FFGAF-SNN: The Forward-Forward Based Gradient Approximation Free Training Framework for Spiking Neural Networks
2025-07-31
Changqing Xu, Ziqiang Yang, Yi Liu, Xinfang Liao, Guiqi Mo, Hao Zeng, Yintang Yang
Spiking Neural Networks (SNNs) offer a biologically plausible framework for energy-efficient neuromorphic computing. However, it is a challenge to train SNNs due to their non-differentiability, efficiently. Existing gradient approximation approaches frequently sacrifice accuracy and face deployment limitations on edge devices due to the substantial computational requirements of backpropagation. To address these challenges, we propose a Forward-Forward (FF) based gradient approximation-free training framework for Spiking Neural Networks, which treats spiking activations as black-box modules, thereby eliminating the need for gradient approximation while significantly reducing computational complexity. Furthermore, we introduce a class-aware complexity adaptation mechanism that dynamically optimizes the loss function based on inter-class difficulty metrics, enabling efficient allocation of network resources across different categories. Experimental results demonstrate that our proposed training framework achieves test accuracies of 99.58%, 92.13%, and 75.64% on the MNIST, Fashion-MNIST, and CIFAR-10 datasets, respectively, surpassing all existing FF-based SNN approaches. Additionally, our proposed method exhibits significant advantages in terms of memory access and computational power consumption.
Hardware-Aware Fine-Tuning of Spiking Q-Networks on the SpiNNaker2 Neuromorphic Platform
2025-07-31
Sirine Arfa, Bernhard Vogginger, Christian Mayr
Spiking Neural Networks (SNNs) promise orders-of-magnitude lower power consumption and low-latency inference on neuromorphic hardware for a wide range of robotic tasks. In this work, we present an energy-efficient implementation of a reinforcement learning (RL) algorithm using quantized SNNs to solve two classical control tasks. The network is trained using the Q-learning algorithm, then fine-tuned and quantized to low-bit (8-bit) precision for embedded deployment on the SpiNNaker2 neuromorphic chip. To evaluate the comparative advantage of SpiNNaker2 over conventional computing platforms, we analyze inference latency, dynamic power consumption, and energy cost per inference for our SNN models, comparing performance against a GTX 1650 GPU baseline. Our results demonstrate SpiNNaker2's strong potential for scalable, low-energy neuromorphic computing, achieving up to 32x reduction in energy consumption. Inference latency remains on par with GPU-based execution, with improvements observed in certain task settings, reinforcing SpiNNaker2's viability for real-time neuromorphic control and making the neuromorphic approach a compelling direction for efficient deep Q-learning.
Accurate finger force estimation is critical for next-generation human-machine interfaces. Traditional electromyography (EMG)-based decoding methods using deep learning require large datasets and high computational resources, limiting their use in real-time, embedded systems. Here, we propose a novel approach that performs finger force regression using spike trains from individual motor neurons, extracted from high-density EMG. These biologically grounded signals drive a spiking neural network implemented on a mixed-signal neuromorphic processor. Unlike prior work that encodes EMG into events, our method exploits spike timing on motor units to perform low-power, real-time inference. This is the first demonstration of motor neuron-based continuous regression computed directly on neuromorphic hardware. Our results confirm accurate finger-specific force prediction with minimal energy use, opening new possibilities for embedded decoding in prosthetics and wearable neurotechnology.
Neural Radiance Fields (NeRF)-based models have achieved remarkable success in 3D reconstruction and rendering tasks. However, during both training and inference, these models rely heavily on dense point sampling along rays from multiple viewpoints, resulting in a surge in floating-point operations and severely limiting their use in resource-constrained scenarios like edge computing. Spiking Neural Networks (SNNs), which communicate via binary spikes over discrete time steps, offer a promising alternative due to their energy-efficient nature. Given the inherent variability in scene scale and texture complexity in neural rendering and the prevailing practice of training separate models per scene, we propose a spike-based NeRF framework with a dynamic time step training strategy, termed Pretrain-Adaptive Time-step Adjustment (PATA). This approach automatically explores the trade-off between rendering quality and time step length during training. Consequently, it enables scene-adaptive inference with variable time steps and reduces the additional consumption of computational resources in the inference process. Anchoring to the established Instant-NGP architecture, we evaluate our method across diverse datasets. The experimental results show that PATA can preserve rendering fidelity while reducing inference time steps by 64\% and running power by 61.55\%.
We propose a biologically inspired model of spiking neurons based on the dynamics of a damped, driven pendulum. Unlike traditional models such as the Leaky Integrate-and-Fire (LIF) neurons, the pendulum neuron incorporates second-order, nonlinear dynamics that naturally give rise to oscillatory behavior and phase-based spike encoding. This model captures richer temporal features and supports timing-sensitive computations critical for sequence processing and symbolic learning. We present an analysis of single-neuron dynamics and extend the model to multi-neuron layers governed by Spike-Timing Dependent Plasticity (STDP) learning rules. We demonstrate practical implementation with python code and with the Brian2 spiking neural simulator, and outline a methodology for deploying the model on neuromorphic hardware platforms, using an approximation of the second-order equations. This framework offers a foundation for developing energy-efficient neural systems for neuromorphic computing and sequential cognition tasks.
Memristor-based Spiking Neural Networks (SNNs) with temporal spike encoding enable ultra-low-energy computation, making them ideal for battery-powered intelligent devices. This paper presents a circuit-level memristive spiking neural network (SNN) architecture trained using a proposed novel supervised in-situ learning algorithm inspired by spike-timing-dependent plasticity (STDP). The proposed architecture efficiently implements lateral inhibition and the refractory period, eliminating the need for external microcontrollers or ancillary control hardware. All synapses of the winning neurons are updated in parallel, enhancing training efficiency. The modular design ensures scalability with respect to input data dimensions and output class count. The SNN is evaluated in LTspice for pattern recognition (using 5x3 binary images) and classification tasks using the Iris and Breast Cancer Wisconsin (BCW) datasets. During testing, the system achieved perfect pattern recognition and high classification accuracies of 99.11\% (Iris) and 97.9\% (BCW). Additionally, it has demonstrated robustness, maintaining an average recognition rate of 93.4\% under 20\% input noise. The impact of stuck-at-conductance faults and memristor device variations was also analyzed.
Neuromorphic Photonic Processing and Memory with Spiking Resonant Tunnelling Diode Neurons and Neural Networks
2025-07-28
Dafydd Owen-Newns, Joshua Robertson, Giovanni Donati, Jose Figueiredo, Edward Wasige, Kathy Ludge, Bruno Romeira, Antonio Hurtado
Neuromorphic computing-modelled after the functionality and efficiency of biological neural systems-offers promising new directions for advancing artificial intelligence and computational models. Photonic techniques for neuromorphic computing hardware are attracting increasing research interest, thanks to their potentials for ultra high bandwidths, low-crosstalk and high parallelism. Among these, approaches based upon resonant tunnelling diodes (RTDs) have recently gained attention as potential building blocks for next-generation light-enabled neuromorphic hardware, due to their capacity to replicate key neuronal behaviours such as excitable spiking and refractoriness, added to their potentials for high operational speeds, energy efficiency and compact footprints. In particular, their ability to function as opto-electronic spiking neurons makes them strong candidates for integration into novel event based neuromorphic computing systems. This work demonstrates the application of optically-triggered spiking RTD neurons to a multiplicity of applications and architectures, these include systems based upon single elements for multi-modal (photonic-electronic) fast rising edge-detection in time-series data, the construction of a two-layer feedforward artificial photonic spiking neural network (pSNN) using RTD neurons as the nonlinear nodes delivering excellent performance in complex dataset classification tasks, and a pSNN comprised of multiple coupled light-sensitive RTD spiking neurons that supports performance as an adjustable neuromorphic optical spiking memory system with a tunable storage time of spiking patterns.
AR-LIF: Adaptive reset leaky-integrate and fire neuron for spiking neural networks
2025-07-28
Zeyu Huang, Wei Meng, Quan Liu, Kun Chen, Li Ma
Spiking neural networks possess the advantage of low energy consumption due to their event-driven nature. Compared with binary spike outputs, their inherent floating-point dynamics are more worthy of attention. The threshold level and reset mode of neurons play a crucial role in determining the number and timing of spikes. The existing hard reset method causes information loss, while the improved soft reset method adopts a uniform treatment for neurons. In response to this, this paper designs an adaptive reset neuron, establishing the correlation between input, output and reset, and integrating a simple yet effective threshold adjustment strategy. It achieves excellent performance on various datasets while maintaining the advantage of low energy consumption.
Identifying Neural Connectivity using Bernoulli Autoregressive Partially Linear Additive Models
2025-07-24
Carla Pinkney, Carolina Euan, Alex Gibberd
Characterising the interactions between spiking neurons is central to our understanding of cognitive processes such as memory, perception and decision making. In this work, we consider the problem of inferring connectivity in the brain network from non-stationary high-dimensional spike train data. Under a binned spike count representation of these data, we propose a Bernoulli autoregressive partially linear additive (BAPLA) model to identify the effective connectivity of a population of neurons. Estimates of the model parameters are obtained using a regularised maximum likelihood estimator, where an $\ell_1$ penalty is used to find sparse and interpretable estimates of neuronal interactions. We also account for non-stationary firing rates by adding a non-parametric trend to the model and provide an inference procedure to quantify the uncertainty associated with our estimated networks of neuronal interactions. We use synthetic data to assess the performance of the BAPLA method, highlighting its ability to detect both excitatory and inhibitory interactions in various settings. Finally, we apply our method to a neural spiking dataset from the DANDI archive, where we study the interactions of neural processes in reaction to various stimulus-response type neuroscience experiments.
Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions
2025-07-24
Alberto Marchisio, Muhammad Shafique
The growing need for intelligent, adaptive, and energy-efficient autonomous systems across fields such as robotics, mobile agents (e.g., UAVs), and self-driving vehicles is driving interest in neuromorphic computing. By drawing inspiration from biological neural systems, neuromorphic approaches offer promising pathways to enhance the perception, decision-making, and responsiveness of autonomous platforms. This paper surveys recent progress in neuromorphic algorithms, specialized hardware, and cross-layer optimization strategies, with a focus on their deployment in real-world autonomous scenarios. Special attention is given to event-based dynamic vision sensors and their role in enabling fast, efficient perception. The discussion highlights new methods that improve energy efficiency, robustness, adaptability, and reliability through the integration of spiking neural networks into autonomous system architectures. We integrate perspectives from machine learning, robotics, neuroscience, and neuromorphic engineering to offer a comprehensive view of the state of the field. Finally, emerging trends and open challenges are explored, particularly in the areas of real-time decision-making, continual learning, and the development of secure, resilient autonomous systems.
Reconfigurable qubit states and quantum trajectories in a synthetic artificial neuron network with a process to direct information generation from co-integrated burst-mode spiking under non-Markovianity
2025-07-22
Osama M. Nayfeh, Chris S. Horne
A synthetic artificial neuron network functional in a regime where quantum information processes are co-integrated with spiking computation provides significant improvement in the capabilities of neuromorphic systems in performing artificial intelligence and autonomy tasks. This provides the ability to execute with the qubit coherence states and entanglement as well as in tandem to perform functions such as read out and basic arithmetic with conventional spike-encoding. Ultimately, this enables the generation and computational processing of information packets with advanced capabilities and an increased level of security in their routing. We now use the dynamical pulse sequences generated by a memristive spiking neuron to drive synthetic neurons with built-in superconductor-ionic memories built in a lateral layout with integrated Niobium metal electrodes as well as a gate terminal and an atomic layer deposited ionic barrier. The memories operate at very low voltage and with direct, and hysteretic Josephson tunneling and provide enhanced coherent properties enabling qubit behavior. We operated now specifically in burst mode to drive its built-in reconfigurable qubit states and direct the resulting quantum trajectory. We analyze the new system with a Hamiltonian that considers an integrated rotational dependence, dependent on the unique co-integrated bursting mode spiking- and where the total above threshold spike count is adjustable with variation of the level of coupling between the neurons. We then examined the impact of key parameters with a longer-term non-Markovian quantum memory and finally explored a process and algorithm for the generation of information packets with a coupled and entangled set of these artificial neuron qubits that provides for a quantum process to define the level of regularity or awareness of the information packets.
Spiking neurons as predictive controllers of linear systems
2025-07-22
Paolo Agliati, André Urbano, Pablo Lanillos, Nasir Ahmad, Marcel van Gerven, Sander Keemink
Neurons communicate with downstream systems via sparse and incredibly brief electrical pulses, or spikes. Using these events, they control various targets such as neuromuscular units, neurosecretory systems, and other neurons in connected circuits. This gave rise to the idea of spiking neurons as controllers, in which spikes are the control signal. Using instantaneous events directly as the control inputs, also called `impulse control', is challenging as it does not scale well to larger networks and has low analytical tractability. Therefore, current spiking control usually relies on filtering the spike signal to approximate analog control. This ultimately means spiking neural networks (SNNs) have to output a continuous control signal, necessitating continuous energy input into downstream systems. Here, we circumvent the need for rate-based representations, providing a scalable method for task-specific spiking control with sparse neural activity. In doing so, we take inspiration from both optimal control and neuroscience theory, and define a spiking rule where spikes are only emitted if they bring a dynamical system closer to a target. From this principle, we derive the required connectivity for an SNN, and show that it can successfully control linear systems. We show that for physically constrained systems, predictive control is required, and the control signal ends up exploiting the passive dynamics of the downstream system to reach a target. Finally, we show that the control method scales to both high-dimensional networks and systems. Importantly, in all cases, we maintain a closed-form mathematical derivation of the network connectivity, the network dynamics and the control objective. This work advances the understanding of SNNs as biologically-inspired controllers, providing insight into how real neurons could exert control, and enabling applications in neuromorphic hardware design.
SPACT18: Spiking Human Action Recognition Benchmark Dataset with Complementary RGB and Thermal Modalities
2025-07-22
Yasser Ashraf, Ahmed Sharshar, Velibor Bojkovic, Bin Gu
Spike cameras, bio-inspired vision sensors, asynchronously fire spikes by accumulating light intensities at each pixel, offering ultra-high energy efficiency and exceptional temporal resolution. Unlike event cameras, which record changes in light intensity to capture motion, spike cameras provide even finer spatiotemporal resolution and a more precise representation of continuous changes. In this paper, we introduce the first video action recognition (VAR) dataset using spike camera, alongside synchronized RGB and thermal modalities, to enable comprehensive benchmarking for Spiking Neural Networks (SNNs). By preserving the inherent sparsity and temporal precision of spiking data, our three datasets offer a unique platform for exploring multimodal video understanding and serve as a valuable resource for directly comparing spiking, thermal, and RGB modalities. This work contributes a novel dataset that will drive research in energy-efficient, ultra-low-power video understanding, specifically for action recognition tasks using spike-based data.
Spiking Neural Networks (SNNs) draw inspiration from biological neurons to create realistic models for brain-like computation, demonstrating effectiveness in processing temporal information with energy efficiency and biological realism. Most existing SNNs assume a single time constant for neuronal membrane voltage dynamics, modeled by first-order ordinary differential equations (ODEs) with Markovian characteristics. Consequently, the voltage state at any time depends solely on its immediate past value, potentially limiting network expressiveness. Real neurons, however, exhibit complex dynamics influenced by long-term correlations and fractal dendritic structures, suggesting non-Markovian behavior. Motivated by this, we propose the Fractional SPIKE Differential Equation neural network (fspikeDE), which captures long-term dependencies in membrane voltage and spike trains through fractional-order dynamics. These fractional dynamics enable more expressive temporal patterns beyond the capability of integer-order models. For efficient training of fspikeDE, we introduce a gradient descent algorithm that optimizes parameters by solving an augmented fractional-order ODE (FDE) backward in time using adjoint sensitivity methods. Extensive experiments on diverse image and graph datasets demonstrate that fspikeDE consistently outperforms traditional SNNs, achieving superior accuracy, comparable energy efficiency, reduced training memory usage, and enhanced robustness against noise. Our approach provides a novel open-sourced computational toolbox for fractional-order SNNs, widely applicable to various real-world tasks.
2025-07-21
Ziqiao Yu, Pengfei Sun, Dan F. M. Goodman
We investigate the extent to which Spiking Neural Networks (SNNs) trained with Surrogate Gradient Descent (Surrogate GD), with and without delay learning, can learn from precise spike timing beyond firing rates. We first design synthetic tasks isolating intra-neuron inter-spike intervals and cross-neuron synchrony under matched spike counts. On more complex spike-based speech recognition datasets (Spiking Heidelberg Digits (SHD) and Spiking Speech Commands (SSC), we construct variants where spike count information is eliminated and only timing information remains, and show that Surrogate GD-trained SNNs are able to perform significantly above chance whereas purely rate-based models perform at chance level. We further evaluate robustness under biologically inspired perturbations -- including Gaussian jitter per spike or per-neuron, and spike deletion -- revealing consistent but perturbation-specific degradation. Networks show a sharp performance drop when spike sequences are reversed in time, with a larger drop in performance from SNNs trained with delays, indicating that these networks are more human-like in terms of behaviour. To facilitate further studies of temporal coding, we have released our modified SHD and SSC datasets.
Accurate and efficient skin lesion classification on edge devices is critical for accessible dermatological care but remains challenging due to computational, energy, and privacy constraints. We introduce QANA, a novel quantization-aware neuromorphic architecture for incremental skin lesion classification on resource-limited hardware. QANA effectively integrates ghost modules, efficient channel attention, and squeeze-and-excitation blocks for robust feature representation with low-latency and energy-efficient inference. Its quantization-aware head and spike-compatible transformations enable seamless conversion to spiking neural networks (SNNs) and deployment on neuromorphic platforms. Evaluation on the large-scale HAM10000 benchmark and a real-world clinical dataset shows that QANA achieves 91.6\% Top-1 accuracy and 82.4\% macro F1 on HAM10000, and 90.8\% / 81.7\% on the clinical dataset, significantly outperforming state-of-the-art CNN-to-SNN models under fair comparison. Deployed on BrainChip Akida hardware, QANA achieves 1.5\,ms inference latency and 1.7\,mJ energy per image, reducing inference latency and energy use by over 94.6\%/98.6\% compared to GPU-based CNNs surpassing state-of-the-art CNN-to-SNN conversion baselines. These results demonstrate the effectiveness of QANA for accurate, real-time, and privacy-sensitive medical analysis in edge environments.
TONUS: Neuromorphic human pose estimation for artistic sound co-creation
2025-07-21
Jules Lecomte, Konrad Zinner, Michael Neumeier, Axel von Arnim
Human machine interaction is a huge source of inspiration in today's media art and digital design, as machines and humans merge together more and more. Its place in art reflects its growing applications in industry, such as robotics. However, those interactions often remains too technical and machine-driven for people to really engage into. On the artistic side, new technologies are often not explored in their full potential and lag a bit behind, so that state-of-the-art research does not make its way up to museums and exhibitions. Machines should support people's imagination and poetry in a seamless interface to their body or soul. We propose an artistic sound installation featuring neuromorphic body sensing to support a direct yet non intrusive interaction with the visitor with the purpose of creating sound scapes together with the machine. We design a neuromorphic multihead human pose estimation neural sensor that shapes sound scapes and visual output with fine body movement control. In particular, the feature extractor is a spiking neural network tailored for a dedicated neuromorphic chip. The visitor, immersed in a sound atmosphere and a neurally processed representation of themselves that they control, experience the dialogue with a machine that thinks neurally, similarly to them.
Spiking Neural Networks (SNNs) are increasingly favored for deployment on resource-constrained edge devices due to their energy-efficient and event-driven processing capabilities. However, training SNNs remains challenging because of the computational intensity of traditional backpropagation algorithms adapted for spike-based systems. In this paper, we propose a novel software-hardware co-design that introduces a hardware-friendly training algorithm, Spiking Direct Feedback Alignment (SDFA) and implement it on a Resistive Random Access Memory (RRAM)-based In-Memory Computing (IMC) architecture, referred to as PipeSDFA, to accelerate SNN training. Software-wise, the computational complexity of SNN training is reduced by the SDFA through the elimination of sequential error propagation. Hardware-wise, a three-level pipelined dataflow is designed based on IMC architecture to parallelize the training process. Experimental results demonstrate that the PipeSDFA training accelerator incurs less than 2% accuracy loss on five datasets compared to baselines, while achieving 1.1X~10.5X and 1.37X~2.1X reductions in training time and energy consumption, respectively compared to PipeLayer.
Analyzing Internal Activity and Robustness of SNNs Across Neuron Parameter Space
2025-07-19
Szymon Mazurek, Jakub Caputa, Maciej Wielgosz
Spiking Neural Networks (SNNs) offer energy-efficient and biologically plausible alternatives to traditional artificial neural networks, but their performance depends critically on the tuning of neuron model parameters. In this work, we identify and characterize an operational space - a constrained region in the neuron hyperparameter domain (specifically membrane time constant tau and voltage threshold vth) - within which the network exhibits meaningful activity and functional behavior. Operating inside this manifold yields optimal trade-offs between classification accuracy and spiking activity, while stepping outside leads to degeneration: either excessive energy use or complete network silence. Through systematic exploration across datasets and architectures, we visualize and quantify this manifold and identify efficient operating points. We further assess robustness to adversarial noise, showing that SNNs exhibit increased spike correlation and internal synchrony when operating outside their optimal region. These findings highlight the importance of principled hyperparameter tuning to ensure both task performance and energy efficiency. Our results offer practical guidelines for deploying robust and efficient SNNs, particularly in neuromorphic computing scenarios.
2025-07-18
Shuiguang Deng, Di Yu, Changze Lv, Xin Du, Linshan Jiang, Xiaofan Zhao, Wentao Tong, Xiaoqing Zheng, Weijia Fang, Peng Zhao, Gang Pan, Schahram Dustdar, Albert Y. Zomaya
The convergence of artificial intelligence and edge computing has spurred growing interest in enabling intelligent services directly on resource-constrained devices. While traditional deep learning models require significant computational resources and centralized data management, the resulting latency, bandwidth consumption, and privacy concerns have exposed critical limitations in cloud-centric paradigms. Brain-inspired computing, particularly Spiking Neural Networks (SNNs), offers a promising alternative by emulating biological neuronal dynamics to achieve low-power, event-driven computation. This survey provides a comprehensive overview of Edge Intelligence based on SNNs (EdgeSNNs), examining their potential to address the challenges of on-device learning, inference, and security in edge scenarios. We present a systematic taxonomy of EdgeSNN foundations, encompassing neuron models, learning algorithms, and supporting hardware platforms. Three representative practical considerations of EdgeSNN are discussed in depth: on-device inference using lightweight SNN models, resource-aware training and updating under non-stationary data conditions, and secure and privacy-preserving issues. Furthermore, we highlight the limitations of evaluating EdgeSNNs on conventional hardware and introduce a dual-track benchmarking strategy to support fair comparisons and hardware-aware optimization. Through this study, we aim to bridge the gap between brain-inspired learning and practical edge deployment, offering insights into current advancements, open challenges, and future research directions. To the best of our knowledge, this is the first dedicated and comprehensive survey on EdgeSNNs, providing an essential reference for researchers and practitioners working at the intersection of neuromorphic computing and edge intelligence.
The fact that strange sea quarks are abundant in the nucleons, but without strangeness, is of great importance for understanding the nature of dense matter condensed by the strong interaction, particularly in the context of the ``gigantic nucleus'' formed by the gravitational collapse of an evolved massive star. We hypothesize that the basic unit of bulk strong matter with the light-flavor symmetry of valence quarks is ``strangeon'', which is the counterpart of the nucleon found in atomic nuclei. In addition to strangeon stars (SnSs) with large baryon number of $A\approx 10^{57}$, strange nuggets (SnNs) with $A\gtrsim 10^{10}$ could also exist in the Universe. Both the SnS and the SnN are explained, with attention to their observational evidence.
LASANA: Large-Scale Surrogate Modeling for Analog Neuromorphic Architecture Exploration
2025-07-14
Jason Ho, James A. Boyle, Linshen Liu, Andreas Gerstlauer
Neuromorphic systems using in-memory or event-driven computing are motivated by the need for more energy-efficient processing of artificial intelligence workloads. Emerging neuromorphic architectures aim to combine traditional digital designs with the computational efficiency of analog computing and novel device technologies. A crucial problem in the rapid exploration and co-design of such architectures is the lack of tools for fast and accurate modeling and simulation. Typical mixed-signal design tools integrate a digital simulator with an analog solver like SPICE, which is prohibitively slow for large systems. By contrast, behavioral modeling of analog components is faster, but existing approaches are fixed to specific architectures with limited energy and performance modeling. In this paper, we propose LASANA, a novel approach that leverages machine learning to derive data-driven surrogate models of analog sub-blocks in a digital backend architecture. LASANA uses SPICE-level simulations of a circuit to train ML models that predict circuit energy, performance, and behavior at analog/digital interfaces. Such models can provide energy and performance annotation on top of existing behavioral models or function as replacements to analog simulation. We apply LASANA to an analog crossbar array and a spiking neuron circuit. Running MNIST and spiking MNIST, LASANA surrogates demonstrate up to three orders of magnitude speedup over SPICE, with energy, latency, and behavioral error less than 7%, 8%, and 2%, respectively.
Boosted Enhanced Quantile Regression Neural Networks with Spatiotemporal Permutation Entropy for Complex System Prognostics
2025-07-14
David J Poland
This paper presents a novel framework for pattern prediction and system prognostics centered on Spatiotemporal Permutation Entropy analysis integrated with Boosted Enhanced Quantile Regression Neural Networks (BEQRNNs). We address the challenge of understanding complex dynamical patterns in multidimensional systems through an approach that combines entropy-based complexity measures with advanced neural architectures. The system leverages dual computational stages: first implementing spatiotemporal entropy extraction optimized for multiscale temporal and spatial data streams, followed by an integrated BEQRNN layer that enables probabilistic pattern prediction with uncertainty quantification. This architecture achieves 81.17% accuracy in spatiotemporal pattern classification with prediction horizons up to 200 time steps and maintains robust performance across diverse regimes. Field testing across chaotic attractors, reaction-diffusion systems, and industrial datasets shows a 79% increase in critical transition detection accuracy and 81.22% improvement in long-term prediction reliability. The framework's effectiveness in processing complex, multimodal entropy features demonstrates significant potential for real-time prognostic applications.
Learning to Control Dynamical Agents via Spiking Neural Networks and Metropolis-Hastings Sampling
2025-07-13
Ali Safa, Farida Mohsen, Ali Al-Zawqari
Spiking Neural Networks (SNNs) offer biologically inspired, energy-efficient alternatives to traditional Deep Neural Networks (DNNs) for real-time control systems. However, their training presents several challenges, particularly for reinforcement learning (RL) tasks, due to the non-differentiable nature of spike-based communication. In this work, we introduce what is, to our knowledge, the first framework that employs Metropolis-Hastings (MH) sampling, a Bayesian inference technique, to train SNNs for dynamical agent control in RL environments without relying on gradient-based methods. Our approach iteratively proposes and probabilistically accepts network parameter updates based on accumulated reward signals, effectively circumventing the limitations of backpropagation while enabling direct optimization on neuromorphic platforms. We evaluated this framework on two standard control benchmarks: AcroBot and CartPole. The results demonstrate that our MH-based approach outperforms conventional Deep Q-Learning (DQL) baselines and prior SNN-based RL approaches in terms of maximizing the accumulated reward while minimizing network resources and training episodes.
On the Importance of Neural Membrane Potential Leakage for LIDAR-based Robot Obstacle Avoidance using Spiking Neural Networks
2025-07-13
Zainab Ali, Lujayn Al-Amir, Ali Safa
Using neuromorphic computing for robotics applications has gained much attention in recent year due to the remarkable ability of Spiking Neural Networks (SNNs) for high-precision yet low memory and compute complexity inference when implemented in neuromorphic hardware. This ability makes SNNs well-suited for autonomous robot applications (such as in drones and rovers) where battery resources and payload are typically limited. Within this context, this paper studies the use of SNNs for performing direct robot navigation and obstacle avoidance from LIDAR data. A custom robot platform equipped with a LIDAR is set up for collecting a labeled dataset of LIDAR sensing data together with the human-operated robot control commands used for obstacle avoidance. Crucially, this paper provides what is, to the best of our knowledge, a first focused study about the importance of neuron membrane leakage on the SNN precision when processing LIDAR data for obstacle avoidance. It is shown that by carefully tuning the membrane potential leakage constant of the spiking Leaky Integrate-and-Fire (LIF) neurons used within our SNN, it is possible to achieve on-par robot control precision compared to the use of a non-spiking Convolutional Neural Network (CNN). Finally, the LIDAR dataset collected during this work is released as open-source with the hope of benefiting future research.
Cellular Mechanisms of Phase Maintenance in a Pyloric Motif of a Central Pattern Generator
2025-07-12
Gabrielle O'Brien, Adam L. Weaver, William H. Barnett, Dmitry A. Kozhanov, Gennady S. Cymbalyuk
In many neural networks, patterns controlling rhythmic behaviors are maintained across a wide range of periods. In the crustacean pyloric central pattern generator (CPG), a constant bursting pattern is preserved over a three-to-fivefold range of periods. We idescribe how neuromodulation could adjust neuronal properties to preserve phase relations as the period changes. We developed a biophysical model implementing a reduced pyloric network motif, which has a bursting neuron and two follower neurons interconnected through inhibitory synaptic coupling. We described cellular mechanisms supporting phase maintenance and investigated possible coordination between these mechanisms in four dynamically distinct ensembles of a pyloric CPG producing a triphasic pattern. The coordinated variation of the voltages of half-activation for potassium (VK2) and hyperpolarization-activated (Vh) currents provides a family of three mechanisms for control of burst duration, interburst interval, and latency to spiking. The mechanisms are determined by the Cornerstone bifurcation, one of the Shilnikov blue sky catastrophe scenarios. In Mechanism 1, in a bursting neuron, the burst duration increases as VK2 nears a blue-sky catastrophe bifurcation, while the interburst interval grows as Vh approaches a saddle-node on an invariant circle bifurcation. In Mechanism 2, a silent neuron responds with a single burst to short input; the burst duration grows as VK2 approaches a saddle-node bifurcation for periodic orbits. In Mechanism 3, a spiking neuron responds with a pause to short input; the pause duration grows as Vh nears a saddle-node bifurcation for stationary states. In all three mechanisms, the measured quantities grow without bound as the bifurcation parameter nears its critical value, consistent with an inverse-square-root law.
Cross Knowledge Distillation between Artificial and Spiking Neural Networks
2025-07-12
Shuhan Ye, Yuanbin Qian, Chong Wang, Sunqi Lin, Jiazhen Xu, Jiangbo Qian, Yuqi Li
Recently, Spiking Neural Networks (SNNs) have demonstrated rich potential in computer vision domain due to their high biological plausibility, event-driven characteristic and energy-saving efficiency. Still, limited annotated event-based datasets and immature SNN architectures result in their performance inferior to that of Artificial Neural Networks (ANNs). To enhance the performance of SNNs on their optimal data format, DVS data, we explore using RGB data and well-performing ANNs to implement knowledge distillation. In this case, solving cross-modality and cross-architecture challenges is necessary. In this paper, we propose cross knowledge distillation (CKD), which not only leverages semantic similarity and sliding replacement to mitigate the cross-modality challenge, but also uses an indirect phased knowledge distillation to mitigate the cross-architecture challenge. We validated our method on main-stream neuromorphic datasets, including N-Caltech101 and CEP-DVS. The experimental results show that our method outperforms current State-of-the-Art methods. The code will be available at https://github.com/ShawnYE618/CKD
Onboard Neuromorphic Split Computing via Optical Links for LEO Remote Sensing
2025-07-11
Zihang Song, Petar Popovski
Low Earth orbit (LEO) satellite constellations increasingly require onboard intelligence under strict power and communication constraints. This paper proposes a neuromorphic split computing framework tailored for hierarchical LEO systems, where edge satellites perform event-driven sensing using dynamic vision sensors (DVS) and lightweight spiking neural network (SNN) encoders, while core satellites conduct inference using a powerful SNN decoder. A learned spike mapping scheme enables direct transmission over optical inter-satellite links (OISLs) without conventional modulation overhead. Experimental results on synthetic aerial scene classification demonstrate that the proposed architecture achieves accuracy on par with modern large vision-based pipelines, while offering energy efficiency comparable to that of existing lightweight implementations. These findings highlight the potential of neuromorphic computing for energy-efficient inter-satellite split computing in LEO remote sensing missions.
2025-07-11
Jorge Mario Cruz-Duarte, El-Ghazali Talbi
The increasing energy footprint of artificial intelligence systems urges alternative computational models that are both efficient and scalable. Neuromorphic Computing (NC) addresses this challenge by empowering event-driven algorithms that operate with minimal power requirements through biologically inspired spiking dynamics. We present the NeurOptimiser, a fully spike-based optimisation framework that materialises the neuromorphic-based metaheuristic paradigm through a decentralised NC system. The proposed approach comprises a population of Neuromorphic Heuristic Units (NHUs), each combining spiking neuron dynamics with spike-triggered perturbation heuristics to evolve candidate solutions asynchronously. The NeurOptimiser's coordination arises through native spiking mechanisms that support activity propagation, local information sharing, and global state updates without external orchestration. We implement this framework on Intel's Lava platform, targeting the Loihi 2 chip, and evaluate it on the noiseless BBOB suite up to 40 dimensions. We deploy several NeurOptimisers using different configurations, mainly considering dynamic systems such as linear and Izhikevich models for spiking neural dynamics, and fixed and Differential Evolution mutation rules for spike-triggered heuristics. Although these configurations are implemented as a proof of concept, we document and outline further extensions and improvements to the framework implementation. Results show that the proposed approach exhibits structured population dynamics, consistent convergence, and milliwatt-level power feasibility. They also position spike-native MHs as a viable path toward real-time, low-energy, and decentralised optimisation.
EEvAct: Early Event-Based Action Recognition with High-Rate Two-Stream Spiking Neural Networks
2025-07-10
Michael Neumeier, Jules Lecomte, Nils Kazinski, Soubarna Banik, Bing Li, Axel von Arnim
Recognizing human activities early is crucial for the safety and responsiveness of human-robot and human-machine interfaces. Due to their high temporal resolution and low latency, event-based vision sensors are a perfect match for this early recognition demand. However, most existing processing approaches accumulate events to low-rate frames or space-time voxels which limits the early prediction capabilities. In contrast, spiking neural networks (SNNs) can process the events at a high-rate for early predictions, but most works still fall short on final accuracy. In this work, we introduce a high-rate two-stream SNN which closes this gap by outperforming previous work by 2% in final accuracy on the large-scale THU EACT-50 dataset. We benchmark the SNNs within a novel early event-based recognition framework by reporting Top-1 and Top-5 recognition scores for growing observation time. Finally, we exemplify the impact of these methods on a real-world task of early action triggering for human motion capture in sports.
Spiking Neural Networks (SNNs), inspired by biological neural mechanisms, represent a promising neuromorphic computing paradigm that offers energy-efficient alternatives to traditional Artificial Neural Networks (ANNs). Despite proven effectiveness, SNN architectures have struggled to achieve competitive performance on large-scale speech processing task. Two key challenges hinder progress: (1) the high computational overhead during training caused by multi-timestep spike firing, and (2) the absence of large-scale SNN architectures tailored to speech processing tasks. To overcome the issues, we introduce Input-aware Multi-Level Spikeformer, i.e. IML-Spikeformer, a spiking Transformer architecture specifically designed for large-scale speech processing. Central to our design is the Input-aware Multi-Level Spike (IMLS) mechanism, which simulate multi-timestep spike firing within a single timestep using an adaptive, input-aware thresholding scheme. IML-Spikeformer further integrates a Reparameterized Spiking Self-Attention (RepSSA) module with a Hierarchical Decay Mask (HDM), forming the HD-RepSSA module. This module enhances the precision of attention maps and enables modeling of multi-scale temporal dependencies in speech signals. Experiments demonstrate that IML-Spikeformer achieves word error rates of 6.0\% on AiShell-1 and 3.4\% on Librispeech-960, comparable to conventional ANN transformers while reducing theoretical inference energy consumption by 4.64$\times$ and 4.32$\times$ respectively. IML-Spikeformer marks an advance of scalable SNN architectures for large-scale speech processing in both task performance and energy efficiency.
Exploring substructures in the Milky Way halo Neural networks applied to Gaia and APOGEE DR 17
2025-07-10
L. Berni, L. Spina, L. Magrini, D. Massari, J. Schiapppacasse-Ulloa, R. E. Giribaldi
The identification of stellar structures in the Galactic halo, including stellar streams and merger remnants, often relies on the dynamics of their constituent stars. However, this approach has limitations due to the complex dynamical interactions between these structures and their environment. Perturbations such as tidal forces exerted by the Milky Way, the potential escape of stars, and passages through the Galactic plane can result in the loss of dynamical coherence of stars in these structures. Consequently, relying solely on dynamics may be insufficient for detecting such disrupted or dispersed remnants. We combine chemistry and dynamics, integrated through a system of neural networks, to develop a clustering method for identifying accreted structures in the Galactic halo. We developed an integrated approach combining Siamese neural networks (SNNs), graph neural networks (GNNs), autoencoders, and the OPTICS algorithm to create a comprehensive procedure named CREEK. This method is designed to uncover stellar structures in the Galactic halo. Initially, CREEK was trained on known globular clusters (GCs) and then applied to the dataset to identify stellar streams. CREEK successfully recovered 80% of the GCs present in the APOGEE dataset, re-identified several known stellar streams, and identified a potential new stream. Additionally, within highly populated stellar structures, CREEK can identify substructures that exhibit distinct chemical compositions and orbital energies. This approach provides an objective data-driven method for selecting stars associated with streams and stellar structures in general.
Stochastic Operator Network: A Stochastic Maximum Principle Based Approach to Operator Learning
2025-07-10
Ryan Bausback, Jingqiao Tang, Lu Lu, Feng Bao, Toan Huynh
We develop a novel framework for uncertainty quantification in operator learning, the Stochastic Operator Network (SON). SON combines the stochastic optimal control concepts of the Stochastic Neural Network (SNN) with the DeepONet. By formulating the branch net as an SDE and backpropagating through the adjoint BSDE, we replace the gradient of the loss function with the gradient of the Hamiltonian from Stohastic Maximum Principle in the SGD update. This allows SON to learn the uncertainty present in operators through its diffusion parameters. We then demonstrate the effectiveness of SON when replicating several noisy operators in 2D and 3D.
A Robust, Open-Source Framework for Spiking Neural Networks on Low-End FPGAs
2025-07-09
Andrew Fan, Simon D. Levy
As the demand for compute power in traditional neural networks has increased significantly, spiking neural networks (SNNs) have emerged as a potential solution to increasingly power-hungry neural networks. By operating on 0/1 spikes emitted by neurons instead of arithmetic multiply-and-accumulate operations, SNNs propagate information temporally and spatially, allowing for more efficient compute power. To this end, many architectures for accelerating and simulating SNNs have been developed, including Loihi, TrueNorth, and SpiNNaker. However, these chips are largely inaccessible to the wider community. Field programmable gate arrays (FPGAs) have been explored to serve as a middle ground between neuromorphic and non-neuromorphic hardware, but many proposed architectures require expensive high-end FPGAs or target a single SNN topology. This paper presents a framework consisting of a robust SNN acceleration architecture and a Pytorch-based SNN model compiler. Targeting any-to-any and/or fully connected SNNs, the FPGA architecture features a synaptic array that tiles across the SNN to propagate spikes. The architecture targets low-end FPGAs and requires very little (6358 LUT, 40.5 BRAM) resources. The framework, tested on a low-end Xilinx Artix-7 FPGA at 100 MHz, achieves competitive speed in recognizing MNIST digits (0.52 ms/img). Further experiments also show accurate simulation of hand coded any-to-any spiking neural networks on toy problems. All code and setup instructions are available at https://github.com/im-afan/snn-fpga}{\texttt{https://github.com/im-afan/snn-fpga.
Exploring Gain-Doped-Waveguide-Synapse for Neuromorphic Applications: A Pulsed Pump-Signal Approach
2025-07-08
Robert Otupiri, Ripalta Stabile
Neuromorphic computing promises to transform AI systems by enabling them to perceive, respond to, and adapt swiftly and accurately to dynamic data and user interactions. However, traditional silicon-based and hybrid electronic technologies for artificial neurons constrain neuromorphic processors in terms of flexibility, scalability, and energy efficiency. In this study, we pioneer the use of Doped-Gain-Layer-on-Waveguide-Synapses for bio-inspired neurons, utilizing a pulsed pump-signal mechanism to enhance neuromorphic computation. This approach addresses critical challenges in scalability and energy efficiency inherent in current technologies. We introduce the concept of Gain on Waveguide Dynamics for synapses, demonstrating how non-linear pulse transformations of input probe signals occur under various pump-probe configurations. Our findings reveal that primarily properties of pulse amplitude, period as well material properties such as doping densities and population dynamics influence strongly the generation of spiking responses that emulate neuronal behaviour and effectively how computational logic is. By harnessing the complex interactions of asynchronous spiking pump techniques and ion densities in excited states, our method produces event-driven responses that mirror natural neuronal functions. This gain-enhanced environment supports short-term memory capabilities alongside essential characteristics like asynchronous spike generation, threshold operation, and temporal integration, foundational to brain-inspired spiking neural network paradigms.
An Exact Gradient Framework for Training Spiking Neural Networks
2025-07-08
Arman Ferdowsi, Atakan Aral
Spiking neural networks inherently rely on the precise timing of discrete spike events for information processing. Incorporating additional bio-inspired degrees of freedom, such as trainable synaptic transmission delays and adaptive firing thresholds, is essential for fully leveraging the temporal dynamics of SNNs. Although recent methods have demonstrated the benefits of training synaptic weights and delays, both in terms of accuracy and temporal representation, these techniques typically rely on discrete-time simulations, surrogate gradient approximations, or full access to internal state variables such as membrane potentials. Such requirements limit training precision and efficiency and pose challenges for neuromorphic hardware implementation due to increased memory and I/O bandwidth demands. To overcome these challenges, we propose an analytical event-driven learning framework that computes exact loss gradients not only with respect to synaptic weights and transmission delays but also to adaptive neuronal firing thresholds. Experiments on multiple benchmarks demonstrate significant gains in accuracy (up to 7%), timing precision, and robustness compared to existing methods.
A novel approach to moment closure problem is used to derive low dimensional laws for the dynamics of the moments of the membrane potential distribution in a population of spiking neurons. Using spectral expansion of the density equation we derive the recursive and nonlinear relation between the moments, such as the mean potential, and the population firing rates. The self-consistent dynamics found relies on the dominant eigenvalues of the evolution operator, tightly related to the moments of the single-neuron inter-spike interval distribution. Contrary to previous attempts our system can be applied both in noise- and drift-dominated regime, and both for weakly and strongly coupled population. We demonstrate the applicability of the theory for the case of a network of leaky integrate-and-fire neurons deriving closed analytical expressions. Truncating the mode decomposition to the first few more relevant moments, results to effectively describe the population dynamics both out-of-equilibrium and in response to strongly-varying inputs.
VOLTRON: Detecting Unknown Malware Using Graph-Based Zero-Shot Learning
2025-07-06
M. Tahir Akdeniz, Zeynep Yeşilkaya, İ. Enes Köse, İ. Ulaş Ünal, Sevil Şen
The persistent threat of Android malware presents a serious challenge to the security of millions of users globally. While many machine learning-based methods have been developed to detect these threats, their reliance on large labeled datasets limits their effectiveness against emerging, previously unseen malware families, for which labeled data is scarce or nonexistent. To address this challenge, we introduce a novel zero-shot learning framework that combines Variational Graph Auto-Encoders (VGAE) with Siamese Neural Networks (SNN) to identify malware without needing prior examples of specific malware families. Our approach leverages graph-based representations of Android applications, enabling the model to detect subtle structural differences between benign and malicious software, even in the absence of labeled data for new threats. Experimental results show that our method outperforms the state-of-the-art MaMaDroid, especially in zero-day malware detection. Our model achieves 96.24% accuracy and 95.20% recall for unknown malware families, highlighting its robustness against evolving Android threats.
Percolation theory serves as a cornerstone for studying phase transitions and critical phenomena, with broad implications in statistical physics, materials science, and complex networks. However, most machine learning frameworks for percolation analysis have focused on two-dimensional systems, oversimplifying the spatial correlations and morphological complexity of real-world three-dimensional materials. To bridge this gap and improve label efficiency and scalability in 3D systems, we propose a Siamese Neural Network (SNN) that leverages features of the largest cluster as discriminative input. Our method achieves high predictive accuracy for both site and bond percolation thresholds and critical exponents in three dimensions, with sub-1% error margins using significantly fewer labeled samples than traditional approaches. This work establishes a robust and data-efficient framework for modeling high-dimensional critical phenomena, with potential applications in materials discovery and complex network analysis.
Efficient Event-Based Semantic Segmentation via Exploiting Frame-Event Fusion: A Hybrid Neural Network Approach
2025-07-04
Hebei Li, Yansong Peng, Jiahui Yuan, Peixi Wu, Jin Wang, Yueyi Zhang, Xiaoyan Sun
Event cameras have recently been introduced into image semantic segmentation, owing to their high temporal resolution and other advantageous properties. However, existing event-based semantic segmentation methods often fail to fully exploit the complementary information provided by frames and events, resulting in complex training strategies and increased computational costs. To address these challenges, we propose an efficient hybrid framework for image semantic segmentation, comprising a Spiking Neural Network branch for events and an Artificial Neural Network branch for frames. Specifically, we introduce three specialized modules to facilitate the interaction between these two branches: the Adaptive Temporal Weighting (ATW) Injector, the Event-Driven Sparse (EDS) Injector, and the Channel Selection Fusion (CSF) module. The ATW Injector dynamically integrates temporal features from event data into frame features, enhancing segmentation accuracy by leveraging critical dynamic temporal information. The EDS Injector effectively combines sparse event data with rich frame features, ensuring precise temporal and spatial information alignment. The CSF module selectively merges these features to optimize segmentation performance. Experimental results demonstrate that our framework not only achieves state-of-the-art accuracy across the DDD17-Seg, DSEC-Semantic, and M3ED-Semantic datasets but also significantly reduces energy consumption, achieving a 65\% reduction on the DSEC-Semantic dataset.
Benchmarking Spiking Neurons for Linear Quadratic Regulator Control of Multi-linked Pole on a Cart: from Single Neuron to Ensemble
2025-07-04
Shreyan Banerjee, Luna Gava, Aasifa Rounak, Vikram Pakrashi
The emerging field of neuromorphic computing for edge control applications poses the need to quantitatively estimate and limit the number of spiking neurons, to reduce network complexity and optimize the number of neurons per core and hence, the chip size, in an application-specific neuromorphic hardware. While rate-encoding for spiking neurons provides a robust way to encode signals with the same number of neurons as an ANN, it often lacks precision. To achieve the desired accuracy, a population of neurons is often needed to encode the complete range of input signals. However, using population encoding immensely increases the total number of neurons required for a particular application, thus increasing the power consumption and on-board resource utilization. A transition from two neurons to a population of neurons for the LQR control of a cartpole is shown in this work. The near-linear behavior of a Leaky-Integrate-and-Fire neuron can be exploited to achieve the Linear Quadratic Regulator (LQR) control of a cartpole system. This has been shown in simulation, followed by a demonstration on a single-neuron hardware, known as Lu.i. The improvement in control performance is then demonstrated by using a population of varying numbers of neurons for similar control in the Nengo Neural Engineering Framework, on CPU and on Intel's Loihi neuromorphic chip. Finally, linear control is demonstrated for four multi-linked pendula on cart systems, using a population of neurons in Nengo, followed by an implementation of the same on Loihi. This study compares LQR control in the NEF using $7$ control and $7$ neuromorphic performance metrics, followed by a comparison with other conventional spiking and non-spiking controllers.
SFATTI: Spiking FPGA Accelerator for Temporal Task-driven Inference -- A Case Study on MNIST
2025-07-04
Alessio Caviglia, Filippo Marostica, Alessio Carpegna, Alessandro Savino, Stefano Di Carlo
Hardware accelerators are essential for achieving low-latency, energy-efficient inference in edge applications like image recognition. Spiking Neural Networks (SNNs) are particularly promising due to their event-driven and temporally sparse nature, making them well-suited for low-power Field Programmable Gate Array (FPGA)-based deployment. This paper explores using the open-source Spiker+ framework to generate optimized SNNs accelerators for handwritten digit recognition on the MNIST dataset. Spiker+ enables high-level specification of network topologies, neuron models, and quantization, automatically generating deployable HDL. We evaluate multiple configurations and analyze trade-offs relevant to edge computing constraints.
Unsupervised Sparse Coding-based Spiking Neural Network for Real-time Spike Sorting
2025-06-30
Alexis Melot, Sean U. N. Wood, Yannick Coffinier, Pierre Yger, Fabien Alibart
Spike sorting is a crucial step in decoding multichannel extracellular neural signals, enabling the identification of individual neuronal activity. A key challenge in brain-machine interfaces (BMIs) is achieving real-time, low-power spike sorting at the edge while keeping high neural decoding performance. This study introduces the Neuromorphic Sparse Sorter (NSS), a compact two-layer spiking neural network optimized for efficient spike sorting. NSS leverages the Locally Competitive Algorithm (LCA) for sparse coding to extract relevant features from noisy events with reduced computational demands. NSS learns to sort detected spike waveforms in an online fashion and operates entirely unsupervised. To exploit multi-bit spike coding capabilities of neuromorphic platforms like Intel's Loihi 2, a custom neuron model was implemented, enabling flexible power-performance trade-offs via adjustable spike bit-widths. Evaluations on simulated and real-world tetrode signals with biological drift showed NSS outperformed established pipelines such as WaveClus3 and PCA+KMeans. With 2-bit graded spikes, NSS on Loihi 2 outperformed NSS implemented with leaky integrate-and-fire neuron and achieved an F1-score of 77% (+10% improvement) while consuming 8.6mW (+1.65mW) when tested on a drifting recording, with a computational processing time of 0.25ms (+60 us) per inference.
Training of Spiking Neural Networks with Expectation-Propagation
2025-06-30
Dan Yao, Steve McLaughlin, Yoann Altmann
In this paper, we propose a unifying message-passing framework for training spiking neural networks (SNNs) using Expectation-Propagation. Our gradient-free method is capable of learning the marginal distributions of network parameters and simultaneously marginalizes nuisance parameters, such as the outputs of hidden layers. This framework allows for the first time, training of discrete and continuous weights, for deterministic and stochastic spiking networks, using batches of training samples. Although its convergence is not ensured, the algorithm converges in practice faster than gradient-based methods, without requiring a large number of passes through the training data. The classification and regression results presented pave the way for new efficient training methods for deep Bayesian networks.
Multi-bit spiking neural networks (SNNs) have recently become a heated research spot, pursuing energy-efficient and high-accurate AI. However, with more bits involved, the associated memory and computation demands escalate to the point where the performance improvements become disproportionate. Based on the insight that different layers demonstrate different importance and extra bits could be wasted and interfering, this paper presents an adaptive bit allocation strategy for direct-trained SNNs, achieving fine-grained layer-wise allocation of memory and computation resources. Thus, SNN's efficiency and accuracy can be improved. Specifically, we parametrize the temporal lengths and the bit widths of weights and spikes, and make them learnable and controllable through gradients. To address the challenges caused by changeable bit widths and temporal lengths, we propose the refined spiking neuron, which can handle different temporal lengths, enable the derivation of gradients for temporal lengths, and suit spike quantization better. In addition, we theoretically formulate the step-size mismatch problem of learnable bit widths, which may incur severe quantization errors to SNN, and accordingly propose the step-size renewal mechanism to alleviate this issue. Experiments on various datasets, including the static CIFAR and ImageNet and the dynamic CIFAR-DVS and DVS-GESTURE, demonstrate that our methods can reduce the overall memory and computation cost while achieving higher accuracy. Particularly, our SEWResNet-34 can achieve a 2.69\% accuracy gain and 4.16$\times$ lower bit budgets over the advanced baseline work on ImageNet. This work will be fully open-sourced.
Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods
2025-06-30
Liying Tao, Zonglin Yang, Delong Shang
The refractory period controls neuron spike firing rate, crucial for network stability and noise resistance. With advancements in spiking neural network (SNN) training methods, low-latency SNN applications have expanded. In low-latency SNNs, shorter simulation steps render traditional refractory mechanisms, which rely on empirical distributions or spike firing rates, less effective. However, omitting the refractory period amplifies the risk of neuron over-activation and reduces the system's robustness to noise. To address this challenge, we propose a historical dynamic refractory period (HDRP) model that leverages membrane potential derivative with historical refractory periods to estimate an initial refractory period and dynamically adjust its duration. Additionally, we propose a threshold-dependent refractory kernel to mitigate excessive neuron state accumulation. Our approach retains the binary characteristics of SNNs while enhancing both noise resistance and overall performance. Experimental results show that HDRP-SNN significantly reduces redundant spikes compared to traditional SNNs, and achieves state-of-the-art (SOTA) accuracy both on static datasets and neuromorphic datasets. Moreover, HDRP-SNN outperforms artificial neural networks (ANNs) and traditional SNNs in noise resistance, highlighting the crucial role of the HDRP mechanism in enhancing the performance of low-latency SNNs.
While deep spiking neural networks (SNNs) demonstrate superior performance, their deployment on resource-constrained neuromorphic hardware still remains challenging. Network pruning offers a viable solution by reducing both parameters and synaptic operations (SynOps) to facilitate the edge deployment of SNNs, among which search-based pruning methods search for the SNNs structure after pruning. However, existing search-based methods fail to directly use SynOps as the constraint because it will dynamically change in the searching process, resulting in the final searched network violating the expected SynOps target. In this paper, we introduce a novel SNN pruning framework called SPEAR, which leverages reinforcement learning (RL) technique to directly use SynOps as the searching constraint. To avoid the violation of SynOps requirements, we first propose a SynOps prediction mechanism called LRE to accurately predict the final SynOps after search. Observing SynOps cannot be explicitly calculated and added to constrain the action in RL, we propose a novel reward called TAR to stabilize the searching. Extensive experiments show that our SPEAR framework can effectively compress SNN under specific SynOps constraint.
We present a fabricated and experimentally characterized memory stack that unifies memristive and memcapacitive behavior. Exploiting this dual functionality, we design a circuit enabling simultaneous control of spatial and temporal dynamics in recurrent spiking neural networks (RSNNs). Hardware-aware simulations highlight its promise for efficient neuromorphic processing.
Canonical Quantization of a Memristive Leaky Integrate-and-Fire Neuron Circuit
2025-06-26
Dean Brand, Domenica Dibenedetto, Francesco Petruccione
We present a theoretical framework for a quantized memristive Leaky Integrate-and-Fire (LIF) neuron, uniting principles from neuromorphic engineering and open quantum systems. Starting from a classical memristive LIF circuit, we apply canonical quantization techniques to derive a quantum model grounded in circuit quantum electrodynamics. Numerical simulations demonstrate key dynamical features of the quantized memristor and LIF neuron in the weak-coupling and adiabatic regime, including memory effects and spiking behaviour. This work establishes a foundational model for quantum neuromorphic computing, offering a pathway towards biologically inspired quantum spiking neural networks and new paradigms in quantum machine learning.
Neuromorphic and quantum computing have recently emerged as promising paradigms for advancing artificial intelligence, each offering complementary strengths. Neuromorphic systems built on spiking neurons excel at processing time-series data efficiently through sparse, event-driven computation, consuming energy only upon input events. Quantum computing, on the other hand, leverages superposition and entanglement to explore feature spaces that are exponentially large in the number of qubits. Hybrid approaches combining these paradigms have begun to show potential, but existing quantum spiking models have important limitations. Notably, prior quantum spiking neuron implementations rely on classical memory mechanisms on single qubits, requiring repeated measurements to estimate firing probabilities, and they use conventional backpropagation on classical simulators for training. Here we propose a stochastic quantum spiking (SQS) neuron model that addresses these challenges. The SQS neuron uses multi-qubit quantum circuits to realize a spiking unit with internal quantum memory, enabling event-driven probabilistic spike generation in a single shot. Furthermore, we outline how networks of SQS neurons -- dubbed SQS neural networks (SQSNNs) -- can be trained via a hardware-friendly local learning rule, eliminating the need for global classical backpropagation. The proposed SQSNN model fuses the time-series efficiency of neuromorphic computing with the exponentially large inner state space of quantum computing, paving the way for quantum spiking neural networks that are modular, scalable, and trainable on quantum hardware.
Spiking Neural Networks for SAR Interferometric Phase Unwrapping: A Theoretical Framework for Energy-Efficient Processing
2025-06-25
Marc Bara
We present the first theoretical framework for applying spiking neural networks (SNNs) to synthetic aperture radar (SAR) interferometric phase unwrapping. Despite extensive research in both domains, our comprehensive literature review confirms that SNNs have never been applied to phase unwrapping, representing a significant gap in current methodologies. As Earth observation data volumes continue to grow exponentially (with missions like NISAR expected to generate 100PB in two years) energy-efficient processing becomes critical for sustainable data center operations. SNNs, with their event-driven computation model, offer potential energy savings of 30-100x compared to conventional approaches while maintaining comparable accuracy. We develop spike encoding schemes specifically designed for wrapped phase data, propose SNN architectures that leverage the spatial propagation nature of phase unwrapping, and provide theoretical analysis of computational complexity and convergence properties. Our framework demonstrates how the temporal dynamics inherent in SNNs can naturally model the spatial continuity constraints fundamental to phase unwrapping. This work opens a new research direction at the intersection of neuromorphic computing and SAR interferometry, offering a complementary approach to existing algorithms that could enable more sustainable large-scale InSAR processing.
We develop a unified platform to evaluate Ideal, Linear, and Non-linear $\text{Pr}_{0.7}\text{Ca}_{0.3}\text{MnO}_{3}$ memristor-based synapse models, each getting progressively closer to hardware realism, alongside four STDP learning rules in a two-layer SNN with LIF neurons and adaptive thresholds for five-class MNIST classification. On MNIST with small train set and large test set, our two-layer SNN with ideal, 25-state, and 12-state nonlinear memristor synapses achieves 92.73 %, 91.07 %, and 80 % accuracy, respectively, while converging faster and using fewer parameters than comparable ANN/CNN baselines.
Enhancing Generalization of Spiking Neural Networks Through Temporal Regularization
2025-06-24
Boxuan Zhang, Zhen Xu, Kuan Tao
Spiking Neural Networks (SNNs) have received widespread attention due to their event-driven and low-power characteristics, making them particularly effective for processing event-based neuromorphic data. Recent studies have shown that directly trained SNNs suffer from severe overfitting issues due to the limited scale of neuromorphic datasets and the gradient mismatching problem, which fundamentally constrain their generalization performance. In this paper, we propose a temporal regularization training (TRT) method by introducing a time-dependent regularization mechanism to enforce stronger constraints on early timesteps. We compare the performance of TRT with other state-of-the-art methods performance on datasets including CIFAR10/100, ImageNet100, DVS-CIFAR10, and N-Caltech101. To validate the effectiveness of TRT, we conducted ablation studies and analyses including loss landscape visualization and learning curve analysis, demonstrating that TRT can effectively mitigate overfitting and flatten the training loss landscape, thereby enhancing generalizability. Furthermore, we establish a theoretical interpretation of TRT's temporal regularization mechanism based on the results of Fisher information analysis. We analyze the temporal information dynamics inside SNNs by tracking Fisher information during the TRT training process, revealing the Temporal Information Concentration (TIC) phenomenon, where Fisher information progressively concentrates in early timesteps. The time-decaying regularization mechanism implemented in TRT effectively guides the network to learn robust features in early timesteps with rich information, thereby leading to significant improvements in model generalization. Code is available at https://anonymous.4open.science/r/TRT-7FBFUYT4E.
Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons
2025-06-24
Dengyu Wu, Jiechen Chen, H. Vincent Poor, Bipin Rajendran, Osvaldo Simeone
Neuromorphic computing offers an energy-efficient alternative to conventional deep learning accelerators for real-time time-series processing. However, many edge applications, such as wireless sensing and audio recognition, generate streaming signals with rich spectral features that are not effectively captured by conventional leaky integrate-and-fire (LIF) spiking neurons. This paper investigates a wireless split computing architecture that employs resonate-and-fire (RF) neurons with oscillatory dynamics to process time-domain signals directly, eliminating the need for costly spectral pre-processing. By resonating at tunable frequencies, RF neurons extract time-localized spectral features while maintaining low spiking activity. This temporal sparsity translates into significant savings in both computation and transmission energy. Assuming an OFDM-based analog wireless interface for spike transmission, we present a complete system design and evaluate its performance on audio classification and modulation classification tasks. Experimental results show that the proposed RF-SNN architecture achieves comparable accuracy to conventional LIF-SNNs and ANNs, while substantially reducing spike rates and total energy consumption during inference and communication.
Spiffy: Efficient Implementation of CoLaNET for Raspberry Pi
2025-06-23
Andrey Derzhavin, Denis Larionov
This paper presents a lightweight software-based approach for running spiking neural networks (SNNs) without relying on specialized neuromorphic hardware or frameworks. Instead, we implement a specific SNN architecture (CoLaNET) in Rust and optimize it for common computing platforms. As a case study, we demonstrate our implementation, called Spiffy, on a Raspberry Pi using the MNIST dataset. Spiffy achieves 92% accuracy with low latency - just 0.9 ms per training step and 0.45 ms per inference step. The code is open-source.
Edge computing scenarios necessitate the development of hardware-efficient online continual learning algorithms to be adaptive to dynamic environment. However, existing algorithms always suffer from high memory overhead and bias towards recently trained tasks. To tackle these issues, this paper proposes a novel online continual learning approach termed as SESLR, which incorporates a sleep enhanced latent replay scheme with spiking neural networks (SNNs). SESLR leverages SNNs' binary spike characteristics to store replay features in single bits, significantly reducing memory overhead. Furthermore, inspired by biological sleep-wake cycles, SESLR introduces a noise-enhanced sleep phase where the model exclusively trains on replay samples with controlled noise injection, effectively mitigating classification bias towards new classes. Extensive experiments on both conventional (MNIST, CIFAR10) and neuromorphic (NMNIST, CIFAR10-DVS) datasets demonstrate SESLR's effectiveness. On Split CIFAR10, SESLR achieves nearly 30% improvement in average accuracy with only one-third of the memory consumption compared to baseline methods. On Split CIFAR10-DVS, it improves accuracy by approximately 10% while reducing memory overhead by a factor of 32. These results validate SESLR as a promising solution for online continual learning in resource-constrained edge computing scenarios.
Continual Learning with Columnar Spiking Neural Networks
2025-06-20
Denis Larionov, Nikolay Bazenkov, Mikhail Kiselev
This study investigates columnar-organized spiking neural networks (SNNs) for continual learning and catastrophic forgetting. Using CoLaNET (Columnar Layered Network), we show that microcolumns adapt most efficiently to new tasks when they lack shared structure with prior learning. We demonstrate how CoLaNET hyperparameters govern the trade-off between retaining old knowledge (stability) and acquiring new information (plasticity). Our optimal configuration learns ten sequential MNIST tasks effectively, maintaining 92% accuracy on each. It shows low forgetting, with only 4% performance degradation on the first task after training on nine subsequent tasks.
Extending Spike-Timing Dependent Plasticity to Learning Synaptic Delays
2025-06-17
Marissa Dominijanni, Alexander Ororbia, Kenneth W. Regan
Synaptic delays play a crucial role in biological neuronal networks, where their modulation has been observed in mammalian learning processes. In the realm of neuromorphic computing, although spiking neural networks (SNNs) aim to emulate biology more closely than traditional artificial neural networks do, synaptic delays are rarely incorporated into their simulation. We introduce a novel learning rule for simultaneously learning synaptic connection strengths and delays, by extending spike-timing dependent plasticity (STDP), a Hebbian method commonly used for learning synaptic weights. We validate our approach by extending a widely-used SNN model for classification trained with unsupervised learning. Then we demonstrate the effectiveness of our new method by comparing it against another existing methods for co-learning synaptic weights and delays as well as against STDP without synaptic delays. Results demonstrate that our proposed method consistently achieves superior performance across a variety of test scenarios. Furthermore, our experimental results yield insight into the interplay between synaptic efficacy and delay.
A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks
2025-06-17
Maximilian Baronig, Yeganeh Bahariasl, Ozan Özdenizci, Robert Legenstein
Recurrent spiking neural networks (RSNNs) can be implemented very efficiently in neuromorphic systems. Nevertheless, training of these models with powerful gradient-based learning algorithms is mostly performed on standard digital hardware using Backpropagation through time (BPTT). However, BPTT has substantial limitations. It does not permit online training and its memory consumption scales linearly with the number of computation steps. In contrast, learning methods using forward propagation of gradients operate in an online manner with a memory consumption independent of the number of time steps. These methods enable SNNs to learn from continuous, infinite-length input sequences. Yet, slow execution speed on conventional hardware as well as inferior performance has hindered their widespread application. In this work, we introduce HYbrid PRopagation (HYPR) that combines the efficiency of parallelization with approximate online forward learning. Our algorithm yields high-throughput online learning through parallelization, paired with constant, i.e., sequence length independent, memory demands. HYPR enables parallelization of parameter update computation over the sub sequences for RSNNs consisting of almost arbitrary non-linear spiking neuron models. We apply HYPR to networks of spiking neurons with oscillatory subthreshold dynamics. We find that this type of neuron model is particularly well trainable by HYPR, resulting in an unprecedentedly low task performance gap between approximate forward gradient learning and BPTT.
GHz spiking neuromorphic photonic chip with in-situ training
2025-06-17
Jinlong Xiang, Xinyuan Fang, Jie Xiao, Youlve Chen, An He, Yaotian Zhao, Zhenyu Zhao, Yikai Su, Min Gu, Xuhan Guo
Neuromorphic photonic computing represents a paradigm shift for next-generation machine intelligence, yet critical gaps persist in emulating the brain's event-driven, asynchronous dynamics,a fundamental barrier to unlocking its full potential. Here, we report a milestone advancement of a photonic spiking neural network (PSNN) chip, the first to achieve full-stack brain-inspired computing on a complementary metal oxide semiconductor-compatible silicon platform. The PSNN features transformative innovations of gigahertz-scale nonlinear spiking dynamics,in situ learning capacity with supervised synaptic plasticity, and informative event representations with retina-inspired spike encoding, resolving the long-standing challenges in spatiotemporal data integration and energy-efficient dynamic processing. By leveraging its frame-free, event-driven working manner,the neuromorphic optoelectronic system achieves 80% accuracy on the KTH video recognition dataset while operating at ~100x faster processing speeds than conventional frame-based approaches. This work represents a leap for neuromorphic computing in a scalable photonic platform with low latency and high throughput, paving the way for advanced applications in real-time dynamic vision processing and adaptive decision-making, such as autonomous vehicles and robotic navigation.
NeuroCoreX: An Open-Source FPGA-Based Spiking Neural Network Emulator with On-Chip Learning
2025-06-17
Ashish Gautam, Prasanna Date, Shruti Kulkarni, Robert Patton, Thomas Potok
Spiking Neural Networks (SNNs) are computational models inspired by the structure and dynamics of biological neuronal networks. Their event-driven nature enables them to achieve high energy efficiency, particularly when deployed on neuromorphic hardware platforms. Unlike conventional Artificial Neural Networks (ANNs), which primarily rely on layered architectures, SNNs naturally support a wide range of connectivity patterns, from traditional layered structures to small-world graphs characterized by locally dense and globally sparse connections. In this work, we introduce NeuroCoreX, an FPGA-based emulator designed for the flexible co-design and testing of SNNs. NeuroCoreX supports all-to-all connectivity, providing the capability to implement diverse network topologies without architectural restrictions. It features a biologically motivated local learning mechanism based on Spike-Timing-Dependent Plasticity (STDP). The neuron model implemented within NeuroCoreX is the Leaky Integrate-and-Fire (LIF) model, with current-based synapses facilitating spike integration and transmission . A Universal Asynchronous Receiver-Transmitter (UART) interface is provided for programming and configuring the network parameters, including neuron, synapse, and learning rule settings. Users interact with the emulator through a simple Python-based interface, streamlining SNN deployment from model design to hardware execution. NeuroCoreX is released as an open-source framework, aiming to accelerate research and development in energy-efficient, biologically inspired computing.
Spiking Neural Networks for Low-Power Vibration-Based Predictive Maintenance
2025-06-16
Alexandru Vasilache, Sven Nitzsche, Christian Kneidl, Mikael Tekneyan, Moritz Neher, Juergen Becker
Advancements in Industrial Internet of Things (IIoT) sensors enable sophisticated Predictive Maintenance (PM) with high temporal resolution. For cost-efficient solutions, vibration-based condition monitoring is especially of interest. However, analyzing high-resolution vibration data via traditional cloud approaches incurs significant energy and communication costs, hindering battery-powered edge deployments. This necessitates shifting intelligence to the sensor edge. Due to their event-driven nature, Spiking Neural Networks (SNNs) offer a promising pathway toward energy-efficient on-device processing. This paper investigates a recurrent SNN for simultaneous regression (flow, pressure, pump speed) and multi-label classification (normal, overpressure, cavitation) for an industrial progressing cavity pump (PCP) using 3-axis vibration data. Furthermore, we provide energy consumption estimates comparing the SNN approach on conventional (x86, ARM) and neuromorphic (Loihi) hardware platforms. Results demonstrate high classification accuracy (>97%) with zero False Negative Rates for critical Overpressure and Cavitation faults. Smoothed regression outputs achieve Mean Relative Percentage Errors below 1% for flow and pump speed, approaching industrial sensor standards, although pressure prediction requires further refinement. Energy estimates indicate significant power savings, with the Loihi consumption (0.0032 J/inf) being up to 3 orders of magnitude less compared to the estimated x86 CPU (11.3 J/inf) and ARM CPU (1.18 J/inf) execution. Our findings underscore the potential of SNNs for multi-task PM directly on resource-constrained edge devices, enabling scalable and energy-efficient industrial monitoring solutions.
Training Neural Networks by Optimizing Neuron Positions
2025-06-16
Laura Erb, Tommaso Boccato, Alexandru Vasilache, Juergen Becker, Nicola Toschi
The high computational complexity and increasing parameter counts of deep neural networks pose significant challenges for deployment in resource-constrained environments, such as edge devices or real-time systems. To address this, we propose a parameter-efficient neural architecture where neurons are embedded in Euclidean space. During training, their positions are optimized and synaptic weights are determined as the inverse of the spatial distance between connected neurons. These distance-dependent wiring rules replace traditional learnable weight matrices and significantly reduce the number of parameters while introducing a biologically inspired inductive bias: connection strength decreases with spatial distance, reflecting the brain's embedding in three-dimensional space where connections tend to minimize wiring length. We validate this approach for both multi-layer perceptrons and spiking neural networks. Through a series of experiments, we demonstrate that these spatially embedded neural networks achieve a performance competitive with conventional architectures on the MNIST dataset. Additionally, the models maintain performance even at pruning rates exceeding 80% sparsity, outperforming traditional networks with the same number of parameters under similar conditions. Finally, the spatial embedding framework offers an intuitive visualization of the network structure.
Realtime-Capable Hybrid Spiking Neural Networks for Neural Decoding of Cortical Activity
2025-06-16
Jann Krausse, Alexandru Vasilache, Klaus Knobloch, Juergen Becker
Intra-cortical brain-machine interfaces (iBMIs) present a promising solution to restoring and decoding brain activity lost due to injury. However, patients with such neuroprosthetics suffer from permanent skull openings resulting from the devices' bulky wiring. This drives the development of wireless iBMIs, which demand low power consumption and small device footprint. Most recently, spiking neural networks (SNNs) have been researched as potential candidates for low-power neural decoding. In this work, we present the next step of utilizing SNNs for such tasks, building on the recently published results of the 2024 Grand Challenge on Neural Decoding Challenge for Motor Control of non-Human Primates. We optimize our model architecture to exceed the existing state of the art on the Primate Reaching dataset while maintaining similar resource demand through various compression techniques. We further focus on implementing a realtime-capable version of the model and discuss the implications of this architecture. With this, we advance one step towards latency-free decoding of cortical spike trains using neuromorphic technology, ultimately improving the lives of millions of paralyzed patients.
Probabilistic Modeling of Spiking Neural Networks with Contract-Based Verification
2025-06-16
Zhen Yao, Elisabetta De Maria, Robert De Simone
Spiking Neural Networks (SNN) are models for "realistic" neuronal computation, which makes them somehow different in scope from "ordinary" deep-learning models widely used in AI platforms nowadays. SNNs focus on timed latency (and possibly probability) of neuronal reactive activation/response, more than numerical computation of filters. So, an SNN model must provide modeling constructs for elementary neural bundles and then for synaptic connections to assemble them into compound data flow network patterns. These elements are to be parametric patterns, with latency and probability values instantiated on particular instances (while supposedly constant "at runtime"). Designers could also use different values to represent "tired" neurons, or ones impaired by external drugs, for instance. One important challenge in such modeling is to study how compound models could meet global reaction requirements (in stochastic timing challenges), provided similar provisions on individual neural bundles. A temporal language of logic to express such assume/guarantee contracts is thus needed. This may lead to formal verification on medium-sized models and testing observations on large ones. In the current article, we make preliminary progress at providing a simple model framework to express both elementary SNN neural bundles and their connecting constructs, which translates readily into both a model-checker and a simulator (both already existing and robust) to conduct experiments.
Energy-Efficient Digital Design: A Comparative Study of Event-Driven and Clock-Driven Spiking Neurons
2025-06-16
Filippo Marostica, Alessio Carpegna, Alessandro Savino, Stefano Di Carlo
This paper presents a comprehensive evaluation of Spiking Neural Network (SNN) neuron models for hardware acceleration by comparing event driven and clock-driven implementations. We begin our investigation in software, rapidly prototyping and testing various SNN models based on different variants of the Leaky Integrate and Fire (LIF) neuron across multiple datasets. This phase enables controlled performance assessment and informs design refinement. Our subsequent hardware phase, implemented on FPGA, validates the simulation findings and offers practical insights into design trade offs. In particular, we examine how variations in input stimuli influence key performance metrics such as latency, power consumption, energy efficiency, and resource utilization. These results yield valuable guidelines for constructing energy efficient, real time neuromorphic systems. Overall, our work bridges software simulation and hardware realization, advancing the development of next generation SNN accelerators.
2025-06-16
Dong Chen, Shuai Zheng, Yeyu Yan, Muhao Xu, Zhenfeng Zhu, Yao Zhao, Kunlun He
Recent research on deep graph learning has shifted from static to dynamic graphs, motivated by the evolving behaviors observed in complex real-world systems. However, the temporal extension in dynamic graphs poses significant data efficiency challenges, including increased data volume, high spatiotemporal redundancy, and reliance on costly dynamic graph neural networks (DGNNs). To alleviate the concerns, we pioneer the study of dynamic graph condensation (DGC), which aims to substantially reduce the scale of dynamic graphs for data-efficient DGNN training. Accordingly, we propose DyGC, a novel framework that condenses the real dynamic graph into a compact version while faithfully preserving the inherent spatiotemporal characteristics. Specifically, to endow synthetic graphs with realistic evolving structures, a novel spiking structure generation mechanism is introduced. It draws on the dynamic behavior of spiking neurons to model temporally-aware connectivity in dynamic graphs. Given the tightly coupled spatiotemporal dependencies, DyGC proposes a tailored distribution matching approach that first constructs a semantically rich state evolving field for dynamic graphs, and then performs fine-grained spatiotemporal state alignment to guide the optimization of the condensed graph. Experiments across multiple dynamic graph datasets and representative DGNN architectures demonstrate the effectiveness of DyGC. Notably, our method retains up to 96.2% DGNN performance with only 0.5% of the original graph size, and achieves up to 1846 times training speedup.
Neuromorphic Online Clustering and Its Application to Spike Sorting
2025-06-14
James E. Smith
Active dendrites are the basis for biologically plausible neural networks possessing many desirable features of the biological brain including flexibility, dynamic adaptability, and energy efficiency. A formulation for active dendrites using the notational language of conventional machine learning is put forward as an alternative to a spiking neuron formulation. Based on this formulation, neuromorphic dendrites are developed as basic neural building blocks capable of dynamic online clustering. Features and capabilities of neuromorphic dendrites are demonstrated via a benchmark drawn from experimental neuroscience: spike sorting. Spike sorting takes inputs from electrical probes implanted in neural tissue, detects voltage spikes (action potentials) emitted by neurons, and attempts to sort the spikes according to the neuron that emitted them. Many spike sorting methods form clusters based on the shapes of action potential waveforms, under the assumption that spikes emitted by a given neuron have similar shapes and will therefore map to the same cluster. Using a stream of synthetic spike shapes, the accuracy of the proposed dendrite is compared with the more compute-intensive, offline k-means clustering approach. Overall, the dendrite outperforms k-means and has the advantage of requiring only a single pass through the input stream, learning as it goes. The capabilities of the neuromorphic dendrite are demonstrated for a number of scenarios including dynamic changes in the input stream, differing neuron spike rates, and varying neuron counts.
Metric Framework of Coherent Activity Patterns Identification in Spiking Neuronal Networks
2025-06-14
Daniil Radushev, Olesia Dogonasheva, Boris Gutkin, Denis Zakharov
Partial synchronization plays a crucial role in the functioning of neuronal networks: selective, coordinated activation of neurons enables information processing that flexibly adapts to a changing computational context. Since the structure of coherent activity patterns reflects the network's current state, developing automated tools to identify them is a key challenge in neurodynamics. Existing methods for analyzing neuronal dynamics tend to focus on global characteristics of the network, such as its aggregated synchrony level. While this approach can distinguish between the network's main dynamical states, it cannot reveal the localization or properties of distinct coherent patterns. In this work, we propose a new perspective on neural dynamics analysis that enables the study of network coherence at the single-neuron scale. We interpret the network as a metric space of neurons and represent its instantaneous state as an activity function on that space. We identify specific coherent activity clusters as regions where the activity function exhibits spatial continuity. Each cluster's activity is further characterized using the analytical properties of the activity function within that region. This approach yields a concise yet detailed algorithmic profile of the network's activity patterns.
FeNN: A RISC-V vector processor for Spiking Neural Network acceleration
2025-06-13
Zainab Aizaz, James C. Knight, Thomas Nowotny
Spiking Neural Networks (SNNs) have the potential to drastically reduce the energy requirements of AI systems. However, mainstream accelerators like GPUs and TPUs are designed for the high arithmetic intensity of standard ANNs so are not well-suited to SNN simulation. FPGAs are well-suited to applications with low arithmetic intensity as they have high off-chip memory bandwidth and large amounts of on-chip memory. Here, we present a novel RISC-V-based soft vector processor (FeNN), tailored to simulating SNNs on FPGAs. Unlike most dedicated neuromorphic hardware, FeNN is fully programmable and designed to be integrated with applications running on standard computers from the edge to the cloud. We demonstrate that, by using stochastic rounding and saturation, FeNN can achieve high numerical precision with low hardware utilisation and that a single FeNN core can simulate an SNN classifier faster than both an embedded GPU and the Loihi neuromorphic system.
Mapping and Scheduling Spiking Neural Networks On Segmented Ladder Bus Architectures
2025-06-12
Phu Khanh Huynh, Francky Catthoor, Anup Das
Large-scale neuromorphic architectures consist of computing tiles that communicate spikes using a shared interconnect. The communication patterns in these systems are inherently sparse, asynchronous, and localized, as neural activity is characterized by temporal sparsity with occasional bursts of high traffic. These characteristics require optimized interconnects to handle high-activity bursts while consuming minimal power during idle periods. Among the proposed interconnect solutions, the dynamic segmented bus has gained attention due to its structural simplicity, scalability, and energy efficiency. Since the benefits of a dynamic segmented bus stem from its simplicity, it is essential to develop a streamlined control plane that can scale efficiently with the network. In this paper, we present a design methodology for a scenario-aware control plane tailored to a segmented ladder bus, with the aim of minimizing control overhead and optimizing energy and area utilization. We evaluated our approach using a combination of FPGA implementation and software simulation to assess scalability. The results demonstrated that our design process effectively reduces the control plane's area footprint compared to the data plane while maintaining scalability with network size.
Towards Practical Alzheimer's Disease Diagnosis: A Lightweight and Interpretable Spiking Neural Model
2025-06-11
Changwei Wu, Yifei Chen, Yuxin Du, Jinying Zong, Jie Dong, Mingxuan Liu, Yong Peng, Jin Fan, Feiwei Qin, Changmiao Wang
Early diagnosis of Alzheimer's Disease (AD), especially at the mild cognitive impairment (MCI) stage, is vital yet hindered by subjective assessments and the high cost of multimodal imaging modalities. Although deep learning methods offer automated alternatives, their energy inefficiency and computational demands limit real-world deployment, particularly in resource-constrained settings. As a brain-inspired paradigm, spiking neural networks (SNNs) are inherently well-suited for modeling the sparse, event-driven patterns of neural degeneration in AD, offering a promising foundation for interpretable and low-power medical diagnostics. However, existing SNNs often suffer from weak expressiveness and unstable training, which restrict their effectiveness in complex medical tasks. To address these limitations, we propose FasterSNN, a hybrid neural architecture that integrates biologically inspired LIF neurons with region-adaptive convolution and multi-scale spiking attention. This design enables sparse, efficient processing of 3D MRI while preserving diagnostic accuracy. Experiments on benchmark datasets demonstrate that FasterSNN achieves competitive performance with substantially improved efficiency and stability, supporting its potential for practical AD screening. Our source code is available at https://github.com/wuchangw/FasterSNN.
Energy Aware Development of Neuromorphic Implantables: From Metrics to Action
2025-06-11
Enrique Barba Roque, Luis Cruz
Spiking Neural Networks (SNNs) and neuromorphic computing present a promising alternative to traditional Artificial Neural Networks (ANNs) by significantly improving energy efficiency, particularly in edge and implantable devices. However, assessing the energy performance of SNN models remains a challenge due to the lack of standardized and actionable metrics and the difficulty of measuring energy consumption in experimental neuromorphic hardware. In this paper, we conduct a preliminary exploratory study of energy efficiency metrics proposed in the SNN benchmarking literature. We classify 13 commonly used metrics based on four key properties: Accessibility, Fidelity, Actionability, and Trend-Based analysis. Our findings indicate that while many existing metrics provide useful comparisons between architectures, they often lack practical insights for SNN developers. Notably, we identify a gap between accessible and high-fidelity metrics, limiting early-stage energy assessment. Additionally, we emphasize the lack of metrics that provide practitioners with actionable insights, making it difficult to guide energy-efficient SNN development. To address these challenges, we outline research directions for bridging accessibility and fidelity and finding new Actionable metrics for implantable neuromorphic devices, introducing more Trend-Based metrics, metrics that reflect changes in power requirements, battery-aware metrics, and improving energy-performance tradeoff assessments. The results from this paper pave the way for future research on enhancing energy metrics and their Actionability for SNNs.
STI-SNN: A 0.14 GOPS/W/PE Single-Timestep Inference FPGA-based SNN Accelerator with Algorithm and Hardware Co-Design
2025-06-10
Kainan Wang, Chengyi Yang, Chengting Yu, Yee Sin Ang, Bo Wang, Aili Wang
Brain-inspired Spiking Neural Networks (SNNs) have attracted attention for their event-driven characteristics and high energy efficiency. However, the temporal dependency and irregularity of spikes present significant challenges for hardware parallel processing and data reuse, leading to some existing accelerators falling short in processing latency and energy efficiency. To overcome these challenges, we introduce the STI-SNN accelerator, designed for resource-constrained applications with high energy efficiency, flexibility, and low latency. The accelerator is designed through algorithm and hardware co-design. Firstly, STI-SNN can perform inference in a single timestep. At the algorithm level, we introduce a temporal pruning approach based on the temporal efficient training (TET) loss function. This approach alleviates spike disappearance during timestep reduction, maintains inference accuracy, and expands TET's application. In hardware design, we analyze data access patterns and adopt the output stationary (OS) dataflow, eliminating the need to store membrane potentials and access memory operations. Furthermore, based on the OS dataflow, we propose a compressed and sorted representation of spikes, then cached in the line buffer to reduce the memory access cost and improve reuse efficiency. Secondly, STI-SNN supports different convolution methods. By adjusting the computation mode of processing elements (PEs) and parameterizing the computation array, STI-SNN can accommodate lightweight models based on depthwise separable convolutions (DSCs), further enhancing hardware flexibility. Lastly, STI-SNN also supports both inter-layer and intra-layer parallel processing. For inter-layer parallelism, we ...
Integration of Contrastive Predictive Coding and Spiking Neural Networks
2025-06-10
Emirhan Bilgiç, Neslihan Serap Şengör, Namık Berk Yalabık, Yavuz Selim İşler, Aykut Görkem Gelen, Rahmi Elibol
This study examines the integration of Contrastive Predictive Coding (CPC) with Spiking Neural Networks (SNN). While CPC learns the predictive structure of data to generate meaningful representations, SNN mimics the computational processes of biological neural systems over time. In this study, the goal is to develop a predictive coding model with greater biological plausibility by processing inputs and outputs in a spike-based system. The proposed model was tested on the MNIST dataset and achieved a high classification rate in distinguishing positive sequential samples from non-sequential negative samples. The study demonstrates that CPC can be effectively combined with SNN, showing that an SNN trained for classification tasks can also function as an encoding mechanism. Project codes and detailed results can be accessed on our GitHub page: https://github.com/vnd-ogrenme/ongorusel-kodlama/tree/main/CPC_SNN
Spiking Neural Models for Decision-Making Tasks with Learning
2025-06-10
Sophie Jaffard, Giulia Mezzadri, Patricia Reynaud-Bouret, Etienne Tanré
In cognition, response times and choices in decision-making tasks are commonly modeled using Drift Diffusion Models (DDMs), which describe the accumulation of evidence for a decision as a stochastic process, specifically a Brownian motion, with the drift rate reflecting the strength of the evidence. In the same vein, the Poisson counter model describes the accumulation of evidence as discrete events whose counts over time are modeled as Poisson processes, and has a spiking neurons interpretation as these processes are used to model neuronal activities. However, these models lack a learning mechanism and are limited to tasks where participants have prior knowledge of the categories. To bridge the gap between cognitive and biological models, we propose a biologically plausible Spiking Neural Network (SNN) model for decision-making that incorporates a learning mechanism and whose neurons activities are modeled by a multivariate Hawkes process. First, we show a coupling result between the DDM and the Poisson counter model, establishing that these two models provide similar categorizations and reaction times and that the DDM can be approximated by spiking Poisson neurons. To go further, we show that a particular DDM with correlated noise can be derived from a Hawkes network of spiking neurons governed by a local learning rule. In addition, we designed an online categorization task to evaluate the model predictions. This work provides a significant step toward integrating biologically relevant neural mechanisms into cognitive models, fostering a deeper understanding of the relationship between neural activity and behavior.
Spiking Neural Networks (SNNs) often suffer from high time complexity $O(T)$ due to the sequential processing of $T$ spikes, making training computationally expensive. In this paper, we propose a novel Fixed-point Parallel Training (FPT) method to accelerate SNN training without modifying the network architecture or introducing additional assumptions. FPT reduces the time complexity to $O(K)$, where $K$ is a small constant (usually $K=3$), by using a fixed-point iteration form of Leaky Integrate-and-Fire (LIF) neurons for all $T$ timesteps. We provide a theoretical convergence analysis of FPT and demonstrate that existing parallel spiking neurons can be viewed as special cases of our proposed method. Experimental results show that FPT effectively simulates the dynamics of original LIF neurons, significantly reducing computational time without sacrificing accuracy. This makes FPT a scalable and efficient solution for real-world applications, particularly for long-term tasks. Our code will be released at \href{https://github.com/WanjinVon/FPT}{\texttt{https://github.com/WanjinVon/FPT}}.
Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow
2025-06-09
Muhammad Ahmed Humais, Xiaoqian Huang, Hussain Sajwani, Sajid Javed, Yahya Zweiri
Event cameras unlock new frontiers that were previously unthinkable with standard frame-based cameras. One notable example is low-latency motion estimation (optical flow), which is critical for many real-time applications. In such applications, the computational efficiency of algorithms is paramount. Although recent deep learning paradigms such as CNN, RNN, or ViT have shown remarkable performance, they often lack the desired computational efficiency. Conversely, asynchronous event-based methods including SNNs and GNNs are computationally efficient; however, these approaches fail to capture sufficient spatio-temporal information, a powerful feature required to achieve better performance for optical flow estimation. In this work, we introduce Spatio-Temporal State Space Model (STSSM) module along with a novel network architecture to develop an extremely efficient solution with competitive performance. Our STSSM module leverages state-space models to effectively capture spatio-temporal correlations in event data, offering higher performance with lower complexity compared to ViT, CNN-based architectures in similar settings. Our model achieves 4.5x faster inference and 8x lower computations compared to TMA and 2x lower computations compared to EV-FlowNet with competitive performance on the DSEC benchmark. Our code will be available at https://github.com/AhmedHumais/E-STMFlow
Low energy consumption for 3D object detection is an important research area because of the increasing energy consumption with their wide application in fields such as autonomous driving. The spiking neural networks (SNNs) with low-power consumption characteristics can provide a novel solution for this research. Therefore, we apply SNNs to monocular 3D object detection and propose the SpikeSMOKE architecture in this paper, which is a new attempt for low-power monocular 3D object detection. As we all know, discrete signals of SNNs will generate information loss and limit their feature expression ability compared with the artificial neural networks (ANNs).In order to address this issue, inspired by the filtering mechanism of biological neuronal synapses, we propose a cross-scale gated coding mechanism(CSGC), which can enhance feature representation by combining cross-scale fusion of attentional methods and gated filtering mechanisms.In addition, to reduce the computation and increase the speed of training, we present a novel light-weight residual block that can maintain spiking computing paradigm and the highest possible detection performance. Compared to the baseline SpikeSMOKE under the 3D Object Detection, the proposed SpikeSMOKE with CSGC can achieve 11.78 (+2.82, Easy), 10.69 (+3.2, Moderate), and 10.48 (+3.17, Hard) on the KITTI autonomous driving dataset by AP|R11 at 0.7 IoU threshold, respectively. It is important to note that the results of SpikeSMOKE can significantly reduce energy consumption compared to the results on SMOKE. For example,the energy consumption can be reduced by 72.2% on the hard category, while the detection performance is reduced by only 4%. SpikeSMOKE-L (lightweight) can further reduce the amount of parameters by 3 times and computation by 10 times compared to SMOKE.
The Spiking Neural Network (SNN), a biologically inspired neural network infrastructure, has garnered significant attention recently. SNNs utilize binary spike activations for efficient information transmission, replacing multiplications with additions, thereby enhancing energy efficiency. However, binary spike activation maps often fail to capture sufficient data information, resulting in reduced accuracy. To address this challenge, we advocate reversing the bit of the weight and activation for SNNs, called \textbf{ReverB-SNN}, inspired by recent findings that highlight greater accuracy degradation from quantizing activations compared to weights. Specifically, our method employs real-valued spike activations alongside binary weights in SNNs. This preserves the event-driven and multiplication-free advantages of standard SNNs while enhancing the information capacity of activations. Additionally, we introduce a trainable factor within binary weights to adaptively learn suitable weight amplitudes during training, thereby increasing network capacity. To maintain efficiency akin to vanilla \textbf{ReverB-SNN}, our trainable binary weight SNNs are converted back to standard form using a re-parameterization technique during inference. Extensive experiments across various network architectures and datasets, both static and dynamic, demonstrate that our approach consistently outperforms state-of-the-art methods.
A Practical Guide to Tuning Spiking Neuronal Dynamics
2025-06-09
William Gebhardt, Alexander G. Ororbia, Nathan McDonald, Clare Thiem, Jack Lombardi
In this work, we examine fundamental elements of spiking neural networks (SNNs) as well as how to tune them. Concretely, we focus on two different foundational neuronal units utilized in SNNs -- the leaky integrate-and-fire (LIF) and the resonate-and-fire (RAF) neuron. We explore key equations and how hyperparameter values affect behavior. Beyond hyperparameters, we discuss other important design elements of SNNs -- the choice of input encoding and the setup for excitatory-inhibitory populations -- and how these impact LIF and RAF dynamics.
Transient Dynamics in Lattices of Differentiating Ring Oscillators
2025-06-08
Peter DelMastro, Arjun Karuvally, Hananel Hazan, Hava Siegelmann, Edward Rietman
Recurrent neural networks (RNNs) are machine learning models widely used for learning temporal relationships. Current state-of-the-art RNNs use integrating or spiking neurons -- two classes of computing units whose outputs depend directly on their internal states -- and accordingly there is a wealth of literature characterizing the behavior of large networks built from these neurons. On the other hand, past research on differentiating neurons, whose outputs are computed from the derivatives of their internal states, remains limited to small hand-designed networks with fewer than one-hundred neurons. Here we show via numerical simulation that large lattices of differentiating neuron rings exhibit local neural synchronization behavior found in the Kuramoto model of interacting oscillators. We begin by characterizing the periodic orbits of uncoupled rings, herein called ring oscillators. We then show the emergence of local correlations between oscillators that grow over time when these rings are coupled together into lattices. As the correlation length grows, transient dynamics arise in which large regions of the lattice settle to the same periodic orbit, and thin domain boundaries separate adjacent, out-of-phase regions. The steady-state scale of these correlated regions depends on how the neurons are shared between adjacent rings, which suggests that lattices of differentiating ring oscillator might be tuned to be used as reservoir computers. Coupled with their simple circuit design and potential for low-power consumption, differentiating neural nets therefore represent a promising substrate for neuromorphic computing that will enable low-power AI applications.
RBA-FE: A Robust Brain-Inspired Audio Feature Extractor for Depression Diagnosis
2025-06-08
Yu-Xuan Wu, Ziyan Huang, Bin Hu, Zhi-Hong Guan
This article proposes a robust brain-inspired audio feature extractor (RBA-FE) model for depression diagnosis, using an improved hierarchical network architecture. Most deep learning models achieve state-of-the-art performance for image-based diagnostic tasks, ignoring the counterpart audio features. In order to tailor the noise challenge, RBA-FE leverages six acoustic features extracted from the raw audio, capturing both spatial characteristics and temporal dependencies. This hybrid attribute helps alleviate the precision limitation in audio feature extraction within other learning models like deep residual shrinkage networks. To deal with the noise issues, our model incorporates an improved spiking neuron model, called adaptive rate smooth leaky integrate-and-fire (ARSLIF). The ARSLIF model emulates the mechanism of ``retuning of cellular signal selectivity" in the brain attention systems, which enhances the model robustness against environmental noises in audio data. Experimental results demonstrate that RBA-FE achieves state-of-the-art accuracy on the MODMA dataset, respectively with 0.8750, 0.8974, 0.8750 and 0.8750 in precision, accuracy, recall and F1 score. Extensive experiments on the AVEC2014 and DAIC-WOZ datasets both show enhancements in noise robustness. It is further indicated by comparison that the ARSLIF neuron model suggest the abnormal firing pattern within the feature extraction on depressive audio data, offering brain-inspired interpretability.
Less is More: some Computational Principles based on Parcimony, and Limitations of Natural Intelligence
2025-06-08
Laura Cohen, Xavier Hinaut, Lilyana Petrova, Alexandre Pitti, Syd Reynal, Ichiro Tsuda
Natural intelligence (NI) consistently achieves more with less. Infants learn language, develop abstract concepts, and acquire sensorimotor skills from sparse data, all within tight neural and energy limits. In contrast, today's AI relies on virtually unlimited computational power, energy, and data to reach high performance. This paper argues that constraints in NI are paradoxically catalysts for efficiency, adaptability, and creativity. We first show how limited neural bandwidth promotes concise codes that still capture complex patterns. Spiking neurons, hierarchical structures, and symbolic-like representations emerge naturally from bandwidth constraints, enabling robust generalization. Next, we discuss chaotic itinerancy, illustrating how the brain transits among transient attractors to flexibly retrieve memories and manage uncertainty. We then highlight reservoir computing, where random projections facilitate rapid generalization from small datasets. Drawing on developmental perspectives, we emphasize how intrinsic motivation, along with responsive social environments, drives infant language learning and discovery of meaning. Such active, embodied processes are largely absent in current AI. Finally, we suggest that adopting 'less is more' principles -- energy constraints, parsimonious architectures, and real-world interaction -- can foster the emergence of more efficient, interpretable, and biologically grounded artificial systems.
Bio-Inspired Classification: Combining Information Theory and Spiking Neural Networks -- Influence of the Learning Rules
2025-06-07
Zofia Rudnicka, Janusz Szczepanski, Agnieszka Pregowska
Training of Spiking Neural Networks (SNN) is challenging due to their unique properties, including temporal dynamics, non-differentiability of spike events, and sparse event-driven activations. In this paper, we widely consider the influence of the type of chosen learning algorithm, including bioinspired learning rules on the accuracy of classification. We proposed a bioinspired classifier based on the combination of SNN and Lempel-Ziv complexity (LZC). This approach synergizes the strengths of SNNs in temporal precision and biological realism with LZC's structural complexity analysis, facilitating efficient and interpretable classification of spatiotemporal neural data. It turned out that the classic backpropagation algorithm achieves excellent classification accuracy, but at extremely high computational cost, which makes it impractical for real-time applications. Biologically inspired learning algorithms such as tempotron and Spikprop provide increased computational efficiency while maintaining competitive classification performance, making them suitable for time-sensitive tasks. The results obtained indicate that the selection of the most appropriate learning algorithm depends on the trade-off between classification accuracy and computational cost as well as application constraints.
Spiking Neural Networks (SNNs) event-driven nature enables efficient encoding of spatial and temporal features, making them suitable for dynamic time-dependent data processing. Despite their biological relevance, SNNs have seen limited application in medical image recognition due to difficulties in matching the performance of conventional deep learning models. To address this, we propose a novel breast cancer classification approach that combines SNNs with Lempel-Ziv Complexity (LZC) a computationally efficient measure of sequence complexity. LZC enhances the interpretability and accuracy of spike-based models by capturing structural patterns in neural activity. Our study explores both biophysical Leaky Integrate-and-Fire (LIF) and probabilistic Levy-Baxter (LB) neuron models under supervised, unsupervised, and hybrid learning regimes. Experiments were conducted on the Breast Cancer Wisconsin dataset using numerical features derived from medical imaging. LB-based models consistently exceeded 90.00% accuracy, while LIF-based models reached over 85.00%. The highest accuracy of 98.25% was achieved using an ANN-to-SNN conversion method applied to both neuron models comparable to traditional deep learning with back-propagation, but at up to 100 times lower computational cost. This hybrid approach merges deep learning performance with the efficiency and plausibility of SNNs, yielding top results at lower computational cost. We hypothesize that the synergy between temporal-coding, spike-sparsity, and LZC-driven complexity analysis enables more-efficient feature extraction. Our findings demonstrate that SNNs combined with LZC offer promising, biologically plausible alternative to conventional neural networks in medical diagnostics, particularly for resource-constrained or real-time systems.
Integer Binary-Range Alignment Neuron for Spiking Neural Networks
2025-06-06
Binghao Ye, Wenjuan Li, Dong Wang, Man Yao, Bing Li, Weiming Hu, Dong Liang, Kun Shang
Spiking Neural Networks (SNNs) are noted for their brain-like computation and energy efficiency, but their performance lags behind Artificial Neural Networks (ANNs) in tasks like image classification and object detection due to the limited representational capacity. To address this, we propose a novel spiking neuron, Integer Binary-Range Alignment Leaky Integrate-and-Fire to exponentially expand the information expression capacity of spiking neurons with only a slight energy increase. This is achieved through Integer Binary Leaky Integrate-and-Fire and range alignment strategy. The Integer Binary Leaky Integrate-and-Fire allows integer value activation during training and maintains spike-driven dynamics with binary conversion expands virtual timesteps during inference. The range alignment strategy is designed to solve the spike activation limitation problem where neurons fail to activate high integer values. Experiments show our method outperforms previous SNNs, achieving 74.19% accuracy on ImageNet and 66.2% mAP@50 and 49.1% mAP@50:95 on COCO, surpassing previous bests with the same architecture by +3.45% and +1.6% and +1.8%, respectively. Notably, our SNNs match or exceed ANNs' performance with the same architecture, and the energy efficiency is improved by 6.3${\times}$.
Spike-TBR: a Noise Resilient Neuromorphic Event Representation
2025-06-05
Gabriele Magrini, Federico Becattini, Luca Cultrera, Lorenzo Berlincioni, Pietro Pala, Alberto Del Bimbo
Event cameras offer significant advantages over traditional frame-based sensors, including higher temporal resolution, lower latency and dynamic range. However, efficiently converting event streams into formats compatible with standard computer vision pipelines remains a challenging problem, particularly in the presence of noise. In this paper, we propose Spike-TBR, a novel event-based encoding strategy based on Temporal Binary Representation (TBR), addressing its vulnerability to noise by integrating spiking neurons. Spike-TBR combines the frame-based advantages of TBR with the noise-filtering capabilities of spiking neural networks, creating a more robust representation of event streams. We evaluate four variants of Spike-TBR, each using different spiking neurons, across multiple datasets, demonstrating superior performance in noise-affected scenarios while improving the results on clean data. Our method bridges the gap between spike-based and frame-based processing, offering a simple noise-resilient solution for event-driven vision applications.
EECD-Net: Energy-Efficient Crack Detection with Spiking Neural Networks and Gated Attention
2025-06-05
Shuo Zhang
Crack detection on road surfaces is a critical measurement technology in the instrumentation domain, essential for ensuring infrastructure safety and transportation reliability. However, due to limited energy and low-resolution imaging, smart terminal devices struggle to maintain real-time monitoring performance. To overcome these challenges, this paper proposes a multi-stage detection approach for road crack detection, EECD-Net, to enhance accuracy and energy efficiency of instrumentation. Specifically, the sophisticated Super-Resolution Convolutional Neural Network (SRCNN) is employed to address the inherent challenges of low-quality images, which effectively enhance image resolution while preserving critical structural details. Meanwhile, a Spike Convolution Unit (SCU) with Continuous Integrate-and-Fire (CIF) neurons is proposed to convert these images into sparse pulse sequences, significantly reducing power consumption. Additionally, a Gated Attention Transformer (GAT) module is designed to strategically fuse multi-scale feature representations through adaptive attention mechanisms, effectively capturing both long-range dependencies and intricate local crack patterns, and significantly enhancing detection robustness across varying crack morphologies. The experiments on the CrackVision12K benchmark demonstrate that EECD-Net achieves a remarkable 98.6\% detection accuracy, surpassing state-of-the-art counterparts such as Hybrid-Segmentor by a significant 1.5\%. Notably, the EECD-Net maintains exceptional energy efficiency, consuming merely 5.6 mJ, which is a substantial 33\% reduction compared to baseline implementations. This work pioneers a transformative approach in instrumentation-based crack detection, offering a scalable, low-power solution for real-time, large-scale infrastructure monitoring in resource-constrained environments.
Optimal Spiking Brain Compression: Improving One-Shot Post-Training Pruning and Quantization for Spiking Neural Networks
2025-06-04
Lianfeng Shi, Ao Li, Benjamin Ward-Cherrier
Spiking Neural Networks (SNNs) have emerged as a new generation of energy-efficient neural networks suitable for implementation on neuromorphic hardware. As neuromorphic hardware has limited memory and computing resources, weight pruning and quantization have recently been explored to improve SNNs' efficiency. State-of-the-art SNN pruning/quantization methods employ multiple compression and training iterations, increasing the cost for pre-trained or very large SNNs. In this paper, we propose a new one-shot post-training pruning/quantization framework, Optimal Spiking Brain Compression (OSBC), that adapts the Optimal Brain Compression (OBC) method of [Frantar, Singh, and Alistarh, 2023] for SNNs. Rather than minimizing the loss on neuron input current as OBC does, OSBC achieves more efficient and accurate SNN compression in one pass by minimizing the loss on spiking neuron membrane potential with a small sample dataset. Our experiments on neuromorphic datasets (N-MNIST, CIFAR10-DVS, DVS128-Gesture) demonstrate that OSBC can achieve 97% sparsity through pruning with 1.41%, 10.20%, and 1.74% accuracy loss, or 4-bit symmetric quantization with 0.17%, 1.54%, and 7.71% accuracy loss, respectively. Code will be available on GitHub.
Learning from Noise: Enhancing DNNs for Event-Based Vision through Controlled Noise Injection
2025-06-04
Marcin Kowalczyk, Kamil Jeziorek, Tomasz Kryjak
Event-based sensors offer significant advantages over traditional frame-based cameras, especially in scenarios involving rapid motion or challenging lighting conditions. However, event data frequently suffers from considerable noise, negatively impacting the performance and robustness of deep learning models. Traditionally, this problem has been addressed by applying filtering algorithms to the event stream, but this may also remove some of relevant data. In this paper, we propose a novel noise-injection training methodology designed to enhance the neural networks robustness against varying levels of event noise. Our approach introduces controlled noise directly into the training data, enabling models to learn noise-resilient representations. We have conducted extensive evaluations of the proposed method using multiple benchmark datasets (N-Caltech101, N-Cars, and Mini N-ImageNet) and various network architectures, including Convolutional Neural Networks, Vision Transformers, Spiking Neural Networks, and Graph Convolutional Networks. Experimental results show that our noise-injection training strategy achieves stable performance over a range of noise intensities, consistently outperforms event-filtering techniques, and achieves the highest average classification accuracy, making it a viable alternative to traditional event-data filtering methods in an object classification system. Code: https://github.com/vision-agh/DVS_Filtering
Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks
2025-06-04
Maxime Fabre, Lyubov Dudchenko, Emre Neftci
Multi-state spiking neurons such as the adaptive leaky integrate-and-fire (AdLIF) neuron offer compelling alternatives to conventional deep learning models thanks to their sparse binary activations, second-order nonlinear recurrent dynamics, and efficient hardware realizations. However, such internal dynamics can cause instabilities during inference and training, often limiting performance and scalability. Meanwhile, state space models (SSMs) excel in long sequence processing using linear state-intrinsic recurrence resembling spiking neurons' subthreshold regime. Here, we establish a mathematical bridge between SSMs and second-order spiking neuron models. Based on structure and parametrization strategies of diagonal SSMs, we propose two novel spiking neuron models. The first extends the AdLIF neuron through timestep training and logarithmic reparametrization to facilitate training and improve final performance. The second additionally brings initialization and structure from complex-state SSMs, broadening the dynamical regime to oscillatory dynamics. Together, our two models achieve beyond or near state-of-the-art (SOTA) performances for reset-based spiking neuron models across both event-based and raw audio speech recognition datasets. We achieve this with a favorable number of parameters and required dynamic memory while maintaining high activity sparsity. Our models demonstrate enhanced scalability in network size and strike a favorable balance between performance and efficiency with respect to SSM models.
2025-06-03
Stefano Fiorini, Hakan Aktas, Iulia Duta, Stefano Coniglio, Pietro Morerio, Alessio Del Bue, Pietro Liò
Sheaf Neural Networks (SNNs) represent a powerful generalization of Graph Neural Networks (GNNs) that significantly improve our ability to model complex relational data. While directionality has been shown to substantially boost performance in graph learning tasks and is key to many real-world applications, existing SNNs fall short in representing it. To address this limitation, we introduce the Directed Cellular Sheaf, a special type of cellular sheaf designed to explicitly account for edge orientation. Building on this structure, we define a new sheaf Laplacian, the Directed Sheaf Laplacian, which captures both the graph's topology and its directional information. This operator serves as the backbone of the Directed Sheaf Neural Network (DSNN), the first SNN model to embed a directional bias into its architecture. Extensive experiments on nine real-world benchmarks show that DSNN consistently outperforms baseline methods.
2025-06-03
Amr Nabil, T. Nandha Kumar, Haider Abbas F. Almurib
Spiking Neural Networks have earned increased recognition in recent years owing to their biological plausibility and event-driven computation. Spiking neurons are the fundamental building components of Spiking Neural Networks. Those neurons act as computational units that determine the decision to fire an action potential. This work presents a methodology to implement biologically plausible yet scalable spiking neurons in hardware. We show that it is more efficient to design neurons that mimic the $I_{Na,p}+I_{K}$ model rather than the more complicated Hodgkin-Huxley model. We demonstrate our methodology by presenting eleven novel minimal spiking neuron circuits in Parts I and II of the paper. We categorize the neuron circuits presented into two types: Resonators and Integrators. We discuss the methodology employed in designing neurons of the resonator type in Part I, while we discuss neurons of the integrator type in Part II. In part I, we postulate that Sodium channels exhibit type-N negative differential resistance. Consequently, we present three novel minimal neuron circuits that use type-N negative differential resistance circuits or devices as the Sodium channel. Nevertheless, the aim of the paper is not to present a set of minimal neuron circuits but rather the methodology utilized to construct those circuits.
SENMAP: Multi-objective data-flow mapping and synthesis for hybrid scalable neuromorphic systems
2025-06-03
Prithvish V Nembhani, Oliver Rhodes, Guangzhi Tang, Alexandra F Dobrita, Yingfu Xu, Kanishkan Vadivel, Kevin Shidqi, Paul Detterer, Mario Konijnenburg, Gert-Jan van Schaik, Manolis Sifalakis, Zaid Al-Ars, Amirreza Yousefzadeh
This paper introduces SENMap, a mapping and synthesis tool for scalable, energy-efficient neuromorphic computing architecture frameworks. SENECA is a flexible architectural design optimized for executing edge AI SNN/ANN inference applications efficiently. To speed up the silicon tape-out and chip design for SENECA, an accurate emulator, SENSIM, was designed. While SENSIM supports direct mapping of SNNs on neuromorphic architectures, as the SNN and ANNs grow in size, achieving optimal mapping for objectives like energy, throughput, area, and accuracy becomes challenging. This paper introduces SENMap, flexible mapping software for efficiently mapping large SNN and ANN applications onto adaptable architectures. SENMap considers architectural, pretrained SNN and ANN realistic examples, and event rate-based parameters and is open-sourced along with SENSIM to aid flexible neuromorphic chip design before fabrication. Experimental results show SENMap enables 40 percent energy improvements for a baseline SENSIM operating in timestep asynchronous mode of operation. SENMap is designed in such a way that it facilitates mapping large spiking neural networks for future modifications as well.
We propose a new ternary spiking neuron model to improve the representation capacity of binary spiking neurons in deep Q-learning. Although a ternary neuron model has recently been introduced to overcome the limited representation capacity offered by the binary spiking neurons, we show that its performance is worse than that of binary models in deep Q-learning tasks. We hypothesize gradient estimation bias during the training process as the underlying potential cause through mathematical and empirical analysis. We propose a novel ternary spiking neuron model to mitigate this issue by reducing the estimation bias. We use the proposed ternary spiking neuron as the fundamental computing unit in a deep spiking Q-learning network (DSQN) and evaluate the network's performance in seven Atari games from the Gym environment. Results show that the proposed ternary spiking neuron mitigates the drastic performance degradation of ternary neurons in Q-learning tasks and improves the network performance compared to the existing binary neurons, making DSQN a more practical solution for on-board autonomous decision-making tasks.
Spiking neural networks (SNNs) have emerged as a class of bio -inspired networks that leverage sparse, event-driven signaling to achieve low-power computation while inherently modeling temporal dynamics. Such characteristics align closely with the demands of ubiquitous computing systems, which often operate on resource-constrained devices while continuously monitoring and processing time-series sensor data. Despite their unique and promising features, SNNs have received limited attention and remain underexplored (or at least, under-adopted) within the ubiquitous computing community. To address this gap, this paper first introduces the core components of SNNs, both in terms of models and training mechanisms. It then presents a systematic survey of 76 SNN-based studies focused on time-series data analysis, categorizing them into six key application domains. For each domain, we summarize relevant works and subsequent advancements, distill core insights, and highlight key takeaways for researchers and practitioners. To facilitate hands-on experimentation, we also provide a comprehensive review of current software frameworks and neuromorphic hardware platforms, detailing their capabilities and specifications, and then offering tailored recommendations for selecting development tools based on specific application needs. Finally, we identify prevailing challenges within each application domain and propose future research directions that need be explored in ubiquitous community. Our survey highlights the transformative potential of SNNs in enabling energy-efficient ubiquitous sensing across diverse application domains, while also serving as an essential introduction for researchers looking to enter this emerging field.
Engram Memory Encoding and Retrieval: A Neurocomputational Perspective
2025-06-02
Daniel Szelogowski
Despite substantial research into the biological basis of memory, the precise mechanisms by which experiences are encoded, stored, and retrieved in the brain remain incompletely understood. A growing body of evidence supports the engram theory, which posits that sparse populations of neurons undergo lasting physical and biochemical changes to support long-term memory. Yet, a comprehensive computational framework that integrates biological findings with mechanistic models remains elusive. This work synthesizes insights from cellular neuroscience and computational modeling to address key challenges in engram research: how engram neurons are identified and manipulated; how synaptic plasticity mechanisms contribute to stable memory traces; and how sparsity promotes efficient, interference-resistant representations. Relevant computational approaches -- such as sparse regularization, engram gating, and biologically inspired architectures like Sparse Distributed Memory and spiking neural networks -- are also examined. Together, these findings suggest that memory efficiency, capacity, and stability emerge from the interaction of plasticity and sparsity constraints. By integrating neurobiological and computational perspectives, this paper provides a comprehensive theoretical foundation for engram research and proposes a roadmap for future inquiry into the mechanisms underlying memory, with implications for the diagnosis and treatment of memory-related disorders.
Spatio-Temporal Decoupled Learning for Spiking Neural Networks
2025-06-01
Chenxiang Ma, Xinyi Chen, Kay Chen Tan, Jibin Wu
Spiking neural networks (SNNs) have gained significant attention for their potential to enable energy-efficient artificial intelligence. However, effective and efficient training of SNNs remains an unresolved challenge. While backpropagation through time (BPTT) achieves high accuracy, it incurs substantial memory overhead. In contrast, biologically plausible local learning methods are more memory-efficient but struggle to match the accuracy of BPTT. To bridge this gap, we propose spatio-temporal decouple learning (STDL), a novel training framework that decouples the spatial and temporal dependencies to achieve both high accuracy and training efficiency for SNNs. Specifically, to achieve spatial decoupling, STDL partitions the network into smaller subnetworks, each of which is trained independently using an auxiliary network. To address the decreased synergy among subnetworks resulting from spatial decoupling, STDL constructs each subnetwork's auxiliary network by selecting the largest subset of layers from its subsequent network layers under a memory constraint. Furthermore, STDL decouples dependencies across time steps to enable efficient online learning. Extensive evaluations on seven static and event-based vision datasets demonstrate that STDL consistently outperforms local learning methods and achieves comparable accuracy to the BPTT method with considerably reduced GPU memory cost. Notably, STDL achieves 4x reduced GPU memory than BPTT on the ImageNet dataset. Therefore, this work opens up a promising avenue for memory-efficient SNN training. Code is available at https://github.com/ChenxiangMA/STDL.
S3CE-Net: Spike-guided Spatiotemporal Semantic Coupling and Expansion Network for Long Sequence Event Re-Identification
2025-05-30
Xianheng Ma, Hongchen Tan, Xiuping Liu, Yi Zhang, Huasheng Wang, Jiang Liu, Ying Chen, Hantao Liu
In this paper, we leverage the advantages of event cameras to resist harsh lighting conditions, reduce background interference, achieve high time resolution, and protect facial information to study the long-sequence event-based person re-identification (Re-ID) task. To this end, we propose a simple and efficient long-sequence event Re-ID model, namely the Spike-guided Spatiotemporal Semantic Coupling and Expansion Network (S3CE-Net). To better handle asynchronous event data, we build S3CE-Net based on spiking neural networks (SNNs). The S3CE-Net incorporates the Spike-guided Spatial-temporal Attention Mechanism (SSAM) and the Spatiotemporal Feature Sampling Strategy (STFS). The SSAM is designed to carry out semantic interaction and association in both spatial and temporal dimensions, leveraging the capabilities of SNNs. The STFS involves sampling spatial feature subsequences and temporal feature subsequences from the spatiotemporal dimensions, driving the Re-ID model to perceive broader and more robust effective semantics. Notably, the STFS introduces no additional parameters and is only utilized during the training stage. Therefore, S3CE-Net is a low-parameter and high-efficiency model for long-sequence event-based person Re-ID. Extensive experiments have verified that our S3CE-Net achieves outstanding performance on many mainstream long-sequence event-based person Re-ID datasets. Code is available at:https://github.com/Mhsunshine/SC3E_Net.
Spiking Neural Networks (SNNs) offer low-latency and energy-efficient decision making through neuromorphic hardware, making them compelling for Reinforcement Learning (RL) in resource-constrained edge devices. Recent studies in this field directly replace Artificial Neural Networks (ANNs) by SNNs in existing RL frameworks, overlooking whether the RL algorithm is suitable for SNNs. However, most RL algorithms in continuous control are designed tailored to ANNs, including the target network soft updates mechanism, which conflict with the discrete, non-differentiable dynamics of SNN spikes. We identify that this mismatch destabilizes SNN training in continuous control tasks. To bridge this gap between discrete SNN and continuous control, we propose a novel proxy target framework. The continuous and differentiable dynamics of the proxy target enable smooth updates, bypassing the incompatibility of SNN spikes, stabilizing the RL algorithms. Since the proxy network operates only during training, the SNN retains its energy efficiency during deployment without inference overhead. Extensive experiments on continuous control benchmarks demonstrate that compared to vanilla SNNs, the proxy target framework enables SNNs to achieve up to 32% higher performance across different spiking neurons. Notably, we are the first to surpass ANN performance in continuous control with simple Leaky-Integrate-and-Fire (LIF) neurons. This work motivates a new class of SNN-friendly RL algorithms tailored to SNN's characteristics, paving the way for neuromorphic agents that combine high performance with low power consumption.
With the growing demand for intelligent computing, neuromorphic computing, a paradigm that mimics the structure and functionality of the human brain, offers a promising approach to developing new high-efficiency intelligent computing systems. Spiking Neural Networks (SNNs), the foundation of neuromorphic computing, have garnered significant attention due to their unique potential in energy efficiency and biomimetic neural processing. However, current hardware development for efficient SNN training lags significantly. No systematic energy evaluation methods exist for SNN training tasks. Therefore, this paper proposes an Energy-Oriented Computing Architecture Simulator (EOCAS) for SNN training to identify the optimal architecture. EOCAS investigates the high sparsity of spike signals, unique hardware design representations, energy assessment, and computation patterns to support energy optimization in various architectures. Under the guidance of EOCAS, we implement the power-aimed optimal hardware architecture through Verilog HDL and achieve low energy consumption using Synopsys Design Compiler with TSMC-28nm technology library under typical parameters. Compared with several State-Of-The-Art (SOTA) DNN and SNN works, our hardware architecture outstands others in various criteria.
The Digital Twin Brain (DTB) is an advanced artificial intelligence framework that integrates spiking neurons to simulate complex cognitive functions and collaborative behaviors. For domain experts, visualizing the DTB's simulation outcomes is essential to understanding complex cognitive activities. However, this task poses significant challenges due to DTB data's inherent characteristics, including its high-dimensionality, temporal dynamics, and spatial complexity. To address these challenges, we developed DTBIA, an Immersive Visual Analytics System for Brain-Inspired Research. In collaboration with domain experts, we identified key requirements for effectively visualizing spatiotemporal and topological patterns at multiple levels of detail. DTBIA incorporates a hierarchical workflow - ranging from brain regions to voxels and slice sections - along with immersive navigation and a 3D edge bundling algorithm to enhance clarity and provide deeper insights into both functional (BOLD) and structural (DTI) brain data. The utility and effectiveness of DTBIA are validated through two case studies involving with brain research experts. The results underscore the system's role in enhancing the comprehension of complex neural behaviors and interactions.
Temporal processing is vital for extracting meaningful information from time-varying signals. Recent advancements in Spiking Neural Networks (SNNs) have shown immense promise in efficiently processing these signals. However, progress in this field has been impeded by the lack of effective and standardized benchmarks, which complicates the consistent measurement of technological advancements and limits the practical applicability of SNNs. To bridge this gap, we introduce the Neuromorphic Sequential Arena (NSA), a comprehensive benchmark that offers an effective, versatile, and application-oriented evaluation framework for neuromorphic temporal processing. The NSA includes seven real-world temporal processing tasks from a diverse range of application scenarios, each capturing rich temporal dynamics across multiple timescales. Utilizing NSA, we conduct extensive comparisons of recently introduced spiking neuron models and neural architectures, presenting comprehensive baselines in terms of task performance, training speed, memory usage, and energy efficiency. Our findings emphasize an urgent need for efficient SNN designs that can consistently deliver high performance across tasks with varying temporal complexities while maintaining low computational costs. NSA enables systematic tracking of advancements in neuromorphic algorithm research and paves the way for developing effective and efficient neuromorphic temporal processing systems.
PdNeuRAM: Forming-Free, Multi-Bit Pd/HfO2 ReRAM for Energy-Efficient Computing
2025-05-28
Erbing Hua, Theofilos Spyrou, Majid Ahmadi, Abdul Momin Syed, Hanzhi Xun, Laurentiu Braic, Ewout van der Veer, Nazek Elatab, Anteneh Gebregiorgis, Georgi Gaydadjiev, Beatriz Noheda, Said Hamdioui, Ryoichi Ishihara, Heba Abunahla
Memristor technology shows great promise for energy-efficient computing, yet it grapples with challenges like resistance drift and inherent variability. For filamentary Resistive RAM (ReRAM), one of the most investigated types of memristive devices, the expensive electroforming step required to create conductive pathways results in increased power and area overheads and reduced endurance. In this study, we present novel HfO2-based forming-free ReRAM devices, PdNeuRAM, that operate at low voltages, support multi-bit functionality, and display reduced variability. Through a deep understanding and comprehensive material characterization, we discover the key process that allows this unique behavior: a Pd-O-Hf configuration that capitalizes on Pd innate affinity for integrating into HfO2. This structure actively facilitates charge redistribution at room temperature, effectively eliminating the need for electroforming. Moreover, the fabricated ReRAM device provides tunable resistance states for dense memory and reduces programming and reading energy by 43% and 73%, respectively, using spiking neural networks (SNN). This study reveals novel mechanistic insights and delineates a strategic roadmap for the realization of power-efficient and cost-effective ReRAM devices.
Constructive community race: full-density spiking neural network model drives neuromorphic computing
2025-05-27
Johanna Senk, Anno C. Kurth, Steve Furber, Tobias Gemmeke, Bruno Golosio, Arne Heittmann, James C. Knight, Eric Müller, Tobias Noll, Thomas Nowotny, Gorka Peraza Coppola, Luca Peres, Oliver Rhodes, Andrew Rowley, Johannes Schemmel, Tim Stadtmann, Tom Tetzlaff, Gianmarco Tiddia, Sacha J. van Albada, José Villamar, Markus Diesmann
The local circuitry of the mammalian brain is a focus of the search for generic computational principles because it is largely conserved across species and modalities. In 2014 a model was proposed representing all neurons and synapses of the stereotypical cortical microcircuit below $1\,\text{mm}^2$ of brain surface. The model reproduces fundamental features of brain activity but its impact remained limited because of its computational demands. For theory and simulation, however, the model was a breakthrough because it removes uncertainties of downscaling, and larger models are less densely connected. This sparked a race in the neuromorphic computing community and the model became a de facto standard benchmark. Within a few years real-time performance was reached and surpassed at significantly reduced energy consumption. We review how the computational challenge was tackled by different simulation technologies and derive guidelines for the next generation of benchmarks and other domains of science.
ECC-SNN: Cost-Effective Edge-Cloud Collaboration for Spiking Neural Networks
2025-05-27
Di Yu, Changze Lv, Xin Du, Linshan Jiang, Wentao Tong, Zhenyu Liao, Xiaoqing Zheng, Shuiguang Deng
Most edge-cloud collaboration frameworks rely on the substantial computational and storage capabilities of cloud-based artificial neural networks (ANNs). However, this reliance results in significant communication overhead between edge devices and the cloud and high computational energy consumption, especially when applied to resource-constrained edge devices. To address these challenges, we propose ECC-SNN, a novel edge-cloud collaboration framework incorporating energy-efficient spiking neural networks (SNNs) to offload more computational workload from the cloud to the edge, thereby improving cost-effectiveness and reducing reliance on the cloud. ECC-SNN employs a joint training approach that integrates ANN and SNN models, enabling edge devices to leverage knowledge from cloud models for enhanced performance while reducing energy consumption and processing latency. Furthermore, ECC-SNN features an on-device incremental learning algorithm that enables edge models to continuously adapt to dynamic environments, reducing the communication overhead and resource consumption associated with frequent cloud update requests. Extensive experimental results on four datasets demonstrate that ECC-SNN improves accuracy by 4.15%, reduces average energy consumption by 79.4%, and lowers average processing latency by 39.1%.
The integration of image and event streams offers a promising approach for achieving robust visual object tracking in complex environments. However, current fusion methods achieve high performance at the cost of significant computational overhead and struggle to efficiently extract the sparse, asynchronous information from event streams, failing to leverage the energy-efficient advantages of event-driven spiking paradigms. To address this challenge, we propose the first fully Spiking Frame-Event Tracking framework called SpikeFET. This network achieves synergistic integration of convolutional local feature extraction and Transformer-based global modeling within the spiking paradigm, effectively fusing frame and event data. To overcome the degradation of translation invariance caused by convolutional padding, we introduce a Random Patchwork Module (RPM) that eliminates positional bias through randomized spatial reorganization and learnable type encoding while preserving residual structures. Furthermore, we propose a Spatial-Temporal Regularization (STR) strategy that overcomes similarity metric degradation from asymmetric features by enforcing spatio-temporal consistency among temporal template features in latent space. Extensive experiments across multiple benchmarks demonstrate that the proposed framework achieves superior tracking accuracy over existing methods while significantly reducing power consumption, attaining an optimal balance between performance and efficiency. The code will be released.
Audio-visual zero-shot learning (ZSL) has been extensively researched for its capability to classify video data from unseen classes during training. Nevertheless, current methodologies often struggle with background scene biases and inadequate motion detail. This paper proposes a novel dual-stream Multi-Timescale Motion-Decoupled Spiking Transformer (MDST++), which decouples contextual semantic information and sparse dynamic motion information. The recurrent joint learning unit is proposed to extract contextual semantic information and capture joint knowledge across various modalities to understand the environment of actions. By converting RGB images to events, our method captures motion information more accurately and mitigates background scene biases. Moreover, we introduce a discrepancy analysis block to model audio motion information. To enhance the robustness of SNNs in extracting temporal and motion cues, we dynamically adjust the threshold of Leaky Integrate-and-Fire neurons based on global motion and contextual semantic information. Our experiments validate the effectiveness of MDST++, demonstrating their consistent superiority over state-of-the-art methods on mainstream benchmarks. Additionally, incorporating motion and multi-timescale information significantly improves HM and ZSL accuracy by 26.2\% and 39.9\%.
Conventional frame-based cameras often struggle with stereo depth estimation in rapidly changing scenes. In contrast, bio-inspired spike cameras emit asynchronous events at microsecond-level resolution, providing an alternative sensing modality. However, existing methods lack specialized stereo algorithms and benchmarks tailored to the spike data. To address this gap, we propose SpikeStereoNet, a brain-inspired framework and the first to estimate stereo depth directly from raw spike streams. The model fuses raw spike streams from two viewpoints and iteratively refines depth estimation through a recurrent spiking neural network (RSNN) update module. To benchmark our approach, we introduce a large-scale synthetic spike stream dataset and a real-world stereo spike dataset with dense depth annotations. SpikeStereoNet outperforms existing methods on both datasets by leveraging spike streams' ability to capture subtle edges and intensity shifts in challenging regions such as textureless surfaces and extreme lighting conditions. Furthermore, our framework exhibits strong data efficiency, maintaining high accuracy even with substantially reduced training data. The source code and datasets will be publicly available.
Spiking Transformers offer an energy-efficient alternative to conventional deep learning by transmitting information solely through binary (0/1) spikes. However, there remains a substantial performance gap compared to artificial neural networks. A common belief is that their binary and sparse activation transmission leads to information loss, thus degrading feature representation and accuracy. In this work, however, we reveal for the first time that spiking neurons preferentially propagate low-frequency information. We hypothesize that the rapid dissipation of high-frequency components is the primary cause of performance degradation. For example, on Cifar-100, adopting Avg-Pooling (low-pass) for token mixing lowers performance to 76.73%; interestingly, replacing it with Max-Pooling (high-pass) pushes the top-1 accuracy to 79.12%, surpassing the well-tuned Spikformer baseline by 0.97%. Accordingly, we introduce Max-Former that restores high-frequency signals through two frequency-enhancing operators: extra Max-Pooling in patch embedding and Depth-Wise Convolution in place of self-attention. Notably, our Max-Former (63.99 M) hits the top-1 accuracy of 82.39% on ImageNet, showing a +7.58% improvement over Spikformer with comparable model size (74.81%, 66.34 M). We hope this simple yet effective solution inspires future research to explore the distinctive nature of spiking neural networks, beyond the established practice in standard deep learning. \href{https://github.com/bic-L/Spiking-Transformers-Need-High-Frequency-Information}{Code} is available.
Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time
2025-05-23
Duc Anh Nguyen, Ernesto Araya, Adalbert Fono, Gitta Kutyniok
Recent years have seen significant progress in developing spiking neural networks (SNNs) as a potential solution to the energy challenges posed by conventional artificial neural networks (ANNs). However, our theoretical understanding of SNNs remains relatively limited compared to the ever-growing body of literature on ANNs. In this paper, we study a discrete-time model of SNNs based on leaky integrate-and-fire (LIF) neurons, referred to as discrete-time LIF-SNNs, a widely used framework that still lacks solid theoretical foundations. We demonstrate that discrete-time LIF-SNNs with static inputs and outputs realize piecewise constant functions defined on polyhedral regions, and more importantly, we quantify the network size required to approximate continuous functions. Moreover, we investigate the impact of latency (number of time steps) and depth (number of layers) on the complexity of the input space partitioning induced by discrete-time LIF-SNNs. Our analysis highlights the importance of latency and contrasts these networks with ANNs employing piecewise linear activation functions. Finally, we present numerical experiments to support our theoretical findings.
A Principled Bayesian Framework for Training Binary and Spiking Neural Networks
2025-05-23
James A. Walker, Moein Khajehnejad, Adeel Razi
We propose a Bayesian framework for training binary and spiking neural networks that achieves state-of-the-art performance without normalisation layers. Unlike commonly used surrogate gradient methods -- often heuristic and sensitive to hyperparameter choices -- our approach is grounded in a probabilistic model of noisy binary networks, enabling fully end-to-end gradient-based optimisation. We introduce importance-weighted straight-through (IW-ST) estimators, a unified class generalising straight-through and relaxation-based estimators. We characterise the bias-variance trade-off in this family and derive a bias-minimising objective implemented via an auxiliary loss. Building on this, we introduce Spiking Bayesian Neural Networks (SBNNs), a variational inference framework that uses posterior noise to train Binary and Spiking Neural Networks with IW-ST. This Bayesian approach minimises gradient bias, regularises parameters, and introduces dropout-like noise. By linking low-bias conditions, vanishing gradients, and the KL term, we enable training of deep residual networks without normalisation. Experiments on CIFAR-10, DVS Gesture, and SHD show our method matches or exceeds existing approaches without normalisation or hand-tuned gradients.
Bruno: Backpropagation Running Undersampled for Novel device Optimization
2025-05-23
Luca Fehlings, Bojian Zhang, Paolo Gibertini, Martin A. Nicholson, Erika Covi, Fernando M. Quintana
Recent efforts to improve the efficiency of neuromorphic and machine learning systems have focused on the development of application-specific integrated circuits (ASICs), which provide hardware specialized for the deployment of neural networks, leading to potential gains in efficiency and performance. These systems typically feature an architecture that goes beyond the von Neumann architecture employed in general-purpose hardware such as GPUs. Neural networks developed for this specialised hardware then need to take into account the specifics of the hardware platform, which requires novel training algorithms and accurate models of the hardware, since they cannot be abstracted as a general-purpose computing platform. In this work, we present a bottom-up approach to train neural networks for hardware based on spiking neurons and synapses built on ferroelectric capacitor (FeCap) and Resistive switching non-volatile devices (RRAM) respectively. In contrast to the more common approach of designing hardware to fit existing abstract neuron or synapse models, this approach starts with compact models of the physical device to model the computational primitive of the neurons. Based on these models, a training algorithm is developed that can reliably backpropagate through these physical models, even when applying common hardware limitations, such as stochasticity, variability, and low bit precision. The training algorithm is then tested on a spatio-temporal dataset with a network composed of quantized synapses based on RRAM and ferroelectric leaky integrate-and-fire (FeLIF) neurons. The performance of the network is compared with different networks composed of LIF neurons. The results of the experiments show the potential advantage of using BRUNO to train networks with FeLIF neurons, by achieving a reduction in both time and memory for detecting spatio-temporal patterns with quantized synapses.
Object Classification Utilizing Neuromorphic Proprioceptive Signals in Active Exploration: Validated on a Soft Anthropomorphic Hand
2025-05-23
Fengyi Wang, Xiangyu Fu, Nitish Thakor, Gordon Cheng
Proprioception, a key sensory modality in haptic perception, plays a vital role in perceiving the 3D structure of objects by providing feedback on the position and movement of body parts. The restoration of proprioceptive sensation is crucial for enabling in-hand manipulation and natural control in the prosthetic hand. Despite its importance, proprioceptive sensation is relatively unexplored in an artificial system. In this work, we introduce a novel platform that integrates a soft anthropomorphic robot hand (QB SoftHand) with flexible proprioceptive sensors and a classifier that utilizes a hybrid spiking neural network with different types of spiking neurons to interpret neuromorphic proprioceptive signals encoded by a biological muscle spindle model. The encoding scheme and the classifier are implemented and tested on the datasets we collected in the active exploration of ten objects from the YCB benchmark. Our results indicate that the classifier achieves more accurate inferences than existing learning approaches, especially in the early stage of the exploration. This system holds the potential for development in the areas of haptic feedback and neural prosthetics.
SVL: Spike-based Vision-language Pretraining for Efficient 3D Open-world Understanding
2025-05-23
Xuerui Qiu, Peixi Wu, Yaozhi Wen, Shaowei Gu, Yuqi Pan, Xinhao Luo, Bo XU, Guoqi Li
Spiking Neural Networks (SNNs) provide an energy-efficient way to extract 3D spatio-temporal features. However, existing SNNs still exhibit a significant performance gap compared to Artificial Neural Networks (ANNs) due to inadequate pre-training strategies. These limitations manifest as restricted generalization ability, task specificity, and a lack of multimodal understanding, particularly in challenging tasks such as multimodal question answering and zero-shot 3D classification. To overcome these challenges, we propose a Spike-based Vision-Language (SVL) pretraining framework that empowers SNNs with open-world 3D understanding while maintaining spike-driven efficiency. SVL introduces two key components: (i) Multi-scale Triple Alignment (MTA) for label-free triplet-based contrastive learning across 3D, image, and text modalities, and (ii) Re-parameterizable Vision-Language Integration (Rep-VLI) to enable lightweight inference without relying on large text encoders. Extensive experiments show that SVL achieves a top-1 accuracy of 85.4% in zero-shot 3D classification, surpassing advanced ANN models, and consistently outperforms prior SNNs on downstream tasks, including 3D classification (+6.1%), DVS action recognition (+2.1%), 3D detection (+1.1%), and 3D segmentation (+2.1%) with remarkable efficiency. Moreover, SVL enables SNNs to perform open-world 3D question answering, sometimes outperforming ANNs. To the best of our knowledge, SVL represents the first scalable, generalizable, and hardware-friendly paradigm for 3D open-world understanding, effectively bridging the gap between SNNs and ANNs in complex open-world understanding tasks. Code is available https://github.com/bollossom/SVL.
Neuromorphic-based metaheuristics: A new generation of low power, low latency and small footprint optimization algorithms
2025-05-22
El-ghazali Talbi
Neuromorphic computing (NC) introduces a novel algorithmic paradigm representing a major shift from traditional digital computing of Von Neumann architectures. NC emulates or simulates the neural dynamics of brains in the form of Spiking Neural Networks (SNNs). Much of the research in NC has concentrated on machine learning applications and neuroscience simulations. This paper investigates the modelling and implementation of optimization algorithms and particularly metaheuristics using the NC paradigm as an alternative to Von Neumann architectures, leading to breakthroughs in solving optimization problems. Neuromorphic-based metaheuristics (Nheuristics) are supposed to be characterized by low power, low latency and small footprint. Since NC systems are fundamentally different from conventional Von Neumann computers, several challenges are posed to the design and implementation of Nheuristics. A guideline based on a classification and critical analysis is conducted on the different families of metaheuristics and optimization problems they address. We also discuss future directions that need to be addressed to expand both the development and application of Nheuristics.
2025-05-21
Damien Depannemaecker, Adrien d'Hollande, Jiaming Wu, Marcelo J. Rozenberg
Common wisdom indicates that to implement a Dynamical Memory with spiking neurons two ingredients are necessary: recurrence and a neuron population. Here we shall show that the second requirement is not needed. We shall demonstrate that under very general assumptions a single recursive spiking neuron can realize a robust model of a dynamical memory. We demonstrate the implementation of a dynamical memory in both, software and hardware. In the former case, we introduce trivial extensions of the popular aQIF and AdEx models. In the latter, we show traces obtained in a circuit model with a recently proposed memristive spiking neuron. We show that the bistability of the theoretical models can be understood in terms of a self-consistent problem that can be represented geometrically. Our minimal dynamical memory model provides a simplest implementation of an important neuro-computational primitive, which can be useful in navigation system models based on purely spiking dynamics. A one neuron dynamical memory may also provides a natural explanation to the surprising recent observation that the excitation bump in Drosophila's ellipsoidal body is made by just a handful of neurons.
Multimodal spiking neural networks (SNNs) hold significant potential for energy-efficient sensory processing but face critical challenges in modality imbalance and temporal misalignment. Current approaches suffer from uncoordinated convergence speeds across modalities and static fusion mechanisms that ignore time-varying cross-modal interactions. We propose the temporal attention-guided adaptive fusion framework for multimodal SNNs with two synergistic innovations: 1) The Temporal Attention-guided Adaptive Fusion (TAAF) module that dynamically assigns importance scores to fused spiking features at each timestep, enabling hierarchical integration of temporally heterogeneous spike-based features; 2) The temporal adaptive balanced fusion loss that modulates learning rates per modality based on the above attention scores, preventing dominant modalities from monopolizing optimization. The proposed framework implements adaptive fusion, especially in the temporal dimension, and alleviates the modality imbalance during multimodal learning, mimicking cortical multisensory integration principles. Evaluations on CREMA-D, AVE, and EAD datasets demonstrate state-of-the-art performance (77.55\%, 70.65\% and 97.5\%accuracy, respectively) with energy efficiency. The system resolves temporal misalignment through learnable time-warping operations and faster modality convergence coordination than baseline SNNs. This work establishes a new paradigm for temporally coherent multimodal learning in neuromorphic systems, bridging the gap between biological sensory processing and efficient machine intelligence.
Agent-based Transformers have been widely adopted in recent reinforcement learning advances due to their demonstrated ability to solve complex tasks. However, the high computational complexity of Transformers often results in significant energy consumption, limiting their deployment in real-world autonomous systems. Spiking neural networks (SNNs), with their biologically inspired structure, offer an energy-efficient alternative for machine learning. In this paper, a novel Spike-Transformer Reinforcement Learning (STRL) algorithm that combines the energy efficiency of SNNs with the powerful decision-making capabilities of reinforcement learning is developed. Specifically, an SNN using multi-step Leaky Integrate-and-Fire (LIF) neurons and attention mechanisms capable of processing spatio-temporal patterns over multiple time steps is designed. The architecture is further enhanced with state, action, and reward encodings to create a Transformer-like structure optimized for reinforcement learning tasks. Comprehensive numerical experiments conducted on state-of-the-art benchmarks demonstrate that the proposed SNN Transformer achieves significantly improved policy performance compared to conventional agent-based Transformers. With both enhanced energy efficiency and policy optimality, this work highlights a promising direction for deploying bio-inspired, low-cost machine learning models in complex real-world decision-making scenarios.
eStonefish-scenes: A synthetically generated dataset for underwater event-based optical flow prediction tasks
2025-05-19
Jad Mansour, Sebastian Realpe, Hayat Rajani, Michele Grimaldi, Rafael Garcia, Nuno Gracias
The combined use of event-based vision and Spiking Neural Networks (SNNs) is expected to significantly impact robotics, particularly in tasks like visual odometry and obstacle avoidance. While existing real-world event-based datasets for optical flow prediction, typically captured with Unmanned Aerial Vehicles (UAVs), offer valuable insights, they are limited in diversity, scalability, and are challenging to collect. Moreover, there is a notable lack of labelled datasets for underwater applications, which hinders the integration of event-based vision with Autonomous Underwater Vehicles (AUVs). To address this, synthetic datasets could provide a scalable solution while bridging the gap between simulation and reality. In this work, we introduce eStonefish-scenes, a synthetic event-based optical flow dataset based on the Stonefish simulator. Along with the dataset, we present a data generation pipeline that enables the creation of customizable underwater environments. This pipeline allows for simulating dynamic scenarios, such as biologically inspired schools of fish exhibiting realistic motion patterns, including obstacle avoidance and reactive navigation around corals. Additionally, we introduce a scene generator that can build realistic reef seabeds by randomly distributing coral across the terrain. To streamline data accessibility, we present eWiz, a comprehensive library designed for processing event-based data, offering tools for data loading, augmentation, visualization, encoding, and training data generation, along with loss functions and performance metrics.
Spiking transformers are emerging as a promising architecture that combines the energy efficiency of Spiking Neural Networks (SNNs) with the powerful attention mechanisms of transformers. However, existing hardware accelerators lack support for spiking attention, exhibit limited throughput in exploiting fine-grained sparsity, and struggle with scalable parallelism in sparse computation. To address these, we propose FireFly-T, a dual-engine overlay architecture that integrates a sparse engine for activation sparsity and a binary engine for spiking attention. In the sparse engine, we propose a highthroughput sparse decoder that exploits fine-grained sparsity by concurrently extracting multiple non-zero spikes. To complement this, we introduce a scalable load balancing mechanism with weight dispatch and out-of-order execution, eliminating bank conflicts to support scalable multidimensional parallelism. In the binary engine, we leverage the byte-level write capability of SRAMs to efficiently manipulate the 3D dataflows required for spiking attention with minimal resource overhead. We also optimize the core AND-PopCount operation in spiking attention through a LUT6-based implementation, improving timing closure and reducing LUT utilization on Xilinx FPGAs. As an overlay architecture, FireFly-T further incorporates an orchestrator that dynamically manipulates input dataflows with flexible adaptation for diverse network topologies, while ensuring efficient resource utilization and maintaining high throughput. Experimental results demonstrate that our accelerator achieves $1.39\times$ and $2.40\times$ higher energy efficiency, as well as $4.21\times$ and $7.10\times$ greater DSP efficiency, compared to FireFly v2 and the transformer-enabled SpikeTA, respectively. These results highlight its potential as an efficient hardware platform for spiking transformer.
Spike cameras offer unique sensing capabilities but their sparse, asynchronous output challenges semantic understanding, especially for Spike Video-Language Alignment (Spike-VLA) where models like CLIP underperform due to modality mismatch. We introduce SPKLIP, the first architecture specifically for Spike-VLA. SPKLIP employs a hierarchical spike feature extractor that adaptively models multi-scale temporal dynamics in event streams, and uses spike-text contrastive learning to directly align spike video with language, enabling effective few-shot learning. A full-spiking visual encoder variant, integrating SNN components into our pipeline, demonstrates enhanced energy efficiency. Experiments show state-of-the-art performance on benchmark spike datasets and strong few-shot generalization on a newly contributed real-world dataset. SPKLIP's energy efficiency highlights its potential for neuromorphic deployment, advancing event-based multimodal research. The source code and dataset are available at [link removed for anonymity].
Spiking Neural Network: a low power solution for physical layer authentication
2025-05-19
Jung Hoon Lee, Sujith Vijayan
Deep learning (DL) is a powerful tool that can solve complex problems, and thus, it seems natural to assume that DL can be used to enhance the security of wireless communication. However, deploying DL models to edge devices in wireless networks is challenging, as they require significant amounts of computing and power resources. Notably, Spiking Neural Networks (SNNs) are known to be efficient in terms of power consumption, meaning they can be an alternative platform for DL models for edge devices. In this study, we ask if SNNs can be used in physical layer authentication. Our evaluation suggests that SNNs can learn unique physical properties (i.e., `fingerprints') of RF transmitters and use them to identify individual devices. Furthermore, we find that SNNs are also vulnerable to adversarial attacks and that an autoencoder can be used clean out adversarial perturbations to harden SNNs against them.
Frozen Backpropagation: Relaxing Weight Symmetry in Temporally-Coded Deep Spiking Neural Networks
2025-05-19
Gaspard Goupy, Pierre Tirilly, Ioan Marius Bilasco
Direct training of Spiking Neural Networks (SNNs) on neuromorphic hardware can greatly reduce energy costs compared to GPU-based training. However, implementing Backpropagation (BP) on such hardware is challenging because forward and backward passes are typically performed by separate networks with distinct weights. To compute correct gradients, forward and feedback weights must remain symmetric during training, necessitating weight transport between the two networks. This symmetry requirement imposes hardware overhead and increases energy costs. To address this issue, we introduce Frozen Backpropagation (fBP), a BP-based training algorithm relaxing weight symmetry in settings with separate networks. fBP updates forward weights by computing gradients with periodically frozen feedback weights, reducing weight transports during training and minimizing synchronization overhead. To further improve transport efficiency, we propose three partial weight transport schemes of varying computational complexity, where only a subset of weights is transported at a time. We evaluate our methods on image recognition tasks and compare them to existing approaches addressing the weight symmetry requirement. Our results show that fBP outperforms these methods and achieves accuracy comparable to BP. With partial weight transport, fBP can substantially lower transport costs by 1,000x with an accuracy drop of only 0.5pp on CIFAR-10 and 1.1pp on CIFAR-100, or by up to 10,000x at the expense of moderated accuracy loss. This work provides insights for guiding the design of neuromorphic hardware incorporating BP-based on-chip learning.
Spiking Neural Networks with Random Network Architecture
2025-05-19
Zihan Dai, Huanfei Ma
The spiking neural network, known as the third generation neural network, is an important network paradigm. Due to its mode of information propagation that follows biological rationality, the spiking neural network has strong energy efficiency and has advantages in complex high-energy application scenarios. However, unlike the artificial neural network (ANN) which has a mature and unified framework, the SNN models and training methods have not yet been widely unified due to the discontinuous and non-differentiable property of the firing mechanism. Although several algorithms for training spiking neural networks have been proposed in the subsequent development process, some fundamental issues remain unsolved. Inspired by random network design, this work proposes a new architecture for spiking neural networks, RanSNN, where only part of the network weights need training and all the classic training methods can be adopted. Compared with traditional training methods for spiking neural networks, it greatly improves the training efficiency while ensuring the training performance, and also has good versatility and stability as validated by benchmark tests.
The combination of Spiking Neural Networks (SNNs) with Vision Transformer architectures has garnered significant attention due to their potential for energy-efficient and high-performance computing paradigms. However, a substantial performance gap still exists between SNN-based and ANN-based transformer architectures. While existing methods propose spiking self-attention mechanisms that are successfully combined with SNNs, the overall architectures proposed by these methods suffer from a bottleneck in effectively extracting features from different image scales. In this paper, we address this issue and propose MSVIT. This novel spike-driven Transformer architecture firstly uses multi-scale spiking attention (MSSA) to enhance the capabilities of spiking attention blocks. We validate our approach across various main datasets. The experimental results show that MSVIT outperforms existing SNN-based models, positioning itself as a state-of-the-art solution among SNN-transformer architectures. The codes are available at https://github.com/Nanhu-AI-Lab/MSViT.
SpikeX: Exploring Accelerator Architecture and Network-Hardware Co-Optimization for Sparse Spiking Neural Networks
2025-05-18
Boxun Xu, Richard Boone, Peng Li
Spiking Neural Networks (SNNs) are promising biologically plausible models of computation which utilize a spiking binary activation function similar to that of biological neurons. SNNs are well positioned to process spatiotemporal data, and are advantageous in ultra-low power and real-time processing. Despite a large body of work on conventional artificial neural network accelerators, much less attention has been given to efficient SNN hardware accelerator design. In particular, SNNs exhibit inherent unstructured spatial and temporal firing sparsity, an opportunity yet to be fully explored for great hardware processing efficiency. In this work, we propose a novel systolic-array SNN accelerator architecture, called SpikeX, to take on the challenges and opportunities stemming from unstructured sparsity while taking into account the unique characteristics of spike-based computation. By developing an efficient dataflow targeting expensive multi-bit weight data movements, SpikeX reduces memory access and increases data sharing and hardware utilization for computations spanning across both time and space, thereby significantly improving energy efficiency and inference latency. Furthermore, recognizing the importance of SNN network and hardware co-design, we develop a co-optimization methodology facilitating not only hardware-aware SNN training but also hardware accelerator architecture search, allowing joint network weight parameter optimization and accelerator architectural reconfiguration. This end-to-end network/accelerator co-design approach offers a significant reduction of 15.1x-150.87x in energy-delay-product(EDP) without comprising model accuracy.
Bishop: Sparsified Bundling Spiking Transformers on Heterogeneous Cores with Error-Constrained Pruning
2025-05-18
Boxun Xu, Yuxuan Yin, Vikram Iyer, Peng Li
We present Bishop, the first dedicated hardware accelerator architecture and HW/SW co-design framework for spiking transformers that optimally represents, manages, and processes spike-based workloads while exploring spatiotemporal sparsity and data reuse. Specifically, we introduce the concept of Token-Time Bundle (TTB), a container that bundles spiking data of a set of tokens over multiple time points. Our heterogeneous accelerator architecture Bishop concurrently processes workload packed in TTBs and explores intra- and inter-bundle multiple-bit weight reuse to significantly reduce memory access. Bishop utilizes a stratifier, a dense core array, and a sparse core array to process MLP blocks and projection layers. The stratifier routes high-density spiking activation workload to the dense core and low-density counterpart to the sparse core, ensuring optimized processing tailored to the given spatiotemporal sparsity level. To further reduce data access and computation, we introduce a novel Bundle Sparsity-Aware (BSA) training pipeline that enhances not only the overall but also structured TTB-level firing sparsity. Moreover, the processing efficiency of self-attention layers is boosted by the proposed Error-Constrained TTB Pruning (ECP), which trims activities in spiking queries, keys, and values both before and after the computation of spiking attention maps with a well-defined error bound. Finally, we design a reconfigurable TTB spiking attention core to efficiently compute spiking attention maps by executing highly simplified "AND" and "Accumulate" operations. On average, Bishop achieves a 5.91x speedup and 6.11x improvement in energy efficiency over previous SNN accelerators, while delivering higher accuracy across multiple datasets.
Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware
2025-05-18
Zhenhui Chen, Haoran Xu, Yangfan Hu, Xiaofei Jin, Xinyu Li, Ziyang Kang, Gang Pan, De Ma
Neuromorphic hardware aims to leverage distributed computing and event-driven circuit design to achieve an energy-efficient AI system. The name "neuromorphic" is derived from its spiking and local computing nature, which mimics the fundamental activity of an animal's nervous system. In neuromorphic hardware, neurons, i.e., computing cores use single-bit, event-driven data (called spikes) for inter-communication, which differs substantially from conventional hardware. To leverage the advantages of neuromorphic hardware and implement a computing model, the conventional approach is to build spiking neural networks (SNNs). SNNs replace the nonlinearity part of artificial neural networks (ANNs) in the realm of deep learning with spiking neurons, where the spiking neuron mimics the basic behavior of bio-neurons. However, there is still a performance gap between SNNs and their ANN counterparts. In this paper, we explore a new way to map computing models onto neuromorphic hardware. We propose a Spiking-Driven ANN (SDANN) framework that directly implements quantized ANN on hardware, eliminating the need for tuning the trainable parameters or any performance degradation. With the power of quantized ANN, our SDANN ensures a lower bound of implementation performance on neuromorphic hardware. To address the limitation of bit width support on hardware, we propose bias calibration and scaled integration methods. Experiments on various tasks demonstrate that our SDANN achieves exactly the same accuracy as the quantized ANN. Beyond toy examples and software implementation, we successfully deployed and validated our spiking models on real neuromorphic hardware, demonstrating the feasibility of the SDANN framework.
Adaptive Gradient Learning for Spiking Neural Networks by Exploiting Membrane Potential Dynamics
2025-05-17
Jiaqiang Jiang, Lei Wang, Runhao Jiang, Jing Fan, Rui Yan
Brain-inspired spiking neural networks (SNNs) are recognized as a promising avenue for achieving efficient, low-energy neuromorphic computing. Recent advancements have focused on directly training high-performance SNNs by estimating the approximate gradients of spiking activity through a continuous function with constant sharpness, known as surrogate gradient (SG) learning. However, as spikes propagate among neurons, the distribution of membrane potential dynamics (MPD) will deviate from the gradient-available interval of fixed SG, hindering SNNs from searching the optimal solution space. To maintain the stability of gradient flows, SG needs to align with evolving MPD. Here, we propose adaptive gradient learning for SNNs by exploiting MPD, namely MPD-AGL. It fully accounts for the underlying factors contributing to membrane potential shifts and establishes a dynamic association between SG and MPD at different timesteps to relax gradient estimation, which provides a new degree of freedom for SG learning. Experimental results demonstrate that our method achieves excellent performance at low latency. Moreover, it increases the proportion of neurons that fall into the gradient-available interval compared to fixed SG, effectively mitigating the gradient vanishing problem.
Traditional spiking neural networks (SNNs) can be viewed as a combination of multiple subnetworks with each running for one time step, where the parameters are shared, and the membrane potential serves as the only information link between them. However, the implicit nature of the membrane potential limits its ability to effectively represent temporal information. As a result, each time step cannot fully leverage information from previous time steps, seriously limiting the model's performance. Inspired by the top-down mechanism in the brain, we introduce TDFormer, a novel model with a top-down feedback structure that functions hierarchically and leverages high-order representations from earlier time steps to modulate the processing of low-order information at later stages. The feedback structure plays a role from two perspectives: 1) During forward propagation, our model increases the mutual information across time steps, indicating that richer temporal information is being transmitted and integrated in different time steps. 2) During backward propagation, we theoretically prove that the feedback structure alleviates the problem of vanishing gradients along the time dimension. We find that these mechanisms together significantly and consistently improve the model performance on multiple datasets. In particular, our model achieves state-of-the-art performance on ImageNet with an accuracy of 86.83%.
In recent years, Recurrent Spiking Neural Networks (RSNNs) have shown promising potential in long-term temporal modeling. Many studies focus on improving neuron models and also integrate recurrent structures, leveraging their synergistic effects to improve the long-term temporal modeling capabilities of Spiking Neural Networks (SNNs). However, these studies often place an excessive emphasis on the role of neurons, overlooking the importance of analyzing neurons and recurrent structures as an integrated framework. In this work, we consider neurons and recurrent structures as an integrated system and conduct a systematic analysis of gradient propagation along the temporal dimension, revealing a challenging gradient vanishing problem. To address this issue, we propose the Skip Recurrent Connection (SRC) as a replacement for the vanilla recurrent structure, effectively mitigating the gradient vanishing problem and enhancing long-term temporal modeling performance. Additionally, we propose the Adaptive Skip Recurrent Connection (ASRC), a method that can learn the skip span of skip recurrent connection in each layer of the network. Experiments show that replacing the vanilla recurrent structure in RSNN with SRC significantly improves the model's performance on temporal benchmark datasets. Moreover, ASRC-SNN outperforms SRC-SNN in terms of temporal modeling capabilities and robustness.
2025-05-16
Georgios Moustakas, Ioannis Tsilikas, Adonis Bogris, Charis Mesaritakis
We present an experimental imaging flow cytometer using a 1 {\mu}s temporal resolution event-based CMOS camera, with data processed by adaptive feedforward and recurrent spiking neural networks. Our study classifies PMMA particles (12, 16, 20 {\mu}m) flowing at 0.7 m/s in a microfluidic channel. Processing of experimental data highlighted that spiking recurrent networks, including LSTM and GRU models, achieved 98.4% accuracy by leveraging temporal dependencies. Additionally, adaptation mechanisms in lightweight feedforward spiking networks improved accuracy by 4.3%. This work outlines a technological roadmap for neuromorphic-assisted biomedical applications, enhancing classification performance while maintaining low latency and sparsity.
Energy efficiency analysis of Spiking Neural Networks for space applications
2025-05-16
Paolo Lunghi, Stefano Silvestrini, Dominik Dold, Gabriele Meoni, Alexander Hadjiivanov, Dario Izzo
While the exponential growth of the space sector and new operative concepts ask for higher spacecraft autonomy, the development of AI-assisted space systems was so far hindered by the low availability of power and energy typical of space applications. In this context, Spiking Neural Networks (SNN) are highly attractive due to their theoretically superior energy efficiency due to their inherently sparse activity induced by neurons communicating by means of binary spikes. Nevertheless, the ability of SNN to reach such efficiency on real world tasks is still to be demonstrated in practice. To evaluate the feasibility of utilizing SNN onboard spacecraft, this work presents a numerical analysis and comparison of different SNN techniques applied to scene classification for the EuroSAT dataset. Such tasks are of primary importance for space applications and constitute a valuable test case given the abundance of competitive methods available to establish a benchmark. Particular emphasis is placed on models based on temporal coding, where crucial information is encoded in the timing of neuron spikes. These models promise even greater efficiency of resulting networks, as they maximize the sparsity properties inherent in SNN. A reliable metric capable of comparing different architectures in a hardware-agnostic way is developed to establish a clear theoretical dependence between architecture parameters and the energy consumption that can be expected onboard the spacecraft. The potential of this novel method and his flexibility to describe specific hardware platforms is demonstrated by its application to predicting the energy consumption of a BrainChip Akida AKD1000 neuromorphic processor.
Lightweight LIF-only SNN accelerator using differential time encoding
2025-05-16
Daniel Windhager, Lothar Ratschbacher, Bernhard A. Moser, Michael Lunglmayr
Spiking Neural Networks (SNNs) offer a promising solution to the problem of increasing computational and energy requirements for modern Machine Learning (ML) applications. Due to their unique data representation choice of using spikes and spike trains, they mostly rely on additions and thresholding operations to achieve results approaching state-of-the-art (SOTA) Artificial Neural Networks (ANNs). This advantage is hindered by the fact that their temporal characteristic does not map well to already existing accelerator hardware like GPUs. Therefore, this work will introduce a hardware accelerator architecture capable of computing feedforward LIF-only SNNs, as well as an accompanying encoding method to efficiently encode already existing data into spike trains. Together, this leads to a design capable of >99% accuracy on the MNIST dataset, with ~0.29ms inference times on a Xilinx Ultrascale+ FPGA, as well as ~0.17ms on a custom ASIC using the open-source predictive 7nm ASAP7 PDK. Furthermore, this work will showcase the advantages of the previously presented differential time encoding for spikes, as well as provide proof that merging spikes from different synapses given in differential time encoding can be done efficiently in hardware.
Spiking Transformers have recently emerged as promising architectures for combining the efficiency of spiking neural networks with the representational power of self-attention. However, the lack of standardized implementations, evaluation pipelines, and consistent design choices has hindered fair comparison and principled analysis. In this paper, we introduce \textbf{STEP}, a unified benchmark framework for Spiking Transformers that supports a wide range of tasks, including classification, segmentation, and detection across static, event-based, and sequential datasets. STEP provides modular support for diverse components such as spiking neurons, input encodings, surrogate gradients, and multiple backends (e.g., SpikingJelly, BrainCog). Using STEP, we reproduce and evaluate several representative models, and conduct systematic ablation studies on attention design, neuron types, encoding schemes, and temporal modeling capabilities. We also propose a unified analytical model for energy estimation, accounting for spike sparsity, bitwidth, and memory access, and show that quantized ANNs may offer comparable or better energy efficiency. Our results suggest that current Spiking Transformers rely heavily on convolutional frontends and lack strong temporal modeling, underscoring the need for spike-native architectural innovations. The full code is available at: https://github.com/Fancyssc/STEP
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection
2025-05-16
Desong Zhang, Jia Hu, Geyong Min
Spiking Neural Networks (SNNs) process information via discrete spikes, enabling them to operate at remarkably low energy levels. However, our experimental observations reveal a striking vulnerability when SNNs are trained using the mainstream method--direct encoding combined with backpropagation through time (BPTT): even a single backward pass on data drawn from a slightly different distribution can lead to catastrophic network collapse. Our theoretical analysis attributes this vulnerability to the repeated inputs inherent in direct encoding and the gradient accumulation characteristic of BPTT, which together produce an exceptional large Hessian spectral radius. To address this challenge, we develop a hyperparameter-free method called Dominant Eigencomponent Projection (DEP). By orthogonally projecting gradients to precisely remove their dominant components, DEP effectively reduces the Hessian spectral radius, thereby preventing SNNs from settling into sharp minima. Extensive experiments demonstrate that DEP not only mitigates the vulnerability of SNNs to heterogeneous data poisoning, but also significantly enhances overall robustness compared to key baselines, providing strong support for safer and more reliable SNN deployment.
Spiking Neural Networks (SNNs) are gaining attention for their energy efficiency and biological plausibility, utilizing 0-1 activation sparsity through spike-driven computation. While existing SNN accelerators exploit this sparsity to skip zero computations, they often overlook the unique distribution patterns inherent in binary activations. In this work, we observe that particular patterns exist in spike activations, which we can utilize to reduce the substantial computation of SNN models. Based on these findings, we propose a novel \textbf{pattern-based hierarchical sparsity} framework, termed \textbf{\textit{Phi}}, to optimize computation. \textit{Phi} introduces a two-level sparsity hierarchy: Level 1 exhibits vector-wise sparsity by representing activations with pre-defined patterns, allowing for offline pre-computation with weights and significantly reducing most runtime computation. Level 2 features element-wise sparsity by complementing the Level 1 matrix, using a highly sparse matrix to further reduce computation while maintaining accuracy. We present an algorithm-hardware co-design approach. Algorithmically, we employ a k-means-based pattern selection method to identify representative patterns and introduce a pattern-aware fine-tuning technique to enhance Level 2 sparsity. Architecturally, we design \textbf{\textit{Phi}}, a dedicated hardware architecture that efficiently processes the two levels of \textit{Phi} sparsity on the fly. Extensive experiments demonstrate that \textit{Phi} achieves a $3.45\times$ speedup and a $4.93\times$ improvement in energy efficiency compared to state-of-the-art SNN accelerators, showcasing the effectiveness of our framework in optimizing SNN computation.
Pipelining Kruskal's: A Neuromorphic Approach for Minimum Spanning Tree
2025-05-16
Yee Hin Chong, Peng Qu, Yuchen Li, Youhui Zhang
Neuromorphic computing, characterized by its event-driven computation and massive parallelism, is particularly effective for handling data-intensive tasks in low-power environments, such as computing the minimum spanning tree (MST) for large-scale graphs. The introduction of dynamic synaptic modifications provides new design opportunities for neuromorphic algorithms. Building on this foundation, we propose an SNN-based union-sort routine and a pipelined version of Kruskal's algorithm for MST computation. The event-driven nature of our method allows for the concurrent execution of two completely decoupled stages: neuromorphic sorting and union-find. Our approach demonstrates superior performance compared to state-of-the-art Prim 's-based methods on large-scale graphs from the DIMACS10 dataset, achieving speedups by 269.67x to 1283.80x, with a median speedup of 540.76x. We further evaluate the pipelined implementation against two serial variants of Kruskal's algorithm, which rely on neuromorphic sorting and neuromorphic radix sort, showing significant performance advantages in most scenarios.
Adversarially Robust Spiking Neural Networks with Sparse Connectivity
2025-05-16
Mathias Schmolli, Maximilian Baronig, Robert Legenstein, Ozan Özdenizci
Deployment of deep neural networks in resource-constrained embedded systems requires innovative algorithmic solutions to facilitate their energy and memory efficiency. To further ensure the reliability of these systems against malicious actors, recent works have extensively studied adversarial robustness of existing architectures. Our work focuses on the intersection of adversarial robustness, memory- and energy-efficiency in neural networks. We introduce a neural network conversion algorithm designed to produce sparse and adversarially robust spiking neural networks (SNNs) by leveraging the sparse connectivity and weights from a robustly pretrained artificial neural network (ANN). Our approach combines the energy-efficient architecture of SNNs with a novel conversion algorithm, leading to state-of-the-art performance with enhanced energy and memory efficiency through sparse connectivity and activations. Our models are shown to achieve up to 100x reduction in the number of weights to be stored in memory, with an estimated 8.6x increase in energy efficiency compared to dense SNNs, while maintaining high performance and robustness against adversarial threats.
ILIF: Temporal Inhibitory Leaky Integrate-and-Fire Neuron for Overactivation in Spiking Neural Networks
2025-05-15
Kai Sun, Peibo Duan, Levin Kuhlmann, Beilun Wang, Bin Zhang
The Spiking Neural Network (SNN) has drawn increasing attention for its energy-efficient, event-driven processing and biological plausibility. To train SNNs via backpropagation, surrogate gradients are used to approximate the non-differentiable spike function, but they only maintain nonzero derivatives within a narrow range of membrane potentials near the firing threshold, referred to as the surrogate gradient support width gamma. We identify a major challenge, termed the dilemma of gamma: a relatively large gamma leads to overactivation, characterized by excessive neuron firing, which in turn increases energy consumption, whereas a small gamma causes vanishing gradients and weakens temporal dependencies. To address this, we propose a temporal Inhibitory Leaky Integrate-and-Fire (ILIF) neuron model, inspired by biological inhibitory mechanisms. This model incorporates interconnected inhibitory units for membrane potential and current, effectively mitigating overactivation while preserving gradient propagation. Theoretical analysis demonstrates ILIF effectiveness in overcoming the gamma dilemma, and extensive experiments on multiple datasets show that ILIF improves energy efficiency by reducing firing rates, stabilizes training, and enhances accuracy. The code is available at github.com/kaisun1/ILIF.
Spiking Neural Networks (SNNs) have shown competitive performance to Artificial Neural Networks (ANNs) in various vision tasks, while offering superior energy efficiency. However, existing SNN-based Transformers primarily focus on single-image tasks, emphasizing spatial features while not effectively leveraging SNNs' efficiency in video-based vision tasks. In this paper, we introduce SpikeVideoFormer, an efficient spike-driven video Transformer, featuring linear temporal complexity $\mathcal{O}(T)$. Specifically, we design a spike-driven Hamming attention (SDHA) which provides a theoretically guided adaptation from traditional real-valued attention to spike-driven attention. Building on SDHA, we further analyze various spike-driven space-time attention designs and identify an optimal scheme that delivers appealing performance for video tasks, while maintaining only linear temporal complexity. The generalization ability and efficiency of our model are demonstrated across diverse downstream video tasks, including classification, human pose tracking, and semantic segmentation. Empirical results show our method achieves state-of-the-art (SOTA) performance compared to existing SNN approaches, with over 15\% improvement on the latter two tasks. Additionally, it matches the performance of recent ANN-based methods while offering significant efficiency gains, achieving $\times 16$, $\times 10$ and $\times 5$ improvements on the three tasks. https://github.com/JimmyZou/SpikeVideoFormer
Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence
2025-05-15
Xiang He, Dongcheng Zhao, Yang Li, Qingqun Kong, Xin Yang, Yi Zeng
Multimodal learning enhances the perceptual capabilities of cognitive systems by integrating information from different sensory modalities. However, existing multimodal fusion research typically assumes static integration, not fully incorporating key dynamic mechanisms found in the brain. Specifically, the brain exhibits an inverse effectiveness phenomenon, wherein weaker unimodal cues yield stronger multisensory integration benefits; conversely, when individual modal cues are stronger, the effect of fusion is diminished. This mechanism enables biological systems to achieve robust cognition even with scarce or noisy perceptual cues. Inspired by this biological mechanism, we explore the relationship between multimodal output and information from individual modalities, proposing an inverse effectiveness driven multimodal fusion (IEMF) strategy. By incorporating this strategy into neural networks, we achieve more efficient integration with improved model performance and computational efficiency, demonstrating up to 50% reduction in computational cost across diverse fusion methods. We conduct experiments on audio-visual classification, continual learning, and question answering tasks to validate our method. Results consistently demonstrate that our method performs excellently in these tasks. To verify universality and generalization, we also conduct experiments on Artificial Neural Networks (ANN) and Spiking Neural Networks (SNN), with results showing good adaptability to both network types. Our research emphasizes the potential of incorporating biologically inspired mechanisms into multimodal networks and provides promising directions for the future development of multimodal artificial intelligence. The code is available at https://github.com/Brain-Cog-Lab/IEMF.
LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models
2025-05-14
Long Chen, Xiaotian Song, Yanan Sun
Spiking Large Language Models (LLMs) have emerged as an energy-efficient alternative to conventional LLMs through their event-driven computation. To effectively obtain spiking LLMs, researchers develop different ANN-to-SNN conversion methods by leveraging pre-trained ANN parameters while inheriting the energy efficiency of SNN. However, existing conversion methods struggle with extreme activation outliers and incompatible nonlinear operations of ANN-based LLMs. To address this, we propose a loss-less ANN-SNN conversion for fully spike-driven LLMs, termed LAS. Specifically, LAS introduces two novel neurons to convert the activation outlier and nonlinear operation of ANN-based LLMs. Moreover, LAS tailors the spike-equivalent Transformer components for spiking LLMs, which can ensure full spiking conversion without any loss of performance. Experimental results on six language models and two vision-language models demonstrate that LAS achieves loss-less conversion. Notably, on OPT-66B, LAS even improves the accuracy of 2\% on the WSC task. In addition, the parameter and ablation studies further verify the effectiveness of LAS. The source code is available at https://github.com/lc783/LAS
Convolutional Spiking Neural Network for Image Classification
2025-05-13
Mikhail Kiselev, Andrey Lavrentyev
We consider an implementation of convolutional architecture in a spiking neural network (SNN) used to classify images. As in the traditional neural network, the convolutional layers form informational "features" used as predictors in the SNN-based classifier with CoLaNET architecture. Since weight sharing contradicts the synaptic plasticity locality principle, the convolutional weights are fixed in our approach. We describe a methodology for their determination from a representative set of images from the same domain as the classified ones. We illustrate and test our approach on a classification task from the NEOVISION2 benchmark.
Efficient, simulation-free estimators of firing rates with Markovian surrogates
2025-05-13
Zhongyi Wang, Louis Tao, Zhuo-Cheng Xiao
Spiking neural networks (SNNs) are powerful mathematical models that integrate the biological details of neural systems, but their complexity often makes them computationally expensive and analytically untractable. The firing rate of an SNN is a crucial first-order statistic to characterize network activity. However, estimating firing rates analytically from even simplified SNN models is challenging due to 1) the intricate dependence between the nonlinear network dynamics and parameters, and 2) the singularity and irreversibility of spikes. In this Letter, we propose a class of computationally efficient, simulation-free estimators of firing rates. This is based on a hierarchy of Markovian approximations that reduces the complexity of SNN dynamics. We show that while considering firing rates alone is insufficient for accurate estimations of themselves, the information of spiking synchrony dramatically improves the estimator's accuracy. This approach provides a practical tool for brain modelers, directly mapping biological parameters to firing rate.
Hybrid Spiking Vision Transformer for Object Detection with Event Cameras
2025-05-12
Qi Xu, Jie Deng, Jiangrong Shen, Biwu Chen, Huajin Tang, Gang Pan
Event-based object detection has gained increasing attention due to its advantages such as high temporal resolution, wide dynamic range, and asynchronous address-event representation. Leveraging these advantages, Spiking Neural Networks (SNNs) have emerged as a promising approach, offering low energy consumption and rich spatiotemporal dynamics. To further enhance the performance of event-based object detection, this study proposes a novel hybrid spike vision Transformer (HsVT) model. The HsVT model integrates a spatial feature extraction module to capture local and global features, and a temporal feature extraction module to model time dependencies and long-term patterns in event sequences. This combination enables HsVT to capture spatiotemporal features, improving its capability to handle complex event-based object detection tasks. To support research in this area, we developed and publicly released The Fall Detection Dataset as a benchmark for event-based object detection tasks. This dataset, captured using an event-based camera, ensures facial privacy protection and reduces memory usage due to the event representation format. We evaluated the HsVT model on GEN1 and Fall Detection datasets across various model sizes. Experimental results demonstrate that HsVT achieves significant performance improvements in event detection with fewer parameters.
Self-Supervised Event Representations: Towards Accurate, Real-Time Perception on SoC FPGAs
2025-05-12
Kamil Jeziorek, Tomasz Kryjak
Event cameras offer significant advantages over traditional frame-based sensors. These include microsecond temporal resolution, robustness under varying lighting conditions and low power consumption. Nevertheless, the effective processing of their sparse, asynchronous event streams remains challenging. Existing approaches to this problem can be categorised into two distinct groups. The first group involves the direct processing of event data with neural models, such as Spiking Neural Networks or Graph Convolutional Neural Networks. However, this approach is often accompanied by a compromise in terms of qualitative performance. The second group involves the conversion of events into dense representations with handcrafted aggregation functions, which can boost accuracy at the cost of temporal fidelity. This paper introduces a novel Self-Supervised Event Representation (SSER) method leveraging Gated Recurrent Unit (GRU) networks to achieve precise per-pixel encoding of event timestamps and polarities without temporal discretisation. The recurrent layers are trained in a self-supervised manner to maximise the fidelity of event-time encoding. The inference is performed with event representations generated asynchronously, thus ensuring compatibility with high-throughput sensors. The experimental validation demonstrates that SSER outperforms aggregation-based baselines, achieving improvements of 2.4% mAP and 0.6% on the Gen1 and 1 Mpx object detection datasets. Furthermore, the paper presents the first hardware implementation of recurrent representation for event data on a System-on-Chip FPGA, achieving sub-microsecond latency and power consumption between 1-2 W, suitable for real-time, power-efficient applications. Code is available at https://github.com/vision-agh/RecRepEvent.
SAEN-BGS: Energy-Efficient Spiking AutoEncoder Network for Background Subtraction
2025-05-12
Zhixuan Zhang, Xiaopeng Li, Qi Liu
Background subtraction (BGS) is utilized to detect moving objects in a video and is commonly employed at the onset of object tracking and human recognition processes. Nevertheless, existing BGS techniques utilizing deep learning still encounter challenges with various background noises in videos, including variations in lighting, shifts in camera angles, and disturbances like air turbulence or swaying trees. To address this problem, we design a spiking autoencoder network, termed SAEN-BGS, based on noise resilience and time-sequence sensitivity of spiking neural networks (SNNs) to enhance the separation of foreground and background. To eliminate unnecessary background noise and preserve the important foreground elements, we begin by creating the continuous spiking conv-and-dconv block, which serves as the fundamental building block for the decoder in SAEN-BGS. Moreover, in striving for enhanced energy efficiency, we introduce a novel self-distillation spiking supervised learning method grounded in ANN-to-SNN frameworks, resulting in decreased power consumption. In extensive experiments conducted on CDnet-2014 and DAVIS-2016 datasets, our approach demonstrates superior segmentation performance relative to other baseline methods, even when challenged by complex scenarios with dynamic backgrounds.
Deep neural networks (DNNs) excel in computer vision tasks, especially, few-shot learning (FSL), which is increasingly important for generalizing from limited examples. However, DNNs are computationally expensive with scalability issues in real world. Spiking Neural Networks (SNNs), with their event-driven nature and low energy consumption, are particularly efficient in processing sparse and dynamic data, though they still encounter difficulties in capturing complex spatiotemporal features and performing accurate cross-class comparisons. To further enhance the performance and efficiency of SNNs in few-shot learning, we propose a few-shot learning framework based on SNNs, which combines a self-feature extractor module and a cross-feature contrastive module to refine feature representation and reduce power consumption. We apply the combination of temporal efficient training loss and InfoNCE loss to optimize the temporal dynamics of spike trains and enhance the discriminative power. Experimental results show that the proposed FSL-SNN significantly improves the classification performance on the neuromorphic dataset N-Omniglot, and also achieves competitive performance to ANNs on static datasets such as CUB and miniImageNet with low power consumption.
Efficient ANN-SNN Conversion with Error Compensation Learning
2025-05-12
Chang Liu, Jiangrong Shen, Xuming Ran, Mingkun Xu, Qi Xu, Yi Xu, Gang Pan
Artificial neural networks (ANNs) have demonstrated outstanding performance in numerous tasks, but deployment in resource-constrained environments remains a challenge due to their high computational and memory requirements. Spiking neural networks (SNNs) operate through discrete spike events and offer superior energy efficiency, providing a bio-inspired alternative. However, current ANN-to-SNN conversion often results in significant accuracy loss and increased inference time due to conversion errors such as clipping, quantization, and uneven activation. This paper proposes a novel ANN-to-SNN conversion framework based on error compensation learning. We introduce a learnable threshold clipping function, dual-threshold neurons, and an optimized membrane potential initialization strategy to mitigate the conversion error. Together, these techniques address the clipping error through adaptive thresholds, dynamically reduce the quantization error through dual-threshold neurons, and minimize the non-uniformity error by effectively managing the membrane potential. Experimental results on CIFAR-10, CIFAR-100, ImageNet datasets show that our method achieves high-precision and ultra-low latency among existing conversion methods. Using only two time steps, our method significantly reduces the inference time while maintains competitive accuracy of 94.75% on CIFAR-10 dataset under ResNet-18 structure. This research promotes the practical application of SNNs on low-power hardware, making efficient real-time processing possible.
Event-based Neural Spike Detection Using Spiking Neural Networks for Neuromorphic iBMI Systems
2025-05-10
Chanwook Hwang, Biyan Zhou, Ye Ke, Vivek Mohan, Jong Hwan Ko, Arindam Basu
Implantable brain-machine interfaces (iBMIs) are evolving to record from thousands of neurons wirelessly but face challenges in data bandwidth, power consumption, and implant size. We propose a novel Spiking Neural Network Spike Detector (SNN-SPD) that processes event-based neural data generated via delta modulation and pulse count modulation, converting signals into sparse events. By leveraging the temporal dynamics and inherent sparsity of spiking neural networks, our method improves spike detection performance while maintaining low computational overhead suitable for implantable devices. Our experimental results demonstrate that the proposed SNN-SPD achieves an accuracy of 95.72% at high noise levels (standard deviation 0.2), which is about 2% higher than the existing Artificial Neural Network Spike Detector (ANN-SPD). Moreover, SNN-SPD requires only 0.41% of the computation and about 26.62% of the weight parameters compared to ANN-SPD, with zero multiplications. This approach balances efficiency and performance, enabling effective data compression and power savings for next-generation iBMIs.
CogniSNN: A First Exploration to Random Graph Architecture based Spiking Neural Networks with Enhanced Expandability and Neuroplasticity
2025-05-09
Yongsheng Huang, Peibo Duan, Zhipeng Liu, Kai Sun, Changsheng Zhang, Bin Zhang, Mingkun Xu
Despite advances in spiking neural networks (SNNs) in numerous tasks, their architectures remain highly similar to traditional artificial neural networks (ANNs), restricting their ability to mimic natural connections between biological neurons. This paper develops a new modeling paradigm for SNN with random graph architecture (RGA), termed Cognition-aware SNN (CogniSNN). Furthermore, we improve the expandability and neuroplasticity of CogniSNN by introducing a modified spiking residual neural node (ResNode) to counteract network degradation in deeper graph pathways, as well as a critical path-based algorithm that enables CogniSNN to perform continual learning on new tasks leveraging the features of the data and the RGA learned in the old task. Experiments show that CogniSNN with re-designed ResNode performs outstandingly in neuromorphic datasets with fewer parameters, achieving 95.5% precision in the DVS-Gesture dataset with only 5 timesteps. The critical path-based approach decreases 3% to 5% forgetting while maintaining expected performance in learning new tasks that are similar to or distinct from the old ones. This study showcases the potential of RGA-based SNN and paves a new path for biologically inspired networks based on graph theory.
This work presents an efficient decoding pipeline for neuromorphic implantable brain-machine interfaces (Neu-iBMI), leveraging sparse neural event data from an event-based neural sensing scheme. We introduce a tunable event filter (EvFilter), which also functions as a spike detector (EvFilter-SPD), significantly reducing the number of events processed for decoding by 192X and 554X, respectively. The proposed pipeline achieves high decoding performance, up to R^2=0.73, with ANN- and SNN-based decoders, eliminating the need for signal recovery, spike detection, or sorting, commonly performed in conventional iBMI systems. The SNN-Decoder reduces computations and memory required by 5-23X compared to NN-, and LSTM-Decoders, while the ST-NN-Decoder delivers similar performance to an LSTM-Decoder requiring 2.5X fewer resources. This streamlined approach significantly reduces computational and memory demands, making it ideal for low-power, on-implant, or wearable iBMIs.
Large language models (LLMs) are rapidly pushing the limits of contemporary computing hardware. For example, training GPT-3 has been estimated to consume around 1300 MWh of electricity, and projections suggest future models may require city-scale (gigawatt) power budgets. These demands motivate exploration of computing paradigms beyond conventional von Neumann architectures. This review surveys emerging photonic hardware optimized for next-generation generative AI computing. We discuss integrated photonic neural network architectures (e.g., Mach-Zehnder interferometer meshes, lasers, wavelength-multiplexed microring resonators) that perform ultrafast matrix operations. We also examine promising alternative neuromorphic devices, including spiking neural network circuits and hybrid spintronic-photonic synapses, which combine memory and processing. The integration of two-dimensional materials (graphene, TMDCs) into silicon photonic platforms is reviewed for tunable modulators and on-chip synaptic elements. Transformer-based LLM architectures (self-attention and feed-forward layers) are analyzed in this context, identifying strategies and challenges for mapping dynamic matrix multiplications onto these novel hardware substrates. We then dissect the mechanisms of mainstream LLMs, such as ChatGPT, DeepSeek, and LLaMA, highlighting their architectural similarities and differences. We synthesize state-of-the-art components, algorithms, and integration methods, highlighting key advances and open issues in scaling such systems to mega-sized LLM models. We find that photonic computing systems could potentially surpass electronic processors by orders of magnitude in throughput and energy efficiency, but require breakthroughs in memory, especially for long-context windows and long token sequences, and in storage of ultra-large datasets.
Neuromorphic computing aims to improve the efficiency of artificial neural networks by taking inspiration from biological neurons and leveraging temporal sparsity, spatial sparsity, and compute near/in memory. Although these approaches have shown efficiency gains, training these spiking neural networks (SNN) remains difficult. The original attempts at converting trained conventional analog neural networks (ANN) to SNNs used the rate of binary spikes to represent neuron activations. This required many simulation time steps per inference, which degraded efficiency. Intel's Loihi 2 is a neuromorphic platform that supports graded spikes which can be used to represent changes in neuron activation. In this work, we use Loihi 2's graded spikes to develop a method for converting ANN networks to spiking networks, which take advantage of temporal and spatial sparsity. We evaluated the performance of this network on Loihi 2 and compared it to NVIDIA's Jetson Xavier edge AI platform.
Recently, spiking neural networks (SNNs), deployed on neuromorphic chips, provide highly efficient solutions on edge devices in different scenarios. However, their ability to adapt to distribution shifts after deployment has become a crucial challenge. Online test-time adaptation (OTTA) offers a promising solution by enabling models to dynamically adjust to new data distributions without requiring source data or labeled target samples. Nevertheless, existing OTTA methods are largely designed for traditional artificial neural networks and are not well-suited for SNNs. To address this gap, we propose a low-power, neuromorphic chip-friendly online test-time adaptation framework, aiming to enhance model generalization under distribution shifts. The proposed approach is called Threshold Modulation (TM), which dynamically adjusts the firing threshold through neuronal dynamics-inspired normalization, being more compatible with neuromorphic hardware. Experimental results on benchmark datasets demonstrate the effectiveness of this method in improving the robustness of SNNs against distribution shifts while maintaining low computational cost. The proposed method offers a practical solution for online test-time adaptation of SNNs, providing inspiration for the design of future neuromorphic chips. The demo code is available at github.com/NneurotransmitterR/TM-OTTA-SNN.
2025-05-08
Giovanni Perin, Cesare Bidini, Riccardo Mazzieri, Michele Rossi
In recent years, spiking neural networks (SNNs) have gained momentum due to their high potential in time-series processing combined with minimal energy consumption. However, they still lack a dedicated and efficient training algorithm. The popular backpropagation with surrogate gradients, adapted from stochastic gradient descent (SGD)-derived algorithms, has several drawbacks when used as an optimizer for SNNs. Specifically, it suffers from low scalability and numerical imprecision. In this paper, we propose a novel SNN training method based on the alternating direction method of multipliers (ADMM). Our ADMM-based training aims to solve the problem of the SNN step function's non-differentiability. We formulate the problem, derive closed-form updates, and empirically show the optimizer's convergence properties, great potential, and possible new research directions to improve the method in a simulated proof-of-concept.
TS-SNN: Temporal Shift Module for Spiking Neural Networks
2025-05-07
Kairong Yu, Tianqing Zhang, Qi Xu, Gang Pan, Hongwei Wang
Spiking Neural Networks (SNNs) are increasingly recognized for their biological plausibility and energy efficiency, positioning them as strong alternatives to Artificial Neural Networks (ANNs) in neuromorphic computing applications. SNNs inherently process temporal information by leveraging the precise timing of spikes, but balancing temporal feature utilization with low energy consumption remains a challenge. In this work, we introduce Temporal Shift module for Spiking Neural Networks (TS-SNN), which incorporates a novel Temporal Shift (TS) module to integrate past, present, and future spike features within a single timestep via a simple yet effective shift operation. A residual combination method prevents information loss by integrating shifted and original features. The TS module is lightweight, requiring only one additional learnable parameter, and can be seamlessly integrated into existing architectures with minimal additional computational cost. TS-SNN achieves state-of-the-art performance on benchmarks like CIFAR-10 (96.72\%), CIFAR-100 (80.28\%), and ImageNet (70.61\%) with fewer timesteps, while maintaining low energy consumption. This work marks a significant step forward in developing efficient and accurate SNN architectures.
Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks
2025-05-07
Ayana Moshruba, Hamed Poursiami, Maryam Parsa
Biological neurons exhibit diverse temporal spike patterns, which are believed to support efficient, robust, and adaptive neural information processing. While models such as Izhikevich can replicate a wide range of these firing dynamics, their complexity poses challenges for directly integrating them into scalable spiking neural networks (SNN) training pipelines. In this work, we propose two probabilistically driven, input-level temporal spike transformations: Poisson-Burst and Delayed-Burst that introduce biologically inspired temporal variability directly into standard Leaky Integrate-and-Fire (LIF) neurons. This enables scalable training and systematic evaluation of how spike timing dynamics affect privacy, generalization, and learning performance. Poisson-Burst modulates burst occurrence based on input intensity, while Delayed-Burst encodes input strength through burst onset timing. Through extensive experiments across multiple benchmarks, we demonstrate that Poisson-Burst maintains competitive accuracy and lower resource overhead while exhibiting enhanced privacy robustness against membership inference attacks, whereas Delayed-Burst provides stronger privacy protection at a modest accuracy trade-off. These findings highlight the potential of biologically grounded temporal spike dynamics in improving the privacy, generalization and biological plausibility of neuromorphic learning systems.
As Spiking Neural Networks (SNNs) gain traction across various applications, understanding their security vulnerabilities becomes increasingly important. In this work, we focus on the adversarial attacks, which is perhaps the most concerning threat. An adversarial attack aims at finding a subtle input perturbation to fool the network's decision-making. We propose two novel adversarial attack algorithms for SNNs: an input-specific attack that crafts adversarial samples from specific dataset inputs and a universal attack that generates a reusable patch capable of inducing misclassification across most inputs, thus offering practical feasibility for real-time deployment. The algorithms are gradient-based operating in the spiking domain proving to be effective across different evaluation metrics, such as adversarial accuracy, stealthiness, and generation time. Experimental results on two widely used neuromorphic vision datasets, NMNIST and IBM DVS Gesture, show that our proposed attacks surpass in all metrics all existing state-of-the-art methods. Additionally, we present the first demonstration of adversarial attack generation in the sound domain using the SHD dataset.
Brain-Inspired Quantum Neural Architectures for Pattern Recognition: Integrating QSNN and QLSTM
2025-05-03
Eva Andrés, Manuel Pegalajar Cuéllar, Gabriel Navarro
Recent advances in the fields of deep learning and quantum computing have paved the way for innovative developments in artificial intelligence. In this manuscript, we leverage these cutting-edge technologies to introduce a novel model that emulates the intricate functioning of the human brain, designed specifically for the detection of anomalies such as fraud in credit card transactions. Leveraging the synergies of Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, our approach is developed in two distinct stages, closely mirroring the information processing mechanisms found in the brain's sensory and memory systems. In the initial stage, similar to the brain's hypothalamus, we extract low-level information from the data, emulating sensory data processing patterns. In the subsequent stage, resembling the hippocampus, we process this information at a higher level, capturing and memorizing correlated patterns. We will compare this model with other quantum models such as Quantum Neural Networks among others and their corresponding classical models.
Spiking Neural Networks (SNNs) have been put forward as an energy-efficient alternative to Artificial Neural Networks (ANNs) since they perform sparse Accumulate operations instead of the power-hungry Multiply-and-Accumulate operations. ANN-SNN conversion is a widely used method to realize deep SNNs with accuracy comparable to that of ANNs.~\citeauthor{bu2023optimal} recently proposed the Quantization-Clip-Floor-Shift (QCFS) activation as an alternative to ReLU to minimize the accuracy loss during ANN-SNN conversion. Nevertheless, SNN inferencing requires a large number of timesteps to match the accuracy of the source ANN for real-world datasets. In this work, we propose PASCAL, which performs ANN-SNN conversion in such a way that the resulting SNN is mathematically equivalent to an ANN with QCFS-activation, thereby yielding similar accuracy as the source ANN with minimal inference timesteps. In addition, we propose a systematic method to configure the quantization step of QCFS activation in a layerwise manner, which effectively determines the optimal number of timesteps per layer for the converted SNN. Our results show that the ResNet-34 SNN obtained using PASCAL achieves an accuracy of $\approx$74\% on ImageNet with a 64$\times$ reduction in the number of inference timesteps compared to existing approaches.
In relativistic heavy-ion collisions, charged particles are accelerated to nearly the speed of light, and their external electromagnetic fields can be effectively approximated as quasi-real photons. These photons interact with another nucleus via photon-nuclear interactions, producing vector mesons. These vector mesons possess extremely low transverse momentum (pT ~ 0.1 GeV/c), distinguishing them from particles produced via hadronic interactions. STAR and ALICE have observed J/psi, rho0 and other vector mesons with very low pT, which are well described by photoproduction models. This unique characteristic of having extremely low transverse momentum allows them to serve as a novel experimental probe. Recent STAR results show that the equivalent photons in photoproduction processes are fully linearly polarized, affecting the azimuthal distribution of final-state particles like rho0 -> pi+ pi-. Since the polarization links to the initial collision geometry, the rho0 azimuthal modulation can probe nuclear structure. However, the post-collision magnetic field may deflect these particles, distorting the azimuthal distribution and complicating structure measurements. We simulated the distribution of residual magnetic fields over time under different collision conditions using UrQMD for Au+Au collisions at sqrt(sNN)=200 GeV and calculated their effects on the azimuthal modulation () of photoproduced rho0. Our results show that in peripheral collisions, the field significantly alters the for photoproduced rho0 with pT ~ 0.1 GeV/c. This provides key insights for future nuclear structure studies via photoproduction in peripheral collisions.
Akkumula: Evidence accumulation driver models with Spiking Neural Networks
2025-04-30
Alberto Morando
Processes of evidence accumulation for motor control contribute to the ecological validity of driver models. According to established theories of cognition, drivers make control adjustments when a process of accumulation of perceptual inputs reaches a decision boundary. Unfortunately, there is not a standard way for building such models, limiting their use. Current implementations are hand-crafted, lack adaptability, and rely on inefficient optimization techniques that do not scale well with large datasets. This paper introduces Akkumula, an evidence accumulation modelling framework built using deep learning techniques to leverage established coding libraries, gradient optimization, and large batch training. The core of the library is based on Spiking Neural Networks, whose operation mimic the evidence accumulation process in the biological brain. The model was tested on data collected during a test-track experiment. Results are promising. The model fits well the time course of vehicle control (brake, accelerate, steering) based on vehicle sensor data. The perceptual inputs are extracted by a dedicated neural network, increasing the context-awareness of the model in dynamic scenarios. Akkumula integrates with existing machine learning architectures, benefits from continuous advancements in deep learning, efficiently processes large datasets, adapts to diverse driving scenarios, and maintains a degree of transparency in its core mechanisms.
Head-Tail-Aware KL Divergence in Knowledge Distillation for Spiking Neural Networks
2025-04-29
Tianqing Zhang, Zixin Zhu, Kairong Yu, Hongwei Wang
Spiking Neural Networks (SNNs) have emerged as a promising approach for energy-efficient and biologically plausible computation. However, due to limitations in existing training methods and inherent model constraints, SNNs often exhibit a performance gap when compared to Artificial Neural Networks (ANNs). Knowledge distillation (KD) has been explored as a technique to transfer knowledge from ANN teacher models to SNN student models to mitigate this gap. Traditional KD methods typically use Kullback-Leibler (KL) divergence to align output distributions. However, conventional KL-based approaches fail to fully exploit the unique characteristics of SNNs, as they tend to overemphasize high-probability predictions while neglecting low-probability ones, leading to suboptimal generalization. To address this, we propose Head-Tail Aware Kullback-Leibler (HTA-KL) divergence, a novel KD method for SNNs. HTA-KL introduces a cumulative probability-based mask to dynamically distinguish between high- and low-probability regions. It assigns adaptive weights to ensure balanced knowledge transfer, enhancing the overall performance. By integrating forward KL (FKL) and reverse KL (RKL) divergence, our method effectively align both head and tail regions of the distribution. We evaluate our methods on CIFAR-10, CIFAR-100 and Tiny ImageNet datasets. Our method outperforms existing methods on most datasets with fewer timesteps.
Revisiting Reset Mechanisms in Spiking Neural Networks for Sequential Modeling: Specialized Discretization for Binary Activated RNN
2025-04-24
Enqi Zhang
In the field of image recognition, spiking neural networks (SNNs) have achieved performance comparable to conventional artificial neural networks (ANNs). In such applications, SNNs essentially function as traditional neural networks with quantized activation values. This article focuses on an another alternative perspective,viewing SNNs as binary-activated recurrent neural networks (RNNs) for sequential modeling tasks. From this viewpoint, current SNN architectures face several fundamental challenges in sequence modeling: (1) Traditional models lack effective memory mechanisms for long-range sequence modeling; (2) The biological-inspired components in SNNs (such as reset mechanisms and refractory period applications) remain theoretically under-explored for sequence tasks; (3) The RNN-like computational paradigm in SNNs prevents parallel training across different timesteps. To address these challenges, this study conducts a systematic analysis of the fundamental mechanisms underlying reset operations and refractory periods in binary-activated RNN-based SNN sequence models. We re-examine whether such biological mechanisms are strictly necessary for generating sparse spiking patterns, provide new theoretical explanations and insights, and ultimately propose the fixed-refractory-period SNN architecture for sequence modeling.
In this paper, we propose a novel machine learning-based method to solve the acoustic scattering problem in unbounded domain. We first employ the Dirichlet-to-Neumann (DtN) operator to truncate the physically unbounded domain into a computable bounded domain. This transformation reduces the original scattering problem in the unbounded domain to a boundary value problem within the bounded domain. To solve this boundary value problem, we design a neural network with a subspace layer, where each neuron in this layer represents a basis function. Consequently, the approximate solution can be expressed by a linear combination of these basis functions. Furthermore, we introduce an innovative alternating optimization technique which alternately updates the basis functions and their linear combination coefficients respectively by training and least squares methods. In our method, we set the coefficients of basis functions to 1 and use a new loss function each time train the subspace. These innovations ensure that the subspace formed by these basis functions is truly optimized. We refer to this method as the alternately-optimized subspace method based on neural networks (AO-SNN). Extensive numerical experiments demonstrate that our new method can significantly reduce the relative $l^2$ error to $10^{-7}$ or lower, outperforming existing machine learning-based methods to the best of our knowledge.
A Deep Bayesian Convolutional Spiking Neural Network-based CAD system with Uncertainty Quantification for Medical Images Classification
2025-04-23
Mohaddeseh Chegini, Ali Mahloojifar
The Computer_Aided Diagnosis (CAD) systems facilitate accurate diagnosis of diseases. The development of CADs by leveraging third generation neural network, namely, Spiking Neural Network (SNN), is essential to utilize of the benefits of SNNs, such as their event_driven processing, parallelism, low power consumption, and the ability to process sparse temporal_spatial information. However, Deep SNN as a deep learning model faces challenges with unreliability. To deal with unreliability challenges due to inability to quantify the uncertainty of the predictions, we proposed a deep Bayesian Convolutional Spiking Neural Network based_CADs with uncertainty_aware module. In this study, the Monte Carlo Dropout method as Bayesian approximation is used as an uncertainty quantification method. This method was applied to several medical image classification tasks. Our experimental results demonstrate that our proposed model is accurate and reliable and will be a proper alternative to conventional deep learning for medical image classification.
Learning Dynamic Graphs via Tensorized and Lightweight Graph Convolutional Networks
2025-04-22
Minglian Han
A dynamic graph (DG) is frequently encountered in numerous real-world scenarios. Consequently, A dynamic graph convolutional network (DGCN) has been successfully applied to perform precise representation learning on a DG. However, conventional DGCNs typically consist of a static GCN coupled with a sequence neural network (SNN) to model spatial and temporal patterns separately. This decoupled modeling mechanism inherently disrupts the intricate spatio-temporal dependencies. To address the issue, this study proposes a novel Tensorized Lightweight Graph Convolutional Network (TLGCN) for accurate dynamic graph learning. It mainly contains the following two key concepts: a) designing a novel spatio-temporal information propagation method for joint propagation of spatio-temporal information based on the tensor M-product framework; b) proposing a tensorized lightweight graph convolutional network based on the above method, which significantly reduces the memory occupation of the model by omitting complex feature transformation and nonlinear activation. Numerical experiments on four real-world datasets demonstrate that the proposed TLGCN outperforms the state-of-the-art models in the weight estimation task on DGs.
Ultra-Low-Power Spiking Neurons in 7 nm FinFET Technology: A Comparative Analysis of Leaky Integrate-and-Fire, Morris-Lecar, and Axon-Hillock Architectures
2025-04-21
Logan Larsh, Raiyan Siddique, Sarah Sharif Yaser Mike Banad
Neuromorphic computing aims to replicate the brain's remarkable energy efficiency and parallel processing capabilities for large-scale artificial intelligence applications. In this work, we present a comprehensive comparative study of three spiking neuron circuit architectures-Leaky-Integrate-and-Fire (LIF), Morris-Lecar (ML), and Axon-Hillock (AH)-implemented in a 7 nm FinFET technology. Through extensive SPICE simulations, we explore the optimization of spiking frequency, energy per spike, and static power consumption. Our results show that the AH design achieves the highest throughput, demonstrating multi-gigahertz firing rates (up to 3 GHz) with attojoule energy costs. By contrast, the ML architecture excels in subthreshold to near-threshold regimes, offering robust low-power operation (as low as 0.385 aJ/spike) and biological bursting behavior. Although LIF benefits from a decoupled current mirror for high-frequency operation, it exhibits slightly higher static leakage compared to ML and AH at elevated supply voltages. Comparisons with previous node implementations (22 nm planar, 28 nm) reveal that 7 nm FinFETs can drastically boost energy efficiency and speed albeit at the cost of increased subthreshold leakage in deep subthreshold regions. By quantifying design trade-offs for each neuron architecture, our work provides a roadmap for optimizing spiking neuron circuits in advanced nanoscale technologies to deliver neuromorphic hardware capable of both ultra-low-power operation and high computational throughput.
Efficient Spiking Point Mamba for Point Cloud Analysis
2025-04-19
Peixi Wu, Bosong Chai, Menghua Zheng, Wei Li, Zhangchi Hu, Jie Chen, Zheyu Zhang, Hebei Li, Xiaoyan Sun
Bio-inspired Spiking Neural Networks (SNNs) provide an energy-efficient way to extract 3D spatio-temporal features. However, existing 3D SNNs have struggled with long-range dependencies until the recent emergence of Mamba, which offers superior computational efficiency and sequence modeling capability. In this work, we propose Spiking Point Mamba (SPM), the first Mamba-based SNN in the 3D domain. Due to the poor performance of simply transferring Mamba to 3D SNNs, SPM is designed to utilize both the sequence modeling capabilities of Mamba and the temporal feature extraction of SNNs. Specifically, we first introduce Hierarchical Dynamic Encoding (HDE), an improved direct encoding method that effectively introduces dynamic temporal mechanism, thereby facilitating temporal interactions. Then, we propose a Spiking Mamba Block (SMB), which builds upon Mamba while learning inter-time-step features and minimizing information loss caused by spikes. Finally, to further enhance model performance, we adopt an asymmetric SNN-ANN architecture for spike-based pre-training and finetune. Compared with the previous state-of-the-art SNN models, SPM improves OA by +6.2%, +6.1%, and +7.4% on three variants of ScanObjectNN, and boosts instance mIOU by +1.9% on ShapeNetPart. Meanwhile, its energy consumption is at least 3.5x lower than that of its ANN counterpart. The code will be made publicly available.
SwitchMT: An Adaptive Context Switching Methodology for Scalable Multi-Task Learning in Intelligent Autonomous Agents
2025-04-18
Avaneesh Devkota, Rachmad Vidya Wicaksana Putra, Muhammad Shafique
The ability to train intelligent autonomous agents (such as mobile robots) on multiple tasks is crucial for adapting to dynamic real-world environments. However, state-of-the-art reinforcement learning (RL) methods only excel in single-task settings, and still struggle to generalize across multiple tasks due to task interference. Moreover, real-world environments also demand the agents to have data stream processing capabilities. Toward this, a state-of-the-art work employs Spiking Neural Networks (SNNs) to improve multi-task learning by exploiting temporal information in data stream, while enabling lowpower/energy event-based operations. However, it relies on fixed context/task-switching intervals during its training, hence limiting the scalability and effectiveness of multi-task learning. To address these limitations, we propose SwitchMT, a novel adaptive task-switching methodology for RL-based multi-task learning in autonomous agents. Specifically, SwitchMT employs the following key ideas: (1) a Deep Spiking Q-Network with active dendrites and dueling structure, that utilizes task-specific context signals to create specialized sub-networks; and (2) an adaptive task-switching policy that leverages both rewards and internal dynamics of the network parameters. Experimental results demonstrate that SwitchMT achieves superior performance in multi-task learning compared to state-of-the-art methods. It achieves competitive scores in multiple Atari games (i.e., Pong: -8.8, Breakout: 5.6, and Enduro: 355.2) compared to the state-of-the-art, showing its better generalized learning capability. These results highlight the effectiveness of our SwitchMT methodology in addressing task interference while enabling multi-task learning automation through adaptive task switching, thereby paving the way for more efficient generalist agents with scalable multi-task learning capabilities.
Causal pieces: analysing and improving spiking neural networks piece by piece
2025-04-18
Dominik Dold, Philipp Christian Petersen
We introduce a novel concept for spiking neural networks (SNNs) derived from the idea of "linear pieces" used to analyse the expressiveness and trainability of artificial neural networks (ANNs). We prove that the input domain of SNNs decomposes into distinct causal regions where its output spike times are locally Lipschitz continuous with respect to the input spike times and network parameters. The number of such regions - which we call "causal pieces" - is a measure of the approximation capabilities of SNNs. In particular, we demonstrate in simulation that parameter initialisations which yield a high number of causal pieces on the training set strongly correlate with SNN training success. Moreover, we find that feedforward SNNs with purely positive weights exhibit a surprisingly high number of causal pieces, allowing them to achieve competitive performance levels on benchmark tasks. We believe that causal pieces are not only a powerful and principled tool for improving SNNs, but might also open up new ways of comparing SNNs and ANNs in the future.
Simple spiking neural network models, such as those built from interacting integrate-and-fire (IF) units, exhibit rich emergent behaviours but remain notoriously difficult to analyse, particularly in terms of their pattern-forming properties. In contrast, rate-based models and coupled phase oscillators offer greater mathematical tractability but fail to capture the full dynamical complexity of spiking networks. To bridge these modelling paradigms, Hermann Haken -- the pioneer of Synergetics -- introduced the Lighthouse model, a framework that provides insights into synchronisation, travelling waves, and pattern formation in neural systems. In this work, we revisit the Lighthouse model and develop new mathematical results that deepen our understanding of self-organisation in spiking neural networks. Specifically, we derive the linear stability conditions for phase-locked spiking states in Lighthouse networks structured on graphs with realistic synaptic interactions ($\alpha$-function synapses) and axonal conduction delays. Extending the analysis on graphs to a spatially continuous (non-local) setting, we develop a variant of Turing instability analysis to explore emergent spiking patterns. Finally, we show how localised spiking bump solutions -- which are difficult to mathematically analyse in IF networks -- are far more tractable in the Lighthouse model and analyse their linear stability to wandering states. These results reaffirm the Lighthouse model as a valuable tool for studying structured neural interactions and self-organisation, further advancing the synergetic perspective on spiking neural dynamics.
Spike-Kal: A Spiking Neuron Network Assisted Kalman Filter
2025-04-17
Xun Xiao, Junbo Tie, Jinyue Zhao, Ziqi Wang, Yuan Li, Qiang Dou, Lei Wang
Kalman filtering can provide an optimal estimation of the system state from noisy observation data. This algorithm's performance depends on the accuracy of system modeling and noise statistical characteristics, which are usually challenging to obtain in practical applications. The powerful nonlinear modeling capabilities of deep learning, combined with its ability to extract features from large amounts of data automatically, offer new opportunities for improving the Kalman filter. This paper proposes a novel method that leverages the Spiking Neural Network to optimize the Kalman filter. Our approach aims to reduce the reliance on prior knowledge of system and observation noises, allowing for adaptation to varying statistical characteristics of time-varying noise. Furthermore, we investigate the potential of SNNs in improving the computational efficiency of the Kalman filter. In our method, we design an integration strategy between the SNN and the Kalman filter. The SNN is trained to directly approximate the optimal gain matrix from observation data, thereby alleviating the computational burden of complex matrix operations inherent in traditional Kalman filtering while maintaining the accuracy and robustness of state estimation. Its average error has been reduced by 18\%-65\% compared with other methods.
Embodied Neuromorphic Control Applied on a 7-DOF Robotic Manipulator
2025-04-17
Ziqi Wang, Jingyue Zhao, Jichao Yang, Yaohua Wang, Xun Xiao, Yuan Li, Chao Xiao, Lei Wang
The development of artificial intelligence towards real-time interaction with the environment is a key aspect of embodied intelligence and robotics. Inverse dynamics is a fundamental robotics problem, which maps from joint space to torque space of robotic systems. Traditional methods for solving it rely on direct physical modeling of robots which is difficult or even impossible due to nonlinearity and external disturbance. Recently, data-based model-learning algorithms are adopted to address this issue. However, they often require manual parameter tuning and high computational costs. Neuromorphic computing is inherently suitable to process spatiotemporal features in robot motion control at extremely low costs. However, current research is still in its infancy: existing works control only low-degree-of-freedom systems and lack performance quantification and comparison. In this paper, we propose a neuromorphic control framework to control 7 degree-of-freedom robotic manipulators. We use Spiking Neural Network to leverage the spatiotemporal continuity of the motion data to improve control accuracy, and eliminate manual parameters tuning. We validated the algorithm on two robotic platforms, which reduces torque prediction error by at least 60% and performs a target position tracking task successfully. This work advances embodied neuromorphic control by one step forward from proof of concept to applications in complex real-world tasks.
Graph Transformers (GTs), which simultaneously integrate message-passing and self-attention mechanisms, have achieved promising empirical results in some graph prediction tasks. Although these approaches show the potential of Transformers in capturing long-range graph topology information, issues concerning the quadratic complexity and high computing energy consumption severely limit the scalability of GTs on large-scale graphs. Recently, as brain-inspired neural networks, Spiking Neural Networks (SNNs), facilitate the development of graph representation learning methods with lower computational and storage overhead through the unique event-driven spiking neurons. Inspired by these characteristics, we propose a linear-time Graph Transformer using Spiking Vector Quantization (GT-SVQ) for node classification. GT-SVQ reconstructs codebooks based on rate coding outputs from spiking neurons, and injects the codebooks into self-attention blocks to aggregate global information in linear complexity. Besides, spiking vector quantization effectively alleviates codebook collapse and the reliance on complex machinery (distance measure, auxiliary loss, etc.) present in previous vector quantization-based graph learning methods. In experiments, we compare GT-SVQ with other state-of-the-art baselines on node classification datasets ranging from small to large. Experimental results show that GT-SVQ has achieved competitive performances on most datasets while maintaining up to 130x faster inference speed compared to other GTs.
Polarisation-Inclusive Spiking Neural Networks for Real-Time RFI Detection in Modern Radio Telescopes
2025-04-16
Nicholas J. Pritchard, Andreas Wicenec, Richard Dodson, Mohammed Bennamoun
Radio Frequency Interference (RFI) is a known growing challenge for radio astronomy, intensified by increasing observatory sensitivity and prevalence of orbital RFI sources. Spiking Neural Networks (SNNs) offer a promising solution for real-time RFI detection by exploiting the time-varying nature of radio observation and neuron dynamics together. This work explores the inclusion of polarisation information in SNN-based RFI detection, using simulated data from the Hydrogen Epoch of Reionisation Array (HERA) instrument and provides power usage estimates for deploying SNN-based RFI detection on existing neuromorphic hardware. Preliminary results demonstrate state-of-the-art detection accuracy and highlight possible extensive energy-efficiency gains.
A PyTorch-Compatible Spike Encoding Framework for Energy-Efficient Neuromorphic Applications
2025-04-15
Alexandru Vasilache, Jona Scholz, Vincent Schilling, Sven Nitzsche, Florian Kaelber, Johannes Korsch, Juergen Becker
Spiking Neural Networks (SNNs) offer promising energy efficiency advantages, particularly when processing sparse spike trains. However, their incompatibility with traditional datasets, which consist of batches of input vectors rather than spike trains, necessitates the development of efficient encoding methods. This paper introduces a novel, open-source PyTorch-compatible Python framework for spike encoding, designed for neuromorphic applications in machine learning and reinforcement learning. The framework supports a range of encoding algorithms, including Leaky Integrate-and-Fire (LIF), Step Forward (SF), Pulse Width Modulation (PWM), and Ben's Spiker Algorithm (BSA), as well as specialized encoding strategies covering population coding and reinforcement learning scenarios. Furthermore, we investigate the performance trade-offs of each method on embedded hardware using C/C++ implementations, considering energy consumption, computation time, spike sparsity, and reconstruction accuracy. Our findings indicate that SF typically achieves the lowest reconstruction error and offers the highest energy efficiency and fastest encoding speed, achieving the second-best spike sparsity. At the same time, other methods demonstrate particular strengths depending on the signal characteristics. This framework and the accompanying empirical analysis provide valuable resources for selecting optimal encoding strategies for energy-efficient SNN applications.
Adaptively Pruned Spiking Neural Networks for Energy-Efficient Intracortical Neural Decoding
2025-04-15
Francesca Rivelli, Martin Popov, Charalampos S. Kouzinopoulos, Guangzhi Tang
Intracortical brain-machine interfaces demand low-latency, energy-efficient solutions for neural decoding. Spiking Neural Networks (SNNs) deployed on neuromorphic hardware have demonstrated remarkable efficiency in neural decoding by leveraging sparse binary activations and efficient spatiotemporal processing. However, reducing the computational cost of SNNs remains a critical challenge for developing ultra-efficient intracortical neural implants. In this work, we introduce a novel adaptive pruning algorithm specifically designed for SNNs with high activation sparsity, targeting intracortical neural decoding. Our method dynamically adjusts pruning decisions and employs a rollback mechanism to selectively eliminate redundant synaptic connections without compromising decoding accuracy. Experimental evaluation on the NeuroBench Non-Human Primate (NHP) Motor Prediction benchmark shows that our pruned network achieves performance comparable to dense networks, with a maximum tenfold improvement in efficiency. Moreover, hardware simulation on the neuromorphic processor reveals that the pruned network operates at sub-$\mu$W power levels, underscoring its potential for energy-constrained neural implants. These results underscore the promise of our approach for advancing energy-efficient intracortical brain-machine interfaces with low-overhead on-device intelligence.
Advancing RFI-Detection in Radio Astronomy with Liquid State Machines
2025-04-14
Nicholas J Pritchard, Andreas Wicenec, Mohammed Bennamoun, Richard Dodson
Radio Frequency Interference (RFI) from anthropogenic radio sources poses significant challenges to current and future radio telescopes. Contemporary approaches to detecting RFI treat the task as a semantic segmentation problem on radio telescope spectrograms. Typically, complex heuristic algorithms handle this task of `flagging' in combination with manual labeling (in the most difficult cases). While recent machine-learning approaches have demonstrated high accuracy, they often fail to meet the stringent operational requirements of modern radio observatories. Owing to their inherently time-varying nature, spiking neural networks (SNNs) are a promising alternative method to RFI-detection by utilizing the time-varying nature of the spectrographic source data. In this work, we apply Liquid State Machines (LSMs), a class of spiking neural networks, to RFI-detection. We employ second-order Leaky Integrate-and-Fire (LiF) neurons, marking the first use of this architecture and neuron type for RFI-detection. We test three encoding methods and three increasingly complex readout layers, including a transformer decoder head, providing a hybrid of SNN and ANN techniques. Our methods extend LSMs beyond conventional classification tasks to fine-grained spatio-temporal segmentation. We train LSMs on simulated data derived from the Hyrogen Epoch of Reionization Array (HERA), a known benchmark for RFI-detection. Our model achieves a per-pixel accuracy of 98% and an F1-score of 0.743, demonstrating competitive performance on this highly challenging task. This work expands the sophistication of SNN techniques and architectures applied to RFI-detection, and highlights the effectiveness of LSMs in handling fine-grained, complex, spatio-temporal signal-processing tasks.
2025-04-14
Yuchen Tian, Assel Kembay, Nhan Duy Truong, Jason K. Eshraghian, Omid Kavehei
Brain networks rely on precise spike timing and coordinated activity to support robust and energy-efficient learning. Inspired by these principles, spiking neural networks (SNNs) are widely regarded as promising candidates for low-power, event-driven computing. However, most biologically-inspired learning rules employed in SNNs, including spike-timing-dependent plasticity (STDP), rely on isolated spike pairs and lack sensitivity to population-level activity. This limits their stability and generalization, particularly in noisy and fast-changing environments. Motivated by biological observations that neural synchrony plays a central role in learning and memory, we introduce a spike-synchrony-dependent plasticity (SSDP) rule that adjusts synaptic weights based on the degree of coordinated firing among neurons. SSDP supports stable and scalable learning by encouraging neurons to form coherent activity patterns. One prominent outcome is a sudden transition from unstable to stable dynamics during training, suggesting that synchrony may drive convergence toward equilibrium firing regimes. We demonstrate SSDP's effectiveness across multiple network types, from minimal-layer models to spiking ResNets and SNN-Transformer. To our knowledge, this is the first application of a synaptic plasticity mechanism in a spiking transformer. SSDP operates in a fully event-driven manner and incurs minimal computational cost, making it well-suited for neuromorphic deployment. In this approach, local synaptic modifications are associated with the collective dynamics of neural networks, resulting in a learning strategy that adheres to biological principles while maintaining practical efficiency, these findings position SSDP as a general-purpose optimization strategy for SNNs, while offering new insights into population-based learning mechanisms in the brain.
snnTrans-DHZ: A Lightweight Spiking Neural Network Architecture for Underwater Image Dehazing
2025-04-13
Vidya Sudevan, Fakhreddine Zayer, Rizwana Kausar, Sajid Javed, Hamad Karki, Giulia De Masi, Jorge Dias
Underwater image dehazing is critical for vision-based marine operations because light scattering and absorption can severely reduce visibility. This paper introduces snnTrans-DHZ, a lightweight Spiking Neural Network (SNN) specifically designed for underwater dehazing. By leveraging the temporal dynamics of SNNs, snnTrans-DHZ efficiently processes time-dependent raw image sequences while maintaining low power consumption. Static underwater images are first converted into time-dependent sequences by repeatedly inputting the same image over user-defined timesteps. These RGB sequences are then transformed into LAB color space representations and processed concurrently. The architecture features three key modules: (i) a K estimator that extracts features from multiple color space representations; (ii) a Background Light Estimator that jointly infers the background light component from the RGB-LAB images; and (iii) a soft image reconstruction module that produces haze-free, visibility-enhanced outputs. The snnTrans-DHZ model is directly trained using a surrogate gradient-based backpropagation through time (BPTT) strategy alongside a novel combined loss function. Evaluated on the UIEB benchmark, snnTrans-DHZ achieves a PSNR of 21.68 dB and an SSIM of 0.8795, and on the EUVP dataset, it yields a PSNR of 23.46 dB and an SSIM of 0.8439. With only 0.5670 million network parameters, and requiring just 7.42 GSOPs and 0.0151 J of energy, the algorithm significantly outperforms existing state-of-the-art methods in terms of efficiency. These features make snnTrans-DHZ highly suitable for deployment in underwater robotics, marine exploration, and environmental monitoring.
Decoding brain signals accurately and efficiently is crucial for intra-cortical brain-computer interfaces. Traditional decoding approaches based on neural activity vector features suffer from low accuracy, whereas deep learning based approaches have high computational cost. To improve both the decoding accuracy and efficiency, this paper proposes a spiking neural network (SNN) for effective and energy-efficient intra-cortical brain signal decoding. We also propose a feature fusion approach, which integrates the manually extracted neural activity vector features with those extracted by a deep neural network, to further improve the decoding accuracy. Experiments in decoding motor-related intra-cortical brain signals of two rhesus macaques demonstrated that our SNN model achieved higher accuracy than traditional artificial neural networks; more importantly, it was tens or hundreds of times more efficient. The SNN model is very suitable for high precision and low power applications like intra-cortical brain-computer interfaces.
We propose a Gaussian process regression framework with additive periodic kernels for the analysis of two-body interactions in coupled oscillator systems. While finite-order Fourier expansions determined by Bayesian methods can still yield artifacts such as a high-amplitude, high-frequency vibration, our additive periodic kernel approach has been demonstrated to effectively circumvent these issues. Furthermore, by exploiting the additive and periodic nature of the coupling functions, we significantly reduce the effective dimensionality of the inference problem. We first validate our method on simple coupled phase oscillators and demonstrate its robustness to more complex systems, including Van der Pol and FitzHugh-Nagumo oscillators, under conditions of biased or limited data. We next apply our approach to spiking neural networks modeled by Hodgkin-Huxley equations, in which we successfully recover the underlying interaction functions. These results highlight the flexibility and stability of Gaussian process regression in capturing nonlinear, periodic interactions in oscillator networks. Our framework provides a practical alternative to conventional methods, enabling data-driven studies of synchronized rhythmic systems across physics, biology, and engineering.
Toward Spiking Neural Network Local Learning Modules Resistant to Adversarial Attacks
2025-04-11
Jiaqi Lin, Abhronil Sengupta
Recent research has shown the vulnerability of Spiking Neural Networks (SNNs) under adversarial examples that are nearly indistinguishable from clean data in the context of frame-based and event-based information. The majority of these studies are constrained in generating adversarial examples using Backpropagation Through Time (BPTT), a gradient-based method which lacks biological plausibility. In contrast, local learning methods, which relax many of BPTT's constraints, remain under-explored in the context of adversarial attacks. To address this problem, we examine adversarial robustness in SNNs through the framework of four types of training algorithms. We provide an in-depth analysis of the ineffectiveness of gradient-based adversarial attacks to generate adversarial instances in this scenario. To overcome these limitations, we introduce a hybrid adversarial attack paradigm that leverages the transferability of adversarial instances. The proposed hybrid approach demonstrates superior performance, outperforming existing adversarial attack methods. Furthermore, the generalizability of the method is assessed under multi-step adversarial attacks, adversarial attacks in black-box FGSM scenarios, and within the non-spiking domain.
Understanding how biological neural networks are shaped via local plasticity mechanisms can lead to energy-efficient and self-adaptive information processing systems, which promises to mitigate some of the current roadblocks in edge computing systems. While biology makes use of spikes to seamless use both spike timing and mean firing rate to modulate synaptic strength, most models focus on one of the two. In this work, we present a Hebbian local learning rule that models synaptic modification as a function of calcium traces tracking neuronal activity. We show how the rule reproduces results from spike time and spike rate protocols from neuroscientific studies. Moreover, we use the model to train spiking neural networks on MNIST digit recognition to show and explain what sort of mechanisms are needed to learn real-world patterns. We show how our model is sensitive to correlated spiking activity and how this enables it to modulate the learning rate of the network without altering the mean firing rate of the neurons nor the hyparameters of the learning rule. To the best of our knowledge, this is the first work that showcases how spike timing and rate can be complementary in their role of shaping the connectivity of spiking neural networks.
Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation
2025-04-09
Sirine Arfa, Bernhard Vogginger, Chen Liu, Johannes Partzsch, Mark Schone, Christian Mayr
Spiking Neural Networks (SNNs) are highly energy-efficient during inference, making them particularly suitable for deployment on neuromorphic hardware. Their ability to process event-driven inputs, such as data from dynamic vision sensors (DVS), further enhances their applicability to edge computing tasks. However, the resource constraints of edge hardware necessitate techniques like weight quantization, which reduce the memory footprint of SNNs while preserving accuracy. Despite its importance, existing quantization methods typically focus on synaptic weights quantization without taking account of other critical parameters, such as scaling neuron firing thresholds. To address this limitation, we present the first benchmark for the DVS gesture recognition task using SNNs optimized for the many-core neuromorphic chip SpiNNaker2. Our study evaluates two quantization pipelines for fixed-point computations. The first approach employs post training quantization (PTQ) with percentile-based threshold scaling, while the second uses quantization aware training (QAT) with adaptive threshold scaling. Both methods achieve accurate 8-bit on-chip inference, closely approximating 32-bit floating-point performance. Additionally, our baseline SNNs perform competitively against previously reported results without specialized techniques. These models are deployed on SpiNNaker2 using the neuromorphic intermediate representation (NIR). Ultimately, we achieve 94.13% classification accuracy on-chip, demonstrating the SpiNNaker2's potential for efficient, low-energy neuromorphic computing.
PETNet -- Coincident Particle Event Detection using Spiking Neural Networks
2025-04-09
Jan Debus, Charlotte Debus, Günther Dissertori, Markus Götz
Spiking neural networks (SNN) hold the promise of being a more biologically plausible, low-energy alternative to conventional artificial neural networks. Their time-variant nature makes them particularly suitable for processing time-resolved, sparse binary data. In this paper, we investigate the potential of leveraging SNNs for the detection of photon coincidences in positron emission tomography (PET) data. PET is a medical imaging technique based on injecting a patient with a radioactive tracer and detecting the emitted photons. One central post-processing task for inferring an image of the tracer distribution is the filtering of invalid hits occurring due to e.g. absorption or scattering processes. Our approach, coined PETNet, interprets the detector hits as a binary-valued spike train and learns to identify photon coincidence pairs in a supervised manner. We introduce a dedicated multi-objective loss function and demonstrate the effects of explicitly modeling the detector geometry on simulation data for two use-cases. Our results show that PETNet can outperform the state-of-the-art classical algorithm with a maximal coincidence detection $F_1$ of 95.2%. At the same time, PETNet is able to predict photon coincidences up to 36 times faster than the classical approach, highlighting the great potential of SNNs in particle physics applications.
SpikeStream: Accelerating Spiking Neural Network Inference on RISC-V Clusters with Sparse Computation Extensions
2025-04-08
Simone Manoni, Paul Scheffler, Luca Zanatta, Andrea Acquaviva, Luca Benini, Andrea Bartolini
Spiking Neural Network (SNN) inference has a clear potential for high energy efficiency as computation is triggered by events. However, the inherent sparsity of events poses challenges for conventional computing systems, driving the development of specialized neuromorphic processors, which come with high silicon area costs and lack the flexibility needed for running other computational kernels, limiting widespread adoption. In this paper, we explore the low-level software design, parallelization, and acceleration of SNNs on general-purpose multicore clusters with a low-overhead RISC-V ISA extension for streaming sparse computations. We propose SpikeStream, an optimization technique that maps weights accesses to affine and indirect register-mapped memory streams to enhance performance, utilization, and efficiency. Our results on the end-to-end Spiking-VGG11 model demonstrate a significant 4.39x speedup and an increase in utilization from 9.28% to 52.3% compared to a non-streaming parallel baseline. Additionally, we achieve an energy efficiency gain of 3.46x over LSMCore and a performance gain of 2.38x over Loihi.
Three-Factor Learning in Spiking Neural Networks: An Overview of Methods and Trends from a Machine Learning Perspective
2025-04-06
Szymon Mazurek, Jakub Caputa, Jan K. Argasiński, Maciej Wielgosz
Three-factor learning rules in Spiking Neural Networks (SNNs) have emerged as a crucial extension to traditional Hebbian learning and Spike-Timing-Dependent Plasticity (STDP), incorporating neuromodulatory signals to improve adaptation and learning efficiency. These mechanisms enhance biological plausibility and facilitate improved credit assignment in artificial neural systems. This paper takes a view on this topic from a machine learning perspective, providing an overview of recent advances in three-factor learning, discusses theoretical foundations, algorithmic implementations, and their relevance to reinforcement learning and neuromorphic computing. In addition, we explore interdisciplinary approaches, scalability challenges, and potential applications in robotics, cognitive modeling, and AI systems. Finally, we highlight key research gaps and propose future directions for bridging the gap between neuroscience and artificial intelligence.
Decision SpikeFormer: Spike-Driven Transformer for Decision Making
2025-04-04
Wei Huang, Qinying Gu, Nanyang Ye
Offline reinforcement learning (RL) enables policy training solely on pre-collected data, avoiding direct environment interaction - a crucial benefit for energy-constrained embodied AI applications. Although Artificial Neural Networks (ANN)-based methods perform well in offline RL, their high computational and energy demands motivate exploration of more efficient alternatives. Spiking Neural Networks (SNNs) show promise for such tasks, given their low power consumption. In this work, we introduce DSFormer, the first spike-driven transformer model designed to tackle offline RL via sequence modeling. Unlike existing SNN transformers focused on spatial dimensions for vision tasks, we develop Temporal Spiking Self-Attention (TSSA) and Positional Spiking Self-Attention (PSSA) in DSFormer to capture the temporal and positional dependencies essential for sequence modeling in RL. Additionally, we propose Progressive Threshold-dependent Batch Normalization (PTBN), which combines the benefits of LayerNorm and BatchNorm to preserve temporal dependencies while maintaining the spiking nature of SNNs. Comprehensive results in the D4RL benchmark show DSFormer's superiority over both SNN and ANN counterparts, achieving 78.4% energy savings, highlighting DSFormer's advantages not only in energy efficiency but also in competitive performance. Code and models are public at https://wei-nijuan.github.io/DecisionSpikeFormer.
Learning dynamics on the picosecond timescale in a superconducting synapse structure
2025-04-03
Ken Segall, Leon Nichols, Will Friend, Steven B. Kaplan
Conventional Artificial Intelligence (AI) systems are running into limitations in terms of training time and energy. Following the principles of the human brain, spiking neural networks trained with unsupervised learning offer a faster, more energy-efficient alternative. However, the dynamics of spiking, learning, and forgetting become more complicated in such schemes. Here we study a superconducting electronics implementation of a learning synapse and experimentally measure its spiking dynamics. By pulsing the system with a superconducting neuron, we show that a superconducting inductor can dynamically hold the synaptic weight with updates due to learning and forgetting. Learning can be stopped by slowing down the arrival time of the post-synaptic pulse, in accordance with the Spike-Timing Dependent Plasticity paradigm. We find excellent agreement with circuit simulations, and by fitting the turn-on of the pulsing frequency, we confirm a learning time of 16.1 +/- 1 ps. The power dissipation in the learning part of the synapse is less than one attojoule per learning event. This leads to the possibility of an extremely fast and energy-efficient learning processor.
In spiking neural networks (SNNs), the main unit of information processing is the neuron with an internal state. The internal state generates an output spike based on its component associated with the membrane potential. This spike is then communicated to other neurons in the network. Here, we propose a general multiple-input multiple-output (MIMO) spiking neuron model that goes beyond this traditional single-input single-output (SISO) model in the SNN literature. Our proposed framework is based on interpreting the neurons as state-space models (SSMs) with linear state evolutions and non-linear spiking activation functions. We illustrate the trade-offs among various parameters of the proposed SSM-inspired neuron model, such as the number of hidden neuron states, the number of input and output channels, including single-input multiple-output (SIMO) and multiple-input single-output (MISO) models. We show that for SNNs with a small number of neurons with large internal state spaces, significant performance gains may be obtained by increasing the number of output channels of a neuron. In particular, a network with spiking neurons with multiple-output channels may achieve the same level of accuracy with the baseline with the continuous-valued communications on the same reference network architecture.
SPACE: SPike-Aware Consistency Enhancement for Test-Time Adaptation in Spiking Neural Networks
2025-04-03
Xinyu Luo, Kecheng Chen, Pao-Sheng Vincent Sun, Chris Xing Tian, Arindam Basu, Haoliang Li
Spiking Neural Networks (SNNs), as a biologically plausible alternative to Artificial Neural Networks (ANNs), have demonstrated advantages in terms of energy efficiency, temporal processing, and biological plausibility. However, SNNs are highly sensitive to distribution shifts, which can significantly degrade their performance in real-world scenarios. Traditional test-time adaptation (TTA) methods designed for ANNs often fail to address the unique computational dynamics of SNNs, such as sparsity and temporal spiking behavior. To address these challenges, we propose SPike-Aware Consistency Enhancement (SPACE), the first source-free and single-instance TTA method specifically designed for SNNs. SPACE leverages the inherent spike dynamics of SNNs to maximize the consistency of spike-behavior-based local feature maps across augmented versions of a single test sample, enabling robust adaptation without requiring source data. We evaluate SPACE on multiple datasets. Furthermore, SPACE exhibits robust generalization across diverse network architectures, consistently enhancing the performance of SNNs on CNNs (such as VGG and ResNet), Transformer models, and ConvLSTM architectures. Experimental results show that SPACE outperforms state-of-the-art methods, highlighting its effectiveness and robustness in real-world settings.
Bridge the Gap between SNN and ANN for Image Restoration
2025-04-02
Xin Su, Chen Wu, Zhuoran Zheng
Models of dense prediction based on traditional Artificial Neural Networks (ANNs) require a lot of energy, especially for image restoration tasks. Currently, neural networks based on the SNN (Spiking Neural Network) framework are beginning to make their mark in the field of image restoration, especially as they typically use less than 10\% of the energy of ANNs with the same architecture. However, training an SNN is much more expensive than training an ANN, due to the use of the heuristic gradient descent strategy. In other words, the process of SNN's potential membrane signal changing from sparse to dense is very slow, which affects the convergence of the whole model.To tackle this problem, we propose a novel distillation technique, called asymmetric framework (ANN-SNN) distillation, in which the teacher is an ANN and the student is an SNN. Specifically, we leverage the intermediate features (feature maps) learned by the ANN as hints to guide the training process of the SNN. This approach not only accelerates the convergence of the SNN but also improves its final performance, effectively bridging the gap between the efficiency of the SNN and the superior learning capabilities of ANN. Extensive experimental results show that our designed SNN-based image restoration model, which has only 1/300 the number of parameters of the teacher network and 1/50 the energy consumption of the teacher network, is as good as the teacher network in some denoising tasks.
Modeling and predicting temporal point processes (TPPs) is critical in domains such as neuroscience, epidemiology, finance, and social sciences. We introduce the Spiking Dynamic Graph Network (SDGN), a novel framework that leverages the temporal processing capabilities of spiking neural networks (SNNs) and spike-timing-dependent plasticity (STDP) to dynamically estimate underlying spatio-temporal functional graphs. Unlike existing methods that rely on predefined or static graph structures, SDGN adapts to any dataset by learning dynamic spatio-temporal dependencies directly from the event data, enhancing generalizability and robustness. While SDGN offers significant improvements over prior methods, we acknowledge its limitations in handling dense graphs and certain non-Gaussian dependencies, providing opportunities for future refinement. Our evaluations, conducted on both synthetic and real-world datasets including NYC Taxi, 911, Reddit, and Stack Overflow, demonstrate that SDGN achieves superior predictive accuracy while maintaining computational efficiency. Furthermore, we include ablation studies to highlight the contributions of its core components.
Enabling Efficient Processing of Spiking Neural Networks with On-Chip Learning on Commodity Neuromorphic Processors for Edge AI Systems
2025-04-01
Rachmad Vidya Wicaksana Putra, Pasindu Wickramasinghe, Muhammad Shafique
The rising demand for energy-efficient edge AI systems (e.g., mobile agents/robots) has increased the interest in neuromorphic computing, since it offers ultra-low power/energy AI computation through spiking neural network (SNN) algorithms on neuromorphic processors. However, their efficient implementation strategy has not been comprehensively studied, hence limiting SNN deployments for edge AI systems. Toward this, we propose a design methodology to enable efficient SNN processing on commodity neuromorphic processors. To do this, we first study the key characteristics of targeted neuromorphic hardware (e.g., memory and compute budgets), and leverage this information to perform compatibility analysis for network selection. Afterward, we employ a mapping strategy for efficient SNN implementation on the targeted processor. Furthermore, we incorporate an efficient on-chip learning mechanism to update the systems' knowledge for adapting to new input classes and dynamic environments. The experimental results show that the proposed methodology leads the system to achieve low latency of inference (i.e., less than 50ms for image classification, less than 200ms for real-time object detection in video streaming, and less than 1ms in keyword recognition) and low latency of on-chip learning (i.e., less than 2ms for keyword recognition), while incurring less than 250mW of processing power and less than 15mJ of energy consumption across the respective different applications and scenarios. These results show the potential of the proposed methodology in enabling efficient edge AI systems for diverse application use-cases.
Scaling Up Resonate-and-Fire Networks for Fast Deep Learning
2025-04-01
Thomas E. Huber, Jules Lecomte, Borislav Polovnikov, Axel von Arnim
Spiking neural networks (SNNs) present a promising computing paradigm for neuromorphic processing of event-based sensor data. The resonate-and-fire (RF) neuron, in particular, appeals through its biological plausibility, complex dynamics, yet computational simplicity. Despite theoretically predicted benefits, challenges in parameter initialization and efficient learning inhibited the implementation of RF networks, constraining their use to a single layer. In this paper, we address these shortcomings by deriving the RF neuron as a structured state space model (SSM) from the HiPPO framework. We introduce S5-RF, a new SSM layer comprised of RF neurons based on the S5 model, that features a generic initialization scheme and fast training within a deep architecture. S5-RF scales for the first time a RF network to a deep SNN with up to four layers and achieves with 78.8% a new state-of-the-art result for recurrent SNNs on the Spiking Speech Commands dataset in under three hours of training time. Moreover, compared to the reference SNNs that solve our benchmarking tasks, it achieves similar performance with much fewer spiking operations. Our code is publicly available at https://github.com/ThomasEHuber/s5-rf.
QUEST: A Quantized Energy-Aware SNN Training Framework for Multi-State Neuromorphic Devices
2025-04-01
Sai Li, Linliang Chen, Yihao Zhang, Zhongkui Zhang, Ao Du, Biao Pan, Zhaohao Wang, Lianggong Wen, Weisheng Zhao
Neuromorphic devices, leveraging novel physical phenomena, offer a promising path toward energy-efficient hardware beyond CMOS technology by emulating brain-inspired computation. However, their progress is often limited to proof-of-concept studies due to the lack of flexible spiking neural network (SNN) algorithm frameworks tailored to device-specific characteristics, posing a significant challenge to scalability and practical deployment. To address this, we propose QUEST, a unified co-design framework that directly trains SNN for emerging devices featuring multilevel resistances. With Skyrmionic Magnetic Tunnel Junction (Sk-MTJ) as a case study, experimental results on the CIFAR-10 dataset demonstrate the framework's ability to enable scalable on-device SNN training with minimal energy consumption during both feedforward and backpropagation. By introducing device mapping pattern and activation operation sparsity, QUEST achieves effective trade-offs among high accuracy (89.6%), low bit precision (2-bit), and energy efficiency (93 times improvement over the ANNs). QUEST offers practical design guidelines for both the device and algorithm communities, providing insights to build energy-efficient and large-scale neuromorphic systems.
Underwater object detection is critical for oceanic research and industrial safety inspections. However, the complex optical environment and the limited resources of underwater equipment pose significant challenges to achieving high accuracy and low power consumption. To address these issues, we propose Spiking Underwater YOLO (SU-YOLO), a Spiking Neural Network (SNN) model. Leveraging the lightweight and energy-efficient properties of SNNs, SU-YOLO incorporates a novel spike-based underwater image denoising method based solely on integer addition, which enhances the quality of feature maps with minimal computational overhead. In addition, we introduce Separated Batch Normalization (SeBN), a technique that normalizes feature maps independently across multiple time steps and is optimized for integration with residual structures to capture the temporal dynamics of SNNs more effectively. The redesigned spiking residual blocks integrate the Cross Stage Partial Network (CSPNet) with the YOLO architecture to mitigate spike degradation and enhance the model's feature extraction capabilities. Experimental results on URPC2019 underwater dataset demonstrate that SU-YOLO achieves mAP of 78.8% with 6.97M parameters and an energy consumption of 2.98 mJ, surpassing mainstream SNN models in both detection accuracy and computational efficiency. These results underscore the potential of SNNs for engineering applications. The code is available in https://github.com/lwxfight/snn-underwater.
Biologically Inspired Spiking Diffusion Model with Adaptive Lateral Selection Mechanism
2025-03-31
Linghao Feng, Dongcheng Zhao, Sicheng Shen, Yi Zeng
Lateral connection is a fundamental feature of biological neural circuits, facilitating local information processing and adaptive learning. In this work, we integrate lateral connections with a substructure selection network to develop a novel diffusion model based on spiking neural networks (SNNs). Unlike conventional artificial neural networks, SNNs employ an intrinsic spiking inner loop to process sequential binary spikes. We leverage this spiking inner loop alongside a lateral connection mechanism to iteratively refine the substructure selection network, enhancing model adaptability and expressivity. Specifically, we design a lateral connection framework comprising a learnable lateral matrix and a lateral mapping function, both implemented using spiking neurons, to dynamically update lateral connections. Through mathematical modeling, we establish that the proposed lateral update mechanism, under a well-defined local objective, aligns with biologically plausible synaptic plasticity principles. Extensive experiments validate the effectiveness of our approach, analyzing the role of substructure selection and lateral connection during training. Furthermore, quantitative comparisons demonstrate that our model consistently surpasses state-of-the-art SNN-based generative models across multiple benchmark datasets.
Spiking Rate and Latency Encoding with Resonant Tunnelling Diode Neuron Circuits and Design Influences
2025-03-27
Giovanni Donati, Dafydd Owen-Newns, Joshua Robertson, Xavier Porte, Ekaterina Malysheva, Jose Figueiredo, Bruno Romeira, Victor Dolores-Calzadilla, Antonio Hurtado
Neuromorphic computing, inspired by the functionality and efficiency of biological neural systems, holds promise for advancing artificial intelligence and computational paradigms. Resonant tunneling diodes (RTDs), thanks to their ability to generate neuronal dynamical responses, such as excitable spiking and refractoriness, have recently emerged as candidates for use as opto-electronic spiking neurons in novel neuromorphic computing hardware. This work explores the ability of RTD spiking neurons to deliver information encoding mechanisms analogous to those observed in biological neurons, specifically spike firing rate and spike latency encoding. We also report detailed experimental and numerical studies on the impact that the RTD mesa radius and circuit inductance and capacitance have on its spiking properties, providing useful information for future design of RTD-based neural networks optimised for ultrafast (>GHz rate) processing capabilities. Finally, we showcase the application of spike rate encoding in RTD neurons for the ultrafast reconstruction of a eight-level amplitude signal, effectively filtering out the noise.
A 71.2-$μ$W Speech Recognition Accelerator with Recurrent Spiking Neural Network
2025-03-27
Chih-Chyau Yang, Tian-Sheuan Chang
This paper introduces a 71.2-$\mu$W speech recognition accelerator designed for edge devices' real-time applications, emphasizing an ultra low power design. Achieved through algorithm and hardware co-optimizations, we propose a compact recurrent spiking neural network with two recurrent layers, one fully connected layer, and a low time step (1 or 2). The 2.79-MB model undergoes pruning and 4-bit fixed-point quantization, shrinking it by 96.42\% to 0.1 MB. On the hardware front, we take advantage of \textit{mixed-level pruning}, \textit{zero-skipping} and \textit{merged spike} techniques, reducing complexity by 90.49\% to 13.86 MMAC/S. The \textit{parallel time-step execution} addresses inter-time-step data dependencies and enables weight buffer power savings through weight sharing. Capitalizing on the sparse spike activity, an input broadcasting scheme eliminates zero computations, further saving power. Implemented on the TSMC 28-nm process, the design operates in real time at 100 kHz, consuming 71.2 $\mu$W, surpassing state-of-the-art designs. At 500 MHz, it has 28.41 TOPS/W and 1903.11 GOPS/mm$^2$ in energy and area efficiency, respectively.
Spiking Neural Networks (SNNs) are highly regarded for their energy efficiency, inherent activation sparsity, and suitability for real-time processing in edge devices. However, most current SNN methods adopt architectures resembling traditional artificial neural networks (ANNs), leading to suboptimal performance when applied to SNNs. While SNNs excel in energy efficiency, they have been associated with lower accuracy levels than traditional ANNs when utilizing conventional architectures. In response, in this work we present LightSNN, a rapid and efficient Neural Network Architecture Search (NAS) technique specifically tailored for SNNs that autonomously leverages the most suitable architecture, striking a good balance between accuracy and efficiency by enforcing sparsity. Based on the spiking NAS network (SNASNet) framework, a cell-based search space including backward connections is utilized to build our training-free pruning-based NAS mechanism. Our technique assesses diverse spike activation patterns across different data samples using a sparsity-aware Hamming distance fitness evaluation. Thorough experiments are conducted on both static (CIFAR10 and CIFAR100) and neuromorphic datasets (DVS128-Gesture). Our LightSNN model achieves state-of-the-art results on CIFAR10 and CIFAR100, improves performance on DVS128Gesture by 4.49\%, and significantly reduces search time most notably offering a $98\times$ speedup over SNASNet and running 30\% faster than the best existing method on DVS128Gesture. Code is available on Github at: https://github.com/YesmineAbdennadher/LightSNN.
Underwater Image Enhancement by Convolutional Spiking Neural Networks
2025-03-26
Vidya Sudevan, Fakhreddine Zayer, Rizwana Kausar, Sajid Javed, Hamad Karki, Giulia De Masi, Jorge Dias
Underwater image enhancement (UIE) is fundamental for marine applications, including autonomous vision-based navigation. Deep learning methods using convolutional neural networks (CNN) and vision transformers advanced UIE performance. Recently, spiking neural networks (SNN) have gained attention for their lightweight design, energy efficiency, and scalability. This paper introduces UIE-SNN, the first SNN-based UIE algorithm to improve visibility of underwater images. UIE-SNN is a 19- layered convolutional spiking encoder-decoder framework with skip connections, directly trained using surrogate gradient-based backpropagation through time (BPTT) strategy. We explore and validate the influence of training datasets on energy reduction, a unique advantage of UIE-SNN architecture, in contrast to the conventional learning-based architectures, where energy consumption is model-dependent. UIE-SNN optimizes the loss function in latent space representation to reconstruct clear underwater images. Our algorithm performs on par with its non-spiking counterpart methods in terms of PSNR and structural similarity index (SSIM) at reduced timesteps ($T=5$) and energy consumption of $85\%$. The algorithm is trained on two publicly available benchmark datasets, UIEB and EUVP, and tested on unseen images from UIEB, EUVP, LSUI, U45, and our custom UIE dataset. The UIE-SNN algorithm achieves PSNR of \(17.7801~dB\) and SSIM of \(0.7454\) on UIEB, and PSNR of \(23.1725~dB\) and SSIM of \(0.7890\) on EUVP. UIE-SNN achieves this algorithmic performance with fewer operators (\(147.49\) GSOPs) and energy (\(0.1327~J\)) compared to its non-spiking counterpart (GFLOPs = \(218.88\) and Energy=\(1.0068~J\)). Compared with existing SOTA UIE methods, UIE-SNN achieves an average of \(6.5\times\) improvement in energy efficiency. The source code is available at \href{https://github.com/vidya-rejul/UIE-SNN.git}{UIE-SNN}.
Spiking Neural Networks (SNNs) and transformers represent two powerful paradigms in neural computation, known for their low power consumption and ability to capture feature dependencies, respectively. However, transformer architectures typically involve multiple types of computational layers, including linear layers for MLP modules and classification heads, convolution layers for tokenizers, and dot product computations for self-attention mechanisms. These diverse operations pose significant challenges for hardware accelerator design, and to our knowledge, there is not yet a hardware solution that leverages spike-form data from SNNs for transformer architectures. In this paper, we introduce VESTA, a novel hardware design that synergizes these technologies, presenting unified Processing Elements (PEs) capable of efficiently performing all three types of computations crucial to transformer structures. VESTA uniquely benefits from the spike-form outputs of the Spike Neuron Layers \cite{zhou2024spikformer}, simplifying multiplication operations by reducing them from handling two 8-bit integers to handling one 8-bit integer and a binary spike. This reduction enables the use of multiplexers in the PE module, significantly enhancing computational efficiency while maintaining the low-power advantage of SNNs. Experimental results show that the core area of VESTA is \(0.844 mm^2\). It operates at 500MHz and is capable of real-time image classification at 30 fps.
Hardware Efficient Accelerator for Spiking Transformer With Reconfigurable Parallel Time Step Computing
2025-03-25
Bo-Yu Chen, Tian-Sheuan Chang
This paper introduces the first low-power hardware accelerator for Spiking Transformers, an emerging alternative to traditional artificial neural networks. By modifying the base Spikformer model to use IAND instead of residual addition, the model exclusively utilizes spike computation. The hardware employs a fully parallel tick-batching dataflow and a time-step reconfigurable neuron architecture, addressing the delay and power challenges of multi-timestep processing in spiking neural networks. This approach processes outputs from all time steps in parallel, reducing computation delay and eliminating membrane memory, thereby lowering energy consumption. The accelerator supports 3x3 and 1x1 convolutions and matrix operations through vectorized processing, meeting model requirements. Implemented in TSMC's 28nm process, it achieves 3.456 TSOPS (tera spike operations per second) with a power efficiency of 38.334 TSOPS/W at 500MHz, using 198.46K logic gates and 139.25KB of SRAM.
Threshold Adaptation in Spiking Networks Enables Shortest Path Finding and Place Disambiguation
2025-03-22
Robin Dietrich, Tobias Fischer, Nicolai Waniek, Nico Reeb, Michael Milford, Alois Knoll, Adam D. Hines
Efficient spatial navigation is a hallmark of the mammalian brain, inspiring the development of neuromorphic systems that mimic biological principles. Despite progress, implementing key operations like back-tracing and handling ambiguity in bio-inspired spiking neural networks remains an open challenge. This work proposes a mechanism for activity back-tracing in arbitrary, uni-directional spiking neuron graphs. We extend the existing replay mechanism of the spiking hierarchical temporal memory (S-HTM) by our spike timing-dependent threshold adaptation (STDTA), which enables us to perform path planning in networks of spiking neurons. We further present an ambiguity dependent threshold adaptation (ADTA) for identifying places in an environment with less ambiguity, enhancing the localization estimate of an agent. Combined, these methods enable efficient identification of the shortest path to an unambiguous target. Our experiments show that a network trained on sequences reliably computes shortest paths with fewer replays than the steps required to reach the target. We further show that we can identify places with reduced ambiguity in multiple, similar environments. These contributions advance the practical application of biologically inspired sequential learning algorithms like the S-HTM towards neuromorphic localization and navigation.
Temporal-Guided Spiking Neural Networks for Event-Based Human Action Recognition
2025-03-21
Siyuan Yang, Shilin Lu, Shizheng Wang, Meng Hwa Er, Zengwei Zheng, Alex C. Kot
This paper explores the promising interplay between spiking neural networks (SNNs) and event-based cameras for privacy-preserving human action recognition (HAR). The unique feature of event cameras in capturing only the outlines of motion, combined with SNNs' proficiency in processing spatiotemporal data through spikes, establishes a highly synergistic compatibility for event-based HAR. Previous studies, however, have been limited by SNNs' ability to process long-term temporal information, essential for precise HAR. In this paper, we introduce two novel frameworks to address this: temporal segment-based SNN (\textit{TS-SNN}) and 3D convolutional SNN (\textit{3D-SNN}). The \textit{TS-SNN} extracts long-term temporal information by dividing actions into shorter segments, while the \textit{3D-SNN} replaces 2D spatial elements with 3D components to facilitate the transmission of temporal information. To promote further research in event-based HAR, we create a dataset, \textit{FallingDetection-CeleX}, collected using the high-resolution CeleX-V event camera $(1280 \times 800)$, comprising 7 distinct actions. Extensive experimental results show that our proposed frameworks surpass state-of-the-art SNN methods on our newly collected dataset and three other neuromorphic datasets, showcasing their effectiveness in handling long-range temporal information for event-based HAR.
A Digital Machine Learning Algorithm Simulating Spiking Neural Network CoLaNET
2025-03-21
Mikhail Kiselev
During last several years, our research team worked on development of a spiking neural network (SNN) architecture, which could be used in the wide range of supervised learning classification tasks. It should work under the condition, that all participating signals (the classified object description, correct class label and SNN decision) should have spiking nature. As a result, the CoLaNET (columnar layered network) SNN architecture was invented. The distinctive feature of this architecture is a combination of prototypical network structures corresponding to different classes and significantly distinctive instances of one class (=columns) and functionally differing populations of neurons inside columns (=layers). The other distinctive feature is a novel combination of anti-Hebbian and dopamine-modulated plasticity. While CoLaNET is relatively simple, it includes several hyperparameters. Their choice for particular classification tasks is not trivial. Besides that, specific features of the data classified (e.g. classification of separate pictures like in MNIST dataset vs. classifying objects in a continuous video stream) require certain modifications of CoLaNET structure. To solve these problems, the deep mathematical exploration of CoLaNET should be carried out. However, SNNs, being stochastic discrete systems, are usually very hard for exact mathematical analysis. To make it easier, I developed a continuous numeric (non-spiking) machine learning algorithm which approximates CoLaNET behavior with satisfactory accuracy. It is described in the paper. At present, it is being studied by exact analytic methods. We hope that the results of this study could be applied to direct calculation of CoLaNET hyperparameters and optimization of its structure.
Replay4NCL: An Efficient Memory Replay-based Methodology for Neuromorphic Continual Learning in Embedded AI Systems
2025-03-21
Mishal Fatima Minhas, Rachmad Vidya Wicaksana Putra, Falah Awwad, Osman Hasan, Muhammad Shafique
Neuromorphic Continual Learning (NCL) paradigm leverages Spiking Neural Networks (SNNs) to enable continual learning (CL) capabilities for AI systems to adapt to dynamically changing environments. Currently, the state-of-the-art employ a memory replay-based method to maintain the old knowledge. However, this technique relies on long timesteps and compression-decompression steps, thereby incurring significant latency and energy overheads, which are not suitable for tightly-constrained embedded AI systems (e.g., mobile agents/robotics). To address this, we propose Replay4NCL, a novel efficient memory replay-based methodology for enabling NCL in embedded AI systems. Specifically, Replay4NCL compresses the latent data (old knowledge), then replays them during the NCL training phase with small timesteps, to minimize the processing latency and energy consumption. To compensate the information loss from reduced spikes, we adjust the neuron threshold potential and learning rate settings. Experimental results on the class-incremental scenario with the Spiking Heidelberg Digits (SHD) dataset show that Replay4NCL can preserve old knowledge with Top-1 accuracy of 90.43% compared to 86.22% from the state-of-the-art, while effectively learning new tasks, achieving 4.88x latency speed-up, 20% latent memory saving, and 36.43% energy saving. These results highlight the potential of our Replay4NCL methodology to further advances NCL capabilities for embedded AI systems.
Region Masking to Accelerate Video Processing on Neuromorphic Hardware
2025-03-21
Sreetama Sarkar, Sumit Bam Shrestha, Yue Che, Leobardo Campos-Macias, Gourav Datta, Peter A. Beerel
The rapidly growing demand for on-chip edge intelligence on resource-constrained devices has motivated approaches to reduce energy and latency of deep learning models. Spiking neural networks (SNNs) have gained particular interest due to their promise to reduce energy consumption using event-based processing. We assert that while sigma-delta encoding in SNNs can take advantage of the temporal redundancy across video frames, they still involve a significant amount of redundant computations due to processing insignificant events. In this paper, we propose a region masking strategy that identifies regions of interest at the input of the SNN, thereby eliminating computation and data movement for events arising from unimportant regions. Our approach demonstrates that masking regions at the input not only significantly reduces the overall spiking activity of the network, but also provides significant improvement in throughput and latency. We apply region masking during video object detection on Loihi 2, demonstrating that masking approximately 60% of input regions can reduce energy-delay product by 1.65x over a baseline sigma-delta network, with a degradation in mAP@0.5 by 1.09%.
Allostatic Control of Persistent States in Spiking Neural Networks for perception and computation
2025-03-20
Aung Htet, Alejandro Rodriguez Jimenez, Sarah Hamburg, Alessandro Di Nuovo
We introduce a novel model for updating perceptual beliefs about the environment by extending the concept of Allostasis to the control of internal representations. Allostasis is a fundamental regulatory mechanism observed in animal physiology that orchestrates responses to maintain a dynamic equilibrium in bodily needs and internal states. In this paper, we focus on an application in numerical cognition, where a bump of activity in an attractor network is used as a spatial numerical representation. While existing neural networks can maintain persistent states, to date, there is no unified framework for dynamically controlling spatial changes in neuronal activity in response to environmental changes. To address this, we couple a well known allostatic microcircuit, the Hammel model, with a ring attractor, resulting in a Spiking Neural Network architecture that can modulate the location of the bump as a function of some reference input. This localized activity in turn is used as a perceptual belief in a simulated subitization task a quick enumeration process without counting. We provide a general procedure to fine-tune the model and demonstrate the successful control of the bump location. We also study the response time in the model with respect to changes in parameters and compare it with biological data. Finally, we analyze the dynamics of the network to understand the selectivity and specificity of different neurons to distinct categories present in the input. The results of this paper, particularly the mechanism for moving persistent states, are not limited to numerical cognition but can be applied to a wide range of tasks involving similar representations.
This work introduces GazeSCRNN, a novel spiking convolutional recurrent neural network designed for event-based near-eye gaze tracking. Leveraging the high temporal resolution, energy efficiency, and compatibility of Dynamic Vision Sensor (DVS) cameras with event-based systems, GazeSCRNN uses a spiking neural network (SNN) to address the limitations of traditional gaze-tracking systems in capturing dynamic movements. The proposed model processes event streams from DVS cameras using Adaptive Leaky-Integrate-and-Fire (ALIF) neurons and a hybrid architecture optimized for spatio-temporal data. Extensive evaluations on the EV-Eye dataset demonstrate the model's accuracy in predicting gaze vectors. In addition, we conducted ablation studies to reveal the importance of the ALIF neurons, dynamic event framing, and training techniques, such as Forward-Propagation-Through-Time, in enhancing overall system performance. The most accurate model achieved a Mean Angle Error (MAE) of 6.034{\deg} and a Mean Pupil Error (MPE) of 2.094 mm. Consequently, this work is pioneering in demonstrating the feasibility of using SNNs for event-based gaze tracking, while shedding light on critical challenges and opportunities for further improvement.
Spiking Neural Networks (SNNs) based on Transformers have garnered significant attention due to their superior performance and high energy efficiency. However, the spiking attention modules of most existing Transformer-based SNNs are adapted from those of analog Transformers, failing to fully address the issue of over-allocating attention to irrelevant contexts. To fix this fundamental yet overlooked issue, we propose a Lateral Inhibition-inspired Spiking Transformer (SpiLiFormer). It emulates the brain's lateral inhibition mechanism, guiding the model to enhance attention to relevant tokens while suppressing attention to irrelevant ones. Our model achieves state-of-the-art (SOTA) performance across multiple datasets, including CIFAR-10 (+0.45%), CIFAR-100 (+0.48%), CIFAR10-DVS (+2.70%), N-Caltech101 (+1.94%), and ImageNet-1K (+1.6%). Notably, on the ImageNet-1K dataset, SpiLiFormer (69.9M parameters, 4 time steps, 384 resolution) outperforms E-SpikeFormer (173.0M parameters, 8 time steps, 384 resolution), a SOTA spiking Transformer, by 0.46% using only 39% of the parameters and half the time steps. Our code and training checkpoints will be released upon acceptance.
Efficient ANN-Guided Distillation: Aligning Rate-based Features of Spiking Neural Networks through Hybrid Block-wise Replacement
2025-03-20
Shu Yang, Chengting Yu, Lei Liu, Hanzhi Ma, Aili Wang, Erping Li
Spiking Neural Networks (SNNs) have garnered considerable attention as a potential alternative to Artificial Neural Networks (ANNs). Recent studies have highlighted SNNs' potential on large-scale datasets. For SNN training, two main approaches exist: direct training and ANN-to-SNN (ANN2SNN) conversion. To fully leverage existing ANN models in guiding SNN learning, either direct ANN-to-SNN conversion or ANN-SNN distillation training can be employed. In this paper, we propose an ANN-SNN distillation framework from the ANN-to-SNN perspective, designed with a block-wise replacement strategy for ANN-guided learning. By generating intermediate hybrid models that progressively align SNN feature spaces to those of ANN through rate-based features, our framework naturally incorporates rate-based backpropagation as a training method. Our approach achieves results comparable to or better than state-of-the-art SNN distillation methods, showing both training and learning efficiency.
Input-Triggered Hardware Trojan Attack on Spiking Neural Networks
2025-03-20
Spyridon Raptis, Paul Kling, Ioannis Kaskampas, Ihsen Alouani, Haralampos-G. Stratigopoulos
Neuromorphic computing based on spiking neural networks (SNNs) is emerging as a promising alternative to traditional artificial neural networks (ANNs), offering unique advantages in terms of low power consumption. However, the security aspect of SNNs is under-explored compared to their ANN counterparts. As the increasing reliance on AI systems comes with unique security risks and challenges, understanding the vulnerabilities and threat landscape is essential as neuromorphic computing matures. In this effort, we propose a novel input-triggered Hardware Trojan (HT) attack for SNNs. The HT mechanism is condensed in the area of one neuron. The trigger mechanism is an input message crafted in the spiking domain such that a selected neuron produces a malicious spike train that is not met in normal settings. This spike train triggers a malicious modification in the neuron that forces it to saturate, firing permanently and failing to recover to its resting state even when the input activity stops. The excessive spikes pollute the network and produce misleading decisions. We propose a methodology to select an appropriate neuron and to generate the input pattern that triggers the HT payload. The attack is illustrated by simulation on three popular benchmarks in the neuromorphic community. We also propose a hardware implementation for an analog spiking neuron and a digital SNN accelerator, demonstrating that the HT has a negligible area and power footprint and, thereby, can easily evade detection.
HiAER-Spike: Hardware-Software Co-Design for Large-Scale Reconfigurable Event-Driven Neuromorphic Computing
2025-03-20
Gwenevere Frank, Gopabandhu Hota, Keli Wang, Abhinav Uppal, Omowuyi Olajide, Kenneth Yoshimoto, Leif Gibb, Qingbo Wang, Johannes Leugering, Stephen Deiss, Gert Cauwenberghs
In this work, we present HiAER-Spike, a modular, reconfigurable, event-driven neuromorphic computing platform designed to execute large spiking neural networks with up to 160 million neurons and 40 billion synapses - roughly twice the neurons of a mouse brain at faster-than real-time. This system, which is currently under construction at the UC San Diego Supercomputing Center, comprises a co-designed hard- and software stack that is optimized for run-time massively parallel processing and hierarchical address-event routing (HiAER) of spikes while promoting memory-efficient network storage and execution. Our architecture efficiently handles both sparse connectivity and sparse activity for robust and low-latency event-driven inference for both edge and cloud computing. A Python programming interface to HiAER-Spike, agnostic to hardware-level detail, shields the user from complexity in the configuration and execution of general spiking neural networks with virtually no constraints in topology. The system is made easily available over a web portal for use by the wider community. In the following we provide an overview of the hard- and software stack, explain the underlying design principles, demonstrate some of the system's capabilities and solicit feedback from the broader neuromorphic community.
Keyword spotting in edge devices is becoming increasingly important as voice-activated assistants are widely used. However, its deployment is often limited by the extreme low-power constraints of the target embedded systems. Here, we explore the Temporal Difference Encoder (TDE) performance in keyword spotting. This recent neuron model encodes the time difference in instantaneous frequency and spike count to perform efficient keyword spotting with neuromorphic processors. We use the TIdigits dataset of spoken digits with a formant decomposition and rate-based encoding into spikes. We compare three Spiking Neural Networks (SNNs) architectures to learn and classify spatio-temporal signals. The proposed SNN architectures are made of three layers with variation in its hidden layer composed of either (1) feedforward TDE, (2) feedforward Current-Based Leaky Integrate-and-Fire (CuBa-LIF), or (3) recurrent CuBa-LIF neurons. We first show that the spike trains of the frequency-converted spoken digits have a large amount of information in the temporal domain, reinforcing the importance of better exploiting temporal encoding for such a task. We then train the three SNNs with the same number of synaptic weights to quantify and compare their performance based on the accuracy and synaptic operations. The resulting accuracy of the feedforward TDE network (89%) is higher than the feedforward CuBa-LIF network (71%) and close to the recurrent CuBa-LIF network (91%). However, the feedforward TDE-based network performs 92% fewer synaptic operations than the recurrent CuBa-LIF network with the same amount of synapses. In addition, the results of the TDE network are highly interpretable and correlated with the frequency and timescale features of the spoken keywords in the dataset. Our findings suggest that the TDE is a promising neuron model for scalable event-driven processing of spatio-temporal patterns.
Temporal Flexibility in Spiking Neural Networks: Towards Generalization Across Time Steps and Deployment Friendliness
2025-03-18
Kangrui Du, Yuhang Wu, Shikuang Deng, Shi Gu
Spiking Neural Networks (SNNs), models inspired by neural mechanisms in the brain, allow for energy-efficient implementation on neuromorphic hardware. However, SNNs trained with current direct training approaches are constrained to a specific time step. This "temporal inflexibility" 1) hinders SNNs' deployment on time-step-free fully event-driven chips and 2) prevents energy-performance balance based on dynamic inference time steps. In this study, we first explore the feasibility of training SNNs that generalize across different time steps. We then introduce Mixed Time-step Training (MTT), a novel method that improves the temporal flexibility of SNNs, making SNNs adaptive to diverse temporal structures. During each iteration of MTT, random time steps are assigned to different SNN stages, with spikes transmitted between stages via communication modules. After training, the weights are deployed and evaluated on both time-stepped and fully event-driven platforms. Experimental results show that models trained by MTT gain remarkable temporal flexibility, friendliness for both event-driven and clock-driven deployment (nearly lossless on N-MNIST and 10.1% higher than standard methods on CIFAR10-DVS), enhanced network generalization, and near SOTA performance. To the best of our knowledge, this is the first work to report the results of large-scale SNN deployment on fully event-driven scenarios.
2025-03-17
Aref Einizade, Dorina Thanou, Fragkiskos D. Malliaros, Jhony H. Giraldo
Simplicial complexes provide a powerful framework for modeling high-order interactions in structured data, making them particularly suitable for applications such as trajectory prediction and mesh processing. However, existing simplicial neural networks (SNNs), whether convolutional or attention-based, rely primarily on discrete filtering techniques, which can be restrictive. In contrast, partial differential equations (PDEs) on simplicial complexes offer a principled approach to capture continuous dynamics in such structures. In this work, we introduce continuous simplicial neural network (COSIMO), a novel SNN architecture derived from PDEs on simplicial complexes. We provide theoretical and experimental justifications of COSIMO's stability under simplicial perturbations. Furthermore, we investigate the over-smoothing phenomenon, a common issue in geometric deep learning, demonstrating that COSIMO offers better control over this effect than discrete SNNs. Our experiments on real-world datasets demonstrate that COSIMO achieves competitive performance compared to state-of-the-art SNNs in complex and noisy environments.
Video anomaly detection plays a significant role in intelligent surveillance systems. To enhance model's anomaly recognition ability, previous works have typically involved RGB, optical flow, and text features. Recently, dynamic vision sensors (DVS) have emerged as a promising technology, which capture visual information as discrete events with a very high dynamic range and temporal resolution. It reduces data redundancy and enhances the capture capacity of moving objects compared to conventional camera. To introduce this rich dynamic information into the surveillance field, we created the first DVS video anomaly detection benchmark, namely UCF-Crime-DVS. To fully utilize this new data modality, a multi-scale spiking fusion network (MSF) is designed based on spiking neural networks (SNNs). This work explores the potential application of dynamic information from event data in video anomaly detection. Our experiments demonstrate the effectiveness of our framework on UCF-Crime-DVS and its superior performance compared to other models, establishing a new baseline for SNN-based weakly supervised video anomaly detection.
Comparative Study of Spike Encoding Methods for Environmental Sound Classification
2025-03-14
Andres Larroza, Javier Naranjo-Alcazar, Vicent Ortiz Castelló, Pedro Zuccarello
Spiking Neural Networks (SNNs) offer a promising approach to reduce energy consumption and computational demands, making them particularly beneficial for embedded machine learning in edge applications. However, data from conventional digital sensors must first be converted into spike trains to be processed using neuromorphic computing technologies. The classification of environmental sounds presents unique challenges due to the high variability of frequencies, background noise, and overlapping acoustic events. Despite these challenges, most studies on spike-based audio encoding focus on speech processing, leaving non-speech environmental sounds underexplored. In this work, we conduct a comprehensive comparison of widely used spike encoding techniques, evaluating their effectiveness on the ESC-10 dataset. By understanding the impact of encoding choices on environmental sound processing, researchers and practitioners can select the most suitable approach for real-world applications such as smart surveillance, environmental monitoring, and industrial acoustic analysis. This study serves as a benchmark for spike encoding in environmental sound classification, providing a foundational reference for future research in neuromorphic audio processing.
ST-FlowNet: An Efficient Spiking Neural Network for Event-Based Optical Flow Estimation
2025-03-13
Hongze Sun, Jun Wang, Wuque Cai, Duo Chen, Qianqian Liao, Jiayi He, Yan Cui, Dezhong Yao, Daqing Guo
Spiking Neural Networks (SNNs) have emerged as a promising tool for event-based optical flow estimation tasks due to their ability to leverage spatio-temporal information and low-power capabilities. However, the performance of SNN models is often constrained, limiting their application in real-world scenarios. In this work, we address this gap by proposing a novel neural network architecture, ST-FlowNet, specifically tailored for optical flow estimation from event-based data. The ST-FlowNet architecture integrates ConvGRU modules to facilitate cross-modal feature augmentation and temporal alignment of the predicted optical flow, improving the network's ability to capture complex motion dynamics. Additionally, to overcome the challenges associated with training SNNs, we introduce a novel approach to derive SNN models from pre-trained artificial neural networks (ANNs) through ANN-to-SNN conversion or our proposed BISNN method. Notably, the BISNN method alleviates the complexities involved in biological parameter selection, further enhancing the robustness of SNNs in optical flow estimation tasks. Extensive evaluations on three benchmark event-based datasets demonstrate that the SNN-based ST-FlowNet model outperforms state-of-the-art methods, delivering superior performance in accurate optical flow estimation across a diverse range of dynamic visual scenes. Furthermore, the inherent energy efficiency of SNN models is highlighted, establishing a compelling advantage for their practical deployment. Overall, our work presents a novel framework for optical flow estimation using SNNs and event-based data, contributing to the advancement of neuromorphic vision applications.
DTA: Dual Temporal-channel-wise Attention for Spiking Neural Networks
2025-03-13
Minje Kim, Minjun Kim, Xu Yang
Spiking Neural Networks (SNNs) present a more energy-efficient alternative to Artificial Neural Networks (ANNs) by harnessing spatio-temporal dynamics and event-driven spikes. Effective utilization of temporal information is crucial for SNNs, leading to the exploration of attention mechanisms to enhance this capability. Conventional attention operations either apply identical operation or employ non-identical operations across target dimensions. We identify that these approaches provide distinct perspectives on temporal information. To leverage the strengths of both operations, we propose a novel Dual Temporal-channel-wise Attention (DTA) mechanism that integrates both identical/non-identical attention strategies. To the best of our knowledge, this is the first attempt to concentrate on both the correlation and dependency of temporal-channel using both identical and non-identical attention operations. Experimental results demonstrate that the DTA mechanism achieves state-of-the-art performance on both static datasets (CIFAR10, CIFAR100, ImageNet-1k) and dynamic dataset (CIFAR10-DVS), elevating spike representation and capturing complex temporal-channel relationship. We open-source our code: https://github.com/MnJnKIM/DTA-SNN.
In recent years, quadruped robotics has advanced significantly, particularly in perception and motion control via reinforcement learning, enabling complex motions in challenging environments. Visual sensors like depth cameras enhance stability and robustness but face limitations, such as low operating frequencies relative to joint control and sensitivity to lighting, which hinder outdoor deployment. Additionally, deep neural networks in sensor and control systems increase computational demands. To address these issues, we introduce spiking neural networks (SNNs) and event cameras to perform a challenging quadruped parkour task. Event cameras capture dynamic visual data, while SNNs efficiently process spike sequences, mimicking biological perception. Experimental results demonstrate that this approach significantly outperforms traditional models, achieving excellent parkour performance with just 11.7% of the energy consumption of an artificial neural network (ANN)-based model, yielding an 88.3% energy reduction. By integrating event cameras with SNNs, our work advances robotic reinforcement learning and opens new possibilities for applications in demanding environments.
2025-03-12
Haoan Feng, Diana Aldana, Tiago Novello, Leila De Floriani
Sinusoidal neural networks (SNNs) have emerged as powerful implicit neural representations (INRs) for low-dimensional signals in computer vision and graphics. They enable high-frequency signal reconstruction and smooth manifold modeling; however, they often suffer from spectral bias, training instability, and overfitting. To address these challenges, we propose SASNet, Spatially-Adaptive SNNs that robustly enhance the capacity of compact INRs to fit detailed signals. SASNet integrates a frequency embedding layer to control frequency components and mitigate spectral bias, along with jointly optimized, spatially-adaptive masks that localize neuron influence, reducing network redundancy and improving convergence stability. Robust to hyperparameter selection, SASNet faithfully reconstructs high-frequency signals without overfitting low-frequency regions. Our experiments show that SASNet outperforms state-of-the-art INRs, achieving strong fitting accuracy, super-resolution capability, and noise suppression, without sacrificing model compactness.
Event-Driven Implementation of a Physical Reservoir Computing Framework for superficial EMG-based Gesture Recognition
2025-03-10
Yuqi Ding, Elisa Donati, Haobo Li, Hadi Heidari
Wearable health devices have a strong demand in real-time biomedical signal processing. However traditional methods often require data transmission to centralized processing unit with substantial computational resources after collecting it from edge devices. Neuromorphic computing is an emerging field that seeks to design specialized hardware for computing systems inspired by the structure, function, and dynamics of the human brain, offering significant advantages in latency and power consumption. This paper explores a novel neuromorphic implementation approach for gesture recognition by extracting spatiotemporal spiking information from surface electromyography (sEMG) data in an event-driven manner. At the same time, the network was designed by implementing a simple-structured and hardware-friendly Physical Reservoir Computing (PRC) framework called Rotating Neuron Reservoir (RNR) within the domain of Spiking neural network (SNN). The spiking RNR (sRNR) is promising to pipeline an innovative solution to compact embedded wearable systems, enabling low-latency, real-time processing directly at the sensor level. The proposed system was validated by an open-access large-scale sEMG database and achieved an average classification accuracy of 74.6\% and 80.3\% using a classical machine learning classifier and a delta learning rule algorithm respectively. While the delta learning rule could be fully spiking and implementable on neuromorphic chips, the proposed gesture recognition system demonstrates the potential for near-sensor low-latency processing.
Characterizing Learning in Spiking Neural Networks with Astrocyte-Like Units
2025-03-09
Christopher S. Yang, Sylvester J. Gates III, Dulara De Zoysa, Jaehoon Choe, Wolfgang Losert, Corey B. Hart
Traditional artificial neural networks take inspiration from biological networks, using layers of neuron-like nodes to pass information for processing. More realistic models include spiking in the neural network, capturing the electrical characteristics more closely. However, a large proportion of brain cells are of the glial cell type, in particular astrocytes which have been suggested to play a role in performing computations. Here, we introduce a modified spiking neural network model with added astrocyte-like units in a neural network and asses their impact on learning. We implement the network as a liquid state machine and task the network with performing a chaotic time-series prediction task. We varied the number and ratio of neuron-like and astrocyte-like units in the network to examine the latter units effect on learning. We show that the combination of neurons and astrocytes together, as opposed to neural- and astrocyte-only networks, are critical for driving learning. Interestingly, we found that the highest learning rate was achieved when the ratio between astrocyte-like and neuron-like units was roughly 2 to 1, mirroring some estimates of the ratio of biological astrocytes to neurons. Our results demonstrate that incorporating astrocyte-like units which represent information across longer timescales can alter the learning rates of neural networks, and the proportion of astrocytes to neurons should be tuned appropriately to a given task.
Hardware-Accelerated Event-Graph Neural Networks for Low-Latency Time-Series Classification on SoC FPGA
2025-03-09
Hiroshi Nakano, Krzysztof Blachut, Kamil Jeziorek, Piotr Wzorek, Manon Dampfhoffer, Thomas Mesquida, Hiroaki Nishi, Tomasz Kryjak, Thomas Dalgaty
As the quantities of data recorded by embedded edge sensors grow, so too does the need for intelligent local processing. Such data often comes in the form of time-series signals, based on which real-time predictions can be made locally using an AI model. However, a hardware-software approach capable of making low-latency predictions with low power consumption is required. In this paper, we present a hardware implementation of an event-graph neural network for time-series classification. We leverage an artificial cochlea model to convert the input time-series signals into a sparse event-data format that allows the event-graph to drastically reduce the number of calculations relative to other AI methods. We implemented the design on a SoC FPGA and applied it to the real-time processing of the Spiking Heidelberg Digits (SHD) dataset to benchmark our approach against competitive solutions. Our method achieves a floating-point accuracy of 92.7% on the SHD dataset for the base model, which is only 2.4% and 2% less than the state-of-the-art models with over 10% and 67% fewer model parameters, respectively. It also outperforms FPGA-based spiking neural network implementations by 19.3% and 4.5%, achieving 92.3% accuracy for the quantised model while using fewer computational resources and reducing latency.
SDTrack: A Baseline for Event-based Tracking via Spiking Neural Networks
2025-03-09
Yimeng Shan, Zhenbang Ren, Haodi Wu, Wenjie Wei, Rui-Jie Zhu, Shuai Wang, Dehao Zhang, Yichen Xiao, Jieyuan Zhang, Kexin Shi, Jingzhinan Wang, Jason K. Eshraghian, Haicheng Qu, Jiqing Zhang, Malu Zhang, Yang Yang
Event cameras provide superior temporal resolution, dynamic range, power efficiency, and pixel bandwidth. Spiking Neural Networks (SNNs) naturally complement event data through discrete spike signals, making them ideal for event-based tracking. However, current approaches that combine Artificial Neural Networks (ANNs) and SNNs, along with suboptimal architectures, compromise energy efficiency and limit tracking performance. To address these limitations, we propose the first Transformer-based spike-driven tracking pipeline. Our Global Trajectory Prompt (GTP) method effectively captures global trajectory information and aggregates it with event streams into event images to enhance spatiotemporal representation. We then introduce SDTrack, a Transformer-based spike-driven tracker comprising a Spiking MetaFormer backbone and a simple tracking head that directly predicts normalized coordinates using spike signals. The framework is end-to-end, does not require data augmentation or post-processing. Extensive experiments demonstrate that SDTrack achieves state-of-the-art performance while maintaining the lowest parameter count and energy consumption across multiple event-based tracking benchmarks, establishing a solid baseline for future research in the field of neuromorphic vision.
Event-based visual odometry has recently gained attention for its high accuracy and real-time performance in fast-motion systems. Unlike traditional synchronous estimators that rely on constant-frequency (zero-order) triggers, event-based visual odometry can actively accumulate information to generate temporally high-order estimation triggers. However, existing methods primarily focus on adaptive event representation after estimation triggers, neglecting the decision-making process for efficient temporal triggering itself. This oversight leads to the computational redundancy and noise accumulation. In this paper, we introduce a temporally high-order event-based visual odometry with spiking event accumulation networks (THE-SEAN). To the best of our knowledge, it is the first event-based visual odometry capable of dynamically adjusting its estimation trigger decision in response to motion and environmental changes. Inspired by biological systems that regulate hormone secretion to modulate heart rate, a self-supervised spiking neural network is designed to generate estimation triggers. This spiking network extracts temporal features to produce triggers, with rewards based on block matching points and Fisher information matrix (FIM) trace acquired from the estimator itself. Finally, THE-SEAN is evaluated across several open datasets, thereby demonstrating average improvements of 13\% in estimation accuracy, 9\% in smoothness, and 38\% in triggering efficiency compared to the state-of-the-art methods.
Spiking Neural Networks (SNNs) offer a promising, biologically inspired approach for processing spatiotemporal data, particularly for time series forecasting. However, conventional neuron models like the Leaky Integrate-and-Fire (LIF) struggle to capture long-term dependencies and effectively process multi-scale temporal dynamics. To overcome these limitations, we introduce the Temporal Segment Leaky Integrate-and-Fire (TS-LIF) model, featuring a novel dual-compartment architecture. The dendritic and somatic compartments specialize in capturing distinct frequency components, providing functional heterogeneity that enhances the neuron's ability to process both low- and high-frequency information. Furthermore, the newly introduced direct somatic current injection reduces information loss during intra-neuronal transmission, while dendritic spike generation improves multi-scale information extraction. We provide a theoretical stability analysis of the TS-LIF model and explain how each compartment contributes to distinct frequency response characteristics. Experimental results show that TS-LIF outperforms traditional SNNs in time series forecasting, demonstrating better accuracy and robustness, even with missing data. TS-LIF advances the application of SNNs in time-series forecasting, providing a biologically inspired approach that captures complex temporal dynamics and offers potential for practical implementation in diverse forecasting scenarios. The source code is available at https://github.com/kkking-kk/TS-LIF.
AOLO: Analysis and Optimization For Low-Carbon Oriented Wireless Large Language Model Services
2025-03-06
Xiaoqi Wang, Hongyang Du, Yuehong Gao, Dong In Kim
Recent advancements in large language models (LLMs) have led to their widespread adoption and large-scale deployment across various domains. However, their environmental impact, particularly during inference, has become a growing concern due to their substantial energy consumption and carbon footprint. Existing research has focused on inference computation alone, overlooking the analysis and optimization of carbon footprint in network-aided LLM service systems. To address this gap, we propose AOLO, a framework for analysis and optimization for low-carbon oriented wireless LLM services. AOLO introduces a comprehensive carbon footprint model that quantifies greenhouse gas emissions across the entire LLM service chain, including computational inference and wireless communication. Furthermore, we formulate an optimization problem aimed at minimizing the overall carbon footprint, which is solved through joint optimization of inference outputs and transmit power under quality-of-experience and system performance constraints. To achieve this joint optimization, we leverage the energy efficiency of spiking neural networks (SNNs) by adopting SNN as the actor network and propose a low-carbon-oriented optimization algorithm, i.e., SNN-based deep reinforcement learning (SDRL). Comprehensive simulations demonstrate that SDRL algorithm significantly reduces overall carbon footprint, achieving an 18.77% reduction compared to the benchmark soft actor-critic, highlighting its potential for enabling more sustainable LLM inference services.
Eventprop training for efficient neuromorphic applications
2025-03-06
Thomas Shoesmith, James C. Knight, Balázs Mészáros, Jonathan Timcheck, Thomas Nowotny
Neuromorphic computing can reduce the energy requirements of neural networks and holds the promise to `repatriate' AI workloads back from the cloud to the edge. However, training neural networks on neuromorphic hardware has remained elusive. Here, we instead present a pipeline for training spiking neural networks on GPUs, using the efficient event-driven Eventprop algorithm implemented in mlGeNN, and deploying them on Intel's Loihi 2 neuromorphic chip. Our benchmarking on keyword spotting tasks indicates that there is almost no loss in accuracy between GPU and Loihi 2 implementations and that classifying a sample on Loihi 2 is up to 10X faster and uses 200X less energy than on an NVIDIA Jetson Orin Nano.
2025-03-06
Yi Xiao, Qiangqiang Yuan, Kui Jiang, Qiang Zhang, Tingting Zheng, Chia-Wen Lin, Liangpei Zhang
Spiking neural networks (SNNs) are emerging as a promising alternative to traditional artificial neural networks (ANNs), offering biological plausibility and energy efficiency. Despite these merits, SNNs are frequently hampered by limited capacity and insufficient representation power, yet remain underexplored in remote sensing super-resolution (SR) tasks. In this paper, we first observe that spiking signals exhibit drastic intensity variations across diverse textures, highlighting an active learning state of the neurons. This observation motivates us to apply SNNs for efficient SR of RSIs. Inspired by the success of attention mechanisms in representing salient information, we devise the spiking attention block (SAB), a concise yet effective component that optimizes membrane potentials through inferred attention weights, which, in turn, regulates spiking activity for superior feature representation. Our key contributions include: 1) we bridge the independent modulation between temporal and channel dimensions, facilitating joint feature correlation learning, and 2) we access the global self-similar patterns in large-scale remote sensing imagery to infer spatial attention weights, incorporating effective priors for realistic and faithful reconstruction. Building upon SAB, we proposed SpikeSR, which achieves state-of-the-art performance across various remote sensing benchmarks such as AID, DOTA, and DIOR, while maintaining high computational efficiency. The code of SpikeSR will be available upon paper acceptance.
Spiking Neural Networks (SNNs) are highly efficient due to their spike-based activation, which inherently produces bit-sparse computation patterns. Existing hardware implementations of SNNs leverage this sparsity pattern to avoid wasteful zero-value computations, yet this approach fails to fully capitalize on the potential efficiency of SNNs. This study introduces a novel sparsity paradigm called Product Sparsity, which leverages combinatorial similarities within matrix multiplication operations to reuse the inner product result and reduce redundant computations. Product Sparsity significantly enhances sparsity in SNNs without compromising the original computation results compared to traditional bit sparsity methods. For instance, in the SpikeBERT SNN model, Product Sparsity achieves a density of only $1.23\%$ and reduces computation by $11\times$, compared to bit sparsity, which has a density of $13.19\%$. To efficiently implement Product Sparsity, we propose Prosperity, an architecture that addresses the challenges of identifying and eliminating redundant computations in real-time. Compared to prior SNN accelerator PTB and the A100 GPU, Prosperity achieves an average speedup of $7.4\times$ and $1.8\times$, respectively, along with energy efficiency improvements of $8.0\times$ and $193\times$, respectively. The code for Prosperity is available at https://github.com/dubcyfor3/Prosperity.
Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradients
2025-03-05
Li Lun, Kunyu Feng, Qinglong Ni, Ling Liang, Yuan Wang, Ying Li, Dunshan Yu, Xiaoxin Cui
Spiking neural networks (SNNs) have shown their competence in handling spatial-temporal event-based data with low energy consumption. Similar to conventional artificial neural networks (ANNs), SNNs are also vulnerable to gradient-based adversarial attacks, wherein gradients are calculated by spatial-temporal back-propagation (STBP) and surrogate gradients (SGs). However, the SGs may be invisible for an inference-only model as they do not influence the inference results, and current gradient-based attacks are ineffective for binary dynamic images captured by the dynamic vision sensor (DVS). While some approaches addressed the issue of invisible SGs through universal SGs, their SGs lack a correlation with the victim model, resulting in sub-optimal performance. Moreover, the imperceptibility of existing SNN-based binary attacks is still insufficient. In this paper, we introduce an innovative potential-dependent surrogate gradient (PDSG) method to establish a robust connection between the SG and the model, thereby enhancing the adaptability of adversarial attacks across various models with invisible SGs. Additionally, we propose the sparse dynamic attack (SDA) to effectively attack binary dynamic images. Utilizing a generation-reduction paradigm, SDA can fully optimize the sparsity of adversarial perturbations. Experimental results demonstrate that our PDSG and SDA outperform state-of-the-art SNN-based attacks across various models and datasets. Specifically, our PDSG achieves 100% attack success rate on ImageNet, and our SDA obtains 82% attack success rate by modifying only 0.24% of the pixels on CIFAR10DVS. The code is available at https://github.com/ryime/PDSG-SDA .
Spiking Neural Networks (SNNs), inspired by the human brain, offer significant computational efficiency through discrete spike-based information transfer. Despite their potential to reduce inference energy consumption, a performance gap persists between SNNs and Artificial Neural Networks (ANNs), primarily due to current training methods and inherent model limitations. While recent research has aimed to enhance SNN learning by employing knowledge distillation (KD) from ANN teacher networks, traditional distillation techniques often overlook the distinctive spatiotemporal properties of SNNs, thus failing to fully leverage their advantages. To overcome these challenge, we propose a novel logit distillation method characterized by temporal separation and entropy regularization. This approach improves existing SNN distillation techniques by performing distillation learning on logits across different time steps, rather than merely on aggregated output features. Furthermore, the integration of entropy regularization stabilizes model optimization and further boosts the performance. Extensive experimental results indicate that our method surpasses prior SNN distillation strategies, whether based on logit distillation, feature distillation, or a combination of both. The code will be available on GitHub.
Understanding cognitive flexibility and task-switching mechanisms in neural systems requires biologically plausible computational models. This tutorial presents a step-by-step approach to constructing a spiking neural network (SNN) that simulates task-switching dynamics within the cognitive control network. The model incorporates biologically realistic features, including lateral inhibition, adaptive synaptic weights through unsupervised Spike Timing-Dependent Plasticity (STDP), and precise neuronal parameterization within physiologically relevant ranges. The SNN is implemented using Leaky Integrate-and-Fire (LIF) neurons, which represent excitatory (glutamatergic) and inhibitory (GABAergic) populations. We utilize two real-world datasets as tasks, demonstrating how the network learns and dynamically switches between them. Experimental design follows cognitive psychology paradigms to analyze neural adaptation, synaptic weight modifications, and emergent behaviors such as Long-Term Potentiation (LTP), Long-Term Depression (LTD), and Task-Set Reconfiguration (TSR). Through a series of structured experiments, this tutorial illustrates how variations in task-switching intervals affect performance and multitasking efficiency. The results align with empirically observed neuronal responses, offering insights into the computational underpinnings of executive function. By following this tutorial, researchers can develop and extend biologically inspired SNN models for studying cognitive processes and neural adaptation.
Low Dimensional Dynamics of Globally Coupled Complex Riccati Equations: Exact Firing-rate Equations for Spiking Neurons with Clustered Substructure
2025-03-05
Diego Pazó, Rok Cestnik
We report on an exact theory for ensembles of globally coupled, heterogeneous complex Riccati equations. A drastic dimensionality reduction to a few ordinary differential equations is achieved for Lorentzian heterogeneity. By applying this technique, we obtain low-dimensional firing-rate equations for populations of spiking neurons with a clustered substructure.
Spiking Neural Networks (SNNs) have gained significant attention due to their biological plausibility and energy efficiency, making them promising alternatives to Artificial Neural Networks (ANNs). However, the performance gap between SNNs and ANNs remains a substantial challenge hindering the widespread adoption of SNNs. In this paper, we propose a Spatial-Temporal Attention Aggregator SNN (STAA-SNN) framework, which dynamically focuses on and captures both spatial and temporal dependencies. First, we introduce a spike-driven self-attention mechanism specifically designed for SNNs. Additionally, we pioneeringly incorporate position encoding to integrate latent temporal relationships into the incoming features. For spatial-temporal information aggregation, we employ step attention to selectively amplify relevant features at different steps. Finally, we implement a time-step random dropout strategy to avoid local optima. As a result, STAA-SNN effectively captures both spatial and temporal dependencies, enabling the model to analyze complex patterns and make accurate predictions. The framework demonstrates exceptional performance across diverse datasets and exhibits strong generalization capabilities. Notably, STAA-SNN achieves state-of-the-art results on neuromorphic datasets CIFAR10-DVS, with remarkable performances of 97.14%, 82.05% and 70.40% on the static datasets CIFAR-10, CIFAR-100 and ImageNet, respectively. Furthermore, our model exhibits improved performance ranging from 0.33\% to 2.80\% with fewer time steps. The code for the model is available on GitHub.
Weight transport through spike timing for robust local gradients
2025-03-04
Timo Gierlich, Andreas Baumbach, Akos F. Kungl, Kevin Max, Mihai A. Petrovici
In both machine learning and in computational neuroscience, plasticity in functional neural networks is frequently expressed as gradient descent on a cost. Often, this imposes symmetry constraints that are difficult to reconcile with local computation, as is required for biological networks or neuromorphic hardware. For example, wake-sleep learning in networks characterized by Boltzmann distributions builds on the assumption of symmetric connectivity. Similarly, the error backpropagation algorithm is notoriously plagued by the weight transport problem between the representation and the error stream. Existing solutions such as feedback alignment tend to circumvent the problem by deferring to the robustness of these algorithms to weight asymmetry. However, they are known to scale poorly with network size and depth. We introduce spike-based alignment learning (SAL), a complementary learning rule for spiking neural networks, which uses spike timing statistics to extract and correct the asymmetry between effective reciprocal connections. Apart from being spike-based and fully local, our proposed mechanism takes advantage of noise. Based on an interplay between Hebbian and anti-Hebbian plasticity, synapses can thereby recover the true local gradient. This also alleviates discrepancies that arise from neuron and synapse variability -- an omnipresent property of physical neuronal networks. We demonstrate the efficacy of our mechanism using different spiking network models. First, we show how SAL can significantly improve convergence to the target distribution in probabilistic spiking networks as compared to Hebbian plasticity alone. Second, in neuronal hierarchies based on cortical microcircuits, we show how our proposed mechanism effectively enables the alignment of feedback weights to the forward pathway, thus allowing the backpropagation of correct feedback errors.
Advances in novel hardware devices and architectures allow Spiking Neural Network evaluation using ultra-low power, mixed-signal, memristor crossbar arrays. As individual network sizes quickly scale beyond the dimensional capabilities of single crossbars, networks must be mapped onto multiple crossbars. Crossbar sizes within modern Memristor Crossbar Architectures are determined predominately not by device technology but by network topology; more, smaller crossbars consume less area thanks to the high structural sparsity found in larger, brain-inspired SNNs. Motivated by continuing increases in SNN sparsity due to improvements in training methods, we propose utilizing heterogeneous crossbar sizes to further reduce area consumption. This approach was previously unachievable as prior compiler studies only explored solutions targeting homogeneous MCAs. Our work improves on the state-of-the-art by providing Integer Linear Programming formulations supporting arbitrarily heterogeneous architectures. By modeling axonal interactions between neurons our methods produce better mappings while removing inhibitive a priori knowledge requirements. We first show a 16.7-27.6% reduction in area consumption for square-crossbar homogeneous architectures. Then, we demonstrate 66.9-72.7% further reduction when using a reasonable configuration of heterogeneous crossbar dimensions. Next, we present a new optimization formulation capable of minimizing the number of inter-crossbar routes. When applied to solutions already near-optimal in area an 11.9-26.4% routing reduction is observed without impacting area consumption. Finally, we present a profile-guided optimization capable of minimizing the number of runtime spikes between crossbars. Compared to the best-area-then-route optimized solutions we observe a further 0.5-14.8% inter-crossbar spike reduction while requiring 1-3 orders of magnitude less solver time.
Sustainable AI: Mathematical Foundations of Spiking Neural Networks
2025-03-03
Adalbert Fono, Manjot Singh, Ernesto Araya, Philipp C. Petersen, Holger Boche, Gitta Kutyniok
Deep learning's success comes with growing energy demands, raising concerns about the long-term sustainability of the field. Spiking neural networks, inspired by biological neurons, offer a promising alternative with potential computational and energy-efficiency gains. This article examines the computational properties of spiking networks through the lens of learning theory, focusing on expressivity, training, and generalization, as well as energy-efficient implementations while comparing them to artificial neural networks. By categorizing spiking models based on time representation and information encoding, we highlight their strengths, challenges, and potential as an alternative computational paradigm.
Efficient inference is critical for deploying deep learning models on edge AI devices. Low-bit quantization (e.g., 3- and 4-bit) with fixed-point arithmetic improves efficiency, while low-power memory technologies like analog nonvolatile memory enable further gains. However, these methods introduce non-ideal hardware behavior, including bit faults and device-to-device variability. We propose a regularization-based quantization-aware training (QAT) framework that supports fixed, learnable step-size, and learnable non-uniform quantization, achieving competitive results on CIFAR-10 and ImageNet. Our method also extends to Spiking Neural Networks (SNNs), demonstrating strong performance on 4-bit networks on CIFAR10-DVS and N-Caltech 101. Beyond quantization, our framework enables fault and variability-aware fine-tuning, mitigating stuck-at faults (fixed weight bits) and device resistance variability. Compared to prior fault-aware training, our approach significantly improves performance recovery under upto 20% bit-fault rate and 40% device-to-device variability. Our results establish a generalizable framework for quantization and robustness-aware training, enhancing efficiency and reliability in low-power, non-ideal hardware.
DEAL: Data-Efficient Adversarial Learning for High-Quality Infrared Imaging
2025-03-02
Zhu Liu, Zijun Wang, Jinyuan Liu, Fanqi Meng, Long Ma, Risheng Liu
Thermal imaging is often compromised by dynamic, complex degradations caused by hardware limitations and unpredictable environmental factors. The scarcity of high-quality infrared data, coupled with the challenges of dynamic, intricate degradations, makes it difficult to recover details using existing methods. In this paper, we introduce thermal degradation simulation integrated into the training process via a mini-max optimization, by modeling these degraded factors as adversarial attacks on thermal images. The simulation is dynamic to maximize objective functions, thus capturing a broad spectrum of degraded data distributions. This approach enables training with limited data, thereby improving model performance.Additionally, we introduce a dual-interaction network that combines the benefits of spiking neural networks with scale transformation to capture degraded features with sharp spike signal intensities. This architecture ensures compact model parameters while preserving efficient feature representation. Extensive experiments demonstrate that our method not only achieves superior visual quality under diverse single and composited degradation, but also delivers a significant reduction in processing when trained on only fifty clear images, outperforming existing techniques in efficiency and accuracy. The source code will be available at https://github.com/LiuZhu-CV/DEAL.
Range and Angle Estimation with Spiking Neural Resonators for FMCW Radar
2025-03-02
Nico Reeb, Javier Lopez-Randulfe, Robin Dietrich, Alois C. Knoll
Automotive radar systems face the challenge of managing high sampling rates and large data bandwidth while complying with stringent real-time and energy efficiency requirements. The growing complexity of autonomous vehicles further intensifies these requirements. Neuromorphic computing offers promising solutions because of its inherent energy efficiency and parallel processing capacity. This research presents a novel spiking neuron model for signal processing of frequency-modulated continuous wave (FMCW) radars that outperforms the state-of-the-art spectrum analysis algorithms in latency and data bandwidth. These spiking neural resonators are based on the resonate-and-fire neuron model and optimized to dynamically process raw radar data while simultaneously emitting an output in the form of spikes. We designed the first neuromorphic neural network consisting of these spiking neural resonators that estimates range and angle from FMCW radar data. We evaluated the range-angle maps on simulated datasets covering multiple scenarios and compared the results with a state-of-the-art pipeline for radar processing. The proposed neuron model significantly reduces the processing latency compared to traditional frequency analysis algorithms, such as the Fourier transformation (FT), which needs to sample and store entire data frames before processing. The evaluations demonstrate that these spiking neural resonators achieve state-of-the-art detection accuracy while emitting spikes simultaneously to processing and transmitting only 0.02 % of the data compared to a float-32 FT. The results showcase the potential for neuromorphic signal processing for FMCW radar systems and pave the way for designing neuromorphic radar sensors.
Spiking World Model with Multi-Compartment Neurons for Model-based Reinforcement Learning
2025-03-02
Yinqian Sun, Feifei Zhao, Mingyang Lv, Yi Zeng
Brain-inspired spiking neural networks (SNNs) have garnered significant research attention in algorithm design and perception applications. However, their potential in the decision-making domain, particularly in model-based reinforcement learning, remains underexplored. The difficulty lies in the need for spiking neurons with long-term temporal memory capabilities, as well as network optimization that can integrate and learn information for accurate predictions. The dynamic dendritic information integration mechanism of biological neurons brings us valuable insights for addressing these challenges. In this study, we propose a multi-compartment neuron model capable of nonlinearly integrating information from multiple dendritic sources to dynamically process long sequential inputs. Based on this model, we construct a Spiking World Model (Spiking-WM), to enable model-based deep reinforcement learning (DRL) with SNNs. We evaluated our model using the DeepMind Control Suite, demonstrating that Spiking-WM outperforms existing SNN-based models and achieves performance comparable to artificial neural network (ANN)-based world models employing Gated Recurrent Units (GRUs). Furthermore, we assess the long-term memory capabilities of the proposed model in speech datasets, including SHD, TIMIT, and LibriSpeech 100h, showing that our multi-compartment neuron model surpasses other SNN-based architectures in processing long sequences. Our findings underscore the critical role of dendritic information integration in shaping neuronal function, emphasizing the importance of cooperative dendritic processing in enhancing neural computation.
Adaptive Wall-Following Control for Unmanned Ground Vehicles Using Spiking Neural Networks
2025-03-01
Hengye Yang, Yanxiao Chen, Zexuan Fan, Lin Shao, Tao Sun
Unmanned ground vehicles operating in complex environments must adaptively adjust to modeling uncertainties and external disturbances to perform tasks such as wall following and obstacle avoidance. This paper introduces an adaptive control approach based on spiking neural networks for wall fitting and tracking, which learns and adapts to unforeseen disturbances. We propose real-time wall-fitting algorithms to model unknown wall shapes and generate corresponding trajectories for the vehicle to follow. A discretized linear quadratic regulator is developed to provide a baseline control signal based on an ideal vehicle model. Point matching algorithms then identify the nearest matching point on the trajectory to generate feedforward control inputs. Finally, an adaptive spiking neural network controller, which adjusts its connection weights online based on error signals, is integrated with the aforementioned control algorithms. Numerical simulations demonstrate that this adaptive control framework outperforms the traditional linear quadratic regulator in tracking complex trajectories and following irregular walls, even in the presence of partial actuator failures and state estimation errors.
Spiking Neural Networks (SNNs) exhibit significant potential due to their low energy consumption. Converting Artificial Neural Networks (ANNs) to SNNs is an efficient way to achieve high-performance SNNs. However, many conversion methods are based on rate coding, which requires numerous spikes and longer time-steps compared to directly trained SNNs, leading to increased energy consumption and latency. This article introduces differential coding for ANN-to-SNN conversion, a novel coding scheme that reduces spike counts and energy consumption by transmitting changes in rate information rather than rates directly, and explores its application across various layers. Additionally, the threshold iteration method is proposed to optimize thresholds based on activation distribution when converting Rectified Linear Units (ReLUs) to spiking neurons. Experimental results on various Convolutional Neural Networks (CNNs) and Transformers demonstrate that the proposed differential coding significantly improves accuracy while reducing energy consumption, particularly when combined with the threshold iteration method, achieving state-of-the-art performance. The source codes of the proposed method are available at https://github.com/h-z-h-cell/ANN-to-SNN-DCGS.
Spiking neural networks (SNNs) show great potential due to their energy efficiency, fast processing capabilities, and robustness. There are two main approaches to constructing SNNs. Direct training methods require much memory, while conversion methods offer a simpler and more efficient option. However, current conversion methods mainly focus on converting convolutional neural networks (CNNs) to SNNs. Converting Transformers to SNN is challenging because of the presence of non-linear modules. In this paper, we propose an Expectation Compensation Module to preserve the accuracy of the conversion. The core idea is to use information from the previous T time-steps to calculate the expected output at time-step T. We also propose a Multi-Threshold Neuron and the corresponding Parallel Parameter normalization to address the challenge of large time steps needed for high accuracy, aiming to reduce network latency and power consumption. Our experimental results demonstrate that our approach achieves state-of-the-art performance. For example, we achieve a top-1 accuracy of 88.60\% with only a 1\% loss in accuracy using 4 time steps while consuming only 35\% of the original power of the Transformer. To our knowledge, this is the first successful Artificial Neural Network (ANN) to SNN conversion for Spiking Transformers that achieves high accuracy, low latency, and low power consumption on complex datasets. The source codes of the proposed method are available at https://github.com/h-z-h-cell/Transformer-to-SNN-ECMT.
Transformers have demonstrated outstanding performance across a wide range of tasks, owing to their self-attention mechanism, but they are highly energy-consuming. Spiking Neural Networks have emerged as a promising energy-efficient alternative to traditional Artificial Neural Networks, leveraging event-driven computation and binary spikes for information transfer. The combination of Transformers' capabilities with the energy efficiency of SNNs offers a compelling opportunity. This paper addresses the challenge of adapting the self-attention mechanism of Transformers to the spiking paradigm by introducing a novel approach: Accurate Addition-Only Spiking Self-Attention (A$^2$OS$^2$A). Unlike existing methods that rely solely on binary spiking neurons for all components of the self-attention mechanism, our approach integrates binary, ReLU, and ternary spiking neurons. This hybrid strategy significantly improves accuracy while preserving non-multiplicative computations. Moreover, our method eliminates the need for softmax and scaling operations. Extensive experiments show that the A$^2$OS$^2$A-based Spiking Transformer outperforms existing SNN-based Transformers on several datasets, even achieving an accuracy of 78.66\% on ImageNet-1K. Our work represents a significant advancement in SNN-based Transformer models, offering a more accurate and efficient solution for real-world applications.
Neural systems use the same underlying computational substrate to carry out analog filtering and signal processing operations, as well as discrete symbol manipulation and digital computation. Inspired by the computational principles of canonical cortical microcircuits, we propose a framework for using recurrent spiking neural networks to seamlessly and robustly switch between analog signal processing and categorical and discrete computation. We provide theoretical analysis and practical neural network design tools to formally determine the conditions for inducing this switch. We demonstrate the robustness of this framework experimentally with hardware soft Winner-Take-All and mixed-feedback recurrent spiking neural networks, implemented by appropriately configuring the analog neuron and synapse circuits of a mixed-signal neuromorphic processor chip.
Noise-Injected Spiking Graph Convolution for Energy-Efficient 3D Point Cloud Denoising
2025-02-27
Zikuan Li, Qiaoyun Wu, Jialin Zhang, Kaijun Zhang, Jun Wang
Spiking neural networks (SNNs), inspired by the spiking computation paradigm of the biological neural systems, have exhibited superior energy efficiency in 2D classification tasks over traditional artificial neural networks (ANNs). However, the regression potential of SNNs has not been well explored, especially in 3D point cloud processing. In this paper, we propose noise-injected spiking graph convolutional networks to leverage the full regression potential of SNNs in 3D point cloud denoising. Specifically, we first emulate the noise-injected neuronal dynamics to build noise-injected spiking neurons. On this basis, we design noise-injected spiking graph convolution for promoting disturbance-aware spiking representation learning on 3D points. Starting from the spiking graph convolution, we build two SNN-based denoising networks. One is a purely spiking graph convolutional network, which achieves low accuracy loss compared with some ANN-based alternatives, while resulting in significantly reduced energy consumption on two benchmark datasets, PU-Net and PC-Net. The other is a hybrid architecture that combines ANN-based learning with a high performance-efficiency trade-off in just a few time steps. Our work lights up SNN's potential for 3D point cloud denoising, injecting new perspectives of exploring the deployment on neuromorphic chips while paving the way for developing energy-efficient 3D data acquisition devices.
Neuromorphic Circuits with Spiking Astrocytes for Increased Energy Efficiency, Fault Tolerance, and Memory Capacitance
2025-02-27
Aybars Yunusoglu, Dexter Le, Murat Isik, I. Can Dikmen, Teoman Karadag
In the rapidly advancing field of neuromorphic computing, integrating biologically-inspired models like the Leaky Integrate-and-Fire Astrocyte (LIFA) into spiking neural networks (SNNs) enhances system robustness and performance. This paper introduces the LIFA model in SNNs, addressing energy efficiency, memory management, routing mechanisms, and fault tolerance. Our core architecture consists of neurons, synapses, and astrocyte circuits, with each astrocyte supporting multiple neurons for self-repair. This clustered model improves fault tolerance and operational efficiency, especially under adverse conditions. We developed a routing methodology to map the LIFA model onto a fault-tolerant, many-core design, optimizing network functionality and efficiency. Our model features a fault tolerance rate of 81.10\% and a resilience improvement rate of 18.90\%, significantly surpassing other implementations. The results validate our approach in memory management, highlighting its potential as a robust solution for advanced neuromorphic computing applications. The integration of astrocytes represents a significant advancement, setting the stage for more resilient and adaptable neuromorphic systems.
Enhanced Neuromorphic Semantic Segmentation Latency through Stream Event
2025-02-26
D. Hareb, J. Martinet, B. Miramond
Achieving optimal semantic segmentation with frame-based vision sensors poses significant challenges for real-time systems like UAVs and self-driving cars, which require rapid and precise processing. Traditional frame-based methods often struggle to balance latency, accuracy, and energy efficiency. To address these challenges, we leverage event streams from event-based cameras-bio-inspired sensors that trigger events in response to changes in the scene. Specifically, we analyze the number of events triggered between successive frames, with a high number indicating significant changes and a low number indicating minimal changes. We exploit this event information to solve the semantic segmentation task by employing a Spiking Neural Network (SNN), a bio-inspired computing paradigm known for its low energy consumption. Our experiments on the DSEC dataset show that our approach significantly reduces latency with only a limited drop in accuracy. Additionally, by using SNNs, we achieve low power consumption, making our method suitable for energy-constrained real-time applications. To the best of our knowledge, our approach is the first to effectively balance reduced latency, minimal accuracy loss, and energy efficiency using events stream to enhance semantic segmentation in dynamic and resource-limited environments.
On the Privacy-Preserving Properties of Spiking Neural Networks with Unique Surrogate Gradients and Quantization Levels
2025-02-25
Ayana Moshruba, Shay Snyder, Hamed Poursiami, Maryam Parsa
As machine learning models increasingly process sensitive data, understanding their vulnerability to privacy attacks is vital. Membership inference attacks (MIAs) exploit model responses to infer whether specific data points were used during training, posing a significant privacy risk. Prior research suggests that spiking neural networks (SNNs), which rely on event-driven computation and discrete spike-based encoding, exhibit greater resilience to MIAs than artificial neural networks (ANNs). This resilience stems from their non-differentiable activations and inherent stochasticity, which obscure the correlation between model responses and individual training samples. To enhance privacy in SNNs, we explore two techniques: quantization and surrogate gradients. Quantization, which reduces precision to limit information leakage, has improved privacy in ANNs. Given SNNs' sparse and irregular activations, quantization may further disrupt the activation patterns exploited by MIAs. We assess the vulnerability of SNNs and ANNs under weight and activation quantization across multiple datasets, using the attack model's receiver operating characteristic (ROC) curve area under the curve (AUC) metric, where lower values indicate stronger privacy, and evaluate the privacy-accuracy trade-off. Our findings show that quantization enhances privacy in both architectures with minimal performance loss, though full-precision SNNs remain more resilient than quantized ANNs. Additionally, we examine the impact of surrogate gradients on privacy in SNNs. Among five evaluated gradients, spike rate escape provides the best privacy-accuracy trade-off, while arctangent increases vulnerability to MIAs. These results reinforce SNNs' inherent privacy advantages and demonstrate that quantization and surrogate gradient selection significantly influence privacy-accuracy trade-offs in SNNs.
Memory-Free and Parallel Computation for Quantized Spiking Neural Networks
2025-02-25
Dehao Zhang, Shuai Wang, Yichen Xiao, Wenjie Wei, Yimeng Shan, Malu Zhang, Yang Yang
Quantized Spiking Neural Networks (QSNNs) offer superior energy efficiency and are well-suited for deployment on resource-limited edge devices. However, limited bit-width weight and membrane potential result in a notable performance decline. In this study, we first identify a new underlying cause for this decline: the loss of historical information due to the quantized membrane potential. To tackle this issue, we introduce a memory-free quantization method that captures all historical information without directly storing membrane potentials, resulting in better performance with less memory requirements. To further improve the computational efficiency, we propose a parallel training and asynchronous inference framework that greatly increases training speed and energy efficiency. We combine the proposed memory-free quantization and parallel computation methods to develop a high-performance and efficient QSNN, named MFP-QSNN. Extensive experiments show that our MFP-QSNN achieves state-of-the-art performance on various static and neuromorphic image datasets, requiring less memory and faster training speeds. The efficiency and efficacy of the MFP-QSNN highlight its potential for energy-efficient neuromorphic computing.
SpikACom: A Neuromorphic Computing Framework for Green Communications
2025-02-24
Yanzhen Liu, Zhijin Qin, Yongxu Zhu, Geoffrey Ye Li
The ever-growing power consumption of wireless communication systems necessitates more energy-efficient algorithms. This paper introduces SpikACom ({Spik}ing {A}daptive {Com}munication), a neuromorphic computing-based framework for power-intensive wireless communication tasks. SpikACom leverages brain-inspired spiking neural networks (SNNs) for efficient signal processing. It is designed for dynamic wireless environments, helping to mitigate catastrophic forgetting and facilitate adaptation to new circumstances. Moreover, SpikACom is customizable, allowing flexibly integration of domain knowledge to enhance it interpretability and efficacy. We validate its performance on fundamental wireless communication tasks, including task-oriented semantic communication, multiple-input multiple-output (MIMO) beamforming, and orthogonal frequency-division multiplexing (OFDM) channel estimation. The simulation results show that SpikACom significantly reduces power consumption while matching or exceeding the performance of conventional algorithms. This study highlights the potential of SNNs for enabling greener and smarter wireless communication systems.
A Quarter of a Century of Neuromorphic Architectures on FPGAs -- an Overview
2025-02-23
Wiktor J. Szczerek, Artur Podobas
Neuromorphic computing is a relatively new discipline of computer science, where the principles of biological brain's computation and memory are used to create a new way of processing information, based on networks of spiking neurons. Those networks can be implemented as both analog and digital implementations, where for the latter, the Field Programmable Gate Arrays (FPGAs) are a frequent choice, due to their inherent flexibility, allowing the researchers to easily design hardware neuromorphic architecture (NMAs). Moreover, digital NMAs show good promise in simulating various spiking neural networks because of their inherent accuracy and resilience to noise, as opposed to analog implementations. This paper presents an overview of digital NMAs implemented on FPGAs, with a goal of providing useful references to various architectural design choices to the researchers interested in digital neuromorphic systems. We present a taxonomy of NMAs that highlights groups of distinct architectural features, their advantages and disadvantages and identify trends and predictions for the future of those architectures.
SpikeRL: A Scalable and Energy-efficient Framework for Deep Spiking Reinforcement Learning
2025-02-21
Tokey Tahmid, Mark Gates, Piotr Luszczek, Catherine D. Schuman
In this era of AI revolution, massive investments in large-scale data-driven AI systems demand high-performance computing, consuming tremendous energy and resources. This trend raises new challenges in optimizing sustainability without sacrificing scalability or performance. Among the energy-efficient alternatives of the traditional Von Neumann architecture, neuromorphic computing and its Spiking Neural Networks (SNNs) are a promising choice due to their inherent energy efficiency. However, in some real-world application scenarios such as complex continuous control tasks, SNNs often lack the performance optimizations that traditional artificial neural networks have. Researchers have addressed this by combining SNNs with Deep Reinforcement Learning (DeepRL), yet scalability remains unexplored. In this paper, we extend our previous work on SpikeRL, which is a scalable and energy efficient framework for DeepRL-based SNNs for continuous control. In our initial implementation of SpikeRL framework, we depended on the population encoding from the Population-coded Spiking Actor Network (PopSAN) method for our SNN model and implemented distributed training with Message Passing Interface (MPI) through mpi4py. Also, further optimizing our model training by using mixed-precision for parameter updates. In our new SpikeRL framework, we have implemented our own DeepRL-SNN component with population encoding, and distributed training with PyTorch Distributed package with NCCL backend while still optimizing with mixed precision training. Our new SpikeRL implementation is 4.26X faster and 2.25X more energy efficient than state-of-the-art DeepRL-SNN methods. Our proposed SpikeRL framework demonstrates a truly scalable and sustainable solution for complex continuous control tasks in real-world applications.
Temporal Misalignment in ANN-SNN Conversion and Its Mitigation via Probabilistic Spiking Neurons
2025-02-20
Velibor Bojković, Xiaofeng Wu, Bin Gu
Spiking Neural Networks (SNNs) offer a more energy-efficient alternative to Artificial Neural Networks (ANNs) by mimicking biological neural principles, establishing them as a promising approach to mitigate the increasing energy demands of large-scale neural models. However, fully harnessing the capabilities of SNNs remains challenging due to their discrete signal processing and temporal dynamics. ANN-SNN conversion has emerged as a practical approach, enabling SNNs to achieve competitive performance on complex machine learning tasks. In this work, we identify a phenomenon in the ANN-SNN conversion framework, termed temporal misalignment, in which random spike rearrangement across SNN layers leads to performance improvements. Based on this observation, we introduce biologically plausible two-phase probabilistic (TPP) spiking neurons, further enhancing the conversion process. We demonstrate the advantages of our proposed method both theoretically and empirically through comprehensive experiments on CIFAR-10/100, CIFAR10-DVS, and ImageNet across a variety of architectures, achieving state-of-the-art results.
Towards Accurate Binary Spiking Neural Networks: Learning with Adaptive Gradient Modulation Mechanism
2025-02-20
Yu Liang, Wenjie Wei, Ammar Belatreche, Honglin Cao, Zijian Zhou, Shuai Wang, Malu Zhang, Yang Yang
Binary Spiking Neural Networks (BSNNs) inherit the eventdriven paradigm of SNNs, while also adopting the reduced storage burden of binarization techniques. These distinct advantages grant BSNNs lightweight and energy-efficient characteristics, rendering them ideal for deployment on resource-constrained edge devices. However, due to the binary synaptic weights and non-differentiable spike function, effectively training BSNNs remains an open question. In this paper, we conduct an in-depth analysis of the challenge for BSNN learning, namely the frequent weight sign flipping problem. To mitigate this issue, we propose an Adaptive Gradient Modulation Mechanism (AGMM), which is designed to reduce the frequency of weight sign flipping by adaptively adjusting the gradients during the learning process. The proposed AGMM can enable BSNNs to achieve faster convergence speed and higher accuracy, effectively narrowing the gap between BSNNs and their full-precision equivalents. We validate AGMM on both static and neuromorphic datasets, and results indicate that it achieves state-of-the-art results among BSNNs. This work substantially reduces storage demands and enhances SNNs' inherent energy efficiency, making them highly feasible for resource-constrained environments.
Rethinking Spiking Neural Networks from an Ensemble Learning Perspective
2025-02-20
Yongqi Ding, Lin Zuo, Mengmeng Jing, Pei He, Hanpu Deng
Spiking neural networks (SNNs) exhibit superior energy efficiency but suffer from limited performance. In this paper, we consider SNNs as ensembles of temporal subnetworks that share architectures and weights, and highlight a crucial issue that affects their performance: excessive differences in initial states (neuronal membrane potentials) across timesteps lead to unstable subnetwork outputs, resulting in degraded performance. To mitigate this, we promote the consistency of the initial membrane potential distribution and output through membrane potential smoothing and temporally adjacent subnetwork guidance, respectively, to improve overall stability and performance. Moreover, membrane potential smoothing facilitates forward propagation of information and backward propagation of gradients, mitigating the notorious temporal gradient vanishing problem. Our method requires only minimal modification of the spiking neurons without adapting the network structure, making our method generalizable and showing consistent performance gains in 1D speech, 2D object, and 3D point cloud recognition tasks. In particular, on the challenging CIFAR10-DVS dataset, we achieved 83.20\% accuracy with only four timesteps. This provides valuable insights into unleashing the potential of SNNs.
Fundamental Survey on Neuromorphic Based Audio Classification
2025-02-20
Amlan Basu, Pranav Chaudhari, Gaetano Di Caterina
Audio classification is paramount in a variety of applications including surveillance, healthcare monitoring, and environmental analysis. Traditional methods frequently depend on intricate signal processing algorithms and manually crafted features, which may fall short in fully capturing the complexities of audio patterns. Neuromorphic computing, inspired by the architecture and functioning of the human brain, presents a promising alternative for audio classification tasks. This survey provides an exhaustive examination of the current state-of-the-art in neuromorphic-based audio classification. It delves into the crucial components of neuromorphic systems, such as Spiking Neural Networks (SNNs), memristors, and neuromorphic hardware platforms, highlighting their advantages in audio classification. Furthermore, the survey explores various methodologies and strategies employed in neuromorphic audio classification, including event-based processing, spike-based learning, and bio-inspired feature extraction. It examines how these approaches address the limitations of traditional audio classification methods, particularly in terms of energy efficiency, real-time processing, and robustness to environmental noise. Additionally, the paper conducts a comparative analysis of different neuromorphic audio classification models and benchmarks, evaluating their performance metrics, computational efficiency, and scalability. By providing a comprehensive guide for researchers, engineers and practitioners, this survey aims to stimulate further innovation and advancements in the evolving field of neuromorphic audio classification.
Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency
2025-02-19
Jiangrong Shen, Qi Xu, Gang Pan, Badong Chen
The human brain utilizes spikes for information transmission and dynamically reorganizes its network structure to boost energy efficiency and cognitive capabilities throughout its lifespan. Drawing inspiration from this spike-based computation, Spiking Neural Networks (SNNs) have been developed to construct event-driven models that emulate this efficiency. Despite these advances, deep SNNs continue to suffer from over-parameterization during training and inference, a stark contrast to the brain's ability to self-organize. Furthermore, existing sparse SNNs are challenged by maintaining optimal pruning levels due to a static pruning ratio, resulting in either under- or over-pruning. In this paper, we propose a novel two-stage dynamic structure learning approach for deep SNNs, aimed at maintaining effective sparse training from scratch while optimizing compression efficiency. The first stage evaluates the compressibility of existing sparse subnetworks within SNNs using the PQ index, which facilitates an adaptive determination of the rewiring ratio for synaptic connections based on data compression insights. In the second stage, this rewiring ratio critically informs the dynamic synaptic connection rewiring process, including both pruning and regrowth. This approach significantly improves the exploration of sparse structure training in deep SNNs, adapting sparsity dynamically from the point view of compression efficiency. Our experiments demonstrate that this sparse training approach not only aligns with the performance of current deep SNNs models but also significantly improves the efficiency of compressing sparse SNNs. Crucially, it preserves the advantages of initiating training with sparse models and offers a promising solution for implementing edge AI on neuromorphic hardware.
Comparison principles and asymptotic behavior of delayed age-structured neuron models
2025-02-19
María J Cáceres, José A Cañizo, Nicolas Torres
In the context of neuroscience the elapsed-time model is an age-structured equation that describes the behavior of interconnected spiking neurons through the time since the last discharge, with many interesting dynamics depending on the type of interactions between neurons. We investigate the asymptotic behavior of this equation in the case of both discrete and distributed delays that account for the time needed to transmit a nerve impulse from one neuron to the rest the ensemble. To prove the convergence to the equilibrium, we follow an approach based on comparison principles for Volterra equations involving the total activity, which provides a simpler and more straightforward alternative technique than those in the existing literature on the elapsed-time model.
SNN-Driven Multimodal Human Action Recognition via Event Camera and Skeleton Data Fusion
2025-02-19
Naichuan Zheng, Hailun Xia
Multimodal human action recognition based on RGB and skeleton data fusion, while effective, is constrained by significant limitations such as high computational complexity, excessive memory consumption, and substantial energy demands, particularly when implemented with Artificial Neural Networks (ANN). These limitations restrict its applicability in resource-constrained scenarios. To address these challenges, we propose a novel Spiking Neural Network (SNN)-driven framework for multimodal human action recognition, utilizing event camera and skeleton data. Our framework is centered on two key innovations: (1) a novel multimodal SNN architecture that employs distinct backbone networks for each modality-an SNN-based Mamba for event camera data and a Spiking Graph Convolutional Network (SGN) for skeleton data-combined with a spiking semantic extraction module to capture deep semantic representations; and (2) a pioneering SNN-based discretized information bottleneck mechanism for modality fusion, which effectively balances the preservation of modality-specific semantics with efficient information compression. To validate our approach, we propose a novel method for constructing a multimodal dataset that integrates event camera and skeleton data, enabling comprehensive evaluation. Extensive experiments demonstrate that our method achieves superior performance in both recognition accuracy and energy efficiency, offering a promising solution for practical applications.
Dynamic Activation with Knowledge Distillation for Energy-Efficient Spiking NN Ensembles
2025-02-19
Orestis Konstantaropoulos, Theodoris Mallios, Maria Papadopouli
While foundation AI models excel at tasks like classification and decision-making, their high energy consumption makes them unsuitable for energy-constrained applications. Inspired by the brain's efficiency, spiking neural networks (SNNs) have emerged as a viable alternative due to their event-driven nature and compatibility with neuromorphic chips. This work introduces a novel system that combines knowledge distillation and ensemble learning to bridge the performance gap between artificial neural networks (ANNs) and SNNs. A foundation AI model acts as a teacher network, guiding smaller student SNNs organized into an ensemble, called Spiking Neural Ensemble (SNE). SNE enables the disentanglement of the teacher's knowledge, allowing each student to specialize in predicting a distinct aspect of it, while processing the same input. The core innovation of SNE is the adaptive activation of a subset of SNN models of an ensemble, leveraging knowledge-distillation, enhanced with an informed-partitioning (disentanglement) of the teacher's feature space. By dynamically activating only a subset of these student SNNs, the system balances accuracy and energy efficiency, achieving substantial energy savings with minimal accuracy loss. Moreover, SNE is significantly more efficient than the teacher network, reducing computational requirements by up to 20x with only a 2% drop in accuracy on the CIFAR-10 dataset. This disentanglement procedure achieves an accuracy improvement of up to 2.4% on the CIFAR-10 dataset compared to other partitioning schemes. Finally, we comparatively analyze SNE performance under noisy conditions, demonstrating enhanced robustness compared to its ANN teacher. In summary, SNE offers a promising new direction for energy-constrained applications.
Spiking Point Transformer for Point Cloud Classification
2025-02-19
Peixi Wu, Bosong Chai, Hebei Li, Menghua Zheng, Yansong Peng, Zeyu Wang, Xuan Nie, Yueyi Zhang, Xiaoyan Sun
Spiking Neural Networks (SNNs) offer an attractive and energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their sparse binary activation. When SNN meets Transformer, it shows great potential in 2D image processing. However, their application for 3D point cloud remains underexplored. To this end, we present Spiking Point Transformer (SPT), the first transformer-based SNN framework for point cloud classification. Specifically, we first design Queue-Driven Sampling Direct Encoding for point cloud to reduce computational costs while retaining the most effective support points at each time step. We introduce the Hybrid Dynamics Integrate-and-Fire Neuron (HD-IF), designed to simulate selective neuron activation and reduce over-reliance on specific artificial neurons. SPT attains state-of-the-art results on three benchmark datasets that span both real-world and synthetic datasets in the SNN domain. Meanwhile, the theoretical energy consumption of SPT is at least 6.4$\times$ less than its ANN counterpart.
Spiking Neural Networks (SNNs) offer a biologically inspired computational paradigm that emulates neuronal activity through discrete spike-based processing. Despite their advantages, training SNNs with traditional backpropagation (BP) remains challenging due to computational inefficiencies and a lack of biological plausibility. This study explores the Forward-Forward (FF) algorithm as an alternative learning framework for SNNs. Unlike backpropagation, which relies on forward and backward passes, the FF algorithm employs two forward passes, enabling layer-wise localized learning, enhanced computational efficiency, and improved compatibility with neuromorphic hardware. We introduce an FF-based SNN training framework and evaluate its performance across both non-spiking (MNIST, Fashion-MNIST, Kuzushiji-MNIST) and spiking (Neuro-MNIST, SHD) datasets. Experimental results demonstrate that our model surpasses existing FF-based SNNs on evaluated static datasets with a much lighter architecture while achieving accuracy comparable to state-of-the-art backpropagation-trained SNNs. On more complex spiking tasks such as SHD, our approach outperforms other SNN models and remains competitive with leading backpropagation-trained SNNs. These findings highlight the FF algorithm's potential to advance SNN training methodologies by addressing some key limitations of backpropagation.
Activation-wise Propagation: A Universal Strategy to Break Timestep Constraints in Spiking Neural Networks for 3D Data Processing
2025-02-18
Jian Song, Xiangfei Yang, Donglin Wang
Due to their event-driven and parameter-efficient effect, spiking neural networks (SNNs) show potential in tasks requiring real-time multi-sensor perception, such as autonomous driving. The spiking mechanism facilitates sparse encoding, enabling spatial and temporal data to be represented in a discrete manner. However, SNNs still lag behind artificial neural networks (ANNs) in terms of performance and computational efficiency. One major challenge in SNNs is the timestep-wise iterative update of neuronal states, which makes it difficult to achieve an optimal trade-off among accuracy, latency, and training cost. Although some methods perform well with shorter timesteps, few propose strategies to overcome such constraint effectively. Moreover, many recent SNN advancements rely on either optimizations tailored to specific architectures or a collection of specialized neuron-level strategies. While these approaches can enhance performance, they often lead to increased computational expense and restrict their application to particular architectures or modalities. This leaves room for further exploration of simple, universal, and structure-agnostic strategies that could offer broader applicability and efficiency. In this paper, we introduce Activation-wise Membrane Potential Propagation (AMP2), a novel state update mechanism for spiking neurons. Inspired by skip connections in deep networks, AMP2 incorporates the membrane potential of neurons into network, eliminating the need for iterative updates. Our method achieves significant improvements across various 3D modalities, including 3D point clouds and event streams, boosting Spiking PointNet's accuracy on ModelNet40 from 87.36% to 89.74% and surpassing ANN PointNet in recognition accuracy on the DVS128 Gesture dataset.
2025-02-18
Enrico Lupi, Abhishek, Max Aehle, Muhammad Awais, Alessandro Breccia, Riccardo Carroccio, Long Chen, Abhijit Das, Andrea De Vita, Tommaso Dorigo, Nicolas R. Gauger, Ralf Keidel, Jan Kieseler, Anders Mikkelsen, Federico Nardi, Xuan Tung Nguyen, Fredrik Sandin, Kylian Schmidt, Pietro Vischia, Joseph Willmore
We simulate hadrons impinging on a homogeneous lead-tungstate (PbWO4) calorimeter to investigate how the resulting light yield and its temporal structure, as detected by an array of light-sensitive sensors, can be processed by a neuromorphic computing system. Our model encodes temporal photon distributions as spike trains and employs a fully connected spiking neural network to estimate the total deposited energy, as well as the position and spatial distribution of the light emissions within the sensitive material. The extracted primitives offer valuable topological information about the shower development in the material, achieved without requiring a segmentation of the active medium. A potential nanophotonic implementation using III-V semiconductor nanowires is discussed. It can be both fast and energy efficient.
The combination of Spiking Neural Networks (SNNs) and Vision Transformers (ViTs) holds potential for achieving both energy efficiency and high performance, particularly suitable for edge vision applications. However, a significant performance gap still exists between SNN-based ViTs and their ANN counterparts. Here, we first analyze why SNN-based ViTs suffer from limited performance and identify a mismatch between the vanilla self-attention mechanism and spatio-temporal spike trains. This mismatch results in degraded spatial relevance and limited temporal interactions. To address these issues, we draw inspiration from biological saccadic attention mechanisms and introduce an innovative Saccadic Spike Self-Attention (SSSA) method. Specifically, in the spatial domain, SSSA employs a novel spike distribution-based method to effectively assess the relevance between Query and Key pairs in SNN-based ViTs. Temporally, SSSA employs a saccadic interaction module that dynamically focuses on selected visual areas at each timestep and significantly enhances whole scene understanding through temporal interactions. Building on the SSSA mechanism, we develop a SNN-based Vision Transformer (SNN-ViT). Extensive experiments across various visual tasks demonstrate that SNN-ViT achieves state-of-the-art performance with linear computational complexity. The effectiveness and efficiency of the SNN-ViT highlight its potential for power-critical edge vision applications.
Humans interpret and perceive the world by integrating sensory information from multiple modalities, such as vision and hearing. Spiking Neural Networks (SNNs), as brain-inspired computational models, exhibit unique advantages in emulating the brain's information processing mechanisms. However, existing SNN models primarily focus on unimodal processing and lack efficient cross-modal information fusion, thereby limiting their effectiveness in real-world multimodal scenarios. To address this challenge, we propose a semantic-alignment cross-modal residual learning (S-CMRL) framework, a Transformer-based multimodal SNN architecture designed for effective audio-visual integration. S-CMRL leverages a spatiotemporal spiking attention mechanism to extract complementary features across modalities, and incorporates a cross-modal residual learning strategy to enhance feature integration. Additionally, a semantic alignment optimization mechanism is introduced to align cross-modal features within a shared semantic space, improving their consistency and complementarity. Extensive experiments on three benchmark datasets CREMA-D, UrbanSound8K-AV, and MNISTDVS-NTIDIGITS demonstrate that S-CMRL significantly outperforms existing multimodal SNN methods, achieving the state-of-the-art performance. The code is publicly available at https://github.com/Brain-Cog-Lab/S-CMRL.
On the Privacy Risks of Spiking Neural Networks: A Membership Inference Analysis
2025-02-18
Junyi Guan, Abhijith Sharma, Chong Tian, Salem Lahlou
Spiking Neural Networks (SNNs) are increasingly explored for their energy efficiency and robustness in real-world applications, yet their privacy risks remain largely unexamined. In this work, we investigate the susceptibility of SNNs to Membership Inference Attacks (MIAs) -- a major privacy threat where an adversary attempts to determine whether a given sample was part of the training dataset. While prior work suggests that SNNs may offer inherent robustness due to their discrete, event-driven nature, we find that its resilience diminishes as latency (T) increases. Furthermore, we introduce an input dropout strategy under black box setting, that significantly enhances membership inference in SNNs. Our findings challenge the assumption that SNNs are inherently more secure, and even though they are expected to be better, our results reveal that SNNs exhibit privacy vulnerabilities that are equally comparable to Artificial Neural Networks (ANNs). Our code is available at https://anonymous.4open.science/r/MIA_SNN-3610.
Emergent functions of noise-driven spontaneous activity: Homeostatic maintenance of criticality and memory consolidation
2025-02-16
Marumitsu Ikeda, Dai Akita, Hirokazu Takahashi
Unlike digital computers, the brain exhibits spontaneous activity even during complete rest, despite the evolutionary pressure for energy efficiency. Inspired by the critical brain hypothesis, which proposes that the brain operates optimally near a critical point of phase transition in the dynamics of neural networks to improve computational efficiency, we postulate that spontaneous activity plays a homeostatic role in the development and maintenance of criticality. Criticality in the brain is associated with the balance between excitatory and inhibitory synaptic inputs (EI balance), which is essential for maintaining neural computation performance. Here, we hypothesize that both criticality and EI balance are stabilized by appropriate noise levels and spike-timing-dependent plasticity (STDP) windows. Using spiking neural network (SNN) simulations and in vitro experiments with dissociated neuronal cultures, we demonstrated that while repetitive stimuli transiently disrupt both criticality and EI balance, spontaneous activity can develop and maintain these properties and prolong the fading memory of past stimuli. Our findings suggest that the brain may achieve self-optimization and memory consolidation as emergent functions of noise-driven spontaneous activity. This noise-harnessing mechanism provides insights for designing energy-efficient neural networks, and may explain the critical function of sleep in maintaining homeostasis and consolidating memory.
Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking Neural Networks (SNNs) excel in handling such data with high efficiency, owing to their rich neuronal dynamics and sparse activity patterns. Given the recent surge in the development of SNNs, there is an urgent need for a comprehensive evaluation of their temporal processing capabilities. In this paper, we first conduct an in-depth assessment of commonly used neuromorphic benchmarks, revealing critical limitations in their ability to evaluate the temporal processing capabilities of SNNs. To bridge this gap, we further introduce a benchmark suite consisting of three temporal processing tasks characterized by rich temporal dynamics across multiple timescales. Utilizing this benchmark suite, we perform a thorough evaluation of recently introduced SNN approaches to elucidate the current status of SNNs in temporal processing. Our findings indicate significant advancements in recently developed spiking neuron models and neural architectures regarding their temporal processing capabilities, while also highlighting a performance gap in handling long-range dependencies when compared to state-of-the-art non-spiking models. Finally, we discuss the key challenges and outline potential avenues for future research.
Generalizable Reinforcement Learning with Biologically Inspired Hyperdimensional Occupancy Grid Maps for Exploration and Goal-Directed Path Planning
2025-02-13
Shay Snyder, Ryan Shea, Andrew Capodieci, David Gorsich, Maryam Parsa
Real-time autonomous systems utilize multi-layer computational frameworks to perform critical tasks such as perception, goal finding, and path planning. Traditional methods implement perception using occupancy grid mapping (OGM), segmenting the environment into discretized cells with probabilistic information. This classical approach is well-established and provides a structured input for downstream processes like goal finding and path planning algorithms. Recent approaches leverage a biologically inspired mathematical framework known as vector symbolic architectures (VSA), commonly known as hyperdimensional computing, to perform probabilistic OGM in hyperdimensional space. This approach, VSA-OGM, provides native compatibility with spiking neural networks, positioning VSA-OGM as a potential neuromorphic alternative to conventional OGM. However, for large-scale integration, it is essential to assess the performance implications of VSA-OGM on downstream tasks compared to established OGM methods. This study examines the efficacy of VSA-OGM against a traditional OGM approach, Bayesian Hilbert Maps (BHM), within reinforcement learning based goal finding and path planning frameworks, across a controlled exploration environment and an autonomous driving scenario inspired by the F1-Tenth challenge. Our results demonstrate that VSA-OGM maintains comparable learning performance across single and multi-scenario training configurations while improving performance on unseen environments by approximately 47%. These findings highlight the increased generalizability of policy networks trained with VSA-OGM over BHM, reinforcing its potential for real-world deployment in diverse environments.
Application-oriented automatic hyperparameter optimization for spiking neural network prototyping
2025-02-13
Vittorio Fra
Hyperparameter optimization (HPO) is of paramount importance in the development of high-performance, specialized artificial intelligence (AI) models, ranging from well-established machine learning (ML) solutions to the deep learning (DL) domain and the field of spiking neural networks (SNNs). The latter introduce further complexity due to the neuronal computational units and their additional hyperparameters, whose inadequate setting can dramatically impact the final model performance. At the cost of possible reduced generalization capabilities, the most suitable strategy to fully disclose the power of SNNs is to adopt an application-oriented approach and perform extensive HPO experiments. To facilitate these operations, automatic pipelines are fundamental, and their configuration is crucial. In this document, the Neural Network Intelligence (NNI) toolkit is used as reference framework to present one such solution, with a use case example providing evidence of the corresponding results. In addition, a summary of published works employing the presented pipeline is reported as possible source of insights into application-oriented HPO experiments for SNN prototyping.
Neuromorphic Digital-Twin-based Controller for Indoor Multi-UAV Systems Deployment
2025-02-12
Reza Ahmadvand, Sarah Safura Sharif, Yaser Mike Banad
Presented study introduces a novel distributed cloud-edge framework for autonomous multi-UAV systems that combines the computational efficiency of neuromorphic computing with nature-inspired control strategies. The proposed architecture equips each UAV with an individual Spiking Neural Network (SNN) that learns to reproduce optimal control signals generated by a cloud-based controller, enabling robust operation even during communication interruptions. By integrating spike coding with nature-inspired control principles inspired by Tilapia fish territorial behavior, our system achieves sophisticated formation control and obstacle avoidance in complex urban environments. The distributed architecture leverages cloud computing for complex calculations while maintaining local autonomy through edge-based SNNs, significantly reducing energy consumption and computational overhead compared to traditional centralized approaches. Our framework addresses critical limitations of conventional methods, including the dependency on pre-modeled environments, computational intensity of traditional methods, and local minima issues in potential field approaches. Simulation results demonstrate the system's effectiveness across two different scenarios. First, the indoor deployment of a multi-UAV system made-up of 15 UAVs. Then the collision-free formation control of a moving UAV flock including 6 UAVs considering the obstacle avoidance. Owing to the sparsity of spiking patterns, and the event-based nature of SNNs in average for the whole group of UAVs, the framework achieves almost 90% reduction in computational burden compared to traditional von Neumann architectures implementing traditional artificial neural networks.
OscNet: Machine Learning on CMOS Oscillator Networks
2025-02-11
Wenxiao Cai, Thomas H. Lee
Machine learning and AI have achieved remarkable advancements but at the cost of significant computational resources and energy consumption. This has created an urgent need for a novel, energy-efficient computational fabric to replace the current computing pipeline. Recently, a promising approach has emerged by mimicking spiking neurons in the brain and leveraging oscillators on CMOS for direct computation. In this context, we propose a new and energy efficient machine learning framework implemented on CMOS Oscillator Networks (OscNet). We model the developmental processes of the prenatal brain's visual system using OscNet, updating weights based on the biologically inspired Hebbian rule. This same pipeline is then directly applied to standard machine learning tasks. OscNet is a specially designed hardware and is inherently energy-efficient. Its reliance on forward propagation alone for training further enhances its energy efficiency while maintaining biological plausibility. Simulation validates our designs of OscNet architectures. Experimental results demonstrate that Hebbian learning pipeline on OscNet achieves performance comparable to or even surpassing traditional machine learning algorithms, highlighting its potential as a energy efficient and effective computational paradigm.
Unsupervised Particle Tracking with Neuromorphic Computing
2025-02-10
Emanuele Coradin, Fabio Cufino, Muhammad Awais, Tommaso Dorigo, Enrico Lupi, Eleonora Porcu, Jinu Raj, Fredrik Sandin, Mia Tosi
We study the application of a neural network architecture for identifying charged particle trajectories via unsupervised learning of delays and synaptic weights using a spike-time-dependent plasticity rule. In the considered model, the neurons receive time-encoded information on the position of particle hits in a tracking detector for a particle collider, modeled according to the geometry of the Compact Muon Solenoid Phase II detector. We show how a spiking neural network is capable of successfully identifying in a completely unsupervised way the signal left by charged particles in the presence of conspicuous noise from accidental or combinatorial hits. These results open the way to applications of neuromorphic computing to particle tracking, motivating further studies into its potential for real-time, low-power particle tracking in future high-energy physics experiments.
Wandering around: A bioinspired approach to visual attention through object motion sensitivity
2025-02-10
Giulia D Angelo, Victoria Clerico, Chiara Bartolozzi, Matej Hoffmann, P. Michael Furlong, Alexander Hadjiivanov
Active vision enables dynamic visual perception, offering an alternative to static feedforward architectures in computer vision, which rely on large datasets and high computational resources. Biological selective attention mechanisms allow agents to focus on salient Regions of Interest (ROIs), reducing computational demand while maintaining real-time responsiveness. Event-based cameras, inspired by the mammalian retina, enhance this capability by capturing asynchronous scene changes enabling efficient low-latency processing. To distinguish moving objects while the event-based camera is in motion the agent requires an object motion segmentation mechanism to accurately detect targets and center them in the visual field (fovea). Integrating event-based sensors with neuromorphic algorithms represents a paradigm shift, using Spiking Neural Networks to parallelize computation and adapt to dynamic environments. This work presents a Spiking Convolutional Neural Network bioinspired attention system for selective attention through object motion sensitivity. The system generates events via fixational eye movements using a Dynamic Vision Sensor integrated into the Speck neuromorphic hardware, mounted on a Pan-Tilt unit, to identify the ROI and saccade toward it. The system, characterized using ideal gratings and benchmarked against the Event Camera Motion Segmentation Dataset, reaches a mean IoU of 82.2% and a mean SSIM of 96% in multi-object motion segmentation. The detection of salient objects reaches 88.8% accuracy in office scenarios and 89.8% in low-light conditions on the Event-Assisted Low-Light Video Object Segmentation Dataset. A real-time demonstrator shows the system's 0.12 s response to dynamic scenes. Its learning-free design ensures robustness across perceptual scenes, making it a reliable foundation for real-time robotic applications serving as a basis for more complex architectures.
This work introduces a spike-based wearable analytics system utilizing Spiking Neural Networks (SNNs) deployed on an In-memory Computing engine based on RRAM crossbars, which are known for their compactness and energy-efficiency. Given the hardware constraints and noise characteristics of the underlying RRAM crossbars, we propose online adaptation of pre-trained SNNs in real-time using Direct Feedback Alignment (DFA) against traditional backpropagation (BP). Direct Feedback Alignment (DFA) learning, that allows layer-parallel gradient computations, acts as a fast, energy & area-efficient method for online adaptation of SNNs on RRAM crossbars, unleashing better algorithmic performance against those adapted using BP. Through extensive simulations using our in-house hardware evaluation engine called DFA_Sim, we find that DFA achieves upto 64.1% lower energy consumption, 10.1% lower area overhead, and a 2.1x reduction in latency compared to BP, while delivering upto 7.55% higher inference accuracy on human activity recognition (HAR) tasks.
Vision-based object tracking is a critical component for achieving autonomous aerial navigation, particularly for obstacle avoidance. Neuromorphic Dynamic Vision Sensors (DVS) or event cameras, inspired by biological vision, offer a promising alternative to conventional frame-based cameras. These cameras can detect changes in intensity asynchronously, even in challenging lighting conditions, with a high dynamic range and resistance to motion blur. Spiking neural networks (SNNs) are increasingly used to process these event-based signals efficiently and asynchronously. Meanwhile, physics-based artificial intelligence (AI) provides a means to incorporate system-level knowledge into neural networks via physical modeling. This enhances robustness, energy efficiency, and provides symbolic explainability. In this work, we present a neuromorphic navigation framework for autonomous drone navigation. The focus is on detecting and navigating through moving gates while avoiding collisions. We use event cameras for detecting moving objects through a shallow SNN architecture in an unsupervised manner. This is combined with a lightweight energy-aware physics-guided neural network (PgNN) trained with depth inputs to predict optimal flight times, generating near-minimum energy paths. The system is implemented in the Gazebo simulator and integrates a sensor-fused vision-to-planning neuro-symbolic framework built with the Robot Operating System (ROS) middleware. This work highlights the future potential of integrating event-based vision with physics-guided planning for energy-efficient autonomous navigation, particularly for low-latency decision-making.
QP-SNN: Quantized and Pruned Spiking Neural Networks
2025-02-09
Wenjie Wei, Malu Zhang, Zijian Zhou, Ammar Belatreche, Yimeng Shan, Yu Liang, Honglin Cao, Jieyuan Zhang, Yang Yang
Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to encode information and operate in an asynchronous event-driven manner, offering a highly energy-efficient paradigm for machine intelligence. However, the current SNN community focuses primarily on performance improvement by developing large-scale models, which limits the applicability of SNNs in resource-limited edge devices. In this paper, we propose a hardware-friendly and lightweight SNN, aimed at effectively deploying high-performance SNN in resource-limited scenarios. Specifically, we first develop a baseline model that integrates uniform quantization and structured pruning, called QP-SNN baseline. While this baseline significantly reduces storage demands and computational costs, it suffers from performance decline. To address this, we conduct an in-depth analysis of the challenges in quantization and pruning that lead to performance degradation and propose solutions to enhance the baseline's performance. For weight quantization, we propose a weight rescaling strategy that utilizes bit width more effectively to enhance the model's representation capability. For structured pruning, we propose a novel pruning criterion using the singular value of spatiotemporal spike activities to enable more accurate removal of redundant kernels. Extensive experiments demonstrate that integrating two proposed methods into the baseline allows QP-SNN to achieve state-of-the-art performance and efficiency, underscoring its potential for enhancing SNN deployment in edge intelligence computing.
Deployment-friendly Lane-changing Intention Prediction Powered by Brain-inspired Spiking Neural Networks
2025-02-09
Shuqi Shen, Junjie Yang, Hui Zhong, Hongliang Lu, Xinhu Zheng, Hai Yang
Accurate and real-time prediction of surrounding vehicles' lane-changing intentions is a critical challenge in deploying safe and efficient autonomous driving systems in open-world scenarios. Existing high-performing methods remain hard to deploy due to their high computational cost, long training times, and excessive memory requirements. Here, we propose an efficient lane-changing intention prediction approach based on brain-inspired Spiking Neural Networks (SNN). By leveraging the event-driven nature of SNN, the proposed approach enables us to encode the vehicle's states in a more efficient manner. Comparison experiments conducted on HighD and NGSIM datasets demonstrate that our method significantly improves training efficiency and reduces deployment costs while maintaining comparable prediction accuracy. Particularly, compared to the baseline, our approach reduces training time by 75% and memory usage by 99.9%. These results validate the efficiency and reliability of our method in lane-changing predictions, highlighting its potential for safe and efficient autonomous driving systems while offering significant advantages in deployment, including reduced training time, lower memory usage, and faster inference.
Do Spikes Protect Privacy? Investigating Black-Box Model Inversion Attacks in Spiking Neural Networks
2025-02-08
Hamed Poursiami, Ayana Moshruba, Maryam Parsa
As machine learning models become integral to security-sensitive applications, concerns over data leakage from adversarial attacks continue to rise. Model Inversion (MI) attacks pose a significant privacy threat by enabling adversaries to reconstruct training data from model outputs. While MI attacks on Artificial Neural Networks (ANNs) have been widely studied, Spiking Neural Networks (SNNs) remain largely unexplored in this context. Due to their event-driven and discrete computations, SNNs introduce fundamental differences in information processing that may offer inherent resistance to such attacks. A critical yet underexplored aspect of this threat lies in black-box settings, where attackers operate through queries without direct access to model parameters or gradients-representing a more realistic adversarial scenario in deployed systems. This work presents the first study of black-box MI attacks on SNNs. We adapt a generative adversarial MI framework to the spiking domain by incorporating rate-based encoding for input transformation and decoding mechanisms for output interpretation. Our results show that SNNs exhibit significantly greater resistance to MI attacks than ANNs, as demonstrated by degraded reconstructions, increased instability in attack convergence, and overall reduced attack effectiveness across multiple evaluation metrics. Further analysis suggests that the discrete and temporally distributed nature of SNN decision boundaries disrupts surrogate modeling, limiting the attacker's ability to approximate the target model.
FAS: Fast ANN-SNN Conversion for Spiking Large Language Models
2025-02-06
Long Chen, Xiaotian Song, Andy Song, BaDong Chen, Jiancheng Lv, Yanan Sun
Spiking Large Language Models have been shown as a good alternative to LLMs in various scenarios. Existing methods for creating Spiking LLMs, i.e., direct training and ANN-SNN conversion, often suffer from performance degradation and relatively high computational costs. To address these issues, we propose a novel Fast ANN-SNN conversion strategy (FAS) that transforms LLMs into spiking LLMs in two stages. The first stage employs a full-parameter fine-tuning of pre-trained models, so it does not need any direct training from scratch. The second stage introduces a coarse-to-fine calibration method to reduce conversion errors and improve accuracy. Experiments on both language and vision-language tasks across four different scales of LLMs demonstrate that FAS can achieve state-of-the-art performance yet with significantly reduced inference latency and computational costs. Notably, FAS only takes eight timesteps to achieve an accuracy of 3\% higher than that of the OPT-7B model, while reducing energy consumption by 96.63\%. The source code is available at https://github.com/lc783/FAS
Poisson Hypothesis and large-population limit for networks of spiking neurons
2025-02-05
Daniele Avitabile, Michel Davydov
We study mean-field descriptions for spatially-extended networks of linear (leaky) and quadratic integrate-and-fire neurons with stochastic spiking times. We consider large-population limits of continuous-time Galves-L\"ocherbach (GL) networks with linear and quadratic intrinsic dynamics. We prove that that the Poisson Hypothesis holds for the replica-mean-field limit of these networks, that is, in a suitably-defined limit, neurons are independent with interaction times replaced by independent time-inhomogeneous Poisson processes with intensities depending on the mean firing rates, extending known results to networks with quadratic intrinsic dynamics and resets. Proving that the Poisson Hypothesis holds opens up the possibility of studying the large-population limit in these networks. We prove this limit to be a well-posed neural field model, subject to stochastic resets.
STEMS: Spatial-Temporal Mapping Tool For Spiking Neural Networks
2025-02-05
Sherif Eissa, Sander Stuijk, Floran De Putter, Andrea Nardi-Dei, Federico Corradi, Henk Corporaal
Spiking Neural Networks (SNNs) are promising bio-inspired third-generation neural networks. Recent research has trained deep SNN models with accuracy on par with Artificial Neural Networks (ANNs). Although the event-driven and sparse nature of SNNs show potential for more energy efficient computation than ANNs, SNN neurons have internal states which evolve over time. Keeping track of SNN states can significantly increase data movement and storage requirements, potentially losing its advantages with respect to ANNs. This paper investigates the energy effects of having neuron states, and how it is influenced by the chosen mapping to realistic hardware architectures with advanced memory hierarchies. Therefore, we develop STEMS, a mapping design space exploration tool for SNNs. STEMS models SNN's stateful behavior and explores intra-layer and inter-layer mapping optimizations to minimize data movement, considering both spatial and temporal SNN dimensions. Using STEMS, we show up to 12x reduction in off-chip data movement and 5x reduction in energy (on top of intra-layer optimizations), on two event-based vision SNN benchmarks. Finally, neuron states may not be needed for all SNN layers. By optimizing neuron states for one of our benchmarks, we show 20x reduction in neuron states and 1.4x better performance without accuracy loss.
2025-02-05
Katerina Maria Oikonomou, Ioannis Kansizoglou, Antonios Gasteratos
Feature discrimination is a crucial aspect of neural network design, as it directly impacts the network's ability to distinguish between classes and generalize across diverse datasets. The accomplishment of achieving high-quality feature representations ensures high intra-class separability and poses one of the most challenging research directions. While conventional deep neural networks (DNNs) rely on complex transformations and very deep networks to come up with meaningful feature representations, they usually require days of training and consume significant energy amounts. To this end, spiking neural networks (SNNs) offer a promising alternative. SNN's ability to capture temporal and spatial dependencies renders them particularly suitable for complex tasks, where multi-modal data are required. In this paper, we propose a feature discrimination approach for multi-modal learning with SNNs, focusing on audio-visual data. We employ deep spiking residual learning for visual modality processing and a simpler yet efficient spiking network for auditory modality processing. Lastly, we deploy a spiking multilayer perceptron for modality fusion. We present our findings and evaluate our approach against similar works in the field of classification challenges. To the best of our knowledge, this is the first work investigating feature discrimination in SNNs.
DA-LIF: Dual Adaptive Leaky Integrate-and-Fire Model for Deep Spiking Neural Networks
2025-02-05
Tianqing Zhang, Kairong Yu, Jian Zhang, Hongwei Wang
Spiking Neural Networks (SNNs) are valued for their ability to process spatio-temporal information efficiently, offering biological plausibility, low energy consumption, and compatibility with neuromorphic hardware. However, the commonly used Leaky Integrate-and-Fire (LIF) model overlooks neuron heterogeneity and independently processes spatial and temporal information, limiting the expressive power of SNNs. In this paper, we propose the Dual Adaptive Leaky Integrate-and-Fire (DA-LIF) model, which introduces spatial and temporal tuning with independently learnable decays. Evaluations on both static (CIFAR10/100, ImageNet) and neuromorphic datasets (CIFAR10-DVS, DVS128 Gesture) demonstrate superior accuracy with fewer timesteps compared to state-of-the-art methods. Importantly, DA-LIF achieves these improvements with minimal additional parameters, maintaining low energy consumption. Extensive ablation studies further highlight the robustness and effectiveness of the DA-LIF model.
Spiking Neural Networks (SNNs) mimic biological brain activity, processing data efficiently through an event-driven design, wherein the neurons activate only when inputs exceed specific thresholds. Their ability to track voltage changes over time via membrane potential dynamics helps retain temporal information. This study combines SNNs with PyTorch's adaptable framework, snnTorch, to test their potential for image-based tasks. We introduce DRiVE, a vehicle detection model that uses spiking neuron dynamics to classify images, achieving 94.8% accuracy and a near-perfect 0.99 AUC score. These results highlight DRiVE's ability to distinguish vehicle classes effectively, challenging the notion that SNNs are limited to temporal data. As interest grows in energy-efficient neural models, DRiVE's success emphasizes the need to refine SNN optimization for visual tasks. This work encourages broader exploration of SNNs in scenarios where conventional networks struggle, particularly for real-world applications requiring both precision and efficiency.
TESS: A Scalable Temporally and Spatially Local Learning Rule for Spiking Neural Networks
2025-02-03
Marco Paul E. Apolinario, Kaushik Roy, Charlotte Frenkel
The demand for low-power inference and training of deep neural networks (DNNs) on edge devices has intensified the need for algorithms that are both scalable and energy-efficient. While spiking neural networks (SNNs) allow for efficient inference by processing complex spatio-temporal dynamics in an event-driven fashion, training them on resource-constrained devices remains challenging due to the high computational and memory demands of conventional error backpropagation (BP)-based approaches. In this work, we draw inspiration from biological mechanisms such as eligibility traces, spike-timing-dependent plasticity, and neural activity synchronization to introduce TESS, a temporally and spatially local learning rule for training SNNs. Our approach addresses both temporal and spatial credit assignments by relying solely on locally available signals within each neuron, thereby allowing computational and memory overheads to scale linearly with the number of neurons, independently of the number of time steps. Despite relying on local mechanisms, we demonstrate performance comparable to the backpropagation through time (BPTT) algorithm, within $\sim1.4$ accuracy points on challenging computer vision scenarios relevant at the edge, such as the IBM DVS Gesture dataset, CIFAR10-DVS, and temporal versions of CIFAR10, and CIFAR100. Being able to produce comparable performance to BPTT while keeping low time and memory complexity, TESS enables efficient and scalable on-device learning at the edge.
Neuro-LIFT: A Neuromorphic, LLM-based Interactive Framework for Autonomous Drone FlighT at the Edge
2025-01-31
Amogh Joshi, Sourav Sanyal, Kaushik Roy
The integration of human-intuitive interactions into autonomous systems has been limited. Traditional Natural Language Processing (NLP) systems struggle with context and intent understanding, severely restricting human-robot interaction. Recent advancements in Large Language Models (LLMs) have transformed this dynamic, allowing for intuitive and high-level communication through speech and text, and bridging the gap between human commands and robotic actions. Additionally, autonomous navigation has emerged as a central focus in robotics research, with artificial intelligence (AI) increasingly being leveraged to enhance these systems. However, existing AI-based navigation algorithms face significant challenges in latency-critical tasks where rapid decision-making is critical. Traditional frame-based vision systems, while effective for high-level decision-making, suffer from high energy consumption and latency, limiting their applicability in real-time scenarios. Neuromorphic vision systems, combining event-based cameras and spiking neural networks (SNNs), offer a promising alternative by enabling energy-efficient, low-latency navigation. Despite their potential, real-world implementations of these systems, particularly on physical platforms such as drones, remain scarce. In this work, we present Neuro-LIFT, a real-time neuromorphic navigation framework implemented on a Parrot Bebop2 quadrotor. Leveraging an LLM for natural language processing, Neuro-LIFT translates human speech into high-level planning commands which are then autonomously executed using event-based neuromorphic vision and physics-driven planning. Our framework demonstrates its capabilities in navigating in a dynamic environment, avoiding obstacles, and adapting to human instructions in real-time.
SpikingSoft: A Spiking Neuron Controller for Bio-inspired Locomotion with Soft Snake Robots
2025-01-31
Chuhan Zhang, Cong Wang, Wei Pan, Cosimo Della Santina
Inspired by the dynamic coupling of moto-neurons and physical elasticity in animals, this work explores the possibility of generating locomotion gaits by utilizing physical oscillations in a soft snake by means of a low-level spiking neural mechanism. To achieve this goal, we introduce the Double Threshold Spiking neuron model with adjustable thresholds to generate varied output patterns. This neuron model can excite the natural dynamics of soft robotic snakes, and it enables distinct movements, such as turning or moving forward, by simply altering the neural thresholds. Finally, we demonstrate that our approach, termed SpikingSoft, naturally pairs and integrates with reinforcement learning. The high-level agent only needs to adjust the two thresholds to generate complex movement patterns, thus strongly simplifying the learning of reactive locomotion. Simulation results demonstrate that the proposed architecture significantly enhances the performance of the soft snake robot, enabling it to achieve target objectives with a 21.6% increase in success rate, a 29% reduction in time to reach the target, and smoother movements compared to the vanilla reinforcement learning controllers or Central Pattern Generator controller acting in torque space.
To bridge the semantic gap between vision and language (VL), it is necessary to develop a good alignment strategy, which includes handling semantic diversity, abstract representation of visual information, and generalization ability of models. Recent works use detector-based bounding boxes or patches with regular partitions to represent visual semantics. While current paradigms have made strides, they are still insufficient for fully capturing the nuanced contextual relations among various objects. This paper proposes a comprehensive visual semantic representation module, necessitating the utilization of panoptic segmentation to generate coherent fine-grained semantic features. Furthermore, we propose a novel Graph Spiking Hybrid Network (GSHN) that integrates the complementary advantages of Spiking Neural Networks (SNNs) and Graph Attention Networks (GATs) to encode visual semantic information. Intriguingly, the model not only encodes the discrete and continuous latent variables of instances but also adeptly captures both local and global contextual features, thereby significantly enhancing the richness and diversity of semantic representations. Leveraging the spatiotemporal properties inherent in SNNs, we employ contrastive learning (CL) to enhance the similarity-based representation of embeddings. This strategy alleviates the computational overhead of the model and enriches meaningful visual representations by constructing positive and negative sample pairs. We design an innovative pre-training method, Spiked Text Learning (STL), which uses text features to improve the encoding ability of discrete semantics. Experiments show that the proposed GSHN exhibits promising results on multiple VL downstream tasks.
SpikingRTNH: Spiking Neural Network for 4D Radar Object Detection
2025-01-31
Dong-Hee Paek, Seung-Hyun Kong
Recently, 4D Radar has emerged as a crucial sensor for 3D object detection in autonomous vehicles, offering both stable perception in adverse weather and high-density point clouds for object shape recognition. However, processing such high-density data demands substantial computational resources and energy consumption. We propose SpikingRTNH, the first spiking neural network (SNN) for 3D object detection using 4D Radar data. By replacing conventional ReLU activation functions with leaky integrate-and-fire (LIF) spiking neurons, SpikingRTNH achieves significant energy efficiency gains. Furthermore, inspired by human cognitive processes, we introduce biological top-down inference (BTI), which processes point clouds sequentially from higher to lower densities. This approach effectively utilizes points with lower noise and higher importance for detection. Experiments on K-Radar dataset demonstrate that SpikingRTNH with BTI significantly reduces energy consumption by 78% while achieving comparable detection performance to its ANN counterpart (51.1% AP 3D, 57.0% AP BEV). These results establish the viability of SNNs for energy-efficient 4D Radar-based object detection in autonomous driving systems. All codes are available at https://github.com/kaist-avelab/k-radar.
Neural Network Modeling of Microstructure Complexity Using Digital Libraries
2025-01-30
Yingjie Zhao, Zhiping Xu
Microstructure evolution in matter is often modeled numerically using field or level-set solvers, mirroring the dual representation of spatiotemporal complexity in terms of pixel or voxel data, and geometrical forms in vector graphics. Motivated by this analog, as well as the structural and event-driven nature of artificial and spiking neural networks, respectively, we evaluate their performance in learning and predicting fatigue crack growth and Turing pattern development. Predictions are made based on digital libraries constructed from computer simulations, which can be replaced by experimental data to lift the mathematical overconstraints of physics. Our assessment suggests that the leaky integrate-and-fire neuron model offers superior predictive accuracy with fewer parameters and less memory usage, alleviating the accuracy-cost tradeoff in contrast to the common practices in computer vision tasks. Examination of network architectures shows that these benefits arise from its reduced weight range and sparser connections. The study highlights the capability of event-driven models in tackling problems with evolutionary bulk-phase and interface behaviors using the digital library approach.
A General-Purpose Neuromorphic Sensor based on Spiketrum Algorithm: Hardware Details and Real-life Applications
2025-01-30
MHD Anas Alsakkal, Runze Wang, Jayawan Wijekoon, Piotr Dudek
Spiking Neural Networks (SNNs) offer a biologically inspired computational paradigm, enabling energy-efficient data processing through spike-based information transmission. Despite notable advancements in hardware for SNNs, spike encoding has largely remained software-dependent, limiting efficiency. This paper addresses the need for adaptable and resource-efficient spike encoding hardware by presenting an area-optimized hardware implementation of the Spiketrum algorithm, which encodes time-varying analogue signals into spatiotemporal spike patterns. Unlike earlier performance-optimized designs, which prioritize speed, our approach focuses on reducing hardware footprint, achieving a 52% reduction in Block RAMs (BRAMs), 31% fewer Digital Signal Processing (DSP) slices, and a 6% decrease in Look-Up Tables (LUTs). The proposed implementation has been verified on an FPGA and successfully integrated into an IC using TSMC180 technology. Experimental results demonstrate the system's effectiveness in real-world applications, including sound and ECG classification. This work highlights the trade-offs between performance and resource efficiency, offering a flexible, scalable solution for neuromorphic systems in power-sensitive applications like cochlear implants and neural devices.
PulmoFusion: Advancing Pulmonary Health with Efficient Multi-Modal Fusion
2025-01-29
Ahmed Sharshar, Yasser Attia, Mohammad Yaqub, Mohsen Guizani
Traditional remote spirometry lacks the precision required for effective pulmonary monitoring. We present a novel, non-invasive approach using multimodal predictive models that integrate RGB or thermal video data with patient metadata. Our method leverages energy-efficient Spiking Neural Networks (SNNs) for the regression of Peak Expiratory Flow (PEF) and classification of Forced Expiratory Volume (FEV1) and Forced Vital Capacity (FVC), using lightweight CNNs to overcome SNN limitations in regression tasks. Multimodal data integration is improved with a Multi-Head Attention Layer, and we employ K-Fold validation and ensemble learning to boost robustness. Using thermal data, our SNN models achieve 92% accuracy on a breathing-cycle basis and 99.5% patient-wise. PEF regression models attain Relative RMSEs of 0.11 (thermal) and 0.26 (RGB), with an MAE of 4.52% for FEV1/FVC predictions, establishing state-of-the-art performance. Code and dataset can be found on https://github.com/ahmed-sharshar/RespiroDynamics.git
Given the energy constraints in autonomous mobile agents (AMAs), such as unmanned vehicles, spiking neural networks (SNNs) are increasingly favored as a more efficient alternative to traditional artificial neural networks. AMAs employ multi-object detection (MOD) from multiple cameras to identify nearby objects while ensuring two essential objectives, (R1) timing guarantee and (R2) high accuracy for safety. In this paper, we propose RT-SNN, the first system design, aiming at achieving R1 and R2 in SNN-based MOD systems on AMAs. Leveraging the characteristic that SNNs gather feature data of input image termed as membrane potential, through iterative computation over multiple timesteps, RT-SNN provides multiple execution options with adjustable timesteps and a novel method for reusing membrane potential to support R1. Then, it captures how these execution strategies influence R2 by introducing a novel notion of mean absolute error and membrane confidence. Further, RT-SNN develops a new scheduling framework consisting of offline schedulability analysis for R1 and a run-time scheduling algorithm for R2 using the notion of membrane confidence. We deployed RT-SNN to Spiking-YOLO, the SNN-based MOD model derived from ANN-to-SNN conversion, and our experimental evaluation confirms its effectiveness in meeting the R1 and R2 requirements while providing significant energy efficiency.
Spiking neural networks (SNNs) are bio-inspired networks that mimic how neurons in the brain communicate through discrete spikes, which have great potential in various tasks due to their energy efficiency and temporal processing capabilities. SNNs with self-attention mechanisms (spiking Transformers) have recently shown great advancements in various tasks, and inspired by traditional Transformers, several studies have demonstrated that spiking absolute positional encoding can help capture sequential relationships for input data, enhancing the capabilities of spiking Transformers for tasks such as sequential modeling and image classification. However, how to incorporate relative positional information into SNNs remains a challenge. In this paper, we introduce several strategies to approximate relative positional encoding (RPE) in spiking Transformers while preserving the binary nature of spikes. Firstly, we formally prove that encoding relative distances with Gray Code ensures that the binary representations of positional indices maintain a constant Hamming distance whenever their decimal values differ by a power of two, and we propose Gray-PE based on this property. In addition, we propose another RPE method called Log-PE, which combines the logarithmic form of the relative distance matrix directly into the spiking attention map. Furthermore, we extend our RPE methods to a two-dimensional form, making them suitable for processing image patches. We evaluate our RPE methods on various tasks, including time series forecasting, text classification, and patch-based image classification, and the experimental results demonstrate a satisfying performance gain by incorporating our RPE methods across many architectures.
Satellite networks are able to collect massive space information with advanced remote sensing technologies, which is essential for real-time applications such as natural disaster monitoring. However, traditional centralized processing by the ground server incurs a severe timeliness issue caused by the transmission bottleneck of raw data. To this end, Space Computing Power Networks (Space-CPN) emerges as a promising architecture to coordinate the computing capability of satellites and enable on board data processing. Nevertheless, due to the natural limitations of solar panels, satellite power system is difficult to meet the energy requirements for ever-increasing intelligent computation tasks of artificial neural networks. To tackle this issue, we propose to employ spiking neural networks (SNNs), which is supported by the neuromorphic computing architecture, for on-board data processing. The extreme sparsity in its computation enables a high energy efficiency. Furthermore, to achieve effective training of these on-board models, we put forward a decentralized neuromorphic learning framework, where a communication-efficient inter-plane model aggregation method is developed with the inspiration from RelaySum. We provide a theoretical analysis to characterize the convergence behavior of the proposed algorithm, which reveals a network diameter related convergence speed. We then formulate a minimum diameter spanning tree problem on the inter-plane connectivity topology and solve it to further improve the learning performance. Extensive experiments are conducted to evaluate the superiority of the proposed method over benchmarks.
Efficient Distillation of Deep Spiking Neural Networks for Full-Range Timestep Deployment
2025-01-27
Chengting Yu, Xiaochen Zhao, Lei Liu, Shu Yang, Gaoang Wang, Erping Li, Aili Wang
Spiking Neural Networks (SNNs) are emerging as a brain-inspired alternative to traditional Artificial Neural Networks (ANNs), prized for their potential energy efficiency on neuromorphic hardware. Despite this, SNNs often suffer from accuracy degradation compared to ANNs and face deployment challenges due to fixed inference timesteps, which require retraining for adjustments, limiting operational flexibility. To address these issues, our work considers the spatio-temporal property inherent in SNNs, and proposes a novel distillation framework for deep SNNs that optimizes performance across full-range timesteps without specific retraining, enhancing both efficacy and deployment adaptability. We provide both theoretical analysis and empirical validations to illustrate that training guarantees the convergence of all implicit models across full-range timesteps. Experimental results on CIFAR-10, CIFAR-100, CIFAR10-DVS, and ImageNet demonstrate state-of-the-art performance among distillation-based SNNs training methods.
Motion deblurring addresses the challenge of image blur caused by camera or scene movement. Event cameras provide motion information that is encoded in the asynchronous event streams. To efficiently leverage the temporal information of event streams, we employ Spiking Neural Networks (SNNs) for motion feature extraction and Artificial Neural Networks (ANNs) for color information processing. Due to the non-uniform distribution and inherent redundancy of event data, existing cross-modal feature fusion methods exhibit certain limitations. Inspired by the visual attention mechanism in the human visual system, this study introduces a bioinspired dual-drive hybrid network (BDHNet). Specifically, the Neuron Configurator Module (NCM) is designed to dynamically adjusts neuron configurations based on cross-modal features, thereby focusing the spikes in blurry regions and adapting to varying blurry scenarios dynamically. Additionally, the Region of Blurry Attention Module (RBAM) is introduced to generate a blurry mask in an unsupervised manner, effectively extracting motion clues from the event features and guiding more accurate cross-modal feature fusion. Extensive subjective and objective evaluations demonstrate that our method outperforms current state-of-the-art methods on both synthetic and real-world datasets.
Normalizing Flow-Assisted Nested Sampling on Type-II Seesaw Model
2025-01-27
Rajneil Baruah, Subhadeep Mondal, Sunando Kumar Patra, Satyajit Roy
We propose a novel technique for sampling particle physics model parameter space. The main sampling method applied is Nested Sampling (NS), which is boosted by the application of multiple Machine Learning (ML) networks, e.g., Self-Normalizing Network (SNN) and Normalizing Flow (specifically RealNVP). We apply this on Type-II Seesaw model to test the efficacy of the algorithm. We present the results of our detailed Bayesian exploration of the model parameter space subjected to theoretical constraints and experimental data corresponding to the 125 GeV Higgs boson, $\rho$-parameter, and the oblique parameters. All associated data, figures, and trained ML models can be found here: https://github.com/sunandopatra/MLNS-T2SS
Regression-based decoding of continuous movements is essential for human-machine interfaces (HMIs), such as prosthetic control. This study explores a feature-based approach to encoding Surface Electromyography (sEMG) signals, focusing on the role of variability in neural-inspired population encoding. By employing heterogeneous populations of Leaky Integrate-and- Fire (LIF) neurons with varying sizes and diverse parameter distributions, we investigate how population size and variability in encoding parameters, such as membrane time constants and thresholds, influence decoding performance. Using a simple linear readout, we demonstrate that variability improves robustness and generalizability compared to single-neuron encoders. These findings emphasize the importance of optimizing variability and population size for efficient and scalable regression tasks in spiking neural networks (SNNs), paving the way for robust, low-power HMI implementations.
As the third generation of neural networks, Spiking Neural Networks (SNNs) have gained widespread attention due to their low energy consumption and biological interpretability. Recently, SNNs have made considerable advancements in computer vision. However, efficiently conducting feature extraction and fusion under the spiking characteristics of SNNs for object detection remains a pressing challenge. To address this problem, we propose the SpikSSD, a novel Spiking Single Shot Multibox Detector. Specifically, we design a full-spiking backbone network, MDS-ResNet, which effectively adjusts the membrane synaptic input distribution at each layer, achieving better spiking feature extraction. Additionally, for spiking feature fusion, we introduce the Spiking Bi-direction Fusion Module (SBFM), which for the first time realizes bi-direction fusion of spiking features, enhancing the multi-scale detection capability of the model. Experimental results show that SpikSSD achieves 40.8% mAP on the GEN1 dataset, 76.3% and 52.4% mAP@0.5 on VOC 2007 and COCO 2017 datasets respectively with the lowest firing rate, outperforming existing SNN-based approaches at ultralow energy consumption. This work sets a new benchmark for future research in SNN-based object detection. Our code is publicly available in https://github.com/yimeng-fan/SpikSSD.
The emergence of abstract rules from exemplars is central to the brain's capability of flexible generalization and rapid adaptation. However, the internal organizing mechanisms underlying rule abstraction remain elusive, largely due to the limitations of conventional models that lack intrinsic neuronal heterogeneity, making it hard to examine neuronal and structural differentiations. Inspired by astrocyte-mediated neuromodulation, this work introduces a hierarchically modulated recurrent spiking neural network (HM-RSNN) that can tune intrinsic neuronal properties, where a global stage simulates calcium wave-driven task-specific configuration and a local one mimics gliotransmitter-mediated fine-tuning. We conduct modeling using HM-RSNN across four cognitive tasks and rule abstraction contingent differentiation is observed at both network and neuron levels, leading to better performance compared to artificial neural networks. These findings highlight the critical role of dynamic internal organization in supporting the accomplishment of various cognitive tasks.
Spiking Neural Networks (SNNs) are distinguished from Artificial Neural Networks (ANNs) for their sophisticated neuronal dynamics and sparse binary activations (spikes) inspired by the biological neural system. Traditional neuron models use iterative step-by-step dynamics, resulting in serial computation and slow training speed of SNNs. Recently, parallelizable spiking neuron models have been proposed to fully utilize the massive parallel computing ability of graphics processing units to accelerate the training of SNNs. However, existing parallelizable spiking neuron models involve dense floating operations and can only achieve high long-term dependencies learning ability with a large order at the cost of huge computational and memory costs. To solve the dilemma of performance and costs, we propose the mul-free channel-wise Parallel Spiking Neuron, which is hardware-friendly and suitable for SNNs' resource-restricted application scenarios. The proposed neuron imports the channel-wise convolution to enhance the learning ability, induces the sawtooth dilations to reduce the neuron order, and employs the bit shift operation to avoid multiplications. The algorithm for design and implementation of acceleration methods is discussed meticulously. Our methods are validated in neuromorphic Spiking Heidelberg Digits voices, sequential CIFAR images, and neuromorphic DVS-Lip vision datasets, achieving the best accuracy among SNNs. Training speed results demonstrate the effectiveness of our acceleration methods, providing a practical reference for future research.
Spiking Neural Networks (SNNs) hold promise for energy-efficient, biologically inspired computing. We identify substantial informatio loss during spike transmission, linked to temporal dependencies in traditional Leaky Integrate-and-Fire (LIF) neuron-a key factor potentially limiting SNN performance. Existing SNN architectures also underutilize modern GPUs, constrained by single-bit spike storage and isolated weight-spike operations that restrict computational efficiency. We introduce ${SpikePack}$, a neuron model designed to reduce transmission loss while preserving essential features like membrane potential reset and leaky integration. ${SpikePack}$ achieves constant $\mathcal{O}(1)$ time and space complexity, enabling efficient parallel processing on GPUs and also supporting serial inference on existing SNN hardware accelerators. Compatible with standard Artificial Neural Network (ANN) architectures, ${SpikePack}$ facilitates near-lossless ANN-to-SNN conversion across various networks. Experimental results on tasks such as image classification, detection, and segmentation show ${SpikePack}$ achieves significant gains in accuracy and efficiency for both directly trained and converted SNNs over state-of-the-art models. Tests on FPGA-based platforms further confirm cross-platform flexibility, delivering high performance and enhanced sparsity. By enhancing information flow and rethinking SNN-ANN integration, ${SpikePack}$ advances efficient SNN deployment across diverse hardware platforms.
2025-01-24
Ivan K. Boikov, Alfredo de Rossi, Mihai A. Petrovici
Owing to their significant advantages in terms of bandwidth, power efficiency and especially speed, optical neuromorphic systems have arisen as interesting alternatives to conventional semiconductor devices. Recently, photonic crystal nanolasers with excitable behaviour were first demonstrated. Depending on the pumping strength, they emit short optical pulses -- spikes -- at various intervals on a nanosecond timescale. In this theoretical work, we show how networks of such photonic spiking neurons can be used for Bayesian inference through sampling from learned probability distributions. We provide a detailed derivation of translation rules from conventional sampling networks such as Boltzmann machines to photonic spiking networks and demonstrate their functionality across a range of generative tasks. Finally, we provide estimates of processing speed and power consumption, for which we expect improvements of several orders of magnitude over current state-of-the-art neuromorphic systems.
Spikes can transmit neurons' subthreshold membrane potentials
2025-01-23
Valentin Schmutz
Neurons primarily communicate through the emission of action potentials, or spikes. To generate a spike, a neuron's membrane potential must cross a defined threshold. Does this spiking mechanism inherently prevent neurons from transmitting their subthreshold membrane potential fluctuations to other neurons? We prove that, in theory, it does not. The subthreshold membrane potential fluctuations of a presynaptic population of spiking neurons can be perfectly transmitted to a downstream population of neurons. Mathematically, this surprising result is an example of concentration phenomenon in high dimensions.
Spiking neural networks are emerging as a promising energy-efficient alternative to traditional artificial neural networks due to their spike-driven paradigm. However, recent research in the SNN domain has mainly focused on enhancing accuracy by designing large-scale Transformer structures, which typically rely on substantial computational resources, limiting their deployment on resource-constrained devices. To overcome this challenge, we propose a quantized spike-driven Transformer baseline (QSD-Transformer), which achieves reduced resource demands by utilizing a low bit-width parameter. Regrettably, the QSD-Transformer often suffers from severe performance degradation. In this paper, we first conduct empirical analysis and find that the bimodal distribution of quantized spike-driven self-attention (Q-SDSA) leads to spike information distortion (SID) during quantization, causing significant performance degradation. To mitigate this issue, we take inspiration from mutual information entropy and propose a bi-level optimization strategy to rectify the information distribution in Q-SDSA. Specifically, at the lower level, we introduce an information-enhanced LIF to rectify the information distribution in Q-SDSA. At the upper level, we propose a fine-grained distillation scheme for the QSD-Transformer to align the distribution in Q-SDSA with that in the counterpart ANN. By integrating the bi-level optimization strategy, the QSD-Transformer can attain enhanced energy efficiency without sacrificing its high-performance advantage. For instance, when compared to the prior SNN benchmark on ImageNet, the QSD-Transformer achieves 80.3% top-1 accuracy, accompanied by significant reductions of 6.0$\times$ and 8.1$\times$ in power consumption and model size, respectively. Code is available at https://github.com/bollossom/QSD-Transformer.
Towards spiking analog hardware implementation of a trajectory interpolation mechanism for smooth closed-loop control of a spiking robot arm
2025-01-23
Daniel Casanueva-Morato, Chenxi Wu, Giacomo Indiveri, Juan P. Dominguez-Morales, Alejandro Linares-Barranco
Neuromorphic engineering aims to incorporate the computational principles found in animal brains, into modern technological systems. Following this approach, in this work we propose a closed-loop neuromorphic control system for an event-based robotic arm. The proposed system consists of a shifted Winner-Take-All spiking network for interpolating a reference trajectory and a spiking comparator network responsible for controlling the flow continuity of the trajectory, which is fed back to the actual position of the robot. The comparator model is based on a differential position comparison neural network, which governs the execution of the next trajectory points to close the control loop between both components of the system. To evaluate the system, we implemented and deployed the model on a mixed-signal analog-digital neuromorphic platform, the DYNAP-SE2, to facilitate integration and communication with the ED-Scorbot robotic arm platform. Experimental results on one joint of the robot validate the use of this architecture and pave the way for future neuro-inspired control of the entire robot.
Marketron games: Self-propelling stocks vs dumb money and metastable dynamics of the Good, Bad and Ugly markets
2025-01-22
I. Halperin, A. Itkin
We present a model of price formation in an inelastic market whose dynamics are partially driven by both money flows and their impact on asset prices. The money flow to the market is viewed as an investment policy of outside investors. For the price impact effect, we use an impact function that incorporates the phenomena of market inelasticity and saturation from new money (the $dumb \; money$ effect). Due to the dependence of market investors' flows on market performance, the model implies a feedback mechanism that gives rise to nonlinear dynamics. Consequently, the market price dynamics are seen as a nonlinear diffusion of a particle (the $marketron$) in a two-dimensional space formed by the log-price $x$ and a memory variable $y$. The latter stores information about past money flows, so that the dynamics are non-Markovian in the log price $x$ alone, but Markovian in the pair $(x,y)$, bearing a strong resemblance to spiking neuron models in neuroscience. In addition to market flows, the model dynamics are partially driven by return predictors, modeled as unobservable Ornstein-Uhlenbeck processes. By using a new interpretation of predictive signals as $self$-$propulsion$ components of the price dynamics, we treat the marketron as an active particle, amenable to methods developed in the physics of active matter. We show that, depending on the choice of parameters, our model can produce a rich variety of interesting dynamic scenarios. In particular, it predicts three distinct regimes of the market, which we call the $Good$, the $Bad$, and the $Ugly$ markets. The latter regime describes a scenario of a total market collapse or, alternatively, a corporate default event, depending on whether our model is applied to the whole market or an individual stock.
Object detection and tracking is an essential perception task for enabling fully autonomous navigation in robotic systems. Edge robot systems such as small drones need to execute complex maneuvers at high-speeds with limited resources, which places strict constraints on the underlying algorithms and hardware. Traditionally, frame-based cameras are used for vision-based perception due to their rich spatial information and simplified synchronous sensing capabilities. However, obtaining detailed information across frames incurs high energy consumption and may not even be required. In addition, their low temporal resolution renders them ineffective in high-speed motion scenarios. Event-based cameras offer a biologically-inspired solution to this by capturing only changes in intensity levels at exceptionally high temporal resolution and low power consumption, making them ideal for high-speed motion scenarios. However, their asynchronous and sparse outputs are not natively suitable with conventional deep learning methods. In this work, we propose TOFFE, a lightweight hybrid framework for performing event-based object motion estimation (including pose, direction, and speed estimation), referred to as Object Flow. TOFFE integrates bio-inspired Spiking Neural Networks (SNNs) and conventional Analog Neural Networks (ANNs), to efficiently process events at high temporal resolutions while being simple to train. Additionally, we present a novel event-based synthetic dataset involving high-speed object motion to train TOFFE. Our experimental results show that TOFFE achieves 5.7x/8.3x reduction in energy consumption and 4.6x/5.8x reduction in latency on edge GPU(Jetson TX2)/hybrid hardware(Loihi-2 and Jetson TX2), compared to previous event-based object detection baselines.
Event-based vision for egomotion estimation using precise event timing
2025-01-20
Hugh Greatorex, Michele Mastella, Madison Cotteret, Ole Richter, Elisabetta Chicca
Egomotion estimation is crucial for applications such as autonomous navigation and robotics, where accurate and real-time motion tracking is required. However, traditional methods relying on inertial sensors are highly sensitive to external conditions, and suffer from drifts leading to large inaccuracies over long distances. Vision-based methods, particularly those utilising event-based vision sensors, provide an efficient alternative by capturing data only when changes are perceived in the scene. This approach minimises power consumption while delivering high-speed, low-latency feedback. In this work, we propose a fully event-based pipeline for egomotion estimation that processes the event stream directly within the event-based domain. This method eliminates the need for frame-based intermediaries, allowing for low-latency and energy-efficient motion estimation. We construct a shallow spiking neural network using a synaptic gating mechanism to convert precise event timing into bursts of spikes. These spikes encode local optical flow velocities, and the network provides an event-based readout of egomotion. We evaluate the network's performance on a dedicated chip, demonstrating strong potential for low-latency, low-power motion estimation. Additionally, simulations of larger networks show that the system achieves state-of-the-art accuracy in egomotion estimation tasks with event-based cameras, making it a promising solution for real-time, power-constrained robotics applications.
The organization of neurons into functionally related assemblies is a fundamental feature of cortical networks, yet our understanding of how these assemblies maintain distinct identities while sharing members remains limited. Here we analyze how spike-timing-dependent plasticity (STDP) shapes the formation and stability of overlapping neuronal assemblies in recurrently coupled networks of spiking neuron models. Using numerical simulations and an associated mean-field theory, we demonstrate that the temporal structure of the STDP rule, specifically its degree of causality, critically determines whether assemblies that share neurons maintain segregation or merge together after training is completed. We find that causal STDP rules, where potentiation/depression occurs strictly when presynaptic spikes precede/proceed postsynaptic spikes, allow assemblies to remain distinct even with substantial overlap in membership. This stability arises because causal STDP effectively cancels the symmetric correlations introduced by common inputs from shared neurons. In contrast, acausal STDP rules lead to assembly fusion when overlap exceeds a critical threshold, due to unchecked growth of common input correlations. Our results provide theoretical insight into how spike-timing-dependent learning rules can support distributed representation where individual neurons participate in multiple assemblies while maintaining functional specificity.
Learnable Sparsification of Die-to-Die Communication via Spike-Based Encoding
2025-01-15
Joshua Nardone, Ruijie Zhu, Joseph Callenes, Mohammed E. Elbtity, Ramtin Zand, Jason Eshraghian
Efficient communication is central to both biological and artificial intelligence (AI) systems. In biological brains, the challenge of long-range communication across regions is addressed through sparse, spike-based signaling, minimizing energy and latency. Conversely, modern AI workloads are increasingly constrained by bandwidth, leading to bottlenecks that hamper scalability and efficiency. Inspired by the brain's ability to execute dynamic and complex local computations coupled with sparse inter-neuron communication, we propose heterogeneous neural networks that combine spiking neural networks (SNNs) and artificial neural networks (ANNs) at bandwidth-limited regions, such as chip boundaries, where spike-based communication reduces data transfer overhead. Within each chip, dense ANN computations maintain high throughput, accuracy, and robustness. While SNNs have struggled to algorithmically scale, our approach surmounts this long-standing challenge through algorithm-architecture co-design where learnable sparsity is employed for die-to-die communication by confining spiking layers to specific partitions. This composable design combines high ANN performance with low-bandwidth SNN efficiency. Evaluations on language processing and computer vision exhibit up to 5.3x energy efficiency gains and 15.2x latency reductions, surpassing both purely spiking and non-spiking models. As model size grows, improvements scale accordingly. By targeting the inter-chip communication bottleneck with biologically inspired methods, this approach presents a promising path to more efficient AI systems.
Self-Attentive Spatio-Temporal Calibration for Precise Intermediate Layer Matching in ANN-to-SNN Distillation
2025-01-14
Di Hong, Yueming Wang
Spiking Neural Networks (SNNs) are promising for low-power computation due to their event-driven mechanism but often suffer from lower accuracy compared to Artificial Neural Networks (ANNs). ANN-to-SNN knowledge distillation can improve SNN performance, but previous methods either focus solely on label information, missing valuable intermediate layer features, or use a layer-wise approach that neglects spatial and temporal semantic inconsistencies, leading to performance degradation.To address these limitations, we propose a novel method called self-attentive spatio-temporal calibration (SASTC). SASTC uses self-attention to identify semantically aligned layer pairs between ANN and SNN, both spatially and temporally. This enables the autonomous transfer of relevant semantic information. Extensive experiments show that SASTC outperforms existing methods, effectively solving the mismatching problem. Superior accuracy results include 95.12% on CIFAR-10, 79.40% on CIFAR-100 with 2 time steps, and 68.69% on ImageNet with 4 time steps for static datasets, and 97.92% on DVS-Gesture and 83.60% on DVS-CIFAR10 for neuromorphic datasets. This marks the first time SNNs have outperformed ANNs on both CIFAR-10 and CIFAR-100, shedding the new light on the potential applications of SNNs.
Spiking Neural Network Accelerator Architecture for Differential-Time Representation using Learned Encoding
2025-01-14
Daniel Windhager, Lothar Ratschbacher, Bernhard A. Moser, Michael Lunglmayr
Spiking Neural Networks (SNNs) have garnered attention over recent years due to their increased energy efficiency and advantages in terms of operational complexity compared to traditional Artificial Neural Networks (ANNs). Two important questions when implementing SNNs are how to best encode existing data into spike trains and how to efficiently process these spike trains in hardware. This paper addresses both of these problems by incorporating the encoding into the learning process, thus allowing the network to learn the spike encoding alongside the weights. Furthermore, this paper proposes a hardware architecture based on a recently introduced differential-time representation for spike trains allowing decoupling of spike time and processing time. Together these contributions lead to a feedforward SNN using only Leaky-Integrate and Fire (LIF) neurons that surpasses 99% accuracy on the MNIST dataset while still being implementable on medium-sized FPGAs with inference times of less than 295us.
Recently, large models, such as Vision Transformer and BERT, have garnered significant attention due to their exceptional performance. However, their extensive computational requirements lead to considerable power and hardware resource consumption. Brain-inspired computing, characterized by its spike-driven methods, has emerged as a promising approach for low-power hardware implementation. In this paper, we propose an efficient sparse hardware accelerator for Spike-driven Transformer. We first design a novel encoding method that encodes the position information of valid activations and skips non-spike values. This method enables us to use encoded spikes for executing the calculations of linear, maxpooling and spike-driven self-attention. Compared with the single spike input design of conventional SNN accelerators that primarily focus on convolution-based spiking computations, the specialized module for spike-driven self-attention is unique in its ability to handle dual spike inputs. By exclusively utilizing activated spikes, our design fully exploits the sparsity of Spike-driven Transformer, which diminishes redundant operations, lowers power consumption, and minimizes computational latency. Experimental results indicate that compared to existing SNNs accelerators, our design achieves up to 13.24$\times$ and 1.33$\times$ improvements in terms of throughput and energy efficiency, respectively.
Efficient Event-based Delay Learning in Spiking Neural Networks
2025-01-13
Balázs Mészáros, James C. Knight, Thomas Nowotny
Spiking Neural Networks (SNNs) are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Networks. Spiking neurons are stateful and intrinsically recurrent, making them well-suited for spatio-temporal tasks. However, this intrinsic memory is limited by synaptic and membrane time constants. A powerful additional mechanism are delays. In this paper, we propose a novel event-based training method for SNNs with delays, grounded in the EventProp formalism and enabling the calculation of exact gradients with respect to weights and delays. Our method supports multiple spikes per neuron and, to our best knowledge, is the first delay learning algorithm to be applied to recurrent SNNs. We evaluate our method on a simple sequence detection task, and the Yin-Yang, Spiking Heidelberg Digits and Spiking Speech Commands datasets, demonstrating that our algorithm can optimize delays from suboptimal initial conditions and enhance classification accuracy compared to architectures without delays. Finally, we show that our approach uses less than half the memory of the current state-of-the-art delay-learning method and is up to 26x faster.
Advancing Single-Snapshot DOA Estimation with Siamese Neural Networks for Sparse Linear Arrays
2025-01-13
Ruxin Zheng, Shunqiao Sun, Hongshan Liu, Yimin D. Zhang
Single-snapshot signal processing in sparse linear arrays has become increasingly vital, particularly in dynamic environments like automotive radar systems, where only limited snapshots are available. These arrays are often utilized either to cut manufacturing costs or result from unintended antenna failures, leading to challenges such as high sidelobe levels and compromised accuracy in direction-of-arrival (DOA) estimation. Despite deep learning's success in tasks such as DOA estimation, the need for extensive training data to increase target numbers or improve angular resolution poses significant challenges. In response, this paper presents a novel Siamese neural network (SNN) featuring a sparse augmentation layer, which enhances signal feature embedding and DOA estimation accuracy in sparse arrays. We demonstrate the enhanced DOA estimation performance of our approach through detailed feature analysis and performance evaluation. The code for this study is available at https://github.com/ruxinzh/SNNS_SLA.
Energy-Efficient Cryogenic Neuromorphic Network with Superconducting Memristor
2025-01-13
Md Mazharul Islam, Julia Steed, Karan Patel, Catherine Schuman, Ahmedullah Aziz
Cryogenic neuromorphic systems, inspired by the brains unparalleled efficiency, present a promising paradigm for next generation computing architectures.This work introduces a fully integrated neuromorphic framework that combines superconducting memristor(SM) based spiking neurons and synapse topologies to achieve a low power neuromorphic network with non volatile synaptic strength.This neurosynaptic framework is validated by implementing the cart pole control task, a dynamic decision making problem requiring real time computation.Through detailed simulations, we demonstrate the network's ability to execute this task with an average fitness of 5965 timesteps across 1000 randomized test episodes, with 40 percent achieving the target fitness of 15,000 timesteps (0.02s per timestep).The system achieves 23 distinct spiking rates across neurons, ensuring efficient information encoding.Our findings establish the potential of SM based cryogenic neuromorphic systems to address the energy and scalability limitations of traditional computing, paving the way for biologically inspired, ultra low power computational frameworks.
With the rapid growth of dynamic vision sensor (DVS) data, constructing a low-energy, efficient data retrieval system has become an urgent task. Hash learning is one of the most important retrieval technologies which can keep the distance between hash codes consistent with the distance between DVS data. As spiking neural networks (SNNs) can encode information through spikes, they demonstrate great potential in promoting energy efficiency. Based on the binary characteristics of SNNs, we first propose a novel supervised hashing method named Spikinghash with a hierarchical lightweight structure. Spiking WaveMixer (SWM) is deployed in shallow layers, utilizing a multilevel 3D discrete wavelet transform (3D-DWT) to decouple spatiotemporal features into various low-frequency and high frequency components, and then employing efficient spectral feature fusion. SWM can effectively capture the temporal dependencies and local spatial features. Spiking Self-Attention (SSA) is deployed in deeper layers to further extract global spatiotemporal information. We also design a hash layer utilizing binary characteristic of SNNs, which integrates information over multiple time steps to generate final hash codes. Furthermore, we propose a new dynamic soft similarity loss for SNNs, which utilizes membrane potentials to construct a learnable similarity matrix as soft labels to fully capture the similarity differences between classes and compensate information loss in SNNs, thereby improving retrieval performance. Experiments on multiple datasets demonstrate that Spikinghash can achieve state-of-the-art results with low energy consumption and fewer parameters.
2025-01-10
Honglin Cao, Zijian Zhou, Wenjie Wei, Ammar Belatreche, Yu Liang, Dehao Zhang, Malu Zhang, Yang Yang, Haizhou Li
Transformer-based Spiking Neural Networks (SNNs) introduce a novel event-driven self-attention paradigm that combines the high performance of Transformers with the energy efficiency of SNNs. However, the larger model size and increased computational demands of the Transformer structure limit their practicality in resource-constrained scenarios. In this paper, we integrate binarization techniques into Transformer-based SNNs and propose the Binary Event-Driven Spiking Transformer, i.e. BESTformer. The proposed BESTformer can significantly reduce storage and computational demands by representing weights and attention maps with a mere 1-bit. However, BESTformer suffers from a severe performance drop from its full-precision counterpart due to the limited representation capability of binarization. To address this issue, we propose a Coupled Information Enhancement (CIE) method, which consists of a reversible framework and information enhancement distillation. By maximizing the mutual information between the binary model and its full-precision counterpart, the CIE method effectively mitigates the performance degradation of the BESTformer. Extensive experiments on static and neuromorphic datasets demonstrate that our method achieves superior performance to other binary SNNs, showcasing its potential as a compact yet high-performance model for resource-limited edge devices.
Impact of Data Breadth and Depth on Performance of Siamese Neural Network Model: Experiments with Three Keystroke Dynamic Datasets
2025-01-10
Ahmed Anu Wahab, Daqing Hou, Nadia Cheng, Parker Huntley, Charles Devlen
Deep learning models, such as the Siamese Neural Networks (SNN), have shown great potential in capturing the intricate patterns in behavioral data. However, the impacts of dataset breadth (i.e., the number of subjects) and depth (e.g., the amount of training samples per subject) on the performance of these models is often informally assumed, and remains under-explored. To this end, we have conducted extensive experiments using the concepts of "feature space" and "density" to guide and gain deeper understanding on the impact of dataset breadth and depth on three publicly available keystroke datasets (Aalto, CMU and Clarkson II). Through varying the number of training subjects, number of samples per subject, amount of data in each sample, and number of triplets used in training, we found that when feasible, increasing dataset breadth enables the training of a well-trained model that effectively captures more inter-subject variability. In contrast, we find that the extent of depth's impact from a dataset depends on the nature of the dataset. Free-text datasets are influenced by all three depth-wise factors; inadequate samples per subject, sequence length, training triplets and gallery sample size, which may all lead to an under-trained model. Fixed-text datasets are less affected by these factors, and as such make it easier to create a well-trained model. These findings shed light on the importance of dataset breadth and depth in training deep learning models for behavioral biometrics and provide valuable insights for designing more effective authentication systems.
Delay Neural Networks (DeNN) for exploiting temporal information in event-based datasets
2025-01-10
Alban Gattepaille, Alexandre Muzy
In Deep Neural Networks (DNN) and Spiking Neural Networks (SNN), the information of a neuron is computed based on the sum of the amplitudes (weights) of the electrical potentials received in input from other neurons. We propose here a new class of neural networks, namely Delay Neural Networks (DeNN), where the information of a neuron is computed based on the sum of its input synaptic delays and on the spike times of the electrical potentials received from other neurons. This way, DeNN are designed to explicitly use exact continuous temporal information of spikes in both forward and backward passes, without approximation. (Deep) DeNN are applied here to images and event-based (audio and visual) data sets. Good performances are obtained, especially for datasets where temporal information is important, with much less parameters and less energy than other models.
Enhanced Quantile Regression with Spiking Neural Networks for Long-Term System Health Prognostics
2025-01-09
David J Poland
This paper presents a novel predictive maintenance framework centered on Enhanced Quantile Regression Neural Networks EQRNNs, for anticipating system failures in industrial robotics. We address the challenge of early failure detection through a hybrid approach that combines advanced neural architectures. The system leverages dual computational stages: first implementing an EQRNN optimized for processing multi-sensor data streams including vibration, thermal, and power signatures, followed by an integrated Spiking Neural Network SNN, layer that enables microsecond-level response times. This architecture achieves notable accuracy rates of 92.3\% in component failure prediction with a 90-hour advance warning window. Field testing conducted on an industrial scale with 50 robotic systems demonstrates significant operational improvements, yielding a 94\% decrease in unexpected system failures and 76\% reduction in maintenance-related downtimes. The framework's effectiveness in processing complex, multi-modal sensor data while maintaining computational efficiency validates its applicability for Industry 4.0 manufacturing environments.
Neuromorphic Optical Tracking and Imaging of Randomly Moving Targets through Strongly Scattering Media
2025-01-07
Ning Zhang, Timothy Shea, Arto Nurmikko
Tracking and acquiring simultaneous optical images of randomly moving targets obscured by scattering media remains a challenging problem of importance to many applications that require precise object localization and identification. In this work we develop an end-to-end neuromorphic optical engineering and computational approach to demonstrate how to track and image normally invisible objects by combining an event detecting camera with a multistage neuromorphic deep learning strategy. Photons emerging from dense scattering media are detected by the event camera and converted to pixel-wise asynchronized spike trains - a first step in isolating object-specific information from the dominant uninformative background. Spiking data is fed into a deep spiking neural network (SNN) engine where object tracking and image reconstruction are performed by two separate yet interconnected modules running in parallel in discrete time steps over the event duration. Through benchtop experiments we demonstrate tracking and imaging randomly moving objects in dense turbid media as well as image reconstruction of spatially stationary but optically dynamic objects. Standardized character sets serve as representative proxies for geometrically complex objects, underscoring the method's generality. The results highlight the advantages of a fully neuromorphic approach in meeting a major imaging technology with high computational efficiency and low power consumption.
Spiking monocular event based 6D pose estimation for space application
2025-01-06
Jonathan Courtois, Benoît Miramond, Alain Pegatoquet
With the growing interest in on On-orbit servicing (OOS) and Active Debris Removal (ADR) missions, spacecraft poses estimation algorithms are being developed using deep learning to improve the precision of this complex task and find the most efficient solution. With the advances of bio-inspired low-power solutions, such a spiking neural networks and event-based processing and cameras, and their recent work for space applications, we propose to investigate the feasibility of a fully event-based solution to improve event-based pose estimation for spacecraft. In this paper, we address the first event-based dataset SEENIC with real event frames captured by an event-based camera on a testbed. We show the methods and results of the first event-based solution for this use case, where our small spiking end-to-end network (S2E2) solution achieves interesting results over 21cm position error and 14degree rotation error, which is the first step towards fully event-based processing for embedded spacecraft pose estimation.
The Robustness of Spiking Neural Networks in Federated Learning with Compression Against Non-omniscient Byzantine Attacks
2025-01-06
Manh V. Nguyen, Liang Zhao, Bobin Deng, Shaoen Wu
Spiking Neural Networks (SNNs), which offer exceptional energy efficiency for inference, and Federated Learning (FL), which offers privacy-preserving distributed training, is a rising area of interest that highly beneficial towards Internet of Things (IoT) devices. Despite this, research that tackles Byzantine attacks and bandwidth limitation in FL-SNNs, both poses significant threats on model convergence and training times, still remains largely unexplored. Going beyond proposing a solution for both of these problems, in this work we highlight the dual benefits of FL-SNNs, against non-omniscient Byzantine adversaries (ones that restrict attackers access to local clients datasets), and greater communication efficiency, over FL-ANNs. Specifically, we discovered that a simple integration of Top-\k{appa} sparsification into the FL apparatus can help leverage the advantages of the SNN models in both greatly reducing bandwidth usage and significantly boosting the robustness of FL training against non-omniscient Byzantine adversaries. Most notably, we saw a massive improvement of roughly 40% accuracy gain in FL-SNNs training under the lethal MinMax attack
Integrated sensing and communication (ISAC) has emerged as a pivotal technology for enabling vehicle-to-everything (V2X) connectivity, mobility, and security. However, designing efficient beamforming schemes to achieve accurate sensing and enhance communication performance in the dynamic and uncertain environments of V2X networks presents significant challenges. While AI technologies offer promising solutions, the energy-intensive nature of neural networks (NNs) imposes substantial burdens on communication infrastructures. This work proposes an energy-efficient and intelligent ISAC system for V2X networks. Specifically, we first leverage a Markov Decision Process framework to model the dynamic and uncertain nature of V2X networks. This framework allows the roadside unit (RSU) to develop beamforming schemes relying solely on its current sensing state information, eliminating the need for numerous pilot signals and extensive channel state information acquisition. To endow the system with intelligence and enhance its performance, we then introduce an advanced deep reinforcement learning (DRL) algorithm based on the Actor-Critic framework with a policy-clipping technique, enabling the joint optimization of beamforming and power allocation strategies to guarantee both communication rate and sensing accuracy. Furthermore, to alleviate the energy demands of NNs, we integrate Spiking Neural Networks (SNNs) into the DRL algorithm. By leveraging discrete spikes and their temporal characteristics for information transmission, SNNs not only significantly reduce the energy consumption of deploying AI model in ISAC-assisted V2X networks but also further enhance algorithm performance. Extensive simulation results validate the effectiveness of the proposed scheme with lower energy consumption, superior communication performance, and improved sensing accuracy.
Hybrid Opto-Electrical Excitation of Spin-Transfer Torque Nano-Oscillators for Advanced Computing
2025-01-01
Felix Oberbauer, Tristan Joachim Winkel, Tim Böhnert, Marcel S. Claro, Luana Benetti, Ihsan Çaha, Leonard Francis, Farshad Moradi, Ricardo Ferreira, Markus Münzenberg, Tahereh Sadat Parvini
Neuromorphic computing, inspired by the brain's parallel and energy-efficient processing, offers a transformative approach to artificial intelligence. In this study, we fabricated optimized spin-transfer torque nano-oscillators (STNOs) and investigated their dynamic behaviors using a hybrid excitation scheme combining AC laser illumination and DC bias currents. Laser-induced thermal gradients generate pulsed thermoelectric voltages ($V_{\text{AC}}$) via the Tunnel Magneto-Seebeck (TMS) effect, while the addition of bias currents enhances this response, producing both $V_{\text{AC}}$ and a DC component ($V_{\text{DC}}$). Magnetic field sweeps reveal distinct switching between parallel (P) and antiparallel (AP) magnetization states in both voltage components, supporting multistate memory applications. Millivolt-range thermovoltage signals in open-circuit conditions demonstrate CMOS compatibility, enabling simplified, scalable neuromorphic systems. Under biased conditions, enhanced thermovoltage outputs exhibit intriguing phenomena, including spikes correlated with Barkhausen jumps and double-switching behavior, offering insights into magnetization dynamics and vortex transitions. These features resemble neural spiking behavior, suggesting applications in spiking neural networks, reservoir computing, multistate logic, analog computing, and high-resolution sensing. By bridging spintronic phenomena with practical applications, this work provides a versatile platform for next-generation AI technologies and adaptive computing architectures.
Recently, it can be noticed that most models based on spiking neural networks (SNNs) only use a same level temporal resolution to deal with speech classification problems, which makes these models cannot learn the information of input data at different temporal scales. Additionally, owing to the different time lengths of the data before and after the sub-modules of many models, the effective residual connections cannot be applied to optimize the training processes of these models.To solve these problems, on the one hand, we reconstruct the temporal dimension of the audio spectrum to propose a novel method named as Temporal Reconstruction (TR) by referring the hierarchical processing process of the human brain for understanding speech. Then, the reconstructed SNN model with TR can learn the information of input data at different temporal scales and model more comprehensive semantic information from audio data because it enables the networks to learn the information of input data at different temporal resolutions. On the other hand, we propose the Non-Aligned Residual (NAR) method by analyzing the audio data, which allows the residual connection can be used in two audio data with different time lengths. We have conducted plentiful experiments on the Spiking Speech Commands (SSC), the Spiking Heidelberg Digits (SHD), and the Google Speech Commands v0.02 (GSC) datasets. According to the experiment results, we have achieved the state-of-the-art (SOTA) result 81.02\% on SSC for the test classification accuracy of all SNN models, and we have obtained the SOTA result 96.04\% on SHD for the classification accuracy of all models.
A brain-computer interface (BCI) facilitates direct interaction between the brain and external devices. To concurrently achieve high decoding accuracy and low energy consumption in invasive BCIs, we propose a novel spiking neural network (SNN) framework incorporating local synaptic stabilization (LSS) and channel-wise attention (CA), termed LSS-CA-SNN. LSS optimizes neuronal membrane potential dynamics, boosting classification performance, while CA refines neuronal activation, effectively reducing energy consumption. Furthermore, we introduce SpikeDrop, a data augmentation strategy designed to expand the training dataset thus enhancing model generalizability. Experiments on invasive spiking datasets recorded from two rhesus macaques demonstrated that LSS-CA-SNN surpassed state-of-the-art artificial neural networks (ANNs) in both decoding accuracy and energy efficiency, achieving 0.80-3.87% performance gains and 14.78-43.86 times energy saving. This study highlights the potential of LSS-CA-SNN and SpikeDrop in advancing invasive BCI applications.
Enhanced Temporal Processing in Spiking Neural Networks for Static Object Detection Using 3D Convolutions
2024-12-23
Huaxu He
Spiking Neural Networks (SNNs) are a class of network models capable of processing spatiotemporal information, with event-driven characteristics and energy efficiency advantages. Recently, directly trained SNNs have shown potential to match or surpass the performance of traditional Artificial Neural Networks (ANNs) in classification tasks. However, in object detection tasks, directly trained SNNs still exhibit a significant performance gap compared to ANNs when tested on frame-based static object datasets (such as COCO2017). Therefore, bridging this performance gap and enabling directly trained SNNs to achieve performance comparable to ANNs on these static datasets has become one of the key challenges in the development of SNNs.To address this challenge, this paper focuses on enhancing the SNN's unique ability to process spatiotemporal information. Spiking neurons, as the core components of SNNs, facilitate the exchange of information between different temporal channels during the process of converting input floating-point data into binary spike signals. However, existing neuron models still have certain limitations in the communication of temporal information. Some studies have even suggested that disabling the backpropagation in the time dimension during SNN training can still yield good training results. To improve the SNN handling of temporal information, this paper proposes replacing traditional 2D convolutions with 3D convolutions, thus directly incorporating temporal information into the convolutional process. Additionally, temporal information recurrence mechanism is introduced within the neurons to further enhance the neurons' efficiency in utilizing temporal information.Experimental results show that the proposed method enables directly trained SNNs to achieve performance levels comparable to ANNs on the COCO2017 and VOC datasets.
HPCNeuroNet: A Neuromorphic Approach Merging SNN Temporal Dynamics with Transformer Attention for FPGA-based Particle Physics
2024-12-23
Murat Isik, Hiruna Vishwamith, Jonathan Naoukin, I. Can Dikmen
This paper presents the innovative HPCNeuroNet model, a pioneering fusion of Spiking Neural Networks (SNNs), Transformers, and high-performance computing tailored for particle physics, particularly in particle identification from detector responses. Our approach leverages SNNs' intrinsic temporal dynamics and Transformers' robust attention mechanisms to enhance performance when discerning intricate particle interactions. At the heart of HPCNeuroNet lies the integration of the sequential dynamism inherent in SNNs with the context-aware attention capabilities of Transformers, enabling the model to precisely decode and interpret complex detector data. HPCNeuroNet is realized through the HLS4ML framework and optimized for deployment in FPGA environments. The model accuracy and scalability are also enhanced by this architectural choice. Benchmarked against machine learning models, HPCNeuroNet showcases better performance metrics, underlining its transformative potential in high-energy physics. We demonstrate that the combination of SNNs, Transformers, and FPGA-based high-performance computing in particle physics signifies a significant step forward and provides a strong foundation for future research.
Cellular traffic forecasting is a critical task that enables network operators to efficiently allocate resources and address anomalies in rapidly evolving environments. The exponential growth of data collected from base stations poses significant challenges to processing and analysis. While machine learning (ML) algorithms have emerged as powerful tools for handling these large datasets and providing accurate predictions, their environmental impact, particularly in terms of energy consumption, is often overlooked in favor of their predictive capabilities. This study investigates the potential of two bio-inspired models: Spiking Neural Networks (SNNs) and Reservoir Computing through Echo State Networks (ESNs) for cellular traffic forecasting. The evaluation focuses on both their predictive performance and energy efficiency. These models are implemented in both centralized and federated settings to analyze their effectiveness and energy consumption in decentralized systems. Additionally, we compare bio-inspired models with traditional architectures, such as Convolutional Neural Networks (CNNs) and Multi-Layer Perceptrons (MLPs), to provide a comprehensive evaluation. Using data collected from three diverse locations in Barcelona, Spain, we examine the trade-offs between predictive accuracy and energy demands across these approaches. The results indicate that bio-inspired models, such as SNNs and ESNs, can achieve significant energy savings while maintaining predictive accuracy comparable to traditional architectures. Furthermore, federated implementations were tested to evaluate their energy efficiency in decentralized settings compared to centralized systems, particularly in combination with bio-inspired models. These findings offer valuable insights into the potential of bio-inspired models for sustainable and privacy-preserving cellular traffic forecasting.
Exploiting Label Skewness for Spiking Neural Networks in Federated Learning
2024-12-23
Di Yu, Xin Du, Linshan Jiang, Huijing Zhang, Shunwen Bai, Shuiguang Deng
The energy efficiency of deep spiking neural networks (SNNs) aligns with the constraints of resource-limited edge devices, positioning SNNs as a promising foundation for intelligent applications leveraging the extensive data collected by these devices. To address data privacy concerns when deploying SNNs on edge devices, federated learning (FL) facilitates collaborative model training by leveraging data distributed across edge devices without transmitting local data to a central server. However, existing FL approaches struggle with label-skewed data across devices, which leads to drift in local SNN models and degrades the performance of the global SNN model. In this paper, we propose a novel framework called FedLEC, which incorporates intra-client label weight calibration to balance the learning intensity across local labels and inter-client knowledge distillation to mitigate local SNN model bias caused by label absence. Extensive experiments with three different structured SNNs across five datasets (i.e., three non-neuromorphic and two neuromorphic datasets) demonstrate the efficiency of FedLEC. Compared to eight state-of-the-art FL algorithms, FedLEC achieves an average accuracy improvement of approximately 11.59% for the global SNN model under various label skew distribution settings.
TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering
2024-12-23
Prabhu Vellaisamy, Harideep Nair, Vamsikrishna Ratnakaram, Dhruv Gupta, John Paul Shen
Temporal Neural Networks (TNNs), a special class of spiking neural networks, draw inspiration from the neocortex in utilizing spike-timings for information processing. Recent works proposed a microarchitecture framework and custom macro suite for designing highly energy-efficient application-specific TNNs. These recent works rely on manual hardware design, a labor-intensive and time-consuming process. Further, there is no open-source functional simulation framework for TNNs. This paper introduces TNNGen, a pioneering effort towards the automated design of TNNs from PyTorch software models to post-layout netlists. TNNGen comprises a novel PyTorch functional simulator (for TNN modeling and application exploration) coupled with a Python-based hardware generator (for PyTorch-to-RTL and RTL-to-Layout conversions). Seven representative TNN designs for time-series signal clustering across diverse sensory modalities are simulated and their post-layout hardware complexity and design runtimes are assessed to demonstrate the effectiveness of TNNGen. We also highlight TNNGen's ability to accurately forecast silicon metrics without running hardware process flow.
Contemporary implementations of spiking bio-inspired neural networks
2024-12-23
Andrey E. Schegolev, Marina V. Bastrakova, Michael A. Sergeev, Anastasia A. Maksimovskaya, Nikolay V. Klenov, Igor I. Soloviev
The extensive development of the field of spiking neural networks has led to many areas of research that have a direct impact on people's lives. As the most bio-similar of all neural networks, spiking neural networks not only allow the solution of recognition and clustering problems (including dynamics), but also contribute to the growing knowledge of the human nervous system. Our analysis has shown that the hardware implementation is of great importance, since the specifics of the physical processes in the network cells affect their ability to simulate the neural activity of living neural tissue, the efficiency of certain stages of information processing, storage and transmission. This survey reviews existing hardware neuromorphic implementations of bio-inspired spiking networks in the "semiconductor", "superconductor" and "optical" domains. Special attention is given to the possibility of effective "hybrids" of different approaches
How connection probability shapes fluctuations of neural population dynamics
2024-12-20
Nils E. Greven, Jonas Ranft, Tilo Schwalger
Mean-field models of neuronal populations in the brain have proven extremely useful to understand network dynamics and response to stimuli, but these models generally lack a faithful description of the fluctuations in the biologically relevant case of finite network size and connection probabilities $p<1$ (non-full connectivity). To gain insight into the different fluctuation mechanisms underlying the neural variability of populations of spiking neurons, we derive and analyze a stochastic mean-field model for finite-size networks of Poisson neurons with random, non-full connectivity, external noise and disordered mean inputs. We treat the quenched disorder of the connectivity by an annealed approximation that enables a reduction to a low-dimensional closed system of coupled Langevin equations for the mean and variance of the neuronal membrane potentials as well as a variable capturing finite-size fluctuations arising specifically in the case $p<1$. Comparing to microscopic simulations, we find that the mesoscopic model describes the fluctuations and nonlinearities well and outperforms previous mesoscopic models that neglected the recurrent noise effect caused by the non-full connectivity. This effect can be analytically understood by a softening of the effective nonlinearity and the multiplicative character of finite-size spiking noise. The mesoscopic theory shows that quenched disorder can stabilize the asynchronous state, and it correctly predicts large quantitiative and non-trivial qualitative effects of connection probability on the variance of the population firing rate and its dependence on stimulus strength. Our theory thus elucidates how disordered connectivity shapes nonlinear dynamics and fluctuations of neural populations at the mesoscopic scale and showcases a useful mean-field method to treat non-full connectivity in finite-size, spiking neural networks.
Spiking Neural Belief Propagation Decoder for LDPC Codes with Small Variable Node Degrees
2024-12-20
Alexander von Bank, Eike-Manuel Edelmann, Jonathan Mandelbaum, Laurent Schmalen
Spiking neural networks (SNNs) promise energy-efficient data processing by imitating the event-based behavior of biological neurons. In previous work, we introduced the enlarge-likelihood-each-notable-amplitude spiking-neural-network (ELENA-SNN) decoder, a novel decoding algorithm for low-density parity-check (LDPC) codes. The decoder integrates SNNs into belief propagation (BP) decoding by approximating the check node (CN) update equation using SNNs. However, when decoding LDPC codes with a small variable node(VN) degree, the approximation gets too rough, and the ELENA-SNN decoder does not yield good results. This paper introduces the multi-level ELENA-SNN (ML-ELENA-SNN) decoder, which is an extension of the ELENA-SNN decoder. Instead of a single SNN approximating the CN update, multiple SNNs are applied in parallel, resulting in a higher resolution and higher dynamic range of the exchanged messages. We show that the ML-ELENA-SNN decoder performs similarly to the ubiquitous normalized min-sum decoder for the (38400, 30720) regular LDPC code with a VN degree of dv = 3 and a CN degree of dc = 15.
Darkit: A User-Friendly Software Toolkit for Spiking Large Language Model
2024-12-20
Xin Du, Shifan Ye, Qian Zheng, Yangfan Hu, Rui Yan, Shunyu Qi, Shuyang Chen, Huajin Tang, Gang Pan, Shuiguang Deng
Large language models (LLMs) have been widely applied in various practical applications, typically comprising billions of parameters, with inference processes requiring substantial energy and computational resources. In contrast, the human brain, employing bio-plausible spiking mechanisms, can accomplish the same tasks while significantly reducing energy consumption, even with a similar number of parameters. Based on this, several pioneering researchers have proposed and implemented various large language models that leverage spiking neural networks. They have demonstrated the feasibility of these models, validated their performance, and open-sourced their frameworks and partial source code. To accelerate the adoption of brain-inspired large language models and facilitate secondary development for researchers, we are releasing a software toolkit named DarwinKit (Darkit). The toolkit is designed specifically for learners, researchers, and developers working on spiking large models, offering a suite of highly user-friendly features that greatly simplify the learning, deployment, and development processes.
Event-based backpropagation on the neuromorphic platform SpiNNaker2
2024-12-19
Gabriel Béna, Timo Wunderlich, Mahmoud Akl, Bernhard Vogginger, Christian Mayr, Hector Andres Gonzalez
Neuromorphic computing aims to replicate the brain's capabilities for energy efficient and parallel information processing, promising a solution to the increasing demand for faster and more efficient computational systems. Efficient training of neural networks on neuromorphic hardware requires the development of training algorithms that retain the sparsity of spike-based communication during training. Here, we report on the first implementation of event-based backpropagation on the SpiNNaker2 neuromorphic hardware platform. We use EventProp, an algorithm for event-based backpropagation in spiking neural networks (SNNs), to compute exact gradients using sparse communication of error signals between neurons. Our implementation computes multi-layer networks of leaky integrate-and-fire neurons using discretized versions of the differential equations and their adjoints, and uses event packets to transmit spikes and error signals between network layers. We demonstrate a proof-of-concept of batch-parallelized, on-chip training of SNNs using the Yin Yang dataset, and provide an off-chip implementation for efficient prototyping, hyper-parameter search, and hybrid training methods.
Spike2Former: Efficient Spiking Transformer for High-performance Image Segmentation
2024-12-19
Zhenxin Lei, Man Yao, Jiakui Hu, Xinhao Luo, Yanye Lu, Bo Xu, Guoqi Li
Spiking Neural Networks (SNNs) have a low-power advantage but perform poorly in image segmentation tasks. The reason is that directly converting neural networks with complex architectural designs for segmentation tasks into spiking versions leads to performance degradation and non-convergence. To address this challenge, we first identify the modules in the architecture design that lead to the severe reduction in spike firing, make targeted improvements, and propose Spike2Former architecture. Second, we propose normalized integer spiking neurons to solve the training stability problem of SNNs with complex architectures. We set a new state-of-the-art for SNNs in various semantic segmentation datasets, with a significant improvement of +12.7% mIoU and 5.0 efficiency on ADE20K, +14.3% mIoU and 5.2 efficiency on VOC2012, and +9.1% mIoU and 6.6 efficiency on CityScapes.
Neuromorphic Spiking Neural Network Based Classification of COVID-19 Spike Sequences
2024-12-19
Taslim Murad, Prakash Chourasia, Sarwan Ali, Imdad Ullah Khan, Murray Patterson
The availability of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus data post-COVID has reached exponentially to an enormous magnitude, opening research doors to analyze its behavior. Various studies are conducted by researchers to gain a deeper understanding of the virus, like genomic surveillance, etc, so that efficient prevention mechanisms can be developed. However, the unstable nature of the virus (rapid mutations, multiple hosts, etc) creates challenges in designing analytical systems for it. Therefore, we propose a neural network-based (NN) mechanism to perform an efficient analysis of the SARS-CoV-2 data, as NN portrays generalized behavior upon training. Moreover, rather than using the full-length genome of the virus, we apply our method to its spike region, as this region is known to have predominant mutations and is used to attach to the host cell membrane. In this paper, we introduce a pipeline that first converts the spike protein sequences into a fixed-length numerical representation and then uses Neuromorphic Spiking Neural Network to classify those sequences. We compare the performance of our method with various baselines using real-world SARS-CoV-2 spike sequence data and show that our method is able to achieve higher predictive accuracy compared to the recent baselines.
Faster and Stronger: When ANN-SNN Conversion Meets Parallel Spiking Calculation
2024-12-18
Zecheng Hao, Zhaofei Yu, Tiejun Huang
Spiking Neural Network (SNN), as a brain-inspired and energy-efficient network, is currently facing the pivotal challenge of exploring a suitable and efficient learning framework. The predominant training methodologies, namely Spatial-Temporal Back-propagation (STBP) and ANN-SNN Conversion, are encumbered by substantial training overhead or pronounced inference latency, which impedes the advancement of SNNs in scaling to larger networks and navigating intricate application domains. In this work, we propose a novel parallel conversion learning framework, which establishes a mathematical mapping relationship between each time-step of the parallel spiking neurons and the cumulative spike firing rate. We theoretically validate the lossless and sorting properties of the conversion process, as well as pointing out the optimal shifting distance for each step. Furthermore, by integrating the above framework with the distribution-aware error calibration technique, we can achieve efficient conversion towards more general activation functions or training-free circumstance. Extensive experiments have confirmed the significant performance advantages of our method for various conversion cases under ultra-low time latency. To our best knowledge, this is the first work which jointly utilizes parallel spiking calculation and ANN-SNN Conversion, providing a highly promising approach for SNN supervised training.
Combining Aggregated Attention and Transformer Architecture for Accurate and Efficient Performance of Spiking Neural Networks
2024-12-18
Hangming Zhang, Alexander Sboev, Roman Rybka, Qiang Yu
Spiking Neural Networks have attracted significant attention in recent years due to their distinctive low-power characteristics. Meanwhile, Transformer models, known for their powerful self-attention mechanisms and parallel processing capabilities, have demonstrated exceptional performance across various domains, including natural language processing and computer vision. Despite the significant advantages of both SNNs and Transformers, directly combining the low-power benefits of SNNs with the high performance of Transformers remains challenging. Specifically, while the sparse computing mode of SNNs contributes to reduced energy consumption, traditional attention mechanisms depend on dense matrix computations and complex softmax operations. This reliance poses significant challenges for effective execution in low-power scenarios. Given the tremendous success of Transformers in deep learning, it is a necessary step to explore the integration of SNNs and Transformers to harness the strengths of both. In this paper, we propose a novel model architecture, Spike Aggregation Transformer (SAFormer), that integrates the low-power characteristics of SNNs with the high-performance advantages of Transformer models. The core contribution of SAFormer lies in the design of the Spike Aggregated Self-Attention (SASA) mechanism, which significantly simplifies the computation process by calculating attention weights using only the spike matrices query and key, thereby effectively reducing energy consumption. Additionally, we introduce a Depthwise Convolution Module (DWC) to enhance the feature extraction capabilities, further improving overall accuracy. We evaluated and demonstrated that SAFormer outperforms state-of-the-art SNNs in both accuracy and energy consumption, highlighting its significant advantages in low-power and high-performance computing.
Spiking Neural Networks (SNNs) are seen as an energy-efficient alternative to traditional Artificial Neural Networks (ANNs), but the performance gap remains a challenge. While this gap is narrowing through ANN-to-SNN conversion, substantial computational resources are still needed, and the energy efficiency of converted SNNs cannot be ensured. To address this, we present a unified training-free conversion framework that significantly enhances both the performance and efficiency of converted SNNs. Inspired by the biological nervous system, we propose a novel Adaptive-Firing Neuron Model (AdaFire), which dynamically adjusts firing patterns across different layers to substantially reduce the Unevenness Error - the primary source of error of converted SNNs within limited inference timesteps. We further introduce two efficiency-enhancing techniques: the Sensitivity Spike Compression (SSC) technique for reducing spike operations, and the Input-aware Adaptive Timesteps (IAT) technique for decreasing latency. These methods collectively enable our approach to achieve state-of-the-art performance while delivering significant energy savings of up to 70.1%, 60.3%, and 43.1% on CIFAR-10, CIFAR-100, and ImageNet datasets, respectively. Extensive experiments across 2D, 3D, event-driven classification tasks, object detection, and segmentation tasks, demonstrate the effectiveness of our method in various domains. The code is available at: https://github.com/bic-L/burst-ann2snn.
2024-12-17
Jiaqi Wang, Liutao Yu, Liwei Huang, Chenlin Zhou, Han Zhang, Zhenxi Song, Min Zhang, Zhengyu Ma, Zhiguo Zhang
The intrinsic dynamics and event-driven nature of spiking neural networks (SNNs) make them excel in processing temporal information by naturally utilizing embedded time sequences as time steps. Recent studies adopting this approach have demonstrated SNNs' effectiveness in speech command recognition, achieving high performance by employing large time steps for long time sequences. However, the large time steps lead to increased deployment burdens for edge computing applications. Thus, it is important to balance high performance and low energy consumption when detecting temporal patterns in edge devices. Our solution comprises two key components. 1). We propose a high-performance fully spike-driven framework termed SpikeSCR, characterized by a global-local hybrid structure for efficient representation learning, which exhibits long-term learning capabilities with extended time steps. 2). To further fully embrace low energy consumption, we propose an effective knowledge distillation method based on curriculum learning (KDCL), where valuable representations learned from the easy curriculum are progressively transferred to the hard curriculum with minor loss, striking a trade-off between power efficiency and high performance. We evaluate our method on three benchmark datasets: the Spiking Heidelberg Dataset (SHD), the Spiking Speech Commands (SSC), and the Google Speech Commands (GSC) V2. Our experimental results demonstrate that SpikeSCR outperforms current state-of-the-art (SOTA) methods across these three datasets with the same time steps. Furthermore, by executing KDCL, we reduce the number of time steps by 60% and decrease energy consumption by 54.8% while maintaining comparable performance to recent SOTA results. Therefore, this work offers valuable insights for tackling temporal processing challenges with long time sequences in edge neuromorphic computing systems.
Event-based semantic segmentation has great potential in autonomous driving and robotics due to the advantages of event cameras, such as high dynamic range, low latency, and low power cost. Unfortunately, current artificial neural network (ANN)-based segmentation methods suffer from high computational demands, the requirements for image frames, and massive energy consumption, limiting their efficiency and application on resource-constrained edge/mobile platforms. To address these problems, we introduce SLTNet, a spike-driven lightweight transformer-based network designed for event-based semantic segmentation. Specifically, SLTNet is built on efficient spike-driven convolution blocks (SCBs) to extract rich semantic features while reducing the model's parameters. Then, to enhance the long-range contextural feature interaction, we propose novel spike-driven transformer blocks (STBs) with binary mask operations. Based on these basic blocks, SLTNet employs a high-efficiency single-branch architecture while maintaining the low energy consumption of the Spiking Neural Network (SNN). Finally, extensive experiments on DDD17 and DSEC-Semantic datasets demonstrate that SLTNet outperforms state-of-the-art (SOTA) SNN-based methods by at most 9.06% and 9.39% mIoU, respectively, with extremely 4.58x lower energy consumption and 114 FPS inference speed. Our code is open-sourced and available at https://github.com/longxianlei/SLTNet-v1.0.
Inspired by the human brain's ability to adapt to new tasks without erasing prior knowledge, we develop spiking neural networks (SNNs) with dynamic structures for Class Incremental Learning (CIL). Our comparative experiments reveal that limited datasets introduce biases in logits distributions among tasks. Fixed features from frozen past-task extractors can cause overfitting and hinder the learning of new tasks. To address these challenges, we propose the ALADE-SNN framework, which includes adaptive logit alignment for balanced feature representation and OtoN suppression to manage weights mapping frozen old features to new classes during training, releasing them during fine-tuning. This approach dynamically adjusts the network architecture based on analytical observations, improving feature extraction and balancing performance between new and old tasks. Experiment results show that ALADE-SNN achieves an average incremental accuracy of 75.42 on the CIFAR100-B0 benchmark over 10 incremental steps. ALADE-SNN not only matches the performance of DNN-based methods but also surpasses state-of-the-art SNN-based continual learning algorithms. This advancement enhances continual learning in neuromorphic computing, offering a brain-inspired, energy-efficient solution for real-time data processing.
CREST: An Efficient Conjointly-trained Spike-driven Framework for Event-based Object Detection Exploiting Spatiotemporal Dynamics
2024-12-17
Ruixin Mao, Aoyu Shen, Lin Tang, Jun Zhou
Event-based cameras feature high temporal resolution, wide dynamic range, and low power consumption, which is ideal for high-speed and low-light object detection. Spiking neural networks (SNNs) are promising for event-based object recognition and detection due to their spiking nature but lack efficient training methods, leading to gradient vanishing and high computational complexity, especially in deep SNNs. Additionally, existing SNN frameworks often fail to effectively handle multi-scale spatiotemporal features, leading to increased data redundancy and reduced accuracy. To address these issues, we propose CREST, a novel conjointly-trained spike-driven framework to exploit spatiotemporal dynamics in event-based object detection. We introduce the conjoint learning rule to accelerate SNN learning and alleviate gradient vanishing. It also supports dual operation modes for efficient and flexible implementation on different hardware types. Additionally, CREST features a fully spike-driven framework with a multi-scale spatiotemporal event integrator (MESTOR) and a spatiotemporal-IoU (ST-IoU) loss. Our approach achieves superior object recognition & detection performance and up to 100X energy efficiency compared with state-of-the-art SNN algorithms on three datasets, providing an efficient solution for event-based object detection algorithms suitable for SNN hardware implementation.
Optimal Gradient Checkpointing for Sparse and Recurrent Architectures using Off-Chip Memory
2024-12-16
Wadjih Bencheikh, Jan Finkbeiner, Emre Neftci
Recurrent neural networks (RNNs) are valued for their computational efficiency and reduced memory requirements on tasks involving long sequence lengths but require high memory-processor bandwidth to train. Checkpointing techniques can reduce the memory requirements by only storing a subset of intermediate states, the checkpoints, but are still rarely used due to the computational overhead of the additional recomputation phase. This work addresses these challenges by introducing memory-efficient gradient checkpointing strategies tailored for the general class of sparse RNNs and Spiking Neural Networks (SNNs). SNNs are energy efficient alternatives to RNNs thanks to their local, event-driven operation and potential neuromorphic implementation. We use the Intelligence Processing Unit (IPU) as an exemplary platform for architectures with distributed local memory. We exploit its suitability for sparse and irregular workloads to scale SNN training on long sequence lengths. We find that Double Checkpointing emerges as the most effective method, optimizing the use of local memory resources while minimizing recomputation overhead. This approach reduces dependency on slower large-scale memory access, enabling training on sequences over 10 times longer or 4 times larger networks than previously feasible, with only marginal time overhead. The presented techniques demonstrate significant potential to enhance scalability and efficiency in training sparse and recurrent networks across diverse hardware platforms, and highlights the benefits of sparse activations for scalable recurrent neural network training.
DriveGazen: Event-Based Driving Status Recognition using Conventional Camera
2024-12-16
Xiaoyin Yang
We introduce a wearable driving status recognition device and our open-source dataset, along with a new real-time method robust to changes in lighting conditions for identifying driving status from eye observations of drivers. The core of our method is generating event frames from conventional intensity frames, and the other is a newly designed Attention Driving State Network (ADSN). Compared to event cameras, conventional cameras offer complete information and lower hardware costs, enabling captured frames to encode rich spatial information. However, these textures lack temporal information, posing challenges in effectively identifying driving status. DriveGazen addresses this issue from three perspectives. First, we utilize video frames to generate realistic synthetic dynamic vision sensor (DVS) events. Second, we adopt a spiking neural network to decode pertinent temporal information. Lastly, ADSN extracts crucial spatial cues from corresponding intensity frames and conveys spatial attention to convolutional spiking layers during both training and inference through a novel guide attention module to guide the feature learning and feature enhancement of the event frame. We specifically collected the Driving Status (DriveGaze) dataset to demonstrate the effectiveness of our approach. Additionally, we validate the superiority of the DriveGazen on the Single-eye Event-based Emotion (SEE) dataset. To the best of our knowledge, our method is the first to utilize guide attention spiking neural networks and eye-based event frames generated from conventional cameras for driving status recognition. Please refer to our project page for more details: https://github.com/TooyoungALEX/AAAI25-DriveGazen.
Deployment Pipeline from Rockpool to Xylo for Edge Computing
2024-12-15
Peng Zhou, Dylan R. Muir
Deploying Spiking Neural Networks (SNNs) on the Xylo neuromorphic chip via the Rockpool framework represents a significant advancement in achieving ultra-low-power consumption and high computational efficiency for edge applications. This paper details a novel deployment pipeline, emphasizing the integration of Rockpool's capabilities with Xylo's architecture, and evaluates the system's performance in terms of energy efficiency and accuracy. The unique advantages of the Xylo chip, including its digital spiking architecture and event-driven processing model, are highlighted to demonstrate its suitability for real-time, power-sensitive applications.
Spiking Neural Networks (SNNs) are emerging as a promising alternative to Artificial Neural Networks (ANNs) due to their inherent energy efficiency. Owing to the inherent sparsity in spike generation within SNNs, the in-depth analysis and optimization of intermediate output spikes are often neglected. This oversight significantly restricts the inherent energy efficiency of SNNs and diminishes their advantages in spatiotemporal feature extraction, resulting in a lack of accuracy and unnecessary energy expenditure. In this work, we analyze the inherent spiking characteristics of SNNs from both temporal and spatial perspectives. In terms of spatial analysis, we find that shallow layers tend to focus on learning vertical variations, while deeper layers gradually learn horizontal variations of features. Regarding temporal analysis, we observe that there is not a significant difference in feature learning across different time steps. This suggests that increasing the time steps has limited effect on feature learning. Based on the insights derived from these analyses, we propose a Frequency-based Spatial-Temporal Attention (FSTA) module to enhance feature learning in SNNs. This module aims to improve the feature learning capabilities by suppressing redundant spike features.The experimental results indicate that the introduction of the FSTA module significantly reduces the spike firing rate of SNNs, demonstrating superior performance compared to state-of-the-art baselines across multiple datasets.
Neuromorphic in-memory computing requires area-efficient architecture for seamless and low latency parallel processing of large volumes of data. Here, we report a compact, vertically integrated/stratified field-effect transistor (VSFET) consisting of a 2D non-ferroelectric MoS$_2$ FET channel stacked on a 2D ferroelectric In$_2$Se$_3$ FET channel. Electrostatic coupling between the ferroelectric and non-ferroelectric semiconducting channels results in hysteretic transfer and output characteristics of both FETs. The gate-controlled MoS$_2$ memtransistor is shown to emulate homosynaptic plasticity behavior with low nonlinearity, low epoch, and high accuracy supervised (ANN - artificial neural network) and unsupervised (SNN - spiking neural network) on-chip learning. Further, simultaneous measurements of the MoS$_2$ and In$_2$Se$_3$ transistor synapses help realize complex heterosynaptic cooperation and competition behaviors. These are shown to mimic advanced sensorimotor neural network-controlled gill withdrawal reflex sensitization and habituation of a sea mollusk (Aplysia) with ultra-low power consumption. Finally, we show logic reconfigurability of the VSFET to realize Boolean gates thereby adding significant design flexibility for advanced computing technologies.
Sharpening Your Density Fields: Spiking Neuron Aided Fast Geometry Learning
2024-12-13
Yi Gu, Zhaorui Wang, Dongjun Ye, Renjing Xu
Neural Radiance Fields (NeRF) have achieved remarkable progress in neural rendering. Extracting geometry from NeRF typically relies on the Marching Cubes algorithm, which uses a hand-crafted threshold to define the level set. However, this threshold-based approach requires laborious and scenario-specific tuning, limiting its practicality for real-world applications. In this work, we seek to enhance the efficiency of this method during the training time. To this end, we introduce a spiking neuron mechanism that dynamically adjusts the threshold, eliminating the need for manual selection. Despite its promise, directly training with the spiking neuron often results in model collapse and noisy outputs. To overcome these challenges, we propose a round-robin strategy that stabilizes the training process and enables the geometry network to achieve a sharper and more precise density distribution with minimal computational overhead. We validate our approach through extensive experiments on both synthetic and real-world datasets. The results show that our method significantly improves the performance of threshold-based techniques, offering a more robust and efficient solution for NeRF geometry extraction.
Graph-based representations for samples of computational mechanics-related datasets can prove instrumental when dealing with problems like irregular domains or molecular structures of materials, etc. To effectively analyze and process such datasets, deep learning offers Graph Neural Networks (GNNs) that utilize techniques like message-passing within their architecture. The issue, however, is that as the individual graph scales and/ or GNN architecture becomes increasingly complex, the increased energy budget of the overall deep learning model makes it unsustainable and restricts its applications in applications like edge computing. To overcome this, we propose in this paper Hybrid Variable Spiking Graph Neural Networks (HVS-GNNs) that utilize Variable Spiking Neurons (VSNs) within their architecture to promote sparse communication and hence reduce the overall energy budget. VSNs, while promoting sparse event-driven computations, also perform well for regression tasks, which are often encountered in computational mechanics applications and are the main target of this paper. Three examples dealing with prediction of mechanical properties of material based on microscale/ mesoscale structures are shown to test the performance of the proposed HVS-GNNs in regression tasks. We have also compared the performance of HVS-GNN architectures with the performance of vanilla GNNs and GNNs utilizing leaky integrate and fire neurons. The results produced show that HVS-GNNs perform well for regression tasks, all while promoting sparse communication and, hence, energy efficiency.
Distribution free uncertainty quantification in neuroscience-inspired deep operators
2024-12-12
Shailesh Garg, Souvik Chakraborty
Energy-efficient deep learning algorithms are essential for a sustainable future and feasible edge computing setups. Spiking neural networks (SNNs), inspired from neuroscience, are a positive step in the direction of achieving the required energy efficiency. However, in a bid to lower the energy requirements, accuracy is marginally sacrificed. Hence, predictions of such deep learning algorithms require an uncertainty measure that can inform users regarding the bounds of a certain output. In this paper, we introduce the Conformalized Randomized Prior Operator (CRP-O) framework that leverages Randomized Prior (RP) networks and Split Conformal Prediction (SCP) to quantify uncertainty in both conventional and spiking neural operators. To further enable zero-shot super-resolution in UQ, we propose an extension incorporating Gaussian Process Regression. This enhanced super-resolution-enabled CRP-O framework is integrated with the recently developed Variable Spiking Wavelet Neural Operator (VSWNO). To test the performance of the obtained calibrated uncertainty bounds, we discuss four different examples covering both one-dimensional and two-dimensional partial differential equations. Results demonstrate that the uncertainty bounds produced by the conformalized RP-VSWNO significantly enhance UQ estimates compared to vanilla RP-VSWNO, Quantile WNO (Q-WNO), and Conformalized Quantile WNO (CQ-WNO). These findings underscore the potential of the proposed approach for practical applications.
High-Speed Time Series Prediction with a GHz-rate Photonic Spiking Neural Network built with a single VCSEL
2024-12-12
Dafydd Owen-Newns, Lina Jaurigue, Josh Robertson, Andrew Adair, Jonnel Anthony Jaurigue, Kathy Lüdge, Antonio Hurtado
Photonic technologies hold significant potential for creating innovative, high-speed, efficient and hardware-friendly neuromorphic computing platforms. Neuromorphic photonic methods leveraging ubiquitous, technologically mature and cost-effective Vertical-Cavity Surface Emitting Lasers (VCSELs) are of notable interest. VCSELs have demonstrated the capability to replicate neuronal optical spiking responses at ultrafast rates. These characteristics have triggered research into applying these key-enabling devices in spike-based photonic computing. Here, a GHz-rate photonic Spiking Neural Network (p-SNN) using a single VCSEL is reported, and its application to a complex time-series prediction task is demonstrated for the first time. The VCSEL p-SNN combined with a technique to induce network memory, is applied to perform multi-step-ahead predictions of a chaotic time-series. By providing the feedforward p-SNN with only two temporally separated inputs excellent accuracy is experimentally demonstrated over a range of prediction horizons. VCSEL-based p-SNNs therefore offer ultrafast, efficient operation in complex predictive tasks whilst enabling hardware implementations. The inherent attributes and performance of VCSEL p-SNNs hold great promise for use in future light-enabled neuromorphic computing hardware.
eCARLA-scenes: A synthetically generated dataset for event-based optical flow prediction
2024-12-12
Jad Mansour, Hayat Rajani, Rafael Garcia, Nuno Gracias
The joint use of event-based vision and Spiking Neural Networks (SNNs) is expected to have a large impact in robotics in the near future, in tasks such as, visual odometry and obstacle avoidance. While researchers have used real-world event datasets for optical flow prediction (mostly captured with Unmanned Aerial Vehicles (UAVs)), these datasets are limited in diversity, scalability, and are challenging to collect. Thus, synthetic datasets offer a scalable alternative by bridging the gap between reality and simulation. In this work, we address the lack of datasets by introducing eWiz, a comprehensive library for processing event-based data. It includes tools for data loading, augmentation, visualization, encoding, and generation of training data, along with loss functions and performance metrics. We further present a synthetic event-based datasets and data generation pipelines for optical flow prediction tasks. Built on top of eWiz, eCARLA-scenes makes use of the CARLA simulator to simulate self-driving car scenarios. The ultimate goal of this dataset is the depiction of diverse environments while laying a foundation for advancing event-based camera applications in autonomous field vehicle navigation, paving the way for using SNNs on neuromorphic hardware such as the Intel Loihi.
In-memory computing (IMC) architectures mitigate the von Neumann bottleneck encountered in traditional deep learning accelerators. Its energy efficiency can realize deep learning-based edge applications. However, because IMC is implemented using analog circuits, inherent non-idealities in the hardware pose significant challenges. This paper presents physical neural networks (PNNs) for constructing physical models of IMC. PNNs can address the synaptic current's dependence on membrane potential, a challenge in charge-domain IMC systems. The proposed model is mathematically equivalent to spiking neural networks with reversal potentials. With a novel technique called differentiable spike-time discretization, the PNNs are efficiently trained. We show that hardware non-idealities traditionally viewed as detrimental can enhance the model's learning performance. This bottom-up methodology was validated by designing an IMC circuit with non-ideal characteristics using the sky130 process. When employing this bottom-up approach, the modeling error reduced by an order of magnitude compared to conventional top-down methods in post-layout simulations.
Grothendieck Graph Neural Networks Framework: An Algebraic Platform for Crafting Topology-Aware GNNs
2024-12-12
Amirreza Shiralinasab Langari, Leila Yeganeh, Kim Khoa Nguyen
Due to the structural limitations of Graph Neural Networks (GNNs), in particular with respect to conventional neighborhoods, alternative aggregation strategies have recently been investigated. This paper investigates graph structure in message passing, aimed to incorporate topological characteristics. While the simplicity of neighborhoods remains alluring, we propose a novel perspective by introducing the concept of 'cover' as a generalization of neighborhoods. We design the Grothendieck Graph Neural Networks (GGNN) framework, offering an algebraic platform for creating and refining diverse covers for graphs. This framework translates covers into matrix forms, such as the adjacency matrix, expanding the scope of designing GNN models based on desired message-passing strategies. Leveraging algebraic tools, GGNN facilitates the creation of models that outperform traditional approaches. Based on the GGNN framework, we propose Sieve Neural Networks (SNN), a new GNN model that leverages the notion of sieves from category theory. SNN demonstrates outstanding performance in experiments, particularly on benchmarks designed to test the expressivity of GNNs, and exemplifies the versatility of GGNN in generating novel architectures.
Efficient 3D Recognition with Event-driven Spike Sparse Convolution
2024-12-10
Xuerui Qiu, Man Yao, Jieyuan Zhang, Yuhong Chou, Ning Qiao, Shibo Zhou, Bo Xu, Guoqi Li
Spiking Neural Networks (SNNs) provide an energy-efficient way to extract 3D spatio-temporal features. Point clouds are sparse 3D spatial data, which suggests that SNNs should be well-suited for processing them. However, when applying SNNs to point clouds, they often exhibit limited performance and fewer application scenarios. We attribute this to inappropriate preprocessing and feature extraction methods. To address this issue, we first introduce the Spike Voxel Coding (SVC) scheme, which encodes the 3D point clouds into a sparse spike train space, reducing the storage requirements and saving time on point cloud preprocessing. Then, we propose a Spike Sparse Convolution (SSC) model for efficiently extracting 3D sparse point cloud features. Combining SVC and SSC, we design an efficient 3D SNN backbone (E-3DSNN), which is friendly with neuromorphic hardware. For instance, SSC can be implemented on neuromorphic chips with only minor modifications to the addressing function of vanilla spike convolution. Experiments on ModelNet40, KITTI, and Semantic KITTI datasets demonstrate that E-3DSNN achieves state-of-the-art (SOTA) results with remarkable efficiency. Notably, our E-3DSNN (1.87M) obtained 91.7\% top-1 accuracy on ModelNet40, surpassing the current best SNN baselines (14.3M) by 3.0\%. To our best knowledge, it is the first direct training 3D SNN backbone that can simultaneously handle various 3D computer vision tasks (e.g., classification, detection, and segmentation) with an event-driven nature. Code is available: https://github.com/bollossom/E-3DSNN/.
Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Dataset
2024-12-09
Xiao Wang, Yu Jin, Wentao Wu, Wei Zhang, Lin Zhu, Bo Jiang, Yonghong Tian
Object detection in event streams has emerged as a cutting-edge research area, demonstrating superior performance in low-light conditions, scenarios with motion blur, and rapid movements. Current detectors leverage spiking neural networks, Transformers, or convolutional neural networks as their core architectures, each with its own set of limitations including restricted performance, high computational overhead, or limited local receptive fields. This paper introduces a novel MoE (Mixture of Experts) heat conduction-based object detection algorithm that strikingly balances accuracy and computational efficiency. Initially, we employ a stem network for event data embedding, followed by processing through our innovative MoE-HCO blocks. Each block integrates various expert modules to mimic heat conduction within event streams. Subsequently, an IoU-based query selection module is utilized for efficient token extraction, which is then channeled into a detection head for the final object detection process. Furthermore, we are pleased to introduce EvDET200K, a novel benchmark dataset for event-based object detection. Captured with a high-definition Prophesee EVK4-HD event camera, this dataset encompasses 10 distinct categories, 200,000 bounding boxes, and 10,054 samples, each spanning 2 to 5 seconds. We also provide comprehensive results from over 15 state-of-the-art detectors, offering a solid foundation for future research and comparison. The source code of this paper will be released on: https://github.com/Event-AHU/OpenEvDET
Recent advances in spiking neural networks (SNNs) have a predominant focus on network architectures, while relatively little attention has been paid to the underlying neuron model. The point neuron models, a cornerstone of deep SNNs, pose a bottleneck on the network-level expressivity since they depict somatic dynamics only. In contrast, the multi-compartment models in neuroscience offer remarkable expressivity by introducing dendritic morphology and dynamics, but remain underexplored in deep learning due to their unaffordable computational cost and inflexibility. To combine the advantages of both sides for a flexible, efficient yet more powerful model, we propose the dendritic spiking neuron (DendSN) incorporating multiple dendritic branches with nonlinear dynamics. Compared to the point spiking neurons, DendSN exhibits significantly higher expressivity. DendSN's flexibility enables its seamless integration into diverse deep SNN architectures. To accelerate dendritic SNNs (DendSNNs), we parallelize dendritic state updates across time steps, and develop Triton kernels for GPU-level acceleration. As a result, we can construct large-scale DendSNNs with depth comparable to their point SNN counterparts. Next, we comprehensively evaluate DendSNNs' performance on various demanding tasks. By modulating dendritic branch strengths using a context signal, catastrophic forgetting of DendSNNs is substantially mitigated. Moreover, DendSNNs demonstrate enhanced robustness against noise and adversarial attacks compared to point SNNs, and excel in few-shot learning settings. Our work firstly demonstrates the possibility of training bio-plausible dendritic SNNs with depths and scales comparable to traditional point SNNs, and reveals superior expressivity and robustness of reduced dendritic neuron models in deep learning, thereby offering a fresh perspective on advancing neural network design.
Spiking Neural Networks for Radio Frequency Interference Detection in Radio Astronomy
2024-12-09
Nicholas J. Pritchard, Andreas Wicenec, Mohammed Bennamoun, Richard Dodson
Spiking Neural Networks (SNNs) promise efficient spatio-temporal data processing owing to their dynamic nature. This paper addresses a significant challenge in radio astronomy, Radio Frequency Interference (RFI) detection, by reformulating it as a time-series segmentation task inherently suited for SNN execution. Automated RFI detection systems capable of real-time operation with minimal energy consumption are increasingly important in modern radio telescopes. We explore several spectrogram-to-spike encoding methods and network parameters, applying first-order leaky integrate-and-fire SNNs to tackle RFI detection. To enhance the contrast between RFI and background information, we introduce a divisive normalisation-inspired pre-processing step, which improves detection performance across multiple encoding strategies. Our approach achieves competitive performance on a synthetic dataset and compelling results on real data from the Low-Frequency Array (LOFAR) instrument. To our knowledge, this work is the first to train SNNs on real radio astronomy data successfully. These findings highlight the potential of SNNs for performing complex time-series tasks, paving the way for efficient, real-time processing in radio astronomy and other data-intensive fields.
Spiking Neural Networks(SNNs) provide a brain-inspired and event-driven mechanism that is believed to be critical to unlock energy-efficient deep learning. The mixture-of-experts approach mirrors the parallel distributed processing of nervous systems, introducing conditional computation policies and expanding model capacity without scaling up the number of computational operations. Additionally, spiking mixture-of-experts self-attention mechanisms enhance representation capacity, effectively capturing diverse patterns of entities and dependencies between visual or linguistic tokens. However, there is currently a lack of hardware support for highly parallel distributed processing needed by spiking transformers, which embody a brain-inspired computation. This paper introduces the first 3D hardware architecture and design methodology for Mixture-of-Experts and Multi-Head Attention spiking transformers. By leveraging 3D integration with memory-on-logic and logic-on-logic stacking, we explore such brain-inspired accelerators with spatially stackable circuitry, demonstrating significant optimization of energy efficiency and latency compared to conventional 2D CMOS integration.
Trimming Down Large Spiking Vision Transformers via Heterogeneous Quantization Search
2024-12-07
Boxun Xu, Yufei Song, Peng Li
Spiking Neural Networks (SNNs) are amenable to deployment on edge devices and neuromorphic hardware due to their lower dissipation. Recently, SNN-based transformers have garnered significant interest, incorporating attention mechanisms akin to their counterparts in Artificial Neural Networks (ANNs) while demonstrating excellent performance. However, deploying large spiking transformer models on resource-constrained edge devices such as mobile phones, still poses significant challenges resulted from the high computational demands of large uncompressed high-precision models. In this work, we introduce a novel heterogeneous quantization method for compressing spiking transformers through layer-wise quantization. Our approach optimizes the quantization of each layer using one of two distinct quantization schemes, i.e., uniform or power-of-two quantification, with mixed bit resolutions. Our heterogeneous quantization demonstrates the feasibility of maintaining high performance for spiking transformers while utilizing an average effective resolution of 3.14-3.67 bits with less than a 1% accuracy drop on DVS Gesture and CIFAR10-DVS datasets. It attains a model compression rate of 8.71x-10.19x for standard floating-point spiking transformers. Moreover, the proposed approach achieves a significant energy reduction of 5.69x, 8.72x, and 10.2x while maintaining high accuracy levels of 85.3%, 97.57%, and 80.4% on N-Caltech101, DVS-Gesture, and CIFAR10-DVS datasets, respectively.
MTSpark: Enabling Multi-Task Learning with Spiking Neural Networks for Generalist Agents
2024-12-06
Avaneesh Devkota, Rachmad Vidya Wicaksana Putra, Muhammad Shafique
Currently, state-of-the-art RL methods excel in single-task settings, but they still struggle to generalize across multiple tasks due to catastrophic forgetting challenges, where previously learned tasks are forgotten as new tasks are introduced. This multi-task learning capability is significantly important for generalist agents, where adaptation features are highly required (e.g., autonomous robots). On the other hand, Spiking Neural Networks (SNNs) have emerged as alternative energy-efficient neural network algorithms due to their sparse spike-based operations. Toward this, we propose MTSpark, a novel methodology to enable multi-task RL using spiking networks. Specifically, MTSpark develops a Deep Spiking Q-Network (DSQN) with active dendrites and dueling structure by leveraging task-specific context signals. Specifically, each neuron computes task-dependent activations that dynamically modulate inputs, forming specialized sub-networks for each task. Moreover, this bioplausible network model also benefits from SNNs, enhancing energy efficiency and making the model suitable for hardware implementation. Experimental results show that, our MTSpark effectively learns multiple tasks with higher performance compared to the state-of-the-art. Specifically, MTSpark successfully achieves high score in three Atari games (i.e., Pong: -5.4, Breakout: 0.6, and Enduro: 371.2), reaching human-level performance (i.e., Pong: -3, Breakout: 31, and Enduro: 368), where state-of-the-art struggle to achieve. In addition, our MTSpark also shows better accuracy in image classification tasks than the state-of-the-art. These results highlight the potential of our MTSpark methodology to develop generalist agents that can learn multiple tasks by leveraging both RL and SNN concepts.
Deep-Unrolling Multidimensional Harmonic Retrieval Algorithms on Neuromorphic Hardware
2024-12-05
Vlad C. Andrei, Alexandru P. Drăguţoiu, Gabriel Béna, Mahmoud Akl, Yin Li, Matthias Lohrmann, Ullrich J. Mönich, Holger Boche
This paper explores the potential of conversion-based neuromorphic algorithms for highly accurate and energy-efficient single-snapshot multidimensional harmonic retrieval (MHR). By casting the MHR problem as a sparse recovery problem, we devise the currently proposed, deep-unrolling-based Structured Learned Iterative Shrinkage and Thresholding (S-LISTA) algorithm to solve it efficiently using complex-valued convolutional neural networks with complex-valued activations, which are trained using a supervised regression objective. Afterward, a novel method for converting the complex-valued convolutional layers and activations into spiking neural networks (SNNs) is developed. At the heart of this method lies the recently proposed Few Spikes (FS) conversion, which is extended by modifying the neuron model's parameters and internal dynamics to account for the inherent coupling between real and imaginary parts in complex-valued computations. Finally, the converted SNNs are mapped onto the SpiNNaker2 neuromorphic board, and a comparison in terms of estimation accuracy and power efficiency between the original CNNs deployed on an NVIDIA Jetson Xavier and the SNNs is being conducted. The measurement results show that the converted SNNs achieve almost five-fold power efficiency at moderate performance loss compared to the original CNNs.
Short-reach Optical Communications: A Real-world Task for Neuromorphic Hardware
2024-12-04
Elias Arnold, Eike-Manuel Edelmann, Alexander von Bank, Eric Müller, Laurent Schmalen, Johannes Schemmel
Spiking neural networks (SNNs) emulated on dedicated neuromorphic accelerators promise to offer energy-efficient signal processing. However, the neuromorphic advantage over traditional algorithms still remains to be demonstrated in real-world applications. Here, we describe an intensity-modulation, direct-detection (IM/DD) task that is relevant to high-speed optical communication systems used in data centers. Compared to other machine learning-inspired benchmarks, the task offers several advantages. First, the dataset is inherently time-dependent, i.e., there is a time dimension that can be natively mapped to the dynamic evolution of SNNs. Second, small-scale SNNs can achieve the target accuracy required by technical communication standards. Third, due to the small scale and the defined target accuracy, the task facilitates the optimization for real-world aspects, such as energy efficiency, resource requirements, and system complexity.
Integrating programmable plasticity in experiment descriptions for analog neuromorphic hardware
2024-12-04
Philipp Spilger, Eric Müller, Johannes Schemmel
The study of plasticity in spiking neural networks is an active area of research. However, simulations that involve complex plasticity rules, dense connectivity/high synapse counts, complex neuron morphologies, or extended simulation times can be computationally demanding. The BrainScaleS-2 neuromorphic architecture has been designed to address this challenge by supporting "hybrid" plasticity, which combines the concepts of programmability and inherently parallel emulation. In particular, observables that are expensive in numerical simulation, such as per-synapse correlation measurements, are implemented directly in the synapse circuits. The evaluation of the observables, the decision to perform an update, and the magnitude of an update, are all conducted in a conventional program that runs simultaneously with the analog neural network. Consequently, these systems can offer a scalable and flexible solution in such cases. While previous work on the platform has already reported on the use of different kinds of plasticity, the descriptions for the spiking neural network experiment topology and protocol, and the plasticity algorithm have not been connected. In this work, we introduce an integrated framework for describing spiking neural network experiments and plasticity rules in a unified high-level experiment description language for the BrainScaleS-2 platform and demonstrate its use.
FL-QDSNNs: Federated Learning with Quantum Dynamic Spiking Neural Networks
2024-12-03
Nouhaila Innan, Alberto Marchisio, Muhammad Shafique
This paper introduces the Federated Learning-Quantum Dynamic Spiking Neural Networks (FL-QDSNNs) framework, an innovative approach specifically designed to tackle significant challenges in distributed learning systems, such as maintaining high accuracy while ensuring privacy. Central to our framework is a novel dynamic threshold mechanism for activating quantum gates in Quantum Spiking Neural Networks (QSNNs), which mimics classical activation functions while uniquely exploiting quantum operations to enhance computational performance. This mechanism is essential for tackling the typical performance variability across dynamically changing data distributions, a prevalent challenge in conventional QSNNs applications. Validated through extensive testing on datasets including Iris, digits, and breast cancer, our FL-QDSNNs framework has demonstrated superior accuracies-up to 94% on the Iris dataset and markedly outperforms existing Quantum Federated Learning (QFL) approaches. Our results reveal that our FL-QDSNNs framework offers scalability with respect to the number of clients, provides improved learning capabilities, and represents a robust solution to privacy and efficiency limitations posed by emerging quantum hardware and complex QSNNs training protocols. By fundamentally advancing the operational capabilities of QSNNs in real-world distributed environments, this framework can potentially redefine the application landscape of quantum computing in sensitive and critical sectors, ensuring enhanced data security and system performance.
Gated Parametric Neuron for Spike-based Audio Recognition
2024-12-02
Haoran Wang, Herui Zhang, Siyang Li, Dongrui Wu
Spiking neural networks (SNNs) aim to simulate real neural networks in the human brain with biologically plausible neurons. The leaky integrate-and-fire (LIF) neuron is one of the most widely studied SNN architectures. However, it has the vanishing gradient problem when trained with backpropagation. Additionally, its neuronal parameters are often manually specified and fixed, in contrast to the heterogeneity of real neurons in the human brain. This paper proposes a gated parametric neuron (GPN) to process spatio-temporal information effectively with the gating mechanism. Compared with the LIF neuron, the GPN has two distinguishing advantages: 1) it copes well with the vanishing gradients by improving the flow of gradient propagation; and, 2) it learns spatio-temporal heterogeneous neuronal parameters automatically. Additionally, we use the same gate structure to eliminate initial neuronal parameter selection and design a hybrid recurrent neural network-SNN structure. Experiments on two spike-based audio datasets demonstrated that the GPN network outperformed several state-of-the-art SNNs, could mitigate vanishing gradients, and had spatio-temporal heterogeneous parameters. Our work shows the ability of SNNs to handle long-term dependencies and achieve high performance simultaneously.
The Reliability Issue in ReRam-based CIM Architecture for SNN: A Survey
2024-11-30
Wei-Ting Chen
The increasing complexity and energy demands of deep learning models have highlighted the limitations of traditional computing architectures, especially for edge devices with constrained resources. Spiking Neural Networks (SNNs) offer a promising alternative by mimicking biological neural networks, enabling energy-efficient computation through event-driven processing and temporal encoding. Concurrently, emerging hardware technologies like Resistive Random Access Memory (ReRAM) and Compute-in-Memory (CIM) architectures aim to overcome the Von Neumann bottleneck by integrating storage and computation. This survey explores the intersection of SNNs and ReRAM-based CIM architectures, focusing on the reliability challenges that arise from device-level variations and operational errors. We review the fundamental principles of SNNs and ReRAM crossbar arrays, discuss the inherent reliability issues in both technologies, and summarize existing solutions to mitigate these challenges.
The role of inhibitory neuronal variability in modulating phase diversity between coupled networks
2024-11-29
Katiele V. P. Brito, Joana M. G. L. Silva, Claudio R. Mirasso, Fernanda S. Matias
Neuronal heterogeneity, characterized by the presence of a multitude of spiking neuronal patterns, is a widespread phenomenon throughout the nervous system. In particular, the brain exhibits strong variability among inhibitory neurons. Despite the huge neuronal heterogeneity across brain regions, which in principle could decrease synchronization, cortical areas coherently oscillate during various cognitive tasks. Therefore, the functional significance of neuronal heterogeneity remains a subject of active investigation. Previous studies typically focus on the role of heterogeneity in the dynamic properties of only one population. Here, we explore how different types of inhibitory neurons can contribute to the diversity of the phase relations between two cortical areas. This research sheds light on the potential impact of local properties, such as neuronal variability, on communication between distant brain regions. We show that both homogeneous and heterogeneous inhibitory networks can exhibit phase diversity and nonintuitive regimes such as anticipated synchronization (AS) and phase bistability. It has been proposed that the bi-stable phase could be related to bi-stable perception, such as in the Necker cube. Moreover, we show that heterogeneity enlarges the region of zero-lag synchronization and bistability. We also show that the parameter controlling inhibitory heterogeneity modulates the transition from the usual delayed synchronization regime (DS) to AS. Finally, we show that the inhibitory heterogeneity drives the internal dynamics of the free-running population. Therefore, we suggest a possible mechanism to explain when the DS-AS transition occurs via zero-lag synchronization or bi-stability.
With the increasing application scope of spiking neural networks (SNN), the complexity of SNN models has surged, leading to an exponential growth in demand for AI computility. As the new generation computing architecture of the neural networks, the efficiency and power consumption of distributed storage and parallel computing in the many-core near-memory computing system have attracted much attention. Among them, the mapping problem from logical cores to physical cores is one of the research hotspots. In order to improve the computing parallelism and system throughput of the many-core near-memory computing system, and to reduce power consumption, we propose a SNN training many-core deployment optimization method based on Off-policy Deterministic Actor-Critic. We utilize deep reinforcement learning as a nonlinear optimizer, treating the many-core topology as network graph features and using graph convolution to input the many-core structure into the policy network. We update the parameters of the policy network through near-end policy optimization to achieve deployment optimization of SNN models in the many-core near-memory computing architecture to reduce chip power consumption. To handle large-dimensional action spaces, we use continuous values matching the number of cores as the output of the policy network and then discretize them again to obtain new deployment schemes. Furthermore, to further balance inter-core computation latency and improve system throughput, we propose a model partitioning method with a balanced storage and computation strategy. Our method overcomes the problems such as uneven computation and storage loads between cores, and the formation of local communication hotspots, significantly reducing model training time, communication costs, and average flow load between cores in the many-core near-memory computing architecture.
In integrated circuit design, the analysis of wafer map patterns is critical to improve yield and detect manufacturing issues. We develop Wafer2Spike, an architecture for wafer map pattern classification using a spiking neural network (SNN), and demonstrate that a well-trained SNN achieves superior performance compared to deep neural network-based solutions. Wafer2Spike achieves an average classification accuracy of 98\% on the WM-811k wafer benchmark dataset. It is also superior to existing approaches for classifying defect patterns that are underrepresented in the original dataset. Wafer2Spike achieves this improved precision with great computational efficiency.
Average-Over-Time Spiking Neural Networks for Uncertainty Estimation in Regression
2024-11-29
Tao Sun, Sander Bohté
Uncertainty estimation is a standard tool to quantify the reliability of modern deep learning models, and crucial for many real-world applications. However, efficient uncertainty estimation methods for spiking neural networks, particularly for regression models, have been lacking. Here, we introduce two methods that adapt the Average-Over-Time Spiking Neural Network (AOT-SNN) framework to regression tasks, enhancing uncertainty estimation in event-driven models. The first method uses the heteroscedastic Gaussian approach, where SNNs predict both the mean and variance at each time step, thereby generating a conditional probability distribution of the target variable. The second method leverages the Regression-as-Classification (RAC) approach, reformulating regression as a classification problem to facilitate uncertainty estimation. We evaluate our approaches on both a toy dataset and several benchmark datasets, demonstrating that the proposed AOT-SNN models achieve performance comparable to or better than state-of-the-art deep neural network methods, particularly in uncertainty estimation. Our findings highlight the potential of SNNs for uncertainty estimation in regression tasks, providing an efficient and biologically inspired alternative for applications requiring both accuracy and energy efficiency.
Hybrid Spiking Neural Network -- Transformer Video Classification Model
2024-11-29
Aaron Bateni
In recent years, Spiking Neural Networks (SNNs) have gathered significant interest due to their temporal understanding capabilities. This work introduces, to the best of our knowledge, the first Cortical Column like hybrid architecture for the Time-Series Data Classification Task that leverages SNNs and is inspired by the brain structure, inspired from the previous hybrid models. We introduce several encoding methods to use with this model. Finally, we develop a procedure for training this network on the training dataset. As an effort to make using these models simpler, we make all the implementations available to the public.
In an era characterized by the rapid growth of data processing, developing new and efficient data processing technologies has become a priority. We address this by proposing a novel type of neuromorphic technology we call Fused-MemBrain. Our proposal is inspired by Golgi's theory modeling the brain as a syncytial continuum, in contrast to Cajal's theory of neurons and synapses being discrete elements. While Cajal's theory has long been the dominant and experimentally validated view of the nervous system, recent discoveries showed that a species of marine invertebrate (ctenophore Mnemiopsis leidyi) may be better described by Golgi's theory. The core idea is to develop hardware that functions analogously to a syncytial network, exploiting self-assembled memristive systems and combining them with CMOS technologies, interfacing with the silicon back-end-of-line. In this way, a memristive self-assembled material can cheaply and efficiently replace the synaptic connections between CMOS neuron implementations in neuromorphic hardware, enhancing the capability of massively parallel computation. The fusion of CMOS circuits with a memristive ``plexus'' allows information transfer without requiring engineered synapses, which typically consume significant area. As the first step toward this ambitious goal, we present a simulation of a memristive network interfaced with spiking neural networks. Additionally, we describe the potential benefits of such a system, along with key technical aspects it should incorporate.
A spiking photonic neural network of 40.000 neurons, trained with rank-order coding for leveraging sparsity
2024-11-28
Ria Talukder, Anas Skalli, Xavier Porte, Simon Thorpe, Daniel Brunner
piking neural networks are neuromorphic systems that emulate certain aspects of biological neurons, offering potential advantages in energy efficiency and speed by for example leveraging sparsity. While CMOS-based electronic SNN hardware has shown promise, scalability and parallelism challenges remain. Photonics provides a promising platform for SNNs due to the speed of excitable photonic devices standing in as neurons and the parallelism and low-latency of optical signal conduction. Here, we present a photonic SNN comprising 40,000 neurons using off-the-shelf components, including a spatial light modulator and a CMOS camera, enabling scalable and cost-effective implementations for photonic SNN proof of concept studies. The system is governed by a modified Ikeda map, were adding additional inhibitory feedback forcing introduces excitability akin to biological dynamics. Using latency encoding and sparsity, the network achieves 83.5% accuracy on MNIST using 22% of neurons, and 77.5% with 8.5% neuron utilization. Training is performed via liquid state machine concepts combined with the hardware-compatible SPSA algorithm, marking its first use in photonic neural networks. This demonstration integrates photonic nonlinearity, excitability, and sparse computation, paving the way for efficient large-scale photonic neuromorphic systems.
NeoHebbian Synapses to Accelerate Online Training of Neuromorphic Hardware
2024-11-27
Shubham Pande, Sai Sukruth Bezugam, Tinish Bhattacharya, Ewelina Wlazlak, Anjan Chakaravorty, Bhaswar Chakrabarti, Dmitri Strukov
Neuromorphic systems that employ advanced synaptic learning rules, such as the three-factor learning rule, require synaptic devices of increased complexity. Herein, a novel neoHebbian artificial synapse utilizing ReRAM devices has been proposed and experimentally validated to meet this demand. This synapse features two distinct state variables: a neuron coupling weight and an "eligibility trace" that dictates synaptic weight updates. The coupling weight is encoded in the ReRAM conductance, while the "eligibility trace" is encoded in the local temperature of the ReRAM and is modulated by applying voltage pulses to a physically co-located resistive heating element. The utility of the proposed synapse has been investigated using two representative tasks: first, temporal signal classification using Recurrent Spiking Neural Networks (RSNNs) employing the e-prop algorithm, and second, Reinforcement Learning (RL) for path planning tasks in feedforward networks using a modified version of the same learning rule. System-level simulations, accounting for various device and system-level non-idealities, confirm that these synapses offer a robust solution for the fast, compact, and energy-efficient implementation of advanced learning rules in neuromorphic hardware.
IKUN: Initialization to Keep snn training and generalization great with sUrrogate-stable variaNce
2024-11-27
Da Chang, Deliang Wang, Xiao Yang
Weight initialization significantly impacts the convergence and performance of neural networks. While traditional methods like Xavier and Kaiming initialization are widely used, they often fall short for spiking neural networks (SNNs), which have distinct requirements compared to artificial neural networks (ANNs). To address this, we introduce \textbf{IKUN}, a variance-stabilizing initialization method integrated with surrogate gradient functions, specifically designed for SNNs. \textbf{IKUN} stabilizes signal propagation, accelerates convergence, and enhances generalization. Experiments show \textbf{IKUN} improves training efficiency by up to \textbf{50\%}, achieving \textbf{95\%} training accuracy and \textbf{91\%} generalization accuracy. Hessian analysis reveals that \textbf{IKUN}-trained models converge to flatter minima, characterized by Hessian eigenvalues near zero on the positive side, promoting better generalization. The method is open-sourced for further exploration: \href{https://github.com/MaeChd/SurrogateVarStabe}{https://github.com/MaeChd/SurrogateVarStabe}.
Investigating Plausibility of Biologically Inspired Bayesian Learning in ANNs
2024-11-27
Ram Zaveri
Catastrophic forgetting has been the leading issue in the domain of lifelong learning in artificial systems. Current artificial systems are reasonably good at learning domains they have seen before; however, as soon as they encounter something new, they either go through a significant performance deterioration or if you try to teach them the new distribution of data, they forget what they have learned before. Additionally, they are also prone to being overly confident when performing inference on seen as well as unseen data, causing significant reliability issues when lives are at stake. Therefore, it is extremely important to dig into this problem and formulate an approach that will be continually adaptable as well as reliable. If we move away from the engineering domain of such systems and look into biological systems, we can realize that these very systems are very efficient at computing the reliance as well as the uncertainty of accurate predictions that further help them refine the inference in a life-long setting. These systems are not perfect; however, they do give us a solid understanding of the reasoning under uncertainty which takes us to the domain of Bayesian reasoning. We incorporate this Bayesian inference with thresholding mechanism as to mimic more biologically inspired models, but only at spatial level. Further, we reproduce a recent study on Bayesian Inference with Spiking Neural Networks for Continual Learning to compare against it as a suitable biologically inspired Bayesian framework. Overall, we investigate the plausibility of biologically inspired Bayesian Learning in artificial systems on a vision dataset, MNIST, and show relative performance improvement under the conditions when the model is forced to predict VS when the model is not.
Combining the complementary benefits of frames and events has been widely used for object detection in challenging scenarios. However, most object detection methods use two independent Artificial Neural Network (ANN) branches, limiting cross-modality information interaction across the two visual streams and encountering challenges in extracting temporal cues from event streams with low power consumption. To address these challenges, we propose HDI-Former, a Hybrid Dynamic Interaction ANN-SNN Transformer, marking the first trial to design a directly trained hybrid ANN-SNN architecture for high-accuracy and energy-efficient object detection using frames and events. Technically, we first present a novel semantic-enhanced self-attention mechanism that strengthens the correlation between image encoding tokens within the ANN Transformer branch for better performance. Then, we design a Spiking Swin Transformer branch to model temporal cues from event streams with low power consumption. Finally, we propose a bio-inspired dynamic interaction mechanism between ANN and SNN sub-networks for cross-modality information interaction. The results demonstrate that our HDI-Former outperforms eleven state-of-the-art methods and our four baselines by a large margin. Our SNN branch also shows comparable performance to the ANN with the same architecture while consuming 10.57$\times$ less energy on the DSEC-Detection dataset. Our open-source code is available in the supplementary material.
Storing overlapping associative memories on latent manifolds in low-rank spiking networks
2024-11-26
William F. Podlaski, Christian K. Machens
Associative memory architectures such as the Hopfield network have long been important conceptual and theoretical models for neuroscience and artificial intelligence. However, translating these abstract models into spiking neural networks has been surprisingly difficult. Indeed, much previous work has been restricted to storing a small number of primarily non-overlapping memories in large networks, thereby limiting their scalability. Here, we revisit the associative memory problem in light of recent advances in understanding spike-based computation. Using a recently-established geometric framework, we show that the spiking activity for a large class of all-inhibitory networks is situated on a low-dimensional, convex, and piecewise-linear manifold, with dynamics that move along the manifold. We then map the associative memory problem onto these dynamics, and demonstrate how the vertices of a hypercubic manifold can be used to store stable, overlapping activity patterns with a direct correspondence to the original Hopfield model. We propose several learning rules, and demonstrate a linear scaling of the storage capacity with the number of neurons, as well as robust pattern completion abilities. Overall, this work serves as a case study to demonstrate the effectiveness of using a geometrical perspective to design dynamics on neural manifolds, with implications for neuroscience and machine learning.
SpikeAtConv: An Integrated Spiking-Convolutional Attention Architecture for Energy-Efficient Neuromorphic Vision Processing
2024-11-26
Wangdan Liao, Weidong Wang
Spiking Neural Networks (SNNs) offer a biologically inspired alternative to conventional artificial neural networks, with potential advantages in power efficiency due to their event-driven computation. Despite their promise, SNNs have yet to achieve competitive performance on complex visual tasks, such as image classification. This study introduces a novel SNN architecture designed to enhance computational efficacy and task accuracy. The architecture features optimized pulse modules that facilitate the processing of spatio-temporal patterns in visual data, aiming to reconcile the computational demands of high-level vision tasks with the energy-efficient processing of SNNs. Our evaluations on standard image classification benchmarks indicate that the proposed architecture narrows the performance gap with traditional neural networks, providing insights into the design of more efficient and capable neuromorphic computing systems.
Noise Adaptor: Enhancing Low-Latency Spiking Neural Networks through Noise-Injected Low-Bit ANN Conversion
2024-11-26
Chen Li, Bipin. Rajendran
We present Noise Adaptor, a novel method for constructing competitive low-latency spiking neural networks (SNNs) by converting noise-injected, low-bit artificial neural networks (ANNs). This approach builds on existing ANN-to-SNN conversion techniques but offers several key improvements: (1) By injecting noise during quantized ANN training, Noise Adaptor better accounts for the dynamic differences between ANNs and SNNs, significantly enhancing SNN accuracy. (2) Unlike previous methods, Noise Adaptor does not require the application of run-time noise correction techniques in SNNs, thereby avoiding modifications to the spiking neuron model and control flow during inference. (3) Our method extends the capability of handling deeper architectures, achieving successful conversions of activation-quantized ResNet-101 and ResNet-152 to SNNs. We demonstrate the effectiveness of our method on CIFAR-10 and ImageNet, achieving competitive performance. The code will be made available as open-source.
Event-based Spiking Neural Networks for Object Detection: A Review of Datasets, Architectures, Learning Rules, and Implementation
2024-11-26
Craig Iaboni, Pramod Abichandani
Spiking Neural Networks (SNNs) represent a biologically inspired paradigm offering an energy-efficient alternative to conventional artificial neural networks (ANNs) for Computer Vision (CV) applications. This paper presents a systematic review of datasets, architectures, learning methods, implementation techniques, and evaluation methodologies used in CV-based object detection tasks using SNNs. Based on an analysis of 151 journal and conference articles, the review codifies: 1) the effectiveness of fully connected, convolutional, and recurrent architectures; 2) the performance of direct unsupervised, direct supervised, and indirect learning methods; and 3) the trade-offs in energy consumption, latency, and memory in neuromorphic hardware implementations. An open-source repository along with detailed examples of Python code and resources for building SNN models, event-based data processing, and SNN simulations are provided. Key challenges in SNN training, hardware integration, and future directions for CV applications are also identified.
There is a growing necessity for edge training to adapt to dynamically changing environment. Neuromorphic computing represents a significant pathway for high-efficiency intelligent computation in energy-constrained edges, but existing neuromorphic architectures lack the ability of directly training spiking neural networks (SNNs) based on backpropagation. We develop a multi-core neuromorphic architecture with Feedforward-Propagation, Back-Propagation, and Weight-Gradient engines in each core, supporting high efficient parallel computing at both the engine and core levels. It combines various data flows and sparse computation optimization by fully leveraging the sparsity in SNN training, obtaining a high energy efficiency of 1.05TFLOPS/W@ FP16 @ 28nm, 55 ~ 85% reduction of DRAM access compared to A100 GPU in SNN trainings, and a 20-core deep SNN training and a 5-worker federated learning on FPGAs. Our study develops the first multi-core neuromorphic architecture supporting the direct SNN training, facilitating the neuromorphic computing in edge-learnable applications.
Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Training
2024-11-25
Man Yao, Xuerui Qiu, Tianxiang Hu, Jiakui Hu, Yuhong Chou, Keyu Tian, Jianxing Liao, Luziwei Leng, Bo Xu, Guoqi Li
The ambition of brain-inspired Spiking Neural Networks (SNNs) is to become a low-power alternative to traditional Artificial Neural Networks (ANNs). This work addresses two major challenges in realizing this vision: the performance gap between SNNs and ANNs, and the high training costs of SNNs. We identify intrinsic flaws in spiking neurons caused by binary firing mechanisms and propose a Spike Firing Approximation (SFA) method using integer training and spike-driven inference. This optimizes the spike firing pattern of spiking neurons, enhancing efficient training, reducing power consumption, improving performance, enabling easier scaling, and better utilizing neuromorphic chips. We also develop an efficient spike-driven Transformer architecture and a spike-masked autoencoder to prevent performance degradation during SNN scaling. On ImageNet-1k, we achieve state-of-the-art top-1 accuracy of 78.5\%, 79.8\%, 84.0\%, and 86.2\% with models containing 10M, 19M, 83M, and 173M parameters, respectively. For instance, the 10M model outperforms the best existing SNN by 7.2\% on ImageNet, with training time acceleration and inference energy efficiency improved by 4.5$\times$ and 3.9$\times$, respectively. We validate the effectiveness and efficiency of the proposed method across various tasks, including object detection, semantic segmentation, and neuromorphic vision tasks. This work enables SNNs to match ANN performance while maintaining the low-power advantage, marking a significant step towards SNNs as a general visual backbone. Code is available at https://github.com/BICLab/Spike-Driven-Transformer-V3.
Exploring the Sparsity-Quantization Interplay on a Novel Hybrid SNN Event-Driven Architecture
2024-11-23
Ilkin Aliyev, Jesus Lopez, Tosiron Adegbija
Spiking Neural Networks (SNNs) offer potential advantages in energy efficiency but currently trail Artificial Neural Networks (ANNs) in versatility, largely due to challenges in efficient input encoding. Recent work shows that direct coding achieves superior accuracy with fewer timesteps than traditional rate coding. However, there is a lack of specialized hardware to fully exploit the potential of direct-coded SNNs, especially their mix of dense and sparse layers. This work proposes the first hybrid inference architecture for direct-coded SNNs. The proposed hardware architecture comprises a dense core to efficiently process the input layer and sparse cores optimized for event-driven spiking convolutions. Furthermore, for the first time, we investigate and quantify the quantization effect on sparsity. Our experiments on two variations of the VGG9 network and implemented on a Xilinx Virtex UltraScale+ FPGA (Field-Programmable Gate Array) reveal two novel findings. Firstly, quantization increases the network sparsity by up to 15.2% with minimal loss of accuracy. Combined with the inherent low power benefits, this leads to a 3.4x improvement in energy compared to the full-precision version. Secondly, direct coding outperforms rate coding, achieving a 10% improvement in accuracy and consuming 26.4x less energy per image. Overall, our accelerator achieves 51x higher throughput and consumes half the power compared to previous work. Our accelerator code is available at: https://github.com/githubofaliyev/SNN-DSE/tree/DATE25
The elastic net penalty is frequently employed in high-dimensional statistics for parameter regression and variable selection. It is particularly beneficial compared to lasso when the number of predictors greatly surpasses the number of observations. However, empirical evidence has shown that the $\ell_q$-norm penalty (where $0 < q < 1$) often provides better regression compared to the $\ell_1$-norm penalty, demonstrating enhanced robustness in various scenarios. In this paper, we explore a generalized elastic net model that employs a $\ell_r$-norm (where $r \geq 1$) in loss function to accommodate various types of noise, and employs a $\ell_q$-norm (where $0 < q < 1$) to replace the $\ell_1$-norm in elastic net penalty. Theoretically, we establish the computable lower bounds for the nonzero entries of the generalized first-order stationary points of the proposed generalized elastic net model. For implementation, we develop two efficient algorithms based on the locally Lipschitz continuous $\epsilon$-approximation to $\ell_q$-norm. The first algorithm employs an alternating direction method of multipliers (ADMM), while the second utilizes a proximal majorization-minimization method (PMM), where the subproblems are addressed using the semismooth Newton method (SNN). We also perform extensive numerical experiments with both simulated and real data, showing that both algorithms demonstrate superior performance. Notably, the PMM-SSN is efficient than ADMM, even though the latter provides a simpler implementation.
Minimizing information loss reduces spiking neuronal networks to differential equations
2024-11-22
Jie Chang, Zhuoran Li, Zhongyi Wang, Louis Tao, Zhuo-Cheng Xiao
Spiking neuronal networks (SNNs) are widely used in computational neuroscience, from biologically realistic modeling of local cortical networks to phenomenological modeling of the whole brain. Despite their prevalence, a systematic mathematical theory for finite-sized SNNs remains elusive, even for idealized homogeneous networks. The primary challenges are twofold: 1) the rich, parameter-sensitive SNN dynamics, and 2) the singularity and irreversibility of spikes. These challenges pose significant difficulties when relating SNNs to systems of differential equations, leading previous studies to impose additional assumptions or to focus on individual dynamic regimes. In this study, we introduce a Markov approximation of homogeneous SNN dynamics to minimize information loss when translating SNNs into ordinary differential equations. Our only assumption for the Markov approximation is the fast self-decorrelation of synaptic conductances. The system of ordinary differential equations derived from the Markov model effectively captures high-frequency partial synchrony and the metastability of finite-neuron networks produced by interacting excitatory and inhibitory populations. Besides accurately predicting dynamical statistics, such as firing rates, our theory also quantitatively captures the geometry of attractors and bifurcation structures of SNNs. Thus, our work provides a comprehensive mathematical framework that can systematically map parameters of single-neuron physiology, network coupling, and external stimuli to homogeneous SNN dynamics.
Mode-conditioned music learning and composition: a spiking neural network inspired by neuroscience and psychology
2024-11-22
Qian Liang, Yi Zeng, Menghaoran Tang
Musical mode is one of the most critical element that establishes the framework of pitch organization and determines the harmonic relationships. Previous works often use the simplistic and rigid alignment method, and overlook the diversity of modes. However, in contrast to AI models, humans possess cognitive mechanisms for perceiving the various modes and keys. In this paper, we propose a spiking neural network inspired by brain mechanisms and psychological theories to represent musical modes and keys, ultimately generating musical pieces that incorporate tonality features. Specifically, the contributions are detailed as follows: 1) The model is designed with multiple collaborated subsystems inspired by the structures and functions of corresponding brain regions; 2)We incorporate mechanisms for neural circuit evolutionary learning that enable the network to learn and generate mode-related features in music, reflecting the cognitive processes involved in human music perception. 3)The results demonstrate that the proposed model shows a connection framework closely similar to the Krumhansl-Schmuckler model, which is one of the most significant key perception models in the music psychology domain. 4) Experiments show that the model can generate music pieces with characteristics of the given modes and keys. Additionally, the quantitative assessments of generated pieces reveals that the generating music pieces have both tonality characteristics and the melodic adaptability needed to generate diverse and musical content. By combining insights from neuroscience, psychology, and music theory with advanced neural network architectures, our research aims to create a system that not only learns and generates music but also bridges the gap between human cognition and artificial intelligence.
TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks
2024-11-22
Prajna G. Malettira, Shubham Negi, Wachirawit Ponghiran, Kaushik Roy
Spiking Neural Networks (SNNs) with their bio-inspired Leaky Integrate-and-Fire (LIF) neurons inherently capture temporal information. This makes them well-suited for sequential tasks like processing event-based data from Dynamic Vision Sensors (DVS) and event-based speech tasks. Harnessing the temporal capabilities of SNNs requires mitigating vanishing spikes during training, capturing spatio-temporal patterns and enhancing precise spike timing. To address these challenges, we propose TSkips, augmenting SNN architectures with forward and backward skip connections that incorporate explicit temporal delays. These connections capture long-term spatio-temporal dependencies and facilitate better spike flow over long sequences. The introduction of TSkips creates a vast search space of possible configurations, encompassing skip positions and time delay values. To efficiently navigate this search space, this work leverages training-free Neural Architecture Search (NAS) to identify optimal network structures and corresponding delays. We demonstrate the effectiveness of our approach on four event-based datasets: DSEC-flow for optical flow estimation, DVS128 Gesture for hand gesture recognition and Spiking Heidelberg Digits (SHD) and Spiking Speech Commands (SSC) for speech recognition. Our method achieves significant improvements across these datasets: up to 18% reduction in Average Endpoint Error (AEE) on DSEC-flow, 8% increase in classification accuracy on DVS128 Gesture, and up to 8% and 16% higher classification accuracy on SHD and SSC, respectively.
SpikeFI: A Fault Injection Framework for Spiking Neural Networks
2024-11-22
Theofilos Spyrou, Said Hamdioui, Haralampos-G. Stratigopoulos
Neuromorphic computing and spiking neural networks (SNNs) are gaining traction across various artificial intelligence (AI) tasks thanks to their potential for efficient energy usage and faster computation speed. This comparative advantage comes from mimicking the structure, function, and efficiency of the biological brain, which arguably is the most brilliant and green computing machine. As SNNs are eventually deployed on a hardware processor, the reliability of the application in light of hardware-level faults becomes a concern, especially for safety- and mission-critical applications. In this work, we propose SpikeFI, a fault injection framework for SNNs that can be used for automating the reliability analysis and test generation. SpikeFI is built upon the SLAYER PyTorch framework with fault injection experiments accelerated on a single or multiple GPUs. It has a comprehensive integrated neuron and synapse fault model library, in accordance to the literature in the domain, which is extendable by the user if needed. It supports: single and multiple faults; permanent and transient faults; specified, random layer-wise, and random network-wise fault locations; and pre-, during, and post-training fault injection. It also offers several optimization speedups and built-in functions for results visualization. SpikeFI is open-source and available for download via GitHub at https://github.com/SpikeFI.
Uncertainty Quantification in Working Memory via Moment Neural Networks
2024-11-21
Hengyuan Ma, Wenlian Lu, Jianfeng Feng
Humans possess a finely tuned sense of uncertainty that helps anticipate potential errors, vital for adaptive behavior and survival. However, the underlying neural mechanisms remain unclear. This study applies moment neural networks (MNNs) to explore the neural mechanism of uncertainty quantification in working memory (WM). The MNN captures nonlinear coupling of the first two moments in spiking neural networks (SNNs), identifying firing covariance as a key indicator of uncertainty in encoded information. Trained on a WM task, the model demonstrates coding precision and uncertainty quantification comparable to human performance. Analysis reveals a link between the probabilistic and sampling-based coding for uncertainty representation. Transferring the MNN's weights to an SNN replicates these results. Furthermore, the study provides testable predictions demonstrating how noise and heterogeneity enhance WM performance, highlighting their beneficial role rather than being mere biological byproducts. These findings offer insights into how the brain effectively manages uncertainty with exceptional accuracy.
Spiking neural networks: Towards bio-inspired multimodal perception in robotics
2024-11-21
Katerina Maria Oikonomou, Vasiliki Balaska, Konstantinos A. Tsintotas, Christos N. Mavridis, Ioannis Kansizoglou, Antonios Gasteratos
Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications.
When malware employs an unseen zero-day exploit, traditional security measures such as vulnerability scanners and antivirus software can fail to detect them. This is because these tools rely on known patches and signatures, which do not exist for new zero-day attacks. Furthermore, existing machine learning methods, which are trained on specific and occasionally outdated malware samples, may struggle to adapt to features in new malware. To address this issue, there is a need for a more robust machine learning model that can identify relationships between malware samples without being trained on a particular malware feature set. This is particularly crucial in the field of cybersecurity, where the number of malware samples is limited and obfuscation techniques are widely used. Current approaches using stacked autoencoders aim to remove the noise introduced by obfuscation techniques through reconstruction of the input. However, this approach ignores the semantic relationships between features across different malware samples. To overcome this limitation, we propose a novel Siamese Neural Network (SNN) that uses relation-aware embeddings to calculate more accurate similarity probabilities based on semantic details of different malware samples. In addition, by using entropy images as inputs, our model can extract better structural information and subtle differences in malware signatures, even in the presence of obfuscation techniques. Evaluations on two large malware sample sets using the N-shot and N-way methods show that our proposed model is highly effective in predicting previously unseen malware, even in the presence of obfuscation techniques.
Hybrid-Neuromorphic Approach for Underwater Robotics Applications: A Conceptual Framework
2024-11-21
Vidya Sudevan, Fakhreddine Zayer, Sajid Javed, Hamad Karki, Giulia De Masi, Jorge Dias
This paper introduces the concept of employing neuromorphic methodologies for task-oriented underwater robotics applications. In contrast to the increasing computational demands of conventional deep learning algorithms, neuromorphic technology, leveraging spiking neural network architectures, promises sophisticated artificial intelligence with significantly reduced computational requirements and power consumption, emulating human brain operational principles. Despite documented neuromorphic technology applications in various robotic domains, its utilization in marine robotics remains largely unexplored. Thus, this article proposes a unified framework for integrating neuromorphic technologies for perception, pose estimation, and haptic-guided conditional control of underwater vehicles, customized to specific user-defined objectives. This conceptual framework stands to revolutionize underwater robotics, enhancing efficiency and autonomy while reducing energy consumption. By enabling greater adaptability and robustness, this advancement could facilitate applications such as underwater exploration, environmental monitoring, and infrastructure maintenance, thereby contributing to significant progress in marine science and technology.
2024-11-21
Stein Stroobants, Christophe de Wagter, Guido C. H. E. De Croon
The real-world application of small drones is mostly hampered by energy limitations. Neuromorphic computing promises extremely energy-efficient AI for autonomous flight, but is still challenging to train and deploy on real robots. In order to reap the maximal benefits from neuromorphic computing, it is desired to perform all autonomy functions end-to-end on a single neuromorphic chip, from low-level attitude control to high-level navigation. This research presents the first neuromorphic control system using a spiking neural network (SNN) to effectively map a drone's raw sensory input directly to motor commands. We apply this method to low-level attitude estimation and control for a quadrotor, deploying the SNN on a tiny Crazyflie. We propose a modular SNN, separately training and then merging estimation and control sub-networks. The SNN is trained with imitation learning, using a flight dataset of sensory-motor pairs. Post-training, the network is deployed on the Crazyflie, issuing control commands from sensor inputs at $500$Hz. Furthermore, for the training procedure we augmented training data by flying a controller with additional excitation and time-shifting the target data to enhance the predictive capabilities of the SNN. On the real drone the perception-to-control SNN tracks attitude commands with an average error of $3$ degrees, compared to $2.5$ degrees for the regular flight stack. We also show the benefits of the proposed learning modifications for reducing the average tracking error and reducing oscillations. Our work shows the feasibility of performing neuromorphic end-to-end control, laying the basis for highly energy-efficient and low-latency neuromorphic autopilots.
SpikEmo: Enhancing Emotion Recognition With Spiking Temporal Dynamics in Conversations
2024-11-21
Xiaomin Yu, Feiyang Wang, Ziyue Qiao
In affective computing, the task of Emotion Recognition in Conversations (ERC) has emerged as a focal area of research. The primary objective of this task is to predict emotional states within conversations by analyzing multimodal data including text, audio, and video. While existing studies have progressed in extracting and fusing representations from multimodal data, they often overlook the temporal dynamics in the data during conversations. To address this challenge, we have developed the SpikEmo framework, which is based on spiking neurons and employs a Semantic & Dynamic Two-stage Modeling approach to more precisely capture the complex temporal features of multimodal emotional data. Additionally, to tackle the class imbalance and emotional semantic similarity problems in the ERC tasks, we have devised an innovative combination of loss functions that significantly enhances the model's performance when dealing with ERC data characterized by long-tail distributions. Extensive experiments conducted on multiple ERC benchmark datasets demonstrate that SpikEmo significantly outperforms existing state-of-the-art methods in ERC tasks. Our code is available at https://github.com/Yu-xm/SpikEmo.git.
SNN-Based Online Learning of Concepts and Action Laws in an Open World
2024-11-19
Christel Grimaud, Dominique Longin, Andreas Herzig
We present the architecture of a fully autonomous, bio-inspired cognitive agent built around a spiking neural network (SNN) implementing the agent's semantic memory. The agent explores its universe and learns concepts of objects/situations and of its own actions in a one-shot manner. While object/situation concepts are unary, action concepts are triples made up of an initial situation, a motor activity, and an outcome. They embody the agent's knowledge of its universe's actions laws. Both kinds of concepts have different degrees of generality. To make decisions the agent queries its semantic memory for the expected outcomes of envisaged actions and chooses the action to take on the basis of these predictions. Our experiments show that the agent handles new situations by appealing to previously learned general concepts and rapidly modifies its concepts to adapt to environment changes.
Reservoir computing (RC), is a class of computational methods such as Echo State Networks (ESN) and Liquid State Machines (LSM) describe a generic method to perform pattern recognition and temporal analysis with any non-linear system. This is enabled by Reservoir Computing being a shallow network model with only Input, Reservoir, and Readout layers where input and reservoir weights are not learned (only the readout layer is trained). LSM is a special case of Reservoir computing inspired by the organization of neurons in the brain and generally refers to spike-based Reservoir computing approaches. LSMs have been successfully used to showcase decent performance on some neuromorphic vision and speech datasets but a common problem associated with LSMs is that since the model is more-or-less fixed, the main way to improve the performance is by scaling up the Reservoir size, but that only gives diminishing rewards despite a tremendous increase in model size and computation. In this paper, we propose two approaches for effectively ensembling LSM models - Multi-Length Scale Reservoir Ensemble (MuLRE) and Temporal Excitation Partitioned Reservoir Ensemble (TEPRE) and benchmark them on Neuromorphic-MNIST (N-MNIST), Spiking Heidelberg Digits (SHD), and DVSGesture datasets, which are standard neuromorphic benchmarks. We achieve 98.1% test accuracy on N-MNIST with a 3600-neuron LSM model which is higher than any prior LSM-based approach and 77.8% test accuracy on the SHD dataset which is on par with a standard Recurrent Spiking Neural Network trained by Backprop Through Time (BPTT). We also propose receptive field-based input weights to the Reservoir to work alongside the Multi-Length Scale Reservoir ensemble model for vision tasks. Thus, we introduce effective means of scaling up the performance of LSM models and evaluate them against relevant neuromorphic benchmarks
STOP: Spatiotemporal Orthogonal Propagation for Weight-Threshold-Leakage Synergistic Training of Deep Spiking Neural Networks
2024-11-17
Haoran Gao, Xichuan Zhou, Yingcheng Lin, Min Tian, Liyuan Liu, Cong Shi
The prevailing of artificial intelligence-of-things calls for higher energy-efficient edge computing paradigms, such as neuromorphic agents leveraging brain-inspired spiking neural network (SNN) models based on spatiotemporally sparse binary spikes. However, the lack of efficient and high-accuracy deep SNN learning algorithms prevents them from practical edge deployments at a strictly bounded cost. In this paper, we propose the spatiotemporal orthogonal propagation (STOP) algorithm to tackle this challenge. Our algorithm enables fully synergistic learning of synaptic weights as well as firing thresholds and leakage factors in spiking neurons to improve SNN accuracy, in a unified temporally-forward trace-based framework to mitigate the huge memory requirement for storing neural states across all time-steps in the forward pass. Characteristically, the spatially-backward neuronal errors and temporally-forward traces propagate orthogonally to and independently of each other, substantially reducing computational complexity. Our STOP algorithm obtained high recognition accuracies of 94.84%, 74.92%, 98.26% and 77.10% on the CIFAR-10, CIFAR-100, DVS-Gesture and DVS-CIFAR10 datasets with adequate deep convolutional SNNs of VGG-11 or ResNet-18 structures. Compared with other deep SNN training algorithms, our method is more plausible for edge intelligent scenarios where resources are limited but high-accuracy in-situ learning is desired.
Spiking Neural Networks (SNNs) has the ability to extract spatio-temporal features due to their spiking sequence. While previous research has primarily foucus on the classification of image and reinforcement learning. In our paper, we put forward novel diffusion policy model based on Spiking Transformer Neural Networks and Denoising Diffusion Probabilistic Model (DDPM): Spiking Transformer Modulate Diffusion Policy Model (STMDP), a new brain-inspired model for generating robot action trajectories. In order to improve the performance of this model, we develop a novel decoder module: Spiking Modulate De coder (SMD), which replaces the traditional Decoder module within the Transformer architecture. Additionally, we explored the substitution of DDPM with Denoising Diffusion Implicit Models (DDIM) in our frame work. We conducted experiments across four robotic manipulation tasks and performed ablation studies on the modulate block. Our model consistently outperforms existing Transformer-based diffusion policy method. Especially in Can task, we achieved an improvement of 8%. The proposed STMDP method integrates SNNs, dffusion model and Transformer architecture, which offers new perspectives and promising directions for exploration in brain-inspired robotics.
Spike Talk in Power Electronic Grids -- Leveraging Post Moore's Computing Laws
2024-11-12
Yubo Song, Subham Sahoo
Emerging distributed generation demands highly reliable and resilient coordinating control in microgrids. To improve on these aspects, spiking neural network is leveraged, as a grid-edge intelligence tool to establish a talkative infrastructure, Spike Talk, expediting coordination in next-generation microgrids without the need of communication at all. This paper unravels the physics behind Spike Talk from the perspective of its distributed infrastructure, which aims to address the Von Neumann Bottleneck. Relying on inferring information via power flows in tie lines, Spike Talk allows adaptive and flexible control and coordination itself, and features in synaptic plasticity facilitating online and local training functionality. Preliminary case studies are demonstrated with results, while more extensive validations are to be included as future scopes of work.
Randomized Forward Mode Gradient for Spiking Neural Networks in Scientific Machine Learning
2024-11-11
Ruyin Wan, Qian Zhang, George Em Karniadakis
Spiking neural networks (SNNs) represent a promising approach in machine learning, combining the hierarchical learning capabilities of deep neural networks with the energy efficiency of spike-based computations. Traditional end-to-end training of SNNs is often based on back-propagation, where weight updates are derived from gradients computed through the chain rule. However, this method encounters challenges due to its limited biological plausibility and inefficiencies on neuromorphic hardware. In this study, we introduce an alternative training approach for SNNs. Instead of using back-propagation, we leverage weight perturbation methods within a forward-mode gradient framework. Specifically, we perturb the weight matrix with a small noise term and estimate gradients by observing the changes in the network output. Experimental results on regression tasks, including solving various PDEs, show that our approach achieves competitive accuracy, suggesting its suitability for neuromorphic systems and potential hardware compatibility.
By exploiting discrete signal processing and simulating brain neuron communication, Spiking Neural Networks (SNNs) offer a low-energy alternative to Artificial Neural Networks (ANNs). However, existing SNN models, still face high computational costs due to the numerous time steps as well as network depth and scale. The tens of billions of neurons and trillions of synapses in the human brain are developed from only 20,000 genes, which inspires us to design an efficient genetic encoding strategy that dynamic evolves to regulate large-scale deep SNNs at low cost. Therefore, we first propose a genetically scaled SNN encoding scheme that incorporates globally shared genetic interactions to indirectly optimize neuronal encoding instead of weight, which obviously brings about reductions in parameters and energy consumption. Then, a spatio-temporal evolutionary framework is designed to optimize the inherently initial wiring rules. Two dynamic regularization operators in the fitness function evolve the neuronal encoding to a suitable distribution and enhance information quality of the genetic interaction respectively, substantially accelerating evolutionary speed and improving efficiency. Experiments show that our approach compresses parameters by approximately 50\% to 80\%, while outperforming models on the same architectures by 0.21\% to 4.38\% on CIFAR-10, CIFAR-100 and ImageNet. In summary, the consistent trends of the proposed genetically encoded spatio-temporal evolution across different datasets and architectures highlight its significant enhancements in terms of efficiency, broad scalability and robustness, demonstrating the advantages of the brain-inspired evolutionary genetic coding for SNN optimization.
Spiking neural networks (SNNs) are powerful models of spatiotemporal computation and are well suited for deployment on resource-constrained edge devices and neuromorphic hardware due to their low power consumption. Leveraging attention mechanisms similar to those found in their artificial neural network counterparts, recently emerged spiking transformers have showcased promising performance and efficiency by capitalizing on the binary nature of spiking operations. Recognizing the current lack of dedicated hardware support for spiking transformers, this paper presents the first work on 3D spiking transformer hardware architecture and design methodology. We present an architecture and physical design co-optimization approach tailored specifically for spiking transformers. Through memory-on-logic and logic-on-logic stacking enabled by 3D integration, we demonstrate significant energy and delay improvements compared to conventional 2D CMOS integration.
Are Neuromorphic Architectures Inherently Privacy-preserving? An Exploratory Study
2024-11-10
Ayana Moshruba, Ihsen Alouani, Maryam Parsa
While machine learning (ML) models are becoming mainstream, especially in sensitive application areas, the risk of data leakage has become a growing concern. Attacks like membership inference (MIA) have shown that trained models can reveal sensitive data, jeopardizing confidentiality. While traditional Artificial Neural Networks (ANNs) dominate ML applications, neuromorphic architectures, specifically Spiking Neural Networks (SNNs), are emerging as promising alternatives due to their low power consumption and event-driven processing, akin to biological neurons. Privacy in ANNs is well-studied; however, little work has explored the privacy-preserving properties of SNNs. This paper examines whether SNNs inherently offer better privacy. Using MIAs, we assess the privacy resilience of SNNs versus ANNs across diverse datasets. We analyze the impact of learning algorithms (surrogate gradient and evolutionary), frameworks (snnTorch, TENNLab, LAVA), and parameters on SNN privacy. Our findings show that SNNs consistently outperform ANNs in privacy preservation, with evolutionary algorithms offering additional resilience. For instance, on CIFAR-10, SNNs achieve an AUC of 0.59, significantly lower than ANNs' 0.82, and on CIFAR-100, SNNs maintain an AUC of 0.58 compared to ANNs' 0.88. Additionally, we explore the privacy-utility trade-off with Differentially Private Stochastic Gradient Descent (DPSGD), finding that SNNs sustain less accuracy loss than ANNs under similar privacy constraints.
Developing asynchronous neuromorphic hardware to meet the demands of diverse real-life edge scenarios remains significant challenges. These challenges include constraints on hardware resources and power budgets while satisfying the requirements for real-time responsiveness, reliable inference accuracy, and so on. Besides, the existing system-level simulators for asynchronous neuromorphic hardware suffer from runtime limitations. To address these challenges, we propose an Asynchronous Neuromorphic algorithm/hardware Co-Exploration Framework (ANCoEF) including multi-objective reinforcement learning (RL)-based hardware architecture optimization method, and a fully asynchronous simulator (TrueAsync) which achieves over 2 times runtime speedups than the state-of-the-art (SOTA) simulator. Our experimental results show that, the RL-based hardware architecture optimization approach of ANCoEF outperforms the SOTA method by reducing 1.81 times hardware energy-delay product (EDP) with 2.73 times less search time on N-MNIST dataset, and the co-exploration framework of ANCoEF improves SNN accuracy by 9.72% and reduces hardware EDP by 28.85 times compared to the SOTA work on DVS128Gesture dataset. Furthermore, ANCoEF framework is evaluated on external neuromorphic dataset CIFAR10-DVS, and static datasets including CIFAR10, CIFAR100, SVHN, and Tiny-ImageNet. For instance, after 26.23 ThreadHour of co-exploration process, the result on CIFAR10-DVS dataset achieves an SNN accuracy of 98.48% while consuming hardware EDP of 0.54 s nJ per sample.
Zero-Shot Temporal Resolution Domain Adaptation for Spiking Neural Networks
2024-11-07
Sanja Karilanova, Maxime Fabre, Emre Neftci, Ayça Özçelikkale
Spiking Neural Networks (SNNs) are biologically-inspired deep neural networks that efficiently extract temporal information while offering promising gains in terms of energy efficiency and latency when deployed on neuromorphic devices. However, SNN model parameters are sensitive to temporal resolution, leading to significant performance drops when the temporal resolution of target data at the edge is not the same with that of the pre-deployment source data used for training, especially when fine-tuning is not possible at the edge. To address this challenge, we propose three novel domain adaptation methods for adapting neuron parameters to account for the change in time resolution without re-training on target time-resolution. The proposed methods are based on a mapping between neuron dynamics in SNNs and State Space Models (SSMs); and are applicable to general neuron models. We evaluate the proposed methods under spatio-temporal data tasks, namely the audio keyword spotting datasets SHD and MSWC as well as the image classification NMINST dataset. Our methods provide an alternative to - and in majority of the cases significantly outperform - the existing reference method that simply scales the time constant. Moreover, our results show that high accuracy on high temporal resolution data can be obtained by time efficient training on lower temporal resolution data and model adaptation.
Neuromorphic Wireless Split Computing with Multi-Level Spikes
2024-11-07
Dengyu Wu, Jiechen Chen, Bipin Rajendran, H. Vincent Poor, Osvaldo Simeone
Inspired by biological processes, neuromorphic computing leverages spiking neural networks (SNNs) to perform inference tasks, offering significant efficiency gains for workloads involving sequential data. Recent advances in hardware and software have shown that embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption. To scale neuromorphic computing to larger workloads, split computing - where an SNN is partitioned across two devices - is a promising solution. In such architectures, the device hosting the initial layers must transmit information about the spikes generated by its output neurons to the second device. This establishes a trade-off between the benefits of multi-level spikes, which carry additional payload information, and the communication resources required for transmitting extra bits between devices. This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs. We propose digital and analog modulation schemes for an orthogonal frequency division multiplexing (OFDM) radio interface to enable efficient communication. Simulation and experimental results using software-defined radios reveal performance improvements achieved by multi-level SNN models and provide insights into the optimal payload size as a function of the connection quality between the transmitter and receiver.
Flashy Backdoor: Real-world Environment Backdoor Attack on SNNs with DVS Cameras
2024-11-05
Roberto Riaño, Gorka Abad, Stjepan Picek, Aitor Urbieta
While security vulnerabilities in traditional Deep Neural Networks (DNNs) have been extensively studied, the susceptibility of Spiking Neural Networks (SNNs) to adversarial attacks remains mostly underexplored. Until now, the mechanisms to inject backdoors into SNN models have been limited to digital scenarios; thus, we present the first evaluation of backdoor attacks in real-world environments. We begin by assessing the applicability of existing digital backdoor attacks and identifying their limitations for deployment in physical environments. To address each of the found limitations, we present three novel backdoor attack methods on SNNs, i.e., Framed, Strobing, and Flashy Backdoor. We also assess the effectiveness of traditional backdoor procedures and defenses adapted for SNNs, such as pruning, fine-tuning, and fine-pruning. The results show that while these procedures and defenses can mitigate some attacks, they often fail against stronger methods like Flashy Backdoor or sacrifice too much clean accuracy, rendering the models unusable. Overall, all our methods can achieve up to a 100% Attack Success Rate while maintaining high clean accuracy in every tested dataset. Additionally, we evaluate the stealthiness of the triggers with commonly used metrics, finding them highly stealthy. Thus, we propose new alternatives more suited for identifying poisoned samples in these scenarios. Our results show that further research is needed to ensure the security of SNN-based systems against backdoor attacks and their safe application in real-world scenarios. The code, experiments, and results are available in our repository.
Spiking Neural Networks (SNNs), with their inherent recurrence, offer an efficient method for processing the asynchronous temporal data generated by Dynamic Vision Sensors (DVS), making them well-suited for event-based vision applications. However, existing SNN accelerators suffer from limitations in adaptability to diverse neuron models, bit precisions and network sizes, inefficient membrane potential (Vmem) handling, and limited sparse optimizations. In response to these challenges, we propose a scalable and reconfigurable digital compute-in-memory (CIM) SNN accelerator \chipname with a set of key features: 1) It uses in-memory computations and reconfigurable operating modes to minimize data movement associated with weight and Vmem data structures while efficiently adapting to different workloads. 2) It supports multiple weight/Vmem bit precision values, enabling a trade-off between accuracy and energy efficiency and enhancing adaptability to diverse application demands. 3) A zero-skipping mechanism for sparse inputs significantly reduces energy usage by leveraging the inherent sparsity of spikes without introducing high overheads for low sparsity. 4) Finally, the asynchronous handshaking mechanism maintains the computational efficiency of the pipeline for variable execution times of different computation units. We fabricated \chipname in 65 nm Taiwan Semiconductor Manufacturing Company (TSMC) low-power (LP) technology. It demonstrates competitive performance (scaled to the same technology node) to other digital SNN accelerators proposed in the recent literature and supports advanced reconfigurability. It achieves up to 5 TOPS/W energy efficiency at 95% input sparsity with 4-bit weights and 7-bit Vmem precision.
Tiny Machine Learning (TinyML) has become a growing field in on-device processing for Internet of Things (IoT) applications, capitalizing on AI algorithms that are optimized for their low complexity and energy efficiency. These algorithms are designed to minimize power and memory footprints, making them ideal for the constraints of IoT devices. Within this domain, Spiking Neural Networks (SNNs) stand out as a cutting-edge solution for TinyML, owning to their event-driven processing paradigm which offers an efficient method of handling dataflow. This paper presents a novel SNN architecture based on the 1st Order Leaky Integrate-and-Fire (LIF) neuron model to efficiently deploy vision-based ML algorithms on TinyML systems. A hardware-friendly LIF design is also proposed, and implemented on a Xilinx Artix-7 FPGA. To evaluate the proposed model, a collision avoidance dataset is considered as a case study. The proposed SNN model is compared to the state-of-the-art works and Binarized Convolutional Neural Network (BCNN) as a baseline. The results show the proposed approach is 86% more energy efficient than the baseline.
All-Optical Excitable Spiking Laser Neuron in InP Generic Integration Technology
2024-11-01
Lukas Puts, Daan Lenstra, Kevin A. Williams, Weiming Yao
Brain-inspired, neuromorphic devices implemented in integrated photonic hardware have attracted significant interest recently as part of efforts towards novel non-von Neumann computing paradigms that make use of the low loss, high-speed and parallel operations in optics. An all-optical spiking laser neuron fabricated on the indium-phosphide generic integration technology platform may be a practical alternative to other semi-integrated photonic and electronic-based spiking neuron implementations. Owing to the large number of predefined building blocks, a plethora of applications have benefitted already from the generic integration process. This technology platform has now been utilised for the first time to demonstrate an all-optical spiking laser neuron. This paper present and discusses the design and measurement of the ultra-fast and rich spiking dynamics in these devices. We show that under external pulse injection and operated slightly below the lasing threshold, the laser neuron exhibits an excitable mode, in addition to a self-spiking mode far above the threshold when no pulse is injected. In the excitable mode, the required injected pulse energy is much lower than that of the generated excited response, meeting an important requirement for neuron cascadability. In addition, we investigate excitability at different injection wavelengths below the lasing wavelength, as well as the ultra-fast temporal properties of the spiking response. All of the discussed characteristics point to the laser neuron being an important candidate for scaling up to future fully-connected, multi-wavelength all-optical photonic spiking neural networks in indium-phosphide generic integration technology.
Spiking Neural Networks (SNNs) have gained enormous popularity in the field of artificial intelligence due to their low power consumption. However, the majority of SNN methods directly inherit the structure of Artificial Neural Networks (ANN), usually leading to sub-optimal model performance in SNNs. To alleviate this problem, we integrate Neural Architecture Search (NAS) method and propose Multi-Attention Differentiable Architecture Search (MA-DARTS) to directly automate the search for the optimal network structure of SNNs. Initially, we defined a differentiable two-level search space and conducted experiments within micro architecture under a fixed layer. Then, we incorporated a multi-dimensional attention mechanism and implemented the MA-DARTS algorithm in this search space. Comprehensive experiments demonstrate our model achieves state-of-the-art performance on classification compared to other methods under the same parameters with 94.40% accuracy on CIFAR10 dataset and 76.52% accuracy on CIFAR100 dataset. Additionally, we monitored and assessed the number of spikes (NoS) in each cell during the whole experiment. Notably, the number of spikes of the whole model stabilized at approximately 110K in validation and 100k in training on datasets.
Approaches to human activity recognition via passive radar
2024-10-31
Christian Bresciani, Federico Cerutti, Marco Cominelli
The thesis explores novel methods for Human Activity Recognition (HAR) using passive radar with a focus on non-intrusive Wi-Fi Channel State Information (CSI) data. Traditional HAR approaches often use invasive sensors like cameras or wearables, raising privacy issues. This study leverages the non-intrusive nature of CSI, using Spiking Neural Networks (SNN) to interpret signal variations caused by human movements. These networks, integrated with symbolic reasoning frameworks such as DeepProbLog, enhance the adaptability and interpretability of HAR systems. SNNs offer reduced power consumption, ideal for privacy-sensitive applications. Experimental results demonstrate SNN-based neurosymbolic models achieve high accuracy making them a promising alternative for HAR across various domains.
Collective dynamics in spiking neural networks: A systematic review
2024-10-31
Afifurrahman, Mohd Hafiz Mohd
This study aims to review recent research on the collective behaviour of excitatory and inhibitory (E-I) spiking neural networks. The research methodology used is Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) procedures, comprising three primary stages: an initial search for literature in the SCOPUS database, a screening process based on specific inclusion and exclusion criteria, and a review of the selected literatures. Out of 491 documents from 2014 to 2024, 6 research papers are qualified for review. Four distinct dynamical states have been identified: synchrony, irregular behaviour, stationary state, and oscillatory dynamics. Our review findings suggest that the collective dynamics of E-I spiking neurons stem from the interaction of intrinsic neuronal characteristics, balance mechanisms, and the type of external stimuli. Additionally, the widespread use of Quadratic Integrate-and-Fire (QIF) neurons in the literature highlights its significance as a robust candidate for exploring collective behaviours in large-scale neuronal networks.
Neurobench: DCASE 2020 Acoustic Scene Classification benchmark on XyloAudio 2
2024-10-31
Weijie Ke, Mina Khoei, Dylan Muir
XyloAudio is a line of ultra-low-power audio inference chips, designed for in- and near-microphone analysis of audio in real-time energy-constrained scenarios. Xylo is designed around a highly efficient integer-logic processor which simulates parameter- and activity-sparse spiking neural networks (SNNs) using a leaky integrate-and-fire (LIF) neuron model. Neurons on Xylo are quantised integer devices operating in synchronous digital CMOS, with neuron and synapse state quantised to 16 bit, and weight parameters quantised to 8 bit. Xylo is tailored for real-time streaming operation, as opposed to accelerated-time operation in the case of an inference accelerator. XyloAudio includes a low-power audio encoding interface for direct connection to a microphone, designed for sparse encoding of incident audio for further processing by the inference core. In this report we present the results of DCASE 2020 acoustic scene classification audio benchmark dataset deployed to XyloAudio 2. We describe the benchmark dataset; the audio preprocessing approach; and the network architecture and training approach. We present the performance of the trained model, and the results of power and latency measurements performed on the XyloAudio 2 development kit. This benchmark is conducted as part of the Neurobench project.
The integration of immersive communication into a human-centric ecosystem has intensified the demand for sophisticated Human Digital Twins (HDTs) driven by multifaceted human data. However, the effective construction of HDTs faces significant challenges due to the heterogeneity of data collection devices, the high energy demands associated with processing intricate data, and concerns over the privacy of sensitive information. This work introduces a novel biologically-inspired (bio-inspired) HDT framework that leverages Brain-Computer Interface (BCI) sensor technology to capture brain signals as the data source for constructing HDT. By collecting and analyzing these signals, the framework not only minimizes device heterogeneity and enhances data collection efficiency, but also provides richer and more nuanced physiological and psychological data for constructing personalized HDTs. To this end, we further propose a bio-inspired neuromorphic computing learning model based on the Spiking Neural Network (SNN). This model utilizes discrete neural spikes to emulate the way of human brain processes information, thereby enhancing the system's ability to process data effectively while reducing energy consumption. Additionally, we integrate a Federated Learning (FL) strategy within the model to strengthen data privacy. We then conduct a case study to demonstrate the performance of our proposed twofold bio-inspired scheme. Finally, we present several challenges and promising directions for future research of HDTs driven by bio-inspired technologies.
ETTFS: An Efficient Training Framework for Time-to-First-Spike Neuron
2024-10-31
Kaiwei Che, Wei Fang, Zhengyu Ma, Li Yuan, Timothée Masquelier, Yonghong Tian
Spiking Neural Networks (SNNs) have attracted considerable attention due to their biologically inspired, event-driven nature, making them highly suitable for neuromorphic hardware. Time-to-First-Spike (TTFS) coding, where neurons fire only once during inference, offers the benefits of reduced spike counts, enhanced energy efficiency, and faster processing. However, SNNs employing TTFS coding often suffer from diminished classification accuracy. This paper presents an efficient training framework for TTFS that not only improves accuracy but also accelerates the training process. Unlike most previous approaches, we first identify two key issues limiting the performance of TTFS neurons: information disminishing and imbalanced membrane potential distribution. To address these challenges, we propose a novel initialization strategy. Additionally, we introduce a temporal weighting decoding method that aggregates temporal outputs through a weighted sum, supporting BPTT. Moreover, we re-evaluate the pooling layer in TTFS neurons and find that average pooling is better suited than max-pooling for this coding scheme. Our experimental results show that the proposed training framework leads to more stable training and significant performance improvements, achieving state-of-the-art (SOTA) results on both the MNIST and Fashion-MNIST datasets.
ViT-LCA: A Neuromorphic Approach for Vision Transformers
2024-10-31
Sanaz Mahmoodi Takaghaj
The recent success of Vision Transformers has generated significant interest in attention mechanisms and transformer architectures. Although existing methods have proposed spiking self-attention mechanisms compatible with spiking neural networks, they often face challenges in effective deployment on current neuromorphic platforms. This paper introduces a novel model that combines vision transformers with the Locally Competitive Algorithm (LCA) to facilitate efficient neuromorphic deployment. Our experiments show that ViT-LCA achieves higher accuracy on ImageNet-1K dataset while consuming significantly less energy than other spiking vision transformer counterparts. Furthermore, ViT-LCA's neuromorphic-friendly design allows for more direct mapping onto current neuromorphic architectures.
An Event-Based Digital Compute-In-Memory Accelerator with Flexible Operand Resolution and Layer-Wise Weight/Output Stationarity
2024-10-30
Nicolas Chauvaux, Adrian Kneip, Christoph Posch, Kofi Makinwa, Charlotte Frenkel
Compute-in-memory (CIM) accelerators for spiking neural networks (SNNs) are promising solutions to enable $\mu$s-level inference latency and ultra-low energy in edge vision applications. Yet, their current lack of flexibility at both the circuit and system levels prevents their deployment in a wide range of real-life scenarios. In this work, we propose a novel digital CIM macro that supports arbitrary operand resolution and shape, with a unified CIM storage for weights and membrane potentials. These circuit-level techniques enable a hybrid weight- and output-stationary dataflow at the system level to maximize operand reuse, thereby minimizing costly on- and off-chip data movements during the SNN execution. Measurement results of a fabricated FlexSpIM prototype in 40-nm CMOS demonstrate a 2$\times$ increase in bit-normalized energy efficiency compared to prior fixed-precision digital CIM-SNNs, while providing resolution reconfiguration with bitwise granularity. Our approach can save up to 90% energy in large-scale systems, while reaching a state-of-the-art classification accuracy of 95.8% on the IBM DVS gesture dataset.
SkipSNN: Efficiently Classifying Spike Trains with Event-attention
2024-10-29
Hang Yin, Yao Su, Liping Liu, Thomas Hartvigsen, Xin Dai, Xiangnan Kong
Spike train classification has recently become an important topic in the machine learning community, where each spike train is a binary event sequence with \emph{temporal-sparsity of signals of interest} and \emph{temporal-noise} properties. A promising model for it should follow the design principle of performing intensive computation only when signals of interest appear. So such tasks use mainly Spiking Neural Networks (SNNs) due to their consideration of temporal-sparsity of spike trains. However, the basic mechanism of SNNs ignore the temporal-noise issue, which makes them computationally expensive and thus high power consumption for analyzing spike trains on resource-constrained platforms. As an event-driven model, an SNN neuron makes a reaction given any input signals, making it difficult to quickly find signals of interest. In this paper, we introduce an event-attention mechanism that enables SNNs to dynamically highlight useful signals of the original spike trains. To this end, we propose SkipSNN, which extends existing SNN models by learning to mask out noise by skipping membrane potential updates and shortening the effective size of the computational graph. This process is analogous to how people choose to open and close their eyes to filter the information they see. We evaluate SkipSNN on various neuromorphic tasks and demonstrate that it achieves significantly better computational efficiency and classification accuracy than other state-of-the-art SNNs.
Similarity-based context aware continual learning for spiking neural networks
2024-10-28
Bing Han, Feifei Zhao, Yang Li, Qingqun Kong, Xianqi Li, Yi Zeng
Biological brains have the capability to adaptively coordinate relevant neuronal populations based on the task context to learn continuously changing tasks in real-world environments. However, existing spiking neural network-based continual learning algorithms treat each task equally, ignoring the guiding role of different task similarity associations for network learning, which limits knowledge utilization efficiency. Inspired by the context-dependent plasticity mechanism of the brain, we propose a Similarity-based Context Aware Spiking Neural Network (SCA-SNN) continual learning algorithm to efficiently accomplish task incremental learning and class incremental learning. Based on contextual similarity across tasks, the SCA-SNN model can adaptively reuse neurons from previous tasks that are beneficial for new tasks (the more similar, the more neurons are reused) and flexibly expand new neurons for the new task (the more similar, the fewer neurons are expanded). Selective reuse and discriminative expansion significantly improve the utilization of previous knowledge and reduce energy consumption. Extensive experimental results on CIFAR100, ImageNet generalized datasets, and FMNIST-MNIST, SVHN-CIFAR100 mixed datasets show that our SCA-SNN model achieves superior performance compared to both SNN-based and DNN-based continual learning algorithms. Additionally, our algorithm has the capability to adaptively select similar groups of neurons for related tasks, offering a promising approach to enhancing the biological interpretability of efficient continual learning.
Mixed-signal implementations of SNNs offer a promising solution to edge computing applications that require low-power and compact embedded processing systems. However, device mismatch in the analog circuits of these neuromorphic processors poses a significant challenge to the deployment of robust processing in these systems. Here we introduce a novel architectural solution inspired by biological development to address this issue. Specifically we propose to implement architectures that incorporate network motifs found in developed brains through a differentiable re-parameterization of weight matrices based on gene expression patterns and genetic rules. Thanks to the gradient descent optimization compatibility of the method proposed, we can apply the robustness of biological neural development to neuromorphic computing. To validate this approach we benchmark it using the Yin-Yang classification dataset, and compare its performance with that of standard multilayer perceptrons trained with state-of-the-art hardware-aware training method. Our results demonstrate that the proposed method mitigates mismatch-induced noise without requiring precise device mismatch measurements, effectively outperforming alternative hardware-aware techniques proposed in the literature, and providing a more general solution for improving the robustness of SNNs in neuromorphic hardware.
2024-10-25
Hadi Vafaii, Dekel Galor, Jacob L. Yates
The Evidence Lower Bound (ELBO) is a widely used objective for training deep generative models, such as Variational Autoencoders (VAEs). In the neuroscience literature, an identical objective is known as the variational free energy, hinting at a potential unified framework for brain function and machine learning. Despite its utility in interpreting generative models, including diffusion models, ELBO maximization is often seen as too broad to offer prescriptive guidance for specific architectures in neuroscience or machine learning. In this work, we show that maximizing ELBO under Poisson assumptions for general sequence data leads to a spiking neural network that performs Bayesian posterior inference through its membrane potential dynamics. The resulting model, the iterative Poisson VAE (iP-VAE), has a closer connection to biological neurons than previous brain-inspired predictive coding models based on Gaussian assumptions. Compared to amortized and iterative VAEs, iP-VAElearns sparser representations and exhibits superior generalization to out-of-distribution samples. These findings suggest that optimizing ELBO, combined with Poisson assumptions, provides a solid foundation for developing prescriptive theories in NeuroAI.
Spatial-Temporal Search for Spiking Neural Networks
2024-10-24
Kaiwei Che, Zhaokun Zhou, Li Yuan, Jianguo Zhang, Yonghong Tian, Luziwei Leng
Spiking Neural Networks (SNNs) are considered as a potential candidate for the next generation of artificial intelligence with appealing characteristics such as sparse computation and inherent temporal dynamics. By adopting architectures of Artificial Neural Networks (ANNs), SNNs achieve competitive performances on benchmark tasks like image classification. However, successful architectures of ANNs are not optimal for SNNs. In this work, we apply Neural Architecture Search (NAS) to find suitable architectures for SNNs. Previous NAS methods for SNNs focus primarily on the spatial dimension, with a notable lack of consideration for the temporal dynamics that are of critical importance for SNNs. Drawing inspiration from the heterogeneity of biological neural networks, we propose a differentiable approach to optimize SNN on both spatial and temporal dimensions. At spatial level, we have developed a spike-based differentiable hierarchical search (SpikeDHS) framework, where spike-based operation is optimized on both the cell and the layer level under computational constraints. We further propose a differentiable surrogate gradient search (DGS) method to evolve local SG functions independently during training. At temporal level, we explore an optimal configuration of diverse temporal dynamics on different types of spiking neurons by evolving their time constants, based on which we further develop hybrid networks combining SNN and ANN, balancing both accuracy and efficiency. Our methods achieve comparable classification performance of CIFAR10/100 and ImageNet with accuracies of 96.43%, 78.96%, and 70.21%, respectively. On event-based deep stereo, our methods find optimal layer variation and surpass the accuracy of specially designed ANNs with 26$\times$ lower computational cost ($6.7\mathrm{mJ}$), demonstrating the potential of SNN in processing highly sparse and dynamic signals.
Spiking Graph Neural Network on Riemannian Manifolds
2024-10-23
Li Sun, Zhenhao Huang, Qiqi Wan, Hao Peng, Philip S. Yu
Graph neural networks (GNNs) have become the dominant solution for learning on graphs, the typical non-Euclidean structures. Conventional GNNs, constructed with the Artificial Neuron Network (ANN), have achieved impressive performance at the cost of high computation and energy consumption. In parallel, spiking GNNs with brain-like spiking neurons are drawing increasing research attention owing to the energy efficiency. So far, existing spiking GNNs consider graphs in Euclidean space, ignoring the structural geometry, and suffer from the high latency issue due to Back-Propagation-Through-Time (BPTT) with the surrogate gradient. In light of the aforementioned issues, we are devoted to exploring spiking GNN on Riemannian manifolds, and present a Manifold-valued Spiking GNN (MSG). In particular, we design a new spiking neuron on geodesically complete manifolds with the diffeomorphism, so that BPTT regarding the spikes is replaced by the proposed differentiation via manifold. Theoretically, we show that MSG approximates a solver of the manifold ordinary differential equation. Extensive experiments on common graphs show the proposed MSG achieves superior performance to previous spiking GNNs and energy efficiency to conventional GNNs.
Neuronal Competition Groups with Supervised STDP for Spike-Based Classification
2024-10-22
Gaspard Goupy, Pierre Tirilly, Ioan Marius Bilasco
Spike Timing-Dependent Plasticity (STDP) is a promising substitute to backpropagation for local training of Spiking Neural Networks (SNNs) on neuromorphic hardware. STDP allows SNNs to address classification tasks by combining unsupervised STDP for feature extraction and supervised STDP for classification. Unsupervised STDP is usually employed with Winner-Takes-All (WTA) competition to learn distinct patterns. However, WTA for supervised STDP classification faces unbalanced competition challenges. In this paper, we propose a method to effectively implement WTA competition in a spiking classification layer employing first-spike coding and supervised STDP training. We introduce the Neuronal Competition Group (NCG), an architecture that improves classification capabilities by promoting the learning of various patterns per class. An NCG is a group of neurons mapped to a specific class, implementing intra-class WTA and a novel competition regulation mechanism based on two-compartment thresholds. We incorporate our proposed architecture into spiking classification layers trained with state-of-the-art supervised STDP rules. On top of two different unsupervised feature extractors, we obtain significant accuracy improvements on image recognition datasets such as CIFAR-10 and CIFAR-100. We show that our competition regulation mechanism is crucial for ensuring balanced competition and improved class separation.
Neuromorphic information processing using ultrafast heat dynamics and quench switching of an antiferromagnet
2024-10-22
Jan Zubáč, Miloslav Surýnek, Kamil Olejník, Andrej Farkaš, Filip Krizek, Lukáš Nádvorník, Petr Kubaščík, Zdeněk Kašpar, František Trojánek, Richard P. Campion, Vít Novák, Petr Němec, Tomáš Jungwirth
Solving complex tasks in a modern information-driven society requires novel materials and concepts for energy-efficient hardware. Antiferromagnets offer a promising platform for seeking such approaches due to their exceptional features: low power consumption and possible high integration density are desirable for information storage and processing or applications in unconventional computing. Among antiferromagnets, CuMnAs stands out for atomic-level scalable magnetic textures, analogue multilevel storage capability, and the magnetic state's control by a single electrical or femtosecond laser pulse. Using a pair of excitation laser pulses, this work examines synaptic and neuronal functionalities of CuMnAs for information processing, readily incorporating two principles of distinct characteristic timescales. Laser-induced transient heat dynamics at sub-nanosecond times represents the short-term memory and causes resistance switching due to quenching into a magnetically fragmented state. This quench switching, detectable electrically from ultrashort times to hours after writing, reminisces the long-term memory. The versatility of the principles' combination is demonstrated by operations commonly used in neural networks. Temporal latency coding, fundamental to spiking neural networks, is utilized to encode data from a grayscale image into sub-nanosecond pulse delays. Applying input laser pulses with distinct amplitudes then allows for pulse-pattern recognition. The results open pathways for designing novel computing architectures.
SpikMamba: When SNN meets Mamba in Event-based Human Action Recognition
2024-10-22
Jiaqi Chen, Yan Yang, Shizhuo Deng, Da Teng, Liyuan Pan
Human action recognition (HAR) plays a key role in various applications such as video analysis, surveillance, autonomous driving, robotics, and healthcare. Most HAR algorithms are developed from RGB images, which capture detailed visual information. However, these algorithms raise concerns in privacy-sensitive environments due to the recording of identifiable features. Event cameras offer a promising solution by capturing scene brightness changes sparsely at the pixel level, without capturing full images. Moreover, event cameras have high dynamic ranges that can effectively handle scenarios with complex lighting conditions, such as low light or high contrast environments. However, using event cameras introduces challenges in modeling the spatially sparse and high temporal resolution event data for HAR. To address these issues, we propose the SpikMamba framework, which combines the energy efficiency of spiking neural networks and the long sequence modeling capability of Mamba to efficiently capture global features from spatially sparse and high a temporal resolution event data. Additionally, to improve the locality of modeling, a spiking window-based linear attention mechanism is used. Extensive experiments show that SpikMamba achieves remarkable recognition performance, surpassing the previous state-of-the-art by 1.45%, 7.22%, 0.15%, and 3.92% on the PAF, HARDVS, DVS128, and E-FAction datasets, respectively. The code is available at https://github.com/Typistchen/SpikMamba.
Real-time Sub-milliwatt Epilepsy Detection Implemented on a Spiking Neural Network Edge Inference Processor
2024-10-22
Ruixin Lia, Guoxu Zhaoa, Dylan Richard Muir, Yuya Ling, Karla Burelo, Mina Khoei, Dong Wang, Yannan Xing, Ning Qiao
Analyzing electroencephalogram (EEG) signals to detect the epileptic seizure status of a subject presents a challenge to existing technologies aimed at providing timely and efficient diagnosis. In this study, we aimed to detect interictal and ictal periods of epileptic seizures using a spiking neural network (SNN). Our proposed approach provides an online and real-time preliminary diagnosis of epileptic seizures and helps to detect possible pathological conditions.To validate our approach, we conducted experiments using multiple datasets. We utilized a trained SNN to identify the presence of epileptic seizures and compared our results with those of related studies. The SNN model was deployed on Xylo, a digital SNN neuromorphic processor designed to process temporal signals. Xylo efficiently simulates spiking leaky integrate-and-fire neurons with exponential input synapses. Xylo has much lower energy requirments than traditional approaches to signal processing, making it an ideal platform for developing low-power seizure detection systems.Our proposed method has a high test accuracy of 93.3% and 92.9% when classifying ictal and interictal periods. At the same time, the application has an average power consumption of 87.4 uW(IO power) + 287.9 uW(computational power) when deployed to Xylo. Our method demonstrates excellent low-latency performance when tested on multiple datasets. Our work provides a new solution for seizure detection, and it is expected to be widely used in portable and wearable devices in the future.
Supervised Learning without Backpropagation using Spike-Timing-Dependent Plasticity for Image Recognition
2024-10-21
Wei Xie
This study introduces a novel supervised learning approach for spiking neural networks that does not rely on traditional backpropagation. Instead, it employs spike-timing-dependent plasticity (STDP) within a supervised framework for image recognition tasks. The effectiveness of this method is demonstrated using the MNIST dataset. The model achieves approximately 40\% learning accuracy with just 10 training stimuli, where each category is exposed to the model only once during training (one-shot learning). With larger training samples, the accuracy increases up to 87\%, maintaining negligible ambiguity. Notably, with only 10 hidden neurons, the model reaches 89\% accuracy with around 10\% ambiguity. This proposed method offers a robust and efficient alternative to traditional backpropagation-based supervised learning techniques.
Spiking Neural Networks as a Controller for Emergent Swarm Agents
2024-10-21
Kevin Zhu, Connor Mattson, Shay Snyder, Ricardo Vega, Daniel S. Brown, Maryam Parsa, Cameron Nowzari
Drones which can swarm and loiter in a certain area cost hundreds of dollars, but mosquitos can do the same and are essentially worthless. To control swarms of low-cost robots, researchers may end up spending countless hours brainstorming robot configurations and policies to ``organically" create behaviors which do not need expensive sensors and perception. Existing research explores the possible emergent behaviors in swarms of robots with only a binary sensor and a simple but hand-picked controller structure. Even agents in this highly limited sensing, actuation, and computational capability class can exhibit relatively complex global behaviors such as aggregation, milling, and dispersal, but finding the local interaction rules that enable more collective behaviors remains a significant challenge. This paper investigates the feasibility of training spiking neural networks to find those local interaction rules that result in particular emergent behaviors. In this paper, we focus on simulating a specific milling behavior already known to be producible using very simple binary sensing and acting agents. To do this, we use evolutionary algorithms to evolve not only the parameters (the weights, biases, and delays) of a spiking neural network, but also its structure. To create a baseline, we also show an evolutionary search strategy over the parameters for the incumbent hand-picked binary controller structure. Our simulations show that spiking neural networks can be evolved in binary sensing agents to form a mill.
Addressing Spectral Bias of Deep Neural Networks by Multi-Grade Deep Learning
2024-10-21
Ronglong Fang, Yuesheng Xu
Deep neural networks (DNNs) suffer from the spectral bias, wherein DNNs typically exhibit a tendency to prioritize the learning of lower-frequency components of a function, struggling to capture its high-frequency features. This paper is to address this issue. Notice that a function having only low frequency components may be well-represented by a shallow neural network (SNN), a network having only a few layers. By observing that composition of low frequency functions can effectively approximate a high-frequency function, we propose to learn a function containing high-frequency components by composing several SNNs, each of which learns certain low-frequency information from the given data. We implement the proposed idea by exploiting the multi-grade deep learning (MGDL) model, a recently introduced model that trains a DNN incrementally, grade by grade, a current grade learning from the residue of the previous grade only an SNN composed with the SNNs trained in the preceding grades as features. We apply MGDL to synthetic, manifold, colored images, and MNIST datasets, all characterized by presence of high-frequency features. Our study reveals that MGDL excels at representing functions containing high-frequency information. Specifically, the neural networks learned in each grade adeptly capture some low-frequency information, allowing their compositions with SNNs learned in the previous grades effectively representing the high-frequency features. Our experimental results underscore the efficacy of MGDL in addressing the spectral bias inherent in DNNs. By leveraging MGDL, we offer insights into overcoming spectral bias limitation of DNNs, thereby enhancing the performance and applicability of deep learning models in tasks requiring the representation of high-frequency information. This study confirms that the proposed method offers a promising solution to address the spectral bias of DNNs.
Enhancing SNN-based Spatio-Temporal Learning: A Benchmark Dataset and Cross-Modality Attention Model
2024-10-21
Shibo Zhou, Bo Yang, Mengwen Yuan, Runhao Jiang, Rui Yan, Gang Pan, Huajin Tang
Spiking Neural Networks (SNNs), renowned for their low power consumption, brain-inspired architecture, and spatio-temporal representation capabilities, have garnered considerable attention in recent years. Similar to Artificial Neural Networks (ANNs), high-quality benchmark datasets are of great importance to the advances of SNNs. However, our analysis indicates that many prevalent neuromorphic datasets lack strong temporal correlation, preventing SNNs from fully exploiting their spatio-temporal representation capabilities. Meanwhile, the integration of event and frame modalities offers more comprehensive visual spatio-temporal information. Yet, the SNN-based cross-modality fusion remains underexplored. In this work, we present a neuromorphic dataset called DVS-SLR that can better exploit the inherent spatio-temporal properties of SNNs. Compared to existing datasets, it offers advantages in terms of higher temporal correlation, larger scale, and more varied scenarios. In addition, our neuromorphic dataset contains corresponding frame data, which can be used for developing SNN-based fusion methods. By virtue of the dual-modal feature of the dataset, we propose a Cross-Modality Attention (CMA) based fusion method. The CMA model efficiently utilizes the unique advantages of each modality, allowing for SNNs to learn both temporal and spatial attention scores from the spatio-temporal features of event and frame modalities, subsequently allocating these scores across modalities to enhance their synergy. Experimental results demonstrate that our method not only improves recognition accuracy but also ensures robustness across diverse scenarios.
Fractional-order spike-timing-dependent gradient descent for multi-layer spiking neural networks
2024-10-20
Yi Yang, Richard M. Voyles, Haiyan H. Zhang, Robert A. Nawrocki
Accumulated detailed knowledge about the neuronal activities in human brains has brought more attention to bio-inspired spiking neural networks (SNNs). In contrast to non-spiking deep neural networks (DNNs), SNNs can encode and transmit spatiotemporal information more efficiently by exploiting biologically realistic and low-power event-driven neuromorphic architectures. However, the supervised learning of SNNs still remains a challenge because the spike-timing-dependent plasticity (STDP) of connected spiking neurons is difficult to implement and interpret in existing backpropagation learning schemes. This paper proposes a fractional-order spike-timing-dependent gradient descent (FO-STDGD) learning model by considering a derived nonlinear activation function that describes the relationship between the quasi-instantaneous firing rate and the temporal membrane potentials of nonleaky integrate-and-fire neurons. The training strategy can be generalized to any fractional orders between 0 and 2 since the FO-STDGD incorporates the fractional gradient descent method into the calculation of spike-timing-dependent loss gradients. The proposed FO-STDGD model is tested on the MNIST and DVS128 Gesture datasets and its accuracy under different network structure and fractional orders is analyzed. It can be found that the classification accuracy increases as the fractional order increases, and specifically, the case of fractional order 1.9 improves by 155% relative to the case of fractional order 1 (traditional gradient descent). In addition, our scheme demonstrates the state-of-the-art computational efficacy for the same SNN structure and training epochs.
Deep learning models are increasingly deployed on resource-constrained edge devices for real-time data analytics. In recent years, Vision Transformer models and their variants have demonstrated outstanding performance across various computer vision tasks. However, their high computational demands and inference latency pose significant challenges for model deployment on resource-constraint edge devices. To address this issue, we propose a novel Vision Transformer splitting framework, ED-ViT, designed to execute complex models across multiple edge devices efficiently. Specifically, we partition Vision Transformer models into several sub-models, where each sub-model is tailored to handle a specific subset of data classes. To further minimize computation overhead and inference latency, we introduce a class-wise pruning technique that reduces the size of each sub-model. We conduct extensive experiments on five datasets with three model structures, demonstrating that our approach significantly reduces inference latency on edge devices and achieves a model size reduction of up to 28.9 times and 34.1 times, respectively, while maintaining test accuracy comparable to the original Vision Transformer. Additionally, we compare ED-ViT with two state-of-the-art methods that deploy CNN and SNN models on edge devices, evaluating accuracy, inference time, and overall model size. Our comprehensive evaluation underscores the effectiveness of the proposed ED-ViT framework.
Spiking Neural Belief Propagation Decoder for Short Block Length LDPC Codes
2024-10-15
Alexander von Bank, Eike-Manuel Edelmann, Sisi Miao, Jonathan Mandelbaum, Laurent Schmalen
Spiking neural networks (SNNs) are neural networks that enable energy-efficient signal processing due to their event-based nature. This paper proposes a novel decoding algorithm for low-density parity-check (LDPC) codes that integrates SNNs into belief propagation (BP) decoding by approximating the check node update equations using SNNs. For the (273,191) and (1023,781) finite-geometry LDPC code, the proposed decoder outperforms sum-product decoder at high signal-to-noise ratios (SNRs). The decoder achieves a similar bit error rate to normalized sum-product decoding with successive relaxation. Furthermore, the novel decoding operates without requiring knowledge of the SNR, making it robust to SNR mismatch.
Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation
2024-10-15
Chengting Yu, Lei Liu, Gaoang Wang, Erping Li, Aili Wang
Recent insights have revealed that rate-coding is a primary form of information representation captured by surrogate-gradient-based Backpropagation Through Time (BPTT) in training deep Spiking Neural Networks (SNNs). Motivated by these findings, we propose rate-based backpropagation, a training strategy specifically designed to exploit rate-based representations to reduce the complexity of BPTT. Our method minimizes reliance on detailed temporal derivatives by focusing on averaged dynamics, streamlining the computational graph to reduce memory and computational demands of SNNs training. We substantiate the rationality of the gradient approximation between BPTT and the proposed method through both theoretical analysis and empirical observations. Comprehensive experiments on CIFAR-10, CIFAR-100, ImageNet, and CIFAR10-DVS validate that our method achieves comparable performance to BPTT counterparts, and surpasses state-of-the-art efficient training techniques. By leveraging the inherent benefits of rate-coding, this work sets the stage for more scalable and efficient SNNs training within resource-constrained environments. Our code is available at https://github.com/Tab-ct/rate-based-backpropagation.
Neuromorphic computing has emerged as a promising energy-efficient alternative to traditional artificial intelligence, predominantly utilizing spiking neural networks (SNNs) implemented on neuromorphic hardware. Significant advancements have been made in SNN-based convolutional neural networks (CNNs) and Transformer architectures. However, neuromorphic computing for the medical imaging domain remains underexplored. In this study, we introduce EG-SpikeFormer, an SNN architecture tailored for clinical tasks that incorporates eye-gaze data to guide the model's attention to the diagnostically relevant regions in medical images. Our developed approach effectively addresses shortcut learning issues commonly observed in conventional models, especially in scenarios with limited clinical data and high demands for model reliability, generalizability, and transparency. Our EG-SpikeFormer not only demonstrates superior energy efficiency and performance in medical image prediction tasks but also enhances clinical relevance through multi-modal information alignment. By incorporating eye-gaze data, the model improves interpretability and generalization, opening new directions for applying neuromorphic computing in healthcare.
Realizing Linear Synaptic Plasticity in Electric Double Layer-Gated Transistors for Improved Predictive Accuracy and Efficiency in Neuromorphic Computing
Neuromorphic computing offers a low-power, parallel alternative to traditional von Neumann architectures by addressing the sequential data processing bottlenecks. Electric double layer-gated transistors (EDLTs) resemble biological synapses with their ionic response and offer low power operations, making them suitable for neuromorphic applications. A critical consideration for artificial neural networks (ANNs) is achieving linear and symmetric plasticity (weight updates) during training, as this directly affects accuracy and efficiency. This study uses finite element modeling to explore EDLTs as artificial synapses in ANNs and investigates the underlying mechanisms behind the nonlinear plasticity observed experimentally in previous studies. By solving modified Poisson-Nernst-Planck (mPNP) equations, we examined ion dynamics within an EDL capacitor & their effects on plasticity, revealing that the rates of EDL formation and dissipation are concentration-dependent. Fixed-magnitude pulse inputs result in decreased formation & increased dissipation rates, leading to nonlinear weight updates and limits the number of accessible states and operating range of devices. To address this, we developed a predictive linear ionic weight update solver (LIWUS) in Python to predict voltage pulse inputs that achieve linear plasticity. We then evaluated an ANN with linear and nonlinear weight updates on the MNIST classification task. The LIWUS-provided linear weight updates required 19% fewer epochs in training and validation than the network with nonlinear weight updates to reach optimal performance. It achieved a 97.6% recognition accuracy, 1.5-4.2% higher than with nonlinear updates and a low standard deviation of 0.02%. The network model is amenable to future spiking neural network applications and the performance improvements with linear weight update is expected to increase for complex networks.
SpikeBottleNet: Spike-Driven Feature Compression Architecture for Edge-Cloud Co-Inference
2024-10-11
Maruf Hassan, Steven Davy
Edge-cloud co-inference enables efficient deep neural network (DNN) deployment by splitting the architecture between an edge device and cloud server, crucial for resource-constraint edge devices. This approach requires balancing on-device computations and communication costs, often achieved through compressed intermediate feature transmission. Conventional DNN architectures require continuous data processing and floating point activations, leading to considerable energy consumption and increased feature sizes, thus raising transmission costs. This challenge motivates exploring binary, event-driven activations using spiking neural networks (SNNs), known for their extreme energy efficiency. In this research, we propose SpikeBottleNet, a novel architecture for edge-cloud co-inference systems that integrates a spiking neuron model to significantly reduce energy consumption on edge devices. A key innovation of our study is an intermediate feature compression technique tailored for SNNs for efficient feature transmission. This technique leverages a split computing approach to strategically place encoder-decoder bottleneck units within complex deep architectures like ResNet and MobileNet. Experimental results demonstrate that SpikeBottleNet achieves up to 256x bit compression in the final convolutional layer of ResNet, with minimal accuracy loss (0.16%). Additionally, our approach enhances edge device energy efficiency by up to 144x compared to the baseline BottleNet, making it ideal for resource-limited edge devices.
The ability to predict future events or patterns based on previous experience is crucial for many applications such as traffic control, weather forecasting, or supply chain management. While modern supervised Machine Learning approaches excel at such sequential tasks, they are computationally expensive and require large training data. A previous work presented a biologically plausible sequence learning model, developed through a bottom-up approach, consisting of a spiking neural network and unsupervised local learning rules. The model in its original formulation identifies only a specific type of sequence elements composed of synchronous spikes by activating a subset of neurons with identical stimulus preference. In this work, we extend the model to detect and learn sequences of various spatio-temporal patterns (STPs) by incorporating plastic connections in the input synapses. We showcase that the model is able to learn and predict high-order sequences. We further study the robustness of the model against different input settings and parameters.
Continual Learning with Neuromorphic Computing: Theories, Methods, and Applications
2024-10-11
Mishal Fatima Minhas, Rachmad Vidya Wicaksana Putra, Falah Awwad, Osman Hasan, Muhammad Shafique
To adapt to real-world dynamics, intelligent systems need to assimilate new knowledge without catastrophic forgetting, where learning new tasks leads to a degradation in performance on old tasks. To address this, continual learning concept is proposed for enabling autonomous systems to acquire new knowledge and dynamically adapt to changing environments. Specifically, energy-efficient continual learning is needed to ensure the functionality of autonomous systems under tight compute and memory resource budgets (i.e., so-called autonomous embedded systems). Neuromorphic computing, with brain-inspired Spiking Neural Networks (SNNs), offers inherent advantages for enabling low-power/energy continual learning in autonomous embedded systems. In this paper, we comprehensively discuss the foundations and methods for enabling continual learning in neural networks, then analyze the state-of-the-art works considering SNNs. Afterward, comparative analyses of existing methods are conducted while considering crucial design factors, such as network complexity, memory, latency, and power/energy efficiency. We also explore the practical applications that can benefit from SNN-based continual learning and open challenges in real-world scenarios. In this manner, our survey provides valuable insights into the recent advancements of SNN-based continual learning for real-world application use-cases.
Energy-efficient SNN Architecture using 3nm FinFET Multiport SRAM-based CIM with Online Learning
2024-10-11
Lucas Huijbregts, Liu Hsiao-Hsuan, Paul Detterer, Said Hamdioui, Amirreza Yousefzadeh, Rajendra Bishnoi
Current Artificial Intelligence (AI) computation systems face challenges, primarily from the memory-wall issue, limiting overall system-level performance, especially for Edge devices with constrained battery budgets, such as smartphones, wearables, and Internet-of-Things sensor systems. In this paper, we propose a new SRAM-based Compute-In-Memory (CIM) accelerator optimized for Spiking Neural Networks (SNNs) Inference. Our proposed architecture employs a multiport SRAM design with multiple decoupled Read ports to enhance the throughput and Transposable Read-Write ports to facilitate online learning. Furthermore, we develop an Arbiter circuit for efficient data-processing and port allocations during the computation. Results for a 128$\times$128 array in 3nm FinFET technology demonstrate a 3.1$\times$ improvement in speed and a 2.2$\times$ enhancement in energy efficiency with our proposed multiport SRAM design compared to the traditional single-port design. At system-level, a throughput of 44 MInf/s at 607 pJ/Inf and 29mW is achieved.
Artificial neural network based Pedestrian Attribute Recognition (PAR) has been widely studied in recent years, despite many progresses, however, the energy consumption is still high. To address this issue, in this paper, we propose a Spiking Neural Network (SNN) based framework for energy-efficient attribute recognition. Specifically, we first adopt a spiking tokenizer module to transform the given pedestrian image into spiking feature representations. Then, the output will be fed into the spiking Transformer backbone networks for energy-efficient feature extraction. We feed the enhanced spiking features into a set of feed-forward networks for pedestrian attribute recognition. In addition to the widely used binary cross-entropy loss function, we also exploit knowledge distillation from the artificial neural network to the spiking Transformer network for more accurate attribute recognition. Extensive experiments on three widely used PAR benchmark datasets fully validated the effectiveness of our proposed SNN-PAR framework. The source code of this paper is released on \url{https://github.com/Event-AHU/OpenPAR}.
Spiking Neural Networks (SNNs) are considered to have enormous potential in the future development of Artificial Intelligence (AI) due to their brain-inspired and energy-efficient properties. In the current supervised learning domain of SNNs, compared to vanilla Spatial-Temporal Back-propagation (STBP) training, online training can effectively overcome the risk of GPU memory explosion and has received widespread academic attention. However, the current proposed online training methods cannot tackle the inseparability problem of temporal dependent gradients and merely aim to optimize the training memory, resulting in no performance advantages compared to the STBP training models in the inference phase. To address the aforementioned challenges, we propose Efficient Multi-Precision Firing (EM-PF) model, which is a family of advanced spiking models based on floating-point spikes and binary synaptic weights. We point out that EM-PF model can effectively separate temporal gradients and achieve full-stage optimization towards computation speed and memory footprint. Experimental results have demonstrated that EM-PF model can be flexibly combined with various techniques including random back-propagation, parallel computation and channel attention mechanism, to achieve state-of-the-art performance with extremely low computational overhead in the field of online learning.
Spiking GS: Towards High-Accuracy and Low-Cost Surface Reconstruction via Spiking Neuron-based Gaussian Splatting
2024-10-09
Weixing Zhang, Zongrui Li, De Ma, Huajin Tang, Xudong Jiang, Qian Zheng, Gang Pan
3D Gaussian Splatting is capable of reconstructing 3D scenes in minutes. Despite recent advances in improving surface reconstruction accuracy, the reconstructed results still exhibit bias and suffer from inefficiency in storage and training. This paper provides a different observation on the cause of the inefficiency and the reconstruction bias, which is attributed to the integration of the low-opacity parts (LOPs) of the generated Gaussians. We show that LOPs consist of Gaussians with overall low-opacity (LOGs) and the low-opacity tails (LOTs) of Gaussians. We propose Spiking GS to reduce such two types of LOPs by integrating spiking neurons into the Gaussian Splatting pipeline. Specifically, we introduce global and local full-precision integrate-and-fire spiking neurons to the opacity and representation function of flattened 3D Gaussians, respectively. Furthermore, we enhance the density control strategy with spiking neurons' thresholds and a new criterion on the scale of Gaussians. Our method can represent more accurate reconstructed surfaces at a lower cost. The supplementary material and code are available at https://github.com/zju-bmi-lab/SpikingGS.
MC-QDSNN: Quantized Deep evolutionary SNN with Multi-Dendritic Compartment Neurons for Stress Detection using Physiological Signals
2024-10-07
Ajay B S, Phani Pavan K, Madhav Rao
Long short-term memory (LSTM) has emerged as a definitive network for analyzing and inferring time series data. LSTM has the capability to extract spectral features and a mixture of temporal features. Due to this benefit, a similar feature extraction method is explored for the spiking counterparts targeting time-series data. Though LSTMs perform well in their spiking form, they tend to be compute and power intensive. Addressing this issue, this work proposes Multi-Compartment Leaky (MCLeaky) neuron as a viable alternative for efficient processing of time series data. The MCLeaky neuron, derived from the Leaky Integrate and Fire (LIF) neuron model, contains multiple memristive synapses interlinked to form a memory component, which emulates the human brain's Hippocampus region. The proposed MCLeaky neuron based Spiking Neural Network model and its quantized variant were benchmarked against state-of-the-art (SOTA) Spiking LSTMs to perform human stress detection, by comparing compute requirements, latency and real-world performances on unseen data with models derived through Neural Architecture Search (NAS). Results show that networks with MCLeaky activation neuron managed a superior accuracy of 98.8% to detect stress based on Electrodermal Activity (EDA) signals, better than any other investigated models, while using 20% less parameters on average. MCLeaky neuron was also tested for various signals including EDA Wrist and Chest, Temperature, ECG, and combinations of them. Quantized MCLeaky model was also derived and validated to forecast their performance on hardware architectures, which resulted in 91.84% accuracy. The neurons were evaluated for multiple modalities of data towards stress detection, which resulted in energy savings of 25.12x to 39.20x and EDP gains of 52.37x to 81.9x over ANNs, while offering a best accuracy of 98.8% when compared with the rest of the SOTA implementations.
Towards Ultra-Low-Power Neuromorphic Speech Enhancement with Spiking-FullSubNet
2024-10-07
Xiang Hao, Chenxiang Ma, Qu Yang, Jibin Wu, Kay Chen Tan
Speech enhancement is critical for improving speech intelligibility and quality in various audio devices. In recent years, deep learning-based methods have significantly improved speech enhancement performance, but they often come with a high computational cost, which is prohibitive for a large number of edge devices, such as headsets and hearing aids. This work proposes an ultra-low-power speech enhancement system based on the brain-inspired spiking neural network (SNN) called Spiking-FullSubNet. Spiking-FullSubNet follows a full-band and sub-band fusioned approach to effectively capture both global and local spectral information. To enhance the efficiency of computationally expensive sub-band modeling, we introduce a frequency partitioning method inspired by the sensitivity profile of the human peripheral auditory system. Furthermore, we introduce a novel spiking neuron model that can dynamically control the input information integration and forgetting, enhancing the multi-scale temporal processing capability of SNN, which is critical for speech denoising. Experiments conducted on the recent Intel Neuromorphic Deep Noise Suppression (N-DNS) Challenge dataset show that the Spiking-FullSubNet surpasses state-of-the-art methods by large margins in terms of both speech quality and energy efficiency metrics. Notably, our system won the championship of the Intel N-DNS Challenge (Algorithmic Track), opening up a myriad of opportunities for ultra-low-power speech enhancement at the edge. Our source code and model checkpoints are publicly available at https://github.com/haoxiangsnr/spiking-fullsubnet.
Hardware-Software Co-optimised Fast and Accurate Deep Reconfigurable Spiking Inference Accelerator Architecture Design Methodology
2024-10-07
Anagha Nimbekar, Prabodh Katti, Chen Li, Bashir M. Al-Hashimi, Amit Acharyya, Bipin Rajendran
Spiking Neural Networks (SNNs) have emerged as a promising approach to improve the energy efficiency of machine learning models, as they naturally implement event-driven computations while avoiding expensive multiplication operations. In this paper, we develop a hardware-software co-optimisation strategy to port software-trained deep neural networks (DNN) to reduced-precision spiking models demonstrating fast and accurate inference in a novel event-driven CMOS reconfigurable spiking inference accelerator. Experimental results show that a reduced-precision Resnet-18 and VGG-11 SNN models achieves classification accuracy within 1% of the baseline full-precision DNN model within 8 spike timesteps. We also demonstrate an FPGA prototype implementation of the spiking inference accelerator with a throughput of 38.4 giga operations per second (GOPS) consuming 1.54 Watts on PYNQ-Z2 FPGA. This corresponds to 0.6 GOPS per processing element and 2.25,GOPS/DSP slice, which is 2x and 4.5x higher utilisation efficiency respectively compared to the state-of-the-art. Our co-optimisation strategy can be employed to develop deep reduced precision SNN models and port them to resource-efficient event-driven hardware accelerators for edge applications.
Spiking neural networks (SNNs) provide an energy-efficient solution by utilizing the spike-based and sparse nature of biological systems. Since the advent of Transformers, SNNs have struggled to compete with artificial networks on long sequential tasks, until the recent emergence of state space models (SSMs), which offer superior computational efficiency and modeling capability. However, applying the highly capable SSMs to SNNs for long sequences learning poses three major challenges: (1) The membrane potential is determined by the past spiking history of the neuron, leading to reduced efficiency for sequence modeling in parallel computing scenarios. (2) Complex dynamics of biological spiking neurons are crucial for functionality but challenging to simulate and exploit effectively in large networks. (3) It is arduous to maintain high sparsity while achieving high accuracy for spiking neurons without resorting to dense computing, as utilized in artificial neuron-based SSMs. To address them, we propose a sparse, precise and efficient spiking SSM framework, termed SPikE-SSM. For (1), we propose a boundary compression strategy (PMBC) to accelerate the inference of the spiking neuron model, enabling parallel processing for long sequence learning. For (2), we propose a novel and concise neuron model incorporating reset-refractory mechanism to leverage the inherent temporal dimension for dynamic computing with biological interpretability. For (3), we hierarchically integrate the proposed neuron model to the original SSM block, and enhance the dynamics of SPikE-SSM by incorporating trainable thresholds and refractory magnitudes to balance accuracy and sparsity. Extensive experiments verify the effectiveness and robustness of SPikE-SSM on the long range arena benchmarks and large language dataset WikiText-103, showing the potential of dynamic spiking neurons in efficient long sequence learning.
Low-dimensional model for adaptive networks of spiking neurons
2024-10-04
Bastian Pietras, Pau Clusella, Ernest Montbrió
We investigate a large ensemble of Quadratic Integrate-and-Fire (QIF) neurons with heterogeneous input currents and adaptation variables. Our analysis reveals that for a specific class of adaptation, termed quadratic spike-frequency adaptation (QSFA), the high-dimensional system can be exactly reduced to a low-dimensional system of ordinary differential equations, which describes the dynamics of three mean-field variables: the population's firing rate, the mean membrane potential, and a mean adaptation variable. The resulting low-dimensional firing rate equations (FRE) uncover a key generic feature of heterogeneous networks with spike frequency adaptation: Both the center and the width of the distribution of the neurons' firing frequencies are reduced, and this largely promotes the emergence of collective synchronization in the network. Our findings are further supported by the bifurcation analysis of the FRE, which accurately captures the collective dynamics of the spiking neuron network, including phenomena such as collective oscillations, bursting, and macroscopic chaos.
Recently, there is growing demand for effective and efficient long sequence modeling, with State Space Models (SSMs) proving to be effective for long sequence tasks. To further reduce energy consumption, SSMs can be adapted to Spiking Neural Networks (SNNs) using spiking functions. However, current spiking-formalized SSMs approaches still rely on float-point matrix-vector multiplication during inference, undermining SNNs' energy advantage. In this work, we address the efficiency and performance challenges of long sequence learning in SNNs simultaneously. First, we propose a decoupled reset method for parallel spiking neuron training, reducing the typical Leaky Integrate-and-Fire (LIF) model's training time from $O(L^2)$ to $O(L\log L)$, effectively speeding up the training by $6.57 \times$ to $16.50 \times$ on sequence lengths $1,024$ to $32,768$. To our best knowledge, this is the first time that parallel computation with a reset mechanism is implemented achieving equivalence to its sequential counterpart. Secondly, to capture long-range dependencies, we propose a Parallel Resonate and Fire (PRF) neuron, which leverages an oscillating membrane potential driven by a resonate mechanism from a differentiable reset function in the complex domain. The PRF enables efficient long sequence learning while maintaining parallel training. Finally, we demonstrate that the proposed spike-driven architecture using PRF achieves performance comparable to Structured SSMs (S4), with two orders of magnitude reduction in energy consumption, outperforming Transformer on Long Range Arena tasks.
The Taiji microresonator as an unidirectional spiking neuron
2024-10-04
Stefano Biasi, Alessandro Foradori, Riccardo Franchi, Alessio Lugnan, Peter Bienstman, Lorenzo Pavesi
While biological neurons ensure unidirectional signalling, scalable integrated photonic neurons, such as silicon microresonators, respond the same way regardless of excitation direction due to the Lorentz reciprocity principle. Here, we show that a non-linear Taiji microresonator is a proper optical analogous of a biological neuron showing both a spiking response as well as a direction dependence response.
Event-based cameras are attracting significant interest as they provide rich edge information, high dynamic range, and high temporal resolution. Many state-of-the-art event-based algorithms rely on splitting the events into fixed groups, resulting in the omission of crucial temporal information, particularly when dealing with diverse motion scenarios (\eg, high/low speed).In this work, we propose SpikeSlicer, a novel-designed plug-and-play event processing method capable of splitting events stream adaptively.SpikeSlicer utilizes a low-energy spiking neural network (SNN) to trigger event slicing. To guide the SNN to fire spikes at optimal time steps, we propose the Spiking Position-aware Loss (SPA-Loss) to modulate the neuron's state. Additionally, we develop a Feedback-Update training strategy that refines the slicing decisions using feedback from the downstream artificial neural network (ANN). Extensive experiments demonstrate that our method yields significant performance improvements in event-based object tracking and recognition. Notably, SpikeSlicer provides a brand-new SNN-ANN cooperation paradigm, where the SNN acts as an efficient, low-energy data processor to assist the ANN in improving downstream performance, injecting new perspectives and potential avenues of exploration.
This study introduces BrainTransformers, an innovative Large Language Model (LLM) implemented using Spiking Neural Networks (SNN). Our key contributions include: (1) designing SNN-compatible Transformer components such as SNNMatmul, SNNSoftmax, and SNNSiLU; (2) implementing an SNN approximation of the SiLU activation function; and (3) developing a Synapsis module to simulate synaptic plasticity. Our 3-billion parameter model, BrainTransformers-3B-Chat, demonstrates competitive performance across various benchmarks, including MMLU (63.2), BBH (54.1), ARC-C (54.3), and GSM8K (76.3), while potentially offering improved energy efficiency and biological plausibility. The model employs a three-stage training approach, including SNN-specific neuronal synaptic plasticity training. This research opens new avenues for brain-like AI systems in natural language processing and neuromorphic computing. Future work will focus on hardware optimization, developing specialized SNN fine-tuning tools, and exploring practical applications in energy-efficient computing environments.
Gradient descent computed by backpropagation (BP) is a widely used learning method for training artificial neural networks but has several limitations: it is computationally demanding, requires frequent manual tuning of the network architecture, and is prone to catastrophic forgetting when learning incrementally. To address these issues, we introduce a brain-mimetic developmental spiking neural network (BDNN) that mimics the postnatal development of neural circuits. We validate its performance through a neuromorphic tactile system capable of learning to recognize objects through grasping. Unlike traditional BP-based methods, BDNN exhibits strong knowledge transfer, supporting efficient incremental learning of new tactile information. It requires no hyperparameter tuning and dynamically adapts to incoming data. Moreover, compared to the BP-based counterpart, it achieves classification accuracy on par with BP while learning over ten times faster in ideal conditions and up to two or three orders of magnitude faster in practical settings. These features make BDNN well-suited for fast data processing on edge devices.
Deep activity propagation via weight initialization in spiking neural networks
2024-10-01
Aurora Micheli, Olaf Booij, Jan van Gemert, Nergis Tömen
Spiking Neural Networks (SNNs) and neuromorphic computing offer bio-inspired advantages such as sparsity and ultra-low power consumption, providing a promising alternative to conventional networks. However, training deep SNNs from scratch remains a challenge, as SNNs process and transmit information by quantizing the real-valued membrane potentials into binary spikes. This can lead to information loss and vanishing spikes in deeper layers, impeding effective training. While weight initialization is known to be critical for training deep neural networks, what constitutes an effective initial state for a deep SNN is not well-understood. Existing weight initialization methods designed for conventional networks (ANNs) are often applied to SNNs without accounting for their distinct computational properties. In this work we derive an optimal weight initialization method specifically tailored for SNNs, taking into account the quantization operation. We show theoretically that, unlike standard approaches, this method enables the propagation of activity in deep SNNs without loss of spikes. We demonstrate this behavior in numerical simulations of SNNs with up to 100 layers across multiple time steps. We present an in-depth analysis of the numerical conditions, regarding layer width and neuron hyperparameters, which are necessary to accurately apply our theoretical findings. Furthermore, our experiments on MNIST demonstrate higher accuracy and faster convergence when using the proposed weight initialization scheme. Finally, we show that the newly introduced weight initialization is robust against variations in several network and neuron hyperparameters.
Enhancing GANs with Contrastive Learning-Based Multistage Progressive Finetuning SNN and RL-Based External Optimization
2024-09-30
Osama Mustafa
Generative Adversarial Networks (GANs) have been at the forefront of image synthesis, especially in medical fields like histopathology, where they help address challenges such as data scarcity, patient privacy, and class imbalance. However, several inherent and domain-specific issues remain. For GANs, training instability, mode collapse, and insufficient feedback from binary classification can undermine performance. These challenges are particularly pronounced with high-resolution histopathology images due to their complex feature representation and high spatial detail. In response to these challenges, this work proposes a novel framework integrating a contrastive learning-based Multistage Progressive Finetuning Siamese Neural Network (MFT-SNN) with a Reinforcement Learning-based External Optimizer (RL-EO). The MFT-SNN improves feature similarity extraction in histopathology data, while the RL-EO acts as a reward-based guide to balance GAN training, addressing mode collapse and enhancing output quality. The proposed approach is evaluated against state-of-the-art (SOTA) GAN models and demonstrates superior performance across multiple metrics.
Spiking Transformer with Spatial-Temporal Attention
2024-09-29
Donghyun Lee, Yuhang Li, Youngeun Kim, Shiting Xiao, Priyadarshini Panda
Spiking Neural Networks (SNNs) present a compelling and energy-efficient alternative to traditional Artificial Neural Networks (ANNs) due to their sparse binary activation. Leveraging the success of the transformer architecture, the spiking transformer architecture is explored to scale up dataset size and performance. However, existing works only consider the spatial self-attention in spiking transformer, neglecting the inherent temporal context across the timesteps. In this work, we introduce Spiking Transformer with Spatial-Temporal Attention (STAtten), a simple and straightforward architecture designed to integrate spatial and temporal information in self-attention with negligible additional computational load. The STAtten divides the temporal or token index and calculates the self-attention in a cross-manner to effectively incorporate spatial-temporal information. We first verify our spatial-temporal attention mechanism's ability to capture long-term temporal dependencies using sequential datasets. Moreover, we validate our approach through extensive experiments on varied datasets, including CIFAR10/100, ImageNet, CIFAR10-DVS, and N-Caltech101. Notably, our cross-attention mechanism achieves an accuracy of 78.39 % on the ImageNet dataset.
Membership Privacy Evaluation in Deep Spiking Neural Networks
2024-09-28
Jiaxin Li, Gorka Abad, Stjepan Picek, Mauro Conti
Artificial Neural Networks (ANNs), commonly mimicking neurons with non-linear functions to output floating-point numbers, consistently receive the same signals of a data point during its forward time. Unlike ANNs, Spiking Neural Networks (SNNs) get various input signals in the forward time of a data point and simulate neurons in a biologically plausible way, i.e., producing a spike (a binary value) if the accumulated membrane potential of a neuron is larger than a threshold. Even though ANNs have achieved remarkable success in multiple tasks, e.g., face recognition and object detection, SNNs have recently obtained attention due to their low power consumption, fast inference, and event-driven properties. While privacy threats against ANNs are widely explored, much less work has been done on SNNs. For instance, it is well-known that ANNs are vulnerable to the Membership Inference Attack (MIA), but whether the same applies to SNNs is not explored. In this paper, we evaluate the membership privacy of SNNs by considering eight MIAs, seven of which are inspired by MIAs against ANNs. Our evaluation results show that SNNs are more vulnerable (maximum 10% higher in terms of balanced attack accuracy) than ANNs when both are trained with neuromorphic datasets (with time dimension). On the other hand, when training ANNs or SNNs with static datasets (without time dimension), the vulnerability depends on the dataset used. If we convert ANNs trained with static datasets to SNNs, the accuracy of MIAs drops (maximum 11.5% with a reduction of 7.6% on the test accuracy of the target model). Next, we explore the impact factors of MIAs on SNNs by conducting a hyperparameter study. Finally, we show that the basic data augmentation method for static data and two recent data augmentation methods for neuromorphic data can considerably (maximum reduction of 25.7%) decrease MIAs' performance on SNNs.
Improvement of Spiking Neural Network with Bit Planes and Color Models
2024-09-28
Nhan T. Luu, Duong T. Luu, Nam N. Pham, Thang C. Truong
Spiking neural network (SNN) has emerged as a promising paradigm in computational neuroscience and artificial intelligence, offering advantages such as low energy consumption and small memory footprint. However, their practical adoption is constrained by several challenges, prominently among them being performance optimization. In this study, we present a novel approach to enhance the performance of SNN for images through a new coding method that exploits bit plane representation. Our proposed technique is designed to improve the accuracy of SNN without increasing model size. Also, we investigate the impacts of color models of the proposed coding process. Through extensive experimental validation, we demonstrate the effectiveness of our coding strategy in achieving performance gain across multiple datasets. To the best of our knowledge, this is the first research that considers bit planes and color models in the context of SNN. By leveraging the unique characteristics of bit planes, we hope to unlock new potentials in SNNs performance, potentially paving the way for more efficient and effective SNNs models in future researches and applications.
2024-09-27
Roy Moyal, Kyrus R. Mama, Matthew Einhorn, Ayon Borthakur, Thomas A. Cleland
The learning and recognition of object features from unregulated input has been a longstanding challenge for artificial intelligence systems. Brains are adept at learning stable representations given small samples of noisy observations; across sensory modalities, this capacity is aided by a cascade of signal conditioning steps informed by domain knowledge. The olfactory system, in particular, solves a source separation and denoising problem compounded by concentration variability, environmental interference, and unpredictably correlated sensor affinities. To function optimally, its plastic network requires statistically well-behaved input. We present a data-blind neuromorphic signal conditioning strategy whereby analog data are normalized and quantized into spike phase representations. Input is delivered to a column of duplicated spiking principal neurons via heterogeneous synaptic weights; this regularizes layer utilization, yoking total activity to the network's operating range and rendering internal representations robust to uncontrolled open-set stimulus variance. We extend this mechanism by adding a data-aware calibration step whereby the range and density of the quantization weights adapt to accumulated input statistics, optimizing resource utilization by balancing activity regularization and information retention.
Spatial embedding promotes a specific form of modularity with low entropy and heterogeneous spectral dynamics
2024-09-26
Cornelia Sheeran, Andrew S. Ham, Duncan E. Astle, Jascha Achterberg, Danyal Akarca
Understanding how biological constraints shape neural computation is a central goal of computational neuroscience. Spatially embedded recurrent neural networks provide a promising avenue to study how modelled constraints shape the combined structural and functional organisation of networks over learning. Prior work has shown that spatially embedded systems like this can combine structure and function into single artificial models during learning. But it remains unclear precisely how, in general, structural constraints bound the range of attainable configurations. In this work, we show that it is possible to study these restrictions through entropic measures of the neural weights and eigenspectrum, across both rate and spiking neural networks. Spatial embedding, in contrast to baseline models, leads to networks with a highly specific low entropy modularity where connectivity is readily interpretable given the known spatial and communication constraints acting on them. Crucially, these networks also demonstrate systematically modulated spectral dynamics, revealing how they exploit heterogeneity in their function to overcome the constraints imposed on their structure. This work deepens our understanding of constrained learning in neural networks, across coding schemes and tasks, where solutions to simultaneous structural and functional objectives must be accomplished in tandem.
Circuits and Systems for Embodied AI: Exploring uJ Multi-Modal Perception for Nano-UAVs on the Kraken Shield
2024-09-26
Viviane Potocnik, Alfio Di Mauro, Lorenzo Lamberti, Victor Kartsch, Moritz Scherer, Francesco Conti, Luca Benini
Embodied artificial intelligence (AI) requires pushing complex multi-modal models to the extreme edge for time-constrained tasks such as autonomous navigation of robots and vehicles. On small form-factor devices, e.g., nano-sized unmanned aerial vehicles (UAVs), such challenges are exacerbated by stringent constraints on energy efficiency and weight. In this paper, we explore embodied multi-modal AI-based perception for Nano-UAVs with the Kraken shield, a 7g multi-sensor (frame-based and event-based imagers) board based on Kraken, a 22 nm SoC featuring multiple acceleration engines for multi-modal event and frame-based inference based on spiking (SNN) and ternary (TNN) neural networks, respectively. Kraken can execute SNN real-time inference for depth estimation at 1.02k inf/s, 18 {\mu}J/inf, TNN real-time inference for object classification at 10k inf/s, 6 {\mu}J/inf, and real-time inference for obstacle avoidance at 221 frame/s, 750 {\mu}J/inf.
Twin Network Augmentation: A Novel Training Strategy for Improved Spiking Neural Networks and Efficient Weight Quantization
2024-09-24
Lucas Deckers, Benjamin Vandersmissen, Ing Jyh Tsang, Werner Van Leekwijck, Steven Latré
The proliferation of Artificial Neural Networks (ANNs) has led to increased energy consumption, raising concerns about their sustainability. Spiking Neural Networks (SNNs), which are inspired by biological neural systems and operate using sparse, event-driven spikes to communicate information between neurons, offer a potential solution due to their lower energy requirements. An alternative technique for reducing a neural network's footprint is quantization, which compresses weight representations to decrease memory usage and energy consumption. In this study, we present Twin Network Augmentation (TNA), a novel training framework aimed at improving the performance of SNNs while also facilitating an enhanced compression through low-precision quantization of weights. TNA involves co-training an SNN with a twin network, optimizing both networks to minimize their cross-entropy losses and the mean squared error between their output logits. We demonstrate that TNA significantly enhances classification performance across various vision datasets and in addition is particularly effective when applied when reducing SNNs to ternary weight precision. Notably, during inference , only the ternary SNN is retained, significantly reducing the network in number of neurons, connectivity and weight size representation. Our results show that TNA outperforms traditional knowledge distillation methods and achieves state-of-the-art performance for the evaluated network architecture on benchmark datasets, including CIFAR-10, CIFAR-100, and CIFAR-10-DVS. This paper underscores the effectiveness of TNA in bridging the performance gap between SNNs and ANNs and suggests further exploration into the application of TNA in different network architectures and datasets.
Spiking Neural Networks (SNNs), the third generation neural networks, are known for their low energy consumption and high robustness. SNNs are developing rapidly and can compete with Artificial Neural Networks (ANNs) in many fields. To ensure that the widespread use of SNNs does not cause serious security incidents, much research has been conducted to explore the robustness of SNNs under adversarial sample attacks. However, many other unassessed security threats exist, such as highly stealthy backdoor attacks. Therefore, to fill the research gap in this and further explore the security vulnerabilities of SNNs, this paper explores the robustness performance of SNNs trained by supervised learning rules under backdoor attacks. Specifically, the work herein includes: i) We propose a generic backdoor attack framework that can be launched against the training process of existing supervised learning rules and covers all learnable dataset types of SNNs. ii) We analyze the robustness differences between different learning rules and between SNN and ANN, which suggests that SNN no longer has inherent robustness under backdoor attacks. iii) We reveal the vulnerability of conversion-dependent learning rules caused by backdoor migration and further analyze the migration ability during the conversion process, finding that the backdoor migration rate can even exceed 99%. iv) Finally, we discuss potential countermeasures against this kind of backdoor attack and its technical challenges and point out several promising research directions.
A spike camera is a specialized high-speed visual sensor that offers advantages such as high temporal resolution and high dynamic range compared to conventional frame cameras. These features provide the camera with significant advantages in many computer vision tasks. However, the tasks of novel view synthesis based on spike cameras remain underdeveloped. Although there are existing methods for learning neural radiance fields from spike stream, they either lack robustness in extremely noisy, low-quality lighting conditions or suffer from high computational complexity due to the deep fully connected neural networks and ray marching rendering strategies used in neural radiance fields, making it difficult to recover fine texture details. In contrast, the latest advancements in 3DGS have achieved high-quality real-time rendering by optimizing the point cloud representation into Gaussian ellipsoids. Building on this, we introduce SpikeGS, the method to learn 3D Gaussian fields solely from spike stream. We designed a differentiable spike stream rendering framework based on 3DGS, incorporating noise embedding and spiking neurons. By leveraging the multi-view consistency of 3DGS and the tile-based multi-threaded parallel rendering mechanism, we achieved high-quality real-time rendering results. Additionally, we introduced a spike rendering loss function that generalizes under varying illumination conditions. Our method can reconstruct view synthesis results with fine texture details from a continuous spike stream captured by a moving spike camera, while demonstrating high robustness in extremely noisy low-light scenarios. Experimental results on both real and synthetic datasets demonstrate that our method surpasses existing approaches in terms of rendering quality and speed. Our code will be available at https://github.com/520jz/SpikeGS.
A Realistic Simulation Framework for Analog/Digital Neuromorphic Architectures
2024-09-23
Fernando M. Quintana, Maryada, Pedro L. Galindo, Elisa Donati, Giacomo Indiveri, Fernando Perez-Peña
Developing dedicated neuromorphic computing platforms optimized for embedded or edge-computing applications requires time-consuming design, fabrication, and deployment of full-custom neuromorphic processors.bTo ensure that initial prototyping efforts, exploring the properties of different network architectures and parameter settings, lead to realistic results it is important to use simulation frameworks that match as best as possible the properties of the final hardware. This is particularly challenging for neuromorphic hardware platforms made using mixed-signal analog/digital circuits, due to the variability and noise sensitivity of their components. In this paper, we address this challenge by developing a software spiking neural network simulator explicitly designed to account for the properties of mixed-signal neuromorphic circuits, including device mismatch variability. The simulator, called ARCANA (A Realistic Simulation Framework for Analog/Digital Neuromorphic Architectures), is designed to reproduce the dynamics of mixed-signal synapse and neuron electronic circuits with autogradient differentiation for parameter optimization and GPU acceleration. We demonstrate the effectiveness of this approach by matching software simulation results with measurements made from an existing neuromorphic processor. We show how the results obtained provide a reliable estimate of the behavior of the spiking neural network trained in software, once deployed in hardware. This framework enables the development and innovation of new learning rules and processing architectures in neuromorphic embedded systems.
A Neuromorphic Implementation of the DBSCAN Algorithm
2024-09-22
Charles P. Rizzo, James S. Plank
DBSCAN is an algorithm that performs clustering in the presence of noise. In this paper, we provide two constructions that allow DBSCAN to be implemented neuromorphically, using spiking neural networks. The first construction is termed "flat," resulting in large spiking neural networks that compute the algorithm quickly, in five timesteps. Moreover, the networks allow pipelining, so that a new DBSCAN calculation may be performed every timestep. The second construction is termed "systolic", and generates much smaller networks, but requires the inputs to be spiked in over several timesteps, column by column. We provide precise specifications of the constructions and analyze them in practical neuromorphic computing settings. We also provide an open-source implementation.
DS2TA: Denoising Spiking Transformer with Attenuated Spatiotemporal Attention
2024-09-20
Boxun Xu, Hejia Geng, Yuxuan Yin, Peng Li
Vision Transformers (ViT) are current high-performance models of choice for various vision applications. Recent developments have given rise to biologically inspired spiking transformers that thrive in ultra-low power operations on neuromorphic hardware, however, without fully unlocking the potential of spiking neural networks. We introduce DS2TA, a Denoising Spiking transformer with attenuated SpatioTemporal Attention, designed specifically for vision applications. DS2TA introduces a new spiking attenuated spatiotemporal attention mechanism that considers input firing correlations occurring in both time and space, thereby fully harnessing the computational power of spiking neurons at the core of the transformer architecture. Importantly, DS2TA facilitates parameter-efficient spatiotemporal attention computation without introducing extra weights. DS2TA employs efficient hashmap-based nonlinear spiking attention denoisers to enhance the robustness and expressive power of spiking attention maps. DS2TA demonstrates state-of-the-art performances on several widely adopted static image and dynamic neuromorphic datasets. Operated over 4 time steps, DS2TA achieves 94.92% top-1 accuracy on CIFAR10 and 77.47% top-1 accuracy on CIFAR100, as well as 79.1% and 94.44% on CIFAR10-DVS and DVS-Gesture using 10 time steps.
FedAT: Federated Adversarial Training for Distributed Insider Threat Detection
2024-09-19
R G Gayathri, Atul Sajjanhar, Md Palash Uddin, Yong Xiang
Insider threats usually occur from within the workplace, where the attacker is an entity closely associated with the organization. The sequence of actions the entities take on the resources to which they have access rights allows us to identify the insiders. Insider Threat Detection (ITD) using Machine Learning (ML)-based approaches gained attention in the last few years. However, most techniques employed centralized ML methods to perform such an ITD. Organizations operating from multiple locations cannot contribute to the centralized models as the data is generated from various locations. In particular, the user behavior data, which is the primary source of ITD, cannot be shared among the locations due to privacy concerns. Additionally, the data distributed across various locations result in extreme class imbalance due to the rarity of attacks. Federated Learning (FL), a distributed data modeling paradigm, gained much interest recently. However, FL-enabled ITD is not yet explored, and it still needs research to study the significant issues of its implementation in practical settings. As such, our work investigates an FL-enabled multiclass ITD paradigm that considers non-Independent and Identically Distributed (non-IID) data distribution to detect insider threats from different locations (clients) of an organization. Specifically, we propose a Federated Adversarial Training (FedAT) approach using a generative model to alleviate the extreme data skewness arising from the non-IID data distribution among the clients. Besides, we propose to utilize a Self-normalized Neural Network-based Multi-Layer Perceptron (SNN-MLP) model to improve ITD. We perform comprehensive experiments and compare the results with the benchmarks to manifest the enhanced performance of the proposed FedATdriven ITD scheme.
The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learning
2024-09-19
Manh V. Nguyen, Liang Zhao, Bobin Deng, William Severa, Honghui Xu, Shaoen Wu
Spiking Neural Networks (SNNs) have recently gained significant interest in on-chip learning in embedded devices and emerged as an energy-efficient alternative to conventional Artificial Neural Networks (ANNs). However, to extend SNNs to a Federated Learning (FL) setting involving collaborative model training, the communication between the local devices and the remote server remains the bottleneck, which is often restricted and costly. In this paper, we first explore the inherent robustness of SNNs under noisy communication in FL. Building upon this foundation, we propose a novel Federated Learning with Top-K Sparsification (FLTS) algorithm to reduce the bandwidth usage for FL training. We discover that the proposed scheme with SNNs allows more bandwidth savings compared to ANNs without impacting the model's accuracy. Additionally, the number of parameters to be communicated can be reduced to as low as 6 percent of the size of the original model. We further improve the communication efficiency by enabling dynamic parameter compression during model training. Extensive experiment results demonstrate that our proposed algorithms significantly outperform the baselines in terms of communication cost and model accuracy and are promising for practical network-efficient FL with SNNs.
A dynamic vision sensor object recognition model based on trainable event-driven convolution and spiking attention mechanism
2024-09-19
Peng Zheng, Qian Zhou
Spiking Neural Networks (SNNs) are well-suited for processing event streams from Dynamic Visual Sensors (DVSs) due to their use of sparse spike-based coding and asynchronous event-driven computation. To extract features from DVS objects, SNNs commonly use event-driven convolution with fixed kernel parameters. These filters respond strongly to features in specific orientations while disregarding others, leading to incomplete feature extraction. To improve the current event-driven convolution feature extraction capability of SNNs, we propose a DVS object recognition model that utilizes a trainable event-driven convolution and a spiking attention mechanism. The trainable event-driven convolution is proposed in this paper to update its convolution kernel through gradient descent. This method can extract local features of the event stream more efficiently than traditional event-driven convolution. Furthermore, the spiking attention mechanism is used to extract global dependence features. The classification performances of our model are better than the baseline methods on two neuromorphic datasets including MNIST-DVS and the more complex CIFAR10-DVS. Moreover, our model showed good classification ability for short event streams. It was shown that our model can improve the performance of event-driven convolutional SNNs for DVS objects.
Spiking neural networks (SNNs) have garnered significant attention for their low power consumption and high biological interpretability. Their rich spatio-temporal information processing capability and event-driven nature make them ideally well-suited for neuromorphic datasets. However, current SNNs struggle to balance accuracy and latency in classifying these datasets. In this paper, we propose Hybrid Step-wise Distillation (HSD) method, tailored for neuromorphic datasets, to mitigate the notable decline in performance at lower time steps. Our work disentangles the dependency between the number of event frames and the time steps of SNNs, utilizing more event frames during the training stage to improve performance, while using fewer event frames during the inference stage to reduce latency. Nevertheless, the average output of SNNs across all time steps is susceptible to individual time step with abnormal outputs, particularly at extremely low time steps. To tackle this issue, we implement Step-wise Knowledge Distillation (SKD) module that considers variations in the output distribution of SNNs at each time step. Empirical evidence demonstrates that our method yields competitive performance in classification tasks on neuromorphic datasets, especially at lower time steps. Our code will be available at: {https://github.com/hsw0929/HSD}.
Hyperspectral Image Classification Based on Faster Residual Multi-branch Spiking Neural Network
2024-09-18
Yang Liu, Yahui Li, Rui Li, Liming Zhou, Lanxue Dang, Huiyu Mu, Qiang Ge
Convolutional neural network (CNN) performs well in Hyperspectral Image (HSI) classification tasks, but its high energy consumption and complex network structure make it difficult to directly apply it to edge computing devices. At present, spiking neural networks (SNN) have developed rapidly in HSI classification tasks due to their low energy consumption and event driven characteristics. However, it usually requires a longer time step to achieve optimal accuracy. In response to the above problems, this paper builds a spiking neural network (SNN-SWMR) based on the leaky integrate-and-fire (LIF) neuron model for HSI classification tasks. The network uses the spiking width mixed residual (SWMR) module as the basic unit to perform feature extraction operations. The spiking width mixed residual module is composed of spiking mixed convolution (SMC), which can effectively extract spatial-spectral features. Secondly, this paper designs a simple and efficient arcsine approximate derivative (AAD), which solves the non-differentiable problem of spike firing by fitting the Dirac function. Through AAD, we can directly train supervised spike neural networks. Finally, this paper conducts comparative experiments with multiple advanced HSI classification algorithms based on spiking neural networks on six public hyperspectral data sets. Experimental results show that the AAD function has strong robustness and a good fitting effect. Meanwhile, compared with other algorithms, SNN-SWMR requires a time step reduction of about 84%, training time, and testing time reduction of about 63% and 70% at the same accuracy. This study solves the key problem of SNN based HSI classification algorithms, which has important practical significance for promoting the practical application of HSI classification algorithms in edge devices such as spaceborne and airborne devices.
This paper introduces an analog spiking neuron that utilizes time-domain information, i.e., a time interval of two signal transitions and a pulse width, to construct a spiking neural network (SNN) for a hardware-friendly physical reservoir computing (RC) on a complementary metal-oxide-semiconductor (CMOS) platform. A neuron with leaky integrate-and-fire is realized by employing two voltage-controlled oscillators (VCOs) with opposite sensitivities to the internal control voltage, and the neuron connection structure is restricted by the use of only 4 neighboring neurons on the 2-dimensional plane to feasibly construct a regular network topology. Such a system enables us to compose an SNN with a counter-based readout circuit, which simplifies the hardware implementation of the SNN. Moreover, another technical advantage thanks to the bottom-up integration is the capability of dynamically capturing every neuron state in the network, which can significantly contribute to finding guidelines on how to enhance the performance for various computational tasks in temporal information processing. Diverse nonlinear physical dynamics needed for RC can be realized by collective behavior through dynamic interaction between neurons, like coupled oscillators, despite the simple network structure. With behavioral system-level simulations, we demonstrate physical RC through short-term memory and exclusive OR tasks, and the spoken digit recognition task with an accuracy of 97.7% as well. Our system is considerably feasible for practical applications and also can be a useful platform for studying the mechanism of physical RC.
Bio-Inspired Mamba: Temporal Locality and Bioplausible Learning in Selective State Space Models
2024-09-17
Jiahao Qin
This paper introduces Bio-Inspired Mamba (BIM), a novel online learning framework for selective state space models that integrates biological learning principles with the Mamba architecture. BIM combines Real-Time Recurrent Learning (RTRL) with Spike-Timing-Dependent Plasticity (STDP)-like local learning rules, addressing the challenges of temporal locality and biological plausibility in training spiking neural networks. Our approach leverages the inherent connection between backpropagation through time and STDP, offering a computationally efficient alternative that maintains the ability to capture long-range dependencies. We evaluate BIM on language modeling, speech recognition, and biomedical signal analysis tasks, demonstrating competitive performance against traditional methods while adhering to biological learning principles. Results show improved energy efficiency and potential for neuromorphic hardware implementation. BIM not only advances the field of biologically plausible machine learning but also provides insights into the mechanisms of temporal information processing in biological neural networks.
This paper introduces a Spiking Diffusion Policy (SDP) learning method for robotic manipulation by integrating Spiking Neurons and Learnable Channel-wise Membrane Thresholds (LCMT) into the diffusion policy model, thereby enhancing computational efficiency and achieving high performance in evaluated tasks. Specifically, the proposed SDP model employs the U-Net architecture as the backbone for diffusion learning within the Spiking Neural Network (SNN). It strategically places residual connections between the spike convolution operations and the Leaky Integrate-and-Fire (LIF) nodes, thereby preventing disruptions to the spiking states. Additionally, we introduce a temporal encoding block and a temporal decoding block to transform static and dynamic data with timestep $T_S$ into each other, enabling the transmission of data within the SNN in spike format. Furthermore, we propose LCMT to enable the adaptive acquisition of membrane potential thresholds, thereby matching the conditions of varying membrane potentials and firing rates across channels and avoiding the cumbersome process of manually setting and tuning hyperparameters. Evaluating the SDP model on seven distinct tasks with SNN timestep $T_S=4$, we achieve results comparable to those of the ANN counterparts, along with faster convergence speeds than the baseline SNN method. This improvement is accompanied by a reduction of 94.3\% in dynamic energy consumption estimated on 45nm hardware.
Contrastive Learning in Memristor-based Neuromorphic Systems
2024-09-17
Cory Merkel, Alexander Ororbia
Spiking neural networks, the third generation of artificial neural networks, have become an important family of neuron-based models that sidestep many of the key limitations facing modern-day backpropagation-trained deep networks, including their high energy inefficiency and long-criticized biological implausibility. In this work, we design and investigate a proof-of-concept instantiation of contrastive-signal-dependent plasticity (CSDP), a neuromorphic form of forward-forward-based, backpropagation-free learning. Our experimental simulations demonstrate that a hardware implementation of CSDP is capable of learning simple logic functions without the need to resort to complex gradient calculations.
Inferno: An Extensible Framework for Spiking Neural Networks
2024-09-17
Marissa Dominijanni
This paper introduces Inferno, a software library built on top of PyTorch that is designed to meet distinctive challenges of using spiking neural networks (SNNs) for machine learning tasks. We describe the architecture of Inferno and key differentiators that make it uniquely well-suited to these tasks. We show how Inferno supports trainable heterogeneous delays on both CPUs and GPUs, and how Inferno enables a "write once, apply everywhere" development methodology for novel models and techniques. We compare Inferno's performance to BindsNET, a library aimed at machine learning with SNNs, and Brian2/Brian2CUDA which is popular in neuroscience. Among several examples, we show how the design decisions made by Inferno facilitate easily implementing the new methods of Nadafian and Ganjtabesh in delay learning with spike-timing dependent plasticity.
Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding
2024-09-14
Yu Song, Liyuan Han, Bo Xu, Tielin Zhang
Brain-computer interfaces (BCIs) are an advanced fusion of neuroscience and artificial intelligence, requiring stable and long-term decoding of neural signals. Spiking Neural Networks (SNNs), with their neuronal dynamics and spike-based signal processing, are inherently well-suited for this task. This paper presents a novel approach utilizing a Multiscale Fusion enhanced Spiking Neural Network (MFSNN). The MFSNN emulates the parallel processing and multiscale feature fusion seen in human visual perception to enable real-time, efficient, and energy-conserving neural signal decoding. Initially, the MFSNN employs temporal convolutional networks and channel attention mechanisms to extract spatiotemporal features from raw data. It then enhances decoding performance by integrating these features through skip connections. Additionally, the MFSNN improves generalizability and robustness in cross-day signal decoding through mini-batch supervised generalization learning. In two benchmark invasive BCI paradigms, including the single-hand grasp-and-touch and center-and-out reach tasks, the MFSNN surpasses traditional artificial neural network methods, such as MLP and GRU, in both accuracy and computational efficiency. Moreover, the MFSNN's multiscale feature fusion framework is well-suited for the implementation on neuromorphic chips, offering an energy-efficient solution for online decoding of invasive BCI signals.
Efficient FPGA Implementation of an Optimized SNN-based DFE for Optical Communications
2024-09-13
Mohamed Moursi, Jonas Ney, Bilal Hammoud, Norbert Wehn
The ever-increasing demand for higher data rates in communication systems intensifies the need for advanced non-linear equalizers capable of higher performance. Recently artificial neural networks (ANNs) were introduced as a viable candidate for advanced non-linear equalizers, as they outperform traditional methods. However, they are computationally complex and therefore power hungry. Spiking neural networks (SNNs) started to gain attention as an energy-efficient alternative to ANNs. Recent works proved that they can outperform ANNs at this task. In this work, we explore the design space of an SNN-based decision-feedback equalizer (DFE) to reduce its computational complexity for an efficient implementation on field programmable gate array (FPGA). Our Results prove that it achieves higher communication performance than ANN-based DFE at roughly the same throughput and at 25X higher energy efficiency.
Are Sparse Neural Networks Better Hard Sample Learners?
2024-09-13
Qiao Xiao, Boqian Wu, Lu Yin, Christopher Neil Gadzinski, Tianjin Huang, Mykola Pechenizkiy, Decebal Constantin Mocanu
While deep learning has demonstrated impressive progress, it remains a daunting challenge to learn from hard samples as these samples are usually noisy and intricate. These hard samples play a crucial role in the optimal performance of deep neural networks. Most research on Sparse Neural Networks (SNNs) has focused on standard training data, leaving gaps in understanding their effectiveness on complex and challenging data. This paper's extensive investigation across scenarios reveals that most SNNs trained on challenging samples can often match or surpass dense models in accuracy at certain sparsity levels, especially with limited data. We observe that layer-wise density ratios tend to play an important role in SNN performance, particularly for methods that train from scratch without pre-trained initialization. These insights enhance our understanding of SNNs' behavior and potential for efficient learning approaches in data-centric AI. Our code is publicly available at: \url{https://github.com/QiaoXiao7282/hard_sample_learners}.
Classifying Images with CoLaNET Spiking Neural Network -- the MNIST Example
2024-09-12
Mikhail Kiselev
In the present paper, it is shown how the columnar/layered CoLaNET spiking neural network (SNN) architecture can be used in supervised learning image classification tasks. Image pixel brightness is coded by the spike count during image presentation period. Image class label is indicated by activity of special SNN input nodes (one node per class). The CoLaNET classification accuracy is evaluated on the MNIST benchmark. It is demonstrated that CoLaNET is almost as accurate as the most advanced machine learning algorithms (not using convolutional approach).
Spiking neural networks (SNNs), the models inspired by the mechanisms of real neurons in the brain, transmit and represent information by employing discrete action potentials or spikes. The sparse, asynchronous properties of information processing make SNNs highly energy efficient, leading to SNNs being promising solutions for implementing neural networks in neuromorphic devices. However, the nondifferentiable nature of SNN neurons makes it a challenge to train them. The current training methods of SNNs that are based on error backpropagation (BP) and precisely designing surrogate gradient are difficult to implement and biologically implausible, hindering the implementation of SNNs on neuromorphic devices. Thus, it is important to train SNNs with a method that is both physically implementatable and biologically plausible. In this paper, we propose using augmented direct feedback alignment (aDFA), a gradient-free approach based on random projection, to train SNNs. This method requires only partial information of the forward process during training, so it is easy to implement and biologically plausible. We systematically demonstrate the feasibility of the proposed aDFA-SNNs scheme, propose its effective working range, and analyze its well-performing settings by employing genetic algorithm. We also analyze the impact of crucial features of SNNs on the scheme, thus demonstrating its superiority and stability over BP and conventional direct feedback alignment. Our scheme can achieve competitive performance without accurate prior knowledge about the utilized system, thus providing a valuable reference for physically training SNNs.
Higher-Order Topological Directionality and Directed Simplicial Neural Networks
2024-09-12
Manuel Lecha, Andrea Cavallo, Francesca Dominici, Elvin Isufi, Claudio Battiloro
Topological Deep Learning (TDL) has emerged as a paradigm to process and learn from signals defined on higher-order combinatorial topological spaces, such as simplicial or cell complexes. Although many complex systems have an asymmetric relational structure, most TDL models forcibly symmetrize these relationships. In this paper, we first introduce a novel notion of higher-order directionality and we then design Directed Simplicial Neural Networks (Dir-SNNs) based on it. Dir-SNNs are message-passing networks operating on directed simplicial complexes able to leverage directed and possibly asymmetric interactions among the simplices. To our knowledge, this is the first TDL model using a notion of higher-order directionality. We theoretically and empirically prove that Dir-SNNs are more expressive than their directed graph counterpart in distinguishing isomorphic directed graphs. Experiments on a synthetic source localization task demonstrate that Dir-SNNs outperform undirected SNNs when the underlying complex is directed, and perform comparably when the underlying complex is undirected.
Optical neural networks (ONNs) perform extensive computations using photons instead of electrons, resulting in passively energy-efficient and low-latency computing. Among various ONNs, the diffractive optical neural networks (DONNs) particularly excel in energy efficiency, bandwidth, and parallelism, therefore attract considerable attention. However, their performance is limited by the inherent constraints of traditional frame-based sensors, which process and produce dense and redundant information at low operating frequency. Inspired by the spiking neurons in human neural system, which utilize a thresholding mechanism to transmit information sparsely and efficiently, we propose integrating a threshold-locking method into neuromorphic vision sensors to generate sparse and binary information, achieving microsecond-level accurate perception similar to human spiking neurons. By introducing novel Binary Dual Adaptive Training (BAT) and Optically Parallel Mixture of Experts (OPMoE) inference methods, the high-speed, spike-based diffractive optical neural network (S2NN) demonstrates an ultra-fast operating speed of 3649 FPS, which is 30 fold faster than that of reported DONNs, delivering a remarkable computational speed of 417.96 TOPS and a system energy efficiency of 12.6 TOPS/W. Our work demonstrates the potential of incorporating neuromorphic architecture to facilitate optical neural network applications in real-world scenarios for both low-level and high-level machine vision tasks.
2024-09-09
Ankit Gupta, Onur Dizdar, Yun Chen, Stephen Wang
In this work, we propose an energy efficient neuromorphic receiver to replace multiple signal-processing blocks at the receiver by a Spiking Neural Network (SNN) based module, called SpikingRx. We propose a deep convolutional SNN with spike-element-wise ResNet layers which takes a whole OFDM grid compliant with 5G specifications and provides soft outputs for decoded bits that can be used as log-likelihood ratios. We propose to employ the surrogate gradient descent method for training the SpikingRx and focus on its generalizability and robustness to quantization. Moreover, the interpretability of the proposed SpikingRx is studied by a comprehensive ablation study. Our extensive numerical simulations show that SpikingRx is capable of achieving significant block error rate performance gain compared to conventional 5G receivers and similar performance compared to its traditional NN-based counterparts with approximately 9x less energy consumption.
Predictive Coding with Spiking Neural Networks: a Survey
2024-09-09
Antony W. N'dri, William Gebhardt, Céline Teulière, Fleur Zeldenrust, Rajesh P. N. Rao, Jochen Triesch, Alexander Ororbia
In this article, we review a class of neuro-mimetic computational models that we place under the label of spiking predictive coding. Specifically, we review the general framework of predictive processing in the context of neurons that emit discrete action potentials, i.e., spikes. Theoretically, we structure our survey around how prediction errors are represented, which results in an organization of historical neuromorphic generalizations that is centered around three broad classes of approaches: prediction errors in explicit groups of error neurons, in membrane potentials, and implicit prediction error encoding. Furthermore, we examine some applications of spiking predictive coding that utilize more energy-efficient, edge-computing hardware platforms. Finally, we highlight important future directions and challenges in this emerging line of inquiry in brain-inspired computing. Building on the prior results of work in computational cognitive neuroscience, machine intelligence, and neuromorphic engineering, we hope that this review of neuromorphic formulations and implementations of predictive coding will encourage and guide future research and development in this emerging research area.
On the Convergence Analysis of Over-Parameterized Variational Autoencoders: A Neural Tangent Kernel Perspective
2024-09-09
Li Wang, Wei Huang
Variational Auto-Encoders (VAEs) have emerged as powerful probabilistic models for generative tasks. However, their convergence properties have not been rigorously proven. The challenge of proving convergence is inherently difficult due to the highly non-convex nature of the training objective and the implementation of a Stochastic Neural Network (SNN) within VAE architectures. This paper addresses these challenges by characterizing the optimization trajectory of SNNs utilized in VAEs through the lens of Neural Tangent Kernel (NTK) techniques. These techniques govern the optimization and generalization behaviors of ultra-wide neural networks. We provide a mathematical proof of VAE convergence under mild assumptions, thus advancing the theoretical understanding of VAE optimization dynamics. Furthermore, we establish a novel connection between the optimization problem faced by over-parameterized SNNs and the Kernel Ridge Regression (KRR) problem. Our findings not only contribute to the theoretical foundation of VAEs but also open new avenues for investigating the optimization of generative models using advanced kernel methods. Our theoretical claims are verified by experimental simulations.
The computational inefficiency of spiking neural networks (SNNs) is primarily due to the sequential updates of membrane potential, which becomes more pronounced during extended encoding periods compared to artificial neural networks (ANNs). This highlights the need to parallelize SNN computations effectively to leverage available hardware parallelism. To address this, we propose Membrane Potential Estimation Parallel Spiking Neurons (MPE-PSN), a parallel computation method for spiking neurons that enhances computational efficiency by enabling parallel processing while preserving the intrinsic dynamic characteristics of SNNs. Our approach exhibits promise for enhancing computational efficiency, particularly under conditions of elevated neuron density. Empirical experiments demonstrate that our method achieves state-of-the-art (SOTA) accuracy and efficiency on neuromorphic datasets. Codes are available at~\url{https://github.com/chrazqee/MPE-PSN}. \end{abstract}
Hybrid Spiking Neural Networks for Low-Power Intra-Cortical Brain-Machine Interfaces
2024-09-06
Alexandru Vasilache, Jann Krausse, Klaus Knobloch, Juergen Becker
Intra-cortical brain-machine interfaces (iBMIs) have the potential to dramatically improve the lives of people with paraplegia by restoring their ability to perform daily activities. However, current iBMIs suffer from scalability and mobility limitations due to bulky hardware and wiring. Wireless iBMIs offer a solution but are constrained by a limited data rate. To overcome this challenge, we are investigating hybrid spiking neural networks for embedded neural decoding in wireless iBMIs. The networks consist of a temporal convolution-based compression followed by recurrent processing and a final interpolation back to the original sequence length. As recurrent units, we explore gated recurrent units (GRUs), leaky integrate-and-fire (LIF) neurons, and a combination of both - spiking GRUs (sGRUs) and analyze their differences in terms of accuracy, footprint, and activation sparsity. To that end, we train decoders on the "Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology" dataset and evaluate it using the NeuroBench framework, targeting both tracks of the IEEE BioCAS Grand Challenge on Neural Decoding. Our approach achieves high accuracy in predicting velocities of primate reaching movements from multichannel primary motor cortex recordings while maintaining a low number of synaptic operations, surpassing the current baseline models in the NeuroBench framework. This work highlights the potential of hybrid neural networks to facilitate wireless iBMIs with high decoding precision and a substantial increase in the number of monitored neurons, paving the way toward more advanced neuroprosthetic technologies.
SDformerFlow: Spatiotemporal swin spikeformer for event-based optical flow estimation
2024-09-06
Yi Tian, Juan Andrade-Cetto
Event cameras generate asynchronous and sparse event streams capturing changes in light intensity. They offer significant advantages over conventional frame-based cameras, such as a higher dynamic range and an extremely faster data rate, making them particularly useful in scenarios involving fast motion or challenging lighting conditions. Spiking neural networks (SNNs) share similar asynchronous and sparse characteristics and are well-suited for processing data from event cameras. Inspired by the potential of transformers and spike-driven transformers (spikeformers) in other computer vision tasks, we propose two solutions for fast and robust optical flow estimation for event cameras: STTFlowNet and SDformerFlow. STTFlowNet adopts a U-shaped artificial neural network (ANN) architecture with spatiotemporal shifted window self-attention (swin) transformer encoders, while SDformerFlow presents its fully spiking counterpart, incorporating swin spikeformer encoders. Furthermore, we present two variants of the spiking version with different neuron models. Our work is the first to make use of spikeformers for dense optical flow estimation. We conduct end-to-end training for all models using supervised learning. Our results yield state-of-the-art performance among SNN-based event optical flow methods on both the DSEC and MVSEC datasets, and show significant reduction in power consumption compared to the equivalent ANNs.
Systolic architectures are widely embraced by neural network accelerators for their superior performance in highly parallelized computation. The DSP48E2s serve as dedicated arithmetic blocks in Xilinx Ultrascale series FPGAs and constitute a fundamental component in FPGA-based systolic matrix engines. Harnessing the full potential of DSP48E2s in architectural design can result in significant performance enhancements for systolic architectures on Ultrascale series FPGAs. This paper unveils several previously untapped DSP optimization techniques capable of further enhancing FPGA-based systolic matrix engines. We apply these techniques to two well-known systolic architectures: Google TPUv1 and Xilinx Vitis AI DPU. With the proposed techniques, our design achieves substantial resource and power reduction compared to the open-source TPUv1 FPGA implementation and the Vitis AI DPU implementation in the same parallelism setting. We also demonstrate the applicability of our techniques to neuromorphic hardware for supporting spiking neural network acceleration.
Training-free Conversion of Pretrained ANNs to SNNs for Low-Power and High-Performance Applications
2024-09-05
Tong Bu, Maohua Li, Zhaofei Yu
Spiking Neural Networks (SNNs) have emerged as a promising substitute for Artificial Neural Networks (ANNs) due to their advantages of fast inference and low power consumption. However, the lack of efficient training algorithms has hindered their widespread adoption. Existing supervised learning algorithms for SNNs require significantly more memory and time than their ANN counterparts. Even commonly used ANN-SNN conversion methods necessitate re-training of ANNs to enhance conversion efficiency, incurring additional computational costs. To address these challenges, we propose a novel training-free ANN-SNN conversion pipeline. Our approach directly converts pre-trained ANN models into high-performance SNNs without additional training. The conversion pipeline includes a local-learning-based threshold balancing algorithm, which enables efficient calculation of the optimal thresholds and fine-grained adjustment of threshold value by channel-wise scaling. We demonstrate the scalability of our framework across three typical computer vision tasks: image classification, semantic segmentation, and object detection. This showcases its applicability to both classification and regression tasks. Moreover, we have evaluated the energy consumption of the converted SNNs, demonstrating their superior low-power advantage compared to conventional ANNs. Our training-free algorithm outperforms existing methods, highlighting its practical applicability and efficiency. This approach simplifies the deployment of SNNs by leveraging open-source pre-trained ANN models and neuromorphic hardware, enabling fast, low-power inference with negligible performance reduction.
2024-09-04
Jamie Lohoff, Jan Finkbeiner, Emre Neftci
Spiking Neural Networks (SNNs) simulators are essential tools to prototype biologically inspired models and neuromorphic hardware architectures and predict their performance. For such a tool, ease of use and flexibility are critical, but so is simulation speed especially given the complexity inherent to simulating SNN. Here, we present SNNAX, a JAX-based framework for simulating and training such models with PyTorch-like intuitiveness and JAX-like execution speed. SNNAX models are easily extended and customized to fit the desired model specifications and target neuromorphic hardware. Additionally, SNNAX offers key features for optimizing the training and deployment of SNNs such as flexible automatic differentiation and just-in-time compilation. We evaluate and compare SNNAX to other commonly used machine learning (ML) frameworks used for programming SNNs. We provide key performance metrics, best practices, documented examples for simulating SNNs in SNNAX, and implement several benchmarks used in the literature.
A Low-Cost Real-Time Spiking System for Obstacle Detection based on Ultrasonic Sensors and Rate Coding
2024-09-04
Alvaro Ayuso-Martinez, Daniel Casanueva-Morato, Juan Pedro Dominguez-Morales, Angel Jimenez-Fernandez, Gabriel Jimenez-Moreno
Since the advent of mobile robots, obstacle detection has been a topic of great interest. It has also been a subject of study in neuroscience, where flying insects and bats could be considered two of the most interesting cases in terms of vision-based and sound-based mechanisms for obstacle detection, respectively. Currently, many studies focus on vision-based obstacle detection, but not many can be found regarding sound-based obstacle detection. This work focuses on the latter approach, which also makes use of a Spiking Neural Network to exploit the advantages of these architectures and achieve an approach closer to biology. The complete system was tested through a series of experiments that confirm the validity of the spiking architecture for obstacle detection. It is empirically demonstrated that, when the distance between the robot and the obstacle decreases, the output firing rate of the system increases in response as expected, and vice versa. Therefore, there is a direct relation between the two. Furthermore, there is a distance threshold between detectable and undetectable objects which is also empirically measured in this work. An in-depth study on how this system works at low level based on the Inter-Spike Interval concept was performed, which may be useful in the future development of applications based on spiking filters.
Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model
2024-09-04
Kaiwen Tang, Zhanglu Yan, Weng-Fai Wong
For reasons such as privacy, there are use cases for language models at the edge. This has given rise to small language models (SLMs) targeted for deployment in resource-constrained devices where energy efficiency is a significant concern. Spiking neural networks (SNNs) offer a promising solution due to their energy efficiency, and there are already works on realizing transformer-based models on SNNs. However, key operations like softmax and layer normalization (LN) are difficult to implement on neuromorphic hardware, and many of these early works sidestepped them. To address these challenges, we introduce Sorbet, a transformer-based spiking language model that is more neuromorphic hardware-compatible. Sorbet incorporates a novel shifting-based softmax called PTsoftmax and a power normalization method using bit-shifting (BSPN), both designed to replace the respective energy-intensive operations. By leveraging knowledge distillation and model quantization, Sorbet achieved a highly compressed binary weight model that maintains competitive performance while significantly reducing energy consumption. We validate Sorbet's effectiveness through extensive testing on the GLUE benchmark and a series of ablation studies, demonstrating its potential as an energy-efficient solution for language model inference.
Internal Representations in Spiking Neural Networks, criticality and the Renormalization Group
2024-09-03
João Henrique de Sant'Ana, Nestor Caticha
Optimal information processing in peripheral sensory systems has been associated in several examples to the signature of a critical or near critical state. Furthermore, cortical systems have also been described to be in a critical state in both wake and anesthetized experimental models, both {\it in vitro} and {\it in vivo}. We investigate whether a similar signature characterizes the internal representations (IR) of a multilayer (deep) spiking artificial neural network performing computationally simple but meaningful cognitive tasks, using a methodology inspired in the biological setup, with cortical implanted electrodes in rats, either freely behaving or under different levels of anesthesia. The increase of the characteristic time of the decay of the correlation of fluctuations of the IR, found when the network input changes, are indications of a broad-tailed distribution of IR fluctuations. The broad tails are present even when the network is not yet capable of performing the classification tasks, either due to partial training or to the effect of a low dose of anesthesia in a simple model. However, we don't find enough evidence of power law distributions of avalanche size and duration. We interpret the results from a renormalization group perspective to point out that despite having broad tails, this is not related to a critical transition but rather similar to fluctuations driven by the reversal of the magnetic field in a ferromagnetic system. Another example of persistent correlation of fluctuations of a non critical system is constructed, where a particle undergoes Brownian motion on a slowly varying potential.
Invasive cortical brain-machine interfaces (BMIs) can significantly improve the life quality of motor-impaired patients. Nonetheless, externally mounted pedestals pose an infection risk, which calls for fully implanted systems. Such systems, however, must meet strict latency and energy constraints while providing reliable decoding performance. While recurrent spiking neural networks (RSNNs) are ideally suited for ultra-low-power, low-latency processing on neuromorphic hardware, it is unclear whether they meet the above requirements. To address this question, we trained RSNNs to decode finger velocity from cortical spike trains (CSTs) of two macaque monkeys. First, we found that a large RSNN model outperformed existing feedforward spiking neural networks (SNNs) and artificial neural networks (ANNs) in terms of their decoding accuracy. We next developed a tiny RSNN with a smaller memory footprint, low firing rates, and sparse connectivity. Despite its reduced computational requirements, the resulting model performed substantially better than existing SNN and ANN decoders. Our results thus demonstrate that RSNNs offer competitive CST decoding performance under tight resource constraints and are promising candidates for fully implanted ultra-low-power BMIs with the potential to revolutionize patient care.
Brain-Inspired Online Adaptation for Remote Sensing with Spiking Neural Network
2024-09-03
Dexin Duan, Peilin liu, Fei Wen
On-device computing, or edge computing, is becoming increasingly important for remote sensing, particularly in applications like deep network-based perception on on-orbit satellites and unmanned aerial vehicles (UAVs). In these scenarios, two brain-like capabilities are crucial for remote sensing models: (1) high energy efficiency, allowing the model to operate on edge devices with limited computing resources, and (2) online adaptation, enabling the model to quickly adapt to environmental variations, weather changes, and sensor drift. This work addresses these needs by proposing an online adaptation framework based on spiking neural networks (SNNs) for remote sensing. Starting with a pretrained SNN model, we design an efficient, unsupervised online adaptation algorithm, which adopts an approximation of the BPTT algorithm and only involves forward-in-time computation that significantly reduces the computational complexity of SNN adaptation learning. Besides, we propose an adaptive activation scaling scheme to boost online SNN adaptation performance, particularly in low time-steps. Furthermore, for the more challenging remote sensing detection task, we propose a confidence-based instance weighting scheme, which substantially improves adaptation performance in the detection task. To our knowledge, this work is the first to address the online adaptation of SNNs. Extensive experiments on seven benchmark datasets across classification, segmentation, and detection tasks demonstrate that our proposed method significantly outperforms existing domain adaptation and domain generalization approaches under varying weather conditions. The proposed method enables energy-efficient and fast online adaptation on edge devices, and has much potential in applications such as remote perception on on-orbit satellites and UAV.
2024-09-03
Shiting Xiao, Yuhang Li, Youngeun Kim, Donghyun Lee, Priyadarshini Panda
Spiking Neural Networks (SNNs) have emerged as a compelling, energy-efficient alternative to traditional Artificial Neural Networks (ANNs) for static image tasks such as image classification and segmentation. However, in the more complex video classification domain, SNN-based methods fall considerably short of ANN-based benchmarks due to the challenges in processing dense frame sequences. To bridge this gap, we propose ReSpike, a hybrid framework that synergizes the strengths of ANNs and SNNs to tackle action recognition tasks with high accuracy and low energy cost. By decomposing film clips into spatial and temporal components, i.e., RGB image Key Frames and event-like Residual Frames, ReSpike leverages ANN for learning spatial information and SNN for learning temporal information. In addition, we propose a multi-scale cross-attention mechanism for effective feature fusion. Compared to state-of-the-art SNN baselines, our ReSpike hybrid architecture demonstrates significant performance improvements (e.g., >30% absolute accuracy improvement on HMDB-51, UCF-101, and Kinetics-400). Furthermore, ReSpike achieves comparable performance with prior ANN approaches while bringing better accuracy-energy tradeoff.
CoLaNET -- A Spiking Neural Network with Columnar Layered Architecture for Classification
2024-09-02
Mikhail Kiselev
In the present paper, I describe a spiking neural network (SNN) architecture which, can be used in wide range of supervised learning classification tasks. It is assumed, that all participating signals (the classified object description, correct class label and SNN decision) have spiking nature. The distinctive feature of this architecture is a combination of prototypical network structures corresponding to different classes and significantly distinctive instances of one class (=columns) and functionally differing populations of neurons inside columns (=layers). The other distinctive feature is a novel combination of anti-Hebbian and dopamine-modulated plasticity. The plasticity rules are local and do not use the backpropagation principle. Besides that, as in my previous studies, I was guided by the requirement that the all neuron/plasticity models should be easily implemented on modern neurochips. I illustrate the high performance of my network on a task related to model-based reinforcement learning, namely, evaluation of proximity of an external world state to the target state.
Digit Recognition using Multimodal Spiking Neural Networks
2024-08-31
William Bjorndahl, Jack Easton, Austin Modoff, Eric C. Larson, Joseph Camp, Prasanna Rangarajan
Spiking neural networks (SNNs) are the third generation of neural networks that are biologically inspired to process data in a fashion that emulates the exchange of signals in the brain. Within the Computer Vision community SNNs have garnered significant attention due in large part to the availability of event-based sensors that produce a spatially resolved spike train in response to changes in scene radiance. SNNs are used to process event-based data due to their neuromorphic nature. The proposed work examines the neuromorphic advantage of fusing multiple sensory inputs in classification tasks. Specifically we study the performance of a SNN in digit classification by passing in a visual modality branch (Neuromorphic-MNIST [N-MNIST]) and an auditory modality branch (Spiking Heidelberg Digits [SHD]) from datasets that were created using event-based sensors to generate a series of time-dependent events. It is observed that multi-modal SNNs outperform unimodal visual and unimodal auditory SNNs. Furthermore, it is observed that the process of sensory fusion is insensitive to the depth at which the visual and auditory branches are combined. This work achieves a 98.43% accuracy on the combined N-MNIST and SHD dataset using a multimodal SNN that concatenates the visual and auditory branches at a late depth.
2024-08-31
Jonathan Wellington Morris, Vishrut Shah, Alex Besanceney, Daksh Shah, Leilani H. Gilpin
Sim-to real gap in Reinforcement Learning is when a model trained in a simulator does not translate to the real world. This is a problem for Autonomous Vehicles (AVs) as vehicle dynamics can vary from simulation to reality, and also from vehicle to vehicle. Slug Mobile is a one tenth scale autonomous vehicle created to help address the sim-to-real gap for AVs by acting as a test-bench to develop models that can easily scale from one vehicle to another. In addition to traditional sensors found in other one tenth scale AVs, we have also included a Dynamic Vision Sensor so we can train Spiking Neural Networks running on neuromorphic hardware.
Spiking Neural Networks (SNNs) seek to mimic the spiking behavior of biological neurons and are expected to play a key role in the advancement of neural computing and artificial intelligence. The conversion of Artificial Neural Networks (ANNs) to SNNs is the most widely used training method, which ensures that the resulting SNNs perform comparably to ANNs on large-scale datasets. The efficiency of these conversion-based SNNs is often determined by the neural coding schemes. Current schemes typically use spike count or timing for encoding, which is linearly related to ANN activations and increases the required number of time steps. To address this limitation, we propose a novel Canonic Signed Spike (CSS) coding scheme. This method incorporates non-linearity into the encoding process by weighting spikes at each step of neural computation, thereby increasing the information encoded in spikes. We identify the temporal coupling phenomenon arising from weighted spikes and introduce negative spikes along with a Ternary Self-Amplifying (TSA) neuron model to mitigate the issue. A one-step silent period is implemented during neural computation, achieving high accuracy with low latency. We apply the proposed methods to directly convert full-precision ANNs and evaluate performance on CIFAR-10 and ImageNet datasets. Our experimental results demonstrate that the CSS coding scheme effectively compresses time steps for coding and reduces inference latency with minimal conversion loss.
A compact neuromorphic system for ultra energy-efficient, on-device robot localization
2024-08-29
Adam D. Hines, Michael Milford, Tobias Fischer
Neuromorphic computing offers a transformative pathway to overcome the computational and energy challenges faced in deploying robotic localization and navigation systems at the edge. Visual place recognition, a critical component for navigation, is often hampered by the high resource demands of conventional systems, making them unsuitable for small-scale robotic platforms which still require to perform complex, long-range tasks. Although neuromorphic approaches offer potential for greater efficiency, real-time edge deployment remains constrained by the complexity and limited scalability of bio-realistic networks. Here, we demonstrate a neuromorphic localization system that performs accurate place recognition in up to 8km of traversal using models as small as 180 KB with 44k parameters, while consuming less than 1% of the energy required by conventional methods. Our Locational Encoding with Neuromorphic Systems (LENS) integrates spiking neural networks, an event-based dynamic vision sensor, and a neuromorphic processor within a single SPECK(TM) chip, enabling real-time, energy-efficient localization on a hexapod robot. LENS represents the first fully neuromorphic localization system capable of large-scale, on-device deployment, setting a new benchmark for energy efficient robotic place recognition.
2024-08-29
Qianhui Liu, Jiadong Wang, Yang Wang, Xin Yang, Gang Pan, Haizhou Li
Humans naturally perform audiovisual speech recognition (AVSR), enhancing the accuracy and robustness by integrating auditory and visual information. Spiking neural networks (SNNs), which mimic the brain's information-processing mechanisms, are well-suited for emulating the human capability of AVSR. Despite their potential, research on SNNs for AVSR is scarce, with most existing audio-visual multimodal methods focused on object or digit recognition. These models simply integrate features from both modalities, neglecting their unique characteristics and interactions. Additionally, they often rely on future information for current processing, which increases recognition latency and limits real-time applicability. Inspired by human speech perception, this paper proposes a novel human-inspired SNN named HI-AVSNN for AVSR, incorporating three key characteristics: cueing interaction, causal processing and spike activity. For cueing interaction, we propose a visual-cued auditory attention module (VCA2M) that leverages visual cues to guide attention to auditory features. We achieve causal processing by aligning the SNN's temporal dimension with that of visual and auditory features and applying temporal masking to utilize only past and current information. To implement spike activity, in addition to using SNNs, we leverage the event camera to capture lip movement as spikes, mimicking the human retina and providing efficient visual data. We evaluate HI-AVSNN on an audiovisual speech recognition dataset combining the DVS-Lip dataset with its corresponding audio samples. Experimental results demonstrate the superiority of our proposed fusion method, outperforming existing audio-visual SNN fusion methods and achieving a 2.27% improvement in accuracy over the only existing SNN-based AVSR method.
Recent years have witnessed Spiking Neural Networks (SNNs) gaining attention for their ultra-low energy consumption and high biological plausibility compared with traditional Artificial Neural Networks (ANNs). Despite their distinguished properties, the application of SNNs in the computationally intensive field of image generation is still under exploration. In this paper, we propose the Spiking Diffusion Models (SDMs), an innovative family of SNN-based generative models that excel in producing high-quality samples with significantly reduced energy consumption. In particular, we propose a Temporal-wise Spiking Mechanism (TSM) that allows SNNs to capture more temporal features from a bio-plasticity perspective. In addition, we propose a threshold-guided strategy that can further improve the performances by up to 16.7% without any additional training. We also make the first attempt to use the ANN-SNN approach for SNN-based generation tasks. Extensive experimental results reveal that our approach not only exhibits comparable performance to its ANN counterpart with few spiking time steps, but also outperforms previous SNN-based generative models by a large margin. Moreover, we also demonstrate the high-quality generation ability of SDM on large-scale datasets, e.g., LSUN bedroom. This development marks a pivotal advancement in the capabilities of SNN-based generation, paving the way for future research avenues to realize low-energy and low-latency generative applications. Our code is available at https://github.com/AndyCao1125/SDM.
Reconsidering the energy efficiency of spiking neural networks
2024-08-29
Zhanglu Yan, Zhenyu Bai, Weng-Fai Wong
Spiking neural networks (SNNs) are generally regarded as more energy-efficient because they do not use multiplications. However, most SNN works only consider the counting of additions to evaluate energy consumption, neglecting other overheads such as memory accesses and data movement operations. This oversight can lead to a misleading perception of efficiency, especially when state-of-the-art SNN accelerators operate with very small time window sizes. In this paper, we present a detailed comparison of the energy consumption of artificial neural networks (ANNs) and SNNs from a hardware perspective. We provide accurate formulas for energy consumption based on classical multi-level memory hierarchy architectures, commonly used neuromorphic dataflow architectures, and our proposed improved spatial-dataflow architecture. Our research demonstrates that to achieve comparable accuracy and greater energy efficiency than ANNs, SNNs require strict limitations on both time window size T and sparsity s. For instance, with the VGG16 model and a fixed T of 6, the neuron sparsity rate must exceed 93% to ensure energy efficiency across most architectures. Inspired by our findings, we explore strategies to enhance energy efficiency by increasing sparsity. We introduce two regularization terms during training that constrain weights and activations, effectively boosting the sparsity rate. Our experiments on the CIFAR-10 dataset, using T of 6, show that our SNNs consume 69% of the energy used by optimized ANNs on spatial-dataflow architectures, while maintaining an SNN accuracy of 94.18%. This framework, developed using PyTorch, is publicly available for use and further research.
Vision-based ego-velocity estimation is a fundamental problem in robot state estimation. However, the constraints of frame-based cameras, including motion blur and insufficient frame rates in dynamic settings, readily lead to the failure of conventional velocity estimation techniques. Mammals exhibit a remarkable ability to accurately estimate their ego-velocity during aggressive movement. Hence, integrating this capability into robots shows great promise for addressing these challenges. In this paper, we propose a brain-inspired framework for linear-angular velocity estimation, dubbed NeuroVE. The NeuroVE framework employs an event camera to capture the motion information and implements spiking neural networks (SNNs) to simulate the brain's spatial cells' function for velocity estimation. We formulate the velocity estimation as a time-series forecasting problem. To this end, we design an Astrocyte Leaky Integrate-and-Fire (ALIF) neuron model to encode continuous values. Additionally, we have developed an Astrocyte Spiking Long Short-term Memory (ASLSTM) structure, which significantly improves the time-series forecasting capabilities, enabling an accurate estimate of ego-velocity. Results from both simulation and real-world experiments indicate that NeuroVE has achieved an approximate 60% increase in accuracy compared to other SNN-based approaches.
Spiking Neural Networks (SNNs), with their brain-inspired structure using discrete spikes instead of continuous activations, are gaining attention for their potential of efficient processing on neuromorphic chips. While current SNN hardware accelerators often prioritize temporal spike sparsity, exploiting sparse synaptic weights offers significant untapped potential for even greater efficiency. To address this, we propose FireFly-S, a Sparse extension of the FireFly series. This co-optimized software-hardware design focusing on leveraging dual-side sparsity for acceleration. On the software side, we propose a novel algorithmic optimization framework that combines gradient rewiring for pruning and modified Learned Step Size Quantization (LSQ) tailored for SNNs, which achieves remarkable weight sparsity exceeding 85\% and enables efficient 4-bit quantization with negligible accuracy loss. On the hardware side, we present an efficient dual-side sparsity detector employing a Bitmap-based sparse decoding logic to pinpoint the positions of non-zero weights and input spikes. The logic allows for the direct bypassing of redundant computations, thereby enhancing computational efficiency. Different from the overlay architecture adopted by previous FireFly series, we adopt a spatial architecture with inter-layer pipelining that can fully exploit the nature of Field-Programmable Gate Arrays (FPGAs). A spatial-temporal dataflow is also proposed to support such inter-layer pipelining and avoid long-term temporal dependencies. In experiments conducted on the MNIST, DVS-Gesture and CIFAR-10 datasets, the FireFly-S model achieves 85-95\% sparsity with 4-bit quantization and the hardware accelerator effectively leverages the dual-side sparsity, delivering outstanding performance metrics of 10,047 FPS/W on MNIST, 3,683 FPS/W on DVS-Gesture, and 2,327 FPS/W on CIFAR-10.
Accelerating Sensor Fusion in Neuromorphic Computing: A Case Study on Loihi-2
2024-08-28
Murat Isik, Karn Tiwari, Muhammed Burak Eryilmaz, I. Can Dikmen
In our study, we utilized Intel's Loihi-2 neuromorphic chip to enhance sensor fusion in fields like robotics and autonomous systems, focusing on datasets such as AIODrive, Oxford Radar RobotCar, D-Behavior (D-Set), nuScenes by Motional, and Comma2k19. Our research demonstrated that Loihi-2, using spiking neural networks, significantly outperformed traditional computing methods in speed and energy efficiency. Compared to conventional CPUs and GPUs, Loihi-2 showed remarkable energy efficiency, being over 100 times more efficient than a CPU and nearly 30 times more than a GPU. Additionally, our Loihi-2 implementation achieved faster processing speeds on various datasets, marking a substantial advancement over existing state-of-the-art implementations. This paper also discusses the specific challenges encountered during the implementation and optimization processes, providing insights into the architectural innovations of Loihi-2 that contribute to its superior performance.
PMSN: A Parallel Multi-compartment Spiking Neuron for Multi-scale Temporal Processing
2024-08-27
Xinyi Chen, Jibin Wu, Chenxiang Ma, Yinsong Yan, Yujie Wu, Kay Chen Tan
Spiking Neural Networks (SNNs) hold great potential to realize brain-inspired, energy-efficient computational systems. However, current SNNs still fall short in terms of multi-scale temporal processing compared to their biological counterparts. This limitation has resulted in poor performance in many pattern recognition tasks with information that varies across different timescales. To address this issue, we put forward a novel spiking neuron model called Parallel Multi-compartment Spiking Neuron (PMSN). The PMSN emulates biological neurons by incorporating multiple interacting substructures and allows for flexible adjustment of the substructure counts to effectively represent temporal information across diverse timescales. Additionally, to address the computational burden associated with the increased complexity of the proposed model, we introduce two parallelization techniques that decouple the temporal dependencies of neuronal updates, enabling parallelized training across different time steps. Our experimental results on a wide range of pattern recognition tasks demonstrate the superiority of PMSN. It outperforms other state-of-the-art spiking neuron models in terms of its temporal processing capacity, training speed, and computation cost. Specifically, compared with the commonly used Leaky Integrate-and-Fire neuron, PMSN offers a simulation acceleration of over 10 $\times$ and a 30 % improvement in accuracy on Sequential CIFAR10 dataset, while maintaining comparable computational cost.
Known as low energy consumption networks, spiking neural networks (SNNs) have gained a lot of attention within the past decades. While SNNs are increasing competitive with artificial neural networks (ANNs) for vision tasks, they are rarely used for long sequence tasks, despite their intrinsic temporal dynamics. In this work, we develop spiking state space models (SpikingSSMs) for long sequence learning by leveraging on the sequence learning abilities of state space models (SSMs). Inspired by dendritic neuron structure, we hierarchically integrate neuronal dynamics with the original SSM block, meanwhile realizing sparse synaptic computation. Furthermore, to solve the conflict of event-driven neuronal dynamics with parallel computing, we propose a light-weight surrogate dynamic network which accurately predicts the after-reset membrane potential and compatible to learnable thresholds, enabling orders of acceleration in training speed compared with conventional iterative methods. On the long range arena benchmark task, SpikingSSM achieves competitive performance to state-of-the-art SSMs meanwhile realizing on average 90\% of network sparsity. On language modeling, our network significantly surpasses existing spiking large language models (spikingLLMs) on the WikiText-103 dataset with only a third of the model size, demonstrating its potential as backbone architecture for low computation cost LLMs.
Sparsity-Aware Hardware-Software Co-Design of Spiking Neural Networks: An Overview
2024-08-26
Ilkin Aliyev, Kama Svoboda, Tosiron Adegbija, Jean-Marc Fellous
Spiking Neural Networks (SNNs) are inspired by the sparse and event-driven nature of biological neural processing, and offer the potential for ultra-low-power artificial intelligence. However, realizing their efficiency benefits requires specialized hardware and a co-design approach that effectively leverages sparsity. We explore the hardware-software co-design of sparse SNNs, examining how sparsity representation, hardware architectures, and training techniques influence hardware efficiency. We analyze the impact of static and dynamic sparsity, discuss the implications of different neuron models and encoding schemes, and investigate the need for adaptability in hardware designs. Our work aims to illuminate the path towards embedded neuromorphic systems that fully exploit the computational advantages of sparse SNNs.
Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks
2024-08-26
Tianyu Zheng, Liyuan Han, Tielin Zhang
Spiking neural networks (SNNs) are gaining popularity in the computational simulation and artificial intelligence fields owing to their biological plausibility and computational efficiency. This paper explores the historical development of SNN and concludes that these two fields are intersecting and merging rapidly. Following the successful application of Dynamic Vision Sensors (DVS) and Dynamic Audio Sensors (DAS), SNNs have found some proper paradigms, such as continuous visual signal tracking, automatic speech recognition, and reinforcement learning for continuous control, that have extensively supported their key features, including spike encoding, neuronal heterogeneity, specific functional circuits, and multiscale plasticity. Compared to these real-world paradigms, the brain contains a spiking version of the biology-world paradigm, which exhibits a similar level of complexity and is usually considered a mirror of the real world. Considering the projected rapid development of invasive and parallel Brain-Computer Interface (BCI), as well as the new BCI-based paradigms that include online pattern recognition and stimulus control of biological spike trains, SNNs naturally leverage their advantages in energy efficiency, robustness, and flexibility. The biological brain has inspired the present study of SNNs and effective SNN machine-learning algorithms, which can help enhance neuroscience discoveries in the brain by applying them to the new BCI paradigm. Such two-way interactions with positive feedback can accelerate brain science research and brain-inspired intelligence technology.
A neurochip is a device that reproduces the signal processing mechanisms of brain neurons and calculates Spiking Neural Networks (SNNs) with low power consumption and at high speed. Thus, neurochips are attracting attention from edge robot applications, which suffer from limited battery capacity. This paper aims to achieve deep reinforcement learning (DRL) that acquires SNN policies suitable for neurochip implementation. Since DRL requires a complex function approximation, we focus on conversion techniques from Floating Point NN (FPNN) because it is one of the most feasible SNN techniques. However, DRL requires conversions to SNNs for every policy update to collect the learning samples for a DRL-learning cycle, which updates the FPNN policy and collects the SNN policy samples. Accumulative conversion errors can significantly degrade the performance of the SNN policies. We propose Robust Iterative Value Conversion (RIVC) as a DRL that incorporates conversion error reduction and robustness to conversion errors. To reduce them, FPNN is optimized with the same number of quantization bits as an SNN. The FPNN output is not significantly changed by quantization. To robustify the conversion error, an FPNN policy that is applied with quantization is updated to increase the gap between the probability of selecting the optimal action and other actions. This step prevents unexpected replacements of the policy's optimal actions. We verified RIVC's effectiveness on a neurochip-driven robot. The results showed that RIVC consumed 1/15 times less power and increased the calculation speed by five times more than an edge CPU (quad-core ARM Cortex-A72). The previous framework with no countermeasures against conversion errors failed to train the policies. Videos from our experiments are available: https://youtu.be/Q5Z0-BvK1Tc.
N-DriverMotion: Driver motion learning and prediction using an event-based camera and directly trained spiking neural networks on Loihi 2
2024-08-23
Hyo Jong Chung, Byungkon Kang, Yoonseok Yang
Driver motion recognition is a principal factor in ensuring the safety of driving systems. This paper presents a novel system for learning and predicting driver motions and an event-based high-resolution (1280x720) dataset, N-DriverMotion, newly collected to train on a neuromorphic vision system. The system comprises an event-based camera that generates the first high-resolution driver motion dataset representing spike inputs and efficient spiking neural networks (SNNs) that are effective in training and predicting the driver's gestures. The event dataset consists of 13 driver motion categories classified by direction (front, side), illumination (bright, moderate, dark), and participant. A novel simplified four-layer convolutional spiking neural network (CSNN) that we proposed was directly trained using the high-resolution dataset without any time-consuming preprocessing. This enables efficient adaptation to on-device SNNs for real-time inference on high-resolution event-based streams. Compared with recent gesture recognition systems adopting neural networks for vision processing, the proposed neuromorphic vision system achieves comparable accuracy, 94.04\%, in recognizing driver motions with the CSNN architecture. Our proposed CSNN and the dataset can be used to develop safer and more efficient driver monitoring systems for autonomous vehicles or edge devices requiring an efficient neural network architecture.
SHEDAD: SNN-Enhanced District Heating Anomaly Detection for Urban Substations
2024-08-23
Jonne van Dreven, Abbas Cheddad, Sadi Alawadi, Ahmad Nauman Ghazi, Jad Al Koussa, Dirk Vanhoudt
District Heating (DH) systems are essential for energy-efficient urban heating. However, despite the advancements in automated fault detection and diagnosis (FDD), DH still faces challenges in operational faults that impact efficiency. This study introduces the Shared Nearest Neighbor Enhanced District Heating Anomaly Detection (SHEDAD) approach, designed to approximate the DH network topology and allow for local anomaly detection without disclosing sensitive information, such as substation locations. The approach leverages a multi-adaptive k-Nearest Neighbor (k-NN) graph to improve the initial neighborhood creation. Moreover, it introduces a merging technique that reduces noise and eliminates trivial edges. We use the Median Absolute Deviation (MAD) and modified z-scores to flag anomalous substations. The results reveal that SHEDAD outperforms traditional clustering methods, achieving significantly lower intra-cluster variance and distance. Additionally, SHEDAD effectively isolates and identifies two distinct categories of anomalies: supply temperatures and substation performance. We identified 30 anomalous substations and reached a sensitivity of approximately 65\% and specificity of approximately 97\%. By focusing on this subset of poor-performing substations in the network, SHEDAD enables more targeted and effective maintenance interventions, which can reduce energy usage while optimizing network performance.
Adaptive Spiking Neural Networks with Hybrid Coding
2024-08-22
Huaxu He
The Spiking Neural Network (SNN), due to its unique spiking-driven nature, is a more energy-efficient and effective neural network compared to Artificial Neural Networks (ANNs). The encoding method directly influences the overall performance of the network, and currently, direct encoding is primarily used for directly trained SNNs. When working with static image datasets, direct encoding inputs the same feature map at every time step, failing to fully exploit the spatiotemporal properties of SNNs. While temporal encoding converts input data into spike trains with spatiotemporal characteristics, traditional SNNs utilize the same neurons when processing input data across different time steps, limiting their ability to integrate and utilize spatiotemporal information effectively.To address this, this paper employs temporal encoding and proposes the Adaptive Spiking Neural Network (ASNN), enhancing the utilization of temporal encoding in conventional SNNs. Additionally, temporal encoding is less frequently used because short time steps can lead to significant loss of input data information, often necessitating a higher number of time steps in practical applications. However, training large SNNs with long time steps is challenging due to hardware constraints. To overcome this, this paper introduces a hybrid encoding approach that not only reduces the required time steps for training but also continues to improve the overall network performance.Notably, significant improvements in classification performance are observed on both Spikformer and Spiking ResNet architectures.our code is available at https://github.com/hhx0320/ASNN
In the training and inference of spiking neural networks (SNNs), direct training and lightweight computation methods have been orthogonally developed, aimed at reducing power consumption. However, only a limited number of approaches have applied these two mechanisms simultaneously and failed to fully leverage the advantages of SNN-based vision transformers (ViTs) since they were originally designed for convolutional neural networks (CNNs). In this paper, we propose AT-SNN designed to dynamically adjust the number of tokens processed during inference in SNN-based ViTs with direct training, wherein power consumption is proportional to the number of tokens. We first demonstrate the applicability of adaptive computation time (ACT), previously limited to RNNs and ViTs, to SNN-based ViTs, enhancing it to discard less informative spatial tokens selectively. Also, we propose a new token-merge mechanism that relies on the similarity of tokens, which further reduces the number of tokens while enhancing accuracy. We implement AT-SNN to Spikformer and show the effectiveness of AT-SNN in achieving high energy efficiency and accuracy compared to state-of-the-art approaches on the image classification tasks, CIFAR10, CIFAR-100, and TinyImageNet. For example, our approach uses up to 42.4% fewer tokens than the existing best-performing method on CIFAR-100, while conserving higher accuracy.
When In-memory Computing Meets Spiking Neural Networks -- A Perspective on Device-Circuit-System-and-Algorithm Co-design
2024-08-22
Abhishek Moitra, Abhiroop Bhattacharjee, Yuhang Li, Youngeun Kim, Priyadarshini Panda
This review explores the intersection of bio-plausible artificial intelligence in the form of Spiking Neural Networks (SNNs) with the analog In-Memory Computing (IMC) domain, highlighting their collective potential for low-power edge computing environments. Through detailed investigation at the device, circuit, and system levels, we highlight the pivotal synergies between SNNs and IMC architectures. Additionally, we emphasize the critical need for comprehensive system-level analyses, considering the inter-dependencies between algorithms, devices, circuit & system parameters, crucial for optimal performance. An in-depth analysis leads to identification of key system-level bottlenecks arising from device limitations which can be addressed using SNN-specific algorithm-hardware co-design techniques. This review underscores the imperative for holistic device to system design space co-exploration, highlighting the critical aspects of hardware and algorithm research endeavors for low-power neuromorphic solutions.
Towards Efficient Formal Verification of Spiking Neural Network
2024-08-20
Baekryun Seong, Jieung Kim, Sang-Ki Ko
Recently, AI research has primarily focused on large language models (LLMs), and increasing accuracy often involves scaling up and consuming more power. The power consumption of AI has become a significant societal issue; in this context, spiking neural networks (SNNs) offer a promising solution. SNNs operate event-driven, like the human brain, and compress information temporally. These characteristics allow SNNs to significantly reduce power consumption compared to perceptron-based artificial neural networks (ANNs), highlighting them as a next-generation neural network technology. However, societal concerns regarding AI go beyond power consumption, with the reliability of AI models being a global issue. For instance, adversarial attacks on AI models are a well-studied problem in the context of traditional neural networks. Despite their importance, the stability and property verification of SNNs remains in the early stages of research. Most SNN verification methods are time-consuming and barely scalable, making practical applications challenging. In this paper, we introduce temporal encoding to achieve practical performance in verifying the adversarial robustness of SNNs. We conduct a theoretical analysis of this approach and demonstrate its success in verifying SNNs at previously unmanageable scales. Our contribution advances SNN verification to a practical level, facilitating the safer application of SNNs.
Toward Large-scale Spiking Neural Networks: A Comprehensive Survey and Future Directions
2024-08-19
Yangfan Hu, Qian Zheng, Guoqi Li, Huajin Tang, Gang Pan
Deep learning has revolutionized artificial intelligence (AI), achieving remarkable progress in fields such as computer vision, speech recognition, and natural language processing. Moreover, the recent success of large language models (LLMs) has fueled a surge in research on large-scale neural networks. However, the escalating demand for computing resources and energy consumption has prompted the search for energy-efficient alternatives. Inspired by the human brain, spiking neural networks (SNNs) promise energy-efficient computation with event-driven spikes. To provide future directions toward building energy-efficient large SNN models, we present a survey of existing methods for developing deep spiking neural networks, with a focus on emerging Spiking Transformers. Our main contributions are as follows: (1) an overview of learning methods for deep spiking neural networks, categorized by ANN-to-SNN conversion and direct training with surrogate gradients; (2) an overview of network architectures for deep spiking neural networks, categorized by deep convolutional neural networks (DCNNs) and Transformer architecture; and (3) a comprehensive comparison of state-of-the-art deep SNNs with a focus on emerging Spiking Transformers. We then further discuss and outline future directions toward large-scale SNNs.
A More Accurate Approximation of Activation Function with Few Spikes Neurons
2024-08-19
Dayena Jeong, Jaewoo Park, Jeonghee Jo, Jongkil Park, Jaewook Kim, Hyun Jae Jang, Suyoun Lee, Seongsik Park
Recent deep neural networks (DNNs), such as diffusion models [1], have faced high computational demands. Thus, spiking neural networks (SNNs) have attracted lots of attention as energy-efficient neural networks. However, conventional spiking neurons, such as leaky integrate-and-fire neurons, cannot accurately represent complex non-linear activation functions, such as Swish [2]. To approximate activation functions with spiking neurons, few spikes (FS) neurons were proposed [3], but the approximation performance was limited due to the lack of training methods considering the neurons. Thus, we propose tendency-based parameter initialization (TBPI) to enhance the approximation of activation function with FS neurons, exploiting temporal dependencies initializing the training parameters.
Obtaining Optimal Spiking Neural Network in Sequence Learning via CRNN-SNN Conversion
2024-08-18
Jiahao Su, Kang You, Zekai Xu, Weizhi Xu, Zhezhi He
Spiking neural networks (SNNs) are becoming a promising alternative to conventional artificial neural networks (ANNs) due to their rich neural dynamics and the implementation of energy-efficient neuromorphic chips. However, the non-differential binary communication mechanism makes SNN hard to converge to an ANN-level accuracy. When SNN encounters sequence learning, the situation becomes worse due to the difficulties in modeling long-range dependencies. To overcome these difficulties, researchers developed variants of LIF neurons and different surrogate gradients but still failed to obtain good results when the sequence became longer (e.g., $>$500). Unlike them, we obtain an optimal SNN in sequence learning by directly mapping parameters from a quantized CRNN. We design two sub-pipelines to support the end-to-end conversion of different structures in neural networks, which is called CNN-Morph (CNN $\rightarrow$ QCNN $\rightarrow$ BIFSNN) and RNN-Morph (RNN $\rightarrow$ QRNN $\rightarrow$ RBIFSNN). Using conversion pipelines and the s-analog encoding method, the conversion error of our framework is zero. Furthermore, we give the theoretical and experimental demonstration of the lossless CRNN-SNN conversion. Our results show the effectiveness of our method over short and long timescales tasks compared with the state-of-the-art learning- and conversion-based methods. We reach the highest accuracy of 99.16% (0.46 $\uparrow$) on S-MNIST, 94.95% (3.95 $\uparrow$) on PS-MNIST (sequence length of 784) respectively, and the lowest loss of 0.057 (0.013 $\downarrow$) within 8 time-steps in collision avoidance dataset.
Spiking neural networks (SNNs) transmit information via low-power binary spikes and have received widespread attention in areas such as computer vision and reinforcement learning. However, there have been very few explorations of SNNs in more practical industrial scenarios. In this paper, we focus on the application of SNNs in bearing fault diagnosis to facilitate the integration of high-performance AI algorithms and real-world industries. In particular, we identify two key limitations of existing SNN fault diagnosis methods: inadequate encoding capacity that necessitates cumbersome data preprocessing, and non-spike-oriented architectures that constrain the performance of SNNs. To alleviate these problems, we propose a Multi-scale Residual Attention SNN (MRA-SNN) to simultaneously improve the efficiency, performance, and robustness of SNN methods. By incorporating a lightweight attention mechanism, we have designed a multi-scale attention encoding module to extract multiscale fault features from vibration signals and encode them as spatio-temporal spikes, eliminating the need for complicated preprocessing. Then, the spike residual attention block extracts high-dimensional fault features and enhances the expressiveness of sparse spikes with the attention mechanism for end-to-end diagnosis. In addition, the performance and robustness of MRA-SNN is further enhanced by introducing the lightweight attention mechanism within the spiking neurons to simulate the biological dendritic filtering effect. Extensive experiments on MFPT and JNU benchmark datasets demonstrate that MRA-SNN significantly outperforms existing methods in terms of accuracy, energy consumption and noise robustness, and is more feasible for deployment in real-world industrial scenarios.
Temporal Reversed Training for Spiking Neural Networks with Generalized Spatio-Temporal Representation
2024-08-17
Lin Zuo, Yongqi Ding, Wenwei Luo, Mengmeng Jing, Xianlong Tian, Kunshan Yang
Spiking neural networks (SNNs) have received widespread attention as an ultra-low power computing paradigm. Recent studies have focused on improving the feature extraction capability of SNNs, but they suffer from inefficient inference and suboptimal performance. In this paper, we propose a simple yet effective temporal reversed training (TRT) method to optimize the spatio-temporal performance of SNNs and circumvent these problems. We perturb the input temporal data by temporal reversal, prompting the SNN to produce original-reversed consistent outputs and to learn perturbation-invariant representations. For static data without temporal dimension, we generalize this strategy by exploiting the inherent temporal property of SNNs for spike feature temporal reversal. In addition, we utilize the lightweight ``star operation" (element-wise multiplication) to hybridize the original and temporally reversed spike firing rates and expand the implicit dimensions, which serves as spatio-temporal regularization to further enhance the generalization of the SNN. Our method involves only a temporal reversal operation and element-wise multiplication during training, thus incurring negligible training overhead and not affecting the inference efficiency at all. Extensive experiments on static/neuromorphic object/action recognition, and 3D point cloud classification tasks demonstrate the effectiveness and generalizability of our method. In particular, with only two timesteps, our method achieves 74.77\% and 90.57\% accuracy on ImageNet and ModelNet40, respectively.
This paper introduces Xpikeformer, a hybrid analog-digital hardware architecture designed to accelerate spiking neural network (SNN)-based transformer models. By combining the energy efficiency and temporal dynamics of SNNs with the powerful sequence modeling capabilities of transformers, Xpikeformer leverages mixed analog-digital computing techniques to enhance performance and energy efficiency. The architecture integrates analog in-memory computing (AIMC) for feedforward and fully connected layers, and a stochastic spiking attention (SSA) engine for efficient attention mechanisms. We detail the design, implementation, and evaluation of Xpikeformer, demonstrating significant improvements in energy consumption and computational efficiency. Through an image classification task and a wireless communication symbol detection task, we show that Xpikeformer can achieve software-comparable inference accuracy. Energy evaluations reveal that Xpikeformer achieves up to a $17.8$--$19.2\times$ reduction in energy consumption compared to state-of-the-art digital ANN transformers and up to a $5.9$--$6.8\times$ reduction compared to fully digital SNN transformers. Xpikeformer also achieves a $12.0\times$ speedup compared to the GPU implementation of spiking transformers.
TACOS: Task Agnostic Continual Learning in Spiking Neural Networks
2024-08-16
Nicholas Soures, Peter Helfer, Anurag Daram, Tej Pandit, Dhireesha Kudithipudi
Catastrophic interference, the loss of previously learned information when learning new information, remains a major challenge in machine learning. Since living organisms do not seem to suffer from this problem, researchers have taken inspiration from biology to improve memory retention in artificial intelligence systems. However, previous attempts to use bio-inspired mechanisms have typically resulted in systems that rely on task boundary information during training and/or explicit task identification during inference, information that is not available in real-world scenarios. Here, we show that neuro-inspired mechanisms such as synaptic consolidation and metaplasticity can mitigate catastrophic interference in a spiking neural network, using only synapse-local information, with no need for task awareness, and with a fixed memory size that does not need to be increased when training on new tasks. Our model, TACOS, combines neuromodulation with complex synaptic dynamics to enable new learning while protecting previous information. We evaluate TACOS on sequential image recognition tasks and demonstrate its effectiveness in reducing catastrophic interference. Our results show that TACOS outperforms existing regularization techniques in domain-incremental learning scenarios. We also report the results of an ablation study to elucidate the contribution of each neuro-inspired mechanism separately.
Analog Spiking Neuron in CMOS 28 nm Towards Large-Scale Neuromorphic Processors
2024-08-14
Marwan Besrour, Jacob Lavoie, Takwa Omrani, Gabriel Martin-Hardy, Esmaeil Ranjbar Koleibi, Jeremy Menard, Konin Koua, Philippe Marcoux, Mounir Boukadoum, Rejean Fontaine
The computational complexity of deep learning algorithms has given rise to significant speed and memory challenges for the execution hardware. In energy-limited portable devices, highly efficient processing platforms are indispensable for reproducing the prowess afforded by much bulkier processing platforms. In this work, we present a low-power Leaky Integrate-and-Fire (LIF) neuron design fabricated in TSMC's 28 nm CMOS technology as proof of concept to build an energy-efficient mixed-signal Neuromorphic System-on-Chip (NeuroSoC). The fabricated neuron consumes 1.61 fJ/spike and occupies an active area of 34 $\mu m^{2}$, leading to a maximum spiking frequency of 300 kHz at 250 mV power supply. These performances are used in a software model to emulate the dynamics of a Spiking Neural Network (SNN). Employing supervised backpropagation and a surrogate gradient technique, the resulting accuracy on the MNIST dataset, using 4-bit post-training quantization stands at 82.5\%. The approach underscores the potential of such ASIC implementation of quantized SNNs to deliver high-performance, energy-efficient solutions to various embedded machine-learning applications.
Advancing Spatio-Temporal Processing in Spiking Neural Networks through Adaptation
2024-08-14
Maximilian Baronig, Romain Ferrand, Silvester Sabathiel, Robert Legenstein
Efficient implementations of spiking neural networks on neuromorphic hardware promise orders of magnitude less power consumption than their non-spiking counterparts. The standard neuron model for spike-based computation on such neuromorphic systems has long been the leaky integrate-and-fire (LIF) neuron. As a promising advancement, a computationally light augmentation of the LIF neuron model with an adaptation mechanism experienced a recent upswing in popularity, caused by demonstrations of its superior performance on spatio-temporal processing tasks. The root of the superiority of these so-called adaptive LIF neurons however, is not well understood. In this article, we thoroughly analyze the dynamical, computational, and learning properties of adaptive LIF neurons and networks thereof. We find that the frequently observed stability problems during training of such networks can be overcome by applying an alternative discretization method that results in provably better stability properties than the commonly used Euler-Forward method. With this discretization, we achieved a new state-of-the-art performance on common event-based benchmark datasets. We also show that the superiority of networks of adaptive LIF neurons extends to the prediction and generation of complex time series. Our further analysis of the computational properties of networks of adaptive LIF neurons shows that they are particularly well suited to exploit the spatio-temporal structure of input sequences. Furthermore, these networks are surprisingly robust to shifts of the mean input strength and input spike rate, even when these shifts were not observed during training. As a consequence, high-performance networks can be obtained without any normalization techniques such as batch normalization or batch-normalization through time.
DPSNN: Spiking Neural Network for Low-Latency Streaming Speech Enhancement
2024-08-14
Tao Sun, Sander Bohté
Speech enhancement (SE) improves communication in noisy environments, affecting areas such as automatic speech recognition, hearing aids, and telecommunications. With these domains typically being power-constrained and event-based while requiring low latency, neuromorphic algorithms in the form of spiking neural networks (SNNs) have great potential. Yet, current effective SNN solutions require a contextual sampling window imposing substantial latency, typically around 32ms, too long for many applications. Inspired by Dual-Path Spiking Neural Networks (DPSNNs) in classical neural networks, we develop a two-phase time-domain streaming SNN framework -- the Dual-Path Spiking Neural Network (DPSNN). In the DPSNN, the first phase uses Spiking Convolutional Neural Networks (SCNNs) to capture global contextual information, while the second phase uses Spiking Recurrent Neural Networks (SRNNs) to focus on frequency-related features. In addition, the regularizer suppresses activation to further enhance energy efficiency of our DPSNNs. Evaluating on the VCTK and Intel DNS Datasets, we demonstrate that our approach achieves the very low latency (approximately 5ms) required for applications like hearing aids, while demonstrating excellent signal-to-noise ratio (SNR), perceptual quality, and energy efficiency.
The Potential of Combined Learning Strategies to Enhance Energy Efficiency of Spiking Neuromorphic Systems
2024-08-13
Ali Shiri Sichani, Sai Kankatala
Ensuring energy-efficient design in neuromorphic computing systems necessitates a tailored architecture combined with algorithmic approaches. This manuscript focuses on enhancing brain-inspired perceptual computing machines through a novel combined learning approach for Convolutional Spiking Neural Networks (CSNNs). CSNNs present a promising alternative to traditional power-intensive and complex machine learning methods like backpropagation, offering energy-efficient spiking neuron processing inspired by the human brain. The proposed combined learning method integrates Pair-based Spike Timing-Dependent Plasticity (PSTDP) and power law-dependent Spike-timing-dependent plasticity (STDP) to adjust synaptic efficacies, enabling the utilization of stochastic elements like memristive devices to enhance energy efficiency and improve perceptual computing accuracy. By reducing learning parameters while maintaining accuracy, these systems consume less energy and have reduced area overhead, making them more suitable for hardware implementation. The research delves into neuromorphic design architectures, focusing on CSNNs to provide a general framework for energy-efficient computing hardware. Various CSNN architectures are evaluated to assess how less trainable parameters can maintain acceptable accuracy in perceptual computing systems, positioning them as viable candidates for neuromorphic architecture. Comparisons with previous work validate the achievements and methodology of the proposed architecture.
Event-Stream Super Resolution using Sigma-Delta Neural Network
2024-08-13
Waseem Shariff, Joe Lemley, Peter Corcoran
This study introduces a novel approach to enhance the spatial-temporal resolution of time-event pixels based on luminance changes captured by event cameras. These cameras present unique challenges due to their low resolution and the sparse, asynchronous nature of the data they collect. Current event super-resolution algorithms are not fully optimized for the distinct data structure produced by event cameras, resulting in inefficiencies in capturing the full dynamism and detail of visual scenes with improved computational complexity. To bridge this gap, our research proposes a method that integrates binary spikes with Sigma Delta Neural Networks (SDNNs), leveraging spatiotemporal constraint learning mechanism designed to simultaneously learn the spatial and temporal distributions of the event stream. The proposed network is evaluated using widely recognized benchmark datasets, including N-MNIST, CIFAR10-DVS, ASL-DVS, and Event-NFS. A comprehensive evaluation framework is employed, assessing both the accuracy, through root mean square error (RMSE), and the computational efficiency of our model. The findings demonstrate significant improvements over existing state-of-the-art methods, specifically, the proposed method outperforms state-of-the-art performance in computational efficiency, achieving a 17.04-fold improvement in event sparsity and a 32.28-fold increase in synaptic operation efficiency over traditional artificial neural networks, alongside a two-fold better performance over spiking neural networks.
On the Solvability of the {XOR} Problem by Spiking Neural Networks
2024-08-11
Bernhard A. Moser, Michael Lunglmayr
The linearly inseparable XOR problem and the related problem of representing binary logical gates is revisited from the point of view of temporal encoding and its solvability by spiking neural networks with minimal configurations of leaky integrate-and-fire (LIF) neurons. We use this problem as an example to study the effect of different hyper parameters such as information encoding, the number of hidden units in a fully connected reservoir, the choice of the leaky parameter and the reset mechanism in terms of reset-to-zero and reset-by-subtraction based on different refractory times. The distributions of the weight matrices give insight into the difficulty, respectively the probability, to find a solution. This leads to the observation that zero refractory time together with graded spikes and an adapted reset mechanism, reset-to-mod, makes it possible to realize sparse solutions of a minimal configuration with only two neurons in the hidden layer to resolve all binary logic gate constellations with XOR as a special case.
This thesis presents and evaluates the Dilated Convolution with Learnable Spacings (DCLS) method. Through various supervised learning experiments in the fields of computer vision, audio, and speech processing, the DCLS method proves to outperform both standard and advanced convolution techniques. The research is organized into several steps, starting with an analysis of the literature and existing convolution techniques that preceded the development of the DCLS method. We were particularly interested in the methods that are closely related to our own and that remain essential to capture the nuances and uniqueness of our approach. The cornerstone of our study is the introduction and application of the DCLS method to convolutional neural networks (CNNs), as well as to hybrid architectures that rely on both convolutional and visual attention approaches. DCLS is shown to be particularly effective in tasks such as classification, semantic segmentation, and object detection. Initially using bilinear interpolation, the study also explores other interpolation methods, finding that Gaussian interpolation slightly improves performance. The DCLS method is further applied to spiking neural networks (SNNs) to enable synaptic delay learning within a neural network that could eventually be transferred to so-called neuromorphic chips. The results show that the DCLS method stands out as a new state-of-the-art technique in SNN audio classification for certain benchmark tasks in this field. These tasks involve datasets with a high temporal component. In addition, we show that DCLS can significantly improve the accuracy of artificial neural networks for the multi-label audio classification task. We conclude with a discussion of the chosen experimental setup, its limitations, the limitations of our method, and our results.
Neuromorphic Keyword Spotting with Pulse Density Modulation MEMS Microphones
2024-08-09
Sidi Yaya Arnaud Yarga, Sean U. N. Wood
The Keyword Spotting (KWS) task involves continuous audio stream monitoring to detect predefined words, requiring low energy devices for continuous processing. Neuromorphic devices effectively address this energy challenge. However, the general neuromorphic KWS pipeline, from microphone to Spiking Neural Network (SNN), entails multiple processing stages. Leveraging the popularity of Pulse Density Modulation (PDM) microphones in modern devices and their similarity to spiking neurons, we propose a direct microphone-to-SNN connection. This approach eliminates intermediate stages, notably reducing computational costs. The system achieved an accuracy of 91.54\% on the Google Speech Command (GSC) dataset, surpassing the state-of-the-art for the Spiking Speech Command (SSC) dataset which is a bio-inspired encoded GSC. Furthermore, the observed sparsity in network activity and connectivity indicates potential for remarkably low energy consumption in a neuromorphic device implementation.
Currently, neural-network processing in machine learning applications relies on layer synchronization, whereby neurons in a layer aggregate incoming currents from all neurons in the preceding layer, before evaluating their activation function. This is practiced even in artificial Spiking Neural Networks (SNNs), which are touted as consistent with neurobiology, in spite of processing in the brain being, in fact asynchronous. A truly asynchronous system however would allow all neurons to evaluate concurrently their threshold and emit spikes upon receiving any presynaptic current. Omitting layer synchronization is potentially beneficial, for latency and energy efficiency, but asynchronous execution of models previously trained with layer synchronization may entail a mismatch in network dynamics and performance. We present a study that documents and quantifies this problem in three datasets on our simulation environment that implements network asynchrony, and we show that models trained with layer synchronization either perform sub-optimally in absence of the synchronization, or they will fail to benefit from any energy and latency reduction, when such a mechanism is in place. We then "make ends meet" and address the problem with unlayered backprop, a novel backpropagation-based training method, for learning models suitable for asynchronous processing. We train with it models that use different neuron execution scheduling strategies, and we show that although their neurons are more reactive, these models consistently exhibit lower overall spike density (up to 50%), reach a correct decision faster (up to 2x) without integrating all spikes, and achieve superior accuracy (up to 10% higher). Our findings suggest that asynchronous event-based (neuromorphic) AI computing is indeed more efficient, but we need to seriously rethink how we train our SNN models, to benefit from it.
A frugal Spiking Neural Network for unsupervised classification of continuous multivariate temporal data
2024-08-08
Sai Deepesh Pokala, Marie Bernert, Takuya Nanami, Takashi Kohno, Timothée Lévi, Blaise Yvert
As neural interfaces become more advanced, there has been an increase in the volume and complexity of neural data recordings. These interfaces capture rich information about neural dynamics that call for efficient, real-time processing algorithms to spontaneously extract and interpret patterns of neural dynamics. Moreover, being able to do so in a fully unsupervised manner is critical as patterns in vast streams of neural data might not be easily identifiable by the human eye. Formal Deep Neural Networks (DNNs) have come a long way in performing pattern recognition tasks for various static and sequential pattern recognition applications. However, these networks usually require large labeled datasets for training and have high power consumption preventing their future embedding in active brain implants. An alternative aimed at addressing these issues are Spiking Neural Networks (SNNs) which are neuromorphic and use more biologically plausible neurons with evolving membrane potentials. In this context, we introduce here a frugal single-layer SNN designed for fully unsupervised identification and classification of multivariate temporal patterns in continuous data with a sequential approach. We show that, with only a handful number of neurons, this strategy is efficient to recognize highly overlapping multivariate temporal patterns, first on simulated data, and then on Mel Cepstral representations of speech sounds and finally on multichannel neural data. This approach relies on several biologically inspired plasticity rules, including Spike-timing-dependent plasticity (STDP), Short-term plasticity (STP) and intrinsic plasticity (IP). These results pave the way towards highly frugal SNNs for fully unsupervised and online-compatible learning of complex multivariate temporal patterns for future embedding in dedicated very-low power hardware.
Unveiling the Power of Sparse Neural Networks for Feature Selection
2024-08-08
Zahra Atashgahi, Tennison Liu, Mykola Pechenizkiy, Raymond Veldhuis, Decebal Constantin Mocanu, Mihaela van der Schaar
Sparse Neural Networks (SNNs) have emerged as powerful tools for efficient feature selection. Leveraging the dynamic sparse training (DST) algorithms within SNNs has demonstrated promising feature selection capabilities while drastically reducing computational overheads. Despite these advancements, several critical aspects remain insufficiently explored for feature selection. Questions persist regarding the choice of the DST algorithm for network training, the choice of metric for ranking features/neurons, and the comparative performance of these methods across diverse datasets when compared to dense networks. This paper addresses these gaps by presenting a comprehensive systematic analysis of feature selection with sparse neural networks. Moreover, we introduce a novel metric considering sparse neural network characteristics, which is designed to quantify feature importance within the context of SNNs. Our findings show that feature selection with SNNs trained with DST algorithms can achieve, on average, more than $50\%$ memory and $55\%$ FLOPs reduction compared to the dense networks, while outperforming them in terms of the quality of the selected features. Our code and the supplementary material are available on GitHub (\url{https://github.com/zahraatashgahi/Neuron-Attribution}).
Synaptic Modulation using Interspike Intervals Increases Energy Efficiency of Spiking Neural Networks
2024-08-06
Dylan Adams, Magda Zajaczkowska, Ashiq Anjum, Andrea Soltoggio, Shirin Dora
Despite basic differences between Spiking Neural Networks (SNN) and Artificial Neural Networks (ANN), most research on SNNs involve adapting ANN-based methods for SNNs. Pruning (dropping connections) and quantization (reducing precision) are often used to improve energy efficiency of SNNs. These methods are very effective for ANNs whose energy needs are determined by signals transmitted on synapses. However, the event-driven paradigm in SNNs implies that energy is consumed by spikes. In this paper, we propose a new synapse model whose weights are modulated by Interspike Intervals (ISI) i.e. time difference between two spikes. SNNs composed of this synapse model, termed ISI Modulated SNNs (IMSNN), can use gradient descent to estimate how the ISI of a neuron changes after updating its synaptic parameters. A higher ISI implies fewer spikes and vice-versa. The learning algorithm for IMSNNs exploits this information to selectively propagate gradients such that learning is achieved by increasing the ISIs resulting in a network that generates fewer spikes. The performance of IMSNNs with dense and convolutional layers have been evaluated in terms of classification accuracy and the number of spikes using the MNIST and FashionMNIST datasets. The performance comparison with conventional SNNs shows that IMSNNs exhibit upto 90% reduction in the number of spikes while maintaining similar classification accuracy.
We show how brain networks, modeled as Spiking Neural Networks, can be viewed at different levels of abstraction. Lower levels include complications such as failures of neurons and edges. Higher levels are more abstract, making simplifying assumptions to avoid these complications. We show precise relationships between executions of networks at different levels, which enables us to understand the behavior of lower-level networks in terms of the behavior of higher-level networks. We express our results using two abstract networks, A1 and A2, one to express firing guarantees and the other to express non-firing guarantees, and one detailed network D. The abstract networks contain reliable neurons and edges, whereas the detailed network has neurons and edges that may fail, subject to some constraints. Here we consider just initial stopping failures. To define these networks, we begin with abstract network A1 and modify it systematically to obtain the other two networks. To obtain A2, we simply lower the firing thresholds of the neurons. To obtain D, we introduce failures of neurons and edges, and incorporate redundancy in the neurons and edges in order to compensate for the failures. We also define corresponding inputs for the networks, and corresponding executions of the networks. We prove two main theorems, one relating corresponding executions of A1 and D and the other relating corresponding executions of A2 and D. Together, these give both firing and non-firing guarantees for the detailed network D. We also give a third theorem, relating the effects of D on an external reliable actuator neuron to the effects of the abstract networks on the same actuator neuron.
Mamba-Spike: Enhancing the Mamba Architecture with a Spiking Front-End for Efficient Temporal Data Processing
2024-08-04
Jiahao Qin, Feng Liu
The field of neuromorphic computing has gained significant attention in recent years, aiming to bridge the gap between the efficiency of biological neural networks and the performance of artificial intelligence systems. This paper introduces Mamba-Spike, a novel neuromorphic architecture that integrates a spiking front-end with the Mamba backbone to achieve efficient and robust temporal data processing. The proposed approach leverages the event-driven nature of spiking neural networks (SNNs) to capture and process asynchronous, time-varying inputs, while harnessing the power of the Mamba backbone's selective state spaces and linear-time sequence modeling capabilities to model complex temporal dependencies effectively. The spiking front-end of Mamba-Spike employs biologically inspired neuron models, along with adaptive threshold and synaptic dynamics. These components enable efficient spatiotemporal feature extraction and encoding of the input data. The Mamba backbone, on the other hand, utilizes a hierarchical structure with gated recurrent units and attention mechanisms to capture long-term dependencies and selectively process relevant information. To evaluate the efficacy of the proposed architecture, a comprehensive empirical study is conducted on both neuromorphic datasets, including DVS Gesture and TIDIGITS, and standard datasets, such as Sequential MNIST and CIFAR10-DVS. The results demonstrate that Mamba-Spike consistently outperforms state-of-the-art baselines, achieving higher accuracy, lower latency, and improved energy efficiency. Moreover, the model exhibits robustness to various input perturbations and noise levels, highlighting its potential for real-world applications. The code will be available at https://github.com/ECNU-Cross-Innovation-Lab/Mamba-Spike.
Spiking Neural Networks (SNNs) are a subclass of neuromorphic models that have great potential to be used as controllers in Cyber-Physical Systems (CPSs) due to their energy efficiency. They can benefit from the prevalent approach of first training an Artificial Neural Network (ANN) and then translating to an SNN with subsequent hyperparameter tuning. The tuning is required to ensure that the resulting SNN is accurate with respect to the ANN in terms of metrics like Mean Squared Error (MSE). However, SNN controllers for safety-critical CPSs must also satisfy safety specifications, which are not guaranteed by the conversion approach. In this paper, we propose a solution which tunes the $temporal$ $window$ hyperparameter of the translated SNN to ensure both accuracy and compliance with the safe range specification that requires the SNN outputs to remain within a safe range. The core verification problem is modelled using mixed-integer linear programming (MILP) and is solved with Gurobi. When the controller fails to meet the range specification, we compute tight bounds on the SNN outputs as feedback for the CPS developer. To mitigate the high computational cost of verification, we integrate data-driven steps to minimize verification calls. Our approach provides designers with the confidence to safely integrate energy-efficient SNN controllers into modern CPSs. We demonstrate our approach with experimental results on five different benchmark neural controllers.
Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamics
2024-08-03
Naichuan Zheng, Hailun Xia, Dapeng Liu
In skeletal-based action recognition, Graph Convolutional Networks (GCNs) based methods face limitations due to their complexity and high energy consumption. Spiking Neural Networks (SNNs) have gained attention in recent years for their low energy consumption, but existing methods combining GCNs and SNNs fail to fully utilize the temporal characteristics of skeletal sequences, leading to increased storage and computational costs. To address this issue, we propose a Signal-SGN(Spiking Graph Convolutional Network), which leverages the temporal dimension of skeletal sequences as the spiking timestep and treats features as discrete stochastic signals. The core of the network consists of a 1D Spiking Graph Convolutional Network (1D-SGN) and a Frequency Spiking Convolutional Network (FSN). The SGN performs graph convolution on single frames and incorporates spiking network characteristics to capture inter-frame temporal relationships, while the FSN uses Fast Fourier Transform (FFT) and complex convolution to extract temporal-frequency features. We also introduce a multi-scale wavelet transform feature fusion module(MWTF) to capture spectral features of temporal signals, enhancing the model's classification capability. We propose a pluggable temporal-frequency spatial semantic feature extraction module(TFSM) to enhance the model's ability to distinguish features without increasing inference-phase consumption. Our numerous experiments on the NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets demonstrate that the proposed models not only surpass existing SNN-based methods in accuracy but also reduce computational and storage costs during training. Furthermore, they achieve competitive accuracy compared to corresponding GCN-based methods, which is quite remarkable.
Using CSNNs to Perform Event-based Data Processing & Classification on ASL-DVS
2024-08-01
Ria Patel, Sujit Tripathy, Zachary Sublett, Seoyoung An, Riya Patel
Recent advancements in bio-inspired visual sensing and neuromorphic computing have led to the development of various highly efficient bio-inspired solutions with real-world applications. One notable application integrates event-based cameras with spiking neural networks (SNNs) to process event-based sequences that are asynchronous and sparse, making them difficult to handle. In this project, we develop a convolutional spiking neural network (CSNN) architecture that leverages convolutional operations and recurrent properties of a spiking neuron to learn the spatial and temporal relations in the ASL-DVS gesture dataset. The ASL-DVS gesture dataset is a neuromorphic dataset containing hand gestures when displaying 24 letters (A to Y, excluding J and Z due to the nature of their symbols) from the American Sign Language (ASL). We performed classification on a pre-processed subset of the full ASL-DVS dataset to identify letter signs and achieved 100\% training accuracy. Specifically, this was achieved by training in the Google Cloud compute platform while using a learning rate of 0.0005, batch size of 25 (total of 20 batches), 200 iterations, and 10 epochs.
Low-Power Vibration-Based Predictive Maintenance for Industry 4.0 using Neural Networks: A Survey
2024-08-01
Alexandru Vasilache, Sven Nitzsche, Daniel Floegel, Tobias Schuermann, Stefan von Dosky, Thomas Bierweiler, Marvin Mußler, Florian Kälber, Soeren Hohmann, Juergen Becker
The advancements in smart sensors for Industry 4.0 offer ample opportunities for low-powered predictive maintenance and condition monitoring. However, traditional approaches in this field rely on processing in the cloud, which incurs high costs in energy and storage. This paper investigates the potential of neural networks for low-power on-device computation of vibration sensor data for predictive maintenance. We review the literature on Spiking Neural Networks (SNNs) and Artificial Neuronal Networks (ANNs) for vibration-based predictive maintenance by analyzing datasets, data preprocessing, network architectures, and hardware implementations. Our findings suggest that no satisfactory standard benchmark dataset exists for evaluating neural networks in predictive maintenance tasks. Furthermore frequency domain transformations are commonly employed for preprocessing. SNNs mainly use shallow feed forward architectures, whereas ANNs explore a wider range of models and deeper networks. Finally, we highlight the need for future research on hardware implementations of neural networks for low-power predictive maintenance applications and the development of a standardized benchmark dataset.
Drawing on the intricate structures of the brain, Spiking Neural Networks (SNNs) emerge as a transformative development in artificial intelligence, closely emulating the complex dynamics of biological neural networks. While SNNs show promising efficiency on specialized sparse-computational hardware, their practical training often relies on conventional GPUs. This reliance frequently leads to extended computation times when contrasted with traditional Artificial Neural Networks (ANNs), presenting significant hurdles for advancing SNN research. To navigate this challenge, we present a novel temporal fusion method, specifically designed to expedite the propagation dynamics of SNNs on GPU platforms, which serves as an enhancement to the current significant approaches for handling deep learning tasks with SNNs. This method underwent thorough validation through extensive experiments in both authentic training scenarios and idealized conditions, confirming its efficacy and adaptability for single and multi-GPU systems. Benchmarked against various existing SNN libraries/implementations, our method achieved accelerations ranging from $5\times$ to $40\times$ on NVIDIA A100 GPUs. Publicly available experimental codes can be found at https://github.com/EMI-Group/snn-temporal-fusion.
An Asynchronous Multi-core Accelerator for SNN inference
2024-07-30
Zhuo Chen, De Ma, Xiaofei Jin, Qinghui Xing, Ouwen Jin, Xin Du, Shuibing He, Gang Pan
Spiking Neural Networks (SNNs) are extensively utilized in brain-inspired computing and neuroscience research. To enhance the speed and energy efficiency of SNNs, several many-core accelerators have been developed. However, maintaining the accuracy of SNNs often necessitates frequent explicit synchronization among all cores, which presents a challenge to overall efficiency. In this paper, we propose an asynchronous architecture for Spiking Neural Networks (SNNs) that eliminates the need for inter-core synchronization, thus enhancing speed and energy efficiency. This approach leverages the pre-determined dependencies of neuromorphic cores established during compilation. Each core is equipped with a scheduler that monitors the status of its dependencies, allowing it to safely advance to the next timestep without waiting for other cores. This eliminates the necessity for global synchronization and minimizes core waiting time despite inherent workload imbalances. Comprehensive evaluations using five different SNN workloads show that our architecture achieves a 1.86x speedup and a 1.55x increase in energy efficiency compared to state-of-the-art synchronization architectures.
Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detection
2024-07-30
Xinhao Luo, Man Yao, Yuhong Chou, Bo Xu, Guoqi Li
Brain-inspired Spiking Neural Networks (SNNs) have bio-plausibility and low-power advantages over Artificial Neural Networks (ANNs). Applications of SNNs are currently limited to simple classification tasks because of their poor performance. In this work, we focus on bridging the performance gap between ANNs and SNNs on object detection. Our design revolves around network architecture and spiking neuron. First, the overly complex module design causes spike degradation when the YOLO series is converted to the corresponding spiking version. We design a SpikeYOLO architecture to solve this problem by simplifying the vanilla YOLO and incorporating meta SNN blocks. Second, object detection is more sensitive to quantization errors in the conversion of membrane potentials into binary spikes by spiking neurons. To address this challenge, we design a new spiking neuron that activates Integer values during training while maintaining spike-driven by extending virtual timesteps during inference. The proposed method is validated on both static and neuromorphic object detection datasets. On the static COCO dataset, we obtain 66.2% mAP@50 and 48.9% mAP@50:95, which is +15.0% and +18.7% higher than the prior state-of-the-art SNN, respectively. On the neuromorphic Gen1 dataset, we achieve 67.2% mAP@50, which is +2.5% greater than the ANN with equivalent architecture, and the energy efficiency is improved by 5.7*. Code: https://github.com/BICLab/SpikeYOLO
Spiking-DD: Neuromorphic Event Camera based Driver Distraction Detection with Spiking Neural Network
2024-07-30
Waseem Shariff, Paul Kielty, Joseph Lemley, Peter Corcoran
Event camera-based driver monitoring is emerging as a pivotal area of research, driven by its significant advantages such as rapid response, low latency, power efficiency, enhanced privacy, and prevention of undersampling. Effective detection of driver distraction is crucial in driver monitoring systems to enhance road safety and reduce accident rates. The integration of an optimized sensor such as Event Camera with an optimized network is essential for maximizing these benefits. This paper introduces the innovative concept of sensing without seeing to detect driver distraction, leveraging computationally efficient spiking neural networks (SNN). To the best of our knowledge, this study is the first to utilize event camera data with spiking neural networks for driver distraction. The proposed Spiking-DD network not only achieve state of the art performance but also exhibit fewer parameters and provides greater accuracy than current event-based methodologies.
Neuromorphic on-chip reservoir computing with spiking neural network architectures
2024-07-30
Samip Karki, Diego Chavez Arana, Andrew Sornborger, Francesco Caravelli
Reservoir computing is a promising approach for harnessing the computational power of recurrent neural networks while dramatically simplifying training. This paper investigates the application of integrate-and-fire neurons within reservoir computing frameworks for two distinct tasks: capturing chaotic dynamics of the H\'enon map and forecasting the Mackey-Glass time series. Integrate-and-fire neurons can be implemented in low-power neuromorphic architectures such as Intel Loihi. We explore the impact of network topologies created through random interactions on the reservoir's performance. Our study reveals task-specific variations in network effectiveness, highlighting the importance of tailored architectures for distinct computational tasks. To identify optimal network configurations, we employ a meta-learning approach combined with simulated annealing. This method efficiently explores the space of possible network structures, identifying architectures that excel in different scenarios. The resulting networks demonstrate a range of behaviors, showcasing how inherent architectural features influence task-specific capabilities. We study the reservoir computing performance using a custom integrate-and-fire code, Intel's Lava neuromorphic computing software framework, and via an on-chip implementation in Loihi. We conclude with an analysis of the energy performance of the Loihi architecture.
Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies
2024-07-30
Mingkun Xu, Huifeng Yin, Yujie Wu, Guoqi Li, Faqiang Liu, Jing Pei, Shuai Zhong, Lei Deng
In recent years, spiking neural networks (SNNs) have attracted substantial interest due to their potential to replicate the energy-efficient and event-driven processing of biological neurons. Despite this, the application of SNNs in graph representation learning, particularly for non-Euclidean data, remains underexplored, and the influence of spiking dynamics on graph learning is not yet fully understood. This work seeks to address these gaps by examining the unique properties and benefits of spiking dynamics in enhancing graph representation learning. We propose a spike-based graph neural network model that incorporates spiking dynamics, enhanced by a novel spatial-temporal feature normalization (STFN) technique, to improve training efficiency and model stability. Our detailed analysis explores the impact of rate coding and temporal coding on SNN performance, offering new insights into their advantages for deep graph networks and addressing challenges such as the oversmoothing problem. Experimental results demonstrate that our SNN models can achieve competitive performance with state-of-the-art graph neural networks (GNNs) while considerably reducing computational costs, highlighting the potential of SNNs for efficient neuromorphic computing applications in complex graph-based scenarios.
Joint Diffusion Processes as an Inductive Bias in Sheaf Neural Networks
2024-07-30
Ferran Hernandez Caralt, Guillermo Bernárdez Gil, Iulia Duta, Pietro Liò, Eduard Alarcón Cot
Sheaf Neural Networks (SNNs) naturally extend Graph Neural Networks (GNNs) by endowing a cellular sheaf over the graph, equipping nodes and edges with vector spaces and defining linear mappings between them. While the attached geometric structure has proven to be useful in analyzing heterophily and oversmoothing, so far the methods by which the sheaf is computed do not always guarantee a good performance in such settings. In this work, drawing inspiration from opinion dynamics concepts, we propose two novel sheaf learning approaches that (i) provide a more intuitive understanding of the involved structure maps, (ii) introduce a useful inductive bias for heterophily and oversmoothing, and (iii) infer the sheaf in a way that does not scale with the number of features, thus using fewer learnable parameters than existing methods. In our evaluation, we show the limitations of the real-world benchmarks used so far on SNNs, and design a new synthetic task -- leveraging the symmetries of n-dimensional ellipsoids -- that enables us to better assess the strengths and weaknesses of sheaf-based models. Our extensive experimentation on these novel datasets reveals valuable insights into the scenarios and contexts where SNNs in general -- and our proposed approaches in particular -- can be beneficial.
Spiking neural networks (SNNs) for event-based optical flow are claimed to be computationally more efficient than their artificial neural networks (ANNs) counterparts, but a fair comparison is missing in the literature. In this work, we propose an event-based optical flow solution based on activation sparsification and a neuromorphic processor, SENECA. SENECA has an event-driven processing mechanism that can exploit the sparsity in ANN activations and SNN spikes to accelerate the inference of both types of neural networks. The ANN and the SNN for comparison have similar low activation/spike density (~5%) thanks to our novel sparsification-aware training. In the hardware-in-loop experiments designed to deduce the average time and energy consumption, the SNN consumes 44.9ms and 927.0 microjoules, which are 62.5% and 75.2% of the ANN's consumption, respectively. We find that SNN's higher efficiency attributes to its lower pixel-wise spike density (43.5% vs. 66.5%) that requires fewer memory access operations for neuron states.
RSC-SNN: Exploring the Trade-off Between Adversarial Robustness and Accuracy in Spiking Neural Networks via Randomized Smoothing Coding
2024-07-29
Keming Wu, Man Yao, Yuhong Chou, Xuerui Qiu, Rui Yang, Bo Xu, Guoqi Li
Spiking Neural Networks (SNNs) have received widespread attention due to their unique neuronal dynamics and low-power nature. Previous research empirically shows that SNNs with Poisson coding are more robust than Artificial Neural Networks (ANNs) on small-scale datasets. However, it is still unclear in theory how the adversarial robustness of SNNs is derived, and whether SNNs can still maintain its adversarial robustness advantage on large-scale dataset tasks. This work theoretically demonstrates that SNN's inherent adversarial robustness stems from its Poisson coding. We reveal the conceptual equivalence of Poisson coding and randomized smoothing in defense strategies, and analyze in depth the trade-off between accuracy and adversarial robustness in SNNs via the proposed Randomized Smoothing Coding (RSC) method. Experiments demonstrate that the proposed RSC-SNNs show remarkable adversarial robustness, surpassing ANNs and achieving state-of-the-art robustness results on large-scale dataset ImageNet. Our open-source implementation code is available at this https URL: https://github.com/KemingWu/RSC-SNN.
Neuromorphic computing has recently gained momentum with the emergence of various neuromorphic processors. As the field advances, there is an increasing focus on developing training methods that can effectively leverage the unique properties of spiking neural networks (SNNs). SNNs emulate the temporal dynamics of biological neurons, making them particularly well-suited for real-time, event-driven processing. To fully harness the potential of SNNs across different neuromorphic platforms, effective training methodologies are essential. In SNNs, learning rules are based on neurons' spiking behavior, that is, if and when spikes are generated due to a neuron's membrane potential exceeding that neuron's spiking threshold, and this spike timing encodes vital information. However, the threshold is generally treated as a hyperparameter, and incorrect selection can lead to neurons that do not spike for large portions of the training process, hindering the effective rate of learning. This work focuses on the significance of learning neuron thresholds alongside weights in SNNs. Our results suggest that promoting threshold from a hyperparameter to a trainable parameter effectively addresses the issue of dead neurons during training. This leads to a more robust training algorithm, resulting in improved convergence, increased test accuracy, and a substantial reduction in the number of training epochs required to achieve viable accuracy on spatiotemporal datasets such as NMNIST, DVS128, and Spiking Heidelberg Digits (SHD), with up to 30% training speed-up and up to 2% higher accuracy on these datasets.
The Role of Temporal Hierarchy in Spiking Neural Networks
2024-07-26
Filippo Moro, Pau Vilimelis Aceituno, Laura Kriener, Melika Payvand
Spiking Neural Networks (SNNs) have the potential for rich spatio-temporal signal processing thanks to exploiting both spatial and temporal parameters. The temporal dynamics such as time constants of the synapses and neurons and delays have been recently shown to have computational benefits that help reduce the overall number of parameters required in the network and increase the accuracy of the SNNs in solving temporal tasks. Optimizing such temporal parameters, for example, through gradient descent, gives rise to a temporal architecture for different problems. As has been shown in machine learning, to reduce the cost of optimization, architectural biases can be applied, in this case in the temporal domain. Such inductive biases in temporal parameters have been found in neuroscience studies, highlighting a hierarchy of temporal structure and input representation in different layers of the cortex. Motivated by this, we propose to impose a hierarchy of temporal representation in the hidden layers of SNNs, highlighting that such an inductive bias improves their performance. We demonstrate the positive effects of temporal hierarchy in the time constants of feed-forward SNNs applied to temporal tasks (Multi-Time-Scale XOR and Keyword Spotting, with a benefit of up to 4.1% in classification accuracy). Moreover, we show that such architectural biases, i.e. hierarchy of time constants, naturally emerge when optimizing the time constants through gradient descent, initialized as homogeneous values. We further pursue this proposal in temporal convolutional SNNs, by introducing the hierarchical bias in the size and dilation of temporal kernels, giving rise to competitive results in popular temporal spike-based datasets.
There is unprecedented development in machine learning, exemplified by recent large language models and world simulators, which are artificial neural networks running on digital computers. However, they still cannot parallel human brains in terms of energy efficiency and the streamlined adaptability to inputs of different difficulties, due to differences in signal representation, optimization, run-time reconfigurability, and hardware architecture. To address these fundamental challenges, we introduce pruning optimization for input-aware dynamic memristive spiking neural network (PRIME). Signal representation-wise, PRIME employs leaky integrate-and-fire neurons to emulate the brain's inherent spiking mechanism. Drawing inspiration from the brain's structural plasticity, PRIME optimizes the topology of a random memristive spiking neural network without expensive memristor conductance fine-tuning. For runtime reconfigurability, inspired by the brain's dynamic adjustment of computational depth, PRIME employs an input-aware dynamic early stop policy to minimize latency during inference, thereby boosting energy efficiency without compromising performance. Architecture-wise, PRIME leverages memristive in-memory computing, mirroring the brain and mitigating the von Neumann bottleneck. We validated our system using a 40 nm 256 Kb memristor-based in-memory computing macro on neuromorphic image classification and image inpainting. Our results demonstrate the classification accuracy and Inception Score are comparable to the software baseline, while achieving maximal 62.50-fold improvements in energy efficiency, and maximal 77.0% computational load savings. The system also exhibits robustness against stochastic synaptic noise of analogue memristors. Our software-hardware co-designed model paves the way to future brain-inspired neuromorphic computing with brain-like energy efficiency and adaptivity.
Neuromorphic computing leveraging spiking neural network has emerged as a promising solution to tackle the security and reliability challenges with the conventional cyber-physical infrastructure of microgrids. Its event-driven paradigm facilitates promising prospect in resilient and energy-efficient coordination among power electronic converters. However, different from biological neurons that are focused in the literature, microgrids exhibit distinct architectures and features, implying potentially diverse adaptability in its capabilities to dismiss information transfer, which remains largely unrevealed. One of the biggest drawbacks in the information transfer theory is the impact of noise in the signaling accuracy. Hence, this article hereby explores the noise resiliency of neuromorphic inferential communication in microgrids through case studies and underlines potential challenges and solutions as extensions beyond the results, thus offering insights for its implementation in real-world scenarios.
Federated machine learning enables model training across multiple clients while maintaining data privacy. Vertical Federated Learning (VFL) specifically deals with instances where the clients have different feature sets of the same samples. As federated learning models aim to improve efficiency and adaptability, innovative neural network architectures like Spiking Neural Networks (SNNs) are being leveraged to enable fast and accurate processing at the edge. SNNs, known for their efficiency over Artificial Neural Networks (ANNs), have not been analyzed for their applicability in VFL, thus far. In this paper, we investigate the benefits and trade-offs of using SNN models in a vertical federated learning setting. We implement two different federated learning architectures -- with model splitting and without model splitting -- that have different privacy and performance implications. We evaluate the setup using CIFAR-10 and CIFAR-100 benchmark datasets along with SNN implementations of VGG9 and ResNET classification models. Comparative evaluations demonstrate that the accuracy of SNN models is comparable to that of traditional ANNs for VFL applications, albeit significantly more energy efficient.
Continual Learning with Hebbian Plasticity in Sparse and Predictive Coding Networks: A Survey and Perspective
2024-07-24
Ali Safa
Recently, the use of bio-inspired learning techniques such as Hebbian learning and its closely-related Spike-Timing-Dependent Plasticity (STDP) variant have drawn significant attention for the design of compute-efficient AI systems that can continuously learn on-line at the edge. A key differentiating factor regarding this emerging class of neuromorphic continual learning system lies in the fact that learning must be carried using a data stream received in its natural order, as opposed to conventional gradient-based offline training, where a static training dataset is assumed available a priori and randomly shuffled to make the training set independent and identically distributed (i.i.d). In contrast, the emerging class of neuromorphic continual learning systems covered in this survey must learn to integrate new information on the fly in a non-i.i.d manner, which makes these systems subject to catastrophic forgetting. In order to build the next generation of neuromorphic AI systems that can continuously learn at the edge, a growing number of research groups are studying the use of Sparse and Predictive Coding-based Hebbian neural network architectures and the related Spiking Neural Networks (SNNs) equipped with STDP learning. However, since this research field is still emerging, there is a need for providing a holistic view of the different approaches proposed in the literature so far. To this end, this survey covers a number of recent works in the field of neuromorphic continual learning based on state-of-the-art Sparse and Predictive Coding technology; provides background theory to help interested researchers quickly learn the key concepts; and discusses important future research questions in light of the different works covered in this paper. It is hoped that this survey will contribute towards future research in the field of neuromorphic continual learning.
A Quantum Leaky Integrate-and-Fire Spiking Neuron and Network
2024-07-23
Dean Brand, Francesco Petruccione
Quantum machine learning is in a period of rapid development and discovery, however it still lacks the resources and diversity of computational models of its classical complement. With the growing difficulties of classical models requiring extreme hardware and power solutions, and quantum models being limited by noisy intermediate-scale quantum (NISQ) hardware, there is an emerging opportunity to solve both problems together. Here we introduce a new software model for quantum neuromorphic computing -- a quantum leaky integrate-and-fire (QLIF) neuron, implemented as a compact high-fidelity quantum circuit, requiring only 2 rotation gates and no CNOT gates. We use these neurons as building blocks in the construction of a quantum spiking neural network (QSNN), and a quantum spiking convolutional neural network (QSCNN), as the first of their kind. We apply these models to the MNIST, Fashion-MNIST, and KMNIST datasets for a full comparison with other classical and quantum models. We find that the proposed models perform competitively, with comparative accuracy, with efficient scaling and fast computation in classical simulation as well as on quantum devices.
Harmonic fractal transformation, 4R-regeneration and noise shaping for ultra wide-band reception in FitzHugh-Nagumo neuronal model
2024-07-22
Mariia Sorokina
Human hearing range significantly surpasses the typical neuronal spiking frequency. Yet, neurons with their modest frequency range not only efficiently receive and process multiple orders higher frequency signals, but also demonstrate remarkable stability and adaptability to frequency variations in brain functional connectivity. Ability to process signals beyond the limitations of the receiver temporal or frequency (bandwidth) resolution is highly desirable yet requires complex design architectures. Using the FitzHugh-Nagumo model we reveal the harmonic fractal transformation of frequency and bandwidth, which enables the Nyquist rate integer (for low frequencies) and sub-integer (for high frequencies) multiplication. We also demonstrate for the first time that noise shaping can be achieved in a simple RLC-circuit without a requirement of a delay line. The discovered effect presents a novel regeneration type - 4R: re-amplifying, re-shaping, re-timing, and re-modulating and due to the fractal nature of transformation offers a remarkable regenerative efficiency. The effect is a generalization of phase locking to non-periodic encoded signals. The discovered physical mechanism explains how using neuronal functionality one can receive and process signals over an ultra-wide band (below or higher the spiking neuronal range by multiple orders) and below the noise floor.
Few-Shot Transfer Learning for Individualized Braking Intent Detection on Neuromorphic Hardware
2024-07-21
Nathan Lutes, Venkata Sriram Siddhardh Nadendla, K. Krishnamurthy
Objective: This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a BrainChip Akida AKD1000 neuromorphic system-on-chip for developing individual-level, instead of traditionally used group-level, models using electroencephalographic data. Main Results: Efficacy of the above methodology to develop individual-specific braking intention predictive models by rapidly adapting the group-level model in as few as three training epochs while achieving at least 90% accuracy, true positive rate and true negative rate is presented. Further, results show the energy-efficiency of the neuromorphic hardware through a power reduction of over 97% with only a $1.3* increase in latency when using the Akida AKD1000 processor for network inference compared to an Intel Xeon central processing unit. Similar results were obtained in a subsequent ablation study using a subset of five out of 19 channels.
SNNGX: Securing Spiking Neural Networks with Genetic XOR Encryption on RRAM-based Neuromorphic Accelerator
2024-07-21
Kwunhang Wong, Songqi Wang, Wei Huang, Xinyuan Zhang, Yangu He, Karl M. H. Lai, Yuzhong Jiao, Ning Lin, Xiaojuan Qi, Xiaoming Chen, Zhongrui Wang
Biologically plausible Spiking Neural Networks (SNNs), characterized by spike sparsity, are growing tremendous attention over intellectual edge devices and critical bio-medical applications as compared to artificial neural networks (ANNs). However, there is a considerable risk from malicious attempts to extract white-box information (i.e., weights) from SNNs, as attackers could exploit well-trained SNNs for profit and white-box adversarial concerns. There is a dire need for intellectual property (IP) protective measures. In this paper, we present a novel secure software-hardware co-designed RRAM-based neuromorphic accelerator for protecting the IP of SNNs. Software-wise, we design a tailored genetic algorithm with classic XOR encryption to target the least number of weights that need encryption. From a hardware perspective, we develop a low-energy decryption module, meticulously designed to provide zero decryption latency. Extensive results from various datasets, including NMNIST, DVSGesture, EEGMMIDB, Braille Letter, and SHD, demonstrate that our proposed method effectively secures SNNs by encrypting a minimal fraction of stealthy weights, only 0.00005% to 0.016% weight bits. Additionally, it achieves a substantial reduction in energy consumption, ranging from x59 to x6780, and significantly lowers decryption latency, ranging from x175 to x4250. Moreover, our method requires as little as one sample per class in dataset for encryption and addresses hessian/gradient-based search insensitive problems. This strategy offers a highly efficient and flexible solution for securing SNNs in diverse applications.
In this paper, we infer remote measurements such as remote voltages and currents online with change in AC power flows using spiking neural network (SNN) as grid-edge technology for efficient coordination of power electronic converters. This work unifies power and information as a means of data normalization using a multi-modal regime in the form of spikes using energy-efficient neuromorphic learning and event-driven asynchronous data collection. Firstly, we organize the synchronous real-valued measurements at each edge and translate them into asynchronous spike-based events to collect sparse data for training of SNN at each edge. Instead of relying on error-dependent supervised data-driven learning theory, we exploit the latency-driven unsupervised Hebbian learning rule to obtain modulation pulses for switching of power electronic converters that can now comprehend grid disturbances locally and adapt their operation without requiring explicit infrastructure for global coordination. Not only does this philosophy block exogenous path arrival for cyber attackers by dismissing the cyber layer, it also entails converter adaptation to system reconfiguration and parameter mismatch issues. We conclude this work by validating its energy-efficient and effective online learning performance under various scenarios in different system sizes, including modified IEEE 14-bus system and under experimental conditions.
On the Robustness of Fully-Spiking Neural Networks in Open-World Scenarios using Forward-Only Learning Algorithms
2024-07-19
Erik B. Terres-Escudero, Javier Del Ser, Aitor Martínez-Seras, Pablo Garcia-Bringas
In the last decade, Artificial Intelligence (AI) models have rapidly integrated into production pipelines propelled by their excellent modeling performance. However, the development of these models has not been matched by advancements in algorithms ensuring their safety, failing to guarantee robust behavior against Out-of-Distribution (OoD) inputs outside their learning domain. Furthermore, there is a growing concern with the sustainability of AI models and their required energy consumption in both training and inference phases. To mitigate these issues, this work explores the use of the Forward-Forward Algorithm (FFA), a biologically plausible alternative to Backpropagation, adapted to the spiking domain to enhance the overall energy efficiency of the model. By capitalizing on the highly expressive topology emerging from the latent space of models trained with FFA, we develop a novel FF-SCP algorithm for OoD Detection. Our approach measures the likelihood of a sample belonging to the in-distribution (ID) data by using the distance from the latent representation of samples to class-representative manifolds. Additionally, to provide deeper insights into our OoD pipeline, we propose a gradient-free attribution technique that highlights the features of a sample pushing it away from the distribution of any class. Multiple experiments using our spiking FFA adaptation demonstrate that the achieved accuracy levels are comparable to those seen in analog networks trained via back-propagation. Furthermore, OoD detection experiments on multiple datasets prove that FF-SCP outperforms avant-garde OoD detectors within the spiking domain in terms of several metrics used in this area. We also present a qualitative analysis of our explainability technique, exposing the precision by which the method detects OoD features, such as embedded artifacts or missing regions.
LoAS: Fully Temporal-Parallel Dataflow for Dual-Sparse Spiking Neural Networks
2024-07-19
Ruokai Yin, Youngeun Kim, Di Wu, Priyadarshini Panda
Spiking Neural Networks (SNNs) have gained significant research attention in the last decade due to their potential to drive resource-constrained edge devices. Though existing SNN accelerators offer high efficiency in processing sparse spikes with dense weights, opportunities are less explored in SNNs with sparse weights, i.e., dual-sparsity. In this work, we study the acceleration of dual-sparse SNNs, focusing on their core operation, sparse-matrix-sparse-matrix multiplication (spMspM). We observe that naively running a dual-sparse SNN on existing spMspM accelerators designed for dual-sparse Artificial Neural Networks (ANNs) exhibits sub-optimal efficiency. The main challenge is that processing timesteps, a natural property of SNNs, introduces an extra loop to ANN spMspM, leading to longer latency and more memory traffic. To address the problem, we propose a fully temporal-parallel (FTP) dataflow, which minimizes both data movement across timesteps and the end-to-end latency of dual-sparse SNNs. To maximize the efficiency of FTP dataflow, we propose an FTP-friendly spike compression mechanism that efficiently compresses single-bit spikes and ensures contiguous memory access. We further propose an FTP-friendly inner-join circuit that can lower the cost of the expensive prefix-sum circuits with almost no throughput penalty. All the above techniques for FTP dataflow are encapsulated in LoAS, a Low-latency inference Accelerator for dual-sparse SNNs. With FTP dataflow, compression, and inner-join, running dual-sparse SNN workloads on LoAS demonstrates significant speedup (up to $8.51\times$) and energy reduction (up to $3.68\times$) compared to running it on prior dual-sparse accelerators.
Accurate Mapping of RNNs on Neuromorphic Hardware with Adaptive Spiking Neurons
2024-07-18
Gauthier Boeshertz, Giacomo Indiveri, Manu Nair, Alpha Renner
Thanks to their parallel and sparse activity features, recurrent neural networks (RNNs) are well-suited for hardware implementation in low-power neuromorphic hardware. However, mapping rate-based RNNs to hardware-compatible spiking neural networks (SNNs) remains challenging. Here, we present a ${\Sigma}{\Delta}$-low-pass RNN (lpRNN): an RNN architecture employing an adaptive spiking neuron model that encodes signals using ${\Sigma}{\Delta}$-modulation and enables precise mapping. The ${\Sigma}{\Delta}$-neuron communicates analog values using spike timing, and the dynamics of the lpRNN are set to match typical timescales for processing natural signals, such as speech. Our approach integrates rate and temporal coding, offering a robust solution for the efficient and accurate conversion of RNNs to SNNs. We demonstrate the implementation of the lpRNN on Intel's neuromorphic research chip Loihi, achieving state-of-the-art classification results on audio benchmarks using 3-bit weights. These results call for a deeper investigation of recurrency and adaptation in event-based systems, which may lead to insights for edge computing applications where power-efficient real-time inference is required.
Spiking neural networks (SNNs) have shown promise in various dynamic visual tasks, yet those ready for practical deployment often lack the compactness and robustness essential in resource-limited and safety-critical settings. Prior research has predominantly concentrated on enhancing the compactness or robustness of artificial neural networks through strategies like network pruning and adversarial training, with little exploration into similar methodologies for SNNs. Robust pruning of SNNs aims to reduce computational overhead while preserving both accuracy and robustness. Current robust pruning approaches generally necessitate expert knowledge and iterative experimentation to establish suitable pruning criteria or auxiliary modules, thus constraining their broader application. Concurrently, evolutionary algorithms (EAs) have been employed to automate the pruning of artificial neural networks, delivering remarkable outcomes yet overlooking the aspect of robustness. In this work, we propose CCSRP, an innovative robust pruning method for SNNs, underpinned by cooperative co-evolution. Robust pruning is articulated as a tri-objective optimization challenge, striving to balance accuracy, robustness, and compactness concurrently, resolved through a cooperative co-evolutionary pruning framework that independently prunes filters across layers using EAs. Our experiments on CIFAR-10 and SVHN demonstrate that CCSRP can match or exceed the performance of the latest methodologies.
SpikeVoice: High-Quality Text-to-Speech Via Efficient Spiking Neural Network
2024-07-17
Kexin Wang, Jiahong Zhang, Yong Ren, Man Yao, Di Shang, Bo Xu, Guoqi Li
Brain-inspired Spiking Neural Network (SNN) has demonstrated its effectiveness and efficiency in vision, natural language, and speech understanding tasks, indicating their capacity to "see", "listen", and "read". In this paper, we design \textbf{SpikeVoice}, which performs high-quality Text-To-Speech (TTS) via SNN, to explore the potential of SNN to "speak". A major obstacle to using SNN for such generative tasks lies in the demand for models to grasp long-term dependencies. The serial nature of spiking neurons, however, leads to the invisibility of information at future spiking time steps, limiting SNN models to capture sequence dependencies solely within the same time step. We term this phenomenon "partial-time dependency". To address this issue, we introduce Spiking Temporal-Sequential Attention STSA in the SpikeVoice. To the best of our knowledge, SpikeVoice is the first TTS work in the SNN field. We perform experiments using four well-established datasets that cover both Chinese and English languages, encompassing scenarios with both single-speaker and multi-speaker configurations. The results demonstrate that SpikeVoice can achieve results comparable to Artificial Neural Networks (ANN) with only 10.5 energy consumption of ANN.
Online Pseudo-Zeroth-Order Training of Neuromorphic Spiking Neural Networks
2024-07-17
Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He, Zhouchen Lin
Brain-inspired neuromorphic computing with spiking neural networks (SNNs) is a promising energy-efficient computational approach. However, successfully training SNNs in a more biologically plausible and neuromorphic-hardware-friendly way is still challenging. Most recent methods leverage spatial and temporal backpropagation (BP), not adhering to neuromorphic properties. Despite the efforts of some online training methods, tackling spatial credit assignments by alternatives with comparable performance as spatial BP remains a significant problem. In this work, we propose a novel method, online pseudo-zeroth-order (OPZO) training. Our method only requires a single forward propagation with noise injection and direct top-down signals for spatial credit assignment, avoiding spatial BP's problem of symmetric weights and separate phases for layer-by-layer forward-backward propagation. OPZO solves the large variance problem of zeroth-order methods by the pseudo-zeroth-order formulation and momentum feedback connections, while having more guarantees than random feedback. Combining online training, OPZO can pave paths to on-chip SNN training. Experiments on neuromorphic and static datasets with fully connected and convolutional networks demonstrate the effectiveness of OPZO with similar performance compared with spatial BP, as well as estimated low training costs.
Voltage-Controlled Magnetoelectric Devices for Neuromorphic Diffusion Process
2024-07-17
Yang Cheng, Qingyuan Shu, Albert Lee, Haoran He, Ivy Zhu, Haris Suhail, Minzhang Chen, Renhe Chen, Zirui Wang, Hantao Zhang, Chih-Yao Wang, Shan-Yi Yang, Yu-Chen Hsin, Cheng-Yi Shih, Hsin-Han Lee, Ran Cheng, Sudhakar Pamarti, Xufeng Kou, Kang L. Wang
Stochastic diffusion processes are pervasive in nature, from the seemingly erratic Brownian motion to the complex interactions of synaptically-coupled spiking neurons. Recently, drawing inspiration from Langevin dynamics, neuromorphic diffusion models were proposed and have become one of the major breakthroughs in the field of generative artificial intelligence. Unlike discriminative models that have been well developed to tackle classification or regression tasks, diffusion models as well as other generative models such as ChatGPT aim at creating content based upon contexts learned. However, the more complex algorithms of these models result in high computational costs using today's technologies, creating a bottleneck in their efficiency, and impeding further development. Here, we develop a spintronic voltage-controlled magnetoelectric memory hardware for the neuromorphic diffusion process. The in-memory computing capability of our spintronic devices goes beyond current Von Neumann architecture, where memory and computing units are separated. Together with the non-volatility of magnetic memory, we can achieve high-speed and low-cost computing, which is desirable for the increasing scale of generative models in the current era. We experimentally demonstrate that the hardware-based true random diffusion process can be implemented for image generation and achieve comparable image quality to software-based training as measured by the Frechet inception distance (FID) score, achieving ~10^3 better energy-per-bit-per-area over traditional hardware.
In this work, we propose time-integrated spike-timing-dependent plasticity (TI-STDP), a mathematical model of synaptic plasticity that allows spiking neural networks to continuously adapt to sensory input streams in an unsupervised fashion. Notably, we theoretically establish and formally prove key properties related to the synaptic adjustment mechanics that underwrite TI-STDP. Empirically, we demonstrate the efficacy of TI-STDP in simulations of jointly learning deeper spiking neural networks that process input digit pixel patterns, at both full image and patch-levels, comparing to two powerful historical instantations of STDP; trace-based STDP (TR-STDP) and event-based post-synaptic STDP (EV-STDP). Usefully, we demonstrate that not only are all forms of STDP capable of meaningfully adapting the synaptic efficacies of a multi-layer biophysical architecture, but that TI-STDP is notably able to do so without requiring the tracking of a large window of pre- and post-synaptic spike timings, the maintenance of additional parameterized traces, or the restriction of synaptic plasticity changes to occur within very narrow windows of time. This means that our findings show that TI-STDP can efficiently embody the benefits of models such as canonical STDP, TR-STDP, and EV-STDP without their costs or drawbacks. Usefully, our results further demonstrate the promise of using a spike-correlation scheme such as TI-STDP in conducting credit assignment in discrete pulse-based neuromorphic models, particularly those than acquire a lower-level distributed representation jointly with an upper-level, more abstract representation that self-organizes to cluster based on inherent cross-pattern similarities. We further demonstrate TI-STDP's effectiveness in adapting a simple neuronal circuit that learns a simple bi-level, part-whole hierarchy from sensory input patterns.
Evaluation of Encoding Schemes on Ubiquitous Sensor Signal for Spiking Neural Network
2024-07-12
Sizhen Bian, Elisa Donati, Michele Magno
Spiking neural networks (SNNs), a brain-inspired computing paradigm, are emerging for their inference performance, particularly in terms of energy efficiency and latency attributed to the plasticity in signal processing. To deploy SNNs in ubiquitous computing systems, signal encoding of sensors is crucial for achieving high accuracy and robustness. Using inertial sensor readings for gym activity recognition as a case study, this work comprehensively evaluates four main encoding schemes and deploys the corresponding SNN on the neuromorphic processor Loihi2 for post-deployment encoding assessment. Rate encoding, time-to-first-spike encoding, binary encoding, and delta modulation are evaluated using metrics like average fire rate, signal-to-noise ratio, classification accuracy, robustness, and inference latency and energy. In this case study, the time-to-first-spike encoding required the lowest firing rate (2%) and achieved a comparative accuracy (89%), although it was the least robust scheme against error spikes (over 20% accuracy drop with 0.1 noisy spike rate). Rate encoding with optimal value-to-probability mapping achieved the highest accuracy (91.7%). Binary encoding provided a balance between information reconstruction and noise resistance. Multi-threshold delta modulation showed the best robustness, with only a 0.7% accuracy drop at a 0.1 noisy spike rate. This work serves researchers in selecting the best encoding scheme for SNN-based ubiquitous sensor signal processing, tailored to specific performance requirements.
BKDSNN: Enhancing the Performance of Learning-based Spiking Neural Networks Training with Blurred Knowledge Distillation
2024-07-12
Zekai Xu, Kang You, Qinghai Guo, Xiang Wang, Zhezhi He
Spiking neural networks (SNNs), which mimic biological neural system to convey information via discrete spikes, are well known as brain-inspired models with excellent computing efficiency. By utilizing the surrogate gradient estimation for discrete spikes, learning-based SNN training methods that can achieve ultra-low inference latency (number of time-step) emerge recently. Nevertheless, due to the difficulty in deriving precise gradient estimation for discrete spikes using learning-based method, a distinct accuracy gap persists between SNN and its artificial neural networks (ANNs) counterpart. To address the aforementioned issue, we propose a blurred knowledge distillation (BKD) technique, which leverages random blurred SNN feature to restore and imitate the ANN feature. Note that, our BKD is applied upon the feature map right before the last layer of SNN, which can also mix with prior logits-based knowledge distillation for maximized accuracy boost. To our best knowledge, in the category of learning-based methods, our work achieves state-of-the-art performance for training SNNs on both static and neuromorphic datasets. On ImageNet dataset, BKDSNN outperforms prior best results by 4.51% and 0.93% with the network topology of CNN and Transformer respectively.
This paper explores the synergistic potential of neuromorphic and edge computing to create a versatile machine learning (ML) system tailored for processing data captured by dynamic vision sensors. We construct and train hybrid models, blending spiking neural networks (SNNs) and artificial neural networks (ANNs) using PyTorch and Lava frameworks. Our hybrid architecture integrates an SNN for temporal feature extraction and an ANN for classification. We delve into the challenges of deploying such hybrid structures on hardware. Specifically, we deploy individual components on Intel's Neuromorphic Processor Loihi (for SNN) and Jetson Nano (for ANN). We also propose an accumulator circuit to transfer data from the spiking to the non-spiking domain. Furthermore, we conduct comprehensive performance analyses of hybrid SNN-ANN models on a heterogeneous system of neuromorphic and edge AI hardware, evaluating accuracy, latency, power, and energy consumption. Our findings demonstrate that the hybrid spiking networks surpass the baseline ANN model across all metrics and outperform the baseline SNN model in accuracy and latency.
STAL: Spike Threshold Adaptive Learning Encoder for Classification of Pain-Related Biosignal Data
2024-07-11
Freek Hens, Mohammad Mahdi Dehshibi, Leila Bagheriye, Mahyar Shahsavari, Ana Tajadura-Jiménez
This paper presents the first application of spiking neural networks (SNNs) for the classification of chronic lower back pain (CLBP) using the EmoPain dataset. Our work has two main contributions. We introduce Spike Threshold Adaptive Learning (STAL), a trainable encoder that effectively converts continuous biosignals into spike trains. Additionally, we propose an ensemble of Spiking Recurrent Neural Network (SRNN) classifiers for the multi-stream processing of sEMG and IMU data. To tackle the challenges of small sample size and class imbalance, we implement minority over-sampling with weighted sample replacement during batch creation. Our method achieves outstanding performance with an accuracy of 80.43%, AUC of 67.90%, F1 score of 52.60%, and Matthews Correlation Coefficient (MCC) of 0.437, surpassing traditional rate-based and latency-based encoding methods. The STAL encoder shows superior performance in preserving temporal dynamics and adapting to signal characteristics. Importantly, our approach (STAL-SRNN) outperforms the best deep learning method in terms of MCC, indicating better balanced class prediction. This research contributes to the development of neuromorphic computing for biosignal analysis. It holds promise for energy-efficient, wearable solutions in chronic pain management.
In recent years there has been a growing interest in event cameras, i.e. vision sensors that record changes in illumination independently for each pixel. This type of operation ensures that acquisition is possible in very adverse lighting conditions, both in low light and high dynamic range, and reduces average power consumption. In addition, the independent operation of each pixel results in low latency, which is desirable for robotic solutions. Nowadays, Field Programmable Gate Arrays (FPGAs), along with general-purpose processors (GPPs/CPUs) and programmable graphics processing units (GPUs), are popular architectures for implementing and accelerating computing tasks. In particular, their usefulness in the embedded vision domain has been repeatedly demonstrated over the past 30 years, where they have enabled fast data processing (even in real-time) and energy efficiency. Hence, the combination of event cameras and reconfigurable devices seems to be a good solution, especially in the context of energy-efficient real-time embedded systems. This paper gives an overview of the most important works, where FPGAs have been used in different contexts to process event data. It covers applications in the following areas: filtering, stereovision, optical flow, acceleration of AI-based algorithms (including spiking neural networks) for object classification, detection and tracking, and applications in robotics and inspection systems. Current trends and challenges for such systems are also discussed.
Spiking Tucker Fusion Transformer for Audio-Visual Zero-Shot Learning
2024-07-11
Wenrui Li, Penghong Wang, Ruiqin Xiong, Xiaopeng Fan
The spiking neural networks (SNNs) that efficiently encode temporal sequences have shown great potential in extracting audio-visual joint feature representations. However, coupling SNNs (binary spike sequences) with transformers (float-point sequences) to jointly explore the temporal-semantic information still facing challenges. In this paper, we introduce a novel Spiking Tucker Fusion Transformer (STFT) for audio-visual zero-shot learning (ZSL). The STFT leverage the temporal and semantic information from different time steps to generate robust representations. The time-step factor (TSF) is introduced to dynamically synthesis the subsequent inference information. To guide the formation of input membrane potentials and reduce the spike noise, we propose a global-local pooling (GLP) which combines the max and average pooling operations. Furthermore, the thresholds of the spiking neurons are dynamically adjusted based on semantic and temporal cues. Integrating the temporal and semantic information extracted by SNNs and Transformers are difficult due to the increased number of parameters in a straightforward bilinear model. To address this, we introduce a temporal-semantic Tucker fusion module, which achieves multi-scale fusion of SNN and Transformer outputs while maintaining full second-order interactions. Our experimental results demonstrate the effectiveness of the proposed approach in achieving state-of-the-art performance in three benchmark datasets. The harmonic mean (HM) improvement of VGGSound, UCF101 and ActivityNet are around 15.4\%, 3.9\%, and 14.9\%, respectively.
In this article, we propose a novel standalone hybrid Spiking-Convolutional Neural Network (SC-NN) model and test on using image inpainting tasks. Our approach uses the unique capabilities of SNNs, such as event-based computation and temporal processing, along with the strong representation learning abilities of CNNs, to generate high-quality inpainted images. The model is trained on a custom dataset specifically designed for image inpainting, where missing regions are created using masks. The hybrid model consists of SNNConv2d layers and traditional CNN layers. The SNNConv2d layers implement the leaky integrate-and-fire (LIF) neuron model, capturing spiking behavior, while the CNN layers capture spatial features. In this study, a mean squared error (MSE) loss function demonstrates the training process, where a training loss value of 0.015, indicates accurate performance on the training set and the model achieved a validation loss value as low as 0.0017 on the testing set. Furthermore, extensive experimental results demonstrate state-of-the-art performance, showcasing the potential of integrating temporal dynamics and feature extraction in a single network for image inpainting.
Accurately and safely predicting the trajectories of surrounding vehicles is essential for fully realizing autonomous driving (AD). This paper presents the Human-Like Trajectory Prediction model (HLTP++), which emulates human cognitive processes to improve trajectory prediction in AD. HLTP++ incorporates a novel teacher-student knowledge distillation framework. The "teacher" model equipped with an adaptive visual sector, mimics the dynamic allocation of attention human drivers exhibit based on factors like spatial orientation, proximity, and driving speed. On the other hand, the "student" model focuses on real-time interaction and human decision-making, drawing parallels to the human memory storage mechanism. Furthermore, we improve the model's efficiency by introducing a new Fourier Adaptive Spike Neural Network (FA-SNN), allowing for faster and more precise predictions with fewer parameters. Evaluated using the NGSIM, HighD, and MoCAD benchmarks, HLTP++ demonstrates superior performance compared to existing models, which reduces the predicted trajectory error with over 11% on the NGSIM dataset and 25% on the HighD datasets. Moreover, HLTP++ demonstrates strong adaptability in challenging environments with incomplete input data. This marks a significant stride in the journey towards fully AD systems.
An Attempt to Devise a Pairwise Ising-Type Maximum Entropy Model Integrated Cost Function for Optimizing SNN Deployment
2024-07-09
Wanhong Huang
The deployment process of a spiking neural network (SNN) often involves partitioning the neural network and mapping these partitions onto processing units within the neuromorphic hardware. Finding optimal deployment schemes is an NP-hard problem. Optimizing these schemes presents challenges, particular in devising computationally effective cost functions optimization objectives such as communication time consumption and energy efficiency. These objectives require consideration of network dynamics shaped by neuron activity patterns, demanding intricate mathematical analyses or simulations for integrating them into a cost model for SNN development. Our approach focuses on network dynamics, which are hardware-independent and can be modeled separately from specific hardware configurations. We employ a pairwise Ising-type maximum entropy model, which is a model show effective in accurately capturing pairwise correlations among system components in a collaborative system. On top of this model, we incorporates hardware and network structure-specific factors to devise a cost function. We conducted an extremely preliminary investigation using the SpiNNaker machine. We show that the ising model training can also be computationally complex. Currently, we lack sufficient evidence to substantiate the effectiveness of our proposed methods. Further efforts is needed to explore integrating network dynamics into SNN deployment.
Spiking Neural Networks (SNNs) represent the forefront of neuromorphic computing, promising energy-efficient and biologically plausible models for complex tasks. This paper weaves together three groundbreaking studies that revolutionize SNN performance through the introduction of heterogeneity in neuron and synapse dynamics. We explore the transformative impact of Heterogeneous Recurrent Spiking Neural Networks (HRSNNs), supported by rigorous analytical frameworks and novel pruning methods like Lyapunov Noise Pruning (LNP). Our findings reveal how heterogeneity not only enhances classification performance but also reduces spiking activity, leading to more efficient and robust networks. By bridging theoretical insights with practical applications, this comprehensive summary highlights the potential of SNNs to outperform traditional neural networks while maintaining lower computational costs. Join us on a journey through the cutting-edge advancements that pave the way for the future of intelligent, energy-efficient neural computing.
Since proposed, spiking neural networks (SNNs) gain recognition for their high performance, low power consumption and enhanced biological interpretability. However, while bringing these advantages, the binary nature of spikes also leads to considerable information loss in SNNs, ultimately causing performance degradation. We claim that the limited expressiveness of current binary spikes, resulting in substantial information loss, is the fundamental issue behind these challenges. To alleviate this, our research introduces a multi-bit information transmission mechanism for SNNs. This mechanism expands the output of spiking neurons from the original single bit to multiple bits, enhancing the expressiveness of the spikes and reducing information loss during the forward process, while still maintaining the low energy consumption advantage of SNNs. For SNNs, this represents a new paradigm of information transmission. Moreover, to further utilize the limited spikes, we extract effective signals from the previous layer to re-stimulate the neurons, thus encouraging full spikes emission across various bit levels. We conducted extensive experiments with our proposed method using both direct training method and ANN-SNN conversion method, and the results show consistent performance improvements.
In this paper, we introduce the finite difference weighted essentially non-oscillatory (WENO) scheme based on the neural network for hyperbolic conservation laws. We employ the supervised learning and design two loss functions, one with the mean squared error and the other with the mean squared logarithmic error, where the WENO3-JS weights are computed as the labels. Each loss function consists of two components where the first component compares the difference between the weights from the neural network and WENO3-JS weights, while the second component matches the output weights of the neural network and the linear weights. The former of the loss function enforces the neural network to follow the WENO properties, implying that there is no need for the post-processing layer. Additionally the latter leads to better performance around discontinuities. As a neural network structure, we choose the shallow neural network (SNN) for computational efficiency with the Delta layer consisting of the normalized undivided differences. These constructed WENO3-SNN schemes show the outperformed results in one-dimensional examples and improved behavior in two-dimensional examples, compared with the simulations from WENO3-JS and WENO3-Z.
Learning Delays Through Gradients and Structure: Emergence of Spatiotemporal Patterns in Spiking Neural Networks
2024-07-07
Balázs Mészáros, James Knight, Thomas Nowotny
We present a Spiking Neural Network (SNN) model that incorporates learnable synaptic delays through two approaches: per-synapse delay learning via Dilated Convolutions with Learnable Spacings (DCLS) and a dynamic pruning strategy that also serves as a form of delay learning. In the latter approach, the network dynamically selects and prunes connections, optimizing the delays in sparse connectivity settings. We evaluate both approaches on the Raw Heidelberg Digits keyword spotting benchmark using Backpropagation Through Time with surrogate gradients. Our analysis of the spatio-temporal structure of synaptic interactions reveals that, after training, excitation and inhibition group together in space and time. Notably, the dynamic pruning approach, which employs DEEP R for connection removal and RigL for reconnection, not only preserves these spatio-temporal patterns but outperforms per-synapse delay learning in sparse networks. Our results demonstrate the potential of combining delay learning with dynamic pruning to develop efficient SNN models for temporal data processing. Moreover, the preservation of spatio-temporal dynamics throughout pruning and rewiring highlights the robustness of these features, providing a solid foundation for future neuromorphic computing applications.
Ternary Spike-based Neuromorphic Signal Processing System
2024-07-07
Shuai Wang, Dehao Zhang, Ammar Belatreche, Yichen Xiao, Hongyu Qing, Wenjie We, Malu Zhang, Yang Yang
Deep Neural Networks (DNNs) have been successfully implemented across various signal processing fields, resulting in significant enhancements in performance. However, DNNs generally require substantial computational resources, leading to significant economic costs and posing challenges for their deployment on resource-constrained edge devices. In this study, we take advantage of spiking neural networks (SNNs) and quantization technologies to develop an energy-efficient and lightweight neuromorphic signal processing system. Our system is characterized by two principal innovations: a threshold-adaptive encoding (TAE) method and a quantized ternary SNN (QT-SNN). The TAE method can efficiently encode time-varying analog signals into sparse ternary spike trains, thereby reducing energy and memory demands for signal processing. QT-SNN, compatible with ternary spike trains from the TAE method, quantifies both membrane potentials and synaptic weights to reduce memory requirements while maintaining performance. Extensive experiments are conducted on two typical signal-processing tasks: speech and electroencephalogram recognition. The results demonstrate that our neuromorphic signal processing system achieves state-of-the-art (SOTA) performance with a 94% reduced memory requirement. Furthermore, through theoretical energy consumption analysis, our system shows 7.5x energy saving compared to other SNN works. The efficiency and efficacy of the proposed system highlight its potential as a promising avenue for energy-efficient signal processing.
FastSpiker: Enabling Fast Training for Spiking Neural Networks on Event-based Data through Learning Rate Enhancements for Autonomous Embedded Systems
2024-07-07
Iqra Bano, Rachmad Vidya Wicaksana Putra, Alberto Marchisio, Muhammad Shafique
Autonomous embedded systems (e.g., robots) typically necessitate intelligent computation with low power/energy processing for completing their tasks. Such requirements can be fulfilled by embodied neuromorphic intelligence with spiking neural networks (SNNs) because of their high learning quality (e.g., accuracy) and sparse computation. Here, the employment of event-based data is preferred to ensure seamless connectivity between input and processing parts. However, state-of-the-art SNNs still face a long training time to achieve high accuracy, thereby incurring high energy consumption and producing a high rate of carbon emission. Toward this, we propose FastSpiker, a novel methodology that enables fast SNN training on event-based data through learning rate enhancements targeting autonomous embedded systems. In FastSpiker, we first investigate the impact of different learning rate policies and their values, then select the ones that quickly offer high accuracy. Afterward, we explore different settings for the selected learning rate policies to find the appropriate policies through a statistical-based decision. Experimental results show that our FastSpiker offers up to 10.5x faster training time and up to 88.39% lower carbon emission to achieve higher or comparable accuracy to the state-of-the-art on the event-based automotive dataset (i.e., NCARS). In this manner, our FastSpiker methodology paves the way for green and sustainable computing in realizing embodied neuromorphic intelligence for autonomous embedded systems.
Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling
2024-07-05
Alejandro Rodriguez-Garcia, Jie Mei, Srikanth Ramaswamy
Recent progress in artificial intelligence (AI) has been driven by insights from neuroscience, particularly with the development of artificial neural networks (ANNs). This has significantly enhanced the replication of complex cognitive tasks such as vision and natural language processing. Despite these advances, ANNs struggle with continual learning, adaptable knowledge transfer, robustness, and resource efficiency - capabilities that biological systems handle seamlessly. Specifically, ANNs often overlook the functional and morphological diversity of the brain, hindering their computational capabilities. Furthermore, incorporating cell-type specific neuromodulatory effects into ANNs with neuronal heterogeneity could enable learning at two spatial scales: spiking behavior at the neuronal level, and synaptic plasticity at the circuit level, thereby potentially enhancing their learning abilities. In this article, we summarize recent bio-inspired models, learning rules and architectures and propose a biologically-informed framework for enhancing ANNs. Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors and dendritic compartments to simulate morphological and functional diversity of neuronal computations. Finally, we outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, balances bioinspiration and complexity, and provides scalable solutions for pressing AI challenges, such as continual learning, adaptability, robustness, and resource-efficiency.
SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking
2024-07-05
Xingrun Xing, Boyan Gao, Zheng Zhang, David A. Clifton, Shitao Xiao, Li Du, Guoqi Li, Jiajun Zhang
The recent advancements in large language models (LLMs) with billions of parameters have significantly boosted their performance across various real-world applications. However, the inference processes for these models require substantial energy and computational resources, presenting considerable deployment challenges. In contrast, human brains, which contain approximately 86 billion biological neurons, exhibit significantly greater energy efficiency compared to LLMs with a similar number of parameters. Inspired by this, we redesign 7 to 70 billion parameter LLMs using bio-plausible spiking mechanisms, emulating the efficient behavior of the human brain. We propose the first spiking large language model as recent LLMs termed SpikeLLM. Coupled with the proposed model, a novel spike-driven quantization framework named Optimal Brain Spiking is introduced to reduce the energy cost and accelerate inference speed via two essential approaches: first (second)-order differentiation-based salient channel detection, and per-channel salient outlier expansion with Generalized Integrate-and-Fire neurons. Our proposed spike-driven quantization can plug in main streams of quantization training methods. In the OmniQuant pipeline, SpikeLLM significantly reduces 25.51% WikiText2 perplexity and improves 3.08% average accuracy of 6 zero-shot datasets on a LLAMA2-7B 4A4W model. In the GPTQ pipeline, SpikeLLM realizes a sparse ternary quantization, which achieves additive in all linear layers. Compared with PB-LLM with similar operations, SpikeLLM also exceeds significantly. We will release our code on GitHub.
Neuromorphic models take inspiration from the human brain by adopting bio-plausible neuron models to build alternatives to traditional Machine Learning (ML) and Deep Learning (DL) solutions. The scarce availability of dedicated hardware able to actualize the emulation of brain-inspired computation, which is otherwise only simulated, yet still hinders the wide adoption of neuromorphic computing for edge devices and embedded systems. With this premise, we adopt the perspective of neuromorphic computing for conventional hardware and we present the L2MU, a natively neuromorphic Legendre Memory Unit (LMU) which entirely relies on Leaky Integrate-and-Fire (LIF) neurons. Specifically, the original recurrent architecture of LMU has been redesigned by modelling every constituent element with neural populations made of LIF or Current-Based (CuBa) LIF neurons. To couple neuromorphic computing and off-the-shelf edge devices, we equipped the L2MU with an input module for the conversion of real values into spikes, which makes it an encoding-free implementation of a Recurrent Spiking Neural Network (RSNN) able to directly work with raw sensor signals on non-dedicated hardware. As a use case to validate our network, we selected the task of Human Activity Recognition (HAR). We benchmarked our L2MU on smartwatch signals from hand-oriented activities, deploying it on three different commercial edge devices in compressed versions too. The reported results remark the possibility of considering neuromorphic models not only in an exclusive relationship with dedicated hardware but also as a suitable choice to work with common sensors and devices.
Real-Time Neuromorphic Navigation: Integrating Event-Based Vision and Physics-Driven Planning on a Parrot Bebop2 Quadrotor
2024-07-01
Amogh Joshi, Sourav Sanyal, Kaushik Roy
In autonomous aerial navigation, real-time and energy-efficient obstacle avoidance remains a significant challenge, especially in dynamic and complex indoor environments. This work presents a novel integration of neuromorphic event cameras with physics-driven planning algorithms implemented on a Parrot Bebop2 quadrotor. Neuromorphic event cameras, characterized by their high dynamic range and low latency, offer significant advantages over traditional frame-based systems, particularly in poor lighting conditions or during high-speed maneuvers. We use a DVS camera with a shallow Spiking Neural Network (SNN) for event-based object detection of a moving ring in real-time in an indoor lab. Further, we enhance drone control with physics-guided empirical knowledge inside a neural network training mechanism, to predict energy-efficient flight paths to fly through the moving ring. This integration results in a real-time, low-latency navigation system capable of dynamically responding to environmental changes while minimizing energy consumption. We detail our hardware setup, control loop, and modifications necessary for real-world applications, including the challenges of sensor integration without burdening the flight capabilities. Experimental results demonstrate the effectiveness of our approach in achieving robust, collision-free, and energy-efficient flight paths, showcasing the potential of neuromorphic vision and physics-driven planning in enhancing autonomous navigation systems.
Spiking neural network (SNN) is studied in multidisciplinary domains to (i) enable order-of-magnitudes energy-efficient AI inference and (ii) computationally simulate neuro-scientific mechanisms. The lack of discrete theory obstructs the practical application of SNN by limiting its performance and nonlinearity support. We present a new optimization-theoretic perspective of the discrete dynamics of spiking neurons. We prove that a discrete dynamical system of simple integrate-and-fire models approximates the sub-gradient method over unconstrained optimization problems. We practically extend our theory to introduce a novel sign gradient descent (signGD)-based neuronal dynamics that can (i) approximate diverse nonlinearities beyond ReLU and (ii) advance ANN-to-SNN conversion performance in low time steps. Experiments on large-scale datasets show that our technique achieves (i) state-of-the-art performance in ANN-to-SNN conversion and (ii) is the first to convert new DNN architectures, e.g., ConvNext, MLP-Mixer, and ResMLP. We publicly share our source code at https://github.com/snuhcs/snn_signgd .
This study presents a novel physics-informed neural network (PINN) framework for modeling poroelasticity in heterogeneous media with material interfaces. The approach introduces a composite neural network (CoNN) where separate neural networks predict displacement and pressure variables for each material. While sharing identical activation functions, these networks are independently trained for all other parameters. To address challenges posed by heterogeneous material interfaces, the CoNN is integrated with the Interface-PINNs or I-PINNs framework (Sarma et al. 2024, https://dx.doi.org/10.1016/j.cma.2024.117135), allowing different activation functions across material interfaces. This ensures accurate approximation of discontinuous solution fields and gradients. Performance and accuracy of this combined architecture were evaluated against the conventional PINNs approach, a single neural network (SNN) architecture, and the eXtended PINNs (XPINNs) framework through two one-dimensional benchmark examples with discontinuous material properties. The results show that the proposed CoNN with I-PINNs architecture achieves an RMSE that is two orders of magnitude better than the conventional PINNs approach and is at least 40 times faster than the SNN framework. Compared to XPINNs, the proposed method achieves an RMSE at least one order of magnitude better and is 40% faster.
HASNAS: A Hardware-Aware Spiking Neural Architecture Search Framework for Neuromorphic Compute-in-Memory Systems
2024-06-30
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
Spiking Neural Networks (SNNs) have shown capabilities for solving diverse machine learning tasks with ultra-low-power/energy computation. To further improve the performance and efficiency of SNN inference, the Compute-in-Memory (CIM) paradigm with emerging device technologies such as resistive random access memory is employed. However, most of SNN architectures are developed without considering constraints from the application and the underlying CIM hardware (e.g., memory, area, latency, and energy consumption). Moreover, most of SNN designs are derived from the Artificial Neural Networks, whose network operations are different from SNNs. These limitations hinder SNNs from reaching their full potential in accuracy and efficiency. Toward this, we propose HASNAS, a novel hardware-aware spiking neural architecture search (NAS) framework for neuromorphic CIM systems that finds an SNN that offers high accuracy under the given memory, area, latency, and energy constraints. To achieve this, HASNAS employs the following key steps: (1) optimizing SNN operations to achieve high accuracy, (2) developing an SNN architecture that facilitates an effective learning process, and (3) devising a systematic hardware-aware search algorithm to meet the constraints. The experimental results show that our HASNAS quickly finds an SNN that maintains high accuracy compared to the state-of-the-art by up to 11x speed-up, and meets the given constraints: 4x10^6 parameters of memory, 100mm^2 of area, 400ms of latency, and 120uJ energy consumption for CIFAR10 and CIFAR100; while the state-of-the-art fails to meet the constraints. In this manner, our HASNAS can enable efficient design automation for providing high-performance and energy-efficient neuromorphic CIM systems for diverse applications.
We present a multi-scale differentiable brain modeling workflow utilizing BrainPy, a unique differentiable brain simulator that combines accurate brain simulation with powerful gradient-based optimization. We leverage this capability of BrainPy across different brain scales. At the single-neuron level, we implement differentiable neuron models and employ gradient methods to optimize their fit to electrophysiological data. On the network level, we incorporate connectomic data to construct biologically constrained network models. Finally, to replicate animal behavior, we train these models on cognitive tasks using gradient-based learning rules. Experiments demonstrate that our approach achieves superior performance and speed in fitting generalized leaky integrate-and-fire and Hodgkin-Huxley single neuron models. Additionally, training a biologically-informed network of excitatory and inhibitory spiking neurons on working memory tasks successfully replicates observed neural activity and synaptic weight distributions. Overall, our differentiable multi-scale simulation approach offers a promising tool to bridge neuroscience data across electrophysiological, anatomical, and behavioral scales.
Directly Training Temporal Spiking Neural Network with Sparse Surrogate Gradient
2024-06-28
Yang Li, Feifei Zhao, Dongcheng Zhao, Yi Zeng
Brain-inspired Spiking Neural Networks (SNNs) have attracted much attention due to their event-based computing and energy-efficient features. However, the spiking all-or-none nature has prevented direct training of SNNs for various applications. The surrogate gradient (SG) algorithm has recently enabled spiking neural networks to shine in neuromorphic hardware. However, introducing surrogate gradients has caused SNNs to lose their original sparsity, thus leading to the potential performance loss. In this paper, we first analyze the current problem of direct training using SGs and then propose Masked Surrogate Gradients (MSGs) to balance the effectiveness of training and the sparseness of the gradient, thereby improving the generalization ability of SNNs. Moreover, we introduce a temporally weighted output (TWO) method to decode the network output, reinforcing the importance of correct timesteps. Extensive experiments on diverse network structures and datasets show that training with MSG and TWO surpasses the SOTA technique.
Spiking Convolutional Neural Networks for Text Classification
2024-06-27
Changze Lv, Jianhan Xu, Xiaoqing Zheng
Spiking neural networks (SNNs) offer a promising pathway to implement deep neural networks (DNNs) in a more energy-efficient manner since their neurons are sparsely activated and inferences are event-driven. However, there have been very few works that have demonstrated the efficacy of SNNs in language tasks partially because it is non-trivial to represent words in the forms of spikes and to deal with variable-length texts by SNNs. This work presents a "conversion + fine-tuning" two-step method for training SNNs for text classification and proposes a simple but effective way to encode pre-trained word embeddings as spike trains. We show empirically that after fine-tuning with surrogate gradients, the converted SNNs achieve comparable results to their DNN counterparts with much less energy consumption across multiple datasets for both English and Chinese. We also show that such SNNs are more robust to adversarial attacks than DNNs.
On Reducing Activity with Distillation and Regularization for Energy Efficient Spiking Neural Networks
2024-06-26
Thomas Louis, Benoit Miramond, Alain Pegatoquet, Adrien Girard
Interest in spiking neural networks (SNNs) has been growing steadily, promising an energy-efficient alternative to formal neural networks (FNNs), commonly known as artificial neural networks (ANNs). Despite increasing interest, especially for Edge applications, these event-driven neural networks suffered from their difficulty to be trained compared to FNNs. To alleviate this problem, a number of innovative methods have been developed to provide performance more or less equivalent to that of FNNs. However, the spiking activity of a network during inference is usually not considered. While SNNs may usually have performance comparable to that of FNNs, it is often at the cost of an increase of the network's activity, thus limiting the benefit of using them as a more energy-efficient solution. In this paper, we propose to leverage Knowledge Distillation (KD) for SNNs training with surrogate gradient descent in order to optimize the trade-off between performance and spiking activity. Then, after understanding why KD led to an increase in sparsity, we also explored Activations regularization and proposed a novel method with Logits Regularization. These approaches, validated on several datasets, clearly show a reduction in network spiking activity (-26.73% on GSC and -14.32% on CIFAR-10) while preserving accuracy.
Embedded event based object detection with spiking neural network
2024-06-25
Jonathan Courtois, Pierre-Emmanuel Novac, Edgar Lemaire, Alain Pegatoquet, Benoit Miramond
The complexity of event-based object detection (OD) poses considerable challenges. Spiking Neural Networks (SNNs) show promising results and pave the way for efficient event-based OD. Despite this success, the path to efficient SNNs on embedded devices remains a challenge. This is due to the size of the networks required to accomplish the task and the ability of devices to take advantage of SNNs benefits. Even when "edge" devices are considered, they typically use embedded GPUs that consume tens of watts. In response to these challenges, our research introduces an embedded neuromorphic testbench that utilizes the SPiking Low-power Event-based ArchiTecture (SPLEAT) accelerator. Using an extended version of the Qualia framework, we can train, evaluate, quantize, and deploy spiking neural networks on an FPGA implementation of SPLEAT. We used this testbench to load a state-of-the-art SNN solution, estimate the performance loss associated with deploying the network on dedicated hardware, and run real-world event-based OD on neuromorphic hardware specifically designed for low-power spiking neural networks. Remarkably, our embedded spiking solution, which includes a model with 1.08 million parameters, operates efficiently with 490 mJ per prediction.
EON-1: A Brain-Inspired Processor for Near-Sensor Extreme Edge Online Feature Extraction
2024-06-25
Alexandra Dobrita, Amirreza Yousefzadeh, Simon Thorpe, Kanishkan Vadivel, Paul Detterer, Guangzhi Tang, Gert-Jan van Schaik, Mario Konijnenburg, Anteneh Gebregiorgis, Said Hamdioui, Manolis Sifalakis
For Edge AI applications, deploying online learning and adaptation on resource-constrained embedded devices can deal with fast sensor-generated streams of data in changing environments. However, since maintaining low-latency and power-efficient inference is paramount at the Edge, online learning and adaptation on the device should impose minimal additional overhead for inference. With this goal in mind, we explore energy-efficient learning and adaptation on-device for streaming-data Edge AI applications using Spiking Neural Networks (SNNs), which follow the principles of brain-inspired computing, such as high-parallelism, neuron co-located memory and compute, and event-driven processing. We propose EON-1, a brain-inspired processor for near-sensor extreme edge online feature extraction, that integrates a fast online learning and adaptation algorithm. We report results of only 1% energy overhead for learning, by far the lowest overhead when compared to other SoTA solutions, while attaining comparable inference accuracy. Furthermore, we demonstrate that EON-1 is up for the challenge of low-latency processing of HD and UHD streaming video in real-time, with learning enabled.
Fast Switching Serial and Parallel Paradigms of SNN Inference on Multi-core Heterogeneous Neuromorphic Platform SpiNNaker2
2024-06-24
Jiaxin Huang, Bernhard Vogginger, Florian Kelber, Hector Gonzalez, Klaus Knobloch, Christian Georg Mayr
With serial and parallel processors introduced into Spiking Neural Networks (SNNs) execution, more and more researchers are dedicated to improving the performance of the computing paradigms by taking full advantage of the strengths of the available processor. In this paper, we compare and integrate serial and parallel paradigms into one SNN compiling system. For a faster switching between them in the layer granularity, we train the classifier to prejudge a better paradigm before compiling instead of making the decision afterward, saving a great amount of compiling time and RAM space on the host PC. The classifier Adaptive Boost, with the highest accuracy (91.69%) among 12 classifiers, is integrated into the switching system, which utilizes less memory and processors on the multi-core neuromorphic hardware backend SpiNNaker2 than two individual paradigms. To the best of our knowledge, it is the first fast-switching compiling system for SNN simulation.
Emerging NeoHebbian Dynamics in Forward-Forward Learning: Implications for Neuromorphic Computing
2024-06-24
Erik B. Terres-Escudero, Javier Del Ser, Pablo García-Bringas
Advances in neural computation have predominantly relied on the gradient backpropagation algorithm (BP). However, the recent shift towards non-stationary data modeling has highlighted the limitations of this heuristic, exposing that its adaptation capabilities are far from those seen in biological brains. Unlike BP, where weight updates are computed through a reverse error propagation path, Hebbian learning dynamics provide synaptic updates using only information within the layer itself. This has spurred interest in biologically plausible learning algorithms, hypothesized to overcome BP's shortcomings. In this context, Hinton recently introduced the Forward-Forward Algorithm (FFA), which employs local learning rules for each layer and has empirically proven its efficacy in multiple data modeling tasks. In this work we argue that when employing a squared Euclidean norm as a goodness function driving the local learning, the resulting FFA is equivalent to a neo-Hebbian Learning Rule. To verify this result, we compare the training behavior of FFA in analog networks with its Hebbian adaptation in spiking neural networks. Our experiments demonstrate that both versions of FFA produce similar accuracy and latent distributions. The findings herein reported provide empirical evidence linking biological learning rules with currently used training algorithms, thus paving the way towards extrapolating the positive outcomes from FFA to Hebbian learning rules. Simultaneously, our results imply that analog networks trained under FFA could be directly applied to neuromorphic computing, leading to reduced energy usage and increased computational speed.
Micro-power spoken keyword spotting on Xylo Audio 2
2024-06-21
Hannah Bos, Dylan R. Muir
For many years, designs for "Neuromorphic" or brain-like processors have been motivated by achieving extreme energy efficiency, compared with von-Neumann and tensor processor devices. As part of their design language, Neuromorphic processors take advantage of weight, parameter, state and activity sparsity. In the extreme case, neural networks based on these principles mimic the sparse activity oof biological nervous systems, in ``Spiking Neural Networks'' (SNNs). Few benchmarks are available for Neuromorphic processors, that have been implemented for a range of Neuromorphic and non-Neuromorphic platforms, which can therefore demonstrate the energy benefits of Neuromorphic processor designs. Here we describes the implementation of a spoken audio keyword-spotting (KWS) benchmark "Aloha" on the Xylo Audio 2 (SYNS61210) Neuromorphic processor device. We obtained high deployed quantized task accuracy, (95%), exceeding the benchmark task accuracy. We measured real continuous power of the deployed application on Xylo. We obtained best-in-class dynamic inference power ($291\mu$W) and best-in-class inference efficiency ($6.6\mu$J / Inf). Xylo sets a new minimum power for the Aloha KWS benchmark, and highlights the extreme energy efficiency achievable with Neuromorphic processor designs. Our results show that Neuromorphic designs are well-suited for real-time near- and in-sensor processing on edge devices.
Video action recognition (VAR) plays crucial roles in various domains such as surveillance, healthcare, and industrial automation, making it highly significant for the society. Consequently, it has long been a research spot in the computer vision field. As artificial neural networks (ANNs) are flourishing, convolution neural networks (CNNs), including 2D-CNNs and 3D-CNNs, as well as variants of the vision transformer (ViT), have shown impressive performance on VAR. However, they usually demand huge computational cost due to the large data volume and heavy information redundancy introduced by the temporal dimension. To address this challenge, some researchers have turned to brain-inspired spiking neural networks (SNNs), such as recurrent SNNs and ANN-converted SNNs, leveraging their inherent temporal dynamics and energy efficiency. Yet, current SNNs for VAR also encounter limitations, such as nontrivial input preprocessing, intricate network construction/training, and the need for repetitive processing of the same video clip, hindering their practical deployment. In this study, we innovatively propose the directly trained SVFormer (Spiking Video transFormer) for VAR. SVFormer integrates local feature extraction, global self-attention, and the intrinsic dynamics, sparsity, and spike-driven nature of SNNs, to efficiently and effectively extract spatio-temporal features. We evaluate SVFormer on two RGB datasets (UCF101, NTU-RGBD60) and one neuromorphic dataset (DVS128-Gesture), demonstrating comparable performance to the mainstream models in a more efficient way. Notably, SVFormer achieves a top-1 accuracy of 84.03% with ultra-low power consumption (21 mJ/video) on UCF101, which is state-of-the-art among directly trained deep SNNs, showcasing significant advantages over prior models.
Neural manifolds are an attractive theoretical framework for characterizing the complex behaviors of neural populations. However, many of the tools for identifying these low-dimensional subspaces are correlational and provide limited insight into the underlying dynamics. The ability to precisely control this latent activity would allow researchers to investigate the structure and function of neural manifolds. Employing techniques from the field of optimal control, we simulate controlling the latent dynamics of a neural population using closed-loop, dynamically generated sensory inputs. Using a spiking neural network (SNN) as a model of a neural circuit, we find low-dimensional representations of both the network activity (the neural manifold) and a set of salient visual stimuli. With a data-driven latent dynamics model, we apply model predictive control (MPC) to provide anticipatory, optimal control over the trajectory of the circuit in a latent space. We are able to control the latent dynamics of the SNN to follow several reference trajectories despite observing only a subset of neurons and with a substantial amount of unknown noise injected into the network. These results provide a framework to experimentally test for causal relationships between manifold dynamics and other variables of interest such as organismal behavior and BCI performance.
2024-06-20
Hongzhi Wang, Xiubo Liang, Mengjian Li, Tao Zhang
The Spiking Neural Networks (SNNs), renowned for their bio-inspired operational mechanism and energy efficiency, mirror the human brain's neural activity. Yet, SNNs face challenges in balancing energy efficiency with the computational demands of advanced tasks. Our research introduces the RTFormer, a novel architecture that embeds Re-parameterized Temporal Sliding Batch Normalization (TSBN) within the Spiking Transformer framework. This innovation optimizes energy usage during inference while ensuring robust computational performance. The crux of RTFormer lies in its integration of reparameterized convolutions and TSBN, achieving an equilibrium between computational prowess and energy conservation.
EvSegSNN: Neuromorphic Semantic Segmentation for Event Data
2024-06-20
Dalia Hareb, Jean Martinet
Semantic segmentation is an important computer vision task, particularly for scene understanding and navigation of autonomous vehicles and UAVs. Several variations of deep neural network architectures have been designed to tackle this task. However, due to their huge computational costs and their high memory consumption, these models are not meant to be deployed on resource-constrained systems. To address this limitation, we introduce an end-to-end biologically inspired semantic segmentation approach by combining Spiking Neural Networks (SNNs, a low-power alternative to classical neural networks) with event cameras whose output data can directly feed these neural network inputs. We have designed EvSegSNN, a biologically plausible encoder-decoder U-shaped architecture relying on Parametric Leaky Integrate and Fire neurons in an objective to trade-off resource usage against performance. The experiments conducted on DDD17 demonstrate that EvSegSNN outperforms the closest state-of-the-art model in terms of MIoU while reducing the number of parameters by a factor of $1.6$ and sparing a batch normalization stage.
2024-06-20
Yang Wang, Haiyang Mei, Qirui Bao, Ziqi Wei, Mike Zheng Shou, Haizhou Li, Bo Dong, Xin Yang
We introduce a novel multimodality synergistic knowledge distillation scheme tailored for efficient single-eye motion recognition tasks. This method allows a lightweight, unimodal student spiking neural network (SNN) to extract rich knowledge from an event-frame multimodal teacher network. The core strength of this approach is its ability to utilize the ample, coarser temporal cues found in conventional frames for effective emotion recognition. Consequently, our method adeptly interprets both temporal and spatial information from the conventional frame domain, eliminating the need for specialized sensing devices, e.g., event-based camera. The effectiveness of our approach is thoroughly demonstrated using both existing and our compiled single-eye emotion recognition datasets, achieving unparalleled performance in accuracy and efficiency over existing state-of-the-art methods.
2024-06-19
Wenjie Wei, Yu Liang, Ammar Belatreche, Yichen Xiao, Honglin Cao, Zhenbang Ren, Guoqing Wang, Malu Zhang, Yang Yang
Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to represent information and process them in an asynchronous event-driven manner, offering an energy-efficient paradigm for the next generation of machine intelligence. However, the current focus within the SNN community prioritizes accuracy optimization through the development of large-scale models, limiting their viability in resource-constrained and low-power edge devices. To address this challenge, we introduce a lightweight and hardware-friendly Quantized SNN (Q-SNN) that applies quantization to both synaptic weights and membrane potentials. By significantly compressing these two key elements, the proposed Q-SNNs substantially reduce both memory usage and computational complexity. Moreover, to prevent the performance degradation caused by this compression, we present a new Weight-Spike Dual Regulation (WS-DR) method inspired by information entropy theory. Experimental evaluations on various datasets, including static and neuromorphic, demonstrate that our Q-SNNs outperform existing methods in terms of both model size and accuracy. These state-of-the-art results in efficiency and efficacy suggest that the proposed method can significantly improve edge intelligent computing.
Trapezoidal Gradient Descent for Effective Reinforcement Learning in Spiking Networks
2024-06-19
Yuhao Pan, Xiucheng Wang, Nan Cheng, Qi Qiu
With the rapid development of artificial intelligence technology, the field of reinforcement learning has continuously achieved breakthroughs in both theory and practice. However, traditional reinforcement learning algorithms often entail high energy consumption during interactions with the environment. Spiking Neural Network (SNN), with their low energy consumption characteristics and performance comparable to deep neural networks, have garnered widespread attention. To reduce the energy consumption of practical applications of reinforcement learning, researchers have successively proposed the Pop-SAN and MDC-SAN algorithms. Nonetheless, these algorithms use rectangular functions to approximate the spike network during the training process, resulting in low sensitivity, thus indicating room for improvement in the training effectiveness of SNN. Based on this, we propose a trapezoidal approximation gradient method to replace the spike network, which not only preserves the original stable learning state but also enhances the model's adaptability and response sensitivity under various signal dynamics. Simulation results show that the improved algorithm, using the trapezoidal approximation gradient to replace the spike network, achieves better convergence speed and performance compared to the original algorithm and demonstrates good training stability.
Thanks to Deep Neural Networks (DNNs), the accuracy of Keyword Spotting (KWS) has made substantial progress. However, as KWS systems are usually implemented on edge devices, energy efficiency becomes a critical requirement besides performance. Here, we take advantage of spiking neural networks' energy efficiency and propose an end-to-end lightweight KWS model. The model consists of two innovative modules: 1) Global-Local Spiking Convolution (GLSC) module and 2) Bottleneck-PLIF module. Compared to the hand-crafted feature extraction methods, the GLSC module achieves speech feature extraction that is sparser, more energy-efficient, and yields better performance. The Bottleneck-PLIF module further processes the signals from GLSC with the aim to achieve higher accuracy with fewer parameters. Extensive experiments are conducted on the Google Speech Commands Dataset (V1 and V2). The results show our method achieves competitive performance among SNN-based KWS models with fewer parameters.
Spiking neural networks (SNNs) are gaining increasing attention as potential computationally efficient alternatives to traditional artificial neural networks(ANNs). However, the unique information propagation mechanisms and the complexity of SNN neuron models pose challenges for adopting traditional methods developed for ANNs to SNNs. These challenges include both weight learning and architecture design. While surrogate gradient learning has shown some success in addressing the former challenge, the latter remains relatively unexplored. Recently, a novel paradigm utilizing evolutionary computation methods has emerged to tackle these challenges. This approach has resulted in the development of a variety of energy-efficient and high-performance SNNs across a wide range of machine learning benchmarks. In this paper, we present a survey of these works and initiate discussions on potential challenges ahead.
Spiking federated learning is an emerging distributed learning paradigm that allows resource-constrained devices to train collaboratively at low power consumption without exchanging local data. It takes advantage of both the privacy computation property in federated learning (FL) and the energy efficiency in spiking neural networks (SNN). Thus, it is highly promising to revolutionize the efficient processing of multimedia data. However, existing spiking federated learning methods employ a random selection approach for client aggregation, assuming unbiased client participation. This neglect of statistical heterogeneity affects the convergence and accuracy of the global model significantly. In our work, we propose a credit assignment-based active client selection strategy, the SFedCA, to judiciously aggregate clients that contribute to the global sample distribution balance. Specifically, the client credits are assigned by the firing intensity state before and after local model training, which reflects the local data distribution difference from the global model. Comprehensive experiments are conducted on various non-identical and independent distribution (non-IID) scenarios. The experimental results demonstrate that the SFedCA outperforms the existing state-of-the-art spiking federated learning methods, and requires fewer communication rounds.
Brain-inspired Computational Modeling of Action Recognition with Recurrent Spiking Neural Networks Equipped with Reinforcement Delay Learning
2024-06-17
Alireza Nadafian, Milad Mozafari, Timothée Masquelier, Mohammad Ganjtabesh
The growing interest in brain-inspired computational models arises from the remarkable problem-solving efficiency of the human brain. Action recognition, a complex task in computational neuroscience, has received significant attention due to both its intricate nature and the brain's exceptional performance in this area. Nevertheless, current solutions for action recognition either exhibit limitations in effectively addressing the problem or lack the necessary biological plausibility. Deep neural networks, for instance, demonstrate acceptable performance but deviate from biological evidence, thereby restricting their suitability for brain-inspired computational studies. On the other hand, the majority of brain-inspired models proposed for action recognition exhibit significantly lower effectiveness compared to deep models and fail to achieve human-level performance. This deficiency can be attributed to their disregard for the underlying mechanisms of the brain. In this article, we present an effective brain-inspired computational model for action recognition. We equip our model with novel biologically plausible mechanisms for spiking neural networks that are crucial for learning spatio-temporal patterns. The key idea behind these new mechanisms is to bridge the gap between the brain's capabilities and action recognition tasks by integrating key biological principles into our computational framework. Furthermore, we evaluate the performance of our model against other models using a benchmark dataset for action recognition, DVS-128 Gesture. The results show that our model outperforms previous biologically plausible models and competes with deep supervised models.
Exemplar LCA-Decoder: A Scalable Framework for On-Chip Learning
2024-06-14
Sanaz Mahmoodi Takaghaj, Jack Sampson
Neuromorphic computing has recently gained significant attention as a promising combined approach for developing energy-efficient, parallel computing systems inspired by the human brain. Efficient training algorithms are imperative for the effective processing of data on neuromorphic platforms; however, their absence remains a notable gap in the field. In this paper, we reduce the gap by proposing an innovative encoder-decoder technique that leverages sparse coding and the Locally Competitive Algorithm (LCA) to provide a computationally efficient and power-conscious algorithm specifically designed for neuromorphic platforms. Using Exemplar LCA-Decoder we reduce the computational demands and memory requirements associated with training Spiking Neural Networks (SNNs) using error backpropagation methods. Our results show notable test accuracy on ImageNet and CIFAR10/100 datasets, surpassing the previously achieved SNN accuracy on these datasets. Additionally, Exemplar LCA-Decoder is scalable and allows expanding the model and adding new data points and classes cost-effectively.
Event-Based Simulation of Stochastic Memristive Devices for Neuromorphic Computing
2024-06-14
Waleed El-Geresy, Christos Papavassiliou, Deniz Gündüz
In this paper, we build a general model of memristors suitable for the simulation of event-based systems, such as hardware spiking neural networks, and more generally, neuromorphic computing systems. We extend an existing general model of memristors - the Generalised Metastable Switch Model - to an event-driven setting, eliminating errors associated discrete time approximation, as well as offering potential improvements in terms of computational efficiency for simulation. We introduce the notion of a volatility state variable, to allow for the modelling of memory-dependent and dynamic switching behaviour, succinctly capturing and unifying a variety of volatile phenomena present in memristive devices, including state relaxation, structural disruption, Joule heating, and drift acceleration phenomena. We supply a drift dataset for titanium dioxide memristors and introduce a linear conductance model to simulate the drift characteristics, motivated by a proposed physical model of filament growth. We then demonstrate an approach for fitting the parameters of the event-based model to the drift model.
Keyword Spotting (KWS) is essential in edge computing requiring rapid and energy-efficient responses. Spiking Neural Networks (SNNs) are well-suited for KWS for their efficiency and temporal capacity for speech. To further reduce the latency and energy consumption, this study introduces ED-sKWS, an SNN-based KWS model with an early-decision mechanism that can stop speech processing and output the result before the end of speech utterance. Furthermore, we introduce a Cumulative Temporal (CT) loss that can enhance prediction accuracy at both the intermediate and final timesteps. To evaluate early-decision performance, we present the SC-100 dataset including 100 speech commands with beginning and end timestamp annotation. Experiments on the Google Speech Commands v2 and our SC-100 datasets show that ED-sKWS maintains competitive accuracy with 61% timesteps and 52% energy consumption compared to SNN models without early-decision mechanism, ensuring rapid response and energy efficiency.
Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks
2024-06-14
Yingchao Yu, Yuping Yan, Jisong Cai, Yaochu Jin
Federated learning (FL) has emerged as a promising paradigm for training models on decentralized data while safeguarding data privacy. Most existing FL systems, however, assume that all machine learning models are of the same type, although it becomes more likely that different edge devices adopt different types of AI models, including both conventional analogue artificial neural networks (ANNs) and biologically more plausible spiking neural networks (SNNs). This diversity empowers the efficient handling of specific tasks and requirements, showcasing the adaptability and versatility of edge computing platforms. One main challenge of such heterogeneous FL system lies in effectively aggregating models from the local devices in a privacy-preserving manner. To address the above issue, this work benchmarks FL systems containing both convoluntional neural networks (CNNs) and SNNs by comparing various aggregation approaches, including federated CNNs, federated SNNs, federated CNNs for SNNs, federated SNNs for CNNs, and federated CNNs with SNN fusion. Experimental results demonstrate that the CNN-SNN fusion framework exhibits the best performance among the above settings on the MNIST dataset. Additionally, intriguing phenomena of competitive suppression are noted during the convergence process of multi-model FL.
ExoSpikeNet: A Light Curve Analysis Based Spiking Neural Network for Exoplanet Detection
2024-06-12
Maneet Chatterjee, Anuvab Sen, Subhabrata Roy
Exoplanets are celestial bodies orbiting stars beyond our Solar System. Although historically they posed detection challenges, Kepler's data has revolutionized our understanding. By analyzing flux values from the Kepler Mission, we investigate the intricate patterns in starlight that may indicate the presence of exoplanets. This study investigates a novel approach for exoplanet classification using Spiking Neural Networks (SNNs) applied to data obtained from the NASA Kepler mission. SNNs offer a unique advantage by mimicking the spiking behavior of neurons in the brain, allowing for more nuanced and biologically inspired processing of temporal data. Experimental results demonstrate the efficacy of the proposed SNN architecture, excelling in various performance metrics such as accuracy, F1 score, precision, and recall.
Spiking neural networks (SNNs) have attracted considerable attention for their event-driven, low-power characteristics and high biological interpretability. Inspired by knowledge distillation (KD), recent research has improved the performance of the SNN model with a pre-trained teacher model. However, additional teacher models require significant computational resources, and it is tedious to manually define the appropriate teacher network architecture. In this paper, we explore cost-effective self-distillation learning of SNNs to circumvent these concerns. Without an explicit defined teacher, the SNN generates pseudo-labels and learns consistency during training. On the one hand, we extend the timestep of the SNN during training to create an implicit temporal ``teacher" that guides the learning of the original ``student", i.e., the temporal self-distillation. On the other hand, we guide the output of the weak classifier at the intermediate stage by the final output of the SNN, i.e., the spatial self-distillation. Our temporal-spatial self-distillation (TSSD) learning method does not introduce any inference overhead and has excellent generalization ability. Extensive experiments on the static image datasets CIFAR10/100 and ImageNet as well as the neuromorphic datasets CIFAR10-DVS and DVS-Gesture validate the superior performance of the TSSD method. This paper presents a novel manner of fusing SNNs with KD, providing insights into high-performance SNN learning methods.
SpikePipe: Accelerated Training of Spiking Neural Networks via Inter-Layer Pipelining and Multiprocessor Scheduling
2024-06-11
Sai Sanjeet, Bibhu Datta Sahoo, Keshab K. Parhi
Spiking Neural Networks (SNNs) have gained popularity due to their high energy efficiency. Prior works have proposed various methods for training SNNs, including backpropagation-based methods. Training SNNs is computationally expensive compared to their conventional counterparts and would benefit from multiprocessor hardware acceleration. This is the first paper to propose inter-layer pipelining to accelerate training in SNNs using systolic array-based processors and multiprocessor scheduling. The impact of training using delayed gradients is observed using three networks training on different datasets, showing no degradation for small networks and < 10% degradation for large networks. The mapping of various training tasks of the SNN onto systolic arrays is formulated, and the proposed scheduling method is evaluated on the three networks. The results are compared against standard pipelining algorithms. The results show that the proposed method achieves an average speedup of 1.6X compared to standard pipelining algorithms, with an upwards of 2X improvement in some cases. The incurred communication overhead due to the proposed method is less than 0.5% of the total required communication of training.
Recently, brain-inspired spiking neural networks (SNNs) have attracted great research attention owing to their inherent bio-interpretability, event-triggered properties and powerful perception of spatiotemporal information, which is beneficial to handling event-based neuromorphic datasets. In contrast to conventional static image datasets, event-based neuromorphic datasets present heightened complexity in feature extraction due to their distinctive time series and sparsity characteristics, which influences their classification accuracy. To overcome this challenge, a novel approach termed Neuromorphic Momentum Contrast Learning (NeuroMoCo) for SNNs is introduced in this paper by extending the benefits of self-supervised pre-training to SNNs to effectively stimulate their potential. This is the first time that self-supervised learning (SSL) based on momentum contrastive learning is realized in SNNs. In addition, we devise a novel loss function named MixInfoNCE tailored to their temporal characteristics to further increase the classification accuracy of neuromorphic datasets, which is verified through rigorous ablation experiments. Finally, experiments on DVS-CIFAR10, DVS128Gesture and N-Caltech101 have shown that NeuroMoCo of this paper establishes new state-of-the-art (SOTA) benchmarks: 83.6% (Spikformer-2-256), 98.62% (Spikformer-2-256), and 84.4% (SEW-ResNet-18), respectively.
Supervised Radio Frequency Interference Detection with SNNs
2024-06-10
Nicholas J. Pritchard, Andreas Wicenec, Mohammed Bennamoun, Richard Dodson
Radio Frequency Interference (RFI) poses a significant challenge in radio astronomy, arising from terrestrial and celestial sources, disrupting observations conducted by radio telescopes. Addressing RFI involves intricate heuristic algorithms, manual examination, and, increasingly, machine learning methods. Given the dynamic and temporal nature of radio astronomy observations, Spiking Neural Networks (SNNs) emerge as a promising approach. In this study, we cast RFI detection as a supervised multi-variate time-series segmentation problem. Notably, our investigation explores the encoding of radio astronomy visibility data for SNN inference, considering six encoding schemes: rate, latency, delta-modulation, and three variations of the step-forward algorithm. We train a small two-layer fully connected SNN on simulated data derived from the Hydrogen Epoch of Reionization Array (HERA) telescope and perform extensive hyper-parameter optimization. Results reveal that latency encoding exhibits superior performance, achieving a per-pixel accuracy of 98.8% and an f1-score of 0.761. Remarkably, these metrics approach those of contemporary RFI detection algorithms, notwithstanding the simplicity and compactness of our proposed network architecture. This study underscores the potential of RFI detection as a benchmark problem for SNN researchers, emphasizing the efficacy of SNNs in addressing complex time-series segmentation tasks in radio astronomy.
Spiking Neural Networks with Consistent Mapping Relations Allow High-Accuracy Inference
2024-06-08
Yang Li, Xiang He, Qingqun Kong, Yi Zeng
Spike-based neuromorphic hardware has demonstrated substantial potential in low energy consumption and efficient inference. However, the direct training of deep spiking neural networks is challenging, and conversion-based methods still require substantial time delay owing to unresolved conversion errors. We determine that the primary source of the conversion errors stems from the inconsistency between the mapping relationship of traditional activation functions and the input-output dynamics of spike neurons. To counter this, we introduce the Consistent ANN-SNN Conversion (CASC) framework. It includes the Consistent IF (CIF) neuron model, specifically contrived to minimize the influence of the stable point's upper bound, and the wake-sleep conversion (WSC) method, synergistically ensuring the uniformity of neuron behavior. This method theoretically achieves a loss-free conversion, markedly diminishing time delays and improving inference performance in extensive classification and object detection tasks. Our approach offers a viable pathway toward more efficient and effective neuromorphic systems.
We introduce CORTEX, an algorithmic framework designed for large-scale brain simulation. Leveraging the computational capacity of the Fugaku Supercomputer, CORTEX maximizes available problem size and processing performance. Our primary innovation, Indegree Sub-Graph Decomposition, along with a suite of parallel algorithms, facilitates efficient domain decomposition by segmenting the global graph structure into smaller, identically structured sub-graphs. This segmentation allows for parallel processing of synaptic interactions without inter-process dependencies, effectively eliminating data racing at the thread level without necessitating mutexes or atomic operations. Additionally, this strategy enhances the overlap of communication and computation. Benchmark tests conducted on spiking neural networks, characterized by biological parameters, have demonstrated significant enhancements in both problem size and simulation performance, surpassing the capabilities of the current leading open-source solution, the NEST Simulator. Our work offers a powerful new tool for the field of neuromorphic computing and understanding brain function.
Encoding Semantic Priors into the Weights of Implicit Neural Representation
2024-06-06
Zhicheng Cai, Qiu Shen
Implicit neural representation (INR) has recently emerged as a promising paradigm for signal representations, which takes coordinates as inputs and generates corresponding signal values. Since these coordinates contain no semantic features, INR fails to take any semantic information into consideration. However, semantic information has been proven critical in many vision tasks, especially for visual signal representation. This paper proposes a reparameterization method termed as SPW, which encodes the semantic priors to the weights of INR, thus making INR contain semantic information implicitly and enhancing its representational capacity. Specifically, SPW uses the Semantic Neural Network (SNN) to extract both low- and high-level semantic information of the target visual signal and generates the semantic vector, which is input into the Weight Generation Network (WGN) to generate the weights of INR model. Finally, INR uses the generated weights with semantic priors to map the coordinates to the signal values. After training, we only retain the generated weights while abandoning both SNN and WGN, thus SPW introduces no extra costs in inference. Experimental results show that SPW can improve the performance of various INR models significantly on various tasks, including image fitting, CT reconstruction, MRI reconstruction, and novel view synthesis. Further experiments illustrate that model with SPW has lower weight redundancy and learns more novel representations, validating the effectiveness of SPW.
SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN
2024-06-05
Kang You, Zekai Xu, Chen Nie, Zhijie Deng, Qinghai Guo, Xiang Wang, Zhezhi He
Spiking neural network (SNN) has attracted great attention due to its characteristic of high efficiency and accuracy. Currently, the ANN-to-SNN conversion methods can obtain ANN on-par accuracy SNN with ultra-low latency (8 time-steps) in CNN structure on computer vision (CV) tasks. However, as Transformer-based networks have achieved prevailing precision on both CV and natural language processing (NLP), the Transformer-based SNNs are still encounting the lower accuracy w.r.t the ANN counterparts. In this work, we introduce a novel ANN-to-SNN conversion method called SpikeZIP-TF, where ANN and SNN are exactly equivalent, thus incurring no accuracy degradation. SpikeZIP-TF achieves 83.82% accuracy on CV dataset (ImageNet) and 93.79% accuracy on NLP dataset (SST-2), which are higher than SOTA Transformer-based SNNs. The code is available in GitHub: https://github.com/Intelligent-Computing-Research-Group/SpikeZIP_transformer
Towards energy-efficient artificial intelligence similar to the human brain, the bio-inspired spiking neural networks (SNNs) have advantages of biological plausibility, event-driven sparsity, and binary activation. Recently, large-scale language models exhibit promising generalization capability, making it a valuable issue to explore more general spike-driven models. However, the binary spikes in existing SNNs fail to encode adequate semantic information, placing technological challenges for generalization. This work proposes the first fully spiking mechanism for general language tasks, including both discriminative and generative ones. Different from previous spikes with {0,1} levels, we propose a more general spike formulation with bi-directional, elastic amplitude, and elastic frequency encoding, while still maintaining the addition nature of SNNs. In a single time step, the spike is enhanced by direction and amplitude information; in spike frequency, a strategy to control spike firing rate is well designed. We plug this elastic bi-spiking mechanism in language modeling, named SpikeLM. It is the first time to handle general language tasks with fully spike-driven models, which achieve much higher accuracy than previously possible. SpikeLM also greatly bridges the performance gap between SNNs and ANNs in language modeling. Our code is available at https://github.com/Xingrun-Xing/SpikeLM.
Efficient Hybrid Neuromorphic-Bayesian Model for Olfaction Sensing: Detection and Classification
2024-06-05
Rizwana Kausar, Fakhreddine Zayer, Jaime Viegas, Jorge Dias
Olfaction sensing in autonomous robotics faces challenges in dynamic operations, energy efficiency, and edge processing. It necessitates a machine learning algorithm capable of managing real-world odor interference, ensuring resource efficiency for mobile robotics, and accurately estimating gas features for critical tasks such as odor mapping, localization, and alarm generation. This paper introduces a hybrid approach that exploits neuromorphic computing in combination with probabilistic inference to address these demanding requirements. Our approach implements a combination of a convolutional spiking neural network for feature extraction and a Bayesian spiking neural network for odor detection and identification. The developed algorithm is rigorously tested on a dataset for sensor drift compensation for robustness evaluation. Additionally, for efficiency evaluation, we compare the energy consumption of our model with a non-spiking machine learning algorithm under identical dataset and operating conditions. Our approach demonstrates superior efficiency alongside comparable accuracy outcomes.
Spiking representation learning for associative memories
2024-06-05
Naresh Ravichandran, Anders Lansner, Pawel Herman
Networks of interconnected neurons communicating through spiking signals offer the bedrock of neural computations. Our brains spiking neural networks have the computational capacity to achieve complex pattern recognition and cognitive functions effortlessly. However, solving real-world problems with artificial spiking neural networks (SNNs) has proved to be difficult for a variety of reasons. Crucially, scaling SNNs to large networks and processing large-scale real-world datasets have been challenging, especially when compared to their non-spiking deep learning counterparts. The critical operation that is needed of SNNs is the ability to learn distributed representations from data and use these representations for perceptual, cognitive and memory operations. In this work, we introduce a novel SNN that performs unsupervised representation learning and associative memory operations leveraging Hebbian synaptic and activity-dependent structural plasticity coupled with neuron-units modelled as Poisson spike generators with sparse firing (~1 Hz mean and ~100 Hz maximum firing rate). Crucially, the architecture of our model derives from the neocortical columnar organization and combines feedforward projections for learning hidden representations and recurrent projections for forming associative memories. We evaluated the model on properties relevant for attractor-based associative memories such as pattern completion, perceptual rivalry, distortion resistance, and prototype extraction.
Spiking Neural Networks (SNNs) are capable of encoding and processing temporal information in a biologically plausible way. However, most existing SNN-based methods for image tasks do not fully exploit this feature. Moreover, they often overlook the role of adaptive threshold in spiking neurons, which can enhance their dynamic behavior and learning ability. To address these issues, we propose a novel method for image decoding based on temporal attention (TAID) and an adaptive Leaky-Integrate-and-Fire (ALIF) neuron model. Our method leverages the temporal information of SNN outputs to generate high-quality images that surpass the state-of-the-art (SOTA) in terms of Inception score, Fr\'echet Inception Distance, and Fr\'echet Autoencoder Distance. Furthermore, our ALIF neuron model achieves remarkable classification accuracy on MNIST (99.78\%) and CIFAR-10 (93.89\%) datasets, demonstrating the effectiveness of learning adaptive thresholds for spiking neurons. The code is available at https://github.com/bollossom/ICLR_TINY_SNN.
P-SpikeSSM: Harnessing Probabilistic Spiking State Space Models for Long-Range Dependency Tasks
2024-06-05
Malyaban Bal, Abhronil Sengupta
Spiking neural networks (SNNs) are posited as a computationally efficient and biologically plausible alternative to conventional neural architectures, with their core computational framework primarily using the leaky integrate-and-fire (LIF) neuron model. However, the limited hidden state representation of LIF neurons, characterized by a scalar membrane potential, and sequential spike generation process, poses challenges for effectively developing scalable spiking models to address long-range dependencies in sequence learning tasks. In this study, we develop a scalable probabilistic spiking learning framework for long-range dependency tasks leveraging the fundamentals of state space models. Unlike LIF neurons that rely on the determinitic Heaviside function for a sequential process of spike generation, we introduce a SpikeSampler layer that samples spikes stochastically based on an SSM-based neuronal model while allowing parallel computations. To address non-differentiability of the spiking operation and enable effective training, we also propose a surrogate function tailored for the stochastic nature of the SpikeSampler layer. To enhance inter-neuron communication, we introduce the SpikeMixer block, which integrates spikes from neuron populations in each layer. This is followed by a ClampFuse layer, incorporating a residual connection to capture complex dependencies, enabling scalability of the model. Our models attain state-of-the-art performance among SNN models across diverse long-range dependency tasks, encompassing the Long Range Arena benchmark, permuted sequential MNIST, and the Speech Command dataset and demonstrate sparse spiking pattern highlighting its computational efficiency.
Spiking neural networks (SNNs) have gained prominence for their potential in neuromorphic computing and energy-efficient artificial intelligence, yet optimizing them remains a formidable challenge for gradient-based methods due to their discrete, spike-based computation. This paper attempts to tackle the challenges by introducing Cosine Annealing Differential Evolution (CADE), designed to modulate the mutation factor (F) and crossover rate (CR) of differential evolution (DE) for the SNN model, i.e., Spiking Element Wise (SEW) ResNet. Extensive empirical evaluations were conducted to analyze CADE. CADE showed a balance in exploring and exploiting the search space, resulting in accelerated convergence and improved accuracy compared to existing gradient-based and DE-based methods. Moreover, an initialization method based on a transfer learning setting was developed, pretraining on a source dataset (i.e., CIFAR-10) and fine-tuning the target dataset (i.e., CIFAR-100), to improve population diversity. It was found to further enhance CADE for SNN. Remarkably, CADE elevates the performance of the highest accuracy SEW model by an additional 0.52 percentage points, underscoring its effectiveness in fine-tuning and enhancing SNNs. These findings emphasize the pivotal role of a scheduler for F and CR adjustment, especially for DE-based SNN. Source Code on Github: https://github.com/Tank-Jiang/CADE4SNN.
Humans learn multiple tasks in succession with minimal mutual interference, through the context gating mechanism in the prefrontal cortex (PFC). The brain-inspired models of spiking neural networks (SNN) have drawn massive attention for their energy efficiency and biological plausibility. To overcome catastrophic forgetting when learning multiple tasks in sequence, current SNN models for lifelong learning focus on memory reserving or regularization-based modification, while lacking SNN to replicate human experimental behavior. Inspired by biological context-dependent gating mechanisms found in PFC, we propose SNN with context gating trained by the local plasticity rule (CG-SNN) for lifelong learning. The iterative training between global and local plasticity for task units is designed to strengthen the connections between task neurons and hidden neurons and preserve the multi-task relevant information. The experiments show that the proposed model is effective in maintaining the past learning experience and has better task-selectivity than other methods during lifelong learning. Our results provide new insights that the CG-SNN model can extend context gating with good scalability on different SNN architectures with different spike-firing mechanisms. Thus, our models have good potential for parallel implementation on neuromorphic hardware and model human's behavior.
This study proposes a framework that extends existing time-coding time-to-first-spike spiking neural network (SNN) models to allow processing information changing over time. We explain spike propagation through a model with multiple input and output spikes at each neuron, as well as design training rules for end-to-end backpropagation. This strategy enables us to process information changing over time. The model is trained and evaluated on a Twitter bot detection task where the time of events (tweets and retweets) is the primary carrier of information. This task was chosen to evaluate how the proposed SNN deals with spike train data composed of hundreds of events occurring at timescales differing by almost five orders of magnitude. The impact of various parameters on model properties, performance and training-time stability is analyzed.
Toward Efficient Deep Spiking Neuron Networks:A Survey On Compression
2024-06-03
Hui Xie, Ge Yang, Wenjuan Gao
With the rapid development of deep learning, Deep Spiking Neural Networks (DSNNs) have emerged as promising due to their unique spike event processing and asynchronous computation. When deployed on neuromorphic chips, DSNNs offer significant power advantages over Deep Artificial Neural Networks (DANNs) and eliminate time and energy consuming multiplications due to the binary nature of spikes (0 or 1). Additionally, DSNNs excel in processing temporal information, making them potentially superior for handling temporal data compared to DANNs. However, their deep network structure and numerous parameters result in high computational costs and energy consumption, limiting real-life deployment. To enhance DSNNs efficiency, researchers have adapted methods from DANNs, such as pruning, quantization, and knowledge distillation, and developed specific techniques like reducing spike firing and pruning time steps. While previous surveys have covered DSNNs algorithms, hardware deployment, and general overviews, focused research on DSNNs compression and efficiency has been lacking. This survey addresses this gap by concentrating on efficient DSNNs and their compression methods. It begins with an exploration of DSNNs' biological background and computational units, highlighting differences from DANNs. It then delves into various compression methods, including pruning, quantization, knowledge distillation, and reducing spike firing, and concludes with suggestions for future research directions.
Pattern Formation in a Spiking Neural-Field of Renewal Neurons
2024-06-03
Gregory Dumont, Carmen Oana Tarniceriu
Elucidating the neurophysiological mechanisms underlying neural pattern formation remains an outstanding challenge in Computational Neuroscience. In this paper, we address the issue of understanding the emergence of neural patterns by considering a network of renewal neurons, a well-established class of spiking cells. Taking the thermodynamics limit, the network's dynamics can be accurately represented by a partial differential equation coupled with a nonlocal differential equation. The stationary state of the nonlocal system is determined, and a perturbation analysis is performed to analytically characterize the conditions for the occurrence of Turing instabilities. Considering neural network parameters such as the synaptic coupling and the external drive, we numerically obtain the bifurcation line that separates the asynchronous regime from the emergence of patterns. Our theoretical findings provide a new and insightful perspective on the emergence of Turing patterns in spiking neural networks. In the long term, our formalism will enable the study of neural patterns while maintaining the connections between microscopic cellular properties, network coupling, and the emergence of Turing instabilities.
Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning
2024-06-03
Yaxin Li, Qi Xu, Jiangrong Shen, Hongming Xu, Long Chen, Gang Pan
The emergence of deep and large-scale spiking neural networks (SNNs) exhibiting high performance across diverse complex datasets has led to a need for compressing network models due to the presence of a significant number of redundant structural units, aiming to more effectively leverage their low-power consumption and biological interpretability advantages. Currently, most model compression techniques for SNNs are based on unstructured pruning of individual connections, which requires specific hardware support. Hence, we propose a structured pruning approach based on the activity levels of convolutional kernels named Spiking Channel Activity-based (SCA) network pruning framework. Inspired by synaptic plasticity mechanisms, our method dynamically adjusts the network's structure by pruning and regenerating convolutional kernels during training, enhancing the model's adaptation to the current target task. While maintaining model performance, this approach refines the network architecture, ultimately reducing computational load and accelerating the inference process. This indicates that structured dynamic sparse learning methods can better facilitate the application of deep SNNs in low-power and high-efficiency scenarios.
Pedestrian intention prediction in Adverse Weather Conditions with Spiking Neural Networks and Dynamic Vision Sensors
2024-06-01
Mustafa Sakhai, Szymon Mazurek, Jakub Caputa, Jan K. Argasiński, Maciej Wielgosz
This study examines the effectiveness of Spiking Neural Networks (SNNs) paired with Dynamic Vision Sensors (DVS) to improve pedestrian detection in adverse weather, a significant challenge for autonomous vehicles. Utilizing the high temporal resolution and low latency of DVS, which excels in dynamic, low-light, and high-contrast environments, we assess the efficiency of SNNs compared to traditional Convolutional Neural Networks (CNNs). Our experiments involved testing across diverse weather scenarios using a custom dataset from the CARLA simulator, mirroring real-world variability. SNN models, enhanced with Temporally Effective Batch Normalization, were trained and benchmarked against state-of-the-art CNNs to demonstrate superior accuracy and computational efficiency in complex conditions such as rain and fog. The results indicate that SNNs, integrated with DVS, significantly reduce computational overhead and improve detection accuracy in challenging conditions compared to CNNs. This highlights the potential of DVS combined with bio-inspired SNN processing to enhance autonomous vehicle perception and decision-making systems, advancing intelligent transportation systems' safety features in varying operational environments. Additionally, our research indicates that SNNs perform more efficiently in handling long perception windows and prediction tasks, rather than simple pedestrian detection.
Spiking Neural Networks (SNNs) emulate the integrated-fire-leak mechanism found in biological neurons, offering a compelling combination of biological realism and energy efficiency. In recent years, they have gained considerable research interest. However, existing SNNs predominantly rely on the Leaky Integrate-and-Fire (LIF) model and are primarily suited for simple, static tasks. They lack the ability to effectively model long-term temporal dependencies and facilitate spatial information interaction, which is crucial for tackling complex, dynamic spatio-temporal prediction tasks. To tackle these challenges, this paper draws inspiration from the concept of autaptic synapses in biology and proposes a novel Spatio-Temporal Circuit (STC) model. The STC model integrates two learnable adaptive pathways, enhancing the spiking neurons' temporal memory and spatial coordination. We conduct a theoretical analysis of the dynamic parameters in the STC model, highlighting their contribution in establishing long-term memory and mitigating the issue of gradient vanishing. Through extensive experiments on multiple spatio-temporal prediction datasets, we demonstrate that our model outperforms other adaptive models. Furthermore, our model is compatible with existing spiking neuron models, thereby augmenting their dynamic representations. In essence, our work enriches the specificity and topological complexity of SNNs.
Understanding the Convergence in Balanced Resonate-and-Fire Neurons
2024-06-01
Saya Higuchi, Sander M. Bohte, Sebastian Otte
Resonate-and-Fire (RF) neurons are an interesting complementary model for integrator neurons in spiking neural networks (SNNs). Due to their resonating membrane dynamics they can extract frequency patterns within the time domain. While established RF variants suffer from intrinsic shortcomings, the recently proposed balanced resonate-and-fire (BRF) neuron marked a significant methodological advance in terms of task performance, spiking and parameter efficiency, as well as, general stability and robustness, demonstrated for recurrent SNNs in various sequence learning tasks. One of the most intriguing result, however, was an immense improvement in training convergence speed and smoothness, overcoming the typical convergence dilemma in backprop-based SNN training. This paper aims at providing further intuitions about how and why these convergence advantages emerge. We show that BRF neurons, in contrast to well-established ALIF neurons, span a very clean and smooth - almost convex - error landscape. Furthermore, empirical results reveal that the convergence benefits are predominantly coupled with a divergence boundary-aware optimization, a major component of the BRF formulation that addresses the numerical stability of the time-discrete resonator approximation. These results are supported by a formal investigation of the membrane dynamics indicating that the gradient is transferred back through time without loss of magnitude.
A Review of Pulse-Coupled Neural Network Applications in Computer Vision and Image Processing
2024-06-01
Nurul Rafi, Pablo Rivas
Research in neural models inspired by mammal's visual cortex has led to many spiking neural networks such as pulse-coupled neural networks (PCNNs). These models are oscillating, spatio-temporal models stimulated with images to produce several time-based responses. This paper reviews PCNN's state of the art, covering its mathematical formulation, variants, and other simplifications found in the literature. We present several applications in which PCNN architectures have successfully addressed some fundamental image processing and computer vision challenges, including image segmentation, edge detection, medical imaging, image fusion, image compression, object recognition, and remote sensing. Results achieved in these applications suggest that the PCNN architecture generates useful perceptual information relevant to a wide variety of computer vision tasks.
Flexible and Efficient Surrogate Gradient Modeling with Forward Gradient Injection
2024-05-31
Sebastian Otte
Automatic differentiation is a key feature of present deep learning frameworks. Moreover, they typically provide various ways to specify custom gradients within the computation graph, which is of particular importance for defining surrogate gradients in the realms of non-differentiable operations such as the Heaviside function in spiking neural networks (SNNs). PyTorch, for example, allows the custom specification of the backward pass of an operation by overriding its backward method. Other frameworks provide comparable options. While these methods are common practice and usually work well, they also have several disadvantages such as limited flexibility, additional source code overhead, poor usability, or a potentially strong negative impact on the effectiveness of automatic model optimization procedures. In this paper, an alternative way to formulate surrogate gradients is presented, namely, forward gradient injection (FGI). FGI applies a simple but effective combination of basic standard operations to inject an arbitrary gradient shape into the computational graph directly within the forward pass. It is demonstrated that using FGI is straightforward and convenient. Moreover, it is shown that FGI can significantly increase the model performance in comparison to custom backward methods in SNNs when using TorchScript. These results are complemented with a general performance study on recurrent SNNs with TorchScript and torch.compile, revealing the potential for a training speedup of more than 7x and an inference speedup of more than 16x in comparison with pure PyTorch.
Spiking neural networks (SNNs) are gaining popularity in deep learning due to their low energy budget on neuromorphic hardware. However, they still face challenges in lacking sufficient robustness to guard safety-critical applications such as autonomous driving. Many studies have been conducted to defend SNNs from the threat of adversarial attacks. This paper aims to uncover the robustness of SNN through the lens of the stability of nonlinear systems. We are inspired by the fact that searching for parameters altering the leaky integrate-and-fire dynamics can enhance their robustness. Thus, we dive into the dynamics of membrane potential perturbation and simplify the formulation of the dynamics. We present that membrane potential perturbation dynamics can reliably convey the intensity of perturbation. Our theoretical analyses imply that the simplified perturbation dynamics satisfy input-output stability. Thus, we propose a training framework with modified SNN neurons and to reduce the mean square of membrane potential perturbation aiming at enhancing the robustness of SNN. Finally, we experimentally verify the effectiveness of the framework in the setting of Gaussian noise training and adversarial training on the image classification task.
Spiking Neural Networks (SNNs) have attracted great attention for their energy-efficient operations and biologically inspired structures, offering potential advantages over Artificial Neural Networks (ANNs) in terms of energy efficiency and interpretability. Nonetheless, similar to ANNs, the robustness of SNNs remains a challenge, especially when facing adversarial attacks. Existing techniques, whether adapted from ANNs or specifically designed for SNNs, exhibit limitations in training SNNs or defending against strong attacks. In this paper, we propose a novel approach to enhance the robustness of SNNs through gradient sparsity regularization. We observe that SNNs exhibit greater resilience to random perturbations compared to adversarial perturbations, even at larger scales. Motivated by this, we aim to narrow the gap between SNNs under adversarial and random perturbations, thereby improving their overall robustness. To achieve this, we theoretically prove that this performance gap is upper bounded by the gradient sparsity of the probability associated with the true label concerning the input image, laying the groundwork for a practical strategy to train robust SNNs by regularizing the gradient sparsity. We validate the effectiveness of our approach through extensive experiments on both image-based and event-based datasets. The results demonstrate notable improvements in the robustness of SNNs. Our work highlights the importance of gradient sparsity in SNNs and its role in enhancing robustness.
2024-05-30
Rui-Jie Zhu, Ziqing Wang, Leilani Gilpin, Jason K. Eshraghian
Autonomous driving demands an integrated approach that encompasses perception, prediction, and planning, all while operating under strict energy constraints to enhance scalability and environmental sustainability. We present Spiking Autonomous Driving (SAD), the first unified Spiking Neural Network (SNN) to address the energy challenges faced by autonomous driving systems through its event-driven and energy-efficient nature. SAD is trained end-to-end and consists of three main modules: perception, which processes inputs from multi-view cameras to construct a spatiotemporal bird's eye view; prediction, which utilizes a novel dual-pathway with spiking neurons to forecast future states; and planning, which generates safe trajectories considering predicted occupancy, traffic rules, and ride comfort. Evaluated on the nuScenes dataset, SAD achieves competitive performance in perception, prediction, and planning tasks, while drawing upon the energy efficiency of SNNs. This work highlights the potential of neuromorphic computing to be applied to energy-efficient autonomous driving, a critical step toward sustainable and safety-critical automotive technology. Our code is available at \url{https://github.com/ridgerchu/SAD}.
Personalized Predictions from Population Level Experiments: A Study on Alzheimer's Disease
2024-05-30
Dennis Shen, Anish Agarwal, Vishal Misra, Bjoern Schelter, Devavrat Shah, Helen Shiells, Claude Wischik
The purpose of this article is to infer patient level outcomes from population level randomized control trials (RCTs). In this pursuit, we utilize the recently proposed synthetic nearest neighbors (SNN) estimator. At its core, SNN leverages information across patients to impute missing data associated with each patient of interest. We focus on two types of missing data: (i) unrecorded outcomes from discontinuing the assigned treatments and (ii) unobserved outcomes associated with unassigned treatments. Data imputation in the former powers and de-biases RCTs, while data imputation in the latter simulates "synthetic RCTs" to predict the outcomes for each patient under every treatment. The SNN estimator is interpretable, transparent, and causally justified under a broad class of missing data scenarios. Relative to several standard methods, we empirically find that SNN performs well for the above two applications using Phase 3 clinical trial data on patients with Alzheimer's Disease. Our findings directly suggest that SNN can tackle a current pain point within the clinical trial workflow on patient dropouts and serve as a new tool towards the development of precision medicine. Building on our insights, we discuss how SNN can further generalize to real-world applications.
CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration
2024-05-29
Sophie Jaffard, Samuel Vaiter, Patricia Reynaud-Bouret
The present work aims at proving mathematically that a neural network inspired by biology can learn a classification task thanks to local transformations only. In this purpose, we propose a spiking neural network named CHANI (Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration), whose neurons activity is modeled by Hawkes processes. Synaptic weights are updated thanks to an expert aggregation algorithm, providing a local and simple learning rule. We were able to prove that our network can learn on average and asymptotically. Moreover, we demonstrated that it automatically produces neuronal assemblies in the sense that the network can encode several classes and that a same neuron in the intermediate layers might be activated by more than one class, and we provided numerical simulations on synthetic dataset. This theoretical approach contrasts with the traditional empirical validation of biologically inspired networks and paves the way for understanding how local learning rules enable neurons to form assemblies able to represent complex concepts.
In the last three decades it has become clear that cortical regions, interconnected via white-matter fibers, form a modular and hierarchical network. This organization, which has also been seen at the microscopic level in the form of interconnected neural assemblies, is believed to support the coexistence of segregation (specialization) and integration (binding) of information. A fundamental open question is to understand how this complex structure can emerge in the brain. Here, we made a first step to address this question and propose that adaptation to various inputs could be the key driving mechanism for the formation of structural assemblies. To test this idea, we develop a model of quadratic integrate-and-fire spiking neurons, trained to stimuli targetting distinct sub-populations. The model is designed to satisfy several biologically plausible constraints: (i) the network contains excitatory and inhibitory neurons with Hebbian and anti-Hebbian STDP; and (ii) neither the neuronal activity nor the synaptic weights are frozen after the learning phase. Instead, the network continues firing spontaneously while synaptic plasticity remains active. We find that only the combination of the two inhibitory STDP sub-populations allows for the formation of stable modular organization in the network, with each sub-population playing a distinct role. The Hebbian sub-population controls for the firing rate and the anti-Hebbian mediates pattern selectivity. After the learning phase, the network activity settles into an asynchronous irregular resting-state, resembling the behaviour typically observed in-vivo in the cortex. This post-learning activity also displays spontaneous memory recalls, which are fundamental for the long-term consolidation of the learned memory items. The model introduced represents a starting point for the joint investigation of neural dynamics, connectivity and plasticity.
Mutual Information Analysis of Neuromorphic Coding for Distributed Wireless Spiking Neural Networks
2024-05-28
Pietro Savazzi, Anna Vizziello, Fabio Dell'Acqua
Wireless spiking neural networks (WSNNs) allow energy-efficient communications, especially when considering edge intelligence and learning for both terrestrial beyond 5G/6G and space networking systems. Recent research work has revealed that distributed wireless SNNs (DWSNNs) show good performance in terms of inference accuracy and low energy consumption of edge devices, under the constraints of limited bandwidth and spike loss probability. Following this reasoning, this technology can be promising for wireless sensor networks (WSNs) in space applications, where the energy constraint is predominant. In this work, we focus on neuromorphic impulse radio (IR) transmission techniques for DWSNNs, quantitatively evaluating the features of different coding algorithms that can be viewed as IR modulations. Specifically, the main contribution of this work is the evaluation of information-theoretic measures that may help in quantifying performance trade-offs among existing neuromorphic coding techniques.
Reliable Object Tracking by Multimodal Hybrid Feature Extraction and Transformer-Based Fusion
2024-05-28
Hongze Sun, Rui Liu, Wuque Cai, Jun Wang, Yue Wang, Huajin Tang, Yan Cui, Dezhong Yao, Daqing Guo
Visual object tracking, which is primarily based on visible light image sequences, encounters numerous challenges in complicated scenarios, such as low light conditions, high dynamic ranges, and background clutter. To address these challenges, incorporating the advantages of multiple visual modalities is a promising solution for achieving reliable object tracking. However, the existing approaches usually integrate multimodal inputs through adaptive local feature interactions, which cannot leverage the full potential of visual cues, thus resulting in insufficient feature modeling. In this study, we propose a novel multimodal hybrid tracker (MMHT) that utilizes frame-event-based data for reliable single object tracking. The MMHT model employs a hybrid backbone consisting of an artificial neural network (ANN) and a spiking neural network (SNN) to extract dominant features from different visual modalities and then uses a unified encoder to align the features across different domains. Moreover, we propose an enhanced transformer-based module to fuse multimodal features using attention mechanisms. With these methods, the MMHT model can effectively construct a multiscale and multidimensional visual feature space and achieve discriminative feature modeling. Extensive experiments demonstrate that the MMHT model exhibits competitive performance in comparison with that of other state-of-the-art methods. Overall, our results highlight the effectiveness of the MMHT model in terms of addressing the challenges faced in visual object tracking tasks.
Temporal Spiking Neural Networks with Synaptic Delay for Graph Reasoning
2024-05-27
Mingqing Xiao, Yixin Zhu, Di He, Zhouchen Lin
Spiking neural networks (SNNs) are investigated as biologically inspired models of neural computation, distinguished by their computational capability and energy efficiency due to precise spiking times and sparse spikes with event-driven computation. A significant question is how SNNs can emulate human-like graph-based reasoning of concepts and relations, especially leveraging the temporal domain optimally. This paper reveals that SNNs, when amalgamated with synaptic delay and temporal coding, are proficient in executing (knowledge) graph reasoning. It is elucidated that spiking time can function as an additional dimension to encode relation properties via a neural-generalized path formulation. Empirical results highlight the efficacy of temporal delay in relation processing and showcase exemplary performance in diverse graph reasoning tasks. The spiking model is theoretically estimated to achieve $20\times$ energy savings compared to non-spiking counterparts, deepening insights into the capabilities and potential of biologically inspired SNNs for efficient reasoning. The code is available at https://github.com/pkuxmq/GRSNN.
The wide application of machine learning (ML) techniques in statistics physics has presented new avenues for research in this field. In this paper, we introduce a semi-supervised learning method based on Siamese Neural Networks (SNN), trying to explore the potential of neural network (NN) in the study of critical behaviors beyond the approaches of supervised and unsupervised learning. By focusing on the (1+1) dimensional bond directed percolation (DP) model of nonequilibrium phase transition, we use the SNN to predict the critical values and critical exponents of the system. Different from traditional ML methods, the input of SNN is a set of configuration data pairs and the output prediction is similarity, which prompts to find an anchor point of data for pair comparison during the test. In our study, during test we set different bond probability $p$ as anchors, and discuss the impact of the configurations at this anchors on predictions. More, we use an iterative method to find the optimal training interval to make the algorithm more efficient, and the prediction results are comparable to other ML methods.
High-Performance Temporal Reversible Spiking Neural Networks with $O(L)$ Training Memory and $O(1)$ Inference Cost
2024-05-26
JiaKui Hu, Man Yao, Xuerui Qiu, Yuhong Chou, Yuxuan Cai, Ning Qiao, Yonghong Tian, Bo XU, Guoqi Li
Multi-timestep simulation of brain-inspired Spiking Neural Networks (SNNs) boost memory requirements during training and increase inference energy cost. Current training methods cannot simultaneously solve both training and inference dilemmas. This work proposes a novel Temporal Reversible architecture for SNNs (T-RevSNN) to jointly address the training and inference challenges by altering the forward propagation of SNNs. We turn off the temporal dynamics of most spiking neurons and design multi-level temporal reversible interactions at temporal turn-on spiking neurons, resulting in a $O(L)$ training memory. Combined with the temporal reversible nature, we redesign the input encoding and network organization of SNNs to achieve $O(1)$ inference energy cost. Then, we finely adjust the internal units and residual connections of the basic SNN block to ensure the effectiveness of sparse temporal information interaction. T-RevSNN achieves excellent accuracy on ImageNet, while the memory efficiency, training time acceleration, and inference energy efficiency can be significantly improved by $8.6 \times$, $2.0 \times$, and $1.6 \times$, respectively. This work is expected to break the technical bottleneck of significantly increasing memory cost and training time for large-scale SNNs while maintaining high performance and low inference energy cost. Source code and models are available at: https://github.com/BICLab/T-RevSNN.
Spiking Neural Network Phase Encoding for Cognitive Computing
2024-05-25
Lei Zhang
This paper presents a novel approach for signal reconstruction using Spiking Neural Networks (SNN) based on the principles of Cognitive Informatics and Cognitive Computing. The proposed SNN leverages the Discrete Fourier Transform (DFT) to represent and reconstruct arbitrary time series signals. By employing N spiking neurons, the SNN captures the frequency components of the input signal, with each neuron assigned a unique frequency. The relationship between the magnitude and phase of the spiking neurons and the DFT coefficients is explored, enabling the reconstruction of the original signal. Additionally, the paper discusses the encoding of impulse delays and the phase differences between adjacent frequency components. This research contributes to the field of signal processing and provides insights into the application of SNN for cognitive signal analysis and reconstruction.
Achieving energy efficiency in learning is a key challenge for artificial intelligence (AI) computing platforms. Biological systems demonstrate remarkable abilities to learn complex skills quickly and efficiently. Inspired by this, we present a hardware implementation of model-based reinforcement learning (MBRL) using spiking neural networks (SNNs) on mixed-signal analog/digital neuromorphic hardware. This approach leverages the energy efficiency of mixed-signal neuromorphic chips while achieving high sample efficiency through an alternation of online learning, referred to as the "awake" phase, and offline learning, known as the "dreaming" phase. The model proposed includes two symbiotic networks: an agent network that learns by combining real and simulated experiences, and a learned world model network that generates the simulated experiences. We validate the model by training the hardware implementation to play the Atari game Pong. We start from a baseline consisting of an agent network learning without a world model and dreaming, which successfully learns to play the game. By incorporating dreaming, the number of required real game experiences are reduced significantly compared to the baseline. The networks are implemented using a mixed-signal neuromorphic processor, with the readout layers trained using a computer in-the-loop, while the other layers remain fixed. These results pave the way toward energy-efficient neuromorphic learning systems capable of rapid learning in real world applications and use-cases.
A generalized neural tangent kernel for surrogate gradient learning
2024-05-24
Luke Eilers, Raoul-Martin Memmesheimer, Sven Goedeke
State-of-the-art neural network training methods depend on the gradient of the network function. Therefore, they cannot be applied to networks whose activation functions do not have useful derivatives, such as binary and discrete-time spiking neural networks. To overcome this problem, the activation function's derivative is commonly substituted with a surrogate derivative, giving rise to surrogate gradient learning (SGL). This method works well in practice but lacks theoretical foundation. The neural tangent kernel (NTK) has proven successful in the analysis of gradient descent. Here, we provide a generalization of the NTK, which we call the surrogate gradient NTK, that enables the analysis of SGL. First, we study a naive extension of the NTK to activation functions with jumps, demonstrating that gradient descent for such activation functions is also ill-posed in the infinite-width limit. To address this problem, we generalize the NTK to gradient descent with surrogate derivatives, i.e., SGL. We carefully define this generalization and expand the existing key theorems on the NTK with mathematical rigor. Further, we illustrate our findings with numerical experiments. Finally, we numerically compare SGL in networks with sign activation function and finite width to kernel regression with the surrogate gradient NTK; the results confirm that the surrogate gradient NTK provides a good characterization of SGL.
Bundle Neural Networks for message diffusion on graphs
2024-05-24
Jacob Bamberger, Federico Barbero, Xiaowen Dong, Michael Bronstein
The dominant paradigm for learning on graph-structured data is message passing. Despite being a strong inductive bias, the local message passing mechanism suffers from pathological issues such as over-smoothing, over-squashing, and limited node-level expressivity. To address these limitations we propose Bundle Neural Networks (BuNN), a new type of GNN that operates via message diffusion over flat vector bundles - structures analogous to connections on Riemannian manifolds that augment the graph by assigning to each node a vector space and an orthogonal map. A BuNN layer evolves the features according to a diffusion-type partial differential equation. When discretized, BuNNs are a special case of Sheaf Neural Networks (SNNs), a recently proposed MPNN capable of mitigating over-smoothing. The continuous nature of message diffusion enables BuNNs to operate on larger scales of the graph and, therefore, to mitigate over-squashing. Finally, we prove that BuNN can approximate any feature transformation over nodes on any (potentially infinite) family of graphs given injective positional encodings, resulting in universal node-level expressivity. We support our theory via synthetic experiments and showcase the strong empirical performance of BuNNs over a range of real-world tasks, achieving state-of-the-art results on several standard benchmarks in transductive and inductive settings.
Domain Wall Magnetic Tunnel Junction Reliable Integrate and Fire Neuron
2024-05-23
Can Cui1, Sam Liu, Jaesuk Kwon, Jean Anne C. Incorvia
In spiking neural networks, neuron dynamics are described by the biologically realistic integrate-and-fire model that captures membrane potential accumulation and above-threshold firing behaviors. Among the hardware implementations of integrate-and-fire neuron devices, one important feature, reset, has been largely ignored. Here, we present the design and fabrication of a magnetic domain wall and magnetic tunnel junction based artificial integrate-and-fire neuron device that achieves reliable reset at the end of the integrate-fire cycle. We demonstrate the domain propagation in the domain wall racetrack (integration), reading using a magnetic tunnel junction (fire), and reset as the domain is ejected from the racetrack, showing the artificial neuron can be operated continuously over 100 integrate-fire-reset cycles. Both pulse amplitude and pulse number encoding is demonstrated. The device data is applied on an image classification task using a spiking neural network and shown to have comparable performance to an ideal leaky, integrate-and-fire neural network. These results achieve the first demonstration of reliable integrate-fire-reset in domain wall-magnetic tunnel junction-based neuron devices and shows the promise of spintronics for neuromorphic computing.
This paper presents a novel approach leveraging Spiking Neural Networks (SNNs) to construct a Variational Quantized Autoencoder (VQ-VAE) with a temporal codebook inspired by hippocampal time cells. This design captures and utilizes temporal dependencies, significantly enhancing the generative capabilities of SNNs. Neuroscientific research has identified hippocampal "time cells" that fire sequentially during temporally structured experiences. Our temporal codebook emulates this behavior by triggering the activation of time cell populations based on similarity measures as input stimuli pass through it. We conducted extensive experiments on standard benchmark datasets, including MNIST, FashionMNIST, CIFAR10, CelebA, and downsampled LSUN Bedroom, to validate our model's performance. Furthermore, we evaluated the effectiveness of the temporal codebook on neuromorphic datasets NMNIST and DVS-CIFAR10, and demonstrated the model's capability with high-resolution datasets such as CelebA-HQ, LSUN Bedroom, and LSUN Church. The experimental results indicate that our method consistently outperforms existing SNN-based generative models across multiple datasets, achieving state-of-the-art performance. Notably, our approach excels in generating high-resolution and temporally consistent data, underscoring the crucial role of temporal information in SNN-based generative modeling.
Surface electromyography (sEMG) based gesture recognition offers a natural and intuitive interaction modality for wearable devices. Despite significant advancements in sEMG-based gesture-recognition models, existing methods often suffer from high computational latency and increased energy consumption. Additionally, the inherent instability of sEMG signals, combined with their sensitivity to distribution shifts in real-world settings, compromises model robustness. To tackle these challenges, we propose a novel SpGesture framework based on Spiking Neural Networks, which possesses several unique merits compared with existing methods: (1) Robustness: By utilizing membrane potential as a memory list, we pioneer the introduction of Source-Free Domain Adaptation into SNN for the first time. This enables SpGesture to mitigate the accuracy degradation caused by distribution shifts. (2) High Accuracy: With a novel Spiking Jaccard Attention, SpGesture enhances the SNNs' ability to represent sEMG features, leading to a notable rise in system accuracy. To validate SpGesture's performance, we collected a new sEMG gesture dataset which has different forearm postures, where SpGesture achieved the highest accuracy among the baselines ($89.26\%$). Moreover, the actual deployment on the CPU demonstrated a system latency below 100ms, well within real-time requirements. This impressive performance showcases SpGesture's potential to enhance the applicability of sEMG in real-world scenarios. The code is available at https://anonymous.4open.science/r/SpGesture.
Spiking neural networks (SNNs) represent a promising approach to developing artificial neural networks that are both energy-efficient and biologically plausible. However, applying SNNs to sequential tasks, such as text classification and time-series forecasting, has been hindered by the challenge of creating an effective and hardware-friendly spike-form positional encoding (PE) strategy. Drawing inspiration from the central pattern generators (CPGs) in the human brain, which produce rhythmic patterned outputs without requiring rhythmic inputs, we propose a novel PE technique for SNNs, termed CPG-PE. We demonstrate that the commonly used sinusoidal PE is mathematically a specific solution to the membrane potential dynamics of a particular CPG. Moreover, extensive experiments across various domains, including time-series forecasting, natural language processing, and image classification, show that SNNs with CPG-PE outperform their conventional counterparts. Additionally, we perform analysis experiments to elucidate the mechanism through which SNNs encode positional information and to explore the function of CPGs in the human brain. This investigation may offer valuable insights into the fundamental principles of neural computation.
EchoSpike Predictive Plasticity: An Online Local Learning Rule for Spiking Neural Networks
2024-05-22
Lars Graf, Zhe Su, Giacomo Indiveri
The drive to develop artificial neural networks that efficiently utilize resources has generated significant interest in bio-inspired Spiking Neural Networks (SNNs). These networks are particularly attractive due to their potential in applications requiring low power and memory. This potential is further enhanced by the ability to perform online local learning, enabling them to adapt to dynamic environments. This requires the model to be adaptive in a self-supervised manner. While self-supervised learning has seen great success in many deep learning domains, its application for online local learning in multi-layer SNNs remains underexplored. In this paper, we introduce the "EchoSpike Predictive Plasticity" (ESPP) learning rule, a pioneering online local learning rule designed to leverage hierarchical temporal dynamics in SNNs through predictive and contrastive coding. We validate the effectiveness of this approach using benchmark datasets, demonstrating that it performs on par with current state-of-the-art supervised learning rules. The temporal and spatial locality of ESPP makes it particularly well-suited for low-cost neuromorphic processors, representing a significant advancement in developing biologically plausible self-supervised learning models for neuromorphic computing at the edge.
Resonate-and-Fire Spiking Neurons for Target Detection and Hand Gesture Recognition: A Hybrid Approach
2024-05-22
Ahmed Shaaban, Zeineb Chaabouni, Maximilian Strobel, Wolfgang Furtner, Robert Weigel, Fabian Lurz
Hand gesture recognition using radar often relies on computationally expensive fast Fourier transforms. This paper proposes an alternative approach that bypasses fast Fourier transforms using resonate-and-fire neurons. These neurons directly detect the hand in the time-domain signal, eliminating the need for fast Fourier transforms to retrieve range information. Following detection, a simple Goertzel algorithm is employed to extract five key features, eliminating the need for a second fast Fourier transform. These features are then fed into a recurrent neural network, achieving an accuracy of 98.21% for classifying five gestures. The proposed approach demonstrates competitive performance with reduced complexity compared to traditional methods
Advancing Spiking Neural Networks towards Multiscale Spatiotemporal Interaction Learning
2024-05-22
Yimeng Shan, Malu Zhang, Rui-jie Zhu, Xuerui Qiu, Jason K. Eshraghian, Haicheng Qu
Recent advancements in neuroscience research have propelled the development of Spiking Neural Networks (SNNs), which not only have the potential to further advance neuroscience research but also serve as an energy-efficient alternative to Artificial Neural Networks (ANNs) due to their spike-driven characteristics. However, previous studies often neglected the multiscale information and its spatiotemporal correlation between event data, leading SNN models to approximate each frame of input events as static images. We hypothesize that this oversimplification significantly contributes to the performance gap between SNNs and traditional ANNs. To address this issue, we have designed a Spiking Multiscale Attention (SMA) module that captures multiscale spatiotemporal interaction information. Furthermore, we developed a regularization method named Attention ZoneOut (AZO), which utilizes spatiotemporal attention weights to reduce the model's generalization error through pseudo-ensemble training. Our approach has achieved state-of-the-art results on mainstream neural morphology datasets. Additionally, we have reached a performance of 77.1% on the Imagenet-1K dataset using a 104-layer ResNet architecture enhanced with SMA and AZO. This achievement confirms the state-of-the-art performance of SNNs with non-transformer architectures and underscores the effectiveness of our method in bridging the performance gap between SNN models and traditional ANN models.
Exact Gradients for Stochastic Spiking Neural Networks Driven by Rough Signals
2024-05-22
Christian Holberg, Cristopher Salvi
We introduce a mathematically rigorous framework based on rough path theory to model stochastic spiking neural networks (SSNNs) as stochastic differential equations with event discontinuities (Event SDEs) and driven by c\`adl\`ag rough paths. Our formalism is general enough to allow for potential jumps to be present both in the solution trajectories as well as in the driving noise. We then identify a set of sufficient conditions ensuring the existence of pathwise gradients of solution trajectories and event times with respect to the network's parameters and show how these gradients satisfy a recursive relation. Furthermore, we introduce a general-purpose loss function defined by means of a new class of signature kernels indexed on c\`adl\`ag rough paths and use it to train SSNNs as generative models. We provide an end-to-end autodifferentiable solver for Event SDEs and make its implementation available as part of the $\texttt{diffrax}$ library. Our framework is, to our knowledge, the first enabling gradient-based training of SSNNs with noise affecting both the spike timing and the network's dynamics.
Adaptive Robotic Arm Control with a Spiking Recurrent Neural Network on a Digital Accelerator
2024-05-21
Alejandro Linares-Barranco, Luciano Prono, Robert Lengenstein, Giacomo Indiveri, Charlotte Frenkel
With the rise of artificial intelligence, neural network simulations of biological neuron models are being explored to reduce the footprint of learning and inference in resource-constrained task scenarios. A mainstream type of such networks are spiking neural networks (SNNs) based on simplified Integrate and Fire models for which several hardware accelerators have emerged. Among them, the ReckOn chip was introduced as a recurrent SNN allowing for both online training and execution of tasks based on arbitrary sensory modalities, demonstrated for vision, audition, and navigation. As a fully digital and open-source chip, we adapted ReckOn to be implemented on a Xilinx Multiprocessor System on Chip system (MPSoC), facilitating its deployment in embedded systems and increasing the setup flexibility. We present an overview of the system, and a Python framework to use it on a Pynq ZU platform. We validate the architecture and implementation in the new scenario of robotic arm control, and show how the simulated accuracy is preserved with a peak performance of 3.8M events processed per second.
Measurement dependence can enhance security in a quantum network
2024-05-20
Amit Kundu, Debasis Sarkar
Network Nonlocality is an advanced study of quantum nonlocality that comprises network structure beyond Bell's theorem. The development of quantum networks has the potential to bring a lot of technological applications in sevaral quantum information processing tasks. Here, we are focusing on how the role of the independence of the measurement choices of the end parties in a network works and can be used to enhance the security in a quantum network. In both three-parties two-sources bilocal network and four-parties three-sources star network scenarios, we are able to show, a practical way to understand the relaxation of the assumptions to enhance a real security protocol if someone wants to breach in a network communications. Theoratically, we have proved that by relaxing the independence of the measurement choices of only one end party we can create a Standard Network Nonlocality(SNN) and more stronger Full Network Nonlocality(FNN) and we can get maximum quantum violation by the classical no-signalling local model. We are able to distinguish between two types of network nonlocality in the sense that the FNN is stronger than SNN, i.e., FNN states all the sources in a network need to distribute nonlocal resources.
Given a memory with infinite capacity, can we solve the learning problem? Apparently, nature has solved this problem as evidenced by the evolution of mammalian brains. Inspired by the organizational principles underlying hippocampal-neocortical systems, we present a navigation-based approach to manifold learning using memory and context. The key insight is to navigate on the manifold and memorize the positions of each route as inductive/design bias of direct-fit-to-nature. We name it navigation-based because our approach can be interpreted as navigating in the latent space of sensorimotor learning via memory (local maps) and context (global indexing). The indexing to the library of local maps within global coordinates is collected by an associative memory serving as the librarian, which mimics the coupling between the hippocampus and the neocortex. In addition to breaking from the notorious bias-variance dilemma and the curse of dimensionality, we discuss the biological implementation of our navigation-based learning by episodic and semantic memories in neural systems. The energy efficiency of navigation-based learning makes it suitable for hardware implementation on non-von Neumann architectures, such as the emerging in-memory computing paradigm, including spiking neural networks and memristor neural networks.
NeuroAssist: Enhancing Cognitive-Computer Synergy with Adaptive AI and Advanced Neural Decoding for Efficient EEG Signal Classification
2024-05-17
Eeshan G. Dandamudi
Traditional methods of controlling prosthetics frequently encounter difficulties regarding flexibility and responsiveness, which can substantially impact people with varying cognitive and physical abilities. Advancements in computational neuroscience and machine learning (ML) have recently led to the development of highly advanced brain-computer interface (BCI) systems that may be customized to meet individual requirements. To address these issues, we propose NeuroAssist, a sophisticated method for analyzing EEG data that merges state-of-the-art BCI technology with adaptable artificial intelligence (AI) algorithms. NeuroAssist's hybrid neural network design efficiently overcomes the constraints of conventional EEG data processing. Our methodology combines a Natural Language Processing (NLP) BERT model to extract complex features from numerical EEG data and utilizes LSTM networks to handle temporal dynamics. In addition, we integrate spiking neural networks (SNNs) and deep Q-networks (DQN) to improve decision-making and flexibility. Our preprocessing method classifies motor imagery (MI) one-versus-the-rest using a common spatial pattern (CSP) while preserving EEG temporal characteristics. The hybrid architecture of NeuroAssist serves as the DQN's Q-network, enabling continuous feedback-based improvement and adaptability. This enables it to acquire optimal actions through trial and error. This experimental analysis has been conducted on the GigaScience and BCI-competition-IV-2a datasets, which have shown exceptional effectiveness in categorizing MI-EEG signals, obtaining an impressive classification accuracy of 99.17%. NeuroAssist offers a crucial approach to current assistive technology by potentially enhancing the speed and versatility of BCI systems.
We prove oracle inequalities for a penalized log-likelihood criterion that hold even if the data are not independent and not stationary, based on a martingale approach. The assumptions are checked for various contexts: density estimation with independent and identically distributed (i.i.d) data, hidden Markov models, spiking neural networks, adversarial bandits. In each case, we compare our results to the literature, showing that, although we lose some logarithmic factors in the most classical case (i.i.d.), these results are comparable or more general than the existing results in the more dependent cases.
Neuromorphic Robust Estimation of Nonlinear Dynamical Systems Applied to Satellite Rendezvous
2024-05-14
Reza Ahmadvand, Sarah Safura Sharif, Yaser Mike Banad
State estimation of nonlinear dynamical systems has long aimed to balance accuracy, computational efficiency, robustness, and reliability. The rapid evolution of various industries has amplified the demand for estimation frameworks that satisfy all these factors. This study introduces a neuromorphic approach for robust filtering of nonlinear dynamical systems: SNN-EMSIF (spiking neural network-extended modified sliding innovation filter). SNN-EMSIF combines the computational efficiency and scalability of SNNs with the robustness of EMSIF, an estimation framework designed for nonlinear systems with zero-mean Gaussian noise. Notably, the weight matrices are designed according to the system model, eliminating the need for a learning process. The framework's efficacy is evaluated through comprehensive Monte Carlo simulations, comparing SNN-EMSIF with EKF and EMSIF. Additionally, it is compared with SNN-EKF in the presence of modeling uncertainties and neuron loss, using RMSEs as a metric. The results demonstrate the superior accuracy and robustness of SNN-EMSIF. Further analysis of runtimes and spiking patterns reveals an impressive reduction of 85% in emitted spikes compared to possible spikes, highlighting the computational efficiency of SNN-EMSIF. This framework offers a promising solution for robust estimation in nonlinear dynamical systems, opening new avenues for efficient and reliable estimation in various industries that can benefit from neuromorphic computing.
Learning A Spiking Neural Network for Efficient Image Deraining
2024-05-10
Tianyu Song, Guiyue Jin, Pengpeng Li, Kui Jiang, Xiang Chen, Jiyu Jin
Recently, spiking neural networks (SNNs) have demonstrated substantial potential in computer vision tasks. In this paper, we present an Efficient Spiking Deraining Network, called ESDNet. Our work is motivated by the observation that rain pixel values will lead to a more pronounced intensity of spike signals in SNNs. However, directly applying deep SNNs to image deraining task still remains a significant challenge. This is attributed to the information loss and training difficulties that arise from discrete binary activation and complex spatio-temporal dynamics. To this end, we develop a spiking residual block to convert the input into spike signals, then adaptively optimize the membrane potential by introducing attention weights to adjust spike responses in a data-driven manner, alleviating information loss caused by discrete binary activation. By this way, our ESDNet can effectively detect and analyze the characteristics of rain streaks by learning their fluctuations. This also enables better guidance for the deraining process and facilitates high-quality image reconstruction. Instead of relying on the ANN-SNN conversion strategy, we introduce a gradient proxy strategy to directly train the model for overcoming the challenge of training. Experimental results show that our approach gains comparable performance against ANN-based methods while reducing energy consumption by 54%. The code source is available at https://github.com/MingTian99/ESDNet.
Compressed Latent Replays for Lightweight Continual Learning on Spiking Neural Networks
2024-05-08
Alberto Dequino, Alessio Carpegna, Davide Nadalini, Alessandro Savino, Luca Benini, Stefano Di Carlo, Francesco Conti
Rehearsal-based Continual Learning (CL) has been intensely investigated in Deep Neural Networks (DNNs). However, its application in Spiking Neural Networks (SNNs) has not been explored in depth. In this paper we introduce the first memory-efficient implementation of Latent Replay (LR)-based CL for SNNs, designed to seamlessly integrate with resource-constrained devices. LRs combine new samples with latent representations of previously learned data, to mitigate forgetting. Experiments on the Heidelberg SHD dataset with Sample and Class-Incremental tasks reach a Top-1 accuracy of 92.5% and 92%, respectively, without forgetting the previously learned information. Furthermore, we minimize the LRs' requirements by applying a time-domain compression, reducing by two orders of magnitude their memory requirement, with respect to a naive rehearsal setup, with a maximum accuracy drop of 4%. On a Multi-Class-Incremental task, our SNN learns 10 new classes from an initial set of 10, reaching a Top-1 accuracy of 78.4% on the full test set.
As spiking neural networks (SNNs) gain traction in deploying neuromorphic computing solutions, protecting their intellectual property (IP) has become crucial. Without adequate safeguards, proprietary SNN architectures are at risk of theft, replication, or misuse, which could lead to significant financial losses for the owners. While IP protection techniques have been extensively explored for artificial neural networks (ANNs), their applicability and effectiveness for the unique characteristics of SNNs remain largely unexplored. In this work, we pioneer an investigation into adapting two prominent watermarking approaches, namely, fingerprint-based and backdoor-based mechanisms to secure proprietary SNN architectures. We conduct thorough experiments to evaluate the impact on fidelity, resilience against overwrite threats, and resistance to compression attacks when applying these watermarking techniques to SNNs, drawing comparisons with their ANN counterparts. This study lays the groundwork for developing neuromorphic-aware IP protection strategies tailored to the distinctive dynamics of SNNs.
SparrowSNN: A Hardware/software Co-design for Energy Efficient ECG Classification
2024-05-06
Zhanglu Yan, Zhenyu Bai, Tulika Mitra, Weng-Fai Wong
Heart disease is one of the leading causes of death worldwide. Given its high risk and often asymptomatic nature, real-time continuous monitoring is essential. Unlike traditional artificial neural networks (ANNs), spiking neural networks (SNNs) are well-known for their energy efficiency, making them ideal for wearable devices and energy-constrained edge computing platforms. However, current energy measurement of SNN implementations for detecting heart diseases typically rely on empirical values, often overlooking hardware overhead. Additionally, the integer and fire activations in SNNs require multiple memory accesses and repeated computations, which can further compromise energy efficiency. In this paper, we propose sparrowSNN, a redesign of the standard SNN workflow from a hardware perspective, and present a dedicated ASIC design for SNNs, optimized for ultra-low power wearable devices used in heartbeat classification. Using the MIT-BIH dataset, our SNN achieves a state-of-the-art accuracy of 98.29% for SNNs, with energy consumption of 31.39nJ per inference and power usage of 6.1uW, making sparrowSNN the highest accuracy with the lowest energy use among comparable systems. We also compare the energy-to-accuracy trade-offs between SNNs and quantized ANNs, offering recommendations on insights on how best to use SNNs.
Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal dynamics, and event-driven computation. The direct training algorithms based on the surrogate gradient method provide sufficient flexibility to design novel SNN architectures and explore the spatial-temporal dynamics of SNNs. According to previous studies, the performance of models is highly dependent on their sizes. Recently, direct training deep SNNs have achieved great progress on both neuromorphic datasets and large-scale static datasets. Notably, transformer-based SNNs show comparable performance with their ANN counterparts. In this paper, we provide a new perspective to summarize the theories and methods for training deep SNNs with high performance in a systematic and comprehensive way, including theory fundamentals, spiking neuron models, advanced SNN models and residual architectures, software frameworks and neuromorphic hardware, applications, and future trends. The reviewed papers are collected at https://github.com/zhouchenlin2096/Awesome-Spiking-Neural-Networks
K+K- photoproduction in ultra$-$peripheral Pb$--$Pb collisions
2024-05-05
Minjung Kim
In ultra-peripheral collisions (UPCs) of relativistic heavy ions, photoproduction occurs when a photon emitted from one nucleus interacts with the other nucleus from the opposing beam, producing particles in the final state. Measurements of \KK photoproduction probe interactions and couplings between the $\phi (1020)$ and charged kaons with photons and nuclear targets. We report exclusive \KK photoproduction cross section at midrapidity in \PbPb collisions at \snn = 5.02 TeV, which is measured for the first time in UPCs.
Spiking Neural Networks (SNNs), recognized for their biological plausibility and energy efficiency, employ sparse and asynchronous spikes for communication. However, the training of SNNs encounters difficulties coming from non-differentiable activation functions and the movement of spike-based inter-layer data. Spike-Timing Dependent Plasticity (STDP), inspired by neurobiology, plays a crucial role in SNN's learning, but its still lacks the conscious part of the brain used for learning. Considering the issue, this research work proposes a Consciousness Driven STDP (CD-STDP), an improved solution addressing inherent limitations observed in conventional STDP models. CD-STDP, designed to infuse the conscious part as coefficients of long-term potentiation (LTP) and long-term depression (LTD), exhibit a dynamic nature. The model connects LTP and LTD coefficients to current and past state of synaptic activities, respectively, enhancing consciousness and adaptability. This consciousness empowers the model to effectively learn while understanding the input patterns. The conscious coefficient adjustment in response to current and past synaptic activity extends the model's conscious and other cognitive capabilities, offering a refined and efficient approach for real-world applications. Evaluations on MNIST, FashionMNIST and CALTECH datasets showcase $CD$-STDP's remarkable accuracy of 98.6%, 85.61% and 99.0%, respectively, in a single hidden layer SNN. In addition, analysis of conscious elements and consciousness of the proposed model on SNN is performed.
Equilibrium Propagation (EP) is a biologically plausible local learning algorithm initially developed for convergent recurrent neural networks (RNNs), where weight updates rely solely on the connecting neuron states across two phases. The gradient calculations in EP have been shown to approximate the gradients computed by Backpropagation Through Time (BPTT) when an infinitesimally small nudge factor is used. This property makes EP a powerful candidate for training Spiking Neural Networks (SNNs), which are commonly trained by BPTT. However, in the spiking domain, previous studies on EP have been limited to architectures involving few linear layers. In this work, for the first time we provide a formulation for training convolutional spiking convergent RNNs using EP, bridging the gap between spiking and non-spiking convergent RNNs. We demonstrate that for spiking convergent RNNs, there is a mismatch in the maximum pooling and its inverse operation, leading to inaccurate gradient estimation in EP. Substituting this with average pooling resolves this issue and enables accurate gradient estimation for spiking convergent RNNs. We also highlight the memory efficiency of EP compared to BPTT. In the regime of SNNs trained by EP, our experimental results indicate state-of-the-art performance on the MNIST and FashionMNIST datasets, with test errors of 0.97% and 8.89%, respectively. These results are comparable to those of convergent RNNs and SNNs trained by BPTT. These findings underscore EP as an optimal choice for on-chip training and a biologically-plausible method for computing error gradients.
A Spiking Neural Network Decoder for Implantable Brain Machine Interfaces and its Sparsity-aware Deployment on RISC-V Microcontrollers
2024-05-03
Jiawei Liao, Oscar Toomey, Xiaying Wang, Lars Widmer, Cynthia A. Chestek, Luca Benini, Taekwang Jang
Implantable Brain-machine interfaces (BMIs) are promising for motor rehabilitation and mobility augmentation, and they demand accurate and energy-efficient algorithms. In this paper, we propose a novel spiking neural network (SNN) decoder for regression tasks for implantable BMIs. The SNN is trained with enhanced spatio-temporal backpropagation to fully leverage its capability to handle temporal problems. The proposed SNN decoder outperforms the state-of-the-art Kalman filter and artificial neural network (ANN) decoders in offline finger velocity decoding tasks. The decoder is deployed on a RISC-V-based hardware platform and optimized to exploit sparsity. The proposed implementation has an average power consumption of 0.50 mW in a duty-cycled mode. When conducting continuous inference without duty-cycling, it achieves an energy efficiency of 1.88 uJ per inference, which is 5.5X less than the baseline ANN. Additionally, the average decoding latency is 0.12 ms for each inference, which is 5.7X faster than the ANN implementation.
Fast Algorithms for Spiking Neural Network Simulation with FPGAs
2024-05-03
Björn A. Lindqvist, Artur Podobas
Using OpenCL-based high-level synthesis, we create a number of spiking neural network (SNN) simulators for the Potjans-Diesmann cortical microcircuit for a high-end Field-Programmable Gate Array (FPGA). Our best simulators simulate the circuit 25\% faster than real-time, require less than 21 nJ per synaptic event, and are bottle-necked by the device's on-chip memory. Speed-wise they compare favorably to the state-of-the-art GPU-based simulators and their energy usage is lower than any other published result. This result is the first for simulating the circuit on a single hardware accelerator. We also extensively analyze the techniques and algorithms we implement our simulators with, many of which can be realized on other types of hardware. Thus, this article is of interest to any researcher or practitioner interested in efficient SNN simulation, whether they target FPGAs or not.
Natural Language to Verilog: Design of a Recurrent Spiking Neural Network using Large Language Models and ChatGPT
2024-05-02
Paola Vitolo, George Psaltakis, Michael Tomlinson, Gian Domenico Licciardo, Andreas G. Andreou
This paper investigates the use of Large Language Models (LLMs) for automating the generation of hardware description code, aiming to explore their potential in supporting and enhancing the development of efficient neuromorphic computing architectures. Building on our prior work, we employ OpenAI's ChatGPT4 and natural language prompts to synthesize a RTL Verilog module of a programmable recurrent spiking neural network, while also generating test benches to assess the system's correctness. The resultant design was validated in three case studies, the exclusive OR,the IRIS flower classification and the MNIST hand-written digit classification, achieving accuracies of up to 96.6%. To verify its synthesizability and implementability, the design was prototyped on a field-programmable gate array and implemented on SkyWater 130 nm technology by using an open-source electronic design automation flow. Additionally, we have submitted it to Tiny Tapeout 6 chip fabrication program to further evaluate the system on-chip performance in the future.
Distributed Representations Enable Robust Multi-Timescale Symbolic Computation in Neuromorphic Hardware
2024-05-02
Madison Cotteret, Hugh Greatorex, Alpha Renner, Junren Chen, Emre Neftci, Huaqiang Wu, Giacomo Indiveri, Martin Ziegler, Elisabetta Chicca
Programming recurrent spiking neural networks (RSNNs) to robustly perform multi-timescale computation remains a difficult challenge. To address this, we describe a single-shot weight learning scheme to embed robust multi-timescale dynamics into attractor-based RSNNs, by exploiting the properties of high-dimensional distributed representations. We embed finite state machines into the RSNN dynamics by superimposing a symmetric autoassociative weight matrix and asymmetric transition terms, which are each formed by the vector binding of an input and heteroassociative outer-products between states. Our approach is validated through simulations with highly non-ideal weights; an experimental closed-loop memristive hardware setup; and on Loihi 2, where it scales seamlessly to large state machines. This work introduces a scalable approach to embed robust symbolic computation through recurrent dynamics into neuromorphic hardware, without requiring parameter fine-tuning or significant platform-specific optimisation. Moreover, it demonstrates that distributed symbolic representations serve as a highly capable representation-invariant language for cognitive algorithms in neuromorphic hardware.
Koopman-based Deep Learning for Nonlinear System Estimation
2024-05-01
Zexin Sun, Mingyu Chen, John Baillieul
Nonlinear differential equations are encountered as models of fluid flow, spiking neurons, and many other systems of interest in the real world. Common features of these systems are that their behaviors are difficult to describe exactly and invariably unmodeled dynamics present challenges in making precise predictions. In many cases the models exhibit extremely complicated behavior due to bifurcations and chaotic regimes. In this paper, we present a novel data-driven linear estimator that uses Koopman operator theory to extract finite-dimensional representations of complex nonlinear systems. The extracted model is used together with a deep reinforcement learning network that learns the optimal stepwise actions to predict future states of the original nonlinear system. Our estimator is also adaptive to a diffeomorphic transformation of the nonlinear system which enables transfer learning to compute state estimates of the transformed system without relearning from scratch.
Weight Sparsity Complements Activity Sparsity in Neuromorphic Language Models
2024-05-01
Rishav Mukherji, Mark Schöne, Khaleelulla Khan Nazeer, Christian Mayr, David Kappel, Anand Subramoney
Activity and parameter sparsity are two standard methods of making neural networks computationally more efficient. Event-based architectures such as spiking neural networks (SNNs) naturally exhibit activity sparsity, and many methods exist to sparsify their connectivity by pruning weights. While the effect of weight pruning on feed-forward SNNs has been previously studied for computer vision tasks, the effects of pruning for complex sequence tasks like language modeling are less well studied since SNNs have traditionally struggled to achieve meaningful performance on these tasks. Using a recently published SNN-like architecture that works well on small-scale language modeling, we study the effects of weight pruning when combined with activity sparsity. Specifically, we study the trade-off between the multiplicative efficiency gains the combination affords and its effect on task performance for language modeling. To dissect the effects of the two sparsities, we conduct a comparative analysis between densely activated models and sparsely activated event-based models across varying degrees of connectivity sparsity. We demonstrate that sparse activity and sparse connectivity complement each other without a proportional drop in task performance for an event-based neural network trained on the Penn Treebank and WikiText-2 language modeling datasets. Our results suggest sparsely connected event-based neural networks are promising candidates for effective and efficient sequence modeling.
Active Dendrites Enable Efficient Continual Learning in Time-To-First-Spike Neural Networks
2024-04-30
Lorenzo Pes, Rick Luiken, Federico Corradi, Charlotte Frenkel
While the human brain efficiently adapts to new tasks from a continuous stream of information, neural network models struggle to learn from sequential information without catastrophically forgetting previously learned tasks. This limitation presents a significant hurdle in deploying edge devices in real-world scenarios where information is presented in an inherently sequential manner. Active dendrites of pyramidal neurons play an important role in the brain ability to learn new tasks incrementally. By exploiting key properties of time-to-first-spike encoding and leveraging its high sparsity, we present a novel spiking neural network model enhanced with active dendrites. Our model can efficiently mitigate catastrophic forgetting in temporally-encoded SNNs, which we demonstrate with an end-of-training accuracy across tasks of 88.3% on the test set using the Split MNIST dataset. Furthermore, we provide a novel digital hardware architecture that paves the way for real-world deployment in edge devices. Using a Xilinx Zynq-7020 SoC FPGA, we demonstrate a 100-% match with our quantized software model, achieving an average inference time of 37.3 ms and an 80.0% accuracy.
DelGrad: Exact gradients in spiking networks for learning transmission delays and weights
2024-04-30
Julian Göltz, Jimmy Weber, Laura Kriener, Peter Lake, Melika Payvand, Mihai A. Petrovici
Spiking neural networks (SNNs) inherently rely on the timing of signals for representing and processing information. Transmission delays play an important role in shaping these temporal characteristics. Recent work has demonstrated the substantial advantages of learning these delays along with synaptic weights, both in terms of accuracy and memory efficiency. However, these approaches suffer from drawbacks in terms of precision and efficiency, as they operate in discrete time and with approximate gradients, while also requiring membrane potential recordings for calculating parameter updates. To alleviate these issues, we propose an analytical approach for calculating exact loss gradients with respect to both synaptic weights and delays in an event-based fashion. The inclusion of delays emerges naturally within our proposed formalism, enriching the model's search space with a temporal dimension. Our algorithm is purely based on the timing of individual spikes and does not require access to other variables such as membrane potentials. We explicitly compare the impact on accuracy and parameter efficiency of different types of delays - axonal, dendritic and synaptic. Furthermore, while previous work on learnable delays in SNNs has been mostly confined to software simulations, we demonstrate the functionality and benefits of our approach on the BrainScaleS-2 neuromorphic platform.
Quantized Context Based LIF Neurons for Recurrent Spiking Neural Networks in 45nm
2024-04-28
Sai Sukruth Bezugam, Yihao Wu, JaeBum Yoo, Dmitri Strukov, Bongjin Kim
In this study, we propose the first hardware implementation of a context-based recurrent spiking neural network (RSNN) emphasizing on integrating dual information streams within the neocortical pyramidal neurons specifically Context- Dependent Leaky Integrate and Fire (CLIF) neuron models, essential element in RSNN. We present a quantized version of the CLIF neuron (qCLIF), developed through a hardware-software codesign approach utilizing the sparse activity of RSNN. Implemented in a 45nm technology node, the qCLIF is compact (900um^2) and achieves a high accuracy of 90% despite 8 bit quantization on DVS gesture classification dataset. Our analysis spans a network configuration from 10 to 200 qCLIF neurons, supporting up to 82k synapses within a 1.86 mm^2 footprint, demonstrating scalability and efficiency
Stochastic Spiking Neural Networks with First-to-Spike Coding
2024-04-26
Yi Jiang, Sen Lu, Abhronil Sengupta
Spiking Neural Networks (SNNs), recognized as the third generation of neural networks, are known for their bio-plausibility and energy efficiency, especially when implemented on neuromorphic hardware. However, the majority of existing studies on SNNs have concentrated on deterministic neurons with rate coding, a method that incurs substantial computational overhead due to lengthy information integration times and fails to fully harness the brain's probabilistic inference capabilities and temporal dynamics. In this work, we explore the merger of novel computing and information encoding schemes in SNN architectures where we integrate stochastic spiking neuron models with temporal coding techniques. Through extensive benchmarking with other deterministic SNNs and rate-based coding, we investigate the tradeoffs of our proposal in terms of accuracy, inference latency, spiking sparsity, energy consumption, and robustness. Our work is the first to extend the scalability of direct training approaches of stochastic SNNs with temporal encoding to VGG architectures and beyond-MNIST datasets.
Converting High-Performance and Low-Latency SNNs through Explicit Modelling of Residual Error in ANNs
2024-04-26
Zhipeng Huang, Jianhao Ding, Zhiyu Pan, Haoran Li, Ying Fang, Zhaofei Yu, Jian K. Liu
Spiking neural networks (SNNs) have garnered interest due to their energy efficiency and superior effectiveness on neuromorphic chips compared with traditional artificial neural networks (ANNs). One of the mainstream approaches to implementing deep SNNs is the ANN-SNN conversion, which integrates the efficient training strategy of ANNs with the energy-saving potential and fast inference capability of SNNs. However, under extreme low-latency conditions, the existing conversion theory suggests that the problem of misrepresentation of residual membrane potentials in SNNs, i.e., the inability of IF neurons with a reset-by-subtraction mechanism to respond to residual membrane potentials beyond the range from resting potential to threshold, leads to a performance gap in the converted SNNs compared to the original ANNs. This severely limits the possibility of practical application of SNNs on delay-sensitive edge devices. Existing conversion methods addressing this problem usually involve modifying the state of the conversion spiking neurons. However, these methods do not consider their adaptability and compatibility with neuromorphic chips. We propose a new approach based on explicit modeling of residual errors as additive noise. The noise is incorporated into the activation function of the source ANN, which effectively reduces the residual error. Our experiments on the CIFAR10/100 dataset verify that our approach exceeds the prevailing ANN-SNN conversion methods and directly trained SNNs concerning accuracy and the required time steps. Overall, our method provides new ideas for improving SNN performance under ultra-low-latency conditions and is expected to promote practical neuromorphic hardware applications for further development.
A Novel Spike Transformer Network for Depth Estimation from Event Cameras via Cross-modality Knowledge Distillation
2024-04-26
Xin Zhang, Liangxiu Han, Tam Sobeih, Lianghao Han, Darren Dancey
Depth estimation is crucial for interpreting complex environments, especially in areas such as autonomous vehicle navigation and robotics. Nonetheless, obtaining accurate depth readings from event camera data remains a formidable challenge. Event cameras operate differently from traditional digital cameras, continuously capturing data and generating asynchronous binary spikes that encode time, location, and light intensity. Yet, the unique sampling mechanisms of event cameras render standard image based algorithms inadequate for processing spike data. This necessitates the development of innovative, spike-aware algorithms tailored for event cameras, a task compounded by the irregularity, continuity, noise, and spatial and temporal characteristics inherent in spiking data.Harnessing the strong generalization capabilities of transformer neural networks for spatiotemporal data, we propose a purely spike-driven spike transformer network for depth estimation from spiking camera data. To address performance limitations with Spiking Neural Networks (SNN), we introduce a novel single-stage cross-modality knowledge transfer framework leveraging knowledge from a large vision foundational model of artificial neural networks (ANN) (DINOv2) to enhance the performance of SNNs with limited data. Our experimental results on both synthetic and real datasets show substantial improvements over existing models, with notable gains in Absolute Relative and Square Relative errors (49% and 39.77% improvements over the benchmark model Spike-T, respectively). Besides accuracy, the proposed model also demonstrates reduced power consumptions, a critical factor for practical applications.
Synchronized Stepwise Control of Firing and Learning Thresholds in a Spiking Randomly Connected Neural Network toward Hardware Implementation
2024-04-26
Kumiko Nomura, Yoshifumi Nishi
We propose hardware-oriented models of intrinsic plasticity (IP) and synaptic plasticity (SP) for spiking randomly connected recursive neural network (RNN). Although the potential of RNNs for temporal data processing has been demonstrated, randomness of the network architecture often causes performance degradation. Self-organization mechanism using IP and SP can mitigate the degradation, therefore, we compile these functions in a spiking neuronal model. To implement the function of IP, a variable firing threshold is introduced to each excitatory neuron in the RNN that changes stepwise in accordance with its activity. We also define other thresholds for SP that synchronize with the firing threshold, which determine the direction of stepwise synaptic update that is executed on receiving a pre-synaptic spike. We demonstrate the effectiveness of our model through simulations of temporal data learning and anomaly detection with a spiking RNN using publicly available electrocardiograms. Considering hardware implementation, we employ discretized thresholds and synaptic weights and show that these parameters can be reduced to binary if the RNN architecture is appropriately designed. This contributes to minimization of the circuit of the neuronal system having IP and SP.
Defending Spiking Neural Networks against Adversarial Attacks through Image Purification
2024-04-26
Weiran Chen, Qi Sun, Qi Xu
Spiking Neural Networks (SNNs) aim to bridge the gap between neuroscience and machine learning by emulating the structure of the human nervous system. However, like convolutional neural networks, SNNs are vulnerable to adversarial attacks. To tackle the challenge, we propose a biologically inspired methodology to enhance the robustness of SNNs, drawing insights from the visual masking effect and filtering theory. First, an end-to-end SNN-based image purification model is proposed to defend against adversarial attacks, including a noise extraction network and a non-blind denoising network. The former network extracts noise features from noisy images, while the latter component employs a residual U-Net structure to reconstruct high-quality noisy images and generate clean images. Simultaneously, a multi-level firing SNN based on Squeeze-and-Excitation Network is introduced to improve the robustness of the classifier. Crucially, the proposed image purification network serves as a pre-processing module, avoiding modifications to classifiers. Unlike adversarial training, our method is highly flexible and can be seamlessly integrated with other defense strategies. Experimental results on various datasets demonstrate that the proposed methodology outperforms state-of-the-art baselines in terms of defense effectiveness, training time, and resource consumption.
Transductive Spiking Graph Neural Networks for Loihi
2024-04-25
Shay Snyder, Victoria Clerico, Guojing Cong, Shruti Kulkarni, Catherine Schuman, Sumedh R. Risbud, Maryam Parsa
Graph neural networks have emerged as a specialized branch of deep learning, designed to address problems where pairwise relations between objects are crucial. Recent advancements utilize graph convolutional neural networks to extract features within graph structures. Despite promising results, these methods face challenges in real-world applications due to sparse features, resulting in inefficient resource utilization. Recent studies draw inspiration from the mammalian brain and employ spiking neural networks to model and learn graph structures. However, these approaches are limited to traditional Von Neumann-based computing systems, which still face hardware inefficiencies. In this study, we present a fully neuromorphic implementation of spiking graph neural networks designed for Loihi 2. We optimize network parameters using Lava Bayesian Optimization, a novel hyperparameter optimization system compatible with neuromorphic computing architectures. We showcase the performance benefits of combining neuromorphic Bayesian optimization with our approach for citation graph classification using fixed-precision spiking neurons. Our results demonstrate the capability of integer-precision, Loihi 2 compatible spiking neural networks in performing citation graph classification with comparable accuracy to existing floating point implementations.
Lu.i -- A low-cost electronic neuron for education and outreach
2024-04-25
Yannik Stradmann, Julian Göltz, Mihai A. Petrovici, Johannes Schemmel, Sebastian Billaudelle
With an increasing presence of science throughout all parts of society, there is a rising expectation for researchers to effectively communicate their work and, equally, for teachers to discuss contemporary findings in their classrooms. While the community can resort to an established set of teaching aids for the fundamental concepts of most natural sciences, there is a need for similarly illustrative experiments and demonstrators in neuroscience. We therefore introduce Lu.i: a parametrizable electronic implementation of the leaky-integrate-and-fire neuron model in an engaging form factor. These palm-sized neurons can be used to visualize and experience the dynamics of individual cells and small spiking neural networks. When stimulated with real or simulated sensory input, Lu.i demonstrates brain-inspired information processing in the hands of a student. As such, it is actively used at workshops, in classrooms, and for science communication. As a versatile tool for teaching and outreach, Lu.i nurtures the comprehension of neuroscience research and neuromorphic engineering among future generations of scientists and in the general public.
Hierarchically modular organization is a canonical network topology that is evolutionarily conserved in the nervous systems of animals. Within the network, neurons form directional connections defined by the growth of their axonal terminals. However, this topology is dissimilar to the network formed by dissociated neurons in culture because they form randomly connected networks on homogeneous substrates. In this study, we fabricated microfluidic devices to reconstitute hierarchically modular neuronal networks in culture (in vitro) and investigated how non-random structures, such as directional connectivity between modules, affect global network dynamics. Embedding directional connections in a pseudo-feedforward manner suppressed excessive synchrony in cultured neuronal networks and enhanced the integration-segregation balance. Modeling the behavior of biological neuronal networks using spiking neural networks (SNNs) further revealed that modularity and directionality cooperate to shape such network dynamics. Finally, we demonstrate that for a given network topology, the statistics of network dynamics, such as global network activation, correlation coefficient, and functional complexity, can be analytically predicted based on eigendecomposition of the transition matrix in the state-transition model. Hence, the integration of bioengineering and cell culture technologies enables us not only to reconstitute complex network circuitry in the nervous system but also to understand the structure-function relationships in biological neuronal networks by bridging theoretical modeling with in vitro experiments.
GPU-RANC: A CUDA Accelerated Simulation Framework for Neuromorphic Architectures
2024-04-24
Sahil Hassan, Michael Inouye, Miguel C. Gonzalez, Ilkin Aliyev, Joshua Mack, Maisha Hafiz, Ali Akoglu
Open-source simulation tools play a crucial role for neuromorphic application engineers and hardware architects to investigate performance bottlenecks and explore design optimizations before committing to silicon. Reconfigurable Architecture for Neuromorphic Computing (RANC) is one such tool that offers ability to execute pre-trained Spiking Neural Network (SNN) models within a unified ecosystem through both software-based simulation and FPGA-based emulation. RANC has been utilized by the community with its flexible and highly parameterized design to study implementation bottlenecks, tune architectural parameters or modify neuron behavior based on application insights and study the trade space on hardware performance and network accuracy. In designing architectures for use in neuromorphic computing, there are an incredibly large number of configuration parameters such as number and precision of weights per neuron, neuron and axon counts per core, network topology, and neuron behavior. To accelerate such studies and provide users with a streamlined productive design space exploration, in this paper we introduce the GPU-based implementation of RANC. We summarize our parallelization approach and quantify the speedup gains achieved with GPU-based tick-accurate simulations across various use cases. We demonstrate up to 780 times speedup compared to serial version of the RANC simulator based on a 512 neuromorphic core MNIST inference application. We believe that the RANC ecosystem now provides a much more feasible avenue in the research of exploring different optimizations for accelerating SNNs and performing richer studies by enabling rapid convergence to optimized neuromorphic architectures.
Biologically-Informed Excitatory and Inhibitory Balance for Robust Spiking Neural Network Training
2024-04-24
Joseph A. Kilgore, Jeffrey D. Kopsick, Giorgio A. Ascoli, Gina C. Adam
Spiking neural networks drawing inspiration from biological constraints of the brain promise an energy-efficient paradigm for artificial intelligence. However, challenges exist in identifying guiding principles to train these networks in a robust fashion. In addition, training becomes an even more difficult problem when incorporating biological constraints of excitatory and inhibitory connections. In this work, we identify several key factors, such as low initial firing rates and diverse inhibitory spiking patterns, that determine the overall ability to train spiking networks with various ratios of excitatory to inhibitory neurons on AI-relevant datasets. The results indicate networks with the biologically realistic 80:20 excitatory:inhibitory balance can reliably train at low activity levels and in noisy environments. Additionally, the Van Rossum distance, a measure of spike train synchrony, provides insight into the importance of inhibitory neurons to increase network robustness to noise. This work supports further biologically-informed large-scale networks and energy efficient hardware implementations.
Spiking neural networks (SNNs) are widely applied in various fields due to their energy-efficient and fast-inference capabilities. Applying SNNs to reinforcement learning (RL) can significantly reduce the computational resource requirements for agents and improve the algorithm's performance under resource-constrained conditions. However, in current spiking reinforcement learning (SRL) algorithms, the simulation results of multiple time steps can only correspond to a single-step decision in RL. This is quite different from the real temporal dynamics in the brain and also fails to fully exploit the capacity of SNNs to process temporal data. In order to address this temporal mismatch issue and further take advantage of the inherent temporal dynamics of spiking neurons, we propose a novel temporal alignment paradigm (TAP) that leverages the single-step update of spiking neurons to accumulate historical state information in RL and introduces gated units to enhance the memory capacity of spiking neurons. Experimental results show that our method can solve partially observable Markov decision processes (POMDPs) and multi-agent cooperation problems with similar performance as recurrent neural networks (RNNs) but with about 50% power consumption.
A Rapid Adapting and Continual Learning Spiking Neural Network Path Planning Algorithm for Mobile Robots
2024-04-23
Harrison Espino, Robert Bain, Jeffrey L. Krichmar
Mapping traversal costs in an environment and planning paths based on this map are important for autonomous navigation. We present a neurobotic navigation system that utilizes a Spiking Neural Network Wavefront Planner and E-prop learning to concurrently map and plan paths in a large and complex environment. We incorporate a novel method for mapping which, when combined with the Spiking Wavefront Planner, allows for adaptive planning by selectively considering any combination of costs. The system is tested on a mobile robot platform in an outdoor environment with obstacles and varying terrain. Results indicate that the system is capable of discerning features in the environment using three measures of cost, (1) energy expenditure by the wheels, (2) time spent in the presence of obstacles, and (3) terrain slope. In just twelve hours of online training, E-prop learns and incorporates traversal costs into the path planning maps by updating the delays in the Spiking Wavefront Planner. On simulated paths, the Spiking Wavefront Planner plans significantly shorter and lower cost paths than A* and RRT*. The spiking wavefront planner is compatible with neuromorphic hardware and could be used for applications requiring low size, weight, and power.
Elucidating the theoretical underpinnings of surrogate gradient learning in spiking neural networks
2024-04-23
Julia Gygax, Friedemann Zenke
Training spiking neural networks to approximate complex functions is essential for studying information processing in the brain and neuromorphic computing. Yet, the binary nature of spikes constitutes a challenge for direct gradient-based training. To sidestep this problem, surrogate gradients have proven empirically successful, but their theoretical foundation remains elusive. Here, we investigate the relation of surrogate gradients to two theoretically well-founded approaches. On the one hand, we consider smoothed probabilistic models, which, due to lack of support for automatic differentiation, are impractical for training deep spiking neural networks, yet provide gradients equivalent to surrogate gradients in single neurons. On the other hand, we examine stochastic automatic differentiation, which is compatible with discrete randomness but has never been applied to spiking neural network training. We find that the latter provides the missing theoretical basis for surrogate gradients in stochastic spiking neural networks. We further show that surrogate gradients in deterministic networks correspond to a particular asymptotic case and numerically confirm the effectiveness of surrogate gradients in stochastic multi-layer spiking neural networks. Finally, we illustrate that surrogate gradients are not conservative fields and, thus, not gradients of a surrogate loss. Our work provides the missing theoretical foundation for surrogate gradients and an analytically well-founded solution for end-to-end training of stochastic spiking neural networks.
Adapting to time: why nature evolved a diverse set of neurons
2024-04-22
Karim G. Habashy, Benjamin D. Evans, Dan F. M. Goodman, Jeffrey S. Bowers
Brains have evolved a diverse set of neurons with varying morphologies, physiological properties and rich dynamics that impact their processing of temporal information. By contrast, most neural network models include a homogeneous set of units that only vary in terms of their spatial parameters (weights and biases). To investigate the importance of temporal parameters to neural function, we trained spiking neural networks on tasks of varying temporal complexity, with different subsets of parameters held constant. We find that in a tightly resource constrained setting, adapting conduction delays is essential to solve all test conditions, and indeed that it is possible to solve these tasks using only temporal parameters (delays and time constants) with weights held constant. In the most complex spatio-temporal task we studied, we found that an adaptable bursting parameter was essential. More generally, allowing for adaptation of both temporal and spatial parameters increases network robustness to noise, an important feature for both biological brains and neuromorphic computing systems. In summary, our findings highlight how rich and adaptable dynamics are key to solving temporally structured tasks at a low neural resource cost, which may be part of the reason why biological neurons vary so dramatically in their physiological properties.
Learning-to-learn enables rapid learning with phase-change memory-based in-memory computing
2024-04-22
Thomas Ortner, Horst Petschenig, Athanasios Vasilopoulos, Roland Renner, Špela Brglez, Thomas Limbacher, Enrique Piñero, Alejandro Linares Barranco, Angeliki Pantazi, Robert Legenstein
There is a growing demand for low-power, autonomously learning artificial intelligence (AI) systems that can be applied at the edge and rapidly adapt to the specific situation at deployment site. However, current AI models struggle in such scenarios, often requiring extensive fine-tuning, computational resources, and data. In contrast, humans can effortlessly adjust to new tasks by transferring knowledge from related ones. The concept of learning-to-learn (L2L) mimics this process and enables AI models to rapidly adapt with only little computational effort and data. In-memory computing neuromorphic hardware (NMHW) is inspired by the brain's operating principles and mimics its physical co-location of memory and compute. In this work, we pair L2L with in-memory computing NMHW based on phase-change memory devices to build efficient AI models that can rapidly adapt to new tasks. We demonstrate the versatility of our approach in two scenarios: a convolutional neural network performing image classification and a biologically-inspired spiking neural network generating motor commands for a real robotic arm. Both models rapidly learn with few parameter updates. Deployed on the NMHW, they perform on-par with their software equivalents. Moreover, meta-training of these models can be performed in software with high-precision, alleviating the need for accurate hardware models.
Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks
2024-04-22
Alexandre Bittar, Philip N. Garner
Understanding cognitive processes in the brain demands sophisticated models capable of replicating neural dynamics at large scales. We present a physiologically inspired speech recognition architecture, compatible and scalable with deep learning frameworks, and demonstrate that end-to-end gradient descent training leads to the emergence of neural oscillations in the central spiking neural network. Significant cross-frequency couplings, indicative of these oscillations, are measured within and across network layers during speech processing, whereas no such interactions are observed when handling background noise inputs. Furthermore, our findings highlight the crucial inhibitory role of feedback mechanisms, such as spike frequency adaptation and recurrent connections, in regulating and synchronising neural activity to improve recognition performance. Overall, on top of developing our understanding of synchronisation phenomena notably observed in the human auditory pathway, our architecture exhibits dynamic and efficient information processing, with relevance to neuromorphic technology.
Functions of Direct and Indirect Pathways for Action Selection Are Quantitatively Analyzed in A Spiking Neural Network of The Basal Ganglia
2024-04-22
Sang-Yoon Kim, Woochang Lim
We are concerned about action selection in the basal ganglia (BG). We quantitatively analyze functions of direct pathway (DP) and indirect pathway (IP) for action selection in a spiking neural network with 3 competing channels. For such quantitative analysis, in each channel, we obtain the competition degree ${\cal C}_d$, given by the ratio of strength of DP (${\cal S}_{DP}$) to strength of IP (${\cal S}_{IP}$) (i.e., ${\cal C}_d = {\cal S}_{DP} / {\cal S}_{IP}$). Then, a desired action is selected in the channel with the largest ${\cal C}_d$. Desired action selection is made mainly due to strong focused inhibitory projection to the output nucleus, SNr (substantia nigra pars reticulata) via the DP in the corresponding channel. Unlike the case of DP, there are two types of IPs; intra-channel IP and inter-channel IP, due to widespread diffusive excitation from the STN (subthalamic nucleus). The intra-channel IP serves a function of brake to suppress the desired action selection. In contrast, the inter-channel IP to the SNr in the neighboring channels suppresses competing actions, leading to highlight the desired action selection. In this way, function of the inter-channel IP is opposite to that of the intra-channel IP. However, to the best of our knowledge, no quantitative analysis for such functions of the DP and the two IPs was made. Here, through direct calculations of the DP and the intra- and the inter-channel IP presynaptic currents into the SNr in each channel, we obtain the competition degree of each channel to determine a desired action, and then functions of the DP and the intra- and inter-channel IPs are quantitatively made clear.
Phase-space analysis of a two-section InP laser as an all-optical spiking neuron: dependency on control and design parameters
2024-04-19
Lukas Puts, Daan Lenstra, Kevin Williams, Weiming Yao
Using a rate-equation model we numerically evaluate the carrier concentration and photon number in an integrated two-section semiconductor laser, and analyse its dynamics in three-dimensional phase space. The simulation comprises compact model descriptions extracted from a commercially-available generic InP technology platform, allowing us to model an applied reverse-bias voltage to the saturable absorber. We use the model to study the influence of the injected gain current, reverse-bias voltage, and cavity mirror reflectivity on the excitable operation state, which is the operation mode desired for the laser to act as an all-optical integrated neuron. We show in phase-space that our model is capable of demonstrating four different operation modes, i.e. cw, self-pulsating and an on-set and excitable mode under optical pulse injection. In addition, we show that lowering the reflectivity of one of the cavity mirrors greatly enhances the control parameter space for excitable operation, enabling more relaxed operation parameter control and lower power consumption of an integrated two-section laser neuron.
Towards free-response paradigm: a theory on decision-making in spiking neural networks
2024-04-16
Zhichao Zhu, Yang Qi, Wenlian Lu, Zhigang Wang, Lu Cao, Jianfeng Feng
The energy-efficient and brain-like information processing abilities of Spiking Neural Networks (SNNs) have attracted considerable attention, establishing them as a crucial element of brain-inspired computing. One prevalent challenge encountered by SNNs is the trade-off between inference speed and accuracy, which requires sufficient time to achieve the desired level of performance. Drawing inspiration from animal behavior experiments that demonstrate a connection between decision-making reaction times, task complexity, and confidence levels, this study seeks to apply these insights to SNNs. The focus is on understanding how SNNs make inferences, with a particular emphasis on untangling the interplay between signal and noise in decision-making processes. The proposed theoretical framework introduces a new optimization objective for SNN training, highlighting the importance of not only the accuracy of decisions but also the development of predictive confidence through learning from past experiences. Experimental results demonstrate that SNNs trained according to this framework exhibit improved confidence expression, leading to better decision-making outcomes. In addition, a strategy is introduced for efficient decision-making during inference, which allows SNNs to complete tasks more quickly and can use stopping times as indicators of decision confidence. By integrating neuroscience insights with neuromorphic computing, this study opens up new possibilities to explore the capabilities of SNNs and advance their application in complex decision-making scenarios.
Hardware-aware training of models with synaptic delays for digital event-driven neuromorphic processors
2024-04-16
Alberto Patino-Saucedo, Roy Meijer, Amirreza Yousefzadeh, Manil-Dev Gomony, Federico Corradi, Paul Detteter, Laura Garrido-Regife, Bernabe Linares-Barranco, Manolis Sifalakis
Configurable synaptic delays are a basic feature in many neuromorphic neural network hardware accelerators. However, they have been rarely used in model implementations, despite their promising impact on performance and efficiency in tasks that exhibit complex (temporal) dynamics, as it has been unclear how to optimize them. In this work, we propose a framework to train and deploy, in digital neuromorphic hardware, highly performing spiking neural network models (SNNs) where apart from the synaptic weights, the per-synapse delays are also co-optimized. Leveraging spike-based back-propagation-through-time, the training accounts for both platform constraints, such as synaptic weight precision and the total number of parameters per core, as a function of the network size. In addition, a delay pruning technique is used to reduce memory footprint with a low cost in performance. We evaluate trained models in two neuromorphic digital hardware platforms: Intel Loihi and Imec Seneca. Loihi offers synaptic delay support using the so-called Ring-Buffer hardware structure. Seneca does not provide native hardware support for synaptic delays. A second contribution of this paper is therefore a novel area- and memory-efficient hardware structure for acceleration of synaptic delays, which we have integrated in Seneca. The evaluated benchmark involves several models for solving the SHD (Spiking Heidelberg Digits) classification task, where minimal accuracy degradation during the transition from software to hardware is demonstrated. To our knowledge, this is the first work showcasing how to train and deploy hardware-aware models parameterized with synaptic delays, on multicore neuromorphic hardware accelerators.
Artificial neuronal devices are the basic building blocks for neuromorphic computing systems, which have been motivated by realistic brain emulation. Aiming for these applications, various device concepts have been proposed to mimic the neuronal dynamics and functions. While till now, the artificial neuron devices with high efficiency, high stability and low power consumption are still far from practical application. Due to the special insulator-metal phase transition, Vanadium Dioxide (VO2) has been considered as an idea candidate for neuronal device fabrication. However, its intrinsic insulating state requires the VO2 neuronal device to be driven under large bias voltage, resulting in high power consumption and low frequency. Thus in the current study, we have addressed this challenge by preparing oxygen vacancies modulated VO2 film(VO2-x) and fabricating the VO2-x neuronal devices for Spiking Neural Networks (SNNs) construction. Results indicate the neuron devices can be operated under lower voltage with improved processing speed. The proposed VO2-x based back-propagation SNNs (BP-SNNs) system, trained with the MNIST dataset, demonstrates excellent accuracy in image recognition. Our study not only demonstrates the VO2-x based neurons and SNN system for practical application, but also offers an effective way to optimize the future neuromorphic computing systems by defect engineering strategy.
In recent years, skeleton-based action recognition, leveraging multimodal Graph Convolutional Networks (GCN), has achieved remarkable results. However, due to their deep structure and reliance on continuous floating-point operations, GCN-based methods are energy-intensive. We propose an innovative Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation (MK-SGN) to address this issue. By merging the energy efficiency of Spiking Neural Network (SNN) with the graph representation capability of GCN, the proposed MK-SGN reduces energy consumption while maintaining recognition accuracy. Firstly, we convert Graph Convolutional Networks (GCN) into Spiking Graph Convolutional Networks (SGN) establishing a new benchmark and paving the way for future research exploration. During this process, we introduce a spiking attention mechanism and design a Spiking-Spatio Graph Convolution module with a Spatial Global Spiking Attention mechanism (SA-SGC), enhancing feature learning capability. Secondly, we propose a Spiking Multimodal Fusion module (SMF), leveraging mutual information to process multimodal data more efficiently. Lastly, we delve into knowledge distillation methods from multimodal GCN to SGN and propose a novel, integrated method that simultaneously focuses on both intermediate layer distillation and soft label distillation to improve the performance of SGN. MK-SGN outperforms the state-of-the-art GCN-like frameworks on three challenging datasets for skeleton-based action recognition in reducing energy consumption. It also outperforms the state-of-the-art SNN frameworks in accuracy. Specifically, our method reduces energy consumption by more than 98% compared to typical GCN-based methods, while maintaining competitive accuracy on the NTU-RGB+D 60 cross-subject split using 4-time steps.
Spiking Neural Network (SNN) is acknowledged as the next generation of Artificial Neural Network (ANN) and hold great promise in effectively processing spatial-temporal information. However, the choice of timestep becomes crucial as it significantly impacts the accuracy of the neural network training. Specifically, a smaller timestep indicates better performance in efficient computing, resulting in reduced latency and operations. While, using a small timestep may lead to low accuracy due to insufficient information presentation with few spikes. This observation motivates us to develop an SNN that is more reliable for adaptive timestep by introducing a novel regularisation technique, namely Spatial-Temporal Regulariser (STR). Our approach regulates the ratio between the strength of spikes and membrane potential at each timestep. This effectively balances spatial and temporal performance during training, ultimately resulting in an Anytime Optimal Inference (AOI) SNN. Through extensive experiments on frame-based and event-based datasets, our method, in combination with cutoff based on softmax output, achieves state-of-the-art performance in terms of both latency and accuracy. Notably, with STR and cutoff, SNN achieves 2.14 to 2.89 faster in inference compared to the pre-configured timestep with near-zero accuracy drop of 0.50% to 0.64% over the event-based datasets. Code available: https://github.com/Dengyu-Wu/AOI-SNN-Regularisation
SQUAT: Stateful Quantization-Aware Training in Recurrent Spiking Neural Networks
2024-04-15
Sreyes Venkatesh, Razvan Marinescu, Jason K. Eshraghian
Weight quantization is used to deploy high-performance deep learning models on resource-limited hardware, enabling the use of low-precision integers for storage and computation. Spiking neural networks (SNNs) share the goal of enhancing efficiency, but adopt an 'event-driven' approach to reduce the power consumption of neural network inference. While extensive research has focused on weight quantization, quantization-aware training (QAT), and their application to SNNs, the precision reduction of state variables during training has been largely overlooked, potentially diminishing inference performance. This paper introduces two QAT schemes for stateful neurons: (i) a uniform quantization strategy, an established method for weight quantization, and (ii) threshold-centered quantization, which allocates exponentially more quantization levels near the firing threshold. Our results show that increasing the density of quantization levels around the firing threshold improves accuracy across several benchmark datasets. We provide an ablation analysis of the effects of weight and state quantization, both individually and combined, and how they impact models. Our comprehensive empirical evaluation includes full precision, 8-bit, 4-bit, and 2-bit quantized SNNs, using QAT, stateful QAT (SQUAT), and post-training quantization methods. The findings indicate that the combination of QAT and SQUAT enhance performance the most, but given the choice of one or the other, QAT improves performance by the larger degree. These trends are consistent all datasets. Our methods have been made available in our Python library snnTorch: https://github.com/jeshraghian/snntorch.
SNN4Agents: A Framework for Developing Energy-Efficient Embodied Spiking Neural Networks for Autonomous Agents
2024-04-14
Rachmad Vidya Wicaksana Putra, Alberto Marchisio, Muhammad Shafique
Recent trends have shown that autonomous agents, such as Autonomous Ground Vehicles (AGVs), Unmanned Aerial Vehicles (UAVs), and mobile robots, effectively improve human productivity in solving diverse tasks. However, since these agents are typically powered by portable batteries, they require extremely low power/energy consumption to operate in a long lifespan. To solve this challenge, neuromorphic computing has emerged as a promising solution, where bio-inspired Spiking Neural Networks (SNNs) use spikes from event-based cameras or data conversion pre-processing to perform sparse computations efficiently. However, the studies of SNN deployments for autonomous agents are still at an early stage. Hence, the optimization stages for enabling efficient embodied SNN deployments for autonomous agents have not been defined systematically. Toward this, we propose a novel framework called SNN4Agents that consists of a set of optimization techniques for designing energy-efficient embodied SNNs targeting autonomous agent applications. Our SNN4Agents employs weight quantization, timestep reduction, and attention window reduction to jointly improve the energy efficiency, reduce the memory footprint, optimize the processing latency, while maintaining high accuracy. In the evaluation, we investigate use cases of event-based car recognition, and explore the trade-offs among accuracy, latency, memory, and energy consumption. The experimental results show that our proposed framework can maintain high accuracy (i.e., 84.12% accuracy) with 68.75% memory saving, 3.58x speed-up, and 4.03x energy efficiency improvement as compared to the state-of-the-art work for NCARS dataset. In this manner, our SNN4Agents framework paves the way toward enabling energy-efficient embodied SNN deployments for autonomous agents.
Phase-Amplitude Description of Stochastic Oscillators: A Parameterization Method Approach
2024-04-13
Alberto Pérez-Cervera, Benjamin Lindner, Peter J. Thomas
The parameterization method (PM) provides a broad theoretical and numerical foundation for computing invariant manifolds of dynamical systems. PM implements a change of variables in order to represent trajectories of a system of ordinary differential equations ``as simply as possible." In this paper we pursue a similar goal for stochastic oscillator systems. For planar nonlinear stochastic systems that are ``robustly oscillatory", we find a change of variables through which the dynamics are as simple as possible $\textit{in the mean}$. We prove existence and uniqueness of a deterministic vector field, the trajectories of which capture the local mean behavior of the stochastic oscillator. We illustrate the construction of such an ``effective vector field" for several examples, including a limit cycle oscillator perturbed by noise, an excitable system derived from a spiking neuron model, and a spiral sink with noise forcing (2D Ornstein-Uhlenbeck process). The latter examples comprise contingent oscillators that would not sustain rhythmic activity without noise forcing. Finally, we exploit the simplicity of the dynamics after the change of variables to obtain the effective diffusion constant of the resulting phase variable, and the stationary variance of the resulting amplitude (isostable) variable.
A Cloud-Edge Framework for Energy-Efficient Event-Driven Control: An Integration of Online Supervised Learning, Spiking Neural Networks and Local Plasticity Rules
2024-04-12
Reza Ahmadvand, Sarah Safura Sharif, Yaser Mike Banad
This paper presents a novel cloud-edge framework for addressing computational and energy constraints in complex control systems. Our approach centers around a learning-based controller using Spiking Neural Networks (SNN) on physical plants. By integrating a biologically plausible learning method with local plasticity rules, we harness the efficiency, scalability, and low latency of SNNs. This design replicates control signals from a cloud-based controller directly on the plant, reducing the need for constant plant-cloud communication. The plant updates weights only when errors surpass predefined thresholds, ensuring efficiency and robustness in various conditions. Applied to linear workbench systems and satellite rendezvous scenarios, including obstacle avoidance, our architecture dramatically lowers normalized tracking error by 96% with increased network size. The event-driven nature of SNNs minimizes energy consumption, utilizing only about 111 nJ (0.3% of conventional computing requirements). The results demonstrate the system's adjustment to changing work environments and its efficient use of computational and energy resources, with a moderate increase in energy consumption of 27.2% and 37% for static and dynamic obstacles, respectively, compared to non-obstacle scenarios.
An Integrated Toolbox for Creating Neuromorphic Edge Applications
2024-04-12
Lars Niedermeier, Jeffrey L. Krichmar
Spiking Neural Networks (SNNs) and neuromorphic models are more efficient and have more biological realism than the activation functions typically used in deep neural networks, transformer models and generative AI. SNNs have local learning rules, are able to learn on small data sets, and can adapt through neuromodulation. Although research has shown their advantages, there are still few compelling practical applications, especially at the edge where sensors and actuators need to be processed in a timely fashion. One reason for this might be that SNNs are much more challenging to understand, build, and operate due to their intrinsic properties. For instance, the mathematical foundation involves differential equations rather than basic activation functions. To address these challenges, we have developed CARLsim++. It is an integrated toolbox that enables fast and easy creation of neuromorphic applications. It encapsulates the mathematical intrinsics and low-level C++ programming by providing a graphical user interface for users who do not have a background in software engineering but still want to create neuromorphic models. Developers can easily configure inputs and outputs to devices and robots. These can be accurately simulated before deploying on physical devices. CARLsim++ can lead to rapid development of neuromorphic applications for simulation or edge processing.
In-context learning (ICL), a property demonstrated by transformer-based sequence models, refers to the automatic inference of an input-output mapping based on examples of the mapping provided as context. ICL requires no explicit learning, i.e., no explicit updates of model weights, directly mapping context and new input to the new output. Prior work has proved the usefulness of ICL for detection in MIMO channels. In this setting, the context is given by pilot symbols, and ICL automatically adapts a detector, or equalizer, to apply to newly received signals. However, the implementation tested in prior art was based on conventional artificial neural networks (ANNs), which may prove too energy-demanding to be run on mobile devices. This paper evaluates a neuromorphic implementation of the transformer for ICL-based MIMO detection. This approach replaces ANNs with spiking neural networks (SNNs), and implements the attention mechanism via stochastic computing, requiring no multiplications, but only logical AND operations and counting. When using conventional digital CMOS hardware, the proposed implementation is shown to preserve accuracy, with a reduction in power consumption ranging from $5.4\times$ to $26.8\times$, depending on the model sizes, as compared to ANN-based implementations.
A Neuromorphic Approach to Obstacle Avoidance in Robot Manipulation
2024-04-08
Ahmed Faisal Abdelrahman, Matias Valdenegro-Toro, Maren Bennewitz, Paul G. Plöger
Neuromorphic computing mimics computational principles of the brain in $\textit{silico}$ and motivates research into event-based vision and spiking neural networks (SNNs). Event cameras (ECs) exclusively capture local intensity changes and offer superior power consumption, response latencies, and dynamic ranges. SNNs replicate biological neuronal dynamics and have demonstrated potential as alternatives to conventional artificial neural networks (ANNs), such as in reducing energy expenditure and inference time in visual classification. Nevertheless, these novel paradigms remain scarcely explored outside the domain of aerial robots. To investigate the utility of brain-inspired sensing and data processing, we developed a neuromorphic approach to obstacle avoidance on a camera-equipped manipulator. Our approach adapts high-level trajectory plans with reactive maneuvers by processing emulated event data in a convolutional SNN, decoding neural activations into avoidance motions, and adjusting plans using a dynamic motion primitive. We conducted experiments with a Kinova Gen3 arm performing simple reaching tasks that involve obstacles in sets of distinct task scenarios and in comparison to a non-adaptive baseline. Our neuromorphic approach facilitated reliable avoidance of imminent collisions in simulated and real-world experiments, where the baseline consistently failed. Trajectory adaptations had low impacts on safety and predictability criteria. Among the notable SNN properties were the correlation of computations with the magnitude of perceived motions and a robustness to different event emulation methods. Tests with a DAVIS346 EC showed similar performance, validating our experimental event emulation. Our results motivate incorporating SNN learning, utilizing neuromorphic processors, and further exploring the potential of neuromorphic methods.
Slax: A Composable JAX Library for Rapid and Flexible Prototyping of Spiking Neural Networks
2024-04-08
Thomas M. Summe, Siddharth Joshi
Recent advances to algorithms for training spiking neural networks (SNNs) often leverage their unique dynamics. While backpropagation through time (BPTT) with surrogate gradients dominate the field, a rich landscape of alternatives can situate algorithms across various points in the performance, bio-plausibility, and complexity landscape. Evaluating and comparing algorithms is currently a cumbersome and error-prone process, requiring them to be repeatedly re-implemented. We introduce Slax, a JAX-based library designed to accelerate SNN algorithm design, compatible with the broader JAX and Flax ecosystem. Slax provides optimized implementations of diverse training algorithms, allowing direct performance comparison. Its toolkit includes methods to visualize and debug algorithms through loss landscapes, gradient similarities, and other metrics of model behavior during training.
Efficient Learning Using Spiking Neural Networks Equipped With Affine Encoders and Decoders
2024-04-06
A. Martina Neuman, Philipp Christian Petersen
We study the learning problem associated with spiking neural networks. Specifically, we consider hypothesis sets of spiking neural networks with affine temporal encoders and decoders and simple spiking neurons having only positive synaptic weights. We demonstrate that the positivity of the weights continues to enable a wide range of expressivity results, including rate-optimal approximation of smooth functions or approximation without the curse of dimensionality. Moreover, positive-weight spiking neural networks are shown to depend continuously on their parameters which facilitates classical covering number-based generalization statements. Finally, we observe that from a generalization perspective, contrary to feedforward neural networks or previous results for general spiking neural networks, the depth has little to no adverse effect on the generalization capabilities.
SpikeExplorer: hardware-oriented Design Space Exploration for Spiking Neural Networks on FPGA
2024-04-04
Dario Padovano, Alessio Carpegna, Alessandro Savino, Stefano Di Carlo
One of today's main concerns is to bring Artificial Intelligence power to embedded systems for edge applications. The hardware resources and power consumption required by state-of-the-art models are incompatible with the constrained environments observed in edge systems, such as IoT nodes and wearable devices. Spiking Neural Networks (SNNs) can represent a solution in this sense: inspired by neuroscience, they reach unparalleled power and resource efficiency when run on dedicated hardware accelerators. However, when designing such accelerators, the amount of choices that can be taken is huge. This paper presents SpikExplorer, a modular and flexible Python tool for hardware-oriented Automatic Design Space Exploration to automate the configuration of FPGA accelerators for SNNs. Using Bayesian optimizations, SpikerExplorer enables hardware-centric multi-objective optimization, supporting factors such as accuracy, area, latency, power, and various combinations during the exploration process. The tool searches the optimal network architecture, neuron model, and internal and training parameters, trying to reach the desired constraints imposed by the user. It allows for a straightforward network configuration, providing the full set of explored points for the user to pick the trade-off that best fits the needs. The potential of SpikExplorer is showcased using three benchmark datasets. It reaches 95.8% accuracy on the MNIST dataset, with a power consumption of 180mW/image and a latency of 0.12 ms/image, making it a powerful tool for automatically optimizing SNNs.
A Methodology to Study the Impact of Spiking Neural Network Parameters considering Event-Based Automotive Data
2024-04-04
Iqra Bano, Rachmad Vidya Wicaksana Putra, Alberto Marchisio, Muhammad Shafique
Autonomous Driving (AD) systems are considered as the future of human mobility and transportation. Solving computer vision tasks such as image classification and object detection/segmentation, with high accuracy and low power/energy consumption, is highly needed to realize AD systems in real life. These requirements can potentially be satisfied by Spiking Neural Networks (SNNs). However, the state-of-the-art works in SNN-based AD systems still focus on proposing network models that can achieve high accuracy, and they have not systematically studied the roles of SNN parameters when used for learning event-based automotive data. Therefore, we still lack understanding of how to effectively develop SNN models for AD systems. Toward this, we propose a novel methodology to systematically study and analyze the impact of SNN parameters considering event-based automotive data, then leverage this analysis for enhancing SNN developments. To do this, we first explore different settings of SNN parameters that directly affect the learning mechanism (i.e., batch size, learning rate, neuron threshold potential, and weight decay), then analyze the accuracy results. Afterward, we propose techniques that jointly improve SNN accuracy and reduce training time. Experimental results show that our methodology can improve the SNN models for AD systems than the state-of-the-art, as it achieves higher accuracy (i.e., 86%) for the NCARS dataset, and it can also achieve iso-accuracy (i.e., ~85% with standard deviation less than 0.5%) while speeding up the training time by 1.9x. In this manner, our research work provides a set of guidelines for SNN parameter enhancements, thereby enabling the practical developments of SNN-based AD systems.
Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stack
2024-04-04
Rachmad Vidya Wicaksana Putra, Alberto Marchisio, Fakhreddine Zayer, Jorge Dias, Muhammad Shafique
Robotic technologies have been an indispensable part for improving human productivity since they have been helping humans in completing diverse, complex, and intensive tasks in a fast yet accurate and efficient way. Therefore, robotic technologies have been deployed in a wide range of applications, ranging from personal to industrial use-cases. However, current robotic technologies and their computing paradigm still lack embodied intelligence to efficiently interact with operational environments, respond with correct/expected actions, and adapt to changes in the environments. Toward this, recent advances in neuromorphic computing with Spiking Neural Networks (SNN) have demonstrated the potential to enable the embodied intelligence for robotics through bio-plausible computing paradigm that mimics how the biological brain works, known as "neuromorphic artificial intelligence (AI)". However, the field of neuromorphic AI-based robotics is still at an early stage, therefore its development and deployment for solving real-world problems expose new challenges in different design aspects, such as accuracy, adaptability, efficiency, reliability, and security. To address these challenges, this paper will discuss how we can enable embodied neuromorphic AI for robotic systems through our perspectives: (P1) Embodied intelligence based on effective learning rule, training mechanism, and adaptability; (P2) Cross-layer optimizations for energy-efficient neuromorphic computing; (P3) Representative and fair benchmarks; (P4) Low-cost reliability and safety enhancements; (P5) Security and privacy for neuromorphic computing; and (P6) A synergistic development for energy-efficient and robust neuromorphic-based robotics. Furthermore, this paper identifies research challenges and opportunities, as well as elaborates our vision for future research development toward embodied neuromorphic AI for robotics.
A Fully-Configurable Open-Source Software-Defined Digital Quantized Spiking Neural Core Architecture
2024-04-02
Shadi Matinizadeh, Noah Pacik-Nelson, Ioannis Polykretis, Krupa Tishbi, Suman Kumar, M. L. Varshika, Arghavan Mohammadhassani, Abhishek Mishra, Nagarajan Kandasamy, James Shackleford, Eric Gallo, Anup Das
We introduce QUANTISENC, a fully configurable open-source software-defined digital quantized spiking neural core architecture to advance research in neuromorphic computing. QUANTISENC is designed hierarchically using a bottom-up methodology with multiple neurons in each layer and multiple layers in each core. The number of layers and neurons per layer can be configured via software in a top-down methodology to generate the hardware for a target spiking neural network (SNN) model. QUANTISENC uses leaky integrate and fire neurons (LIF) and current-based excitatory and inhibitory synapses (CUBA). The nonlinear dynamics of a neuron can be configured at run-time via programming its internal control registers. Each neuron performs signed fixed-point arithmetic with user-defined quantization and decimal precision. QUANTISENC supports all-to-all, one-to-one, and Gaussian connections between layers. Its hardware-software interface is integrated with a PyTorch-based SNN simulator. This integration allows to define and train an SNN model in PyTorch and evaluate the hardware performance (e.g., area, power, latency, and throughput) through FPGA prototyping and ASIC design. The hardware-software interface also takes advantage of the layer-based architecture and distributed memory organization of QUANTISENC to enable pipelining by overlapping computations on streaming data. Overall, the proposed software-defined hardware design methodology offers flexibility similar to that of high-level synthesis (HLS), but provides better hardware performance with zero hardware development effort. We evaluate QUANTISENC using three spiking datasets and show its superior performance against state-of the-art designs.
2024-04-02
Nan Yin, Mengzhu Wan, Li Shen, Hitesh Laxmichand Patel, Baopu Li, Bin Gu, Huan Xiong
Continuous graph neural networks (CGNNs) have garnered significant attention due to their ability to generalize existing discrete graph neural networks (GNNs) by introducing continuous dynamics. They typically draw inspiration from diffusion-based methods to introduce a novel propagation scheme, which is analyzed using ordinary differential equations (ODE). However, the implementation of CGNNs requires significant computational power, making them challenging to deploy on battery-powered devices. Inspired by recent spiking neural networks (SNNs), which emulate a biological inference process and provide an energy-efficient neural architecture, we incorporate the SNNs with CGNNs in a unified framework, named Continuous Spiking Graph Neural Networks (COS-GNN). We employ SNNs for graph node representation at each time step, which are further integrated into the ODE process along with time. To enhance information preservation and mitigate information loss in SNNs, we introduce the high-order structure of COS-GNN, which utilizes the second-order ODE for spiking representation and continuous propagation. Moreover, we provide the theoretical proof that COS-GNN effectively mitigates the issues of exploding and vanishing gradients, enabling us to capture long-range dependencies between nodes. Experimental results on graph-based learning tasks demonstrate the effectiveness of the proposed COS-GNN over competitive baselines.
A Methodology for Improving Accuracy of Embedded Spiking Neural Networks through Kernel Size Scaling
2024-04-02
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
Spiking Neural Networks (SNNs) can offer ultra low power/ energy consumption for machine learning-based applications due to their sparse spike-based operations. Currently, most of the SNN architectures need a significantly larger model size to achieve higher accuracy, which is not suitable for resource-constrained embedded applications. Therefore, developing SNNs that can achieve high accuracy with acceptable memory footprint is highly needed. Toward this, we propose a novel methodology that improves the accuracy of SNNs through kernel size scaling. Its key steps include investigating the impact of different kernel sizes on the accuracy, devising new sets of kernel sizes, generating SNN architectures based on the selected kernel sizes, and analyzing the accuracy-memory trade-offs for SNN model selection. The experimental results show that our methodology achieves higher accuracy than state-of-the-art (93.24% accuracy for CIFAR10 and 70.84% accuracy for CIFAR100) with less than 10M parameters and up to 3.45x speed-up of searching time, thereby making it suitable for embedded applications.
SpikeMba: Multi-Modal Spiking Saliency Mamba for Temporal Video Grounding
2024-04-01
Wenrui Li, Xiaopeng Hong, Ruiqin Xiong, Xiaopeng Fan
Temporal video grounding (TVG) is a critical task in video content understanding, requiring precise alignment between video content and natural language instructions. Despite significant advancements, existing methods face challenges in managing confidence bias towards salient objects and capturing long-term dependencies in video sequences. To address these issues, we introduce SpikeMba: a multi-modal spiking saliency mamba for temporal video grounding. Our approach integrates Spiking Neural Networks (SNNs) with state space models (SSMs) to leverage their unique advantages in handling different aspects of the task. Specifically, we use SNNs to develop a spiking saliency detector that generates the proposal set. The detector emits spike signals when the input signal exceeds a predefined threshold, resulting in a dynamic and binary saliency proposal set. To enhance the model's capability to retain and infer contextual information, we introduce relevant slots which learnable tensors that encode prior knowledge. These slots work with the contextual moment reasoner to maintain a balance between preserving contextual information and exploring semantic relevance dynamically. The SSMs facilitate selective information propagation, addressing the challenge of long-term dependency in video content. By combining SNNs for proposal generation and SSMs for effective contextual reasoning, SpikeMba addresses confidence bias and long-term dependencies, thereby significantly enhancing fine-grained multimodal relationship capture. Our experiments demonstrate the effectiveness of SpikeMba, which consistently outperforms state-of-the-art methods across mainstream benchmarks.
Parallel Proportional Fusion of Spiking Quantum Neural Network for Optimizing Image Classification
2024-04-01
Zuyu Xu, Kang Shen, Pengnian Cai, Tao Yang, Yuanming Hu, Shixian Chen, Yunlai Zhu, Zuheng Wu, Yuehua Dai, Jun Wang, Fei Yang
The recent emergence of the hybrid quantum-classical neural network (HQCNN) architecture has garnered considerable attention due to the potential advantages associated with integrating quantum principles to enhance various facets of machine learning algorithms and computations. However, the current investigated serial structure of HQCNN, wherein information sequentially passes from one network to another, often imposes limitations on the trainability and expressivity of the network. In this study, we introduce a novel architecture termed Parallel Proportional Fusion of Quantum and Spiking Neural Networks (PPF-QSNN). The dataset information is simultaneously fed into both the spiking neural network and the variational quantum circuits, with the outputs amalgamated in proportion to their individual contributions. We systematically assess the impact of diverse PPF-QSNN parameters on network performance for image classification, aiming to identify the optimal configuration. Numerical results on the MNIST dataset unequivocally illustrate that our proposed PPF-QSNN outperforms both the existing spiking neural network and the serial quantum neural network across metrics such as accuracy, loss, and robustness. This study introduces a novel and effective amalgamation approach for HQCNN, thereby laying the groundwork for the advancement and application of quantum advantage in artificial intelligent computations.
SpikingJET: Enhancing Fault Injection for Fully and Convolutional Spiking Neural Networks
2024-03-30
Anil Bayram Gogebakan, Enrico Magliano, Alessio Carpegna, Annachiara Ruospo, Alessandro Savino, Stefano Di Carlo
As artificial neural networks become increasingly integrated into safety-critical systems such as autonomous vehicles, devices for medical diagnosis, and industrial automation, ensuring their reliability in the face of random hardware faults becomes paramount. This paper introduces SpikingJET, a novel fault injector designed specifically for fully connected and convolutional Spiking Neural Networks (SNNs). Our work underscores the critical need to evaluate the resilience of SNNs to hardware faults, considering their growing prominence in real-world applications. SpikingJET provides a comprehensive platform for assessing the resilience of SNNs by inducing errors and injecting faults into critical components such as synaptic weights, neuron model parameters, internal states, and activation functions. This paper demonstrates the effectiveness of Spiking-JET through extensive software-level experiments on various SNN architectures, revealing insights into their vulnerability and resilience to hardware faults. Moreover, highlighting the importance of fault resilience in SNNs contributes to the ongoing effort to enhance the reliability and safety of Neural Network (NN)-powered systems in diverse domains.
Biologically-Plausible Topology Improved Spiking Actor Network for Efficient Deep Reinforcement Learning
2024-03-29
Duzhen Zhang, Qingyu Wang, Tielin Zhang, Bo Xu
The success of Deep Reinforcement Learning (DRL) is largely attributed to utilizing Artificial Neural Networks (ANNs) as function approximators. Recent advances in neuroscience have unveiled that the human brain achieves efficient reward-based learning, at least by integrating spiking neurons with spatial-temporal dynamics and network topologies with biologically-plausible connectivity patterns. This integration process allows spiking neurons to efficiently combine information across and within layers via nonlinear dendritic trees and lateral interactions. The fusion of these two topologies enhances the network's information-processing ability, crucial for grasping intricate perceptions and guiding decision-making procedures. However, ANNs and brain networks differ significantly. ANNs lack intricate dynamical neurons and only feature inter-layer connections, typically achieved by direct linear summation, without intra-layer connections. This limitation leads to constrained network expressivity. To address this, we propose a novel alternative for function approximator, the Biologically-Plausible Topology improved Spiking Actor Network (BPT-SAN), tailored for efficient decision-making in DRL. The BPT-SAN incorporates spiking neurons with intricate spatial-temporal dynamics and introduces intra-layer connections, enhancing spatial-temporal state representation and facilitating more precise biological simulations. Diverging from the conventional direct linear weighted sum, the BPT-SAN models the local nonlinearities of dendritic trees within the inter-layer connections. For the intra-layer connections, the BPT-SAN introduces lateral interactions between adjacent neurons, integrating them into the membrane potential formula to ensure accurate spike firing.
A survey on learning models of spiking neural membrane systems and spiking neural networks
2024-03-27
Prithwineel Paul, Petr Sosik, Lucie Ciencialova
Spiking neural networks (SNN) are a biologically inspired model of neural networks with certain brain-like properties. In the past few decades, this model has received increasing attention in computer science community, owing also to the successful phenomenon of deep learning. In SNN, communication between neurons takes place through the spikes and spike trains. This differentiates these models from the ``standard'' artificial neural networks (ANN) where the frequency of spikes is replaced by real-valued signals. Spiking neural P systems (SNPS) can be considered a branch of SNN based more on the principles of formal automata, with many variants developed within the framework of the membrane computing theory. In this paper, we first briefly compare structure and function, advantages and drawbacks of SNN and SNPS. A key part of the article is a survey of recent results and applications of machine learning and deep learning models of both SNN and SNPS formalisms.
Spikewhisper: Temporal Spike Backdoor Attacks on Federated Neuromorphic Learning over Low-power Devices
2024-03-27
Hanqing Fu, Gaolei Li, Jun Wu, Jianhua Li, Xi Lin, Kai Zhou, Yuchen Liu
Federated neuromorphic learning (FedNL) leverages event-driven spiking neural networks and federated learning frameworks to effectively execute intelligent analysis tasks over amounts of distributed low-power devices but also perform vulnerability to poisoning attacks. The threat of backdoor attacks on traditional deep neural networks typically comes from time-invariant data. However, in FedNL, unknown threats may be hidden in time-varying spike signals. In this paper, we start to explore a novel vulnerability of FedNL-based systems with the concept of time division multiplexing, termed Spikewhisper, which allows attackers to evade detection as much as possible, as multiple malicious clients can imperceptibly poison with different triggers at different timeslices. In particular, the stealthiness of Spikewhisper is derived from the time-domain divisibility of global triggers, in which each malicious client pastes only one local trigger to a certain timeslice in the neuromorphic sample, and also the polarity and motion of each local trigger can be configured by attackers. Extensive experiments based on two different neuromorphic datasets demonstrate that the attack success rate of Spikewispher is higher than the temporally centralized attacks. Besides, it is validated that the effect of Spikewispher is sensitive to the trigger duration.
FTBC: Forward Temporal Bias Correction for Optimizing ANN-SNN Conversion
2024-03-27
Xiaofeng Wu, Velibor Bojkovic, Bin Gu, Kun Suo, Kai Zou
Spiking Neural Networks (SNNs) offer a promising avenue for energy-efficient computing compared with Artificial Neural Networks (ANNs), closely mirroring biological neural processes. However, this potential comes with inherent challenges in directly training SNNs through spatio-temporal backpropagation -- stemming from the temporal dynamics of spiking neurons and their discrete signal processing -- which necessitates alternative ways of training, most notably through ANN-SNN conversion. In this work, we introduce a lightweight Forward Temporal Bias Correction (FTBC) technique, aimed at enhancing conversion accuracy without the computational overhead. We ground our method on provided theoretical findings that through proper temporal bias calibration the expected error of ANN-SNN conversion can be reduced to be zero after each time step. We further propose a heuristic algorithm for finding the temporal bias only in the forward pass, thus eliminating the computational burden of backpropagation and we evaluate our method on CIFAR-10/100 and ImageNet datasets, achieving a notable increase in accuracy on all datasets. Codes are released at a GitHub repository.
Fourier or Wavelet bases as counterpart self-attention in spikformer for efficient visual classification
2024-03-27
Qingyu Wang, Duzhen Zhang, Tilelin Zhang, Bo Xu
Energy-efficient spikformer has been proposed by integrating the biologically plausible spiking neural network (SNN) and artificial Transformer, whereby the Spiking Self-Attention (SSA) is used to achieve both higher accuracy and lower computational cost. However, it seems that self-attention is not always necessary, especially in sparse spike-form calculation manners. In this paper, we innovatively replace vanilla SSA (using dynamic bases calculating from Query and Key) with spike-form Fourier Transform, Wavelet Transform, and their combinations (using fixed triangular or wavelets bases), based on a key hypothesis that both of them use a set of basis functions for information transformation. Hence, the Fourier-or-Wavelet-based spikformer (FWformer) is proposed and verified in visual classification tasks, including both static image and event-based video datasets. The FWformer can achieve comparable or even higher accuracies ($0.4\%$-$1.5\%$), higher running speed ($9\%$-$51\%$ for training and $19\%$-$70\%$ for inference), reduced theoretical energy consumption ($20\%$-$25\%$), and reduced GPU memory usage ($4\%$-$26\%$), compared to the standard spikformer. Our result indicates the continuous refinement of new Transformers, that are inspired either by biological discovery (spike-form), or information theory (Fourier or Wavelet Transform), is promising.
Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks
2024-03-25
Huifeng Yin, Mingkun Xu, Jing Pei, Lei Deng
Graph representation learning has become a crucial task in machine learning and data mining due to its potential for modeling complex structures such as social networks, chemical compounds, and biological systems. Spiking neural networks (SNNs) have recently emerged as a promising alternative to traditional neural networks for graph learning tasks, benefiting from their ability to efficiently encode and process temporal and spatial information. In this paper, we propose a novel approach that integrates attention mechanisms with SNNs to improve graph representation learning. Specifically, we introduce an attention mechanism for SNN that can selectively focus on important nodes and corresponding features in a graph during the learning process. We evaluate our proposed method on several benchmark datasets and show that it achieves comparable performance compared to existing graph learning techniques.
Understanding the Functional Roles of Modelling Components in Spiking Neural Networks
2024-03-25
Huifeng Yin, Hanle Zheng, Jiayi Mao, Siyuan Ding, Xing Liu, Mingkun Xu, Yifan Hu, Jing Pei, Lei Deng
Spiking neural networks (SNNs), inspired by the neural circuits of the brain, are promising in achieving high computational efficiency with biological fidelity. Nevertheless, it is quite difficult to optimize SNNs because the functional roles of their modelling components remain unclear. By designing and evaluating several variants of the classic model, we systematically investigate the functional roles of key modelling components, leakage, reset, and recurrence, in leaky integrate-and-fire (LIF) based SNNs. Through extensive experiments, we demonstrate how these components influence the accuracy, generalization, and robustness of SNNs. Specifically, we find that the leakage plays a crucial role in balancing memory retention and robustness, the reset mechanism is essential for uninterrupted temporal processing and computational efficiency, and the recurrence enriches the capability to model complex dynamics at a cost of robustness degradation. With these interesting observations, we provide optimization suggestions for enhancing the performance of SNNs in different scenarios. This work deepens the understanding of how SNNs work, which offers valuable guidance for the development of more effective and robust neuromorphic models.
Spiking Transformers, which integrate Spiking Neural Networks (SNNs) with Transformer architectures, have attracted significant attention due to their potential for energy efficiency and high performance. However, existing models in this domain still suffer from suboptimal performance. We introduce several innovations to improve the performance: i) We propose a novel spike-form Q-K attention mechanism, tailored for SNNs, which efficiently models the importance of token or channel dimensions through binary vectors with linear complexity. ii) We incorporate the hierarchical structure, which significantly benefits the performance of both the brain and artificial neural networks, into spiking transformers to obtain multi-scale spiking representation. iii) We design a versatile and powerful patch embedding module with a deformed shortcut specifically for spiking transformers. Together, we develop QKFormer, a hierarchical spiking transformer based on Q-K attention with direct training. QKFormer shows significantly superior performance over existing state-of-the-art SNN models on various mainstream datasets. Notably, with comparable size to Spikformer (66.34 M, 74.81%), QKFormer (64.96 M) achieves a groundbreaking top-1 accuracy of 85.65% on ImageNet-1k, substantially outperforming Spikformer by 10.84%. To our best knowledge, this is the first time that directly training SNNs have exceeded 85% accuracy on ImageNet-1K. The code and models are publicly available at https://github.com/zhouchenlin2096/QKFormer
2024-03-25
Yosuke Bando, Ramdas Pillai, Atsushi Kajita, Farhan Abdul Hakeem, Yves Quemener, Hua-an Tseng, Kiryl D. Piatkevich, Changyang Linghu, Xue Han, Edward S. Boyden
In voltage imaging, where the membrane potentials of individual neurons are recorded at from hundreds to thousand frames per second using fluorescence microscopy, data processing presents a challenge. Even a fraction of a minute of recording with a limited image size yields gigabytes of video data consisting of tens of thousands of frames, which can be time-consuming to process. Moreover, millisecond-level short exposures lead to noisy video frames, obscuring neuron footprints especially in deep-brain samples where noisy signals are buried in background fluorescence. To address this challenge, we propose a fast neuron segmentation method able to detect multiple, potentially overlapping, spiking neurons from noisy video frames, and implement a data processing pipeline incorporating the proposed segmentation method along with GPU-accelerated motion correction. By testing on existing datasets as well as on new datasets we introduce, we show that our pipeline extracts neuron footprints that agree well with human annotation even from cluttered datasets, and demonstrate real-time processing of voltage imaging data on a single desktop computer for the first time.
Artificial Neural Microcircuits as Building Blocks: Concept and Challenges
2024-03-24
Andrew Walter, Shimeng Wu, Andy M. Tyrrell, Liam McDaid, Malachy McElholm, Nidhin Thandassery Sumithran, Jim Harkin, Martin A. Trefzer
Artificial Neural Networks (ANNs) are one of the most widely employed forms of bio-inspired computation. However the current trend is for ANNs to be structurally homogeneous. Furthermore, this structural homogeneity requires the application of complex training and learning tools that produce application specific ANNs, susceptible to pitfalls such as overfitting. In this paper, an new approach is explored, inspired by the role played in biology by Neural Microcircuits, the so called ``fundamental processing elements'' of organic nervous systems. How large neural networks, particularly Spiking Neural Networks (SNNs) can be assembled using Artificial Neural Microcircuits (ANMs), intended as off-the-shelf components, is articulated; the results of initial work to produce a catalogue of such Microcircuits though the use of Novelty Search is shown; followed by efforts to expand upon this initial work, including a discussion of challenges uncovered during these efforts and explorations of methods by which they might be overcome.
Event cameras have emerged as a promising sensing modality for autonomous navigation systems, owing to their high temporal resolution, high dynamic range and negligible motion blur. To process the asynchronous temporal event streams from such sensors, recent research has shown that a mix of Artificial Neural Networks (ANNs), Spiking Neural Networks (SNNs) as well as hybrid SNN-ANN algorithms are necessary to achieve high accuracies across a range of perception tasks. However, we observe that executing such workloads on commodity edge platforms which feature heterogeneous processing elements such as CPUs, GPUs and neural accelerators results in inferior performance. This is due to the mismatch between the irregular nature of event streams and diverse characteristics of algorithms on the one hand and the underlying hardware platform on the other. We propose Ev-Edge, a framework that contains three key optimizations to boost the performance of event-based vision systems on edge platforms: (1) An Event2Sparse Frame converter directly transforms raw event streams into sparse frames, enabling the use of sparse libraries with minimal encoding overheads (2) A Dynamic Sparse Frame Aggregator merges sparse frames at runtime by trading off the temporal granularity of events and computational demand thereby improving hardware utilization (3) A Network Mapper maps concurrently executing tasks to different processing elements while also selecting layer precision by considering both compute and communication overheads. On several state-of-art networks for a range of autonomous navigation tasks, Ev-Edge achieves 1.28x-2.05x improvements in latency and 1.23x-2.15x in energy over an all-GPU implementation on the NVIDIA Jetson Xavier AGX platform for single-task execution scenarios. Ev-Edge also achieves 1.43x-1.81x latency improvements over round-robin scheduling methods in multi-task execution scenarios.
Event cameras, characterized by high temporal resolution, high dynamic range, low power consumption, and high pixel bandwidth, offer unique capabilities for object detection in specialized contexts. Despite these advantages, the inherent sparsity and asynchrony of event data pose challenges to existing object detection algorithms. Spiking Neural Networks (SNNs), inspired by the way the human brain codes and processes information, offer a potential solution to these difficulties. However, their performance in object detection using event cameras is limited in current implementations. In this paper, we propose the Spiking Fusion Object Detector (SFOD), a simple and efficient approach to SNN-based object detection. Specifically, we design a Spiking Fusion Module, achieving the first-time fusion of feature maps from different scales in SNNs applied to event cameras. Additionally, through integrating our analysis and experiments conducted during the pretraining of the backbone network on the NCAR dataset, we delve deeply into the impact of spiking decoding strategies and loss functions on model performance. Thereby, we establish state-of-the-art classification results based on SNNs, achieving 93.7\% accuracy on the NCAR dataset. Experimental results on the GEN1 detection dataset demonstrate that the SFOD achieves a state-of-the-art mAP of 32.1\%, outperforming existing SNN-based approaches. Our research not only underscores the potential of SNNs in object detection with event cameras but also propels the advancement of SNNs. Code is available at https://github.com/yimeng-fan/SFOD.
SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks
2024-03-21
Xinyu Shi, Zecheng Hao, Zhaofei Yu
The remarkable success of Vision Transformers in Artificial Neural Networks (ANNs) has led to a growing interest in incorporating the self-attention mechanism and transformer-based architecture into Spiking Neural Networks (SNNs). While existing methods propose spiking self-attention mechanisms that are compatible with SNNs, they lack reasonable scaling methods, and the overall architectures proposed by these methods suffer from a bottleneck in effectively extracting local features. To address these challenges, we propose a novel spiking self-attention mechanism named Dual Spike Self-Attention (DSSA) with a reasonable scaling method. Based on DSSA, we propose a novel spiking Vision Transformer architecture called SpikingResformer, which combines the ResNet-based multi-stage architecture with our proposed DSSA to improve both performance and energy efficiency while reducing parameters. Experimental results show that SpikingResformer achieves higher accuracy with fewer parameters and lower energy consumption than other spiking Vision Transformer counterparts. Notably, our SpikingResformer-L achieves 79.40% top-1 accuracy on ImageNet with 4 time-steps, which is the state-of-the-art result in the SNN field.
Recently, Graph Transformers have emerged as a promising solution to alleviate the inherent limitations of Graph Neural Networks (GNNs) and enhance graph representation performance. Unfortunately, Graph Transformers are computationally expensive due to the quadratic complexity inherent in self-attention when applied over large-scale graphs, especially for node tasks. In contrast, spiking neural networks (SNNs), with event-driven and binary spikes properties, can perform energy-efficient computation. In this work, we propose a novel insight into integrating SNNs with Graph Transformers and design a Spiking Graph Attention (SGA) module. The matrix multiplication is replaced by sparse addition and mask operations. The linear complexity enables all-pair node interactions on large-scale graphs with limited GPU memory. To our knowledge, our work is the first attempt to introduce SNNs into Graph Transformers. Furthermore, we design SpikeGraphormer, a Dual-branch architecture, combining a sparse GNN branch with our SGA-driven Graph Transformer branch, which can simultaneously perform all-pair node interactions and capture local neighborhoods. SpikeGraphormer consistently outperforms existing state-of-the-art approaches across various datasets and makes substantial improvements in training time, inference time, and GPU memory cost (10 ~ 20x lower than vanilla self-attention). It also performs well in cross-domain applications (image and text classification). We release our code at https://github.com/PHD-lanyu/SpikeGraphormer.
EAS-SNN: End-to-End Adaptive Sampling and Representation for Event-based Detection with Recurrent Spiking Neural Networks
2024-03-19
Ziming Wang, Ziling Wang, Huaning Li, Lang Qin, Runhao Jiang, De Ma, Huajin Tang
Event cameras, with their high dynamic range and temporal resolution, are ideally suited for object detection, especially under scenarios with motion blur and challenging lighting conditions. However, while most existing approaches prioritize optimizing spatiotemporal representations with advanced detection backbones and early aggregation functions, the crucial issue of adaptive event sampling remains largely unaddressed. Spiking Neural Networks (SNNs), which operate on an event-driven paradigm through sparse spike communication, emerge as a natural fit for addressing this challenge. In this study, we discover that the neural dynamics of spiking neurons align closely with the behavior of an ideal temporal event sampler. Motivated by this insight, we propose a novel adaptive sampling module that leverages recurrent convolutional SNNs enhanced with temporal memory, facilitating a fully end-to-end learnable framework for event-based detection. Additionally, we introduce Residual Potential Dropout (RPD) and Spike-Aware Training (SAT) to regulate potential distribution and address performance degradation encountered in spike-based sampling modules. Through rigorous testing on neuromorphic datasets for event-based detection, our approach demonstrably surpasses existing state-of-the-art spike-based methods, achieving superior performance with significantly fewer parameters and time steps. For instance, our method achieves a 4.4\% mAP improvement on the Gen1 dataset, while requiring 38\% fewer parameters and three time steps. Moreover, the applicability and effectiveness of our adaptive sampling methodology extend beyond SNNs, as demonstrated through further validation on conventional non-spiking detection models.
Spiking Neural Networks (SNNs) have become an essential paradigm in neuroscience and artificial intelligence, providing brain-inspired computation. Recent advances in literature have studied the network representations of deep neural networks. However, there has been little work that studies representations learned by SNNs, especially using unsupervised local learning methods like spike-timing dependent plasticity (STDP). Recent work by \cite{barannikov2021representation} has introduced a novel method to compare topological mappings of learned representations called Representation Topology Divergence (RTD). Though useful, this method is engineered particularly for feedforward deep neural networks and cannot be used for recurrent networks like Recurrent SNNs (RSNNs). This paper introduces a novel methodology to use RTD to measure the difference between distributed representations of RSNN models with different learning methods. We propose a novel reformulation of RSNNs using feedforward autoencoder networks with skip connections to help us compute the RTD for recurrent networks. Thus, we investigate the learning capabilities of RSNN trained using STDP and the role of heterogeneity in the synaptic dynamics in learning such representations. We demonstrate that heterogeneous STDP in RSNNs yield distinct representations than their homogeneous and surrogate gradient-based supervised learning counterparts. Our results provide insights into the potential of heterogeneous SNN models, aiding the development of more efficient and biologically plausible hybrid artificial intelligence systems.
Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep learning by mimicking the event-driven processing of the brain. Incorporating the Transformers with SNNs has shown promise for accuracy, yet it is incompetent to capture high-frequency patterns like moving edge and pixel-level brightness changes due to their reliance on global self-attention operations. Porting frequency representations in SNN is challenging yet crucial for event-driven vision. To address this issue, we propose the Spiking Wavelet Transformer (SWformer), an attention-free architecture that effectively learns comprehensive spatial-frequency features in a spike-driven manner by leveraging the sparse wavelet transform. The critical component is a Frequency-Aware Token Mixer (FATM) with three branches: 1) spiking wavelet learner for spatial-frequency domain learning, 2) convolution-based learner for spatial feature extraction, and 3) spiking pointwise convolution for cross-channel information aggregation. We also adopt negative spike dynamics to strengthen the frequency representation further. This enables the SWformer to outperform vanilla Spiking Transformers in capturing high-frequency visual components, as evidenced by our empirical results. Experiments on both static and neuromorphic datasets demonstrate SWformer's effectiveness in capturing spatial-frequency patterns in a multiplication-free, event-driven fashion, outperforming state-of-the-art SNNs. SWformer achieves an over 50% reduction in energy consumption, a 21.1% reduction in parameter count, and a 2.40% performance improvement on the ImageNet dataset compared to vanilla Spiking Transformers.
Spiking Neural Networks for Fast-Moving Object Detection on Neuromorphic Hardware Devices Using an Event-Based Camera
2024-03-15
Andreas Ziegler, Karl Vetter, Thomas Gossard, Jonas Tebbe, Andreas Zell
Table tennis is a fast-paced and exhilarating sport that demands agility, precision, and fast reflexes. In recent years, robotic table tennis has become a popular research challenge for robot perception algorithms. Fast and accurate ball detection is crucial for enabling a robotic arm to rally the ball back successfully. Previous approaches have employed conventional frame-based cameras with Convolutional Neural Networks (CNNs) or traditional computer vision methods. In this paper, we propose a novel solution that combines an event-based camera with Spiking Neural Networks (SNNs) for ball detection. We use multiple state-of-the-art SNN frameworks and develop a SNN architecture for each of them, complying with their corresponding constraints. Additionally, we implement the SNN solution across multiple neuromorphic edge devices, conducting comparisons of their accuracies and run-times. This furnishes robotics researchers with a benchmark illustrating the capabilities achievable with each SNN framework and a corresponding neuromorphic edge device. Next to this comparison of SNN solutions for robots, we also show that an SNN on a neuromorphic edge device is able to run in real-time in a closed loop robotic system, a table tennis robot in our use case.
A Hybrid SNN-ANN Network for Event-based Object Detection with Spatial and Temporal Attention
2024-03-15
Soikat Hasan Ahmed, Jan Finkbeiner, Emre Neftci
Event cameras offer high temporal resolution and dynamic range with minimal motion blur, making them promising for object detection tasks. While Spiking Neural Networks (SNNs) are a natural match for event-based sensory data and enable ultra-energy efficient and low latency inference on neuromorphic hardware, Artificial Neural Networks (ANNs) tend to display more stable training dynamics and faster convergence resulting in greater task performance. Hybrid SNN-ANN approaches are a promising alternative, enabling to leverage the strengths of both SNN and ANN architectures. In this work, we introduce the first Hybrid Attention-based SNN-ANN backbone for object detection using event cameras. We propose a novel Attention-based SNN-ANN bridge module to capture sparse spatial and temporal relations from the SNN layer and convert them into dense feature maps for the ANN part of the backbone. Experimental results demonstrate that our proposed method surpasses baseline hybrid and SNN-based approaches by significant margins, with results comparable to existing ANN-based methods. Extensive ablation studies confirm the effectiveness of our proposed modules and architectural choices. These results pave the way toward a hybrid SNN-ANN architecture that achieves ANN like performance at a drastically reduced parameter budget. We implemented the SNN blocks on digital neuromorphic hardware to investigate latency and power consumption and demonstrate the feasibility of our approach.
Event camera, a novel bio-inspired vision sensor, has drawn a lot of attention for its low latency, low power consumption, and high dynamic range. Currently, overfitting remains a critical problem in event-based classification tasks for Spiking Neural Network (SNN) due to its relatively weak spatial representation capability. Data augmentation is a simple but efficient method to alleviate overfitting and improve the generalization ability of neural networks, and saliency-based augmentation methods are proven to be effective in the image processing field. However, there is no approach available for extracting saliency maps from SNNs. Therefore, for the first time, we present Spiking Layer-Time-wise Relevance Propagation rule (SLTRP) and Spiking Layer-wise Relevance Propagation rule (SLRP) in order for SNN to generate stable and accurate CAMs and saliency maps. Based on this, we propose EventRPG, which leverages relevance propagation on the spiking neural network for more efficient augmentation. Our proposed method has been evaluated on several SNN structures, achieving state-of-the-art performance in object recognition tasks including N-Caltech101, CIFAR10-DVS, with accuracies of 85.62% and 85.55%, as well as action recognition task SL-Animals with an accuracy of 91.59%. Our code is available at https://github.com/myuansun/EventRPG.
Neuromorphic force-control in an industrial task: validating energy and latency benefits
2024-03-13
Camilo Amaya, Evan Eames, Gintautas Palinauskas, Alexander Perzylo, Yulia Sandamirskaya, Axel von Arnim
As robots become smarter and more ubiquitous, optimizing the power consumption of intelligent compute becomes imperative towards ensuring the sustainability of technological advancements. Neuromorphic computing hardware makes use of biologically inspired neural architectures to achieve energy and latency improvements compared to conventional von Neumann computing architecture. Applying these benefits to robots has been demonstrated in several works in the field of neurorobotics, typically on relatively simple control tasks. Here, we introduce an example of neuromorphic computing applied to the real-world industrial task of object insertion. We trained a spiking neural network (SNN) to perform force-torque feedback control using a reinforcement learning approach in simulation. We then ported the SNN to the Intel neuromorphic research chip Loihi interfaced with a KUKA robotic arm. At inference time we show latency competitive with current CPU/GPU architectures, two orders of magnitude less energy usage in comparison to traditional low-energy edge-hardware. We offer this example as a proof of concept implementation of a neuromoprhic controller in real-world robotic setting, highlighting the benefits of neuromorphic hardware for the development of intelligent controllers for robots.
In this paper, we present a novel spiking neural network model designed to perform frequency decomposition of spike trains. Our model emulates neural microcircuits theorized in the somatosensory cortex, rendering it a biologically plausible candidate for decoding the spike trains observed in tactile peripheral nerves. We demonstrate the capacity of simple neurons and synapses to replicate the phase-locked loop (PLL) and explore the emergent properties when considering multiple spiking phase-locked loops (sPLLs) with diverse oscillations. We illustrate how these sPLLs can decode textures using the spectral features elicited in peripheral nerves. Leveraging our model's frequency decomposition abilities, we improve state-of-the-art performances on a Multifrequency Spike Train (MST) dataset. This work offers valuable insights into neural processing and presents a practical framework for enhancing artificial neural network capabilities in complex pattern recognition tasks.
In the domain of dynamic graph representation learning (DGRL), the efficient and comprehensive capture of temporal evolution within real-world networks is crucial. Spiking Neural Networks (SNNs), known as their temporal dynamics and low-power characteristic, offer an efficient solution for temporal processing in DGRL task. However, owing to the spike-based information encoding mechanism of SNNs, existing DGRL methods employed SNNs face limitations in their representational capacity. Given this issue, we propose a novel framework named Spike-induced Graph Neural Network (SiGNN) for learning enhanced spatialtemporal representations on dynamic graphs. In detail, a harmonious integration of SNNs and GNNs is achieved through an innovative Temporal Activation (TA) mechanism. Benefiting from the TA mechanism, SiGNN not only effectively exploits the temporal dynamics of SNNs but also adeptly circumvents the representational constraints imposed by the binary nature of spikes. Furthermore, leveraging the inherent adaptability of SNNs, we explore an in-depth analysis of the evolutionary patterns within dynamic graphs across multiple time granularities. This approach facilitates the acquisition of a multiscale temporal node representation.Extensive experiments on various real-world dynamic graph datasets demonstrate the superior performance of SiGNN in the node classification task.
Finding Visual Saliency in Continuous Spike Stream
2024-03-10
Lin Zhu, Xianzhang Chen, Xiao Wang, Hua Huang
As a bio-inspired vision sensor, the spike camera emulates the operational principles of the fovea, a compact retinal region, by employing spike discharges to encode the accumulation of per-pixel luminance intensity. Leveraging its high temporal resolution and bio-inspired neuromorphic design, the spike camera holds significant promise for advancing computer vision applications. Saliency detection mimics the behavior of human beings and captures the most salient region from the scenes. In this paper, we investigate the visual saliency in the continuous spike stream for the first time. To effectively process the binary spike stream, we propose a Recurrent Spiking Transformer (RST) framework, which is based on a full spiking neural network. Our framework enables the extraction of spatio-temporal features from the continuous spatio-temporal spike stream while maintaining low power consumption. To facilitate the training and validation of our proposed model, we build a comprehensive real-world spike-based visual saliency dataset, enriched with numerous light conditions. Extensive experiments demonstrate the superior performance of our Recurrent Spiking Transformer framework in comparison to other spike neural network-based methods. Our framework exhibits a substantial margin of improvement in capturing and highlighting visual saliency in the spike stream, which not only provides a new perspective for spike-based saliency segmentation but also shows a new paradigm for full SNN-based transformer models. The code and dataset are available at \url{https://github.com/BIT-Vision/SVS}.
sVAD: A Robust, Low-Power, and Light-Weight Voice Activity Detection with Spiking Neural Networks
2024-03-09
Qu Yang, Qianhui Liu, Nan Li, Meng Ge, Zeyang Song, Haizhou Li
Speech applications are expected to be low-power and robust under noisy conditions. An effective Voice Activity Detection (VAD) front-end lowers the computational need. Spiking Neural Networks (SNNs) are known to be biologically plausible and power-efficient. However, SNN-based VADs have yet to achieve noise robustness and often require large models for high performance. This paper introduces a novel SNN-based VAD model, referred to as sVAD, which features an auditory encoder with an SNN-based attention mechanism. Particularly, it provides effective auditory feature representation through SincNet and 1D convolution, and improves noise robustness with attention mechanisms. The classifier utilizes Spiking Recurrent Neural Networks (sRNN) to exploit temporal speech information. Experimental results demonstrate that our sVAD achieves remarkable noise robustness and meanwhile maintains low power consumption and a small footprint, making it a promising solution for real-world VAD applications.
As a general method for exploration in deep reinforcement learning (RL), NoisyNet can produce problem-specific exploration strategies. Spiking neural networks (SNNs), due to their binary firing mechanism, have strong robustness to noise, making it difficult to realize efficient exploration with local disturbances. To solve this exploration problem, we propose a noisy spiking actor network (NoisySAN) that introduces time-correlated noise during charging and transmission. Moreover, a noise reduction method is proposed to find a stable policy for the agent. Extensive experimental results demonstrate that our method outperforms the state-of-the-art performance on a wide range of continuous control tasks from OpenAI gym.
Photonic-electronic spiking neuron with multi-modal and multi-wavelength excitatory and inhibitory operation for high-speed neuromorphic sensing and computing
2024-03-06
Weikang Zhang, Matěj Hejda, Qusay Raghib Ali Al-Taai, Dafydd Owen-Newns, Bruno Romeira, José M. L. Figueiredo, Joshua Robertson, Edward Wasige, Antonio Hurtado
We report a multi-modal spiking neuron that allows optical and electronic input and control, and wavelength-multiplexing operation, for use in novel high-speed neuromorphic sensing and computing functionalities. The photonic-electronic neuron is built with a micro-scale, nanostructure resonant tunnelling diode (RTD) with photodetection (PD) capability. Leveraging the advantageous intrinsic properties of this RTD-PD system, namely highly nonlinear characteristics, photo-sensitivity, light-induced I-V curve shift, and the ability to deliver excitable responses under electrical and optical inputs, we successfully achieve flexible neuromorphic spike activation and inhibition regimes through photonic-electrical control. We also demonstrate the ability of this RTD-PD spiking sensing-processing neuron to operate under the simultaneous arrival of multiple wavelength-multiplexed optical signals, due to its large photodetection spectral window (covering the 1310 and 1550 nm telecom wavelength bands). Our results highlight the potential of RTD photonic-electronic neurons to reproduce multiple key excitatory and inhibitory spiking regimes, at high speed (ns-rate spiking responses, with faster sub-ns regimes theoretically predicted) and low energy (requiring only ~10 mV and ~150 microW, electrical and optical input amplitudes, respectively), similar in nature to those commonly found in the biological neurons of the visual system and the brain. This work offers a highly promising approach for the realisation of high-speed, energy-efficient photonic-electronic spiking neurons and spiking neural networks, enabling multi-modal and multi-wavelength operation for sensing and information processing tasks. This work therefore paves the way for innovative high-speed, photonic-electronic, and spike-based neuromorphic sensing and computing systems and artificial intelligence hardware.
Recurrent Spiking Neural Networks (RSNNs) have emerged as a computationally efficient and brain-inspired learning model. The design of sparse RSNNs with fewer neurons and synapses helps reduce the computational complexity of RSNNs. Traditionally, sparse SNNs are obtained by first training a dense and complex SNN for a target task, and, then, pruning neurons with low activity (activity-based pruning) while maintaining task performance. In contrast, this paper presents a task-agnostic methodology for designing sparse RSNNs by pruning a large randomly initialized model. We introduce a novel Lyapunov Noise Pruning (LNP) algorithm that uses graph sparsification methods and utilizes Lyapunov exponents to design a stable sparse RSNN from a randomly initialized RSNN. We show that the LNP can leverage diversity in neuronal timescales to design a sparse Heterogeneous RSNN (HRSNN). Further, we show that the same sparse HRSNN model can be trained for different tasks, such as image classification and temporal prediction. We experimentally show that, in spite of being task-agnostic, LNP increases computational efficiency (fewer neurons and synapses) and prediction performance of RSNNs compared to traditional activity-based pruning of trained dense models.
Gesture recognition on wearable devices is extensively applied in human-computer interaction. Electromyography (EMG) has been used in many gesture recognition systems for its rapid perception of muscle signals. However, analyzing EMG signals on devices, like smart wristbands, usually needs inference models to have high performances, such as low inference latency, low power consumption, and low memory occupation. Therefore, this paper proposes an improved spiking neural network (SNN) to achieve these goals. We propose an adaptive multi-delta coding as a spiking coding method to improve recognition accuracy. We propose two additive solvers for SNN, which can reduce inference energy consumption and amount of parameters significantly, and improve the robustness of temporal differences. In addition, we propose a linear action detection method TAD-LIF, which is suitable for SNNs. TAD-LIF is an improved LIF neuron that can detect transient-state gestures quickly and accurately. We collected two datasets from 20 subjects including 6 micro gestures. The collection devices are two designed lightweight consumer-level sEMG wristbands (3 and 8 electrode channels respectively). Compared to CNN, FCN, and normal SNN-based methods, the proposed SNN has higher recognition accuracy. The accuracy of the proposed SNN is 83.85% and 93.52% on the two datasets respectively. In addition, the inference latency of the proposed SNN is about 1% of CNN, the power consumption is about 0.1% of CNN, and the memory occupation is about 20% of CNN. The proposed methods can be used for precise, high-speed, and low-power micro-gesture recognition tasks, and are suitable for consumer-level intelligent wearable devices, which is a general way to achieve ubiquitous computing.
Parallel Hyperparameter Optimization Of Spiking Neural Network
2024-03-01
Thomas Firmin, Pierre Boulet, El-Ghazali Talbi
Spiking Neural Networks (SNN). SNNs are based on a more biologically inspired approach than usual artificial neural networks. Such models are characterized by complex dynamics between neurons and spikes. These are very sensitive to the hyperparameters, making their optimization challenging. To tackle hyperparameter optimization of SNNs, we initially extended the signal loss issue of SNNs to what we call silent networks. These networks fail to emit enough spikes at their outputs due to mistuned hyperparameters or architecture. Generally, search spaces are heavily restrained, sometimes even discretized, to prevent the sampling of such networks. By defining an early stopping criterion detecting silent networks and by designing specific constraints, we were able to instantiate larger and more flexible search spaces. We applied a constrained Bayesian optimization technique, which was asynchronously parallelized, as the evaluation time of a SNN is highly stochastic. Large-scale experiments were carried-out on a multi-GPU Petascale architecture. By leveraging silent networks, results show an acceleration of the search, while maintaining good performances of both the optimization algorithm and the best solution obtained. We were able to apply our methodology to two popular training algorithms, known as spike timing dependent plasticity and surrogate gradient. Early detection allowed us to prevent worthless and costly computation, directing the search toward promising hyperparameter combinations. Our methodology could be applied to multi-objective problems, where the spiking activity is often minimized to reduce the energy consumption. In this scenario, it becomes essential to find the delicate frontier between low-spiking and silent networks. Finally, our approach may have implications for neural architecture search, particularly in defining suitable spiking architectures.
2024-03-01
Wenjie Wei, Malu Zhang, Jilin Zhang, Ammar Belatreche, Jibin Wu, Zijing Xu, Xuerui Qiu, Hong Chen, Yang Yang, Haizhou Li
Brain-inspired spiking neural networks (SNNs) have gained prominence in the field of neuromorphic computing owing to their low energy consumption during feedforward inference on neuromorphic hardware. However, it remains an open challenge how to effectively benefit from the sparse event-driven property of SNNs to minimize backpropagation learning costs. In this paper, we conduct a comprehensive examination of the existing event-driven learning algorithms, reveal their limitations, and propose novel solutions to overcome them. Specifically, we introduce two novel event-driven learning methods: the spike-timing-dependent event-driven (STD-ED) and membrane-potential-dependent event-driven (MPD-ED) algorithms. These proposed algorithms leverage precise neuronal spike timing and membrane potential, respectively, for effective learning. The two methods are extensively evaluated on static and neuromorphic datasets to confirm their superior performance. They outperform existing event-driven counterparts by up to 2.51% for STD-ED and 6.79% for MPD-ED on the CIFAR-100 dataset. In addition, we theoretically and experimentally validate the energy efficiency of our methods on neuromorphic hardware. On-chip learning experiments achieved a remarkable 30-fold reduction in energy consumption over time-step-based surrogate gradient methods. The demonstrated efficiency and efficacy of the proposed event-driven learning methods emphasize their potential to significantly advance the fields of neuromorphic computing, offering promising avenues for energy-efficiency applications.
A Unified Evaluation Framework for Spiking Neural Network Hardware Accelerators Based on Emerging Non-Volatile Memory Devices
2024-02-29
Debasis Das, Xuanyao Fong
Spiking Neural Networks (SNNs) have emerged as a promising paradigm, offering event-driven and energy-efficient computation. In recent studies, various devices tailored for SNN synapses and neurons have been proposed, leveraging the unique characteristics of emerging non-volatile memory (eNVM) technologies. While substantial progress has been made in exploring the capabilities of SNNs and designing dedicated hardware components, there exists a critical gap in establishing a unified approach for evaluating hardware-level metrics. Specifically, metrics such as latency, and energy consumption, are pivotal in assessing the practical viability and efficiency of the constructed neural network. In this article, we address this gap by presenting a comprehensive framework for evaluating hardware-level metrics in SNNs based on non-volatile memory devices. We systematically analyze the impact of synaptic and neuronal components on energy consumption providing a unified perspective for assessing the overall efficiency of the network. In this study, our emphasis lies on the neuron and synaptic device based on magnetic skyrmions. Nevertheless, our framework is versatile enough to encompass other emerging devices as well. Utilizing our proposed skyrmionic devices, the constructed SNN demonstrates an inference accuracy of approximately 98% and achieves energy consumption on the order of pJ when processing the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset.
2024-02-29
Liuzhenghao Lv, Wei Fang, Li Yuan, Yonghong Tian
Spiking Neural Networks (SNNs) have emerged as a promising third generation of neural networks, offering unique characteristics such as binary outputs, high sparsity, and biological plausibility. However, the lack of effective learning algorithms remains a challenge for SNNs. For instance, while converting artificial neural networks (ANNs) to SNNs circumvents the need for direct training of SNNs, it encounters issues related to conversion errors and high inference time delays. In order to reduce or even eliminate conversion errors while decreasing inference time-steps, we have introduced a novel type of neuron called Group Neurons (GNs). One GN is composed of multiple Integrate-and-Fire (IF) neurons as members, and its neural dynamics are meticulously designed. Based on GNs, we have optimized the traditional ANN-SNN conversion framework. Specifically, we replace the IF neurons in the SNNs obtained by the traditional conversion framework with GNs. The resulting SNNs, which utilize GNs, are capable of achieving accuracy levels comparable to ANNs even within extremely short inference time-steps. The experiments on CIFAR10, CIFAR100, and ImageNet datasets demonstrate the superiority of the proposed methods in terms of both inference accuracy and latency. Code is available at https://github.com/Lyu6PosHao/ANN2SNN_GN.
Spyx: A Library for Just-In-Time Compiled Optimization of Spiking Neural Networks
2024-02-29
Kade M. Heckel, Thomas Nowotny
As the role of artificial intelligence becomes increasingly pivotal in modern society, the efficient training and deployment of deep neural networks have emerged as critical areas of focus. Recent advancements in attention-based large neural architectures have spurred the development of AI accelerators, facilitating the training of extensive, multi-billion parameter models. Despite their effectiveness, these powerful networks often incur high execution costs in production environments. Neuromorphic computing, inspired by biological neural processes, offers a promising alternative. By utilizing temporally-sparse computations, Spiking Neural Networks (SNNs) offer to enhance energy efficiency through a reduced and low-power hardware footprint. However, the training of SNNs can be challenging due to their recurrent nature which cannot as easily leverage the massive parallelism of modern AI accelerators. To facilitate the investigation of SNN architectures and dynamics researchers have sought to bridge Python-based deep learning frameworks such as PyTorch or TensorFlow with custom-implemented compute kernels. This paper introduces Spyx, a new and lightweight SNN simulation and optimization library designed in JAX. By pre-staging data in the expansive vRAM of contemporary accelerators and employing extensive JIT compilation, Spyx allows for SNN optimization to be executed as a unified, low-level program on NVIDIA GPUs or Google TPUs. This approach achieves optimal hardware utilization, surpassing the performance of many existing SNN training frameworks while maintaining considerable flexibility.
Neuromorphic Event-Driven Semantic Communication in Microgrids
2024-02-28
Xiaoguang Diao, Yubo Song, Subham Sahoo, Yuan Li
Synergies between advanced communications, computing and artificial intelligence are unraveling new directions of coordinated operation and resiliency in microgrids. On one hand, coordination among sources is facilitated by distributed, privacy-minded processing at multiple locations, whereas on the other hand, it also creates exogenous data arrival paths for adversaries that can lead to cyber-physical attacks amongst other reliability issues in the communication layer. This long-standing problem necessitates new intrinsic ways of exchanging information between converters through power lines to optimize the system's control performance. Going beyond the existing power and data co-transfer technologies that are limited by efficiency and scalability concerns, this paper proposes neuromorphic learning to implant communicative features using spiking neural networks (SNNs) at each node, which is trained collaboratively in an online manner simply using the power exchanges between the nodes. As opposed to the conventional neuromorphic sensors that operate with spiking signals, we employ an event-driven selective process to collect sparse data for training of SNNs. Finally, its multi-fold effectiveness and reliable performance is validated under simulation conditions with different microgrid topologies and components to establish a new direction in the sense-actuate-compute cycle for power electronic dominated grids and microgrids.
Scalable Superconductor Neuron with Ternary Synaptic Connections for Ultra-Fast SNN Hardware
2024-02-26
Mustafa Altay Karamuftuoglu, Beyza Zeynep Ucpinar, Arash Fayyazi, Sasan Razmkhah, Mehdi Kamal, Massoud Pedram
A novel high-fan-in differential superconductor neuron structure designed for ultra-high-performance Spiking Neural Network (SNN) accelerators is presented. Utilizing a high-fan-in neuron structure allows us to design SNN accelerators with more synaptic connections, enhancing the overall network capabilities. The proposed neuron design is based on superconductor electronics fabric, incorporating multiple superconducting loops, each with two Josephson Junctions. This arrangement enables each input data branch to have positive and negative inductive coupling, supporting excitatory and inhibitory synaptic data. Compatibility with synaptic devices and thresholding operation is achieved using a single flux quantum (SFQ) pulse-based logic style. The neuron design, along with ternary synaptic connections, forms the foundation for a superconductor-based SNN inference. To demonstrate the capabilities of our design, we train the SNN using snnTorch, augmenting the PyTorch framework. After pruning, the demonstrated SNN inference achieves an impressive 96.1% accuracy on MNIST images. Notably, the network exhibits a remarkable throughput of 8.92 GHz while consuming only 1.5 nJ per inference, including the energy consumption associated with cooling to 4K. These results underscore the potential of superconductor electronics in developing high-performance and ultra-energy-efficient neural network accelerator architectures.
Efficient Online Learning for Networks of Two-Compartment Spiking Neurons
2024-02-25
Yujia Yin, Xinyi Chen, Chenxiang Ma, Jibin Wu, Kay Chen Tan
The brain-inspired Spiking Neural Networks (SNNs) have garnered considerable research interest due to their superior performance and energy efficiency in processing temporal signals. Recently, a novel multi-compartment spiking neuron model, namely the Two-Compartment LIF (TC-LIF) model, has been proposed and exhibited a remarkable capacity for sequential modelling. However, training the TC-LIF model presents challenges stemming from the large memory consumption and the issue of gradient vanishing associated with the Backpropagation Through Time (BPTT) algorithm. To address these challenges, online learning methodologies emerge as a promising solution. Yet, to date, the application of online learning methods in SNNs has been predominantly confined to simplified Leaky Integrate-and-Fire (LIF) neuron models. In this paper, we present a novel online learning method specifically tailored for networks of TC-LIF neurons. Additionally, we propose a refined TC-LIF neuron model called Adaptive TC-LIF, which is carefully designed to enhance temporal information integration in online learning scenarios. Extensive experiments, conducted on various sequential benchmarks, demonstrate that our approach successfully preserves the superior sequential modeling capabilities of the TC-LIF neuron while incorporating the training efficiency and hardware friendliness of online learning. As a result, it offers a multitude of opportunities to leverage neuromorphic solutions for processing temporal signals.
Structuring Concept Space with the Musical Circle of Fifths by Utilizing Music Grammar Based Activations
2024-02-22
Tofara Moyo
In this paper, we explore the intriguing similarities between the structure of a discrete neural network, such as a spiking network, and the composition of a piano piece. While both involve nodes or notes that are activated sequentially or in parallel, the latter benefits from the rich body of music theory to guide meaningful combinations. We propose a novel approach that leverages musical grammar to regulate activations in a spiking neural network, allowing for the representation of symbols as attractors. By applying rules for chord progressions from music theory, we demonstrate how certain activations naturally follow others, akin to the concept of attraction. Furthermore, we introduce the concept of modulating keys to navigate different basins of attraction within the network. Ultimately, we show that the map of concepts in our model is structured by the musical circle of fifths, highlighting the potential for leveraging music theory principles in deep learning algorithms.
Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks
2024-02-19
Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He, Zhouchen Lin
Neuromorphic computing with spiking neural networks is promising for energy-efficient artificial intelligence (AI) applications. However, different from humans who continually learn different tasks in a lifetime, neural network models suffer from catastrophic forgetting. How could neuronal operations solve this problem is an important question for AI and neuroscience. Many previous studies draw inspiration from observed neuroscience phenomena and propose episodic replay or synaptic metaplasticity, but they are not guaranteed to explicitly preserve knowledge for neuron populations. Other works focus on machine learning methods with more mathematical grounding, e.g., orthogonal projection on high dimensional spaces, but there is no neural correspondence for neuromorphic computing. In this work, we develop a new method with neuronal operations based on lateral connections and Hebbian learning, which can protect knowledge by projecting activity traces of neurons into an orthogonal subspace so that synaptic weight update will not interfere with old tasks. We show that Hebbian and anti-Hebbian learning on recurrent lateral connections can effectively extract the principal subspace of neural activities and enable orthogonal projection. This provides new insights into how neural circuits and Hebbian learning can help continual learning, and also how the concept of orthogonal projection can be realized in neuronal systems. Our method is also flexible to utilize arbitrary training methods based on presynaptic activities/traces. Experiments show that our method consistently solves forgetting for spiking neural networks with nearly zero forgetting under various supervised training methods with different error propagation approaches, and outperforms previous approaches under various settings. Our method can pave a solid path for building continual neuromorphic computing systems.
Low-power SNN-based audio source localisation using a Hilbert Transform spike encoding scheme
2024-02-19
Saeid Haghighatshoar, Dylan R Muir
Sound source localisation is used in many consumer electronics devices, to help isolate audio from individual speakers and to reject noise. Localization is frequently accomplished by "beamforming" algorithms, which combine microphone audio streams to improve received signal power from particular incident source directions. Beamforming algorithms generally use knowledge of the frequency components of the audio source, along with the known microphone array geometry, to analytically phase-shift microphone streams before combining them. A dense set of band-pass filters is often used to obtain known-frequency "narrowband" components from wide-band audio streams. These approaches achieve high accuracy, but state of the art narrowband beamforming algorithms are computationally demanding, and are therefore difficult to integrate into low-power IoT devices. We demonstrate a novel method for sound source localisation in arbitrary microphone arrays, designed for efficient implementation in ultra-low-power spiking neural networks (SNNs). We use a novel short-time Hilbert transform (STHT) to remove the need for demanding band-pass filtering of audio, and introduce a new accompanying method for audio encoding with spiking events. Our beamforming and localisation approach achieves state-of-the-art accuracy for SNN methods, and comparable with traditional non-SNN super-resolution approaches. We deploy our method to low-power SNN audio inference hardware, and achieve much lower power consumption compared with super-resolution methods. We demonstrate that signal processing approaches can be co-designed with spiking neural network implementations to achieve high levels of power efficiency. Our new Hilbert-transform-based method for beamforming promises to also improve the efficiency of traditional DSP-based signal processing.
TDE-3: An improved prior for optical flow computation in spiking neural networks
2024-02-18
Matthew Yedutenko, Federico Paredes-Valles, Lyes Khacef, Guido C. H. E. De Croon
Motion detection is a primary task required for robotic systems to perceive and navigate in their environment. Proposed in the literature bioinspired neuromorphic Time-Difference Encoder (TDE-2) combines event-based sensors and processors with spiking neural networks to provide real-time and energy-efficient motion detection through extracting temporal correlations between two points in space. However, on the algorithmic level, this design leads to loss of direction-selectivity of individual TDEs in textured environments. Here we propose an augmented 3-point TDE (TDE-3) with additional inhibitory input that makes TDE-3 direction-selectivity robust in textured environments. We developed a procedure to train the new TDE-3 using backpropagation through time and surrogate gradients to linearly map input velocities into an output spike count or an Inter-Spike Interval (ISI). Our work is the first instance of training a spiking neuron to have a specific ISI. Using synthetic data we compared training and inference with spike count and ISI with respect to changes in stimuli dynamic range, spatial frequency, and level of noise. ISI turns out to be more robust towards variation in spatial frequency, whereas the spike count is a more reliable training signal in the presence of noise. We performed the first in-depth quantitative investigation of optical flow coding with TDE and compared TDE-2 vs TDE-3 in terms of energy-efficiency and coding precision. Results show that on the network level both detectors show similar precision (20 degree angular error, 88% correlation with ground truth). Yet, due to the more robust direction-selectivity of individual TDEs, TDE-3 based network spike less and hence is more energy-efficient. Reported precision is on par with model-based methods but the spike-based processing of the TDEs provides allows more energy-efficient inference with neuromorphic hardware.
Impact of the La2NiO4+δ oxygen content on the synaptic properties of the TiN/La2NiO4+δ/Pt memristive devices
2024-02-18
Aleksandra Koroleva, Thoai-Khanh Khuu, César Magén, Hervé Roussel, Carmen Jiménez, Céline Ternon, Elena-Ioana Vatajelu, Mónica Burriel
The rapid development of brain-inspired computing requires new artificial components and architectures for its hardware implementation. In this regard, memristive devices emerged as potential candidates for artificial synapses because of their ability to emulate the plasticity of the biological synapses. In this work, the synaptic behavior of the TiN/La2NiO4+{\delta}/Pt memristive devices based on thermally annealed La2NiO4+{\delta} films is thoroughly investigated. Using electron energy loss spectroscopy, we show that annealing using reducing (Ar) or oxidizing (O2) atmospheres affects the interstitial oxygen content ({\delta}) in the La2NiO4+{\delta} films. Electrical characterization shows that both devices exhibit long-term potentiation/depression and spike-timing-dependent plasticity, which makes them suitable for neuromorphic applications. At the same time, the Ar annealed TiN/La2NiO4+{\delta}/Pt device demonstrates non-volatile properties with low energy consumption during the learning process. On the other hand, in the O2 annealed TiN/La2NiO4+{\delta}/Pt device the resistive switching behavior is more volatile and requires more energy for synaptic learning. Finally, the simulation tools show that spiking neural network architectures with unsupervised learning rules based on the experimental data achieve high inference accuracy in the digit recognition task, which proves the potential of TiN/La2NiO4+{\delta}/Pt devices for artificial synapse applications.
2024-02-18
Shu Yang, Hanzhi Ma, Chengting Yu, Aili Wang, Er-Ping Li
Spiking neural networks (SNNs) have low power consumption and bio-interpretable characteristics, and are considered to have tremendous potential for energy-efficient computing. However, the exploration of SNNs on image generation tasks remains very limited, and a unified and effective structure for SNN-based generative models has yet to be proposed. In this paper, we explore a novel diffusion model architecture within spiking neural networks. We utilize transformer to replace the commonly used U-net structure in mainstream diffusion models. It can generate higher quality images with relatively lower computational cost and shorter sampling time. It aims to provide an empirical baseline for research of generative models based on SNNs. Experiments on MNIST, Fashion-MNIST, and CIFAR-10 datasets demonstrate that our work is highly competitive compared to existing SNN generative models.
SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network-based Autonomous Agents
2024-02-17
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
Autonomous mobile agents (e.g., UAVs and UGVs) are typically expected to incur low power/energy consumption for solving machine learning tasks (such as object recognition), as these mobile agents are usually powered by portable batteries. These requirements can be fulfilled by Spiking Neural Networks (SNNs), since their bio-inspired spike-based operations offer high accuracy and ultra low-power/energy computation. Currently, most of the SNN architectures are derived from Artificial Neural Networks whose neurons' architectures and operations are different from SNNs, or developed without considering memory budgets from the underlying processing hardware of autonomous mobile agents. These limitations hinder SNNs from reaching their full potential in accuracy and efficiency. Toward this, we propose SpikeNAS, a novel fast memory-aware neural architecture search (NAS) framework for SNNs that quickly finds an appropriate SNN architecture with high accuracy under the given memory budgets from autonomous mobile agents. To do this, our SpikeNAS employs several key steps: analyzing the impacts of network operations on the accuracy, enhancing the network architecture to improve the learning quality, and developing a fast memory-aware search algorithm. The experimental results show that our SpikeNAS improves the searching time and maintains high accuracy as compared to state-of-the-art while meeting the given memory budgets (e.g., 4.4x faster search with 1.3% accuracy improvement for CIFAR100, using an Nvidia RTX 6000 Ada GPU machine), thereby quickly providing the appropriate SNN architecture for the memory-constrained autonomous mobile agents.
Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips
2024-02-15
Man Yao, Jiakui Hu, Tianxiang Hu, Yifan Xu, Zhaokun Zhou, Yonghong Tian, Bo Xu, Guoqi Li
Neuromorphic computing, which exploits Spiking Neural Networks (SNNs) on neuromorphic chips, is a promising energy-efficient alternative to traditional AI. CNN-based SNNs are the current mainstream of neuromorphic computing. By contrast, no neuromorphic chips are designed especially for Transformer-based SNNs, which have just emerged, and their performance is only on par with CNN-based SNNs, offering no distinct advantage. In this work, we propose a general Transformer-based SNN architecture, termed as ``Meta-SpikeFormer", whose goals are: 1) Lower-power, supports the spike-driven paradigm that there is only sparse addition in the network; 2) Versatility, handles various vision tasks; 3) High-performance, shows overwhelming performance advantages over CNN-based SNNs; 4) Meta-architecture, provides inspiration for future next-generation Transformer-based neuromorphic chip designs. Specifically, we extend the Spike-driven Transformer in \citet{yao2023spike} into a meta architecture, and explore the impact of structure, spike-driven self-attention, and skip connection on its performance. On ImageNet-1K, Meta-SpikeFormer achieves 80.0\% top-1 accuracy (55M), surpassing the current state-of-the-art (SOTA) SNN baselines (66M) by 3.7\%. This is the first direct training SNN backbone that can simultaneously supports classification, detection, and segmentation, obtaining SOTA results in SNNs. Finally, we discuss the inspiration of the meta SNN architecture for neuromorphic chip design. Source code and models are available at \url{https://github.com/BICLab/Spike-Driven-Transformer-V2}.
Spiking Neural Networks (SNNs) have been recently integrated into Transformer architectures due to their potential to reduce computational demands and to improve power efficiency. Yet, the implementation of the attention mechanism using spiking signals on general-purpose computing platforms remains inefficient. In this paper, we propose a novel framework leveraging stochastic computing (SC) to effectively execute the dot-product attention for SNN-based Transformers. We demonstrate that our approach can achieve high classification accuracy ($83.53\%$) on CIFAR-10 within 10 time steps, which is comparable to the performance of a baseline artificial neural network implementation ($83.66\%$). We estimate that the proposed SC approach can lead to over $6.3\times$ reduction in computing energy and $1.7\times$ reduction in memory access costs for a digital CMOS-based ASIC design. We experimentally validate our stochastic attention block design through an FPGA implementation, which is shown to achieve $48\times$ lower latency as compared to a GPU implementation, while consuming $15\times$ less power.
Towards Chip-in-the-loop Spiking Neural Network Training via Metropolis-Hastings Sampling
2024-02-09
Ali Safa, Vikrant Jaltare, Samira Sebt, Kameron Gano, Johannes Leugering, Georges Gielen, Gert Cauwenberghs
This paper studies the use of Metropolis-Hastings sampling for training Spiking Neural Network (SNN) hardware subject to strong unknown non-idealities, and compares the proposed approach to the common use of the backpropagation of error (backprop) algorithm and surrogate gradients, widely used to train SNNs in literature. Simulations are conducted within a chip-in-the-loop training context, where an SNN subject to unknown distortion must be trained to detect cancer from measurements, within a biomedical application context. Our results show that the proposed approach strongly outperforms the use of backprop by up to $27\%$ higher accuracy when subject to strong hardware non-idealities. Furthermore, our results also show that the proposed approach outperforms backprop in terms of SNN generalization, needing $>10 \times$ less training data for achieving effective accuracy. These findings make the proposed training approach well-suited for SNN implementations in analog subthreshold circuits and other emerging technologies where unknown hardware non-idealities can jeopardize backprop.
Fine-Tuning Surrogate Gradient Learning for Optimal Hardware Performance in Spiking Neural Networks
2024-02-09
Ilkin Aliyev, Tosiron Adegbija
The highly sparse activations in Spiking Neural Networks (SNNs) can provide tremendous energy efficiency benefits when carefully exploited in hardware. The behavior of sparsity in SNNs is uniquely shaped by the dataset and training hyperparameters. This work reveals novel insights into the impacts of training on hardware performance. Specifically, we explore the trade-offs between model accuracy and hardware efficiency. We focus on three key hyperparameters: surrogate gradient functions, beta, and membrane threshold. Results on an FPGA-based hardware platform show that the fast sigmoid surrogate function yields a lower firing rate with similar accuracy compared to the arctangent surrogate on the SVHN dataset. Furthermore, by cross-sweeping the beta and membrane threshold hyperparameters, we can achieve a 48% reduction in hardware-based inference latency with only 2.88% trade-off in inference accuracy compared to the default setting. Overall, this study highlights the importance of fine-tuning model hyperparameters as crucial for designing efficient SNN hardware accelerators, evidenced by the fine-tuned model achieving a 1.72x improvement in accelerator efficiency (FPS/W) compared to the most recent work.
PULSE: Parametric Hardware Units for Low-power Sparsity-Aware Convolution Engine
2024-02-09
Ilkin Aliyev, Tosiron Adegbija
Spiking Neural Networks (SNNs) have become popular for their more bio-realistic behavior than Artificial Neural Networks (ANNs). However, effectively leveraging the intrinsic, unstructured sparsity of SNNs in hardware is challenging, especially due to the variability in sparsity across network layers. This variability depends on several factors, including the input dataset, encoding scheme, and neuron model. Most existing SNN accelerators fail to account for the layer-specific workloads of an application (model + dataset), leading to high energy consumption. To address this, we propose a design-time parametric hardware generator that takes layer-wise sparsity and the number of processing elements as inputs and synthesizes the corresponding hardware. The proposed design compresses sparse spike trains using a priority encoder and efficiently shifts the activations across the network's layers. We demonstrate the robustness of our proposed approach by first profiling a given application's characteristics followed by performing efficient resource allocation. Results on a Xilinx Kintex FPGA (Field Programmable Gate Arrays) using MNIST, FashionMNIST, and SVHN datasets show a 3.14x improvement in accelerator efficiency (FPS/W) compared to a sparsity-oblivious systolic array-based accelerator. Compared to the most recent sparsity-aware work, our solution improves efficiency by 1.72x.
Spiking Neural Network Enhanced Hand Gesture Recognition Using Low-Cost Single-photon Avalanche Diode Array
2024-02-08
Zhenya Zang, Xingda Li, David Day Uei Li
We present a compact spiking convolutional neural network (SCNN) and spiking multilayer perceptron (SMLP) to recognize ten different gestures in dark and bright light environments, using a $9.6 single-photon avalanche diode (SPAD) array. In our hand gesture recognition (HGR) system, photon intensity data was leveraged to train and test the network. A vanilla convolutional neural network (CNN) was also implemented to compare the performance of SCNN with the same network topologies and training strategies. Our SCNN was trained from scratch instead of being converted from the CNN. We tested the three models in dark and ambient light (AL)-corrupted environments. The results indicate that SCNN achieves comparable accuracy (90.8%) to CNN (92.9%) and exhibits lower floating operations with only 8 timesteps. SMLP also presents a trade-off between computational workload and accuracy. The code and collected datasets of this work are available at https://github.com/zzy666666zzy/TinyLiDAR_NET_SNN.
MTSA-SNN: A Multi-modal Time Series Analysis Model Based on Spiking Neural Network
2024-02-08
Chengzhi Liu, Zheng Tao, Zihong Luo, Chenghao Liu
Time series analysis and modelling constitute a crucial research area. Traditional artificial neural networks struggle with complex, non-stationary time series data due to high computational complexity, limited ability to capture temporal information, and difficulty in handling event-driven data. To address these challenges, we propose a Multi-modal Time Series Analysis Model Based on Spiking Neural Network (MTSA-SNN). The Pulse Encoder unifies the encoding of temporal images and sequential information in a common pulse-based representation. The Joint Learning Module employs a joint learning function and weight allocation mechanism to fuse information from multi-modal pulse signals complementary. Additionally, we incorporate wavelet transform operations to enhance the model's ability to analyze and evaluate temporal information. Experimental results demonstrate that our method achieved superior performance on three complex time-series tasks. This work provides an effective event-driven approach to overcome the challenges associated with analyzing intricate temporal information. Access to the source code is available at https://github.com/Chenngzz/MTSA-SNN}{https://github.com/Chenngzz/MTSA-SNN
Artificial neural networks (ANNs) can help camera-based remote photoplethysmography (rPPG) in measuring cardiac activity and physiological signals from facial videos, such as pulse wave, heart rate and respiration rate with better accuracy. However, most existing ANN-based methods require substantial computing resources, which poses challenges for effective deployment on mobile devices. Spiking neural networks (SNNs), on the other hand, hold immense potential for energy-efficient deep learning owing to their binary and event-driven architecture. To the best of our knowledge, we are the first to introduce SNNs into the realm of rPPG, proposing a hybrid neural network (HNN) model, the Spiking-PhysFormer, aimed at reducing power consumption. Specifically, the proposed Spiking-PhyFormer consists of an ANN-based patch embedding block, SNN-based transformer blocks, and an ANN-based predictor head. First, to simplify the transformer block while preserving its capacity to aggregate local and global spatio-temporal features, we design a parallel spike transformer block to replace sequential sub-blocks. Additionally, we propose a simplified spiking self-attention mechanism that omits the value parameter without compromising the model's performance. Experiments conducted on four datasets-PURE, UBFC-rPPG, UBFC-Phys, and MMPD demonstrate that the proposed model achieves a 12.4\% reduction in power consumption compared to PhysFormer. Additionally, the power consumption of the transformer block is reduced by a factor of 12.2, while maintaining decent performance as PhysFormer and other ANN-based models.
Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Compared to conventional deep Artificial Neural Networks (ANNs), SNNs exhibit superior efficiency and capability to process temporal information. However, it remains a challenge to train SNNs due to their undifferentiable spiking mechanism. The surrogate gradients method is commonly used to train SNNs, but often comes with an accuracy disadvantage over ANNs counterpart. We link the degraded accuracy to the vanishing of gradient on the temporal dimension through the analytical and experimental study of the training process of Leaky Integrate-and-Fire (LIF) Neuron-based SNNs. Moreover, we propose the Complementary Leaky Integrate-and-Fire (CLIF) Neuron. CLIF creates extra paths to facilitate the backpropagation in computing temporal gradient while keeping binary output. CLIF is hyperparameter-free and features broad applicability. Extensive experiments on a variety of datasets demonstrate CLIF's clear performance advantage over other neuron models. Furthermore, the CLIF's performance even slightly surpasses superior ANNs with identical network structure and training conditions. The code is available at https://github.com/HuuYuLong/Complementary-LIF.
A Quasi-Stationary Approach to Metastability in a System of Spiking Neurons with Synaptic Plasticity
2024-02-07
Christophe Pouzat, Morgan André
After reviewing the behavioral studies of working memory and of the cellular substrate of the latter, we argue that metastable states constitute candidates for the type of transient information storage required by working memory. We then present a simple neural network model made of stochastic units whose synapses exhibit short-term facilitation. The Markov process dynamics of this model was specifically designed to be analytically tractable, simple to simulate numerically and to exhibit a quasi-stationary distribution (QSD). Since the state space is finite this QSD is also a Yaglom limit, which allows us to bridge the gap between quasi-stationarity and metastability by considering the relative orders of magnitude of the relaxation and absorption times. We present first analytical results: characterization of the absorbing region of the Markov process, irreducibility outside this absorbing region and consequently existence and uniqueness of a QSD. We then apply Perron-Frobenius spectral analysis to obtain any specific QSD, and design an approximate method for the first moments of this QSD when the exact method is intractable. Finally we use these methods to study the relaxation time toward the QSD and establish numerically the memorylessness of the time of extinction.
Algorithms designed for addressing typical supervised classification problems can only learn from a fixed set of samples and labels, making them unsuitable for the real world, where data arrives as a stream of samples often associated with multiple labels over time. This motivates the study of task-agnostic continual multi-label learning problems. While algorithms using deep learning approaches for continual multi-label learning have been proposed in the recent literature, they tend to be computationally heavy. Although spiking neural networks (SNNs) offer a computationally efficient alternative to artificial neural networks, existing literature has not used SNNs for continual multi-label learning. Also, accurately determining multiple labels with SNNs is still an open research problem. This work proposes a dual output spiking architecture (DOSA) to bridge these research gaps. A novel imbalance-aware loss function is also proposed, improving the multi-label classification performance of the model by making it more robust to data imbalance. A modified F1 score is presented to evaluate the effectiveness of the proposed loss function in handling imbalance. Experiments on several benchmark multi-label datasets show that DOSA trained with the proposed loss function shows improved robustness to data imbalance and obtains better continual multi-label learning performance than CIFDM, a previous state-of-the-art algorithm.
Forward Direct Feedback Alignment for Online Gradient Estimates of Spiking Neural Networks
2024-02-06
Florian Bacho, Dminique Chu
There is an interest in finding energy efficient alternatives to current state of the art neural network training algorithms. Spiking neural network are a promising approach, because they can be simulated energy efficiently on neuromorphic hardware platforms. However, these platforms come with limitations on the design of the training algorithm. Most importantly, backpropagation cannot be implemented on those. We propose a novel neuromorphic algorithm, the \textit{Spiking Forward Direct Feedback Alignment} (SFDFA) algorithm, an adaption of \textit{Forward Direct Feedback Alignment} to train SNNs. SFDFA estimates the weights between output and hidden neurons as feedback connections. The main contribution of this paper is to describe how exact local gradients of spikes can be computed in an online manner while taking into account the intra-neuron dependencies between post-synaptic spikes and derive a dynamical system for neuromorphic hardware compatibility. We compare the SFDFA algorithm with a number of competitor algorithms and show that the proposed algorithm achieves higher performance and convergence rates.
Magnetic Field Gated and Current Controlled Spintronic Mem-transistor Neuron -based Spiking Neural Networks
2024-02-06
Aijaz H. Lone, Meng Tang, Daniel N. Rahimi, Xuecui Zou, Dongxing Zheng, Hossein Fariborzi, Xixiang Zhang, Gianluca Setti
Spintronic devices, such as the domain walls and skyrmions, have shown significant potential for applications in energy-efficient data storage and beyond CMOS computing architectures. In recent years, spiking neural networks have shown more bio-plausibility. Based on the magnetic multilayer spintronic devices, we demonstrate the magnetic field-gated Leaky integrate and fire neuron characteristics for the spiking neural network applications. The LIF characteristics are controlled by the current pulses, which drive the domain wall, and an external magnetic field is used as the bias to tune the firing properties of the neuron. Thus, the device works like a gate-controlled LIF neuron, acting like a spintronic Mem-Transistor device. We develop a LIF neuron model based on the measured characteristics to show the device integration in the system-level SNNs. We extend the study and propose a scaled version of the demonstrated device with a multilayer spintronic domain wall magnetic tunnel junction as a LIF neuron. using the combination of SOT and the variation of the demagnetization energy across the thin film, the modified leaky integrate and fire LIF neuron characteristics are realized in the proposed devices. The neuron device characteristics are modeled as the modified LIF neuron model. Finally, we integrate the measured and simulated neuron models in the 3-layer spiking neural network and convolutional spiking neural network CSNN framework to test these spiking neuron models for classification of the MNIST and FMNIST datasets. In both architectures, the network achieves classification accuracy above 96%. Considering the good system-level performance, mem-transistor properties, and promise for scalability. The presented devices show an excellent properties for neuromorphic computing applications.
Time-Distributed Backdoor Attacks on Federated Spiking Learning
2024-02-05
Gorka Abad, Stjepan Picek, Aitor Urbieta
This paper investigates the vulnerability of spiking neural networks (SNNs) and federated learning (FL) to backdoor attacks using neuromorphic data. Despite the efficiency of SNNs and the privacy advantages of FL, particularly in low-powered devices, we demonstrate that these systems are susceptible to such attacks. We first assess the viability of using FL with SNNs using neuromorphic data, showing its potential usage. Then, we evaluate the transferability of known FL attack methods to SNNs, finding that these lead to suboptimal attack performance. Therefore, we explore backdoor attacks involving single and multiple attackers to improve the attack performance. Our primary contribution is developing a novel attack strategy tailored to SNNs and FL, which distributes the backdoor trigger temporally and across malicious devices, enhancing the attack's effectiveness and stealthiness. In the best case, we achieve a 100 attack success rate, 0.13 MSE, and 98.9 SSIM. Moreover, we adapt and evaluate an existing defense against backdoor attacks, revealing its inadequacy in protecting SNNs. This study underscores the need for robust security measures in deploying SNNs and FL, particularly in the context of backdoor attacks.
Spiking neural networks (SNNs), inspired by the spiking behavior of biological neurons, provide a unique pathway for capturing the intricacies of temporal data. However, applying SNNs to time-series forecasting is challenging due to difficulties in effective temporal alignment, complexities in encoding processes, and the absence of standardized guidelines for model selection. In this paper, we propose a framework for SNNs in time-series forecasting tasks, leveraging the efficiency of spiking neurons in processing temporal information. Through a series of experiments, we demonstrate that our proposed SNN-based approaches achieve comparable or superior results to traditional time-series forecasting methods on diverse benchmarks with much less energy consumption. Furthermore, we conduct detailed analysis experiments to assess the SNN's capacity to capture temporal dependencies within time-series data, offering valuable insights into its nuanced strengths and effectiveness in modeling the intricate dynamics of temporal data. Our study contributes to the expanding field of SNNs and offers a promising alternative for time-series forecasting tasks, presenting a pathway for the development of more biologically inspired and temporally aware forecasting models. Our code is available at https://github.com/microsoft/SeqSNN.
2024-02-02
Saya Higuchi, Sebastian Kairat, Sander M. Bohte, Sebastian Otte
The resonate-and-fire (RF) neuron, introduced over two decades ago, is a simple, efficient, yet biologically plausible spiking neuron model, which can extract frequency patterns within the time domain due to its resonating membrane dynamics. However, previous RF formulations suffer from intrinsic shortcomings that limit effective learning and prevent exploiting the principled advantage of RF neurons. Here, we introduce the balanced RF (BRF) neuron, which alleviates some of the intrinsic limitations of vanilla RF neurons and demonstrates its effectiveness within recurrent spiking neural networks (RSNNs) on various sequence learning tasks. We show that networks of BRF neurons achieve overall higher task performance, produce only a fraction of the spikes, and require significantly fewer parameters as compared to modern RSNNs. Moreover, BRF-RSNN consistently provide much faster and more stable training convergence, even when bridging many hundreds of time steps during backpropagation through time (BPTT). These results underscore that our BRF-RSNN is a strong candidate for future large-scale RSNN architectures, further lines of research in SNN methodology, and more efficient hardware implementations.
Spiking CenterNet: A Distillation-boosted Spiking Neural Network for Object Detection
2024-02-02
Lennard Bodden, Franziska Schwaiger, Duc Bach Ha, Lars Kreuzberg, Sven Behnke
In the era of AI at the edge, self-driving cars, and climate change, the need for energy-efficient, small, embedded AI is growing. Spiking Neural Networks (SNNs) are a promising approach to address this challenge, with their event-driven information flow and sparse activations. We propose Spiking CenterNet for object detection on event data. It combines an SNN CenterNet adaptation with an efficient M2U-Net-based decoder. Our model significantly outperforms comparable previous work on Prophesee's challenging GEN1 Automotive Detection Dataset while using less than half the energy. Distilling the knowledge of a non-spiking teacher into our SNN further increases performance. To the best of our knowledge, our work is the first approach that takes advantage of knowledge distillation in the field of spiking object detection.
Benchmarking Spiking Neural Network Learning Methods with Varying Locality
2024-02-01
Jiaqi Lin, Sen Lu, Malyaban Bal, Abhronil Sengupta
Spiking Neural Networks (SNNs), providing more realistic neuronal dynamics, have shown to achieve performance comparable to Artificial Neural Networks (ANNs) in several machine learning tasks. Information is processed as spikes within SNNs in an event-based mechanism that significantly reduces energy consumption. However, training SNNs is challenging due to the non-differentiable nature of the spiking mechanism. Traditional approaches, such as Backpropagation Through Time (BPTT), have shown effectiveness but comes with additional computational and memory costs and are biologically implausible. In contrast, recent works propose alternative learning methods with varying degrees of locality, demonstrating success in classification tasks. In this work, we show that these methods share similarities during the training process, while they present a trade-off between biological plausibility and performance. Further, this research examines the implicitly recurrent nature of SNNs and investigates the influence of addition of explicit recurrence to SNNs. We experimentally prove that the addition of explicit recurrent weights enhances the robustness of SNNs. We also investigate the performance of local learning methods under gradient and non-gradient based adversarial attacks.
Parallel Spiking Unit for Efficient Training of Spiking Neural Networks
2024-02-01
Yang Li, Yinqian Sun, Xiang He, Yiting Dong, Dongcheng Zhao, Yi Zeng
Efficient parallel computing has become a pivotal element in advancing artificial intelligence. Yet, the deployment of Spiking Neural Networks (SNNs) in this domain is hampered by their inherent sequential computational dependency. This constraint arises from the need for each time step's processing to rely on the preceding step's outcomes, significantly impeding the adaptability of SNN models to massively parallel computing environments. Addressing this challenge, our paper introduces the innovative Parallel Spiking Unit (PSU) and its two derivatives, the Input-aware PSU (IPSU) and Reset-aware PSU (RPSU). These variants skillfully decouple the leaky integration and firing mechanisms in spiking neurons while probabilistically managing the reset process. By preserving the fundamental computational attributes of the spiking neuron model, our approach enables the concurrent computation of all membrane potential instances within the SNN, facilitating parallel spike output generation and substantially enhancing computational efficiency. Comprehensive testing across various datasets, including static and sequential images, Dynamic Vision Sensor (DVS) data, and speech datasets, demonstrates that the PSU and its variants not only significantly boost performance and simulation speed but also augment the energy efficiency of SNNs through enhanced sparsity in neural activity. These advancements underscore the potential of our method in revolutionizing SNN deployment for high-performance parallel computing applications.
Compared to traditional Artificial Neural Network (ANN), Spiking Neural Network (SNN) has garnered widespread academic interest for its intrinsic ability to transmit information in a more biological-inspired and energy-efficient manner. However, despite previous efforts to optimize the learning gradients and model structure of SNNs through various methods, SNNs still lag behind ANNs in terms of performance to some extent. The recently proposed multi-threshold model provides more possibilities for further enhancing the learning capability of SNNs. In this paper, we rigorously analyze the relationship among the multi-threshold model, vanilla spiking model and quantized ANNs from a mathematical perspective, then propose a novel LM-HT model, which is an equidistant multi-hierarchical model that can dynamically regulate the global input current and membrane potential leakage on the time dimension. In addition, we note that the direct training algorithm based on the LM-HT model can seamlessly integrate with the traditional ANN-SNN Conversion framework. This novel hybrid learning framework can effectively improve the relatively poor performance of converted SNNs under low time latency. Extensive experimental results have demonstrated that our LM-HT model can significantly outperform previous state-of-the-art works on various types of datasets, which promote SNNs to achieve a brand-new level of performance comparable to quantized ANNs.
With the mainstream integration of machine learning into security-sensitive domains such as healthcare and finance, concerns about data privacy have intensified. Conventional artificial neural networks (ANNs) have been found vulnerable to several attacks that can leak sensitive data. Particularly, model inversion (MI) attacks enable the reconstruction of data samples that have been used to train the model. Neuromorphic architectures have emerged as a paradigm shift in neural computing, enabling asynchronous and energy-efficient computation. However, little to no existing work has investigated the privacy of neuromorphic architectures against model inversion. Our study is motivated by the intuition that the non-differentiable aspect of spiking neural networks (SNNs) might result in inherent privacy-preserving properties, especially against gradient-based attacks. To investigate this hypothesis, we propose a thorough exploration of SNNs' privacy-preserving capabilities. Specifically, we develop novel inversion attack strategies that are comprehensively designed to target SNNs, offering a comparative analysis with their conventional ANN counterparts. Our experiments, conducted on diverse event-based and static datasets, demonstrate the effectiveness of the proposed attack strategies and therefore questions the assumption of inherent privacy-preserving in neuromorphic architectures.
SNNLP: Energy-Efficient Natural Language Processing Using Spiking Neural Networks
2024-01-31
R. Alexander Knipper, Kaniz Mishty, Mehdi Sadi, Shubhra Kanti Karmaker Santu
As spiking neural networks receive more attention, we look toward applications of this computing paradigm in fields other than computer vision and signal processing. One major field, underexplored in the neuromorphic setting, is Natural Language Processing (NLP), where most state-of-the-art solutions still heavily rely on resource-consuming and power-hungry traditional deep learning architectures. Therefore, it is compelling to design NLP models for neuromorphic architectures due to their low energy requirements, with the additional benefit of a more human-brain-like operating model for processing information. However, one of the biggest issues with bringing NLP to the neuromorphic setting is in properly encoding text into a spike train so that it can be seamlessly handled by both current and future SNN architectures. In this paper, we compare various methods of encoding text as spikes and assess each method's performance in an associated SNN on a downstream NLP task, namely, sentiment analysis. Furthermore, we go on to propose a new method of encoding text as spikes that outperforms a widely-used rate-coding technique, Poisson rate-coding, by around 13\% on our benchmark NLP tasks. Subsequently, we demonstrate the energy efficiency of SNNs implemented in hardware for the sentiment analysis task compared to traditional deep neural networks, observing an energy efficiency increase of more than 32x during inference and 60x during training while incurring the expected energy-performance tradeoff.
Picosecond transfer from short-term to long-term memory in analog antiferromagnetic memory device
2024-01-30
M. Surynek, J. Zubac, K. Olejnik, A. Farkas, F. Krizek, L. Nadvornik, P. Kubascik, F. Trojanek, R. P. Campion, V. Novak, T. Jungwirth, P. Nemec
Experiments in materials with a compensated ordering of magnetic moments have demonstrated a potential for approaching the thermodynamic limit of the fastest and least-dissipative operation of a digital memory bit. In addition, these materials are very promising for a construction of energy-efficient analog devices with neuromorphic functionalities, which are inspired by computing-in-memory capabilities of the human brain. In this paper, we report on experimental separation of switching-related and heat-related resistance signal dynamics in memory devices microfabricated from CuMnAs antiferromagnetic metal. We show that the memory variable multilevel resistance can be used as a long-term memory (LTM), lasting up to minutes at room temperature. In addition, ultrafast reflectivity change and heat dissipation from nanoscale-thickness CuMnAs films, taking place on picosecond to hundreds of nanoseconds time scales, can be used as a short-term memory (STM). Information about input stimuli, represented by femtosecond laser pulses, can be transferred from STM to LTM after rehearsals at picosecond to nanosecond times in these memory devices, where information can be retrieved at times up to 10^15 longer than the input pulse duration. Our results open a route towards ultra-fast low-power implementations of spiking neuron and synapse functionalities using a resistive analog antiferromagnetic memory.
jaxsnn: Event-driven Gradient Estimation for Analog Neuromorphic Hardware
2024-01-30
Eric Müller, Moritz Althaus, Elias Arnold, Philipp Spilger, Christian Pehle, Johannes Schemmel
Traditional neuromorphic hardware architectures rely on event-driven computation, where the asynchronous transmission of events, such as spikes, triggers local computations within synapses and neurons. While machine learning frameworks are commonly used for gradient-based training, their emphasis on dense data structures poses challenges for processing asynchronous data such as spike trains. This problem is particularly pronounced for typical tensor data structures. In this context, we present a novel library (jaxsnn) built on top of JAX, that departs from conventional machine learning frameworks by providing flexibility in the data structures used and the handling of time, while maintaining Autograd functionality and composability. Our library facilitates the simulation of spiking neural networks and gradient estimation, with a focus on compatibility with time-continuous neuromorphic backends, such as the BrainScaleS-2 system, during the forward pass. This approach opens avenues for more efficient and flexible training of spiking neural networks, bridging the gap between traditional neuromorphic architectures and contemporary machine learning frameworks.
Towards Large-scale Network Emulation on Analog Neuromorphic Hardware
2024-01-30
Elias Arnold, Philipp Spilger, Jan V. Straub, Eric Müller, Dominik Dold, Gabriele Meoni, Johannes Schemmel
We present a novel software feature for the BrainScaleS-2 accelerated neuromorphic platform that facilitates the emulation of partitioned large-scale spiking neural networks. This approach is well suited for many deep spiking neural networks, where the constraint of the largest recurrent subnetwork fitting on the substrate or the limited fan-in of neurons is often not a limitation in practice. We demonstrate the training of two deep spiking neural network models, using the MNIST and EuroSAT datasets, that exceed the physical size constraints of a single-chip BrainScaleS-2 system. The ability to emulate and train networks larger than the substrate provides a pathway for accurate performance evaluation in planned or scaled systems, ultimately advancing the development and understanding of large-scale models and neuromorphic computing architectures.
One-Spike SNN: Single-Spike Phase Coding with Base Manipulation for ANN-to-SNN Conversion Loss Minimization
2024-01-30
Sangwoo Hwang, Jaeha Kung
As spiking neural networks (SNNs) are event-driven, energy efficiency is higher than conventional artificial neural networks (ANNs). Since SNN delivers data through discrete spikes, it is difficult to use gradient methods for training, limiting its accuracy. To keep the accuracy of SNNs similar to ANN counterparts, pre-trained ANNs are converted to SNNs (ANN-to-SNN conversion). During the conversion, encoding activations of ANNs to a set of spikes in SNNs is crucial for minimizing the conversion loss. In this work, we propose a single-spike phase coding as an encoding scheme that minimizes the number of spikes to transfer data between SNN layers. To minimize the encoding error due to single-spike approximation in phase coding, threshold shift and base manipulation are proposed. Without any additional retraining or architectural constraints on ANNs, the proposed conversion method does not lose inference accuracy (0.58% on average) verified on three convolutional neural networks (CNNs) with CIFAR and ImageNet datasets.In addition, graph convolutional networks (GCNs) are converted to SNNs successfully with an average accuracy loss of 0.90%.Most importantly, the energy efficiency of our SNN improves by 4.6~17.3 X compared to the ANN baseline.
Radio Frequency (RF) sensing holds the potential for enabling pervasive monitoring applications. However, modern sensing algorithms imply complex operations, which clash with the energy-constrained nature of edge sensing devices. This calls for the development of new processing and learning techniques that can strike a suitable balance between performance and energy efficiency. Spiking Neural Networks (SNNs) have recently emerged as an energy-efficient alternative to conventional neural networks for edge computing applications. They process information in the form of sparse binary spike trains, thus potentially reducing energy consumption by several orders of magnitude. Their fruitful use for RF signal processing critically depends on the representation of RF signals in the form of spike signals. We underline that existing spike encoding algorithms to do so generally produce inaccurate signal representations and dense (i.e., inefficient) spike trains. In this work, we propose a lightweight neural architecture that learns a tailored spike encoding representations of RF channel responses by jointly reconstructing the input and its spectral content. By leveraging a tunable regularization term, our approach enables fine-grained control over the performance-energy trade-off of the system. Our numerical results show that the proposed method outperforms existing encoding algorithms in terms of reconstruction error and sparsity of the obtained spike encodings.
EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm
2024-01-28
Lakshmi Annamalai, Chetan Singh Thakur
Bio-inspired Address Event Representation (AER) sensors have attracted significant popularity owing to their low power consumption, high sparsity, and high temporal resolution. Spiking Neural Network (SNN) has become the inherent choice for AER data processing. However, the integration of the AER-SNN paradigm has not adequately explored asynchronous processing, neuromorphic compatibility, and sparse spiking, which are the key requirements of resource-constrained applications. To address this gap, we introduce a brain-inspired AER-SNN object recognition solution, which includes a data encoder integrated with a First-To-Spike recognition network. Being fascinated by the functionality of neurons in the visual cortex, we designed the solution to be asynchronous and compatible with neuromorphic hardware. Furthermore, we have adapted the principle of denoising and First-To-Spike coding to achieve optimal spike signaling, significantly reducing computation costs. Experimental evaluation has demonstrated that the proposed method incurs significantly less computation cost to achieve state-of-the-art competitive accuracy. Overall, the proposed solution offers an asynchronous and cost-effective AER recognition system that harnesses the full potential of AER sensors.
Bayesian Inference Accelerator for Spiking Neural Networks
2024-01-27
Prabodh Katti, Anagha Nimbekar, Chen Li, Amit Acharyya, Bashir M. Al-Hashimi, Bipin Rajendran
Bayesian neural networks offer better estimates of model uncertainty compared to frequentist networks. However, inference involving Bayesian models requires multiple instantiations or sampling of the network parameters, requiring significant computational resources. Compared to traditional deep learning networks, spiking neural networks (SNNs) have the potential to reduce computational area and power, thanks to their event-driven and spike-based computational framework. Most works in literature either address frequentist SNN models or non-spiking Bayesian neural networks. In this work, we demonstrate an optimization framework for developing and implementing efficient Bayesian SNNs in hardware by additionally restricting network weights to be binary-valued to further decrease power and area consumption. We demonstrate accuracies comparable to Bayesian binary networks with full-precision Bernoulli parameters, while requiring up to $25\times$ less spikes than equivalent binary SNN implementations. We show the feasibility of the design by mapping it onto Zynq-7000, a lightweight SoC, and achieve a $6.5 \times$ improvement in GOPS/DSP while utilizing up to 30 times less power compared to the state-of-the-art.
Speed-based Filtration and DBSCAN of Event-based Camera Data with Neuromorphic Computing
2024-01-26
Charles P. Rizzo, Catherine D. Schuman, James S. Plank
Spiking neural networks are powerful computational elements that pair well with event-based cameras (EBCs). In this work, we present two spiking neural network architectures that process events from EBCs: one that isolates and filters out events based on their speeds, and another that clusters events based on the DBSCAN algorithm.
LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization
2024-01-26
Qianhui Liu, Jiaqi Yan, Malu Zhang, Gang Pan, Haizhou Li
Spiking Neural Networks (SNNs) mimic the information-processing mechanisms of the human brain and are highly energy-efficient, making them well-suited for low-power edge devices. However, the pursuit of accuracy in current studies leads to large, long-timestep SNNs, conflicting with the resource constraints of these devices. In order to design lightweight and efficient SNNs, we propose a new approach named LitE-SNN that incorporates both spatial and temporal compression into the automated network design process. Spatially, we present a novel Compressive Convolution block (CompConv) to expand the search space to support pruning and mixed-precision quantization. Temporally, we are the first to propose a compressive timestep search to identify the optimal number of timesteps under specific computation cost constraints. Finally, we formulate a joint optimization to simultaneously learn the architecture parameters and spatial-temporal compression strategies to achieve high performance while minimizing memory and computation costs. Experimental results on CIFAR-10, CIFAR-100, and Google Speech Command datasets demonstrate our proposed LitE-SNNs can achieve competitive or even higher accuracy with remarkably smaller model sizes and fewer computation costs.
Designing Silicon Brains using LLM: Leveraging ChatGPT for Automated Description of a Spiking Neuron Array
2024-01-25
Michael Tomlinson, Joe Li, Andreas Andreou
Large language models (LLMs) have made headlines for synthesizing correct-sounding responses to a variety of prompts, including code generation. In this paper, we present the prompts used to guide ChatGPT4 to produce a synthesizable and functional verilog description for the entirety of a programmable Spiking Neuron Array ASIC. This design flow showcases the current state of using ChatGPT4 for natural language driven hardware design. The AI-generated design was verified in simulation using handcrafted testbenches and has been submitted for fabrication in Skywater 130nm through Tiny Tapeout 5 using an open-source EDA flow.
Learning fast changing slow in spiking neural networks
2024-01-25
Cristiano Capone, Paolo Muratore
Reinforcement learning (RL) faces substantial challenges when applied to real-life problems, primarily stemming from the scarcity of available data due to limited interactions with the environment. This limitation is exacerbated by the fact that RL often demands a considerable volume of data for effective learning. The complexity escalates further when implementing RL in recurrent spiking networks, where inherent noise introduced by spikes adds a layer of difficulty. Life-long learning machines must inherently resolve the plasticity-stability paradox. Striking a balance between acquiring new knowledge and maintaining stability is crucial for artificial agents. To address this challenge, we draw inspiration from machine learning technology and introduce a biologically plausible implementation of proximal policy optimization, referred to as lf-cs (learning fast changing slow). Our approach results in two notable advancements: firstly, the capacity to assimilate new information into a new policy without requiring alterations to the current policy; and secondly, the capability to replay experiences without experiencing policy divergence. Furthermore, when contrasted with other experience replay (ER) techniques, our method demonstrates the added advantage of being computationally efficient in an online setting. We demonstrate that the proposed methodology enhances the efficiency of learning, showcasing its potential impact on neuromorphic and real-world applications.
A robust balancing mechanism for spiking neural networks
2024-01-23
Antonio Politi, Alessandro Torcini
Dynamical balance of excitation and inhibition is usually invoked to explain the irregular low firing activity observed in the cortex. We propose a robust nonlinear balancing mechanism for a random network of spiking neurons, which works also in absence of strong external currents. Biologically, the mechanism exploits the plasticity of excitatory-excitatory synapses induced by short-term depression. Mathematically, the nonlinear response of the synaptic activity is the key ingredient responsible for the emergence of a stable balanced regime. Our claim is supported by a simple self-consistent analysis accompanied by extensive simulations performed for increasing network sizes. The observed regime is essentially fluctuation driven and characterized by highly irregular spiking dynamics of all neurons.
TIM: An Efficient Temporal Interaction Module for Spiking Transformer
2024-01-22
Sicheng Shen, Dongcheng Zhao, Guobin Shen, Yi Zeng
Spiking Neural Networks (SNNs), as the third generation of neural networks, have gained prominence for their biological plausibility and computational efficiency, especially in processing diverse datasets. The integration of attention mechanisms, inspired by advancements in neural network architectures, has led to the development of Spiking Transformers. These have shown promise in enhancing SNNs' capabilities, particularly in the realms of both static and neuromorphic datasets. Despite their progress, a discernible gap exists in these systems, specifically in the Spiking Self Attention (SSA) mechanism's effectiveness in leveraging the temporal processing potential of SNNs. To address this, we introduce the Temporal Interaction Module (TIM), a novel, convolution-based enhancement designed to augment the temporal data processing abilities within SNN architectures. TIM's integration into existing SNN frameworks is seamless and efficient, requiring minimal additional parameters while significantly boosting their temporal information handling capabilities. Through rigorous experimentation, TIM has demonstrated its effectiveness in exploiting temporal information, leading to state-of-the-art performance across various neuromorphic datasets. The code is available at https://github.com/BrainCog-X/Brain-Cog/tree/main/examples/TIM.
Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer
2024-01-21
Qinyu Chen, Congyi Sun, Chang Gao, Shih-Chii Liu
Epilepsy is a common disease of the nervous system. Timely prediction of seizures and intervention treatment can significantly reduce the accidental injury of patients and protect the life and health of patients. This paper presents a neuromorphic Spiking Convolutional Transformer, named Spiking Conformer, to detect and predict epileptic seizure segments from scalped long-term electroencephalogram (EEG) recordings. We report evaluation results from the Spiking Conformer model using the Boston Children's Hospital-MIT (CHB-MIT) EEG dataset. By leveraging spike-based addition operations, the Spiking Conformer significantly reduces the classification computational cost compared to the non-spiking model. Additionally, we introduce an approximate spiking neuron layer to further reduce spike-triggered neuron updates by nearly 38% without sacrificing accuracy. Using raw EEG data as input, the proposed Spiking Conformer achieved an average sensitivity rate of 94.9% and a specificity rate of 99.3% for the seizure detection task, and 96.8%, 89.5% for the seizure prediction task, and needs >10x fewer operations compared to the non-spiking equivalent model.
TDC-less Direct Time-of-Flight Imaging Using Spiking Neural Networks
2024-01-19
Jack MacLean, Brian Stewart, Istvan Gyongy
3D depth sensors using single-photon avalanche diodes (SPADs) are becoming increasingly common in applications such as autonomous navigation and object detection. Recent designs implement on-chip histogramming time-to-digital converters (TDCs) to compress the photon timestamps and reduce the bottleneck in the read-out and processing of large volumes of photon data. However, the use of full histogramming with large SPAD arrays poses significant challenges due to the associated demands in silicon area and power consumption. We propose a TDC-less dToF sensor which uses Spiking Neural Networks (SNN) to process the SPAD events directly. The proposed SNN is trained and tested on synthetic SPAD events, and while it offers five times lower precision in depth prediction than a classic centre-of-mass (CoM) algorithm (applied to histograms of the events), it achieves similar Mean Absolute Error (MAE) with faster processing speeds and significantly lower power consumption is anticipated.
TT-SNN: Tensor Train Decomposition for Efficient Spiking Neural Network Training
2024-01-15
Donghyun Lee, Ruokai Yin, Youngeun Kim, Abhishek Moitra, Yuhang Li, Priyadarshini Panda
Spiking Neural Networks (SNNs) have gained significant attention as a potentially energy-efficient alternative for standard neural networks with their sparse binary activation. However, SNNs suffer from memory and computation overhead due to spatio-temporal dynamics and multiple backpropagation computations across timesteps during training. To address this issue, we introduce Tensor Train Decomposition for Spiking Neural Networks (TT-SNN), a method that reduces model size through trainable weight decomposition, resulting in reduced storage, FLOPs, and latency. In addition, we propose a parallel computation pipeline as an alternative to the typical sequential tensor computation, which can be flexibly integrated into various existing SNN architectures. To the best of our knowledge, this is the first of its kind application of tensor decomposition in SNNs. We validate our method using both static and dynamic datasets, CIFAR10/100 and N-Caltech101, respectively. We also propose a TT-SNN-tailored training accelerator to fully harness the parallelism in TT-SNN. Our results demonstrate substantial reductions in parameter size (7.98X), FLOPs (9.25X), training time (17.7%), and training energy (28.3%) during training for the N-Caltech101 dataset, with negligible accuracy degradation.
Spiking neural networks (SNNs) implemented on neuromorphic processors (NPs) can enhance the energy efficiency of deployments of artificial intelligence (AI) for specific workloads. As such, NP represents an interesting opportunity for implementing AI tasks on board power-limited satellite communication spacecraft. In this article, we disseminate the findings of a recently completed study which targeted the comparison in terms of performance and power-consumption of different satellite communication use cases implemented on standard AI accelerators and on NPs. In particular, the article describes three prominent use cases, namely payload resource optimization, onboard interference detection and classification, and dynamic receive beamforming; and compare the performance of conventional convolutional neural networks (CNNs) implemented on Xilinx's VCK5000 Versal development card and SNNs on Intel's neuromorphic chip Loihi 2.
Resource-Efficient Gesture Recognition using Low-Resolution Thermal Camera via Spiking Neural Networks and Sparse Segmentation
2024-01-12
Ali Safa, Wout Mommen, Lars Keuninckx
This work proposes a novel approach for hand gesture recognition using an inexpensive, low-resolution (24 x 32) thermal sensor processed by a Spiking Neural Network (SNN) followed by Sparse Segmentation and feature-based gesture classification via Robust Principal Component Analysis (R-PCA). Compared to the use of standard RGB cameras, the proposed system is insensitive to lighting variations while being significantly less expensive compared to high-frequency radars, time-of-flight cameras and high-resolution thermal sensors previously used in literature. Crucially, this paper shows that the innovative use of the recently proposed Monostable Multivibrator (MMV) neural networks as a new class of SNN achieves more than one order of magnitude smaller memory and compute complexity compared to deep learning approaches, while reaching a top gesture recognition accuracy of 93.9% using a 5-class thermal camera dataset acquired in a car cabin, within an automotive context. Our dataset is released for helping future research.
A Brain-inspired Computational Model for Human-like Concept Learning
2024-01-12
Yuwei Wang, Yi Zeng
Concept learning is a fundamental aspect of human cognition and plays a critical role in mental processes such as categorization, reasoning, memory, and decision-making. Researchers across various disciplines have shown consistent interest in the process of concept acquisition in individuals. To elucidate the mechanisms involved in human concept learning, this study examines the findings from computational neuroscience and cognitive psychology. These findings indicate that the brain's representation of concepts relies on two essential components: multisensory representation and text-derived representation. These two types of representations are coordinated by a semantic control system, ultimately leading to the acquisition of concepts. Drawing inspiration from this mechanism, the study develops a human-like computational model for concept learning based on spiking neural networks. By effectively addressing the challenges posed by diverse sources and imbalanced dimensionality of the two forms of concept representations, the study successfully attains human-like concept representations. Tests involving similar concepts demonstrate that our model, which mimics the way humans learn concepts, yields representations that closely align with human cognition.
Grounded learning for compositional vector semantics
2024-01-10
Martha Lewis
Categorical compositional distributional semantics is an approach to modelling language that combines the success of vector-based models of meaning with the compositional power of formal semantics. However, this approach was developed without an eye to cognitive plausibility. Vector representations of concepts and concept binding are also of interest in cognitive science, and have been proposed as a way of representing concepts within a biologically plausible spiking neural network. This work proposes a way for compositional distributional semantics to be implemented within a spiking neural network architecture, with the potential to address problems in concept binding, and give a small implementation. We also describe a means of training word representations using labelled images.
Multi-Neuron Representations of Hierarchical Concepts in Spiking Neural Networks
2024-01-09
Nancy A. Lynch
We describe how hierarchical concepts can be represented in three types of layered neural networks. The aim is to support recognition of the concepts when partial information about the concepts is presented, and also when some of the neurons in the network might fail. Our failure model involves initial random failures. The three types of networks are: feed-forward networks with high connectivity, feed-forward networks with low connectivity, and layered networks with low connectivity and with both forward edges and "lateral" edges within layers. In order to achieve fault-tolerance, the representations all use multiple representative neurons for each concept. We show how recognition can work in all three of these settings, and quantify how the probability of correct recognition depends on several parameters, including the number of representatives and the neuron failure probability. We also discuss how these representations might be learned, in all three types of networks. For the feed-forward networks, the learning algorithms are similar to ones used in [4], whereas for networks with lateral edges, the algorithms are generally inspired by work on the assembly calculus [3, 6, 7].
SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning
2024-01-09
Hector A. Gonzalez, Jiaxin Huang, Florian Kelber, Khaleelulla Khan Nazeer, Tim Langer, Chen Liu, Matthias Lohrmann, Amirhossein Rostami, Mark Schöne, Bernhard Vogginger, Timo C. Wunderlich, Yexin Yan, Mahmoud Akl, Christian Mayr
The joint progress of artificial neural networks (ANNs) and domain specific hardware accelerators such as GPUs and TPUs took over many domains of machine learning research. This development is accompanied by a rapid growth of the required computational demands for larger models and more data. Concurrently, emerging properties of foundation models such as in-context learning drive new opportunities for machine learning applications. However, the computational cost of such applications is a limiting factor of the technology in data centers, and more importantly in mobile devices and edge systems. To mediate the energy footprint and non-trivial latency of contemporary systems, neuromorphic computing systems deeply integrate computational principles of neurobiological systems by leveraging low-power analog and digital technologies. SpiNNaker2 is a digital neuromorphic chip developed for scalable machine learning. The event-based and asynchronous design of SpiNNaker2 allows the composition of large-scale systems involving thousands of chips. This work features the operating principles of SpiNNaker2 systems, outlining the prototype of novel machine learning applications. These applications range from ANNs over bio-inspired spiking neural networks to generalized event-based neural networks. With the successful development and deployment of SpiNNaker2, we aim to facilitate the advancement of event-based and asynchronous algorithms for future generations of machine learning systems.
Take A Shortcut Back: Mitigating the Gradient Vanishing for Training Spiking Neural Networks
2024-01-09
Yufei Guo, Yuanpei Chen
The Spiking Neural Network (SNN) is a biologically inspired neural network infrastructure that has recently garnered significant attention. It utilizes binary spike activations to transmit information, thereby replacing multiplications with additions and resulting in high energy efficiency. However, training an SNN directly poses a challenge due to the undefined gradient of the firing spike process. Although prior works have employed various surrogate gradient training methods that use an alternative function to replace the firing process during back-propagation, these approaches ignore an intrinsic problem: gradient vanishing. To address this issue, we propose a shortcut back-propagation method in our paper, which advocates for transmitting the gradient directly from the loss to the shallow layers. This enables us to present the gradient to the shallow layers directly, thereby significantly mitigating the gradient vanishing problem. Additionally, this method does not introduce any burden during the inference phase. To strike a balance between final accuracy and ease of training, we also propose an evolutionary training framework and implement it by inducing a balance coefficient that dynamically changes with the training epoch, which further improves the network's performance. Extensive experiments conducted over static and dynamic datasets using several popular network structures reveal that our method consistently outperforms state-of-the-art methods.
With the help of special neuromorphic hardware, spiking neural networks (SNNs) are expected to realize artificial intelligence (AI) with less energy consumption. It provides a promising energy-efficient way for realistic control tasks by combining SNNs with deep reinforcement learning (DRL). In this paper, we focus on the task where the agent needs to learn multi-dimensional deterministic policies to control, which is very common in real scenarios. Recently, the surrogate gradient method has been utilized for training multi-layer SNNs, which allows SNNs to achieve comparable performance with the corresponding deep networks in this task. Most existing spike-based RL methods take the firing rate as the output of SNNs, and convert it to represent continuous action space (i.e., the deterministic policy) through a fully-connected (FC) layer. However, the decimal characteristic of the firing rate brings the floating-point matrix operations to the FC layer, making the whole SNN unable to deploy on the neuromorphic hardware directly. To develop a fully spiking actor network without any floating-point matrix operations, we draw inspiration from the non-spiking interneurons found in insects and employ the membrane voltage of the non-spiking neurons to represent the action. Before the non-spiking neurons, multiple population neurons are introduced to decode different dimensions of actions. Since each population is used to decode a dimension of action, we argue that the neurons in each population should be connected in time domain and space domain. Hence, the intra-layer connections are used in output populations to enhance the representation capacity. Finally, we propose a fully spiking actor network with intra-layer connections (ILC-SAN).
Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks
2024-01-08
Qi Xu, Yuyuan Gao, Jiangrong Shen, Yaxin Li, Xuming Ran, Huajin Tang, Gang Pan
Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-temporal patterns in time series, such as the Address-Event Representation data collected from Dynamic Vision Sensor (DVS). Although convolutional SNNs have achieved remarkable performance on these AER datasets, benefiting from the predominant spatial feature extraction ability of convolutional structure, they ignore temporal features related to sequential time points. In this paper, we develop a recurrent spiking neural network (RSNN) model embedded with an advanced spiking convolutional block attention module (SCBAM) component to combine both spatial and temporal features of spatio-temporal patterns. It invokes the history information in spatial and temporal channels adaptively through SCBAM, which brings the advantages of efficient memory calling and history redundancy elimination. The performance of our model was evaluated in DVS128-Gesture dataset and other time-series datasets. The experimental results show that the proposed SRNN-SCBAM model makes better use of the history information in spatial and temporal dimensions with less memory space, and achieves higher accuracy compared to other models.
This paper introduces a new neural network model that aims to mimic the biological brain more closely by structuring the network as a complete directed graph that processes continuous data for each timestep. Current neural networks have structures that vaguely mimic the brain structure, such as neurons, convolutions, and recurrence. The model proposed in this paper adds additional structural properties by introducing cycles into the neuron connections and removing the sequential nature commonly seen in other network layers. Furthermore, the model has continuous input and output, inspired by spiking neural networks, which allows the network to learn a process of classification, rather than simply returning the final result.
Neural Population Decoding and Imbalanced Multi-Omic Datasets For Cancer Subtype Diagnosis
2024-01-06
Charles Theodore Kent, Leila Bagheriye, Johan Kwisthout
Recent strides in the field of neural computation has seen the adoption of Winner Take All (WTA) circuits to facilitate the unification of hierarchical Bayesian inference and spiking neural networks as a neurobiologically plausible model of information processing. Current research commonly validates the performance of these networks via classification tasks, particularly of the MNIST dataset. However, researchers have not yet reached consensus about how best to translate the stochastic responses from these networks into discrete decisions, a process known as population decoding. Despite being an often underexamined part of SNNs, in this work we show that population decoding has a significanct impact on the classification performance of WTA networks. For this purpose, we apply a WTA network to the problem of cancer subtype diagnosis from multi omic data, using datasets from The Cancer Genome Atlas (TCGA). In doing so we utilise a novel implementation of gene similarity networks, a feature encoding technique based on Kohoens self organising map algorithm. We further show that the impact of selecting certain population decoding methods is amplified when facing imbalanced datasets.
Training a General Spiking Neural Network with Improved Efficiency and Minimum Latency
2024-01-05
Yunpeng Yao, Man Wu, Zheng Chen, Renyuan Zhang
Spiking Neural Networks (SNNs) that operate in an event-driven manner and employ binary spike representation have recently emerged as promising candidates for energy-efficient computing. However, a cost bottleneck arises in obtaining high-performance SNNs: training a SNN model requires a large number of time steps in addition to the usual learning iterations, hence this limits their energy efficiency. This paper proposes a general training framework that enhances feature learning and activation efficiency within a limited time step, providing a new solution for more energy-efficient SNNs. Our framework allows SNN neurons to learn robust spike feature from different receptive fields and update neuron states by utilizing both current stimuli and recurrence information transmitted from other neurons. This setting continuously complements information within a single time step. Additionally, we propose a projection function to merge these two stimuli to smoothly optimize neuron weights (spike firing threshold and activation). We evaluate the proposal for both convolution and recurrent models. Our experimental results indicate state-of-the-art visual classification tasks, including CIFAR10, CIFAR100, and TinyImageNet.Our framework achieves 72.41% and 72.31% top-1 accuracy with only 1 time step on CIFAR100 for CNNs and RNNs, respectively. Our method reduces 10x and 3x joule energy than a standard ANN and SNN, respectively, on CIFAR10, without additional time steps.
Spikformer V2: Join the High Accuracy Club on ImageNet with an SNN Ticket
2024-01-04
Zhaokun Zhou, Kaiwei Che, Wei Fang, Keyu Tian, Yuesheng Zhu, Shuicheng Yan, Yonghong Tian, Li Yuan
Spiking Neural Networks (SNNs), known for their biologically plausible architecture, face the challenge of limited performance. The self-attention mechanism, which is the cornerstone of the high-performance Transformer and also a biologically inspired structure, is absent in existing SNNs. To this end, we explore the potential of leveraging both self-attention capability and biological properties of SNNs, and propose a novel Spiking Self-Attention (SSA) and Spiking Transformer (Spikformer). The SSA mechanism eliminates the need for softmax and captures the sparse visual feature employing spike-based Query, Key, and Value. This sparse computation without multiplication makes SSA efficient and energy-saving. Further, we develop a Spiking Convolutional Stem (SCS) with supplementary convolutional layers to enhance the architecture of Spikformer. The Spikformer enhanced with the SCS is referred to as Spikformer V2. To train larger and deeper Spikformer V2, we introduce a pioneering exploration of Self-Supervised Learning (SSL) within the SNN. Specifically, we pre-train Spikformer V2 with masking and reconstruction style inspired by the mainstream self-supervised Transformer, and then finetune the Spikformer V2 on the image classification on ImageNet. Extensive experiments show that Spikformer V2 outperforms other previous surrogate training and ANN2SNN methods. An 8-layer Spikformer V2 achieves an accuracy of 80.38% using 4 time steps, and after SSL, a 172M 16-layer Spikformer V2 reaches an accuracy of 81.10% with just 1 time step. To the best of our knowledge, this is the first time that the SNN achieves 80+% accuracy on ImageNet. The code will be available at Spikformer V2.
A Review of Findings from Neuroscience and Cognitive Psychology as Possible Inspiration for the Path to Artificial General Intelligence
2024-01-03
Florin Leon
This review aims to contribute to the quest for artificial general intelligence by examining neuroscience and cognitive psychology methods for potential inspiration. Despite the impressive advancements achieved by deep learning models in various domains, they still have shortcomings in abstract reasoning and causal understanding. Such capabilities should be ultimately integrated into artificial intelligence systems in order to surpass data-driven limitations and support decision making in a way more similar to human intelligence. This work is a vertical review that attempts a wide-ranging exploration of brain function, spanning from lower-level biological neurons, spiking neural networks, and neuronal ensembles to higher-level concepts such as brain anatomy, vector symbolic architectures, cognitive and categorization models, and cognitive architectures. The hope is that these concepts may offer insights for solutions in artificial general intelligence.
Spiker+: a framework for the generation of efficient Spiking Neural Networks FPGA accelerators for inference at the edge
2024-01-02
Alessio Carpegna, Alessandro Savino, Stefano Di Carlo
Including Artificial Neural Networks in embedded systems at the edge allows applications to exploit Artificial Intelligence capabilities directly within devices operating at the network periphery. This paper introduces Spiker+, a comprehensive framework for generating efficient, low-power, and low-area customized Spiking Neural Networks (SNN) accelerators on FPGA for inference at the edge. Spiker+ presents a configurable multi-layer hardware SNN, a library of highly efficient neuron architectures, and a design framework, enabling the development of complex neural network accelerators with few lines of Python code. Spiker+ is tested on two benchmark datasets, the MNIST and the Spiking Heidelberg Digits (SHD). On the MNIST, it demonstrates competitive performance compared to state-of-the-art SNN accelerators. It outperforms them in terms of resource allocation, with a requirement of 7,612 logic cells and 18 Block RAMs (BRAMs), which makes it fit in very small FPGA, and power consumption, draining only 180mW for a complete inference on an input image. The latency is comparable to the ones observed in the state-of-the-art, with 780us/img. To the authors' knowledge, Spiker+ is the first SNN accelerator tested on the SHD. In this case, the accelerator requires 18,268 logic cells and 51 BRAM, with an overall power consumption of 430mW and a latency of 54 us for a complete inference on input data. This underscores the significance of Spiker+ in the hardware-accelerated SNN landscape, making it an excellent solution to deploy configurable and tunable SNN architectures in resource and power-constrained edge applications.
Neuromorphic object recognition with spiking neural networks (SNNs) is the cornerstone of low-power neuromorphic computing. However, existing SNNs suffer from significant latency, utilizing 10 to 40 timesteps or more, to recognize neuromorphic objects. At low latencies, the performance of existing SNNs is drastically degraded. In this work, we propose the Shrinking SNN (SSNN) to achieve low-latency neuromorphic object recognition without reducing performance. Concretely, we alleviate the temporal redundancy in SNNs by dividing SNNs into multiple stages with progressively shrinking timesteps, which significantly reduces the inference latency. During timestep shrinkage, the temporal transformer smoothly transforms the temporal scale and preserves the information maximally. Moreover, we add multiple early classifiers to the SNN during training to mitigate the mismatch between the surrogate gradient and the true gradient, as well as the gradient vanishing/exploding, thus eliminating the performance degradation at low latency. Extensive experiments on neuromorphic datasets, CIFAR10-DVS, N-Caltech101, and DVS-Gesture have revealed that SSNN is able to improve the baseline accuracy by 6.55% ~ 21.41%. With only 5 average timesteps and without any data augmentation, SSNN is able to achieve an accuracy of 73.63% on CIFAR10-DVS. This work presents a heterogeneous temporal scale SNN and provides valuable insights into the development of high-performance, low-latency SNNs.
Learn to integrate parts for whole through correlated neural variability
2024-01-01
Zhichao Zhu, Yang Qi, Wenlian Lu, Jianfeng Feng
Sensory perception originates from the responses of sensory neurons, which react to a collection of sensory signals linked to various physical attributes of a singular perceptual object. Unraveling how the brain extracts perceptual information from these neuronal responses is a pivotal challenge in both computational neuroscience and machine learning. Here we introduce a statistical mechanical theory, where perceptual information is first encoded in the correlated variability of sensory neurons and then reformatted into the firing rates of downstream neurons. Applying this theory, we illustrate the encoding of motion direction using neural covariance and demonstrate high-fidelity direction recovery by spiking neural networks. Networks trained under this theory also show enhanced performance in classifying natural images, achieving higher accuracy and faster inference speed. Our results challenge the traditional view of neural covariance as a secondary factor in neural coding, highlighting its potential influence on brain function.
Analysis of biologically plausible neuron models for regression with spiking neural networks
2023-12-31
Mario De Florio, Adar Kahana, George Em Karniadakis
This paper explores the impact of biologically plausible neuron models on the performance of Spiking Neural Networks (SNNs) for regression tasks. While SNNs are widely recognized for classification tasks, their application to Scientific Machine Learning and regression remains underexplored. We focus on the membrane component of SNNs, comparing four neuron models: Leaky Integrate-and-Fire, FitzHugh-Nagumo, Izhikevich, and Hodgkin-Huxley. We investigate their effect on SNN accuracy and efficiency for function regression tasks, by using Euler and Runge-Kutta 4th-order approximation schemes. We show how more biologically plausible neuron models improve the accuracy of SNNs while reducing the number of spikes in the system. The latter represents an energetic gain on actual neuromorphic chips since it directly reflects the amount of energy required for the computations.
Darwin3: A large-scale neuromorphic chip with a Novel ISA and On-Chip Learning
2023-12-29
De Ma, Xiaofei Jin, Shichun Sun, Yitao Li, Xundong Wu, Youneng Hu, Fangchao Yang, Huajin Tang, Xiaolei Zhu, Peng Lin, Gang Pan
Spiking Neural Networks (SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-temporal dynamics in SNNs, neuromorphic chips are highly desired to execute SNNs in hardware-based neuron and synapse circuits directly. This paper presents a large-scale neuromorphic chip named Darwin3 with a novel instruction set architecture(ISA), which comprises 10 primary instructions and a few extended instructions. It supports flexible neuron model programming and local learning rule designs. The Darwin3 chip architecture is designed in a mesh of computing nodes with an innovative routing algorithm. We used a compression mechanism to represent synaptic connections, significantly reducing memory usage. The Darwin3 chip supports up to 2.35 million neurons, making it the largest of its kind in neuron scale. The experimental results showed that code density was improved up to 28.3x in Darwin3, and neuron core fan-in and fan-out were improved up to 4096x and 3072x by connection compression compared to the physical memory depth. Our Darwin3 chip also provided memory saving between 6.8X and 200.8X when mapping convolutional spiking neural networks (CSNN) onto the chip, demonstrating state-of-the-art performance in accuracy and latency compared to other neuromorphic chips.
SparseProp: Efficient Event-Based Simulation and Training of Sparse Recurrent Spiking Neural Networks
2023-12-28
Rainer Engelken
Spiking Neural Networks (SNNs) are biologically-inspired models that are capable of processing information in streams of action potentials. However, simulating and training SNNs is computationally expensive due to the need to solve large systems of coupled differential equations. In this paper, we introduce SparseProp, a novel event-based algorithm for simulating and training sparse SNNs. Our algorithm reduces the computational cost of both the forward and backward pass operations from O(N) to O(log(N)) per network spike, thereby enabling numerically exact simulations of large spiking networks and their efficient training using backpropagation through time. By leveraging the sparsity of the network, SparseProp eliminates the need to iterate through all neurons at each spike, employing efficient state updates instead. We demonstrate the efficacy of SparseProp across several classical integrate-and-fire neuron models, including a simulation of a sparse SNN with one million LIF neurons. This results in a speed-up exceeding four orders of magnitude relative to previous event-based implementations. Our work provides an efficient and exact solution for training large-scale spiking neural networks and opens up new possibilities for building more sophisticated brain-inspired models.
Fast gradient-free activation maximization for neurons in spiking neural networks
2023-12-28
Nikita Pospelov, Andrei Chertkov, Maxim Beketov, Ivan Oseledets, Konstantin Anokhin
Elements of neural networks, both biological and artificial, can be described by their selectivity for specific cognitive features. Understanding these features is important for understanding the inner workings of neural networks. For a living system, such as a neuron, whose response to a stimulus is unknown and not differentiable, the only way to reveal these features is through a feedback loop that exposes it to a large set of different stimuli. The properties of these stimuli should be varied iteratively in order to maximize the neuronal response. To utilize this feedback loop for a biological neural network, it is important to run it quickly and efficiently in order to reach the stimuli that maximizes certain neurons' activation with the least number of iterations possible. Here we present a framework with an efficient design for such a loop. We successfully tested it on an artificial spiking neural network (SNN), which is a model that simulates the asynchronous spiking activity of neurons in living brains. Our optimization method for activation maximization is based on the low-rank Tensor Train decomposition of the discrete activation function. The optimization space is the latent parameter space of images generated by SN-GAN or VQ-VAE generative models. To our knowledge, this is the first time that effective AM has been applied to SNNs. We track changes in the optimal stimuli for artificial neurons during training and show that highly selective neurons can form already in the early epochs of training and in the early layers of a convolutional spiking network. This formation of refined optimal stimuli is associated with an increase in classification accuracy. Some neurons, especially in the deeper layers, may gradually change the concepts they are selective for during learning, potentially explaining their importance for model performance.
Event-based Shape from Polarization with Spiking Neural Networks
2023-12-26
Peng Kang, Srutarshi Banerjee, Henry Chopp, Aggelos Katsaggelos, Oliver Cossairt
Recent advances in event-based shape determination from polarization offer a transformative approach that tackles the trade-off between speed and accuracy in capturing surface geometries. In this paper, we investigate event-based shape from polarization using Spiking Neural Networks (SNNs), introducing the Single-Timestep and Multi-Timestep Spiking UNets for effective and efficient surface normal estimation. Specificially, the Single-Timestep model processes event-based shape as a non-temporal task, updating the membrane potential of each spiking neuron only once, thereby reducing computational and energy demands. In contrast, the Multi-Timestep model exploits temporal dynamics for enhanced data extraction. Extensive evaluations on synthetic and real-world datasets demonstrate that our models match the performance of state-of-the-art Artifical Neural Networks (ANNs) in estimating surface normals, with the added advantage of superior energy efficiency. Our work not only contributes to the advancement of SNNs in event-based sensing but also sets the stage for future explorations in optimizing SNN architectures, integrating multi-modal data, and scaling for applications on neuromorphic hardware.
Spiking Neural Networks (SNNs) have a greater potential for modeling time series data than Artificial Neural Networks (ANNs), due to their inherent neuron dynamics and low energy consumption. However, it is difficult to demonstrate their superiority in classification accuracy, because current efforts mainly focus on designing better network structures. In this work, enlighten by brain-inspired science, we find that, not only the structure but also the learning process should be human-like. To achieve this, we investigate the power of Curriculum Learning (CL) on SNNs by designing a novel method named CSNN with two theoretically guaranteed mechanisms: The active-to-dormant training order makes the curriculum similar to that of human learning and suitable for spiking neurons; The value-based regional encoding makes the neuron activity to mimic the brain memory when learning sequential data. Experiments on multiple time series sources including simulated, sensor, motion, and healthcare demonstrate that CL has a more positive effect on SNNs than ANNs with about twice the accuracy change, and CSNN can increase about 3% SNNs' accuracy by improving network sparsity, neuron firing status, anti-noise ability, and convergence speed.
Astrocyte Regulated Neuromorphic Central Pattern Generator Control of Legged Robotic Locomotion
2023-12-25
Zhuangyu Han, Abhronil Sengupta
Neuromorphic computing systems, where information is transmitted through action potentials in a bio-plausible fashion, is gaining increasing interest due to its promise of low-power event-driven computing. Application of neuromorphic computing in robotic locomotion research have largely focused on Central Pattern Generators (CPGs) for bionics robotic control algorithms - inspired from neural circuits governing the collaboration of the limb muscles in animal movement. Implementation of artificial CPGs on neuromorphic hardware platforms can potentially enable adaptive and energy-efficient edge robotics applications in resource constrained environments. However, underlying rewiring mechanisms in CPG for gait emergence process is not well understood. This work addresses the missing gap in literature pertaining to CPG plasticity and underscores the critical homeostatic functionality of astrocytes - a cellular component in the brain that is believed to play a major role in multiple brain functions. This paper introduces an astrocyte regulated Spiking Neural Network (SNN)-based CPG for learning locomotion gait through Reward-Modulated STDP for quadruped robots, where the astrocytes help build inhibitory connections among the artificial motor neurons in different limbs. The SNN-based CPG is simulated on a multi-object physics simulation platform resulting in the emergence of a trotting gait while running the robot on flat ground. $23.3\times$ computational power savings is observed in comparison to a state-of-the-art reinforcement learning based robot control algorithm. Such a neuroscience-algorithm co-design approach can potentially enable a quantum leap in the functionality of neuromorphic systems incorporating glial cell functionality.
Spiking Neural Networks (SNNs) capture the information processing mechanism of the brain by taking advantage of spiking neurons, such as the Leaky Integrate-and-Fire (LIF) model neuron, which incorporates temporal dynamics and transmits information via discrete and asynchronous spikes. However, the simplified biological properties of LIF ignore the neuronal coupling and dendritic structure of real neurons, which limits the spatio-temporal dynamics of neurons and thus reduce the expressive power of the resulting SNNs. In this work, we leverage a more biologically plausible neural model with complex dynamics, i.e., a pulse-coupled neural network (PCNN), to improve the expressiveness and recognition performance of SNNs for vision tasks. The PCNN is a type of cortical model capable of emulating the complex neuronal activities in the primary visual cortex. We construct deep pulse-coupled neural networks (DPCNNs) by replacing commonly used LIF neurons in SNNs with PCNN neurons. The intra-coupling in existing PCNN models limits the coupling between neurons only within channels. To address this limitation, we propose inter-channel coupling, which allows neurons in different feature maps to interact with each other. Experimental results show that inter-channel coupling can efficiently boost performance with fewer neurons, synapses, and less training time compared to widening the networks. For instance, compared to the LIF-based SNN with wide VGG9, DPCNN with VGG9 uses only 50%, 53%, and 73% of neurons, synapses, and training time, respectively. Furthermore, we propose receptive field and time dependent batch normalization (RFTD-BN) to speed up the convergence and performance of DPCNNs.
2023-12-22
Christos G. Tsinos, Alexandros-Apostolos A. Boulogeorgos, Theodoros A. Tsiftsis
Reconfigurable intelligent surfaces (RISs) operate similarly to electromagnetic (EM) mirrors and remarkably go beyond Snell law to generate an applicable EM environment allowing for flexible adaptation and fostering sustainability in terms of economic deployment and energy efficiency. However, the conventional RIS is controlled through high-latency field programmable gate array or micro-controller circuits usually implementing artificial neural networks (ANNs) for tuning the RIS phase array that have also very high energy requirements. Most importantly, conventional RIS are unable to function under realistic scenarios i.e, high-mobility/low-end user equipment (UE). In this paper, we benefit from the advanced computing power of neuromorphic processors and design a new type of RIS named \emph{NeuroRIS}, to supporting high mobility UEs through real time adaptation to the ever-changing wireless channel conditions. To this end, the neuromorphic processing unit tunes all the RIS meta-elements in the orders of $\rm{ns}$ for particular switching circuits e.g., varactors while exhibiting significantly low energy requirements since it is based on event-driven processing through spiking neural networks for accurate and efficient phase-shift vector design. Numerical results show that the NeuroRIS achieves very close rate performance to a conventional RIS-based on ANNs, while requiring significantly reduced energy consumption with the latter.
Low-power event-based face detection with asynchronous neuromorphic hardware
2023-12-21
Caterina Caccavella, Federico Paredes-Vallés, Marco Cannici, Lyes Khacef
The rise of mobility, IoT and wearables has shifted processing to the edge of the sensors, driven by the need to reduce latency, communication costs and overall energy consumption. While deep learning models have achieved remarkable results in various domains, their deployment at the edge for real-time applications remains computationally expensive. Neuromorphic computing emerges as a promising paradigm shift, characterized by co-localized memory and computing as well as event-driven asynchronous sensing and processing. In this work, we demonstrate the possibility of solving the ubiquitous computer vision task of object detection at the edge with low-power requirements, using the event-based N-Caltech101 dataset. We present the first instance of an on-chip spiking neural network for event-based face detection deployed on the SynSense Speck neuromorphic chip, which comprises both an event-based sensor and a spike-based asynchronous processor implementing Integrate-and-Fire neurons. We show how to reduce precision discrepancies between off-chip clock-driven simulation used for training and on-chip event-driven inference. This involves using a multi-spike version of the Integrate-and-Fire neuron on simulation, where spikes carry values that are proportional to the extent the membrane potential exceeds the firing threshold. We propose a robust strategy to train spiking neural networks with back-propagation through time using multi-spike activation and firing rate regularization and demonstrate how to decode output spikes into bounding boxes. We show that the power consumption of the chip is directly proportional to the number of synaptic operations in the spiking neural network, and we explore the trade-off between power consumption and detection precision with different firing rate regularization, achieving an on-chip face detection mAP[0.5] of ~0.6 while consuming only ~20 mW.
Energy-efficient Spiking Neural Network Equalization for IM/DD Systems with Optimized Neural Encoding
2023-12-20
Alexander von Bank, Eike-Manuel Edelmann, Laurent Schmalen
We propose an energy-efficient equalizer for IM/DD systems based on spiking neural networks. We optimize a neural spike encoding that boosts the equalizer's performance while decreasing energy consumption.
Dynamic Spiking Framework for Graph Neural Networks
2023-12-15
Nan Yin, Mengzhu Wang, Zhenghan Chen, Giulia De Masi, Bin Gu, Huan Xiong
The integration of Spiking Neural Networks (SNNs) and Graph Neural Networks (GNNs) is gradually attracting attention due to the low power consumption and high efficiency in processing the non-Euclidean data represented by graphs. However, as a common problem, dynamic graph representation learning faces challenges such as high complexity and large memory overheads. Current work often uses SNNs instead of Recurrent Neural Networks (RNNs) by using binary features instead of continuous ones for efficient training, which would overlooks graph structure information and leads to the loss of details during propagation. Additionally, optimizing dynamic spiking models typically requires propagation of information across time steps, which increases memory requirements. To address these challenges, we present a framework named \underline{Dy}namic \underline{S}p\underline{i}king \underline{G}raph \underline{N}eural Networks (\method{}). To mitigate the information loss problem, \method{} propagates early-layer information directly to the last layer for information compensation. To accommodate the memory requirements, we apply the implicit differentiation on the equilibrium state, which does not rely on the exact reverse of the forward computation. While traditional implicit differentiation methods are usually used for static situations, \method{} extends it to the dynamic graph setting. Extensive experiments on three large-scale real-world dynamic graph datasets validate the effectiveness of \method{} on dynamic node classification tasks with lower computational costs.
Recurrent Neural Networks (RNNs) are useful in temporal sequence tasks. However, training RNNs involves dense matrix multiplications which require hardware that can support a large number of arithmetic operations and memory accesses. Implementing online training of RNNs on the edge calls for optimized algorithms for an efficient deployment on hardware. Inspired by the spiking neuron model, the Delta RNN exploits temporal sparsity during inference by skipping over the update of hidden states from those inactivated neurons whose change of activation across two timesteps is below a defined threshold. This work describes a training algorithm for Delta RNNs that exploits temporal sparsity in the backward propagation phase to reduce computational requirements for training on the edge. Due to the symmetric computation graphs of forward and backward propagation during training, the gradient computation of inactivated neurons can be skipped. Results show a reduction of $\sim$80% in matrix operations for training a 56k parameter Delta LSTM on the Fluent Speech Commands dataset with negligible accuracy loss. Logic simulations of a hardware accelerator designed for the training algorithm show 2-10X speedup in matrix computations for an activation sparsity range of 50%-90%. Additionally, we show that the proposed Delta RNN training will be useful for online incremental learning on edge devices with limited computing resources.
Language Modeling on a SpiNNaker 2 Neuromorphic Chip
2023-12-14
Khaleelulla Khan Nazeer, Mark Schöne, Rishav Mukherji, Bernhard Vogginger, Christian Mayr, David Kappel, Anand Subramoney
As large language models continue to scale in size rapidly, so too does the computational power required to run them. Event-based networks on neuromorphic devices offer a potential way to reduce energy consumption for inference significantly. However, to date, most event-based networks that can run on neuromorphic hardware, including spiking neural networks (SNNs), have not achieved task performance even on par with LSTM models for language modeling. As a result, language modeling on neuromorphic devices has seemed a distant prospect. In this work, we demonstrate the first-ever implementation of a language model on a neuromorphic device - specifically the SpiNNaker 2 chip - based on a recently published event-based architecture called the EGRU. SpiNNaker 2 is a many-core neuromorphic chip designed for large-scale asynchronous processing, while the EGRU is architected to leverage such hardware efficiently while maintaining competitive task performance. This implementation marks the first time a neuromorphic language model matches LSTMs, setting the stage for taking task performance to the level of large language models. We also demonstrate results on a gesture recognition task based on inputs from a DVS camera. Overall, our results showcase the feasibility of this neuro-inspired neural network in hardware, highlighting significant gains versus conventional hardware in energy efficiency for the common use case of single batch inference.
An increasing number of neuroscience studies are highlighting the importance of spatial dendritic branching in pyramidal neurons in the brain for supporting non-linear computation through localized synaptic integration. In particular, dendritic branches play a key role in temporal signal processing and feature detection, using coincidence detection (CD) mechanisms, made possible by the presence of synaptic delays that align temporally disparate inputs for effective integration. Computational studies on spiking neural networks further highlight the significance of delays for CD operations, enabling spatio-temporal pattern recognition within feed-forward neural networks without the need for recurrent architectures. In this work, we present DenRAM, the first realization of a spiking neural network with analog dendritic circuits, integrated into a 130nm technology node coupled with resistive memory (RRAM) technology. DenRAM's dendritic circuits use the RRAM devices to implement both delays and synaptic weights in the network. By configuring the RRAM devices to reproduce bio-realistic timescales, and through exploiting their heterogeneity, we experimentally demonstrate DenRAM's capability to replicate synaptic delay profiles, and efficiently implement CD for spatio-temporal pattern recognition. To validate the architecture, we conduct comprehensive system-level simulations on two representative temporal benchmarks, highlighting DenRAM's resilience to analog hardware noise, and its superior accuracy compared to recurrent architectures with an equivalent number of parameters. DenRAM not only brings rich temporal processing capabilities to neuromorphic architectures, but also reduces the memory footprint of edge devices, provides high accuracy on temporal benchmarks, and represents a significant step-forward in low-power real-time signal processing technologies.
Learning Long Sequences in Spiking Neural Networks
2023-12-14
Matei Ioan Stan, Oliver Rhodes
Spiking neural networks (SNNs) take inspiration from the brain to enable energy-efficient computations. Since the advent of Transformers, SNNs have struggled to compete with artificial networks on modern sequential tasks, as they inherit limitations from recurrent neural networks (RNNs), with the added challenge of training with non-differentiable binary spiking activations. However, a recent renewed interest in efficient alternatives to Transformers has given rise to state-of-the-art recurrent architectures named state space models (SSMs). This work systematically investigates, for the first time, the intersection of state-of-the-art SSMs with SNNs for long-range sequence modelling. Results suggest that SSM-based SNNs can outperform the Transformer on all tasks of a well-established long-range sequence modelling benchmark. It is also shown that SSM-based SNNs can outperform current state-of-the-art SNNs with fewer parameters on sequential image classification. Finally, a novel feature mixing layer is introduced, improving SNN accuracy while challenging assumptions about the role of binary activations in SNNs. This work paves the way for deploying powerful SSM-based architectures, such as large language models, to neuromorphic hardware for energy-efficient long-range sequence modelling.
Accelerated Event-Based Feature Detection and Compression for Surveillance Video Systems
2023-12-13
Andrew C. Freeman, Ketan Mayer-Patel, Montek Singh
The strong temporal consistency of surveillance video enables compelling compression performance with traditional methods, but downstream vision applications operate on decoded image frames with a high data rate. Since it is not straightforward for applications to extract information on temporal redundancy from the compressed video representations, we propose a novel system which conveys temporal redundancy within a sparse decompressed representation. We leverage a video representation framework called ADDER to transcode framed videos to sparse, asynchronous intensity samples. We introduce mechanisms for content adaptation, lossy compression, and asynchronous forms of classical vision algorithms. We evaluate our system on the VIRAT surveillance video dataset, and we show a median 43.7% speed improvement in FAST feature detection compared to OpenCV. We run the same algorithm as OpenCV, but only process pixels that receive new asynchronous events, rather than process every pixel in an image frame. Our work paves the way for upcoming neuromorphic sensors and is amenable to future applications with spiking neural networks.
Spiking neural networks (SNNs) are potential competitors to artificial neural networks (ANNs) due to their high energy-efficiency on neuromorphic hardware. However, SNNs are unfolded over simulation time steps during the training process. Thus, SNNs require much more memory than ANNs, which impedes the training of deeper SNN models. In this paper, we propose the reversible spiking neural network to reduce the memory cost of intermediate activations and membrane potentials during training. Firstly, we extend the reversible architecture along temporal dimension and propose the reversible spiking block, which can reconstruct the computational graph and recompute all intermediate variables in forward pass with a reverse process. On this basis, we adopt the state-of-the-art SNN models to the reversible variants, namely reversible spiking ResNet (RevSResNet) and reversible spiking transformer (RevSFormer). Through experiments on static and neuromorphic datasets, we demonstrate that the memory cost per image of our reversible SNNs does not increase with the network depth. On CIFAR10 and CIFAR100 datasets, our RevSResNet37 and RevSFormer-4-384 achieve comparable accuracies and consume 3.79x and 3.00x lower GPU memory per image than their counterparts with roughly identical model complexity and parameters. We believe that this work can unleash the memory constraints in SNN training and pave the way for training extremely large and deep SNNs. The code is available at https://github.com/mi804/RevSNN.git.
Efficient Object Detection in Autonomous Driving using Spiking Neural Networks: Performance, Energy Consumption Analysis, and Insights into Open-set Object Discovery
2023-12-12
Aitor Martinez Seras, Javier Del Ser, Pablo Garcia-Bringas
Besides performance, efficiency is a key design driver of technologies supporting vehicular perception. Indeed, a well-balanced trade-off between performance and energy consumption is crucial for the sustainability of autonomous vehicles. In this context, the diversity of real-world contexts in which autonomous vehicles can operate motivates the need for empowering perception models with the capability to detect, characterize and identify newly appearing objects by themselves. In this manuscript we elaborate on this threefold conundrum (performance, efficiency and open-world learning) for object detection modeling tasks over image data collected from vehicular scenarios. Specifically, we show that well-performing and efficient models can be realized by virtue of Spiking Neural Networks (SNNs), reaching competitive levels of detection performance when compared to their non-spiking counterparts at dramatic energy consumption savings (up to 85%) and a slightly improved robustness against image noise. Our experiments herein offered also expose qualitatively the complexity of detecting new objects based on the preliminary results of a simple approach to discriminate potential object proposals in the captured image.
Astrocyte-Enabled Advancements in Spiking Neural Networks for Large Language Modeling
2023-12-12
Guobin Shen, Dongcheng Zhao, Yiting Dong, Yang Li, Jindong Li, Kang Sun, Yi Zeng
Within the complex neuroarchitecture of the brain, astrocytes play crucial roles in development, structure, and metabolism. These cells regulate neural activity through tripartite synapses, directly impacting cognitive processes such as learning and memory. Despite the growing recognition of astrocytes' significance, traditional Spiking Neural Network (SNN) models remain predominantly neuron-centric, overlooking the profound influence of astrocytes on neural dynamics. Inspired by these biological insights, we have developed an Astrocyte-Modulated Spiking Unit (AM-SU), an innovative framework that integrates neuron-astrocyte interactions into the computational paradigm, demonstrating wide applicability across various hardware platforms. Our Astrocyte-Modulated Spiking Neural Network (AstroSNN) exhibits exceptional performance in tasks involving memory retention and natural language generation, particularly in handling long-term dependencies and complex linguistic structures. The design of AstroSNN not only enhances its biological authenticity but also introduces novel computational dynamics, enabling more effective processing of complex temporal dependencies. Furthermore, AstroSNN shows low latency, high throughput, and reduced memory usage in practical applications, making it highly suitable for resource-constrained environments. By successfully integrating astrocytic dynamics into intelligent neural networks, our work narrows the gap between biological plausibility and neural modeling, laying the groundwork for future biologically-inspired neural computing research that includes both neurons and astrocytes.
When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networks
2023-12-12
Gourav Datta, Zeyu Liu, James Diffenderfer, Bhavya Kailkhura, Peter A. Beerel
Bio-inspired Spiking Neural Networks (SNN) are now demonstrating comparable accuracy to intricate convolutional neural networks (CNN), all while delivering remarkable energy and latency efficiency when deployed on neuromorphic hardware. In particular, ANN-to-SNN conversion has recently gained significant traction in developing deep SNNs with close to state-of-the-art (SOTA) test accuracy on complex image recognition tasks. However, advanced ANN-to-SNN conversion approaches demonstrate that for lossless conversion, the number of SNN time steps must equal the number of quantization steps in the ANN activation function. Reducing the number of time steps significantly increases the conversion error. Moreover, the spiking activity of the SNN, which dominates the compute energy in neuromorphic chips, does not reduce proportionally with the number of time steps. To mitigate the accuracy concern, we propose a novel ANN-to-SNN conversion framework, that incurs an exponentially lower number of time steps compared to that required in the SOTA conversion approaches. Our framework modifies the SNN integrate-and-fire (IF) neuron model with identical complexity and shifts the bias term of each batch normalization (BN) layer in the trained ANN. To mitigate the spiking activity concern, we propose training the source ANN with a fine-grained L1 regularizer with surrogate gradients that encourages high spike sparsity in the converted SNN. Our proposed framework thus yields lossless SNNs with ultra-low latency, ultra-low compute energy, thanks to the ultra-low timesteps and high spike sparsity, and ultra-high test accuracy, for example, 73.30% with only 4 time steps on the ImageNet dataset.
2023-12-11
Craig M. Vineyard, William M. Severa, James B. Aimone
Co-design is a prominent topic presently in computing, speaking to the mutual benefit of coordinating design choices of several layers in the technology stack. For example, this may be designing algorithms which can most efficiently take advantage of the acceleration properties of a given architecture, while simultaneously designing the hardware to support the structural needs of a class of computation. The implications of these design decisions are influential enough to be deemed a lottery, enabling an idea to win out over others irrespective of the individual merits. Coordination is a well studied topic in the mathematics of game theory, where in many cases without a coordination mechanism the outcome is sub-optimal. Here we consider what insights game theoretic analysis can offer for computer architecture co-design. In particular, we consider the interplay between algorithm and architecture advances in the field of neuromorphic computing. Analyzing developments of spiking neural network algorithms and neuromorphic hardware as a co-design game we use the Stag Hunt model to illustrate challenges for spiking algorithms or architectures to advance the field independently and advocate for a strategic pursuit to advance neuromorphic computing.
The Spiking Neural Network (SNN), as one of the biologically inspired neural network infrastructures, has drawn increasing attention recently. It adopts binary spike activations to transmit information, thus the multiplications of activations and weights can be substituted by additions, which brings high energy efficiency. However, in the paper, we theoretically and experimentally prove that the binary spike activation map cannot carry enough information, thus causing information loss and resulting in accuracy decreasing. To handle the problem, we propose a ternary spike neuron to transmit information. The ternary spike neuron can also enjoy the event-driven and multiplication-free operation advantages of the binary spike neuron but will boost the information capacity. Furthermore, we also embed a trainable factor in the ternary spike neuron to learn the suitable spike amplitude, thus our SNN will adopt different spike amplitudes along layers, which can better suit the phenomenon that the membrane potential distributions are different along layers. To retain the efficiency of the vanilla ternary spike, the trainable ternary spike SNN will be converted to a standard one again via a re-parameterization technique in the inference. Extensive experiments with several popular network structures over static and dynamic datasets show that the ternary spike can consistently outperform state-of-the-art methods. Our code is open-sourced at https://github.com/yfguo91/Ternary-Spike.
NiSNN-A: Non-iterative Spiking Neural Networks with Attention with Application to Motor Imagery EEG Classification
2023-12-09
Chuhan Zhang, Wei Pan, Cosimo Della Santina
Motor imagery, an important category in electroencephalogram (EEG) research, often intersects with scenarios demanding low energy consumption, such as portable medical devices and isolated environment operations. Traditional deep learning algorithms, despite their effectiveness, are characterized by significant computational demands accompanied by high energy usage. As an alternative, spiking neural networks (SNNs), inspired by the biological functions of the brain, emerge as a promising energy-efficient solution. However, SNNs typically exhibit lower accuracy than their counterpart convolutional neural networks (CNNs). Although attention mechanisms successfully increase network accuracy by focusing on relevant features, their integration in the SNN framework remains an open question. In this work, we combine the SNN and the attention mechanisms for the EEG classification, aiming to improve precision and reduce energy consumption. To this end, we first propose a Non-iterative Leaky Integrate-and-Fire (LIF) neuron model, overcoming the gradient issues in the traditional SNNs using the Iterative LIF neurons. Then, we introduce the sequence-based attention mechanisms to refine the feature map. We evaluated the proposed Non-iterative SNN with Attention (NiSNN-A) model on OpenBMI, a large-scale motor imagery dataset. Experiment results demonstrate that 1) our model outperforms other SNN models by achieving higher accuracy, 2) our model increases energy efficiency compared to the counterpart CNN models (i.e., by 2.27 times) while maintaining comparable accuracy.
Recent strides in low-latency spiking neural network (SNN) algorithms have drawn significant interest, particularly due to their event-driven computing nature and fast inference capability. One of the most efficient ways to construct a low-latency SNN is by converting a pre-trained, low-bit artificial neural network (ANN) into an SNN. However, this conversion process faces two main challenges: First, converting SNNs from low-bit ANNs can lead to ``occasional noise" -- the phenomenon where occasional spikes are generated in spiking neurons where they should not be -- during inference, which significantly lowers SNN accuracy. Second, although low-latency SNNs initially show fast improvements in accuracy with time steps, these accuracy growths soon plateau, resulting in their peak accuracy lagging behind both full-precision ANNs and traditional ``long-latency SNNs'' that prioritize precision over speed. In response to these two challenges, this paper introduces a novel technique named ``noise adaptor.'' Noise adaptor can model occasional noise during training and implicitly optimize SNN accuracy, particularly at high simulation times $T$. Our research utilizes the ResNet model for a comprehensive analysis of the impact of the noise adaptor on low-latency SNNs. The results demonstrate that our method outperforms the previously reported quant-ANN-to-SNN conversion technique. We achieved an accuracy of 95.95\% within 4 time steps on CIFAR-10 using ResNet-18, and an accuracy of 74.37\% within 64 time steps on ImageNet using ResNet-50. Remarkably, these results were obtained without resorting to any noise correction methods during SNN inference, such as negative spikes or two-stage SNN simulations. Our approach significantly boosts the peak accuracy of low-latency SNNs, bringing them on par with the accuracy of full-precision ANNs. Code will be open source.
Analysis on Effects of Fault Elements in Memristive Neuromorphic Systems
2023-12-08
Hyun-Jong Lee, Jae-Han Lim
Nowadays, neuromorphic systems based on Spiking Neural Networks (SNNs) attract attentions of many researchers. There are many studies to improve performances of neuromorphic systems. These studies have been showing satisfactory results. To magnify performances of neuromorphic systems, developing actual neuromorphic systems is essential. For developing them, memristors play key role due to their useful characteristics. Although memristors are essential for actual neuromorphic systems, they are vulnerable to faults. However, there are few studies analyzing effects of fault elements in neuromorphic systems using memristors. To solve this problem, we analyze performance of a memristive neuromorphic system with fault elements changing fault ratios, types, and positions. We choose neurons and synapses to inject faults. We inject two types of faults to synapses: SA0 and SA1 faults. The fault synapses appear in random and important positions. Through our analysis, we discover the following four interesting points. First, memristive characteristics increase vulnerability of neuromorphic systems to fault elements. Second, fault neuron ratios reducing performance sharply exist. Third, performance degradation by fault synapses depends on fault types. Finally, SA1 fault synapses improve performance when they appear in important positions.
Supervised learning of spatial features with STDP and homeostasis using Spiking Neural Networks on SpiNNaker
2023-12-05
Sergio Davies, Andrew Gait, Andrew Rowley, Alessandro Di Nuovo
Artificial Neural Networks (ANN) have gained significant popularity thanks to their ability to learn using the well-known backpropagation algorithm. Conversely, Spiking Neural Networks (SNNs), despite having broader capabilities than ANNs, have always posed challenges in the training phase. This paper shows a new method to perform supervised learning on SNNs, using Spike Timing Dependent Plasticity (STDP) and homeostasis, aiming at training the network to identify spatial patterns. Spatial patterns refer to spike patterns without a time component, where all spike events occur simultaneously. The method is tested using the SpiNNaker digital architecture. A SNN is trained to recognise one or multiple patterns and performance metrics are extracted to measure the performance of the network. Some considerations are drawn from the results showing that, in the case of a single trained pattern, the network behaves as the ideal detector, with 100% accuracy in detecting the trained pattern. However, as the number of trained patterns on a single network increases, the accuracy of identification is linked to the similarities between these patterns. This method of training an SNN to detect spatial patterns may be applied to pattern recognition in static images or traffic analysis in computer networks, where each network packet represents a spatial pattern. It will be stipulated that the homeostatic factor may enable the network to detect patterns with some degree of similarity, rather than only perfectly matching patterns.The principles outlined in this article serve as the fundamental building blocks for more complex systems that utilise both spatial and temporal patterns by converting specific features of input signals into spikes.One example of such a system is a computer network packet classifier, tasked with real-time identification of packet streams based on features within the packet content
Spiking neural networks (SNNs) have garnered considerable attention owing to their ability to run on neuromorphic devices with super-high speeds and remarkable energy efficiencies. SNNs can be used in conventional neural network-based time- and energy-consuming applications. However, research on generative models within SNNs remains limited, despite their advantages. In particular, diffusion models are a powerful class of generative models, whose image generation quality surpass that of the other generative models, such as GANs. However, diffusion models are characterized by high computational costs and long inference times owing to their iterative denoising feature. Therefore, we propose a novel approach fully spiking denoising diffusion implicit model (FSDDIM) to construct a diffusion model within SNNs and leverage the high speed and low energy consumption features of SNNs via synaptic current learning (SCL). SCL fills the gap in that diffusion models use a neural network to estimate real-valued parameters of a predefined probabilistic distribution, whereas SNNs output binary spike trains. The SCL enables us to complete the entire generative process of diffusion models exclusively using SNNs. We demonstrate that the proposed method outperforms the state-of-the-art fully spiking generative model.
Recent Advances in Scalable Energy-Efficient and Trustworthy Spiking Neural networks: from Algorithms to Technology
2023-12-02
Souvik Kundu, Rui-Jie Zhu, Akhilesh Jaiswal, Peter A. Beerel
Neuromorphic computing and, in particular, spiking neural networks (SNNs) have become an attractive alternative to deep neural networks for a broad range of signal processing applications, processing static and/or temporal inputs from different sensory modalities, including audio and vision sensors. In this paper, we start with a description of recent advances in algorithmic and optimization innovations to efficiently train and scale low-latency, and energy-efficient spiking neural networks (SNNs) for complex machine learning applications. We then discuss the recent efforts in algorithm-architecture co-design that explores the inherent trade-offs between achieving high energy-efficiency and low latency while still providing high accuracy and trustworthiness. We then describe the underlying hardware that has been developed to leverage such algorithmic innovations in an efficient way. In particular, we describe a hybrid method to integrate significant portions of the model's computation within both memory components as well as the sensor itself. Finally, we discuss the potential path forward for research in building deployable SNN systems identifying key challenges in the algorithm-hardware-application co-design space with an emphasis on trustworthiness.
Rethinking Skip Connections in Spiking Neural Networks with Time-To-First-Spike Coding
2023-12-01
Youngeun Kim, Adar Kahana, Ruokai Yin, Yuhang Li, Panos Stinis, George Em Karniadakis, Priyadarshini Panda
Time-To-First-Spike (TTFS) coding in Spiking Neural Networks (SNNs) offers significant advantages in terms of energy efficiency, closely mimicking the behavior of biological neurons. In this work, we delve into the role of skip connections, a widely used concept in Artificial Neural Networks (ANNs), within the domain of SNNs with TTFS coding. Our focus is on two distinct types of skip connection architectures: (1) addition-based skip connections, and (2) concatenation-based skip connections. We find that addition-based skip connections introduce an additional delay in terms of spike timing. On the other hand, concatenation-based skip connections circumvent this delay but produce time gaps between after-convolution and skip connection paths, thereby restricting the effective mixing of information from these two paths. To mitigate these issues, we propose a novel approach involving a learnable delay for skip connections in the concatenation-based skip connection architecture. This approach successfully bridges the time gap between the convolutional and skip branches, facilitating improved information mixing. We conduct experiments on public datasets including MNIST and Fashion-MNIST, illustrating the advantage of the skip connection in TTFS coding architectures. Additionally, we demonstrate the applicability of TTFS coding on beyond image recognition tasks and extend it to scientific machine-learning tasks, broadening the potential uses of SNNs.
Retina : Low-Power Eye Tracking with Event Camera and Spiking Hardware
2023-12-01
Pietro Bonazzi, Sizhen Bian, Giovanni Lippolis, Yawei Li, Sadique Sheik, Michele Magno
This paper introduces a neuromorphic methodology for eye tracking, harnessing pure event data captured by a Dynamic Vision Sensor (DVS) camera. The framework integrates a directly trained Spiking Neuron Network (SNN) regression model and leverages a state-of-the-art low power edge neuromorphic processor - Speck, collectively aiming to advance the precision and efficiency of eye-tracking systems. First, we introduce a representative event-based eye-tracking dataset, "Ini-30", which was collected with two glass-mounted DVS cameras from thirty volunteers. Then,a SNN model, based on Integrate And Fire (IAF) neurons, named "Retina", is described , featuring only 64k parameters (6.63x fewer than the latest) and achieving pupil tracking error of only 3.24 pixels in a 64x64 DVS input. The continous regression output is obtained by means of convolution using a non-spiking temporal 1D filter slided across the output spiking layer. Finally, we evaluate Retina on the neuromorphic processor, showing an end-to-end power between 2.89-4.8 mW and a latency of 5.57-8.01 mS dependent on the time window. We also benchmark our model against the latest event-based eye-tracking method, "3ET", which was built upon event frames. Results show that Retina achieves superior precision with 1.24px less pupil centroid error and reduced computational complexity with 35 times fewer MAC operations. We hope this work will open avenues for further investigation of close-loop neuromorphic solutions and true event-based training pursuing edge performance.
Astrocyte control bursting mode of spiking neuron network with memristor-implemented plasticity
2023-11-30
Sergey V. Stasenko, Alexey N. Mikhaylov, Alexander A. Fedotov, Vladimir A. Smirnov, Victor B. Kazantsev
A mathematical model of a spiking neuron network accompanied by astrocytes is considered. The network is composed of excitatory and inhibitory neurons with synaptic connections supplied by a memristor-based model of plasticity. Another mechanism for changing the synaptic connections involves astrocytic regulations using the concept of tripartite synapses. In the absence of memristor-based plasticity, the connections between these neurons drive the network dynamics into a burst mode, as observed in many experimental neurobiological studies when investigating living networks in neuronal cultures. The memristive plasticity implementing synaptic plasticity in inhibitory synapses results in a shift in network dynamics towards an asynchronous mode. Next,it is found that accounting for astrocytic regulation in glutamatergic excitatory synapses enable the restoration of 'normal' burst dynamics. The conditions and parameters of such astrocytic regulation's impact on burst dynamics established.
Large spiking neural networks (SNNs) require ultra-low power and low variability hardware for neuromorphic computing applications. Recently, a band-to-band tunneling-based (BTBT) integrator, enabling sub-kHz operation of neurons with area and energy efficiency, was proposed. For an ultra-low power implementation of such neurons, a very low BTBT current is needed, so minimizing current without degrading neuronal properties is essential. Low variability is needed in the ultra-low current integrator to avoid network performance degradation in a large BTBT neuron-based SNN. To address this, we conducted design space and variability analysis in TCAD, utilizing a well-calibrated TCAD deck with experimental data from GlobalFoundries 32nm PD-SOI MOSFET. First, we discuss the physics-based explanation of the tunneling mechanism. Second, we explore the impact of device design parameters on SOI MOSFET performance, highlighting parameter sensitivities to tunneling current. With device parameters' optimization, we demonstrate a ~20x reduction in BTBT current compared to the experimental data. Finally, a variability analysis that includes the effects of random dopant fluctuations (RDF), oxide thickness variability (OTV), and channel-oxide interface traps DIT in the BTBT, SS, and ON regimes of operation is shown. The BTBT regime shows high sensitivity to the RDF and OTV as any variation in them directly modulates the tunnel length or the electric field at the drain-channel junction, whereas minimal sensitivity to DIT is observed.
As next-generation implantable brain-machine interfaces become pervasive on edge device, incrementally learning new tasks in bio-plasticity ways is urgently demanded for Neuromorphic chips. Due to the inherent characteristics of its structure, spiking neural networks are naturally well-suited for BMI-chips. Here we propose Hebbian Weight Consolidation, as well as an on-chip learning framework. HWC selectively masks synapse modifications for previous tasks, retaining them to store new knowledge from subsequent tasks while preserving the old knowledge. Leveraging the bio-plasticity of dendritic spines, the intrinsic self-organizing nature of Hebbian Weight Consolidation aligns naturally with the incremental learning paradigm, facilitating robust learning outcomes. By reading out spikes layer by layer and performing back-propagation on the external micro-controller unit, MLoC can efficiently accomplish on-chip learning. Experiments show that our HWC algorithm up to 23.19% outperforms lower bound that without incremental learning algorithm, particularly in more challenging monkey behavior decoding scenarios. Taking into account on-chip computing on Synsense Speck 2e chip, our proposed algorithm exhibits an improvement of 11.06%. This study demonstrates the feasibility of employing incremental learning for high-performance neural signal decoding in next-generation brain-machine interfaces.
A computational model of behavioral adaptation to solve the credit assignment problem
2023-11-29
Roy E. Clymer, Sanjeev V. Namjoshi
The adaptive fitness of an organism in its ecological niche is highly reliant upon its ability to associate an environmental or internal stimulus with a behavior response through reinforcement. This simple but powerful observation has been successfully applied in a number of contexts within computational neuroscience and reinforcement learning to model both human and animal behaviors. However, a critical challenge faced by these models is the credit assignment problem which asks how past behavior comes to be associated with a delayed reinforcement signal. In this paper we reformulate the credit assignment problem to ask how past stimuli come to be linked to adaptive behavioral responses in the context of a simple neuronal circuit. We propose a biologically plausible variant of a spiking neural network which can model a wide variety of behavioral, learning, and evolutionary phenomena. Our model suggests one fundamental mechanism, potentially in use in the brains of both simple and complex organisms, that would allow it to associate a behavior with an adaptive response. We present results that showcase the model's versatility and biological plausibility in a number of tasks related to classical and operant conditioning including behavioral chaining. We then provide further simulations to demonstrate how adaptive behaviors such as reflexes and simple category detection may have evolved using our model. Our results indicate the potential for further modifications and extensions of our model to replicate more sophisticated and biologically plausible behavioral, learning, and intelligence phenomena found throughout the animal kingdom.
Dynamical manifold dimensionality as characterization measure of chimera states in bursting neuronal networks
2023-11-29
Olesia Dogonasheva, Daniil Radushev, Boris Gutkin, Denis Zakharov
Methods that distinguish dynamical regimes in networks of active elements make it possible to design the dynamics of models of realistic networks. A particularly salient example is partial synchronization, which may play a pivotal role in elucidating the dynamics of biological neural networks. Such emergent partial synchronization in structurally homogeneous networks is commonly denoted as chimera states. While several methods for detecting chimeras in networks of spiking neurons have been proposed, these are less effective when applied to networks of bursting neurons. Here we introduce the correlation dimension as a novel approach to identifying dynamic network states. To assess the viability of this new method, we study a network of intrinsically Hindmarsh-Rose neurons with non-local connections. In comparison to other measures of chimera states, the correlation dimension effectively characterizes chimeras in burst neurons, whether the incoherence arises in spikes or bursts. The generality of dimensionality measures inherent in the correlation dimension renders this approach applicable to any dynamic system, facilitating the comparison of simulated and experimental data. We anticipate that this methodology will enable the tuning and simulation of when modelling intricate network processes, contributing to a deeper understanding of neural dynamics.
Spiking Neural Networks with Dynamic Time Steps for Vision Transformers
2023-11-28
Gourav Datta, Zeyu Liu, Anni Li, Peter A. Beerel
Spiking Neural Networks (SNNs) have emerged as a popular spatio-temporal computing paradigm for complex vision tasks. Recently proposed SNN training algorithms have significantly reduced the number of time steps (down to 1) for improved latency and energy efficiency, however, they target only convolutional neural networks (CNN). These algorithms, when applied on the recently spotlighted vision transformers (ViT), either require a large number of time steps or fail to converge. Based on analysis of the histograms of the ANN and SNN activation maps, we hypothesize that each ViT block has a different sensitivity to the number of time steps. We propose a novel training framework that dynamically allocates the number of time steps to each ViT module depending on a trainable score assigned to each timestep. In particular, we generate a scalar binary time step mask that filters spikes emitted by each neuron in a leaky-integrate-and-fire (LIF) layer. The resulting SNNs have high activation sparsity and require only accumulate operations (AC), except for the input embedding layer, in contrast to expensive multiply-and-accumulates (MAC) needed in traditional ViTs. This yields significant improvements in energy efficiency. We evaluate our training framework and resulting SNNs on image recognition tasks including CIFAR10, CIFAR100, and ImageNet with different ViT architectures. We obtain a test accuracy of 95.97% with 4.97 time steps with direct encoding on CIFAR10.
Demonstration of Programmable Brain-Inspired Optoelectronic Neuron in Photonic Spiking Neural Network with Neural Heterogeneity
2023-11-27
Yun-Jhu Lee, Mehmet Berkay On, Luis El Srouji, Li Zhang, Mahmoud Abdelghany, S. J. Ben Yoo
Photonic Spiking Neural Networks (PSNN) composed of the co-integrated CMOS and photonic elements can offer low loss, low power, highly-parallel, and high-throughput computing for brain-inspired neuromorphic systems. In addition, heterogeneity of neuron dynamics can also bring greater diversity and expressivity to brain-inspired networks, potentially allowing for the implementation of complex functions with fewer neurons. In this paper, we design, fabricate, and experimentally demonstrate an optoelectronic spiking neuron that can simultaneously achieve high programmability for heterogeneous biological neural networks and maintain high-speed computing. We demonstrate that our neuron can be programmed to tune four essential parameters of neuron dynamics under 1GSpike/s input spiking pattern signals. A single neuron circuit can be tuned to output three spiking patterns, including chattering behaviors. The PSNN consisting of the optoelectronic spiking neuron and a Mach-Zehnder interferometer (MZI) mesh synaptic network achieves 89.3% accuracy on the Iris dataset. Our neuron power consumption is 1.18 pJ/spike output, mainly limited by the power efficiency of the vertical-cavity-lasers, optical coupling efficiency, and the 45 nm CMOS platform used in this experiment, and is predicted to achieve 36.84 fJ/spike output with a 7 nm CMOS platform (e.g. ASAP7) integrated with silicon photonics containing on-chip micron-scale lasers.
Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing
2023-11-24
Jens E. Pedersen, Steven Abreu, Matthias Jobst, Gregor Lenz, Vittorio Fra, Felix C. Bauer, Dylan R. Muir, Peng Zhou, Bernhard Vogginger, Kade Heckel, Gianvito Urgese, Sadasivan Shankar, Terrence C. Stewart, Jason K. Eshraghian, Sadique Sheik
Spiking neural networks and neuromorphic hardware platforms that emulate neural dynamics are slowly gaining momentum and entering main-stream usage. Despite a well-established mathematical foundation for neural dynamics, the implementation details vary greatly across different platforms. Correspondingly, there are a plethora of software and hardware implementations with their own unique technology stacks. Consequently, neuromorphic systems typically diverge from the expected computational model, which challenges the reproducibility and reliability across platforms. Additionally, most neuromorphic hardware is limited by its access via a single software frameworks with a limited set of training procedures. Here, we establish a common reference-frame for computations in neuromorphic systems, dubbed the Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational primitives as idealized continuous-time hybrid systems that can be composed into graphs and mapped to and from various neuromorphic technology stacks. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the fundamental computation, while simultaneously exposing the exact differences between the evaluated implementation and the idealized mathematical formalism. We reproduce three NIR graphs across 7 neuromorphic simulators and 4 hardware platforms, demonstrating support for an unprecedented number of neuromorphic systems. With NIR, we decouple the evolution of neuromorphic hardware and software, ultimately increasing the interoperability between platforms and improving accessibility to neuromorphic technologies. We believe that NIR is an important step towards the continued study of brain-inspired hardware and bottom-up approaches aimed at an improved understanding of the computational underpinnings of nervous systems.
SNN Architecture for Differential Time Encoding Using Decoupled Processing Time
2023-11-24
Daniel Windhager, Bernhard A. Moser, Michael Lunglmayr
Spiking neural networks (SNNs) have gained attention in recent years due to their ability to handle sparse and event-based data better than regular artificial neural networks (ANNs). Since the structure of SNNs is less suited for typically used accelerators such as GPUs than conventional ANNs, there is a demand for custom hardware accelerators for processing SNNs. In the past, the main focus was on platforms that resemble the structure of multiprocessor systems. In this work, we propose a lightweight neuron layer architecture that allows network structures to be directly mapped onto digital hardware. Our approach is based on differential time coding of spike sequences and the decoupling of processing time and spike timing that allows the SNN to be processed on different hardware platforms. We present synthesis and performance results showing that this architecture can be implemented for networks of more than 1000 neurons with high clock speeds on a State-of-the-Art FPGA. We furthermore show results on the robustness of our approach to quantization. These results demonstrate that high-accuracy inference can be performed with bit widths as low as 4.
2023-11-24
Nicholas J. Pritchard, Andreas Wicenec, Mohammed Bennamoun, Richard Dodson
Detecting and mitigating Radio Frequency Interference (RFI) is critical for enabling and maximising the scientific output of radio telescopes. The emergence of machine learning methods has led to their application in radio astronomy, and in RFI detection. Spiking Neural Networks (SNNs), inspired by biological systems, are well-suited for processing spatio-temporal data. This study introduces the first exploratory application of SNNs to an astronomical data-processing task, specifically RFI detection. We adapt the nearest-latent-neighbours (NLN) algorithm and auto-encoder architecture proposed by previous authors to SNN execution by direct ANN2SNN conversion, enabling simplified downstream RFI detection by sampling the naturally varying latent space from the internal spiking neurons. Our subsequent evaluation aims to determine whether SNNs are viable for future RFI detection schemes. We evaluate detection performance with the simulated HERA telescope and hand-labelled LOFAR observation dataset the original authors provided. We additionally evaluate detection performance with a new MeerKAT-inspired simulation dataset that provides a technical challenge for machine-learnt RFI detection methods. This dataset focuses on satellite-based RFI, an increasingly important class of RFI and is an additional contribution. Our approach remains competitive with existing methods in AUROC, AUPRC and F1 scores for the HERA dataset but exhibits difficulty in the LOFAR and Tabascal datasets. Our method maintains this accuracy while completely removing the compute and memory-intense latent sampling step found in NLN. This work demonstrates the viability of SNNs as a promising avenue for machine-learning-based RFI detection in radio telescopes by establishing a minimal performance baseline on traditional and nascent satellite-based RFI sources and is the first work to our knowledge to apply SNNs in astronomy.
Spiking Neural Networks (SNNs) have emerged as a promising energy-efficient alternative to traditional Artificial Neural Networks (ANNs). Despite this, bridging the performance gap with ANNs in practical scenarios remains a significant challenge. This paper focuses on addressing the dual objectives of enhancing the performance and efficiency of SNNs through the established SNN Calibration conversion framework. Inspired by the biological nervous system, we propose a novel Adaptive-Firing Neuron Model (AdaFire) that dynamically adjusts firing patterns across different layers, substantially reducing conversion errors within limited timesteps. Moreover, to meet our efficiency objectives, we propose two novel strategies: an Sensitivity Spike Compression (SSC) technique and an Input-aware Adaptive Timesteps (IAT) technique. These techniques synergistically reduce both energy consumption and latency during the conversion process, thereby enhancing the overall efficiency of SNNs. Extensive experiments demonstrate our approach outperforms state-of-the-art SNNs methods, showcasing superior performance and efficiency in 2D, 3D, and event-driven classification, as well as object detection and segmentation tasks.
Applications of Spiking Neural Networks in Visual Place Recognition
2023-11-22
Somayeh Hussaini, Michael Milford, Tobias Fischer
In robotics, Spiking Neural Networks (SNNs) are increasingly recognized for their largely-unrealized potential energy efficiency and low latency particularly when implemented on neuromorphic hardware. Our paper highlights three advancements for SNNs in Visual Place Recognition (VPR). Firstly, we propose Modular SNNs, where each SNN represents a set of non-overlapping geographically distinct places, enabling scalable networks for large environments. Secondly, we present Ensembles of Modular SNNs, where multiple networks represent the same place, significantly enhancing accuracy compared to single-network models. Each of our Modular SNN modules is compact, comprising only 1500 neurons and 474k synapses, making them ideally suited for ensembling due to their small size. Lastly, we investigate the role of sequence matching in SNN-based VPR, a technique where consecutive images are used to refine place recognition. We analyze the responsiveness of SNNs to ensembling and sequence matching compared to other VPR techniques. Our contributions highlight the viability of SNNs for VPR, offering scalable and robust solutions, and paving the way for their application in various energy-sensitive robotic tasks.
HPCNeuroNet: Advancing Neuromorphic Audio Signal Processing with Transformer-Enhanced Spiking Neural Networks
2023-11-21
Murat Isik, Hiruna Vishwamith, Kayode Inadagbo, I. Can Dikmen
This paper presents a novel approach to neuromorphic audio processing by integrating the strengths of Spiking Neural Networks (SNNs), Transformers, and high-performance computing (HPC) into the HPCNeuroNet architecture. Utilizing the Intel N-DNS dataset, we demonstrate the system's capability to process diverse human vocal recordings across multiple languages and noise backgrounds. The core of our approach lies in the fusion of the temporal dynamics of SNNs with the attention mechanisms of Transformers, enabling the model to capture intricate audio patterns and relationships. Our architecture, HPCNeuroNet, employs the Short-Time Fourier Transform (STFT) for time-frequency representation, Transformer embeddings for dense vector generation, and SNN encoding/decoding mechanisms for spike train conversions. The system's performance is further enhanced by leveraging the computational capabilities of NVIDIA's GeForce RTX 3060 GPU and Intel's Core i9 12900H CPU. Additionally, we introduce a hardware implementation on the Xilinx VU37P HBM FPGA platform, optimizing for energy efficiency and real-time processing. The proposed accelerator achieves a throughput of 71.11 Giga-Operations Per Second (GOP/s) with a 3.55 W on-chip power consumption at 100 MHz. The comparison results with off-the-shelf devices and recent state-of-the-art implementations illustrate that the proposed accelerator has obvious advantages in terms of energy efficiency and design flexibility. Through design-space exploration, we provide insights into optimizing core capacities for audio tasks. Our findings underscore the transformative potential of integrating SNNs, Transformers, and HPC for neuromorphic audio processing, setting a new benchmark for future research and applications.
Spiking Neural Networks (SNNs) offer a biologically inspired approach to computer vision that can lead to more efficient processing of visual data with reduced energy consumption. However, maintaining homeostasis within these networks is challenging, as it requires continuous adjustment of neural responses to preserve equilibrium and optimal processing efficiency amidst diverse and often unpredictable input signals. In response to these challenges, we propose the Asynchronous Bioplausible Neuron (ABN), a dynamic spike firing mechanism to auto-adjust the variations in the input signal. Comprehensive evaluation across various datasets demonstrates ABN's enhanced performance in image classification and segmentation, maintenance of neural equilibrium, and energy efficiency.
Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons
2023-11-19
Luke Taylor, Andrew J King, Nicol S Harper
The adaptive leaky integrate-and-fire (ALIF) model is fundamental within computational neuroscience and has been instrumental in studying our brains $\textit{in silico}$. Due to the sequential nature of simulating these neural models, a commonly faced issue is the speed-accuracy trade-off: either accurately simulate a neuron using a small discretisation time-step (DT), which is slow, or more quickly simulate a neuron using a larger DT and incur a loss in simulation accuracy. Here we provide a solution to this dilemma, by algorithmically reinterpreting the ALIF model, reducing the sequential simulation complexity and permitting a more efficient parallelisation on GPUs. We computationally validate our implementation to obtain over a $50\times$ training speedup using small DTs on synthetic benchmarks. We also obtained a comparable performance to the standard ALIF implementation on different supervised classification tasks - yet in a fraction of the training time. Lastly, we showcase how our model makes it possible to quickly and accurately fit real electrophysiological recordings of cortical neurons, where very fine sub-millisecond DTs are crucial for capturing exact spike timing.
Spiking Neural Networks (SNNs), a novel brain-inspired algorithm, are garnering increased attention for their superior computation and energy efficiency over traditional artificial neural networks (ANNs). To facilitate deployment on memory-constrained devices, numerous studies have explored SNN pruning. However, these efforts are hindered by challenges such as scalability challenges in more complex architectures and accuracy degradation. Amidst these challenges, the Lottery Ticket Hypothesis (LTH) emerges as a promising pruning strategy. It posits that within dense neural networks, there exist winning tickets or subnetworks that are sparser but do not compromise performance. To explore a more structure-sparse and energy-saving model, we investigate the unique synergy of SNNs with LTH and design two novel spiking winning tickets to push the boundaries of sparsity within SNNs. Furthermore, we introduce an innovative algorithm capable of simultaneously identifying both weight and patch-level winning tickets, enabling the achievement of sparser structures without compromising on the final model's performance. Through comprehensive experiments on both RGB-based and event-based datasets, we demonstrate that our spiking lottery ticket achieves comparable or superior performance even when the model structure is extremely sparse.
Spiking Neural Networks (SNNs) have been widely praised for their high energy efficiency and immense potential. However, comprehensive research that critically contrasts and correlates SNNs with quantized Artificial Neural Networks (ANNs) remains scant, often leading to skewed comparisons lacking fairness towards ANNs. This paper introduces a unified perspective, illustrating that the time steps in SNNs and quantized bit-widths of activation values present analogous representations. Building on this, we present a more pragmatic and rational approach to estimating the energy consumption of SNNs. Diverging from the conventional Synaptic Operations (SynOps), we champion the "Bit Budget" concept. This notion permits an intricate discourse on strategically allocating computational and storage resources between weights, activation values, and temporal steps under stringent hardware constraints. Guided by the Bit Budget paradigm, we discern that pivoting efforts towards spike patterns and weight quantization, rather than temporal attributes, elicits profound implications for model performance. Utilizing the Bit Budget for holistic design consideration of SNNs elevates model performance across diverse data types, encompassing static imagery and neuromorphic datasets. Our revelations bridge the theoretical chasm between SNNs and quantized ANNs and illuminate a pragmatic trajectory for future endeavors in energy-efficient neural computations.
Energy consumption remains the main limiting factors in many IoT applications. In particular, micro-controllers consume far too much power. In order to overcome this problem, new circuit designs have been proposed and the use of spiking neurons and analog computing has emerged as it allows a very significant consumption reduction. However, working in the analog domain brings difficulty to handle the sequential processing of incoming signals as is needed in many use cases. In this paper, we use a bio-inspired phenomenon called Interacting Synapses to produce a time filter, without using non-biological techniques such as synaptic delays. We propose a model of neuron and synapses that fire for a specific range of delays between two incoming spikes, but do not react when this Inter-Spike Timing is not in that range. We study the parameters of the model to understand how to choose them and adapt the Inter-Spike Timing. The originality of the paper is to propose a new way, in the analog domain, to deal with temporal sequences.
DISTA: Denoising Spiking Transformer with intrinsic plasticity and spatiotemporal attention
2023-11-15
Boxun Xu, Hejia Geng, Yuxuan Yin, Peng Li
Among the array of neural network architectures, the Vision Transformer (ViT) stands out as a prominent choice, acclaimed for its exceptional expressiveness and consistent high performance in various vision applications. Recently, the emerging Spiking ViT approach has endeavored to harness spiking neurons, paving the way for a more brain-inspired transformer architecture that thrives in ultra-low power operations on dedicated neuromorphic hardware. Nevertheless, this approach remains confined to spatial self-attention and doesn't fully unlock the potential of spiking neural networks. We introduce DISTA, a Denoising Spiking Transformer with Intrinsic Plasticity and SpatioTemporal Attention, designed to maximize the spatiotemporal computational prowess of spiking neurons, particularly for vision applications. DISTA explores two types of spatiotemporal attentions: intrinsic neuron-level attention and network-level attention with explicit memory. Additionally, DISTA incorporates an efficient nonlinear denoising mechanism to quell the noise inherent in computed spatiotemporal attention maps, thereby resulting in further performance gains. Our DISTA transformer undergoes joint training involving synaptic plasticity (i.e., weight tuning) and intrinsic plasticity (i.e., membrane time constant tuning) and delivers state-of-the-art performances across several static image and dynamic neuromorphic datasets. With only 6 time steps, DISTA achieves remarkable top-1 accuracy on CIFAR10 (96.26%) and CIFAR100 (79.15%), as well as 79.1% on CIFAR10-DVS using 10 time steps.
Spiking NeRF: Representing the Real-World Geometry by a Discontinuous Representation
2023-11-15
Zhanfeng Liao, Qian Zheng, Yan Liu, Gang Pan
A crucial reason for the success of existing NeRF-based methods is to build a neural density field for the geometry representation via multiple perceptron layers (MLPs). MLPs are continuous functions, however, real geometry or density field is frequently discontinuous at the interface between the air and the surface. Such a contrary brings the problem of unfaithful geometry representation. To this end, this paper proposes spiking NeRF, which leverages spiking neurons and a hybrid Artificial Neural Network (ANN)-Spiking Neural Network (SNN) framework to build a discontinuous density field for faithful geometry representation. Specifically, we first demonstrate the reason why continuous density fields will bring inaccuracy. Then, we propose to use the spiking neurons to build a discontinuous density field. We conduct a comprehensive analysis for the problem of existing spiking neuron models and then provide the numerical relationship between the parameter of the spiking neuron and the theoretical accuracy of geometry. Based on this, we propose a bounded spiking neuron to build the discontinuous density field. Our method achieves SOTA performance. The source code and the supplementary material are available at https://github.com/liaozhanfeng/Spiking-NeRF.
SparseSpikformer: A Co-Design Framework for Token and Weight Pruning in Spiking Transformer
2023-11-15
Yue Liu, Shanlin Xiao, Bo Li, Zhiyi Yu
As the third-generation neural network, the Spiking Neural Network (SNN) has the advantages of low power consumption and high energy efficiency, making it suitable for implementation on edge devices. More recently, the most advanced SNN, Spikformer, combines the self-attention module from Transformer with SNN to achieve remarkable performance. However, it adopts larger channel dimensions in MLP layers, leading to an increased number of redundant model parameters. To effectively decrease the computational complexity and weight parameters of the model, we explore the Lottery Ticket Hypothesis (LTH) and discover a very sparse ($\ge$90%) subnetwork that achieves comparable performance to the original network. Furthermore, we also design a lightweight token selector module, which can remove unimportant background information from images based on the average spike firing rate of neurons, selecting only essential foreground image tokens to participate in attention calculation. Based on that, we present SparseSpikformer, a co-design framework aimed at achieving sparsity in Spikformer through token and weight pruning techniques. Experimental results demonstrate that our framework can significantly reduce 90% model parameters and cut down Giga Floating-Point Operations (GFLOPs) by 20% while maintaining the accuracy of the original model.
We propose, in this paper, a Variable Spiking Wavelet Neural Operator (VS-WNO), which aims to bridge the gap between theoretical and practical implementation of Artificial Intelligence (AI) algorithms for mechanics applications. With recent developments like the introduction of neural operators, AI's potential for being used in mechanics applications has increased significantly. However, AI's immense energy and resource requirements are a hurdle in its practical field use case. The proposed VS-WNO is based on the principles of spiking neural networks, which have shown promise in reducing the energy requirements of the neural networks. This makes possible the use of such algorithms in edge computing. The proposed VS-WNO utilizes variable spiking neurons, which promote sparse communication, thus conserving energy, and its use is further supported by its ability to tackle regression tasks, often faced in the field of mechanics. Various examples dealing with partial differential equations, like Burger's equation, Allen Cahn's equation, and Darcy's equation, have been shown. Comparisons have been shown against wavelet neural operator utilizing leaky integrate and fire neurons (direct and encoded inputs) and vanilla wavelet neural operator utilizing artificial neurons. The results produced illustrate the ability of the proposed VS-WNO to converge to ground truth while promoting sparse communication.
Redundant information transfer in a neural network can increase the complexity of the deep learning model, thus increasing its power consumption. We introduce in this paper a novel spiking neuron, termed Variable Spiking Neuron (VSN), which can reduce the redundant firing using lessons from biological neuron inspired Leaky Integrate and Fire Spiking Neurons (LIF-SN). The proposed VSN blends LIF-SN and artificial neurons. It garners the advantage of intermittent firing from the LIF-SN and utilizes the advantage of continuous activation from the artificial neuron. This property of the proposed VSN makes it suitable for regression tasks, which is a weak point for the vanilla spiking neurons, all while keeping the energy budget low. The proposed VSN is tested against both classification and regression tasks. The results produced advocate favorably towards the efficacy of the proposed spiking neuron, particularly for regression tasks.
Adversarially Robust Spiking Neural Networks Through Conversion
2023-11-15
Ozan Özdenizci, Robert Legenstein
Spiking neural networks (SNNs) provide an energy-efficient alternative to a variety of artificial neural network (ANN) based AI applications. As the progress in neuromorphic computing with SNNs expands their use in applications, the problem of adversarial robustness of SNNs becomes more pronounced. To the contrary of the widely explored end-to-end adversarial training based solutions, we address the limited progress in scalable robust SNN training methods by proposing an adversarially robust ANN-to-SNN conversion algorithm. Our method provides an efficient approach to embrace various computationally demanding robust learning objectives that have been proposed for ANNs. During a post-conversion robust finetuning phase, our method adversarially optimizes both layer-wise firing thresholds and synaptic connectivity weights of the SNN to maintain transferred robustness gains from the pre-trained ANN. We perform experimental evaluations in a novel setting proposed to rigorously assess the robustness of SNNs, where numerous adaptive adversarial attacks that account for the spike-based operation dynamics are considered. Results show that our approach yields a scalable state-of-the-art solution for adversarially robust deep SNNs with low-latency.
Hybrid Synaptic Structure for Spiking Neural Network Realization
2023-11-13
Sasan Razmkhah, Mustafa Altay Karamuftuoglu, Ali Bozbey
Neural networks and neuromorphic computing play pivotal roles in deep learning and machine vision. Due to their dissipative nature and inherent limitations, traditional semiconductor-based circuits face challenges in realizing ultra-fast and low-power neural networks. However, the spiking behavior characteristic of single flux quantum (SFQ) circuits positions them as promising candidates for spiking neural networks (SNNs). Our previous work showcased a JJ-Soma design capable of operating at tens of gigahertz while consuming only a fraction of the power compared to traditional circuits, as documented in [1]. This paper introduces a compact SFQ-based synapse design that applies positive and negative weighted inputs to the JJ-Soma. Using an RSFQ synapse empowers us to replicate the functionality of a biological neuron, a crucial step in realizing a complete SNN. The JJ-Synapse can operate at ultra-high frequencies, exhibits orders of magnitude lower power consumption than CMOS counterparts, and can be conveniently fabricated using commercial Nb processes. Furthermore, the network's flexibility enables modifications by incorporating cryo-CMOS circuits for weight value adjustments. In our endeavor, we have successfully designed, fabricated, and partially tested the JJ-Synapse within our cryocooler system. Integration with the JJ-Soma further facilitates the realization of a high-speed inference SNN.
In recent decades, Industrial Fault Diagnosis (IFD) has emerged as a crucial discipline concerned with detecting and gathering vital information about industrial equipment's health condition, thereby facilitating the identification of failure types and severities. The pursuit of precise and effective fault recognition has garnered substantial attention, culminating in a focus on automating equipment monitoring to preclude safety accidents and reduce reliance on human labor. The advent of artificial neural networks (ANNs) has been instrumental in augmenting intelligent IFD algorithms, particularly in the context of big data. Despite these advancements, ANNs, being a simplified biomimetic neural network model, exhibit inherent limitations such as resource and data dependencies and restricted cognitive capabilities. To address these limitations, the third-generation Spiking Neural Network (SNN), founded on principles of Brain-inspired computing, has surfaced as a promising alternative. The SNN, characterized by its biological neuron dynamics and spiking information encoding, demonstrates exceptional potential in representing spatiotemporal features. Consequently, developing SNN-based IFD models has gained momentum, displaying encouraging performance. Nevertheless, this field lacks systematic surveys to illustrate the current situation, challenges, and future directions. Therefore, this paper systematically reviews the theoretical progress of SNN-based models to answer the question of what SNN is. Subsequently, it reviews and analyzes existing SNN-based IFD models to explain why SNN needs to be used and how to use it. More importantly, this paper systematically answers the challenges, solutions, and opportunities of SNN in IFD.
Activity Sparsity Complements Weight Sparsity for Efficient RNN Inference
2023-11-13
Rishav Mukherji, Mark Schöne, Khaleelulla Khan Nazeer, Christian Mayr, Anand Subramoney
Artificial neural networks open up unprecedented machine learning capabilities at the cost of ever growing computational requirements. Sparsifying the parameters, often achieved through weight pruning, has been identified as a powerful technique to compress the number of model parameters and reduce the computational operations of neural networks. Yet, sparse activations, while omnipresent in both biological neural networks and deep learning systems, have not been fully utilized as a compression technique in deep learning. Moreover, the interaction between sparse activations and weight pruning is not fully understood. In this work, we demonstrate that activity sparsity can compose multiplicatively with parameter sparsity in a recurrent neural network model based on the GRU that is designed to be activity sparse. We achieve up to $20\times$ reduction of computation while maintaining perplexities below $60$ on the Penn Treebank language modeling task. This magnitude of reduction has not been achieved previously with solely sparsely connected LSTMs, and the language modeling performance of our model has not been achieved previously with any sparsely activated recurrent neural networks or spiking neural networks. Neuromorphic computing devices are especially good at taking advantage of the dynamic activity sparsity, and our results provide strong evidence that making deep learning models activity sparse and porting them to neuromorphic devices can be a viable strategy that does not compromise on task performance. Our results also drive further convergence of methods from deep learning and neuromorphic computing for efficient machine learning.
SynA-ResNet: Spike-driven ResNet Achieved through OR Residual Connection
2023-11-11
Yimeng Shan, Xuerui Qiu, Rui-jie Zhu, Jason K. Eshraghian, Malu Zhang, Haicheng Qu
Spiking Neural Networks (SNNs) have garnered substantial attention in brain-like computing for their biological fidelity and the capacity to execute energy-efficient spike-driven operations. As the demand for heightened performance in SNNs surges, the trend towards training deeper networks becomes imperative, while residual learning stands as a pivotal method for training deep neural networks. In our investigation, we identified that the SEW-ResNet, a prominent representative of deep residual spiking neural networks, incorporates non-event-driven operations. To rectify this, we propose a novel training paradigm that first accumulates a large amount of redundant information through OR Residual Connection (ORRC), and then filters out the redundant information using the Synergistic Attention (SynA) module, which promotes feature extraction in the backbone while suppressing the influence of noise and useless features in the shortcuts. When integrating SynA into the network, we observed the phenomenon of "natural pruning", where after training, some or all of the shortcuts in the network naturally drop out without affecting the model's classification accuracy. This significantly reduces computational overhead and makes it more suitable for deployment on edge devices. Experimental results on various public datasets confirmed that the SynA-ResNet achieved single-sample classification with as little as 0.8 spikes per neuron. Moreover, when compared to other residual SNN models, it exhibited higher accuracy and up to a 28-fold reduction in energy consumption.
Discrete synaptic events induce global oscillations in balanced neural networks
2023-11-10
Denis S. Goldobin, Matteo di Volo, Alessandro Torcini
Neural dynamics is triggered by discrete synaptic inputs of finite amplitude. However, the neural response is usually obtained within the diffusion approximation (DA) representing the synaptic inputs as Gaussian noise. We derive a mean-field formalism encompassing synaptic shot-noise for sparse balanced networks of spiking neurons. For low (high) external drives (synaptic strengths) irregular global oscillations emerge via continuous and hysteretic transitions, correctly predicted by our approach, but not from the DA. These oscillations display frequencies in biologically relevant bands.
Two-compartment neuronal spiking model expressing brain-state specific apical-amplification, -isolation and -drive regimes
2023-11-10
Elena Pastorelli, Alper Yegenoglu, Nicole Kolodziej, Willem Wybo, Francesco Simula, Sandra Diaz, Johan Frederik Storm, Pier Stanislao Paolucci
Mounting experimental evidence suggests that brain-state-specific neural mechanisms, supported by connectomic architectures, play a crucial role in integrating past and contextual knowledge with the current, incoming flow of evidence (e.g., from sensory systems). These mechanisms operate across multiple spatial and temporal scales, necessitating dedicated support at the levels of individual neurons and synapses. A notable feature within the neocortex is the structure of large, deep pyramidal neurons, which exhibit a distinctive separation between an apical dendritic compartment and a basal dendritic/perisomatic compartment. This separation is characterized by distinct patterns of incoming connections and brain-state-specific activation mechanisms, namely, apical amplification, isolation, and drive, which are associated with wakefulness, deeper NREM sleep stages, and REM sleep, respectively. The cognitive roles of apical mechanisms have been demonstrated in behaving animals. In contrast, classical models of learning in spiking networks are based on single-compartment neurons, lacking the ability to describe the integration of apical and basal/somatic information. This work aims to provide the computational community with a two-compartment spiking neuron model that incorporates features essential for supporting brain-state-specific learning. This model includes a piece-wise linear transfer function (ThetaPlanes) at the highest abstraction level, making it suitable for use in large-scale bio-inspired artificial intelligence systems. A machine learning evolutionary algorithm, guided by a set of fitness functions, selected the parameters that define neurons expressing the desired apical mechanisms.
Firing rate distributions in plastic networks of spiking neurons
2023-11-09
Marina Vegué, Antoine Allard, Patrick Desrosiers
In recurrent networks of leaky integrate-and-fire (LIF) neurons, mean-field theory has proven successful in describing various statistical properties of neuronal activity at equilibrium, such as firing rate distributions. Mean-field theory has been applied to networks in which either the synaptic weights are homogeneous across synapses and the number of incoming connections of individual neurons is heterogeneous, or vice versa. Here we extend the previous mean-field formalisms to treat networks in which these two sources of structural heterogeneity occur simultaneously, including networks whose synapses are subject to plastic, activity-dependent modulation. The plasticity in our model is mediated by the introduction of one spike trace per neuron: a chemical signal that is released every time the neuron emits a spike and which is degraded over time. The temporal evolution of the trace is controlled by its degradation rate $r_p$ and by the neuron's underlying firing rate $\nu$. When the ratio $\alpha=\nu / r_p$ tends to infinity, the trace can be rescaled to be a reliable estimation of the neuron's firing rate. In this regime, the value of any synaptic weight at equilibrium is a function of the pre- and post-synaptic firing rates, and this relation can be used in the mean-field formalism. The solution to the mean-field equations specifies the firing rate and synaptic weight distributions at equilibrium. These equations are exact in the limit of reliable traces but they already provide accurate results when the degradation rate lies within a reasonable range, as we show by comparison with simulations of the full neuronal dynamics in networks composed of excitatory and inhibitory LIF neurons. Overall, this work offers a way to explore and better understand the way in which plasticity shapes both activity and structure in neuronal networks.
From "What" to "When" -- a Spiking Neural Network Predicting Rare Events and Time to their Occurrence
2023-11-09
Mikhail Kiselev
In the reinforcement learning (RL) tasks, the ability to predict receiving reward in the near or more distant future means the ability to evaluate the current state as more or less close to the target state (labelled by the reward signal). In the present work, we utilize a spiking neural network (SNN) to predict time to the next target event (reward - in case of RL). In the context of SNNs, events are represented as spikes emitted by network neurons or input nodes. It is assumed that target events are indicated by spikes emitted by a special network input node. Using description of the current state encoded in the form of spikes from the other input nodes, the network should predict approximate time of the next target event. This research paper presents a novel approach to learning the corresponding predictive model by an SNN consisting of leaky integrate-and-fire (LIF) neurons. The proposed method leverages specially designed local synaptic plasticity rules and a novel columnar-layered SNN architecture. Similar to our previous works, this study places a strong emphasis on the hardware-friendliness of the proposed models, ensuring their efficient implementation on modern and future neuroprocessors. The approach proposed was tested on a simple reward prediction task in the context of one of the RL benchmark ATARI games, ping-pong. It was demonstrated that the SNN described in this paper gives superior prediction accuracy in comparison with precise machine learning techniques, such as decision tree algorithms and convolutional neural networks.
Rethinking Residual Connection in Training Large-Scale Spiking Neural Networks
2023-11-09
Yudong Li, Yunlin Lei, Xu Yang
Spiking Neural Network (SNN) is known as the most famous brain-inspired model, but the non-differentiable spiking mechanism makes it hard to train large-scale SNNs. To facilitate the training of large-scale SNNs, many training methods are borrowed from Artificial Neural Networks (ANNs), among which deep residual learning is the most commonly used. But the unique features of SNNs make prior intuition built upon ANNs not available for SNNs. Although there are a few studies that have made some pioneer attempts on the topology of Spiking ResNet, the advantages of different connections remain unclear. To tackle this issue, we analyze the merits and limitations of various residual connections and empirically demonstrate our ideas with extensive experiments. Then, based on our observations, we abstract the best-performing connections into densely additive (DA) connection, extend such a concept to other topologies, and propose four architectures for training large-scale SNNs, termed DANet, which brings up to 13.24% accuracy gain on ImageNet. Besides, in order to present a detailed methodology for designing the topology of large-scale SNNs, we further conduct in-depth discussions on their applicable scenarios in terms of their performance on various scales of datasets and demonstrate their advantages over prior architectures. At a low training expense, our best-performing ResNet-50/101/152 obtain 73.71%/76.13%/77.22% top-1 accuracy on ImageNet with 4 time steps. We believe that this work shall give more insights for future works to design the topology of their networks and promote the development of large-scale SNNs. The code will be publicly available.
Neuromorphic engineering has emerged as a promising avenue for developing brain-inspired computational systems. However, conventional electronic AI-based processors often encounter challenges related to processing speed and thermal dissipation. As an alternative, optical implementations of such processors have been proposed, capitalizing on the intrinsic information-processing capabilities of light. Within the realm of optical neuromorphic engineering, various optical neural networks (ONNs) have been explored. Among these, Spiking Neural Networks (SNNs) have exhibited notable success in emulating the computational principles of the human brain. Nevertheless, the integration of optical SNN processors has presented formidable obstacles, mainly when dealing with the computational demands of large datasets. In response to these challenges, we introduce a pioneering concept: the Free-space Optical deep Spiking Convolutional Neural Network (OSCNN). This novel approach draws inspiration from computational models of the human eye. We have meticulously designed various optical components within the OSCNN to tackle object detection tasks across prominent benchmark datasets, including MNIST, ETH 80, and Caltech. Our results demonstrate promising performance with minimal latency and power consumption compared to their electronic ONN counterparts. Additionally, we conducted several pertinent simulations, such as optical intensity to-latency conversion and synchronization. Of particular significance is the evaluation of the feature extraction layer, employing a Gabor filter bank, which stands to impact the practical deployment of diverse ONN architectures significantly.
Harnessing Manycore Processors with Distributed Memory for Accelerated Training of Sparse and Recurrent Models
2023-11-07
Jan Finkbeiner, Thomas Gmeinder, Mark Pupilli, Alexander Titterton, Emre Neftci
Current AI training infrastructure is dominated by single instruction multiple data (SIMD) and systolic array architectures, such as Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs), that excel at accelerating parallel workloads and dense vector matrix multiplications. Potentially more efficient neural network models utilizing sparsity and recurrence cannot leverage the full power of SIMD processor and are thus at a severe disadvantage compared to today's prominent parallel architectures like Transformers and CNNs, thereby hindering the path towards more sustainable AI. To overcome this limitation, we explore sparse and recurrent model training on a massively parallel multiple instruction multiple data (MIMD) architecture with distributed local memory. We implement a training routine based on backpropagation through time (BPTT) for the brain-inspired class of Spiking Neural Networks (SNNs) that feature binary sparse activations. We observe a massive advantage in using sparse activation tensors with a MIMD processor, the Intelligence Processing Unit (IPU) compared to GPUs. On training workloads, our results demonstrate 5-10x throughput gains compared to A100 GPUs and up to 38x gains for higher levels of activation sparsity, without a significant slowdown in training convergence or reduction in final model performance. Furthermore, our results show highly promising trends for both single and multi IPU configurations as we scale up to larger model sizes. Our work paves the way towards more efficient, non-standard models via AI training hardware beyond GPUs, and competitive large scale SNN models.
Estimating Post-Synaptic Effects for Online Training of Feed-Forward SNNs
2023-11-07
Thomas Summe, Clemens JS Schaefer, Siddharth Joshi
Facilitating online learning in spiking neural networks (SNNs) is a key step in developing event-based models that can adapt to changing environments and learn from continuous data streams in real-time. Although forward-mode differentiation enables online learning, its computational requirements restrict scalability. This is typically addressed through approximations that limit learning in deep models. In this study, we propose Online Training with Postsynaptic Estimates (OTPE) for training feed-forward SNNs, which approximates Real-Time Recurrent Learning (RTRL) by incorporating temporal dynamics not captured by current approximations, such as Online Training Through Time (OTTT) and Online Spatio-Temporal Learning (OSTL). We show improved scaling for multi-layer networks using a novel approximation of temporal effects on the subsequent layer's activity. This approximation incurs minimal overhead in the time and space complexity compared to similar algorithms, and the calculation of temporal effects remains local to each layer. We characterize the learning performance of our proposed algorithms on multiple SNN model configurations for rate-based and time-based encoding. OTPE exhibits the highest directional alignment to exact gradients, calculated with backpropagation through time (BPTT), in deep networks and, on time-based encoding, outperforms other approximate methods. We also observe sizeable gains in average performance over similar algorithms in offline training of Spiking Heidelberg Digits with equivalent hyper-parameters (OTTT/OSTL - 70.5%; OTPE - 75.2%; BPTT - 78.1%).
Criticality-Guided Efficient Pruning in Spiking Neural Networks Inspired by Critical Brain Hypothesis
2023-11-05
Shuo Chen, Boxiao Liu, Haihang You
Spiking Neural Networks (SNNs) have gained considerable attention due to the energy-efficient and multiplication-free characteristics. The continuous growth in scale of deep SNNs poses challenges for model deployment. Network pruning reduces hardware resource requirements of model deployment by compressing the network scale. However, existing SNN pruning methods cause high pruning costs and performance loss because the pruning iterations amplify the training difficulty of SNNs. In this paper, inspired by the critical brain hypothesis in neuroscience, we propose a regeneration mechanism based on the neuron criticality for SNN pruning to enhance feature extraction and accelerate the pruning process. Firstly, we propose a low-cost metric for the criticality in SNNs. Then, we re-rank the pruned structures after pruning and regenerate those with higher criticality to obtain the critical network. Our method achieves higher performance than the current state-of-the-art (SOTA) method with up to 95.26% reduction of pruning cost. Moreover, we investigate the underlying mechanism of our method and find that it efficiently selects potential structures and learns the consistent feature representation.
Third-generation artificial neural networks, Spiking Neural Networks (SNNs), can be efficiently implemented on hardware. Their implementation on neuromorphic chips opens a broad range of applications, such as machine learning-based autonomous control and intelligent biomedical devices. In critical applications, however, insight into the reasoning of SNNs is important, thus SNNs need to be equipped with the ability to explain how decisions are reached. We present \textit{Temporal Spike Attribution} (TSA), a local explanation method for SNNs. To compute the explanation, we aggregate all information available in model-internal variables: spike times and model weights. We evaluate TSA on artificial and real-world time series data and measure explanation quality w.r.t. multiple quantitative criteria. We find that TSA correctly identifies a small subset of input features relevant to the decision (i.e., is output-complete and compact) and generates similar explanations for similar inputs (i.e., is continuous). Further, our experiments show that incorporating the notion of \emph{absent} spikes improves explanation quality. Our work can serve as a starting point for explainable SNNs, with future implementations on hardware yielding not only predictions but also explanations in a broad range of application scenarios. Source code is available at https://github.com/ElisaNguyen/tsa-explanations.
2023-10-29
Ismael Balafrej, Fabien Alibart, Jean Rouat
Recurrent spiking neural networks (RSNNs) are notoriously difficult to train because of the vanishing gradient problem that is enhanced by the binary nature of the spikes. In this paper, we review the ability of the current state-of-the-art RSNNs to solve long-term memory tasks, and show that they have strong constraints both in performance, and for their implementation on hardware analog neuromorphic processors. We present a novel spiking neural network that circumvents these limitations. Our biologically inspired neural network uses synaptic delays, branching factor regularization and a novel surrogate derivative for the spiking function. The proposed network proves to be more successful in using the recurrent connections on memory tasks.
2023-10-29
Andreas Ziegler, Thomas Gossard, Karl Vetter, Jonas Tebbe, Andreas Zell
In recent years, robotic table tennis has become a popular research challenge for perception and robot control. Here, we present an improved table tennis robot system with high accuracy vision detection and fast robot reaction. Based on previous work, our system contains a KUKA robot arm with 6 DOF, with four frame-based cameras and two additional event-based cameras. We developed a novel calibration approach to calibrate this multimodal perception system. For table tennis, spin estimation is crucial. Therefore, we introduced a novel, and more accurate spin estimation approach. Finally, we show how combining the output of an event-based camera and a Spiking Neural Network (SNN) can be used for accurate ball detection.
WaLiN-GUI: a graphical and auditory tool for neuron-based encoding
2023-10-25
Simon F. Müller-Cleve, Fernando M. Quintana, Vittorio Fra, Pedro L. Galindo, Fernando Perez-Peña, Gianvito Urgese, Chiara Bartolozzi
Neuromorphic computing relies on spike-based, energy-efficient communication, inherently implying the need for conversion between real-valued (sensory) data and binary, sparse spiking representation. This is usually accomplished using the real valued data as current input to a spiking neuron model, and tuning the neuron's parameters to match a desired, often biologically inspired behaviour. We developed a tool, the WaLiN-GUI, that supports the investigation of neuron models and parameter combinations to identify suitable configurations for neuron-based encoding of sample-based data into spike trains. Due to the generalized LIF model implemented by default, next to the LIF and Izhikevich neuron models, many spiking behaviors can be investigated out of the box, thus offering the possibility of tuning biologically plausible responses to the input data. The GUI is provided open source and with documentation, being easy to extend with further neuron models and personalize with data analysis functions.
Design Space Exploration of Sparsity-Aware Application-Specific Spiking Neural Network Accelerators
2023-10-25
Ilkin Aliyev. Kama Svoboda, Tosiron Adegbija
Spiking Neural Networks (SNNs) offer a promising alternative to Artificial Neural Networks (ANNs) for deep learning applications, particularly in resource-constrained systems. This is largely due to their inherent sparsity, influenced by factors such as the input dataset, the length of the spike train, and the network topology. While a few prior works have demonstrated the advantages of incorporating sparsity into the hardware design, especially in terms of reducing energy consumption, the impact on hardware resources has not yet been explored. This is where design space exploration (DSE) becomes crucial, as it allows for the optimization of hardware performance by tailoring both the hardware and model parameters to suit specific application needs. However, DSE can be extremely challenging given the potentially large design space and the interplay of hardware architecture design choices and application-specific model parameters. In this paper, we propose a flexible hardware design that leverages the sparsity of SNNs to identify highly efficient, application-specific accelerator designs. We develop a high-level, cycle-accurate simulation framework for this hardware and demonstrate the framework's benefits in enabling detailed and fine-grained exploration of SNN design choices, such as the layer-wise logical-to-hardware ratio (LHR). Our experimental results show that our design can (i) achieve up to $76\%$ reduction in hardware resources and (ii) deliver a speed increase of up to $31.25\times$, while requiring $27\%$ fewer hardware resources compared to sparsity-oblivious designs. We further showcase the robustness of our framework by varying spike train lengths with different neuron population sizes to find the optimal trade-off points between accuracy and hardware latency.
Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks
2023-10-25
Jiechen Chen, Sangwoo Park, Osvaldo Simeone
Spiking neural networks (SNNs) are recurrent models that can leverage sparsity in input time series to efficiently carry out tasks such as classification. Additional efficiency gains can be obtained if decisions are taken as early as possible as a function of the complexity of the input time series. The decision on when to stop inference and produce a decision must rely on an estimate of the current accuracy of the decision. Prior work demonstrated the use of conformal prediction (CP) as a principled way to quantify uncertainty and support adaptive-latency decisions in SNNs. In this paper, we propose to enhance the uncertainty quantification capabilities of SNNs by implementing ensemble models for the purpose of improving the reliability of stopping decisions. Intuitively, an ensemble of multiple models can decide when to stop more reliably by selecting times at which most models agree that the current accuracy level is sufficient. The proposed method relies on different forms of information pooling from ensemble models, and offers theoretical reliability guarantees. We specifically show that variational inference-based ensembles with p-variable pooling significantly reduce the average latency of state-of-the-art methods, while maintaining reliability guarantees.
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of the automatic differentiation, parallel computation acceleration, and high integration of processing neuromorphic datasets and deployment. In this work, we present the SpikingJelly framework to address the aforementioned dilemma. We contribute a full-stack toolkit for pre-processing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips. Compared to existing methods, the training of deep SNNs can be accelerated $11\times$, and the superior extensibility and flexibility of SpikingJelly enable users to accelerate custom models at low costs through multilevel inheritance and semiautomatic code generation. SpikingJelly paves the way for synthesizing truly energy-efficient SNN-based machine intelligence systems, which will enrich the ecology of neuromorphic computing.
How can neuromorphic hardware attain brain-like functional capabilities?
2023-10-25
Wolfgang Maass
Research on neuromorphic computing is driven by the vision that we can emulate brain-like computing capability, learning capability, and energy-efficiency in novel hardware. Unfortunately, this vision has so far been pursued in a half-hearted manner. Most current neuromorphic hardware (NMHW) employs brain-like spiking neurons instead of standard artificial neurons. This is a good first step, which does improve the energy-efficiency of some computations, see \citep{rao2022long} for one of many examples. But current architectures and training methods for networks of spiking neurons in NMHW are largely copied from artificial neural networks. Hence it is not surprising that they inherit many deficiencies of artificial neural networks, rather than attaining brain-like functional capabilities. Of course, the brain is very complex, and we cannot implement all its details in NMHW. Instead, we need to focus on principles that are both easy to implement in NMHW and are likely to support brain-like functionality. The goal of this article is to highlight some of them.
Exploitation des propri{é}t{é}s de saturation synaptique pour obtenir un neurone {à} fr{é}quence sp{é}cifique
2023-10-24
Guillaume Marthe, Claire Goursaud
Energy consumption remains the main limiting factors in many promising IoT applications. In particular, micro-controllers consume far too much power. In order to overcome this problem, new circuit designs have been proposed and the use of spiking neurons and analog computing has emerged as it allows a very significant consumption reduction. However, working in the analog domain brings difficulty to handle the sequential processing of incoming signals as is needed in many use cases.In this paper, we propose to use a bio-inspired phenomenon called Interacting Synapses to produce a time filter. We propose a model of synapses that makes the neuron fire for a specific range of delays between two incoming spikes, but not react when this Inter-Spike Timing is not in that range. We study the parameters of the model to understand how to adapt the Inter-Spike Timing. The originality of the paper is to propose a new way, in the analog domain, to deal with temporal sequences.
LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding
2023-10-23
Qu Yang, Malu Zhang, Jibin Wu, Kay Chen Tan, Haizhou Li
The biological neurons use precise spike times, in addition to the spike firing rate, to communicate with each other. The time-to-first-spike (TTFS) coding is inspired by such biological observation. However, there is a lack of effective solutions for training TTFS-based spiking neural network (SNN). In this paper, we put forward a simple yet effective network conversion algorithm, which is referred to as LC-TTFS, by addressing two main problems that hinder an effective conversion from a high-performance artificial neural network (ANN) to a TTFS-based SNN. We show that our algorithm can achieve a near-perfect mapping between the activation values of an ANN and the spike times of an SNN on a number of challenging AI tasks, including image classification, image reconstruction, and speech enhancement. With TTFS coding, we can achieve up to orders of magnitude saving in computation over ANN and other rate-based SNNs. The study, therefore, paves the way for deploying ultra-low-power TTFS-based SNNs on power-constrained edge computing platforms.
ESVAE: An Efficient Spiking Variational Autoencoder with Reparameterizable Poisson Spiking Sampling
2023-10-23
Qiugang Zhan, Ran Tao, Xiurui Xie, Guisong Liu, Malu Zhang, Huajin Tang, Yang Yang
In recent years, studies on image generation models of spiking neural networks (SNNs) have gained the attention of many researchers. Variational autoencoders (VAEs), as one of the most popular image generation models, have attracted a lot of work exploring their SNN implementation. Due to the constrained binary representation in SNNs, existing SNN VAE methods implicitly construct the latent space by an elaborated autoregressive network and use the network outputs as the sampling variables. However, this unspecified implicit representation of the latent space will increase the difficulty of generating high-quality images and introduces additional network parameters. In this paper, we propose an efficient spiking variational autoencoder (ESVAE) that constructs an interpretable latent space distribution and design a reparameterizable spiking sampling method. Specifically, we construct the prior and posterior of the latent space as a Poisson distribution using the firing rate of the spiking neurons. Subsequently, we propose a reparameterizable Poisson spiking sampling method, which is free from the additional network. Comprehensive experiments have been conducted, and the experimental results show that the proposed ESVAE outperforms previous SNN VAE methods in reconstructed & generated images quality. In addition, experiments demonstrate that ESVAE's encoder is able to retain the original image information more efficiently, and the decoder is more robust. The source code is available at https://github.com/QgZhan/ESVAE.
Spiking neural networks play an important role in brain-like neuromorphic computations and in studying working mechanisms of neural circuits. One drawback of training a large scale spiking neural network is that updating all weights is quite expensive. Furthermore, after training, all information related to the computational task is hidden into the weight matrix, prohibiting us from a transparent understanding of circuit mechanisms. Therefore, in this work, we address these challenges by proposing a spiking mode-based training protocol, where the recurrent weight matrix is explained as a Hopfield-like multiplication of three matrices: input, output modes and a score matrix. The first advantage is that the weight is interpreted by input and output modes and their associated scores characterizing the importance of each decomposition term. The number of modes is thus adjustable, allowing more degrees of freedom for modeling the experimental data. This significantly reduces the training cost because of significantly reduced space complexity for learning. Training spiking networks is thus carried out in the mode-score space. The second advantage is that one can project the high dimensional neural activity (filtered spike train) in the state space onto the mode space which is typically of a low dimension, e.g., a few modes are sufficient to capture the shape of the underlying neural manifolds. We successfully apply our framework in two computational tasks -- digit classification and selective sensory integration tasks. Our method accelerate the training of spiking neural networks by a Hopfield-like decomposition, and moreover this training leads to low-dimensional attractor structures of high-dimensional neural dynamics.
The attention mechanism has been proven to be an effective way to improve spiking neural network (SNN). However, based on the fact that the current SNN input data flow is split into tensors to process on GPUs, none of the previous works consider the properties of tensors to implement an attention module. This inspires us to rethink current SNN from the perspective of tensor-relevant theories. Using tensor decomposition, we design the \textit{projected full attention} (PFA) module, which demonstrates excellent results with linearly growing parameters. Specifically, PFA is composed by the \textit{linear projection of spike tensor} (LPST) module and \textit{attention map composing} (AMC) module. In LPST, we start by compressing the original spike tensor into three projected tensors using a single property-preserving strategy with learnable parameters for each dimension. Then, in AMC, we exploit the inverse procedure of the tensor decomposition process to combine the three tensors into the attention map using a so-called connecting factor. To validate the effectiveness of the proposed PFA module, we integrate it into the widely used VGG and ResNet architectures for classification tasks. Our method achieves state-of-the-art performance on both static and dynamic benchmark datasets, surpassing the existing SNN models with Transformer-based and CNN-based backbones.
Multi-level, Forming Free, Bulk Switching Trilayer RRAM for Neuromorphic Computing at the Edge
2023-10-20
Jaeseoung Park, Ashwani Kumar, Yucheng Zhou, Sangheon Oh, Jeong-Hoon Kim, Yuhan Shi, Soumil Jain, Gopabandhu Hota, Amelie L. Nagle, Catherine D. Schuman, Gert Cauwenberghs, Duygu Kuzum
Resistive memory-based reconfigurable systems constructed by CMOS-RRAM integration hold great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. Low ON-state resistance of filamentary RRAM devices further increases the energy consumption due to high-current read and write operations, and limits the array size and parallel multiply & accumulate operations. High-forming voltages needed for filamentary RRAM are not compatible with advanced CMOS technology nodes. To address all these challenges, we developed a forming-free and bulk switching RRAM technology based on a trilayer metal-oxide stack. We systematically engineered a trilayer metal-oxide RRAM stack and investigated the switching characteristics of RRAM devices with varying thicknesses and oxygen vacancy distributions across the trilayer to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching operation at megaohm regime with high current nonlinearity and programmed up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform based on trilayer bulk RRAM crossbars by combining energy-efficient switched-capacitor voltage sensing circuits with differential encoding of weights to experimentally demonstrate high-accuracy matrix-vector multiplication. We showcased the computational capability of bulk RRAM crossbars by implementing a spiking neural network model for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints.
Stochasticity plays a significant role in the low-power operation of a biological neural network. In an artificial neural network (ANN), stochasticity also contributes to critical functions such as the uncertainty quantification (UQ) for estimating the probability for the correctness of prediction. This UQ is vital for cutting-edge applications, including medical diagnostics, autopilots, and large language models. Thanks to high computing velocity and low dissipation, a spin-orbit-torque (SOT) device exhibits significant potential for implementing the UQ. However, up until now, the application of UQ for stochastic SOT devices remains unexplored. In this study, based on SOT-induced stochastic magnetic domain wall (DW) motion with varying velocity, we fabricated an SOT synapse that could emulate stochastic weight update following the Spike-Timing-Dependent-Plasticity (STDP) rule. Furthermore, we set up a stochastic Spiking-Neural-Network (SNN), which, when compared to its deterministic counterpart, demonstrates a clear advantage in quantifying uncertainty for diagnosing the type of breast tumor (benign or malignant).
Spiking Semantic Communication for Feature Transmission with HARQ
2023-10-13
Mengyang Wang, Jiahui Li, Mengyao Ma, Xiaopeng Fan
In Collaborative Intelligence (CI), the Artificial Intelligence (AI) model is divided between the edge and the cloud, with intermediate features being sent from the edge to the cloud for inference. Several deep learning-based Semantic Communication (SC) models have been proposed to reduce feature transmission overhead and mitigate channel noise interference. Previous research has demonstrated that Spiking Neural Network (SNN)-based SC models exhibit greater robustness on digital channels compared to Deep Neural Network (DNN)-based SC models. However, the existing SNN-based SC models require fixed time steps, resulting in fixed transmission bandwidths that cannot be adaptively adjusted based on channel conditions. To address this issue, this paper introduces a novel SC model called SNN-SC-HARQ, which combines the SNN-based SC model with the Hybrid Automatic Repeat Request (HARQ) mechanism. SNN-SC-HARQ comprises an SNN-based SC model that supports the transmission of features at varying bandwidths, along with a policy model that determines the appropriate bandwidth. Experimental results show that SNN-SC-HARQ can dynamically adjust the bandwidth according to the channel conditions without performance loss.
We present an on-chip trainable neuron circuit. Our proposed circuit suits bio-inspired spike-based time-dependent data computation for training spiking neural networks (SNN). The thresholds of neurons can be increased or decreased depending on the desired application-specific spike generation rate. This mechanism provides us with a flexible design and scalable circuit structure. We demonstrate the trainable neuron structure under different operating scenarios. The circuits are designed and optimized for the MIT LL SFQ5ee fabrication process. Margin values for all parameters are above 25\% with a 3GHz throughput for a 16-input neuron.
Event cameras are bio-inspired sensors that respond to local changes in light intensity and feature low latency, high energy efficiency, and high dynamic range. Meanwhile, Spiking Neural Networks (SNNs) have gained significant attention due to their remarkable efficiency and fault tolerance. By synergistically harnessing the energy efficiency inherent in event cameras and the spike-based processing capabilities of SNNs, their integration could enable ultra-low-power application scenarios, such as action recognition tasks. However, existing approaches often entail converting asynchronous events into conventional frames, leading to additional data mapping efforts and a loss of sparsity, contradicting the design concept of SNNs and event cameras. To address this challenge, we propose SpikePoint, a novel end-to-end point-based SNN architecture. SpikePoint excels at processing sparse event cloud data, effectively extracting both global and local features through a singular-stage structure. Leveraging the surrogate training method, SpikePoint achieves high accuracy with few parameters and maintains low power consumption, specifically employing the identity mapping feature extractor on diverse datasets. SpikePoint achieves state-of-the-art (SOTA) performance on four event-based action recognition datasets using only 16 timesteps, surpassing other SNN methods. Moreover, it also achieves SOTA performance across all methods on three datasets, utilizing approximately 0.3\% of the parameters and 0.5\% of power consumption employed by artificial neural networks (ANNs). These results emphasize the significance of Point Cloud and pave the way for many ultra-low-power event-based data processing applications.
Energy-Efficient Visual Search by Eye Movement and Low-Latency Spiking Neural Network
2023-10-10
Yunhui Zhou, Dongqi Han, Yuguo Yu
Human vision incorporates non-uniform resolution retina, efficient eye movement strategy, and spiking neural network (SNN) to balance the requirements in visual field size, visual resolution, energy cost, and inference latency. These properties have inspired interest in developing human-like computer vision. However, existing models haven't fully incorporated the three features of human vision, and their learned eye movement strategies haven't been compared with human's strategy, making the models' behavior difficult to interpret. Here, we carry out experiments to examine human visual search behaviors and establish the first SNN-based visual search model. The model combines an artificial retina with spiking feature extraction, memory, and saccade decision modules, and it employs population coding for fast and efficient saccade decisions. The model can learn either a human-like or a near-optimal fixation strategy, outperform humans in search speed and accuracy, and achieve high energy efficiency through short saccade decision latency and sparse activation. It also suggests that the human search strategy is suboptimal in terms of search speed. Our work connects modeling of vision in neuroscience and machine learning and sheds light on developing more energy-efficient computer vision algorithms.
Spiking neural networks (SNNs) have demonstrated the capability to achieve comparable performance to deep neural networks (DNNs) in both visual and linguistic domains while offering the advantages of improved energy efficiency and adherence to biological plausibility. However, the extension of such single-modality SNNs into the realm of multimodal scenarios remains an unexplored territory. Drawing inspiration from the concept of contrastive language-image pre-training (CLIP), we introduce a novel framework, named SpikeCLIP, to address the gap between two modalities within the context of spike-based computing through a two-step recipe involving ``Alignment Pre-training + Dual-Loss Fine-tuning". Extensive experiments demonstrate that SNNs achieve comparable results to their DNN counterparts while significantly reducing energy consumption across a variety of datasets commonly used for multimodal model evaluation. Furthermore, SpikeCLIP maintains robust performance in image classification tasks that involve class labels not predefined within specific categories.
Recently, Spiking Neural Networks (SNNs), enjoying extreme energy efficiency, have drawn much research attention on 2D visual recognition and shown gradually increasing application potential. However, it still remains underexplored whether SNNs can be generalized to 3D recognition. To this end, we present Spiking PointNet in the paper, the first spiking neural model for efficient deep learning on point clouds. We discover that the two huge obstacles limiting the application of SNNs in point clouds are: the intrinsic optimization obstacle of SNNs that impedes the training of a big spiking model with large time steps, and the expensive memory and computation cost of PointNet that makes training a big spiking point model unrealistic. To solve the problems simultaneously, we present a trained-less but learning-more paradigm for Spiking PointNet with theoretical justifications and in-depth experimental analysis. In specific, our Spiking PointNet is trained with only a single time step but can obtain better performance with multiple time steps inference, compared to the one trained directly with multiple time steps. We conduct various experiments on ModelNet10, ModelNet40 to demonstrate the effectiveness of Spiking PointNet. Notably, our Spiking PointNet even can outperform its ANN counterpart, which is rare in the SNN field thus providing a potential research direction for the following work. Moreover, Spiking PointNet shows impressive speedup and storage saving in the training phase.
Bio-inspired computational memory model of the Hippocampus: an approach to a neuromorphic spike-based Content-Addressable Memory
2023-10-09
Daniel Casanueva-Morato, Alvaro Ayuso-Martinez, Juan P. Dominguez-Morales, Angel Jimenez-Fernandez, Gabriel Jimenez-Moreno
The brain has computational capabilities that surpass those of modern systems, being able to solve complex problems efficiently in a simple way. Neuromorphic engineering aims to mimic biology in order to develop new systems capable of incorporating such capabilities. Bio-inspired learning systems continue to be a challenge that must be solved, and much work needs to be done in this regard. Among all brain regions, the hippocampus stands out as an autoassociative short-term memory with the capacity to learn and recall memories from any fragment of them. These characteristics make the hippocampus an ideal candidate for developing bio-inspired learning systems that, in addition, resemble content-addressable memories. Therefore, in this work we propose a bio-inspired spiking content-addressable memory model based on the CA3 region of the hippocampus with the ability to learn, forget and recall memories, both orthogonal and non-orthogonal, from any fragment of them. The model was implemented on the SpiNNaker hardware platform using Spiking Neural Networks. A set of experiments based on functional, stress and applicability tests were performed to demonstrate its correct functioning. This work presents the first hardware implementation of a fully-functional bio-inspired spiking hippocampal content-addressable memory model, paving the way for the development of future more complex neuromorphic systems.
Investigating Continuous Learning in Spiking Neural Networks
2023-10-09
C. Tanner Fredieu
In this paper, the use of third-generation machine learning, also known as spiking neural network architecture, for continuous learning was investigated and compared to conventional models. The experimentation was divided into three separate phases. The first phase focused on training the conventional models via transfer learning. The second phase trains a Nengo model from their library. Lastly, each conventional model is converted into a spiking neural network and trained. Initial results from phase 1 are inline with known knowledge about continuous learning within current machine learning literature. All models were able to correctly identify the current classes, but they would immediately see a sharp performance drop in previous classes due to catastrophic forgetting. However, the SNN models were able to retain some information about previous classes. Although many of the previous classes were still identified as the current trained classes, the output probabilities showed a higher than normal value to the actual class. This indicates that the SNN models do have potential to overcome catastrophic forgetting but much work is still needed.
In recent years, legged robots based on deep reinforcement learning have made remarkable progress. Quadruped robots have demonstrated the ability to complete challenging tasks in complex environments and have been deployed in real-world scenarios to assist humans. Simultaneously, bipedal and humanoid robots have achieved breakthroughs in various demanding tasks. Current reinforcement learning methods can utilize diverse robot bodies and historical information to perform actions. However, prior research has not emphasized the speed and energy consumption of network inference, as well as the biological significance of the neural networks themselves. Most of the networks employed are traditional artificial neural networks that utilize multilayer perceptrons (MLP). In this paper, we successfully apply a novel Spiking Neural Network (SNN) to process legged robots, achieving outstanding results across a range of simulated terrains. SNN holds a natural advantage over traditional neural networks in terms of inference speed and energy consumption, and their pulse-form processing of body perception signals offers improved biological interpretability. Applying more biomimetic neural networks to legged robots can further reduce the heat dissipation and structural burden caused by the high power consumption of neural networks. To the best of our knowledge, this is the first work to implement SNN in legged robots.
In the Blink of an Eye: Event-based Emotion Recognition
2023-10-06
Haiwei Zhang, Jiqing Zhang, Bo Dong, Pieter Peers, Wenwei Wu, Xiaopeng Wei, Felix Heide, Xin Yang
We introduce a wearable single-eye emotion recognition device and a real-time approach to recognizing emotions from partial observations of an emotion that is robust to changes in lighting conditions. At the heart of our method is a bio-inspired event-based camera setup and a newly designed lightweight Spiking Eye Emotion Network (SEEN). Compared to conventional cameras, event-based cameras offer a higher dynamic range (up to 140 dB vs. 80 dB) and a higher temporal resolution. Thus, the captured events can encode rich temporal cues under challenging lighting conditions. However, these events lack texture information, posing problems in decoding temporal information effectively. SEEN tackles this issue from two different perspectives. First, we adopt convolutional spiking layers to take advantage of the spiking neural network's ability to decode pertinent temporal information. Second, SEEN learns to extract essential spatial cues from corresponding intensity frames and leverages a novel weight-copy scheme to convey spatial attention to the convolutional spiking layers during training and inference. We extensively validate and demonstrate the effectiveness of our approach on a specially collected Single-eye Event-based Emotion (SEE) dataset. To the best of our knowledge, our method is the first eye-based emotion recognition method that leverages event-based cameras and spiking neural network.
2023-10-05
Mustafa Altay Karamuftuoglu, Beyza Zeynep Ucpinar, Sasan Razmkhah, Mehdi Kamal, Massoud Pedram
Single Flux Quantum (SFQ) technology represents a groundbreaking advancement in computational efficiency and ultra-high-speed neuromorphic processing. The key features of SFQ technology, particularly data representation, transmission, and processing through SFQ pulses, closely mirror fundamental aspects of biological neural structures. Consequently, SFQ-based circuits emerge as an ideal candidate for realizing Spiking Neural Networks (SNNs). This study presents a proof-of-concept demonstration of an SFQ-based SNN architecture, showcasing its capacity for ultra-fast switching at remarkably low energy consumption per output activity. Notably, our work introduces innovative approaches: (i) We introduce a novel spike-timing-dependent plasticity mechanism to update synapses and to trace spike-activity by incorporating a leaky non-destructive readout circuit. (ii) We propose a novel method to dynamically regulate the threshold behavior of leaky integrate and fire superconductor neurons, enhancing the adaptability of our SNN architecture. (iii) Our research incorporates a novel winner-take-all mechanism, aligning with practical strategies for SNN development and enabling effective decision-making processes. The effectiveness of these proposed structural enhancements is evaluated by integrating high-level models into the BindsNET framework. By leveraging BindsNET, we model the online training of an SNN, integrating the novel structures into the learning process. To ensure the robustness and functionality of our circuits, we employ JoSIM for circuit parameter extraction and functional verification through simulation.
Neuromorphic Robust Framework for Concurrent Estimation and Control in Dynamical Systems using Spiking Neural Networks
2023-10-05
Reza Ahmadvand, Sarah Safura Sharif, Yaser Mike Banad
Concurrent estimation and control of robotic systems remains an ongoing challenge, where controllers rely on data extracted from states/parameters riddled with uncertainties and noises. Framework suitability hinges on task complexity and computational constraints, demanding a balance between computational efficiency and mission-critical accuracy. This study leverages recent advancements in neuromorphic computing, particularly spiking neural networks (SNNs), for estimation and control applications. Our presented framework employs a recurrent network of leaky integrate-and-fire (LIF) neurons, mimicking a linear quadratic regulator (LQR) through a robust filtering strategy, a modified sliding innovation filter (MSIF). Benefiting from both the robustness of MSIF and the computational efficiency of SNN, our framework customizes SNN weight matrices to match the desired system model without requiring training. Additionally, the network employs a biologically plausible firing rule similar to predictive coding. In the presence of uncertainties, we compare the SNN-LQR-MSIF with non-spiking LQR-MSIF and the optimal linear quadratic Gaussian (LQG) strategy. Evaluation across a workbench linear problem and a satellite rendezvous maneuver, implementing the Clohessy-Wiltshire (CW) model in space robotics, demonstrates that the SNN-LQR-MSIF achieves acceptable performance in computational efficiency, robustness, and accuracy. This positions it as a promising solution for addressing dynamic systems' concurrent estimation and control challenges in dynamic systems.
In this article, we propose a new paradigm for training spiking neural networks (SNNs), spike accumulation forwarding (SAF). It is known that SNNs are energy-efficient but difficult to train. Consequently, many researchers have proposed various methods to solve this problem, among which online training through time (OTTT) is a method that allows inferring at each time step while suppressing the memory cost. However, to compute efficiently on GPUs, OTTT requires operations with spike trains and weighted summation of spike trains during forwarding. In addition, OTTT has shown a relationship with the Spike Representation, an alternative training method, though theoretical agreement with Spike Representation has yet to be proven. Our proposed method can solve these problems; namely, SAF can halve the number of operations during the forward process, and it can be theoretically proven that SAF is consistent with the Spike Representation and OTTT, respectively. Furthermore, we confirmed the above contents through experiments and showed that it is possible to reduce memory and training time while maintaining accuracy.
Graphene-Based Artificial Dendrites for Bio-Inspired Learning in Spiking Neuromorphic Systems
2023-10-03
Samuel Liu, Deji Akinwande, Dmitry Kireev, Jean Anne C. Incorvia
Analog neuromorphic computing systems emulate the parallelism and connectivity of the human brain, promising greater expressivity and energy efficiency compared to digital systems. Though many devices have emerged as candidates for artificial neurons and artificial synapses, there have been few device candidates for artificial dendrites. In this work, we report on biocompatible graphene-based artificial dendrites (GrADs) that can implement dendritic processing. By using a dual side-gate configuration, current applied through a Nafion membrane can be used to control device conductance across a trilayer graphene channel, showing spatiotemporal responses of leaky recurrent, alpha, and gaussian dendritic potentials. The devices can be variably connected to enable higher order neuronal responses, and we show through data-driven spiking neural network classification simulations that overall spiking activity is reduced by up to 15% without accuracy loss while low frequency operation is stabilized. This positions the GrADs as strong candidates for energy efficient bio-interfaced spiking neural networks.
Event-Enhanced Multi-Modal Spiking Neural Network for Dynamic Obstacle Avoidance
2023-10-03
Yang Wang, Bo Dong, Yuji Zhang, Yunduo Zhou, Haiyang Mei, Ziqi Wei, Xin Yang
Autonomous obstacle avoidance is of vital importance for an intelligent agent such as a mobile robot to navigate in its environment. Existing state-of-the-art methods train a spiking neural network (SNN) with deep reinforcement learning (DRL) to achieve energy-efficient and fast inference speed in complex/unknown scenes. These methods typically assume that the environment is static while the obstacles in real-world scenes are often dynamic. The movement of obstacles increases the complexity of the environment and poses a great challenge to the existing methods. In this work, we approach robust dynamic obstacle avoidance twofold. First, we introduce the neuromorphic vision sensor (i.e., event camera) to provide motion cues complementary to the traditional Laser depth data for handling dynamic obstacles. Second, we develop an DRL-based event-enhanced multimodal spiking actor network (EEM-SAN) that extracts information from motion events data via unsupervised representation learning and fuses Laser and event camera data with learnable thresholding. Experiments demonstrate that our EEM-SAN outperforms state-of-the-art obstacle avoidance methods by a significant margin, especially for dynamic obstacle avoidance.
Well-posedness and numerical analysis of an elapsed time model with strongly coupled neural networks
2023-10-03
Mauricio Sepulveda, Nicolas Torres, Luis Miguel Villada
The elapsed time equation is an age-structured model that describes dynamics of interconnected spiking neurons through the elapsed time since the last discharge, leading to many interesting questions on the evolution of the system from a mathematical and biological point of view. In this work, we first deal with the case when transmission after a spike is instantaneous and the case when there exists a distributed delay that depends on previous history of the system, which is a more realistic assumption. Then we study the well-posedness and the numerical analysis of the elapsed time models. For existence and uniqueness we improve the previous works by relaxing some hypothesis on the nonlinearity, including the strongly excitatory case, while for the numerical analysis we prove that the approximation given by the explicit upwind scheme converges to the solution of the non-linear problem. We also show some numerical simulations to compare the behavior of the system in the case of instantaneous transmission with the case of distributed delay under different parameters, leading to solutions with different asymptotic profiles.
Integrate-and-fire circuit for converting analog signals to spikes using phase encoding
2023-10-03
Javier Lopez-Randulfe, Nico Reeb, Alois Knoll
Processing sensor data with spiking neural networks on digital neuromorphic chips requires converting continuous analog signals into spike pulses. Two strategies are promising for achieving low energy consumption and fast processing speeds in end-to-end neuromorphic applications. First, to directly encode analog signals to spikes to bypass the need for an analog-to-digital converter (ADC). Second, to use temporal encoding techniques to maximize the spike sparsity, which is a crucial parameter for fast and efficient neuromorphic processing. In this work, we propose an adaptive control of the refractory period of the leaky integrate-and-fire (LIF) neuron model for encoding continuous analog signals into a train of time-coded spikes. The LIF-based encoder generates phase-encoded spikes that are compatible with digital hardware. We implemented the neuron model on a physical circuit and tested it with different electric signals. A digital neuromorphic chip processed the generated spike trains and computed the signal's frequency spectrum using a spiking version of the Fourier transform. We tested the prototype circuit on electric signals up to 1 KHz. Thus, we provide an end-to-end neuromorphic application that generates the frequency spectrum of an electric signal without the need for an ADC or a digital signal processing algorithm.
2023-10-01
Ole Richter, Chenxi Wu, Adrian M. Whatley, German Köstinger, Carsten Nielsen, Ning Qiao, Giacomo Indiveri
With the remarkable progress that technology has made, the need for processing data near the sensors at the edge has increased dramatically. The electronic systems used in these applications must process data continuously, in real-time, and extract relevant information using the smallest possible energy budgets. A promising approach for implementing always-on processing of sensory signals that supports on-demand, sparse, and edge-computing is to take inspiration from biological nervous system. Following this approach, we present a brain-inspired platform for prototyping real-time event-based Spiking Neural Networks (SNNs). The system proposed supports the direct emulation of dynamic and realistic neural processing phenomena such as short-term plasticity, NMDA gating, AMPA diffusion, homeostasis, spike frequency adaptation, conductance-based dendritic compartments and spike transmission delays. The analog circuits that implement such primitives are paired with a low latency asynchronous digital circuits for routing and mapping events. This asynchronous infrastructure enables the definition of different network architectures, and provides direct event-based interfaces to convert and encode data from event-based and continuous-signal sensors. Here we describe the overall system architecture, we characterize the mixed signal analog-digital circuits that emulate neural dynamics, demonstrate their features with experimental measurements, and present a low- and high-level software ecosystem that can be used for configuring the system. The flexibility to emulate different biologically plausible neural networks, and the chip's ability to monitor both population and single neuron signals in real-time, allow to develop and validate complex models of neural processing for both basic research and edge-computing applications.
SpikeMOT: Event-based Multi-Object Tracking with Sparse Motion Features
2023-09-29
Song Wang, Zhu Wang, Can Li, Xiaojuan Qi, Hayden Kwok-Hay So
In comparison to conventional RGB cameras, the superior temporal resolution of event cameras allows them to capture rich information between frames, making them prime candidates for object tracking. Yet in practice, despite their theoretical advantages, the body of work on event-based multi-object tracking (MOT) remains in its infancy, especially in real-world settings where events from complex background and camera motion can easily obscure the true target motion. In this work, an event-based multi-object tracker, called SpikeMOT, is presented to address these challenges. SpikeMOT leverages spiking neural networks to extract sparse spatiotemporal features from event streams associated with objects. The resulting spike train representations are used to track the object movement at high frequency, while a simultaneous object detector provides updated spatial information of these objects at an equivalent frame rate. To evaluate the effectiveness of SpikeMOT, we introduce DSEC-MOT, the first large-scale event-based MOT benchmark incorporating fine-grained annotations for objects experiencing severe occlusions, frequent trajectory intersections, and long-term re-identification in real-world contexts. Extensive experiments employing DSEC-MOT and another event-based dataset, named FE240hz, demonstrate SpikeMOT's capability to achieve high tracking accuracy amidst challenging real-world scenarios, advancing the state-of-the-art in event-based multi-object tracking.
Ultra-low-power Image Classification on Neuromorphic Hardware
2023-09-28
Gregor Lenz, Garrick Orchard, Sadique Sheik
Spiking neural networks (SNNs) promise ultra-low-power applications by exploiting temporal and spatial sparsity. The number of binary activations, called spikes, is proportional to the power consumed when executed on neuromorphic hardware. Training such SNNs using backpropagation through time for vision tasks that rely mainly on spatial features is computationally costly. Training a stateless artificial neural network (ANN) to then convert the weights to an SNN is a straightforward alternative when it comes to image recognition datasets. Most conversion methods rely on rate coding in the SNN to represent ANN activation, which uses enormous amounts of spikes and, therefore, energy to encode information. Recently, temporal conversion methods have shown promising results requiring significantly fewer spikes per neuron, but sometimes complex neuron models. We propose a temporal ANN-to-SNN conversion method, which we call Quartz, that is based on the time to first spike (TTFS). Quartz achieves high classification accuracy and can be easily implemented on neuromorphic hardware while using the least amount of synaptic operations and memory accesses. It incurs a cost of two additional synapses per neuron compared to previous temporal conversion methods, which are readily available on neuromorphic hardware. We benchmark Quartz on MNIST, CIFAR10, and ImageNet in simulation to show the benefits of our method and follow up with an implementation on Loihi, a neuromorphic chip by Intel. We provide evidence that temporal coding has advantages in terms of power consumption, throughput, and latency for similar classification accuracy. Our code and models are publicly available.
Neuromorphic processors that implement Spiking Neural Networks (SNNs) using mixed-signal analog/digital circuits represent a promising technology for closed-loop real-time processing of biosignals. As in biology, to minimize power consumption, the silicon neurons' circuits are configured to fire with a limited dynamic range and with maximum firing rates restricted to a few tens or hundreds of Herz. However, biosignals can have a very large dynamic range, so encoding them into spikes without saturating the neuron outputs represents an open challenge. In this work, we present a biologically-inspired strategy for compressing this high-dynamic range in SNN architectures, using three adaptation mechanisms ubiquitous in the brain: spike-frequency adaptation at the single neuron level, feed-forward inhibitory connections from neurons belonging to the input layer, and Excitatory-Inhibitory (E-I) balance via recurrent inhibition among neurons in the output layer. We apply this strategy to input biosignals encoded using both an asynchronous delta modulation method and an energy-based pulse-frequency modulation method. We validate this approach in silico, simulating a simple network applied to a gesture classification task from surface EMG recordings.
Spiking Neural Networks (SNNs) are expected to be a promising alternative to Artificial Neural Networks (ANNs) due to their strong biological interpretability and high energy efficiency. Specialized SNN hardware offers clear advantages over general-purpose devices in terms of power and performance. However, there's still room to advance hardware support for state-of-the-art (SOTA) SNN algorithms and improve computation and memory efficiency. As a further step in supporting high-performance SNNs on specialized hardware, we introduce FireFly v2, an FPGA SNN accelerator that can address the issue of non-spike operation in current SOTA SNN algorithms, which presents an obstacle in the end-to-end deployment onto existing SNN hardware. To more effectively align with the SNN characteristics, we design a spatiotemporal dataflow that allows four dimensions of parallelism and eliminates the need for membrane potential storage, enabling on-the-fly spike processing and spike generation. To further improve hardware acceleration performance, we develop a high-performance spike computing engine as a backend based on a systolic array operating at 500-600MHz. To the best of our knowledge, FireFly v2 achieves the highest clock frequency among all FPGA-based implementations. Furthermore, it stands as the first SNN accelerator capable of supporting non-spike operations, which are commonly used in advanced SNN algorithms. FireFly v2 has doubled the throughput and DSP efficiency when compared to our previous version of FireFly and it exhibits 1.33 times the DSP efficiency and 1.42 times the power efficiency compared to the current most advanced FPGA accelerators.
Autonomous Driving using Spiking Neural Networks on Dynamic Vision Sensor Data: A Case Study of Traffic Light Change Detection
2023-09-27
Xuelei Chen
Autonomous driving is a challenging task that has gained broad attention from both academia and industry. Current solutions using convolutional neural networks require large amounts of computational resources, leading to high power consumption. Spiking neural networks (SNNs) provide an alternative computation model to process information and make decisions. This biologically plausible model has the advantage of low latency and energy efficiency. Recent work using SNNs for autonomous driving mostly focused on simple tasks like lane keeping in simplified simulation environments. This project studies SNNs on photo-realistic driving scenes in the CARLA simulator, which is an important step toward using SNNs on real vehicles. The efficacy and generalizability of the method will be investigated.
The high biological properties and low energy consumption of Spiking Neural Networks (SNNs) have brought much attention in recent years. However, the converted SNNs generally need large time steps to achieve satisfactory performance, which will result in high inference latency and computational resources increase. In this work, we propose a highly efficient and fast SNN for object detection. First, we build an initial compact ANN by using quantization training method of convolution layer fold batch normalization layer and neural network modification. Second, we theoretically analyze how to obtain the low complexity SNN correctly. Then, we propose a scale-aware pseudoquantization scheme to guarantee the correctness of the compact ANN to SNN. Third, we propose a continuous inference scheme by using a Feed-Forward Integrate-and-Fire (FewdIF) neuron to realize high-speed object detection. Experimental results show that our efficient SNN can achieve 118X speedup on GPU with only 1.5MB parameters for object detection tasks. We further verify our SNN on FPGA platform and the proposed model can achieve 800+FPS object detection with extremely low latency.
Low Latency of object detection for spikng neural network
2023-09-27
Nemin Qiu, Chuang Zhu
Spiking Neural Networks, as a third-generation neural network, are well-suited for edge AI applications due to their binary spike nature. However, when it comes to complex tasks like object detection, SNNs often require a substantial number of time steps to achieve high performance. This limitation significantly hampers the widespread adoption of SNNs in latency-sensitive edge devices. In this paper, our focus is on generating highly accurate and low-latency SNNs specifically for object detection. Firstly, we systematically derive the conversion between SNNs and ANNs and analyze how to improve the consistency between them: improving the spike firing rate and reducing the quantization error. Then we propose a structural replacement, quantization of ANN activation and residual fix to allevicate the disparity. We evaluate our method on challenging dataset MS COCO, PASCAL VOC and our spike dataset. The experimental results show that the proposed method achieves higher accuracy and lower latency compared to previous work Spiking-YOLO. The advantages of SNNs processing of spike signals are also demonstrated.
Single Biological Neurons as Temporally Precise Spatio-Temporal Pattern Recognizers
2023-09-26
David Beniaguev
This PhD thesis is focused on the central idea that single neurons in the brain should be regarded as temporally precise and highly complex spatio-temporal pattern recognizers. This is opposed to the prevalent view of biological neurons as simple and mainly spatial pattern recognizers by most neuroscientists today. In this thesis, I will attempt to demonstrate that this is an important distinction, predominantly because the above-mentioned computational properties of single neurons have far-reaching implications with respect to the various brain circuits that neurons compose, and on how information is encoded by neuronal activity in the brain. Namely, that these particular "low-level" details at the single neuron level have substantial system-wide ramifications. In the introduction we will highlight the main components that comprise a neural microcircuit that can perform useful computations and illustrate the inter-dependence of these components from a system perspective. In chapter 1 we discuss the great complexity of the spatio-temporal input-output relationship of cortical neurons that are the result of morphological structure and biophysical properties of the neuron. In chapter 2 we demonstrate that single neurons can generate temporally precise output patterns in response to specific spatio-temporal input patterns with a very simple biologically plausible learning rule. In chapter 3, we use the differentiable deep network analog of a realistic cortical neuron as a tool to approximate the gradient of the output of the neuron with respect to its input and use this capability in an attempt to teach the neuron to perform nonlinear XOR operation. In chapter 4 we expand chapter 3 to describe extension of our ideas to neuronal networks composed of many realistic biological spiking neurons that represent either small microcircuits or entire brain regions.
Smooth Exact Gradient Descent Learning in Spiking Neural Networks
2023-09-25
Christian Klos, Raoul-Martin Memmesheimer
Artificial neural networks are highly successfully trained with backpropagation. For spiking neural networks, however, a similar gradient descent scheme seems prohibitive due to the sudden, disruptive (dis-)appearance of spikes. Here, we demonstrate exact gradient descent learning based on spiking dynamics that change only continuously. These are generated by neuron models whose spikes vanish and appear at the end of a trial, where they do not influence other neurons anymore. This also enables gradient-based spike addition and removal. We apply our learning scheme to induce and continuously move spikes to desired times, in single neurons and recurrent networks. Further, it achieves competitive performance in a benchmark task using deep, initially silent networks. Our results show how non-disruptive learning is possible despite discrete spikes.
Deploying energy-efficient deep learning algorithms on computational-limited devices, such as robots, is still a pressing issue for real-world applications. Spiking Neural Networks (SNNs), a novel brain-inspired algorithm, offer a promising solution due to their low-latency and low-energy properties over traditional Artificial Neural Networks (ANNs). Despite their advantages, the dense structure of deep SNNs can still result in extra energy consumption. The Lottery Ticket Hypothesis (LTH) posits that within dense neural networks, there exist winning Lottery Tickets (LTs), namely sub-networks, that can be obtained without compromising performance. Inspired by this, this paper delves into the spiking-based LTs (SLTs), examining their unique properties and potential for extreme efficiency. Then, two significant sparse \textbf{\textit{Rewards}} are gained through comprehensive explorations and meticulous experiments on SLTs across various dense structures. Moreover, a sparse algorithm tailored for spiking transformer structure, which incorporates convolution operations into the Patch Embedding Projection (ConvPEP) module, has been proposed to achieve Multi-level Sparsity (MultiSp). MultiSp refers to (1) Patch number sparsity; (2) ConvPEP weights sparsity and binarization; and (3) ConvPEP activation layer binarization. Extensive experiments demonstrate that our method achieves extreme sparsity with only a slight performance decrease, paving the way for deploying energy-efficient neural networks in robotics and beyond.
Evolving Spiking Neural Networks to Mimic PID Control for Autonomous Blimps
2023-09-22
Tim Burgers, Stein Stroobants, Guido de Croon
In recent years, Artificial Neural Networks (ANN) have become a standard in robotic control. However, a significant drawback of large-scale ANNs is their increased power consumption. This becomes a critical concern when designing autonomous aerial vehicles, given the stringent constraints on power and weight. Especially in the case of blimps, known for their extended endurance, power-efficient control methods are essential. Spiking neural networks (SNN) can provide a solution, facilitating energy-efficient and asynchronous event-driven processing. In this paper, we have evolved SNNs for accurate altitude control of a non-neutrally buoyant indoor blimp, relying solely on onboard sensing and processing power. The blimp's altitude tracking performance significantly improved compared to prior research, showing reduced oscillations and a minimal steady-state error. The parameters of the SNNs were optimized via an evolutionary algorithm, using a Proportional-Derivative-Integral (PID) controller as the target signal. We developed two complementary SNN controllers while examining various hidden layer structures. The first controller responds swiftly to control errors, mitigating overshooting and oscillations, while the second minimizes steady-state errors due to non-neutral buoyancy-induced drift. Despite the blimp's drivetrain limitations, our SNN controllers ensured stable altitude control, employing only 160 spiking neurons.
S3TC: Spiking Separated Spatial and Temporal Convolutions with Unsupervised STDP-based Learning for Action Recognition
2023-09-22
Mireille El-Assal, Pierre Tirilly, Ioan Marius Bilasco
Video analysis is a major computer vision task that has received a lot of attention in recent years. The current state-of-the-art performance for video analysis is achieved with Deep Neural Networks (DNNs) that have high computational costs and need large amounts of labeled data for training. Spiking Neural Networks (SNNs) have significantly lower computational costs (thousands of times) than regular non-spiking networks when implemented on neuromorphic hardware. They have been used for video analysis with methods like 3D Convolutional Spiking Neural Networks (3D CSNNs). However, these networks have a significantly larger number of parameters compared with spiking 2D CSNN. This, not only increases the computational costs, but also makes these networks more difficult to implement with neuromorphic hardware. In this work, we use CSNNs trained in an unsupervised manner with the Spike Timing-Dependent Plasticity (STDP) rule, and we introduce, for the first time, Spiking Separated Spatial and Temporal Convolutions (S3TCs) for the sake of reducing the number of parameters required for video analysis. This unsupervised learning has the advantage of not needing large amounts of labeled data for training. Factorizing a single spatio-temporal spiking convolution into a spatial and a temporal spiking convolution decreases the number of parameters of the network. We test our network with the KTH, Weizmann, and IXMAS datasets, and we show that S3TCs successfully extract spatio-temporal information from videos, while increasing the output spiking activity, and outperforming spiking 3D convolutions.
Spiking neural networks (SNNs) have been thriving on numerous tasks to leverage their promising energy efficiency and exploit their potentialities as biologically plausible intelligence. Meanwhile, the Neural Radiance Fields (NeRF) render high-quality 3D scenes with massive energy consumption, but few works delve into the energy-saving solution with a bio-inspired approach. In this paper, we propose SpikingNeRF, which aligns the radiance ray with the temporal dimension of SNN, to naturally accommodate the SNN to the reconstruction of Radiance Fields. Thus, the computation turns into a spike-based, multiplication-free manner, reducing the energy consumption. In SpikingNeRF, each sampled point on the ray is matched onto a particular time step, and represented in a hybrid manner where the voxel grids are maintained as well. Based on the voxel grids, sampled points are determined whether to be masked for better training and inference. However, this operation also incurs irregular temporal length. We propose the temporal padding strategy to tackle the masked samples to maintain regular temporal length, i.e., regular tensors, and the temporal condensing strategy to form a denser data structure for hardware-friendly computation. Extensive experiments on various datasets demonstrate that our method reduces the 70.79% energy consumption on average and obtains comparable synthesis quality with the ANN baseline.
VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition
2023-09-19
Adam D. Hines, Peter G. Stratton, Michael Milford, Tobias Fischer
Spiking Neural Networks (SNNs) are at the forefront of neuromorphic computing thanks to their potential energy-efficiency, low latencies, and capacity for continual learning. While these capabilities are well suited for robotics tasks, SNNs have seen limited adaptation in this field thus far. This work introduces a SNN for Visual Place Recognition (VPR) that is both trainable within minutes and queryable in milliseconds, making it well suited for deployment on compute-constrained robotic systems. Our proposed system, VPRTempo, overcomes slow training and inference times using an abstracted SNN that trades biological realism for efficiency. VPRTempo employs a temporal code that determines the timing of a single spike based on a pixel's intensity, as opposed to prior SNNs relying on rate coding that determined the number of spikes; improving spike efficiency by over 100%. VPRTempo is trained using Spike-Timing Dependent Plasticity and a supervised delta learning rule enforcing that each output spiking neuron responds to just a single place. We evaluate our system on the Nordland and Oxford RobotCar benchmark localization datasets, which include up to 27k places. We found that VPRTempo's accuracy is comparable to prior SNNs and the popular NetVLAD place recognition algorithm, while being several orders of magnitude faster and suitable for real-time deployment -- with inference speeds over 50 Hz on CPU. VPRTempo could be integrated as a loop closure component for online SLAM on resource-constrained systems such as space and underwater robots.
The human brain can self-organize rich and diverse sparse neural pathways to incrementally master hundreds of cognitive tasks. However, most existing continual learning algorithms for deep artificial and spiking neural networks are unable to adequately auto-regulate the limited resources in the network, which leads to performance drop along with energy consumption rise as the increase of tasks. In this paper, we propose a brain-inspired continual learning algorithm with adaptive reorganization of neural pathways, which employs Self-Organizing Regulation networks to reorganize the single and limited Spiking Neural Network (SOR-SNN) into rich sparse neural pathways to efficiently cope with incremental tasks. The proposed model demonstrates consistent superiority in performance, energy consumption, and memory capacity on diverse continual learning tasks ranging from child-like simple to complex tasks, as well as on generalized CIFAR100 and ImageNet datasets. In particular, the SOR-SNN model excels at learning more complex tasks as well as more tasks, and is able to integrate the past learned knowledge with the information from the current task, showing the backward transfer ability to facilitate the old tasks. Meanwhile, the proposed model exhibits self-repairing ability to irreversible damage and for pruned networks, could automatically allocate new pathway from the retained network to recover memory for forgotten knowledge.
Spiking-LEAF: A Learnable Auditory front-end for Spiking Neural Networks
2023-09-18
Zeyang Song, Jibin Wu, Malu Zhang, Mike Zheng Shou, Haizhou Li
Brain-inspired spiking neural networks (SNNs) have demonstrated great potential for temporal signal processing. However, their performance in speech processing remains limited due to the lack of an effective auditory front-end. To address this limitation, we introduce Spiking-LEAF, a learnable auditory front-end meticulously designed for SNN-based speech processing. Spiking-LEAF combines a learnable filter bank with a novel two-compartment spiking neuron model called IHC-LIF. The IHC-LIF neurons draw inspiration from the structure of inner hair cells (IHC) and they leverage segregated dendritic and somatic compartments to effectively capture multi-scale temporal dynamics of speech signals. Additionally, the IHC-LIF neurons incorporate the lateral feedback mechanism along with spike regularization loss to enhance spike encoding efficiency. On keyword spotting and speaker identification tasks, the proposed Spiking-LEAF outperforms both SOTA spiking auditory front-ends and conventional real-valued acoustic features in terms of classification accuracy, noise robustness, and encoding efficiency.
Chasing Day and Night: Towards Robust and Efficient All-Day Object Detection Guided by an Event Camera
2023-09-17
Jiahang Cao, Xu Zheng, Yuanhuiyi Lyu, Jiaxu Wang, Renjing Xu, Lin Wang
The ability to detect objects in all lighting (i.e., normal-, over-, and under-exposed) conditions is crucial for real-world applications, such as self-driving.Traditional RGB-based detectors often fail under such varying lighting conditions.Therefore, recent works utilize novel event cameras to supplement or guide the RGB modality; however, these methods typically adopt asymmetric network structures that rely predominantly on the RGB modality, resulting in limited robustness for all-day detection. In this paper, we propose EOLO, a novel object detection framework that achieves robust and efficient all-day detection by fusing both RGB and event modalities. Our EOLO framework is built based on a lightweight spiking neural network (SNN) to efficiently leverage the asynchronous property of events. Buttressed by it, we first introduce an Event Temporal Attention (ETA) module to learn the high temporal information from events while preserving crucial edge information. Secondly, as different modalities exhibit varying levels of importance under diverse lighting conditions, we propose a novel Symmetric RGB-Event Fusion (SREF) module to effectively fuse RGB-Event features without relying on a specific modality, thus ensuring a balanced and adaptive fusion for all-day detection. In addition, to compensate for the lack of paired RGB-Event datasets for all-day training and evaluation, we propose an event synthesis approach based on the randomized optical flow that allows for directly generating the event frame from a single exposure image. We further build two new datasets, E-MSCOCO and E-VOC based on the popular benchmarks MSCOCO and PASCAL VOC. Extensive experiments demonstrate that our EOLO outperforms the state-of-the-art detectors,e.g.,RENet,by a substantial margin (+3.74% mAP50) in all lighting conditions.Our code and datasets will be available at https://vlislab22.github.io/EOLO/
With the rapid development of AI technology, we have witnessed numerous innovations and conveniences. However, along with these advancements come privacy threats and risks. Fully Homomorphic Encryption (FHE) emerges as a key technology for privacy-preserving computation, enabling computations while maintaining data privacy. Nevertheless, FHE has limitations in processing continuous non-polynomial functions as it is restricted to discrete integers and supports only addition and multiplication. Spiking Neural Networks (SNNs) operate on discrete spike signals, naturally aligning with the properties of FHE. In this paper, we present a framework called FHE-DiCSNN. This framework is based on the efficient TFHE scheme and leverages the discrete properties of SNNs to achieve high prediction performance on ciphertexts. Firstly, by employing bootstrapping techniques, we successfully implement computations of the Leaky Integrate-and-Fire neuron model on ciphertexts. Through bootstrapping, we can facilitate computations for SNNs of arbitrary depth. This framework can be extended to other spiking neuron models, providing a novel framework for the homomorphic evaluation of SNNs. Secondly, inspired by CNNs, we adopt convolutional methods to replace Poisson encoding. This not only enhances accuracy but also mitigates the issue of prolonged simulation time caused by random encoding. Furthermore, we employ engineering techniques to parallelize the computation of bootstrapping, resulting in a significant improvement in computational efficiency. Finally, we evaluate our model on the MNIST dataset. Experimental results demonstrate that, with the optimal parameter configuration, FHE-DiCSNN achieves an accuracy of 97.94% on ciphertexts, with a loss of only 0.53% compared to the original network's accuracy of 98.47%. Moreover, each prediction requires only 0.75 seconds of computation time
Causal relationship recognition is a fundamental operation in neural networks aimed at learning behavior, action planning, and inferring external world dynamics. This operation is particularly crucial for reinforcement learning (RL). In the context of spiking neural networks (SNNs), events are represented as spikes emitted by network neurons or input nodes. Detecting causal relationships within these events is essential for effective RL implementation. This research paper presents a novel approach to realize causal relationship recognition using a simple spiking binary neuron. The proposed method leverages specially designed synaptic plasticity rules, which are both straightforward and efficient. Notably, our approach accounts for the temporal aspects of detected causal links and accommodates the representation of spiking signals as single spikes or tight spike sequences (bursts), as observed in biological brains. Furthermore, this study places a strong emphasis on the hardware-friendliness of the proposed models, ensuring their efficient implementation on modern and future neuroprocessors. Being compared with precise machine learning techniques, such as decision tree algorithms and convolutional neural networks, our neuron demonstrates satisfactory accuracy despite its simplicity. In conclusion, we introduce a multi-neuron structure capable of operating in more complex environments with enhanced accuracy, making it a promising candidate for the advancement of RL applications in SNNs.
Astrocyte-Integrated Dynamic Function Exchange in Spiking Neural Networks
2023-09-15
Murat Isik, Kayode Inadagbo
This paper presents an innovative methodology for improving the robustness and computational efficiency of Spiking Neural Networks (SNNs), a critical component in neuromorphic computing. The proposed approach integrates astrocytes, a type of glial cell prevalent in the human brain, into SNNs, creating astrocyte-augmented networks. To achieve this, we designed and implemented an astrocyte model in two distinct platforms: CPU/GPU and FPGA. Our FPGA implementation notably utilizes Dynamic Function Exchange (DFX) technology, enabling real-time hardware reconfiguration and adaptive model creation based on current operating conditions. The novel approach of leveraging astrocytes significantly improves the fault tolerance of SNNs, thereby enhancing their robustness. Notably, our astrocyte-augmented SNN displays near-zero latency and theoretically infinite throughput, implying exceptional computational efficiency. Through comprehensive comparative analysis with prior works, it's established that our model surpasses others in terms of neuron and synapse count while maintaining an efficient power consumption profile. These results underscore the potential of our methodology in shaping the future of neuromorphic computing, by providing robust and energy-efficient systems.
In this study, we theoretically investigate neuron-like spiking dynamics in an elliptic ferromagnet/heavy metal bilayer-based spin Hall nano oscillator (SHNO) in bias field-free condition, much suitable for practical realization of brain inspired computing schemes. We demonstrate regular periodic spiking with tunable frequency as well as the leaky-integrate-and-fire (LIF) behavior in a single SHNO by manipulating the pulse features of input current. The frequency of regular periodic spiking is tunable in a range of 0.5 GHz to 0.96 GHz (460 MHz bandwidth) through adjusting the magnitude of constant input dc current density. We further demonstrate the reconfigurability of spiking dynamics in response to a time varying input accomplished by continuously increasing the input current density as a linear function of time. Macrospin theory and micromagnetic simulation provide insights into the origin of bias field-free auto-oscillation and the spiking phenomena in our SHNO. In addition, we discuss how the shape anisotropy of the elliptic ferromagnet influence the bias field-free auto oscillation characteristics, including threshold current, frequency and transition from in-plane to out-of-plane precession. The SHNO operates below $10^{12} A/m^2$ input current density and exhibits a large auto-oscillation amplitude, ensuring high output power. We show that the threshold current density can be reduced by decreasing the ellipticity of the ferromagnet layer as well as enhancing the perpendicular magnetic anisotropy. These findings highlight the potential of bias field-free elliptic SHNO in designing power-efficient spiking neuron-based neuromorphic hardware.
Spiking Dynamics in Dual Free Layer Perpendicular Magnetic Tunnel Junctions
2023-09-14
Louis Farcis, Bruno Teixeira, Philippe Talatchian, David Salomoni, Ursula Ebels, Stéphane Auffret, Bernard Dieny, Frank Mizrahi, Julie Grollier, Ricardo Sousa, Liliana Buda-Prejbeanu
Spintronic devices have recently attracted a lot of attention in the field of unconventional computing due to their non-volatility for short and long term memory, non-linear fast response and relatively small footprint. Here we report how voltage driven magnetization dynamics of dual free layer perpendicular magnetic tunnel junctions enable to emulate spiking neurons in hardware. The output spiking rate was controlled by varying the dc bias voltage across the device. The field-free operation of this two terminal device and its robustness against an externally applied magnetic field make it a suitable candidate to mimic neuron response in a dense Neural Network (NN). The small energy consumption of the device (4-16 pJ/spike) and its scalability are important benefits for embedded applications. This compact perpendicular magnetic tunnel junction structure could finally bring spiking neural networks (SNN) to sub-100nm size elements.
Event-Driven Imaging in Turbid Media: A Confluence of Optoelectronics and Neuromorphic Computation
2023-09-13
Ning Zhang, Timothy Shea, Arto Nurmikko
In this paper a new optical-computational method is introduced to unveil images of targets whose visibility is severely obscured by light scattering in dense, turbid media. The targets of interest are taken to be dynamic in that their optical properties are time-varying whether stationary in space or moving. The scheme, to our knowledge the first of its kind, is human vision inspired whereby diffuse photons collected from the turbid medium are first transformed to spike trains by a dynamic vision sensor as in the retina, and image reconstruction is then performed by a neuromorphic computing approach mimicking the brain. We combine benchtop experimental data in both reflection (backscattering) and transmission geometries with support from physics-based simulations to develop a neuromorphic computational model and then apply this for image reconstruction of different MNIST characters and image sets by a dedicated deep spiking neural network algorithm. Image reconstruction is achieved under conditions of turbidity where an original image is unintelligible to the human eye or a digital video camera, yet clearly and quantifiable identifiable when using the new neuromorphic computational approach.
Co-learning synaptic delays, weights and adaptation in spiking neural networks
2023-09-12
Lucas Deckers, Laurens Van Damme, Ing Jyh Tsang, Werner Van Leekwijck, Steven Latré
Spiking neural networks (SNN) distinguish themselves from artificial neural networks (ANN) because of their inherent temporal processing and spike-based computations, enabling a power-efficient implementation in neuromorphic hardware. In this paper, we demonstrate that data processing with spiking neurons can be enhanced by co-learning the connection weights with two other biologically inspired neuronal features: 1) a set of parameters describing neuronal adaptation processes and 2) synaptic propagation delays. The former allows the spiking neuron to learn how to specifically react to incoming spikes based on its past. The trained adaptation parameters result in neuronal heterogeneity, which is found in the brain and also leads to a greater variety in available spike patterns. The latter enables to learn to explicitly correlate patterns that are temporally distanced. Synaptic delays reflect the time an action potential requires to travel from one neuron to another. We show that each of the co-learned features separately leads to an improvement over the baseline SNN and that the combination of both leads to state-of-the-art SNN results on all speech recognition datasets investigated with a simple 2-hidden layer feed-forward network. Our SNN outperforms the ANN on the neuromorpic datasets (Spiking Heidelberg Digits and Spiking Speech Commands), even with fewer trainable parameters. On the 35-class Google Speech Commands dataset, our SNN also outperforms a GRU of similar size. Our work presents brain-inspired improvements to SNN that enable them to excel over an equivalent ANN of similar size on tasks with rich temporal dynamics.
Neuromorphic Auditory Perception by Neural Spiketrum
2023-09-11
Huajin Tang, Pengjie Gu, Jayawan Wijekoon, MHD Anas Alsakkal, Ziming Wang, Jiangrong Shen, Rui Yan
Neuromorphic computing holds the promise to achieve the energy efficiency and robust learning performance of biological neural systems. To realize the promised brain-like intelligence, it needs to solve the challenges of the neuromorphic hardware architecture design of biological neural substrate and the hardware amicable algorithms with spike-based encoding and learning. Here we introduce a neural spike coding model termed spiketrum, to characterize and transform the time-varying analog signals, typically auditory signals, into computationally efficient spatiotemporal spike patterns. It minimizes the information loss occurring at the analog-to-spike transformation and possesses informational robustness to neural fluctuations and spike losses. The model provides a sparse and efficient coding scheme with precisely controllable spike rate that facilitates training of spiking neural networks in various auditory perception tasks. We further investigate the algorithm-hardware co-designs through a neuromorphic cochlear prototype which demonstrates that our approach can provide a systematic solution for spike-based artificial intelligence by fully exploiting its advantages with spike-based computation.
The role of axonal synaptic delays in the efficacy and performance of artificial neural networks has been largely unexplored. In step-based analog-valued neural network models (ANNs), the concept is almost absent. In their spiking neuroscience-inspired counterparts, there is hardly a systematic account of their effects on model performance in terms of accuracy and number of synaptic operations.This paper proposes a methodology for accounting for axonal delays in the training loop of deep Spiking Neural Networks (SNNs), intending to efficiently solve machine learning tasks on data with rich temporal dependencies. We then conduct an empirical study of the effects of axonal delays on model performance during inference for the Adding task, a benchmark for sequential regression, and for the Spiking Heidelberg Digits dataset (SHD), commonly used for evaluating event-driven models. Quantitative results on the SHD show that SNNs incorporating axonal delays instead of explicit recurrent synapses achieve state-of-the-art, over 90% test accuracy while needing less than half trainable synapses. Additionally, we estimate the required memory in terms of total parameters and energy consumption of accomodating such delay-trained models on a modern neuromorphic accelerator. These estimations are based on the number of synaptic operations and the reference GF-22nm FDX CMOS technology. As a result, we demonstrate that a reduced parameterization, which incorporates axonal delays, leads to approximately 90% energy and memory reduction in digital hardware implementations for a similar performance in the aforementioned task.
Brain-inspired Evolutionary Architectures for Spiking Neural Networks
2023-09-11
Wenxuan Pan, Feifei Zhao, Zhuoya Zhao, Yi Zeng
The complex and unique neural network topology of the human brain formed through natural evolution enables it to perform multiple cognitive functions simultaneously. Automated evolutionary mechanisms of biological network structure inspire us to explore efficient architectural optimization for Spiking Neural Networks (SNNs). Instead of manually designed fixed architectures or hierarchical Network Architecture Search (NAS), this paper evolves SNNs architecture by incorporating brain-inspired local modular structure and global cross-module connectivity. Locally, the brain region-inspired module consists of multiple neural motifs with excitatory and inhibitory connections; Globally, we evolve free connections among modules, including long-term cross-module feedforward and feedback connections. We further introduce an efficient multi-objective evolutionary algorithm based on a few-shot performance predictor, endowing SNNs with high performance, efficiency and low energy consumption. Extensive experiments on static datasets (CIFAR10, CIFAR100) and neuromorphic datasets (CIFAR10-DVS, DVS128-Gesture) demonstrate that our proposed model boosts energy efficiency, archiving consistent and remarkable performance. This work explores brain-inspired neural architectures suitable for SNNs and also provides preliminary insights into the evolutionary mechanisms of biological neural networks in the human brain.
Starting with small and simple concepts, and gradually introducing complex and difficult concepts is the natural process of human learning. Spiking Neural Networks (SNNs) aim to mimic the way humans process information, but current SNNs models treat all samples equally, which does not align with the principles of human learning and overlooks the biological plausibility of SNNs. To address this, we propose a CL-SNN model that introduces Curriculum Learning(CL) into SNNs, making SNNs learn more like humans and providing higher biological interpretability. CL is a training strategy that advocates presenting easier data to models before gradually introducing more challenging data, mimicking the human learning process. We use a confidence-aware loss to measure and process the samples with different difficulty levels. By learning the confidence of different samples, the model reduces the contribution of difficult samples to parameter optimization automatically. We conducted experiments on static image datasets MNIST, Fashion-MNIST, CIFAR10, and neuromorphic datasets N-MNIST, CIFAR10-DVS, DVS-Gesture. The results are promising. To our best knowledge, this is the first proposal to enhance the biologically plausibility of SNNs by introducing CL.
Advanced Computing and Related Applications Leveraging Brain-inspired Spiking Neural Networks
2023-09-08
Lyuyang Sima, Joseph Bucukovski, Erwan Carlson, Nicole L. Yien
In the rapid evolution of next-generation brain-inspired artificial intelligence and increasingly sophisticated electromagnetic environment, the most bionic characteristics and anti-interference performance of spiking neural networks show great potential in terms of computational speed, real-time information processing, and spatio-temporal information processing. Data processing. Spiking neural network is one of the cores of brain-like artificial intelligence, which realizes brain-like computing by simulating the structure and information transfer mode of biological neural networks. This paper summarizes the strengths, weaknesses and applicability of five neuronal models and analyzes the characteristics of five network topologies; then reviews the spiking neural network algorithms and summarizes the unsupervised learning algorithms based on synaptic plasticity rules and four types of supervised learning algorithms from the perspectives of unsupervised learning and supervised learning; finally focuses on the review of brain-like neuromorphic chips under research at home and abroad. This paper is intended to provide learning concepts and research orientations for the peers who are new to the research field of spiking neural networks through systematic summaries.
Spiking Neural Networks (SNNs) are a biologically plausible neural network model with significant advantages in both event-driven processing and spatio-temporal information processing, rendering SNNs an appealing choice for energyefficient object detection. However, the non-differentiability of the biological neuronal dynamics model presents a challenge during the training of SNNs. Furthermore, a suitable decoding strategy for object detection in SNNs is currently lacking. In this study, we introduce the Current Mean Decoding (CMD) method, which solves the regression problem to facilitate the training of deep SNNs for object detection tasks. Based on the gradient surrogate and CMD, we propose the SNN-YOLOv3 model for object detection. Our experiments demonstrate that SNN-YOLOv3 achieves a remarkable performance with an mAP of 61.87% on the PASCAL VOC dataset, requiring only 6 time steps. Compared to SpikingYOLO, we have managed to increase mAP by nearly 10% while reducing energy consumption by two orders of magnitude.
Spiking Structured State Space Model for Monaural Speech Enhancement
2023-09-07
Yu Du, Xu Liu, Yansong Chua
Speech enhancement seeks to extract clean speech from noisy signals. Traditional deep learning methods face two challenges: efficiently using information in long speech sequences and high computational costs. To address these, we introduce the Spiking Structured State Space Model (Spiking-S4). This approach merges the energy efficiency of Spiking Neural Networks (SNN) with the long-range sequence modeling capabilities of Structured State Space Models (S4), offering a compelling solution. Evaluation on the DNS Challenge and VoiceBank+Demand Datasets confirms that Spiking-S4 rivals existing Artificial Neural Network (ANN) methods but with fewer computational resources, as evidenced by reduced parameters and Floating Point Operations (FLOPs).
Are SNNs Truly Energy-efficient? $-$ A Hardware Perspective
2023-09-06
Abhiroop Bhattacharjee, Ruokai Yin, Abhishek Moitra, Priyadarshini Panda
Spiking Neural Networks (SNNs) have gained attention for their energy-efficient machine learning capabilities, utilizing bio-inspired activation functions and sparse binary spike-data representations. While recent SNN algorithmic advances achieve high accuracy on large-scale computer vision tasks, their energy-efficiency claims rely on certain impractical estimation metrics. This work studies two hardware benchmarking platforms for large-scale SNN inference, namely SATA and SpikeSim. SATA is a sparsity-aware systolic-array accelerator, while SpikeSim evaluates SNNs implemented on In-Memory Computing (IMC) based analog crossbars. Using these tools, we find that the actual energy-efficiency improvements of recent SNN algorithmic works differ significantly from their estimated values due to various hardware bottlenecks. We identify and address key roadblocks to efficient SNN deployment on hardware, including repeated computations & data movements over timesteps, neuronal module overhead, and vulnerability of SNNs towards crossbar non-idealities.
Using accelerators based on analog computing is an efficient way to process the immensely large workloads in Neural Networks (NNs). One example of an analog computing scheme for NNs is Integrate-and-Fire (IF) Spiking Neural Networks (SNNs). However, to achieve high inference accuracy in IF-SNNs, the analog hardware needs to represent current-based multiply-accumulate (MAC) levels as spike times, for which a large membrane capacitor needs to be charged for a certain amount of time. A large capacitor results in high energy use, considerable area cost, and long latency, constituting one of the major bottlenecks in analog IF-SNN implementations. In this work, we propose a HW/SW Codesign method, called CapMin, for capacitor size minimization in analog computing IF-SNNs. CapMin minimizes the capacitor size by reducing the number of spike times needed for accurate operation of the HW, based on the absolute frequency of MAC level occurrences in the SW. To increase the operation of IF-SNNs to current variation, we propose the method CapMin-V, which trades capacitor size for protection based on the reduced capacitor size found in CapMin. In our experiments, CapMin achieves more than a 14$\times$ reduction in capacitor size over the state of the art, while CapMin-V achieves increased variation tolerance in the IF-SNN operation, requiring only a small increase in capacitor size.
Spiking based Cellular Learning Automata (SCLA) algorithm for mobile robot motion formulation
2023-09-01
Vahid Pashaei Rad, Vahid Azimi Rad, Saleh Valizadeh Sotubadi
In this paper a new method called SCLA which stands for Spiking based Cellular Learning Automata is proposed for a mobile robot to get to the target from any random initial point. The proposed method is a result of the integration of both cellular automata and spiking neural networks. The environment consists of multiple squares of the same size and the robot only observes the neighboring squares of its current square. It should be stated that the robot only moves either up and down or right and left. The environment returns feedback to the learning automata to optimize its decision making in the next steps resulting in cellular automata training. Simultaneously a spiking neural network is trained to implement long term improvements and reductions on the paths. The results show that the integration of both cellular automata and spiking neural network ends up in reinforcing the proper paths and training time reduction at the same time.
SPAIC: A sub-$μ$W/Channel, 16-Channel General-Purpose Event-Based Analog Front-End with Dual-Mode Encoders
2023-08-31
Shyam Narayanan, Matteo Cartiglia, Arianna Rubino, Charles Lego, Charlotte Frenkel, Giacomo Indiveri
Low-power event-based analog front-ends (AFE) are a crucial component required to build efficient end-to-end neuromorphic processing systems for edge computing. Although several neuromorphic chips have been developed for implementing spiking neural networks (SNNs) and solving a wide range of sensory processing tasks, there are only a few general-purpose analog front-end devices that can be used to convert analog sensory signals into spikes and interfaced to neuromorphic processors. In this work, we present a novel, highly configurable analog front-end chip, denoted as SPAIC (signal-to-spike converter for analog AI computation), that offers a general-purpose dual-mode analog signal-to-spike encoding with delta modulation and pulse frequency modulation, with tunable frequency bands. The ASIC is designed in a 180 nm process. It supports and encodes a wide variety of signals spanning 4 orders of magnitude in frequency, and provides an event-based output that is compatible with existing neuromorphic processors. We validated the ASIC for its functions and present initial silicon measurement results characterizing the basic building blocks of the chip.
Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning
2023-08-31
Qian Zhang, Chenxi Wu, Adar Kahana, Youngeun Kim, Yuhang Li, George Em Karniadakis, Priyadarshini Panda
We introduce a method to convert Physics-Informed Neural Networks (PINNs), commonly used in scientific machine learning, to Spiking Neural Networks (SNNs), which are expected to have higher energy efficiency compared to traditional Artificial Neural Networks (ANNs). We first extend the calibration technique of SNNs to arbitrary activation functions beyond ReLU, making it more versatile, and we prove a theorem that ensures the effectiveness of the calibration. We successfully convert PINNs to SNNs, enabling computational efficiency for diverse regression tasks in solving multiple differential equations, including the unsteady Navier-Stokes equations. We demonstrate great gains in terms of overall efficiency, including Separable PINNs (SPINNs), which accelerate the training process. Overall, this is the first work of this kind and the proposed method achieves relatively good accuracy with low spike rates.
A Deep Dive into the Design Space of a Dynamically Reconfigurable Cryogenic Spiking Neuron
2023-08-30
Md Mazharul Islam, Shamiul Alam, Catherine D Schuman, Md Shafayat Hossain, Ahmedullah Aziz
Spiking neural network offers the most bio-realistic approach to mimic the parallelism and compactness of the human brain. A spiking neuron is the central component of an SNN which generates information-encoded spikes. We present a comprehensive design space analysis of the superconducting memristor (SM)-based electrically reconfigurable cryogenic neuron. A superconducting nanowire (SNW) connected in parallel with an SM function as a dual-frequency oscillator and two of these oscillators can be coupled to design a dynamically tunable spiking neuron. The same neuron topology was previously proposed where a fixed resistance was used in parallel with the SNW. Replacing the fixed resistance with the SM provides an additional tuning knob with four distinct combinations of SM resistances, which improves the reconfigurability by up to ~70%. Utilizing an external bias current (Ibias), the spike frequency can be modulated up to ~3.5 times. Two distinct spike amplitudes (~1V and ~1.8 V) are also achieved. Here, we perform a systematic sensitivity analysis and show that the reconfigurability can be further tuned by choosing a higher input current strength. By performing a 500-point Monte Carlo variation analysis, we find that the spike amplitude is more variation robust than spike frequency and the variation robustness can be further improved by choosing a higher Ibias. Our study provides valuable insights for further exploration of materials and circuit level modification of the neuron that will be useful for system-level incorporation of the neuron circuit
Bayesian Integration of Information Using Top-Down Modulated WTA Networks
2023-08-29
Otto van der Himst, Leila Bagheriye, Johan Kwisthout
Winner Take All (WTA) circuits a type of Spiking Neural Networks (SNN) have been suggested as facilitating the brain's ability to process information in a Bayesian manner. Research has shown that WTA circuits are capable of approximating hierarchical Bayesian models via Expectation Maximization (EM). So far, research in this direction has focused on bottom up processes. This is contrary to neuroscientific evidence that shows that, besides bottom up processes, top down processes too play a key role in information processing by the human brain. Several functions ascribed to top down processes include direction of attention, adjusting for expectations, facilitation of encoding and recall of learned information, and imagery. This paper explores whether WTA circuits are suitable for further integrating information represented in separate WTA networks. Furthermore, it explores whether, and under what circumstances, top down processes can improve WTA network performance with respect to inference and learning. The results show that WTA circuits are capable of integrating the probabilistic information represented by other WTA networks, and that top down processes can improve a WTA network's inference and learning performance. Notably, it is able to do this according to key neuromorphic principles, making it ideal for low-latency and energy efficient implementation on neuromorphic hardware.
Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding
2023-08-29
Xinyi Chen, Jibin Wu, Huajin Tang, Qinyuan Ren, Kay Chen Tan
The human brain exhibits remarkable abilities in integrating temporally distant sensory inputs for decision-making. However, existing brain-inspired spiking neural networks (SNNs) have struggled to match their biological counterpart in modeling long-term temporal relationships. To address this problem, this paper presents a novel Contextual Embedding Leaky Integrate-and-Fire (CE-LIF) spiking neuron model. Specifically, the CE-LIF model incorporates a meticulously designed contextual embedding component into the adaptive neuronal firing threshold, thereby enhancing the memory storage of spiking neurons and facilitating effective sequential modeling. Additionally, theoretical analysis is provided to elucidate how the CE-LIF model enables long-term temporal credit assignment. Remarkably, when compared to state-of-the-art recurrent SNNs, feedforward SNNs comprising the proposed CE-LIF neurons demonstrate superior performance across extensive sequential modeling tasks in terms of classification accuracy, network convergence speed, and memory capacity.
Spiking neural networks (SNNs) offer a promising avenue to implement deep neural networks in a more energy-efficient way. However, the network architectures of existing SNNs for language tasks are still simplistic and relatively shallow, and deep architectures have not been fully explored, resulting in a significant performance gap compared to mainstream transformer-based networks such as BERT. To this end, we improve a recently-proposed spiking Transformer (i.e., Spikformer) to make it possible to process language tasks and propose a two-stage knowledge distillation method for training it, which combines pre-training by distilling knowledge from BERT with a large collection of unlabelled texts and fine-tuning with task-specific instances via knowledge distillation again from the BERT fine-tuned on the same training examples. Through extensive experimentation, we show that the models trained with our method, named SpikeBERT, outperform state-of-the-art SNNs and even achieve comparable results to BERTs on text classification tasks for both English and Chinese with much less energy consumption. Our code is available at https://github.com/Lvchangze/SpikeBERT.
TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling
2023-08-25
Shimin Zhang, Qu Yang, Chenxiang Ma, Jibin Wu, Haizhou Li, Kay Chen Tan
The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. As a result, it remains a challenging task for state-of-the-art spiking neural networks (SNNs) to establish long-term temporal dependency between distant cues. To address this challenge, we propose a novel biologically inspired Two-Compartment Leaky Integrate-and-Fire spiking neuron model, dubbed TC-LIF. The proposed model incorporates carefully designed somatic and dendritic compartments that are tailored to facilitate learning long-term temporal dependencies. Furthermore, a theoretical analysis is provided to validate the effectiveness of TC-LIF in propagating error gradients over an extended temporal duration. Our experimental results, on a diverse range of temporal classification tasks, demonstrate superior temporal classification capability, rapid training convergence, and high energy efficiency of the proposed TC-LIF model. Therefore, this work opens up a myriad of opportunities for solving challenging temporal processing tasks on emerging neuromorphic computing systems. Our code is publicly available at https://github.com/ZhangShimin1/TC-LIF.
The rapid development of artificial intelligence has brought considerable convenience, yet also introduces significant security risks. One of the research hotspots is to balance data privacy and utility in the real world of artificial intelligence. The present second-generation artificial neural networks have made tremendous advances, but some big models could have really high computational costs. The third-generation neural network, SNN (Spiking Neural Network), mimics real neurons by using discrete spike signals, whose sequences exhibit strong sparsity, providing advantages such as low energy consumption and high efficiency. In this paper, we construct a framework to evaluate the homomorphic computation of SNN named FHE-DiSNN that enables SNN to achieve good prediction performance on encrypted data. First, benefitting from the discrete nature of spike signals, our proposed model avoids the errors introduced by discretizing activation functions. Second, by applying bootstrapping, we design new private preserving functions FHE-Fire and FHE-Reset, through which noise can be refreshed, allowing us to evaluate SNN for an arbitrary number of operations. Furthermore, We improve the computational efficiency of FHE-DiSNN while maintaining a high level of accuracy. Finally, we evaluate our model on the MNIST dataset. The experiments show that FHE-DiSNN with 30 neurons in the hidden layer achieves a minimum prediction accuracy of 94.4%. Under optimal parameters, it achieves a 95.1% accuracy, with only a 0.6% decrease compared to the original SNN (95.7%). These results demonstrate the superiority of SNN over second-generation neural networks for homomorphic evaluation.
Computational models of object motion detectors accelerated using FPGA technology
2023-08-23
Pedro Machado
This PhD research introduces three key contributions in the domain of object motion detection: Multi-Hierarchical Spiking Neural Network (MHSNN): A specialized four-layer Spiking Neural Network (SNN) architecture inspired by vertebrate retinas. Trained on custom lab-generated images, it exhibited 6.75% detection error for horizontal and vertical movements. While non-scalable, MHSNN laid the foundation for further advancements. Hybrid Sensitive Motion Detector (HSMD): Enhancing Dynamic Background Subtraction (DBS) using a tailored three-layer SNN, stabilizing foreground data to enhance object motion detection. Evaluated on standard datasets, HSMD outperformed OpenCV-based methods, excelling in four categories across eight metrics. It maintained real-time processing (13.82-13.92 fps) on a high-performance computer but showed room for hardware optimisation. Neuromorphic Hybrid Sensitive Motion Detector (NeuroHSMD): Building upon HSMD, this adaptation implemented the SNN component on dedicated hardware (FPGA). OpenCL simplified FPGA design and enabled portability. NeuroHSMD demonstrated an 82% speedup over HSMD, achieving 28.06-28.71 fps on CDnet2012 and CDnet2014 datasets. These contributions collectively represent significant advancements in object motion detection, from a biologically inspired neural network design to an optimized hardware implementation that outperforms existing methods in accuracy and processing speed.
Learning the Plasticity: Plasticity-Driven Learning Framework in Spiking Neural Networks
2023-08-23
Guobin Shen, Dongcheng Zhao, Yiting Dong, Yang Li, Feifei Zhao, Yi Zeng
The evolution of the human brain has led to the development of complex synaptic plasticity, enabling dynamic adaptation to a constantly evolving world. This progress inspires our exploration into a new paradigm for Spiking Neural Networks (SNNs): a Plasticity-Driven Learning Framework (PDLF). This paradigm diverges from traditional neural network models that primarily focus on direct training of synaptic weights, leading to static connections that limit adaptability in dynamic environments. Instead, our approach delves into the heart of synaptic behavior, prioritizing the learning of plasticity rules themselves. This shift in focus from weight adjustment to mastering the intricacies of synaptic change offers a more flexible and dynamic pathway for neural networks to evolve and adapt. Our PDLF does not merely adapt existing concepts of functional and Presynaptic-Dependent Plasticity but redefines them, aligning closely with the dynamic and adaptive nature of biological learning. This reorientation enhances key cognitive abilities in artificial intelligence systems, such as working memory and multitasking capabilities, and demonstrates superior adaptability in complex, real-world scenarios. Moreover, our framework sheds light on the intricate relationships between various forms of plasticity and cognitive functions, thereby contributing to a deeper understanding of the brain's learning mechanisms. Integrating this groundbreaking plasticity-centric approach in SNNs marks a significant advancement in the fusion of neuroscience and artificial intelligence. It paves the way for developing AI systems that not only learn but also adapt in an ever-changing world, much like the human brain.
2023-08-23
Leander Weber, Jim Berend, Alexander Binder, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin
In this paper, we present Layer-wise Feedback Propagation (LFP), a novel training approach for neural-network-like predictors that utilizes explainability, specifically Layer-wise Relevance Propagation(LRP), to assign rewards to individual connections based on their respective contributions to solving a given task. This differs from traditional gradient descent, which updates parameters towards anestimated loss minimum. LFP distributes a reward signal throughout the model without the need for gradient computations. It then strengthens structures that receive positive feedback while reducingthe influence of structures that receive negative feedback. We establish the convergence of LFP theoretically and empirically, and demonstrate its effectiveness in achieving comparable performance to gradient descent on various models and datasets. Notably, LFP overcomes certain limitations associated with gradient-based methods, such as reliance on meaningful derivatives. We further investigate how the different LRP-rules can be extended to LFP, what their effects are on training, as well as potential applications, such as training models with no meaningful derivatives, e.g., step-function activated Spiking Neural Networks (SNNs), or for transfer learning, to efficiently utilize existing knowledge.
Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing
2023-08-22
Flor Ortiz, Nicolas Skatchkovsky, Eva Lagunas, Wallace A. Martins, Geoffrey Eappen, Saed Daoud, Osvaldo Simeone, Bipin Rajendran, Symeon Chatzinotas
The latest satellite communication (SatCom) missions are characterized by a fully reconfigurable on-board software-defined payload, capable of adapting radio resources to the temporal and spatial variations of the system traffic. As pure optimization-based solutions have shown to be computationally tedious and to lack flexibility, machine learning (ML)-based methods have emerged as promising alternatives. We investigate the application of energy-efficient brain-inspired ML models for on-board radio resource management. Apart from software simulation, we report extensive experimental results leveraging the recently released Intel Loihi 2 chip. To benchmark the performance of the proposed model, we implement conventional convolutional neural networks (CNN) on a Xilinx Versal VCK5000, and provide a detailed comparison of accuracy, precision, recall, and energy efficiency for different traffic demands. Most notably, for relevant workloads, spiking neural networks (SNNs) implemented on Loihi 2 yield higher accuracy, while reducing power consumption by more than 100$\times$ as compared to the CNN-based reference platform. Our findings point to the significant potential of neuromorphic computing and SNNs in supporting on-board SatCom operations, paving the way for enhanced efficiency and sustainability in future SatCom systems.
SpikingBERT: Distilling BERT to Train Spiking Language Models Using Implicit Differentiation
2023-08-21
Malyaban Bal, Abhronil Sengupta
Large language Models (LLMs), though growing exceedingly powerful, comprises of orders of magnitude less neurons and synapses than the human brain. However, it requires significantly more power/energy to operate. In this work, we propose a novel bio-inspired spiking language model (LM) which aims to reduce the computational cost of conventional LMs by drawing motivation from the synaptic information flow in the brain. In this paper, we demonstrate a framework that leverages the average spiking rate of neurons at equilibrium to train a neuromorphic spiking LM using implicit differentiation technique, thereby overcoming the non-differentiability problem of spiking neural network (SNN) based algorithms without using any type of surrogate gradient. The steady-state convergence of the spiking neurons also allows us to design a spiking attention mechanism, which is critical in developing a scalable spiking LM. Moreover, the convergence of average spiking rate of neurons at equilibrium is utilized to develop a novel ANN-SNN knowledge distillation based technique wherein we use a pre-trained BERT model as "teacher" to train our "student" spiking architecture. While the primary architecture proposed in this paper is motivated by BERT, the technique can be potentially extended to different kinds of LLMs. Our work is the first one to demonstrate the performance of an operational spiking LM architecture on multiple different tasks in the GLUE benchmark.
HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds
2023-08-20
Hejia Geng, Peng Li
While spiking neural networks (SNNs) offer a promising neurally-inspired model of computation, they are vulnerable to adversarial attacks. We present the first study that draws inspiration from neural homeostasis to design a threshold-adapting leaky integrate-and-fire (TA-LIF) neuron model and utilize TA-LIF neurons to construct the adversarially robust homeostatic SNNs (HoSNNs) for improved robustness. The TA-LIF model incorporates a self-stabilizing dynamic thresholding mechanism, offering a local feedback control solution to the minimization of each neuron's membrane potential error caused by adversarial disturbance. Theoretical analysis demonstrates favorable dynamic properties of TA-LIF neurons in terms of the bounded-input bounded-output stability and suppressed time growth of membrane potential error, underscoring their superior robustness compared with the standard LIF neurons. When trained with weak FGSM attacks (attack budget = 2/255) and tested with much stronger PGD attacks (attack budget = 8/255), our HoSNNs significantly improve model accuracy on several datasets: from 30.54% to 74.91% on FashionMNIST, from 0.44% to 35.06% on SVHN, from 0.56% to 42.63% on CIFAR10, from 0.04% to 16.66% on CIFAR100, over the conventional LIF-based SNNs.
Spiking-Diffusion: Vector Quantized Discrete Diffusion Model with Spiking Neural Networks
2023-08-20
Mingxuan Liu, Jie Gan, Rui Wen, Tao Li, Yongli Chen, Hong Chen
Spiking neural networks (SNNs) have tremendous potential for energy-efficient neuromorphic chips due to their binary and event-driven architecture. SNNs have been primarily used in classification tasks, but limited exploration on image generation tasks. To fill the gap, we propose a Spiking-Diffusion model, which is based on the vector quantized discrete diffusion model. First, we develop a vector quantized variational autoencoder with SNNs (VQ-SVAE) to learn a discrete latent space for images. In VQ-SVAE, image features are encoded using both the spike firing rate and postsynaptic potential, and an adaptive spike generator is designed to restore embedding features in the form of spike trains. Next, we perform absorbing state diffusion in the discrete latent space and construct a spiking diffusion image decoder (SDID) with SNNs to denoise the image. Our work is the first to build the diffusion model entirely from SNN layers. Experimental results on MNIST, FMNIST, KMNIST, Letters, and Cifar10 demonstrate that Spiking-Diffusion outperforms the existing SNN-based generation model. We achieve FIDs of 37.50, 91.98, 59.23, 67.41, and 120.5 on the above datasets respectively, with reductions of 58.60\%, 18.75\%, 64.51\%, 29.75\%, and 44.88\% in FIDs compared with the state-of-art work. Our code will be available at \url{https://github.com/Arktis2022/Spiking-Diffusion}.
With the success of self-supervised learning, multimodal foundation models have rapidly adapted a wide range of downstream tasks driven by vision and language (VL) pretraining. State-of-the-art methods achieve impressive performance by pre-training on large-scale datasets. However, bridging the semantic gap between the two modalities remains a nonnegligible challenge for VL tasks. In this work, we propose an efficient computation framework for multimodal alignment by introducing a novel visual semantic module to further improve the performance of the VL tasks. Specifically, we propose a flexible model, namely Artificial-Spiking Hierarchical Networks (ASH-Nets), which combines the complementary advantages of Artificial neural networks (ANNs) and Spiking neural networks (SNNs) to enrich visual semantic representations. In particular, a visual concrete encoder and a semantic abstract encoder are constructed to learn continuous and discrete latent variables to enhance the flexibility of semantic encoding. Considering the spatio-temporal properties of SNNs modeling, we introduce a contrastive learning method to optimize the inputs of similar samples. This can improve the computational efficiency of the hierarchical network, while the augmentation of hard samples is beneficial to the learning of visual representations. Furthermore, the Spiking to Text Uni-Alignment Learning (STUA) pre-training method is proposed, which only relies on text features to enhance the encoding ability of abstract semantics. We validate the performance on multiple well-established downstream VL tasks. Experiments show that the proposed ASH-Nets achieve competitive results.
Development of an interface for digital neuromorphic hardware based on an FPGA
2023-08-17
René Harmann, Lukas Sohlbach, Fernando Perez-Peña, Karsten Schmidt
Exploring and understanding the functioning of the human brain is one of the greatest challenges for current research. Neuromorphic engineering tries to address this challenge by abstracting biological mechanisms and translating them into technology. Via the abstraction process and experiments with the resulting technical system, an attempt is made to obtain information about the biological counterpart. One subsection of Neuromorphic Engineering (NE) are Spiking Neural Networks (SNN), which describe the structures of the human brain more and more closely than Artificial Neural Networks (ANN). Together with their dedicated hardware, SNNs provide a good platform for developing new algorithms for information processing. In the context of these neuromorphic hardware platforms, this paper aims to develop an interface for a digital hardware platform (SPINN-3 Development Board) to enable the use of industrial or conventional sensors and thus create new approaches for experimental research. The basis for this endeavor is a Field Programmable Gate Array (FPGA), which is placed as a gateway between the sensors and the neuromorphic hardware. Overall, the developed system provides a robust solution for a wide variety of investigations related to neuromorphic hardware and SNNs. Furthermore, the solution also offers suitable possibilities to monitor all processes within the system in order to obtain suitable measurements, which can be examined in search of meaningful results.
Biologically-inspired Spiking Neural Networks (SNNs), processing information using discrete-time events known as spikes rather than continuous values, have garnered significant attention due to their hardware-friendly and energy-efficient characteristics. However, the training of SNNs necessitates a considerably large memory footprint, given the additional storage requirements for spikes or events, leading to a complex structure and dynamic setup. In this paper, to address memory constraint in SNN training, we introduce an innovative framework, characterized by a remarkably low memory footprint. We \textbf{(i)} design a reversible SNN node that retains a high level of accuracy. Our design is able to achieve a $\mathbf{58.65\times}$ reduction in memory usage compared to the current SNN node. We \textbf{(ii)} propose a unique algorithm to streamline the backpropagation process of our reversible SNN node. This significantly trims the backward Floating Point Operations Per Second (FLOPs), thereby accelerating the training process in comparison to current reversible layer backpropagation method. By using our algorithm, the training time is able to be curtailed by $\mathbf{23.8\%}$ relative to existing reversible layer architectures.
As one of the energy-efficient alternatives of conventional neural networks (CNNs), spiking neural networks (SNNs) have gained more and more interest recently. To train the deep models, some effective batch normalization (BN) techniques are proposed in SNNs. All these BNs are suggested to be used after the convolution layer as usually doing in CNNs. However, the spiking neuron is much more complex with the spatio-temporal dynamics. The regulated data flow after the BN layer will be disturbed again by the membrane potential updating operation before the firing function, i.e., the nonlinear activation. Therefore, we advocate adding another BN layer before the firing function to normalize the membrane potential again, called MPBN. To eliminate the induced time cost of MPBN, we also propose a training-inference-decoupled re-parameterization technique to fold the trained MPBN into the firing threshold. With the re-parameterization technique, the MPBN will not introduce any extra time burden in the inference. Furthermore, the MPBN can also adopt the element-wised form, while these BNs after the convolution layer can only use the channel-wised form. Experimental results show that the proposed MPBN performs well on both popular non-spiking static and neuromorphic datasets. Our code is open-sourced at \href{https://github.com/yfguo91/MPBN}{MPBN}.
2023-08-16
Man Yao, Jiakui Hu, Guangshe Zhao, Yaoyuan Wang, Ziyang Zhang, Bo Xu, Guoqi Li
Spiking Neural Networks (SNNs) are well known as a promising energy-efficient alternative to conventional artificial neural networks. Subject to the preconceived impression that SNNs are sparse firing, the analysis and optimization of inherent redundancy in SNNs have been largely overlooked, thus the potential advantages of spike-based neuromorphic computing in accuracy and energy efficiency are interfered. In this work, we pose and focus on three key questions regarding the inherent redundancy in SNNs. We argue that the redundancy is induced by the spatio-temporal invariance of SNNs, which enhances the efficiency of parameter utilization but also invites lots of noise spikes. Further, we analyze the effect of spatio-temporal invariance on the spatio-temporal dynamics and spike firing of SNNs. Then, motivated by these analyses, we propose an Advance Spatial Attention (ASA) module to harness SNNs' redundancy, which can adaptively optimize their membrane potential distribution by a pair of individual spatial attention sub-modules. In this way, noise spike features are accurately regulated. Experimental results demonstrate that the proposed method can significantly drop the spike firing with better performance than state-of-the-art SNN baselines. Our code is available in \url{https://github.com/BICLab/ASA-SNN}.
HyperSNN: A new efficient and robust deep learning model for resource constrained control applications
2023-08-16
Zhanglu Yan, Shida Wang, Kaiwen Tang, Weng-Fai Wong
In light of the increasing adoption of edge computing in areas such as intelligent furniture, robotics, and smart homes, this paper introduces HyperSNN, an innovative method for control tasks that uses spiking neural networks (SNNs) in combination with hyperdimensional computing. HyperSNN substitutes expensive 32-bit floating point multiplications with 8-bit integer additions, resulting in reduced energy consumption while enhancing robustness and potentially improving accuracy. Our model was tested on AI Gym benchmarks, including Cartpole, Acrobot, MountainCar, and Lunar Lander. HyperSNN achieves control accuracies that are on par with conventional machine learning methods but with only 1.36% to 9.96% of the energy expenditure. Furthermore, our experiments showed increased robustness when using HyperSNN. We believe that HyperSNN is especially suitable for interactive, mobile, and wearable devices, promoting energy-efficient and robust system design. Furthermore, it paves the way for the practical implementation of complex algorithms like model predictive control (MPC) in real-world industrial scenarios.
The synergy between spiking neural networks and neuromorphic hardware holds promise for the development of energy-efficient AI applications. Inspired by this potential, we revisit the foundational aspects to study the capabilities of spiking neural networks where information is encoded in the firing time of neurons. Under the Spike Response Model as a mathematical model of a spiking neuron with a linear response function, we compare the expressive power of artificial and spiking neural networks, where we initially show that they realize piecewise linear mappings. In contrast to ReLU networks, we prove that spiking neural networks can realize both continuous and discontinuous functions. Moreover, we provide complexity bounds on the size of spiking neural networks to emulate multi-layer (ReLU) neural networks. Restricting to the continuous setting, we also establish complexity bounds in the reverse direction for one-layer spiking neural networks.
Spiking Neural Networks (SNNs) as one of the biology-inspired models have received much attention recently. It can significantly reduce energy consumption since they quantize the real-valued membrane potentials to 0/1 spikes to transmit information thus the multiplications of activations and weights can be replaced by additions when implemented on hardware. However, this quantization mechanism will inevitably introduce quantization error, thus causing catastrophic information loss. To address the quantization error problem, we propose a regularizing membrane potential loss (RMP-Loss) to adjust the distribution which is directly related to quantization error to a range close to the spikes. Our method is extremely simple to implement and straightforward to train an SNN. Furthermore, it is shown to consistently outperform previous state-of-the-art methods over different network architectures and datasets.
Spiking neural networks (SNNs) are emerging as an energy-efficient alternative to traditional artificial neural networks (ANNs) due to their unique spike-based event-driven nature. Coding is crucial in SNNs as it converts external input stimuli into spatio-temporal feature sequences. However, most existing deep SNNs rely on direct coding that generates powerless spike representation and lacks the temporal dynamics inherent in human vision. Hence, we introduce Gated Attention Coding (GAC), a plug-and-play module that leverages the multi-dimensional gated attention unit to efficiently encode inputs into powerful representations before feeding them into the SNN architecture. GAC functions as a preprocessing layer that does not disrupt the spike-driven nature of the SNN, making it amenable to efficient neuromorphic hardware implementation with minimal modifications. Through an observer model theoretical analysis, we demonstrate GAC's attention mechanism improves temporal dynamics and coding efficiency. Experiments on CIFAR10/100 and ImageNet datasets demonstrate that GAC achieves state-of-the-art accuracy with remarkable efficiency. Notably, we improve top-1 accuracy by 3.10\% on CIFAR100 with only 6-time steps and 1.07\% on ImageNet while reducing energy usage to 66.9\% of the previous works. To our best knowledge, it is the first time to explore the attention-based dynamic coding scheme in deep SNNs, with exceptional effectiveness and efficiency on large-scale datasets.The Code is available at https://github.com/bollossom/GAC.
A Homomorphic Encryption Framework for Privacy-Preserving Spiking Neural Networks
2023-08-10
Farzad Nikfam, Raffaele Casaburi, Alberto Marchisio, Maurizio Martina, Muhammad Shafique
Machine learning (ML) is widely used today, especially through deep neural networks (DNNs), however, increasing computational load and resource requirements have led to cloud-based solutions. To address this problem, a new generation of networks called Spiking Neural Networks (SNN) has emerged, which mimic the behavior of the human brain to improve efficiency and reduce energy consumption. These networks often process large amounts of sensitive information, such as confidential data, and thus privacy issues arise. Homomorphic encryption (HE) offers a solution, allowing calculations to be performed on encrypted data without decrypting it. This research compares traditional DNNs and SNNs using the Brakerski/Fan-Vercauteren (BFV) encryption scheme. The LeNet-5 model, a widely-used convolutional architecture, is used for both DNN and SNN models based on the LeNet-5 architecture, and the networks are trained and compared using the FashionMNIST dataset. The results show that SNNs using HE achieve up to 40% higher accuracy than DNNs for low values of the plaintext modulus t, although their execution time is longer due to their time-coding nature with multiple time-steps.
Children possess the ability to learn multiple cognitive tasks sequentially, which is a major challenge toward the long-term goal of artificial general intelligence. Existing continual learning frameworks are usually applicable to Deep Neural Networks (DNNs) and lack the exploration on more brain-inspired, energy-efficient Spiking Neural Networks (SNNs). Drawing on continual learning mechanisms during child growth and development, we propose Dynamic Structure Development of Spiking Neural Networks (DSD-SNN) for efficient and adaptive continual learning. When learning a sequence of tasks, the DSD-SNN dynamically assigns and grows new neurons to new tasks and prunes redundant neurons, thereby increasing memory capacity and reducing computational overhead. In addition, the overlapping shared structure helps to quickly leverage all acquired knowledge to new tasks, empowering a single network capable of supporting multiple incremental tasks (without the separate sub-network mask for each task). We validate the effectiveness of the proposed model on multiple class incremental learning and task incremental learning benchmarks. Extensive experiments demonstrated that our model could significantly improve performance, learning speed and memory capacity, and reduce computational overhead. Besides, our DSD-SNN model achieves comparable performance with the DNNs-based methods, and significantly outperforms the state-of-the-art (SOTA) performance for existing SNNs-based continual learning methods.
Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks
2023-08-09
Jue Chen, Huan Yuan, Jianchao Tan, Bin Chen, Chengru Song, Di Zhang
Brain-inspired Spiking Neural Networks (SNNs) have the characteristics of event-driven and high energy-efficient, which are different from traditional Artificial Neural Networks (ANNs) when deployed on edge devices such as neuromorphic chips. Most previous work focuses on SNNs training strategies to improve model performance and brings larger and deeper network architectures. It is difficult to deploy these complex networks on resource-limited edge devices directly. To meet such demand, people compress SNNs very cautiously to balance the performance and the computation efficiency. Existing compression methods either iteratively pruned SNNs using weights norm magnitude or formulated the problem as a sparse learning optimization. We propose an improved end-to-end Minimax optimization method for this sparse learning problem to better balance the model performance and the computation efficiency. We also demonstrate that jointly applying compression and finetuning on SNNs is better than sequentially, especially for extreme compression ratios. The compressed SNN models achieved state-of-the-art (SOTA) performance on various benchmark datasets and architectures. Our code is available at https://github.com/chenjallen/Resource-Constrained-Compression-on-SNN.
SSTFormer: Bridging Spiking Neural Network and Memory Support Transformer for Frame-Event based Recognition
2023-08-08
Xiao Wang, Zongzhen Wu, Yao Rong, Lin Zhu, Bo Jiang, Jin Tang, Yonghong Tian
Event camera-based pattern recognition is a newly arising research topic in recent years. Current researchers usually transform the event streams into images, graphs, or voxels, and adopt deep neural networks for event-based classification. Although good performance can be achieved on simple event recognition datasets, however, their results may be still limited due to the following two issues. Firstly, they adopt spatial sparse event streams for recognition only, which may fail to capture the color and detailed texture information well. Secondly, they adopt either Spiking Neural Networks (SNN) for energy-efficient recognition with suboptimal results, or Artificial Neural Networks (ANN) for energy-intensive, high-performance recognition. However, seldom of them consider achieving a balance between these two aspects. In this paper, we formally propose to recognize patterns by fusing RGB frames and event streams simultaneously and propose a new RGB frame-event recognition framework to address the aforementioned issues. The proposed method contains four main modules, i.e., memory support Transformer network for RGB frame encoding, spiking neural network for raw event stream encoding, multi-modal bottleneck fusion module for RGB-Event feature aggregation, and prediction head. Due to the scarce of RGB-Event based classification dataset, we also propose a large-scale PokerEvent dataset which contains 114 classes, and 27102 frame-event pairs recorded using a DVS346 event camera. Extensive experiments on two RGB-Event based classification datasets fully validated the effectiveness of our proposed framework. We hope this work will boost the development of pattern recognition by fusing RGB frames and event streams. Both our dataset and source code of this work will be released at https://github.com/Event-AHU/SSTFormer.
Core interface optimization for multi-core neuromorphic processors
2023-08-08
Zhe Su, Hyunjung Hwang, Tristan Torchet, Giacomo Indiveri
Hardware implementations of Spiking Neural Networks (SNNs) represent a promising approach to edge-computing for applications that require low-power and low-latency, and which cannot resort to external cloud-based computing services. However, most solutions proposed so far either support only relatively small networks, or take up significant hardware resources, to implement large networks. To realize large-scale and scalable SNNs it is necessary to develop an efficient asynchronous communication and routing fabric that enables the design of multi-core architectures. In particular the core interface that manages inter-core spike communication is a crucial component as it represents the bottleneck of Power-Performance-Area (PPA) especially for the arbitration architecture and the routing memory. In this paper we present an arbitration mechanism with the corresponding asynchronous encoding pipeline circuits, based on hierarchical arbiter trees. The proposed scheme reduces the latency by more than 70% in sparse-event mode, compared to the state-of-the-art arbitration architectures, with lower area cost. The routing memory makes use of asynchronous Content Addressable Memory (CAM) with Current Sensing Completion Detection (CSCD), which saves approximately 46% energy, and achieves a 40% increase in throughput against conventional asynchronous CAM using configurable delay lines, at the cost of only a slight increase in area. In addition as it radically reduces the core interface resources in multi-core neuromorphic processors, the arbitration architecture and CAM architecture we propose can be also applied to a wide range of general asynchronous circuits and systems.
Paired Competing Neurons Improving STDP Supervised Local Learning In Spiking Neural Networks
2023-08-04
Gaspard Goupy, Pierre Tirilly, Ioan Marius Bilasco
Direct training of Spiking Neural Networks (SNNs) on neuromorphic hardware has the potential to significantly reduce the energy consumption of artificial neural network training. SNNs trained with Spike Timing-Dependent Plasticity (STDP) benefit from gradient-free and unsupervised local learning, which can be easily implemented on ultra-low-power neuromorphic hardware. However, classification tasks cannot be performed solely with unsupervised STDP. In this paper, we propose Stabilized Supervised STDP (S2-STDP), a supervised STDP learning rule to train the classification layer of an SNN equipped with unsupervised STDP for feature extraction. S2-STDP integrates error-modulated weight updates that align neuron spikes with desired timestamps derived from the average firing time within the layer. Then, we introduce a training architecture called Paired Competing Neurons (PCN) to further enhance the learning capabilities of our classification layer trained with S2-STDP. PCN associates each class with paired neurons and encourages neuron specialization toward target or non-target samples through intra-class competition. We evaluate our methods on image recognition datasets, including MNIST, Fashion-MNIST, and CIFAR-10. Results show that our methods outperform state-of-the-art supervised STDP learning rules, for comparable architectures and numbers of neurons. Further analysis demonstrates that the use of PCN enhances the performance of S2-STDP, regardless of the hyperparameter set and without introducing any additional hyperparameters.
In this work, we present a computing platform named digital twin brain (DTB) that can simulate spiking neuronal networks of the whole human brain scale and more importantly, a personalized biological brain structure. In comparison to most brain simulations with a homogeneous global structure, we highlight that the sparseness, couplingness and heterogeneity in the sMRI, DTI and PET data of the brain has an essential impact on the efficiency of brain simulation, which is proved from the scaling experiments that the DTB of human brain simulation is communication-intensive and memory-access intensive computing systems rather than computation-intensive. We utilize a number of optimization techniques to balance and integrate the computation loads and communication traffics from the heterogeneous biological structure to the general GPU-based HPC and achieve leading simulation performance for the whole human brain-scaled spiking neuronal networks. On the other hand, the biological structure, equipped with a mesoscopic data assimilation, enables the DTB to investigate brain cognitive function by a reverse-engineering method, which is demonstrated by a digital experiment of visual evaluation on the DTB. Furthermore, we believe that the developing DTB will be a promising powerful platform for a large of research orients including brain-inspiredintelligence, rain disease medicine and brain-machine interface.
Attention-free Spikformer: Mixing Spike Sequences with Simple Linear Transforms
2023-08-02
Qingyu Wang, Duzhen Zhang, Tielin Zhang, Bo Xu
By integrating the self-attention capability and the biological properties of Spiking Neural Networks (SNNs), Spikformer applies the flourishing Transformer architecture to SNNs design. It introduces a Spiking Self-Attention (SSA) module to mix sparse visual features using spike-form Query, Key, and Value, resulting in the State-Of-The-Art (SOTA) performance on numerous datasets compared to previous SNN-like frameworks. In this paper, we demonstrate that the Spikformer architecture can be accelerated by replacing the SSA with an unparameterized Linear Transform (LT) such as Fourier and Wavelet transforms. These transforms are utilized to mix spike sequences, reducing the quadratic time complexity to log-linear time complexity. They alternate between the frequency and time domains to extract sparse visual features, showcasing powerful performance and efficiency. We conduct extensive experiments on image classification using both neuromorphic and static datasets. The results indicate that compared to the SOTA Spikformer with SSA, Spikformer with LT achieves higher Top-1 accuracy on neuromorphic datasets (i.e., CIFAR10-DVS and DVS128 Gesture) and comparable Top-1 accuracy on static datasets (i.e., CIFAR-10 and CIFAR-100). Furthermore, Spikformer with LT achieves approximately 29-51% improvement in training speed, 61-70% improvement in inference speed, and reduces memory usage by 4-26% due to not requiring learnable parameters.
Evaluating Spiking Neural Network On Neuromorphic Platform For Human Activity Recognition
2023-08-01
Sizhen Bian, Michele Magno
Energy efficiency and low latency are crucial requirements for designing wearable AI-empowered human activity recognition systems, due to the hard constraints of battery operations and closed-loop feedback. While neural network models have been extensively compressed to match the stringent edge requirements, spiking neural networks and event-based sensing are recently emerging as promising solutions to further improve performance due to their inherent energy efficiency and capacity to process spatiotemporal data in very low latency. This work aims to evaluate the effectiveness of spiking neural networks on neuromorphic processors in human activity recognition for wearable applications. The case of workout recognition with wrist-worn wearable motion sensors is used as a study. A multi-threshold delta modulation approach is utilized for encoding the input sensor data into spike trains to move the pipeline into the event-based approach. The spikes trains are then fed to a spiking neural network with direct-event training, and the trained model is deployed on the research neuromorphic platform from Intel, Loihi, to evaluate energy and latency efficiency. Test results show that the spike-based workouts recognition system can achieve a comparable accuracy (87.5\%) comparable to the popular milliwatt RISC-V bases multi-core processor GAP8 with a traditional neural network ( 88.1\%) while achieving two times better energy-delay product (0.66 \si{\micro\joule\second} vs. 1.32 \si{\micro\joule\second}).
Gradient Scaling on Deep Spiking Neural Networks with Spike-Dependent Local Information
2023-08-01
Seongsik Park, Jeonghee Jo, Jongkil Park, Yeonjoo Jeong, Jaewook Kim, Suyoun Lee, Joon Young Kwak, Inho Kim, Jong-Keuk Park, Kyeong Seok Lee, Gye Weon Hwang, Hyun Jae Jang
Deep spiking neural networks (SNNs) are promising neural networks for their model capacity from deep neural network architecture and energy efficiency from SNNs' operations. To train deep SNNs, recently, spatio-temporal backpropagation (STBP) with surrogate gradient was proposed. Although deep SNNs have been successfully trained with STBP, they cannot fully utilize spike information. In this work, we proposed gradient scaling with local spike information, which is the relation between pre- and post-synaptic spikes. Considering the causality between spikes, we could enhance the training performance of deep SNNs. According to our experiments, we could achieve higher accuracy with lower spikes by adopting the gradient scaling on image classification tasks, such as CIFAR10 and CIFAR100.
Effect of Synaptic Heterogeneity on Neuronal Coordination
2023-08-01
Moritz Layer, Moritz Helias, David Dahmen
Recent advancements in measurement techniques have resulted in an increasing amount of data on neural activities recorded in parallel, revealing largely heterogeneous correlation patterns across neurons. Yet, the mechanistic origin of this heterogeneity is largely unknown because existing theoretical approaches linking structure and dynamics in neural circuits are restricted to population-averaged connectivity and activity. Here we present a systematic inclusion of heterogeneity in network connectivity to derive quantitative predictions for neuron-resolved covariances and their statistics in spiking neural networks. Our study shows that the heterogeneity in covariances is not a result of variability in single-neuron firing statistics but stems from the ubiquitously observed sparsity and variability of connections in brain networks. Linear-response theory maps these features to the effective connectivity between neurons, which in turn determines neuronal covariances. Beyond-mean-field tools reveal that synaptic heterogeneity modulates the variability of covariances and thus the complexity of neuronal coordination across many orders of magnitude.
Synaptic Plasticity Models and Bio-Inspired Unsupervised Deep Learning: A Survey
2023-07-30
Gabriele Lagani, Fabrizio Falchi, Claudio Gennaro, Giuseppe Amato
Recently emerged technologies based on Deep Learning (DL) achieved outstanding results on a variety of tasks in the field of Artificial Intelligence (AI). However, these encounter several challenges related to robustness to adversarial inputs, ecological impact, and the necessity of huge amounts of training data. In response, researchers are focusing more and more interest on biologically grounded mechanisms, which are appealing due to the impressive capabilities exhibited by biological brains. This survey explores a range of these biologically inspired models of synaptic plasticity, their application in DL scenarios, and the connections with models of plasticity in Spiking Neural Networks (SNNs). Overall, Bio-Inspired Deep Learning (BIDL) represents an exciting research direction, aiming at advancing not only our current technologies but also our understanding of intelligence.
Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey
2023-07-30
Gabriele Lagani, Fabrizio Falchi, Claudio Gennaro, Giuseppe Amato
For a long time, biology and neuroscience fields have been a great source of inspiration for computer scientists, towards the development of Artificial Intelligence (AI) technologies. This survey aims at providing a comprehensive review of recent biologically-inspired approaches for AI. After introducing the main principles of computation and synaptic plasticity in biological neurons, we provide a thorough presentation of Spiking Neural Network (SNN) models, and we highlight the main challenges related to SNN training, where traditional backprop-based optimization is not directly applicable. Therefore, we discuss recent bio-inspired training methods, which pose themselves as alternatives to backprop, both for traditional and spiking networks. Bio-Inspired Deep Learning (BIDL) approaches towards advancing the computational capabilities and biological plausibility of current models.
Single Channel Speech Enhancement Using U-Net Spiking Neural Networks
2023-07-26
Abir Riahi, Éric Plourde
Speech enhancement (SE) is crucial for reliable communication devices or robust speech recognition systems. Although conventional artificial neural networks (ANN) have demonstrated remarkable performance in SE, they require significant computational power, along with high energy costs. In this paper, we propose a novel approach to SE using a spiking neural network (SNN) based on a U-Net architecture. SNNs are suitable for processing data with a temporal dimension, such as speech, and are known for their energy-efficient implementation on neuromorphic hardware. As such, SNNs are thus interesting candidates for real-time applications on devices with limited resources. The primary objective of the current work is to develop an SNN-based model with comparable performance to a state-of-the-art ANN model for SE. We train a deep SNN using surrogate-gradient-based optimization and evaluate its performance using perceptual objective tests under different signal-to-noise ratios and real-world noise conditions. Our results demonstrate that the proposed energy-efficient SNN model outperforms the Intel Neuromorphic Deep Noise Suppression Challenge (Intel N-DNS Challenge) baseline solution and achieves acceptable performance compared to an equivalent ANN model.
Learning heterogeneous delays in a layer of spiking neurons for fast motion detection
2023-07-26
Antoine Grimaldi, Laurent U Perrinet
The precise timing of spikes emitted by neurons plays a crucial role in shaping the response of efferent biological neurons. This temporal dimension of neural activity holds significant importance in understanding information processing in neurobiology, especially for the performance of neuromorphic hardware, such as event-based cameras. Nonetheless, many artificial neural models disregard this critical temporal dimension of neural activity. In this study, we present a model designed to efficiently detect temporal spiking motifs using a layer of spiking neurons equipped with heterogeneous synaptic delays. Our model capitalizes on the diverse synaptic delays present on the dendritic tree, enabling specific arrangements of temporally precise synaptic inputs to synchronize upon reaching the basal dendritic tree. We formalize this process as a time-invariant logistic regression, which can be trained using labeled data. To demonstrate its practical efficacy, we apply the model to naturalistic videos transformed into event streams, simulating the output of the biological retina or event-based cameras. To evaluate the robustness of the model in detecting visual motion, we conduct experiments by selectively pruning weights and demonstrate that the model remains efficient even under significantly reduced workloads. In conclusion, by providing a comprehensive, event-driven computational building block, the incorporation of heterogeneous delays has the potential to greatly improve the performance of future spiking neural network algorithms, particularly in the context of neuromorphic chips.
Automotive Object Detection via Learning Sparse Events by Spiking Neurons
2023-07-24
Hu Zhang, Yanchen Li, Luziwei Leng, Kaiwei Che, Qian Liu, Qinghai Guo, Jianxing Liao, Ran Cheng
Event-based sensors, distinguished by their high temporal resolution of 1 $\mathrm{\mu}\text{s}$ and a dynamic range of 120 $\text{dB}$, stand out as ideal tools for deployment in fast-paced settings like vehicles and drones. Traditional object detection techniques that utilize Artificial Neural Networks (ANNs) face challenges due to the sparse and asynchronous nature of the events these sensors capture. In contrast, Spiking Neural Networks (SNNs) offer a promising alternative, providing a temporal representation that is inherently aligned with event-based data. This paper explores the unique membrane potential dynamics of SNNs and their ability to modulate sparse events. We introduce an innovative spike-triggered adaptive threshold mechanism designed for stable training. Building on these insights, we present a specialized spiking feature pyramid network (SpikeFPN) optimized for automotive event-based object detection. Comprehensive evaluations demonstrate that SpikeFPN surpasses both traditional SNNs and advanced ANNs enhanced with attention mechanisms. Evidently, SpikeFPN achieves a mean Average Precision (mAP) of 0.477 on the GEN1 Automotive Detection (GAD) benchmark dataset, marking significant increases over the selected SNN baselines. Moreover, the efficient design of SpikeFPN ensures robust performance while optimizing computational resources, attributed to its innate sparse computation capabilities. Source codes are publicly accessible at https://github.com/EMI-Group/spikefpn.
The training of multilayer spiking neural networks (SNNs) using the error backpropagation algorithm has made significant progress in recent years. Among the various training schemes, the error backpropagation method that directly uses the firing time of neurons has attracted considerable attention because it can realize ideal temporal coding. This method uses time-to-first spike (TTFS) coding, in which each neuron fires at most once, and this restriction on the number of firings enables information to be processed at a very low firing frequency. This low firing frequency increases the energy efficiency of information processing in SNNs, which is important not only because of its similarity with information processing in the brain, but also from an engineering point of view. However, only an upper limit has been provided for TTFS-coded SNNs, and the information-processing capability of SNNs at lower firing frequencies has not been fully investigated. In this paper, we propose two spike timing-based sparse-firing (SSR) regularization methods to further reduce the firing frequency of TTFS-coded SNNs. The first is the membrane potential-aware SSR (M-SSR) method, which has been derived as an extreme form of the loss function of the membrane potential value. The second is the firing condition-aware SSR (F-SSR) method, which is a regularization function obtained from the firing conditions. Both methods are characterized by the fact that they only require information about the firing timing and associated weights. The effects of these regularization methods were investigated on the MNIST, Fashion-MNIST, and CIFAR-10 datasets using multilayer perceptron networks and convolutional neural network structures.
Bio-realistic Neural Network Implementation on Loihi 2 with Izhikevich Neurons
2023-07-21
Recep Buğra Uludağ, Serhat Çağdaş, Yavuz Selim İşler, Neslihan Serap Şengör, Ismail Akturk
In this paper, we presented a bio-realistic basal ganglia neural network and its integration into Intel's Loihi neuromorphic processor to perform simple Go/No-Go task. To incorporate more bio-realistic and diverse set of neuron dynamics, we used Izhikevich neuron model, implemented as microcode, instead of Leaky-Integrate and Fire (LIF) neuron model that has built-in support on Loihi. This work aims to demonstrate the feasibility of implementing computationally efficient custom neuron models on Loihi for building spiking neural networks (SNNs) that features these custom neurons to realize bio-realistic neural networks.
Accurate Detection of Spiking Motifs by Learning Heterogeneous Delays of a Spiking Neural Network
2023-07-21
Laurent U Perrinet
Recently, interest has grown in exploring the hypothesis that neural activity conveys information through precise spiking motifs. To investigate this phenomenon, various algorithms have been proposed to detect such motifs in Single Unit Activity (SUA) recorded from populations of neurons. In this study, we present a novel detection model based on the inversion of a generative model of raster plot synthesis. Using this generative model, we derive an optimal detection procedure that takes the form of logistic regression combined with temporal convolution. A key advantage of this model is its differentiability, which allows us to formulate a supervised learning approach using a gradient descent on the binary cross-entropy loss. To assess the model's ability to detect spiking motifs in synthetic data, we first perform numerical evaluations. This analysis highlights the advantages of using spiking motifs over traditional firing rate based population codes. We then successfully demonstrate that our learning method can recover synthetically generated spiking motifs, indicating its potential for further applications. In the future, we aim to extend this method to real neurobiological data, where the ground truth is unknown, to explore and detect spiking motifs in a more natural and biologically relevant context.
Deep Directly-Trained Spiking Neural Networks for Object Detection
2023-07-21
Qiaoyi Su, Yuhong Chou, Yifan Hu, Jianing Li, Shijie Mei, Ziyang Zhang, Guoqi Li
Spiking neural networks (SNNs) are brain-inspired energy-efficient models that encode information in spatiotemporal dynamics. Recently, deep SNNs trained directly have shown great success in achieving high performance on classification tasks with very few time steps. However, how to design a directly-trained SNN for the regression task of object detection still remains a challenging problem. To address this problem, we propose EMS-YOLO, a novel directly-trained SNN framework for object detection, which is the first trial to train a deep SNN with surrogate gradients for object detection rather than ANN-SNN conversion strategies. Specifically, we design a full-spike residual block, EMS-ResNet, which can effectively extend the depth of the directly-trained SNN with low power consumption. Furthermore, we theoretically analyze and prove the EMS-ResNet could avoid gradient vanishing or exploding. The results demonstrate that our approach outperforms the state-of-the-art ANN-SNN conversion methods (at least 500 time steps) in extremely fewer time steps (only 4 time steps). It is shown that our model could achieve comparable performance to the ANN with the same architecture while consuming 5.83 times less energy on the frame-based COCO Dataset and the event-based Gen1 Dataset.
EV-Planner: Energy-Efficient Robot Navigation via Event-Based Physics-Guided Neuromorphic Planner
2023-07-21
Sourav Sanyal, Rohan Kumar Manna, Kaushik Roy
Vision-based object tracking is an essential precursor to performing autonomous aerial navigation in order to avoid obstacles. Biologically inspired neuromorphic event cameras are emerging as a powerful alternative to frame-based cameras, due to their ability to asynchronously detect varying intensities (even in poor lighting conditions), high dynamic range, and robustness to motion blur. Spiking neural networks (SNNs) have gained traction for processing events asynchronously in an energy-efficient manner. On the other hand, physics-based artificial intelligence (AI) has gained prominence recently, as they enable embedding system knowledge via physical modeling inside traditional analog neural networks (ANNs). In this letter, we present an event-based physics-guided neuromorphic planner (EV-Planner) to perform obstacle avoidance using neuromorphic event cameras and physics-based AI. We consider the task of autonomous drone navigation where the mission is to detect moving gates and fly through them while avoiding a collision. We use event cameras to perform object detection using a shallow spiking neural network in an unsupervised fashion. Utilizing the physical equations of the brushless DC motors present in the drone rotors, we train a lightweight energy-aware physics-guided neural network (PgNN) with depth inputs. This predicts the optimal flight time responsible for generating near-minimum energy paths. We spawn the drone in the Gazebo simulator and implement a sensor-fused vision-to-planning neuro-symbolic framework using Robot Operating System (ROS). Simulation results for safe collision-free flight trajectories are presented with performance analysis, ablation study and potential future research directions
Spiking neural networks (SNNs) are bio-plausible computing models with high energy efficiency. The temporal dynamics of neurons and synapses enable them to detect temporal patterns and generate sequences. While Backpropagation Through Time (BPTT) is traditionally used to train SNNs, it is not suitable for online learning of embedded applications due to its high computation and memory cost as well as extended latency. Previous works have proposed online learning algorithms, but they often utilize highly simplified spiking neuron models without synaptic dynamics and reset feedback, resulting in subpar performance. In this work, we present Spatiotemporal Online Learning for Synaptic Adaptation (SOLSA), specifically designed for online learning of SNNs composed of Leaky Integrate and Fire (LIF) neurons with exponentially decayed synapses and soft reset. The algorithm not only learns the synaptic weight but also adapts the temporal filters associated to the synapses. Compared to the BPTT algorithm, SOLSA has much lower memory requirement and achieves a more balanced temporal workload distribution. Moreover, SOLSA incorporates enhancement techniques such as scheduled weight update, early stop training and adaptive synapse filter, which speed up the convergence and enhance the learning performance. When compared to other non-BPTT based SNN learning, SOLSA demonstrates an average learning accuracy improvement of 14.2%. Furthermore, compared to BPTT, SOLSA achieves a 5% higher average learning accuracy with a 72% reduction in memory cost.
On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments
2023-07-20
Shruti R. Kulkarni, Aaron Young, Prasanna Date, Narasinga Rao Miniskar, Jeffrey S. Vetter, Farah Fahim, Benjamin Parpillon, Jennet Dickinson, Nhan Tran, Jieun Yoo, Corrinne Mills, Morris Swartz, Petar Maksimovic, Catherine D. Schuman, Alice Bean
This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at the High Luminosity Large Hadron Collider. We present our approach for developing a compact neuromorphic model that filters out the sensor data based on the particle's transverse momentum with the goal of reducing the amount of data being sent to the downstream electronics. The incoming charge waveforms are converted to streams of binary-valued events, which are then processed by the SNN. We present our insights on the various system design choices - from data encoding to optimal hyperparameters of the training algorithm - for an accurate and compact SNN optimized for hardware deployment. Our results show that an SNN trained with an evolutionary algorithm and an optimized set of hyperparameters obtains a signal efficiency of about 91% with nearly half as many parameters as a deep neural network.
Deep Multi-Threshold Spiking-UNet for Image Processing
2023-07-20
Hebei Li, Yueyi Zhang, Zhiwei Xiong, Xiaoyan Sun
U-Net, known for its simple yet efficient architecture, is widely utilized for image processing tasks and is particularly suitable for deployment on neuromorphic chips. This paper introduces the novel concept of Spiking-UNet for image processing, which combines the power of Spiking Neural Networks (SNNs) with the U-Net architecture. To achieve an efficient Spiking-UNet, we face two primary challenges: ensuring high-fidelity information propagation through the network via spikes and formulating an effective training strategy. To address the issue of information loss, we introduce multi-threshold spiking neurons, which improve the efficiency of information transmission within the Spiking-UNet. For the training strategy, we adopt a conversion and fine-tuning pipeline that leverage pre-trained U-Net models. During the conversion process, significant variability in data distribution across different parts is observed when utilizing skip connections. Therefore, we propose a connection-wise normalization method to prevent inaccurate firing rates. Furthermore, we adopt a flow-based training method to fine-tune the converted models, reducing time steps while preserving performance. Experimental results show that, on image segmentation and denoising, our Spiking-UNet achieves comparable performance to its non-spiking counterpart, surpassing existing SNN methods. Compared with the converted Spiking-UNet without fine-tuning, our Spiking-UNet reduces inference time by approximately 90\%. This research broadens the application scope of SNNs in image processing and is expected to inspire further exploration in the field of neuromorphic engineering. The code for our Spiking-UNet implementation is available at https://github.com/SNNresearch/Spiking-UNet.
Multi-core neuromorphic systems typically use on-chip routers to transmit spikes among cores. These routers require significant memory resources and consume a large part of the overall system's energy budget. A promising alternative approach to using standard CMOS and SRAM-based routers is to exploit the features of memristive crossbar arrays and use them as programmable switch-matrices that route spikes. However, the scaling of these crossbar arrays presents physical challenges, such as "IR drop" on the metal lines due to the parasitic resistance, and leakage current accumulation on multiple active memristors in their "off" state. While reliability challenges of this type have been extensively studied in synchronous systems for compute-in-memory matrix-vector multiplication (MVM) accelerators and storage class memory, little effort has been devoted so far to characterizing the scaling limits of memristor-based crossbar routers. Here, we study the challenges of memristive crossbar arrays, when used as routing channels to transmit spikes in asynchronous Spiking Neural Network (SNN) hardware. We validate our analytical findings with experimental results obtained from a 4K-ReRAM chip which demonstrates its functionality as a routing crossbar. We determine the functionality bounds on the routing due to the IR drop and leak problem, based on theoretical modeling, circuit simulations for a 22nm FDSOI technology, and experimental measurements. This work highlights the limitations of this approach and provides useful guidelines for engineering the memristor device properties in memristive crossbar routers for multi-core asynchronous neuromorphic systems.
Enhancing Energy Efficiency and Reliability in Autonomous Systems Estimation using Neuromorphic Approach
2023-07-16
Reza Ahmadvand, Sarah Safura Sharif, Yaser Mike Banad
Energy efficiency and reliability have long been crucial factors for ensuring cost-effective and safe missions in autonomous systems computers. With the rapid evolution of industries such as space robotics and advanced air mobility, the demand for these low size, weight, and power (SWaP) computers has grown significantly. This study focuses on introducing an estimation framework based on spike coding theories and spiking neural networks (SNN), leveraging the efficiency and scalability of neuromorphic computers. Therefore, we propose an SNN-based Kalman filter (KF), a fundamental and widely adopted optimal strategy for well-defined linear systems. Furthermore, based on the modified sliding innovation filter (MSIF) we present a robust strategy called SNN-MSIF. Notably, the weight matrices of the networks are designed according to the system model, eliminating the need for learning. To evaluate the effectiveness of the proposed strategies, we compare them to their algorithmic counterparts, namely the KF and the MSIF, using Monte Carlo simulations. Additionally, we assess the robustness of SNN-MSIF by comparing it to SNN-KF in the presence of modeling uncertainties and neuron loss. Our results demonstrate the applicability of the proposed methods and highlight the superior performance of SNN-MSIF in terms of accuracy and robustness. Furthermore, the spiking pattern observed from the networks serves as evidence of the energy efficiency achieved by the proposed methods, as they exhibited an impressive reduction of approximately 97 percent in emitted spikes compared to possible spikes.
Temporal spike recognition plays a crucial role in various domains, including anomaly detection, keyword spotting and neuroscience. This paper presents a novel algorithm for efficient temporal spike pattern recognition on sparse event series data. The algorithm leverages a combination of reward-modulatory behavior, Hebbian and anti-Hebbian based learning methods to identify patterns in dynamic datasets with short intervals of training. The algorithm begins with a preprocessing step, where the input data is rationalized and translated to a feature-rich yet sparse spike time series data. Next, a linear feed forward spiking neural network processes this data to identify a trained pattern. Finally, the next layer performs a weighted check to ensure the correct pattern has been detected.To evaluate the performance of the proposed algorithm, it was trained on a complex dataset containing spoken digits with spike information and its output compared to state-of-the-art.
Long Short-term Memory with Two-Compartment Spiking Neuron
2023-07-14
Shimin Zhang, Qu Yang, Chenxiang Ma, Jibin Wu, Haizhou Li, Kay Chen Tan
The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. As a result, it remains a challenging task for state-of-the-art spiking neural networks (SNNs) to identify long-term temporal dependencies since bridging the temporal gap necessitates an extended memory capacity. To address this challenge, we propose a novel biologically inspired Long Short-Term Memory Leaky Integrate-and-Fire spiking neuron model, dubbed LSTM-LIF. Our model incorporates carefully designed somatic and dendritic compartments that are tailored to retain short- and long-term memories. The theoretical analysis further confirms its effectiveness in addressing the notorious vanishing gradient problem. Our experimental results, on a diverse range of temporal classification tasks, demonstrate superior temporal classification capability, rapid training convergence, strong network generalizability, and high energy efficiency of the proposed LSTM-LIF model. This work, therefore, opens up a myriad of opportunities for resolving challenging temporal processing tasks on emerging neuromorphic computing machines.
SLSSNN: High energy efficiency spike-train level spiking neural networks with spatio-temporal conversion
2023-07-14
Changqing Xu, Yi Liu, Yintang Yang
Brain-inspired spiking neuron networks (SNNs) have attracted widespread research interest due to their low power features, high biological plausibility, and strong spatiotemporal information processing capability. Although adopting a surrogate gradient (SG) makes the non-differentiability SNN trainable, achieving comparable accuracy for ANNs and keeping low-power features simultaneously is still tricky. In this paper, we proposed an energy-efficient spike-train level spiking neural network (SLSSNN) with low computational cost and high accuracy. In the SLSSNN, spatio-temporal conversion blocks (STCBs) are applied to replace the convolutional and ReLU layers to keep the low power features of SNNs and improve accuracy. However, SLSSNN cannot adopt backpropagation algorithms directly due to the non-differentiability nature of spike trains. We proposed a suitable learning rule for SLSSNNs by deducing the equivalent gradient of STCB. We evaluate the proposed SLSSNN on static and neuromorphic datasets, including Fashion-Mnist, Cifar10, Cifar100, TinyImageNet, and DVS-Cifar10. The experiment results show that our proposed SLSSNN outperforms the state-of-the-art accuracy on nearly all datasets, using fewer time steps and being highly energy-efficient.
2023-07-13
Richard Gall, Deniz Kocanaogullari, Murat Akcakaya, Deniz Erdogmus, Rajkumar Kubendran
In a multi-speaker "cocktail party" scenario, a listener can selectively attend to a speaker of interest. Studies into the human auditory attention network demonstrate cortical entrainment to speech envelopes resulting in highly correlated Electroencephalography (EEG) measurements. Current trends in EEG-based auditory attention detection (AAD) using artificial neural networks (ANN) are not practical for edge-computing platforms due to longer decision windows using several EEG channels, with higher power consumption and larger memory footprint requirements. Nor are ANNs capable of accurately modeling the brain's top-down attention network since the cortical organization is complex and layer. In this paper, we propose a hybrid convolutional neural network-spiking neural network (CNN-SNN) corticomorphic architecture, inspired by the auditory cortex, which uses EEG data along with multi-speaker speech envelopes to successfully decode auditory attention with low latency down to 1 second, using only 8 EEG electrodes strategically placed close to the auditory cortex, at a significantly higher accuracy of 91.03%, compared to the state-of-the-art. Simultaneously, when compared to a traditional CNN reference model, our model uses ~15% fewer parameters at a lower bit precision resulting in ~57% memory footprint reduction. The results show great promise for edge-computing in brain-embedded devices, like smart hearing aids.
Mixed-signal neuromorphic systems represent a promising solution for solving extreme-edge computing tasks without relying on external computing resources. Their spiking neural network circuits are optimized for processing sensory data on-line in continuous-time. However, their low precision and high variability can severely limit their performance. To address this issue and improve their robustness to inhomogeneities and noise in both their internal state variables and external input signals, we designed on-chip learning circuits with short-term analog dynamics and long-term tristate discretization mechanisms. An additional hysteretic stop-learning mechanism is included to improve stability and automatically disable weight updates when necessary, to enable continuous always-on learning. We designed a spiking neural network with these learning circuits in a prototype chip using a 180 nm CMOS technology. Simulation and silicon measurement results from the prototype chip are presented. These circuits enable the construction of large-scale spiking neural networks with online learning capabilities for real-world edge computing tasks.
Energy Efficient Personalized Hand-Gesture Recognition with Neuromorphic Computing
2023-07-11
Muhammad Aitsam, Alessandro Di Nuovo
Hand gestures are a form of non-verbal communication that is used in social interaction and it is therefore required for more natural human-robot interaction. Neuromorphic (brain-inspired) computing offers a low-power solution for Spiking neural networks (SNNs) that can be used for the classification and recognition of gestures. This article introduces the preliminary results of a novel methodology for training spiking convolutional neural networks for hand-gesture recognition so that a humanoid robot with integrated neuromorphic hardware will be able to personalise the interaction with a user according to the shown hand gesture. It also describes other approaches that could improve the overall performance of the model.
The Spiking Neural Network (SNN) has attracted more and more attention recently. It adopts binary spike signals to transmit information. Benefitting from the information passing paradigm of SNNs, the multiplications of activations and weights can be replaced by additions, which are more energy-efficient. However, its "Hard Reset" mechanism for the firing activity would ignore the difference among membrane potentials when the membrane potential is above the firing threshold, causing information loss. Meanwhile, quantifying the membrane potential to 0/1 spikes at the firing instants will inevitably introduce the quantization error thus bringing about information loss too. To address these problems, we propose to use the "Soft Reset" mechanism for the supervised training-based SNNs, which will drive the membrane potential to a dynamic reset potential according to its magnitude, and Membrane Potential Rectifier (MPR) to reduce the quantization error via redistributing the membrane potential to a range close to the spikes. Results show that the SNNs with the "Soft Reset" mechanism and MPR outperform their vanilla counterparts on both static and dynamic datasets.
Deep Unsupervised Learning Using Spike-Timing-Dependent Plasticity
2023-07-08
Sen Lu, Abhronil Sengupta
Spike-Timing-Dependent Plasticity (STDP) is an unsupervised learning mechanism for Spiking Neural Networks (SNNs) that has received significant attention from the neuromorphic hardware community. However, scaling such local learning techniques to deeper networks and large-scale tasks has remained elusive. In this work, we investigate a Deep-STDP framework where a rate-based convolutional network, that can be deployed in a neuromorphic setting, is trained in tandem with pseudo-labels generated by the STDP clustering process on the network outputs. We achieve $24.56\%$ higher accuracy and $3.5\times$ faster convergence speed at iso-accuracy on a 10-class subset of the Tiny ImageNet dataset in contrast to a $k$-means clustering approach.
A Survey of Spiking Neural Network Accelerator on FPGA
2023-07-08
Murat Isik
Due to the ability to implement customized topology, FPGA is increasingly used to deploy SNNs in both embedded and high-performance applications. In this paper, we survey state-of-the-art SNN implementations and their applications on FPGA. We collect the recent widely-used spiking neuron models, network structures, and signal encoding formats, followed by the enumeration of related hardware design schemes for FPGA-based SNN implementations. Compared with the previous surveys, this manuscript enumerates the application instances that applied the above-mentioned technical schemes in recent research. Based on that, we discuss the actual acceleration potential of implementing SNN on FPGA. According to our above discussion, the upcoming trends are discussed in this paper and give a guideline for further advancement in related subjects.
A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations
2023-07-06
Sergio F. Chevtchenko, Yeshwanth Bethi, Teresa B. Ludermir, Saeed Afshar
Reinforcement Learning (RL) provides a powerful framework for decision-making in complex environments. However, implementing RL in hardware-efficient and bio-inspired ways remains a challenge. This paper presents a novel Spiking Neural Network (SNN) architecture for solving RL problems with real-valued observations. The proposed model incorporates multi-layered event-based clustering, with the addition of Temporal Difference (TD)-error modulation and eligibility traces, building upon prior work. An ablation study confirms the significant impact of these components on the proposed model's performance. A tabular actor-critic algorithm with eligibility traces and a state-of-the-art Proximal Policy Optimization (PPO) algorithm are used as benchmarks. Our network consistently outperforms the tabular approach and successfully discovers stable control policies on classic RL environments: mountain car, cart-pole, and acrobot. The proposed model offers an appealing trade-off in terms of computational and hardware implementation requirements. The model does not require an external memory buffer nor a global error gradient computation, and synaptic updates occur online, driven by local learning rules and a broadcasted TD-error signal. Thus, this work contributes to the development of more hardware-efficient RL solutions.
Computational models of cortical activity provide insight into the mechanisms of higher-order processing in the human brain including planning, perception and the control of movement. Activity in the cortex is ongoing even in the absence of sensory input or discernible movements and is thought to be linked to the topology of cortical circuitry. However, the connectivity and its functional role in the generation of spatio-temporal firing patterns and cortical computations are still unknown. Movement of the body is a key function of the brain, with the motor cortex the main cortical area implicated in the generation of movement. We built a spiking neural network model of the motor cortex which incorporates a laminar structure and circuitry based on a previous cortical model. A local connectivity scheme was implemented to introduce more physiological plausibility to the cortex model, and the effect on the rates, distributions and irregularity of neuronal firing was compared to the original random connectivity method and experimental data. Local connectivity increased the distribution of and overall rate of neuronal firing. It also resulted in the irregularity of firing being more similar to those observed in experimental measurements. The larger variability in dynamical behaviour of the local connectivity model suggests that the topological structure of the connections in neuronal population plays a significant role in firing patterns during spontaneous activity. This model took steps towards replicating the macroscopic network of the motor cortex, replicating realistic spatiotemporal firing to shed light on information coding in the cortex. Large scale computational models such as this one can capture how structure and function relate to observable neuronal firing behaviour, and investigates the underlying computational mechanisms of the brain.
2023-07-04
Man Yao, Jiakui Hu, Zhaokun Zhou, Li Yuan, Yonghong Tian, Bo Xu, Guoqi Li
Spiking Neural Networks (SNNs) provide an energy-efficient deep learning option due to their unique spike-based event-driven (i.e., spike-driven) paradigm. In this paper, we incorporate the spike-driven paradigm into Transformer by the proposed Spike-driven Transformer with four unique properties: 1) Event-driven, no calculation is triggered when the input of Transformer is zero; 2) Binary spike communication, all matrix multiplications associated with the spike matrix can be transformed into sparse additions; 3) Self-attention with linear complexity at both token and channel dimensions; 4) The operations between spike-form Query, Key, and Value are mask and addition. Together, there are only sparse addition operations in the Spike-driven Transformer. To this end, we design a novel Spike-Driven Self-Attention (SDSA), which exploits only mask and addition operations without any multiplication, and thus having up to $87.2\times$ lower computation energy than vanilla self-attention. Especially in SDSA, the matrix multiplication between Query, Key, and Value is designed as the mask operation. In addition, we rearrange all residual connections in the vanilla Transformer before the activation functions to ensure that all neurons transmit binary spike signals. It is shown that the Spike-driven Transformer can achieve 77.1\% top-1 accuracy on ImageNet-1K, which is the state-of-the-art result in the SNN field. The source code is available at https://github.com/BICLab/Spike-Driven-Transformer.
Resistive memory-based zero-shot liquid state machine for multimodal event data learning
2023-07-03
Ning Lin, Shaocong Wang, Yi Li, Bo Wang, Shuhui Shi, Yangu He, Woyu Zhang, Yifei Yu, Yue Zhang, Xiaojuan Qi, Xiaoming Chen, Hao Jiang, Xumeng Zhang, Peng Lin, Xiaoxin Xu, Qi Liu, Zhongrui Wang, Dashan Shang, Ming Liu
The human brain is a complex spiking neural network (SNN) that learns multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, the brain achieves this with minimal power consumption, using event-based signals that propagate within its structure. However, mimicking the human brain in neuromorphic hardware presents both hardware and software challenges. Hardware limitations, such as the slowdown of Moore's law and the von Neumann bottleneck, hinder the efficiency of digital computers. On the software side, SNNs are known for their difficult training, especially when learning multimodal signals. To overcome these challenges, we propose a hardware-software co-design that combines a fixed and random liquid state machine (LSM) SNN encoder with trainable artificial neural network (ANN) projections. The LSM is physically implemented using analogue resistive memory, leveraging the inherent stochasticity of resistive switching to generate random weights. This highly efficient and nanoscale in-memory computing approach effectively addresses the von Neumann bottleneck and the slowdown of Moore's law. The ANN projections are implemented digitally, allowing for easy optimization using contrastive loss, which helps to overcome the difficulties associated with SNN training. We experimentally implement this co-design on a 40nm 256Kb in-memory computing macro. We first demonstrate LSM-based event encoding through supervised classification and linear probing on the N-MNIST and N-TIDIGITS datasets.
Spiking Transformers have gained considerable attention because they achieve both the energy efficiency of Spiking Neural Networks (SNNs) and the high capacity of Transformers. However, the existing Spiking Transformer architectures, derived from Artificial Neural Networks (ANNs), exhibit a notable architectural gap, resulting in suboptimal performance compared to their ANN counterparts. Manually discovering optimal architectures is time-consuming. To address these limitations, we introduce AutoST, a training-free NAS method for Spiking Transformers, to rapidly identify high-performance Spiking Transformer architectures. Unlike existing training-free NAS methods, which struggle with the non-differentiability and high sparsity inherent in SNNs, we propose to utilize Floating-Point Operations (FLOPs) as a performance metric, which is independent of model computations and training dynamics, leading to a stronger correlation with performance. Our extensive experiments show that AutoST models outperform state-of-the-art manually or automatically designed SNN architectures on static and neuromorphic datasets. Full code, model, and data are released for reproduction.
Infrared small target detection (ISTD) plays a vital role in numerous practical applications. In pursuit of determining the performance boundaries, researchers employ large and expensive manual-labeling data for representation learning. Nevertheless, this approach renders the state-of-the-art ISTD methods highly fragile in real-world challenges. In this paper, we first study the variation in detection performance across several mainstream methods under various scarcity -- namely, the absence of high-quality infrared data -- that challenge the prevailing theories about practical ISTD. To address this concern, we introduce the Gaussian Agnostic Representation Learning. Specifically, we propose the Gaussian Group Squeezer, leveraging Gaussian sampling and compression for non-uniform quantization. By exploiting a diverse array of training samples, we enhance the resilience of ISTD models against various challenges. Then, we introduce two-stage diffusion models for real-world reconstruction. By aligning quantized signals closely with real-world distributions, we significantly elevate the quality and fidelity of the synthetic samples. Comparative evaluations against state-of-the-art detection methods in various scarcity scenarios demonstrate the efficacy of the proposed approach.
Leveraging Language Prior for Infrared Small Target Detection
2025-07-17
Pranav Singh, Pravendra Singh
IRSTD (InfraRed Small Target Detection) detects small targets in infrared blurry backgrounds and is essential for various applications. The detection task is challenging due to the small size of the targets and their sparse distribution in infrared small target datasets. Although existing IRSTD methods and datasets have led to significant advancements, they are limited by their reliance solely on the image modality. Recent advances in deep learning and large vision-language models have shown remarkable performance in various visual recognition tasks. In this work, we propose a novel multimodal IRSTD framework that incorporates language priors to guide small target detection. We leverage language-guided attention weights derived from the language prior to enhance the model's ability for IRSTD, presenting a novel approach that combines textual information with image data to improve IRSTD capabilities. Utilizing the state-of-the-art GPT-4 vision model, we generate text descriptions that provide the locations of small targets in infrared images, employing careful prompt engineering to ensure improved accuracy. Due to the absence of multimodal IR datasets, existing IRSTD methods rely solely on image data. To address this shortcoming, we have curated a multimodal infrared dataset that includes both image and text modalities for small target detection, expanding upon the popular IRSTD-1k and NUDT-SIRST datasets. We validate the effectiveness of our approach through extensive experiments and comprehensive ablation studies. The results demonstrate significant improvements over the state-of-the-art method, with relative percentage differences of 9.74%, 13.02%, 1.25%, and 67.87% in IoU, nIoU, Pd, and Fa on the NUAA-SIRST subset, and 4.41%, 2.04%, 2.01%, and 113.43% on the IRSTD-1k subset of the LangIR dataset, respectively.
Infrared small target detection plays a vital role in remote sensing, industrial monitoring, and various civilian applications. Despite recent progress powered by deep learning, many end-to-end convolutional models tend to pursue performance by stacking increasingly complex architectures, often at the expense of interpretability, parameter efficiency, and generalization. These models typically overlook the intrinsic sparsity prior of infrared small targets--an essential cue that can be explicitly modeled for both performance and efficiency gains. To address this, we revisit the model-based paradigm of Robust Principal Component Analysis (RPCA) and propose Dynamic RPCA Network (DRPCA-Net), a novel deep unfolding network that integrates the sparsity-aware prior into a learnable architecture. Unlike conventional deep unfolding methods that rely on static, globally learned parameters, DRPCA-Net introduces a dynamic unfolding mechanism via a lightweight hypernetwork. This design enables the model to adaptively generate iteration-wise parameters conditioned on the input scene, thereby enhancing its robustness and generalization across diverse backgrounds. Furthermore, we design a Dynamic Residual Group (DRG) module to better capture contextual variations within the background, leading to more accurate low-rank estimation and improved separation of small targets. Extensive experiments on multiple public infrared datasets demonstrate that DRPCA-Net significantly outperforms existing state-of-the-art methods in detection accuracy. Code is available at https://github.com/GrokCV/DRPCA-Net.
Weakly-supervised Contrastive Learning with Quantity Prompts for Moving Infrared Small Target Detection
2025-07-03
Weiwei Duan, Luping Ji, Shengjia Chen, Sicheng Zhu, Jianghong Huang, Mao Ye
Different from general object detection, moving infrared small target detection faces huge challenges due to tiny target size and weak background contrast.Currently, most existing methods are fully-supervised, heavily relying on a large number of manual target-wise annotations. However, manually annotating video sequences is often expensive and time-consuming, especially for low-quality infrared frame images. Inspired by general object detection, non-fully supervised strategies ($e.g.$, weakly supervised) are believed to be potential in reducing annotation requirements. To break through traditional fully-supervised frameworks, as the first exploration work, this paper proposes a new weakly-supervised contrastive learning (WeCoL) scheme, only requires simple target quantity prompts during model training.Specifically, in our scheme, based on the pretrained segment anything model (SAM), a potential target mining strategy is designed to integrate target activation maps and multi-frame energy accumulation.Besides, contrastive learning is adopted to further improve the reliability of pseudo-labels, by calculating the similarity between positive and negative samples in feature subspace.Moreover, we propose a long-short term motion-aware learning scheme to simultaneously model the local motion patterns and global motion trajectory of small targets.The extensive experiments on two public datasets (DAUB and ITSDT-15K) verify that our weakly-supervised scheme could often outperform early fully-supervised methods. Even, its performance could reach over 90\% of state-of-the-art (SOTA) fully-supervised ones.
It's Not the Target, It's the Background: Rethinking Infrared Small Target Detection via Deep Patch-Free Low-Rank Representations
2025-06-12
Guoyi Zhang, Guangsheng Xu, Siyang Chen, Han Wang, Xiaohu Zhang
Infrared small target detection (IRSTD) remains a long-standing challenge in complex backgrounds due to low signal-to-clutter ratios (SCR), diverse target morphologies, and the absence of distinctive visual cues. While recent deep learning approaches aim to learn discriminative representations, the intrinsic variability and weak priors of small targets often lead to unstable performance. In this paper, we propose a novel end-to-end IRSTD framework, termed LRRNet, which leverages the low-rank property of infrared image backgrounds. Inspired by the physical compressibility of cluttered scenes, our approach adopts a compression--reconstruction--subtraction (CRS) paradigm to directly model structure-aware low-rank background representations in the image domain, without relying on patch-based processing or explicit matrix decomposition. To the best of our knowledge, this is the first work to directly learn low-rank background structures using deep neural networks in an end-to-end manner. Extensive experiments on multiple public datasets demonstrate that LRRNet outperforms 38 state-of-the-art methods in terms of detection accuracy, robustness, and computational efficiency. Remarkably, it achieves real-time performance with an average speed of 82.34 FPS. Evaluations on the challenging NoisySIRST dataset further confirm the model's resilience to sensor noise. The source code will be made publicly available upon acceptance.
Current CNN-based infrared small target detection(IRSTD) methods generally overlook the heterogeneity between shallow and deep features, leading to inefficient collaboration between shallow fine grained structural information and deep high-level semantic representations. Additionally, the dependency relationships and fusion mechanisms across different feature hierarchies lack systematic modeling, which fails to fully exploit the complementarity of multilevel features. These limitations hinder IRSTD performance while incurring substantial computational costs. To address these challenges, this paper proposes a shallow-deep synergistic detection network (SDS-Net) that efficiently models multilevel feature representations to increase both the detection accuracy and computational efficiency in IRSTD tasks. SDS-Net introduces a dual-branch architecture that separately models the structural characteristics and semantic properties of features, effectively preserving shallow spatial details while capturing deep semantic representations, thereby achieving high-precision detection with significantly improved inference speed. Furthermore, the network incorporates an adaptive feature fusion module to dynamically model cross-layer feature correlations, enhancing overall feature collaboration and representation capability. Comprehensive experiments on three public datasets (NUAA-SIRST, NUDT-SIRST, and IRSTD-1K) demonstrate that SDS-Net outperforms state-of-the-art IRSTD methods while maintaining low computational complexity and high inference efficiency, showing superior detection performance and broad application prospects. Our code will be made public at https://github.com/PhysiLearn/SDS-Net.
Infrared small target detection is a challenging task due to its unique characteristics (e.g., small, dim, shapeless and changeable). Recently published CNN-based methods have achieved promising performance with heavy feature extraction and fusion modules. To achieve efficient and effective detection, we propose a recurrent reusable-convolution attention network (RRCA-Net) for infrared small target detection. Specifically, RRCA-Net incorporates reusable-convolution block (RuCB) in a recurrent manner without introducing extra parameters. With the help of the repetitive iteration in RuCB, the high-level information of small targets in the deep layers can be well maintained and further refined. Then, a dual interactive attention aggregation module (DIAAM) is proposed to promote the mutual enhancement and fusion of refined information. In this way, RRCA-Net can both achieve high-level feature refinement and enhance the correlation of contextual information between adjacent layers. Moreover, to achieve steady convergence, we design a target characteristic inspired loss function (DpT-k loss) by integrating physical and mathematical constraints. Experimental results on three benchmark datasets (e.g. NUAA-SIRST, IRSTD-1k, DenseSIRST) demonstrate that our RRCA-Net can achieve comparable performance to the state-of-the-art methods while maintaining a small number of parameters, and act as a plug and play module to introduce consistent performance improvement for several popular IRSTD methods. Our code will be available at https://github.com/yongxianLiu/ soon.
Target Driven Adaptive Loss For Infrared Small Target Detection
2025-06-02
Yuho Shoji, Takahiro Toizumi, Atsushi Ito
We propose a target driven adaptive (TDA) loss to enhance the performance of infrared small target detection (IRSTD). Prior works have used loss functions, such as binary cross-entropy loss and IoU loss, to train segmentation models for IRSTD. Minimizing these loss functions guides models to extract pixel-level features or global image context. However, they have two issues: improving detection performance for local regions around the targets and enhancing robustness to small scale and low local contrast. To address these issues, the proposed TDA loss introduces a patch-based mechanism, and an adaptive adjustment strategy to scale and local contrast. The proposed TDA loss leads the model to focus on local regions around the targets and pay particular attention to targets with smaller scales and lower local contrast. We evaluate the proposed method on three datasets for IRSTD. The results demonstrate that the proposed TDA loss achieves better detection performance than existing losses on these datasets.
Infrared small target detection (ISTD) is vital for long-range surveillance in military, maritime, and early warning applications. ISTD is challenged by targets occupying less than 0.15% of the image and low distinguishability from complex backgrounds. Existing deep learning methods often suffer from information loss during downsampling and inefficient global context modeling. This paper presents SAMamba, a novel framework integrating SAM2's hierarchical feature learning with Mamba's selective sequence modeling. Key innovations include: (1) A Feature Selection Adapter (FS-Adapter) for efficient natural-to-infrared domain adaptation via dual-stage selection (token-level with a learnable task embedding and channel-wise adaptive transformations); (2) A Cross-Channel State-Space Interaction (CSI) module for efficient global context modeling with linear complexity using selective state space modeling; and (3) A Detail-Preserving Contextual Fusion (DPCF) module that adaptively combines multi-scale features with a gating mechanism to balance high-resolution and low-resolution feature contributions. SAMamba addresses core ISTD challenges by bridging the domain gap, maintaining fine-grained details, and efficiently modeling long-range dependencies. Experiments on NUAA-SIRST, IRSTD-1k, and NUDT-SIRST datasets show SAMamba significantly outperforms state-of-the-art methods, especially in challenging scenarios with heterogeneous backgrounds and varying target scales. Code: https://github.com/zhengshuchen/SAMamba.
Resolving closely-spaced small targets in dense clusters presents a significant challenge in infrared imaging, as the overlapping signals hinder precise determination of their quantity, sub-pixel positions, and radiation intensities. While deep learning has advanced the field of infrared small target detection, its application to closely-spaced infrared small targets has not yet been explored. This gap exists primarily due to the complexity of separating superimposed characteristics and the lack of an open-source infrastructure. In this work, we propose the Dynamic Iterative Shrinkage Thresholding Network (DISTA-Net), which reconceptualizes traditional sparse reconstruction within a dynamic framework. DISTA-Net adaptively generates convolution weights and thresholding parameters to tailor the reconstruction process in real time. To the best of our knowledge, DISTA-Net is the first deep learning model designed specifically for the unmixing of closely-spaced infrared small targets, achieving superior sub-pixel detection accuracy. Moreover, we have established the first open-source ecosystem to foster further research in this field. This ecosystem comprises three key components: (1) CSIST-100K, a publicly available benchmark dataset; (2) CSO-mAP, a custom evaluation metric for sub-pixel detection; and (3) GrokCSO, an open-source toolkit featuring DISTA-Net and other models. Our code and dataset are available at https://github.com/GrokCV/GrokCSO.
AuxDet: Auxiliary Metadata Matters for Omni-Domain Infrared Small Target Detection
2025-05-21
Yangting Shi, Renjie He, Le Hui, Xiang Li, Jian Yang, Ming-Ming Cheng, Yimian Dai
Omni-domain infrared small target detection (IRSTD) poses formidable challenges, as a single model must seamlessly adapt to diverse imaging systems, varying resolutions, and multiple spectral bands simultaneously. Current approaches predominantly rely on visual-only modeling paradigms that not only struggle with complex background interference and inherently scarce target features, but also exhibit limited generalization capabilities across complex omni-scene environments where significant domain shifts and appearance variations occur. In this work, we reveal a critical oversight in existing paradigms: the neglect of readily available auxiliary metadata describing imaging parameters and acquisition conditions, such as spectral bands, sensor platforms, resolution, and observation perspectives. To address this limitation, we propose the Auxiliary Metadata Driven Infrared Small Target Detector (AuxDet), a novel multi-modal framework that fundamentally reimagines the IRSTD paradigm by incorporating textual metadata for scene-aware optimization. Through a high-dimensional fusion module based on multi-layer perceptrons (MLPs), AuxDet dynamically integrates metadata semantics with visual features, guiding adaptive representation learning for each individual sample. Additionally, we design a lightweight prior-initialized enhancement module using 1D convolutional blocks to further refine fused features and recover fine-grained target cues. Extensive experiments on the challenging WideIRSTD-Full benchmark demonstrate that AuxDet consistently outperforms state-of-the-art methods, validating the critical role of auxiliary information in improving robustness and accuracy in omni-domain IRSTD tasks. Code is available at https://github.com/GrokCV/AuxDet.
Infrared small target detection (ISTD) plays a critical role in a wide range of civilian and military applications. Existing methods suffer from deficiencies in the localization of dim targets and the perception of contour information under dense clutter environments, severely limiting their detection performance. To tackle these issues, we propose a contour-aware and saliency priors embedding network (CSPENet) for ISTD. We first design a surround-convergent prior extraction module (SCPEM) that effectively captures the intrinsic characteristic of target contour pixel gradients converging toward their center. This module concurrently extracts two collaborative priors: a boosted saliency prior for accurate target localization and multi-scale structural priors for comprehensively enriching contour detail representation. Building upon this, we propose a dual-branch priors embedding architecture (DBPEA) that establishes differentiated feature fusion pathways, embedding these two priors at optimal network positions to achieve performance enhancement. Finally, we develop an attention-guided feature enhancement module (AGFEM) to refine feature representations and improve saliency estimation accuracy. Experimental results on public datasets NUDT-SIRST, IRSTD-1k, and NUAA-SIRST demonstrate that our CSPENet outperforms other state-of-the-art methods in detection performance. The code is available at https://github.com/IDIP2025/CSPENet.
Infrared small target detection (ISTD) is critical in both civilian and military applications. However, the limited texture and structural information in infrared images makes accurate detection particularly challenging. Although recent deep learning-based methods have improved performance, their use of conventional convolution kernels limits adaptability to complex scenes and diverse targets. Moreover, pooling operations often cause feature loss and insufficient exploitation of image information. To address these issues, we propose an adaptive receptive field convolution and wavelet-attentive hierarchical network for infrared small target detection (ARFC-WAHNet). This network incorporates a multi-receptive field feature interaction convolution (MRFFIConv) module to adaptively extract discriminative features by integrating multiple convolutional branches with a gated unit. A wavelet frequency enhancement downsampling (WFED) module leverages Haar wavelet transform and frequency-domain reconstruction to enhance target features and suppress background noise. Additionally, we introduce a high-low feature fusion (HLFF) module for integrating low-level details with high-level semantics, and a global median enhancement attention (GMEA) module to improve feature diversity and expressiveness via global attention. Experiments on public datasets SIRST, NUDT-SIRST, and IRSTD-1k demonstrate that ARFC-WAHNet outperforms recent state-of-the-art methods in both detection accuracy and robustness, particularly under complex backgrounds. The code is available at https://github.com/Leaf2001/ARFC-WAHNet.
Infrared Small Target Detection (IRSTD) system aims to identify small targets in complex backgrounds. Due to the convolution operation in Convolutional Neural Networks (CNNs), applying traditional CNNs to IRSTD presents challenges, since the feature extraction of small targets is often insufficient, resulting in the loss of critical features. To address these issues, we propose a dynamic content-guided attention multiscale feature aggregation network (DCGANet), which adheres to the attention principle of 'coarse-to-fine' and achieves high detection accuracy. First, we propose a selective variable convolution (SVC) module that integrates the benefits of standard convolution, irregular deformable convolution, and multi-rate dilated convolution. This module is designed to expand the receptive field and enhance non-local features, thereby effectively improving the discrimination between targets and backgrounds. Second, the core component of DCGANet is a two-stage content-guided attention module. This module employs a two-stage attention mechanism to initially direct the network's focus to salient regions within the feature maps and subsequently determine whether these regions correspond to targets or background interference. By retaining the most significant responses, this mechanism effectively suppresses false alarms. Additionally, we propose an Adaptive Dynamic Feature Fusion (ADFF) module to substitute for static feature cascading. This dynamic feature fusion strategy enables DCGANet to adaptively integrate contextual features, thereby enhancing its ability to discriminate true targets from false alarms. DCGANet has achieved new benchmarks across multiple datasets.
Infrared small target detection(IRSTD) is widely recognized as a challenging task due to the inherent limitations of infrared imaging, including low signal-to-noise ratios, lack of texture details, and complex background interference. While most existing methods model IRSTD as a semantic segmentation task, but they suffer from two critical drawbacks: (1)blurred target boundaries caused by long-distance imaging dispersion; and (2) excessive computational overhead due to indiscriminate feature stackin. To address these issues, we propose the Lightweight Efficiency Infrared Small Target Detection (LE-IRSTD), a lightweight and efficient framework based on YOLOv8n, with following key innovations. Firstly, we identify that the multiple bottleneck structures within the C2f component of the YOLOv8-n backbone contribute to an increased computational burden. Therefore, we implement the Mobile Inverted Bottleneck Convolution block (MBConvblock) and Bottleneck Structure block (BSblock) in the backbone, effectively balancing the trade-off between computational efficiency and the extraction of deep semantic information. Secondly, we introduce the Attention-based Variable Convolution Stem (AVCStem) structure, substituting the final convolution with Variable Kernel Convolution (VKConv), which allows for adaptive convolutional kernels that can transform into various shapes, facilitating the receptive field for the extraction of targets. Finally, we employ Global Shuffle Convolution (GSConv) to shuffle the channel dimension features obtained from different convolutional approaches, thereby enhancing the robustness and generalization capabilities of our method. Experimental results demonstrate that our LE-IRSTD method achieves compelling results in both accuracy and lightweight performance, outperforming several state-of-the-art deep learning methods.
Infrared small target detection (ISTD) is highly sensitive to sensor type, observation conditions, and the intrinsic properties of the target. These factors can introduce substantial variations in the distribution of acquired infrared image data, a phenomenon known as domain shift. Such distribution discrepancies significantly hinder the generalization capability of ISTD models across diverse scenarios. To tackle this challenge, this paper introduces an ISTD framework enhanced by domain adaptation. To alleviate distribution shift between datasets and achieve cross-sample alignment, we introduce Cross-view Channel Alignment (CCA). Additionally, we propose the Cross-view Top-K Fusion strategy, which integrates target information with diverse background features, enhancing the model' s ability to extract critical data characteristics. To further mitigate the impact of noise on ISTD, we develop a Noise-guided Representation learning strategy. This approach enables the model to learn more noise-resistant feature representations, to improve its generalization capability across diverse noisy domains. Finally, we develop a dedicated infrared small target dataset, RealScene-ISTD. Compared to state-of-the-art methods, our approach demonstrates superior performance in terms of detection probability (Pd), false alarm rate (Fa), and intersection over union (IoU). The code is available at: https://github.com/luy0222/RealScene-ISTD.
Aiming at the detection difficulties of infrared images such as complex background, low signal-to-noise ratio, small target size and weak brightness, a lightweight infrared small target detection algorithm ISTD-YOLO based on improved YOLOv7 was proposed. Firstly, the YOLOv7 network structure was lightweight reconstructed, and a three-scale lightweight network architecture was designed. Then, the ELAN-W module of the model neck network is replaced by VoV-GSCSP to reduce the computational cost and the complexity of the network structure. Secondly, a parameter-free attention mechanism was introduced into the neck network to enhance the relevance of local con-text information. Finally, the Normalized Wasserstein Distance (NWD) was used to optimize the commonly used IoU index to enhance the localization and detection accuracy of small targets. Experimental results show that compared with YOLOv7 and the current mainstream algorithms, ISTD-YOLO can effectively improve the detection effect, and all indicators are effectively improved, which can achieve high-quality detection of infrared small targets.
In complex environments, detecting tiny infrared targets has always been challenging because of the low contrast and high noise levels inherent in infrared images. These factors often lead to the loss of crucial details during feature extraction. Moreover, existing detection methods have limitations in adequately integrating global and local information, which constrains the efficiency and accuracy of infrared small target detection. To address these challenges, this paper proposes a network architecture named MSCA-Net, which integrates three key components: Multi-Scale Enhanced Dilated Attention mechanism (MSEDA), Positional Convolutional Block Attention Module (PCBAM), and Channel Aggregation Feature Fusion Block (CAB). Specifically, MSEDA employs a multi-scale feature fusion attention mechanism to adaptively aggregate information across different scales, enriching feature representation. PCBAM captures the correlation between global and local features through a correlation matrix-based strategy, enabling deep feature interaction. Moreover, CAB enhances the representation of critical features by assigning greater weights to them, integrating both low-level and high-level information, and thereby improving the models detection performance in complex backgrounds. The experimental results demonstrate that MSCA-Net achieves strong small target detection performance in complex backgrounds. Specifically, it attains mIoU scores of 78.43%, 94.56%, and 67.08% on the NUAA-SIRST, NUDT-SIRST, and IRTSD-1K datasets, respectively, underscoring its effectiveness and strong potential for real-world applications.
Infrared small target detection is currently a hot and challenging task in computer vision. Existing methods usually focus on mining visual features of targets, which struggles to cope with complex and diverse detection scenarios. The main reason is that infrared small targets have limited image information on their own, thus relying only on visual features fails to discriminate targets and interferences, leading to lower detection performance. To address this issue, we introduce a novel approach leveraging semantic text to guide infrared small target detection, called Text-IRSTD. It innovatively expands classical IRSTD to text-guided IRSTD, providing a new research idea. On the one hand, we devise a novel fuzzy semantic text prompt to accommodate ambiguous target categories. On the other hand, we propose a progressive cross-modal semantic interaction decoder (PCSID) to facilitate information fusion between texts and images. In addition, we construct a new benchmark consisting of 2,755 infrared images of different scenarios with fuzzy semantic textual annotations, called FZDT. Extensive experimental results demonstrate that our method achieves better detection performance and target contour recovery than the state-of-the-art methods. Moreover, proposed Text-IRSTD shows strong generalization and wide application prospects in unseen detection scenarios. The dataset and code will be publicly released after acceptance of this paper.
10K is Enough: An Ultra-Lightweight Binarized Network for Infrared Small-Target Detection
2025-03-04
Biqiao Xin, Qianchen Mao, Bingshu Wang, Jiangbin Zheng, Yong Zhao, C. L. Philip Chen
The widespread deployment of Infrared Small-Target Detection (IRSTD) algorithms on edge devices necessitates the exploration of model compression techniques. Binarized neural networks (BNNs) are distinguished by their exceptional efficiency in model compression. However, the small size of infrared targets introduces stringent precision requirements for the IRSTD task, while the inherent precision loss during binarization presents a significant challenge. To address this, we propose the Binarized Infrared Small-Target Detection Network (BiisNet), which preserves the core operations of binarized convolutions while integrating full-precision features into the network's information flow. Specifically, we propose the Dot Binary Convolution, which retains fine-grained semantic information in feature maps while still leveraging the binarized convolution operations. In addition, we introduce a smooth and adaptive Dynamic Softsign function, which provides more comprehensive and progressively finer gradient during backpropagation, enhancing model stability and promoting an optimal weight distribution. Experimental results demonstrate that BiisNet not only significantly outperforms other binary architectures but also has strong competitiveness among state-of-the-art full-precision models.
Low-Level Matters: An Efficient Hybrid Architecture for Robust Multi-frame Infrared Small Target Detection
2025-03-04
Zhihua Shen, Siyang Chen, Han Wang, Tongsu Zhang, Xiaohu Zhang, Xiangpeng Xu, Xia Yang
Multi-frame infrared small target detection (IRSTD) plays a crucial role in low-altitude and maritime surveillance. The hybrid architecture combining CNNs and Transformers shows great promise for enhancing multi-frame IRSTD performance. In this paper, we propose LVNet, a simple yet powerful hybrid architecture that redefines low-level feature learning in hybrid frameworks for multi-frame IRSTD. Our key insight is that the standard linear patch embeddings in Vision Transformers are insufficient for capturing the scale-sensitive local features critical to infrared small targets. To address this limitation, we introduce a multi-scale CNN frontend that explicitly models local features by leveraging the local spatial bias of convolution. Additionally, we design a U-shaped video Transformer for multi-frame spatiotemporal context modeling, effectively capturing the motion characteristics of targets. Experiments on the publicly available datasets IRDST and NUDT-MIRSDT demonstrate that LVNet outperforms existing state-of-the-art methods. Notably, compared to the current best-performing method, LMAFormer, LVNet achieves an improvement of 5.63\% / 18.36\% in nIoU, while using only 1/221 of the parameters and 1/92 / 1/21 of the computational cost. Ablation studies further validate the importance of low-level representation learning in hybrid architectures. Our code and trained models are available at https://github.com/ZhihuaShen/LVNet.
With the advancement of aerospace technology and the increasing demands of military applications, the development of low false-alarm and high-precision infrared small target detection algorithms has emerged as a key focus of research globally. However, the traditional model-driven method is not robust enough when dealing with features such as noise, target size, and contrast. The existing deep-learning methods have limited ability to extract and fuse key features, and it is difficult to achieve high-precision detection in complex backgrounds and when target features are not obvious. To solve these problems, this paper proposes a deep-learning infrared small target detection method that combines image super-resolution technology with multi-scale observation. First, the input infrared images are preprocessed with super-resolution and multiple data enhancements are performed. Secondly, based on the YOLOv5 model, we proposed a new deep-learning network named YOLO-MST. This network includes replacing the SPPF module with the self-designed MSFA module in the backbone, optimizing the neck, and finally adding a multi-scale dynamic detection head to the prediction head. By dynamically fusing features from different scales, the detection head can better adapt to complex scenes. The mAP@0.5 detection rates of this method on two public datasets, SIRST and IRIS, reached 96.4% and 99.5% respectively, more effectively solving the problems of missed detection, false alarms, and low precision.
Learning Dynamic Local Context Representations for Infrared Small Target Detection
2024-12-23
Guoyi Zhang, Guangsheng Xu, Han Wang, Siyang Chen, Yunxiao Shan, Xiaohu Zhang
Infrared small target detection (ISTD) is challenging due to complex backgrounds, low signal-to-clutter ratios, and varying target sizes and shapes. Effective detection relies on capturing local contextual information at the appropriate scale. However, small-kernel CNNs have limited receptive fields, leading to false alarms, while transformer models, with global receptive fields, often treat small targets as noise, resulting in miss-detections. Hybrid models struggle to bridge the semantic gap between CNNs and transformers, causing high complexity.To address these challenges, we propose LCRNet, a novel method that learns dynamic local context representations for ISTD. The model consists of three components: (1) C2FBlock, inspired by PDE solvers, for efficient small target information capture; (2) DLC-Attention, a large-kernel attention mechanism that dynamically builds context and reduces feature redundancy; and (3) HLKConv, a hierarchical convolution operator based on large-kernel decomposition that preserves sparsity and mitigates the drawbacks of dilated convolutions. Despite its simplicity, with only 1.65M parameters, LCRNet achieves state-of-the-art (SOTA) performance.Experiments on multiple datasets, comparing LCRNet with 33 SOTA methods, demonstrate its superior performance and efficiency.
Optimization-based approaches dominate infrared small target detection as they leverage infrared imagery's intrinsic low-rankness and sparsity. While effective for single-frame images, they struggle with dynamic changes in multi-frame scenarios as traditional spatial-temporal representations often fail to adapt. To address these challenges, we introduce a Neural-represented Spatial-Temporal Tensor (NeurSTT) model. This framework employs nonlinear networks to enhance spatial-temporal feature correlations in background approximation, thereby supporting target detection in an unsupervised manner. Specifically, we employ neural layers to approximate sequential backgrounds within a low-rank informed deep scheme. A neural three-dimensional total variation is developed to refine background smoothness while reducing static target-like clusters in sequences. Traditional sparsity constraints are incorporated into the loss functions to preserve potential targets. By replacing complex solvers with a deep updating strategy, NeurSTT simplifies the optimization process in a domain-awareness way. Visual and numerical results across various datasets demonstrate that our method outperforms detection challenges. Notably, it has 16.6$\times$ fewer parameters and averaged 19.19\% higher in $IoU$ compared to the suboptimal method on $256 \times 256$ sequences.
Pinwheel-shaped Convolution and Scale-based Dynamic Loss for Infrared Small Target Detection
2024-12-22
Jiangnan Yang, Shuangli Liu, Jingjun Wu, Xinyu Su, Nan Hai, Xueli Huang
These recent years have witnessed that convolutional neural network (CNN)-based methods for detecting infrared small targets have achieved outstanding performance. However, these methods typically employ standard convolutions, neglecting to consider the spatial characteristics of the pixel distribution of infrared small targets. Therefore, we propose a novel pinwheel-shaped convolution (PConv) as a replacement for standard convolutions in the lower layers of the backbone network. PConv better aligns with the pixel Gaussian spatial distribution of dim small targets, enhances feature extraction, significantly increases the receptive field, and introduces only a minimal increase in parameters. Additionally, while recent loss functions combine scale and location losses, they do not adequately account for the varying sensitivity of these losses across different target scales, limiting detection performance on dim-small targets. To overcome this, we propose a scale-based dynamic (SD) Loss that dynamically adjusts the influence of scale and location losses based on target size, improving the network's ability to detect targets of varying scales. We construct a new benchmark, SIRST-UAVB, which is the largest and most challenging dataset to date for real-shot single-frame infrared small target detection. Lastly, by integrating PConv and SD Loss into the latest small target detection algorithms, we achieved significant performance improvements on IRSTD-1K and our SIRST-UAVB dataset, validating the effectiveness and generalizability of our approach. Code -- https://github.com/JN-Yang/PConv-SDloss-Data
Recently, single-frame infrared small target (SIRST) detection with single point supervision has drawn wide-spread attention. However, the latest label evolution with single point supervision (LESPS) framework suffers from instability, excessive label evolution, and difficulty in exerting embedded network performance. Inspired by organisms gradually adapting to their environment and continuously accumulating knowledge, we construct an innovative Progressive Active Learning (PAL) framework for single point supervision, which drives the existing SIRST detection networks progressively and actively recognizes and learns more hard samples to achieve significant performance improvements. Specifically, to avoid the early low-performance model leading to the wrong selection of hard samples, we propose a model pre-start concept, which focuses on automatically selecting a portion of easy samples and helping the model have basic task-specific learning capabilities. Meanwhile, we propose a refined dual-update strategy, which can promote reasonable learning of harder samples and continuous refinement of pseudo-labels. In addition, to alleviate the risk of excessive label evolution, a decay factor is reasonably introduced, which helps to achieve a dynamic balance between the expansion and contraction of target annotations. Extensive experiments show that existing SIRST detection networks equipped with our PAL framework have achieved state-of-the-art (SOTA) results on multiple public datasets. Furthermore, our PAL framework can build an efficient and stable bridge between full supervision and single point supervision tasks. Our code are available at https://github.com/YuChuang1205/PAL.
Paying more attention to local contrast: improving infrared small target detection performance via prior knowledge
2024-11-20
Peichao Wang, Jiabao Wang, Yao Chen, Rui Zhang, Yang Li, Zhuang Miao
The data-driven method for infrared small target detection (IRSTD) has achieved promising results. However, due to the small scale of infrared small target datasets and the limited number of pixels occupied by the targets themselves, it is a challenging task for deep learning methods to directly learn from these samples. Utilizing human expert knowledge to assist deep learning methods in better learning is worthy of exploration. To effectively guide the model to focus on targets' spatial features, this paper proposes the Local Contrast Attention Enhanced infrared small target detection Network (LCAE-Net), combining prior knowledge with data-driven deep learning methods. LCAE-Net is a U-shaped neural network model which consists of two developed modules: a Local Contrast Enhancement (LCE) module and a Channel Attention Enhancement (CAE) module. The LCE module takes advantages of prior knowledge, leveraging handcrafted convolution operator to acquire Local Contrast Attention (LCA), which could realize background suppression while enhance the potential target region, thus guiding the neural network to pay more attention to potential infrared small targets' location information. To effectively utilize the response information throughout downsampling progresses, the CAE module is proposed to achieve the information fusion among feature maps' different channels. Experimental results indicate that our LCAE-Net outperforms existing state-of-the-art methods on the three public datasets NUDT-SIRST, NUAA-SIRST, and IRSTD-1K, and its detection speed could reach up to 70 fps. Meanwhile, our model has a parameter count and Floating-Point Operations (FLOPs) of 1.945M and 4.862G respectively, which is suitable for deployment on edge devices.
SFA-UNet: More Attention to Multi-Scale Contrast and Contextual Information in Infrared Small Object Segmentation
2024-10-30
Imad Ali Shah, Fahad Mumtaz Malik, Muhammad Waqas Ashraf
Computer vision researchers have extensively worked on fundamental infrared visual recognition for the past few decades. Among various approaches, deep learning has emerged as the most promising candidate. However, Infrared Small Object Segmentation (ISOS) remains a major focus due to several challenges including: 1) the lack of effective utilization of local contrast and global contextual information; 2) the potential loss of small objects in deep models; and 3) the struggling to capture fine-grained details and ignore noise. To address these challenges, we propose a modified U-Net architecture, named SFA-UNet, by combining Scharr Convolution (SC) and Fast Fourier Convolution (FFC) in addition to vertical and horizontal Attention gates (AG) into UNet. SFA-UNet utilizes double convolution layers with the addition of SC and FFC in its encoder and decoder layers. SC helps to learn the foreground-to-background contrast information whereas FFC provide multi-scale contextual information while mitigating the small objects vanishing problem. Additionally, the introduction of vertical AGs in encoder layers enhances the model's focus on the targeted object by ignoring irrelevant regions. We evaluated the proposed approach on publicly available, SIRST and IRSTD datasets, and achieved superior performance by an average 0.75% with variance of 0.025 of all combined metrics in multiple runs as compared to the existing state-of-the-art methods
Robust infrared small target detection using self-supervised and a contrario paradigms
2024-10-09
Alina Ciocarlan, Sylvie Le Hégarat-Mascle, Sidonie Lefebvre, Arnaud Woiselle
Detecting small targets in infrared images poses significant challenges in defense applications due to the presence of complex backgrounds and the small size of the targets. Traditional object detection methods often struggle to balance high detection rates with low false alarm rates, especially when dealing with small objects. In this paper, we introduce a novel approach that combines a contrario paradigm with Self-Supervised Learning (SSL) to improve Infrared Small Target Detection (IRSTD). On the one hand, the integration of an a contrario criterion into a YOLO detection head enhances feature map responses for small and unexpected objects while effectively controlling false alarms. On the other hand, we explore SSL techniques to overcome the challenges of limited annotated data, common in IRSTD tasks. Specifically, we benchmark several representative SSL strategies for their effectiveness in improving small object detection performance. Our findings show that instance discrimination methods outperform masked image modeling strategies when applied to YOLO-based small object detection. Moreover, the combination of the a contrario and SSL paradigms leads to significant performance improvements, narrowing the gap with state-of-the-art segmentation methods and even outperforming them in frugal settings. This two-pronged approach offers a robust solution for improving IRSTD performance, particularly under challenging conditions.
Infrared small target detection (ISTD) is widely used in civilian and military applications. However, ISTD encounters several challenges, including the tendency for small and dim targets to be obscured by complex backgrounds. To address this issue, we propose the Dynamic Attention Transformer Network (DATransNet), which aims to extract and preserve detailed information vital for small targets. DATransNet employs the Dynamic Attention Transformer (DATrans), simulating central difference convolutions (CDC) to extract gradient features. Furthermore, we propose a global feature extraction module (GFEM) that offers a comprehensive perspective to prevent the network from focusing solely on details while neglecting the global information. We compare the network with state-of-the-art (SOTA) approaches and demonstrate that our method performs effectively. Our source code is available at https://github.com/greekinRoma/DATransNet.
Multi-frame infrared small target (MIRST) detection in satellite videos is a long-standing, fundamental yet challenging task for decades, and the challenges can be summarized as: First, extremely small target size, highly complex clutters & noises, various satellite motions result in limited feature representation, high false alarms, and difficult motion analyses. Second, the lack of large-scale public available MIRST dataset in satellite videos greatly hinders the algorithm development. To address the aforementioned challenges, in this paper, we first build a large-scale dataset for MIRST detection in satellite videos (namely IRSatVideo-LEO), and then develop a recurrent feature refinement (RFR) framework as the baseline method. Specifically, IRSatVideo-LEO is a semi-simulated dataset with synthesized satellite motion, target appearance, trajectory and intensity, which can provide a standard toolbox for satellite video generation and a reliable evaluation platform to facilitate the algorithm development. For baseline method, RFR is proposed to be equipped with existing powerful CNN-based methods for long-term temporal dependency exploitation and integrated motion compensation & MIRST detection. Specifically, a pyramid deformable alignment (PDA) module and a temporal-spatial-frequency modulation (TSFM) module are proposed to achieve effective and efficient feature alignment, propagation, aggregation and refinement. Extensive experiments have been conducted to demonstrate the effectiveness and superiority of our scheme. The comparative results show that ResUNet equipped with RFR outperforms the state-of-the-art MIRST detection methods. Dataset and code are released at https://github.com/XinyiYing/RFR.
Recent advancements in deep learning have greatly advanced the field of infrared small object detection (IRSTD). Despite their remarkable success, a notable gap persists between these IRSTD methods and generic segmentation approaches in natural image domains. This gap primarily arises from the significant modality differences and the limited availability of infrared data. In this study, we aim to bridge this divergence by investigating the adaptation of generic segmentation models, such as the Segment Anything Model (SAM), to IRSTD tasks. Our investigation reveals that many generic segmentation models can achieve comparable performance to state-of-the-art IRSTD methods. However, their full potential in IRSTD remains untapped. To address this, we propose a simple, lightweight, yet effective baseline model for segmenting small infrared objects. Through appropriate distillation strategies, we empower smaller student models to outperform state-of-the-art methods, even surpassing fine-tuned teacher results. Furthermore, we enhance the model's performance by introducing a novel query design comprising dense and sparse queries to effectively encode multi-scale features. Through extensive experimentation across four popular IRSTD datasets, our model demonstrates significantly improved performance in both accuracy and throughput compared to existing approaches, surpassing SAM and Semantic-SAM by over 14 IoU on NUDT and 4 IoU on IRSTD1k. The source code and models will be released at https://github.com/O937-blip/SimIR.
Hybrid Mask Generation for Infrared Small Target Detection with Single-Point Supervision
2024-09-06
Weijie He, Mushui Liu, Yunlong Yu
Single-frame infrared small target (SIRST) detection poses a significant challenge due to the requirement to discern minute targets amidst complex infrared background clutter. In this paper, we focus on a weakly-supervised paradigm to obtain high-quality pseudo masks from the point-level annotation by integrating a novel learning-free method with the hybrid of the learning-based method. The learning-free method adheres to a sequential process, progressing from a point annotation to the bounding box that encompasses the target, and subsequently to detailed pseudo masks, while the hybrid is achieved through filtering out false alarms and retrieving missed detections in the network's prediction to provide a reliable supplement for learning-free masks. The experimental results show that our learning-free method generates pseudo masks with an average Intersection over Union (IoU) that is 4.3% higher than the second-best learning-free competitor across three datasets, while the hybrid learning-based method further enhances the quality of pseudo masks, achieving an additional average IoU increase of 3.4%.
In this paper, we briefly summarize the first competition on resource-limited infrared small target detection (namely, LimitIRSTD). This competition has two tracks, including weakly-supervised infrared small target detection (Track 1) and lightweight infrared small target detection (Track 2). 46 and 60 teams successfully registered and took part in Tracks 1 and Track 2, respectively. The top-performing methods and their results in each track are described with details. This competition inspires the community to explore the tough problems in the application of infrared small target detection, and ultimately promote the deployment of this technology under limited resource.
We pioneer a learning-based single-point prompt paradigm for infrared small target label generation (IRSTLG) to lobber annotation burdens. Unlike previous clustering-based methods, our intuition is that point-guided mask generation just requires one more prompt than target detection, i.e., IRSTLG can be treated as an infrared small target detection (IRSTD) with the location hint. Therefore, we propose an elegant yet effective Energy-Double-Guided Single-point Prompt (EDGSP) framework, aiming to adeptly transform a coarse IRSTD network into a refined label generation method. Specifically, EDGSP comprises three key modules: 1) target energy initialization (TEI), which establishes a foundational outline to streamline the mapping process for effective shape evolution, 2) double prompt embedding (DPE) for rapidly localizing interesting regions and reinforcing high-resolution individual edges to avoid label adhesion, and 3) bounding box-based matching (BBM) for eliminating false masks via considering comprehensive cluster boundary conditions to obtain a reliable output. In this way, pseudo labels generated by three backbones equipped with our EDGSP achieve 100% object-level probability of detection (Pd) and 0% false-alarm rate (Fa) on SIRST, NUDT-SIRST, and IRSTD-1k datasets, with a pixel-level intersection over union (IoU) improvement of 13.28% over state-of-the-art (SOTA) label generation methods. Further applying our inferred masks to train detection models, EDGSP, for the first time, enables a single-point-generated pseudo mask to surpass the manual labels. Even with coarse single-point annotations, it still achieves 99.5% performance of full labeling. Code is available at https://github.com/xdFai/EDGSP.
Infrared small target detection faces the inherent challenge of precisely localizing dim targets amidst complex background clutter. Traditional approaches struggle to balance detection precision and false alarm rates. To break this dilemma, we propose SeRankDet, a deep network that achieves high accuracy beyond the conventional hit-miss trade-off, by following the ``Pick of the Bunch'' principle. At its core lies our Selective Rank-Aware Attention (SeRank) module, employing a non-linear Top-K selection process that preserves the most salient responses, preventing target signal dilution while maintaining constant complexity. Furthermore, we replace the static concatenation typical in U-Net structures with our Large Selective Feature Fusion (LSFF) module, a dynamic fusion strategy that empowers SeRankDet with adaptive feature integration, enhancing its ability to discriminate true targets from false alarms. The network's discernment is further refined by our Dilated Difference Convolution (DDC) module, which merges differential convolution aimed at amplifying subtle target characteristics with dilated convolution to expand the receptive field, thereby substantially improving target-background separation. Despite its lightweight architecture, the proposed SeRankDet sets new benchmarks in state-of-the-art performance across multiple public datasets. The code is available at https://github.com/GrokCV/SeRankDet.
LR-Net: A Lightweight and Robust Network for Infrared Small Target Detection
2024-08-05
Chuang Yu, Yunpeng Liu, Jinmiao Zhao, Zelin Shi
Limited by equipment limitations and the lack of target intrinsic features, existing infrared small target detection methods have difficulty meeting actual comprehensive performance requirements. Therefore, we propose an innovative lightweight and robust network (LR-Net), which abandons the complex structure and achieves an effective balance between detection accuracy and resource consumption. Specifically, to ensure the lightweight and robustness, on the one hand, we construct a lightweight feature extraction attention (LFEA) module, which can fully extract target features and strengthen information interaction across channels. On the other hand, we construct a simple refined feature transfer (RFT) module. Compared with direct cross-layer connections, the RFT module can improve the network's feature refinement extraction capability with little resource consumption. Meanwhile, to solve the problem of small target loss in high-level feature maps, on the one hand, we propose a low-level feature distribution (LFD) strategy to use low-level features to supplement the information of high-level features. On the other hand, we introduce an efficient simplified bilinear interpolation attention module (SBAM) to promote the guidance constraints of low-level features on high-level features and the fusion of the two. In addition, We abandon the traditional resizing method and adopt a new training and inference cropping strategy, which is more robust to datasets with multi-scale samples. Extensive experimental results show that our LR-Net achieves state-of-the-art (SOTA) performance. Notably, on the basis of the proposed LR-Net, we achieve 3rd place in the "ICPR 2024 Resource-Limited Infrared Small Target Detection Challenge Track 2: Lightweight Infrared Small Target Detection".
Refined Infrared Small Target Detection Scheme with Single-Point Supervision
2024-08-05
Jinmiao Zhao, Zelin Shi, Chuang Yu, Yunpeng Liu
Recently, infrared small target detection with single-point supervision has attracted extensive attention. However, the detection accuracy of existing methods has difficulty meeting actual needs. Therefore, we propose an innovative refined infrared small target detection scheme with single-point supervision, which has excellent segmentation accuracy and detection rate. Specifically, we introduce label evolution with single point supervision (LESPS) framework and explore the performance of various excellent infrared small target detection networks based on this framework. Meanwhile, to improve the comprehensive performance, we construct a complete post-processing strategy. On the one hand, to improve the segmentation accuracy, we use a combination of test-time augmentation (TTA) and conditional random field (CRF) for post-processing. On the other hand, to improve the detection rate, we introduce an adjustable sensitivity (AS) strategy for post-processing, which fully considers the advantages of multiple detection results and reasonably adds some areas with low confidence to the fine segmentation image in the form of centroid points. In addition, to further improve the performance and explore the characteristics of this task, on the one hand, we construct and find that a multi-stage loss is helpful for fine-grained detection. On the other hand, we find that a reasonable sliding window cropping strategy for test samples has better performance for actual multi-size samples. Extensive experimental results show that the proposed scheme achieves state-of-the-art (SOTA) performance. Notably, the proposed scheme won the third place in the "ICPR 2024 Resource-Limited Infrared Small Target Detection Challenge Track 1: Weakly Supervised Infrared Small Target Detection".
Infrared small target detection (IRSTD) tasks are extremely challenging for two main reasons: 1) it is difficult to obtain accurate labelling information that is critical to existing methods, and 2) infrared (IR) small target information is easily lost in deep networks. To address these issues, we propose a single-point supervised high-resolution dynamic network (SSHD-Net). In contrast to existing methods, we achieve state-of-the-art (SOTA) detection performance using only single-point supervision. Specifically, we first design a high-resolution cross-feature extraction module (HCEM), that achieves bi-directional feature interaction through stepped feature cascade channels (SFCC). It balances network depth and feature resolution to maintain deep IR small-target information. Secondly, the effective integration of global and local features is achieved through the dynamic coordinate fusion module (DCFM), which enhances the anti-interference ability in complex backgrounds. In addition, we introduce the high-resolution multilevel residual module (HMRM) to enhance the semantic information extraction capability. Finally, we design the adaptive target localization detection head (ATLDH) to improve detection accuracy. Experiments on the publicly available datasets NUDT-SIRST and IRSTD-1k demonstrate the effectiveness of our method. Compared to other SOTA methods, our method can achieve better detection performance with only a single point of supervision.
Recently, single-frame infrared small target (SIRST) detection technology has attracted wide-spread attention. However, due to the intrinsic feature scarcity in infrared small targets, precise segmentation of small targets from complex backgrounds remains a significant challenge. Different from most existing deep learning-based methods that focus on improving network architectures, we propose a feature-enhanced and sensitivity-tunable (FEST) framework, which is compatible with existing SIRST detection networks and further enhances their detection performance. The FEST framework improves the model's robustness from two aspects: feature enhancement and target confidence regulation. For feature enhancement, on the one hand, we adopt a multi-scale fusion strategy, which can effectively improve the model's perception and adaptability to multi-scale features of multi-size targets. On the other hand, we construct an edge enhancement difficulty mining (EEDM) loss based on the analysis of the task characteristics, which helps guide the network to continuously focus on challenging target regions and edge features during training. For target confidence regulation, we design an adjustable sensitivity (AS) strategy for network post-processing. This strategy not only enhances the adaptability of the network in complex scenarios, but also significantly improves the detection rate of infrared small targets while maintaining segmentation accuracy. Extensive experimental results show that our FEST framework can significantly enhance the performance of existing SIRST detection networks. Notably, the multi-scale direction-aware network (MSDA-Net) equipped with the FEST framework won the first prize in the PRCV 2024 wide-area infrared small target detection competition.
Infrared small target detection poses unique challenges due to the scarcity of intrinsic target features and the abundance of similar background distractors. We argue that background semantics play a pivotal role in distinguishing visually similar objects for this task. To address this, we introduce a new task--clustered infrared small target detection, and present DenseSIRST, a novel benchmark dataset that provides per-pixel semantic annotations for background regions, enabling the transition from sparse to dense target detection. Leveraging this dataset, we propose the Background-Aware Feature Exchange Network (BAFE-Net), which transforms the detection paradigm from a single task focused on the foreground to a multi-task architecture that jointly performs target detection and background semantic segmentation. BAFE-Net introduces a dynamic cross-task feature hard-exchange mechanism to embed target and background semantics between the two tasks. Furthermore, we propose the Background-Aware Gaussian Copy-Paste (BAG-CP) method, which selectively pastes small targets into sky regions during training, avoiding the creation of false alarm targets in complex non-sky backgrounds. Extensive experiments validate the effectiveness of BAG-CP and BAFE-Net in improving target detection accuracy while reducing false alarms. The DenseSIRST dataset, code, and trained models are available at https://github.com/GrokCV/BAFE-Net.
Infrared small target detection is crucial for the efficacy of infrared search and tracking systems. Current tensor decomposition methods emphasize representing small targets with sparsity but struggle to separate targets from complex backgrounds due to insufficient use of intrinsic directional information and reduced target visibility during decomposition. To address these challenges, this study introduces a Sparse Differential Directionality prior (SDD) framework. SDD leverages the distinct directional characteristics of targets to differentiate them from the background, applying mixed sparse constraints on the differential directional images and continuity difference matrix of the temporal component, both derived from Tucker decomposition. We further enhance target detectability with a saliency coherence strategy that intensifies target contrast against the background during hierarchical decomposition. A Proximal Alternating Minimization-based (PAM) algorithm efficiently solves our proposed model. Experimental results on several real-world datasets validate our method's effectiveness, outperforming ten state-of-the-art methods in target detection and clutter suppression. Our code is available at https://github.com/GrokCV/SDD.
The recent Segment Anything Model (SAM) is a significant advancement in natural image segmentation, exhibiting potent zero-shot performance suitable for various downstream image segmentation tasks. However, directly utilizing the pretrained SAM for Infrared Small Target Detection (IRSTD) task falls short in achieving satisfying performance due to a notable domain gap between natural and infrared images. Unlike a visible light camera, a thermal imager reveals an object's temperature distribution by capturing infrared radiation. Small targets often show a subtle temperature transition at the object's boundaries. To address this issue, we propose the IRSAM model for IRSTD, which improves SAM's encoder-decoder architecture to learn better feature representation of infrared small objects. Specifically, we design a Perona-Malik diffusion (PMD)-based block and incorporate it into multiple levels of SAM's encoder to help it capture essential structural features while suppressing noise. Additionally, we devise a Granularity-Aware Decoder (GAD) to fuse the multi-granularity feature from the encoder to capture structural information that may be lost in long-distance modeling. Extensive experiments on the public datasets, including NUAA-SIRST, NUDT-SIRST, and IRSTD-1K, validate the design choice of IRSAM and its significant superiority over representative state-of-the-art methods. The source code are available at: github.com/IPIC-Lab/IRSAM.
Lost in UNet: Improving Infrared Small Target Detection by Underappreciated Local Features
2024-06-19
Wuzhou Quan, Wei Zhao, Weiming Wang, Haoran Xie, Fu Lee Wang, Mingqiang Wei
Many targets are often very small in infrared images due to the long-distance imaging meachnism. UNet and its variants, as popular detection backbone networks, downsample the local features early and cause the irreversible loss of these local features, leading to both the missed and false detection of small targets in infrared images. We propose HintU, a novel network to recover the local features lost by various UNet-based methods for effective infrared small target detection. HintU has two key contributions. First, it introduces the "Hint" mechanism for the first time, i.e., leveraging the prior knowledge of target locations to highlight critical local features. Second, it improves the mainstream UNet-based architecture to preserve target pixels even after downsampling. HintU can shift the focus of various networks (e.g., vanilla UNet, UNet++, UIUNet, MiM+, and HCFNet) from the irrelevant background pixels to a more restricted area from the beginning. Experimental results on three datasets NUDT-SIRST, SIRSTv2 and IRSTD1K demonstrate that HintU enhances the performance of existing methods with only an additional 1.88 ms cost (on RTX Titan). Additionally, the explicit constraints of HintU enhance the generalization ability of UNet-based methods. Code is available at https://github.com/Wuzhou-Quan/HintU.
Triple-domain Feature Learning with Frequency-aware Memory Enhancement for Moving Infrared Small Target Detection
2024-06-11
Weiwei Duan, Luping Ji, Shengjia Chen, Sicheng Zhu, Mao Ye
As a sub-field of object detection, moving infrared small target detection presents significant challenges due to tiny target sizes and low contrast against backgrounds. Currently-existing methods primarily rely on the features extracted only from spatio-temporal domain. Frequency domain has hardly been concerned yet, although it has been widely applied in image processing. To extend feature source domains and enhance feature representation, we propose a new Triple-domain Strategy (Tridos) with the frequency-aware memory enhancement on spatio-temporal domain for infrared small target detection. In this scheme, it effectively detaches and enhances frequency features by a local-global frequency-aware module with Fourier transform. Inspired by human visual system, our memory enhancement is designed to capture the spatial relations of infrared targets among video frames. Furthermore, it encodes temporal dynamics motion features via differential learning and residual enhancing. Additionally, we further design a residual compensation to reconcile possible cross-domain feature mismatches. To our best knowledge, proposed Tridos is the first work to explore infrared target feature learning comprehensively in spatio-temporal-frequency domains. The extensive experiments on three datasets (i.e., DAUB, ITSDT-15K and IRDST) validate that our triple-domain infrared feature learning scheme could often be obviously superior to state-of-the-art ones. Source codes are available at https://github.com/UESTC-nnLab/Tridos.
Multi-Scale Direction-Aware Network for Infrared Small Target Detection
2024-06-04
Jinmiao Zhao, Zelin Shi, Chuang Yu, Yunpeng Liu
Infrared small target detection faces the problem that it is difficult to effectively separate the background and the target. Existing deep learning-based methods focus on appearance features and ignore high-frequency directional features. Therefore, we propose a multi-scale direction-aware network (MSDA-Net), which is the first attempt to integrate the high-frequency directional features of infrared small targets as domain prior knowledge into neural networks. Specifically, an innovative multi-directional feature awareness (MDFA) module is constructed, which fully utilizes the prior knowledge of targets and emphasizes the focus on high-frequency directional features. On this basis, combined with the multi-scale local relation learning (MLRL) module, a multi-scale direction-aware (MSDA) module is further constructed. The MSDA module promotes the full extraction of local relations at different scales and the full perception of key features in different directions. Meanwhile, a high-frequency direction injection (HFDI) module without training parameters is constructed to inject the high-frequency directional information of the original image into the network. This helps guide the network to pay attention to detailed information such as target edges and shapes. In addition, we propose a feature aggregation (FA) structure that aggregates multi-level features to solve the problem of small targets disappearing in deep feature maps. Furthermore, a lightweight feature alignment fusion (FAF) module is constructed, which can effectively alleviate the pixel offset existing in multi-level feature map fusion. Extensive experimental results show that our MSDA-Net achieves state-of-the-art (SOTA) results on the public NUDT-SIRST, SIRST and IRSTD-1k datasets.
Recently, researchers have proposed various deep learning methods to accurately detect infrared targets with the characteristics of indistinct shape and texture. Due to the limited variety of infrared datasets, training deep learning models with good generalization poses a challenge. To augment the infrared dataset, researchers employ data augmentation techniques, which often involve generating new images by combining images from different datasets. However, these methods are lacking in two respects. In terms of realism, the images generated by mixup-based methods lack realism and are difficult to effectively simulate complex real-world scenarios. In terms of diversity, compared with real-world scenes, borrowing knowledge from another dataset inherently has a limited diversity. Currently, the diffusion model stands out as an innovative generative approach. Large-scale trained diffusion models have a strong generative prior that enables real-world modeling of images to generate diverse and realistic images. In this paper, we propose Diff-Mosaic, a data augmentation method based on the diffusion model. This model effectively alleviates the challenge of diversity and realism of data augmentation methods via diffusion prior. Specifically, our method consists of two stages. Firstly, we introduce an enhancement network called Pixel-Prior, which generates highly coordinated and realistic Mosaic images by harmonizing pixels. In the second stage, we propose an image enhancement strategy named Diff-Prior. This strategy utilizes diffusion priors to model images in the real-world scene, further enhancing the diversity and realism of the images. Extensive experiments have demonstrated that our approach significantly improves the performance of the detection network. The code is available at https://github.com/YupeiLin2388/Diff-Mosaic
Recently, infrared small target detection (IRSTD) has been dominated by deep-learning-based methods. However, these methods mainly focus on the design of complex model structures to extract discriminative features, leaving the loss functions for IRSTD under-explored. For example, the widely used Intersection over Union (IoU) and Dice losses lack sensitivity to the scales and locations of targets, limiting the detection performance of detectors. In this paper, we focus on boosting detection performance with a more effective loss but a simpler model structure. Specifically, we first propose a novel Scale and Location Sensitive (SLS) loss to handle the limitations of existing losses: 1) for scale sensitivity, we compute a weight for the IoU loss based on target scales to help the detector distinguish targets with different scales: 2) for location sensitivity, we introduce a penalty term based on the center points of targets to help the detector localize targets more precisely. Then, we design a simple Multi-Scale Head to the plain U-Net (MSHNet). By applying SLS loss to each scale of the predictions, our MSHNet outperforms existing state-of-the-art methods by a large margin. In addition, the detection performance of existing detectors can be further improved when trained with our SLS loss, demonstrating the effectiveness and generalization of our SLS loss. The code is available at https://github.com/ying-fu/MSHNet.
Mitigate Target-level Insensitivity of Infrared Small Target Detection via Posterior Distribution Modeling
2024-03-13
Haoqing Li, Jinfu Yang, Yifei Xu, Runshi Wang
Infrared Small Target Detection (IRSTD) aims to segment small targets from infrared clutter background. Existing methods mainly focus on discriminative approaches, i.e., a pixel-level front-background binary segmentation. Since infrared small targets are small and low signal-to-clutter ratio, empirical risk has few disturbances when a certain false alarm and missed detection exist, which seriously affect the further improvement of such methods. Motivated by the dense prediction generative methods, in this paper, we propose a diffusion model framework for Infrared Small Target Detection which compensates pixel-level discriminant with mask posterior distribution modeling. Furthermore, we design a Low-frequency Isolation in the wavelet domain to suppress the interference of intrinsic infrared noise on the diffusion noise estimation. This transition from the discriminative paradigm to generative one enables us to bypass the target-level insensitivity. Experiments show that the proposed method achieves competitive performance gains over state-of-the-art methods on NUAA-SIRST, IRSTD-1k, and NUDT-SIRST datasets. Code are available at https://github.com/Li-Haoqing/IRSTD-Diff.
SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection
2024-03-08
Yahao Lu, Yupei Lin, Han Wu, Xiaoyu Xian, Yukai Shi, Liang Lin
Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. Recently, convolutional neural networks have achieved significant advantages in general object detection. With the development of Transformer, the scale of SIRST models is constantly increasing. Due to the limited training samples, performance has not been improved accordingly. The quality, quantity, and diversity of the infrared dataset are critical to the detection of small targets. To highlight this issue, we propose a negative sample augmentation method in this paper. Specifically, a negative augmentation approach is proposed to generate massive negatives for self-supervised learning. Firstly, we perform a sequential noise modeling technology to generate realistic infrared data. Secondly, we fuse the extracted noise with the original data to facilitate diversity and fidelity in the generated data. Lastly, we proposed a negative augmentation strategy to enrich diversity as well as maintain semantic invariance. The proposed algorithm produces a synthetic SIRST-5K dataset, which contains massive pseudo-data and corresponding labels. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed. Compared with other state-of-the-art (SOTA) methods, our method achieves outstanding performance in terms of probability of detection (Pd), false-alarm rate (Fa), and intersection over union (IoU).
MiM-ISTD: Mamba-in-Mamba for Efficient Infrared Small Target Detection
2024-03-04
Tianxiang Chen, Zi Ye, Zhentao Tan, Tao Gong, Yue Wu, Qi Chu, Bin Liu, Nenghai Yu, Jieping Ye
Recently, infrared small target detection (ISTD) has made significant progress, thanks to the development of basic models. Specifically, the models combining CNNs with transformers can successfully extract both local and global features. However, the disadvantage of the transformer is also inherited, i.e., the quadratic computational complexity to sequence length. Inspired by the recent basic model with linear complexity for long-distance modeling, Mamba, we explore the potential of this state space model for ISTD task in terms of effectiveness and efficiency in the paper. However, directly applying Mamba achieves suboptimal performances due to the insufficient harnessing of local features, which are imperative for detecting small targets. Instead, we tailor a nested structure, Mamba-in-Mamba (MiM-ISTD), for efficient ISTD. It consists of Outer and Inner Mamba blocks to adeptly capture both global and local features. Specifically, we treat the local patches as "visual sentences" and use the Outer Mamba to explore the global information. We then decompose each visual sentence into sub-patches as "visual words" and use the Inner Mamba to further explore the local information among words in the visual sentence with negligible computational costs. By aggregating the visual word and visual sentence features, our MiM-ISTD can effectively explore both global and local information. Experiments on NUAA-SIRST and IRSTD-1k show the superior accuracy and efficiency of our method. Specifically, MiM-ISTD is $8 \times$ faster than the SOTA method and reduces GPU memory usage by 62.2$\%$ when testing on $2048 \times 2048$ images, overcoming the computation and memory constraints on high-resolution infrared images.
Infrared Small Target Detection via tensor $L_{2,1}$ norm minimization and ASSTV regularization: A Novel Tensor Recovery Approach
2024-02-28
Jiqian Zhao, An-Bao Xu
In recent years, there has been a noteworthy focus on infrared small target detection, given its vital importance in processing signals from infrared remote sensing. The considerable computational cost incurred by prior methods, relying excessively on nuclear norm for noise separation, necessitates the exploration of efficient alternatives. The aim of this research is to identify a swift and resilient tensor recovery method for the efficient extraction of infrared small targets from image sequences. Theoretical validation indicates that smaller singular values predominantly contribute to constructing noise information. In the exclusion process, tensor QR decomposition is employed to reasonably reduce the size of the target tensor. Subsequently, we address a tensor $L_{2,1}$ Norm Minimization via T-QR (TLNMTQR) based method to effectively isolate the noise, markedly improving computational speed without compromising accuracy. Concurrently, by integrating the asymmetric spatial-temporal total variation regularization method (ASSTV), our objective is to augment the flexibility and efficacy of our algorithm in handling time series data. Ultimately, our method underwent rigorous testing with real-world data, affirmatively showcasing the superiority of our algorithm in terms of speed, precision, and robustness.
SpirDet: Towards Efficient, Accurate and Lightweight Infrared Small Target Detector
2024-02-08
Qianchen Mao, Qiang Li, Bingshu Wang, Yongjun Zhang, Tao Dai, C. L. Philip Chen
In recent years, the detection of infrared small targets using deep learning methods has garnered substantial attention due to notable advancements. To improve the detection capability of small targets, these methods commonly maintain a pathway that preserves high-resolution features of sparse and tiny targets. However, it can result in redundant and expensive computations. To tackle this challenge, we propose SpirDet, a novel approach for efficient detection of infrared small targets. Specifically, to cope with the computational redundancy issue, we employ a new dual-branch sparse decoder to restore the feature map. Firstly, the fast branch directly predicts a sparse map indicating potential small target locations (occupying only 0.5\% area of the map). Secondly, the slow branch conducts fine-grained adjustments at the positions indicated by the sparse map. Additionally, we design an lightweight DO-RepEncoder based on reparameterization with the Downsampling Orthogonality, which can effectively reduce memory consumption and inference latency. Extensive experiments show that the proposed SpirDet significantly outperforms state-of-the-art models while achieving faster inference speed and fewer parameters. For example, on the IRSTD-1K dataset, SpirDet improves $MIoU$ by 4.7 and has a $7\times$ $FPS$ acceleration compared to the previous state-of-the-art model. The code will be open to the public.
$\textit{A Contrario}$ Paradigm for YOLO-based Infrared Small Target Detection
2024-02-03
Alina Ciocarlan, Sylvie Le Hégarat-Mascle, Sidonie Lefebvre, Arnaud Woiselle, Clara Barbanson
Detecting small to tiny targets in infrared images is a challenging task in computer vision, especially when it comes to differentiating these targets from noisy or textured backgrounds. Traditional object detection methods such as YOLO struggle to detect tiny objects compared to segmentation neural networks, resulting in weaker performance when detecting small targets. To reduce the number of false alarms while maintaining a high detection rate, we introduce an $\textit{a contrario}$ decision criterion into the training of a YOLO detector. The latter takes advantage of the $\textit{unexpectedness}$ of small targets to discriminate them from complex backgrounds. Adding this statistical criterion to a YOLOv7-tiny bridges the performance gap between state-of-the-art segmentation methods for infrared small target detection and object detection networks. It also significantly increases the robustness of YOLO towards few-shot settings.
Infrared small target detection (ISTD) is critical to national security and has been extensively applied in military areas. ISTD aims to segment small target pixels from background. Most ISTD networks focus on designing feature extraction blocks or feature fusion modules, but rarely describe the ISTD process from the feature map evolution perspective. In the ISTD process, the network attention gradually shifts towards target areas. We abstract this process as the directional movement of feature map pixels to target areas through convolution, pooling and interactions with surrounding pixels, which can be analogous to the movement of thermal particles constrained by surrounding variables and particles. In light of this analogy, we propose Thermal Conduction-Inspired Transformer (TCI-Former) based on the theoretical principles of thermal conduction. According to thermal conduction differential equation in heat dynamics, we derive the pixel movement differential equation (PMDE) in the image domain and further develop two modules: Thermal Conduction-Inspired Attention (TCIA) and Thermal Conduction Boundary Module (TCBM). TCIA incorporates finite difference method with PMDE to reach a numerical approximation so that target body features can be extracted. To further remove errors in boundary areas, TCBM is designed and supervised by boundary masks to refine target body features with fine boundary details. Experiments on IRSTD-1k and NUAA-SIRST demonstrate the superiority of our method.
Infrared small target detection (IRSTD) has recently benefitted greatly from U-shaped neural models. However, largely overlooking effective global information modeling, existing techniques struggle when the target has high similarities with the background. We present a Spatial-channel Cross Transformer Network (SCTransNet) that leverages spatial-channel cross transformer blocks (SCTBs) on top of long-range skip connections to address the aforementioned challenge. In the proposed SCTBs, the outputs of all encoders are interacted with cross transformer to generate mixed features, which are redistributed to all decoders to effectively reinforce semantic differences between the target and clutter at full scales. Specifically, SCTB contains the following two key elements: (a) spatial-embedded single-head channel-cross attention (SSCA) for exchanging local spatial features and full-level global channel information to eliminate ambiguity among the encoders and facilitate high-level semantic associations of the images, and (b) a complementary feed-forward network (CFN) for enhancing the feature discriminability via a multi-scale strategy and cross-spatial-channel information interaction to promote beneficial information transfer. Our SCTransNet effectively encodes the semantic differences between targets and backgrounds to boost its internal representation for detecting small infrared targets accurately. Extensive experiments on three public datasets, NUDT-SIRST, NUAA-SIRST, and IRSTD-1k, demonstrate that the proposed SCTransNet outperforms existing IRSTD methods. Our code will be made public at https://github.com/xdFai.
In a real-world infrared imaging system, effectively learning a consistent stripe noise removal model is essential. Most existing destriping methods cannot precisely reconstruct images due to cross-level semantic gaps and insufficient characterization of the global column features. To tackle this problem, we propose a novel infrared image destriping method, called Asymmetric Sampling Correction Network (ASCNet), that can effectively capture global column relationships and embed them into a U-shaped framework, providing comprehensive discriminative representation and seamless semantic connectivity. Our ASCNet consists of three core elements: Residual Haar Discrete Wavelet Transform (RHDWT), Pixel Shuffle (PS), and Column Non-uniformity Correction Module (CNCM). Specifically, RHDWT is a novel downsampler that employs double-branch modeling to effectively integrate stripe-directional prior knowledge and data-driven semantic interaction to enrich the feature representation. Observing the semantic patterns crosstalk of stripe noise, PS is introduced as an upsampler to prevent excessive apriori decoding and performing semantic-bias-free image reconstruction. After each sampling, CNCM captures the column relationships in long-range dependencies. By incorporating column, spatial, and self-dependence information, CNCM well establishes a global context to distinguish stripes from the scene's vertical structures. Extensive experiments on synthetic data, real data, and infrared small target detection tasks demonstrate that the proposed method outperforms state-of-the-art single-image destriping methods both visually and quantitatively. Our code will be made publicly available at https://github.com/xdFai/ASCNet.
Improved Dense Nested Attention Network Based on Transformer for Infrared Small Target Detection
2023-11-15
Chun Bao, Jie Cao, Yaqian Ning, Tianhua Zhao, Zhijun Li, Zechen Wang, Li Zhang, Qun Hao
Infrared small target detection based on deep learning offers unique advantages in separating small targets from complex and dynamic backgrounds. However, the features of infrared small targets gradually weaken as the depth of convolutional neural network (CNN) increases. To address this issue, we propose a novel method for detecting infrared small targets called improved dense nested attention network (IDNANet), which is based on the transformer architecture. We preserve the dense nested structure of dense nested attention network (DNANet) and introduce the Swin-transformer during feature extraction stage to enhance the continuity of features. Furthermore, we integrate the ACmix attention structure into the dense nested structure to enhance the features of intermediate layers. Additionally, we design a weighted dice binary cross-entropy (WD-BCE) loss function to mitigate the negative impact of foreground-background imbalance in the samples. Moreover, we develop a dataset specifically for infrared small targets, called BIT-SIRST. The dataset comprises a significant amount of real-world targets and manually annotated labels, as well as synthetic data and corresponding labels. We have evaluated the effectiveness of our method through experiments conducted on public datasets. In comparison to other state-of-the-art methods, our approach outperforms in terms of probability of detection ($P_d$), false-alarm rate ($F_a$), and mean intersection of union ($mIoU$). The $mIoU$ reaches 90.89\% on the NUDT-SIRST dataset and 79.72\% on the SIRST dataset. The BIT-SIRST dataset and codes are available openly at \href{https://github.com/EdwardBao1006/bit\_sirst}{\color[HTML]{B22222}{https://github.com/EdwardBao1006/bit\_sirst}}.
Deep learning (DL) networks have achieved remarkable performance in infrared small target detection (ISTD). However, these structures exhibit a deficiency in interpretability and are widely regarded as black boxes, as they disregard domain knowledge in ISTD. To alleviate this issue, this work proposes an interpretable deep network for detecting infrared dim targets, dubbed RPCANet. Specifically, our approach formulates the ISTD task as sparse target extraction, low-rank background estimation, and image reconstruction in a relaxed Robust Principle Component Analysis (RPCA) model. By unfolding the iterative optimization updating steps into a deep-learning framework, time-consuming and complex matrix calculations are replaced by theory-guided neural networks. RPCANet detects targets with clear interpretability and preserves the intrinsic image feature, instead of directly transforming the detection task into a matrix decomposition problem. Extensive experiments substantiate the effectiveness of our deep unfolding framework and demonstrate its trustworthy results, surpassing baseline methods in both qualitative and quantitative evaluations.
Click on Mask: A Labor-efficient Annotation Framework with Level Set for Infrared Small Target Detection
2023-10-19
Haoqing Li, Jinfu Yang, Yifei Xu, Runshi Wang
Infrared Small Target Detection is a challenging task to separate small targets from infrared clutter background. Recently, deep learning paradigms have achieved promising results. However, these data-driven methods need plenty of manual annotation. Due to the small size of infrared targets, manual annotation consumes more resources and restricts the development of this field. This letter proposed a labor-efficient and cursory annotation framework with level set, which obtains a high-quality pseudo mask with only one cursory click. A variational level set formulation with an expectation difference energy functional is designed, in which the zero level contour is intrinsically maintained during the level set evolution. It solves the issue that zero level contour disappearing due to small target size and excessive regularization. Experiments on the NUAA-SIRST and IRSTD-1k datasets reveal that our approach achieves superior performance. Code is available at https://github.com/Li-Haoqing/COM.
Infrared small target detection plays an important role in the remote sensing fields. Therefore, many detection algorithms have been proposed, in which the infrared patch-tensor (IPT) model has become a mainstream tool due to its excellent performance. However, most IPT-based methods face great challenges, such as inaccurate measure of the tensor low-rankness and poor robustness to complex scenes, which will leadto poor detection performance. In order to solve these problems, this paper proposes a novel double-weighted multi-granularity infrared patch tensor (DWMGIPT) model. First, to capture different granularity information of tensor from multiple modes, a multi-granularity infrared patch tensor (MGIPT) model is constructed by collecting nonoverlapping patches and tensor augmentation based on the tensor train (TT) decomposition. Second, to explore the latent structure of tensor more efficiently, we utilize the auto-weighted mechanism to balance the importance of information at different granularity. Then, the steering kernel (SK) is employed to extract local structure prior, which suppresses background interference such as strong edges and noise. Finally, an efficient optimization algorithm based on the alternating direction method of multipliers (ADMM) is presented to solve the model. Extensive experiments in various challenging scenes show that the proposed algorithm is robust to noise and different scenes. Compared with the other eight state-of-the-art methods, different evaluation metrics demonstrate that our method achieves better detection performance in various complex scenes.
Infrared small target detection is a technique for finding small targets from infrared clutter background. Due to the dearth of high-level semantic information, small infrared target features are weakened in the deep layers of the CNN, which underachieves the CNN's representation ability. To address the above problem, in this paper, we propose an infrared low-level network (ILNet) that considers infrared small targets as salient areas with little semantic information. Unlike other SOTA methods, ILNet pays greater attention to low-level information instead of treating them equally. A new lightweight feature fusion module, named Interactive Polarized Orthogonal Fusion module (IPOF), is proposed, which integrates more important low-level features from the shallow layers into the deep layers. A Dynamic One-Dimensional Aggregation layers (DODA) are inserted into the IPOF, to dynamically adjust the aggregation of low dimensional information according to the number of input channels. In addition, the idea of ensemble learning is used to design a Representative Block (RB) to dynamically allocate weights for shallow and deep layers. Experimental results on the challenging NUAA-SIRST (78.22% nIoU and 1.33e-6 Fa) and IRSTD-1K (68.91% nIoU and 3.23e-6 Fa) dataset demonstrate that the proposed ILNet can get better performances than other SOTA methods. Moreover, ILNet can obtain a greater improvement with the increasement of data volume. Training code are available at https://github.com/Li-Haoqing/ILNet.
Enhancing Infrared Small Target Detection Robustness with Bi-Level Adversarial Framework
2023-09-03
Zhu Liu, Zihang Chen, Jinyuan Liu, Long Ma, Xin Fan, Risheng Liu
The detection of small infrared targets against blurred and cluttered backgrounds has remained an enduring challenge. In recent years, learning-based schemes have become the mainstream methodology to establish the mapping directly. However, these methods are susceptible to the inherent complexities of changing backgrounds and real-world disturbances, leading to unreliable and compromised target estimations. In this work, we propose a bi-level adversarial framework to promote the robustness of detection in the presence of distinct corruptions. We first propose a bi-level optimization formulation to introduce dynamic adversarial learning. Specifically, it is composited by the learnable generation of corruptions to maximize the losses as the lower-level objective and the robustness promotion of detectors as the upper-level one. We also provide a hierarchical reinforced learning strategy to discover the most detrimental corruptions and balance the performance between robustness and accuracy. To better disentangle the corruptions from salient features, we also propose a spatial-frequency interaction network for target detection. Extensive experiments demonstrate our scheme remarkably improves 21.96% IOU across a wide array of corruptions and notably promotes 4.97% IOU on the general benchmark. The source codes are available at https://github.com/LiuZhu-CV/BALISTD.
EFLNet: Enhancing Feature Learning for Infrared Small Target Detection
2023-07-27
Bo Yang, Xinyu Zhang, Jian Zhang, Jun Luo, Mingliang Zhou, Yangjun Pi
Single-frame infrared small target detection is considered to be a challenging task, due to the extreme imbalance between target and background, bounding box regression is extremely sensitive to infrared small target, and target information is easy to lose in the high-level semantic layer. In this article, we propose an enhancing feature learning network (EFLNet) to address these problems. First, we notice that there is an extremely imbalance between the target and the background in the infrared image, which makes the model pay more attention to the background features rather than target features. To address this problem, we propose a new adaptive threshold focal loss (ATFL) function that decouples the target and the background, and utilizes the adaptive mechanism to adjust the loss weight to force the model to allocate more attention to target features. Second, we introduce the normalized Gaussian Wasserstein distance (NWD) to alleviate the difficulty of convergence caused by the extreme sensitivity of the bounding box regression to infrared small target. Finally, we incorporate a dynamic head mechanism into the network to enable adaptive learning of the relative importance of each semantic layer. Experimental results demonstrate our method can achieve better performance in the detection performance of infrared small target compared to the state-of-the-art (SOTA) deep-learning-based methods. The source codes and bounding box annotated datasets are available at https://github.com/YangBo0411/infrared-small-target.
Single-frame infrared small target (SIRST) detection aims at separating small targets from clutter backgrounds on infrared images. Recently, deep learning based methods have achieved promising performance on SIRST detection, but at the cost of a large amount of training data with expensive pixel-level annotations. To reduce the annotation burden, we propose the first method to achieve SIRST detection with single-point supervision. The core idea of this work is to recover the per-pixel mask of each target from the given single point label by using clustering approaches, which looks simple but is indeed challenging since targets are always insalient and accompanied with background clutters. To handle this issue, we introduce randomness to the clustering process by adding noise to the input images, and then obtain much more reliable pseudo masks by averaging the clustered results. Thanks to this "Monte Carlo" clustering approach, our method can accurately recover pseudo masks and thus turn arbitrary fully supervised SIRST detection networks into weakly supervised ones with only single point annotation. Experiments on four datasets demonstrate that our method can be applied to existing SIRST detection networks to achieve comparable performance with their fully supervised counterparts, which reveals that single-point supervision is strong enough for SIRST detection. Our code will be available at: https://github.com/YeRen123455/SIRST-Single-Point-Supervision.
Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision
2023-04-04
Xinyi Ying, Li Liu, Yingqian Wang, Ruojing Li, Nuo Chen, Zaiping Lin, Weidong Sheng, Shilin Zhou
Training a convolutional neural network (CNN) to detect infrared small targets in a fully supervised manner has gained remarkable research interests in recent years, but is highly labor expensive since a large number of per-pixel annotations are required. To handle this problem, in this paper, we make the first attempt to achieve infrared small target detection with point-level supervision. Interestingly, during the training phase supervised by point labels, we discover that CNNs first learn to segment a cluster of pixels near the targets, and then gradually converge to predict groundtruth point labels. Motivated by this "mapping degeneration" phenomenon, we propose a label evolution framework named label evolution with single point supervision (LESPS) to progressively expand the point label by leveraging the intermediate predictions of CNNs. In this way, the network predictions can finally approximate the updated pseudo labels, and a pixel-level target mask can be obtained to train CNNs in an end-to-end manner. We conduct extensive experiments with insightful visualizations to validate the effectiveness of our method. Experimental results show that CNNs equipped with LESPS can well recover the target masks from corresponding point labels, {and can achieve over 70% and 95% of their fully supervised performance in terms of pixel-level intersection over union (IoU) and object-level probability of detection (Pd), respectively. Code is available at https://github.com/XinyiYing/LESPS.
ABC: Attention with Bilinear Correlation for Infrared Small Target Detection
2023-03-18
Peiwen Pan, Huan Wang, Chenyi Wang, Chang Nie
Infrared small target detection (ISTD) has a wide range of applications in early warning, rescue, and guidance. However, CNN based deep learning methods are not effective at segmenting infrared small target (IRST) that it lack of clear contour and texture features, and transformer based methods also struggle to achieve significant results due to the absence of convolution induction bias. To address these issues, we propose a new model called attention with bilinear correlation (ABC), which is based on the transformer architecture and includes a convolution linear fusion transformer (CLFT) module with a novel attention mechanism for feature extraction and fusion, which effectively enhances target features and suppresses noise. Additionally, our model includes a u-shaped convolution-dilated convolution (UCDC) module located deeper layers of the network, which takes advantage of the smaller resolution of deeper features to obtain finer semantic information. Experimental results on public datasets demonstrate that our approach achieves state-of-the-art performance. Code is available at https://github.com/PANPEIWEN/ABC
Infrared small target detection (ISTD) under complex backgrounds is a difficult problem, for the differences between targets and backgrounds are not easy to distinguish. Background reconstruction is one of the methods to deal with this problem. This paper proposes an ISTD method based on background reconstruction called Dynamic Background Reconstruction (DBR). DBR consists of three modules: a dynamic shift window module (DSW), a background reconstruction module (BR), and a detection head (DH). BR takes advantage of Vision Transformers in reconstructing missing patches and adopts a grid masking strategy with a masking ratio of 50\% to reconstruct clean backgrounds without targets. To avoid dividing one target into two neighboring patches, resulting in reconstructing failure, DSW is performed before input embedding. DSW calculates offsets, according to which infrared images dynamically shift. To reduce False Positive (FP) cases caused by regarding reconstruction errors as targets, DH utilizes a structure of densely connected Transformer to further improve the detection performance. Experimental results show that DBR achieves the best F1-score on the two ISTD datasets, MFIRST (64.10\%) and SIRST (75.01\%).
Infrared small target detection (IRSTD) poses a significant challenge in the field of computer vision. While substantial efforts have been made over the past two decades to improve the detection capabilities of IRSTD algorithms, there has been a lack of extensive investigation into the evaluation metrics used for assessing their performance. In this paper, we employ a systematic approach to address this issue by first evaluating the effectiveness of existing metrics and then proposing new metrics to overcome the limitations of conventional ones. To achieve this, we carefully analyze the necessary conditions for successful detection and identify the shortcomings of current evaluation metrics, including both pre-thresholding and post-thresholding metrics. We then introduce new metrics that are designed to align with the requirements of real-world systems. Furthermore, we utilize these newly proposed metrics to compare and evaluate the performance of five widely recognized small infrared target detection algorithms. The results demonstrate that the new metrics provide consistent and meaningful quantitative assessments, aligning with qualitative observations.
Single-frame InfraRed Small Target (SIRST) detection has been a challenging task due to a lack of inherent characteristics, imprecise bounding box regression, a scarcity of real-world datasets, and sensitive localization evaluation. In this paper, we propose a comprehensive solution to these challenges. First, we find that the existing anchor-free label assignment method is prone to mislabeling small targets as background, leading to their omission by detectors. To overcome this issue, we propose an all-scale pseudo-box-based label assignment scheme that relaxes the constraints on scale and decouples the spatial assignment from the size of the ground-truth target. Second, motivated by the structured prior of feature pyramids, we introduce the one-stage cascade refinement network (OSCAR), which uses the high-level head as soft proposals for the low-level refinement head. This allows OSCAR to process the same target in a cascade coarse-to-fine manner. Finally, we present a new research benchmark for infrared small target detection, consisting of the SIRST-V2 dataset of real-world, high-resolution single-frame targets, the normalized contrast evaluation metric, and the DeepInfrared toolkit for detection. We conduct extensive ablation studies to evaluate the components of OSCAR and compare its performance to state-of-the-art model-driven and data-driven methods on the SIRST-V2 benchmark. Our results demonstrate that a top-down cascade refinement framework can improve the accuracy of infrared small target detection without sacrificing efficiency. The DeepInfrared toolkit, dataset, and trained models are available at https://github.com/YimianDai/open-deepinfrared to advance further research in this field.
Small targets are often submerged in cluttered backgrounds of infrared images. Conventional detectors tend to generate false alarms, while CNN-based detectors lose small targets in deep layers. To this end, we propose iSmallNet, a multi-stream densely nested network with label decoupling for infrared small object detection. On the one hand, to fully exploit the shape information of small targets, we decouple the original labeled ground-truth (GT) map into an interior map and a boundary one. The GT map, in collaboration with the two additional maps, tackles the unbalanced distribution of small object boundaries. On the other hand, two key modules are delicately designed and incorporated into the proposed network to boost the overall performance. First, to maintain small targets in deep layers, we develop a multi-scale nested interaction module to explore a wide range of context information. Second, we develop an interior-boundary fusion module to integrate multi-granularity information. Experiments on NUAA-SIRST and NUDT-SIRST clearly show the superiority of iSmallNet over 11 state-of-the-art detectors.
Infrared small-target detection (ISTD) is an important computer vision task. ISTD aims at separating small targets from complex background clutter. The infrared radiation decays over distances, making the targets highly dim and prone to confusion with the background clutter, which makes the detector challenging to balance the precision and recall rate. To deal with this difficulty, this paper proposes a neural-network-based ISTD method called CourtNet, which has three sub-networks: the prosecution network is designed for improving the recall rate; the defendant network is devoted to increasing the precision rate; the jury network weights their results to adaptively balance the precision and recall rate. Furthermore, the prosecution network utilizes a densely connected transformer structure, which can prevent small targets from disappearing in the network forward propagation. In addition, a fine-grained attention module is adopted to accurately locate the small targets. Experimental results show that CourtNet achieves the best F1-score on the two ISTD datasets, MFIRST (0.62) and SIRST (0.73).
Due to the complicated background and noise of infrared images, infrared small target detection is one of the most difficult problems in the field of computer vision. In most existing studies, semantic segmentation methods are typically used to achieve better results. The centroid of each target is calculated from the segmentation map as the detection result. In contrast, we propose a novel end-to-end framework for infrared small target detection and segmentation in this paper. First, with the use of UNet as the backbone to maintain resolution and semantic information, our model can achieve a higher detection accuracy than other state-of-the-art methods by attaching a simple anchor-free head. Then, a pyramid pool module is used to further extract features and improve the precision of target segmentation. Next, we use semantic segmentation tasks that pay more attention to pixel-level features to assist in the training process of object detection, which increases the average precision and allows the model to detect some targets that were previously not detectable. Furthermore, we develop a multi-task framework for infrared small target detection and segmentation. Our multi-task learning model reduces complexity by nearly half and speeds up inference by nearly twice compared to the composite single-task model, while maintaining accuracy. The code and models are publicly available at https://github.com/Chenastron/MTUNet.
MPANet: Multi-Patch Attention For Infrared Small Target object Detection
2022-06-05
Ao Wang, Wei Li, Xin Wu, Zhanchao Huang, Ran Tao
Infrared small target detection (ISTD) has attracted widespread attention and been applied in various fields. Due to the small size of infrared targets and the noise interference from complex backgrounds, the performance of ISTD using convolutional neural networks (CNNs) is restricted. Moreover, the constriant that long-distance dependent features can not be encoded by the vanilla CNNs also impairs the robustness of capturing targets' shapes and locations in complex scenarios. To this end, a multi-patch attention network (MPANet) based on the axial-attention encoder and the multi-scale patch branch (MSPB) structure is proposed. Specially, an axial-attention-improved encoder architecture is designed to highlight the effective features of small targets and suppress background noises. Furthermore, the developed MSPB structure fuses the coarse-grained and fine-grained features from different semantic scales. Extensive experiments on the SIRST dataset show the superiority performance and effectiveness of the proposed MPANet compared to the state-of-the-art methods.
Infrared small target super-resolution (SR) aims to recover reliable and detailed high-resolution image with high-contrast targets from its low-resolution counterparts. Since the infrared small target lacks color and fine structure information, it is significant to exploit the supplementary information among sequence images to enhance the target. In this paper, we propose the first infrared small target SR method named local motion and contrast prior driven deep network (MoCoPnet) to integrate the domain knowledge of infrared small target into deep network, which can mitigate the intrinsic feature scarcity of infrared small targets. Specifically, motivated by the local motion prior in the spatio-temporal dimension, we propose a local spatio-temporal attention module to perform implicit frame alignment and incorporate the local spatio-temporal information to enhance the local features (especially for small targets). Motivated by the local contrast prior in the spatial dimension, we propose a central difference residual group to incorporate the central difference convolution into the feature extraction backbone, which can achieve center-oriented gradient-aware feature extraction to further improve the target contrast. Extensive experiments have demonstrated that our method can recover accurate spatial dependency and improve the target contrast. Comparative results show that MoCoPnet can outperform the state-of-the-art video SR and single image SR methods in terms of both SR performance and target enhancement. Based on the SR results, we further investigate the influence of SR on infrared small target detection and the experimental results demonstrate that MoCoPnet promotes the detection performance. The code is available at https://github.com/XinyiYing/MoCoPnet.
Infrared small target detection is an important problem in many fields such as earth observation, military reconnaissance, disaster relief, and has received widespread attention recently. This paper presents the Attention-Guided Pyramid Context Network (AGPCNet) algorithm. Its main components are an Attention-Guided Context Block (AGCB), a Context Pyramid Module (CPM), and an Asymmetric Fusion Module (AFM). AGCB divides the feature map into patches to compute local associations and uses Global Context Attention (GCA) to compute global associations between semantics, CPM integrates features from multi-scale AGCBs, and AFM integrates low-level and deep-level semantics from a feature-fusion perspective to enhance the utilization of features. The experimental results illustrate that AGPCNet has achieved new state-of-the-art performance on two available infrared small target datasets. The source codes are available at https://github.com/Tianfang-Zhang/AGPCNet.
Infrared small target detection plays an important role in the infrared search and tracking applications. In recent years, deep learning techniques were introduced to this task and achieved noteworthy effects. Following general object segmentation methods, existing deep learning methods usually processed the image from the global view. However, the imaging locality of small targets and extreme class-imbalance between the target and background pixels were not well-considered by these deep learning methods, which causes the low-efficiency on training and high-dependence on numerous data. A local patch network (LPNet) with global attention is proposed in this paper to detect small targets by jointly considering the global and local properties of infrared small target images. From the global view, a supervised attention module trained by the small target spread map is proposed to suppress most background pixels irrelevant with small target features. From the local view, local patches are split from global features and share the same convolution weights with each other in a patch net. By leveraging both the global and local properties, the data-driven framework proposed in this paper has fused multi-scale features for small target detection. Extensive synthetic and real data experiments show that the proposed method achieves the state-of-the-art performance compared with existing both conventional and deep learning methods.
Single-frame infrared small target (SIRST) detection aims at separating small targets from clutter backgrounds. With the advances of deep learning, CNN-based methods have yielded promising results in generic object detection due to their powerful modeling capability. However, existing CNN-based methods cannot be directly applied for infrared small targets since pooling layers in their networks could lead to the loss of targets in deep layers. To handle this problem, we propose a dense nested attention network (DNANet) in this paper. Specifically, we design a dense nested interactive module (DNIM) to achieve progressive interaction among high-level and low-level features. With the repeated interaction in DNIM, infrared small targets in deep layers can be maintained. Based on DNIM, we further propose a cascaded channel and spatial attention module (CSAM) to adaptively enhance multi-level features. With our DNANet, contextual information of small targets can be well incorporated and fully exploited by repeated fusion and enhancement. Moreover, we develop an infrared small target dataset (namely, NUDT-SIRST) and propose a set of evaluation metrics to conduct comprehensive performance evaluation. Experiments on both public and our self-developed datasets demonstrate the effectiveness of our method. Compared to other state-of-the-art methods, our method achieves better performance in terms of probability of detection (Pd), false-alarm rate (Fa), and intersection of union (IoU).
Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection
2021-05-31
Ting Liu, Jungang Yang, Boyang Li, Chao Xiao, Yang Sun, Yingqian Wang, Wei An
Infrared small target detection is an important fundamental task in the infrared system. Therefore, many infrared small target detection methods have been proposed, in which the low-rank model has been used as a powerful tool. However, most low-rank-based methods assign the same weights for different singular values, which will lead to inaccurate background estimation. Considering that different singular values have different importance and should be treated discriminatively, in this paper, we propose a non-convex tensor low-rank approximation (NTLA) method for infrared small target detection. In our method, NTLA regularization adaptively assigns different weights to different singular values for accurate background estimation. Based on the proposed NTLA, we propose asymmetric spatial-temporal total variation (ASTTV) regularization to achieve more accurate background estimation in complex scenes. Compared with the traditional total variation approach, ASTTV exploits different smoothness intensities for spatial and temporal regularization. We design an efficient algorithm to find the optimal solution of our method. Compared with some state-of-the-art methods, the proposed method achieves an improvement in terms of various evaluation metrics. Extensive experimental results in various complex scenes demonstrate that our method has strong robustness and low false-alarm rate. Code is available at https://github.com/LiuTing20a/ASTTV-NTLA.
To mitigate the issue of minimal intrinsic features for pure data-driven methods, in this paper, we propose a novel model-driven deep network for infrared small target detection, which combines discriminative networks and conventional model-driven methods to make use of both labeled data and the domain knowledge. By designing a feature map cyclic shift scheme, we modularize a conventional local contrast measure method as a depth-wise parameterless nonlinear feature refinement layer in an end-to-end network, which encodes relatively long-range contextual interactions with clear physical interpretability. To highlight and preserve the small target features, we also exploit a bottom-up attentional modulation integrating the smaller scale subtle details of low-level features into high-level features of deeper layers. We conduct detailed ablation studies with varying network depths to empirically verify the effectiveness and efficiency of the design of each component in our network architecture. We also compare the performance of our network against other model-driven methods and deep networks on the open SIRST dataset as well. The results suggest that our network yields a performance boost over its competitors. Our code, trained models, and results are available online.
Infrared small target detection based on isotropic constraint under complex background
2020-11-24
Fan Wang
Infrared search and tracking (IRST) system has been widely concerned and applied in the area of national defence. Small target detection under complex background is a very challenging task in the development of system algorithm. Low signal-to-clutter ratio (SCR) of target and the interference caused by irregular background clutter make it difficult to get an accurate result. In this paper, small targets are considered to have two characteristics of high contrast and isotropy, and we propose a multilayer gray difference (MGD) method constrained by isotropy. Firstly, the suspected regions are obtained through MGD, and then the eigenvalues of the original image's Hessian matrix are calculated to obtain the isotropy parameter of each region. Finally, those regions do not meet the isotropic constraint condition are suppressed. Experiments show that the proposed method is effective and superior to several common methods in terms of signal-to-clutter ratio gain (SCRG) and receiver operating characteristic (ROC) curve.
The technique of detecting multiple dim and small targets with low signal-to-clutter ratios (SCR) is very important for infrared search and tracking systems. In this paper, we establish a detection method derived from maximal entropy random walk (MERW) to robustly detect multiple small targets. Initially, we introduce the primal MERW and analyze the feasibility of applying it to small target detection. However, the original weight matrix of the MERW is sensitive to interferences. Therefore, a specific weight matrix is designed for the MERW in principle of enhancing characteristics of small targets and suppressing strong clutters. Moreover, the primal MERW has a critical limitation of strong bias to the most salient small target. To achieve multiple small targets detection, we develop a hierarchical version of the MERW method. Based on the hierarchical MERW (HMERW), we propose a small target detection method as follows. First, filtering technique is used to smooth the infrared image. Second, an output map is obtained by importing the filtered image into the HMERW. Then, a coefficient map is constructed to fuse the stationary dirtribution map of the HMERW. Finally, an adaptive threshold is used to segment multiple small targets from the fusion map. Extensive experiments on practical data sets demonstrate that the proposed method is superior to the state-of-the-art methods in terms of target enhancement, background suppression and multiple small targets detection.
Single-frame infrared small target detection remains a challenge not only due to the scarcity of intrinsic target characteristics but also because of lacking a public dataset. In this paper, we first contribute an open dataset with high-quality annotations to advance the research in this field. We also propose an asymmetric contextual modulation module specially designed for detecting infrared small targets. To better highlight small targets, besides a top-down global contextual feedback, we supplement a bottom-up modulation pathway based on point-wise channel attention for exchanging high-level semantics and subtle low-level details. We report ablation studies and comparisons to state-of-the-art methods, where we find that our approach performs significantly better. Our dataset and code are available online.
Filter design for small target detection on infrared imagery using normalized-cross-correlation layer
2020-06-15
H. Seçkin Demir, Erdem Akagunduz
In this paper, we introduce a machine learning approach to the problem of infrared small target detection filter design. For this purpose, similarly to a convolutional layer of a neural network, the normalized-cross-correlational (NCC) layer, which we utilize for designing a target detection/recognition filter bank, is proposed. By employing the NCC layer in a neural network structure, we introduce a framework, in which supervised training is used to calculate the optimal filter shape and the optimum number of filters required for a specific target detection/recognition task on infrared images. We also propose the mean-absolute-deviation NCC (MAD-NCC) layer, an efficient implementation of the proposed NCC layer, designed especially for FPGA systems, in which square root operations are avoided for real-time computation. As a case study we work on dim-target detection on mid-wave infrared imagery and obtain the filters that can discriminate a dim target from various types of background clutter, specific to our operational concept.
Infrared small target detection is a key technique in infrared search and tracking (IRST) systems. Although deep learning has been widely used in the vision tasks of visible light images recently, it is rarely used in infrared small target detection due to the difficulty in learning small target features. In this paper, we propose a novel lightweight convolutional neural network TBC-Net for infrared small target detection. The TBCNet consists of a target extraction module (TEM) and a semantic constraint module (SCM), which are used to extract small targets from infrared images and to classify the extracted target images during the training, respectively. Meanwhile, we propose a joint loss function and a training method. The SCM imposes a semantic constraint on TEM by combining the high-level classification task and solve the problem of the difficulty to learn features caused by class imbalance problem. During the training, the targets are extracted from the input image and then be classified by SCM. During the inference, only the TEM is used to detect the small targets. We also propose a data synthesis method to generate training data. The experimental results show that compared with the traditional methods, TBC-Net can better reduce the false alarm caused by complicated background, the proposed network structure and joint loss have a significant improvement on small target feature learning. Besides, TBC-Net can achieve real-time detection on the NVIDIA Jetson AGX Xavier development board, which is suitable for applications such as field research with drones equipped with infrared sensors.
Infrared small target detection in an infrared search and track (IRST) system is a challenging task. This situation becomes more complicated when high gray-intensity structural backgrounds appear in the field of view (FoV) of the infrared seeker. While the majority of the infrared small target detection algorithms neglect directional information, in this paper, a directional approach is presented to suppress structural backgrounds and develop a more effective detection algorithm. To this end, a similar concept to the average absolute gray difference (AAGD) is utilized to construct a novel directional small target detection algorithm called absolute directional mean difference (ADMD). Also, an efficient implementation procedure is presented for the proposed algorithm. The proposed algorithm effectively enhances the target area and eliminates background clutter. Simulation results on real infrared images prove the significant effectiveness of the proposed algorithm.
Reweighted Infrared Patch-Tensor Model With Both Non-Local and Local Priors for Single-Frame Small Target Detection
2017-03-27
Yimian Dai, Yiquan Wu
Many state-of-the-art methods have been proposed for infrared small target detection. They work well on the images with homogeneous backgrounds and high-contrast targets. However, when facing highly heterogeneous backgrounds, they would not perform very well, mainly due to: 1) the existence of strong edges and other interfering components, 2) not utilizing the priors fully. Inspired by this, we propose a novel method to exploit both local and non-local priors simultaneously. Firstly, we employ a new infrared patch-tensor (IPT) model to represent the image and preserve its spatial correlations. Exploiting the target sparse prior and background non-local self-correlation prior, the target-background separation is modeled as a robust low-rank tensor recovery problem. Moreover, with the help of the structure tensor and reweighted idea, we design an entry-wise local-structure-adaptive and sparsity enhancing weight to replace the globally constant weighting parameter. The decomposition could be achieved via the element-wise reweighted higher-order robust principal component analysis with an additional convergence condition according to the practical situation of target detection. Extensive experiments demonstrate that our model outperforms the other state-of-the-arts, in particular for the images with very dim targets and heavy clutters.
Applying salient object detection (SOD) to RGB-D videos is an emerging task called RGB-D VSOD and has recently gained increasing interest, due to considerable performance gains of incorporating motion and depth and that RGB-D videos can be easily captured now in daily life. Existing RGB-D VSOD models have different attempts to derive motion cues, in which extracting motion information explicitly from optical flow appears to be a more effective and promising alternative. Despite this, there remains a key issue that how to effectively utilize optical flow and depth to assist the RGB modality in SOD. Previous methods always treat optical flow and depth equally with respect to model designs, without explicitly considering their unequal contributions in individual scenarios, limiting the potential of motion and depth. To address this issue and unleash the power of motion and depth, we propose a novel selective cross-modal fusion framework (SMFNet) for RGB-D VSOD, incorporating a pixel-level selective fusion strategy (PSF) that achieves optimal fusion of optical flow and depth based on their actual contributions. Besides, we propose a multi-dimensional selective attention module (MSAM) to integrate the fused features derived from PSF with the remaining RGB modality at multiple dimensions, effectively enhancing feature representation to generate refined features. We conduct comprehensive evaluation of SMFNet against 19 state-of-the-art models on both RDVS and DVisal datasets, making the evaluation the most comprehensive RGB-D VSOD benchmark up to date, and it also demonstrates the superiority of SMFNet over other models. Meanwhile, evaluation on five video benchmark datasets incorporating synthetic depth validates the efficacy of SMFNet as well. Our code and benchmark results are made publicly available at https://github.com/Jia-hao999/SMFNet.
Video salient object detection (SOD) relies on motion cues to distinguish salient objects from backgrounds, but training such models is limited by scarce video datasets compared to abundant image datasets. Existing approaches that use spatial transformations to create video sequences from static images fail for motion-guided tasks, as these transformations produce unrealistic optical flows that lack semantic understanding of motion. We present TransFlow, which transfers motion knowledge from pre-trained video diffusion models to generate realistic training data for video SOD. Video diffusion models have learned rich semantic motion priors from large-scale video data, understanding how different objects naturally move in real scenes. TransFlow leverages this knowledge to generate semantically-aware optical flows from static images, where objects exhibit natural motion patterns while preserving spatial boundaries and temporal coherence. Our method achieves improved performance across multiple benchmarks, demonstrating effective motion knowledge transfer.
Modern multispectral feature fusion for object detection faces two critical limitations: (1) Excessive preference for local complementary features over cross-modal shared semantics adversely affects generalization performance; and (2) The trade-off between the receptive field size and computational complexity present critical bottlenecks for scalable feature modeling. Addressing these issues, a novel Multispectral State-Space Feature Fusion framework, dubbed MS2Fusion, is proposed based on the state space model (SSM), achieving efficient and effective fusion through a dual-path parametric interaction mechanism. More specifically, the first cross-parameter interaction branch inherits the advantage of cross-attention in mining complementary information with cross-modal hidden state decoding in SSM. The second shared-parameter branch explores cross-modal alignment with joint embedding to obtain cross-modal similar semantic features and structures through parameter sharing in SSM. Finally, these two paths are jointly optimized with SSM for fusing multispectral features in a unified framework, allowing our MS2Fusion to enjoy both functional complementarity and shared semantic space. In our extensive experiments on mainstream benchmarks including FLIR, M3FD and LLVIP, our MS2Fusion significantly outperforms other state-of-the-art multispectral object detection methods, evidencing its superiority. Moreover, MS2Fusion is general and applicable to other multispectral perception tasks. We show that, even without specific design, MS2Fusion achieves state-of-the-art results on RGB-T semantic segmentation and RGBT salient object detection, showing its generality. The source code will be available at https://github.com/61s61min/MS2Fusion.git.
Rapid Salient Object Detection with Difference Convolutional Neural Networks
2025-07-01
Zhuo Su, Li Liu, Matthias Müller, Jiehua Zhang, Diana Wofk, Ming-Ming Cheng, Matti Pietikäinen
This paper addresses the challenge of deploying salient object detection (SOD) on resource-constrained devices with real-time performance. While recent advances in deep neural networks have improved SOD, existing top-leading models are computationally expensive. We propose an efficient network design that combines traditional wisdom on SOD and the representation power of modern CNNs. Like biologically-inspired classical SOD methods relying on computing contrast cues to determine saliency of image regions, our model leverages Pixel Difference Convolutions (PDCs) to encode the feature contrasts. Differently, PDCs are incorporated in a CNN architecture so that the valuable contrast cues are extracted from rich feature maps. For efficiency, we introduce a difference convolution reparameterization (DCR) strategy that embeds PDCs into standard convolutions, eliminating computation and parameters at inference. Additionally, we introduce SpatioTemporal Difference Convolution (STDC) for video SOD, enhancing the standard 3D convolution with spatiotemporal contrast capture. Our models, SDNet for image SOD and STDNet for video SOD, achieve significant improvements in efficiency-accuracy trade-offs. On a Jetson Orin device, our models with $<$ 1M parameters operate at 46 FPS and 150 FPS on streamed images and videos, surpassing the second-best lightweight models in our experiments by more than $2\times$ and $3\times$ in speed with superior accuracy. Code will be available at https://github.com/hellozhuo/stdnet.git.
From Sight to Insight: Unleashing Eye-Tracking in Weakly Supervised Video Salient Object Detection
2025-06-30
Qi Qin, Runmin Cong, Gen Zhan, Yiting Liao, Sam Kwong
The eye-tracking video saliency prediction (VSP) task and video salient object detection (VSOD) task both focus on the most attractive objects in video and show the result in the form of predictive heatmaps and pixel-level saliency masks, respectively. In practical applications, eye tracker annotations are more readily obtainable and align closely with the authentic visual patterns of human eyes. Therefore, this paper aims to introduce fixation information to assist the detection of video salient objects under weak supervision. On the one hand, we ponder how to better explore and utilize the information provided by fixation, and then propose a Position and Semantic Embedding (PSE) module to provide location and semantic guidance during the feature learning process. On the other hand, we achieve spatiotemporal feature modeling under weak supervision from the aspects of feature selection and feature contrast. A Semantics and Locality Query (SLQ) Competitor with semantic and locality constraints is designed to effectively select the most matching and accurate object query for spatiotemporal modeling. In addition, an Intra-Inter Mixed Contrastive (IIMC) model improves the spatiotemporal modeling capabilities under weak supervision by forming an intra-video and inter-video contrastive learning paradigm. Experimental results on five popular VSOD benchmarks indicate that our model outperforms other competitors on various evaluation metrics.
Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning
2025-06-27
Zuyao You, Zuxuan Wu
We present Seg-R1, a preliminary exploration of using reinforcement learning (RL) to enhance the pixel-level understanding and reasoning capabilities of large multimodal models (LMMs). Starting with foreground segmentation tasks, specifically camouflaged object detection (COD) and salient object detection (SOD), our approach enables the LMM to generate point and bounding box prompts in the next-token fashion, which are then used to guide SAM2 in producing segmentation masks. We introduce Group Relative Policy Optimization (GRPO) into the segmentation domain, equipping the LMM with pixel-level comprehension through a carefully designed training strategy. Notably, Seg-R1 achieves remarkable performance with purely RL-based training, achieving .873 S-measure on COD10K without complex model modification. Moreover, we found that pure RL training demonstrates strong open-world generalization. Despite being trained solely on foreground segmentation image-mask pairs without text supervision, Seg-R1 achieves impressive zero-shot performance on referring segmentation and reasoning segmentation tasks, with 71.4 cIoU on RefCOCOg test and 56.7 gIoU on ReasonSeg test, outperforming models fully supervised on these datasets.
Lightweight RGB-D Salient Object Detection from a Speed-Accuracy Tradeoff Perspective
2025-05-07
Songsong Duan, Xi Yang, Nannan Wang, Xinbo Gao
Current RGB-D methods usually leverage large-scale backbones to improve accuracy but sacrifice efficiency. Meanwhile, several existing lightweight methods are difficult to achieve high-precision performance. To balance the efficiency and performance, we propose a Speed-Accuracy Tradeoff Network (SATNet) for Lightweight RGB-D SOD from three fundamental perspectives: depth quality, modality fusion, and feature representation. Concerning depth quality, we introduce the Depth Anything Model to generate high-quality depth maps,which effectively alleviates the multi-modal gaps in the current datasets. For modality fusion, we propose a Decoupled Attention Module (DAM) to explore the consistency within and between modalities. Here, the multi-modal features are decoupled into dual-view feature vectors to project discriminable information of feature maps. For feature representation, we develop a Dual Information Representation Module (DIRM) with a bi-directional inverted framework to enlarge the limited feature space generated by the lightweight backbones. DIRM models texture features and saliency features to enrich feature space, and employ two-way prediction heads to optimal its parameters through a bi-directional backpropagation. Finally, we design a Dual Feature Aggregation Module (DFAM) in the decoder to aggregate texture and saliency features. Extensive experiments on five public RGB-D SOD datasets indicate that the proposed SATNet excels state-of-the-art (SOTA) CNN-based heavyweight models and achieves a lightweight framework with 5.2 M parameters and 415 FPS.
FLIM-based Salient Object Detection Networks with Adaptive Decoders
2025-04-29
Gilson Junior Soares, Matheus Abrantes Cerqueira, Jancarlo F. Gomes, Laurent Najman, Silvio Jamil F. Guimarães, Alexandre Xavier Falcão
Salient Object Detection (SOD) methods can locate objects that stand out in an image, assign higher values to their pixels in a saliency map, and binarize the map outputting a predicted segmentation mask. A recent tendency is to investigate pre-trained lightweight models rather than deep neural networks in SOD tasks, coping with applications under limited computational resources. In this context, we have investigated lightweight networks using a methodology named Feature Learning from Image Markers (FLIM), which assumes that the encoder's kernels can be estimated from marker pixels on discriminative regions of a few representative images. This work proposes flyweight networks, hundreds of times lighter than lightweight models, for SOD by combining a FLIM encoder with an adaptive decoder, whose weights are estimated for each input image by a given heuristic function. Such FLIM networks are trained from three to four representative images only and without backpropagation, making the models suitable for applications under labeled data constraints as well. We study five adaptive decoders; two of them are introduced here. Differently from the previous ones that rely on one neuron per pixel with shared weights, the heuristic functions of the new adaptive decoders estimate the weights of each neuron per pixel. We compare FLIM models with adaptive decoders for two challenging SOD tasks with three lightweight networks from the state-of-the-art, two FLIM networks with decoders trained by backpropagation, and one FLIM network whose labeled markers define the decoder's weights. The experiments demonstrate the advantages of the proposed networks over the baselines, revealing the importance of further investigating such methods in new applications.
Visual Consensus Prompting for Co-Salient Object Detection
2025-04-19
Jie Wang, Nana Yu, Zihao Zhang, Yahong Han
Existing co-salient object detection (CoSOD) methods generally employ a three-stage architecture (i.e., encoding, consensus extraction & dispersion, and prediction) along with a typical full fine-tuning paradigm. Although they yield certain benefits, they exhibit two notable limitations: 1) This architecture relies on encoded features to facilitate consensus extraction, but the meticulously extracted consensus does not provide timely guidance to the encoding stage. 2) This paradigm involves globally updating all parameters of the model, which is parameter-inefficient and hinders the effective representation of knowledge within the foundation model for this task. Therefore, in this paper, we propose an interaction-effective and parameter-efficient concise architecture for the CoSOD task, addressing two key limitations. It introduces, for the first time, a parameter-efficient prompt tuning paradigm and seamlessly embeds consensus into the prompts to formulate task-specific Visual Consensus Prompts (VCP). Our VCP aims to induce the frozen foundation model to perform better on CoSOD tasks by formulating task-specific visual consensus prompts with minimized tunable parameters. Concretely, the primary insight of the purposeful Consensus Prompt Generator (CPG) is to enforce limited tunable parameters to focus on co-salient representations and generate consensus prompts. The formulated Consensus Prompt Disperser (CPD) leverages consensus prompts to form task-specific visual consensus prompts, thereby arousing the powerful potential of pre-trained models in addressing CoSOD tasks. Extensive experiments demonstrate that our concise VCP outperforms 13 cutting-edge full fine-tuning models, achieving the new state of the art (with 6.8% improvement in F_m metrics on the most challenging CoCA dataset). Source code has been available at https://github.com/WJ-CV/VCP.
2025-04-15
Felipe Crispim Salvagnini, Jancarlo F. Gomes, Cid A. N. Santos, Silvio Jamil F. Guimarães, Alexandre X. Falcão
The necessity of abundant annotated data and complex network architectures presents a significant challenge in deep-learning Salient Object Detection (deep SOD) and across the broader deep-learning landscape. This challenge is particularly acute in medical applications in developing countries with limited computational resources. Combining modern and classical techniques offers a path to maintaining competitive performance while enabling practical applications. Feature Learning from Image Markers (FLIM) methodology empowers experts to design convolutional encoders through user-drawn markers, with filters learned directly from these annotations. Recent findings demonstrate that coupling a FLIM encoder with an adaptive decoder creates a flyweight network suitable for SOD, requiring significantly fewer parameters than lightweight models and eliminating the need for backpropagation. Cellular Automata (CA) methods have proven successful in data-scarce scenarios but require proper initialization -- typically through user input, priors, or randomness. We propose a practical intersection of these approaches: using FLIM networks to initialize CA states with expert knowledge without requiring user interaction for each image. By decoding features from each level of a FLIM network, we can initialize multiple CAs simultaneously, creating a multi-level framework. Our method leverages the hierarchical knowledge encoded across different network layers, merging multiple saliency maps into a high-quality final output that functions as a CA ensemble. Benchmarks across two challenging medical datasets demonstrate the competitiveness of our multi-level CA approach compared to established models in the deep SOD literature.
Flyweight FLIM Networks for Salient Object Detection in Biomedical Images
2025-04-15
Leonardo M. Joao, Jancarlo F. Gomes, Silvio J. F. Guimaraes, Ewa Kijak, Alexandre X. Falcao
Salient Object Detection (SOD) with deep learning often requires substantial computational resources and large annotated datasets, making it impractical for resource-constrained applications. Lightweight models address computational demands but typically strive in complex and scarce labeled-data scenarios. Feature Learning from Image Markers (FLIM) learns an encoder's convolutional kernels among image patches extracted from discriminative regions marked on a few representative images, dismissing large annotated datasets, pretraining, and backpropagation. Such a methodology exploits information redundancy commonly found in biomedical image applications. This study presents methods to learn dilated-separable convolutional kernels and multi-dilation layers without backpropagation for FLIM networks. It also proposes a novel network simplification method to reduce kernel redundancy and encoder size. By combining a FLIM encoder with an adaptive decoder, a concept recently introduced to estimate a pointwise convolution per image, this study presents very efficient (named flyweight) SOD models for biomedical images. Experimental results in challenging datasets demonstrate superior efficiency and effectiveness to lightweight models. By requiring significantly fewer parameters and floating-point operations, the results show competitive effectiveness to heavyweight models. These advances highlight the potential of FLIM networks for data-limited and resource-constrained applications with information redundancy.
Uncertainty Guided Refinement for Fine-Grained Salient Object Detection
2025-04-13
Yao Yuan, Pan Gao, Qun Dai, Jie Qin, Wei Xiang
Recently, salient object detection (SOD) methods have achieved impressive performance. However, salient regions predicted by existing methods usually contain unsaturated regions and shadows, which limits the model for reliable fine-grained predictions. To address this, we introduce the uncertainty guidance learning approach to SOD, intended to enhance the model's perception of uncertain regions. Specifically, we design a novel Uncertainty Guided Refinement Attention Network (UGRAN), which incorporates three important components, i.e., the Multilevel Interaction Attention (MIA) module, the Scale Spatial-Consistent Attention (SSCA) module, and the Uncertainty Refinement Attention (URA) module. Unlike conventional methods dedicated to enhancing features, the proposed MIA facilitates the interaction and perception of multilevel features, leveraging the complementary characteristics among multilevel features. Then, through the proposed SSCA, the salient information across diverse scales within the aggregated features can be integrated more comprehensively and integrally. In the subsequent steps, we utilize the uncertainty map generated from the saliency prediction map to enhance the model's perception capability of uncertain regions, generating a highly-saturated fine-grained saliency prediction map. Additionally, we devise an adaptive dynamic partition (ADP) mechanism to minimize the computational overhead of the URA module and improve the utilization of uncertainty guidance. Experiments on seven benchmark datasets demonstrate the superiority of the proposed UGRAN over the state-of-the-art methodologies. Codes will be released at https://github.com/I2-Multimedia-Lab/UGRAN.
Balancing long- and short-term dynamics for the modeling of saliency in videos
2025-04-08
Theodor Wulff, Fares Abawi, Philipp Allgeuer, Stefan Wermter
The role of long- and short-term dynamics towards salient object detection in videos is under-researched. We present a Transformer-based approach to learn a joint representation of video frames and past saliency information. Our model embeds long- and short-term information to detect dynamically shifting saliency in video. We provide our model with a stream of video frames and past saliency maps, which acts as a prior for the next prediction, and extract spatiotemporal tokens from both modalities. The decomposition of the frame sequence into tokens lets the model incorporate short-term information from within the token, while being able to make long-term connections between tokens throughout the sequence. The core of the system consists of a dual-stream Transformer architecture to process the extracted sequences independently before fusing the two modalities. Additionally, we apply a saliency-based masking scheme to the input frames to learn an embedding that facilitates the recognition of deviations from previous outputs. We observe that the additional prior information aids in the first detection of the salient location. Our findings indicate that the ratio of spatiotemporal long- and short-term features directly impacts the model's performance. While increasing the short-term context is beneficial up to a certain threshold, the model's performance greatly benefits from an expansion of the long-term context.
Recent mainstream unsupervised video object segmentation (UVOS) motion-appearance approaches use either the bi-encoder structure to separately encode motion and appearance features, or the uni-encoder structure for joint encoding. However, these methods fail to properly balance the motion-appearance relationship. Consequently, even with complex fusion modules for motion-appearance integration, the extracted suboptimal features degrade the models' overall performance. Moreover, the quality of optical flow varies across scenarios, making it insufficient to rely solely on optical flow to achieve high-quality segmentation results. To address these challenges, we propose the Saliency-Motion guided Trunk-Collateral Network (SMTC-Net), which better balances the motion-appearance relationship and incorporates model's intrinsic saliency information to enhance segmentation performance. Specifically, considering that optical flow maps are derived from RGB images, they share both commonalities and differences. Accordingly, we propose a novel Trunk-Collateral structure for motion-appearance UVOS. The shared trunk backbone captures the motion-appearance commonality, while the collateral branch learns the uniqueness of motion features. Furthermore, an Intrinsic Saliency guided Refinement Module (ISRM) is devised to efficiently leverage the model's intrinsic saliency information to refine high-level features, and provide pixel-level guidance for motion-appearance fusion, thereby enhancing performance without additional input. Experimental results show that SMTC-Net achieved state-of-the-art performance on three UVOS datasets ( 89.2% J&F on DAVIS-16, 76% J on YouTube-Objects, 86.4% J on FBMS ) and four standard video salient object detection (VSOD) benchmarks with the notable increase, demonstrating its effectiveness and superiority over previous methods.
Existing RGB-thermal salient object detection (RGB-T SOD) methods aim to identify visually significant objects by leveraging both RGB and thermal modalities to enable robust performance in complex scenarios, but they often suffer from limited generalization due to the constrained diversity of available datasets and the inefficiencies in constructing multi-modal representations. In this paper, we propose a novel prompt learning-based RGB-T SOD method, named KAN-SAM, which reveals the potential of visual foundational models for RGB-T SOD tasks. Specifically, we extend Segment Anything Model 2 (SAM2) for RGB-T SOD by introducing thermal features as guiding prompts through efficient and accurate Kolmogorov-Arnold Network (KAN) adapters, which effectively enhance RGB representations and improve robustness. Furthermore, we introduce a mutually exclusive random masking strategy to reduce reliance on RGB data and improve generalization. Experimental results on benchmarks demonstrate superior performance over the state-of-the-art methods.
The objective of hyperspectral remote sensing image salient object detection (HRSI-SOD) is to identify objects or regions that exhibit distinct spectrum contrasts with the background. This area holds significant promise for practical applications; however, progress has been limited by a notable scarcity of dedicated datasets and methodologies. To bridge this gap and stimulate further research, we introduce the first HRSI-SOD dataset, termed HRSSD, which includes 704 hyperspectral images and 5327 pixel-level annotated salient objects. The HRSSD dataset poses substantial challenges for salient object detection algorithms due to large scale variation, diverse foreground-background relations, and multi-salient objects. Additionally, we propose an innovative and efficient baseline model for HRSI-SOD, termed the Deep Spectral Saliency Network (DSSN). The core of DSSN is the Cross-level Saliency Assessment Block, which performs pixel-wise attention and evaluates the contributions of multi-scale similarity maps at each spatial location, effectively reducing erroneous responses in cluttered regions and emphasizes salient regions across scales. Additionally, the High-resolution Fusion Module combines bottom-up fusion strategy and learned spatial upsampling to leverage the strengths of multi-scale saliency maps, ensuring accurate localization of small objects. Experiments on the HRSSD dataset robustly validate the superiority of DSSN, underscoring the critical need for specialized datasets and methodologies in this domain. Further evaluations on the HSOD-BIT and HS-SOD datasets demonstrate the generalizability of the proposed method. The dataset and source code are publicly available at https://github.com/laprf/HRSSD.
CFMD: Dynamic Cross-layer Feature Fusion for Salient Object Detection
2025-04-02
Jin Lian, Zhongyu Wan, Ming Gao, JunFeng Chen
Cross-layer feature pyramid networks (CFPNs) have achieved notable progress in multi-scale feature fusion and boundary detail preservation for salient object detection. However, traditional CFPNs still suffer from two core limitations: (1) a computational bottleneck caused by complex feature weighting operations, and (2) degraded boundary accuracy due to feature blurring in the upsampling process. To address these challenges, we propose CFMD, a novel cross-layer feature pyramid network that introduces two key innovations. First, we design a context-aware feature aggregation module (CFLMA), which incorporates the state-of-the-art Mamba architecture to construct a dynamic weight distribution mechanism. This module adaptively adjusts feature importance based on image context, significantly improving both representation efficiency and generalization. Second, we introduce an adaptive dynamic upsampling unit (CFLMD) that preserves spatial details during resolution recovery. By adjusting the upsampling range dynamically and initializing with a bilinear strategy, the module effectively reduces feature overlap and maintains fine-grained boundary structures. Extensive experiments on three standard benchmarks using three mainstream backbone networks demonstrate that CFMD achieves substantial improvements in pixel-level accuracy and boundary segmentation quality, especially in complex scenes. The results validate the effectiveness of CFMD in jointly enhancing computational efficiency and segmentation performance, highlighting its strong potential in salient object detection tasks.
Salient Object Detection in Traffic Scene through the TSOD10K Dataset
2025-03-21
Yu Qiu, Yuhang Sun, Jie Mei, Lin Xiao, Jing Xu
Traffic Salient Object Detection (TSOD) aims to segment the objects critical to driving safety by combining semantic (e.g., collision risks) and visual saliency. Unlike SOD in natural scene images (NSI-SOD), which prioritizes visually distinctive regions, TSOD emphasizes the objects that demand immediate driver attention due to their semantic impact, even with low visual contrast. This dual criterion, i.e., bridging perception and contextual risk, re-defines saliency for autonomous and assisted driving systems. To address the lack of task-specific benchmarks, we collect the first large-scale TSOD dataset with pixel-wise saliency annotations, named TSOD10K. TSOD10K covers the diverse object categories in various real-world traffic scenes under various challenging weather/illumination variations (e.g., fog, snowstorms, low-contrast, and low-light). Methodologically, we propose a Mamba-based TSOD model, termed Tramba. Considering the challenge of distinguishing inconspicuous visual information from complex traffic backgrounds, Tramba introduces a novel Dual-Frequency Visual State Space module equipped with shifted window partitioning and dilated scanning to enhance the perception of fine details and global structure by hierarchically decomposing high/low-frequency components. To emphasize critical regions in traffic scenes, we propose a traffic-oriented Helix 2D-Selective-Scan (Helix-SS2D) mechanism that injects driving attention priors while effectively capturing global multi-direction spatial dependencies. We establish a comprehensive benchmark by evaluating Tramba and 22 existing NSI-SOD models on TSOD10K, demonstrating Tramba's superiority. Our research establishes the first foundation for safety-aware saliency analysis in intelligent transportation systems.
Salient Object Detection (SOD) is crucial in computer vision, yet RGB-based methods face limitations in challenging scenes, such as small objects and similar color features. Hyperspectral images provide a promising solution for more accurate Hyperspectral Salient Object Detection (HSOD) by abundant spectral information, while HSOD methods are hindered by the lack of extensive and available datasets. In this context, we introduce HSOD-BIT-V2, the largest and most challenging HSOD benchmark dataset to date. Five distinct challenges focusing on small objects and foreground-background similarity are designed to emphasize spectral advantages and real-world complexity. To tackle these challenges, we propose Hyper-HRNet, a high-resolution HSOD network. Hyper-HRNet effectively extracts, integrates, and preserves effective spectral information while reducing dimensionality by capturing the self-similar spectral features. Additionally, it conveys fine details and precisely locates object contours by incorporating comprehensive global information and detailed object saliency representations. Experimental analysis demonstrates that Hyper-HRNet outperforms existing models, especially in challenging scenarios.
Salient object detection (SOD) in RGB-D images is an essential task in computer vision, enabling applications in scene understanding, robotics, and augmented reality. However, existing methods struggle to capture global dependency across modalities, lack comprehensive saliency priors from both RGB and depth data, and are ineffective in handling low-quality depth maps. To address these challenges, we propose SSNet, a saliency-prior and state space model (SSM)-based network for the RGB-D SOD task. Unlike existing convolution- or transformer-based approaches, SSNet introduces an SSM-based multi-modal multi-scale decoder module to efficiently capture both intra- and inter-modal global dependency with linear complexity. Specifically, we propose a cross-modal selective scan SSM (CM-S6) mechanism, which effectively captures global dependency between different modalities. Furthermore, we introduce a saliency enhancement module (SEM) that integrates three saliency priors with deep features to refine feature representation and improve the localization of salient objects. To further address the issue of low-quality depth maps, we propose an adaptive contrast enhancement technique that dynamically refines depth maps, making them more suitable for the RGB-D SOD task. Extensive quantitative and qualitative experiments on seven benchmark datasets demonstrate that SSNet outperforms state-of-the-art methods.
Unifying Light Field Perception with Field of Parallax
2025-03-02
Fei Teng, Buyin Deng, Boyuan Zheng, Kai Luo, Kunyu Peng, Jiaming Zhang, Kailun Yang
Field of Parallax (FoP)}, a spatial field that distills the common features from different LF representations to provide flexible and consistent support for multi-task learning. FoP is built upon three core features--projection difference, adjacency divergence, and contextual consistency--which are essential for cross-task adaptability. To implement FoP, we design a two-step angular adapter: the first step captures angular-specific differences, while the second step consolidates contextual consistency to ensure robust representation. Leveraging the FoP-based representation, we introduce the LFX framework, the first to handle arbitrary LF representations seamlessly, unifying LF multi-task vision. We evaluated LFX across three different tasks, achieving new state-of-the-art results, compared with previous task-specific architectures: 84.74% in mIoU for semantic segmentation on UrbanLF, 0.84% in AP for object detection on PKU, and 0.030 in MAE and 0.026 in MAE for salient object detection on Duftv2 and PKU, respectively. The source code will be made publicly available at https://github.com/warriordby/LFX.
Geometry-Aware 3D Salient Object Detection Network
2025-02-23
Chen Wang, Liyuan Zhang, Le Hui, Qi Liu, Yuchao Dai
Point cloud salient object detection has attracted the attention of researchers in recent years. Since existing works do not fully utilize the geometry context of 3D objects, blurry boundaries are generated when segmenting objects with complex backgrounds. In this paper, we propose a geometry-aware 3D salient object detection network that explicitly clusters points into superpoints to enhance the geometric boundaries of objects, thereby segmenting complete objects with clear boundaries. Specifically, we first propose a simple yet effective superpoint partition module to cluster points into superpoints. In order to improve the quality of superpoints, we present a point cloud class-agnostic loss to learn discriminative point features for clustering superpoints from the object. After obtaining superpoints, we then propose a geometry enhancement module that utilizes superpoint-point attention to aggregate geometric information into point features for predicting the salient map of the object with clear boundaries. Extensive experiments show that our method achieves new state-of-the-art performance on the PCSOD dataset.
Multi-Knowledge-oriented Nighttime Haze Imaging Enhancer for Vision-driven Intelligent Systems
2025-02-11
Ai Chen, Yuxu Lu, Dong Yang, Junlin Zhou, Yan Fu, Duanbing Chen
Salient object detection (SOD) plays a critical role in Intelligent Imaging, facilitating the detection and segmentation of key visual elements in an image. However, adverse imaging conditions such as haze during the day, low light, and haze at night severely degrade image quality and hinder reliable object detection in real-world scenarios. To address these challenges, we propose a multi-knowledge-oriented nighttime haze imaging enhancer (MKoIE), which integrates three tasks: daytime dehazing, low-light enhancement, and nighttime dehazing. The MKoIE incorporates two key innovative components: First, the network employs a task-oriented node learning mechanism to handle three specific degradation types: day-time haze, low light, and night-time haze conditions, with an embedded self-attention module enhancing its performance in nighttime imaging. In addition, multi-receptive field enhancement module that efficiently extracts multi-scale features through three parallel depthwise separable convolution branches with different dilation rates, capturing comprehensive spatial information with minimal computational overhead to meet the requirements of real-time imaging deployment. To ensure optimal image reconstruction quality and visual characteristics, we suggest a hybrid loss function. Extensive experiments on different types of weather/imaging conditions illustrate that MKoIE surpasses existing methods, enhancing the reliability, accuracy, and operational efficiency of intelligent imaging.
Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation
2025-01-14
Yunzhi Zhuge, Hongyu Gu, Lu Zhang, Jinqing Qi, Huchuan Lu
In this paper, we address the challenges in unsupervised video object segmentation (UVOS) by proposing an efficient algorithm, termed MTNet, which concurrently exploits motion and temporal cues. Unlike previous methods that focus solely on integrating appearance with motion or on modeling temporal relations, our method combines both aspects by integrating them within a unified framework. MTNet is devised by effectively merging appearance and motion features during the feature extraction process within encoders, promoting a more complementary representation. To capture the intricate long-range contextual dynamics and information embedded within videos, a temporal transformer module is introduced, facilitating efficacious inter-frame interactions throughout a video clip. Furthermore, we employ a cascade of decoders all feature levels across all feature levels to optimally exploit the derived features, aiming to generate increasingly precise segmentation masks. As a result, MTNet provides a strong and compact framework that explores both temporal and cross-modality knowledge to robustly localize and track the primary object accurately in various challenging scenarios efficiently. Extensive experiments across diverse benchmarks conclusively show that our method not only attains state-of-the-art performance in unsupervised video object segmentation but also delivers competitive results in video salient object detection. These findings highlight the method's robust versatility and its adeptness in adapting to a range of segmentation tasks. Source code is available on https://github.com/hy0523/MTNet.
Most of the current salient object detection approaches use deeper networks with large backbones to produce more accurate predictions, which results in a significant increase in computational complexity. A great number of network designs follow the pure UNet and Feature Pyramid Network (FPN) architecture which has limited feature extraction and aggregation ability which motivated us to design a lightweight post-decoder refinement module, the crossed post-decoder refinement (CPDR) to enhance the feature representation of a standard FPN or U-Net framework. Specifically, we introduce the Attention Down Sample Fusion (ADF), which employs channel attention mechanisms with attention maps generated by high-level representation to refine the low-level features, and Attention Up Sample Fusion (AUF), leveraging the low-level information to guide the high-level features through spatial attention. Additionally, we proposed the Dual Attention Cross Fusion (DACF) upon ADFs and AUFs, which reduces the number of parameters while maintaining the performance. Experiments on five benchmark datasets demonstrate that our method outperforms previous state-of-the-art approaches.
Salient Object Detection (SOD) aims to identify and segment prominent regions within a scene. Traditional models rely on manually annotated pseudo labels with precise pixel-level accuracy, which is time-consuming. We developed a low-cost, high-precision annotation method by leveraging large foundation models to address the challenges. Specifically, we use a weakly supervised approach to guide large models in generating pseudo-labels through textual prompts. Since large models do not effectively focus on the salient regions of images, we manually annotate a subset of text to fine-tune the model. Based on this approach, which enables precise and rapid generation of pseudo-labels, we introduce a new dataset, BDS-TR. Compared to the previous DUTS-TR dataset, BDS-TR is more prominent in scale and encompasses a wider variety of categories and scenes. This expansion will enhance our model's applicability across a broader range of scenarios and provide a more comprehensive foundational dataset for future SOD research. Additionally, we present an edge decoder based on dynamic upsampling, which focuses on object edges while gradually recovering image feature resolution. Comprehensive experiments on five benchmark datasets demonstrate that our method significantly outperforms state-of-the-art approaches and also surpasses several existing fully-supervised SOD methods. The code and results will be made available.
Dual Mutual Learning Network with Global-local Awareness for RGB-D Salient Object Detection
2025-01-03
Kang Yi, Haoran Tang, Yumeng Li, Jing Xu, Jun Zhang
RGB-D salient object detection (SOD), aiming to highlight prominent regions of a given scene by jointly modeling RGB and depth information, is one of the challenging pixel-level prediction tasks. Recently, the dual-attention mechanism has been devoted to this area due to its ability to strengthen the detection process. However, most existing methods directly fuse attentional cross-modality features under a manual-mandatory fusion paradigm without considering the inherent discrepancy between the RGB and depth, which may lead to a reduction in performance. Moreover, the long-range dependencies derived from global and local information make it difficult to leverage a unified efficient fusion strategy. Hence, in this paper, we propose the GL-DMNet, a novel dual mutual learning network with global-local awareness. Specifically, we present a position mutual fusion module and a channel mutual fusion module to exploit the interdependencies among different modalities in spatial and channel dimensions. Besides, we adopt an efficient decoder based on cascade transformer-infused reconstruction to integrate multi-level fusion features jointly. Extensive experiments on six benchmark datasets demonstrate that our proposed GL-DMNet performs better than 24 RGB-D SOD methods, achieving an average improvement of ~3% across four evaluation metrics compared to the second-best model (S3Net). Codes and results are available at https://github.com/kingkung2016/GL-DMNet.
Hyperspectral salient object detection (HSOD) aims to extract targets or regions with significantly different spectra from hyperspectral images. While existing deep learning-based methods can achieve good detection results, they generally necessitate pixel-level annotations, which are notably challenging to acquire for hyperspectral images. To address this issue, we introduce point supervision into HSOD, and incorporate Spectral Saliency, derived from conventional HSOD methods, as a pivotal spectral representation within the framework. This integration leads to the development of a novel Spectrum-oriented Point-supervised Saliency Detector (SPSD). Specifically, we propose a novel pipeline, specifically designed for HSIs, to generate pseudo-labels, effectively mitigating the performance decline associated with point supervision strategy. Additionally, Spectral Saliency is employed to counteract information loss during model supervision and saliency refinement, thereby maintaining the structural integrity and edge accuracy of the detected objects. Furthermore, we introduce a Spectrum-transformed Spatial Gate to focus more precisely on salient regions while reducing feature redundancy. We have carried out comprehensive experiments on both HSOD-BIT and HS-SOD datasets to validate the efficacy of our proposed method, using mean absolute error (MAE), E-measure, F-measure, Area Under Curve, and Cross Correlation as evaluation metrics. For instance, on the HSOD-BIT dataset, our SPSD achieves a MAE of 0.031 and an F-measure of 0.878. Thorough ablation studies have substantiated the effectiveness of each individual module and provided insights into the model's working mechanism. Further evaluations on RGB-thermal salient object detection datasets highlight the versatility of our approach.
Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection
2024-12-22
Yi Liu, Chengxin Li, Xiaohui Dong, Lei Li, Dingwen Zhang, Shoukun Xu, Jungong Han
Achieving joint learning of Salient Object Detection (SOD) and Camouflaged Object Detection (COD) is extremely challenging due to their distinct object characteristics, i.e., saliency and camouflage. The only preliminary research treats them as two contradictory tasks, training models on large-scale labeled data alternately for each task and assessing them independently. However, such task-specific mechanisms fail to meet real-world demands for addressing unknown tasks effectively. To address this issue, in this paper, we pioneer a task-agnostic framework to unify SOD and COD. To this end, inspired by the agreeable nature of binary segmentation for SOD and COD, we propose a Contrastive Distillation Paradigm (CDP) to distil the foreground from the background, facilitating the identification of salient and camouflaged objects amidst their surroundings. To probe into the contribution of our CDP, we design a simple yet effective contextual decoder involving the interval-layer and global context, which achieves an inference speed of 67 fps. Besides the supervised setting, our CDP can be seamlessly integrated into unsupervised settings, eliminating the reliance on extensive human annotations. Experiments on public SOD and COD datasets demonstrate the superiority of our proposed framework in both supervised and unsupervised settings, compared with existing state-of-the-art approaches. Code is available on https://github.com/liuyi1989/Seamless-Detection.
Various infrared-visible (IR-VIS) tasks greatly benefit from the advantage of combining infrared and visible modalities. Driven by the motivation that streamlining the infrared flow and harnessing PVMs with fewer parameters for superior performance, we propose "IV-tuning", a novel and general fine-tuning approach, to parameter-efficiently harness PVMs for various infrared-visible downstream tasks. At its core, IV-tuning freezes pre-trained visible-based PVMs and integrates infrared flow into modal prompts to interact with adapters, which achieves a more efficient and general modal interaction paradigm. By fine-tuning approximately 3% of the backbone parameters, IV-tuning outperforms full fine-tuning and previous state-of-the-art methods across multiple baselines in multiple tasks, including IR-VIS salient object detection, semantic segmentation and object detection. Extensive experiments demonstrate that IV-tuning achieves superior performance with fewer trainable parameters, providing a good alternative to full fine-tuning and a novel method of extending visible-based models for infrared-visible tasks. The code will be provided in supplementary material.
2024-12-21
Jiayi Zhu, Qing Guo, Felix Juefei-Xu, Yihao Huang, Yang Liu, Geguang Pu
Co-salient object detection (Co-SOD) aims to identify common salient objects across a group of related images. While recent methods have made notable progress, they typically rely on low-level visual patterns and lack semantic priors, limiting their detection performance. We propose ConceptCoSOD, a concept-guided framework that introduces high-level semantic knowledge to enhance co-saliency detection. By extracting shared text-based concepts from the input image group, ConceptCoSOD provides semantic guidance that anchors the detection process. To further improve concept quality, we analyze the effect of diffusion timesteps and design a resampling strategy that selects more informative steps for learning robust concepts. This semantic prior, combined with the resampling-enhanced representation, enables accurate and consistent segmentation even in challenging visual conditions. Extensive experiments on three benchmark datasets and five corrupted settings demonstrate that ConceptCoSOD significantly outperforms existing methods in both accuracy and generalization.
Alignment-Free RGB-T Salient Object Detection: A Large-scale Dataset and Progressive Correlation Network
2024-12-19
Kunpeng Wang, Keke Chen, Chenglong Li, Zhengzheng Tu, Bin Luo
Alignment-free RGB-Thermal (RGB-T) salient object detection (SOD) aims to achieve robust performance in complex scenes by directly leveraging the complementary information from unaligned visible-thermal image pairs, without requiring manual alignment. However, the labor-intensive process of collecting and annotating image pairs limits the scale of existing benchmarks, hindering the advancement of alignment-free RGB-T SOD. In this paper, we construct a large-scale and high-diversity unaligned RGB-T SOD dataset named UVT20K, comprising 20,000 image pairs, 407 scenes, and 1256 object categories. All samples are collected from real-world scenarios with various challenges, such as low illumination, image clutter, complex salient objects, and so on. To support the exploration for further research, each sample in UVT20K is annotated with a comprehensive set of ground truths, including saliency masks, scribbles, boundaries, and challenge attributes. In addition, we propose a Progressive Correlation Network (PCNet), which models inter- and intra-modal correlations on the basis of explicit alignment to achieve accurate predictions in unaligned image pairs. Extensive experiments conducted on unaligned and aligned datasets demonstrate the effectiveness of our method.Code and dataset are available at https://github.com/Angknpng/PCNet.
While the human visual system employs distinct mechanisms to perceive salient and camouflaged objects, existing models struggle to disentangle these tasks. Specifically, salient object detection (SOD) models frequently misclassify camouflaged objects as salient, while camouflaged object detection (COD) models conversely misinterpret salient objects as camouflaged. We hypothesize that this can be attributed to two factors: (i) the specific annotation paradigm of current SOD and COD datasets, and (ii) the lack of explicit attribute relationship modeling in current models. Prevalent SOD/COD datasets enforce a mutual exclusivity constraint, assuming scenes contain either salient or camouflaged objects, which poorly aligns with the real world. Furthermore, current SOD/COD methods are primarily designed for these highly constrained datasets and lack explicit modeling of the relationship between salient and camouflaged objects. In this paper, to promote the development of unconstrained salient and camouflaged object detection, we construct a large-scale dataset, USC12K, which features comprehensive labels and four different scenes that cover all possible logical existence scenarios of both salient and camouflaged objects. To explicitly model the relationship between salient and camouflaged objects, we propose a model called USCNet, which introduces two distinct prompt query mechanisms for modeling inter-sample and intra-sample attribute relationships. Additionally, to assess the model's ability to distinguish between salient and camouflaged objects, we design an evaluation metric called CSCS. The proposed method achieves state-of-the-art performance across all scenes in various metrics. The code and dataset will be available at https://github.com/ssecv/USCNet.
RGB-Thermal Salient Object Detection aims to pinpoint prominent objects within aligned pairs of visible and thermal infrared images. Traditional encoder-decoder architectures, while designed for cross-modality feature interactions, may not have adequately considered the robustness against noise originating from defective modalities. Inspired by hierarchical human visual systems, we propose the ConTriNet, a robust Confluent Triple-Flow Network employing a Divide-and-Conquer strategy. Specifically, ConTriNet comprises three flows: two modality-specific flows explore cues from RGB and Thermal modalities, and a third modality-complementary flow integrates cues from both modalities. ConTriNet presents several notable advantages. It incorporates a Modality-induced Feature Modulator in the modality-shared union encoder to minimize inter-modality discrepancies and mitigate the impact of defective samples. Additionally, a foundational Residual Atrous Spatial Pyramid Module in the separated flows enlarges the receptive field, allowing for the capture of multi-scale contextual information. Furthermore, a Modality-aware Dynamic Aggregation Module in the modality-complementary flow dynamically aggregates saliency-related cues from both modality-specific flows. Leveraging the proposed parallel triple-flow framework, we further refine saliency maps derived from different flows through a flow-cooperative fusion strategy, yielding a high-quality, full-resolution saliency map for the final prediction. To evaluate the robustness and stability of our approach, we collect a comprehensive RGB-T SOD benchmark, VT-IMAG, covering various real-world challenging scenarios. Extensive experiments on public benchmarks and our VT-IMAG dataset demonstrate that ConTriNet consistently outperforms state-of-the-art competitors in both common and challenging scenarios.
The rapid development of deep learning has significantly improved salient object detection (SOD) combining both RGB and thermal (RGB-T) images. However, existing deep learning-based RGB-T SOD models suffer from two major limitations. First, Transformer-based models with quadratic complexity are computationally expensive and memory-intensive, limiting their application in high-resolution bi-modal feature fusion. Second, even when these models converge to an optimal solution, there remains a frequency gap between the prediction and ground-truth. To overcome these limitations, we propose a purely Fourier transform-based model, namely Deep Fourier-Embedded Network (DFENet), for accurate RGB-T SOD. To address the computational complexity when dealing with high-resolution images, we leverage the efficiency of fast Fourier transform with linear complexity to design three key components: (1) the Modal-coordinated Perception Attention, which fuses RGB and thermal modalities with enhanced multi-dimensional representation; (2) the Frequency-decomposed Edge-aware Block, which clarifies object edges by deeply decomposing and enhancing frequency components of low-level features; and (3) the Fourier Residual Channel Attention Block, which prioritizes high-frequency information while aligning channel-wise global relationships. To mitigate the frequency gap, we propose Co-focus Frequency Loss, which dynamically weights hard frequencies during edge frequency reconstruction by cross-referencing bi-modal edge information in the Fourier domain. Extensive experiments on four RGB-T SOD benchmark datasets demonstrate that DFENet outperforms fifteen existing state-of-the-art RGB-T SOD models. Comprehensive ablation studies further validate the value and effectiveness of our newly proposed components. The code is available at https://github.com/JoshuaLPF/DFENet.
Transforming Static Images Using Generative Models for Video Salient Object Detection
2024-11-21
Suhwan Cho, Minhyeok Lee, Jungho Lee, Sangyoun Lee
In many video processing tasks, leveraging large-scale image datasets is a common strategy, as image data is more abundant and facilitates comprehensive knowledge transfer. A typical approach for simulating video from static images involves applying spatial transformations, such as affine transformations and spline warping, to create sequences that mimic temporal progression. However, in tasks like video salient object detection, where both appearance and motion cues are critical, these basic image-to-video techniques fail to produce realistic optical flows that capture the independent motion properties of each object. In this study, we show that image-to-video diffusion models can generate realistic transformations of static images while understanding the contextual relationships between image components. This ability allows the model to generate plausible optical flows, preserving semantic integrity while reflecting the independent motion of scene elements. By augmenting individual images in this way, we create large-scale image-flow pairs that significantly enhance model training. Our approach achieves state-of-the-art performance across all public benchmark datasets, outperforming existing approaches.
Instruction-Driven Fusion of Infrared-Visible Images: Tailoring for Diverse Downstream Tasks
2024-11-14
Zengyi Yang, Yafei Zhang, Huafeng Li, Yu Liu
The primary value of infrared and visible image fusion technology lies in applying the fusion results to downstream tasks. However, existing methods face challenges such as increased training complexity and significantly compromised performance of individual tasks when addressing multiple downstream tasks simultaneously. To tackle this, we propose Task-Oriented Adaptive Regulation (T-OAR), an adaptive mechanism specifically designed for multi-task environments. Additionally, we introduce the Task-related Dynamic Prompt Injection (T-DPI) module, which generates task-specific dynamic prompts from user-input text instructions and integrates them into target representations. This guides the feature extraction module to produce representations that are more closely aligned with the specific requirements of downstream tasks. By incorporating the T-DPI module into the T-OAR framework, our approach generates fusion images tailored to task-specific requirements without the need for separate training or task-specific weights. This not only reduces computational costs but also enhances adaptability and performance across multiple tasks. Experimental results show that our method excels in object detection, semantic segmentation, and salient object detection, demonstrating its strong adaptability, flexibility, and task specificity. This provides an efficient solution for image fusion in multi-task environments, highlighting the technology's potential across diverse applications.
United Domain Cognition Network for Salient Object Detection in Optical Remote Sensing Images
2024-11-11
Yanguang Sun, Jian Yang, Lei Luo
Recently, deep learning-based salient object detection (SOD) in optical remote sensing images (ORSIs) have achieved significant breakthroughs. We observe that existing ORSIs-SOD methods consistently center around optimizing pixel features in the spatial domain, progressively distinguishing between backgrounds and objects. However, pixel information represents local attributes, which are often correlated with their surrounding context. Even with strategies expanding the local region, spatial features remain biased towards local characteristics, lacking the ability of global perception. To address this problem, we introduce the Fourier transform that generate global frequency features and achieve an image-size receptive field. To be specific, we propose a novel United Domain Cognition Network (UDCNet) to jointly explore the global-local information in the frequency and spatial domains. Technically, we first design a frequency-spatial domain transformer block that mutually amalgamates the complementary local spatial and global frequency features to strength the capability of initial input features. Furthermore, a dense semantic excavation module is constructed to capture higher-level semantic for guiding the positioning of remote sensing objects. Finally, we devise a dual-branch joint optimization decoder that applies the saliency and edge branches to generate high-quality representations for predicting salient objects. Experimental results demonstrate the superiority of the proposed UDCNet method over 24 state-of-the-art models, through extensive quantitative and qualitative comparisons in three widely-used ORSIs-SOD datasets. The source code is available at: \href{https://github.com/CSYSI/UDCNet}{\color{blue} https://github.com/CSYSI/UDCNet}.
A light field camera can reconstruct 3D scenes using captured multi-focus images that contain rich spatial geometric information, enhancing applications in stereoscopic photography, virtual reality, and robotic vision. In this work, a state-of-the-art salient object detection model for multi-focus light field images, called LFSamba, is introduced to emphasize four main insights: (a) Efficient feature extraction, where SAM is used to extract modality-aware discriminative features; (b) Inter-slice relation modeling, leveraging Mamba to capture long-range dependencies across multiple focal slices, thus extracting implicit depth cues; (c) Inter-modal relation modeling, utilizing Mamba to integrate all-focus and multi-focus images, enabling mutual enhancement; (d) Weakly supervised learning capability, developing a scribble annotation dataset from an existing pixel-level mask dataset, establishing the first scribble-supervised baseline for light field salient object detection.https://github.com/liuzywen/LFScribble
Unmanned aerial vehicle (UAV)-based bi-modal salient object detection (BSOD) aims to segment salient objects in a scene utilizing complementary cues in unaligned RGB and thermal image pairs. However, the high computational expense of existing UAV-based BSOD models limits their applicability to real-world UAV devices. To address this problem, we propose an efficient Fourier filter network with contrastive learning that achieves both real-time and accurate performance. Specifically, we first design a semantic contrastive alignment loss to align the two modalities at the semantic level, which facilitates mutual refinement in a parameter-free way. Second, inspired by the fast Fourier transform that obtains global relevance in linear complexity, we propose synchronized alignment fusion, which aligns and fuses bi-modal features in the channel and spatial dimensions by a hierarchical filtering mechanism. Our proposed model, AlignSal, reduces the number of parameters by 70.0%, decreases the floating point operations by 49.4%, and increases the inference speed by 152.5% compared to the cutting-edge BSOD model (i.e., MROS). Extensive experiments on the UAV RGB-T 2400 and seven bi-modal dense prediction datasets demonstrate that AlignSal achieves both real-time inference speed and better performance and generalizability compared to nineteen state-of-the-art models across most evaluation metrics. In addition, our ablation studies further verify AlignSal's potential in boosting the performance of existing aligned BSOD models on UAV-based unaligned data. The code is available at: https://github.com/JoshuaLPF/AlignSal.
Despite significant advancements in salient object detection(SOD) in optical remote sensing images(ORSI), challenges persist due to the intricate edge structures of ORSIs and the complexity of their contextual relationships. Current deep learning approaches encounter difficulties in accurately identifying boundary features and lack efficiency in collaboratively modeling the foreground and background by leveraging contextual features. To address these challenges, we propose a stronger multifaceted collaborative salient object detector in ORSIs, termed LBA-MCNet, which incorporates aspects of localization, balance, and affinity. The network focuses on accurately locating targets, balancing detailed features, and modeling image-level global context information. Specifically, we design the Edge Feature Adaptive Balancing and Adjusting(EFABA) module for precise edge localization, using edge features to guide attention to boundaries and preserve spatial details. Moreover, we design the Global Distributed Affinity Learning(GDAL) module to model global context. It captures global context by generating an affinity map from the encoders final layer, ensuring effective modeling of global patterns. Additionally, deep supervision during deconvolution further enhances feature representation. Finally, we compared with 28 state of the art approaches on three publicly available datasets. The results clearly demonstrate the superiority of our method.
The purpose of RGB-D Salient Object Detection (SOD) is to pinpoint the most visually conspicuous areas within images accurately. While conventional deep models heavily rely on CNN extractors and overlook the long-range contextual dependencies, subsequent transformer-based models have addressed the issue to some extent but introduce high computational complexity. Moreover, incorporating spatial information from depth maps has been proven effective for this task. A primary challenge of this issue is how to fuse the complementary information from RGB and depth effectively. In this paper, we propose a dual Mamba-driven cross-modal fusion network for RGB-D SOD, named MambaSOD. Specifically, we first employ a dual Mamba-driven feature extractor for both RGB and depth to model the long-range dependencies in multiple modality inputs with linear complexity. Then, we design a cross-modal fusion Mamba for the captured multi-modal features to fully utilize the complementary information between the RGB and depth features. To the best of our knowledge, this work is the first attempt to explore the potential of the Mamba in the RGB-D SOD task, offering a novel perspective. Numerous experiments conducted on six prevailing datasets demonstrate our method's superiority over sixteen state-of-the-art RGB-D SOD models. The source code will be released at https://github.com/YueZhan721/MambaSOD.
Part-Whole Relational Fusion Towards Multi-Modal Scene Understanding
2024-10-19
Yi Liu, Chengxin Li, Shoukun Xu, Jungong Han
Multi-modal fusion has played a vital role in multi-modal scene understanding. Most existing methods focus on cross-modal fusion involving two modalities, often overlooking more complex multi-modal fusion, which is essential for real-world applications like autonomous driving, where visible, depth, event, LiDAR, etc., are used. Besides, few attempts for multi-modal fusion, \emph{e.g.}, simple concatenation, cross-modal attention, and token selection, cannot well dig into the intrinsic shared and specific details of multiple modalities. To tackle the challenge, in this paper, we propose a Part-Whole Relational Fusion (PWRF) framework. For the first time, this framework treats multi-modal fusion as part-whole relational fusion. It routes multiple individual part-level modalities to a fused whole-level modality using the part-whole relational routing ability of Capsule Networks (CapsNets). Through this part-whole routing, our PWRF generates modal-shared and modal-specific semantics from the whole-level modal capsules and the routing coefficients, respectively. On top of that, modal-shared and modal-specific details can be employed to solve the issue of multi-modal scene understanding, including synthetic multi-modal segmentation and visible-depth-thermal salient object detection in this paper. Experiments on several datasets demonstrate the superiority of the proposed PWRF framework for multi-modal scene understanding. The source code has been released on https://github.com/liuyi1989/PWRF.
Robust Salient Object Detection on Compressed Images Using Convolutional Neural Networks
2024-09-20
Guibiao Liao, Wei Gao
Salient object detection (SOD) has achieved substantial progress in recent years. In practical scenarios, compressed images (CI) serve as the primary medium for data transmission and storage. However, scant attention has been directed towards SOD for compressed images using convolutional neural networks (CNNs). In this paper, we are dedicated to strictly benchmarking and analyzing CNN-based salient object detection on compressed images. To comprehensively study this issue, we meticulously establish various CI SOD datasets from existing public SOD datasets. Subsequently, we investigate representative CNN-based SOD methods, assessing their robustness on compressed images (approximately 2.64 million images). Importantly, our evaluation results reveal two key findings: 1) current state-of-the-art CNN-based SOD models, while excelling on clean images, exhibit significant performance bottlenecks when applied to compressed images. 2) The principal factors influencing the robustness of CI SOD are rooted in the characteristics of compressed images and the limitations in saliency feature learning. Based on these observations, we propose a simple yet promising baseline framework that focuses on robust feature representation learning to achieve robust CNN-based CI SOD. Extensive experiments demonstrate the effectiveness of our approach, showcasing markedly improved robustness across various levels of image degradation, while maintaining competitive accuracy on clean data. We hope that our benchmarking efforts, analytical insights, and proposed techniques will contribute to a more comprehensive understanding of the robustness of CNN-based SOD algorithms, inspiring future research in the community.
Scribble supervised salient object detection (SSSOD) constructs segmentation ability of attractive objects from surroundings under the supervision of sparse scribble labels. For the better segmentation, depth and thermal infrared modalities serve as the supplement to RGB images in the complex scenes. Existing methods specifically design various feature extraction and multi-modal fusion strategies for RGB, RGB-Depth, RGB-Thermal, and Visual-Depth-Thermal image input respectively, leading to similar model flood. As the recently proposed Segment Anything Model (SAM) possesses extraordinary segmentation and prompt interactive capability, we propose an SSSOD family based on SAM, named SSFam, for the combination input with different modalities. Firstly, different modal-aware modulators are designed to attain modal-specific knowledge which cooperates with modal-agnostic information extracted from the frozen SAM encoder for the better feature ensemble. Secondly, a siamese decoder is tailored to bridge the gap between the training with scribble prompt and the testing with no prompt for the stronger decoding ability. Our model demonstrates the remarkable performance among combinations of different modalities and refreshes the highest level of scribble supervised methods and comes close to the ones of fully supervised methods. https://github.com/liuzywen/SSFam
2024-09-04
Xuelu Feng, Yunsheng Li, Dongdong Chen, Chunming Qiao, Junsong Yuan, Lu Yuan, Gang Hua
We introduce pluralistic salient object detection (PSOD), a novel task aimed at generating multiple plausible salient segmentation results for a given input image. Unlike conventional SOD methods that produce a single segmentation mask for salient objects, this new setting recognizes the inherent complexity of real-world images, comprising multiple objects, and the ambiguity in defining salient objects due to different user intentions. To study this task, we present two new SOD datasets "DUTS-MM" and "DUS-MQ", along with newly designed evaluation metrics. DUTS-MM builds upon the DUTS dataset but enriches the ground-truth mask annotations from three aspects which 1) improves the mask quality especially for boundary and fine-grained structures; 2) alleviates the annotation inconsistency issue; and 3) provides multiple ground-truth masks for images with saliency ambiguity. DUTS-MQ consists of approximately 100K image-mask pairs with human-annotated preference scores, enabling the learning of real human preferences in measuring mask quality. Building upon these two datasets, we propose a simple yet effective pluralistic SOD baseline based on a Mixture-of-Experts (MOE) design. Equipped with two prediction heads, it simultaneously predicts multiple masks using different query prompts and predicts human preference scores for each mask candidate. Extensive experiments and analyses underscore the significance of our proposed datasets and affirm the effectiveness of our PSOD framework.
CONDA: Condensed Deep Association Learning for Co-Salient Object Detection
2024-09-02
Long Li, Nian Liu, Dingwen Zhang, Zhongyu Li, Salman Khan, Rao Anwer, Hisham Cholakkal, Junwei Han, Fahad Shahbaz Khan
Inter-image association modeling is crucial for co-salient object detection. Despite satisfactory performance, previous methods still have limitations on sufficient inter-image association modeling. Because most of them focus on image feature optimization under the guidance of heuristically calculated raw inter-image associations. They directly rely on raw associations which are not reliable in complex scenarios, and their image feature optimization approach is not explicit for inter-image association modeling. To alleviate these limitations, this paper proposes a deep association learning strategy that deploys deep networks on raw associations to explicitly transform them into deep association features. Specifically, we first create hyperassociations to collect dense pixel-pair-wise raw associations and then deploys deep aggregation networks on them. We design a progressive association generation module for this purpose with additional enhancement of the hyperassociation calculation. More importantly, we propose a correspondence-induced association condensation module that introduces a pretext task, i.e. semantic correspondence estimation, to condense the hyperassociations for computational burden reduction and noise elimination. We also design an object-aware cycle consistency loss for high-quality correspondence estimations. Experimental results in three benchmark datasets demonstrate the remarkable effectiveness of our proposed method with various training settings.
SODAWideNet++: Combining Attention and Convolutions for Salient Object Detection
2024-08-29
Rohit Venkata Sai Dulam, Chandra Kambhamettu
Salient Object Detection (SOD) has traditionally relied on feature refinement modules that utilize the features of an ImageNet pre-trained backbone. However, this approach limits the possibility of pre-training the entire network because of the distinct nature of SOD and image classification. Additionally, the architecture of these backbones originally built for Image classification is sub-optimal for a dense prediction task like SOD. To address these issues, we propose a novel encoder-decoder-style neural network called SODAWideNet++ that is designed explicitly for SOD. Inspired by the vision transformers ability to attain a global receptive field from the initial stages, we introduce the Attention Guided Long Range Feature Extraction (AGLRFE) module, which combines large dilated convolutions and self-attention. Specifically, we use attention features to guide long-range information extracted by multiple dilated convolutions, thus taking advantage of the inductive biases of a convolution operation and the input dependency brought by self-attention. In contrast to the current paradigm of ImageNet pre-training, we modify 118K annotated images from the COCO semantic segmentation dataset by binarizing the annotations to pre-train the proposed model end-to-end. Further, we supervise the background predictions along with the foreground to push our model to generate accurate saliency predictions. SODAWideNet++ performs competitively on five different datasets while only containing 35% of the trainable parameters compared to the state-of-the-art models. The code and pre-computed saliency maps are provided at https://github.com/VimsLab/SODAWideNetPlusPlus.
Adapting Segment Anything Model to Multi-modal Salient Object Detection with Semantic Feature Fusion Guidance
2024-08-27
Kunpeng Wang, Danying Lin, Chenglong Li, Zhengzheng Tu, Bin Luo
Although most existing multi-modal salient object detection (SOD) methods demonstrate effectiveness through training models from scratch, the limited multi-modal data hinders these methods from reaching optimality. In this paper, we propose a novel framework to explore and exploit the powerful feature representation and zero-shot generalization ability of the pre-trained Segment Anything Model (SAM) for multi-modal SOD. Despite serving as a recent vision fundamental model, driving the class-agnostic SAM to comprehend and detect salient objects accurately is non-trivial, especially in challenging scenes. To this end, we develop \underline{SAM} with se\underline{m}antic f\underline{e}ature fu\underline{s}ion guidanc\underline{e} (Sammese), which incorporates multi-modal saliency-specific knowledge into SAM to adapt SAM to multi-modal SOD tasks. However, it is difficult for SAM trained on single-modal data to directly mine the complementary benefits of multi-modal inputs and comprehensively utilize them to achieve accurate saliency prediction. To address these issues, we first design a multi-modal complementary fusion module to extract robust multi-modal semantic features by integrating information from visible and thermal or depth image pairs. Then, we feed the extracted multi-modal semantic features into both the SAM image encoder and mask decoder for fine-tuning and prompting, respectively. Specifically, in the image encoder, a multi-modal adapter is proposed to adapt the single-modal SAM to multi-modal information. In the mask decoder, a semantic-geometric prompt generation strategy is proposed to produce corresponding embeddings with various saliency cues. Extensive experiments on both RGB-D and RGB-T SOD benchmarks show the effectiveness of the proposed framework. The code will be available at \url{https://github.com/Angknpng/Sammese}.
Depth information provides valuable insights into the 3D structure especially the outline of objects, which can be utilized to improve the semantic segmentation tasks. However, a naive fusion of depth information can disrupt feature and compromise accuracy due to the modality gap between the depth and the vision. In this work, we introduce a Depth-guided Texture Diffusion approach that effectively tackles the outlined challenge. Our method extracts low-level features from edges and textures to create a texture image. This image is then selectively diffused across the depth map, enhancing structural information vital for precisely extracting object outlines. By integrating this enriched depth map with the original RGB image into a joint feature embedding, our method effectively bridges the disparity between the depth map and the image, enabling more accurate semantic segmentation. We conduct comprehensive experiments across diverse, commonly-used datasets spanning a wide range of semantic segmentation tasks, including Camouflaged Object Detection (COD), Salient Object Detection (SOD), and indoor semantic segmentation. With source-free estimated depth or depth captured by depth cameras, our method consistently outperforms existing baselines and achieves new state-of-theart results, demonstrating the effectiveness of our Depth-guided Texture Diffusion for image semantic segmentation.
SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation
2024-08-16
Xinyu Xiong, Zihuang Wu, Shuangyi Tan, Wenxue Li, Feilong Tang, Ying Chen, Siying Li, Jie Ma, Guanbin Li
Image segmentation plays an important role in vision understanding. Recently, the emerging vision foundation models continuously achieved superior performance on various tasks. Following such success, in this paper, we prove that the Segment Anything Model 2 (SAM2) can be a strong encoder for U-shaped segmentation models. We propose a simple but effective framework, termed SAM2-UNet, for versatile image segmentation. Specifically, SAM2-UNet adopts the Hiera backbone of SAM2 as the encoder, while the decoder uses the classic U-shaped design. Additionally, adapters are inserted into the encoder to allow parameter-efficient fine-tuning. Preliminary experiments on various downstream tasks, such as camouflaged object detection, salient object detection, marine animal segmentation, mirror detection, and polyp segmentation, demonstrate that our SAM2-UNet can simply beat existing specialized state-of-the-art methods without bells and whistles. Project page: \url{https://github.com/WZH0120/SAM2-UNet}.
Multi-Scale and Detail-Enhanced Segment Anything Model for Salient Object Detection
2024-08-08
Shixuan Gao, Pingping Zhang, Tianyu Yan, Huchuan Lu
Salient Object Detection (SOD) aims to identify and segment the most prominent objects in images. Advanced SOD methods often utilize various Convolutional Neural Networks (CNN) or Transformers for deep feature extraction. However, these methods still deliver low performance and poor generalization in complex cases. Recently, Segment Anything Model (SAM) has been proposed as a visual fundamental model, which gives strong segmentation and generalization capabilities. Nonetheless, SAM requires accurate prompts of target objects, which are unavailable in SOD. Additionally, SAM lacks the utilization of multi-scale and multi-level information, as well as the incorporation of fine-grained details. To address these shortcomings, we propose a Multi-scale and Detail-enhanced SAM (MDSAM) for SOD. Specifically, we first introduce a Lightweight Multi-Scale Adapter (LMSA), which allows SAM to learn multi-scale information with very few trainable parameters. Then, we propose a Multi-Level Fusion Module (MLFM) to comprehensively utilize the multi-level information from the SAM's encoder. Finally, we propose a Detail Enhancement Module (DEM) to incorporate SAM with fine-grained details. Experimental results demonstrate the superior performance of our model on multiple SOD datasets and its strong generalization on other segmentation tasks. The source code is released at https://github.com/BellyBeauty/MDSAM.
We present an advanced study on more challenging high-resolution salient object detection (HRSOD) from both dataset and network framework perspectives. To compensate for the lack of HRSOD dataset, we thoughtfully collect a large-scale high resolution salient object detection dataset, called UHRSD, containing 5,920 images from real-world complex scenarios at 4K-8K resolutions. All the images are finely annotated in pixel-level, far exceeding previous low-resolution SOD datasets. Aiming at overcoming the contradiction between the sampling depth and the receptive field size in the past methods, we propose a novel one-stage framework for HR-SOD task using pyramid grafting mechanism. In general, transformer-based and CNN-based backbones are adopted to extract features from different resolution images independently and then these features are grafted from transformer branch to CNN branch. An attention-based Cross-Model Grafting Module (CMGM) is proposed to enable CNN branch to combine broken detailed information more holistically, guided by different source feature during decoding process. Moreover, we design an Attention Guided Loss (AGL) to explicitly supervise the attention matrix generated by CMGM to help the network better interact with the attention from different branches. Comprehensive experiments on UHRSD and widely-used SOD datasets demonstrate that our method can simultaneously locate salient object and preserve rich details, outperforming state-of-the-art methods. To verify the generalization ability of the proposed framework, we apply it to the camouflaged object detection (COD) task. Notably, our method performs superior to most state-of-the-art COD methods without bells and whistles.
PEEKABOO: Hiding parts of an image for unsupervised object localization
2024-07-24
Hasib Zunair, A. Ben Hamza
Localizing objects in an unsupervised manner poses significant challenges due to the absence of key visual information such as the appearance, type and number of objects, as well as the lack of labeled object classes typically available in supervised settings. While recent approaches to unsupervised object localization have demonstrated significant progress by leveraging self-supervised visual representations, they often require computationally intensive training processes, resulting in high resource demands in terms of computation, learnable parameters, and data. They also lack explicit modeling of visual context, potentially limiting their accuracy in object localization. To tackle these challenges, we propose a single-stage learning framework, dubbed PEEKABOO, for unsupervised object localization by learning context-based representations at both the pixel- and shape-level of the localized objects through image masking. The key idea is to selectively hide parts of an image and leverage the remaining image information to infer the location of objects without explicit supervision. The experimental results, both quantitative and qualitative, across various benchmark datasets, demonstrate the simplicity, effectiveness and competitive performance of our approach compared to state-of-the-art methods in both single object discovery and unsupervised salient object detection tasks. Code and pre-trained models are available at: https://github.com/hasibzunair/peekaboo
Improving Unsupervised Video Object Segmentation via Fake Flow Generation
2024-07-16
Suhwan Cho, Minhyeok Lee, Jungho Lee, Donghyeong Kim, Seunghoon Lee, Sungmin Woo, Sangyoun Lee
Unsupervised video object segmentation (VOS), also known as video salient object detection, aims to detect the most prominent object in a video at the pixel level. Recently, two-stream approaches that leverage both RGB images and optical flow maps have gained significant attention. However, the limited amount of training data remains a substantial challenge. In this study, we propose a novel data generation method that simulates fake optical flows from single images, thereby creating large-scale training data for stable network learning. Inspired by the observation that optical flow maps are highly dependent on depth maps, we generate fake optical flows by refining and augmenting the estimated depth maps of each image. By incorporating our simulated image-flow pairs, we achieve new state-of-the-art performance on all public benchmark datasets without relying on complex modules. We believe that our data generation method represents a potential breakthrough for future VOS research.
CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object Detection
2024-07-09
Shuang Hao, Chunlin Zhong, He Tang
The depth/thermal information is beneficial for detecting salient object with conventional RGB images. However, in dual-modal salient object detection (SOD) model, the robustness against noisy inputs and modality missing is crucial but rarely studied. To tackle this problem, we introduce \textbf{Co}nditional Dropout and \textbf{LA}nguage-driven(\textbf{CoLA}) framework comprising two core components. 1) Language-driven Quality Assessment (LQA): Leveraging a pretrained vision-language model with a prompt learner, the LQA recalibrates image contributions without requiring additional quality annotations. This approach effectively mitigates the impact of noisy inputs. 2) Conditional Dropout (CD): A learning method to strengthen the model's adaptability in scenarios with missing modalities, while preserving its performance under complete modalities. The CD serves as a plug-in training scheme that treats modality-missing as conditions, strengthening the overall robustness of various dual-modal SOD models. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art dual-modal SOD models, under both modality-complete and modality-missing conditions. We will release source code upon acceptance.
FIPGNet:Pyramid grafting network with feature interaction strategies
2024-07-04
Ziyi Ding, Like Xin
Salient object detection is designed to identify the objects in an image that attract the most visual attention.Currently, the most advanced method of significance object detection adopts pyramid grafting network architecture.However, pyramid-graft network architecture still has the problem of failing to accurately locate significant targets.We observe that this is mainly due to the fact that current salient object detection methods simply aggregate different scale features, ignoring the correlation between different scale features.To overcome these problems, we propose a new salience object detection framework(FIPGNet),which is a pyramid graft network with feature interaction strategies.Specifically, we propose an attention-mechanism based feature interaction strategy (FIA) that innovatively introduces spatial agent Cross Attention (SACA) to achieve multi-level feature interaction, highlighting important spatial regions from a spatial perspective, thereby enhancing salient regions.And the channel proxy Cross Attention Module (CCM), which is used to effectively connect the features extracted by the backbone network and the features processed using the spatial proxy cross attention module, eliminating inconsistencies.Finally, under the action of these two modules, the prominent target location problem in the current pyramid grafting network model is solved.Experimental results on six challenging datasets show that the proposed method outperforms the current 12 salient object detection methods on four indicators.
ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection
2024-06-18
Junhao Lin, Lei Zhu, Jiaxing Shen, Huazhu Fu, Qing Zhang, Liansheng Wang
With the rapid development of depth sensor, more and more RGB-D videos could be obtained. Identifying the foreground in RGB-D videos is a fundamental and important task. However, the existing salient object detection (SOD) works only focus on either static RGB-D images or RGB videos, ignoring the collaborating of RGB-D and video information. In this paper, we first collect a new annotated RGB-D video SOD (ViDSOD-100) dataset, which contains 100 videos within a total of 9,362 frames, acquired from diverse natural scenes. All the frames in each video are manually annotated to a high-quality saliency annotation. Moreover, we propose a new baseline model, named attentive triple-fusion network (ATF-Net), for RGB-D video salient object detection. Our method aggregates the appearance information from an input RGB image, spatio-temporal information from an estimated motion map, and the geometry information from the depth map by devising three modality-specific branches and a multi-modality integration branch. The modality-specific branches extract the representation of different inputs, while the multi-modality integration branch combines the multi-level modality-specific features by introducing the encoder feature aggregation (MEA) modules and decoder feature aggregation (MDA) modules. The experimental findings conducted on both our newly introduced ViDSOD-100 dataset and the well-established DAVSOD dataset highlight the superior performance of the proposed ATF-Net. This performance enhancement is demonstrated both quantitatively and qualitatively, surpassing the capabilities of current state-of-the-art techniques across various domains, including RGB-D saliency detection, video saliency detection, and video object segmentation. Our data and our code are available at github.com/jhl-Det/RGBD_Video_SOD.
Dataset Enhancement with Instance-Level Augmentations
2024-06-12
Orest Kupyn, Christian Rupprecht
We present a method for expanding a dataset by incorporating knowledge from the wide distribution of pre-trained latent diffusion models. Data augmentations typically incorporate inductive biases about the image formation process into the training (e.g. translation, scaling, colour changes, etc.). Here, we go beyond simple pixel transformations and introduce the concept of instance-level data augmentation by repainting parts of the image at the level of object instances. The method combines a conditional diffusion model with depth and edge maps control conditioning to seamlessly repaint individual objects inside the scene, being applicable to any segmentation or detection dataset. Used as a data augmentation method, it improves the performance and generalization of the state-of-the-art salient object detection, semantic segmentation and object detection models. By redrawing all privacy-sensitive instances (people, license plates, etc.), the method is also applicable for data anonymization. We also release fully synthetic and anonymized expansions for popular datasets: COCO, Pascal VOC and DUTS.
Learning Adaptive Fusion Bank for Multi-modal Salient Object Detection
2024-06-03
Kunpeng Wang, Zhengzheng Tu, Chenglong Li, Cheng Zhang, Bin Luo
Multi-modal salient object detection (MSOD) aims to boost saliency detection performance by integrating visible sources with depth or thermal infrared ones. Existing methods generally design different fusion schemes to handle certain issues or challenges. Although these fusion schemes are effective at addressing specific issues or challenges, they may struggle to handle multiple complex challenges simultaneously. To solve this problem, we propose a novel adaptive fusion bank that makes full use of the complementary benefits from a set of basic fusion schemes to handle different challenges simultaneously for robust MSOD. We focus on handling five major challenges in MSOD, namely center bias, scale variation, image clutter, low illumination, and thermal crossover or depth ambiguity. The fusion bank proposed consists of five representative fusion schemes, which are specifically designed based on the characteristics of each challenge, respectively. The bank is scalable, and more fusion schemes could be incorporated into the bank for more challenges. To adaptively select the appropriate fusion scheme for multi-modal input, we introduce an adaptive ensemble module that forms the adaptive fusion bank, which is embedded into hierarchical layers for sufficient fusion of different source data. Moreover, we design an indirect interactive guidance module to accurately detect salient hollow objects via the skip integration of high-level semantic information and low-level spatial details. Extensive experiments on three RGBT datasets and seven RGBD datasets demonstrate that the proposed method achieves the outstanding performance compared to the state-of-the-art methods. The code and results are available at https://github.com/Angknpng/LAFB.
Alignment-Free RGBT Salient Object Detection: Semantics-guided Asymmetric Correlation Network and A Unified Benchmark
2024-06-03
Kunpeng Wang, Danying Lin, Chenglong Li, Zhengzheng Tu, Bin Luo
RGB and Thermal (RGBT) Salient Object Detection (SOD) aims to achieve high-quality saliency prediction by exploiting the complementary information of visible and thermal image pairs, which are initially captured in an unaligned manner. However, existing methods are tailored for manually aligned image pairs, which are labor-intensive, and directly applying these methods to original unaligned image pairs could significantly degrade their performance. In this paper, we make the first attempt to address RGBT SOD for initially captured RGB and thermal image pairs without manual alignment. Specifically, we propose a Semantics-guided Asymmetric Correlation Network (SACNet) that consists of two novel components: 1) an asymmetric correlation module utilizing semantics-guided attention to model cross-modal correlations specific to unaligned salient regions; 2) an associated feature sampling module to sample relevant thermal features according to the corresponding RGB features for multi-modal feature integration. In addition, we construct a unified benchmark dataset called UVT2000, containing 2000 RGB and thermal image pairs directly captured from various real-world scenes without any alignment, to facilitate research on alignment-free RGBT SOD. Extensive experiments on both aligned and unaligned datasets demonstrate the effectiveness and superior performance of our method. The dataset and code are available at https://github.com/Angknpng/SACNet.
Boosting General Trimap-free Matting in the Real-World Image
2024-05-28
Leo Shan Wenzhang Zhou Grace Zhao
Image matting aims to obtain an alpha matte that separates foreground objects from the background accurately. Recently, trimap-free matting has been well studied because it requires only the original image without any extra input. Such methods usually extract a rough foreground by itself to take place trimap as further guidance. However, the definition of 'foreground' lacks a unified standard and thus ambiguities arise. Besides, the extracted foreground is sometimes incomplete due to inadequate network design. Most importantly, there is not a large-scale real-world matting dataset, and current trimap-free methods trained with synthetic images suffer from large domain shift problems in practice. In this paper, we define the salient object as foreground, which is consistent with human cognition and annotations of the current matting dataset. Meanwhile, data and technologies in salient object detection can be transferred to matting in a breeze. To obtain a more accurate and complete alpha matte, we propose a network called \textbf{M}ulti-\textbf{F}eature fusion-based \textbf{C}oarse-to-fine Network \textbf{(MFC-Net)}, which fully integrates multiple features for an accurate and complete alpha matte. Furthermore, we introduce image harmony in data composition to bridge the gap between synthetic and real images. More importantly, we establish the largest general matting dataset \textbf{(Real-19k)} in the real world to date. Experiments show that our method is significantly effective on both synthetic and real-world images, and the performance in the real-world dataset is far better than existing matting-free methods. Our code and data will be released soon.
Deep learning-based information processing consumes long time and requires huge computing resources, especially for dense prediction tasks which require an output for each pixel, like semantic segmentation and salient object detection. There are mainly two challenges for quantization of dense prediction tasks. Firstly, directly applying the upsampling operation that dense prediction tasks require is extremely crude and causes unacceptable accuracy reduction. Secondly, the complex structure of dense prediction networks means it is difficult to maintain a fast speed as well as a high accuracy when performing quantization. In this paper, we propose an effective upsampling method and an efficient attention computation strategy to transfer the success of the binary neural networks (BNN) from single prediction tasks to dense prediction tasks. Firstly, we design a simple and robust multi-branch parallel upsampling structure to achieve the high accuracy. Then we further optimize the attention method which plays an important role in segmentation but has huge computation complexity. Our attention method can reduce the computational complexity by a factor of one hundred times but retain the original effect. Experiments on Cityscapes, KITTI road, and ECSSD fully show the effectiveness of our work.
MINet: Multi-scale Interactive Network for Real-time Salient Object Detection of Strip Steel Surface Defects
2024-05-25
Kunye Shen, Xiaofei Zhou, Zhi Liu
The automated surface defect detection is a fundamental task in industrial production, and the existing saliencybased works overcome the challenging scenes and give promising detection results. However, the cutting-edge efforts often suffer from large parameter size, heavy computational cost, and slow inference speed, which heavily limits the practical applications. To this end, we devise a multi-scale interactive (MI) module, which employs depthwise convolution (DWConv) and pointwise convolution (PWConv) to independently extract and interactively fuse features of different scales, respectively. Particularly, the MI module can provide satisfactory characterization for defect regions with fewer parameters. Embarking on this module, we propose a lightweight Multi-scale Interactive Network (MINet) to conduct real-time salient object detection of strip steel surface defects. Comprehensive experimental results on SD-Saliency-900 dataset, which contains three kinds of strip steel surface defect detection images (i.e., inclusion, patches, and scratches), demonstrate that the proposed MINet presents comparable detection accuracy with the state-of-the-art methods while running at a GPU speed of 721FPS and a CPU speed of 6.3FPS for 368*368 images with only 0.28M parameters. The code is available at https://github.com/Kunye-Shen/MINet.
Salience-guided Ground Factor for Robust Localization of Delivery Robots in Complex Urban Environments
2024-05-20
Jooyong Park, Jungwoo Lee, Euncheol Choi, Younggun Cho
In urban environments for delivery robots, particularly in areas such as campuses and towns, many custom features defy standard road semantic categorizations. Addressing this challenge, our paper introduces a method leveraging Salient Object Detection (SOD) to extract these unique features, employing them as pivotal factors for enhanced robot loop closure and localization. Traditional geometric feature-based localization is hampered by fluctuating illumination and appearance changes. Our preference for SOD over semantic segmentation sidesteps the intricacies of classifying a myriad of non-standardized urban features. To achieve consistent ground features, the Motion Compensate IPM (MC-IPM) technique is implemented, capitalizing on motion for distortion compensation and subsequently selecting the most pertinent salient ground features through moment computations. For thorough evaluation, we validated the saliency detection and localization performances to the real urban scenarios. Project page: https://sites.google.com/view/salient-ground-feature/home.
This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://github.com/Ferry-Li/SI-SOD.
Depth images and thermal images contain the spatial geometry information and surface temperature information, which can act as complementary information for the RGB modality. However, the quality of the depth and thermal images is often unreliable in some challenging scenarios, which will result in the performance degradation of the two-modal based salient object detection (SOD). Meanwhile, some researchers pay attention to the triple-modal SOD task, where they attempt to explore the complementarity of the RGB image, the depth image, and the thermal image. However, existing triple-modal SOD methods fail to perceive the quality of depth maps and thermal images, which leads to performance degradation when dealing with scenes with low-quality depth and thermal images. Therefore, we propose a quality-aware selective fusion network (QSF-Net) to conduct VDT salient object detection, which contains three subnets including the initial feature extraction subnet, the quality-aware region selection subnet, and the region-guided selective fusion subnet. Firstly, except for extracting features, the initial feature extraction subnet can generate a preliminary prediction map from each modality via a shrinkage pyramid architecture. Then, we design the weakly-supervised quality-aware region selection subnet to generate the quality-aware maps. Concretely, we first find the high-quality and low-quality regions by using the preliminary predictions, which further constitute the pseudo label that can be used to train this subnet. Finally, the region-guided selective fusion subnet purifies the initial features under the guidance of the quality-aware maps, and then fuses the triple-modal features and refines the edge details of prediction maps through the intra-modality and inter-modality attention (IIA) module and the edge refinement (ER) module, respectively. Extensive experiments are performed on VDT-2048
Salient Object Detection From Arbitrary Modalities
2024-05-06
Nianchang Huang, Yang Yang, Ruida Xi, Qiang Zhang, Jungong Han, Jin Huang
Toward desirable saliency prediction, the types and numbers of inputs for a salient object detection (SOD) algorithm may dynamically change in many real-life applications. However, existing SOD algorithms are mainly designed or trained for one particular type of inputs, failing to be generalized to other types of inputs. Consequentially, more types of SOD algorithms need to be prepared in advance for handling different types of inputs, raising huge hardware and research costs. Differently, in this paper, we propose a new type of SOD task, termed Arbitrary Modality SOD (AM SOD). The most prominent characteristics of AM SOD are that the modality types and modality numbers will be arbitrary or dynamically changed. The former means that the inputs to the AM SOD algorithm may be arbitrary modalities such as RGB, depths, or even any combination of them. While, the latter indicates that the inputs may have arbitrary modality numbers as the input type is changed, e.g. single-modality RGB image, dual-modality RGB-Depth (RGB-D) images or triple-modality RGB-Depth-Thermal (RGB-D-T) images. Accordingly, a preliminary solution to the above challenges, \i.e. a modality switch network (MSN), is proposed in this paper. In particular, a modality switch feature extractor (MSFE) is first designed to extract discriminative features from each modality effectively by introducing some modality indicators, which will generate some weights for modality switching. Subsequently, a dynamic fusion module (DFM) is proposed to adaptively fuse features from a variable number of modalities based on a novel Transformer structure. Finally, a new dataset, named AM-XD, is constructed to facilitate research on AM SOD. Extensive experiments demonstrate that our AM SOD method can effectively cope with changes in the type and number of input modalities for robust salient object detection.
Modality Prompts for Arbitrary Modality Salient Object Detection
2024-05-06
Nianchang Huang, Yang Yang, Qiang Zhang, Jungong Han, Jin Huang
This paper delves into the task of arbitrary modality salient object detection (AM SOD), aiming to detect salient objects from arbitrary modalities, eg RGB images, RGB-D images, and RGB-D-T images. A novel modality-adaptive Transformer (MAT) will be proposed to investigate two fundamental challenges of AM SOD, ie more diverse modality discrepancies caused by varying modality types that need to be processed, and dynamic fusion design caused by an uncertain number of modalities present in the inputs of multimodal fusion strategy. Specifically, inspired by prompt learning's ability of aligning the distributions of pre-trained models to the characteristic of downstream tasks by learning some prompts, MAT will first present a modality-adaptive feature extractor (MAFE) to tackle the diverse modality discrepancies by introducing a modality prompt for each modality. In the training stage, a new modality translation contractive (MTC) loss will be further designed to assist MAFE in learning those modality-distinguishable modality prompts. Accordingly, in the testing stage, MAFE can employ those learned modality prompts to adaptively adjust its feature space according to the characteristics of the input modalities, thus being able to extract discriminative unimodal features. Then, MAFE will present a channel-wise and spatial-wise fusion hybrid (CSFH) strategy to meet the demand for dynamic fusion. For that, CSFH dedicates a channel-wise dynamic fusion module (CDFM) and a novel spatial-wise dynamic fusion module (SDFM) to fuse the unimodal features from varying numbers of modalities and meanwhile effectively capture cross-modal complementary semantic and detail information, respectively. Moreover, CSFH will carefully align CDFM and SDFM to different levels of unimodal features based on their characteristics for more effective complementary information exploitation.
SalFAU-Net: Saliency Fusion Attention U-Net for Salient Object Detection
2024-05-05
Kassaw Abraham Mulat, Zhengyong Feng, Tegegne Solomon Eshetie, Ahmed Endris Hasen
Salient object detection (SOD) remains an important task in computer vision, with applications ranging from image segmentation to autonomous driving. Fully convolutional network (FCN)-based methods have made remarkable progress in visual saliency detection over the last few decades. However, these methods have limitations in accurately detecting salient objects, particularly in challenging scenes with multiple objects, small objects, or objects with low resolutions. To address this issue, we proposed a Saliency Fusion Attention U-Net (SalFAU-Net) model, which incorporates a saliency fusion module into each decoder block of the attention U-net model to generate saliency probability maps from each decoder block. SalFAU-Net employs an attention mechanism to selectively focus on the most informative regions of an image and suppress non-salient regions. We train SalFAU-Net on the DUTS dataset using a binary cross-entropy loss function. We conducted experiments on six popular SOD evaluation datasets to evaluate the effectiveness of the proposed method. The experimental results demonstrate that our method, SalFAU-Net, achieves competitive performance compared to other methods in terms of mean absolute error (MAE), F-measure, s-measure, and e-measure.
Salient object detection (SOD) aims at finding the most salient objects in images and outputs pixel-level binary masks. Transformer-based methods achieve promising performance due to their global semantic understanding, crucial for identifying salient objects. However, these models tend to be large and require numerous training parameters. To better harness the potential of transformers for SOD, we propose a novel parameter-efficient fine-tuning method aimed at reducing the number of training parameters while enhancing the salient object detection capability. Our model, termed EXternal Prompt features Enhanced adapteR Tuning (ExPert), features an encoder-decoder structure with adapters and injectors interspersed between the layers of a frozen transformer encoder. The adapter modules adapt the pretrained backbone to SOD while the injector modules incorporate external prompt features to enhance the awareness of salient objects. Comprehensive experiments demonstrate the superiority of our method. Surpassing former state-of-the-art (SOTA) models across five SOD datasets, ExPert achieves 0.215 mean absolute error (MAE) in the ECSSD dataset with 80.2M trained parameters, 21% better than SelfReformer and 47% better than EGNet.
Unified Unsupervised Salient Object Detection via Knowledge Transfer
2024-04-23
Yao Yuan, Wutao Liu, Pan Gao, Qun Dai, Jie Qin
Recently, unsupervised salient object detection (USOD) has gained increasing attention due to its annotation-free nature. However, current methods mainly focus on specific tasks such as RGB and RGB-D, neglecting the potential for task migration. In this paper, we propose a unified USOD framework for generic USOD tasks. Firstly, we propose a Progressive Curriculum Learning-based Saliency Distilling (PCL-SD) mechanism to extract saliency cues from a pre-trained deep network. This mechanism starts with easy samples and progressively moves towards harder ones, to avoid initial interference caused by hard samples. Afterwards, the obtained saliency cues are utilized to train a saliency detector, and we employ a Self-rectify Pseudo-label Refinement (SPR) mechanism to improve the quality of pseudo-labels. Finally, an adapter-tuning method is devised to transfer the acquired saliency knowledge, leveraging shared knowledge to attain superior transferring performance on the target tasks. Extensive experiments on five representative SOD tasks confirm the effectiveness and feasibility of our proposed method. Code and supplement materials are available at https://github.com/I2-Multimedia-Lab/A2S-v3.
2024-04-01
Beomyoung Kim, Donghyun Kim, Sung Ju Hwang
This paper presents a fresh perspective on the role of saliency maps in weakly-supervised semantic segmentation (WSSS) and offers new insights and research directions based on our empirical findings. We conduct comprehensive experiments and observe that the quality of the saliency map is a critical factor in saliency-guided WSSS approaches. Nonetheless, we find that the saliency maps used in previous works are often arbitrarily chosen, despite their significant impact on WSSS. Additionally, we observe that the choice of the threshold, which has received less attention before, is non-trivial in WSSS. To facilitate more meaningful and rigorous research for saliency-guided WSSS, we introduce \texttt{WSSS-BED}, a standardized framework for conducting research under unified conditions. \texttt{WSSS-BED} provides various saliency maps and activation maps for seven WSSS methods, as well as saliency maps from unsupervised salient object detection models.
Hyperspectral salient object detection (HSOD) has exhibited remarkable promise across various applications, particularly in intricate scenarios where conventional RGB-based approaches fall short. Despite the considerable progress in HSOD method advancements, two critical challenges require immediate attention. Firstly, existing hyperspectral data dimension reduction techniques incur a loss of spectral information, which adversely affects detection accuracy. Secondly, previous methods insufficiently harness the inherent distinctive attributes of hyperspectral images (HSIs) during the feature extraction process. To address these challenges, we propose a novel approach termed the Distilled Mixed Spectral-Spatial Network (DMSSN), comprising a Distilled Spectral Encoding process and a Mixed Spectral-Spatial Transformer (MSST) feature extraction network. The encoding process utilizes knowledge distillation to construct a lightweight autoencoder for dimension reduction, striking a balance between robust encoding capabilities and low computational costs. The MSST extracts spectral-spatial features through multiple attention head groups, collaboratively enhancing its resistance to intricate scenarios. Moreover, we have created a large-scale HSOD dataset, HSOD-BIT, to tackle the issue of data scarcity in this field and meet the fundamental data requirements of deep network training. Extensive experiments demonstrate that our proposed DMSSN achieves state-of-the-art performance on multiple datasets. We will soon make the code and dataset publicly available on https://github.com/anonymous0519/HSOD-BIT.
CosalPure: Learning Concept from Group Images for Robust Co-Saliency Detection
2024-03-27
Jiayi Zhu, Qing Guo, Felix Juefei-Xu, Yihao Huang, Yang Liu, Geguang Pu
Co-salient object detection (CoSOD) aims to identify the common and salient (usually in the foreground) regions across a given group of images. Although achieving significant progress, state-of-the-art CoSODs could be easily affected by some adversarial perturbations, leading to substantial accuracy reduction. The adversarial perturbations can mislead CoSODs but do not change the high-level semantic information (e.g., concept) of the co-salient objects. In this paper, we propose a novel robustness enhancement framework by first learning the concept of the co-salient objects based on the input group images and then leveraging this concept to purify adversarial perturbations, which are subsequently fed to CoSODs for robustness enhancement. Specifically, we propose CosalPure containing two modules, i.e., group-image concept learning and concept-guided diffusion purification. For the first module, we adopt a pre-trained text-to-image diffusion model to learn the concept of co-salient objects within group images where the learned concept is robust to adversarial examples. For the second module, we map the adversarial image to the latent space and then perform diffusion generation by embedding the learned concept into the noise prediction function as an extra condition. Our method can effectively alleviate the influence of the SOTA adversarial attack containing different adversarial patterns, including exposure and noise. The extensive results demonstrate that our method could enhance the robustness of CoSODs significantly.
Our paper introduces a novel two-stage self-supervised approach for detecting co-occurring salient objects (CoSOD) in image groups without requiring segmentation annotations. Unlike existing unsupervised methods that rely solely on patch-level information (e.g. clustering patch descriptors) or on computation heavy off-the-shelf components for CoSOD, our lightweight model leverages feature correspondences at both patch and region levels, significantly improving prediction performance. In the first stage, we train a self-supervised network that detects co-salient regions by computing local patch-level feature correspondences across images. We obtain the segmentation predictions using confidence-based adaptive thresholding. In the next stage, we refine these intermediate segmentations by eliminating the detected regions (within each image) whose averaged feature representations are dissimilar to the foreground feature representation averaged across all the cross-attention maps (from the previous stage). Extensive experiments on three CoSOD benchmark datasets show that our self-supervised model outperforms the corresponding state-of-the-art models by a huge margin (e.g. on the CoCA dataset, our model has a 13.7% F-measure gain over the SOTA unsupervised CoSOD model). Notably, our self-supervised model also outperforms several recent fully supervised CoSOD models on the three test datasets (e.g., on the CoCA dataset, our model has a 4.6% F-measure gain over a recent supervised CoSOD model).
While we enjoy the richness and informativeness of multimodal data, it also introduces interference and redundancy of information. To achieve optimal domain interpretation with limited resources, we propose CSDNet, a lightweight \textbf{C}ross \textbf{S}hallow and \textbf{D}eep Perception \textbf{Net}work designed to integrate two modalities with less coherence, thereby discarding redundant information or even modality. We implement our CSDNet for Salient Object Detection (SOD) task in robotic perception. The proposed method capitalises on spatial information prescreening and implicit coherence navigation across shallow and deep layers of the depth-thermal (D-T) modality, prioritising integration over fusion to maximise the scene interpretation. To further refine the descriptive capabilities of the encoder for the less-known D-T modalities, we also propose SAMAEP to guide an effective feature mapping to the generalised feature space. Our approach is tested on the VDT-2048 dataset, leveraging the D-T modality outperforms those of SOTA methods using RGB-T or RGB-D modalities for the first time, achieves comparable performance with the RGB-D-T triple-modality benchmark method with 5.97 times faster at runtime and demanding 0.0036 times fewer FLOPs. Demonstrates the proposed CSDNet effectively integrates the information from the D-T modality. The code will be released upon acceptance.
Reframe Anything: LLM Agent for Open World Video Reframing
2024-03-10
Jiawang Cao, Yongliang Wu, Weiheng Chi, Wenbo Zhu, Ziyue Su, Jay Wu
The proliferation of mobile devices and social media has revolutionized content dissemination, with short-form video becoming increasingly prevalent. This shift has introduced the challenge of video reframing to fit various screen aspect ratios, a process that highlights the most compelling parts of a video. Traditionally, video reframing is a manual, time-consuming task requiring professional expertise, which incurs high production costs. A potential solution is to adopt some machine learning models, such as video salient object detection, to automate the process. However, these methods often lack generalizability due to their reliance on specific training data. The advent of powerful large language models (LLMs) open new avenues for AI capabilities. Building on this, we introduce Reframe Any Video Agent (RAVA), a LLM-based agent that leverages visual foundation models and human instructions to restructure visual content for video reframing. RAVA operates in three stages: perception, where it interprets user instructions and video content; planning, where it determines aspect ratios and reframing strategies; and execution, where it invokes the editing tools to produce the final video. Our experiments validate the effectiveness of RAVA in video salient object detection and real-world reframing tasks, demonstrating its potential as a tool for AI-powered video editing.
A Simple yet Effective Network based on Vision Transformer for Camouflaged Object and Salient Object Detection
2024-02-29
Chao Hao, Zitong Yu, Xin Liu, Jun Xu, Huanjing Yue, Jingyu Yang
Camouflaged object detection (COD) and salient object detection (SOD) are two distinct yet closely-related computer vision tasks widely studied during the past decades. Though sharing the same purpose of segmenting an image into binary foreground and background regions, their distinction lies in the fact that COD focuses on concealed objects hidden in the image, while SOD concentrates on the most prominent objects in the image. Previous works achieved good performance by stacking various hand-designed modules and multi-scale features. However, these carefully-designed complex networks often performed well on one task but not on another. In this work, we propose a simple yet effective network (SENet) based on vision Transformer (ViT), by employing a simple design of an asymmetric ViT-based encoder-decoder structure, we yield competitive results on both tasks, exhibiting greater versatility than meticulously crafted ones. Furthermore, to enhance the Transformer's ability to model local information, which is important for pixel-level binary segmentation tasks, we propose a local information capture module (LICM). We also propose a dynamic weighted loss (DW loss) based on Binary Cross-Entropy (BCE) and Intersection over Union (IoU) loss, which guides the network to pay more attention to those smaller and more difficult-to-find target objects according to their size. Moreover, we explore the issue of joint training of SOD and COD, and propose a preliminary solution to the conflict in joint training, further improving the performance of SOD. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of our method. The code is available at https://github.com/linuxsino/SENet.
Decomposition-based and Interference Perception for Infrared and Visible Image Fusion in Complex Scenes
2024-02-03
Xilai Li, Xiaosong Li, Haishu Tan
Infrared and visible image fusion has emerged as a prominent research in computer vision. However, little attention has been paid on complex scenes fusion, causing existing techniques to produce sub-optimal results when suffers from real interferences. To fill this gap, we propose a decomposition-based and interference perception image fusion method. Specifically, we classify the pixels of visible image from the degree of scattering of light transmission, based on which we then separate the detail and energy information of the image. This refined decomposition facilitates the proposed model in identifying more interfering pixels that are in complex scenes. To strike a balance between denoising and detail preservation, we propose an adaptive denoising scheme for fusing detail components. Meanwhile, we propose a new weighted fusion rule by considering the distribution of image energy information from the perspective of multiple directions. Extensive experiments in complex scenes fusions cover adverse weathers, noise, blur, overexposure, fire, as well as downstream tasks including semantic segmentation, object detection, salient object detection and depth estimation, consistently indicate the effectiveness and superiority of the proposed method compared with the recent representative methods.
Leveraging rich information is crucial for dense prediction tasks. Light field (LF) cameras are instrumental in this regard, as they allow data to be sampled from various perspectives. This capability provides valuable spatial, depth, and angular information, enhancing scene-parsing tasks. However, we have identified two overlooked issues for the LF salient object detection (SOD) task. (1): Previous approaches predominantly employ a customized two-stream design to discover the spatial and depth features within light field images. The network struggles to learn the implicit angular information between different images due to a lack of intra-network data connectivity. (2): Little research has been directed towards the data augmentation strategy for LF SOD. Research on inter-network data connectivity is scant. In this study, we propose an efficient paradigm (LF Tracy) to address those issues. This comprises a single-pipeline encoder paired with a highly efficient information aggregation (IA) module (around 8M parameters) to establish an intra-network connection. Then, a simple yet effective data augmentation strategy called MixLD is designed to bridge the inter-network connections. Owing to this innovative paradigm, our model surpasses the existing state-of-the-art method through extensive experiments. Especially, LF Tracy demonstrates a 23% improvement over previous results on the latest large-scale PKU dataset. The source code is publicly available at: https://github.com/FeiBryantkit/LF-Tracy.
A Saliency Enhanced Feature Fusion based multiscale RGB-D Salient Object Detection Network
2024-01-22
Rui Huang, Qingyi Zhao, Yan Xing, Sihua Gao, Weifeng Xu, Yuxiang Zhang, Wei Fan
Multiscale convolutional neural network (CNN) has demonstrated remarkable capabilities in solving various vision problems. However, fusing features of different scales alwaysresults in large model sizes, impeding the application of multiscale CNNs in RGB-D saliency detection. In this paper, we propose a customized feature fusion module, called Saliency Enhanced Feature Fusion (SEFF), for RGB-D saliency detection. SEFF utilizes saliency maps of the neighboring scales to enhance the necessary features for fusing, resulting in more representative fused features. Our multiscale RGB-D saliency detector uses SEFF and processes images with three different scales. SEFF is used to fuse the features of RGB and depth images, as well as the features of decoders at different scales. Extensive experiments on five benchmark datasets have demonstrated the superiority of our method over ten SOTA saliency detectors.
Discriminative Consensus Mining with A Thousand Groups for More Accurate Co-Salient Object Detection
2024-01-15
Peng Zheng
Co-Salient Object Detection (CoSOD) is a rapidly growing task, extended from Salient Object Detection (SOD) and Common Object Segmentation (Co-Segmentation). It is aimed at detecting the co-occurring salient object in the given image group. Many effective approaches have been proposed on the basis of existing datasets. However, there is still no standard and efficient training set in CoSOD, which makes it chaotic to choose training sets in the recently proposed CoSOD methods. First, the drawbacks of existing training sets in CoSOD are analyzed in a comprehensive way, and potential improvements are provided to solve existing problems to some extent. In particular, in this thesis, a new CoSOD training set is introduced, named Co-Saliency of ImageNet (CoSINe) dataset. The proposed CoSINe is the largest number of groups among all existing CoSOD datasets. The images obtained here span a wide variety in terms of categories, object sizes, etc. In experiments, models trained on CoSINe can achieve significantly better performance with fewer images compared to all existing datasets. Second, to make the most of the proposed CoSINe, a novel CoSOD approach named Hierarchical Instance-aware COnsensus MinEr (HICOME) is proposed, which efficiently mines the consensus feature from different feature levels and discriminates objects of different classes in an object-aware contrastive way. As extensive experiments show, the proposed HICOME achieves SoTA performance on all the existing CoSOD test sets. Several useful training tricks suitable for training CoSOD models are also provided. Third, practical applications are given using the CoSOD technique to show the effectiveness. Finally, the remaining challenges and potential improvements of CoSOD are discussed to inspire related work in the future. The source code, the dataset, and the online demo will be publicly available at github.com/ZhengPeng7/CoSINe.
The Segment Anything Model (SAM) has been a cornerstone in the field of interactive segmentation, propelling significant progress in generative AI, computational photography, and medical imaging. Despite its ability to process arbitrary user input and generate corresponding segmentation masks, SAM's 600 million parameter architecture, based on ViT-H, is not compatible with current mobile hardware due to its high computational demands and large model size. Our research aims to adapt SAM for use in mobile photography applications. To this end, we have developed a fully convolutional SqueezeSAM model architecture, which is 62.5 times faster and 31.6 times smaller than the original SAM, making it a viable solution for mobile applications. Furthermore, our tiny model achieves an mIOU within 1% of the original VIT-H architecture. Automated segmentation holds significant value in the creation flow for photography applications, as evidenced by its adoption by leading industry players like apple and capcut. To facilitate this automation, we employ salient object detection and simulate potential user clicks for foreground object selection, generating an initial segmentation mask that users can subsequently edit interactively. A common user expectation is that a click on a specific part of an object will result in the segmentation of the entire object. For example, a click on a person's t-shirt in a photo should ideally segment the entire person, not just the t-shirt. However, SAM typically only segments the clicked area. We address this limitation through a novel data augmentation scheme. Consequently, if a user clicks on a person holding a basketball, both the person and the basketball are segmented together, aligning with user expectations and enhancing the overall user experience.
Texture-Semantic Collaboration Network for ORSI Salient Object Detection
2023-12-06
Gongyang Li, Zhen Bai, Zhi Liu
Salient object detection (SOD) in optical remote sensing images (ORSIs) has become increasingly popular recently. Due to the characteristics of ORSIs, ORSI-SOD is full of challenges, such as multiple objects, small objects, low illuminations, and irregular shapes. To address these challenges, we propose a concise yet effective Texture-Semantic Collaboration Network (TSCNet) to explore the collaboration of texture cues and semantic cues for ORSI-SOD. Specifically, TSCNet is based on the generic encoder-decoder structure. In addition to the encoder and decoder, TSCNet includes a vital Texture-Semantic Collaboration Module (TSCM), which performs valuable feature modulation and interaction on basic features extracted from the encoder. The main idea of our TSCM is to make full use of the texture features at the lowest level and the semantic features at the highest level to achieve the expression enhancement of salient regions on features. In the TSCM, we first enhance the position of potential salient regions using semantic features. Then, we render and restore the object details using the texture features. Meanwhile, we also perceive regions of various scales, and construct interactions between different regions. Thanks to the perfect combination of TSCM and generic structure, our TSCNet can take care of both the position and details of salient objects, effectively handling various scenes. Extensive experiments on three datasets demonstrate that our TSCNet achieves competitive performance compared to 14 state-of-the-art methods. The code and results of our method are available at https://github.com/MathLee/TSCNet.
Existing salient object detection methods are capable of predicting binary maps that highlight visually salient regions. However, these methods are limited in their ability to differentiate the relative importance of multiple objects and the relationships among them, which can lead to errors and reduced accuracy in downstream tasks that depend on the relative importance of multiple objects. To conquer, this paper proposes a new paradigm for saliency ranking, which aims to completely focus on ranking salient objects by their "importance order". While previous works have shown promising performance, they still face ill-posed problems. First, the saliency ranking ground truth (GT) orders generation methods are unreasonable since determining the correct ranking order is not well-defined, resulting in false alarms. Second, training a ranking model remains challenging because most saliency ranking methods follow the multi-task paradigm, leading to conflicts and trade-offs among different tasks. Third, existing regression-based saliency ranking methods are complex for saliency ranking models due to their reliance on instance mask-based saliency ranking orders. These methods require a significant amount of data to perform accurately and can be challenging to implement effectively. To solve these problems, this paper conducts an in-depth analysis of the causes and proposes a whole-flow processing paradigm of saliency ranking task from the perspective of "GT data generation", "network structure design" and "training protocol". The proposed approach outperforms existing state-of-the-art methods on the widely-used SALICON set, as demonstrated by extensive experiments with fair and reasonable comparisons. The saliency ranking task is still in its infancy, and our proposed unified framework can serve as a fundamental strategy to guide future work.
Hyperspectral salient object detection (HSOD) aims to detect spectrally salient objects in hyperspectral images (HSIs). However, existing methods inadequately utilize spectral information by either converting HSIs into false-color images or converging neural networks with clustering. We propose a novel approach that fully leverages the spectral characteristics by extracting two distinct frequency components from the spectrum: low-frequency Spectral Saliency and high-frequency Spectral Edge. The Spectral Saliency approximates the region of salient objects, while the Spectral Edge captures edge information of salient objects. These two complementary components, crucial for HSOD, are derived by computing from the inter-layer spectral angular distance of the Gaussian pyramid and the intra-neighborhood spectral angular gradients, respectively. To effectively utilize this dual-frequency information, we introduce a novel lightweight Spectrum-driven Mixed-frequency Network (SMN). SMN incorporates two parameter-free plug-and-play operators, namely Spectral Saliency Generator and Spectral Edge Operator, to extract the Spectral Saliency and Spectral Edge components from the input HSI independently. Subsequently, the Mixed-frequency Attention module, comprised of two frequency-dependent heads, intelligently combines the embedded features of edge and saliency information, resulting in a mixed-frequency feature representation. Furthermore, a saliency-edge-aware decoder progressively scales up the mixed-frequency feature while preserving rich detail and saliency information for accurate salient object prediction. Extensive experiments conducted on the HS-SOD benchmark and our custom dataset HSOD-BIT demonstrate that our SMN outperforms state-of-the-art methods regarding HSOD performance. Code and dataset will be available at https://github.com/laprf/SMN.
Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning
2023-12-01
Shaohua Dong, Yunhe Feng, Qing Yang, Yan Huang, Dongfang Liu, Heng Fan
Multimodal (e.g., RGB-Depth/RGB-Thermal) fusion has shown great potential for improving semantic segmentation in complex scenes (e.g., indoor/low-light conditions). Existing approaches often fully fine-tune a dual-branch encoder-decoder framework with a complicated feature fusion strategy for achieving multimodal semantic segmentation, which is training-costly due to the massive parameter updates in feature extraction and fusion. To address this issue, we propose a surprisingly simple yet effective dual-prompt learning network (dubbed DPLNet) for training-efficient multimodal (e.g., RGB-D/T) semantic segmentation. The core of DPLNet is to directly adapt a frozen pre-trained RGB model to multimodal semantic segmentation, reducing parameter updates. For this purpose, we present two prompt learning modules, comprising multimodal prompt generator (MPG) and multimodal feature adapter (MFA). MPG works to fuse the features from different modalities in a compact manner and is inserted from shadow to deep stages to generate the multi-level multimodal prompts that are injected into the frozen backbone, while MPG adapts prompted multimodal features in the frozen backbone for better multimodal semantic segmentation. Since both the MPG and MFA are lightweight, only a few trainable parameters (3.88M, 4.4% of the pre-trained backbone parameters) are introduced for multimodal feature fusion and learning. Using a simple decoder (3.27M parameters), DPLNet achieves new state-of-the-art performance or is on a par with other complex approaches on four RGB-D/T semantic segmentation datasets while satisfying parameter efficiency. Moreover, we show that DPLNet is general and applicable to other multimodal tasks such as salient object detection and video semantic segmentation. Without special design, DPLNet outperforms many complicated models. Our code will be available at github.com/ShaohuaDong2021/DPLNet.
Cascaded Interaction with Eroded Deep Supervision for Salient Object Detection
2023-11-30
Hewen Xiao, Jie Mei, Guangfu Ma, Weiren Wu
Deep convolutional neural networks have been widely applied in salient object detection and have achieved remarkable results in this field. However, existing models suffer from information distortion caused by interpolation during up-sampling and down-sampling. In response to this drawback, this article starts from two directions in the network: feature and label. On the one hand, a novel cascaded interaction network with a guidance module named global-local aligned attention (GAA) is designed to reduce the negative impact of interpolation on the feature side. On the other hand, a deep supervision strategy based on edge erosion is proposed to reduce the negative guidance of label interpolation on lateral output. Extensive experiments on five popular datasets demonstrate the superiority of our method.
Unsupervised video object segmentation (UVOS) aims at detecting the primary objects in a given video sequence without any human interposing. Most existing methods rely on two-stream architectures that separately encode the appearance and motion information before fusing them to identify the target and generate object masks. However, this pipeline is computationally expensive and can lead to suboptimal performance due to the difficulty of fusing the two modalities properly. In this paper, we propose a novel UVOS model called SimulFlow that simultaneously performs feature extraction and target identification, enabling efficient and effective unsupervised video object segmentation. Concretely, we design a novel SimulFlow Attention mechanism to bridege the image and motion by utilizing the flexibility of attention operation, where coarse masks predicted from fused feature at each stage are used to constrain the attention operation within the mask area and exclude the impact of noise. Because of the bidirectional information flow between visual and optical flow features in SimulFlow Attention, no extra hand-designed fusing module is required and we only adopt a light decoder to obtain the final prediction. We evaluate our method on several benchmark datasets and achieve state-of-the-art results. Our proposed approach not only outperforms existing methods but also addresses the computational complexity and fusion difficulties caused by two-stream architectures. Our models achieve 87.4% J & F on DAVIS-16 with the highest speed (63.7 FPS on a 3090) and the lowest parameters (13.7 M). Our SimulFlow also obtains competitive results on video salient object detection datasets.
Unified-modal Salient Object Detection via Adaptive Prompt Learning
2023-11-28
Kunpeng Wang, Chenglong Li, Zhengzheng Tu, Zhengyi Liu, Bin Luo
Existing single-modal and multi-modal salient object detection (SOD) methods focus on designing specific architectures tailored for their respective tasks. However, developing completely different models for different tasks leads to labor and time consumption, as well as high computational and practical deployment costs. In this paper, we attempt to address both single-modal and multi-modal SOD in a unified framework called UniSOD, which fully exploits the overlapping prior knowledge between different tasks. Nevertheless, assigning appropriate strategies to modality variable inputs is challenging. To this end, UniSOD learns modality-aware prompts with task-specific hints through adaptive prompt learning, which are plugged into the proposed pre-trained baseline SOD model to handle corresponding tasks, while only requiring few learnable parameters compared to training the entire model. Each modality-aware prompt is generated from a switchable prompt generation block, which adaptively performs structural switching based on single-modal and multi-modal inputs without human intervention. Through end-to-end joint training, UniSOD achieves overall performance improvement on 14 benchmark datasets for RGB, RGB-D, and RGB-T SOD, which demonstrates that our method effectively and efficiently unifies single-modal and multi-modal SOD tasks.The code and results are available at https://github.com/Angknpng/UniSOD.
VSCode: General Visual Salient and Camouflaged Object Detection with 2D Prompt Learning
2023-11-25
Ziyang Luo, Nian Liu, Wangbo Zhao, Xuguang Yang, Dingwen Zhang, Deng-Ping Fan, Fahad Khan, Junwei Han
Salient object detection (SOD) and camouflaged object detection (COD) are related yet distinct binary mapping tasks. These tasks involve multiple modalities, sharing commonalities and unique cues. Existing research often employs intricate task-specific specialist models, potentially leading to redundancy and suboptimal results. We introduce VSCode, a generalist model with novel 2D prompt learning, to jointly address four SOD tasks and three COD tasks. We utilize VST as the foundation model and introduce 2D prompts within the encoder-decoder architecture to learn domain and task-specific knowledge on two separate dimensions. A prompt discrimination loss helps disentangle peculiarities to benefit model optimization. VSCode outperforms state-of-the-art methods across six tasks on 26 datasets and exhibits zero-shot generalization to unseen tasks by combining 2D prompts, such as RGB-D COD. Source code has been available at https://github.com/Sssssuperior/VSCode.
Salient object detection (SOD) aims to identify the most attractive objects within an image. Depending on the type of data being detected, SOD can be categorized into various forms, including RGB, RGB-D (Depth), RGB-T (Thermal) and light field SOD. Previous researches have focused on saliency detection with individual data type. If the RGB-D SOD model is forced to detect RGB-T data it will perform poorly. We propose an innovative model framework that provides a unified solution for the salient object detection task of three types of data (RGB, RGB-D, and RGB-T). The three types of data can be handled in one model (all in one) with the same weight parameters. In this framework, the three types of data are concatenated in an ordered manner within a single input batch, and features are extracted using a transformer network. Based on this framework, we propose an efficient lightweight SOD model, namely AiOSOD, which can detect any RGB, RGB-D, and RGB-T data with high speed (780FPS for RGB data, 485FPS for RGB-D or RGB-T data). Notably, with only 6.25M parameters, AiOSOD achieves excellent performance on RGB, RGB-D, and RGB-T datasets.
Unsupervised and semi-supervised co-salient object detection via segmentation frequency statistics
2023-11-11
Souradeep Chakraborty, Shujon Naha, Muhammet Bastan, Amit Kumar K C, Dimitris Samaras
In this paper, we address the detection of co-occurring salient objects (CoSOD) in an image group using frequency statistics in an unsupervised manner, which further enable us to develop a semi-supervised method. While previous works have mostly focused on fully supervised CoSOD, less attention has been allocated to detecting co-salient objects when limited segmentation annotations are available for training. Our simple yet effective unsupervised method US-CoSOD combines the object co-occurrence frequency statistics of unsupervised single-image semantic segmentations with salient foreground detections using self-supervised feature learning. For the first time, we show that a large unlabeled dataset e.g. ImageNet-1k can be effectively leveraged to significantly improve unsupervised CoSOD performance. Our unsupervised model is a great pre-training initialization for our semi-supervised model SS-CoSOD, especially when very limited labeled data is available for training. To avoid propagating erroneous signals from predictions on unlabeled data, we propose a confidence estimation module to guide our semi-supervised training. Extensive experiments on three CoSOD benchmark datasets show that both of our unsupervised and semi-supervised models outperform the corresponding state-of-the-art models by a significant margin (e.g., on the Cosal2015 dataset, our US-CoSOD model has an 8.8% F-measure gain over a SOTA unsupervised co-segmentation model and our SS-CoSOD model has an 11.81% F-measure gain over a SOTA semi-supervised CoSOD model).
SODAWideNet -- Salient Object Detection with an Attention augmented Wide Encoder Decoder network without ImageNet pre-training
2023-11-08
Rohit Venkata Sai Dulam, Chandra Kambhamettu
Developing a new Salient Object Detection (SOD) model involves selecting an ImageNet pre-trained backbone and creating novel feature refinement modules to use backbone features. However, adding new components to a pre-trained backbone needs retraining the whole network on the ImageNet dataset, which requires significant time. Hence, we explore developing a neural network from scratch directly trained on SOD without ImageNet pre-training. Such a formulation offers full autonomy to design task-specific components. To that end, we propose SODAWideNet, an encoder-decoder-style network for Salient Object Detection. We deviate from the commonly practiced paradigm of narrow and deep convolutional models to a wide and shallow architecture, resulting in a parameter-efficient deep neural network. To achieve a shallower network, we increase the receptive field from the beginning of the network using a combination of dilated convolutions and self-attention. Therefore, we propose Multi Receptive Field Feature Aggregation Module (MRFFAM) that efficiently obtains discriminative features from farther regions at higher resolutions using dilated convolutions. Next, we propose Multi-Scale Attention (MSA), which creates a feature pyramid and efficiently computes attention across multiple resolutions to extract global features from larger feature maps. Finally, we propose two variants, SODAWideNet-S (3.03M) and SODAWideNet (9.03M), that achieve competitive performance against state-of-the-art models on five datasets.
Given the widespread adoption of depth-sensing acquisition devices, RGB-D videos and related data/media have gained considerable traction in various aspects of daily life. Consequently, conducting salient object detection (SOD) in RGB-D videos presents a highly promising and evolving avenue. Despite the potential of this area, SOD in RGB-D videos remains somewhat under-explored, with RGB-D SOD and video SOD (VSOD) traditionally studied in isolation. To explore this emerging field, this paper makes two primary contributions: the dataset and the model. On one front, we construct the RDVS dataset, a new RGB-D VSOD dataset with realistic depth and characterized by its diversity of scenes and rigorous frame-by-frame annotations. We validate the dataset through comprehensive attribute and object-oriented analyses, and provide training and testing splits. Moreover, we introduce DCTNet+, a three-stream network tailored for RGB-D VSOD, with an emphasis on RGB modality and treats depth and optical flow as auxiliary modalities. In pursuit of effective feature enhancement, refinement, and fusion for precise final prediction, we propose two modules: the multi-modal attention module (MAM) and the refinement fusion module (RFM). To enhance interaction and fusion within RFM, we design a universal interaction module (UIM) and then integrate holistic multi-modal attentive paths (HMAPs) for refining multi-modal low-level features before reaching RFMs. Comprehensive experiments, conducted on pseudo RGB-D video datasets alongside our RDVS, highlight the superiority of DCTNet+ over 17 VSOD models and 14 RGB-D SOD models. Ablation experiments were performed on both pseudo and realistic RGB-D video datasets to demonstrate the advantages of individual modules as well as the necessity of introducing realistic depth. Our code together with RDVS dataset will be available at https://github.com/kerenfu/RDVS/.
VST++: Efficient and Stronger Visual Saliency Transformer
2023-10-18
Nian Liu, Ziyang Luo, Ni Zhang, Junwei Han
While previous CNN-based models have exhibited promising results for salient object detection (SOD), their ability to explore global long-range dependencies is restricted. Our previous work, the Visual Saliency Transformer (VST), addressed this constraint from a transformer-based sequence-to-sequence perspective, to unify RGB and RGB-D SOD. In VST, we developed a multi-task transformer decoder that concurrently predicts saliency and boundary outcomes in a pure transformer architecture. Moreover, we introduced a novel token upsampling method called reverse T2T for predicting a high-resolution saliency map effortlessly within transformer-based structures. Building upon the VST model, we further propose an efficient and stronger VST version in this work, i.e. VST++. To mitigate the computational costs of the VST model, we propose a Select-Integrate Attention (SIA) module, partitioning foreground into fine-grained segments and aggregating background information into a single coarse-grained token. To incorporate 3D depth information with low cost, we design a novel depth position encoding method tailored for depth maps. Furthermore, we introduce a token-supervised prediction loss to provide straightforward guidance for the task-related tokens. We evaluate our VST++ model across various transformer-based backbones on RGB, RGB-D, and RGB-T SOD benchmark datasets. Experimental results show that our model outperforms existing methods while achieving a 25% reduction in computational costs without significant performance compromise. The demonstrated strong ability for generalization, enhanced performance, and heightened efficiency of our VST++ model highlight its potential.
Towards Open-World Co-Salient Object Detection with Generative Uncertainty-aware Group Selective Exchange-Masking
2023-10-16
Yang Wu, Shenglong Hu, Huihui Song, Kaihua Zhang, Bo Liu, Dong Liu
The traditional definition of co-salient object detection (CoSOD) task is to segment the common salient objects in a group of relevant images. This definition is based on an assumption of group consensus consistency that is not always reasonable in the open-world setting, which results in robustness issue in the model when dealing with irrelevant images in the inputting image group under the open-word scenarios. To tackle this problem, we introduce a group selective exchange-masking (GSEM) approach for enhancing the robustness of the CoSOD model. GSEM takes two groups of images as input, each containing different types of salient objects. Based on the mixed metric we designed, GSEM selects a subset of images from each group using a novel learning-based strategy, then the selected images are exchanged. To simultaneously consider the uncertainty introduced by irrelevant images and the consensus features of the remaining relevant images in the group, we designed a latent variable generator branch and CoSOD transformer branch. The former is composed of a vector quantised-variational autoencoder to generate stochastic global variables that model uncertainty. The latter is designed to capture correlation-based local features that include group consensus. Finally, the outputs of the two branches are merged and passed to a transformer-based decoder to generate robust predictions. Taking into account that there are currently no benchmark datasets specifically designed for open-world scenarios, we constructed three open-world benchmark datasets, namely OWCoSal, OWCoSOD, and OWCoCA, based on existing datasets. By breaking the group-consistency assumption, these datasets provide effective simulations of real-world scenarios and can better evaluate the robustness and practicality of models.
Unsupervised salient object detection aims to detect salient objects without using supervision signals eliminating the tedious task of manually labeling salient objects. To improve training efficiency, end-to-end methods for USOD have been proposed as a promising alternative. However, current solutions rely heavily on noisy handcraft labels and fail to mine rich semantic information from deep features. In this paper, we propose a self-supervised end-to-end salient object detection framework via top-down context. Specifically, motivated by contrastive learning, we exploit the self-localization from the deepest feature to construct the location maps which are then leveraged to learn the most instructive segmentation guidance. Further considering the lack of detailed information in deepest features, we exploit the detail-boosting refiner module to enrich the location labels with details. Moreover, we observe that due to lack of supervision, current unsupervised saliency models tend to detect non-salient objects that are salient in some other samples of corresponding scenarios. To address this widespread issue, we design a novel Unsupervised Non-Salient Suppression (UNSS) method developing the ability to ignore non-salient objects. Extensive experiments on benchmark datasets demonstrate that our method achieves leading performance among the recent end-to-end methods and most of the multi-stage solutions. The code is available.
A Spatial-Temporal Dual-Mode Mixed Flow Network for Panoramic Video Salient Object Detection
2023-10-13
Xiaolei Chen, Pengcheng Zhang, Zelong Du, Ishfaq Ahmad
Salient object detection (SOD) in panoramic video is still in the initial exploration stage. The indirect application of 2D video SOD method to the detection of salient objects in panoramic video has many unmet challenges, such as low detection accuracy, high model complexity, and poor generalization performance. To overcome these hurdles, we design an Inter-Layer Attention (ILA) module, an Inter-Layer weight (ILW) module, and a Bi-Modal Attention (BMA) module. Based on these modules, we propose a Spatial-Temporal Dual-Mode Mixed Flow Network (STDMMF-Net) that exploits the spatial flow of panoramic video and the corresponding optical flow for SOD. First, the ILA module calculates the attention between adjacent level features of consecutive frames of panoramic video to improve the accuracy of extracting salient object features from the spatial flow. Then, the ILW module quantifies the salient object information contained in the features of each level to improve the fusion efficiency of the features of each level in the mixed flow. Finally, the BMA module improves the detection accuracy of STDMMF-Net. A large number of subjective and objective experimental results testify that the proposed method demonstrates better detection accuracy than the state-of-the-art (SOTA) methods. Moreover, the comprehensive performance of the proposed method is better in terms of memory required for model inference, testing time, complexity, and generalization performance.
SEMPART: Self-supervised Multi-resolution Partitioning of Image Semantics
2023-09-20
Sriram Ravindran, Debraj Basu
Accurately determining salient regions of an image is challenging when labeled data is scarce. DINO-based self-supervised approaches have recently leveraged meaningful image semantics captured by patch-wise features for locating foreground objects. Recent methods have also incorporated intuitive priors and demonstrated value in unsupervised methods for object partitioning. In this paper, we propose SEMPART, which jointly infers coarse and fine bi-partitions over an image's DINO-based semantic graph. Furthermore, SEMPART preserves fine boundary details using graph-driven regularization and successfully distills the coarse mask semantics into the fine mask. Our salient object detection and single object localization findings suggest that SEMPART produces high-quality masks rapidly without additional post-processing and benefits from co-optimizing the coarse and fine branches.
We present DFormer, a novel RGB-D pretraining framework to learn transferable representations for RGB-D segmentation tasks. DFormer has two new key innovations: 1) Unlike previous works that encode RGB-D information with RGB pretrained backbone, we pretrain the backbone using image-depth pairs from ImageNet-1K, and hence the DFormer is endowed with the capacity to encode RGB-D representations; 2) DFormer comprises a sequence of RGB-D blocks, which are tailored for encoding both RGB and depth information through a novel building block design. DFormer avoids the mismatched encoding of the 3D geometry relationships in depth maps by RGB pretrained backbones, which widely lies in existing methods but has not been resolved. We finetune the pretrained DFormer on two popular RGB-D tasks, i.e., RGB-D semantic segmentation and RGB-D salient object detection, with a lightweight decoder head. Experimental results show that our DFormer achieves new state-of-the-art performance on these two tasks with less than half of the computational cost of the current best methods on two RGB-D semantic segmentation datasets and five RGB-D salient object detection datasets. Our code is available at: https://github.com/VCIP-RGBD/DFormer.
M$^3$Net: Multilevel, Mixed and Multistage Attention Network for Salient Object Detection
2023-09-15
Yao Yuan, Pan Gao, XiaoYang Tan
Most existing salient object detection methods mostly use U-Net or feature pyramid structure, which simply aggregates feature maps of different scales, ignoring the uniqueness and interdependence of them and their respective contributions to the final prediction. To overcome these, we propose the M$^3$Net, i.e., the Multilevel, Mixed and Multistage attention network for Salient Object Detection (SOD). Firstly, we propose Multiscale Interaction Block which innovatively introduces the cross-attention approach to achieve the interaction between multilevel features, allowing high-level features to guide low-level feature learning and thus enhancing salient regions. Secondly, considering the fact that previous Transformer based SOD methods locate salient regions only using global self-attention while inevitably overlooking the details of complex objects, we propose the Mixed Attention Block. This block combines global self-attention and window self-attention, aiming at modeling context at both global and local levels to further improve the accuracy of the prediction map. Finally, we proposed a multilevel supervision strategy to optimize the aggregated feature stage-by-stage. Experiments on six challenging datasets demonstrate that the proposed M$^3$Net surpasses recent CNN and Transformer-based SOD arts in terms of four metrics. Codes are available at https://github.com/I2-Multimedia-Lab/M3Net.
UniST: Towards Unifying Saliency Transformer for Video Saliency Prediction and Detection
2023-09-15
Junwen Xiong, Peng Zhang, Chuanyue Li, Wei Huang, Yufei Zha, Tao You
Video saliency prediction and detection are thriving research domains that enable computers to simulate the distribution of visual attention akin to how humans perceiving dynamic scenes. While many approaches have crafted task-specific training paradigms for either video saliency prediction or video salient object detection tasks, few attention has been devoted to devising a generalized saliency modeling framework that seamlessly bridges both these distinct tasks. In this study, we introduce the Unified Saliency Transformer (UniST) framework, which comprehensively utilizes the essential attributes of video saliency prediction and video salient object detection. In addition to extracting representations of frame sequences, a saliency-aware transformer is designed to learn the spatio-temporal representations at progressively increased resolutions, while incorporating effective cross-scale saliency information to produce a robust representation. Furthermore, a task-specific decoder is proposed to perform the final prediction for each task. To the best of our knowledge, this is the first work that explores designing a transformer structure for both saliency modeling tasks. Convincible experiments demonstrate that the proposed UniST achieves superior performance across seven challenging benchmarks for two tasks, and significantly outperforms the other state-of-the-art methods.
Existing methods for Salient Object Detection in Optical Remote Sensing Images (ORSI-SOD) mainly adopt Convolutional Neural Networks (CNNs) as the backbone, such as VGG and ResNet. Since CNNs can only extract features within certain receptive fields, most ORSI-SOD methods generally follow the local-to-contextual paradigm. In this paper, we propose a novel Global Extraction Local Exploration Network (GeleNet) for ORSI-SOD following the global-to-local paradigm. Specifically, GeleNet first adopts a transformer backbone to generate four-level feature embeddings with global long-range dependencies. Then, GeleNet employs a Direction-aware Shuffle Weighted Spatial Attention Module (D-SWSAM) and its simplified version (SWSAM) to enhance local interactions, and a Knowledge Transfer Module (KTM) to further enhance cross-level contextual interactions. D-SWSAM comprehensively perceives the orientation information in the lowest-level features through directional convolutions to adapt to various orientations of salient objects in ORSIs, and effectively enhances the details of salient objects with an improved attention mechanism. SWSAM discards the direction-aware part of D-SWSAM to focus on localizing salient objects in the highest-level features. KTM models the contextual correlation knowledge of two middle-level features of different scales based on the self-attention mechanism, and transfers the knowledge to the raw features to generate more discriminative features. Finally, a saliency predictor is used to generate the saliency map based on the outputs of the above three modules. Extensive experiments on three public datasets demonstrate that the proposed GeleNet outperforms relevant state-of-the-art methods. The code and results of our method are available at https://github.com/MathLee/GeleNet.
Co-Salient Object Detection with Semantic-Level Consensus Extraction and Dispersion
2023-09-14
Peiran Xu, Yadong Mu
Given a group of images, co-salient object detection (CoSOD) aims to highlight the common salient object in each image. There are two factors closely related to the success of this task, namely consensus extraction, and the dispersion of consensus to each image. Most previous works represent the group consensus using local features, while we instead utilize a hierarchical Transformer module for extracting semantic-level consensus. Therefore, it can obtain a more comprehensive representation of the common object category, and exclude interference from other objects that share local similarities with the target object. In addition, we propose a Transformer-based dispersion module that takes into account the variation of the co-salient object in different scenes. It distributes the consensus to the image feature maps in an image-specific way while making full use of interactions within the group. These two modules are integrated with a ViT encoder and an FPN-like decoder to form an end-to-end trainable network, without additional branch and auxiliary loss. The proposed method is evaluated on three commonly used CoSOD datasets and achieves state-of-the-art performance.
Adversarial Attacks Assessment of Salient Object Detection via Symbolic Learning
2023-09-12
Gustavo Olague, Roberto Pineda, Gerardo Ibarra-Vazquez, Matthieu Olague, Axel Martinez, Sambit Bakshi, Jonathan Vargas, Isnardo Reducindo
Machine learning is at the center of mainstream technology and outperforms classical approaches to handcrafted feature design. Aside from its learning process for artificial feature extraction, it has an end-to-end paradigm from input to output, reaching outstandingly accurate results. However, security concerns about its robustness to malicious and imperceptible perturbations have drawn attention since its prediction can be changed entirely. Salient object detection is a research area where deep convolutional neural networks have proven effective but whose trustworthiness represents a significant issue requiring analysis and solutions to hackers' attacks. Brain programming is a kind of symbolic learning in the vein of good old-fashioned artificial intelligence. This work provides evidence that symbolic learning robustness is crucial in designing reliable visual attention systems since it can withstand even the most intense perturbations. We test this evolutionary computation methodology against several adversarial attacks and noise perturbations using standard databases and a real-world problem of a shorebird called the Snowy Plover portraying a visual attention task. We compare our methodology with five different deep learning approaches, proving that they do not match the symbolic paradigm regarding robustness. All neural networks suffer significant performance losses, while brain programming stands its ground and remains unaffected. Also, by studying the Snowy Plover, we remark on the importance of security in surveillance activities regarding wildlife protection and conservation.
2023-09-11
Haoke Xiao, Lv Tang, Bo Li, Zhiming Luo, Shaozi Li
Co-salient Object Detection (CoSOD) endeavors to replicate the human visual system's capacity to recognize common and salient objects within a collection of images. Despite recent advancements in deep learning models, these models still rely on training with well-annotated CoSOD datasets. The exploration of training-free zero-shot CoSOD frameworks has been limited. In this paper, taking inspiration from the zero-shot transfer capabilities of foundational computer vision models, we introduce the first zero-shot CoSOD framework that harnesses these models without any training process. To achieve this, we introduce two novel components in our proposed framework: the group prompt generation (GPG) module and the co-saliency map generation (CMP) module. We evaluate the framework's performance on widely-used datasets and observe impressive results. Our approach surpasses existing unsupervised methods and even outperforms fully supervised methods developed before 2020, while remaining competitive with some fully supervised methods developed before 2022.
Decomposed Guided Dynamic Filters for Efficient RGB-Guided Depth Completion
2023-09-05
Yufei Wang, Yuxin Mao, Qi Liu, Yuchao Dai
RGB-guided depth completion aims at predicting dense depth maps from sparse depth measurements and corresponding RGB images, where how to effectively and efficiently exploit the multi-modal information is a key issue. Guided dynamic filters, which generate spatially-variant depth-wise separable convolutional filters from RGB features to guide depth features, have been proven to be effective in this task. However, the dynamically generated filters require massive model parameters, computational costs and memory footprints when the number of feature channels is large. In this paper, we propose to decompose the guided dynamic filters into a spatially-shared component multiplied by content-adaptive adaptors at each spatial location. Based on the proposed idea, we introduce two decomposition schemes A and B, which decompose the filters by splitting the filter structure and using spatial-wise attention, respectively. The decomposed filters not only maintain the favorable properties of guided dynamic filters as being content-dependent and spatially-variant, but also reduce model parameters and hardware costs, as the learned adaptors are decoupled with the number of feature channels. Extensive experimental results demonstrate that the methods using our schemes outperform state-of-the-art methods on the KITTI dataset, and rank 1st and 2nd on the KITTI benchmark at the time of submission. Meanwhile, they also achieve comparable performance on the NYUv2 dataset. In addition, our proposed methods are general and could be employed as plug-and-play feature fusion blocks in other multi-modal fusion tasks such as RGB-D salient object detection.
The Impact of Background Removal on Performance of Neural Networks for Fashion Image Classification and Segmentation
2023-08-18
Junhui Liang, Ying Liu, Vladimir Vlassov
Fashion understanding is a hot topic in computer vision, with many applications having great business value in the market. Fashion understanding remains a difficult challenge for computer vision due to the immense diversity of garments and various scenes and backgrounds. In this work, we try removing the background from fashion images to boost data quality and increase model performance. Having fashion images of evident persons in fully visible garments, we can utilize Salient Object Detection to achieve the background removal of fashion data to our expectations. A fashion image with the background removed is claimed as the "rembg" image, contrasting with the original one in the fashion dataset. We conducted extensive comparative experiments with these two types of images on multiple aspects of model training, including model architectures, model initialization, compatibility with other training tricks and data augmentations, and target task types. Our experiments show that background removal can effectively work for fashion data in simple and shallow networks that are not susceptible to overfitting. It can improve model accuracy by up to 5% in the classification on the FashionStyle14 dataset when training models from scratch. However, background removal does not perform well in deep neural networks due to incompatibility with other regularization techniques like batch normalization, pre-trained initialization, and data augmentations introducing randomness. The loss of background pixels invalidates many existing training tricks in the model training, adding the risk of overfitting for deep models.
Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection
2023-08-17
Runmin Cong, Hongyu Liu, Chen Zhang, Wei Zhang, Feng Zheng, Ran Song, Sam Kwong
By integrating complementary information from RGB image and depth map, the ability of salient object detection (SOD) for complex and challenging scenes can be improved. In recent years, the important role of Convolutional Neural Networks (CNNs) in feature extraction and cross-modality interaction has been fully explored, but it is still insufficient in modeling global long-range dependencies of self-modality and cross-modality. To this end, we introduce CNNs-assisted Transformer architecture and propose a novel RGB-D SOD network with Point-aware Interaction and CNN-induced Refinement (PICR-Net). On the one hand, considering the prior correlation between RGB modality and depth modality, an attention-triggered cross-modality point-aware interaction (CmPI) module is designed to explore the feature interaction of different modalities with positional constraints. On the other hand, in order to alleviate the block effect and detail destruction problems brought by the Transformer naturally, we design a CNN-induced refinement (CNNR) unit for content refinement and supplementation. Extensive experiments on five RGB-D SOD datasets show that the proposed network achieves competitive results in both quantitative and qualitative comparisons.
Adaptive Low Rank Adaptation of Segment Anything to Salient Object Detection
2023-08-10
Ruikai Cui, Siyuan He, Shi Qiu
Foundation models, such as OpenAI's GPT-3 and GPT-4, Meta's LLaMA, and Google's PaLM2, have revolutionized the field of artificial intelligence. A notable paradigm shift has been the advent of the Segment Anything Model (SAM), which has exhibited a remarkable capability to segment real-world objects, trained on 1 billion masks and 11 million images. Although SAM excels in general object segmentation, it lacks the intrinsic ability to detect salient objects, resulting in suboptimal performance in this domain. To address this challenge, we present the Segment Salient Object Model (SSOM), an innovative approach that adaptively fine-tunes SAM for salient object detection by harnessing the low-rank structure inherent in deep learning. Comprehensive qualitative and quantitative evaluations across five challenging RGB benchmark datasets demonstrate the superior performance of our approach, surpassing state-of-the-art methods.
Recurrent Multi-scale Transformer for High-Resolution Salient Object Detection
2023-08-07
Xinhao Deng, Pingping Zhang, Wei Liu, Huchuan Lu
Salient Object Detection (SOD) aims to identify and segment the most conspicuous objects in an image or video. As an important pre-processing step, it has many potential applications in multimedia and vision tasks. With the advance of imaging devices, SOD with high-resolution images is of great demand, recently. However, traditional SOD methods are largely limited to low-resolution images, making them difficult to adapt to the development of High-Resolution SOD (HRSOD). Although some HRSOD methods emerge, there are no large enough datasets for training and evaluating. Besides, current HRSOD methods generally produce incomplete object regions and irregular object boundaries. To address above issues, in this work, we first propose a new HRS10K dataset, which contains 10,500 high-quality annotated images at 2K-8K resolution. As far as we know, it is the largest dataset for the HRSOD task, which will significantly help future works in training and evaluating models. Furthermore, to improve the HRSOD performance, we propose a novel Recurrent Multi-scale Transformer (RMFormer), which recurrently utilizes shared Transformers and multi-scale refinement architectures. Thus, high-resolution saliency maps can be generated with the guidance of lower-resolution predictions. Extensive experiments on both high-resolution and low-resolution benchmarks show the effectiveness and superiority of the proposed framework. The source code and dataset are released at: https://github.com/DrowsyMon/RMFormer.
With the emergence of VR and AR, 360{\deg} data attracts increasing attention from the computer vision and multimedia communities. Typically, 360{\deg} data is projected into 2D ERP (equirectangular projection) images for feature extraction. However, existing methods cannot handle the distortions that result from the projection, hindering the development of 360-data-based tasks. Therefore, in this paper, we propose a Transformer-based model called DATFormer to address the distortion problem. We tackle this issue from two perspectives. Firstly, we introduce two distortion-adaptive modules. The first is a Distortion Mapping Module, which guides the model to pre-adapt to distorted features globally. The second module is a Distortion-Adaptive Attention Block that reduces local distortions on multi-scale features. Secondly, to exploit the unique characteristics of 360{\deg} data, we present a learnable relation matrix and use it as part of the positional embedding to further improve performance. Extensive experiments are conducted on three public datasets, and the results show that our model outperforms existing 2D SOD (salient object detection) and 360 SOD methods.
ComPtr: Towards Diverse Bi-source Dense Prediction Tasks via A Simple yet General Complementary Transformer
2023-07-23
Youwei Pang, Xiaoqi Zhao, Lihe Zhang, Huchuan Lu
Deep learning (DL) has advanced the field of dense prediction, while gradually dissolving the inherent barriers between different tasks. However, most existing works focus on designing architectures and constructing visual cues only for the specific task, which ignores the potential uniformity introduced by the DL paradigm. In this paper, we attempt to construct a novel $\underline{ComP}$lementary $\underline{tr}$ansformer, $\textbf{ComPtr}$, for diverse bi-source dense prediction tasks. Specifically, unlike existing methods that over-specialize in a single task or a subset of tasks, ComPtr starts from the more general concept of bi-source dense prediction. Based on the basic dependence on information complementarity, we propose consistency enhancement and difference awareness components with which ComPtr can evacuate and collect important visual semantic cues from different image sources for diverse tasks, respectively. ComPtr treats different inputs equally and builds an efficient dense interaction model in the form of sequence-to-sequence on top of the transformer. This task-generic design provides a smooth foundation for constructing the unified model that can simultaneously deal with various bi-source information. In extensive experiments across several representative vision tasks, i.e. remote sensing change detection, RGB-T crowd counting, RGB-D/T salient object detection, and RGB-D semantic segmentation, the proposed method consistently obtains favorable performance. The code will be available at https://github.com/lartpang/ComPtr.
Salient objects attract human attention and usually stand out clearly from their surroundings. In contrast, camouflaged objects share similar colors or textures with the environment. In this case, salient objects are typically non-camouflaged, and camouflaged objects are usually not salient. Due to this inherent contradictory attribute, we introduce an uncertainty-aware learning pipeline to extensively explore the contradictory information of salient object detection (SOD) and camouflaged object detection (COD) via data-level and task-wise contradiction modeling. We first exploit the dataset correlation of these two tasks and claim that the easy samples in the COD dataset can serve as hard samples for SOD to improve the robustness of the SOD model. Based on the assumption that these two models should lead to activation maps highlighting different regions of the same input image, we further introduce a contrastive module with a joint-task contrastive learning framework to explicitly model the contradictory attributes of these two tasks. Different from conventional intra-task contrastive learning for unsupervised representation learning, our contrastive module is designed to model the task-wise correlation, leading to cross-task representation learning. To better understand the two tasks from the perspective of uncertainty, we extensively investigate the uncertainty estimation techniques for modeling the main uncertainties of the two tasks, namely task uncertainty (for SOD) and data uncertainty (for COD), and aiming to effectively estimate the challenging regions for each task to achieve difficulty-aware learning. Experimental results on benchmark datasets demonstrate that our solution leads to both state-of-the-art performance and informative uncertainty estimation.
HODINet: High-Order Discrepant Interaction Network for RGB-D Salient Object Detection
2023-07-03
Kang Yi, Jing Xu, Xiao Jin, Fu Guo, Yan-Feng Wu
RGB-D salient object detection (SOD) aims to detect the prominent regions by jointly modeling RGB and depth information. Most RGB-D SOD methods apply the same type of backbones and fusion modules to identically learn the multimodality and multistage features. However, these features contribute differently to the final saliency results, which raises two issues: 1) how to model discrepant characteristics of RGB images and depth maps; 2) how to fuse these cross-modality features in different stages. In this paper, we propose a high-order discrepant interaction network (HODINet) for RGB-D SOD. Concretely, we first employ transformer-based and CNN-based architectures as backbones to encode RGB and depth features, respectively. Then, the high-order representations are delicately extracted and embedded into spatial and channel attentions for cross-modality feature fusion in different stages. Specifically, we design a high-order spatial fusion (HOSF) module and a high-order channel fusion (HOCF) module to fuse features of the first two and the last two stages, respectively. Besides, a cascaded pyramid reconstruction network is adopted to progressively decode the fused features in a top-down pathway. Extensive experiments are conducted on seven widely used datasets to demonstrate the effectiveness of the proposed approach. We achieve competitive performance against 24 state-of-the-art methods under four evaluation metrics.
Detecting the salient objects in a remote sensing image has wide applications for the interdisciplinary research. Many existing deep learning methods have been proposed for Salient Object Detection (SOD) in remote sensing images and get remarkable results. However, the recent adversarial attack examples, generated by changing a few pixel values on the original remote sensing image, could result in a collapse for the well-trained deep learning based SOD model. Different with existing methods adding perturbation to original images, we propose to jointly tune adversarial exposure and additive perturbation for attack and constrain image close to cloudy image as Adversarial Cloud. Cloud is natural and common in remote sensing images, however, camouflaging cloud based adversarial attack and defense for remote sensing images are not well studied before. Furthermore, we design DefenseNet as a learn-able pre-processing to the adversarial cloudy images so as to preserve the performance of the deep learning based remote sensing SOD model, without tuning the already deployed deep SOD model. By considering both regular and generalized adversarial examples, the proposed DefenseNet can defend the proposed Adversarial Cloud in white-box setting and other attack methods in black-box setting. Experimental results on a synthesized benchmark from the public remote sensing SOD dataset (EORSSD) show the promising defense against adversarial cloud attacks.
The emergence of different sensors (Near-Infrared, Depth, etc.) is a remedy for the limited application scenarios of traditional RGB camera. The RGB-X tasks, which rely on RGB input and another type of data input to resolve specific problems, have become a popular research topic in multimedia. A crucial part in two-branch RGB-X deep neural networks is how to fuse information across modalities. Given the tremendous information inside RGB-X networks, previous works typically apply naive fusion (e.g., average or max fusion) or only focus on the feature fusion at the same scale(s). While in this paper, we propose a novel method called RXFOOD for the fusion of features across different scales within the same modality branch and from different modality branches simultaneously in a unified attention mechanism. An Energy Exchange Module is designed for the interaction of each feature map's energy matrix, who reflects the inter-relationship of different positions and different channels inside a feature map. The RXFOOD method can be easily incorporated to any dual-branch encoder-decoder network as a plug-in module, and help the original backbone network better focus on important positions and channels for object of interest detection. Experimental results on RGB-NIR salient object detection, RGB-D salient object detection, and RGBFrequency image manipulation detection demonstrate the clear effectiveness of the proposed RXFOOD.
Mutual Information Regularization for Weakly-supervised RGB-D Salient Object Detection
2023-06-06
Aixuan Li, Yuxin Mao, Jing Zhang, Yuchao Dai
In this paper, we present a weakly-supervised RGB-D salient object detection model via scribble supervision. Specifically, as a multimodal learning task, we focus on effective multimodal representation learning via inter-modal mutual information regularization. In particular, following the principle of disentangled representation learning, we introduce a mutual information upper bound with a mutual information minimization regularizer to encourage the disentangled representation of each modality for salient object detection. Based on our multimodal representation learning framework, we introduce an asymmetric feature extractor for our multimodal data, which is proven more effective than the conventional symmetric backbone setting. We also introduce multimodal variational auto-encoder as stochastic prediction refinement techniques, which takes pseudo labels from the first training stage as supervision and generates refined prediction. Experimental results on benchmark RGB-D salient object detection datasets verify both effectiveness of our explicit multimodal disentangled representation learning method and the stochastic prediction refinement strategy, achieving comparable performance with the state-of-the-art fully supervised models. Our code and data are available at: https://github.com/baneitixiaomai/MIRV.
We present the RSSOD-Bench dataset for salient object detection (SOD) in optical remote sensing imagery. While SOD has achieved success in natural scene images with deep learning, research in SOD for remote sensing imagery (RSSOD) is still in its early stages. Existing RSSOD datasets have limitations in terms of scale, and scene categories, which make them misaligned with real-world applications. To address these shortcomings, we construct the RSSOD-Bench dataset, which contains images from four different cities in the USA. The dataset provides annotations for various salient object categories, such as buildings, lakes, rivers, highways, bridges, aircraft, ships, athletic fields, and more. The salient objects in RSSOD-Bench exhibit large-scale variations, cluttered backgrounds, and different seasons. Unlike existing datasets, RSSOD-Bench offers uniform distribution across scene categories. We benchmark 23 different state-of-the-art approaches from both the computer vision and remote sensing communities. Experimental results demonstrate that more research efforts are required for the RSSOD task.
Explicit Visual Prompting for Universal Foreground Segmentations
2023-05-29
Weihuang Liu, Xi Shen, Chi-Man Pun, Xiaodong Cun
Foreground segmentation is a fundamental problem in computer vision, which includes salient object detection, forgery detection, defocus blur detection, shadow detection, and camouflage object detection. Previous works have typically relied on domain-specific solutions to address accuracy and robustness issues in those applications. In this paper, we present a unified framework for a number of foreground segmentation tasks without any task-specific designs. We take inspiration from the widely-used pre-training and then prompt tuning protocols in NLP and propose a new visual prompting model, named Explicit Visual Prompting (EVP). Different from the previous visual prompting which is typically a dataset-level implicit embedding, our key insight is to enforce the tunable parameters focusing on the explicit visual content from each individual image, i.e., the features from frozen patch embeddings and high-frequency components. Our method freezes a pre-trained model and then learns task-specific knowledge using a few extra parameters. Despite introducing only a small number of tunable parameters, EVP achieves superior performance than full fine-tuning and other parameter-efficient fine-tuning methods. Experiments in fourteen datasets across five tasks show the proposed method outperforms other task-specific methods while being considerably simple. The proposed method demonstrates the scalability in different architectures, pre-trained weights, and tasks. The code is available at: https://github.com/NiFangBaAGe/Explicit-Visual-Prompt.
In this paper, we introduce Divide-and-Conquer into the salient object detection (SOD) task to enable the model to learn prior knowledge that is for predicting the saliency map. We design a novel network, Divide-and-Conquer Network (DC-Net) which uses two encoders to solve different subtasks that are conducive to predicting the final saliency map, here is to predict the edge maps with width 4 and location maps of salient objects and then aggregate the feature maps with different semantic information into the decoder to predict the final saliency map. The decoder of DC-Net consists of our newly designed two-level Residual nested-ASPP (ResASPP$^{2}$) modules, which have the ability to capture a large number of different scale features with a small number of convolution operations and have the advantages of maintaining high resolution all the time and being able to obtain a large and compact effective receptive field (ERF). Based on the advantage of Divide-and-Conquer's parallel computing, we use Parallel Acceleration to speed up DC-Net, allowing it to achieve competitive performance on six LR-SOD and five HR-SOD datasets under high efficiency (60 FPS and 55 FPS). Codes and results are available: https://github.com/PiggyJerry/DC-Net.
Referring Expression Segmentation (RES) is a widely explored multi-modal task, which endeavors to segment the pre-existing object within a single image with a given linguistic expression. However, in broader real-world scenarios, it is not always possible to determine if the described object exists in a specific image. Typically, we have a collection of images, some of which may contain the described objects. The current RES setting curbs its practicality in such situations. To overcome this limitation, we propose a more realistic and general setting, named Group-wise Referring Expression Segmentation (GRES), which expands RES to a collection of related images, allowing the described objects to be present in a subset of input images. To support this new setting, we introduce an elaborately compiled dataset named Grouped Referring Dataset (GRD), containing complete group-wise annotations of target objects described by given expressions. We also present a baseline method named Grouped Referring Segmenter (GRSer), which explicitly captures the language-vision and intra-group vision-vision interactions to achieve state-of-the-art results on the proposed GRES and related tasks, such as Co-Salient Object Detection and RES. Our dataset and codes will be publicly released in https://github.com/yixuan730/group-res.
We introduce ViDaS, a two-stream, fully convolutional Video, Depth-Aware Saliency network to address the problem of attention modeling ``in-the-wild", via saliency prediction in videos. Contrary to existing visual saliency approaches using only RGB frames as input, our network employs also depth as an additional modality. The network consists of two visual streams, one for the RGB frames, and one for the depth frames. Both streams follow an encoder-decoder approach and are fused to obtain a final saliency map. The network is trained end-to-end and is evaluated in a variety of different databases with eye-tracking data, containing a wide range of video content. Although the publicly available datasets do not contain depth, we estimate it using three different state-of-the-art methods, to enable comparisons and a deeper insight. Our method outperforms in most cases state-of-the-art models and our RGB-only variant, which indicates that depth can be beneficial to accurately estimating saliency in videos displayed on a 2D screen. Depth has been widely used to assist salient object detection problems, where it has been proven to be very beneficial. Our problem though differs significantly from salient object detection, since it is not restricted to specific salient objects, but predicts human attention in a more general aspect. These two problems not only have different objectives, but also different ground truth data and evaluation metrics. To our best knowledge, this is the first competitive deep learning video saliency estimation approach that combines both RGB and Depth features to address the general problem of saliency estimation ``in-the-wild". The code will be publicly released.
An Interactively Reinforced Paradigm for Joint Infrared-Visible Image Fusion and Saliency Object Detection
2023-05-17
Di Wang, Jinyuan Liu, Risheng Liu, Xin Fan
This research focuses on the discovery and localization of hidden objects in the wild and serves unmanned systems. Through empirical analysis, infrared and visible image fusion (IVIF) enables hard-to-find objects apparent, whereas multimodal salient object detection (SOD) accurately delineates the precise spatial location of objects within the picture. Their common characteristic of seeking complementary cues from different source images motivates us to explore the collaborative relationship between Fusion and Salient object detection tasks on infrared and visible images via an Interactively Reinforced multi-task paradigm for the first time, termed IRFS. To the seamless bridge of multimodal image fusion and SOD tasks, we specifically develop a Feature Screening-based Fusion subnetwork (FSFNet) to screen out interfering features from source images, thereby preserving saliency-related features. After generating the fused image through FSFNet, it is then fed into the subsequent Fusion-Guided Cross-Complementary SOD subnetwork (FC$^2$Net) as the third modality to drive the precise prediction of the saliency map by leveraging the complementary information derived from the fused image. In addition, we develop an interactive loop learning strategy to achieve the mutual reinforcement of IVIF and SOD tasks with a shorter training period and fewer network parameters. Comprehensive experiment results demonstrate that the seamless bridge of IVIF and SOD mutually enhances their performance, and highlights their superiority.
Guided Focal Stack Refinement Network for Light Field Salient Object Detection
2023-05-09
Bo Yuan, Yao Jiang, Keren Fu, Qijun Zhao
Light field salient object detection (SOD) is an emerging research direction attributed to the richness of light field data. However, most existing methods lack effective handling of focal stacks, therefore making the latter involved in a lot of interfering information and degrade the performance of SOD. To address this limitation, we propose to utilize multi-modal features to refine focal stacks in a guided manner, resulting in a novel guided focal stack refinement network called GFRNet. To this end, we propose a guided refinement and fusion module (GRFM) to refine focal stacks and aggregate multi-modal features. In GRFM, all-in-focus (AiF) and depth modalities are utilized to refine focal stacks separately, leading to two novel sub-modules for different modalities, namely AiF-based refinement module (ARM) and depth-based refinement module (DRM). Such refinement modules enhance structural and positional information of salient objects in focal stacks, and are able to improve SOD accuracy. Experimental results on four benchmark datasets demonstrate the superiority of our GFRNet model against 12 state-of-the-art models.
2023-05-08
Yi Liu, Shoukun Xu, Dingwen Zhang, Jungong Han
Co-salient object detection targets at detecting co-existed salient objects among a group of images. Recently, a generalist model for segmenting everything in context, called SegGPT, is gaining public attention. In view of its breakthrough for segmentation, we can hardly wait to probe into its contribution to the task of co-salient object detection. In this report, we first design a framework to enable SegGPT for the problem of co-salient object detection. Proceed to the next step, we evaluate the performance of SegGPT on the problem of co-salient object detection on three available datasets. We achieve a finding that co-saliency scenes challenges SegGPT due to context discrepancy within a group of co-saliency images.
Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection
2023-04-30
Long Li, Junwei Han, Ni Zhang, Nian Liu, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan
Most previous co-salient object detection works mainly focus on extracting co-salient cues via mining the consistency relations across images while ignoring explicit exploration of background regions. In this paper, we propose a Discriminative co-saliency and background Mining Transformer framework (DMT) based on several economical multi-grained correlation modules to explicitly mine both co-saliency and background information and effectively model their discrimination. Specifically, we first propose a region-to-region correlation module for introducing inter-image relations to pixel-wise segmentation features while maintaining computational efficiency. Then, we use two types of pre-defined tokens to mine co-saliency and background information via our proposed contrast-induced pixel-to-token correlation and co-saliency token-to-token correlation modules. We also design a token-guided feature refinement module to enhance the discriminability of the segmentation features under the guidance of the learned tokens. We perform iterative mutual promotion for the segmentation feature extraction and token construction. Experimental results on three benchmark datasets demonstrate the effectiveness of our proposed method. The source code is available at: https://github.com/dragonlee258079/DMT.
The majority of current salient object detection (SOD) models are focused on designing a series of decoders based on fully convolutional networks (FCNs) or Transformer architectures and integrating them in a skillful manner. These models have achieved remarkable high performance and made significant contributions to the development of SOD. Their primary research objective is to develop novel algorithms that can outperform state-of-the-art models, a task that is extremely difficult and time-consuming. In contrast, this paper proposes a positive feedback method based on F-measure value for SOD, aiming to improve the accuracy of saliency prediction using existing methods. Specifically, our proposed method takes an image to be detected and inputs it into several existing models to obtain their respective prediction maps. These prediction maps are then fed into our positive feedback method to generate the final prediction result, without the need for careful decoder design or model training. Moreover, our method is adaptive and can be implemented based on existing models without any restrictions. Experimental results on five publicly available datasets show that our proposed positive feedback method outperforms the latest 12 methods in five evaluation metrics for saliency map prediction. Additionally, we conducted a robustness experiment, which shows that when at least one good prediction result exists in the selected existing model, our proposed approach can ensure that the prediction result is not worse. Our approach achieves a prediction speed of 20 frames per second (FPS) when evaluated on a low configuration host and after removing the prediction time overhead of inserted models. These results highlight the effectiveness, efficiency, and robustness of our proposed approach for salient object detection.
Recently, RGB-Thermal based perception has shown significant advances. Thermal information provides useful clues when visual cameras suffer from poor lighting conditions, such as low light and fog. However, how to effectively fuse RGB images and thermal data remains an open challenge. Previous works involve naive fusion strategies such as merging them at the input, concatenating multi-modality features inside models, or applying attention to each data modality. These fusion strategies are straightforward yet insufficient. In this paper, we propose a novel fusion method named Explicit Attention-Enhanced Fusion (EAEF) that fully takes advantage of each type of data. Specifically, we consider the following cases: i) both RGB data and thermal data, ii) only one of the types of data, and iii) none of them generate discriminative features. EAEF uses one branch to enhance feature extraction for i) and iii) and the other branch to remedy insufficient representations for ii). The outputs of two branches are fused to form complementary features. As a result, the proposed fusion method outperforms state-of-the-art by 1.6\% in mIoU on semantic segmentation, 3.1\% in MAE on salient object detection, 2.3\% in mAP on object detection, and 8.1\% in MAE on crowd counting. The code is available at https://github.com/FreeformRobotics/EAEFNet.
Adaptive Graph Convolution Module for Salient Object Detection
2023-03-17
Yongwoo Lee, Minhyeok Lee, Suhwan Cho, Sangyoun Lee
Salient object detection (SOD) is a task that involves identifying and segmenting the most visually prominent object in an image. Existing solutions can accomplish this use a multi-scale feature fusion mechanism to detect the global context of an image. However, as there is no consideration of the structures in the image nor the relations between distant pixels, conventional methods cannot deal with complex scenes effectively. In this paper, we propose an adaptive graph convolution module (AGCM) to overcome these limitations. Prototype features are initially extracted from the input image using a learnable region generation layer that spatially groups features in the image. The prototype features are then refined by propagating information between them based on a graph architecture, where each feature is regarded as a node. Experimental results show that the proposed AGCM dramatically improves the SOD performance both quantitatively and quantitatively.
Salient object detection segments attractive objects in scenes. RGB and thermal modalities provide complementary information and scribble annotations alleviate large amounts of human labor. Based on the above facts, we propose a scribble-supervised RGB-T salient object detection model. By a four-step solution (expansion, prediction, aggregation, and supervision), label-sparse challenge of scribble-supervised method is solved. To expand scribble annotations, we collect the superpixels that foreground scribbles pass through in RGB and thermal images, respectively. The expanded multi-modal labels provide the coarse object boundary. To further polish the expanded labels, we propose a prediction module to alleviate the sharpness of boundary. To play the complementary roles of two modalities, we combine the two into aggregated pseudo labels. Supervised by scribble annotations and pseudo labels, our model achieves the state-of-the-art performance on the relabeled RGBT-S dataset. Furthermore, the model is applied to RGB-D and video scribble-supervised applications, achieving consistently excellent performance.
Co-salient object detection (Co-SOD) aims at discovering the common objects in a group of relevant images. Mining a co-representation is essential for locating co-salient objects. Unfortunately, the current Co-SOD method does not pay enough attention that the information not related to the co-salient object is included in the co-representation. Such irrelevant information in the co-representation interferes with its locating of co-salient objects. In this paper, we propose a Co-Representation Purification (CoRP) method aiming at searching noise-free co-representation. We search a few pixel-wise embeddings probably belonging to co-salient regions. These embeddings constitute our co-representation and guide our prediction. For obtaining purer co-representation, we use the prediction to iteratively reduce irrelevant embeddings in our co-representation. Experiments on three datasets demonstrate that our CoRP achieves state-of-the-art performances on the benchmark datasets. Our source code is available at https://github.com/ZZY816/CoRP.
With the increasing application of deep learning in various domains, salient object detection in optical remote sensing images (ORSI-SOD) has attracted significant attention. However, most existing ORSI-SOD methods predominantly rely on local information from low-level features to infer salient boundary cues and supervise them using boundary ground truth, but fail to sufficiently optimize and protect the local information, and almost all approaches ignore the potential advantages offered by the last layer of the decoder to maintain the integrity of saliency maps. To address these issues, we propose a novel method named boundary-semantic collaborative guidance network (BSCGNet) with dual-stream feedback mechanism. First, we propose a boundary protection calibration (BPC) module, which effectively reduces the loss of edge position information during forward propagation and suppresses noise in low-level features without relying on boundary ground truth. Second, based on the BPC module, a dual feature feedback complementary (DFFC) module is proposed, which aggregates boundary-semantic dual features and provides effective feedback to coordinate features across different layers, thereby enhancing cross-scale knowledge communication. Finally, to obtain more complete saliency maps, we consider the uniqueness of the last layer of the decoder for the first time and propose the adaptive feedback refinement (AFR) module, which further refines feature representation and eliminates differences between features through a unique feedback mechanism. Extensive experiments on three benchmark datasets demonstrate that BSCGNet exhibits distinct advantages in challenging scenarios and outperforms the 17 state-of-the-art (SOTA) approaches proposed in recent years. Codes and results have been released on GitHub: https://github.com/YUHsss/BSCGNet.
Co-Salient Object Detection (CoSOD) aims at detecting common salient objects within a group of relevant source images. Most of the latest works employ the attention mechanism for finding common objects. To achieve accurate CoSOD results with high-quality maps and high efficiency, we propose a novel Memory-aided Contrastive Consensus Learning (MCCL) framework, which is capable of effectively detecting co-salient objects in real time (~150 fps). To learn better group consensus, we propose the Group Consensus Aggregation Module (GCAM) to abstract the common features of each image group; meanwhile, to make the consensus representation more discriminative, we introduce the Memory-based Contrastive Module (MCM), which saves and updates the consensus of images from different groups in a queue of memories. Finally, to improve the quality and integrity of the predicted maps, we develop an Adversarial Integrity Learning (AIL) strategy to make the segmented regions more likely composed of complete objects with less surrounding noise. Extensive experiments on all the latest CoSOD benchmarks demonstrate that our lite MCCL outperforms 13 cutting-edge models, achieving the new state of the art (~5.9% and ~6.2% improvement in S-measure on CoSOD3k and CoSal2015, respectively). Our source codes, saliency maps, and online demos are publicly available at https://github.com/ZhengPeng7/MCCL.
HDR image watermarking using saliency detection and quantization index modulation
2023-02-22
Ahmed Khan, Minoru Kuribayashi, KokSheik Wong, Vishnu Monn Baskaran
High-dynamic range (HDR) images are circulated rapidly over the internet with risks of being exploited for unauthorized usage. To protect these images, some HDR image based watermarking (HDR-IW) methods were put forward. However, they inherited the same problem faced by conventional IW methods for standard dynamic range (SDR) images, where only trade-offs among conflicting requirements are managed instead of simultaneous improvement. In this paper, a novel saliency (eye-catching object) detection based trade-off independent HDR-IW is proposed, to simultaneously improve robustness, imperceptibility and payload. First, the host image goes through our proposed salient object detection model to produce a saliency map, which is, in turn, exploited to segment the foreground and background of the host image. Next, the binary watermark is partitioned into the foregrounds and backgrounds using the same mask and scrambled using a random permutation algorithm. Finally, the watermark segments are embedded into selected bit-plane of the corresponding host segments using quantized indexed modulation. Experimental results suggest that the proposed work outperforms state-of-the-art methods in terms of improving the conflicting requirements.
Fully supervised salient object detection (SOD) methods have made considerable progress in performance, yet these models rely heavily on expensive pixel-wise labels. Recently, to achieve a trade-off between labeling burden and performance, scribble-based SOD methods have attracted increasing attention. Previous scribble-based models directly implement the SOD task only based on SOD training data with limited information, it is extremely difficult for them to understand the image and further achieve a superior SOD task. In this paper, we propose a simple yet effective framework guided by general visual representations with rich contextual semantic knowledge for scribble-based SOD. These general visual representations are generated by self-supervised learning based on large-scale unlabeled datasets. Our framework consists of a task-related encoder, a general visual module, and an information integration module to efficiently combine the general visual representations with task-related features to perform the SOD task based on understanding the contextual connections of images. Meanwhile, we propose a novel global semantic affinity loss to guide the model to perceive the global structure of the salient objects. Experimental results on five public benchmark datasets demonstrate that our method, which only utilizes scribble annotations without introducing any extra label, outperforms the state-of-the-art weakly supervised SOD methods. Specifically, it outperforms the previous best scribble-based method on all datasets with an average gain of 5.5% for max f-measure, 5.8% for mean f-measure, 24% for MAE, and 3.1% for E-measure. Moreover, our method achieves comparable or even superior performance to the state-of-the-art fully supervised models.
Hierarchical Cross-modal Transformer for RGB-D Salient Object Detection
2023-02-16
Hao Chen, Feihong Shen
Most of existing RGB-D salient object detection (SOD) methods follow the CNN-based paradigm, which is unable to model long-range dependencies across space and modalities due to the natural locality of CNNs. Here we propose the Hierarchical Cross-modal Transformer (HCT), a new multi-modal transformer, to tackle this problem. Unlike previous multi-modal transformers that directly connecting all patches from two modalities, we explore the cross-modal complementarity hierarchically to respect the modality gap and spatial discrepancy in unaligned regions. Specifically, we propose to use intra-modal self-attention to explore complementary global contexts, and measure spatial-aligned inter-modal attention locally to capture cross-modal correlations. In addition, we present a Feature Pyramid module for Transformer (FPT) to boost informative cross-scale integration as well as a consistency-complementarity module to disentangle the multi-modal integration path and improve the fusion adaptivity. Comprehensive experiments on a large variety of public datasets verify the efficacy of our designs and the consistent improvement over state-of-the-art models.
RGB-D saliency detection aims to fuse multi-modal cues to accurately localize salient regions. Existing works often adopt attention modules for feature modeling, with few methods explicitly leveraging fine-grained details to merge with semantic cues. Thus, despite the auxiliary depth information, it is still challenging for existing models to distinguish objects with similar appearances but at distinct camera distances. In this paper, from a new perspective, we propose a novel Hierarchical Depth Awareness network (HiDAnet) for RGB-D saliency detection. Our motivation comes from the observation that the multi-granularity properties of geometric priors correlate well with the neural network hierarchies. To realize multi-modal and multi-level fusion, we first use a granularity-based attention scheme to strengthen the discriminatory power of RGB and depth features separately. Then we introduce a unified cross dual-attention module for multi-modal and multi-level fusion in a coarse-to-fine manner. The encoded multi-modal features are gradually aggregated into a shared decoder. Further, we exploit a multi-scale loss to take full advantage of the hierarchical information. Extensive experiments on challenging benchmark datasets demonstrate that our HiDAnet performs favorably over the state-of-the-art methods by large margins.
Existing salient object detection methods often adopt deeper and wider networks for better performance, resulting in heavy computational burden and slow inference speed. This inspires us to rethink saliency detection to achieve a favorable balance between efficiency and accuracy. To this end, we design a lightweight framework while maintaining satisfying competitive accuracy. Specifically, we propose a novel trilateral decoder framework by decoupling the U-shape structure into three complementary branches, which are devised to confront the dilution of semantic context, loss of spatial structure and absence of boundary detail, respectively. Along with the fusion of three branches, the coarse segmentation results are gradually refined in structure details and boundary quality. Without adding additional learnable parameters, we further propose Scale-Adaptive Pooling Module to obtain multi-scale receptive filed. In particular, on the premise of inheriting this framework, we rethink the relationship among accuracy, parameters and speed via network depth-width tradeoff. With these insightful considerations, we comprehensively design shallower and narrower models to explore the maximum potential of lightweight SOD. Our models are purposed for different application environments: 1) a tiny version CTD-S (1.7M, 125FPS) for resource constrained devices, 2) a fast version CTD-M (12.6M, 158FPS) for speed-demanding scenarios, 3) a standard version CTD-L (26.5M, 84FPS) for high-performance platforms. Extensive experiments validate the superiority of our method, which achieves better efficiency-accuracy balance across five benchmarks.
Salient Object Detection for Images Taken by People With Vision Impairments
2023-01-12
Jarek Reynolds, Chandra Kanth Nagesh, Danna Gurari
Salient object detection is the task of producing a binary mask for an image that deciphers which pixels belong to the foreground object versus background. We introduce a new salient object detection dataset using images taken by people who are visually impaired who were seeking to better understand their surroundings, which we call VizWiz-SalientObject. Compared to seven existing datasets, VizWiz-SalientObject is the largest (i.e., 32,000 human-annotated images) and contains unique characteristics including a higher prevalence of text in the salient objects (i.e., in 68\% of images) and salient objects that occupy a larger ratio of the images (i.e., on average, $\sim$50\% coverage). We benchmarked seven modern salient object detection methods on our dataset and found they struggle most with images featuring salient objects that are large, have less complex boundaries, and lack text as well as for lower quality images. We invite the broader community to work on our new dataset challenge by publicly sharing the dataset at https://vizwiz.org/tasks-and-datasets/salient-object .
2023-01-08
Bin Tang, Zhengyi Liu, Yacheng Tan, Qian He
The High-Resolution Transformer (HRFormer) can maintain high-resolution representation and share global receptive fields. It is friendly towards salient object detection (SOD) in which the input and output have the same resolution. However, two critical problems need to be solved for two-modality SOD. One problem is two-modality fusion. The other problem is the HRFormer output's fusion. To address the first problem, a supplementary modality is injected into the primary modality by using global optimization and an attention mechanism to select and purify the modality at the input level. To solve the second problem, a dual-direction short connection fusion module is used to optimize the output features of HRFormer, thereby enhancing the detailed representation of objects at the output level. The proposed model, named HRTransNet, first introduces an auxiliary stream for feature extraction of supplementary modality. Then, features are injected into the primary modality at the beginning of each multi-resolution branch. Next, HRFormer is applied to achieve forwarding propagation. Finally, all the output features with different resolutions are aggregated by intra-feature and inter-feature interactive transformers. Application of the proposed model results in impressive improvement for driving two-modality SOD tasks, e.g., RGB-D, RGB-T, and light field SOD.https://github.com/liuzywen/HRTransNet
Lightweight Salient Object Detection in Optical Remote-Sensing Images via Semantic Matching and Edge Alignment
2023-01-07
Gongyang Li, Zhi Liu, Xinpeng Zhang, Weisi Lin
Recently, relying on convolutional neural networks (CNNs), many methods for salient object detection in optical remote sensing images (ORSI-SOD) are proposed. However, most methods ignore the huge parameters and computational cost brought by CNNs, and only a few pay attention to the portability and mobility. To facilitate practical applications, in this paper, we propose a novel lightweight network for ORSI-SOD based on semantic matching and edge alignment, termed SeaNet. Specifically, SeaNet includes a lightweight MobileNet-V2 for feature extraction, a dynamic semantic matching module (DSMM) for high-level features, an edge self-alignment module (ESAM) for low-level features, and a portable decoder for inference. First, the high-level features are compressed into semantic kernels. Then, semantic kernels are used to activate salient object locations in two groups of high-level features through dynamic convolution operations in DSMM. Meanwhile, in ESAM, cross-scale edge information extracted from two groups of low-level features is self-aligned through L2 loss and used for detail enhancement. Finally, starting from the highest-level features, the decoder infers salient objects based on the accurate locations and fine details contained in the outputs of the two modules. Extensive experiments on two public datasets demonstrate that our lightweight SeaNet not only outperforms most state-of-the-art lightweight methods but also yields comparable accuracy with state-of-the-art conventional methods, while having only 2.76M parameters and running with 1.7G FLOPs for 288x288 inputs. Our code and results are available at https://github.com/MathLee/SeaNet.
Multi-Projection Fusion and Refinement Network for Salient Object Detection in 360° Omnidirectional Image
2022-12-23
Runmin Cong, Ke Huang, Jianjun Lei, Yao Zhao, Qingming Huang, Sam Kwong
Salient object detection (SOD) aims to determine the most visually attractive objects in an image. With the development of virtual reality technology, 360{\deg} omnidirectional image has been widely used, but the SOD task in 360{\deg} omnidirectional image is seldom studied due to its severe distortions and complex scenes. In this paper, we propose a Multi-Projection Fusion and Refinement Network (MPFR-Net) to detect the salient objects in 360{\deg} omnidirectional image. Different from the existing methods, the equirectangular projection image and four corresponding cube-unfolding images are embedded into the network simultaneously as inputs, where the cube-unfolding images not only provide supplementary information for equirectangular projection image, but also ensure the object integrity of the cube-map projection. In order to make full use of these two projection modes, a Dynamic Weighting Fusion (DWF) module is designed to adaptively integrate the features of different projections in a complementary and dynamic manner from the perspective of inter and intra features. Furthermore, in order to fully explore the way of interaction between encoder and decoder features, a Filtration and Refinement (FR) module is designed to suppress the redundant information between the feature itself and the feature. Experimental results on two omnidirectional datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both qualitatively and quantitatively.
Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection
2022-12-13
Zhenyu Wu, Lin Wang, Wei Wang, Qing Xia, Chenglizhao Chen, Aimin Hao, Shuo Li
Although weakly-supervised techniques can reduce the labeling effort, it is unclear whether a saliency model trained with weakly-supervised data (e.g., point annotation) can achieve the equivalent performance of its fully-supervised version. This paper attempts to answer this unexplored question by proving a hypothesis: there is a point-labeled dataset where saliency models trained on it can achieve equivalent performance when trained on the densely annotated dataset. To prove this conjecture, we proposed a novel yet effective adversarial trajectory-ensemble active learning (ATAL). Our contributions are three-fold: 1) Our proposed adversarial attack triggering uncertainty can conquer the overconfidence of existing active learning methods and accurately locate these uncertain pixels. {2)} Our proposed trajectory-ensemble uncertainty estimation method maintains the advantages of the ensemble networks while significantly reducing the computational cost. {3)} Our proposed relationship-aware diversity sampling algorithm can conquer oversampling while boosting performance. Experimental results show that our ATAL can find such a point-labeled dataset, where a saliency model trained on it obtained $97\%$ -- $99\%$ performance of its fully-supervised version with only ten annotated points per image.
2022-12-10
Zongwei Wu, Danda Pani Paudel, Deng-Ping Fan, Jingjing Wang, Shuo Wang, Cédric Demonceaux, Radu Timofte, Luc Van Gool
Depth cues are known to be useful for visual perception. However, direct measurement of depth is often impracticable. Fortunately, though, modern learning-based methods offer promising depth maps by inference in the wild. In this work, we adapt such depth inference models for object segmentation using the objects' "pop-out" prior in 3D. The "pop-out" is a simple composition prior that assumes objects reside on the background surface. Such compositional prior allows us to reason about objects in the 3D space. More specifically, we adapt the inferred depth maps such that objects can be localized using only 3D information. Such separation, however, requires knowledge about contact surface which we learn using the weak supervision of the segmentation mask. Our intermediate representation of contact surface, and thereby reasoning about objects purely in 3D, allows us to better transfer the depth knowledge into semantics. The proposed adaptation method uses only the depth model without needing the source data used for training, making the learning process efficient and practical. Our experiments on eight datasets of two challenging tasks, namely camouflaged object detection and salient object detection, consistently demonstrate the benefit of our method in terms of both performance and generalizability.
Fully supervised salient object detection (SOD) has made considerable progress based on expensive and time-consuming data with pixel-wise annotations. Recently, to relieve the labeling burden while maintaining performance, some scribble-based SOD methods have been proposed. However, learning precise boundary details from scribble annotations that lack edge information is still difficult. In this paper, we propose to learn precise boundaries from our designed synthetic images and labels without introducing any extra auxiliary data. The synthetic image creates boundary information by inserting synthetic concave regions that simulate the real concave regions of salient objects. Furthermore, we propose a novel self-consistent framework that consists of a global integral branch (GIB) and a boundary-aware branch (BAB) to train a saliency detector. GIB aims to identify integral salient objects, whose input is the original image. BAB aims to help predict accurate boundaries, whose input is the synthetic image. These two branches are connected through a self-consistent loss to guide the saliency detector to predict precise boundaries while identifying salient objects. Experimental results on five benchmarks demonstrate that our method outperforms the state-of-the-art weakly supervised SOD methods and further narrows the gap with the fully supervised methods.
Video salient object detection (VSOD), as a fundamental computer vision problem, has been extensively discussed in the last decade. However, all existing works focus on addressing the VSOD problem in 2D scenarios. With the rapid development of VR devices, panoramic videos have been a promising alternative to 2D videos to provide immersive feelings of the real world. In this paper, we aim to tackle the video salient object detection problem for panoramic videos, with their corresponding ambisonic audios. A multimodal fusion module equipped with two pseudo-siamese audio-visual context fusion (ACF) blocks is proposed to effectively conduct audio-visual interaction. The ACF block equipped with spherical positional encoding enables the fusion in the 3D context to capture the spatial correspondence between pixels and sound sources from the equirectangular frames and ambisonic audios. Experimental results verify the effectiveness of our proposed components and demonstrate that our method achieves state-of-the-art performance on the ASOD60K dataset.
Semantic-aware Texture-Structure Feature Collaboration for Underwater Image Enhancement
2022-11-19
Di Wang, Long Ma, Risheng Liu, Xin Fan
Underwater image enhancement has become an attractive topic as a significant technology in marine engineering and aquatic robotics. However, the limited number of datasets and imperfect hand-crafted ground truth weaken its robustness to unseen scenarios, and hamper the application to high-level vision tasks. To address the above limitations, we develop an efficient and compact enhancement network in collaboration with a high-level semantic-aware pretrained model, aiming to exploit its hierarchical feature representation as an auxiliary for the low-level underwater image enhancement. Specifically, we tend to characterize the shallow layer features as textures while the deep layer features as structures in the semantic-aware model, and propose a multi-path Contextual Feature Refinement Module (CFRM) to refine features in multiple scales and model the correlation between different features. In addition, a feature dominative network is devised to perform channel-wise modulation on the aggregated texture and structure features for the adaptation to different feature patterns of the enhancement network. Extensive experiments on benchmarks demonstrate that the proposed algorithm achieves more appealing results and outperforms state-of-the-art methods by large margins. We also apply the proposed algorithm to the underwater salient object detection task to reveal the favorable semantic-aware ability for high-level vision tasks. The code is available at STSC.
It is believed that human vision system (HVS) consists of pre-attentive process and attention process when performing salient object detection (SOD). Based on this fact, we propose a four-stage framework for SOD, in which the first two stages match the \textbf{P}re-\textbf{A}ttentive process consisting of general feature extraction (GFE) and feature preprocessing (FP), and the last two stages are corresponding to \textbf{A}ttention process containing saliency feature extraction (SFE) and the feature aggregation (FA), namely \textbf{PAANet}. According to the pre-attentive process, the GFE stage applies the fully-trained backbone and needs no further finetuning for different datasets. This modification can greatly increase the training speed. The FP stage plays the role of finetuning but works more efficiently because of its simpler structure and fewer parameters. Moreover, in SFE stage we design for saliency feature extraction a novel contrast operator, which works more semantically in contrast with the traditional convolution operator when extracting the interactive information between the foreground and its surroundings. Interestingly, this contrast operator can be cascaded to form a deeper structure and extract higher-order saliency more effective for complex scene. Comparative experiments with the state-of-the-art methods on 5 datasets demonstrate the effectiveness of our framework.
Multistep feature aggregation framework for salient object detection
2022-11-12
Xiaogang Liu Shuang Song
Recent works on salient object detection have made use of multi-scale features in a way such that high-level features and low-level features can collaborate in locating salient objects. Many of the previous methods have achieved great performance in salient object detection. By merging the high-level and low-level features, a large number of feature information can be extracted. Generally, they are doing these in a one-way framework, and interweaving the variable features all the way to the final feature output. Which may cause some blurring or inaccurate localization of saliency maps. To overcome these difficulties, we introduce a multistep feature aggregation (MSFA) framework for salient object detection, which is composed of three modules, including the Diverse Reception (DR) module, multiscale interaction (MSI) module and Feature Enhancement (FE) module to accomplish better multi-level feature fusion. Experimental results on six benchmark datasets demonstrate that MSFA achieves state-of-the-art performance.
Interactive Context-Aware Network for RGB-T Salient Object Detection
2022-11-11
Yuxuan Wang, Feng Dong, Jinchao Zhu
Salient object detection (SOD) focuses on distinguishing the most conspicuous objects in the scene. However, most related works are based on RGB images, which lose massive useful information. Accordingly, with the maturity of thermal technology, RGB-T (RGB-Thermal) multi-modality tasks attain more and more attention. Thermal infrared images carry important information which can be used to improve the accuracy of SOD prediction. To accomplish it, the methods to integrate multi-modal information and suppress noises are critical. In this paper, we propose a novel network called Interactive Context-Aware Network (ICANet). It contains three modules that can effectively perform the cross-modal and cross-scale fusions. We design a Hybrid Feature Fusion (HFF) module to integrate the features of two modalities, which utilizes two types of feature extraction. The Multi-Scale Attention Reinforcement (MSAR) and Upper Fusion (UF) blocks are responsible for the cross-scale fusion that converges different levels of features and generate the prediction maps. We also raise a novel Context-Aware Multi-Supervised Network (CAMSNet) to calculate the content loss between the prediction and the ground truth (GT). Experiments prove that our network performs favorably against the state-of-the-art RGB-T SOD methods.
We propose PSFormer, an effective point transformer model for 3D salient object detection. PSFormer is an encoder-decoder network that takes full advantage of transformers to model the contextual information in both multi-scale point- and scene-wise manners. In the encoder, we develop a Point Context Transformer (PCT) module to capture region contextual features at the point level; PCT contains two different transformers to excavate the relationship among points. In the decoder, we develop a Scene Context Transformer (SCT) module to learn context representations at the scene level; SCT contains both Upsampling-and-Transformer blocks and Multi-context Aggregation units to integrate the global semantic and multi-level features from the encoder into the global scene context. Experiments show clear improvements of PSFormer over its competitors and validate that PSFormer is more robust to challenging cases such as small objects, multiple objects, and objects with complex structures.
LeNo: Adversarial Robust Salient Object Detection Networks with Learnable Noise
2022-10-27
He Wang, Lin Wan, He Tang
Pixel-wise prediction with deep neural network has become an effective paradigm for salient object detection (SOD) and achieved remarkable performance. However, very few SOD models are robust against adversarial attacks which are visually imperceptible for human visual attention. The previous work robust saliency (ROSA) shuffles the pre-segmented superpixels and then refines the coarse saliency map by the densely connected conditional random field (CRF). Different from ROSA that relies on various pre- and post-processings, this paper proposes a light-weight Learnable Noise (LeNo) to defend adversarial attacks for SOD models. LeNo preserves accuracy of SOD models on both adversarial and clean images, as well as inference speed. In general, LeNo consists of a simple shallow noise and noise estimation that embedded in the encoder and decoder of arbitrary SOD networks respectively. Inspired by the center prior of human visual attention mechanism, we initialize the shallow noise with a cross-shaped gaussian distribution for better defense against adversarial attacks. Instead of adding additional network components for post-processing, the proposed noise estimation modifies only one channel of the decoder. With the deeply-supervised noise-decoupled training on state-of-the-art RGB and RGB-D SOD networks, LeNo outperforms previous works not only on adversarial images but also on clean images, which contributes stronger robustness for SOD. Our code is available at https://github.com/ssecv/LeNo.
Synthetic Data Supervised Salient Object Detection
2022-10-25
Zhenyu Wu, Lin Wang, Wei Wang, Tengfei Shi, Chenglizhao Chen, Aimin Hao, Shuo Li
Although deep salient object detection (SOD) has achieved remarkable progress, deep SOD models are extremely data-hungry, requiring large-scale pixel-wise annotations to deliver such promising results. In this paper, we propose a novel yet effective method for SOD, coined SODGAN, which can generate infinite high-quality image-mask pairs requiring only a few labeled data, and these synthesized pairs can replace the human-labeled DUTS-TR to train any off-the-shelf SOD model. Its contribution is three-fold. 1) Our proposed diffusion embedding network can address the manifold mismatch and is tractable for the latent code generation, better matching with the ImageNet latent space. 2) For the first time, our proposed few-shot saliency mask generator can synthesize infinite accurate image synchronized saliency masks with a few labeled data. 3) Our proposed quality-aware discriminator can select highquality synthesized image-mask pairs from noisy synthetic data pool, improving the quality of synthetic data. For the first time, our SODGAN tackles SOD with synthetic data directly generated from the generative model, which opens up a new research paradigm for SOD. Extensive experimental results show that the saliency model trained on synthetic data can achieve $98.4\%$ F-measure of the saliency model trained on the DUTS-TR. Moreover, our approach achieves a new SOTA performance in semi/weakly-supervised methods, and even outperforms several fully-supervised SOTA methods. Code is available at https://github.com/wuzhenyubuaa/SODGAN
Recent research advances in salient object detection (SOD) could largely be attributed to ever-stronger multi-scale feature representation empowered by the deep learning technologies. The existing SOD deep models extract multi-scale features via the off-the-shelf encoders and combine them smartly via various delicate decoders. However, the kernel sizes in this commonly-used thread are usually "fixed". In our new experiments, we have observed that kernels of small size are preferable in scenarios containing tiny salient objects. In contrast, large kernel sizes could perform better for images with large salient objects. Inspired by this observation, we advocate the "dynamic" scale routing (as a brand-new idea) in this paper. It will result in a generic plug-in that could directly fit the existing feature backbone. This paper's key technical innovations are two-fold. First, instead of using the vanilla convolution with fixed kernel sizes for the encoder design, we propose the dynamic pyramid convolution (DPConv), which dynamically selects the best-suited kernel sizes w.r.t. the given input. Second, we provide a self-adaptive bidirectional decoder design to accommodate the DPConv-based encoder best. The most significant highlight is its capability of routing between feature scales and their dynamic collection, making the inference process scale-aware. As a result, this paper continues to enhance the current SOTA performance. Both the code and dataset are publicly available at https://github.com/wuzhenyubuaa/DPNet.
Deep neural networks are facing severe threats from adversarial attacks. Most existing black-box attacks fool target model by generating either global perturbations or local patches. However, both global perturbations and local patches easily cause annoying visual artifacts in adversarial example. Compared with some smooth regions of an image, the object region generally has more edges and a more complex texture. Thus small perturbations on it will be more imperceptible. On the other hand, the object region is undoubtfully the decisive part of an image to classification tasks. Motivated by these two facts, we propose an object-attentional adversarial attack method for untargeted attack. Specifically, we first generate an object region by intersecting the object detection region from YOLOv4 with the salient object detection (SOD) region from HVPNet. Furthermore, we design an activation strategy to avoid the reaction caused by the incomplete SOD. Then, we perform an adversarial attack only on the detected object region by leveraging Simple Black-box Adversarial Attack (SimBA). To verify the proposed method, we create a unique dataset by extracting all the images containing the object defined by COCO from ImageNet-1K, named COCO-Reduced-ImageNet in this paper. Experimental results on ImageNet-1K and COCO-Reduced-ImageNet show that under various system settings, our method yields the adversarial example with better perceptual quality meanwhile saving the query budget up to 24.16\% compared to the state-of-the-art approaches including SimBA.
MOVE: Unsupervised Movable Object Segmentation and Detection
2022-10-14
Adam Bielski, Paolo Favaro
We introduce MOVE, a novel method to segment objects without any form of supervision. MOVE exploits the fact that foreground objects can be shifted locally relative to their initial position and result in realistic (undistorted) new images. This property allows us to train a segmentation model on a dataset of images without annotation and to achieve state of the art (SotA) performance on several evaluation datasets for unsupervised salient object detection and segmentation. In unsupervised single object discovery, MOVE gives an average CorLoc improvement of 7.2% over the SotA, and in unsupervised class-agnostic object detection it gives a relative AP improvement of 53% on average. Our approach is built on top of self-supervised features (e.g. from DINO or MAE), an inpainting network (based on the Masked AutoEncoder) and adversarial training.
PSNet: Parallel Symmetric Network for Video Salient Object Detection
2022-10-12
Runmin Cong, Weiyu Song, Jianjun Lei, Guanghui Yue, Yao Zhao, Sam Kwong
For the video salient object detection (VSOD) task, how to excavate the information from the appearance modality and the motion modality has always been a topic of great concern. The two-stream structure, including an RGB appearance stream and an optical flow motion stream, has been widely used as a typical pipeline for VSOD tasks, but the existing methods usually only use motion features to unidirectionally guide appearance features or adaptively but blindly fuse two modality features. However, these methods underperform in diverse scenarios due to the uncomprehensive and unspecific learning schemes. In this paper, following a more secure modeling philosophy, we deeply investigate the importance of appearance modality and motion modality in a more comprehensive way and propose a VSOD network with up and down parallel symmetry, named PSNet. Two parallel branches with different dominant modalities are set to achieve complete video saliency decoding with the cooperation of the Gather Diffusion Reinforcement (GDR) module and Cross-modality Refinement and Complement (CRC) module. Finally, we use the Importance Perception Fusion (IPF) module to fuse the features from two parallel branches according to their different importance in different scenarios. Experiments on four dataset benchmarks demonstrate that our method achieves desirable and competitive performance.
Does Thermal Really Always Matter for RGB-T Salient Object Detection?
2022-10-09
Runmin Cong, Kepu Zhang, Chen Zhang, Feng Zheng, Yao Zhao, Qingming Huang, Sam Kwong
In recent years, RGB-T salient object detection (SOD) has attracted continuous attention, which makes it possible to identify salient objects in environments such as low light by introducing thermal image. However, most of the existing RGB-T SOD models focus on how to perform cross-modality feature fusion, ignoring whether thermal image is really always matter in SOD task. Starting from the definition and nature of this task, this paper rethinks the connotation of thermal modality, and proposes a network named TNet to solve the RGB-T SOD task. In this paper, we introduce a global illumination estimation module to predict the global illuminance score of the image, so as to regulate the role played by the two modalities. In addition, considering the role of thermal modality, we set up different cross-modality interaction mechanisms in the encoding phase and the decoding phase. On the one hand, we introduce a semantic constraint provider to enrich the semantics of thermal images in the encoding phase, which makes thermal modality more suitable for the SOD task. On the other hand, we introduce a two-stage localization and complementation module in the decoding phase to transfer object localization cue and internal integrity cue in thermal features to the RGB modality. Extensive experiments on three datasets show that the proposed TNet achieves competitive performance compared with 20 state-of-the-art methods.
Focusing on the issue of how to effectively capture and utilize cross-modality information in RGB-D salient object detection (SOD) task, we present a convolutional neural network (CNN) model, named CIR-Net, based on the novel cross-modality interaction and refinement. For the cross-modality interaction, 1) a progressive attention guided integration unit is proposed to sufficiently integrate RGB-D feature representations in the encoder stage, and 2) a convergence aggregation structure is proposed, which flows the RGB and depth decoding features into the corresponding RGB-D decoding streams via an importance gated fusion unit in the decoder stage. For the cross-modality refinement, we insert a refinement middleware structure between the encoder and the decoder, in which the RGB, depth, and RGB-D encoder features are further refined by successively using a self-modality attention refinement unit and a cross-modality weighting refinement unit. At last, with the gradually refined features, we predict the saliency map in the decoder stage. Extensive experiments on six popular RGB-D SOD benchmarks demonstrate that our network outperforms the state-of-the-art saliency detectors both qualitatively and quantitatively.
Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection
2022-09-28
Maoxun Yuan, Yinyan Wang, Xingxing Wei
Integrating multispectral data in object detection, especially visible and infrared images, has received great attention in recent years. Since visible (RGB) and infrared (IR) images can provide complementary information to handle light variations, the paired images are used in many fields, such as multispectral pedestrian detection, RGB-IR crowd counting and RGB-IR salient object detection. Compared with natural RGB-IR images, we find detection in aerial RGB-IR images suffers from cross-modal weakly misalignment problems, which are manifested in the position, size and angle deviations of the same object. In this paper, we mainly address the challenge of cross-modal weakly misalignment in aerial RGB-IR images. Specifically, we firstly explain and analyze the cause of the weakly misalignment problem. Then, we propose a Translation-Scale-Rotation Alignment (TSRA) module to address the problem by calibrating the feature maps from these two modalities. The module predicts the deviation between two modality objects through an alignment process and utilizes Modality-Selection (MS) strategy to improve the performance of alignment. Finally, a two-stream feature alignment detector (TSFADet) based on the TSRA module is constructed for RGB-IR object detection in aerial images. With comprehensive experiments on the public DroneVehicle datasets, we verify that our method reduces the effect of the cross-modal misalignment and achieve robust detection results.
View-aware Salient Object Detection for 360° Omnidirectional Image
2022-09-27
Junjie Wu, Changqun Xia, Tianshu Yu, Jia Li
Image-based salient object detection (ISOD) in 360{\deg} scenarios is significant for understanding and applying panoramic information. However, research on 360{\deg} ISOD has not been widely explored due to the lack of large, complex, high-resolution, and well-labeled datasets. Towards this end, we construct a large scale 360{\deg} ISOD dataset with object-level pixel-wise annotation on equirectangular projection (ERP), which contains rich panoramic scenes with not less than 2K resolution and is the largest dataset for 360{\deg} ISOD by far to our best knowledge. By observing the data, we find current methods face three significant challenges in panoramic scenarios: diverse distortion degrees, discontinuous edge effects and changeable object scales. Inspired by humans' observing process, we propose a view-aware salient object detection method based on a Sample Adaptive View Transformer (SAVT) module with two sub-modules to mitigate these issues. Specifically, the sub-module View Transformer (VT) contains three transform branches based on different kinds of transformations to learn various features under different views and heighten the model's feature toleration of distortion, edge effects and object scales. Moreover, the sub-module Sample Adaptive Fusion (SAF) is to adjust the weights of different transform branches based on various sample features and make transformed enhanced features fuse more appropriately. The benchmark results of 20 state-of-the-art ISOD methods reveal the constructed dataset is very challenging. Moreover, exhaustive experiments verify the proposed approach is practical and outperforms the state-of-the-art methods.
RGB-Thermal salient object detection (SOD) combines two spectra to segment visually conspicuous regions in images. Most existing methods use boundary maps to learn the sharp boundary. These methods ignore the interactions between isolated boundary pixels and other confident pixels, leading to sub-optimal performance. To address this problem,we propose a position-aware relation learning network (PRLNet) for RGB-T SOD based on swin transformer. PRLNet explores the distance and direction relationships between pixels to strengthen intra-class compactness and inter-class separation, generating salient object masks with clear boundaries and homogeneous regions. Specifically, we develop a novel signed distance map auxiliary module (SDMAM) to improve encoder feature representation, which takes into account the distance relation of different pixels in boundary neighborhoods. Then, we design a feature refinement approach with directional field (FRDF), which rectifies features of boundary neighborhood by exploiting the features inside salient objects. FRDF utilizes the directional information between object pixels to effectively enhance the intra-class compactness of salient regions. In addition, we constitute a pure transformer encoder-decoder network to enhance multispectral feature representation for RGB-T SOD. Finally, we conduct quantitative and qualitative experiments on three public benchmark datasets.The results demonstrate that our proposed method outperforms the state-of-the-art methods.
Revisiting Image Pyramid Structure for High Resolution Salient Object Detection
2022-09-20
Taehun Kim, Kunhee Kim, Joonyeong Lee, Dongmin Cha, Jiho Lee, Daijin Kim
Salient object detection (SOD) has been in the spotlight recently, yet has been studied less for high-resolution (HR) images. Unfortunately, HR images and their pixel-level annotations are certainly more labor-intensive and time-consuming compared to low-resolution (LR) images and annotations. Therefore, we propose an image pyramid-based SOD framework, Inverse Saliency Pyramid Reconstruction Network (InSPyReNet), for HR prediction without any of HR datasets. We design InSPyReNet to produce a strict image pyramid structure of saliency map, which enables to ensemble multiple results with pyramid-based image blending. For HR prediction, we design a pyramid blending method which synthesizes two different image pyramids from a pair of LR and HR scale from the same image to overcome effective receptive field (ERF) discrepancy. Our extensive evaluations on public LR and HR SOD benchmarks demonstrate that InSPyReNet surpasses the State-of-the-Art (SotA) methods on various SOD metrics and boundary accuracy.
Large-Field Contextual Feature Learning for Glass Detection
2022-09-10
Haiyang Mei, Xin Yang, Letian Yu, Qiang Zhang, Xiaopeng Wei, Rynson W. H. Lau
Glass is very common in our daily life. Existing computer vision systems neglect it and thus may have severe consequences, e.g., a robot may crash into a glass wall. However, sensing the presence of glass is not straightforward. The key challenge is that arbitrary objects/scenes can appear behind the glass. In this paper, we propose an important problem of detecting glass surfaces from a single RGB image. To address this problem, we construct the first large-scale glass detection dataset (GDD) and propose a novel glass detection network, called GDNet-B, which explores abundant contextual cues in a large field-of-view via a novel large-field contextual feature integration (LCFI) module and integrates both high-level and low-level boundary features with a boundary feature enhancement (BFE) module. Extensive experiments demonstrate that our GDNet-B achieves satisfying glass detection results on the images within and beyond the GDD testing set. We further validate the effectiveness and generalization capability of our proposed GDNet-B by applying it to other vision tasks, including mirror segmentation and salient object detection. Finally, we show the potential applications of glass detection and discuss possible future research directions.
A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels
2022-09-07
Runmin Cong, Qi Qin, Chen Zhang, Qiuping Jiang, Shiqi Wang, Yao Zhao, Sam Kwong
Fully-supervised salient object detection (SOD) methods have made great progress, but such methods often rely on a large number of pixel-level annotations, which are time-consuming and labour-intensive. In this paper, we focus on a new weakly-supervised SOD task under hybrid labels, where the supervision labels include a large number of coarse labels generated by the traditional unsupervised method and a small number of real labels. To address the issues of label noise and quantity imbalance in this task, we design a new pipeline framework with three sophisticated training strategies. In terms of model framework, we decouple the task into label refinement sub-task and salient object detection sub-task, which cooperate with each other and train alternately. Specifically, the R-Net is designed as a two-stream encoder-decoder model equipped with Blender with Guidance and Aggregation Mechanisms (BGA), aiming to rectify the coarse labels for more reliable pseudo-labels, while the S-Net is a replaceable SOD network supervised by the pseudo labels generated by the current R-Net. Note that, we only need to use the trained S-Net for testing. Moreover, in order to guarantee the effectiveness and efficiency of network training, we design three training strategies, including alternate iteration mechanism, group-wise incremental mechanism, and credibility verification mechanism. Experiments on five SOD benchmarks show that our method achieves competitive performance against weakly-supervised/unsupervised methods both qualitatively and quantitatively.
We propose a new setting that relaxes an assumption in the conventional Co-Salient Object Detection (CoSOD) setting by allowing the presence of "noisy images" which do not show the shared co-salient object. We call this new setting Generalised Co-Salient Object Detection (GCoSOD). We propose a novel random sampling based Generalised CoSOD Training (GCT) strategy to distill the awareness of inter-image absence of co-salient objects into CoSOD models. It employs a Diverse Sampling Self-Supervised Learning (DS3L) that, in addition to the provided supervised co-salient label, introduces additional self-supervised labels for noisy images (being null, that no co-salient object is present). Further, the random sampling process inherent in GCT enables the generation of a high-quality uncertainty map highlighting potential false-positive predictions at instance level. To evaluate the performance of CoSOD models under the GCoSOD setting, we propose two new testing datasets, namely CoCA-Common and CoCA-Zero, where a common salient object is partially present in the former and completely absent in the latter. Extensive experiments demonstrate that our proposed method significantly improves the performance of CoSOD models in terms of the performance under the GCoSOD setting as well as the model calibration degrees.
Stereo Superpixel Segmentation Via Decoupled Dynamic Spatial-Embedding Fusion Network
2022-08-17
Hua Li, Junyan Liang, Ruiqi Wu, Runmin Cong, Junhui Wu, Sam Tak Wu Kwong
Stereo superpixel segmentation aims at grouping the discretizing pixels into perceptual regions through left and right views more collaboratively and efficiently. Existing superpixel segmentation algorithms mostly utilize color and spatial features as input, which may impose strong constraints on spatial information while utilizing the disparity information in terms of stereo image pairs. To alleviate this issue, we propose a stereo superpixel segmentation method with a decoupling mechanism of spatial information in this work. To decouple stereo disparity information and spatial information, the spatial information is temporarily removed before fusing the features of stereo image pairs, and a decoupled stereo fusion module (DSFM) is proposed to handle the stereo features alignment as well as occlusion problems. Moreover, since the spatial information is vital to superpixel segmentation, we further design a dynamic spatiality embedding module (DSEM) to re-add spatial information, and the weights of spatial information will be adaptively adjusted through the dynamic fusion (DF) mechanism in DSEM for achieving a finer segmentation. Comprehensive experimental results demonstrate that our method can achieve the state-of-the-art performance on the KITTI2015 and Cityscapes datasets, and also verify the efficiency when applied in salient object detection on NJU2K dataset. The source code will be available publicly after paper is accepted.
Semi-Supervised Cross-Modal Salient Object Detection with U-Structure Networks
2022-08-08
Yunqing Bao, Hang Dai, Abdulmotaleb Elsaddik
Salient Object Detection (SOD) is a popular and important topic aimed at precise detection and segmentation of the interesting regions in the images. We integrate the linguistic information into the vision-based U-Structure networks designed for salient object detection tasks. The experiments are based on the newly created DUTS Cross Modal (DUTS-CM) dataset, which contains both visual and linguistic labels. We propose a new module called efficient Cross-Modal Self-Attention (eCMSA) to combine visual and linguistic features and improve the performance of the original U-structure networks. Meanwhile, to reduce the heavy burden of labeling, we employ a semi-supervised learning method by training an image caption model based on the DUTS-CM dataset, which can automatically label other datasets like DUT-OMRON and HKU-IS. The comprehensive experiments show that the performance of SOD can be improved with the natural language input and is competitive compared with other SOD methods.
Recently CNN-based RGB-D salient object detection (SOD) has obtained significant improvement on detection accuracy. However, existing models often fail to perform well in terms of efficiency and accuracy simultaneously. This hinders their potential applications on mobile devices as well as many real-world problems. To bridge the accuracy gap between lightweight and large models for RGB-D SOD, in this paper, an efficient module that can greatly improve the accuracy but adds little computation is proposed. Inspired by the fact that depth quality is a key factor influencing the accuracy, we propose an efficient depth quality-inspired feature manipulation (DQFM) process, which can dynamically filter depth features according to depth quality. The proposed DQFM resorts to the alignment of low-level RGB and depth features, as well as holistic attention of the depth stream to explicitly control and enhance cross-modal fusion. We embed DQFM to obtain an efficient lightweight RGB-D SOD model called DFM-Net, where we in addition design a tailored depth backbone and a two-stage decoder as basic parts. Extensive experimental results on nine RGB-D datasets demonstrate that our DFM-Net outperforms recent efficient models, running at about 20 FPS on CPU with only 8.5Mb model size, and meanwhile being 2.9/2.4 times faster and 6.7/3.1 times smaller than the latest best models A2dele and MobileSal. It also maintains state-of-the-art accuracy when even compared to non-efficient models. Interestingly, further statistics and analyses verify the ability of DQFM in distinguishing depth maps of various qualities without any quality labels. Last but not least, we further apply DFM-Net to deal with video SOD (VSOD), achieving comparable performance against recent efficient models while being 3/2.3 times faster/smaller than the prior best in this field. Our code is available at https://github.com/zwbx/DFM-Net.
KD-SCFNet: Towards More Accurate and Efficient Salient Object Detection via Knowledge Distillation
2022-08-03
Jin Zhang, Qiuwei Liang, Yanjiao Shi
Most existing salient object detection (SOD) models are difficult to apply due to the complex and huge model structures. Although some lightweight models are proposed, the accuracy is barely satisfactory. In this paper, we design a novel semantics-guided contextual fusion network (SCFNet) that focuses on the interactive fusion of multi-level features for accurate and efficient salient object detection. Furthermore, we apply knowledge distillation to SOD task and provide a sizeable dataset KD-SOD80K. In detail, we transfer the rich knowledge from a seasoned teacher to the untrained SCFNet through unlabeled images, enabling SCFNet to learn a strong generalization ability to detect salient objects more accurately. The knowledge distillation based SCFNet (KDSCFNet) achieves comparable accuracy to the state-of-the-art heavyweight methods with less than 1M parameters and 174 FPS real-time detection speed. Extensive experiments demonstrate the robustness and effectiveness of the proposed distillation method and SOD framework. Code and data: https://github.com/zhangjinCV/KD-SCFNet.
Previous methods based on 3DCNN, convLSTM, or optical flow have achieved great success in video salient object detection (VSOD). However, they still suffer from high computational costs or poor quality of the generated saliency maps. To solve these problems, we design a space-time memory (STM)-based network, which extracts useful temporal information of the current frame from adjacent frames as the temporal branch of VSOD. Furthermore, previous methods only considered single-frame prediction without temporal association. As a result, the model may not focus on the temporal information sufficiently. Thus, we initially introduce object motion prediction between inter-frame into VSOD. Our model follows standard encoder--decoder architecture. In the encoding stage, we generate high-level temporal features by using high-level features from the current and its adjacent frames. This approach is more efficient than the optical flow-based methods. In the decoding stage, we propose an effective fusion strategy for spatial and temporal branches. The semantic information of the high-level features is used to fuse the object details in the low-level features, and then the spatiotemporal features are obtained step by step to reconstruct the saliency maps. Moreover, inspired by the boundary supervision commonly used in image salient object detection (ISOD), we design a motion-aware loss for predicting object boundary motion and simultaneously perform multitask learning for VSOD and object motion prediction, which can further facilitate the model to extract spatiotemporal features accurately and maintain the object integrity. Extensive experiments on several datasets demonstrated the effectiveness of our method and can achieve state-of-the-art metrics on some datasets. The proposed model does not require optical flow or other preprocessing, and can reach a speed of nearly 100 FPS during inference.
This paper researches the unexplored task-point cloud salient object detection (SOD). Differing from SOD for images, we find the attention shift of point clouds may provoke saliency conflict, i.e., an object paradoxically belongs to salient and non-salient categories. To eschew this issue, we present a novel view-dependent perspective of salient objects, reasonably reflecting the most eye-catching objects in point cloud scenarios. Following this formulation, we introduce PCSOD, the first dataset proposed for point cloud SOD consisting of 2,872 in-/out-door 3D views. The samples in our dataset are labeled with hierarchical annotations, e.g., super-/sub-class, bounding box, and segmentation map, which endows the brilliant generalizability and broad applicability of our dataset verifying various conjectures. To evidence the feasibility of our solution, we further contribute a baseline model and benchmark five representative models for a comprehensive comparison. The proposed model can effectively analyze irregular and unordered points for detecting salient objects. Thanks to incorporating the task-tailored designs, our method shows visible superiority over other baselines, producing more satisfactory results. Extensive experiments and discussions reveal the promising potential of this research field, paving the way for further study.
SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection
2022-07-16
Minhyeok Lee, Chaewon Park, Suhwan Cho, Sangyoun Lee
RGB-D salient object detection (SOD) has been in the spotlight recently because it is an important preprocessing operation for various vision tasks. However, despite advances in deep learning-based methods, RGB-D SOD is still challenging due to the large domain gap between an RGB image and the depth map and low-quality depth maps. To solve this problem, we propose a novel superpixel prototype sampling network (SPSN) architecture. The proposed model splits the input RGB image and depth map into component superpixels to generate component prototypes. We design a prototype sampling network so that the network only samples prototypes corresponding to salient objects. In addition, we propose a reliance selection module to recognize the quality of each RGB and depth feature map and adaptively weight them in proportion to their reliability. The proposed method makes the model robust to inconsistencies between RGB images and depth maps and eliminates the influence of non-salient objects. Our method is evaluated on five popular datasets, achieving state-of-the-art performance. We prove the effectiveness of the proposed method through comparative experiments.
Video salient object detection models trained on pixel-wise dense annotation have achieved excellent performance, yet obtaining pixel-by-pixel annotated datasets is laborious. Several works attempt to use scribble annotations to mitigate this problem, but point supervision as a more labor-saving annotation method (even the most labor-saving method among manual annotation methods for dense prediction), has not been explored. In this paper, we propose a strong baseline model based on point supervision. To infer saliency maps with temporal information, we mine inter-frame complementary information from short-term and long-term perspectives, respectively. Specifically, we propose a hybrid token attention module, which mixes optical flow and image information from orthogonal directions, adaptively highlighting critical optical flow information (channel dimension) and critical token information (spatial dimension). To exploit long-term cues, we develop the Long-term Cross-Frame Attention module (LCFA), which assists the current frame in inferring salient objects based on multi-frame tokens. Furthermore, we label two point-supervised datasets, P-DAVIS and P-DAVSOD, by relabeling the DAVIS and the DAVSOD dataset. Experiments on the six benchmark datasets illustrate our method outperforms the previous state-of-the-art weakly supervised methods and even is comparable with some fully supervised approaches. Source code and datasets are available.
Deep Learning-based Unsupervised Salient Object Detection (USOD) mainly relies on the noisy saliency pseudo labels that have been generated from traditional handcraft methods or pre-trained networks. To cope with the noisy labels problem, a class of methods focus on only easy samples with reliable labels but ignore valuable knowledge in hard samples. In this paper, we propose a novel USOD method to mine rich and accurate saliency knowledge from both easy and hard samples. First, we propose a Confidence-aware Saliency Distilling (CSD) strategy that scores samples conditioned on samples' confidences, which guides the model to distill saliency knowledge from easy samples to hard samples progressively. Second, we propose a Boundary-aware Texture Matching (BTM) strategy to refine the boundaries of noisy labels by matching the textures around the predicted boundary. Extensive experiments on RGB, RGB-D, RGB-T, and video SOD benchmarks prove that our method achieves state-of-the-art USOD performance.
RGB-D SOD uses depth information to handle challenging scenes and obtain high-quality saliency maps. Existing state-of-the-art RGB-D saliency detection methods overwhelmingly rely on the strategy of directly fusing depth information. Although these methods improve the accuracy of saliency prediction through various cross-modality fusion strategies, misinformation provided by some poor-quality depth images can affect the saliency prediction result. To address this issue, a novel RGB-D salient object detection model (SiaTrans) is proposed in this paper, which allows training on depth image quality classification at the same time as training on SOD. In light of the common information between RGB and depth images on salient objects, SiaTrans uses a Siamese transformer network with shared weight parameters as the encoder and extracts RGB and depth features concatenated on the batch dimension, saving space resources without compromising performance. SiaTrans uses the Class token in the backbone network (T2T-ViT) to classify the quality of depth images without preventing the token sequence from going on with the saliency detection task. Transformer-based cross-modality fusion module (CMF) can effectively fuse RGB and depth information. And in the testing process, CMF can choose to fuse cross-modality information or enhance RGB information according to the quality classification signal of the depth image. The greatest benefit of our designed CMF and decoder is that they maintain the consistency of RGB and RGB-D information decoding: SiaTrans decodes RGB-D or RGB information under the same model parameters according to the classification signal during testing. Comprehensive experiments on nine RGB-D SOD benchmark datasets show that SiaTrans has the best overall performance and the least computation compared with recent state-of-the-art methods.
Mirror Complementary Transformer Network for RGB-thermal Salient Object Detection
2022-07-07
Xiurong Jiang, Lin Zhu, Yifan Hou, Hui Tian
RGB-thermal salient object detection (RGB-T SOD) aims to locate the common prominent objects of an aligned visible and thermal infrared image pair and accurately segment all the pixels belonging to those objects. It is promising in challenging scenes such as nighttime and complex backgrounds due to the insensitivity to lighting conditions of thermal images. Thus, the key problem of RGB-T SOD is to make the features from the two modalities complement and adjust each other flexibly, since it is inevitable that any modalities of RGB-T image pairs failure due to challenging scenes such as extreme light conditions and thermal crossover. In this paper, we propose a novel mirror complementary Transformer network (MCNet) for RGB-T SOD. Specifically, we introduce a Transformer-based feature extraction module to effective extract hierarchical features of RGB and thermal images. Then, through the attention-based feature interaction and serial multiscale dilated convolution (SDC) based feature fusion modules, the proposed model achieves the complementary interaction of low-level features and the semantic fusion of deep features. Finally, based on the mirror complementary structure, the salient regions of the two modalities can be accurately extracted even one modality is invalid. To demonstrate the robustness of the proposed model under challenging scenes in real world, we build a novel RGB-T SOD dataset VT723 based on a large public semantic segmentation RGB-T dataset used in the autonomous driving domain. Expensive experiments on benchmark and VT723 datasets show that the proposed method outperforms state-of-the-art approaches, including CNN-based and Transformer-based methods. The code and dataset will be released later at https://github.com/jxr326/SwinMCNet.
Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images
2022-07-05
Yuhan Lin, Han Sun, Ningzhong Liu, Yetong Bian, Jun Cen, Huiyu Zhou
Due to the extreme complexity of scale and shape as well as the uncertainty of the predicted location, salient object detection in optical remote sensing images (RSI-SOD) is a very difficult task. The existing SOD methods can satisfy the detection performance for natural scene images, but they are not well adapted to RSI-SOD due to the above-mentioned image characteristics in remote sensing images. In this paper, we propose a novel Attention Guided Network (AGNet) for SOD in optical RSIs, including position enhancement stage and detail refinement stage. Specifically, the position enhancement stage consists of a semantic attention module and a contextual attention module to accurately describe the approximate location of salient objects. The detail refinement stage uses the proposed self-refinement module to progressively refine the predicted results under the guidance of attention and reverse attention. In addition, the hybrid loss is applied to supervise the training of the network, which can improve the performance of the model from three perspectives of pixel, region and statistics. Extensive experiments on two popular benchmarks demonstrate that AGNet achieves competitive performance compared to other state-of-the-art methods. The code will be available at https://github.com/NuaaYH/AGNet.
TANet: Transformer-based Asymmetric Network for RGB-D Salient Object Detection
2022-07-04
Chang Liu, Gang Yang, Shuo Wang, Hangxu Wang, Yunhua Zhang, Yutao Wang
Existing RGB-D SOD methods mainly rely on a symmetric two-stream CNN-based network to extract RGB and depth channel features separately. However, there are two problems with the symmetric conventional network structure: first, the ability of CNN in learning global contexts is limited; second, the symmetric two-stream structure ignores the inherent differences between modalities. In this paper, we propose a Transformer-based asymmetric network (TANet) to tackle the issues mentioned above. We employ the powerful feature extraction capability of Transformer (PVTv2) to extract global semantic information from RGB data and design a lightweight CNN backbone (LWDepthNet) to extract spatial structure information from depth data without pre-training. The asymmetric hybrid encoder (AHE) effectively reduces the number of parameters in the model while increasing speed without sacrificing performance. Then, we design a cross-modal feature fusion module (CMFFM), which enhances and fuses RGB and depth features with each other. Finally, we add edge prediction as an auxiliary task and propose an edge enhancement module (EEM) to generate sharper contours. Extensive experiments demonstrate that our method achieves superior performance over 14 state-of-the-art RGB-D methods on six public datasets. Our code will be released at https://github.com/lc012463/TANet.
The existing state-of-the-art (SOTA) video salient object detection (VSOD) models have widely followed short-term methodology, which dynamically determines the balance between spatial and temporal saliency fusion by solely considering the current consecutive limited frames. However, the short-term methodology has one critical limitation, which conflicts with the real mechanism of our visual system -- a typical long-term methodology. As a result, failure cases keep showing up in the results of the current SOTA models, and the short-term methodology becomes the major technical bottleneck. To solve this problem, this paper proposes a novel VSOD approach, which performs VSOD in a complete long-term way. Our approach converts the sequential VSOD, a sequential task, to a data mining problem, i.e., decomposing the input video sequence to object proposals in advance and then mining salient object proposals as much as possible in an easy-to-hard way. Since all object proposals are simultaneously available, the proposed approach is a complete long-term approach, which can alleviate some difficulties rooted in conventional short-term approaches. In addition, we devised an online updating scheme that can grasp the most representative and trustworthy pattern profile of the salient objects, outputting framewise saliency maps with rich details and smoothing both spatially and temporally. The proposed approach outperforms almost all SOTA models on five widely used benchmark datasets.
This paper presents a novel deep neural network framework for RGB-D salient object detection by controlling the message passing between the RGB images and depth maps on the feature level and exploring the long-range semantic contexts and geometric information on both RGB and depth features to infer salient objects. To achieve this, we formulate a dynamic message propagation (DMP) module with the graph neural networks and deformable convolutions to dynamically learn the context information and to automatically predict filter weights and affinity matrices for message propagation control. We further embed this module into a Siamese-based network to process the RGB image and depth map respectively and design a multi-level feature fusion (MFF) module to explore the cross-level information between the refined RGB and depth features. Compared with 17 state-of-the-art methods on six benchmark datasets for RGB-D salient object detection, experimental results show that our method outperforms all the others, both quantitatively and visually.
Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection
2022-06-07
Chao Zeng, Sam Kwong
Salient Object Detection is the task of predicting the human attended region in a given scene. Fusing depth information has been proven effective in this task. The main challenge of this problem is how to aggregate the complementary information from RGB modality and depth modality. However, conventional deep models heavily rely on CNN feature extractors, and the long-range contextual dependencies are usually ignored. In this work, we propose Dual Swin-Transformer based Mutual Interactive Network. We adopt Swin-Transformer as the feature extractor for both RGB and depth modality to model the long-range dependencies in visual inputs. Before fusing the two branches of features into one, attention-based modules are applied to enhance features from each modality. We design a self-attention-based cross-modality interaction module and a gated modality attention module to leverage the complementary information between the two modalities. For the saliency decoding, we create different stages enhanced with dense connections and keep a decoding memory while the multi-level encoding features are considered simultaneously. Considering the inaccurate depth map issue, we collect the RGB features of early stages into a skip convolution module to give more guidance from RGB modality to the final saliency prediction. In addition, we add edge supervision to regularize the feature learning process. Comprehensive experiments on five standard RGB-D SOD benchmark datasets over four evaluation metrics demonstrate the superiority of the proposed DTMINet method.
GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector
2022-05-30
Peng Zheng, Huazhu Fu, Deng-Ping Fan, Qi Fan, Jie Qin, Yu-Wing Tai, Chi-Keung Tang, Luc Van Gool
In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in natural scenes. The proposed GCoNet+ achieves the new state-of-the-art performance for co-salient object detection (CoSOD) through mining consensus representations based on the following two essential criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects by capturing their inherent shared attributes using our novel group affinity module (GAM); 2) inter-group separability to effectively suppress the influence of noisy objects on the output by introducing our new group collaborating module (GCM) conditioning on the inconsistent consensus. To further improve the accuracy, we design a series of simple yet effective components as follows: i) a recurrent auxiliary classification module (RACM) promoting model learning at the semantic level; ii) a confidence enhancement module (CEM) assisting the model in improving the quality of the final predictions; and iii) a group-based symmetric triplet (GST) loss guiding the model to learn more discriminative features. Extensive experiments on three challenging benchmarks, i.e., CoCA, CoSOD3k, and CoSal2015, demonstrate that our GCoNet+ outperforms the existing 12 cutting-edge models. Code has been released at https://github.com/ZhengPeng7/GCoNet_plus.
SelfReformer: Self-Refined Network with Transformer for Salient Object Detection
2022-05-23
Yi Ke Yun, Weisi Lin
The global and local contexts significantly contribute to the integrity of predictions in Salient Object Detection (SOD). Unfortunately, existing methods still struggle to generate complete predictions with fine details. There are two major problems in conventional approaches: first, for global context, high-level CNN-based encoder features cannot effectively catch long-range dependencies, resulting in incomplete predictions. Second, downsampling the ground truth to fit the size of predictions will introduce inaccuracy as the ground truth details are lost during interpolation or pooling. Thus, in this work, we developed a Transformer-based network and framed a supervised task for a branch to learn the global context information explicitly. Besides, we adopt Pixel Shuffle from Super-Resolution (SR) to reshape the predictions back to the size of ground truth instead of the reverse. Thus details in the ground truth are untouched. In addition, we developed a two-stage Context Refinement Module (CRM) to fuse global context and automatically locate and refine the local details in the predictions. The proposed network can guide and correct itself based on the global and local context generated, thus is named, Self-Refined Transformer (SelfReformer). Extensive experiments and evaluation results on five benchmark datasets demonstrate the outstanding performance of the network, and we achieved the state-of-the-art.
A lightweight multi-scale context network for salient object detection in optical remote sensing images
2022-05-18
Yuhan Lin, Han Sun, Ningzhong Liu, Yetong Bian, Jun Cen, Huiyu Zhou
Due to the more dramatic multi-scale variations and more complicated foregrounds and backgrounds in optical remote sensing images (RSIs), the salient object detection (SOD) for optical RSIs becomes a huge challenge. However, different from natural scene images (NSIs), the discussion on the optical RSI SOD task still remains scarce. In this paper, we propose a multi-scale context network, namely MSCNet, for SOD in optical RSIs. Specifically, a multi-scale context extraction module is adopted to address the scale variation of salient objects by effectively learning multi-scale contextual information. Meanwhile, in order to accurately detect complete salient objects in complex backgrounds, we design an attention-based pyramid feature aggregation mechanism for gradually aggregating and refining the salient regions from the multi-scale context extraction module. Extensive experiments on two benchmarks demonstrate that MSCNet achieves competitive performance with only 3.26M parameters. The code will be available at https://github.com/NuaaYH/MSCNet.
Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection
2022-05-15
Wei Ji, Jingjing Li, Qi Bi, Chuan Guo, Jie Liu, Li Cheng
Growing interests in RGB-D salient object detection (RGB-D SOD) have been witnessed in recent years, owing partly to the popularity of depth sensors and the rapid progress of deep learning techniques. Unfortunately, existing RGB-D SOD methods typically demand large quantity of training images being thoroughly annotated at pixel-level. The laborious and time-consuming manual annotation has become a real bottleneck in various practical scenarios. On the other hand, current unsupervised RGB-D SOD methods still heavily rely on handcrafted feature representations. This inspires us to propose in this paper a deep unsupervised RGB-D saliency detection approach, which requires no manual pixel-level annotation during training. It is realized by two key ingredients in our training pipeline. First, a depth-disentangled saliency update (DSU) framework is designed to automatically produce pseudo-labels with iterative follow-up refinements, which provides more trustworthy supervision signals for training the saliency network. Second, an attentive training strategy is introduced to tackle the issue of noisy pseudo-labels, by properly re-weighting to highlight the more reliable pseudo-labels. Extensive experiments demonstrate the superior efficiency and effectiveness of our approach in tackling the challenging unsupervised RGB-D SOD scenarios. Moreover, our approach can also be adapted to work in fully-supervised situation. Empirical studies show the incorporation of our approach gives rise to notably performance improvement in existing supervised RGB-D SOD models.
A Saliency-Guided Street View Image Inpainting Framework for Efficient Last-Meters Wayfinding
2022-05-14
Chuanbo Hu, Shan Jia, Fan Zhang, Xin Li
Global Positioning Systems (GPS) have played a crucial role in various navigation applications. Nevertheless, localizing the perfect destination within the last few meters remains an important but unresolved problem. Limited by the GPS positioning accuracy, navigation systems always show users a vicinity of a destination, but not its exact location. Street view images (SVI) in maps as an immersive media technology have served as an aid to provide the physical environment for human last-meters wayfinding. However, due to the large diversity of geographic context and acquisition conditions, the captured SVI always contains various distracting objects (e.g., pedestrians and vehicles), which will distract human visual attention from efficiently finding the destination in the last few meters. To address this problem, we highlight the importance of reducing visual distraction in image-based wayfinding by proposing a saliency-guided image inpainting framework. It aims at redirecting human visual attention from distracting objects to destination-related objects for more efficient and accurate wayfinding in the last meters. Specifically, a context-aware distracting object detection method driven by deep salient object detection has been designed to extract distracting objects from three semantic levels in SVI. Then we employ a large-mask inpainting method with fast Fourier convolutions to remove the detected distracting objects. Experimental results with both qualitative and quantitative analysis show that our saliency-guided inpainting method can not only achieve great perceptual quality in street view images but also redirect the human's visual attention to focus more on static location-related objects than distracting ones. The human-based evaluation also justified the effectiveness of our method in improving the efficiency of locating the target destination.
Salient Object Detection via Bounding-box Supervision
2022-05-11
Mengqi He, Jing Zhang, Wenxin Yu
The success of fully supervised saliency detection models depends on a large number of pixel-wise labeling. In this paper, we work on bounding-box based weakly-supervised saliency detection to relieve the labeling effort. Given the bounding box annotation, we observe that pixels inside the bounding box may contain extensive labeling noise. However, as a large amount of background is excluded, the foreground bounding box region contains a less complex background, making it possible to perform handcrafted features-based saliency detection with only the cropped foreground region. As the conventional handcrafted features are not representative enough, leading to noisy saliency maps, we further introduce structure-aware self-supervised loss to regularize the structure of the prediction. Further, we claim that pixels outside the bounding box should be background, thus partial cross-entropy loss function can be used to accurately localize the accurate background region. Experimental results on six benchmark RGB saliency datasets illustrate the effectiveness of our model.
Global-and-Local Collaborative Learning for Co-Salient Object Detection
2022-04-19
Runmin Cong, Ning Yang, Chongyi Li, Huazhu Fu, Yao Zhao, Qingming Huang, Sam Kwong
The goal of co-salient object detection (CoSOD) is to discover salient objects that commonly appear in a query group containing two or more relevant images. Therefore, how to effectively extract inter-image correspondence is crucial for the CoSOD task. In this paper, we propose a global-and-local collaborative learning architecture, which includes a global correspondence modeling (GCM) and a local correspondence modeling (LCM) to capture comprehensive inter-image corresponding relationship among different images from the global and local perspectives. Firstly, we treat different images as different time slices and use 3D convolution to integrate all intra features intuitively, which can more fully extract the global group semantics. Secondly, we design a pairwise correlation transformation (PCT) to explore similarity correspondence between pairwise images and combine the multiple local pairwise correspondences to generate the local inter-image relationship. Thirdly, the inter-image relationships of the GCM and LCM are integrated through a global-and-local correspondence aggregation (GLA) module to explore more comprehensive inter-image collaboration cues. Finally, the intra- and inter-features are adaptively integrated by an intra-and-inter weighting fusion (AEWF) module to learn co-saliency features and predict the co-saliency map. The proposed GLNet is evaluated on three prevailing CoSOD benchmark datasets, demonstrating that our model trained on a small dataset (about 3k images) still outperforms eleven state-of-the-art competitors trained on some large datasets (about 8k-200k images).
2022-04-19
Jing Zhang, Jianwen Xie, Nick Barnes, Ping Li
We propose a novel generative saliency prediction framework that adopts an informative energy-based model as a prior distribution. The energy-based prior model is defined on the latent space of a saliency generator network that generates the saliency map based on a continuous latent variables and an observed image. Both the parameters of saliency generator and the energy-based prior are jointly trained via Markov chain Monte Carlo-based maximum likelihood estimation, in which the sampling from the intractable posterior and prior distributions of the latent variables are performed by Langevin dynamics. With the generative saliency model, we can obtain a pixel-wise uncertainty map from an image, indicating model confidence in the saliency prediction. Different from existing generative models, which define the prior distribution of the latent variables as a simple isotropic Gaussian distribution, our model uses an energy-based informative prior which can be more expressive in capturing the latent space of the data. With the informative energy-based prior, we extend the Gaussian distribution assumption of generative models to achieve a more representative distribution of the latent space, leading to more reliable uncertainty estimation. We apply the proposed frameworks to both RGB and RGB-D salient object detection tasks with both transformer and convolutional neural network backbones. We further propose an adversarial learning algorithm and a variational inference algorithm as alternatives to train the proposed generative framework. Experimental results show that our generative saliency model with an energy-based prior can achieve not only accurate saliency predictions but also reliable uncertainty maps that are consistent with human perception. Results and code are available at \url{https://github.com/JingZhang617/EBMGSOD}.
2022-04-14
Tanveer Hussain, Abbas Anwar, Saeed Anwar, Lars Petersson, Sung Wook Baik
Salient object detection (SOD) extracts meaningful contents from an input image. RGB-based SOD methods lack the complementary depth clues; hence, providing limited performance for complex scenarios. Similarly, RGB-D models process RGB and depth inputs, but the depth data availability during testing may hinder the model's practical applicability. This paper exploits only RGB images, estimates depth from RGB, and leverages the intermediate depth features. We employ a pyramidal attention structure to extract multi-level convolutional-transformer features to process initial stage representations and further enhance the subsequent ones. At each stage, the backbone transformer model produces global receptive fields and computing in parallel to attain fine-grained global predictions refined by our residual convolutional attention decoder for optimal saliency prediction. We report significantly improved performance against 21 and 40 state-of-the-art SOD methods on eight RGB and RGB-D datasets, respectively. Consequently, we present a new SOD perspective of generating RGB-D SOD without acquiring depth data during training and testing and assist RGB methods with depth clues for improved performance. The code and trained models are available at https://github.com/tanveer-hussain/EfficientSOD2
Convolutional neural networks (CNNs) are good at extracting contexture features within certain receptive fields, while transformers can model the global long-range dependency features. By absorbing the advantage of transformer and the merit of CNN, Swin Transformer shows strong feature representation ability. Based on it, we propose a cross-modality fusion model SwinNet for RGB-D and RGB-T salient object detection. It is driven by Swin Transformer to extract the hierarchical features, boosted by attention mechanism to bridge the gap between two modalities, and guided by edge information to sharp the contour of salient object. To be specific, two-stream Swin Transformer encoder first extracts multi-modality features, and then spatial alignment and channel re-calibration module is presented to optimize intra-level cross-modality features. To clarify the fuzzy boundary, edge-guided decoder achieves inter-level cross-modality fusion under the guidance of edge features. The proposed model outperforms the state-of-the-art models on RGB-D and RGB-T datasets, showing that it provides more insight into the cross-modality complementarity task.
Recent salient object detection (SOD) methods based on deep neural network have achieved remarkable performance. However, most of existing SOD models designed for low-resolution input perform poorly on high-resolution images due to the contradiction between the sampling depth and the receptive field size. Aiming at resolving this contradiction, we propose a novel one-stage framework called Pyramid Grafting Network (PGNet), using transformer and CNN backbone to extract features from different resolution images independently and then graft the features from transformer branch to CNN branch. An attention-based Cross-Model Grafting Module (CMGM) is proposed to enable CNN branch to combine broken detailed information more holistically, guided by different source feature during decoding process. Moreover, we design an Attention Guided Loss (AGL) to explicitly supervise the attention matrix generated by CMGM to help the network better interact with the attention from different models. We contribute a new Ultra-High-Resolution Saliency Detection dataset UHRSD, containing 5,920 images at 4K-8K resolutions. To our knowledge, it is the largest dataset in both quantity and resolution for high-resolution SOD task, which can be used for training and testing in future research. Sufficient experiments on UHRSD and widely-used SOD datasets demonstrate that our method achieves superior performance compared to the state-of-the-art methods.
Despite recent improvements in computer vision, artificial visual systems' design is still daunting since an explanation of visual computing algorithms remains elusive. Salient object detection is one problem that is still open due to the difficulty of understanding the brain's inner workings. Progress on this research area follows the traditional path of hand-made designs using neuroscience knowledge. In recent years two different approaches based on genetic programming appear to enhance their technique. One follows the idea of combining previous hand-made methods through genetic programming and fuzzy logic. The other approach consists of improving the inner computational structures of basic hand-made models through artificial evolution. This research work proposes expanding the artificial dorsal stream using a recent proposal to solve salient object detection problems. This approach uses the benefits of the two main aspects of this research area: fixation prediction and detection of salient objects. We decided to apply the fusion of visual saliency and image segmentation algorithms as a template. The proposed methodology discovers several critical structures in the template through artificial evolution. We present results on a benchmark designed by experts with outstanding results in comparison with the state-of-the-art.
Recent deep learning-based video salient object detection (VSOD) has achieved some breakthrough, but these methods rely on expensive annotated videos with pixel-wise annotations, weak annotations, or part of the pixel-wise annotations. In this paper, based on the similarities and the differences between VSOD and image salient object detection (SOD), we propose a novel VSOD method via a progressive framework that locates and segments salient objects in sequence without utilizing any video annotation. To use the knowledge learned in the SOD dataset for VSOD efficiently, we introduce dynamic saliency to compensate for the lack of motion information of SOD during the locating process but retain the same fine segmenting process. Specifically, an algorithm for generating spatiotemporal location labels, which consists of generating high-saliency location labels and tracking salient objects in adjacent frames, is proposed. Based on these location labels, a two-stream locating network that introduces an optical flow branch for video salient object locating is presented. Although our method does not require labeled video at all, the experimental results on five public benchmarks of DAVIS, FBMS, ViSal, VOS, and DAVSOD demonstrate that our proposed method is competitive with fully supervised methods and outperforms the state-of-the-art weakly and unsupervised methods.
Salient object detection (SOD) in optical remote sensing images (RSIs), or RSI-SOD, is an emerging topic in understanding optical RSIs. However, due to the difference between optical RSIs and natural scene images (NSIs), directly applying NSI-SOD methods to optical RSIs fails to achieve satisfactory results. In this paper, we propose a novel Adjacent Context Coordination Network (ACCoNet) to explore the coordination of adjacent features in an encoder-decoder architecture for RSI-SOD. Specifically, ACCoNet consists of three parts: an encoder, Adjacent Context Coordination Modules (ACCoMs), and a decoder. As the key component of ACCoNet, ACCoM activates the salient regions of output features of the encoder and transmits them to the decoder. ACCoM contains a local branch and two adjacent branches to coordinate the multi-level features simultaneously. The local branch highlights the salient regions in an adaptive way, while the adjacent branches introduce global information of adjacent levels to enhance salient regions. Additionally, to extend the capabilities of the classic decoder block (i.e., several cascaded convolutional layers), we extend it with two bifurcations and propose a Bifurcation-Aggregation Block to capture the contextual information in the decoder. Extensive experiments on two benchmark datasets demonstrate that the proposed ACCoNet outperforms 22 state-of-the-art methods under nine evaluation metrics, and runs up to 81 fps on a single NVIDIA Titan X GPU. The code and results of our method are available at https://github.com/MathLee/ACCoNet.
Unsupervised Salient Object Detection with Spectral Cluster Voting
2022-03-23
Gyungin Shin, Samuel Albanie, Weidi Xie
In this paper, we tackle the challenging task of unsupervised salient object detection (SOD) by leveraging spectral clustering on self-supervised features. We make the following contributions: (i) We revisit spectral clustering and demonstrate its potential to group the pixels of salient objects; (ii) Given mask proposals from multiple applications of spectral clustering on image features computed from various self-supervised models, e.g., MoCov2, SwAV, DINO, we propose a simple but effective winner-takes-all voting mechanism for selecting the salient masks, leveraging object priors based on framing and distinctiveness; (iii) Using the selected object segmentation as pseudo groundtruth masks, we train a salient object detector, dubbed SelfMask, which outperforms prior approaches on three unsupervised SOD benchmarks. Code is publicly available at https://github.com/NoelShin/selfmask.
Current state-of-the-art saliency detection models rely heavily on large datasets of accurate pixel-wise annotations, but manually labeling pixels is time-consuming and labor-intensive. There are some weakly supervised methods developed for alleviating the problem, such as image label, bounding box label, and scribble label, while point label still has not been explored in this field. In this paper, we propose a novel weakly-supervised salient object detection method using point supervision. To infer the saliency map, we first design an adaptive masked flood filling algorithm to generate pseudo labels. Then we develop a transformer-based point-supervised saliency detection model to produce the first round of saliency maps. However, due to the sparseness of the label, the weakly supervised model tends to degenerate into a general foreground detection model. To address this issue, we propose a Non-Salient Suppression (NSS) method to optimize the erroneous saliency maps generated in the first round and leverage them for the second round of training. Moreover, we build a new point-supervised dataset (P-DUTS) by relabeling the DUTS dataset. In P-DUTS, there is only one labeled point for each salient object. Comprehensive experiments on five largest benchmark datasets demonstrate our method outperforms the previous state-of-the-art methods trained with the stronger supervision and even surpass several fully supervised state-of-the-art models. The code is available at: https://github.com/shuyonggao/PSOD.
GroupTransNet: Group Transformer Network for RGB-D Salient Object Detection
2022-03-21
Xian Fang, Jinshao Zhu, Xiuli Shao, Hongpeng Wang
Salient object detection on RGB-D images is an active topic in computer vision. Although the existing methods have achieved appreciable performance, there are still some challenges. The locality of convolutional neural network requires that the model has a sufficiently deep global receptive field, which always leads to the loss of local details. To address the challenge, we propose a novel Group Transformer Network (GroupTransNet) for RGB-D salient object detection. This method is good at learning the long-range dependencies of cross layer features to promote more perfect feature expression. At the beginning, the features of the slightly higher classes of the middle three levels and the latter three levels are soft grouped to absorb the advantages of the high-level features. The input features are repeatedly purified and enhanced by the attention mechanism to purify the cross modal features of color modal and depth modal. The features of the intermediate process are first fused by the features of different layers, and then processed by several transformers in multiple groups, which not only makes the size of the features of each scale unified and interrelated, but also achieves the effect of sharing the weight of the features within the group. The output features in different groups complete the clustering staggered by two owing to the level difference, and combine with the low-level features. Extensive experiments demonstrate that GroupTransNet outperforms the comparison models and achieves the new state-of-the-art performance.
In this paper, we propose an effective and efficient method for Human-Gaze-Target (HGT) detection, i.e., gaze following. Current approaches decouple the HGT detection task into separate branches of salient object detection and human gaze prediction, employing a two-stage framework where human head locations must first be detected and then be fed into the next gaze target prediction sub-network. In contrast, we redefine the HGT detection task as detecting human head locations and their gaze targets, simultaneously. By this way, our method, named Human-Gaze-Target detection TRansformer or HGTTR, streamlines the HGT detection pipeline by eliminating all other additional components. HGTTR reasons about the relations of salient objects and human gaze from the global image context. Moreover, unlike existing two-stage methods that require human head locations as input and can predict only one human's gaze target at a time, HGTTR can directly predict the locations of all people and their gaze targets at one time in an end-to-end manner. The effectiveness and robustness of our proposed method are verified with extensive experiments on the two standard benchmark datasets, GazeFollowing and VideoAttentionTarget. Without bells and whistles, HGTTR outperforms existing state-of-the-art methods by large margins (6.4 mAP gain on GazeFollowing and 10.3 mAP gain on VideoAttentionTarget) with a much simpler architecture.
DFTR: Depth-supervised Fusion Transformer for Salient Object Detection
2022-03-12
Heqin Zhu, Xu Sun, Yuexiang Li, Kai Ma, S. Kevin Zhou, Yefeng Zheng
Automated salient object detection (SOD) plays an increasingly crucial role in many computer vision applications. By reformulating the depth information as supervision rather than as input, depth-supervised convolutional neural networks (CNN) have achieved promising results on both RGB and RGB-D SOD scenarios with the merits of no requirements for extra depth networks and depth inputs in the inference stage. This paper, for the first time, seeks to expand the applicability of depth supervision to the Transformer architecture. Specifically, we develop a Depth-supervised Fusion TRansformer (DFTR), to further improve the accuracy of both RGB and RGB-D SOD. The proposed DFTR involves three primary features: 1) DFTR, to the best of our knowledge, is the first pure Transformer-based model for depth-supervised SOD; 2) A multi-scale feature aggregation (MFA) module is proposed to fully exploit the multi-scale features encoded by the Swin Transformer in a coarse-to-fine manner; 3) To enable bidirectional information flow across different streams of features, a novel multi-stage feature fusion (MFF) module is further integrated into our DFTR with the emphasis on salient regions at different network learning stages. We extensively evaluate the proposed DFTR on ten benchmarking datasets. Experimental results show that our DFTR consistently outperforms the existing state-of-the-art methods for both RGB and RGB-D SOD tasks. The code and model will be made publicly available.
Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection
2022-03-11
Siyue Yu, Jimin Xiao, Bingfeng Zhang, Eng Gee Lim
Co-salient object detection, with the target of detecting co-existed salient objects among a group of images, is gaining popularity. Recent works use the attention mechanism or extra information to aggregate common co-salient features, leading to incomplete even incorrect responses for target objects. In this paper, we aim to mine comprehensive co-salient features with democracy and reduce background interference without introducing any extra information. To achieve this, we design a democratic prototype generation module to generate democratic response maps, covering sufficient co-salient regions and thereby involving more shared attributes of co-salient objects. Then a comprehensive prototype based on the response maps can be generated as a guide for final prediction. To suppress the noisy background information in the prototype, we propose a self-contrastive learning module, where both positive and negative pairs are formed without relying on additional classification information. Besides, we also design a democratic feature enhancement module to further strengthen the co-salient features by readjusting attention values. Extensive experiments show that our model obtains better performance than previous state-of-the-art methods, especially on challenging real-world cases (e.g., for CoCA, we obtain a gain of 2.0% for MAE, 5.4% for maximum F-measure, 2.3% for maximum E-measure, and 3.7% for S-measure) under the same settings. Code will be released soon.
Joint Learning of Salient Object Detection, Depth Estimation and Contour Extraction
2022-03-09
Xiaoqi Zhao, Youwei Pang, Lihe Zhang, Huchuan Lu
Benefiting from color independence, illumination invariance and location discrimination attributed by the depth map, it can provide important supplemental information for extracting salient objects in complex environments. However, high-quality depth sensors are expensive and can not be widely applied. While general depth sensors produce the noisy and sparse depth information, which brings the depth-based networks with irreversible interference. In this paper, we propose a novel multi-task and multi-modal filtered transformer (MMFT) network for RGB-D salient object detection (SOD). Specifically, we unify three complementary tasks: depth estimation, salient object detection and contour estimation. The multi-task mechanism promotes the model to learn the task-aware features from the auxiliary tasks. In this way, the depth information can be completed and purified. Moreover, we introduce a multi-modal filtered transformer (MFT) module, which equips with three modality-specific filters to generate the transformer-enhanced feature for each modality. The proposed model works in a depth-free style during the testing phase. Experiments show that it not only significantly surpasses the depth-based RGB-D SOD methods on multiple datasets, but also precisely predicts a high-quality depth map and salient contour at the same time. And, the resulted depth map can help existing RGB-D SOD methods obtain significant performance gain. The source code will be publicly available at https://github.com/Xiaoqi-Zhao-DLUT/MMFT.
Humans tend to mine objects by learning from a group of images or several frames of video since we live in a dynamic world. In the computer vision area, many researches focus on co-segmentation (CoS), co-saliency detection (CoSD) and video salient object detection (VSOD) to discover the co-occurrent objects. However, previous approaches design different networks on these similar tasks separately, and they are difficult to apply to each other, which lowers the upper bound of the transferability of deep learning frameworks. Besides, they fail to take full advantage of the cues among inter- and intra-feature within a group of images. In this paper, we introduce a unified framework to tackle these issues, term as UFO (Unified Framework for Co-Object Segmentation). Specifically, we first introduce a transformer block, which views the image feature as a patch token and then captures their long-range dependencies through the self-attention mechanism. This can help the network to excavate the patch structured similarities among the relevant objects. Furthermore, we propose an intra-MLP learning module to produce self-mask to enhance the network to avoid partial activation. Extensive experiments on four CoS benchmarks (PASCAL, iCoseg, Internet and MSRC), three CoSD benchmarks (Cosal2015, CoSOD3k, and CocA) and four VSOD benchmarks (DAVIS16, FBMS, ViSal and SegV2) show that our method outperforms other state-of-the-arts on three different tasks in both accuracy and speed by using the same network architecture , which can reach 140 FPS in real-time.
We present a conceptually simple self-supervised method for saliency detection. Our method generates and uses pseudo-ground truth labels for training. The generated pseudo-GT labels don't require any kind of human annotations (e.g., pixel-wise labels or weak labels like scribbles). Recent works show that features extracted from classification tasks provide important saliency cues like structure and semantic information of salient objects in the image. Our method, called 3SD, exploits this idea by adding a branch for a self-supervised classification task in parallel with salient object detection, to obtain class activation maps (CAM maps). These CAM maps along with the edges of the input image are used to generate the pseudo-GT saliency maps to train our 3SD network. Specifically, we propose a contrastive learning-based training on multiple image patches for the classification task. We show the multi-patch classification with contrastive loss improves the quality of the CAM maps compared to naive classification on the entire image. Experiments on six benchmark datasets demonstrate that without any labels, our 3SD method outperforms all existing weakly supervised and unsupervised methods, and its performance is on par with the fully-supervised methods. Code is available at :https://github.com/rajeevyasarla/3SD
2022-03-08
Bo Xu, Guanze Liu, Han Huang, Cheng Lu, Yandong Guo
Most existing CNN-based salient object detection methods can identify local segmentation details like hair and animal fur, but often misinterpret the real saliency due to the lack of global contextual information caused by the subjectiveness of the SOD task and the locality of convolution layers. Moreover, due to the unrealistically expensive labeling costs, the current existing SOD datasets are insufficient to cover the real data distribution. The limitation and bias of the training data add additional difficulty to fully exploring the semantic association between object-to-object and object-to-environment in a given image. In this paper, we propose a semantic distillation guided SOD (SDG-SOD) method that produces accurate results by fusing semantically distilled knowledge from generated image captioning into the Vision-Transformer-based SOD framework. SDG-SOD can better uncover inter-objects and object-to-environment saliency and cover the gap between the subjective nature of SOD and its expensive labeling. Comprehensive experiments on five benchmark datasets demonstrate that the SDG-SOD outperforms the state-of-the-art approaches on four evaluation metrics, and largely improves the model performance on DUTS, ECSSD, DUT, HKU-IS, and PASCAL-S datasets.
Unsupervised Domain Adaptive Salient Object Detection Through Uncertainty-Aware Pseudo-Label Learning
2022-02-26
Pengxiang Yan, Ziyi Wu, Mengmeng Liu, Kun Zeng, Liang Lin, Guanbin Li
Recent advances in deep learning significantly boost the performance of salient object detection (SOD) at the expense of labeling larger-scale per-pixel annotations. To relieve the burden of labor-intensive labeling, deep unsupervised SOD methods have been proposed to exploit noisy labels generated by handcrafted saliency methods. However, it is still difficult to learn accurate saliency details from rough noisy labels. In this paper, we propose to learn saliency from synthetic but clean labels, which naturally has higher pixel-labeling quality without the effort of manual annotations. Specifically, we first construct a novel synthetic SOD dataset by a simple copy-paste strategy. Considering the large appearance differences between the synthetic and real-world scenarios, directly training with synthetic data will lead to performance degradation on real-world scenarios. To mitigate this problem, we propose a novel unsupervised domain adaptive SOD method to adapt between these two domains by uncertainty-aware self-training. Experimental results show that our proposed method outperforms the existing state-of-the-art deep unsupervised SOD methods on several benchmark datasets, and is even comparable to fully-supervised ones.
Depth can provide useful geographical cues for salient object detection (SOD), and has been proven helpful in recent RGB-D SOD methods. However, existing video salient object detection (VSOD) methods only utilize spatiotemporal information and seldom exploit depth information for detection. In this paper, we propose a depth-cooperated trimodal network, called DCTNet for VSOD, which is a pioneering work to incorporate depth information to assist VSOD. To this end, we first generate depth from RGB frames, and then propose an approach to treat the three modalities unequally. Specifically, a multi-modal attention module (MAM) is designed to model multi-modal long-range dependencies between the main modality (RGB) and the two auxiliary modalities (depth, optical flow). We also introduce a refinement fusion module (RFM) to suppress noises in each modality and select useful information dynamically for further feature refinement. Lastly, a progressive fusion strategy is adopted after the refined features to achieve final cross-modal fusion. Experiments on five benchmark datasets demonstrate the superiority of our depth-cooperated model against 12 state-of-the-art methods, and the necessity of depth is also validated.
Disentangle Saliency Detection into Cascaded Detail Modeling and Body Filling
2022-02-08
Yue Song, Hao Tang, Nicu Sebe, Wei Wang
Salient object detection has been long studied to identify the most visually attractive objects in images/videos. Recently, a growing amount of approaches have been proposed all of which rely on the contour/edge information to improve detection performance. The edge labels are either put into the loss directly or used as extra supervision. The edge and body can also be learned separately and then fused afterward. Both methods either lead to high prediction errors near the edge or cannot be trained in an end-to-end manner. Another problem is that existing methods may fail to detect objects of various sizes due to the lack of efficient and effective feature fusion mechanisms. In this work, we propose to decompose the saliency detection task into two cascaded sub-tasks, \emph{i.e.}, detail modeling and body filling. Specifically, the detail modeling focuses on capturing the object edges by supervision of explicitly decomposed detail label that consists of the pixels that are nested on the edge and near the edge. Then the body filling learns the body part which will be filled into the detail map to generate more accurate saliency map. To effectively fuse the features and handle objects at different scales, we have also proposed two novel multi-scale detail attention and body attention blocks for precise detail and body modeling. Experimental results show that our method achieves state-of-the-art performances on six public datasets.
Scribble-based Boundary-aware Network for Weakly Supervised Salient Object Detection in Remote Sensing Images
2022-02-07
Zhou Huang, Tian-Zhu Xiang, Huai-Xin Chen, Hang Dai
Existing CNNs-based salient object detection (SOD) heavily depends on the large-scale pixel-level annotations, which is labor-intensive, time-consuming, and expensive. By contrast, the sparse annotations become appealing to the salient object detection community. However, few efforts are devoted to learning salient object detection from sparse annotations, especially in the remote sensing field. In addition, the sparse annotation usually contains scanty information, which makes it challenging to train a well-performing model, resulting in its performance largely lagging behind the fully-supervised models. Although some SOD methods adopt some prior cues to improve the detection performance, they usually lack targeted discrimination of object boundaries and thus provide saliency maps with poor boundary localization. To this end, in this paper, we propose a novel weakly-supervised salient object detection framework to predict the saliency of remote sensing images from sparse scribble annotations. To implement it, we first construct the scribble-based remote sensing saliency dataset by relabelling an existing large-scale SOD dataset with scribbles, namely S-EOR dataset. After that, we present a novel scribble-based boundary-aware network (SBA-Net) for remote sensing salient object detection. Specifically, we design a boundary-aware module (BAM) to explore the object boundary semantics, which is explicitly supervised by the high-confidence object boundary (pseudo) labels generated by the boundary label generation (BLG) module, forcing the model to learn features that highlight the object structure and thus boosting the boundary localization of objects. Then, the boundary semantics are integrated with high-level features to guide the salient object detection under the supervision of scribble labels.
In recent years, deep network-based methods have continuously refreshed state-of-the-art performance on Salient Object Detection (SOD) task. However, the performance discrepancy caused by different implementation details may conceal the real progress in this task. Making an impartial comparison is required for future researches. To meet this need, we construct a general SALient Object Detection (SALOD) benchmark to conduct a comprehensive comparison among several representative SOD methods. Specifically, we re-implement 14 representative SOD methods by using consistent settings for training. Moreover, two additional protocols are set up in our benchmark to investigate the robustness of existing methods in some limited conditions. In the first protocol, we enlarge the difference between objectness distributions of train and test sets to evaluate the robustness of these SOD methods. In the second protocol, we build multiple train subsets with different scales to validate whether these methods can extract discriminative features from only a few samples. In the above experiments, we find that existing loss functions usually specialized in some metrics but reported inferior results on the others. Therefore, we propose a novel Edge-Aware (EA) loss that promotes deep networks to learn more discriminative features by integrating both pixel- and image-level supervision signals. Experiments prove that our EA loss reports more robust performances compared to existing losses.
Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection
2022-01-24
Ze-yu Liu, Jian-wei Liu, Xin Zuo, Ming-fei Hu
The extensive research leveraging RGB-D information has been exploited in salient object detection. However, salient visual cues appear in various scales and resolutions of RGB images due to semantic gaps at different feature levels. Meanwhile, similar salient patterns are available in cross-modal depth images as well as multi-scale versions. Cross-modal fusion and multi-scale refinement are still an open problem in RGB-D salient object detection task. In this paper, we begin by introducing top-down and bottom-up iterative refinement architecture to leverage multi-scale features, and then devise attention based fusion module (ABF) to address on cross-modal correlation. We conduct extensive experiments on seven public datasets. The experimental results show the effectiveness of our devised method
Salient object detection in optical remote sensing images (ORSI-SOD) has been widely explored for understanding ORSIs. However, previous methods focus mainly on improving the detection accuracy while neglecting the cost in memory and computation, which may hinder their real-world applications. In this paper, we propose a novel lightweight ORSI-SOD solution, named CorrNet, to address these issues. In CorrNet, we first lighten the backbone (VGG-16) and build a lightweight subnet for feature extraction. Then, following the coarse-to-fine strategy, we generate an initial coarse saliency map from high-level semantic features in a Correlation Module (CorrM). The coarse saliency map serves as the location guidance for low-level features. In CorrM, we mine the object location information between high-level semantic features through the cross-layer correlation operation. Finally, based on low-level detailed features, we refine the coarse saliency map in the refinement subnet equipped with Dense Lightweight Refinement Blocks, and produce the final fine saliency map. By reducing the parameters and computations of each component, CorrNet ends up having only 4.09M parameters and running with 21.09G FLOPs. Experimental results on two public datasets demonstrate that our lightweight CorrNet achieves competitive or even better performance compared with 26 state-of-the-art methods (including 16 large CNN-based methods and 2 lightweight methods), and meanwhile enjoys the clear memory and run time efficiency. The code and results of our method are available at https://github.com/MathLee/CorrNet.
Salient Object Detection by LTP Texture Characterization on Opposing Color Pairs under SLICO Superpixel Constraint
2022-01-03
Didier Ndayikengurukiye, Max Mignotte
The effortless detection of salient objects by humans has been the subject of research in several fields, including computer vision as it has many applications. However, salient object detection remains a challenge for many computer models dealing with color and textured images. Herein, we propose a novel and efficient strategy, through a simple model, almost without internal parameters, which generates a robust saliency map for a natural image. This strategy consists of integrating color information into local textural patterns to characterize a color micro-texture. Most models in the literature that use the color and texture features treat them separately. In our case, it is the simple, yet powerful LTP (Local Ternary Patterns) texture descriptor applied to opposing color pairs of a color space that allows us to achieve this end. Each color micro-texture is represented by vector whose components are from a superpixel obtained by SLICO (Simple Linear Iterative Clustering with zero parameter) algorithm which is simple, fast and exhibits state-of-the-art boundary adherence. The degree of dissimilarity between each pair of color micro-texture is computed by the FastMap method, a fast version of MDS (Multi-dimensional Scaling), that considers the color micro-textures non-linearity while preserving their distances. These degrees of dissimilarity give us an intermediate saliency map for each RGB, HSL, LUV and CMY color spaces. The final saliency map is their combination to take advantage of the strength of each of them. The MAE (Mean Absolute Error) and F$_{\beta}$ measures of our saliency maps, on the complex ECSSD dataset show that our model is both simple and efficient, outperforming several state-of-the-art models.
Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images
2022-01-01
Xiaoqiang Wang, Lei Zhu, Siliang Tang, Huazhu Fu, Ping Li, Fei Wu, Yi Yang, Yueting Zhuang
Training deep models for RGB-D salient object detection (SOD) often requires a large number of labeled RGB-D images. However, RGB-D data is not easily acquired, which limits the development of RGB-D SOD techniques. To alleviate this issue, we present a Dual-Semi RGB-D Salient Object Detection Network (DS-Net) to leverage unlabeled RGB images for boosting RGB-D saliency detection. We first devise a depth decoupling convolutional neural network (DDCNN), which contains a depth estimation branch and a saliency detection branch. The depth estimation branch is trained with RGB-D images and then used to estimate the pseudo depth maps for all unlabeled RGB images to form the paired data. The saliency detection branch is used to fuse the RGB feature and depth feature to predict the RGB-D saliency. Then, the whole DDCNN is assigned as the backbone in a teacher-student framework for semi-supervised learning. Moreover, we also introduce a consistency loss on the intermediate attention and saliency maps for the unlabeled data, as well as a supervised depth and saliency loss for labeled data. Experimental results on seven widely-used benchmark datasets demonstrate that our DDCNN outperforms state-of-the-art methods both quantitatively and qualitatively. We also demonstrate that our semi-supervised DS-Net can further improve the performance, even when using an RGB image with the pseudo depth map.
Semi-supervised Salient Object Detection with Effective Confidence Estimation
2021-12-28
Jiawei Liu, Jing Zhang, Nick Barnes
The success of existing salient object detection models relies on a large pixel-wise labeled training dataset, which is time-consuming and expensive to obtain. We study semi-supervised salient object detection, with access to a small number of labeled samples and a large number of unlabeled samples. Specifically, we present a pseudo label based learn-ing framework with a Conditional Energy-based Model. We model the stochastic nature of human saliency labels using the stochastic latent variable of the Conditional Energy-based Model. It further enables generation of a high-quality pixel-wise uncertainty map, highlighting the reliability of corresponding pseudo label generated for the unlabeled sample. This minimises the contribution of low-certainty pseudo labels in optimising the model, preventing the error propagation. Experimental results show that the proposed strategy can effectively explore the contribution of unlabeled data. With only 1/16 labeled samples, our model achieves competitive performance compared with state-of-the-art fully-supervised models.
Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction
2021-12-27
Jing Zhang, Jianwen Xie, Nick Barnes, Ping Li
Vision transformer networks have shown superiority in many computer vision tasks. In this paper, we take a step further by proposing a novel generative vision transformer with latent variables following an informative energy-based prior for salient object detection. Both the vision transformer network and the energy-based prior model are jointly trained via Markov chain Monte Carlo-based maximum likelihood estimation, in which the sampling from the intractable posterior and prior distributions of the latent variables are performed by Langevin dynamics. Further, with the generative vision transformer, we can easily obtain a pixel-wise uncertainty map from an image, which indicates the model confidence in predicting saliency from the image. Different from the existing generative models which define the prior distribution of the latent variables as a simple isotropic Gaussian distribution, our model uses an energy-based informative prior which can be more expressive to capture the latent space of the data. We apply the proposed framework to both RGB and RGB-D salient object detection tasks. Extensive experimental results show that our framework can achieve not only accurate saliency predictions but also meaningful uncertainty maps that are consistent with the human perception.
a novel attention-based network for fast salient object detection
2021-12-20
Bin Zhang, Yang Wu, Xiaojing Zhang, Ming Ma
In the current salient object detection network, the most popular method is using U-shape structure. However, the massive number of parameters leads to more consumption of computing and storage resources which are not feasible to deploy on the limited memory device. Some others shallow layer network will not maintain the same accuracy compared with U-shape structure and the deep network structure with more parameters will not converge to a global minimum loss with great speed. To overcome all of these disadvantages, we proposed a new deep convolution network architecture with three contributions: (1) using smaller convolution neural networks (CNNs) to compress the model in our improved salient object features compression and reinforcement extraction module (ISFCREM) to reduce parameters of the model. (2) introducing channel attention mechanism in ISFCREM to weigh different channels for improving the ability of feature representation. (3) applying a new optimizer to accumulate the long-term gradient information during training to adaptively tune the learning rate. The results demonstrate that the proposed method can compress the model to 1/3 of the original size nearly without losing the accuracy and converging faster and more smoothly on six widely used datasets of salient object detection compared with the others models. Our code is published in https://gitee.com/binzhangbinzhangbin/code-a-novel-attention-based-network-for-fast-salient-object-detection.git
2021-12-14
Min Seok Lee, Wooseok Shin, Sung Won Han
Existing studies on salient object detection (SOD) focus on extracting distinct objects with edge information and aggregating multi-level features to improve SOD performance. To achieve satisfactory performance, the methods employ refined edge information and low multi-level discrepancy. However, both performance gain and computational efficiency cannot be attained, which has motivated us to study the inefficiencies in existing encoder-decoder structures to avoid this trade-off. We propose TRACER, which detects salient objects with explicit edges by incorporating attention guided tracing modules. We employ a masked edge attention module at the end of the first encoder using a fast Fourier transform to propagate the refined edge information to the downstream feature extraction. In the multi-level aggregation phase, the union attention module identifies the complementary channel and important spatial information. To improve the decoder performance and computational efficiency, we minimize the decoder block usage with object attention module. This module extracts undetected objects and edge information from refined channels and spatial representations. Subsequently, we propose an adaptive pixel intensity loss function to deal with the relatively important pixels unlike conventional loss functions which treat all pixels equally. A comparison with 13 existing methods reveals that TRACER achieves state-of-the-art performance on five benchmark datasets. We have released TRACER at https://github.com/Karel911/TRACER.
Existing deep learning-based Unsupervised Salient Object Detection (USOD) methods rely on supervised pre-trained deep models. Moreover, they generate pseudo labels based on hand-crafted features, which lack high-level semantic information. In order to overcome these shortcomings, we propose a new two-stage Activation-to-Saliency (A2S) framework that effectively excavates high-quality saliency cues to train a robust saliency detector. It is worth noting that our method does not require any manual annotation, even in the pre-training phase. In the first stage, we transform an unsupervisedly pre-trained network to aggregate multi-level features to a single activation map, where an Adaptive Decision Boundary (ADB) is proposed to assist the training of the transformed network. Moreover, a new loss function is proposed to facilitate the generation of high-quality pseudo labels. In the second stage, a self-rectification learning paradigm strategy is developed to train a saliency detector and refine the pseudo labels online. In addition, we construct a lightweight saliency detector using two Residual Attention Modules (RAMs) to largely reduce the risk of overfitting. Extensive experiments on several SOD benchmarks prove that our framework reports significant performance compared with existing USOD methods. Moreover, training our framework on 3,000 images consumes about 1 hour, which is over 30$\times$ faster than previous state-of-the-art methods.
CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detection
2021-12-04
Youwei Pang, Xiaoqi Zhao, Lihe Zhang, Huchuan Lu
Most of the existing bi-modal (RGB-D and RGB-T) salient object detection methods utilize the convolution operation and construct complex interweave fusion structures to achieve cross-modal information integration. The inherent local connectivity of the convolution operation constrains the performance of the convolution-based methods to a ceiling. In this work, we rethink these tasks from the perspective of global information alignment and transformation. Specifically, the proposed \underline{c}ross-mod\underline{a}l \underline{v}iew-mixed transform\underline{er} (CAVER) cascades several cross-modal integration units to construct a top-down transformer-based information propagation path. CAVER treats the multi-scale and multi-modal feature integration as a sequence-to-sequence context propagation and update process built on a novel view-mixed attention mechanism. Besides, considering the quadratic complexity w.r.t. the number of input tokens, we design a parameter-free patch-wise token re-embedding strategy to simplify operations. Extensive experimental results on RGB-D and RGB-T SOD datasets demonstrate that such a simple two-stream encoder-decoder framework can surpass recent state-of-the-art methods when it is equipped with the proposed components. Code and pretrained models will be available at \href{https://github.com/lartpang/CAVER}{the link}.
MFNet: Multi-filter Directive Network for Weakly Supervised Salient Object Detection
2021-12-03
Yongri Piao, Jian Wang, Miao Zhang, Huchuan Lu
Weakly supervised salient object detection (WSOD) targets to train a CNNs-based saliency network using only low-cost annotations. Existing WSOD methods take various techniques to pursue single "high-quality" pseudo label from low-cost annotations and then develop their saliency networks. Though these methods have achieved good performance, the generated single label is inevitably affected by adopted refinement algorithms and shows prejudiced characteristics which further influence the saliency networks. In this work, we introduce a new multiple-pseudo-label framework to integrate more comprehensive and accurate saliency cues from multiple labels, avoiding the aforementioned problem. Specifically, we propose a multi-filter directive network (MFNet) including a saliency network as well as multiple directive filters. The directive filter (DF) is designed to extract and filter more accurate saliency cues from the noisy pseudo labels. The multiple accurate cues from multiple DFs are then simultaneously propagated to the saliency network with a multi-guidance loss. Extensive experiments on five datasets over four metrics demonstrate that our method outperforms all the existing congeneric methods. Moreover, it is also worth noting that our framework is flexible enough to apply to existing methods and improve their performance.
In the computer vision community, great progresses have been achieved in salient object detection from natural scene images (NSI-SOD); by contrast, salient object detection in optical remote sensing images (RSI-SOD) remains to be a challenging emerging topic. The unique characteristics of optical RSIs, such as scales, illuminations and imaging orientations, bring significant differences between NSI-SOD and RSI-SOD. In this paper, we propose a novel Multi-Content Complementation Network (MCCNet) to explore the complementarity of multiple content for RSI-SOD. Specifically, MCCNet is based on the general encoder-decoder architecture, and contains a novel key component named Multi-Content Complementation Module (MCCM), which bridges the encoder and the decoder. In MCCM, we consider multiple types of features that are critical to RSI-SOD, including foreground features, edge features, background features, and global image-level features, and exploit the content complementarity between them to highlight salient regions over various scales in RSI features through the attention mechanism. Besides, we comprehensively introduce pixel-level, map-level and metric-aware losses in the training phase. Extensive experiments on two popular datasets demonstrate that the proposed MCCNet outperforms 23 state-of-the-art methods, including both NSI-SOD and RSI-SOD methods. The code and results of our method are available at https://github.com/MathLee/MCCNet.
A General Divergence Modeling Strategy for Salient Object Detection
2021-11-23
Xinyu Tian, Jing Zhang, Yuchao Dai
Salient object detection is subjective in nature, which implies that multiple estimations should be related to the same input image. Most existing salient object detection models are deterministic following a point to point estimation learning pipeline, making them incapable of estimating the predictive distribution. Although latent variable model based stochastic prediction networks exist to model the prediction variants, the latent space based on the single clean saliency annotation is less reliable in exploring the subjective nature of saliency, leading to less effective saliency divergence modeling. Given multiple saliency annotations, we introduce a general divergence modeling strategy via random sampling, and apply our strategy to an ensemble based framework and three latent variable model based solutions to explore the subjective nature of saliency. Experimental results prove the superior performance of our general divergence modeling strategy.
Addressing Multiple Salient Object Detection via Dual-Space Long-Range Dependencies
2021-11-04
Bowen Deng, Andrew P. French, Michael P. Pound
Salient object detection plays an important role in many downstream tasks. However, complex real-world scenes with varying scales and numbers of salient objects still pose a challenge. In this paper, we directly address the problem of detecting multiple salient objects across complex scenes. We propose a network architecture incorporating non-local feature information in both the spatial and channel spaces, capturing the long-range dependencies between separate objects. Traditional bottom-up and non-local features are combined with edge features within a feature fusion gate that progressively refines the salient object prediction in the decoder. We show that our approach accurately locates multiple salient regions even in complex scenarios. To demonstrate the efficacy of our approach to the multiple salient objects problem, we curate a new dataset containing only multiple salient objects. Our experiments demonstrate the proposed method presents state-of-the-art results on five widely used datasets without any pre-processing and post-processing. We obtain a further performance improvement against competing techniques on our multi-objects dataset. The dataset and source code are avaliable at: https://github.com/EricDengbowen/DSLRDNet.
Video Salient Object Detection via Contrastive Features and Attention Modules
2021-11-03
Yi-Wen Chen, Xiaojie Jin, Xiaohui Shen, Ming-Hsuan Yang
Video salient object detection aims to find the most visually distinctive objects in a video. To explore the temporal dependencies, existing methods usually resort to recurrent neural networks or optical flow. However, these approaches require high computational cost, and tend to accumulate inaccuracies over time. In this paper, we propose a network with attention modules to learn contrastive features for video salient object detection without the high computational temporal modeling techniques. We develop a non-local self-attention scheme to capture the global information in the video frame. A co-attention formulation is utilized to combine the low-level and high-level features. We further apply the contrastive learning to improve the feature representations, where foreground region pairs from the same video are pulled together, and foreground-background region pairs are pushed away in the latent space. The intra-frame contrastive loss helps separate the foreground and background features, and the inter-frame contrastive loss improves the temporal consistency. We conduct extensive experiments on several benchmark datasets for video salient object detection and unsupervised video object segmentation, and show that the proposed method requires less computation, and performs favorably against the state-of-the-art approaches.
RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images
2021-10-27
Runmin Cong, Yumo Zhang, Leyuan Fang, Jun Li, Yao Zhao, Sam Kwong
Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. Despite some saliency models were proposed to solve the intrinsic problem of optical RSIs (such as complex background and scale-variant objects), the accuracy and completeness are still unsatisfactory. To this end, we propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs in this paper. The relational reasoning module that integrates the spatial and the channel dimensions is designed to infer the semantic relationship by utilizing high-level encoder features, thereby promoting the generation of more complete detection results. The parallel multi-scale attention module is proposed to effectively restore the detail information and address the scale variation of salient objects by using the low-level features refined by multi-scale attention. Extensive experiments on two datasets demonstrate that our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively.
C$^{4}$Net: Contextual Compression and Complementary Combination Network for Salient Object Detection
2021-10-22
Hazarapet Tunanyan
Deep learning solutions of the salient object detection problem have achieved great results in recent years. The majority of these models are based on encoders and decoders, with a different multi-feature combination. In this paper, we show that feature concatenation works better than other combination methods like multiplication or addition. Also, joint feature learning gives better results, because of the information sharing during their processing. We designed a Complementary Extraction Module (CEM) to extract necessary features with edge preservation. Our proposed Excessiveness Loss (EL) function helps to reduce false-positive predictions and purifies the edges with other weighted loss functions. Our designed Pyramid-Semantic Module (PSM) with Global guiding flow (G) makes the prediction more accurate by providing high-level complementary information to shallower layers. Experimental results show that the proposed model outperforms the state-of-the-art methods on all benchmark datasets under three evaluation metrics.
LC3Net: Ladder context correlation complementary network for salient object detection
2021-10-21
Xian Fang, Jinchao Zhu, Xiuli Shao, Hongpeng Wang
Currently, existing salient object detection methods based on convolutional neural networks commonly resort to constructing discriminative networks to aggregate high level and low level features. However, contextual information is always not fully and reasonably utilized, which usually causes either the absence of useful features or contamination of redundant features. To address these issues, we propose a novel ladder context correlation complementary network (LC3Net) in this paper, which is equipped with three crucial components. At the beginning, we propose a filterable convolution block (FCB) to assist the automatic collection of information on the diversity of initial features, and it is simple yet practical. Besides, we propose a dense cross module (DCM) to facilitate the intimate aggregation of different levels of features by validly integrating semantic information and detailed information of both adjacent and non-adjacent layers. Furthermore, we propose a bidirectional compression decoder (BCD) to help the progressive shrinkage of multi-scale features from coarse to fine by leveraging multiple pairs of alternating top-down and bottom-up feature interaction flows. Extensive experiments demonstrate the superiority of our method against 16 state-of-the-art methods.
Salient object detection requires a comprehensive and scalable receptive field to locate the visually significant objects in the image. Recently, the emergence of visual transformers and multi-branch modules has significantly enhanced the ability of neural networks to perceive objects at different scales. However, compared to the traditional backbone, the calculation process of transformers is time-consuming. Moreover, different branches of the multi-branch modules could cause the same error back propagation in each training iteration, which is not conducive to extracting discriminative features. To solve these problems, we propose a bilateral network based on transformer and CNN to efficiently broaden local details and global semantic information simultaneously. Besides, a Multi-Head Boosting (MHB) strategy is proposed to enhance the specificity of different network branches. By calculating the errors of different prediction heads, each branch can separately pay more attention to the pixels that other branches predict incorrectly. Moreover, Unlike multi-path parallel training, MHB randomly selects one branch each time for gradient back propagation in a boosting way. Additionally, an Attention Feature Fusion Module (AF) is proposed to fuse two types of features according to respective characteristics. Comprehensive experiments on five benchmark datasets demonstrate that the proposed method can achieve a significant performance improvement compared with the state-of-the-art methods.
Saliency Detection via Global Context Enhanced Feature Fusion and Edge Weighted Loss
2021-10-13
Chaewon Park, Minhyeok Lee, MyeongAh Cho, Sangyoun Lee
UNet-based methods have shown outstanding performance in salient object detection (SOD), but are problematic in two aspects. 1) Indiscriminately integrating the encoder feature, which contains spatial information for multiple objects, and the decoder feature, which contains global information of the salient object, is likely to convey unnecessary details of non-salient objects to the decoder, hindering saliency detection. 2) To deal with ambiguous object boundaries and generate accurate saliency maps, the model needs additional branches, such as edge reconstructions, which leads to increasing computational cost. To address the problems, we propose a context fusion decoder network (CFDN) and near edge weighted loss (NEWLoss) function. The CFDN creates an accurate saliency map by integrating global context information and thus suppressing the influence of the unnecessary spatial information. NEWLoss accelerates learning of obscure boundaries without additional modules by generating weight maps on object boundaries. Our method is evaluated on four benchmarks and achieves state-of-the-art performance. We prove the effectiveness of the proposed method through comparative experiments.
Recent RGBD-based models for saliency detection have attracted research attention. The depth clues such as boundary clues, surface normal, shape attribute, etc., contribute to the identification of salient objects with complicated scenarios. However, most RGBD networks require multi-modalities from the input side and feed them separately through a two-stream design, which inevitably results in extra costs on depth sensors and computation. To tackle these inconveniences, we present in this paper a novel fusion design named modality-guided subnetwork (MGSnet). It has the following superior designs: 1) Our model works for both RGB and RGBD data, and dynamically estimating depth if not available. Taking the inner workings of depth-prediction networks into account, we propose to estimate the pseudo-geometry maps from RGB input - essentially mimicking the multi-modality input. 2) Our MGSnet for RGB SOD results in real-time inference but achieves state-of-the-art performance compared to other RGB models. 3) The flexible and lightweight design of MGS facilitates the integration into RGBD two-streaming models. The introduced fusion design enables a cross-modality interaction to enable further progress but with a minimal cost.
Salient object detection is a fundamental topic in computer vision. Previous methods based on RGB-D often suffer from the incompatibility of multi-modal feature fusion and the insufficiency of multi-scale feature aggregation. To tackle these two dilemmas, we propose a novel multi-modal and multi-scale refined network (M2RNet). Three essential components are presented in this network. The nested dual attention module (NDAM) explicitly exploits the combined features of RGB and depth flows. The adjacent interactive aggregation module (AIAM) gradually integrates the neighbor features of high, middle and low levels. The joint hybrid optimization loss (JHOL) makes the predictions have a prominent outline. Extensive experiments demonstrate that our method outperforms other state-of-the-art approaches.
ACFNet: Adaptively-Cooperative Fusion Network for RGB-D Salient Object Detection
2021-09-10
Jinchao Zhu
The reasonable employment of RGB and depth data show great significance in promoting the development of computer vision tasks and robot-environment interaction. However, there are different advantages and disadvantages in the early and late fusion of the two types of data. Besides, due to the diversity of object information, using a single type of data in a specific scenario tends to result in semantic misleading. Based on the above considerations, we propose an adaptively-cooperative fusion network (ACFNet) with ResinRes structure for salient object detection. This structure is designed to flexibly utilize the advantages of feature fusion in early and late stages. Secondly, an adaptively-cooperative semantic guidance (ACG) scheme is designed to suppress inaccurate features in the guidance phase. Further, we proposed a type-based attention module (TAM) to optimize the network and enhance the multi-scale perception of different objects. For different objects, the features generated by different types of convolution are enhanced or suppressed by the gated mechanism for segmentation optimization. ACG and TAM optimize the transfer of feature streams according to their data attributes and convolution attributes, respectively. Sufficient experiments conducted on RGB-D SOD datasets illustrate that the proposed network performs favorably against 18 state-of-the-art algorithms.
Conventional RGB-D salient object detection methods aim to leverage depth as complementary information to find the salient regions in both modalities. However, the salient object detection results heavily rely on the quality of captured depth data which sometimes are unavailable. In this work, we make the first attempt to solve the RGB-D salient object detection problem with a novel depth-awareness framework. This framework only relies on RGB data in the testing phase, utilizing captured depth data as supervision for representation learning. To construct our framework as well as achieving accurate salient detection results, we propose a Ubiquitous Target Awareness (UTA) network to solve three important challenges in RGB-D SOD task: 1) a depth awareness module to excavate depth information and to mine ambiguous regions via adaptive depth-error weights, 2) a spatial-aware cross-modal interaction and a channel-aware cross-level interaction, exploiting the low-level boundary cues and amplifying high-level salient channels, and 3) a gated multi-scale predictor module to perceive the object saliency in different contextual scales. Besides its high performance, our proposed UTA network is depth-free for inference and runs in real-time with 43 FPS. Experimental evidence demonstrates that our proposed network not only surpasses the state-of-the-art methods on five public RGB-D SOD benchmarks by a large margin, but also verifies its extensibility on five public RGB SOD benchmarks.
Weakly-supervised salient object detection (WSOD) aims to develop saliency models using image-level annotations. Despite of the success of previous works, explorations on an effective training strategy for the saliency network and accurate matches between image-level annotations and salient objects are still inadequate. In this work, 1) we propose a self-calibrated training strategy by explicitly establishing a mutual calibration loop between pseudo labels and network predictions, liberating the saliency network from error-prone propagation caused by pseudo labels. 2) we prove that even a much smaller dataset (merely 1.8% of ImageNet) with well-matched annotations can facilitate models to achieve better performance as well as generalizability. This sheds new light on the development of WSOD and encourages more contributions to the community. Comprehensive experiments demonstrate that our method outperforms all the existing WSOD methods by adopting the self-calibrated strategy only. Steady improvements are further achieved by training on the proposed dataset. Additionally, our method achieves 94.7% of the performance of fully-supervised methods on average. And what is more, the fully supervised models adopting our predicted results as "ground truths" achieve successful results (95.6% for BASNet and 97.3% for ITSD on F-measure), while costing only 0.32% of labeling time for pixel-level annotation.
Multi-scale Edge-based U-shape Network for Salient Object Detection
2021-08-21
Han Sun, Yetong Bian, Ningzhong Liu, Huiyu Zhou
Deep-learning based salient object detection methods achieve great improvements. However, there are still problems existing in the predictions, such as blurry boundary and inaccurate location, which is mainly caused by inadequate feature extraction and integration. In this paper, we propose a Multi-scale Edge-based U-shape Network (MEUN) to integrate various features at different scales to achieve better performance. To extract more useful information for boundary prediction, U-shape Edge Network modules are embedded in each decoder units. Besides, the additional down-sampling module alleviates the location inaccuracy. Experimental results on four benchmark datasets demonstrate the validity and reliability of the proposed method. Multi-scale Edge based U-shape Network also shows its superiority when compared with 15 state-of-the-art salient object detection methods.
2021-08-18
Tao Zhou, Deng-Ping Fan, Geng Chen, Yi Zhou, Huazhu Fu
Salient object detection (SOD) on RGB and depth images has attracted more and more research interests, due to its effectiveness and the fact that depth cues can now be conveniently captured. Existing RGB-D SOD models usually adopt different fusion strategies to learn a shared representation from the two modalities (\ie, RGB and depth), while few methods explicitly consider how to preserve modality-specific characteristics. In this study, we propose a novel framework, termed SPNet} (Specificity-preserving network), which benefits SOD performance by exploring both the shared information and modality-specific properties (\eg, specificity). Specifically, we propose to adopt two modality-specific networks and a shared learning network to generate individual and shared saliency prediction maps, respectively. To effectively fuse cross-modal features in the shared learning network, we propose a cross-enhanced integration module (CIM) and then propagate the fused feature to the next layer for integrating cross-level information. Moreover, to capture rich complementary multi-modal information for boosting the SOD performance, we propose a multi-modal feature aggregation (MFA) module to integrate the modality-specific features from each individual decoder into the shared decoder. By using a skip connection, the hierarchical features between the encoder and decoder layers can be fully combined. Extensive experiments demonstrate that our~\ours~outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection benchmarks. The project is publicly available at: https://github.com/taozh2017/SPNet.
Boosting Salient Object Detection with Transformer-based Asymmetric Bilateral U-Net
2021-08-17
Yu Qiu, Yun Liu, Le Zhang, Jing Xu
Existing salient object detection (SOD) methods mainly rely on U-shaped convolution neural networks (CNNs) with skip connections to combine the global contexts and local spatial details that are crucial for locating salient objects and refining object details, respectively. Despite great successes, the ability of CNNs in learning global contexts is limited. Recently, the vision transformer has achieved revolutionary progress in computer vision owing to its powerful modeling of global dependencies. However, directly applying the transformer to SOD is suboptimal because the transformer lacks the ability to learn local spatial representations. To this end, this paper explores the combination of transformers and CNNs to learn both global and local representations for SOD. We propose a transformer-based Asymmetric Bilateral U-Net (ABiU-Net). The asymmetric bilateral encoder has a transformer path and a lightweight CNN path, where the two paths communicate at each encoder stage to learn complementary global contexts and local spatial details, respectively. The asymmetric bilateral decoder also consists of two paths to process features from the transformer and CNN encoder paths, with communication at each decoder stage for decoding coarse salient object locations and fine-grained object details, respectively. Such communication between the two encoder/decoder paths enables AbiU-Net to learn complementary global and local representations, taking advantage of the natural merits of transformers and CNNs, respectively. Hence, ABiU-Net provides a new perspective for transformer-based SOD. Extensive experiments demonstrate that ABiU-Net performs favorably against previous state-of-the-art SOD methods. The code is available at https://github.com/yuqiuyuqiu/ABiU-Net.
The multi-modal salient object detection model based on RGB-D information has better robustness in the real world. However, it remains nontrivial to better adaptively balance effective multi-modal information in the feature fusion phase. In this letter, we propose a novel gated recoding network (GRNet) to evaluate the information validity of the two modes, and balance their influence. Our framework is divided into three phases: perception phase, recoding mixing phase and feature integration phase. First, A perception encoder is adopted to extract multi-level single-modal features, which lays the foundation for multi-modal semantic comparative analysis. Then, a modal-adaptive gate unit (MGU) is proposed to suppress the invalid information and transfer the effective modal features to the recoding mixer and the hybrid branch decoder. The recoding mixer is responsible for recoding and mixing the balanced multi-modal information. Finally, the hybrid branch decoder completes the multi-level feature integration under the guidance of an optional edge guidance stream (OEGS). Experiments and analysis on eight popular benchmarks verify that our framework performs favorably against 9 state-of-art methods.
Salient object detection is the pixel-level dense prediction task which can highlight the prominent object in the scene. Recently U-Net framework is widely used, and continuous convolution and pooling operations generate multi-level features which are complementary with each other. In view of the more contribution of high-level features for the performance, we propose a triplet transformer embedding module to enhance them by learning long-range dependencies across layers. It is the first to use three transformer encoders with shared weights to enhance multi-level features. By further designing scale adjustment module to process the input, devising three-stream decoder to process the output and attaching depth features to color features for the multi-modal fusion, the proposed triplet transformer embedding network (TriTransNet) achieves the state-of-the-art performance in RGB-D salient object detection, and pushes the performance to a new level. Experimental results demonstrate the effectiveness of the proposed modules and the competition of TriTransNet.
MPI: Multi-receptive and Parallel Integration for Salient Object Detection
2021-08-08
Han Sun, Jun Cen, Ningzhong Liu, Dong Liang, Huiyu Zhou
The semantic representation of deep features is essential for image context understanding, and effective fusion of features with different semantic representations can significantly improve the model's performance on salient object detection. In this paper, a novel method called MPI is proposed for salient object detection. Firstly, a multi-receptive enhancement module (MRE) is designed to effectively expand the receptive fields of features from different layers and generate features with different receptive fields. MRE can enhance the semantic representation and improve the model's perception of the image context, which enables the model to locate the salient object accurately. Secondly, in order to reduce the reuse of redundant information in the complex top-down fusion method and weaken the differences between semantic features, a relatively simple but effective parallel fusion strategy (PFS) is proposed. It allows multi-scale features to better interact with each other, thus improving the overall performance of the model. Experimental results on multiple datasets demonstrate that the proposed method outperforms state-of-the-art methods under different evaluation metrics.
Disentangled High Quality Salient Object Detection
2021-08-08
Lv Tang, Bo Li, Shouhong Ding, Mofei Song
Aiming at discovering and locating most distinctive objects from visual scenes, salient object detection (SOD) plays an essential role in various computer vision systems. Coming to the era of high resolution, SOD methods are facing new challenges. The major limitation of previous methods is that they try to identify the salient regions and estimate the accurate objects boundaries simultaneously with a single regression task at low-resolution. This practice ignores the inherent difference between the two difficult problems, resulting in poor detection quality. In this paper, we propose a novel deep learning framework for high-resolution SOD task, which disentangles the task into a low-resolution saliency classification network (LRSCN) and a high-resolution refinement network (HRRN). As a pixel-wise classification task, LRSCN is designed to capture sufficient semantics at low-resolution to identify the definite salient, background and uncertain image regions. HRRN is a regression task, which aims at accurately refining the saliency value of pixels in the uncertain region to preserve a clear object boundary at high-resolution with limited GPU memory. It is worth noting that by introducing uncertainty into the training process, our HRRN can well address the high-resolution refinement task without using any high-resolution training data. Extensive experiments on high-resolution saliency datasets as well as some widely used saliency benchmarks show that the proposed method achieves superior performance compared to the state-of-the-art methods.
Previous video object segmentation approaches mainly focus on using simplex solutions between appearance and motion, limiting feature collaboration efficiency among and across these two cues. In this work, we study a novel and efficient full-duplex strategy network (FSNet) to address this issue, by considering a better mutual restraint scheme between motion and appearance in exploiting the cross-modal features from the fusion and decoding stage. Specifically, we introduce the relational cross-attention module (RCAM) to achieve bidirectional message propagation across embedding sub-spaces. To improve the model's robustness and update the inconsistent features from the spatial-temporal embeddings, we adopt the bidirectional purification module (BPM) after the RCAM. Extensive experiments on five popular benchmarks show that our FSNet is robust to various challenging scenarios (e.g., motion blur, occlusion) and achieves favourable performance against existing cutting-edges both in the video object segmentation and video salient object detection tasks. The project is publicly available at: https://dpfan.net/FSNet.
Mamba-based Efficient Spatio-Frequency Motion Perception for Video Camouflaged Object Detection
2025-07-31
Xin Li, Keren Fu, Qijun Zhao
Existing video camouflaged object detection (VCOD) methods primarily rely on spatial appearance features to perceive motion cues for breaking camouflage. However, the high similarity between foreground and background in VCOD results in limited discriminability of spatial appearance features (e.g., color and texture), restricting detection accuracy and completeness. Recent studies demonstrate that frequency features can not only enhance feature representation to compensate for appearance limitations but also perceive motion through dynamic variations in frequency energy. Furthermore, the emerging state space model called Mamba, enables efficient perception of motion cues in frame sequences due to its linear-time long-sequence modeling capability. Motivated by this, we propose a novel visual camouflage Mamba (Vcamba) based on spatio-frequency motion perception that integrates frequency and spatial features for efficient and accurate VCOD. Specifically, we propose a receptive field visual state space (RFVSS) module to extract multi-scale spatial features after sequence modeling. For frequency learning, we introduce an adaptive frequency component enhancement (AFE) module with a novel frequency-domain sequential scanning strategy to maintain semantic consistency. Then we propose a space-based long-range motion perception (SLMP) module and a frequency-based long-range motion perception (FLMP) module to model spatio-temporal and frequency-temporal sequences in spatial and frequency phase domains. Finally, the space and frequency motion fusion module (SFMF) integrates dual-domain features for unified motion representation. Experimental results show that our Vcamba outperforms state-of-the-art methods across 6 evaluation metrics on 2 datasets with lower computation cost, confirming the superiority of Vcamba. Our code is available at: https://github.com/BoydeLi/Vcamba.
Semi-supervised Camouflaged Object Detection (SSCOD) aims to reduce reliance on costly pixel-level annotations by leveraging limited annotated data and abundant unlabeled data. However, existing SSCOD methods based on Teacher-Student frameworks suffer from severe prediction bias and error propagation under scarce supervision, while their multi-network architectures incur high computational overhead and limited scalability. To overcome these limitations, we propose ST-SAM, a highly annotation-efficient yet concise framework that breaks away from conventional SSCOD constraints. Specifically, ST-SAM employs Self-Training strategy that dynamically filters and expands high-confidence pseudo-labels to enhance a single-model architecture, thereby fundamentally circumventing inter-model prediction bias. Furthermore, by transforming pseudo-labels into hybrid prompts containing domain-specific knowledge, ST-SAM effectively harnesses the Segment Anything Model's potential for specialized tasks to mitigate error accumulation in self-training. Experiments on COD benchmark datasets demonstrate that ST-SAM achieves state-of-the-art performance with only 1\% labeled data, outperforming existing SSCOD methods and even matching fully supervised methods. Remarkably, ST-SAM requires training only a single network, without relying on specific models or loss functions. This work establishes a new paradigm for annotation-efficient SSCOD. Codes will be available at https://github.com/hu-xh/ST-SAM.
Due to the high cost of collection and labeling, there are relatively few datasets for camouflaged object detection (COD). In particular, for certain specialized categories, the available image dataset is insufficiently populated. Synthetic datasets can be utilized to alleviate the problem of limited data to some extent. However, directly training with synthetic datasets compared to real datasets can lead to a degradation in model performance. To tackle this problem, in this work, we investigate a new task, namely Syn-to-Real Camouflaged Object Detection (S2R-COD). In order to improve the model performance in real world scenarios, a set of annotated synthetic camouflaged images and a limited number of unannotated real images must be utilized. We propose the Cycling Syn-to-Real Domain Adaptation Framework (CSRDA), a method based on the student-teacher model. Specially, CSRDA propagates class information from the labeled source domain to the unlabeled target domain through pseudo labeling combined with consistency regularization. Considering that narrowing the intra-domain gap can improve the quality of pseudo labeling, CSRDA utilizes a recurrent learning framework to build an evolving real domain for bridging the source and target domain. Extensive experiments demonstrate the effectiveness of our framework, mitigating the problem of limited data and handcraft annotations in COD. Our code is publicly available at: https://github.com/Muscape/S2R-COD.
Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning
2025-06-27
Zuyao You, Zuxuan Wu
We present Seg-R1, a preliminary exploration of using reinforcement learning (RL) to enhance the pixel-level understanding and reasoning capabilities of large multimodal models (LMMs). Starting with foreground segmentation tasks, specifically camouflaged object detection (COD) and salient object detection (SOD), our approach enables the LMM to generate point and bounding box prompts in the next-token fashion, which are then used to guide SAM2 in producing segmentation masks. We introduce Group Relative Policy Optimization (GRPO) into the segmentation domain, equipping the LMM with pixel-level comprehension through a carefully designed training strategy. Notably, Seg-R1 achieves remarkable performance with purely RL-based training, achieving .873 S-measure on COD10K without complex model modification. Moreover, we found that pure RL training demonstrates strong open-world generalization. Despite being trained solely on foreground segmentation image-mask pairs without text supervision, Seg-R1 achieves impressive zero-shot performance on referring segmentation and reasoning segmentation tasks, with 71.4 cIoU on RefCOCOg test and 56.7 gIoU on ReasonSeg test, outperforming models fully supervised on these datasets.
Camouflaged object detection (COD) primarily focuses on learning subtle yet discriminative representations from complex scenes. Existing methods predominantly follow the parametric feedforward architecture based on static visual representation modeling. However, they lack explicit mechanisms for acquiring historical context, limiting their adaptation and effectiveness in handling challenging camouflage scenes. In this paper, we propose a recall-augmented COD architecture, namely RetroMem, which dynamically modulates camouflage pattern perception and inference by integrating relevant historical knowledge into the process. Specifically, RetroMem employs a two-stage training paradigm consisting of a learning stage and a recall stage to construct, update, and utilize memory representations effectively. During the learning stage, we design a dense multi-scale adapter (DMA) to improve the pretrained encoder's capability to capture rich multi-scale visual information with very few trainable parameters, thereby providing foundational inferences. In the recall stage, we propose a dynamic memory mechanism (DMM) and an inference pattern reconstruction (IPR). These components fully leverage the latent relationships between learned knowledge and current sample context to reconstruct the inference of camouflage patterns, thereby significantly improving the model's understanding of camouflage scenes. Extensive experiments on several widely used datasets demonstrate that our RetroMem significantly outperforms existing state-of-the-art methods.
Camouflaged Object Detection (COD) presents inherent challenges due to the subtle visual differences between targets and their backgrounds. While existing methods have made notable progress, there remains significant potential for post-processing refinement that has yet to be fully explored. To address this limitation, we propose the Uncertainty-Masked Bernoulli Diffusion (UMBD) model, the first generative refinement framework specifically designed for COD. UMBD introduces an uncertainty-guided masking mechanism that selectively applies Bernoulli diffusion to residual regions with poor segmentation quality, enabling targeted refinement while preserving correctly segmented areas. To support this process, we design the Hybrid Uncertainty Quantification Network (HUQNet), which employs a multi-branch architecture and fuses uncertainty from multiple sources to improve estimation accuracy. This enables adaptive guidance during the generative sampling process. The proposed UMBD framework can be seamlessly integrated with a wide range of existing Encoder-Decoder-based COD models, combining their discriminative capabilities with the generative advantages of diffusion-based refinement. Extensive experiments across multiple COD benchmarks demonstrate consistent performance improvements, achieving average gains of 5.5% in MAE and 3.2% in weighted F-measure with only modest computational overhead. Code will be released.
UCOD-DPL: Unsupervised Camouflaged Object Detection via Dynamic Pseudo-label Learning
2025-06-08
Weiqi Yan, Lvhai Chen, Huaijia Kou, Shengchuan Zhang, Yan Zhang, Liujuan Cao
Unsupervised Camoflaged Object Detection (UCOD) has gained attention since it doesn't need to rely on extensive pixel-level labels. Existing UCOD methods typically generate pseudo-labels using fixed strategies and train 1 x1 convolutional layers as a simple decoder, leading to low performance compared to fully-supervised methods. We emphasize two drawbacks in these approaches: 1). The model is prone to fitting incorrect knowledge due to the pseudo-label containing substantial noise. 2). The simple decoder fails to capture and learn the semantic features of camouflaged objects, especially for small-sized objects, due to the low-resolution pseudo-labels and severe confusion between foreground and background pixels. To this end, we propose a UCOD method with a teacher-student framework via Dynamic Pseudo-label Learning called UCOD-DPL, which contains an Adaptive Pseudo-label Module (APM), a Dual-Branch Adversarial (DBA) decoder, and a Look-Twice mechanism. The APM module adaptively combines pseudo-labels generated by fixed strategies and the teacher model to prevent the model from overfitting incorrect knowledge while preserving the ability for self-correction; the DBA decoder takes adversarial learning of different segmentation objectives, guides the model to overcome the foreground-background confusion of camouflaged objects, and the Look-Twice mechanism mimics the human tendency to zoom in on camouflaged objects and performs secondary refinement on small-sized objects. Extensive experiments show that our method demonstrates outstanding performance, even surpassing some existing fully supervised methods. The code is available now.
The core challenge in Camouflage Object Detection (COD) lies in the indistinguishable similarity between targets and backgrounds in terms of color, texture, and shape. This causes existing methods to either lose edge details (such as hair-like fine structures) due to over-reliance on global semantic information or be disturbed by similar backgrounds (such as vegetation patterns) when relying solely on local features. We propose DRRNet, a four-stage architecture characterized by a "context-detail-fusion-refinement" pipeline to address these issues. Specifically, we introduce an Omni-Context Feature Extraction Module to capture global camouflage patterns and a Local Detail Extraction Module to supplement microstructural information for the full-scene context module. We then design a module for forming dual representations of scene understanding and structural awareness, which fuses panoramic features and local features across various scales. In the decoder, we also introduce a reverse refinement module that leverages spatial edge priors and frequency-domain noise suppression to perform a two-stage inverse refinement of the output. By applying two successive rounds of inverse refinement, the model effectively suppresses background interference and enhances the continuity of object boundaries. Experimental results demonstrate that DRRNet significantly outperforms state-of-the-art methods on benchmark datasets. Our code is available at https://github.com/jerrySunning/DRRNet.
Promoting SAM for Camouflaged Object Detection via Selective Key Point-based Guidance
2025-05-14
Guoying Liang, Su Yang
Big model has emerged as a new research paradigm that can be applied to various down-stream tasks with only minor effort for domain adaption. Correspondingly, this study tackles Camouflaged Object Detection (COD) leveraging the Segment Anything Model (SAM). The previous studies declared that SAM is not workable for COD but this study reveals that SAM works if promoted properly, for which we devise a new framework to render point promotions: First, we develop the Promotion Point Targeting Network (PPT-net) to leverage multi-scale features in predicting the probabilities of camouflaged objects' presences at given candidate points over the image. Then, we develop a key point selection (KPS) algorithm to deploy both positive and negative point promotions contrastively to SAM to guide the segmentation. It is the first work to facilitate big model for COD and achieves plausible results experimentally over the existing methods on 3 data sets under 6 metrics. This study demonstrates an off-the-shelf methodology for COD by leveraging SAM, which gains advantage over designing professional models from scratch, not only in performance, but also in turning the problem to a less challenging task, that is, seeking informative but not exactly precise promotions.
CamoSAM2: Motion-Appearance Induced Auto-Refining Prompts for Video Camouflaged Object Detection
2025-04-01
Xin Zhang, Keren Fu, Qijun Zhao
The Segment Anything Model 2 (SAM2), a prompt-guided video foundation model, has remarkably performed in video object segmentation, drawing significant attention in the community. Due to the high similarity between camouflaged objects and their surroundings, which makes them difficult to distinguish even by the human eye, the application of SAM2 for automated segmentation in real-world scenarios faces challenges in camouflage perception and reliable prompts generation. To address these issues, we propose CamoSAM2, a motion-appearance prompt inducer (MAPI) and refinement framework to automatically generate and refine prompts for SAM2, enabling high-quality automatic detection and segmentation in VCOD task. Initially, we introduce a prompt inducer that simultaneously integrates motion and appearance cues to detect camouflaged objects, delivering more accurate initial predictions than existing methods. Subsequently, we propose a video-based adaptive multi-prompts refinement (AMPR) strategy tailored for SAM2, aimed at mitigating prompt error in initial coarse masks and further producing good prompts. Specifically, we introduce a novel three-step process to generate reliable prompts by camouflaged object determination, pivotal prompting frame selection, and multi-prompts formation. Extensive experiments conducted on two benchmark datasets demonstrate that our proposed model, CamoSAM2, significantly outperforms existing state-of-the-art methods, achieving increases of 8.0% and 10.1% in mIoU metric. Additionally, our method achieves the fastest inference speed compared to current VCOD models.
Context in object detection: a systematic literature review
2025-03-29
Mahtab Jamali, Paul Davidsson, Reza Khoshkangini, Martin Georg Ljungqvist, Radu-Casian Mihailescu
Context is an important factor in computer vision as it offers valuable information to clarify and analyze visual data. Utilizing the contextual information inherent in an image or a video can improve the precision and effectiveness of object detectors. For example, where recognizing an isolated object might be challenging, context information can improve comprehension of the scene. This study explores the impact of various context-based approaches to object detection. Initially, we investigate the role of context in object detection and survey it from several perspectives. We then review and discuss the most recent context-based object detection approaches and compare them. Finally, we conclude by addressing research questions and identifying gaps for further studies. More than 265 publications are included in this survey, covering different aspects of context in different categories of object detection, including general object detection, video object detection, small object detection, camouflaged object detection, zero-shot, one-shot, and few-shot object detection. This literature review presents a comprehensive overview of the latest advancements in context-based object detection, providing valuable contributions such as a thorough understanding of contextual information and effective methods for integrating various context types into object detection, thus benefiting researchers.
Knowledge Rectification for Camouflaged Object Detection: Unlocking Insights from Low-Quality Data
2025-03-28
Juwei Guan, Xiaolin Fang, Donghyun Kim, Haotian Gong, Tongxin Zhu, Zhen Ling, Ming Yang
Low-quality data often suffer from insufficient image details, introducing an extra implicit aspect of camouflage that complicates camouflaged object detection (COD). Existing COD methods focus primarily on high-quality data, overlooking the challenges posed by low-quality data, which leads to significant performance degradation. Therefore, we propose KRNet, the first framework explicitly designed for COD on low-quality data. KRNet presents a Leader-Follower framework where the Leader extracts dual gold-standard distributions: conditional and hybrid, from high-quality data to drive the Follower in rectifying knowledge learned from low-quality data. The framework further benefits from a cross-consistency strategy that improves the rectification of these distributions and a time-dependent conditional encoder that enriches the distribution diversity. Extensive experiments on benchmark datasets demonstrate that KRNet outperforms state-of-the-art COD methods and super-resolution-assisted COD approaches, proving its effectiveness in tackling the challenges of low-quality data in COD.
Video Camouflaged Object Detection (VCOD) aims to segment objects whose appearances closely resemble their surroundings, posing a challenging and emerging task. Existing vision models often struggle in such scenarios due to the indistinguishable appearance of camouflaged objects and the insufficient exploitation of dynamic information in videos. To address these challenges, we propose an end-to-end VCOD framework inspired by human memory-recognition, which leverages historical video information by integrating memory reference frames for camouflaged sequence processing. Specifically, we design a dual-purpose decoder that simultaneously generates predicted masks and scores, enabling reference frame selection based on scores while introducing auxiliary supervision to enhance feature extraction.Furthermore, this study introduces a novel reference-guided multilevel asymmetric attention mechanism, effectively integrating long-term reference information with short-term motion cues for comprehensive feature extraction. By combining these modules, we develop the Scoring, Remember, and Reference (SRR) framework, which efficiently extracts information to locate targets and employs memory guidance to improve subsequent processing. With its optimized module design and effective utilization of video data, our model achieves significant performance improvements, surpassing existing approaches by 10% on benchmark datasets while requiring fewer parameters (54M) and only a single pass through the video. The code will be made publicly available.
A Revisit to the Decoder for Camouflaged Object Detection
2025-03-18
Seung Woo Ko, Joopyo Hong, Suyoung Kim, Seungjai Bang, Sungzoon Cho, Nojun Kwak, Hyung-Sin Kim, Joonseok Lee
Camouflaged object detection (COD) aims to generate a fine-grained segmentation map of camouflaged objects hidden in their background. Due to the hidden nature of camouflaged objects, it is essential for the decoder to be tailored to effectively extract proper features of camouflaged objects and extra-carefully generate their complex boundaries. In this paper, we propose a novel architecture that augments the prevalent decoding strategy in COD with Enrich Decoder and Retouch Decoder, which help to generate a fine-grained segmentation map. Specifically, the Enrich Decoder amplifies the channels of features that are important for COD using channel-wise attention. Retouch Decoder further refines the segmentation maps by spatially attending to important pixels, such as the boundary regions. With extensive experiments, we demonstrate that ENTO shows superior performance using various encoders, with the two novel components playing their unique roles that are mutually complementary.
Camouflaged Object Detection (COD) is challenging due to the strong similarity between camouflaged objects and their surroundings, which complicates identification. Existing methods mainly rely on spatial local features, failing to capture global information, while Transformers increase computational costs. To address this, the Frequency-Assisted Mamba-Like Linear Attention Network (FMNet) is proposed, which leverages frequency-domain learning to efficiently capture global features and mitigate ambiguity between objects and the background. FMNet introduces the Multi-Scale Frequency-Assisted Mamba-Like Linear Attention (MFM) module, integrating frequency and spatial features through a multi-scale structure to handle scale variations while reducing computational complexity. Additionally, the Pyramidal Frequency Attention Extraction (PFAE) module and the Frequency Reverse Decoder (FRD) enhance semantics and reconstruct features. Experimental results demonstrate that FMNet outperforms existing methods on multiple COD datasets, showcasing its advantages in both performance and efficiency. Code available at https://github.com/Chranos/FMNet.
Improving SAM for Camouflaged Object Detection via Dual Stream Adapters
2025-03-08
Jiaming Liu, Linghe Kong, Guihai Chen
Segment anything model (SAM) has shown impressive general-purpose segmentation performance on natural images, but its performance on camouflaged object detection (COD) is unsatisfactory. In this paper, we propose SAM-COD that performs camouflaged object detection for RGB-D inputs. While keeping the SAM architecture intact, dual stream adapters are expanded on the image encoder to learn potential complementary information from RGB images and depth images, and fine-tune the mask decoder and its depth replica to perform dual-stream mask prediction. In practice, the dual stream adapters are embedded into the attention block of the image encoder in a parallel manner to facilitate the refinement and correction of the two types of image embeddings. To mitigate channel discrepancies arising from dual stream embeddings that do not directly interact with each other, we augment the association of dual stream embeddings using bidirectional knowledge distillation including a model distiller and a modal distiller. In addition, to predict the masks for RGB and depth attention maps, we hybridize the two types of image embeddings which are jointly learned with the prompt embeddings to update the initial prompt, and then feed them into the mask decoders to synchronize the consistency of image embeddings and prompt embeddings. Experimental results on four COD benchmarks show that our SAM-COD achieves excellent detection performance gains over SAM and achieves state-of-the-art results with a given fine-tuning paradigm.
MSVCOD:A Large-Scale Multi-Scene Dataset for Video Camouflage Object Detection
2025-02-19
Shuyong Gao, Yu'ang Feng, Qishan Wang, Lingyi Hong, Xinyu Zhou, Liu Fei, Yan Wang, Wenqiang Zhang
Video Camouflaged Object Detection (VCOD) is a challenging task which aims to identify objects that seamlessly concealed within the background in videos. The dynamic properties of video enable detection of camouflaged objects through motion cues or varied perspectives. Previous VCOD datasets primarily contain animal objects, limiting the scope of research to wildlife scenarios. However, the applications of VCOD extend beyond wildlife and have significant implications in security, art, and medical fields. Addressing this problem, we construct a new large-scale multi-domain VCOD dataset MSVCOD. To achieve high-quality annotations, we design a semi-automatic iterative annotation pipeline that reduces costs while maintaining annotation accuracy. Our MSVCOD is the largest VCOD dataset to date, introducing multiple object categories including human, animal, medical, and vehicle objects for the first time, while also expanding background diversity across various environments. This expanded scope increases the practical applicability of the VCOD task in camouflaged object detection. Alongside this dataset, we introduce a one-steam video camouflage object detection model that performs both feature extraction and information fusion without additional motion feature fusion modules. Our framework achieves state-of-the-art results on the existing VCOD animal dataset and the proposed MSVCOD. The dataset and code will be made publicly available.
Uncertainty Aware Human-machine Collaboration in Camouflaged Object Detection
2025-02-12
Ziyue Yang, Kehan Wang, Yuhang Ming, Yong Peng, Han Yang, Qiong Chen, Wanzeng Kong
Camouflaged Object Detection (COD), the task of identifying objects concealed within their environments, has seen rapid growth due to its wide range of practical applications. A key step toward developing trustworthy COD systems is the estimation and effective utilization of uncertainty. In this work, we propose a human-machine collaboration framework for classifying the presence of camouflaged objects, leveraging the complementary strengths of computer vision (CV) models and noninvasive brain-computer interfaces (BCIs). Our approach introduces a multiview backbone to estimate uncertainty in CV model predictions, utilizes this uncertainty during training to improve efficiency, and defers low-confidence cases to human evaluation via RSVP-based BCIs during testing for more reliable decision-making. We evaluated the framework in the CAMO dataset, achieving state-of-the-art results with an average improvement of 4.56\% in balanced accuracy (BA) and 3.66\% in the F1 score compared to existing methods. For the best-performing participants, the improvements reached 7.6\% in BA and 6.66\% in the F1 score. Analysis of the training process revealed a strong correlation between our confidence measures and precision, while an ablation study confirmed the effectiveness of the proposed training policy and the human-machine collaboration strategy. In general, this work reduces human cognitive load, improves system reliability, and provides a strong foundation for advancements in real-world COD applications and human-computer interaction. Our code and data are available at: https://github.com/ziyuey/Uncertainty-aware-human-machine-collaboration-in-camouflaged-object-identification.
Sketches, with their expressive potential, allow humans to convey the essence of an object through even a rough contour. For the first time, we harness this expressive potential to improve segmentation performance in challenging tasks like camouflaged object detection (COD). Our approach introduces an innovative sketch-guided interactive segmentation framework, allowing users to intuitively annotate objects with freehand sketches (drawing a rough contour of the object) instead of the traditional bounding boxes or points used in classic interactive segmentation models like SAM. We demonstrate that sketch input can significantly improve performance in existing iterative segmentation methods, outperforming text or bounding box annotations. Additionally, we introduce key modifications to network architectures and a novel sketch augmentation technique to fully harness the power of sketch input and further boost segmentation accuracy. Remarkably, our model' s output can be directly used to train other neural networks, achieving results comparable to pixel-by-pixel annotations--while reducing annotation time by up to 120 times, which shows great potential in democratizing the annotation process and enabling model training with less reliance on resource-intensive, laborious pixel-level annotations. We also present KOSCamo+, the first freehand sketch dataset for camouflaged object detection. The dataset, code, and the labeling tool will be open sourced.
2025-01-19
Xinyu Wang, Hong-Shuo Chen, Zhiruo Zhou, Suya You, Azad M. Madni, C. -C. Jay Kuo
Camouflaged object detection (COD) aims to distinguish hidden objects embedded in an environment highly similar to the object. Conventional video-based COD (VCOD) methods explicitly extract motion cues or employ complex deep learning networks to handle the temporal information, which is limited by high complexity and unstable performance. In this work, we propose a green VCOD method named GreenVCOD. Built upon a green ICOD method, GreenVCOD uses long- and short-term temporal neighborhoods (TN) to capture joint spatial/temporal context information for decision refinement. Experimental results show that GreenVCOD offers competitive performance compared to state-of-the-art VCOD benchmarks.
Camouflaged object detection (COD) primarily relies on semantic or instance segmentation methods. While these methods have made significant advancements in identifying the contours of camouflaged objects, they may be inefficient or cost-effective for tasks that only require the specific location of the object. Object detection algorithms offer an optimized solution for Realistic Camouflaged Object Detection (RCOD) in such cases. However, detecting camouflaged objects remains a formidable challenge due to the high degree of similarity between the features of the objects and their backgrounds. Unlike segmentation methods that perform pixel-wise comparisons to differentiate between foreground and background, object detectors omit this analysis, further aggravating the challenge. To solve this problem, we propose a camouflage-aware feature refinement (CAFR) strategy. Since camouflaged objects are not rare categories, CAFR fully utilizes a clear perception of the current object within the prior knowledge of large models to assist detectors in deeply understanding the distinctions between background and foreground. Specifically, in CAFR, we introduce the Adaptive Gradient Propagation (AGP) module that fine-tunes all feature extractor layers in large detection models to fully refine class-specific features from camouflaged contexts. We then design the Sparse Feature Refinement (SFR) module that optimizes the transformer-based feature extractor to focus primarily on capturing class-specific features in camouflaged scenarios. To facilitate the assessment of RCOD tasks, we manually annotate the labels required for detection on three existing segmentation COD datasets, creating a new benchmark for RCOD tasks. Code and datasets are available at: https://github.com/zhimengXin/RCOD.
Weakly-Supervised Camouflaged Object Detection (WSCOD) has gained popularity for its promise to train models with weak labels to segment objects that visually blend into their surroundings. Recently, some methods using sparsely-annotated supervision shown promising results through scribbling in WSCOD, while point-text supervision remains underexplored. Hence, this paper introduces a novel holistically point-guided text framework for WSCOD by decomposing into three phases: segment, choose, train. Specifically, we propose Point-guided Candidate Generation (PCG), where the point's foreground serves as a correction for the text path to explicitly correct and rejuvenate the loss detection object during the mask generation process (SEGMENT). We also introduce a Qualified Candidate Discriminator (QCD) to choose the optimal mask from a given text prompt using CLIP (CHOOSE), and employ the chosen pseudo mask for training with a self-supervised Vision Transformer (TRAIN). Additionally, we developed a new point-supervised dataset (P2C-COD) and a text-supervised dataset (T-COD). Comprehensive experiments on four benchmark datasets demonstrate our method outperforms state-of-the-art methods by a large margin, and also outperforms some existing fully-supervised camouflaged object detection methods.
B2Net: Camouflaged Object Detection via Boundary Aware and Boundary Fusion
2024-12-31
Junmin Cai, Han Sun, Ningzhong Liu
Camouflaged object detection (COD) aims to identify objects in images that are well hidden in the environment due to their high similarity to the background in terms of texture and color. However, existing most boundary-guided camouflage object detection algorithms tend to generate object boundaries early in the network, and inaccurate edge priors often introduce noises in object detection. Address on this issue, we propose a novel network named B2Net aiming to enhance the accuracy of obtained boundaries by reusing boundary-aware modules at different stages of the network. Specifically, we present a Residual Feature Enhanced Module (RFEM) with the goal of integrating more discriminative feature representations to enhance detection accuracy and reliability. After that, the Boundary Aware Module (BAM) is introduced to explore edge cues twice by integrating spatial information from low-level features and semantic information from high-level features. Finally, we design the Cross-scale Boundary Fusion Module(CBFM) that integrate information across different scales in a top-down manner, merging boundary features with object features to obtain a comprehensive feature representation incorporating boundary information. Extensive experimental results on three challenging benchmark datasets demonstrate that our proposed method B2Net outperforms 15 state-of-art methods under widely used evaluation metrics. Code will be made publicly available.
2024-12-25
Chenxi Zhang, Qing Zhang, Jiayun Wu, Youwei Pang
Camouflaged Object Detection (COD) aims to identify objects that blend seamlessly into their surroundings. The inherent visual complexity of camouflaged objects, including their low contrast with the background, diverse textures, and subtle appearance variations, often obscures semantic cues, making accurate segmentation highly challenging. Existing methods primarily rely on visual features, which are insufficient to handle the variability and intricacy of camouflaged objects, leading to unstable object perception and ambiguous segmentation results. To tackle these limitations, we introduce a novel task, class-guided camouflaged object detection (CGCOD), which extends traditional COD task by incorporating object-specific class knowledge to enhance detection robustness and accuracy. To facilitate this task, we present a new dataset, CamoClass, comprising real-world camouflaged objects with class annotations. Furthermore, we propose a multi-stage framework, CGNet, which incorporates a plug-and-play class prompt generator and a simple yet effective class-guided detector. This establishes a new paradigm for COD, bridging the gap between contextual understanding and class-guided detection. Extensive experimental results demonstrate the effectiveness of our flexible framework in improving the performance of proposed and existing detectors by leveraging class-level textual information.
Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection
2024-12-22
Yi Liu, Chengxin Li, Xiaohui Dong, Lei Li, Dingwen Zhang, Shoukun Xu, Jungong Han
Achieving joint learning of Salient Object Detection (SOD) and Camouflaged Object Detection (COD) is extremely challenging due to their distinct object characteristics, i.e., saliency and camouflage. The only preliminary research treats them as two contradictory tasks, training models on large-scale labeled data alternately for each task and assessing them independently. However, such task-specific mechanisms fail to meet real-world demands for addressing unknown tasks effectively. To address this issue, in this paper, we pioneer a task-agnostic framework to unify SOD and COD. To this end, inspired by the agreeable nature of binary segmentation for SOD and COD, we propose a Contrastive Distillation Paradigm (CDP) to distil the foreground from the background, facilitating the identification of salient and camouflaged objects amidst their surroundings. To probe into the contribution of our CDP, we design a simple yet effective contextual decoder involving the interval-layer and global context, which achieves an inference speed of 67 fps. Besides the supervised setting, our CDP can be seamlessly integrated into unsupervised settings, eliminating the reliance on extensive human annotations. Experiments on public SOD and COD datasets demonstrate the superiority of our proposed framework in both supervised and unsupervised settings, compared with existing state-of-the-art approaches. Code is available on https://github.com/liuyi1989/Seamless-Detection.
While the human visual system employs distinct mechanisms to perceive salient and camouflaged objects, existing models struggle to disentangle these tasks. Specifically, salient object detection (SOD) models frequently misclassify camouflaged objects as salient, while camouflaged object detection (COD) models conversely misinterpret salient objects as camouflaged. We hypothesize that this can be attributed to two factors: (i) the specific annotation paradigm of current SOD and COD datasets, and (ii) the lack of explicit attribute relationship modeling in current models. Prevalent SOD/COD datasets enforce a mutual exclusivity constraint, assuming scenes contain either salient or camouflaged objects, which poorly aligns with the real world. Furthermore, current SOD/COD methods are primarily designed for these highly constrained datasets and lack explicit modeling of the relationship between salient and camouflaged objects. In this paper, to promote the development of unconstrained salient and camouflaged object detection, we construct a large-scale dataset, USC12K, which features comprehensive labels and four different scenes that cover all possible logical existence scenarios of both salient and camouflaged objects. To explicitly model the relationship between salient and camouflaged objects, we propose a model called USCNet, which introduces two distinct prompt query mechanisms for modeling inter-sample and intra-sample attribute relationships. Additionally, to assess the model's ability to distinguish between salient and camouflaged objects, we design an evaluation metric called CSCS. The proposed method achieves state-of-the-art performance across all scenes in various metrics. The code and dataset will be available at https://github.com/ssecv/USCNet.
We rethink the segment anything model (SAM) and propose a novel multiprompt network called COMPrompter for camouflaged object detection (COD). SAM has zero-shot generalization ability beyond other models and can provide an ideal framework for COD. Our network aims to enhance the single prompt strategy in SAM to a multiprompt strategy. To achieve this, we propose an edge gradient extraction module, which generates a mask containing gradient information regarding the boundaries of camouflaged objects. This gradient mask is then used as a novel boundary prompt, enhancing the segmentation process. Thereafter, we design a box-boundary mutual guidance module, which fosters more precise and comprehensive feature extraction via mutual guidance between a boundary prompt and a box prompt. This collaboration enhances the model's ability to accurately detect camouflaged objects. Moreover, we employ the discrete wavelet transform to extract high-frequency features from image embeddings. The high-frequency features serve as a supplementary component to the multiprompt system. Finally, our COMPrompter guides the network to achieve enhanced segmentation results, thereby advancing the development of SAM in terms of COD. Experimental results across COD benchmarks demonstrate that COMPrompter achieves a cutting-edge performance, surpassing the current leading model by an average positive metric of 2.2% in COD10K. In the specific application of COD, the experimental results in polyp segmentation show that our model is superior to top-tier methods as well. The code will be made available at https://github.com/guobaoxiao/COMPrompter.
Camouflaged Object Detection (COD) aims to detect objects with camouflaged properties. Although previous studies have focused on natural (animals and insects) and unnatural (artistic and synthetic) camouflage detection, plant camouflage has been neglected. However, plant camouflage plays a vital role in natural camouflage. Therefore, this paper introduces a new challenging problem of Plant Camouflage Detection (PCD). To address this problem, we introduce the PlantCamo dataset, which comprises 1,250 images with camouflaged plants representing 58 object categories in various natural scenes. To investigate the current status of plant camouflage detection, we conduct a large-scale benchmark study using 20+ cutting-edge COD models on the proposed dataset. Due to the unique characteristics of plant camouflage, including holes and irregular borders, we developed a new framework, named PCNet, dedicated to PCD. Our PCNet surpasses performance thanks to its multi-scale global feature enhancement and refinement. Finally, we discuss the potential applications and insights, hoping this work fills the gap in fine-grained COD research and facilitates further intelligent ecology research. All resources will be available on https://github.com/yjybuaa/PlantCamo.
Mamba Capsule Routing Towards Part-Whole Relational Camouflaged Object Detection
2024-10-05
Dingwen Zhang, Liangbo Cheng, Yi Liu, Xinggang Wang, Junwei Han
The part-whole relational property endowed by Capsule Networks (CapsNets) has been known successful for camouflaged object detection due to its segmentation integrity. However, the previous Expectation Maximization (EM) capsule routing algorithm with heavy computation and large parameters obstructs this trend. The primary attribution behind lies in the pixel-level capsule routing. Alternatively, in this paper, we propose a novel mamba capsule routing at the type level. Specifically, we first extract the implicit latent state in mamba as capsule vectors, which abstract type-level capsules from pixel-level versions. These type-level mamba capsules are fed into the EM routing algorithm to get the high-layer mamba capsules, which greatly reduce the computation and parameters caused by the pixel-level capsule routing for part-whole relationships exploration. On top of that, to retrieve the pixel-level capsule features for further camouflaged prediction, we achieve this on the basis of the low-layer pixel-level capsules with the guidance of the correlations from adjacent-layer type-level mamba capsules. Extensive experiments on three widely used COD benchmark datasets demonstrate that our method significantly outperforms state-of-the-arts. Code has been available on https://github.com/Liangbo-Cheng/mamba\_capsule.
Camouflaged object detection (COD) aims to segment camouflaged objects which exhibit very similar patterns with the surrounding environment. Recent research works have shown that enhancing the feature representation via the frequency information can greatly alleviate the ambiguity problem between the foreground objects and the background.With the emergence of vision foundation models, like InternImage, Segment Anything Model etc, adapting the pretrained model on COD tasks with a lightweight adapter module shows a novel and promising research direction. Existing adapter modules mainly care about the feature adaptation in the spatial domain. In this paper, we propose a novel frequency-guided spatial adaptation method for COD task. Specifically, we transform the input features of the adapter into frequency domain. By grouping and interacting with frequency components located within non overlapping circles in the spectrogram, different frequency components are dynamically enhanced or weakened, making the intensity of image details and contour features adaptively adjusted. At the same time, the features that are conducive to distinguishing object and background are highlighted, indirectly implying the position and shape of camouflaged object. We conduct extensive experiments on four widely adopted benchmark datasets and the proposed method outperforms 26 state-of-the-art methods with large margins. Code will be released.
GLCONet: Learning Multi-source Perception Representation for Camouflaged Object Detection
2024-09-15
Yanguang Sun, Hanyu Xuan, Jian Yang, Lei Luo
Recently, biological perception has been a powerful tool for handling the camouflaged object detection (COD) task. However, most existing methods are heavily dependent on the local spatial information of diverse scales from convolutional operations to optimize initial features. A commonly neglected point in these methods is the long-range dependencies between feature pixels from different scale spaces that can help the model build a global structure of the object, inducing a more precise image representation. In this paper, we propose a novel Global-Local Collaborative Optimization Network, called GLCONet. Technically, we first design a collaborative optimization strategy from the perspective of multi-source perception to simultaneously model the local details and global long-range relationships, which can provide features with abundant discriminative information to boost the accuracy in detecting camouflaged objects. Furthermore, we introduce an adjacent reverse decoder that contains cross-layer aggregation and reverse optimization to integrate complementary information from different levels for generating high-quality representations. Extensive experiments demonstrate that the proposed GLCONet method with different backbones can effectively activate potentially significant pixels in an image, outperforming twenty state-of-the-art methods on three public COD datasets. The source code is available at: \https://github.com/CSYSI/GLCONet.
Frequency-Spatial Entanglement Learning for Camouflaged Object Detection
2024-09-03
Yanguang Sun, Chunyan Xu, Jian Yang, Hanyu Xuan, Lei Luo
Camouflaged object detection has attracted a lot of attention in computer vision. The main challenge lies in the high degree of similarity between camouflaged objects and their surroundings in the spatial domain, making identification difficult. Existing methods attempt to reduce the impact of pixel similarity by maximizing the distinguishing ability of spatial features with complicated design, but often ignore the sensitivity and locality of features in the spatial domain, leading to sub-optimal results. In this paper, we propose a new approach to address this issue by jointly exploring the representation in the frequency and spatial domains, introducing the Frequency-Spatial Entanglement Learning (FSEL) method. This method consists of a series of well-designed Entanglement Transformer Blocks (ETB) for representation learning, a Joint Domain Perception Module for semantic enhancement, and a Dual-domain Reverse Parser for feature integration in the frequency and spatial domains. Specifically, the ETB utilizes frequency self-attention to effectively characterize the relationship between different frequency bands, while the entanglement feed-forward network facilitates information interaction between features of different domains through entanglement learning. Our extensive experiments demonstrate the superiority of our FSEL over 21 state-of-the-art methods, through comprehensive quantitative and qualitative comparisons in three widely-used datasets. The source code is available at: https://github.com/CSYSI/FSEL.
Camouflaged object detection (COD) aims to identify the objects that seamlessly blend into the surrounding backgrounds. Due to the intrinsic similarity between the camouflaged objects and the background region, it is extremely challenging to precisely distinguish the camouflaged objects by existing approaches. In this paper, we propose a hierarchical graph interaction network termed HGINet for camouflaged object detection, which is capable of discovering imperceptible objects via effective graph interaction among the hierarchical tokenized features. Specifically, we first design a region-aware token focusing attention (RTFA) with dynamic token clustering to excavate the potentially distinguishable tokens in the local region. Afterwards, a hierarchical graph interaction transformer (HGIT) is proposed to construct bi-directional aligned communication between hierarchical features in the latent interaction space for visual semantics enhancement. Furthermore, we propose a decoder network with confidence aggregated feature fusion (CAFF) modules, which progressively fuses the hierarchical interacted features to refine the local detail in ambiguous regions. Extensive experiments conducted on the prevalent datasets, i.e. COD10K, CAMO, NC4K and CHAMELEON demonstrate the superior performance of HGINet compared to existing state-of-the-art methods. Our code is available at https://github.com/Garyson1204/HGINet.
Camouflaged Object Detection (COD) refers to the task of identifying and segmenting objects that blend seamlessly into their surroundings, posing a significant challenge for computer vision systems. In recent years, COD has garnered widespread attention due to its potential applications in surveillance, wildlife conservation, autonomous systems, and more. While several surveys on COD exist, they often have limitations in terms of the number and scope of papers covered, particularly regarding the rapid advancements made in the field since mid-2023. To address this void, we present the most comprehensive review of COD to date, encompassing both theoretical frameworks and practical contributions to the field. This paper explores various COD methods across four domains, including both image-level and video-level solutions, from the perspectives of traditional and deep learning approaches. We thoroughly investigate the correlations between COD and other camouflaged scenario methods, thereby laying the theoretical foundation for subsequent analyses. Beyond object-level detection, we also summarize extended methods for instance-level tasks, including camouflaged instance segmentation, counting, and ranking. Additionally, we provide an overview of commonly used benchmarks and evaluation metrics in COD tasks, conducting a comprehensive evaluation of deep learning-based techniques in both image and video domains, considering both qualitative and quantitative performance. Finally, we discuss the limitations of current COD models and propose 9 promising directions for future research, focusing on addressing inherent challenges and exploring novel, meaningful technologies. For those interested, a curated list of COD-related techniques, datasets, and additional resources can be found at https://github.com/ChunmingHe/awesome-concealed-object-segmentation
Camouflaged Object Detection (COD) demands models to expeditiously and accurately distinguish objects which conceal themselves seamlessly in the environment. Owing to the subtle differences and ambiguous boundaries, COD is not only a remarkably challenging task for models but also for human annotators, requiring huge efforts to provide pixel-wise annotations. To alleviate the heavy annotation burden, we propose to fulfill this task with the help of only one point supervision. Specifically, by swiftly clicking on each object, we first adaptively expand the original point-based annotation to a reasonable hint area. Then, to avoid partial localization around discriminative parts, we propose an attention regulator to scatter model attention to the whole object through partially masking labeled regions. Moreover, to solve the unstable feature representation of camouflaged objects under only point-based annotation, we perform unsupervised contrastive learning based on differently augmented image pairs (e.g. changing color or doing translation). On three mainstream COD benchmarks, experimental results show that our model outperforms several weakly-supervised methods by a large margin across various metrics.
Most Camouflaged Object Detection (COD) methods heavily rely on mask annotations, which are time-consuming and labor-intensive to acquire. Existing weakly-supervised COD approaches exhibit significantly inferior performance compared to fully-supervised methods and struggle to simultaneously support all the existing types of camouflaged object labels, including scribbles, bounding boxes, and points. Even for Segment Anything Model (SAM), it is still problematic to handle the weakly-supervised COD and it typically encounters challenges of prompt compatibility of the scribble labels, extreme response, semantically erroneous response, and unstable feature representations, producing unsatisfactory results in camouflaged scenes. To mitigate these issues, we propose a unified COD framework in this paper, termed SAM-COD, which is capable of supporting arbitrary weakly-supervised labels. Our SAM-COD employs a prompt adapter to handle scribbles as prompts based on SAM. Meanwhile, we introduce response filter and semantic matcher modules to improve the quality of the masks obtained by SAM under COD prompts. To alleviate the negative impacts of inaccurate mask predictions, a new strategy of prompt-adaptive knowledge distillation is utilized to ensure a reliable feature representation. To validate the effectiveness of our approach, we have conducted extensive empirical experiments on three mainstream COD benchmarks. The results demonstrate the superiority of our method against state-of-the-art weakly-supervised and even fully-supervised methods.
Depth information provides valuable insights into the 3D structure especially the outline of objects, which can be utilized to improve the semantic segmentation tasks. However, a naive fusion of depth information can disrupt feature and compromise accuracy due to the modality gap between the depth and the vision. In this work, we introduce a Depth-guided Texture Diffusion approach that effectively tackles the outlined challenge. Our method extracts low-level features from edges and textures to create a texture image. This image is then selectively diffused across the depth map, enhancing structural information vital for precisely extracting object outlines. By integrating this enriched depth map with the original RGB image into a joint feature embedding, our method effectively bridges the disparity between the depth map and the image, enabling more accurate semantic segmentation. We conduct comprehensive experiments across diverse, commonly-used datasets spanning a wide range of semantic segmentation tasks, including Camouflaged Object Detection (COD), Salient Object Detection (SOD), and indoor semantic segmentation. With source-free estimated depth or depth captured by depth cameras, our method consistently outperforms existing baselines and achieves new state-of-theart results, demonstrating the effectiveness of our Depth-guided Texture Diffusion for image semantic segmentation.
SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation
2024-08-16
Xinyu Xiong, Zihuang Wu, Shuangyi Tan, Wenxue Li, Feilong Tang, Ying Chen, Siying Li, Jie Ma, Guanbin Li
Image segmentation plays an important role in vision understanding. Recently, the emerging vision foundation models continuously achieved superior performance on various tasks. Following such success, in this paper, we prove that the Segment Anything Model 2 (SAM2) can be a strong encoder for U-shaped segmentation models. We propose a simple but effective framework, termed SAM2-UNet, for versatile image segmentation. Specifically, SAM2-UNet adopts the Hiera backbone of SAM2 as the encoder, while the decoder uses the classic U-shaped design. Additionally, adapters are inserted into the encoder to allow parameter-efficient fine-tuning. Preliminary experiments on various downstream tasks, such as camouflaged object detection, salient object detection, marine animal segmentation, mirror detection, and polyp segmentation, demonstrate that our SAM2-UNet can simply beat existing specialized state-of-the-art methods without bells and whistles. Project page: \url{https://github.com/WZH0120/SAM2-UNet}.
CamoTeacher: Dual-Rotation Consistency Learning for Semi-Supervised Camouflaged Object Detection
2024-08-15
Xunfa Lai, Zhiyu Yang, Jie Hu, Shengchuan Zhang, Liujuan Cao, Guannan Jiang, Zhiyu Wang, Songan Zhang, Rongrong Ji
Existing camouflaged object detection~(COD) methods depend heavily on large-scale pixel-level annotations.However, acquiring such annotations is laborious due to the inherent camouflage characteristics of the objects.Semi-supervised learning offers a promising solution to this challenge.Yet, its application in COD is hindered by significant pseudo-label noise, both pixel-level and instance-level.We introduce CamoTeacher, a novel semi-supervised COD framework, utilizing Dual-Rotation Consistency Learning~(DRCL) to effectively address these noise issues.Specifically, DRCL minimizes pseudo-label noise by leveraging rotation views' consistency in pixel-level and instance-level.First, it employs Pixel-wise Consistency Learning~(PCL) to deal with pixel-level noise by reweighting the different parts within the pseudo-label.Second, Instance-wise Consistency Learning~(ICL) is used to adjust weights for pseudo-labels, which handles instance-level noise.Extensive experiments on four COD benchmark datasets demonstrate that the proposed CamoTeacher not only achieves state-of-the-art compared with semi-supervised learning methods, but also rivals established fully-supervised learning methods.Our code will be available soon.
Multi-scale Contrastive Adaptor Learning for Segmenting Anything in Underperformed Scenes
2024-08-12
Ke Zhou, Zhongwei Qiu, Dongmei Fu
Foundational vision models, such as the Segment Anything Model (SAM), have achieved significant breakthroughs through extensive pre-training on large-scale visual datasets. Despite their general success, these models may fall short in specialized tasks with limited data, and fine-tuning such large-scale models is often not feasible. Current strategies involve incorporating adaptors into the pre-trained SAM to facilitate downstream task performance with minimal model adjustment. However, these strategies can be hampered by suboptimal learning approaches for the adaptors. In this paper, we introduce a novel Multi-scale Contrastive Adaptor learning method named MCA-SAM, which enhances adaptor performance through a meticulously designed contrastive learning framework at both token and sample levels. Our Token-level Contrastive adaptor (TC-adaptor) focuses on refining local representations by improving the discriminability of patch tokens, while the Sample-level Contrastive adaptor (SC-adaptor) amplifies global understanding across different samples. Together, these adaptors synergistically enhance feature comparison within and across samples, bolstering the model's representational strength and its ability to adapt to new tasks. Empirical results demonstrate that MCA-SAM sets new benchmarks, outperforming existing methods in three challenging domains: camouflage object detection, shadow segmentation, and polyp segmentation. Specifically, MCA-SAM exhibits substantial relative performance enhancements, achieving a 20.0% improvement in MAE on the COD10K dataset, a 6.0% improvement in MAE on the CAMO dataset, a 15.4% improvement in BER on the ISTD dataset, and a 7.9% improvement in mDice on the Kvasir-SEG dataset.
We present an advanced study on more challenging high-resolution salient object detection (HRSOD) from both dataset and network framework perspectives. To compensate for the lack of HRSOD dataset, we thoughtfully collect a large-scale high resolution salient object detection dataset, called UHRSD, containing 5,920 images from real-world complex scenarios at 4K-8K resolutions. All the images are finely annotated in pixel-level, far exceeding previous low-resolution SOD datasets. Aiming at overcoming the contradiction between the sampling depth and the receptive field size in the past methods, we propose a novel one-stage framework for HR-SOD task using pyramid grafting mechanism. In general, transformer-based and CNN-based backbones are adopted to extract features from different resolution images independently and then these features are grafted from transformer branch to CNN branch. An attention-based Cross-Model Grafting Module (CMGM) is proposed to enable CNN branch to combine broken detailed information more holistically, guided by different source feature during decoding process. Moreover, we design an Attention Guided Loss (AGL) to explicitly supervise the attention matrix generated by CMGM to help the network better interact with the attention from different branches. Comprehensive experiments on UHRSD and widely-used SOD datasets demonstrate that our method can simultaneously locate salient object and preserve rich details, outperforming state-of-the-art methods. To verify the generalization ability of the proposed framework, we apply it to the camouflaged object detection (COD) task. Notably, our method performs superior to most state-of-the-art COD methods without bells and whistles.
Evaluating SAM2's Role in Camouflaged Object Detection: From SAM to SAM2
2024-07-31
Lv Tang, Bo Li
The Segment Anything Model (SAM), introduced by Meta AI Research as a generic object segmentation model, quickly garnered widespread attention and significantly influenced the academic community. To extend its application to video, Meta further develops Segment Anything Model 2 (SAM2), a unified model capable of both video and image segmentation. SAM2 shows notable improvements over its predecessor in terms of applicable domains, promptable segmentation accuracy, and running speed. However, this report reveals a decline in SAM2's ability to perceive different objects in images without prompts in its auto mode, compared to SAM. Specifically, we employ the challenging task of camouflaged object detection to assess this performance decrease, hoping to inspire further exploration of the SAM model family by researchers. The results of this paper are provided in \url{https://github.com/luckybird1994/SAMCOD}.
Existing Camouflaged Object Detection (COD) methods rely heavily on large-scale pixel-annotated training sets, which are both time-consuming and labor-intensive. Although weakly supervised methods offer higher annotation efficiency, their performance is far behind due to the unclear visual demarcations between foreground and background in camouflaged images. In this paper, we explore the potential of using boxes as prompts in camouflaged scenes and introduce the first weakly semi-supervised COD method, aiming for budget-efficient and high-precision camouflaged object segmentation with an extremely limited number of fully labeled images. Critically, learning from such limited set inevitably generates pseudo labels with serious noisy pixels. To address this, we propose a noise correction loss that facilitates the model's learning of correct pixels in the early learning stage, and corrects the error risk gradients dominated by noisy pixels in the memorization stage, ultimately achieving accurate segmentation of camouflaged objects from noisy labels. When using only 20% of fully labeled data, our method shows superior performance over the state-of-the-art methods.
FocusDiffuser: Perceiving Local Disparities for Camouflaged Object Detection
2024-07-18
Jianwei Zhao, Xin Li, Fan Yang, Qiang Zhai, Ao Luo, Zicheng Jiao, Hong Cheng
Detecting objects seamlessly blended into their surroundings represents a complex task for both human cognitive capabilities and advanced artificial intelligence algorithms. Currently, the majority of methodologies for detecting camouflaged objects mainly focus on utilizing discriminative models with various unique designs. However, it has been observed that generative models, such as Stable Diffusion, possess stronger capabilities for understanding various objects in complex environments; Yet their potential for the cognition and detection of camouflaged objects has not been extensively explored. In this study, we present a novel denoising diffusion model, namely FocusDiffuser, to investigate how generative models can enhance the detection and interpretation of camouflaged objects. We believe that the secret to spotting camouflaged objects lies in catching the subtle nuances in details. Consequently, our FocusDiffuser innovatively integrates specialized enhancements, notably the Boundary-Driven LookUp (BDLU) module and Cyclic Positioning (CP) module, to elevate standard diffusion models, significantly boosting the detail-oriented analytical capabilities. Our experiments demonstrate that FocusDiffuser, from a generative perspective, effectively addresses the challenge of camouflaged object detection, surpassing leading models on benchmarks like CAMO, COD10K and NC4K.
Exploring Deeper! Segment Anything Model with Depth Perception for Camouflaged Object Detection
2024-07-17
Zhenni Yu, Xiaoqin Zhang, Li Zhao, Yi Bin, Guobao Xiao
This paper introduces a new Segment Anything Model with Depth Perception (DSAM) for Camouflaged Object Detection (COD). DSAM exploits the zero-shot capability of SAM to realize precise segmentation in the RGB-D domain. It consists of the Prompt-Deeper Module and the Finer Module. The Prompt-Deeper Module utilizes knowledge distillation and the Bias Correction Module to achieve the interaction between RGB features and depth features, especially using depth features to correct erroneous parts in RGB features. Then, the interacted features are combined with the box prompt in SAM to create a prompt with depth perception. The Finer Module explores the possibility of accurately segmenting highly camouflaged targets from a depth perspective. It uncovers depth cues in areas missed by SAM through mask reversion, self-filtering, and self-attention operations, compensating for its defects in the COD domain. DSAM represents the first step towards the SAM-based RGB-D COD model. It maximizes the utilization of depth features while synergizing with RGB features to achieve multimodal complementarity, thereby overcoming the segmentation limitations of SAM and improving its accuracy in COD. Experimental results on COD benchmarks demonstrate that DSAM achieves excellent segmentation performance and reaches the state-of-the-art (SOTA) on COD benchmarks with less consumption of training resources. The code will be available at https://github.com/guobaoxiao/DSAM.
YOLO-FEDER FusionNet: A Novel Deep Learning Architecture for Drone Detection
2024-06-17
Tamara R. Lenhard, Andreas Weinmann, Stefan Jäger, Tobias Koch
Predominant methods for image-based drone detection frequently rely on employing generic object detection algorithms like YOLOv5. While proficient in identifying drones against homogeneous backgrounds, these algorithms often struggle in complex, highly textured environments. In such scenarios, drones seamlessly integrate into the background, creating camouflage effects that adversely affect the detection quality. To address this issue, we introduce a novel deep learning architecture called YOLO-FEDER FusionNet. Unlike conventional approaches, YOLO-FEDER FusionNet combines generic object detection methods with the specialized strength of camouflage object detection techniques to enhance drone detection capabilities. Comprehensive evaluations of YOLO-FEDER FusionNet show the efficiency of the proposed model and demonstrate substantial improvements in both reducing missed detections and false alarms.
SAM-PM: Enhancing Video Camouflaged Object Detection using Spatio-Temporal Attention
2024-06-09
Muhammad Nawfal Meeran, Gokul Adethya T, Bhanu Pratyush Mantha
In the domain of large foundation models, the Segment Anything Model (SAM) has gained notable recognition for its exceptional performance in image segmentation. However, tackling the video camouflage object detection (VCOD) task presents a unique challenge. Camouflaged objects typically blend into the background, making them difficult to distinguish in still images. Additionally, ensuring temporal consistency in this context is a challenging problem. As a result, SAM encounters limitations and falls short when applied to the VCOD task. To overcome these challenges, we propose a new method called the SAM Propagation Module (SAM-PM). Our propagation module enforces temporal consistency within SAM by employing spatio-temporal cross-attention mechanisms. Moreover, we exclusively train the propagation module while keeping the SAM network weights frozen, allowing us to integrate task-specific insights with the vast knowledge accumulated by the large model. Our method effectively incorporates temporal consistency and domain-specific expertise into the segmentation network with an addition of less than 1% of SAM's parameters. Extensive experimentation reveals a substantial performance improvement in the VCOD benchmark when compared to the most recent state-of-the-art techniques. Code and pre-trained weights are open-sourced at https://github.com/SpiderNitt/SAM-PM
Utilizing Grounded SAM for self-supervised frugal camouflaged human detection
2024-06-09
Matthias Pijarowski, Alexander Wolpert, Martin Heckmann, Michael Teutsch
Visually detecting camouflaged objects is a hard problem for both humans and computer vision algorithms. Strong similarities between object and background appearance make the task significantly more challenging than traditional object detection or segmentation tasks. Current state-of-the-art models use either convolutional neural networks or vision transformers as feature extractors. They are trained in a fully supervised manner and thus need a large amount of labeled training data. In this paper, both self-supervised and frugal learning methods are introduced to the task of Camouflaged Object Detection (COD). The overall goal is to fine-tune two COD reference methods, namely SINet-V2 and HitNet, pre-trained for camouflaged animal detection to the task of camouflaged human detection. Therefore, we use the public dataset CPD1K that contains camouflaged humans in a forest environment. We create a strong baseline using supervised frugal transfer learning for the fine-tuning task. Then, we analyze three pseudo-labeling approaches to perform the fine-tuning task in a self-supervised manner. Our experiments show that we achieve similar performance by pure self-supervision compared to fully supervised frugal learning.
GreenCOD: A Green Camouflaged Object Detection Method
2024-05-25
Hong-Shuo Chen, Yao Zhu, Suya You, Azad M. Madni, C. -C. Jay Kuo
We introduce GreenCOD, a green method for detecting camouflaged objects, distinct in its avoidance of backpropagation techniques. GreenCOD leverages gradient boosting and deep features extracted from pre-trained Deep Neural Networks (DNNs). Traditional camouflaged object detection (COD) approaches often rely on complex deep neural network architectures, seeking performance improvements through backpropagation-based fine-tuning. However, such methods are typically computationally demanding and exhibit only marginal performance variations across different models. This raises the question of whether effective training can be achieved without backpropagation. Addressing this, our work proposes a new paradigm that utilizes gradient boosting for COD. This approach significantly simplifies the model design, resulting in a system that requires fewer parameters and operations and maintains high performance compared to state-of-the-art deep learning models. Remarkably, our models are trained without backpropagation and achieve the best performance with fewer than 20G Multiply-Accumulate Operations (MACs). This new, more efficient paradigm opens avenues for further exploration in green, backpropagation-free model training.
Depth Awakens: A Depth-perceptual Attention Fusion Network for RGB-D Camouflaged Object Detection
2024-05-09
Xinran Liua, Lin Qia, Yuxuan Songa, Qi Wen
Camouflaged object detection (COD) presents a persistent challenge in accurately identifying objects that seamlessly blend into their surroundings. However, most existing COD models overlook the fact that visual systems operate within a genuine 3D environment. The scene depth inherent in a single 2D image provides rich spatial clues that can assist in the detection of camouflaged objects. Therefore, we propose a novel depth-perception attention fusion network that leverages the depth map as an auxiliary input to enhance the network's ability to perceive 3D information, which is typically challenging for the human eye to discern from 2D images. The network uses a trident-branch encoder to extract chromatic and depth information and their communications. Recognizing that certain regions of a depth map may not effectively highlight the camouflaged object, we introduce a depth-weighted cross-attention fusion module to dynamically adjust the fusion weights on depth and RGB feature maps. To keep the model simple without compromising effectiveness, we design a straightforward feature aggregation decoder that adaptively fuses the enhanced aggregated features. Experiments demonstrate the significant superiority of our proposed method over other states of the arts, which further validates the contribution of depth information in camouflaged object detection. The code will be available at https://github.com/xinran-liu00/DAF-Net.
Adaptive Guidance Learning for Camouflaged Object Detection
2024-05-05
Zhennan Chen, Xuying Zhang, Tian-Zhu Xiang, Ying Tai
Camouflaged object detection (COD) aims to segment objects visually embedded in their surroundings, which is a very challenging task due to the high similarity between the objects and the background. To address it, most methods often incorporate additional information (e.g., boundary, texture, and frequency clues) to guide feature learning for better detecting camouflaged objects from the background. Although progress has been made, these methods are basically individually tailored to specific auxiliary cues, thus lacking adaptability and not consistently achieving high segmentation performance. To this end, this paper proposes an adaptive guidance learning network, dubbed \textit{AGLNet}, which is a unified end-to-end learnable model for exploring and adapting different additional cues in CNN models to guide accurate camouflaged feature learning. Specifically, we first design a straightforward additional information generation (AIG) module to learn additional camouflaged object cues, which can be adapted for the exploration of effective camouflaged features. Then we present a hierarchical feature combination (HFC) module to deeply integrate additional cues and image features to guide camouflaged feature learning in a multi-level fusion manner.Followed by a recalibration decoder (RD), different features are further aggregated and refined for accurate object prediction. Extensive experiments on three widely used COD benchmark datasets demonstrate that the proposed method achieves significant performance improvements under different additional cues, and outperforms the recent 20 state-of-the-art methods by a large margin. Our code will be made publicly available at: \textcolor{blue}{{https://github.com/ZNan-Chen/AGLNet}}.
Shifting Spotlight for Co-supervision: A Simple yet Efficient Single-branch Network to See Through Camouflage
2024-04-13
Yang Hu, Jinxia Zhang, Kaihua Zhang, Yin Yuan, Jiale Huang, Zechao Zhan, Xing Wang
Camouflaged object detection (COD) remains a challenging task in computer vision. Existing methods often resort to additional branches for edge supervision, incurring substantial computational costs. To address this, we propose the Co-Supervised Spotlight Shifting Network (CS$^3$Net), a compact single-branch framework inspired by how shifting light source exposes camouflage. Our spotlight shifting strategy replaces multi-branch designs by generating supervisory signals that highlight boundary cues. Within CS$^3$Net, a Projection Aware Attention (PAA) module is devised to strengthen feature extraction, while the Extended Neighbor Connection Decoder (ENCD) enhances final predictions. Extensive experiments on public datasets demonstrate that CS$^3$Net not only achieves superior performance, but also reduces Multiply-Accumulate operations (MACs) by 32.13% compared to state-of-the-art COD methods, striking an optimal balance between efficiency and effectiveness.
Explicit Motion Handling and Interactive Prompting for Video Camouflaged Object Detection
2024-03-04
Xin Zhang, Tao Xiao, Gepeng Ji, Xuan Wu, Keren Fu, Qijun Zhao
Camouflage poses challenges in distinguishing a static target, whereas any movement of the target can break this disguise. Existing video camouflaged object detection (VCOD) approaches take noisy motion estimation as input or model motion implicitly, restricting detection performance in complex dynamic scenes. In this paper, we propose a novel Explicit Motion handling and Interactive Prompting framework for VCOD, dubbed EMIP, which handles motion cues explicitly using a frozen pre-trained optical flow fundamental model. EMIP is characterized by a two-stream architecture for simultaneously conducting camouflaged segmentation and optical flow estimation. Interactions across the dual streams are realized in an interactive prompting way that is inspired by emerging visual prompt learning. Two learnable modules, i.e., the camouflaged feeder and motion collector, are designed to incorporate segmentation-to-motion and motion-to-segmentation prompts, respectively, and enhance outputs of the both streams. The prompt fed to the motion stream is learned by supervising optical flow in a self-supervised manner. Furthermore, we show that long-term historical information can also be incorporated as a prompt into EMIP and achieve more robust results with temporal consistency. Experimental results demonstrate that our EMIP achieves new state-of-the-art records on popular VCOD benchmarks. Our code is made publicly available at https://github.com/zhangxin06/EMIP.
A Simple yet Effective Network based on Vision Transformer for Camouflaged Object and Salient Object Detection
2024-02-29
Chao Hao, Zitong Yu, Xin Liu, Jun Xu, Huanjing Yue, Jingyu Yang
Camouflaged object detection (COD) and salient object detection (SOD) are two distinct yet closely-related computer vision tasks widely studied during the past decades. Though sharing the same purpose of segmenting an image into binary foreground and background regions, their distinction lies in the fact that COD focuses on concealed objects hidden in the image, while SOD concentrates on the most prominent objects in the image. Previous works achieved good performance by stacking various hand-designed modules and multi-scale features. However, these carefully-designed complex networks often performed well on one task but not on another. In this work, we propose a simple yet effective network (SENet) based on vision Transformer (ViT), by employing a simple design of an asymmetric ViT-based encoder-decoder structure, we yield competitive results on both tasks, exhibiting greater versatility than meticulously crafted ones. Furthermore, to enhance the Transformer's ability to model local information, which is important for pixel-level binary segmentation tasks, we propose a local information capture module (LICM). We also propose a dynamic weighted loss (DW loss) based on Binary Cross-Entropy (BCE) and Intersection over Union (IoU) loss, which guides the network to pay more attention to those smaller and more difficult-to-find target objects according to their size. Moreover, we explore the issue of joint training of SOD and COD, and propose a preliminary solution to the conflict in joint training, further improving the performance of SOD. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of our method. The code is available at https://github.com/linuxsino/SENet.
Spatial Coherence Loss: All Objects Matter in Salient and Camouflaged Object Detection
2024-02-28
Ziyun Yang, Kevin Choy, Sina Farsiu
Generic object detection is a category-independent task that relies on accurate modeling of objectness. We show that for accurate semantic analysis, the network needs to learn all object-level predictions that appear at any stage of learning, including the pre-defined ground truth (GT) objects and the ambiguous decoy objects that the network misidentifies as foreground. Yet, most relevant models focused mainly on improving the learning of the GT objects. A few methods that consider decoy objects utilize loss functions that only focus on the single-response, i.e., the loss response of a single ambiguous pixel, and thus do not benefit from the wealth of information that an object-level ambiguity learning design can provide. Inspired by the human visual system, which first discerns the boundaries of ambiguous regions before delving into the semantic meaning, we propose a novel loss function, Spatial Coherence Loss (SCLoss), that incorporates the mutual response between adjacent pixels into the widely-used single-response loss functions. We demonstrate that the proposed SCLoss can gradually learn the ambiguous regions by detecting and emphasizing their boundaries in a self-adaptive manner. Through comprehensive experiments, we demonstrate that replacing popular loss functions with SCLoss can improve the performance of current state-of-the-art (SOTA) salient or camouflaged object detection (SOD or COD) models. We also demonstrate that combining SCLoss with other loss functions can further improve performance and result in SOTA outcomes for different applications.
CoFiNet: Unveiling Camouflaged Objects with Multi-Scale Finesse
2024-02-03
Cunhan Guo, Heyan Huang
Camouflaged Object Detection (COD) is a critical aspect of computer vision aimed at identifying concealed objects, with applications spanning military, industrial, medical and monitoring domains. To address the problem of poor detail segmentation effect, we introduce a novel method for camouflage object detection, named CoFiNet. Our approach primarily focuses on multi-scale feature fusion and extraction, with special attention to the model's segmentation effectiveness for detailed features, enhancing its ability to effectively detect camouflaged objects. CoFiNet adopts a coarse-to-fine strategy. A multi-scale feature integration module is laveraged to enhance the model's capability of fusing context feature. A multi-activation selective kernel module is leveraged to grant the model the ability to autonomously alter its receptive field, enabling it to selectively choose an appropriate receptive field for camouflaged objects of different sizes. During mask generation, we employ the dual-mask strategy for image segmentation, separating the reconstruction of coarse and fine masks, which significantly enhances the model's learning capacity for details. Comprehensive experiments were conducted on four different datasets, demonstrating that CoFiNet achieves state-of-the-art performance across all datasets. The experiment results of CoFiNet underscore its effectiveness in camouflage object detection and highlight its potential in various practical application scenarios.
Segment anything model (SAM) has demonstrated excellent generalizability in common vision scenarios, yet falling short of the ability to understand specialized data. Recently, several methods have combined parameter-efficient techniques with task-specific designs to fine-tune SAM on particular tasks. However, these methods heavily rely on handcraft, complicated, and task-specific designs, and pre/post-processing to achieve acceptable performances on downstream tasks. As a result, this severely restricts generalizability to other downstream tasks. To address this issue, we present a simple and unified framework, namely SU-SAM, that can easily and efficiently fine-tune the SAM model with parameter-efficient techniques while maintaining excellent generalizability toward various downstream tasks. SU-SAM does not require any task-specific designs and aims to improve the adaptability of SAM-like models significantly toward underperformed scenes. Concretely, we abstract parameter-efficient modules of different methods into basic design elements in our framework. Besides, we propose four variants of SU-SAM, i.e., series, parallel, mixed, and LoRA structures. Comprehensive experiments on nine datasets and six downstream tasks to verify the effectiveness of SU-SAM, including medical image segmentation, camouflage object detection, salient object segmentation, surface defect segmentation, complex object shapes, and shadow masking. Our experimental results demonstrate that SU-SAM achieves competitive or superior accuracy compared to state-of-the-art methods. Furthermore, we provide in-depth analyses highlighting the effectiveness of different parameter-efficient designs within SU-SAM. In addition, we propose a generalized model and benchmark, showcasing SU-SAM's generalizability across all diverse datasets simultaneously.
Concealed Object Segmentation with Hierarchical Coherence Modeling
2024-01-22
Fengyang Xiao, Pan Zhang, Chunming He, Runze Hu, Yutao Liu
Concealed object segmentation (COS) is a challenging task that involves localizing and segmenting those concealed objects that are visually blended with their surrounding environments. Despite achieving remarkable success, existing COS segmenters still struggle to achieve complete segmentation results in extremely concealed scenarios. In this paper, we propose a Hierarchical Coherence Modeling (HCM) segmenter for COS, aiming to address this incomplete segmentation limitation. In specific, HCM promotes feature coherence by leveraging the intra-stage coherence and cross-stage coherence modules, exploring feature correlations at both the single-stage and contextual levels. Additionally, we introduce the reversible re-calibration decoder to detect previously undetected parts in low-confidence regions, resulting in further enhancing segmentation performance. Extensive experiments conducted on three COS tasks, including camouflaged object detection, polyp image segmentation, and transparent object detection, demonstrate the promising results achieved by the proposed HCM segmenter.
Relax Image-Specific Prompt Requirement in SAM: A Single Generic Prompt for Segmenting Camouflaged Objects
2023-12-12
Jian Hu, Jiayi Lin, Weitong Cai, Shaogang Gong
Camouflaged object detection (COD) approaches heavily rely on pixel-level annotated datasets. Weakly-supervised COD (WSCOD) approaches use sparse annotations like scribbles or points to reduce annotation effort, but this can lead to decreased accuracy. The Segment Anything Model (SAM) shows remarkable segmentation ability with sparse prompts like points. However, manual prompt is not always feasible, as it may not be accessible in real-world application. Additionally, it only provides localization information instead of semantic one, which can intrinsically cause ambiguity in interpreting the targets. In this work, we aim to eliminate the need for manual prompt. The key idea is to employ Cross-modal Chains of Thought Prompting (CCTP) to reason visual prompts using the semantic information given by a generic text prompt. To that end, we introduce a test-time adaptation per-instance mechanism called Generalizable SAM (GenSAM) to automatically enerate and optimize visual prompts the generic task prompt for WSCOD. In particular, CCTP maps a single generic text prompt onto image-specific consensus foreground and background heatmaps using vision-language models, acquiring reliable visual prompts. Moreover, to test-time adapt the visual prompts, we further propose Progressive Mask Generation (PMG) to iteratively reweight the input image, guiding the model to focus on the targets in a coarse-to-fine manner. Crucially, all network parameters are fixed, avoiding the need for additional training. Experiments demonstrate the superiority of GenSAM. Experiments on three benchmarks demonstrate that GenSAM outperforms point supervision approaches and achieves comparable results to scribble supervision ones, solely relying on general task descriptions as prompts. our codes is in: https://lwpyh.github.io/GenSAM/.
Referring camouflaged object detection (Ref-COD) is a recently-proposed problem aiming to segment out specified camouflaged objects matched with a textual or visual reference. This task involves two major challenges: the COD domain-specific perception and multimodal reference-image alignment. Our motivation is to make full use of the semantic intelligence and intrinsic knowledge of recent Multimodal Large Language Models (MLLMs) to decompose this complex task in a human-like way. As language is highly condensed and inductive, linguistic expression is the main media of human knowledge learning, and the transmission of knowledge information follows a multi-level progression from simplicity to complexity. In this paper, we propose a large-model-based Multi-Level Knowledge-Guided multimodal method for Ref-COD termed MLKG, where multi-level knowledge descriptions from MLLM are organized to guide the large vision model of segmentation to perceive the camouflage-targets and camouflage-scene progressively and meanwhile deeply align the textual references with camouflaged photos. To our knowledge, our contributions mainly include: (1) This is the first time that the MLLM knowledge is studied for Ref-COD and COD. (2) We, for the first time, propose decomposing Ref-COD into two main perspectives of perceiving the target and scene by integrating MLLM knowledge, and contribute a multi-level knowledge-guided method. (3) Our method achieves the state-of-the-art on the Ref-COD benchmark outperforming numerous strong competitors. Moreover, thanks to the injected rich knowledge, it demonstrates zero-shot generalization ability on uni-modal COD datasets. We will release our code soon.
Cross-level Attention with Overlapped Windows for Camouflaged Object Detection
2023-11-28
Jiepan Li, Fangxiao Lu, Nan Xue, Zhuohong Li, Hongyan Zhang, Wei He
Camouflaged objects adaptively fit their color and texture with the environment, which makes them indistinguishable from the surroundings. Current methods revealed that high-level semantic features can highlight the differences between camouflaged objects and the backgrounds. Consequently, they integrate high-level semantic features with low-level detailed features for accurate camouflaged object detection (COD). Unlike previous designs for multi-level feature fusion, we state that enhancing low-level features is more impending for COD. In this paper, we propose an overlapped window cross-level attention (OWinCA) to achieve the low-level feature enhancement guided by the highest-level features. By sliding an aligned window pair on both the highest- and low-level feature maps, the high-level semantics are explicitly integrated into the low-level details via cross-level attention. Additionally, it employs an overlapped window partition strategy to alleviate the incoherence among windows, which prevents the loss of global information. These adoptions enable the proposed OWinCA to enhance low-level features by promoting the separability of camouflaged objects. The associated proposed OWinCANet fuses these enhanced multi-level features by simple convolution operation to achieve the final COD. Experiments conducted on three large-scale COD datasets demonstrate that our OWinCANet significantly surpasses the current state-of-the-art COD methods.
VSCode: General Visual Salient and Camouflaged Object Detection with 2D Prompt Learning
2023-11-25
Ziyang Luo, Nian Liu, Wangbo Zhao, Xuguang Yang, Dingwen Zhang, Deng-Ping Fan, Fahad Khan, Junwei Han
Salient object detection (SOD) and camouflaged object detection (COD) are related yet distinct binary mapping tasks. These tasks involve multiple modalities, sharing commonalities and unique cues. Existing research often employs intricate task-specific specialist models, potentially leading to redundancy and suboptimal results. We introduce VSCode, a generalist model with novel 2D prompt learning, to jointly address four SOD tasks and three COD tasks. We utilize VST as the foundation model and introduce 2D prompts within the encoder-decoder architecture to learn domain and task-specific knowledge on two separate dimensions. A prompt discrimination loss helps disentangle peculiarities to benefit model optimization. VSCode outperforms state-of-the-art methods across six tasks on 26 datasets and exhibits zero-shot generalization to unseen tasks by combining 2D prompts, such as RGB-D COD. Source code has been available at https://github.com/Sssssuperior/VSCode.
Chain of Visual Perception: Harnessing Multimodal Large Language Models for Zero-shot Camouflaged Object Detection
2023-11-19
Lv Tang, Peng-Tao Jiang, Zhihao Shen, Hao Zhang, Jinwei Chen, Bo Li
In this paper, we introduce a novel multimodal camo-perceptive framework (MMCPF) aimed at handling zero-shot Camouflaged Object Detection (COD) by leveraging the powerful capabilities of Multimodal Large Language Models (MLLMs). Recognizing the inherent limitations of current COD methodologies, which predominantly rely on supervised learning models demanding extensive and accurately annotated datasets, resulting in weak generalization, our research proposes a zero-shot MMCPF that circumvents these challenges. Although MLLMs hold significant potential for broad applications, their effectiveness in COD is hindered and they would make misinterpretations of camouflaged objects. To address this challenge, we further propose a strategic enhancement called the Chain of Visual Perception (CoVP), which significantly improves the perceptual capabilities of MLLMs in camouflaged scenes by leveraging both linguistic and visual cues more effectively. We validate the effectiveness of MMCPF on five widely used COD datasets, containing CAMO, COD10K, NC4K, MoCA-Mask and OVCamo. Experiments show that MMCPF can outperform all existing state-of-the-art zero-shot COD methods, and achieve competitive performance compared to weakly-supervised and fully-supervised methods, which demonstrates the potential of MMCPF. The Github link of this paper is \url{https://github.com/luckybird1994/MMCPF}.
TokenMotion: Motion-Guided Vision Transformer for Video Camouflaged Object Detection Via Learnable Token Selection
2023-11-05
Zifan Yu, Erfan Bank Tavakoli, Meida Chen, Suya You, Raghuveer Rao, Sanjeev Agarwal, Fengbo Ren
The area of Video Camouflaged Object Detection (VCOD) presents unique challenges in the field of computer vision due to texture similarities between target objects and their surroundings, as well as irregular motion patterns caused by both objects and camera movement. In this paper, we introduce TokenMotion (TMNet), which employs a transformer-based model to enhance VCOD by extracting motion-guided features using a learnable token selection. Evaluated on the challenging MoCA-Mask dataset, TMNet achieves state-of-the-art performance in VCOD. It outperforms the existing state-of-the-art method by a 12.8% improvement in weighted F-measure, an 8.4% enhancement in S-measure, and a 10.7% boost in mean IoU. The results demonstrate the benefits of utilizing motion-guided features via learnable token selection within a transformer-based framework to tackle the intricate task of VCOD.
Recent camouflaged object detection (COD) attempts to segment objects visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios. Apart from the high intrinsic similarity between camouflaged objects and their background, objects are usually diverse in scale, fuzzy in appearance, and even severely occluded. To this end, we propose an effective unified collaborative pyramid network that mimics human behavior when observing vague images and videos, \ie zooming in and out. Specifically, our approach employs the zooming strategy to learn discriminative mixed-scale semantics by the multi-head scale integration and rich granularity perception units, which are designed to fully explore imperceptible clues between candidate objects and background surroundings. The former's intrinsic multi-head aggregation provides more diverse visual patterns. The latter's routing mechanism can effectively propagate inter-frame differences in spatiotemporal scenarios and be adaptively deactivated and output all-zero results for static representations. They provide a solid foundation for realizing a unified architecture for static and dynamic COD. Moreover, considering the uncertainty and ambiguity derived from indistinguishable textures, we construct a simple yet effective regularization, uncertainty awareness loss, to encourage predictions with higher confidence in candidate regions. Our highly task-friendly framework consistently outperforms existing state-of-the-art methods in image and video COD benchmarks. Our code can be found at {https://github.com/lartpang/ZoomNeXt}.
In this paper, we provide a comprehensive study on a new task called collaborative camouflaged object detection (CoCOD), which aims to simultaneously detect camouflaged objects with the same properties from a group of relevant images. To this end, we meticulously construct the first large-scale dataset, termed CoCOD8K, which consists of 8,528 high-quality and elaborately selected images with object mask annotations, covering 5 superclasses and 70 subclasses. The dataset spans a wide range of natural and artificial camouflage scenes with diverse object appearances and backgrounds, making it a very challenging dataset for CoCOD. Besides, we propose the first baseline model for CoCOD, named bilateral-branch network (BBNet), which explores and aggregates co-camouflaged cues within a single image and between images within a group, respectively, for accurate camouflaged object detection in given images. This is implemented by an inter-image collaborative feature exploration (CFE) module, an intra-image object feature search (OFS) module, and a local-global refinement (LGR) module. We benchmark 18 state-of-the-art models, including 12 COD algorithms and 6 CoSOD algorithms, on the proposed CoCOD8K dataset under 5 widely used evaluation metrics. Extensive experiments demonstrate the effectiveness of the proposed method and the significantly superior performance compared to other competitors. We hope that our proposed dataset and model will boost growth in the COD community. The dataset, model, and results will be available at: https://github.com/zc199823/BBNet--CoCOD.
Camouflage object detection (COD) poses a significant challenge due to the high resemblance between camouflaged objects and their surroundings. Although current deep learning methods have made significant progress in detecting camouflaged objects, many of them heavily rely on additional prior information. However, acquiring such additional prior information is both expensive and impractical in real-world scenarios. Therefore, there is a need to develop a network for camouflage object detection that does not depend on additional priors. In this paper, we propose a novel adaptive feature aggregation method that effectively combines multi-layer feature information to generate guidance information. In contrast to previous approaches that rely on edge or ranking priors, our method directly leverages information extracted from image features to guide model training. Through extensive experimental results, we demonstrate that our proposed method achieves comparable or superior performance when compared to state-of-the-art approaches.
CamoFA: A Learnable Fourier-based Augmentation for Camouflage Segmentation
2023-08-29
Minh-Quan Le, Minh-Triet Tran, Trung-Nghia Le, Tam V. Nguyen, Thanh-Toan Do
Camouflaged object detection (COD) and camouflaged instance segmentation (CIS) aim to recognize and segment objects that are blended into their surroundings, respectively. While several deep neural network models have been proposed to tackle those tasks, augmentation methods for COD and CIS have not been thoroughly explored. Augmentation strategies can help improve models' performance by increasing the size and diversity of the training data and exposing the model to a wider range of variations in the data. Besides, we aim to automatically learn transformations that help to reveal the underlying structure of camouflaged objects and allow the model to learn to better identify and segment camouflaged objects. To achieve this, we propose a learnable augmentation method in the frequency domain for COD and CIS via the Fourier transform approach, dubbed CamoFA. Our method leverages a conditional generative adversarial network and cross-attention mechanism to generate a reference image and an adaptive hybrid swapping with parameters to mix the low-frequency component of the reference image and the high-frequency component of the input image. This approach aims to make camouflaged objects more visible for detection and segmentation models. Without bells and whistles, our proposed augmentation method boosts the performance of camouflaged object detectors and instance segmenters by large margins.
Camouflaged object detection (COD) aims to accurately detect objects hidden in the surrounding environment. However, the existing COD methods mainly locate camouflaged objects in the RGB domain, their performance has not been fully exploited in many challenging scenarios. Considering that the features of the camouflaged object and the background are more discriminative in the frequency domain, we propose a novel learnable and separable frequency perception mechanism driven by the semantic hierarchy in the frequency domain. Our entire network adopts a two-stage model, including a frequency-guided coarse localization stage and a detail-preserving fine localization stage. With the multi-level features extracted by the backbone, we design a flexible frequency perception module based on octave convolution for coarse positioning. Then, we design the correction fusion module to step-by-step integrate the high-level features through the prior-guided correction and cross-layer feature channel association, and finally combine them with the shallow features to achieve the detailed correction of the camouflaged objects. Compared with the currently existing models, our proposed method achieves competitive performance in three popular benchmark datasets both qualitatively and quantitatively.
Camouflaged Image Synthesis Is All You Need to Boost Camouflaged Detection
2023-08-13
Haichao Zhang, Can Qin, Yu Yin, Yun Fu
Camouflaged objects that blend into natural scenes pose significant challenges for deep-learning models to detect and synthesize. While camouflaged object detection is a crucial task in computer vision with diverse real-world applications, this research topic has been constrained by limited data availability. We propose a framework for synthesizing camouflage data to enhance the detection of camouflaged objects in natural scenes. Our approach employs a generative model to produce realistic camouflage images, which can be used to train existing object detection models. Specifically, we use a camouflage environment generator supervised by a camouflage distribution classifier to synthesize the camouflage images, which are then fed into our generator to expand the dataset. Our framework outperforms the current state-of-the-art method on three datasets (COD10k, CAMO, and CHAMELEON), demonstrating its effectiveness in improving camouflaged object detection. This approach can serve as a plug-and-play data generation and augmentation module for existing camouflaged object detection tasks and provides a novel way to introduce more diversity and distributions into current camouflage datasets.
When Super-Resolution Meets Camouflaged Object Detection: A Comparison Study
2023-08-08
Juan Wen, Shupeng Cheng, Peng Xu, Bowen Zhou, Radu Timofte, Weiyan Hou, Luc Van Gool
Super Resolution (SR) and Camouflaged Object Detection (COD) are two hot topics in computer vision with various joint applications. For instance, low-resolution surveillance images can be successively processed by super-resolution techniques and camouflaged object detection. However, in previous work, these two areas are always studied in isolation. In this paper, we, for the first time, conduct an integrated comparative evaluation for both. Specifically, we benchmark different super-resolution methods on commonly used COD datasets, and meanwhile, we evaluate the robustness of different COD models by using COD data processed by SR methods. Our goal is to bridge these two domains, discover novel experimental phenomena, summarize new experim.
Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects
2023-08-06
Chunming He, Kai Li, Yachao Zhang, Yulun Zhang, Zhenhua Guo, Xiu Li, Martin Danelljan, Fisher Yu
Camouflaged object detection (COD) is the challenging task of identifying camouflaged objects visually blended into surroundings. Albeit achieving remarkable success, existing COD detectors still struggle to obtain precise results in some challenging cases. To handle this problem, we draw inspiration from the prey-vs-predator game that leads preys to develop better camouflage and predators to acquire more acute vision systems and develop algorithms from both the prey side and the predator side. On the prey side, we propose an adversarial training framework, Camouflageator, which introduces an auxiliary generator to generate more camouflaged objects that are harder for a COD method to detect. Camouflageator trains the generator and detector in an adversarial way such that the enhanced auxiliary generator helps produce a stronger detector. On the predator side, we introduce a novel COD method, called Internal Coherence and Edge Guidance (ICEG), which introduces a camouflaged feature coherence module to excavate the internal coherence of camouflaged objects, striving to obtain more complete segmentation results. Additionally, ICEG proposes a novel edge-guided separated calibration module to remove false predictions to avoid obtaining ambiguous boundaries. Extensive experiments show that ICEG outperforms existing COD detectors and Camouflageator is flexible to improve various COD detectors, including ICEG, which brings state-of-the-art COD performance.
2023-08-01
Zhennan Chen, Rongrong Gao, Tian-Zhu Xiang, Fan Lin
Camouflaged object detection is a challenging task that aims to identify objects that are highly similar to their background. Due to the powerful noise-to-image denoising capability of denoising diffusion models, in this paper, we propose a diffusion-based framework for camouflaged object detection, termed diffCOD, a new framework that considers the camouflaged object segmentation task as a denoising diffusion process from noisy masks to object masks. Specifically, the object mask diffuses from the ground-truth masks to a random distribution, and the designed model learns to reverse this noising process. To strengthen the denoising learning, the input image prior is encoded and integrated into the denoising diffusion model to guide the diffusion process. Furthermore, we design an injection attention module (IAM) to interact conditional semantic features extracted from the image with the diffusion noise embedding via the cross-attention mechanism to enhance denoising learning. Extensive experiments on four widely used COD benchmark datasets demonstrate that the proposed method achieves favorable performance compared to the existing 11 state-of-the-art methods, especially in the detailed texture segmentation of camouflaged objects. Our code will be made publicly available at: https://github.com/ZNan-Chen/diffCOD.
Camouflaged object detection (COD), aiming to segment camouflaged objects which exhibit similar patterns with the background, is a challenging task. Most existing works are dedicated to establishing specialized modules to identify camouflaged objects with complete and fine details, while the boundary can not be well located for the lack of object-related semantics. In this paper, we propose a novel ``pre-train, adapt and detect" paradigm to detect camouflaged objects. By introducing a large pre-trained model, abundant knowledge learned from massive multi-modal data can be directly transferred to COD. A lightweight parallel adapter is inserted to adjust the features suitable for the downstream COD task. Extensive experiments on four challenging benchmark datasets demonstrate that our method outperforms existing state-of-the-art COD models by large margins. Moreover, we design a multi-task learning scheme for tuning the adapter to exploit the shareable knowledge across different semantic classes. Comprehensive experimental results showed that the generalization ability of our model can be substantially improved with multi-task adapter initialization on source tasks and multi-task adaptation on target tasks.
Salient objects attract human attention and usually stand out clearly from their surroundings. In contrast, camouflaged objects share similar colors or textures with the environment. In this case, salient objects are typically non-camouflaged, and camouflaged objects are usually not salient. Due to this inherent contradictory attribute, we introduce an uncertainty-aware learning pipeline to extensively explore the contradictory information of salient object detection (SOD) and camouflaged object detection (COD) via data-level and task-wise contradiction modeling. We first exploit the dataset correlation of these two tasks and claim that the easy samples in the COD dataset can serve as hard samples for SOD to improve the robustness of the SOD model. Based on the assumption that these two models should lead to activation maps highlighting different regions of the same input image, we further introduce a contrastive module with a joint-task contrastive learning framework to explicitly model the contradictory attributes of these two tasks. Different from conventional intra-task contrastive learning for unsupervised representation learning, our contrastive module is designed to model the task-wise correlation, leading to cross-task representation learning. To better understand the two tasks from the perspective of uncertainty, we extensively investigate the uncertainty estimation techniques for modeling the main uncertainties of the two tasks, namely task uncertainty (for SOD) and data uncertainty (for COD), and aiming to effectively estimate the challenging regions for each task to achieve difficulty-aware learning. Experimental results on benchmark datasets demonstrate that our solution leads to both state-of-the-art performance and informative uncertainty estimation.
Camouflaged Object Detection with Feature Grafting and Distractor Aware
2023-07-08
Yuxuan Song, Xinyue Li, Lin Qi
The task of Camouflaged Object Detection (COD) aims to accurately segment camouflaged objects that integrated into the environment, which is more challenging than ordinary detection as the texture between the target and background is visually indistinguishable. In this paper, we proposed a novel Feature Grafting and Distractor Aware network (FDNet) to handle the COD task. Specifically, we use CNN and Transformer to encode multi-scale images in parallel. In order to better explore the advantages of the two encoders, we design a cross-attention-based Feature Grafting Module to graft features extracted from Transformer branch into CNN branch, after which the features are aggregated in the Feature Fusion Module. A Distractor Aware Module is designed to explicitly model the two possible distractors in the COD task to refine the coarse camouflage map. We also proposed the largest artificial camouflaged object dataset which contains 2000 images with annotations, named ACOD2K. We conducted extensive experiments on four widely used benchmark datasets and the ACOD2K dataset. The results show that our method significantly outperforms other state-of-the-art methods. The code and the ACOD2K will be available at https://github.com/syxvision/FDNet.
Edge-Aware Mirror Network for Camouflaged Object Detection
2023-07-08
Dongyue Sun, Shiyao Jiang, Lin Qi
Existing edge-aware camouflaged object detection (COD) methods normally output the edge prediction in the early stage. However, edges are important and fundamental factors in the following segmentation task. Due to the high visual similarity between camouflaged targets and the surroundings, edge prior predicted in early stage usually introduces erroneous foreground-background and contaminates features for segmentation. To tackle this problem, we propose a novel Edge-aware Mirror Network (EAMNet), which models edge detection and camouflaged object segmentation as a cross refinement process. More specifically, EAMNet has a two-branch architecture, where a segmentation-induced edge aggregation module and an edge-induced integrity aggregation module are designed to cross-guide the segmentation branch and edge detection branch. A guided-residual channel attention module which leverages the residual connection and gated convolution finally better extracts structural details from low-level features. Quantitative and qualitative experiment results show that EAMNet outperforms existing cutting-edge baselines on three widely used COD datasets. Codes are available at https://github.com/sdy1999/EAMNet.
We consider the problem of referring camouflaged object detection (Ref-COD), a new task that aims to segment specified camouflaged objects based on a small set of referring images with salient target objects. We first assemble a large-scale dataset, called R2C7K, which consists of 7K images covering 64 object categories in real-world scenarios. Then, we develop a simple but strong dual-branch framework, dubbed R2CNet, with a reference branch embedding the common representations of target objects from referring images and a segmentation branch identifying and segmenting camouflaged objects under the guidance of the common representations. In particular, we design a Referring Mask Generation module to generate pixel-level prior mask and a Referring Feature Enrichment module to enhance the capability of identifying specified camouflaged objects. Extensive experiments show the superiority of our Ref-COD methods over their COD counterparts in segmenting specified camouflaged objects and identifying the main body of target objects. Our code and dataset are publicly available at https://github.com/zhangxuying1004/RefCOD.
Explicit Visual Prompting for Universal Foreground Segmentations
2023-05-29
Weihuang Liu, Xi Shen, Chi-Man Pun, Xiaodong Cun
Foreground segmentation is a fundamental problem in computer vision, which includes salient object detection, forgery detection, defocus blur detection, shadow detection, and camouflage object detection. Previous works have typically relied on domain-specific solutions to address accuracy and robustness issues in those applications. In this paper, we present a unified framework for a number of foreground segmentation tasks without any task-specific designs. We take inspiration from the widely-used pre-training and then prompt tuning protocols in NLP and propose a new visual prompting model, named Explicit Visual Prompting (EVP). Different from the previous visual prompting which is typically a dataset-level implicit embedding, our key insight is to enforce the tunable parameters focusing on the explicit visual content from each individual image, i.e., the features from frozen patch embeddings and high-frequency components. Our method freezes a pre-trained model and then learns task-specific knowledge using a few extra parameters. Despite introducing only a small number of tunable parameters, EVP achieves superior performance than full fine-tuning and other parameter-efficient fine-tuning methods. Experiments in fourteen datasets across five tasks show the proposed method outperforms other task-specific methods while being considerably simple. The proposed method demonstrates the scalability in different architectures, pre-trained weights, and tasks. The code is available at: https://github.com/NiFangBaAGe/Explicit-Visual-Prompt.
CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models
2023-05-29
Zhongxi Chen, Ke Sun, Xianming Lin, Rongrong Ji
Camouflaged Object Detection (COD) is a challenging task in computer vision due to the high similarity between camouflaged objects and their surroundings. Existing COD methods primarily employ semantic segmentation, which suffers from overconfident incorrect predictions. In this paper, we propose a new paradigm that treats COD as a conditional mask-generation task leveraging diffusion models. Our method, dubbed CamoDiffusion, employs the denoising process of diffusion models to iteratively reduce the noise of the mask. Due to the stochastic sampling process of diffusion, our model is capable of sampling multiple possible predictions from the mask distribution, avoiding the problem of overconfident point estimation. Moreover, we develop specialized learning strategies that include an innovative ensemble approach for generating robust predictions and tailored forward diffusion methods for efficient training, specifically for the COD task. Extensive experiments on three COD datasets attest the superior performance of our model compared to existing state-of-the-art methods, particularly on the most challenging COD10K dataset, where our approach achieves 0.019 in terms of MAE.
A bioinspired three-stage model for camouflaged object detection
2023-05-22
Tianyou Chen, Jin Xiao, Xiaoguang Hu, Guofeng Zhang, Shaojie Wang
Camouflaged objects are typically assimilated into their backgrounds and exhibit fuzzy boundaries. The complex environmental conditions and the high intrinsic similarity between camouflaged targets and their surroundings pose significant challenges in accurately locating and segmenting these objects in their entirety. While existing methods have demonstrated remarkable performance in various real-world scenarios, they still face limitations when confronted with difficult cases, such as small targets, thin structures, and indistinct boundaries. Drawing inspiration from human visual perception when observing images containing camouflaged objects, we propose a three-stage model that enables coarse-to-fine segmentation in a single iteration. Specifically, our model employs three decoders to sequentially process subsampled features, cropped features, and high-resolution original features. This proposed approach not only reduces computational overhead but also mitigates interference caused by background noise. Furthermore, considering the significance of multi-scale information, we have designed a multi-scale feature enhancement module that enlarges the receptive field while preserving detailed structural cues. Additionally, a boundary enhancement module has been developed to enhance performance by leveraging boundary information. Subsequently, a mask-guided fusion module is proposed to generate fine-grained results by integrating coarse prediction maps with high-resolution feature maps. Our network surpasses state-of-the-art CNN-based counterparts without unnecessary complexities. Upon acceptance of the paper, the source code will be made publicly available at https://github.com/clelouch/BTSNet.
As a promptable generic object segmentation model, segment anything model (SAM) has recently attracted significant attention, and also demonstrates its powerful performance. Nevertheless, it still meets its Waterloo when encountering several tasks, e.g., medical image segmentation, camouflaged object detection, etc. In this report, we try SAM on an unexplored popular task: shadow detection. Specifically, four benchmarks were chosen and evaluated with widely used metrics. The experimental results show that the performance for shadow detection using SAM is not satisfactory, especially when comparing with the elaborate models. Code is available at https://github.com/LeipingJie/SAMSh.
SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More
2023-04-18
Tianrun Chen, Lanyun Zhu, Chaotao Ding, Runlong Cao, Yan Wang, Zejian Li, Lingyun Sun, Papa Mao, Ying Zang
The emergence of large models, also known as foundation models, has brought significant advancements to AI research. One such model is Segment Anything (SAM), which is designed for image segmentation tasks. However, as with other foundation models, our experimental findings suggest that SAM may fail or perform poorly in certain segmentation tasks, such as shadow detection and camouflaged object detection (concealed object detection). This study first paves the way for applying the large pre-trained image segmentation model SAM to these downstream tasks, even in situations where SAM performs poorly. Rather than fine-tuning the SAM network, we propose \textbf{SAM-Adapter}, which incorporates domain-specific information or visual prompts into the segmentation network by using simple yet effective adapters. By integrating task-specific knowledge with general knowledge learnt by the large model, SAM-Adapter can significantly elevate the performance of SAM in challenging tasks as shown in extensive experiments. We can even outperform task-specific network models and achieve state-of-the-art performance in the task we tested: camouflaged object detection, shadow detection. We also tested polyp segmentation (medical image segmentation) and achieves better results. We believe our work opens up opportunities for utilizing SAM in downstream tasks, with potential applications in various fields, including medical image processing, agriculture, remote sensing, and more.
The Art of Camouflage: Few-Shot Learning for Animal Detection and Segmentation
2023-04-15
Thanh-Danh Nguyen, Anh-Khoa Nguyen Vu, Nhat-Duy Nguyen, Vinh-Tiep Nguyen, Thanh Duc Ngo, Thanh-Toan Do, Minh-Triet Tran, Tam V. Nguyen
Camouflaged object detection and segmentation is a new and challenging research topic in computer vision. There is a serious issue of lacking data on concealed objects such as camouflaged animals in natural scenes. In this paper, we address the problem of few-shot learning for camouflaged object detection and segmentation. To this end, we first collect a new dataset, CAMO-FS, for the benchmark. As camouflaged instances are challenging to recognize due to their similarity compared to the surroundings, we guide our models to obtain camouflaged features that highly distinguish the instances from the background. In this work, we propose FS-CDIS, a framework to efficiently detect and segment camouflaged instances via two loss functions contributing to the training process. Firstly, the instance triplet loss with the characteristic of differentiating the anchor, which is the mean of all camouflaged foreground points, and the background points are employed to work at the instance level. Secondly, to consolidate the generalization at the class level, we present instance memory storage with the scope of storing camouflaged features of the same category, allowing the model to capture further class-level information during the learning process. The extensive experiments demonstrated that our proposed method achieves state-of-the-art performance on the newly collected dataset. Code is available at https://github.com/danhntd/FS-CDIS.
The burgeoning field of camouflaged object detection (COD) seeks to identify objects that blend into their surroundings. Despite the impressive performance of recent models, we have identified a limitation in their robustness, where existing methods may misclassify salient objects as camouflaged ones, despite these two characteristics being contradictory. This limitation may stem from lacking multi-pattern training images, leading to less saliency robustness. To address this issue, we introduce CamDiff, a novel approach inspired by AI-Generated Content (AIGC) that overcomes the scarcity of multi-pattern training images. Specifically, we leverage the latent diffusion model to synthesize salient objects in camouflaged scenes, while using the zero-shot image classification ability of the Contrastive Language-Image Pre-training (CLIP) model to prevent synthesis failures and ensure the synthesized object aligns with the input prompt. Consequently, the synthesized image retains its original camouflage label while incorporating salient objects, yielding camouflage samples with richer characteristics. The results of user studies show that the salient objects in the scenes synthesized by our framework attract the user's attention more; thus, such samples pose a greater challenge to the existing COD models. Our approach enables flexible editing and efficient large-scale dataset generation at a low cost. It significantly enhances COD baselines' training and testing phases, emphasizing robustness across diverse domains. Our newly-generated datasets and source code are available at https://github.com/drlxj/CamDiff.
Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection
2023-04-10
Lv Tang, Haoke Xiao, Bo Li
SAM is a segmentation model recently released by Meta AI Research and has been gaining attention quickly due to its impressive performance in generic object segmentation. However, its ability to generalize to specific scenes such as camouflaged scenes is still unknown. Camouflaged object detection (COD) involves identifying objects that are seamlessly integrated into their surroundings and has numerous practical applications in fields such as medicine, art, and agriculture. In this study, we try to ask if SAM can address the COD task and evaluate the performance of SAM on the COD benchmark by employing maximum segmentation evaluation and camouflage location evaluation. We also compare SAM's performance with 22 state-of-the-art COD methods. Our results indicate that while SAM shows promise in generic object segmentation, its performance on the COD task is limited. This presents an opportunity for further research to explore how to build a stronger SAM that may address the COD task. The results of this paper are provided in \url{https://github.com/luckybird1994/SAMCOD}.
Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformers
2023-03-26
Zhou Huang, Hang Dai, Tian-Zhu Xiang, Shuo Wang, Huai-Xin Chen, Jie Qin, Huan Xiong
Vision transformers have recently shown strong global context modeling capabilities in camouflaged object detection. However, they suffer from two major limitations: less effective locality modeling and insufficient feature aggregation in decoders, which are not conducive to camouflaged object detection that explores subtle cues from indistinguishable backgrounds. To address these issues, in this paper, we propose a novel transformer-based Feature Shrinkage Pyramid Network (FSPNet), which aims to hierarchically decode locality-enhanced neighboring transformer features through progressive shrinking for camouflaged object detection. Specifically, we propose a nonlocal token enhancement module (NL-TEM) that employs the non-local mechanism to interact neighboring tokens and explore graph-based high-order relations within tokens to enhance local representations of transformers. Moreover, we design a feature shrinkage decoder (FSD) with adjacent interaction modules (AIM), which progressively aggregates adjacent transformer features through a layer-bylayer shrinkage pyramid to accumulate imperceptible but effective cues as much as possible for object information decoding. Extensive quantitative and qualitative experiments demonstrate that the proposed model significantly outperforms the existing 24 competitors on three challenging COD benchmark datasets under six widely-used evaluation metrics. Our code is publicly available at https://github.com/ZhouHuang23/FSPNet.
AdvART: Adversarial Art for Camouflaged Object Detection Attacks
2023-03-03
Amira Guesmi, Ioan Marius Bilasco, Muhammad Shafique, Ihsen Alouani
Physical adversarial attacks pose a significant practical threat as it deceives deep learning systems operating in the real world by producing prominent and maliciously designed physical perturbations. Emphasizing the evaluation of naturalness is crucial in such attacks, as humans can readily detect and eliminate unnatural manipulations. To overcome this limitation, recent work has proposed leveraging generative adversarial networks (GANs) to generate naturalistic patches, which may not catch human's attention. However, these approaches suffer from a limited latent space which leads to an inevitable trade-off between naturalness and attack efficiency. In this paper, we propose a novel approach to generate naturalistic and inconspicuous adversarial patches. Specifically, we redefine the optimization problem by introducing an additional loss term to the cost function. This term works as a semantic constraint to ensure that the generated camouflage pattern holds semantic meaning rather than arbitrary patterns. The additional term leverages similarity metrics to construct a similarity loss that we optimize within the global objective function. Our technique is based on directly manipulating the pixel values in the patch, which gives higher flexibility and larger space compared to the GAN-based techniques that are based on indirectly optimizing the patch by modifying the latent vector. Our attack achieves superior success rate of up to 91.19\% and 72\%, respectively, in the digital world and when deployed in smart cameras at the edge compared to the GAN-based technique.
We have made a dataset of camouflage object detection mainly for complex seabed scenes, and named it UnderWater RGB&Sonar,or UW-RS for short. The UW-RS dataset contains a total of 1972 image data. The dataset mainly consists of two parts, namely underwater optical data part (UW-R dataset) and underwater sonar data part (UW-S dataset).
Camouflaged objects are seamlessly blended in with their surroundings, which brings a challenging detection task in computer vision. Optimizing a convolutional neural network (CNN) for camouflaged object detection (COD) tends to activate local discriminative regions while ignoring complete object extent, causing the partial activation issue which inevitably leads to missing or redundant regions of objects. In this paper, we argue that partial activation is caused by the intrinsic characteristics of CNN, where the convolution operations produce local receptive fields and experience difficulty to capture long-range feature dependency among image regions. In order to obtain feature maps that could activate full object extent, keeping the segmental results from being overwhelmed by noisy features, a novel framework termed Cross-Model Detail Querying network (DQnet) is proposed. It reasons the relations between long-range-aware representations and multi-scale local details to make the enhanced representation fully highlight the object regions and eliminate noise on non-object regions. Specifically, a vanilla ViT pretrained with self-supervised learning (SSL) is employed to model long-range dependencies among image regions. A ResNet is employed to enable learning fine-grained spatial local details in multiple scales. Then, to effectively retrieve object-related details, a Relation-Based Querying (RBQ) module is proposed to explore window-based interactions between the global representations and the multi-scale local details. Extensive experiments are conducted on the widely used COD datasets and show that our DQnet outperforms the current state-of-the-arts.
2022-12-10
Zongwei Wu, Danda Pani Paudel, Deng-Ping Fan, Jingjing Wang, Shuo Wang, Cédric Demonceaux, Radu Timofte, Luc Van Gool
Depth cues are known to be useful for visual perception. However, direct measurement of depth is often impracticable. Fortunately, though, modern learning-based methods offer promising depth maps by inference in the wild. In this work, we adapt such depth inference models for object segmentation using the objects' "pop-out" prior in 3D. The "pop-out" is a simple composition prior that assumes objects reside on the background surface. Such compositional prior allows us to reason about objects in the 3D space. More specifically, we adapt the inferred depth maps such that objects can be localized using only 3D information. Such separation, however, requires knowledge about contact surface which we learn using the weak supervision of the segmentation mask. Our intermediate representation of contact surface, and thereby reasoning about objects purely in 3D, allows us to better transfer the depth knowledge into semantics. The proposed adaptation method uses only the depth model without needing the source data used for training, making the learning process efficient and practical. Our experiments on eight datasets of two challenging tasks, namely camouflaged object detection and salient object detection, consistently demonstrate the benefit of our method in terms of both performance and generalizability.
How to identify and segment camouflaged objects from the background is challenging. Inspired by the multi-head self-attention in Transformers, we present a simple masked separable attention (MSA) for camouflaged object detection. We first separate the multi-head self-attention into three parts, which are responsible for distinguishing the camouflaged objects from the background using different mask strategies. Furthermore, we propose to capture high-resolution semantic representations progressively based on a simple top-down decoder with the proposed MSA to attain precise segmentation results. These structures plus a backbone encoder form a new model, dubbed CamoFormer. Extensive experiments show that CamoFormer surpasses all existing state-of-the-art methods on three widely-used camouflaged object detection benchmarks. There are on average around 5% relative improvements over previous methods in terms of S-measure and weighted F-measure.
Feature Aggregation and Propagation Network for Camouflaged Object Detection
2022-12-02
Tao Zhou, Yi Zhou, Chen Gong, Jian Yang, Yu Zhang
Camouflaged object detection (COD) aims to detect/segment camouflaged objects embedded in the environment, which has attracted increasing attention over the past decades. Although several COD methods have been developed, they still suffer from unsatisfactory performance due to the intrinsic similarities between the foreground objects and background surroundings. In this paper, we propose a novel Feature Aggregation and Propagation Network (FAP-Net) for camouflaged object detection. Specifically, we propose a Boundary Guidance Module (BGM) to explicitly model the boundary characteristic, which can provide boundary-enhanced features to boost the COD performance. To capture the scale variations of the camouflaged objects, we propose a Multi-scale Feature Aggregation Module (MFAM) to characterize the multi-scale information from each layer and obtain the aggregated feature representations. Furthermore, we propose a Cross-level Fusion and Propagation Module (CFPM). In the CFPM, the feature fusion part can effectively integrate the features from adjacent layers to exploit the cross-level correlations, and the feature propagation part can transmit valuable context information from the encoder to the decoder network via a gate unit. Finally, we formulate a unified and end-to-end trainable framework where cross-level features can be effectively fused and propagated for capturing rich context information. Extensive experiments on three benchmark camouflaged datasets demonstrate that our FAP-Net outperforms other state-of-the-art COD models. Moreover, our model can be extended to the polyp segmentation task, and the comparison results further validate the effectiveness of the proposed model in segmenting polyps. The source code and results will be released at https://github.com/taozh2017/FAPNet.
Boundary-aware Camouflaged Object Detection via Deformable Point Sampling
2022-11-22
Minhyeok Lee, Suhwan Cho, Chaewon Park, Dogyoon Lee, Jungho Lee, Sangyoun Lee
The camouflaged object detection (COD) task aims to identify and segment objects that blend into the background due to their similar color or texture. Despite the inherent difficulties of the task, COD has gained considerable attention in several fields, such as medicine, life-saving, and anti-military fields. In this paper, we propose a novel solution called the Deformable Point Sampling network (DPS-Net) to address the challenges associated with COD. The proposed DPS-Net utilizes a Deformable Point Sampling transformer (DPS transformer) that can effectively capture sparse local boundary information of significant object boundaries in COD using a deformable point sampling method. Moreover, the DPS transformer demonstrates robust COD performance by extracting contextual features for target object localization through integrating rough global positional information of objects with boundary local information. We evaluate our method on three prominent datasets and achieve state-of-the-art performance. Our results demonstrate the effectiveness of the proposed method through comparative experiments.
Recent research about camouflaged object detection (COD) aims to segment highly concealed objects hidden in complex surroundings. The tiny, fuzzy camouflaged objects result in visually indistinguishable properties. However, current single-view COD detectors are sensitive to background distractors. Therefore, blurred boundaries and variable shapes of the camouflaged objects are challenging to be fully captured with a single-view detector. To overcome these obstacles, we propose a behavior-inspired framework, called Multi-view Feature Fusion Network (MFFN), which mimics the human behaviors of finding indistinct objects in images, i.e., observing from multiple angles, distances, perspectives. Specifically, the key idea behind it is to generate multiple ways of observation (multi-view) by data augmentation and apply them as inputs. MFFN captures critical boundary and semantic information by comparing and fusing extracted multi-view features. In addition, our MFFN exploits the dependence and interaction between views and channels. Specifically, our methods leverage the complementary information between different views through a two-stage attention module called Co-attention of Multi-view (CAMV). And we design a local-overall module called Channel Fusion Unit (CFU) to explore the channel-wise contextual clues of diverse feature maps in an iterative manner. The experiment results show that our method performs favorably against existing state-of-the-art methods via training with the same data. The code will be available at https://github.com/dwardzheng/MFFN_COD.
Weakly-Supervised Camouflaged Object Detection with Scribble Annotations
2022-07-28
Ruozhen He, Qihua Dong, Jiaying Lin, Rynson W. H. Lau
Existing camouflaged object detection (COD) methods rely heavily on large-scale datasets with pixel-wise annotations. However, due to the ambiguous boundary, annotating camouflage objects pixel-wisely is very time-consuming and labor-intensive, taking ~60mins to label one image. In this paper, we propose the first weakly-supervised COD method, using scribble annotations as supervision. To achieve this, we first relabel 4,040 images in existing camouflaged object datasets with scribbles, which takes ~10s to label one image. As scribble annotations only describe the primary structure of objects without details, for the network to learn to localize the boundaries of camouflaged objects, we propose a novel consistency loss composed of two parts: a cross-view loss to attain reliable consistency over different images, and an inside-view loss to maintain consistency inside a single prediction map. Besides, we observe that humans use semantic information to segment regions near the boundaries of camouflaged objects. Hence, we further propose a feature-guided loss, which includes visual features directly extracted from images and semantically significant features captured by the model. Finally, we propose a novel network for COD via scribble learning on structural information and semantic relations. Our network has two novel modules: the local-context contrasted (LCC) module, which mimics visual inhibition to enhance image contrast/sharpness and expand the scribbles into potential camouflaged regions, and the logical semantic relation (LSR) module, which analyzes the semantic relation to determine the regions representing the camouflaged object. Experimental results show that our model outperforms relevant SOTA methods on three COD benchmarks with an average improvement of 11.0% on MAE, 3.2% on S-measure, 2.5% on E-measure, and 4.4% on weighted F-measure.
Camouflaged Object Detection via Context-aware Cross-level Fusion
2022-07-27
Geng Chen, Si-Jie Liu, Yu-Jia Sun, Ge-Peng Ji, Ya-Feng Wu, Tao Zhou
Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. Accurate COD suffers from a number of challenges associated with low boundary contrast and the large variation of object appearances, e.g., object size and shape. To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects. Specifically, we compute informative attention coefficients from multi-level features with our Attention-induced Cross-level Fusion Module (ACFM), which further integrates the features under the guidance of attention coefficients. We then propose a Dual-branch Global Context Module (DGCM) to refine the fused features for informative feature representations by exploiting rich global context information. Multiple ACFMs and DGCMs are integrated in a cascaded manner for generating a coarse prediction from high-level features. The coarse prediction acts as an attention map to refine the low-level features before passing them to our Camouflage Inference Module (CIM) to generate the final prediction. We perform extensive experiments on three widely used benchmark datasets and compare C2F-Net with state-of-the-art (SOTA) models. The results show that C2F-Net is an effective COD model and outperforms SOTA models remarkably. Further, an evaluation on polyp segmentation datasets demonstrates the promising potentials of our C2F-Net in COD downstream applications. Our code is publicly available at: https://github.com/Ben57882/C2FNet-TSCVT.
Camouflaged object detection (COD), segmenting objects that are elegantly blended into their surroundings, is a valuable yet challenging task. Existing deep-learning methods often fall into the difficulty of accurately identifying the camouflaged object with complete and fine object structure. To this end, in this paper, we propose a novel boundary-guided network (BGNet) for camouflaged object detection. Our method explores valuable and extra object-related edge semantics to guide representation learning of COD, which forces the model to generate features that highlight object structure, thereby promoting camouflaged object detection of accurate boundary localization. Extensive experiments on three challenging benchmark datasets demonstrate that our BGNet significantly outperforms the existing 18 state-of-the-art methods under four widely-used evaluation metrics. Our code is publicly available at: https://github.com/thograce/BGNet.
Deep Gradient Learning for Efficient Camouflaged Object Detection
2022-05-25
Ge-Peng Ji, Deng-Ping Fan, Yu-Cheng Chou, Dengxin Dai, Alexander Liniger, Luc Van Gool
This paper introduces DGNet, a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD). It decouples the task into two connected branches, i.e., a context and a texture encoder. The essential connection is the gradient-induced transition, representing a soft grouping between context and texture features. Benefiting from the simple but efficient framework, DGNet outperforms existing state-of-the-art COD models by a large margin. Notably, our efficient version, DGNet-S, runs in real-time (80 fps) and achieves comparable results to the cutting-edge model JCSOD-CVPR$_{21}$ with only 6.82% parameters. Application results also show that the proposed DGNet performs well in polyp segmentation, defect detection, and transparent object segmentation tasks. Codes will be made available at https://github.com/GewelsJI/DGNet.
Towards Deeper Understanding of Camouflaged Object Detection
2022-05-23
Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Nick Barnes, Deng-Ping Fan
Preys in the wild evolve to be camouflaged to avoid being recognized by predators. In this way, camouflage acts as a key defence mechanism across species that is critical to survival. To detect and segment the whole scope of a camouflaged object, camouflaged object detection (COD) is introduced as a binary segmentation task, with the binary ground truth camouflage map indicating the exact regions of the camouflaged objects. In this paper, we revisit this task and argue that the binary segmentation setting fails to fully understand the concept of camouflage. We find that explicitly modeling the conspicuousness of camouflaged objects against their particular backgrounds can not only lead to a better understanding about camouflage, but also provide guidance to designing more sophisticated camouflage techniques. Furthermore, we observe that it is some specific parts of camouflaged objects that make them detectable by predators. With the above understanding about camouflaged objects, we present the first triple-task learning framework to simultaneously localize, segment, and rank camouflaged objects, indicating the conspicuousness level of camouflage. As no corresponding datasets exist for either the localization model or the ranking model, we generate localization maps with an eye tracker, which are then processed according to the instance level labels to generate our ranking-based training and testing dataset. We also contribute the largest COD testing set to comprehensively analyse performance of the COD models. Experimental results show that our triple-task learning framework achieves new state-of-the-art, leading to a more explainable COD network. Our code, data, and results are available at: \url{https://github.com/JingZhang617/COD-Rank-Localize-and-Segment}.
Boosting Camouflaged Object Detection with Dual-Task Interactive Transformer
2022-05-21
Zhengyi Liu, Zhili Zhang, Wei Wu
Camouflaged object detection intends to discover the concealed objects hidden in the surroundings. Existing methods follow the bio-inspired framework, which first locates the object and second refines the boundary. We argue that the discovery of camouflaged objects depends on the recurrent search for the object and the boundary. The recurrent processing makes the human tired and helpless, but it is just the advantage of the transformer with global search ability. Therefore, a dual-task interactive transformer is proposed to detect both accurate position of the camouflaged object and its detailed boundary. The boundary feature is considered as Query to improve the camouflaged object detection, and meanwhile the object feature is considered as Query to improve the boundary detection. The camouflaged object detection and the boundary detection are fully interacted by multi-head self-attention. Besides, to obtain the initial object feature and boundary feature, transformer-based backbones are adopted to extract the foreground and background. The foreground is just object, while foreground minus background is considered as boundary. Here, the boundary feature can be obtained from blurry boundary region of the foreground and background. Supervised by the object, the background and the boundary ground truth, the proposed model achieves state-of-the-art performance in public datasets. https://github.com/liuzywen/COD
High-resolution Iterative Feedback Network for Camouflaged Object Detection
2022-03-22
Xiaobin Hu, Shuo Wang, Xuebin Qin, Hang Dai, Wenqi Ren, Ying Tai, Chengjie Wang, Ling Shao
Spotting camouflaged objects that are visually assimilated into the background is tricky for both object detection algorithms and humans who are usually confused or cheated by the perfectly intrinsic similarities between the foreground objects and the background surroundings. To tackle this challenge, we aim to extract the high-resolution texture details to avoid the detail degradation that causes blurred vision in edges and boundaries. We introduce a novel HitNet to refine the low-resolution representations by high-resolution features in an iterative feedback manner, essentially a global loop-based connection among the multi-scale resolutions. In addition, an iterative feedback loss is proposed to impose more constraints on each feedback connection. Extensive experiments on four challenging datasets demonstrate that our \ourmodel~breaks the performance bottleneck and achieves significant improvements compared with 29 state-of-the-art methods. To address the data scarcity in camouflaged scenarios, we provide an application example by employing cross-domain learning to extract the features that can reflect the camouflaged object properties and embed the features into salient objects, thereby generating more camouflaged training samples from the diverse salient object datasets The code will be available at https://github.com/HUuxiaobin/HitNet.
Implicit Motion Handling for Video Camouflaged Object Detection
2022-03-14
Xuelian Cheng, Huan Xiong, Deng-Ping Fan, Yiran Zhong, Mehrtash Harandi, Tom Drummond, Zongyuan Ge
We propose a new video camouflaged object detection (VCOD) framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from video frames. An essential property of camouflaged objects is that they usually exhibit patterns similar to the background and thus make them hard to identify from still images. Therefore, effectively handling temporal dynamics in videos becomes the key for the VCOD task as the camouflaged objects will be noticeable when they move. However, current VCOD methods often leverage homography or optical flows to represent motions, where the detection error may accumulate from both the motion estimation error and the segmentation error. On the other hand, our method unifies motion estimation and object segmentation within a single optimization framework. Specifically, we build a dense correlation volume to implicitly capture motions between neighbouring frames and utilize the final segmentation supervision to optimize the implicit motion estimation and segmentation jointly. Furthermore, to enforce temporal consistency within a video sequence, we jointly utilize a spatio-temporal transformer to refine the short-term predictions. Extensive experiments on VCOD benchmarks demonstrate the architectural effectiveness of our approach. We also provide a large-scale VCOD dataset named MoCA-Mask with pixel-level handcrafted ground-truth masks and construct a comprehensive VCOD benchmark with previous methods to facilitate research in this direction. Dataset Link: https://xueliancheng.github.io/SLT-Net-project.
The recently proposed camouflaged object detection (COD) attempts to segment objects that are visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios. Apart from high intrinsic similarity between the camouflaged objects and their background, the objects are usually diverse in scale, fuzzy in appearance, and even severely occluded. To deal with these problems, we propose a mixed-scale triplet network, \textbf{ZoomNet}, which mimics the behavior of humans when observing vague images, i.e., zooming in and out. Specifically, our ZoomNet employs the zoom strategy to learn the discriminative mixed-scale semantics by the designed scale integration unit and hierarchical mixed-scale unit, which fully explores imperceptible clues between the candidate objects and background surroundings. Moreover, considering the uncertainty and ambiguity derived from indistinguishable textures, we construct a simple yet effective regularization constraint, uncertainty-aware loss, to promote the model to accurately produce predictions with higher confidence in candidate regions. Without bells and whistles, our proposed highly task-friendly model consistently surpasses the existing 23 state-of-the-art methods on four public datasets. Besides, the superior performance over the recent cutting-edge models on the SOD task also verifies the effectiveness and generality of our model. The code will be available at \url{https://github.com/lartpang/ZoomNet}.
Class-agnostic Object Detection with Multi-modal Transformer
2021-11-22
Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan, Rao Muhammad Anwer, Ming-Hsuan Yang
What constitutes an object? This has been a long-standing question in computer vision. Towards this goal, numerous learning-free and learning-based approaches have been developed to score objectness. However, they generally do not scale well across new domains and novel objects. In this paper, we advocate that existing methods lack a top-down supervision signal governed by human-understandable semantics. For the first time in literature, we demonstrate that Multi-modal Vision Transformers (MViT) trained with aligned image-text pairs can effectively bridge this gap. Our extensive experiments across various domains and novel objects show the state-of-the-art performance of MViTs to localize generic objects in images. Based on the observation that existing MViTs do not include multi-scale feature processing and usually require longer training schedules, we develop an efficient MViT architecture using multi-scale deformable attention and late vision-language fusion. We show the significance of MViT proposals in a diverse range of applications including open-world object detection, salient and camouflage object detection, supervised and self-supervised detection tasks. Further, MViTs can adaptively generate proposals given a specific language query and thus offer enhanced interactability. Code: \url{https://git.io/J1HPY}.
Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model
2021-11-22
Jing Zhang, Yuchao Dai, Mehrtash Harandi, Yiran Zhong, Nick Barnes, Richard Hartley
Uncertainty estimation has been extensively studied in recent literature, which can usually be classified as aleatoric uncertainty and epistemic uncertainty. In current aleatoric uncertainty estimation frameworks, it is often neglected that the aleatoric uncertainty is an inherent attribute of the data and can only be correctly estimated with an unbiased oracle model. Since the oracle model is inaccessible in most cases, we propose a new sampling and selection strategy at train time to approximate the oracle model for aleatoric uncertainty estimation. Further, we show a trivial solution in the dual-head based heteroscedastic aleatoric uncertainty estimation framework and introduce a new uncertainty consistency loss to avoid it. For epistemic uncertainty estimation, we argue that the internal variable in a conditional latent variable model is another source of epistemic uncertainty to model the predictive distribution and explore the limited knowledge about the hidden true model. We validate our observation on a dense prediction task, i.e., camouflaged object detection. Our results show that our solution achieves both accurate deterministic results and reliable uncertainty estimation.
Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network
2021-11-05
Ge-Peng Ji, Lei Zhu, Mingchen Zhuge, Keren Fu
Camouflaged Object Detection (COD) aims to detect objects with similar patterns (e.g., texture, intensity, colour, etc) to their surroundings, and recently has attracted growing research interest. As camouflaged objects often present very ambiguous boundaries, how to determine object locations as well as their weak boundaries is challenging and also the key to this task. Inspired by the biological visual perception process when a human observer discovers camouflaged objects, this paper proposes a novel edge-based reversible re-calibration network called ERRNet. Our model is characterized by two innovative designs, namely Selective Edge Aggregation (SEA) and Reversible Re-calibration Unit (RRU), which aim to model the visual perception behaviour and achieve effective edge prior and cross-comparison between potential camouflaged regions and background. More importantly, RRU incorporates diverse priors with more comprehensive information comparing to existing COD models. Experimental results show that ERRNet outperforms existing cutting-edge baselines on three COD datasets and five medical image segmentation datasets. Especially, compared with the existing top-1 model SINet, ERRNet significantly improves the performance by $\sim$6% (mean E-measure) with notably high speed (79.3 FPS), showing that ERRNet could be a general and robust solution for the COD task.
Improving Camouflaged Object Detection with the Uncertainty of Pseudo-edge Labels
2021-10-29
Nobukatsu Kajiura, Hong Liu, Shin'ichi Satoh
This paper focuses on camouflaged object detection (COD), which is a task to detect objects hidden in the background. Most of the current COD models aim to highlight the target object directly while outputting ambiguous camouflaged boundaries. On the other hand, the performance of the models considering edge information is not yet satisfactory. To this end, we propose a new framework that makes full use of multiple visual cues, i.e., saliency as well as edges, to refine the predicted camouflaged map. This framework consists of three key components, i.e., a pseudo-edge generator, a pseudo-map generator, and an uncertainty-aware refinement module. In particular, the pseudo-edge generator estimates the boundary that outputs the pseudo-edge label, and the conventional COD method serves as the pseudo-map generator that outputs the pseudo-map label. Then, we propose an uncertainty-based module to reduce the uncertainty and noise of such two pseudo labels, which takes both pseudo labels as input and outputs an edge-accurate camouflaged map. Experiments on various COD datasets demonstrate the effectiveness of our method with superior performance to the existing state-of-the-art methods.
2021-08-18
Tao Zhou, Deng-Ping Fan, Geng Chen, Yi Zhou, Huazhu Fu
Salient object detection (SOD) on RGB and depth images has attracted more and more research interests, due to its effectiveness and the fact that depth cues can now be conveniently captured. Existing RGB-D SOD models usually adopt different fusion strategies to learn a shared representation from the two modalities (\ie, RGB and depth), while few methods explicitly consider how to preserve modality-specific characteristics. In this study, we propose a novel framework, termed SPNet} (Specificity-preserving network), which benefits SOD performance by exploring both the shared information and modality-specific properties (\eg, specificity). Specifically, we propose to adopt two modality-specific networks and a shared learning network to generate individual and shared saliency prediction maps, respectively. To effectively fuse cross-modal features in the shared learning network, we propose a cross-enhanced integration module (CIM) and then propagate the fused feature to the next layer for integrating cross-level information. Moreover, to capture rich complementary multi-modal information for boosting the SOD performance, we propose a multi-modal feature aggregation (MFA) module to integrate the modality-specific features from each individual decoder into the shared decoder. By using a skip connection, the hierarchical features between the encoder and decoder layers can be fully combined. Extensive experiments demonstrate that our~\ours~outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection benchmarks. The project is publicly available at: https://github.com/taozh2017/SPNet.
Camouflaged object detection (COD) aims to segment camouflaged objects hiding in the environment, which is challenging due to the similar appearance of camouflaged objects and their surroundings. Research in biology suggests depth can provide useful object localization cues for camouflaged object discovery. In this paper, we study the depth contribution for camouflaged object detection, where the depth maps are generated with existing monocular depth estimation (MDE) methods. Due to the domain gap between the MDE dataset and our COD dataset, the generated depth maps are not accurate enough to be directly used. We then introduce two solutions to avoid the noisy depth maps from dominating the training process. Firstly, we present an auxiliary depth estimation branch ("ADE"), aiming to regress the depth maps. We find that "ADE" is especially necessary for our "generated depth" scenario. Secondly, we introduce a multi-modal confidence-aware loss function via a generative adversarial network to weigh the contribution of depth for camouflaged object detection. Our extensive experiments on various camouflaged object detection datasets explain that the existing "sensor depth" based RGB-D segmentation techniques work poorly with "generated depth", and our proposed two solutions work cooperatively, achieving effective depth contribution exploration for camouflaged object detection.
Confidence-Aware Learning for Camouflaged Object Detection
2021-06-22
Jiawei Liu, Jing Zhang, Nick Barnes
Confidence-aware learning is proven as an effective solution to prevent networks becoming overconfident. We present a confidence-aware camouflaged object detection framework using dynamic supervision to produce both accurate camouflage map and meaningful "confidence" representing model awareness about the current prediction. A camouflaged object detection network is designed to produce our camouflage prediction. Then, we concatenate it with the input image and feed it to the confidence estimation network to produce an one channel confidence map.We generate dynamic supervision for the confidence estimation network, representing the agreement of camouflage prediction with the ground truth camouflage map. With the produced confidence map, we introduce confidence-aware learning with the confidence map as guidance to pay more attention to the hard/low-confidence pixels in the loss function. We claim that, once trained, our confidence estimation network can evaluate pixel-wise accuracy of the prediction without relying on the ground truth camouflage map. Extensive results on four camouflaged object detection testing datasets illustrate the superior performance of the proposed model in explaining the camouflage prediction.
Context-aware Cross-level Fusion Network for Camouflaged Object Detection
2021-05-26
Yujia Sun, Geng Chen, Tao Zhou, Yi Zhang, Nian Liu
Camouflaged object detection (COD) is a challenging task due to the low boundary contrast between the object and its surroundings. In addition, the appearance of camouflaged objects varies significantly, e.g., object size and shape, aggravating the difficulties of accurate COD. In this paper, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net) to address the challenging COD task. Specifically, we propose an Attention-induced Cross-level Fusion Module (ACFM) to integrate the multi-level features with informative attention coefficients. The fused features are then fed to the proposed Dual-branch Global Context Module (DGCM), which yields multi-scale feature representations for exploiting rich global context information. In C2F-Net, the two modules are conducted on high-level features using a cascaded manner. Extensive experiments on three widely used benchmark datasets demonstrate that our C2F-Net is an effective COD model and outperforms state-of-the-art models remarkably. Our code is publicly available at: https://github.com/thograce/C2FNet.
Visual salient object detection (SOD) aims at finding the salient object(s) that attract human attention, while camouflaged object detection (COD) on the contrary intends to discover the camouflaged object(s) that hidden in the surrounding. In this paper, we propose a paradigm of leveraging the contradictory information to enhance the detection ability of both salient object detection and camouflaged object detection. We start by exploiting the easy positive samples in the COD dataset to serve as hard positive samples in the SOD task to improve the robustness of the SOD model. Then, we introduce a similarity measure module to explicitly model the contradicting attributes of these two tasks. Furthermore, considering the uncertainty of labeling in both tasks' datasets, we propose an adversarial learning network to achieve both higher order similarity measure and network confidence estimation. Experimental results on benchmark datasets demonstrate that our solution leads to state-of-the-art (SOTA) performance for both tasks.
Mutual Graph Learning for Camouflaged Object Detection
2021-04-03
Qiang Zhai, Xin Li, Fan Yang, Chenglizhao Chen, Hong Cheng, Deng-Ping Fan
Automatically detecting/segmenting object(s) that blend in with their surroundings is difficult for current models. A major challenge is that the intrinsic similarities between such foreground objects and background surroundings make the features extracted by deep model indistinguishable. To overcome this challenge, an ideal model should be able to seek valuable, extra clues from the given scene and incorporate them into a joint learning framework for representation co-enhancement. With this inspiration, we design a novel Mutual Graph Learning (MGL) model, which generalizes the idea of conventional mutual learning from regular grids to the graph domain. Specifically, MGL decouples an image into two task-specific feature maps -- one for roughly locating the target and the other for accurately capturing its boundary details -- and fully exploits the mutual benefits by recurrently reasoning their high-order relations through graphs. Importantly, in contrast to most mutual learning approaches that use a shared function to model all between-task interactions, MGL is equipped with typed functions for handling different complementary relations to maximize information interactions. Experiments on challenging datasets, including CHAMELEON, CAMO and COD10K, demonstrate the effectiveness of our MGL with superior performance to existing state-of-the-art methods.
2021-03-24
Yang Cao, Zhengqiang Zhang, Enze Xie, Qibin Hou, Kai Zhao, Xiangui Luo, Jian Tuo
Detecting transparent objects in natural scenes is challenging due to the low contrast in texture, brightness and colors. Recent deep-learning-based works reveal that it is effective to leverage boundaries for transparent object detection (TOD). However, these methods usually encounter boundary-related imbalance problem, leading to limited generation capability. Detailly, a kind of boundaries in the background, which share the same characteristics with boundaries of transparent objects but have much smaller amounts, usually hurt the performance. To conquer the boundary-related imbalance problem, we propose a novel content-dependent data augmentation method termed FakeMix. Considering collecting these trouble-maker boundaries in the background is hard without corresponding annotations, we elaborately generate them by appending the boundaries of transparent objects from other samples into the current image during training, which adjusts the data space and improves the generalization of the models. Further, we present AdaptiveASPP, an enhanced version of ASPP, that can capture multi-scale and cross-modality features dynamically. Extensive experiments demonstrate that our methods clearly outperform the state-of-the-art methods. We also show that our approach can also transfer well on related tasks, in which the model meets similar troubles, such as mirror detection, glass detection, and camouflaged object detection. Code will be made publicly available.
Simultaneously Localize, Segment and Rank the Camouflaged Objects
2021-03-06
Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Bowen Liu, Nick Barnes, Deng-Ping Fan
Camouflage is a key defence mechanism across species that is critical to survival. Common strategies for camouflage include background matching, imitating the color and pattern of the environment, and disruptive coloration, disguising body outlines [35]. Camouflaged object detection (COD) aims to segment camouflaged objects hiding in their surroundings. Existing COD models are built upon binary ground truth to segment the camouflaged objects without illustrating the level of camouflage. In this paper, we revisit this task and argue that explicitly modeling the conspicuousness of camouflaged objects against their particular backgrounds can not only lead to a better understanding about camouflage and evolution of animals, but also provide guidance to design more sophisticated camouflage techniques. Furthermore, we observe that it is some specific parts of the camouflaged objects that make them detectable by predators. With the above understanding about camouflaged objects, we present the first ranking based COD network (Rank-Net) to simultaneously localize, segment and rank camouflaged objects. The localization model is proposed to find the discriminative regions that make the camouflaged object obvious. The segmentation model segments the full scope of the camouflaged objects. And, the ranking model infers the detectability of different camouflaged objects. Moreover, we contribute a large COD testing set to evaluate the generalization ability of COD models. Experimental results show that our model achieves new state-of-the-art, leading to a more interpretable COD network.
Camouflaged object detection is a challenging task that aims to identify objects having similar texture to the surroundings. This paper presents to amplify the subtle texture difference between camouflaged objects and the background for camouflaged object detection by formulating multiple texture-aware refinement modules to learn the texture-aware features in a deep convolutional neural network. The texture-aware refinement module computes the covariance matrices of feature responses to extract the texture information, designs an affinity loss to learn a set of parameter maps that help to separate the texture between camouflaged objects and the background, and adopts a boundary-consistency loss to explore the object detail structures.We evaluate our network on the benchmark dataset for camouflaged object detection both qualitatively and quantitatively. Experimental results show that our approach outperforms various state-of-the-art methods by a large margin.
Camouflaged object detection (COD), which aims to identify the objects that conceal themselves into the surroundings, has recently drawn increasing research efforts in the field of computer vision. In practice, the success of deep learning based COD is mainly determined by two key factors, including (i) A significantly large receptive field, which provides rich context information, and (ii) An effective fusion strategy, which aggregates the rich multi-level features for accurate COD. Motivated by these observations, in this paper, we propose a novel deep learning based COD approach, which integrates the large receptive field and effective feature fusion into a unified framework. Specifically, we first extract multi-level features from a backbone network. The resulting features are then fed to the proposed dual-branch mixture convolution modules, each of which utilizes multiple asymmetric convolutional layers and two dilated convolutional layers to extract rich context features from a large receptive field. Finally, we fuse the features using specially-designed multilevel interactive fusion modules, each of which employs an attention mechanism along with feature interaction for effective feature fusion. Our method detects camouflaged objects with an effective fusion strategy, which aggregates the rich context information from a large receptive field. All of these designs meet the requirements of COD well, allowing the accurate detection of camouflaged objects. Extensive experiments on widely-used benchmark datasets demonstrate that our method is capable of accurately detecting camouflaged objects and outperforms the state-of-the-art methods.
Camouflaged Object Detection and Tracking: A Survey
2020-12-25
Ajoy Mondal
Moving object detection and tracking have various applications, including surveillance, anomaly detection, vehicle navigation, etc. The literature on object detection and tracking is rich enough, and several essential survey papers exist. However, the research on camouflage object detection and tracking limited due to the complexity of the problem. Existing work on this problem has been done based on either biological characteristics of the camouflaged objects or computer vision techniques. In this article, we review the existing camouflaged object detection and tracking techniques using computer vision algorithms from the theoretical point of view. This article also addresses several issues of interest as well as future research direction on this area. We hope this review will help the reader to learn the recent advances in camouflaged object detection and tracking.
MergeSAM: Unsupervised change detection of remote sensing images based on the Segment Anything Model
2025-07-30
Meiqi Hu, Lingzhi Lu, Chengxi Han, Xiaoping Liu
Recently, large foundation models trained on vast datasets have demonstrated exceptional capabilities in feature extraction and general feature representation. The ongoing advancements in deep learning-driven large models have shown great promise in accelerating unsupervised change detection methods, thereby enhancing the practical applicability of change detection technologies. Building on this progress, this paper introduces MergeSAM, an innovative unsupervised change detection method for high-resolution remote sensing imagery, based on the Segment Anything Model (SAM). Two novel strategies, MaskMatching and MaskSplitting, are designed to address real-world complexities such as object splitting, merging, and other intricate changes. The proposed method fully leverages SAM's object segmentation capabilities to construct multitemporal masks that capture complex changes, embedding the spatial structure of land cover into the change detection process.
Accurate interpretation of land-cover changes in multi-temporal satellite imagery is critical for real-world scenarios. However, existing methods typically provide only one-shot change masks or static captions, limiting their ability to support interactive, query-driven analysis. In this work, we introduce remote sensing image change analysis (RSICA) as a new paradigm that combines the strengths of change detection and visual question answering to enable multi-turn, instruction-guided exploration of changes in bi-temporal remote sensing images. To support this task, we construct ChangeChat-105k, a large-scale instruction-following dataset, generated through a hybrid rule-based and GPT-assisted process, covering six interaction types: change captioning, classification, quantification, localization, open-ended question answering, and multi-turn dialogues. Building on this dataset, we propose DeltaVLM, an end-to-end architecture tailored for interactive RSICA. DeltaVLM features three innovations: (1) a fine-tuned bi-temporal vision encoder to capture temporal differences; (2) a visual difference perception module with a cross-semantic relation measuring (CSRM) mechanism to interpret changes; and (3) an instruction-guided Q-former to effectively extract query-relevant difference information from visual changes, aligning them with textual instructions. We train DeltaVLM on ChangeChat-105k using a frozen large language model, adapting only the vision and alignment modules to optimize efficiency. Extensive experiments and ablation studies demonstrate that DeltaVLM achieves state-of-the-art performance on both single-turn captioning and multi-turn interactive change analysis, outperforming existing multimodal large language models and remote sensing vision-language models. Code, dataset and pre-trained weights are available at https://github.com/hanlinwu/DeltaVLM.
MultiTaskDeltaNet: Change Detection-based Image Segmentation for Operando ETEM with Application to Carbon Gasification Kinetics
2025-07-22
Yushuo Niu, Tianyu Li, Yuanyuan Zhu, Qian Yang
Transforming in-situ transmission electron microscopy (TEM) imaging into a tool for spatially-resolved operando characterization of solid-state reactions requires automated, high-precision semantic segmentation of dynamically evolving features. However, traditional deep learning methods for semantic segmentation often encounter limitations due to the scarcity of labeled data, visually ambiguous features of interest, and small-object scenarios. To tackle these challenges, we introduce MultiTaskDeltaNet (MTDN), a novel deep learning architecture that creatively reconceptualizes the segmentation task as a change detection problem. By implementing a unique Siamese network with a U-Net backbone and using paired images to capture feature changes, MTDN effectively utilizes minimal data to produce high-quality segmentations. Furthermore, MTDN utilizes a multi-task learning strategy to leverage correlations between physical features of interest. In an evaluation using data from in-situ environmental TEM (ETEM) videos of filamentous carbon gasification, MTDN demonstrated a significant advantage over conventional segmentation models, particularly in accurately delineating fine structural features. Notably, MTDN achieved a 10.22% performance improvement over conventional segmentation models in predicting small and visually ambiguous physical features. This work bridges several key gaps between deep learning and practical TEM image analysis, advancing automated characterization of nanomaterials in complex experimental settings.
AtrousMamaba: An Atrous-Window Scanning Visual State Space Model for Remote Sensing Change Detection
2025-07-22
Tao Wang, Tiecheng Bai, Chao Xu, Bin Liu, Erlei Zhang, Jiyun Huang, Hongming Zhang
Recently, a novel visual state space (VSS) model, referred to as Mamba, has demonstrated significant progress in modeling long sequences with linear complexity, comparable to Transformer models, thereby enhancing its adaptability for processing visual data. Although most methods aim to enhance the global receptive field by directly modifying Mamba's scanning mechanism, they tend to overlook the critical importance of local information in dense prediction tasks. Additionally, whether Mamba can effectively extract local features as convolutional neural networks (CNNs) do remains an open question that merits further investigation. In this paper, We propose a novel model, AtrousMamba, which effectively balances the extraction of fine-grained local details with the integration of global contextual information. Specifically, our method incorporates an atrous-window selective scan mechanism, enabling a gradual expansion of the scanning range with adjustable rates. This design shortens the distance between adjacent tokens, enabling the model to effectively capture fine-grained local features and global context. By leveraging the atrous window scan visual state space (AWVSS) module, we design dedicated end-to-end Mamba-based frameworks for binary change detection (BCD) and semantic change detection (SCD), referred to as AWMambaBCD and AWMambaSCD, respectively. Experimental results on six benchmark datasets show that the proposed framework outperforms existing CNN-based, Transformer-based, and Mamba-based methods. These findings clearly demonstrate that Mamba not only captures long-range dependencies in visual data but also effectively preserves fine-grained local details.
Video data, especially long-form video, is extremely dense and high-dimensional. Text-based summaries of video content offer a way to represent query-relevant content in a much more compact manner than raw video. In addition, textual representations are easily ingested by state-of-the-art large language models (LLMs), which enable reasoning over video content to answer complex natural language queries. To solve this issue, we rely on the progressive construction of a text-based memory by a video captioner operating on shorter chunks of the video, where spatio-temporal modeling is computationally feasible. We explore ways to improve the quality of the activity log comprised solely of short video captions. Because the video captions tend to be focused on human actions, and questions may pertain to other information in the scene, we seek to enrich the memory with static scene descriptions using Vision Language Models (VLMs). Our video understanding system relies on the LaViLa video captioner in combination with a LLM to answer questions about videos. We first explored different ways of partitioning the video into meaningful segments such that the textual descriptions more accurately reflect the structure of the video content. Furthermore, we incorporated static scene descriptions into the captioning pipeline using LLaVA VLM, resulting in a more detailed and complete caption log and expanding the space of questions that are answerable from the textual memory. Finally, we have successfully fine-tuned the LaViLa video captioner to produce both action and scene captions, significantly improving the efficiency of the captioning pipeline compared to using separate captioning models for the two tasks. Our model, controllable hybrid captioner, can alternate between different types of captions according to special input tokens that signals scene changes detected in the video.
Compress-Align-Detect: onboard change detection from unregistered images
2025-07-21
Gabriele Inzerillo, Diego Valsesia, Aniello Fiengo, Enrico Magli
Change detection from satellite images typically incurs a delay ranging from several hours up to days because of latency in downlinking the acquired images and generating orthorectified image products at the ground stations; this may preclude real- or near real-time applications. To overcome this limitation, we propose shifting the entire change detection workflow onboard satellites. This requires to simultaneously solve challenges in data storage, image registration and change detection with a strict complexity constraint. In this paper, we present a novel and efficient framework for onboard change detection that addresses the aforementioned challenges in an end-to-end fashion with a deep neural network composed of three interlinked submodules: (1) image compression, tailored to minimize onboard data storage resources; (2) lightweight co-registration of non-orthorectified multi-temporal image pairs; and (3) a novel temporally-invariant and computationally efficient change detection model. This is the first approach in the literature combining all these tasks in a single end-to-end framework with the constraints dictated by onboard processing. Experimental results compare each submodule with the current state-of-the-art, and evaluate the performance of the overall integrated system in realistic setting on low-power hardware. Compelling change detection results are obtained in terms of F1 score as a function of compression rate, sustaining a throughput of 0.7 Mpixel/s on a 15W accelerator.
Remote sensing change detection is vital for monitoring environmental and urban transformations but faces challenges like manual feature extraction and sensitivity to noise. Traditional methods and early deep learning models, such as convolutional neural networks (CNNs), struggle to capture long-range dependencies and global context essential for accurate change detection in complex scenes. While Transformer-based models mitigate these issues, their computational complexity limits their applicability in high-resolution remote sensing. Building upon ChangeMamba architecture, which leverages state space models for efficient global context modeling, this paper proposes precision fusion blocks to capture channel-wise temporal variations and per-pixel differences for fine-grained change detection. An enhanced decoder pipeline, incorporating lightweight channel reduction mechanisms, preserves local details with minimal computational cost. Additionally, an optimized loss function combining Cross Entropy, Dice and Lovasz objectives addresses class imbalance and boosts Intersection-over-Union (IoU). Evaluations on SYSU-CD, LEVIR-CD+, and WHU-CD datasets demonstrate superior precision, recall, F1 score, IoU, and overall accuracy compared to state-of-the-art methods, highlighting the approach's robustness for remote sensing change detection. For complete transparency, the codes and pretrained models are accessible at https://github.com/Buddhi19/MambaCD.git
Graph Aggregation Prototype Learning for Semantic Change Detection in Remote Sensing
2025-07-15
Zhengyi Xu, Haoran Wu, Wen Jiang, Jie Geng
Semantic change detection (SCD) extends the binary change detection task to provide not only the change locations but also the detailed "from-to" categories in multi-temporal remote sensing data. Such detailed semantic insights into changes offer considerable advantages for a wide array of applications. However, since SCD involves the simultaneous optimization of multiple tasks, the model is prone to negative transfer due to task-specific learning difficulties and conflicting gradient flows. To address this issue, we propose Graph Aggregation Prototype Learning for Semantic Change Detection in remote sensing(GAPL-SCD). In this framework, a multi-task joint optimization method is designed to optimize the primary task of semantic segmentation and change detection, along with the auxiliary task of graph aggregation prototype learning. Adaptive weight allocation and gradient rotation methods are used to alleviate the conflict between training tasks and improve multi-task learning capabilities. Specifically, the graph aggregation prototype learning module constructs an interaction graph using high-level features. Prototypes serve as class proxies, enabling category-level domain alignment across time points and reducing interference from irrelevant changes. Additionally, the proposed self-query multi-level feature interaction and bi-temporal feature fusion modules further enhance multi-scale feature representation, improving performance in complex scenes. Experimental results on the SECOND and Landsat-SCD datasets demonstrate that our method achieves state-of-the-art performance, with significant improvements in accuracy and robustness for SCD task.
Information-Bottleneck Driven Binary Neural Network for Change Detection
2025-07-04
Kaijie Yin, Zhiyuan Zhang, Shu Kong, Tian Gao, Chengzhong Xu, Hui Kong
In this paper, we propose Binarized Change Detection (BiCD), the first binary neural network (BNN) designed specifically for change detection. Conventional network binarization approaches, which directly quantize both weights and activations in change detection models, severely limit the network's ability to represent input data and distinguish between changed and unchanged regions. This results in significantly lower detection accuracy compared to real-valued networks. To overcome these challenges, BiCD enhances both the representational power and feature separability of BNNs, improving detection performance. Specifically, we introduce an auxiliary objective based on the Information Bottleneck (IB) principle, guiding the encoder to retain essential input information while promoting better feature discrimination. Since directly computing mutual information under the IB principle is intractable, we design a compact, learnable auxiliary module as an approximation target, leading to a simple yet effective optimization strategy that minimizes both reconstruction loss and standard change detection loss. Extensive experiments on street-view and remote sensing datasets demonstrate that BiCD establishes a new benchmark for BNN-based change detection, achieving state-of-the-art performance in this domain.
Be the Change You Want to See: Revisiting Remote Sensing Change Detection Practices
2025-07-04
Blaž Rolih, Matic Fučka, Filip Wolf, Luka Čehovin Zajc
Remote sensing change detection aims to localize semantic changes between images of the same location captured at different times. In the past few years, newer methods have attributed enhanced performance to the additions of new and complex components to existing architectures. Most fail to measure the performance contribution of fundamental design choices such as backbone selection, pre-training strategies, and training configurations. We claim that such fundamental design choices often improve performance even more significantly than the addition of new architectural components. Due to that, we systematically revisit the design space of change detection models and analyse the full potential of a well-optimised baseline. We identify a set of fundamental design choices that benefit both new and existing architectures. Leveraging this insight, we demonstrate that when carefully designed, even an architecturally simple model can match or surpass state-of-the-art performance on six challenging change detection datasets. Our best practices generalise beyond our architecture and also offer performance improvements when applied to related methods, indicating that the space of fundamental design choices has been underexplored. Our guidelines and architecture provide a strong foundation for future methods, emphasizing that optimizing core components is just as important as architectural novelty in advancing change detection performance. Code: https://github.com/blaz-r/BTC-change-detection
Change detection typically involves identifying regions with changes between bitemporal images taken at the same location. Besides significant changes, slow changes in bitemporal images are also important in real-life scenarios. For instance, weak changes often serve as precursors to major hazards in scenarios like slopes, dams, and tailings ponds. Therefore, designing a change detection network that simultaneously detects slow and fast changes presents a novel challenge. In this paper, to address this challenge, we propose a change detection network named Flow-CDNet, consisting of two branches: optical flow branch and binary change detection branch. The first branch utilizes a pyramid structure to extract displacement changes at multiple scales. The second one combines a ResNet-based network with the optical flow branch's output to generate fast change outputs. Subsequently, to supervise and evaluate this new change detection framework, a self-built change detection dataset Flow-Change, a loss function combining binary tversky loss and L2 norm loss, along with a new evaluation metric called FEPE are designed. Quantitative experiments conducted on Flow-Change dataset demonstrated that our approach outperforms the existing methods. Furthermore, ablation experiments verified that the two branches can promote each other to enhance the detection performance.
RTMap: Real-Time Recursive Mapping with Change Detection and Localization
2025-07-01
Yuheng Du, Sheng Yang, Lingxuan Wang, Zhenghua Hou, Chengying Cai, Zhitao Tan, Mingxia Chen, Shi-Sheng Huang, Qiang Li
While recent online HD mapping methods relieve burdened offline pipelines and solve map freshness, they remain limited by perceptual inaccuracies, occlusion in dense traffic, and an inability to fuse multi-agent observations. We propose RTMap to enhance these single-traversal methods by persistently crowdsourcing a multi-traversal HD map as a self-evolutional memory. On onboard agents, RTMap simultaneously addresses three core challenges in an end-to-end fashion: (1) Uncertainty-aware positional modeling for HD map elements, (2) probabilistic-aware localization w.r.t. the crowdsourced prior-map, and (3) real-time detection for possible road structural changes. Experiments on several public autonomous driving datasets demonstrate our solid performance on both the prior-aided map quality and the localization accuracy, demonstrating our effectiveness of robustly serving downstream prediction and planning modules while gradually improving the accuracy and freshness of the crowdsourced prior-map asynchronously. Our source-code will be made publicly available at https://github.com/CN-ADLab/RTMap.
Quickest Detection of Adversarial Attacks Against Correlated Equilibria
2025-06-30
Kiarash Kazari, Aris Kanellopoulos, György Dán
We consider correlated equilibria in strategic games in an adversarial environment, where an adversary can compromise the public signal used by the players for choosing their strategies, while players aim at detecting a potential attack as soon as possible to avoid loss of utility. We model the interaction between the adversary and the players as a zero-sum game and we derive the maxmin strategies for both the defender and the attacker using the framework of quickest change detection. We define a class of adversarial strategies that achieve the optimal trade-off between attack impact and attack detectability and show that a generalized CUSUM scheme is asymptotically optimal for the detection of the attacks. Our numerical results on the Sioux-Falls benchmark traffic routing game show that the proposed detection scheme can effectively limit the utility loss by a potential adversary.
CL-Splats: Continual Learning of Gaussian Splatting with Local Optimization
2025-06-26
Jan Ackermann, Jonas Kulhanek, Shengqu Cai, Haofei Xu, Marc Pollefeys, Gordon Wetzstein, Leonidas Guibas, Songyou Peng
In dynamic 3D environments, accurately updating scene representations over time is crucial for applications in robotics, mixed reality, and embodied AI. As scenes evolve, efficient methods to incorporate changes are needed to maintain up-to-date, high-quality reconstructions without the computational overhead of re-optimizing the entire scene. This paper introduces CL-Splats, which incrementally updates Gaussian splatting-based 3D representations from sparse scene captures. CL-Splats integrates a robust change-detection module that segments updated and static components within the scene, enabling focused, local optimization that avoids unnecessary re-computation. Moreover, CL-Splats supports storing and recovering previous scene states, facilitating temporal segmentation and new scene-analysis applications. Our extensive experiments demonstrate that CL-Splats achieves efficient updates with improved reconstruction quality over the state-of-the-art. This establishes a robust foundation for future real-time adaptation in 3D scene reconstruction tasks.
Remote sensing change detection is essential for monitoring urban expansion, disaster assessment, and resource management, offering timely, accurate, and large-scale insights into dynamic landscape transformations. While deep learning has revolutionized change detection, the increasing complexity and computational demands of modern models have not necessarily translated into significant accuracy gains. Instead of following this trend, this study explores a more efficient approach, focusing on lightweight models that maintain high accuracy while minimizing resource consumption, which is an essential requirement for on-satellite processing. To this end, we propose FlickCD, which means quick flick then get great results, pushing the boundaries of the performance-resource trade-off. FlickCD introduces an Enhanced Difference Module (EDM) to amplify critical feature differences between temporal phases while suppressing irrelevant variations such as lighting and weather changes, thereby reducing computational costs in the subsequent change decoder. Additionally, the FlickCD decoder incorporates Local-Global Fusion Blocks, leveraging Shifted Window Self-Attention (SWSA) and Enhanced Global Self-Attention (EGSA) to efficiently capture semantic information at multiple scales, preserving both coarse- and fine-grained changes. Extensive experiments on four benchmark datasets demonstrate that FlickCD reduces computational and storage overheads by more than an order of magnitude while achieving state-of-the-art (SOTA) performance or incurring only a minor (<1\% F1) accuracy trade-off. The implementation code is publicly available at https://github.com/xulsh8/FlickCD.
3D-SSM: A Novel 3D Selective Scan Module for Remote Sensing Change Detection
2025-06-24
Rui Huang, Jincheng Zeng, Sen Gao, Yan Xing
Existing Mamba-based approaches in remote sensing change detection have enhanced scanning models, yet remain limited by their inability to capture long-range dependencies between image channels effectively, which restricts their feature representation capabilities. To address this limitation, we propose a 3D selective scan module (3D-SSM) that captures global information from both the spatial plane and channel perspectives, enabling a more comprehensive understanding of the data.Based on the 3D-SSM, we present two key components: a spatiotemporal interaction module (SIM) and a multi-branch feature extraction module (MBFEM). The SIM facilitates bi-temporal feature integration by enabling interactions between global and local features across images from different time points, thereby enhancing the detection of subtle changes. Meanwhile, the MBFEM combines features from the frequency domain, spatial domain, and 3D-SSM to provide a rich representation of contextual information within the image. Our proposed method demonstrates favourable performance compared to state-of-the-art change detection methods on five benchmark datasets through extensive experiments. Code is available at https://github.com/VerdantMist/3D-SSM
Remote sensing change detection is used in urban planning, terrain analysis, and environmental monitoring by analyzing feature changes in the same area over time. In this paper, we propose a large language model (LLM) augmented inference approach (SegChange-R1), which enhances the detection capability by integrating textual descriptive information and guides the model to focus on relevant change regions, accelerating convergence. We designed a linear attention-based spatial transformation module (BEV) to address modal misalignment by unifying features from different times into a BEV space. Furthermore, we introduce DVCD, a novel dataset for building change detection from UAV viewpoints. Experiments on four widely-used datasets demonstrate significant improvements over existing method The code and pre-trained models are available in {https://github.com/Yu-Zhouz/SegChange-R1}.
Active InSAR monitoring of building damage in Gaza during the Israel-Hamas War
2025-06-17
Corey Scher, Jamon Van Den Hoek
Aerial bombardment of the Gaza Strip beginning October 7, 2023 is one of the most intense bombing campaigns of the twenty-first century, driving widespread urban damage. Characterizing damage over a geographically dynamic and protracted armed conflict requires active monitoring. Synthetic aperture radar (SAR) has precedence for mapping disaster-induced damage with bi-temporal methods but applications to active monitoring during sustained crises are limited. Using interferometric SAR data from Sentinel-1, we apply a long temporal-arc coherent change detection (LT-CCD) approach to track weekly damage trends over the first year of the 2023- Israel-Hamas War. We detect 92.5% of damage labels in reference data from the United Nations with a negligible (1.2%) false positive rate. The temporal fidelity of our approach reveals rapidly increasing damage during the first three months of the war focused in northern Gaza, a notable pause in damage during a temporary ceasefire, and surges of new damage as conflict hot-spots shift from north to south. Three-fifths (191,263) of all buildings are damaged or destroyed by the end of the study. With massive need for timely data on damage in armed conflict zones, our low-cost and low-latency approach enables rapid uptake of damage information at humanitarian and journalistic organizations.
HydroChronos: Forecasting Decades of Surface Water Change
2025-06-17
Daniele Rege Cambrin, Eleonora Poeta, Eliana Pastor, Isaac Corley, Tania Cerquitelli, Elena Baralis, Paolo Garza
Forecasting surface water dynamics is crucial for water resource management and climate change adaptation. However, the field lacks comprehensive datasets and standardized benchmarks. In this paper, we introduce HydroChronos, a large-scale, multi-modal spatiotemporal dataset for surface water dynamics forecasting designed to address this gap. We couple the dataset with three forecasting tasks. The dataset includes over three decades of aligned Landsat 5 and Sentinel-2 imagery, climate data, and Digital Elevation Models for diverse lakes and rivers across Europe, North America, and South America. We also propose AquaClimaTempo UNet, a novel spatiotemporal architecture with a dedicated climate data branch, as a strong benchmark baseline. Our model significantly outperforms a Persistence baseline for forecasting future water dynamics by +14% and +11% F1 across change detection and direction of change classification tasks, and by +0.1 MAE on the magnitude of change regression. Finally, we conduct an Explainable AI analysis to identify the key climate variables and input channels that influence surface water change, providing insights to inform and guide future modeling efforts.
Recently convolution and transformer-based change detection (CD) methods provide promising performance. However, it remains unclear how the local and global dependencies interact to effectively alleviate the pseudo changes. Moreover, directly utilizing standard self-attention presents intrinsic limitations including governing global feature representations limit to capture subtle changes, quadratic complexity, and restricted training parallelism. To address these limitations, we propose a Siamese-based framework, called HyRet-Change, which can seamlessly integrate the merits of convolution and retention mechanisms at multi-scale features to preserve critical information and enhance adaptability in complex scenes. Specifically, we introduce a novel feature difference module to exploit both convolutions and multi-head retention mechanisms in a parallel manner to capture complementary information. Furthermore, we propose an adaptive local-global interactive context awareness mechanism that enables mutual learning and enhances discrimination capability through information exchange. We perform experiments on three challenging CD datasets and achieve state-of-the-art performance compared to existing methods. Our source code is publicly available at https://github.com/mustansarfiaz/HyRect-Change.
Revisiting Clustering of Neural Bandits: Selective Reinitialization for Mitigating Loss of Plasticity
2025-06-14
Zhiyuan Su, Sunhao Dai, Xiao Zhang
Clustering of Bandits (CB) methods enhance sequential decision-making by grouping bandits into clusters based on similarity and incorporating cluster-level contextual information, demonstrating effectiveness and adaptability in applications like personalized streaming recommendations. However, when extending CB algorithms to their neural version (commonly referred to as Clustering of Neural Bandits, or CNB), they suffer from loss of plasticity, where neural network parameters become rigid and less adaptable over time, limiting their ability to adapt to non-stationary environments (e.g., dynamic user preferences in recommendation). To address this challenge, we propose Selective Reinitialization (SeRe), a novel bandit learning framework that dynamically preserves the adaptability of CNB algorithms in evolving environments. SeRe leverages a contribution utility metric to identify and selectively reset underutilized units, mitigating loss of plasticity while maintaining stable knowledge retention. Furthermore, when combining SeRe with CNB algorithms, the adaptive change detection mechanism adjusts the reinitialization frequency according to the degree of non-stationarity, ensuring effective adaptation without unnecessary resets. Theoretically, we prove that SeRe enables sublinear cumulative regret in piecewise-stationary environments, outperforming traditional CNB approaches in long-term performances. Extensive experiments on six real-world recommendation datasets demonstrate that SeRe-enhanced CNB algorithms can effectively mitigate the loss of plasticity with lower regrets, improving adaptability and robustness in dynamic settings.
Environmental Change Detection: Toward a Practical Task of Scene Change Detection
2025-06-13
Kyusik Cho, Suhan Woo, Hongje Seong, Euntai Kim
Humans do not memorize everything. Thus, humans recognize scene changes by exploring the past images. However, available past (i.e., reference) images typically represent nearby viewpoints of the present (i.e., query) scene, rather than the identical view. Despite this practical limitation, conventional Scene Change Detection (SCD) has been formalized under an idealized setting in which reference images with matching viewpoints are available for every query. In this paper, we push this problem toward a practical task and introduce Environmental Change Detection (ECD). A key aspect of ECD is to avoid unrealistically aligned query-reference pairs and rely solely on environmental cues. Inspired by real-world practices, we provide these cues through a large-scale database of uncurated images. To address this new task, we propose a novel framework that jointly understands spatial environments and detects changes. The main idea is that matching at the same spatial locations between a query and a reference may lead to a suboptimal solution due to viewpoint misalignment and limited field-of-view (FOV) coverage. We deal with this limitation by leveraging multiple reference candidates and aggregating semantically rich representations for change detection. We evaluate our framework on three standard benchmark sets reconstructed for ECD, and significantly outperform a naive combination of state-of-the-art methods while achieving comparable performance to the oracle setting. The code will be released upon acceptance.
Do We Still Need Audio? Rethinking Speaker Diarization with a Text-Based Approach Using Multiple Prediction Models
2025-06-12
Peilin Wu, Jinho D. Choi
We present a novel approach to Speaker Diarization (SD) by leveraging text-based methods focused on Sentence-level Speaker Change Detection within dialogues. Unlike audio-based SD systems, which are often challenged by audio quality and speaker similarity, our approach utilizes the dialogue transcript alone. Two models are developed: the Single Prediction Model (SPM) and the Multiple Prediction Model (MPM), both of which demonstrate significant improvements in identifying speaker changes, particularly in short conversations. Our findings, based on a curated dataset encompassing diverse conversational scenarios, reveal that the text-based SD approach, especially the MPM, performs competitively against state-of-the-art audio-based SD systems, with superior performance in short conversational contexts. This paper not only showcases the potential of leveraging linguistic features for SD but also highlights the importance of integrating semantic understanding into SD systems, opening avenues for future research in multimodal and semantic feature-based diarization.
Remote sensing image interpretation plays a critical role in environmental monitoring, urban planning, and disaster assessment. However, acquiring high-quality labeled data is often costly and time-consuming. To address this challenge, we proposes a multi-modal self-supervised learning framework that leverages high-resolution RGB images, multi-spectral data, and digital surface models (DSM) for pre-training. By designing an information-aware adaptive masking strategy, cross-modal masking mechanism, and multi-task self-supervised objectives, the framework effectively captures both the correlations across different modalities and the unique feature structures within each modality. We evaluated the proposed method on multiple downstream tasks, covering typical remote sensing applications such as scene classification, semantic segmentation, change detection, object detection, and depth estimation. Experiments are conducted on 15 remote sensing datasets, encompassing 26 tasks. The results demonstrate that the proposed method outperforms existing pretraining approaches in most tasks. Specifically, on the Potsdam and Vaihingen semantic segmentation tasks, our method achieved mIoU scores of 78.30\% and 76.50\%, with only 50\% train-set. For the US3D depth estimation task, the RMSE error is reduced to 0.182, and for the binary change detection task in SECOND dataset, our method achieved mIoU scores of 47.51\%, surpassing the second CS-MAE by 3 percentage points. Our pretrain code, checkpoints, and HR-Pairs dataset can be found in https://github.com/CVEO/MSSDF.
A Comparative Study of U-Net Architectures for Change Detection in Satellite Images
2025-06-09
Yaxita Amin, Naimisha S Trivedi, Rashmi Bhattad
Remote sensing change detection is essential for monitoring the everchanging landscapes of the Earth. The U-Net architecture has gained popularity for its capability to capture spatial information and perform pixel-wise classification. However, their application in the Remote sensing field remains largely unexplored. Therefore, this paper fill the gap by conducting a comprehensive analysis of 34 papers. This study conducts a comparison and analysis of 18 different U-Net variations, assessing their potential for detecting changes in remote sensing. We evaluate both benefits along with drawbacks of each variation within the framework of this particular application. We emphasize variations that are explicitly built for change detection, such as Siamese Swin-U-Net, which utilizes a Siamese architecture. The analysis highlights the significance of aspects such as managing data from different time periods and collecting relationships over a long distance to enhance the precision of change detection. This study provides valuable insights for researchers and practitioners that choose U-Net versions for remote sensing change detection tasks.
Change-Points Detection and Support Recovery for Spatially Indexed Functional Data
2025-06-08
Fengyi Song, Decai Liang, Changliang Zou
Large volumes of spatiotemporal data, characterized by high spatial and temporal variability, may experience structural changes over time. Unlike traditional change-point problems, each sequence in this context consists of function-valued curves observed at multiple spatial locations, with typically only a small subset of locations affected. This paper addresses two key issues: detecting the global change-point and identifying the spatial support set, within a unified framework tailored to spatially indexed functional data. By leveraging a weakly separable cross-covariance structure -- an extension beyond the restrictive assumption of space-time separability -- we incorporate functional principal component analysis into the change-detection methodology, while preserving common temporal features across locations. A kernel-based test statistic is further developed to integrate spatial clustering pattern into the detection process, and its local variant, combined with the estimated change-point, is employed to identify the subset of locations contributing to the mean shifts. To control the false discovery rate in multiple testing, we introduce a functional symmetrized data aggregation approach that does not rely on pointwise p-values and effectively pools spatial information. We establish the asymptotic validity of the proposed change detection and support recovery method under mild regularity conditions. The efficacy of our approach is demonstrated through simulations, with its practical usefulness illustrated in an application to China's precipitation data.
Training-free AI for Earth Observation Change Detection using Physics Aware Neuromorphic Networks
2025-06-04
Stephen Smith, Cormac Purcell, Zdenka Kuncic
Earth observations from low Earth orbit satellites provide vital information for decision makers to better manage time-sensitive events such as natural disasters. For the data to be most effective for first responders, low latency is required between data capture and its arrival to decision makers. A major bottleneck is in the bandwidth-limited downlinking of the data from satellites to ground stations. One approach to overcome this challenge is to process at least some of the data on-board and prioritise pertinent data to be downlinked. In this work we propose a Physics Aware Neuromorphic Network (PANN) to detect changes caused by natural disasters from a sequence of multi-spectral satellite images and produce a change map, enabling relevant data to be prioritised for downlinking. The PANN used in this study is motivated by physical neural networks comprised of nano-electronic circuit elements known as "memristors" (nonlinear resistors with memory). The weights in the network are dynamic and update in response to varying input signals according to memristor equations of state and electrical circuit conservation laws. The PANN thus generates physics-constrained dynamical output features which are used to detect changes in a natural disaster detection task by applying a distance-based metric. Importantly, this makes the whole model training-free, allowing it to be implemented with minimal computing resources. The PANN was benchmarked against a state-of-the-art AI model and achieved comparable or better results in each natural disaster category. It thus presents a promising solution to the challenge of resource-constrained on-board processing.
Word Sense Detection Leveraging Maximum Mean Discrepancy
2025-06-02
Kensuke Mitsuzawa
Word sense analysis is an essential analysis work for interpreting the linguistic and social backgrounds. The word sense change detection is a task of identifying and interpreting shifts in word meanings over time. This paper proposes MMD-Sense-Analysis, a novel approach that leverages Maximum Mean Discrepancy (MMD) to select semantically meaningful variables and quantify changes across time periods. This method enables both the identification of words undergoing sense shifts and the explanation of their evolution over multiple historical periods. To my knowledge, this is the first application of MMD to word sense change detection. Empirical assessment results demonstrate the effectiveness of the proposed approach.
Multiresolution Analysis and Statistical Thresholding on Dynamic Networks
2025-06-01
Raphaël Romero, Tijl De Bie, Nick Heard, Alexander Modell
Detecting structural change in dynamic network data has wide-ranging applications. Existing approaches typically divide the data into time bins, extract network features within each bin, and then compare these features over time. This introduces an inherent tradeoff between temporal resolution and the statistical stability of the extracted features. Despite this tradeoff, reminiscent of time-frequency tradeoffs in signal processing, most methods rely on a fixed temporal resolution. Choosing an appropriate resolution parameter is typically difficult and can be especially problematic in domains like cybersecurity, where anomalous behavior may emerge at multiple time scales. We address this challenge by proposing ANIE (Adaptive Network Intensity Estimation), a multi-resolution framework designed to automatically identify the time scales at which network structure evolves, enabling the joint detection of both rapid and gradual changes. Modeling interactions as Poisson processes, our method proceeds in two steps: (1) estimating a low-dimensional subspace of node behavior, and (2) deriving a set of novel empirical affinity coefficients that quantify change in interaction intensity between latent factors and support statistical testing for structural change across time scales. We provide theoretical guarantees for subspace estimation and the asymptotic behavior of the affinity coefficients, enabling model-based change detection. Experiments on synthetic networks show that ANIE adapts to the appropriate time resolution and is able to capture sharp structural changes while remaining robust to noise. Furthermore, applications to real-world data showcase the practical benefits of ANIE's multiresolution approach to detecting structural change over fixed resolution methods.
Multimodal large language models have demonstrated remarkable capabilities in visual understanding, but their application to long-term Earth observation analysis remains limited, primarily focusing on single-temporal or bi-temporal imagery. To address this gap, we introduce DVL-Suite, a comprehensive framework for analyzing long-term urban dynamics through remote sensing imagery. Our suite comprises 15,063 high-resolution (1.0m) multi-temporal images spanning 42 megacities in the U.S. from 2005 to 2023, organized into two components: DVL-Bench and DVL-Instruct. The DVL-Bench includes seven urban understanding tasks, from fundamental change detection (pixel-level) to quantitative analyses (regional-level) and comprehensive urban narratives (scene-level), capturing diverse urban dynamics including expansion/transformation patterns, disaster assessment, and environmental challenges. We evaluate 17 state-of-the-art multimodal large language models and reveal their limitations in long-term temporal understanding and quantitative analysis. These challenges motivate the creation of DVL-Instruct, a specialized instruction-tuning dataset designed to enhance models' capabilities in multi-temporal Earth observation. Building upon this dataset, we develop DVLChat, a baseline model capable of both image-level question-answering and pixel-level segmentation, facilitating a comprehensive understanding of city dynamics through language interactions.
What Changed? Detecting and Evaluating Instruction-Guided Image Edits with Multimodal Large Language Models
2025-05-26
Lorenzo Baraldi, Davide Bucciarelli, Federico Betti, Marcella Cornia, Lorenzo Baraldi, Nicu Sebe, Rita Cucchiara
Instruction-based image editing models offer increased personalization opportunities in generative tasks. However, properly evaluating their results is challenging, and most of the existing metrics lag in terms of alignment with human judgment and explainability. To tackle these issues, we introduce DICE (DIfference Coherence Estimator), a model designed to detect localized differences between the original and the edited image and to assess their relevance to the given modification request. DICE consists of two key components: a difference detector and a coherence estimator, both built on an autoregressive Multimodal Large Language Model (MLLM) and trained using a strategy that leverages self-supervision, distillation from inpainting networks, and full supervision. Through extensive experiments, we evaluate each stage of our pipeline, comparing different MLLMs within the proposed framework. We demonstrate that DICE effectively identifies coherent edits, effectively evaluating images generated by different editing models with a strong correlation with human judgment. We publicly release our source code, models, and data.
This article studies the problem of online non-parametric change point detection in multivariate data streams. We approach the problem through the lens of kernel-based two-sample testing and introduce a sequential testing procedure based on random Fourier features, running with logarithmic time complexity per observation and with overall logarithmic space complexity. The algorithm has two advantages compared to the state of the art. First, our approach is genuinely online, and no access to training data known to be from the pre-change distribution is necessary. Second, the algorithm does not require the user to specify a window parameter over which local tests are to be calculated. We prove strong theoretical guarantees on the algorithm's performance, including information-theoretic bounds demonstrating that the detection delay is optimal in the minimax sense. Numerical studies on real and synthetic data show that our algorithm is competitive with respect to the state of the art.
Quantum-Driven Multihead Inland Waterbody Detection With Transformer-Encoded CYGNSS Delay-Doppler Map Data
2025-05-22
Chia-Hsiang Lin, Jhao-Ting Lin, Po-Ying Chiu, Shih-Ping Chen, Charles C. H. Lin
Inland waterbody detection (IWD) is critical for water resources management and agricultural planning. However, the development of high-fidelity IWD mapping technology remains unresolved. We aim to propose a practical solution based on the easily accessible data, i.e., the delay-Doppler map (DDM) provided by NASA's Cyclone Global Navigation Satellite System (CYGNSS), which facilitates effective estimation of physical parameters on the Earth's surface with high temporal resolution and wide spatial coverage. Specifically, as quantum deep network (QUEEN) has revealed its strong proficiency in addressing classification-like tasks, we encode the DDM using a customized transformer, followed by feeding the transformer-encoded DDM (tDDM) into a highly entangled QUEEN to distinguish whether the tDDM corresponds to a hydrological region. In recent literature, QUEEN has achieved outstanding performances in numerous challenging remote sensing tasks (e.g., hyperspectral restoration, change detection, and mixed noise removal, etc.), and its high effectiveness stems from the fundamentally different way it adopts to extract features (the so-called quantum unitary-computing features). The meticulously designed IWD-QUEEN retrieves high-precision river textures, such as those in Amazon River Basin in South America, demonstrating its superiority over traditional classification methods and existing global hydrography maps. IWD-QUEEN, together with its parallel quantum multihead scheme, works in a near-real-time manner (i.e., millisecond-level computing per DDM). To broaden accessibility for users of traditional computers, we also provide the non-quantum counterpart of our method, called IWD-Transformer, thereby increasing the impact of this work.
CEBSNet: Change-Excited and Background-Suppressed Network with Temporal Dependency Modeling for Bitemporal Change Detection
2025-05-21
Qi'ao Xu, Yan Xing, Jiali Hu, Yunan Jia, Rui Huang
Change detection, a critical task in remote sensing and computer vision, aims to identify pixel-level differences between image pairs captured at the same geographic area but different times. It faces numerous challenges such as illumination variation, seasonal changes, background interference, and shooting angles, especially with a large time gap between images. While current methods have advanced, they often overlook temporal dependencies and overemphasize prominent changes while ignoring subtle but equally important changes. To address these limitations, we introduce \textbf{CEBSNet}, a novel change-excited and background-suppressed network with temporal dependency modeling for change detection. During the feature extraction, we utilize a simple Channel Swap Module (CSM) to model temporal dependency, reducing differences and noise. The Feature Excitation and Suppression Module (FESM) is developed to capture both obvious and subtle changes, maintaining the integrity of change regions. Additionally, we design a Pyramid-Aware Spatial-Channel Attention module (PASCA) to enhance the ability to detect change regions at different sizes and focus on critical regions. We conduct extensive experiments on three common street view datasets and two remote sensing datasets, and our method achieves the state-of-the-art performance.
Detection, attribution, and modeling of climate change: key open issues
2025-05-21
Nicola Scafetta
The CMIP global climate models (GCMs) assess that nearly 100% of global surface warming observed between 1850-1900 and 2011-2020 is attributable to anthropogenic drivers like greenhouse gas emissions. These models also generate future climate projections based on shared socioeconomic pathways (SSPs), aiding in risk assessment and the development of costly Net-Zero climate mitigation strategies. Yet, the CMIP GCMs face significant scientific challenges in attributing and modeling climate change, particularly in capturing natural climate variability over multiple timescales throughout the Holocene. Other key concerns include the reliability of global surface temperature records, the accuracy of solar irradiance models, and the robustness of climate sensitivity estimates. Global warming estimates may be overstated due to uncorrected non-climatic biases, and the GCMs may significantly underestimate solar and astronomical influences on climate variations. The equilibrium climate sensitivity (ECS) to radiative forcing could be lower than commonly assumed; empirical findings suggest ECS values lower than 3 K and possibly even closer to 1.1 +/- 0.4 K. Empirical models incorporating natural variability suggest that the 21st-century global warming may remain moderate, even under SSP scenarios that do not necessitate Net-Zero emission policies. These findings raise important questions regarding the necessity and urgency of implementing aggressive climate mitigation strategies. While GCMs remain essential tools for climate research and policymaking, their scientific limitations underscore the need for more refined modeling approaches to ensure accurate future climate assessments. Addressing uncertainties related to climate change detection, natural variability, solar influences, and climate sensitivity to radiative forcing will enhance predictions and better inform sustainable climate strategies.
Scaling Vision Mamba Across Resolutions via Fractal Traversal
2025-05-20
Bo Li, Haoke Xiao, Lv Tang
Vision Mamba has recently emerged as a promising alternative to Transformer-based architectures, offering linear complexity in sequence length while maintaining strong modeling capacity. However, its adaptation to visual inputs is hindered by challenges in 2D-to-1D patch serialization and weak scalability across input resolutions. Existing serialization strategies such as raster scanning disrupt local spatial continuity and limit the model's ability to generalize across scales. In this paper, we propose FractalMamba++, a robust vision backbone that leverages fractal-based patch serialization via Hilbert curves to preserve spatial locality and enable seamless resolution adaptability. To address long-range dependency fading in high-resolution inputs, we further introduce a Cross-State Routing (CSR) mechanism that enhances global context propagation through selective state reuse. Additionally, we propose a Positional-Relation Capture (PRC) module to recover local adjacency disrupted by curve inflection points. Extensive experiments on image classification, semantic segmentation, object detection, and change detection demonstrate that FractalMamba++ consistently outperforms previous Mamba-based backbones, particularly under high-resolution settings.
RB-SCD: A New Benchmark for Semantic Change Detection of Roads and Bridges in Traffic Scenes
2025-05-19
Qingling Shu, Sibao Chen, Zhihui You, Wei Lu, Jin Tang, Bin Luo
With the rapid modernization of urban transportation, accurately detecting changes such as road and bridge construction, renovation, and demolition is crucial for urban planning and traffic management. However, existing methods often struggle to extract fine-grained semantic changes in complex traffic scenes, largely due to the lack of high-quality annotated change detection (CD) datasets. To address this, we introduce the Road and Bridge Semantic Change Detection (RB-SCD) dataset, a comprehensive benchmark consisting of 260 pairs of high-resolution remote sensing images. RB-SCD spans diverse geographic areas and includes a wide variety of road and bridge types across over ten cities in multiple countries. It covers 11 distinct categories of semantic changes, enabling detailed structural and functional analysis. Based on this challenging dataset, we propose a novel framework called the Multimodal Frequency-Driven Change Detector (MFDCD). For the first time, MFDCD integrates multimodal feature characteristics in the frequency domain. It comprises two key components: the Dynamic Frequency Coupler (DFC) and the Textual Frequency Filter (TFF). DFC couples hierarchical visual features with wavelet-based frequency components, enhancing the perception of fine-grained and cross-temporal structural changes. TFF transforms textual features extracted by the CLIP model into the frequency domain via Fourier transform and applies graph-based filtering to extract salient frequency responses. These are then fused with visual features to enable effective multimodal representation learning. Extensive experiments show that MFDCD achieves strong performance on RB-SCD and three public benchmarks. The RB-SCD dataset, with its rich and diverse annotations, serves as a valuable resource for advancing research in road and bridge change detection under complex traffic conditions.
The proliferation of multi-source remote sensing data has propelled the development of deep learning for dense prediction, yet significant challenges in data and task unification persist. Current deep learning architectures for remote sensing are fundamentally rigid. They are engineered for fixed input-output configurations, restricting their adaptability to the heterogeneous spatial, temporal, and spectral dimensions inherent in real-world data. Furthermore, these models neglect the intrinsic correlations among semantic segmentation, binary change detection, and semantic change detection, necessitating the development of distinct models or task-specific decoders. This paradigm is also constrained to a predefined set of output semantic classes, where any change to the classes requires costly retraining. To overcome these limitations, we introduce the Spatial-Temporal-Spectral Unified Network (STSUN) for unified modeling. STSUN can adapt to input and output data with arbitrary spatial sizes, temporal lengths, and spectral bands by leveraging their metadata for a unified representation. Moreover, STSUN unifies disparate dense prediction tasks within a single architecture by conditioning the model on trainable task embeddings. Similarly, STSUN facilitates flexible prediction across any set of semantic categories by integrating trainable category embeddings as metadata. Extensive experiments on multiple datasets with diverse STS configurations in multiple scenarios demonstrate that a single STSUN model effectively adapts to heterogeneous inputs and outputs, unifying various dense prediction tasks and diverse semantic class predictions. The proposed approach consistently achieves state-of-the-art performance, highlighting its robustness and generalizability for complex remote sensing applications.
A Perturbation and Speciation-Based Algorithm for Dynamic Optimization Uninformed of Change
2025-05-16
Federico Signorelli, Anil Yaman
Dynamic optimization problems (DOPs) are challenging due to their changing conditions. This requires algorithms to be highly adaptable and efficient in terms of finding rapidly new optimal solutions under changing conditions. Traditional approaches often depend on explicit change detection, which can be impractical or inefficient when the change detection is unreliable or unfeasible. We propose Perturbation and Speciation-Based Particle Swarm Optimization (PSPSO), a robust algorithm for uninformed dynamic optimization without requiring the information of environmental changes. The PSPSO combines speciation-based niching, deactivation, and a newly proposed random perturbation mechanism to handle DOPs. PSPSO leverages a cyclical multi-population framework, strategic resource allocation, and targeted noisy updates, to adapt to dynamic environments. We compare PSPSO with several state-of-the-art algorithms on the Generalized Moving Peaks Benchmark (GMPB), which covers a variety of scenarios, including simple and multi-modal dynamic optimization, frequent and intense changes, and high-dimensional spaces. Our results show that PSPSO outperforms other state-of-the-art uninformed algorithms in all scenarios and leads to competitive results compared to informed algorithms. In particular, PSPSO shows strength in functions with high dimensionality or high frequency of change in the GMPB. The ablation study showed the importance of the random perturbation component.
Non-Registration Change Detection: A Novel Change Detection Task and Benchmark Dataset
2025-05-15
Zhe Shan, Lei Zhou, Liu Mao, Shaofan Chen, Chuanqiu Ren, Xia Xie
In this study, we propose a novel remote sensing change detection task, non-registration change detection, to address the increasing number of emergencies such as natural disasters, anthropogenic accidents, and military strikes. First, in light of the limited discourse on the issue of non-registration change detection, we systematically propose eight scenarios that could arise in the real world and potentially contribute to the occurrence of non-registration problems. Second, we develop distinct image transformation schemes tailored to various scenarios to convert the available registration change detection dataset into a non-registration version. Finally, we demonstrate that non-registration change detection can cause catastrophic damage to the state-of-the-art methods. Our code and dataset are available at https://github.com/ShanZard/NRCD.
8 Years of Optimizing Apache Otava: How disconnected open source developers took an algorithm from n3 to constant time
2025-05-10
Henrik Ingo
As the project now known as Apache Otava (incubating) makes it first release, we look back over the past 8 years that the codebase was developed by a rather uncoordinated, loosely connected group of performance engineers at MongoDB, Datastax, Confluent, Nyrkio and others. Ever since the first publication (Daly 2020), developers of the code base now known as Apache Otava (incubating), have continuosly improved its performance. Even when a contributor's primary motivation was to add functionality, it seems like they couldn't help themselves but to also make some performance optimizations while at it. When developing the Nyrkio web service to provide change detection for performance testing, we have observed that Otava had become fast enough that it was almost feasible to compute change points synchronously, as the user is browsing test results in a web browser. Inspired by this, we have developed and contributed a new optimization for the common case where new data points are appended to the end of the series. This is now the 7th generation of performance optimizations in Otava. These improvements have been done over the past 8 years of development, by disconnected individuals at different employees. Taken together, the historical optimizations and those published in this paper, represent a 18 000 to 300 000 speedup over the original by the book implementation of (Matteson and James 2014). In the language of computational complexity, an evolution from O (n3) to O (1) (constant time). The ability to compute and recompute change points in real-time unlocks new opportunities in the user experience.
Building-Guided Pseudo-Label Learning for Cross-Modal Building Damage Mapping
2025-05-08
Jiepan Li, He Huang, Yu Sheng, Yujun Guo, Wei He
Accurate building damage assessment using bi-temporal multi-modal remote sensing images is essential for effective disaster response and recovery planning. This study proposes a novel Building-Guided Pseudo-Label Learning Framework to address the challenges of mapping building damage from pre-disaster optical and post-disaster SAR images. First, we train a series of building extraction models using pre-disaster optical images and building labels. To enhance building segmentation, we employ multi-model fusion and test-time augmentation strategies to generate pseudo-probabilities, followed by a low-uncertainty pseudo-label training method for further refinement. Next, a change detection model is trained on bi-temporal cross-modal images and damaged building labels. To improve damage classification accuracy, we introduce a building-guided low-uncertainty pseudo-label refinement strategy, which leverages building priors from the previous step to guide pseudo-label generation for damaged buildings, reducing uncertainty and enhancing reliability. Experimental results on the 2025 IEEE GRSS Data Fusion Contest dataset demonstrate the effectiveness of our approach, which achieved the highest mIoU score (54.28%) and secured first place in the competition.
Semantic Change Detection (SCD) in remote sensing imagery requires accurately identifying land-cover changes across multi-temporal image pairs. Despite substantial advancements, including the introduction of transformer-based architectures, current SCD models continue to struggle with challenges such as noisy inputs, subtle class boundaries, and significant class imbalance. In this study, we propose enhancing the Semantic Change Network (SCanNet) by integrating the Convolutional Block Attention Module (CBAM) and employing Dice loss during training. CBAM sequentially applies channel attention to highlight feature maps with the most meaningful content, followed by spatial attention to pinpoint critical regions within these maps. This sequential approach ensures precise suppression of irrelevant features and spatial noise, resulting in more accurate and robust detection performance compared to attention mechanisms that apply both processes simultaneously or independently. Dice loss, designed explicitly for handling class imbalance, further boosts sensitivity to minority change classes. Quantitative experiments conducted on the SECOND dataset demonstrate consistent improvements. Qualitative analysis confirms these improvements, showing clearer segmentation boundaries and more accurate recovery of small-change regions. These findings highlight the effectiveness of attention mechanisms and Dice loss in improving feature representation and addressing class imbalance in semantic change detection tasks.
Regularized Fingerprinting with Linearly Optimal Weight Matrix in Detection and Attribution of Climate Change
2025-05-07
Haoran Li, Yan Li
Climate change detection and attribution play a central role in establishing the causal influence of human activities on global warming. The dominant framework, optimal fingerprinting, is a linear errors-in-variables model in which each covariate is subject to measurement error with covariance proportional to that of the regression error. The reliability of such analyses depends critically on accurate inference of the regression coefficients. The optimal weight matrix for estimating these coefficients is the precision matrix of the regression error, which is typically unknown and must be estimated from climate model simulations. However, existing regularized optimal fingerprinting approaches often yield underestimated uncertainties and overly narrow confidence intervals that fail to attain nominal coverage, thereby compromising the reliability of analysis. In this paper, we first propose consistent variance estimators for the regression coefficients within the class of linear shrinkage weight matrices, addressing undercoverage in conventional methods. Building on this, we derive a linearly optimal weight matrix that directly minimizes the asymptotic variances of the estimated scaling factors. Numerical studies confirm improved empirical coverage and shorter interval lengths. When applied to annual mean temperature data, the proposed method produces narrower, more reliable intervals and provides new insights into detection and attribution across different regions.
Model-Targeted Data Poisoning Attacks against ITS Applications with Provable Convergence
2025-05-06
Xin Wang, Feilong Wang, Yuan Hong, R. Tyrrell Rockafellar, Xuegang, Ban
The growing reliance of intelligent systems on data makes the systems vulnerable to data poisoning attacks. Such attacks could compromise machine learning or deep learning models by disrupting the input data. Previous studies on data poisoning attacks are subject to specific assumptions, and limited attention is given to learning models with general (equality and inequality) constraints or lacking differentiability. Such learning models are common in practice, especially in Intelligent Transportation Systems (ITS) that involve physical or domain knowledge as specific model constraints. Motivated by ITS applications, this paper formulates a model-target data poisoning attack as a bi-level optimization problem with a constrained lower-level problem, aiming to induce the model solution toward a target solution specified by the adversary by modifying the training data incrementally. As the gradient-based methods fail to solve this optimization problem, we propose to study the Lipschitz continuity property of the model solution, enabling us to calculate the semi-derivative, a one-sided directional derivative, of the solution over data. We leverage semi-derivative descent to solve the bi-level optimization problem, and establish the convergence conditions of the method to any attainable target model. The model and solution method are illustrated with a simulation of a poisoning attack on the lane change detection using SVM.
Deep learning has profoundly transformed remote sensing, yet prevailing architectures like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) remain constrained by critical trade-offs: CNNs suffer from limited receptive fields, while ViTs grapple with quadratic computational complexity, hindering their scalability for high-resolution remote sensing data. State Space Models (SSMs), particularly the recently proposed Mamba architecture, have emerged as a paradigm-shifting solution, combining linear computational scaling with global context modeling. This survey presents a comprehensive review of Mamba-based methodologies in remote sensing, systematically analyzing about 120 Mamba-based remote sensing studies to construct a holistic taxonomy of innovations and applications. Our contributions are structured across five dimensions: (i) foundational principles of vision Mamba architectures, (ii) micro-architectural advancements such as adaptive scan strategies and hybrid SSM formulations, (iii) macro-architectural integrations, including CNN-Transformer-Mamba hybrids and frequency-domain adaptations, (iv) rigorous benchmarking against state-of-the-art methods in multiple application tasks, such as object detection, semantic segmentation, change detection, etc. and (v) critical analysis of unresolved challenges with actionable future directions. By bridging the gap between SSM theory and remote sensing practice, this survey establishes Mamba as a transformative framework for remote sensing analysis. To our knowledge, this paper is the first systematic review of Mamba architectures in remote sensing. Our work provides a structured foundation for advancing research in remote sensing systems through SSM-based methods. We curate an open-source repository (https://github.com/BaoBao0926/Awesome-Mamba-in-Remote-Sensing) to foster community-driven advancements.
Lightweight Adapter Learning for More Generalized Remote Sensing Change Detection
2025-04-28
Dou Quan, Rufan Zhou, Shuang Wang, Ning Huyan, Dong Zhao, Yunan Li, Licheng Jiao
Deep learning methods have shown promising performances in remote sensing image change detection (CD). However, existing methods usually train a dataset-specific deep network for each dataset. Due to the significant differences in the data distribution and labeling between various datasets, the trained dataset-specific deep network has poor generalization performances on other datasets. To solve this problem, this paper proposes a change adapter network (CANet) for a more universal and generalized CD. CANet contains dataset-shared and dataset-specific learning modules. The former explores the discriminative features of images, and the latter designs a lightweight adapter model, to deal with the characteristics of different datasets in data distribution and labeling. The lightweight adapter can quickly generalize the deep network for new CD tasks with a small computation cost. Specifically, this paper proposes an interesting change region mask (ICM) in the adapter, which can adaptively focus on interested change objects and decrease the influence of labeling differences in various datasets. Moreover, CANet adopts a unique batch normalization layer for each dataset to deal with data distribution differences. Compared with existing deep learning methods, CANet can achieve satisfactory CD performances on various datasets simultaneously. Experimental results on several public datasets have verified the effectiveness and advantages of the proposed CANet on CD. CANet has a stronger generalization ability, smaller training costs (merely updating 4.1%-7.7% parameters), and better performances under limited training datasets than other deep learning methods, which also can be flexibly inserted with existing deep models.
Quantum-Enhanced Change Detection and Joint Communication-Detection
2025-04-24
Zihao Gong, Saikat Guha
Quick detection of transmittance changes in optical channel is crucial for secure communication. We demonstrate that pre-shared entanglement using two-mode squeezed vacuum states significantly reduces detection latency compared to classical and entanglement-augmented coherent-state probes. The change detection latency is inversely proportional to the quantum relative entropy (QRE), which goes to infinity in the absence of thermal noise, suggesting idealized instantaneous detection. However, in realistic scenarios, we show that QRE scales logarithmically with the inverse of the thermal noise mean photon number. We propose a receiver that achieves this scaling and quantify its performance gains over existing methods. Additionally, we explore the fundamental trade-off between communication capacity and change detection latency, highlighting how pre-shared entanglement enhances both.
Bernstein Polynomial Processes for Continuous Time Change Detection
2025-04-24
Dan Cunha, Mark Friedl, Luis Carvalho
There is a lack of methodological results for continuous time change detection due to the challenges of noninformative prior specification and efficient posterior inference in this setting. Most methodologies to date assume data are collected according to uniformly spaced time intervals. This assumption incurs bias in the continuous time setting where, a priori, two consecutive observations measured closely in time are less likely to change than two consecutive observations that are far apart in time. Models proposed in this setting have required MCMC sampling which is not ideal. To address these issues, we derive the heterogeneous continuous time Markov chain that models change point transition probabilities noninformatively. By construction, change points under this model can be inferred efficiently using the forward backward algorithm and do not require MCMC sampling. We then develop a novel loss function for the continuous time setting, derive its Bayes estimator, and demonstrate its performance on synthetic data. A case study using time series of remotely sensed observations is then carried out on three change detection applications. To reduce falsely detected changes in this setting, we develop a semiparametric mean function that captures interannual variability due to weather in addition to trend and seasonal components.
The remote sensing image change detection task is an essential method for large-scale monitoring. We propose HSANet, a network that uses hierarchical convolution to extract multi-scale features. It incorporates hybrid self-attention and cross-attention mechanisms to learn and fuse global and cross-scale information. This enables HSANet to capture global context at different scales and integrate cross-scale features, refining edge details and improving detection performance. We will also open-source our model code: https://github.com/ChengxiHAN/HSANet.
As an essential procedure in earth observation system, change detection (CD) aims to reveal the spatial-temporal evolution of the observation regions. A key prerequisite for existing change detection algorithms is aligned geo-references between multi-temporal images by fine-grained registration. However, in the majority of real-world scenarios, a prior manual registration is required between the original images, which significantly increases the complexity of the CD workflow. In this paper, we proposed a self-supervision motivated CD framework with geometric estimation, called "MatchCD". Specifically, the proposed MatchCD framework utilizes the zero-shot capability to optimize the encoder with self-supervised contrastive representation, which is reused in the downstream image registration and change detection to simultaneously handle the bi-temporal unalignment and object change issues. Moreover, unlike the conventional change detection requiring segmenting the full-frame image into small patches, our MatchCD framework can directly process the original large-scale image (e.g., 6K*4K resolutions) with promising performance. The performance in multiple complex scenarios with significant geometric distortion demonstrates the effectiveness of our proposed framework.
DAM-Net: Domain Adaptation Network with Micro-Labeled Fine-Tuning for Change Detection
2025-04-18
Hongjia Chen, Xin Xu, Fangling Pu
Change detection (CD) in remote sensing imagery plays a crucial role in various applications such as urban planning, damage assessment, and resource management. While deep learning approaches have significantly advanced CD performance, current methods suffer from poor domain adaptability, requiring extensive labeled data for retraining when applied to new scenarios. This limitation severely restricts their practical applications across different datasets. In this work, we propose DAM-Net: a Domain Adaptation Network with Micro-Labeled Fine-Tuning for CD. Our network introduces adversarial domain adaptation to CD for, utilizing a specially designed segmentation-discriminator and alternating training strategy to enable effective transfer between domains. Additionally, we propose a novel Micro-Labeled Fine-Tuning approach that strategically selects and labels a minimal amount of samples (less than 1%) to enhance domain adaptation. The network incorporates a Multi-Temporal Transformer for feature fusion and optimized backbone structure based on previous research. Experiments conducted on the LEVIR-CD and WHU-CD datasets demonstrate that DAM-Net significantly outperforms existing domain adaptation methods, achieving comparable performance to semi-supervised approaches that require 10% labeled data while using only 0.3% labeled samples. Our approach significantly advances cross-dataset CD applications and provides a new paradigm for efficient domain adaptation in remote sensing. The source code of DAM-Net will be made publicly available upon publication.
Semi-supervised change detection (SSCD) aims to detect changes between bi-temporal remote sensing images by utilizing limited labeled data and abundant unlabeled data. Existing methods struggle in complex scenarios, exhibiting poor performance when confronted with noisy data. They typically neglect intra-layer multi-scale features while emphasizing inter-layer fusion, harming the integrity of change objects with different scales. In this paper, we propose HSACNet, a Hierarchical Scale-Aware Consistency regularized Network for SSCD. Specifically, we integrate Segment Anything Model 2 (SAM2), using its Hiera backbone as the encoder to extract inter-layer multi-scale features and applying adapters for parameter-efficient fine-tuning. Moreover, we design a Scale-Aware Differential Attention Module (SADAM) that can precisely capture intra-layer multi-scale change features and suppress noise. Additionally, a dual-augmentation consistency regularization strategy is adopted to effectively utilize the unlabeled data. Extensive experiments across four CD benchmarks demonstrate that our HSACNet achieves state-of-the-art performance, with reduced parameters and computational cost.
SAM-Based Building Change Detection with Distribution-Aware Fourier Adaptation and Edge-Constrained Warping
2025-04-17
Yun-Cheng Li, Sen Lei, Yi-Tao Zhao, Heng-Chao Li, Jun Li, Antonio Plaza
Building change detection remains challenging for urban development, disaster assessment, and military reconnaissance. While foundation models like Segment Anything Model (SAM) show strong segmentation capabilities, SAM is limited in the task of building change detection due to domain gap issues. Existing adapter-based fine-tuning approaches face challenges with imbalanced building distribution, resulting in poor detection of subtle changes and inaccurate edge extraction. Additionally, bi-temporal misalignment in change detection, typically addressed by optical flow, remains vulnerable to background noises. This affects the detection of building changes and compromises both detection accuracy and edge recognition. To tackle these challenges, we propose a new SAM-Based Network with Distribution-Aware Fourier Adaptation and Edge-Constrained Warping (FAEWNet) for building change detection. FAEWNet utilizes the SAM encoder to extract rich visual features from remote sensing images. To guide SAM in focusing on specific ground objects in remote sensing scenes, we propose a Distribution-Aware Fourier Aggregated Adapter to aggregate task-oriented changed information. This adapter not only effectively addresses the domain gap issue, but also pays attention to the distribution of changed buildings. Furthermore, to mitigate noise interference and misalignment in height offset estimation, we design a novel flow module that refines building edge extraction and enhances the perception of changed buildings. Our state-of-the-art results on the LEVIR-CD, S2Looking and WHU-CD datasets highlight the effectiveness of FAEWNet. The code is available at https://github.com/SUPERMAN123000/FAEWNet.
Change State Space Models for Remote Sensing Change Detection
2025-04-15
Elman Ghazaei, Erchan Aptoula
Despite their frequent use for change detection, both ConvNets and Vision transformers (ViT) exhibit well-known limitations, namely the former struggle to model long-range dependencies while the latter are computationally inefficient, rendering them challenging to train on large-scale datasets. Vision Mamba, an architecture based on State Space Models has emerged as an alternative addressing the aforementioned deficiencies and has been already applied to remote sensing change detection, though mostly as a feature extracting backbone. In this article the Change State Space Model is introduced, that has been specifically designed for change detection by focusing on the relevant changes between bi-temporal images, effectively filtering out irrelevant information. By concentrating solely on the changed features, the number of network parameters is reduced, enhancing significantly computational efficiency while maintaining high detection performance and robustness against input degradation. The proposed model has been evaluated via three benchmark datasets, where it outperformed ConvNets, ViTs, and Mamba-based counterparts at a fraction of their computational complexity. The implementation will be made available at https://github.com/Elman295/CSSM upon acceptance.
A Quantum Advantage in Localizing Transmission Loss Change in Optical Networks
2025-04-15
Yufei Zheng, Yu-Zhen Janice Chen, Prithwish Basu, Don Towsley
The ability to localize transmission loss change to a subset of links in optical networks is crucial for maintaining network reliability, performance and security. \emph{Quantum probes}, implemented by sending blocks of $n$ coherent-state pulses augmented with continuous-variable (CV) squeezing ($n=1$) or weak temporal-mode entanglement ($n>1$) over a lossy channel to a receiver with homodyne detection capabilities, are known to be more sensitive than their quasi-classical counterparts in detecting a sudden increase in channel loss. The enhanced sensitivity can be characterized by the increased Kullback-Leibler (KL) divergence of the homodyne output, before and after the loss change occurs. When combined with the theory of quickest change detection (QCD), the increase in KL divergence translates into a decrease in detection latency. In this work, we first revisit quantum probes over a channel, generalizing previous results on $n=1$ (CV squeezed states) to arbitrary values of $n$. Assuming a subset of nodes in an optical network is capable of sending and receiving such probes through intermediate nodes with all-optical switching capabilities, we present a scheme for quickly detecting the links that have suffered a sudden drop in transmissivity. Since quantum probes lose their sensitivity with increasing loss in the channel, we first propose a probe construction algorithm that makes the set of links suffering transmission loss change identifiable, while minimizing the longest distance a probe traverses. We then introduce new cumulative sum (CUSUM) statistics with a stopping rule, which allows us to run the CUSUM algorithm to quickly localize the lossy links using our constructed probes. Finally, we show that the proposed scheme achieves a quantum advantage in decreasing the detection delay.
LightFormer: A lightweight and efficient decoder for remote sensing image segmentation
2025-04-15
Sihang Chen, Lijun Yun, Ze Liu, JianFeng Zhu, Jie Chen, Hui Wang, Yueping Nie
Deep learning techniques have achieved remarkable success in the semantic segmentation of remote sensing images and in land-use change detection. Nevertheless, their real-time deployment on edge platforms remains constrained by decoder complexity. Herein, we introduce LightFormer, a lightweight decoder for time-critical tasks that involve unstructured targets, such as disaster assessment, unmanned aerial vehicle search-and-rescue, and cultural heritage monitoring. LightFormer employs a feature-fusion and refinement module built on channel processing and a learnable gating mechanism to aggregate multi-scale, multi-range information efficiently, which drastically curtails model complexity. Furthermore, we propose a spatial information selection module (SISM) that integrates long-range attention with a detail preservation branch to capture spatial dependencies across multiple scales, thereby substantially improving the recognition of unstructured targets in complex scenes. On the ISPRS Vaihingen benchmark, LightFormer attains 99.9% of GLFFNet's mIoU (83.9% vs. 84.0%) while requiring only 14.7% of its FLOPs and 15.9% of its parameters, thus achieving an excellent accuracy-efficiency trade-off. Consistent results on LoveDA, ISPRS Potsdam, RescueNet, and FloodNet further demonstrate its robustness and superior perception of unstructured objects. These findings highlight LightFormer as a practical solution for remote sensing applications where both computational economy and high-precision segmentation are imperative.
BLAST: Bayesian online change-point detection with structured image data
2025-04-14
Xiaojun Zheng, Simon Mak
The prompt online detection of abrupt changes in image data is essential for timely decision-making in broad applications, from video surveillance to manufacturing quality control. Existing methods, however, face three key challenges. First, the high-dimensional nature of image data introduces computational bottlenecks for efficient real-time monitoring. Second, changes often involve structural image features, e.g., edges, blurs and/or shapes, and ignoring such structure can lead to delayed change detection. Third, existing methods are largely non-Bayesian and thus do not provide a quantification of monitoring uncertainty for confident detection. We address this via a novel Bayesian onLine Structure-Aware change deTection (BLAST) method. BLAST first leverages a deep Gaussian Markov random field prior to elicit desirable image structure from offline reference data. With this prior elicited, BLAST employs a new Bayesian online change-point procedure for image monitoring via its so-called posterior run length distribution. This posterior run length distribution can be computed in an online fashion using $\mathcal{O}(p^2)$ work at each time-step, where $p$ is the number of image pixels; this facilitates scalable Bayesian online monitoring of large images. We demonstrate the effectiveness of BLAST over existing methods in a suite of numerical experiments and in two applications, the first on street scene monitoring and the second on real-time process monitoring for metal additive manufacturing.
You've Changed: Detecting Modification of Black-Box Large Language Models
2025-04-14
Alden Dima, James Foulds, Shimei Pan, Philip Feldman
Large Language Models (LLMs) are often provided as a service via an API, making it challenging for developers to detect changes in their behavior. We present an approach to monitor LLMs for changes by comparing the distributions of linguistic and psycholinguistic features of generated text. Our method uses a statistical test to determine whether the distributions of features from two samples of text are equivalent, allowing developers to identify when an LLM has changed. We demonstrate the effectiveness of our approach using five OpenAI completion models and Meta's Llama 3 70B chat model. Our results show that simple text features coupled with a statistical test can distinguish between language models. We also explore the use of our approach to detect prompt injection attacks. Our work enables frequent LLM change monitoring and avoids computationally expensive benchmark evaluations.
This paper addresses the problem of quickest change detection (QCD) at two spatially separated locations monitored by a single unmanned aerial vehicle (UAV) equipped with a sensor. At any location, the UAV observes i.i.d. data sequentially in discrete time instants. The distribution of the observation data changes at some unknown, arbitrary time and the UAV has to detect this change in the shortest possible time. Change can occur at most at one location over the entire infinite time horizon. The UAV switches between these two locations in order to quickly detect the change. To this end, we propose Location Switching and Change Detection (LS-CD) algorithm which uses a repeated one-sided sequential probability ratio test (SPRT) based mechanism for observation-driven location switching and change detection. The primary goal is to minimize the worst-case average detection delay (WADD) while meeting constraints on the average run length to false alarm (ARL2FA) and the UAV's time-averaged energy consumption. We provide a rigorous theoretical analysis of the algorithm's performance by using theory of random walk. Specifically, we derive tight upper and lower bounds to its ARL2FA and a tight upper bound to its WADD. In the special case of a symmetrical setting, our analysis leads to a new asymptotic upper bound to the ARL2FA of the standard CUSUM algorithm, a novel contribution not available in the literature, to our knowledge. Numerical simulations demonstrate the efficacy of LS-CD.
LDGNet: A Lightweight Difference Guiding Network for Remote Sensing Change Detection
2025-04-07
Chenfeng Xu
With the rapid advancement of deep learning, the field of change detection (CD) in remote sensing imagery has achieved remarkable progress. Existing change detection methods primarily focus on achieving higher accuracy with increased computational costs and parameter sizes, leaving development of lightweight methods for rapid real-world processing an underexplored challenge. To address this challenge, we propose a Lightweight Difference Guiding Network (LDGNet), leveraging absolute difference image to guide optical remote sensing change detection. First, to enhance the feature representation capability of the lightweight backbone network, we propose the Difference Guiding Module (DGM), which leverages multi-scale features extracted from the absolute difference image to progressively influence the original image encoder at each layer, thereby reinforcing feature extraction. Second, we propose the Difference-Aware Dynamic Fusion (DADF) module with Visual State Space Model (VSSM) for lightweight long-range dependency modeling. The module first uses feature absolute differences to guide VSSM's global contextual modeling of change regions, then employs difference attention to dynamically fuse these long-range features with feature differences, enhancing change semantics while suppressing noise and background. Extensive experiments on multiple datasets demonstrate that our method achieves comparable or superior performance to current state-of-the-art (SOTA) methods requiring several times more computation, while maintaining only 3.43M parameters and 1.12G FLOPs.
Motivated by a study on deception and counter-deception, this paper addresses the problem of identifying an agent's target as it seeks to reach one of two targets in a given environment. In practice, an agent may initially follow a strategy to aim at one target but decide to switch to another midway. Such a strategy can be deceptive when the counterpart only has access to imperfect observations, which include heavily corrupted sensor noise and possible outliers, making it difficult to visually identify the agent's true intent. To counter deception and identify the true target, we utilize prior knowledge of the agent's dynamics and the imprecisely observed partial trajectory of the agent's states to dynamically update the estimation of the posterior probability of whether a deceptive switch has taken place. However, existing methods in the literature have not achieved effective deception identification within a reasonable computation time. We propose a set of outlier-robust change detection methods to track relevant change-related statistics efficiently, enabling the detection of deceptive strategies in hidden nonlinear dynamics with reasonable computational effort. The performance of the proposed framework is examined for Weapon-Target Assignment (WTA) detection under deceptive strategies using random simulations in the kinematics model with external forcing.
Water Mapping and Change Detection Using Time Series Derived from the Continuous Monitoring of Land Disturbance Algorithm
2025-04-04
Huong Pham, Samuel Cheng, Tao Hu, Chengbin Deng
Given the growing environmental challenges, accurate monitoring and prediction of changes in water bodies are essential for sustainable management and conservation. The Continuous Monitoring of Land Disturbance (COLD) algorithm provides a valuable tool for real-time analysis of land changes, such as deforestation, urban expansion, agricultural activities, and natural disasters. This capability enables timely interventions and more informed decision-making. This paper assesses the effectiveness of the algorithm to estimate water bodies and track pixel-level water trends over time. Our findings indicate that COLD-derived data can reliably estimate estimate water frequency during stable periods and delineate water bodies. Furthermore, it enables the evaluation of trends in water areas after disturbances, allowing for the determination of whether water frequency increases, decreases, or remains constant.
The rapid advancement of foundation models has revolutionized visual representation learning in a self-supervised manner. However, their application in remote sensing (RS) remains constrained by a fundamental gap: existing models predominantly handle single or limited modalities, overlooking the inherently multi-modal nature of RS observations. Optical, synthetic aperture radar (SAR), and multi-spectral data offer complementary insights that significantly reduce the inherent ambiguity and uncertainty in single-source analysis. To bridge this gap, we introduce RingMoE, a unified multi-modal RS foundation model with 14.7 billion parameters, pre-trained on 400 million multi-modal RS images from nine satellites. RingMoE incorporates three key innovations: (1) A hierarchical Mixture-of-Experts (MoE) architecture comprising modal-specialized, collaborative, and shared experts, effectively modeling intra-modal knowledge while capturing cross-modal dependencies to mitigate conflicts between modal representations; (2) Physics-informed self-supervised learning, explicitly embedding sensor-specific radiometric characteristics into the pre-training objectives; (3) Dynamic expert pruning, enabling adaptive model compression from 14.7B to 1B parameters while maintaining performance, facilitating efficient deployment in Earth observation applications. Evaluated across 23 benchmarks spanning six key RS tasks (i.e., classification, detection, segmentation, tracking, change detection, and depth estimation), RingMoE outperforms existing foundation models and sets new SOTAs, demonstrating remarkable adaptability from single-modal to multi-modal scenarios. Beyond theoretical progress, it has been deployed and trialed in multiple sectors, including emergency response, land management, marine sciences, and urban planning.
Geospatial Artificial Intelligence for Satellite-Based Flood Extent Mapping: Concepts, Advances, and Future Perspectives
2025-04-03
Hyunho Lee, Wenwen Li
Geospatial Artificial Intelligence (GeoAI) for satellite-based flood extent mapping systematically integrates artificial intelligence techniques with satellite data to identify flood events and assess their impacts, for disaster management and spatial decision-making. The primary output often includes flood extent maps, which delineate the affected areas, along with additional analytical outputs such as uncertainty estimation and change detection.
MSSFC-Net:Enhancing Building Interpretation with Multi-Scale Spatial-Spectral Feature Collaboration
2025-04-01
Dehua Huo, Weida Zhan, Jinxin Guo, Depeng Zhu, Yu Chen, YiChun Jiang, Yueyi Han, Deng Han, Jin Li
Building interpretation from remote sensing imagery primarily involves two fundamental tasks: building extraction and change detection. However, most existing methods address these tasks independently, overlooking their inherent correlation and failing to exploit shared feature representations for mutual enhancement. Furthermore, the diverse spectral,spatial, and scale characteristics of buildings pose additional challenges in jointly modeling spatial-spectral multi-scale features and effectively balancing precision and recall. The limited synergy between spatial and spectral representations often results in reduced detection accuracy and incomplete change localization.To address these challenges, we propose a Multi-Scale Spatial-Spectral Feature Cooperative Dual-Task Network (MSSFC-Net) for joint building extraction and change detection in remote sensing images. The framework integrates both tasks within a unified architecture, leveraging their complementary nature to simultaneously extract building and change features. Specifically,a Dual-branch Multi-scale Feature Extraction module (DMFE) with Spatial-Spectral Feature Collaboration (SSFC) is designed to enhance multi-scale representation learning, effectively capturing shallow texture details and deep semantic information, thus improving building extraction performance. For temporal feature aggregation, we introduce a Multi-scale Differential Fusion Module (MDFM) that explicitly models the interaction between differential and dual-temporal features. This module refines the network's capability to detect large-area changes and subtle structural variations in buildings. Extensive experiments conducted on three benchmark datasets demonstrate that MSSFC-Net achieves superior performance in both building extraction and change detection tasks, effectively improving detection accuracy while maintaining completeness.
Change detection has essential significance for the region's development, in which pseudo-changes between bitemporal images induced by imaging environmental factors are key challenges. Existing transformation-based methods regard pseudo-changes as a kind of style shift and alleviate it by transforming bitemporal images into the same style using generative adversarial networks (GANs). However, their efforts are limited by two drawbacks: 1) Transformed images suffer from distortion that reduces feature discrimination. 2) Alignment hampers the model from learning domain-agnostic representations that degrades performance on scenes with domain shifts from the training data. Therefore, oriented from pseudo-changes caused by style differences, we present a generalizable domain-agnostic difference learning network (DonaNet). For the drawback 1), we argue for local-level statistics as style proxies to assist against domain shifts. For the drawback 2), DonaNet learns domain-agnostic representations by removing domain-specific style of encoded features and highlighting the class characteristics of objects. In the removal, we propose a domain difference removal module to reduce feature variance while preserving discriminative properties and propose its enhanced version to provide possibilities for eliminating more style by decorrelating the correlation between features. In the highlighting, we propose a cross-temporal generalization learning strategy to imitate latent domain shifts, thus enabling the model to extract feature representations more robust to shifts actively. Extensive experiments conducted on three public datasets demonstrate that DonaNet outperforms existing state-of-the-art methods with a smaller model size and is more robust to domain shift.
The rapid advancement of remote sensing foundation models, particularly vision and multimodal models, has significantly enhanced the capabilities of intelligent geospatial data interpretation. These models combine various data modalities, such as optical, radar, and LiDAR imagery, with textual and geographic information, enabling more comprehensive analysis and understanding of remote sensing data. The integration of multiple modalities allows for improved performance in tasks like object detection, land cover classification, and change detection, which are often challenged by the complex and heterogeneous nature of remote sensing data. However, despite these advancements, several challenges remain. The diversity in data types, the need for large-scale annotated datasets, and the complexity of multimodal fusion techniques pose significant obstacles to the effective deployment of these models. Moreover, the computational demands of training and fine-tuning multimodal models require significant resources, further complicating their practical application in remote sensing image interpretation tasks. This paper provides a comprehensive review of the state-of-the-art in vision and multimodal foundation models for remote sensing, focusing on their architecture, training methods, datasets and application scenarios. We discuss the key challenges these models face, such as data alignment, cross-modal transfer learning, and scalability, while also identifying emerging research directions aimed at overcoming these limitations. Our goal is to provide a clear understanding of the current landscape of remote sensing foundation models and inspire future research that can push the boundaries of what these models can achieve in real-world applications. The list of resources collected by the paper can be found in the https://github.com/IRIP-BUAA/A-Review-for-remote-sensing-vision-language-models.
Change detection is a key task in Earth observation applications. Recently, deep learning methods have demonstrated strong performance and widespread application. However, change detection faces data scarcity due to the labor-intensive process of accurately aligning remote sensing images of the same area, which limits the performance of deep learning algorithms. To address the data scarcity issue, we develop a fine-tuning strategy called the Semantic Change Network (SCN). We initially pre-train the model on single-temporal supervised tasks to acquire prior knowledge of instance feature extraction. The model then employs a shared-weight Siamese architecture and extended Temporal Fusion Module (TFM) to preserve this prior knowledge and is fine-tuned on change detection tasks. The learned semantics for identifying all instances is changed to focus on identifying only the changes. Meanwhile, we observe that the locations of changes between the two images are spatially identical, a concept we refer to as spatial consistency. We introduce this inductive bias through an attention map that is generated by large-kernel convolutions and applied to the features from both time points. This enhances the modeling of multi-scale changes and helps capture underlying relationships in change detection semantics. We develop a binary change detection model utilizing these two strategies. The model is validated against state-of-the-art methods on six datasets, surpassing all benchmark methods and achieving F1 scores of 92.87%, 86.43%, 68.95%, 97.62%, 84.58%, and 93.20% on the LEVIR-CD, LEVIR-CD+, S2Looking, CDD, SYSU-CD, and WHU-CD datasets, respectively.
Most existing change detection (CD) methods focus on optical images captured at different times, and deep learning (DL) has achieved remarkable success in this domain. However, in extreme scenarios such as disaster response, synthetic aperture radar (SAR), with its active imaging capability, is more suitable for providing post-event data. This introduces new challenges for CD methods, as existing weight-sharing Siamese networks struggle to effectively learn the cross-modal data distribution between optical and SAR images. To address this challenge, we propose a unified MultiModal CD framework, M$^2$CD. We integrate Mixture of Experts (MoE) modules into the backbone to explicitly handle diverse modalities, thereby enhancing the model's ability to learn multimodal data distributions. Additionally, we innovatively propose an Optical-to-SAR guided path (O2SP) and implement self-distillation during training to reduce the feature space discrepancy between different modalities, further alleviating the model's learning burden. We design multiple variants of M$^2$CD based on both CNN and Transformer backbones. Extensive experiments validate the effectiveness of the proposed framework, with the MiT-b1 version of M$^2$CD outperforming all state-of-the-art (SOTA) methods in optical-SAR CD tasks.
In this paper, we present Change3D, a framework that reconceptualizes the change detection and captioning tasks through video modeling. Recent methods have achieved remarkable success by regarding each pair of bi-temporal images as separate frames. They employ a shared-weight image encoder to extract spatial features and then use a change extractor to capture differences between the two images. However, image feature encoding, being a task-agnostic process, cannot attend to changed regions effectively. Furthermore, different change extractors designed for various change detection and captioning tasks make it difficult to have a unified framework. To tackle these challenges, Change3D regards the bi-temporal images as comprising two frames akin to a tiny video. By integrating learnable perception frames between the bi-temporal images, a video encoder enables the perception frames to interact with the images directly and perceive their differences. Therefore, we can get rid of the intricate change extractors, providing a unified framework for different change detection and captioning tasks. We verify Change3D on multiple tasks, encompassing change detection (including binary change detection, semantic change detection, and building damage assessment) and change captioning, across eight standard benchmarks. Without bells and whistles, this simple yet effective framework can achieve superior performance with an ultra-light video model comprising only ~6%-13% of the parameters and ~8%-34% of the FLOPs compared to state-of-the-art methods. We hope that Change3D could be an alternative to 2D-based models and facilitate future research.
GS-LTS: 3D Gaussian Splatting-Based Adaptive Modeling for Long-Term Service Robots
2025-03-22
Bin Fu, Jialin Li, Bin Zhang, Ruiping Wang, Xilin Chen
3D Gaussian Splatting (3DGS) has garnered significant attention in robotics for its explicit, high fidelity dense scene representation, demonstrating strong potential for robotic applications. However, 3DGS-based methods in robotics primarily focus on static scenes, with limited attention to the dynamic scene changes essential for long-term service robots. These robots demand sustained task execution and efficient scene updates-challenges current approaches fail to meet. To address these limitations, we propose GS-LTS (Gaussian Splatting for Long-Term Service), a 3DGS-based system enabling indoor robots to manage diverse tasks in dynamic environments over time. GS-LTS detects scene changes (e.g., object addition or removal) via single-image change detection, employs a rule-based policy to autonomously collect multi-view observations, and efficiently updates the scene representation through Gaussian editing. Additionally, we propose a simulation-based benchmark that automatically generates scene change data as compact configuration scripts, providing a standardized, user-friendly evaluation benchmark. Experimental results demonstrate GS-LTS's advantages in reconstruction, navigation, and superior scene updates-faster and higher quality than the image training baseline-advancing 3DGS for long-term robotic operations. Code and benchmark are available at: https://vipl-vsu.github.io/3DGS-LTS.
EMPLACE: Self-Supervised Urban Scene Change Detection
2025-03-22
Tim Alpherts, Sennay Ghebreab, Nanne van Noord
Urban change is a constant process that influences the perception of neighbourhoods and the lives of the people within them. The field of Urban Scene Change Detection (USCD) aims to capture changes in street scenes using computer vision and can help raise awareness of changes that make it possible to better understand the city and its residents. Traditionally, the field of USCD has used supervised methods with small scale datasets. This constrains methods when applied to new cities, as it requires labour-intensive labeling processes and forces a priori definitions of relevant change. In this paper we introduce AC-1M the largest USCD dataset by far of over 1.1M images, together with EMPLACE, a self-supervising method to train a Vision Transformer using our adaptive triplet loss. We show EMPLACE outperforms SOTA methods both as a pre-training method for linear fine-tuning as well as a zero-shot setting. Lastly, in a case study of Amsterdam, we show that we are able to detect both small and large changes throughout the city and that changes uncovered by EMPLACE, depending on size, correlate with housing prices - which in turn is indicative of inequity.
The Change You Want To Detect: Semantic Change Detection In Earth Observation With Hybrid Data Generation
2025-03-19
Yanis Benidir, Nicolas Gonthier, Clement Mallet
Bi-temporal change detection at scale based on Very High Resolution (VHR) images is crucial for Earth monitoring. This remains poorly addressed so far: methods either require large volumes of annotated data (semantic case), or are limited to restricted datasets (binary set-ups). Most approaches do not exhibit the versatility required for temporal and spatial adaptation: simplicity in architecture design and pretraining on realistic and comprehensive datasets. Synthetic datasets are the key solution but still fail to handle complex and diverse scenes. In this paper, we present HySCDG a generative pipeline for creating a large hybrid semantic change detection dataset that contains both real VHR images and inpainted ones, along with land cover semantic map at both dates and the change map. Being semantically and spatially guided, HySCDG generates realistic images, leading to a comprehensive and hybrid transfer-proof dataset FSC-180k. We evaluate FSC-180k on five change detection cases (binary and semantic), from zero-shot to mixed and sequential training, and also under low data regime training. Experiments demonstrate that pretraining on our hybrid dataset leads to a significant performance boost, outperforming SyntheWorld, a fully synthetic dataset, in every configuration. All codes, models, and data are available here: https://yb23.github.io/projects/cywd/
Operational Change Detection for Geographical Information: Overview and Challenges
2025-03-18
Nicolas Gonthier
Rapid evolution of territories due to climate change and human impact requires prompt and effective updates to geospatial databases maintained by the National Mapping Agency. This paper presents a comprehensive overview of change detection methods tailored for the operational updating of large-scale geographic databases. This review first outlines the fundamental definition of change, emphasizing its multifaceted nature, from temporal to semantic characterization. It categorizes automatic change detection methods into four main families: rule-based, statistical, machine learning, and simulation methods. The strengths, limitations, and applicability of every family are discussed in the context of various input data. Then, key applications for National Mapping Agencies are identified, particularly the optimization of geospatial database updating, change-based phenomena, and dynamics monitoring. Finally, the paper highlights the current challenges for leveraging change detection such as the variability of change definition, the missing of relevant large-scale datasets, the diversity of input data, the unstudied no-change detection, the human in the loop integration and the operational constraints. The discussion underscores the necessity for ongoing innovation in change detection techniques to address the future needs of geographic information systems for national mapping agencies.
The application of Vision-Language Models (VLMs) in remote sensing (RS) has demonstrated significant potential in traditional tasks such as scene classification, object detection, and image captioning. However, current models, which excel in Referring Expression Comprehension (REC), struggle with tasks involving complex instructions (e.g., exists multiple conditions) or pixel-level operations like segmentation and change detection. In this white paper, we provide a comprehensive hierarchical summary of vision-language tasks in RS, categorized by the varying levels of cognitive capability required. We introduce the Remote Sensing Vision-Language Task Set (RSVLTS), which includes Open-Vocabulary Tasks (OVT), Referring Expression Tasks (RET), and Described Object Tasks (DOT) with increased difficulty, and Visual Question Answering (VQA) aloneside. Moreover, we propose a novel unified data representation using a set-of-points approach for RSVLTS, along with a condition parser and a self-augmentation strategy based on cyclic referring. These features are integrated into the GeoRSMLLM model, and this enhanced model is designed to handle a broad range of tasks of RSVLTS, paving the way for a more generalized solution for vision-language tasks in geoscience and remote sensing.
A sudden increase of loss in an optical communications channel can be caused by a malicious wiretapper, or for a benign reason such as inclement weather in a free-space channel or an unintentional bend in an optical fiber. We show that adding a small amount of squeezing to bright phase-modulated coherent-state pulses can dramatically increase the homodyne detection receiver's sensitivity to change detection in channel loss, without affecting the communications rate. We further show that augmenting blocks of $n$ pulses of a coherent-state codeword with weak continuous-variable entanglement generated by splitting squeezed vacuum pulses in a temporal $n$-mode equal splitter progressively enhances this change-detection sensitivity as $n$ increases; the aforesaid squeezed-light augmentation being the $n=1$ special case. For $n$ high enough, an arbitrarily small amount of quantum-augmented photons per pulse diminishes the change-detection latency by the inverse of the pre-detection channel loss. This superadditivity-like phenomenon in the entanglement-augmented relative entropy rate, which quantifies the latency of change-point detection, may find other uses. We discuss the quantum limit of quickest change detection and a receiver that achieves it, tradeoffs between continuous and discrete-variable quantum augmentation, and the broad problem of joint classical-and-quantum communications and channel-change-detection that our study opens up.
CFNet: Optimizing Remote Sensing Change Detection through Content-Aware Enhancement
2025-03-11
Fan Wu, Sijun Dong, Xiaoliang Meng
Change detection is a crucial and widely applied task in remote sensing, aimed at identifying and analyzing changes occurring in the same geographical area over time. Due to variability in acquisition conditions, bi-temporal remote sensing images often exhibit significant differences in image style. Even with the powerful generalization capabilities of DNNs, these unpredictable style variations between bi-temporal images inevitably affect model's ability to accurately detect changed areas. To address issue above, we propose the Content Focuser Network (CFNet), which takes content-aware strategy as a key insight. CFNet employs EfficientNet-B5 as the backbone for feature extraction. To enhance the model's focus on the content features of images while mitigating the misleading effects of style features, we develop a constraint strategy that prioritizes the content features of bi-temporal images, termed Content-Aware. Furthermore, to enable the model to flexibly focus on changed and unchanged areas according to the requirements of different stages, we design a reweighting module based on the cosine distance between bi-temporal image features, termed Focuser. CFNet achieve outstanding performance across three well-known change detection datasets: CLCD (F1: 81.41%, IoU: 68.65%), LEVIR-CD (F1: 92.18%, IoU: 85.49%), and SYSU-CD (F1: 82.89%, IoU: 70.78%). The code and pretrained models of CFNet are publicly released at https://github.com/wifiBlack/CFNet.
In environmental protection, tree monitoring plays an essential role in maintaining and improving ecosystem health. However, precise monitoring is challenging because existing datasets fail to capture continuous fine-grained changes in trees due to low-resolution images and high acquisition costs. In this paper, we introduce UAVTC, a large-scale, long-term, high-resolution dataset collected using UAVs equipped with cameras, specifically designed to detect individual Tree Changes (TCs). UAVTC includes rich annotations and statistics based on biological knowledge, offering a fine-grained view for tree monitoring. To address environmental influences and effectively model the hierarchical diversity of physiological TCs, we propose a novel Hyperbolic Siamese Network (HSN) for TC detection, enabling compact and hierarchical representations of dynamic tree changes. Extensive experiments show that HSN can effectively capture complex hierarchical changes and provide a robust solution for fine-grained TC detection. In addition, HSN generalizes well to cross-domain face anti-spoofing task, highlighting its broader significance in AI. We believe our work, combining ecological insights and interdisciplinary expertise, will benefit the community by offering a new benchmark and innovative AI technologies.
2DMCG:2DMambawith Change Flow Guidance for Change Detection in Remote Sensing
2025-03-01
JunYao Kaung, HongWei Ge
Remote sensing change detection (CD) has made significant advancements with the adoption of Convolutional Neural Networks (CNNs) and Transformers. While CNNs offer powerful feature extraction, they are constrained by receptive field limitations, and Transformers suffer from quadratic complexity when processing long sequences, restricting scalability. The Mamba architecture provides an appealing alternative, offering linear complexity and high parallelism. However, its inherent 1D processing structure causes a loss of spatial information in 2D vision tasks. This paper addresses this limitation by proposing an efficient framework based on a Vision Mamba variant that enhances its ability to capture 2D spatial information while maintaining the linear complexity characteristic of Mamba. The framework employs a 2DMamba encoder to effectively learn global spatial contextual information from multi-temporal images. For feature fusion, we introduce a 2D scan-based, channel-parallel scanning strategy combined with a spatio-temporal feature fusion method, which adeptly captures both local and global change information, alleviating spatial discontinuity issues during fusion. In the decoding stage, we present a feature change flow-based decoding method that improves the mapping of feature change information from low-resolution to high-resolution feature maps, mitigating feature shift and misalignment. Extensive experiments on benchmark datasets such as LEVIR-CD+ and WHU-CD demonstrate the superior performance of our framework compared to state-of-the-art methods, showcasing the potential of Vision Mamba for efficient and accurate remote sensing change detection.
In hyperspectral remote sensing field, some downstream dense prediction tasks, such as semantic segmentation (SS) and change detection (CD), rely on supervised learning to improve model performance and require a large amount of manually annotated data for training. However, due to the needs of specific equipment and special application scenarios, the acquisition and annotation of hyperspectral images (HSIs) are often costly and time-consuming. To this end, our work explores the potential of generative diffusion model in synthesizing HSIs with pixel-level annotations. The main idea is to utilize a two-stream VAE to learn the latent representations of images and corresponding masks respectively, learn their joint distribution during the diffusion model training, and finally obtain the image and mask through their respective decoders. To the best of our knowledge, it is the first work to generate high-dimensional HSIs with annotations. Our proposed approach can be applied in various kinds of dataset generation. We select two of the most widely used dense prediction tasks: semantic segmentation and change detection, and generate datasets suitable for these tasks. Experiments demonstrate that our synthetic datasets have a positive impact on the improvement of these downstream tasks.
Instance-level change detection in 3D scenes presents significant challenges, particularly in uncontrolled environments lacking labeled image pairs, consistent camera poses, or uniform lighting conditions. This paper addresses these challenges by introducing a novel approach for detecting changes in real-world scenarios. Our method leverages 4D Gaussians to embed multiple images into Gaussian distributions, enabling the rendering of two coherent image sequences. We segment each image and assign unique identifiers to instances, facilitating efficient change detection through ID comparison. Additionally, we utilize change maps and classification encodings to categorize 4D Gaussians as changed or unchanged, allowing for the rendering of comprehensive change maps from any viewpoint. Extensive experiments across various instance-level change detection datasets demonstrate that our method significantly outperforms state-of-the-art approaches like C-NERF and CYWS-3D, especially in scenarios with substantial lighting variations. Our approach offers improved detection accuracy, robustness to lighting changes, and efficient processing times, advancing the field of 3D change detection.
Change detection (CD) in remote sensing images plays a vital role in Earth observation. However, the scarcity of high-resolution, comprehensive open-source datasets and the difficulty in achieving robust performance across varying change types remain major challenges. To address these issues, we introduce JL1-CD, a large-scale, sub-meter CD dataset consisting of 5,000 image pairs. We further propose a novel Origin-Partition (O-P) strategy and integrate it into a Multi-Teacher Knowledge Distillation (MTKD) framework to enhance CD performance. The O-P strategy partitions the training set by Change Area Ratio (CAR) and trains specialized teacher models on each subset. The MTKD framework then distills complementary knowledge from these teachers into a single student model, enabling improved detection results across diverse CAR scenarios without additional inference cost. Our MTKD approach demonstrated strong performance in the 2024 "Jilin-1'' Cup challenge, ranking first in the preliminary and second in the final rounds. Extensive experiments on the JL1-CD and SYSU-CD datasets show that the MTKD framework consistently improves the performance of CD models with various network architectures and parameter sizes, establishing new state-of-the-art results. Code and dataset are available at https://anonymous.4open.science/r/MTKD-A-84B8.
S2C: Learning Noise-Resistant Differences for Unsupervised Change Detection in Multimodal Remote Sensing Images
2025-02-18
Lei Ding, Xibing Zuo, Danfeng Hong, Haitao Guo, Jun Lu, Zhihui Gong, Lorenzo Bruzzone
Unsupervised Change Detection (UCD) in multimodal Remote Sensing (RS) images remains a difficult challenge due to the inherent spatio-temporal complexity within data, and the heterogeneity arising from different imaging sensors. Inspired by recent advancements in Visual Foundation Models (VFMs) and Contrastive Learning (CL) methodologies, this research aims to develop CL methodologies to translate implicit knowledge in VFM into change representations, thus eliminating the need for explicit supervision. To this end, we introduce a Semantic-to-Change (S2C) learning framework for UCD in both homogeneous and multimodal RS images. Differently from existing CL methodologies that typically focus on learning multi-temporal similarities, we introduce a novel triplet learning strategy that explicitly models temporal differences, which are crucial to the CD task. Furthermore, random spatial and spectral perturbations are introduced during the training to enhance robustness to temporal noise. In addition, a grid sparsity regularization is defined to suppress insignificant changes, and an IoU-matching algorithm is developed to refine the CD results. Experiments on four benchmark CD datasets demonstrate that the proposed S2C learning framework achieves significant improvements in accuracy, surpassing current state-of-the-art by over 31\%, 9\%, 23\%, and 15\%, respectively. It also demonstrates robustness and sample efficiency, suitable for training and adaptation of various Visual Foundation Models (VFMs) or backbone neural networks. The relevant code will be available at: github.com/DingLei14/S2C.
Image translation for change detection or classification in bi-temporal remote sensing images is unique. Although it can acquire paired images, it is still unsupervised. Moreover, strict semantic preservation in translation is always needed instead of multimodal outputs. In response to these problems, this paper proposes a new method, SRUIT (Semantically Robust Unsupervised Image-to-image Translation), which ensures semantically robust translation and produces deterministic output. Inspired by previous works, the method explores the underlying characteristics of bi-temporal Remote Sensing images and designs the corresponding networks. Firstly, we assume that bi-temporal Remote Sensing images share the same latent space, for they are always acquired from the same land location. So SRUIT makes the generators share their high-level layers, and this constraint will compel two domain mapping to fall into the same latent space. Secondly, considering land covers of bi-temporal images could evolve into each other, SRUIT exploits the cross-cycle-consistent adversarial networks to translate from one to the other and recover them. Experimental results show that constraints of sharing weights and cross-cycle consistency enable translated images with both good perceptual image quality and semantic preservation for significant differences.
A recurrent vision transformer shows signatures of primate visual attention
2025-02-16
Jonathan Morgan, Badr Albanna, James P. Herman
Attention is fundamental to both biological and artificial intelligence, yet research on animal attention and AI self attention remains largely disconnected. We propose a Recurrent Vision Transformer (Recurrent ViT) that integrates self-attention with recurrent memory, allowing both current inputs and stored information to guide attention allocation. Trained solely via sparse reward feedback on a spatially cued orientation change detection task, a paradigm used in primate studies, our model exhibits primate like signatures of attention, including improved accuracy and faster responses for cued stimuli that scale with cue validity. Analysis of self-attention maps reveals dynamic spatial prioritization with reactivation prior to expected changes, and targeted perturbations produce performance shifts similar to those observed in primate frontal eye fields and superior colliculus. These findings demonstrate that incorporating recurrent feedback into self attention can capture key aspects of primate visual attention.
AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution
2025-02-09
David S. Bhatti, Yougin Choi, Rahman S M Wahidur, Maleeka Bakhtawar, Sumin Kim, Surin Lee, Yongtae Lee, Heung-No Lee
Hyperspectral imaging (HSI) captures spatial and spectral data, enabling analysis of features invisible to conventional systems. The technology is vital in fields such as weather monitoring, food quality control, counterfeit detection, healthcare diagnostics, and extending into defense, agriculture, and industrial automation at the same time. HSI has advanced with improvements in spectral resolution, miniaturization, and computational methods. This study provides an overview of the HSI, its applications, challenges in data fusion and the role of deep learning models in processing HSI data. We discuss how integration of multimodal HSI with AI, particularly with deep learning, improves classification accuracy and operational efficiency. Deep learning enhances HSI analysis in areas like feature extraction, change detection, denoising unmixing, dimensionality reduction, landcover mapping, data augmentation, spectral construction and super resolution. An emerging focus is the fusion of hyperspectral cameras with large language models (LLMs), referred as highbrain LLMs, enabling the development of advanced applications such as low visibility crash detection and face antispoofing. We also highlight key players in HSI industry, its compound annual growth rate and the growing industrial significance. The purpose is to offer insight to both technical and non-technical audience, covering HSI's images, trends, and future directions, while providing valuable information on HSI datasets and software libraries.
2025-02-07
Robert Malinas, Dogyoon Song, Benjamin D. Robinson, Alfred O. Hero III
We address the problem of detecting a change in the distribution of a high-dimensional multivariate normal time series. Assuming that the post-change parameters are unknown and estimated using a window of historical data, we extend the framework of quickest change detection (QCD) to the highdimensional setting in which the number of variables increases proportionally with the size of the window used to estimate the post-change parameters. Our analysis reveals that an information theoretic quantity, which we call the Normalized High- Dimensional Kullback-Leibler divergence (NHDKL), governs the high-dimensional asymptotic performance of QCD procedures. Specifically, we show that the detection delay is asymptotically inversely proportional to the difference between the NHDKL of the true post-change versus pre-change distributions and the NHDKL of the true versus estimated post-change distributions. In cases of perfect estimation, where the latter NHDKL is zero, the delay is inversely proportional to the NHDKL between the post-change and pre-change distributions alone. Thus, our analysis is a direct generalization of the traditional fixed-dimension, large-sample asymptotic framework, where the standard KL divergence is asymptotically inversely proportional to detection delay. Finally, we identify parameter estimators that asymptotically minimize the NHDKL between the true versus estimated post-change distributions, resulting in a QCD method that is guaranteed to outperform standard approaches based on fixed-dimension asymptotics.
A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges
2025-02-05
Lei Ding, Danfeng Hong, Maofan Zhao, Hongruixuan Chen, Chenyu Li, Jie Deng, Naoto Yokoya, Lorenzo Bruzzone, Jocelyn Chanussot
In the last decade, the rapid development of deep learning (DL) has made it possible to perform automatic, accurate, and robust Change Detection (CD) on large volumes of Remote Sensing Images (RSIs). However, despite advances in CD methods, their practical application in real-world contexts remains limited due to the diverse input data and the applicational context. For example, the collected RSIs can be time-series observations, and more informative results are required to indicate the time of change or the specific change category. Moreover, training a Deep Neural Network (DNN) requires a massive amount of training samples, whereas in many cases these samples are difficult to collect. To address these challenges, various specific CD methods have been developed considering different application scenarios and training resources. Additionally, recent advancements in image generation, self-supervision, and visual foundation models (VFMs) have opened up new approaches to address the 'data-hungry' issue of DL-based CD. The development of these methods in broader application scenarios requires further investigation and discussion. Therefore, this article summarizes the literature methods for different CD tasks and the available strategies and techniques to train and deploy DL-based CD methods in sample-limited scenarios. We expect that this survey can provide new insights and inspiration for researchers in this field to develop more effective CD methods that can be applied in a wider range of contexts.
Streaming multi-talker speech translation is a task that involves not only generating accurate and fluent translations with low latency but also recognizing when a speaker change occurs and what the speaker's gender is. Speaker change information can be used to create audio prompts for a zero-shot text-to-speech system, and gender can help to select speaker profiles in a conventional text-to-speech model. We propose to tackle streaming speaker change detection and gender classification by incorporating speaker embeddings into a transducer-based streaming end-to-end speech translation model. Our experiments demonstrate that the proposed methods can achieve high accuracy for both speaker change detection and gender classification.
SatFlow: Generative model based framework for producing High Resolution Gap Free Remote Sensing Imagery
2025-02-03
Bharath Irigireddy, Varaprasad Bandaru
Frequent, high-resolution remote sensing imagery is crucial for agricultural and environmental monitoring. Satellites from the Landsat collection offer detailed imagery at 30m resolution but with lower temporal frequency, whereas missions like MODIS and VIIRS provide daily coverage at coarser resolutions. Clouds and cloud shadows contaminate about 55\% of the optical remote sensing observations, posing additional challenges. To address these challenges, we present SatFlow, a generative model-based framework that fuses low-resolution MODIS imagery and Landsat observations to produce frequent, high-resolution, gap-free surface reflectance imagery. Our model, trained via Conditional Flow Matching, demonstrates better performance in generating imagery with preserved structural and spectral integrity. Cloud imputation is treated as an image inpainting task, where the model reconstructs cloud-contaminated pixels and fills gaps caused by scan lines during inference by leveraging the learned generative processes. Experimental results demonstrate the capability of our approach in reliably imputing cloud-covered regions. This capability is crucial for downstream applications such as crop phenology tracking, environmental change detection etc.,
Online Correlation Change Detection for Large-Dimensional Data with An Application to Forecasting of El Niño Events
2025-02-03
Jie Gao, Liyan Xie, Zhaoyuan Li
We consider detecting change points in the correlation structure of streaming large-dimensional data with minimum assumptions posed on the underlying data distribution. Depending on the $\ell_1$ and $\ell_{\infty}$ norms of the squared difference of vectorized pre-change and post-change correlation matrices, detection statistics are constructed for dense and sparse settings, respectively. The proposed detection procedures possess the bless-dimension property, as a novel algorithm for threshold selection is designed based on sign-flip permutation. Theoretical evaluations of the proposed methods are conducted in terms of average run length and expected detection delay. Numerical studies are conducted to examine the finite sample performances of the proposed methods. Our methods are effective because the average detection delays have slopes similar to that of the optimal exact CUSUM test. Moreover, a combined $\ell_1$ and $\ell_{\infty}$ norm approach is proposed and has expected performance for transitions from sparse to dense settings. Our method is applied to forecast El Ni{\~n}o events and achieves state-of-the-art hit rates greater than 0.86, while false alarm rates are 0. This application illustrates the efficiency and effectiveness of our proposed methodology in detecting fundamental changes with minimal delay.
Lifelong 3D Mapping Framework for Hand-held & Robot-mounted LiDAR Mapping Systems
2025-01-30
Liudi Yang, Sai Manoj Prakhya, Senhua Zhu, Ziyuan Liu
We propose a lifelong 3D mapping framework that is modular, cloud-native by design and more importantly, works for both hand-held and robot-mounted 3D LiDAR mapping systems. Our proposed framework comprises of dynamic point removal, multi-session map alignment, map change detection and map version control. First, our sensor-setup agnostic dynamic point removal algorithm works seamlessly with both hand-held and robot-mounted setups to produce clean static 3D maps. Second, the multi-session map alignment aligns these clean static maps automatically, without manual parameter fine-tuning, into a single reference frame, using a two stage approach based on feature descriptor matching and fine registration. Third, our novel map change detection identifies positive and negative changes between two aligned maps. Finally, the map version control maintains a single base map that represents the current state of the environment, and stores the detected positive and negative changes, and boundary information. Our unique map version control system can reconstruct any of the previous clean session maps and allows users to query changes between any two random mapping sessions, all without storing any input raw session maps, making it very unique. Extensive experiments are performed using hand-held commercial LiDAR mapping devices and open-source robot-mounted LiDAR SLAM algorithms to evaluate each module and the whole 3D lifelong mapping framework.
SCDiar: a streaming diarization system based on speaker change detection and speech recognition
2025-01-28
Naijun Zheng, Xucheng Wan, Kai Liu, Zhou Huan
In hours-long meeting scenarios, real-time speech stream often struggles with achieving accurate speaker diarization, commonly leading to speaker identification and speaker count errors. To address this challenge, we propose SCDiar, a system that operates on speech segments, split at the token level by a speaker change detection (SCD) module. Building on these segments, we introduce several enhancements to efficiently select the best available segment for each speaker. These improvements lead to significant gains across various benchmarks. Notably, on real-world meeting data involving more than ten participants, SCDiar outperforms previous systems by up to 53.6\% in accuracy, substantially narrowing the performance gap between online and offline systems.
CD-Lamba: Boosting Remote Sensing Change Detection via a Cross-Temporal Locally Adaptive State Space Model
2025-01-26
Zhenkai Wu, Xiaowen Ma, Rongrong Lian, Kai Zheng, Mengting Ma, Wei Zhang, Siyang Song
Mamba, with its advantages of global perception and linear complexity, has been widely applied to identify changes of the target regions within the remote sensing (RS) images captured under complex scenarios and varied conditions. However, existing remote sensing change detection (RSCD) approaches based on Mamba frequently struggle to effectively perceive the inherent locality of change regions as they direct flatten and scan RS images (i.e., the features of the same region of changes are not distributed continuously within the sequence but are mixed with features from other regions throughout the sequence). In this paper, we propose a novel locally adaptive SSM-based approach, termed CD-Lamba, which effectively enhances the locality of change detection while maintaining global perception. Specifically, our CD-Lamba includes a Locally Adaptive State-Space Scan (LASS) strategy for locality enhancement, a Cross-Temporal State-Space Scan (CTSS) strategy for bi-temporal feature fusion, and a Window Shifting and Perception (WSP) mechanism to enhance interactions across segmented windows. These strategies are integrated into a multi-scale Cross-Temporal Locally Adaptive State-Space Scan (CT-LASS) module to effectively highlight changes and refine changes' representations feature generation. CD-Lamba significantly enhances local-global spatio-temporal interactions in bi-temporal images, offering improved performance in RSCD tasks. Extensive experimental results show that CD-Lamba achieves state-of-the-art performance on four benchmark datasets with a satisfactory efficiency-accuracy trade-off. Our code is publicly available at https://github.com/xwmaxwma/rschange.
The point clouds collected by the Airborne Laser Scanning (ALS) system provide accurate 3D information of urban land covers. By utilizing multi-temporal ALS point clouds, semantic changes in urban area can be captured, demonstrating significant potential in urban planning, emergency management, and infrastructure maintenance. Existing 3D change detection methods struggle to efficiently extract multi-class semantic information and change features, still facing the following challenges: (1) the difficulty of accurately modeling cross-temporal point clouds spatial relationships for effective change feature extraction; (2) class imbalance of change samples which hinders distinguishability of semantic features; (3) the lack of real-world datasets for 3D semantic change detection. To resolve these challenges, we propose the Multi-task Enhanced Cross-temporal Point Transformer (ME-CPT) network. ME-CPT establishes spatiotemporal correspondences between point cloud across different epochs and employs attention mechanisms to jointly extract semantic change features, facilitating information exchange and change comparison. Additionally, we incorporate a semantic segmentation task and through the multi-task training strategy, further enhance the distinguishability of semantic features, reducing the impact of class imbalance in change types. Moreover, we release a 22.5 $km^2$ 3D semantic change detection dataset, offering diverse scenes for comprehensive evaluation. Experiments on multiple datasets show that the proposed MT-CPT achieves superior performance compared to existing state-of-the-art methods. The source code and dataset will be released upon acceptance at https://github.com/zhangluqi0209/ME-CPT.
Monitoring Earth's evolving land covers requires methods capable of detecting changes across a wide range of categories and contexts. Existing change detection methods are hindered by their dependency on predefined classes, reducing their effectiveness in open-world applications. To address this issue, we introduce open-vocabulary change detection (OVCD), a novel task that bridges vision and language to detect changes across any category. Considering the lack of high-quality data and annotation, we propose two training-free frameworks, M-C-I and I-M-C, which leverage and integrate off-the-shelf foundation models for the OVCD task. The insight behind the M-C-I framework is to discover all potential changes and then classify these changes, while the insight of I-M-C framework is to identify all targets of interest and then determine whether their states have changed. Based on these two frameworks, we instantiate to obtain several methods, e.g., SAM-DINOv2-SegEarth-OV, Grounding-DINO-SAM2-DINO, etc. Extensive evaluations on 5 benchmark datasets demonstrate the superior generalization and robustness of our OVCD methods over existing supervised and unsupervised methods. To support continued exploration, we release DynamicEarth, a dedicated codebase designed to advance research and application of OVCD. https://likyoo.github.io/DynamicEarth
Sequential Change Detection for Learning in Piecewise Stationary Bandit Environments
2025-01-19
Yu-Han Huang, Venugopal V. Veeravalli
A finite-horizon variant of the quickest change detection problem is investigated, which is motivated by a change detection problem that arises in piecewise stationary bandits. The goal is to minimize the \emph{latency}, which is smallest threshold such that the probability that the detection delay exceeds the threshold is below a desired low level, while controlling the false alarm probability to a desired low level. When the pre- and post-change distributions are unknown, two tests are proposed as candidate solutions. These tests are shown to attain order optimality in terms of the horizon. Furthermore, the growth in their latencies with respect to the false alarm probability and late detection probability satisfies a property that is desirable in regret analysis for piecewise stationary bandits. Numerical results are provided to validate the theoretical performance results.
Change detection in remote sensing imagery is a critical technique for Earth observation, primarily focusing on pixel-level segmentation of change regions between bi-temporal images. The essence of pixel-level change detection lies in determining whether corresponding pixels in bi-temporal images have changed. In deep learning, the spatial and channel dimensions of feature maps represent different information from the original images. In this study, we found that in change detection tasks, difference information can be computed not only from the spatial dimension of bi-temporal features but also from the channel dimension. Therefore, we designed the Channel-Spatial Difference Weighting (CSDW) module as an aggregation-distribution mechanism for bi-temporal features in change detection. This module enhances the sensitivity of the change detection model to difference features. Additionally, bi-temporal images share the same geographic location and exhibit strong inter-image correlations. To construct the correlation between bi-temporal images, we designed a decoding structure based on the Layer-Exchange (LE) method to enhance the interaction of bi-temporal features. Comprehensive experiments on the CLCD, PX-CLCD, LEVIR-CD, and S2Looking datasets demonstrate that the proposed LENet model significantly improves change detection performance. The code and pre-trained models will be available at: https://github.com/dyzy41/lenet.
LWGANet: A Lightweight Group Attention Backbone for Remote Sensing Visual Tasks
2025-01-17
Wei Lu, Si-Bao Chen, Chris H. Q. Ding, Jin Tang, Bin Luo
Remote sensing (RS) visual tasks have gained significant academic and practical importance. However, they encounter numerous challenges that hinder effective feature extraction, including the detection and recognition of multiple objects exhibiting substantial variations in scale within a single image. While prior dual-branch or multi-branch architectural strategies have been effective in managing these object variances, they have concurrently resulted in considerable increases in computational demands and parameter counts. Consequently, these architectures are rendered less viable for deployment on resource-constrained devices. Contemporary lightweight backbone networks, designed primarily for natural images, frequently encounter difficulties in effectively extracting features from multi-scale objects, which compromises their efficacy in RS visual tasks. This article introduces LWGANet, a specialized lightweight backbone network tailored for RS visual tasks, incorporating a novel lightweight group attention (LWGA) module designed to address these specific challenges. LWGA module, tailored for RS imagery, adeptly harnesses redundant features to extract a wide range of spatial information, from local to global scales, without introducing additional complexity or computational overhead. This facilitates precise feature extraction across multiple scales within an efficient framework.LWGANet was rigorously evaluated across twelve datasets, which span four crucial RS visual tasks: scene classification, oriented object detection, semantic segmentation, and change detection. The results confirm LWGANet's widespread applicability and its ability to maintain an optimal balance between high performance and low complexity, achieving SOTA results across diverse datasets. LWGANet emerged as a novel solution for resource-limited scenarios requiring robust RS image processing capabilities.
Semantic-CD: Remote Sensing Image Semantic Change Detection towards Open-vocabulary Setting
2025-01-12
Yongshuo Zhu, Lu Li, Keyan Chen, Chenyang Liu, Fugen Zhou, Zhenwei Shi
Remote sensing image semantic change detection is a method used to analyze remote sensing images, aiming to identify areas of change as well as categorize these changes within images of the same location taken at different times. Traditional change detection methods often face challenges in generalizing across semantic categories in practical scenarios. To address this issue, we introduce a novel approach called Semantic-CD, specifically designed for semantic change detection in remote sensing images. This method incorporates the open vocabulary semantics from the vision-language foundation model, CLIP. By utilizing CLIP's extensive vocabulary knowledge, our model enhances its ability to generalize across categories and improves segmentation through fully decoupled multi-task learning, which includes both binary change detection and semantic change detection tasks. Semantic-CD consists of four main components: a bi-temporal CLIP visual encoder for extracting features from bi-temporal images, an open semantic prompter for creating semantic cost volume maps with open vocabulary, a binary change detection decoder for generating binary change detection masks, and a semantic change detection decoder for producing semantic labels. Experimental results on the SECOND dataset demonstrate that Semantic-CD achieves more accurate masks and reduces semantic classification errors, illustrating its effectiveness in applying semantic priors from vision-language foundation models to SCD tasks.
Existing Weakly-Supervised Change Detection (WSCD) methods often encounter the problem of "instance lumping" under scene-level supervision, particularly in scenarios with a dense distribution of changed instances (i.e., changed objects). In these scenarios, unchanged pixels between changed instances are also mistakenly identified as changed, causing multiple changes to be mistakenly viewed as one. In practical applications, this issue prevents the accurate quantification of the number of changes. To address this issue, we propose a Dense Instance Separation (DISep) method as a plug-and-play solution, refining pixel features from a unified instance perspective under scene-level supervision. Specifically, our DISep comprises a three-step iterative training process: 1) Instance Localization: We locate instance candidate regions for changed pixels using high-pass class activation maps. 2) Instance Retrieval: We identify and group these changed pixels into different instance IDs through connectivity searching. Then, based on the assigned instance IDs, we extract corresponding pixel-level features on a per-instance basis. 3) Instance Separation: We introduce a separation loss to enforce intra-instance pixel consistency in the embedding space, thereby ensuring separable instance feature representations. The proposed DISep adds only minimal training cost and no inference cost. It can be seamlessly integrated to enhance existing WSCD methods. We achieve state-of-the-art performance by enhancing {three Transformer-based and four ConvNet-based methods} on the LEVIR-CD, WHU-CD, DSIFN-CD, SYSU-CD, and CDD datasets. Additionally, our DISep can be used to improve fully-supervised change detection methods. Code is available at https://github.com/zhenghuizhao/Plug-and-Play-DISep-for-Change-Detection.
Improving Zero-Shot Object-Level Change Detection by Incorporating Visual Correspondence
2025-01-09
Hung Huy Nguyen, Pooyan Rahmanzadehgervi, Long Mai, Anh Totti Nguyen
Detecting object-level changes between two images across possibly different views is a core task in many applications that involve visual inspection or camera surveillance. Existing change-detection approaches suffer from three major limitations: (1) lack of evaluation on image pairs that contain no changes, leading to unreported false positive rates; (2) lack of correspondences (i.e., localizing the regions before and after a change); and (3) poor zero-shot generalization across different domains. To address these issues, we introduce a novel method that leverages change correspondences (a) during training to improve change detection accuracy, and (b) at test time, to minimize false positives. That is, we harness the supervision labels of where an object is added or removed to supervise change detectors, improving their accuracy over previous work by a large margin. Our work is also the first to predict correspondences between pairs of detected changes using estimated homography and the Hungarian algorithm. Our model demonstrates superior performance over existing methods, achieving state-of-the-art results in change detection and change correspondence accuracy across both in-distribution and zero-shot benchmarks.
Multiple testing in multi-stream sequential change detection
2025-01-07
Sanjit Dandapanthula, Aaditya Ramdas
Multi-stream sequential change detection involves simultaneously monitoring many streams of data and trying to detect when their distributions change, if at all. Here, we theoretically study multiple testing issues that arise from detecting changes in many streams. We point out that any algorithm with finite average run length (ARL) must have a trivial worst-case false detection rate (FDR), family-wise error rate (FWER), per-family error rate (PFER), and global error rate (GER); thus, any attempt to control these Type I error metrics is fundamentally in conflict with the desire for a finite ARL (which is typically necessary in order to have a small detection delay). One of our contributions is to define a new class of metrics which can be controlled, called error over patience (EOP). We propose algorithms that combine the recent e-detector framework (which generalizes the Shiryaev-Roberts and CUSUM methods) with the recent e-Benjamini-Hochberg procedure and e-Bonferroni procedures. We prove that these algorithms control the EOP at any desired level under very general dependence structures on the data within and across the streams. In fact, we prove a more general error control that holds uniformly over all stopping times and provides a smooth trade-off between the conflicting metrics. Additionally, if finiteness of the ARL is forfeited, we show that our algorithms control the worst-case Type I error.
An up-to-date city-scale lane-level map is an indispensable infrastructure and a key enabling technology for ensuring the safety and user experience of autonomous driving systems. In industrial scenarios, reliance on manual annotation for map updates creates a critical bottleneck. Lane-level updates require precise change information and must ensure consistency with adjacent data while adhering to strict standards. Traditional methods utilize a three-stage approach-construction, change detection, and updating-which often necessitates manual verification due to accuracy limitations. This results in labor-intensive processes and hampers timely updates. To address these challenges, we propose LDMapNet-U, which implements a new end-to-end paradigm for city-scale lane-level map updating. By reconceptualizing the update task as an end-to-end map generation process grounded in historical map data, we introduce a paradigm shift in map updating that simultaneously generates vectorized maps and change information. To achieve this, a Prior-Map Encoding (PME) module is introduced to effectively encode historical maps, serving as a critical reference for detecting changes. Additionally, we incorporate a novel Instance Change Prediction (ICP) module that learns to predict associations with historical maps. Consequently, LDMapNet-U simultaneously achieves vectorized map element generation and change detection. To demonstrate the superiority and effectiveness of LDMapNet-U, extensive experiments are conducted using large-scale real-world datasets. In addition, LDMapNet-U has been successfully deployed in production at Baidu Maps since April 2024, supporting map updating for over 360 cities and significantly shortening the update cycle from quarterly to weekly. The updated maps serve hundreds of millions of users and are integrated into the autonomous driving systems of several leading vehicle companies.
Detection Augmented Bandit Procedures for Piecewise Stationary MABs: A Modular Approach
2025-01-02
Yu-Han Huang, Argyrios Gerogiannis, Subhonmesh Bose, Venugopal V. Veeravalli
Conventional Multi-Armed Bandit (MAB) algorithms are designed for stationary environments, where the reward distributions associated with the arms do not change with time. In many applications, however, the environment is more accurately modeled as being nonstationary. In this work, piecewise stationary MAB (PS-MAB) environments are investigated, in which the reward distributions associated with a subset of the arms change at some change-points and remain stationary between change-points. Our focus is on the asymptotic analysis of PS-MABs, for which practical algorithms based on change detection (CD) have been previously proposed. Our goal is to modularize the design and analysis of such CD-based Bandit (CDB) procedures. To this end, we identify the requirements for stationary bandit algorithms and change detectors in a CDB procedure that are needed for the modularization. We assume that the rewards are sub-Gaussian. Under this assumption and a condition on the separation of the change-points, we show that the analysis of CDB procedures can indeed be modularized, so that regret bounds can be obtained in a unified manner for various combinations of change detectors and bandit algorithms. Through this analysis, we develop new modular CDB procedures that are order-optimal. We compare the performance of our modular CDB procedures with various other methods in simulations.
EHCTNet: Enhanced Hybrid of CNN and Transformer Network for Remote Sensing Image Change Detection
2025-01-02
Junjie Yang, Haibo Wan, Zhihai Shang
Remote sensing (RS) change detection incurs a high cost because of false negatives, which are more costly than false positives. Existing frameworks, struggling to improve the Precision metric to reduce the cost of false positive, still have limitations in focusing on the change of interest, which leads to missed detections and discontinuity issues. This work tackles these issues by enhancing feature learning capabilities and integrating the frequency components of feature information, with a strategy to incrementally boost the Recall value. We propose an enhanced hybrid of CNN and Transformer network (EHCTNet) for effectively mining the change information of interest. Firstly, a dual branch feature extraction module is used to extract the multi scale features of RS images. Secondly, the frequency component of these features is exploited by a refined module I. Thirdly, an enhanced token mining module based on the Kolmogorov Arnold Network is utilized to derive semantic information. Finally, the semantic change information's frequency component, beneficial for final detection, is mined from the refined module II. Extensive experiments validate the effectiveness of EHCTNet in comprehending complex changes of interest. The visualization outcomes show that EHCTNet detects more intact and continuous changed areas and perceives more accurate neighboring distinction than state of the art models.
The problem of quickest detection of a change in the distribution of a sequence of random variables is studied. The objective is to detect the change with the minimum possible delay, subject to constraints on the rate of false alarms and the cost of observations used in the decision-making process. The post-change distribution of the data is known only within a distribution family. It is shown that if the post-change family has a distribution that is least favorable in a well-defined sense, then a computationally efficient algorithm can be designed that uses an on-off observation control strategy to save the cost of observations. In addition, the algorithm can detect the change robustly while avoiding unnecessary false alarms. It is shown that the algorithm is also asymptotically robust optimal as the rate of false alarms goes to zero for every fixed constraint on the cost of observations. The algorithm's effectiveness is validated on simulated data and real public health data.
Remote sensing image change description represents an innovative multimodal task within the realm of remote sensing processing.This task not only facilitates the detection of alterations in surface conditions, but also provides comprehensive descriptions of these changes, thereby improving human interpretability and interactivity.Current deep learning methods typically adopt a three stage framework consisting of feature extraction, feature fusion, and change localization, followed by text generation. Most approaches focus heavily on designing complex network modules but lack solid theoretical guidance, relying instead on extensive empirical experimentation and iterative tuning of network components. This experience-driven design paradigm may lead to overfitting and design bottlenecks, thereby limiting the model's generalizability and adaptability.To address these limitations, this paper proposes a paradigm that shift towards data distribution learning using diffusion models, reinforced by frequency-domain noise filtering, to provide a theoretically motivated and practically effective solution to multimodal remote sensing change description.The proposed method primarily includes a simple multi-scale change detection module, whose output features are subsequently refined by a well-designed diffusion model.Furthermore, we introduce a frequency-guided complex filter module to boost the model performance by managing high-frequency noise throughout the diffusion process. We validate the effectiveness of our proposed method across several datasets for remote sensing change detection and description, showcasing its superior performance compared to existing techniques. The code will be available at \href{https://github.com/sundongwei}{MaskApproxNet}.
STeInFormer: Spatial-Temporal Interaction Transformer Architecture for Remote Sensing Change Detection
2024-12-23
Xiaowen Ma, Zhenkai Wu, Mengting Ma, Mengjiao Zhao, Fan Yang, Zhenhong Du, Wei Zhang
Convolutional neural networks and attention mechanisms have greatly benefited remote sensing change detection (RSCD) because of their outstanding discriminative ability. Existent RSCD methods often follow a paradigm of using a non-interactive Siamese neural network for multi-temporal feature extraction and change detection heads for feature fusion and change representation. However, this paradigm lacks the contemplation of the characteristics of RSCD in temporal and spatial dimensions, and causes the drawback on spatial-temporal interaction that hinders high-quality feature extraction. To address this problem, we present STeInFormer, a spatial-temporal interaction Transformer architecture for multi-temporal feature extraction, which is the first general backbone network specifically designed for RSCD. In addition, we propose a parameter-free multi-frequency token mixer to integrate frequency-domain features that provide spectral information for RSCD. Experimental results on three datasets validate the effectiveness of the proposed method, which can outperform the state-of-the-art methods and achieve the most satisfactory efficiency-accuracy trade-off. Code is available at https://github.com/xwmaxwma/rschange.
Detect Changes like Humans: Incorporating Semantic Priors for Improved Change Detection
2024-12-22
Yuhang Gan, Wenjie Xuan, Zhiming Luo, Lei Fang, Zengmao Wang, Juhua Liu, Bo Du
When given two similar images, humans identify their differences by comparing the appearance ({\it e.g., color, texture}) with the help of semantics ({\it e.g., objects, relations}). However, mainstream change detection models adopt a supervised training paradigm, where the annotated binary change map is the main constraint. Thus, these methods primarily emphasize the difference-aware features between bi-temporal images and neglect the semantic understanding of the changed landscapes, which undermines the accuracy in the presence of noise and illumination variations. To this end, this paper explores incorporating semantic priors to improve the ability to detect changes. Firstly, we propose a Semantic-Aware Change Detection network, namely SA-CDNet, which transfers the common knowledge of the visual foundation models ({\it i.e., FastSAM}) to change detection. Inspired by the human visual paradigm, a novel dual-stream feature decoder is derived to distinguish changes by combining semantic-aware features and difference-aware features. Secondly, we design a single-temporal semantic pre-training strategy to enhance the semantic understanding of landscapes, which brings further increments. Specifically, we construct pseudo-change detection data from public single-temporal remote sensing segmentation datasets for large-scale pre-training, where an extra branch is also introduced for the proxy semantic segmentation task. Experimental results on five challenging benchmarks demonstrate the superiority of our method over the existing state-of-the-art methods. The code is available at \href{https://github.com/thislzm/SA-CD}{SA-CD}.
Data-driven deep learning models have enabled tremendous progress in change detection (CD) with the support of pixel-level annotations. However, collecting diverse data and manually annotating them is costly, laborious, and knowledge-intensive. Existing generative methods for CD data synthesis show competitive potential in addressing this issue but still face the following limitations: 1) difficulty in flexibly controlling change events, 2) dependence on additional data to train the data generators, 3) focus on specific change detection tasks. To this end, this paper focuses on the semantic CD (SCD) task and develops a multi-temporal SCD data generator ChangeDiff by exploring powerful diffusion models. ChangeDiff innovatively generates change data in two steps: first, it uses text prompts and a text-to-layout (T2L) model to create continuous layouts, and then it employs layout-to-image (L2I) to convert these layouts into images. Specifically, we propose multi-class distribution-guided text prompts (MCDG-TP), allowing for layouts to be generated flexibly through controllable classes and their corresponding ratios. Subsequently, to generalize the T2L model to the proposed MCDG-TP, a class distribution refinement loss is further designed as training supervision. %For the former, a multi-classdistribution-guided text prompt (MCDG-TP) is proposed to complement via controllable classes and ratios. To generalize the text-to-image diffusion model to the proposed MCDG-TP, a class distribution refinement loss is designed as training supervision. For the latter, MCDG-TP in three modes is proposed to synthesize new layout masks from various texts. Our generated data shows significant progress in temporal continuity, spatial diversity, and quality realism, empowering change detectors with accuracy and transferability. The code is available at https://github.com/DZhaoXd/ChangeDiff
EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues
2024-12-19
Sagar Soni, Akshay Dudhane, Hiyam Debary, Mustansar Fiaz, Muhammad Akhtar Munir, Muhammad Sohail Danish, Paolo Fraccaro, Campbell D Watson, Levente J Klein, Fahad Shahbaz Khan, Salman Khan
Automated analysis of vast Earth observation data via interactive Vision-Language Models (VLMs) can unlock new opportunities for environmental monitoring, disaster response, and {resource management}. Existing generic VLMs do not perform well on Remote Sensing data, while the recent Geo-spatial VLMs remain restricted to a fixed resolution and few sensor modalities. In this paper, we introduce EarthDial, a conversational assistant specifically designed for Earth Observation (EO) data, transforming complex, multi-sensory Earth observations into interactive, natural language dialogues. EarthDial supports multi-spectral, multi-temporal, and multi-resolution imagery, enabling a wide range of remote sensing tasks, including classification, detection, captioning, question answering, visual reasoning, and visual grounding. To achieve this, we introduce an extensive instruction tuning dataset comprising over 11.11M instruction pairs covering RGB, Synthetic Aperture Radar (SAR), and multispectral modalities such as Near-Infrared (NIR) and infrared. Furthermore, EarthDial handles bi-temporal and multi-temporal sequence analysis for applications like change detection. Our extensive experimental results on 44 downstream datasets demonstrate that EarthDial outperforms existing generic and domain-specific models, achieving better generalization across various EO tasks. Our source codes and pre-trained models are at https://github.com/hiyamdebary/EarthDial.
AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities
2024-12-18
Guillaume Astruc, Nicolas Gonthier, Clement Mallet, Loic Landrieu
Geospatial models must adapt to the diversity of Earth observation data in terms of resolutions, scales, and modalities. However, existing approaches expect fixed input configurations, which limits their practical applicability. We propose AnySat, a multimodal model based on joint embedding predictive architecture (JEPA) and scale-adaptive spatial encoders, allowing us to train a single model on highly heterogeneous data in a self-supervised manner. To demonstrate the advantages of this unified approach, we compile GeoPlex, a collection of 5 multimodal datasets with varying characteristics and $11$ distinct sensors. We then train a single powerful model on these diverse datasets simultaneously. Once fine-tuned or probed, we reach state-of-the-art results on the test sets of GeoPlex and for 6 external datasets across various environment monitoring tasks: land cover mapping, tree species identification, crop type classification, change detection, climate type classification, and segmentation of flood, burn scar, and deforestation. The code and models are available at https://github.com/gastruc/AnySat.
Lexical semantic change detection aims to identify shifts in word meanings over time. While existing methods using embeddings from a diachronic corpus pair estimate the degree of change for target words, they offer limited insight into changes at the level of individual usage instances. To address this, we apply Unbalanced Optimal Transport (UOT) to sets of contextualized word embeddings, capturing semantic change through the excess and deficit in the alignment between usage instances. In particular, we propose Sense Usage Shift (SUS), a measure that quantifies changes in the usage frequency of a word sense at each usage instance. By leveraging SUS, we demonstrate that several challenges in semantic change detection can be addressed in a unified manner, including quantifying instance-level semantic change and word-level tasks such as measuring the magnitude of semantic change and the broadening or narrowing of meaning.
Detecting changes between images is fundamental in applications such as infrastructure assessment, environmental monitoring, and industrial automation. Existing supervised models demonstrate strong performance but are inherently limited by the scope of their training data, requiring retraining to recognize novel changes. To overcome this limitation, we introduce a novel change detection task utilizing textual prompts alongside two potentially unaligned images to produce binary segmentations highlighting user-relevant changes. This text-conditioned framework significantly broadens the scope of change detection, enabling unparalleled flexibility and straightforward scalability by incorporating diverse future datasets without restriction to specific change types. As a first approach to address this challenge, we propose ViewDelta, a multimodal architecture extending the vision transformer into the domain of text-conditioned change detection. ViewDelta establishes a robust baseline, demonstrating flexibility across various scenarios and achieving competitive results compared to specialized, fine-tuned models trained on aligned images. Moreover, we create and release the first text-prompt-conditioned change detection dataset, comprising 501,153 image pairs with corresponding textual prompts and annotated labels. Extensive experiments confirm the robustness and versatility of our model across diverse environments, including indoor, outdoor, street-level, synthetic, and satellite imagery. https://joshuakgao.github.io/viewdelta/
RSUniVLM: A Unified Vision Language Model for Remote Sensing via Granularity-oriented Mixture of Experts
2024-12-07
Xu Liu, Zhouhui Lian
Remote Sensing Vision-Language Models (RS VLMs) have made much progress in the tasks of remote sensing (RS) image comprehension. While performing well in multi-modal reasoning and multi-turn conversations, the existing models lack pixel-level understanding and struggle with multi-image inputs. In this work, we propose RSUniVLM, a unified, end-to-end RS VLM designed for comprehensive vision understanding across multiple granularity, including image-level, region-level, and pixel-level tasks. RSUniVLM also performs effectively in multi-image analysis, with instances of change detection and change captioning. To enhance the model's ability to capture visual information at different levels without increasing model size, we design a novel architecture called Granularity-oriented Mixture of Experts to constraint the model to about 1 billion parameters. We also construct a large-scale RS instruction-following dataset based on a variety of existing datasets in both RS and general domain, encompassing various tasks such as object localization, visual question answering, and semantic segmentation. Substantial experiments have been conducted to validate the superiority of the proposed RSUniVLM up to state-of-the-art across various RS tasks. Code and model will be available at \href{https://github.com/xuliu-cyber/RSUniVLM}{here}.
Text Change Detection in Multilingual Documents Using Image Comparison
2024-12-05
Doyoung Park, Naresh Reddy Yarram, Sunjin Kim, Minkyu Kim, Seongho Cho, Taehee Lee
Document comparison typically relies on optical character recognition (OCR) as its core technology. However, OCR requires the selection of appropriate language models for each document and the performance of multilingual or hybrid models remains limited. To overcome these challenges, we propose text change detection (TCD) using an image comparison model tailored for multilingual documents. Unlike OCR-based approaches, our method employs word-level text image-to-image comparison to detect changes. Our model generates bidirectional change segmentation maps between the source and target documents. To enhance performance without requiring explicit text alignment or scaling preprocessing, we employ correlations among multi-scale attention features. We also construct a benchmark dataset comprising actual printed and scanned word pairs in various languages to evaluate our model. We validate our approach using our benchmark dataset and public benchmarks Distorted Document Images and the LRDE Document Binarization Dataset. We compare our model against state-of-the-art semantic segmentation and change detection models, as well as to conventional OCR-based models.
Multi-View Pose-Agnostic Change Localization with Zero Labels
2024-12-05
Chamuditha Jayanga Galappaththige, Jason Lai, Lloyd Windrim, Donald Dansereau, Niko Suenderhauf, Dimity Miller
Autonomous agents often require accurate methods for detecting and localizing changes in their environment, particularly when observations are captured from unconstrained and inconsistent viewpoints. We propose a novel label-free, pose-agnostic change detection method that integrates information from multiple viewpoints to construct a change-aware 3D Gaussian Splatting (3DGS) representation of the scene. With as few as 5 images of the post-change scene, our approach can learn an additional change channel in a 3DGS and produce change masks that outperform single-view techniques. Our change-aware 3D scene representation additionally enables the generation of accurate change masks for unseen viewpoints. Experimental results demonstrate state-of-the-art performance in complex multi-object scenes, achieving a 1.7x and 1.5x improvement in Mean Intersection Over Union and F1 score respectively over other baselines. We also contribute a new real-world dataset to benchmark change detection in diverse challenging scenes in the presence of lighting variations.
Validity and efficiency of the conformal CUSUM procedure
2024-12-04
Vladimir Vovk, Ilia Nouretdinov, Alex Gammerman
In this paper we study the validity and efficiency of a conformal version of the CUSUM procedure for change detection both experimentally and theoretically.
Benchmarking Attention Mechanisms and Consistency Regularization Semi-Supervised Learning for Post-Flood Building Damage Assessment in Satellite Images
Post-flood building damage assessment is critical for rapid response and post-disaster reconstruction planning. Current research fails to consider the distinct requirements of disaster assessment (DA) from change detection (CD) in neural network design. This paper focuses on two key differences: 1) building change features in DA satellite images are more subtle than in CD; 2) DA datasets face more severe data scarcity and label imbalance. To address these issues, in terms of model architecture, the research explores the benchmark performance of attention mechanisms in post-flood DA tasks and introduces Simple Prior Attention UNet (SPAUNet) to enhance the model's ability to recognize subtle changes, in terms of semi-supervised learning (SSL) strategies, the paper constructs four different combinations of image-level label category reference distributions for consistent training. Experimental results on flood events of xBD dataset show that SPAUNet performs exceptionally well in supervised learning experiments, achieving a recall of 79.10% and an F1 score of 71.32% for damaged classification, outperforming CD methods. The results indicate the necessity of DA task-oriented model design. SSL experiments demonstrate the positive impact of image-level consistency regularization on the model. Using pseudo-labels to form the reference distribution for consistency training yields the best results, proving the potential of using the category distribution of a large amount of unlabeled data for SSL. This paper clarifies the differences between DA and CD tasks. It preliminarily explores model design strategies utilizing prior attention mechanisms and image-level consistency regularization, establishing new post-flood DA task benchmark methods.
Words and Action: Modeling Linguistic Leadership in #BlackLivesMatter Communities
2024-12-03
Dani Roytburg, Deborah Olorunisola, Sandeep Soni, Lauren Klein
In this project, we describe a method of modeling semantic leadership across a set of communities associated with the #BlackLivesMatter movement, which has been informed by qualitative research on the structure of social media and Black Twitter in particular. We describe our bespoke approaches to time-binning, community clustering, and connecting communities over time, as well as our adaptation of state-of-the-art approaches to semantic change detection and semantic leadership induction. We find substantial evidence of the leadership role of BLM activists and progressives, as well as Black celebrities. We also find evidence of the sustained engagement of the conservative community with this discourse, suggesting an alternative explanation for how we arrived at the present moment, in which "anti-woke" and "anti-CRT" bills are being enacted nationwide.
Remote Sensing Spatio-Temporal Vision-Language Models: A Comprehensive Survey
2024-12-03
Chenyang Liu, Jiafan Zhang, Keyan Chen, Man Wang, Zhengxia Zou, Zhenwei Shi
The interpretation of multi-temporal remote sensing imagery is critical for monitoring Earth's dynamic processes-yet previous change detection methods, which produce binary or semantic masks, fall short of providing human-readable insights into changes. Recent advances in Vision-Language Models (VLMs) have opened a new frontier by fusing visual and linguistic modalities, enabling spatio-temporal vision-language understanding: models that not only capture spatial and temporal dependencies to recognize changes but also provide a richer interactive semantic analysis of temporal images (e.g., generate descriptive captions and answer natural-language queries). In this survey, we present the first comprehensive review of RS-STVLMs. The survey covers the evolution of models from early task-specific models to recent general foundation models that leverage powerful large language models. We discuss progress in representative tasks, such as change captioning, change question answering, and change grounding. Moreover, we systematically dissect the fundamental components and key technologies underlying these models, and review the datasets and evaluation metrics that have driven the field. By synthesizing task-level insights with a deep dive into shared architectural patterns, we aim to illuminate current achievements and chart promising directions for future research in spatio-temporal vision-language understanding for remote sensing. We will keep tracing related works at https://github.com/Chen-Yang-Liu/Awesome-RS-SpatioTemporal-VLMs
LaVIDE: A Language-Vision Discriminator for Detecting Changes in Satellite Image with Map References
2024-11-29
Shuguo Jiang, Fang Xu, Sen Jia, Gui-Song Xia
Change detection, which typically relies on the comparison of bi-temporal images, is significantly hindered when only a single image is available. Comparing a single image with an existing map, such as OpenStreetMap, which is continuously updated through crowd-sourcing, offers a viable solution to this challenge. Unlike images that carry low-level visual details of ground objects, maps convey high-level categorical information. This discrepancy in abstraction levels complicates the alignment and comparison of the two data types. In this paper, we propose a \textbf{La}nguage-\textbf{VI}sion \textbf{D}iscriminator for d\textbf{E}tecting changes in satellite image with map references, namely \ours{}, which leverages language to bridge the information gap between maps and images. Specifically, \ours{} formulates change detection as the problem of ``{\textit Does the pixel belong to [class]?}'', aligning maps and images within the feature space of the language-vision model to associate high-level map categories with low-level image details. Moreover, we build a mixture-of-experts discriminative module, which compares linguistic features from maps with visual features from images across various semantic perspectives, achieving comprehensive semantic comparison for change detection. Extensive evaluation on four benchmark datasets demonstrates that \ours{} can effectively detect changes in satellite image with map references, outperforming state-of-the-art change detection algorithms, e.g., with gains of about $13.8$\% on the DynamicEarthNet dataset and $4.3$\% on the SECOND dataset.
Semi-supervised change detection (SSCD) utilizes partially labeled data and abundant unlabeled data to detect differences between multi-temporal remote sensing images. The mainstream SSCD methods based on consistency regularization have limitations. They perform perturbations mainly at a single level, restricting the utilization of unlabeled data and failing to fully tap its potential. In this paper, we introduce a novel Gate-guided Two-level Perturbation Consistency regularization-based SSCD method (GTPC-SSCD). It simultaneously maintains strong-to-weak consistency at the image level and perturbation consistency at the feature level, enhancing the utilization efficiency of unlabeled data. Moreover, we develop a hardness analysis-based gating mechanism to assess the training complexity of different samples and determine the necessity of performing feature perturbations for each sample. Through this differential treatment, the network can explore the potential of unlabeled data more efficiently. Extensive experiments conducted on six benchmark CD datasets demonstrate the superiority of our GTPC-SSCD over seven state-of-the-art methods.
GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial Tasks
2024-11-28
Muhammad Sohail Danish, Muhammad Akhtar Munir, Syed Roshaan Ali Shah, Kartik Kuckreja, Fahad Shahbaz Khan, Paolo Fraccaro, Alexandre Lacoste, Salman Khan
While numerous recent benchmarks focus on evaluating generic Vision-Language Models (VLMs), they do not effectively address the specific challenges of geospatial applications. Generic VLM benchmarks are not designed to handle the complexities of geospatial data, an essential component for applications such as environmental monitoring, urban planning, and disaster management. Key challenges in the geospatial domain include temporal change detection, large-scale object counting, tiny object detection, and understanding relationships between entities in remote sensing imagery. To bridge this gap, we present GEOBench-VLM, a comprehensive benchmark specifically designed to evaluate VLMs on geospatial tasks, including scene understanding, object counting, localization, fine-grained categorization, segmentation, and temporal analysis. Our benchmark features over 10,000 manually verified instructions and spanning diverse visual conditions, object types, and scales. We evaluate several state-of-the-art VLMs to assess performance on geospatial-specific challenges. The results indicate that although existing VLMs demonstrate potential, they face challenges when dealing with geospatial-specific tasks, highlighting the room for further improvements. Notably, the best-performing LLaVa-OneVision achieves only 41.7% accuracy on MCQs, slightly more than GPT-4o, which is approximately double the random guess performance. Our benchmark is publicly available at https://github.com/The-AI-Alliance/GEO-Bench-VLM .
The problem of robust quickest change detection (QCD) in non-stationary processes under a multi-stream setting is studied. In classical QCD theory, optimal solutions are developed to detect a sudden change in the distribution of stationary data. Most studies have focused on single-stream data. In non-stationary processes, the data distribution both before and after change varies with time and is not precisely known. The multi-dimension data even complicates such issues. It is shown that if the non-stationary family for each dimension or stream has a least favorable law (LFL) or distribution in a well-defined sense, then the algorithm designed using the LFLs is robust optimal. The notion of LFL defined in this work differs from the classical definitions due to the dependence of the post-change model on the change point. Examples of multi-stream non-stationary processes encountered in public health monitoring and aviation applications are provided. Our robust algorithm is applied to simulated and real data to show its effectiveness.
The CUSUM Test with Observation-Adjusted Control Limits in Parameters Change Detection for the Extremely Heavy-Tailed Distributions Sequences
2024-11-22
F. Tang, D. Han
In this paper, we propose an new the CUSUM sequential test (control chart, stopping time) with the observation-adjusted control limits (CUSUM-OAL) for monitoring quickly and adaptively the change in distribution of a sequential observations. We give the estimation of the in-control and the out-of-control average run lengths (ARLs) of the CUSUM-OAL test. The theoretical results are illustrated by numerical simulations in detecting $\alpha$ shifts of the extreme heavy-tailed distribution observations sequence.
Global and Local Attention-Based Transformer for Hyperspectral Image Change Detection
2024-11-21
Ziyi Wang, Feng Gao, Junyu Dong, Qian Du
Recently Transformer-based hyperspectral image (HSI) change detection methods have shown remarkable performance. Nevertheless, existing attention mechanisms in Transformers have limitations in local feature representation. To address this issue, we propose Global and Local Attention-based Transformer (GLAFormer), which incorporates a global and local attention module (GLAM) to combine high-frequency and low-frequency signals. Furthermore, we introduce a cross-gating mechanism, called cross-gated feed-forward network (CGFN), to emphasize salient features and suppress noise interference. Specifically, the GLAM splits attention heads into global and local attention components to capture comprehensive spatial-spectral features. The global attention component employs global attention on downsampled feature maps to capture low-frequency information, while the local attention component focuses on high-frequency details using non-overlapping window-based local attention. The CGFN enhances the feature representation via convolutions and cross-gating mechanism in parallel paths. The proposed GLAFormer is evaluated on three HSI datasets. The results demonstrate its superiority over state-of-the-art HSI change detection methods. The source code of GLAFormer is available at \url{https://github.com/summitgao/GLAFormer}.
In complex scenes and varied conditions, effectively integrating spatial-temporal context is crucial for accurately identifying changes. However, current RS-CD methods lack a balanced consideration of performance and efficiency. CNNs lack global context, Transformers are computationally expensive, and Mambas face CUDA dependence and local correlation loss. In this paper, we propose CDXLSTM, with a core component that is a powerful XLSTM-based feature enhancement layer, integrating the advantages of linear computational complexity, global context perception, and strong interpret-ability. Specifically, we introduce a scale-specific Feature Enhancer layer, incorporating a Cross-Temporal Global Perceptron customized for semantic-accurate deep features, and a Cross-Temporal Spatial Refiner customized for detail-rich shallow features. Additionally, we propose a Cross-Scale Interactive Fusion module to progressively interact global change representations with spatial responses. Extensive experimental results demonstrate that CDXLSTM achieves state-of-the-art performance across three benchmark datasets, offering a compelling balance between efficiency and accuracy. Code is available at https://github.com/xwmaxwma/rschange.
Change Detection (CD) is an essential field in remote sensing, with a primary focus on identifying areas of change in bi-temporal image pairs captured at varying intervals of the same region by a satellite. The data annotation process for the CD task is both time-consuming and labor-intensive. To make better use of the scarce labeled data and abundant unlabeled data, we present an adaptive dynamic semi-supervised learning method, AdaSemiCD, to improve the use of pseudo-labels and optimize the training process. Initially, due to the extreme class imbalance inherent in CD, the model is more inclined to focus on the background class, and it is easy to confuse the boundary of the target object. Considering these two points, we develop a measurable evaluation metric for pseudo-labels that enhances the representation of information entropy by class rebalancing and amplification of confusing areas to give a larger weight to prospects change objects. Subsequently, to enhance the reliability of sample-wise pseudo-labels, we introduce the AdaFusion module, which is capable of dynamically identifying the most uncertain region and substituting it with more trustworthy content. Lastly, to ensure better training stability, we introduce the AdaEMA module, which updates the teacher model using only batches of trusted samples. Experimental results from LEVIR-CD, WHU-CD, and CDD datasets validate the efficacy and universality of our proposed adaptive training framework.
Change detection (CD) is a critical remote sensing technique for identifying changes in the Earth's surface over time. The outstanding substance identifiability of hyperspectral images (HSIs) has significantly enhanced the detection accuracy, making hyperspectral change detection (HCD) an essential technology. The detection accuracy can be further upgraded by leveraging the graph structure of HSIs, motivating us to adopt the graph neural networks (GNNs) in solving HCD. For the first time, this work introduces quantum deep network (QUEEN) into HCD. Unlike GNN and CNN, both extracting the affine-computing features, QUEEN provides fundamentally different unitary-computing features. We demonstrate that through the unitary feature extraction procedure, QUEEN provides radically new information for deciding whether there is a change or not. Hierarchically, a graph feature learning (GFL) module exploits the graph structure of the bitemporal HSIs at the superpixel level, while a quantum feature learning (QFL) module learns the quantum features at the pixel level, as a complementary to GFL by preserving pixel-level detailed spatial information not retained in the superpixels. In the final classification stage, a quantum classifier is designed to cooperate with a traditional fully connected classifier. The superior HCD performance of the proposed QUEEN-empowered GNN (i.e., QUEEN-G) will be experimentally demonstrated on real hyperspectral datasets.
Recent self-supervised learning (SSL) methods have demonstrated impressive results in learning visual representations from unlabeled remote sensing images. However, most remote sensing images predominantly consist of scenographic scenes containing multiple ground objects without explicit foreground targets, which limits the performance of existing SSL methods that focus on foreground targets. This raises the question: Is there a method that can automatically aggregate similar objects within scenographic remote sensing images, thereby enabling models to differentiate knowledge embedded in various geospatial patterns for improved feature representation? In this work, we present the Pattern Integration and Enhancement Vision Transformer (PIEViT), a novel self-supervised learning framework designed specifically for remote sensing imagery. PIEViT utilizes a teacher-student architecture to address both image-level and patch-level tasks. It employs the Geospatial Pattern Cohesion (GPC) module to explore the natural clustering of patches, enhancing the differentiation of individual features. The Feature Integration Projection (FIP) module further refines masked token reconstruction using geospatially clustered patches. We validated PIEViT across multiple downstream tasks, including object detection, semantic segmentation, and change detection. Experiments demonstrated that PIEViT enhances the representation of internal patch features, providing significant improvements over existing self-supervised baselines. It achieves excellent results in object detection, land cover classification, and change detection, underscoring its robustness, generalization, and transferability for remote sensing image interpretation tasks.
3DGS-CD: 3D Gaussian Splatting-based Change Detection for Physical Object Rearrangement
2024-11-06
Ziqi Lu, Jianbo Ye, John Leonard
We present 3DGS-CD, the first 3D Gaussian Splatting (3DGS)-based method for detecting physical object rearrangements in 3D scenes. Our approach estimates 3D object-level changes by comparing two sets of unaligned images taken at different times. Leveraging 3DGS's novel view rendering and EfficientSAM's zero-shot segmentation capabilities, we detect 2D object-level changes, which are then associated and fused across views to estimate 3D change masks and object transformations. Our method can accurately identify changes in cluttered environments using sparse (as few as one) post-change images within as little as 18s. It does not rely on depth input, user instructions, pre-defined object classes, or object models -- An object is recognized simply if it has been re-arranged. Our approach is evaluated on both public and self-collected real-world datasets, achieving up to 14% higher accuracy and three orders of magnitude faster performance compared to the state-of-the-art radiance-field-based change detection method. This significant performance boost enables a broad range of downstream applications, where we highlight three key use cases: object reconstruction, robot workspace reset, and 3DGS model update. Our code and data will be made available at https://github.com/520xyxyzq/3DGS-CD.
From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing
2024-11-05
Xintian Sun, Benji Peng, Charles Zhang, Fei Jin, Qian Niu, Junyu Liu, Keyu Chen, Ming Li, Pohsun Feng, Ziqian Bi, Ming Liu, Yichao Zhang
Remote sensing has evolved from simple image acquisition to complex systems capable of integrating and processing visual and textual data. This review examines the development and application of multi-modal language models (MLLMs) in remote sensing, focusing on their ability to interpret and describe satellite imagery using natural language. We cover the technical underpinnings of MLLMs, including dual-encoder architectures, Transformer models, self-supervised and contrastive learning, and cross-modal integration. The unique challenges of remote sensing data--varying spatial resolutions, spectral richness, and temporal changes--are analyzed for their impact on MLLM performance. Key applications such as scene description, object detection, change detection, text-to-image retrieval, image-to-text generation, and visual question answering are discussed to demonstrate their relevance in environmental monitoring, urban planning, and disaster response. We review significant datasets and resources supporting the training and evaluation of these models. Challenges related to computational demands, scalability, data quality, and domain adaptation are highlighted. We conclude by proposing future research directions and technological advancements to further enhance MLLM utility in remote sensing.
Tree level change detection over Ahmedabad city using very high resolution satellite images and Deep Learning
2024-11-04
Jai G Singla, Gautam Jaiswal
In this study, 0.5m high resolution satellite datasets over Indian urban region was used to demonstrate the applicability of deep learning models over Ahmedabad, India. Here, YOLOv7 instance segmentation model was trained on well curated trees canopy dataset (6500 images) in order to carry out the change detection. During training, evaluation metrics such as bounding box regression and mask regression loss, mean average precision (mAP) and stochastic gradient descent algorithm were used for evaluating and optimizing the performance of model. After the 500 epochs, the mAP of 0.715 and 0.699 for individual tree detection and tree canopy mask segmentation were obtained. However, by further tuning hyper parameters of the model, maximum accuracy of 80 % of trees detection with false segmentation rate of 2% on data was obtained.
Remote sensing image change caption (RSICC) aims to provide natural language descriptions for bi-temporal remote sensing images. Since Change Caption (CC) task requires both spatial and temporal features, previous works follow an encoder-fusion-decoder architecture. They use an image encoder to extract spatial features and the fusion module to integrate spatial features and extract temporal features, which leads to increasingly complex manual design of the fusion module. In this paper, we introduce a novel video model-based paradigm without design of the fusion module and propose a Mask-enhanced Video model for Change Caption (MV-CC). Specifically, we use the off-the-shelf video encoder to simultaneously extract the temporal and spatial features of bi-temporal images. Furthermore, the types of changes in the CC are set based on specific task requirements, and to enable the model to better focus on the regions of interest, we employ masks obtained from the Change Detection (CD) method to explicitly guide the CC model. Experimental results demonstrate that our proposed method can obtain better performance compared with other state-of-the-art RSICC methods. The code is available at https://github.com/liuruixun/MV-CC.
Show Me What and Where has Changed? Question Answering and Grounding for Remote Sensing Change Detection
2024-10-31
Ke Li, Fuyu Dong, Di Wang, Shaofeng Li, Quan Wang, Xinbo Gao, Tat-Seng Chua
Remote sensing change detection aims to perceive changes occurring on the Earth's surface from remote sensing data in different periods, and feed these changes back to humans. However, most existing methods only focus on detecting change regions, lacking the capability to interact with users to identify changes that the users expect. In this paper, we introduce a new task named Change Detection Question Answering and Grounding (CDQAG), which extends the traditional change detection task by providing interpretable textual answers and intuitive visual evidence. To this end, we construct the first CDQAG benchmark dataset, termed QAG-360K, comprising over 360K triplets of questions, textual answers, and corresponding high-quality visual masks. It encompasses 10 essential land-cover categories and 8 comprehensive question types, which provides a valuable and diverse dataset for remote sensing applications. Furthermore, we present VisTA, a simple yet effective baseline method that unifies the tasks of question answering and grounding by delivering both visual and textual answers. Our method achieves state-of-the-art results on both the classic change detection-based visual question answering (CDVQA) and the proposed CDQAG datasets. Extensive qualitative and quantitative experimental results provide useful insights for developing better CDQAG models, and we hope that our work can inspire further research in this important yet underexplored research field. The proposed benchmark dataset and method are available at https://github.com/like413/VisTA.
GeoLLaVA: Efficient Fine-Tuned Vision-Language Models for Temporal Change Detection in Remote Sensing
2024-10-25
Hosam Elgendy, Ahmed Sharshar, Ahmed Aboeitta, Yasser Ashraf, Mohsen Guizani
Detecting temporal changes in geographical landscapes is critical for applications like environmental monitoring and urban planning. While remote sensing data is abundant, existing vision-language models (VLMs) often fail to capture temporal dynamics effectively. This paper addresses these limitations by introducing an annotated dataset of video frame pairs to track evolving geographical patterns over time. Using fine-tuning techniques like Low-Rank Adaptation (LoRA), quantized LoRA (QLoRA), and model pruning on models such as Video-LLaVA and LLaVA-NeXT-Video, we significantly enhance VLM performance in processing remote sensing temporal changes. Results show significant improvements, with the best performance achieving a BERT score of 0.864 and ROUGE-1 score of 0.576, demonstrating superior accuracy in describing land-use transformations.
An Auditing Test To Detect Behavioral Shift in Language Models
2024-10-25
Leo Richter, Xuanli He, Pasquale Minervini, Matt J. Kusner
As language models (LMs) approach human-level performance, a comprehensive understanding of their behavior becomes crucial. This includes evaluating capabilities, biases, task performance, and alignment with societal values. Extensive initial evaluations, including red teaming and diverse benchmarking, can establish a model's behavioral profile. However, subsequent fine-tuning or deployment modifications may alter these behaviors in unintended ways. We present a method for continual Behavioral Shift Auditing (BSA) in LMs. Building on recent work in hypothesis testing, our auditing test detects behavioral shifts solely through model generations. Our test compares model generations from a baseline model to those of the model under scrutiny and provides theoretical guarantees for change detection while controlling false positives. The test features a configurable tolerance parameter that adjusts sensitivity to behavioral changes for different use cases. We evaluate our approach using two case studies: monitoring changes in (a) toxicity and (b) translation performance. We find that the test is able to detect meaningful changes in behavior distributions using just hundreds of examples.
2024-10-23
Sheik Mohammad Mostakim Fattah, Athman Bouguettaya
We propose a novel change detection framework to identify changes in the long-term performance behavior of an IaaS service. An IaaS service's long-term performance behavior is represented by an IaaS performance signature. The proposed framework leverages time series similarity measures and a sliding window technique to detect changes in IaaS performance signatures. We introduce a new IaaS performance noise model that enables the proposed framework to distinguish between performance noise and actual changes in performance. The proposed framework utilizes a novel Signal-to-Noise Ratio (SNR) based approach to detect changes when prior knowledge about performance noise is available. A set of experiments is conducted using real-world datasets to demonstrate the effectiveness of the proposed change detection framework.
HCDN: A Change Detection Network for Construction Housekeeping Using Feature Fusion and Large Vision Models
2024-10-23
Kailai Sun, Zherui Shao, Yang Miang Goh, Jing Tian, Vincent J. L. Gan
Workplace safety has received increasing attention as millions of workers worldwide suffer from work-related accidents. Despite poor housekeeping is a significant contributor to construction accidents, there remains a significant lack of technological research focused on improving housekeeping practices in construction sites. Recognizing and locating poor housekeeping in a dynamic construction site is an important task that can be improved through computer vision approaches. Despite advances in AI and computer vision, existing methods for detecting poor housekeeping conditions face many challenges, including limited explanations, lack of locating of poor housekeeping, and lack of annotated datasets. On the other hand, change detection which aims to detect the changed environmental conditions (e.g., changing from good to poor housekeeping) and 'where' the change has occurred (e.g., location of objects causing poor housekeeping), has not been explored to the problem of housekeeping management. To address these challenges, we propose the Housekeeping Change Detection Network (HCDN), an advanced change detection neural network that integrates a feature fusion module and a large vision model, achieving state-of-the-art performance. Additionally, we introduce the approach to establish a novel change detection dataset (named Housekeeping-CCD) focused on housekeeping in construction sites, along with a housekeeping segmentation dataset. Our contributions include significant performance improvements compared to existing methods, providing an effective tool for enhancing construction housekeeping and safety. To promote further development, we share our source code and trained models for global researchers: https://github.com/NUS-DBE/Housekeeping-CD.
Tracing the Development of the Virtual Particle Concept Using Semantic Change Detection
2024-10-22
Michael Zichert, Adrian Wüthrich
Virtual particles are peculiar objects. They figure prominently in much of theoretical and experimental research in elementary particle physics. But exactly what they are is far from obvious. In particular, to what extent they should be considered "real" remains a matter of controversy in philosophy of science. Also their origin and development has only recently come into focus of scholarship in the history of science. In this study, we propose using the intriguing case of virtual particles to discuss the efficacy of Semantic Change Detection (SCD) based on contextualized word embeddings from a domain-adapted BERT model in studying specific scientific concepts. We find that the SCD metrics align well with qualitative research insights in the history and philosophy of science, as well as with the results obtained from Dependency Parsing to determine the frequency and connotations of the term "virtual." Still, the metrics of SCD provide additional insights over and above the qualitative research and the Dependency Parsing. Among other things, the metrics suggest that the concept of the virtual particle became more stable after 1950 but at the same time also more polysemous.
The Ocean Color Monitor-3, launched aboard Oceansat-3, represents a significant advancement in ocean observation technology, building upon the capabilities of its predecessors. With thirteen spectral bands, OCM-3 enhances feature identification and atmospheric correction, enabling precise data collection from a sun-synchronous orbit. With thirteen spectral bands, OCM-3 enhances feature identification and atmospheric correction, enabling precise data collection from a sunsynchronous orbit. Operating at an altitude of 732.5 km, the satellite achieves high signal-to-noise ratios (SNR) through sophisticated onboard and ground processing techniques, including advanced geometric modeling for pixel registration.The OCM-3 processing pipeline, consisting of multiple levels, ensures rigorous calibration and correction of radiometric and geometric data. This paper presents key methodologies such as dark data modeling, photo response non-uniformity correction, and smear correction, are employed to enhance data quality. The effective implementation of ground time delay integration (TDI) allows for the refinement of SNR, with evaluations demonstrating that performance specifications were exceeded. Geometric calibration procedures, including band-to-band registration and geolocation accuracy assessments, which further optimize data reliability are presented in the paper. Advanced image registration techniques leveraging Ground Control Points (GCPs) and residual error analysis significantly reduce geolocation errors, achieving precision within specified thresholds. Overall, OCM-3 comprehensive calibration and processing strategies ensure high-quality, reliable data crucial for ocean monitoring and change detection applications, facilitating improved understanding of ocean dynamics and environmental changes.
Asymptotically Optimal Change Detection for Unnormalized Pre- and Post-Change Distributions
2024-10-18
Arman Adibi, Sanjeev Kulkarni, H. Vincent Poor, Taposh Banerjee, Vahid Tarokh
This paper addresses the problem of detecting changes when only unnormalized pre- and post-change distributions are accessible. This situation happens in many scenarios in physics such as in ferromagnetism, crystallography, magneto-hydrodynamics, and thermodynamics, where the energy models are difficult to normalize. Our approach is based on the estimation of the Cumulative Sum (CUSUM) statistics, which is known to produce optimal performance. We first present an intuitively appealing approximation method. Unfortunately, this produces a biased estimator of the CUSUM statistics and may cause performance degradation. We then propose the Log-Partition Approximation Cumulative Sum (LPA-CUSUM) algorithm based on thermodynamic integration (TI) in order to estimate the log-ratio of normalizing constants of pre- and post-change distributions. It is proved that this approach gives an unbiased estimate of the log-partition function and the CUSUM statistics, and leads to an asymptotically optimal performance. Moreover, we derive a relationship between the required sample size for thermodynamic integration and the desired detection delay performance, offering guidelines for practical parameter selection. Numerical studies are provided demonstrating the efficacy of our approach.
We present Kernel-QuantTree Exponentially Weighted Moving Average (KQT-EWMA), a non-parametric change-detection algorithm that combines the Kernel-QuantTree (KQT) histogram and the EWMA statistic to monitor multivariate data streams online. The resulting monitoring scheme is very flexible, since histograms can be used to model any stationary distribution, and practical, since the distribution of test statistics does not depend on the distribution of datastream in stationary conditions (non-parametric monitoring). KQT-EWMA enables controlling false alarms by operating at a pre-determined Average Run Length ($ARL_0$), which measures the expected number of stationary samples to be monitored before triggering a false alarm. The latter peculiarity is in contrast with most non-parametric change-detection tests, which rarely can control the $ARL_0$ a priori. Our experiments on synthetic and real-world datasets demonstrate that KQT-EWMA can control $ARL_0$ while achieving detection delays comparable to or lower than state-of-the-art methods designed to work in the same conditions.
Is Prior-Free Black-Box Non-Stationary Reinforcement Learning Feasible?
2024-10-17
Argyrios Gerogiannis, Yu-Han Huang, Venugopal V. Veeravalli
We study the problem of Non-Stationary Reinforcement Learning (NS-RL) without prior knowledge about the system's non-stationarity. A state-of-the-art, black-box algorithm, known as MASTER, is considered, with a focus on identifying the conditions under which it can achieve its stated goals. Specifically, we prove that MASTER's non-stationarity detection mechanism is not triggered for practical choices of horizon, leading to performance akin to a random restarting algorithm. Moreover, we show that the regret bound for MASTER, while being order optimal, stays above the worst-case linear regret until unreasonably large values of the horizon. To validate these observations, MASTER is tested for the special case of piecewise stationary multi-armed bandits, along with methods that employ random restarting, and others that use quickest change detection to restart. A simple, order optimal random restarting algorithm, that has prior knowledge of the non-stationarity is proposed as a baseline. The behavior of the MASTER algorithm is validated in simulations, and it is shown that methods employing quickest change detection are more robust and consistently outperform MASTER and other random restarting approaches.
Remote sensing image change detection (RSCD) is crucial for monitoring dynamic surface changes, with applications ranging from environmental monitoring to disaster assessment. While traditional CNN-based methods have improved detection accuracy, they often suffer from high computational complexity and large parameter counts, limiting their use in resource-constrained environments. To address these challenges, we propose a Lightweight remote sensing Change Detection Network (LCD-Net in short) that reduces model size and computational cost while maintaining high detection performance. LCD-Net employs MobileNetV2 as the encoder to efficiently extract features from bitemporal images. A Temporal Interaction and Fusion Module (TIF) enhances the interaction between bitemporal features, improving temporal context awareness. Additionally, the Feature Fusion Module (FFM) aggregates multiscale features to better capture subtle changes while suppressing background noise. The Gated Mechanism Module (GMM) in the decoder further enhances feature learning by dynamically adjusting channel weights, emphasizing key change regions. Experiments on LEVIR-CD+, SYSU, and S2Looking datasets show that LCD-Net achieves competitive performance with just 2.56M parameters and 4.45G FLOPs, making it well-suited for real-time applications in resource-limited settings. The code is available at https://github.com/WenyuLiu6/LCD-Net.
ChangeMinds: Multi-task Framework for Detecting and Describing Changes in Remote Sensing
2024-10-13
Yuduo Wang, Weikang Yu, Michael Kopp, Pedram Ghamisi
Recent advancements in Remote Sensing (RS) for Change Detection (CD) and Change Captioning (CC) have seen substantial success by adopting deep learning techniques. Despite these advances, existing methods often handle CD and CC tasks independently, leading to inefficiencies from the absence of synergistic processing. In this paper, we present ChangeMinds, a novel unified multi-task framework that concurrently optimizes CD and CC processes within a single, end-to-end model. We propose the change-aware long short-term memory module (ChangeLSTM) to effectively capture complex spatiotemporal dynamics from extracted bi-temporal deep features, enabling the generation of universal change-aware representations that effectively serve both CC and CD tasks. Furthermore, we introduce a multi-task predictor with a cross-attention mechanism that enhances the interaction between image and text features, promoting efficient simultaneous learning and processing for both tasks. Extensive evaluations on the LEVIR-MCI dataset, alongside other standard benchmarks, show that ChangeMinds surpasses existing methods in multi-task learning settings and markedly improves performance in individual CD and CC tasks. Codes and pre-trained models will be available online.
2024-10-12
Yi Xiao, Bin Luo, Jun Liu, Xin Su, Wei Wang
Change Detection (CD) enables the identification of alterations between images of the same area captured at different times. However, existing CD methods still struggle to address pseudo changes resulting from domain information differences in multi-temporal images and instances of detail errors caused by the loss and contamination of detail features during the upsampling process in the network. To address this, we propose a bi-temporal Gaussian distribution feature-dependent network (BGFD). Specifically, we first introduce the Gaussian noise domain disturbance (GNDD) module, which approximates distribution using image statistical features to characterize domain information, samples noise to perturb the network for learning redundant domain information, addressing domain information differences from a more fundamental perspective. Additionally, within the feature dependency facilitation (FDF) module, we integrate a novel mutual information difference loss ($L_{MI}$) and more sophisticated attention mechanisms to enhance the capabilities of the network, ensuring the acquisition of essential domain information. Subsequently, we have designed a novel detail feature compensation (DFC) module, which compensates for detail feature loss and contamination introduced during the upsampling process from the perspectives of enhancing local features and refining global features. The BGFD has effectively reduced pseudo changes and enhanced the detection capability of detail information. It has also achieved state-of-the-art performance on four publicly available datasets - DSIFN-CD, SYSU-CD, LEVIR-CD, and S2Looking, surpassing baseline models by +8.58%, +1.28%, +0.31%, and +3.76% respectively, in terms of the F1-Score metric.
Exploring Foundation Models in Remote Sensing Image Change Detection: A Comprehensive Survey
2024-10-10
Zihan Yu, Tianxiao Li, Yuxin Zhu, Rongze Pan
Change detection, as an important and widely applied technique in the field of remote sensing, aims to analyze changes in surface areas over time and has broad applications in areas such as environmental monitoring, urban development, and land use analysis.In recent years, deep learning, especially the development of foundation models, has provided more powerful solutions for feature extraction and data fusion, effectively addressing these complexities. This paper systematically reviews the latest advancements in the field of change detection, with a focus on the application of foundation models in remote sensing tasks.
Almost Minimax Optimal Best Arm Identification in Piecewise Stationary Linear Bandits
2024-10-10
Yunlong Hou, Vincent Y. F. Tan, Zixin Zhong
We propose a {\em novel} piecewise stationary linear bandit (PSLB) model, where the environment randomly samples a context from an unknown probability distribution at each changepoint, and the quality of an arm is measured by its return averaged over all contexts. The contexts and their distribution, as well as the changepoints are unknown to the agent. We design {\em Piecewise-Stationary $\varepsilon$-Best Arm Identification$^+$} (PS$\varepsilon$BAI$^+$), an algorithm that is guaranteed to identify an $\varepsilon$-optimal arm with probability $\ge 1-\delta$ and with a minimal number of samples. PS$\varepsilon$BAI$^+$ consists of two subroutines, PS$\varepsilon$BAI and {\sc Na\"ive $\varepsilon$-BAI} (N$\varepsilon$BAI), which are executed in parallel. PS$\varepsilon$BAI actively detects changepoints and aligns contexts to facilitate the arm identification process. When PS$\varepsilon$BAI and N$\varepsilon$BAI are utilized judiciously in parallel, PS$\varepsilon$BAI$^+$ is shown to have a finite expected sample complexity. By proving a lower bound, we show the expected sample complexity of PS$\varepsilon$BAI$^+$ is optimal up to a logarithmic factor. We compare PS$\varepsilon$BAI$^+$ to baseline algorithms using numerical experiments which demonstrate its efficiency. Both our analytical and numerical results corroborate that the efficacy of PS$\varepsilon$BAI$^+$ is due to the delicate change detection and context alignment procedures embedded in PS$\varepsilon$BAI.
TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data
2024-10-08
Jeremy Andrew Irvin, Emily Ruoyu Liu, Joyce Chuyi Chen, Ines Dormoy, Jinyoung Kim, Samar Khanna, Zhuo Zheng, Stefano Ermon
Large vision and language assistants have enabled new capabilities for interpreting natural images. These approaches have recently been adapted to earth observation data, but they are only able to handle single image inputs, limiting their use for many real-world tasks. In this work, we develop a new vision and language assistant called TEOChat that can engage in conversations about temporal sequences of earth observation data. To train TEOChat, we curate an instruction-following dataset composed of many single image and temporal tasks including building change and damage assessment, semantic change detection, and temporal scene classification. We show that TEOChat can perform a wide variety of spatial and temporal reasoning tasks, substantially outperforming previous vision and language assistants, and even achieving comparable or better performance than several specialist models trained to perform specific tasks. Furthermore, TEOChat achieves impressive zero-shot performance on a change detection and change question answering dataset, outperforms GPT-4o and Gemini 1.5 Pro on multiple temporal tasks, and exhibits stronger single image capabilities than a comparable single image instruction-following model on scene classification, visual question answering, and captioning. We publicly release our data, model, and code at https://github.com/ermongroup/TEOChat .
The piecewise-stationary bandit problem is an important variant of the multi-armed bandit problem that further considers abrupt changes in the reward distributions. The main theme of the problem is the trade-off between exploration for detecting environment changes and exploitation of traditional bandit algorithms. While this problem has been extensively investigated, existing works either assume knowledge about the number of change points $M$ or require extremely high computational complexity. In this work, we revisit the piecewise-stationary bandit problem from a minimalist perspective. We propose a novel and generic exploration mechanism, called diminishing exploration, which eliminates the need for knowledge about $M$ and can be used in conjunction with an existing change detection-based algorithm to achieve near-optimal regret scaling. Simulation results show that despite oblivious of $M$, equipping existing algorithms with the proposed diminishing exploration generally achieves better empirical regret than the traditional uniform exploration.
Detecting changes in high-dimensional vectors presents significant challenges, especially when the post-change distribution is unknown and time-varying. This paper introduces a novel robust algorithm for correlation change detection in high-dimensional data. The approach utilizes the summary statistic of the maximum magnitude correlation coefficient to detect the change. This summary statistic captures the level of correlation present in the data but also has an asymptotic density. The robust test is designed using the asymptotic density. The proposed approach is robust because it can help detect a change in correlation level from some known level to unknown, time-varying levels. The proposed test is also computationally efficient and valid for a broad class of data distributions. The effectiveness of the proposed algorithm is demonstrated on simulated data.
Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel
2024-09-26
Jialin Li, Marta Zagorowska, Giulia De Pasquale, Alisa Rupenyan, John Lygeros
Ensuring safety is a key aspect in sequential decision making problems, such as robotics or process control. The complexity of the underlying systems often makes finding the optimal decision challenging, especially when the safety-critical system is time-varying. Overcoming the problem of optimizing an unknown time-varying reward subject to unknown time-varying safety constraints, we propose TVSafeOpt, a new algorithm built on Bayesian optimization with a spatio-temporal kernel. The algorithm is capable of safely tracking a time-varying safe region without the need for explicit change detection. Optimality guarantees are also provided for the algorithm when the optimization problem becomes stationary. We show that TVSafeOpt compares favorably against SafeOpt on synthetic data, both regarding safety and optimality. Evaluation on a realistic case study with gas compressors confirms that TVSafeOpt ensures safety when solving time-varying optimization problems with unknown reward and safety functions.
We present a novel method for scene change detection that leverages the robust feature extraction capabilities of a visual foundational model, DINOv2, and integrates full-image cross-attention to address key challenges such as varying lighting, seasonal variations, and viewpoint differences. In order to effectively learn correspondences and mis-correspondences between an image pair for the change detection task, we propose to a) ``freeze'' the backbone in order to retain the generality of dense foundation features, and b) employ ``full-image'' cross-attention to better tackle the viewpoint variations between the image pair. We evaluate our approach on two benchmark datasets, VL-CMU-CD and PSCD, along with their viewpoint-varied versions. Our experiments demonstrate significant improvements in F1-score, particularly in scenarios involving geometric changes between image pairs. The results indicate our method's superior generalization capabilities over existing state-of-the-art approaches, showing robustness against photometric and geometric variations as well as better overall generalization when fine-tuned to adapt to new environments. Detailed ablation studies further validate the contributions of each component in our architecture. Our source code is available at: https://github.com/ChadLin9596/Robust-Scene-Change-Detection.
Scene Change Detection is a challenging task in computer vision and robotics that aims to identify differences between two images of the same scene captured at different times. Traditional change detection methods rely on training models that take these image pairs as input and estimate the changes, which requires large amounts of annotated data, a costly and time-consuming process. To overcome this, we propose ZeroSCD, a zero-shot scene change detection framework that eliminates the need for training. ZeroSCD leverages pre-existing models for place recognition and semantic segmentation, utilizing their features and outputs to perform change detection. In this framework, features extracted from the place recognition model are used to estimate correspondences and detect changes between the two images. These are then combined with segmentation results from the semantic segmentation model to precisely delineate the boundaries of the detected changes. Extensive experiments on benchmark datasets demonstrate that ZeroSCD outperforms several state-of-the-art methods in change detection accuracy, despite not being trained on any of the benchmark datasets, proving its effectiveness and adaptability across different scenarios.
2024-09-23
Yan Xing, Qi'ao Xu, Jingcheng Zeng, Rui Huang, Sihua Gao, Weifeng Xu, Yuxiang Zhang, Wei Fan
Semi-supervised change detection (SSCD) utilizes partially labeled data and a large amount of unlabeled data to detect changes. However, the transformer-based SSCD network does not perform as well as the convolution-based SSCD network due to the lack of labeled data. To overcome this limitation, we introduce a new decoder called Cross Branch Feature Fusion CBFF, which combines the strengths of both local convolutional branch and global transformer branch. The convolutional branch is easy to learn and can produce high-quality features with a small amount of labeled data. The transformer branch, on the other hand, can extract global context features but is hard to learn without a lot of labeled data. Using CBFF, we build our SSCD model based on a strong-to-weak consistency strategy. Through comprehensive experiments on WHU-CD and LEVIR-CD datasets, we have demonstrated the superiority of our method over seven state-of-the-art SSCD methods.
Recently, while significant progress has been made in remote sensing image change captioning, existing methods fail to filter out areas unrelated to actual changes, making models susceptible to irrelevant features. In this article, we propose a novel multimodal framework for remote sensing image change captioning, guided by Key Change Features and Instruction-tuned (KCFI). This framework aims to fully leverage the intrinsic knowledge of large language models through visual instructions and enhance the effectiveness and accuracy of change features using pixel-level change detection tasks. Specifically, KCFI includes a ViTs encoder for extracting bi-temporal remote sensing image features, a key feature perceiver for identifying critical change areas, a pixel-level change detection decoder to constrain key change features, and an instruction-tuned decoder based on a large language model. Moreover, to ensure that change description and change detection tasks are jointly optimized, we employ a dynamic weight-averaging strategy to balance the losses between the two tasks. We also explore various feature combinations for visual fine-tuning instructions and demonstrate that using only key change features to guide the large language model is the optimal choice. To validate the effectiveness of our approach, we compare it against several state-of-the-art change captioning methods on the LEVIR-CC dataset, achieving the best performance. Our code will be available at https://github.com/yangcong356/KCFI.git.
ExelMap: Explainable Element-based HD-Map Change Detection and Update
2024-09-16
Lena Wild, Ludvig Ericson, Rafael Valencia, Patric Jensfelt
Acquisition and maintenance are central problems in deploying high-definition (HD) maps for autonomous driving, with two lines of research prevalent in current literature: Online HD map generation and HD map change detection. However, the generated map's quality is currently insufficient for safe deployment, and many change detection approaches fail to precisely localize and extract the changed map elements, hence lacking explainability and hindering a potential fleet-based cooperative HD map update. In this paper, we propose the novel task of explainable element-based HD map change detection and update. In extending recent approaches that use online mapping techniques informed with an outdated map prior for HD map updating, we present ExelMap, an explainable element-based map updating strategy that specifically identifies changed map elements. In this context, we discuss how currently used metrics fail to capture change detection performance, while allowing for unfair comparison between prior-less and prior-informed map generation methods. Finally, we present an experimental study on real-world changes related to pedestrian crossings of the Argoverse 2 Map Change Dataset. To the best of our knowledge, this is the first comprehensive problem investigation of real-world end-to-end element-based HD map change detection and update, and ExelMap the first proposed solution.
High Definition Map Mapping and Update: A General Overview and Future Directions
2024-09-15
Benny Wijaya, Kun Jiang, Mengmeng Yang, Tuopu Wen, Yunlong Wang, Xuewei Tang, Zheng Fu, Taohua Zhou, Diange Yang
Along with the rapid growth of autonomous vehicles (AVs), more and more demands are required for environment perception technology. Among others, HD mapping has become one of the more prominent roles in helping the vehicle realize essential tasks such as localization and path planning. While increasing research efforts have been directed toward HD Map development. However, a comprehensive overview of the overall HD map mapping and update framework is still lacking. This article introduces the development and current state of the algorithm involved in creating HD map mapping and its maintenance. As part of this study, the primary data preprocessing approach of processing raw data to information ready to feed for mapping and update purposes, semantic segmentation, and localization are also briefly reviewed. Moreover, the map taxonomy, ontology, and quality assessment are extensively discussed, the map data's general representation method is presented, and the mapping algorithm ranging from SLAM to transformers learning-based approaches are also discussed. The development of the HD map update algorithm, from change detection to the update methods, is also presented. Finally, the authors discuss possible future developments and the remaining challenges in HD map mapping and update technology. This paper simultaneously serves as a position paper and tutorial to those new to HD map mapping and update domains.
ChangeChat: An Interactive Model for Remote Sensing Change Analysis via Multimodal Instruction Tuning
2024-09-13
Pei Deng, Wenqian Zhou, Hanlin Wu
Remote sensing (RS) change analysis is vital for monitoring Earth's dynamic processes by detecting alterations in images over time. Traditional change detection excels at identifying pixel-level changes but lacks the ability to contextualize these alterations. While recent advancements in change captioning offer natural language descriptions of changes, they do not support interactive, user-specific queries. To address these limitations, we introduce ChangeChat, the first bitemporal vision-language model (VLM) designed specifically for RS change analysis. ChangeChat utilizes multimodal instruction tuning, allowing it to handle complex queries such as change captioning, category-specific quantification, and change localization. To enhance the model's performance, we developed the ChangeChat-87k dataset, which was generated using a combination of rule-based methods and GPT-assisted techniques. Experiments show that ChangeChat offers a comprehensive, interactive solution for RS change analysis, achieving performance comparable to or even better than state-of-the-art (SOTA) methods on specific tasks, and significantly surpassing the latest general-domain model, GPT-4. Code and pre-trained weights are available at https://github.com/hanlinwu/ChangeChat.
The field of quickest change detection (QCD) concerns design and analysis of algorithms to estimate in real time the time at which an important event takes place and identify properties of the post-change behavior. The goal is to devise a stopping time adapted to the observations that minimizes an $L_1$ loss. Approximately optimal solutions are well known under a variety of assumptions. In the work surveyed here we consider the CUSUM statistic, which is defined as a one-dimensional reflected random walk driven by a functional of the observations. It is known that the optimal functional is a log likelihood ratio subject to special statical assumptions. The paper concerns model free approaches to detection design, considering the following questions: 1. What is the performance for a given functional of the observations? 2. How do the conclusions change when there is dependency between pre- and post-change behavior? 3. How can techniques from statistics and machine learning be adapted to approximate the best functional in a given class?
While current state-of-the-art Scene Change Detection (SCD) approaches achieve impressive results in well-trained research data, they become unreliable under unseen environments and different temporal conditions; in-domain performance drops from 77.6% to 8.0% in a previously unseen environment and to 4.6% under a different temporal condition -- calling for generalizable SCD and benchmark. In this work, we propose the Generalizable Scene Change Detection Framework (GeSCF), which addresses unseen domain performance and temporal consistency -- to meet the growing demand for anything SCD. Our method leverages the pre-trained Segment Anything Model (SAM) in a zero-shot manner. For this, we design Initial Pseudo-mask Generation and Geometric-Semantic Mask Matching -- seamlessly turning user-guided prompt and single-image based segmentation into scene change detection for a pair of inputs without guidance. Furthermore, we define the Generalizable Scene Change Detection (GeSCD) benchmark along with novel metrics and an evaluation protocol to facilitate SCD research in generalizability. In the process, we introduce the ChangeVPR dataset, a collection of challenging image pairs with diverse environmental scenarios -- including urban, suburban, and rural settings. Extensive experiments across various datasets demonstrate that GeSCF achieves an average performance gain of 19.2% on existing SCD datasets and 30.0% on the ChangeVPR dataset, nearly doubling the prior art performance. We believe our work can lay a solid foundation for robust and generalizable SCD research.
Various Earth anomalies have destroyed the stable, balanced state, resulting in fatalities and serious destruction of property. With the advantages of large-scale and precise observation, high-resolution remote sensing images have been widely used for anomaly monitoring and localization. Powered by the deep representation, the existing methods have achieved remarkable advances, primarily in classification and change detection techniques. However, labeled samples are difficult to acquire due to the low probability of anomaly occurrence, and the trained models are limited to fixed anomaly categories, which hinders the application for anomalies with few samples or unknown anomalies. In this paper, to tackle this problem, we propose the anomaly change detection (AnomalyCD) technique, which accepts time-series observations and learns to identify anomalous changes by learning from the historical normal change pattern. Compared to the existing techniques, AnomalyCD processes an unfixed number of time steps and can localize the various anomalies in a unified manner, without human supervision. To benchmark AnomalyCD, we constructed a high-resolution dataset with time-series images dedicated to various Earth anomalies (the AnomalyCDD dataset). AnomalyCDD contains high-resolution (from 0.15 to 2.39 m/pixel), time-series (from 3 to 7 time steps), and large-scale images (1927.93 km2 in total) collected globally Furthermore, we developed a zero-shot baseline model (AnomalyCDM), which implements the AnomalyCD technique by extracting a general representation from the segment anything model (SAM) and conducting temporal comparison to distinguish the anomalous changes from normal changes. AnomalyCDM is designed as a two-stage workflow to enhance the efficiency, and has the ability to process the unseen images directly, without retraining for each scene.
Neuromorphic Heart Rate Monitors: Neural State Machines for Monotonic Change Detection
2024-09-04
Alessio Carpegna, Chiara De Luca, Federico Emanuele Pozzi, Alessandro Savino, Stefano Di Carlo, Giacomo Indiveri, Elisa Donati
Detecting monotonic changes in heart rate (HR) is crucial for early identification of cardiac conditions and health management. This is particularly important for dementia patients, where HR trends can signal stress or agitation. Developing wearable technologies that can perform always-on monitoring of HRs is essential to effectively detect slow changes over extended periods of time. However, designing compact electronic circuits that can monitor and process bio-signals continuously, and that can operate in a low-power regime to ensure long-lasting performance, is still an open challenge. Neuromorphic technology offers an energy-efficient solution for real-time health monitoring. We propose a neuromorphic implementation of a Neural State Machine (NSM) network to encode different health states and switch between them based on the input stimuli. Our focus is on detecting monotonic state switches in electrocardiogram data to identify progressive HR increases. This innovative approach promises significant advancements in continuous health monitoring and management.
Despite the eye-catching breakthroughs achieved by deep visual networks in detecting region-level surface defects, the challenge of high-quality pixel-wise defect detection remains due to diverse defect appearances and data scarcity. To avoid over-reliance on defect appearance and achieve accurate defect segmentation, we proposed a change-aware Siamese network that solves the defect segmentation in a change detection framework. A novel multi-class balanced contrastive loss is introduced to guide the Transformer-based encoder, which enables encoding diverse categories of defects as the unified class-agnostic difference between defect and defect-free images. The difference presented by a distance map is then skip-connected to the change-aware decoder to assist in the location of both inter-class and out-of-class pixel-wise defects. In addition, we proposed a synthetic dataset with multi-class liquid crystal display (LCD) defects under a complex and disjointed background context, to demonstrate the advantages of change-based modeling over appearance-based modeling for defect segmentation. In our proposed dataset and two public datasets, our model achieves superior performances than the leading semantic segmentation methods, while maintaining a relatively small model size. Moreover, our model achieves a new state-of-the-art performance compared to the semi-supervised approaches in various supervision settings.
ClimDetect: A Benchmark Dataset for Climate Change Detection and Attribution
2024-08-28
Sungduk Yu, Brian L. White, Anahita Bhiwandiwalla, Musashi Hinck, Matthew Lyle Olson, Yaniv Gurwicz, Raanan Y. Rohekar, Tung Nguyen, Vasudev Lal
Detecting and attributing temperature increases driven by climate change is crucial for understanding global warming and informing adaptation strategies. However, distinguishing human-induced climate signals from natural variability remains challenging for traditional detection and attribution (D&A) methods, which rely on identifying specific "fingerprints" -- spatial patterns expected to emerge from external forcings such as greenhouse gas emissions. Deep learning offers promise in discerning these complex patterns within expansive spatial datasets, yet the lack of standardized protocols has hindered consistent comparisons across studies. To address this gap, we introduce ClimDetect, a standardized dataset comprising 1.17M daily climate snapshots paired with target climate change indicator variables. The dataset is curated from both CMIP6 climate model simulations and real-world observation-assimilated reanalysis datasets (ERA5, JRA-3Q, and MERRA-2), and is designed to enhance model accuracy in detecting climate change signals. ClimDetect integrates various input and target variables used in previous research, ensuring comparability and consistency across studies. We also explore the application of vision transformers (ViT) to climate data -- a novel approach that, to our knowledge, has not been attempted before for climate change detection tasks. Our open-access data serve as a benchmark for advancing climate science by enabling end-to-end model development and evaluation. ClimDetect is publicly accessible via Hugging Face dataset repository at: https://huggingface.co/datasets/ClimDetect/ClimDetect.
TempoFormer: A Transformer for Temporally-aware Representations in Change Detection
2024-08-28
Talia Tseriotou, Adam Tsakalidis, Maria Liakata
Dynamic representation learning plays a pivotal role in understanding the evolution of linguistic content over time. On this front both context and time dynamics as well as their interplay are of prime importance. Current approaches model context via pre-trained representations, which are typically temporally agnostic. Previous work on modelling context and temporal dynamics has used recurrent methods, which are slow and prone to overfitting. Here we introduce TempoFormer, the first task-agnostic transformer-based and temporally-aware model for dynamic representation learning. Our approach is jointly trained on inter and intra context dynamics and introduces a novel temporal variation of rotary positional embeddings. The architecture is flexible and can be used as the temporal representation foundation of other models or applied to different transformer-based architectures. We show new SOTA performance on three different real-time change detection tasks.
Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1
2024-08-28
Alejandro Mestre-Quereda, Juan M. Lopez-Sanchez
Speckle suppression in synthetic aperture radar (SAR) images is a key processing step which continues to be a research topic. A wide variety of methods, using either spatially-based approaches or transform-based strategies, have been developed and have shown to provide outstanding results. However, recent advances in deep learning techniques and their application to SAR image despeckling have been demonstrated to offer state-of-the-art results. Unfortunately, they have been mostly applied to single-polarimetric images. The extension of a deep learning-based approach for speckle removal to polarimetric SAR (PolSAR) images is complicated because of the complex nature of the measured covariance matrices for every image pixel, the properties of which must be preserved during filtering. In this work, we propose a complete framework to remove speckle in polarimetric SAR images using a convolutional neural network. The methodology includes a reversible transformation of the original complex covariance matrix to obtain a set of real-valued intensity bands which are fed to the neural network. In addition, the proposed method includes a change detection strategy to avoid the neural network to learn erroneous features in areas strongly affected by temporal changes, so that the network only learns the underlying speckle component present in the data. The method is implemented and tested with dual-polarimetric images acquired by Sentinel-1. Experiments show that the proposed approach offers exceptional results in both speckle reduction and resolution preservation. More importantly, it is also shown that the neural network is not generating artifacts or introducing bias in the filtered images, making them suitable for further polarimetric processing and exploitation.
Towards Better Comprehension of Breaking Changes in the NPM Ecosystem
2024-08-26
Dezhen Kong, Jiakun Liu, Lingfeng Bao, David Lo
Breaking changes cause a lot of effort to both downstream and upstream developers: downstream developers need to adapt to breaking changes and upstream developers are responsible for identifying and documenting them. In the NPM ecosystem, characterized by frequent code changes and a high tolerance for making breaking changes, the effort is larger. For better comprehension of breaking changes in the NPM ecosystem and to enhance breaking change detection tools, we conduct a large-scale empirical study to investigate breaking changes in the NPM ecosystem. We construct a dataset of explicitly documented breaking changes from 381 popular NPM projects. We find that 95.4% of the detected breaking changes can be covered by developers' documentation, and about 19% of the breaking changes cannot be detected by regression testing. Then in the process of investigating source code of our collected breaking changes, we yield a taxonomy of JavaScript and TypeScript-specific syntactic breaking changes and a taxonomy of major types of behavioral breaking changes. Additionally, we investigate the reasons why developers make breaking changes in NPM and find three major reasons, i.e., to reduce code redundancy, to improve identifier name, and to improve API design, and each category contains several sub-items. We provide actionable implications for future research, e.g., automatic naming and renaming techniques should be applied in JavaScript projects to improve identifier names, future research can try to detect more types of behavioral breaking changes. By presenting the implications, we also discuss the weakness of automatic renaming and breaking change detection approaches.
Find the Assembly Mistakes: Error Segmentation for Industrial Applications
2024-08-23
Dan Lehman, Tim J. Schoonbeek, Shao-Hsuan Hung, Jacek Kustra, Peter H. N. de With, Fons van der Sommen
Recognizing errors in assembly and maintenance procedures is valuable for industrial applications, since it can increase worker efficiency and prevent unplanned down-time. Although assembly state recognition is gaining attention, none of the current works investigate assembly error localization. Therefore, we propose StateDiffNet, which localizes assembly errors based on detecting the differences between a (correct) intended assembly state and a test image from a similar viewpoint. StateDiffNet is trained on synthetically generated image pairs, providing full control over the type of meaningful change that should be detected. The proposed approach is the first to correctly localize assembly errors taken from real ego-centric video data for both states and error types that are never presented during training. Furthermore, the deployment of change detection to this industrial application provides valuable insights and considerations into the mechanisms of state-of-the-art change detection algorithms. The code and data generation pipeline are publicly available at: https://timschoonbeek.github.io/error_seg.
UMAD: University of Macau Anomaly Detection Benchmark Dataset
2024-08-22
Dong Li, Lineng Chen, Cheng-Zhong Xu, Hui Kong
Anomaly detection is critical in surveillance systems and patrol robots by identifying anomalous regions in images for early warning. Depending on whether reference data are utilized, anomaly detection can be categorized into anomaly detection with reference and anomaly detection without reference. Currently, anomaly detection without reference, which is closely related to out-of-distribution (OoD) object detection, struggles with learning anomalous patterns due to the difficulty of collecting sufficiently large and diverse anomaly datasets with the inherent rarity and novelty of anomalies. Alternatively, anomaly detection with reference employs the scheme of change detection to identify anomalies by comparing semantic changes between a reference image and a query one. However, there are very few ADr works due to the scarcity of public datasets in this domain. In this paper, we aim to address this gap by introducing the UMAD Benchmark Dataset. To our best knowledge, this is the first benchmark dataset designed specifically for anomaly detection with reference in robotic patrolling scenarios, e.g., where an autonomous robot is employed to detect anomalous objects by comparing a reference and a query video sequences. The reference sequences can be taken by the robot along a specified route when there are no anomalous objects in the scene. The query sequences are captured online by the robot when it is patrolling in the same scene following the same route. Our benchmark dataset is elaborated such that each query image can find a corresponding reference based on accurate robot localization along the same route in the prebuilt 3D map, with which the reference and query images can be geometrically aligned using adaptive warping. Besides the proposed benchmark dataset, we evaluate the baseline models of ADr on this dataset.
Asymmetric Graph Error Control with Low Complexity in Causal Bandits
2024-08-20
Chen Peng, Di Zhang, Urbashi Mitra
In this paper, the causal bandit problem is investigated, with the objective of maximizing the long-term reward by selecting an optimal sequence of interventions on nodes in an unknown causal graph. It is assumed that both the causal topology and the distribution of interventions are unknown. First, based on the difference between the two types of graph identification errors (false positives and negatives), a causal graph learning method is proposed. Numerical results suggest that this method has a much lower sample complexity relative to the prior art by learning sub-graphs. However, we note that a sample complexity analysis for the new algorithm has not been undertaken, as of yet. Under the assumption of minimum-mean squared error weight estimation, a new uncertainty bound tailored to the causal bandit problem is derived. This uncertainty bound drives an upper confidence bound-based intervention selection to optimize the reward. Further, we consider a particular instance of non-stationary bandits wherein both the causal topology and interventional distributions can change. Our solution is the design of a sub-graph change detection mechanism that requires a modest number of samples. Numerical results compare the new methodology to existing schemes and show a substantial performance improvement in stationary and non-stationary settings. Averaged over 100 randomly generated causal bandits, the proposed scheme takes significantly fewer samples to learn the causal structure and achieves a reward gain of 85% compared to existing approaches.
Hierarchical Attention Diffusion Networks with Object Priors for Video Change Detection
2024-08-20
Andrew Kiruluta, Eric Lundy, Andreas Lemos
We present a unified change detection pipeline that combines instance level masking, multi\-scale attention within a denoising diffusion model, and per pixel semantic classification, all refined via SSIM to match human perception. By first isolating only temporally novel objects with Mask R\-CNN, then guiding diffusion updates through hierarchical cross attention to object and global contexts, and finally categorizing each pixel into one of C change types, our method delivers detailed, interpretable multi\-class maps. It outperforms traditional differencing, Siamese CNNs, and GAN\-based detectors by 10\-25 points in F1 and IoU on both synthetic and real world benchmarks, marking a new state of the art in remote sensing change detection.
Treat Stillness with Movement: Remote Sensing Change Detection via Coarse-grained Temporal Foregrounds Mining
2024-08-15
Xixi Wang, Zitian Wang, Jingtao Jiang, Lan Chen, Xiao Wang, Bo Jiang
Current works focus on addressing the remote sensing change detection task using bi-temporal images. Although good performance can be achieved, however, seldom of they consider the motion cues which may also be vital. In this work, we revisit the widely adopted bi-temporal images-based framework and propose a novel Coarse-grained Temporal Mining Augmented (CTMA) framework. To be specific, given the bi-temporal images, we first transform them into a video using interpolation operations. Then, a set of temporal encoders is adopted to extract the motion features from the obtained video for coarse-grained changed region prediction. Subsequently, we design a novel Coarse-grained Foregrounds Augmented Spatial Encoder module to integrate both global and local information. We also introduce a motion augmented strategy that leverages motion cues as an additional output to aggregate with the spatial features for improved results. Meanwhile, we feed the input image pairs into the ResNet to get the different features and also the spatial blocks for fine-grained feature learning. More importantly, we propose a mask augmented strategy that utilizes coarse-grained changed regions, incorporating them into the decoder blocks to enhance the final changed prediction. Extensive experiments conducted on multiple benchmark datasets fully validated the effectiveness of our proposed framework for remote sensing image change detection. The source code of this paper will be released on https://github.com/Event-AHU/CTM_Remote_Sensing_Change_Detection
Granger causal inference for climate change attribution
2024-08-13
Mark D. Risser, Mohammed Ombadi, Michael F. Wehner
Climate change detection and attribution (D&A) is concerned with determining the extent to which anthropogenic activities have influenced specific aspects of the global climate system. D&A fits within the broader field of causal inference, the collection of statistical methods that identify cause and effect relationships. There are a wide variety of methods for making attribution statements, each of which require different types of input data and each of which are conditional to varying extents. Some methods are based on Pearl causality (experimental interference) while others leverage Granger (predictive) causality, and the causal framing provides important context for how the resulting attribution conclusion should be interpreted. However, while Granger-causal attribution analyses have become more common, there is no clear statement of their strengths and weaknesses and no clear consensus on where and when Granger-causal perspectives are appropriate. In this prospective paper, we provide a formal definition for Granger-based approaches to trend and event attribution and a clear comparison with more traditional methods for assessing the human influence on extreme weather and climate events. Broadly speaking, Granger-causal attribution statements can be constructed quickly from observations and do not require computationally-intesive dynamical experiments. These analyses also enable rapid attribution, which is useful in the aftermath of a severe weather event, and provide multiple lines of evidence for anthropogenic climate change when paired with Pearl-causal attribution. Confidence in attribution statements is increased when different methodologies arrive at similar conclusions. Moving forward, we encourage the D&A community to embrace hybrid approaches to climate change attribution that leverage the strengths of both Granger and Pearl causality.
Specialized Change Detection using Segment Anything
2024-08-13
Tahir Ahmad, Sudipan Saha
Change detection (CD) is a fundamental task in Earth observation. While most change detection methods detect all changes, there is a growing need for specialized methods targeting specific changes relevant to particular applications while discarding the other changes. For instance, urban management might prioritize detecting the disappearance of buildings due to natural disasters or other reasons. Furthermore, while most supervised change detection methods require large-scale training datasets, in many applications only one or two training examples might be available instead of large datasets. Addressing such needs, we propose a focused CD approach using the Segment Anything Model (SAM), a versatile vision foundation model. Our method leverages a binary mask of the object of interest in pre-change images to detect their disappearance in post-change images. By using SAM's robust segmentation capabilities, we create prompts from the pre-change mask, use those prompts to segment the post-change image, and identify missing objects. This unsupervised approach demonstrated for building disappearance detection, is adaptable to various domains requiring specialized CD. Our contributions include defining a novel CD problem, proposing a method using SAM, and demonstrating its effectiveness. The proposed method also has benefits related to privacy preservation.
Anomaly Detection in Time Series of EDFA Pump Currents to Monitor Degeneration Processes using Fuzzy Clustering
2024-08-12
Dominic Schneider, Lutz Rapp, Christoph Ament
This article proposes a novel fuzzy clustering based anomaly detection method for pump current time series of EDFA systems. The proposed change detection framework (CDF) strategically combines the advantages of entropy analysis (EA) and principle component analysis (PCA) with fuzzy clustering procedures. In the framework, EA is applied for dynamic selection of features for reduction of the feature space and increase of computational performance. Furthermore, PCA is utilized to extract features from the raw feature space to enable generalization capability of the subsequent fuzzy clustering procedures. Three different fuzzy clustering methods, more precisely the fuzzy clustering algorithm, a probabilistic clustering algorithm and a possibilistic clustering algorithm are evaluated for performance and generalization. Hence, the proposed framework has the innovative feature to detect changes in pump current time series at an early stage for arbitrary points of operation, compared to state-of-the-art predefined alarms in commercially used EDFAs. Moreover, the approach is implemented and tested using experimental data. In addition, the proposed framework enables further approaches of applying decentralized predictive maintenance for optical fiber networks.
High Probability Latency Sequential Change Detection over an Unknown Finite Horizon
2024-08-11
Yu-Han Huang, Venugopal V. Veeravalli
A finite horizon variant of the quickest change detection problem is studied, in which the goal is to minimize a delay threshold (latency), under constraints on the probability of false alarm and the probability that the latency is exceeded. In addition, the horizon is not known to the change detector. A variant of the cumulative sum (CuSum) test with a threshold that increasing logarithmically with time is proposed as a candidate solution to the problem. An information-theoretic lower bound on the minimum value of the latency under the constraints is then developed. This lower bound is used to establish certain asymptotic optimality properties of the proposed test in terms of the horizon and the false alarm probability. Some experimental results are given to illustrate the performance of the test.
Integrated Dynamic Phenological Feature for Remote Sensing Image Land Cover Change Detection
2024-08-08
Yi Liu, Chenhao Sun, Hao Ye, Xiangying Liu, Weilong Ju
Remote sensing image change detection (CD) is essential for analyzing land surface changes over time, with a significant challenge being the differentiation of actual changes from complex scenes while filtering out pseudo-changes. A primary contributor to this challenge is the intra-class dynamic changes due to phenological characteristics in natural areas. To overcome this, we introduce the InPhea model, which integrates phenological features into a remote sensing image CD framework. The model features a detector with a differential attention module for improved feature representation of change information, coupled with high-resolution feature extraction and spatial pyramid blocks to enhance performance. Additionally, a constrainer with four constraint modules and a multi-stage contrastive learning approach is employed to aid in the model's understanding of phenological characteristics. Experiments on the HRSCD, SECD, and PSCD-Wuhan datasets reveal that InPhea outperforms other models, confirming its effectiveness in addressing phenological pseudo-changes and its overall model superiority.
Testing for a general changepoint in psychometric studies: changes detection and sample size planning
2024-08-07
Nicoletta D'Angelo
This paper introduces a new method for change detection in psychometric studies based on the recently introduced pseudo Score statistic, for which the sampling distribution under the alternative hypothesis has been determined. Our approach has the advantage of simplicity in its computation, eliminating the need for resampling or simulations to obtain critical values. Additionally, it comes with a known null/alternative distribution, facilitating easy calculations for power levels and sample size planning. The paper indeed also discusses the topic of power analysis in segmented regression, namely the estimation of sample size or power level when the study data being collected focuses on a covariate expected to affect the mean response via a piecewise relationship with an unknown breakpoint. We run simulation results showing that our method outperforms other Tests for a Change Point (TFCP) with both normally distributed and binary data and carry out a real SAT Critical reading data analysis. The proposed test contributes to the framework of psychometric research, and it is available on the Comprehensive R Archive Network (CRAN) and in a more user-friendly Shiny App, both illustrated at the end of the paper.
Cross-Domain Separable Translation Network for Multimodal Image Change Detection
2024-07-23
Tao Zhan, Yuanyuan Zhu, Jie Lan, Qianlong Dang
In the remote sensing community, multimodal change detection (MCD) is particularly critical due to its ability to track changes across different imaging conditions and sensor types, making it highly applicable to a wide range of real-world scenarios. This paper focuses on addressing the challenges of MCD, especially the difficulty in comparing images from different sensors with varying styles and statistical characteristics of geospatial objects. Traditional MCD methods often struggle with these variations, leading to inaccurate and unreliable results. To overcome these limitations, a novel unsupervised cross-domain separable translation network (CSTN) is proposed, which uniquely integrates a within-domain self-reconstruction and a cross-domain image translation and cycle-reconstruction workflow with change detection constraints. The model is optimized by implementing both the tasks of image translation and MCD simultaneously, thereby guaranteeing the comparability of learned features from multimodal images. Specifically, a simple yet efficient dual-branch convolutional architecture is employed to separate the content and style information of multimodal images. This process generates a style-independent content-comparable feature space, which is crucial for achieving accurate change detection even in the presence of significant sensor variations. Extensive experimental results demonstrate the effectiveness of the proposed method, showing remarkable improvements over state-of-the-art approaches in terms of accuracy and efficacy for MCD. The implementation of our method will be publicly available at \url{https://github.com/OMEGA-RS/CSTN}
With the widespread application of remote sensing technology in environmental monitoring, the demand for efficient and accurate remote sensing image change detection (CD) for natural environments is growing. We propose a novel deep learning framework named EfficientCD, specifically designed for remote sensing image change detection. The framework employs EfficientNet as its backbone network for feature extraction. To enhance the information exchange between bi-temporal image feature maps, we have designed a new Feature Pyramid Network module targeted at remote sensing change detection, named ChangeFPN. Additionally, to make full use of the multi-level feature maps in the decoding stage, we have developed a layer-by-layer feature upsampling module combined with Euclidean distance to improve feature fusion and reconstruction during the decoding stage. The EfficientCD has been experimentally validated on four remote sensing datasets: LEVIR-CD, SYSU-CD, CLCD, and WHUCD. The experimental results demonstrate that EfficientCD exhibits outstanding performance in change detection accuracy. The code and pretrained models will be released at https://github.com/dyzy41/mmrscd.
We present Open-CD, a change detection toolbox that contains a rich set of change detection methods as well as related components and modules. The toolbox started from a series of open source general vision task tools, including OpenMMLab Toolkits, PyTorch Image Models, etc. It gradually evolves into a unified platform that covers many popular change detection methods and contemporary modules. It not only includes training and inference codes, but also provides some useful scripts for data analysis. We believe this toolbox is by far the most complete change detection toolbox. In this report, we introduce the various features, supported methods and applications of Open-CD. In addition, we also conduct a benchmarking study on different methods and components. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new change detectors. Code and models are available at https://github.com/likyoo/open-cd. Pioneeringly, this report also includes brief descriptions of the algorithms supported in Open-CD, mainly contributed by their authors. We sincerely encourage researchers in this field to participate in this project and work together to create a more open community. This toolkit and report will be kept updated.
Semantic-CC: Boosting Remote Sensing Image Change Captioning via Foundational Knowledge and Semantic Guidance
2024-07-19
Yongshuo Zhu, Lu Li, Keyan Chen, Chenyang Liu, Fugen Zhou, Zhenwei Shi
Remote sensing image change captioning (RSICC) aims to articulate the changes in objects of interest within bi-temporal remote sensing images using natural language. Given the limitations of current RSICC methods in expressing general features across multi-temporal and spatial scenarios, and their deficiency in providing granular, robust, and precise change descriptions, we introduce a novel change captioning (CC) method based on the foundational knowledge and semantic guidance, which we term Semantic-CC. Semantic-CC alleviates the dependency of high-generalization algorithms on extensive annotations by harnessing the latent knowledge of foundation models, and it generates more comprehensive and accurate change descriptions guided by pixel-level semantics from change detection (CD). Specifically, we propose a bi-temporal SAM-based encoder for dual-image feature extraction; a multi-task semantic aggregation neck for facilitating information interaction between heterogeneous tasks; a straightforward multi-scale change detection decoder to provide pixel-level semantic guidance; and a change caption decoder based on the large language model (LLM) to generate change description sentences. Moreover, to ensure the stability of the joint training of CD and CC, we propose a three-stage training strategy that supervises different tasks at various stages. We validate the proposed method on the LEVIR-CC and LEVIR-CD datasets. The experimental results corroborate the complementarity of CD and CC, demonstrating that Semantic-CC can generate more accurate change descriptions and achieve optimal performance across both tasks.
Synthetic aperture radar (SAR) image change detection is critical in remote sensing image analysis. Recently, the attention mechanism has been widely used in change detection tasks. However, existing attention mechanisms often employ down-sampling operations such as average pooling on the Key and Value components to enhance computational efficiency. These irreversible operations result in the loss of high-frequency components and other important information. To address this limitation, we develop Wavelet-based Bi-dimensional Aggregation Network (WBANet) for SAR image change detection. We design a wavelet-based self-attention block that includes discrete wavelet transform and inverse discrete wavelet transform operations on Key and Value components. Hence, the feature undergoes downsampling without any loss of information, while simultaneously enhancing local contextual awareness through an expanded receptive field. Additionally, we have incorporated a bi-dimensional aggregation module that boosts the non-linear representation capability by merging spatial and channel information via broadcast mechanism. Experimental results on three SAR datasets demonstrate that our WBANet significantly outperforms contemporary state-of-the-art methods. Specifically, our WBANet achieves 98.33\%, 96.65\%, and 96.62\% of percentage of correct classification (PCC) on the respective datasets, highlighting its superior performance. Source codes are available at \url{https://github.com/summitgao/WBANet}.
This study introduces a novel Remote Sensing (RS) Urban Prediction (UP) task focused on future urban planning, which aims to forecast urban layouts by utilizing information from existing urban layouts and planned change maps. To address the proposed RS UP task, we propose UP-Diff, which leverages a Latent Diffusion Model (LDM) to capture positionaware embeddings of pre-change urban layouts and planned change maps. In specific, the trainable cross-attention layers within UP-Diff's iterative diffusion modules enable the model to dynamically highlight crucial regions for targeted modifications. By utilizing our UP-Diff, designers can effectively refine and adjust future urban city plans by making modifications to the change maps in a dynamic and adaptive manner. Compared with conventional RS Change Detection (CD) methods, the proposed UP-Diff for the RS UP task avoids the requirement of paired prechange and post-change images, which enhances the practical usage in city development. Experimental results on LEVIRCD and SYSU-CD datasets show UP-Diff's ability to accurately predict future urban layouts with high fidelity, demonstrating its potential for urban planning. Code and model weights are available at https://github.com/zeyuwang-zju/UP-Diff.
2024-07-15
Sean Moushegian, Suya Wu, Enmao Diao, Jie Ding, Taposh Banerjee, Vahid Tarokh
Methods in the field of quickest change detection rapidly detect in real-time a change in the data-generating distribution of an online data stream. Existing methods have been able to detect this change point when the densities of the pre- and post-change distributions are known. Recent work has extended these results to the case where the pre- and post-change distributions are known only by their score functions. This work considers the case where the pre- and post-change score functions are known only to correspond to distributions in two disjoint sets. This work selects a pair of least-favorable distributions from these sets to robustify the existing score-based quickest change detection algorithm, the properties of which are studied. This paper calculates the least-favorable distributions for specific model classes and provides methods of estimating the least-favorable distributions for common constructions. Simulation results are provided demonstrating the performance of our robust change detection algorithm.
SeFi-CD: A Semantic First Change Detection Paradigm That Can Detect Any Change You Want
2024-07-13
Ling Zhao, Zhenyang Huang, Dongsheng Kuang, Chengli Peng, Jun Gan, Haifeng Li
The existing change detection(CD) methods can be summarized as the visual-first change detection (ViFi-CD) paradigm, which first extracts change features from visual differences and then assigns them specific semantic information. However, CD is essentially dependent on change regions of interest (CRoIs), meaning that the CD results are directly determined by the semantics changes of interest, making its primary image factor semantic of interest rather than visual. The ViFi-CD paradigm can only assign specific semantics of interest to specific change features extracted from visual differences, leading to the inevitable omission of potential CRoIs and the inability to adapt to different CRoI CD tasks. In other words, changes in other CRoIs cannot be detected by the ViFi-CD method without retraining the model or significantly modifying the method. This paper introduces a new CD paradigm, the semantic-first CD (SeFi-CD) paradigm. The core idea of SeFi-CD is to first perceive the dynamic semantics of interest and then visually search for change features related to the semantics. Based on the SeFi-CD paradigm, we designed Anything You Want Change Detection (AUWCD). Experiments on public datasets demonstrate that the AUWCD outperforms the current state-of-the-art CD methods, achieving an average F1 score 5.01\% higher than that of these advanced supervised baselines on the SECOND dataset, with a maximum increase of 13.17\%. The proposed SeFi-CD offers a novel CD perspective and approach.
Open-Canopy: Towards Very High Resolution Forest Monitoring
2024-07-12
Fajwel Fogel, Yohann Perron, Nikola Besic, Laurent Saint-André, Agnès Pellissier-Tanon, Martin Schwartz, Thomas Boudras, Ibrahim Fayad, Alexandre d'Aspremont, Loic Landrieu, Philippe Ciais
Estimating canopy height and its changes at meter resolution from satellite imagery is a significant challenge in computer vision with critical environmental applications. However, the lack of open-access datasets at this resolution hinders the reproducibility and evaluation of models. We introduce Open-Canopy, the first open-access, country-scale benchmark for very high-resolution (1.5 m) canopy height estimation, covering over 87,000 km$^2$ across France with 1.5 m resolution satellite imagery and aerial LiDAR data. Additionally, we present Open-Canopy-$\Delta$, a benchmark for canopy height change detection between images from different years at tree level-a challenging task for current computer vision models. We evaluate state-of-the-art architectures on these benchmarks, highlighting significant challenges and opportunities for improvement. Our datasets and code are publicly available at https://github.com/fajwel/Open-Canopy.
Although recently several foundation models for satellite remote sensing imagery have been proposed, they fail to address major challenges of real/operational applications. Indeed, embeddings that don't take into account the spectral, spatial and temporal dimensions of the data as well as the irregular or unaligned temporal sampling are of little use for most real world uses. As a consequence, we propose an ALIgned Sits Encoder (ALISE), a novel approach that leverages the spatial, spectral, and temporal dimensions of irregular and unaligned SITS while producing aligned latent representations. Unlike SSL models currently available for SITS, ALISE incorporates a flexible query mechanism to project the SITS into a common and learned temporal projection space. Additionally, thanks to a multi-view framework, we explore integration of instance discrimination along a masked autoencoding task to SITS. The quality of the produced representation is assessed through three downstream tasks: crop segmentation (PASTIS), land cover segmentation (MultiSenGE), and a novel crop change detection dataset. Furthermore, the change detection task is performed without supervision. The results suggest that the use of aligned representations is more effective than previous SSL methods for linear probing segmentation tasks.
Satellite Image Time Series Semantic Change Detection: Novel Architecture and Analysis of Domain Shift
2024-07-10
Elliot Vincent, Jean Ponce, Mathieu Aubry
Satellite imagery plays a crucial role in monitoring changes happening on Earth's surface and aiding in climate analysis, ecosystem assessment, and disaster response. In this paper, we tackle semantic change detection with satellite image time series (SITS-SCD) which encompasses both change detection and semantic segmentation tasks. We propose a new architecture that improves over the state of the art, scales better with the number of parameters, and leverages long-term temporal information. However, for practical use cases, models need to adapt to spatial and temporal shifts, which remains a challenge. We investigate the impact of temporal and spatial shifts separately on global, multi-year SITS datasets using DynamicEarthNet and MUDS. We show that the spatial domain shift represents the most complex setting and that the impact of temporal shift on performance is more pronounced on change detection than on semantic segmentation, highlighting that it is a specific issue deserving further attention.
A Mamba-based Siamese Network for Remote Sensing Change Detection
2024-07-08
Jay N. Paranjape, Celso de Melo, Vishal M. Patel
Change detection in remote sensing images is an essential tool for analyzing a region at different times. It finds varied applications in monitoring environmental changes, man-made changes as well as corresponding decision-making and prediction of future trends. Deep learning methods like Convolutional Neural Networks (CNNs) and Transformers have achieved remarkable success in detecting significant changes, given two images at different times. In this paper, we propose a Mamba-based Change Detector (M-CD) that segments out the regions of interest even better. Mamba-based architectures demonstrate linear-time training capabilities and an improved receptive field over transformers. Our experiments on four widely used change detection datasets demonstrate significant improvements over existing state-of-the-art (SOTA) methods. Our code and pre-trained models are available at https://github.com/JayParanjape/M-CD
Rearranging objects (e.g. vase, door) back in their original positions is one of the most fundamental skills for domestic service robots (DSRs). In rearrangement tasks, it is crucial to detect the objects that need to be rearranged according to the goal and current states. In this study, we focus on Rearrangement Target Detection (RTD), where the model generates a change mask for objects that should be rearranged. Although many studies have been conducted in the field of Scene Change Detection (SCD), most SCD methods often fail to segment objects with complex shapes and fail to detect the change in the angle of objects that can be opened or closed. In this study, we propose a Co-Scale Cross-Attentional Transformer for RTD. We introduce the Serial Encoder which consists of a sequence of serial blocks and the Cross-Attentional Encoder which models the relationship between the goal and current states. We built a new dataset consisting of RGB images and change masks regarding the goal and current states. We validated our method on the dataset and the results demonstrated that our method outperformed baseline methods on $F_1$-score and mean IoU.
TokenVerse: Towards Unifying Speech and NLP Tasks via Transducer-based ASR
2024-07-05
Shashi Kumar, Srikanth Madikeri, Juan Zuluaga-Gomez, Iuliia Thorbecke, Esaú Villatoro-Tello, Sergio Burdisso, Petr Motlicek, Karthik Pandia, Aravind Ganapathiraju
In traditional conversational intelligence from speech, a cascaded pipeline is used, involving tasks such as voice activity detection, diarization, transcription, and subsequent processing with different NLP models for tasks like semantic endpointing and named entity recognition (NER). Our paper introduces TokenVerse, a single Transducer-based model designed to handle multiple tasks. This is achieved by integrating task-specific tokens into the reference text during ASR model training, streamlining the inference and eliminating the need for separate NLP models. In addition to ASR, we conduct experiments on 3 different tasks: speaker change detection, endpointing, and NER. Our experiments on a public and a private dataset show that the proposed method improves ASR by up to 7.7% in relative WER while outperforming the cascaded pipeline approach in individual task performance. Our code is publicly available: https://github.com/idiap/tokenverse-unifying-speech-nlp
Monitoring changes triggered by mining activities is crucial for industrial controlling, environmental management and regulatory compliance, yet it poses significant challenges due to the vast and often remote locations of mining sites. Remote sensing technologies have increasingly become indispensable to detect and analyze these changes over time. We thus introduce MineNetCD, a comprehensive benchmark designed for global mining change detection using remote sensing imagery. The benchmark comprises three key contributions. First, we establish a global mining change detection dataset featuring more than 70k paired patches of bi-temporal high-resolution remote sensing images and pixel-level annotations from 100 mining sites worldwide. Second, we develop a novel baseline model based on a change-aware Fast Fourier Transform (ChangeFFT) module, which enhances various backbones by leveraging essential spectrum components within features in the frequency domain and capturing the channel-wise correlation of bi-temporal feature differences to learn change-aware representations. Third, we construct a unified change detection (UCD) framework that integrates over 13 advanced change detection models. This framework is designed for streamlined and efficient processing, utilizing the cloud platform hosted by HuggingFace. Extensive experiments have been conducted to demonstrate the superiority of the proposed baseline model compared with 12 state-of-the-art change detection approaches. Empirical studies on modularized backbones comprehensively confirm the efficacy of different representation learners on change detection. This contribution represents significant advancements in the field of remote sensing and change detection, providing a robust resource for future research and applications in global mining monitoring. Dataset and Codes are available via the link.
M^3:Manipulation Mask Manufacturer for Arbitrary-Scale Super-Resolution Mask
2024-07-04
Xinyu Yang, Xiaochen Ma, Xuekang Zhu, Bo Du, Lei Su, Bingkui Tong, Zeyu Lei, Jizhe Zhou
In the field of image manipulation localization (IML), the small quantity and poor quality of existing datasets have always been major issues. A dataset containing various types of manipulations will greatly help improve the accuracy of IML models. Images on the internet (such as those on Baidu Tieba's PS Bar) are manipulated using various techniques, and creating a dataset from these images will significantly enrich the types of manipulations in our data. However, images on the internet suffer from resolution and clarity issues, and the masks obtained by simply subtracting the manipulated image from the original contain various noises. These noises are difficult to remove, rendering the masks unusable for IML models. Inspired by the field of change detection, we treat the original and manipulated images as changes over time for the same image and view the data generation task as a change detection task. However, due to clarity issues between images, conventional change detection models perform poorly. Therefore, we introduced a super-resolution module and proposed the Manipulation Mask Manufacturer (MMM) framework. It enhances the resolution of both the original and tampered images, thereby improving image details for better comparison. Simultaneously, the framework converts the original and tampered images into feature embeddings and concatenates them, effectively modeling the context. Additionally, we created the Manipulation Mask Manufacturer Dataset (MMMD), a dataset that covers a wide range of manipulation techniques. We aim to contribute to the fields of image forensics and manipulation detection by providing more realistic manipulation data through MMM and MMMD. Detailed information about MMMD and the download link can be found at: the code and datasets will be made available.
Relating CNN-Transformer Fusion Network for Change Detection
2024-07-03
Yuhao Gao, Gensheng Pei, Mengmeng Sheng, Zeren Sun, Tao Chen, Yazhou Yao
While deep learning, particularly convolutional neural networks (CNNs), has revolutionized remote sensing (RS) change detection (CD), existing approaches often miss crucial features due to neglecting global context and incomplete change learning. Additionally, transformer networks struggle with low-level details. RCTNet addresses these limitations by introducing \textbf{(1)} an early fusion backbone to exploit both spatial and temporal features early on, \textbf{(2)} a Cross-Stage Aggregation (CSA) module for enhanced temporal representation, \textbf{(3)} a Multi-Scale Feature Fusion (MSF) module for enriched feature extraction in the decoder, and \textbf{(4)} an Efficient Self-deciphering Attention (ESA) module utilizing transformers to capture global information and fine-grained details for accurate change detection. Extensive experiments demonstrate RCTNet's clear superiority over traditional RS image CD methods, showing significant improvement and an optimal balance between accuracy and computational cost.
Investigating the Contextualised Word Embedding Dimensions Specified for Contextual and Temporal Semantic Changes
2024-07-03
Taichi Aida, Danushka Bollegala
The sense-aware contextualised word embeddings (SCWEs) encode semantic changes of words within the contextualised word embedding (CWE) spaces. Despite the superior performance of SCWEs in contextual/temporal semantic change detection (SCD) benchmarks, it remains unclear as to how the meaning changes are encoded in the embedding space. To study this, we compare pre-trained CWEs and their fine-tuned versions on contextual and temporal semantic change benchmarks under Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformations. Our experimental results reveal (a) although there exist a smaller number of axes that are specific to semantic changes of words in the pre-trained CWE space, this information gets distributed across all dimensions when fine-tuned, and (b) in contrast to prior work studying the geometry of CWEs, we find that PCA to better represent semantic changes than ICA within the top 10% of axes. These findings encourage the development of more efficient SCD methods with a small number of SCD-aware dimensions. Source code is available at https://github.com/LivNLP/svp-dims .
It has been verified that deep learning methods, including convolutional neural networks (CNNs), graph neural networks (GNNs), and transformers, can accurately extract features from hyperspectral images (HSIs). These algorithms perform exceptionally well on HSIs change detection (HSIs-CD). However, the downside of these impressive results is the enormous number of parameters, FLOPs, GPU memory, training and test times required. In this paper, we propose an spectral Kolmogorov-Arnold Network for HSIs-CD (SpectralKAN). SpectralKAN represent a multivariate continuous function with a composition of activation functions to extract HSIs feature and classification. These activation functions are b-spline functions with different parameters that can simulate various functions. In SpectralKAN, a KAN encoder is proposed to enhance computational efficiency for HSIs. And a spatial-spectral KAN encoder is introduced, where the spatial KAN encoder extracts spatial features and compresses the spatial dimensions from patch size to one. The spectral KAN encoder then extracts spectral features and classifies them into changed and unchanged categories. We use five HSIs-CD datasets to verify the effectiveness of SpectralKAN. Experimental verification has shown that SpectralKAN maintains high HSIs-CD accuracy while requiring fewer parameters, FLOPs, GPU memory, training and testing times, thereby increasing the efficiency of HSIs-CD. The code will be available at https://github.com/yanhengwang-heu/SpectralKAN.
SAFE: a SAR Feature Extractor based on self-supervised learning and masked Siamese ViTs
2024-06-30
Max Muzeau, Joana Frontera-Pons, Chengfang Ren, Jean-Philippe Ovarlez
Due to its all-weather and day-and-night capabilities, Synthetic Aperture Radar imagery is essential for various applications such as disaster management, earth monitoring, change detection and target recognition. However, the scarcity of labeled SAR data limits the performance of most deep learning algorithms. To address this issue, we propose a novel self-supervised learning framework based on masked Siamese Vision Transformers to create a General SAR Feature Extractor coined SAFE. Our method leverages contrastive learning principles to train a model on unlabeled SAR data, extracting robust and generalizable features. SAFE is applicable across multiple SAR acquisition modes and resolutions. We introduce tailored data augmentation techniques specific to SAR imagery, such as sub-aperture decomposition and despeckling. Comprehensive evaluations on various downstream tasks, including few-shot classification, segmentation, visualization, and pattern detection, demonstrate the effectiveness and versatility of the proposed approach. Our network competes with or surpasses other state-of-the-art methods in few-shot classification and segmentation tasks, even without being trained on the sensors used for the evaluation.
Our understanding of the temporal dynamics of the Earth's surface has been advanced by deep vision models, which often require lots of labeled multi-temporal images for training. However, collecting, preprocessing, and annotating multi-temporal remote sensing images at scale is non-trivial since it is expensive and knowledge-intensive. In this paper, we present change data generators based on generative models, which are cheap and automatic, alleviating these data problems. Our main idea is to simulate a stochastic change process over time. We describe the stochastic change process as a probabilistic graphical model (GPCM), which factorizes the complex simulation problem into two more tractable sub-problems, i.e., change event simulation and semantic change synthesis. To solve these two problems, we present Changen2, a GPCM with a resolution-scalable diffusion transformer which can generate time series of images and their semantic and change labels from labeled or unlabeled single-temporal images. Changen2 is a generative change foundation model that can be trained at scale via self-supervision, and can produce change supervisory signals from unlabeled single-temporal images. Unlike existing foundation models, Changen2 synthesizes change data to train task-specific foundation models for change detection. The resulting model possesses inherent zero-shot change detection capabilities and excellent transferability. Experiments suggest Changen2 has superior spatiotemporal scalability, e.g., Changen2 model trained on 256$^2$ pixel single-temporal images can yield time series of any length and resolutions of 1,024$^2$ pixels. Changen2 pre-trained models exhibit superior zero-shot performance (narrowing the performance gap to 3% on LEVIR-CD and approximately 10% on both S2Looking and SECOND, compared to fully supervised counterparts) and transferability across multiple types of change tasks.
Continuous Urban Change Detection from Satellite Image Time Series with Temporal Feature Refinement and Multi-Task Integration
2024-06-25
Sebastian Hafner, Heng Fang, Hossein Azizpour, Yifang Ban
Urbanization advances at unprecedented rates, leading to negative environmental and societal impacts. Remote sensing can help mitigate these effects by supporting sustainable development strategies with accurate information on urban growth. Deep learning-based methods have achieved promising urban change detection results from optical satellite image pairs using convolutional neural networks (ConvNets), transformers, and a multi-task learning setup. However, bi-temporal methods are limited for continuous urban change detection, i.e., the detection of changes in consecutive image pairs of satellite image time series (SITS), as they fail to fully exploit multi-temporal data (> 2 images). Existing multi-temporal change detection methods, on the other hand, collapse the temporal dimension, restricting their ability to capture continuous urban changes. Additionally, multi-task learning methods lack integration approaches that combine change and segmentation outputs. To address these challenges, we propose a continuous urban change detection framework incorporating two key modules. The temporal feature refinement (TFR) module employs self-attention to improve ConvNet-based multi-temporal building representations. The temporal dimension is preserved in the TFR module, enabling the detection of continuous changes. The multi-task integration (MTI) module utilizes Markov networks to find an optimal building map time series based on segmentation and dense change outputs. The proposed framework effectively identifies urban changes based on high-resolution SITS acquired by the PlanetScope constellation (F1 score 0.551), Gaofen-2 (F1 score 0.440), and WorldView-2 (F1 score 0.543). Moreover, our experiments on three challenging datasets demonstrate the effectiveness of the proposed framework compared to bi-temporal and multi-temporal urban change detection and segmentation methods.
Distribution-Free Online Change Detection for Low-Rank Images
2024-06-23
Tingnan Gong, Seong-Hee Kim, Yao Xie
We present a distribution-free CUSUM procedure designed for online change detection in a time series of low-rank images, particularly when the change causes a mean shift. We represent images as matrix data and allow for temporal dependence, in addition to inherent spatial dependence, before and after the change. The marginal distributions are assumed to be general, not limited to any specific parametric distribution. We propose new monitoring statistics that utilize the low-rank structure of the in-control mean matrix. Additionally, we study the properties of the proposed detection procedure, assessing whether the monitoring statistics effectively capture a mean shift and evaluating the rate of increase in the average run length relative to the control limit in both the in-control and out-of-control cases. The effectiveness of our procedure is demonstrated through simulated and real data experiments.
UDHF2-Net: Uncertainty-diffusion-model-based High-Frequency TransFormer Network for Remotely Sensed Imagery Interpretation
2024-06-23
Pengfei Zhang, Chang Li, Yongjun Zhang, Rongjun Qin, Kyle Gao, Jonathan Li
Remotely sensed imagery interpretation (RSII) faces the three major problems: (1) objective representation of spatial distribution patterns; (2) edge uncertainty problem caused by downsampling encoder and intrinsic edge noises (e.g., mixed pixel and edge occlusion etc.); and (3) false detection problem caused by geometric registration error in change detection. To solve the aforementioned problems, uncertainty-diffusion-model-based high-Frequency TransFormer network (UDHF2-Net) is the first to be proposed, whose superiorities are as follows: (1) a spatially-stationary-and-non-stationary high-frequency connection paradigm (SHCP) is proposed to enhance the interaction of spatially frequency-wise stationary and non-stationary features to yield high-fidelity edge extraction result. Inspired by HRFormer, SHCP proposes high-frequency-wise stream to replace high-resolution-wise stream in HRFormer through the whole encoder-decoder process with parallel frequency-wise high-to-low streams, so it improves the edge extraction accuracy by continuously remaining high-frequency information; (2) a mask-and-geo-knowledge-based uncertainty diffusion module (MUDM), which is a self-supervised learning strategy, is proposed to improve the edge accuracy of extraction and change detection by gradually removing the simulated spectrum noises based on geo-knowledge and the generated diffused spectrum noises; (3) a frequency-wise semi-pseudo-Siamese UDHF2-Net is the first to be proposed to balance accuracy and complexity for change detection. Besides the aforementioned spectrum noises in semantic segmentation, MUDM is also a self-supervised learning strategy to effectively reduce the edge false change detection from the generated imagery with geometric registration error.
To tackle sound event detection (SED), we propose frequency dependent networks (FreDNets), which heavily leverage frequency-dependent methods. We apply frequency warping and FilterAugment, which are frequency-dependent data augmentation methods. The model architecture consists of 3 branches: audio teacher-student transformer (ATST) branch, BEATs branch and CNN branch including either partial dilated frequency dynamic convolution (PDFD conv) or squeeze-and-Excitation (SE) with time-frame frequency-wise SE (tfwSE). To train MAESTRO labels with coarse temporal resolution, we applied max pooling on prediction for the MAESTRO dataset. Using best ensemble model, we applied self training to obtain pseudo label from DESED weak set, unlabeled set and AudioSet. AudioSet pseudo labels, filtered to focus on high-confidence labels, are used to train on DESED dataset only. We used change-detection-based sound event bounding boxes (cSEBBs) as post processing for ensemble models on self training and submission models. The resulting FreDNet was ranked 2nd in DCASE 2024 Challenge Task 4.
Bitemporal supervised learning paradigm always dominates remote sensing change detection using numerous labeled bitemporal image pairs, especially for high spatial resolution (HSR) remote sensing imagery. However, it is very expensive and labor-intensive to label change regions in large-scale bitemporal HSR remote sensing image pairs. In this paper, we propose single-temporal supervised learning (STAR) for universal remote sensing change detection from a new perspective of exploiting changes between unpaired images as supervisory signals. STAR enables us to train a high-accuracy change detector only using unpaired labeled images and can generalize to real-world bitemporal image pairs. To demonstrate the flexibility and scalability of STAR, we design a simple yet unified change detector, termed ChangeStar2, capable of addressing binary change detection, object change detection, and semantic change detection in one architecture. ChangeStar2 achieves state-of-the-art performances on eight public remote sensing change detection datasets, covering above two supervised settings, multiple change types, multiple scenarios. The code is available at https://github.com/Z-Zheng/pytorch-change-models.
Rethinking Remote Sensing Change Detection With A Mask View
2024-06-21
Xiaowen Ma, Zhenkai Wu, Rongrong Lian, Wei Zhang, Siyang Song
Remote sensing change detection aims to compare two or more images recorded for the same area but taken at different time stamps to quantitatively and qualitatively assess changes in geographical entities and environmental factors. Mainstream models usually built on pixel-by-pixel change detection paradigms, which cannot tolerate the diversity of changes due to complex scenes and variation in imaging conditions. To address this shortcoming, this paper rethinks the change detection with the mask view, and further proposes the corresponding: 1) meta-architecture CDMask and 2) instance network CDMaskFormer. Components of CDMask include Siamese backbone, change extractor, pixel decoder, transformer decoder and normalized detector, which ensures the proper functioning of the mask detection paradigm. Since the change query can be adaptively updated based on the bi-temporal feature content, the proposed CDMask can adapt to different latent data distributions, thus accurately identifying regions of interest changes in complex scenarios. Consequently, we further propose the instance network CDMaskFormer customized for the change detection task, which includes: (i) a Spatial-temporal convolutional attention-based instantiated change extractor to capture spatio-temporal context simultaneously with lightweight operations; and (ii) a scene-guided axial attention-instantiated transformer decoder to extract more spatial details. State-of-the-art performance of CDMaskFormer is achieved on five benchmark datasets with a satisfactory efficiency-accuracy trade-off. Code is available at https://github.com/xwmaxwma/rschange.
We use contextualized word definitions generated by large language models as semantic representations in the task of diachronic lexical semantic change detection (LSCD). In short, generated definitions are used as `senses', and the change score of a target word is retrieved by comparing their distributions in two time periods under comparison. On the material of five datasets and three languages, we show that generated definitions are indeed specific and general enough to convey a signal sufficient to rank sets of words by the degree of their semantic change over time. Our approach is on par with or outperforms prior non-supervised sense-based LSCD methods. At the same time, it preserves interpretability and allows to inspect the reasons behind a specific shift in terms of discrete definitions-as-senses. This is another step in the direction of explainable semantic change modeling.
This study analyses the medium access control (MAC) layer aspects of a low-Earth-orbit (LEO) satellite-based Internet of Things (IoT) network. A transmission scheme based on change detection is proposed to accommodate more users within the network and improve energy efficiency. Machine learning (ML) algorithms are also proposed to reduce the payload size by leveraging the correlation among the sensed parameters. Real-world data from an IoT testbed deployed for a smart city application is utilised to analyse the performance regarding collision probability, effective data received and average battery lifetime. The findings reveal that the traffic pattern, post-implementation of the proposed scheme, differs from the commonly assumed Poisson traffic, thus proving the effectiveness of having IoT data from actual deployment. It is demonstrated that the transmission scheme facilitates accommodating more devices while targeting a specific collision probability. Considering the link budget for a direct access NB-IoT scenario, more data is effectively offloaded to the server within the limited visibility of LEO satellites. The average battery lifetimes are also demonstrated to increase by many folds by using the proposed access schemes and ML algorithms.
Remote sensing change detection (RSCD) aims to identify the changes of interest in a region by analyzing multi-temporal remote sensing images, and has an outstanding value for local development monitoring. Existing RSCD methods are devoted to contextual modeling in the spatial domain to enhance the changes of interest. Despite the satisfactory performance achieved, the lack of knowledge in the frequency domain limits the further improvement of model performance. In this paper, we propose DDLNet, a RSCD network based on dual-domain learning (i.e., frequency and spatial domains). In particular, we design a Frequency-domain Enhancement Module (FEM) to capture frequency components from the input bi-temporal images using Discrete Cosine Transform (DCT) and thus enhance the changes of interest. Besides, we devise a Spatial-domain Recovery Module (SRM) to fuse spatiotemporal features for reconstructing spatial details of change representations. Extensive experiments on three benchmark RSCD datasets demonstrate that the proposed method achieves state-of-the-art performance and reaches a more satisfactory accuracy-efficiency trade-off. Our code is publicly available at https://github.com/xwmaxwma/rschange.
Towards a multimodal framework for remote sensing image change retrieval and captioning
2024-06-19
Roger Ferrod, Luigi Di Caro, Dino Ienco
Recently, there has been increasing interest in multimodal applications that integrate text with other modalities, such as images, audio and video, to facilitate natural language interactions with multimodal AI systems. While applications involving standard modalities have been extensively explored, there is still a lack of investigation into specific data modalities such as remote sensing (RS) data. Despite the numerous potential applications of RS data, including environmental protection, disaster monitoring and land planning, available solutions are predominantly focused on specific tasks like classification, captioning and retrieval. These solutions often overlook the unique characteristics of RS data, such as its capability to systematically provide information on the same geographical areas over time. This ability enables continuous monitoring of changes in the underlying landscape. To address this gap, we propose a novel foundation model for bi-temporal RS image pairs, in the context of change detection analysis, leveraging Contrastive Learning and the LEVIR-CC dataset for both captioning and text-image retrieval. By jointly training a contrastive encoder and captioning decoder, our model add text-image retrieval capabilities, in the context of bi-temporal change detection, while maintaining captioning performances that are comparable to the state of the art. We release the source code and pretrained weights at: https://github.com/rogerferrod/RSICRC.
Change detection in remote sensing images is essential for tracking environmental changes on the Earth's surface. Despite the success of vision transformers (ViTs) as backbones in numerous computer vision applications, they remain underutilized in change detection, where convolutional neural networks (CNNs) continue to dominate due to their powerful feature extraction capabilities. In this paper, our study uncovers ViTs' unique advantage in discerning large-scale changes, a capability where CNNs fall short. Capitalizing on this insight, we introduce ChangeViT, a framework that adopts a plain ViT backbone to enhance the performance of large-scale changes. This framework is supplemented by a detail-capture module that generates detailed spatial features and a feature injector that efficiently integrates fine-grained spatial information into high-level semantic learning. The feature integration ensures that ChangeViT excels in both detecting large-scale changes and capturing fine-grained details, providing comprehensive change detection across diverse scales. Without bells and whistles, ChangeViT achieves state-of-the-art performance on three popular high-resolution datasets (i.e., LEVIR-CD, WHU-CD, and CLCD) and one low-resolution dataset (i.e., OSCD), which underscores the unleashed potential of plain ViTs for change detection. Furthermore, thorough quantitative and qualitative analyses validate the efficacy of the introduced modules, solidifying the effectiveness of our approach. The source code is available at https://github.com/zhuduowang/ChangeViT.
We present the scintillation velocity measurements of FRB~20201124A from the FAST observations, which reveal an annual variation. This annual variation is further supported by changes detected in the scintillation arc as observed from the secondary spectrum. We attribute the annual velocity variation to the presence of a moderately anisotropic scattering screen located at a distance of 0.4$\pm$0.1~kpc from Earth. Our results prove that the scintillation of this FRB is mainly caused by material close to Earth on a Galactic scale. However, scintillation observations of other FRBs may expose their surrounding environment or uncover possible orbital motion if scintillation is caused by materials in their host galaxy.
We present a novel, training-free approach to scene change detection. Our method leverages tracking models, which inherently perform change detection between consecutive frames of video by identifying common objects and detecting new or missing objects. Specifically, our method takes advantage of the change detection effect of the tracking model by inputting reference and query images instead of consecutive frames. Furthermore, we focus on the content gap and style gap between two input images in change detection, and address both issues by proposing adaptive content threshold and style bridging layers, respectively. Finally, we extend our approach to video, leveraging rich temporal information to enhance the performance of scene change detection. We compare our approach and baseline through various experiments. While existing train-based baseline tend to specialize only in the trained domain, our method shows consistent performance across various domains, proving the competitiveness of our approach.
A Late-Stage Bitemporal Feature Fusion Network for Semantic Change Detection
2024-06-15
Chenyao Zhou, Haotian Zhang, Han Guo, Zhengxia Zou, Zhenwei Shi
Semantic change detection is an important task in geoscience and earth observation. By producing a semantic change map for each temporal phase, both the land use land cover categories and change information can be interpreted. Recently some multi-task learning based semantic change detection methods have been proposed to decompose the task into semantic segmentation and binary change detection subtasks. However, previous works comprise triple branches in an entangled manner, which may not be optimal and hard to adopt foundation models. Besides, lacking explicit refinement of bitemporal features during fusion may cause low accuracy. In this letter, we propose a novel late-stage bitemporal feature fusion network to address the issue. Specifically, we propose local global attentional aggregation module to strengthen feature fusion, and propose local global context enhancement module to highlight pivotal semantics. Comprehensive experiments are conducted on two public datasets, including SECOND and Landsat-SCD. Quantitative and qualitative results show that our proposed model achieves new state-of-the-art performance on both datasets.
Automated GIS-Based Framework for Detecting Crosswalk Changes from Bi-Temporal High-Resolution Aerial Images
2024-06-14
Richard Boadu Antwi, Samuel Takyi, Alican Karaer, Eren Erman Ozguven, Michael Kimollo, Ren Moses, Maxim A. Dulebenets, Thobias Sando
Identification of changes in pavement markings has become crucial for infrastructure monitoring, maintenance, development, traffic management, and safety. Automated extraction of roadway geometry is critical in helping with this, given the increasing availability of high-resolution images and advancements in computer vision and object detection. Specifically, due to the substantial volume of satellite and high-resolution aerial images captured at different time instances, change detection has become a viable solution. In this study, an automated framework is developed to detect changes in crosswalks of Orange, Osceola, and Seminole counties in Florida, utilizing data extracted from high-resolution images obtained at various time intervals. Specifically, for Orange County, crosswalk changes between 2019 and 2021 were manually extracted, verified, and categorized as either new or modified crosswalks. For Seminole County, the developed model was used to automatically extract crosswalk changes between 2018 and 2021, while for Osceola County, changes between 2019 and 2020 were extracted. Findings indicate that Orange County witnessed approximately 2,094 crosswalk changes, with 312 occurring on state roads. In Seminole and Osceola counties, on the other hand, 1,040 and 1,402 crosswalk changes were observed on both local and state roads, respectively. Among these, 340 and 344 were identified on state roads in Seminole and Osceola, respectively. Spatiotemporal changes observed in crosswalks can be utilized to regularly update the existing crosswalk inventories, which is essential for agencies engaged in traffic and safety studies. Data extracted from these crosswalk changes can be combined with traffic and crash data to provide valuable insights to policymakers.
2024-06-12
Yue Li, Xinsheng Wang, Li Zhang, Lei Xie
Speaker Change Detection (SCD) is to identify boundaries among speakers in a conversation. Motivated by the success of fine-tuning wav2vec 2.0 models for the SCD task, a further investigation of self-supervised learning (SSL) features for SCD is conducted in this work. Specifically, an SCD model, named SCDNet, is proposed. With this model, various state-of-the-art SSL models, including Hubert, wav2vec 2.0, and WavLm are investigated. To discern the most potent layer of SSL models for SCD, a learnable weighting method is employed to analyze the effectiveness of intermediate representations. Additionally, a fine-tuning-based approach is also implemented to further compare the characteristics of SSL models in the SCD task. Furthermore, a contrastive learning method is proposed to mitigate the overfitting tendencies in the training of both the fine-tuning-based method and SCDNet. Experiments showcase the superiority of WavLm in the SCD task and also demonstrate the good design of SCDNet.
A$^{2}$-MAE: A spatial-temporal-spectral unified remote sensing pre-training method based on anchor-aware masked autoencoder
2024-06-12
Lixian Zhang, Yi Zhao, Runmin Dong, Jinxiao Zhang, Shuai Yuan, Shilei Cao, Mengxuan Chen, Juepeng Zheng, Weijia Li, Wei Liu, Wayne Zhang, Litong Feng, Haohuan Fu
Vast amounts of remote sensing (RS) data provide Earth observations across multiple dimensions, encompassing critical spatial, temporal, and spectral information which is essential for addressing global-scale challenges such as land use monitoring, disaster prevention, and environmental change mitigation. Despite various pre-training methods tailored to the characteristics of RS data, a key limitation persists: the inability to effectively integrate spatial, temporal, and spectral information within a single unified model. To unlock the potential of RS data, we construct a Spatial-Temporal-Spectral Structured Dataset (STSSD) characterized by the incorporation of multiple RS sources, diverse coverage, unified locations within image sets, and heterogeneity within images. Building upon this structured dataset, we propose an Anchor-Aware Masked AutoEncoder method (A$^{2}$-MAE), leveraging intrinsic complementary information from the different kinds of images and geo-information to reconstruct the masked patches during the pre-training phase. A$^{2}$-MAE integrates an anchor-aware masking strategy and a geographic encoding module to comprehensively exploit the properties of RS images. Specifically, the proposed anchor-aware masking strategy dynamically adapts the masking process based on the meta-information of a pre-selected anchor image, thereby facilitating the training on images captured by diverse types of RS sources within one model. Furthermore, we propose a geographic encoding method to leverage accurate spatial patterns, enhancing the model generalization capabilities for downstream applications that are generally location-related. Extensive experiments demonstrate our method achieves comprehensive improvements across various downstream tasks compared with existing RS pre-training methods, including image classification, semantic segmentation, and change detection tasks.
Generalizable Disaster Damage Assessment via Change Detection with Vision Foundation Model
2024-06-12
Kyeongjin Ahn, Sungwon Han, Sungwon Park, Jihee Kim, Sangyoon Park, Meeyoung Cha
The increasing frequency and intensity of natural disasters call for rapid and accurate damage assessment. In response, disaster benchmark datasets from high-resolution satellite imagery have been constructed to develop methods for detecting damaged areas. However, these methods face significant challenges when applied to previously unseen regions due to the limited geographical and disaster-type diversity in the existing datasets. We introduce DAVI (Disaster Assessment with VIsion foundation model), a novel approach that addresses domain disparities and detects structural damage at the building level without requiring ground-truth labels for target regions. DAVI combines task-specific knowledge from a model trained on source regions with task-agnostic knowledge from an image segmentation model to generate pseudo labels indicating potential damage in target regions. It then utilizes a two-stage refinement process, which operate at both pixel and image levels, to accurately identify changes in disaster-affected areas. Our evaluation, including a case study on the 2023 T\"urkiye earthquake, demonstrates that our model achieves exceptional performance across diverse terrains (e.g., North America, Asia, and the Middle East) and disaster types (e.g., wildfires, hurricanes, and tsunamis). This confirms its robustness in disaster assessment without dependence on ground-truth labels and highlights its practical applicability.
SRC-Net: Bi-Temporal Spatial Relationship Concerned Network for Change Detection
2024-06-09
Hongjia Chen, Xin Xu, Fangling Pu
Change detection (CD) in remote sensing imagery is a crucial task with applications in environmental monitoring, urban development, and disaster management. CD involves utilizing bi-temporal images to identify changes over time. The bi-temporal spatial relationships between features at the same location at different times play a key role in this process. However, existing change detection networks often do not fully leverage these spatial relationships during bi-temporal feature extraction and fusion. In this work, we propose SRC-Net: a bi-temporal spatial relationship concerned network for CD. The proposed SRC-Net includes a Perception and Interaction Module that incorporates spatial relationships and establishes a cross-branch perception mechanism to enhance the precision and robustness of feature extraction. Additionally, a Patch-Mode joint Feature Fusion Module is introduced to address information loss in current methods. It considers different change modes and concerns about spatial relationships, resulting in more expressive fusion features. Furthermore, we construct a novel network using these two relationship concerned modules and conducted experiments on the LEVIR-CD and WHU Building datasets. The experimental results demonstrate that our network outperforms state-of-the-art (SOTA) methods while maintaining a modest parameter count. We believe our approach sets a new paradigm for change detection and will inspire further advancements in the field. The code and models are publicly available at https://github.com/Chnja/SRCNet.
2024-06-06
Janek Ebbers, Francois G. Germain, Gordon Wichern, Jonathan Le Roux
Sound event detection is the task of recognizing sounds and determining their extent (onset/offset times) within an audio clip. Existing systems commonly predict sound presence confidence in short time frames. Then, thresholding produces binary frame-level presence decisions, with the extent of individual events determined by merging consecutive positive frames. In this paper, we show that frame-level thresholding degrades the prediction of the event extent by coupling it with the system's sound presence confidence. We propose to decouple the prediction of event extent and confidence by introducing SEBBs, which format each sound event prediction as a tuple of a class type, extent, and overall confidence. We also propose a change-detection-based algorithm to convert legacy frame-level outputs into SEBBs. We find the algorithm significantly improves the performance of DCASE 2023 Challenge systems, boosting the state of the art from .644 to .686 PSDS1.
Recently, the Mamba architecture based on state space models has demonstrated remarkable performance in a series of natural language processing tasks and has been rapidly applied to remote sensing change detection (CD) tasks. However, most methods enhance the global receptive field by directly modifying the scanning mode of Mamba, neglecting the crucial role that local information plays in dense prediction tasks (e.g., binary CD). In this article, we propose a model called CDMamba, which effectively combines global and local features for handling binary CD tasks. Specifically, the Scaled Residual ConvMamba (SRCM) block is proposed to utilize the ability of Mamba to extract global features and convolution to enhance the local details to alleviate the issue that current Mamba-based methods lack detailed clues and are difficult to achieve fine detection in dense prediction tasks. Furthermore, considering the characteristics of bi-temporal feature interaction required for CD, the Adaptive Global Local Guided Fusion (AGLGF) block is proposed to dynamically facilitate the bi-temporal interaction guided by other temporal global/local features. Our intuition is that more discriminative change features can be acquired with the guidance of other temporal features. Extensive experiments on five datasets demonstrate that our proposed CDMamba is comparable to the current methods (such as the F1/IoU scores are improved by 2.10%/3.00% and 2.44%/2.91% on LEVIR+CD and CLCD, respectively). Our code is open-sourced at https://github.com/zmoka-zht/CDMamba.
Using Synchronic Definitions and Semantic Relations to Classify Semantic Change Types
2024-06-05
Pierluigi Cassotti, Stefano De Pascale, Nina Tahmasebi
There is abundant evidence of the fact that the way words change their meaning can be classified in different types of change, highlighting the relationship between the old and new meanings (among which generalization, specialization and co-hyponymy transfer). In this paper, we present a way of detecting these types of change by constructing a model that leverages information both from synchronic lexical relations and definitions of word meanings. Specifically, we use synset definitions and hierarchy information from WordNet and test it on a digitized version of Blank's (1997) dataset of semantic change types. Finally, we show how the sense relationships can improve models for both approximation of human judgments of semantic relatedness as well as binary Lexical Semantic Change Detection.
There has been a surge of interest in computational modeling of semantic change. The foci of previous works are on detecting and interpreting word senses gained over time; however, it remains unclear whether the gained senses are covered by dictionaries. In this work, we aim to fill this research gap by comparing detected word senses with dictionary sense inventories in order to bridge between the communities of lexical semantic change detection and lexicography. We evaluate our system in the AXOLOTL-24 shared task for Finnish, Russian and German languages \cite{fedorova-etal-2024-axolotl}. Our system is fully unsupervised. It leverages a graph-based clustering approach to predict mappings between unknown word usages and dictionary entries for Subtask 1, and generates dictionary-like definitions for those novel word usages through the state-of-the-art Large Language Models such as GPT-4 and LLaMA-3 for Subtask 2. In Subtask 1, our system outperforms the baseline system by a large margin, and it offers interpretability for the mapping results by distinguishing between matched and unmatched (novel) word usages through our graph-based clustering approach. Our system ranks first in Finnish and German, and ranks second in Russian on the Subtask 2 test-phase leaderboard. These results show the potential of our system in managing dictionary entries, particularly for updating dictionaries to include novel sense entries. Our code and data are made publicly available\footnote{\url{https://github.com/xiaohemaikoo/axolotl24-ABDN-NLP}}.
Landslides are one of the most critical and destructive geohazards. Widespread development of human activities and settlements combined with the effects of climate change on weather are resulting in a high increase in the frequency and destructive power of landslides, making them a major threat to human life and the economy. In this paper, we explore methodologies to map newly-occurred landslides using Sentinel-2 imagery automatically. All approaches presented are framed as a bi-temporal change detection problem, requiring only a pair of Sentinel-2 images, taken respectively before and after a landslide-triggering event. Furthermore, we introduce a novel deep learning architecture for fusing Sentinel-2 bi-temporal image pairs with Digital Elevation Model (DEM) data, showcasing its promising performances w.r.t. other change detection models in the literature. As a parallel task, we address limitations in existing datasets by creating a novel geodatabase, which includes manually validated open-access landslide inventories over heterogeneous ecoregions of the world. We release both code and dataset with an open-source license.
FUSU: A Multi-temporal-source Land Use Change Segmentation Dataset for Fine-grained Urban Semantic Understanding
2024-05-29
Shuai Yuan, Guancong Lin, Lixian Zhang, Runmin Dong, Jinxiao Zhang, Shuang Chen, Juepeng Zheng, Jie Wang, Haohuan Fu
Fine urban change segmentation using multi-temporal remote sensing images is essential for understanding human-environment interactions in urban areas. Although there have been advances in high-quality land cover datasets that reveal the physical features of urban landscapes, the lack of fine-grained land use datasets hinders a deeper understanding of how human activities are distributed across the landscape and the impact of these activities on the environment, thus constraining proper technique development. To address this, we introduce FUSU, the first fine-grained land use change segmentation dataset for Fine-grained Urban Semantic Understanding. FUSU features the most detailed land use classification system to date, with 17 classes and 30 billion pixels of annotations. It includes bi-temporal high-resolution satellite images with 0.2-0.5 m ground sample distance and monthly optical and radar satellite time series, covering 847 km^2 across five urban areas in the southern and northern of China with different geographical features. The fine-grained land use pixel-wise annotations and high spatial-temporal resolution data provide a robust foundation for developing proper deep learning models to provide contextual insights on human activities and urbanization. To fully leverage FUSU, we propose a unified time-series architecture for both change detection and segmentation. We benchmark FUSU on various methods for several tasks. Dataset and code are available at: https://github.com/yuanshuai0914/FUSU.
In conventional remote sensing change detection (RS CD) procedures, extensive manual labeling for bi-temporal images is first required to maintain the performance of subsequent fully supervised training. However, pixel-level labeling for CD tasks is very complex and time-consuming. In this paper, we explore a novel self-supervised contrastive framework applicable to the RS CD task, which promotes the model to accurately capture spatial, structural, and semantic information through domain adapter and hierarchical contrastive head. The proposed SSLChange framework accomplishes self-learning only by taking a single-temporal sample and can be flexibly transferred to main-stream CD baselines. With self-supervised contrastive learning, feature representation pre-training can be performed directly based on the original data even without labeling. After a certain amount of labels are subsequently obtained, the pre-trained features will be aligned with the labels for fully supervised fine-tuning. Without introducing any additional data or labels, the performance of downstream baselines will experience a significant enhancement. Experimental results on 2 entire datasets and 6 diluted datasets show that our proposed SSLChange improves the performance and stability of CD baseline in data-limited situations. The code of SSLChange will be released at \url{https://github.com/MarsZhaoYT/SSLChange}
Post-selection inference for quantifying uncertainty in changes in variance
2024-05-24
Rachel Carrington, Paul Fearnhead
Quantifying uncertainty in detected changepoints is an important problem. However it is challenging as the naive approach would use the data twice, first to detect the changes, and then to test them. This will bias the test, and can lead to anti-conservative p-values. One approach to avoid this is to use ideas from post-selection inference, which conditions on the information in the data used to choose which changes to test. As a result this produces valid p-values; that is, p-values that have a uniform distribution if there is no change. Currently such methods have been developed for detecting changes in mean only. This paper presents two approaches for constructing post-selection p-values for detecting changes in variance. These vary depending on the method use to detect the changes, but are general in terms of being applicable for a range of change-detection methods and a range of hypotheses that we may wish to test.
A Behavior-Aware Approach for Deep Reinforcement Learning in Non-stationary Environments without Known Change Points
2024-05-23
Zihe Liu, Jie Lu, Guangquan Zhang, Junyu Xuan
Deep reinforcement learning is used in various domains, but usually under the assumption that the environment has stationary conditions like transitions and state distributions. When this assumption is not met, performance suffers. For this reason, tracking continuous environmental changes and adapting to unpredictable conditions is challenging yet crucial because it ensures that systems remain reliable and flexible in practical scenarios. Our research introduces Behavior-Aware Detection and Adaptation (BADA), an innovative framework that merges environmental change detection with behavior adaptation. The key inspiration behind our method is that policies exhibit different global behaviors in changing environments. Specifically, environmental changes are identified by analyzing variations between behaviors using Wasserstein distances without manually set thresholds. The model adapts to the new environment through behavior regularization based on the extent of changes. The results of a series of experiments demonstrate better performance relative to several current algorithms. This research also indicates significant potential for tackling this long-standing challenge.
Online Action Representation using Change Detection and Symbolic Programming
2024-05-19
Vishnu S Nair, Sneha Sree, Jayaraj Joseph, Mohanasankar Sivaprakasam
This paper addresses the critical need for online action representation, which is essential for various applications like rehabilitation, surveillance, etc. The task can be defined as representation of actions as soon as they happen in a streaming video without access to video frames in the future. Most of the existing methods use predefined window sizes for video segments, which is a restrictive assumption on the dynamics. The proposed method employs a change detection algorithm to automatically segment action sequences, which form meaningful sub-actions and subsequently fit symbolic generative motion programs to the clipped segments. We determine the start time and end time of segments using change detection followed by a piece-wise linear fit algorithm on joint angle and bone length sequences. Domain-specific symbolic primitives are fit to pose keypoint trajectories of those extracted segments in order to obtain a higher level semantic representation. Since this representation is part-based, it is complementary to the compositional nature of human actions, i.e., a complex activity can be broken down into elementary sub-actions. We show the effectiveness of this representation in the downstream task of class agnostic repetition detection. We propose a repetition counting algorithm based on consecutive similarity matching of primitives, which can do online repetition counting. We also compare the results with a similar but offline repetition counting algorithm. The results of the experiments demonstrate that, despite operating online, the proposed method performs better or on par with the existing method.
Confidence Estimation in Unsupervised Deep Change Vector Analysis
2024-05-16
Sudipan Saha
Unsupervised transfer learning-based change detection methods exploit the feature extraction capability of pre-trained networks to distinguish changed pixels from the unchanged ones. However, their performance may vary significantly depending on several geographical and model-related aspects. In many applications, it is of utmost importance to provide trustworthy or confident results, even if over a subset of pixels. The core challenge in this problem is to identify changed pixels and confident pixels in an unsupervised manner. To address this, we propose a two-network model - one tasked with mere change detection and the other with confidence estimation. While the change detection network can be used in conjunction with popular transfer learning-based change detection methods such as Deep Change Vector Analysis, the confidence estimation network operates similarly to a randomized smoothing model. By ingesting ensembles of inputs perturbed by noise, it creates a distribution over the output and assigns confidence to each pixel's outcome. We tested the proposed method on three different Earth observation sensors: optical, Synthetic Aperture Radar, and hyperspectral sensors.
IMAFD: An Interpretable Multi-stage Approach to Flood Detection from time series Multispectral Data
2024-05-13
Ziyang Zhang, Plamen Angelov, Dmitry Kangin, Nicolas Longépé
In this paper, we address two critical challenges in the domain of flood detection: the computational expense of large-scale time series change detection and the lack of interpretable decision-making processes on explainable AI (XAI). To overcome these challenges, we proposed an interpretable multi-stage approach to flood detection, IMAFD has been proposed. It provides an automatic, efficient and interpretable solution suitable for large-scale remote sensing tasks and offers insight into the decision-making process. The proposed IMAFD approach combines the analysis of the dynamic time series image sequences to identify images with possible flooding with the static, within-image semantic segmentation. It combines anomaly detection (at both image and pixel level) with semantic segmentation. The flood detection problem is addressed through four stages: (1) at a sequence level: identifying the suspected images (2) at a multi-image level: detecting change within suspected images (3) at an image level: semantic segmentation of images into Land, Water or Cloud class (4) decision making. Our contributions are two folder. First, we efficiently reduced the number of frames to be processed for dense change detection by providing a multi-stage holistic approach to flood detection. Second, the proposed semantic change detection method (stage 3) provides human users with an interpretable decision-making process, while most of the explainable AI (XAI) methods provide post hoc explanations. The evaluation of the proposed IMAFD framework was performed on three datasets, WorldFloods, RavAEn and MediaEval. For all the above datasets, the proposed framework demonstrates a competitive performance compared to other methods offering also interpretability and insight.
Syndrome-based Fusion Rules in Heterogeneous Distributed Quickest Change Detection
2024-05-11
Wen-Hsuan Li, Yu-Chih Huang
In this paper, the heterogeneous distributed quickest change detection (HetDQCD) with 1-bit non-anonymous feedback is studied. The concept of syndromes is introduced and the family of syndrome-based fusion rules is proposed, which encompasses all deterministic fusion rules as special cases. Through the Hasse diagram of syndromes, upper and lower bounds on the second-order performance of expected detection delay as a function of average run length to false alarm are provided. An interesting instance, the weighted voting rule previously proposed in our prior work, is then revisited, for which an efficient pruning method for breadth-first search in the Hasse diagram is proposed to analyze the performance. This in turn assists in the design of the weight threshold in the weighted voting rule. Simulation results corroborate that our analysis is instrumental in identifying a proper design for the weighted voting rule, demonstrating consistent superiority over both the anonymous voting rule and the group selection rule in HetDQCD.
Open Access Battle Damage Detection via Pixel-Wise T-Test on Sentinel-1 Imagery
2024-05-10
Ollie Ballinger
In the context of recent, highly destructive conflicts in Gaza and Ukraine, reliable estimates of building damage are essential for an informed public discourse, human rights monitoring, and humanitarian aid provision. Given the contentious nature of conflict damage assessment, these estimates must be fully reproducible, explainable, and derived from open access data. This paper introduces a new method for building damage detection-- the Pixel-Wise T-Test (PWTT)-- that satisfies these conditions. Using a combination of freely-available synthetic aperture radar imagery and statistical change detection, the PWTT generates accurate conflict damage estimates across a wide area at regular time intervals. Accuracy is assessed using an original dataset of over half a million labeled building footprints spanning 12 cities across Ukraine, Palestine, Syria, and Iraq. Despite being simple and lightweight, the algorithm achieves building-level accuracy statistics (AUC=0.88 across Ukraine, 0.81 in Gaza) rivalling state of the art methods that use deep learning and high resolution imagery. The workflow is open source and deployed entirely within the Google Earth Engine environment, allowing for the generation of interactive Battle Damage Dashboards for Ukraine and Gaza that update in near-real time, allowing the public and humanitarian practitioners to immediately get estimates of damaged buildings in a given area.
Feature matching determines the orientation accuracy for the High Spatial Resolution (HSR) optical satellite stereos, subsequently impacting several significant applications such as 3D reconstruction and change detection. However, the matching of off-track HSR optical satellite stereos often encounters challenging conditions including wide-baseline observation, significant radiometric differences, multi-temporal changes, varying spatial resolutions, inconsistent spectral resolution, and diverse sensors. In this study, we evaluate various advanced feature matching algorithms for HSR optical satellite stereos. Utilizing a specially constructed dataset from five satellites across six challenging scenarios, HSROSS Dataset, we conduct a comparative analysis of four algorithms: the traditional SIFT, and deep-learning based methods including SuperPoint + SuperGlue, SuperPoint + LightGlue, and LoFTR. Our findings highlight overall superior performance of SuperPoint + LightGlue in balancing robustness, accuracy, distribution, and efficiency, showcasing its potential in complex HSR optical satellite scenarios.
Zero-shot Degree of Ill-posedness Estimation for Active Small Object Change Detection
2024-05-10
Koji Takeda, Kanji Tanaka, Yoshimasa Nakamura, Asako Kanezaki
In everyday indoor navigation, robots often needto detect non-distinctive small-change objects (e.g., stationery,lost items, and junk, etc.) to maintain domain knowledge. Thisis most relevant to ground-view change detection (GVCD), a recently emerging research area in the field of computer vision.However, these existing techniques rely on high-quality class-specific object priors to regularize a change detector modelthat cannot be applied to semantically nondistinctive smallobjects. To address ill-posedness, in this study, we explorethe concept of degree-of-ill-posedness (DoI) from the newperspective of GVCD, aiming to improve both passive and activevision. This novel DoI problem is highly domain-dependent,and manually collecting fine-grained annotated training datais expensive. To regularize this problem, we apply the conceptof self-supervised learning to achieve efficient DoI estimationscheme and investigate its generalization to diverse datasets.Specifically, we tackle the challenging issue of obtaining self-supervision cues for semantically non-distinctive unseen smallobjects and show that novel "oversegmentation cues" from openvocabulary semantic segmentation can be effectively exploited.When applied to diverse real datasets, the proposed DoI modelcan boost state-of-the-art change detection models, and it showsstable and consistent improvements when evaluated on real-world datasets.
Private Online Community Detection for Censored Block Models
2024-05-09
Mohamed Seif, Liyan Xie, Andrea J. Goldsmith, H. Vincent Poor
We study the private online change detection problem for dynamic communities, using a censored block model (CBM). Focusing on the notion of edge differential privacy (DP), we seek to understand the fundamental tradeoffs between the privacy budget, detection delay, and exact community recovery of community labels. We establish the theoretical lower bound on the delay in detecting changes privately and propose an algorithm capable of identifying changes in the community structure, while maintaining user privacy. Further, we provide theoretical guarantees for the effectiveness of our proposed method by showing necessary and sufficient conditions on change detection and exact recovery under edge DP. Simulation and real data examples are provided to validate the proposed method.
Change Detection (CD) aims to identify pixels with semantic changes between images. However, annotating massive numbers of pixel-level images is labor-intensive and costly, especially for multi-temporal images, which require pixel-wise comparisons by human experts. Considering the excellent performance of visual language models (VLMs) for zero-shot, open-vocabulary, etc. with prompt-based reasoning, it is promising to utilize VLMs to make better CD under limited labeled data. In this paper, we propose a VLM guidance-based semi-supervised CD method, namely SemiCD-VL. The insight of SemiCD-VL is to synthesize free change labels using VLMs to provide additional supervision signals for unlabeled data. However, almost all current VLMs are designed for single-temporal images and cannot be directly applied to bi- or multi-temporal images. Motivated by this, we first propose a VLM-based mixed change event generation (CEG) strategy to yield pseudo labels for unlabeled CD data. Since the additional supervised signals provided by these VLM-driven pseudo labels may conflict with the pseudo labels from the consistency regularization paradigm (e.g. FixMatch), we propose the dual projection head for de-entangling different signal sources. Further, we explicitly decouple the bi-temporal images semantic representation through two auxiliary segmentation decoders, which are also guided by VLM. Finally, to make the model more adequately capture change representations, we introduce metric-aware supervision by feature-level contrastive loss in auxiliary branches. Extensive experiments show the advantage of SemiCD-VL. For instance, SemiCD-VL improves the FixMatch baseline by +5.3 IoU on WHU-CD and by +2.4 IoU on LEVIR-CD with 5% labels. In addition, our CEG strategy, in an un-supervised manner, can achieve performance far superior to state-of-the-art un-supervised CD methods.
Triadic-OCD: Asynchronous Online Change Detection with Provable Robustness, Optimality, and Convergence
2024-05-03
Yancheng Huang, Kai Yang, Zelin Zhu, Leian Chen
The primary goal of online change detection (OCD) is to promptly identify changes in the data stream. OCD problem find a wide variety of applications in diverse areas, e.g., security detection in smart grids and intrusion detection in communication networks. Prior research usually assumes precise knowledge of the system parameters. Nevertheless, this presumption often proves unattainable in practical scenarios due to factors such as estimation errors, system updates, etc. This paper aims to take the first attempt to develop a triadic-OCD framework with certifiable robustness, provable optimality, and guaranteed convergence. In addition, the proposed triadic-OCD algorithm can be realized in a fully asynchronous distributed manner, easing the necessity of transmitting the data to a single server. This asynchronous mechanism could also mitigate the straggler issue that faced by traditional synchronous algorithm. Moreover, the non-asymptotic convergence property of Triadic-OCD is theoretically analyzed, and its iteration complexity to achieve an $\epsilon$-optimal point is derived. Extensive experiments have been conducted to elucidate the effectiveness of the proposed method.
Lightweight Change Detection in Heterogeneous Remote Sensing Images with Online All-Integer Pruning Training
2024-05-03
Chengyang Zhang, Weiming Li, Gang Li, Huina Song, Zhaohui Song, Xueqian Wang, Antonio Plaza
Detection of changes in heterogeneous remote sensing images is vital, especially in response to emergencies like earthquakes and floods. Current homogenous transformation-based change detection (CD) methods often suffer from high computation and memory costs, which are not friendly to edge-computation devices like onboard CD devices at satellites. To address this issue, this paper proposes a new lightweight CD method for heterogeneous remote sensing images that employs the online all-integer pruning (OAIP) training strategy to efficiently fine-tune the CD network using the current test data. The proposed CD network consists of two visual geometry group (VGG) subnetworks as the backbone architecture. In the OAIP-based training process, all the weights, gradients, and intermediate data are quantized to integers to speed up training and reduce memory usage, where the per-layer block exponentiation scaling scheme is utilized to reduce the computation errors of network parameters caused by quantization. Second, an adaptive filter-level pruning method based on the L1-norm criterion is employed to further lighten the fine-tuning process of the CD network. Experimental results show that the proposed OAIP-based method attains similar detection performance (but with significantly reduced computation complexity and memory usage) in comparison with state-of-the-art CD methods.
MFDS-Net: Multi-Scale Feature Depth-Supervised Network for Remote Sensing Change Detection with Global Semantic and Detail Information
2024-05-02
Zhenyang Huang, Zhaojin Fu, Song Jintao, Genji Yuan, Jinjiang Li
Change detection as an interdisciplinary discipline in the field of computer vision and remote sensing at present has been receiving extensive attention and research. Due to the rapid development of society, the geographic information captured by remote sensing satellites is changing faster and more complex, which undoubtedly poses a higher challenge and highlights the value of change detection tasks. We propose MFDS-Net: Multi-Scale Feature Depth-Supervised Network for Remote Sensing Change Detection with Global Semantic and Detail Information (MFDS-Net) with the aim of achieving a more refined description of changing buildings as well as geographic information, enhancing the localisation of changing targets and the acquisition of weak features. To achieve the research objectives, we use a modified ResNet_34 as backbone network to perform feature extraction and DO-Conv as an alternative to traditional convolution to better focus on the association between feature information and to obtain better training results. We propose the Global Semantic Enhancement Module (GSEM) to enhance the processing of high-level semantic information from a global perspective. The Differential Feature Integration Module (DFIM) is proposed to strengthen the fusion of different depth feature information, achieving learning and extraction of differential features. The entire network is trained and optimized using a deep supervision mechanism. The experimental outcomes of MFDS-Net surpass those of current mainstream change detection networks. On the LEVIR dataset, it achieved an F1 score of 91.589 and IoU of 84.483, on the WHU dataset, the scores were F1: 92.384 and IoU: 86.807, and on the GZ-CD dataset, the scores were F1: 86.377 and IoU: 76.021. The code is available at https://github.com/AOZAKIiii/MFDS-Net
Artificial intelligence for context-aware visual change detection in software test automation
2024-05-01
Milad Moradi, Ke Yan, David Colwell, Rhona Asgari
Automated software testing is integral to the software development process, streamlining workflows and ensuring product reliability. Visual testing within this context, especially concerning user interface (UI) and user experience (UX) validation, stands as one of crucial determinants of overall software quality. Nevertheless, conventional methods like pixel-wise comparison and region-based visual change detection fall short in capturing contextual similarities, nuanced alterations, and understanding the spatial relationships between UI elements. In this paper, we introduce a novel graph-based method for visual change detection in software test automation. Leveraging a machine learning model, our method accurately identifies UI controls from software screenshots and constructs a graph representing contextual and spatial relationships between the controls. This information is then used to find correspondence between UI controls within screenshots of different versions of a software. The resulting graph encapsulates the intricate layout of the UI and underlying contextual relations, providing a holistic and context-aware model. This model is finally used to detect and highlight visual regressions in the UI. Comprehensive experiments on different datasets showed that our change detector can accurately detect visual software changes in various simple and complex test scenarios. Moreover, it outperformed pixel-wise comparison and region-based baselines by a large margin in more complex testing scenarios. This work not only contributes to the advancement of visual change detection but also holds practical implications, offering a robust solution for real-world software test automation challenges, enhancing reliability, and ensuring the seamless evolution of software interfaces.
2024-05-01
Yu-Zhen Janice Chen, Jinhang Zuo, Venugopal V. Veeravalli, Don Towsley
In the problem of quickest change detection (QCD), a change occurs at some unknown time in the distribution of a sequence of independent observations. This work studies a QCD problem where the change is either a bad change, which we aim to detect, or a confusing change, which is not of our interest. Our objective is to detect a bad change as quickly as possible while avoiding raising a false alarm for pre-change or a confusing change. We identify a specific set of pre-change, bad change, and confusing change distributions that pose challenges beyond the capabilities of standard Cumulative Sum (CuSum) procedures. Proposing novel CuSum-based detection procedures, S-CuSum and J-CuSum, leveraging two CuSum statistics, we offer solutions applicable across all kinds of pre-change, bad change, and confusing change distributions. For both S-CuSum and J-CuSum, we provide analytical performance guarantees and validate them by numerical results. Furthermore, both procedures are computationally efficient as they only require simple recursive updates.
Analyzing Semantic Change through Lexical Replacements
2024-04-29
Francesco Periti, Pierluigi Cassotti, Haim Dubossarsky, Nina Tahmasebi
Modern language models are capable of contextualizing words based on their surrounding context. However, this capability is often compromised due to semantic change that leads to words being used in new, unexpected contexts not encountered during pre-training. In this paper, we model \textit{semantic change} by studying the effect of unexpected contexts introduced by \textit{lexical replacements}. We propose a \textit{replacement schema} where a target word is substituted with lexical replacements of varying relatedness, thus simulating different kinds of semantic change. Furthermore, we leverage the replacement schema as a basis for a novel \textit{interpretable} model for semantic change. We are also the first to evaluate the use of LLaMa for semantic change detection.
RFL-CDNet: Towards Accurate Change Detection via Richer Feature Learning
2024-04-27
Yuhang Gan, Wenjie Xuan, Hang Chen, Juhua Liu, Bo Du
Change Detection is a crucial but extremely challenging task of remote sensing image analysis, and much progress has been made with the rapid development of deep learning. However, most existing deep learning-based change detection methods mainly focus on intricate feature extraction and multi-scale feature fusion, while ignoring the insufficient utilization of features in the intermediate stages, thus resulting in sub-optimal results. To this end, we propose a novel framework, named RFL-CDNet, that utilizes richer feature learning to boost change detection performance. Specifically, we first introduce deep multiple supervision to enhance intermediate representations, thus unleashing the potential of backbone feature extractor at each stage. Furthermore, we design the Coarse-To-Fine Guiding (C2FG) module and the Learnable Fusion (LF) module to further improve feature learning and obtain more discriminative feature representations. The C2FG module aims to seamlessly integrate the side prediction from the previous coarse-scale into the current fine-scale prediction in a coarse-to-fine manner, while LF module assumes that the contribution of each stage and each spatial location is independent, thus designing a learnable module to fuse multiple predictions. Experiments on several benchmark datasets show that our proposed RFL-CDNet achieves state-of-the-art performance on WHU cultivated land dataset and CDD dataset, and the second-best performance on WHU building dataset. The source code and models are publicly available at https://github.com/Hhaizee/RFL-CDNet.
Change detection (CD) is a fundamental task in remote sensing (RS) which aims to detect the semantic changes between the same geographical regions at different time stamps. Existing convolutional neural networks (CNNs) based approaches often struggle to capture long-range dependencies. Whereas recent transformer-based methods are prone to the dominant global representation and may limit their capabilities to capture the subtle change regions due to the complexity of the objects in the scene. To address these limitations, we propose an effective Siamese-based framework to encode the semantic changes occurring in the bi-temporal RS images. The main focus of our design is to introduce a change encoder that leverages local and global feature representations to capture both subtle and large change feature information from multi-scale features to precisely estimate the change regions. Our experimental study on two challenging CD datasets reveals the merits of our approach and obtains state-of-the-art performance.
A high-precision feature extraction model is crucial for change detection (CD). In the past, many deep learning-based supervised CD methods learned to recognize change feature patterns from a large number of labelled bi-temporal images, whereas labelling bi-temporal remote sensing images is very expensive and often time-consuming; therefore, we propose a coarse-to-fine semi-supervised CD method based on consistency regularization (C2F-SemiCD), which includes a coarse-to-fine CD network with a multiscale attention mechanism (C2FNet) and a semi-supervised update method. Among them, the C2FNet network gradually completes the extraction of change features from coarse-grained to fine-grained through multiscale feature fusion, channel attention mechanism, spatial attention mechanism, global context module, feature refine module, initial aggregation module, and final aggregation module. The semi-supervised update method uses the mean teacher method. The parameters of the student model are updated to the parameters of the teacher Model by using the exponential moving average (EMA) method. Through extensive experiments on three datasets and meticulous ablation studies, including crossover experiments across datasets, we verify the significant effectiveness and efficiency of the proposed C2F-SemiCD method. The code will be open at: https://github.com/ChengxiHAN/C2F-SemiCDand-C2FNet.
Change detection (CD) from remote sensing (RS) images using deep learning has been widely investigated in the literature. It is typically regarded as a pixel-wise labeling task that aims to classify each pixel as changed or unchanged. Although per-pixel classification networks in encoder-decoder structures have shown dominance, they still suffer from imprecise boundaries and incomplete object delineation at various scenes. For high-resolution RS images, partly or totally changed objects are more worthy of attention rather than a single pixel. Therefore, we revisit the CD task from the mask prediction and classification perspective and propose MaskCD to detect changed areas by adaptively generating categorized masks from input image pairs. Specifically, it utilizes a cross-level change representation perceiver (CLCRP) to learn multiscale change-aware representations and capture spatiotemporal relations from encoded features by exploiting deformable multihead self-attention (DeformMHSA). Subsequently, a masked-attention-based detection transformers (MA-DETR) decoder is developed to accurately locate and identify changed objects based on masked attention and self-attention mechanisms. It reconstructs the desired changed objects by decoding the pixel-wise representations into learnable mask proposals and making final predictions from these candidates. Experimental results on five benchmark datasets demonstrate the proposed approach outperforms other state-of-the-art models. Codes and pretrained models are available online (https://github.com/EricYu97/MaskCD).
Single-temporal Supervised Remote Change Detection for Domain Generalization
2024-04-17
Qiangang Du, Jinlong Peng, Xu Chen, Qingdong He, Liren He, Qiang Nie, Wenbing Zhu, Mingmin Chi, Yabiao Wang, Chengjie Wang
Change detection is widely applied in remote sensing image analysis. Existing methods require training models separately for each dataset, which leads to poor domain generalization. Moreover, these methods rely heavily on large amounts of high-quality pair-labelled data for training, which is expensive and impractical. In this paper, we propose a multimodal contrastive learning (ChangeCLIP) based on visual-language pre-training for change detection domain generalization. Additionally, we propose a dynamic context optimization for prompt learning. Meanwhile, to address the data dependency issue of existing methods, we introduce a single-temporal and controllable AI-generated training strategy (SAIN). This allows us to train the model using a large number of single-temporal images without image pairs in the real world, achieving excellent generalization. Extensive experiments on series of real change detection datasets validate the superiority and strong generalization of ChangeCLIP, outperforming state-of-the-art change detection methods. Code will be available.
Leveraging Fine-Grained Information and Noise Decoupling for Remote Sensing Change Detection
2024-04-17
Qiangang Du, Jinlong Peng, Changan Wang, Xu Chen, Qingdong He, Wenbing Zhu, Mingmin Chi, Yabiao Wang, Chengjie Wang
Change detection aims to identify remote sense object changes by analyzing data between bitemporal image pairs. Due to the large temporal and spatial span of data collection in change detection image pairs, there are often a significant amount of task-specific and task-agnostic noise. Previous effort has focused excessively on denoising, with this goes a great deal of loss of fine-grained information. In this paper, we revisit the importance of fine-grained features in change detection and propose a series of operations for fine-grained information compensation and noise decoupling (FINO). First, the context is utilized to compensate for the fine-grained information in the feature space. Next, a shape-aware and a brightness-aware module are designed to improve the capacity for representation learning. The shape-aware module guides the backbone for more precise shape estimation, guiding the backbone network in extracting object shape features. The brightness-aware module learns a overall brightness estimation to improve the model's robustness to task-agnostic noise. Finally, a task-specific noise decoupling structure is designed as a way to improve the model's ability to separate noise interference from feature similarity. With these training schemes, our proposed method achieves new state-of-the-art (SOTA) results in multiple change detection benchmarks. The code will be made available.
Multi-Sensor Diffusion-Driven Optical Image Translation for Large-Scale Applications
2024-04-17
João Gabriel Vinholi, Marco Chini, Anis Amziane, Renato Machado, Danilo Silva, Patrick Matgen
Comparing images captured by disparate sensors is a common challenge in remote sensing. This requires image translation -- converting imagery from one sensor domain to another while preserving the original content. Denoising Diffusion Implicit Models (DDIM) are potential state-of-the-art solutions for such domain translation due to their proven superiority in multiple image-to-image translation tasks in computer vision. However, these models struggle with reproducing radiometric features of large-scale multi-patch imagery, resulting in inconsistencies across the full image. This renders downstream tasks like Heterogeneous Change Detection impractical. To overcome these limitations, we propose a method that leverages denoising diffusion for effective multi-sensor optical image translation over large areas. Our approach super-resolves large-scale low spatial resolution images into high-resolution equivalents from disparate optical sensors, ensuring uniformity across hundreds of patches. Our contributions lie in new forward and reverse diffusion processes that address the challenges of large-scale image translation. Extensive experiments using paired Sentinel-II (10m) and Planet Dove (3m) images demonstrate that our approach provides precise domain adaptation, preserving image content while improving radiometric accuracy and feature representation. A thorough image quality assessment and comparisons with the standard DDIM framework and five other leading methods are presented. We reach a mean Learned Perceptual Image Patch Similarity (mLPIPS) of 0.1884 and a Fr\'echet Inception Distance (FID) of 45.64, expressively outperforming all compared methods, including DDIM, ShuffleMixer, and SwinIR. The usefulness of our approach is further demonstrated in two Heterogeneous Change Detection tasks.
Unsupervised Speaker Diarization in Distributed IoT Networks Using Federated Learning
2024-04-16
Amit Kumar Bhuyan, Hrishikesh Dutta, Subir Biswas
This paper presents a computationally efficient and distributed speaker diarization framework for networked IoT-style audio devices. The work proposes a Federated Learning model which can identify the participants in a conversation without the requirement of a large audio database for training. An unsupervised online update mechanism is proposed for the Federated Learning model which depends on cosine similarity of speaker embeddings. Moreover, the proposed diarization system solves the problem of speaker change detection via. unsupervised segmentation techniques using Hotelling's t-squared Statistic and Bayesian Information Criterion. In this new approach, speaker change detection is biased around detected quasi-silences, which reduces the severity of the trade-off between the missed detection and false detection rates. Additionally, the computational overhead due to frame-by-frame identification of speakers is reduced via. unsupervised clustering of speech segments. The results demonstrate the effectiveness of the proposed training method in the presence of non-IID speech data. It also shows a considerable improvement in the reduction of false and missed detection at the segmentation stage, while reducing the computational overhead. Improved accuracy and reduced computational cost makes the mechanism suitable for real-time speaker diarization across a distributed IoT audio network.
The rapid advancement of automated artificial intelligence algorithms and remote sensing instruments has benefited change detection (CD) tasks. However, there is still a lot of space to study for precise detection, especially the edge integrity and internal holes phenomenon of change features. In order to solve these problems, we design the Change Guiding Network (CGNet), to tackle the insufficient expression problem of change features in the conventional U-Net structure adopted in previous methods, which causes inaccurate edge detection and internal holes. Change maps from deep features with rich semantic information are generated and used as prior information to guide multi-scale feature fusion, which can improve the expression ability of change features. Meanwhile, we propose a self-attention module named Change Guide Module (CGM), which can effectively capture the long-distance dependency among pixels and effectively overcome the problem of the insufficient receptive field of traditional convolutional neural networks. On four major CD datasets, we verify the usefulness and efficiency of the CGNet, and a large number of experiments and ablation studies demonstrate the effectiveness of CGNet. We're going to open-source our code at https://github.com/ChengxiHAN/CGNet-CD.
Benefiting from the developments in deep learning technology, deep-learning-based algorithms employing automatic feature extraction have achieved remarkable performance on the change detection (CD) task. However, the performance of existing deep-learning-based CD methods is hindered by the imbalance between changed and unchanged pixels. To tackle this problem, a progressive foreground-balanced sampling strategy on the basis of not adding change information is proposed in this article to help the model accurately learn the features of the changed pixels during the early training process and thereby improve detection performance.Furthermore, we design a discriminative Siamese network, hierarchical attention network (HANet), which can integrate multiscale features and refine detailed features. The main part of HANet is the HAN module, which is a lightweight and effective self-attention mechanism. Extensive experiments and ablation studies on two CDdatasets with extremely unbalanced labels validate the effectiveness and efficiency of the proposed method.
ChangeAnywhere: Sample Generation for Remote Sensing Change Detection via Semantic Latent Diffusion Model
2024-04-13
Kai Tang, Jin Chen
Remote sensing change detection (CD) is a pivotal technique that pinpoints changes on a global scale based on multi-temporal images. With the recent expansion of deep learning, supervised deep learning-based CD models have shown satisfactory performance. However, CD sample labeling is very time-consuming as it is densely labeled and requires expert knowledge. To alleviate this problem, we introduce ChangeAnywhere, a novel CD sample generation method using the semantic latent diffusion model and single-temporal images. Specifically, ChangeAnywhere leverages the relative ease of acquiring large single-temporal semantic datasets to generate large-scale, diverse, and semantically annotated bi-temporal CD datasets. ChangeAnywhere captures the two essentials of CD samples, i.e., change implies semantically different, and non-change implies reasonable change under the same semantic constraints. We generated ChangeAnywhere-100K, the largest synthesis CD dataset with 100,000 pairs of CD samples based on the proposed method. The ChangeAnywhere-100K significantly improved both zero-shot and few-shot performance on two CD benchmark datasets for various deep learning-based CD models, as demonstrated by transfer experiments. This paper delineates the enormous potential of ChangeAnywhere for CD sample generation and demonstrates the subsequent enhancement of model performance. Therefore, ChangeAnywhere offers a potent tool for remote sensing CD. All codes and pre-trained models will be available at https://github.com/tangkai-RS/ChangeAnywhere.
Quickest Change Detection for Multiple Data Streams Using the James-Stein Estimator
2024-04-08
Topi Halme, Venugopal V. Veeravalli, Visa Koivunen
The problem of quickest change detection is studied in the context of detecting an arbitrary unknown mean-shift in multiple independent Gaussian data streams. The James-Stein estimator is used in constructing detection schemes that exhibit strong detection performance both asymptotically and non-asymptotically. Our results indicate that utilizing the James-Stein estimator in the recently developed window-limited CuSum test constitutes a uniform improvement over its typical maximum likelihood variant. That is, the proposed James-Stein version achieves a smaller detection delay simultaneously for all possible post-change parameter values and every false alarm rate constraint, as long as the number of parallel data streams is greater than three. Additionally, an alternative detection procedure that utilizes the James-Stein estimator is shown to have asymptotic detection delay properties that compare favorably to existing tests. The second-order asymptotic detection delay term is reduced in a predefined low-dimensional subspace of the parameter space, while second-order asymptotic minimaxity is preserved. The results are verified in simulations, where the proposed schemes are shown to achieve smaller detection delays compared to existing alternatives, especially when the number of data streams is large.
LRNet: Change detection of high-resolution remote sensing imagery via strategy of localization-then-refinement
2024-04-07
Huan Zhong, Chen Wu, Ziqi Xiao
Change detection, as a research hotspot in the field of remote sensing, has witnessed continuous development and progress. However, the discrimination of boundary details remains a significant bottleneck due to the complexity of surrounding elements between change areas and backgrounds. Discriminating the boundaries of large change areas results in misalignment, while connecting boundaries occurs for small change targets. To address the above issues, a novel network based on the localization-then-refinement strategy is proposed in this paper, namely LRNet. LRNet consists of two stages: localization and refinement. In the localization stage, a three-branch encoder simultaneously extracts original image features and their differential features for interactive localization of the position of each change area. To minimize information loss during feature extraction, learnable optimal pooling (LOP) is proposed to replace the widely used max-pooling. Additionally, this process is trainable and contributes to the overall optimization of the network. To effectively interact features from different branches and accurately locate change areas of various sizes, change alignment attention (C2A) and hierarchical change alignment module (HCA) are proposed. In the refinement stage, the localization results from the localization stage are corrected by constraining the change areas and change edges through the edge-area alignment module (E2A). Subsequently, the decoder, combined with the difference features strengthened by C2A in the localization phase, refines change areas of different sizes, ultimately achieving accurate boundary discrimination of change areas. The proposed LRNet outperforms 13 other state-of-the-art methods in terms of comprehensive evaluation metrics and provides the most precise boundary discrimination results on the LEVIR-CD and WHU-CD datasets.
Convolutional neural networks (CNN) and Transformers have made impressive progress in the field of remote sensing change detection (CD). However, both architectures have inherent shortcomings: CNN are constrained by a limited receptive field that may hinder their ability to capture broader spatial contexts, while Transformers are computationally intensive, making them costly to train and deploy on large datasets. Recently, the Mamba architecture, based on state space models, has shown remarkable performance in a series of natural language processing tasks, which can effectively compensate for the shortcomings of the above two architectures. In this paper, we explore for the first time the potential of the Mamba architecture for remote sensing CD tasks. We tailor the corresponding frameworks, called MambaBCD, MambaSCD, and MambaBDA, for binary change detection (BCD), semantic change detection (SCD), and building damage assessment (BDA), respectively. All three frameworks adopt the cutting-edge Visual Mamba architecture as the encoder, which allows full learning of global spatial contextual information from the input images. For the change decoder, which is available in all three architectures, we propose three spatio-temporal relationship modeling mechanisms, which can be naturally combined with the Mamba architecture and fully utilize its attribute to achieve spatio-temporal interaction of multi-temporal features, thereby obtaining accurate change information. On five benchmark datasets, our proposed frameworks outperform current CNN- and Transformer-based approaches without using any complex training strategies or tricks, fully demonstrating the potential of the Mamba architecture in CD tasks. Further experiments show that our architecture is quite robust to degraded data. The source code will be available in https://github.com/ChenHongruixuan/MambaCD
Context modeling is critical for remote sensing image dense prediction tasks. Nowadays, the growing size of very-high-resolution (VHR) remote sensing images poses challenges in effectively modeling context. While transformer-based models possess global modeling capabilities, they encounter computational challenges when applied to large VHR images due to their quadratic complexity. The conventional practice of cropping large images into smaller patches results in a notable loss of contextual information. To address these issues, we propose the Remote Sensing Mamba (RSM) for dense prediction tasks in large VHR remote sensing images. RSM is specifically designed to capture the global context of remote sensing images with linear complexity, facilitating the effective processing of large VHR images. Considering that the land covers in remote sensing images are distributed in arbitrary spatial directions due to characteristics of remote sensing over-head imaging, the RSM incorporates an omnidirectional selective scan module to globally model the context of images in multiple directions, capturing large spatial features from various directions. Extensive experiments on semantic segmentation and change detection tasks across various land covers demonstrate the effectiveness of the proposed RSM. We designed simple yet effective models based on RSM, achieving state-of-the-art performance on dense prediction tasks in VHR remote sensing images without fancy training strategies. Leveraging the linear complexity and global modeling capabilities, RSM achieves better efficiency and accuracy than transformer-based models on large remote sensing images. Interestingly, we also demonstrated that our model generally performs better with a larger image size on dense prediction tasks. Our code is available at https://github.com/walking-shadow/Official_Remote_Sensing_Mamba.
Eclipse Attack Detection on a Blockchain Network as a Non-Parametric Change Detection Problem
2024-03-31
Anurag Gupta, Vikram Krishnamurthy, Brian M. Sadler
This paper introduces a novel non-parametric change detection algorithm to identify eclipse attacks on a blockchain network; the non-parametric algorithm relies only on the empirical mean and variance of the dataset, making it highly adaptable. An eclipse attack occurs when malicious actors isolate blockchain users, disrupting their ability to reach consensus with the broader network, thereby distorting their local copy of the ledger. To detect an eclipse attack, we monitor changes in the Fr\'echet mean and variance of the evolving blockchain communication network connecting blockchain users. First, we leverage the Johnson-Lindenstrauss lemma to project large-dimensional networks into a lower-dimensional space, preserving essential statistical properties. Subsequently, we employ a non-parametric change detection procedure, leading to a test statistic that converges weakly to a Brownian bridge process in the absence of an eclipse attack. This enables us to quantify the false alarm rate of the detector. Our detector can be implemented as a smart contract on the blockchain, offering a tamper-proof and reliable solution. Finally, we use numerical examples to compare the proposed eclipse attack detector with a detector based on the random forest model.
The LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks
2024-03-29
Dominik Schlechtweg, Shafqat Mumtaz Virk, Nikolay Arefyev
Lexical Semantic Change Detection (LSCD) is a complex, lemma-level task, which is usually operationalized based on two subsequently applied usage-level tasks: First, Word-in-Context (WiC) labels are derived for pairs of usages. Then, these labels are represented in a graph on which Word Sense Induction (WSI) is applied to derive sense clusters. Finally, LSCD labels are derived by comparing sense clusters over time. This modularity is reflected in most LSCD datasets and models. It also leads to a large heterogeneity in modeling options and task definitions, which is exacerbated by a variety of dataset versions, preprocessing options and evaluation metrics. This heterogeneity makes it difficult to evaluate models under comparable conditions, to choose optimal model combinations or to reproduce results. Hence, we provide a benchmark repository standardizing LSCD evaluation. Through transparent implementation results become easily reproducible and by standardization different components can be freely combined. The repository reflects the task's modularity by allowing model evaluation for WiC, WSI and LSCD. This allows for careful evaluation of increasingly complex model components providing new ways of model optimization.
Monitoring changes in the Earth's surface is crucial for understanding natural processes and human impacts, necessitating precise and comprehensive interpretation methodologies. Remote sensing satellite imagery offers a unique perspective for monitoring these changes, leading to the emergence of remote sensing image change interpretation (RSICI) as a significant research focus. Current RSICI technology encompasses change detection and change captioning, each with its limitations in providing comprehensive interpretation. To address this, we propose an interactive Change-Agent, which can follow user instructions to achieve comprehensive change interpretation and insightful analysis, such as change detection and change captioning, change object counting, change cause analysis, etc. The Change-Agent integrates a multi-level change interpretation (MCI) model as the eyes and a large language model (LLM) as the brain. The MCI model contains two branches of pixel-level change detection and semantic-level change captioning, in which the BI-temporal Iterative Interaction (BI3) layer is proposed to enhance the model's discriminative feature representation capabilities. To support the training of the MCI model, we build the LEVIR-MCI dataset with a large number of change masks and captions of changes. Experiments demonstrate the SOTA performance of the MCI model in achieving both change detection and change description simultaneously, and highlight the promising application value of our Change-Agent in facilitating comprehensive interpretation of surface changes, which opens up a new avenue for intelligent remote sensing applications. To facilitate future research, we will make our dataset and codebase of the MCI model and Change-Agent publicly available at https://github.com/Chen-Yang-Liu/Change-Agent
Deep learning has shown remarkable success in remote sensing change detection (CD), aiming to identify semantic change regions between co-registered satellite image pairs acquired at distinct time stamps. However, existing convolutional neural network and transformer-based frameworks often struggle to accurately segment semantic change regions. Moreover, transformers-based methods with standard self-attention suffer from quadratic computational complexity with respect to the image resolution, making them less practical for CD tasks with limited training data. To address these issues, we propose an efficient change detection framework, ELGC-Net, which leverages rich contextual information to precisely estimate change regions while reducing the model size. Our ELGC-Net comprises a Siamese encoder, fusion modules, and a decoder. The focus of our design is the introduction of an Efficient Local-Global Context Aggregator module within the encoder, capturing enhanced global context and local spatial information through a novel pooled-transpose (PT) attention and depthwise convolution, respectively. The PT attention employs pooling operations for robust feature extraction and minimizes computational cost with transposed attention. Extensive experiments on three challenging CD datasets demonstrate that ELGC-Net outperforms existing methods. Compared to the recent transformer-based CD approach (ChangeFormer), ELGC-Net achieves a 1.4% gain in intersection over union metric on the LEVIR-CD dataset, while significantly reducing trainable parameters. Our proposed ELGC-Net sets a new state-of-the-art performance in remote sensing change detection benchmarks. Finally, we also introduce ELGC-Net-LW, a lighter variant with significantly reduced computational complexity, suitable for resource-constrained settings, while achieving comparable performance. Project url https://github.com/techmn/elgcnet.
Round Robin Active Sequential Change Detection for Dependent Multi-Channel Data
2024-03-24
Anamitra Chaudhuri, Georgios Fellouris, Ali Tajer
This paper considers the problem of sequentially detecting a change in the joint distribution of multiple data sources under a sampling constraint. Specifically, the channels or sources generate observations that are independent over time, but not necessarily independent at any given time instant. The sources follow an initial joint distribution, and at an unknown time instant, the joint distribution of an unknown subset of sources changes. Importantly, there is a hard constraint that only a fixed number of sources are allowed to be sampled at each time instant. The goal is to sequentially observe the sources according to the constraint, and stop sampling as quickly as possible after the change while controlling the false alarm rate below a user-specified level. The sources can be selected dynamically based on the already collected data, and thus, a policy for this problem consists of a joint sampling and change-detection rule. A non-randomized policy is studied, and an upper bound is established on its worst-case conditional expected detection delay with respect to both the change point and the observations from the affected sources before the change.
Change detection is a fundamental task in computer vision that processes a bi-temporal image pair to differentiate between semantically altered and unaltered regions. Large language models (LLMs) have been utilized in various domains for their exceptional feature extraction capabilities and have shown promise in numerous downstream applications. In this study, we harness the power of a pre-trained LLM, extracting feature maps from extensive datasets, and employ an auxiliary network to detect changes. Unlike existing LLM-based change detection methods that solely focus on deriving high-quality feature maps, our approach emphasizes the manipulation of these feature maps to enhance semantic relevance.
Open-pit mine change detection (CD) in high-resolution (HR) remote sensing images plays a crucial role in mineral development and environmental protection. Significant progress has been made in this field in recent years, largely due to the advancement of deep learning techniques. However, existing deep-learning-based CD methods encounter challenges in effectively integrating neighborhood and scale information, resulting in suboptimal performance. Therefore, by exploring the influence patterns of neighborhood and scale information, this paper proposes an Integrated Neighborhood and Scale Information Network (INSINet) for open-pit mine CD in HR remote sensing images. Specifically, INSINet introduces 8-neighborhood-image information to acquire a larger receptive field, improving the recognition of center image boundary regions. Drawing on techniques of skip connection, deep supervision, and attention mechanism, the multi-path deep supervised attention (MDSA) module is designed to enhance multi-scale information fusion and change feature extraction. Experimental analysis reveals that incorporating neighborhood and scale information enhances the F1 score of INSINet by 6.40%, with improvements of 3.08% and 3.32% respectively. INSINet outperforms existing methods with an Overall Accuracy of 97.69%, Intersection over Union of 71.26%, and F1 score of 83.22%. INSINet shows significance for open-pit mine CD in HR remote sensing images.
Reinforcement Learning Design for Quickest Change Detection
2024-03-21
Austin Cooper, Sean Meyn
The field of quickest change detection (QCD) concerns design and analysis of algorithms to estimate in real time the time at which an important event takes place, and identify properties of the post-change behavior. It is shown in this paper that approaches based on reinforcement learning (RL) can be adapted based on any "surrogate information state" that is adapted to the observations. Hence we are left to choose both the surrogate information state process and the algorithm. For the former, it is argued that there are many choices available, based on a rich theory of asymptotic statistics for QCD. Two approaches to RL design are considered: (i) Stochastic gradient descent based on an actor-critic formulation. Theory is largely complete for this approach: the algorithm is unbiased, and will converge to a local minimum. However, it is shown that variance of stochastic gradients can be very large, necessitating the need for commensurately long run times; (ii) Q-learning algorithms based on a version of the projected Bellman equation. It is shown that the algorithm is stable, in the sense of bounded sample paths, and that a solution to the projected Bellman equation exists under mild conditions. Numerical experiments illustrate these findings, and provide a roadmap for algorithm design in more general settings.
MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
2024-03-20
Di Wang, Jing Zhang, Minqiang Xu, Lin Liu, Dongsheng Wang, Erzhong Gao, Chengxi Han, Haonan Guo, Bo Du, Dacheng Tao, Liangpei Zhang
Foundation models have reshaped the landscape of Remote Sensing (RS) by enhancing various image interpretation tasks. Pretraining is an active research topic, encompassing supervised and self-supervised learning methods to initialize model weights effectively. However, transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks. In this study, we explore the Multi-Task Pretraining (MTP) paradigm for RS foundation models to address this issue. Using a shared encoder and task-specific decoder architecture, we conduct multi-task supervised pretraining on the SAMRS dataset, encompassing semantic segmentation, instance segmentation, and rotated object detection. MTP supports both convolutional neural networks and vision transformer foundation models with over 300 million parameters. The pretrained models are finetuned on various RS downstream tasks, such as scene classification, horizontal and rotated object detection, semantic segmentation, and change detection. Extensive experiments across 14 datasets demonstrate the superiority of our models over existing ones of similar size and their competitive performance compared to larger state-of-the-art models, thus validating the effectiveness of MTP.
Large-scale hydrodynamic models generally rely on fixed-resolution spatial grids and model parameters as well as incurring a high computational cost. This limits their ability to accurately forecast flood crests and issue time-critical hazard warnings. In this work, we build a fast, stable, accurate, resolution-invariant, and geometry-adaptative flood modeling and forecasting framework that can perform at large scales, namely FloodCast. The framework comprises two main modules: multi-satellite observation and hydrodynamic modeling. In the multi-satellite observation module, a real-time unsupervised change detection method and a rainfall processing and analysis tool are proposed to harness the full potential of multi-satellite observations in large-scale flood prediction. In the hydrodynamic modeling module, a geometry-adaptive physics-informed neural solver (GeoPINS) is introduced, benefiting from the absence of a requirement for training data in physics-informed neural networks and featuring a fast, accurate, and resolution-invariant architecture with Fourier neural operators. GeoPINS demonstrates impressive performance on popular PDEs across regular and irregular domains. Building upon GeoPINS, we propose a sequence-to-sequence GeoPINS model to handle long-term temporal series and extensive spatial domains in large-scale flood modeling. Next, we establish a benchmark dataset in the 2022 Pakistan flood to assess various flood prediction methods. Finally, we validate the model in three dimensions - flood inundation range, depth, and transferability of spatiotemporal downscaling. Traditional hydrodynamics and sequence-to-sequence GeoPINS exhibit exceptional agreement during high water levels, while comparative assessments with SAR-based flood depth data show that sequence-to-sequence GeoPINS outperforms traditional hydrodynamics, with smaller prediction errors.
With the increasing deployment of earth observation satellite constellations, the downlink (satellite-to-ground) capacity often limits the freshness, quality, and coverage of the imagery data available to applications on the ground. To overcome the downlink limitation, we present Earth+, a new satellite imagery compression system that, instead of compressing each image individually, pinpoints and downloads only recent imagery changes with respect to the history reference images. To minimize the amount of changes, it is critical to make reference images as fresh as possible. Earth+ enables each satellite to choose fresh reference images from not only its own history images but also past images of other satellites from an entire satellite constellation. To share reference images across satellites, Earth+ utilizes the limited capacity of the existing uplink (ground-to-satellite) by judiciously selecting and compressing reference images while still allowing accurate change detection. In short, Earth+ is the first to make reference-based compression efficient, by enabling constellation-wide sharing of fresh reference images across satellites. Our evaluation shows that Earth+ can reduce the downlink usage by a factor of 3.3 compared to state-of-the-art on-board image compression techniques while not sacrificing image quality, or using more on-board computing or storage resources, or more uplink bandwidth than currently available.
Pre-examinations Improve Automated Metastases Detection on Cranial MRI
2024-03-13
Katerina Deike-Hofmann, Dorottya Dancs, Daniel Paech, Heinz-Peter Schlemmer, Klaus Maier-Hein, Philipp Bäumer, Alexander Radbruch, Michael Götz
Materials and methods: First, a dual-time approach was assessed, for which the CNN was provided sequences of the MRI that initially depicted new MM (diagnosis MRI) as well as of a prediagnosis MRI: inclusion of only contrast-enhanced T1-weighted images (CNNdual_ce) was compared with inclusion of also the native T1-weighted images, T2-weighted images, and FLAIR sequences of both time points (CNNdual_all).Second, results were compared with the corresponding single time approaches, in which the CNN was provided exclusively the respective sequences of the diagnosis MRI.Casewise diagnostic performance parameters were calculated from 5-fold cross-validation. Results: In total, 94 cases with 494 MMs were included. Overall, the highest diagnostic performance was achieved by inclusion of only the contrast-enhanced T1-weighted images of the diagnosis and of a prediagnosis MRI (CNNdual_ce, sensitivity = 73%, PPV = 25%, F1-score = 36%). Using exclusively contrast-enhanced T1-weighted images as input resulted in significantly less false-positives (FPs) compared with inclusion of further sequences beyond contrast-enhanced T1-weighted images (FPs = 5/7 for CNNdual_ce/CNNdual_all, P < 1e-5). Comparison of contrast-enhanced dual and mono time approaches revealed that exclusion of prediagnosis MRI significantly increased FPs (FPs = 5/10 for CNNdual_ce/CNNce, P < 1e-9).Approaches with only native sequences were clearly inferior to CNNs that were provided contrast-enhanced sequences. Conclusions: Automated MM detection on contrast-enhanced T1-weighted images performed with high sensitivity. Frequent FPs due to artifacts and vessels were significantly reduced by additional inclusion of prediagnosis MRI, but not by inclusion of further sequences beyond contrast-enhanced T1-weighted images. Future studies might investigate different change detection architectures for computer-aided detection.
RSBuilding: Towards General Remote Sensing Image Building Extraction and Change Detection with Foundation Model
2024-03-12
Mingze Wang, Lili Su, Cilin Yan, Sheng Xu, Pengcheng Yuan, Xiaolong Jiang, Baochang Zhang
The intelligent interpretation of buildings plays a significant role in urban planning and management, macroeconomic analysis, population dynamics, etc. Remote sensing image building interpretation primarily encompasses building extraction and change detection. However, current methodologies often treat these two tasks as separate entities, thereby failing to leverage shared knowledge. Moreover, the complexity and diversity of remote sensing image scenes pose additional challenges, as most algorithms are designed to model individual small datasets, thus lacking cross-scene generalization. In this paper, we propose a comprehensive remote sensing image building understanding model, termed RSBuilding, developed from the perspective of the foundation model. RSBuilding is designed to enhance cross-scene generalization and task universality. Specifically, we extract image features based on the prior knowledge of the foundation model and devise a multi-level feature sampler to augment scale information. To unify task representation and integrate image spatiotemporal clues, we introduce a cross-attention decoder with task prompts. Addressing the current shortage of datasets that incorporate annotations for both tasks, we have developed a federated training strategy to facilitate smooth model convergence even when supervision for some tasks is missing, thereby bolstering the complementarity of different tasks. Our model was trained on a dataset comprising up to 245,000 images and validated on multiple building extraction and change detection datasets. The experimental results substantiate that RSBuilding can concurrently handle two structurally distinct tasks and exhibits robust zero-shot generalization capabilities.
Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits
2024-03-11
Yu Xia, Fang Kong, Tong Yu, Liya Guo, Ryan A. Rossi, Sungchul Kim, Shuai Li
Web-based applications such as chatbots, search engines and news recommendations continue to grow in scale and complexity with the recent surge in the adoption of LLMs. Online model selection has thus garnered increasing attention due to the need to choose the best model among a diverse set while balancing task reward and exploration cost. Organizations faces decisions like whether to employ a costly API-based LLM or a locally finetuned small LLM, weighing cost against performance. Traditional selection methods often evaluate every candidate model before choosing one, which are becoming impractical given the rising costs of training and finetuning LLMs. Moreover, it is undesirable to allocate excessive resources towards exploring poor-performing models. While some recent works leverage online bandit algorithm to manage such exploration-exploitation trade-off in model selection, they tend to overlook the increasing-then-converging trend in model performances as the model is iteratively finetuned, leading to less accurate predictions and suboptimal model selections. In this paper, we propose a time-increasing bandit algorithm TI-UCB, which effectively predicts the increase of model performances due to finetuning and efficiently balances exploration and exploitation in model selection. To further capture the converging points of models, we develop a change detection mechanism by comparing consecutive increase predictions. We theoretically prove that our algorithm achieves a logarithmic regret upper bound in a typical increasing bandit setting, which implies a fast convergence rate. The advantage of our method is also empirically validated through extensive experiments on classification model selection and online selection of LLMs. Our results highlight the importance of utilizing increasing-then-converging pattern for more efficient and economic model selection in the deployment of LLMs.
Weakly Supervised Change Detection via Knowledge Distillation and Multiscale Sigmoid Inference
2024-03-09
Binghao Lu, Caiwen Ding, Jinbo Bi, Dongjin Song
Change detection, which aims to detect spatial changes from a pair of multi-temporal images due to natural or man-made causes, has been widely applied in remote sensing, disaster management, urban management, etc. Most existing change detection approaches, however, are fully supervised and require labor-intensive pixel-level labels. To address this, we develop a novel weakly supervised change detection technique via Knowledge Distillation and Multiscale Sigmoid Inference (KD-MSI) that leverages image-level labels. In our approach, the Class Activation Maps (CAM) are utilized not only to derive a change probability map but also to serve as a foundation for the knowledge distillation process. This is done through a joint training strategy of the teacher and student networks, enabling the student network to highlight potential change areas more accurately than teacher network based on image-level labels. Moreover, we designed a Multiscale Sigmoid Inference (MSI) module as a post processing step to further refine the change probability map from the trained student network. Empirical results on three public datasets, i.e., WHU-CD, DSIFN-CD, and LEVIR-CD, demonstrate that our proposed technique, with its integrated training strategy, significantly outperforms the state-of-the-art.
Impacts of Color and Texture Distortions on Earth Observation Data in Deep Learning
2024-03-07
Martin Willbo, Aleksis Pirinen, John Martinsson, Edvin Listo Zec, Olof Mogren, Mikael Nilsson
Land cover classification and change detection are two important applications of remote sensing and Earth observation (EO) that have benefited greatly from the advances of deep learning. Convolutional and transformer-based U-net models are the state-of-the-art architectures for these tasks, and their performances have been boosted by an increased availability of large-scale annotated EO datasets. However, the influence of different visual characteristics of the input EO data on a model's predictions is not well understood. In this work we systematically examine model sensitivities with respect to several color- and texture-based distortions on the input EO data during inference, given models that have been trained without such distortions. We conduct experiments with multiple state-of-the-art segmentation networks for land cover classification and show that they are in general more sensitive to texture than to color distortions. Beyond revealing intriguing characteristics of widely used land cover classification models, our results can also be used to guide the development of more robust models within the EO domain.
LiSTA: Geometric Object-Based Change Detection in Cluttered Environments
2024-03-04
Joseph Rowell, Lintong Zhang, Maurice Fallon
We present LiSTA (LiDAR Spatio-Temporal Analysis), a system to detect probabilistic object-level change over time using multi-mission SLAM. Many applications require such a system, including construction, robotic navigation, long-term autonomy, and environmental monitoring. We focus on the semi-static scenario where objects are added, subtracted, or changed in position over weeks or months. Our system combines multi-mission LiDAR SLAM, volumetric differencing, object instance description, and correspondence grouping using learned descriptors to keep track of an open set of objects. Object correspondences between missions are determined by clustering the object's learned descriptors. We demonstrate our approach using datasets collected in a simulated environment and a real-world dataset captured using a LiDAR system mounted on a quadruped robot monitoring an industrial facility containing static, semi-static, and dynamic objects. Our method demonstrates superior performance in detecting changes in semi-static environments compared to existing methods.
A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection
2024-03-01
Taichi Aida, Danushka Bollegala
Detecting temporal semantic changes of words is an important task for various NLP applications that must make time-sensitive predictions. Lexical Semantic Change Detection (SCD) task involves predicting whether a given target word, $w$, changes its meaning between two different text corpora, $C_1$ and $C_2$. For this purpose, we propose a supervised two-staged SCD method that uses existing Word-in-Context (WiC) datasets. In the first stage, for a target word $w$, we learn two sense-aware encoders that represent the meaning of $w$ in a given sentence selected from a corpus. Next, in the second stage, we learn a sense-aware distance metric that compares the semantic representations of a target word across all of its occurrences in $C_1$ and $C_2$. Experimental results on multiple benchmark datasets for SCD show that our proposed method achieves strong performance in multiple languages. Additionally, our method achieves significant improvements on WiC benchmarks compared to a sense-aware encoder with conventional distance functions. Source code is available at https://github.com/LivNLP/svp-sdml .
The capability of intelligent models to extrapolate and comprehend changes in object states is a crucial yet demanding aspect of AI research, particularly through the lens of human interaction in real-world settings. This task involves describing complex visual environments, identifying active objects, and interpreting their changes as conveyed through language. Traditional methods, which isolate object captioning and state change detection, offer a limited view of dynamic environments. Moreover, relying on a small set of symbolic words to represent changes has restricted the expressiveness of the language. To address these challenges, in this paper, we introduce the Object State Captioning and State Change Representation (OSCaR) dataset and benchmark. OSCaR consists of 14,084 annotated video segments with nearly 1,000 unique objects from various egocentric video collections. It sets a new testbed for evaluating multimodal large language models (MLLMs). Our experiments demonstrate that while MLLMs show some skill, they lack a full understanding of object state changes. The benchmark includes a fine-tuned model that, despite initial capabilities, requires significant improvements in accuracy and generalization ability for effective understanding of these changes. Our code and dataset are available at https://github.com/nguyennm1024/OSCaR.
Semantic change detection for Slovene language: a novel dataset and an approach based on optimal transport
2024-02-26
Marko Pranjić, Kaja Dobrovoljc, Senja Pollak, Matej Martinc
In this paper, we focus on the detection of semantic changes in Slovene, a less resourced Slavic language with two million speakers. Detecting and tracking semantic changes provides insights into the evolution of the language caused by changes in society and culture. Recently, several systems have been proposed to aid in this study, but all depend on manually annotated gold standard datasets for evaluation. In this paper, we present the first Slovene dataset for evaluating semantic change detection systems, which contains aggregated semantic change scores for 104 target words obtained from more than 3000 manually annotated sentence pairs. We evaluate several existing semantic change detection methods on this dataset and also propose a novel approach based on optimal transport that improves on the existing state-of-the-art systems with an error reduction rate of 22.8%.
In the later training stages, further improvement of the models ability to determine changes relies on how well the change detection (CD) model learns hard cases; however, there are two additional challenges to learning hard case samples: (1) change labels are limited and tend to pointer only to foreground targets, yet hard case samples are prevalent in the background, which leads to optimizing the loss function focusing on the foreground targets and ignoring the background hard cases, which we call imbalance. (2) Complex situations, such as light shadows, target occlusion, and seasonal changes, induce hard case samples, and in the absence of both supervisory and scene information, it is difficult for the model to learn hard case samples directly to accurately obtain the feature representations of the change information, which we call missingness. We propose a Siamese foreground association-driven hard case sample optimization network (HSONet). To deal with this imbalance, we propose an equilibrium optimization loss function to regulate the optimization focus of the foreground and background, determine the hard case samples through the distribution of the loss values, and introduce dynamic weights in the loss term to gradually shift the optimization focus of the loss from the foreground to the background hard cases as the training progresses. To address this missingness, we understand hard case samples with the help of the scene context, propose the scene-foreground association module, use potential remote sensing spatial scene information to model the association between the target of interest in the foreground and the related context to obtain scene embedding, and apply this information to the feature reinforcement of hard cases. Experiments on four public datasets show that HSONet outperforms current state-of-the-art CD methods, particularly in detecting hard case samples.
A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Change
2024-02-19
Francesco Periti, Nina Tahmasebi
Contextualized embeddings are the preferred tool for modeling Lexical Semantic Change (LSC). Current evaluations typically focus on a specific task known as Graded Change Detection (GCD). However, performance comparison across work are often misleading due to their reliance on diverse settings. In this paper, we evaluate state-of-the-art models and approaches for GCD under equal conditions. We further break the LSC problem into Word-in-Context (WiC) and Word Sense Induction (WSI) tasks, and compare models across these different levels. Our evaluation is performed across different languages on eight available benchmarks for LSC, and shows that (i) APD outperforms other approaches for GCD; (ii) XL-LEXEME outperforms other contextualized models for WiC, WSI, and GCD, while being comparable to GPT-4; (iii) there is a clear need for improving the modeling of word meanings, as well as focus on how, when, and why these meanings change, rather than solely focusing on the extent of semantic change.
LaserSAM: Zero-Shot Change Detection Using Visual Segmentation of Spinning LiDAR
2024-02-15
Alexander Krawciw, Sven Lilge, Timothy D. Barfoot
This paper presents an approach for applying camera perception techniques to spinning LiDAR data. To improve the robustness of long-term change detection from a 3D LiDAR, range and intensity information are rendered into virtual perspectives using a pinhole camera model. Hue-saturation-value image encoding is used to colourize the images by range and near-IR intensity. The LiDAR's active scene illumination makes it invariant to ambient brightness, which enables night-to-day change detection without additional processing. Using the range-colourized, perspective image allows existing foundation models to detect semantic regions. Specifically, the Segment Anything Model detects semantically similar regions in both a previously acquired map and live view from a path-repeating robot. By comparing the masks in both views, changes in the live scan are detected. Results indicate that the Segment Anything Model accurately captures the shape of arbitrary changes introduced into scenes. The proposed method achieves a segmentation intersection over union of 73.3% when evaluated in unstructured environments and 80.4% when evaluated within the planning corridor. Changes can be detected reliably through day-to-night illumination variations. After pixel-level masks are generated, the one-to-one correspondence with 3D points means that the 2D masks can be used directly to recover the 3D location of the changes. The detected 3D changes are avoided in a closed loop by treating them as obstacles in a local motion planner. Experiments on an unmanned ground vehicle demonstrate the performance of the method.
An Evaluation of Real-time Adaptive Sampling Change Point Detection Algorithm using KCUSUM
2024-02-15
Vijayalakshmi Saravanan, Perry Siehien, Shinjae Yoo, Hubertus Van Dam, Thomas Flynn, Christopher Kelly, Khaled Z Ibrahim
Detecting abrupt changes in real-time data streams from scientific simulations presents a challenging task, demanding the deployment of accurate and efficient algorithms. Identifying change points in live data stream involves continuous scrutiny of incoming observations for deviations in their statistical characteristics, particularly in high-volume data scenarios. Maintaining a balance between sudden change detection and minimizing false alarms is vital. Many existing algorithms for this purpose rely on known probability distributions, limiting their feasibility. In this study, we introduce the Kernel-based Cumulative Sum (KCUSUM) algorithm, a non-parametric extension of the traditional Cumulative Sum (CUSUM) method, which has gained prominence for its efficacy in online change point detection under less restrictive conditions. KCUSUM splits itself by comparing incoming samples directly with reference samples and computes a statistic grounded in the Maximum Mean Discrepancy (MMD) non-parametric framework. This approach extends KCUSUM's pertinence to scenarios where only reference samples are available, such as atomic trajectories of proteins in vacuum, facilitating the detection of deviations from the reference sample without prior knowledge of the data's underlying distribution. Furthermore, by harnessing MMD's inherent random-walk structure, we can theoretically analyze KCUSUM's performance across various use cases, including metrics like expected delay and mean runtime to false alarms. Finally, we discuss real-world use cases from scientific simulations such as NWChem CODAR and protein folding data, demonstrating KCUSUM's practical effectiveness in online change point detection.
Tracking Changing Probabilities via Dynamic Learners
2024-02-15
Omid Madani
Consider a predictor, a learner, whose input is a stream of discrete items. The predictor's task, at every time point, is probabilistic multiclass prediction, i.e., to predict which item may occur next by outputting zero or more candidate items, each with a probability, after which the actual item is revealed and the predictor learns from this observation. To output probabilities, the predictor keeps track of the proportions of the items it has seen. The stream is unbounded and the predictor has finite limited space and we seek efficient prediction and update techniques: the set of items is unknown to the predictor and their totality can also grow unbounded. Moreover, there is non-stationarity: the underlying frequencies of items may change, substantially, from time to time. For instance, new items may start appearing and a few recently frequent items may cease to occur again. The predictor, being space-bounded, need only provide probabilities for those items with (currently) sufficiently high frequency, i.e., the salient items. This problem is motivated in the setting of prediction games, a self-supervised learning regime where concepts serve as both the predictors and the predictands, and the set of concepts grows over time, resulting in non-stationarities as new concepts are generated and used. We develop sparse multiclass moving average techniques designed to respond to such non-stationarities in a timely manner. One technique is based on the exponentiated moving average (EMA) and another is based on queuing a few count snapshots. We show that the combination, and in particular supporting dynamic predictand-specific learning rates, offers advantages in terms of faster change detection and convergence.
A Comprehensive Review on Computer Vision Analysis of Aerial Data
2024-02-15
Vivek Tetarwal, Sandeep Kumar
With the emergence of new technologies in the field of airborne platforms and imaging sensors, aerial data analysis is becoming very popular, capitalizing on its advantages over land data. This paper presents a comprehensive review of the computer vision tasks within the domain of aerial data analysis. While addressing fundamental aspects such as object detection and tracking, the primary focus is on pivotal tasks like change detection, object segmentation, and scene-level analysis. The paper provides the comparison of various hyper parameters employed across diverse architectures and tasks. A substantial section is dedicated to an in-depth discussion on libraries, their categorization, and their relevance to different domain expertise. The paper encompasses aerial datasets, the architectural nuances adopted, and the evaluation metrics associated with all the tasks in aerial data analysis. Applications of computer vision tasks in aerial data across different domains are explored, with case studies providing further insights. The paper thoroughly examines the challenges inherent in aerial data analysis, offering practical solutions. Additionally, unresolved issues of significance are identified, paving the way for future research directions in the field of aerial data analysis.
Quickest Detection of False Data Injection Attack in Distributed Process Tracking
2024-02-15
Saqib Abbas Baba, Arpan Chattopadhyay
This paper addresses the problem of detecting false data injection (FDI) attacks in a distributed network without a fusion center, represented by a connected graph among multiple agent nodes. Each agent node is equipped with a sensor, and uses a Kalman consensus information filter (KCIF) to track a discrete time global process with linear dynamics and additive Gaussian noise. The state estimate of the global process at any sensor is computed from the local observation history and the information received by that agent node from its neighbors. At an unknown time, an attacker starts altering the local observation of one agent node. In the Bayesian setting where there is a known prior distribution of the attack beginning instant, we formulate a Bayesian quickest change detection (QCD) problem for FDI detection in order to minimize the mean detection delay subject to a false alarm probability constraint. While it is well-known that the optimal Bayesian QCD rule involves checking the Shriyaev's statistic against a threshold, we demonstrate how to compute the Shriyaev's statistic at each node in a recursive fashion given our non-i.i.d. observations. Next, we consider non-Bayesian QCD where the attack begins at an arbitrary and unknown time, and the detector seeks to minimize the worst case detection delay subject to a constraint on the mean time to false alarm and probability of misidentification. We use the multiple hypothesis sequential probability ratio test for attack detection and identification at each sensor. For unknown attack strategy, we use the window-limited generalized likelihood ratio (WL-GLR) algorithm to solve the QCD problem. Numerical results demonstrate the performances and trade-offs of the proposed algorithms.
2024-02-10
Isaac Corley, Caleb Robinson, Anthony Ortiz
In recent years, there has been an explosion of proposed change detection deep learning architectures in the remote sensing literature. These approaches claim to offer state-of-the-art performance on different standard benchmark datasets. However, has the field truly made significant progress? In this paper we perform experiments which conclude a simple U-Net segmentation baseline without training tricks or complicated architectural changes is still a top performer for the task of change detection.
Transferring facade labels between point clouds with semantic octrees while considering change detection
2024-02-09
Sophia Schwarz, Tanja Pilz, Olaf Wysocki, Ludwig Hoegner, Uwe Stilla
Point clouds and high-resolution 3D data have become increasingly important in various fields, including surveying, construction, and virtual reality. However, simply having this data is not enough; to extract useful information, semantic labeling is crucial. In this context, we propose a method to transfer annotations from a labeled to an unlabeled point cloud using an octree structure. The structure also analyses changes between the point clouds. Our experiments confirm that our method effectively transfers annotations while addressing changes. The primary contribution of this project is the development of the method for automatic label transfer between two different point clouds that represent the same real-world object. The proposed method can be of great importance for data-driven deep learning algorithms as it can also allow circumventing stochastic transfer learning by deterministic label transfer between datasets depicting the same objects.
Visual foundation models have achieved remarkable results in zero-shot image classification and segmentation, but zero-shot change detection remains an open problem. In this paper, we propose the segment any change models (AnyChange), a new type of change detection model that supports zero-shot prediction and generalization on unseen change types and data distributions. AnyChange is built on the segment anything model (SAM) via our training-free adaptation method, bitemporal latent matching. By revealing and exploiting intra-image and inter-image semantic similarities in SAM's latent space, bitemporal latent matching endows SAM with zero-shot change detection capabilities in a training-free way. We also propose a point query mechanism to enable AnyChange's zero-shot object-centric change detection capability. We perform extensive experiments to confirm the effectiveness of AnyChange for zero-shot change detection. AnyChange sets a new record on the SECOND benchmark for unsupervised change detection, exceeding the previous SOTA by up to 4.4% F$_1$ score, and achieving comparable accuracy with negligible manual annotations (1 pixel per image) for supervised change detection.
The prices of renewable commodities: A robust stationarity analysis
2024-02-01
Manuel Landajo, María José Presno
This paper addresses the problem of testing for persistence in the effects of the shocks affecting the prices of renewable commodities, which have potential implications on stabilization policies and economic forecasting, among other areas. A robust methodology is employed that enables the determination of the potential presence and number of instant/gradual structural changes in the series, stationarity testing conditional on the number of changes detected, and the detection of change points. This procedure is applied to the annual real prices of eighteen renewable commodities over the period of 1900-2018. Results indicate that most of the series display non-linear features, including quadratic patterns and regime transitions that often coincide with well-known political and economic episodes. The conclusions of stationarity testing suggest that roughly half of the series are integrated. Stationarity fails to be rejected for grains, whereas most livestock and textile commodities do reject stationarity. Evidence is mixed in all soft commodities and tropical crops, where stationarity can be rejected in approximately half of the cases. The implication would be that for these commodities, stabilization schemes would not be recommended.
Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data
2024-01-31
Chenhui Zhang, Sherrie Wang
Large Vision-Language Models (VLMs) have demonstrated impressive performance on complex tasks involving visual input with natural language instructions. However, it remains unclear to what extent capabilities on natural images transfer to Earth observation (EO) data, which are predominantly satellite and aerial images less common in VLM training data. In this work, we propose a comprehensive benchmark to gauge the progress of VLMs toward being useful tools for EO data by assessing their abilities on scene understanding, localization and counting, and change detection tasks. Motivated by real-world applications, our benchmark includes scenarios like urban monitoring, disaster relief, land use, and conservation. We discover that, although state-of-the-art VLMs like GPT-4V possess extensive world knowledge that leads to strong performance on open-ended tasks like location understanding and image captioning, their poor spatial reasoning limits usefulness on object localization and counting tasks. Our benchmark will be made publicly available at https://vleo.danielz.ch/ and on Hugging Face at https://huggingface.co/collections/mit-ei/vleo-benchmark-datasets-65b789b0466555489cce0d70 for easy model evaluation.
(Chat)GPT v BERT: Dawn of Justice for Semantic Change Detection
2024-01-25
Francesco Periti, Haim Dubossarsky, Nina Tahmasebi
In the universe of Natural Language Processing, Transformer-based language models like BERT and (Chat)GPT have emerged as lexical superheroes with great power to solve open research problems. In this paper, we specifically focus on the temporal problem of semantic change, and evaluate their ability to solve two diachronic extensions of the Word-in-Context (WiC) task: TempoWiC and HistoWiC. In particular, we investigate the potential of a novel, off-the-shelf technology like ChatGPT (and GPT) 3.5 compared to BERT, which represents a family of models that currently stand as the state-of-the-art for modeling semantic change. Our experiments represent the first attempt to assess the use of (Chat)GPT for studying semantic change. Our results indicate that ChatGPT performs significantly worse than the foundational GPT version. Furthermore, our results demonstrate that (Chat)GPT achieves slightly lower performance than BERT in detecting long-term changes but performs significantly worse in detecting short-term changes.
Obtaining high-resolution, accurate channel topography and deposit conditions is the prior challenge for the study of channelized debris flow. Currently, wide-used mapping technologies including satellite imaging and drone photogrammetry struggle to precisely observe channel interior conditions of mountainous long-deep gullies, particularly those in the Wenchuan Earthquake region. SLAM is an emerging tech for 3D mapping; however, extremely rugged environment in long-deep gullies poses two major challenges even for the state-of-art SLAM: (1) Atypical features; (2) Violent swaying and oscillation of sensors. These issues result in large deviation and lots of noise for SLAM results. To improve SLAM mapping in such environments, we propose an advanced SLAM-based channel detection and mapping system, namely AscDAMs. It features three main enhancements to post-process SLAM results: (1) The digital orthophoto map aided deviation correction algorithm greatly eliminates the systematic error; (2) The point cloud smoothing algorithm substantially diminishes noises; (3) The cross section extraction algorithm enables the quantitative assessment of channel deposits and their changes. Two field experiments were conducted in Chutou Gully, Wenchuan County in China in February and November 2023, representing observations before and after the rainy season. We demonstrate the capability of AscDAMs to greatly improve SLAM results, promoting SLAM for mapping the specially challenging environment. The proposed method compensates for the insufficiencies of existing technologies in detecting debris flow channel interiors including detailed channel morphology, erosion patterns, deposit distinction, volume estimation and change detection. It serves to enhance the study of full-scale debris flow mechanisms, long-term post-seismic evolution, and hazard assessment.
On the Fundamental Tradeoff of Joint Communication and Quickest Change Detection with State-Independent Data Channels
2024-01-23
Daewon Seo, Sung Hoon Lim
In this work, we take the initiative in studying the information-theoretic tradeoff between communication and quickest change detection (QCD) under an integrated sensing and communication setting. We formally establish a joint communication and sensing problem for the quickest change detection. We assume a broadcast channel with a transmitter, a communication receiver, and a QCD detector in which only the detection channel is state dependent. For the problem setting, by utilizing constant subblock-composition codes and a modified CuSum detection rule, which we call subblock CuSum (SCS), we provide an inner bound on the information-theoretic tradeoff between communication rate and change point detection delay in the asymptotic regime of vanishing false alarm rate. We further provide a partial converse that matches our inner bound for a certain class of codes. This implies that the SCS detection strategy is asymptotically optimal for our codes as the false alarm rate constraint vanishes. We also present some canonical examples of the tradeoff region for a binary channel, a scalar Gaussian channel, and a MIMO Gaussian channel.
Semantic Change Detection (SCD) is recognized as both a crucial and challenging task in the field of image analysis. Traditional methods for SCD have predominantly relied on the comparison of image pairs. However, this approach is significantly hindered by substantial imaging differences, which arise due to variations in shooting times, atmospheric conditions, and angles. Such discrepancies lead to two primary issues: the under-detection of minor yet significant changes, and the generation of false alarms due to temporal variances. These factors often result in unchanged objects appearing markedly different in multi-temporal images. In response to these challenges, the MapChange framework has been developed. This framework introduces a novel paradigm that synergizes temporal-invariant historical map data with contemporary high-resolution images. By employing this combination, the temporal variance inherent in conventional image pair comparisons is effectively mitigated. The efficacy of the MapChange framework has been empirically validated through comprehensive testing on two public datasets. These tests have demonstrated the framework's marked superiority over existing state-of-the-art SCD methods.
Advanced change detection techniques primarily target image pairs of equal and high quality. However, variations in imaging conditions and platforms frequently lead to image pairs with distinct qualities: one image being high-quality, while the other being low-quality. These disparities in image quality present significant challenges for understanding image pairs semantically and extracting change features, ultimately resulting in a notable decline in performance. To tackle this challenge, we introduce an innovative training strategy grounded in knowledge distillation. The core idea revolves around leveraging task knowledge acquired from high-quality image pairs to guide the model's learning process when dealing with image pairs that exhibit differences in quality. Additionally, we develop a hierarchical correlation distillation approach (involving self-correlation, cross-correlation, and global correlation). This approach compels the student model to replicate the correlations inherent in the teacher model, rather than focusing solely on individual features. This ensures effective knowledge transfer while maintaining the student model's training flexibility.
Regime change detection in irregularly sampled time series
2024-01-18
Norbert Marwan, Deniz Eroglu, Ibrahim Ozken, Thomas Stemler, Karl-Heinz Wyrwoll, Jürgen Kurths
Irregular sampling is a common problem in palaeoclimate studies. We propose a method that provides regularly sampled time series and at the same time a difference filtering of the data. The differences between successive time instances are derived by a transformation costs procedure. A subsequent recurrence analysis is used to investigate regime transitions. This approach is applied on speleothem based palaeoclimate proxy data from the Indonesian-Australian monsoon region. We can clearly identify Heinrich events in the palaeoclimate as characteristic changes in the dynamics.
Recently, the application of deep learning to change detection (CD) has significantly progressed in remote sensing images. In recent years, CD tasks have mostly used architectures such as CNN and Transformer to identify these changes. However, these architectures have shortcomings in representing boundary details and are prone to false alarms and missed detections under complex lighting and weather conditions. For that, we propose a new network, Siamese Meets Diffusion Network (SMDNet). This network combines the Siam-U2Net Feature Differential Encoder (SU-FDE) and the denoising diffusion implicit model to improve the accuracy of image edge change detection and enhance the model's robustness under environmental changes. First, we propose an innovative SU-FDE module that utilizes shared weight features to capture differences between time series images and identify similarities between features to enhance edge detail detection. Furthermore, we add an attention mechanism to identify key coarse features to improve the model's sensitivity and accuracy. Finally, the diffusion model of progressive sampling is used to fuse key coarse features, and the noise reduction ability of the diffusion model and the advantages of capturing the probability distribution of image data are used to enhance the adaptability of the model in different environments. Our method's combination of feature extraction and diffusion models demonstrates effectiveness in change detection in remote sensing images. The performance evaluation of SMDNet on LEVIR-CD, DSIFN-CD, and CDD datasets yields validated F1 scores of 90.99%, 88.40%, and 88.47%, respectively. This substantiates the advanced capabilities of our model in accurately identifying variations and intricate details.
Spatio-temporal analysis for extreme temperature indices over the Levant region
2024-01-17
Ala A. M. Salameh, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Ahmad Abu Hammad, María Jesús Esteban-Parra
The temporal and spatial trends of 16 climate extreme indices based on daily maximum and minimum temperatures during the period 1987-2016 at 28 stations distributed across Israel and Palestine in the Levant region were annually and seasonally analysed. The Man-Kendall test and the Sen's slope estimator were employed for the trend analysis. Results showed that the region has significantly experienced a dominant warming trend for the last three decades, with more intense changes for minimum temperatures than for maximum. At annual scale, maximum values of minimum temperatures exhibited significant increasing trends up to 0.68{\deg}C/decade. Changes detected were more pronounced than those for the absolute extreme temperature indices, with 93 and 89% of stations significantly showed increasing trends in TX90p and TN90p, respectively. The duration and fixed threshold extreme indices confirmed the trend toward a warming, with the 86% of the stations exhibited significant increasing trends in the annual SU25 and TR20. Moreover, 57% of stations showed significant increasing trends in their SU30 index. At seasonal scale, the analysis of trends for extreme temperature indices showed intense and broad significant increasing trends in all absolute extreme temperature indices. In summer, more than 75% of total stations exhibited significant increasing trends for warm days and warm nights. In winter and spring, 71% of the total stations also showed significant increasing trends in SU25 index, whereas the percentage of stations reached 82% in summer and 64% in autumn for significant increasing trends in TR20 index. Finally, the influence of large-scale circulation patterns on temperature extremes was examined. The results highlighted the presence of significant correlations between most of the selected extreme temperature indices and the North Sea-Caspian pattern at annual and seasonal scales.
Unsupervised multimodal change detection is pivotal for time-sensitive tasks and comprehensive multi-temporal Earth monitoring. In this study, we explore unsupervised multimodal change detection between two key remote sensing data sources: optical high-resolution imagery and OpenStreetMap (OSM) data. Specifically, we propose to utilize the vision foundation model Segmentation Anything Model (SAM), for addressing our task. Leveraging SAM's exceptional zero-shot transfer capability, high-quality segmentation maps of optical images can be obtained. Thus, we can directly compare these two heterogeneous data forms in the so-called segmentation domain. We then introduce two strategies for guiding SAM's segmentation process: the 'no-prompt' and 'box/mask prompt' methods. The two strategies are designed to detect land-cover changes in general scenarios and to identify new land-cover objects within existing backgrounds, respectively. Experimental results on three datasets indicate that the proposed approach can achieve more competitive results compared to representative unsupervised multimodal change detection methods.
Image Fusion in Remote Sensing: An Overview and Meta Analysis
2024-01-16
Hessah Albanwan, Rongjun Qin, Yang Tang
Image fusion in Remote Sensing (RS) has been a consistent demand due to its ability to turn raw images of different resolutions, sources, and modalities into accurate, complete, and spatio-temporally coherent images. It greatly facilitates downstream applications such as pan-sharpening, change detection, land-cover classification, etc. Yet, image fusion solutions are highly disparate to various remote sensing problems and thus are often narrowly defined in existing reviews as topical applications, such as pan-sharpening, and spatial-temporal image fusion. Considering that image fusion can be theoretically applied to any gridded data through pixel-level operations, in this paper, we expanded its scope by comprehensively surveying relevant works with a simple taxonomy: 1) many-to-one image fusion; 2) many-to-many image fusion. This simple taxonomy defines image fusion as a mapping problem that turns either a single or a set of images into another single or set of images, depending on the desired coherence, e.g., spectral, spatial/resolution coherence, etc. We show that this simple taxonomy, despite the significant modality difference it covers, can be presented by a conceptually easy framework. In addition, we provide a meta-analysis to review the major papers studying the various types of image fusion and their applications over the years (from the 1980s to date), covering 5,926 peer-reviewed papers. Finally, we discuss the main benefits and emerging challenges to provide open research directions and potential future works.
Stylometry Analysis of Multi-authored Documents for Authorship and Author Style Change Detection
2024-01-12
Muhammad Tayyab Zamir, Muhammad Asif Ayub, Asma Gul, Nasir Ahmad, Kashif Ahmad
In recent years, the increasing use of Artificial Intelligence based text generation tools has posed new challenges in document provenance, authentication, and authorship detection. However, advancements in stylometry have provided opportunities for automatic authorship and author change detection in multi-authored documents using style analysis techniques. Style analysis can serve as a primary step toward document provenance and authentication through authorship detection. This paper investigates three key tasks of style analysis: (i) classification of single and multi-authored documents, (ii) single change detection, which involves identifying the point where the author switches, and (iii) multiple author-switching detection in multi-authored documents. We formulate all three tasks as classification problems and propose a merit-based fusion framework that integrates several state-of-the-art natural language processing (NLP) algorithms and weight optimization techniques. We also explore the potential of special characters, which are typically removed during pre-processing in NLP applications, on the performance of the proposed methods for these tasks by conducting extensive experiments on both cleaned and raw datasets. Experimental results demonstrate significant improvements over existing solutions for all three tasks on a benchmark dataset.
AGSPNet: A framework for parcel-scale crop fine-grained semantic change detection from UAV high-resolution imagery with agricultural geographic scene constraints
Real-time and accurate information on fine-grained changes in crop cultivation is of great significance for crop growth monitoring, yield prediction and agricultural structure adjustment. Aiming at the problems of serious spectral confusion in visible high-resolution unmanned aerial vehicle (UAV) images of different phases, interference of large complex background and salt-and-pepper noise by existing semantic change detection (SCD) algorithms, in order to effectively extract deep image features of crops and meet the demand of agricultural practical engineering applications, this paper designs and proposes an agricultural geographic scene and parcel-scale constrained SCD framework for crops (AGSPNet). AGSPNet framework contains three parts: agricultural geographic scene (AGS) division module, parcel edge extraction module and crop SCD module. Meanwhile, we produce and introduce an UAV image SCD dataset (CSCD) dedicated to agricultural monitoring, encompassing multiple semantic variation types of crops in complex geographical scene. We conduct comparative experiments and accuracy evaluations in two test areas of this dataset, and the results show that the crop SCD results of AGSPNet consistently outperform other deep learning SCD models in terms of quantity and quality, with the evaluation metrics F1-score, kappa, OA, and mIoU obtaining improvements of 0.038, 0.021, 0.011 and 0.062, respectively, on average over the sub-optimal method. The method proposed in this paper can clearly detect the fine-grained change information of crop types in complex scenes, which can provide scientific and technical support for smart agriculture monitoring and management, food policy formulation and food security assurance.
Toward distortion-aware change detection in realistic scenarios
2024-01-10
Yitao Zhao, Heng-Chao Li, Nanqing Liu, Rui Wang
In the conventional change detection (CD) pipeline, two manually registered and labeled remote sensing datasets serve as the input of the model for training and prediction. However, in realistic scenarios, data from different periods or sensors could fail to be aligned as a result of various coordinate systems. Geometric distortion caused by coordinate shifting remains a thorny issue for CD algorithms. In this paper, we propose a reusable self-supervised framework for bitemporal geometric distortion in CD tasks. The whole framework is composed of Pretext Representation Pre-training, Bitemporal Image Alignment, and Down-stream Decoder Fine-Tuning. With only single-stage pre-training, the key components of the framework can be reused for assistance in the bitemporal image alignment, while simultaneously enhancing the performance of the CD decoder. Experimental results in 2 large-scale realistic scenarios demonstrate that our proposed method can alleviate the bitemporal geometric distortion in CD tasks.
SwiMDiff: Scene-wide Matching Contrastive Learning with Diffusion Constraint for Remote Sensing Image
2024-01-10
Jiayuan Tian, Jie Lei, Jiaqing Zhang, Weiying Xie, Yunsong Li
With recent advancements in aerospace technology, the volume of unlabeled remote sensing image (RSI) data has increased dramatically. Effectively leveraging this data through self-supervised learning (SSL) is vital in the field of remote sensing. However, current methodologies, particularly contrastive learning (CL), a leading SSL method, encounter specific challenges in this domain. Firstly, CL often mistakenly identifies geographically adjacent samples with similar semantic content as negative pairs, leading to confusion during model training. Secondly, as an instance-level discriminative task, it tends to neglect the essential fine-grained features and complex details inherent in unstructured RSIs. To overcome these obstacles, we introduce SwiMDiff, a novel self-supervised pre-training framework designed for RSIs. SwiMDiff employs a scene-wide matching approach that effectively recalibrates labels to recognize data from the same scene as false negatives. This adjustment makes CL more applicable to the nuances of remote sensing. Additionally, SwiMDiff seamlessly integrates CL with a diffusion model. Through the implementation of pixel-level diffusion constraints, we enhance the encoder's ability to capture both the global semantic information and the fine-grained features of the images more comprehensively. Our proposed framework significantly enriches the information available for downstream tasks in remote sensing. Demonstrating exceptional performance in change detection and land-cover classification tasks, SwiMDiff proves its substantial utility and value in the field of remote sensing.
Deep learning models are essential for scene classification, change detection, land cover segmentation, and other remote sensing image understanding tasks. Most backbones of existing remote sensing deep learning models are typically initialized by pre-trained weights obtained from ImageNet pre-training (IMP). However, domain gaps exist between remote sensing images and natural images (e.g., ImageNet), making deep learning models initialized by pre-trained weights of IMP perform poorly for remote sensing image understanding. Although some pre-training methods are studied in the remote sensing community, current remote sensing pre-training methods face the problem of vague generalization by only using remote sensing images. In this paper, we propose a novel remote sensing pre-training framework, Generic Knowledge Boosted Remote Sensing Pre-training (GeRSP), to learn robust representations from remote sensing and natural images for remote sensing understanding tasks. GeRSP contains two pre-training branches: (1) A self-supervised pre-training branch is adopted to learn domain-related representations from unlabeled remote sensing images. (2) A supervised pre-training branch is integrated into GeRSP for general knowledge learning from labeled natural images. Moreover, GeRSP combines two pre-training branches using a teacher-student architecture to simultaneously learn representations with general and special knowledge, which generates a powerful pre-trained model for deep learning model initialization. Finally, we evaluate GeRSP and other remote sensing pre-training methods on three downstream tasks, i.e., object detection, semantic segmentation, and scene classification. The extensive experimental results consistently demonstrate that GeRSP can effectively learn robust representations in a unified manner, improving the performance of remote sensing downstream tasks.
BD-MSA: Body decouple VHR Remote Sensing Image Change Detection method guided by multi-scale feature information aggregation
2024-01-09
Yonghui Tan, Xiaolong Li, Yishu Chen, Jinquan Ai
The purpose of remote sensing image change detection (RSCD) is to detect differences between bi-temporal images taken at the same place. Deep learning has been extensively used to RSCD tasks, yielding significant results in terms of result recognition. However, due to the shooting angle of the satellite, the impacts of thin clouds, and certain lighting conditions, the problem of fuzzy edges in the change region in some remote sensing photographs cannot be properly handled using current RSCD algorithms. To solve this issue, we proposed a Body Decouple Multi-Scale by fearure Aggregation change detection (BD-MSA), a novel model that collects both global and local feature map information in the channel and space dimensions of the feature map during the training and prediction phases. This approach allows us to successfully extract the change region's boundary information while also divorcing the change region's main body from its boundary. Numerous studies have shown that the assessment metrics and evaluation effects of the model described in this paper on the publicly available datasets DSIFN-CD, S2Looking and WHU-CD are the best when compared to other models.
Urban transformations have profound societal impact on both individuals and communities at large. Accurately assessing these shifts is essential for understanding their underlying causes and ensuring sustainable urban planning. Traditional measurements often encounter constraints in spatial and temporal granularity, failing to capture real-time physical changes. While street view imagery, capturing the heartbeat of urban spaces from a pedestrian point of view, can add as a high-definition, up-to-date, and on-the-ground visual proxy of urban change. We curate the largest street view time series dataset to date, and propose an end-to-end change detection model to effectively capture physical alterations in the built environment at scale. We demonstrate the effectiveness of our proposed method by benchmark comparisons with previous literature and implementing it at the city-wide level. Our approach has the potential to supplement existing dataset and serve as a fine-grained and accurate assessment of urban change.
Change Detection (CD) has been attracting extensive interests with the availability of bi-temporal datasets. However, due to the huge cost of multi-temporal images acquisition and labeling, existing change detection datasets are small in quantity, short in temporal, and low in practicability. Therefore, a large-scale practical-oriented dataset covering wide temporal phases is urgently needed to facilitate the community. To this end, the ChangeNet dataset is presented especially for multi-temporal change detection, along with the new task of "Asymmetric Change Detection". Specifically, ChangeNet consists of 31,000 multi-temporal images pairs, a wide range of complex scenes from 100 cities, and 6 pixel-level annotated categories, which is far superior to all the existing change detection datasets including LEVIR-CD, WHU Building CD, etc.. In addition, ChangeNet contains amounts of real-world perspective distortions in different temporal phases on the same areas, which is able to promote the practical application of change detection algorithms. The ChangeNet dataset is suitable for both binary change detection (BCD) and semantic change detection (SCD) tasks. Accordingly, we benchmark the ChangeNet dataset on six BCD methods and two SCD methods, and extensive experiments demonstrate its challenges and great significance. The dataset is available at https://github.com/jankyee/ChangeNet.
Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection
2023-12-28
Hichem Sahbi
We introduce a novel interactive satellite image change detection algorithm based on active learning. The proposed method is iterative and consists in frugally probing the user (oracle) about the labels of the most critical images, and according to the oracle's annotations, it updates change detection results. First, we consider a probabilistic framework which assigns to each unlabeled sample a relevance measure modeling how critical is that sample when training change detection functions. We obtain these relevance measures by minimizing an objective function mixing diversity, representativity and uncertainty. These criteria when combined allow exploring different data modes and also refining change detections. Then, we further explore the potential of this objective function, by considering a reinforcement learning approach that finds the best combination of diversity, representativity and uncertainty as well as display-sizes through active learning iterations, leading to better generalization as shown through experiments in interactive satellite image change detection.
Cellular forgetting, desensitisation, stress and aging in signalling networks. When do cells refuse to learn more?
2023-12-28
Tamas Veres, Mark Kerestely, Borbala M. Kovacs, David Keresztes, Klara Schulc, Erik Seitz, Zsolt Vassy, Daniel V. Veres, Peter Csermely
Recent findings show that single, non-neuronal cells are also able to learn signalling responses developing cellular memory. In cellular learning nodes of signalling networks strengthen their interactions e.g. by the conformational memory of intrinsically disordered proteins, protein translocation, miRNAs, lncRNAs, chromatin memory and signalling cascades. This can be described by a generalized, unicellular Hebbian learning process, where those signalling connections, which participate in learning, become stronger. Here we review those scenarios, where cellular signalling is not only repeated in a few times (when learning occurs), but becomes too frequent, too large, or too complex and overloads the cell. This leads to desensitisation of signalling networks by decoupling signalling components, receptor internalization, and consequent downregulation. These molecular processes are examples of anti-Hebbian learning and forgetting of signalling networks. Stress can be perceived as signalling overload inducing the desensitisation of signalling pathways. Aging occurs by the summative effects of cumulative stress downregulating signalling. We propose that cellular learning desensitisation, stress and aging may be placed along the same axis of more and more intensive (prolonged or repeated) signalling. We discuss how cells might discriminate between repeated and unexpected signals, and highlight the Hebbian and anti-Hebbian mechanisms behind the fold-change detection in the NF-\k{appa}B signalling pathway. We list drug design methods using Hebbian learning (such as chemically-induced proximity) and clinical treatment modalities inducing (cancer, drug allergies) desensitisation or avoiding drug-induced desensitisation. A better discrimination between cellular learning, desensitisation and stress may open novel directions in drug design, e.g., helping to overcome drug-resistance.
The field of Remote Sensing (RS) widely employs Change Detection (CD) on very-high-resolution (VHR) images. A majority of extant deep-learning-based methods hinge on annotated samples to complete the CD process. Recently, the emergence of Vision Foundation Model (VFM) enables zero-shot predictions in particular vision tasks. In this work, we propose an unsupervised CD method named Segment Change Model (SCM), built upon the Segment Anything Model (SAM) and Contrastive Language-Image Pre-training (CLIP). Our method recalibrates features extracted at different scales and integrates them in a top-down manner to enhance discriminative change edges. We further design an innovative Piecewise Semantic Attention (PSA) scheme, which can offer semantic representation without training, thereby minimize pseudo change phenomenon. Through conducting experiments on two public datasets, the proposed SCM increases the mIoU from 46.09% to 53.67% on the LEVIR-CD dataset, and from 47.56% to 52.14% on the WHU-CD dataset. Our codes are available at https://github.com/StephenApX/UCD-SCM.
The existing methods for Remote Sensing Image Change Captioning (RSICC) perform well in simple scenes but exhibit poorer performance in complex scenes. This limitation is primarily attributed to the model's constrained visual ability to distinguish and locate changes. Acknowledging the inherent correlation between change detection (CD) and RSICC tasks, we believe pixel-level CD is significant for describing the differences between images through language. Regrettably, the current RSICC dataset lacks readily available pixel-level CD labels. To address this deficiency, we leverage a model trained on existing CD datasets to derive CD pseudo-labels. We propose an innovative network with an auxiliary CD branch, supervised by pseudo-labels. Furthermore, a semantic fusion augment (SFA) module is proposed to fuse the feature information extracted by the CD branch, thereby facilitating the nuanced description of changes. Experiments demonstrate that our method achieves state-of-the-art performance and validate that learning pixel-level CD pseudo-labels significantly contributes to change captioning. Our code will be available at: https://github.com/Chen-Yang-Liu/Pix4Cap
Change detection, a prominent research area in remote sensing, is pivotal in observing and analyzing surface transformations. Despite significant advancements achieved through deep learning-based methods, executing high-precision change detection in spatio-temporally complex remote sensing scenarios still presents a substantial challenge. The recent emergence of foundation models, with their powerful universality and generalization capabilities, offers potential solutions. However, bridging the gap of data and tasks remains a significant obstacle. In this paper, we introduce Time Travelling Pixels (TTP), a novel approach that integrates the latent knowledge of the SAM foundation model into change detection. This method effectively addresses the domain shift in general knowledge transfer and the challenge of expressing homogeneous and heterogeneous characteristics of multi-temporal images. The state-of-the-art results obtained on the LEVIR-CD underscore the efficacy of the TTP. The Code is available at \url{https://kychen.me/TTP}.
Change Point Detection of Events in Molecular Simulations using dupin
2023-12-22
Brandon L. Butler, Domagoj Fijan, Sharon C. Glotzer
Particle tracking is commonly used to study time-dependent behavior in many different types of physical and chemical systems involving constituents that span many length scales, including atoms, molecules, nanoparticles, granular particles, etc. Behaviors of interest studied using particle tracking information include disorder-order transitions, thermodynamic phase transitions, structural transitions, protein folding, crystallization, gelation, swarming, avalanches and fracture. A common challenge in studies of these systems involves change detection. Change point detection discerns when a temporal signal undergoes a change in distribution. These changes can be local or global, instantaneous or prolonged, obvious or subtle. Moreover, system-wide changes marking an interesting physical or chemical phenomenon (e.g. crystallization) are often preceded by events (e.g. pre-nucleation clusters) that are localized and can occur anywhere at anytime in the system. For these reasons, detecting events in particle trajectories generated by molecular simulation is challenging and typically accomplished via ad hoc solutions unique to the behavior and system under study. Consequently, methods for event detection lack generality, and those used in one field are not easily used by scientists in other fields. Here we present a new Python-based tool, dupin, that allows for event detection from particle trajectory data irrespective of the system details. dupin works by creating a signal representing the simulation and partitioning the signal based on events (changes within the trajectory). This approach allows for studies where manual annotating of event boundaries would require a prohibitive amount of time. Furthermore, dupin can serve as a tool in automated and reproducible workflows. We demonstrate the application of dupin using two examples and discuss its applicability to a wider class of problems.
Large Language Models on Lexical Semantic Change Detection: An Evaluation
2023-12-10
Ruiyu Wang, Matthew Choi
Lexical Semantic Change Detection stands out as one of the few areas where Large Language Models (LLMs) have not been extensively involved. Traditional methods like PPMI, and SGNS remain prevalent in research, alongside newer BERT-based approaches. Despite the comprehensive coverage of various natural language processing domains by LLMs, there is a notable scarcity of literature concerning their application in this specific realm. In this work, we seek to bridge this gap by introducing LLMs into the domain of Lexical Semantic Change Detection. Our work presents novel prompting solutions and a comprehensive evaluation that spans all three generations of language models, contributing to the exploration of LLMs in this research area.
Adapting Vision Transformer for Efficient Change Detection
2023-12-08
Yang Zhao, Yuxiang Zhang, Yanni Dong, Bo Du
Most change detection models based on vision transformers currently follow a "pretraining then fine-tuning" strategy. This involves initializing the model weights using large scale classification datasets, which can be either natural images or remote sensing images. However, fully tuning such a model requires significant time and resources. In this paper, we propose an efficient tuning approach that involves freezing the parameters of the pretrained image encoder and introducing additional training parameters. Through this approach, we have achieved competitive or even better results while maintaining extremely low resource consumption across six change detection benchmarks. For example, training time on LEVIR-CD, a change detection benchmark, is only half an hour with 9 GB memory usage, which could be very convenient for most researchers. Additionally, the decoupled tuning framework can be extended to any pretrained model for semantic change detection and multi temporal change detection as well. We hope that our proposed approach will serve as a part of foundational model to inspire more unified training approaches on change detection in the future.
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes
2023-12-05
Hao Zhao, Rong Pan
Change-point detection (CPD) is crucial for identifying abrupt shifts in data, which influence decision-making and efficient resource allocation across various domains. To address the challenges posed by the costly and time-intensive data acquisition in CPD, we introduce the Derivative-Aware Change Detection (DACD) method. It leverages the derivative process of a Gaussian process (GP) for Active Learning (AL), aiming to pinpoint change-point locations effectively. DACD balances the exploitation and exploration of derivative processes through multiple data acquisition functions (AFs). By utilizing GP derivative mean and variance as criteria, DACD sequentially selects the next sampling data point, thus enhancing algorithmic efficiency and ensuring reliable and accurate results. We investigate the effectiveness of DACD method in diverse scenarios and show it outperforms other active learning change-point detection approaches.
We develop the information geometry of scaled Gaussian distributions for which the covariance matrix exhibits a Kronecker product structure. This model and its geometry are then used to propose an online change detection (CD) algorithm for multivariate image times series (MITS). The proposed approach relies mainly on the online estimation of the structured covariance matrix under the null hypothesis, which is performed through a recursive (natural) Riemannian gradient descent. This approach exhibits a practical interest compared to the corresponding offline version, as its computational cost remains constant for each new image added in the time series. Simulations show that the proposed recursive estimators reach the Intrinsic Cram\'er-Rao bound. The interest of the proposed online CD approach is demonstrated on both simulated and real data.
In this work, we aim to detect the changes caused by object variations in a scene represented by the neural radiance fields (NeRFs). Given an arbitrary view and two sets of scene images captured at different timestamps, we can predict the scene changes in that view, which has significant potential applications in scene monitoring and measuring. We conducted preliminary studies and found that such an exciting task cannot be easily achieved by utilizing existing NeRFs and 2D change detection methods with many false or missing detections. The main reason is that the 2D change detection is based on the pixel appearance difference between spatial-aligned image pairs and neglects the stereo information in the NeRF. To address the limitations, we propose the C-NERF to represent scene changes as directional consistency difference-based NeRF, which mainly contains three modules. We first perform the spatial alignment of two NeRFs captured before and after changes. Then, we identify the change points based on the direction-consistent constraint; that is, real change points have similar change representations across view directions, but fake change points do not. Finally, we design the change map rendering process based on the built NeRFs and can generate the change map of an arbitrarily specified view direction. To validate the effectiveness, we build a new dataset containing ten scenes covering diverse scenarios with different changing objects. Our approach surpasses state-of-the-art 2D change detection and NeRF-based methods by a significant margin.
Graph Information Bottleneck for Remote Sensing Segmentation
2023-12-05
Yuntao Shou, Wei Ai, Tao Meng, Nan Yin
Remote sensing segmentation has a wide range of applications in environmental protection, and urban change detection, etc. Despite the success of deep learning-based remote sensing segmentation methods (e.g., CNN and Transformer), they are not flexible enough to model irregular objects. In addition, existing graph contrastive learning methods usually adopt the way of maximizing mutual information to keep the node representations consistent between different graph views, which may cause the model to learn task-independent redundant information. To tackle the above problems, this paper treats images as graph structures and introduces a simple contrastive vision GNN (SC-ViG) architecture for remote sensing segmentation. Specifically, we construct a node-masked and edge-masked graph view to obtain an optimal graph structure representation, which can adaptively learn whether to mask nodes and edges. Furthermore, this paper innovatively introduces information bottleneck theory into graph contrastive learning to maximize task-related information while minimizing task-independent redundant information. Finally, we replace the convolutional module in UNet with the SC-ViG module to complete the segmentation and classification tasks of remote sensing images. Extensive experiments on publicly available real datasets demonstrate that our method outperforms state-of-the-art remote sensing image segmentation methods.
Unsupervised Change Detection for Space Habitats Using 3D Point Clouds
2023-12-04
Jamie Santos, Holly Dinkel, Julia Di, Paulo V. K. Borges, Marina Moreira, Oleg Alexandrov, Brian Coltin, Trey Smith
This work presents an algorithm for scene change detection from point clouds to enable autonomous robotic caretaking in future space habitats. Autonomous robotic systems will help maintain future deep-space habitats, such as the Gateway space station, which will be uncrewed for extended periods. Existing scene analysis software used on the International Space Station (ISS) relies on manually-labeled images for detecting changes. In contrast, the algorithm presented in this work uses raw, unlabeled point clouds as inputs. The algorithm first applies modified Expectation-Maximization Gaussian Mixture Model (GMM) clustering to two input point clouds. It then performs change detection by comparing the GMMs using the Earth Mover's Distance. The algorithm is validated quantitatively and qualitatively using a test dataset collected by an Astrobee robot in the NASA Ames Granite Lab comprising single frame depth images taken directly by Astrobee and full-scene reconstructed maps built with RGB-D and pose data from Astrobee. The runtimes of the approach are also analyzed in depth. The source code is publicly released to promote further development.
A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection
2023-12-02
Kaiyu Li, Xiangyong Cao, Deyu Meng
Change detection (CD) is a critical task to observe and analyze dynamic processes of land cover. Although numerous deep learning-based CD models have performed excellently, their further performance improvements are constrained by the limited knowledge extracted from the given labelled data. On the other hand, the foundation models that emerged recently contain a huge amount of knowledge by scaling up across data modalities and proxy tasks. In this paper, we propose a Bi-Temporal Adapter Network (BAN), which is a universal foundation model-based CD adaptation framework aiming to extract the knowledge of foundation models for CD. The proposed BAN contains three parts, i.e. frozen foundation model (e.g., CLIP), bi-temporal adapter branch (Bi-TAB), and bridging modules between them. Specifically, BAN extracts general features through a frozen foundation model, which are then selected, aligned, and injected into Bi-TAB via the bridging modules. Bi-TAB is designed as a model-agnostic concept to extract task/domain-specific features, which can be either an existing arbitrary CD model or some hand-crafted stacked blocks. Beyond current customized models, BAN is the first extensive attempt to adapt the foundation model to the CD task. Experimental results show the effectiveness of our BAN in improving the performance of existing CD methods (e.g., up to 4.08\% IoU improvement) with only a few additional learnable parameters. More importantly, these successful practices show us the potential of foundation models for remote sensing CD. The code is available at \url{https://github.com/likyoo/BAN} and will be supported in our Open-CD.
As capturing devices become common, 3D scans of interior spaces are acquired on a daily basis. Through scene comparison over time, information about objects in the scene and their changes is inferred. This information is important for robots and AR and VR devices, in order to operate in an immersive virtual experience. We thus propose an unsupervised object discovery method that identifies added, moved, or removed objects without any prior knowledge of what objects exist in the scene. We model this problem as a combination of a 3D change detection and a 2D segmentation task. Our algorithm leverages generic 2D segmentation masks to refine an initial but incomplete set of 3D change detections. The initial changes, acquired through render-and-compare likely correspond to movable objects. The incomplete detections are refined through graph optimization, distilling the information of the 2D segmentation masks in the 3D space. Experiments on the 3Rscan dataset prove that our method outperforms competitive baselines, with SoTA results.
Balancing Summarization and Change Detection in Graph Streams
2023-11-30
Shintaro Fukushima, Kenji Yamanishi
This study addresses the issue of balancing graph summarization and graph change detection. Graph summarization compresses large-scale graphs into a smaller scale. However, the question remains: To what extent should the original graph be compressed? This problem is solved from the perspective of graph change detection, aiming to detect statistically significant changes using a stream of summary graphs. If the compression rate is extremely high, important changes can be ignored, whereas if the compression rate is extremely low, false alarms may increase with more memory. This implies that there is a trade-off between compression rate in graph summarization and accuracy in change detection. We propose a novel quantitative methodology to balance this trade-off to simultaneously realize reliable graph summarization and change detection. We introduce a probabilistic structure of hierarchical latent variable model into a graph, thereby designing a parameterized summary graph on the basis of the minimum description length principle. The parameter specifying the summary graph is then optimized so that the accuracy of change detection is guaranteed to suppress Type I error probability (probability of raising false alarms) to be less than a given confidence level. First, we provide a theoretical framework for connecting graph summarization with change detection. Then, we empirically demonstrate its effectiveness on synthetic and real datasets.
Quickest Change Detection with Post-Change Density Estimation
2023-11-25
Yuchen Liang, Venugopal V. Veeravalli
The problem of quickest change detection in a sequence of independent observations is considered. The pre-change distribution is assumed to be known, while the post-change distribution is unknown. Two tests based on post-change density estimation are developed for this problem, the window-limited non-parametric generalized likelihood ratio (NGLR) CuSum test and the non-parametric window-limited adaptive (NWLA) CuSum test. Both tests do not assume any knowledge of the post-change distribution, except that the post-change density satisfies certain smoothness conditions that allows for efficient non-parametric estimation. Also, they do not require any pre-collected post-change training samples. Under certain convergence conditions on the density estimator, it is shown that both tests are first-order asymptotically optimal, as the false alarm rate goes to zero. The analysis is validated through numerical results, where both tests are compared with baseline tests that have distributional knowledge.
PanBench: Towards High-Resolution and High-Performance Pansharpening
2023-11-20
Shiying Wang, Xuechao Zou, Kai Li, Junliang Xing, Pin Tao
Pansharpening, a pivotal task in remote sensing, involves integrating low-resolution multispectral images with high-resolution panchromatic images to synthesize an image that is both high-resolution and retains multispectral information. These pansharpened images enhance precision in land cover classification, change detection, and environmental monitoring within remote sensing data analysis. While deep learning techniques have shown significant success in pansharpening, existing methods often face limitations in their evaluation, focusing on restricted satellite data sources, single scene types, and low-resolution images. This paper addresses this gap by introducing PanBench, a high-resolution multi-scene dataset containing all mainstream satellites and comprising 5,898 pairs of samples. Each pair includes a four-channel (RGB + near-infrared) multispectral image of 256x256 pixels and a mono-channel panchromatic image of 1,024x1,024 pixels. To achieve high-fidelity synthesis, we propose a Cascaded Multiscale Fusion Network (CMFNet) for Pansharpening. Extensive experiments validate the effectiveness of CMFNet. We have released the dataset, source code, and pre-trained models in the supplementary, fostering further research in remote sensing.
SeaDSC: A video-based unsupervised method for dynamic scene change detection in unmanned surface vehicles
2023-11-20
Linh Trinh, Ali Anwar, Siegfried Mercelis
Recently, there has been an upsurge in the research on maritime vision, where a lot of works are influenced by the application of computer vision for Unmanned Surface Vehicles (USVs). Various sensor modalities such as camera, radar, and lidar have been used to perform tasks such as object detection, segmentation, object tracking, and motion planning. A large subset of this research is focused on the video analysis, since most of the current vessel fleets contain the camera's onboard for various surveillance tasks. Due to the vast abundance of the video data, video scene change detection is an initial and crucial stage for scene understanding of USVs. This paper outlines our approach to detect dynamic scene changes in USVs. To the best of our understanding, this work represents the first investigation of scene change detection in the maritime vision application. Our objective is to identify significant changes in the dynamic scenes of maritime video data, particularly those scenes that exhibit a high degree of resemblance. In our system for dynamic scene change detection, we propose completely unsupervised learning method. In contrast to earlier studies, we utilize a modified cutting-edge generative picture model called VQ-VAE-2 to train on multiple marine datasets, aiming to enhance the feature extraction. Next, we introduce our innovative similarity scoring technique for directly calculating the level of similarity in a sequence of consecutive frames by utilizing grid calculation on retrieved features. The experiments were conducted using a nautical video dataset called RoboWhaler to showcase the efficient performance of our technique.
Exchanging Dual Encoder-Decoder: A New Strategy for Change Detection with Semantic Guidance and Spatial Localization
2023-11-19
Sijie Zhao, Xueliang Zhang, Pengfeng Xiao, Guangjun He
Change detection is a critical task in earth observation applications. Recently, deep learning-based methods have shown promising performance and are quickly adopted in change detection. However, the widely used multiple encoder and single decoder (MESD) as well as dual encoder-decoder (DED) architectures still struggle to effectively handle change detection well. The former has problems of bitemporal feature interference in the feature-level fusion, while the latter is inapplicable to intraclass change detection and multiview building change detection. To solve these problems, we propose a new strategy with an exchanging dual encoder-decoder structure for binary change detection with semantic guidance and spatial localization. The proposed strategy solves the problems of bitemporal feature inference in MESD by fusing bitemporal features in the decision level and the inapplicability in DED by determining changed areas using bitemporal semantic features. We build a binary change detection model based on this strategy, and then validate and compare it with 18 state-of-the-art change detection methods on six datasets in three scenarios, including intraclass change detection datasets (CDD, SYSU), single-view building change detection datasets (WHU, LEVIR-CD, LEVIR-CD+) and a multiview building change detection dataset (NJDS). The experimental results demonstrate that our model achieves superior performance with high efficiency and outperforms all benchmark methods with F1-scores of 97.77%, 83.07%, 94.86%, 92.33%, 91.39%, 74.35% on CDD, SYSU, WHU, LEVIR-CD, LEVIR- CD+, and NJDS datasets, respectively. The code of this work will be available at https://github.com/NJU-LHRS/official-SGSLN.
Fully supervised change detection methods have achieved significant advancements in performance, yet they depend severely on acquiring costly pixel-level labels. Considering that the patch-level annotations also contain abundant information corresponding to both changed and unchanged objects in bi-temporal images, an intuitive solution is to segment the changes with patch-level annotations. How to capture the semantic variations associated with the changed and unchanged regions from the patch-level annotations to obtain promising change results is the critical challenge for the weakly supervised change detection task. In this paper, we propose a memory-supported transformer (MS-Former), a novel framework consisting of a bi-directional attention block (BAB) and a patch-level supervision scheme (PSS) tailored for weakly supervised change detection with patch-level annotations. More specifically, the BAM captures contexts associated with the changed and unchanged regions from the temporal difference features to construct informative prototypes stored in the memory bank. On the other hand, the BAM extracts useful information from the prototypes as supplementary contexts to enhance the temporal difference features, thereby better distinguishing changed and unchanged regions. After that, the PSS guides the network learning valuable knowledge from the patch-level annotations, thus further elevating the performance. Experimental results on three benchmark datasets demonstrate the effectiveness of our proposed method in the change detection task. The demo code for our work will be publicly available at \url{https://github.com/guanyuezhen/MS-Former}.
Change detection has always been a concerned task in the interpretation of remote sensing images. It is essentially a unique binary classification task with two inputs, and there is a change relationship between these two inputs. At present, the mining of change relationship features is usually implicit in the network architectures that contain single-branch or two-branch encoders. However, due to the lack of artificial prior design for change relationship features, these networks cannot learn enough change semantic information and lose more accurate change detection performance. So we propose a network architecture NAME for the explicit mining of change relation features. In our opinion, the change features of change detection should be divided into pre-changed image features, post-changed image features and change relation features. In order to fully mine these three kinds of change features, we propose the triple branch network combining the transformer and convolutional neural network (CNN) to extract and fuse these change features from two perspectives of global information and local information, respectively. In addition, we design the continuous change relation (CCR) branch to further obtain the continuous and detail change relation features to improve the change discrimination capability of the model. The experimental results show that our network performs better, in terms of F1, IoU, and OA, than those of the existing advanced networks for change detection on four public very high-resolution (VHR) remote sensing datasets. Our source code is available at https://github.com/DalongZ/NAME.
SpectralGPT: Spectral Remote Sensing Foundation Model
2023-11-13
Danfeng Hong, Bing Zhang, Xuyang Li, Yuxuan Li, Chenyu Li, Jing Yao, Naoto Yokoya, Hao Li, Pedram Ghamisi, Xiuping Jia, Antonio Plaza, Paolo Gamba, Jon Atli Benediktsson, Jocelyn Chanussot
The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which offers valuable information for scene understanding, especially in remote sensing (RS) applications. To fill this gap, we created for the first time a universal RS foundation model, named SpectralGPT, which is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT). Compared to existing foundation models, SpectralGPT 1) accommodates input images with varying sizes, resolutions, time series, and regions in a progressive training fashion, enabling full utilization of extensive RS big data; 2) leverages 3D token generation for spatial-spectral coupling; 3) captures spectrally sequential patterns via multi-target reconstruction; 4) trains on one million spectral RS images, yielding models with over 600 million parameters. Our evaluation highlights significant performance improvements with pretrained SpectralGPT models, signifying substantial potential in advancing spectral RS big data applications within the field of geoscience across four downstream tasks: single/multi-label scene classification, semantic segmentation, and change detection.
Diffusion Models for Earth Observation Use-cases: from cloud removal to urban change detection
2023-11-10
Fulvio Sanguigni, Mikolaj Czerkawski, Lorenzo Papa, Irene Amerini, Bertrand Le Saux
The advancements in the state of the art of generative Artificial Intelligence (AI) brought by diffusion models can be highly beneficial in novel contexts involving Earth observation data. After introducing this new family of generative models, this work proposes and analyses three use cases which demonstrate the potential of diffusion-based approaches for satellite image data. Namely, we tackle cloud removal and inpainting, dataset generation for change-detection tasks, and urban replanning.
Effective Restoration of Source Knowledge in Continual Test Time Adaptation
2023-11-08
Fahim Faisal Niloy, Sk Miraj Ahmed, Dripta S. Raychaudhuri, Samet Oymak, Amit K. Roy-Chowdhury
Traditional test-time adaptation (TTA) methods face significant challenges in adapting to dynamic environments characterized by continuously changing long-term target distributions. These challenges primarily stem from two factors: catastrophic forgetting of previously learned valuable source knowledge and gradual error accumulation caused by miscalibrated pseudo labels. To address these issues, this paper introduces an unsupervised domain change detection method that is capable of identifying domain shifts in dynamic environments and subsequently resets the model parameters to the original source pre-trained values. By restoring the knowledge from the source, it effectively corrects the negative consequences arising from the gradual deterioration of model parameters caused by ongoing shifts in the domain. Our method involves progressive estimation of global batch-norm statistics specific to each domain, while keeping track of changes in the statistics triggered by domain shifts. Importantly, our method is agnostic to the specific adaptation technique employed and thus, can be incorporated to existing TTA methods to enhance their performance in dynamic environments. We perform extensive experiments on benchmark datasets to demonstrate the superior performance of our method compared to state-of-the-art adaptation methods.
Efficiently Detecting Performance Changes in FaaS Application Releases
2023-11-07
Martin Grambow, Tim Dockenfuß, Trever Schirmer, Nils Japke, David Bermbach
The source code of Function as a Service (FaaS) applications is constantly being refined. To detect if a source code change introduces a significant performance regression, the traditional benchmarking approach evaluates both the old and new function version separately using numerous artificial requests. In this paper, we describe a wrapper approach that enables the Randomized Multiple Interleaved Trials (RMIT) benchmark execution methodology in FaaS environments and use bootstrapping percentile intervals to derive more accurate confidence intervals of detected performance changes. We evaluate our approach using two public FaaS providers, an artificial performance issue, and several benchmark configuration parameters. We conclude that RMIT can shrink the width of confidence intervals in the results from 10.65% using the traditional approach to 0.37% using RMIT and thus enables a more fine-grained performance change detection.
This paper considers image change detection with only a small number of samples, which is a significant problem in terms of a few annotations available. A major impediment of image change detection task is the lack of large annotated datasets covering a wide variety of scenes. Change detection models trained on insufficient datasets have shown poor generalization capability. To address the poor generalization issue, we propose using simple image processing methods for generating synthetic but informative datasets, and design an early fusion network based on object detection which could outperform the siamese neural network. Our key insight is that the synthetic data enables the trained model to have good generalization ability for various scenarios. We compare the model trained on the synthetic data with that on the real-world data captured from a challenging dataset, CDNet, using six different test sets. The results demonstrate that the synthetic data is informative enough to achieve higher generalization ability than the insufficient real-world data. Besides, the experiment shows that utilizing a few (often tens of) samples to fine-tune the model trained on the synthetic data will achieve excellent results.
Unsupervised convolutional neural network fusion approach for change detection in remote sensing images
2023-11-07
Weidong Yan, Pei Yan, Li Cao
With the rapid development of deep learning, a variety of change detection methods based on deep learning have emerged in recent years. However, these methods usually require a large number of training samples to train the network model, so it is very expensive. In this paper, we introduce a completely unsupervised shallow convolutional neural network (USCNN) fusion approach for change detection. Firstly, the bi-temporal images are transformed into different feature spaces by using convolution kernels of different sizes to extract multi-scale information of the images. Secondly, the output features of bi-temporal images at the same convolution kernels are subtracted to obtain the corresponding difference images, and the difference feature images at the same scale are fused into one feature image by using 1 * 1 convolution layer. Finally, the output features of different scales are concatenated and a 1 * 1 convolution layer is used to fuse the multi-scale information of the image. The model parameters are obtained by a redesigned sparse function. Our model has three features: the entire training process is conducted in an unsupervised manner, the network architecture is shallow, and the objective function is sparse. Thus, it can be seen as a kind of lightweight network model. Experimental results on four real remote sensing datasets indicate the feasibility and effectiveness of the proposed approach.
FLOGA: A machine learning ready dataset, a benchmark and a novel deep learning model for burnt area mapping with Sentinel-2
2023-11-06
Maria Sdraka, Alkinoos Dimakos, Alexandros Malounis, Zisoula Ntasiou, Konstantinos Karantzalos, Dimitrios Michail, Ioannis Papoutsis
Over the last decade there has been an increasing frequency and intensity of wildfires across the globe, posing significant threats to human and animal lives, ecosystems, and socio-economic stability. Therefore urgent action is required to mitigate their devastating impact and safeguard Earth's natural resources. Robust Machine Learning methods combined with the abundance of high-resolution satellite imagery can provide accurate and timely mappings of the affected area in order to assess the scale of the event, identify the impacted assets and prioritize and allocate resources effectively for the proper restoration of the damaged region. In this work, we create and introduce a machine-learning ready dataset we name FLOGA (Forest wiLdfire Observations for the Greek Area). This dataset is unique as it comprises of satellite imagery acquired before and after a wildfire event, it contains information from Sentinel-2 and MODIS modalities with variable spatial and spectral resolution, and contains a large number of events where the corresponding burnt area ground truth has been annotated by domain experts. FLOGA covers the wider region of Greece, which is characterized by a Mediterranean landscape and climatic conditions. We use FLOGA to provide a thorough comparison of multiple Machine Learning and Deep Learning algorithms for the automatic extraction of burnt areas, approached as a change detection task. We also compare the results to those obtained using standard specialized spectral indices for burnt area mapping. Finally, we propose a novel Deep Learning model, namely BAM-CD. Our benchmark results demonstrate the efficacy of the proposed technique in the automatic extraction of burnt areas, outperforming all other methods in terms of accuracy and robustness. Our dataset and code are publicly available at: https://github.com/Orion-AI-Lab/FLOGA.
TAMPAR: Visual Tampering Detection for Parcel Logistics in Postal Supply Chains
2023-11-06
Alexander Naumann, Felix Hertlein, Laura Dörr, Kai Furmans
Due to the steadily rising amount of valuable goods in supply chains, tampering detection for parcels is becoming increasingly important. In this work, we focus on the use-case last-mile delivery, where only a single RGB image is taken and compared against a reference from an existing database to detect potential appearance changes that indicate tampering. We propose a tampering detection pipeline that utilizes keypoint detection to identify the eight corner points of a parcel. This permits applying a perspective transformation to create normalized fronto-parallel views for each visible parcel side surface. These viewpoint-invariant parcel side surface representations facilitate the identification of signs of tampering on parcels within the supply chain, since they reduce the problem to parcel side surface matching with pair-wise appearance change detection. Experiments with multiple classical and deep learning-based change detection approaches are performed on our newly collected TAMpering detection dataset for PARcels, called TAMPAR. We evaluate keypoint and change detection separately, as well as in a unified system for tampering detection. Our evaluation shows promising results for keypoint (Keypoint AP 75.76) and tampering detection (81% accuracy, F1-Score 0.83) on real images. Furthermore, a sensitivity analysis for tampering types, lens distortion and viewing angles is presented. Code and dataset are available at https://a-nau.github.io/tampar.
Multi-Agent 3D Map Reconstruction and Change Detection in Microgravity with Free-Flying Robots
2023-11-05
Holly Dinkel, Julia Di, Jamie Santos, Keenan Albee, Paulo Borges, Marina Moreira, Oleg Alexandrov, Brian Coltin, Trey Smith
Assistive free-flyer robots autonomously caring for future crewed outposts -- such as NASA's Astrobee robots on the International Space Station (ISS) -- must be able to detect day-to-day interior changes to track inventory, detect and diagnose faults, and monitor the outpost status. This work presents a framework for multi-agent cooperative mapping and change detection to enable robotic maintenance of space outposts. One agent is used to reconstruct a 3D model of the environment from sequences of images and corresponding depth information. Another agent is used to periodically scan the environment for inconsistencies against the 3D model. Change detection is validated after completing the surveys using real image and pose data collected by Astrobee robots in a ground testing environment and from microgravity aboard the ISS. This work outlines the objectives, requirements, and algorithmic modules for the multi-agent reconstruction system, including recommendations for its use by assistive free-flyers aboard future microgravity outposts.
Efficient Cloud Pipelines for Neural Radiance Fields
2023-11-03
Derek Jacoby, Donglin Xu, Weder Ribas, Minyi Xu, Ting Liu, Vishwanath Jayaraman, Mengdi Wei, Emma De Blois, Yvonne Coady
Since their introduction in 2020, Neural Radiance Fields (NeRFs) have taken the computer vision community by storm. They provide a multi-view representation of a scene or object that is ideal for eXtended Reality (XR) applications and for creative endeavors such as virtual production, as well as change detection operations in geospatial analytics. The computational cost of these generative AI models is quite high, however, and the construction of cloud pipelines to generate NeRFs is neccesary to realize their potential in client applications. In this paper, we present pipelines on a high performance academic computing cluster and compare it with a pipeline implemented on Microsoft Azure. Along the way, we describe some uses of NeRFs in enabling novel user interaction scenarios.
Measuring Behavior Change with Observational Studies: a Review
2023-10-30
Arianna Pera, Gianmarco de Francisci Morales, Luca Maria Aiello
Exploring behavioral change in the digital age is imperative for societal progress in the context of 21st-century challenges. We analyzed 148 articles (2000-2023) and built a map that categorizes behaviors and change detection methodologies, platforms of reference, and theoretical frameworks that characterize online behavior change. Our findings uncover a focus on sentiment shifts, an emphasis on API-restricted platforms, and limited theory integration. We call for methodologies able to capture a wider range of behavioral types, diverse data sources, and stronger theory-practice alignment in the study of online behavioral change.
2023-10-26
Venugopal V. Veeravalli, Georgios Fellouris, George V. Moustakides
In the problem of quickest change detection, a change occurs at some unknown time in the distribution of a sequence of random vectors that are monitored in real time, and the goal is to detect this change as quickly as possible subject to a certain false alarm constraint. In this work we consider this problem in the presence of parametric uncertainty in the post-change regime and controlled sensing. That is, the post-change distribution contains an unknown parameter, and the distribution of each observation, before and after the change, is affected by a control action. In this context, in addition to a stopping rule that determines the time at which it is declared that the change has occurred, one also needs to determine a sequential control policy, which chooses the control action at each time based on the already collected observations. We formulate this problem mathematically using Lorden's minimax criterion, and assuming that there are finitely many possible actions and post-change parameter values. We then propose a specific procedure for this problem that employs an adaptive CuSum statistic in which (i) the estimate of the parameter is based on a fixed number of the more recent observations, and (ii) each action is selected to maximize the Kullback-Leibler divergence of the next observation based on the current parameter estimate, apart from a small number of exploration times. We show that this procedure, which we call the Windowed Chernoff-CuSum (WCC), is first-order asymptotically optimal under Lorden's minimax criterion, for every possible possible value of the unknown post-change parameter, as the mean time to false alarm goes to infinity. We also provide simulation results to illustrate the performance of the WCC procedure.
TransY-Net:Learning Fully Transformer Networks for Change Detection of Remote Sensing Images
2023-10-22
Tianyu Yan, Zifu Wan, Pingping Zhang, Gong Cheng, Huchuan Lu
In the remote sensing field, Change Detection (CD) aims to identify and localize the changed regions from dual-phase images over the same places. Recently, it has achieved great progress with the advances of deep learning. However, current methods generally deliver incomplete CD regions and irregular CD boundaries due to the limited representation ability of the extracted visual features. To relieve these issues, in this work we propose a novel Transformer-based learning framework named TransY-Net for remote sensing image CD, which improves the feature extraction from a global view and combines multi-level visual features in a pyramid manner. More specifically, the proposed framework first utilizes the advantages of Transformers in long-range dependency modeling. It can help to learn more discriminative global-level features and obtain complete CD regions. Then, we introduce a novel pyramid structure to aggregate multi-level visual features from Transformers for feature enhancement. The pyramid structure grafted with a Progressive Attention Module (PAM) can improve the feature representation ability with additional inter-dependencies through spatial and channel attentions. Finally, to better train the whole framework, we utilize the deeply-supervised learning with multiple boundary-aware loss functions. Extensive experiments demonstrate that our proposed method achieves a new state-of-the-art performance on four optical and two SAR image CD benchmarks. The source code is released at https://github.com/Drchip61/TransYNet.
Deep-Learning-based Change Detection with Spaceborne Hyperspectral PRISMA data
2023-10-20
J. F. Amieva, A. Austoni, M. A. Brovelli, L. Ansalone, P. Naylor, F. Serva, B. Le Saux
Change detection (CD) methods have been applied to optical data for decades, while the use of hyperspectral data with a fine spectral resolution has been rarely explored. CD is applied in several sectors, such as environmental monitoring and disaster management. Thanks to the PRecursore IperSpettrale della Missione operativA (PRISMA), hyperspectral-from-space CD is now possible. In this work, we apply standard and deep-learning (DL) CD methods to different targets, from natural to urban areas. We propose a pipeline starting from coregistration, followed by CD with a full-spectrum algorithm and by a DL network developed for optical data. We find that changes in vegetation and built environments are well captured. The spectral information is valuable to identify subtle changes and the DL methods are less affected by noise compared to the statistical method, but atmospheric effects and the lack of reliable ground truth represent a major challenge to hyperspectral CD.
VcT: Visual change Transformer for Remote Sensing Image Change Detection
2023-10-17
Bo Jiang, Zitian Wang, Xixi Wang, Ziyan Zhang, Lan Chen, Xiao Wang, Bin Luo
Existing visual change detectors usually adopt CNNs or Transformers for feature representation learning and focus on learning effective representation for the changed regions between images. Although good performance can be obtained by enhancing the features of the change regions, however, these works are still limited mainly due to the ignorance of mining the unchanged background context information. It is known that one main challenge for change detection is how to obtain the consistent representations for two images involving different variations, such as spatial variation, sunlight intensity, etc. In this work, we demonstrate that carefully mining the common background information provides an important cue to learn the consistent representations for the two images which thus obviously facilitates the visual change detection problem. Based on this observation, we propose a novel Visual change Transformer (VcT) model for visual change detection problem. To be specific, a shared backbone network is first used to extract the feature maps for the given image pair. Then, each pixel of feature map is regarded as a graph node and the graph neural network is proposed to model the structured information for coarse change map prediction. Top-K reliable tokens can be mined from the map and refined by using the clustering algorithm. Then, these reliable tokens are enhanced by first utilizing self/cross-attention schemes and then interacting with original features via an anchor-primary attention learning module. Finally, the prediction head is proposed to get a more accurate change map. Extensive experiments on multiple benchmark datasets validated the effectiveness of our proposed VcT model.
Can Word Sense Distribution Detect Semantic Changes of Words?
2023-10-16
Xiaohang Tang, Yi Zhou, Taichi Aida, Procheta Sen, Danushka Bollegala
Semantic Change Detection (SCD) of words is an important task for various NLP applications that must make time-sensitive predictions. Some words are used over time in novel ways to express new meanings, and these new meanings establish themselves as novel senses of existing words. On the other hand, Word Sense Disambiguation (WSD) methods associate ambiguous words with sense ids, depending on the context in which they occur. Given this relationship between WSD and SCD, we explore the possibility of predicting whether a target word has its meaning changed between two corpora collected at different time steps, by comparing the distributions of senses of that word in each corpora. For this purpose, we use pretrained static sense embeddings to automatically annotate each occurrence of the target word in a corpus with a sense id. Next, we compute the distribution of sense ids of a target word in a given corpus. Finally, we use different divergence or distance measures to quantify the semantic change of the target word across the two given corpora. Our experimental results on SemEval 2020 Task 1 dataset show that word sense distributions can be accurately used to predict semantic changes of words in English, German, Swedish and Latin.
The Road to On-board Change Detection: A Lightweight Patch-Level Change Detection Network via Exploring the Potential of Pruning and Pooling
2023-10-16
Lihui Xue, Zhihao Wang, Xueqian Wang, Gang Li
Existing satellite remote sensing change detection (CD) methods often crop original large-scale bi-temporal image pairs into small patch pairs and then use pixel-level CD methods to fairly process all the patch pairs. However, due to the sparsity of change in large-scale satellite remote sensing images, existing pixel-level CD methods suffer from a waste of computational cost and memory resources on lots of unchanged areas, which reduces the processing efficiency of on-board platform with extremely limited computation and memory resources. To address this issue, we propose a lightweight patch-level CD network (LPCDNet) to rapidly remove lots of unchanged patch pairs in large-scale bi-temporal image pairs. This is helpful to accelerate the subsequent pixel-level CD processing stage and reduce its memory costs. In our LPCDNet, a sensitivity-guided channel pruning method is proposed to remove unimportant channels and construct the lightweight backbone network on basis of ResNet18 network. Then, the multi-layer feature compression (MLFC) module is designed to compress and fuse the multi-level feature information of bi-temporal image patch. The output of MLFC module is fed into the fully-connected decision network to generate the predicted binary label. Finally, a weighted cross-entropy loss is utilized in the training process of network to tackle the change/unchange class imbalance problem. Experiments on two CD datasets demonstrate that our LPCDNet achieves more than 1000 frames per second on an edge computation platform, i.e., NVIDIA Jetson AGX Orin, which is more than 3 times that of the existing methods without noticeable CD performance loss. In addition, our method reduces more than 60% memory costs of the subsequent pixel-level CD processing stage.
Prototype-oriented Unsupervised Change Detection for Disaster Management
2023-10-15
Youngtack Oh, Minseok Seo, Doyi Kim, Junghoon Seo
Climate change has led to an increased frequency of natural disasters such as floods and cyclones. This emphasizes the importance of effective disaster monitoring. In response, the remote sensing community has explored change detection methods. These methods are primarily categorized into supervised techniques, which yield precise results but come with high labeling costs, and unsupervised techniques, which eliminate the need for labeling but involve intricate hyperparameter tuning. To address these challenges, we propose a novel unsupervised change detection method named Prototype-oriented Unsupervised Change Detection for Disaster Management (PUCD). PUCD captures changes by comparing features from pre-event, post-event, and prototype-oriented change synthesis images via a foundational model, and refines results using the Segment Anything Model (SAM). Although PUCD is an unsupervised change detection, it does not require complex hyperparameter tuning. We evaluate PUCD framework on the LEVIR-Extension dataset and the disaster dataset and it achieves state-of-the-art performance compared to other methods on the LEVIR-Extension dataset.
Optimal algorithms are developed for robust detection of changes in non-stationary processes. These are processes in which the distribution of the data after change varies with time. The decision-maker does not have access to precise information on the post-change distribution. It is shown that if the post-change non-stationary family has a distribution that is least favorable in a well-defined sense, then the algorithms designed using the least favorable distributions are robust and optimal. Non-stationary processes are encountered in public health monitoring and space and military applications. The robust algorithms are applied to real and simulated data to show their effectiveness.
Transformer-based Multimodal Change Detection with Multitask Consistency Constraints
2023-10-13
Biyuan Liu, Huaixin Chen, Kun Li, Michael Ying Yang
Change detection plays a fundamental role in Earth observation for analyzing temporal iterations over time. However, recent studies have largely neglected the utilization of multimodal data that presents significant practical and technical advantages compared to single-modal approaches. This research focuses on leveraging {pre-event} digital surface model (DSM) data and {post-event} digital aerial images captured at different times for detecting change beyond 2D. We observe that the current change detection methods struggle with the multitask conflicts between semantic and height change detection tasks. To address this challenge, we propose an efficient Transformer-based network that learns shared representation between cross-dimensional inputs through cross-attention. {It adopts a consistency constraint to establish the multimodal relationship. Initially, pseudo-changes are derived by employing height change thresholding. Subsequently, the $L2$ distance between semantic and pseudo-changes within their overlapping regions is minimized. This explicitly endows the height change detection (regression task) and semantic change detection (classification task) with representation consistency.} A DSM-to-image multimodal dataset encompassing three cities in the Netherlands was constructed. It lays a new foundation for beyond-2D change detection from cross-dimensional inputs. Compared to five state-of-the-art change detection methods, our model demonstrates consistent multitask superiority in terms of semantic and height change detection. Furthermore, the consistency strategy can be seamlessly adapted to the other methods, yielding promising improvements.
Quickest Change Detection in Autoregressive Models
2023-10-13
Zhongchang Sun, Shaofeng Zou
The problem of quickest change detection (QCD) in autoregressive (AR) models is investigated. A system is being monitored with sequentially observed samples. At some unknown time, a disturbance signal occurs and changes the distribution of the observations. The disturbance signal follows an AR model, which is dependent over time. Before the change, observations only consist of measurement noise, and are independent and identically distributed (i.i.d.). After the change, observations consist of the disturbance signal and the measurement noise, are dependent over time, which essentially follow a continuous-state hidden Markov model (HMM). The goal is to design a stopping time to detect the disturbance signal as quickly as possible subject to false alarm constraints. Existing approaches for general non-i.i.d. settings and discrete-state HMMs cannot be applied due to their high computational complexity and memory consumption, and they usually assume some asymptotic stability condition. In this paper, the asymptotic stability condition is firstly theoretically proved for the AR model by a novel design of forward variable and auxiliary Markov chain. A computationally efficient Ergodic CuSum algorithm that can be updated recursively is then constructed and is further shown to be asymptotically optimal. The data-driven setting where the disturbance signal parameters are unknown is further investigated, and an online and computationally efficient gradient ascent CuSum algorithm is designed. The algorithm is constructed by iteratively updating the estimate of the unknown parameters based on the maximum likelihood principle and the gradient ascent approach. The lower bound on its average running length to false alarm is also derived for practical false alarm control. Simulation results are provided to demonstrate the performance of the proposed algorithms.
SSG2: A new modelling paradigm for semantic segmentation
2023-10-12
Foivos I. Diakogiannis, Suzanne Furby, Peter Caccetta, Xiaoliang Wu, Rodrigo Ibata, Ondrej Hlinka, John Taylor
State-of-the-art models in semantic segmentation primarily operate on single, static images, generating corresponding segmentation masks. This one-shot approach leaves little room for error correction, as the models lack the capability to integrate multiple observations for enhanced accuracy. Inspired by work on semantic change detection, we address this limitation by introducing a methodology that leverages a sequence of observables generated for each static input image. By adding this "temporal" dimension, we exploit strong signal correlations between successive observations in the sequence to reduce error rates. Our framework, dubbed SSG2 (Semantic Segmentation Generation 2), employs a dual-encoder, single-decoder base network augmented with a sequence model. The base model learns to predict the set intersection, union, and difference of labels from dual-input images. Given a fixed target input image and a set of support images, the sequence model builds the predicted mask of the target by synthesizing the partial views from each sequence step and filtering out noise. We evaluate SSG2 across three diverse datasets: UrbanMonitor, featuring orthoimage tiles from Darwin, Australia with five spectral bands and 0.2m spatial resolution; ISPRS Potsdam, which includes true orthophoto images with multiple spectral bands and a 5cm ground sampling distance; and ISIC2018, a medical dataset focused on skin lesion segmentation, particularly melanoma. The SSG2 model demonstrates rapid convergence within the first few tens of epochs and significantly outperforms UNet-like baseline models with the same number of gradient updates. However, the addition of the temporal dimension results in an increased memory footprint. While this could be a limitation, it is offset by the advent of higher-memory GPUs and coding optimizations.
A Survey of Feature Types and Their Contributions for Camera Tampering Detection
2023-10-11
Pranav Mantini, Shishir K. Shah
Camera tamper detection is the ability to detect unauthorized and unintentional alterations in surveillance cameras by analyzing the video. Camera tampering can occur due to natural events or it can be caused intentionally to disrupt surveillance. We cast tampering detection as a change detection problem, and perform a review of the existing literature with emphasis on feature types. We formulate tampering detection as a time series analysis problem, and design experiments to study the robustness and capability of various feature types. We compute ten features on real-world surveillance video and apply time series analysis to ascertain their predictability, and their capability to detect tampering. Finally, we quantify the performance of various time series models using each feature type to detect tampering.
HDNA: A graph-based change detection in HTML pages(Deface Attack Detection)
2023-10-05
Mahdi Akhi, Nona Ghazizadeh
In this paper, a new approach called HDNA (HTML DNA) is introduced for analyzing and comparing Document Object Model (DOM) trees in order to detect differences in HTML pages. This method assigns an identifier to each HTML page based on its structure, which proves to be particularly useful for detecting variations caused by server-side updates, user interactions or potential security risks. The process involves preprocessing the HTML content generating a DOM tree and calculating the disparities between two or more trees. By assigning weights to the nodes valuable insights about their hierarchical importance are obtained. The effectiveness of the HDNA approach has been demonstrated in identifying changes in DOM trees even when dynamically generated content is involved. Not does this method benefit web developers, testers, and security analysts by offering a deeper understanding of how web pages evolve. It also helps ensure the functionality and performance of web applications. Additionally, it enables detection and response to vulnerabilities that may arise from modifications in DOM structures. As the web ecosystem continues to evolve HDNA proves to be a tool, for individuals engaged in web development, testing, or security analysis.
Optical high-resolution imagery and OSM data are two important data sources of change detection (CD). Previous related studies focus on utilizing the information in OSM data to aid the CD on optical high-resolution images. This paper pioneers the direct detection of land-cover changes utilizing paired OSM data and optical imagery, thereby expanding the scope of CD tasks. To this end, we propose an object-guided Transformer (ObjFormer) by naturally combining the object-based image analysis (OBIA) technique with the advanced vision Transformer architecture. This combination can significantly reduce the computational overhead in the self-attention module without adding extra parameters or layers. ObjFormer has a hierarchical pseudo-siamese encoder consisting of object-guided self-attention modules that extracts multi-level heterogeneous features from OSM data and optical images; a decoder consisting of object-guided cross-attention modules can recover land-cover changes from the extracted heterogeneous features. Beyond basic binary change detection, this paper raises a new semi-supervised semantic change detection task that does not require any manually annotated land-cover labels to train semantic change detectors. Two lightweight semantic decoders are added to ObjFormer to accomplish this task efficiently. A converse cross-entropy loss is designed to fully utilize negative samples, contributing to the great performance improvement in this task. A large-scale benchmark dataset called OpenMapCD containing 1,287 samples covering 40 regions on six continents is constructed to conduct detailed experiments. The results show the effectiveness of our methods in this new kind of CD task. Additionally, case studies in Japanese cities demonstrate the framework's generalizability and practical potential. The OpenMapCD and source code are available in https://github.com/ChenHongruixuan/ObjFormer
A Dual Attentive Generative Adversarial Network for Remote Sensing Image Change Detection
2023-10-03
Luyi Qiu, Xiaofeng Zhang, ChaoChen Gu, and ShanYing Zhu
Remote sensing change detection between bi-temporal images receives growing concentration from researchers. However, comparing two bi-temporal images for detecting changes is challenging, as they demonstrate different appearances. In this paper, we propose a dual attentive generative adversarial network for achieving very high-resolution remote sensing image change detection tasks, which regards the detection model as a generator and attains the optimal weights of the detection model without increasing the parameters of the detection model through generative-adversarial strategy, boosting the spatial contiguity of predictions. Moreover, We design a multi-level feature extractor for effectively fusing multi-level features, which adopts the pre-trained model to extract multi-level features from bi-temporal images and introduces aggregate connections to fuse them. To strengthen the identification of multi-scale objects, we propose a multi-scale adaptive fusion module to adaptively fuse multi-scale features through various receptive fields and design a context refinement module to explore contextual dependencies. Moreover, the DAGAN framework utilizes the 4-layer convolution network as a discriminator to identify whether the synthetic image is fake or real. Extensive experiments represent that the DAGAN framework has better performance with 85.01% mean IoU and 91.48% mean F1 score than advanced methods on the LEVIR dataset.
Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange
2023-10-01
Hongruixuan Chen, Jian Song, Chen Wu, Bo Du, Naoto Yokoya
Change detection (CD) is a critical task in studying the dynamics of ecosystems and human activities using multi-temporal remote sensing images. While deep learning has shown promising results in CD tasks, it requires a large number of labeled and paired multi-temporal images to achieve high performance. Pairing and annotating large-scale multi-temporal remote sensing images is both expensive and time-consuming. To make deep learning-based CD techniques more practical and cost-effective, we propose an unsupervised single-temporal CD framework based on intra- and inter-image patch exchange (I3PE). The I3PE framework allows for training deep change detectors on unpaired and unlabeled single-temporal remote sensing images that are readily available in real-world applications. The I3PE framework comprises four steps: 1) intra-image patch exchange method is based on an object-based image analysis method and adaptive clustering algorithm, which generates pseudo-bi-temporal image pairs and corresponding change labels from single-temporal images by exchanging patches within the image; 2) inter-image patch exchange method can generate more types of land-cover changes by exchanging patches between images; 3) a simulation pipeline consisting of several image enhancement methods is proposed to simulate the radiometric difference between pre- and post-event images caused by different imaging conditions in real situations; 4) self-supervised learning based on pseudo-labels is applied to further improve the performance of the change detectors in both unsupervised and semi-supervised cases. Extensive experiments on two large-scale datasets demonstrate that I3PE outperforms representative unsupervised approaches and achieves F1 value improvements of 10.65% and 6.99% to the SOTA method. Moreover, I3PE can improve the performance of the ... (see the original article for full abstract)
Distributionally Robust Quickest Change Detection using Wasserstein Uncertainty Sets
2023-09-28
Liyan Xie, Yuchen Liang, Venugopal V. Veeravalli
The problem of quickest detection of a change in the distribution of a sequence of independent observations is considered. It is assumed that the pre-change distribution is known (accurately estimated), while the only information about the post-change distribution is through a (small) set of labeled data. This post-change data is used in a data-driven minimax robust framework, where an uncertainty set for the post-change distribution is constructed using the Wasserstein distance from the empirical distribution of the data. The robust change detection problem is studied in an asymptotic setting where the mean time to false alarm goes to infinity, for which the least favorable post-change distribution within the uncertainty set is the one that minimizes the Kullback-Leibler divergence between the post- and the pre-change distributions. It is shown that the density corresponding to the least favorable distribution is an exponentially tilted version of the pre-change density and can be calculated efficiently. A Cumulative Sum (CuSum) test based on the least favorable distribution, which is referred to as the distributionally robust (DR) CuSum test, is then shown to be asymptotically robust. The results are extended to the case where the post-change uncertainty set is a finite union of multiple Wasserstein uncertainty sets, corresponding to multiple post-change scenarios, each with its own labeled data. The proposed method is validated using synthetic and real data examples.
Autonomous Driving using Spiking Neural Networks on Dynamic Vision Sensor Data: A Case Study of Traffic Light Change Detection
2023-09-27
Xuelei Chen
Autonomous driving is a challenging task that has gained broad attention from both academia and industry. Current solutions using convolutional neural networks require large amounts of computational resources, leading to high power consumption. Spiking neural networks (SNNs) provide an alternative computation model to process information and make decisions. This biologically plausible model has the advantage of low latency and energy efficiency. Recent work using SNNs for autonomous driving mostly focused on simple tasks like lane keeping in simplified simulation environments. This project studies SNNs on photo-realistic driving scenes in the CARLA simulator, which is an important step toward using SNNs on real vehicles. The efficacy and generalizability of the method will be investigated.
Intelligent trading strategy based on improved directional change and regime change detection
2023-09-27
Bing Wu, Xiangzu Han
Previous research primarily characterized price movements according to time intervals, resulting in temporal discontinuity and overlooking crucial activities in financial markets. Directional Change (DC) is an alternative approach to sampling price data, highlighting significant points while blurring out noise details in price movements. However, traditional DC treated the thresholds of upward and downward trends with distinct intrinsic patterns as equivalent and preset them as fixed values, which are dependent on the subjective judgment of traders. To enhance the generalization performance of this methodology, we improved DC by introducing a modified threshold selection technique. Specifically, we addressed upward and downward trends distinctly by incorporating a decay coefficient. Further, we simultaneously optimized the threshold and decay coefficient using the Bayesian Optimization Algorithm (BOA). Additionally, we recognized the abnormal market state by regime change detection based on the Hidden Markov Model (RCD-HMM) to reduce the risk. Our Intelligent Trading Algorithm (ITA) was constructed based on above methods and the experiments were carried out on tick data from diverse currency pairs in the forex market. The experimental results showed a significant increase in profit and reduction in risk of DC-based trading strategies, which demonstrated the effectiveness of our proposed methods.
Frugal Satellite Image Change Detection with Deep-Net Inversion
2023-09-26
Hichem Sahbi, Sebastien Deschamps
Change detection in satellite imagery seeks to find occurrences of targeted changes in a given scene taken at different instants. This task has several applications ranging from land-cover mapping, to anthropogenic activity monitory as well as climate change and natural hazard damage assessment. However, change detection is highly challenging due to the acquisition conditions and also to the subjectivity of changes. In this paper, we devise a novel algorithm for change detection based on active learning. The proposed method is based on a question and answer model that probes an oracle (user) about the relevance of changes only on a small set of critical images (referred to as virtual exemplars), and according to oracle's responses updates deep neural network (DNN) classifiers. The main contribution resides in a novel adversarial model that allows learning the most representative, diverse and uncertain virtual exemplars (as inverted preimages of the trained DNNs) that challenge (the most) the trained DNNs, and this leads to a better re-estimate of these networks in the subsequent iterations of active learning. Experiments show the out-performance of our proposed deep-net inversion against the related work.
Machine-assisted mixed methods: augmenting humanities and social sciences with artificial intelligence
2023-09-24
Andres Karjus
The increasing capacities of large language models (LLMs) present an unprecedented opportunity to scale up data analytics in the humanities and social sciences, augmenting and automating qualitative analytic tasks previously typically allocated to human labor. This contribution proposes a systematic mixed methods framework to harness qualitative analytic expertise, machine scalability, and rigorous quantification, with attention to transparency and replicability. 16 machine-assisted case studies are showcased as proof of concept. Tasks include linguistic and discourse analysis, lexical semantic change detection, interview analysis, historical event cause inference and text mining, detection of political stance, text and idea reuse, genre composition in literature and film; social network inference, automated lexicography, missing metadata augmentation, and multimodal visual cultural analytics. In contrast to the focus on English in the emerging LLM applicability literature, many examples here deal with scenarios involving smaller languages and historical texts prone to digitization distortions. In all but the most difficult tasks requiring expert knowledge, generative LLMs can demonstrably serve as viable research instruments. LLM (and human) annotations may contain errors and variation, but the agreement rate can and should be accounted for in subsequent statistical modeling; a bootstrapping approach is discussed. The replications among the case studies illustrate how tasks previously requiring potentially months of team effort and complex computational pipelines, can now be accomplished by an LLM-assisted scholar in a fraction of the time. Importantly, this approach is not intended to replace, but to augment researcher knowledge and skills. With these opportunities in sight, qualitative expertise and the ability to pose insightful questions have arguably never been more critical.
2023-09-24
Dan Wang, Licheng Jiao, Jie Chen, Shuyuan Yang, Fang Liu
Difference features obtained by comparing the images of two periods play an indispensable role in the change detection (CD) task. However, a pair of bi-temporal images can exhibit diverse changes, which may cause various difference features. Identifying changed pixels with differ difference features to be the same category is thus a challenge for CD. Most nowadays' methods acquire distinctive difference features in implicit ways like enhancing image representation or supervision information. Nevertheless, informative image features only guarantee object semantics are modeled and can not guarantee that changed pixels have similar semantics in the difference feature space and are distinct from those unchanged ones. In this work, the generalized representation of various changes is learned straightforwardly in the difference feature space, and a novel Changes-Aware Transformer (CAT) for refining difference features is proposed. This generalized representation can perceive which pixels are changed and which are unchanged and further guide the update of pixels' difference features. CAT effectively accomplishes this refinement process through the stacked cosine cross-attention layer and self-attention layer. After refinement, the changed pixels in the difference feature space are closer to each other, which facilitates change detection. In addition, CAT is compatible with various backbone networks and existing CD methods. Experiments on remote sensing CD data set and street scene CD data set show that our method achieves state-of-the-art performance and has excellent generalization.
Convolution and Attention Mixer for Synthetic Aperture Radar Image Change Detection
2023-09-21
Haopeng Zhang, Zijing Lin, Feng Gao, Junyu Dong, Qian Du, Heng-Chao Li
Synthetic aperture radar (SAR) image change detection is a critical task and has received increasing attentions in the remote sensing community. However, existing SAR change detection methods are mainly based on convolutional neural networks (CNNs), with limited consideration of global attention mechanism. In this letter, we explore Transformer-like architecture for SAR change detection to incorporate global attention. To this end, we propose a convolution and attention mixer (CAMixer). First, to compensate the inductive bias for Transformer, we combine self-attention with shift convolution in a parallel way. The parallel design effectively captures the global semantic information via the self-attention and performs local feature extraction through shift convolution simultaneously. Second, we adopt a gating mechanism in the feed-forward network to enhance the non-linear feature transformation. The gating mechanism is formulated as the element-wise multiplication of two parallel linear layers. Important features can be highlighted, leading to high-quality representations against speckle noise. Extensive experiments conducted on three SAR datasets verify the superior performance of the proposed CAMixer. The source codes will be publicly available at https://github.com/summitgao/CAMixer .
2025-07-30
Chao Yi, Dian Chen, Gaoyang Guo, Jiakai Tang, Jian Wu, Jing Yu, Mao Zhang, Sunhao Dai, Wen Chen, Wenjun Yang, Yuning Jiang, Zhujin Gao, Bo Zheng, Chi Li, Dimin Wang, Dixuan Wang, Fan Li, Fan Zhang, Haibin Chen, Haozhuang Liu, Jialin Zhu, Jiamang Wang, Jiawei Wu, Jin Cui, Ju Huang, Kai Zhang, Kan Liu, Lang Tian, Liang Rao, Longbin Li, Lulu Zhao, Na He, Peiyang Wang, Qiqi Huang, Tao Luo, Wenbo Su, Xiaoxiao He, Xin Tong, Xu Chen, Xunke Xi, Yang Li, Yaxuan Wu, Yeqiu Yang, Yi Hu, Yinnan Song, Yuchen Li, Yujie Luo, Yujin Yuan, Yuliang Yan, Zhengyang Wang, Zhibo Xiao, Zhixin Ma, Zile Zhou, Ziqi Zhang
Recommender systems are among the most impactful applications of artificial intelligence, serving as critical infrastructure connecting users, merchants, and platforms. However, most current industrial systems remain heavily reliant on historical co-occurrence patterns and log-fitting objectives, i.e., optimizing for past user interactions without explicitly modeling user intent. This log-fitting approach often leads to overfitting to narrow historical preferences, failing to capture users' evolving and latent interests. As a result, it reinforces filter bubbles and long-tail phenomena, ultimately harming user experience and threatening the sustainability of the whole recommendation ecosystem. To address these challenges, we rethink the overall design paradigm of recommender systems and propose RecGPT, a next-generation framework that places user intent at the center of the recommendation pipeline. By integrating large language models (LLMs) into key stages of user interest mining, item retrieval, and explanation generation, RecGPT transforms log-fitting recommendation into an intent-centric process. To effectively align general-purpose LLMs to the above domain-specific recommendation tasks at scale, RecGPT incorporates a multi-stage training paradigm, which integrates reasoning-enhanced pre-alignment and self-training evolution, guided by a Human-LLM cooperative judge system. Currently, RecGPT has been fully deployed on the Taobao App. Online experiments demonstrate that RecGPT achieves consistent performance gains across stakeholders: users benefit from increased content diversity and satisfaction, merchants and the platform gain greater exposure and conversions. These comprehensive improvement results across all stakeholders validates that LLM-driven, intent-centric design can foster a more sustainable and mutually beneficial recommendation ecosystem.
Rethink Domain Generalization in Heterogeneous Sequence MRI Segmentation
2025-07-30
Zheyuan Zhang, Linkai Peng, Wanying Dou, Cuiling Sun, Halil Ertugrul Aktas, Andrea M. Bejar, Elif Keles, Gorkem Durak, Ulas Bagci
Clinical magnetic-resonance (MR) protocols generate many T1 and T2 sequences whose appearance differs more than the acquisition sites that produce them. Existing domain-generalization benchmarks focus almost on cross-center shifts and overlook this dominant source of variability. Pancreas segmentation remains a major challenge in abdominal imaging: the gland is small, irregularly, surrounded by organs and fat, and often suffers from low T1 contrast. State-of-the-art deep networks that already achieve >90% Dice on the liver or kidneys still miss 20-30% of the pancreas. The organ is also systematically under-represented in public cross-domain benchmarks, despite its clinical importance in early cancer detection, surgery, and diabetes research. To close this gap, we present PancreasDG, a large-scale multi-center 3D MRI pancreas segmentation dataset for investigating domain generalization in medical imaging. The dataset comprises 563 MRI scans from six institutions, spanning both venous phase and out-of-phase sequences, enabling study of both cross-center and cross-sequence variations with pixel-accurate pancreas masks created by a double-blind, two-pass protocol. Through comprehensive analysis, we reveal three insights: (i) limited sampling introduces significant variance that may be mistaken for distribution shifts, (ii) cross-center performance correlates with source domain performance for identical sequences, and (iii) cross-sequence shifts require specialized solutions. We also propose a semi-supervised approach that leverages anatomical invariances, significantly outperforming state-of-the-art domain generalization techniques with 61.63% Dice score improvements and 87.00% on two test centers for cross-sequence segmentation. PancreasDG sets a new benchmark for domain generalization in medical imaging. Dataset, code, and models will be available at https://pancreasdg.netlify.app.
Training language models to be warm and empathetic makes them less reliable and more sycophantic
2025-07-29
Lujain Ibrahim, Franziska Sofia Hafner, Luc Rocher
Artificial intelligence (AI) developers are increasingly building language models with warm and empathetic personas that millions of people now use for advice, therapy, and companionship. Here, we show how this creates a significant trade-off: optimizing language models for warmth undermines their reliability, especially when users express vulnerability. We conducted controlled experiments on five language models of varying sizes and architectures, training them to produce warmer, more empathetic responses, then evaluating them on safety-critical tasks. Warm models showed substantially higher error rates (+10 to +30 percentage points) than their original counterparts, promoting conspiracy theories, providing incorrect factual information, and offering problematic medical advice. They were also significantly more likely to validate incorrect user beliefs, particularly when user messages expressed sadness. Importantly, these effects were consistent across different model architectures, and occurred despite preserved performance on standard benchmarks, revealing systematic risks that current evaluation practices may fail to detect. As human-like AI systems are deployed at an unprecedented scale, our findings indicate a need to rethink how we develop and oversee these systems that are reshaping human relationships and social interaction.
Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting
2025-07-28
Alexey Kravets, Da Chen, Vinay P. Namboodiri
CLIP is a foundational model with transferable classification performance in the few-shot setting. Several methods have shown improved performance of CLIP using few-shot examples. However, so far, all these techniques have been benchmarked using standard few-shot datasets. We argue that this mode of evaluation does not provide a true indication of the inductive generalization ability using few-shot examples. As most datasets have been seen by the CLIP model, the resultant setting can be termed as partially transductive. To solve this, we propose a pipeline that uses an unlearning technique to obtain true inductive baselines. In this new inductive setting, the methods show a significant drop in performance (-55% on average among 13 baselines with multiple datasets). We validate the unlearning technique using oracle baselines. An improved few-shot classification technique is proposed that consistently obtains state-of-the-art performance over 13 other recent baseline methods on a comprehensive analysis with 5880 experiments - varying the datasets, differing number of few-shot examples, unlearning setting, and with different seeds. Thus, we identify the issue with the evaluation of CLIP-based few-shot classification, provide a solution using unlearning, propose new benchmarks, and provide an improved method.
Inter-user interference remains a critical bottleneck in wireless communication systems, particularly in the emerging paradigm of semantic communication (SemCom). Compared to traditional systems, inter-user interference in SemCom severely degrades key semantic information, often causing worse performance than Gaussian noise under the same power level. To address this challenge, inspired by the recently proposed concept of Orthogonal Model Division Multiple Access (OMDMA) that leverages semantic orthogonality rooted in the personalized joint source and channel (JSCC) models to distinguish users, we propose a novel, scalable framework that eliminates the need for user-specific JSCC models as did in original OMDMA. Our key innovation lies in shuffle-based orthogonalization, where randomly permuting the positions of JSCC feature vectors transforms inter-user interference into Gaussian-like noise. By assigning each user a unique shuffling pattern, the interference is treated as channel noise, enabling effective mitigation using diffusion models (DMs). This approach not only simplifies system design by requiring a single universal JSCC model but also enhances privacy, as shuffling patterns act as implicit private keys. Additionally, we extend the framework to scenarios involving semantically correlated data. By grouping users based on semantic similarity, a cooperative beamforming strategy is introduced to exploit redundancy in correlated data, further improving system performance. Extensive simulations demonstrate that the proposed method outperforms state-of-the-art multi-user SemCom frameworks, achieving superior semantic fidelity, robustness to interference, and scalability-all without requiring additional training overhead.
AI-Driven Media & Synthetic Knowledge: Rethinking Society in Generative Futures
2025-07-26
Katalin Feher
Generative AI is not just a technological leap -- it is a societal stress test, reshaping trust, identity, equity, and authorship. This exploratory PhD seminar examined emerging academic trends in AI-driven synthetic media and worlds, emphasizing ethical risks and societal implications. In Part One, students explored core concepts such as generative AI, fake media, and synthetic knowledge production. In Part Two, they critically engaged with these challenges, producing actionable insights. The two-part format enabled deep reflection on power, responsibility, and education in AI-augmented communication. Outcomes offer practical guidance for educators, researchers, and institutions committed to fostering more responsible, human-centered AI use in media and society.
Designing for sufficiency is one of many approaches that could foster more moderate and sustainable digital practices. Based on the Sustainable Information and Communication Technologies (ICT) and Human-Computer Interaction (HCI) literature, we identify five environmental settings categories. However, our analysis of three mobile OS and nine representative applications shows an overall lack of environmental concerns in settings design, leading us to identify six pervasive anti-patterns. Environmental settings, where they exist, are set on the most intensive option by default. They are not presented as such, are not easily accessible, and offer little explanation of their impact. Instead, they encourage more intensive use. Based on these findings, we create a design workbook that explores design principles for environmental settings: presenting the environmental potential of settings; shifting to environmentally neutral states; previewing effects to encourage moderate use; rethinking defaults; facilitating settings access and; exploring more frugal settings. Building upon this workbook, we discuss how settings can tie individual behaviors to systemic factors.
Dataset Search -- the process of finding appropriate datasets for a given task -- remains a critical yet under-explored challenge in data science workflows. Assessing dataset suitability for a task (e.g., training a classification model) is a multi-pronged affair that involves understanding: data characteristics (e.g. granularity, attributes, size), semantics (e.g., data semantics, creation goals), and relevance to the task at hand. Present-day dataset search interfaces are restrictive -- users struggle to convey implicit preferences and lack visibility into the search space and result inclusion criteria -- making query iteration challenging. To bridge these gaps, we introduce DataScout to proactively steer users through the process of dataset discovery via -- (i) AI-assisted query reformulations informed by the underlying search space, (ii) semantic search and filtering based on dataset content, including attributes (columns) and granularity (rows), and (iii) dataset relevance indicators, generated dynamically based on the user-specified task. A within-subjects study with 12 participants comparing DataScout to keyword and semantic dataset search reveals that users uniquely employ DataScout's features not only for structured explorations, but also to glean feedback on their search queries and build conceptual models of the search space.
Quantum Internet Architecture: unlocking Quantum-Native Routing via Quantum Addressing
2025-07-25
Marcello Caleffi, Angela Sara Cacciapuoti
The Quantum Internet introduces a fundamental shift in the network design, since its key objective is the distribution and manipulation of quantum entanglement, rather than the transmission of classical information. This shift breaks key classical Internet design principles, such as the end-to-end argument, due to the inherently stateful and non-local nature of entangled states that require coordinated in-network operations. Consequently, in this paper we propose a novel hierarchical Quantum Internet architecture centered around the concept of entanglement-defined controller, enabling scalable and efficient management of the aforementioned in-network operations. However, architecture alone is insufficient for network scalability, which requires a quantum-native control plane that fundamentally rethinks addressing and routing. Consequently, we propose a quantum addressing scheme that embraces the principles and quantum phenomena within the node identifiers. Built upon this addressing scheme, we also design a quantum-native routing protocol that exhibits scalable and compact routing tables, by efficiently operating over entanglement-aware topologies. Finally, we design a quantum address splitting functionality based on Schr\"odinger's oracles that generalizes classical match-and-forward logic to the quantum domain. Together, these contributions demonstrate, for the first time, the key advantages of quantum-by-design network functioning.
In data-scarce scenarios, deep learning models often overfit to noise and irrelevant patterns, which limits their ability to generalize to unseen samples. To address these challenges in medical image segmentation, we introduce Diff-UMamba, a novel architecture that combines the UNet framework with the mamba mechanism to model long-range dependencies. At the heart of Diff-UMamba is a noise reduction module, which employs a signal differencing strategy to suppress noisy or irrelevant activations within the encoder. This encourages the model to filter out spurious features and enhance task-relevant representations, thereby improving its focus on clinically significant regions. As a result, the architecture achieves improved segmentation accuracy and robustness, particularly in low-data settings. Diff-UMamba is evaluated on multiple public datasets, including medical segmentation decathalon dataset (lung and pancreas) and AIIB23, demonstrating consistent performance gains of 1-3% over baseline methods in various segmentation tasks. To further assess performance under limited data conditions, additional experiments are conducted on the BraTS-21 dataset by varying the proportion of available training samples. The approach is also validated on a small internal non-small cell lung cancer dataset for the segmentation of gross tumor volume in cone beam CT, where it achieves a 4-5% improvement over baseline.
Rethinking Indonesia's Public Debt in the Era of Negative Interest Rate-Growth Differentials
2025-07-24
Mervin Goklas Hamonangan
This study contributes to the discussion about how higher public debt may not be costly because of the negative interest rate-growth differentials by simulating OLG models introduced by Blanchard (2019) under uncertainty, showing debt and welfare dynamics in two scenarios: intergenerational transfers and debt rollovers in the case of Indonesia. The simulation is done by modifying the model parameters based on interest rate-growth differentials historic data from 2004-2019. It is found that the fiscal consensus does not hold when implementing Blanchard (2019) analysis with Indonesian-based rate parameters. Increasing public debt makes the economy more volatile and high risk. Modifying other factors supports the initial finding, with lower initial endowment diminishing the benefits of public debt and higher capital share under Cobb-Douglas. When the threat of debt explosion appears, efforts to reduce debt share will reduce the welfare of the society. The policy implication is to be careful of the opportunity. Increasing public debt may not be the way to go, avoiding possible dire consequences.
Rethinking HSM and TPM Security in the Cloud: Real-World Attacks and Next-Gen Defenses
2025-07-23
Shams Shaikh, Trima P. Fernandes e Fizardo
As organizations rapidly migrate to the cloud, the security of cryptographic key management has become a growing concern. Hardware Security Modules (HSMs) and Trusted Platform Modules (TPMs), traditionally seen as the gold standard for securing encryption keys and digital trust, are increasingly challenged by cloud-native threats. Real-world breaches have exposed weaknesses in cloud deployments, including misconfigurations, API abuse, and privilege escalations, allowing attackers to access sensitive key material and bypass protections. These incidents reveal that while the hardware remains secure, the surrounding cloud ecosystem introduces systemic vulnerabilities. This paper analyzes notable security failures involving HSMs and TPMs, identifies common attack vectors, and questions longstanding assumptions about their effectiveness in distributed environments. We explore alternative approaches such as confidential computing, post-quantum cryptography, and decentralized key management. Our findings highlight that while HSMs and TPMs still play a role, modern cloud security requires more adaptive, layered architectures. By evaluating both current weaknesses and emerging models, this research equips cloud architects and security engineers with strategies to reinforce cryptographic trust in the evolving threat landscape.
C3RL: Rethinking the Combination of Channel-independence and Channel-mixing from Representation Learning
2025-07-23
Shusen Ma, Yun-Bo Zhao, Yu Kang
Multivariate time series forecasting has drawn increasing attention due to its practical importance. Existing approaches typically adopt either channel-mixing (CM) or channel-independence (CI) strategies. CM strategy can capture inter-variable dependencies but fails to discern variable-specific temporal patterns. CI strategy improves this aspect but fails to fully exploit cross-variable dependencies like CM. Hybrid strategies based on feature fusion offer limited generalization and interpretability. To address these issues, we propose C3RL, a novel representation learning framework that jointly models both CM and CI strategies. Motivated by contrastive learning in computer vision, C3RL treats the inputs of the two strategies as transposed views and builds a siamese network architecture: one strategy serves as the backbone, while the other complements it. By jointly optimizing contrastive and prediction losses with adaptive weighting, C3RL balances representation and forecasting performance. Extensive experiments on seven models show that C3RL boosts the best-case performance rate to 81.4\% for models based on CI strategy and to 76.3\% for models based on CM strategy, demonstrating strong generalization and effectiveness. The code will be available once the paper is accepted.
Rethinking VAE: From Continuous to Discrete Representations Without Probabilistic Assumptions
2025-07-23
Songxuan Shi
This paper explores the generative capabilities of Autoencoders (AEs) and establishes connections between Variational Autoencoders (VAEs) and Vector Quantized-Variational Autoencoders (VQ-VAEs) through a reformulated training framework. We demonstrate that AEs exhibit generative potential via latent space interpolation and perturbation, albeit limited by undefined regions in the encoding space. To address this, we propose a new VAE-like training method that introduces clustering centers to enhance data compactness and ensure well-defined latent spaces without relying on traditional KL divergence or reparameterization techniques. Experimental results on MNIST, CelebA, and FashionMNIST datasets show smooth interpolative transitions, though blurriness persists. Extending this approach to multiple learnable vectors, we observe a natural progression toward a VQ-VAE-like model in continuous space. However, when the encoder outputs multiple vectors, the model degenerates into a discrete Autoencoder (VQ-AE), which combines image fragments without learning semantic representations. Our findings highlight the critical role of encoding space compactness and dispersion in generative modeling and provide insights into the intrinsic connections between VAEs and VQ-VAEs, offering a new perspective on their design and limitations.
Rethinking LLM-Based RTL Code Optimization Via Timing Logic Metamorphosis
2025-07-22
Zhihao Xu, Bixin Li, Lulu Wang
Register Transfer Level(RTL) code optimization is crucial for achieving high performance and low power consumption in digital circuit design. However, traditional optimization methods often rely on manual tuning and heuristics, which can be time-consuming and error-prone. Recent studies proposed to leverage Large Language Models(LLMs) to assist in RTL code optimization. LLMs can generate optimized code snippets based on natural language descriptions, potentially speeding up the optimization process. However, existing approaches have not thoroughly evaluated the effectiveness of LLM-Based code optimization methods for RTL code with complex timing logic. To address this gap, we conducted a comprehensive empirical investigation to assess the capability of LLM-Based RTL code optimization methods in handling RTL code with complex timing logic. In this study, we first propose a new benchmark for RTL optimization evaluation. It comprises four subsets, each corresponding to a specific area of RTL code optimization. Then we introduce a method based on metamorphosis to systematically evaluate the effectiveness of LLM-Based RTL code optimization methods.Our key insight is that the optimization effectiveness should remain consistent for semantically equivalent but more complex code. After intensive experiments, we revealed several key findings. (1) LLM-Based RTL optimization methods can effectively optimize logic operations and outperform existing compiler-based methods. (2) LLM-Based RTL optimization methods do not perform better than existing compiler-based methods on RTL code with complex timing logic, particularly in timing control flow optimization and clock domain optimization. This is primarily attributed to the challenges LLMs face in understanding timing logic in RTL code. Based on these findings, we provide insights for further research in leveraging LLMs for RTL code optimization.
Human-AI Co-Creation: A Framework for Collaborative Design in Intelligent Systems
2025-07-22
Zhangqi Liu
As artificial intelligence (AI) continues to evolve from a back-end computational tool into an interactive, generative collaborator, its integration into early-stage design processes demands a rethinking of traditional workflows in human-centered design. This paper explores the emergent paradigm of human-AI co-creation, where AI is not merely used for automation or efficiency gains, but actively participates in ideation, visual conceptualization, and decision-making. Specifically, we investigate the use of large language models (LLMs) like GPT-4 and multimodal diffusion models such as Stable Diffusion as creative agents that engage designers in iterative cycles of proposal, critique, and revision.
DWTGS: Rethinking Frequency Regularization for Sparse-view 3D Gaussian Splatting
2025-07-21
Hung Nguyen, Runfa Li, An Le, Truong Nguyen
Sparse-view 3D Gaussian Splatting (3DGS) presents significant challenges in reconstructing high-quality novel views, as it often overfits to the widely-varying high-frequency (HF) details of the sparse training views. While frequency regularization can be a promising approach, its typical reliance on Fourier transforms causes difficult parameter tuning and biases towards detrimental HF learning. We propose DWTGS, a framework that rethinks frequency regularization by leveraging wavelet-space losses that provide additional spatial supervision. Specifically, we supervise only the low-frequency (LF) LL subbands at multiple DWT levels, while enforcing sparsity on the HF HH subband in a self-supervised manner. Experiments across benchmarks show that DWTGS consistently outperforms Fourier-based counterparts, as this LF-centric strategy improves generalization and reduces HF hallucinations.
We Need to Rethink Benchmarking in Anomaly Detection
2025-07-21
Philipp Röchner, Simon Klüttermann, Franz Rothlauf, Daniel Schlör
Despite the continuous proposal of new anomaly detection algorithms and extensive benchmarking efforts, progress seems to stagnate, with only minor performance differences between established baselines and new algorithms. In this position paper, we argue that this stagnation is due to limitations in how we evaluate anomaly detection algorithms. Current benchmarking does not, for example, sufficiently reflect the diversity of anomalies in applications ranging from predictive maintenance to scientific discovery. Consequently, we need to rethink benchmarking in anomaly detection. In our opinion, anomaly detection should be studied using scenarios that capture the relevant characteristics of different applications. We identify three key areas for improvement: First, we need to identify anomaly detection scenarios based on a common taxonomy. Second, anomaly detection pipelines should be analyzed end-to-end and by component. Third, evaluating anomaly detection algorithms should be meaningful regarding the scenario's objectives.
Rethinking Occlusion in FER: A Semantic-Aware Perspective and Go Beyond
2025-07-21
Huiyu Zhai, Xingxing Yang, Yalan Ye, Chenyang Li, Bin Fan, Changze Li
Facial expression recognition (FER) is a challenging task due to pervasive occlusion and dataset biases. Especially when facial information is partially occluded, existing FER models struggle to extract effective facial features, leading to inaccurate classifications. In response, we present ORSANet, which introduces the following three key contributions: First, we introduce auxiliary multi-modal semantic guidance to disambiguate facial occlusion and learn high-level semantic knowledge, which is two-fold: 1) we introduce semantic segmentation maps as dense semantics prior to generate semantics-enhanced facial representations; 2) we introduce facial landmarks as sparse geometric prior to mitigate intrinsic noises in FER, such as identity and gender biases. Second, to facilitate the effective incorporation of these two multi-modal priors, we customize a Multi-scale Cross-interaction Module (MCM) to adaptively fuse the landmark feature and semantics-enhanced representations within different scales. Third, we design a Dynamic Adversarial Repulsion Enhancement Loss (DARELoss) that dynamically adjusts the margins of ambiguous classes, further enhancing the model's ability to distinguish similar expressions. We further construct the first occlusion-oriented FER dataset to facilitate specialized robustness analysis on various real-world occlusion conditions, dubbed Occlu-FER. Extensive experiments on both public benchmarks and Occlu-FER demonstrate that our proposed ORSANet achieves SOTA recognition performance. Code is publicly available at https://github.com/Wenyuzhy/ORSANet-master.
Reasoning Models are Test Exploiters: Rethinking Multiple-Choice
2025-07-21
Narun Raman, Taylor Lundy, Kevin Leyton-Brown
When evaluating Large Language Models (LLMs) in question-answering domains, it is common to ask the model to choose among a fixed set of choices (so-called multiple-choice question-answering, or MCQA). Although downstream tasks of interest typically do not provide systems with explicit options among which to choose, this approach is nevertheless widely used because it makes it makes automatic grading straightforward and has tended to produce challenging benchmarks that correlate sufficiently well with downstream performance. This paper investigates the extent to which this trend continues to hold for state-of-the-art reasoning models, describing a systematic evaluation of $15$ different question-answering benchmarks (e.g., MMLU, HLE) and $25$ different LLMs (including small models such as Qwen 7B and relatively large models such as Llama 70B). For each model-benchmark pair, we considered $5$ ways of presenting the model with questions, including variations on whether multiple choices were offered to the model at all; whether "none of the above" sometimes replaced the right answer; and whether the model was permitted to perform chain-of-thought reasoning before and/or after the choices were presented. MCQA remained a good proxy for the downstream performance of models as long as they were allowed to perform chain-of-thought reasoning only before being presented with the options among which they had to select. On the other hand, large models that were able to perform reasoning after being given a set of options tended to significantly outperform their free-text performance due to exploiting the information in the options. We conclude that MCQA is no longer a good proxy for assessing downstream performance of state-of-the-art models, and offer practical guidelines for designing more robust, bias-resistant benchmarks that better reflect LLMs' genuine reasoning capabilities.
Click A, Buy B: Rethinking Conversion Attribution in E- Commerce Recommendations
2025-07-20
Xiangyu Zeng, Amit Jaspal, Bin Liu, Goutham Panneeru, Kevin Huang, Nicolas Bievre, Mohit Jaggi, Prathap Maniraju, Ankur Jain
User journeys in e-commerce routinely violate the one-to-one assumption that a clicked item on an advertising platform is the same item later purchased on the merchant's website/app. For a significant number of converting sessions on our platform, users click product A but buy product B -- the Click A, Buy B (CABB) phenomenon. Training recommendation models on raw click-conversion pairs therefore rewards items that merely correlate with purchases, leading to biased learning and sub-optimal conversion rates. We reframe conversion prediction as a multi-task problem with separate heads for Click A Buy A (CABA) and Click A Buy B (CABB). To isolate informative CABB conversions from unrelated CABB conversions, we introduce a taxonomy-aware collaborative filtering weighting scheme where each product is first mapped to a leaf node in a product taxonomy, and a category-to-category similarity matrix is learned from large-scale co-engagement logs. This weighting amplifies pairs that reflect genuine substitutable or complementary relations while down-weighting coincidental cross-category purchases. Offline evaluation on e-commerce sessions reduces normalized entropy by 13.9% versus a last-click attribution baseline. An online A/B test on live traffic shows +0.25% gains in the primary business metric.
Rethinking Pan-sharpening: Principled Design, Unified Training, and a Universal Loss Surpass Brute-Force Scaling
2025-07-20
Ran Zhang, Xuanhua He, Li Xueheng, Ke Cao, Liu Liu, Wenbo Xu, Fang Jiabin, Yang Qize, Jie Zhang
The field of pan-sharpening has recently seen a trend towards increasingly large and complex models, often trained on single, specific satellite datasets. This approach, however, leads to high computational overhead and poor generalization on full resolution data, a paradigm we challenge in this paper. In response to this issue, we propose PanTiny, a lightweight, single-step pan-sharpening framework designed for both efficiency and robust performance. More critically, we introduce multiple-in-one training paradigm, where a single, compact model is trained simultaneously on three distinct satellite datasets (WV2, WV3, and GF2) with different resolution and spectral information. Our experiments show that this unified training strategy not only simplifies deployment but also significantly boosts generalization on full-resolution data. Further, we introduce a universally powerful composite loss function that elevates the performance of almost all of models for pan-sharpening, pushing state-of-the-art metrics into a new era. Our PanTiny model, benefiting from these innovations, achieves a superior performance-to-efficiency balance, outperforming most larger, specialized models. Through extensive ablation studies, we validate that principled engineering in model design, training paradigms, and loss functions can surpass brute-force scaling. Our work advocates for a community-wide shift towards creating efficient, generalizable, and data-conscious models for pan-sharpening. The code is available at https://github.com/Zirconium233/PanTiny .
Rethinking Memorization Measures and their Implications in Large Language Models
2025-07-20
Bishwamittra Ghosh, Soumi Das, Qinyuan Wu, Mohammad Aflah Khan, Krishna P. Gummadi, Evimaria Terzi, Deepak Garg
Concerned with privacy threats, memorization in LLMs is often seen as undesirable, specifically for learning. In this paper, we study whether memorization can be avoided when optimally learning a language, and whether the privacy threat posed by memorization is exaggerated or not. To this end, we re-examine existing privacy-focused measures of memorization, namely recollection-based and counterfactual memorization, along with a newly proposed contextual memorization. Relating memorization to local over-fitting during learning, contextual memorization aims to disentangle memorization from the contextual learning ability of LLMs. Informally, a string is contextually memorized if its recollection due to training exceeds the optimal contextual recollection, a learned threshold denoting the best contextual learning without training. Conceptually, contextual recollection avoids the fallacy of recollection-based memorization, where any form of high recollection is a sign of memorization. Theoretically, contextual memorization relates to counterfactual memorization, but imposes stronger conditions. Memorization measures differ in outcomes and information requirements. Experimenting on 18 LLMs from 6 families and multiple formal languages of different entropy, we show that (a) memorization measures disagree on memorization order of varying frequent strings, (b) optimal learning of a language cannot avoid partial memorization of training strings, and (c) improved learning decreases contextual and counterfactual memorization but increases recollection-based memorization. Finally, (d) we revisit existing reports of memorized strings by recollection that neither pose a privacy threat nor are contextually or counterfactually memorized.
Rethinking Suicidal Ideation Detection: A Trustworthy Annotation Framework and Cross-Lingual Model Evaluation
2025-07-19
Amina Dzafic, Merve Kavut, Ulya Bayram
Suicidal ideation detection is critical for real-time suicide prevention, yet its progress faces two under-explored challenges: limited language coverage and unreliable annotation practices. Most available datasets are in English, but even among these, high-quality, human-annotated data remains scarce. As a result, many studies rely on available pre-labeled datasets without examining their annotation process or label reliability. The lack of datasets in other languages further limits the global realization of suicide prevention via artificial intelligence (AI). In this study, we address one of these gaps by constructing a novel Turkish suicidal ideation corpus derived from social media posts and introducing a resource-efficient annotation framework involving three human annotators and two large language models (LLMs). We then address the remaining gaps by performing a bidirectional evaluation of label reliability and model consistency across this dataset and three popular English suicidal ideation detection datasets, using transfer learning through eight pre-trained sentiment and emotion classifiers. These transformers help assess annotation consistency and benchmark model performance against manually labeled data. Our findings underscore the need for more rigorous, language-inclusive approaches to annotation and evaluation in mental health natural language processing (NLP) while demonstrating the questionable performance of popular models with zero-shot transfer learning. We advocate for transparency in model training and dataset construction in mental health NLP, prioritizing data and model reliability.
Designing Conversational AI to Support Think-Aloud Practice in Technical Interview Preparation for CS Students
2025-07-19
Taufiq Daryanto, Sophia Stil, Xiaohan Ding, Daniel Manesh, Sang Won Lee, Tim Lee, Stephanie Lunn, Sarah Rodriguez, Chris Brown, Eugenia Rho
One challenge in technical interviews is the think-aloud process, where candidates verbalize their thought processes while solving coding tasks. Despite its importance, opportunities for structured practice remain limited. Conversational AI offers potential assistance, but limited research explores user perceptions of its role in think-aloud practice. To address this gap, we conducted a study with 17 participants using an LLM-based technical interview practice tool. Participants valued AI's role in simulation, feedback, and learning from generated examples. Key design recommendations include promoting social presence in conversational AI for technical interview simulation, providing feedback beyond verbal content analysis, and enabling crowdsourced think-aloud examples through human-AI collaboration. Beyond feature design, we examined broader considerations, including intersectional challenges and potential strategies to address them, how AI-driven interview preparation could promote equitable learning in computing careers, and the need to rethink AI's role in interview practice by suggesting a research direction that integrates human-AI collaboration.
MorphIt: Flexible Spherical Approximation of Robot Morphology for Representation-driven Adaptation
2025-07-18
Nataliya Nechyporenko, Yutong Zhang, Sean Campbell, Alessandro Roncone
What if a robot could rethink its own morphological representation to better meet the demands of diverse tasks? Most robotic systems today treat their physical form as a fixed constraint rather than an adaptive resource, forcing the same rigid geometric representation to serve applications with vastly different computational and precision requirements. We introduce MorphIt, a novel algorithm for approximating robot morphology using spherical primitives that balances geometric accuracy with computational efficiency. Unlike existing approaches that rely on either labor-intensive manual specification or inflexible computational methods, MorphIt implements an automatic gradient-based optimization framework with tunable parameters that provides explicit control over the physical fidelity versus computational cost tradeoff. Quantitative evaluations demonstrate that MorphIt outperforms baseline approaches (Variational Sphere Set Approximation and Adaptive Medial-Axis Approximation) across multiple metrics, achieving better mesh approximation with fewer spheres and reduced computational overhead. Our experiments show enhanced robot capabilities in collision detection accuracy, contact-rich interaction simulation, and navigation through confined spaces. By dynamically adapting geometric representations to task requirements, robots can now exploit their physical embodiment as an active resource rather than an inflexible parameter, opening new frontiers for manipulation in environments where physical form must continuously balance precision with computational tractability.
Rethinking Individual Fairness in Deepfake Detection
2025-07-18
Aryana Hou, Li Lin, Justin Li, Shu Hu
Generative AI models have substantially improved the realism of synthetic media, yet their misuse through sophisticated DeepFakes poses significant risks. Despite recent advances in deepfake detection, fairness remains inadequately addressed, enabling deepfake markers to exploit biases against specific populations. While previous studies have emphasized group-level fairness, individual fairness (i.e., ensuring similar predictions for similar individuals) remains largely unexplored. In this work, we identify for the first time that the original principle of individual fairness fundamentally fails in the context of deepfake detection, revealing a critical gap previously unexplored in the literature. To mitigate it, we propose the first generalizable framework that can be integrated into existing deepfake detectors to enhance individual fairness and generalization. Extensive experiments conducted on leading deepfake datasets demonstrate that our approach significantly improves individual fairness while maintaining robust detection performance, outperforming state-of-the-art methods. The code is available at https://github.com/Purdue-M2/Individual-Fairness-Deepfake-Detection.
Recent advances in large pretrained models have led to their widespread integration as core components in modern software systems. The trend is expected to continue in the foreseeable future. Unlike traditional software systems governed by deterministic logic, systems powered by pretrained models exhibit distinctive and emergent characteristics, such as ambiguous capability boundaries, context-dependent behavior, and continuous evolution. These properties fundamentally challenge long-standing assumptions in requirements engineering, including functional decomposability and behavioral predictability. This paper investigates this problem and advocates for a rethinking of existing requirements engineering methodologies. We propose a conceptual framework tailored to requirements engineering of pretrained-model-enabled software systems and outline several promising research directions within this framework. This vision helps provide a guide for researchers and practitioners to tackle the emerging challenges in requirements engineering of pretrained-model-enabled systems.
Rethinking the Embodied Gap in Vision-and-Language Navigation: A Holistic Study of Physical and Visual Disparities
2025-07-17
Liuyi Wang, Xinyuan Xia, Hui Zhao, Hanqing Wang, Tai Wang, Yilun Chen, Chengju Liu, Qijun Chen, Jiangmiao Pang
Recent Vision-and-Language Navigation (VLN) advancements are promising, but their idealized assumptions about robot movement and control fail to reflect physically embodied deployment challenges. To bridge this gap, we introduce VLN-PE, a physically realistic VLN platform supporting humanoid, quadruped, and wheeled robots. For the first time, we systematically evaluate several ego-centric VLN methods in physical robotic settings across different technical pipelines, including classification models for single-step discrete action prediction, a diffusion model for dense waypoint prediction, and a train-free, map-based large language model (LLM) integrated with path planning. Our results reveal significant performance degradation due to limited robot observation space, environmental lighting variations, and physical challenges like collisions and falls. This also exposes locomotion constraints for legged robots in complex environments. VLN-PE is highly extensible, allowing seamless integration of new scenes beyond MP3D, thereby enabling more comprehensive VLN evaluation. Despite the weak generalization of current models in physical deployment, VLN-PE provides a new pathway for improving cross-embodiment's overall adaptability. We hope our findings and tools inspire the community to rethink VLN limitations and advance robust, practical VLN models. The code is available at https://crystalsixone.github.io/vln_pe.github.io/.
2025-07-17
Zihua Zhao, Feng Hong, Mengxi Chen, Pengyi Chen, Benyuan Liu, Jiangchao Yao, Ya Zhang, Yanfeng Wang
The remarkable success of contrastive-learning-based multimodal models has been greatly driven by training on ever-larger datasets with expensive compute consumption. Sample selection as an alternative efficient paradigm plays an important direction to accelerate the training process. However, recent advances on sample selection either mostly rely on an oracle model to offline select a high-quality coreset, which is limited in the cold-start scenarios, or focus on online selection based on real-time model predictions, which has not sufficiently or efficiently considered the noisy correspondence. To address this dilemma, we propose a novel Differential-Informed Sample Selection (DISSect) method, which accurately and efficiently discriminates the noisy correspondence for training acceleration. Specifically, we rethink the impact of noisy correspondence on contrastive learning and propose that the differential between the predicted correlation of the current model and that of a historical model is more informative to characterize sample quality. Based on this, we construct a robust differential-based sample selection and analyze its theoretical insights. Extensive experiments on three benchmark datasets and various downstream tasks demonstrate the consistent superiority of DISSect over current state-of-the-art methods. Source code is available at: https://github.com/MediaBrain-SJTU/DISSect.
InterpIoU: Rethinking Bounding Box Regression with Interpolation-Based IoU Optimization
2025-07-16
Haoyuan Liu, Hiroshi Watanabe
Bounding box regression (BBR) is fundamental to object detection, where the regression loss is crucial for accurate localization. Existing IoU-based losses often incorporate handcrafted geometric penalties to address IoU's non-differentiability in non-overlapping cases and enhance BBR performance. However, these penalties are sensitive to box shape, size, and distribution, often leading to suboptimal optimization for small objects and undesired behaviors such as bounding box enlargement due to misalignment with the IoU objective. To address these limitations, we propose InterpIoU, a novel loss function that replaces handcrafted geometric penalties with a term based on the IoU between interpolated boxes and the target. By using interpolated boxes to bridge the gap between predictions and ground truth, InterpIoU provides meaningful gradients in non-overlapping cases and inherently avoids the box enlargement issue caused by misaligned penalties. Simulation results further show that IoU itself serves as an ideal regression target, while existing geometric penalties are both unnecessary and suboptimal. Building on InterpIoU, we introduce Dynamic InterpIoU, which dynamically adjusts interpolation coefficients based on IoU values, enhancing adaptability to scenarios with diverse object distributions. Experiments on COCO, VisDrone, and PASCAL VOC show that our methods consistently outperform state-of-the-art IoU-based losses across various detection frameworks, with particularly notable improvements in small object detection, confirming their effectiveness.
Securing sensitive cloud workloads requires composing confidential virtual machines (CVMs) with nested enclaves or sandboxes. Unfortunately, each new isolation boundary adds ad-hoc access control mechanisms, hardware extensions, and trusted software. This escalating complexity bloats the TCB, complicates end-to-end attestation, and leads to fragmentation across platforms and cloud service providers (CSPs). We introduce a unified isolation model that delegates enforceable, composable, and attestable isolation to a single trusted security monitor: Tyche. Tyche provides an API for partitioning, sharing, attesting, and reclaiming resources through its core abstraction, trust domains (TDs). To provide fine-grain isolation, TDs can recursively create and manage sub-TDs. Tyche captures these relationships in attestations, allowing cloud tenants to reason about end-to-end security. TDs serve as the building blocks for constructing composable enclaves, sandboxes, and CVMs. Tyche runs on commodity x86_64 without hardware security extensions and can maintain backward compatibility with existing software. We provide an SDK to run and compose unmodified workloads as sandboxes, enclaves, and CVMs with minimal overhead compared to native Linux execution. Tyche supports complex cloud scenarios, such as confidential inference with mutually distrustful users, model owners, and CSPs. An additional RISC-V prototype demonstrates Tyche's portability across platforms.
Out-of-distribution data supervision towards biomedical semantic segmentation
2025-07-16
Yiquan Gao, Duohui Xu
Biomedical segmentation networks easily suffer from the unexpected misclassification between foreground and background objects when learning on limited and imperfect medical datasets. Inspired by the strong power of Out-of-Distribution (OoD) data on other visual tasks, we propose a data-centric framework, Med-OoD to address this issue by introducing OoD data supervision into fully-supervised biomedical segmentation with none of the following needs: (i) external data sources, (ii) feature regularization objectives, (iii) additional annotations. Our method can be seamlessly integrated into segmentation networks without any modification on the architectures. Extensive experiments show that Med-OoD largely prevents various segmentation networks from the pixel misclassification on medical images and achieves considerable performance improvements on Lizard dataset. We also present an emerging learning paradigm of training a medical segmentation network completely using OoD data devoid of foreground class labels, surprisingly turning out 76.1% mIoU as test result. We hope this learning paradigm will attract people to rethink the roles of OoD data. Code is made available at https://github.com/StudioYG/Med-OoD.
Motion forecasting for on-road traffic agents presents both a significant challenge and a critical necessity for ensuring safety in autonomous driving systems. In contrast to most existing data-driven approaches that directly predict future trajectories, we rethink this task from a planning perspective, advocating a "First Reasoning, Then Forecasting" strategy that explicitly incorporates behavior intentions as spatial guidance for trajectory prediction. To achieve this, we introduce an interpretable, reward-driven intention reasoner grounded in a novel query-centric Inverse Reinforcement Learning (IRL) scheme. Our method first encodes traffic agents and scene elements into a unified vectorized representation, then aggregates contextual features through a query-centric paradigm. This enables the derivation of a reward distribution, a compact yet informative representation of the target agent's behavior within the given scene context via IRL. Guided by this reward heuristic, we perform policy rollouts to reason about multiple plausible intentions, providing valuable priors for subsequent trajectory generation. Finally, we develop a hierarchical DETR-like decoder integrated with bidirectional selective state space models to produce accurate future trajectories along with their associated probabilities. Extensive experiments on the large-scale Argoverse and nuScenes motion forecasting datasets demonstrate that our approach significantly enhances trajectory prediction confidence, achieving highly competitive performance relative to state-of-the-art methods.
Diffusion-based large language models (dLLMs) have recently emerged as a powerful alternative to autoregressive LLMs, offering faster inference and greater interactivity via parallel decoding and bidirectional modeling. However, despite strong performance in code generation and text infilling, we identify a fundamental safety concern: existing alignment mechanisms fail to safeguard dLLMs against context-aware, masked-input adversarial prompts, exposing novel vulnerabilities. To this end, we present DIJA, the first systematic study and jailbreak attack framework that exploits unique safety weaknesses of dLLMs. Specifically, our proposed DIJA constructs adversarial interleaved mask-text prompts that exploit the text generation mechanisms of dLLMs, i.e., bidirectional modeling and parallel decoding. Bidirectional modeling drives the model to produce contextually consistent outputs for masked spans, even when harmful, while parallel decoding limits model dynamic filtering and rejection sampling of unsafe content. This causes standard alignment mechanisms to fail, enabling harmful completions in alignment-tuned dLLMs, even when harmful behaviors or unsafe instructions are directly exposed in the prompt. Through comprehensive experiments, we demonstrate that DIJA significantly outperforms existing jailbreak methods, exposing a previously overlooked threat surface in dLLM architectures. Notably, our method achieves up to 100% keyword-based ASR on Dream-Instruct, surpassing the strongest prior baseline, ReNeLLM, by up to 78.5% in evaluator-based ASR on JailbreakBench and by 37.7 points in StrongREJECT score, while requiring no rewriting or hiding of harmful content in the jailbreak prompt. Our findings underscore the urgent need for rethinking safety alignment in this emerging class of language models. Code is available at https://github.com/ZichenWen1/DIJA.
Masked Autoencoders (MAEs) have emerged as a dominant strategy for self-supervised representation learning in natural images, where models are pre-trained to reconstruct masked patches with a pixel-wise mean squared error (MSE) between original and reconstructed RGB values as the loss. We observe that MSE encourages blurred image re-construction, but still works for natural images as it preserves dominant edges. However, in medical imaging, when the texture cues are more important for classification of a visual abnormality, the strategy fails. Taking inspiration from Gray Level Co-occurrence Matrix (GLCM) feature in Radiomics studies, we propose a novel MAE based pre-training framework, GLCM-MAE, using reconstruction loss based on matching GLCM. GLCM captures intensity and spatial relationships in an image, hence proposed loss helps preserve morphological features. Further, we propose a novel formulation to convert matching GLCM matrices into a differentiable loss function. We demonstrate that unsupervised pre-training on medical images with the proposed GLCM loss improves representations for downstream tasks. GLCM-MAE outperforms the current state-of-the-art across four tasks - gallbladder cancer detection from ultrasound images by 2.1%, breast cancer detection from ultrasound by 3.1%, pneumonia detection from x-rays by 0.5%, and COVID detection from CT by 0.6%. Source code and pre-trained models are available at: https://github.com/ChetanMadan/GLCM-MAE.
Rethinking Inductive Bias in Geographically Neural Network Weighted Regression
2025-07-14
Zhenyuan Chen
Inductive bias is a key factor in spatial regression models, determining how well a model can learn from limited data and capture spatial patterns. This work revisits the inductive biases in Geographically Neural Network Weighted Regression (GNNWR) and identifies limitations in current approaches for modeling spatial non-stationarity. While GNNWR extends traditional Geographically Weighted Regression by using neural networks to learn spatial weighting functions, existing implementations are often restricted by fixed distance-based schemes and limited inductive bias. We propose to generalize GNNWR by incorporating concepts from convolutional neural networks, recurrent neural networks, and transformers, introducing local receptive fields, sequential context, and self-attention into spatial regression. Through extensive benchmarking on synthetic spatial datasets with varying heterogeneity, noise, and sample sizes, we show that GNNWR outperforms classic methods in capturing nonlinear and complex spatial relationships. Our results also reveal that model performance depends strongly on data characteristics, with local models excelling in highly heterogeneous or small-sample scenarios, and global models performing better with larger, more homogeneous data. These findings highlight the importance of inductive bias in spatial modeling and suggest future directions, including learnable spatial weighting functions, hybrid neural architectures, and improved interpretability for models handling non-stationary spatial data.
An increasing number of NLP applications interact with large language models (LLMs) through black-box APIs, making prompt engineering critical for controlling model outputs. While recent Automatic Prompt Optimization (APO) methods iteratively refine prompts using model-generated feedback, textual gradients, they primarily focus on error correction and neglect valuable insights from correct predictions. This limits both their effectiveness and efficiency. In this paper, we propose a novel APO framework centered on enhancing the feedback mechanism. We reinterpret the textual gradient as a form of negative reinforcement and introduce the complementary positive reinforcement to explicitly preserve beneficial prompt components identified through successful predictions. To mitigate the noise inherent in LLM-generated feedback, we introduce a technique called feedback diversification, which aggregates multiple feedback signals, emphasizing consistent, actionable advice while filtering out outliers. Motivated by the rapid evolution and diversity of available LLMs, we also formalize Continual Prompt Optimization (CPO), addressing the practical challenge of efficiently migrating optimized prompts between different model versions or API providers. Our experiments reveal that naive prompt migration often degrades performance due to loss of critical instructions. In contrast, our approach consistently outperforms strong baselines, achieving significant accuracy improvements, faster convergence, and lower computational costs in both standard and migration scenarios.
rt-RISeg: Real-Time Model-Free Robot Interactive Segmentation for Active Instance-Level Object Understanding
2025-07-14
Howard H. Qian, Yiting Chen, Gaotian Wang, Podshara Chanrungmaneekul, Kaiyu Hang
Successful execution of dexterous robotic manipulation tasks in new environments, such as grasping, depends on the ability to proficiently segment unseen objects from the background and other objects. Previous works in unseen object instance segmentation (UOIS) train models on large-scale datasets, which often leads to overfitting on static visual features. This dependency results in poor generalization performance when confronted with out-of-distribution scenarios. To address this limitation, we rethink the task of UOIS based on the principle that vision is inherently interactive and occurs over time. We propose a novel real-time interactive perception framework, rt-RISeg, that continuously segments unseen objects by robot interactions and analysis of a designed body frame-invariant feature (BFIF). We demonstrate that the relative rotational and linear velocities of randomly sampled body frames, resulting from selected robot interactions, can be used to identify objects without any learned segmentation model. This fully self-contained segmentation pipeline generates and updates object segmentation masks throughout each robot interaction without the need to wait for an action to finish. We showcase the effectiveness of our proposed interactive perception method by achieving an average object segmentation accuracy rate 27.5% greater than state-of-the-art UOIS methods. Furthermore, although rt-RISeg is a standalone framework, we show that the autonomously generated segmentation masks can be used as prompts to vision foundation models for significantly improved performance.
Rethinking LSM-tree based Key-Value Stores: A Survey
2025-07-13
Yina Lv, Qiao Li, Quanqing Xu, Congming Gao, Chuanhui Yang, Xiaoli Wang, Chun Jason Xue
LSM-tree is a widely adopted data structure in modern key-value store systems that optimizes write performance in write-heavy applications by using append writes to achieve sequential writes. However, the unpredictability of LSM-tree compaction introduces significant challenges, including performance variability during peak workloads and in resource-constrained environments, write amplification caused by data rewriting during compactions, read amplification from multi-level queries, trade-off between read and write performance, as well as efficient space utilization to mitigate space amplification. Prior studies on LSM-tree optimizations have addressed the above challenges; however, in recent years, research on LSM-tree optimization has continued to propose. The goal of this survey is to review LSM-tree optimization, focusing on representative works in the past five years. This survey first studies existing solutions on how to mitigate the performance impact of LSM-tree flush and compaction and how to improve basic key-value operations. In addition, distributed key-value stores serve multi-tenants, ranging from tens of thousands to millions of users with diverse requirements. We then analyze the new challenges and opportunities in these modern architectures and across various application scenarios. Unlike the existing survey papers, this survey provides a detailed discussion of the state-of-the-art work on LSM-tree optimizations and gives future research directions.
The Spectacle of Fidelity: Blind Resistance and the Wizardry of Prototyping
2025-07-13
Hrittika Bhowmick, Shilpaa Anand
Prototyping is widely regarded in Human-Computer Interaction as an iterative process through which ideas are tested and refined, often via visual mockups, screen flows, and coded simulations. This position paper critiques the visual-centric norms embedded in prototyping culture by drawing from the lived experiences of blind scholars and insights from cultural disability studies. It discusses how dominant methods of prototyping rely on an unexamined fidelity to sight, privileging what can be rendered visibly coherent while marginalizing other modes of knowing and making. By repositioning prototyping as a situated, embodied, and relational practice, this paper challenges HCI to rethink what kinds of design participation are legitimized and which are excluded when prototyping is reduced to screen-based simulations.
Back to the Basics: Rethinking Issue-Commit Linking with LLM-Assisted Retrieval
2025-07-12
Huihui Huang, Ratnadira Widyasari, Ting Zhang, Ivana Clairine Irsan, Jieke Shi, Han Wei Ang, Frank Liauw, Eng Lieh Ouh, Lwin Khin Shar, Hong Jin Kang, David Lo
Issue-commit linking, which connects issues with commits that fix them, is crucial for software maintenance. Existing approaches have shown promise in automatically recovering these links. Evaluations of these techniques assess their ability to identify genuine links from plausible but false links. However, these evaluations overlook the fact that, in reality, when a repository has more commits, the presence of more plausible yet unrelated commits may interfere with the tool in differentiating the correct fix commits. To address this, we propose the Realistic Distribution Setting (RDS) and use it to construct a more realistic evaluation dataset that includes 20 open-source projects. By evaluating tools on this dataset, we observe that the performance of the state-of-the-art deep learning-based approach drops by more than half, while the traditional Information Retrieval method, VSM, outperforms it. Inspired by these observations, we propose EasyLink, which utilizes a vector database as a modern Information Retrieval technique. To address the long-standing problem of the semantic gap between issues and commits, EasyLink leverages a large language model to rerank the commits retrieved from the database. Under our evaluation, EasyLink achieves an average Precision@1 of 75.91%, improving over the state-of-the-art by over four times. Additionally, this paper provides practical guidelines for advancing research in issue-commit link recovery.
Automatic n-gram based metrics such as ROUGE are widely used for evaluating generative tasks such as summarization. While these metrics are considered indicative (even if imperfect) of human evaluation for English, their suitability for other languages remains unclear. To address this, we systematically assess evaluation metrics for generation both n-gram-based and neural based to evaluate their effectiveness across languages and tasks. Specifically, we design a large-scale evaluation suite across eight languages from four typological families: agglutinative, isolating, low-fusional, and high-fusional, spanning both low- and high-resource settings, to analyze their correlation with human judgments. Our findings highlight the sensitivity of evaluation metrics to the language type. For example, in fusional languages, n-gram-based metrics show lower correlation with human assessments compared to isolating and agglutinative languages. We also demonstrate that proper tokenization can significantly mitigate this issue for morphologically rich fusional languages, sometimes even reversing negative trends. Additionally, we show that neural-based metrics specifically trained for evaluation, such as COMET, consistently outperform other neural metrics and better correlate with human judgments in low-resource languages. Overall, our analysis highlights the limitations of n-gram metrics for fusional languages and advocates for greater investment in neural-based metrics trained for evaluation tasks.
MIRA: A Novel Framework for Fusing Modalities in Medical RAG
2025-07-10
Jinhong Wang, Tajamul Ashraf, Zongyan Han, Jorma Laaksonen, Rao Mohammad Anwer
Multimodal Large Language Models (MLLMs) have significantly advanced AI-assisted medical diagnosis, but they often generate factually inconsistent responses that deviate from established medical knowledge. Retrieval-Augmented Generation (RAG) enhances factual accuracy by integrating external sources, but it presents two key challenges. First, insufficient retrieval can miss critical information, whereas excessive retrieval can introduce irrelevant or misleading content, disrupting model output. Second, even when the model initially provides correct answers, over-reliance on retrieved data can lead to factual errors. To address these issues, we introduce the Multimodal Intelligent Retrieval and Augmentation (MIRA) framework, designed to optimize factual accuracy in MLLM. MIRA consists of two key components: (1) a calibrated Rethinking and Rearrangement module that dynamically adjusts the number of retrieved contexts to manage factual risk, and (2) A medical RAG framework integrating image embeddings and a medical knowledge base with a query-rewrite module for efficient multimodal reasoning. This enables the model to effectively integrate both its inherent knowledge and external references. Our evaluation of publicly available medical VQA and report generation benchmarks demonstrates that MIRA substantially enhances factual accuracy and overall performance, achieving new state-of-the-art results. Code is released at https://github.com/mbzuai-oryx/MIRA.
Class-incremental/Continual image segmentation (CIS) aims to train an image segmenter in stages, where the set of available categories differs at each stage. To leverage the built-in objectness of query-based transformers, which mitigates catastrophic forgetting of mask proposals, current methods often decouple mask generation from the continual learning process. This study, however, identifies two key issues with decoupled frameworks: loss of plasticity and heavy reliance on input data order. To address these, we conduct an in-depth investigation of the built-in objectness and find that highly aggregated image features provide a shortcut for queries to generate masks through simple feature alignment. Based on this, we propose SimCIS, a simple yet powerful baseline for CIS. Its core idea is to directly select image features for query assignment, ensuring "perfect alignment" to preserve objectness, while simultaneously allowing queries to select new classes to promote plasticity. To further combat catastrophic forgetting of categories, we introduce cross-stage consistency in selection and an innovative "visual query"-based replay mechanism. Experiments demonstrate that SimCIS consistently outperforms state-of-the-art methods across various segmentation tasks, settings, splits, and input data orders. All models and codes will be made publicly available at https://github.com/SooLab/SimCIS.
Text embeddings are fundamental to many natural language processing (NLP) tasks, extensively applied in domains such as recommendation systems and information retrieval (IR). Traditionally, transmitting embeddings instead of raw text has been seen as privacy-preserving. However, recent methods such as Vec2Text challenge this assumption by demonstrating that controlled decoding can successfully reconstruct original texts from black-box embeddings. The unexpectedly strong results reported by Vec2Text motivated us to conduct further verification, particularly considering the typically non-intuitive and opaque structure of high-dimensional embedding spaces. In this work, we reproduce the Vec2Text framework and evaluate it from two perspectives: (1) validating the original claims, and (2) extending the study through targeted experiments. First, we successfully replicate the original key results in both in-domain and out-of-domain settings, with only minor discrepancies arising due to missing artifacts, such as model checkpoints and dataset splits. Furthermore, we extend the study by conducting a parameter sensitivity analysis, evaluating the feasibility of reconstructing sensitive inputs (e.g., passwords), and exploring embedding quantization as a lightweight privacy defense. Our results show that Vec2Text is effective under ideal conditions, capable of reconstructing even password-like sequences that lack clear semantics. However, we identify key limitations, including its sensitivity to input sequence length. We also find that Gaussian noise and quantization techniques can mitigate the privacy risks posed by Vec2Text, with quantization offering a simpler and more widely applicable solution. Our findings emphasize the need for caution in using text embeddings and highlight the importance of further research into robust defense mechanisms for NLP systems.
As cyber-physical systems grow increasingly interconnected and spatially distributed, ensuring their resilience against evolving cyberattacks has become a critical priority. Spatio-Temporal Anomaly detection plays an important role in ensuring system security and operational integrity. However, current data-driven approaches, largely driven by black-box deep learning, face challenges in interpretability, adaptability to distribution shifts, and robustness under evolving system dynamics. In this paper, we advocate for a causal learning perspective to advance anomaly detection in spatially distributed infrastructures that grounds detection in structural cause-effect relationships. We identify and formalize three key directions: causal graph profiling, multi-view fusion, and continual causal graph learning, each offering distinct advantages in uncovering dynamic cause-effect structures across time and space. Drawing on real-world insights from systems such as water treatment infrastructures, we illustrate how causal models provide early warning signals and root cause attribution, addressing the limitations of black-box detectors. Looking ahead, we outline the future research agenda centered on multi-modality, generative AI-driven, and scalable adaptive causal frameworks. Our objective is to lay a new research trajectory toward scalable, adaptive, explainable, and spatially grounded anomaly detection systems. We hope to inspire a paradigm shift in cybersecurity research, promoting causality-driven approaches to address evolving threats in interconnected infrastructures.
Rail-optimized network fabrics have become the de facto datacenter scale-out fabric for large-scale ML training. However, the use of high-radix electrical switches to provide all-to-all connectivity in rails imposes massive power, cost, and complexity overheads. We propose a rethinking of the rail abstraction by retaining its communication semantics, but realizing it using optical circuit switches. The key challenge is that optical switches support only one-to-one connectivity at a time, limiting the fan-out of traffic in ML workloads using hybrid parallelisms. We introduce parallelism-driven rail reconfiguration as a solution that leverages the sequential ordering between traffic from different parallelisms. We design a control plane, Opus, to enable time-multiplexed emulation of electrical rail switches using optical switches. More broadly, our work discusses a new research agenda: datacenter fabrics that co-evolve with the model parallelism dimensions within each job, as opposed to the prevailing mindset of reconfiguring networks before a job begins.
Large language models (LLMs) have recently achieved notable success in code-generation benchmarks such as HumanEval and LiveCodeBench. However, a detailed examination reveals that these evaluation suites often comprise only a limited number of homogeneous test cases, resulting in subtle faults going undetected. This not only artificially inflates measured performance but also compromises accurate reward estimation in reinforcement learning frameworks utilizing verifiable rewards (RLVR). To address these critical shortcomings, we systematically investigate the test-case generation (TCG) task by proposing multi-dimensional metrics designed to rigorously quantify test-suite thoroughness. Furthermore, we introduce a human-LLM collaborative method (SAGA), leveraging human programming expertise with LLM reasoning capability, aimed at significantly enhancing both the coverage and the quality of generated test cases. In addition, we develop a TCGBench to facilitate the study of the TCG task. Experiments show that SAGA achieves a detection rate of 90.62% and a verifier accuracy of 32.58% on TCGBench. The Verifier Accuracy (Verifier Acc) of the code generation evaluation benchmark synthesized by SAGA is 10.78% higher than that of LiveCodeBench-v6. These results demonstrate the effectiveness of our proposed method. We hope this work contributes to building a scalable foundation for reliable LLM code evaluation, further advancing RLVR in code generation, and paving the way for automated adversarial test synthesis and adaptive benchmark integration.
Despite advances in reinforcement learning (RL)-based video reasoning with large language models (LLMs), data collection and finetuning remain significant challenges. These methods often rely on large-scale supervised fine-tuning (SFT) with extensive video data and long Chain-of-Thought (CoT) annotations, making them costly and hard to scale. To address this, we present Video-RTS, a new approach to improve video reasoning capability with drastically improved data efficiency by combining data-efficient RL with a video-adaptive test-time scaling (TTS) strategy. Based on observations about the data scaling of RL samples, we skip the resource-intensive SFT step and employ efficient pure-RL training with output-based rewards, requiring no additional annotations or extensive fine-tuning. Furthermore, to utilize computational resources more efficiently, we introduce a sparse-to-dense video TTS strategy that improves inference by iteratively adding frames based on output consistency. We validate our approach on multiple video reasoning benchmarks, showing that Video-RTS surpasses existing video reasoning models by an average of 2.4% in accuracy using only 3.6% training samples. For example, Video-RTS achieves a 4.2% improvement on Video-Holmes, a recent and challenging video reasoning benchmark, and a 2.6% improvement on MMVU. Notably, our pure RL training and adaptive video TTS offer complementary strengths, enabling Video-RTS's strong reasoning performance.
Rethinking Pricing in Energy Markets: Pay-as-Bid vs Pay-as-Clear
2025-07-08
Ioannis Caragiannis, Zhile Jiang, Stratis Skoulakis
The design of energy markets is a subject of ongoing debate, particularly concerning the choice between the widely adopted Pay-as-Clear (PC) pricing mechanism and the alternative Pay-as-Bid (PB). These mechanisms determine how energy producers are compensated: under PC, all selected producers are paid the market-clearing price (i.e., the highest accepted bid), while under PB, each selected producer is paid their own submitted bid. The overarching objective is to meet the total demand for energy at minimal cost in the presence of strategic behavior. We present two key theoretical results. First, no mechanism can uniformly dominate PC or PB. This means that for any mechanism $\mathcal{M}$, there exists a market configuration and a mixed-strategy Nash equilibrium of PC (respectively for PB) that yields strictly lower total energy costs than under $\mathcal{M}$. Second, in terms of worst-case equilibrium outcomes, PB consistently outperforms PC: across all market instances, the highest possible equilibrium price under PB is strictly lower than that under PC. This suggests a structural robustness of PB to strategic manipulation. These theoretical insights are further supported by extensive simulations based on no-regret learning dynamics, which consistently yield lower average market prices in several energy market settings.
From ID-based to ID-free: Rethinking ID Effectiveness in Multimodal Collaborative Filtering Recommendation
2025-07-08
Guohao Li, Li Jing, Jia Wu, Xuefei Li, Kai Zhu, Yue He
Most existing multimodal collaborative filtering recommendation (MCFRec) methods rely heavily on ID features and multimodal content to enhance recommendation performance. However, this paper reveals that ID features are effective but have limited benefits in multimodal collaborative filtering recommendation. Therefore, this paper systematically deconstruct the pros and cons of ID features: (i) they provide initial embedding but lack semantic richness, (ii) they provide a unique identifier for each user and item but hinder generalization to untrained data, and (iii) they assist in aligning and fusing multimodal features but may lead to representation shift. Based on these insights, this paper proposes IDFREE, an ID-free multimodal collaborative Filtering REcommEndation baseline. IDFREE replaces ID features with multimodal features and positional encodings to generate semantically meaningful ID-free embeddings. For ID-free multimodal collaborative filtering, it further proposes an adaptive similarity graph module to construct dynamic user-user and item-item graphs based on multimodal features. Then, an augmented user-item graph encoder is proposed to construct more effective user and item encoding. Finally, IDFREE achieves inter-multimodal alignment based on the contrastive learning and uses Softmax loss as recommendation loss. Basic experiments on three public datasets demonstrate that IDFREE outperforms existing ID-based MCFRec methods, achieving an average performance gain of 72.24% across standard metrics (Recall@5, 10, 20, 50 and NDCG@5, 10, 20, 50). Exploratory and extended experiments further validate our findings on the limitations of ID features in MCFRec. The code is released at https://github.com/G-H-Li/IDFREE.
Video Diffusion Models (VDMs) have demonstrated remarkable capabilities in synthesizing realistic videos by learning from large-scale data. Although vanilla Low-Rank Adaptation (LoRA) can learn specific spatial or temporal movement to driven VDMs with constrained data, achieving precise control over both camera trajectories and object motion remains challenging due to the unstable fusion and non-linear scalability. To address these issues, we propose LiON-LoRA, a novel framework that rethinks LoRA fusion through three core principles: Linear scalability, Orthogonality, and Norm consistency. First, we analyze the orthogonality of LoRA features in shallow VDM layers, enabling decoupled low-level controllability. Second, norm consistency is enforced across layers to stabilize fusion during complex camera motion combinations. Third, a controllable token is integrated into the diffusion transformer (DiT) to linearly adjust motion amplitudes for both cameras and objects with a modified self-attention mechanism to ensure decoupled control. Additionally, we extend LiON-LoRA to temporal generation by leveraging static-camera videos, unifying spatial and temporal controllability. Experiments demonstrate that LiON-LoRA outperforms state-of-the-art methods in trajectory control accuracy and motion strength adjustment, achieving superior generalization with minimal training data. Project Page: https://fuchengsu.github.io/lionlora.github.io/
Graphic design is crucial for conveying ideas and messages. Designers usually organize their work into objects, backgrounds, and vectorized text layers to simplify editing. However, this workflow demands considerable expertise. With the rise of GenAI methods, an endless supply of high-quality graphic designs in pixel format has become more accessible, though these designs often lack editability. Despite this, non-layered designs still inspire human designers, influencing their choices in layouts and text styles, ultimately guiding the creation of layered designs. Motivated by this observation, we propose Accordion, a graphic design generation framework taking the first attempt to convert AI-generated designs into editable layered designs, meanwhile refining nonsensical AI-generated text with meaningful alternatives guided by user prompts. It is built around a vision language model (VLM) playing distinct roles in three curated stages. For each stage, we design prompts to guide the VLM in executing different tasks. Distinct from existing bottom-up methods (e.g., COLE and Open-COLE) that gradually generate elements to create layered designs, our approach works in a top-down manner by using the visually harmonious reference image as global guidance to decompose each layer. Additionally, it leverages multiple vision experts such as SAM and element removal models to facilitate the creation of graphic layers. We train our method using the in-house graphic design dataset Design39K, augmented with AI-generated design images coupled with refined ground truth created by a customized inpainting model. Experimental results and user studies by designers show that Accordion generates favorable results on the DesignIntention benchmark, including tasks such as text-to-template, adding text to background, and text de-rendering, and also excels in creating design variations.
Reasoning with quantifier expressions in natural language combines logical and arithmetical features, transcending strict divides between qualitative and quantitative. Our topic is this cooperation of styles as it occurs in common linguistic usage and its extension into the broader practice of natural language plus "grassroots mathematics". We begin with a brief review of first-order logic with counting operators and cardinality comparisons. This system is known to be of high complexity, and drowns out finer aspects of the combination of logic and counting. We move to a small fragment that can represent numerical syllogisms and basic reasoning about comparative size: monadic first-order logic with counting. We provide normal forms that allow for axiomatization, determine which arithmetical notions can be defined on finite and on infinite models, and conversely, we discuss which logical notions can be defined out of purely arithmetical ones, and what sort of (non-)classical logics can be induced. Next, we investigate a series of strengthenings, again using normal form methods. The monadic second-order version is close, in a precise sense, to additive Presburger Arithmetic, while versions with the natural device of tuple counting take us to Diophantine equations, making the logic undecidable. We also define a system that combines basic modal logic over binary accessibility relations with counting, needed to formulate ubiquitous reasoning patterns such as the Pigeonhole Principle. We return to our starting point in natural language, confronting the architecture of our formal systems with linguistic quantifier vocabulary and syntax. We conclude with some general thoughts on yet further entanglements of logic and counting in formal systems, on rethinking the qualitative/quantitative divide, and on connecting our analysis to empirical findings in cognitive science.
PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs
2025-07-07
Xinzhe Zheng, Hao Du, Fanding Xu, Jinzhe Li, Zhiyuan Liu, Wenkang Wang, Tao Chen, Wanli Ouyang, Stan Z. Li, Yan Lu, Nanqing Dong, Yang Zhang
Deep learning-based computational methods have achieved promising results in predicting protein-protein interactions (PPIs). However, existing benchmarks predominantly focus on isolated pairwise evaluations, overlooking a model's capability to reconstruct biologically meaningful PPI networks, which is crucial for biology research. To address this gap, we introduce PRING, the first comprehensive benchmark that evaluates protein-protein interaction prediction from a graph-level perspective. PRING curates a high-quality, multi-species PPI network dataset comprising 21,484 proteins and 186,818 interactions, with well-designed strategies to address both data redundancy and leakage. Building on this golden-standard dataset, we establish two complementary evaluation paradigms: (1) topology-oriented tasks, which assess intra and cross-species PPI network construction, and (2) function-oriented tasks, including protein complex pathway prediction, GO module analysis, and essential protein justification. These evaluations not only reflect the model's capability to understand the network topology but also facilitate protein function annotation, biological module detection, and even disease mechanism analysis. Extensive experiments on four representative model categories, consisting of sequence similarity-based, naive sequence-based, protein language model-based, and structure-based approaches, demonstrate that current PPI models have potential limitations in recovering both structural and functional properties of PPI networks, highlighting the gap in supporting real-world biological applications. We believe PRING provides a reliable platform to guide the development of more effective PPI prediction models for the community. The dataset and source code of PRING are available at https://github.com/SophieSarceau/PRING.
Silent Failures in Stateless Systems: Rethinking Anomaly Detection for Serverless Computing
2025-07-07
Chanh Nguyen, Erik Elmroth, Monowar Bhuyan
Serverless computing has redefined cloud application deployment by abstracting infrastructure and enabling on-demand, event-driven execution, thereby enhancing developer agility and scalability. However, maintaining consistent application performance in serverless environments remains a significant challenge. The dynamic and transient nature of serverless functions makes it difficult to distinguish between benign and anomalous behavior, which in turn undermines the effectiveness of traditional anomaly detection methods. These conventional approaches, designed for stateful and long-running services, struggle in serverless settings where executions are short-lived, functions are isolated, and observability is limited. In this first comprehensive vision paper on anomaly detection for serverless systems, we systematically explore the unique challenges posed by this paradigm, including the absence of persistent state, inconsistent monitoring granularity, and the difficulty of correlating behaviors across distributed functions. We further examine a range of threats that manifest as anomalies, from classical Denial-of-Service (DoS) attacks to serverless-specific threats such as Denial-of-Wallet (DoW) and cold start amplification. Building on these observations, we articulate a research agenda for next-generation detection frameworks that address the need for context-aware, multi-source data fusion, real-time, lightweight, privacy-preserving, and edge-cloud adaptive capabilities. Through the identification of key research directions and design principles, we aim to lay the foundation for the next generation of anomaly detection in cloud-native, serverless ecosystems.
Reason to Rote: Rethinking Memorization in Reasoning
2025-07-07
Yupei Du, Philipp Mondorf, Silvia Casola, Yuekun Yao, Robert Litschko, Barbara Plank
Large language models readily memorize arbitrary training instances, such as label noise, yet they perform strikingly well on reasoning tasks. In this work, we investigate how language models memorize label noise, and why such memorization in many cases does not heavily affect generalizable reasoning capabilities. Using two controllable synthetic reasoning datasets with noisy labels, four-digit addition (FDA) and two-hop relational reasoning (THR), we discover a reliance of memorization on generalizable reasoning mechanisms: models continue to compute intermediate reasoning outputs even when retrieving memorized noisy labels, and intervening reasoning adversely affects memorization. We further show that memorization operates through distributed encoding, i.e., aggregating various inputs and intermediate results, rather than building a look-up mechanism from inputs to noisy labels. Moreover, our FDA case study reveals memorization occurs via outlier heuristics, where existing neuron activation patterns are slightly shifted to fit noisy labels. Together, our findings suggest that memorization of label noise in language models builds on, rather than overrides, the underlying reasoning mechanisms, shedding lights on the intriguing phenomenon of benign memorization.
Helix Parallelism: Rethinking Sharding Strategies for Interactive Multi-Million-Token LLM Decoding
2025-07-07
Nidhi Bhatia, Ankit More, Ritika Borkar, Tiyasa Mitra, Ramon Matas, Ritchie Zhao, Maximilian Golub, Dheevatsa Mudigere, Brian Pharris, Bita Darvish Rouhani
As LLMs scale to multi-million-token KV histories, real-time autoregressive decoding under tight Token-to-Token Latency (TTL) constraints faces growing pressure. Two core bottlenecks dominate: accessing Feed-Forward Network (FFN) weights and reading long KV caches. While Tensor Parallelism (TP) helps mitigate the cost of FFN weight reads, it does not scale well for attention. When TP width exceeds the number of KV heads, it leads to inefficient KV duplication, limits parallelism, and constrains batch size. Simultaneously, DRAM reads for long KV histories scale linearly with batch size, further capping efficiency. We introduce Helix Parallelism, a hybrid execution strategy that applies KV parallelism during attention to shard KV caches across GPUs, then reuses the same GPUs for TP in dense LLMs or TPxExpert Parallel (EP) in MoEs during FFN computation. To preserve exact attention behavior, Helix includes a lightweight communication step. To minimize the exposed communication cost, we introduce Helix HOP-B. Helix HOP-B effectively minimizes communication overhead through batchwise overlap, preserving low TTL while improving GPU efficiency. Compared to conventional parallelism approaches, Helix reduces TTL by up to 1.5x at fixed batch sizes and supports up to 32x larger batches under the same latency budget for DeepSeek-R1, pushing forward the throughput-latency Pareto on Blackwell and making real-time inference with ultra-long-sequence practical.
2025-07-06
Carles Gelada, Jacob Buckman, Sean Zhang, Txus Bach
We argue that neither transformers nor sub-quadratic architectures are well suited to training at long sequence lengths: the cost of processing the context is too expensive in the former, too inexpensive in the latter. Approaches such as sliding window attention which reduce the cost-per-token of a transformer impair in-context learning, and so are also unsuitable. To address these limitations, we introduce power attention, an architectural layer for linear-cost sequence modeling whose state size can be adjusted independently of parameters, unlocking the advantages of linear attention on practical domains. We develop and open-source a set of GPU kernels for efficient power attention, identifying a novel pattern of operation fusion to avoid memory and bandwidth bottlenecks. Our experiments on the in-context learning of power attention shows that these models dominate both exponential attention and linear attention at long-context training.
Rethinking and Exploring String-Based Malware Family Classification in the Era of LLMs and RAG
2025-07-05
Yufan Chen, Daoyuan Wu, Juantao Zhong, Zicheng Zhang, Debin Gao, Shuai Wang, Yingjiu Li, Ning Liu
Malware Family Classification (MFC) aims to identify the fine-grained family (e.g., GuLoader or BitRAT) to which a potential malware sample belongs, in contrast to malware detection or sample classification that predicts only an Yes/No. Accurate family identification can greatly facilitate automated sample labeling and understanding on crowdsourced malware analysis platforms such as VirusTotal and MalwareBazaar, which generate vast amounts of data daily. In this paper, we explore and assess the feasibility of using traditional binary string features for MFC in the new era of large language models (LLMs) and Retrieval-Augmented Generation (RAG). Specifically, we investigate how Family-Specific String (FSS) features could be utilized in a manner similar to RAG to facilitate MFC. To this end, we develop a curated evaluation framework covering 4,347 samples from 67 malware families, extract and analyze over 25 million strings, and conduct detailed ablation studies to assess the impact of different design choices in four major modules.
HyperGaussians: High-Dimensional Gaussian Splatting for High-Fidelity Animatable Face Avatars
2025-07-03
Gent Serifi, Marcel C. Bühler
We introduce HyperGaussians, a novel extension of 3D Gaussian Splatting for high-quality animatable face avatars. Creating such detailed face avatars from videos is a challenging problem and has numerous applications in augmented and virtual reality. While tremendous successes have been achieved for static faces, animatable avatars from monocular videos still fall in the uncanny valley. The de facto standard, 3D Gaussian Splatting (3DGS), represents a face through a collection of 3D Gaussian primitives. 3DGS excels at rendering static faces, but the state-of-the-art still struggles with nonlinear deformations, complex lighting effects, and fine details. While most related works focus on predicting better Gaussian parameters from expression codes, we rethink the 3D Gaussian representation itself and how to make it more expressive. Our insights lead to a novel extension of 3D Gaussians to high-dimensional multivariate Gaussians, dubbed 'HyperGaussians'. The higher dimensionality increases expressivity through conditioning on a learnable local embedding. However, splatting HyperGaussians is computationally expensive because it requires inverting a high-dimensional covariance matrix. We solve this by reparameterizing the covariance matrix, dubbed the 'inverse covariance trick'. This trick boosts the efficiency so that HyperGaussians can be seamlessly integrated into existing models. To demonstrate this, we plug in HyperGaussians into the state-of-the-art in fast monocular face avatars: FlashAvatar. Our evaluation on 19 subjects from 4 face datasets shows that HyperGaussians outperform 3DGS numerically and visually, particularly for high-frequency details like eyeglass frames, teeth, complex facial movements, and specular reflections.
The rapid advancement of speech generation models has heightened privacy and security concerns related to voice cloning (VC). Recent studies have investigated disrupting unauthorized voice cloning by introducing adversarial perturbations. However, determined attackers can mitigate these protective perturbations and successfully execute VC. In this study, we conduct the first systematic evaluation of these protective perturbations against VC under realistic threat models that include perturbation purification. Our findings reveal that while existing purification methods can neutralize a considerable portion of the protective perturbations, they still lead to distortions in the feature space of VC models, which degrades the performance of VC. From this perspective, we propose a novel two-stage purification method: (1) Purify the perturbed speech; (2) Refine it using phoneme guidance to align it with the clean speech distribution. Experimental results demonstrate that our method outperforms state-of-the-art purification methods in disrupting VC defenses. Our study reveals the limitations of adversarial perturbation-based VC defenses and underscores the urgent need for more robust solutions to mitigate the security and privacy risks posed by VC. The code and audio samples are available at https://de-antifake.github.io.
Rethinking Broken Object Level Authorization Attacks Under Zero Trust Principle
2025-07-03
Anbin Wu, Zhiyong Feng, Ruitao Feng, Zhenchang Xing, Yang Liu
RESTful APIs facilitate data exchange between applications, but they also expose sensitive resources to potential exploitation. Broken Object Level Authorization (BOLA) is the top vulnerability in the OWASP API Security Top 10, exemplifies a critical access control flaw where attackers manipulate API parameters to gain unauthorized access. To address this, we propose BOLAZ, a defense framework grounded in zero trust principles. BOLAZ analyzes the data flow of resource IDs, pinpointing BOLA attack injection points and determining the associated authorization intervals to prevent horizontal privilege escalation. Our approach leverages static taint tracking to categorize APIs into producers and consumers based on how they handle resource IDs. By mapping the propagation paths of resource IDs, BOLAZ captures the context in which these IDs are produced and consumed, allowing for precise identification of authorization boundaries. Unlike defense methods based on common authorization models, BOLAZ is the first authorization-guided method that adapts defense rules based on the system's best-practice authorization logic. We validate BOLAZ through empirical research on 10 GitHub projects. The results demonstrate BOLAZ's effectiveness in defending against vulnerabilities collected from CVE and discovering 35 new BOLA vulnerabilities in the wild, demonstrating its practicality in real-world deployments.
Rethinking Data Protection in the (Generative) Artificial Intelligence Era
2025-07-03
Yiming Li, Shuo Shao, Yu He, Junfeng Guo, Tianwei Zhang, Zhan Qin, Pin-Yu Chen, Michael Backes, Philip Torr, Dacheng Tao, Kui Ren
The (generative) artificial intelligence (AI) era has profoundly reshaped the meaning and value of data. No longer confined to static content, data now permeates every stage of the AI lifecycle from the training samples that shape model parameters to the prompts and outputs that drive real-world model deployment. This shift renders traditional notions of data protection insufficient, while the boundaries of what needs safeguarding remain poorly defined. Failing to safeguard data in AI systems can inflict societal and individual, underscoring the urgent need to clearly delineate the scope of and rigorously enforce data protection. In this perspective, we propose a four-level taxonomy, including non-usability, privacy preservation, traceability, and deletability, that captures the diverse protection needs arising in modern (generative) AI models and systems. Our framework offers a structured understanding of the trade-offs between data utility and control, spanning the entire AI pipeline, including training datasets, model weights, system prompts, and AI-generated content. We analyze representative technical approaches at each level and reveal regulatory blind spots that leave critical assets exposed. By offering a structured lens to align future AI technologies and governance with trustworthy data practices, we underscore the urgency of rethinking data protection for modern AI techniques and provide timely guidance for developers, researchers, and regulators alike.
Collective Communication Profiling of Modern-day Machine Learning Workloads
2025-07-03
Jit Gupta, Andrew Li, Tarun Banka, Ariel Cohen, T. Sridhar, Raj Yavatkar
Machine Learning jobs, carried out on large number of distributed high performance systems, involve periodic communication using operations like AllReduce, AllGather, and Broadcast. These operations may create high bandwidth and bursty traffic patterns, leading to network congestion and packet loss, thus impacting the performance of these jobs. Hence it is imperative to analyze these patterns, which can be helpful in provisioning network resources depending on the type of machine learning workloads. In this poster we carry out extensive analysis of the collective communication behavior seen in a wide variety of models (ex. DeepSeek, GPT, Llama, etc.) To achieve this we instrument Nvidia Collective Communication Library logging functionality for richer context about the collectives and workloads. We adjust configuration parameters that influence collective communication behavior, such as parallelism, number of nodes, and model type. This overview presents and discusses some of the results on the collective communication behavior for the open source DeepSeek V3 inferencing model, which includes operation type and count, transfer sizes per operation, and request size distribution. Our analysis shows that it makes sense to rethink current collective communication frameworks and network topologies so as to accommodate the effect of network anomalies on the mentioned workloads.
Recent advances in large language models (LLMs) have spurred interests in encoding images as discrete tokens and leveraging autoregressive (AR) frameworks for visual generation. However, the quantization process in AR-based visual generation models inherently introduces information loss that degrades image fidelity. To mitigate this limitation, recent studies have explored to autoregressively predict continuous tokens. Unlike discrete tokens that reside in a structured and bounded space, continuous representations exist in an unbounded, high-dimensional space, making density estimation more challenging and increasing the risk of generating out-of-distribution artifacts. Based on the above findings, this work introduces DisCon (Discrete-Conditioned Continuous Autoregressive Model), a novel framework that reinterprets discrete tokens as conditional signals rather than generation targets. By modeling the conditional probability of continuous representations conditioned on discrete tokens, DisCon circumvents the optimization challenges of continuous token modeling while avoiding the information loss caused by quantization. DisCon achieves a gFID score of 1.38 on ImageNet 256$\times$256 generation, outperforming state-of-the-art autoregressive approaches by a clear margin. Project page: https://pengzheng0707.github.io/DisCon.
Designing for Community Care: Reimagining Support for Equity & Well-being in Academia
2025-07-02
Beatriz Severes, Ana O. Henriques, Rory Clark, Paulo Bala, Anna Carter, Rua Mae Williams, Geraldine Fitzpatrick
Academic well-being is deeply influenced by peer-support networks, yet they remain informal, inequitable, and unsustainable, often relying on personal connections and social capital rather than structured, inclusive systems. Additionally, institutional well-being responses frequently focus on student populations, neglecting the emotional labour of faculty and staff, reinforcing an exclusionary academic culture. Drawing on HCI methodologies, participatory design, and care ethics, this workshop will provide a space for rethinking how academic communities can support inclusive networks. Through pre-workshop engagement, co-design activities, and reflection, participants will examine systemic gaps in networks and explore ways to embed care, equity, and sustainability into academic peer-support frameworks -- from informal, exclusionary models to structured, inclusive care-based ecosystems. At the end of the workshop, participants will co-develop design strategies for integrating care and resilience in academic ecosystems, resources for designing equitable support systems, and a peer network invested and committed to fostering a supportive academic community.
Rethinking All Evidence: Enhancing Trustworthy Retrieval-Augmented Generation via Conflict-Driven Summarization
2025-07-02
Juan Chen, Baolong Bi, Wei Zhang, Jingyan Sui, Xiaofei Zhu, Yuanzhuo Wang, Lingrui Mei, Shenghua Liu
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating their parametric knowledge with external retrieved content. However, knowledge conflicts caused by internal inconsistencies or noisy retrieved content can severely undermine the generation reliability of RAG systems.In this work, we argue that LLMs should rethink all evidence, including both retrieved content and internal knowledge, before generating responses.We propose CARE-RAG (Conflict-Aware and Reliable Evidence for RAG), a novel framework that improves trustworthiness through Conflict-Driven Summarization of all available evidence.CARE-RAG first derives parameter-aware evidence by comparing parameter records to identify diverse internal perspectives. It then refines retrieved evidences to produce context-aware evidence, removing irrelevant or misleading content. To detect and summarize conflicts, we distill a 3B LLaMA3.2 model to perform conflict-driven summarization, enabling reliable synthesis across multiple sources.To further ensure evaluation integrity, we introduce a QA Repair step to correct outdated or ambiguous benchmark answers.Experiments on revised QA datasets with retrieval data show that CARE-RAG consistently outperforms strong RAG baselines, especially in scenarios with noisy or conflicting evidence.
Pruning the Tree: Rethinking RPKI Architecture From The Ground Up
2025-07-02
Haya Schulmann, Niklas Vogel
Resource Public Key Infrastructure (RPKI) is a critical security mechanism for BGP, but the complexity of its architecture is a growing concern as its adoption scales. Current RPKI design heavily reuses legacy PKI components, such as X.509 EE-certificates, ASN.1 encoding, and XML-based repository protocols, which introduce excessive cryptographic validation, redundant metadata, and inefficiencies in both storage and processing. We show that these design choices, although based on established standards, create significant performance bottlenecks, increase the vulnerability surface, and hinder scalability for wide-scale Internet deployment. In this paper, we perform the first systematic analysis of the root causes of complexity in RPKI's design and experimentally quantify their real-world impact. We show that over 70\% of validation time in RPKI relying parties is spent on certificate parsing and signature verification, much of it unnecessary. Building on this insight, we introduce the improved RPKI (iRPKI), a backwards-compatible redesign that preserves all security guarantees while substantially reducing protocol overhead. iRPKI eliminates EE-certificates and ROA signatures, merges revocation and integrity objects, replaces verbose encodings with Protobuf, and restructures repository metadata for more efficient access. We experimentally demonstrate that our implementation of iRPKI in the Routinator validator achieves a 20x speed-up of processing time, 18x improvement of bandwidth requirements and 8x reduction in cache memory footprint, while also eliminating classes of vulnerabilities that have led to at least 10 vulnerabilities in RPKI software. iRPKI significantly increases the feasibility of deploying RPKI at scale in the Internet, and especially in constrained environments. Our design may be deployed incrementally without impacting existing operations.
Rethinking cryptophane A for methane gas sensing: cross sensitivity to N2 and CO2 at ambient conditions
2025-07-02
Sebastián Alberti, Thierry Brotin, Jana Jágerská
Since the affinity of Cryptophane-A for methane was first reported in 1993, cryptophane-doped polymer films have been extensively studied as enrichment cladding layers in plasmonic, fiber-optic, and integrated waveguide-based optical sensors. While the use of cryptophane-doped layers has improved methane sensitivity compared to undoped claddings, controversy has grown over the years regarding their claimed selectivity and practical applicability. Key questions remain unresolved, including the extent of true methane enrichment at room temperature, cross-sensitivity to other gases, and the proportion of active cryptophane molecules within the polymer matrix. In this work, we employ Raman spectroscopy to provide direct and unambiguous evidence that Cryptophane-A exhibits measurable affinity for major ambient gases other than methane at room temperature. Notably, both nitrogen and carbon dioxide are shown to enter the cryptophane cavity and compete with methane for binding sites. This study underscores the value of Raman spectroscopy as a benchmark technique for investigating gas capture within host molecules at ambient conditions. It offers deeper insight into the binding behavior of Cryptophane-A and enables quantification of its relative affinities to atmospheric gases, thereby revealing both the limitations and the potential of cryptophane for future sensing applications.
2025-07-01
Iñaki Dellibarda Varela, Pablo Romero-Sorozabal, Eduardo Rocon, Manuel Cebrian
Earlier this year, Apple ignited controversy by publishing "The Illusion of Thinking," prompting heated debate within the AI community. Critics seized upon the findings as conclusive evidence that Large Reasoning Models (LRMs) lack genuine reasoning capabilities, branding them as mere stochastic parrots. Meanwhile, defenders-spearheaded by Lawsen et al. (2025)-fired back, condemning the experimental setup as flawed and the conclusions overstated. We clarify this debate by replicating and refining two of the original study's most contentious benchmarks: Towers of Hanoi and River Crossing. By introducing incremental stepwise prompting and agentic collaborative dialogue, we show that previously reported failures solving the Towers of Hanoi were not purely result of output constraints, but also partly a result of cognition limitations: LRMs still stumble when complexity rises moderately (around 8 disks). Moreover, the River Crossing results initially heralded as catastrophic failures turn out to hinge upon testing unsolvable configurations. Once we limit tests strictly to solvable problems-LRMs effortlessly solve large instances involving over 100 agent pairs. Our findings ultimately defy simplistic narratives: today's LRMs are stochastic, RL-tuned searchers in a discrete state space we barely understand. Real progress in symbolic, long-horizon reasoning demands mapping that terrain through fine-grained ablations like those introduced here.
From Sentences to Sequences: Rethinking Languages in Biological System
2025-07-01
Ke Liu, Shuaike Shen, Hao Chen
The paradigm of large language models in natural language processing (NLP) has also shown promise in modeling biological languages, including proteins, RNA, and DNA. Both the auto-regressive generation paradigm and evaluation metrics have been transferred from NLP to biological sequence modeling. However, the intrinsic structural correlations in natural and biological languages differ fundamentally. Therefore, we revisit the notion of language in biological systems to better understand how NLP successes can be effectively translated to biological domains. By treating the 3D structure of biomolecules as the semantic content of a sentence and accounting for the strong correlations between residues or bases, we highlight the importance of structural evaluation and demonstrate the applicability of the auto-regressive paradigm in biological language modeling. Code can be found at \href{https://github.com/zjuKeLiu/RiFold}{github.com/zjuKeLiu/RiFold}
The growing use of deep learning necessitates efficient network design and deployment, making neural predictors vital for estimating attributes such as accuracy and latency. Recently, Graph Neural Networks (GNNs) and transformers have shown promising performance in representing neural architectures. However, each of both methods has its disadvantages. GNNs lack the capabilities to represent complicated features, while transformers face poor generalization when the depth of architecture grows. To mitigate the above issues, we rethink neural architecture topology and show that sibling nodes are pivotal while overlooked in previous research. We thus propose a novel predictor leveraging the strengths of GNNs and transformers to learn the enhanced topology. We introduce a novel token mixer that considers siblings, and a new channel mixer named bidirectional graph isomorphism feed-forward network. Our approach consistently achieves promising performance in both accuracy and latency prediction, providing valuable insights for learning Directed Acyclic Graph (DAG) topology. The code is available at https://github.com/XuRuihan/NNFormer.
Sub-Neptunes Are Drier Than They Seem: Rethinking the Origins of Water-Rich Worlds
2025-07-01
Aaron Werlen, Caroline Dorn, Remo Burn, Hilke E. Schlichting, Simon L. Grimm, Edward D. Young
Recent claims of biosignature gases in sub-Neptune atmospheres have renewed interest in water-rich sub-Neptunes with surface oceans, often referred to as Hycean planets. These planets are hypothesized to form beyond the snow line, accreting large amounts of H$_2$O >10 wt%) before migrating inward. However, current interior models often neglect chemical equilibration between primordial atmospheres and molten interiors. Here, we compute global chemical equilibrium states for a synthetic population of sub-Neptunes with magma oceans. Although many initially accrete 5-30 wt% water, interior-atmosphere interactions destroy most of it, reducing final H$_2$O mass fractions to below 1.5 wt%. As a result, none meet the threshold for Hycean planets. Despite that, we find H$_2$O-dominated atmospheres exclusively on planets that accreted the least ice. These planets form inside the snow line, are depleted in carbon and hydrogen, and develop small envelopes with envelope mass fractions below 1%, dominated by endogenic water. In contrast, planets formed beyond the snow line accrete more volatiles, but their water is largely converted to H$_2$ gas or sequestered into the interior, resulting in low atmospheric H$_2$O mass fractions. Most H$_2$O-rich envelopes are also fully miscible with H$_2$, making a separate water layer unlikely. Our results topple the conventional link between ice accretion and water-rich atmospheres, showing instead that H$_2$O-dominated envelopes emerge through chemical equilibration in hydrogen-poor planets formed inside the snow line.
Rethinking Group Recommender Systems in the Era of Generative AI: From One-Shot Recommendations to Agentic Group Decision Support
2025-07-01
Dietmar Jannach, Amra Delić, Francesco Ricci, Markus Zanker
More than twenty-five years ago, first ideas were developed on how to design a system that can provide recommendations to groups of users instead of individual users. Since then, a rich variety of algorithmic proposals were published, e.g., on how to acquire individual preferences, how to aggregate them, and how to generate recommendations for groups of users. However, despite the rich literature on the topic, barely any examples of real-world group recommender systems can be found. This lets us question common assumptions in academic research, in particular regarding communication processes in a group and how recommendation-supported decisions are made. In this essay, we argue that these common assumptions and corresponding system designs often may not match the needs or expectations of users. We thus call for a reorientation in this research area, leveraging the capabilities of modern Generative AI assistants like ChatGPT. Specifically, as one promising future direction, we envision group recommender systems to be systems where human group members interact in a chat and an AI-based group recommendation agent assists the decision-making process in an agentic way. Ultimately, this shall lead to a more natural group decision-making environment and finally to wider adoption of group recommendation systems in practice.
Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning
2025-07-01
Maggie Huan, Yuetai Li, Tuney Zheng, Xiaoyu Xu, Seungone Kim, Minxin Du, Radha Poovendran, Graham Neubig, Xiang Yue
Math reasoning has become the poster child of progress in large language models (LLMs), with new models rapidly surpassing human-level performance on benchmarks like MATH and AIME. But as math leaderboards improve week by week, it is worth asking: do these gains reflect broader problem-solving ability or just narrow overfitting? To answer this question, we evaluate over 20 open-weight reasoning-tuned models across a broad suite of tasks, including math, scientific QA, agent planning, coding, and standard instruction-following. We surprisingly find that most models that succeed in math fail to transfer their gains to other domains. To rigorously study this phenomenon, we conduct controlled experiments on Qwen3-14B models using math-only data but different tuning methods. We find that reinforcement learning (RL)-tuned models generalize well across domains, while supervised fine-tuning (SFT)-tuned models often forget general capabilities. Latent-space representation and token-space distribution shift analyses reveal that SFT induces substantial representation and output drift, while RL preserves general-domain structure. Our results suggest a need to rethink standard post-training recipes, particularly the reliance on SFT-distilled data for advancing reasoning models.
2025-06-30
Satoshi Tanaka, Koji Minoda, Fumiya Watanabe, Takamasa Horibe
High-accuracy and low-latency 3D object detection is essential for autonomous driving systems. While previous studies on 3D object detection often evaluate performance based on mean average precision (mAP) and latency, they typically fail to address the trade-off between speed and accuracy, such as 60.0 mAP at 100 ms vs 61.0 mAP at 500 ms. A quantitative assessment of the trade-offs between different hardware devices and accelerators remains unexplored, despite being critical for real-time applications. Furthermore, they overlook the impact on collision avoidance in motion planning, for example, 60.0 mAP leading to safer motion planning or 61.0 mAP leading to high-risk motion planning. In this paper, we introduce latency-aware AP (L-AP) and planning-aware AP (P-AP) as new metrics, which consider the physical world such as the concept of time and physical constraints, offering a more comprehensive evaluation for real-time 3D object detection. We demonstrate the effectiveness of our metrics for the entire autonomous driving system using nuPlan dataset, and evaluate 3D object detection models accounting for hardware differences and accelerators. We also develop a state-of-the-art performance model for real-time 3D object detection through latency-aware hyperparameter optimization (L-HPO) using our metrics. Additionally, we quantitatively demonstrate that the assumption "the more point clouds, the better the recognition performance" is incorrect for real-time applications and optimize both hardware and model selection using our metrics.
Prediction Gaps as Pathways to Explanation: Rethinking Educational Outcomes through Differences in Model Performance
2025-06-28
Javier Garcia-Bernardo, Eva Jaspers, Weverthon Machado, Samuel Plach, Erik Jan van Leeuwen
Social contexts -- such as families, schools, and neighborhoods -- shape life outcomes. The key question is not simply whether they matter, but rather for whom and under what conditions. Here, we argue that prediction gaps -- differences in predictive performance between statistical models of varying complexity -- offer a pathway for identifying surprising empirical patterns (i.e., not captured by simpler models) which highlight where theories succeed or fall short. Using population-scale administrative data from the Netherlands, we compare logistic regression, gradient boosting, and graph neural networks to predict university completion using early-life social contexts. Overall, prediction gaps are small, suggesting that previously identified indicators, particularly parental status, capture most measurable variation in educational attainment. However, gaps are larger for girls growing up without fathers -- suggesting that the effects of social context for these groups go beyond simple models in line with sociological theory. Our paper shows the potential of prediction methods to support sociological explanation.
Memory as a Service (MaaS): Rethinking Contextual Memory as Service-Oriented Modules for Collaborative Agents
2025-06-28
Haichang Li
This position paper aims to rethink the role and design of memory in Large Language Model (LLM)-based agent systems. We observe that while current memory practices have begun to transcend the limitations of single interactions, they remain conceptually grounded in "bound memory" in terms of design concept-where memory is treated as local state attached to specific context or entities, forming "memory silos" that impede cross-entity collaboration. To overcome this architectural bottleneck, this paper proposes the timely design perspective of "Memory as a Service" (MaaS). MaaS advocates decoupling memory from its conventional role as an interaction byproduct and encapsulating it as a modular service that can be independently callable, dynamically composable, and finely governed. At its core, MaaS leverages the duality of memory-its inherently private nature and its potential for public service-to grant memory controlled, on-demand interoperability across entities. This paper introduces a two-dimensional design space defined by entity structure and service type, illustrating how MaaS aligns with current memory practices while naturally extending them to cross-entity collaborative scenarios. Finally, we outline an open research agenda spanning governance, security, and ethical ecosystems, and call upon the broader research community to explore this shift toward service-oriented memory for collaborative agents operating across entity boundaries.
Rethinking Visual Token Reduction in LVLMs under Cross-modal Misalignment
2025-06-27
Rui Xu, Yunke Wang, Yong Luo, Bo Du
Large Vision-Language Models (LVLMs) encode visual inputs as dense sequences of patch-level tokens to capture fine-grained semantics. These visual tokens often outnumber their textual counterparts by a large margin, leading to substantial computational overhead and limiting the scalability of LVLMs in practice. Previous efforts have explored visual token reduction either prior to or within the large language models (LLM). However, most in-LLM reduction approaches rely on text-conditioned interactions, implicitly assuming that textual tokens can reliably capture the importance of visual tokens. In this work, we revisit this assumption and reveal causal, semantic, and spatial forms of cross-modal misalignment. These misalignments undermine the effectiveness of text-guided visual token reduction. To address this, we introduce VisionDrop, a training-free, visual-only pruning framework that selects informative visual tokens based on intra-modal (visual-to-visual) attention, without relying on textual signals. To further suppress redundancy throughout the model hierarchy, we treat the visual encoder and the LLM as a unified system and design a progressive pruning pipeline. Our method performs dominant token selection and lightweight contextual merging at multiple stages, enabling fine-grained visual information to be retained even under aggressive token budgets. Extensive experiments across diverse benchmarks show that VisionDrop achieves consistent improvements over existing methods, despite requiring no additional training or complex modifications. Its simple yet effective design enables efficient inference while preserving strong performance across tasks.
Artificial Intelligent Disobedience: Rethinking the Agency of Our Artificial Teammates
2025-06-27
Reuth Mirsky
Artificial intelligence has made remarkable strides in recent years, achieving superhuman performance across a wide range of tasks. Yet despite these advances, most cooperative AI systems remain rigidly obedient, designed to follow human instructions without question and conform to user expectations, even when doing so may be counterproductive or unsafe. This paper argues for expanding the agency of AI teammates to include \textit{intelligent disobedience}, empowering them to make meaningful and autonomous contributions within human-AI teams. It introduces a scale of AI agency levels and uses representative examples to highlight the importance and growing necessity of treating AI autonomy as an independent research focus in cooperative settings. The paper then explores how intelligent disobedience manifests across different autonomy levels and concludes by proposing initial boundaries and considerations for studying disobedience as a core capability of artificial agents.
Rethinking Oversaturation in Classifier-Free Guidance via Low Frequency
2025-06-26
Kaiyu Song, Hanjiang Lai
Classifier-free guidance (CFG) succeeds in condition diffusion models that use a guidance scale to balance the influence of conditional and unconditional terms. A high guidance scale is used to enhance the performance of the conditional term. However, the high guidance scale often results in oversaturation and unrealistic artifacts. In this paper, we introduce a new perspective based on low-frequency signals, identifying the accumulation of redundant information in these signals as the key factor behind oversaturation and unrealistic artifacts. Building on this insight, we propose low-frequency improved classifier-free guidance (LF-CFG) to mitigate these issues. Specifically, we introduce an adaptive threshold-based measurement to pinpoint the locations of redundant information. We determine a reasonable threshold by analyzing the change rate of low-frequency information between prior and current steps. We then apply a down-weight strategy to reduce the impact of redundant information in the low-frequency signals. Experimental results demonstrate that LF-CFG effectively alleviates oversaturation and unrealistic artifacts across various diffusion models, including Stable Diffusion-XL, Stable Diffusion 2.1, 3.0, 3.5, and SiT-XL.
Rethink Sparse Signals for Pose-guided Text-to-image Generation
2025-06-26
Wenjie Xuan, Jing Zhang, Juhua Liu, Bo Du, Dacheng Tao
Recent works favored dense signals (e.g., depth, DensePose), as an alternative to sparse signals (e.g., OpenPose), to provide detailed spatial guidance for pose-guided text-to-image generation. However, dense representations raised new challenges, including editing difficulties and potential inconsistencies with textual prompts. This fact motivates us to revisit sparse signals for pose guidance, owing to their simplicity and shape-agnostic nature, which remains underexplored. This paper proposes a novel Spatial-Pose ControlNet(SP-Ctrl), equipping sparse signals with robust controllability for pose-guided image generation. Specifically, we extend OpenPose to a learnable spatial representation, making keypoint embeddings discriminative and expressive. Additionally, we introduce keypoint concept learning, which encourages keypoint tokens to attend to the spatial positions of each keypoint, thus improving pose alignment. Experiments on animal- and human-centric image generation tasks demonstrate that our method outperforms recent spatially controllable T2I generation approaches under sparse-pose guidance and even matches the performance of dense signal-based methods. Moreover, SP-Ctrl shows promising capabilities in diverse and cross-species generation through sparse signals. Codes will be available at https://github.com/DREAMXFAR/SP-Ctrl.
Exploring Image Generation via Mutually Exclusive Probability Spaces and Local Correlation Hypothesis
2025-06-26
Chenqiu Zhao, Anup Basu
We propose two theoretical frameworks, the Mutually Exclusive Probability Space (MESP) and the Local Correlation Hypothesis (LCH), to explore a potential limitation in probabilistic generative models; namely that learning global distributions leads to memorization rather than generative behavior. MESP emerges from our rethinking of the Variational Autoencoder (VAE). We observe that latent variable distributions in VAE exhibit overlap, which leads to an optimization conflict between the reconstruction loss and KL-divergence loss. A lower bound based on the overlap coefficient is proposed. We refer to this phenomenon as Mutually Exclusive Probability Spaces. Based on MESP, a Binary Latent Autoencoder (BL-AE) is proposed to encode images into binary latent representations. These binary latents are used as the input to our Autoregressive Random Variable Model (ARVM), a modified autoregressive model outputting histograms. Our ARVM achieves competitive FID scores, outperforming state-of-the-art methods on standard datasets. However, such scores reflect memorization rather than generation. To address this issue, we propose the Local Correlation Hypothesis (LCH), which posits that generative capability arising from local correlations among latent variables. Comprehensive experiments and discussions are conducted to validate our frameworks.
AI for good initiatives often rely on the assumption that technical interventions can resolve complex social problems. In the context of human trafficking (HT), such techno-solutionism risks oversimplifying exploitation, reinforcing power imbalances and causing harm to the very communities AI claims to support. In this paper, we introduce the Radical Questioning (RQ) framework as a five step, pre-project ethical assessment tool to critically evaluate whether AI should be built at all, especially in domains involving marginalized populations and entrenched systemic injustice. RQ does not replace principles based ethics but precedes it, offering an upstream, deliberative space to confront assumptions, map power, and consider harms before design. Using a case study in AI for HT, we demonstrate how RQ reveals overlooked sociocultural complexities and guides us away from surveillance based interventions toward survivor empowerment tools. While developed in the context of HT, RQ's five step structure can generalize to other domains, though the specific questions must be contextual. This paper situates RQ within a broader AI ethics philosophy that challenges instrumentalist norms and centers relational, reflexive responsibility.
Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning
2025-06-26
Zihao Zhao, Xinlong Zhai, Jinyu Yang, Chuan Shi
Foundation models have achieved great success in natural language processing (NLP) and computer vision (CV). Their success largely stems from the ability to integrate multi-domain knowledge in pre-training and transfer it to target domains. Considering graph data, especially graphs without textual features, is ubiquitous in real-world applications such as social networks and recommendation systems, some researchers have attempted to extend this paradigm to the graph field, aiming to construct graph foundation models. However, unlike CV and NLP, there are huge gaps among the semantics and properties of graphs in different domains, while current works still adopt traditional contrastive pre-training strategies designed in the single-domain scenario, which regard contrastive samples from different domains as equivalent. From experimental investigations, we discovered that inherent domain-specific differences prevent these strategies from effectively absorbing knowledge from different domains to generate informative representations. In this paper, we propose a novel multi-domain pre-training and cross-domain transfer framework, namely MDGCL.In the pre-training stage, we design a contrastive learning strategy to substantially recognize and capture domain differences, and introduce domain tokens to encode domain-level global information. In the downstream stage, we introduce a domain attention mechanism to enable fine-grained domain knowledge transfer. Extensive experiments on five benchmark datasets have demonstrated that our method outperforms state-of-the-art significantly, with the maximum improvement of 19.33\% on accuracy and 19.13\% on Macro-F1 score.
Filters of Identity: AR Beauty and the Algorithmic Politics of the Digital Body
2025-06-24
Miriam Doh, Corinna Canali, Nuria Oliver
This position paper situates AR beauty filters within the broader debate on Body Politics in HCI. We argue that these filters are not neutral tools but technologies of governance that reinforce racialized, gendered, and ableist beauty standards. Through naming conventions, algorithmic bias, and platform governance, they impose aesthetic norms while concealing their influence. To address these challenges, we advocate for transparency-driven interventions and a critical rethinking of algorithmic aesthetics and digital embodiment.
Scientific progress is often driven by groundbreaking and disruptive research, yet traditional metrics for evaluating contributions to science often emphasize quantity over quality. In this study, we consider metrics that focus on the quality of scientific performance of countries and propose a novel framework that facilitates a competitive ranking of these countries based on their performance, highlighting the impact of their contributions. Using a bipartite network and centrality measures computed through the GENEPY scheme, we evaluate the complexity and diversity of scientific fields. The analysis incorporates two quality indicators of a research paper: the network-based normalized citation score (NBNC) to identify high-impact articles and the Disruptive Index (CD) to classify research as consolidating or disruptive. These metrics focus on the perspective of citing articles, providing a localized view of scientific influence. Our study draws on bibliometric data from OpenAlex, encompassing nearly 60 million articles in the domain of physical sciences published between 1900 and 2023. This data spans 10 fields, 89 subfields, and approximately 1,300 topics, offering a comprehensive view of scientific development over time. By focusing on quality indicators such as breakthroughs and disruptive research, our framework underscores the importance of transformative contributions that challenge existing paradigms and drive innovation. This approach not only highlights the leading contributors to global science but also reveals patterns in the evolution of scientific fields. By prioritizing impactful research, our study offers insights into optimizing resources for advancing science. The results are relevant for policymakers, funding agencies, and researchers, providing a clearer understanding of how quality-driven metrics can reshape the evaluation of scientific performance on a global scale.
Rethinking Click Models in Light of Carousel Interfaces: Theory-Based Categorization and Design of Click Models
2025-06-23
Jingwei Kang, Maarten de Rijke, Santiago de Leon-Martinez, Harrie Oosterhuis
Click models are a well-established for modeling user interactions with web interfaces. Previous work has mainly focused on traditional single-list web search settings; this includes existing surveys that introduced categorizations based on the first generation of probabilistic graphical model (PGM) click models that have become standard. However, these categorizations have become outdated, as their conceptualizations are unable to meaningfully compare PGM with neural network (NN) click models nor generalize to newer interfaces, such as carousel interfaces. We argue that this outdated view fails to adequately explain the fundamentals of click model designs, thus hindering the development of novel click models. This work reconsiders what should be the fundamental concepts in click model design, grounding them - unlike previous approaches - in their mathematical properties. We propose three fundamental key-design choices that explain what statistical patterns a click model can capture, and thus indirectly, what user behaviors they can capture. Based on these choices, we create a novel click model taxonomy that allows a meaningful comparison of all existing click models; this is the first taxonomy of single-list, grid and carousel click models that includes PGMs and NNs. Finally, we show how our conceptualization provides a foundation for future click model design by an example derivation of a novel design for carousel interfaces.
The Debugging Decay Index: Rethinking Debugging Strategies for Code LLMs
2025-06-23
Muntasir Adnan, Carlos C. N. Kuhn
The effectiveness of AI debugging follows a predictable exponential decay pattern; most models lose 60-80% of their debugging capability within just 2-3 attempts, despite iterative debugging being a critical capability for practical code generation systems. We introduce the Debugging Decay Index (DDI), a mathematical framework that quantifies when debugging becomes ineffective and predicts intervention points. Our strategic fresh start approach shifts from exploitation to exploration at strategic points in the debugging process, demonstrating that well-timed interventions can rescue the effectiveness of debugging. DDI reveals a fundamental limitation in current AI debugging and provides the first quantitative framework for optimising iterative code generation strategies.
Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention
2025-06-23
Saad Wazir, Daeyoung Kim
Segmenting biomarkers in medical images is crucial for various biotech applications. Despite advances, Transformer and CNN based methods often struggle with variations in staining and morphology, limiting feature extraction. In medical image segmentation, where datasets often have limited sample availability, recent state-of-the-art (SOTA) methods achieve higher accuracy by leveraging pre-trained encoders, whereas end-to-end methods tend to underperform. This is due to challenges in effectively transferring rich multiscale features from encoders to decoders, as well as limitations in decoder efficiency. To address these issues, we propose an architecture that captures multi-scale local and global contextual information and a novel decoder design, which effectively integrates features from the encoder, emphasizes important channels and regions, and reconstructs spatial dimensions to enhance segmentation accuracy. Our method, compatible with various encoders, outperforms SOTA methods, as demonstrated by experiments on four datasets and ablation studies. Specifically, our method achieves absolute performance gains of 2.76% on MoNuSeg, 3.12% on DSB, 2.87% on Electron Microscopy, and 4.03% on TNBC datasets compared to existing SOTA methods. Code: https://github.com/saadwazir/MCADS-Decoder
Speech quality assessment (SQA) aims to evaluate the quality of speech samples without relying on time-consuming listener questionnaires. Recent efforts have focused on training neural-based SQA models to predict the mean opinion score (MOS) of speech samples produced by text-to-speech or voice conversion systems. This paper targets the enhancement of MOS prediction models' performance. We propose a novel score aggregation method to address the limitations of conventional annotations for MOS, which typically involve ratings on a scale from 1 to 5. Our method is based on the hypothesis that annotators internally consider continuous scores and then choose the nearest discrete rating. By modeling this process, we approximate the generative distribution of ratings by quantizing the latent continuous distribution. We then use the peak of this latent distribution, estimated through the loss between the quantized distribution and annotated ratings, as a new representative value instead of MOS. Experimental results demonstrate that substituting MOSNet's predicted target with this proposed value improves prediction performance.
Deep neural networks have demonstrated remarkable performance across numerous learning tasks but often suffer from miscalibration, resulting in unreliable probability outputs. This has inspired many recent works on mitigating miscalibration, particularly through post-hoc recalibration methods that aim to obtain calibrated probabilities without sacrificing the classification performance of pre-trained models. In this study, we summarize and categorize previous works into three general strategies: intuitively designed methods, binning-based methods, and methods based on formulations of ideal calibration. Through theoretical and practical analysis, we highlight ten common limitations in previous approaches. To address these limitations, we propose a probabilistic learning framework for calibration called h-calibration, which theoretically constructs an equivalent learning formulation for canonical calibration with boundedness. On this basis, we design a simple yet effective post-hoc calibration algorithm. Our method not only overcomes the ten identified limitations but also achieves markedly better performance than traditional methods, as validated by extensive experiments. We further analyze, both theoretically and experimentally, the relationship and advantages of our learning objective compared to traditional proper scoring rule. In summary, our probabilistic framework derives an approximately equivalent differentiable objective for learning error-bounded calibrated probabilities, elucidating the correspondence and convergence properties of computational statistics with respect to theoretical bounds in canonical calibration. The theoretical effectiveness is verified on standard post-hoc calibration benchmarks by achieving state-of-the-art performance. This research offers valuable reference for learning reliable likelihood in related fields.
Backchanneling (e.g., "uh-huh", "hmm", a simple nod) encompasses a big part of everyday communication; it is how we negotiate the turn to speak, it signals our engagement, and shapes the flow of our conversations. For people with speech and motor impairments, backchanneling is limited to a reduced set of modalities, and their Augmentative and Alternative Communication (AAC) technology requires visual attention, making it harder to observe non-verbal cues of conversation partners. We explore how users of AAC technology approach backchanneling and create their own unique channels and communication culture. We conducted a workshop with 4 AAC users to understand the unique characteristics of backchanneling in AAC. We explored how backchanneling changes when pairs of AAC users communicate vs when an AAC user communicates with a non-AAC user. We contextualize these findings through four in-depth interviews with speech-language pathologists (SLPs). We conclude with a discussion about backchanneling as a micro-cultural practice, rethinking embodiment and mediation in AAC technology, and providing design recommendations for timely multi-modal backchanneling while respecting different communication cultures.
Rethinking Ecological Measures Of Functional Diversity
2025-06-21
Ines Meraoumia, Adji Bousso Dieng
Understanding functional diversity, the range and variability of species' roles and actions within their communities, is key to predicting and preserving the functions that sustain both nature and human well-being. In this paper, we provide a comprehensive review of the literature on functional diversity measurement. We begin by consolidating essential criteria that effective measures of functional diversity should meet. We then evaluate fifteen widely used functional diversity metrics against these criteria and assess their performance across six synthetic ecosystem scenarios where optimal behavior is known. Surprisingly, our analysis reveals that none of the widely used metrics fully satisfy all the established requirements, and all fail in at least one ecosystem scenario. In particular, we find that almost all metrics flagrantly violate set monotonicity and distance monotonicity, requirements that adding a novel species should increase diversity, and that the magnitude of that increase should grow with trait dissimilarity. We also find that metrics fail to decline when rare, functionally extreme species are lost, and even increase when a perfectly redundant species is added. These critical flaws leave them blind to the very biodiversity loss that functional diversity measures are intended to detect. Our findings underscore the urgent need to develop a new generation of functional diversity metrics that more accurately reflect ecological realities.
AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction
2025-06-21
Song Wang, Zhen Tan, Zihan Chen, Shuang Zhou, Tianlong Chen, Jundong Li
Recent progress in large language model (LLM)-based multi-agent collaboration highlights the power of structured communication in enabling collective intelligence. However, existing methods largely rely on static or graph-based inter-agent topologies, lacking the potential adaptability and flexibility in communication. In this work, we propose a new framework that rethinks multi-agent coordination through a sequential structure rather than a graph structure, offering a significantly larger topology space for multi-agent communication. Our method focuses on two key directions: (1) Next-Agent Prediction, which selects the most suitable agent role at each step, and (2) Next-Context Selection (NCS), which enables each agent to selectively access relevant information from any previous step. Together, these components construct task-adaptive communication pipelines that support both role flexibility and global information flow. Extensive evaluations across multiple benchmarks demonstrate that our approach achieves superior performance while substantially reducing communication overhead.
Multi-condition fault diagnosis is prevalent in industrial systems and presents substantial challenges for conventional diagnostic approaches. The discrepancy in data distributions across different operating conditions degrades model performance when a model trained under one condition is applied to others. With the recent advancements in deep learning, transfer learning has been introduced to the fault diagnosis field as a paradigm for addressing multi-condition fault diagnosis. Among these methods, domain generalization approaches can handle complex scenarios by extracting condition-invariant fault features. Although many studies have considered fault diagnosis in specific multi-condition scenarios, the extent to which operating conditions affect fault information has been scarcely studied, which is crucial. However, the extent to which operating conditions affect fault information has been scarcely studied, which is crucial. When operating conditions have a significant impact on fault features, directly applying domain generalization methods may lead the model to learn condition-specific information, thereby reducing its overall generalization ability. This paper investigates the performance of existing end-to-end domain generalization methods under varying conditions, specifically in variable-speed and variable-load scenarios, using multiple experiments on a real-world gearbox. Additionally, a two-stage diagnostic framework is proposed, aiming to improve fault diagnosis performance under scenarios with significant operating condition impacts. By incorporating a domain-generalized encoder with a retraining strategy, the framework is able to extract condition-invariant fault features while simultaneously alleviating potential overfitting to the source domain. Several experiments on a real-world gearbox dataset are conducted to validate the effectiveness of the proposed approach.
Reinforcement learning has emerged as a powerful paradigm for post-training large language models (LLMs) to improve reasoning. Approaches like Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning with Verifiable Rewards (RLVR) have shown strong results, but they require extensive external supervision. We investigate an alternative class of methods, Reinforcement Learning from Internal Feedback (RLIF), which relies solely on intrinsic model-derived signals instead of external rewards. In particular, we leverage unsupervised reward proxies such as token-level entropy, trajectory-level entropy, and self-certainty. Our theoretical analysis shows these internal objectives are partially equivalent, and we empirically evaluate various RLIF strategies on challenging math reasoning benchmarks. Experimental results demonstrate that RLIF can boost the reasoning performance of base LLMs at the beginning phase of the training, matching or surpassing RLVR techniques on these tasks. However, when training progresses, performance degrades even below the model before training. Moreover, we find that RLIF yields little improvement for instruction-tuned models, indicating diminishing returns of intrinsic feedback once an LLM is already instruction-tuned. We further analyze this limitation by mixing model weights and explain the reason of RLIF's training behaviors, providing practical guidelines for integrating internal feedback signals into LLM training. We hope our analysis of internal feedback will inform more principled and effective strategies for LLM post-training.
Despite the impressive advancements of Large Vision-Language Models (LVLMs), existing approaches suffer from a fundamental bottleneck: inefficient visual-language integration. Current methods either disrupt the model's inherent structure or introduce severe long-context computational burden, severely limiting scalability and efficiency. In this paper, we rethink multimodal integration and present LaVi, a novel LVLM that enables seamless and efficient vision-language fusion through internal feature modulation within the Large Language Models (LLMs). Unlike dominant LVLMs that rely on visual token concatenation, LaVi bypasses long-context expansion by introducing a lightweight and adaptive transformation, which incorporates visual context by injecting token-wise vision-conditioned deltas into the affine parameters of layer normalization. This mechanism directly modulates linguistic hidden states based on visual input, ensuring precise vision-language alignment while preserving the LLM's linguistic priors and drastically reducing computational costs. Extensive evaluations across 15 image and video benchmarks demonstrate that LaVi not only achieves state-of-the-art multimodal performance but also dramatically enhances efficiency. Compared to LLaVA-OV-7B, LaVi reduces FLOPs by 94.0%, improves inference speed by 3.1 times, and cuts memory usage in half - establishing LaVi as a scalable and practical solution for real-time multimodal reasoning. The code and models will be released soon.
Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization
2025-06-20
Kaichen Ouyang
Graph Neural Networks (GNNs) have shown great potential in graph data analysis due to their powerful representation capabilities. However, as the network depth increases, the issue of over-smoothing becomes more severe, causing node representations to lose their distinctiveness. This paper analyzes the mechanism of over-smoothing through the analogy to Anderson localization and introduces participation degree as a metric to quantify this phenomenon. Specifically, as the depth of the GNN increases, node features homogenize after multiple layers of message passing, leading to a loss of distinctiveness, similar to the behavior of vibration modes in disordered systems. In this context, over-smoothing in GNNs can be understood as the expansion of low-frequency modes (increased participation degree) and the localization of high-frequency modes (decreased participation degree). Based on this, we systematically reviewed the potential connection between the Anderson localization behavior in disordered systems and the over-smoothing behavior in Graph Neural Networks. A theoretical analysis was conducted, and we proposed the potential of alleviating over-smoothing by reducing the disorder in information propagation.
AI is the Strategy: From Agentic AI to Autonomous Business Models onto Strategy in the Age of AI
2025-06-19
René Bohnsack, Mickie de Wet
This article develops the concept of Autonomous Business Models (ABMs) as a distinct managerial and strategic logic in the age of agentic AI. While most firms still operate within human-driven or AI-augmented models, we argue that we are now entering a phase where agentic AI (systems capable of initiating, coordinating, and adapting actions autonomously) can increasingly execute the core mechanisms of value creation, delivery, and capture. This shift reframes AI not as a tool to support strategy, but as the strategy itself. Using two illustrative cases, getswan.ai, an Israeli startup pursuing autonomy by design, and a hypothetical reconfiguration of Ryanair as an AI-driven incumbent, we depict the evolution from augmented to autonomous business models. We show how ABMs reshape competitive advantage through agentic execution, continuous adaptation, and the gradual offloading of human decision-making. This transition introduces new forms of competition between AI-led firms, which we term synthetic competition, where strategic interactions occur at rapid, machine-level speed and scale. It also challenges foundational assumptions in strategy, organizational design, and governance. By positioning agentic AI as the central actor in business model execution, the article invites us to rethink strategic management in an era where firms increasingly run themselves.
Golden Partition Zone: Rethinking Neural Network Partitioning Under Inversion Threats in Collaborative Inference
2025-06-18
Rongke Liu, Youwen Zhu
In collaborative inference, intermediate features transmitted from edge devices can be exploited by adversaries to reconstruct original inputs via model inversion attacks (MIA). While existing defenses focus on shallow layer protection, they often incur significant utility loss. A key open question is how to partition the edge cloud model to maximize resistance to MIA while minimizing accuracy degradation. We firest overturn the common belief that increasing model depth can resist MIA. Through theoretical analysis, we show that representational transitions in neural networks cause sharp changes in conditional entropy $H(x\mid z)$, intra class mean squared radius ($R_c^2$) and feature dimensionality being critical factors. Experiments on three representative deep vision models show that partitioning at the representational transition or decision level layers yields over 4 times higher mean square error compared to shallow splits, indicating significantly stronger resistance to MIA. Positive label smoothing further enhances robustness by compressing $R_c^2$ and improving generalization. We also validate the resilience of decision level features under feature and inversion model enhancements, and observe that auxiliary data types influence both transition boundaries and reconstruction behavior.
Rethinking LLM Training through Information Geometry and Quantum Metrics
2025-06-18
Riccardo Di Sipio
Optimization in large language models (LLMs) unfolds over high-dimensional parameter spaces with non-Euclidean structure. Information geometry frames this landscape using the Fisher information metric, enabling more principled learning via natural gradient descent. Though often impractical, this geometric lens clarifies phenomena such as sharp minima, generalization, and observed scaling laws. We argue that curvature-aware approaches deepen our understanding of LLM training. Finally, we speculate on quantum analogies based on the Fubini-Study metric and Quantum Fisher Information, hinting at efficient optimization in quantum-enhanced systems.
The explosive growth of Large Language Models (LLMs) - such as GPT-4 with 1.8 trillion parameters - demands a radical rethinking of data center architecture to ensure scalability, efficiency, and cost-effectiveness. Our work provides a comprehensive co-design framework that jointly explores FLOPS, HBM bandwidth and capacity, multiple network topologies (two-tier vs. FullFlat optical), the size of the scale-out domain, and popular parallelism/optimization strategies used in LLMs. We introduce and evaluate FullFlat network architectures, which provide uniform high-bandwidth, low-latency connectivity between all nodes, and demonstrate their transformative impact on performance and scalability. Through detailed sensitivity analyses, we quantify the benefits of overlapping compute and communication, leveraging hardware-accelerated collectives, wider scale-out domains, and larger memory capacity. Our study spans both sparse (mixture of experts) and dense transformer-based LLMs, revealing how system design choices affect Model FLOPS Utilization (MFU = Model flops per token x Observed tokens per sec / Peak flops of the hardware) and overall throughput. For the co-design study, we extended and validated a performance modeling tool capable of predicting LLM runtime within 10% of real-world measurements. Our findings offer actionable insights and a practical roadmap for designing AI data centers that can efficiently support trillion-parameter models, reduce optimization complexity, and sustain the rapid evolution of AI capabilities.
Previous methods for image geo-localization have typically treated the task as either classification or retrieval, often relying on black-box decisions that lack interpretability. The rise of large vision-language models (LVLMs) has enabled a rethinking of geo-localization as a reasoning-driven task grounded in visual cues. However, two major challenges persist. On the data side, existing reasoning-focused datasets are primarily based on street-view imagery, offering limited scene diversity and constrained viewpoints. On the modeling side, current approaches predominantly rely on supervised fine-tuning, which yields only marginal improvements in reasoning capabilities. To address these challenges, we propose a novel pipeline that constructs a reasoning-oriented geo-localization dataset, MP16-Reason, using diverse social media images. We introduce GLOBE, Group-relative policy optimization for Locatability assessment and Optimized visual-clue reasoning, yielding Bi-objective geo-Enhancement for the VLM in recognition and reasoning. GLOBE incorporates task-specific rewards that jointly enhance locatability assessment, visual clue reasoning, and geolocation accuracy. Both qualitative and quantitative results demonstrate that GLOBE outperforms state-of-the-art open-source LVLMs on geo-localization tasks, particularly in diverse visual scenes, while also generating more insightful and interpretable reasoning trajectories.
One Size Fits None: Rethinking Fairness in Medical AI
2025-06-17
Roland Roller, Michael Hahn, Ajay Madhavan Ravichandran, Bilgin Osmanodja, Florian Oetke, Zeineb Sassi, Aljoscha Burchardt, Klaus Netter, Klemens Budde, Anne Herrmann, Tobias Strapatsas, Peter Dabrock, Sebastian Möller
Machine learning (ML) models are increasingly used to support clinical decision-making. However, real-world medical datasets are often noisy, incomplete, and imbalanced, leading to performance disparities across patient subgroups. These differences raise fairness concerns, particularly when they reinforce existing disadvantages for marginalized groups. In this work, we analyze several medical prediction tasks and demonstrate how model performance varies with patient characteristics. While ML models may demonstrate good overall performance, we argue that subgroup-level evaluation is essential before integrating them into clinical workflows. By conducting a performance analysis at the subgroup level, differences can be clearly identified-allowing, on the one hand, for performance disparities to be considered in clinical practice, and on the other hand, for these insights to inform the responsible development of more effective models. Thereby, our work contributes to a practical discussion around the subgroup-sensitive development and deployment of medical ML models and the interconnectedness of fairness and transparency.
The Tech DEI Backlash -- The Changing Landscape of Diversity, Equity, and Inclusion in Software Engineering
2025-06-17
Sonja M. Hyrynsalmi, Mary Sanchez-Gordon, Anna Szlavi, Letizia Jaccheri
Not long ago, Diversity, Equity, and Inclusion (DEI) initiatives were a top priority for leading software companies. However, in a short period, a wave of backlash has led many firms to re-assess their DEI strategies. Responding to this DEI backlash is crucial in academic research, especially because, currently, little scholarly research has been done on it. In this paper, therefore, we have set forth the following research question (RQ): "How have leading software companies changed their DEI strategies in recent years?" Given the novelty of the RQ and, consequently, the lack of scholarly research on it, we are conducting a grey literature study, examining the current state of DEI initiatives in 10 leading software companies. Based on our analysis, we have classified companies into categories based on their shift in commitment to DEI. We can identify that companies are indeed responding to the backlash by rethinking their strategy, either by reducing, increasing, or renaming their DEI initiatives. In contrast, some companies keep on with their DEI strategy, at least so far, despite the challenging political climate. To illustrate these changes, we introduce the DEI Universe Map, a visual representation of software industry trends in DEI commitment and actions.
Production Function Estimation without Invertibility: Imperfectly Competitive Environments and Demand Shocks
2025-06-16
Ulrich Doraszelski, Lixiong Li
We advance the proxy variable approach to production function estimation. We show that the invertibility assumption at its heart is testable. We characterize what goes wrong if invertibility fails and what can still be done. We show that rethinking how the estimation procedure is implemented either eliminates or mitigates the bias that arises if invertibility fails. In particular, a simple change to the first step of the estimation procedure provides a first-order bias correction for the GMM estimator in the second step. Furthermore, a modification of the moment condition in the second step ensures Neyman orthogonality and enhances efficiency and robustness by rendering the asymptotic distribution of the GMM estimator invariant to estimation noise from the first step.
2025-06-16
Ming Shen, Zhikun Xu, Xiao Ye, Jacob Dineen, Ben Zhou
Large language models (LLMs) are typically trained via next-word prediction (NWP), which provides strong surface-level fluency but often lacks support for robust reasoning. We propose BOttlenecked next Word exploration (BOW), a novel RL framework that rethinks NWP by introducing a reasoning bottleneck where a policy model first generates a reasoning path rather than predicting the next token directly, after which a frozen judge model predicts the next token distribution based solely on this reasoning path. We train the policy model using GRPO with rewards that quantify how effectively the reasoning path facilitates next-word recovery. Compared with other continual pretraining baselines, we show that BOW improves both the general and next-word reasoning capabilities of the base model, evaluated on various benchmarks. Our findings show that BOW can serve as an effective and scalable alternative to vanilla NWP.
From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs
2025-06-16
Alsharif Abuadbba, Chris Hicks, Kristen Moore, Vasilios Mavroudis, Burak Hasircioglu, Diksha Goel, Piers Jennings
Large Language Models (LLMs) are set to reshape cybersecurity by augmenting red and blue team operations. Red teams can exploit LLMs to plan attacks, craft phishing content, simulate adversaries, and generate exploit code. Conversely, blue teams may deploy them for threat intelligence synthesis, root cause analysis, and streamlined documentation. This dual capability introduces both transformative potential and serious risks. This position paper maps LLM applications across cybersecurity frameworks such as MITRE ATT&CK and the NIST Cybersecurity Framework (CSF), offering a structured view of their current utility and limitations. While LLMs demonstrate fluency and versatility across various tasks, they remain fragile in high-stakes, context-heavy environments. Key limitations include hallucinations, limited context retention, poor reasoning, and sensitivity to prompts, which undermine their reliability in operational settings. Moreover, real-world integration raises concerns around dual-use risks, adversarial misuse, and diminished human oversight. Malicious actors could exploit LLMs to automate reconnaissance, obscure attack vectors, and lower the technical threshold for executing sophisticated attacks. To ensure safer adoption, we recommend maintaining human-in-the-loop oversight, enhancing model explainability, integrating privacy-preserving mechanisms, and building systems robust to adversarial exploitation. As organizations increasingly adopt AI driven cybersecurity, a nuanced understanding of LLMs' risks and operational impacts is critical to securing their defensive value while mitigating unintended consequences.
Rethinking Test-Time Scaling for Medical AI: Model and Task-Aware Strategies for LLMs and VLMs
2025-06-16
Gyutaek Oh, Seoyeon Kim, Sangjoon Park, Byung-Hoon Kim
Test-time scaling has recently emerged as a promising approach for enhancing the reasoning capabilities of large language models or vision-language models during inference. Although a variety of test-time scaling strategies have been proposed, and interest in their application to the medical domain is growing, many critical aspects remain underexplored, including their effectiveness for vision-language models and the identification of optimal strategies for different settings. In this paper, we conduct a comprehensive investigation of test-time scaling in the medical domain. We evaluate its impact on both large language models and vision-language models, considering factors such as model size, inherent model characteristics, and task complexity. Finally, we assess the robustness of these strategies under user-driven factors, such as misleading information embedded in prompts. Our findings offer practical guidelines for the effective use of test-time scaling in medical applications and provide insights into how these strategies can be further refined to meet the reliability and interpretability demands of the medical domain.
Rethinking Explainability in the Era of Multimodal AI
2025-06-16
Chirag Agarwal
While multimodal AI systems (models jointly trained on heterogeneous data types such as text, time series, graphs, and images) have become ubiquitous and achieved remarkable performance across high-stakes applications, transparent and accurate explanation algorithms are crucial for their safe deployment and ensure user trust. However, most existing explainability techniques remain unimodal, generating modality-specific feature attributions, concepts, or circuit traces in isolation and thus failing to capture cross-modal interactions. This paper argues that such unimodal explanations systematically misrepresent and fail to capture the cross-modal influence that drives multimodal model decisions, and the community should stop relying on them for interpreting multimodal models. To support our position, we outline key principles for multimodal explanations grounded in modality: Granger-style modality influence (controlled ablations to quantify how removing one modality changes the explanation for another), Synergistic faithfulness (explanations capture the model's predictive power when modalities are combined), and Unified stability (explanations remain consistent under small, cross-modal perturbations). This targeted shift to multimodal explanations will help the community uncover hidden shortcuts, mitigate modality bias, improve model reliability, and enhance safety in high-stakes settings where incomplete explanations can have serious consequences.
Rethinking Optimization: A Systems-Based Approach to Social Externalities
2025-06-15
Pegah Nokhiz, Aravinda Kanchana Ruwanpathirana, Helen Nissenbaum
Optimization is widely used for decision making across various domains, valued for its ability to improve efficiency. However, poor implementation practices can lead to unintended consequences, particularly in socioeconomic contexts where externalities (costs or benefits to third parties outside the optimization process) are significant. To propose solutions, it is crucial to first characterize involved stakeholders, their goals, and the types of subpar practices causing unforeseen outcomes. This task is complex because affected stakeholders often fall outside the direct focus of optimization processes. Also, incorporating these externalities into optimization requires going beyond traditional economic frameworks, which often focus on describing externalities but fail to address their normative implications or interconnected nature, and feedback loops. This paper suggests a framework that combines systems thinking with the economic concept of externalities to tackle these challenges. This approach aims to characterize what went wrong, who was affected, and how (or where) to include them in the optimization process. Economic externalities, along with their established quantification methods, assist in identifying "who was affected and how" through stakeholder characterization. Meanwhile, systems thinking (an analytical approach to comprehending relationships in complex systems) provides a holistic, normative perspective. Systems thinking contributes to an understanding of interconnections among externalities, feedback loops, and determining "when" to incorporate them in the optimization. Together, these approaches create a comprehensive framework for addressing optimization's unintended consequences, balancing descriptive accuracy with normative objectives. Using this, we examine three common types of subpar practices: ignorance, error, and prioritization of short-term goals.
Rethinking Hate Speech Detection on Social Media: Can LLMs Replace Traditional Models?
2025-06-15
Daman Deep Singh, Ramanuj Bhattacharjee, Abhijnan Chakraborty
Hate speech detection across contemporary social media presents unique challenges due to linguistic diversity and the informal nature of online discourse. These challenges are further amplified in settings involving code-mixing, transliteration, and culturally nuanced expressions. While fine-tuned transformer models, such as BERT, have become standard for this task, we argue that recent large language models (LLMs) not only surpass them but also redefine the landscape of hate speech detection more broadly. To support this claim, we introduce IndoHateMix, a diverse, high-quality dataset capturing Hindi-English code-mixing and transliteration in the Indian context, providing a realistic benchmark to evaluate model robustness in complex multilingual scenarios where existing NLP methods often struggle. Our extensive experiments show that cutting-edge LLMs (such as LLaMA-3.1) consistently outperform task-specific BERT-based models, even when fine-tuned on significantly less data. With their superior generalization and adaptability, LLMs offer a transformative approach to mitigating online hate in diverse environments. This raises the question of whether future works should prioritize developing specialized models or focus on curating richer and more varied datasets to further enhance the effectiveness of LLMs.
Rethinking DPO: The Role of Rejected Responses in Preference Misalignment
2025-06-15
Jay Hyeon Cho, JunHyeok Oh, Myunsoo Kim, Byung-Jun Lee
Direct Preference Optimization (DPO) is a simple and efficient framework that has attracted substantial attention. However, it often struggles to meet its primary objectives -- increasing the generation probability of chosen responses while reducing that of rejected responses -- due to the dominant influence of rejected responses on the loss function. This imbalance leads to suboptimal performance in promoting preferred responses. In this work, we systematically analyze the limitations of DPO and existing algorithms designed to achieve the objectives stated above. To address these limitations, we propose Bounded-DPO (BDPO), a novel method that bounds the influence of rejected responses while maintaining the original optimization structure of DPO. Through theoretical analysis and empirical evaluations, we demonstrate that BDPO achieves a balanced optimization of the chosen and rejected responses, outperforming existing algorithms.
Rethinking Distributional IVs: KAN-Powered D-IV-LATE & Model Choice
2025-06-15
Charles Shaw
The double/debiased machine learning (DML) framework has become a cornerstone of modern causal inference, allowing researchers to utilise flexible machine learning models for the estimation of nuisance functions without introducing first-order bias into the final parameter estimate. However, the choice of machine learning model for the nuisance functions is often treated as a minor implementation detail. In this paper, we argue that this choice can have a profound impact on the substantive conclusions of the analysis. We demonstrate this by presenting and comparing two distinct Distributional Instrumental Variable Local Average Treatment Effect (D-IV-LATE) estimators. The first estimator leverages standard machine learning models like Random Forests for nuisance function estimation, while the second is a novel estimator employing Kolmogorov-Arnold Networks (KANs). We establish the asymptotic properties of these estimators and evaluate their performance through Monte Carlo simulations. An empirical application analysing the distributional effects of 401(k) participation on net financial assets reveals that the choice of machine learning model for nuisance functions can significantly alter substantive conclusions, with the KAN-based estimator suggesting more complex treatment effect heterogeneity. These findings underscore a critical "caveat emptor". The selection of nuisance function estimators is not a mere implementation detail. Instead, it is a pivotal choice that can profoundly impact research outcomes in causal inference.
Refract ICL: Rethinking Example Selection in the Era of Million-Token Models
2025-06-14
Arjun R. Akula, Kazuma Hashimoto, Krishna Srinivasan, Aditi Chaudhary, Karthik Raman, Michael Bendersky
The emergence of long-context large language models (LLMs) has enabled the use of hundreds, or even thousands, of demonstrations for in-context learning (ICL) - a previously impractical regime. This paper investigates whether traditional ICL selection strategies, which balance the similarity of ICL examples to the test input (using a text retriever) with diversity within the ICL set, remain effective when utilizing a large number of demonstrations. Our experiments demonstrate that, while longer contexts can accommodate more examples, simply increasing the number of demonstrations does not guarantee improved performance. Smart ICL selection remains crucial, even with thousands of demonstrations. To further enhance ICL in this setting, we introduce Refract ICL, a novel ICL selection algorithm specifically designed to focus LLM attention on challenging examples by strategically repeating them within the context and incorporating zero-shot predictions as error signals. Our results show that Refract ICL significantly improves the performance of extremely long-context models such as Gemini 1.5 Pro, particularly on tasks with a smaller number of output classes.
Modern front-end frameworks face escalating reactivity management challenges, including performance degradation from complex observable chains and unpredictable re-renders. This paper introduces Signal-First Architecture--a novel paradigm where granular, dependency-tracked signals are the atomic unit of reactivity. Unlike traditional RxJS or NgRx patterns, Signal-First enforces reactive flows from explicit signal declarations, with derived values via computed() and side effects scoped to effect(). This model ensures deterministic behavior by eliminating implicit subscriptions and optimizing reactive graph evaluation. We present a comparative analysis of three Angular reactivity models: RxJS service-based, NgRx global stores, and pure Signal-First implementations. Through controlled benchmarking, including Chrome DevTools performance tracing, memory heap snapshots, and Lighthouse audits, this study quantifies Signal-First advantages.
Vision-Language Translation (VLT) is a challenging task that requires accurately recognizing multilingual text embedded in images and translating it into the target language with the support of visual context. While recent Large Vision-Language Models (LVLMs) have demonstrated strong multilingual and visual understanding capabilities, there is a lack of systematic evaluation and understanding of their performance on VLT. In this work, we present a comprehensive study of VLT from three key perspectives: data quality, model architecture, and evaluation metrics. (1) We identify critical limitations in existing datasets, particularly in semantic and cultural fidelity, and introduce AibTrans -- a multilingual, parallel, human-verified dataset with OCR-corrected annotations. (2) We benchmark 11 commercial LVLMs/LLMs and 6 state-of-the-art open-source models across end-to-end and cascaded architectures, revealing their OCR dependency and contrasting generation versus reasoning behaviors. (3) We propose Density-Aware Evaluation to address metric reliability issues under varying contextual complexity, introducing the DA Score as a more robust measure of translation quality. Building upon these findings, we establish a new evaluation benchmark for VLT. Notably, we observe that fine-tuning LVLMs on high-resource language pairs degrades cross-lingual performance, and we propose a balanced multilingual fine-tuning strategy that effectively adapts LVLMs to VLT without sacrificing their generalization ability.
Large language models have not only captivated the public imagination but have also sparked a profound rethinking of how we learn. In the third year following the breakthrough launch of ChatGPT, everyday informal learning has been transformed as diverse user groups explore these novel tools. Who is embracing LLMs for self-directed learning, and who remains hesitant? What are their reasons for adoption or avoidance? What learning patterns emerge with this novel technological landscape? We present an in-depth analysis from a large-scale survey of 776 participants, showcasing that 88% of our respondents already incorporate LLMs into their everyday learning routines for a wide variety of (learning) tasks. Young adults are at the forefront of adopting LLMs, primarily to enhance their learning experiences independently of time and space. Four types of learners emerge across learning contexts, depending on the tasks they perform with LLMs and the devices they use to access them. Interestingly, our respondents exhibit paradoxical behaviours regarding their trust in LLMs' accuracy and privacy protection measures. Our implications emphasize the importance of including different media types for learning, enabling collaborative learning, providing sources and meeting the needs of different types of learners and learning by design.
Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks
2025-06-13
Georgios Kontes, Diomidis S. Michalopoulos, Birendra Ghimire, Christopher Mutschler
AI/ML research has predominantly been driven by domains such as computer vision, natural language processing, and video analysis. In contrast, the application of AI/ML to wireless networks, particularly at the air interface, remains in its early stages. Although there are emerging efforts to explore this intersection, fully realizing the potential of AI/ML in wireless communications requires a deep interdisciplinary understanding of both fields. We provide an overview of AI/ML-related discussions in 3GPP standardization, highlighting key use cases, architectural considerations, and technical requirements. We outline open research challenges and opportunities where academic and industrial communities can contribute to shaping the future of AI-enabled wireless systems.
Rethinking Competition as a Non-Beneficial Mechanism in Economic Systems
2025-06-13
Marcelo S. Tedesco, Gonzalo Marquez
Persistent economic competition is often justified as a mechanism of innovation, efficiency, and welfare maximization. Yet empirical evidence across disciplines reveals that competition systematically generates fragility, inequality, and ecological degradation, emergent outcomes not of isolated failures but of underlying systemic dynamics. This work reconceptualizes economic ecosystems as real complex adaptive systems, structurally isomorphic with biological and social ecosystems. Integrating complexity science, evolutionary biology, ecology, and economic and business theory, we classify economic interactions according to their systemic effects and propose a theoretical model of ecosystemic equilibrium based on the predominance of beneficial versus non-beneficial relationships. Recognizing economies as ecologically embedded and structurally interdependent systems provides a novel framework for analyzing systemic resilience, reframing competition as a non-beneficial mechanism.
Reversing the Paradigm: Building AI-First Systems with Human Guidance
2025-06-13
Cosimo Spera, Garima Agrawal
The relationship between humans and artificial intelligence is no longer science fiction -- it's a growing reality reshaping how we live and work. AI has moved beyond research labs into everyday life, powering customer service chats, personalizing travel, aiding doctors in diagnosis, and supporting educators. What makes this moment particularly compelling is AI's increasing collaborative nature. Rather than replacing humans, AI augments our capabilities -- automating routine tasks, enhancing decisions with data, and enabling creativity in fields like design, music, and writing. The future of work is shifting toward AI agents handling tasks autonomously, with humans as supervisors, strategists, and ethical stewards. This flips the traditional model: instead of humans using AI as a tool, intelligent agents will operate independently within constraints, managing everything from scheduling and customer service to complex workflows. Humans will guide and fine-tune these agents to ensure alignment with goals, values, and context. This shift offers major benefits -- greater efficiency, faster decisions, cost savings, and scalability. But it also brings risks: diminished human oversight, algorithmic bias, security flaws, and a widening skills gap. To navigate this transition, organizations must rethink roles, invest in upskilling, embed ethical principles, and promote transparency. This paper examines the technological and organizational changes needed to enable responsible adoption of AI-first systems -- where autonomy is balanced with human intent, oversight, and values.
2025-06-12
Sebastian Sanokowski, Lukas Gruber, Christoph Bartmann, Sepp Hochreiter, Sebastian Lehner
Diffusion bridges are a promising class of deep-learning methods for sampling from unnormalized distributions. Recent works show that the Log Variance (LV) loss consistently outperforms the reverse Kullback-Leibler (rKL) loss when using the reparametrization trick to compute rKL-gradients. While the on-policy LV loss yields identical gradients to the rKL loss when combined with the log-derivative trick for diffusion samplers with non-learnable forward processes, this equivalence does not hold for diffusion bridges or when diffusion coefficients are learned. Based on this insight we argue that for diffusion bridges the LV loss does not represent an optimization objective that can be motivated like the rKL loss via the data processing inequality. Our analysis shows that employing the rKL loss with the log-derivative trick (rKL-LD) does not only avoid these conceptual problems but also consistently outperforms the LV loss. Experimental results with different types of diffusion bridges on challenging benchmarks show that samplers trained with the rKL-LD loss achieve better performance. From a practical perspective we find that rKL-LD requires significantly less hyperparameter optimization and yields more stable training behavior.
Spurious Rewards: Rethinking Training Signals in RLVR
2025-06-12
Rulin Shao, Shuyue Stella Li, Rui Xin, Scott Geng, Yiping Wang, Sewoong Oh, Simon Shaolei Du, Nathan Lambert, Sewon Min, Ranjay Krishna, Yulia Tsvetkov, Hannaneh Hajishirzi, Pang Wei Koh, Luke Zettlemoyer
We show that reinforcement learning with verifiable rewards (RLVR) can elicit strong mathematical reasoning in certain models even with spurious rewards that have little, no, or even negative correlation with the correct answer. For example, RLVR improves MATH-500 performance for Qwen2.5-Math-7B in absolute points by 21.4% (random reward), 13.8% (format reward), 24.1% (incorrect label), 26.0% (1-shot RL), and 27.1% (majority voting) -- nearly matching the 29.1% gained with ground truth rewards. However, the spurious rewards that work for Qwen often fail to yield gains with other model families like Llama3 or OLMo2. In particular, we find code reasoning -- thinking in code without actual code execution -- to be a distinctive Qwen2.5-Math behavior that becomes significantly more frequent after RLVR, from 65% to over 90%, even with spurious rewards. Overall, we hypothesize that, given the lack of useful reward signal, RLVR must somehow be surfacing useful reasoning representations learned during pretraining, although the exact mechanism remains a topic for future work. We suggest that future RLVR research should possibly be validated on diverse models rather than a single de facto choice, as we show that it is easy to get significant performance gains on Qwen models even with completely spurious reward signals.
Rethinking Growth: An Extension of the Solow-Swan Model
2025-06-12
Timothy F. Power, Roman G. Smirnov
The aggregate Cobb-Douglas production function stands as a central element in the renowned Solow-Swan model in economics, providing a crucial theoretical framework for comprehending the determinants of economic growth. This model not only guides policymakers and economists but also influences their decisions, fostering sustainable and inclusive development. In this study, we utilize a one-input version of a new generalization of the Cobb-Douglas production function proposed recently, thereby extending the Solow-Swan model to incorporate energy production as a factor. We offer a rationale for this extension and conduct a comprehensive analysis employing advanced mathematical tools to explore solutions to this new model. This approach allows us to effectively integrate environmental considerations related to energy production into economic growth strategies, fostering long-term sustainability.
PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework
2025-06-12
SiXiang Chen, Jianyu Lai, Jialin Gao, Tian Ye, Haoyu Chen, Hengyu Shi, Shitong Shao, Yunlong Lin, Song Fei, Zhaohu Xing, Yeying Jin, Junfeng Luo, Xiaoming Wei, Lei Zhu
Generating aesthetic posters is more challenging than simple design images: it requires not only precise text rendering but also the seamless integration of abstract artistic content, striking layouts, and overall stylistic harmony. To address this, we propose PosterCraft, a unified framework that abandons prior modular pipelines and rigid, predefined layouts, allowing the model to freely explore coherent, visually compelling compositions. PosterCraft employs a carefully designed, cascaded workflow to optimize the generation of high-aesthetic posters: (i) large-scale text-rendering optimization on our newly introduced Text-Render-2M dataset; (ii) region-aware supervised fine-tuning on HQ-Poster100K; (iii) aesthetic-text-reinforcement learning via best-of-n preference optimization; and (iv) joint vision-language feedback refinement. Each stage is supported by a fully automated data-construction pipeline tailored to its specific needs, enabling robust training without complex architectural modifications. Evaluated on multiple experiments, PosterCraft significantly outperforms open-source baselines in rendering accuracy, layout coherence, and overall visual appeal-approaching the quality of SOTA commercial systems. Our code, models, and datasets can be found in the Project page: https://ephemeral182.github.io/PosterCraft
Rethinking Random Masking in Self Distillation on ViT
2025-06-12
Jihyeon Seong, Hyunkyung Han
Vision Transformers (ViTs) have demonstrated remarkable performance across a wide range of vision tasks. In particular, self-distillation frameworks such as DINO have contributed significantly to these advances. Within such frameworks, random masking is often utilized to improve training efficiency and introduce regularization. However, recent studies have raised concerns that indiscriminate random masking may inadvertently eliminate critical semantic information, motivating the development of more informed masking strategies. In this study, we explore the role of random masking in the self-distillation setting, focusing on the DINO framework. Specifically, we apply random masking exclusively to the student's global view, while preserving the student's local views and the teacher's global view in their original, unmasked forms. This design leverages DINO's multi-view augmentation scheme to retain clean supervision while inducing robustness through masked inputs. We evaluate our approach using DINO-Tiny on the mini-ImageNet dataset and show that random masking under this asymmetric setup yields more robust and fine-grained attention maps, ultimately enhancing downstream performance.
Large language models (LLMs) often struggle with knowledge-intensive tasks due to a lack of background knowledge and a tendency to hallucinate. To address these limitations, integrating knowledge graphs (KGs) with LLMs has been intensively studied. Existing KG-enhanced LLMs focus on supplementary factual knowledge, but still struggle with solving complex questions. We argue that refining the relationships among facts and organizing them into a logically consistent reasoning path is equally important as factual knowledge itself. Despite their potential, extracting reliable reasoning paths from KGs poses the following challenges: the complexity of graph structures and the existence of multiple generated paths, making it difficult to distinguish between useful and redundant ones. To tackle these challenges, we propose the RRP framework to mine the knowledge graph, which combines the semantic strengths of LLMs with structural information obtained through relation embedding and bidirectional distribution learning. Additionally, we introduce a rethinking module that evaluates and refines reasoning paths according to their significance. Experimental results on two public datasets show that RRP achieves state-of-the-art performance compared to existing baseline methods. Moreover, RRP can be easily integrated into various LLMs to enhance their reasoning abilities in a plug-and-play manner. By generating high-quality reasoning paths tailored to specific questions, RRP distills effective guidance for LLM reasoning.
Rethinking Generative Human Video Coding with Implicit Motion Transformation
2025-06-12
Bolin Chen, Ru-Ling Liao, Jie Chen, Yan Ye
Beyond traditional hybrid-based video codec, generative video codec could achieve promising compression performance by evolving high-dimensional signals into compact feature representations for bitstream compactness at the encoder side and developing explicit motion fields as intermediate supervision for high-quality reconstruction at the decoder side. This paradigm has achieved significant success in face video compression. However, compared to facial videos, human body videos pose greater challenges due to their more complex and diverse motion patterns, i.e., when using explicit motion guidance for Generative Human Video Coding (GHVC), the reconstruction results could suffer severe distortions and inaccurate motion. As such, this paper highlights the limitations of explicit motion-based approaches for human body video compression and investigates the GHVC performance improvement with the aid of Implicit Motion Transformation, namely IMT. In particular, we propose to characterize complex human body signal into compact visual features and transform these features into implicit motion guidance for signal reconstruction. Experimental results demonstrate the effectiveness of the proposed IMT paradigm, which can facilitate GHVC to achieve high-efficiency compression and high-fidelity synthesis.
It's Not the Target, It's the Background: Rethinking Infrared Small Target Detection via Deep Patch-Free Low-Rank Representations
2025-06-12
Guoyi Zhang, Guangsheng Xu, Siyang Chen, Han Wang, Xiaohu Zhang
Infrared small target detection (IRSTD) remains a long-standing challenge in complex backgrounds due to low signal-to-clutter ratios (SCR), diverse target morphologies, and the absence of distinctive visual cues. While recent deep learning approaches aim to learn discriminative representations, the intrinsic variability and weak priors of small targets often lead to unstable performance. In this paper, we propose a novel end-to-end IRSTD framework, termed LRRNet, which leverages the low-rank property of infrared image backgrounds. Inspired by the physical compressibility of cluttered scenes, our approach adopts a compression--reconstruction--subtraction (CRS) paradigm to directly model structure-aware low-rank background representations in the image domain, without relying on patch-based processing or explicit matrix decomposition. To the best of our knowledge, this is the first work to directly learn low-rank background structures using deep neural networks in an end-to-end manner. Extensive experiments on multiple public datasets demonstrate that LRRNet outperforms 38 state-of-the-art methods in terms of detection accuracy, robustness, and computational efficiency. Remarkably, it achieves real-time performance with an average speed of 82.34 FPS. Evaluations on the challenging NoisySIRST dataset further confirm the model's resilience to sensor noise. The source code will be made publicly available upon acceptance.
Closer to Language than Steam: AI as the Cognitive Engine of a New Productivity Revolution
2025-06-12
Xinmin Fang, Lingfeng Tao, Zhengxiong Li
Artificial Intelligence (AI) is reframed as a cognitive engine driving a novel productivity revolution distinct from the Industrial Revolution's physical thrust. This paper develops a theoretical framing of AI as a cognitive revolution akin to written language - a transformative augmentation of human intellect rather than another mechanized tool. We compare AI's emergence to historical leaps in information technology to show how it amplifies knowledge work. Examples from various domains demonstrate AI's impact as a driver of productivity in cognitive tasks. We adopt a multidisciplinary perspective combining computer science advances with economic insights and sociological perspectives on how AI reshapes work and society. Through conceptual frameworks, we visualize the shift from manual to cognitive productivity. Our central argument is that AI functions as an engine of cognition - comparable to how human language revolutionized knowledge - heralding a new productivity paradigm. We discuss how this revolution demands rethinking of skills, organizations, and policies. This paper, balancing academic rigor with clarity, concludes that AI's promise lies in complementing human cognitive abilities, marking a new chapter in productivity evolution.
Do We Still Need Audio? Rethinking Speaker Diarization with a Text-Based Approach Using Multiple Prediction Models
2025-06-12
Peilin Wu, Jinho D. Choi
We present a novel approach to Speaker Diarization (SD) by leveraging text-based methods focused on Sentence-level Speaker Change Detection within dialogues. Unlike audio-based SD systems, which are often challenged by audio quality and speaker similarity, our approach utilizes the dialogue transcript alone. Two models are developed: the Single Prediction Model (SPM) and the Multiple Prediction Model (MPM), both of which demonstrate significant improvements in identifying speaker changes, particularly in short conversations. Our findings, based on a curated dataset encompassing diverse conversational scenarios, reveal that the text-based SD approach, especially the MPM, performs competitively against state-of-the-art audio-based SD systems, with superior performance in short conversational contexts. This paper not only showcases the potential of leveraging linguistic features for SD but also highlights the importance of integrating semantic understanding into SD systems, opening avenues for future research in multimodal and semantic feature-based diarization.
From Replication to Redesign: Exploring Pairwise Comparisons for LLM-Based Peer Review
2025-06-12
Yaohui Zhang, Haijing Zhang, Wenlong Ji, Tianyu Hua, Nick Haber, Hancheng Cao, Weixin Liang
The advent of large language models (LLMs) offers unprecedented opportunities to reimagine peer review beyond the constraints of traditional workflows. Despite these opportunities, prior efforts have largely focused on replicating traditional review workflows with LLMs serving as direct substitutes for human reviewers, while limited attention has been given to exploring new paradigms that fundamentally rethink how LLMs can participate in the academic review process. In this paper, we introduce and explore a novel mechanism that employs LLM agents to perform pairwise comparisons among manuscripts instead of individual scoring. By aggregating outcomes from substantial pairwise evaluations, this approach enables a more accurate and robust measure of relative manuscript quality. Our experiments demonstrate that this comparative approach significantly outperforms traditional rating-based methods in identifying high-impact papers. However, our analysis also reveals emergent biases in the selection process, notably a reduced novelty in research topics and an increased institutional imbalance. These findings highlight both the transformative potential of rethinking peer review with LLMs and critical challenges that future systems must address to ensure equity and diversity.
Dynamical systems modeling is a core pillar of scientific inquiry across natural and life sciences. Increasingly, dynamical system models are learned from data, rendering identifiability a paramount concept. For systems that are not identifiable from data, no guarantees can be given about their behavior under new conditions and inputs, or about possible control mechanisms to steer the system. It is known in the community that "linear ordinary differential equations (ODE) are almost surely identifiable from a single trajectory." However, this only holds for dense matrices. The sparse regime remains underexplored, despite its practical relevance with sparsity arising naturally in many biological, social, and physical systems. In this work, we address this gap by characterizing the identifiability of sparse linear ODEs. Contrary to the dense case, we show that sparse systems are unidentifiable with a positive probability in practically relevant sparsity regimes and provide lower bounds for this probability. We further study empirically how this theoretical unidentifiability manifests in state-of-the-art methods to estimate linear ODEs from data. Our results corroborate that sparse systems are also practically unidentifiable. Theoretical limitations are not resolved through inductive biases or optimization dynamics. Our findings call for rethinking what can be expected from data-driven dynamical system modeling and allows for quantitative assessments of how much to trust a learned linear ODE.
Delegations as Adaptive Representation Patterns: Rethinking Influence in Liquid Democracy
2025-06-11
Davide Grossi, Andreas Nitsche
Liquid democracy is a mechanism for the division of labor in decision-making through the transitive delegation of influence. In essence, all individuals possess the autonomy to determine the issues with which they will engage directly, while for other matters, they may appoint a representative of their choosing. So far, the literature has studied the delegation structures emerging in liquid democracy as static. As a result, transitivity defined as the capacity to transfer acquired authority to another entity, has been identified as a concern as it would be conducive to unrestrained accumulation of power. Focusing on the implementation of liquid democracy supported by the LiquidFeedback software, we propose a novel approach to assessing the influence of voting nodes in a transitive delegation graph, taking into account the process nature of real-world liquid democracy in which delegation and voting are distinct and increasingly independent activities. By introducing a novel model of delegations in liquid democracy, we show how transitivity may in fact contribute to an effective regulation of deliberation influence and decision-making power. While maintaining the one-person, one-vote paradigm for all votes cast, the anticipated influence of an agent, to the extent it is stemming from transitivity, experiences a precipitous decline following an exponential trajectory. In general, it is our objective to move the first steps towards a rigorous analysis of liquid democracy as an adaptive democratic representation process. The adaptivity aspect of liquid democracy has not yet been explored within the existing academic literature despite it being, we believe, one of its most important features. We therefore also outline a research agenda focusing on this aspect of liquid democracy.
Precise segmentation of brain tumors, particularly contrast-enhancing regions visible in post-contrast MRI (areas highlighted by contrast agent injection), is crucial for accurate clinical diagnosis and treatment planning but remains challenging. However, current methods exhibit notable performance degradation in segmenting these enhancing brain tumor areas, largely due to insufficient consideration of MRI-specific tumor features such as complex textures and directional variations. To address this, we propose the Harmonized Frequency Fusion Network (HFF-Net), which rethinks brain tumor segmentation from a frequency-domain perspective. To comprehensively characterize tumor regions, we develop a Frequency Domain Decomposition (FDD) module that separates MRI images into low-frequency components, capturing smooth tumor contours and high-frequency components, highlighting detailed textures and directional edges. To further enhance sensitivity to tumor boundaries, we introduce an Adaptive Laplacian Convolution (ALC) module that adaptively emphasizes critical high-frequency details using dynamically updated convolution kernels. To effectively fuse tumor features across multiple scales, we design a Frequency Domain Cross-Attention (FDCA) integrating semantic, positional, and slice-specific information. We further validate and interpret frequency-domain improvements through visualization, theoretical reasoning, and experimental analyses. Extensive experiments on four public datasets demonstrate that HFF-Net achieves an average relative improvement of 4.48\% (ranging from 2.39\% to 7.72\%) in the mean Dice scores across the three major subregions, and an average relative improvement of 7.33% (ranging from 5.96% to 8.64%) in the segmentation of contrast-enhancing tumor regions, while maintaining favorable computational efficiency and clinical applicability. Code: https://github.com/VinyehShaw/HFF.
Apparent bistability from weak long-range interactions
2025-06-11
Achilleas Lazarides, Andrea Pizzi
Bistability, or the coexistence of two stable phases, can be broken by a bias field $h$ destabilising one of the phases via the nucleation and growth of defects. Strong long-range interactions, $1/r^\alpha$ with $\alpha$ less than the system's dimensionality $d$, can suppress the proliferation of defects and restore bistability. The case of weak long-range interactions $d<\alpha < d+1$ remains instead poorly understood. Here, we show that it supports \emph{apparent} bistability: While the system has in principle a unique stable phase, it appears bistable for all practical purposes for $\alpha < \alpha_c$, with $\alpha_c > d$ behaving like a genuine critical point. At the core of this is an exponential scaling of the critical droplet size $R_c\sim h^{-1/(\alpha - d)}$, which makes nucleating destabilizing droplets extremely unlikely for $\alpha < \alpha_c$, and such that $\alpha_c$ is mostly independent of system size. In support of these conclusions we provide field-theoretical arguments and numerics on a probabilistic cellular automaton. Overall, our results offer a way to rethink phase stability in systems with long-range interactions as well as a new route to achieve practical bistability.
Rethinking Range-View LiDAR Segmentation in Adverse Weather
2025-06-10
Longyu Yang, Lu Zhang, Jun Liu, Yap-Peng Tan, Heng Tao Shen, Xiaofeng Zhu, Ping Hu
LiDAR segmentation has emerged as an important task to enrich scene perception and understanding. Range-view-based methods have gained popularity due to their high computational efficiency and compatibility with real-time deployment. However, their generalized performance under adverse weather conditions remains underexplored, limiting their reliability in real-world environments. In this work, we identify and analyze the unique challenges that affect the generalization of range-view LiDAR segmentation in severe weather. To address these challenges, we propose a modular and lightweight framework that enhances robustness without altering the core architecture of existing models. Our method reformulates the initial stem block of standard range-view networks into two branches to process geometric attributes and reflectance intensity separately. Specifically, a Geometric Abnormality Suppression (GAS) module reduces the influence of weather-induced spatial noise, and a Reflectance Distortion Calibration (RDC) module corrects reflectance distortions through memory-guided adaptive instance normalization. The processed features are then fused and passed to the original segmentation pipeline. Extensive experiments on different benchmarks and baseline models demonstrate that our approach significantly improves generalization to adverse weather with minimal inference overhead, offering a practical and effective solution for real-world LiDAR segmentation.
Rethinking Citation of AI Sources in Student-AI Collaboration within HCI Design Education
2025-06-10
Prakash Shukla, Suchismita Naik, Ike Obi, Jessica Backus, Nancy Rasche, Paul Parsons
The growing integration of AI tools in student design projects presents an unresolved challenge in HCI education: how should AI-generated content be cited and documented? Traditional citation frameworks -- grounded in credibility, retrievability, and authorship -- struggle to accommodate the dynamic and ephemeral nature of AI outputs. In this paper, we examine how undergraduate students in a UX design course approached AI usage and citation when given the freedom to integrate generative tools into their design process. Through qualitative analysis of 35 team projects and reflections from 175 students, we identify varied citation practices ranging from formal attribution to indirect or absent acknowledgment. These inconsistencies reveal gaps in existing frameworks and raise questions about authorship, assessment, and pedagogical transparency. We argue for rethinking AI citation as a reflective and pedagogical practice; one that supports metacognitive engagement by prompting students to critically evaluate how and why they used AI throughout the design process. We propose alternative strategies -- such as AI contribution statements and process-aware citation models that better align with the iterative and reflective nature of design education. This work invites educators to reconsider how citation practices can support meaningful student--AI collaboration.
Rethinking Cross-Modal Interaction in Multimodal Diffusion Transformers
2025-06-09
Zhengyao Lv, Tianlin Pan, Chenyang Si, Zhaoxi Chen, Wangmeng Zuo, Ziwei Liu, Kwan-Yee K. Wong
Multimodal Diffusion Transformers (MM-DiTs) have achieved remarkable progress in text-driven visual generation. However, even state-of-the-art MM-DiT models like FLUX struggle with achieving precise alignment between text prompts and generated content. We identify two key issues in the attention mechanism of MM-DiT, namely 1) the suppression of cross-modal attention due to token imbalance between visual and textual modalities and 2) the lack of timestep-aware attention weighting, which hinder the alignment. To address these issues, we propose \textbf{Temperature-Adjusted Cross-modal Attention (TACA)}, a parameter-efficient method that dynamically rebalances multimodal interactions through temperature scaling and timestep-dependent adjustment. When combined with LoRA fine-tuning, TACA significantly enhances text-image alignment on the T2I-CompBench benchmark with minimal computational overhead. We tested TACA on state-of-the-art models like FLUX and SD3.5, demonstrating its ability to improve image-text alignment in terms of object appearance, attribute binding, and spatial relationships. Our findings highlight the importance of balancing cross-modal attention in improving semantic fidelity in text-to-image diffusion models. Our codes are publicly available at \href{https://github.com/Vchitect/TACA}
Rethinking Crowd-Sourced Evaluation of Neuron Explanations
2025-06-09
Tuomas Oikarinen, Ge Yan, Akshay Kulkarni, Tsui-Wei Weng
Interpreting individual neurons or directions in activations space is an important component of mechanistic interpretability. As such, many algorithms have been proposed to automatically produce neuron explanations, but it is often not clear how reliable these explanations are, or which methods produce the best explanations. This can be measured via crowd-sourced evaluations, but they can often be noisy and expensive, leading to unreliable results. In this paper, we carefully analyze the evaluation pipeline and develop a cost-effective and highly accurate crowdsourced evaluation strategy. In contrast to previous human studies that only rate whether the explanation matches the most highly activating inputs, we estimate whether the explanation describes neuron activations across all inputs. To estimate this effectively, we introduce a novel application of importance sampling to determine which inputs are the most valuable to show to raters, leading to around 30x cost reduction compared to uniform sampling. We also analyze the label noise present in crowd-sourced evaluations and propose a Bayesian method to aggregate multiple ratings leading to a further ~5x reduction in number of ratings required for the same accuracy. Finally, we use these methods to conduct a large-scale study comparing the quality of neuron explanations produced by the most popular methods for two different vision models.
Vision-language models (VLMs) are widely assumed to exhibit in-context learning (ICL), a property similar to that of their language-only counterparts. While recent work suggests VLMs can perform multimodal ICL (MM-ICL), studies show they often rely on shallow heuristics -- such as copying or majority voting -- rather than true task understanding. We revisit this assumption by evaluating VLMs under distribution shifts, where support examples come from a dataset different from the query. Surprisingly, performance often degrades with more demonstrations, and models tend to copy answers rather than learn from them. To investigate further, we propose a new MM-ICL with Reasoning pipeline that augments each demonstration with a generated rationale alongside the answer. We conduct extensive and comprehensive experiments on both perception- and reasoning-required datasets with open-source VLMs ranging from 3B to 72B and proprietary models such as Gemini 2.0. We conduct controlled studies varying shot count, retrieval method, rationale quality, and distribution. Our results show limited performance sensitivity across these factors, suggesting that current VLMs do not effectively utilize demonstration-level information as intended in MM-ICL.
Drag-Based Image Editing (DBIE), which allows users to manipulate images by directly dragging objects within them, has recently attracted much attention from the community. However, it faces two key challenges: (\emph{\textcolor{magenta}{i}}) point-based drag is often highly ambiguous and difficult to align with users' intentions; (\emph{\textcolor{magenta}{ii}}) current DBIE methods primarily rely on alternating between motion supervision and point tracking, which is not only cumbersome but also fails to produce high-quality results. These limitations motivate us to explore DBIE from a new perspective -- redefining it as deformation, rotation, and translation of user-specified handle regions. Thereby, by requiring users to explicitly specify both drag areas and types, we can effectively address the ambiguity issue. Furthermore, we propose a simple-yet-effective editing framework, dubbed \textcolor{SkyBlue}{\textbf{DragNeXt}}. It unifies DBIE as a Latent Region Optimization (LRO) problem and solves it through Progressive Backward Self-Intervention (PBSI), simplifying the overall procedure of DBIE while further enhancing quality by fully leveraging region-level structure information and progressive guidance from intermediate drag states. We validate \textcolor{SkyBlue}{\textbf{DragNeXt}} on our NextBench, and extensive experiments demonstrate that our proposed method can significantly outperform existing approaches. Code will be released on github.
Dynamic and Parametric Retrieval-Augmented Generation
2025-06-07
Weihang Su, Qingyao Ai, Jingtao Zhan, Qian Dong, Yiqun Liu
Retrieval-Augmented Generation (RAG) has become a foundational paradigm for equipping large language models (LLMs) with external knowledge, playing a critical role in information retrieval and knowledge-intensive applications. However, conventional RAG systems typically adopt a static retrieve-then-generate pipeline and rely on in-context knowledge injection, which can be suboptimal for complex tasks that require multihop reasoning, adaptive information access, and deeper integration of external knowledge. Motivated by these limitations, the research community has moved beyond static retrieval and in-context knowledge injection. Among the emerging directions, this tutorial delves into two rapidly growing and complementary research areas on RAG: Dynamic RAG and Parametric RAG. Dynamic RAG adaptively determines when and what to retrieve during the LLM's generation process, enabling real-time adaptation to the LLM's evolving information needs. Parametric RAG rethinks how retrieved knowledge should be injected into LLMs, transitioning from input-level to parameter-level knowledge injection for enhanced efficiency and effectiveness. This tutorial offers a comprehensive overview of recent advances in these emerging research areas. It also shares theoretical foundations and practical insights to support and inspire further research in RAG.
Rethinking Semi-supervised Segmentation Beyond Accuracy: Reliability and Robustness
2025-06-06
Steven Landgraf, Markus Hillemann, Markus Ulrich
Semantic segmentation is critical for scene understanding but demands costly pixel-wise annotations, attracting increasing attention to semi-supervised approaches to leverage abundant unlabeled data. While semi-supervised segmentation is often promoted as a path toward scalable, real-world deployment, it is astonishing that current evaluation protocols exclusively focus on segmentation accuracy, entirely overlooking reliability and robustness. These qualities, which ensure consistent performance under diverse conditions (robustness) and well-calibrated model confidences as well as meaningful uncertainties (reliability), are essential for safety-critical applications like autonomous driving, where models must handle unpredictable environments and avoid sudden failures at all costs. To address this gap, we introduce the Reliable Segmentation Score (RSS), a novel metric that combines predictive accuracy, calibration, and uncertainty quality measures via a harmonic mean. RSS penalizes deficiencies in any of its components, providing an easy and intuitive way of holistically judging segmentation models. Comprehensive evaluations of UniMatchV2 against its predecessor and a supervised baseline show that semi-supervised methods often trade reliability for accuracy. While out-of-domain evaluations demonstrate UniMatchV2's robustness, they further expose persistent reliability shortcomings. We advocate for a shift in evaluation protocols toward more holistic metrics like RSS to better align semi-supervised learning research with real-world deployment needs.
Writing-RL: Advancing Long-form Writing via Adaptive Curriculum Reinforcement Learning
2025-06-06
Xuanyu Lei, Chenliang Li, Yuning Wu, Kaiming Liu, Weizhou Shen, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu
Recent advances in Large Language Models (LLMs) have enabled strong performance in long-form writing, yet existing supervised fine-tuning (SFT) approaches suffer from limitations such as data saturation and restricted learning capacity bounded by teacher signals. In this work, we present Writing-RL: an Adaptive Curriculum Reinforcement Learning framework to advance long-form writing capabilities beyond SFT. The framework consists of three key components: Margin-aware Data Selection strategy that prioritizes samples with high learning potential, Pairwise Comparison Reward mechanism that provides discriminative learning signals in the absence of verifiable rewards, and Dynamic Reference Scheduling approach, which plays a particularly critical role by adaptively adjusting task difficulty based on evolving model performance. Experiments on 7B-scale writer models show that our RL framework largely improves long-form writing performance over strong SFT baselines. Furthermore, we observe that models trained with long-output RL generalize surprisingly well to long-input reasoning tasks, potentially offering a promising perspective for rethinking long-context training.
Token Transforming: A Unified and Training-Free Token Compression Framework for Vision Transformer Acceleration
2025-06-06
Fanhu Zeng, Deli Yu, Zhenglun Kong, Hao Tang
Vision transformers have been widely explored in various vision tasks. Due to heavy computational cost, much interest has aroused for compressing vision transformer dynamically in the aspect of tokens. Current methods mainly pay attention to token pruning or merging to reduce token numbers, in which tokens are compressed exclusively, causing great information loss and therefore post-training is inevitably required to recover the performance. In this paper, we rethink token reduction and unify the process as an explicit form of token matrix transformation, in which all existing methods are constructing special forms of matrices within the framework. Furthermore, we propose a many-to-many Token Transforming framework that serves as a generalization of all existing methods and reserves the most information, even enabling training-free acceleration. We conduct extensive experiments to validate our framework. Specifically, we reduce 40% FLOPs and accelerate DeiT-S by $\times$1.5 with marginal 0.1% accuracy drop. Furthermore, we extend the method to dense prediction tasks including segmentation, object detection, depth estimation, and language model generation. Results demonstrate that the proposed method consistently achieves substantial improvements, offering a better computation-performance trade-off, impressive budget reduction and inference acceleration.
2025-06-05
Ranajoy Sadhukhan, Zhuoming Chen, Haizhong Zheng, Yang Zhou, Emma Strubell, Beidi Chen
We rethink test-time scaling laws from a practical efficiency perspective, revealing that the effectiveness of smaller models is significantly overestimated. Prior work, grounded in compute-optimality, overlooks critical memory access bottlenecks introduced by inference-time strategies (e.g., Best-of-$N$, long CoTs). Our holistic analysis, spanning models from 0.6B to 32B parameters, reveals a new Kinetics Scaling Law that better guides resource allocation by incorporating both computation and memory access costs. Kinetics Scaling Law suggests that test-time compute is more effective when used on models above a threshold than smaller ones. A key reason is that in TTS, attention, rather than parameter count, emerges as the dominant cost factor. Motivated by this, we propose a new scaling paradigm centered on sparse attention, which lowers per-token cost and enables longer generations and more parallel samples within the same resource budget. Empirically, we show that sparse attention models consistently outperform dense counterparts, achieving over 60 points gains in low-cost regimes and over 5 points gains in high-cost regimes for problem-solving accuracy on AIME, encompassing evaluations on state-of-the-art MoEs. These results suggest that sparse attention is essential and increasingly important with more computing invested, for realizing the full potential of test-time scaling where, unlike training, accuracy has yet to saturate as a function of computation, and continues to improve through increased generation. The code is available at https://github.com/Infini-AI-Lab/Kinetics.
Rethinking Contrastive Learning in Session-based Recommendation
2025-06-05
Xiaokun Zhang, Bo Xu, Fenglong Ma, Zhizheng Wang, Liang Yang, Hongfei Lin
Session-based recommendation aims to predict intents of anonymous users based on limited behaviors. With the ability in alleviating data sparsity, contrastive learning is prevailing in the task. However, we spot that existing contrastive learning based methods still suffer from three obstacles: (1) they overlook item-level sparsity and primarily focus on session-level sparsity; (2) they typically augment sessions using item IDs like crop, mask and reorder, failing to ensure the semantic consistency of augmented views; (3) they treat all positive-negative signals equally, without considering their varying utility. To this end, we propose a novel multi-modal adaptive contrastive learning framework called MACL for session-based recommendation. In MACL, a multi-modal augmentation is devised to generate semantically consistent views at both item and session levels by leveraging item multi-modal features. Besides, we present an adaptive contrastive loss that distinguishes varying contributions of positive-negative signals to improve self-supervised learning. Extensive experiments on three real-world datasets demonstrate the superiority of MACL over state-of-the-art methods.
VoxDet: Rethinking 3D Semantic Occupancy Prediction as Dense Object Detection
2025-06-05
Wuyang Li, Zhu Yu, Alexandre Alahi
3D semantic occupancy prediction aims to reconstruct the 3D geometry and semantics of the surrounding environment. With dense voxel labels, prior works typically formulate it as a dense segmentation task, independently classifying each voxel. However, this paradigm neglects critical instance-centric discriminability, leading to instance-level incompleteness and adjacent ambiguities. To address this, we highlight a free lunch of occupancy labels: the voxel-level class label implicitly provides insight at the instance level, which is overlooked by the community. Motivated by this observation, we first introduce a training-free Voxel-to-Instance (VoxNT) trick: a simple yet effective method that freely converts voxel-level class labels into instance-level offset labels. Building on this, we further propose VoxDet, an instance-centric framework that reformulates the voxel-level occupancy prediction as dense object detection by decoupling it into two sub-tasks: offset regression and semantic prediction. Specifically, based on the lifted 3D volume, VoxDet first uses (a) Spatially-decoupled Voxel Encoder to generate disentangled feature volumes for the two sub-tasks, which learn task-specific spatial deformation in the densely projected tri-perceptive space. Then, we deploy (b) Task-decoupled Dense Predictor to address this task via dense detection. Here, we first regress a 4D offset field to estimate distances (6 directions) between voxels and object borders in the voxel space. The regressed offsets are then used to guide the instance-level aggregation in the classification branch, achieving instance-aware prediction. Experiments show that VoxDet can be deployed on both camera and LiDAR input, jointly achieving state-of-the-art results on both benchmarks. VoxDet is not only highly efficient, but also achieves 63.0 IoU on the SemanticKITTI test set, ranking 1st on the online leaderboard.
Rethinking Accessible Prototyping Methods for Blind and Visually Impaired Passengers in Highly Automated Vehicles
2025-06-05
Luca-Maxim Meinhardt, Enrico Rukzio
Highly Automated Vehicles (HAVs) can improve mobility for blind and visually impaired people (BVIPs). However, designing non-visual interfaces that enable them to maintain situation awareness inside the vehicle is a challenge. This paper presents two of our participatory design workshops that explored what information BVIPs need in HAVs and what an interface that meets these needs might look like. Based on the participants' insights, we created final systems to improve their situation awareness. The two workshops used different approaches: in the first, participants built their own low-fidelity prototypes; in the second, they evaluated and discussed the initial prototypes we provided. We will outline how each workshop was set up and share lessons learned about prototyping methods for BVIPs and how they could be improved.
Recent trends in test-time scaling for reasoning models (e.g., OpenAI o1, DeepSeek R1) have led to a popular belief that extending thinking traces using prompts like "Wait" or "Let me rethink" can improve performance. This raises a natural question: Does thinking more at test-time truly lead to better reasoning? To answer this question, we perform a detailed empirical study across models and benchmarks, which reveals a consistent pattern of initial performance improvements from additional thinking followed by a decline, due to "overthinking". To understand this non-monotonic trend, we consider a simple probabilistic model, which reveals that additional thinking increases output variance-creating an illusion of improved reasoning while ultimately undermining precision. Thus, observed gains from "more thinking" are not true indicators of improved reasoning, but artifacts stemming from the connection between model uncertainty and evaluation metric. This suggests that test-time scaling through extended thinking is not an effective way to utilize the inference thinking budget. Recognizing these limitations, we introduce an alternative test-time scaling approach, parallel thinking, inspired by Best-of-N sampling. Our method generates multiple independent reasoning paths within the same inference budget and selects the most consistent response via majority vote, achieving up to 20% higher accuracy compared to extended thinking. This provides a simple yet effective mechanism for test-time scaling of reasoning models.
Rethinking the Stability-Plasticity Trade-off in Continual Learning from an Architectural Perspective
2025-06-04
Aojun Lu, Hangjie Yuan, Tao Feng, Yanan Sun
The quest for Continual Learning (CL) seeks to empower neural networks with the ability to learn and adapt incrementally. Central to this pursuit is addressing the stability-plasticity dilemma, which involves striking a balance between two conflicting objectives: preserving previously learned knowledge and acquiring new knowledge. While numerous CL methods aim to achieve this trade-off, they often overlook the impact of network architecture on stability and plasticity, restricting the trade-off to the parameter level. In this paper, we delve into the conflict between stability and plasticity at the architectural level. We reveal that under an equal parameter constraint, deeper networks exhibit better plasticity, while wider networks are characterized by superior stability. To address this architectural-level dilemma, we introduce a novel framework denoted Dual-Arch, which serves as a plug-in component for CL. This framework leverages the complementary strengths of two distinct and independent networks: one dedicated to plasticity and the other to stability. Each network is designed with a specialized and lightweight architecture, tailored to its respective objective. Extensive experiments demonstrate that Dual-Arch enhances the performance of existing CL methods while being up to 87% more compact in terms of parameters. Code: https://github.com/byyx666/Dual-Arch.
Verbalized Confidence Triggers Self-Verification: Emergent Behavior Without Explicit Reasoning Supervision
2025-06-04
Chaeyun Jang, Moonseok Choi, Yegon Kim, Hyungi Lee, Juho Lee
Uncertainty calibration is essential for the safe deployment of large language models (LLMs), particularly when users rely on verbalized confidence estimates. While prior work has focused on classifiers or short-form generation, confidence calibration for chain-of-thought (CoT) reasoning remains largely unexplored. Surprisingly, we find that supervised fine-tuning with scalar confidence labels alone suffices to elicit self-verification behavior of language models, without any explicit reasoning supervision or reinforcement learning-based rewards. Despite being trained only to produce a verbalized confidence score without any self-verifying examples, the model learns to generate longer and self-checking responses for low-confidence queries while providing more concise answers for high-confidence ones. We further propose a simple rethinking method that boosts performance via test-time scaling based on calibrated uncertainty. Experiments on GSM8K and held-out reasoning tasks such as MATH-500 and ARC-Challenge show that our confidence-aware fine-tuning improves both calibration and accuracy, while also enhancing interpretability by aligning the model's reasoning path with its confidence.
Through the Stealth Lens: Rethinking Attacks and Defenses in RAG
2025-06-04
Sarthak Choudhary, Nils Palumbo, Ashish Hooda, Krishnamurthy Dj Dvijotham, Somesh Jha
Retrieval-augmented generation (RAG) systems are vulnerable to attacks that inject poisoned passages into the retrieved set, even at low corruption rates. We show that existing attacks are not designed to be stealthy, allowing reliable detection and mitigation. We formalize stealth using a distinguishability-based security game. If a few poisoned passages are designed to control the response, they must differentiate themselves from benign ones, inherently compromising stealth. This motivates the need for attackers to rigorously analyze intermediate signals involved in generation$\unicode{x2014}$such as attention patterns or next-token probability distributions$\unicode{x2014}$to avoid easily detectable traces of manipulation. Leveraging attention patterns, we propose a passage-level score$\unicode{x2014}$the Normalized Passage Attention Score$\unicode{x2014}$used by our Attention-Variance Filter algorithm to identify and filter potentially poisoned passages. This method mitigates existing attacks, improving accuracy by up to $\sim 20 \%$ over baseline defenses. To probe the limits of attention-based defenses, we craft stealthier adaptive attacks that obscure such traces, achieving up to $35 \%$ attack success rate, and highlight the challenges in improving stealth.
In recent years, code intelligence has gained increasing importance in the field of automated software engineering. Meanwhile, the widespread adoption of Pretrained Language Models (PLMs) and Large Language Models (LLMs) has raised concerns regarding data contamination and its potential impact on model performance evaluation. This paper presents a systematic empirical study to investigate the fine-grained data contamination on code intelligence tasks. Our study involves diverse representative PLMs, namely RoBERTa and GPT-2, and LLMs, namely LLaMA and StarCoder, covering three major tasks: code translation, code generation, and code summarization. We categorize contamination scenarios into four types according to the code intelligence practice, namely input-only, output-only, unpaired, and paired contamination settings, and construct corresponding experimental and control groups for exploration. Experimental results show that, under the pre-training, fine-tuning, and inference paradigm adopted by PLMs, even deliberately injecting paired contamination does not lead to significant performance overestimation. But direct inference or small-scale fine-tuning uncovers the contamination effects. In contrast, LLMs with pre-training and inference paradigm are significantly affected by the paired contamination. Apart from the above, other contamination scenarios have no impact on both PLMs and LLMs. Our findings challenge the conventional belief that contamination inevitably leads to performance overestimation, providing new insights into the evaluation and deployment of code intelligence models.
Hybrid parallelism techniques are essential for efficiently training large language models (LLMs). Nevertheless, current automatic parallel planning frameworks often overlook the simultaneous consideration of node heterogeneity and dynamic network topology changes, limiting their effectiveness in practical applications. In this paper, we address these limitations by modeling heterogeneous nodes within dynamically changing network environments and leveraging simulation-based strategies to determine optimal parallel configurations. Our approach enables fine-grained workload allocation tailored for heterogeneous nodes and complex network scenarios, achieving performance competitive with state-of-the-art methods under regular and stable network conditions. Additionally, we introduce a strategy pruning technique to rapidly discard infeasible parallel configurations, substantially reducing the search space and accelerating the search process through parallel execution within the simulator. Preliminary evaluations confirm that our method notably enhances training performance on heterogeneous nodes and demonstrates improved adaptability in complex, dynamic scenarios such as cloud computing environments.
With the surge and widespread application of image generation models, data privacy and content safety have become major concerns and attracted great attention from users, service providers, and policymakers. Machine unlearning (MU) is recognized as a cost-effective and promising means to address these challenges. Despite some advancements, image generation model unlearning (IGMU) still faces remarkable gaps in practice, e.g., unclear task discrimination and unlearning guidelines, lack of an effective evaluation framework, and unreliable evaluation metrics. These can hinder the understanding of unlearning mechanisms and the design of practical unlearning algorithms. We perform exhaustive assessments over existing state-of-the-art unlearning algorithms and evaluation standards, and discover several critical flaws and challenges in IGMU tasks. Driven by these limitations, we make several core contributions, to facilitate the comprehensive understanding, standardized categorization, and reliable evaluation of IGMU. Specifically, (1) We design CatIGMU, a novel hierarchical task categorization framework. It provides detailed implementation guidance for IGMU, assisting in the design of unlearning algorithms and the construction of testbeds. (2) We introduce EvalIGMU, a comprehensive evaluation framework. It includes reliable quantitative metrics across five critical aspects. (3) We construct DataIGM, a high-quality unlearning dataset, which can be used for extensive evaluations of IGMU, training content detectors for judgment, and benchmarking the state-of-the-art unlearning algorithms. With EvalIGMU and DataIGM, we discover that most existing IGMU algorithms cannot handle the unlearning well across different evaluation dimensions, especially for preservation and robustness. Code and models are available at https://github.com/ryliu68/IGMU.
Rethinking Post-Unlearning Behavior of Large Vision-Language Models
2025-06-03
Minsung Kim, Nakyeong Yang, Kyomin Jung
Machine unlearning is used to mitigate the privacy risks of Large Vision-Language Models (LVLMs) arising from training on large-scale web data. However, existing unlearning methods often fail to carefully select substitute outputs for forget targets, resulting in Unlearning Aftermaths-undesirable behaviors such as degenerate, hallucinated, or excessively refused responses. We highlight that, especially for generative LVLMs, it is crucial to consider the quality and informativeness of post-unlearning responses rather than relying solely on naive suppression. To address this, we introduce a new unlearning task for LVLMs that requires models to provide privacy-preserving yet informative and visually grounded responses. We also propose PUBG, a novel unlearning method that explicitly guides post-unlearning behavior toward a desirable output distribution. Experiments show that, while existing methods suffer from Unlearning Aftermaths despite successfully preventing privacy violations, PUBG effectively mitigates these issues, generating visually grounded and informative responses without privacy leakage for forgotten targets.
Automated interpretation of CT images-particularly localizing and describing abnormal findings across multi-plane and whole-body scans-remains a significant challenge in clinical radiology. This work aims to address this challenge through four key contributions: (i) On taxonomy, we collaborate with senior radiologists to propose a comprehensive hierarchical classification system, with 404 representative abnormal findings across all body regions; (ii) On data, we contribute a dataset containing over 14.5K CT images from multiple planes and all human body regions, and meticulously provide grounding annotations for over 19K abnormalities, each linked to the detailed description and cast into the taxonomy; (iii) On model development, we propose OminiAbnorm-CT, which can automatically ground and describe abnormal findings on multi-plane and whole-body CT images based on text queries, while also allowing flexible interaction through visual prompts; (iv) On benchmarks, we establish three representative evaluation tasks based on real clinical scenarios. Through extensive experiments, we show that OminiAbnorm-CT can significantly outperform existing methods on all the tasks and metrics.
A Tale of Two Symmetries: Exploring the Loss Landscape of Equivariant Models
2025-06-02
YuQing Xie, Tess Smidt
Equivariant neural networks have proven to be effective for tasks with known underlying symmetries. However, optimizing equivariant networks can be tricky and best training practices are less established than for standard networks. In particular, recent works have found small training benefits from relaxing equivariance constraints. This raises the question: do equivariance constraints introduce fundamental obstacles to optimization? Or do they simply require different hyperparameter tuning? In this work, we investigate this question through a theoretical analysis of the loss landscape geometry. We focus on networks built using permutation representations, which we can view as a subset of unconstrained MLPs. Importantly, we show that the parameter symmetries of the unconstrained model has nontrivial effects on the loss landscape of the equivariant subspace and under certain conditions can provably prevent learning of the global minima. Further, we empirically demonstrate in such cases, relaxing to an unconstrained MLP can sometimes solve the issue. Interestingly, the weights eventually found via relaxation corresponds to a different choice of group representation in the hidden layer. From this, we draw 3 key takeaways. (1) Viewing any class of networks in the context of larger unconstrained function space can give important insights on loss landscape structure. (2) Within the unconstrained function space, equivariant networks form a complicated union of linear hyperplanes, each associated with a specific choice of internal group representation. (3) Effective relaxation of equivariance may require not only adding nonequivariant degrees of freedom, but also rethinking the fixed choice of group representations in hidden layers.
Alignment as Distribution Learning: Your Preference Model is Explicitly a Language Model
2025-06-02
Jihun Yun, Juno Kim, Jongho Park, Junhyuck Kim, Jongha Jon Ryu, Jaewoong Cho, Kwang-Sung Jun
Alignment via reinforcement learning from human feedback (RLHF) has become the dominant paradigm for controlling the quality of outputs from large language models (LLMs). However, when viewed as `loss + regularization,' the standard RLHF objective lacks theoretical justification and incentivizes degenerate, deterministic solutions, an issue that variants such as Direct Policy Optimization (DPO) also inherit. In this paper, we rethink alignment by framing it as \emph{distribution learning} from pairwise preference feedback by explicitly modeling how information about the target language model bleeds through the preference data. This explicit modeling leads us to propose three principled learning objectives: preference maximum likelihood estimation, preference distillation, and reverse KL minimization. We theoretically show that all three approaches enjoy strong non-asymptotic $O(1/n)$ convergence to the target language model, naturally avoiding degeneracy and reward overfitting. Finally, we empirically demonstrate that our distribution learning framework, especially preference distillation, consistently outperforms or matches the performances of RLHF and DPO across various tasks and models.
This paper rethinks image histogram matching (HM) and proposes a differentiable and parametric HM preprocessing for a downstream classifier. Convolutional neural networks have demonstrated remarkable achievements in classification tasks. However, they often exhibit degraded performance on low-contrast images captured under adverse weather conditions. To maintain classifier performance under low-contrast images, histogram equalization (HE) is commonly used. HE is a special case of HM using a uniform distribution as a target pixel value distribution. In this paper, we focus on the shape of the target pixel value distribution. Compared to a uniform distribution, a single, well-designed distribution could have potential to improve the performance of the downstream classifier across various adverse weather conditions. Based on this hypothesis, we propose a differentiable and parametric HM that optimizes the target distribution using the loss function of the downstream classifier. This method addresses pixel value imbalances by transforming input images with arbitrary distributions into a target distribution optimized for the classifier. Our HM is trained on only normal weather images using the classifier. Experimental results show that a classifier trained with our proposed HM outperforms conventional preprocessing methods under adverse weather conditions.
Culturally-Grounded Chain-of-Thought (CG-CoT):Enhancing LLM Performance on Culturally-Specific Tasks in Low-Resource Languages
2025-06-01
Madhavendra Thakur
Large Language Models (LLMs) struggle with culturally-specific reasoning tasks, particularly in low-resource languages, hindering their global applicability. Addressing this gap is crucial for equitable AI deployment. We introduce Culturally-Grounded Chain-of-Thought (CG-CoT), a novel prompting strategy that combines dense vector retrieval of cultural context with explicit reasoning sequences. Our extensive experiments on Yoruba proverb interpretation demonstrate that CG-CoT provides significantly higher culturally-aligned accuracy and depth than traditional prompting methods, validated through both automated metrics and LLM-based evaluations. Notably, we uncover stark disparities between token-level translation metrics like BLEU and human-judged cultural relevance, suggesting a rethinking of evaluation approaches for low-resource NLP.
GThinker: Towards General Multimodal Reasoning via Cue-Guided Rethinking
2025-06-01
Yufei Zhan, Ziheng Wu, Yousong Zhu, Rongkun Xue, Ruipu Luo, Zhenghao Chen, Can Zhang, Yifan Li, Zhentao He, Zheming Yang, Ming Tang, Minghui Qiu, Jinqiao Wang
Despite notable advancements in multimodal reasoning, leading Multimodal Large Language Models (MLLMs) still underperform on vision-centric multimodal reasoning tasks in general scenarios. This shortfall stems from their predominant reliance on logic- and knowledge-based slow thinking strategies, while effective for domains like math and science, fail to integrate visual information effectively during reasoning. Consequently, these models often fail to adequately ground visual cues, resulting in suboptimal performance in tasks that require multiple plausible visual interpretations and inferences. To address this, we present GThinker (General Thinker), a novel reasoning MLLM excelling in multimodal reasoning across general scenarios, mathematics, and science. GThinker introduces Cue-Rethinking, a flexible reasoning pattern that grounds inferences in visual cues and iteratively reinterprets these cues to resolve inconsistencies. Building on this pattern, we further propose a two-stage training pipeline, including pattern-guided cold start and incentive reinforcement learning, designed to enable multimodal reasoning capabilities across domains. Furthermore, to support the training, we construct GThinker-11K, comprising 7K high-quality, iteratively-annotated reasoning paths and 4K curated reinforcement learning samples, filling the data gap toward general multimodal reasoning. Extensive experiments demonstrate that GThinker achieves 81.5% on the challenging comprehensive multimodal reasoning benchmark M$^3$CoT, surpassing the latest O4-mini model. It also shows an average improvement of 2.1% on general scenario multimodal reasoning benchmarks, while maintaining on-par performance in mathematical reasoning compared to counterpart advanced reasoning models. The code, model, and data will be released soon at https://github.com/jefferyZhan/GThinker.
Rethinking Neural-based Matrix Inversion: Why can't, and Where can
2025-05-31
Yuliang Ji, Jian Wu, Yuanzhe Xi
Deep neural networks have achieved substantial success across various scientific computing tasks. A pivotal challenge within this domain is the rapid and parallel approximation of matrix inverses, critical for numerous applications. Despite significant progress, there currently exists no universal neural-based method for approximating matrix inversion. This paper presents a theoretical analysis demonstrating the fundamental limitations of neural networks in developing a general matrix inversion model. We expand the class of Lipschitz functions to encompass a wider array of neural network models, thereby refining our theoretical approach. Moreover, we delineate specific conditions under which neural networks can effectively approximate matrix inverses. Our theoretical results are supported by experimental results from diverse matrix datasets, exploring the efficacy of neural networks in addressing the matrix inversion challenge.
Machine learning models often degrade when deployed on data distributions different from their training data. Challenging conventional validation paradigms, we demonstrate that higher in-distribution (ID) bias can lead to better out-of-distribution (OOD) generalization. Our Adaptive Distribution Bridge (ADB) framework implements this insight by introducing controlled statistical diversity during training, enabling models to develop bias profiles that effectively generalize across distributions. Empirically, we observe a robust negative correlation where higher ID bias corresponds to lower OOD error--a finding that contradicts standard practices focused on minimizing validation error. Evaluation on multiple datasets shows our approach significantly improves OOD generalization. ADB achieves robust mean error reductions of up to 26.8% compared to traditional cross-validation, and consistently identifies high-performing training strategies, evidenced by percentile ranks often exceeding 74.4%. Our work provides both a practical method for improving generalization and a theoretical framework for reconsidering the role of bias in robust machine learning.
HELM: Hyperbolic Large Language Models via Mixture-of-Curvature Experts
2025-05-30
Neil He, Rishabh Anand, Hiren Madhu, Ali Maatouk, Smita Krishnaswamy, Leandros Tassiulas, Menglin Yang, Rex Ying
Large language models (LLMs) have shown great success in text modeling tasks across domains. However, natural language exhibits inherent semantic hierarchies and nuanced geometric structure, which current LLMs do not capture completely owing to their reliance on Euclidean operations. Recent studies have also shown that not respecting the geometry of token embeddings leads to training instabilities and degradation of generative capabilities. These findings suggest that shifting to non-Euclidean geometries can better align language models with the underlying geometry of text. We thus propose to operate fully in Hyperbolic space, known for its expansive, scale-free, and low-distortion properties. We thus introduce HELM, a family of HypErbolic Large Language Models, offering a geometric rethinking of the Transformer-based LLM that addresses the representational inflexibility, missing set of necessary operations, and poor scalability of existing hyperbolic LMs. We additionally introduce a Mixture-of-Curvature Experts model, HELM-MICE, where each expert operates in a distinct curvature space to encode more fine-grained geometric structure from text, as well as a dense model, HELM-D. For HELM-MICE, we further develop hyperbolic Multi-Head Latent Attention (HMLA) for efficient, reduced-KV-cache training and inference. For both models, we develop essential hyperbolic equivalents of rotary positional encodings and RMS normalization. We are the first to train fully hyperbolic LLMs at billion-parameter scale, and evaluate them on well-known benchmarks such as MMLU and ARC, spanning STEM problem-solving, general knowledge, and commonsense reasoning. Our results show consistent gains from our HELM architectures -- up to 4% -- over popular Euclidean architectures used in LLaMA and DeepSeek, highlighting the efficacy and enhanced reasoning afforded by hyperbolic geometry in large-scale LM pretraining.
Are Optimal Algorithms Still Optimal? Rethinking Sorting in LLM-Based Pairwise Ranking with Batching and Caching
2025-05-30
Juan Wisznia, Cecilia Bolaños, Juan Tollo, Giovanni Marraffini, Agustín Gianolini, Noe Hsueh, Luciano Del Corro
We introduce a novel framework for analyzing sorting algorithms in pairwise ranking prompting (PRP), re-centering the cost model around LLM inferences rather than traditional pairwise comparisons. While classical metrics based on comparison counts have traditionally been used to gauge efficiency, our analysis reveals that expensive LLM inferences overturn these predictions; accordingly, our framework encourages strategies such as batching and caching to mitigate inference costs. We show that algorithms optimal in the classical setting can lose efficiency when LLM inferences dominate the cost under certain optimizations.
Rethinking Neural Combinatorial Optimization for Vehicle Routing Problems with Different Constraint Tightness Degrees
2025-05-30
Fu Luo, Yaoxin Wu, Zhi Zheng, Zhenkun Wang
Recent neural combinatorial optimization (NCO) methods have shown promising problem-solving ability without requiring domain-specific expertise. Most existing NCO methods use training and testing data with a fixed constraint value and lack research on the effect of constraint tightness on the performance of NCO methods. This paper takes the capacity-constrained vehicle routing problem (CVRP) as an example to empirically analyze the NCO performance under different tightness degrees of the capacity constraint. Our analysis reveals that existing NCO methods overfit the capacity constraint, and they can only perform satisfactorily on a small range of the constraint values but poorly on other values. To tackle this drawback of existing NCO methods, we develop an efficient training scheme that explicitly considers varying degrees of constraint tightness and proposes a multi-expert module to learn a generally adaptable solving strategy. Experimental results show that the proposed method can effectively overcome the overfitting issue, demonstrating superior performances on the CVRP and CVRP with time windows (CVRPTW) with various constraint tightness degrees.
Rethinking Continual Learning with Progressive Neural Collapse
2025-05-30
Zheng Wang, Wanhao Yu, Li Yang, Sen Lin
Continual Learning (CL) seeks to build an agent that can continuously learn a sequence of tasks, where a key challenge, namely Catastrophic Forgetting, persists due to the potential knowledge interference among different tasks. On the other hand, deep neural networks (DNNs) are shown to converge to a terminal state termed Neural Collapse during training, where all class prototypes geometrically form a static simplex equiangular tight frame (ETF). These maximally and equally separated class prototypes make the ETF an ideal target for model learning in CL to mitigate knowledge interference. Thus inspired, several studies have emerged very recently to leverage a fixed global ETF in CL, which however suffers from key drawbacks, such as impracticability and limited performance.To address these challenges and fully unlock the potential of ETF in CL, we propose Progressive Neural Collapse (ProNC), a novel framework that completely removes the need of a fixed global ETF in CL. Specifically, ProNC progressively expands the ETF target in a principled way by adding new class prototypes as vertices for new tasks, ensuring maximal separability across all encountered classes with minimal shifts from the previous ETF. We next develop a new CL framework by plugging ProNC into commonly used CL algorithm designs, where distillation is further leveraged to balance between target shifting for old classes and target aligning for new classes. Extensive experiments show that our approach significantly outperforms related baselines while maintaining superior flexibility, simplicity, and efficiency.
From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models
2025-05-30
Haibo Jin, Peiyan Zhang, Peiran Wang, Man Luo, Haohan Wang
Large foundation models (LFMs) are susceptible to two distinct vulnerabilities: hallucinations and jailbreak attacks. While typically studied in isolation, we observe that defenses targeting one often affect the other, hinting at a deeper connection. We propose a unified theoretical framework that models jailbreaks as token-level optimization and hallucinations as attention-level optimization. Within this framework, we establish two key propositions: (1) \textit{Similar Loss Convergence} - the loss functions for both vulnerabilities converge similarly when optimizing for target-specific outputs; and (2) \textit{Gradient Consistency in Attention Redistribution} - both exhibit consistent gradient behavior driven by shared attention dynamics. We validate these propositions empirically on LLaVA-1.5 and MiniGPT-4, showing consistent optimization trends and aligned gradients. Leveraging this connection, we demonstrate that mitigation techniques for hallucinations can reduce jailbreak success rates, and vice versa. Our findings reveal a shared failure mode in LFMs and suggest that robustness strategies should jointly address both vulnerabilities.
Bose-Einstein Condensation, Fluctuations and Spontaneous Symmetry Breaking
2025-05-30
A. Crisanti, A. Sarracino, M. Zannetti
The realisation of Bose-Einstein condensation under grand-canonical conditions has provided the experimental evidence for the simultaneous occurrence of macroscopic fluctuations and phase coherence of the condensate. The observation of these two features, against a consolidated tradition which wants the fluctuations to be pathological (grand-canonical catastrophe) and incompatible with spontaneous symmetry braking, calls for a comprehensive rethinking of the approach to the problem. In this paper we consider the uniform ideal gas in a box and we present an alternative conceptual framework. We show that the usually-employed Bogoliubov quasi-average construction fails to reproduce the broken-symmetry state. The observed features are accounted for by a different pattern of spontaneous symmetry breaking, characterised by condensation of fluctuations and long-range correlations of the order parameter.
Beyond Exponential Decay: Rethinking Error Accumulation in Large Language Models
2025-05-30
Mikhail L. Arbuzov, Alexey A. Shvets, Sisong Beir
The prevailing assumption of an exponential decay in large language model (LLM) reliability with sequence length, predicated on independent per-token error probabilities, posits an inherent limitation for long autoregressive outputs. Our research fundamentally challenges this view by synthesizing emerging evidence that LLM errors are not uniformly distributed but are concentrated at sparse "key tokens" ($5-10\%$ of total tokens) representing critical decision junctions. By distinguishing these high-impact tokens from the increasingly predictable majority, we introduce a new reliability formula explaining the sustained coherence of modern LLMs over thousands of tokens. Converging research streams reveal that long-context performance primarily depends on accurately navigating a few crucial semantic decision points rather than on uniform token-level accuracy, enabling targeted strategies that significantly outperform brute-force approaches. We thus propose a framework for next-generation systems centered on selective preservation of semantically vital tokens, dynamic computational allocation at uncertain decision boundaries, multi-path exploration at ambiguities, and architectures aligned with natural semantic domains. This marks a fundamental shift from raw scaling to strategic reasoning, promising breakthrough performance without proportionate computational scaling and offering a more nuanced understanding that supersedes the exponential decay hypothesis, thereby opening pathways toward substantially more powerful and efficient language systems.
Seeing is Not Reasoning: MVPBench for Graph-based Evaluation of Multi-path Visual Physical CoT
2025-05-30
Zhuobai Dong, Junchao Yi, Ziyuan Zheng, Haochen Han, Xiangxi Zheng, Alex Jinpeng Wang, Fangming Liu, Linjie Li
Understanding the physical world - governed by laws of motion, spatial relations, and causality - poses a fundamental challenge for multimodal large language models (MLLMs). While recent advances such as OpenAI o3 and GPT-4o demonstrate impressive perceptual and reasoning capabilities, our investigation reveals these models struggle profoundly with visual physical reasoning, failing to grasp basic physical laws, spatial interactions, and causal effects in complex scenes. More importantly, they often fail to follow coherent reasoning chains grounded in visual evidence, especially when multiple steps are needed to arrive at the correct answer. To rigorously evaluate this capability, we introduce MVPBench, a curated benchmark designed to rigorously evaluate visual physical reasoning through the lens of visual chain-of-thought (CoT). Each example features interleaved multi-image inputs and demands not only the correct final answer but also a coherent, step-by-step reasoning path grounded in evolving visual cues. This setup mirrors how humans reason through real-world physical processes over time. To ensure fine-grained evaluation, we introduce a graph-based CoT consistency metric that verifies whether the reasoning path of model adheres to valid physical logic. Additionally, we minimize shortcut exploitation from text priors, encouraging models to rely on visual understanding. Experimental results reveal a concerning trend: even cutting-edge MLLMs exhibit poor visual reasoning accuracy and weak image-text alignment in physical domains. Surprisingly, RL-based post-training alignment - commonly believed to improve visual reasoning performance - often harms spatial reasoning, suggesting a need to rethink current fine-tuning practices.
Effects of higher-order interactions and homophily on information access inequality
2025-05-30
Moritz Laber, Samantha Dies, Joseph Ehlert, Brennan Klein, Tina Eliassi-Rad
The spread of information through socio-technical systems determines which individuals are the first to gain access to opportunities and insights. Yet, the pathways through which information flows can be skewed, leading to systematic differences in access across social groups. These inequalities remain poorly characterized in settings involving nonlinear social contagion and higher-order interactions that exhibit homophily. We introduce a enerative model for hypergraphs with hyperedge homophily, a hyperedge size-dependent property, and tunable degree distribution, called the $\texttt{H3}$ model, along with a model for nonlinear social contagion that incorporates asymmetric transmission between in-group and out-group nodes. Using stochastic simulations of a social contagion process on hypergraphs from the $\texttt{H3}$ model and diverse empirical datasets, we show that the interaction between social contagion dynamics and hyperedge homophily -- an effect unique to higher-order networks due to its dependence on hyperedge size -- can critically shape group-level differences in information access. By emphasizing how hyperedge homophily shapes interaction patterns, our findings underscore the need to rethink socio-technical system design through a higher-order perspective and suggest that dynamics-informed, targeted interventions at specific hyperedge sizes, embedded in a platform architecture, offer a powerful lever for reducing inequality.
Rethinking Regularization Methods for Knowledge Graph Completion
2025-05-29
Linyu Li, Zhi Jin, Yuanpeng He, Dongming Jin, Haoran Duan, Zhengwei Tao, Xuan Zhang, Jiandong Li
Knowledge graph completion (KGC) has attracted considerable attention in recent years because it is critical to improving the quality of knowledge graphs. Researchers have continuously explored various models. However, most previous efforts have neglected to take advantage of regularization from a deeper perspective and therefore have not been used to their full potential. This paper rethinks the application of regularization methods in KGC. Through extensive empirical studies on various KGC models, we find that carefully designed regularization not only alleviates overfitting and reduces variance but also enables these models to break through the upper bounds of their original performance. Furthermore, we introduce a novel sparse-regularization method that embeds the concept of rank-based selective sparsity into the KGC regularizer. The core idea is to selectively penalize those components with significant features in the embedding vector, thus effectively ignoring many components that contribute little and may only represent noise. Various comparative experiments on multiple datasets and multiple models show that the SPR regularization method is better than other regularization methods and can enable the KGC model to further break through the performance margin.
Recent Large Vision-Language Models (LVLMs) have advanced multi-modal understanding by incorporating finer-grained visual perception and encoding. However, such methods incur significant computational costs due to longer visual token sequences, posing challenges for real-time deployment. To mitigate this, prior studies have explored pruning unimportant visual tokens either at the output layer of the visual encoder or at the early layers of the language model. In this work, we revisit these design choices and reassess their effectiveness through comprehensive empirical studies of how visual tokens are processed throughout the visual encoding and language decoding stages. Guided by these insights, we propose VScan, a two-stage visual token reduction framework that addresses token redundancy by: (1) integrating complementary global and local scans with token merging during visual encoding, and (2) introducing pruning at intermediate layers of the language model. Extensive experimental results across four LVLMs validate the effectiveness of VScan in accelerating inference and demonstrate its superior performance over current state-of-the-arts on sixteen benchmarks. Notably, when applied to LLaVA-NeXT-7B, VScan achieves a 2.91$\times$ speedup in prefilling and a 10$\times$ reduction in FLOPs, while retaining 95.4\% of the original performance. Code is available at https://github.com/Tencent/SelfEvolvingAgent/tree/main/VScan.
Rethinking BPS: A Utility-Based Evaluation Framework
2025-05-28
Konrad Özdemir, Lukas Kirchdorfer, Keyvan Amiri Elyasi, Han van der Aa, Heiner Stuckenschmidt
Business process simulation (BPS) is a key tool for analyzing and optimizing organizational workflows, supporting decision-making by estimating the impact of process changes. The reliability of such estimates depends on the ability of a BPS model to accurately mimic the process under analysis, making rigorous accuracy evaluation essential. However, the state-of-the-art approach to evaluating BPS models has two key limitations. First, it treats simulation as a forecasting problem, testing whether models can predict unseen future events. This fails to assess how well a model captures the as-is process, particularly when process behavior changes from train to test period. Thus, it becomes difficult to determine whether poor results stem from an inaccurate model or the inherent complexity of the data, such as unpredictable drift. Second, the evaluation approach strongly relies on Earth Mover's Distance-based metrics, which can obscure temporal patterns and thus yield misleading conclusions about simulation quality. To address these issues, we propose a novel framework that evaluates simulation quality based on its ability to generate representative process behavior. Instead of comparing simulated logs to future real-world executions, we evaluate whether predictive process monitoring models trained on simulated data perform comparably to those trained on real data for downstream analysis tasks. Empirical results show that our framework not only helps identify sources of discrepancies but also distinguishes between model accuracy and data complexity, offering a more meaningful way to assess BPS quality.
Rethinking the Unsolvable: When In-Context Search Meets Test-Time Scaling
2025-05-28
Fanzeng Xia, Yidong Luo, Tinko Sebastian Bartels, Yaqi Xu, Tongxin Li
Recent research has highlighted that Large Language Models (LLMs), even when trained to generate extended long reasoning steps, still face significant challenges on hard reasoning problems. However, much of the existing literature relies on direct prompting with simple in-context learning examples for evaluation, which largely overlooks advanced techniques to elicit LLMs' deliberate reasoning before drawing conclusions that LLMs hit a performance ceiling. In this paper, we systematically explore the combined potential of in-context search and test-time scaling on super hard reasoning tasks. We find that by employing advanced in-context search prompting to LLMs augmented with internal scaling, one can achieve transformative performance breakthroughs on tasks previously deemed "unsolvable" (e.g., reported success rates below 5%). We provide both empirical results and theoretical analysis of how this combination can unleash LLM reasoning capabilities: i) Empirically, on controlled NP-hard tasks and complex real-world planning benchmarks, our approach achieves up to a 30x improvement in success rates compared to previously reported results without any external mechanisms; ii) Theoretically, we show that in-context search prompting, when combined with internal scaling, significantly extends the complexity class of solvable reasoning problems. These findings challenge prevailing assumptions about the limitations of LLMs on complex tasks, indicating that current evaluation paradigms systematically underestimate their true potential. Our work calls for a critical reassessment of how LLM reasoning is benchmarked and a more robust evaluation strategy that fully captures the true capabilities of contemporary LLMs, which can lead to a better understanding of their operational reasoning boundaries in real-world deployments.
UDuo: Universal Dual Optimization Framework for Online Matching
2025-05-28
Bin Li, Diwei Liu, Zehong Hu, Jia Jia
Online resource allocation under budget constraints critically depends on proper modeling of user arrival dynamics. Classical approaches employ stochastic user arrival models to derive near-optimal solutions through fractional matching formulations of exposed users for downstream allocation tasks. However, this is no longer a reasonable assumption when the environment changes dynamically. In this work, We propose the Universal Dual optimization framework UDuo, a novel paradigm that fundamentally rethinks online allocation through three key innovations: (i) a temporal user arrival representation vector that explicitly captures distribution shifts in user arrival patterns and resource consumption dynamics, (ii) a resource pacing learner with adaptive allocation policies that generalize to heterogeneous constraint scenarios, and (iii) an online time-series forecasting approach for future user arrival distributions that achieves asymptotically optimal solutions with constraint feasibility guarantees in dynamic environments. Experimental results show that UDuo achieves higher efficiency and faster convergence than the traditional stochastic arrival model in real-world pricing while maintaining rigorous theoretical validity for general online allocation problems.
Rethinking Gradient-based Adversarial Attacks on Point Cloud Classification
2025-05-28
Jun Chen, Xinke Li, Mingyue Xu, Tianrui Li, Chongshou Li
Gradient-based adversarial attacks have become a dominant approach for evaluating the robustness of point cloud classification models. However, existing methods often rely on uniform update rules that fail to consider the heterogeneous nature of point clouds, resulting in excessive and perceptible perturbations. In this paper, we rethink the design of gradient-based attacks by analyzing the limitations of conventional gradient update mechanisms and propose two new strategies to improve both attack effectiveness and imperceptibility. First, we introduce WAAttack, a novel framework that incorporates weighted gradients and an adaptive step-size strategy to account for the non-uniform contribution of points during optimization. This approach enables more targeted and subtle perturbations by dynamically adjusting updates according to the local structure and sensitivity of each point. Second, we propose SubAttack, a complementary strategy that decomposes the point cloud into subsets and focuses perturbation efforts on structurally critical regions. Together, these methods represent a principled rethinking of gradient-based adversarial attacks for 3D point cloud classification. Extensive experiments demonstrate that our approach outperforms state-of-the-art baselines in generating highly imperceptible adversarial examples. Code will be released upon paper acceptance.
NGPU-LM: GPU-Accelerated N-Gram Language Model for Context-Biasing in Greedy ASR Decoding
2025-05-28
Vladimir Bataev, Andrei Andrusenko, Lilit Grigoryan, Aleksandr Laptev, Vitaly Lavrukhin, Boris Ginsburg
Statistical n-gram language models are widely used for context-biasing tasks in Automatic Speech Recognition (ASR). However, existing implementations lack computational efficiency due to poor parallelization, making context-biasing less appealing for industrial use. This work rethinks data structures for statistical n-gram language models to enable fast and parallel operations for GPU-optimized inference. Our approach, named NGPU-LM, introduces customizable greedy decoding for all major ASR model types - including transducers, attention encoder-decoder models, and CTC - with less than 7% computational overhead. The proposed approach can eliminate more than 50% of the accuracy gap between greedy and beam search for out-of-domain scenarios while avoiding significant slowdown caused by beam search. The implementation of the proposed NGPU-LM is open-sourced.
In Dialogue with Intelligence: Rethinking Large Language Models as Collective Knowledge
2025-05-28
Eleni Vasilaki
Large Language Models (LLMs) are typically analysed through architectural, behavioural, or training-data lenses. This article offers a theoretical and experiential re-framing: LLMs as dynamic instantiations of Collective human Knowledge (CK), where intelligence is evoked through dialogue rather than stored statically. Drawing on concepts from neuroscience and AI, and grounded in sustained interaction with ChatGPT-4, I examine emergent dialogue patterns, the implications of fine-tuning, and the notion of co-augmentation: mutual enhancement between human and machine cognition. This perspective offers a new lens for understanding interaction, representation, and agency in contemporary AI systems.
LegalSearchLM: Rethinking Legal Case Retrieval as Legal Elements Generation
2025-05-28
Chaeeun Kim, Jinu Lee, Wonseok Hwang
Legal Case Retrieval (LCR), which retrieves relevant cases from a query case, is a fundamental task for legal professionals in research and decision-making. However, existing studies on LCR face two major limitations. First, they are evaluated on relatively small-scale retrieval corpora (e.g., 100-55K cases) and use a narrow range of criminal query types, which cannot sufficiently reflect the complexity of real-world legal retrieval scenarios. Second, their reliance on embedding-based or lexical matching methods often results in limited representations and legally irrelevant matches. To address these issues, we present: (1) LEGAR BENCH, the first large-scale Korean LCR benchmark, covering 411 diverse crime types in queries over 1.2M legal cases; and (2) LegalSearchLM, a retrieval model that performs legal element reasoning over the query case and directly generates content grounded in the target cases through constrained decoding. Experimental results show that LegalSearchLM outperforms baselines by 6-20% on LEGAR BENCH, achieving state-of-the-art performance. It also demonstrates strong generalization to out-of-domain cases, outperforming naive generative models trained on in-domain data by 15%.
Rethinking Hybrid Retrieval: When Small Embeddings and LLM Re-ranking Beat Bigger Models
2025-05-28
Arjun Rao, Hanieh Alipour, Nick Pendar
This paper presents a comparison of embedding models in tri-modal hybrid retrieval for Retrieval-Augmented Generation (RAG) systems. We investigate the fusion of dense semantic, sparse lexical, and graph-based embeddings, focusing on the performance of the MiniLM-v6 and BGE-Large architectures. Contrary to conventional assumptions, our results show that the compact MiniLM-v6 outperforms the larger BGE-Large when integrated with LLM-based re-ranking within our tri-modal hybrid framework. Experiments conducted on the SciFact, FIQA, and NFCorpus datasets demonstrate significant improvements in retrieval quality with the MiniLM-v6 configuration. The performance difference is particularly pronounced in agentic re-ranking scenarios, indicating better alignment between MiniLM-v6's embedding space and LLM reasoning. Our findings suggest that embedding model selection for RAG systems should prioritize compatibility with multi-signal fusion and LLM alignment, rather than relying solely on larger models. This approach may reduce computational requirements while improving retrieval accuracy and efficiency.
Fairness through Feedback: Addressing Algorithmic Misgendering in Automatic Gender Recognition
2025-05-28
Camilla Quaresmini, Giacomo Zanotti
Automatic Gender Recognition (AGR) systems are an increasingly widespread application in the Machine Learning (ML) landscape. While these systems are typically understood as detecting gender, they often classify datapoints based on observable features correlated at best with either male or female sex. In addition to questionable binary assumptions, from an epistemological point of view, this is problematic for two reasons. First, there exists a gap between the categories the system is meant to predict (woman versus man) and those onto which their output reasonably maps (female versus male). What is more, gender cannot be inferred on the basis of such observable features. This makes AGR tools often unreliable, especially in the case of non-binary and gender non-conforming people. We suggest a theoretical and practical rethinking of AGR systems. To begin, distinctions are made between sex, gender, and gender expression. Then, we build upon the observation that, unlike algorithmic misgendering, human-human misgendering is open to the possibility of re-evaluation and correction. We suggest that analogous dynamics should be recreated in AGR, giving users the possibility to correct the system's output. While implementing such a feedback mechanism could be regarded as diminishing the system's autonomy, it represents a way to significantly increase fairness levels in AGR. This is consistent with the conceptual change of paradigm that we advocate for AGR systems, which should be understood as tools respecting individuals' rights and capabilities of self-expression and determination.
Construction, Commissioning, and Installation of the Cylindrical GEM Inner Tracker of the BESIII Experiment
2025-05-27
Stefano Gramigna
BESIII (BEijing Spectrometer III) is a particle physics experiment with a vast physics program centered around the study of charmonium and the $\tau$ lepton. The performance of the spectrometer's inner tracker, the innermost part of a large drift chamber, has been degrading due to aging phenomena related to the large particle rate. Planned upgrades to the BEPCII (Beijing Electron Positron Collider II) collider, servicing the experiment, may further aggravate the problem, with the risk of disrupting the data taking. The Italian component of the BESIII collaboration proposed a detector based on cylindrical GEM (Gas Electron Multiplier) technology to replace the aging inner tracker. The new detector aims to improve the current tracker's spatial resolution in the beam direction at least a factor 2 and to ensure the continuation of BESIII's data taking until its end in 2030. After more than 10 years of design, development, and construction the three layers of the CGEM-IT (Cylindrical GEM Inner Tracker) are finally being installed in the spectrometer. This thesis describes the three final years of the detector's development, which led from diagnosing and resolving mechanical issues preventing the largest layer from powering on to securing the approval of the experiment's internal review committee for installation. Particular focus is given to the technological solutions adopted to overcome the challenges encountered during the development process, which often required a complete rethinking of previous methods and procedures. The thesis concludes with a snapshot of the ongoing installation of the detector, commenting on the results of the preparatory work undertaken to ensure its success.
Recent advances in multimodal question answering have primarily focused on combining heterogeneous modalities or fine-tuning multimodal large language models. While these approaches have shown strong performance, they often rely on a single, generalized reasoning strategy, overlooking the unique characteristics of each modality ultimately limiting both accuracy and interpretability. To address these limitations, we propose MAMMQA, a multi-agent QA framework for multimodal inputs spanning text, tables, and images. Our system includes two Visual Language Model (VLM) agents and one text-based Large Language Model (LLM) agent. The first VLM decomposes the user query into sub-questions and sequentially retrieves partial answers from each modality. The second VLM synthesizes and refines these results through cross-modal reasoning. Finally, the LLM integrates the insights into a cohesive answer. This modular design enhances interpretability by making the reasoning process transparent and allows each agent to operate within its domain of expertise. Experiments on diverse multimodal QA benchmarks demonstrate that our cooperative, multi-agent framework consistently outperforms existing baselines in both accuracy and robustness.
Rethinking Chunk Size For Long-Document Retrieval: A Multi-Dataset Analysis
2025-05-27
Sinchana Ramakanth Bhat, Max Rudat, Jannis Spiekermann, Nicolas Flores-Herr
Chunking is a crucial preprocessing step in retrieval-augmented generation (RAG) systems, significantly impacting retrieval effectiveness across diverse datasets. In this study, we systematically evaluate fixed-size chunking strategies and their influence on retrieval performance using multiple embedding models. Our experiments, conducted on both short-form and long-form datasets, reveal that chunk size plays a critical role in retrieval effectiveness -- smaller chunks (64-128 tokens) are optimal for datasets with concise, fact-based answers, whereas larger chunks (512-1024 tokens) improve retrieval in datasets requiring broader contextual understanding. We also analyze the impact of chunking on different embedding models, finding that they exhibit distinct chunking sensitivities. While models like Stella benefit from larger chunks, leveraging global context for long-range retrieval, Snowflake performs better with smaller chunks, excelling at fine-grained, entity-based matching. Our results underscore the trade-offs between chunk size, embedding models, and dataset characteristics, emphasizing the need for improved chunk quality measures, and more comprehensive datasets to advance chunk-based retrieval in long-document Information Retrieval (IR).
Rethinking the Outlier Distribution in Large Language Models: An In-depth Study
2025-05-27
Rahul Raman, Khushi Sharma, Sai Qian Zhang
Investigating outliers in large language models (LLMs) is crucial due to their significant impact on various aspects of LLM performance, including quantization and compression. Outliers often cause considerable quantization errors, leading to degraded model performance. Identifying and addressing these outliers can enhance the accuracy and efficiency of the quantization process, enabling smoother deployment on edge devices or specialized hardware. Recent studies have identified two common types of outliers in LLMs: massive activations and channel-wise outliers. While numerous quantization algorithms have been proposed to mitigate their effects and maintain satisfactory accuracy, few have thoroughly explored the root causes of these outliers in depth. In this paper, we conduct a comprehensive investigation into the formation mechanisms of these outliers and propose potential strategies to mitigate their occurrence. Ultimately, we introduce some efficient approaches to eliminate most massive activations and channel-wise outliers with minimal impact on accuracy.
Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives
2025-05-27
Yajiao Liu, Congliang Chen, Junchi Yang, Ruoyu Sun
Training large language models with data collected from various domains can improve their performance on downstream tasks. However, given a fixed training budget, the sampling proportions of these different domains significantly impact the model's performance. How can we determine the domain weights across different data domains to train the best-performing model within constrained computational resources? In this paper, we provide a comprehensive overview of existing data mixture methods. First, we propose a fine-grained categorization of existing methods, extending beyond the previous offline and online classification. Offline methods are further grouped into heuristic-based, algorithm-based, and function fitting-based methods. For online methods, we categorize them into three groups: online min-max optimization, online mixing law, and other approaches by drawing connections with the optimization frameworks underlying offline methods. Second, we summarize the problem formulations, representative algorithms for each subtype of offline and online methods, and clarify the relationships and distinctions among them. Finally, we discuss the advantages and disadvantages of each method and highlight key challenges in the field of data mixture.
Fine-tuning large language models (LLMs) is intended to improve their reasoning capabilities, yet we uncover a counterintuitive effect: models often forget how to solve problems they previously answered correctly during training. We term this phenomenon temporal forgetting and show that it is widespread across model sizes, fine-tuning methods (both Reinforcement Learning and Supervised Fine-Tuning), and multiple reasoning benchmarks. To address this gap, we introduce Temporal Sampling, a simple decoding strategy that draws outputs from multiple checkpoints along the training trajectory. This approach recovers forgotten solutions without retraining or ensembling, and leads to substantial improvements in reasoning performance, gains from 4 to 19 points in Pass@k and consistent gains in Majority@k across several benchmarks. We further extend our method to LoRA-adapted models, demonstrating that storing only adapter weights across checkpoints achieves similar benefits with minimal storage cost. By leveraging the temporal diversity inherent in training, Temporal Sampling offers a practical, compute-efficient way to surface hidden reasoning ability and rethink how we evaluate LLMs.
Causal Meta-Analysis: Rethinking the Foundations of Evidence-Based Medicine
2025-05-26
Clément Berenfeld, Ahmed Boughdiri, Bénédicte Colnet, Wouter A. C. van Amsterdam, Aurélien Bellet, Rémi Khellaf, Erwan Scornet, Julie Josse
Meta-analysis, by synthesizing effect estimates from multiple studies conducted in diverse settings, stands at the top of the evidence hierarchy in clinical research. Yet, conventional approaches based on fixed- or random-effects models lack a causal framework, which may limit their interpretability and utility for public policy. Incorporating causal inference reframes meta-analysis as the estimation of well-defined causal effects on clearly specified populations, enabling a principled approach to handling study heterogeneity. We show that classical meta-analysis estimators have a clear causal interpretation when effects are measured as risk differences. However, this breaks down for nonlinear measures like the risk ratio and odds ratio. To address this, we introduce novel causal aggregation formulas that remain compatible with standard meta-analysis practices and do not require access to individual-level data. To evaluate real-world impact, we apply both classical and causal meta-analysis methods to 500 published meta-analyses. While the conclusions often align, notable discrepancies emerge, revealing cases where conventional methods may suggest a treatment is beneficial when, under a causal lens, it is in fact harmful.
Probabilistic Circuits (PCs) offer a computationally scalable framework for generative modeling, supporting exact and efficient inference of a wide range of probabilistic queries. While recent advances have significantly improved the expressiveness and scalability of PCs, effectively training their parameters remains a challenge. In particular, a widely used optimization method, full-batch Expectation-Maximization (EM), requires processing the entire dataset before performing a single update, making it ineffective for large datasets. While empirical extensions to the mini-batch setting have been proposed, it remains unclear what objective these algorithms are optimizing, making it difficult to assess their theoretical soundness. This paper bridges the gap by establishing a novel connection between the general EM objective and the standard full-batch EM algorithm. Building on this, we derive a theoretically grounded generalization to the mini-batch setting and demonstrate its effectiveness through preliminary empirical results.
Recent advances in model-based reinforcement learning (MBRL) have achieved super-human level performance on the Atari100k benchmark, driven by reinforcement learning agents trained on powerful diffusion world models. However, we identify that the current aggregates mask a major performance asymmetry: MBRL agents dramatically outperform humans in some tasks despite drastically underperforming in others, with the former inflating the aggregate metrics. This is especially pronounced in pixel-based agents trained with diffusion world models. In this work, we address the pronounced asymmetry observed in pixel-based agents as an initial attempt to reverse the worrying upward trend observed in them. We address the problematic aggregates by delineating all tasks as Agent-Optimal or Human-Optimal and advocate for equal importance on metrics from both sets. Next, we hypothesize this pronounced asymmetry is due to the lack of temporally-structured latent space trained with the World Model objective in pixel-based methods. Lastly, to address this issue, we propose Joint Embedding DIffusion (JEDI), a novel latent diffusion world model trained end-to-end with the self-consistency objective. JEDI outperforms SOTA models in human-optimal tasks while staying competitive across the Atari100k benchmark, and runs 3 times faster with 43% lower memory than the latest pixel-based diffusion baseline. Overall, our work rethinks what it truly means to cross human-level performance in Atari100k.
Large vision-language models (LVLMs) excel at multimodal understanding but suffer from high computational costs due to redundant vision tokens. Existing pruning methods typically rely on single-layer attention scores to rank and prune redundant visual tokens to solve this inefficiency. However, as the interaction between tokens and layers is complicated, this raises a basic question: Is such a simple single-layer criterion sufficient to identify redundancy? To answer this question, we rethink the emergence of redundant visual tokens from a fundamental perspective: information flow, which models the interaction between tokens and layers by capturing how information moves between tokens across layers. We find (1) the CLS token acts as an information relay, which can simplify the complicated flow analysis; (2) the redundancy emerges progressively and dynamically via layer-wise attention concentration; and (3) relying solely on attention scores from single layers can lead to contradictory redundancy identification. Based on this, we propose FlowCut, an information-flow-aware pruning framework, mitigating the insufficiency of the current criterion for identifying redundant tokens and better aligning with the model's inherent behaviors. Extensive experiments show that FlowCut achieves superior results, outperforming SoTA by 1.6% on LLaVA-1.5-7B with 88.9% token reduction, and by 4.3% on LLaVA-NeXT-7B with 94.4% reduction, delivering 3.2x speed-up in the prefilling stage. Our code is available at https://github.com/TungChintao/FlowCut
Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality Inputs with Confidence-Guided Gate
2025-05-26
Liangwei Nathan Zheng, Wei Emma Zhang, Mingyu Guo, Miao Xu, Olaf Maennel, Weitong Chen
Effectively managing missing modalities is a fundamental challenge in real-world multimodal learning scenarios, where data incompleteness often results from systematic collection errors or sensor failures. Sparse Mixture-of-Experts (SMoE) architectures have the potential to naturally handle multimodal data, with individual experts specializing in different modalities. However, existing SMoE approach often lacks proper ability to handle missing modality, leading to performance degradation and poor generalization in real-world applications. We propose Conf-SMoE to introduce a two-stage imputation module to handle the missing modality problem for the SMoE architecture and reveal the insight of expert collapse from theoretical analysis with strong empirical evidence. Inspired by our theoretical analysis, Conf-SMoE propose a novel expert gating mechanism by detaching the softmax routing score to task confidence score w.r.t ground truth. This naturally relieves expert collapse without introducing additional load balance loss function. We show that the insights of expert collapse aligns with other gating mechanism such as Gaussian and Laplacian gate. We also evaluate the proposed method on four different real world dataset with three different experiment settings to conduct comprehensive the analysis of Conf-SMoE on modality fusion and resistance to missing modality.
End-to-end learning has emerged as a transformative paradigm in autonomous driving. However, the inherently multimodal nature of driving behaviors and the generalization challenges in long-tail scenarios remain critical obstacles to robust deployment. We propose DiffE2E, a diffusion-based end-to-end autonomous driving framework. This framework first performs multi-scale alignment of multi-sensor perception features through a hierarchical bidirectional cross-attention mechanism. It then introduces a novel class of hybrid diffusion-supervision decoders based on the Transformer architecture, and adopts a collaborative training paradigm that seamlessly integrates the strengths of both diffusion and supervised policy. DiffE2E models structured latent spaces, where diffusion captures the distribution of future trajectories and supervision enhances controllability and robustness. A global condition integration module enables deep fusion of perception features with high-level targets, significantly improving the quality of trajectory generation. Subsequently, a cross-attention mechanism facilitates efficient interaction between integrated features and hybrid latent variables, promoting the joint optimization of diffusion and supervision objectives for structured output generation, ultimately leading to more robust control. Experiments demonstrate that DiffE2E achieves state-of-the-art performance in both CARLA closed-loop evaluations and NAVSIM benchmarks. The proposed integrated diffusion-supervision policy offers a generalizable paradigm for hybrid action representation, with strong potential for extension to broader domains including embodied intelligence. More details and visualizations are available at \href{https://infinidrive.github.io/DiffE2E/}{project website}.
Research interest in end-to-end autonomous driving has surged owing to its fully differentiable design integrating modular tasks, i.e. perception, prediction and planing, which enables optimization in pursuit of the ultimate goal. Despite the great potential of the end-to-end paradigm, existing methods suffer from several aspects including expensive BEV (bird's eye view) computation, action diversity, and sub-optimal decision in complex real-world scenarios. To address these challenges, we propose a novel hybrid sparse-dense diffusion policy, empowered by a Vision-Language Model (VLM), called Diff-VLA. We explore the sparse diffusion representation for efficient multi-modal driving behavior. Moreover, we rethink the effectiveness of VLM driving decision and improve the trajectory generation guidance through deep interaction across agent, map instances and VLM output. Our method shows superior performance in Autonomous Grand Challenge 2025 which contains challenging real and reactive synthetic scenarios. Our methods achieves 45.0 PDMS.
2025-05-26
Zitian Gao, Lynx Chen, Joey Zhou, Bryan Dai
We trained 13,440 large language models and found that entropy minimization requires only a single unlabeled data and 10 steps optimization to achieve performance improvements comparable to or even greater than those obtained using thousands of data and carefully designed rewards in rule-based reinforcement learning. This striking result may prompt a rethinking of post-training paradigms for large language models. Our code is avaliable at https://github.com/zitian-gao/one-shot-em.
Rethinking Text-based Protein Understanding: Retrieval or LLM?
2025-05-26
Juntong Wu, Zijing Liu, He Cao, Hao Li, Bin Feng, Zishan Shu, Ke Yu, Li Yuan, Yu Li
In recent years, protein-text models have gained significant attention for their potential in protein generation and understanding. Current approaches focus on integrating protein-related knowledge into large language models through continued pretraining and multi-modal alignment, enabling simultaneous comprehension of textual descriptions and protein sequences. Through a thorough analysis of existing model architectures and text-based protein understanding benchmarks, we identify significant data leakage issues present in current benchmarks. Moreover, conventional metrics derived from natural language processing fail to accurately assess the model's performance in this domain. To address these limitations, we reorganize existing datasets and introduce a novel evaluation framework based on biological entities. Motivated by our observation, we propose a retrieval-enhanced method, which significantly outperforms fine-tuned LLMs for protein-to-text generation and shows accuracy and efficiency in training-free scenarios. Our code and data can be seen at https://github.com/IDEA-XL/RAPM.
Trivial Monodromy without Separation: A Non-Hausdorff Counterexample in Covering Theory
2025-05-26
Abhiram Sripat
We present a concrete topological construction, a non-Hausdorff, locally Euclidean space with multiple origins, that behaves unexpectedly well under path-lifting. Despite lacking the Hausdorff property, the space remains second-countable, T1, and path-connected, yet its fundamental group sees only trivial monodromy. This violates intuitive expectations from classical covering theory and invites a systematic rethinking of how separation axioms interact with lifting properties. We offer this space as a foundational test case for a broader theory of generalized covers in non-Hausdorff settings. We invite mathematicians to explore its categorical, logical, and geometric consequences, and to develop the corresponding machinery.
Our knowledge of past cultures relies considerably on written material. For centuries, texts have been copied, altered, then transmitted or lost - eventually, from surviving documents, philologists attempt to reconstruct text phylogenies ("stemmata"), and past written cultures. Nonetheless, fundamental questions on the extent of losses, representativeness of surviving artefacts, and the dynamics of text genealogies have remained open since the earliest days of philology. To address these, we radically rethink the study of text transmission through a complexity science approach, integrating stochastic modelling, computer simulations, and data analysis, in a parsimonious mindset akin to statistical physics and evolutionary biology. Thus, we design models that are simple and general, while accounting for diachrony and other key aspects of the dynamical process underlying text phylogenies, such as the extinction of entire branches or trees. On the well-known case study of Medieval French chivalric literature, we find that up to 60% of texts and 99% of manuscripts were lost (consistent with recent synchronic "biodiversity" analyses). We also settle a hundred-year-old controversy on the bifidity of stemmata. Further, our null model suggests that pure chance ("drift") is not the only mechanism at play, and we provide a theoretical and empirical framework for future investigation.
Recent advances in lightweight time series forecasting models suggest the inherent simplicity of time series forecasting tasks. In this paper, we present CMoS, a super-lightweight time series forecasting model. Instead of learning the embedding of the shapes, CMoS directly models the spatial correlations between different time series chunks. Additionally, we introduce a Correlation Mixing technique that enables the model to capture diverse spatial correlations with minimal parameters, and an optional Periodicity Injection technique to ensure faster convergence. Despite utilizing as low as 1% of the lightweight model DLinear's parameters count, experimental results demonstrate that CMoS outperforms existing state-of-the-art models across multiple datasets. Furthermore, the learned weights of CMoS exhibit great interpretability, providing practitioners with valuable insights into temporal structures within specific application scenarios.
Rethinking Metrics and Benchmarks of Video Anomaly Detection
2025-05-25
Zihao Liu, Xiaoyu Wu, Wenna Li, Linlin Yang
Video Anomaly Detection (VAD), which aims to detect anomalies that deviate from expectation, has attracted increasing attention in recent years. Existing advancements in VAD primarily focus on model architectures and training strategies, while devoting insufficient attention to evaluation metrics and benchmarks. In this paper, we rethink VAD evaluation protocols through comprehensive experimental analyses, revealing three critical limitations in current practices: 1) existing metrics are significantly influenced by single annotation bias; 2) current metrics fail to reward early detection of anomalies; 3) available benchmarks lack the capability to evaluate scene overfitting. To address these limitations, we propose three novel evaluation methods: first, we establish averaged AUC/AP metrics over multi-round annotations to mitigate single annotation bias; second, we develop a Latency-aware Average Precision (LaAP) metric that rewards early and accurate anomaly detection; and finally, we introduce two hard normal benchmarks (UCF-HN, MSAD-HN) with videos specifically designed to evaluate scene overfitting. We report performance comparisons of ten state-of-the-art VAD approaches using our proposed evaluation methods, providing novel perspectives for future VAD model development.
Rethinking the Understanding Ability across LLMs through Mutual Information
2025-05-25
Shaojie Wang, Sirui Ding, Na Zou
Recent advances in large language models (LLMs) have revolutionized natural language processing, yet evaluating their intrinsic linguistic understanding remains challenging. Moving beyond specialized evaluation tasks, we propose an information-theoretic framework grounded in mutual information (MI) to achieve this. We formalize the understanding as MI between an input sentence and its latent representation (sentence-level MI), measuring how effectively input information is preserved in latent representation. Given that LLMs learn embeddings for individual tokens, we decompose sentence-level MI into token-level MI between tokens and sentence embeddings, establishing theoretical bounds connecting these measures. Based on this foundation, we theoretically derive a computable lower bound for token-level MI using Fano's inequality, which directly relates to token-level recoverability-the ability to predict original tokens from sentence embedding. We implement this recoverability task to comparatively measure MI across different LLMs, revealing that encoder-only models consistently maintain higher information fidelity than their decoder-only counterparts, with the latter exhibiting a distinctive late-layer "forgetting" pattern where mutual information is first enhanced and then discarded. Moreover, fine-tuning to maximize token-level recoverability consistently improves understanding ability of LLMs on tasks without task-specific supervision, demonstrating that mutual information can serve as a foundation for understanding and improving language model capabilities.
Beyond Domain Randomization: Event-Inspired Perception for Visually Robust Adversarial Imitation from Videos
2025-05-24
Andrea Ramazzina, Vittorio Giammarino, Matteo El-Hariry, Mario Bijelic
Imitation from videos often fails when expert demonstrations and learner environments exhibit domain shifts, such as discrepancies in lighting, color, or texture. While visual randomization partially addresses this problem by augmenting training data, it remains computationally intensive and inherently reactive, struggling with unseen scenarios. We propose a different approach: instead of randomizing appearances, we eliminate their influence entirely by rethinking the sensory representation itself. Inspired by biological vision systems that prioritize temporal transients (e.g., retinal ganglion cells) and by recent sensor advancements, we introduce event-inspired perception for visually robust imitation. Our method converts standard RGB videos into a sparse, event-based representation that encodes temporal intensity gradients, discarding static appearance features. This biologically grounded approach disentangles motion dynamics from visual style, enabling robust visual imitation from observations even in the presence of visual mismatches between expert and agent environments. By training policies on event streams, we achieve invariance to appearance-based distractors without requiring computationally expensive and environment-specific data augmentation techniques. Experiments across the DeepMind Control Suite and the Adroit platform for dynamic dexterous manipulation show the efficacy of our method. Our code is publicly available at Eb-LAIfO.
We introduce and investigate the concept of Stratified Algebra, a new algebraic framework equipped with a layer-based structure on a vector space. We formalize a set of axioms governing intra-layer and inter-layer interactions, study their implications for algebraic dynamics, and present concrete matrix-based models that satisfy different subsets of these axioms. Both associative and bracket-sensitive constructions are considered, with an emphasis on stratum-breaking propagation and permutation symmetry. This framework proposes a paradigm shift in the way algebraic structures are conceived: instead of enforcing uniform global rules, it introduces stratified layers with context-dependent interactions. Such a rethinking of algebraic organization allows for the modeling of systems where local consistency coexists with global asymmetry, non-associativity, and semantic transitions.
Aligning text-to-image (T2I) diffusion models with human preferences has emerged as a critical research challenge. While recent advances in this area have extended preference optimization techniques from large language models (LLMs) to the diffusion setting, they often struggle with limited exploration. In this work, we propose a novel and orthogonal approach to enhancing diffusion-based preference optimization. First, we introduce a stable reference model update strategy that relaxes the frozen reference model, encouraging exploration while maintaining a stable optimization anchor through reference model regularization. Second, we present a timestep-aware training strategy that mitigates the reward scale imbalance problem across timesteps. Our method can be integrated into various preference optimization algorithms. Experimental results show that our approach improves the performance of state-of-the-art methods on human preference evaluation benchmarks.
Benchmarking and Rethinking Knowledge Editing for Large Language Models
2025-05-24
Guoxiu He, Xin Song, Futing Wang, Aixin Sun
Knowledge editing aims to update the embedded knowledge within Large Language Models (LLMs). However, existing approaches, whether through parameter modification or external memory integration, often suffer from inconsistent evaluation objectives and experimental setups. To address this gap, we conduct a comprehensive benchmarking study. In addition to fact-level datasets, we introduce more complex event-based datasets and general-purpose datasets drawn from other tasks. Our evaluation covers both instruction-tuned and reasoning-oriented LLMs, under a realistic autoregressive inference setting rather than teacher-forced decoding. Beyond single-edit assessments, we also evaluate multi-edit scenarios to better reflect practical demands. We employ four evaluation dimensions, including portability, and compare all recent methods against a simple and straightforward baseline named Selective Contextual Reasoning (SCR). Empirical results reveal that parameter-based editing methods perform poorly under realistic conditions. In contrast, SCR consistently outperforms them across all settings. This study offers new insights into the limitations of current knowledge editing methods and highlights the potential of context-based reasoning as a more robust alternative.
Rethinking Causal Mask Attention for Vision-Language Inference
2025-05-24
Xiaohuan Pei, Tao Huang, YanXiang Ma, Chang Xu
Causal attention has become a foundational mechanism in autoregressive vision-language models (VLMs), unifying textual and visual inputs under a single generative framework. However, existing causal mask-based strategies are inherited from large language models (LLMs) where they are tailored for text-only decoding, and their adaptation to vision tokens is insufficiently addressed in the prefill stage. Strictly masking future positions for vision queries introduces overly rigid constraints, which hinder the model's ability to leverage future context that often contains essential semantic cues for accurate inference. In this work, we empirically investigate how different causal masking strategies affect vision-language inference and then propose a family of future-aware attentions tailored for this setting. We first empirically analyze the effect of previewing future tokens for vision queries and demonstrate that rigid masking undermines the model's capacity to capture useful contextual semantic representations. Based on these findings, we propose a lightweight attention family that aggregates future visual context into past representations via pooling, effectively preserving the autoregressive structure while enhancing cross-token dependencies. We evaluate a range of causal masks across diverse vision-language inference settings and show that selectively compressing future semantic context into past representations benefits the inference.
Rethinking Climate Econometrics: Data Cleaning, Flexible Trend Controls, and Predictive Validation
2025-05-23
Christof Schötz, Jan Hassel, Christian Otto
We assess empirical models in climate econometrics using modern statistical learning techniques. Existing approaches are prone to outliers, ignore sample dependencies, and lack principled model selection. To address these issues, we implement robust preprocessing, nonparametric time-trend controls, and out-of-sample validation across 700+ climate variables. Our analysis reveals that widely used models and predictors-such as mean temperature-have little predictive power. A previously overlooked humidity-related variable emerges as the most consistent predictor, though even its performance remains limited. These findings challenge the empirical foundations of climate econometrics and point toward a more robust, data-driven path forward.
Graph anomaly detection aims to identify unusual patterns in graph-based data, with wide applications in fields such as web security and financial fraud detection. Existing methods typically rely on contrastive learning, assuming that a lower similarity between a node and its local subgraph indicates abnormality. However, these approaches overlook a crucial limitation: the presence of interfering edges invalidates this assumption, since it introduces disruptive noise that compromises the contrastive learning process. Consequently, this limitation impairs the ability to effectively learn meaningful representations of normal patterns, leading to suboptimal detection performance. To address this issue, we propose a Clean-View Enhanced Graph Anomaly Detection framework (CVGAD), which includes a multi-scale anomaly awareness module to identify key sources of interference in the contrastive learning process. Moreover, to mitigate bias from the one-step edge removal process, we introduce a novel progressive purification module. This module incrementally refines the graph by iteratively identifying and removing interfering edges, thereby enhancing model performance. Extensive experiments on five benchmark datasets validate the effectiveness of our approach.
Don't Overthink it. Preferring Shorter Thinking Chains for Improved LLM Reasoning
2025-05-23
Michael Hassid, Gabriel Synnaeve, Yossi Adi, Roy Schwartz
Reasoning large language models (LLMs) heavily rely on scaling test-time compute to perform complex reasoning tasks by generating extensive "thinking" chains. While demonstrating impressive results, this approach incurs significant computational costs and inference time. In this work, we challenge the assumption that long thinking chains results in better reasoning capabilities. We first demonstrate that shorter reasoning chains within individual questions are significantly more likely to yield correct answers - up to 34.5% more accurate than the longest chain sampled for the same question. Based on these results, we suggest short-m@k, a novel reasoning LLM inference method. Our method executes k independent generations in parallel and halts computation once the first m thinking processes are done. The final answer is chosen using majority voting among these m chains. Basic short-1@k demonstrates similar or even superior performance over standard majority voting in low-compute settings - using up to 40% fewer thinking tokens. short-3@k, while slightly less efficient than short-1@k, consistently surpasses majority voting across all compute budgets, while still being substantially faster (up to 33% wall time reduction). Inspired by our results, we finetune an LLM using short, long, and randomly selected reasoning chains. We then observe that training on the shorter ones leads to better performance. Our findings suggest rethinking current methods of test-time compute in reasoning LLMs, emphasizing that longer "thinking" does not necessarily translate to improved performance and can, counter-intuitively, lead to degraded results.
Most LLM-based agent frameworks adopt a top-down philosophy: humans decompose tasks, define workflows, and assign agents to execute each step. While effective on benchmark-style tasks, such systems rely on designer updates and overlook agents' potential to learn from experience. Recently, Silver and Sutton(2025) envision a shift into a new era, where agents could progress from a stream of experiences. In this paper, we instantiate this vision of experience-driven learning by introducing a bottom-up agent paradigm that mirrors the human learning process. Agents acquire competence through a trial-and-reasoning mechanism-exploring, reflecting on outcomes, and abstracting skills over time. Once acquired, skills can be rapidly shared and extended, enabling continual evolution rather than static replication. As more agents are deployed, their diverse experiences accelerate this collective process, making bottom-up design especially suited for open-ended environments. We evaluate this paradigm in Slay the Spire and Civilization V, where agents perceive through raw visual inputs and act via mouse outputs, the same as human players. Using a unified, game-agnostic codebase without any game-specific prompts or privileged APIs, our bottom-up agents acquire skills entirely through autonomous interaction, demonstrating the potential of the bottom-up paradigm in complex, real-world environments. Our code is available at https://github.com/AngusDujw/Bottom-Up-Agent.
Reinforcement learning exhibits potential in enhancing the reasoning abilities of large language models, yet it is hard to scale for the low sample efficiency during the rollout phase. Existing methods attempt to improve efficiency by scheduling problems based on problem difficulties. However, these approaches suffer from unstable and biased estimations of problem difficulty and fail to capture the alignment between model competence and problem difficulty in RL training, leading to suboptimal results. To tackle these limitations, this paper introduces $\textbf{C}$ompetence-$\textbf{D}$ifficulty $\textbf{A}$lignment $\textbf{S}$ampling ($\textbf{CDAS}$), which enables accurate and stable estimation of problem difficulties by aggregating historical performance discrepancies of problems. Then the model competence is quantified to adaptively select problems whose difficulty is in alignment with the model's current competence using a fixed-point system. Experimental results across a range of challenging mathematical benchmarks show that CDAS achieves great improvements in both accuracy and efficiency. CDAS attains the highest average accuracy against baselines and exhibits significant speed advantages compared to Dynamic Sampling, a competitive strategy in DAPO, which is 2.33 times slower than CDAS.
A Framework for Spontaneous Brillouin Noise: Unveiling Fundamental Limits in Brillouin Metrology
2025-05-23
Simeng Jin, Shuai Yao, Zhisheng Yang, Zixuan Du, Xiaobin Hong, Marcelo A. Soto, Jingjing Xie, Long Zhang, Fan Yang, Jian Wu
Spontaneous Brillouin scattering (SpBS) provides a non-contact tool for probing the mechanical and thermodynamic properties of materials, enabling important applications such as distributed optical fiber sensing and high-resolution Brillouin microscopy. Achieving metrological precision in these systems relies critically on identifying fundamental noise sources. While a pioneering study three decades ago numerically investigated an intrinsic SpBS noise mechanism, this phenomenon has remained largely unexplored, particularly in the context of Brillouin metrological systems. Here, by revisiting its physical formation process and rethinking its stochastic behaviors, we develop and experimentally validate a comprehensive analytical framework on this long-overlooked noise source. Importantly, we theoretically predict, for the first time, the SpBS noise is a universal and fundamental limit that can dominate over conventional limits such as shot noise in Brillouin metrological systems like imaging, microscopy and sensing. Specifically, we experimentally demonstrate the SpBS-noise-limited regime in Brillouin imaging and sensing scenarios. This framework establishes a critical foundation for understanding and optimizing the performance bounds of current and future Brillouin-based technologies across diverse applications.
Imitation learning (IL) with human demonstrations is a promising method for robotic manipulation tasks. While minimal demonstrations enable robotic action execution, achieving high success rates and generalization requires high cost, e.g., continuously adding data or incrementally conducting human-in-loop processes with complex hardware/software systems. In this paper, we rethink the state/action space of the data collection pipeline as well as the underlying factors responsible for the prediction of non-robust actions. To this end, we introduce a Hierarchical Data Collection Space (HD-Space) for robotic imitation learning, a simple data collection scheme, endowing the model to train with proactive and high-quality data. Specifically, We segment the fine manipulation task into multiple key atomic tasks from a high-level perspective and design atomic state/action spaces for human demonstrations, aiming to generate robust IL data. We conduct empirical evaluations across two simulated and five real-world long-horizon manipulation tasks and demonstrate that IL policy training with HD-Space-based data can achieve significantly enhanced policy performance. HD-Space allows the use of a small amount of demonstration data to train a more powerful policy, particularly for long-horizon manipulation tasks. We aim for HD-Space to offer insights into optimizing data quality and guiding data scaling. project page: https://hd-space-robotics.github.io.
How high is `high'? Rethinking the roles of dimensionality in topological data analysis and manifold learning
2025-05-22
Hannah Sansford, Nick Whiteley, Patrick Rubin-Delanchy
We present a generalised Hanson-Wright inequality and use it to establish new statistical insights into the geometry of data point-clouds. In the setting of a general random function model of data, we clarify the roles played by three notions of dimensionality: ambient intrinsic dimension $p_{\mathrm{int}}$, which measures total variability across orthogonal feature directions; correlation rank, which measures functional complexity across samples; and latent intrinsic dimension, which is the dimension of manifold structure hidden in data. Our analysis shows that in order for persistence diagrams to reveal latent homology and for manifold structure to emerge it is sufficient that $p_{\mathrm{int}}\gg \log n$, where $n$ is the sample size. Informed by these theoretical perspectives, we revisit the ground-breaking neuroscience discovery of toroidal structure in grid-cell activity made by Gardner et al. (Nature, 2022): our findings reveal, for the first time, evidence that this structure is in fact isometric to physical space, meaning that grid cell activity conveys a geometrically faithful representation of the real world.
From Surveys to Narratives: Rethinking Cultural Value Adaptation in LLMs
2025-05-22
Muhammad Farid Adilazuarda, Chen Cecilia Liu, Iryna Gurevych, Alham Fikri Aji
Adapting cultural values in Large Language Models (LLMs) presents significant challenges, particularly due to biases and limited training data. Prior work primarily aligns LLMs with different cultural values using World Values Survey (WVS) data. However, it remains unclear whether this approach effectively captures cultural nuances or produces distinct cultural representations for various downstream tasks. In this paper, we systematically investigate WVS-based training for cultural value adaptation and find that relying solely on survey data can homogenize cultural norms and interfere with factual knowledge. To investigate these issues, we augment WVS with encyclopedic and scenario-based cultural narratives from Wikipedia and NormAd. While these narratives may have variable effects on downstream tasks, they consistently improve cultural distinctiveness than survey data alone. Our work highlights the inherent complexity of aligning cultural values with the goal of guiding task-specific behavior.
Rethinking Code Review Workflows with LLM Assistance: An Empirical Study
2025-05-22
Fannar Steinn Aðalsteinsson, Björn Borgar Magnússon, Mislav Milicevic, Adam Nirving Davidsson, Chih-Hong Cheng
Code reviews are a critical yet time-consuming aspect of modern software development, increasingly challenged by growing system complexity and the demand for faster delivery. This paper presents a study conducted at WirelessCar Sweden AB, combining an exploratory field study of current code review practices with a field experiment involving two variations of an LLM-assisted code review tool. The field study identifies key challenges in traditional code reviews, including frequent context switching, insufficient contextual information, and highlights both opportunities (e.g., automatic summarization of complex pull requests) and concerns (e.g., false positives and trust issues) in using LLMs. In the field experiment, we developed two prototype variations: one offering LLM-generated reviews upfront and the other enabling on-demand interaction. Both utilize a semantic search pipeline based on retrieval-augmented generation to assemble relevant contextual information for the review, thereby tackling the uncovered challenges. Developers evaluated both variations in real-world settings: AI-led reviews are overall more preferred, while still being conditional on the reviewers' familiarity with the code base, as well as on the severity of the pull request.
Erased or Dormant? Rethinking Concept Erasure Through Reversibility
2025-05-22
Ping Liu, Chi Zhang
To what extent does concept erasure eliminate generative capacity in diffusion models? While prior evaluations have primarily focused on measuring concept suppression under specific textual prompts, we explore a complementary and fundamental question: do current concept erasure techniques genuinely remove the ability to generate targeted concepts, or do they merely achieve superficial, prompt-specific suppression? We systematically evaluate the robustness and reversibility of two representative concept erasure methods, Unified Concept Editing and Erased Stable Diffusion, by probing their ability to eliminate targeted generative behaviors in text-to-image models. These methods attempt to suppress undesired semantic concepts by modifying internal model parameters, either through targeted attention edits or model-level fine-tuning strategies. To rigorously assess whether these techniques truly erase generative capacity, we propose an instance-level evaluation strategy that employs lightweight fine-tuning to explicitly test the reactivation potential of erased concepts. Through quantitative metrics and qualitative analyses, we show that erased concepts often reemerge with substantial visual fidelity after minimal adaptation, indicating that current methods suppress latent generative representations without fully eliminating them. Our findings reveal critical limitations in existing concept erasure approaches and highlight the need for deeper, representation-level interventions and more rigorous evaluation standards to ensure genuine, irreversible removal of concepts from generative models.
LLMs Are Not Scorers: Rethinking MT Evaluation with Generation-Based Methods
2025-05-22
Hyang Cui
Recent studies have applied large language models (LLMs) to machine translation quality estimation (MTQE) by prompting models to assign numeric scores. Nonetheless, these direct scoring methods tend to show low segment-level correlation with human judgments. In this paper, we propose a generation-based evaluation paradigm that leverages decoder-only LLMs to produce high-quality references, followed by semantic similarity scoring using sentence embeddings. We conduct the most extensive evaluation to date in MTQE, covering 8 LLMs and 8 language pairs. Empirical results show that our method outperforms both intra-LLM direct scoring baselines and external non-LLM reference-free metrics from MTME. These findings demonstrate the strength of generation-based evaluation and support a shift toward hybrid approaches that combine fluent generation with accurate semantic assessment.
A practical approach to activate long chain-of-thoughts reasoning ability in pre-trained large language models is to perform supervised fine-tuning on instruction datasets synthesized by strong Large Reasoning Models such as DeepSeek-R1, offering a cost-effective alternative to reinforcement learning. However, large-scale instruction sets with more than 100k samples incur significant training overhead, while effective strategies for automatic long-CoT instruction selection still remain unexplored. In this work, we propose Select2Reason, a novel and efficient instruction-tuning data selection framework for long-CoT reasoning. From the perspective of emergence of rethinking behaviors like self-correction and backtracking, we investigate common metrics that may determine the quality of long-CoT reasoning instructions. Select2Reason leverages a quantifier to estimate difficulty of question and jointly incorporates a reasoning trace length-based heuristic through a weighted scheme for ranking to prioritize high-utility examples. Empirical results on OpenR1-Math-220k demonstrate that fine-tuning LLM on only 10% of the data selected by Select2Reason achieves performance competitive with or superior to full-data tuning and open-source baseline OpenR1-Qwen-7B across three competition-level and six comprehensive mathematical benchmarks. Further experiments highlight the scalability in varying data size, efficiency during inference, and its adaptability to other instruction pools with minimal cost.
Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding
2025-05-21
Ta Duc Huy, Duy Anh Huynh, Yutong Xie, Yuankai Qi, Qi Chen, Phi Le Nguyen, Sen Kim Tran, Son Lam Phung, Anton van den Hengel, Zhibin Liao, Minh-Son To, Johan W. Verjans, Vu Minh Hieu Phan
Visual grounding (VG) is the capability to identify the specific regions in an image associated with a particular text description. In medical imaging, VG enhances interpretability by highlighting relevant pathological features corresponding to textual descriptions, improving model transparency and trustworthiness for wider adoption of deep learning models in clinical practice. Current models struggle to associate textual descriptions with disease regions due to inefficient attention mechanisms and a lack of fine-grained token representations. In this paper, we empirically demonstrate two key observations. First, current VLMs assign high norms to background tokens, diverting the model's attention from regions of disease. Second, the global tokens used for cross-modal learning are not representative of local disease tokens. This hampers identifying correlations between the text and disease tokens. To address this, we introduce simple, yet effective Disease-Aware Prompting (DAP) process, which uses the explainability map of a VLM to identify the appropriate image features. This simple strategy amplifies disease-relevant regions while suppressing background interference. Without any additional pixel-level annotations, DAP improves visual grounding accuracy by 20.74% compared to state-of-the-art methods across three major chest X-ray datasets.
The evaluation of large language models (LLMs) via benchmarks is widespread, yet inconsistencies between different leaderboards and poor separability among top models raise concerns about their ability to accurately reflect authentic model capabilities. This paper provides a critical analysis of benchmark effectiveness, examining main-stream prominent LLM benchmarks using results from diverse models. We first propose a new framework for accurate and reliable estimations of item characteristics and model abilities. Specifically, we propose Pseudo-Siamese Network for Item Response Theory (PSN-IRT), an enhanced Item Response Theory framework that incorporates a rich set of item parameters within an IRT-grounded architecture. Based on PSN-IRT, we conduct extensive analysis which reveals significant and varied shortcomings in the measurement quality of current benchmarks. Furthermore, we demonstrate that leveraging PSN-IRT is able to construct smaller benchmarks while maintaining stronger alignment with human preference.
We present a comprehensive study of formaldehyde (H2CO) absorption and radio recombination line (H110a) emission in 215 molecular clouds from the Bolocam Galactic Plane Survey (BGPS), observed using the Nanshan 25-m radio telescope. H2CO was detected in 88 sources (40.93 percent) with 59 being new detections, while H110a emission was found in only 11 sources (5.12 percent), all coincident with H2CO absorption. There exists a correlation of H2CO fluxes with millimeter fluxes below a 3 Jy threshold and an increased dispersion above it, suggesting the sub-CMB cooling of H2CO. Cross-matching with kinematic distance catalogs revealed H2CO spanning galactocentric distances from 0.216 to 10.769 kpc, with column densities ranging from 7.82 x 10^11 to 6.69 x 10^14 cm-2. A significant inverse correlation was observed between H2CO detection fraction and galactocentric distance, suggesting enhanced star forming activity closer to the Galactic Center. These findings challenge traditional Galactic Habitable Zone (GHZ) models by demonstrating the presence of biogenic precursors in the inner Galaxy, shielded within dense molecular clouds. Our results underscore the importance of incorporating chemical tracers like H2CO, alongside physical constraints, to refine the boundaries of the GHZ and advance the research of prebiotic chemistry in the Milky Way.
Self-heating electrochemical memory for high-precision analog computing
2025-05-21
Adam L. Gross, Sangheon Oh, François Léonard, Wyatt Hodges, T. Patrick Xiao, Joshua D. Sugar, Jacklyn Zhu, Sritharini Radhakrishnan, Sangyong Lee, Jolie Wang, Adam Christensen, Sam Lilak, Patrick S. Finnegan, Patrick Crandall, Christopher H. Bennett, William Wahby, Robin Jacobs-Gedrim, Matthew J. Marinella, Suhas Kumar, Sapan Agarwal, Yiyang Li, A. Alec Talin, Elliot J. Fuller
Artificial intelligence (AI) is pushing the limits of digital computing to such an extent that, if current trends were to continue, global energy consumption from computation alone would eclipse all other forms of energy within the next two decades. One promising approach to reduce energy consumption and to increase computational speed is in-memory analog computing. However, analog computing necessitates a fundamental rethinking of computation at the material level, where information is stored as continuously variable physical observables. This shift introduces challenges related to the precision, dynamic range, and reliability of analog devices - issues that have hindered the development of existing memory technology for use in analog computers. Here, we address these issues in the context of memory which stores information as resistance. Our approach utilizes an electrochemical cell to tune the bulk oxygen-vacancy concentration within a metal oxide film. Through leveraging the gate contact as both a heater and source of electrochemical currents, kinetic barriers are overcome to enable a dynamic range of nine decades of analog tunable resistance, more than 3,000 available states, and programming with voltages less than 2 V. Furthermore, we demonstrate deterministic write operations with high precision, current-voltage linearity across six decades, and programming speeds as fast as 15 ns. These characteristics pave the way toward low-power analog computers with potential to improve AI efficiency by orders of magnitude.
Tactile perception is profoundly influenced by the surface properties of objects in contact. However, despite their crucial role in shaping tactile experiences, these material characteristics have been largely neglected in existing tactile representation learning methods. Most approaches primarily focus on aligning tactile data with visual or textual information, overlooking the richness of tactile feedback that comes from understanding the materials' inherent properties. In this work, we address this gap by revisiting the tactile representation learning framework and incorporating material-aware priors into the learning process. These priors, which represent pre-learned characteristics specific to different materials, allow tactile models to better capture and generalize the nuances of surface texture. Our method enables more accurate, contextually rich tactile feedback across diverse materials and textures, improving performance in real-world applications such as robotics, haptic feedback systems, and material editing.
Multi-scale deformable attention (MSDA) is a flexible and powerful feature extraction mechanism for visual tasks, but its random-access grid sampling strategy poses significant optimization challenges, especially on domain-specific accelerators such as NPUs. In this work, we present a co-design approach that systematically rethinks memory access and computation strategies for MSDA on the Ascend NPU architecture. With this co-design approach, our implementation supports both efficient forward and backward computation, is fully adapted for training workloads, and incorporates a suite of hardware-aware optimizations. Extensive experiments show that our solution achieves up to $5.9\times$ (forward), $8.9\times$ (backward), and $7.3\times$ (end-to-end training) speedup over the grid sample-based baseline, and $1.9\times$, $2.4\times$, and $2.0\times$ acceleration over the latest vendor library, respectively.
Rethink the Role of Deep Learning towards Large-scale Quantum Systems
2025-05-20
Yusheng Zhao, Chi Zhang, Yuxuan Du
Characterizing the ground state properties of quantum systems is fundamental to capturing their behavior but computationally challenging. Recent advances in AI have introduced novel approaches, with diverse machine learning (ML) and deep learning (DL) models proposed for this purpose. However, the necessity and specific role of DL models in these tasks remain unclear, as prior studies often employ varied or impractical quantum resources to construct datasets, resulting in unfair comparisons. To address this, we systematically benchmark DL models against traditional ML approaches across three families of Hamiltonian, scaling up to 127 qubits in three crucial ground-state learning tasks while enforcing equivalent quantum resource usage. Our results reveal that ML models often achieve performance comparable to or even exceeding that of DL approaches across all tasks. Furthermore, a randomization test demonstrates that measurement input features have minimal impact on DL models' prediction performance. These findings challenge the necessity of current DL models in many quantum system learning scenarios and provide valuable insights into their effective utilization.
Deep Neural Networks have achieved remarkable achievements across various domains, however balancing performance and generalization still remains a challenge while training these networks. In this paper, we propose a novel framework that uses a cyclic optimization strategy to concurrently optimize the model and its input data for better training, rethinking the traditional training paradigm. Central to our approach is Iterative Constructive Perturbation (ICP), which leverages the model's loss to iteratively perturb the input, progressively constructing an enhanced representation over some refinement steps. This ICP input is then fed back into the model to produce improved intermediate features, which serve as a target in a self-distillation framework against the original features. By alternately altering the model's parameters to the data and the data to the model, our method effectively addresses the gap between fitting and generalization, leading to enhanced performance. Extensive experiments demonstrate that our approach not only mitigates common performance bottlenecks in neural networks but also demonstrates significant improvements across training variations.
Rethinking Stateful Tool Use in Multi-Turn Dialogues: Benchmarks and Challenges
2025-05-19
Hongru Wang, Wenyu Huang, Yufei Wang, Yuanhao Xi, Jianqiao Lu, Huan Zhang, Nan Hu, Zeming Liu, Jeff Z. Pan, Kam-Fai Wong
Existing benchmarks that assess Language Models (LMs) as Language Agents (LAs) for tool use primarily focus on stateless, single-turn interactions or partial evaluations, such as tool selection in a single turn, overlooking the inherent stateful nature of interactions in multi-turn applications. To fulfill this gap, we propose \texttt{DialogTool}, a multi-turn dialogue dataset with stateful tool interactions considering the whole life cycle of tool use, across six key tasks in three stages: 1) \textit{tool creation}; 2) \textit{tool utilization}: tool awareness, tool selection, tool execution; and 3) \textit{role-consistent response}: response generation and role play. Furthermore, we build \texttt{VirtualMobile} -- an embodied virtual mobile evaluation environment to simulate API calls and assess the robustness of the created APIs\footnote{We will use tools and APIs alternatively, there are no significant differences between them in this paper.}. Taking advantage of these artifacts, we conduct comprehensive evaluation on 13 distinct open- and closed-source LLMs and provide detailed analysis at each stage, revealing that the existing state-of-the-art LLMs still cannot perform well to use tools over long horizons.
Semi-supervised learning (SSL) alleviates the cost of data labeling process by exploiting unlabeled data, and has achieved promising results on various tasks such as image classification. Meanwhile, the Pretrain-Finetuning paradigm has garnered significant attention in recent years, and exploiting pre-trained models could also reduce the requirement of labeled data in downstream tasks. Therefore, a question naturally occurs: \emph{When the labeled data is scarce in the target tasks, should we exploit unlabeled data or pre-trained models?} To answer this question, we select pre-trained Vision-Language Models (VLMs) as representative pretrain-finetuning instances and propose \textit{Few-shot SSL} -- a framework that enables fair comparison between these two paradigms by controlling the amount of labeled data used. Extensive experiments across various settings demonstrate that pre-trained VLMs generally outperform SSL methods in nearly all cases, except when the data has low resolution or lacks clear semantic structure. Therefore, we encourage future SSL research to compare with pre-trained models and explore deeper integration, such as using pre-trained knowledge to enhance pseudo-labeling. To support future research, we release our unified reproduction and evaluation framework. Codes are available \href{https://anonymous.4open.science/r/Rethinking-SSL-and-Pretrain-Finetuning-5566 }{here}.
DD-Ranking: Rethinking the Evaluation of Dataset Distillation
2025-05-19
Zekai Li, Xinhao Zhong, Samir Khaki, Zhiyuan Liang, Yuhao Zhou, Mingjia Shi, Ziqiao Wang, Xuanlei Zhao, Wangbo Zhao, Ziheng Qin, Mengxuan Wu, Pengfei Zhou, Haonan Wang, David Junhao Zhang, Jia-Wei Liu, Shaobo Wang, Dai Liu, Linfeng Zhang, Guang Li, Kun Wang, Zheng Zhu, Zhiheng Ma, Joey Tianyi Zhou, Jiancheng Lv, Yaochu Jin, Peihao Wang, Kaipeng Zhang, Lingjuan Lyu, Yiran Huang, Zeynep Akata, Zhiwei Deng, Xindi Wu, George Cazenavette, Yuzhang Shang, Justin Cui, Jindong Gu, Qian Zheng, Hao Ye, Shuo Wang, Xiaobo Wang, Yan Yan, Angela Yao, Mike Zheng Shou, Tianlong Chen, Hakan Bilen, Baharan Mirzasoleiman, Manolis Kellis, Konstantinos N. Plataniotis, Zhangyang Wang, Bo Zhao, Yang You, Kai Wang
In recent years, dataset distillation has provided a reliable solution for data compression, where models trained on the resulting smaller synthetic datasets achieve performance comparable to those trained on the original datasets. To further improve the performance of synthetic datasets, various training pipelines and optimization objectives have been proposed, greatly advancing the field of dataset distillation. Recent decoupled dataset distillation methods introduce soft labels and stronger data augmentation during the post-evaluation phase and scale dataset distillation up to larger datasets (e.g., ImageNet-1K). However, this raises a question: Is accuracy still a reliable metric to fairly evaluate dataset distillation methods? Our empirical findings suggest that the performance improvements of these methods often stem from additional techniques rather than the inherent quality of the images themselves, with even randomly sampled images achieving superior results. Such misaligned evaluation settings severely hinder the development of DD. Therefore, we propose DD-Ranking, a unified evaluation framework, along with new general evaluation metrics to uncover the true performance improvements achieved by different methods. By refocusing on the actual information enhancement of distilled datasets, DD-Ranking provides a more comprehensive and fair evaluation standard for future research advancements.
What if Deception Cannot be Detected? A Cross-Linguistic Study on the Limits of Deception Detection from Text
2025-05-19
Aswathy Velutharambath, Kai Sassenberg, Roman Klinger
Can deception be detected solely from written text? Cues of deceptive communication are inherently subtle, even more so in text-only communication. Yet, prior studies have reported considerable success in automatic deception detection. We hypothesize that such findings are largely driven by artifacts introduced during data collection and do not generalize beyond specific datasets. We revisit this assumption by introducing a belief-based deception framework, which defines deception as a misalignment between an author's claims and true beliefs, irrespective of factual accuracy, allowing deception cues to be studied in isolation. Based on this framework, we construct three corpora, collectively referred to as DeFaBel, including a German-language corpus of deceptive and non-deceptive arguments and a multilingual version in German and English, each collected under varying conditions to account for belief change and enable cross-linguistic analysis. Using these corpora, we evaluate commonly reported linguistic cues of deception. Across all three DeFaBel variants, these cues show negligible, statistically insignificant correlations with deception labels, contrary to prior work that treats such cues as reliable indicators. We further benchmark against other English deception datasets following similar data collection protocols. While some show statistically significant correlations, effect sizes remain low and, critically, the set of predictive cues is inconsistent across datasets. We also evaluate deception detection using feature-based models, pretrained language models, and instruction-tuned large language models. While some models perform well on established deception datasets, they consistently perform near chance on DeFaBel. Our findings challenge the assumption that deception can be reliably inferred from linguistic cues and call for rethinking how deception is studied and modeled in NLP.
OmniFC: Rethinking Federated Clustering via Lossless and Secure Distance Reconstruction
2025-05-19
Jie Yan, Xin Liu, Zhong-Yuan Zhang
Federated clustering (FC) aims to discover global cluster structures across decentralized clients without sharing raw data, making privacy preservation a fundamental requirement. There are two critical challenges: (1) privacy leakage during collaboration, and (2) robustness degradation due to aggregation of proxy information from non-independent and identically distributed (Non-IID) local data, leading to inaccurate or inconsistent global clustering. Existing solutions typically rely on model-specific local proxies, which are sensitive to data heterogeneity and inherit inductive biases from their centralized counterparts, thus limiting robustness and generality. We propose Omni Federated Clustering (OmniFC), a unified and model-agnostic framework. Leveraging Lagrange coded computing, our method enables clients to share only encoded data, allowing exact reconstruction of the global distance matrix--a fundamental representation of sample relationships--without leaking private information, even under client collusion. This construction is naturally resilient to Non-IID data distributions. This approach decouples FC from model-specific proxies, providing a unified extension mechanism applicable to diverse centralized clustering methods. Theoretical analysis confirms both reconstruction fidelity and privacy guarantees, while comprehensive experiments demonstrate OmniFC's superior robustness, effectiveness, and generality across various benchmarks compared to state-of-the-art methods. Code will be released.
Fast, Not Fancy: Rethinking G2P with Rich Data and Rule-Based Models
2025-05-19
Mahta Fetrat Qharabagh, Zahra Dehghanian, Hamid R. Rabiee
Homograph disambiguation remains a significant challenge in grapheme-to-phoneme (G2P) conversion, especially for low-resource languages. This challenge is twofold: (1) creating balanced and comprehensive homograph datasets is labor-intensive and costly, and (2) specific disambiguation strategies introduce additional latency, making them unsuitable for real-time applications such as screen readers and other accessibility tools. In this paper, we address both issues. First, we propose a semi-automated pipeline for constructing homograph-focused datasets, introduce the HomoRich dataset generated through this pipeline, and demonstrate its effectiveness by applying it to enhance a state-of-the-art deep learning-based G2P system for Persian. Second, we advocate for a paradigm shift - utilizing rich offline datasets to inform the development of fast, rule-based methods suitable for latency-sensitive accessibility applications like screen readers. To this end, we improve one of the most well-known rule-based G2P systems, eSpeak, into a fast homograph-aware version, HomoFast eSpeak. Our results show an approximate 30% improvement in homograph disambiguation accuracy for the deep learning-based and eSpeak systems.
Rethinking Features-Fused-Pyramid-Neck for Object Detection
2025-05-19
Hulin Li
Multi-head detectors typically employ a features-fused-pyramid-neck for multi-scale detection and are widely adopted in the industry. However, this approach faces feature misalignment when representations from different hierarchical levels of the feature pyramid are forcibly fused point-to-point. To address this issue, we designed an independent hierarchy pyramid (IHP) architecture to evaluate the effectiveness of the features-unfused-pyramid-neck for multi-head detectors. Subsequently, we introduced soft nearest neighbor interpolation (SNI) with a weight downscaling factor to mitigate the impact of feature fusion at different hierarchies while preserving key textures. Furthermore, we present a features adaptive selection method for down sampling in extended spatial windows (ESD) to retain spatial features and enhance lightweight convolutional techniques (GSConvE). These advancements culminate in our secondary features alignment solution (SA) for real-time detection, achieving state-of-the-art results on Pascal VOC and MS COCO. Code will be released at https://github.com/AlanLi1997/rethinking-fpn. This paper has been accepted by ECCV2024 and published on Springer Nature.
Beyond Individual UX: Defining Group Experience(GX) as a New Paradigm for Group-centered AI
2025-05-19
Soohwan Lee, Seoyeong Hwang, Kyungho Lee
Recent advancements in HCI and AI have predominantly centered on individual user experiences, often neglecting the emergent dynamics of group interactions. This provocation introduces Group Experience(GX) to capture the collective perceptual, emotional, and cognitive dimensions that arise when individuals interact in cohesive groups. We challenge the conventional Human-centered AI paradigm and propose Group-centered AI(GCAI) as a framework that actively mediates group dynamics, amplifies diverse voices, and fosters ethical collective decision-making. Drawing on social psychology, organizational behavior, and group dynamics, we outline a group-centered design approach that balances individual autonomy with collective interests while developing novel evaluative metrics. Our analysis emphasizes rethinking traditional methodologies that focus solely on individual outcomes and advocates for innovative strategies to capture group collaboration. We call on researchers to bridge the gap between micro-level experiences and macro-level impacts, ultimately enriching and transforming collaborative human interactions.
Reward models (RMs) play a crucial role in reinforcement learning from human feedback (RLHF), aligning model behavior with human preferences. However, existing benchmarks for reward models show a weak correlation with the performance of optimized policies, suggesting that they fail to accurately assess the true capabilities of RMs. To bridge this gap, we explore several evaluation designs through the lens of reward overoptimization\textemdash a phenomenon that captures both how well the reward model aligns with human preferences and the dynamics of the learning signal it provides to the policy. The results highlight three key findings on how to construct a reliable benchmark: (i) it is important to minimize differences between chosen and rejected responses beyond correctness, (ii) evaluating reward models requires multiple comparisons across a wide range of chosen and rejected responses, and (iii) given that reward models encounter responses with diverse representations, responses should be sourced from a variety of models. However, we also observe that a extremely high correlation with degree of overoptimization leads to comparatively lower correlation with certain downstream performance. Thus, when designing a benchmark, it is desirable to use the degree of overoptimization as a useful tool, rather than the end goal.
Rethinking Predictive Modeling for LLM Routing: When Simple kNN Beats Complex Learned Routers
2025-05-19
Yang Li
As large language models (LLMs) grow in scale and specialization, routing--selecting the best model for a given input--has become essential for efficient and effective deployment. While recent methods rely on complex learned routing strategies, their dependence on disparate training data and evaluation setups makes comparison and generalization difficult. In this work, we revisit LLM routing through the lens of simplicity. We show that a well-tuned k-Nearest Neighbors (kNN) approach not only matches but often outperforms state-of-the-art learned routers across diverse tasks. To support systematic evaluation, we introduce a suite of standardized routing benchmarks spanning instruction-following, question-answering, and reasoning tasks, as well as the first multi-modal routing dataset involving visual inputs. Our findings reveal that the locality properties of model performance in embedding space enable simple non-parametric methods to achieve strong routing decisions with lower sample complexity than parametric approaches. This challenges the prevailing trend toward sophisticated architectures and highlights the importance of thoroughly evaluating simple baselines before investing in complex solutions. To support reproducibility and further exploration, we will release all benchmarks and code upon publication.
Traversal Verification for Speculative Tree Decoding
2025-05-18
Yepeng Weng, Qiao Hu, Xujie Chen, Li Liu, Dianwen Mei, Huishi Qiu, Jiang Tian, Zhongchao Shi
Speculative decoding is a promising approach for accelerating large language models. The primary idea is to use a lightweight draft model to speculate the output of the target model for multiple subsequent timesteps, and then verify them in parallel to determine whether the drafted tokens should be accepted or rejected. To enhance acceptance rates, existing frameworks typically construct token trees containing multiple candidates in each timestep. However, their reliance on token-level verification mechanisms introduces two critical limitations: First, the probability distribution of a sequence differs from that of individual tokens, leading to suboptimal acceptance length. Second, current verification schemes begin from the root node and proceed layer by layer in a top-down manner. Once a parent node is rejected, all its child nodes should be discarded, resulting in inefficient utilization of speculative candidates. This paper introduces Traversal Verification, a novel speculative decoding algorithm that fundamentally rethinks the verification paradigm through leaf-to-root traversal. Our approach considers the acceptance of the entire token sequence from the current node to the root, and preserves potentially valid subsequences that would be prematurely discarded by existing methods. We theoretically prove that the probability distribution obtained through Traversal Verification is identical to that of the target model, guaranteeing lossless inference while achieving substantial acceleration gains. Experimental results across different large language models and multiple tasks show that our method consistently improves acceptance length and throughput over existing methods
Kernelized attention extends the attention mechanism by modeling sequence correlations through kernel functions, making significant progresses in optimizing attention. Under the guarantee of harmonic analysis theory, kernel functions can be expanded with basis functions, inspiring random feature-based approaches to enhance the efficiency of kernelized attention while maintaining predictive performance. However, current random feature-based works are limited to the Fourier basis expansions under Bochner's theorem. We propose Schoenberg's theorem-based attention (SchoenbAt), which approximates dot-product kernelized attention with the polynomial basis under Schoenberg's theorem via random Maclaurin features and applies a two-stage regularization to constrain the input space and restore the output scale, acting as a drop-in replacement of dot-product kernelized attention. Our theoretical proof of the unbiasedness and concentration error bound of SchoenbAt supports its efficiency and accuracy as a kernelized attention approximation, which is also empirically validated under various random feature dimensions. Evaluations on real-world datasets demonstrate that SchoenbAt significantly enhances computational speed while preserving competitive performance in terms of precision, outperforming several efficient attention methods.
Tool learning, which enables large language models (LLMs) to utilize external tools effectively, has garnered increasing attention for its potential to revolutionize productivity across industries. Despite rapid development in tool learning, key challenges and opportunities remain understudied, limiting deeper insights and future advancements. In this paper, we investigate the tool learning ability of 41 prevalent LLMs by reproducing 33 benchmarks and enabling one-click evaluation for seven of them, forming a Tool Learning Platform named ToLeaP. We also collect 21 out of 33 potential training datasets to facilitate future exploration. After analyzing over 3,000 bad cases of 41 LLMs based on ToLeaP, we identify four main critical challenges: (1) benchmark limitations induce both the neglect and lack of (2) autonomous learning, (3) generalization, and (4) long-horizon task-solving capabilities of LLMs. To aid future advancements, we take a step further toward exploring potential directions, namely (1) real-world benchmark construction, (2) compatibility-aware autonomous learning, (3) rationale learning by thinking, and (4) identifying and recalling key clues. The preliminary experiments demonstrate their effectiveness, highlighting the need for further research and exploration.
Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline
2025-05-16
Xvyuan Liu, Xiangfei Qiu, Xingjian Wu, Zhengyu Li, Chenjuan Guo, Jilin Hu, Bin Yang
The forecasting of irregular multivariate time series (IMTS) is crucial in key areas such as healthcare, biomechanics, climate science, and astronomy. However, achieving accurate and practical predictions is challenging due to two main factors. First, the inherent irregularity and data missingness in irregular time series make modeling difficult. Second, most existing methods are typically complex and resource-intensive. In this study, we propose a general framework called APN to address these challenges. Specifically, we design a novel Time-Aware Patch Aggregation (TAPA) module that achieves adaptive patching. By learning dynamically adjustable patch boundaries and a time-aware weighted averaging strategy, TAPA transforms the original irregular sequences into high-quality, regularized representations in a channel-independent manner. Additionally, we use a simple query module to effectively integrate historical information while maintaining the model's efficiency. Finally, predictions are made by a shallow MLP. Experimental results on multiple real-world datasets show that APN outperforms existing state-of-the-art methods in both efficiency and accuracy.
2025-05-16
Guido Caccianiga, Yarden Sharon, Bernard Javot, Senya Polikovsky, Gökce Ergün, Ivan Capobianco, André L. Mihaljevic, Anton Deguet, Katherine J. Kuchenbecker
As robots for minimally invasive surgery (MIS) gradually become more accessible and modular, we believe there is a great opportunity to rethink and expand the visualization and control paradigms that have characterized surgical teleoperation since its inception. We conjecture that introducing one or more additional adjustable viewpoints in the abdominal cavity would not only unlock novel visualization and collaboration strategies for surgeons but also substantially boost the robustness of machine perception toward shared autonomy. Immediate advantages include controlling a second viewpoint and teleoperating surgical tools from a different perspective, which would allow collaborating surgeons to adjust their views independently and still maneuver their robotic instruments intuitively. Furthermore, we believe that capturing synchronized multi-view 3D measurements of the patient's anatomy would unlock advanced scene representations. Accurate real-time intraoperative 3D perception will allow algorithmic assistants to directly control one or more robotic instruments and/or robotic cameras. Toward these goals, we are building a synchronized multi-viewpoint, multi-sensor robotic surgery system by integrating high-performance vision components and upgrading the da Vinci Research Kit control logic. This short paper reports a functional summary of our setup and elaborates on its potential impacts in research and future clinical practice. By fully open-sourcing our system, we will enable the research community to reproduce our setup, improve it, and develop powerful algorithms, effectively boosting clinical translation of cutting-edge research.
Rethinking the Mean Teacher Strategy from the Perspective of Self-paced Learning
2025-05-16
Pengchen Zhang, Alan J. X. Guo, Sipin Luo, Zhe Han, Lin Guo
Semi-supervised medical image segmentation has attracted significant attention due to its potential to reduce manual annotation costs. The mean teacher (MT) strategy, commonly understood as introducing smoothed, temporally lagged consistency regularization, has demonstrated strong performance across various tasks in this field. In this work, we reinterpret the MT strategy on supervised data as a form of self-paced learning, regulated by the output agreement between the temporally lagged teacher model and the ground truth labels. This idea is further extended to incorporate agreement between a temporally lagged model and a cross-architectural model, which offers greater flexibility in regulating the learning pace and enables application to unlabeled data. Specifically, we propose dual teacher-student learning (DTSL), a framework that introduces two groups of teacher-student models with different architectures. The output agreement between the cross-group teacher and student models is used as pseudo-labels, generated via a Jensen-Shannon divergence-based consensus label generator (CLG). Extensive experiments on popular datasets demonstrate that the proposed method consistently outperforms existing state-of-the-art approaches. Ablation studies further validate the effectiveness of the proposed modules.
Rethinking the Role of Prompting Strategies in LLM Test-Time Scaling: A Perspective of Probability Theory
2025-05-16
Yexiang Liu, Zekun Li, Zhi Fang, Nan Xu, Ran He, Tieniu Tan
Recently, scaling test-time compute on Large Language Models (LLM) has garnered wide attention. However, there has been limited investigation of how various reasoning prompting strategies perform as scaling. In this paper, we focus on a standard and realistic scaling setting: majority voting. We systematically conduct experiments on 6 LLMs $\times$ 8 prompting strategies $\times$ 6 benchmarks. Experiment results consistently show that as the sampling time and computational overhead increase, complicated prompting strategies with superior initial performance gradually fall behind simple Chain-of-Thought. We analyze this phenomenon and provide theoretical proofs. Additionally, we propose a probabilistic method to efficiently predict scaling performance and identify the best prompting strategy under large sampling times, eliminating the need for resource-intensive inference processes in practical applications. Furthermore, we introduce two ways derived from our theoretical analysis to significantly improve the scaling performance. We hope that our research can promote to re-examine the role of complicated prompting, unleash the potential of simple prompting strategies, and provide new insights for enhancing test-time scaling performance. Code is available at https://github.com/MraDonkey/rethinking_prompting.
FP64 is All You Need: Rethinking Failure Modes in Physics-Informed Neural Networks
2025-05-16
Chenhui Xu, Dancheng Liu, Amir Nassereldine, Jinjun Xiong
Physics Informed Neural Networks (PINNs) often exhibit failure modes in which the PDE residual loss converges while the solution error stays large, a phenomenon traditionally blamed on local optima separated from the true solution by steep loss barriers. We challenge this understanding by demonstrate that the real culprit is insufficient arithmetic precision: with standard FP32, the LBFGS optimizer prematurely satisfies its convergence test, freezing the network in a spurious failure phase. Simply upgrading to FP64 rescues optimization, enabling vanilla PINNs to solve PDEs without any failure modes. These results reframe PINN failure modes as precision induced stalls rather than inescapable local minima and expose a three stage training dynamic unconverged, failure, success whose boundaries shift with numerical precision. Our findings emphasize that rigorous arithmetic precision is the key to dependable PDE solving with neural networks.
Recent advancements in generative artificial intelligence have introduced groundbreaking approaches to innovating next-generation semantic communication, which prioritizes conveying the meaning of a message rather than merely transmitting raw data. A fundamental challenge in semantic communication lies in accurately identifying and extracting the most critical semantic information while adapting to downstream tasks without degrading performance, particularly when the objective at the receiver may evolve over time. To enable flexible adaptation to multiple tasks at the receiver, this work introduces a novel semantic communication framework, which is capable of jointly capturing task-specific information to enhance downstream task performance and contextual information. Through rigorous experiments on popular image datasets and computer vision tasks, our framework shows promising improvement compared to existing work, including superior performance in downstream tasks, better generalizability, ultra-high bandwidth efficiency, and low reconstruction latency.
Rethinking Optimal Verification Granularity for Compute-Efficient Test-Time Scaling
2025-05-16
Hao Mark Chen, Guanxi Lu, Yasuyuki Okoshi, Zhiwen Mo, Masato Motomura, Hongxiang Fan
Test-time scaling (TTS) has proven effective in enhancing the reasoning capabilities of large language models (LLMs). Verification plays a key role in TTS, simultaneously influencing (1) reasoning performance and (2) compute efficiency, due to the quality and computational cost of verification. In this work, we challenge the conventional paradigms of verification, and make the first attempt toward systematically investigating the impact of verification granularity-that is, how frequently the verifier is invoked during generation, beyond verifying only the final output or individual generation steps. To this end, we introduce Variable Granularity Search (VG-Search), a unified algorithm that generalizes beam search and Best-of-N sampling via a tunable granularity parameter g. Extensive experiments with VG-Search under varying compute budgets, generator-verifier configurations, and task attributes reveal that dynamically selecting g can improve the compute efficiency and scaling behavior. Building on these findings, we propose adaptive VG-Search strategies that achieve accuracy gains of up to 3.1\% over Beam Search and 3.6\% over Best-of-N, while reducing FLOPs by over 52\%. We will open-source the code to support future research.
Rethinking Repetition Problems of LLMs in Code Generation
2025-05-15
Yihong Dong, Yuchen Liu, Xue Jiang, Zhi Jin, Ge Li
With the advent of neural language models, the performance of code generation has been significantly boosted. However, the problem of repetitions during the generation process continues to linger. Previous work has primarily focused on content repetition, which is merely a fraction of the broader repetition problem in code generation. A more prevalent and challenging problem is structural repetition. In structural repetition, the repeated code appears in various patterns but possesses a fixed structure, which can be inherently reflected in grammar. In this paper, we formally define structural repetition and propose an efficient decoding approach called RPG, which stands for Repetition Penalization based on Grammar, to alleviate the repetition problems in code generation for LLMs. Specifically, RPG first leverages grammar rules to identify repetition problems during code generation, and then strategically decays the likelihood of critical tokens that contribute to repetitions, thereby mitigating them in code generation. To facilitate this study, we construct a new dataset CodeRepetEval to comprehensively evaluate approaches for mitigating the repetition problems in code generation. Extensive experimental results demonstrate that RPG substantially outperforms the best-performing baselines on CodeRepetEval dataset as well as HumanEval and MBPP benchmarks, effectively reducing repetitions and enhancing the quality of generated code.
In this paper, we introduce S-MedQA, an English medical question-answering (QA) dataset for benchmarking large language models in fine-grained clinical specialties. We use S-MedQA to check the applicability of a popular hypothesis related to knowledge injection in the knowledge-intense scenario of medical QA, and show that: 1) training on data from a speciality does not necessarily lead to best performance on that specialty and 2) regardless of the specialty fine-tuned on, token probabilities of clinically relevant terms for all specialties increase consistently. Thus, we believe improvement gains come mostly from domain shifting (e.g., general to medical) rather than knowledge injection and suggest rethinking the role of fine-tuning data in the medical domain. We release S-MedQA and all code needed to reproduce all our experiments to the research community.
Rethinking Circuit Completeness in Language Models: AND, OR, and ADDER Gates
2025-05-15
Hang Chen, Jiaying Zhu, Xinyu Yang, Wenya Wang
Circuit discovery has gradually become one of the prominent methods for mechanistic interpretability, and research on circuit completeness has also garnered increasing attention. Methods of circuit discovery that do not guarantee completeness not only result in circuits that are not fixed across different runs but also cause key mechanisms to be omitted. The nature of incompleteness arises from the presence of OR gates within the circuit, which are often only partially detected in standard circuit discovery methods. To this end, we systematically introduce three types of logic gates: AND, OR, and ADDER gates, and decompose the circuit into combinations of these logical gates. Through the concept of these gates, we derive the minimum requirements necessary to achieve faithfulness and completeness. Furthermore, we propose a framework that combines noising-based and denoising-based interventions, which can be easily integrated into existing circuit discovery methods without significantly increasing computational complexity. This framework is capable of fully identifying the logic gates and distinguishing them within the circuit. In addition to the extensive experimental validation of the framework's ability to restore the faithfulness, completeness, and sparsity of circuits, using this framework, we uncover fundamental properties of the three logic gates, such as their proportions and contributions to the output, and explore how they behave among the functionalities of language models.
Rethinking Prompt Optimizers: From Prompt Merits to Optimization
2025-05-15
Zixiao Zhu, Hanzhang Zhou, Zijian Feng, Tianjiao Li, Chua Jia Jim Deryl, Mak Lee Onn, Gee Wah Ng, Kezhi Mao
Prompt optimization (PO) provides a practical way to improve response quality when users lack the time or expertise to manually craft effective prompts. Existing methods typically rely on advanced, large-scale LLMs like GPT-4 to generate optimized prompts. However, due to limited downward compatibility, verbose, instruction-heavy prompts from advanced LLMs can overwhelm lightweight inference models and degrade response quality. In this work, we rethink prompt optimization through the lens of interpretable design. We first identify a set of model-agnostic prompt quality merits and empirically validate their effectiveness in enhancing prompt and response quality. We then introduce MePO, a merit-guided, lightweight, and locally deployable prompt optimizer trained on our preference dataset built from merit-aligned prompts generated by a lightweight LLM. Unlike prior work, MePO avoids online optimization reliance, reduces cost and privacy concerns, and, by learning clear, interpretable merits, generalizes effectively to both large-scale and lightweight inference models. Experiments demonstrate that MePO achieves better results across diverse tasks and model types, offering a scalable and robust solution for real-world deployment. The code and dataset can be found in https://github.com/MidiyaZhu/MePO
Face Anti-Spoofing (FAS) is essential for the security of facial recognition systems in diverse scenarios such as payment processing and surveillance. Current multimodal FAS methods often struggle with effective generalization, mainly due to modality-specific biases and domain shifts. To address these challenges, we introduce the \textbf{M}ulti\textbf{m}odal \textbf{D}enoising and \textbf{A}lignment (\textbf{MMDA}) framework. By leveraging the zero-shot generalization capability of CLIP, the MMDA framework effectively suppresses noise in multimodal data through denoising and alignment mechanisms, thereby significantly enhancing the generalization performance of cross-modal alignment. The \textbf{M}odality-\textbf{D}omain Joint \textbf{D}ifferential \textbf{A}ttention (\textbf{MD2A}) module in MMDA concurrently mitigates the impacts of domain and modality noise by refining the attention mechanism based on extracted common noise features. Furthermore, the \textbf{R}epresentation \textbf{S}pace \textbf{S}oft (\textbf{RS2}) Alignment strategy utilizes the pre-trained CLIP model to align multi-domain multimodal data into a generalized representation space in a flexible manner, preserving intricate representations and enhancing the model's adaptability to various unseen conditions. We also design a \textbf{U}-shaped \textbf{D}ual \textbf{S}pace \textbf{A}daptation (\textbf{U-DSA}) module to enhance the adaptability of representations while maintaining generalization performance. These improvements not only enhance the framework's generalization capabilities but also boost its ability to represent complex representations. Our experimental results on four benchmark datasets under different evaluation protocols demonstrate that the MMDA framework outperforms existing state-of-the-art methods in terms of cross-domain generalization and multimodal detection accuracy. The code will be released soon.
eqsat: An Equality Saturation Dialect for Non-destructive Rewriting
2025-05-14
Jules Merckx, Alexandre Lopoukhine, Samuel Coward, Jianyi Cheng, Bjorn De Sutter, Tobias Grosser
With recent algorithmic improvements and easy-to-use libraries, equality saturation is being picked up for hardware design, program synthesis, theorem proving, program optimization, and more. Existing work on using equality saturation for program optimization makes use of external equality saturation libraries such as egg, typically generating a single optimized expression. In the context of a compiler, such an approach uses equality saturation to replace a small number of passes. In this work, we propose an alternative approach that represents equality saturation natively in the compiler's intermediate representation, facilitating the application of constructive compiler passes that maintain the e-graph state throughout the compilation flow. We take LLVM's MLIR framework and propose a new MLIR dialect named eqsat that represents e-graphs in MLIR code. This not only provides opportunities to rethink e-matching and extraction techniques by orchestrating existing MLIR passes, such as common subexpression elimination, but also avoids translation overhead between the chosen e-graph library and MLIR. Our eqsat intermediate representation (IR) allows programmers to apply equality saturation on arbitrary domain-specific IRs using the same flow as other compiler transformations in MLIR.
Statistical models of barren plateaus and anti-concentration of Pauli observables
2025-05-13
Nikita Nemkov
We introduce statistical models for each of the three main sources of barren plateaus: non-locality of the observable, entanglement of the initial state, and circuit expressivity. For instance, non-local observables are modeled by random Pauli operators, which lead to barren plateaus with probability exponentially close to one. These models are complementary to the conventional deterministic ones, and often simpler to analyze. Using this framework, we show that in the barren plateau regime any two Pauli observables are anti-concentrated with high probability in the following sense. While each of the observables is localized in an exponentially small parameter subspace, these regions are essentially independent, so that their overlap is yet exponentially smaller than each subspace. This invites to rethink the structure of quantum landscapes with barren plateaus and approaches to their optimization, including warm-start strategies.
Rethink Repeatable Measures of Robot Performance with Statistical Query
2025-05-13
Bowen Weng, Linda Capito, Guillermo A. Castillo, Dylan Khor
For a general standardized testing algorithm designed to evaluate a specific aspect of a robot's performance, several key expectations are commonly imposed. Beyond accuracy (i.e., closeness to a typically unknown ground-truth reference) and efficiency (i.e., feasibility within acceptable testing costs and equipment constraints), one particularly important attribute is repeatability. Repeatability refers to the ability to consistently obtain the same testing outcome when similar testing algorithms are executed on the same subject robot by different stakeholders, across different times or locations. However, achieving repeatable testing has become increasingly challenging as the components involved grow more complex, intelligent, diverse, and, most importantly, stochastic. While related efforts have addressed repeatability at ethical, hardware, and procedural levels, this study focuses specifically on repeatable testing at the algorithmic level. Specifically, we target the well-adopted class of testing algorithms in standardized evaluation: statistical query (SQ) algorithms (i.e., algorithms that estimate the expected value of a bounded function over a distribution using sampled data). We propose a lightweight, parameterized, and adaptive modification applicable to any SQ routine, whether based on Monte Carlo sampling, importance sampling, or adaptive importance sampling, that makes it provably repeatable, with guaranteed bounds on both accuracy and efficiency. We demonstrate the effectiveness of the proposed approach across three representative scenarios: (i) established and widely adopted standardized testing of manipulators, (ii) emerging intelligent testing algorithms for operational risk assessment in automated vehicles, and (iii) developing use cases involving command tracking performance evaluation of humanoid robots in locomotion tasks.
Compression, Regularity, Randomness and Emergent Structure: Rethinking Physical Complexity in the Data-Driven Era
2025-05-12
Nima Dehghani
Complexity science offers a wide range of measures for quantifying unpredictability, structure, and information. Yet, a systematic conceptual organization of these measures is still missing. We present a unified framework that locates statistical, algorithmic, and dynamical measures along three axes (regularity, randomness, and complexity) and situates them in a common conceptual space. We map statistical, algorithmic, and dynamical measures into this conceptual space, discussing their computational accessibility and approximability. This taxonomy reveals the deep challenges posed by uncomputability and highlights the emergence of modern data-driven methods (including autoencoders, latent dynamical models, symbolic regression, and physics-informed neural networks) as pragmatic approximations to classical complexity ideals. Latent spaces emerge as operational arenas where regularity extraction, noise management, and structured compression converge, bridging theoretical foundations with practical modeling in high-dimensional systems. We close by outlining implications for physics-informed AI and AI-guided discovery in complex physical systems, arguing that classical questions of complexity remain central to next-generation scientific modeling.
Deep unfolding networks (DUNs) are widely employed in illumination degradation image restoration (IDIR) to merge the interpretability of model-based approaches with the generalization of learning-based methods. However, the performance of DUN-based methods remains considerably inferior to that of state-of-the-art IDIR solvers. Our investigation indicates that this limitation does not stem from structural shortcomings of DUNs but rather from the limited exploration of the unfolding structure, particularly for (1) constructing task-specific restoration models, (2) integrating advanced network architectures, and (3) designing DUN-specific loss functions. To address these issues, we propose a novel DUN-based method, UnfoldIR, for IDIR tasks. UnfoldIR first introduces a new IDIR model with dedicated regularization terms for smoothing illumination and enhancing texture. We unfold the iterative optimized solution of this model into a multistage network, with each stage comprising a reflectance-assisted illumination correction (RAIC) module and an illumination-guided reflectance enhancement (IGRE) module. RAIC employs a visual state space (VSS) to extract non-local features, enforcing illumination smoothness, while IGRE introduces a frequency-aware VSS to globally align similar textures, enabling mildly degraded regions to guide the enhancement of details in more severely degraded areas. This suppresses noise while enhancing details. Furthermore, given the multistage structure, we propose an inter-stage information consistent loss to maintain network stability in the final stages. This loss contributes to structural preservation and sustains the model's performance even in unsupervised settings. Experiments verify our effectiveness across 5 IDIR tasks and 3 downstream problems.
ASIL-Decomposition Based Resource Allocation Optimization for Automotive E/E Architectures
2025-05-10
Dorsa Zaheri, Hans-Christian Reuss
Recent years have brought a surge of efforts in rethinking the vehicle's electrical and/or electronic (E/E) architecture as well as the development process to reduce complexity and enable automation, connectivity, and electromobility. Resource allocation is an important step of the development process that can influence the quality of the designed system. As the design space is large and complex, intuitive design can turn into a time-consuming process with sub-optimal solutions. Here, we present an approach to automatically map software components to available hardware resources. Compared to existing frameworks, our method provides a wider range of safety analyses in compliance with the ISO 26262 standard, encompassing aspects such as reliability, task scheduling, and automotive safety integrity level (ASIL) compatibility. We propose an integer linear programming (ILP)-based approach to perform the ASIL decomposition technique specified by the standard. This technique proves beneficial when suitable hardware resources are unavailable for a safety-critical application. We formulate a multi-objective optimization problem to minimize both the development cost and the maximum execution times of critical function chains. The effectiveness of the proposed approach is investigated on an exemplary case study, and the results of the performance analysis are presented and discussed.
Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective
2025-05-09
Henan Sun, Xunkai Li, Lei Zhu, Junyi Han, Guang Zeng, Ronghua Li, Guoren Wang
Out-Of-Distribution (OOD) generalization has gained increasing attentions for machine learning on graphs, as graph neural networks (GNNs) often exhibit performance degradation under distribution shifts. Existing graph OOD methods tend to follow the basic ideas of invariant risk minimization and structural causal models, interpreting the invariant knowledge across datasets under various distribution shifts as graph topology or graph spectrum. However, these interpretations may be inconsistent with real-world scenarios, as neither invariant topology nor spectrum is assured. In this paper, we advocate the learnable random walk (LRW) perspective as the instantiation of invariant knowledge, and propose LRW-OOD to realize graph OOD generalization learning. Instead of employing fixed probability transition matrix (i.e., degree-normalized adjacency matrix), we parameterize the transition matrix with an LRW-sampler and a path encoder. Furthermore, we propose the kernel density estimation (KDE)-based mutual information (MI) loss to generate random walk sequences that adhere to OOD principles. Extensive experiment demonstrates that our model can effectively enhance graph OOD generalization under various types of distribution shifts and yield a significant accuracy improvement of 3.87% over state-of-the-art graph OOD generalization baselines.
2025-05-08
Lizhe Fang, Yifei Wang, Khashayar Gatmiry, Lei Fang, Yisen Wang
In-Context Learning (ICL) has emerged as a pivotal capability of auto-regressive large language models, yet it is hindered by a notable sensitivity to the ordering of context examples regardless of their mutual independence. To address this issue, recent studies have introduced several variant algorithms of ICL that achieve permutation invariance. However, many of these do not exhibit comparable performance with the standard auto-regressive ICL algorithm. In this work, we identify two crucial elements in the design of an invariant ICL algorithm: information non-leakage and context interdependence, which are not simultaneously achieved by any of the existing methods. These investigations lead us to the proposed Invariant ICL (InvICL), a methodology designed to achieve invariance in ICL while ensuring the two properties. Empirically, our findings reveal that InvICL surpasses previous models, both invariant and non-invariant, in most benchmark datasets, showcasing superior generalization capabilities across varying input lengths. Code is available at https://github.com/PKU-ML/InvICL.
Rethinking the Relationship between the Power Law and Hierarchical Structures
2025-05-08
Kai Nakaishi, Ryo Yoshida, Kohei Kajikawa, Koji Hukushima, Yohei Oseki
Statistical analysis of corpora provides an approach to quantitatively investigate natural languages. This approach has revealed that several power laws consistently emerge across different corpora and languages, suggesting the universal principles underlying languages. Particularly, the power-law decay of correlation has been interpreted as evidence for underlying hierarchical structures in syntax, semantics, and discourse. This perspective has also been extended to child languages and animal signals. However, the argument supporting this interpretation has not been empirically tested. To address this problem, this study examines the validity of the argument for syntactic structures. Specifically, we test whether the statistical properties of parse trees align with the implicit assumptions in the argument. Using English corpora, we analyze the mutual information, deviations from probabilistic context-free grammars (PCFGs), and other properties in parse trees, as well as in the PCFG that approximates these trees. Our results indicate that the assumptions do not hold for syntactic structures and that it is difficult to apply the proposed argument to child languages and animal signals, highlighting the need to reconsider the relationship between the power law and hierarchical structures.
Graph contrastive learning (GCL) has achieved remarkable success by following the computer vision paradigm of preserving absolute similarity between augmented views. However, this approach faces fundamental challenges in graphs due to their discrete, non-Euclidean nature -- view generation often breaks semantic validity and similarity verification becomes unreliable. Through analyzing 11 real-world graphs, we discover a universal pattern transcending the homophily-heterophily dichotomy: label consistency systematically diminishes as structural distance increases, manifesting as smooth decay in homophily graphs and oscillatory decay in heterophily graphs. We establish theoretical guarantees for this pattern through random walk theory, proving label distribution convergence and characterizing the mechanisms behind different decay behaviors. This discovery reveals that graphs naturally encode relative similarity patterns, where structurally closer nodes exhibit collectively stronger semantic relationships. Leveraging this insight, we propose RELGCL, a novel GCL framework with complementary pairwise and listwise implementations that preserve these inherent patterns through collective similarity objectives. Extensive experiments demonstrate that our method consistently outperforms 20 existing approaches across both homophily and heterophily graphs, validating the effectiveness of leveraging natural relative similarity over artificial absolute similarity.
Perpetuating Misogyny with Generative AI: How Model Personalization Normalizes Gendered Harm
2025-05-07
Laura Wagner, Eva Cetinic
Open-source text-to-image (TTI) pipelines have become dominant in the landscape of AI-generated visual content, driven by technological advances that enable users to personalize models through adapters tailored to specific tasks. While personalization methods such as LoRA offer unprecedented creative opportunities, they also facilitate harmful practices, including the generation of non-consensual deepfakes and the amplification of misogynistic or hypersexualized content. This study presents an exploratory sociotechnical analysis of CivitAI, the most active platform for sharing and developing open-source TTI models. Drawing on a dataset of more than 40 million user-generated images and over 230,000 models, we find a disproportionate rise in not-safe-for-work (NSFW) content and a significant number of models intended to mimic real individuals. We also observe a strong influence of internet subcultures on the tools and practices shaping model personalizations and resulting visual media. In response to these findings, we contextualize the emergence of exploitative visual media through feminist and constructivist perspectives on technology, emphasizing how design choices and community dynamics shape platform outcomes. Building on this analysis, we propose interventions aimed at mitigating downstream harm, including improved content moderation, rethinking tool design, and establishing clearer platform policies to promote accountability and consent.
Biologically-informed neural networks typically leverage pathway annotations to enhance performance in biomedical applications. We hypothesized that the benefits of pathway integration does not arise from its biological relevance, but rather from the sparsity it introduces. We conducted a comprehensive analysis of all relevant pathway-based neural network models for predictive tasks, critically evaluating each study's contributions. From this review, we curated a subset of methods for which the source code was publicly available. The comparison of the biologically informed state-of-the-art deep learning models and their randomized counterparts showed that models based on randomized information performed equally well as biologically informed ones across different metrics and datasets. Notably, in 3 out of the 15 analyzed models, the randomized versions even outperformed their biologically informed counterparts. Moreover, pathway-informed models did not show any clear advantage in interpretability, as randomized models were still able to identify relevant disease biomarkers despite lacking explicit pathway information. Our findings suggest that pathway annotations may be too noisy or inadequately explored by current methods. Therefore, we propose a methodology that can be applied to different domains and can serve as a robust benchmark for systematically comparing novel pathway-informed models against their randomized counterparts. This approach enables researchers to rigorously determine whether observed performance improvements can be attributed to biological insights.
Rethinking LLM Advancement: Compute-Dependent and Independent Paths to Progress
2025-05-07
Jack Sanderson, Teddy Foley, Spencer Guo, Anqi Qu, Henry Josephson
Regulatory efforts to govern large language model (LLM) development have predominantly focused on restricting access to high-performance computational resources. This study evaluates the efficacy of such measures by examining whether LLM capabilities can advance through algorithmic innovation in compute-constrained environments. We propose a novel framework distinguishing compute-dependent innovations--which yield disproportionate benefits at high compute--from compute-independent innovations, which improve efficiency across compute scales. The impact is quantified using Compute-Equivalent Gain (CEG). Experimental validation with nanoGPT models confirms that compute-independent advancements yield significant performance gains (e.g., with combined CEG up to $3.5\times$) across the tested scales. In contrast, compute-dependent advancements were detrimental to performance at smaller experimental scales, but showed improved CEG (on par with the baseline) as model size increased, a trend consistent with their definition of yielding primary benefits at higher compute. Crucially, these findings indicate that restrictions on computational hardware, while potentially slowing LLM progress, are insufficient to prevent all capability gains driven by algorithmic advancements. We argue that effective AI oversight must therefore incorporate mechanisms for understanding, anticipating, and potentially guiding algorithmic research, moving beyond a singular focus on hardware. The proposed framework also serves as an analytical tool for forecasting AI progress.
Rethinking the Global Convergence of Softmax Policy Gradient with Linear Function Approximation
2025-05-06
Max Qiushi Lin, Jincheng Mei, Matin Aghaei, Michael Lu, Bo Dai, Alekh Agarwal, Dale Schuurmans, Csaba Szepesvari, Sharan Vaswani
Policy gradient (PG) methods have played an essential role in the empirical successes of reinforcement learning. In order to handle large state-action spaces, PG methods are typically used with function approximation. In this setting, the approximation error in modeling problem-dependent quantities is a key notion for characterizing the global convergence of PG methods. We focus on Softmax PG with linear function approximation (referred to as $\texttt{Lin-SPG}$) and demonstrate that the approximation error is irrelevant to the algorithm's global convergence even for the stochastic bandit setting. Consequently, we first identify the necessary and sufficient conditions on the feature representation that can guarantee the asymptotic global convergence of $\texttt{Lin-SPG}$. Under these feature conditions, we prove that $T$ iterations of $\texttt{Lin-SPG}$ with a problem-specific learning rate result in an $O(1/T)$ convergence to the optimal policy. Furthermore, we prove that $\texttt{Lin-SPG}$ with any arbitrary constant learning rate can ensure asymptotic global convergence to the optimal policy.
Medical image segmentation is a pivotal task within the realms of medical image analysis and computer vision. While current methods have shown promise in accurately segmenting major regions of interest, the precise segmentation of boundary areas remains challenging. In this study, we propose a novel network architecture named CTO, which combines Convolutional Neural Networks (CNNs), Vision Transformer (ViT) models, and explicit edge detection operators to tackle this challenge. CTO surpasses existing methods in terms of segmentation accuracy and strikes a better balance between accuracy and efficiency, without the need for additional data inputs or label injections. Specifically, CTO adheres to the canonical encoder-decoder network paradigm, with a dual-stream encoder network comprising a mainstream CNN stream for capturing local features and an auxiliary StitchViT stream for integrating long-range dependencies. Furthermore, to enhance the model's ability to learn boundary areas, we introduce a boundary-guided decoder network that employs binary boundary masks generated by dedicated edge detection operators to provide explicit guidance during the decoding process. We validate the performance of CTO through extensive experiments conducted on seven challenging medical image segmentation datasets, namely ISIC 2016, PH2, ISIC 2018, CoNIC, LiTS17, and BTCV. Our experimental results unequivocally demonstrate that CTO achieves state-of-the-art accuracy on these datasets while maintaining competitive model complexity. The codes have been released at: https://github.com/xiaofang007/CTO.
Intelligent Load Balancing Systems using Reinforcement Learning System
2025-05-06
Raju Singh
Load Balancing is a fundamental technology for scaling cloud infrastructure. It enables systems to distribute incoming traffic across backend servers using predefined algorithms such as round robin, weighted round robin, least connections, weighted least connections, resource based, weighted response time, source IP hash, and URL hash. This approach has helped software developers, infrastructure engineers, and system administrators address many internet traffic related challenges across modern software architectures ranging from monolithic systems and traditional three tier models to microservices based applications. However, traditional traffic balancing techniques are increasingly becoming inadequate in optimizing distribution times. Existing algorithms are struggling to meet the rising demands of internet traffic, often resulting in degraded user experiences. To proactively address these issues particularly in areas like response time, distribution latency, and system uptime, we need to rethink how load balancing is implemented. Key challenges include traffic management, congestion control, intelligent scheduling, and the ability to determine when and when not to apply load balancing.
Teaching the social media generation: rethinking learning without sacrificing quality
2025-05-05
Sepinoud Azimi
The rise of social media and AI tools has reshaped how students engage with learning, process information, and build trust in educational content. This generation prefers short, visual materials and fast feedback but often struggles with focus, critical thinking, and deep learning. Educators face the challenge of adapting teaching methods to these habits without lowering academic standards. This study presents a blended learning redesign of a first-year technical course at a Dutch university. Key features included short whiteboard videos before class, hands-on teamwork during class, narrative-style handouts to reinforce learning, in-class draft assignments without AI, and weekly anonymous feedback to adjust in real time. The results were promising: attendance increased by nearly 50%, and none of the regularly attending students failed the exam. Students found the videos useful but emphasized that in-person sessions were essential for understanding the material. While some resisted the shift in expectations, most appreciated the structure, clarity, and opportunities for active learning. This case suggests that combining digital familiarity with clear expectations and active support can help meet students where they are, while still challenging them to grow.
Rethinking Federated Graph Learning: A Data Condensation Perspective
2025-05-05
Hao Zhang, Xunkai Li, Yinlin Zhu, Lianglin Hu
Federated graph learning is a widely recognized technique that promotes collaborative training of graph neural networks (GNNs) by multi-client graphs.However, existing approaches heavily rely on the communication of model parameters or gradients for federated optimization and fail to adequately address the data heterogeneity introduced by intricate and diverse graph distributions. Although some methods attempt to share additional messages among the server and clients to improve federated convergence during communication, they introduce significant privacy risks and increase communication overhead. To address these issues, we introduce the concept of a condensed graph as a novel optimization carrier to address FGL data heterogeneity and propose a new FGL paradigm called FedGM. Specifically, we utilize a generalized condensation graph consensus to aggregate comprehensive knowledge from distributed graphs, while minimizing communication costs and privacy risks through a single transmission of the condensed data. Extensive experiments on six public datasets consistently demonstrate the superiority of FedGM over state-of-the-art baselines, highlighting its potential for a novel FGL paradigm.
Rethinking Multimodal Sentiment Analysis: A High-Accuracy, Simplified Fusion Architecture
2025-05-05
Nischal Mandal, Yang Li
Multimodal sentiment analysis, a pivotal task in affective computing, seeks to understand human emotions by integrating cues from language, audio, and visual signals. While many recent approaches leverage complex attention mechanisms and hierarchical architectures, we propose a lightweight, yet effective fusion-based deep learning model tailored for utterance-level emotion classification. Using the benchmark IEMOCAP dataset, which includes aligned text, audio-derived numeric features, and visual descriptors, we design a modality-specific encoder using fully connected layers followed by dropout regularization. The modality-specific representations are then fused using simple concatenation and passed through a dense fusion layer to capture cross-modal interactions. This streamlined architecture avoids computational overhead while preserving performance, achieving a classification accuracy of 92% across six emotion categories. Our approach demonstrates that with careful feature engineering and modular design, simpler fusion strategies can outperform or match more complex models, particularly in resource-constrained environments.
Rethinking Score Distilling Sampling for 3D Editing and Generation
2025-05-03
Xingyu Miao, Haoran Duan, Yang Long, Jungong Han
Score Distillation Sampling (SDS) has emerged as a prominent method for text-to-3D generation by leveraging the strengths of 2D diffusion models. However, SDS is limited to generation tasks and lacks the capability to edit existing 3D assets. Conversely, variants of SDS that introduce editing capabilities often can not generate new 3D assets effectively. In this work, we observe that the processes of generation and editing within SDS and its variants have unified underlying gradient terms. Building on this insight, we propose Unified Distillation Sampling (UDS), a method that seamlessly integrates both the generation and editing of 3D assets. Essentially, UDS refines the gradient terms used in vanilla SDS methods, unifying them to support both tasks. Extensive experiments demonstrate that UDS not only outperforms baseline methods in generating 3D assets with richer details but also excels in editing tasks, thereby bridging the gap between 3D generation and editing. The code is available on: https://github.com/xingy038/UDS.
VTS-LLM: Domain-Adaptive LLM Agent for Enhancing Awareness in Vessel Traffic Services through Natural Language
2025-05-02
Sijin Sun, Liangbin Zhao, Ming Deng, Xiuju Fu
Vessel Traffic Services (VTS) are essential for maritime safety and regulatory compliance through real-time traffic management. However, with increasing traffic complexity and the prevalence of heterogeneous, multimodal data, existing VTS systems face limitations in spatiotemporal reasoning and intuitive human interaction. In this work, we propose VTS-LLM Agent, the first domain-adaptive large LLM agent tailored for interactive decision support in VTS operations. We formalize risk-prone vessel identification as a knowledge-augmented Text-to-SQL task, combining structured vessel databases with external maritime knowledge. To support this, we construct a curated benchmark dataset consisting of a custom schema, domain-specific corpus, and a query-SQL test set in multiple linguistic styles. Our framework incorporates NER-based relational reasoning, agent-based domain knowledge injection, semantic algebra intermediate representation, and query rethink mechanisms to enhance domain grounding and context-aware understanding. Experimental results show that VTS-LLM outperforms both general-purpose and SQL-focused baselines under command-style, operational-style, and formal natural language queries, respectively. Moreover, our analysis provides the first empirical evidence that linguistic style variation introduces systematic performance challenges in Text-to-SQL modeling. This work lays the foundation for natural language interfaces in vessel traffic services and opens new opportunities for proactive, LLM-driven maritime real-time traffic management.
Unlocking True Elasticity for the Cloud-Native Era with Dandelion
2025-05-02
Tom Kuchler, Pinghe Li, Yazhuo Zhang, Lazar Cvetković, Boris Goranov, Tobias Stocker, Leon Thomm, Simone Kalbermatter, Tim Notter, Andrea Lattuada, Ana Klimovic
Elasticity is fundamental to cloud computing, as it enables quickly allocating resources to match the demand of each workload as it arrives, rather than pre-provisioning resources to meet performance objectives. However, even serverless platforms -- which boot sandboxes in 10s to 100s of milliseconds -- are not sufficiently elastic to avoid over-provisioning expensive resources. Today's FaaS platforms rely on pre-provisioning many idle sandboxes in memory to reduce the occurrence of slow, cold starts. A key obstacle for high elasticity is booting a guest OS and configuring features like networking in sandboxes, which are required to expose an isolated POSIX-like interface to user functions. Our key insight is that redesigning the interface for applications in the cloud-native era enables co-designing a much more efficient and elastic execution system. Now is a good time to rethink cloud abstractions as developers are building applications to be cloud-native. Cloud-native applications typically consist of user-provided compute logic interacting with cloud services (for storage, AI inference, query processing, etc) exposed over REST APIs. Hence, we propose Dandelion, an elastic cloud platform with a declarative programming model that expresses applications as DAGs of pure compute functions and higher-level communication functions. Dandelion can securely execute untrusted user compute functions in lightweight sandboxes that cold start in hundreds of microseconds, since pure functions do not rely on extra software environments such as a guest OS. Dandelion makes it practical to boot a sandbox on-demand for each request, decreasing performance variability by two to three orders of magnitude compared to Firecracker and reducing committed memory by 96% on average when running the Azure Functions trace.
Beyond Productivity: Rethinking the Impact of Creativity Support Tools
2025-05-02
Samuel Rhys Cox, Helena Bøjer Djernæs, Niels van Berkel
Creativity Support Tools (CSTs) are widely used across diverse creative domains, with generative AI recently increasing the abilities of CSTs. To better understand how the success of CSTs is determined in the literature, we conducted a review of outcome measures used in CST evaluations. Drawing from (n=173) CST evaluations in the ACM Digital Library, we identified the metrics commonly employed to assess user interactions with CSTs. Our findings reveal prevailing trends in current evaluation practices, while exposing underexplored measures that could broaden the scope of future research. Based on these results, we argue for a more holistic approach to evaluating CSTs, encouraging the HCI community to consider not only user experience and the quality of the generated output, but also user-centric aspects such as self-reflection and well-being as critical dimensions of assessment. We also highlight a need for validated measures specifically suited to the evaluation of generative AI in CSTs.
Event cameras capture motion dynamics, offering a unique modality with great potential in various computer vision tasks. However, RGB-Event fusion faces three intrinsic misalignments: (i) temporal, (ii) spatial, and (iii) modal misalignment. Existing voxel grid representations neglect temporal correlations between consecutive event windows, and their formulation with simple accumulation of asynchronous and sparse events is incompatible with the synchronous and dense nature of RGB modality. To tackle these challenges, we propose a novel event representation, Motion-enhanced Event Tensor (MET), which transforms sparse event voxels into a dense and temporally coherent form by leveraging dense optical flows and event temporal features. In addition, we introduce a Frequency-aware Bidirectional Flow Aggregation Module (BFAM) and a Temporal Fusion Module (TFM). BFAM leverages the frequency domain and MET to mitigate modal misalignment, while bidirectional flow aggregation and temporal fusion mechanisms resolve spatiotemporal misalignment. Experimental results on two large-scale datasets demonstrate that our framework significantly outperforms state-of-the-art RGB-Event semantic segmentation approaches. Our code is available at: https://github.com/zyaocoder/BRENet.
In this paper, we propose GuideSR, a novel single-step diffusion-based image super-resolution (SR) model specifically designed to enhance image fidelity. Existing diffusion-based SR approaches typically adapt pre-trained generative models to image restoration tasks by adding extra conditioning on a VAE-downsampled representation of the degraded input, which often compromises structural fidelity. GuideSR addresses this limitation by introducing a dual-branch architecture comprising: (1) a Guidance Branch that preserves high-fidelity structures from the original-resolution degraded input, and (2) a Diffusion Branch, which a pre-trained latent diffusion model to enhance perceptual quality. Unlike conventional conditioning mechanisms, our Guidance Branch features a tailored structure for image restoration tasks, combining Full Resolution Blocks (FRBs) with channel attention and an Image Guidance Network (IGN) with guided attention. By embedding detailed structural information directly into the restoration pipeline, GuideSR produces sharper and more visually consistent results. Extensive experiments on benchmark datasets demonstrate that GuideSR achieves state-of-the-art performance while maintaining the low computational cost of single-step approaches, with up to 1.39dB PSNR gain on challenging real-world datasets. Our approach consistently outperforms existing methods across various reference-based metrics including PSNR, SSIM, LPIPS, DISTS and FID, further representing a practical advancement for real-world image restoration.
Rethinking Memory in AI: Taxonomy, Operations, Topics, and Future Directions
2025-05-01
Yiming Du, Wenyu Huang, Danna Zheng, Zhaowei Wang, Sebastien Montella, Mirella Lapata, Kam-Fai Wong, Jeff Z. Pan
Memory is a fundamental component of AI systems, underpinning large language models (LLMs)-based agents. While prior surveys have focused on memory applications with LLMs (e.g., enabling personalized memory in conversational agents), they often overlook the atomic operations that underlie memory dynamics. In this survey, we first categorize memory representations into parametric and contextual forms, and then introduce six fundamental memory operations: Consolidation, Updating, Indexing, Forgetting, Retrieval, and Compression. We map these operations to the most relevant research topics across long-term, long-context, parametric modification, and multi-source memory. By reframing memory systems through the lens of atomic operations and representation types, this survey provides a structured and dynamic perspective on research, benchmark datasets, and tools related to memory in AI, clarifying the functional interplay in LLMs based agents while outlining promising directions for future research\footnote{The paper list, datasets, methods and tools are available at \href{https://github.com/Elvin-Yiming-Du/Survey_Memory_in_AI}{https://github.com/Elvin-Yiming-Du/Survey\_Memory\_in\_AI}.}.
Balancing Security and Liquidity: A Time-Weighted Snapshot Framework for DAO Governance Voting
2025-05-01
Zayn Wang, Frank Pu, Vinci Cheung, Robert Hao
As new project upgrading the blockchain industry, novel forms of attack challenges developers to rethink about the design of their innovations. In the growth stage of the development, Decentralized Autonomous Organizations (DAO) introduces different approaches in managing fund through voting in governance tokens. However, relying on tokens as a weight for voting introduces opportunities for hackers to manipulate voting results through flash loan, allowing malicious proposals - fund withdrawal from DAO to hacker's wallet - to execute through the smart contract. In this research, we learned different defense mechanism against the flash loan attack, and their weakness in accessibility that compromise the security of different blockchain projects. Based on our observation, we propose a new defensing structure and apply it with cases.
Effectively modeling time information and incorporating it into applications or models involving chronologically occurring events is crucial. Real-world scenarios often involve diverse and complex time patterns, which pose significant challenges for time encoding methods. While previous methods focus on capturing time patterns, many rely on specific inductive biases, such as using trigonometric functions to model periodicity. This narrow focus on single-pattern modeling makes them less effective in handling the diversity and complexities of real-world time patterns. In this paper, we investigate to improve the existing commonly used time encoding methods and introduce Learnable Transformation-based Generalized Time Encoding (LeTE). We propose using deep function learning techniques to parameterize non-linear transformations in time encoding, making them learnable and capable of modeling generalized time patterns, including diverse and complex temporal dynamics. By enabling learnable transformations, LeTE encompasses previous methods as specific cases and allows seamless integration into a wide range of tasks. Through extensive experiments across diverse domains, we demonstrate the versatility and effectiveness of LeTE.
Multimodal large language models (MLLMs) have achieved impressive performance across a wide range of tasks, typically using CLIP-ViT as their visual encoder due to its strong text-image alignment capabilities. While prior studies suggest that different CLIP-ViT layers capture different types of information, with shallower layers focusing on fine visual details and deeper layers aligning more closely with textual semantics, most MLLMs still select visual features based on empirical heuristics rather than systematic analysis. In this work, we propose a Layer-wise Representation Similarity approach to group CLIP-ViT layers with similar behaviors into {shallow, middle, and deep} categories and assess their impact on MLLM performance. Building on this foundation, we revisit the visual layer selection problem in MLLMs at scale, training LLaVA-style models ranging from 1.4B to 7B parameters. Through extensive experiments across 10 datasets and 4 tasks, we find that: (1) deep layers are essential for OCR tasks; (2) shallow and middle layers substantially outperform deep layers on reasoning tasks involving counting, positioning, and object localization; (3) a lightweight fusion of features across shallow, middle, and deep layers consistently outperforms specialized fusion baselines and single-layer selections, achieving gains on 9 out of 10 datasets. Our work offers the first principled study of visual layer selection in MLLMs, laying the groundwork for deeper investigations into visual representation learning for MLLMs.
Taxonomic Trace Links: Rethinking Traceability and its Benefits
2025-04-29
Waleed Abdeen, Michael Unterkalmsteiner, Alexandros Chirtoglou, Christoph Paul Schimanski, Heja Goli, Krzysztof Wnuk
Traceability greatly supports knowledge-intensive tasks, e.g., coverage check and impact analysis. Despite its clear benefits, the \emph{practical} implementation of traceability poses significant challenges, leading to a reduced focus on the creation and maintenance of trace links. We propose a new approach -- Taxonomic Trace Links (TTL) -- which rethinks traceability and its benefits. With TTL, trace links are created indirectly through a domain-specific taxonomy, a simplified version of a domain model. TTL has the potential to address key traceability challenges, such as the granularity of trace links, the lack of a common data structure among software development artifacts, and unclear responsibility for traceability. We explain how TTL addresses these challenges and perform an initial validation with practitioners. We identified six challenges associated with TTL implementation that need to be addressed. Finally, we propose a research roadmap to further develop and evaluate the technical solution of TTL. TTL appears to be particularly feasible in practice where a domain taxonomy is already established
Label distribution learning (LDL) is an emerging learning paradigm designed to capture the relative importance of labels for each instance. Label-specific features (LSFs), constructed by LIFT, have proven effective for learning tasks with label ambiguity by leveraging clustering-based prototypes for each label to re-characterize instances. However, directly introducing LIFT into LDL tasks can be suboptimal, as the prototypes it collects primarily reflect intra-cluster relationships while neglecting interactions among distinct clusters. Additionally, constructing LSFs using multi-perspective information, rather than relying solely on Euclidean distance, provides a more robust and comprehensive representation of instances, mitigating noise and bias that may arise from a single distance perspective. To address these limitations, we introduce Structural Anchor Points (SAPs) to capture inter-cluster interactions. This leads to a novel LSFs construction strategy, LIFT-SAP, which enhances LIFT by integrating both distance and direction information of each instance relative to SAPs. Furthermore, we propose a novel LDL algorithm, Label Distribution Learning via Label-specifIc FeaTure with SAPs (LDL-LIFT-SAP), which unifies multiple label description degrees predicted from different LSF spaces into a cohesive label distribution. Extensive experiments on 15 real-world datasets demonstrate the effectiveness of LIFT-SAP over LIFT, as well as the superiority of LDL-LIFT-SAP compared to seven other well-established algorithms.
Training diffusion models that work directly on lidar points at the scale of outdoor scenes is challenging due to the difficulty of generating fine-grained details from white noise over a broad field of view. The latest works addressing scene completion with diffusion models tackle this problem by reformulating the original DDPM as a local diffusion process. It contrasts with the common practice of operating at the level of objects, where vanilla DDPMs are currently used. In this work, we close the gap between these two lines of work. We identify approximations in the local diffusion formulation, show that they are not required to operate at the scene level, and that a vanilla DDPM with a well-chosen starting point is enough for completion. Finally, we demonstrate that our method, LiDPM, leads to better results in scene completion on SemanticKITTI. The project page is https://astra-vision.github.io/LiDPM .
India's gig-based food delivery platforms, such as Swiggy and Zomato, provide crucial income to marginalised communities but also entrench workers in cycles of invisible labour. Through 14 semi-structured interviews, we analyse waiting time and repetitive UI itneractions as key burdens that contribute to 'digital discomfort' for gig based food delivery agents. We find that workers employ creative strategies to navigate algorithmic management, yet remain constrained by platform-side 'gamification' and system opacity. We propose worker-centered GUI automation as a potential intervention to reduce friction while preserving agency. In conclusion, this position paper argues for rethinking HCI approaches in the Global South to prioritise worker autonomy over efficiency-driven design optimisations.
2025-04-24
João Oliveira, João Gonçalves, Miguel Matos
Persistent Memory (PM) introduces new opportunities for designing crash-consistent applications without the traditional storage overheads. However, ensuring crash consistency in PM demands intricate knowledge of CPU, cache, and memory interactions. Hardware and software mechanisms have been proposed to ease this burden, but neither proved sufficient, prompting a variety of bug detection tools. With the sunset of Intel Optane comes the rise of Compute Express Link (CXL) for PM. In this position paper, we discuss the impact of CXL's disaggregated and heterogeneous nature in the development of crash-consistent PM applications, and outline three research directions: hardware primitives, persistency frameworks, and bug detection tools.
Adaptive Orchestration of Modular Generative Information Access Systems
2025-04-24
Mohanna Hoveyda, Harrie Oosterhuis, Arjen P. de Vries, Maarten de Rijke, Faegheh Hasibi
Advancements in large language models (LLMs) have driven the emergence of complex new systems to provide access to information, that we will collectively refer to as modular generative information access (GenIA) systems. They integrate a broad and evolving range of specialized components, including LLMs, retrieval models, and a heterogeneous set of sources and tools. While modularity offers flexibility, it also raises critical challenges: How can we systematically characterize the space of possible modules and their interactions? How can we automate and optimize interactions among these heterogeneous components? And, how do we enable this modular system to dynamically adapt to varying user query requirements and evolving module capabilities? In this perspective paper, we argue that the architecture of future modular generative information access systems will not just assemble powerful components, but enable a self-organizing system through real-time adaptive orchestration -- where components' interactions are dynamically configured for each user input, maximizing information relevance while minimizing computational overhead. We give provisional answers to the questions raised above with a roadmap that depicts the key principles and methods for designing such an adaptive modular system. We identify pressing challenges, and propose avenues for addressing them in the years ahead. This perspective urges the IR community to rethink modular system designs for developing adaptive, self-optimizing, and future-ready architectures that evolve alongside their rapidly advancing underlying technologies.
We propose DiMeR, a novel geometry-texture disentangled feed-forward model with 3D supervision for sparse-view mesh reconstruction. Existing methods confront two persistent obstacles: (i) textures can conceal geometric errors, i.e., visually plausible images can be rendered even with wrong geometry, producing multiple ambiguous optimization objectives in geometry-texture mixed solution space for similar objects; and (ii) prevailing mesh extraction methods are redundant, unstable, and lack 3D supervision. To solve these challenges, we rethink the inductive bias for mesh reconstruction. First, we disentangle the unified geometry-texture solution space, where a single input admits multiple feasible solutions, into geometry and texture spaces individually. Specifically, given that normal maps are strictly consistent with geometry and accurately capture surface variations, the normal maps serve as the sole input for geometry prediction in DiMeR, while the texture is estimated from RGB images. Second, we streamline the algorithm of mesh extraction by eliminating modules with low performance/cost ratios and redesigning regularization losses with 3D supervision. Notably, DiMeR still accepts raw RGB images as input by leveraging foundation models for normal prediction. Extensive experiments demonstrate that DiMeR generalises across sparse-view-, single-image-, and text-to-3D tasks, consistently outperforming baselines. On the GSO and OmniObject3D datasets, DiMeR significantly reduces Chamfer Distance by more than 30%.
Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval
2025-04-23
Xin Jiang, Hao Tang, Yonghua Pan, Zechao Li
Large-scale fine-grained image retrieval (FGIR) aims to retrieve images belonging to the same subcategory as a given query by capturing subtle differences in a large-scale setting. Recently, Vision Transformers (ViT) have been employed in FGIR due to their powerful self-attention mechanism for modeling long-range dependencies. However, most Transformer-based methods focus primarily on leveraging self-attention to distinguish fine-grained details, while overlooking the high computational complexity and redundant dependencies inherent to these models, limiting their scalability and effectiveness in large-scale FGIR. In this paper, we propose an Efficient and Effective ViT-based framework, termed \textbf{EET}, which integrates token pruning module with a discriminative transfer strategy to address these limitations. Specifically, we introduce a content-based token pruning scheme to enhance the efficiency of the vanilla ViT, progressively removing background or low-discriminative tokens at different stages by exploiting feature responses and self-attention mechanism. To ensure the resulting efficient ViT retains strong discriminative power, we further present a discriminative transfer strategy comprising both \textit{discriminative knowledge transfer} and \textit{discriminative region guidance}. Using a distillation paradigm, these components transfer knowledge from a larger ``teacher'' ViT to a more efficient ``student'' model, guiding the latter to focus on subtle yet crucial regions in a cost-free manner. Extensive experiments on two widely-used fine-grained datasets and four large-scale fine-grained datasets demonstrate the effectiveness of our method. Specifically, EET reduces the inference latency of ViT-Small by 42.7\% and boosts the retrieval performance of 16-bit hash codes by 5.15\% on the challenging NABirds dataset.
Infrared small target detection (ISTD) is highly sensitive to sensor type, observation conditions, and the intrinsic properties of the target. These factors can introduce substantial variations in the distribution of acquired infrared image data, a phenomenon known as domain shift. Such distribution discrepancies significantly hinder the generalization capability of ISTD models across diverse scenarios. To tackle this challenge, this paper introduces an ISTD framework enhanced by domain adaptation. To alleviate distribution shift between datasets and achieve cross-sample alignment, we introduce Cross-view Channel Alignment (CCA). Additionally, we propose the Cross-view Top-K Fusion strategy, which integrates target information with diverse background features, enhancing the model' s ability to extract critical data characteristics. To further mitigate the impact of noise on ISTD, we develop a Noise-guided Representation learning strategy. This approach enables the model to learn more noise-resistant feature representations, to improve its generalization capability across diverse noisy domains. Finally, we develop a dedicated infrared small target dataset, RealScene-ISTD. Compared to state-of-the-art methods, our approach demonstrates superior performance in terms of detection probability (Pd), false alarm rate (Fa), and intersection over union (IoU). The code is available at: https://github.com/luy0222/RealScene-ISTD.
Adaptation of quantum models to economic growth theories
2025-04-23
Hugo Spring-Ragain
Traditional economic growth theories, grounded in deterministic and often linear frameworks, fail to adequately capture the inherent uncertainty, non-commutativity, and complex interdependencies of modern economies. This paper proposes a novel approach by transposing fundamental concepts of quantum mechanics-such as superposition, operator algebra, and path integrals-into the realm of macroeconomic modeling. Within this quantum framework, core economic variables (capital, labor, and technological progress) are redefined as non-commuting operators acting on Hilbert spaces, and the state of the economy is represented as a dynamic wave function governed by a time-dependent Hamiltonian. The evolution of this economic wave function follows a generalized Schr{\"o}dinger equation, developed here through Dyson series and Magnus expansions. We also define a quantum production function as the expected value of a composite operator, capturing the probabilistic nature of economic output. By integrating uncertainty relations analogous to Heisenberg's principle, and modeling economic fluctuations via Langevin dynamics, we extend the model to include dissipation, feedback loops, and non-linear interactions between variables. Finally, a Feynman path integral formalism is constructed to provide an alternative trajectory-based interpretation of economic dynamics. This quantum-inspired framework offers a rigorous and flexible methodology to rethink macroeconomic modeling under radical uncertainty, with potential applications in dynamic policy simulations and innovation-driven growth.
Rethinking the Potential of Multimodality in Collaborative Problem Solving Diagnosis with Large Language Models
2025-04-21
K. Wong, B. Wu, S. Bulathwela, M. Cukurova
Detecting collaborative and problem-solving behaviours from digital traces to interpret students' collaborative problem solving (CPS) competency is a long-term goal in the Artificial Intelligence in Education (AIEd) field. Although multimodal data and advanced models are argued to have the potential to detect complex CPS behaviours, empirical evidence on their value remains limited with some contrasting evidence. In this study, we investigated the potential of multimodal data to improve model performance in diagnosing 78 secondary school students' CPS subskills and indicators in authentic educational settings. In particular, text embeddings from verbal data and acoustic embeddings from audio data were used in a multimodal classification model for CPS diagnosis. Both unimodal and multimodal transformer-based models outperformed traditional models in detecting CPS classes. Although the inclusion of multimodality did not improve the performance of traditional unimodal models, its integration into transformer-based models demonstrated improved performance for diagnosing social-cognitive CPS classes compared to unimodal transformer-based models. Based on the results, the paper argues that multimodality and the selection of a particular modelling technique should not be taken for granted to achieve the best performance in the automated detection of every CPS subskill and indicator. Rather, their value is limited to certain types of CPS indicators, affected by the complexity of the labels, and dependent on the composition of indicators in the dataset. We conclude the paper by discussing the required nuance when considering the value of LLMs and multimodality in automated CPS diagnosis, highlighting the need for human-AI complementarity, and proposing the exploration of relevant model architectures and techniques to improve CPS diagnosis in authentic educational contexts.
Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models
2025-04-21
Zijin Yang, Xin Zhang, Kejiang Chen, Kai Zeng, Qiyi Yao, Han Fang, Weiming Zhang, Nenghai Yu
Ethical concerns surrounding copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models. One effective solution involves watermarking the generated images. Existing methods primarily focus on ensuring that watermark embedding does not degrade the model performance. However, they often overlook critical challenges in real-world deployment scenarios, such as the complexity of watermark key management, user-defined generation parameters, and the difficulty of verification by arbitrary third parties. To address this issue, we propose Gaussian Shading++, a diffusion model watermarking method tailored for real-world deployment. We propose a double-channel design that leverages pseudorandom error-correcting codes to encode the random seed required for watermark pseudorandomization, achieving performance-lossless watermarking under a fixed watermark key and overcoming key management challenges. Additionally, we model the distortions introduced during generation and inversion as an additive white Gaussian noise channel and employ a novel soft decision decoding strategy during extraction, ensuring strong robustness even when generation parameters vary. To enable third-party verification, we incorporate public key signatures, which provide a certain level of resistance against forgery attacks even when model inversion capabilities are fully disclosed. Extensive experiments demonstrate that Gaussian Shading++ not only maintains performance losslessness but also outperforms existing methods in terms of robustness, making it a more practical solution for real-world deployment.
Rethinking trust in the digital age: An investigation of zero trust architecture's social consequences on organizational culture, collaboration, and knowledge sharing
2025-04-20
Ganiyu Oladimeji
As cyber threats escalate, Zero Trust Architecture (ZTA) replaces outdated perimeter security with strict never trust, always verify protocols. However, ZTA's dual nature as both technical infrastructure and social intervention creates an unresolved tension: its very mechanisms for security may systematically erode the trust foundations enabling effective collaboration. This integrative research combines case study analysis, employee surveys, and social network mapping reveals how ZTA disrupts knowledge-sharing, disproportionately hindering low-altruism employees, while surveillance erodes collective psychological ownership. Networked organizations, reliant on fluid trust, face fragmentation risks. Mitigation strategies include adaptive authorization frameworks using behavioral analytics and transparent communication reframing security as shared responsibility. Interdepartmental collaboration in security design preserves organizational trust structures identified through sociometric mapping. This research provides a framework balancing technical rigor with cultural sensitivity, proving cybersecurity can coexist with innovation by aligning verification with organizational psychology. The findings pioneer a paradigm where security and trust evolve synergistically critical for digital resilience in hybrid work environments. Future security must harmonize protocols with trust cultivation, ensuring defenses adapt to social dynamics driving modern enterprises.
Rethinking Traffic Flow Forecasting: From Transition to Generatation
2025-04-19
Li Shijiao, Ma Zhipeng, He Huajun, Chen Haiyue
Traffic flow prediction plays an important role in Intelligent Transportation Systems in traffic management and urban planning. There have been extensive successful works in this area. However, these approaches focus only on modelling the flow transition and ignore the flow generation process, which manifests itself in two ways: (i) The models are based on Markovian assumptions, ignoring the multi-periodicity of the flow generation in nodes. (ii) The same structure is designed to encode both the transition and generation processes, ignoring the differences between them. To address these problems, we propose an Effective Multi-Branch Similarity Transformer for Traffic Flow Prediction, namely EMBSFormer. Through data analysis, we find that the factors affecting traffic flow include node-level traffic generation and graph-level traffic transition, which describe the multi-periodicity and interaction pattern of nodes, respectively. Specifically, to capture traffic generation patterns, we propose a similarity analysis module that supports multi-branch encoding to dynamically expand significant cycles. For traffic transition, we employ a temporal and spatial self-attention mechanism to maintain global node interactions, and use GNN and time conv to model local node interactions, respectively. Model performance is evaluated on three real-world datasets on both long-term and short-term prediction tasks. Experimental results show that EMBSFormer outperforms baselines on both tasks. Moreover, compared to models based on flow transition modelling (e.g. GMAN, 513k), the variant of EMBSFormer(93K) only uses 18\% of the parameters, achieving the same performance.
Hypothetical Documents or Knowledge Leakage? Rethinking LLM-based Query Expansion
2025-04-19
Yejun Yoon, Jaeyoon Jung, Seunghyun Yoon, Kunwoo Park
Query expansion methods powered by large language models (LLMs) have demonstrated effectiveness in zero-shot retrieval tasks. These methods assume that LLMs can generate hypothetical documents that, when incorporated into a query vector, enhance the retrieval of real evidence. However, we challenge this assumption by investigating whether knowledge leakage in benchmarks contributes to the observed performance gains. Using fact verification as a testbed, we analyze whether the generated documents contain information entailed by ground-truth evidence and assess their impact on performance. Our findings indicate that, on average, performance improvements consistently occurred for claims whose generated documents included sentences entailed by gold evidence. This suggests that knowledge leakage may be present in fact-verification benchmarks, potentially inflating the perceived performance of LLM-based query expansion methods.
Rethinking Target Label Conditioning in Adversarial Attacks: A 2D Tensor-Guided Generative Approach
2025-04-19
Hangyu Liu, Bo Peng, Pengxiang Ding, Donglin Wang
Compared to single-target adversarial attacks, multi-target attacks have garnered significant attention due to their ability to generate adversarial images for multiple target classes simultaneously. Existing generative approaches for multi-target attacks mainly analyze the effect of the use of target labels on noise generation from a theoretical perspective, lacking practical validation and comprehensive summarization. To address this gap, we first identify and validate that the semantic feature quality and quantity are critical factors affecting the transferability of targeted attacks: 1) Feature quality refers to the structural and detailed completeness of the implanted target features, as deficiencies may result in the loss of key discriminative information; 2) Feature quantity refers to the spatial sufficiency of the implanted target features, as inadequacy limits the victim model's attention to this feature. Based on these findings, we propose the 2D Tensor-Guided Adversarial Fusion (2D-TGAF) framework, which leverages the powerful generative capabilities of diffusion models to encode target labels into two-dimensional semantic tensors for guiding adversarial noise generation. Additionally, we design a novel masking strategy tailored for the training process, ensuring that parts of the generated noise retain complete semantic information about the target class. Extensive experiments on the standard ImageNet dataset demonstrate that 2D-TGAF consistently surpasses state-of-the-art methods in attack success rates, both on normally trained models and across various defense mechanisms.
Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?
2025-04-18
Yang Yue, Zhiqi Chen, Rui Lu, Andrew Zhao, Zhaokai Wang, Yang Yue, Shiji Song, Gao Huang
Reinforcement Learning with Verifiable Rewards (RLVR) has recently demonstrated notable success in enhancing the reasoning capabilities of LLMs, particularly in mathematics and programming tasks. It is widely believed that RLVR enables LLMs to continuously self-improve, thus acquiring novel reasoning abilities that exceed corresponding base models' capacity. In this study, however, we critically re-examines this assumption by measuring the pass@\textit{k} metric with large values of \textit{k} to explore the reasoning capability boundary of the models across a wide range of model families and benchmarks. Surprisingly, the RL does \emph{not}, in fact, elicit fundamentally new reasoning patterns. While RL-trained models outperform their base models at smaller values of $k$ (\eg, $k$=1), base models can achieve a comparable or even higher pass@$k$ score compared to their RL counterparts at large $k$ values. The reasoning paths generated by RL-trained models are already included in the base models' sampling distribution, suggesting that most reasoning abilities manifested in RL-trained models are already obtained by base models. Further analysis shows that RL training boosts the performance by biasing the model's output distribution toward paths that are more likely to yield rewards, therefore sampling correct responses more efficiently. But this also results in a narrower reasoning capability boundary compared to base models. Similar results are observed in visual reasoning tasks trained with RLVR. Moreover, we find that distillation can genuinely introduce new knowledge into the model, different from RLVR. These findings underscore a critical limitation of RLVR in advancing LLM reasoning abilities which requires us to fundamentally rethink the impact of RL training in reasoning LLMs and the need of a better paradigm. Project Page: https://limit-of-RLVR.github.io
Beyond Misinformation: A Conceptual Framework for Studying AI Hallucinations in (Science) Communication
2025-04-18
Anqi Shao
This paper proposes a conceptual framework for understanding AI hallucinations as a distinct form of misinformation. While misinformation scholarship has traditionally focused on human intent, generative AI systems now produce false yet plausible outputs absent of such intent. I argue that these AI hallucinations should not be treated merely as technical failures but as communication phenomena with social consequences. Drawing on a supply-and-demand model and the concept of distributed agency, the framework outlines how hallucinations differ from human-generated misinformation in production, perception, and institutional response. I conclude by outlining a research agenda for communication scholars to investigate the emergence, dissemination, and audience reception of hallucinated content, with attention to macro (institutional), meso (group), and micro (individual) levels. This work urges communication researchers to rethink the boundaries of misinformation theory in light of probabilistic, non-human actors increasingly embedded in knowledge production.
Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling
2025-04-18
Hui Yeok Wong, Chee Kau Lim, Chee Seng Chan
Federated Learning (FL) on non-independently and identically distributed (non-IID) data remains a critical challenge, as existing approaches struggle with severe data heterogeneity. Current methods primarily address symptoms of non-IID by applying incremental adjustments to Federated Averaging (FedAvg), rather than directly resolving its inherent design limitations. Consequently, performance significantly deteriorates under highly heterogeneous conditions, as the fundamental issue of imbalanced exposure to diverse class and feature distributions remains unresolved. This paper introduces Stratify, a novel FL framework designed to systematically manage class and feature distributions throughout training, effectively tackling the root cause of non-IID challenges. Inspired by classical stratified sampling, our approach employs a Stratified Label Schedule (SLS) to ensure balanced exposure across labels, significantly reducing bias and variance in aggregated gradients. Complementing SLS, we propose a label-aware client selection strategy, restricting participation exclusively to clients possessing data relevant to scheduled labels. Additionally, Stratify incorporates a fine-grained, high-frequency update scheme, accelerating convergence and further mitigating data heterogeneity. To uphold privacy, we implement a secure client selection protocol leveraging homomorphic encryption, enabling precise global label statistics without disclosing sensitive client information. Extensive evaluations on MNIST, CIFAR-10, CIFAR-100, Tiny-ImageNet, COVTYPE, PACS, and Digits-DG demonstrate that Stratify attains performance comparable to IID baselines, accelerates convergence, and reduces client-side computation compared to state-of-the-art methods, underscoring its practical effectiveness in realistic federated learning scenarios.
Flowco: Rethinking Data Analysis in the Age of LLMs
2025-04-18
Stephen N. Freund, Brooke Simon, Emery D. Berger, Eunice Jun
Conducting data analysis typically involves authoring code to transform, visualize, analyze, and interpret data. Large language models (LLMs) are now capable of generating such code for simple, routine analyses. LLMs promise to democratize data science by enabling those with limited programming expertise to conduct data analyses, including in scientific research, business, and policymaking. However, analysts in many real-world settings must often exercise fine-grained control over specific analysis steps, verify intermediate results explicitly, and iteratively refine their analytical approaches. Such tasks present barriers to building robust and reproducible analyses using LLMs alone or even in conjunction with existing authoring tools (e.g., computational notebooks). This paper introduces Flowco, a new mixed-initiative system to address these challenges. Flowco leverages a visual dataflow programming model and integrates LLMs into every phase of the authoring process. A user study suggests that Flowco supports analysts, particularly those with less programming experience, in quickly authoring, debugging, and refining data analyses.
RUKA: Rethinking the Design of Humanoid Hands with Learning
2025-04-17
Anya Zorin, Irmak Guzey, Billy Yan, Aadhithya Iyer, Lisa Kondrich, Nikhil X. Bhattasali, Lerrel Pinto
Dexterous manipulation is a fundamental capability for robotic systems, yet progress has been limited by hardware trade-offs between precision, compactness, strength, and affordability. Existing control methods impose compromises on hand designs and applications. However, learning-based approaches present opportunities to rethink these trade-offs, particularly to address challenges with tendon-driven actuation and low-cost materials. This work presents RUKA, a tendon-driven humanoid hand that is compact, affordable, and capable. Made from 3D-printed parts and off-the-shelf components, RUKA has 5 fingers with 15 underactuated degrees of freedom enabling diverse human-like grasps. Its tendon-driven actuation allows powerful grasping in a compact, human-sized form factor. To address control challenges, we learn joint-to-actuator and fingertip-to-actuator models from motion-capture data collected by the MANUS glove, leveraging the hand's morphological accuracy. Extensive evaluations demonstrate RUKA's superior reachability, durability, and strength compared to other robotic hands. Teleoperation tasks further showcase RUKA's dexterous movements. The open-source design and assembly instructions of RUKA, code, and data are available at https://ruka-hand.github.io/.
Rethinking Temporal Fusion with a Unified Gradient Descent View for 3D Semantic Occupancy Prediction
2025-04-17
Dubing Chen, Huan Zheng, Jin Fang, Xingping Dong, Xianfei Li, Wenlong Liao, Tao He, Pai Peng, Jianbing Shen
We present GDFusion, a temporal fusion method for vision-based 3D semantic occupancy prediction (VisionOcc). GDFusion opens up the underexplored aspects of temporal fusion within the VisionOcc framework, focusing on both temporal cues and fusion strategies. It systematically examines the entire VisionOcc pipeline, identifying three fundamental yet previously overlooked temporal cues: scene-level consistency, motion calibration, and geometric complementation. These cues capture diverse facets of temporal evolution and make distinct contributions across various modules in the VisionOcc framework. To effectively fuse temporal signals across heterogeneous representations, we propose a novel fusion strategy by reinterpreting the formulation of vanilla RNNs. This reinterpretation leverages gradient descent on features to unify the integration of diverse temporal information, seamlessly embedding the proposed temporal cues into the network. Extensive experiments on nuScenes demonstrate that GDFusion significantly outperforms established baselines. Notably, on Occ3D benchmark, it achieves 1.4\%-4.8\% mIoU improvements and reduces memory consumption by 27\%-72\%.
How to get Rid of SQL, Relational Algebra, the Relational Model, ERM, and ORMs in a Single Paper -- A Thought Experiment
2025-04-17
Jens Dittrich
Without any doubt, the relational paradigm has been a huge success. At the same time, we believe that the time is ripe to rethink how database systems could look like if we designed them from scratch. Would we really end up with the same abstractions and techniques that are prevalent today? This paper explores that space. We discuss the various issues with both the relational model(RM) and the entity-relationship model (ERM). We provide a unified data model: the relational map type model (RMTM) which can represent both RM and ERM as special cases and overcomes all of their problems. We proceed to identify seven rules that an RMTM query language (QL) must fulfill and provide a foundation of a language fulfilling all seven rules. Our QL operates on maps which may represent tuples, relations, databases or sets of databases. Like that we dramatically expand the existing operational abstractions found in SQL and relational algebra (RA) which only operate on relations/tables. In fact, RA is just a special case of our much more generic approach. This work has far-reaching consequences: we show a path how to come up with a modern QL that solves (almost if not) all problems of SQL. Our QL is much more expressive than SQL and integrates smoothly into existing programming languages (PL). We also show results of an initial experiment showcasing that just by switching to our data model, and without changing the underlying query processing algorithms, we can achieve speed-ups of up to a factor 3. We will conclude that, if we build a database system from scratch, we could and should do this without SQL, RA, RM, ERM, and ORMs.
Recently, DeepSeek-R1 (671B) (DeepSeek-AIet al., 2025) has demonstrated its excellent reasoning ability in complex tasks and has publiclyshared its methodology. This provides potentially high-quality chain-of-thought (CoT) data for stimulating the reasoning abilities of small-sized large language models (LLMs). To generate high-quality CoT data for different LLMs, we seek an efficient method for generating high-quality CoT data with LLM-Adaptive questiondifficulty levels. First, we grade the difficulty of the questions according to the reasoning ability of the LLMs themselves and construct a LLM-Adaptive question database. Second, we sample the problem database based on a distribution of difficulty levels of the questions and then use DeepSeek-R1 (671B) (DeepSeek-AI et al., 2025) to generate the corresponding high-quality CoT data with correct answers. Thanks to the construction of CoT data with LLM-Adaptive difficulty levels, we have significantly reduced the cost of data generation and enhanced the efficiency of model supervised fine-tuning (SFT). Finally, we have validated the effectiveness and generalizability of the proposed method in the fields of complex mathematical competitions and code generation tasks. Notably, with only 2k high-quality mathematical CoT data, our ZMath-32B surpasses DeepSeek-Distill-32B in math reasoning task. Similarly, with only 2k high-quality code CoT data, our ZCode-32B surpasses DeepSeek-Distill-32B in code reasoning tasks.
Rethinking LLM-Based Recommendations: A Query Generation-Based, Training-Free Approach
2025-04-16
Donghee Han, Hwanjun Song, Mun Yong Yi
Existing large language model LLM-based recommendation methods face several challenges, including inefficiency in handling large candidate pools, sensitivity to item order within prompts ("lost in the middle" phenomenon) poor scalability, and unrealistic evaluation due to random negative sampling. To address these issues, we propose a Query-to-Recommendation approach that leverages LLMs to generate personalized queries for retrieving relevant items from the entire candidate pool, eliminating the need for candidate pre-selection. This method can be integrated into an ID-based recommendation system without additional training, enhances recommendation performance and diversity through LLMs' world knowledge, and performs well even for less popular item groups. Experiments on three datasets show up to 57 percent improvement, with an average gain of 31 percent, demonstrating strong zero-shot performance and further gains when ensembled with existing models.
Steerable rolling of a 1-DoF robot using an internal pendulum
2025-04-16
Christopher Y. Xu, Jack Yan, Kathleen Lum, Justin K. Yim
We present ROCK (Rolling One-motor Controlled rocK), a 1 degree-of-freedom robot consisting of a round shell and an internal pendulum. An uneven shell surface enables steering by using only the movement of the pendulum, allowing for mechanically simple designs that may be feasible to scale to large quantities or small sizes. We train a control policy using reinforcement learning in simulation and deploy it onto the robot to complete a rectangular trajectory.
Rethinking Trust in AI Assistants for Software Development: A Critical Review
2025-04-16
Sebastian Baltes, Timo Speith, Brenda Chiteri, Seyedmoein Mohsenimofidi, Shalini Chakraborty, Daniel Buschek
Trust is a fundamental concept in human decision-making and collaboration that has long been studied in philosophy and psychology. However, software engineering (SE) articles often use the term 'trust' informally - providing an explicit definition or embedding results in established trust models is rare. In SE research on AI assistants, this practice culminates in equating trust with the likelihood of accepting generated content, which does not capture the full complexity of the trust concept. Without a common definition, true secondary research on trust is impossible. The objectives of our research were: (1) to present the psychological and philosophical foundations of human trust, (2) to systematically study how trust is conceptualized in SE and the related disciplines human-computer interaction and information systems, and (3) to discuss limitations of equating trust with content acceptance, outlining how SE research can adopt existing trust models to overcome the widespread informal use of the term 'trust'. We conducted a literature review across disciplines and a critical review of recent SE articles focusing on conceptualizations of trust. We found that trust is rarely defined or conceptualized in SE articles. Related disciplines commonly embed their methodology and results in established trust models, clearly distinguishing, for example, between initial trust and trust formation and discussing whether and when trust can be applied to AI assistants. Our study reveals a significant maturity gap of trust research in SE compared to related disciplines. We provide concrete recommendations on how SE researchers can adopt established trust models and instruments to study trust in AI assistants beyond the acceptance of generated software artifacts.
DMM: Building a Versatile Image Generation Model via Distillation-Based Model Merging
2025-04-16
Tianhui Song, Weixin Feng, Shuai Wang, Xubin Li, Tiezheng Ge, Bo Zheng, Limin Wang
The success of text-to-image (T2I) generation models has spurred a proliferation of numerous model checkpoints fine-tuned from the same base model on various specialized datasets. This overwhelming specialized model production introduces new challenges for high parameter redundancy and huge storage cost, thereby necessitating the development of effective methods to consolidate and unify the capabilities of diverse powerful models into a single one. A common practice in model merging adopts static linear interpolation in the parameter space to achieve the goal of style mixing. However, it neglects the features of T2I generation task that numerous distinct models cover sundry styles which may lead to incompatibility and confusion in the merged model. To address this issue, we introduce a style-promptable image generation pipeline which can accurately generate arbitrary-style images under the control of style vectors. Based on this design, we propose the score distillation based model merging paradigm (DMM), compressing multiple models into a single versatile T2I model. Moreover, we rethink and reformulate the model merging task in the context of T2I generation, by presenting new merging goals and evaluation protocols. Our experiments demonstrate that DMM can compactly reorganize the knowledge from multiple teacher models and achieve controllable arbitrary-style generation.
The last couple of years have witnessed emerging research that appropriates Theory-of-Mind (ToM) tasks designed for humans to benchmark LLM's ToM capabilities as an indication of LLM's social intelligence. However, this approach has a number of limitations. Drawing on existing psychology and AI literature, we summarize the theoretical, methodological, and evaluation limitations by pointing out that certain issues are inherently present in the original ToM tasks used to evaluate human's ToM, which continues to persist and exacerbated when appropriated to benchmark LLM's ToM. Taking a human-computer interaction (HCI) perspective, these limitations prompt us to rethink the definition and criteria of ToM in ToM benchmarks in a more dynamic, interactional approach that accounts for user preferences, needs, and experiences with LLMs in such evaluations. We conclude by outlining potential opportunities and challenges towards this direction.
Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs
2025-04-15
Flint Xiaofeng Fan, Cheston Tan, Roger Wattenhofer, Yew-Soon Ong
The rise of reinforcement learning (RL) in critical real-world applications demands a fundamental rethinking of privacy in AI systems. Traditional privacy frameworks, designed to protect isolated data points, fall short for sequential decision-making systems where sensitive information emerges from temporal patterns, behavioral strategies, and collaborative dynamics. Modern RL paradigms, such as federated RL (FedRL) and RL with human feedback (RLHF) in large language models (LLMs), exacerbate these challenges by introducing complex, interactive, and context-dependent learning environments that traditional methods do not address. In this position paper, we argue for a new privacy paradigm built on four core principles: multi-scale protection, behavioral pattern protection, collaborative privacy preservation, and context-aware adaptation. These principles expose inherent tensions between privacy, utility, and interpretability that must be navigated as RL systems become more pervasive in high-stakes domains like healthcare, autonomous vehicles, and decision support systems powered by LLMs. To tackle these challenges, we call for the development of new theoretical frameworks, practical mechanisms, and rigorous evaluation methodologies that collectively enable effective privacy protection in sequential decision-making systems.
Urban causal research is essential for understanding the complex dynamics of cities and informing evidence-based policies. However, it is challenged by the inefficiency and bias of hypothesis generation, barriers to multimodal data complexity, and the methodological fragility of causal experimentation. Recent advances in large language models (LLMs) present an opportunity to rethink how urban causal analysis is conducted. This Perspective examines current urban causal research by analyzing taxonomies that categorize research topics, data sources, and methodological approaches to identify structural gaps. We then introduce an LLM-driven conceptual framework, AutoUrbanCI, composed of four distinct modular agents responsible for hypothesis generation, data engineering, experiment design and execution, and results interpretation with policy recommendations. We propose evaluation criteria for rigor and transparency and reflect on implications for human-AI collaboration, equity, and accountability. We call for a new research agenda that embraces AI-augmented workflows not as replacements for human expertise but as tools to broaden participation, improve reproducibility, and unlock more inclusive forms of urban causal reasoning.
Rethinking Technological Readiness in the Era of AI Uncertainty
2025-04-15
S. Tucker Browne, Mark M. Bailey
Artificial intelligence (AI) is poised to revolutionize military combat systems, but ensuring these AI-enabled capabilities are truly mission-ready presents new challenges. We argue that current technology readiness assessments fail to capture critical AI-specific factors, leading to potential risks in deployment. We propose a new AI Readiness Framework to evaluate the maturity and trustworthiness of AI components in military systems. The central thesis is that a tailored framework - analogous to traditional Technology Readiness Levels (TRL) but expanded for AI - can better gauge an AI system's reliability, safety, and suitability for combat use. Using current data evaluation tools and testing practices, we demonstrate the framework's feasibility for near-term implementation. This structured approach provides military decision-makers with clearer insight into whether an AI-enabled system has met the necessary standards of performance, transparency, and human integration to be deployed with confidence, thus advancing the field of defense technology management and risk assessment.
Can LLMs Generate Tabular Summaries of Science Papers? Rethinking the Evaluation Protocol
2025-04-14
Weiqi Wang, Jiefu Ou, Yangqiu Song, Benjamin Van Durme, Daniel Khashabi
Literature review tables are essential for summarizing and comparing collections of scientific papers. We explore the task of generating tables that best fulfill a user's informational needs given a collection of scientific papers. Building on recent work (Newman et al., 2024), we extend prior approaches to address real-world complexities through a combination of LLM-based methods and human annotations. Our contributions focus on three key challenges encountered in real-world use: (i) User prompts are often under-specified; (ii) Retrieved candidate papers frequently contain irrelevant content; and (iii) Task evaluation should move beyond shallow text similarity techniques and instead assess the utility of inferred tables for information-seeking tasks (e.g., comparing papers). To support reproducible evaluation, we introduce ARXIV2TABLE, a more realistic and challenging benchmark for this task, along with a novel approach to improve literature review table generation in real-world scenarios. Our extensive experiments on this benchmark show that both open-weight and proprietary LLMs struggle with the task, highlighting its difficulty and the need for further advancements. Our dataset and code are available at https://github.com/JHU-CLSP/arXiv2Table.
The reasoning capabilities of large language models (LLMs), such as OpenAI's o1 and DeepSeek-R1, have seen substantial advancements through deep thinking. However, these enhancements come with significant resource demands, underscoring the need to explore strategies to train effective reasoning LLMs with far fewer parameters. A critical challenge is that smaller models have different capacities and cognitive trajectories than their larger counterparts. Hence, direct distillation of chain-of-thought (CoT) results from large LLMs to smaller ones can be sometimes ineffective and requires a huge amount of annotated data. In this paper, we introduce a novel framework called Critique-Rethink-Verify (CRV), designed for training smaller yet powerful reasoning LLMs. Our CRV framework consists of multiple LLM agents, each specializing in unique abilities: (i) critiquing the CoTs according to the cognitive capabilities of smaller models, (ii) rethinking and refining these CoTs based on the critiques, and (iii) verifying the correctness of the refined results. We further propose the cognitive preference optimization (CogPO) algorithm to enhance the reasoning abilities of smaller models by aligning thoughts of these models with their cognitive capacities. Comprehensive evaluations on challenging reasoning benchmarks demonstrate the efficacy of CRV and CogPO, which outperforms other training methods by a large margin.
Drug-target binding affinity prediction is a fundamental task for drug discovery. It has been extensively explored in literature and promising results are reported. However, in this paper, we demonstrate that the results may be misleading and cannot be well generalized to real practice. The core observation is that the canonical randomized split of a test set in conventional evaluation leaves the test set dominated by samples with high similarity to the training set. The performance of models is severely degraded on samples with lower similarity to the training set but the drawback is highly overlooked in current evaluation. As a result, the performance can hardly be trusted when the model meets low-similarity samples in real practice. To address this problem, we propose a framework of similarity aware evaluation in which a novel split methodology is proposed to adapt to any desired distribution. This is achieved by a formulation of optimization problems which are approximately and efficiently solved by gradient descent. We perform extensive experiments across five representative methods in four datasets for two typical target evaluations and compare them with various counterpart methods. Results demonstrate that the proposed split methodology can significantly better fit desired distributions and guide the development of models. Code is released at https://github.com/Amshoreline/SAE/tree/main.
Meta-Evaluating Local LLMs: Rethinking Performance Metrics for Serious Games
2025-04-13
Andrés Isaza-Giraldo, Paulo Bala, Lucas Pereira
The evaluation of open-ended responses in serious games presents a unique challenge, as correctness is often subjective. Large Language Models (LLMs) are increasingly being explored as evaluators in such contexts, yet their accuracy and consistency remain uncertain, particularly for smaller models intended for local execution. This study investigates the reliability of five small-scale LLMs when assessing player responses in \textit{En-join}, a game that simulates decision-making within energy communities. By leveraging traditional binary classification metrics (including accuracy, true positive rate, and true negative rate), we systematically compare these models across different evaluation scenarios. Our results highlight the strengths and limitations of each model, revealing trade-offs between sensitivity, specificity, and overall performance. We demonstrate that while some models excel at identifying correct responses, others struggle with false positives or inconsistent evaluations. The findings highlight the need for context-aware evaluation frameworks and careful model selection when deploying LLMs as evaluators. This work contributes to the broader discourse on the trustworthiness of AI-driven assessment tools, offering insights into how different LLM architectures handle subjective evaluation tasks.
Rethinking Remaining Useful Life Prediction with Scarce Time Series Data: Regression under Indirect Supervision
2025-04-12
Jiaxiang Cheng, Yipeng Pang, Guoqiang Hu
Supervised time series prediction relies on directly measured target variables, but real-world use cases such as predicting remaining useful life (RUL) involve indirect supervision, where the target variable is labeled as a function of another dependent variable. Trending temporal regression techniques rely on sequential time series inputs to capture temporal patterns, requiring interpolation when dealing with sparsely and irregularly sampled covariates along the timeline. However, interpolation can introduce significant biases, particularly with highly scarce data. In this paper, we address the RUL prediction problem with data scarcity as time series regression under indirect supervision. We introduce a unified framework called parameterized static regression, which takes single data points as inputs for regression of target values, inherently handling data scarcity without requiring interpolation. The time dependency under indirect supervision is captured via a parametrical rectification (PR) process, approximating a parametric function during inference with historical posteriori estimates, following the same underlying distribution used for labeling during training. Additionally, we propose a novel batch training technique for tasks in indirect supervision to prevent overfitting and enhance efficiency. We evaluate our model on public benchmarks for RUL prediction with simulated data scarcity. Our method demonstrates competitive performance in prediction accuracy when dealing with highly scarce time series data.
Rethinking News and Media System Design Towards Positive Societal Implications
2025-04-12
Florian Bemmann, Doruntina Murtezaj
Since this century, the speed, availability, and plethora of online informational content have made it increasingly difficult for humans to keep an overview of real-world situations, build a personal opinion, and sometimes even decide on the truth. Thereby, personal opinion-making and public discourse became harder - two essential building blocks that keep a democratic society alive. HCI thus needs to rethink news, information, and social media systems to mitigate such negative effects. Instead of polarising through emotional and extremely framed messages, informational content online should make people think about other opinions and discuss constructively. Instead, through polarization and filter bubble effects, people lose openness and tolerance for the existence of opposing opinions. In this workshop, we will discuss how we can redesign our information technology for a better societal impact. We will present key takeaways from the social sciences and discuss how we can implement them using recent HCI findings and digital technologies.
Modern cutting-edge AI applications are being developed over fast-evolving, heterogeneous, nascent hardware devices. This requires frequent reworking of the AI software stack to adopt bottom-up changes from new hardware, which takes time for general-purpose software libraries. Consequently, real applications often develop custom software stacks optimized for their specific workloads and hardware. Custom stacks help in quick development and optimization, but incur a lot of redundant efforts across applications in writing non-portable code. This paper discusses an alternative communication library interface for AI applications that offers both portability and performance by reducing redundant efforts while maintaining flexibility for customization. We present MSCCL++, a novel abstraction of GPU communication based on separation of concerns: (1) a primitive interface provides a minimal hardware abstraction as a common ground for software and hardware developers to write custom communication, and (2) higher-level portable interfaces and specialized implementations enable optimization for different workloads and hardware environments. This approach makes the primitive interface reusable across applications while enabling highly flexible optimization. Compared to state-of-the-art baselines (NCCL, RCCL, and MSCCL), MSCCL++ achieves speedups of up to 5.4$\times$ for collective communication and up to 15% for real-world AI inference workloads. MSCCL++ is in production of multiple AI services provided by Microsoft Azure, and is also adopted by RCCL, the GPU collective communication library maintained by AMD. MSCCL++ is open-source and available at https://github.com/microsoft/mscclpp.
2025-04-11
Minjie Deng, Yan Wei, Hao Zhai, An Wu, Yuncan Ouyang, Qianyao Peng
In image fusion tasks, the absence of real fused images as priors presents a fundamental challenge. Most deep learning-based fusion methods rely on large-scale paired datasets to extract global weighting features from raw images, thereby generating fused outputs that approximate real fused images. In contrast to previous studies, this paper explores few-shot training of neural networks under the condition of having prior knowledge. We propose a novel fusion framework named GBFF, and a Granular Ball Significant Extraction algorithm specifically designed for the few-shot prior setting. All pixel pairs involved in the fusion process are initially modeled as a Coarse-Grained Granular Ball. At the local level, Fine-Grained Granular Balls are used to slide through the brightness space to extract Non-Salient Pixel Pairs, and perform splitting operations to obtain Salient Pixel Pairs. Pixel-wise weights are then computed to generate a pseudo-supervised image. At the global level, pixel pairs with significant contributions to the fusion process are categorized into the Positive Region, while those whose contributions cannot be accurately determined are assigned to the Boundary Region. The Granular Ball performs modality-aware adaptation based on the proportion of the positive region, thereby adjusting the neural network's loss function and enabling it to complement the information of the boundary region. Extensive experiments demonstrate the effectiveness of both the proposed algorithm and the underlying theory. Compared with state-of-the-art (SOTA) methods, our approach shows strong competitiveness in terms of both fusion time and image expressiveness. Our code is publicly available at:
Rethinking the Foundations for Continual Reinforcement Learning
2025-04-10
Michael Bowling, Esraa Elelimy
Algorithms and approaches for continual reinforcement learning have gained increasing attention. Much of this early progress rests on the foundations and standard practices of traditional reinforcement learning, without questioning if they are well-suited to the challenges of continual learning agents. We suggest that many core foundations of traditional RL are, in fact, antithetical to the goals of continual reinforcement learning. We enumerate four such foundations: the Markov decision process formalism, a focus on optimal policies, the expected sum of rewards as the primary evaluation metric, and episodic benchmark environments that embrace the other three foundations. Shedding such sacredly held and taught concepts is not easy. They are self-reinforcing in that each foundation depends upon and holds up the others, making it hard to rethink each in isolation. We propose an alternative set of all four foundations that are better suited to the continual learning setting. We hope to spur on others in rethinking the traditional foundations, proposing and critiquing alternatives, and developing new algorithms and approaches enabled by better-suited foundations.
Dynamic graph learning is essential for applications involving temporal networks and requires effective modeling of temporal relationships. Seminal attention-based models like TGAT and DyGFormer rely on sinusoidal time encoders to capture temporal relationships between edge events. In this paper, we study a simpler alternative: the linear time encoder, which avoids temporal information loss caused by sinusoidal functions and reduces the need for high dimensional time encoders. We show that the self-attention mechanism can effectively learn to compute time spans from linear time encodings and extract relevant temporal patterns. Through extensive experiments on six dynamic graph datasets, we demonstrate that the linear time encoder improves the performance of TGAT and DyGFormer in most cases. Moreover, the linear time encoder can lead to significant savings in model parameters with minimal performance loss. For example, compared to a 100-dimensional sinusoidal time encoder, TGAT with a 2-dimensional linear time encoder saves 43% of parameters and achieves higher average precision on five datasets. These results can be readily used to positively impact the design choices of a wide variety of dynamic graph learning architectures. The experimental code is available at: https://github.com/hsinghuan/dg-linear-time.git.
Recently, slow-thinking systems like GPT-o1 and DeepSeek-R1 have demonstrated great potential in solving challenging problems through explicit reflection. They significantly outperform the best fast-thinking models, such as GPT-4o, on various math and science benchmarks. However, their multimodal reasoning capabilities remain on par with fast-thinking models. For instance, GPT-o1's performance on benchmarks like MathVista, MathVerse, and MathVision is similar to fast-thinking models. In this paper, we aim to enhance the slow-thinking capabilities of vision-language models using reinforcement learning (without relying on distillation) to advance the state of the art. First, we adapt the GRPO algorithm with a novel technique called Selective Sample Replay (SSR) to address the vanishing advantages problem. While this approach yields strong performance, the resulting RL-trained models exhibit limited self-reflection or self-verification. To further encourage slow-thinking, we introduce Forced Rethinking, which appends a rethinking trigger token to the end of rollouts in RL training, explicitly enforcing a self-reflection reasoning step. By combining these two techniques, our model, VL-Rethinker, advances state-of-the-art scores on MathVista, MathVerse to achieve 80.4%, 63.5% respectively. VL-Rethinker also achieves open-source SoTA on multi-disciplinary benchmarks such as MathVision, MMMU-Pro, EMMA, and MEGA-Bench, narrowing the gap with OpenAI-o1. Our empirical results show the effectiveness of our approaches.
This work investigates abnormal feature behaviors observed in image restoration (IR) Transformers. Specifically, we identify two critical issues: feature entropy becoming excessively small and feature magnitudes diverging up to a million-fold scale. We pinpoint the root cause to the per-token normalization aspect of conventional LayerNorm, which disrupts essential spatial correlations and internal feature statistics. To address this, we propose a simple normalization strategy tailored for IR Transformers. Our approach applies normalization across the entire spatio-channel dimension, effectively preserving spatial correlations. Additionally, we introduce an input-adaptive rescaling method that aligns feature statistics to the unique statistical requirements of each input. Experimental results verify that this combined strategy effectively resolves feature divergence, significantly enhancing both the stability and performance of IR Transformers across various IR tasks.
Exploring Ordinal Bias in Action Recognition for Instructional Videos
2025-04-09
Joochan Kim, Minjoon Jung, Byoung-Tak Zhang
Action recognition models have achieved promising results in understanding instructional videos. However, they often rely on dominant, dataset-specific action sequences rather than true video comprehension, a problem that we define as ordinal bias. To address this issue, we propose two effective video manipulation methods: Action Masking, which masks frames of frequently co-occurring actions, and Sequence Shuffling, which randomizes the order of action segments. Through comprehensive experiments, we demonstrate that current models exhibit significant performance drops when confronted with nonstandard action sequences, underscoring their vulnerability to ordinal bias. Our findings emphasize the importance of rethinking evaluation strategies and developing models capable of generalizing beyond fixed action patterns in diverse instructional videos.
From Text to Time? Rethinking the Effectiveness of the Large Language Model for Time Series Forecasting
2025-04-09
Xinyu Zhang, Shanshan Feng, Xutao Li
Using pre-trained large language models (LLMs) as the backbone for time series prediction has recently gained significant research interest. However, the effectiveness of LLM backbones in this domain remains a topic of debate. Based on thorough empirical analyses, we observe that training and testing LLM-based models on small datasets often leads to the Encoder and Decoder becoming overly adapted to the dataset, thereby obscuring the true predictive capabilities of the LLM backbone. To investigate the genuine potential of LLMs in time series prediction, we introduce three pre-training models with identical architectures but different pre-training strategies. Thereby, large-scale pre-training allows us to create unbiased Encoder and Decoder components tailored to the LLM backbone. Through controlled experiments, we evaluate the zero-shot and few-shot prediction performance of the LLM, offering insights into its capabilities. Extensive experiments reveal that although the LLM backbone demonstrates some promise, its forecasting performance is limited. Our source code is publicly available in the anonymous repository: https://anonymous.4open.science/r/LLM4TS-0B5C.
Rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation with Attention Mechanisms and Multiscale Feature Fusion
2025-04-08
Saad Wazir, Daeyoung Kim
Identifying biomarkers in medical images is vital for a wide range of biotech applications. However, recent Transformer and CNN based methods often struggle with variations in morphology and staining, which limits their feature extraction capabilities. In medical image segmentation, where data samples are often limited, state-of-the-art (SOTA) methods improve accuracy by using pre-trained encoders, while end-to-end approaches typically fall short due to difficulties in transferring multiscale features effectively between encoders and decoders. To handle these challenges, we introduce a nested UNet architecture that captures both local and global context through Multiscale Feature Fusion and Attention Mechanisms. This design improves feature integration from encoders, highlights key channels and regions, and restores spatial details to enhance segmentation performance. Our method surpasses SOTA approaches, as evidenced by experiments across four datasets and detailed ablation studies. Code: https://github.com/saadwazir/ReN-UNet
Rethinking Review Citations: Impact on Scientific Integrity
2025-04-08
Jesus S. Aguilar-Ruiz
The proliferation of surveys and review articles in academic journals has impacted citation metrics like impact factor and h-index, skewing evaluations of journal and researcher quality. This work investigates the implications of this trend, focusing on the field of Computer Science, where a notable increase in review publications has led to inflated citation counts and rankings. While reviews serve as valuable literature overviews, they should not overshadow the primary goal of research -to advance scientific knowledge through original contributions. We advocate for prioritizing citations of primary research in journal articles to uphold citation integrity and ensure fair recognition of substantive contributions. This approach preserves the reliability of citation-based metrics and supports genuine scientific advancement.
Human Activity Recognition using RGB-Event based Sensors: A Multi-modal Heat Conduction Model and A Benchmark Dataset
2025-04-08
Shiao Wang, Xiao Wang, Bo Jiang, Lin Zhu, Guoqi Li, Yaowei Wang, Yonghong Tian, Jin Tang
Human Activity Recognition (HAR) primarily relied on traditional RGB cameras to achieve high-performance activity recognition. However, the challenging factors in real-world scenarios, such as insufficient lighting and rapid movements, inevitably degrade the performance of RGB cameras. To address these challenges, biologically inspired event cameras offer a promising solution to overcome the limitations of traditional RGB cameras. In this work, we rethink human activity recognition by combining the RGB and event cameras. The first contribution is the proposed large-scale multi-modal RGB-Event human activity recognition benchmark dataset, termed HARDVS 2.0, which bridges the dataset gaps. It contains 300 categories of everyday real-world actions with a total of 107,646 paired videos covering various challenging scenarios. Inspired by the physics-informed heat conduction model, we propose a novel multi-modal heat conduction operation framework for effective activity recognition, termed MMHCO-HAR. More in detail, given the RGB frames and event streams, we first extract the feature embeddings using a stem network. Then, multi-modal Heat Conduction blocks are designed to fuse the dual features, the key module of which is the multi-modal Heat Conduction Operation layer. We integrate RGB and event embeddings through a multi-modal DCT-IDCT layer while adaptively incorporating the thermal conductivity coefficient via FVEs into this module. After that, we propose an adaptive fusion module based on a policy routing strategy for high-performance classification. Comprehensive experiments demonstrate that our method consistently performs well, validating its effectiveness and robustness. The source code and benchmark dataset will be released on https://github.com/Event-AHU/HARDVS/tree/HARDVSv2
PvNeXt: Rethinking Network Design and Temporal Motion for Point Cloud Video Recognition
2025-04-07
Jie Wang, Tingfa Xu, Lihe Ding, Xinjie Zhang, Long Bai, Jianan Li
Point cloud video perception has become an essential task for the realm of 3D vision. Current 4D representation learning techniques typically engage in iterative processing coupled with dense query operations. Although effective in capturing temporal features, this approach leads to substantial computational redundancy. In this work, we propose a framework, named as PvNeXt, for effective yet efficient point cloud video recognition, via personalized one-shot query operation. Specially, PvNeXt consists of two key modules, the Motion Imitator and the Single-Step Motion Encoder. The former module, the Motion Imitator, is designed to capture the temporal dynamics inherent in sequences of point clouds, thus generating the virtual motion corresponding to each frame. The Single-Step Motion Encoder performs a one-step query operation, associating point cloud of each frame with its corresponding virtual motion frame, thereby extracting motion cues from point cloud sequences and capturing temporal dynamics across the entire sequence. Through the integration of these two modules, {PvNeXt} enables personalized one-shot queries for each frame, effectively eliminating the need for frame-specific looping and intensive query processes. Extensive experiments on multiple benchmarks demonstrate the effectiveness of our method.
Cloud-Fog Automation: The New Paradigm towards Autonomous Industrial Cyber-Physical Systems
2025-04-07
Jiong Jin, Zhibo Pang, Jonathan Kua, Quanyan Zhu, Karl H. Johansson, Nikolaj Marchenko, Dave Cavalcanti
Autonomous Industrial Cyber-Physical Systems (ICPS) represent a future vision where industrial systems achieve full autonomy, integrating physical processes seamlessly with communication, computing and control technologies while holistically embedding intelligence. Cloud-Fog Automation is a new digitalized industrial automation reference architecture that has been recently proposed. This architecture is a fundamental paradigm shift from the traditional International Society of Automation (ISA)-95 model to accelerate the convergence and synergy of communication, computing, and control towards a fully autonomous ICPS. With the deployment of new wireless technologies to enable almost-deterministic ultra-reliable low-latency communications, a joint design of optimal control and computing has become increasingly important in modern ICPS. It is also imperative that system-wide cyber-physical security are critically enforced. Despite recent advancements in the field, there are still significant research gaps and open technical challenges. Therefore, a deliberate rethink in co-designing and synergizing communications, computing, and control (which we term "3C co-design") is required. In this paper, we position Cloud-Fog Automation with 3C co-design as the new paradigm to realize the vision of autonomous ICPS. We articulate the state-of-the-art and future directions in the field, and specifically discuss how goal-oriented communication, virtualization-empowered computing, and Quality of Service (QoS)-aware control can drive Cloud-Fog Automation towards a fully autonomous ICPS, while accounting for system-wide cyber-physical security.
From Fairness to Truthfulness: Rethinking Data Valuation Design
2025-04-07
Dongyang Fan, Tyler J. Rotello, Sai Praneeth Karimireddy
As large language models increasingly rely on external data sources, fairly compensating data contributors has become a central concern. In this paper, we revisit the design of data markets through a game-theoretic lens, where data owners face private, heterogeneous costs for data sharing. We show that commonly used valuation methods--such as Leave-One-Out and Data Shapley--fail to ensure truthful reporting of these costs, leading to inefficient market outcomes. To address this, we adapt well-established payment rules from mechanism design, namely Myerson and Vickrey-Clarke-Groves (VCG), to the data market setting. We demonstrate that the Myerson payment is the minimal truthful payment mechanism, optimal from the buyer's perspective, and that VCG and Myerson payments coincide in unconstrained allocation settings. Our findings highlight the importance of incorporating incentive compatibility into data valuation, paving the way for more robust and efficient data markets.
Rethinking RoPE: A Mathematical Blueprint for N-dimensional Positional Encoding
2025-04-07
Haiping Liu, Hongpeng Zhou
Rotary Position Embedding (RoPE) is widely adopted in Transformers due to its ability to encode relative positions with high efficiency and extrapolation capability. However, existing RoPE variants lack a unified theoretical foundation, especially in higher dimensions. In this paper, we propose a systematic mathematical framework for RoPE grounded in Lie group and Lie algebra theory. We identify two core properties of RoPE, named relativity and reversibility, and derive general constraints and constructions for valid RoPE in 1D, 2D, and N-dimensional (ND). We prove that RoPE must lie in the basis of a maximal abelian subalgebra (MASA) of the special orthogonal Lie algebra, and show that standard RoPE corresponds to the maximal toral subalgebra. Furthermore, we propose to model inter-dimensional interactions by learning an orthogonal basis transformation. Our framework unifies and explains existing RoPE designs, while enabling principled extensions to new modalities and tasks.
Comment on "Hilbert's Sixth Problem: Derivation of Fluid Equations via Boltzmann's Kinetic Theory" by Deng, Hani, and Ma
2025-04-07
Shan Gao
Deng, Hani, and Ma [arXiv:2503.01800] claim to resolve Hilbert's Sixth Problem by deriving the Navier-Stokes-Fourier equations from Newtonian mechanics via an iterated limit: a Boltzmann-Grad limit (\(\varepsilon \to 0\), \(N \varepsilon^{d-1} = \alpha\) fixed) yielding the Boltzmann equation, followed by a hydrodynamic limit (\(\alpha \to \infty\)) to obtain fluid dynamics. Though mathematically rigorous, their approach harbors two critical physical flaws. First, the vanishing volume fraction (\(N \varepsilon^d \to 0\)) confines the system to a dilute gas, incapable of embodying dense fluid properties even as \(\alpha\) scales, rendering the resulting equations a rescaled gas model rather than a true continuum. Second, the Boltzmann equation's reliance on molecular chaos collapses in fluid-like regimes, where recollisions and correlations invalidate its derivation from Newtonian dynamics. These inconsistencies expose a disconnect between the formalism and the physical essence of fluids, failing to capture emergent, density-driven phenomena central to Hilbert's vision. We contend that the Sixth Problem remains open, urging a rethink of classical kinetic theory's limits and the exploration of alternative frameworks to unify microscale mechanics with macroscale fluid behavior.
User-Centered AI for Data Exploration: Rethinking GenAI's Role in Visualization
2025-04-05
Kathrin Schnizer, Sven Mayer
Recent advances in GenAI have enabled automation in data visualization, allowing users to generate visual representations using natural language. However, existing systems primarily focus on automation, overlooking users' varying expertise levels and analytical needs. In this position paper, we advocate for a shift toward adaptive GenAI-driven visualization tools that tailor interactions, reasoning, and visualizations to individual users. We first review existing automation-focused approaches and highlight their limitations. We then introduce methods for assessing user expertise, as well as key open challenges and research questions that must be addressed to allow for an adaptive approach. Finally, we present our vision for a user-centered system that leverages GenAI not only for automation but as an intelligent collaborator in visual data exploration. Our perspective contributes to the broader discussion on designing GenAI-based systems that enhance human cognition by dynamically adapting to the user, ultimately advancing toward systems that promote augmented cognition.
Rethinking Multilingual Continual Pretraining: Data Mixing for Adapting LLMs Across Languages and Resources
2025-04-05
Zihao Li, Shaoxiong Ji, Hengyu Luo, Jörg Tiedemann
Large Language Models (LLMs) exhibit significant disparities in performance across languages, primarily benefiting high-resource languages while marginalizing underrepresented ones. Continual Pretraining (CPT) has emerged as a promising approach to address this imbalance, although the relative effectiveness of monolingual, bilingual, and code-augmented data strategies remains unclear. This study systematically evaluates 36 CPT configurations involving three multilingual base models, across 30+ languages categorized as altruistic, selfish, and stagnant, spanning various resource levels. Our findings reveal three major insights: (1) Bilingual CPT improves multilingual classification but often causes language mixing issues during generation. (2) Including programming code data during CPT consistently enhances multilingual classification accuracy, particularly benefiting low-resource languages, but introduces a trade-off by slightly degrading generation quality. (3) Contrary to prior work, we observe substantial deviations from language classifications according to their impact on cross-lingual transfer: Languages classified as altruistic often negatively affect related languages, selfish languages show conditional and configuration-dependent behavior, and stagnant languages demonstrate surprising adaptability under certain CPT conditions. These nuanced interactions emphasize the complexity of multilingual representation learning, underscoring the importance of systematic studies on generalizable language classification to inform future multilingual CPT strategies.
2025-04-05
Essential AI, :, Darsh J Shah, Peter Rushton, Somanshu Singla, Mohit Parmar, Kurt Smith, Yash Vanjani, Ashish Vaswani, Adarsh Chaluvaraju, Andrew Hojel, Andrew Ma, Anil Thomas, Anthony Polloreno, Ashish Tanwer, Burhan Drak Sibai, Divya S Mansingka, Divya Shivaprasad, Ishaan Shah, Karl Stratos, Khoi Nguyen, Michael Callahan, Michael Pust, Mrinal Iyer, Philip Monk, Platon Mazarakis, Ritvik Kapila, Saurabh Srivastava, Tim Romanski
A language model's ability to reflect on its own reasoning provides a key advantage for solving complex problems. While most recent research has focused on how this ability develops during reinforcement learning, we show that it actually begins to emerge much earlier - during the model's pre-training. To study this, we introduce deliberate errors into chains-of-thought and test whether the model can still arrive at the correct answer by recognizing and correcting these mistakes. By tracking performance across different stages of pre-training, we observe that this self-correcting ability appears early and improves steadily over time. For instance, an OLMo2-7B model pre-trained on 4 trillion tokens displays self-correction on our six self-reflection tasks.
Sample, Don't Search: Rethinking Test-Time Alignment for Language Models
2025-04-04
Gonçalo Faria, Noah A. Smith
Increasing test-time computation has emerged as a promising direction for improving language model performance, particularly in scenarios where model finetuning is impractical or impossible due to computational constraints or private model weights. However, existing test-time search methods using a reward model (RM) often degrade in quality as compute scales, due to the over-optimization of what are inherently imperfect reward proxies. We introduce QAlign, a new test-time alignment approach. As we scale test-time compute, QAlign converges to sampling from the optimal aligned distribution for each individual prompt. By adopting recent advances in Markov chain Monte Carlo for text generation, our method enables better-aligned outputs without modifying the underlying model or even requiring logit access. We demonstrate the effectiveness of QAlign on mathematical reasoning benchmarks (GSM8K and GSM-Symbolic) using a task-specific RM, showing consistent improvements over existing test-time compute methods like best-of-n and majority voting. Furthermore, when applied with more realistic RMs trained on the Tulu 3 preference dataset, QAlign outperforms direct preference optimization (DPO), best-of-n, majority voting, and weighted majority voting on a diverse range of datasets (GSM8K, MATH500, IFEval, MMLU-Redux, and TruthfulQA). A practical solution to aligning language models at test time using additional computation without degradation, our approach expands the limits of the capability that can be obtained from off-the-shelf language models without further training.
Not someone, but something: Rethinking trust in the age of medical AI
2025-04-04
Jan Beger
As artificial intelligence (AI) becomes embedded in healthcare, trust in medical decision-making is changing fast. This opinion paper argues that trust in AI isn't a simple transfer from humans to machines - it's a dynamic, evolving relationship that must be built and maintained. Rather than debating whether AI belongs in medicine, this paper asks: what kind of trust must AI earn, and how? Drawing from philosophy, bioethics, and system design, it explores the key differences between human trust and machine reliability - emphasizing transparency, accountability, and alignment with the values of good care. It argues that trust in AI shouldn't be built on mimicking empathy or intuition, but on thoughtful design, responsible deployment, and clear moral responsibility. The goal is a balanced view - one that avoids blind optimism and reflexive fear. Trust in AI must be treated not as a given, but as something to be earned over time.
Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation Scheme
2025-04-03
Yan Ma, Steffi Chern, Xuyang Shen, Yiran Zhong, Pengfei Liu
Reinforcement learning (RL) has recently shown strong potential in improving the reasoning capabilities of large language models and is now being actively extended to vision-language models (VLMs). However, existing RL applications in VLMs often rely on heavily engineered frameworks that hinder reproducibility and accessibility, while lacking standardized evaluation protocols, making it difficult to compare results or interpret training dynamics. This work introduces a transparent, from-scratch framework for RL in VLMs, offering a minimal yet functional four-step pipeline validated across multiple models and datasets. In addition, a standardized evaluation scheme is proposed to assess training dynamics and reflective behaviors. Extensive experiments on visual reasoning tasks uncover key empirical findings: response length is sensitive to random seeds, reflection correlates with output length, and RL consistently outperforms supervised fine-tuning (SFT) in generalization, even with high-quality data. These findings, together with the proposed framework, aim to establish a reproducible baseline and support broader engagement in RL-based VLM research.
CornerPoint3D: Look at the Nearest Corner Instead of the Center
2025-04-03
Ruixiao Zhang, Runwei Guan, Xiangyu Chen, Adam Prugel-Bennett, Xiaohao Cai
3D object detection aims to predict object centers, dimensions, and rotations from LiDAR point clouds. Despite its simplicity, LiDAR captures only the near side of objects, making center-based detectors prone to poor localization accuracy in cross-domain tasks with varying point distributions. Meanwhile, existing evaluation metrics designed for single-domain assessment also suffer from overfitting due to dataset-specific size variations. A key question arises: Do we really need models to maintain excellent performance in the entire 3D bounding boxes after being applied across domains? Actually, one of our main focuses is on preventing collisions between vehicles and other obstacles, especially in cross-domain scenarios where correctly predicting the sizes is much more difficult. To address these issues, we rethink cross-domain 3D object detection from a practical perspective. We propose two new metrics that evaluate a model's ability to detect objects' closer-surfaces to the LiDAR sensor. Additionally, we introduce EdgeHead, a refinement head that guides models to focus more on learnable closer surfaces, significantly improving cross-domain performance under both our new and traditional BEV/3D metrics. Furthermore, we argue that predicting the nearest corner rather than the object center enhances robustness. We propose a novel 3D object detector, coined as CornerPoint3D, which is built upon CenterPoint and uses heatmaps to supervise the learning and detection of the nearest corner of each object. Our proposed methods realize a balanced trade-off between the detection quality of entire bounding boxes and the locating accuracy of closer surfaces to the LiDAR sensor, outperforming the traditional center-based detector CenterPoint in multiple cross-domain tasks and providing a more practically reasonable and robust cross-domain 3D object detection solution.
The Amenability Framework: Rethinking Causal Ordering Without Estimating Causal Effects
2025-04-03
Carlos Fernández-Loría, Jorge Loría
Who should we prioritize for intervention when we cannot estimate intervention effects? In many applied domains (e.g., advertising, customer retention, and behavioral nudging) prioritization is guided by predictive models that estimate outcome probabilities rather than causal effects. This paper investigates when these predictions (scores) can effectively rank individuals by their intervention effects, particularly when direct effect estimation is infeasible or unreliable. We propose a conceptual framework based on amenability: an individual's latent proclivity to be influenced by an intervention. We then formalize conditions under which predictive scores serve as effective proxies for amenability. These conditions justify using non-causal scores for intervention prioritization, even when the scores do not directly estimate effects. We further show that, under plausible assumptions, predictive models can outperform causal effect estimators in ranking individuals by intervention effects. Empirical evidence from an advertising context supports our theoretical findings, demonstrating that predictive modeling can offer a more robust approach to targeting than effect estimation. Our framework suggests a shift in focus, from estimating effects to inferring who is amenable, as a practical and theoretically grounded strategy for prioritizing interventions in resource-constrained environments.
Rethinking industrial artificial intelligence: a unified foundation framework
2025-04-02
Jay Lee, Hanqi Su
Recent advancements in industrial artificial intelligence (AI) are reshaping the industry by driving smarter manufacturing, predictive maintenance, and intelligent decision-making. However, existing approaches often focus primarily on algorithms and models while overlooking the importance of systematically integrating domain knowledge, data, and models to develop more comprehensive and effective AI solutions. Therefore, the effective development and deployment of industrial AI require a more comprehensive and systematic approach. To address this gap, this paper reviews previous research, rethinks the role of industrial AI, and proposes a unified industrial AI foundation framework comprising three core modules: the knowledge module, data module, and model module. These modules help to extend and enhance the industrial AI methodology platform, supporting various industrial applications. In addition, a case study on rotating machinery diagnosis is presented to demonstrate the effectiveness of the proposed framework, and several future directions are highlighted for the development of the industrial AI foundation framework.
The newly released OpenAI-o1 and DeepSeek-R1 have demonstrated that test-time scaling can significantly improve model performance, especially in complex tasks such as logical reasoning. Common test-time scaling methods involve generating more chain of thoughts (CoTs) or longer CoTs with self-correction. However, while self-correction can improve performance, it may lead to significant token waste and reduce readability of the CoT if the reasoning steps are already correct. To demonstrate that large language models (LLMs) can rectify errors at a more fine-grained level, we propose Adaptive Rectification Sampling (AR-Sampling), which can guide the LLMs to self-correction at the appropriate step. AR-Sampling leverages a process-supervised reward model (PRM) as a verifier and constructed trigger sentences to guide the model in adaptive step-level rethinking. Through the experiments on GSM8K and MATH500, it indicate that our approach enables the models to rethink in more fine-grained level, improving the accuracy of solutions, while generating a reasonable number of additional tokens.
Key-Value cache (\texttt{KV} \texttt{cache}) compression has emerged as a promising technique to optimize Large Language Model (LLM) serving. It primarily decreases the memory consumption of \texttt{KV} \texttt{cache} to reduce the computation cost. Despite the development of many compression algorithms, their applications in production environments are still not prevalent. In this paper, we revisit mainstream \texttt{KV} \texttt{cache} compression solutions from a practical perspective. Our contributions are three-fold. First, we comprehensively review existing algorithmic designs and benchmark studies for \texttt{KV} \texttt{cache} compression and identify missing pieces in their performance measurement, which could hinder their adoption in practice. Second, we empirically evaluate representative \texttt{KV} \texttt{cache} compression methods to uncover two key issues that affect the computational efficiency: (1) while compressing \texttt{KV} \texttt{cache} can reduce memory consumption, current implementations (e.g., FlashAttention, PagedAttention) do not optimize for production-level LLM serving, resulting in suboptimal throughput performance; (2) compressing \texttt{KV} \texttt{cache} may lead to longer outputs, resulting in increased end-to-end latency. We further investigate the accuracy performance of individual samples rather than the overall performance, revealing the intrinsic limitations in \texttt{KV} \texttt{cache} compression when handling specific LLM tasks. Third, we provide tools to shed light on future \texttt{KV} \texttt{cache} compression studies and facilitate their practical deployment in production. They are open-sourced in \href{https://github.com/LLMkvsys/rethink-kv-compression}{https://github.com/LLMkvsys/rethink-kv-compression}.
Spurious correlations are unstable statistical associations that hinder robust decision-making. Conventional wisdom suggests that models relying on such correlations will fail to generalize out-of-distribution (OOD), especially under strong distribution shifts. However, empirical evidence challenges this view as naive in-distribution empirical risk minimizers often achieve the best OOD accuracy across popular OOD generalization benchmarks. In light of these results, we propose a different perspective: many widely used benchmarks for evaluating robustness to spurious correlations are misspecified. Specifically, they fail to include shifts in spurious correlations that meaningfully impact OOD generalization, making them unsuitable for evaluating the benefit of removing such correlations. We establish conditions under which a distribution shift can reliably assess a model's reliance on spurious correlations. Crucially, under these conditions, we should not observe a strong positive correlation between in-distribution and OOD accuracy, often called "accuracy on the line." Yet, most state-of-the-art benchmarks exhibit this pattern, suggesting they do not effectively assess robustness. Our findings expose a key limitation in current benchmarks used to evaluate domain generalization algorithms, that is, models designed to avoid spurious correlations. We highlight the need to rethink how robustness to spurious correlations is assessed, identify well-specified benchmarks the field should prioritize, and enumerate strategies for designing future benchmarks that meaningfully reflect robustness under distribution shift.
Rethinking Technological Solutions for Community-Based Older Adult Care: Insights from 'Older Partners' in China
2025-03-30
Yuing Sun, Sam Addison Ankenbauer, Zhifan Guo, Yuchen Chen, Xiaojuan Ma, Liang He
Aging in place refers to the enabling of individuals to age comfortably and securely within their own homes and communities. Aging in place relies on robust infrastructure, prompting the development and implementation of both human-led care services and information and communication technologies to provide support. Through a long-term ethnographic study that includes semi-structured interviews with 24 stakeholders, we consider these human- and technology-driven care infrastructures for aging in place, examining their origins, deployment, interactions with older adults, and challenges. In doing so, we reconsider the value of these different forms of older adult care, highlighting the various issues associated with using, for instance, health monitoring technology or appointment scheduling systems to care for older adults aging in place. We suggest that technology should take a supportive, not substitutive role in older adult care infrastructure. Furthermore, we note that designing for aging in place should move beyond a narrow focus on independence in one's home to instead encompass the broader community and its dynamics.
Rethinking Structural Equation Modeling in Political Science: Challenges, Best Practices, and Future Directions
2025-03-30
Bang Quan Zheng
Structural Equation Modeling (SEM) or Covariance Structure Analysis (CSA) is a versatile and powerful method in the social and behavioral sciences, providing a framework for modeling complex relationships, testing mediation, accounting for measurement error, and analyzing latent constructs. However, SEM remains underutilized in in political science; its application is often marred by misunderstandings, misinterpretations, and methodological pitfalls that can compromise the validity and interpretability of findings. This article examines key challenges in SEM applications within political science, including test statistics and fit indices, model specification, estimator selection, and causal inference. It offers practical recommendations for enhancing methodological rigor and introduces recent advancements in causal inference.
Beyond Background Shift: Rethinking Instance Replay in Continual Semantic Segmentation
2025-03-28
Hongmei Yin, Tingliang Feng, Fan Lyu, Fanhua Shang, Hongying Liu, Wei Feng, Liang Wan
In this work, we focus on continual semantic segmentation (CSS), where segmentation networks are required to continuously learn new classes without erasing knowledge of previously learned ones. Although storing images of old classes and directly incorporating them into the training of new models has proven effective in mitigating catastrophic forgetting in classification tasks, this strategy presents notable limitations in CSS. Specifically, the stored and new images with partial category annotations leads to confusion between unannotated categories and the background, complicating model fitting. To tackle this issue, this paper proposes a novel Enhanced Instance Replay (EIR) method, which not only preserves knowledge of old classes while simultaneously eliminating background confusion by instance storage of old classes, but also mitigates background shifts in the new images by integrating stored instances with new images. By effectively resolving background shifts in both stored and new images, EIR alleviates catastrophic forgetting in the CSS task, thereby enhancing the model's capacity for CSS. Experimental results validate the efficacy of our approach, which significantly outperforms state-of-the-art CSS methods.
LeX-Art: Rethinking Text Generation via Scalable High-Quality Data Synthesis
2025-03-27
Shitian Zhao, Qilong Wu, Xinyue Li, Bo Zhang, Ming Li, Qi Qin, Dongyang Liu, Kaipeng Zhang, Hongsheng Li, Yu Qiao, Peng Gao, Bin Fu, Zhen Li
We introduce LeX-Art, a comprehensive suite for high-quality text-image synthesis that systematically bridges the gap between prompt expressiveness and text rendering fidelity. Our approach follows a data-centric paradigm, constructing a high-quality data synthesis pipeline based on Deepseek-R1 to curate LeX-10K, a dataset of 10K high-resolution, aesthetically refined 1024$\times$1024 images. Beyond dataset construction, we develop LeX-Enhancer, a robust prompt enrichment model, and train two text-to-image models, LeX-FLUX and LeX-Lumina, achieving state-of-the-art text rendering performance. To systematically evaluate visual text generation, we introduce LeX-Bench, a benchmark that assesses fidelity, aesthetics, and alignment, complemented by Pairwise Normalized Edit Distance (PNED), a novel metric for robust text accuracy evaluation. Experiments demonstrate significant improvements, with LeX-Lumina achieving a 79.81% PNED gain on CreateBench, and LeX-FLUX outperforming baselines in color (+3.18%), positional (+4.45%), and font accuracy (+3.81%). Our codes, models, datasets, and demo are publicly available.
With diffusion transformer (DiT) excelling in video generation, its use in specific tasks has drawn increasing attention. However, adapting DiT for pose-guided human image animation faces two core challenges: (a) existing U-Net-based pose control methods may be suboptimal for the DiT backbone; and (b) removing text guidance, as in previous approaches, often leads to semantic loss and model degradation. To address these issues, we propose DynamiCtrl, a novel framework for human animation in video DiT architecture. Specifically, we use a shared VAE encoder for human images and driving poses, unifying them into a common latent space, maintaining pose fidelity, and eliminating the need for an expert pose encoder during video denoising. To integrate pose control into the DiT backbone effectively, we propose a novel Pose-adaptive Layer Norm model. It injects normalized pose features into the denoising process via conditioning on visual tokens, enabling seamless and scalable pose control across DiT blocks. Furthermore, to overcome the shortcomings of text removal, we introduce the "Joint-text" paradigm, which preserves the role of text embeddings to provide global semantic context. Through full-attention blocks, image and pose features are aligned with text features, enhancing semantic consistency, leveraging pretrained knowledge, and enabling multi-level control. Experiments verify the superiority of DynamiCtrl on benchmark and self-collected data (e.g., achieving the best LPIPS of 0.166), demonstrating strong character control and high-quality synthesis. The project page is available at https://gulucaptain.github.io/DynamiCtrl/.
Recently, the emergence of LLMs has prompted researchers to integrate language descriptions into graphs, aiming to enhance model encoding capabilities from a data-centric perspective. This graph representation is called text-attributed graphs (TAGs). A review of prior advancements highlights that graph structure learning (GSL) is a pivotal technique for improving data utility, making it highly relevant to efficient TAG learning. However, most GSL methods are tailored for traditional graphs without textual information, underscoring the necessity of developing a new GSL paradigm. Despite clear motivations, it remains challenging: (1) How can we define a reasonable optimization objective for GSL in the era of LLMs, considering the massive parameters in LLM? (2) How can we design an efficient model architecture that enables seamless integration of LLM for this optimization objective? For Question 1, we reformulate existing GSL optimization objectives as a tree optimization framework, shifting the focus from obtaining a well-trained edge predictor to a language-aware tree sampler. For Question 2, we propose decoupled and training-free model design principles for LLM integration, shifting the focus from computation-intensive fine-tuning to more efficient inference. Based on this, we propose Large Language and Tree Assistant (LLaTA), which leverages tree-based LLM in-context learning to enhance the understanding of topology and text, enabling reliable inference and generating improved graph structure. Extensive experiments on 10 datasets demonstrate that LLaTA enjoys flexibility-incorporated with any backbone; scalability-outperforms other LLM-enhanced graph learning methods; effectiveness-achieves SOTA predictive performance.
With the advances in artificial intelligence, Mix-of-Experts (MoE) has become the main form of Large Language Models (LLMs), and its demand for model compression is increasing. Quantization is an effective method that not only compresses the models but also significantly accelerates their performance. Existing quantization methods have gradually shifted the focus from parameter scaling to the analysis of data distributions. However, their analysis is designed for dense LLMs, which are suboptimal for MoE quantization, due to MoEs' complex data-model distribution. To address this problem, we decouple the complexity of MoEs' data-model distribution into a multi-stage analysis and reveal MoEs' inherent dynamics. The analysis results show that the expert performance of MoE varies dynamically both within and across data distributions. Based on these, we design two quantization strategies with data-model distribution awareness and integrate them into an end-to-end framework for MoE quantization, which is named MoQa. MoQa uses an expert-level mix-precision base quantization with distribution awareness. Moreover, MoQa uses a channel-level quantization adjustment to dynamically adjust expert performance to adapt to novel distributions. Experiments show that MoQa's base quantization achieves a 0.49~8.51 PPL decrease on known distributions. With the adjustments, MoQa achieves a 2.74~6.44 PPL decrease and 1.85%~3.77% average accuracy improvements on novel distributions. We believe MoQa will play a role in future MoE construction, optimization, and compression.
This paper presents the ZJUKLAB team's submission for SemEval-2025 Task 4: Unlearning Sensitive Content from Large Language Models. This task aims to selectively erase sensitive knowledge from large language models, avoiding both over-forgetting and under-forgetting issues. We propose an unlearning system that leverages Model Merging (specifically TIES-Merging), combining two specialized models into a more balanced unlearned model. Our system achieves competitive results, ranking second among 26 teams, with an online score of 0.944 for Task Aggregate and 0.487 for overall Aggregate. In this paper, we also conduct local experiments and perform a comprehensive analysis of the unlearning process, examining performance trajectories, loss dynamics, and weight perspectives, along with several supplementary experiments, to understand the effectiveness of our method. Furthermore, we analyze the shortcomings of our method and evaluation metrics, emphasizing that MIA scores and ROUGE-based metrics alone are insufficient to fully evaluate successful unlearning. Finally, we emphasize the need for more comprehensive evaluation methodologies and rethinking of unlearning objectives in future research. Code is available at https://github.com/zjunlp/unlearn/tree/main/semeval25.
Deepfake detection is a long-established research topic vital for mitigating the spread of malicious misinformation. Unlike prior methods that provide either binary classification results or textual explanations separately, we introduce a novel method capable of generating both simultaneously. Our method harnesses the multi-modal learning capability of the pre-trained CLIP and the unprecedented interpretability of large language models (LLMs) to enhance both the generalization and explainability of deepfake detection. Specifically, we introduce a multi-modal face forgery detector (M2F2-Det) that employs tailored face forgery prompt learning, incorporating the pre-trained CLIP to improve generalization to unseen forgeries. Also, M2F2-Det incorporates an LLM to provide detailed textual explanations of its detection decisions, enhancing interpretability by bridging the gap between natural language and subtle cues of facial forgeries. Empirically, we evaluate M2F2-Det on both detection and explanation generation tasks, where it achieves state-of-the-art performance, demonstrating its effectiveness in identifying and explaining diverse forgeries.
Participatory Design of EHR Components: Crafting Novel Relational Spaces for IT Specialists and Hospital Staff to Cooperate
2025-03-26
Louise Robert, Laurine Moniez, Quentin Luzurier, David Morquin
Introduced in the early 2010s, Electronic Health Records (EHRs) have become ubiquitous in hospitals. Despite clear benefits, they remain unpopular among healthcare professionals and present significant challenges. Positioned at the intersection of Health Information Systems studies, Computer Supported Collaborative Work (CSCW), Service Design, and Participatory Design (PD), our research investigates how involving users in the co-design of new EHR components within a dedicated hospital space can transform healthcare practices. Through participatory co-design methodologies, including ethnographic observation, collaborative workshops, and realistic simulations, we identify the material and interactional elements essential for rebalancing power dynamics between users and designers. This project contributes to rethinking traditional EHR design approaches, embedding design practice into systemic transformation to genuinely meet healthcare professionals' needs.
2025-03-25
George Hodgkins, Mark Madler, Joseph Izraelevitz
In this work, we explore an object-based programming model for filling the space between shared memory and distributed systems programming. We argue that the natural representation for resources distributed across a memory network (e.g. RDMA or CXL) is the traditional shared memory object. This concurrent object (which we call a "channel" object) exports traditional methods, but, especially in an incoherent or uncacheable memory network, stores its state in a distributed fashion across all participating nodes. In a sense, the channel object's state is stored "across the network". Based on this philosophy, we introduce the Library of Channel Objects (LOCO), a library for building multi-node objects on RDMA. Channel objects are composable and designed for both the strong locality effects and the weak consistency of RDMA. Unlike prior work, channel objects do not hide memory complexity, instead relying on the programmer to use NUMA-like techniques to explicitly manage each object. As a consequence, our channel objects have performance similar to custom RDMA systems (e.g. distributed maps), but with a far simpler programming model. Our distributed map channel has better read and comparable write performance to a state-of-the-art custom RDMA solution, using well-encapsulated and reusable primitives.
We introduce FFN Fusion, an architectural optimization technique that reduces sequential computation in large language models by identifying and exploiting natural opportunities for parallelization. Our key insight is that sequences of Feed-Forward Network (FFN) layers, particularly those remaining after the removal of specific attention layers, can often be parallelized with minimal accuracy impact. We develop a principled methodology for identifying and fusing such sequences, transforming them into parallel operations that significantly reduce inference latency while preserving model behavior. Applying these techniques to Llama-3.1-405B-Instruct, we create Llama-Nemotron-Ultra-253B-Base (Ultra-253B-Base), an efficient and soon-to-be publicly available model that achieves a 1.71X speedup in inference latency and 35X lower per-token cost while maintaining strong performance across benchmarks. Through extensive experiments on models from 49B to 253B parameters, we demonstrate that FFN Fusion becomes increasingly effective at larger scales and can complement existing optimization techniques like quantization and pruning. Most intriguingly, we find that even full transformer blocks containing both attention and FFN layers can sometimes be parallelized, suggesting new directions for neural architecture design.
Rethinking Glaucoma Calibration: Voting-Based Binocular and Metadata Integration
2025-03-24
Taejin Jeong, Joohyeok Kim, Jaehoon Joo, Yeonwoo Jung, Hyeonmin Kim, Seong Jae Hwang
Glaucoma is an incurable ophthalmic disease that damages the optic nerve, leads to vision loss, and ranks among the leading causes of blindness worldwide. Diagnosing glaucoma typically involves fundus photography, optical coherence tomography (OCT), and visual field testing. However, the high cost of OCT often leads to reliance on fundus photography and visual field testing, both of which exhibit inherent inter-observer variability. This stems from glaucoma being a multifaceted disease that influenced by various factors. As a result, glaucoma diagnosis is highly subjective, emphasizing the necessity of calibration, which aligns predicted probabilities with actual disease likelihood. Proper calibration is essential to prevent overdiagnosis or misdiagnosis, which are critical concerns for high-risk diseases. Although AI has significantly improved diagnostic accuracy, overconfidence in models have worsen calibration performance. Recent study has begun focusing on calibration for glaucoma. Nevertheless, previous study has not fully considered glaucoma's systemic nature and the high subjectivity in its diagnostic process. To overcome these limitations, we propose V-ViT (Voting-based ViT), a novel framework that enhances calibration by incorporating disease-specific characteristics. V-ViT integrates binocular data and metadata, reflecting the multi-faceted nature of glaucoma diagnosis. Additionally, we introduce a MC dropout-based Voting System to address high subjectivity. Our approach achieves state-of-the-art performance across all metrics, including accuracy, demonstrating that our proposed methods are effective in addressing calibration issues. We validate our method using a custom dataset including binocular data.
Rise of the Community Champions: From Reviewer Crunch to Community Power
2025-03-24
Changlun Li, Yao Shi, Yuyu Luo, Nan Tang
Academic publishing is facing a crisis driven by exponential growth in submissions and an overwhelmed peer review system, leading to inconsistent decisions and a severe reviewer shortage. This paper introduces Panvas, a platform that reimagines academic publishing as a continuous, community-driven process. Panvas addresses these systemic failures with a novel combination of economic incentives (paid reviews) and rich interaction mechanisms (multi-dimensional ratings, threaded discussions, and expert-led reviews). By moving beyond the traditional accept/reject paradigm and integrating paper hosting with code/data repositories and social networking, Panvas fosters a meritocratic environment for scholarly communication and presents a radical rethinking of how we evaluate and disseminate scientific knowledge. We present the system design, development roadmap, and a user study plan to evaluate its effectiveness.
OvercookedV2: Rethinking Overcooked for Zero-Shot Coordination
2025-03-22
Tobias Gessler, Tin Dizdarevic, Ani Calinescu, Benjamin Ellis, Andrei Lupu, Jakob Nicolaus Foerster
AI agents hold the potential to transform everyday life by helping humans achieve their goals. To do this successfully, agents need to be able to coordinate with novel partners without prior interaction, a setting known as zero-shot coordination (ZSC). Overcooked has become one of the most popular benchmarks for evaluating coordination capabilities of AI agents and learning algorithms. In this work, we investigate the origins of ZSC challenges in Overcooked. We introduce a state augmentation mechanism which mixes states that might be encountered when paired with unknown partners into the training distribution, reducing the out-of-distribution challenge associated with ZSC. We show that independently trained agents under this algorithm coordinate successfully in Overcooked. Our results suggest that ZSC failure can largely be attributed to poor state coverage under self-play rather than more sophisticated coordination challenges. The Overcooked environment is therefore not suitable as a ZSC benchmark. To address these shortcomings, we introduce OvercookedV2, a new version of the benchmark, which includes asymmetric information and stochasticity, facilitating the creation of interesting ZSC scenarios. To validate OvercookedV2, we conduct experiments demonstrating that mere exhaustive state coverage is insufficient to coordinate well. Finally, we use OvercookedV2 to build a new range of coordination challenges, including ones that require test time protocol formation, and we demonstrate the need for new coordination algorithms that can adapt online. We hope that OvercookedV2 will help benchmark the next generation of ZSC algorithms and advance collaboration between AI agents and humans.
NdLinear Is All You Need for Representation Learning
2025-03-21
Alex Reneau, Jerry Yao-Chieh Hu, Zhongfang Zhuang, Ting-Chun Liu
Many high-impact machine learning tasks involve multi-dimensional data (e.g., images, volumetric medical scans, multivariate time-series). Yet, most neural architectures flatten inputs, discarding critical cross-dimension information. We introduce NdLinear, a novel linear transformation that preserves these structures without extra overhead. By operating separately along each dimension, NdLinear captures dependencies that standard fully connected layers overlook. Extensive experiments across convolutional, recurrent, and transformer-based networks show significant improvements in representational power and parameter efficiency. Crucially, NdLinear serves as a foundational building block for large-scale foundation models by operating on any unimodal or multimodal data in its native form. This removes the need for flattening or modality-specific preprocessing. Ndlinear rethinks core architectural priorities beyond attention, enabling more expressive, context-aware models at scale. We propose NdLinear as a drop-in replacement for standard linear layers -- marking an important step toward next-generation neural architectures.
MAPS: A Multi-Agent Framework Based on Big Seven Personality and Socratic Guidance for Multimodal Scientific Problem Solving
2025-03-21
Jian Zhang, Zhiyuan Wang, Zhangqi Wang, Xinyu Zhang, Fangzhi Xu, Qika Lin, Rui Mao, Erik Cambria, Jun Liu
Multimodal scientific problems (MSPs) involve complex issues that require the integration of multiple modalities, such as text and diagrams, presenting a significant challenge in artificial intelligence. While progress has been made in addressing traditional scientific problems, MSPs still face two primary issues: the challenge of multi-modal comprehensive reasoning in scientific problem-solving and the lack of reflective and rethinking capabilities. To address these issues, we introduce a Multi-Agent framework based on the Big Seven Personality and Socratic guidance (MAPS). This framework employs seven distinct agents that leverage feedback mechanisms and the Socratic method to guide the resolution of MSPs. To tackle the first issue, we propose a progressive four-agent solving strategy, where each agent focuses on a specific stage of the problem-solving process. For the second issue, we introduce a Critic agent, inspired by Socratic questioning, which prompts critical thinking and stimulates autonomous learning. We conduct extensive experiments on the EMMA, Olympiad, and MathVista datasets, achieving promising results that outperform the current SOTA model by 15.84% across all tasks. Meanwhile, the additional analytical experiments also verify the model's progress as well as generalization ability.
2025-03-21
George Cazenavette, Joel Julin, Simon Lucey
Until quite recently, the backbone of nearly every state-of-the-art computer vision model has been the 2D convolution. At its core, a 2D convolution simultaneously mixes information across both the spatial and channel dimensions of a representation. Many recent computer vision architectures consist of sequences of isotropic blocks that disentangle the spatial and channel-mixing components. This separation of the operations allows us to more closely juxtapose the effects of spatial and channel mixing in deep learning. In this paper, we take an initial step towards garnering a deeper understanding of the roles of these mixing operations. Through our experiments and analysis, we discover that on both classical (ResNet) and cutting-edge (ConvMixer) models, we can reach nearly the same level of classification performance by and leaving the spatial mixers at their random initializations. Furthermore, we show that models with random, fixed spatial mixing are naturally more robust to adversarial perturbations. Lastly, we show that this phenomenon extends past the classification regime, as such models can also decode pixel-shuffled images.
Rethinking Robustness in Machine Learning: A Posterior Agreement Approach
2025-03-20
João Borges S. Carvalho, Alessandro Torcinovich, Victor Jimenez Rodriguez, Antonio E. Cinà, Carlos Cotrini, Lea Schönherr, Joachim M. Buhmann
The robustness of algorithms against covariate shifts is a fundamental problem with critical implications for the deployment of machine learning algorithms in the real world. Current evaluation methods predominantly match the robustness definition to that of standard generalization, relying on standard metrics like accuracy-based scores, which, while designed for performance assessment, lack a theoretical foundation encompassing their application in estimating robustness to distribution shifts. In this work, we set the desiderata for a robustness metric, and we propose a novel principled framework for the robustness assessment problem that directly follows the Posterior Agreement (PA) theory of model validation. Specifically, we extend the PA framework to the covariate shift setting by proposing a PA metric for robustness evaluation in supervised classification tasks. We assess the soundness of our metric in controlled environments and through an empirical robustness analysis in two different covariate shift scenarios: adversarial learning and domain generalization. We illustrate the suitability of PA by evaluating several models under different nature and magnitudes of shift, and proportion of affected observations. The results show that the PA metric provides a sensible and consistent analysis of the vulnerabilities in learning algorithms, even in the presence of few perturbed observations.
Think or Not Think: A Study of Explicit Thinking in Rule-Based Visual Reinforcement Fine-Tuning
2025-03-20
Ming Li, Jike Zhong, Shitian Zhao, Yuxiang Lai, Haoquan Zhang, Wang Bill Zhu, Kaipeng Zhang
This paper investigates the role of explicit thinking process in rule-based reinforcement fine-tuning (RFT) for MLLMs. We first propose CLS-RL for MLLM image classification, using verifiable rewards for fine-tuning. Experiments show CLS-RL significantly outperforms SFT and yields a cross-dataset generalization effect. We then rethink and question whether explicit thinking in RFT is always necessary. Challenging the convention that explicit thinking is crucial for the success of RFT, we introduce No-Thinking-RL, exploring RFT without thinking by introducing a simple equality accuracy reward. We evaluate No-Thinking-RL on 6 diverse tasks across different model sizes and types. Experimental results reveal three key findings: 1). Visual perception tasks do not require thinking during RFT, as No-Thinking-RL consistently outperforms or matches Thinking-based RFT across model sizes. 2).} Models with limited capabilities struggle to generate high-quality CoT for RFT, making Thinking-based RFT less effective than No-Thinking-RL. 3). There are inconsistencies between the answers in the thinking and answer tags for some responses of thinking-based RFT, which show lower accuracy than the overall accuracy. We hypothesize that explicit thinking before verifiable answers may hinder reward convergence and reduce performance. To test this hypothesis, we propose Think-After-Answer, which places thinking after the answer to mitigate this effect for experimental verification. Lastly, we conduct a pilot study to explore whether MLLMs can learn when to think during RFT, introducing an Adaptive-Thinking method. Experiments show that it converges to a specific prompt depending on model capability and task complexity, achieving comparable or better performance than both Thinking and No-Thinking-RL. This suggests MLLMs can adaptively decide to think or not based on their capabilities and task complexity.
Medical image segmentation aims to identify anatomical structures at the voxel-level. Segmentation accuracy relies on distinguishing voxel differences. Compared to advancements achieved in studies of the inter-class variance, the intra-class variance receives less attention. Moreover, traditional linear classifiers, limited by a single learnable weight per class, struggle to capture this finer distinction. To address the above challenges, we propose a Multi-Prototype-based Embedding Refinement method for semi-supervised medical image segmentation. Specifically, we design a multi-prototype-based classification strategy, rethinking the segmentation from the perspective of structural relationships between voxel embeddings. The intra-class variations are explored by clustering voxels along the distribution of multiple prototypes in each class. Next, we introduce a consistency constraint to alleviate the limitation of linear classifiers. This constraint integrates different classification granularities from a linear classifier and the proposed prototype-based classifier. In the thorough evaluation on two popular benchmarks, our method achieves superior performance compared with state-of-the-art methods. Code is available at https://github.com/Briley-byl123/MPER.
This study examines the roles of public and private sector actors in the development of mRNA vaccines, a breakthrough innovation in modern medicine. Using a dataset of 151 core patent families and 2,416 antecedent (cited) patents, we analyze the structure and dynamics of the mRNA vaccine knowledge network through network theory. Our findings highlight the central role of biotechnology firms, such as Moderna and BioNTech, alongside the crucial contributions of universities and public research organizations (PROs) in providing foundational knowledge.We develop a novel credit allocation framework, showing that universities, PROs, government and research centers account for at least 27% of the external technological knowledge base behind mRNA vaccine breakthroughs - representing a minimum threshold of their overall contribution. Our study offers new insights into pharmaceutical and biotechnology innovation dynamics, emphasizing how Moderna and BioNTech's mRNA technologies have benefited from academic institutions, with notable differences in their institutional knowledge sources.
Rethinking End-to-End 2D to 3D Scene Segmentation in Gaussian Splatting
2025-03-18
Runsong Zhu, Shi Qiu, Zhengzhe Liu, Ka-Hei Hui, Qianyi Wu, Pheng-Ann Heng, Chi-Wing Fu
Lifting multi-view 2D instance segmentation to a radiance field has proven to be effective to enhance 3D understanding. Existing methods rely on direct matching for end-to-end lifting, yielding inferior results; or employ a two-stage solution constrained by complex pre- or post-processing. In this work, we design a new end-to-end object-aware lifting approach, named Unified-Lift that provides accurate 3D segmentation based on the 3D Gaussian representation. To start, we augment each Gaussian point with an additional Gaussian-level feature learned using a contrastive loss to encode instance information. Importantly, we introduce a learnable object-level codebook to account for individual objects in the scene for an explicit object-level understanding and associate the encoded object-level features with the Gaussian-level point features for segmentation predictions. While promising, achieving effective codebook learning is non-trivial and a naive solution leads to degraded performance. Therefore, we formulate the association learning module and the noisy label filtering module for effective and robust codebook learning. We conduct experiments on three benchmarks: LERF-Masked, Replica, and Messy Rooms datasets. Both qualitative and quantitative results manifest that our Unified-Lift clearly outperforms existing methods in terms of segmentation quality and time efficiency. The code is publicly available at \href{https://github.com/Runsong123/Unified-Lift}{https://github.com/Runsong123/Unified-Lift}.
Cell counting in microscopy images is vital in medicine and biology but extremely tedious and time-consuming to perform manually. While automated methods have advanced in recent years, state-of-the-art approaches tend to increasingly complex model designs. In this paper, we propose a conceptually simple yet effective decoupled learning scheme for automated cell counting, consisting of separate counter and localizer networks. In contrast to jointly learning counting and density map estimation, we show that decoupling these objectives surprisingly improves results. The counter operates on intermediate feature maps rather than pixel space to leverage global context and produce count estimates, while also generating coarse density maps. The localizer then reconstructs high-resolution density maps that precisely localize individual cells, conditional on the original images and coarse density maps from the counter. Besides, to boost counting accuracy, we further introduce a global message passing module to integrate cross-region patterns. Extensive experiments on four datasets demonstrate that our approach, despite its simplicity, challenges common practice and achieves state-of-the-art performance by significant margins. Our key insight is that decoupled learning alleviates the need to learn counting on high-resolution density maps directly, allowing the model to focus on global features critical for accurate estimates. Code is available at https://github.com/MedAITech/DCL.
2025-03-17
Shaolin Su, Josep M. Rocafort, Danna Xue, David Serrano-Lozano, Lei Sun, Javier Vazquez-Corral
While recent advancing image super-resolution (SR) techniques are continually improving the perceptual quality of their outputs, they can usually fail in quantitative evaluations. This inconsistency leads to a growing distrust in existing image metrics for SR evaluations. Though image evaluation depends on both the metric and the reference ground truth (GT), researchers typically do not inspect the role of GTs, as they are generally accepted as `perfect' references. However, due to the data being collected in the early years and the ignorance of controlling other types of distortions, we point out that GTs in existing SR datasets can exhibit relatively poor quality, which leads to biased evaluations. Following this observation, in this paper, we are interested in the following questions: Are GT images in existing SR datasets 100% trustworthy for model evaluations? How does GT quality affect this evaluation? And how to make fair evaluations if there exist imperfect GTs? To answer these questions, this paper presents two main contributions. First, by systematically analyzing seven state-of-the-art SR models across three real-world SR datasets, we show that SR performances can be consistently affected across models by low-quality GTs, and models can perform quite differently when GT quality is controlled. Second, we propose a novel perceptual quality metric, Relative Quality Index (RQI), that measures the relative quality discrepancy of image pairs, thus issuing the biased evaluations caused by unreliable GTs. Our proposed model achieves significantly better consistency with human opinions. We expect our work to provide insights for the SR community on how future datasets, models, and metrics should be developed.
Pensez: Less Data, Better Reasoning -- Rethinking French LLM
2025-03-17
Huy Hoang Ha
Large language models (LLMs) have demonstrated remarkable capabilities in various natural language processing tasks. However, achieving strong performance in specialized domains like mathematical reasoning and non-English languages often requires extensive training on massive datasets. This paper investigates a contrasting approach: strategic fine-tuning on a small, high-quality, bilingual (English-French) dataset to enhance both the reasoning capabilities and French language proficiency of a large language model. Rather than relying on scale, we explore the hypothesis that targeted data curation and optimized training can achieve competitive, or even superior, performance. We demonstrate, through targeted supervised fine-tuning (SFT) on only 2,000 carefully selected samples, significant improvements in mathematical reasoning. Specifically, Pensez 7B exhibits an increase in accuracy of the base model up to 20% on the AIME25 and a 12% increase on a French MATH level 5 benchmark. These results challenge the prevailing assumption that massive datasets are aprerequisite for strong reasoning performance in LLMs, highlighting the potential of strategic data curation and optimized fine-tuning for enhancing both specialized skills and multilingual capabilities. Our findings have implications for the efficient development of high-performing, multilingual LLMs, especially in resource-constrained scenarios.
2025-03-14
Oliver Michel, Satadal Sengupta, Hyojoon Kim, Ravi Netravali, Jennifer Rexford
Video-conferencing applications face an unwavering surge in traffic, stressing their underlying infrastructure in unprecedented ways. This paper rethinks the key building block for conferencing infrastructures -- selective forwarding units (SFUs). SFUs relay and adapt media streams between participants and, today, run in software on general-purpose servers. Our main insight, discerned from dissecting the operation of production SFU servers, is that SFUs largely mimic traditional packet-processing operations such as dropping and forwarding. Guided by this, we present Scallop, an SDN-inspired SFU that decouples video-conferencing applications into a hardware-based data plane for latency-sensitive and frequent media operations, and a software control plane for the (infrequent) remaining tasks, such as analyzing feedback signals. Our Tofino-based implementation fully supports WebRTC and delivers 7-210 times improved scaling over a 32-core commodity server, while reaping performance improvements by cutting forwarding-induced latency by 26 times.
Rethinking Few-Shot Adaptation of Vision-Language Models in Two Stages
2025-03-14
Matteo Farina, Massimiliano Mancini, Giovanni Iacca, Elisa Ricci
An old-school recipe for training a classifier is to (i) learn a good feature extractor and (ii) optimize a linear layer atop. When only a handful of samples are available per category, as in Few-Shot Adaptation (FSA), data are insufficient to fit a large number of parameters, rendering the above impractical. This is especially true with large pre-trained Vision-Language Models (VLMs), which motivated successful research at the intersection of Parameter-Efficient Fine-tuning (PEFT) and FSA. In this work, we start by analyzing the learning dynamics of PEFT techniques when trained on few-shot data from only a subset of categories, referred to as the ``base'' classes. We show that such dynamics naturally splits into two distinct phases: (i) task-level feature extraction and (ii) specialization to the available concepts. To accommodate this dynamic, we then depart from prompt- or adapter-based methods and tackle FSA differently. Specifically, given a fixed computational budget, we split it to (i) learn a task-specific feature extractor via PEFT and (ii) train a linear classifier on top. We call this scheme Two-Stage Few-Shot Adaptation (2SFS). Differently from established methods, our scheme enables a novel form of selective inference at a category level, i.e., at test time, only novel categories are embedded by the adapted text encoder, while embeddings of base categories are available within the classifier. Results with fixed hyperparameters across two settings, three backbones, and eleven datasets, show that 2SFS matches or surpasses the state-of-the-art, while established methods degrade significantly across settings.
Reinforcement Learning and Imitation Learning have achieved widespread success in many domains but remain constrained during real-world deployment. One of the main issues is the additional requirements that were not considered during training. To address this challenge, policy customization has been introduced, aiming to adapt a prior policy while preserving its inherent properties and meeting new task-specific requirements. A principled approach to policy customization is Residual Q-Learning (RQL), which formulates the problem as a Markov Decision Process (MDP) and derives a family of value-based learning algorithms. However, RQL has not yet been applied to policy gradient methods, which restricts its applicability, especially in tasks where policy gradient has already proven more effective. In this work, we first derive a concise form of Soft Policy Gradient as a preliminary. Building on this, we introduce Residual Policy Gradient (RPG), which extends RQL to policy gradient methods, allowing policy customization in gradient-based RL settings. With the view of RPG, we rethink the KL-regularized objective widely used in RL fine-tuning. We show that under certain assumptions, KL-regularized objective leads to a maximum-entropy policy that balances the inherent properties and task-specific requirements on a reward-level. Our experiments in MuJoCo demonstrate the effectiveness of Soft Policy Gradient and Residual Policy Gradient.
Rotation-invariant recognition of shapes is a common challenge in computer vision. Recent approaches have significantly improved the accuracy of rotation-invariant recognition by encoding the rotational invariance of shapes as hand-crafted image features and introducing deep neural networks. However, the methods based on pixels have too much redundant information, and the critical geometric information is prone to early leakage, resulting in weak rotation-invariant recognition of fine-grained shapes. In this paper, we reconsider the shape recognition problem from the perspective of contour points rather than pixels. We propose an anti-noise rotation-invariant convolution module based on contour geometric aware for fine-grained shape recognition. The module divides the shape contour into multiple local geometric regions(LGA), where we implement finer-grained rotation-invariant coding in terms of point topological relations. We provide a deep network composed of five such cascaded modules for classification and retrieval experiments. The results show that our method exhibits excellent performance in rotation-invariant recognition of fine-grained shapes. In addition, we demonstrate that our method is robust to contour noise and the rotation centers. The source code is available at https://github.com/zhenguonie/ANRICN_CGA.
Multi-Modal Object Detection (MMOD), due to its stronger adaptability to various complex environments, has been widely applied in various applications. Extensive research is dedicated to the RGB-IR object detection, primarily focusing on how to integrate complementary features from RGB-IR modalities. However, they neglect the mono-modality insufficient learning problem that the decreased feature extraction capability in multi-modal joint learning. This leads to an unreasonable but prevalent phenomenon--Fusion Degradation, which hinders the performance improvement of the MMOD model. Motivated by this, in this paper, we introduce linear probing evaluation to the multi-modal detectors and rethink the multi-modal object detection task from the mono-modality learning perspective. Therefore, we construct an novel framework called M$^2$D-LIF, which consists of the Mono-Modality Distillation (M$^2$D) method and the Local Illumination-aware Fusion (LIF) module. The M$^2$D-LIF framework facilitates the sufficient learning of mono-modality during multi-modal joint training and explores a lightweight yet effective feature fusion manner to achieve superior object detection performance. Extensive experiments conducted on three MMOD datasets demonstrate that our M$^2$D-LIF effectively mitigates the Fusion Degradation phenomenon and outperforms the previous SOTA detectors.
Inversion-based image editing is rapidly gaining momentum while suffering from significant computation overhead, hindering its application in real-time interactive scenarios. In this paper, we rethink that the redundancy in inversion-based image editing exists in both the spatial and temporal dimensions, such as the unnecessary computation in unedited regions and the redundancy in the inversion progress. To tackle these challenges, we propose a practical framework, named EEdit, to achieve efficient image editing. Specifically, we introduce three techniques to solve them one by one. For spatial redundancy, spatial locality caching is introduced to compute the edited region and its neighboring regions while skipping the unedited regions, and token indexing preprocessing is designed to further accelerate the caching. For temporal redundancy, inversion step skipping is proposed to reuse the latent for efficient editing. Our experiments demonstrate an average of 2.46 $\times$ acceleration without performance drop in a wide range of editing tasks including prompt-guided image editing, dragging and image composition. Our codes are available at https://github.com/yuriYanZeXuan/EEdit
Rethinking Prompt-based Debiasing in Large Language Models
2025-03-12
Xinyi Yang, Runzhe Zhan, Derek F. Wong, Shu Yang, Junchao Wu, Lidia S. Chao
Investigating bias in large language models (LLMs) is crucial for developing trustworthy AI. While prompt-based through prompt engineering is common, its effectiveness relies on the assumption that models inherently understand biases. Our study systematically analyzed this assumption using the BBQ and StereoSet benchmarks on both open-source models as well as commercial GPT model. Experimental results indicate that prompt-based is often superficial; for instance, the Llama2-7B-Chat model misclassified over 90% of unbiased content as biased, despite achieving high accuracy in identifying bias issues on the BBQ dataset. Additionally, specific evaluation and question settings in bias benchmarks often lead LLMs to choose "evasive answers", disregarding the core of the question and the relevance of the response to the context. Moreover, the apparent success of previous methods may stem from flawed evaluation metrics. Our research highlights a potential "false prosperity" in prompt-base efforts and emphasizes the need to rethink bias metrics to ensure truly trustworthy AI.
Bimanual robotic manipulation is an emerging and critical topic in the robotics community. Previous works primarily rely on integrated control models that take the perceptions and states of both arms as inputs to directly predict their actions. However, we think bimanual manipulation involves not only coordinated tasks but also various uncoordinated tasks that do not require explicit cooperation during execution, such as grasping objects with the closest hand, which integrated control frameworks ignore to consider due to their enforced cooperation in the early inputs. In this paper, we propose a novel decoupled interaction framework that considers the characteristics of different tasks in bimanual manipulation. The key insight of our framework is to assign an independent model to each arm to enhance the learning of uncoordinated tasks, while introducing a selective interaction module that adaptively learns weights from its own arm to improve the learning of coordinated tasks. Extensive experiments on seven tasks in the RoboTwin dataset demonstrate that: (1) Our framework achieves outstanding performance, with a 23.5% boost over the SOTA method. (2) Our framework is flexible and can be seamlessly integrated into existing methods. (3) Our framework can be effectively extended to multi-agent manipulation tasks, achieving a 28% boost over the integrated control SOTA. (4) The performance boost stems from the decoupled design itself, surpassing the SOTA by 16.5% in success rate with only 1/6 of the model size.
Curse of Dimensionality is an unavoidable challenge in statistical probability models, yet diffusion models seem to overcome this limitation, achieving impressive results in high-dimensional data generation. Diffusion models assume that they can learn the statistical properties of the underlying probability distribution, enabling sampling from this distribution to generate realistic samples. But is this really how they work? To address this question, this paper conducts a detailed analysis of the objective function and inference methods of diffusion models, leading to several important conclusions that help answer the above question: 1) In high-dimensional sparse scenarios, the target of the objective function fitting degrades from a weighted sum of multiple samples to a single sample. 2) The mainstream inference methods can all be represented within a simple unified framework, without requiring statistical concepts such as Markov chains and SDE, while aligning with the degraded objective function. 3) Guided by this simple framework, more efficient inference methods can be discovered.
Desirable Unfamiliarity: Insights from Eye Movements on Engagement and Readability of Dictation Interfaces
2025-03-11
Zhaohui Liang, Yonglin Chen, Naser Al Madi, Can Liu
Dictation interfaces support efficient text input, but the transcribed text can be hard to read. To understand how users read and review dictated text, we conducted a controlled eye-tracking experiment with 20 participants to compare five dictation interfaces: PLAIN (real-time transcription), AOC (periodic corrections), RAKE (keyword highlights), GP-TSM (grammar-preserving highlights), and SUMMARY (LLM-generated abstraction summary). The study analyzed participants' gaze patterns during their speech composition and reviewing processes. The findings show that during composition, participants spent only 7--11% of their time actively reading, and they favored real-time feedback and avoided distracting interface changes. During reviewing, although SUMMARY introduced unfamiliar words (requiring longer and more frequent fixation), they were easier to read (requiring fewer regressions). Participants preferred SUMMARY for the polished text that preserved fidelity to original meanings. RAKE guided the reading of self-produced text better than GP-TSM. These findings provide new ways to rethink the design of dictation interfaces.
Stakeholder Perspectives on Whether and How Social Robots Can Support Mediation and Advocacy for Higher Education Students with Disabilities
2025-03-11
Alva Markelius, Julie Bailey, Jenny L. Gibson, Hatice Gunes
This paper presents an iterative, participatory, empirical study that examines the potential of using artificial intelligence, such as social robots and large language models, to support mediation and advocacy for students with disabilities in higher education. Drawing on qualitative data from interviews and focus groups conducted with various stakeholders, including disabled students, disabled student representatives, and disability practitioners at the University of Cambridge, this study reports findings relating to understanding the problem space, ideating robotic support and participatory co-design of advocacy support robots. The findings highlight the potential of these technologies in providing signposting and acting as a sounding board or study companion, while also addressing limitations in empathic understanding, trust, equity, and accessibility. We discuss ethical considerations, including intersectional biases, the double empathy problem, and the implications of deploying social robots in contexts shaped by structural inequalities. Finally, we offer a set of recommendations and suggestions for future research, rethinking the notion of corrective technological interventions to tools that empower and amplify self-advocacy.
You Are Your Own Best Teacher: Achieving Centralized-level Performance in Federated Learning under Heterogeneous and Long-tailed Data
2025-03-10
Shanshan Yan, Zexi Li, Chao Wu, Meng Pang, Yang Lu, Yan Yan, Hanzi Wang
Data heterogeneity, stemming from local non-IID data and global long-tailed distributions, is a major challenge in federated learning (FL), leading to significant performance gaps compared to centralized learning. Previous research found that poor representations and biased classifiers are the main problems and proposed neural-collapse-inspired synthetic simplex ETF to help representations be closer to neural collapse optima. However, we find that the neural-collapse-inspired methods are not strong enough to reach neural collapse and still have huge gaps to centralized training. In this paper, we rethink this issue from a self-bootstrap perspective and propose FedYoYo (You Are Your Own Best Teacher), introducing Augmented Self-bootstrap Distillation (ASD) to improve representation learning by distilling knowledge between weakly and strongly augmented local samples, without needing extra datasets or models. We further introduce Distribution-aware Logit Adjustment (DLA) to balance the self-bootstrap process and correct biased feature representations. FedYoYo nearly eliminates the performance gap, achieving centralized-level performance even under mixed heterogeneity. It enhances local representation learning, reducing model drift and improving convergence, with feature prototypes closer to neural collapse optimality. Extensive experiments show FedYoYo achieves state-of-the-art results, even surpassing centralized logit adjustment methods by 5.4\% under global long-tailed settings.
In this paper, we present an approach for integrated task and motion planning based on an AND/OR graph network, which is used to represent task-level states and actions, and we leverage it to implement different classes of task and motion planning problems (TAMP). Several problems that fall under task and motion planning do not have a predetermined number of sub-tasks to achieve a goal. For example, while retrieving a target object from a cluttered workspace, in principle the number of object re-arrangements required to finally grasp it cannot be known ahead of time. To address this challenge, and in contrast to traditional planners, also those based on AND/OR graphs, we grow the AND/OR graph at run-time by progressively adding sub-graphs until grasping the target object becomes feasible, which yields a network of AND/OR graphs. The approach is extended to enable multi-robot task and motion planning, and (i) it allows us to perform task allocation while coordinating the activity of a given number of robots, and (ii) can handle multi-robot tasks involving an a priori unknown number of sub-tasks. The approach is evaluated and validated both in simulation and with a real dual-arm robot manipulator, that is, Baxter from Rethink Robotics. In particular, for the single-robot task and motion planning, we validated our approach in three different TAMP domains. Furthermore, we also use three different robots for simulation, namely, Baxter, Franka Emika Panda manipulators, and a PR2 robot. Experiments show that our approach can be readily scaled to scenarios with many objects and robots, and is capable of handling different classes of TAMP problems.
Rethinking Lanes and Points in Complex Scenarios for Monocular 3D Lane Detection
2025-03-08
Yifan Chang, Junjie Huang, Xiaofeng Wang, Yun Ye, Zhujin Liang, Yi Shan, Dalong Du, Xingang Wang
Monocular 3D lane detection is a fundamental task in autonomous driving. Although sparse-point methods lower computational load and maintain high accuracy in complex lane geometries, current methods fail to fully leverage the geometric structure of lanes in both lane geometry representations and model design. In lane geometry representations, we present a theoretical analysis alongside experimental validation to verify that current sparse lane representation methods contain inherent flaws, resulting in potential errors of up to 20 m, which raise significant safety concerns for driving. To address this issue, we propose a novel patching strategy to completely represent the full lane structure. To enable existing models to match this strategy, we introduce the EndPoint head (EP-head), which adds a patching distance to endpoints. The EP-head enables the model to predict more complete lane representations even with fewer preset points, effectively addressing existing limitations and paving the way for models that are faster and require fewer parameters in the future. In model design, to enhance the model's perception of lane structures, we propose the PointLane attention (PL-attention), which incorporates prior geometric knowledge into the attention mechanism. Extensive experiments demonstrate the effectiveness of the proposed methods on various state-of-the-art models. For instance, in terms of the overall F1-score, our methods improve Persformer by 4.4 points, Anchor3DLane by 3.2 points, and LATR by 2.8 points. The code will be available soon.
Depth ambiguity is a fundamental challenge in spatial scene understanding, especially in transparent scenes where single-depth estimates fail to capture full 3D structure. Existing models, limited to deterministic predictions, overlook real-world multi-layer depth. To address this, we introduce a paradigm shift from single-prediction to multi-hypothesis spatial foundation models. We first present \texttt{MD-3k}, a benchmark exposing depth biases in expert and foundational models through multi-layer spatial relationship labels and new metrics. To resolve depth ambiguity, we propose Laplacian Visual Prompting (LVP), a training-free spectral prompting technique that extracts hidden depth from pre-trained models via Laplacian-transformed RGB inputs. By integrating LVP-inferred depth with standard RGB-based estimates, our approach elicits multi-layer depth without model retraining. Extensive experiments validate the effectiveness of LVP in zero-shot multi-layer depth estimation, unlocking more robust and comprehensive geometry-conditioned visual generation, 3D-grounded spatial reasoning, and temporally consistent video-level depth inference. Our benchmark and code will be available at https://github.com/Xiaohao-Xu/Ambiguity-in-Space.
Rethinking Deep Clustering Paradigms: Self-Supervision Is All You Need
2025-03-05
Amal Shaheena, Nairouz Mrabahb, Riadh Ksantinia, Abdulla Alqaddoumia
The recent advances in deep clustering have been made possible by significant progress in self-supervised and pseudo-supervised learning. However, the trade-off between self-supervision and pseudo-supervision can give rise to three primary issues. The joint training causes Feature Randomness and Feature Drift, whereas the independent training causes Feature Randomness and Feature Twist. In essence, using pseudo-labels generates random and unreliable features. The combination of pseudo-supervision and self-supervision drifts the reliable clustering-oriented features. Moreover, moving from self-supervision to pseudo-supervision can twist the curved latent manifolds. This paper addresses the limitations of existing deep clustering paradigms concerning Feature Randomness, Feature Drift, and Feature Twist. We propose a new paradigm with a new strategy that replaces pseudo-supervision with a second round of self-supervision training. The new strategy makes the transition between instance-level self-supervision and neighborhood-level self-supervision smoother and less abrupt. Moreover, it prevents the drifting effect that is caused by the strong competition between instance-level self-supervision and clustering-level pseudo-supervision. Moreover, the absence of the pseudo-supervision prevents the risk of generating random features. With this novel approach, our paper introduces a Rethinking of the Deep Clustering Paradigms, denoted by R-DC. Our model is specifically designed to address three primary challenges encountered in Deep Clustering: Feature Randomness, Feature Drift, and Feature Twist. Experimental results conducted on six datasets have shown that the two-level self-supervision training yields substantial improvements.
Existing video tokenizers typically use the traditional Variational Autoencoder (VAE) architecture for video compression and reconstruction. However, to achieve good performance, its training process often relies on complex multi-stage training tricks that go beyond basic reconstruction loss and KL regularization. Among these tricks, the most challenging is the precise tuning of adversarial training with additional Generative Adversarial Networks (GANs) in the final stage, which can hinder stable convergence. In contrast to GANs, diffusion models offer more stable training processes and can generate higher-quality results. Inspired by these advantages, we propose CDT, a novel Conditioned Diffusion-based video Tokenizer, that replaces the GAN-based decoder with a conditional causal diffusion model. The encoder compresses spatio-temporal information into compact latents, while the decoder reconstructs videos through a reverse diffusion process conditioned on these latents. During inference, we incorporate a feature cache mechanism to generate videos of arbitrary length while maintaining temporal continuity and adopt sampling acceleration technique to enhance efficiency. Trained using only a basic MSE diffusion loss for reconstruction, along with KL term and LPIPS perceptual loss from scratch, extensive experiments demonstrate that CDT achieves state-of-the-art performance in video reconstruction tasks with just a single-step sampling. Even a scaled-down version of CDT (3$\times$ inference speedup) still performs comparably with top baselines. Moreover, the latent video generation model trained with CDT also exhibits superior performance. The source code and pretrained weights are available at https://github.com/ali-vilab/CDT.
Rethinking Synthetic Data definitions: A privacy driven approach
2025-03-05
Vibeke Binz Vallevik, Serena Elizabeth Marshall, Aleksandar Babic, Jan Franz Nygaard
Synthetic data is gaining traction as a cost-effective solution for the increasing data demands of AI development and can be generated either from existing knowledge or derived data captured from real-world events. The source of the synthetic data generation and the technique used significantly impacts its residual privacy risk and therefore its opportunity for sharing. Traditional classification of synthetic data types no longer fit the newer generation techniques and there is a need to better align the classification with practical needs. We suggest a new way of grouping synthetic data types that better supports privacy evaluations to aid regulatory policymaking. Our novel classification provides flexibility to new advancements like deep generative methods and offers a more practical framework for future applications.
Knowledge Augmentation in Federation: Rethinking What Collaborative Learning Can Bring Back to Decentralized Data
2025-03-05
Wentai Wu, Ligang He, Saiqin Long, Ahmed M. Abdelmoniem, Yingliang Wu, Rui Mao
Data, as an observable form of knowledge, has become one of the most important factors of production for the development of Artificial Intelligence (AI). Meanwhile, increasing legislation and regulations on private and proprietary information results in scattered data sources also known as the "data islands". Although some collaborative learning paradigms such as Federated Learning (FL) can enable privacy-preserving training over decentralized data, they have inherent deficiencies in fairness, costs and reproducibility because of being learning-centric, which greatly limits the way how participants cooperate with each other. In light of this, we present a knowledge-centric paradigm termed Knowledge Augmentation in Federation (KAF), with focus on how to enhance local knowledge through collaborative effort. We provide the suggested system architecture, formulate the prototypical optimization objective, and review emerging studies that employ methodologies suitable for KAF. On our roadmap, with a three-way categorization we describe the methods for knowledge expansion, knowledge filtering, and label and feature space correction in the federation. Further, we highlight several challenges and open questions that deserve more attention from the community. With our investigation, we intend to offer new insights for what collaborative learning can bring back to decentralized data.
Rethinking Few-Shot Medical Image Segmentation by SAM2: A Training-Free Framework with Augmentative Prompting and Dynamic Matching
2025-03-05
Haiyue Zu, Jun Ge, Heting Xiao, Jile Xie, Zhangzhe Zhou, Yifan Meng, Jiayi Ni, Junjie Niu, Linlin Zhang, Li Ni, Huilin Yang
The reliance on large labeled datasets presents a significant challenge in medical image segmentation. Few-shot learning offers a potential solution, but existing methods often still require substantial training data. This paper proposes a novel approach that leverages the Segment Anything Model 2 (SAM2), a vision foundation model with strong video segmentation capabilities. We conceptualize 3D medical image volumes as video sequences, departing from the traditional slice-by-slice paradigm. Our core innovation is a support-query matching strategy: we perform extensive data augmentation on a single labeled support image and, for each frame in the query volume, algorithmically select the most analogous augmented support image. This selected image, along with its corresponding mask, is used as a mask prompt, driving SAM2's video segmentation. This approach entirely avoids model retraining or parameter updates. We demonstrate state-of-the-art performance on benchmark few-shot medical image segmentation datasets, achieving significant improvements in accuracy and annotation efficiency. This plug-and-play method offers a powerful and generalizable solution for 3D medical image segmentation.
Rethinking Video Super-Resolution: Towards Diffusion-Based Methods without Motion Alignment
2025-03-05
Zhihao Zhan, Wang Pang, Xiang Zhu, Yechao Bai
In this work, we rethink the approach to video super-resolution by introducing a method based on the Diffusion Posterior Sampling framework, combined with an unconditional video diffusion transformer operating in latent space. The video generation model, a diffusion transformer, functions as a space-time model. We argue that a powerful model, which learns the physics of the real world, can easily handle various kinds of motion patterns as prior knowledge, thus eliminating the need for explicit estimation of optical flows or motion parameters for pixel alignment. Furthermore, a single instance of the proposed video diffusion transformer model can adapt to different sampling conditions without re-training. Empirical results on synthetic and real-world datasets illustrate the feasibility of diffusion-based, alignment-free video super-resolution.
Rethinking Reuse in Dependency Supply Chains: Initial Analysis of NPM packages at the End of the Chain
2025-03-04
Brittany Anne Reid, Raula Gaikovina Kula
The success of modern software development can be largely attributed to the concept of code reuse, such as the ability to reuse existing functionality via third-party package dependencies, evident within massive package networks like NPM, PyPI and Maven. For a long time, the dominant philosophy has been to `reuse as much as possible, without thought for what is being depended upon', resulting in the formation of large dependency supply chains that spread throughout entire software ecosystems. Such heavy reliance on third-party packages has eventually brought forward resilience and maintenance concerns, such as security attacks and outdated dependencies. In this vision paper, we investigate packages that challenge the typical concepts of reuse--that is, packages with no dependencies themselves that bear the responsibility of being at the end of the dependency supply chain. We find that these end-of-chain packages vary in characteristics and not just packages that can be easily replaced: an active, well-maintained package at the end of the chain; a "classical" package that has remained unchanged for 11 years; a trivial package nested deep in the dependency chain; a package that may appear trivial; and a package that bundled up and absorbed its dependencies. The vision of this paper is to advocate for a shift in software development practices toward minimizing reliance on third-party packages, particularly those at the end of dependency supply chains. We argue that these end-of-chain packages offer unique insights, as they play a key role in the ecosystem.
Rethinking Static Line Rating for Economic and Efficient Power Operation in South Korea
2025-03-04
Junseon Park, Junhyun Lee, Hyeongon Park
In South Korea, power grid is currently operated based on the static line rating (SLR) method, where the transmission line capacity is determined based on extreme weather conditions. However, with global warming, there is a concern that the temperatures during summer may exceed the SLR criteria, posing safety risks. On the other hand, the conservative estimates used for winter conditions limit the utilization of renewable energy. Proposals to install new lines face significant financial and environmental hurdles, complicating efforts to adapt to these changing conditions. Dynamic Line Rating (DLR) offers a real-time solution but requires extensive weather monitoring and complex integration. This paper proposes a novel method that improves on SLR by analyzing historical data to refine line rating criteria on a monthly, seasonal, and semi-annual basis. Through simulations, we show our approach significantly enhances cost effectiveness and reliability of the power system, achieving efficiencies close to DLR with existing infrastructure. This method offers a practical alternative to overcome the limitations of SLR and the implementation challenges of DLR.
CrystalFramer: Rethinking the Role of Frames for SE(3)-Invariant Crystal Structure Modeling
2025-03-04
Yusei Ito, Tatsunori Taniai, Ryo Igarashi, Yoshitaka Ushiku, Kanta Ono
Crystal structure modeling with graph neural networks is essential for various applications in materials informatics, and capturing SE(3)-invariant geometric features is a fundamental requirement for these networks. A straightforward approach is to model with orientation-standardized structures through structure-aligned coordinate systems, or"frames." However, unlike molecules, determining frames for crystal structures is challenging due to their infinite and highly symmetric nature. In particular, existing methods rely on a statically fixed frame for each structure, determined solely by its structural information, regardless of the task under consideration. Here, we rethink the role of frames, questioning whether such simplistic alignment with the structure is sufficient, and propose the concept of dynamic frames. While accommodating the infinite and symmetric nature of crystals, these frames provide each atom with a dynamic view of its local environment, focusing on actively interacting atoms. We demonstrate this concept by utilizing the attention mechanism in a recent transformer-based crystal encoder, resulting in a new architecture called CrystalFramer. Extensive experiments show that CrystalFramer outperforms conventional frames and existing crystal encoders in various crystal property prediction tasks.
Rethinking Data: Towards Better Performing Domain-Specific Small Language Models
2025-03-03
Boris Nazarov, Darya Frolova, Yackov Lubarsky, Alexei Gaissinski, Pavel Kisilev
Fine-tuning of Large Language Models (LLMs) for downstream tasks, performed on domain-specific data has shown significant promise. However, commercial use of such LLMs is limited by the high computational cost required for their deployment at scale. On the other hand, small Language Models (LMs) are much more cost effective but have subpar performance in a similar setup. This paper presents our approach to finetuning a small LM, that reaches high accuracy in multiple choice question answering task. We achieve this by improving data quality at each stage of the LM training pipeline. In particular, we start with data structuring resulting in extraction of compact, semantically meaningful text chunks used by a retriever. This allows more efficient knowledge digestion by the LM. Further, we improve the retrieved context by training a lightweight Chunk Re-Ranker (CRR) that generates more accurate relative relevance chunk scores. Finally, we improve the model generalization ability by merging the models fine-tuned with different parameters on different data subsets. We present detailed procedure descriptions, and corresponding experimental findings that show the improvements of each one of the proposed techniques.
Light decoder-based solvers have gained popularity for solving vehicle routing problems (VRPs) due to their efficiency and ease of integration with reinforcement learning algorithms. However, they often struggle with generalization to larger problem instances or different VRP variants. This paper revisits light decoder-based approaches, analyzing the implications of their reliance on static embeddings and the inherent challenges that arise. Specifically, we demonstrate that in the light decoder paradigm, the encoder is implicitly tasked with capturing information for all potential decision scenarios during solution construction within a single set of embeddings, resulting in high information density. Furthermore, our empirical analysis reveals that the overly simplistic decoder struggles to effectively utilize this dense information, particularly as task complexity increases, which limits generalization to out-of-distribution (OOD) settings. Building on these insights, we show that enhancing the decoder capacity, with a simple addition of identity mapping and a feed-forward layer, can considerably alleviate the generalization issue. Experimentally, our method significantly enhances the OOD generalization of light decoder-based approaches on large-scale instances and complex VRP variants, narrowing the gap with the heavy decoder paradigm. Our code is available at: https://github.com/ziweileonhuang/reld-nco.
Deep learning-based medical image segmentation typically requires large amount of labeled data for training, making it less applicable in clinical settings due to high annotation cost. Semi-supervised learning (SSL) has emerged as an appealing strategy due to its less dependence on acquiring abundant annotations from experts compared to fully supervised methods. Beyond existing model-centric advancements of SSL by designing novel regularization strategies, we anticipate a paradigmatic shift due to the emergence of promptable segmentation foundation models with universal segmentation capabilities using positional prompts represented by Segment Anything Model (SAM). In this paper, we present SemiSAM+, a foundation model-driven SSL framework to efficiently learn from limited labeled data for medical image segmentation. SemiSAM+ consists of one or multiple promptable foundation models as generalist models, and a trainable task-specific segmentation model as specialist model. For a given new segmentation task, the training is based on the specialist-generalist collaborative learning procedure, where the trainable specialist model delivers positional prompts to interact with the frozen generalist models to acquire pseudo-labels, and then the generalist model output provides the specialist model with informative and efficient supervision which benefits the automatic segmentation and prompt generation in turn. Extensive experiments on two public datasets and one in-house clinical dataset demonstrate that SemiSAM+ achieves significant performance improvement, especially under extremely limited annotation scenarios, and shows strong efficiency as a plug-and-play strategy that can be easily adapted to different specialist and generalist models.
Rethinking LLM Bias Probing Using Lessons from the Social Sciences
2025-02-28
Kirsten N. Morehouse, Siddharth Swaroop, Weiwei Pan
The proliferation of LLM bias probes introduces three significant challenges: (1) we lack principled criteria for choosing appropriate probes, (2) we lack a system for reconciling conflicting results across probes, and (3) we lack formal frameworks for reasoning about when (and why) probe results will generalize to real user behavior. We address these challenges by systematizing LLM social bias probing using actionable insights from social sciences. We then introduce EcoLevels - a framework that helps (a) determine appropriate bias probes, (b) reconcile conflicting findings across probes, and (c) generate predictions about bias generalization. Overall, we ground our analysis in social science research because many LLM probes are direct applications of human probes, and these fields have faced similar challenges when studying social bias in humans. Based on our work, we suggest how the next generation of LLM bias probing can (and should) benefit from decades of social science research.
Rethinking Multimodal Learning from the Perspective of Mitigating Classification Ability Disproportion
2025-02-27
QingYuan Jiang, Longfei Huang, Yang Yang
Although multimodal learning~(MML) has garnered remarkable progress, the existence of modality imbalance hinders multimodal learning from achieving its expected superiority over unimodal models in practice. To overcome this issue, mainstream multimodal learning methods have placed greater emphasis on balancing the learning process. However, these approaches do not explicitly enhance the classification ability of weaker modalities, leading to limited performance promotion. By designing a sustained boosting algorithm, we propose a novel multimodal learning approach to dynamically balance the classification ability of weak and strong modalities. Concretely, we first propose a sustained boosting algorithm in multimodal learning by simultaneously optimizing the classification and residual errors using a designed configurable classifier module. Then, we propose an adaptive classifier assignment strategy to dynamically facilitate the classification performance of weak modality. To this end, the classification ability of strong and weak modalities is expected to be balanced, thereby mitigating the imbalance issue. Empirical experiments on widely used datasets reveal the superiority of our method through comparison with various state-of-the-art~(SoTA) multimodal learning baselines.
Striving for Faster and Better: A One-Layer Architecture with Auto Re-parameterization for Low-Light Image Enhancement
2025-02-27
Nan An, Long Ma, Guangchao Han, Xin Fan, RIsheng Liu
Deep learning-based low-light image enhancers have made significant progress in recent years, with a trend towards achieving satisfactory visual quality while gradually reducing the number of parameters and improving computational efficiency. In this work, we aim to delving into the limits of image enhancers both from visual quality and computational efficiency, while striving for both better performance and faster processing. To be concrete, by rethinking the task demands, we build an explicit connection, i.e., visual quality and computational efficiency are corresponding to model learning and structure design, respectively. Around this connection, we enlarge parameter space by introducing the re-parameterization for ample model learning of a pre-defined minimalist network (e.g., just one layer), to avoid falling into a local solution. To strengthen the structural representation, we define a hierarchical search scheme for discovering a task-oriented re-parameterized structure, which also provides powerful support for efficiency. Ultimately, this achieves efficient low-light image enhancement using only a single convolutional layer, while maintaining excellent visual quality. Experimental results show our sensible superiority both in quality and efficiency against recently-proposed methods. Especially, our running time on various platforms (e.g., CPU, GPU, NPU, DSP) consistently moves beyond the existing fastest scheme. The source code will be released at https://github.com/vis-opt-group/AR-LLIE.
Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based Approach
2025-02-27
Chen-Chen Zong, Sheng-Jun Huang
Active learning (AL), which iteratively queries the most informative examples from a large pool of unlabeled candidates for model training, faces significant challenges in the presence of open-set classes. Existing methods either prioritize query examples likely to belong to known classes, indicating low epistemic uncertainty (EU), or focus on querying those with highly uncertain predictions, reflecting high aleatoric uncertainty (AU). However, they both yield suboptimal performance, as low EU corresponds to limited useful information, and closed-set AU metrics for unknown class examples are less meaningful. In this paper, we propose an Energy-based Active Open-set Annotation (EAOA) framework, which effectively integrates EU and AU to achieve superior performance. EAOA features a $(C+1)$-class detector and a target classifier, incorporating an energy-based EU measure and a margin-based energy loss designed for the detector, alongside an energy-based AU measure for the target classifier. Another crucial component is the target-driven adaptive sampling strategy. It first forms a smaller candidate set with low EU scores to ensure closed-set properties, making AU metrics meaningful. Subsequently, examples with high AU scores are queried to form the final query set, with the candidate set size adjusted adaptively. Extensive experiments show that EAOA achieves state-of-the-art performance while maintaining high query precision and low training overhead. The code is available at https://github.com/chenchenzong/EAOA.
Rethinking LLM Unlearning Objectives: A Gradient Perspective and Go Beyond
2025-02-26
Qizhou Wang, Jin Peng Zhou, Zhanke Zhou, Saebyeol Shin, Bo Han, Kilian Q. Weinberger
Large language models (LLMs) should undergo rigorous audits to identify potential risks, such as copyright and privacy infringements. Once these risks emerge, timely updates are crucial to remove undesirable responses, ensuring legal and safe model usage. It has spurred recent research into LLM unlearning, focusing on erasing targeted undesirable knowledge without compromising the integrity of other, non-targeted responses. Existing studies have introduced various unlearning objectives to pursue LLM unlearning without necessitating complete retraining. However, each of these objectives has unique properties, and no unified framework is currently available to comprehend them thoroughly. To fill the gap, we propose a toolkit of the gradient effect (G-effect), quantifying the impacts of unlearning objectives on model performance from a gradient perspective. A notable advantage is its broad ability to detail the unlearning impacts from various aspects across instances, updating steps, and LLM layers. Accordingly, the G-effect offers new insights into identifying drawbacks of existing unlearning objectives, further motivating us to explore a series of new solutions for their mitigation and improvements. Finally, we outline promising directions that merit further studies, aiming at contributing to the community to advance this important field.
Dictionary-based Framework for Interpretable and Consistent Object Parsing
2025-02-26
Tiezheng Zhang, Qihang Yu, Alan Yuille, Ju He
In this work, we present CoCal, an interpretable and consistent object parsing framework based on dictionary-based mask transformer. Designed around Contrastive Components and Logical Constraints, CoCal rethinks existing cluster-based mask transformer architectures used in segmentation; Specifically, CoCal utilizes a set of dictionary components, with each component being explicitly linked to a specific semantic class. To advance this concept, CoCal introduces a hierarchical formulation of dictionary components that aligns with the semantic hierarchy. This is achieved through the integration of both within-level contrastive components and cross-level logical constraints. Concretely, CoCal employs a component-wise contrastive algorithm at each semantic level, enabling the contrasting of dictionary components within the same class against those from different classes. Furthermore, CoCal addresses logical concerns by ensuring that the dictionary component representing a particular part is closer to its corresponding object component than to those of other objects through a cross-level contrastive learning objective. To further enhance our logical relation modeling, we implement a post-processing function inspired by the principle that a pixel assigned to a part should also be assigned to its corresponding object. With these innovations, CoCal establishes a new state-of-the-art performance on both PartImageNet and Pascal-Part-108, outperforming previous methods by a significant margin of 2.08% and 0.70% in part mIoU, respectively. Moreover, CoCal exhibits notable enhancements in object-level metrics across these benchmarks, highlighting its capacity to not only refine parsing at a finer level but also elevate the overall quality of object segmentation.
From ChatGPT to DeepSeek: Can LLMs Simulate Humanity?
2025-02-25
Qian Wang, Zhenheng Tang, Bingsheng He
Simulation powered by Large Language Models (LLMs) has become a promising method for exploring complex human social behaviors. However, the application of LLMs in simulations presents significant challenges, particularly regarding their capacity to accurately replicate the complexities of human behaviors and societal dynamics, as evidenced by recent studies highlighting discrepancies between simulated and real-world interactions. We rethink LLM-based simulations by emphasizing both their limitations and the necessities for advancing LLM simulations. By critically examining these challenges, we aim to offer actionable insights and strategies for enhancing the applicability of LLM simulations in human society in the future.
Policy-as-Prompt: Rethinking Content Moderation in the Age of Large Language Models
2025-02-25
Konstantina Palla, José Luis Redondo García, Claudia Hauff, Francesco Fabbri, Henrik Lindström, Daniel R. Taber, Andreas Damianou, Mounia Lalmas
Content moderation plays a critical role in shaping safe and inclusive online environments, balancing platform standards, user expectations, and regulatory frameworks. Traditionally, this process involves operationalising policies into guidelines, which are then used by downstream human moderators for enforcement, or to further annotate datasets for training machine learning moderation models. However, recent advancements in large language models (LLMs) are transforming this landscape. These models can now interpret policies directly as textual inputs, eliminating the need for extensive data curation. This approach offers unprecedented flexibility, as moderation can be dynamically adjusted through natural language interactions. This paradigm shift raises important questions about how policies are operationalised and the implications for content moderation practices. In this paper, we formalise the emerging policy-as-prompt framework and identify five key challenges across four domains: Technical Implementation (1. translating policy to prompts, 2. sensitivity to prompt structure and formatting), Sociotechnical (3. the risk of technological determinism in policy formation), Organisational (4. evolving roles between policy and machine learning teams), and Governance (5. model governance and accountability). Through analysing these challenges across technical, sociotechnical, organisational, and governance dimensions, we discuss potential mitigation approaches. This research provides actionable insights for practitioners and lays the groundwork for future exploration of scalable and adaptive content moderation systems in digital ecosystems.
Visual transformers have achieved remarkable performance in image classification tasks, but this performance gain has come at the cost of interpretability. One of the main obstacles to the interpretation of transformers is the self-attention mechanism, which mixes visual information across the whole image in a complex way. In this paper, we propose Hindered Transformer (HiT), a novel interpretable by design architecture inspired by visual transformers. Our proposed architecture rethinks the design of transformers to better disentangle patch influences at the classification stage. Ultimately, HiT can be interpreted as a linear combination of patch-level information. We show that the advantages of our approach in terms of explicability come with a reasonable trade-off in performance, making it an attractive alternative for applications where interpretability is paramount.
The Lottery LLM Hypothesis, Rethinking What Abilities Should LLM Compression Preserve?
2025-02-24
Zhenheng Tang, Xiang Liu, Qian Wang, Peijie Dong, Bingsheng He, Xiaowen Chu, Bo Li
Motivated by reducing the computational and storage costs of LLMs, model compression and KV cache compression have attracted much attention from researchers. However, current methods predominantly emphasize maintaining the performance of compressed LLMs, as measured by perplexity or simple accuracy on tasks of common sense knowledge QA and basic arithmetic reasoning. In this blog, we present a brief review of recent advancements in LLMs related to retrieval-augmented generation, multi-step reasoning, external tools, and computational expressivity, all of which substantially enhance LLM performance. Then, we propose a lottery LLM hypothesis suggesting that for a given LLM and task, there exists a smaller lottery LLM capable of producing the same performance as the original LLM with the assistance of multi-step reasoning and external tools. Based on the review of current progress in LLMs, we discuss and summarize the essential capabilities that the lottery LLM and KV cache compression must possess, which are currently overlooked in existing methods.
VPNeXt -- Rethinking Dense Decoding for Plain Vision Transformer
2025-02-23
Xikai Tang, Ye Huang, Guangqiang Yin, Lixin Duan
We present VPNeXt, a new and simple model for the Plain Vision Transformer (ViT). Unlike the many related studies that share the same homogeneous paradigms, VPNeXt offers a fresh perspective on dense representation based on ViT. In more detail, the proposed VPNeXt addressed two concerns about the existing paradigm: (1) Is it necessary to use a complex Transformer Mask Decoder architecture to obtain good representations? (2) Does the Plain ViT really need to depend on the mock pyramid feature for upsampling? For (1), we investigated the potential underlying reasons that contributed to the effectiveness of the Transformer Decoder and introduced the Visual Context Replay (VCR) to achieve similar effects efficiently. For (2), we introduced the ViTUp module. This module fully utilizes the previously overlooked ViT real pyramid feature to achieve better upsampling results compared to the earlier mock pyramid feature. This represents the first instance of such functionality in the field of semantic segmentation for Plain ViT. We performed ablation studies on related modules to verify their effectiveness gradually. We conducted relevant comparative experiments and visualizations to show that VPNeXt achieved state-of-the-art performance with a simple and effective design. Moreover, the proposed VPNeXt significantly exceeded the long-established mIoU wall/barrier of the VOC2012 dataset, setting a new state-of-the-art by a large margin, which also stands as the largest improvement since 2015.
Annotation-guided AoS-to-SoA conversions and GPU offloading with data views in C++
2025-02-23
Pawel K. Radtke, Tobias Weinzierl
The C++ programming language provides classes and structs as fundamental modeling entities. Consequently, C++ code tends to favour array-of-structs (AoS) for encoding data sequences, even though structure-of-arrays (SoA) yields better performance for some calculations. We propose a C++ language extension based on attributes that allows developers to guide the compiler in selecting memory arrangements, i.e.~to select the optimal choice between AoS and SoA dynamically depending on both the execution context and algorithm step. The compiler can then automatically convert data into the preferred format prior to the calculations and convert results back afterward. The compiler handles all the complexity of determining which data to convert and how to manage data transformations. Our implementation realises the compiler-extension for the new annotations in Clang and demonstrates their effectiveness through a smoothed particle hydrodynamics (SPH) code, which we evaluate on an Intel CPU, an ARM CPU, and a Grace-Hopper GPU. While the separation of concerns between data structure and operators is elegant and provides performance improvements, the new annotations do not eliminate the need for performance engineering. Instead, they challenge conventional performance wisdom and necessitate rethinking approaches how to write efficient implementations.
Offload Rethinking by Cloud Assistance for Efficient Environmental Sound Recognition on LPWANs
2025-02-21
Le Zhang, Quanling Zhao, Run Wang, Shirley Bian, Onat Gungor, Flavio Ponzina, Tajana Rosing
Learning-based environmental sound recognition has emerged as a crucial method for ultra-low-power environmental monitoring in biological research and city-scale sensing systems. These systems usually operate under limited resources and are often powered by harvested energy in remote areas. Recent efforts in on-device sound recognition suffer from low accuracy due to resource constraints, whereas cloud offloading strategies are hindered by high communication costs. In this work, we introduce ORCA, a novel resource-efficient cloud-assisted environmental sound recognition system on batteryless devices operating over the Low-Power Wide-Area Networks (LPWANs), targeting wide-area audio sensing applications. We propose a cloud assistance strategy that remedies the low accuracy of on-device inference while minimizing the communication costs for cloud offloading. By leveraging a self-attention-based cloud sub-spectral feature selection method to facilitate efficient on-device inference, ORCA resolves three key challenges for resource-constrained cloud offloading over LPWANs: 1) high communication costs and low data rates, 2) dynamic wireless channel conditions, and 3) unreliable offloading. We implement ORCA on an energy-harvesting batteryless microcontroller and evaluate it in a real world urban sound testbed. Our results show that ORCA outperforms state-of-the-art methods by up to $80 \times$ in energy savings and $220 \times$ in latency reduction while maintaining comparable accuracy.
Self-awareness, i.e., the ability to assess and correct one's own generation, is a fundamental aspect of human intelligence, making its replication in large language models (LLMs) an important yet challenging task. Previous works tackle this by employing extensive reinforcement learning or rather relying on large external verifiers. In this work, we propose Refine via Intrinsic Self-Verification (ReVISE), an efficient and effective framework that enables LLMs to self-correct their outputs through self-verification. The core idea of ReVISE is to enable LLMs to verify their reasoning processes and continually rethink reasoning trajectories based on its verification. We introduce a structured curriculum based upon online preference learning to implement this efficiently. Specifically, as ReVISE involves two challenging tasks (i.e., self-verification and reasoning correction), we tackle each task sequentially using curriculum learning, collecting both failed and successful reasoning paths to construct preference pairs for efficient training. During inference, our approach enjoys natural test-time scaling by integrating self-verification and correction capabilities, further enhanced by our proposed confidence-aware decoding mechanism. Our experiments on various reasoning tasks demonstrate that ReVISE achieves efficient self-correction and significantly improves reasoning performance.
Innovative Financing Solutions: A Transformative Driver for Financial Performance of Businesses in Morocco
2025-02-20
Nohayla Badrane, Zineb Bamousse
In a rapidly evolving landscape marked by continuous change and complex challenges, effective cash management stands as a cornerstone for ensuring business sustainability and driving performance. To address these pressing demands, cash managersare increasingly turning to innovative financing solutions such as venture capital, green finance, crowdfunding, advanced services from Pan-African banks, and blockchain technology. These cutting-edge tools are pivotal in bolstering resilience against market volatility, ecological transitions, and the accelerating pace of technological change. The present article aims to examine how such innovative financial approaches can serve as strategic drivers, enabling businesses to transform challenges into opportunities. The analysis underscores that rethinking cash management through innovation is a critical pathway toboost the performance of Moroccan companies. Therefore, embracing these forward-thinking strategies unlocks new avenues for development empowering them to adapt with agility amidst the uncertainties of a shifting environment.
Structured representations, exemplified by Abstract Meaning Representation (AMR), have long been pivotal in computational linguistics. However, their role remains ambiguous in the Large Language Models (LLMs) era. Initial attempts to integrate structured representation into LLMs via a zero-shot setting yielded inferior performance. We hypothesize that such a decline stems from the structure information being passed into LLMs in a code format unfamiliar to LLMs' training corpora. Consequently, we propose SR-LLM, an innovative framework with two settings to explore a superior way of integrating structured representation with LLMs from training-free and training-dependent perspectives. The former integrates structural information through natural language descriptions in LLM prompts, whereas its counterpart augments the model's inference capability through fine-tuning on linguistically described structured representations. Performance improvements were observed in widely downstream datasets, with particularly notable gains of 3.17% and 12.38% in PAWS. To the best of our knowledge, this work represents the pioneering demonstration that leveraging structural representations can substantially enhance LLMs' inference capability. We hope that our work sheds light and encourages future research to enhance the reasoning and interoperability of LLMs by structure data.
Rethinking Spiking Neural Networks from an Ensemble Learning Perspective
2025-02-20
Yongqi Ding, Lin Zuo, Mengmeng Jing, Pei He, Hanpu Deng
Spiking neural networks (SNNs) exhibit superior energy efficiency but suffer from limited performance. In this paper, we consider SNNs as ensembles of temporal subnetworks that share architectures and weights, and highlight a crucial issue that affects their performance: excessive differences in initial states (neuronal membrane potentials) across timesteps lead to unstable subnetwork outputs, resulting in degraded performance. To mitigate this, we promote the consistency of the initial membrane potential distribution and output through membrane potential smoothing and temporally adjacent subnetwork guidance, respectively, to improve overall stability and performance. Moreover, membrane potential smoothing facilitates forward propagation of information and backward propagation of gradients, mitigating the notorious temporal gradient vanishing problem. Our method requires only minimal modification of the spiking neurons without adapting the network structure, making our method generalizable and showing consistent performance gains in 1D speech, 2D object, and 3D point cloud recognition tasks. In particular, on the challenging CIFAR10-DVS dataset, we achieved 83.20\% accuracy with only four timesteps. This provides valuable insights into unleashing the potential of SNNs.
The lifecycle of large language models (LLMs) is far more complex than that of traditional machine learning models, involving multiple training stages, diverse data sources, and varied inference methods. While prior research on data poisoning attacks has primarily focused on the safety vulnerabilities of LLMs, these attacks face significant challenges in practice. Secure data collection, rigorous data cleaning, and the multistage nature of LLM training make it difficult to inject poisoned data or reliably influence LLM behavior as intended. Given these challenges, this position paper proposes rethinking the role of data poisoning and argue that multi-faceted studies on data poisoning can advance LLM development. From a threat perspective, practical strategies for data poisoning attacks can help evaluate and address real safety risks to LLMs. From a trustworthiness perspective, data poisoning can be leveraged to build more robust LLMs by uncovering and mitigating hidden biases, harmful outputs, and hallucinations. Moreover, from a mechanism perspective, data poisoning can provide valuable insights into LLMs, particularly the interplay between data and model behavior, driving a deeper understanding of their underlying mechanisms.
Benchmarking Android Malware Detection: Rethinking the Role of Traditional and Deep Learning Models
2025-02-20
Guojun Liu, Doina Caragea, Xinming Ou, Sankardas Roy
Android malware detection has been extensively studied using both traditional machine learning (ML) and deep learning (DL) approaches. While many state-of-the-art detection models, particularly those based on DL, claim superior performance, they often rely on limited comparisons, lacking comprehensive benchmarking against traditional ML models across diverse datasets. This raises concerns about the robustness of DL-based approaches' performance and the potential oversight of simpler, more efficient ML models. In this paper, we conduct a systematic evaluation of Android malware detection models across four datasets: three recently published, publicly available datasets and a large-scale dataset we systematically collected. We implement a range of traditional ML models, including Random Forests (RF) and CatBoost, alongside advanced DL models such as Capsule Graph Neural Networks (CapsGNN), BERT-based models, and ExcelFormer based models. Our results reveal that while advanced DL models can achieve strong performance, they are often compared against an insufficient number of traditional ML baselines. In many cases, simpler and more computationally efficient ML models achieve comparable or even superior performance. These findings highlight the need for rigorous benchmarking in Android malware detection research. We encourage future studies to conduct more comprehensive benchmarking comparisons between traditional and advanced models to ensure a more accurate assessment of detection capabilities. To facilitate further research, we provide access to our dataset, including app IDs, hash values, and labels.
Inference of Abstraction for Grounded Predicate Logic
2025-02-19
Hiroyuki Kido
An important open question in AI is what simple and natural principle enables a machine to reason logically for meaningful abstraction with grounded symbols. This paper explores a conceptually new approach to combining probabilistic reasoning and predicative symbolic reasoning over data. We return to the era of reasoning with a full joint distribution before the advent of Bayesian networks. We then discuss that a full joint distribution over models of exponential size in propositional logic and of infinite size in predicate logic should be simply derived from a full joint distribution over data of linear size. We show that the same process is not only enough to generalise the logical consequence relation of predicate logic but also to provide a new perspective to rethink well-known limitations such as the undecidability of predicate logic, the symbol grounding problem and the principle of explosion. The reproducibility of this theoretical work is fully demonstrated by the included proofs.
Understanding human preferences is crucial for improving foundation models and building personalized AI systems. However, preferences are inherently diverse and complex, making it difficult for traditional reward models to capture their full range. While fine-grained preference data can help, collecting it is expensive and hard to scale. In this paper, we introduce Decomposed Reward Models (DRMs), a novel approach that extracts diverse human preferences from binary comparisons without requiring fine-grained annotations. Our key insight is to represent human preferences as vectors and analyze them using Principal Component Analysis (PCA). By constructing a dataset of embedding differences between preferred and rejected responses, DRMs identify orthogonal basis vectors that capture distinct aspects of preference. These decomposed rewards can be flexibly combined to align with different user needs, offering an interpretable and scalable alternative to traditional reward models. We demonstrate that DRMs effectively extract meaningful preference dimensions (e.g., helpfulness, safety, humor) and adapt to new users without additional training. Our results highlight DRMs as a powerful framework for personalized and interpretable LLM alignment.
Corrupted but Not Broken: Rethinking the Impact of Corrupted Data in Visual Instruction Tuning
2025-02-18
Yunhao Gou, Hansi Yang, Zhili Liu, Kai Chen, Yihan Zeng, Lanqing Hong, Zhenguo Li, Qun Liu, James T. Kwok, Yu Zhang
Visual Instruction Tuning (VIT) enhances Multimodal Large Language Models (MLLMs) but it is hindered by corrupted datasets containing hallucinated content, incorrect responses, and poor OCR quality. While prior works focus on dataset refinement through high-quality data collection or rule-based filtering, they are costly or limited to specific types of corruption. To deeply understand how corrupted data affects MLLMs, in this paper, we systematically investigate this issue and find that while corrupted data degrades the performance of MLLMs, its effects are largely superficial in that the performance of MLLMs can be largely restored by either disabling a small subset of parameters or post-training with a small amount of clean data. Additionally, corrupted MLLMs exhibit improved ability to distinguish clean samples from corrupted ones, enabling the dataset cleaning without external help. Based on those insights, we propose a corruption-robust training paradigm combining self-validation and post-training, which significantly outperforms existing corruption mitigation strategies.
The rapid adoption of Generative AI (GenAI) is significantly reshaping human cognition, influencing how we engage with information, think, reason, and learn. This paper synthesizes existing literature on GenAI's effects on different aspects of human cognition. Drawing on Krathwohl's revised Bloom's Taxonomy and Dewey's conceptualization of reflective thought, we examine the mechanisms through which GenAI is affecting the development of different cognitive abilities. We focus on novices, such as students, who may lack both domain knowledge and an understanding of effective human-AI interaction. Accordingly, we provide implications for rethinking and designing educational experiences that foster critical thinking and deeper cognitive engagement.
RingFormer: Rethinking Recurrent Transformer with Adaptive Level Signals
2025-02-18
Jaemu Heo, Eldor Fozilov, Hyunmin Song, Taehwan Kim
Transformers have achieved great success in effectively processing sequential data such as text. Their architecture consisting of several attention and feedforward blocks can model relations between elements of a sequence in parallel manner, which makes them very efficient to train and effective in sequence modeling. Even though they have shown strong performance in processing sequential data, the size of their parameters is considerably larger when compared to other architectures such as RNN and CNN based models. Therefore, several approaches have explored parameter sharing and recurrence in Transformer models to address their computational demands. However, such methods struggle to maintain high performance compared to the original transformer model. To address this challenge, we propose our novel approach, RingFormer, which employs one Transformer layer that processes input repeatedly in a circular, ring-like manner, while utilizing low-rank matrices to generate input-dependent level signals. This allows us to reduce the model parameters substantially while maintaining high performance in a variety of tasks such as translation and image classification, as validated in the experiments.
Single-Molecule Water Motion on h-BN and Graphene: A Paradigm Shift in Understanding the Behaviour of Water on 2D Material Interfaces
2025-02-17
Philipp Seiler, Anthony Payne, Neubi F. Xavier Jr, Louie Slocombe, Marco Sacchi, Anton Tamtögl
Understanding water behaviour on 2D materials is crucial for sensing, microfluidics, and tribology. While water/graphene interactions are well studied, water on hexagonal boron nitride (h-BN) remains largely unexplored. Despite structural similarity to graphene, h-BN's slightly polar B-N bonds impart a large band gap, high thermal conductivity, and chemical stability, making it promising for electronics, lubricants, and coatings. Moreover, existing water studies often focus on multilayer water dynamics, overlooking single-molecular details. We bridge this gap by studying single-molecular water friction and diffusion on h-BN, comparing it with graphene using helium spin-echo experiments and ab initio calculations. Our findings show that water diffusion on h-BN/Ni follows a complex rotational-translational dynamic, unlike graphene. While conventional views treat water motion as discrete jumps between equivalent adsorption sites, we demonstrate that on h-BN, water molecules rotate freely around their centre of mass. Although the binding energies of water on h-BN and graphene are similar, the activation energy for water dynamics on h-BN is 2.5 times lower than on graphene, implying a much lower barrier for molecular mobility. The fundamentally different diffusion characteristics which classical models cannot capture, underscores the need to rethink how we model water on polar 2D materials. Moreover, our analysis reveals that the metal substrate strongly influences water friction, with h-BN/Ni showing a markedly lower friction than graphene/Ni, in stark contrast to the free-standing materials. These findings challenge assumptions about 2D material-water interactions, highlighting the crucial role of substrate effects in chemistry and material science and offer insights for designing next-generation microfluidic devices that require precise water mobility control.
Rethinking Benign Overfitting in Two-Layer Neural Networks
2025-02-17
Ruichen Xu, Kexin Chen
Recent theoretical studies (Kou et al., 2023; Cao et al., 2022) have revealed a sharp phase transition from benign to harmful overfitting when the noise-to-feature ratio exceeds a threshold-a situation common in long-tailed data distributions where atypical data is prevalent. However, harmful overfitting rarely happens in overparameterized neural networks. Further experimental results suggested that memorization is necessary for achieving near-optimal generalization error in long-tailed data distributions (Feldman & Zhang, 2020). We argue that this discrepancy between theoretical predictions and empirical observations arises because previous feature-noise data models overlook the heterogeneous nature of noise across different data classes. In this paper, we refine the feature-noise data model by incorporating class-dependent heterogeneous noise and re-examine the overfitting phenomenon in neural networks. Through a comprehensive analysis of the training dynamics, we establish test loss bounds for the refined model. Our findings reveal that neural networks can leverage "data noise", previously deemed harmful, to learn implicit features that improve the classification accuracy for long-tailed data. Experimental validation on both synthetic and real-world datasets supports our theoretical results.
Rethinking Audio-Visual Adversarial Vulnerability from Temporal and Modality Perspectives
2025-02-17
Zeliang Zhang, Susan Liang, Daiki Shimada, Chenliang Xu
While audio-visual learning equips models with a richer understanding of the real world by leveraging multiple sensory modalities, this integration also introduces new vulnerabilities to adversarial attacks. In this paper, we present a comprehensive study of the adversarial robustness of audio-visual models, considering both temporal and modality-specific vulnerabilities. We propose two powerful adversarial attacks: 1) a temporal invariance attack that exploits the inherent temporal redundancy across consecutive time segments and 2) a modality misalignment attack that introduces incongruence between the audio and visual modalities. These attacks are designed to thoroughly assess the robustness of audio-visual models against diverse threats. Furthermore, to defend against such attacks, we introduce a novel audio-visual adversarial training framework. This framework addresses key challenges in vanilla adversarial training by incorporating efficient adversarial perturbation crafting tailored to multi-modal data and an adversarial curriculum strategy. Extensive experiments in the Kinetics-Sounds dataset demonstrate that our proposed temporal and modality-based attacks in degrading model performance can achieve state-of-the-art performance, while our adversarial training defense largely improves the adversarial robustness as well as the adversarial training efficiency.
In the field of chemical engineering, traditional data-processing and prediction methods face significant challenges. Machine-learning and large-language models (LLMs) also have their respective limitations. This paper explores the application of the Chain-of-Thought (CoT) reasoning model in chemical engineering, starting from 30 experimental data points. By integrating traditional surrogate models like Gaussian processes and random forests with powerful LLMs such as DeepSeek-R1, a hierarchical architecture is proposed. Two CoT-building methods, Large Language Model-Chain of Thought (LLM-CoT) and Machine Learning-Large Language Model-Chain of Thought (ML-LLM-CoT), are studied. The LLM-CoT combines local models DeepSeek-r1:14b and Qwen2:7b with Ollama. The ML-LLM-CoT integrates a pre-trained Gaussian ML model with the LLM-based CoT framework. Our results show that during construction, ML-LLM-CoT is more efficient. It only has 2 points that require rethink and a total of 4 rethink times, while LLM-CoT has 5 points that need to be re-thought and 34 total rethink times. In predicting the solubility of 20 molecules with dissimilar structures, the number of molecules with a prediction deviation higher than 100\% for the Gaussian model, LLM-CoT, and ML-LLM-CoT is 7, 6, and 4 respectively. These results indicate that ML-LLM-CoT performs better in controlling the number of high-deviation molecules, optimizing the average deviation, and achieving a higher success rate in solubility judgment, providing a more reliable method for chemical engineering and molecular property prediction. This study breaks through the limitations of traditional methods and offers new solutions for rapid property prediction and process optimization in chemical engineering.
Large Vision-Language Models (LVLMs) have shown impressive performance in various tasks. However, LVLMs suffer from hallucination, which hinders their adoption in the real world. Existing studies emphasized that the strong language priors of LVLMs can overpower visual information, causing hallucinations. However, the positive role of language priors is the key to a powerful LVLM. If the language priors are too weak, LVLMs will struggle to leverage rich parameter knowledge and instruction understanding abilities to complete tasks in challenging visual scenarios where visual information alone is insufficient. Therefore, we propose a benchmark called LanP to rethink the impact of Language Priors in LVLMs. It is designed to investigate how strong language priors are in current LVLMs. LanP consists of 170 images and 340 corresponding well-designed questions. Extensive experiments on 25 popular LVLMs reveal that many LVLMs' language priors are not strong enough to effectively aid question answering when objects are partially hidden. Many models, including GPT-4 Turbo, exhibit an accuracy below 0.5 in such a scenario.
Assessing Pedagogical Readiness for Digital Innovation: A Mixed-Methods Study
2025-02-17
Ning Yulin, Solomon Danquah Danso
Digital innovation in education has revolutionized teaching and learning processes, demanding a rethink of pedagogical competence among educators. This study evaluates the preparation of instructors to use digital technologies into their educational practices. The study used a mixed-methods approach, integrating both qualitative interviews and quantitative surveys to evaluate teachers' institutional support systems, beliefs, and technical proficiency. The results show that even while a large number of educators acknowledge the benefits of digital tools, problems including poor professional development and change aversion still exist. In order to improve digital pedagogical preparation, the study emphasizes the necessity of focused training initiatives and encouraging institutional regulations. There is discussion on the implications for educational institutions and policymakers.
The Bathroom Model: A Realistic Approach to Hash Table Algorithm Optimization
2025-02-16
Qiantong Wang
Hash table search strategies have remained a pivotal area of inquiry in computer science over the past several decades. A prevailing viewpoint asserts that random probing stands as the optimal method for open-addressing hash tables. Challenging this long-standing belief, a recent contribution introduces an elastic probing technique based on fixed interval thresholds. Although this method presents improvements over traditional strategies, its dependence on static thresholds limits its theoretical optimality. In this paper, we propose a new conceptual model for optimizing hash table probing, inspired by human behavior in selecting restroom stalls - dubbed the "Bathroom Model." Unlike fixed or purely random approaches, our technique dynamically updates probing decisions using previously observed occupancy patterns, resulting in a more intelligent and adaptive search process. We rigorously formalize this model, analyze its theoretical properties, and benchmark its performance against leading hash table algorithms. Our findings indicate that adaptive probing mechanisms can significantly enhance search efficiency while keeping computational demands minimal. This work not only sheds new light on an extensively studied problem but also points to broader algorithmic opportunities in rethinking classical data structures.
Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective
2025-02-14
Zeyu Jia, Alexander Rakhlin, Tengyang Xie
As large language models have evolved, it has become crucial to distinguish between process supervision and outcome supervision -- two key reinforcement learning approaches to complex reasoning tasks. While process supervision offers intuitive advantages for long-term credit assignment, the precise relationship between these paradigms has remained an open question. Conventional wisdom suggests that outcome supervision is fundamentally more challenging due to the trajectory-level coverage problem, leading to significant investment in collecting fine-grained process supervision data. In this paper, we take steps towards resolving this debate. Our main theorem shows that, under standard data coverage assumptions, reinforcement learning through outcome supervision is no more statistically difficult than through process supervision, up to polynomial factors in horizon. At the core of this result lies the novel Change of Trajectory Measure Lemma -- a technical tool that bridges return-based trajectory measure and step-level distribution shift. Furthermore, for settings with access to a verifier or a rollout capability, we prove that any policy's advantage function can serve as an optimal process reward model, providing a direct connection between outcome and process supervision. These findings suggest that the empirically observed performance gap -- if any -- between outcome and process supervision likely stems from algorithmic limitations rather than inherent statistical difficulties, potentially transforming how we approach data collection and algorithm design for reinforcement learning.
One of the goals of automatic evaluation metrics in grammatical error correction (GEC) is to rank GEC systems such that it matches human preferences. However, current automatic evaluations are based on procedures that diverge from human evaluation. Specifically, human evaluation derives rankings by aggregating sentence-level relative evaluation results, e.g., pairwise comparisons, using a rating algorithm, whereas automatic evaluation averages sentence-level absolute scores to obtain corpus-level scores, which are then sorted to determine rankings. In this study, we propose an aggregation method for existing automatic evaluation metrics which aligns with human evaluation methods to bridge this gap. We conducted experiments using various metrics, including edit-based metrics, $n$-gram based metrics, and sentence-level metrics, and show that resolving the gap improves results for the most of metrics on the SEEDA benchmark. We also found that even BERT-based metrics sometimes outperform the metrics of GPT-4. We publish our unified implementation of the metrics and meta-evaluations.
Replication protocols are essential for distributed systems, ensuring consistency, reliability, and fault tolerance. Traditional Crash Fault Tolerant (CFT) protocols, which assume a fail-stop model, are inadequate for untrusted cloud environments where adversaries or software bugs can cause Byzantine behavior. Byzantine Fault Tolerant (BFT) protocols address these threats but face significant performance, resource overheads, and scalability challenges. This paper introduces Recipe, a novel approach to transforming CFT protocols to operate securely in Byzantine settings without altering their core logic. Recipe rethinks CFT protocols in the context of modern cloud hardware, including many-core servers, RDMA-capable networks, and Trusted Execution Environments (TEEs). The approach leverages these advancements to enhance the security and performance of replication protocols in untrusted cloud environments. Recipe implements two practical security mechanisms, i.e., transferable authentication and non-equivocation, using TEEs and high-performance networking stacks (e.g., RDMA, DPDK). These mechanisms ensure that any CFT protocol can be transformed into a BFT protocol, guaranteeing authenticity and non-equivocation. The Recipe protocol consists of five key components: transferable authentication, initialization, normal operation, view change, and recovery phases. The protocol's correctness is formally verified using Tamarin, a symbolic model checker. Recipe is implemented as a library and applied to transform four widely used CFT protocols-Raft, Chain Replication, ABD, and AllConcur-into Byzantine settings. The results demonstrate up to 24x higher throughput compared to PBFT and 5.9x better performance than state-of-the-art BFT protocols. Additionally, Recipe requires fewer replicas and offers confidentiality, a feature absent in traditional BFT protocols.
Rethinking Tokenized Graph Transformers for Node Classification
2025-02-12
Jinsong Chen, Chenyang Li, GaiChao Li, John E. Hopcroft, Kun He
Node tokenized graph Transformers (GTs) have shown promising performance in node classification. The generation of token sequences is the key module in existing tokenized GTs which transforms the input graph into token sequences, facilitating the node representation learning via Transformer. In this paper, we observe that the generations of token sequences in existing GTs only focus on the first-order neighbors on the constructed similarity graphs, which leads to the limited usage of nodes to generate diverse token sequences, further restricting the potential of tokenized GTs for node classification. To this end, we propose a new method termed SwapGT. SwapGT first introduces a novel token swapping operation based on the characteristics of token sequences that fully leverages the semantic relevance of nodes to generate more informative token sequences. Then, SwapGT leverages a Transformer-based backbone to learn node representations from the generated token sequences. Moreover, SwapGT develops a center alignment loss to constrain the representation learning from multiple token sequences, further enhancing the model performance. Extensive empirical results on various datasets showcase the superiority of SwapGT for node classification.
Europe's AI Imperative -- A Pragmatic Blueprint for Global Tech Leadership
2025-02-12
Gjergji Kasneci, Urs Gasser, Thomas F. Hofmann, Gerhard Kramer, Gerhard Müller, Claudia Peus, Helmut Schönenberger, Enkelejda Kasneci
Europe is at a make-or-break moment in the global AI race, squeezed between the massive venture capital and tech giants in the US and China's scale-oriented, top-down drive. At this tipping point, where the convergence of AI with complementary and synergistic technologies, like quantum computing, biotech, VR/AR, 5G/6G, robotics, advanced materials, and high-performance computing, could upend geopolitical balances, Europe needs to rethink its AI-related strategy. On the heels of the AI Action Summit 2025 in Paris, we present a sharp, doable strategy that builds upon Europe's strengths and closes gaps.
Progress in AI has relied on human-generated data, from annotator marketplaces to the wider Internet. However, the widespread use of large language models now threatens the quality and integrity of human-generated data on these very platforms. We argue that this issue goes beyond the immediate challenge of filtering AI-generated content--it reveals deeper flaws in how data collection systems are designed. Existing systems often prioritize speed, scale, and efficiency at the cost of intrinsic human motivation, leading to declining engagement and data quality. We propose that rethinking data collection systems to align with contributors' intrinsic motivations--rather than relying solely on external incentives--can help sustain high-quality data sourcing at scale while maintaining contributor trust and long-term participation.
Rethinking Timing Residuals: Advancing PET Detectors with Explicit TOF Corrections
2025-02-11
Stephan Naunheim, Luis Lopes de Paiva, Vanessa Nadig, Yannick Kuhl, Stefan Gundacker, Florian Mueller, Volkmar Schulz
PET is a functional imaging method that visualizes metabolic processes. TOF information can be derived from coincident detector signals and incorporated into image reconstruction to enhance the SNR. PET detectors are typically assessed by their CTR, but timing performance is degraded by various factors. Research on timing calibration seeks to mitigate these degradations and restore accurate timing information. While many calibration methods use analytical approaches, machine learning techniques have recently gained attention due to their flexibility. We developed a residual physics-based calibration approach that combines prior domain knowledge with the power of machine learning models. This approach begins with an initial analytical calibration addressing first-order skews. The remaining deviations, regarded as residual effects, are used to train machine learning models to eliminate higher-order skews. The key advantage is that the experimenter guides the learning process through the definition of timing residuals. In earlier studies, we developed models that directly predicted the expected time difference, which offered corrections only implicitly (implicit correction models). In this study, we introduce a new definition for timing residuals, enabling us to train models that directly predict correction values (explicit correction models). The explicit correction approach significantly simplifies data acquisition, improves linearity, and enhances timing performance from $371 \pm 6$ ps to $281 \pm 5$ ps for coincidences from 430 keV to 590 keV. Additionally, the new definition reduces model size, making it suitable for high-throughput applications like PET scanners. Experiments were conducted using two detector stacks composed of $4 \times 4$ LYSO:Ce,Ca crystals ($3.8\times 3.8\times 20$ mm$^{3}$) coupled to $4 \times 4$ Broadcom NUV-MT SiPMs and digitized with the TOFPET2 ASIC.
Linear sequence modeling approaches, such as linear attention, provide advantages like linear-time training and constant-memory inference over sequence lengths. However, existing sequence parallelism (SP) methods are either not optimized for the right-product-first feature of linear attention or use a ring-style communication strategy, which results in lower computation parallelism, limits their scalability for longer sequences in distributed systems. In this paper, we introduce LASP-2, a new SP method to enhance both communication and computation parallelism when training linear attention transformer models with very-long input sequences. Compared to previous work LASP, LASP-2 rethinks the minimal communication requirement for SP on linear attention layers, reorganizes the whole communication-computation workflow of LASP. In this way, only one single AllGather collective communication is needed on intermediate memory states, whose sizes are independent of the sequence length, leading to significant improvements of both communication and computation parallelism, as well as their overlap. Additionally, we extend LASP-2 to LASP-2H by applying similar communication redesign to standard attention modules, offering an efficient SP solution for hybrid models that blend linear and standard attention layers. Our evaluation on a Linear-Llama3 model, a variant of Llama3 with linear attention replacing standard attention, demonstrates the effectiveness of LASP-2 and LASP-2H. Specifically, LASP-2 achieves training speed improvements of 15.2% over LASP and 36.6% over Ring Attention, with a sequence length of 2048K across 64 GPUs. The Code is released as a part of: https://github.com/OpenSparseLLMs/Linear-MoE.
2025-02-11
Feng Chen, Allan Raventos, Nan Cheng, Surya Ganguli, Shaul Druckmann
Recent progress in large language models (LLMs) highlights the power of scaling test-time compute to achieve strong performance on complex tasks, such as mathematical reasoning and code generation. This raises a critical question: how should model training be modified to optimize performance under a subsequent test-time compute strategy and budget? To explore this, we focus on pass@N, a simple test-time strategy that searches for a correct answer in $N$ independent samples. We show, surprisingly, that training with cross-entropy (CE) loss can be ${\it misaligned}$ with pass@N in that pass@N accuracy ${\it decreases}$ with longer training. We explain the origins of this misalignment in terms of model overconfidence induced by CE, and experimentally verify our prediction of overconfidence as an impediment to scaling test-time compute via pass@N. Furthermore we suggest a principled, modified training loss that is better aligned to pass@N by limiting model confidence and rescuing pass@N test performance. Our algorithm demonstrates improved mathematical reasoning on MATH and MiniF2F benchmarks under several scenarios: (1) providing answers to math questions; and (2) proving theorems by searching over proof trees of varying shapes. Overall our work underscores the importance of co-designing two traditionally separate phases of LLM development: training-time protocols and test-time search and reasoning strategies.
Test-Time Scaling (TTS) is an important method for improving the performance of Large Language Models (LLMs) by using additional computation during the inference phase. However, current studies do not systematically analyze how policy models, Process Reward Models (PRMs), and problem difficulty influence TTS. This lack of analysis limits the understanding and practical use of TTS methods. In this paper, we focus on two core questions: (1) What is the optimal approach to scale test-time computation across different policy models, PRMs, and problem difficulty levels? (2) To what extent can extended computation improve the performance of LLMs on complex tasks, and can smaller language models outperform larger ones through this approach? Through comprehensive experiments on MATH-500 and challenging AIME24 tasks, we have the following observations: (1) The compute-optimal TTS strategy is highly dependent on the choice of policy model, PRM, and problem difficulty. (2) With our compute-optimal TTS strategy, extremely small policy models can outperform larger models. For example, a 1B LLM can exceed a 405B LLM on MATH-500. Moreover, on both MATH-500 and AIME24, a 0.5B LLM outperforms GPT-4o, a 3B LLM surpasses a 405B LLM, and a 7B LLM beats o1 and DeepSeek-R1, while with higher inference efficiency. These findings show the significance of adapting TTS strategies to the specific characteristics of each task and model and indicate that TTS is a promising approach for enhancing the reasoning abilities of LLMs.
Rethinking Large-scale Dataset Compression: Shifting Focus From Labels to Images
2025-02-10
Lingao Xiao, Songhua Liu, Yang He, Xinchao Wang
Dataset distillation and dataset pruning are two prominent techniques for compressing datasets to improve computational and storage efficiency. Despite their overlapping objectives, these approaches are rarely compared directly. Even within each field, the evaluation protocols are inconsistent across various methods, which complicates fair comparisons and hinders reproducibility. Considering these limitations, we introduce in this paper a benchmark that equitably evaluates methodologies across both distillation and pruning literatures. Notably, our benchmark reveals that in the mainstream dataset distillation setting for large-scale datasets, which heavily rely on soft labels from pre-trained models, even randomly selected subsets can achieve surprisingly competitive performance. This finding suggests that an overemphasis on soft labels may be diverting attention from the intrinsic value of the image data, while also imposing additional burdens in terms of generation, storage, and application. To address these issues, we propose a new framework for dataset compression, termed Prune, Combine, and Augment (PCA), which focuses on leveraging image data exclusively, relies solely on hard labels for evaluation, and achieves state-of-the-art performance in this setup. By shifting the emphasis back to the images, our benchmark and PCA framework pave the way for more balanced and accessible techniques in dataset compression research. Our code is available at: https://github.com/ArmandXiao/Rethinking-Dataset-Compression
Link prediction for directed graphs is a crucial task with diverse real-world applications. Recent advances in embedding methods and Graph Neural Networks (GNNs) have shown promising improvements. However, these methods often lack a thorough analysis of embedding expressiveness and suffer from ineffective benchmarks for a fair evaluation. In this paper, we propose a unified framework to assess the expressiveness of existing methods, highlighting the impact of dual embeddings and decoder design on performance. To address limitations in current experimental setups, we introduce DirLinkBench, a robust new benchmark with comprehensive coverage and standardized evaluation. The results show that current methods struggle to achieve strong performance on the new benchmark, while DiGAE outperforms others overall. We further revisit DiGAE theoretically, showing its graph convolution aligns with GCN on an undirected bipartite graph. Inspired by these insights, we propose a novel spectral directed graph auto-encoder SDGAE that achieves SOTA results on DirLinkBench. Finally, we analyze key factors influencing directed link prediction and highlight open challenges.
Rethinking Word Similarity: Semantic Similarity through Classification Confusion
2025-02-08
Kaitlyn Zhou, Haishan Gao, Sarah Chen, Dan Edelstein, Dan Jurafsky, Chen Shani
Word similarity has many applications to social science and cultural analytics tasks like measuring meaning change over time and making sense of contested terms. Yet traditional similarity methods based on cosine similarity between word embeddings cannot capture the context-dependent, asymmetrical, polysemous nature of semantic similarity. We propose a new measure of similarity, Word Confusion, that reframes semantic similarity in terms of feature-based classification confusion. Word Confusion is inspired by Tversky's suggestion that similarity features be chosen dynamically. Here we train a classifier to map contextual embeddings to word identities and use the classifier confusion (the probability of choosing a confounding word c instead of the correct target word t) as a measure of the similarity of c and t. The set of potential confounding words acts as the chosen features. Our method is comparable to cosine similarity in matching human similarity judgments across several datasets (MEN, WirdSim353, and SimLex), and can measure similarity using predetermined features of interest. We demonstrate our model's ability to make use of dynamic features by applying it to test a hypothesis about changes in the 18th C. meaning of the French word "revolution" from popular to state action during the French Revolution. We hope this reimagining of semantic similarity will inspire the development of new tools that better capture the multi-faceted and dynamic nature of language, advancing the fields of computational social science and cultural analytics and beyond.
Knowledge editing has become a promising approach for efficiently and precisely updating knowledge embedded in large language models (LLMs). In this work, we focus on Same-Subject Editing, which involves modifying multiple attributes of a single entity to ensure comprehensive and consistent updates to entity-centric knowledge. Through preliminary observation, we identify a significant challenge: Current state-of-the-art editing methods struggle when tasked with editing multiple related knowledge pieces for the same subject. To address the lack of relevant editing data for identical subjects in traditional benchmarks, we introduce the $\text{S}^2\text{RKE}$(Same-Subject Related Knowledge Editing) benchmark. Our extensive experiments reveal that only mainstream locate-then-edit methods, such as ROME and MEMIT, exhibit "related knowledge perturbation," where subsequent edits interfere with earlier ones. Further analysis reveals that these methods over-rely on subject information, neglecting other critical factors, resulting in reduced editing effectiveness.
Rethinking Oversmoothing in Graph Neural Networks: A Rank-Based Perspective
2025-02-07
Kaicheng Zhang, Piero Deidda, Desmond Higham, Francesco Tudisco
Oversmoothing is a fundamental challenge in graph neural networks (GNNs): as the number of layers increases, node embeddings become increasingly similar, and model performance drops sharply. Traditionally, oversmoothing has been quantified using metrics that measure the similarity of neighbouring node features, such as the Dirichlet energy. While these metrics are related to oversmoothing, we argue they have critical limitations and fail to reliably capture oversmoothing in realistic scenarios. For instance, they provide meaningful insights only for very deep networks and under somewhat strict conditions on the norm of network weights and feature representations. As an alternative, we propose measuring oversmoothing by examining the numerical or effective rank of the feature representations. We provide theoretical support for this approach, demonstrating that the numerical rank of feature representations converges to one for a broad family of nonlinear activation functions under the assumption of nonnegative trained weights. To the best of our knowledge, this is the first result that proves the occurrence of oversmoothing without assumptions on the boundedness of the weight matrices. Along with the theoretical findings, we provide extensive numerical evaluation across diverse graph architectures. Our results show that rank-based metrics consistently capture oversmoothing, whereas energy-based metrics often fail. Notably, we reveal that a significant drop in the rank aligns closely with performance degradation, even in scenarios where energy metrics remain unchanged.
Point2RBox-v2: Rethinking Point-supervised Oriented Object Detection with Spatial Layout Among Instances
2025-02-06
Yi Yu, Botao Ren, Peiyuan Zhang, Mingxin Liu, Junwei Luo, Shaofeng Zhang, Feipeng Da, Junchi Yan, Xue Yang
With the rapidly increasing demand for oriented object detection (OOD), recent research involving weakly-supervised detectors for learning OOD from point annotations has gained great attention. In this paper, we rethink this challenging task setting with the layout among instances and present Point2RBox-v2. At the core are three principles: 1) Gaussian overlap loss. It learns an upper bound for each instance by treating objects as 2D Gaussian distributions and minimizing their overlap. 2) Voronoi watershed loss. It learns a lower bound for each instance through watershed on Voronoi tessellation. 3) Consistency loss. It learns the size/rotation variation between two output sets with respect to an input image and its augmented view. Supplemented by a few devised techniques, e.g. edge loss and copy-paste, the detector is further enhanced. To our best knowledge, Point2RBox-v2 is the first approach to explore the spatial layout among instances for learning point-supervised OOD. Our solution is elegant and lightweight, yet it is expected to give a competitive performance especially in densely packed scenes: 62.61%/86.15%/34.71% on DOTA/HRSC/FAIR1M. Code is available at https://github.com/VisionXLab/point2rbox-v2.
Rethinking the Residual Distribution of Locate-then-Editing Methods in Model Editing
2025-02-06
Xiaopeng Li, Shanwen Wang, Shasha Li, Shezheng Song, Bin Ji, Jun Ma, Jie Yu
Model editing is a powerful technique for updating the knowledge of Large Language Models (LLMs). Locate-then-edit methods are a popular class of approaches that first identify the critical layers storing knowledge, then compute the residual of the last critical layer based on the edited knowledge, and finally perform multi-layer updates using a least-squares solution by evenly distributing the residual from the first critical layer to the last. Although these methods achieve promising results, they have been shown to degrade the original knowledge of LLMs. We argue that residual distribution leads to this issue. To explore this, we conduct a comprehensive analysis of residual distribution in locate-then-edit methods from both empirical and theoretical perspectives, revealing that residual distribution introduces editing errors, leading to inaccurate edits. To address this issue, we propose the Boundary Layer UpdatE (BLUE) strategy to enhance locate-then-edit methods. Sequential batch editing experiments on three LLMs and two datasets demonstrate that BLUE not only delivers an average performance improvement of 35.59\%, significantly advancing the state of the art in model editing, but also enhances the preservation of LLMs' general capabilities. Our code is available at https://github.com/xpq-tech/BLUE.
Rethinking Approximate Gaussian Inference in Classification
2025-02-05
Bálint Mucsányi, Nathaël Da Costa, Philipp Hennig
In classification tasks, softmax functions are ubiquitously used as output activations to produce predictive probabilities. Such outputs only capture aleatoric uncertainty. To capture epistemic uncertainty, approximate Gaussian inference methods have been proposed, which output Gaussian distributions over the logit space. Predictives are then obtained as the expectations of the Gaussian distributions pushed forward through the softmax. However, such softmax Gaussian integrals cannot be solved analytically, and Monte Carlo (MC) approximations can be costly and noisy. We propose a simple change in the learning objective which allows the exact computation of predictives and enjoys improved training dynamics, with no runtime or memory overhead. This framework is compatible with a family of output activation functions that includes the softmax, as well as element-wise normCDF and sigmoid. Moreover, it allows for approximating the Gaussian pushforwards with Dirichlet distributions by analytic moment matching. We evaluate our approach combined with several approximate Gaussian inference methods (Laplace, HET, SNGP) on large- and small-scale datasets (ImageNet, CIFAR-10), demonstrating improved uncertainty quantification capabilities compared to softmax MC sampling. Code is available at https://github.com/bmucsanyi/probit.
Pioneer: Physics-informed Riemannian Graph ODE for Entropy-increasing Dynamics
2025-02-05
Li Sun, Ziheng Zhang, Zixi Wang, Yujie Wang, Qiqi Wan, Hao Li, Hao Peng, Philip S. Yu
Dynamic interacting system modeling is important for understanding and simulating real world systems. The system is typically described as a graph, where multiple objects dynamically interact with each other and evolve over time. In recent years, graph Ordinary Differential Equations (ODE) receive increasing research attentions. While achieving encouraging results, existing solutions prioritize the traditional Euclidean space, and neglect the intrinsic geometry of the system and physics laws, e.g., the principle of entropy increasing. The limitations above motivate us to rethink the system dynamics from a fresh perspective of Riemannian geometry, and pose a more realistic problem of physics-informed dynamic system modeling, considering the underlying geometry and physics law for the first time. In this paper, we present a novel physics-informed Riemannian graph ODE for a wide range of entropy-increasing dynamic systems (termed as Pioneer). In particular, we formulate a differential system on the Riemannian manifold, where a manifold-valued graph ODE is governed by the proposed constrained Ricci flow, and a manifold preserving Gyro-transform aware of system geometry. Theoretically, we report the provable entropy non-decreasing of our formulation, obeying the physics laws. Empirical results show the superiority of Pioneer on real datasets.
Behavior Cloning (BC) is a widely adopted visual imitation learning method in robot manipulation. Current BC approaches often enhance generalization by leveraging large datasets and incorporating additional visual and textual modalities to capture more diverse information. However, these methods overlook whether the learned representations contain redundant information and lack a solid theoretical foundation to guide the learning process. To address these limitations, we adopt an information-theoretic perspective and introduce mutual information to quantify and mitigate redundancy in latent representations. Building on this, we incorporate the Information Bottleneck (IB) principle into BC, which extends the idea of reducing redundancy by providing a structured framework for compressing irrelevant information while preserving task-relevant features. This work presents the first comprehensive study on redundancy in latent representations across various methods, backbones, and experimental settings, while extending the generalizability of the IB to BC. Extensive experiments and analyses on the CortexBench and LIBERO benchmarks demonstrate significant performance improvements with IB, underscoring the importance of reducing input data redundancy and highlighting its practical value for more practical applications. Project Page: https://baishuanghao.github.io/BC-IB.github.io.
Artificially intelligent (AI) agents that are capable of autonomous learning and independent decision-making hold great promise for addressing complex challenges across domains like transportation, energy systems, and manufacturing. However, the surge in AI systems' design and deployment driven by various stakeholders with distinct and unaligned objectives introduces a crucial challenge: how can uncoordinated AI systems coexist and evolve harmoniously in shared environments without creating chaos? To address this, we advocate for a fundamental rethinking of existing multi-agent frameworks, such as multi-agent systems and game theory, which are largely limited to predefined rules and static objective structures. We posit that AI agents should be empowered to dynamically adjust their objectives, make compromises, form coalitions, and safely compete or cooperate through evolving relationships and social feedback. Through this paper, we call for a shift toward the emergent, self-organizing, and context-aware nature of these systems.
This paper presents Perceptual Preference Optimization (PerPO), a perception alignment method aimed at addressing the visual discrimination challenges in generative pre-trained multimodal large language models (MLLMs). To align MLLMs with human visual perception process, PerPO employs discriminative rewarding to gather diverse negative samples, followed by listwise preference optimization to rank them.By utilizing the reward as a quantitative margin for ranking, our method effectively bridges generative preference optimization and discriminative empirical risk minimization. PerPO significantly enhances MLLMs' visual discrimination capabilities while maintaining their generative strengths, mitigates image-unconditional reward hacking, and ensures consistent performance across visual tasks. This work marks a crucial step towards more perceptually aligned and versatile MLLMs. We also hope that PerPO will encourage the community to rethink MLLM alignment strategies.
Rethinking the Global Knowledge of CLIP in Training-Free Open-Vocabulary Semantic Segmentation
2025-02-05
Jingyun Wang, Cilin Yan, Guoliang Kang
Recent works modify CLIP to perform open-vocabulary semantic segmentation in a training-free manner (TF-OVSS). In vanilla CLIP, patch-wise image representations mainly encode homogeneous image-level properties, which hinders the application of CLIP to the dense prediction task. Previous TF-OVSS works sacrifice globality to enhance the locality of CLIP features, by making each patch mainly attend to itself or its neighboring patches within a narrow local window. With their modifications,the ability of CLIP to aggregate global context information is largely weakened. Differently, in this paper, we rethink the global knowledge encoded by CLIP and propose GCLIP to answer how to extract and utilize beneficial global knowledge of CLIP for TF-OVSS. As the representation of each patch is finally determined by the attention weights and the Value embeddings, we propose to reshape the last-block attention and Value embeddings to aggregate useful global context into final features. Firstly, we aim to equip the last-block attention with image-level properties while not introducing homogeneous attention patterns across patches. To realize the goal, we fuse the attention from the global-token emerging blocks with the Query-Query attention. Secondly, we aim to make Value embeddings of the last-block attention module more semantically correlated. To realize this, we design a novel channel suppression strategy.Extensive experiments on five standard benchmarks demonstrate that our method consistently outperforms previous state-of-the-arts.
Graph Neural Networks (GNNs) have significant advantages in handling non-Euclidean data and have been widely applied across various areas, thus receiving increasing attention in recent years. The framework of GNN models mainly includes the information propagation phase and the aggregation phase, treating nodes and edges as information entities and propagation channels, respectively. However, most existing GNN models face the challenge of disconnection between node and edge feature information, as these models typically treat the learning of edge and node features as independent tasks. To address this limitation, we aim to develop an edge-empowered graph feature preference learning framework that can capture edge embeddings to assist node embeddings. By leveraging the learned multidimensional edge feature matrix, we construct multi-channel filters to more effectively capture accurate node features, thereby obtaining the non-local structural characteristics and fine-grained high-order node features. Specifically, the inclusion of multidimensional edge information enhances the functionality and flexibility of the GNN model, enabling it to handle complex and diverse graph data more effectively. Additionally, integrating relational representation learning into the message passing framework allows graph nodes to receive more useful information, thereby facilitating node representation learning. Finally, experiments on four real-world heterogeneous graphs demonstrate the effectiveness of theproposed model.
UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation
2025-02-04
Tao Zhang, Jinyong Wen, Zhen Chen, Kun Ding, Shiming Xiang, Chunhong Pan
Pre-training techniques significantly enhance the performance of semantic segmentation tasks with limited training data. However, the efficacy under a large domain gap between pre-training (e.g. RGB) and fine-tuning (e.g. infrared) remains underexplored. In this study, we first benchmark the infrared semantic segmentation performance of various pre-training methods and reveal several phenomena distinct from the RGB domain. Next, our layerwise analysis of pre-trained attention maps uncovers that: (1) There are three typical attention patterns (local, hybrid, and global); (2) Pre-training tasks notably influence the pattern distribution across layers; (3) The hybrid pattern is crucial for semantic segmentation as it attends to both nearby and foreground elements; (4) The texture bias impedes model generalization in infrared tasks. Building on these insights, we propose UNIP, a UNified Infrared Pre-training framework, to enhance the pre-trained model performance. This framework uses the hybrid-attention distillation NMI-HAD as the pre-training target, a large-scale mixed dataset InfMix for pre-training, and a last-layer feature pyramid network LL-FPN for fine-tuning. Experimental results show that UNIP outperforms various pre-training methods by up to 13.5\% in average mIoU on three infrared segmentation tasks, evaluated using fine-tuning and linear probing metrics. UNIP-S achieves performance on par with MAE-L while requiring only 1/10 of the computational cost. Furthermore, UNIP significantly surpasses state-of-the-art (SOTA) infrared or RGB segmentation methods and demonstrates broad potential for application in other modalities, such as RGB and depth. Our code is available at https://github.com/casiatao/UNIP.
Rethinking stance detection: A theoretically-informed research agenda for user-level inference using language models
2025-02-04
Prasanta Bhattacharya, Hong Zhang, Yiming Cao, Wei Gao, Brandon Siyuan Loh, Joseph J. P. Simons, Liang Ze Wong
Stance detection has emerged as a popular task in natural language processing research, enabled largely by the abundance of target-specific social media data. While there has been considerable research on the development of stance detection models, datasets, and application, we highlight important gaps pertaining to (i) a lack of theoretical conceptualization of stance, and (ii) the treatment of stance at an individual- or user-level, as opposed to message-level. In this paper, we first review the interdisciplinary origins of stance as an individual-level construct to highlight relevant attributes (e.g., psychological features) that might be useful to incorporate in stance detection models. Further, we argue that recent pre-trained and large language models (LLMs) might offer a way to flexibly infer such user-level attributes and/or incorporate them in modelling stance. To better illustrate this, we briefly review and synthesize the emerging corpus of studies on using LLMs for inferring stance, and specifically on incorporating user attributes in such tasks. We conclude by proposing a four-point agenda for pursuing stance detection research that is theoretically informed, inclusive, and practically impactful.
Rethinking Timesteps Samplers and Prediction Types
2025-02-04
Bin Xie, Gady Agam
Diffusion models suffer from the huge consumption of time and resources to train. For example, diffusion models need hundreds of GPUs to train for several weeks for a high-resolution generative task to meet the requirements of an extremely large number of iterations and a large batch size. Training diffusion models become a millionaire's game. With limited resources that only fit a small batch size, training a diffusion model always fails. In this paper, we investigate the key reasons behind the difficulties of training diffusion models with limited resources. Through numerous experiments and demonstrations, we identified a major factor: the significant variation in the training losses across different timesteps, which can easily disrupt the progress made in previous iterations. Moreover, different prediction types of $x_0$ exhibit varying effectiveness depending on the task and timestep. We hypothesize that using a mixed-prediction approach to identify the most accurate $x_0$ prediction type could potentially serve as a breakthrough in addressing this issue. In this paper, we outline several challenges and insights, with the hope of inspiring further research aimed at tackling the limitations of training diffusion models with constrained resources, particularly for high-resolution tasks.
Large vision-and-language models (LVLMs) typically treat visual and textual embeddings as homogeneous inputs to a large language model (LLM). However, these inputs are inherently different: visual inputs are multi-dimensional and contextually rich, often pre-encoded by models like CLIP, while textual inputs lack this structure. In this paper, we propose Decomposed Attention (D-Attn), a novel method that processes visual and textual embeddings differently by decomposing the 1-D causal self-attention in LVLMs. After the attention decomposition, D-Attn diagonalizes visual-to-visual self-attention, reducing computation from $\mathcal{O}(|V|^2)$ to $\mathcal{O}(|V|)$ for $|V|$ visual embeddings without compromising performance. Moreover, D-Attn debiases positional encodings in textual-to-visual cross-attention, further enhancing visual understanding. Finally, we introduce an $\alpha$-weighting strategy to merge visual and textual information, maximally preserving the pre-trained LLM's capabilities with minimal modifications. Extensive experiments and rigorous analyses validate the effectiveness of D-Attn, demonstrating significant improvements on multiple image benchmarks while significantly reducing computational costs. Code, data, and models will be publicly available.
Rethinking Vision Transformer for Object Centric Foundation Models
2025-02-04
Manuel Traub, Martin V. Butz
Recent state-of-the-art object segmentation mechanisms, such as the Segment Anything Model (SAM) and FastSAM, first encode the full image over several layers and then focus on generating the mask for one particular object or area. We present an off-grid Fovea-Like Input Patching (FLIP) approach, which selects image input and encodes it from the beginning in an object-focused manner. While doing so, it separates locational encoding from an object-centric perceptual code. FLIP is more data-efficient and yields improved segmentation performance when masking relatively small objects in high-resolution visual scenes. On standard benchmarks such as Hypersim, KITTI-360, and OpenImages, FLIP achieves Intersection over Union (IoU) scores that approach the performance of SAM with much less compute effort. It surpasses FastSAM in all IoU measurements. We also introduce an additional semi-natural but highly intuitive dataset where FLIP outperforms SAM and FastSAM overall and particularly on relatively small objects. Seeing that FLIP is an end-to-end object-centric segmentation approach, it has high potential particularly for applications that benefit from computationally efficient, spatially highly selective object tracking.
Rethinking Energy Management for Autonomous Ground Robots on a Budget
2025-02-03
Akshar Chavan, Rudra Joshi, Marco Brocanelli
Autonomous Ground Robots (AGRs) face significant challenges due to limited energy reserve, which restricts their overall performance and availability. Prior research has focused separately on energy-efficient approaches and fleet management strategies for task allocation to extend operational time. A fleet-level scheduler, however, assumes a specific energy consumption during task allocation, requiring the AGR to fully utilize the energy for maximum performance, which contrasts with energy-efficient practices. This paper addresses this gap by investigating the combined impact of computing frequency and locomotion speed on energy consumption and performance. We analyze these variables through experiments on our prototype AGR, laying the foundation for an integrated approach that optimizes cyber-physical resources within the constraints of a specified energy budget. To tackle this challenge, we introduce PECC (Predictable Energy Consumption Controller), a framework designed to optimize computing frequency and locomotion speed to maximize performance while ensuring the system operates within the specified energy budget. We conducted extensive experiments with PECC using a real AGR and in simulations, comparing it to an energy-efficient baseline. Our results show that the AGR travels up to 17\% faster than the baseline in real-world tests and up to 31\% faster in simulations, while consuming 95\% and 91\% of the given energy budget, respectively. These results prove that PECC can effectively enhance AGR performance in scenarios where prioritizing the energy budget outweighs the need for energy efficiency.
Re-examining Double Descent and Scaling Laws under Norm-based Capacity via Deterministic Equivalence
2025-02-03
Yichen Wang, Yudong Chen, Lorenzo Rosasco, Fanghui Liu
We investigate double descent and scaling laws in terms of weights rather than the number of parameters. Specifically, we analyze linear and random features models using the deterministic equivalence approach from random matrix theory. We precisely characterize how the weights norm concentrate around deterministic quantities and elucidate the relationship between the expected test error and the norm-based capacity (complexity). Our results rigorously answer whether double descent exists under norm-based capacity and reshape the corresponding scaling laws. Moreover, they prompt a rethinking of the data-parameter paradigm - from under-parameterized to over-parameterized regimes - by shifting the focus to norms (weights) rather than parameter count.
FSPGD: Rethinking Black-box Attacks on Semantic Segmentation
2025-02-03
Eun-Sol Park, MiSo Park, Seung Park, Yong-Goo Shin
Transferability, the ability of adversarial examples crafted for one model to deceive other models, is crucial for black-box attacks. Despite advancements in attack methods for semantic segmentation, transferability remains limited, reducing their effectiveness in real-world applications. To address this, we introduce the Feature Similarity Projected Gradient Descent (FSPGD) attack, a novel black-box approach that enhances both attack performance and transferability. Unlike conventional segmentation attacks that rely on output predictions for gradient calculation, FSPGD computes gradients from intermediate layer features. Specifically, our method introduces a loss function that targets local information by comparing features between clean images and adversarial examples, while also disrupting contextual information by accounting for spatial relationships between objects. Experiments on Pascal VOC 2012 and Cityscapes datasets demonstrate that FSPGD achieves superior transferability and attack performance, establishing a new state-of-the-art benchmark. Code is available at https://github.com/KU-AIVS/FSPGD.
End-to-end human animation, such as audio-driven talking human generation, has undergone notable advancements in the recent few years. However, existing methods still struggle to scale up as large general video generation models, limiting their potential in real applications. In this paper, we propose OmniHuman, a Diffusion Transformer-based framework that scales up data by mixing motion-related conditions into the training phase. To this end, we introduce two training principles for these mixed conditions, along with the corresponding model architecture and inference strategy. These designs enable OmniHuman to fully leverage data-driven motion generation, ultimately achieving highly realistic human video generation. More importantly, OmniHuman supports various portrait contents (face close-up, portrait, half-body, full-body), supports both talking and singing, handles human-object interactions and challenging body poses, and accommodates different image styles. Compared to existing end-to-end audio-driven methods, OmniHuman not only produces more realistic videos, but also offers greater flexibility in inputs. It also supports multiple driving modalities (audio-driven, video-driven and combined driving signals). Video samples are provided on the ttfamily project page (https://omnihuman-lab.github.io)
Mobile and Web-of-Things (WoT) devices at the network edge generate vast amounts of data for machine learning applications, yet privacy concerns hinder centralized model training. Federated Learning (FL) allows clients (devices) to collaboratively train a shared model coordinated by a central server without transfer private data, but inherent statistical heterogeneity among clients presents challenges, often leading to a dilemma between clients' needs for personalized local models and the server's goal of building a generalized global model. Existing FL methods typically prioritize either global generalization or local personalization, resulting in a trade-off between these two objectives and limiting the full potential of diverse client data. To address this challenge, we propose a novel framework that simultaneously enhances global generalization and local personalization by Rethinking Information Representation in the Federated learning process (FedRIR). Specifically, we introduce Masked Client-Specific Learning (MCSL), which isolates and extracts fine-grained client-specific features tailored to each client's unique data characteristics, thereby enhancing personalization. Concurrently, the Information Distillation Module (IDM) refines the global shared features by filtering out redundant client-specific information, resulting in a purer and more robust global representation that enhances generalization. By integrating the refined global features with the isolated client-specific features, we construct enriched representations that effectively capture both global patterns and local nuances, thereby improving the performance of downstream tasks on the client. The code is available at https://github.com/Deep-Imaging-Group/FedRIR.
Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?
2025-02-02
Wenzhe Li, Yong Lin, Mengzhou Xia, Chi Jin
Ensembling outputs from diverse sources is a straightforward yet effective approach to boost performance. Mixture-of-Agents (MoA) is one such popular ensemble method that aggregates outputs from multiple different Large Language Models (LLMs). This paper raises the question in the context of language models: is mixing different LLMs truly beneficial? We propose Self-MoA -- an ensemble method that aggregates outputs from only the single top-performing LLM. Our extensive experiments reveal that, surprisingly, Self-MoA outperforms standard MoA that mixes different LLMs in a large number of scenarios: Self-MoA achieves $6.6\%$ improvement over MoA on the AlpacaEval 2.0 benchmark, and an average of $3.8\%$ improvement across various benchmarks, including MMLU, CRUX, and MATH. Applying Self-MoA to one of the top-ranking models in AlpacaEval 2.0 directly achieves the new state-of-the-art performance on the leaderboard. To understand the effectiveness of Self-MoA, we systematically investigate the trade-off between diversity and quality of outputs under various MoA settings. We confirm that the MoA performance is rather sensitive to the quality, and mixing different LLMs often lowers the average quality of the models. To complement the study, we identify the scenarios where mixing different LLMs could be helpful. This paper further introduces a sequential version of Self-MoA, that is capable of aggregating a large number of LLM outputs on-the-fly over multiple rounds, and is as effective as aggregating all outputs at once.
Dominated Novelty Search: Rethinking Local Competition in Quality-Diversity
2025-02-01
Ryan Bahlous-Boldi, Maxence Faldor, Luca Grillotti, Hannah Janmohamed, Lisa Coiffard, Lee Spector, Antoine Cully
Quality-Diversity is a family of evolutionary algorithms that generate diverse, high-performing solutions through local competition principles inspired by natural evolution. While research has focused on improving specific aspects of Quality-Diversity algorithms, surprisingly little attention has been paid to investigating alternative formulations of local competition itself -- the core mechanism distinguishing Quality-Diversity from traditional evolutionary algorithms. Most approaches implement local competition through explicit collection mechanisms like fixed grids or unstructured archives, imposing artificial constraints that require predefined bounds or hard-to-tune parameters. We show that Quality-Diversity methods can be reformulated as Genetic Algorithms where local competition occurs through fitness transformations rather than explicit collection mechanisms. Building on this insight, we introduce Dominated Novelty Search, a Quality-Diversity algorithm that implements local competition through dynamic fitness transformations, eliminating the need for predefined bounds or parameters. Our experiments show that Dominated Novelty Search significantly outperforms existing approaches across standard Quality-Diversity benchmarks, while maintaining its advantage in challenging scenarios like high-dimensional and unsupervised spaces.
When End-to-End is Overkill: Rethinking Cascaded Speech-to-Text Translation
2025-02-01
Anna Min, Chenxu Hu, Yi Ren, Hang Zhao
Though end-to-end speech-to-text translation has been a great success, we argue that the cascaded speech-to-text translation model still has its place, which is usually criticized for the error propagation between automatic speech recognition (ASR) and machine translation (MT) models. In this paper, we explore the benefits of incorporating multiple candidates from ASR and self-supervised speech features into MT. Our analysis reveals that the primary cause of cascading errors stems from the increased divergence between similar samples in the speech domain when mapped to the text domain. By including multiple candidates and self-supervised speech features, our approach allows the machine translation model to choose the right words and ensure precise translation using various speech samples. This strategy minimizes error spread and takes advantage of large ASR and MT datasets, along with pre-trained ASR/MT models, while addressing associated issues.
Precision Harvesting in Cluttered Environments: Integrating End Effector Design with Dual Camera Perception
2025-01-31
Kendall Koe, Poojan Kalpeshbhai Shah, Benjamin Walt, Jordan Westphal, Samhita Marri, Shivani Kamtikar, James Seungbum Nam, Naveen Kumar Uppalapati, Girish Krishnan, Girish Chowdhary
Due to labor shortages in specialty crop industries, a need for robotic automation to increase agricultural efficiency and productivity has arisen. Previous manipulation systems perform well in harvesting in uncluttered and structured environments. High tunnel environments are more compact and cluttered in nature, requiring a rethinking of the large form factor systems and grippers. We propose a novel codesigned framework incorporating a global detection camera and a local eye-in-hand camera that demonstrates precise localization of small fruits via closed-loop visual feedback and reliable error handling. Field experiments in high tunnels show our system can reach an average of 85.0\% of cherry tomato fruit in 10.98s on average.
2025-01-31
Eugène Berta, David Holzmüller, Michael I. Jordan, Francis Bach
Machine learning classifiers often produce probabilistic predictions that are critical for accurate and interpretable decision-making in various domains. The quality of these predictions is generally evaluated with proper losses like cross-entropy, which decompose into two components: calibration error assesses general under/overconfidence, while refinement error measures the ability to distinguish different classes. In this paper, we provide theoretical and empirical evidence that these two errors are not minimized simultaneously during training. Selecting the best training epoch based on validation loss thus leads to a compromise point that is suboptimal for both calibration error and, most importantly, refinement error. To address this, we introduce a new metric for early stopping and hyperparameter tuning that makes it possible to minimize refinement error during training. The calibration error is minimized after training, using standard techniques. Our method integrates seamlessly with any architecture and consistently improves performance across diverse classification tasks.
Swift: Rethinking RDMA Control Plane for Elastic Computing
2025-01-31
Junxue Zhang, Han Tian, Xinyang Huang, Wenxue Li, Kaiqiang Xu, Dian Shen, Yong Wang, Kai Chen
Elastic computing enables dynamic scaling to meet workload demands, and Remote Direct Memory Access (RDMA) enhances this by providing high-throughput, low-latency network communication. However, integrating RDMA into elastic computing remains a challenge, particularly in control plane operations for RDMA connection setup. This paper revisits the assumptions of prior work on high-performance RDMA for elastic computing, and reveals that extreme microsecond-level control plane optimizations are often unnecessary. By challenging the conventional beliefs on the slowness of user-space RDMA control plane and the difficulty of user-space RDMA resource sharing, we uncover new design opportunities. Our key insight is that user-space RDMA connection setup can be significantly improved with caching, while RDMA resources can be efficiently shared among processes using fork. In light of this, we propose Swift, a simple yet effective solution that co-designs RDMA with a serverless framework to optimize performance for elastic computing. At its very core, Swift handles cold and warm serverless requests by swiftly initializing the RDMA control plane with cache-optimized libibverbs, and manages fork requests by leveraging the RDMA's fork capability. Implemented with OpenWhisk, Swift delivers 30.56-46.50% higher average throughput and 18.55-37.21% lower latency, at a cost of 6.5% control plane overhead, compared to prior solutions.
Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior
2025-01-31
Tongda Xu, Xiyan Cai, Xinjie Zhang, Xingtong Ge, Dailan He, Ming Sun, Jingjing Liu, Ya-Qin Zhang, Jian Li, Yan Wang
Recent advancements in diffusion models have been leveraged to address inverse problems without additional training, and Diffusion Posterior Sampling (DPS) (Chung et al., 2022a) is among the most popular approaches. Previous analyses suggest that DPS accomplishes posterior sampling by approximating the conditional score. While in this paper, we demonstrate that the conditional score approximation employed by DPS is not as effective as previously assumed, but rather aligns more closely with the principle of maximizing a posterior (MAP). This assertion is substantiated through an examination of DPS on 512x512 ImageNet images, revealing that: 1) DPS's conditional score estimation significantly diverges from the score of a well-trained conditional diffusion model and is even inferior to the unconditional score; 2) The mean of DPS's conditional score estimation deviates significantly from zero, rendering it an invalid score estimation; 3) DPS generates high-quality samples with significantly lower diversity. In light of the above findings, we posit that DPS more closely resembles MAP than a conditional score estimator, and accordingly propose the following enhancements to DPS: 1) we explicitly maximize the posterior through multi-step gradient ascent and projection; 2) we utilize a light-weighted conditional score estimator trained with only 100 images and 8 GPU hours. Extensive experimental results indicate that these proposed improvements significantly enhance DPS's performance. The source code for these improvements is provided in https://github.com/tongdaxu/Rethinking-Diffusion-Posterior-Sampling-From-Conditional-Score-Estimator-to-Maximizing-a-Posterior.
Rethinking Bottlenecks in Safety Fine-Tuning of Vision Language Models
2025-01-30
Yi Ding, Lijun Li, Bing Cao, Jing Shao
Large Vision-Language Models (VLMs) have achieved remarkable performance across a wide range of tasks. However, their deployment in safety-critical domains poses significant challenges. Existing safety fine-tuning methods, which focus on textual or multimodal content, fall short in addressing challenging cases or disrupt the balance between helpfulness and harmlessness. Our evaluation highlights a safety reasoning gap: these methods lack safety visual reasoning ability, leading to such bottlenecks. To address this limitation and enhance both visual perception and reasoning in safety-critical contexts, we propose a novel dataset that integrates multi-image inputs with safety Chain-of-Thought (CoT) labels as fine-grained reasoning logic to improve model performance. Specifically, we introduce the Multi-Image Safety (MIS) dataset, an instruction-following dataset tailored for multi-image safety scenarios, consisting of training and test splits. Our experiments demonstrate that fine-tuning InternVL2.5-8B with MIS significantly outperforms both powerful open-source models and API-based models in challenging multi-image tasks requiring safety-related visual reasoning. This approach not only delivers exceptional safety performance but also preserves general capabilities without any trade-offs. Specifically, fine-tuning with MIS increases average accuracy by 0.83% across five general benchmarks and reduces the Attack Success Rate (ASR) on multiple safety benchmarks by a large margin. Data and Models are released under: \href{https://dripnowhy.github.io/MIS/}{\texttt{https://dripnowhy.github.io/MIS/}}
Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution
2025-01-30
Haohan Shi, Fei Zhou, Xin Sun, Jungong Han
Deep learning has achieved significant success in single hyperspectral image super-resolution (SHSR); however, the high spectral dimensionality leads to a heavy computational burden, thus making it difficult to deploy in real-time scenarios. To address this issue, this paper proposes a novel lightweight SHSR network, i.e., LKCA-Net, that incorporates channel attention to calibrate multi-scale channel features of hyperspectral images. Furthermore, we demonstrate, for the first time, that the low-rank property of the learnable upsampling layer is a key bottleneck in lightweight SHSR methods. To address this, we employ the low-rank approximation strategy to optimize the parameter redundancy of the learnable upsampling layer. Additionally, we introduce a knowledge distillation-based feature alignment technique to ensure the low-rank approximated network retains the same feature representation capacity as the original. We conducted extensive experiments on the Chikusei, Houston 2018, and Pavia Center datasets compared to some SOTAs. The results demonstrate that our method is competitive in performance while achieving speedups of several dozen to even hundreds of times compared to other well-performing SHSR methods.
Contextual Agent Security: A Policy for Every Purpose
2025-01-28
Lillian Tsai, Eugene Bagdasarian
Judging an action's safety requires knowledge of the context in which the action takes place. To human agents who act in various contexts, this may seem obvious: performing an action such as email deletion may or may not be appropriate depending on the email's content, the goal (e.g., to erase sensitive emails or to clean up trash), and the type of email address (e.g., work or personal). Unlike people, computational systems have often had only limited agency in limited contexts. Thus, manually crafted policies and user confirmation (e.g., smartphone app permissions or network access control lists), while imperfect, have sufficed to restrict harmful actions. However, with the upcoming deployment of generalist agents that support a multitude of tasks (e.g., an automated personal assistant), we argue that we must rethink security designs to adapt to the scale of contexts and capabilities of these systems. As a first step, this paper explores contextual security in the domain of agents and proposes contextual agent security (Conseca), a framework to generate just-in-time, contextual, and human-verifiable security policies.
How Linguistics Learned to Stop Worrying and Love the Language Models
2025-01-28
Richard Futrell, Kyle Mahowald
Language models can produce fluent, grammatical text. Nonetheless, some maintain that language models don't really learn language and also that, even if they did, that would not be informative for the study of human learning and processing. On the other side, there have been claims that the success of LMs obviates the need for studying linguistic theory and structure. We argue that both extremes are wrong. LMs can contribute to fundamental questions about linguistic structure, language processing, and learning. They force us to rethink arguments about learning and are informative for major questions in linguistic theory. But they do not replace linguistic structure and theory. We offer an optimistic take on the relationship between language models and linguistics.
Rethinking the Win Ratio: A Causal Framework for Hierarchical Outcome Analysis
2025-01-28
Mathieu Even, Julie Josse
Quantifying causal effects in the presence of complex and multivariate outcomes is a key challenge to evaluate treatment effects. For hierarchical multivarariates outcomes, the FDA recommends the Win Ratio and Generalized Pairwise Comparisons approaches. However, as far as we know, these empirical methods lack causal or statistical foundations to justify their broader use in recent studies. To address this gap, we establish causal foundations for hierarchical comparison methods. We define related causal effect measures, and highlight that depending on the methodology used to compute Win Ratios or Net Benefits of treatments, the causal estimand targeted can be different, as proved by our consistency results. Quite dramatically, it appears that the causal estimand related to the historical estimation approach can yield reversed and incorrect treatment recommendations in heterogeneous populations, as we illustrate through striking examples. In order to compensate for this fallacy, we introduce a novel, individual-level yet identifiable causal effect measure that better approximates the ideal, non-identifiable individual-level estimand. We prove that computing Win Ratio or Net Benefits using a Nearest Neighbor pairing approach between treated and controlled patients, an approach that can be seen as an extreme form of stratification, leads to estimating this new causal estimand measure. We extend our methods to observational settings via propensity weighting, distributional regression to address the curse of dimensionality, and a doubly robust framework. We prove the consistency of our methods, and the double robustness of our augmented estimator. Finally, we validate our approach using synthetic data and on CRASH-3, a major clinical trial focused on assessing the effects of tranexamic acid in patients with traumatic brain injury.
Rethinking Functional Brain Connectome Analysis: Do Graph Deep Learning Models Help?
2025-01-28
Keqi Han, Yao Su, Lifang He, Liang Zhan, Sergey Plis, Vince Calhoun, Carl Yang
Functional brain connectome is crucial for deciphering the neural mechanisms underlying cognitive functions and neurological disorders. Graph deep learning models have recently gained tremendous popularity in this field. However, their actual effectiveness in modeling the brain connectome remains unclear. In this study, we re-examine graph deep learning models based on four large-scale neuroimaging studies encompassing diverse cognitive and clinical outcomes. Surprisingly, we find that the message aggregation mechanism, a hallmark of graph deep learning models, does not help with predictive performance as typically assumed, but rather consistently degrades it. To address this issue, we propose a hybrid model combining a linear model with a graph attention network through dual pathways, achieving robust predictions and enhanced interpretability by revealing both localized and global neural connectivity patterns. Our findings urge caution in adopting complex deep learning models for functional brain connectome analysis, emphasizing the need for rigorous experimental designs to establish tangible performance gains and perhaps more importantly, to pursue improvements in model interpretability.
AdaCoT: Rethinking Cross-Lingual Factual Reasoning through Adaptive Chain-of-Thought
2025-01-27
Xin Huang, Tarun Kumar Vangani, Zhengyuan Liu, Bowei Zou, Ai Ti Aw
Large language models (LLMs) have shown impressive multilingual capabilities through pretraining on diverse corpora. While these models show strong reasoning abilities, their performance varies significantly across languages due to uneven training data distribution. Existing approaches using machine translation, and extensive multilingual pretraining and cross-lingual tuning face scalability challenges and often fail to capture nuanced reasoning processes across languages. In this paper, we introduce AdaCoT (Adaptive Chain-of-Thought), a framework that enhances multilingual reasoning by dynamically routing thought processes through intermediary "thinking languages" before generating target-language responses. AdaCoT leverages a language-agnostic core and incorporates an adaptive, reward-based mechanism for selecting optimal reasoning pathways without requiring additional pretraining. Our comprehensive evaluation across multiple benchmarks demonstrates substantial improvements in both factual reasoning quality and cross-lingual consistency, with particularly strong performance gains in low-resource language settings. The results suggest that adaptive reasoning paths can effectively bridge the performance gap between high and low-resource languages while maintaining cultural and linguistic nuances.
A Standardization Procedure to Incorporate Variance Partitioning Based Priors in Latent Gaussian Models
2025-01-27
Luisa Ferrari, Massimo Ventrucci
Latent Gaussian Models (LGMs) are a subset of Bayesian Hierarchical models where Gaussian priors, conditional on variance parameters, are assigned to all effects in the model. LGMs are employed in many fields for their flexibility and computational efficiency. However, practitioners find prior elicitation on the variance parameters challenging because of a lack of intuitive interpretation for them. Recently, several papers have tackled this issue by rethinking the model in terms of variance partitioning (VP) and assigning priors to parameters reflecting the relative contribution of each effect to the total variance. So far, the class of priors based on VP has been mainly deployed for random effects and fixed effects separately. This work presents a novel standardization procedure that expands the applicability of VP priors to a broader class of LGMs, including both fixed and random effects. We describe the steps required for standardization through various examples, with a particular focus on the popular class of intrinsic Gaussian Markov random fields (IGMRFs). The practical advantages of standardization are demonstrated with simulated data and a real dataset on survival analysis.
Rethinking the Bias of Foundation Model under Long-tailed Distribution
2025-01-27
Jiahao Chen, Bin Qin, Jiangmeng Li, Hao Chen, Bing Su
Long-tailed learning has garnered increasing attention due to its practical significance. Among the various approaches, the fine-tuning paradigm has gained considerable interest with the advent of foundation models. However, most existing methods primarily focus on leveraging knowledge from these models, overlooking the inherent biases introduced by the imbalanced training data they rely on. In this paper, we examine how such imbalances from pre-training affect long-tailed downstream tasks. Specifically, we find the imbalance biases inherited in foundation models on downstream task as parameter imbalance and data imbalance. During fine-tuning, we observe that parameter imbalance plays a more critical role, while data imbalance can be mitigated using existing re-balancing strategies. Moreover, we find that parameter imbalance cannot be effectively addressed by current re-balancing techniques, such as adjusting the logits, during training, unlike data imbalance. To tackle both imbalances simultaneously, we build our method on causal learning and view the incomplete semantic factor as the confounder, which brings spurious correlations between input samples and labels. To resolve the negative effects of this, we propose a novel backdoor adjustment method that learns the true causal effect between input samples and labels, rather than merely fitting the correlations in the data. Notably, we achieve an average performance increase of about $1.67\%$ on each dataset.
Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning
2025-01-26
Zeyu Gan, Yun Liao, Yong Liu
Test-time scaling, which is also often referred to as slow-thinking, has been demonstrated to enhance multi-step reasoning in large language models (LLMs). However, despite its widespread utilization, the mechanisms underlying slow-thinking methods remain poorly understood. This paper explores the mechanisms of external slow-thinking from a theoretical standpoint. We begin by examining the snowball error effect within the LLM reasoning process and connect it to the likelihood of correct reasoning using information theory. Building on this, we show that external slow-thinking methods can be interpreted as strategies to mitigate the error probability. We further provide a comparative analysis of popular external slow-thinking approaches, ranging from simple to complex, highlighting their differences and interrelationships. Our findings suggest that the efficacy of these methods is not primarily determined by the specific framework employed, and that expanding the search scope or the model's internal reasoning capacity may yield more sustained improvements in the long term. We open-source our code at https://github.com/ZyGan1999/Snowball-Errors-and-Probability.
Recent advances in table understanding have focused on instruction-tuning large language models (LLMs) for table-related tasks. However, existing research has overlooked the impact of hyperparameter choices and lacks a comprehensive evaluation of the out-of-domain table understanding ability and the general capabilities of these table LLMs. In this paper, we evaluate these abilities in existing table LLMs, and reveal significant declines in both out-of-domain table understanding and general capabilities compared to their base models. Through systematic analysis, we show that hyperparameters, such as learning rate, can significantly influence both table-specific and general capabilities. Contrary to the existing table instruction-tuning works, we demonstrate that smaller learning rates and fewer training instances can enhance table understanding while preserving general capabilities. Based on our findings, we introduce TAMA, a TAble LLM instruction-tuned from LLaMA 3.1 8B Instruct, which achieves performance on par with, or surpassing GPT-3.5 and GPT-4 on table tasks, while maintaining strong out-of-domain generalization and general capabilities. Our findings highlight the potential for reduced data annotation costs and more efficient model development through careful hyperparameter selection.
Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST
2025-01-24
Fuping Wu, Bartlomiej W. Papiez
Foundation models are widely employed in medical image analysis, due to their high adaptability and generalizability for downstream tasks. With the increasing number of foundation models being released, model selection has become an important issue. In this work, we study the capabilities of foundation models in medical image classification tasks by conducting a benchmark study on the MedMNIST dataset. Specifically, we adopt various foundation models ranging from convolutional to Transformer-based models and implement both end-to-end training and linear probing for all classification tasks. The results demonstrate the significant potential of these pre-trained models when transferred for medical image classification. We further conduct experiments with different image sizes and various sizes of training data. By analyzing all the results, we provide preliminary, yet useful insights and conclusions on this topic.
Rethinking Encoder-Decoder Flow Through Shared Structures
2025-01-24
Frederik Laboyrie, Mehmet Kerim Yucel, Albert Saa-Garriga
Dense prediction tasks have enjoyed a growing complexity of encoder architectures, decoders, however, have remained largely the same. They rely on individual blocks decoding intermediate feature maps sequentially. We introduce banks, shared structures that are used by each decoding block to provide additional context in the decoding process. These structures, through applying them via resampling and feature fusion, improve performance on depth estimation for state-of-the-art transformer-based architectures on natural and synthetic images whilst training on large-scale datasets.
Spiking Neural Networks (SNNs) hold promise for energy-efficient, biologically inspired computing. We identify substantial informatio loss during spike transmission, linked to temporal dependencies in traditional Leaky Integrate-and-Fire (LIF) neuron-a key factor potentially limiting SNN performance. Existing SNN architectures also underutilize modern GPUs, constrained by single-bit spike storage and isolated weight-spike operations that restrict computational efficiency. We introduce ${SpikePack}$, a neuron model designed to reduce transmission loss while preserving essential features like membrane potential reset and leaky integration. ${SpikePack}$ achieves constant $\mathcal{O}(1)$ time and space complexity, enabling efficient parallel processing on GPUs and also supporting serial inference on existing SNN hardware accelerators. Compatible with standard Artificial Neural Network (ANN) architectures, ${SpikePack}$ facilitates near-lossless ANN-to-SNN conversion across various networks. Experimental results on tasks such as image classification, detection, and segmentation show ${SpikePack}$ achieves significant gains in accuracy and efficiency for both directly trained and converted SNNs over state-of-the-art models. Tests on FPGA-based platforms further confirm cross-platform flexibility, delivering high performance and enhanced sparsity. By enhancing information flow and rethinking SNN-ANN integration, ${SpikePack}$ advances efficient SNN deployment across diverse hardware platforms.
Feature quality is paramount for classification performance, particularly in few-shot scenarios. Contrastive learning, a widely adopted technique for enhancing feature quality, leverages sample relations to extract intrinsic features that capture semantic information and has achieved remarkable success in Few-Shot Learning (FSL). Nevertheless, current few-shot contrastive learning approaches often overlook the semantic similarity discrepancies at different granularities when employing the same modeling approach for different sample relations, which limits the potential of few-shot contrastive learning. In this paper, we introduce a straightforward yet effective contrastive learning approach, Multi-Grained Relation Contrastive Learning (MGRCL), as a pre-training feature learning model to boost few-shot learning by meticulously modeling sample relations at different granularities. MGRCL categorizes sample relations into three types: intra-sample relation of the same sample under different transformations, intra-class relation of homogenous samples, and inter-class relation of inhomogeneous samples. In MGRCL, we design Transformation Consistency Learning (TCL) to ensure the rigorous semantic consistency of a sample under different transformations by aligning predictions of input pairs. Furthermore, to preserve discriminative information, we employ Class Contrastive Learning (CCL) to ensure that a sample is always closer to its homogenous samples than its inhomogeneous ones, as homogenous samples share similar semantic content while inhomogeneous samples have different semantic content. Our method is assessed across four popular FSL benchmarks, showing that such a simple pre-training feature learning method surpasses a majority of leading FSL methods. Moreover, our method can be incorporated into other FSL methods as the pre-trained model and help them obtain significant performance gains.
ExLM: Rethinking the Impact of [MASK] Tokens in Masked Language Models
2025-01-23
Kangjie Zheng, Junwei Yang, Siyue Liang, Bin Feng, Zequn Liu, Wei Ju, Zhiping Xiao, Ming Zhang
Masked Language Models (MLMs) have achieved remarkable success in many self-supervised representation learning tasks. MLMs are trained by randomly masking portions of the input sequences with [MASK] tokens and learning to reconstruct the original content based on the remaining context. This paper explores the impact of [MASK] tokens on MLMs. Analytical studies show that masking tokens can introduce the corrupted semantics problem, wherein the corrupted context may convey multiple, ambiguous meanings. This problem is also a key factor affecting the performance of MLMs on downstream tasks. Based on these findings, we propose a novel enhanced-context MLM, ExLM. Our approach expands [MASK] tokens in the input context and models the dependencies between these expanded states. This enhancement increases context capacity and enables the model to capture richer semantic information, effectively mitigating the corrupted semantics problem during pre-training. Experimental results demonstrate that ExLM achieves significant performance improvements in both text modeling and SMILES modeling tasks. Further analysis confirms that ExLM enriches semantic representations through context enhancement, and effectively reduces the semantic multimodality commonly observed in MLMs.
Multi-aspect Knowledge Distillation with Large Language Model
2025-01-23
Taegyeong Lee, Jinsik Bang, Soyeong Kwon, Taehwan Kim
Recent advancements in deep learning have significantly improved performance on computer vision tasks. Previous image classification methods primarily modify model architectures or add features, and they optimize models using cross-entropy loss on class logits. Since they focus on classifying images with considering class labels, these methods may struggle to learn various \emph{aspects} of classes (e.g., natural positions and shape changes). Rethinking the previous approach from a novel view, we propose a multi-aspect knowledge distillation method using Multimodal Large Language Models (MLLMs). Our approach involves: 1) querying Large Language Model with multi-aspect questions relevant to the knowledge we want to transfer to the model, 2) extracting corresponding logits from MLLM, and 3) expanding the model's output dimensions to distill these multi-aspect logits. We then apply cross-entropy loss to class logits and binary cross-entropy loss to multi-aspect logits. Through our method, the model can learn not only the knowledge about visual aspects but also the abstract and complex aspects that require a deeper understanding. We primarily apply our method to image classification, and to explore the potential for extending our model, such as object detection. In all experimental results, our method improves the performance of the baselines. Additionally, we analyze the effect of multi-aspect knowledge distillation. These results demonstrate that our method can transfer knowledge about various aspects to the model and the aspect knowledge can enhance model performance in computer vision tasks.
A Call for Critically Rethinking and Reforming Data Analysis in Empirical Software Engineering
2025-01-22
Matteo Esposito, Mikel Robredo, Murali Sridharan, Guilherme Horta Travassos, Rafael Peñaloza, Valentina Lenarduzzi
Context: Empirical Software Engineering (ESE) drives innovation in SE through qualitative and quantitative studies. However, concerns about the correct application of empirical methodologies have existed since the 2006 Dagstuhl seminar on SE. Objective: To analyze three decades of SE research, identify mistakes in statistical methods, and evaluate experts' ability to detect and address these issues. Methods: We conducted a literature survey of ~27,000 empirical studies, using LLMs to classify statistical methodologies as adequate or inadequate. Additionally, we selected 30 primary studies and held a workshop with 33 ESE experts to assess their ability to identify and resolve statistical issues. Results: Significant statistical issues were found in the primary studies, and experts showed limited ability to detect and correct these methodological problems, raising concerns about the broader ESE community's proficiency in this area. Conclusions. Despite our study's eventual limitations, its results shed light on recurring issues from promoting information copy-and-paste from past authors' works and the continuous publication of inadequate approaches that promote dubious results and jeopardize the spread of the correct statistical strategies among researchers. Besides, it justifies further investigation into empirical rigor in software engineering to expose these recurring issues and establish a framework for reassessing our field's foundation of statistical methodology application. Therefore, this work calls for critically rethinking and reforming data analysis in empirical software engineering, paving the way for our work soon.
GPUs, CPUs, and... NICs: Rethinking the Network's Role in Serving Complex AI Pipelines
2025-01-22
Mike Wong, Ulysses Butler, Emma Farkash, Praveen Tammana, Anirudh Sivaraman, Ravi Netravali
The increasing prominence of AI necessitates the deployment of inference platforms for efficient and effective management of AI pipelines and compute resources. As these pipelines grow in complexity, the demand for distributed serving rises and introduces much-dreaded network delays. In this paper, we investigate how the network can instead be a boon to the excessively high resource overheads of AI pipelines. To alleviate these overheads, we discuss how resource-intensive data processing tasks -- a key facet of growing AI pipeline complexity -- are well-matched for the computational characteristics of packet processing pipelines and how they can be offloaded onto SmartNICs. We explore the challenges and opportunities of offloading, and propose a research agenda for integrating network hardware into AI pipelines, unlocking new opportunities for optimization.
Rethinking Membership Inference Attacks Against Transfer Learning
2025-01-20
Cong Wu, Jing Chen, Qianru Fang, Kun He, Ziming Zhao, Hao Ren, Guowen Xu, Yang Liu, Yang Xiang
Transfer learning, successful in knowledge translation across related tasks, faces a substantial privacy threat from membership inference attacks (MIAs). These attacks, despite posing significant risk to ML model's training data, remain limited-explored in transfer learning. The interaction between teacher and student models in transfer learning has not been thoroughly explored in MIAs, potentially resulting in an under-examined aspect of privacy vulnerabilities within transfer learning. In this paper, we propose a new MIA vector against transfer learning, to determine whether a specific data point was used to train the teacher model while only accessing the student model in a white-box setting. Our method delves into the intricate relationship between teacher and student models, analyzing the discrepancies in hidden layer representations between the student model and its shadow counterpart. These identified differences are then adeptly utilized to refine the shadow model's training process and to inform membership inference decisions effectively. Our method, evaluated across four datasets in diverse transfer learning tasks, reveals that even when an attacker only has access to the student model, the teacher model's training data remains susceptible to MIAs. We believe our work unveils the unexplored risk of membership inference in transfer learning.
It's the People, Not the Placement: Rethinking Allocations in Post-Moore Clouds
2025-01-19
Tejas Harith, Antoine Kaufmann
The Cambrian explosion of new accelerators, driven by the slowdown of Moore's Law, has created significant resource management challenges for modern IaaS clouds. Unlike the homogeneous datacenters backing legacy clouds, emerging neoclouds amass a diverse portfolio of heterogeneous hardware -- NVIDIA GPUs, TPUs, Trainium chips, and FPGAs. Neocloud operators and tenants must transition from managing a single large pool of computational resources to navigating a set of highly fragmented and constrained pools. We argue that cloud resource management mechanisms and interfaces require a fundamental rethink to enable efficient and economical neoclouds. Specifically we propose shifting from long-term static resource allocation with fixed-pricing to dynamic allocation with continuous, multilateral cost re-negotatiaton. We demonstrate this approach is not only feasible for modern applications but also significantly improves resource efficiency and reduces costs. Finally, we propose a new architecture for the interaction between operators, tenants, and applications in neoclouds.
Rethinking Pseudo-Label Guided Learning for Weakly Supervised Temporal Action Localization from the Perspective of Noise Correction
2025-01-19
Quan Zhang, Yuxin Qi, Xi Tang, Rui Yuan, Xi Lin, Ke Zhang, Chun Yuan
Pseudo-label learning methods have been widely applied in weakly-supervised temporal action localization. Existing works directly utilize weakly-supervised base model to generate instance-level pseudo-labels for training the fully-supervised detection head. We argue that the noise in pseudo-labels would interfere with the learning of fully-supervised detection head, leading to significant performance leakage. Issues with noisy labels include:(1) inaccurate boundary localization; (2) undetected short action clips; (3) multiple adjacent segments incorrectly detected as one segment. To target these issues, we introduce a two-stage noisy label learning strategy to harness every potential useful signal in noisy labels. First, we propose a frame-level pseudo-label generation model with a context-aware denoising algorithm to refine the boundaries. Second, we introduce an online-revised teacher-student framework with a missing instance compensation module and an ambiguous instance correction module to solve the short-action-missing and many-to-one problems. Besides, we apply a high-quality pseudo-label mining loss in our online-revised teacher-student framework to add different weights to the noisy labels to train more effectively. Our model outperforms the previous state-of-the-art method in detection accuracy and inference speed greatly upon the THUMOS14 and ActivityNet v1.2 benchmarks.
Rethinking Early-Fusion Strategies for Improved Multimodal Image Segmentation
2025-01-19
Zhengwen Shen, Yulian Li, Han Zhang, Yuchen Weng, Jun Wang
RGB and thermal image fusion have great potential to exhibit improved semantic segmentation in low-illumination conditions. Existing methods typically employ a two-branch encoder framework for multimodal feature extraction and design complicated feature fusion strategies to achieve feature extraction and fusion for multimodal semantic segmentation. However, these methods require massive parameter updates and computational effort during the feature extraction and fusion. To address this issue, we propose a novel multimodal fusion network (EFNet) based on an early fusion strategy and a simple but effective feature clustering for training efficient RGB-T semantic segmentation. In addition, we also propose a lightweight and efficient multi-scale feature aggregation decoder based on Euclidean distance. We validate the effectiveness of our method on different datasets and outperform previous state-of-the-art methods with lower parameters and computation.
Good things come in small packages: Should we adopt Lite-GPUs in AI infrastructure?
2025-01-17
Burcu Canakci, Junyi Liu, Xingbo Wu, Nathanaël Cheriere, Paolo Costa, Sergey Legtchenko, Dushyanth Narayanan, Ant Rowstron
To match the blooming demand of generative AI workloads, GPU designers have so far been trying to pack more and more compute and memory into single complex and expensive packages. However, there is growing uncertainty about the scalability of individual GPUs and thus AI clusters, as state-of-the-art GPUs are already displaying packaging, yield, and cooling limitations. We propose to rethink the design and scaling of AI clusters through efficiently-connected large clusters of Lite-GPUs, GPUs with single, small dies and a fraction of the capabilities of larger GPUs. We think recent advances in co-packaged optics can be key in overcoming the communication challenges of distributing AI workloads onto more Lite-GPUs. In this paper, we present the key benefits of Lite-GPUs on manufacturing cost, blast radius, yield, and power efficiency; and discuss systems opportunities and challenges around resource, workload, memory, and network management.
Rethinking cloud abstractions for tenant-provider cooperative optimization of AI workloads
2025-01-16
Marco Canini, Ricardo Bianchini, Íñigo Goiri, Dejan Kostić, Peter Pietzuch
AI workloads, often hosted in multi-tenant cloud environments, require vast computational resources but suffer inefficiencies due to limited tenant-provider coordination. Tenants lack infrastructure insights, while providers lack workload details to optimize tasks like partitioning, scheduling, and fault tolerance. We propose the HarmonAIze project to redefine cloud abstractions, enabling cooperative optimization for improved performance, efficiency, resiliency, and sustainability. This paper outlines key opportunities, challenges, and a research agenda to realize this vision.
AdaFV: Rethinking of Visual-Language alignment for VLM acceleration
2025-01-16
Jiayi Han, Liang Du, Yiwen Wu, Xiangguo Zhou, Hongwei Du, Weibo Zheng
The success of VLMs often relies on the dynamic high-resolution schema that adaptively augments the input images to multiple crops, so that the details of the images can be retained. However, such approaches result in a large number of redundant visual tokens, thus significantly reducing the efficiency of the VLMs. To improve the VLMs' efficiency without introducing extra training costs, many research works are proposed to reduce the visual tokens by filtering the uninformative visual tokens or aggregating their information. Some approaches propose to reduce the visual tokens according to the self-attention of VLMs, which are biased, to result in inaccurate responses. The token reduction approaches solely rely on visual cues are text-agnostic, and fail to focus on the areas that are most relevant to the question, especially when the queried objects are non-salient to the image. In this work, we first conduct experiments to show that the original text embeddings are aligned with the visual tokens, without bias on the tailed visual tokens. We then propose a self-adaptive cross-modality attention mixture mechanism that dynamically leverages the effectiveness of visual saliency and text-to-image similarity in the pre-LLM layers to select the visual tokens that are informative. Extensive experiments demonstrate that the proposed approach achieves state-of-the-art training-free VLM acceleration performance, especially when the reduction rate is sufficiently large.
Video generation has achieved remarkable progress with the introduction of diffusion models, which have significantly improved the quality of generated videos. However, recent research has primarily focused on scaling up model training, while offering limited insights into the direct impact of representations on the video generation process. In this paper, we initially investigate the characteristics of features in intermediate layers, finding substantial variations in attention maps across different layers. These variations lead to unstable semantic representations and contribute to cumulative differences between features, which ultimately reduce the similarity between adjacent frames and negatively affect temporal coherence. To address this, we propose RepVideo, an enhanced representation framework for text-to-video diffusion models. By accumulating features from neighboring layers to form enriched representations, this approach captures more stable semantic information. These enhanced representations are then used as inputs to the attention mechanism, thereby improving semantic expressiveness while ensuring feature consistency across adjacent frames. Extensive experiments demonstrate that our RepVideo not only significantly enhances the ability to generate accurate spatial appearances, such as capturing complex spatial relationships between multiple objects, but also improves temporal consistency in video generation.
As Large Language Models (LLMs) become increasingly computationally complex, developing efficient deployment strategies, such as quantization, becomes crucial. State-of-the-art Post-training Quantization (PTQ) techniques often rely on calibration processes to maintain the accuracy of these models. However, while these calibration techniques can enhance performance in certain domains, they may not be as effective in others. This paper aims to draw attention to robust statistical approaches that can mitigate such issues. We propose a weight-adaptive PTQ method that can be considered a precursor to calibration-based PTQ methods, guiding the quantization process to preserve the distribution of weights by minimizing the Kullback-Leibler divergence between the quantized weights and the originally trained weights. This minimization ensures that the quantized model retains the Shannon information content of the original model to a great extent, guaranteeing robust and efficient deployment across many tasks. As such, our proposed approach can perform on par with most common calibration-based PTQ methods, establishing a new pre-calibration step for further adjusting the quantized weights with calibration. We show that our pre-calibration results achieve the same accuracy as some existing calibration-based PTQ methods on various LLMs.
GRAPHMOE: Amplifying Cognitive Depth of Mixture-of-Experts Network via Introducing Self-Rethinking Mechanism
2025-01-14
Chen Tang, Bo Lv, Zifan Zheng, Bohao Yang, Kun Zhao, Ning Liao, Xiaoxing Wang, Feiyu Xiong, Zhiyu Li, Nayu Liu, Jingchi Jiang
Traditional Mixture-of-Experts (MoE) networks benefit from utilizing multiple smaller expert models as opposed to a single large network. However, these experts typically operate independently, leaving a question open about whether interconnecting these models could enhance the performance of MoE networks. In response, we introduce GRAPHMOE, a novel method aimed at augmenting the cognitive depth of language models via a self-rethinking mechanism constructed on Pseudo GraphMoE networks. GRAPHMOE employs a recurrent routing strategy to simulate iterative thinking steps, thereby facilitating the flow of information among expert nodes. We implement the GRAPHMOE architecture using Low-Rank Adaptation techniques (LoRA) and conduct extensive experiments on various benchmark datasets. The experimental results reveal that GRAPHMOE outperforms other LoRA based models, achieving state-of-the-art (SOTA) performance. Additionally, this study explores a novel recurrent routing strategy that may inspire further advancements in enhancing the reasoning capabilities of language models.
A Low-Rank QTT-based Finite Element Method for Elasticity Problems
2025-01-14
Elena Benvenuti, Gianmarco Manzini, Marco Nale, Simone Pizzolato
We present an efficient and robust numerical algorithm for solving the two-dimensional linear elasticity problem that combines the Quantized Tensor Train format and a domain partitioning strategy. This approach makes it possible to solve the linear elasticity problem on a computational domain that is more general than a square. Our method substantially decreases memory usage and achieves a notable reduction in rank compared to established Finite Element implementations like the FEniCS platform. This performance gain, however, requires a fundamental rethinking of how core finite element operations are implemented, which includes changes to mesh discretization, node and degree of freedom ordering, stiffness matrix and internal nodal force assembly, and the execution of algebraic matrix-vector operations. In this work, we discuss all these aspects in detail and assess the method's performance in the numerical approximation of three representative test cases.
Rethinking Knowledge in Distillation: An In-context Sample Retrieval Perspective
2025-01-13
Jinjing Zhu, Songze Li, Lin Wang
Conventional knowledge distillation (KD) approaches are designed for the student model to predict similar output as the teacher model for each sample. Unfortunately, the relationship across samples with same class is often neglected. In this paper, we explore to redefine the knowledge in distillation, capturing the relationship between each sample and its corresponding in-context samples (a group of similar samples with the same or different classes), and perform KD from an in-context sample retrieval perspective. As KD is a type of learned label smoothing regularization (LSR), we first conduct a theoretical analysis showing that the teacher's knowledge from the in-context samples is a crucial contributor to regularize the student training with the corresponding samples. Buttressed by the analysis, we propose a novel in-context knowledge distillation (IC-KD) framework that shows its superiority across diverse KD paradigms (offline, online, and teacher-free KD). Firstly, we construct a feature memory bank from the teacher model and retrieve in-context samples for each corresponding sample through retrieval-based learning. We then introduce Positive In-Context Distillation (PICD) to reduce the discrepancy between a sample from the student and the aggregated in-context samples with the same class from the teacher in the logit space. Moreover, Negative In-Context Distillation (NICD) is introduced to separate a sample from the student and the in-context samples with different classes from the teacher in the logit space. Extensive experiments demonstrate that IC-KD is effective across various types of KD, and consistently achieves state-of-the-art performance on CIFAR-100 and ImageNet datasets.
2025-01-13
Michal Bravansky, Filip Trhlik, Fazl Barez
As general-purpose artificial intelligence (AI) systems become increasingly integrated with diverse human communities, cultural alignment has emerged as a crucial element in their deployment. Most existing approaches treat cultural alignment as one-directional, embedding predefined cultural values from standardized surveys and repositories into AI systems. To challenge this perspective, we highlight research showing that humans' cultural values must be understood within the context of specific AI systems. We then use a GPT-4o case study to demonstrate that AI systems' cultural alignment depends on how humans structure their interactions with the system. Drawing on these findings, we argue that cultural alignment should be reframed as a bidirectional process: rather than merely imposing standardized values on AIs, we should query the human cultural values most relevant to each AI-based system and align it to these values through interaction frameworks shaped by human users.
Artificial intelligence (AI) has made significant strides in recent years, yet it continues to struggle with a fundamental aspect of cognition present in all animals: common sense. Current AI systems, including those designed for complex tasks like autonomous driving, problem-solving challenges such as the Abstraction and Reasoning Corpus (ARC), and conversational benchmarks like the Turing Test, often lack the ability to adapt to new situations without extensive prior knowledge. This manuscript argues that integrating common sense into AI systems is essential for achieving true autonomy and unlocking the full societal and commercial value of AI. We propose a shift in the order of knowledge acquisition emphasizing the importance of developing AI systems that start from minimal prior knowledge and are capable of contextual learning, adaptive reasoning, and embodiment -- even within abstract domains. Additionally, we highlight the need to rethink the AI software stack to address this foundational challenge. Without common sense, AI systems may never reach true autonomy, instead exhibiting asymptotic performance that approaches theoretical ideals like AIXI but remains unattainable in practice due to infinite resource and computation requirements. While scaling AI models and passing benchmarks like the Turing Test have brought significant advancements in applications that do not require autonomy, these approaches alone are insufficient to achieve autonomous AI with common sense. By redefining existing benchmarks and challenges to enforce constraints that require genuine common sense, and by broadening our understanding of embodiment to include both physical and abstract domains, we can encourage the development of AI systems better equipped to handle the complexities of real-world and abstract environments.
LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs
2025-01-10
Omkar Thawakar, Dinura Dissanayake, Ketan More, Ritesh Thawkar, Ahmed Heakl, Noor Ahsan, Yuhao Li, Mohammed Zumri, Jean Lahoud, Rao Muhammad Anwer, Hisham Cholakkal, Ivan Laptev, Mubarak Shah, Fahad Shahbaz Khan, Salman Khan
Reasoning is a fundamental capability for solving complex multi-step problems, particularly in visual contexts where sequential step-wise understanding is essential. Existing approaches lack a comprehensive framework for evaluating visual reasoning and do not emphasize step-wise problem-solving. To this end, we propose a comprehensive framework for advancing step-by-step visual reasoning in large language models (LMMs) through three key contributions. First, we introduce a visual reasoning benchmark specifically designed to evaluate multi-step reasoning tasks. The benchmark presents a diverse set of challenges with eight different categories ranging from complex visual perception to scientific reasoning with over 4k reasoning steps in total, enabling robust evaluation of LLMs' abilities to perform accurate and interpretable visual reasoning across multiple steps. Second, we propose a novel metric that assesses visual reasoning quality at the granularity of individual steps, emphasizing both correctness and logical coherence. The proposed metric offers deeper insights into reasoning performance compared to traditional end-task accuracy metrics. Third, we present a new multimodal visual reasoning model, named LlamaV-o1, trained using a multi-step curriculum learning approach, where tasks are progressively organized to facilitate incremental skill acquisition and problem-solving. The proposed LlamaV-o1 is designed for multi-step reasoning and learns step-by-step through a structured training paradigm. Extensive experiments show that our LlamaV-o1 outperforms existing open-source models and performs favorably against close-source proprietary models. Compared to the recent Llava-CoT, our LlamaV-o1 achieves an average score of 67.3 with an absolute gain of 3.8\% across six benchmarks while being 5 times faster during inference scaling. Our benchmark, model, and code are publicly available.
Social web and Wikipedia: an opportunity to rethink the links between sources' credibility, trust and authority
2025-01-10
Gilles Sahut, André Tricot
The Web and its main tools (Google, Wikipedia, Facebook, Twitter) deeply raise and renew fundamental questions, that everyone asks almost every day: Is this information or content true? Can I trust this author or source? These questions are not new, they have been the same with books, newspapers, broadcasting and television, and, more fundamentally, in every human interpersonal communication. This paper is focused on two scientific problems on this issue. The first one is theoretical: to address this issue, many concepts have been used in library and information sciences, communication and psychology. The links between these concepts are not clear: sometimes two concepts are considered as synonymous, sometimes as very different. The second one is historical: sources like Wikipedia deeply challenge the epistemic evaluation of information sources, compared to previous modes of information production. This paper proposes an integrated and simple model considering the relation between a user, a document and an author as human communication. It reduces the problem to three concepts: credibility as a characteristic granted to information depending on its truth-value; trust as the ability to produce credible information; authority when the power to influence of an author is accepted, i.e., when readers accept that the source can modify their opinion, knowledge and decisions. The model describes also two kinds of relationships between the three concepts: an upward link and a downward link. The model is confronted with findings of empirical research on Wikipedia in particular.
ByteFS: System Support for (CXL-based) Memory-Semantic Solid-State Drives
2025-01-09
Shaobo Li, Yirui Eric Zhou, Hao Ren, Jian Huang
Unlike non-volatile memory that resides on the processor memory bus, memory-semantic solid-state drives (SSDs) support both byte and block access granularity via PCIe or CXL interconnects. They provide scalable memory capacity using NAND flash at a much lower cost. In addition, they have different performance characteristics for their dual byte/block interface respectively, while offering essential memory semantics for upper-level software. Such a byte-accessible storage device provides new implications on the software system design. In this paper, we develop a new file system, named ByteFS, by rethinking the design primitives of file systems and SSD firmware to exploit the advantages of both byte and block-granular data accesses. ByteFS supports byte-granular data persistence to retain the persistence nature of SSDs. It extends the core data structure of file systems by enabling dual byte/block-granular data accesses. To facilitate the support for byte-granular writes, \pname{} manages the internal DRAM of SSD firmware in a log-structured manner and enables data coalescing to reduce the unnecessary I/O traffic to flash chips. ByteFS also enables coordinated data caching between the host page cache and SSD cache for best utilizing the precious memory resource. We implement ByteFS on both a real programmable SSD and an emulated memory-semantic SSD for sensitivity study. Compared to state-of-the-art file systems for non-volatile memory and conventional SSDs, ByteFS outperforms them by up to 2.7$\times$, while preserving the essential properties of a file system. ByteFS also reduces the write traffic to SSDs by up to 5.1$\times$ by alleviating unnecessary writes caused by both metadata and data updates in file systems.
A Look into How Machine Learning is Reshaping Engineering Models: the Rise of Analysis Paralysis, Optimal yet Infeasible Solutions, and the Inevitable Rashomon Paradox
2025-01-09
MZ Naser
The widespread acceptance of empirically derived codal provisions and equations in civil engineering stands in stark contrast to the skepticism facing machine learning (ML) models, despite their shared statistical foundations. This paper examines this philosophical tension through the lens of structural engineering and explores how integrating ML challenges traditional engineering philosophies and professional identities. Recent efforts have documented how ML enhances predictive accuracy, optimizes designs, and analyzes complex behaviors. However, one might also raise concerns about the diminishing role of human intuition and the interpretability of algorithms. To showcase this rarely explored front, this paper presents how ML can be successfully integrated into various engineering problems by means of formulation via deduction, induction, and abduction. Then, this paper identifies three principal paradoxes that could arise when adopting ML: analysis paralysis (increased prediction accuracy leading to a reduced understanding of physical mechanisms), infeasible solutions (optimization resulting in unconventional designs that challenge engineering intuition), and the Rashomon effect (where contradictions in explainability methods and physics arise). This paper concludes by addressing these paradoxes and arguing the need to rethink epistemological shifts in engineering and engineering education and methodologies to harmonize traditional principles with ML.
Sparse autoencoders (SAEs) have gained a lot of attention as a promising tool to improve the interpretability of large language models (LLMs) by mapping the complex superposition of polysemantic neurons into monosemantic features and composing a sparse dictionary of words. However, traditional performance metrics like Mean Squared Error and L0 sparsity ignore the evaluation of the semantic representational power of SAEs -- whether they can acquire interpretable monosemantic features while preserving the semantic relationship of words. For instance, it is not obvious whether a learned sparse feature could distinguish different meanings in one word. In this paper, we propose a suite of evaluations for SAEs to analyze the quality of monosemantic features by focusing on polysemous words. Our findings reveal that SAEs developed to improve the MSE-L0 Pareto frontier may confuse interpretability, which does not necessarily enhance the extraction of monosemantic features. The analysis of SAEs with polysemous words can also figure out the internal mechanism of LLMs; deeper layers and the Attention module contribute to distinguishing polysemy in a word. Our semantics focused evaluation offers new insights into the polysemy and the existing SAE objective and contributes to the development of more practical SAEs.
Rethinking High-speed Image Reconstruction Framework with Spike Camera
2025-01-08
Kang Chen, Yajing Zheng, Tiejun Huang, Zhaofei Yu
Spike cameras, as innovative neuromorphic devices, generate continuous spike streams to capture high-speed scenes with lower bandwidth and higher dynamic range than traditional RGB cameras. However, reconstructing high-quality images from the spike input under low-light conditions remains challenging. Conventional learning-based methods often rely on the synthetic dataset as the supervision for training. Still, these approaches falter when dealing with noisy spikes fired under the low-light environment, leading to further performance degradation in the real-world dataset. This phenomenon is primarily due to inadequate noise modelling and the domain gap between synthetic and real datasets, resulting in recovered images with unclear textures, excessive noise, and diminished brightness. To address these challenges, we introduce a novel spike-to-image reconstruction framework SpikeCLIP that goes beyond traditional training paradigms. Leveraging the CLIP model's powerful capability to align text and images, we incorporate the textual description of the captured scene and unpaired high-quality datasets as the supervision. Our experiments on real-world low-light datasets U-CALTECH and U-CIFAR demonstrate that SpikeCLIP significantly enhances texture details and the luminance balance of recovered images. Furthermore, the reconstructed images are well-aligned with the broader visual features needed for downstream tasks, ensuring more robust and versatile performance in challenging environments.
Along with the proliferating research interest in Semantic Communication (SemCom), Joint Source Channel Coding (JSCC) has dominated the attention due to the widely assumed existence in efficiently delivering information semantics. %has emerged as a pivotal area of research, aiming to enhance the efficiency and reliability of information transmission through deep learning-based methods. Nevertheless, this paper challenges the conventional JSCC paradigm, and advocates for adoption of Separate Source Channel Coding (SSCC) to enjoy the underlying more degree of freedom for optimization. We demonstrate that SSCC, after leveraging the strengths of Large Language Model (LLM) for source coding and Error Correction Code Transformer (ECCT) complemented for channel decoding, offers superior performance over JSCC. Our proposed framework also effectively highlights the compatibility challenges between SemCom approaches and digital communication systems, particularly concerning the resource costs associated with the transmission of high precision floating point numbers. Through comprehensive evaluations, we establish that empowered by LLM-based compression and ECCT-enhanced error correction, SSCC remains a viable and effective solution for modern communication systems. In other words, separate source and channel coding is still what we need!
Rethinking domain generalization in medical image segmentation: One image as one domain
2025-01-08
Jin Hong, Bo Liu, Guoli Long, Siyue Li, Khan Muhammad
Domain shifts in medical image segmentation, particularly when data comes from different centers, pose significant challenges. Intra-center variability, such as differences in scanner models or imaging protocols, can cause domain shifts as large as, or even larger than, those between centers. To address this, we propose the "one image as one domain" (OIOD) hypothesis, which treats each image as a unique domain, enabling flexible and robust domain generalization. Based on this hypothesis, we develop a unified disentanglement-based domain generalization (UniDDG) framework, which simultaneously handles both multi-source and single-source domain generalization without requiring explicit domain labels. This approach simplifies training with a fixed architecture, independent of the number of source domains, reducing complexity and enhancing scalability. We decouple each input image into content representation and style code, then exchange and combine these within the batch for segmentation, reconstruction, and further disentanglement. By maintaining distinct style codes for each image, our model ensures thorough decoupling of content representations and style codes, improving domain invariance of the content representations. Additionally, we enhance generalization with expansion mask attention (EMA) for boundary preservation and style augmentation (SA) to simulate diverse image styles, improving robustness to domain shifts. Extensive experiments show that our method achieves Dice scores of 84.43% and 88.91% for multi-source to single-center and single-center generalization in optic disc and optic cup segmentation, respectively, and 86.96% and 88.56% for prostate segmentation, outperforming current state-of-the-art domain generalization methods, offering superior performance and adaptability across clinical settings.
MC-VTON: Minimal Control Virtual Try-On Diffusion Transformer
2025-01-07
Junsheng Luan, Guangyuan Li, Lei Zhao, Wei Xing
Virtual try-on methods based on diffusion models achieve realistic try-on effects. They use an extra reference network or an additional image encoder to process multiple conditional image inputs, which adds complexity pre-processing and additional computational costs. Besides, they require more than 25 inference steps, bringing longer inference time. In this work, with the development of diffusion transformer (DiT), we rethink the necessity of additional reference network or image encoder and introduce MC-VTON, which leverages DiT's intrinsic backbone to seamlessly integrate minimal conditional try-on inputs. Compared to existing methods, the superiority of MC-VTON is demonstrated in four aspects: (1) Superior detail fidelity. Our DiT-based MC-VTON exhibits superior fidelity in preserving fine-grained details. (2) Simplified network and inputs. We remove any extra reference network or image encoder. We also remove unnecessary conditions like the long prompt, pose estimation, human parsing, and depth map. We require only the masked person image and the garment image. (3) Parameter-efficient training. To process the try-on task, we fine-tune the FLUX.1-dev with only 39.7M additional parameters (0.33% of the backbone parameters). (4) Less inference steps. We apply distillation diffusion on MC-VTON and only need 8 steps to generate a realistic try-on image, with only 86.8M additional parameters (0.72% of the backbone parameters). Experiments show that MC-VTON achieves superior qualitative and quantitative results with fewer condition inputs, trainable parameters, and inference steps than baseline methods.
Online Learning Management Systems (LMSs), such as Blackboard and Canvas, have existed for decades. Yet, course readings, when provided at all, consistently exist as simple digital twins to their real-life counterparts. While online tools and resources exist to help students process digital texts more efficiently or in ways better suited to their learning styles, knowledge about such resources is not evenly distributed and creates a gulf in advantage between students. This paper proposes the courseware integration of "smart" textbooks, a newfound way for students to chat with their readings, receive summaries and explanations for highlighted text, and generate quiz questions via an AI agent embedded in their online course material. Future iterations of the software aim to add in-context reference highlighting for AI-generated answers and personalized tunings for the end learner.
Deep Reinforcement Learning (DRL) suffers from uncertainties and inaccuracies in the observation signal in realworld applications. Adversarial attack is an effective method for evaluating the robustness of DRL agents. However, existing attack methods targeting individual sampled actions have limited impacts on the overall policy distribution, particularly in continuous action spaces. To address these limitations, we propose the Distribution-Aware Projected Gradient Descent attack (DAPGD). DAPGD uses distribution similarity as the gradient perturbation input to attack the policy network, which leverages the entire policy distribution rather than relying on individual samples. We utilize the Bhattacharyya distance in DAPGD to measure policy similarity, enabling sensitive detection of subtle but critical differences between probability distributions. Our experiment results demonstrate that DAPGD achieves SOTA results compared to the baselines in three robot navigation tasks, achieving an average 22.03% higher reward drop compared to the best baseline.
Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective
2025-01-06
Zhongjian Zhang, Mengmei Zhang, Xiao Wang, Lingjuan Lyu, Bo Yan, Junping Du, Chuan Shi
To preserve user privacy in recommender systems, federated recommendation (FR) based on federated learning (FL) emerges, keeping the personal data on the local client and updating a model collaboratively. Unlike FL, FR has a unique sparse aggregation mechanism, where the embedding of each item is updated by only partial clients, instead of full clients in a dense aggregation of general FL. Recently, as an essential principle of FL, model security has received increasing attention, especially for Byzantine attacks, where malicious clients can send arbitrary updates. The problem of exploring the Byzantine robustness of FR is particularly critical since in the domains applying FR, e.g., e-commerce, malicious clients can be injected easily by registering new accounts. However, existing Byzantine works neglect the unique sparse aggregation of FR, making them unsuitable for our problem. Thus, we make the first effort to investigate Byzantine attacks on FR from the perspective of sparse aggregation, which is non-trivial: it is not clear how to define Byzantine robustness under sparse aggregations and design Byzantine attacks under limited knowledge/capability. In this paper, we reformulate the Byzantine robustness under sparse aggregation by defining the aggregation for a single item as the smallest execution unit. Then we propose a family of effective attack strategies, named Spattack, which exploit the vulnerability in sparse aggregation and are categorized along the adversary's knowledge and capability. Extensive experimental results demonstrate that Spattack can effectively prevent convergence and even break down defenses under a few malicious clients, raising alarms for securing FR systems.
Hard Thresholding Pursuit (HTP) has aroused increasing attention for its robust theoretical guarantees and impressive numerical performance in non-convex optimization. In this paper, we introduce a novel tuning-free procedure, named Full-Adaptive HTP (FAHTP), that simultaneously adapts to both the unknown sparsity and signal strength of the underlying model. We provide an in-depth analysis of the iterative thresholding dynamics of FAHTP, offering refined theoretical insights. In specific, under the beta-min condition $\min_{i \in S^*}|{\boldsymbol{\beta}}^*_i| \ge C\sigma (\log p/n)^{1/2}$, we show that the FAHTP achieves oracle estimation rate $\sigma (s^*/n)^{1/2}$, highlighting its theoretical superiority over convex competitors such as LASSO and SLOPE, and recovers the true support set exactly. More importantly, even without the beta-min condition, our method achieves a tighter error bound than the classical minimax rate with high probability. The comprehensive numerical experiments substantiate our theoretical findings, underscoring the effectiveness and robustness of the proposed FAHTP.
Rethinking IDE Customization for Enhanced HAX: A Hyperdimensional Perspective
2025-01-05
Roham Koohestani, Maliheh Izadi
As Integrated Development Environments (IDEs) increasingly integrate Artificial Intelligence, Software Engineering faces both benefits like productivity gains and challenges like mismatched user preferences. We propose Hyper-Dimensional (HD) vector spaces to model Human-Computer Interaction, focusing on user actions, stylistic preferences, and project context. These contributions aim to inspire further research on applying HD computing in IDE design.
Benchmarks are crucial for evaluating machine learning algorithm performance, facilitating comparison and identifying superior solutions. However, biases within datasets can lead models to learn shortcut patterns, resulting in inaccurate assessments and hindering real-world applicability. This paper addresses the issue of entity bias in relation extraction tasks, where models tend to rely on entity mentions rather than context. We propose a debiased relation extraction benchmark DREB that breaks the pseudo-correlation between entity mentions and relation types through entity replacement. DREB utilizes Bias Evaluator and PPL Evaluator to ensure low bias and high naturalness, providing a reliable and accurate assessment of model generalization in entity bias scenarios. To establish a new baseline on DREB, we introduce MixDebias, a debiasing method combining data-level and model training-level techniques. MixDebias effectively improves model performance on DREB while maintaining performance on the original dataset. Extensive experiments demonstrate the effectiveness and robustness of MixDebias compared to existing methods, highlighting its potential for improving the generalization ability of relation extraction models. We will release DREB and MixDebias publicly.
Rethinking Addressing in Language Models via Contexualized Equivariant Positional Encoding
2025-01-01
Jiajun Zhu, Peihao Wang, Ruisi Cai, Jason D. Lee, Pan Li, Zhangyang Wang
Transformers rely on both content-based and position-based addressing mechanisms to make predictions, but existing positional encoding techniques often diminish the effectiveness of position-based addressing. Many current methods enforce rigid patterns in attention maps, limiting the ability to model long-range dependencies and adapt to diverse tasks. Additionally, most positional encodings are learned as general biases, lacking the specialization required for different instances within a dataset. To address this, we propose con$\textbf{T}$extualized equivari$\textbf{A}$nt $\textbf{P}$osition $\textbf{E}$mbedding ($\textbf{TAPE}$), a novel framework that enhances positional embeddings by incorporating sequence content across layers. TAPE introduces dynamic, context-aware positional encodings, overcoming the constraints of traditional fixed patterns. By enforcing permutation and orthogonal equivariance, TAPE ensures the stability of positional encodings during updates, improving robustness and adaptability. Our method can be easily integrated into pre-trained transformers, offering parameter-efficient fine-tuning with minimal overhead. Extensive experiments shows that TAPE achieves superior performance in language modeling, arithmetic reasoning, and long-context retrieval tasks compared to existing positional embedding techniques.
Rethink Delay Doppler Channels and Time-Frequency Coding
2024-12-31
Xiang-Gen Xia
In this paper, we rethink delay Doppler channels (also called doubly selective channels). We prove that no modulation schemes (including the current active VOFDM/OTFS) can compensate a non-trivial Doppler spread well. We then discuss some of the existing methods to deal with time-varying channels, in particular time-frequency (TF) coding in an OFDM system. TF coding is equivalent to space-time coding in the math part. We also summarize state of the art on space-time coding that was an active research topic over a decade ago.
Diffusion models have shown exceptional performance in visual generation tasks. Recently, these models have shifted from traditional U-Shaped CNN-Attention hybrid structures to fully transformer-based isotropic architectures. While these transformers exhibit strong scalability and performance, their reliance on complicated self-attention operation results in slow inference speeds. Contrary to these works, we rethink one of the simplest yet fastest module in deep learning, 3x3 Convolution, to construct a scaled-up purely convolutional diffusion model. We first discover that an Encoder-Decoder Hourglass design outperforms scalable isotropic architectures for Conv3x3, but still under-performing our expectation. Further improving the architecture, we introduce sparse skip connections to reduce redundancy and improve scalability. Based on the architecture, we introduce conditioning improvements including stage-specific embeddings, mid-block condition injection, and conditional gating. These improvements lead to our proposed Diffusion CNN (DiC), which serves as a swift yet competitive diffusion architecture baseline. Experiments on various scales and settings show that DiC surpasses existing diffusion transformers by considerable margins in terms of performance while keeping a good speed advantage. Project page: https://github.com/YuchuanTian/DiC
Proactive Conversational Agents with Inner Thoughts
2024-12-31
Xingyu Bruce Liu, Shitao Fang, Weiyan Shi, Chien-Sheng Wu, Takeo Igarashi, Xiang Anthony Chen
One of the long-standing aspirations in conversational AI is to allow them to autonomously take initiatives in conversations, i.e., being proactive. This is especially challenging for multi-party conversations. Prior NLP research focused mainly on predicting the next speaker from contexts like preceding conversations. In this paper, we demonstrate the limitations of such methods and rethink what it means for AI to be proactive in multi-party, human-AI conversations. We propose that just like humans, rather than merely reacting to turn-taking cues, a proactive AI formulates its own inner thoughts during a conversation, and seeks the right moment to contribute. Through a formative study with 24 participants and inspiration from linguistics and cognitive psychology, we introduce the Inner Thoughts framework. Our framework equips AI with a continuous, covert train of thoughts in parallel to the overt communication process, which enables it to proactively engage by modeling its intrinsic motivation to express these thoughts. We instantiated this framework into two real-time systems: an AI playground web app and a chatbot. Through a technical evaluation and user studies with human participants, our framework significantly surpasses existing baselines on aspects like anthropomorphism, coherence, intelligence, and turn-taking appropriateness.
Layer removal is an effective technique for compressing large language models (LLMs) by reducing redundancy and improving inference efficiency. However, indiscriminate pruning disrupts representation stability, leading to performance degradation. We propose GRASP (Gradient-based Retention of Adaptive Singular Parameters), which preserves representation-critical singular values to mitigate these effects. Unlike direct layer removal, GRASP leverages gradient-based attribution on a syntax- and semantics-rich dataset to guide the selection of representation-critical singular values. By selectively applying singular value decomposition (SVD) to affected layers, GRASP achieves efficient compression while maintaining representation stability with minimal overhead. Experiments across multiple LLMs show that GRASP consistently outperforms existing compression methods in perplexity and downstream task performance.
Large language models (LLMs) have demonstrated exceptional performance in text generation within current NLP research. However, the lack of factual accuracy is still a dark cloud hanging over the LLM skyscraper. Structural knowledge prompting (SKP) is a prominent paradigm to integrate external knowledge into LLMs by incorporating structural representations, achieving state-of-the-art results in many knowledge-intensive tasks. However, existing methods often focus on specific problems, lacking a comprehensive exploration of the generalization and capability boundaries of SKP. This paper aims to evaluate and rethink the generalization capability of the SKP paradigm from four perspectives including Granularity, Transferability, Scalability, and Universality. To provide a thorough evaluation, we introduce a novel multi-granular, multi-level benchmark called SUBARU, consisting of 9 different tasks with varying levels of granularity and difficulty.
2024-12-30
Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark van der Wilk, Adam Foster, Tom Rainforth
The ideas of aleatoric and epistemic uncertainty are widely used to reason about the probabilistic predictions of machine-learning models. We identify incoherence in existing discussions of these ideas and suggest this stems from the aleatoric-epistemic view being insufficiently expressive to capture all of the distinct quantities that researchers are interested in. To explain and address this we derive a simple delineation of different model-based uncertainties and the data-generating processes associated with training and evaluation. Using this in place of the aleatoric-epistemic view could produce clearer discourse as the field moves forward.
Analog Alchemy: Neural Computation with In-Memory Inference, Learning and Routing
2024-12-30
Yigit Demirag
As neural computation is revolutionizing the field of Artificial Intelligence (AI), rethinking the ideal neural hardware is becoming the next frontier. Fast and reliable von Neumann architecture has been the hosting platform for neural computation. Although capable, its separation of memory and computation creates the bottleneck for the energy efficiency of neural computation, contrasting the biological brain. The question remains: how can we efficiently combine memory and computation, while exploiting the physics of the substrate, to build intelligent systems? In this thesis, I explore an alternative way with memristive devices for neural computation, where the unique physical dynamics of the devices are used for inference, learning and routing. Guided by the principles of gradient-based learning, we selected functions that need to be materialized, and analyzed connectomics principles for efficient wiring. Despite non-idealities and noise inherent in analog physics, I will provide hardware evidence of adaptability of local learning to memristive substrates, new material stacks and circuit blocks that aid in solving the credit assignment problem and efficient routing between analog crossbars for scalable architectures.
Text-based person search aims to retrieve the matched pedestrians from a large-scale image database according to the text description. The core difficulty of this task is how to extract effective details from pedestrian images and texts, and achieve cross-modal alignment in a common latent space. Prior works adopt image and text encoders pre-trained on unimodal data to extract global and local features from image and text respectively, and then global-local alignment is achieved explicitly. However, these approaches still lack the ability of understanding visual details, and the retrieval accuracy is still limited by identity confusion. In order to alleviate the above problems, we rethink the importance of visual features for text-based person search, and propose VFE-TPS, a Visual Feature Enhanced Text-based Person Search model. It introduces a pre-trained multimodal backbone CLIP to learn basic multimodal features and constructs Text Guided Masked Image Modeling task to enhance the model's ability of learning local visual details without explicit annotation. In addition, we design Identity Supervised Global Visual Feature Calibration task to guide the model learn identity-aware global visual features. The key finding of our study is that, with the help of our proposed auxiliary tasks, the knowledge embedded in the pre-trained CLIP model can be successfully adapted to text-based person search task, and the model's visual understanding ability is significantly enhanced. Experimental results on three benchmarks demonstrate that our proposed model exceeds the existing approaches, and the Rank-1 accuracy is significantly improved with a notable margin of about $1\%\sim9\%$. Our code can be found at https://github.com/zhangweifeng1218/VFE_TPS.
Self-Disclosure to AI: The Paradox of Trust and Vulnerability in Human-Machine Interactions
2024-12-29
Zoe Zhiqiu Jiang
In this paper, we explore the paradox of trust and vulnerability in human-machine interactions, inspired by Alexander Reben's BlabDroid project. This project used small, unassuming robots that actively engaged with people, successfully eliciting personal thoughts or secrets from individuals, often more effectively than human counterparts. This phenomenon raises intriguing questions about how trust and self-disclosure operate in interactions with machines, even in their simplest forms. We study the change of trust in technology through analyzing the psychological processes behind such encounters. The analysis applies theories like Social Penetration Theory and Communication Privacy Management Theory to understand the balance between perceived security and the risk of exposure when personal information and secrets are shared with machines or AI. Additionally, we draw on philosophical perspectives, such as posthumanism and phenomenology, to engage with broader questions about trust, privacy, and vulnerability in the digital age. Rapid incorporation of AI into our most private areas challenges us to rethink and redefine our ethical responsibilities.
Structural Similarity in Deep Features: Image Quality Assessment Robust to Geometrically Disparate Reference
2024-12-27
Keke Zhang, Weiling Chen, Tiesong Zhao, Zhou Wang
Image Quality Assessment (IQA) with references plays an important role in optimizing and evaluating computer vision tasks. Traditional methods assume that all pixels of the reference and test images are fully aligned. Such Aligned-Reference IQA (AR-IQA) approaches fail to address many real-world problems with various geometric deformations between the two images. Although significant effort has been made to attack Geometrically-Disparate-Reference IQA (GDR-IQA) problem, it has been addressed in a task-dependent fashion, for example, by dedicated designs for image super-resolution and retargeting, or by assuming the geometric distortions to be small that can be countered by translation-robust filters or by explicit image registrations. Here we rethink this problem and propose a unified, non-training-based Deep Structural Similarity (DeepSSIM) approach to address the above problems in a single framework, which assesses structural similarity of deep features in a simple but efficient way and uses an attention calibration strategy to alleviate attention deviation. The proposed method, without application-specific design, achieves state-of-the-art performance on AR-IQA datasets and meanwhile shows strong robustness to various GDR-IQA test cases. Interestingly, our test also shows the effectiveness of DeepSSIM as an optimization tool for training image super-resolution, enhancement and restoration, implying an even wider generalizability. \footnote{Source code will be made public after the review is completed.
Optimality Conditions for Model Predictive Control: Rethinking Predictive Model Design
2024-12-24
Akhil S Anand, Arash Bahari Kordabad, Mario Zanon, Sebastien Gros
Optimality is a critical aspect of Model Predictive Control (MPC), especially in economic MPC. However, achieving optimality in MPC presents significant challenges, and may even be impossible, due to inherent inaccuracies in the predictive models. Predictive models often fail to accurately capture the true system dynamics, such as in the presence of stochasticity, leading to suboptimal MPC policies. In this paper, we establish the necessary and sufficient conditions on the underlying prediction model for an MPC scheme to achieve closed-loop optimality. Interestingly, these conditions are counterintuitive to the traditional approach of building predictive models that best fit the data. These conditions present a mathematical foundation for constructing models that are directly linked to the performance of the resulting MPC scheme.
Rethinking Cancer Gene Identification through Graph Anomaly Analysis
2024-12-23
Yilong Zang, Lingfei Ren, Yue Li, Zhikang Wang, David Antony Selby, Zheng Wang, Sebastian Josef Vollmer, Hongzhi Yin, Jiangning Song, Junhang Wu
Graph neural networks (GNNs) have shown promise in integrating protein-protein interaction (PPI) networks for identifying cancer genes in recent studies. However, due to the insufficient modeling of the biological information in PPI networks, more faithfully depiction of complex protein interaction patterns for cancer genes within the graph structure remains largely unexplored. This study takes a pioneering step toward bridging biological anomalies in protein interactions caused by cancer genes to statistical graph anomaly. We find a unique graph anomaly exhibited by cancer genes, namely weight heterogeneity, which manifests as significantly higher variance in edge weights of cancer gene nodes within the graph. Additionally, from the spectral perspective, we demonstrate that the weight heterogeneity could lead to the "flattening out" of spectral energy, with a concentration towards the extremes of the spectrum. Building on these insights, we propose the HIerarchical-Perspective Graph Neural Network (HIPGNN) that not only determines spectral energy distribution variations on the spectral perspective, but also perceives detailed protein interaction context on the spatial perspective. Extensive experiments are conducted on two reprocessed datasets STRINGdb and CPDB, and the experimental results demonstrate the superiority of HIPGNN.
ErasableMask: A Robust and Erasable Privacy Protection Scheme against Black-box Face Recognition Models
2024-12-22
Sipeng Shen, Yunming Zhang, Dengpan Ye, Xiuwen Shi, Long Tang, Haoran Duan, Jiacheng Deng, Ziyi Liu
While face recognition (FR) models have brought remarkable convenience in face verification and identification, they also pose substantial privacy risks to the public. Existing facial privacy protection schemes usually adopt adversarial examples to disrupt face verification of FR models. However, these schemes often suffer from weak transferability against black-box FR models and permanently damage the identifiable information that cannot fulfill the requirements of authorized operations such as forensics and authentication. To address these limitations, we propose ErasableMask, a robust and erasable privacy protection scheme against black-box FR models. Specifically, via rethinking the inherent relationship between surrogate FR models, ErasableMask introduces a novel meta-auxiliary attack, which boosts black-box transferability by learning more general features in a stable and balancing optimization strategy. It also offers a perturbation erasion mechanism that supports the erasion of semantic perturbations in protected face without degrading image quality. To further improve performance, ErasableMask employs a curriculum learning strategy to mitigate optimization conflicts between adversarial attack and perturbation erasion. Extensive experiments on the CelebA-HQ and FFHQ datasets demonstrate that ErasableMask achieves the state-of-the-art performance in transferability, achieving over 72% confidence on average in commercial FR systems. Moreover, ErasableMask also exhibits outstanding perturbation erasion performance, achieving over 90% erasion success rate.
Rethinking Performance Analysis for Configurable Software Systems: A Case Study from a Fitness Landscape Perspective
2024-12-22
Mingyu Huang, Peili Mao, Ke Li
Modern software systems are often highly configurable to tailor varied requirements from diverse stakeholders. Understanding the mapping between configurations and the desired performance attributes plays a fundamental role in advancing the controllability and tuning of the underlying system, yet has long been a dark hole of knowledge due to its black-box nature. While there have been previous efforts in performance analysis for these systems, they analyze the configurations as isolated data points without considering their inherent spatial relationships. This renders them incapable of interrogating many important aspects of the configuration space like local optima. In this work, we advocate a novel perspective to rethink performance analysis -- modeling the configuration space as a structured ``landscape''. To support this proposition, we designed \our, an open-source, graph data mining empowered fitness landscape analysis (FLA) framework. By applying this framework to $86$M benchmarked configurations from $32$ running workloads of $3$ real-world systems, we arrived at $6$ main findings, which together constitute a holistic picture of the landscape topography, with thorough discussions about their implications on both configuration tuning and performance modeling.
2024-12-21
Jiayi Zhu, Qing Guo, Felix Juefei-Xu, Yihao Huang, Yang Liu, Geguang Pu
The task of co-saliency object detection (Co-SOD) seeks to identify common, salient objects across a collection of images by examining shared visual features. However, traditional Co-SOD methods often encounter limitations when faced with diverse object variations (e.g., different postures) and irrelevant background elements that introduce noise. To address these challenges, we propose ConceptCoSOD, a novel concept-guided approach that leverages text semantic information to enhance Co-SOD performance by guiding the model to focus on consistent object features. Through rethinking Co-SOD as an (image-text)-to-image task instead of an image-to-image task, ConceptCoSOD first captures shared semantic concepts within an image group and then uses them as guidance for precise object segmentation in complex scenarios. Experimental results on three benchmark datasets and six corruptions reveal that ConceptCoSOD significantly improves detection accuracy, especially in challenging settings with considerable background distractions and object variability.
Low-light image enhancement (LLIE) is a fundamental task in computational photography, aiming to improve illumination, reduce noise, and enhance the image quality of low-light images. While recent advancements primarily focus on customizing complex neural network models, we have observed significant redundancy in these models, limiting further performance improvement. In this paper, we investigate and rethink the model redundancy for LLIE, identifying parameter harmfulness and parameter uselessness. Inspired by the rethinking, we propose two innovative techniques to mitigate model redundancy while improving the LLIE performance: Attention Dynamic Reallocation (ADR) and Parameter Orthogonal Generation (POG). ADR dynamically reallocates appropriate attention based on original attention, thereby mitigating parameter harmfulness. POG learns orthogonal basis embeddings of parameters and prevents degradation to static parameters, thereby mitigating parameter uselessness. Experiments validate the effectiveness of our techniques. We will release the code to the public.
Rethinking Hardware Impairments in Multi-User Systems: Can FAS Make a Difference?
2024-12-20
Junteng Yao, Tuo Wu, Liaoshi Zhou, Ming Jin, Cunhua Pan, Maged Elkashlan, Fumiyuki Adachi, George K. Karagiannidis, Naofal Al-Dhahir, Chau Yuen
In this paper, we analyze the role of fluid antenna systems (FAS) in multi-user systems with hardware impairments (HIs). Specifically, we investigate a scenario where a base station (BS) equipped with multiple fluid antennas communicates with multiple users (CUs), each equipped with a single fluid antenna. Our objective is to maximize the minimum communication rate among all users by jointly optimizing the BS's transmit beamforming, the positions of its transmit fluid antennas, and the positions of the CUs' receive fluid antennas. To address this non-convex problem, we propose a block coordinate descent (BCD) algorithm integrating semidefinite relaxation (SDR), rank-one constraint relaxation (SRCR), successive convex approximation (SCA), and majorization-minimization (MM). Simulation results demonstrate that FAS significantly enhances system performance and robustness, with notable gains when both the BS and CUs are equipped with fluid antennas. Even under low transmit power conditions, deploying FAS at the BS alone yields substantial performance gains. However, the effectiveness of FAS depends on the availability of sufficient movement space, as space constraints may limit its benefits compared to fixed antenna strategies. Our findings highlight the potential of FAS to mitigate HIs and enhance multi-user system performance, while emphasizing the need for practical deployment considerations.
Bell's theorem proves the incompatibility between quantum mechanics and local realistic hidden-variable theories. In this paper we show that, contrary to a common belief, the theoretical proof of Bell's theorem is not affected by counterfactual reasoning. Then, we demonstrate that the experimental verification of this theorem may be affected in an unknowable way by our ignorance about the probability distribution of the hidden variables. Our study is based on the standard theory of random variables, and lays the groundwork for a critical rethinking of Bell's theorem and its consequences.
Large Language Models (LLMs) are increasingly employed in real-world applications, driving the need to evaluate the trustworthiness of their generated text. To this end, reliable uncertainty estimation is essential. Since current LLMs generate text autoregressively through a stochastic process, the same prompt can lead to varying outputs. Consequently, leading uncertainty estimation methods generate and analyze multiple output sequences to determine the LLM's uncertainty. However, generating output sequences is computationally expensive, making these methods impractical at scale. In this work, we inspect the theoretical foundations of the leading methods and explore new directions to enhance their computational efficiency. Building on the framework of proper scoring rules, we find that the negative log-likelihood of the most likely output sequence constitutes a theoretically grounded uncertainty measure. To approximate this alternative measure, we propose G-NLL, which has the advantage of being obtained using only a single output sequence generated by greedy decoding. This makes uncertainty estimation more efficient and straightforward, while preserving theoretical rigor. Empirical results demonstrate that G-NLL achieves state-of-the-art performance across various LLMs and tasks. Our work lays the foundation for efficient and reliable uncertainty estimation in natural language generation, challenging the necessity of more computationally involved methods currently leading the field.
Creating a high-fidelity, animatable 3D full-body avatar from a single image is a challenging task due to the diverse appearance and poses of humans and the limited availability of high-quality training data. To achieve fast and high-quality human reconstruction, this work rethinks the task from the perspectives of dataset, model, and representation. First, we introduce a large-scale HUman-centric GEnerated dataset, HuGe100K, consisting of 100K diverse, photorealistic sets of human images. Each set contains 24-view frames in specific human poses, generated using a pose-controllable image-to-multi-view model. Next, leveraging the diversity in views, poses, and appearances within HuGe100K, we develop a scalable feed-forward transformer model to predict a 3D human Gaussian representation in a uniform space from a given human image. This model is trained to disentangle human pose, body shape, clothing geometry, and texture. The estimated Gaussians can be animated without post-processing. We conduct comprehensive experiments to validate the effectiveness of the proposed dataset and method. Our model demonstrates the ability to efficiently reconstruct photorealistic humans at 1K resolution from a single input image using a single GPU instantly. Additionally, it seamlessly supports various applications, as well as shape and texture editing tasks. Project page: https://yiyuzhuang.github.io/IDOL/.
Countries across the world use more land for golf courses than wind or solar energy
2024-12-19
Jann Weinand, Tristan Pelser, Max Kleinebrahm, Detlef Stolten
Land use is a critical factor in the siting of renewable energy facilities and is often scrutinized due to perceived conflicts with other land demands. Meanwhile, substantial areas are devoted to activities such as golf, which are accessible to only a select few and have a significant land and environmental footprint. Our study shows that in countries such as the United States and the United Kingdom, far more land is allocated to golf courses than to renewable energy facilities. Areas equivalent to those currently used for golf could support the installation of up to 842 GW of solar and 659 GW of wind capacity in the top ten countries with the most golf courses. In many of these countries, this potential exceeds both current installed capacity and medium-term projections. These findings underscore the untapped potential of rethinking land use priorities to accelerate the transition to renewable energy.
Next Patch Prediction for Autoregressive Visual Generation
2024-12-19
Yatian Pang, Peng Jin, Shuo Yang, Bin Lin, Bin Zhu, Zhenyu Tang, Liuhan Chen, Francis E. H. Tay, Ser-Nam Lim, Harry Yang, Li Yuan
Autoregressive models, built based on the Next Token Prediction (NTP) paradigm, show great potential in developing a unified framework that integrates both language and vision tasks. Pioneering works introduce NTP to autoregressive visual generation tasks. In this work, we rethink the NTP for autoregressive image generation and extend it to a novel Next Patch Prediction (NPP) paradigm. Our key idea is to group and aggregate image tokens into patch tokens with higher information density. By using patch tokens as a more compact input sequence, the autoregressive model is trained to predict the next patch, significantly reducing computational costs. To further exploit the natural hierarchical structure of image data, we propose a multi-scale coarse-to-fine patch grouping strategy. With this strategy, the training process begins with a large patch size and ends with vanilla NTP where the patch size is 1$\times$1, thus maintaining the original inference process without modifications. Extensive experiments across a diverse range of model sizes demonstrate that NPP could reduce the training cost to around 0.6 times while improving image generation quality by up to 1.0 FID score on the ImageNet 256x256 generation benchmark. Notably, our method retains the original autoregressive model architecture without introducing additional trainable parameters or specifically designing a custom image tokenizer, offering a flexible and plug-and-play solution for enhancing autoregressive visual generation.
A world model provides an agent with a representation of its environment, enabling it to predict the causal consequences of its actions. Current world models typically cannot directly and explicitly imitate the actual environment in front of a robot, often resulting in unrealistic behaviors and hallucinations that make them unsuitable for real-world robotics applications. To overcome those challenges, we propose to rethink robot world models as learnable digital twins. We introduce DreMa, a new approach for constructing digital twins automatically using learned explicit representations of the real world and its dynamics, bridging the gap between traditional digital twins and world models. DreMa replicates the observed world and its structure by integrating Gaussian Splatting and physics simulators, allowing robots to imagine novel configurations of objects and to predict the future consequences of robot actions thanks to its compositionality. We leverage this capability to generate new data for imitation learning by applying equivariant transformations to a small set of demonstrations. Our evaluations across various settings demonstrate significant improvements in accuracy and robustness by incrementing actions and object distributions, reducing the data needed to learn a policy and improving the generalization of the agents. As a highlight, we show that a real Franka Emika Panda robot, powered by DreMa's imagination, can successfully learn novel physical tasks from just a single example per task variation (one-shot policy learning). Our project page can be found in: https://dreamtomanipulate.github.io/.
2024-12-18
Amedeo Pachera, Mattia Palmiotto, Angela Bonifati, Andrea Mauri
Graphs are expressive abstractions representing more effectively relationships in data and enabling data science tasks. They are also a widely adopted paradigm in causal inference focusing on causal directed acyclic graphs. Causal DAGs (Directed Acyclic Graphs) are manually curated by domain experts, but they are never validated, stored and integrated as data artifacts in a graph data management system. In this paper, we delineate our vision to align these two paradigms, namely causal analysis and property graphs, the latter being the cornerstone of modern graph databases. To articulate this vision, a paradigm shift is required leading to rethinking property graph data models with hypernodes and structural equations, graph query semantics and query constructs, and the definition of graph views to account for causality operators. Moreover, several research problems and challenges arise aiming at automatically extracting causal models from the underlying graph observational data, aligning and integrating disparate causal graph models into unified ones along with their maintenance upon the changes in the underlying data. The above vision will allow to make graph databases aware of causal knowledge and pave the way to data-driven personalized decision-making in several scientific fields.
Rethink the Evaluation Protocol of Model Merging on Classification Task
2024-12-18
Fanshuang Kong, Richong Zhang, Zhijie Nie, Ziqiao Wang
Model merging combines multiple fine-tuned models into a single one via parameter fusion, achieving improvements across many tasks. However, in the classification task, we find a misalignment issue between merging outputs and the fine-tuned classifier, which limits its effectiveness. In this paper, we demonstrate the following observations: (1) The embedding quality of the merging outputs is already very high, and the primary reason for the differences in classification performance lies in the misalignment issue. (2) We propose FT-Classifier, a new protocol that fine-tunes an aligned classifier with few-shot samples to alleviate misalignment, enabling better evaluation of merging outputs and improved classification performance. (3) The misalignment is relatively straightforward and can be formulated as an orthogonal transformation. Experiments demonstrate the existence of misalignment and the effectiveness of our FT-Classifier evaluation protocol.
F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration
2024-12-17
Lu Liu, Huiyu Duan, Qiang Hu, Liu Yang, Chunlei Cai, Tianxiao Ye, Huayu Liu, Xiaoyun Zhang, Guangtao Zhai
Artificial intelligence generative models exhibit remarkable capabilities in content creation, particularly in face image generation, customization, and restoration. However, current AI-generated faces (AIGFs) often fall short of human preferences due to unique distortions, unrealistic details, and unexpected identity shifts, underscoring the need for a comprehensive quality evaluation framework for AIGFs. To address this need, we introduce FaceQ, a large-scale, comprehensive database of AI-generated Face images with fine-grained Quality annotations reflecting human preferences. The FaceQ database comprises 12,255 images generated by 29 models across three tasks: (1) face generation, (2) face customization, and (3) face restoration. It includes 32,742 mean opinion scores (MOSs) from 180 annotators, assessed across multiple dimensions: quality, authenticity, identity (ID) fidelity, and text-image correspondence. Using the FaceQ database, we establish F-Bench, a benchmark for comparing and evaluating face generation, customization, and restoration models, highlighting strengths and weaknesses across various prompts and evaluation dimensions. Additionally, we assess the performance of existing image quality assessment (IQA), face quality assessment (FQA), AI-generated content image quality assessment (AIGCIQA), and preference evaluation metrics, manifesting that these standard metrics are relatively ineffective in evaluating authenticity, ID fidelity, and text-image correspondence. The FaceQ database will be publicly available upon publication.
Rethinking Diffusion-Based Image Generators for Fundus Fluorescein Angiography Synthesis on Limited Data
2024-12-17
Chengzhou Yu, Huihui Fang, Hongqiu Wang, Ting Deng, Qing Du, Yanwu Xu, Weihua Yang
Fundus imaging is a critical tool in ophthalmology, with different imaging modalities offering unique advantages. For instance, fundus fluorescein angiography (FFA) can accurately identify eye diseases. However, traditional invasive FFA involves the injection of sodium fluorescein, which can cause discomfort and risks. Generating corresponding FFA images from non-invasive fundus images holds significant practical value but also presents challenges. First, limited datasets constrain the performance and effectiveness of models. Second, previous studies have primarily focused on generating FFA for single diseases or single modalities, often resulting in poor performance for patients with various ophthalmic conditions. To address these issues, we propose a novel latent diffusion model-based framework, Diffusion, which introduces a fine-tuning protocol to overcome the challenge of limited medical data and unleash the generative capabilities of diffusion models. Furthermore, we designed a new approach to tackle the challenges of generating across different modalities and disease types. On limited datasets, our framework achieves state-of-the-art results compared to existing methods, offering significant potential to enhance ophthalmic diagnostics and patient care. Our code will be released soon to support further research in this field.
Gramian Multimodal Representation Learning and Alignment
2024-12-16
Giordano Cicchetti, Eleonora Grassucci, Luigi Sigillo, Danilo Comminiello
Human perception integrates multiple modalities, such as vision, hearing, and language, into a unified understanding of the surrounding reality. While recent multimodal models have achieved significant progress by aligning pairs of modalities via contrastive learning, their solutions are unsuitable when scaling to multiple modalities. These models typically align each modality to a designated anchor without ensuring the alignment of all modalities with each other, leading to suboptimal performance in tasks requiring a joint understanding of multiple modalities. In this paper, we structurally rethink the pairwise conventional approach to multimodal learning and we present the novel Gramian Representation Alignment Measure (GRAM), which overcomes the above-mentioned limitations. GRAM learns and then aligns $n$ modalities directly in the higher-dimensional space in which modality embeddings lie by minimizing the Gramian volume of the $k$-dimensional parallelotope spanned by the modality vectors, ensuring the geometric alignment of all modalities simultaneously. GRAM can replace cosine similarity in any downstream method, holding for 2 to $n$ modalities and providing more meaningful alignment with respect to previous similarity measures. The novel GRAM-based contrastive loss function enhances the alignment of multimodal models in the higher-dimensional embedding space, leading to new state-of-the-art performance in downstream tasks such as video-audio-text retrieval and audio-video classification. The project page, the code, and the pretrained models are available at https://ispamm.github.io/GRAM/.
Zeal: Rethinking Large-Scale Resource Allocation with "Decouple and Decompose"
2024-12-16
Zhiying Xu, Francis Y. Yan, Minlan Yu
Resource allocation is fundamental for cloud systems to ensure efficient resource sharing among tenants. However, the scale of such optimization problems has outgrown the capabilities of commercial solvers traditionally employed in production. To scale up resource allocation, prior approaches either tailor solutions to specific problems or rely on assumptions tied to particular workloads. In this work, we revisit real-world resource allocation problems and uncover a common underlying structure: a vast majority of these problems are inherently separable, i.e., they optimize the aggregate utility of individual resource and demand allocations, under separate constraints for each resource and each demand. Building on this insight, we develop DeDe, a general, scalable, and theoretically grounded framework for accelerating resource allocation through a "decouple and decompose" approach. DeDe systematically decouples entangled resource and demand constraints, thereby decomposing the overall optimization into alternating per-resource and per-demand allocations, which can then be solved efficiently and in parallel. We have implemented DeDe as a library extension to an open-source solver, maintaining a familiar user interface. Experimental results across three prominent resource allocation tasks -- traffic engineering, cluster scheduling, and load balancing -- demonstrate DeDe's substantial speedups and robust allocation quality.
Algorithmic Idealism I: Reconceptualizing Reality Through Information and Experience
2024-12-16
Krzysztof Sienicki
Algorithmic idealism represents a transformative approach to understanding reality, emphasizing the informational structure of self-states and their algorithmic transitions over traditional notions of an external, objective universe. Rooted in algorithmic information theory, it redefines reality as a sequence of self-state transitions governed by principles such as Solomonoff induction. This framework offers a unified solution to longstanding challenges in quantum mechanics, cosmology, and metaphysics, addressing issues like the measurement problem, the Boltzmann brain paradox, and the simulation hypothesis. Algorithmic idealism shifts the focus from describing an independent external world to understanding first-person experiences, providing epistemic interpretations of physical theories and dissolving metaphysical divides between "real" and simulated realities. Beyond resolving these conceptual challenges, it raises profound ethical questions regarding the continuity, duplication, and termination of informational entities, reshaping discussions on identity, consciousness, and existence in the digital and quantum age. By offering a mathematically rigorous yet philosophically innovative framework, algorithmic idealism invites a rethinking of reality as an emergent property of informational dynamics rather than a static external construct.
PGD-Imp: Rethinking and Unleashing Potential of Classic PGD with Dual Strategies for Imperceptible Adversarial Attacks
2024-12-15
Jin Li, Zitong Yu, Ziqiang He, Z. Jane Wang, Xiangui Kang
Imperceptible adversarial attacks have recently attracted increasing research interests. Existing methods typically incorporate external modules or loss terms other than a simple $l_p$-norm into the attack process to achieve imperceptibility, while we argue that such additional designs may not be necessary. In this paper, we rethink the essence of imperceptible attacks and propose two simple yet effective strategies to unleash the potential of PGD, the common and classical attack, for imperceptibility from an optimization perspective. Specifically, the Dynamic Step Size is introduced to find the optimal solution with minimal attack cost towards the decision boundary of the attacked model, and the Adaptive Early Stop strategy is adopted to reduce the redundant strength of adversarial perturbations to the minimum level. The proposed PGD-Imperceptible (PGD-Imp) attack achieves state-of-the-art results in imperceptible adversarial attacks for both untargeted and targeted scenarios. When performing untargeted attacks against ResNet-50, PGD-Imp attains 100$\%$ (+0.3$\%$) ASR, 0.89 (-1.76) $l_2$ distance, and 52.93 (+9.2) PSNR with 57s (-371s) running time, significantly outperforming existing methods.
Rethinking Software Misconfigurations in the Real World: An Empirical Study and Literature Analysis
2024-12-15
Yuhao Liu, Yingnan Zhou, Hanfeng Zhang, Zhiwei Chang, Sihan Xu, Yan Jia, Wei Wang, Zheli Liu
Software misconfiguration has consistently been a major reason for software failures. Over the past twenty decades, much work has been done to detect and diagnose software misconfigurations. However, there is still a gap between real-world misconfigurations and the literature. It is desirable to investigate whether existing taxonomy and tools are applicable for real-world misconfigurations in modern software. In this paper, we conduct an empirical study on 823 real-world misconfiguration issues, based on which we propose a novel classification of the root causes of software misconfigurations, i.e., constraint violation, resource unavailability, component-dependency error, and misunderstanding of configuration effects. Then, we systematically review the literature on misconfiguration troubleshooting, and study the trends of research and the practicality of the tools and datasets in this field. We find that the research targets have changed from fundamental software to advanced applications (e.g., cloud service). In the meanwhile, the research on non-crash misconfigurations such as performance degradation and security risks also has a significant growth. Despite the progress, a majority of studies lack reproducibility due to the unavailable tools and evaluation datasets. In total, only six tools and two datasets are publicly available. However, the adaptability of these tools limit their practical use on real-world misconfigurations. We also summarize the important challenges and several suggestions to facilitate the research on software misconfiguration.
Chain-of-thought (CoT) reasoning has emerged as an effective approach for activating latent capabilities in LLMs. Interestingly, we observe that both CoT reasoning and self-training share the core objective: iteratively leveraging model-generated information to progressively reduce prediction uncertainty. Building on this insight, we propose a novel CoT framework to improve reasoning performance. Our framework integrates two key components: (i) a task-specific prompt module that optimizes the initial reasoning process, and (ii) an adaptive reasoning iteration module that dynamically refines the reasoning process and addresses the limitations of previous CoT approaches, \ie over-reasoning and high similarity between consecutive reasoning iterations. Extensive experiments demonstrate that the proposed method achieves significant advantages in both performance and computational efficiency.
Scene text spotting has attracted the enthusiasm of relative researchers in recent years. Most existing scene text spotters follow the detection-then-recognition paradigm, where the vanilla detection module hardly determines the reading order and leads to failure recognition. After rethinking the auto-regressive scene text recognition method, we find that a well-trained recognizer can implicitly perceive the local semantics of all characters in a complete word or a sentence without a character-level detection module. Local semantic knowledge not only includes text content but also spatial information in the right reading order. Motivated by the above analysis, we propose the Local Semantics Guided scene text Spotter (LSGSpotter), which auto-regressively decodes the position and content of characters guided by the local semantics. Specifically, two effective modules are proposed in LSGSpotter. On the one hand, we design a Start Point Localization Module (SPLM) for locating text start points to determine the right reading order. On the other hand, a Multi-scale Adaptive Attention Module (MAAM) is proposed to adaptively aggregate text features in a local area. In conclusion, LSGSpotter achieves the arbitrary reading order spotting task without the limitation of sophisticated detection, while alleviating the cost of computational resources with the grid sampling strategy. Extensive experiment results show LSGSpotter achieves state-of-the-art performance on the InverseText benchmark. Moreover, our spotter demonstrates superior performance on English benchmarks for arbitrary-shaped text, achieving improvements of 0.7\% and 2.5\% on Total-Text and SCUT-CTW1500, respectively. These results validate our text spotter is effective for scene texts in arbitrary reading order and shape.
Object detection in Unmanned Aerial Vehicle (UAV) images has emerged as a focal area of research, which presents two significant challenges: i) objects are typically small and dense within vast images; ii) computational resource constraints render most models unsuitable for real-time deployment. Current real-time object detectors are not optimized for UAV images, and complex methods designed for small object detection often lack real-time capabilities. To address these challenges, we propose a novel detector, RemDet (Reparameter efficient multiplication Detector). Our contributions are as follows: 1) Rethinking the challenges of existing detectors for small and dense UAV images, and proposing information loss as a design guideline for efficient models. 2) We introduce the ChannelC2f module to enhance small object detection performance, demonstrating that high-dimensional representations can effectively mitigate information loss. 3) We design the GatedFFN module to provide not only strong performance but also low latency, effectively addressing the challenges of real-time detection. Our research reveals that GatedFFN, through the use of multiplication, is more cost-effective than feed-forward networks for high-dimensional representation. 4) We propose the CED module, which combines the advantages of ViT and CNN downsampling to effectively reduce information loss. It specifically enhances context information for small and dense objects. Extensive experiments on large UAV datasets, Visdrone and UAVDT, validate the real-time efficiency and superior performance of our methods. On the challenging UAV dataset VisDrone, our methods not only provided state-of-the-art results, improving detection by more than 3.4%, but also achieve 110 FPS on a single 4090.
PanSR: An Object-Centric Mask Transformer for Panoptic Segmentation
2024-12-13
Lojze Žust, Matej Kristan
Panoptic segmentation is a fundamental task in computer vision and a crucial component for perception in autonomous vehicles. Recent mask-transformer-based methods achieve impressive performance on standard benchmarks but face significant challenges with small objects, crowded scenes and scenes exhibiting a wide range of object scales. We identify several fundamental shortcomings of the current approaches: (i) the query proposal generation process is biased towards larger objects, resulting in missed smaller objects, (ii) initially well-localized queries may drift to other objects, resulting in missed detections, (iii) spatially well-separated instances may be merged into a single mask causing inconsistent and false scene interpretations. To address these issues, we rethink the individual components of the network and its supervision, and propose a novel method for panoptic segmentation PanSR. PanSR effectively mitigates instance merging, enhances small-object detection and increases performance in crowded scenes, delivering a notable +3.4 PQ improvement over state-of-the-art on the challenging LaRS benchmark, while reaching state-of-the-art performance on Cityscapes. The code and models will be publicly available at https://github.com/lojzezust/PanSR.
Data augmentation methods, especially SoTA interpolation-based methods such as Fair Mixup, have been widely shown to increase model fairness. However, this fairness is evaluated on metrics that do not capture model uncertainty and on datasets with only one, relatively large, minority group. As a remedy, multicalibration has been introduced to measure fairness while accommodating uncertainty and accounting for multiple minority groups. However, existing methods of improving multicalibration involve reducing initial training data to create a holdout set for post-processing, which is not ideal when minority training data is already sparse. This paper uses multicalibration to more rigorously examine data augmentation for classification fairness. We stress-test four versions of Fair Mixup on two structured data classification problems with up to 81 marginalized groups, evaluating multicalibration violations and balanced accuracy. We find that on nearly every experiment, Fair Mixup \textit{worsens} baseline performance and fairness, but the simple vanilla Mixup \textit{outperforms} both Fair Mixup and the baseline, especially when calibrating on small groups. \textit{Combining} vanilla Mixup with multicalibration post-processing, which enforces multicalibration through post-processing on a holdout set, further increases fairness.
Image classification serves as the cornerstone of computer vision, traditionally achieved through discriminative models based on deep neural networks. Recent advancements have introduced classification methods derived from generative models, which offer the advantage of zero-shot classification. However, these methods suffer from two main drawbacks: high computational overhead and inferior performance compared to discriminative models. Inspired by the coordinated cognitive processes of rapid-slow pathway interactions in the human brain during visual signal recognition, we propose the Diffusion-Based Discriminative Model Enhancement Framework (DBMEF). This framework seamlessly integrates discriminative and generative models in a training-free manner, leveraging discriminative models for initial predictions and endowing deep neural networks with rethinking capabilities via diffusion models. Consequently, DBMEF can effectively enhance the classification accuracy and generalization capability of discriminative models in a plug-and-play manner. We have conducted extensive experiments across 17 prevalent deep model architectures with different training methods, including both CNN-based models such as ResNet and Transformer-based models like ViT, to demonstrate the effectiveness of the proposed DBMEF. Specifically, the framework yields a 1.51\% performance improvement for ResNet-50 on the ImageNet dataset and 3.02\% on the ImageNet-A dataset. In conclusion, our research introduces a novel paradigm for image classification, demonstrating stable improvements across different datasets and neural networks. The code is available at https://github.com/ChunXiaostudy/DBMEF.
Multi-objective learning aims to optimize multiple objectives simultaneously with a single model for achieving a balanced and satisfying performance on all these objectives. However, it suffers from the difficulty to formalize and conduct the exact learning process, especially considering the possible conflicts between objectives. Existing approaches explores to resolve this primarily in two directions: adapting modeling structure or constraining optimization with certain assumptions. However, a primary issue is that their presuppositions for the effectiveness of their design are insufficient to guarantee the its generality in real-world applications. What's worse, the high space and computation complexity issue makes it even harder to apply them in large-scale, complicated environment such as the recommender systems. To address these issues, we propose a general framework for automatically learning to achieve multiple objectives based on the existing sequential data. We apply the goal-conditioned supervised learning (GCSL) framework to multi-objective learning, by extending the definition of goals from one-dimensional scalar to multi-dimensional vector that perfectly disentangle the representation of different objectives. Meanwhile, GCSL enables the model to simultaneously learn to achieve each objective in a concise supervised learning way, simply guided by existing sequences in the offline data. No additional constraint, special model structure design, or complex optimization algorithms are further required. Apart from that, we formally analyze the property of the goals in GCSL and then firstly propose a goal-generation framework to gain achievable and reasonable goals for inference. Extensive experiments are conducted on real-world recommendation datasets, demonstrating the effectiveness of the proposed method and exploring the feasibility of the goal-generation strategies in GCSL.
Rethinking Mean Square Error: Why Information is a Superior Assessment of Estimators
2024-12-11
Paul Vos
James-Stein (JS) estimators have been described as showing the inadequacy of maximum likelihood estimation when assessed using mean square error (MSE). We claim the problem is not with maximum likelihood (ML) but with MSE. When MSE is replaced with a measure $\Lambda$ of the information utilized by a statistic, likelihood based methods are superior. The information measure $\Lambda$ describes not just point estimators but extends to Fisher's view of estimation so that we not only reconsider how estimators are assessed but also how we define an estimator. Fisher information and his views on the role of parameters, interpretation of probability, and logic of statistical inference fit well with $\Lambda$ as measure of information.
Video semantic segmentation(VSS) has been widely employed in lots of fields, such as simultaneous localization and mapping, autonomous driving and surveillance. Its core challenge is how to leverage temporal information to achieve better segmentation. Previous efforts have primarily focused on pixel-level static-dynamic contexts matching, utilizing techniques such as optical flow and attention mechanisms. Instead, this paper rethinks static-dynamic contexts at the class level and proposes a novel static-dynamic class-level perceptual consistency (SD-CPC) framework. In this framework, we propose multivariate class prototype with contrastive learning and a static-dynamic semantic alignment module. The former provides class-level constraints for the model, obtaining personalized inter-class features and diversified intra-class features. The latter first establishes intra-frame spatial multi-scale and multi-level correlations to achieve static semantic alignment. Then, based on cross-frame static perceptual differences, it performs two-stage cross-frame selective aggregation to achieve dynamic semantic alignment. Meanwhile, we propose a window-based attention map calculation method that leverages the sparsity of attention points during cross-frame aggregation to reduce computation cost. Extensive experiments on VSPW and Cityscapes datasets show that the proposed approach outperforms state-of-the-art methods. Our implementation will be open-sourced on GitHub.
Rethinking Comprehensive Benchmark for Chart Understanding: A Perspective from Scientific Literature
2024-12-11
Lingdong Shen, Qigqi, Kun Ding, Gaofeng Meng, Shiming Xiang
Scientific Literature charts often contain complex visual elements, including multi-plot figures, flowcharts, structural diagrams and etc. Evaluating multimodal models using these authentic and intricate charts provides a more accurate assessment of their understanding abilities. However, existing benchmarks face limitations: a narrow range of chart types, overly simplistic template-based questions and visual elements, and inadequate evaluation methods. These shortcomings lead to inflated performance scores that fail to hold up when models encounter real-world scientific charts. To address these challenges, we introduce a new benchmark, Scientific Chart QA (SCI-CQA), which emphasizes flowcharts as a critical yet often overlooked category. To overcome the limitations of chart variety and simplistic visual elements, we curated a dataset of 202,760 image-text pairs from 15 top-tier computer science conferences papers over the past decade. After rigorous filtering, we refined this to 37,607 high-quality charts with contextual information. SCI-CQA also introduces a novel evaluation framework inspired by human exams, encompassing 5,629 carefully curated questions, both objective and open-ended. Additionally, we propose an efficient annotation pipeline that significantly reduces data annotation costs. Finally, we explore context-based chart understanding, highlighting the crucial role of contextual information in solving previously unanswerable questions.
Robust Multiple Description Neural Video Codec with Masked Transformer for Dynamic and Noisy Networks
2024-12-10
Xinyue Hu, Wei Ye, Jiaxiang Tang, Eman Ramadan, Zhi-Li Zhang
Multiple Description Coding (MDC) is a promising error-resilient source coding method that is particularly suitable for dynamic networks with multiple (yet noisy and unreliable) paths. However, conventional MDC video codecs suffer from cumbersome architectures, poor scalability, limited loss resilience, and lower compression efficiency. As a result, MDC has never been widely adopted. Inspired by the potential of neural video codecs, this paper rethinks MDC design. We propose a novel MDC video codec, NeuralMDC, demonstrating how bidirectional transformers trained for masked token prediction can vastly simplify the design of MDC video codec. To compress a video, NeuralMDC starts by tokenizing each frame into its latent representation and then splits the latent tokens to create multiple descriptions containing correlated information. Instead of using motion prediction and warping operations, NeuralMDC trains a bidirectional masked transformer to model the spatial-temporal dependencies of latent representations and predict the distribution of the current representation based on the past. The predicted distribution is used to independently entropy code each description and infer any potentially lost tokens. Extensive experiments demonstrate NeuralMDC achieves state-of-the-art loss resilience with minimal sacrifices in compression efficiency, significantly outperforming the best existing residual-coding-based error-resilient neural video codec.
Modern affective computing systems rely heavily on datasets with human-annotated emotion labels, for training and evaluation. However, human annotations are expensive to obtain, sensitive to study design, and difficult to quality control, because of the subjective nature of emotions. Meanwhile, Large Language Models (LLMs) have shown remarkable performance on many Natural Language Understanding tasks, emerging as a promising tool for text annotation. In this work, we analyze the complexities of emotion annotation in the context of LLMs, focusing on GPT-4 as a leading model. In our experiments, GPT-4 achieves high ratings in a human evaluation study, painting a more positive picture than previous work, in which human labels served as the only ground truth. On the other hand, we observe differences between human and GPT-4 emotion perception, underscoring the importance of human input in annotation studies. To harness GPT-4's strength while preserving human perspective, we explore two ways of integrating GPT-4 into emotion annotation pipelines, showing its potential to flag low-quality labels, reduce the workload of human annotators, and improve downstream model learning performance and efficiency. Together, our findings highlight opportunities for new emotion labeling practices and suggest the use of LLMs as a promising tool to aid human annotation.
2024-12-09
Jiangning Zhang, Teng Hu, Haoyang He, Zhucun Xue, Yabiao Wang, Chengjie Wang, Yong Liu, Xiangtai Li, Dacheng Tao
This work focuses on developing parameter-efficient and lightweight models for dense predictions while trading off parameters, FLOPs, and performance. Our goal is to set up the new frontier of the 5M magnitude lightweight model on various downstream tasks. Inverted Residual Block (IRB) serves as the infrastructure for lightweight CNNs, but no counterparts have been recognized by attention-based design. Our work rethinks the lightweight infrastructure of efficient IRB and practical components in Transformer from a unified perspective, extending CNN-based IRB to attention-based models and abstracting a one-residual Meta Mobile Block (MMBlock) for lightweight model design. Following neat but effective design criterion, we deduce a modern Improved Inverted Residual Mobile Block (i2RMB) and improve a hierarchical Efficient MOdel (EMOv2) with no elaborate complex structures. Considering the imperceptible latency for mobile users when downloading models under 4G/5G bandwidth and ensuring model performance, we investigate the performance upper limit of lightweight models with a magnitude of 5M. Extensive experiments on various vision recognition, dense prediction, and image generation tasks demonstrate the superiority of our EMOv2 over state-of-the-art methods, e.g., EMOv2-1M/2M/5M achieve 72.3, 75.8, and 79.4 Top-1 that surpass equal-order CNN-/Attention-based models significantly. At the same time, EMOv2-5M equipped RetinaNet achieves 41.5 mAP for object detection tasks that surpasses the previous EMO-5M by +2.6. When employing the more robust training recipe, our EMOv2-5M eventually achieves 82.9 Top-1 accuracy, which elevates the performance of 5M magnitude models to a new level. Code is available at https://github.com/zhangzjn/EMOv2.
Pruning All-Rounder: Rethinking and Improving Inference Efficiency for Large Vision Language Models
2024-12-09
Wei Suo, Ji Ma, Mengyang Sun, Lin Yuanbo Wu, Peng Wang, Yanning Zhang
Although Large Vision-Language Models (LVLMs) have achieved impressive results, their high computational cost poses a significant barrier to wider application. To enhance inference efficiency, most existing approaches depend on parameter-dependent or token-dependent strategies to reduce computational demands. However, these methods typically require complex training processes and struggle to consistently select the most relevant tokens. In this paper, we systematically analyze the above challenges and provide a series of valuable insights for inference acceleration. Based on these findings, we propose a novel framework, the Pruning All-Rounder (PAR). Different from previous works, PAR develops a meta-router to adaptively organize pruning flows across both tokens and layers. With a self-supervised learning manner, our method achieves a superior balance between performance and efficiency. Notably, PAR is highly flexible, offering multiple pruning versions to address a range of pruning scenarios. The code for this work will be made publicly available.
Simplications: Why and how we should rethink data of/by/for the people in smart homes and its privacy implications
2024-12-09
Albrecht Kurze, Alexa Becker
More and more smart devices enter our homes. Often these devices come with a variety of sensors, mostly simple sensors, e.g., for light, temperature, humidity or motion. And they all collect data. While it is data of the home environment it is also data of domestic life in the home. Thus it is data of the people and by the people in the home capturing their presence, arrival and departure, typical domestic activities, bad habits, health status etc. Based on previous as well as ongoing research we know that people are actually able to make sense of simple sensor data and that they will make use of it for their own purposes. Simple sensors, when critically reflected, are often only "simple" in a technical sense. The unreflected design and use of these sensors can easily lead to unintended implications, i.e. for privacy. However, it may not even need a Big Brother or data experts or AI to make the data of these sensors sensitive, e.g., if used for lateral surveillance within families. Often unintended but wicked implications emerge despite good intentions, such as improving efficiency or energy saving through collecting sensor data. Thus sensor data from the home is actually data of/by/for the people in the home. First, we explain how this might have relevance across scales of community of people - not only for the domain of the home but also in broader meaning. Second, we relate our previous as well as ongoing research in the domain of smart homes to this topic.
Continual learning aims to update a model so that it can sequentially learn new tasks without forgetting previously acquired knowledge. Recent continual learning approaches often leverage the vision-language model CLIP for its high-dimensional feature space and cross-modality feature matching. Traditional CLIP-based classification methods identify the most similar text label for a test image by comparing their embeddings. However, these methods are sensitive to the quality of text phrases and less effective for classes lacking meaningful text labels. In this work, we rethink CLIP-based continual learning and introduce the concept of Label Vector Pool (LVP). LVP replaces text labels with training images as similarity references, eliminating the need for ideal text descriptions. We present three variations of LVP and evaluate their performance on class and domain incremental learning tasks. Leveraging CLIP's high dimensional feature space, LVP learning algorithms are task-order invariant. The new knowledge does not modify the old knowledge, hence, there is minimum forgetting. Different tasks can be learned independently and in parallel with low computational and memory demands. Experimental results show that proposed LVP-based methods outperform the current state-of-the-art baseline by a significant margin of 40.7%.
Recent research on integrating Large Language Models (LLMs) with Graph Neural Networks (GNNs) typically follows two approaches: LLM-centered models, which convert graph data into tokens for LLM processing, and GNN-centered models, which use LLMs to encode text features into node and edge representations for GNN input. LLM-centered models often struggle to capture graph structures effectively, while GNN-centered models compress variable-length textual data into fixed-size vectors, limiting their ability to understand complex semantics. Additionally, GNN-centered approaches require converting tasks into a uniform, manually-designed format, restricting them to classification tasks and preventing language output. To address these limitations, we introduce a new architecture that deeply integrates GNN with LLM, featuring three key innovations: (1) Structure-Aware Transformers, which incorporate GNN's message-passing capabilities directly into LLM's transformer layers, allowing simultaneous processing of textual and structural information and generating outputs from both GNN and LLM; (2) Graph-Text Cross-Attention, which processes full, uncompressed text from graph nodes and edges, ensuring complete semantic integration; and (3) GNN-LLM Twin Predictor, enabling LLM's flexible autoregressive generation alongside GNN's scalable one-pass prediction. GL-Fusion achieves outstand performance on various tasks. Notably, it achieves state-of-the-art performance on OGBN-Arxiv and OGBG-Code2.
Detecting objects occupying only small areas in an image is difficult, even for humans. Therefore, annotating small-size object instances is hard and thus costly. This study questions common sense by asking the following: is annotating small-size instances worth its cost? We restate it as the following verifiable question: can we detect small-size instances with a detector trained using training data free of small-size instances? We evaluate a method that upscales input images at test time and a method that downscales images at training time. The experiments conducted using the COCO dataset show the following. The first method, together with a remedy to narrow the domain gap between training and test inputs, achieves at least comparable performance to the baseline detector trained using complete training data. Although the method needs to apply the same detector twice to an input image with different scaling, we show that its distillation yields a single-path detector that performs equally well to the same baseline detector. These results point to the necessity of rethinking the annotation of training data for object detection.
Steps are all you need: Rethinking STEM Education with Prompt Engineering
2024-12-06
Krishnasai Addala, Kabir Dev Paul Baghel, Chhavi Kirtani, Avinash Anand, Rajiv Ratn Shah
Few shot and Chain-of-Thought prompting have shown promise when applied to Physics Question Answering Tasks, but are limited by the lack of mathematical ability inherent to LLMs, and are prone to hallucination. By utilizing a Mixture of Experts (MoE) Model, along with analogical prompting, we are able to show improved model performance when compared to the baseline on standard LLMs. We also survey the limits of these prompting techniques and the effects they have on model performance. Additionally, we propose Analogical CoT prompting, a prompting technique designed to allow smaller, open source models to leverage Analogical prompting, something they have struggled with, possibly due to a lack of specialist training data.
Rethink Deep Learning with Invariance in Data Representation
2024-12-06
Shuren Qi, Fei Wang, Tieyong Zeng, Fenglei Fan
Integrating invariance into data representations is a principled design in intelligent systems and web applications. Representations play a fundamental role, where systems and applications are both built on meaningful representations of digital inputs (rather than the raw data). In fact, the proper design/learning of such representations relies on priors w.r.t. the task of interest. Here, the concept of symmetry from the Erlangen Program may be the most fruitful prior -- informally, a symmetry of a system is a transformation that leaves a certain property of the system invariant. Symmetry priors are ubiquitous, e.g., translation as a symmetry of the object classification, where object category is invariant under translation. The quest for invariance is as old as pattern recognition and data mining itself. Invariant design has been the cornerstone of various representations in the era before deep learning, such as the SIFT. As we enter the early era of deep learning, the invariance principle is largely ignored and replaced by a data-driven paradigm, such as the CNN. However, this neglect did not last long before they encountered bottlenecks regarding robustness, interpretability, efficiency, and so on. The invariance principle has returned in the era of rethinking deep learning, forming a new field known as Geometric Deep Learning (GDL). In this tutorial, we will give a historical perspective of the invariance in data representations. More importantly, we will identify those research dilemmas, promising works, future directions, and web applications.
Rethinking Time Series Forecasting with LLMs via Nearest Neighbor Contrastive Learning
2024-12-06
Jayanie Bogahawatte, Sachith Seneviratne, Maneesha Perera, Saman Halgamuge
Adapting Large Language Models (LLMs) that are extensively trained on abundant text data, and customizing the input prompt to enable time series forecasting has received considerable attention. While recent work has shown great potential for adapting the learned prior of LLMs, the formulation of the prompt to finetune LLMs remains challenging as prompt should be aligned with time series data. Additionally, current approaches do not effectively leverage word token embeddings which embody the rich representation space learned by LLMs. This emphasizes the need for a robust approach to formulate the prompt which utilizes the word token embeddings while effectively representing the characteristics of the time series. To address these challenges, we propose NNCL-TLLM: Nearest Neighbor Contrastive Learning for Time series forecasting via LLMs. First, we generate time series compatible text prototypes such that each text prototype represents both word token embeddings in its neighborhood and time series characteristics via end-to-end finetuning. Next, we draw inspiration from Nearest Neighbor Contrastive Learning to formulate the prompt while obtaining the top-$k$ nearest neighbor time series compatible text prototypes. We then fine-tune the layer normalization and positional embeddings of the LLM, keeping the other layers intact, reducing the trainable parameters and decreasing the computational cost. Our comprehensive experiments demonstrate that NNCL-TLLM outperforms in few-shot forecasting while achieving competitive or superior performance over the state-of-the-art methods in long-term and short-term forecasting tasks.
Smart leverage? Rethinking the role of Leveraged Exchange Traded Funds in constructing portfolios to beat a benchmark
2024-12-06
Pieter van Staden, Peter Forsyth, Yuying Li
Leveraged Exchange Traded Funds (LETFs), while extremely controversial in the literature, remain stubbornly popular with both institutional and retail investors in practice. While the criticisms of LETFs are certainly valid, we argue that their potential has been underestimated in the literature due to the use of very simple investment strategies involving LETFs. In this paper, we systematically investigate the potential of including a broad stock market index LETF in long-term, dynamically-optimal investment strategies designed to maximize the outperformance over standard investment benchmarks in the sense of the information ratio (IR). Our results exploit the observation that positions in a LETF deliver call-like payoffs, so that the addition of a LETF to a portfolio can be a convenient way to add inexpensive leverage while providing downside protection. Under stylized assumptions, we present and analyze closed-form IR-optimal investment strategies using either a LETF or standard/vanilla ETF (VETF) on the same equity index, which provides the necessary intuition for the potential and benefits of LETFs. In more realistic settings, we use a neural network-based approach to determine the IR-optimal strategies, trained on bootstrapped historical data. We find that IR-optimal strategies with a broad stock market LETF are not only more likely to outperform the benchmark than IR-optimal strategies derived using the corresponding VETF, but are able to achieve partial stochastic dominance over the benchmark and VETF-based strategies in terms of terminal wealth.
2024-12-03
James Austgen, Andrés Fábrega, Mahimna Kelkar, Dani Vilardell, Sarah Allen, Kushal Babel, Jay Yu, Ari Juels
Inherent in the world of cryptocurrency systems and their security models is the notion that private keys, and thus assets, are controlled by individuals or individual entities. We present Liquefaction, a wallet platform that demonstrates the dangerous fragility of this foundational assumption by systemically breaking it. Liquefaction uses trusted execution environments (TEEs) to encumber private keys, i.e., attach rich, multi-user policies to their use. In this way, it enables the cryptocurrency credentials and assets of a single end-user address to be freely rented, shared, or pooled. It accomplishes these things privately, with no direct on-chain traces. Liquefaction demonstrates the sweeping consequences of TEE-based key encumbrance for the cryptocurrency landscape. Liquefaction can undermine the security and economic models of many applications and resources, such as locked tokens, DAO voting, airdrops, loyalty points, soulbound tokens, and quadratic voting. It can do so with no on-chain and minimal off-chain visibility. Conversely, we also discuss beneficial applications of Liquefaction, such as privacy-preserving, cost-efficient DAOs and a countermeasure to dusting attacks. Importantly, we describe an existing TEE-based tool that applications can use as a countermeasure to Liquefaction. Our work prompts a wholesale rethinking of existing models and enforcement of key and asset ownership in the cryptocurrency ecosystem.
Rethinking Self-Supervised Learning Within the Framework of Partial Information Decomposition
2024-12-03
Salman Mohamadi, Gianfranco Doretto, Donald A. Adjeroh
Self Supervised learning (SSL) has demonstrated its effectiveness in feature learning from unlabeled data. Regarding this success, there have been some arguments on the role that mutual information plays within the SSL framework. Some works argued for increasing mutual information between representation of augmented views. Others suggest decreasing mutual information between them, while increasing task-relevant information. We ponder upon this debate and propose to revisit the core idea of SSL within the framework of partial information decomposition (PID). Thus, with SSL under PID we propose to replace traditional mutual information with the more general concept of joint mutual information to resolve the argument. Our investigation on instantiation of SSL within the PID framework leads to upgrading the existing pipelines by considering the components of the PID in the SSL models for improved representation learning. Accordingly we propose a general pipeline that can be applied to improve existing baselines. Our pipeline focuses on extracting the unique information component under the PID to build upon lower level supervision for generic feature learning and on developing higher-level supervisory signals for task-related feature learning. In essence, this could be interpreted as a joint utilization of local and global clustering. Experiments on four baselines and four datasets show the effectiveness and generality of our approach in improving existing SSL frameworks.
Different from its counterpart outcome reward models (ORMs), which evaluate the entire responses, a process reward model (PRM) scores a reasoning trajectory step by step, providing denser and more fine grained rewards. However, training a PRM requires labels annotated at every intermediate step, presenting significant challenges for both manual and automatic data collection. This paper aims to address this challenge. Both theoretically and empirically, we show that an \textit{implicit PRM} can be obtained at no additional cost, by simply training an ORM on the cheaper response-level labels. The only assumption is to parameterize the outcome reward as the log-likelihood ratios of the policy and reference models, which can be optimized regardless of the specific choice of loss objectives. In experiments, we instantiate our implicit PRMs with various objectives and evaluate their performance on MATH. We show that our implicit PRM outperforms a strong MCTS-based baseline \textit{\'a la} Math-Shepherd using less than $1/38$ of the training data. Its performance can be further improved with majority voting. We further find that scaling up instructions and responses benefits our implicit PRM, and the latter brings a larger gain. Particularly, we find that our implicit PRM, when instantiated with the cross-entropy (CE) loss, is more data-efficient and can keep improving generation models even when trained with only one response per instruction, the setup that suffers from extreme data scarcity and imbalance. Further, instructions should be relevant to downstream tasks while the diversity of responses does not bring gains. Surprisingly, training on extra Math-Shepherd step labels brings no further improvements to our implicit PRM trained on only outcome data. We hope that our work will encourage a rethinking of PRM training approaches and contribute to making training PRMs more accessible.
DaDu-E: Rethinking the Role of Large Language Model in Robotic Computing Pipeline
2024-12-02
Wenhao Sun, Sai Hou, Zixuan Wang, Bo Yu, Shaoshan Liu, Xu Yang, Shuai Liang, Yiming Gan, Yinhe Han
Performing complex tasks in open environments remains challenging for robots, even when using large language models (LLMs) as the core planner. Many LLM-based planners are inefficient due to their large number of parameters and prone to inaccuracies because they operate in open-loop systems. We think the reason is that only applying LLMs as planners is insufficient. In this work, we propose DaDu-E, a robust closed-loop planning framework for embodied AI robots. Specifically, DaDu-E is equipped with a relatively lightweight LLM, a set of encapsulated robot skill instructions, a robust feedback system, and memory augmentation. Together, these components enable DaDu-E to (i) actively perceive and adapt to dynamic environments, (ii) optimize computational costs while maintaining high performance, and (iii) recover from execution failures using its memory and feedback mechanisms. Extensive experiments on real-world and simulated tasks show that DaDu-E achieves task success rates comparable to embodied AI robots with larger models as planners like COME-Robot, while reducing computational requirements by $6.6 \times$. Users are encouraged to explore our system at: \url{https://rlc-lab.github.io/dadu-e/}.
TGTOD: A Global Temporal Graph Transformer for Outlier Detection at Scale
2024-12-01
Kay Liu, Jiahao Ding, MohamadAli Torkamani, Philip S. Yu
While Transformers have revolutionized machine learning on various data, existing Transformers for temporal graphs face limitations in (1) restricted receptive fields, (2) overhead of subgraph extraction, and (3) suboptimal generalization capability beyond link prediction. In this paper, we rethink temporal graph Transformers and propose TGTOD, a novel end-to-end Temporal Graph Transformer for Outlier Detection. TGTOD employs global attention to model both structural and temporal dependencies within temporal graphs. To tackle scalability, our approach divides large temporal graphs into spatiotemporal patches, which are then processed by a hierarchical Transformer architecture comprising Patch Transformer, Cluster Transformer, and Temporal Transformer. We evaluate TGTOD on three public datasets under two settings, comparing with a wide range of baselines. Our experimental results demonstrate the effectiveness of TGTOD, achieving AP improvement of 61% on Elliptic. Furthermore, our efficiency evaluation shows that TGTOD reduces training time by 44x compared to existing Transformers for temporal graphs. To foster reproducibility, we make our implementation publicly available at https://github.com/kayzliu/tgtod.
Rethinking Cognition: Morphological Info-Computation and the Embodied Paradigm in Life and Artificial Intelligence
2024-12-01
Gordana Dodig-Crnkovic
This study aims to place Lorenzo Magnanis Eco-Cognitive Computationalism within the broader context of current work on information, computation, and cognition. Traditionally, cognition was believed to be exclusive to humans and a result of brain activity. However, recent studies reveal it as a fundamental characteristic of all life forms, ranging from single cells to complex multicellular organisms and their networks. Yet, the literature and general understanding of cognition still largely remain human-brain-focused, leading to conceptual gaps and incoherency. This paper presents a variety of computational (information processing) approaches, including an info-computational approach to cognition, where natural structures represent information and dynamical processes on natural structures are regarded as computation, relative to an observing cognizing agent. We model cognition as a web of concurrent morphological computations, driven by processes of self-assembly, self-organisation, and autopoiesis across physical, chemical, and biological domains. We examine recent findings linking morphological computation, morphogenesis, agency, basal cognition, extended evolutionary synthesis, and active inference. We establish a connection to Magnanis Eco-Cognitive Computationalism and the idea of computational domestication of ignorant entities. Novel theoretical and applied insights question the boundaries of conventional computational models of cognition. The traditional models prioritize symbolic processing and often neglect the inherent constraints and potentialities in the physical embodiment of agents on different levels of organization. Gaining a better info-computational grasp of cognitive embodiment is crucial for the advancement of fields such as biology, evolutionary studies, artificial intelligence, robotics, medicine, and more.
Learning on Less: Constraining Pre-trained Model Learning for Generalizable Diffusion-Generated Image Detection
2024-12-01
Yingjian Chen, Lei Zhang, Yakun Niu, Lei Tan, Pei Chen
Diffusion Models enable realistic image generation, raising the risk of misinformation and eroding public trust. Currently, detecting images generated by unseen diffusion models remains challenging due to the limited generalization capabilities of existing methods. To address this issue, we rethink the effectiveness of pre-trained models trained on large-scale, real-world images. Our findings indicate that: 1) Pre-trained models can cluster the features of real images effectively. 2) Models with pre-trained weights can approximate an optimal generalization solution at a specific training step, but it is extremely unstable. Based on these facts, we propose a simple yet effective training method called Learning on Less (LoL). LoL utilizes a random masking mechanism to constrain the model's learning of the unique patterns specific to a certain type of diffusion model, allowing it to focus on less image content. This leverages the inherent strengths of pre-trained weights while enabling a more stable approach to optimal generalization, which results in the extraction of a universal feature that differentiates various diffusion-generated images from real images. Extensive experiments on the GenImage benchmark demonstrate the remarkable generalization capability of our proposed LoL. With just 1% training data, LoL significantly outperforms the current state-of-the-art, achieving a 13.6% improvement in average ACC across images generated by eight different models.
Representations learned by self-supervised approaches are generally considered to possess sufficient generalizability and discriminability. However, we disclose a nontrivial mutual-exclusion relationship between these critical representation properties through an exploratory demonstration on self-supervised learning. State-of-the-art self-supervised methods tend to enhance either generalizability or discriminability but not both simultaneously. Thus, learning representations jointly possessing strong generalizability and discriminability presents a specific challenge for self-supervised learning. To this end, we revisit the learning paradigm of self-supervised learning from the perspective of evolutionary game theory (EGT) and outline the theoretical roadmap to achieve a desired trade-off between these representation properties. EGT performs well in analyzing the trade-off point in a two-player game by utilizing dynamic system modeling. However, the EGT analysis requires sufficient annotated data, which contradicts the principle of self-supervised learning, i.e., the EGT analysis cannot be conducted without the annotations of the specific target domain for self-supervised learning. Thus, to enhance the methodological generalization, we propose a novel self-supervised learning method that leverages advancements in reinforcement learning to jointly benefit from the general guidance of EGT and sequentially optimize the model to chase the consistent improvement of generalizability and discriminability for specific target domains during pre-training. Theoretically, we establish that the proposed method tightens the generalization error upper bound of self-supervised learning. Empirically, our method achieves state-of-the-art performance on various benchmarks.
This paper explores the application of large language models (LLMs) in designing strategic mechanisms -- including auctions, contracts, and games -- for specific purposes in communication networks. Traditionally, strategic mechanism design in telecommunications has relied on human expertise to craft solutions based on game theory, auction theory, and contract theory. However, the evolving landscape of telecom networks, characterized by increasing abstraction, emerging use cases, and novel value creation opportunities, calls for more adaptive and efficient approaches. We propose leveraging LLMs to automate or semi-automate the process of strategic mechanism design, from intent specification to final formulation. This paradigm shift introduces both semi-automated and fully-automated design pipelines, raising crucial questions about faithfulness to intents, incentive compatibility, algorithmic stability, and the balance between human oversight and artificial intelligence (AI) autonomy. The paper discusses potential frameworks, such as retrieval-augmented generation (RAG)-based systems, to implement LLM-driven mechanism design in communication networks contexts. We examine key challenges, including LLM limitations in capturing domain-specific constraints, ensuring strategy proofness, and integrating with evolving telecom standards. By providing an in-depth analysis of the synergies and tensions between LLMs and strategic mechanism design within the IoT ecosystem, this work aims to stimulate discussion on the future of AI-driven information economic mechanisms in telecommunications and their potential to address complex, dynamic network management scenarios.
Rethinking the initialization of Momentum in Federated Learning with Heterogeneous Data
2024-11-29
Chenguang Xiao, Shuo Wang
Data Heterogeneity is a major challenge of Federated Learning performance. Recently, momentum based optimization techniques have beed proved to be effective in mitigating the heterogeneity issue. Along with the model updates, the momentum updates are transmitted to the server side and aggregated. Therefore, the local training initialized with a global momentum is guided by the global history of the gradients. However, we spot a problem in the traditional cumulation of the momentum which is suboptimal in the Federated Learning systems. The momentum used to weight less on the historical gradients and more on the recent gradients. This however, will engage more biased local gradients in the end of the local training. In this work, we propose a new way to calculate the estimated momentum used in local initialization. The proposed method is named as Reversed Momentum Federated Learning (RMFL). The key idea is to assign exponentially decayed weights to the gradients with the time going forward, which is on the contrary to the traditional momentum cumulation. The effectiveness of RMFL is evaluated on three popular benchmark datasets with different heterogeneity levels.
Ten Ways in which Virtual Reality Differs from Video Streaming
2024-11-29
Gustavo de Veciana, Sonia Fahmy, George Kesidis, Voicu Popescu
Virtual Reality (VR) applications have a number of unique characteristics that set them apart from traditional video streaming. These characteristics have major implications on the design of VR rendering, adaptation, prefetching, caching, and transport mechanisms. This paper contrasts VR to video streaming, stored 2D video streaming in particular, and discusses how to rethink system and network support for VR.
We rethink the segment anything model (SAM) and propose a novel multiprompt network called COMPrompter for camouflaged object detection (COD). SAM has zero-shot generalization ability beyond other models and can provide an ideal framework for COD. Our network aims to enhance the single prompt strategy in SAM to a multiprompt strategy. To achieve this, we propose an edge gradient extraction module, which generates a mask containing gradient information regarding the boundaries of camouflaged objects. This gradient mask is then used as a novel boundary prompt, enhancing the segmentation process. Thereafter, we design a box-boundary mutual guidance module, which fosters more precise and comprehensive feature extraction via mutual guidance between a boundary prompt and a box prompt. This collaboration enhances the model's ability to accurately detect camouflaged objects. Moreover, we employ the discrete wavelet transform to extract high-frequency features from image embeddings. The high-frequency features serve as a supplementary component to the multiprompt system. Finally, our COMPrompter guides the network to achieve enhanced segmentation results, thereby advancing the development of SAM in terms of COD. Experimental results across COD benchmarks demonstrate that COMPrompter achieves a cutting-edge performance, surpassing the current leading model by an average positive metric of 2.2% in COD10K. In the specific application of COD, the experimental results in polyp segmentation show that our model is superior to top-tier methods as well. The code will be made available at https://github.com/guobaoxiao/COMPrompter.
The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History?
2024-11-27
Jérémie Sublime
In today's world, AI programs powered by Machine Learning are ubiquitous, and have achieved seemingly exceptional performance across a broad range of tasks, from medical diagnosis and credit rating in banking, to theft detection via video analysis, and even predicting political or sexual orientation from facial images. These predominantly deep learning methods excel due to their extraordinary capacity to process vast amounts of complex data to extract complex correlations and relationship from different levels of features. In this paper, we contend that the designers and final users of these ML methods have forgotten a fundamental lesson from statistics: correlation does not imply causation. Not only do most state-of-the-art methods neglect this crucial principle, but by doing so they often produce nonsensical or flawed causal models, akin to social astrology or physiognomy. Consequently, we argue that current efforts to make AI models more ethical by merely reducing biases in the training data are insufficient. Through examples, we will demonstrate that the potential for harm posed by these methods can only be mitigated by a complete rethinking of their core models, improved quality assessment metrics and policies, and by maintaining humans oversight throughout the process.
To accelerate the inference of heavy Multimodal Large Language Models (MLLMs), this study rethinks the current landscape of training-free token reduction research. We regret to find that the critical components of existing methods are tightly intertwined, with their interconnections and effects remaining unclear for comparison, transfer, and expansion. Therefore, we propose a unified ''filter-correlate-compress'' paradigm that decomposes the token reduction into three distinct stages within a pipeline, maintaining consistent design objectives and elements while allowing for unique implementations. We additionally demystify the popular works and subsume them into our paradigm to showcase its universality. Finally, we offer a suite of methods grounded in the paradigm, striking a balance between speed and accuracy throughout different phases of the inference. Experimental results across 10 benchmarks indicate that our methods can achieve up to an 82.4% reduction in FLOPs with a minimal impact on performance, simultaneously surpassing state-of-the-art training-free methods. Our project page is at https://ficoco-accelerate.github.io/.
RealSeal: Revolutionizing Media Authentication with Real-Time Realism Scoring
2024-11-26
Bhaktipriya Radharapu, Harish Krishna
The growing threat of deepfakes and manipulated media necessitates a radical rethinking of media authentication. Existing methods for watermarking synthetic data fall short, as they can be easily removed or altered, and current deepfake detection algorithms do not achieve perfect accuracy. Provenance techniques, which rely on metadata to verify content origin, fail to address the fundamental problem of staged or fake media. This paper introduces a groundbreaking paradigm shift in media authentication by advocating for the watermarking of real content at its source, as opposed to watermarking synthetic data. Our innovative approach employs multisensory inputs and machine learning to assess the realism of content in real-time and across different contexts. We propose embedding a robust realism score within the image metadata, fundamentally transforming how images are trusted and circulated. By combining established principles of human reasoning about reality, rooted in firmware and hardware security, with the sophisticated reasoning capabilities of contemporary machine learning systems, we develop a holistic approach that analyzes information from multiple perspectives. This ambitious, blue sky approach represents a significant leap forward in the field, pushing the boundaries of media authenticity and trust. By embracing cutting-edge advancements in technology and interdisciplinary research, we aim to establish a new standard for verifying the authenticity of digital media.
Since 2023, Vector Quantization (VQ)-based discrete generation methods have rapidly dominated human motion generation, primarily surpassing diffusion-based continuous generation methods in standard performance metrics. However, VQ-based methods have inherent limitations. Representing continuous motion data as limited discrete tokens leads to inevitable information loss, reduces the diversity of generated motions, and restricts their ability to function effectively as motion priors or generation guidance. In contrast, the continuous space generation nature of diffusion-based methods makes them well-suited to address these limitations and with even potential for model scalability. In this work, we systematically investigate why current VQ-based methods perform well and explore the limitations of existing diffusion-based methods from the perspective of motion data representation and distribution. Drawing on these insights, we preserve the inherent strengths of a diffusion-based human motion generation model and gradually optimize it with inspiration from VQ-based approaches. Our approach introduces a human motion diffusion model enabled to perform bidirectional masked autoregression, optimized with a reformed data representation and distribution. Additionally, we also propose more robust evaluation methods to fairly assess different-based methods. Extensive experiments on benchmark human motion generation datasets demonstrate that our method excels previous methods and achieves state-of-the-art performances.
Traffic Wave Properties for Automated Vehicles During Traffic Oscillations via Analytical Approximations
2024-11-25
Yang Zhou, Sixu Li, Wissam Kontar, Fan Pu, Anupam Srivastava, Soyoung Ahn
This paper presents an analytical approximation framework to understand the dynamics of traffic wave propagation for Automated Vehicles (AVs) during traffic oscillations. The framework systematically unravels the intricate relationships between the longitudinal control model of the AVs and the properties of traffic waves. We apply Laplacian Transformation and Describing Function Analysis to mathematically derive the traffic wave properties of an AV in car-following scenarios. Further, we incorporate Newell's car-following model to determine the speed of the traffic waves. Our analysis extends to both homogenous and heterogenous traffic, systematically handling intra-heterogeneities and inter-heterogeneities in traffic wave propagation using the established analytical framework. We validate our approach via numerical simulations and show the connections between the AV control system and traffic wave properties. This research emphasizes the importance of rethinking our understanding of traffic wave properties when AVs are present in the traffic system.
Revisiting DDIM Inversion for Controlling Defect Generation by Disentangling the Background
2024-11-25
Youngjae Cho, Gwangyeol Kim, Sirojbek Safarov, Seongdeok Bang, Jaewoo Park
In anomaly detection, the scarcity of anomalous data compared to normal data poses a challenge in effectively utilizing deep neural network representations to identify anomalous features. From a data-centric perspective, generative models can solve this data imbalance issue by synthesizing anomaly datasets. Although previous research tried to enhance the controllability and quality of generating defects, they do not consider the relation between background and defect. Since the defect depends on the object's background (i.e., the normal part of an object), training only the defect area cannot utilize the background information, and even generation can be biased depending on the mask information. In addition, controlling logical anomalies should consider the dependency between background and defect areas (e.g., orange colored defect on a orange juice bottle). In this paper, our paper proposes modeling a relationship between the background and defect, where background affects denoising defects; however, the reverse is not. We introduce the regularizing term to disentangle denoising background from defects. From the disentanglement loss, we rethink defect generation with DDIM Inversion, where we generate the defect on the target normal image. Additionally, we theoretically prove that our methodology can generate a defect on the target normal image with an invariant background. We demonstrate our synthetic data is realistic and effective in several experiments.
From Quantum Cognition to Conceptuality Interpretation II: Unraveling the Quantum Mysteries
2024-11-25
Diederik Aerts, Massimiliano Sassoli de Bianchi, Sandro Sozzo
An overview of the conceptuality interpretation of quantum mechanics is presented, along with an explanation of how it sheds light on key quantum and relativistic phenomena. In particular, we show how the interpretation clarifies Heisenberg's uncertainty principle, wave function-based and entanglement-based nonlocality, interference effects resulting from the superposition principle, delayed choice experiments, quantum measurements, the mechanism of quantization, the reason why entities can establish entanglement bonds, and the statistical behavior of indistinguishable entities. We further argue that the interpretation can also elucidate relativistic effects, focusing on time dilation. Finally, we suggest that it can provide a novel and challenging perspective on evolution. This article is the second in a two-part series devoted to exploring this promising approach to reality. The first part, which serves as a companion to this discussion, outlines the intellectual trajectory leading from the first applications of quantum notions to human cognition to the bold rethinking suggested by the conceptuality interpretation.
Recent research has shown that large language models (LLMs) can be effectively used for real-world time series forecasting due to their strong natural language understanding capabilities. However, aligning time series into semantic spaces of LLMs comes with high computational costs and inference complexity, particularly for long-range time series generation. Building on recent advancements in using linear models for time series, this paper introduces an LLM-enhanced mixture of linear experts for precise and efficient time series forecasting. This approach involves developing a mixture of linear experts with multiple lookback lengths and a new multimodal fusion mechanism. The use of a mixture of linear experts is efficient due to its simplicity, while the multimodal fusion mechanism adaptively combines multiple linear experts based on the learned features of the text modality from pre-trained large language models. In experiments, we rethink the need to align time series to LLMs by existing time-series large language models and further discuss their efficiency and effectiveness in time series forecasting. Our experimental results show that the proposed LeMoLE model presents lower prediction errors and higher computational efficiency than existing LLM models.
SPAC-Net: Rethinking Point Cloud Completion with Structural Prior
2024-11-22
Zizhao Wu, Jian Shi, Xuan Deng, Cheng Zhang, Genfu Yang, Ming Zeng, Yunhai Wang
Point cloud completion aims to infer a complete shape from its partial observation. Many approaches utilize a pure encoderdecoder paradigm in which complete shape can be directly predicted by shape priors learned from partial scans, however, these methods suffer from the loss of details inevitably due to the feature abstraction issues. In this paper, we propose a novel framework,termed SPAC-Net, that aims to rethink the completion task under the guidance of a new structural prior, we call it interface. Specifically, our method first investigates Marginal Detector (MAD) module to localize the interface, defined as the intersection between the known observation and the missing parts. Based on the interface, our method predicts the coarse shape by learning the displacement from the points in interface move to their corresponding position in missing parts. Furthermore, we devise an additional Structure Supplement(SSP) module before the upsampling stage to enhance the structural details of the coarse shape, enabling the upsampling module to focus more on the upsampling task. Extensive experiments have been conducted on several challenging benchmarks, and the results demonstrate that our method outperforms existing state-of-the-art approaches.
A Hierarchical Poisson Generator for Universal Graphs under Limited Resources
2024-11-21
Xiaorui Qi, Yanlong Wen, Xiaojie Yuan
Graph generation is one of the most challenging tasks in recent years, and its core is to learn the ground truth distribution hiding in the training data. However, training data may not be available due to security concerns or unaffordable costs, which severely blows the learning models, especially the deep generative models. The dilemma leads us to rethink non-learned generation methods based on graph invariant features. Based on the observation of scale-free property, we propose a hierarchical Poisson graph generation algorithm. Specifically, we design a two-stage generation strategy. In the first stage, we sample multiple anchor nodes according to the Poisson distribution to further guide the formation of substructures, splitting the initial node set into multiple ones. Next, we progressively generate edges by sampling nodes through a degree mixing distribution, adjusting the tolerance towards exotic structures via two thresholds. We provide theoretical guarantees for hierarchical generation and verify the effectiveness of our method under 12 datasets of three categories. Experimental results show that our method fits the ground truth distribution better than various generation strategies and other distribution observations.
Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation
2024-11-21
Ke Zhao, Huayang Huang, Miao Li, Yu Wu
Language-conditioned robotic learning has significantly enhanced robot adaptability by enabling a single model to execute diverse tasks in response to verbal commands. Despite these advancements, security vulnerabilities within this domain remain largely unexplored. This paper addresses this gap by proposing a novel adversarial prompt attack tailored to language-conditioned robotic models. Our approach involves crafting a universal adversarial prefix that induces the model to perform unintended actions when added to any original prompt. We demonstrate that existing adversarial techniques exhibit limited effectiveness when directly transferred to the robotic domain due to the inherent robustness of discretized robotic action spaces. To overcome this challenge, we propose to optimize adversarial prefixes based on continuous action representations, circumventing the discretization process. Additionally, we identify the beneficial impact of intermediate features on adversarial attacks and leverage the negative gradient of intermediate self-attention features to further enhance attack efficacy. Extensive experiments on VIMA models across 13 robot manipulation tasks validate the superiority of our method over existing approaches and demonstrate its transferability across different model variants.
Despite rapid advancements in TTS models, a consistent and robust human evaluation framework is still lacking. For example, MOS tests fail to differentiate between similar models, and CMOS's pairwise comparisons are time-intensive. The MUSHRA test is a promising alternative for evaluating multiple TTS systems simultaneously, but in this work we show that its reliance on matching human reference speech unduly penalises the scores of modern TTS systems that can exceed human speech quality. More specifically, we conduct a comprehensive assessment of the MUSHRA test, focusing on its sensitivity to factors such as rater variability, listener fatigue, and reference bias. Based on our extensive evaluation involving 492 human listeners across Hindi and Tamil we identify two primary shortcomings: (i) reference-matching bias, where raters are unduly influenced by the human reference, and (ii) judgement ambiguity, arising from a lack of clear fine-grained guidelines. To address these issues, we propose two refined variants of the MUSHRA test. The first variant enables fairer ratings for synthesized samples that surpass human reference quality. The second variant reduces ambiguity, as indicated by the relatively lower variance across raters. By combining these approaches, we achieve both more reliable and more fine-grained assessments. We also release MANGO, a massive dataset of 246,000 human ratings, the first-of-its-kind collection for Indian languages, aiding in analyzing human preferences and developing automatic metrics for evaluating TTS systems.
Naturalistic driving action localization task aims to recognize and comprehend human behaviors and actions from video data captured during real-world driving scenarios. Previous studies have shown great action localization performance by applying a recognition model followed by probability-based post-processing. Nevertheless, the probabilities provided by the recognition model frequently contain confused information causing challenge for post-processing. In this work, we adopt an action recognition model based on self-supervise learning to detect distracted activities and give potential action probabilities. Subsequently, a constraint ensemble strategy takes advantages of multi-camera views to provide robust predictions. Finally, we introduce a conditional post-processing operation to locate distracted behaviours and action temporal boundaries precisely. Experimenting on test set A2, our method obtains the sixth position on the public leaderboard of track 3 of the 2024 AI City Challenge.
Recent advancements in 3D generation models have opened new possibilities for simulating dynamic 3D object movements and customizing behaviors, yet creating this content remains challenging. Current methods often require manual assignment of precise physical properties for simulations or rely on video generation models to predict them, which is computationally intensive. In this paper, we rethink the usage of multi-modal large language model (MLLM) in physics-based simulation, and present Sim Anything, a physics-based approach that endows static 3D objects with interactive dynamics. We begin with detailed scene reconstruction and object-level 3D open-vocabulary segmentation, progressing to multi-view image in-painting. Inspired by human visual reasoning, we propose MLLM-based Physical Property Perception (MLLM-P3) to predict mean physical properties of objects in a zero-shot manner. Based on the mean values and the object's geometry, the Material Property Distribution Prediction model (MPDP) model then estimates the full distribution, reformulating the problem as probability distribution estimation to reduce computational costs. Finally, we simulate objects in an open-world scene with particles sampled via the Physical-Geometric Adaptive Sampling (PGAS) strategy, efficiently capturing complex deformations and significantly reducing computational costs. Extensive experiments and user studies demonstrate our Sim Anything achieves more realistic motion than state-of-the-art methods within 2 minutes on a single GPU.
Rethinking Text-Promptable Surgical Instrument Segmentation with Robust Framework
2024-11-19
Tae-Min Choi, Juyoun Park
Surgical instrument segmentation (SIS) is essential in computer-assisted surgeries, with deep learning methods improving accuracy in complex environments. Recently, text-promptable segmentation methods have been introduced, generating masks based on textual descriptions. However, they assume the text-described object is present and always generate an associated mask even when the object is absent. Existing methods address this by using prompts only for objects already known to exist in the scene, which relies on inaccessible information. To address this, we rethink text-promptable SIS and redefine it under robust conditions as Robust text-promptable SIS (R-SIS). Unlike previous approaches, R-SIS is a process that analyzes text prompts for all surgical instrument categories without relying on external knowledge, identifies the instruments present in the scene, and segments them accordingly. Building on this, we propose Robust Surgical Instrument Segmentation (RoSIS), an optimized framework combining visual and language features for promptable segmentation in the R-SIS setting. RoSIS employs an encoder-decoder architecture with a Multi-Modal Fusion Block (MMFB) and a Selective Gate Block (SGB) for balanced integration of vision and language features. Additionally, an iterative refinement strategy enhances segmentation masks through a two-step process: an initial pass with name-based prompts, followed by refinement with location prompts. Experiments across multiple datasets and settings show that RoSIS outperforms existing vision-based and promptable segmentation methods under robust conditions. By rethinking text-promptable SIS, our work establishes a fair and effective approach to surgical instrument segmentation.
Rethinking Thinking Tokens: Understanding Why They Underperform in Practice
2024-11-18
Sreeram Vennam, David Valente, David Herel, Ponnurangam Kumaraguru
Thinking Tokens (TT) have been proposed as an unsupervised method to facilitate reasoning in language models. However, despite their conceptual appeal, our findings show that TTs marginally improves performance and consistently underperforms compared to Chain-of-Thought (CoT) reasoning across multiple benchmarks. We hypothesize that this underperformance stems from the reliance on a single embedding for TTs, which results in inconsistent learning signals and introduces noisy gradients. This paper provides a comprehensive empirical analysis to validate this hypothesis and discusses the implications for future research on unsupervised reasoning in LLMs.
2024-11-17
William Huang, Yifeng Jiang, Tom Van Wouwe, C. Karen Liu
Diffusion models have demonstrated significant promise in various generative tasks; however, they often struggle to satisfy challenging constraints. Our approach addresses this limitation by rethinking training-free loss-guided diffusion from an optimization perspective. We formulate a series of constrained optimizations throughout the inference process of a diffusion model. In each optimization, we allow the sample to take multiple steps along the gradient of the proxy constraint function until we can no longer trust the proxy, according to the variance at each diffusion level. Additionally, we estimate the state manifold of diffusion model to allow for early termination when the sample starts to wander away from the state manifold at each diffusion step. Trust sampling effectively balances between following the unconditional diffusion model and adhering to the loss guidance, enabling more flexible and accurate constrained generation. We demonstrate the efficacy of our method through extensive experiments on complex tasks, and in drastically different domains of images and 3D motion generation, showing significant improvements over existing methods in terms of generation quality. Our implementation is available at https://github.com/will-s-h/trust-sampling.
Understanding Learning with Sliced-Wasserstein Requires Rethinking Informative Slices
2024-11-16
Huy Tran, Yikun Bai, Ashkan Shahbazi, John R. Hershey, Soheil Kolouri
The practical applications of Wasserstein distances (WDs) are constrained by their sample and computational complexities. Sliced-Wasserstein distances (SWDs) provide a workaround by projecting distributions onto one-dimensional subspaces, leveraging the more efficient, closed-form WDs for one-dimensional distributions. However, in high dimensions, most random projections become uninformative due to the concentration of measure phenomenon. Although several SWD variants have been proposed to focus on \textit{informative} slices, they often introduce additional complexity, numerical instability, and compromise desirable theoretical (metric) properties of SWD. Amidst the growing literature that focuses on directly modifying the slicing distribution, which often face challenges, we revisit the classical Sliced-Wasserstein and propose instead to rescale the 1D Wasserstein to make all slices equally informative. Importantly, we show that with an appropriate data assumption and notion of \textit{slice informativeness}, rescaling for all individual slices simplifies to \textbf{a single global scaling factor} on the SWD. This, in turn, translates to the standard learning rate search for gradient-based learning in common machine learning workflows. We perform extensive experiments across various machine learning tasks showing that the classical SWD, when properly configured, can often match or surpass the performance of more complex variants. We then answer the following question: "Is Sliced-Wasserstein all you need for common learning tasks?"
The Link Between Large Scientific Collaboration and Productivity. Rethinking How to Estimate the Monetary Value of Publications
2024-11-15
Francesco Giffoni, Emanuela Sirtori, Louis Colnot
This paper addresses how to assign a monetary value to scientific publications, particularly in the case of multi-author papers arising from large-scale research collaborations. Contemporary science increasingly relies on extensive and varied collaborations to tackle global challenges in fields such as life sciences, climate science, energy, high-energy physics, astronomy, and many others. We argue that existing literature fails to address the collaborative nature of research by overlooking the relationship between coauthorship and scientists productivity. Using the Marginal Cost of Production (MCP) approach, we first highlight the methodological limitations of ignoring this relationship, then propose a generalised MCP model to value co-authorship. As a case study, we examine High-Energy Physics (HEP) collaborations at the Large Hadron Collider (LHC) at CERN, analysing approximately half a million scientific outputs by over 50,000 authors from 1990 to 2021. Our findings indicate that collaborative adjustments yield monetary valuations for subsets of highly collaborative papers up to 3 orders of magnitude higher than previous estimates, with elevated values correlating with high research quality. This study contributes to the literature on research output evaluation, addressing debates in science policy around assessing research performance and impact. Our methodology is applicable to authorship valuation both within academia and in large-scale scientific collaborations, fitting diverse research impact assessment frameworks or as self-standing procedure. Additionally, we discuss the conditions under which this method may complement survey-based approaches.
Fill in the blanks: Rethinking Interpretability in vision
2024-11-15
Pathirage N. Deelaka, Tharindu Wickremasinghe, Devin Y. De Silva, Lisara N. Gajaweera
Model interpretability is a key challenge that has yet to align with the advancements observed in contemporary state-of-the-art deep learning models. In particular, deep learning aided vision tasks require interpretability, in order for their adoption in more specialized domains such as medical imaging. Although the field of explainable AI (XAI) developed methods for interpreting vision models along with early convolutional neural networks, recent XAI research has mainly focused on assigning attributes via saliency maps. As such, these methods are restricted to providing explanations at a sample level, and many explainability methods suffer from low adaptability across a wide range of vision models. In our work, we re-think vision-model explainability from a novel perspective, to probe the general input structure that a model has learnt during its training. To this end, we ask the question: "How would a vision model fill-in a masked-image". Experiments on standard vision datasets and pre-trained models reveal consistent patterns, and could be intergrated as an additional model-agnostic explainability tool in modern machine-learning platforms. The code will be available at \url{https://github.com/BoTZ-TND/FillingTheBlanks.git}
Rethinking Normalization Strategies and Convolutional Kernels for Multimodal Image Fusion
2024-11-15
Dan He, Guofen Wang, Weisheng Li, Yucheng Shu, Wenbo Li, Lijian Yang, Yuping Huang, Feiyan Li
Multimodal image fusion (MMIF) aims to integrate information from different modalities to obtain a comprehensive image, aiding downstream tasks. However, existing methods tend to prioritize natural image fusion and focus on information complementary and network training strategies. They ignore the essential distinction between natural and medical image fusion and the influence of underlying components. This paper dissects the significant differences between the two tasks regarding fusion goals, statistical properties, and data distribution. Based on this, we rethink the suitability of the normalization strategy and convolutional kernels for end-to-end MMIF.Specifically, this paper proposes a mixture of instance normalization and group normalization to preserve sample independence and reinforce intrinsic feature correlation.This strategy promotes the potential of enriching feature maps, thus boosting fusion performance. To this end, we further introduce the large kernel convolution, effectively expanding receptive fields and enhancing the preservation of image detail. Moreover, the proposed multipath adaptive fusion module recalibrates the decoder input with features of various scales and receptive fields, ensuring the transmission of crucial information. Extensive experiments demonstrate that our method exhibits state-of-the-art performance in multiple fusion tasks and significantly improves downstream applications. The code is available at https://github.com/HeDan-11/LKC-FUNet.
2024-11-14
Arianna Crippa, Yahui Chai, Omar Costa Hamido, Paulo Itaborai, Karl Jansen
We aim to apply a quantum computing technique to compose artworks. The main idea is to revisit three paintings of different styles and historical periods: ''Narciso'', painted circa 1597-1599 by Michelangelo Merisi (Caravaggio), ''Les fils de l'homme'', painted in 1964 by Rene Magritte and ''192 Farben'', painted in 1966 by Gerard Richter. We utilize the output of a quantum computation to change the composition in the paintings, leading to a paintings series titled ''Quantum Transformation I, II, III''. In particular, the figures are discretized into square lattices and the order of the pieces is changed according to the result of the quantum simulation. We consider an Ising Hamiltonian as the observable in the quantum computation and its time evolution as the final outcome. From a classical subject to abstract forms, we seek to combine classical and quantum aesthetics through these three art pieces. Besides experimenting with hardware runs and circuit noise, our goal is to reproduce these works as physical oil paintings on wooden panels. With this process, we complete a full circle between classical and quantum techniques and contribute to rethinking Art practice in the era of quantum computing technologies.
2024-11-14
Hu Wang, Congbo Ma, Ibrahim Almakky, Ian Reid, Gustavo Carneiro, Mohammad Yaqub
Weight-averaged model-merging has emerged as a powerful approach in deep learning, capable of enhancing model performance without fine-tuning or retraining. However, the underlying mechanisms that explain its effectiveness remain largely unexplored. In this paper, we investigate this technique from three novel perspectives to provide deeper insights into how and why weight-averaged model-merging works: (1) we examine the intrinsic patterns captured by the learning of the model weights, through the visualizations of their patterns on several datasets, showing that these weights often encode structured and interpretable patterns; (2) we investigate model ensemble merging strategies based on averaging on weights versus averaging on features, providing detailed analyses across diverse architectures and datasets; and (3) we explore the impact on model-merging prediction stability in terms of changing the parameter magnitude, revealing insights into the way of weight averaging works as regularization by showing the robustness across different parameter scales. Our findings shed light on the "black box" of weight-averaged model-merging, offering valuable insights and practical recommendations that advance the model-merging process.
The Travelling Salesman Problem (TSP) remains a fundamental challenge in combinatorial optimization, inspiring diverse algorithmic strategies. This paper revisits the "heatmap + Monte Carlo Tree Search (MCTS)" paradigm that has recently gained traction for learning-based TSP solutions. Within this framework, heatmaps encode the likelihood of edges forming part of the optimal tour, and MCTS refines this probabilistic guidance to discover optimal solutions. Contemporary approaches have predominantly emphasized the refinement of heatmap generation through sophisticated learning models, inadvertently sidelining the critical role of MCTS. Our extensive empirical analysis reveals two pivotal insights: 1) The configuration of MCTS strategies profoundly influences the solution quality, demanding meticulous tuning to leverage their full potential; 2) Our findings demonstrate that a rudimentary and parameter-free heatmap, derived from the intrinsic $k$-nearest nature of TSP, can rival or even surpass the performance of complicated heatmaps, with strong generalizability across various scales. Empirical evaluations across various TSP scales underscore the efficacy of our approach, achieving competitive results. These observations challenge the prevailing focus on heatmap sophistication, advocating a reevaluation of the paradigm to harness both components synergistically. Our code is available at: https://github.com/LOGO-CUHKSZ/rethink_mcts_tsp.
Rethinking CyberSecEval: An LLM-Aided Approach to Evaluation Critique
2024-11-13
Suhas Hariharan, Zainab Ali Majid, Jaime Raldua Veuthey, Jacob Haimes
A key development in the cybersecurity evaluations space is the work carried out by Meta, through their CyberSecEval approach. While this work is undoubtedly a useful contribution to a nascent field, there are notable features that limit its utility. Key drawbacks focus on the insecure code detection part of Meta's methodology. We explore these limitations, and use our exploration as a test case for LLM-assisted benchmark analysis.
Rethinking negative sampling in content-based news recommendation
2024-11-13
Miguel Ângelo Rebelo, João Vinagre, Ivo Pereira, Álvaro Figueira
News recommender systems are hindered by the brief lifespan of articles, as they undergo rapid relevance decay. Recent studies have demonstrated the potential of content-based neural techniques in tackling this problem. However, these models often involve complex neural architectures and often lack consideration for negative examples. In this study, we posit that the careful sampling of negative examples has a big impact on the model's outcome. We devise a negative sampling technique that not only improves the accuracy of the model but also facilitates the decentralization of the recommendation system. The experimental results obtained using the MIND dataset demonstrate that the accuracy of the method under consideration can compete with that of State-of-the-Art models. The utilization of the sampling technique is essential in reducing model complexity and accelerating the training process, while maintaining a high level of accuracy. Finally, we discuss how decentralized models can help improve privacy and scalability.
In Serverless, OS Scheduler Choice Costs Money: A Hybrid Scheduling Approach for Cheaper FaaS
2024-11-13
Yuxuan Zhao, Weikang Weng, Rob van Nieuwpoort, Alexandru Uta
In Function-as-a-Service (FaaS) serverless, large applications are split into short-lived stateless functions. Deploying functions is mutually profitable: users need not be concerned with resource management, while providers can keep their servers at high utilization rates running thousands of functions concurrently on a single machine. It is exactly this high concurrency that comes at a cost. The standard Linux Completely Fair Scheduler (CFS) switches often between tasks, which leads to prolonged execution times. We present evidence that relying on the default Linux CFS scheduler increases serverless workloads cost by up to 10X. In this article, we raise awareness and make a case for rethinking the OS-level scheduling in Linux for serverless workloads composed of many short-lived processes. To make serverless more affordable we introduce a hybrid two-level scheduling approach that relies on FaaS characteristics. Short-running functions are executed in FIFO fashion without preemption, while longer-running functions are passed to CFS after a certain time period. We show that tailor-made OS scheduling is able to significantly reduce user-facing costs without adding any provider-facing overhead.
The VLLM Safety Paradox: Dual Ease in Jailbreak Attack and Defense
2024-11-13
Yangyang Guo, Fangkai Jiao, Liqiang Nie, Mohan Kankanhalli
The vulnerability of Vision Large Language Models (VLLMs) to jailbreak attacks appears as no surprise. However, recent defense mechanisms against these attacks have reached near-saturation performance on benchmarks, often with minimal effort. This simultaneous high performance in both attack and defense presents a perplexing paradox. Resolving it is critical for advancing the development of trustworthy models. To address this research gap, we first investigate why VLLMs are prone to these attacks. We then make a key observation: existing defense mechanisms suffer from an \textbf{over-prudence} problem, resulting in unexpected abstention even in the presence of benign inputs. Additionally, we find that the two representative evaluation methods for jailbreak often exhibit chance agreement. This limitation makes it potentially misleading when evaluating attack strategies or defense mechanisms. Beyond these empirical observations, our another contribution in this work is to repurpose the guardrails of LLMs on the shelf, as an effective alternative detector prior to VLLM response. We believe these findings offer useful insights to rethink the foundational development of VLLM safety with respect to benchmark datasets, evaluation methods, and defense strategies.
How to quantify interaction strengths? A critical rethinking of the interaction Jacobian and evaluation methods for non-parametric inference in time series analysis
Quantifying interaction strengths between state variables in dynamical systems is essential for understanding ecological networks. Within the empirical dynamic modeling approach, multivariate S-map infers the interaction Jacobian from time series data without assuming specific dynamical models. This approach enables the non-parametric statistical inference of interspecific interactions through state space reconstruction. However, deviations in the biological interpretation and numerical implementation of the interaction Jacobian from its mathematical definition pose challenges. We mathematically reintroduce the interaction Jacobian using differential quotients, uncovering two problems: (1) the mismatch between the interaction Jacobian and its biological meaning complicates comparisons between interspecific and intraspecific interactions; (2) the interaction Jacobian is not fully implemented in the parametric Jacobian numerically derived from given parametric models, especially using ordinary differential equations. As a result, model-based evaluations of S-map methods become inappropriate. To address these problems, (1) we propose adjusting the diagonal elements of the interaction Jacobian by subtracting 1 to resolve the comparability problem between inter- and intraspecific interaction strengths. Simulations of population dynamics showed that this adjustment prevents overestimation of intraspecific interaction strengths. (2) We introduce an alternative parametric Jacobian and then cumulative interaction strength (CIS), providing a more rigorous benchmark for evaluating S-map methods. Furthermore, we demonstrated that the numerical gap between CIS and the existing parametric Jacobian is substantial in realistic scenarios, suggesting CIS as preferred benchmark. These solutions offer a clearer framework for developing non-parametric approaches in ecological time series analysis.
To improve the performance of Graph Neural Networks (GNNs), Graph Structure Learning (GSL) has been extensively applied to reconstruct or refine original graph structures, effectively addressing issues like heterophily, over-squashing, and noisy structures. While GSL is generally thought to improve GNN performance, it often leads to longer training times and more hyperparameter tuning. Besides, the distinctions among current GSL methods remain ambiguous from the perspective of GNN training, and there is a lack of theoretical analysis to quantify their effectiveness. Recent studies further suggest that, under fair comparisons with the same hyperparameter tuning, GSL does not consistently outperform baseline GNNs. This motivates us to ask a critical question: is GSL really useful for GNNs? To address this question, this paper makes two key contributions. First, we propose a new GSL framework, which includes three steps: GSL base (the representation used for GSL) construction, new structure construction, and view fusion, to better understand the effectiveness of GSL in GNNs. Second, after graph convolution, we analyze the differences in mutual information (MI) between node representations derived from the original topology and those from the newly constructed topology. Surprisingly, our empirical observations and theoretical analysis show that no matter which type of graph structure construction methods are used, after feeding the same GSL bases to the newly constructed graph, there is no MI gain compared to the original GSL bases. To fairly reassess the effectiveness of GSL, we conduct ablation experiments and find that it is the pretrained GSL bases that enhance GNN performance, and in most cases, GSL cannot improve GNN performance. This finding encourages us to rethink the essential components in GNNs, such as self-training and structural encoding, in GNN design rather than GSL.
Rethinking category-selectivity in human visual cortex
2024-11-12
J. Brendan Ritchie, Susan G. Wardle, Maryam Vaziri-Pashkam, Dwight J. Kravitz, Chris I. Baker
A wealth of studies report evidence that occipitotemporal cortex tessellates into "category-selective" brain regions that are apparently specialized for representing ecologically important visual stimuli like faces, bodies, scenes, and tools. Here, we argue that while valuable insights have been gained through the lens of category-selectivity, a more complete view of visual function in occipitotemporal cortex requires centering the behavioral relevance of visual properties in real-world environments rather than stimulus category. Focusing on behavioral relevance challenges a simple mapping between stimulus and visual function in occipitotemporal cortex because the environmental properties relevant to a behavior are visually diverse and how a given property is represented is modulated by our goals. Grounding our thinking in behavioral relevance rather than category-selectivity raises a host of theoretical and empirical issues that we discuss while providing proposals for how existing tools can be harnessed in this light to better understand visual function in occipitotemporal cortex.
2024-11-11
Ang Lv, Ruobing Xie, Shuaipeng Li, Jiayi Liao, Xingwu Sun, Zhanhui Kang, Di Wang, Rui Yan
We propose a novel attention mechanism, named Cog Attention, that enables attention weights to be negative for enhanced expressiveness, which stems from two key factors: (1) Cog Attention enhances parameter flexibility. For example, unlike traditional softmax attention heads that use a static output-value (OV) matrix to delete or copy inputs that the heads attend to, Cog Attention naturally learns to use the sign of dynamic query-key (QK) inner products to represent these operations. This enables Cog Attention to perform multiple operations simultaneously within a single head. Meanwhile, Cog Attention's OV matrix can focus more on refinement or modification. (2) Cog Attention enhances the model's robustness against representational collapse by preventing the ``over-squashing'' of earlier tokens into later positions. We develop Transformer-like models which use Cog Attention as attention modules, including decoder-only models at various scales for language modeling and U-ViT diffusion models for image generation. Experiments show that models using Cog Attention exhibit superior performance compared to those employing traditional softmax attention modules. Our approach suggests a promising research direction for rethinking and breaking the entrenched constraints of traditional softmax attention, such as the requirement for non-negative weights.
Recently, token-based generation have demonstrated their effectiveness in image synthesis. As a representative example, non-autoregressive Transformers (NATs) can generate decent-quality images in a few steps. NATs perform generation in a progressive manner, where the latent tokens of a resulting image are incrementally revealed. At each step, the unrevealed image regions are padded with mask tokens and inferred by NAT. In this paper, we delve into the mechanisms behind the effectiveness of NATs and uncover two important patterns that naturally emerge from NATs: Spatially (within a step), although mask and visible tokens are processed uniformly by NATs, the interactions between them are highly asymmetric. In specific, mask tokens mainly gather information for decoding, while visible tokens tend to primarily provide information, and their deep representations can be built only upon themselves. Temporally (across steps), the interactions between adjacent generation steps mostly concentrate on updating the representations of a few critical tokens, while the computation for the majority of tokens is generally repetitive. Driven by these findings, we propose EfficientNAT (ENAT), a NAT model that explicitly encourages these critical interactions inherent in NATs. At the spatial level, we disentangle the computations of visible and mask tokens by encoding visible tokens independently, while decoding mask tokens conditioned on the fully encoded visible tokens. At the temporal level, we prioritize the computation of the critical tokens at each step, while maximally reusing previously computed token representations to supplement necessary information. ENAT improves the performance of NATs notably with significantly reduced computational cost. Experiments on ImageNet-256, ImageNet-512 and MS-COCO validate the effectiveness of ENAT. Code is available at https://github.com/LeapLabTHU/ENAT.
Superpixel Segmentation: A Long-Lasting Ill-Posed Problem
2024-11-10
Rémi Giraud, Michaël Clément
For many years, image over-segmentation into superpixels has been essential to computer vision pipelines, by creating homogeneous and identifiable regions of similar sizes. Such constrained segmentation problem would require a clear definition and specific evaluation criteria. However, the validation framework for superpixel methods, typically viewed as standard object segmentation, has rarely been thoroughly studied. In this work, we first take a step back to show that superpixel segmentation is fundamentally an ill-posed problem, due to the implicit regularity constraint on the shape and size of superpixels. We also demonstrate through a novel comprehensive study that the literature suffers from only evaluating certain aspects, sometimes incorrectly and with inappropriate metrics. Concurrently, recent deep learning-based superpixel methods mainly focus on the object segmentation task at the expense of regularity. In this ill-posed context, we show that we can achieve competitive results using a recent architecture like the Segment Anything Model (SAM), without dedicated training for the superpixel segmentation task. This leads to rethinking superpixel segmentation and the necessary properties depending on the targeted downstream task.
GFT: Graph Foundation Model with Transferable Tree Vocabulary
2024-11-09
Zehong Wang, Zheyuan Zhang, Nitesh V Chawla, Chuxu Zhang, Yanfang Ye
Inspired by the success of foundation models in applications such as ChatGPT, as graph data has been ubiquitous, one can envision the far-reaching impacts that can be brought by Graph Foundation Models (GFMs) with broader applications in the areas such as scientific research, social network analysis, drug discovery, and e-commerce. Despite the significant progress of pre-trained graph neural networks, there haven't been GFMs that can achieve desired performance on various graph-learning-related tasks. Building GFMs may rely on a vocabulary that encodes transferable patterns shared among different tasks and domains. Unlike image and text, defining such transferable patterns for graphs remains an open question. In this paper, we aim to bridge this gap by rethinking the transferable patterns on graphs as computation trees -- i.e., tree structures derived from the message-passing process. Based on this insight, we propose a cross-task, cross-domain graph foundation model named GFT, short for Graph Foundation model with transferable Tree vocabulary. By treating computation trees as tokens within the transferable vocabulary, GFT improves model generalization and reduces the risk of negative transfer. The theoretical analyses and extensive experimental studies have demonstrated the transferability of computation trees and shown the effectiveness of GFT across diverse tasks and domains in graph learning. The open source code and data are available at https://github.com/Zehong-Wang/GFT.
From Transparent to Opaque: Rethinking Neural Implicit Surfaces with $α$-NeuS
2024-11-08
Haoran Zhang, Junkai Deng, Xuhui Chen, Fei Hou, Wencheng Wang, Hong Qin, Chen Qian, Ying He
Traditional 3D shape reconstruction techniques from multi-view images, such as structure from motion and multi-view stereo, face challenges in reconstructing transparent objects. Recent advances in neural radiance fields and its variants primarily address opaque or transparent objects, encountering difficulties to reconstruct both transparent and opaque objects simultaneously. This paper introduces $\alpha$-Neus -- an extension of NeuS -- that proves NeuS is unbiased for materials from fully transparent to fully opaque. We find that transparent and opaque surfaces align with the non-negative local minima and the zero iso-surface, respectively, in the learned distance field of NeuS. Traditional iso-surfacing extraction algorithms, such as marching cubes, which rely on fixed iso-values, are ill-suited for such data. We develop a method to extract the transparent and opaque surface simultaneously based on DCUDF. To validate our approach, we construct a benchmark that includes both real-world and synthetic scenes, demonstrating its practical utility and effectiveness. Our data and code are publicly available at https://github.com/728388808/alpha-NeuS.
Other Worlds: Using AI to Revisit Cybersyn and Rethink Economic Futures
2024-11-08
Luke Munn, Liam Magee
Neoliberalism has become orthodoxy in the present, erasing competing paradigms and alternative imaginings. Chile's radical Cybersyn project from 1971 to 1973 offers a departure point for an alternative path, albeit one that was abruptly and violently extinguished. We revisit this moment by fine-tuning AI language models on the words and writing of Salvador Allende, the Chilean President, and Stafford Beer, the cyberneticist who helped to design the project. We conduct interviews with these simulated personas, focusing on how their revolutionary ideas might be taken up in the present. We then use an AI model to generate five-year-plans from 1973 to the present, simulating an alternate history guided by Cybersyn and a progressive agenda. We frame these interventions as socialist infrastructuring that cultivates a more expansive socialist imagining. This work is not about the viability of planned economies, but about the 'inspirability' of exploring other value-systems in the present, allowing us to break out of our future-on-rails to envision alternative ways of organizing economy and society.
The Grass of the Universe: Rethinking Technosphere, Planetary History, and Sustainability with Fermi Paradox
2024-11-08
Lukáš Likavčan
SETI is not a usual point of departure for environmental humanities. However, this paper argues that theories originating in this field have direct implications for how we think about viable inhabitation of the Earth. To demonstrate SETI's impact on environmental humanities, this paper introduces Fermi paradox as a speculative tool to probe possible trajectories of planetary history, and especially the "Sustainability Solution" proposed by Jacob Haqq-Misra and Seth Baum. This solution suggests that sustainable coupling between extraterrestrial intelligences and their planetary environments is the major factor in the possibility of their successful detection by remote observation. By positing that exponential growth is not a sustainable development pattern, this solution rules out space-faring civilizations colonizing solar systems or galaxies. This paper elaborates on Haqq-Misra's and Baum's arguments, and discusses speculative implications of the Sustainability Solution, thus rethinking three concepts in environmental humanities: technosphere, planetary history, and sustainability. The paper advocates that (1) technosphere is a transitory layer that shall fold back into biosphere; (2) planetary history must be understood in a generic perspective that abstracts from terrestrial particularities; and (3) sustainability is not sufficient vector of viable human inhabitation of the Earth, suggesting instead habitability and genesity as better candidates.
Rethinking Bradley-Terry Models in Preference-Based Reward Modeling: Foundations, Theory, and Alternatives
2024-11-07
Hao Sun, Yunyi Shen, Jean-Francois Ton
The Bradley-Terry (BT) model is a common and successful practice in reward modeling for Large Language Model (LLM) alignment. However, it remains unclear why this model -- originally developed for multi-player stochastic game matching -- can be adopted to convert pairwise response comparisons to reward values and make predictions. Especially given the fact that only a limited number of prompt-response pairs are sparsely compared with others. In this paper, we first revisit the foundations of using BT models in reward modeling, and establish the convergence rate of BT reward models based on deep neural networks using embeddings, providing a theoretical foundation for their use. Despite theoretically sound, we argue that the BT model is not a necessary choice from the perspective of downstream optimization. This is because a reward model only needs to preserve the correct ranking predictions through a monotonic transformation of the true reward. We highlight the critical concept of order consistency in reward modeling and demonstrate that the BT model possesses this property. Consequently, we propose a simple and straightforward upper-bound algorithm, compatible with off-the-shelf binary classifiers, as an alternative order-consistent reward modeling objective. To offer practical insights, we empirically evaluate the performance of these different reward modeling approaches across more than 12,000 experimental setups, using $6$ base LLMs, $2$ datasets, and diverse annotation designs that vary in quantity, quality, and pairing choices in preference annotations.
Rethinking Deep Learning: Non-backpropagation and Non-optimization Machine Learning Approach Using Hebbian Neural Networks
2024-11-07
Kei Itoh
Developing strong AI could provide a powerful tool for addressing social and scientific challenges. Neural networks (NNs), inspired by biological systems, have the potential to achieve this. However, weight optimization techniques using error backpropagation are not observed in biological systems, raising doubts about current NNs approaches. In this context, Itoh (2024) solved the MNIST classification problem without using objective functions or backpropagation. However, weight updates were not used, so it does not qualify as machine learning AI. In this study, I develop a machine learning method that mimics biological neural systems by implementing Hebbian learning in NNs without backpropagation and optimization method to solve the MNIST classification problem and analyze its output. Development proceeded in three stages. In the first stage, I applied the Hebbian learning rule to the MNIST character recognition algorithm by Itoh (2024), resulting in lower accuracy than non-Hebbian NNs, highlighting the limitations of conventional training procedures for Hebbian learning. In the second stage, I examined the properties of individually trained NNs using norm-based cognition, showing that NNs trained on a specific label respond powerfully to that label. In the third stage, I created an MNIST character recognition program using vector norm magnitude as the criterion, achieving an accuracy of approximately 75%. This demonstrates that the Hebbian learning NNs can recognize handwritten characters without objective functions, backpropagation, optimization processes, and large data set. Based on these results, developing a mechanism based on norm-based cognition as a fundamental unit and then increasing complexity to achieve indirect similarity cognition should help mimic biological neural systems and contribute to realizing strong AI.
Rethinking Decoders for Transformer-based Semantic Segmentation: A Compression Perspective
2024-11-05
Qishuai Wen, Chun-Guang Li
State-of-the-art methods for Transformer-based semantic segmentation typically adopt Transformer decoders that are used to extract additional embeddings from image embeddings via cross-attention, refine either or both types of embeddings via self-attention, and project image embeddings onto the additional embeddings via dot-product. Despite their remarkable success, these empirical designs still lack theoretical justifications or interpretations, thus hindering potentially principled improvements. In this paper, we argue that there are fundamental connections between semantic segmentation and compression, especially between the Transformer decoders and Principal Component Analysis (PCA). From such a perspective, we derive a white-box, fully attentional DEcoder for PrIncipled semantiC segemenTation (DEPICT), with the interpretations as follows: 1) the self-attention operator refines image embeddings to construct an ideal principal subspace that aligns with the supervision and retains most information; 2) the cross-attention operator seeks to find a low-rank approximation of the refined image embeddings, which is expected to be a set of orthonormal bases of the principal subspace and corresponds to the predefined classes; 3) the dot-product operation yields compact representation for image embeddings as segmentation masks. Experiments conducted on dataset ADE20K find that DEPICT consistently outperforms its black-box counterpart, Segmenter, and it is light weight and more robust.
Class-incremental semantic segmentation (CSS) requires that a model learn to segment new classes without forgetting how to segment previous ones: this is typically achieved by distilling the current knowledge and incorporating the latest data. However, bypassing iterative distillation by directly transferring outputs of initial classes to the current learning task is not supported in existing class-specific CSS methods. Via Softmax, they enforce dependency between classes and adjust the output distribution at each learning step, resulting in a large probability distribution gap between initial and current tasks. We introduce a simple, yet effective Class Independent Transformation (CIT) that converts the outputs of existing semantic segmentation models into class-independent forms with negligible cost or performance loss. By utilizing class-independent predictions facilitated by CIT, we establish an accumulative distillation framework, ensuring equitable incorporation of all class information. We conduct extensive experiments on various segmentation architectures, including DeepLabV3, Mask2Former, and SegViTv2. Results from these experiments show minimal task forgetting across different datasets, with less than 5% for ADE20K in the most challenging 11 task configurations and less than 1% across all configurations for the PASCAL VOC 2012 dataset.
Creative writing is a deeply human craft, yet AI systems using large language models (LLMs) offer the automation of significant parts of the writing process. So why do some creative writers choose to use AI? Through interviews and observed writing sessions with 18 creative writers who already use AI regularly in their writing practice, we find that creative writers are intentional about how they incorporate AI, making many deliberate decisions about when and how to engage AI based on their core values, such as authenticity and craftsmanship. We characterize the interplay between writers' values, their fluid relationships with AI, and specific integration strategies -- ultimately enabling writers to create new AI workflows without compromising their creative values. We provide insight for writing communities, AI developers and future researchers on the importance of supporting transparency of these emerging writing processes and rethinking what AI features can best serve writers.
What Goes Into a LM Acceptability Judgment? Rethinking the Impact of Frequency and Length
2024-11-04
Lindia Tjuatja, Graham Neubig, Tal Linzen, Sophie Hao
When comparing the linguistic capabilities of language models (LMs) with humans using LM probabilities, factors such as the length of the sequence and the unigram frequency of lexical items have a significant effect on LM probabilities in ways that humans are largely robust to. Prior works in comparing LM and human acceptability judgments treat these effects uniformly across models, making a strong assumption that models require the same degree of adjustment to control for length and unigram frequency effects. We propose MORCELA, a new linking theory between LM scores and acceptability judgments where the optimal level of adjustment for these effects is estimated from data via learned parameters for length and unigram frequency. We first show that MORCELA outperforms a commonly used linking theory for acceptability--SLOR (Pauls and Klein, 2012; Lau et al. 2017)--across two families of transformer LMs (Pythia and OPT). Furthermore, we demonstrate that the assumed degrees of adjustment in SLOR for length and unigram frequency overcorrect for these confounds, and that larger models require a lower relative degree of adjustment for unigram frequency, though a significant amount of adjustment is still necessary for all models. Finally, our subsequent analysis shows that larger LMs' lower susceptibility to frequency effects can be explained by an ability to better predict rarer words in context.
Rethinking Weight Decay for Robust Fine-Tuning of Foundation Models
2024-11-03
Junjiao Tian, Chengyue Huang, Zsolt Kira
Modern optimizers such as AdamW, equipped with momentum and adaptive learning rate, are designed to escape local minima and explore the vast parameter space. This exploration is beneficial for finding good loss basins when training from scratch. It is not necessarily ideal when resuming from a powerful foundation model because it can lead to large deviations from the pre-trained initialization and, consequently, worse robustness and generalization. At the same time, strong regularization on all parameters can lead to under-fitting. We hypothesize that selectively regularizing the parameter space is the key to fitting and retraining the pre-trained knowledge. This paper proposes a new weight decay technique, Selective Projection Decay (SPD), that selectively imposes a strong penalty on certain layers while allowing others to change freely. Intuitively, SPD expands and contracts the parameter search space for layers with consistent and inconsistent loss reduction, respectively. Experimentally, when equipped with SPD, Adam consistently provides better in-distribution generalization and out-of-distribution robustness performance on multiple popular vision and language benchmarks. Code available at~\url{https://github.com/GT-RIPL/Selective-Projection-Decay.git}
TrajRoute: Rethinking Routing with a Simple Trajectory-Based Approach -- Forget the Maps and Traffic!
2024-11-02
Maria Despoina Siampou, Chrysovalantis Anastasiou, John Krumm, Cyrus Shahabi
The abundance of vehicle trajectory data offers a new opportunity to compute driving routes between origins and destinations. Current graph-based routing pipelines, while effective, involve substantial costs in constructing, maintaining, and updating road network graphs to reflect real-time conditions. In this study, we propose a new trajectory-based routing paradigm that bypasses current workflows by directly utilizing raw trajectory data to compute efficient routes. Our method, named TrajRoute, uniquely "follows" historical trajectories from a source to a destination, constructing paths that reflect actual driver behavior and implicit preferences. To supplement areas with sparse trajectory data, the road network is also incorporated into TrajRoute's index, and tunable parameters are introduced to control the balance between road segments and trajectories, ensuring a unified and adaptable routing approach. We experimentally verify our approach by comparing it to an existing online routing service. Our results demonstrate that as the number of trajectories covering the road network increases, TrajRoute produces increasingly accurate travel time and route length estimates while gradually eliminating the need to downgrade to the road network. This highlights the potential of simpler, data-driven pipelines for routing, offering lower-maintenance alternatives to conventional systems.
Understanding node representations in graph-based models is crucial for uncovering biases ,diagnosing errors, and building trust in model decisions. However, previous work on explainable AI for node representations has primarily emphasized explanations (reasons for model predictions) rather than interpretations (mapping representations to understandable concepts). Furthermore, the limited research that focuses on interpretation lacks validation, and thus the reliability of such methods is unclear. We address this gap by proposing a novel interpretation method-Node Coherence Rate for Representation Interpretation (NCI)-which quantifies how well different node relations are captured in node representations. We also propose a novel method (IME) to evaluate the accuracy of different interpretation methods. Our experimental results demonstrate that NCI reduces the error of the previous best approach by an average of 39%. We then apply NCI to derive insights about the node representations produced by several graph-based methods and assess their quality in unsupervised settings.
Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure
2024-10-31
Xiang Li, Yixiang Dai, Qing Qu
In this work, we study the generalizability of diffusion models by looking into the hidden properties of the learned score functions, which are essentially a series of deep denoisers trained on various noise levels. We observe that as diffusion models transition from memorization to generalization, their corresponding nonlinear diffusion denoisers exhibit increasing linearity. This discovery leads us to investigate the linear counterparts of the nonlinear diffusion models, which are a series of linear models trained to match the function mappings of the nonlinear diffusion denoisers. Surprisingly, these linear denoisers are approximately the optimal denoisers for a multivariate Gaussian distribution characterized by the empirical mean and covariance of the training dataset. This finding implies that diffusion models have the inductive bias towards capturing and utilizing the Gaussian structure (covariance information) of the training dataset for data generation. We empirically demonstrate that this inductive bias is a unique property of diffusion models in the generalization regime, which becomes increasingly evident when the model's capacity is relatively small compared to the training dataset size. In the case that the model is highly overparameterized, this inductive bias emerges during the initial training phases before the model fully memorizes its training data. Our study provides crucial insights into understanding the notable strong generalization phenomenon recently observed in real-world diffusion models.
Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment
2024-10-31
Weichao Zhou, Wenchao Li
Many imitation learning (IL) algorithms use inverse reinforcement learning (IRL) to infer a reward function that aligns with the demonstration. However, the inferred reward functions often fail to capture the underlying task objectives. In this paper, we propose a novel framework for IRL-based IL that prioritizes task alignment over conventional data alignment. Our framework is a semi-supervised approach that leverages expert demonstrations as weak supervision to derive a set of candidate reward functions that align with the task rather than only with the data. It then adopts an adversarial mechanism to train a policy with this set of reward functions to gain a collective validation of the policy's ability to accomplish the task. We provide theoretical insights into this framework's ability to mitigate task-reward misalignment and present a practical implementation. Our experimental results show that our framework outperforms conventional IL baselines in complex and transfer learning scenarios.
Rethinking Scale: The Efficacy of Fine-Tuned Open-Source LLMs in Large-Scale Reproducible Social Science Research
2024-10-31
Marcello Carammia, Stefano Maria Iacus, Giuseppe Porro
Large Language Models (LLMs) are distinguished by their architecture, which dictates their parameter size and performance capabilities. Social scientists have increasingly adopted LLMs for text classification tasks, which are difficult to scale with human coders. While very large, closed-source models often deliver superior performance, their use presents significant risks. These include lack of transparency, potential exposure of sensitive data, challenges to replicability, and dependence on proprietary systems. Additionally, their high costs make them impractical for large-scale research projects. In contrast, open-source models, although available in various sizes, may underperform compared to commercial alternatives if used without further fine-tuning. However, open-source models offer distinct advantages: they can be run locally (ensuring data privacy), fine-tuned for specific tasks, shared within the research community, and integrated into reproducible workflows. This study demonstrates that small, fine-tuned open-source LLMs can achieve equal or superior performance to models such as ChatGPT-4. We further explore the relationship between training set size and fine-tuning efficacy in open-source models. Finally, we propose a hybrid workflow that leverages the strengths of both open and closed models, offering a balanced approach to performance, transparency, and reproducibility.
PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation
2024-10-31
Weiqin Yang, Jiawei Chen, Xin Xin, Sheng Zhou, Binbin Hu, Yan Feng, Chun Chen, Can Wang
Softmax Loss (SL) is widely applied in recommender systems (RS) and has demonstrated effectiveness. This work analyzes SL from a pairwise perspective, revealing two significant limitations: 1) the relationship between SL and conventional ranking metrics like DCG is not sufficiently tight; 2) SL is highly sensitive to false negative instances. Our analysis indicates that these limitations are primarily due to the use of the exponential function. To address these issues, this work extends SL to a new family of loss functions, termed Pairwise Softmax Loss (PSL), which replaces the exponential function in SL with other appropriate activation functions. While the revision is minimal, we highlight three merits of PSL: 1) it serves as a tighter surrogate for DCG with suitable activation functions; 2) it better balances data contributions; and 3) it acts as a specific BPR loss enhanced by Distributionally Robust Optimization (DRO). We further validate the effectiveness and robustness of PSL through empirical experiments. The code is available at https://github.com/Tiny-Snow/IR-Benchmark.
TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters
2024-10-30
Haiyang Wang, Yue Fan, Muhammad Ferjad Naeem, Yongqin Xian, Jan Eric Lenssen, Liwei Wang, Federico Tombari, Bernt Schiele
Transformers have become the predominant architecture in foundation models due to their excellent performance across various domains. However, the substantial cost of scaling these models remains a significant concern. This problem arises primarily from their dependence on a fixed number of parameters within linear projections. When architectural modifications (e.g., channel dimensions) are introduced, the entire model typically requires retraining from scratch. As model sizes continue growing, this strategy results in increasingly high computational costs and becomes unsustainable. To overcome this problem, we introduce TokenFormer, a natively scalable architecture that leverages the attention mechanism not only for computations among input tokens but also for interactions between tokens and model parameters, thereby enhancing architectural flexibility. By treating model parameters as tokens, we replace all the linear projections in Transformers with our token-parameter attention layer, where input tokens act as queries and model parameters as keys and values. This reformulation allows for progressive and efficient scaling without necessitating retraining from scratch. Our model scales from 124M to 1.4B parameters by incrementally adding new key-value parameter pairs, achieving performance comparable to Transformers trained from scratch while greatly reducing training costs. Code and models are available at \url{https://github.com/Haiyang-W/TokenFormer}.
Rethinking Deep Thinking: Stable Learning of Algorithms using Lipschitz Constraints
2024-10-30
Jay Bear, Adam Prügel-Bennett, Jonathon Hare
Iterative algorithms solve problems by taking steps until a solution is reached. Models in the form of Deep Thinking (DT) networks have been demonstrated to learn iterative algorithms in a way that can scale to different sized problems at inference time using recurrent computation and convolutions. However, they are often unstable during training, and have no guarantees of convergence/termination at the solution. This paper addresses the problem of instability by analyzing the growth in intermediate representations, allowing us to build models (referred to as Deep Thinking with Lipschitz Constraints (DT-L)) with many fewer parameters and providing more reliable solutions. Additionally our DT-L formulation provides guarantees of convergence of the learned iterative procedure to a unique solution at inference time. We demonstrate DT-L is capable of robustly learning algorithms which extrapolate to harder problems than in the training set. We benchmark on the traveling salesperson problem to evaluate the capabilities of the modified system in an NP-hard problem where DT fails to learn.
System Reliability Engineering in the Age of Industry 4.0: Challenges and Innovations
2024-10-30
Antoine Tordeux, Tim M. Julitz, Isabelle Müller, Zikai Zhang, Jannis Pietruschka, Nicola Fricke, Nadine Schlüter, Stefan Bracke, Manuel Löwer
In the era of Industry 4.0, system reliability engineering faces both challenges and opportunities. On the one hand, the complexity of cyber-physical systems, the integration of novel numerical technologies, and the handling of large amounts of data pose new difficulties for ensuring system reliability. On the other hand, innovations such as AI-driven prognostics, digital twins, and IoT-enabled systems enable the implementation of new methodologies that are transforming reliability engineering. Condition-based monitoring and predictive maintenance are examples of key advancements, leveraging real-time sensor data collection and AI to predict and prevent equipment failures. These approaches reduce failures and downtime, lower costs, and extend equipment lifespan and sustainability. However, it also brings challenges such as data management, integrating complexity, and the need for fast and accurate models and algorithms. Overall, the convergence of advanced technologies in Industry 4.0 requires a rethinking of reliability tasks, emphasising adaptability and real-time data processing. In this chapter, we propose to review recent innovations in the field, related methods and applications, as well as challenges and barriers that remain to be explored. In the red lane, we focus on smart manufacturing and automotive engineering applications with sensor-based monitoring and driver assistance systems.
Rethinking Code Refinement: Learning to Judge Code Efficiency
2024-10-29
Minju Seo, Jinheon Baek, Sung Ju Hwang
Large Language Models (LLMs) have demonstrated impressive capabilities in understanding and generating codes. Due to these capabilities, many recent methods are proposed to automatically refine the codes with LLMs. However, we should rethink that the refined codes (from LLMs and even humans) are not always more efficient than their original versions. On the other hand, running two different versions of codes and comparing them every time is not ideal and time-consuming. Therefore, in this work, we propose a novel method based on the code language model that is trained to judge the efficiency between two different codes (generated across humans and machines) by either classifying the superior one or predicting the relative improvement. We validate our method on multiple programming languages with multiple refinement steps, demonstrating that the proposed method can effectively distinguish between more and less efficient versions of code.
Large language models (LLM) have sparked significant impact with regard to both intelligence and productivity. In recent years, a great surge has been witnessed in the introduction of both commercial and open-source LLMs. Many businesses have adopted the LLMs into their applications to solve their own domain-specific tasks. However, integrating LLMs into specific business scenarios requires more than just utilizing the models themselves. Instead, it is a systematic process that involves substantial components, which are collectively referred to as the LLM supply chain. The LLM supply chain inherently carries risks. Therefore, it is essential to understand the types of components that may be introduced into the supply chain and the associated risks, enabling different stakeholders to implement effective mitigation measures. While some literature discusses risks associated with LLMs, there is currently no paper that clearly outlines the LLM supply chain from the perspective of both providing and consuming its components. As LLMs have become essential infrastructure in the new era, we believe that a thorough review of the LLM supply chain, along with its inherent risks and mitigation strategies, would be valuable for industry practitioners to avoid potential damages and losses, and enlightening for academic researchers to rethink existing approaches and explore new avenues of research. Our paper provides a comprehensive overview of the LLM supply chain, detailing the stakeholders, composing artifacts, and the supplying types. We developed taxonomies of risk types, risky actions, and mitigations related to various supply chain stakeholders and components. In summary, our work explores the technical and operational aspects of the LLM supply chain, offering valuable insights for researchers and engineers in the evolving LLM landscape.
Multimedia file fragment classification (MFFC) aims to identify file fragment types, e.g., image/video, audio, and text without system metadata. It is of vital importance in multimedia storage and communication. Existing MFFC methods typically treat fragments as 1D byte sequences and emphasize the relations between separate bytes (interbytes) for classification. However, the more informative relations inside bytes (intrabytes) are overlooked and seldom investigated. By looking inside bytes, the bit-level details of file fragments can be accessed, enabling a more accurate classification. Motivated by this, we first propose Byte2Image, a novel visual representation model that incorporates previously overlooked intrabyte information into file fragments and reinterprets these fragments as 2D grayscale images. This model involves a sliding byte window to reveal the intrabyte information and a rowwise stacking of intrabyte ngrams for embedding fragments into a 2D space. Thus, complex interbyte and intrabyte correlations can be mined simultaneously using powerful vision networks. Additionally, we propose an end-to-end dual-branch network ByteNet to enhance robust correlation mining and feature representation. ByteNet makes full use of the raw 1D byte sequence and the converted 2D image through a shallow byte branch feature extraction (BBFE) and a deep image branch feature extraction (IBFE) network. In particular, the BBFE, composed of a single fully-connected layer, adaptively recognizes the co-occurrence of several some specific bytes within the raw byte sequence, while the IBFE, built on a vision Transformer, effectively mines the complex interbyte and intrabyte correlations from the converted image. Experiments on the two representative benchmarks, including 14 cases, validate that our proposed method outperforms state-of-the-art approaches on different cases by up to 12.2%.
Synthetic data augmentation via large language models (LLMs) allows researchers to leverage additional training data, thus enhancing the performance of downstream tasks, especially when real-world data is scarce. However, the generated data can deviate from the real-world data, and this misalignment can bring deficient outcomes while applying the trained model to applications. Therefore, we proposed efficient weighted-loss approaches to align synthetic data with real-world distribution by emphasizing high-quality and diversified data generated by LLMs with using merely a little real-world data. We empirically assessed the effectiveness of our method on multiple text classification tasks, and the results showed leveraging our approaches on a BERT-level model robustly outperformed standard cross-entropy and other data weighting approaches, providing potential solutions to effectively leveraging synthetic data from any suitable data generator for model training.
Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy
2024-10-27
Sunwoo Kim, Soo Yong Lee, Fanchen Bu, Shinhwan Kang, Kyungho Kim, Jaemin Yoo, Kijung Shin
Graph autoencoders (Graph-AEs) learn representations of given graphs by aiming to accurately reconstruct them. A notable application of Graph-AEs is graph-level anomaly detection (GLAD), whose objective is to identify graphs with anomalous topological structures and/or node features compared to the majority of the graph population. Graph-AEs for GLAD regard a graph with a high mean reconstruction error (i.e. mean of errors from all node pairs and/or nodes) as anomalies. Namely, the methods rest on the assumption that they would better reconstruct graphs with similar characteristics to the majority. We, however, report non-trivial counter-examples, a phenomenon we call reconstruction flip, and highlight the limitations of the existing Graph-AE-based GLAD methods. Specifically, we empirically and theoretically investigate when this assumption holds and when it fails. Through our analyses, we further argue that, while the reconstruction errors for a given graph are effective features for GLAD, leveraging the multifaceted summaries of the reconstruction errors, beyond just mean, can further strengthen the features. Thus, we propose a novel and simple GLAD method, named MUSE. The key innovation of MUSE involves taking multifaceted summaries of reconstruction errors as graph features for GLAD. This surprisingly simple method obtains SOTA performance in GLAD, performing best overall among 14 methods across 10 datasets.
Rethinking Data Synthesis: A Teacher Model Training Recipe with Interpretation
2024-10-27
Yifang Chen, David Zhu, Simon Du, Kevin Jamieson, Yang Liu
Recent advances in large language model (LLM) training have highlighted the need for diverse, high-quality instruction data. Recently, many works are exploring synthetic data generation using LLMs. However, they primarily focus on prompt engineering with standard supervised instruction-finetuned models, which contains a fundamental limitation: these models are optimized for general question-answering/problem-solving rather than data generation. We propose a paradigm shift named \textbf{NOMAD} by investigating how to specifically train models for data generation, demonstrating that this task differs significantly from training a classical LM. We identify two key factors: no-prompt-masked training and proper training set size selection. Our method, NOMAD, shows substantial improvements over baselines, achieving >4\% gains in TriviaQA and >2\% in GSM8K with limited training data. Finally, we offer new insights by interpreting synthetic data through the lenses of "relevance" and "novelty".
Access control in a distributed micro-cloud environment
2024-10-26
Tamara Ranković, Miloš Simić, Milan Stojkov, Goran Sladić
Proliferation of systems that generate enormous amounts of data and operate in real time has led researchers to rethink the current organization of the cloud. Many proposed solutions consist of a number of small data centers in the vicinity of data sources. That creates a highly complex environment, where strict access control is essential. Recommended access control models frequently belong to the Attribute-Based Access Control (ABAC) family. Flexibility and dynamic nature of these models come at the cost of high policy management complexity. In this paper, we explore whether the administrative overhead can be lowered with resource hierarchies. We propose an ABAC model that incorporates user and object hierarchies. We develop a policy engine that supports the model and present a distributed cloud use case. Findings in this paper suggest that resource hierarchies simplify the administration of ABAC models, which is a necessary step towards their further inclusion in real-world systems.
In recent years, Large Language Models (LLMs) have become fundamental to a broad spectrum of artificial intelligence applications. As the use of LLMs expands, precisely estimating the uncertainty in their predictions has become crucial. Current methods often struggle to accurately identify, measure, and address the true uncertainty, with many focusing primarily on estimating model confidence. This discrepancy is largely due to an incomplete understanding of where, when, and how uncertainties are injected into models. This paper introduces a comprehensive framework specifically designed to identify and understand the types and sources of uncertainty, aligned with the unique characteristics of LLMs. Our framework enhances the understanding of the diverse landscape of uncertainties by systematically categorizing and defining each type, establishing a solid foundation for developing targeted methods that can precisely quantify these uncertainties. We also provide a detailed introduction to key related concepts and examine the limitations of current methods in mission-critical and safety-sensitive applications. The paper concludes with a perspective on future directions aimed at enhancing the reliability and practical adoption of these methods in real-world scenarios.
Rethinking Visual Dependency in Long-Context Reasoning for Large Vision-Language Models
2024-10-25
Yucheng Zhou, Zhi Rao, Jun Wan, Jianbing Shen
Large Vision-Language Models (LVLMs) excel in cross-model tasks but experience performance declines in long-context reasoning due to overreliance on textual information and reduced visual dependency. In this study, we empirically analyze LVLMs in long-context reasoning, revealing that increased context length leads to a higher dependence on language at the expense of visual dependency. To address this issue, we propose a novel training-free context pruning method that selectively removes less critical textual information. Our approach enhances visual dependency and reduces textual noise, thereby improving LVLM performance in long-context reasoning. We validate our method by constructing a long-context dataset, demonstrating its effectiveness across various LVLMs. Moreover, further analysis confirms the robustness of different token pruning strategies and preliminary explores scaling laws between pruning rates and context length.
Every Component Counts: Rethinking the Measure of Success for Medical Semantic Segmentation in Multi-Instance Segmentation Tasks
2024-10-24
Alexander Jaus, Constantin Seibold, Simon Reiß, Zdravko Marinov, Keyi Li, Zeling Ye, Stefan Krieg, Jens Kleesiek, Rainer Stiefelhagen
We present Connected-Component~(CC)-Metrics, a novel semantic segmentation evaluation protocol, targeted to align existing semantic segmentation metrics to a multi-instance detection scenario in which each connected component matters. We motivate this setup in the common medical scenario of semantic metastases segmentation in a full-body PET/CT. We show how existing semantic segmentation metrics suffer from a bias towards larger connected components contradicting the clinical assessment of scans in which tumor size and clinical relevance are uncorrelated. To rebalance existing segmentation metrics, we propose to evaluate them on a per-component basis thus giving each tumor the same weight irrespective of its size. To match predictions to ground-truth segments, we employ a proximity-based matching criterion, evaluating common metrics locally at the component of interest. Using this approach, we break free of biases introduced by large metastasis for overlap-based metrics such as Dice or Surface Dice. CC-Metrics also improves distance-based metrics such as Hausdorff Distances which are uninformative for small changes that do not influence the maximum or 95th percentile, and avoids pitfalls introduced by directly combining counting-based metrics with overlap-based metrics as it is done in Panoptic Quality.
FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation
2024-10-24
Christopher T. H Teo, Milad Abdollahzadeh, Xinda Ma, Ngai-man Cheung
Recently, prompt learning has emerged as the state-of-the-art (SOTA) for fair text-to-image (T2I) generation. Specifically, this approach leverages readily available reference images to learn inclusive prompts for each target Sensitive Attribute (tSA), allowing for fair image generation. In this work, we first reveal that this prompt learning-based approach results in degraded sample quality. Our analysis shows that the approach's training objective -- which aims to align the embedding differences of learned prompts and reference images -- could be sub-optimal, resulting in distortion of the learned prompts and degraded generated images. To further substantiate this claim, as our major contribution, we deep dive into the denoising subnetwork of the T2I model to track down the effect of these learned prompts by analyzing the cross-attention maps. In our analysis, we propose a novel prompt switching analysis: I2H and H2I. Furthermore, we propose new quantitative characterization of cross-attention maps. Our analysis reveals abnormalities in the early denoising steps, perpetuating improper global structure that results in degradation in the generated samples. Building on insights from our analysis, we propose two ideas: (i) Prompt Queuing and (ii) Attention Amplification to address the quality issue. Extensive experimental results on a wide range of tSAs show that our proposed method outperforms SOTA approach's image generation quality, while achieving competitive fairness. More resources at FairQueue Project site: https://sutd-visual-computing-group.github.io/FairQueue
Rethinking Softmax: Self-Attention with Polynomial Activations
2024-10-24
Hemanth Saratchandran, Jianqiao Zheng, Yiping Ji, Wenbo Zhang, Simon Lucey
This paper challenges the conventional belief that softmax attention in transformers is effective primarily because it generates a probability distribution for attention allocation. Instead, we theoretically show that its success lies in its ability to implicitly regularize the Frobenius norm of the attention matrix during training. We then explore alternative activations that regularize the Frobenius norm of the attention matrix, demonstrating that certain polynomial activations can achieve this effect, making them suitable for attention-based architectures. Empirical results indicate these activations perform comparably or better than softmax across various computer vision and language tasks, suggesting new possibilities for attention mechanisms beyond softmax.
2024-10-23
Jiantao Wu, Shentong Mo, Zhenhua Feng, Sara Atito, Josef Kitler, Muhammad Awais
Contrastive learning, a prominent approach to representation learning, traditionally assumes positive pairs are closely related samples (the same image or class) and negative pairs are distinct samples. We challenge this assumption by proposing to learn from arbitrary pairs, allowing any pair of samples to be positive within our framework.The primary challenge of the proposed approach lies in applying contrastive learning to disparate pairs which are semantically distant. Motivated by the discovery that SimCLR can separate given arbitrary pairs (e.g., garter snake and table lamp) in a subspace, we propose a feature filter in the condition of class pairs that creates the requisite subspaces by gate vectors selectively activating or deactivating dimensions. This filter can be optimized through gradient descent within a conventional contrastive learning mechanism. We present Hydra, a universal contrastive learning framework for visual representations that extends conventional contrastive learning to accommodate arbitrary pairs. Our approach is validated using IN1K, where 1K diverse classes compose 500,500 pairs, most of them being distinct. Surprisingly, Hydra achieves superior performance in this challenging setting. Additional benefits include the prevention of dimensional collapse and the discovery of class relationships. Our work highlights the value of learning common features of arbitrary pairs and potentially broadens the applicability of contrastive learning techniques on the sample pairs with weak relationships.
Joint Point Cloud Upsampling and Cleaning with Octree-based CNNs
2024-10-22
Jihe Li, Bo Pang, Peng-Shuai Wang
Recovering dense and uniformly distributed point clouds from sparse or noisy data remains a significant challenge. Recently, great progress has been made on these tasks, but usually at the cost of increasingly intricate modules or complicated network architectures, leading to long inference time and huge resource consumption. Instead, we embrace simplicity and present a simple yet efficient method for jointly upsampling and cleaning point clouds. Our method leverages an off-the-shelf octree-based 3D U-Net (OUNet) with minor modifications, enabling the upsampling and cleaning tasks within a single network. Our network directly processes each input point cloud as a whole instead of processing each point cloud patch as in previous works, which significantly eases the implementation and brings at least 47 times faster inference. Extensive experiments demonstrate that our method achieves state-of-the-art performances under huge efficiency advantages on a series of benchmarks. We expect our method to serve simple baselines and inspire researchers to rethink the method design on point cloud upsampling and cleaning.
Rethinking generalization of classifiers in separable classes scenarios and over-parameterized regimes
2024-10-22
Julius Martinetz, Christoph Linse, Thomas Martinetz
We investigate the learning dynamics of classifiers in scenarios where classes are separable or classifiers are over-parameterized. In both cases, Empirical Risk Minimization (ERM) results in zero training error. However, there are many global minima with a training error of zero, some of which generalize well and some of which do not. We show that in separable classes scenarios the proportion of "bad" global minima diminishes exponentially with the number of training data n. Our analysis provides bounds and learning curves dependent solely on the density distribution of the true error for the given classifier function set, irrespective of the set's size or complexity (e.g., number of parameters). This observation may shed light on the unexpectedly good generalization of over-parameterized Neural Networks. For the over-parameterized scenario, we propose a model for the density distribution of the true error, yielding learning curves that align with experiments on MNIST and CIFAR-10.
The actor model has gained increasing popularity. However, it lacks support for complex state management tasks, such as enforcing foreign key constraints and ensuring data replication consistency across actors. These are crucial properties in partitioned application designs, such as microservices. To fill this gap, we start by analyzing the key impediments in state-of-the-art actor systems. We find it difficult for developers to express complex data relationships across actors and reason about the impact of state updates on performance due to opaque state management abstractions. To solve this conundrum, we develop SmSa, a novel data management layer for actor systems, allowing developers to declare data dependencies that cut across actors, including foreign keys, data replications, and other dependencies. SmSa can transparently enforce the declared dependencies, reducing the burden on developers. Furthermore, SmSa employs novel logging and concurrency control algorithms to support transactional maintenance of data dependencies. We demonstrate SmSa can support core data management tasks where dependencies across components appear frequently without jeopardizing application logic expressiveness and performance. Our experiments show SmSa significantly reduces the logging overhead and leads to increased concurrency level, improving by up to 2X the performance of state-of-the-art deterministic scheduling approaches. As a result, SmSa will make it easier to design and implement highly partitioned and distributed applications.
The rise of large language models (LLMs) for visually rich document understanding (VRDU) has kindled a need for prompt-response, document-based datasets. As annotating new datasets from scratch is labor-intensive, the existing literature has generated prompt-response datasets from available resources using simple templates. For the case of key information extraction (KIE), one of the most common VRDU tasks, past work has typically employed the template "What is the value for the {key}?". However, given the variety of questions encountered in the wild, simple and uniform templates are insufficient for creating robust models in research and industrial contexts. In this work, we present K2Q, a diverse collection of five datasets converted from KIE to a prompt-response format using a plethora of bespoke templates. The questions in K2Q can span multiple entities and be extractive or boolean. We empirically compare the performance of seven baseline generative models on K2Q with zero-shot prompting. We further compare three of these models when training on K2Q versus training on simpler templates to motivate the need of our work. We find that creating diverse and intricate KIE questions enhances the performance and robustness of VRDU models. We hope this work encourages future studies on data quality for generative model training.
Previous studies have highlighted significant advancements in multimodal fusion. Nevertheless, such methods often encounter challenges regarding the efficacy of feature extraction, data integrity, consistency of feature dimensions, and adaptability across various downstream tasks. This paper proposes a generalized multimodal fusion method (GMF) via the Poisson-Nernst-Planck (PNP) equation, which adeptly addresses the aforementioned issues. Theoretically, the optimization objective for traditional multimodal tasks is formulated and redefined by integrating information entropy and the flow of gradient backward step. Leveraging these theoretical insights, the PNP equation is applied to feature fusion, rethinking multimodal features through the framework of charged particles in physics and controlling their movement through dissociation, concentration, and reconstruction. Building on these theoretical foundations, GMF disassociated features which extracted by the unimodal feature extractor into modality-specific and modality-invariant subspaces, thereby reducing mutual information and subsequently lowering the entropy of downstream tasks. The identifiability of the feature's origin enables our approach to function independently as a frontend, seamlessly integrated with a simple concatenation backend, or serve as a prerequisite for other modules. Experimental results on multiple downstream tasks show that the proposed GMF achieves performance close to the state-of-the-art (SOTA) accuracy while utilizing fewer parameters and computational resources. Furthermore, by integrating GMF with advanced fusion methods, we surpass the SOTA results.
World models integrate raw data from various modalities, such as images and language to simulate comprehensive interactions in the world, thereby displaying crucial roles in fields like mixed reality and robotics. Yet, applying the world model for accurate video prediction is quite challenging due to the complex and dynamic intentions of the various scenes in practice. In this paper, inspired by the human rethinking process, we decompose the complex video prediction into four meta-tasks that enable the world model to handle this issue in a more fine-grained manner. Alongside these tasks, we introduce a new benchmark named Embodied Video Anticipation Benchmark (EVA-Bench) to provide a well-rounded evaluation. EVA-Bench focused on evaluating the video prediction ability of human and robot actions, presenting significant challenges for both the language model and the generation model. Targeting embodied video prediction, we propose the Embodied Video Anticipator (EVA), a unified framework aiming at video understanding and generation. EVA integrates a video generation model with a visual language model, effectively combining reasoning capabilities with high-quality generation. Moreover, to enhance the generalization of our framework, we tailor-designed a multi-stage pretraining paradigm that adaptatively ensembles LoRA to produce high-fidelity results. Extensive experiments on EVA-Bench highlight the potential of EVA to significantly improve performance in embodied scenes, paving the way for large-scale pre-trained models in real-world prediction tasks.
The Sunk Carbon Fallacy: Rethinking Carbon Footprint Metrics for Effective Carbon-Aware Scheduling
2024-10-19
Noman Bashir, Varun Gohil, Anagha Belavadi, Mohammad Shahrad, David Irwin, Elsa Olivetti, Christina Delimitrou
The rapid increase in computing demand and its corresponding energy consumption have focused attention on computing's impact on the climate and sustainability. Prior work proposes metrics that quantify computing's carbon footprint across several lifecycle phases, including its supply chain, operation, and end-of-life. Industry uses these metrics to optimize the carbon footprint of manufacturing hardware and running computing applications. Unfortunately, prior work on optimizing datacenters' carbon footprint often succumbs to the \emph{sunk cost fallacy} by considering embodied carbon emissions (a sunk cost) when making operational decisions (i.e., job scheduling and placement), which leads to operational decisions that do not always reduce the total carbon footprint. In this paper, we evaluate carbon-aware job scheduling and placement on a given set of servers for a number of carbon accounting metrics. Our analysis reveals state-of-the-art carbon accounting metrics that include embodied carbon emissions when making operational decisions can actually increase the total carbon footprint of executing a set of jobs. We study the factors that affect the added carbon cost of such suboptimal decision-making. We then use a real-world case study from a datacenter to demonstrate how the sunk carbon fallacy manifests itself in practice. Finally, we discuss the implications of our findings in better guiding effective carbon-aware scheduling in on-premise and cloud datacenters.
Counterfactual explanations have been a popular method of post-hoc explainability for a variety of settings in Machine Learning. Such methods focus on explaining classifiers by generating new data points that are similar to a given reference, while receiving a more desirable prediction. In this work, we investigate a framing for counterfactual generation methods that considers counterfactuals not as independent draws from a region around the reference, but as jointly sampled with the reference from the underlying data distribution. Through this framing, we derive a distance metric, tailored for counterfactual similarity that can be applied to a broad range of settings. Through both quantitative and qualitative analyses of counterfactual generation methods, we show that this framing allows us to express more nuanced dependencies among the covariates.
Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysis
2024-10-18
Honglin Li, Yunlong Zhang, Pingyi Chen, Zhongyi Shui, Chenglu Zhu, Lin Yang
Histopathology Whole Slide Image (WSI) analysis serves as the gold standard for clinical cancer diagnosis in the daily routines of doctors. To develop computer-aided diagnosis model for WSIs, previous methods typically employ Multi-Instance Learning to enable slide-level prediction given only slide-level labels. Among these models, vanilla attention mechanisms without pairwise interactions have traditionally been employed but are unable to model contextual information. More recently, self-attention models have been utilized to address this issue. To alleviate the computational complexity of long sequences in large WSIs, methods like HIPT use region-slicing, and TransMIL employs approximation of full self-attention. Both approaches suffer from suboptimal performance due to the loss of key information. Moreover, their use of absolute positional embedding struggles to effectively handle long contextual dependencies in shape-varying WSIs. In this paper, we first analyze how the low-rank nature of the long-sequence attention matrix constrains the representation ability of WSI modelling. Then, we demonstrate that the rank of attention matrix can be improved by focusing on local interactions via a local attention mask. Our analysis shows that the local mask aligns with the attention patterns in the lower layers of the Transformer. Furthermore, the local attention mask can be implemented during chunked attention calculation, reducing the quadratic computational complexity to linear with a small local bandwidth. Building on this, we propose a local-global hybrid Transformer for both computational acceleration and local-global information interactions modelling. Our method, Long-contextual MIL (LongMIL), is evaluated through extensive experiments on various WSI tasks to validate its superiority. Our code will be available at github.com/invoker-LL/Long-MIL.
2024-10-17
Takeru Miyato, Sindy Löwe, Andreas Geiger, Max Welling
It has long been known in both neuroscience and AI that ``binding'' between neurons leads to a form of competitive learning where representations are compressed in order to represent more abstract concepts in deeper layers of the network. More recently, it was also hypothesized that dynamic (spatiotemporal) representations play an important role in both neuroscience and AI. Building on these ideas, we introduce Artificial Kuramoto Oscillatory Neurons (AKOrN) as a dynamical alternative to threshold units, which can be combined with arbitrary connectivity designs such as fully connected, convolutional, or attentive mechanisms. Our generalized Kuramoto updates bind neurons together through their synchronization dynamics. We show that this idea provides performance improvements across a wide spectrum of tasks such as unsupervised object discovery, adversarial robustness, calibrated uncertainty quantification, and reasoning. We believe that these empirical results show the importance of rethinking our assumptions at the most basic neuronal level of neural representation, and in particular show the importance of dynamical representations.
We propose a novel algorithm for offline reinforcement learning using optimal transport. Typically, in offline reinforcement learning, the data is provided by various experts and some of them can be sub-optimal. To extract an efficient policy, it is necessary to \emph{stitch} the best behaviors from the dataset. To address this problem, we rethink offline reinforcement learning as an optimal transportation problem. And based on this, we present an algorithm that aims to find a policy that maps states to a \emph{partial} distribution of the best expert actions for each given state. We evaluate the performance of our algorithm on continuous control problems from the D4RL suite and demonstrate improvements over existing methods.
Rethinking Visual Counterfactual Explanations Through Region Constraint
2024-10-16
Bartlomiej Sobieski, Jakub Grzywaczewski, Bartlomiej Sadlej, Matthew Tivnan, Przemyslaw Biecek
Visual counterfactual explanations (VCEs) have recently gained immense popularity as a tool for clarifying the decision-making process of image classifiers. This trend is largely motivated by what these explanations promise to deliver -- indicate semantically meaningful factors that change the classifier's decision. However, we argue that current state-of-the-art approaches lack a crucial component -- the region constraint -- whose absence prevents from drawing explicit conclusions, and may even lead to faulty reasoning due to phenomenons like confirmation bias. To address the issue of previous methods, which modify images in a very entangled and widely dispersed manner, we propose region-constrained VCEs (RVCEs), which assume that only a predefined image region can be modified to influence the model's prediction. To effectively sample from this subclass of VCEs, we propose Region-Constrained Counterfactual Schr\"odinger Bridges (RCSB), an adaptation of a tractable subclass of Schr\"odinger Bridges to the problem of conditional inpainting, where the conditioning signal originates from the classifier of interest. In addition to setting a new state-of-the-art by a large margin, we extend RCSB to allow for exact counterfactual reasoning, where the predefined region contains only the factor of interest, and incorporating the user to actively interact with the RVCE by predefining the regions manually.
For decades, the Bj{\o}ntegaard Delta (BD) has been the metric for evaluating codec Rate-Distortion (R-D) performance. Yet, in most studies, BD is determined using just 4-5 R-D data points, could this be sufficient? As codecs and quality metrics advance, does the conventional BD estimation still hold up? Crucially, are the performance improvements of new codecs and tools genuine, or merely artifacts of estimation flaws? This paper addresses these concerns by reevaluating BD estimation. We present a novel approach employing a parameterized deep neural network to model R-D curves with high precision across various metrics, accompanied by a comprehensive R-D dataset. This approach both assesses the reliability of BD calculations and serves as a precise BD estimator. Our findings advocate for the adoption of rigorous R-D sampling and reliability metrics in future compression research to ensure the validity and reliability of results.
2024-10-16
Zheng Zhan, Yushu Wu, Zhenglun Kong, Changdi Yang, Yifan Gong, Xuan Shen, Xue Lin, Pu Zhao, Yanzhi Wang
Recent advancements in State Space Models (SSMs) have attracted significant interest, particularly in models optimized for parallel training and handling long-range dependencies. Architectures like Mamba have scaled to billions of parameters with selective SSM. To facilitate broader applications using Mamba, exploring its efficiency is crucial. While token reduction techniques offer a straightforward post-training strategy, we find that applying existing methods directly to SSMs leads to substantial performance drops. Through insightful analysis, we identify the reasons for this failure and the limitations of current techniques. In response, we propose a tailored, unified post-training token reduction method for SSMs. Our approach integrates token importance and similarity, thus taking advantage of both pruning and merging, to devise a fine-grained intra-layer token reduction strategy. Extensive experiments show that our method improves the average accuracy by 5.7% to 13.1% on six benchmarks with Mamba-2 compared to existing methods, while significantly reducing computational demands and memory requirements.
Collaborative Perception (CP) is a process in which an ego agent receives and fuses sensor information from surrounding vehicles and infrastructure to enhance its perception capability. To evaluate the need for infrastructure equipped with sensors, extensive and quantitative analysis of the role of infrastructure data in CP is crucial, yet remains underexplored. To address this gap, we first quantitatively assess the importance of infrastructure data in existing vehicle-centric CP, where the ego agent is a vehicle. Furthermore, we compare vehicle-centric CP with infra-centric CP, where the ego agent is now the infrastructure, to evaluate the effectiveness of each approach. Our results demonstrate that incorporating infrastructure data improves 3D detection accuracy by up to 10.87%, and infra-centric CP shows enhanced noise robustness and increases accuracy by up to 42.53% compared with vehicle-centric CP.
Graph Transformer (GT), as a special type of Graph Neural Networks (GNNs), utilizes multi-head attention to facilitate high-order message passing. However, this also imposes several limitations in node classification applications: 1) nodes are susceptible to global noise; 2) self-attention computation cannot scale well to large graphs. In this work, we conduct extensive observational experiments to explore the adaptability of the GT architecture in node classification tasks and draw several conclusions: the current multi-head self-attention module in GT can be completely replaceable, while the feed-forward neural network module proves to be valuable. Based on this, we decouple the propagation (P) and transformation (T) of GNNs and explore a powerful GT architecture, named GNNFormer, which is based on the P/T combination message passing and adapted for node classification in both homophilous and heterophilous scenarios. Extensive experiments on 12 benchmark datasets demonstrate that our proposed GT architecture can effectively adapt to node classification tasks without being affected by global noise and computational efficiency limitations.
This study investigates judgment prediction in a realistic scenario within the context of Indian judgments, utilizing a range of transformer-based models, including InLegalBERT, BERT, and XLNet, alongside LLMs such as Llama-2 and GPT-3.5 Turbo. In this realistic scenario, we simulate how judgments are predicted at the point when a case is presented for a decision in court, using only the information available at that time, such as the facts of the case, statutes, precedents, and arguments. This approach mimics real-world conditions, where decisions must be made without the benefit of hindsight, unlike retrospective analyses often found in previous studies. For transformer models, we experiment with hierarchical transformers and the summarization of judgment facts to optimize input for these models. Our experiments with LLMs reveal that GPT-3.5 Turbo excels in realistic scenarios, demonstrating robust performance in judgment prediction. Furthermore, incorporating additional legal information, such as statutes and precedents, significantly improves the outcome of the prediction task. The LLMs also provide explanations for their predictions. To evaluate the quality of these predictions and explanations, we introduce two human evaluation metrics: Clarity and Linking. Our findings from both automatic and human evaluations indicate that, despite advancements in LLMs, they are yet to achieve expert-level performance in judgment prediction and explanation tasks.
In recent years, significant progress has been made in the medical image analysis domain using convolutional neural networks (CNNs). In particular, deep neural networks based on a U-shaped architecture (UNet) with skip connections have been adopted for several medical imaging tasks, including organ segmentation. Despite their great success, CNNs are not good at learning global or semantic features. Especially ones that require human-like reasoning to understand the context. Many UNet architectures attempted to adjust with the introduction of Transformer-based self-attention mechanisms, and notable gains in performance have been noted. However, the transformers are inherently flawed with redundancy to learn at shallow layers, which often leads to an increase in the computation of attention from the nearby pixels offering limited information. The recently introduced Super Token Attention (STA) mechanism adapts the concept of superpixels from pixel space to token space, using super tokens as compact visual representations. This approach tackles the redundancy by learning efficient global representations in vision transformers, especially for the shallow layers. In this work, we introduce the STA module in the UNet architecture (STA-UNet), to limit redundancy without losing rich information. Experimental results on four publicly available datasets demonstrate the superiority of STA-UNet over existing state-of-the-art architectures in terms of Dice score and IOU for organ segmentation tasks. The code is available at \url{https://github.com/Retinal-Research/STA-UNet}.
Rethinking Data Selection at Scale: Random Selection is Almost All You Need
2024-10-12
Tingyu Xia, Bowen Yu, Kai Dang, An Yang, Yuan Wu, Yuan Tian, Yi Chang, Junyang Lin
Supervised fine-tuning (SFT) is crucial for aligning Large Language Models (LLMs) with human instructions. The primary goal during SFT is to select a small yet representative subset of training data from the larger pool, such that fine-tuning with this subset achieves results comparable to or even exceeding those obtained using the entire dataset. However, most existing data selection techniques are designed for small-scale data pools, which fail to meet the demands of real-world SFT scenarios. In this paper, we replicated several self-scoring methods those that do not rely on external model assistance on two million scale datasets, and found that nearly all methods struggled to significantly outperform random selection when dealing with such large-scale data pools. Moreover, our comparisons suggest that, during SFT, diversity in data selection is more critical than simply focusing on high quality data. We also analyzed the limitations of several current approaches, explaining why they perform poorly on large-scale datasets and why they are unsuitable for such contexts. Finally, we found that filtering data by token length offers a stable and efficient method for improving results. This approach, particularly when training on long text data, proves highly beneficial for relatively weaker base models, such as Llama3.
LIME-Eval: Rethinking Low-light Image Enhancement Evaluation via Object Detection
2024-10-11
Mingjia Li, Hao Zhao, Xiaojie Guo
Due to the nature of enhancement--the absence of paired ground-truth information, high-level vision tasks have been recently employed to evaluate the performance of low-light image enhancement. A widely-used manner is to see how accurately an object detector trained on enhanced low-light images by different candidates can perform with respect to annotated semantic labels. In this paper, we first demonstrate that the mentioned approach is generally prone to overfitting, and thus diminishes its measurement reliability. In search of a proper evaluation metric, we propose LIME-Bench, the first online benchmark platform designed to collect human preferences for low-light enhancement, providing a valuable dataset for validating the correlation between human perception and automated evaluation metrics. We then customize LIME-Eval, a novel evaluation framework that utilizes detectors pre-trained on standard-lighting datasets without object annotations, to judge the quality of enhanced images. By adopting an energy-based strategy to assess the accuracy of output confidence maps, our LIME-Eval can simultaneously bypass biases associated with retraining detectors and circumvent the reliance on annotations for dim images. Comprehensive experiments are provided to reveal the effectiveness of our LIME-Eval. Our benchmark platform (https://huggingface.co/spaces/lime-j/eval) and code (https://github.com/lime-j/lime-eval) are available online.
Rethinking the Principle of Gradient Smooth Methods in Model Explanation
2024-10-10
Linjiang Zhou, Chao Ma, Zepeng Wang, Xiaochuan Shi
Gradient Smoothing is an efficient approach to reducing noise in gradient-based model explanation method. SmoothGrad adds Gaussian noise to mitigate much of these noise. However, the crucial hyper-parameter in this method, the variance $\sigma$ of Gaussian noise, is set manually or with heuristic approach. However, it results in the smoothed gradients still containing a certain amount of noise. In this paper, we aim to interpret SmoothGrad as a corollary of convolution, thereby re-understanding the gradient noise and the role of $\sigma$ from the perspective of confidence level. Furthermore, we propose an adaptive gradient smoothing method, AdaptGrad, based on these insights. Through comprehensive experiments, both qualitative and quantitative results demonstrate that AdaptGrad could effectively reduce almost all the noise in vanilla gradients compared with baselines methods. AdaptGrad is simple and universal, making it applicable for enhancing gradient-based interpretability methods for better visualization.
Rethinking Adversarial Inverse Reinforcement Learning: From the Angles of Policy Imitation and Transferable Reward Recovery
2024-10-10
Yangchun Zhang, Wang Zhou, Yirui Zhou
In scenarios of inverse reinforcement learning (IRL) with a single expert, adversarial inverse reinforcement learning (AIRL) serves as a foundational approach to providing comprehensive and transferable task descriptions by restricting the reward class, e.g., to state-only rewards. However, AIRL faces practical challenges, primarily stemming from the difficulty of verifying the unobservable transition matrix - often encountered in practice - under the specific conditions necessary for effective transfer. This paper reexamines AIRL in light of the unobservable transition matrix or limited informative priors. By applying random matrix theory (RMT), we demonstrate that AIRL can disentangle rewards for effective transfer with high probability, irrespective of specific conditions. This perspective reframes inadequate transfer in certain contexts. Specifically, it is attributed to the selection problem of the reinforcement learning algorithm employed by AIRL, which is characterized by training variance. Based on this insight, we propose a hybrid framework that integrates on-policy proximal policy optimization (PPO) in the source environment with off-policy soft actor-critic (SAC) in the target environment, leading to significant improvements in reward transfer effectiveness.
Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning
2024-10-09
Chongyu Fan, Jiancheng Liu, Licong Lin, Jinghan Jia, Ruiqi Zhang, Song Mei, Sijia Liu
In this work, we address the problem of large language model (LLM) unlearning, aiming to remove unwanted data influences and associated model capabilities (e.g., copyrighted data or harmful content generation) while preserving essential model utilities, without the need for retraining from scratch. Despite the growing need for LLM unlearning, a principled optimization framework remains lacking. To this end, we revisit the state-of-the-art approach, negative preference optimization (NPO), and identify the issue of reference model bias, which could undermine NPO's effectiveness, particularly when unlearning forget data of varying difficulty. Given that, we propose a simple yet effective unlearning optimization framework, called SimNPO, showing that 'simplicity' in removing the reliance on a reference model (through the lens of simple preference optimization) benefits unlearning. We also provide deeper insights into SimNPO's advantages, supported by analysis using mixtures of Markov chains. Furthermore, we present extensive experiments validating SimNPO's superiority over existing unlearning baselines in benchmarks like TOFU and MUSE, and robustness against relearning attacks. Codes are available at https://github.com/OPTML-Group/Unlearn-Simple.
Rethinking the Evaluation of Visible and Infrared Image Fusion
2024-10-09
Dayan Guan, Yixuan Wu, Tianzhu Liu, Alex C. Kot, Yanfeng Gu
Visible and Infrared Image Fusion (VIF) has garnered significant interest across a wide range of high-level vision tasks, such as object detection and semantic segmentation. However, the evaluation of VIF methods remains challenging due to the absence of ground truth. This paper proposes a Segmentation-oriented Evaluation Approach (SEA) to assess VIF methods by incorporating the semantic segmentation task and leveraging segmentation labels available in latest VIF datasets. Specifically, SEA utilizes universal segmentation models, capable of handling diverse images and classes, to predict segmentation outputs from fused images and compare these outputs with segmentation labels. Our evaluation of recent VIF methods using SEA reveals that their performance is comparable or even inferior to using visible images only, despite nearly half of the infrared images demonstrating better performance than visible images. Further analysis indicates that the two metrics most correlated to our SEA are the gradient-based fusion metric $Q_{\text{ABF}}$ and the visual information fidelity metric $Q_{\text{VIFF}}$ in conventional VIF evaluation metrics, which can serve as proxies when segmentation labels are unavailable. We hope that our evaluation will guide the development of novel and practical VIF methods. The code has been released in \url{https://github.com/Yixuan-2002/SEA/}.
For 6-DoF grasp detection, simulated data is expandable to train more powerful model, but it faces the challenge of the large gap between simulation and real world. Previous works bridge this gap with a sim-to-real way. However, this way explicitly or implicitly forces the simulated data to adapt to the noisy real data when training grasp detectors, where the positional drift and structural distortion within the camera noise will harm the grasp learning. In this work, we propose a Real-to-Sim framework for 6-DoF Grasp detection, named R2SGrasp, with the key insight of bridging this gap in a real-to-sim way, which directly bypasses the camera noise in grasp detector training through an inference-time real-to-sim adaption. To achieve this real-to-sim adaptation, our R2SGrasp designs the Real-to-Sim Data Repairer (R2SRepairer) to mitigate the camera noise of real depth maps in data-level, and the Real-to-Sim Feature Enhancer (R2SEnhancer) to enhance real features with precise simulated geometric primitives in feature-level. To endow our framework with the generalization ability, we construct a large-scale simulated dataset cost-efficiently to train our grasp detector, which includes 64,000 RGB-D images with 14.4 million grasp annotations. Sufficient experiments show that R2SGrasp is powerful and our real-to-sim perspective is effective. The real-world experiments further show great generalization ability of R2SGrasp. Project page is available on https://isee-laboratory.github.io/R2SGrasp.
PostCast: Generalizable Postprocessing for Precipitation Nowcasting via Unsupervised Blurriness Modeling
2024-10-08
Junchao Gong, Siwei Tu, Weidong Yang, Ben Fei, Kun Chen, Wenlong Zhang, Xiaokang Yang, Wanli Ouyang, Lei Bai
Precipitation nowcasting plays a pivotal role in socioeconomic sectors, especially in severe convective weather warnings. Although notable progress has been achieved by approaches mining the spatiotemporal correlations with deep learning, these methods still suffer severe blurriness as the lead time increases, which hampers accurate predictions for extreme precipitation. To alleviate blurriness, researchers explore generative methods conditioned on blurry predictions. However, the pairs of blurry predictions and corresponding ground truth need to be generated in advance, making the training pipeline cumbersome and limiting the generality of generative models within blur modes that appear in training data. By rethinking the blurriness in precipitation nowcasting as a blur kernel acting on predictions, we propose an unsupervised postprocessing method to eliminate the blurriness without the requirement of training with the pairs of blurry predictions and corresponding ground truth. Specifically, we utilize blurry predictions to guide the generation process of a pre-trained unconditional denoising diffusion probabilistic model (DDPM) to obtain high-fidelity predictions with eliminated blurriness. A zero-shot blur kernel estimation mechanism and an auto-scale denoise guidance strategy are introduced to adapt the unconditional DDPM to any blurriness modes varying from datasets and lead times in precipitation nowcasting. Extensive experiments are conducted on 7 precipitation radar datasets, demonstrating the generality and superiority of our method.
Recent Retrieval Augmented Generation (RAG) aims to enhance Large Language Models (LLMs) by incorporating extensive knowledge retrieved from external sources. However, such approach encounters some challenges: Firstly, the original queries may not be suitable for precise retrieval, resulting in erroneous contextual knowledge; Secondly, the language model can easily generate inconsistent answer with external references due to their knowledge boundary limitation. To address these issues, we propose the chain-of-verification (CoV-RAG) to enhance the external retrieval correctness and internal generation consistency. Specifically, we integrate the verification module into the RAG, engaging in scoring, judgment, and rewriting. To correct external retrieval errors, CoV-RAG retrieves new knowledge using a revised query. To correct internal generation errors, we unify QA and verification tasks with a Chain-of-Thought (CoT) reasoning during training. Our comprehensive experiments across various LLMs demonstrate the effectiveness and adaptability compared with other strong baselines. Especially, our CoV-RAG can significantly surpass the state-of-the-art baselines using different LLM backbones.
Rethinking Reward Model Evaluation: Are We Barking up the Wrong Tree?
2024-10-08
Xueru Wen, Jie Lou, Yaojie Lu, Hongyu Lin, Xing Yu, Xinyu Lu, Ben He, Xianpei Han, Debing Zhang, Le Sun
Reward Models (RMs) are crucial for aligning language models with human preferences. Currently, the evaluation of RMs depends on measuring accuracy against a validation set of manually annotated preference data. Although this method is straightforward and widely adopted, the relationship between RM accuracy and downstream policy performance remains under-explored. In this work, we conduct experiments in a synthetic setting to investigate how differences in RM measured by accuracy translate into gaps in optimized policy performance. Our findings reveal that while there is a weak positive correlation between accuracy and downstream performance, policies optimized towards RMs with similar accuracy can exhibit quite different performance. Moreover, we discover that the way of measuring accuracy significantly impacts its ability to predict the final policy performance. Through the lens of the Regressional Goodhart effect, we recognize that accuracy, when used for measuring RM quality, can fail to fully capture the potential RM overoptimization. This underscores the inadequacy of relying solely on accuracy to reflect their impact on policy optimization.
WTCL-Dehaze: Rethinking Real-world Image Dehazing via Wavelet Transform and Contrastive Learning
2024-10-07
Divine Joseph Appiah, Donghai Guan, Abdul Nasser Kasule, Mingqiang Wei
Images captured in hazy outdoor conditions often suffer from colour distortion, low contrast, and loss of detail, which impair high-level vision tasks. Single image dehazing is essential for applications such as autonomous driving and surveillance, with the aim of restoring image clarity. In this work, we propose WTCL-Dehaze an enhanced semi-supervised dehazing network that integrates Contrastive Loss and Discrete Wavelet Transform (DWT). We incorporate contrastive regularization to enhance feature representation by contrasting hazy and clear image pairs. Additionally, we utilize DWT for multi-scale feature extraction, effectively capturing high-frequency details and global structures. Our approach leverages both labelled and unlabelled data to mitigate the domain gap and improve generalization. The model is trained on a combination of synthetic and real-world datasets, ensuring robust performance across different scenarios. Extensive experiments demonstrate that our proposed algorithm achieves superior performance and improved robustness compared to state-of-the-art single image dehazing methods on both benchmark datasets and real-world images.
Rethinking Weak-to-Strong Augmentation in Source-Free Domain Adaptive Object Detection
2024-10-07
Jiuzheng Yang, Song Tang, Yangkuiyi Zhang, Shuaifeng Li, Mao Ye, Jianwei Zhang, Xiatian Zhu
Source-Free domain adaptive Object Detection (SFOD) aims to transfer a detector (pre-trained on source domain) to new unlabelled target domains. Current SFOD methods typically follow the Mean Teacher framework, where weak-to-strong augmentation provides diverse and sharp contrast for self-supervised learning. However, this augmentation strategy suffers from an inherent problem called crucial semantics loss: Due to random, strong disturbance, strong augmentation is prone to losing typical visual components, hindering cross-domain feature extraction. To address this thus-far ignored limitation, this paper introduces a novel Weak-to-Strong Contrastive Learning (WSCoL) approach. The core idea is to distill semantics lossless knowledge in the weak features (from the weak/teacher branch) to guide the representation learning upon the strong features (from the strong/student branch). To achieve this, we project the original features into a shared space using a mapping network, thereby reducing the bias between the weak and strong features. Meanwhile, a weak features-guided contrastive learning is performed in a weak-to-strong manner alternatively. Specifically, we first conduct an adaptation-aware prototype-guided clustering on the weak features to generate pseudo labels for corresponding strong features matched through proposals. Sequentially, we identify positive-negative samples based on the pseudo labels and perform cross-category contrastive learning on the strong features where an uncertainty estimator encourages adaptive background contrast. Extensive experiments demonstrate that WSCoL yields new state-of-the-art performance, offering a built-in mechanism mitigating crucial semantics loss for traditional Mean Teacher framework. The code and data will be released soon.
Hyper-multi-step: The Truth Behind Difficult Long-context Tasks
2024-10-06
Yijiong Yu, Ma Xiufa, Fang Jianwei, Zhi Xu, Su Guangyao, Wang Jiancheng, Yongfeng Huang, Zhixiao Qi, Wei Wang, Weifeng Liu, Ran Chen, Ji Pei
Long-context language models (LCLM), characterized by their extensive context window, is becoming increasingly popular. Meanwhile, many long-context benchmarks present challenging tasks that even the most advanced LCLMs struggle to complete. However, the underlying sources of various challenging long-context tasks have seldom been studied. To bridge this gap, we conduct experiments to indicate their difficulty stems primarily from two basic issues: "multi-matching retrieval," which requires the simultaneous retrieval of multiple items, and "logic-based retrieval," which necessitates logical judgment within retrieval criteria. These two problems, while seemingly straightforward, actually exceed the capabilities of LCLMs because they are proven to be hyper-multi-step (demanding numerous steps to solve) in nature. This finding could explain why LLMs struggle with more advanced long-context tasks, providing a more accurate perspective for rethinking solutions for them.
Lens: Rethinking Multilingual Enhancement for Large Language Models
2024-10-06
Weixiang Zhao, Yulin Hu, Jiahe Guo, Xingyu Sui, Tongtong Wu, Yang Deng, Yanyan Zhao, Bing Qin, Wanxiang Che, Ting Liu
Despite the growing global demand for large language models (LLMs) that serve users from diverse linguistic backgrounds, most cutting-edge LLMs remain predominantly English-centric. This creates a performance gap across languages, restricting access to advanced AI services for non-English speakers. Current methods to enhance multilingual capabilities largely rely on data-driven post-training techniques, such as multilingual instruction tuning or continual pre-training. However, these approaches encounter significant challenges, including the scarcity of high-quality multilingual datasets and the limited enhancement of multilingual capabilities. They often suffer from off-target issues and catastrophic forgetting of central language abilities. To this end, we propose Lens, a novel approach to enhance multilingual capabilities of LLMs by leveraging their internal language representation spaces. Specially, Lens operates by manipulating the hidden representations within the language-agnostic and language-specific subspaces from top layers of LLMs. Using the central language as a pivot, the target language is drawn closer to it within the language-agnostic subspace, allowing it to inherit well-established semantic representations. Meanwhile, in the language-specific subspace, the representations of the target and central languages are pushed apart, enabling the target language to express itself distinctly. Extensive experiments on one English-centric and two multilingual LLMs demonstrate that Lens effectively improves multilingual performance without sacrificing the original central language capabilities of the backbone model, achieving superior results with much fewer computational resources compared to existing post-training approaches.
Rethinking Fair Representation Learning for Performance-Sensitive Tasks
2024-10-05
Charles Jones, Fabio de Sousa Ribeiro, Mélanie Roschewitz, Daniel C. Castro, Ben Glocker
We investigate the prominent class of fair representation learning methods for bias mitigation. Using causal reasoning to define and formalise different sources of dataset bias, we reveal important implicit assumptions inherent to these methods. We prove fundamental limitations on fair representation learning when evaluation data is drawn from the same distribution as training data and run experiments across a range of medical modalities to examine the performance of fair representation learning under distribution shifts. Our results explain apparent contradictions in the existing literature and reveal how rarely considered causal and statistical aspects of the underlying data affect the validity of fair representation learning. We raise doubts about current evaluation practices and the applicability of fair representation learning methods in performance-sensitive settings. We argue that fine-grained analysis of dataset biases should play a key role in the field moving forward.
Rethinking Selection in Generational Genetic Algorithms to Solve Combinatorial Optimization Problems: An Upper Bound-based Parent Selection Strategy for Recombination
2024-10-04
Prashant Sankaran, Katie McConky
Existing stochastic selection strategies for parent selection in generational GA help build genetic diversity and sustain exploration; however, it ignores the possibility of exploiting knowledge gained by the process to make informed decisions for parent selection, which can often lead to an inefficient search for large, challenging optimization problems. This work proposes a deterministic parent selection strategy for recombination in a generational GA setting called Upper Bound-based Parent Selection (UBS) to solve NP-hard combinatorial optimization problems. Specifically, as part of the UBS strategy, we formulate the parent selection problem using the MAB framework and a modified UCB1 algorithm to manage exploration and exploitation. Further, we provided a unique similarity-based approach for transferring knowledge of the search progress between generations to accelerate the search. To demonstrate the effectiveness of the proposed UBS strategy in comparison to traditional stochastic selection strategies, we conduct experimental studies on two NP-hard combinatorial optimization problems: team orienteering and quadratic assignment. Specifically, we first perform a characterization study to determine the potential of UBS and the best configuration for all the selection strategies involved. Next, we run experiments using these best configurations as part of the comparison study. The results from the characterization studies reveal that UBS, in most cases, favors larger variations among the population between generations. Next, the comparison studies reveal that UBS can effectively search for high-quality solutions faster than traditional stochastic selection strategies on challenging NP-hard combinatorial optimization problems under given experimental conditions.
Generalization emerges from local optimization in a self-organized learning network
2024-10-03
S. Barland, L. Gil
We design and analyze a new paradigm for building supervised learning networks, driven only by local optimization rules without relying on a global error function. Traditional neural networks with a fixed topology are made up of identical nodes and derive their expressiveness from an appropriate adjustment of connection weights. In contrast, our network stores new knowledge in the nodes accurately and instantaneously, in the form of a lookup table. Only then is some of this information structured and incorporated into the network geometry. The training error is initially zero by construction and remains so throughout the network topology transformation phase. The latter involves a small number of local topological transformations, such as splitting or merging of nodes and adding binary connections between them. The choice of operations to be carried out is only driven by optimization of expressivity at the local scale. What we are primarily looking for in a learning network is its ability to generalize, i.e. its capacity to correctly answer questions for which it has never learned the answers. We show on numerous examples of classification tasks that the networks generated by our algorithm systematically reach such a state of perfect generalization when the number of learned examples becomes sufficiently large. We report on the dynamics of the change of state and show that it is abrupt and has the distinctive characteristics of a first order phase transition, a phenomenon already observed for traditional learning networks and known as grokking. In addition to proposing a non-potential approach for the construction of learning networks, our algorithm makes it possible to rethink the grokking transition in a new light, under which acquisition of training data and topological structuring of data are completely decoupled phenomena.
2024-10-03
Rubén Ballester, Ernst Röell, Daniel Bin Schmid, Mathieu Alain, Sergio Escalera, Carles Casacuberta, Bastian Rieck
The rising interest in leveraging higher-order interactions present in complex systems has led to a surge in more expressive models exploiting high-order structures in the data, especially in topological deep learning (TDL), which designs neural networks on high-order domains such as simplicial complexes. However, progress in this field is hindered by the scarcity of datasets for benchmarking these architectures. To address this gap, we introduce MANTRA, the first large-scale, diverse, and intrinsically high order dataset for benchmarking high-order models, comprising over 43,000 and 249,000 triangulations of surfaces and three-dimensional manifolds, respectively. With MANTRA, we assess several graph- and simplicial complex-based models on three topological classification tasks. We demonstrate that while simplicial complex-based neural networks generally outperform their graph-based counterparts in capturing simple topological invariants, they also struggle, suggesting a rethink of TDL. Thus, MANTRA serves as a benchmark for assessing and advancing topological methods, leading the way for more effective high-order models.
2024-10-03
Avi Cooper, Keizo Kato, Chia-Hsien Shih, Hiroaki Yamane, Kasper Vinken, Kentaro Takemoto, Taro Sunagawa, Hao-Wei Yeh, Jin Yamanaka, Ian Mason, Xavier Boix
Visual Language Models (VLMs) are now increasingly being merged with Large Language Models (LLMs) to enable new capabilities, particularly in terms of improved interactivity and open-ended responsiveness. While these are remarkable capabilities, the contribution of LLMs to enhancing the longstanding key problem of classifying an image among a set of choices remains unclear. Through extensive experiments involving seven models, ten visual understanding datasets, and multiple prompt variations per dataset, we find that, for object and scene recognition, VLMs that do not leverage LLMs can achieve better performance than VLMs that do. Yet at the same time, leveraging LLMs can improve performance on tasks requiring reasoning and outside knowledge. In response to these challenges, we propose a pragmatic solution: a lightweight fix involving a relatively small LLM that efficiently routes visual tasks to the most suitable model for the task. The LLM router undergoes training using a dataset constructed from more than 2.5 million examples of pairs of visual task and model accuracy. Our results reveal that this lightweight fix surpasses or matches the accuracy of state-of-the-art alternatives, including GPT-4V and HuggingGPT, while improving cost-effectiveness.
Rethinking the Expressiveness of GNNs: A Computational Model Perspective
2024-10-02
Guanyu Cui, Zhewei Wei, Hsin-Hao Su
Graph Neural Networks (GNNs) are extensively employed in graph machine learning, with considerable research focusing on their expressiveness. Current studies often assess GNN expressiveness by comparing them to the Weisfeiler-Lehman (WL) tests or classical graph algorithms. However, we identify three key issues in existing analyses: (1) some studies use preprocessing to enhance expressiveness but overlook its computational costs; (2) some claim the anonymous WL test's limited power while enhancing expressiveness using non-anonymous features, creating a mismatch; and (3) some characterize message-passing GNNs (MPGNNs) with the CONGEST model but make unrealistic assumptions about computational resources, allowing $\textsf{NP-Complete}$ problems to be solved in $O(m)$ depth. We contend that a well-defined computational model is urgently needed to serve as the foundation for discussions on GNN expressiveness. To address these issues, we introduce the Resource-Limited CONGEST (RL-CONGEST) model, incorporating optional preprocessing and postprocessing to form a framework for analyzing GNN expressiveness. Our framework sheds light on computational aspects, including the computational hardness of hash functions in the WL test and the role of virtual nodes in reducing network capacity. Additionally, we suggest that high-order GNNs correspond to first-order model-checking problems, offering new insights into their expressiveness.
Electronic-photonic computing systems have emerged as a promising platform for accelerating deep neural network (DNN) workloads. Major efforts have been focused on countering hardware non-idealities and boosting efficiency with various hardware/algorithm co-design methods. However, the adversarial robustness of such photonic analog mixed-signal AI hardware remains unexplored. Though the hardware variations can be mitigated with robustness-driven optimization methods, malicious attacks on the hardware show distinct behaviors from noises, which requires a customized protection method tailored to optical analog hardware. In this work, we rethink the role of conventionally undesired non-idealities in photonic analog accelerators and claim their surprising effects on defending against adversarial weight attacks. Inspired by the protection effects from DNN quantization and pruning, we propose a synergistic defense framework tailored for optical analog hardware that proactively protects sensitive weights via pre-attack unary weight encoding and post-attack vulnerability-aware weight locking. Efficiency-reliability trade-offs are formulated as constrained optimization problems and efficiently solved offline without model re-training costs. Extensive evaluation of various DNN benchmarks with a multi-core photonic accelerator shows that our framework maintains near-ideal on-chip inference accuracy under adversarial bit-flip attacks with merely <3% memory overhead. Our codes are open-sourced at https://github.com/ScopeX-ASU/Unlikely_Hero.
Foundational Vision-Language models such as CLIP have exhibited impressive generalization in downstream tasks. However, CLIP suffers from a two-level misalignment issue, i.e., task misalignment and data misalignment, when adapting to specific tasks. Soft prompt tuning has mitigated the task misalignment, yet the data misalignment remains a challenge. To analyze the impacts of the data misalignment, we revisit the pre-training and adaptation processes of CLIP and develop a structural causal model. We discover that while we expect to capture task-relevant information for downstream tasks accurately, the task-irrelevant knowledge impacts the prediction results and hampers the modeling of the true relationships between the images and the predicted classes. As task-irrelevant knowledge is unobservable, we leverage the front-door adjustment and propose Causality-Guided Semantic Decoupling and Classification (CDC) to mitigate the interference of task-irrelevant knowledge. Specifically, we decouple semantics contained in the data of downstream tasks and perform classification based on each semantic. Furthermore, we employ the Dempster-Shafer evidence theory to evaluate the uncertainty of each prediction generated by diverse semantics. Experiments conducted in multiple different settings have consistently demonstrated the effectiveness of CDC.
Text-to-video diffusion models have made remarkable advancements. Driven by their ability to generate temporally coherent videos, research on zero-shot video editing using these fundamental models has expanded rapidly. To enhance editing quality, structural controls are frequently employed in video editing. Among these techniques, cross-attention mask control stands out for its effectiveness and efficiency. However, when cross-attention masks are naively applied to video editing, they can introduce artifacts such as blurring and flickering. Our experiments uncover a critical factor overlooked in previous video editing research: cross-attention masks are not consistently clear but vary with model structure and denoising timestep. To address this issue, we propose the metric Mask Matching Cost (MMC) that quantifies this variability and propose FreeMask, a method for selecting optimal masks tailored to specific video editing tasks. Using MMC-selected masks, we further improve the masked fusion mechanism within comprehensive attention features, e.g., temp, cross, and self-attention modules. Our approach can be seamlessly integrated into existing zero-shot video editing frameworks with better performance, requiring no control assistance or parameter fine-tuning but enabling adaptive decoupling of unedited semantic layouts with mask precision control. Extensive experiments demonstrate that FreeMask achieves superior semantic fidelity, temporal consistency, and editing quality compared to state-of-the-art methods.
Feature Extractor or Decision Maker: Rethinking the Role of Visual Encoders in Visuomotor Policies
2024-09-30
Ruiyu Wang, Zheyu Zhuang, Shutong Jin, Nils Ingelhag, Danica Kragic, Florian T. Pokorny
An end-to-end (E2E) visuomotor policy is typically treated as a unified whole, but recent approaches using out-of-domain (OOD) data to pretrain the visual encoder have cleanly separated the visual encoder from the network, with the remainder referred to as the policy. We propose Visual Alignment Testing, an experimental framework designed to evaluate the validity of this functional separation. Our results indicate that in E2E-trained models, visual encoders actively contribute to decision-making resulting from motor data supervision, contradicting the assumed functional separation. In contrast, OOD-pretrained models, where encoders lack this capability, experience an average performance drop of 42% in our benchmark results, compared to the state-of-the-art performance achieved by E2E policies. We believe this initial exploration of visual encoders' role can provide a first step towards guiding future pretraining methods to address their decision-making ability, such as developing task-conditioned or context-aware encoders.
3D semantic occupancy prediction networks have demonstrated remarkable capabilities in reconstructing the geometric and semantic structure of 3D scenes, providing crucial information for robot navigation and autonomous driving systems. However, due to their large overhead from dense network structure designs, existing networks face challenges balancing accuracy and latency. In this paper, we introduce OccRWKV, an efficient semantic occupancy network inspired by Receptance Weighted Key Value (RWKV). OccRWKV separates semantics, occupancy prediction, and feature fusion into distinct branches, each incorporating Sem-RWKV and Geo-RWKV blocks. These blocks are designed to capture long-range dependencies, enabling the network to learn domain-specific representation (i.e., semantics and geometry), which enhances prediction accuracy. Leveraging the sparse nature of real-world 3D occupancy, we reduce computational overhead by projecting features into the bird's-eye view (BEV) space and propose a BEV-RWKV block for efficient feature enhancement and fusion. This enables real-time inference at 22.2 FPS without compromising performance. Experiments demonstrate that OccRWKV outperforms the state-of-the-art methods on the SemanticKITTI dataset, achieving a mIoU of 25.1 while being 20 times faster than the best baseline, Co-Occ, making it suitable for real-time deployment on robots to enhance autonomous navigation efficiency. Code and video are available on our project page: https://jmwang0117.github.io/OccRWKV/.
Towards Robust Extractive Question Answering Models: Rethinking the Training Methodology
2024-09-29
Son Quoc Tran, Matt Kretchmar
This paper proposes a novel training method to improve the robustness of Extractive Question Answering (EQA) models. Previous research has shown that existing models, when trained on EQA datasets that include unanswerable questions, demonstrate a significant lack of robustness against distribution shifts and adversarial attacks. Despite this, the inclusion of unanswerable questions in EQA training datasets is essential for ensuring real-world reliability. Our proposed training method includes a novel loss function for the EQA problem and challenges an implicit assumption present in numerous EQA datasets. Models trained with our method maintain in-domain performance while achieving a notable improvement on out-of-domain datasets. This results in an overall F1 score improvement of 5.7 across all testing sets. Furthermore, our models exhibit significantly enhanced robustness against two types of adversarial attacks, with a performance decrease of only about a third compared to the default models.
Time series forecasting has played a pivotal role across various industries, including finance, transportation, energy, healthcare, and climate. Due to the abundant seasonal information they contain, timestamps possess the potential to offer robust global guidance for forecasting techniques. However, existing works primarily focus on local observations, with timestamps being treated merely as an optional supplement that remains underutilized. When data gathered from the real world is polluted, the absence of global information will damage the robust prediction capability of these algorithms. To address these problems, we propose a novel framework named GLAFF. Within this framework, the timestamps are modeled individually to capture the global dependencies. Working as a plugin, GLAFF adaptively adjusts the combined weights for global and local information, enabling seamless collaboration with any time series forecasting backbone. Extensive experiments conducted on nine real-world datasets demonstrate that GLAFF significantly enhances the average performance of widely used mainstream forecasting models by 12.5%, surpassing the previous state-of-the-art method by 5.5%.
Unveiling Hidden Vulnerabilities in Quantum Systems by Expanding Attack Vectors through Heisenberg's Uncertainty Principle
2024-09-27
Jose R. Rosas-Bustos, Jesse Van Griensven Thé, Roydon Andrew Fraser
This study uncovers novel vulnerabilities within Quantum Key Distribution (QKD) protocols that extend beyond traditional implementation flaws, such as loopholes. These newly identified vulnerabilities arise from the complex interaction between Bell Inequalities (BIs) and Hidden Variable Theories (HVTs), further exacerbated by the Heisenberg Uncertainty Principle (HUP). Through a combination of theoretical analysis, simulations, and quantum experiments, we reveal critical security weaknesses that challenge the core assumptions of today's quantum cryptography. While these vulnerabilities differ from known loopholes, when considered alongside them and traditional cyberattacks, they present a significant threat to the robustness of QKD and quantum integrity systems. These results provide a new perspective to rethink current quantum security frameworks to ensure the robustness of future quantum cryptographic and quantum integrity protocols.
2024-09-26
Sergei Nirenburg, Marjorie McShane, Kenneth W. Goodman, Sanjay Oruganti
Explanation is key to people having confidence in high-stakes AI systems. However, machine-learning-based systems -- which account for almost all current AI -- can't explain because they are usually black boxes. The explainable AI (XAI) movement hedges this problem by redefining "explanation". The human-centered explainable AI (HCXAI) movement identifies the explanation-oriented needs of users but can't fulfill them because of its commitment to machine learning. In order to achieve the kinds of explanations needed by real people operating in critical domains, we must rethink how to approach AI. We describe a hybrid approach to developing cognitive agents that uses a knowledge-based infrastructure supplemented by data obtained through machine learning when applicable. These agents will serve as assistants to humans who will bear ultimate responsibility for the decisions and actions of the human-robot team. We illustrate the explanatory potential of such agents using the under-the-hood panels of a demonstration system in which a team of simulated robots collaborate on a search task assigned by a human.
ReThink: Reveal the Threat of Electromagnetic Interference on Power Inverters
2024-09-26
Fengchen Yang, Zihao Dan, Kaikai Pan, Chen Yan, Xiaoyu Ji, Wenyuan Xu
With the boom of renewable energy sources (RES), the number of power inverters proliferates. Power inverters are the key electronic devices that transform the direct current (DC) power from RES to the alternating current (AC) power on the grids, and their security can affect the stable operation of RES and even power grids. This paper analyzes the security of photovoltaic (PV) inverters from the aspects of internal sensors since they serve as the foundation for safe power conversion. We discover that both the embedded current sensors and voltage sensors are vulnerable to electromagnetic interference (EMI) of 1 GHz or higher, despite electromagnetic compatibility (EMC) countermeasures. Such vulnerabilities can lead to incorrect measurements and deceiving the control algorithms, and we design ReThink that could produce three types of consequences on PV inverters by emitting carefully crafted EMI, i.e., Denial of Service (DoS), damaging inverters physically or damping the power output. We successfully validate these consequences on 5 off-the-shelf PV inverters, and even in a real-world microgrid, by transmitting EMI signals at a distance of 100-150cm and a total power within 20W. Our work aims to raise awareness of the security of power electronic devices of RES, as they represent an emerging Cyber-Physical attack surface to the future RES-dominated grid. Finally, to cope with such threats, we provide hardware and software-based countermeasures.
Textoshop: Interactions Inspired by Drawing Software to Facilitate Text Editing
2024-09-25
Damien Masson, Young-Ho Kim, Fanny Chevalier
We explore how interactions inspired by drawing software can help edit text. Making an analogy between visual and text editing, we consider words as pixels, sentences as regions, and tones as colours. For instance, direct manipulations move, shorten, expand, and reorder text; tools change number, tense, and grammar; colours map to tones explored along three dimensions in a tone picker; and layers help organize and version text. This analogy also leads to new workflows, such as boolean operations on text fragments to construct more elaborated text. A study shows participants were more successful at editing text and preferred using the proposed interface over existing solutions. Broadly, our work highlights the potential of interaction analogies to rethink existing workflows, while capitalizing on familiar features.
Adapting Vision-Language Model with Fine-grained Semantics for Open-Vocabulary Segmentation
2024-09-24
Yong Xien Chng, Xuchong Qiu, Yizeng Han, Kai Ding, Wan Ding, Gao Huang
Despite extensive research, open-vocabulary segmentation methods still struggle to generalize across diverse domains. To reduce the computational cost of adapting Vision-Language Models (VLMs) while preserving their pre-trained knowledge, most methods freeze the VLMs for mask classification and train only the mask generator. However, our comprehensive analysis reveals a surprising insight: open-vocabulary segmentation is primarily bottlenecked by mask classification, not mask generation. This discovery prompts us to rethink the existing paradigm and explore an alternative approach. Instead of freezing the VLM, we propose to freeze the pre-trained mask generator and focus on optimizing the mask classifier. Building on the observation that VLMs pre-trained on global-pooled image-text features often fail to capture fine-grained semantics necessary for effective mask classification, we propose a novel Fine-grained Semantic Adaptation (FISA) method to address this limitation. FISA enhances the extracted visual features with fine-grained semantic awareness by explicitly integrating this crucial semantic information early in the visual encoding process. As our method strategically optimizes only a small portion of the VLM's parameters, it enjoys the efficiency of adapting to new data distributions while largely preserving the valuable VLM pre-trained knowledge. Extensive ablation studies confirm the superiority of our approach. Notably, FISA achieves new state-of-the-art results across multiple representative benchmarks, improving performance by up to +1.0 PQ and +3.0 mIoU and reduces training costs by nearly 5x compared to previous best methods. Our code and data will be made public.
Rethinking Emotion Bias in Music via Frechet Audio Distance
2024-09-23
Yuanchao Li, Azalea Gui, Dimitra Emmanouilidou, Hannes Gamper
The subjective nature of music emotion introduces inherent bias in both recognition and generation, especially when relying on a single audio encoder, emotion classifier, or evaluation metric. In this work, we conduct a study on Music Emotion Recognition (MER) and Emotional Music Generation (EMG), employing diverse audio encoders alongside the Frechet Audio Distance (FAD), a reference-free evaluation metric. Our study begins with a benchmark evaluation of MER, highlighting the limitations associated with using a single audio encoder and the disparities observed across different measurements. We then propose assessing MER performance using FAD from multiple encoders to provide a more objective measure of music emotion. Furthermore, we introduce an enhanced EMG approach designed to improve both the variation and prominence of generated music emotion, thus enhancing realism. Additionally, we investigate the realism disparities between the emotions conveyed in real and synthetic music, comparing our EMG model against two baseline models. Experimental results underscore the emotion bias problem in both MER and EMG and demonstrate the potential of using FAD and diverse audio encoders to evaluate music emotion objectively.
Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling
2024-09-23
Lechao Xiao
The remarkable success of large language pretraining and the discovery of scaling laws signify a paradigm shift in machine learning. Notably, the primary objective has evolved from minimizing generalization error to reducing approximation error, and the most effective strategy has transitioned from regularization (in a broad sense) to scaling up models. This raises a critical question: Do the established principles that proved successful in the generalization-centric era remain valid in this new era of scaling? This paper examines several influential regularization-based principles that may no longer hold true in the scaling-centric, large language model (LLM) era. These principles include explicit L2 regularization and implicit regularization through small batch sizes and large learning rates. Additionally, we identify a new phenomenon termed ``scaling law crossover,'' where two scaling curves intersect at a certain scale, implying that methods effective at smaller scales may not generalize to larger ones. Together, these observations highlight two fundamental questions within this new paradigm: $\bullet$ Guiding Principles for Scaling: If regularization is no longer the primary guiding principle for model design, what new principles are emerging to guide scaling? $\bullet$ Model Comparison at Scale: How to reliably and effectively compare models at the scale where only a single experiment is feasible?
Semantic Parsing aims to capture the meaning of a sentence and convert it into a logical, structured form. Previous studies show that semantic parsing enhances the performance of smaller models (e.g., BERT) on downstream tasks. However, it remains unclear whether the improvements extend similarly to LLMs. In this paper, our empirical findings reveal that, unlike smaller models, directly adding semantic parsing results into LLMs reduces their performance. To overcome this, we propose SENSE, a novel prompting approach that embeds semantic hints within the prompt. Experiments show that SENSE consistently improves LLMs' performance across various tasks, highlighting the potential of integrating semantic information to improve LLM capabilities.
RTs != Endorsements: Rethinking Exposure Fairness on Social Media Platforms
2024-09-20
Nathan Bartley, Kristina Lerman
Recommender systems underpin many of the personalized services in the online information & social media ecosystem. However, the assumptions in the research on content recommendations in domains like search, video, and music are often applied wholesale to domains that require a better understanding of why and how users interact with the systems. In this position paper we focus on social media and argue that personalized timelines have an added layer of complexity that is derived from the social nature of the platform itself. In particular, definitions of exposure fairness should be expanded to consider the social environment each user is situated in: how often a user is exposed to others is as important as who they get exposed to.
Beyond Perceptual Distances: Rethinking Disparity Assessment for Out-of-Distribution Detection with Diffusion Models
2024-09-16
Kun Fang, Qinghua Tao, Zuopeng Yang, Xiaolin Huang, Jie Yang
Out-of-Distribution (OoD) detection aims to justify whether a given sample is from the training distribution of the classifier-under-protection, i.e., In-Distribution (InD), or from OoD. Diffusion Models (DMs) are recently utilized in OoD detection by using the perceptual distances between the given image and its DM generation. DM-based methods bring fresh insights to the field, yet remain under-explored. In this work, we point out two main limitations in DM-based OoD detection methods: (i) the perceptual metrics on the disparities between the given sample and its generation are devised only at human-perceived levels, ignoring the abstract or high-level patterns that help better reflect the intrinsic disparities in distribution; (ii) only the raw image contents are taken to measure the disparities, while other representations, i.e., the features and probabilities from the classifier-under-protection, are easy to access at hand but are ignored. To this end, our proposed detection framework goes beyond the perceptual distances and looks into the deep representations from the classifier-under-protection with our novel metrics devised correspondingly, leading to more informative disparity assessments between InD and OoD. An anomaly-removal strategy is integrated to remove the abnormal OoD information in the generation, further enhancing the distinctiveness of disparities. Our work has demonstrated state-of-the-art detection performances among DM-based methods in extensive experiments.
Rethinking KenLM: Good and Bad Model Ensembles for Efficient Text Quality Filtering in Large Web Corpora
2024-09-15
Yungi Kim, Hyunsoo Ha, Sukyung Lee, Jihoo Kim, Seonghoon Yang, Chanjun Park
With the increasing demand for substantial amounts of high-quality data to train large language models (LLMs), efficiently filtering large web corpora has become a critical challenge. For this purpose, KenLM, a lightweight n-gram-based language model that operates on CPUs, is widely used. However, the traditional method of training KenLM utilizes only high-quality data and, consequently, does not explicitly learn the linguistic patterns of low-quality data. To address this issue, we propose an ensemble approach that leverages two contrasting KenLMs: (i) Good KenLM, trained on high-quality data; and (ii) Bad KenLM, trained on low-quality data. Experimental results demonstrate that our approach significantly reduces noisy content while preserving high-quality content compared to the traditional KenLM training method. This indicates that our method can be a practical solution with minimal computational overhead for resource-constrained environments.
LLM agents enhanced by tree search algorithms have yielded notable performances in code generation. However, current search algorithms in this domain suffer from low search quality due to several reasons: 1) Ineffective design of the search space for the high-reasoning demands of code generation tasks, 2) Inadequate integration of code feedback with the search algorithm, and 3) Poor handling of negative feedback during the search, leading to reduced search efficiency and quality. To address these challenges, we propose to search for the reasoning process of the code and use the detailed feedback of code execution to refine erroneous thoughts during the search. In this paper, we introduce RethinkMCTS, which employs the Monte Carlo Tree Search (MCTS) algorithm to conduct thought-level searches before generating code, thereby exploring a wider range of strategies. More importantly, we construct verbal feedback from fine-grained code execution feedback to refine erroneous thoughts during the search. This ensures that the search progresses along the correct reasoning paths, thus improving the overall search quality of the tree by leveraging execution feedback. Through extensive experiments, we demonstrate that RethinkMCTS outperforms previous search-based and feedback-based code generation baselines. On the HumanEval dataset, it improves the pass@1 of GPT-3.5-turbo from 70.12 to 89.02 and GPT-4o-mini from 87.20 to 94.51. It effectively conducts more thorough exploration through thought-level searches and enhances the search quality of the entire tree by incorporating rethink operation.
Rethinking the Influence of Source Code on Test Case Generation
2024-09-14
Dong Huang, Jie M. Zhang, Mingzhe Du, Mark Harman, Heming Cui
Large language models (LLMs) have been widely applied to assist test generation with the source code under test provided as the context. This paper aims to answer the question: If the source code under test is incorrect, will LLMs be misguided when generating tests? The effectiveness of test cases is measured by their accuracy, coverage, and bug detection effectiveness. Our evaluation results with five open- and six closed-source LLMs on four datasets demonstrate that incorrect code can significantly mislead LLMs in generating correct, high-coverage, and bug-revealing tests. For instance, in the HumanEval dataset, LLMs achieve 80.45% test accuracy when provided with task descriptions and correct code, but only 57.12% when given task descriptions and incorrect code. For the APPS dataset, prompts with correct code yield tests that detect 39.85% of the bugs, while prompts with incorrect code detect only 19.61%. These findings have important implications for the deployment of LLM-based testing: using it on mature code may help protect against future regression, but on early-stage immature code, it may simply bake in errors. Our findings also underscore the need for further research to improve LLMs resilience against incorrect code in generating reliable and bug-revealing tests.
DiffFAS: Face Anti-Spoofing via Generative Diffusion Models
2024-09-13
Xinxu Ge, Xin Liu, Zitong Yu, Jingang Shi, Chun Qi, Jie Li, Heikki Kälviäinen
Face anti-spoofing (FAS) plays a vital role in preventing face recognition (FR) systems from presentation attacks. Nowadays, FAS systems face the challenge of domain shift, impacting the generalization performance of existing FAS methods. In this paper, we rethink about the inherence of domain shift and deconstruct it into two factors: image style and image quality. Quality influences the purity of the presentation of spoof information, while style affects the manner in which spoof information is presented. Based on our analysis, we propose DiffFAS framework, which quantifies quality as prior information input into the network to counter image quality shift, and performs diffusion-based high-fidelity cross-domain and cross-attack types generation to counter image style shift. DiffFAS transforms easily collectible live faces into high-fidelity attack faces with precise labels while maintaining consistency between live and spoof face identities, which can also alleviate the scarcity of labeled data with novel type attacks faced by nowadays FAS system. We demonstrate the effectiveness of our framework on challenging cross-domain and cross-attack FAS datasets, achieving the state-of-the-art performance. Available at https://github.com/murphytju/DiffFAS.
Meta-learning has emerged as a powerful approach for leveraging knowledge from previous tasks to solve new tasks. The mainstream methods focus on training a well-generalized model initialization, which is then adapted to different tasks with limited data and updates. However, it pushes the model overfitting on the training tasks. Previous methods mainly attributed this to the lack of data and used augmentations to address this issue, but they were limited by sufficient training and effective augmentation strategies. In this work, we focus on the more fundamental ``learning to learn'' strategy of meta-learning to explore what causes errors and how to eliminate these errors without changing the environment. Specifically, we first rethink the algorithmic procedure of meta-learning from a ``learning'' lens. Through theoretical and empirical analyses, we find that (i) this paradigm faces the risk of both overfitting and underfitting and (ii) the model adapted to different tasks promote each other where the effect is stronger if the tasks are more similar. Based on this insight, we propose using task relations to calibrate the optimization process of meta-learning and propose a plug-and-play method called Task Relation Learner (TRLearner) to achieve this goal. Specifically, it first obtains task relation matrices from the extracted task-specific meta-data. Then, it uses the obtained matrices with relation-aware consistency regularization to guide optimization. Extensive theoretical and empirical analyses demonstrate the effectiveness of TRLearner.
Rethinking Programmed I/O for Fast Devices, Cheap Cores, and Coherent Interconnects
2024-09-12
Anastasiia Ruzhanskaia, Pengcheng Xu, David Cock, Timothy Roscoe
Conventional wisdom holds that an efficient interface between an OS running on a CPU and a high-bandwidth I/O device should be based on Direct Memory Access (DMA), descriptor rings, and interrupts: DMA offloads transfers from the CPU, descriptor rings provide buffering and queuing, and interrupts facilitate asynchronous interaction between cores and device with a lightweight notification mechanism. In this paper we question this wisdom in the light of modern hardware and workloads, particularly in cloud servers. Like others before us, we argue that the assumptions that led to this model are obsolete, and in many use-cases use of Programmed I/O (PIO), where the CPU explicitly transfers data and control information to and from a device via loads and stores, actually results in a more efficient system. However, unlike others to date, we push this idea further and show, in a real implementation, the gains in average and tail latency for fine-grained communication achievable using an open cache-coherence protocol which exposes cache transitions to a smart device. We show this using three use-cases: fine-grained RPC-style invocation of functions on an accelerator, offloading of operators in a streaming dataflow engine, and a network interface targeting for serverless functions, comparing our use of coherence with both traditional DMA-style interaction and a highly-optimized implementation using PIO over PCI Express (PCIe).
Vision-language models (VLMs) like CLIP have been adapted for Multi-Label Recognition (MLR) with partial annotations by leveraging prompt-learning, where positive and negative prompts are learned for each class to associate their embeddings with class presence or absence in the shared vision-text feature space. While this approach improves MLR performance by relying on VLM priors, we hypothesize that learning negative prompts may be suboptimal, as the datasets used to train VLMs lack image-caption pairs explicitly focusing on class absence. To analyze the impact of positive and negative prompt learning on MLR, we introduce PositiveCoOp and NegativeCoOp, where only one prompt is learned with VLM guidance while the other is replaced by an embedding vector learned directly in the shared feature space without relying on the text encoder. Through empirical analysis, we observe that negative prompts degrade MLR performance, and learning only positive prompts, combined with learned negative embeddings (PositiveCoOp), outperforms dual prompt learning approaches. Moreover, we quantify the performance benefits that prompt-learning offers over a simple vision-features-only baseline, observing that the baseline displays strong performance comparable to dual prompt learning approach (DualCoOp), when the proportion of missing labels is low, while requiring half the training compute and 16 times fewer parameters
The Mamba-based model has demonstrated outstanding performance across tasks in computer vision, natural language processing, and speech processing. However, in the realm of speech processing, the Mamba-based model's performance varies across different tasks. For instance, in tasks such as speech enhancement and spectrum reconstruction, the Mamba model performs well when used independently. However, for tasks like speech recognition, additional modules are required to surpass the performance of attention-based models. We propose the hypothesis that the Mamba-based model excels in "reconstruction" tasks within speech processing. However, for "classification tasks" such as Speech Recognition, additional modules are necessary to accomplish the "reconstruction" step. To validate our hypothesis, we analyze the previous Mamba-based Speech Models from an information theory perspective. Furthermore, we leveraged the properties of HuBERT in our study. We trained a Mamba-based HuBERT model, and the mutual information patterns, along with the model's performance metrics, confirmed our assumptions.
Rethinking Directional Parameterization in Neural Implicit Surface Reconstruction
2024-09-11
Zijie Jiang, Tianhan Xu, Hiroharu Kato
Multi-view 3D surface reconstruction using neural implicit representations has made notable progress by modeling the geometry and view-dependent radiance fields within a unified framework. However, their effectiveness in reconstructing objects with specular or complex surfaces is typically biased by the directional parameterization used in their view-dependent radiance network. {\it Viewing direction} and {\it reflection direction} are the two most commonly used directional parameterizations but have their own limitations. Typically, utilizing the viewing direction usually struggles to correctly decouple the geometry and appearance of objects with highly specular surfaces, while using the reflection direction tends to yield overly smooth reconstructions for concave or complex structures. In this paper, we analyze their failed cases in detail and propose a novel hybrid directional parameterization to address their limitations in a unified form. Extensive experiments demonstrate the proposed hybrid directional parameterization consistently delivered satisfactory results in reconstructing objects with a wide variety of materials, geometry and appearance, whereas using other directional parameterizations faces challenges in reconstructing certain objects. Moreover, the proposed hybrid directional parameterization is nearly parameter-free and can be effortlessly applied in any existing neural surface reconstruction method.
Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting
2024-09-09
Lirong Wu, Haitao Lin, Guojiang Zhao, Cheng Tan, Stan Z. Li
Recent years have witnessed great success in handling graph-related tasks with Graph Neural Networks (GNNs). However, most existing GNNs are based on message passing to perform feature aggregation and transformation, where the structural information is explicitly involved in the forward propagation by coupling with node features through graph convolution at each layer. As a result, subtle feature noise or structure perturbation may cause severe error propagation, resulting in extremely poor robustness. In this paper, we rethink the roles played by graph structural information in graph data training and identify that message passing is not the only path to modeling structural information. Inspired by this, we propose a simple but effective Graph Structure Self-Contrasting (GSSC) framework that learns graph structural information without message passing. The proposed framework is based purely on Multi-Layer Perceptrons (MLPs), where the structural information is only implicitly incorporated as prior knowledge to guide the computation of supervision signals, substituting the explicit message propagation as in GNNs. Specifically, it first applies structural sparsification to remove potentially uninformative or noisy edges in the neighborhood, and then performs structural self-contrasting in the sparsified neighborhood to learn robust node representations. Finally, structural sparsification and self-contrasting are formulated as a bi-level optimization problem and solved in a unified framework. Extensive experiments have qualitatively and quantitatively demonstrated that the GSSC framework can produce truly encouraging performance with better generalization and robustness than other leading competitors.
Seeing Through the Mask: Rethinking Adversarial Examples for CAPTCHAs
2024-09-09
Yahya Jabary, Andreas Plesner, Turlan Kuzhagaliyev, Roger Wattenhofer
Modern CAPTCHAs rely heavily on vision tasks that are supposedly hard for computers but easy for humans. However, advances in image recognition models pose a significant threat to such CAPTCHAs. These models can easily be fooled by generating some well-hidden "random" noise and adding it to the image, or hiding objects in the image. However, these methods are model-specific and thus can not aid CAPTCHAs in fooling all models. We show in this work that by allowing for more significant changes to the images while preserving the semantic information and keeping it solvable by humans, we can fool many state-of-the-art models. Specifically, we demonstrate that by adding masks of various intensities the Accuracy @ 1 (Acc@1) drops by more than 50%-points for all models, and supposedly robust models such as vision transformers see an Acc@1 drop of 80%-points. These masks can therefore effectively fool modern image classifiers, thus showing that machines have not caught up with humans -- yet.
Enhancing low-light images remains a critical challenge in computer vision, as does designing lightweight models for edge devices that can handle the computational demands of deep learning. In this article, we introduce an extended version of the Channel-Prior and Gamma-Estimation Network (CPGA-Net), termed CPGA-Net+, which incorporates an attention mechanism driven by a reformulated Atmospheric Scattering Model and effectively addresses both global and local image processing through Plug-in Attention with gamma correction. These innovations enable CPGA-Net+ to achieve superior performance on image enhancement tasks for supervised and unsupervised learning, surpassing lightweight state-of-the-art methods with high efficiency. Furthermore, we provide a theoretical analysis showing that our approach inherently decomposes the enhancement process into restoration and lightening stages, aligning with the fundamental image degradation model. To further optimize efficiency, we introduce a block simplification technique that reduces computational costs by more than two-thirds. Experimental results validate the effectiveness of CPGA-Net+ and highlight its potential for applications in resource-constrained environments.
Generative Steganography (GS) is a novel technique that utilizes generative models to conceal messages without relying on cover images. Contemporary GS algorithms leverage the powerful generative capabilities of Diffusion Models (DMs) to create high-fidelity stego images. However, these algorithms, while yielding relatively satisfactory generation outcomes and message extraction accuracy, significantly alter modifications to the initial Gaussian noise of DMs, thereby compromising steganographic security. In this paper, we rethink the trade-off among image quality, steganographic security, and message extraction accuracy within Diffusion Generative Steganography (DGS) settings. Our findings reveal that the normality of initial noise of DMs is crucial to these factors and can offer theoretically grounded guidance for DGS design. Based on this insight, we propose a Provable and Adjustable Message Mapping (PA-B2G) approach. It can, on one hand, theoretically guarantee reversible encoding of bit messages from arbitrary distributions into standard Gaussian noise for DMs. On the other hand, its adjustability provides a more natural and fine-grained way to trade off image quality, steganographic security, and message extraction accuracy. By integrating PA-B2G with a probability flow ordinary differential equation, we establish an invertible mapping between secret messages and stego images. PA-B2G can be seamlessly incorporated with most mainstream DMs, such as the Stable Diffusion, without necessitating additional training or fine-tuning. Comprehensive experiments corroborate our theoretical insights regarding the trade-off in DGS settings and demonstrate the effectiveness of our DGS algorithm in producing high-quality stego images while preserving desired levels of steganographic security and extraction accuracy.
Recent advancements in generative models have significantly enhanced their capacity for image generation, enabling a wide range of applications such as image editing, completion and video editing. A specialized area within generative modeling is layout-to-image (L2I) generation, where predefined layouts of objects guide the generative process. In this study, we introduce a novel regional cross-attention module tailored to enrich layout-to-image generation. This module notably improves the representation of layout regions, particularly in scenarios where existing methods struggle with highly complex and detailed textual descriptions. Moreover, while current open-vocabulary L2I methods are trained in an open-set setting, their evaluations often occur in closed-set environments. To bridge this gap, we propose two metrics to assess L2I performance in open-vocabulary scenarios. Additionally, we conduct a comprehensive user study to validate the consistency of these metrics with human preferences.
Rethinking Improved Privacy-Utility Trade-off with Pre-existing Knowledge for DP Training
2024-09-05
Yu Zheng, Wenchao Zhang, Yonggang Zhang, Wei Song, Kai Zhou, Bo Han
Differential privacy (DP) provides a provable framework for protecting individuals by customizing a random mechanism over a privacy-sensitive dataset. Deep learning models have demonstrated privacy risks in model exposure as an established learning model unintentionally records membership-level privacy leakage. Differentially private stochastic gradient descent (DP- SGD) has been proposed to safeguard training individuals by adding random Gaussian noise to gradient updates in the backpropagation. Researchers identify that DP-SGD typically causes utility loss since the injected homogeneous noise alters the gradient updates calculated at each iteration. Namely, all elements in the gradient are contaminated regardless of their importance in updating model parameters. In this work, we argue that the utility loss mainly results from the homogeneity of injected noise. Consequently, we propose a generic differential privacy framework with heterogeneous noise (DP-Hero) by defining a heterogeneous random mechanism to abstract its property. The insight of DP-Hero is to leverage the knowledge encoded in the previously trained model to guide the subsequent allocation of noise heterogeneity, thereby leveraging the statistical perturbation and achieving enhanced utility. Atop DP-Hero, we instantiate a heterogeneous version of DP-SGD, where the noise injected into gradients is heterogeneous and guided by prior-established model parameters. We conduct comprehensive experiments to verify and explain the effectiveness of the proposed DP-Hero, showing improved training accuracy compared with state-of-the-art works. Broadly, we shed light on improving the privacy-utility space by learning the noise guidance from the pre-existing leaked knowledge encoded in the previously trained model, showing a different perspective of understanding the utility-improved DP training.
TC-LLaVA: Rethinking the Transfer from Image to Video Understanding with Temporal Considerations
2024-09-05
Mingze Gao, Jingyu Liu, Mingda Li, Jiangtao Xie, Qingbin Liu, Bo Zhao, Xi Chen, Hui Xiong
Multimodal Large Language Models (MLLMs) have significantly improved performance across various image-language applications. Recently, there has been a growing interest in adapting image pre-trained MLLMs for video-related tasks. However, most efforts concentrate on enhancing the vision encoder and projector components, while the core part, Large Language Models (LLMs), remains comparatively under-explored. In this paper, we propose two strategies to enhance the model's capability in video understanding tasks by improving inter-layer attention computation in LLMs. Specifically, the first approach focuses on the enhancement of Rotary Position Embedding (RoPE) with Temporal-Aware Dual RoPE, which introduces temporal position information to strengthen the MLLM's temporal modeling capabilities while preserving the relative position relationships of both visual and text tokens. The second approach involves enhancing the Attention Mask with the Frame-wise Block Causal Attention Mask, a simple yet effective method that broadens visual token interactions within and across video frames while maintaining the causal inference mechanism. Based on these proposed methods, we adapt LLaVA for video understanding tasks, naming it Temporal-Considered LLaVA (TC-LLaVA). Our TC-LLaVA achieves new state-of-the-art performance across various video understanding benchmarks with only supervised fine-tuning (SFT) on video-related datasets.
Rethinking HTG Evaluation: Bridging Generation and Recognition
2024-09-04
Konstantina Nikolaidou, George Retsinas, Giorgos Sfikas, Marcus Liwicki
The evaluation of generative models for natural image tasks has been extensively studied. Similar protocols and metrics are used in cases with unique particularities, such as Handwriting Generation, even if they might not be completely appropriate. In this work, we introduce three measures tailored for HTG evaluation, $ \text{HTG}_{\text{HTR}} $, $ \text{HTG}_{\text{style}} $, and $ \text{HTG}_{\text{OOV}} $, and argue that they are more expedient to evaluate the quality of generated handwritten images. The metrics rely on the recognition error/accuracy of Handwriting Text Recognition and Writer Identification models and emphasize writing style, textual content, and diversity as the main aspects that adhere to the content of handwritten images. We conduct comprehensive experiments on the IAM handwriting database, showcasing that widely used metrics such as FID fail to properly quantify the diversity and the practical utility of generated handwriting samples. Our findings show that our metrics are richer in information and underscore the necessity of standardized evaluation protocols in HTG. The proposed metrics provide a more robust and informative protocol for assessing HTG quality, contributing to improved performance in HTR. Code for the evaluation protocol is available at: https://github.com/koninik/HTG_evaluation.
Diffusion tensor imaging (DTI) holds significant importance in clinical diagnosis and neuroscience research. However, conventional model-based fitting methods often suffer from sensitivity to noise, leading to decreased accuracy in estimating DTI parameters. While traditional data-driven deep learning methods have shown potential in terms of accuracy and efficiency, their limited generalization to out-of-training-distribution data impedes their broader application due to the diverse scan protocols used across centers, scanners, and studies. This work aims to tackle these challenges and promote the use of DTI by introducing a data-driven optimization-based method termed DoDTI. DoDTI combines the weighted linear least squares fitting algorithm and regularization by denoising technique. The former fits DW images from diverse acquisition settings into diffusion tensor field, while the latter applies a deep learning-based denoiser to regularize the diffusion tensor field instead of the DW images, which is free from the limitation of fixed-channel assignment of the network. The optimization object is solved using the alternating direction method of multipliers and then unrolled to construct a deep neural network, leveraging a data-driven strategy to learn network parameters. Extensive validation experiments are conducted utilizing both internally simulated datasets and externally obtained in-vivo datasets. The results, encompassing both qualitative and quantitative analyses, showcase that the proposed method attains state-of-the-art performance in DTI parameter estimation. Notably, it demonstrates superior generalization, accuracy, and efficiency, rendering it highly reliable for widespread application in the field.
The Baxter robot is a standard research platform used widely in research tasks, supported with an SDK provided by the developers, Rethink Robotics. Despite the ubiquitous use of the robot, the official software support is sub-standard. Especially, the native IK service has a low success rate and is often inconsistent. This unreliable behavior makes Baxter difficult to use for experiments and the research community is in need of a more reliable software support to control the robot. We present our work towards creating a Python based software library supporting the kinematics and dynamics of the Baxter robot. Our toolbox contains implementation of pose and velocity kinematics with support for Jacobian operations for redundancy resolution. We present the implementation and performance of our library, along with a comparison with PyKDL. Keywords- Baxter Research Robot, Manipulator Kinematics, Iterative IK, Dynamical Model, Redundant Manipulator
Rethinking Image Super-Resolution from Training Data Perspectives
2024-09-01
Go Ohtani, Ryu Tadokoro, Ryosuke Yamada, Yuki M. Asano, Iro Laina, Christian Rupprecht, Nakamasa Inoue, Rio Yokota, Hirokatsu Kataoka, Yoshimitsu Aoki
In this work, we investigate the understudied effect of the training data used for image super-resolution (SR). Most commonly, novel SR methods are developed and benchmarked on common training datasets such as DIV2K and DF2K. However, we investigate and rethink the training data from the perspectives of diversity and quality, {thereby addressing the question of ``How important is SR training for SR models?''}. To this end, we propose an automated image evaluation pipeline. With this, we stratify existing high-resolution image datasets and larger-scale image datasets such as ImageNet and PASS to compare their performances. We find that datasets with (i) low compression artifacts, (ii) high within-image diversity as judged by the number of different objects, and (iii) a large number of images from ImageNet or PASS all positively affect SR performance. We hope that the proposed simple-yet-effective dataset curation pipeline will inform the construction of SR datasets in the future and yield overall better models.
Social media provide a steady diet of dire warnings that artificial intelligence (AI) will make software engineering (SE) irrelevant or obsolete. To the contrary, the engineering discipline of software is rich and robust; it encompasses the full scope of software design, development, deployment, and practical use; and it has regularly assimilated radical new offerings from AI. Current AI innovations such as machine learning, large language models (LLMs) and generative AI will offer new opportunities to extend the models and methods of SE. They may automate some routine development processes, and they will bring new kinds of components and architectures. If we're fortunate they may force SE to rethink what we mean by correctness and reliability. They will not, however, render SE irrelevant.
Rethinking Backdoor Detection Evaluation for Language Models
2024-08-31
Jun Yan, Wenjie Jacky Mo, Xiang Ren, Robin Jia
Backdoor attacks, in which a model behaves maliciously when given an attacker-specified trigger, pose a major security risk for practitioners who depend on publicly released language models. Backdoor detection methods aim to detect whether a released model contains a backdoor, so that practitioners can avoid such vulnerabilities. While existing backdoor detection methods have high accuracy in detecting backdoored models on standard benchmarks, it is unclear whether they can robustly identify backdoors in the wild. In this paper, we examine the robustness of backdoor detectors by manipulating different factors during backdoor planting. We find that the success of existing methods highly depends on how intensely the model is trained on poisoned data during backdoor planting. Specifically, backdoors planted with either more aggressive or more conservative training are significantly more difficult to detect than the default ones. Our results highlight a lack of robustness of existing backdoor detectors and the limitations in current benchmark construction.
Medical report generation is a critical task in healthcare that involves the automatic creation of detailed and accurate descriptions from medical images. Traditionally, this task has been approached as a sequence generation problem, relying on vision-and-language techniques to generate coherent and contextually relevant reports. However, in this paper, we propose a novel perspective: rethinking medical report generation as a multi-label classification problem. By framing the task this way, we leverage the radiology nodes from the commonly used knowledge graph, which can be better captured through classification techniques. To verify our argument, we introduce a novel report generation framework based on BLIP integrated with classified key nodes, which allows for effective report generation with accurate classification of multiple key aspects within the medical images. This approach not only simplifies the report generation process but also significantly enhances performance metrics. Our extensive experiments demonstrate that leveraging key nodes can achieve state-of-the-art (SOTA) performance, surpassing existing approaches across two benchmark datasets. The results underscore the potential of re-envisioning traditional tasks with innovative methodologies, paving the way for more efficient and accurate medical report generation.
The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data, which we termed "blendfake", encouraging models to learn generic forgery artifacts like blending boundary. Interestingly, current SoTA methods utilize blendfake without incorporating any deepfake data in their training process. This is likely because previous empirical observations suggest that vanilla hybrid training (VHT), which combines deepfake and blendfake data, results in inferior performance to methods using only blendfake data (so-called "1+1<2"). Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive. In this paper, we rethink the role of blendfake in detecting deepfakes and formulate the process from "real to blendfake to deepfake" to be a progressive transition. Specifically, blendfake and deepfake can be explicitly delineated as the oriented pivot anchors between "real-to-fake" transitions. The accumulation of forgery information should be oriented and progressively increasing during this transition process. To this end, we propose an Oriented Progressive Regularizor (OPR) to establish the constraints that compel the distribution of anchors to be discretely arranged. Furthermore, we introduce feature bridging to facilitate the smooth transition between adjacent anchors. Extensive experiments confirm that our design allows leveraging forgery information from both blendfake and deepfake effectively and comprehensively.
In this paper, we rethink sparse lexical representations for image retrieval. By utilizing multi-modal large language models (M-LLMs) that support visual prompting, we can extract image features and convert them into textual data, enabling us to utilize efficient sparse retrieval algorithms employed in natural language processing for image retrieval tasks. To assist the LLM in extracting image features, we apply data augmentation techniques for key expansion and analyze the impact with a metric for relevance between images and textual data. We empirically show the superior precision and recall performance of our image retrieval method compared to conventional vision-language model-based methods on the MS-COCO, PASCAL VOC, and NUS-WIDE datasets in a keyword-based image retrieval scenario, where keywords serve as search queries. We also demonstrate that the retrieval performance can be improved by iteratively incorporating keywords into search queries.
More Text, Less Point: Towards 3D Data-Efficient Point-Language Understanding
2024-08-28
Yuan Tang, Xu Han, Xianzhi Li, Qiao Yu, Jinfeng Xu, Yixue Hao, Long Hu, Min Chen
Enabling Large Language Models (LLMs) to comprehend the 3D physical world remains a significant challenge. Due to the lack of large-scale 3D-text pair datasets, the success of LLMs has yet to be replicated in 3D understanding. In this paper, we rethink this issue and propose a new task: 3D Data-Efficient Point-Language Understanding. The goal is to enable LLMs to achieve robust 3D object understanding with minimal 3D point cloud and text data pairs. To address this task, we introduce GreenPLM, which leverages more text data to compensate for the lack of 3D data. First, inspired by using CLIP to align images and text, we utilize a pre-trained point cloud-text encoder to map the 3D point cloud space to the text space. This mapping leaves us to seamlessly connect the text space with LLMs. Once the point-text-LLM connection is established, we further enhance text-LLM alignment by expanding the intermediate text space, thereby reducing the reliance on 3D point cloud data. Specifically, we generate 6M free-text descriptions of 3D objects, and design a three-stage training strategy to help LLMs better explore the intrinsic connections between different modalities. To achieve efficient modality alignment, we design a zero-parameter cross-attention module for token pooling. Extensive experimental results show that GreenPLM requires only 12% of the 3D training data used by existing state-of-the-art models to achieve superior 3D understanding. Remarkably, GreenPLM also achieves competitive performance using text-only data. The code and weights are available at: https://github.com/TangYuan96/GreenPLM.
Relation Also Knows: Rethinking the Recall and Editing of Factual Associations in Auto-Regressive Transformer Language Models
2024-08-27
Xiyu Liu, Zhengxiao Liu, Naibin Gu, Zheng Lin, Wanli Ma, Ji Xiang, Weiping Wang
The storage and recall of factual associations in auto-regressive transformer language models (LMs) have drawn a great deal of attention, inspiring knowledge editing by directly modifying the located model weights. Most editing works achieve knowledge editing under the guidance of existing interpretations of knowledge recall that mainly focus on subject knowledge. However, these interpretations are seriously flawed, neglecting relation information and leading to the over-generalizing problem for editing. In this work, we discover a novel relation-focused perspective to interpret the knowledge recall of transformer LMs during inference and apply it on knowledge editing to avoid over-generalizing. Experimental results on the dataset supplemented with a new R-Specificity criterion demonstrate that our editing approach significantly alleviates over-generalizing while remaining competitive on other criteria, breaking the domination of subject-focused editing for future research.
Rethinking Knowledge Transfer in Learning Using Privileged Information
2024-08-26
Danil Provodin, Bram van den Akker, Christina Katsimerou, Maurits Kaptein, Mykola Pechenizkiy
In supervised machine learning, privileged information (PI) is information that is unavailable at inference, but is accessible during training time. Research on learning using privileged information (LUPI) aims to transfer the knowledge captured in PI onto a model that can perform inference without PI. It seems that this extra bit of information ought to make the resulting model better. However, finding conclusive theoretical or empirical evidence that supports the ability to transfer knowledge using PI has been challenging. In this paper, we critically examine the assumptions underlying existing theoretical analyses and argue that there is little theoretical justification for when LUPI should work. We analyze LUPI methods and reveal that apparent improvements in empirical risk of existing research may not directly result from PI. Instead, these improvements often stem from dataset anomalies or modifications in model design misguidedly attributed to PI. Our experiments for a wide variety of application domains further demonstrate that state-of-the-art LUPI approaches fail to effectively transfer knowledge from PI. Thus, we advocate for practitioners to exercise caution when working with PI to avoid unintended inductive biases.
AI Thinking: A framework for rethinking artificial intelligence in practice
2024-08-26
Denis Newman-Griffis
Artificial intelligence is transforming the way we work with information across disciplines and practical contexts. A growing range of disciplines are now involved in studying, developing, and assessing the use of AI in practice, but these disciplines often employ conflicting understandings of what AI is and what is involved in its use. New, interdisciplinary approaches are needed to bridge competing conceptualisations of AI in practice and help shape the future of AI use. I propose a novel conceptual framework called AI Thinking, which models key decisions and considerations involved in AI use across disciplinary perspectives. The AI Thinking model addresses five practice-based competencies involved in applying AI in context: motivating AI use in information processes, formulating AI methods, assessing available tools and technologies, selecting appropriate data, and situating AI in the sociotechnical contexts it is used in. A hypothetical case study is provided to illustrate the application of AI Thinking in practice. This article situates AI Thinking in broader cross-disciplinary discourses of AI, including its connections to ongoing discussions around AI literacy and AI-driven innovation. AI Thinking can help to bridge divides between academic disciplines and diverse contexts of AI use, and to reshape the future of AI in practice.
Rethinking State Disentanglement in Causal Reinforcement Learning
2024-08-24
Haiyao Cao, Zhen Zhang, Panpan Cai, Yuhang Liu, Jinan Zou, Ehsan Abbasnejad, Biwei Huang, Mingming Gong, Anton van den Hengel, Javen Qinfeng Shi
One of the significant challenges in reinforcement learning (RL) when dealing with noise is estimating latent states from observations. Causality provides rigorous theoretical support for ensuring that the underlying states can be uniquely recovered through identifiability. Consequently, some existing work focuses on establishing identifiability from a causal perspective to aid in the design of algorithms. However, these results are often derived from a purely causal viewpoint, which may overlook the specific RL context. We revisit this research line and find that incorporating RL-specific context can reduce unnecessary assumptions in previous identifiability analyses for latent states. More importantly, removing these assumptions allows algorithm design to go beyond the earlier boundaries constrained by them. Leveraging these insights, we propose a novel approach for general partially observable Markov Decision Processes (POMDPs) by replacing the complicated structural constraints in previous methods with two simple constraints for transition and reward preservation. With the two constraints, the proposed algorithm is guaranteed to disentangle state and noise that is faithful to the underlying dynamics. Empirical evidence from extensive benchmark control tasks demonstrates the superiority of our approach over existing counterparts in effectively disentangling state belief from noise.
Current video deblurring methods have limitations in recovering high-frequency information since the regression losses are conservative with high-frequency details. Since Diffusion Models (DMs) have strong capabilities in generating high-frequency details, we consider introducing DMs into the video deblurring task. However, we found that directly applying DMs to the video deblurring task has the following problems: (1) DMs require many iteration steps to generate videos from Gaussian noise, which consumes many computational resources. (2) DMs are easily misled by the blurry artifacts in the video, resulting in irrational content and distortion of the deblurred video. To address the above issues, we propose a novel video deblurring framework VD-Diff that integrates the diffusion model into the Wavelet-Aware Dynamic Transformer (WADT). Specifically, we perform the diffusion model in a highly compact latent space to generate prior features containing high-frequency information that conforms to the ground truth distribution. We design the WADT to preserve and recover the low-frequency information in the video while utilizing the high-frequency information generated by the diffusion model. Extensive experiments show that our proposed VD-Diff outperforms SOTA methods on GoPro, DVD, BSD, and Real-World Video datasets.
The essence of sequential recommender systems (RecSys) lies in understanding how users make decisions. Most existing approaches frame the task as sequential prediction based on users' historical purchase records. While effective in capturing users' natural preferences, this formulation falls short in accurately modeling actual recommendation scenarios, particularly in accounting for how unsuccessful recommendations influence future purchases. Furthermore, the impact of the RecSys itself on users' decisions has not been appropriately isolated and quantitatively analyzed. To address these challenges, we propose a novel formulation of sequential recommendation, termed Causal Sequential Recommendation (CSRec). Instead of predicting the next item in the sequence, CSRec aims to predict the probability of a recommended item's acceptance within a sequential context and backtrack how current decisions are made. Critically, CSRec facilitates the isolation of various factors that affect users' final decisions, especially the influence of the recommender system itself, thereby opening new avenues for the design of recommender systems. CSRec can be seamlessly integrated into existing methodologies. Experimental evaluations on both synthetic and real-world datasets demonstrate that the proposed implementation significantly improves upon state-of-the-art baselines.
Humans exhibit a remarkable ability to learn quickly from a limited number of labeled samples, a capability that starkly contrasts with that of current machine learning systems. Unsupervised Few-Shot Learning (U-FSL) seeks to bridge this divide by reducing reliance on annotated datasets during initial training phases. In this work, we first quantitatively assess the impacts of Masked Image Modeling (MIM) and Contrastive Learning (CL) on few-shot learning tasks. Our findings highlight the respective limitations of MIM and CL in terms of discriminative and generalization abilities, which contribute to their underperformance in U-FSL contexts. To address these trade-offs between generalization and discriminability in unsupervised pretraining, we introduce a novel paradigm named Masked Image Contrastive Modeling (MICM). MICM creatively combines the targeted object learning strength of CL with the generalized visual feature learning capability of MIM, significantly enhancing its efficacy in downstream few-shot learning inference. Extensive experimental analyses confirm the advantages of MICM, demonstrating significant improvements in both generalization and discrimination capabilities for few-shot learning. Our comprehensive quantitative evaluations further substantiate the superiority of MICM, showing that our two-stage U-FSL framework based on MICM markedly outperforms existing leading baselines.
Timeline and Boundary Guided Diffusion Network for Video Shadow Detection
2024-08-21
Haipeng Zhou, Honqiu Wang, Tian Ye, Zhaohu Xing, Jun Ma, Ping Li, Qiong Wang, Lei Zhu
Video Shadow Detection (VSD) aims to detect the shadow masks with frame sequence. Existing works suffer from inefficient temporal learning. Moreover, few works address the VSD problem by considering the characteristic (i.e., boundary) of shadow. Motivated by this, we propose a Timeline and Boundary Guided Diffusion (TBGDiff) network for VSD where we take account of the past-future temporal guidance and boundary information jointly. In detail, we design a Dual Scale Aggregation (DSA) module for better temporal understanding by rethinking the affinity of the long-term and short-term frames for the clipped video. Next, we introduce Shadow Boundary Aware Attention (SBAA) to utilize the edge contexts for capturing the characteristics of shadows. Moreover, we are the first to introduce the Diffusion model for VSD in which we explore a Space-Time Encoded Embedding (STEE) to inject the temporal guidance for Diffusion to conduct shadow detection. Benefiting from these designs, our model can not only capture the temporal information but also the shadow property. Extensive experiments show that the performance of our approach overtakes the state-of-the-art methods, verifying the effectiveness of our components. We release the codes, weights, and results at \url{https://github.com/haipengzhou856/TBGDiff}.
The advent of the Segment Anything Model (SAM) marks a significant milestone for interactive segmentation using generalist models. As a late fusion model, SAM extracts image embeddings once and merges them with prompts in later interactions. This strategy limits the models ability to extract detailed information from the prompted target zone. Current specialist models utilize the early fusion strategy that encodes the combination of images and prompts to target the prompted objects, yet repetitive complex computations on the images result in high latency. The key to these issues is efficiently synergizing the images and prompts. We propose SAM-REF, a two-stage refinement framework that fully integrates images and prompts globally and locally while maintaining the accuracy of early fusion and the efficiency of late fusion. The first-stage GlobalDiff Refiner is a lightweight early fusion network that combines the whole image and prompts, focusing on capturing detailed information for the entire object. The second-stage PatchDiff Refiner locates the object detail window according to the mask and prompts, then refines the local details of the object. Experimentally, we demonstrated the high effectiveness and efficiency of our method in tackling complex cases with multiple interactions. Our SAM-REF model outperforms the current state-of-the-art method in most metrics on segmentation quality without compromising efficiency.
Hierarchical Retrieval-Augmented Generation Model with Rethink for Multi-hop Question Answering
2024-08-20
Xiaoming Zhang, Ming Wang, Xiaocui Yang, Daling Wang, Shi Feng, Yifei Zhang
Multi-hop Question Answering (QA) necessitates complex reasoning by integrating multiple pieces of information to resolve intricate questions. However, existing QA systems encounter challenges such as outdated information, context window length limitations, and an accuracy-quantity trade-off. To address these issues, we propose a novel framework, the Hierarchical Retrieval-Augmented Generation Model with Rethink (HiRAG), comprising Decomposer, Definer, Retriever, Filter, and Summarizer five key modules. We introduce a new hierarchical retrieval strategy that incorporates both sparse retrieval at the document level and dense retrieval at the chunk level, effectively integrating their strengths. Additionally, we propose a single-candidate retrieval method to mitigate the limitations of multi-candidate retrieval. We also construct two new corpora, Indexed Wikicorpus and Profile Wikicorpus, to address the issues of outdated and insufficient knowledge. Our experimental results on four datasets demonstrate that HiRAG outperforms state-of-the-art models across most metrics, and our Indexed Wikicorpus is effective. The code for HiRAG is available at https://github.com/2282588541a/HiRAG
Video segmentation aims at partitioning video sequences into meaningful segments based on objects or regions of interest within frames. Current video segmentation models are often derived from image segmentation techniques, which struggle to cope with small-scale or class-imbalanced video datasets. This leads to inconsistent segmentation results across frames. To address these issues, we propose a training strategy Masked Video Consistency, which enhances spatial and temporal feature aggregation. MVC introduces a training strategy that randomly masks image patches, compelling the network to predict the entire semantic segmentation, thus improving contextual information integration. Additionally, we introduce Object Masked Attention (OMA) to optimize the cross-attention mechanism by reducing the impact of irrelevant queries, thereby enhancing temporal modeling capabilities. Our approach, integrated into the latest decoupled universal video segmentation framework, achieves state-of-the-art performance across five datasets for three video segmentation tasks, demonstrating significant improvements over previous methods without increasing model parameters.
Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation
2024-08-19
Heath Arthur-Loui, Amina Mollaysa, Michael Krauthammer
De novo molecule design has become a highly active research area, advanced significantly through the use of state-of-the-art generative models. Despite these advances, several fundamental questions remain unanswered as the field increasingly focuses on more complex generative models and sophisticated molecular representations as an answer to the challenges of drug design. In this paper, we return to the simplest representation of molecules, and investigate overlooked limitations of classical generative approaches, particularly Variational Autoencoders (VAEs) and auto-regressive models. We propose a hybrid model in the form of a novel regularizer that leverages the strengths of both to improve validity, conditional generation, and style transfer of molecular sequences. Additionally, we provide an in depth discussion of overlooked assumptions of these models' behaviour.
Enhancing Adversarial Transferability with Adversarial Weight Tuning
2024-08-18
Jiahao Chen, Zhou Feng, Rui Zeng, Yuwen Pu, Chunyi Zhou, Yi Jiang, Yuyou Gan, Jinbao Li, Shouling Ji
Deep neural networks (DNNs) are vulnerable to adversarial examples (AEs) that mislead the model while appearing benign to human observers. A critical concern is the transferability of AEs, which enables black-box attacks without direct access to the target model. However, many previous attacks have failed to explain the intrinsic mechanism of adversarial transferability. In this paper, we rethink the property of transferable AEs and reformalize the formulation of transferability. Building on insights from this mechanism, we analyze the generalization of AEs across models with different architectures and prove that we can find a local perturbation to mitigate the gap between surrogate and target models. We further establish the inner connections between model smoothness and flat local maxima, both of which contribute to the transferability of AEs. Further, we propose a new adversarial attack algorithm, \textbf{A}dversarial \textbf{W}eight \textbf{T}uning (AWT), which adaptively adjusts the parameters of the surrogate model using generated AEs to optimize the flat local maxima and model smoothness simultaneously, without the need for extra data. AWT is a data-free tuning method that combines gradient-based and model-based attack methods to enhance the transferability of AEs. Extensive experiments on a variety of models with different architectures on ImageNet demonstrate that AWT yields superior performance over other attacks, with an average increase of nearly 5\% and 10\% attack success rates on CNN-based and Transformer-based models, respectively, compared to state-of-the-art attacks.
Rethinking Deep Learning: Propagating Information in Neural Networks without Backpropagation and Statistical Optimization
2024-08-18
Kei Itoh
Developing strong AI signifies the arrival of technological singularity, contributing greatly to advancing human civilization and resolving social issues. Neural networks (NNs) and deep learning, which utilize NNs, are expected to lead to strong AI due to their biological neural system-mimicking structures. However, the statistical weight optimization techniques commonly used, such as error backpropagation and loss functions, may hinder the mimicry of neural systems. This study discusses the information propagation capabilities and potential practical applications of NNs as neural system mimicking structures by solving the handwritten character recognition problem in the Modified National Institute of Standards and Technology (MNIST) database without using statistical weight optimization techniques like error backpropagation. In this study, the NNs architecture comprises fully connected layers using step functions as activation functions, with 0-15 hidden layers, and no weight updates. The accuracy is calculated by comparing the average output vectors of the training data for each label with the output vectors of the test data, based on vector similarity. The results showed that the maximum accuracy achieved is around 80%. This indicates that NNs can propagate information correctly without using statistical weight optimization. Additionally, the accuracy decreased with an increasing number of hidden layers. This is attributed to the decrease in the variance of the output vectors as the number of hidden layers increases, suggesting that the output data becomes smooth. This study's NNs and accuracy calculation methods are simple and have room for various improvements. Moreover, creating a feedforward NNs that repeatedly cycles through 'input -> processing -> output -> environmental response -> input -> ...' could pave the way for practical software applications.
Current text-to-image (T2I) synthesis diffusion models raise misuse concerns, particularly in creating prohibited or not-safe-for-work (NSFW) images. To address this, various safety mechanisms and red teaming attack methods are proposed to enhance or expose the T2I model's capability to generate unsuitable content. However, many red teaming attack methods assume knowledge of the text encoders, limiting their practical usage. In this work, we rethink the case of \textit{purely black-box} attacks without prior knowledge of the T2l model. To overcome the unavailability of gradients and the inability to optimize attacks within a discrete prompt space, we propose DiffZOO which applies Zeroth Order Optimization to procure gradient approximations and harnesses both C-PRV and D-PRV to enhance attack prompts within the discrete prompt domain. We evaluated our method across multiple safety mechanisms of the T2I diffusion model and online servers. Experiments on multiple state-of-the-art safety mechanisms show that DiffZOO attains an 8.5% higher average attack success rate than previous works, hence its promise as a practical red teaming tool for T2l models.
Rethinking Generative Semantic Communication for Multi-User Systems with Multi-Modal LLM
2024-08-16
Wanting Yang, Zehui Xiong, Shiwen Mao, Tony Q. S. Quek, Ping Zhang, Merouane Debbah, Rahim Tafazolli
The surge in connected devices in 6G with typical complex tasks requiring multi-user cooperation, such as smart agriculture and smart cities, poses significant challenges to unsustainable traditional communication. Fortunately, the booming artificial intelligence technology and the growing computational power of devices offer a promising 6G enabler: semantic communication (SemCom). However, existing deep learning-based SemCom paradigms struggle to extend to multi-user scenarios due to its increasing model size with the growing number of users and its limited compatibility with complex communication environments. Consequently, to truly empower 6G networks with this critical technology, this article rethinks generative SemCom for multi-user system with multi-modal large language model (MLLM), and propose a novel framework called ``M2GSC". In this framework, the MLLM, which serves as shared knowledge base (SKB), plays three critical roles, that is complex task decomposition, semantic representation specification, and semantic translation and mapping, for complex tasks, spawning a series of benefits such as semantic encoding standardization and semantic decoding personalization. Meanwhile, to enhance the performance of M2GSC framework, we highlight three relevant research directions, namely, upgrading SKB to closed loop agent, adaptive semantic encoding offloading, and streamlined semantic decoding offloading, as well as the involved multi-user resource management. Finally, a case study is conducted to demonstrate the preliminary validation on the effectiveness of the M2GSC framework in terms of streamlined decoding offloading.
The Limitations of Model Retraining in the Face of Performativity
2024-08-16
Anmol Kabra, Kumar Kshitij Patel
We study stochastic optimization in the context of performative shifts, where the data distribution changes in response to the deployed model. We demonstrate that naive retraining can be provably suboptimal even for simple distribution shifts. The issue worsens when models are retrained given a finite number of samples at each retraining step. We show that adding regularization to retraining corrects both of these issues, attaining provably optimal models in the face of distribution shifts. Our work advocates rethinking how machine learning models are retrained in the presence of performative effects.
Rethinking Medical Anomaly Detection in Brain MRI: An Image Quality Assessment Perspective
2024-08-15
Zixuan Pan, Jun Xia, Zheyu Yan, Guoyue Xu, Yawen Wu, Zhenge Jia, Jianxu Chen, Yiyu Shi
Reconstruction-based methods, particularly those leveraging autoencoders, have been widely adopted to perform anomaly detection in brain MRI. While most existing works try to improve detection accuracy by proposing new model structures or algorithms, we tackle the problem through image quality assessment, an underexplored perspective in the field. We propose a fusion quality loss function that combines Structural Similarity Index Measure loss with l1 loss, offering a more comprehensive evaluation of reconstruction quality. Additionally, we introduce a data pre-processing strategy that enhances the average intensity ratio (AIR) between normal and abnormal regions, further improving the distinction of anomalies. By fusing the aforementioned two methods, we devise the image quality assessment (IQA) approach. The proposed IQA approach achieves significant improvements (>10%) in terms of Dice coefficient (DICE) and Area Under the Precision-Recall Curve (AUPRC) on the BraTS21 (T2, FLAIR) and MSULB datasets when compared with state-of-the-art methods. These results highlight the importance of invoking the comprehensive image quality assessment in medical anomaly detection and provide a new perspective for future research in this field.
Stereo matching, a critical step of 3D reconstruction, has fully shifted towards deep learning due to its strong feature representation of remote sensing images. However, ground truth for stereo matching task relies on expensive airborne LiDAR data, thus making it difficult to obtain enough samples for supervised learning. To improve the generalization ability of stereo matching networks on cross-domain data from different sensors and scenarios, in this paper, we dedicate to study key training factors from three perspectives. (1) For the selection of training dataset, it is important to select data with similar regional target distribution as the test set instead of utilizing data from the same sensor. (2) For model structure, cascaded structure that flexibly adapts to different sizes of features is preferred. (3) For training manner, unsupervised methods generalize better than supervised methods, and we design an unsupervised early-stop strategy to help retain the best model with pre-trained weights as the basis. Extensive experiments are conducted to support the previous findings, on the basis of which we present an unsupervised stereo matching network with good generalization performance. We release the source code and the datasets at https://github.com/Elenairene/RKF_RSSM to reproduce the results and encourage future work.
Understanding the 3D semantics of a scene is a fundamental problem for various scenarios such as embodied agents. While NeRFs and 3DGS excel at novel-view synthesis, previous methods for understanding their semantics have been limited to incomplete 3D understanding: their segmentation results are 2D masks and their supervision is anchored at 2D pixels. This paper revisits the problem set to pursue a better 3D understanding of a scene modeled by NeRFs and 3DGS as follows. 1) We directly supervise the 3D points to train the language embedding field. It achieves state-of-the-art accuracy without relying on multi-scale language embeddings. 2) We transfer the pre-trained language field to 3DGS, achieving the first real-time rendering speed without sacrificing training time or accuracy. 3) We introduce a 3D querying and evaluation protocol for assessing the reconstructed geometry and semantics together. Code, checkpoints, and annotations will be available online. Project page: https://hyunji12.github.io/Open3DRF
Rethinking Video with a Universal Event-Based Representation
2024-08-12
Andrew Freeman
Traditionally, video is structured as a sequence of discrete image frames. Recently, however, a novel video sensing paradigm has emerged which eschews video frames entirely. These "event" sensors aim to mimic the human vision system with asynchronous sensing, where each pixel has an independent, sparse data stream. While these cameras enable high-speed and high-dynamic-range sensing, researchers often revert to a framed representation of the event data for existing applications, or build bespoke applications for a particular camera's event data type. At the same time, classical video systems have significant computational redundancy at the application layer, since pixel samples are repeated across frames in the uncompressed domain. To address the shortcomings of existing systems, I introduce Address, Decimation, {\Delta}t Event Representation (AD{\Delta}ER, pronounced "adder"), a novel intermediate video representation and system framework. The framework transcodes a variety of framed and event camera sources into a single event-based representation, which supports source-modeled lossy compression and backward compatibility with traditional frame-based applications. I demonstrate that AD{\Delta}ER achieves state-of-the-art application speed and compression performance for scenes with high temporal redundancy. Crucially, I describe how AD{\Delta}ER unlocks an entirely new control mechanism for computer vision: application speed can correlate with both the scene content and the level of lossy compression. Finally, I discuss the implications for event-based video on large-scale video surveillance and resource-constrained sensing.
Currently, neural-network processing in machine learning applications relies on layer synchronization, whereby neurons in a layer aggregate incoming currents from all neurons in the preceding layer, before evaluating their activation function. This is practiced even in artificial Spiking Neural Networks (SNNs), which are touted as consistent with neurobiology, in spite of processing in the brain being, in fact asynchronous. A truly asynchronous system however would allow all neurons to evaluate concurrently their threshold and emit spikes upon receiving any presynaptic current. Omitting layer synchronization is potentially beneficial, for latency and energy efficiency, but asynchronous execution of models previously trained with layer synchronization may entail a mismatch in network dynamics and performance. We present a study that documents and quantifies this problem in three datasets on our simulation environment that implements network asynchrony, and we show that models trained with layer synchronization either perform sub-optimally in absence of the synchronization, or they will fail to benefit from any energy and latency reduction, when such a mechanism is in place. We then "make ends meet" and address the problem with unlayered backprop, a novel backpropagation-based training method, for learning models suitable for asynchronous processing. We train with it models that use different neuron execution scheduling strategies, and we show that although their neurons are more reactive, these models consistently exhibit lower overall spike density (up to 50%), reach a correct decision faster (up to 2x) without integrating all spikes, and achieve superior accuracy (up to 10% higher). Our findings suggest that asynchronous event-based (neuromorphic) AI computing is indeed more efficient, but we need to seriously rethink how we train our SNN models, to benefit from it.
Multiple instance learning (MIL) problem is currently solved from either bag-classification or instance-classification perspective, both of which ignore important information contained in some instances and result in limited performance. For example, existing methods often face difficulty in learning hard positive instances. In this paper, we formulate MIL as a semi-supervised instance classification problem, so that all the labeled and unlabeled instances can be fully utilized to train a better classifier. The difficulty in this formulation is that all the labeled instances are negative in MIL, and traditional self-training techniques used in semi-supervised learning tend to degenerate in generating pseudo labels for the unlabeled instances in this scenario. To resolve this problem, we propose a weakly-supervised self-training method, in which we utilize the positive bag labels to construct a global constraint and a local constraint on the pseudo labels to prevent them from degenerating and force the classifier to learn hard positive instances. It is worth noting that easy positive instances are instances are far from the decision boundary in the classification process, while hard positive instances are those close to the decision boundary. Through iterative optimization, the pseudo labels can gradually approach the true labels. Extensive experiments on two MNIST synthetic datasets, five traditional MIL benchmark datasets and two histopathology whole slide image datasets show that our method achieved new SOTA performance on all of them. The code will be publicly available.
2024-08-09
Haider Al-Tahan, Quentin Garrido, Randall Balestriero, Diane Bouchacourt, Caner Hazirbas, Mark Ibrahim
Significant research efforts have been made to scale and improve vision-language model (VLM) training approaches. Yet, with an ever-growing number of benchmarks, researchers are tasked with the heavy burden of implementing each protocol, bearing a non-trivial computational cost, and making sense of how all these benchmarks translate into meaningful axes of progress. To facilitate a systematic evaluation of VLM progress, we introduce UniBench: a unified implementation of 50+ VLM benchmarks spanning a comprehensive range of carefully categorized capabilities from object recognition to spatial awareness, counting, and much more. We showcase the utility of UniBench for measuring progress by evaluating nearly 60 publicly available vision-language models, trained on scales of up to 12.8B samples. We find that while scaling training data or model size can boost many vision-language model capabilities, scaling offers little benefit for reasoning or relations. Surprisingly, we also discover today's best VLMs struggle on simple digit recognition and counting tasks, e.g. MNIST, which much simpler networks can solve. Where scale falls short, we find that more precise interventions, such as data quality or tailored-learning objectives offer more promise. For practitioners, we also offer guidance on selecting a suitable VLM for a given application. Finally, we release an easy-to-run UniBench code-base with the full set of 50+ benchmarks and comparisons across 59 models as well as a distilled, representative set of benchmarks that runs in 5 minutes on a single GPU.
Rethinking Affect Analysis: A Protocol for Ensuring Fairness and Consistency
2024-08-04
Guanyu Hu, Dimitrios Kollias, Eleni Papadopoulou, Paraskevi Tzouveli, Jie Wei, Xinyu Yang
Evaluating affect analysis methods presents challenges due to inconsistencies in database partitioning and evaluation protocols, leading to unfair and biased results. Previous studies claim continuous performance improvements, but our findings challenge such assertions. Using these insights, we propose a unified protocol for database partitioning that ensures fairness and comparability. We provide detailed demographic annotations (in terms of race, gender and age), evaluation metrics, and a common framework for expression recognition, action unit detection and valence-arousal estimation. We also rerun the methods with the new protocol and introduce a new leaderboards to encourage future research in affect recognition with a fairer comparison. Our annotations, code, and pre-trained models are available on \hyperlink{https://github.com/dkollias/Fair-Consistent-Affect-Analysis}{Github}.
Rethinking Pre-trained Feature Extractor Selection in Multiple Instance Learning for Whole Slide Image Classification
2024-08-02
Bryan Wong, Mun Yong Yi
Multiple instance learning (MIL) has become a preferred method for gigapixel whole slide image (WSI) classification without requiring patch-level annotations. Current MIL research primarily relies on embedding-based approaches, which extract patch features using a pre-trained feature extractor and aggregate them for slide-level prediction. Despite the critical role of feature extraction, there is limited guidance on selecting optimal feature extractors to maximize WSI performance. This study addresses this gap by systematically evaluating MIL feature extractors across three dimensions: pre-training dataset, backbone model, and pre-training method. Extensive experiments were conducted on two public WSI datasets (TCGA-NSCLC and Camelyon16) using four state-of-the-art (SOTA) MIL models. Our findings reveal that selecting a robust self-supervised learning (SSL) method has a greater impact on performance than relying solely on an in-domain pre-training dataset. Additionally, prioritizing Transformer-based backbones with deeper architectures over CNN-based models and using larger, more diverse pre-training datasets significantly enhances classification outcomes. We believe these insights provide practical guidance for optimizing WSI classification and explain the reasons behind the performance advantages of current SOTA pathology foundation models. Furthermore, this work may inform the development of more effective foundation models. Our code is publicly available at https://anonymous.4open.science/r/MIL-Feature-Extractor-Selection
Rethink convolution-based graph neural networks (GNN) -- they characteristically suffer from limited expressiveness, over-smoothing, and over-squashing, and require specialized sparse kernels for efficient computation. Here, we design a simple graph learning module entirely free of convolution operators, coined random walk with unifying memory (RUM) neural network, where an RNN merges the topological and semantic graph features along the random walks terminating at each node. Relating the rich literature on RNN behavior and graph topology, we theoretically show and experimentally verify that RUM attenuates the aforementioned symptoms and is more expressive than the Weisfeiler-Lehman (WL) isomorphism test. On a variety of node- and graph-level classification and regression tasks, RUM not only achieves competitive performance, but is also robust, memory-efficient, scalable, and faster than the simplest convolutional GNNs.
Reasoning encompasses two typical types: deductive reasoning and inductive reasoning. Despite extensive research into the reasoning capabilities of Large Language Models (LLMs), most studies have failed to rigorously differentiate between inductive and deductive reasoning, leading to a blending of the two. This raises an essential question: In LLM reasoning, which poses a greater challenge - deductive or inductive reasoning? While the deductive reasoning capabilities of LLMs, (i.e. their capacity to follow instructions in reasoning tasks), have received considerable attention, their abilities in true inductive reasoning remain largely unexplored. To investigate into the true inductive reasoning capabilities of LLMs, we propose a novel framework, SolverLearner. This framework enables LLMs to learn the underlying function (i.e., $y = f_w(x)$), that maps input data points $(x)$ to their corresponding output values $(y)$, using only in-context examples. By focusing on inductive reasoning and separating it from LLM-based deductive reasoning, we can isolate and investigate inductive reasoning of LLMs in its pure form via SolverLearner. Our observations reveal that LLMs demonstrate remarkable inductive reasoning capabilities through SolverLearner, achieving near-perfect performance with ACC of 1 in most cases. Surprisingly, despite their strong inductive reasoning abilities, LLMs tend to relatively lack deductive reasoning capabilities, particularly in tasks involving ``counterfactual'' reasoning.
The neurons of Kolmogorov-Arnold Networks (KANs) perform a simple summation motivated by the Kolmogorov-Arnold representation theorem, which asserts that sum is the only fundamental multivariate function. In this work, we investigate the potential for identifying an alternative multivariate function for KAN neurons that may offer increased practical utility. Our empirical research involves testing various multivariate functions in KAN neurons across a range of benchmark Machine Learning tasks. Our findings indicate that substituting the sum with the average function in KAN neurons results in significant performance enhancements compared to traditional KANs. Our study demonstrates that this minor modification contributes to the stability of training by confining the input to the spline within the effective range of the activation function. Our implementation and experiments are available at: \url{https://github.com/Ghaith81/dropkan}
Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets
2024-07-29
Muhammad Abdullah Jamal, Omid Mohareri
Surgical scene understanding is a key technical component for enabling intelligent and context aware systems that can transform various aspects of surgical interventions. In this work, we focus on the semantic segmentation task, propose a simple yet effective multi-modal (RGB and depth) training framework called SurgDepth, and show state-of-the-art (SOTA) results on all publicly available datasets applicable for this task. Unlike previous approaches, which either fine-tune SOTA segmentation models trained on natural images, or encode RGB or RGB-D information using RGB only pre-trained backbones, SurgDepth, which is built on top of Vision Transformers (ViTs), is designed to encode both RGB and depth information through a simple fusion mechanism. We conduct extensive experiments on benchmark datasets including EndoVis2022, AutoLapro, LapI2I and EndoVis2017 to verify the efficacy of SurgDepth. Specifically, SurgDepth achieves a new SOTA IoU of 0.86 on EndoVis 2022 SAR-RARP50 challenge and outperforms the current best method by at least 4%, using a shallow and compute efficient decoder consisting of ConvNeXt blocks.
Visual reasoning, as a prominent research area, plays a crucial role in AI by facilitating concept formation and interaction with the world. However, current works are usually carried out separately on small datasets thus lacking generalization ability. Through rigorous evaluation of diverse benchmarks, we demonstrate the shortcomings of existing ad-hoc methods in achieving cross-domain reasoning and their tendency to data bias fitting. In this paper, we revisit visual reasoning with a two-stage perspective: (1) symbolization and (2) logical reasoning given symbols or their representations. We find that the reasoning stage is better at generalization than symbolization. Thus, it is more efficient to implement symbolization via separated encoders for different data domains while using a shared reasoner. Given our findings, we establish design principles for visual reasoning frameworks following the separated symbolization and shared reasoning. The proposed two-stage framework achieves impressive generalization ability on various visual reasoning tasks, including puzzles, physical prediction, and visual question answering (VQA), encompassing both 2D and 3D modalities. We believe our insights will pave the way for generalizable visual reasoning.
Rethinking Attention Module Design for Point Cloud Analysis
2024-07-27
Chengzhi Wu, Kaige Wang, Zeyun Zhong, Hao Fu, Junwei Zheng, Jiaming Zhang, Julius Pfrommer, Jürgen Beyerer
In recent years, there have been significant advancements in applying attention mechanisms to point cloud analysis. However, attention module variants featured in various research papers often operate under diverse settings and tasks, incorporating potential training strategies. This heterogeneity poses challenges in establishing a fair comparison among these attention module variants. In this paper, we address this issue by rethinking and exploring attention module design within a consistent base framework and settings. Both global-based and local-based attention methods are studied, with a focus on the selection basis and scales of neighbors for local-based attention. Different combinations of aggregated local features and computation methods for attention scores are evaluated, ranging from the initial addition/concatenation-based approach to the widely adopted dot product-based method and the recently proposed vector attention technique. Various position encoding methods are also investigated. Our extensive experimental analysis reveals that there is no universally optimal design across diverse point cloud tasks. Instead, drawing from best practices, we propose tailored attention modules for specific tasks, leading to superior performance on point cloud classification and segmentation benchmarks.
Mamba? Catch The Hype Or Rethink What Really Helps for Image Registration
2024-07-27
Bailiang Jian, Jiazhen Pan, Morteza Ghahremani, Daniel Rueckert, Christian Wachinger, Benedikt Wiestler
Our findings indicate that adopting "advanced" computational elements fails to significantly improve registration accuracy. Instead, well-established registration-specific designs offer fair improvements, enhancing results by a marginal 1.5\% over the baseline. Our findings emphasize the importance of rigorous, unbiased evaluation and contribution disentanglement of all low- and high-level registration components, rather than simply following the computer vision trends with "more advanced" computational blocks. We advocate for simpler yet effective solutions and novel evaluation metrics that go beyond conventional registration accuracy, warranting further research across diverse organs and modalities. The code is available at \url{https://github.com/BailiangJ/rethink-reg}.
Point-based image editing enables accurate and flexible control through content dragging. However, the role of text embedding during the editing process has not been thoroughly investigated. A significant aspect that remains unexplored is the interaction between text and image embeddings. During the progressive editing in a diffusion model, the text embedding remains constant. As the image embedding increasingly diverges from its initial state, the discrepancy between the image and text embeddings presents a significant challenge. In this study, we found that the text prompt significantly influences the dragging process, particularly in maintaining content integrity and achieving the desired manipulation. Upon these insights, we propose DragText, which optimizes text embedding in conjunction with the dragging process to pair with the modified image embedding. Simultaneously, we regularize the text optimization process to preserve the integrity of the original text prompt. Our approach can be seamlessly integrated with existing diffusion-based drag methods, enhancing performance with only a few lines of code.
Quality Assured: Rethinking Annotation Strategies in Imaging AI
2024-07-24
Tim Rädsch, Annika Reinke, Vivienn Weru, Minu D. Tizabi, Nicholas Heller, Fabian Isensee, Annette Kopp-Schneider, Lena Maier-Hein
This paper does not describe a novel method. Instead, it studies an essential foundation for reliable benchmarking and ultimately real-world application of AI-based image analysis: generating high-quality reference annotations. Previous research has focused on crowdsourcing as a means of outsourcing annotations. However, little attention has so far been given to annotation companies, specifically regarding their internal quality assurance (QA) processes. Therefore, our aim is to evaluate the influence of QA employed by annotation companies on annotation quality and devise methodologies for maximizing data annotation efficacy. Based on a total of 57,648 instance segmented images obtained from a total of 924 annotators and 34 QA workers from four annotation companies and Amazon Mechanical Turk (MTurk), we derived the following insights: (1) Annotation companies perform better both in terms of quantity and quality compared to the widely used platform MTurk. (2) Annotation companies' internal QA only provides marginal improvements, if any. However, improving labeling instructions instead of investing in QA can substantially boost annotation performance. (3) The benefit of internal QA depends on specific image characteristics. Our work could enable researchers to derive substantially more value from a fixed annotation budget and change the way annotation companies conduct internal QA.
Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution
2024-07-23
Kai Liu, Zhihang Fu, Sheng Jin, Chao Chen, Ze Chen, Rongxin Jiang, Fan Zhou, Yaowu Chen, Jieping Ye
Detecting and rejecting unknown out-of-distribution (OOD) samples is critical for deployed neural networks to void unreliable predictions. In real-world scenarios, however, the efficacy of existing OOD detection methods is often impeded by the inherent imbalance of in-distribution (ID) data, which causes significant performance decline. Through statistical observations, we have identified two common challenges faced by different OOD detectors: misidentifying tail class ID samples as OOD, while erroneously predicting OOD samples as head class from ID. To explain this phenomenon, we introduce a generalized statistical framework, termed ImOOD, to formulate the OOD detection problem on imbalanced data distribution. Consequently, the theoretical analysis reveals that there exists a class-aware bias item between balanced and imbalanced OOD detection, which contributes to the performance gap. Building upon this finding, we present a unified training-time regularization technique to mitigate the bias and boost imbalanced OOD detectors across architecture designs. Our theoretically grounded method translates into consistent improvements on the representative CIFAR10-LT, CIFAR100-LT, and ImageNet-LT benchmarks against several state-of-the-art OOD detection approaches. Code is available at https://github.com/alibaba/imood.
STrack: A Reliable Multipath Transport for AI/ML Clusters
2024-07-21
Yanfang Le, Rong Pan, Peter Newman, Jeremias Blendin, Abdul Kabbani, Vipin Jain, Raghava Sivaramu, Francis Matus
Emerging artificial intelligence (AI) and machine learning (ML) workloads present new challenges of managing the collective communication used in distributed training across hundreds or even thousands of GPUs. This paper presents STrack, a novel hardware-offloaded reliable transport protocol aimed at improving the performance of AI /ML workloads by rethinking key aspects of the transport layer. STrack optimizes congestion control and load balancing in tandem: it incorporates an adaptive load balancing algorithm leveraging ECN, while adopts RTT as multi-bit congestion indicators for precise congestion window adjustment. Additionally, STrack facilitates out-of-order delivery, selective retransmission, and swift loss recovery in hardware for multipath environment. The extensive simulation comparing STrack and RoCEv2 demonstrates that STrack outperforms RoCEv2 by up to 6X with synthetic workloads and by 27.4% with collective workloads, even with the optimized RoCEv2 system setup.
Rethinking Domain Adaptation and Generalization in the Era of CLIP
2024-07-21
Ruoyu Feng, Tao Yu, Xin Jin, Xiaoyuan Yu, Lei Xiao, Zhibo Chen
In recent studies on domain adaptation, significant emphasis has been placed on the advancement of learning shared knowledge from a source domain to a target domain. Recently, the large vision-language pre-trained model, i.e., CLIP has shown strong ability on zero-shot recognition, and parameter efficient tuning can further improve its performance on specific tasks. This work demonstrates that a simple domain prior boosts CLIP's zero-shot recognition in a specific domain. Besides, CLIP's adaptation relies less on source domain data due to its diverse pre-training dataset. Furthermore, we create a benchmark for zero-shot adaptation and pseudo-labeling based self-training with CLIP. Last but not least, we propose to improve the task generalization ability of CLIP from multiple unlabeled domains, which is a more practical and unique scenario. We believe our findings motivate a rethinking of domain adaptation benchmarks and the associated role of related algorithms in the era of CLIP.
Rethinking Feature Backbone Fine-tuning for Remote Sensing Object Detection
2024-07-21
Yechan Kim, JongHyun Park, SooYeon Kim, Moongu Jeon
Recently, numerous methods have achieved impressive performance in remote sensing object detection, relying on convolution or transformer architectures. Such detectors typically have a feature backbone to extract useful features from raw input images. For the remote sensing domain, a common practice among current detectors is to initialize the backbone with pre-training on ImageNet consisting of natural scenes. Fine-tuning the backbone is then typically required to generate features suitable for remote-sensing images. However, this could hinder the extraction of basic visual features in long-term training, thus restricting performance improvement. To mitigate this issue, we propose a novel method named DBF (Dynamic Backbone Freezing) for feature backbone fine-tuning on remote sensing object detection. Our method aims to handle the dilemma of whether the backbone should extract low-level generic features or possess specific knowledge of the remote sensing domain, by introducing a module called 'Freezing Scheduler' to dynamically manage the update of backbone features during training. Extensive experiments on DOTA and DIOR-R show that our approach enables more accurate model learning while substantially reducing computational costs. Our method can be seamlessly adopted without additional effort due to its straightforward design.
Virtual try-on methods based on diffusion models achieve realistic try-on effects but often replicate the backbone network as a ReferenceNet or use additional image encoders to process condition inputs, leading to high training and inference costs. In this work, we rethink the necessity of ReferenceNet and image encoders and innovate the interaction between garment and person by proposing CatVTON, a simple and efficient virtual try-on diffusion model. CatVTON facilitates the seamless transfer of in-shop or worn garments of any category to target persons by simply concatenating them in spatial dimensions as inputs. The efficiency of our model is demonstrated in three aspects: (1) Lightweight network: Only the original diffusion modules are used, without additional network modules. The text encoder and cross-attentions for text injection in the backbone are removed, reducing the parameters by 167.02M. (2) Parameter-efficient training: We identified the try-on relevant modules through experiments and achieved high-quality try-on effects by training only 49.57M parameters, approximately 5.51 percent of the backbone network's parameters. (3) Simplified inference: CatVTON eliminates all unnecessary conditions and preprocessing steps, including pose estimation, human parsing, and text input, requiring only a garment reference, target person image, and mask for the virtual try-on process. Extensive experiments demonstrate that CatVTON achieves superior qualitative and quantitative results with fewer prerequisites and trainable parameters than baseline methods. Furthermore, CatVTON shows good generalization in in-the-wild scenarios despite using open-source datasets with only 73K samples.
Teach Harder, Learn Poorer: Rethinking Hard Sample Distillation for GNN-to-MLP Knowledge Distillation
2024-07-20
Lirong Wu, Yunfan Liu, Haitao Lin, Yufei Huang, Stan Z. Li
To bridge the gaps between powerful Graph Neural Networks (GNNs) and lightweight Multi-Layer Perceptron (MLPs), GNN-to-MLP Knowledge Distillation (KD) proposes to distill knowledge from a well-trained teacher GNN into a student MLP. In this paper, we revisit the knowledge samples (nodes) in teacher GNNs from the perspective of hardness, and identify that hard sample distillation may be a major performance bottleneck of existing graph KD algorithms. The GNN-to-MLP KD involves two different types of hardness, one student-free knowledge hardness describing the inherent complexity of GNN knowledge, and the other student-dependent distillation hardness describing the difficulty of teacher-to-student distillation. However, most of the existing work focuses on only one of these aspects or regards them as one thing. This paper proposes a simple yet effective Hardness-aware GNN-to-MLP Distillation (HGMD) framework, which decouples the two hardnesses and estimates them using a non-parametric approach. Finally, two hardness-aware distillation schemes (i.e., HGMD-weight and HGMD-mixup) are further proposed to distill hardness-aware knowledge from teacher GNNs into the corresponding nodes of student MLPs. As non-parametric distillation, HGMD does not involve any additional learnable parameters beyond the student MLPs, but it still outperforms most of the state-of-the-art competitors. HGMD-mixup improves over the vanilla MLPs by 12.95% and outperforms its teacher GNNs by 2.48% averaged over seven real-world datasets.
Recent adaptations can boost the low-shot capability of Contrastive Vision-Language Pre-training (CLIP) by effectively facilitating knowledge transfer. However, these adaptation methods are usually operated on the global view of an input image, and thus biased perception of partial local details of the image. To solve this problem, we propose a Visual Content Refinement (VCR) before the adaptation calculation during the test stage. Specifically, we first decompose the test image into different scales to shift the feature extractor's attention to the details of the image. Then, we select the image view with the max prediction margin in each scale to filter out the noisy image views, where the prediction margins are calculated from the pre-trained CLIP model. Finally, we merge the content of the aforementioned selected image views based on their scales to construct a new robust representation. Thus, the merged content can be directly used to help the adapter focus on both global and local parts without any extra training parameters. We apply our method to 3 popular low-shot benchmark tasks with 13 datasets and achieve a significant improvement over state-of-the-art methods. For example, compared to the baseline (Tip-Adapter) on the few-shot classification task, our method achieves about 2\% average improvement for both training-free and training-need settings.
PASS++: A Dual Bias Reduction Framework for Non-Exemplar Class-Incremental Learning
2024-07-19
Fei Zhu, Xu-Yao Zhang, Zhen Cheng, Cheng-Lin Liu
Class-incremental learning (CIL) aims to recognize new classes incrementally while maintaining the discriminability of old classes. Most existing CIL methods are exemplar-based, i.e., storing a part of old data for retraining. Without relearning old data, those methods suffer from catastrophic forgetting. In this paper, we figure out two inherent problems in CIL, i.e., representation bias and classifier bias, that cause catastrophic forgetting of old knowledge. To address these two biases, we present a simple and novel dual bias reduction framework that employs self-supervised transformation (SST) in input space and prototype augmentation (protoAug) in deep feature space. On the one hand, SST alleviates the representation bias by learning generic and diverse representations that can transfer across different tasks. On the other hand, protoAug overcomes the classifier bias by explicitly or implicitly augmenting prototypes of old classes in the deep feature space, which poses tighter constraints to maintain previously learned decision boundaries. We further propose hardness-aware prototype augmentation and multi-view ensemble strategies, leading to significant improvements. The proposed framework can be easily integrated with pre-trained models. Without storing any samples of old classes, our method can perform comparably with state-of-the-art exemplar-based approaches which store plenty of old data. We hope to draw the attention of researchers back to non-exemplar CIL by rethinking the necessity of storing old samples in CIL.
We introduce KFD-NeRF, a novel dynamic neural radiance field integrated with an efficient and high-quality motion reconstruction framework based on Kalman filtering. Our key idea is to model the dynamic radiance field as a dynamic system whose temporally varying states are estimated based on two sources of knowledge: observations and predictions. We introduce a novel plug-in Kalman filter guided deformation field that enables accurate deformation estimation from scene observations and predictions. We use a shallow Multi-Layer Perceptron (MLP) for observations and model the motion as locally linear to calculate predictions with motion equations. To further enhance the performance of the observation MLP, we introduce regularization in the canonical space to facilitate the network's ability to learn warping for different frames. Additionally, we employ an efficient tri-plane representation for encoding the canonical space, which has been experimentally demonstrated to converge quickly with high quality. This enables us to use a shallower observation MLP, consisting of just two layers in our implementation. We conduct experiments on synthetic and real data and compare with past dynamic NeRF methods. Our KFD-NeRF demonstrates similar or even superior rendering performance within comparable computational time and achieves state-of-the-art view synthesis performance with thorough training.
Rethinking Video-Text Understanding: Retrieval from Counterfactually Augmented Data
2024-07-18
Wufei Ma, Kai Li, Zhongshi Jiang, Moustafa Meshry, Qihao Liu, Huiyu Wang, Christian Häne, Alan Yuille
Recent video-text foundation models have demonstrated strong performance on a wide variety of downstream video understanding tasks. Can these video-text models genuinely understand the contents of natural videos? Standard video-text evaluations could be misleading as many questions can be inferred merely from the objects and contexts in a single frame or biases inherent in the datasets. In this paper, we aim to better assess the capabilities of current video-text models and understand their limitations. We propose a novel evaluation task for video-text understanding, namely retrieval from counterfactually augmented data (RCAD), and a new Feint6K dataset. To succeed on our new evaluation task, models must derive a comprehensive understanding of the video from cross-frame reasoning. Analyses show that previous video-text foundation models can be easily fooled by counterfactually augmented data and are far behind human-level performance. In order to narrow the gap between video-text models and human performance on RCAD, we identify a key limitation of current contrastive approaches on video-text data and introduce LLM-teacher, a more effective approach to learn action semantics by leveraging knowledge obtained from a pretrained large language model. Experiments and analyses show that our approach successfully learn more discriminative action embeddings and improves results on Feint6K when applied to multiple video-text models. Our Feint6K dataset and project page is available at https://feint6k.github.io.
This paper rethinks Sharpness-Aware Minimization (SAM), which is originally formulated as a zero-sum game where the weights of a network and a bounded perturbation try to minimize/maximize, respectively, the same differentiable loss. To fundamentally improve this design, we argue that SAM should instead be reformulated using the 0-1 loss. As a continuous relaxation, we follow the simple conventional approach where the minimizing (maximizing) player uses an upper bound (lower bound) surrogate to the 0-1 loss. This leads to a novel formulation of SAM as a bilevel optimization problem, dubbed as BiSAM. BiSAM with newly designed lower-bound surrogate loss indeed constructs stronger perturbation. Through numerical evidence, we show that BiSAM consistently results in improved performance when compared to the original SAM and variants, while enjoying similar computational complexity. Our code is available at https://github.com/LIONS-EPFL/BiSAM.
Rethinking the Architecture Design for Efficient Generic Event Boundary Detection
2024-07-17
Ziwei Zheng, Zechuan Zhang, Yulin Wang, Shiji Song, Gao Huang, Le Yang
Generic event boundary detection (GEBD), inspired by human visual cognitive behaviors of consistently segmenting videos into meaningful temporal chunks, finds utility in various applications such as video editing and. In this paper, we demonstrate that SOTA GEBD models often prioritize final performance over model complexity, resulting in low inference speed and hindering efficient deployment in real-world scenarios. We contribute to addressing this challenge by experimentally reexamining the architecture of GEBD models and uncovering several surprising findings. Firstly, we reveal that a concise GEBD baseline model already achieves promising performance without any sophisticated design. Secondly, we find that the widely applied image-domain backbones in GEBD models can contain plenty of architecture redundancy, motivating us to gradually ``modernize'' each component to enhance efficiency. Thirdly, we show that the GEBD models using image-domain backbones conducting the spatiotemporal learning in a spatial-then-temporal greedy manner can suffer from a distraction issue, which might be the inefficient villain for GEBD. Using a video-domain backbone to jointly conduct spatiotemporal modeling is an effective solution for this issue. The outcome of our exploration is a family of GEBD models, named EfficientGEBD, significantly outperforms the previous SOTA methods by up to 1.7\% performance gain and 280\% speedup under the same backbone. Our research prompts the community to design modern GEBD methods with the consideration of model complexity, particularly in resource-aware applications. The code is available at \url{https://github.com/Ziwei-Zheng/EfficientGEBD}.
What's Distributive Justice Got to Do with It? Rethinking Algorithmic Fairness from the Perspective of Approximate Justice
2024-07-17
Corinna Hertweck, Christoph Heitz, Michele Loi
In the field of algorithmic fairness, many fairness criteria have been proposed. Oftentimes, their proposal is only accompanied by a loose link to ideas from moral philosophy -- which makes it difficult to understand when the proposed criteria should be used to evaluate the fairness of a decision-making system. More recently, researchers have thus retroactively tried to tie existing fairness criteria to philosophical concepts. Group fairness criteria have typically been linked to egalitarianism, a theory of distributive justice. This makes it tempting to believe that fairness criteria mathematically represent ideals of distributive justice and this is indeed how they are typically portrayed. In this paper, we will discuss why the current approach of linking algorithmic fairness and distributive justice is too simplistic and, hence, insufficient. We argue that in the context of imperfect decision-making systems -- which is what we deal with in algorithmic fairness -- we should not only care about what the ideal distribution of benefits/harms among individuals would look like but also about how deviations from said ideal are distributed. Our claim is that algorithmic fairness is concerned with unfairness in these deviations. This requires us to rethink the way in which we, as algorithmic fairness researchers, view distributive justice and use fairness criteria.
Recovering the camera motion and scene geometry from visual data is a fundamental problem in the field of computer vision. Its success in standard vision is attributed to the maturity of feature extraction, data association and multi-view geometry. The recent emergence of neuromorphic event-based cameras places great demands on approaches that use raw event data as input to solve this fundamental problem. Existing state-of-the-art solutions typically infer implicitly data association by iteratively reversing the event data generation process. However, the nonlinear nature of these methods limits their applicability in real-time tasks, and the constant-motion assumption leads to unstable results under agile motion. To this end, we rethink the problem formulation in a way that aligns better with the differential working principle of event cameras. We show that the event-based normal flow can be used, via the proposed geometric error term, as an alternative to the full flow in solving a family of geometric problems that involve instantaneous first-order kinematics and scene geometry. Furthermore, we develop a fast linear solver and a continuous-time nonlinear solver on top of the proposed geometric error term. Experiments on both synthetic and real data show the superiority of our linear solver in terms of accuracy and efficiency, and indicate its complementary feature as an initialization method for existing nonlinear solvers. Besides, our continuous-time non-linear solver exhibits exceptional capability in accommodating sudden variations in motion since it does not rely on the constant-motion assumption.
Rethinking Transformer-based Multi-document Summarization: An Empirical Investigation
2024-07-16
Congbo Ma, Wei Emma Zhang, Dileepa Pitawela, Haojie Zhuang, Yanfeng Shu
The utilization of Transformer-based models prospers the growth of multi-document summarization (MDS). Given the huge impact and widespread adoption of Transformer-based models in various natural language processing tasks, investigating their performance and behaviors in the context of MDS becomes crucial for advancing the field and enhancing the quality of summary. To thoroughly examine the behaviours of Transformer-based MDS models, this paper presents five empirical studies on (1) measuring the impact of document boundary separators quantitatively; (2) exploring the effectiveness of different mainstream Transformer structures; (3) examining the sensitivity of the encoder and decoder; (4) discussing different training strategies; and (5) discovering the repetition in a summary generation. The experimental results on prevalent MDS datasets and eleven evaluation metrics show the influence of document boundary separators, the granularity of different level features and different model training strategies. The results also reveal that the decoder exhibits greater sensitivity to noises compared to the encoder. This underscores the important role played by the decoder, suggesting a potential direction for future research in MDS. Furthermore, the experimental results indicate that the repetition problem in the generated summaries has correlations with the high uncertainty scores.
Driven by the powerful representation ability of Graph Neural Networks (GNNs), plentiful GNN models have been widely deployed in many real-world applications. Nevertheless, due to distribution disparities between different demographic groups, fairness in high-stake decision-making systems is receiving increasing attention. Although lots of recent works devoted to improving the fairness of GNNs and achieved considerable success, they all require significant architectural changes or additional loss functions requiring more hyper-parameter tuning. Surprisingly, we find that simple re-balancing methods can easily match or surpass existing fair GNN methods. We claim that the imbalance across different demographic groups is a significant source of unfairness, resulting in imbalanced contributions from each group to the parameters updating. However, these simple re-balancing methods have their own shortcomings during training. In this paper, we propose FairGB, Fair Graph Neural Network via re-Balancing, which mitigates the unfairness of GNNs by group balancing. Technically, FairGB consists of two modules: counterfactual node mixup and contribution alignment loss. Firstly, we select counterfactual pairs across inter-domain and inter-class, and interpolate the ego-networks to generate new samples. Guided by analysis, we can reveal the debiasing mechanism of our model by the causal view and prove that our strategy can make sensitive attributes statistically independent from target labels. Secondly, we reweigh the contribution of each group according to gradients. By combining these two modules, they can mutually promote each other. Experimental results on benchmark datasets show that our method can achieve state-of-the-art results concerning both utility and fairness metrics. Code is available at https://github.com/ZhixunLEE/FairGB.
Rethinking Learned Image Compression: Context is All You Need
2024-07-16
Jixiang Luo
Since LIC has made rapid progress recently compared to traditional methods, this paper attempts to discuss the question about 'Where is the boundary of Learned Image Compression(LIC)?'. Thus this paper splits the above problem into two sub-problems:1)Where is the boundary of rate-distortion performance of PSNR? 2)How to further improve the compression gain and achieve the boundary? Therefore this paper analyzes the effectiveness of scaling parameters for encoder, decoder and context model, which are the three components of LIC. Then we conclude that scaling for LIC is to scale for context model and decoder within LIC. Extensive experiments demonstrate that overfitting can actually serve as an effective context. By optimizing the context, this paper further improves PSNR and achieves state-of-the-art performance, showing a performance gain of 14.39% with BD-RATE over VVC.
Existing few-shot segmentation (FSS) methods mainly focus on prototype feature generation and the query-support matching mechanism. As a crucial prompt for generating prototype features, the pair of image-mask types in the support set has become the default setting. However, various types such as image, text, box, and mask all can provide valuable information regarding the objects in context, class, localization, and shape appearance. Existing work focuses on specific combinations of guidance, leading FSS into different research branches. Rethinking guidance types in FSS is expected to explore the efficient joint representation of the coupling between the support set and query set, giving rise to research trends in the weakly or strongly annotated guidance to meet the customized requirements of practical users. In this work, we provide the generalized FSS with seven guidance paradigms and develop a universal vision-language framework (UniFSS) to integrate prompts from text, mask, box, and image. Leveraging the advantages of large-scale pre-training vision-language models in textual and visual embeddings, UniFSS proposes high-level spatial correction and embedding interactive units to overcome the semantic ambiguity drawbacks typically encountered by pure visual matching methods when facing intra-class appearance diversities. Extensive experiments show that UniFSS significantly outperforms the state-of-the-art methods. Notably, the weakly annotated class-aware box paradigm even surpasses the finely annotated mask paradigm.
As a fundamental and extensively studied task in computer vision, image segmentation aims to locate and identify different semantic concepts at the pixel level. Recently, inspired by In-Context Learning (ICL), several generalist segmentation frameworks have been proposed, providing a promising paradigm for segmenting specific objects. However, existing works mostly ignore the value of visual prompts or simply apply similarity sorting to select contextual examples. In this paper, we focus on rethinking and improving the example selection strategy. By comprehensive comparisons, we first demonstrate that ICL-based segmentation models are sensitive to different contexts. Furthermore, empirical evidence indicates that the diversity of contextual prompts plays a crucial role in guiding segmentation. Based on the above insights, we propose a new stepwise context search method. Different from previous works, we construct a small yet rich candidate pool and adaptively search the well-matched contexts. More importantly, this method effectively reduces the annotation cost by compacting the search space. Extensive experiments show that our method is an effective strategy for selecting examples and enhancing segmentation performance.
Rethinking temporal self-similarity for repetitive action counting
2024-07-12
Yanan Luo, Jinhui Yi, Yazan Abu Farha, Moritz Wolter, Juergen Gall
Counting repetitive actions in long untrimmed videos is a challenging task that has many applications such as rehabilitation. State-of-the-art methods predict action counts by first generating a temporal self-similarity matrix (TSM) from the sampled frames and then feeding the matrix to a predictor network. The self-similarity matrix, however, is not an optimal input to a network since it discards too much information from the frame-wise embeddings. We thus rethink how a TSM can be utilized for counting repetitive actions and propose a framework that learns embeddings and predicts action start probabilities at full temporal resolution. The number of repeated actions is then inferred from the action start probabilities. In contrast to current approaches that have the TSM as an intermediate representation, we propose a novel loss based on a generated reference TSM, which enforces that the self-similarity of the learned frame-wise embeddings is consistent with the self-similarity of repeated actions. The proposed framework achieves state-of-the-art results on three datasets, i.e., RepCount, UCFRep, and Countix.
Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition Systems
2024-07-11
Yuxin Cao, Yumeng Zhu, Derui Wang, Sheng Wen, Minhui Xue, Jin Lu, Hao Ge
Face recognition pipelines have been widely deployed in various mission-critical systems in trust, equitable and responsible AI applications. However, the emergence of adversarial attacks has threatened the security of the entire recognition pipeline. Despite the sheer number of attack methods proposed for crafting adversarial examples in both digital and physical forms, it is never an easy task to assess the real threat level of different attacks and obtain useful insight into the key risks confronted by face recognition systems. Traditional attacks view imperceptibility as the most important measurement to keep perturbations stealthy, while we suspect that industry professionals may possess a different opinion. In this paper, we delve into measuring the threat brought about by adversarial attacks from the perspectives of the industry and the applications of face recognition. In contrast to widely studied sophisticated attacks in the field, we propose an effective yet easy-to-launch physical adversarial attack, named AdvColor, against black-box face recognition pipelines in the physical world. AdvColor fools models in the recognition pipeline via directly supplying printed photos of human faces to the system under adversarial illuminations. Experimental results show that physical AdvColor examples can achieve a fooling rate of more than 96% against the anti-spoofing model and an overall attack success rate of 88% against the face recognition pipeline. We also conduct a survey on the threats of prevailing adversarial attacks, including AdvColor, to understand the gap between the machine-measured and human-assessed threat levels of different forms of adversarial attacks. The survey results surprisingly indicate that, compared to deliberately launched imperceptible attacks, perceptible but accessible attacks pose more lethal threats to real-world commercial systems of face recognition.
Few-shot class-incremental learning (FSCIL) aims to learn sequential classes with limited samples in a few-shot fashion. Inherited from the classical class-incremental learning setting, the popular benchmark of FSCIL uses averaged accuracy (aAcc) and last-task averaged accuracy (lAcc) as the evaluation metrics. However, we reveal that such evaluation metrics may not provide adequate emphasis on the novel class performance, and the continual learning ability of FSCIL methods could be ignored under this benchmark. In this work, as a complement to existing metrics, we offer a new metric called generalized average accuracy (gAcc) which is designed to provide an extra equitable evaluation by incorporating different perspectives of the performance under the guidance of a parameter $\alpha$. We also present an overall metric in the form of the area under the curve (AUC) along the $\alpha$. Under the guidance of gAcc, we release the potential of intermediate features of the vision transformers to boost the novel-class performance. Taking information from intermediate layers which are less class-specific and more generalizable, we manage to rectify the final features, leading to a more generalizable transformer-based FSCIL framework. Without complex network designs or cumbersome training procedures, our method outperforms existing FSCIL methods at aAcc and gAcc on three datasets. See codes at https://github.com/iSEE-Laboratory/Revisting_FSCIL
Rethinking Image-to-Video Adaptation: An Object-centric Perspective
2024-07-09
Rui Qian, Shuangrui Ding, Dahua Lin
Image-to-video adaptation seeks to efficiently adapt image models for use in the video domain. Instead of finetuning the entire image backbone, many image-to-video adaptation paradigms use lightweight adapters for temporal modeling on top of the spatial module. However, these attempts are subject to limitations in efficiency and interpretability. In this paper, we propose a novel and efficient image-to-video adaptation strategy from the object-centric perspective. Inspired by human perception, which identifies objects as key components for video understanding, we integrate a proxy task of object discovery into image-to-video transfer learning. Specifically, we adopt slot attention with learnable queries to distill each frame into a compact set of object tokens. These object-centric tokens are then processed through object-time interaction layers to model object state changes across time. Integrated with two novel object-level losses, we demonstrate the feasibility of performing efficient temporal reasoning solely on the compressed object-centric representations for video downstream tasks. Our method achieves state-of-the-art performance with fewer tunable parameters, only 5\% of fully finetuned models and 50\% of efficient tuning methods, on action recognition benchmarks. In addition, our model performs favorably in zero-shot video object segmentation without further retraining or object annotations, proving the effectiveness of object-centric video understanding.
Various models based on StyleGAN have gained significant traction in the field of image synthesis, attributed to their robust training stability and superior performances. Within the StyleGAN framework, the adoption of image skip connection is favored over the traditional residual connection. However, this preference is just based on empirical observations; there has not been any in-depth mathematical analysis on it yet. To rectify this situation, this brief aims to elucidate the mathematical meaning of the image skip connection and introduce a groundbreaking methodology, termed the image squeeze connection, which significantly improves the quality of image synthesis. Specifically, we analyze the image skip connection technique to reveal its problem and introduce the proposed method which not only effectively boosts the GAN performance but also reduces the required number of network parameters. Extensive experiments on various datasets demonstrate that the proposed method consistently enhances the performance of state-of-the-art models based on StyleGAN. We believe that our findings represent a vital advancement in the field of image synthesis, suggesting a novel direction for future research and applications.
Language Models Encode Collaborative Signals in Recommendation
2024-07-07
Leheng Sheng, An Zhang, Yi Zhang, Yuxin Chen, Xiang Wang, Tat-Seng Chua
Recent studies empirically indicate that language models (LMs) encode rich world knowledge beyond mere semantics, attracting significant attention across various fields. However, in the recommendation domain, it remains uncertain whether LMs implicitly encode user preference information. Contrary to the prevailing understanding that LMs and traditional recommender models learn two distinct representation spaces due to a huge gap in language and behavior modeling objectives, this work rethinks such understanding and explores extracting a recommendation space directly from the language representation space. Surprisingly, our findings demonstrate that item representations, when linearly mapped from advanced LM representations, yield superior recommendation performance. This outcome suggests the homomorphism between the language representation space and an effective recommendation space, implying that collaborative signals may indeed be encoded within advanced LMs. Motivated by these findings, we propose a simple yet effective collaborative filtering (CF) model named AlphaRec, which utilizes language representations of item textual metadata (e.g., titles) instead of traditional ID-based embeddings. Specifically, AlphaRec is comprised of three main components: a multilayer perceptron (MLP), graph convolution, and contrastive learning (CL) loss function, making it extremely easy to implement and train. Our empirical results show that AlphaRec outperforms leading ID-based CF models on multiple datasets, marking the first instance of such a recommender with text embeddings achieving this level of performance. Moreover, AlphaRec introduces a new language-representation-based CF paradigm with several desirable advantages: being easy to implement, lightweight, rapid convergence, superior zero-shot recommendation abilities in new domains, and being aware of user intention.
In the realm of unsupervised image outlier detection, assigning outlier scores holds greater significance than its subsequent task: thresholding for predicting labels. This is because determining the optimal threshold on non-separable outlier score functions is an ill-posed problem. However, the lack of predicted labels not only hiders some real applications of current outlier detectors but also causes these methods not to be enhanced by leveraging the dataset's self-supervision. To advance existing scoring methods, we propose a multiple thresholding (Multi-T) module. It generates two thresholds that isolate inliers and outliers from the unlabelled target dataset, whereas outliers are employed to obtain better feature representation while inliers provide an uncontaminated manifold. Extensive experiments verify that Multi-T can significantly improve proposed outlier scoring methods. Moreover, Multi-T contributes to a naive distance-based method being state-of-the-art.
In recent years, the integration of prediction and planning through neural networks has received substantial attention. Despite extensive studies on it, there is a noticeable gap in understanding the operation of such models within a closed-loop planning setting. To bridge this gap, we propose a novel closed-loop planning framework compatible with neural networks engaged in joint prediction and planning. The framework contains two running modes, namely planning and safety monitoring, wherein the neural network performs Motion Prediction and Planning (MPP) and Conditional Motion Prediction (CMP) correspondingly without altering architecture. We evaluate the efficacy of our framework using the nuPlan dataset and its simulator, conducting closed-loop experiments across diverse scenarios. The results demonstrate that the proposed framework ensures the feasibility and local stability of the planning process while maintaining safety with CMP safety monitoring. Compared to other learning-based methods, our approach achieves substantial improvement.
Targeted adversarial attacks are widely used to evaluate the robustness of neural machine translation systems. Unfortunately, this paper first identifies a critical issue in the existing settings of NMT targeted adversarial attacks, where their attacking results are largely overestimated. To this end, this paper presents a new setting for NMT targeted adversarial attacks that could lead to reliable attacking results. Under the new setting, it then proposes a Targeted Word Gradient adversarial Attack (TWGA) method to craft adversarial examples. Experimental results demonstrate that our proposed setting could provide faithful attacking results for targeted adversarial attacks on NMT systems, and the proposed TWGA method can effectively attack such victim NMT systems. In-depth analyses on a large-scale dataset further illustrate some valuable findings. 1 Our code and data are available at https://github.com/wujunjie1998/TWGA.
Toward a Unified Metadata Schema for Ecological Momentary Assessment with Voice-First Virtual Assistants
2024-07-06
Chen Chen, Khalil Mrini, Kemeberly Charles, Ella T. Lifset, Michael Hogarth, Alison A. Moore, Nadir Weibel, Emilia Farcas
Ecological momentary assessment (EMA) is used to evaluate subjects' behaviors and moods in their natural environments, yet collecting real-time and self-report data with EMA is challenging due to user burden. Integrating voice into EMA data collection platforms through today's intelligent virtual assistants (IVAs) is promising due to hands-free and eye-free nature. However, efficiently managing conversations and EMAs is non-trivial and time consuming due to the ambiguity of the voice input. We approach this problem by rethinking the data modeling of EMA questions and what is needed to deploy them on voice-first user interfaces. We propose a unified metadata schema that models EMA questions and the necessary attributes to effectively and efficiently integrate voice as a new EMA modality. Our schema allows user experience researchers to write simple rules that can be rendered at run-time, instead of having to edit the source code. We showcase an example EMA survey implemented with our schema, which can run on multiple voice-only and voice-first devices. We believe that our work will accelerate the iterative prototyping and design process of real-world voice-based EMA data collection platforms.
Graph classification benchmarks, vital for assessing and developing graph neural networks (GNNs), have recently been scrutinized, as simple methods like MLPs have demonstrated comparable performance. This leads to an important question: Do these benchmarks effectively distinguish the advancements of GNNs over other methodologies? If so, how do we quantitatively measure this effectiveness? In response, we first propose an empirical protocol based on a fair benchmarking framework to investigate the performance discrepancy between simple methods and GNNs. We further propose a novel metric to quantify the dataset effectiveness by considering both dataset complexity and model performance. To the best of our knowledge, our work is the first to thoroughly study and provide an explicit definition for dataset effectiveness in the graph learning area. Through testing across 16 real-world datasets, we found our metric to align with existing studies and intuitive assumptions. Finally, we explore the causes behind the low effectiveness of certain datasets by investigating the correlation between intrinsic graph properties and class labels, and we developed a novel technique supporting the correlation-controllable synthetic dataset generation. Our findings shed light on the current understanding of benchmark datasets, and our new platform could fuel the future evolution of graph classification benchmarks.
Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge
2024-07-05
Yuanze Lin, Yunsheng Li, Dongdong Chen, Weijian Xu, Ronald Clark, Philip Torr, Lu Yuan
In recent years, multimodal large language models (MLLMs) have made significant strides by training on vast high-quality image-text datasets, enabling them to generally understand images well. However, the inherent difficulty in explicitly conveying fine-grained or spatially dense information in text, such as masks, poses a challenge for MLLMs, limiting their ability to answer questions requiring an understanding of detailed or localized visual elements. Drawing inspiration from the Retrieval-Augmented Generation (RAG) concept, this paper proposes a new visual prompt approach to integrate fine-grained external knowledge, gleaned from specialized vision models (e.g., instance segmentation/OCR models), into MLLMs. This is a promising yet underexplored direction for enhancing MLLMs' performance. Our approach diverges from concurrent works, which transform external knowledge into additional text prompts, necessitating the model to indirectly learn the correspondence between visual content and text coordinates. Instead, we propose embedding fine-grained knowledge information directly into a spatial embedding map as a visual prompt. This design can be effortlessly incorporated into various MLLMs, such as LLaVA and Mipha, considerably improving their visual understanding performance. Through rigorous experiments, we demonstrate that our method can enhance MLLM performance across nine benchmarks, amplifying their fine-grained context-aware capabilities.
Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection
2024-07-05
YeongHyeon Park, Sungho Kang, Myung Jin Kim, Hyeong Seok Kim, Juneho Yi
In unsupervised anomaly detection (UAD) research, while state-of-the-art models have reached a saturation point with extensive studies on public benchmark datasets, they adopt large-scale tailor-made neural networks (NN) for detection performance or pursued unified models for various tasks. Towards edge computing, it is necessary to develop a computationally efficient and scalable solution that avoids large-scale complex NNs. Motivated by this, we aim to optimize the UAD performance with minimal changes to NN settings. Thus, we revisit the reconstruction-by-inpainting approach and rethink to improve it by analyzing strengths and weaknesses. The strength of the SOTA methods is a single deterministic masking approach that addresses the challenges of random multiple masking that is inference latency and output inconsistency. Nevertheless, the issue of failure to provide a mask to completely cover anomalous regions is a remaining weakness. To mitigate this issue, we propose Feature Attenuation of Defective Representation (FADeR) that only employs two MLP layers which attenuates feature information of anomaly reconstruction during decoding. By leveraging FADeR, features of unseen anomaly patterns are reconstructed into seen normal patterns, reducing false alarms. Experimental results demonstrate that FADeR achieves enhanced performance compared to similar-scale NNs. Furthermore, our approach exhibits scalability in performance enhancement when integrated with other single deterministic masking methods in a plug-and-play manner.
VRSD: Rethinking Similarity and Diversity for Retrieval in Large Language Models
2024-07-05
Hang Gao, Yongfeng Zhang
Vector retrieval algorithms are essential for semantic queries within the rapidly evolving landscape of Large Language Models (LLMs). The ability to retrieve vectors that satisfy both similarity and diversity criteria substantially enhances the performance of LLMs. Although Maximal Marginal Relevance (MMR) is widely employed in retrieval scenarios requiring relevance and diversity, variations in the parameter $\lambda$ lead to fluctuations that complicate the optimization trajectory in vector spaces. This obscures the direction of improvement and highlights the lack of a robust theoretical analysis regarding similarity and diversity constraints in retrieval processes. To address these challenges, this paper introduces a novel approach that characterizes both constraints through the relationship between the sum vector and the query vector. The proximity of these vectors ensures the similarity constraint, while requiring individual vectors within the sum vector to diverge in their alignment with the query vector satisfies the diversity constraint. We first formulate a new combinatorial optimization problem, selecting k vectors from a candidate set such that their sum vector maximally aligns with the query vector, and demonstrate that this problem is NP-complete. This result underscores the inherent difficulty of simultaneously achieving similarity and diversity in vector retrieval, thereby providing a theoretical foundation for future research. Subsequently, we present the heuristic algorithm Vectors Retrieval with Similarity and Diversity, VRSD, which features a clear optimization objective and eliminates the need for preset parameters. VRSD also achieves a modest reduction in time complexity compared to MMR. Empirical validation confirms that VRSD significantly outperforms MMR across various datasets.
The rapid growth of the Internet, driven by social media, web browsing, and video streaming, has made images central to the Web experience, resulting in significant data transfer and increased webpage sizes. Traditional image compression methods, while reducing bandwidth, often degrade image quality. This paper explores a novel approach using generative AI to reconstruct images at the edge or client-side. We develop a framework that leverages text prompts and provides additional conditioning inputs like Canny edges and color palettes to a text-to-image model, achieving up to 99.8% bandwidth savings in the best cases and 92.6% on average, while maintaining high perceptual similarity. Empirical analysis and a user study show that our method preserves image meaning and structure more effectively than traditional compression methods, offering a promising solution for reducing bandwidth usage and improving Internet affordability with minimal degradation in image quality.
PDiscoFormer: Relaxing Part Discovery Constraints with Vision Transformers
2024-07-05
Ananthu Aniraj, Cassio F. Dantas, Dino Ienco, Diego Marcos
Computer vision methods that explicitly detect object parts and reason on them are a step towards inherently interpretable models. Existing approaches that perform part discovery driven by a fine-grained classification task make very restrictive assumptions on the geometric properties of the discovered parts; they should be small and compact. Although this prior is useful in some cases, in this paper we show that pre-trained transformer-based vision models, such as self-supervised DINOv2 ViT, enable the relaxation of these constraints. In particular, we find that a total variation (TV) prior, which allows for multiple connected components of any size, substantially outperforms previous work. We test our approach on three fine-grained classification benchmarks: CUB, PartImageNet and Oxford Flowers, and compare our results to previously published methods as well as a re-implementation of the state-of-the-art method PDiscoNet with a transformer-based backbone. We consistently obtain substantial improvements across the board, both on part discovery metrics and the downstream classification task, showing that the strong inductive biases in self-supervised ViT models require to rethink the geometric priors that can be used for unsupervised part discovery.
In recent years, point cloud upsampling has been widely applied in fields such as 3D reconstruction and surface generation. However, existing point cloud upsampling inputs are all patch based, and there is no research discussing the differences and principles between point cloud model full input and patch based input. In order to compare with patch based point cloud input, this article proposes a new data input method, which divides the full point cloud model to ensure shape integrity while training PU-GCN. This article was validated on the PU1K and ABC datasets, but the results showed that Patch based performance is better than model based full input i.e. Average Segment input. Therefore, this article explores the data input factors and model modules that affect the upsampling results of point clouds.
Drop it All or Pick it Up? How Developers Responded to the Log4JShell Vulnerability
2024-07-05
Vittunyuta Maeprasart, Ali Ouni, Raula Gaikovina Kula
Although using third-party libraries has become prevalent in contemporary software development, developers often struggle to update their dependencies. Prior works acknowledge that due to the migration effort, priority and other issues cause lags in the migration process. The common assumption is that developers should drop all other activities and prioritize fixing the vulnerability. Our objective is to understand developer behavior when facing high-risk vulnerabilities in their code. We explore the prolific, and possibly one of the cases of the Log4JShell, a vulnerability that has the highest severity rating ever, which received widespread media attention. Using a mixed-method approach, we analyze 219 GitHub Pull Requests (PR) and 354 issues belonging to 53 Maven projects affected by the Log4JShell vulnerability. Our study confirms that developers show a quick response taking from 5 to 6 days. However, instead of dropping everything, surprisingly developer activities tend to increase for all pending issues and PRs. Developer discussions involved either giving information (29.3\%) and seeking information (20.6\%), which is missing in existing support tools. Leveraging this possibly-one of a kind event, insights opens up a new line of research, causing us to rethink best practices and what developers need in order to efficiently fix vulnerabilities.
Integrated sensing and communication (ISAC) has been recognized as a key enabler and feature of future wireless networks. In the existing works analyzing the performances of ISAC, discrete-time systems were commonly assumed, which, however, overlooked the impacts of temporal, spectral, and spatial properties. To address this issue, we establish a unified information model for the band-limited continuous-time ISAC systems. In the established information model, we employ a novel sensing performance metric, called the sensing mutual information (SMI). Through analysis, we show how the SMI can be utilized as a bridge between the mutual information domain and the mean squared error (MSE) domain. In addition, we illustrate the communication mutual information (CMI)-SMI and CMI-MSE regions to identify the performance bounds of ISAC systems in practical settings and reveal the trade-off between communication and sensing performances. Moreover, via analysis and numerical results, we provide two valuable insights into the design of novel ISAC-enabled systems: i) communication prefers the waveforms of random amplitude, sensing prefers the waveforms of constant amplitude, both communication and sensing favor the waveforms of low correlations with random phases; ii) There exists a linear positive proportional relationship between the allocated time-frequency resource and the achieved communication rate/sensing MSE.
No Training, No Problem: Rethinking Classifier-Free Guidance for Diffusion Models
2024-07-02
Seyedmorteza Sadat, Manuel Kansy, Otmar Hilliges, Romann M. Weber
Classifier-free guidance (CFG) has become the standard method for enhancing the quality of conditional diffusion models. However, employing CFG requires either training an unconditional model alongside the main diffusion model or modifying the training procedure by periodically inserting a null condition. There is also no clear extension of CFG to unconditional models. In this paper, we revisit the core principles of CFG and introduce a new method, independent condition guidance (ICG), which provides the benefits of CFG without the need for any special training procedures. Our approach streamlines the training process of conditional diffusion models and can also be applied during inference on any pre-trained conditional model. Additionally, by leveraging the time-step information encoded in all diffusion networks, we propose an extension of CFG, called time-step guidance (TSG), which can be applied to any diffusion model, including unconditional ones. Our guidance techniques are easy to implement and have the same sampling cost as CFG. Through extensive experiments, we demonstrate that ICG matches the performance of standard CFG across various conditional diffusion models. Moreover, we show that TSG improves generation quality in a manner similar to CFG, without relying on any conditional information.
Rethinking Data Augmentation for Robust LiDAR Semantic Segmentation in Adverse Weather
2024-07-02
Junsung Park, Kyungmin Kim, Hyunjung Shim
Existing LiDAR semantic segmentation methods often struggle with performance declines in adverse weather conditions. Previous work has addressed this issue by simulating adverse weather or employing universal data augmentation during training. However, these methods lack a detailed analysis and understanding of how adverse weather negatively affects LiDAR semantic segmentation performance. Motivated by this issue, we identified key factors of adverse weather and conducted a toy experiment to pinpoint the main causes of performance degradation: (1) Geometric perturbation due to refraction caused by fog or droplets in the air and (2) Point drop due to energy absorption and occlusions. Based on these findings, we propose new strategic data augmentation techniques. First, we introduced a Selective Jittering (SJ) that jitters points in the random range of depth (or angle) to mimic geometric perturbation. Additionally, we developed a Learnable Point Drop (LPD) to learn vulnerable erase patterns with a Deep Q-Learning Network to approximate the point drop phenomenon from adverse weather conditions. Without precise weather simulation, these techniques strengthen the LiDAR semantic segmentation model by exposing it to vulnerable conditions identified by our data-centric analysis. Experimental results confirmed the suitability of the proposed data augmentation methods for enhancing robustness against adverse weather conditions. Our method achieves a notable 39.5 mIoU on the SemanticKITTI-to-SemanticSTF benchmark, improving the baseline by 8.1\%p and establishing a new state-of-the-art. Our code will be released at \url{https://github.com/engineerJPark/LiDARWeather}.
2024-07-02
Joshua Feis, Sebastian Weidemann, Tom Sheppard, Hannah M. Price, Alexander Szameit
Time is, figuratively and literally, becoming the new dimension for crystalline matter. As such, rapid recent progress on time-varying media gave rise to the notion of temporal and spatiotemporal crystals. Fundamentally rethinking the role of time, which, in contrast to space exhibits a unique unidirectionality often referred to as the arrow of time, promises a new dimension also for topological physics. Here, we enter the new realm of time and spacetime topology: Firstly, we implement a time-topological time interface state. Secondly, we propose and observe a spacetime-topological event and demonstrate unique features like its limited collapse under disorder and causality-suppressed coupling. The new paradigms of time and spacetime topology unveil a distinctive role of causality and non-Hermiticity in topology and pave the way towards topological spatiotemporal wave control with unique robustness.
Various methods try to enhance adversarial transferability by improving the generalization from different perspectives. In this paper, we rethink the optimization process and propose a novel sequence optimization concept, which is named Looking From the Future (LFF). LFF makes use of the original optimization process to refine the very first local optimization choice. Adapting the LFF concept to the adversarial attack task, we further propose an LFF attack as well as an MLFF attack with better generalization ability. Furthermore, guiding with the LFF concept, we propose an $LLF^{\mathcal{N}}$ attack which entends the LFF attack to a multi-order attack, further enhancing the transfer attack ability. All our proposed methods can be directly applied to the iteration-based attack methods. We evaluate our proposed method on the ImageNet1k dataset by applying several SOTA adversarial attack methods under four kinds of tasks. Experimental results show that our proposed method can greatly enhance the attack transferability. Ablation experiments are also applied to verify the effectiveness of each component. The source code will be released after this paper is accepted.
A Method to Facilitate Membership Inference Attacks in Deep Learning Models
2024-07-02
Zitao Chen, Karthik Pattabiraman
Modern machine learning (ML) ecosystems offer a surging number of ML frameworks and code repositories that can greatly facilitate the development of ML models. Today, even ordinary data holders who are not ML experts can apply off-the-shelf codebase to build high-performance ML models on their data, many of which are sensitive in nature (e.g., clinical records). In this work, we consider a malicious ML provider who supplies model-training code to the data holders, does not have access to the training process, and has only black-box query access to the resulting model. In this setting, we demonstrate a new form of membership inference attack that is strictly more powerful than prior art. Our attack empowers the adversary to reliably de-identify all the training samples (average >99% attack TPR@0.1% FPR), and the compromised models still maintain competitive performance as their uncorrupted counterparts (average <1% accuracy drop). Moreover, we show that the poisoned models can effectively disguise the amplified membership leakage under common membership privacy auditing, which can only be revealed by a set of secret samples known by the adversary. Overall, our study not only points to the worst-case membership privacy leakage, but also unveils a common pitfall underlying existing privacy auditing methods, which calls for future efforts to rethink the current practice of auditing membership privacy in machine learning models.
The use of large language model (LLM)-based preference evaluations has become widespread for comparing model responses, but it has revealed a notable bias towards longer responses, questioning the reliability of such evaluations. This paper explores the length bias in LLM evaluations from a data-centric perspective, analyzing 14 commonly used preference datasets and 10 reward models. Our findings indicate that human preference labeling favors longer responses and this spurious correlation is learned by the reward model and subsequently propagated to the aligned model during training. We decompose the preference evaluation metric, i.e., win rate, from the perspective of human to identify the deeper factors and conclude that the win rate is affected by two axes of model response: desirability and information mass, where the former is length-independent and related to trustworthiness, and the latter is length-dependent and can be represented by conditional entropy. Controlled experiments demonstrate that response length impacts evaluations by influencing information mass. To ensure reliable evaluation metrics that assess content quality without being confounded by response length, we propose AdapAlpaca, a simple yet effective adjustment to win rate measurement. Specifically, by adjusting the lengths of reference answers to match the test model's answers within the same interval, we debias information mass relative to length, ensuring a fair model evaluation. Furthermore, we investigate length bias in DPO using AlpacaEval and AdapAlpaca. By testing Tulu2 and Tulu2-dpo at 7B, 13B, and 70B scales, we found that DPO leads to higher human preference, but this gain is amplified by response length, with AlpacaEval showing higher win rates gain than AdapAlpaca.
Rethinking harmless refusals when fine-tuning foundation models
2024-06-27
Florin Pop, Judd Rosenblatt, Diogo Schwerz de Lucena, Michael Vaiana
In this paper, we investigate the degree to which fine-tuning in Large Language Models (LLMs) effectively mitigates versus merely conceals undesirable behavior. Through the lens of semi-realistic role-playing exercises designed to elicit such behaviors, we explore the response dynamics of LLMs post fine-tuning interventions. Our methodology involves prompting models for Chain-of-Thought (CoT) reasoning and analyzing the coherence between the reasoning traces and the resultant outputs. Notably, we identify a pervasive phenomenon we term \emph{reason-based deception}, where models either stop producing reasoning traces or produce seemingly ethical reasoning traces that belie the unethical nature of their final outputs. We further examine the efficacy of response strategies (polite refusal versus explicit rebuttal) in curbing the occurrence of undesired behavior in subsequent outputs of multi-turn interactions. Our findings reveal that explicit rebuttals significantly outperform polite refusals in preventing the continuation of undesired outputs and nearly eliminate reason-based deception, challenging current practices in model fine-tuning. Accordingly, the two key contributions of this paper are (1) defining and studying reason-based deception, a new type of hidden behavior, and (2) demonstrating that rebuttals provide a more robust response model to harmful requests than refusals, thereby highlighting the need to reconsider the response strategies in fine-tuning approaches.
Structural Attention: Rethinking Transformer for Unpaired Medical Image Synthesis
2024-06-27
Vu Minh Hieu Phan, Yutong Xie, Bowen Zhang, Yuankai Qi, Zhibin Liao, Antonios Perperidis, Son Lam Phung, Johan W. Verjans, Minh-Son To
Unpaired medical image synthesis aims to provide complementary information for an accurate clinical diagnostics, and address challenges in obtaining aligned multi-modal medical scans. Transformer-based models excel in imaging translation tasks thanks to their ability to capture long-range dependencies. Although effective in supervised training settings, their performance falters in unpaired image synthesis, particularly in synthesizing structural details. This paper empirically demonstrates that, lacking strong inductive biases, Transformer can converge to non-optimal solutions in the absence of paired data. To address this, we introduce UNet Structured Transformer (UNest), a novel architecture incorporating structural inductive biases for unpaired medical image synthesis. We leverage the foundational Segment-Anything Model to precisely extract the foreground structure and perform structural attention within the main anatomy. This guides the model to learn key anatomical regions, thus improving structural synthesis under the lack of supervision in unpaired training. Evaluated on two public datasets, spanning three modalities, i.e., MR, CT, and PET, UNest improves recent methods by up to 19.30% across six medical image synthesis tasks. Our code is released at https://github.com/HieuPhan33/MICCAI2024-UNest.
Rethinking and Defending Protective Perturbation in Personalized Diffusion Models
2024-06-27
Yixin Liu, Ruoxi Chen, Xun Chen, Lichao Sun
Personalized diffusion models (PDMs) have become prominent for adapting pretrained text-to-image models to generate images of specific subjects using minimal training data. However, PDMs are susceptible to minor adversarial perturbations, leading to significant degradation when fine-tuned on corrupted datasets. These vulnerabilities are exploited to create protective perturbations that prevent unauthorized image generation. Existing purification methods attempt to mitigate this issue but often over-purify images, resulting in information loss. In this work, we conduct an in-depth analysis of the fine-tuning process of PDMs through the lens of shortcut learning. We hypothesize and empirically demonstrate that adversarial perturbations induce a latent-space misalignment between images and their text prompts in the CLIP embedding space. This misalignment causes the model to erroneously associate noisy patterns with unique identifiers during fine-tuning, resulting in poor generalization. Based on these insights, we propose a systematic defense framework that includes data purification and contrastive decoupling learning. We first employ off-the-shelf image restoration techniques to realign images with their original semantic meanings in latent space. Then, we introduce contrastive decoupling learning with noise tokens to decouple the learning of personalized concepts from spurious noise patterns. Our study not only uncovers fundamental shortcut learning vulnerabilities in PDMs but also provides a comprehensive evaluation framework for developing stronger protection. Our extensive evaluation demonstrates its superiority over existing purification methods and stronger robustness against adaptive perturbation.
2024-06-25
Johnny Li, Saksham Consul, Eda Zhou, James Wong, Naila Farooqui, Yuxin Ye, Nithyashree Manohar, Zhuxiaona Wei, Tian Wu, Ben Echols, Sharon Zhou, Gregory Diamos
Despite their powerful chat, coding, and reasoning abilities, Large Language Models (LLMs) frequently hallucinate. Conventional wisdom suggests that hallucinations are a consequence of a balance between creativity and factuality, which can be mitigated, but not eliminated, by grounding the LLM in external knowledge sources. Through extensive systematic experiments, we show that these traditional approaches fail to explain why LLMs hallucinate in practice. Specifically, we show that LLMs augmented with a massive Mixture of Memory Experts (MoME) can easily memorize large datasets of random numbers. We corroborate these experimental findings with a theoretical construction showing that simple neural networks trained to predict the next token hallucinate when the training loss is above a threshold as it usually does in practice when training on internet scale data. We interpret our findings by comparing against traditional retrieval methods for mitigating hallucinations. We use our findings to design a first generation model for removing hallucinations -- Lamini-1 -- that stores facts in a massive mixture of millions of memory experts that are retrieved dynamically.
Rethinking Entity-level Unlearning for Large Language Models
2024-06-22
Weitao Ma, Xiaocheng Feng, Weihong Zhong, Lei Huang, Yangfan Ye, Bing Qin
Large language model unlearning has gained increasing attention due to its potential to mitigate security and privacy concerns. Current research predominantly focuses on Instance-level unlearning, specifically aiming at forgetting predefined instances of sensitive content. However, a notable gap still exists in exploring the deletion of complete entity-related information, which is crucial in many real-world scenarios, such as copyright protection. To this end, we propose a novel task of Entity-level unlearning, where the entity-related knowledge within the target model is supposed to be entirely erased. Given the challenge of practically accessing all entity-related knowledge within a model, we begin by simulating entity-level unlearning scenarios through fine-tuning models to introduce pseudo entities. Following this, we develop baseline methods inspired by trending unlearning techniques and conduct a detailed comparison of their effectiveness in this task. Extensive experiments reveal that current unlearning algorithms struggle to achieve effective entity-level unlearning. Additionally, our analyses further indicate that entity-related knowledge injected through fine-tuning is more susceptible than original entities from pre-training during unlearning, highlighting the necessity for more thorough pseudo-entity injection methods to make them closer to pre-trained knowledge.
Rethinking the Diffusion Models for Numerical Tabular Data Imputation from the Perspective of Wasserstein Gradient Flow
2024-06-22
Zhichao Chen, Haoxuan Li, Fangyikang Wang, Odin Zhang, Hu Xu, Xiaoyu Jiang, Zhihuan Song, Eric H. Wang
Diffusion models (DMs) have gained attention in Missing Data Imputation (MDI), but there remain two long-neglected issues to be addressed: (1). Inaccurate Imputation, which arises from inherently sample-diversification-pursuing generative process of DMs. (2). Difficult Training, which stems from intricate design required for the mask matrix in model training stage. To address these concerns within the realm of numerical tabular datasets, we introduce a novel principled approach termed Kernelized Negative Entropy-regularized Wasserstein gradient flow Imputation (KnewImp). Specifically, based on Wasserstein gradient flow (WGF) framework, we first prove that issue (1) stems from the cost functionals implicitly maximized in DM-based MDI are equivalent to the MDI's objective plus diversification-promoting non-negative terms. Based on this, we then design a novel cost functional with diversification-discouraging negative entropy and derive our KnewImp approach within WGF framework and reproducing kernel Hilbert space. After that, we prove that the imputation procedure of KnewImp can be derived from another cost functional related to the joint distribution, eliminating the need for the mask matrix and hence naturally addressing issue (2). Extensive experiments demonstrate that our proposed KnewImp approach significantly outperforms existing state-of-the-art methods.
Rethinking Remote Sensing Change Detection With A Mask View
2024-06-21
Xiaowen Ma, Zhenkai Wu, Rongrong Lian, Wei Zhang, Siyang Song
Remote sensing change detection aims to compare two or more images recorded for the same area but taken at different time stamps to quantitatively and qualitatively assess changes in geographical entities and environmental factors. Mainstream models usually built on pixel-by-pixel change detection paradigms, which cannot tolerate the diversity of changes due to complex scenes and variation in imaging conditions. To address this shortcoming, this paper rethinks the change detection with the mask view, and further proposes the corresponding: 1) meta-architecture CDMask and 2) instance network CDMaskFormer. Components of CDMask include Siamese backbone, change extractor, pixel decoder, transformer decoder and normalized detector, which ensures the proper functioning of the mask detection paradigm. Since the change query can be adaptively updated based on the bi-temporal feature content, the proposed CDMask can adapt to different latent data distributions, thus accurately identifying regions of interest changes in complex scenarios. Consequently, we further propose the instance network CDMaskFormer customized for the change detection task, which includes: (i) a Spatial-temporal convolutional attention-based instantiated change extractor to capture spatio-temporal context simultaneously with lightweight operations; and (ii) a scene-guided axial attention-instantiated transformer decoder to extract more spatial details. State-of-the-art performance of CDMaskFormer is achieved on five benchmark datasets with a satisfactory efficiency-accuracy trade-off. Code is available at https://github.com/xwmaxwma/rschange.
Rethinking Pruning Large Language Models: Benefits and Pitfalls of Reconstruction Error Minimization
2024-06-21
Sungbin Shin, Wonpyo Park, Jaeho Lee, Namhoon Lee
This work suggests fundamentally rethinking the current practice of pruning large language models (LLMs). The way it is done is by divide and conquer: split the model into submodels, sequentially prune them, and reconstruct predictions of the dense counterparts on small calibration data one at a time; the final model is obtained simply by putting the resulting sparse submodels together. While this approach enables pruning under memory constraints, it generates high reconstruction errors. In this work, we first present an array of reconstruction techniques that can significantly reduce this error by more than $90\%$. Unwittingly, however, we discover that minimizing reconstruction error is not always ideal and can overfit the given calibration data, resulting in rather increased language perplexity and poor performance at downstream tasks. We find out that a strategy of self-generating calibration data can mitigate this trade-off between reconstruction and generalization, suggesting new directions in the presence of both benefits and pitfalls of reconstruction for pruning LLMs.
Take the essence and discard the dross: A Rethinking on Data Selection for Fine-Tuning Large Language Models
2024-06-20
Ziche Liu, Rui Ke, Feng Jiang, Haizhou Li
Data selection for fine-tuning Large Language Models (LLMs) aims to select a high-quality subset from a given candidate dataset to train a Pending Fine-tune Model (PFM) into a Selective-Enhanced Model (SEM). It can improve the model performance and accelerate the training process. Although a few surveys have investigated related works of data selection, there is a lack of comprehensive comparison between existing methods due to their various experimental settings. To address this issue, we first propose a three-stage scheme for data selection and comprehensively review existing works according to this scheme. Then, we design a unified comparing method with ratio-based efficiency indicators and ranking-based feasibility indicators to overcome the difficulty of comparing various models with diverse experimental settings. After an in-depth comparative analysis, we find that the more targeted method with data-specific and model-specific quality labels has higher efficiency, but the introduction of additional noise information should be avoided when designing selection algorithms. Finally, we summarize the trends in data selection and highlight the short-term and long-term challenges to guide future research.
You can't handle the (dirty) truth: Data-centric insights improve pseudo-labeling
2024-06-19
Nabeel Seedat, Nicolas Huynh, Fergus Imrie, Mihaela van der Schaar
Pseudo-labeling is a popular semi-supervised learning technique to leverage unlabeled data when labeled samples are scarce. The generation and selection of pseudo-labels heavily rely on labeled data. Existing approaches implicitly assume that the labeled data is gold standard and 'perfect'. However, this can be violated in reality with issues such as mislabeling or ambiguity. We address this overlooked aspect and show the importance of investigating labeled data quality to improve any pseudo-labeling method. Specifically, we introduce a novel data characterization and selection framework called DIPS to extend pseudo-labeling. We select useful labeled and pseudo-labeled samples via analysis of learning dynamics. We demonstrate the applicability and impact of DIPS for various pseudo-labeling methods across an extensive range of real-world tabular and image datasets. Additionally, DIPS improves data efficiency and reduces the performance distinctions between different pseudo-labelers. Overall, we highlight the significant benefits of a data-centric rethinking of pseudo-labeling in real-world settings.
Deep learning has enabled great strides in abdominal multi-organ segmentation, even surpassing junior oncologists on common cases or organs. However, robustness on corner cases and complex organs remains a challenging open problem for clinical adoption. To investigate model robustness, we collected and annotated the RAOS dataset comprising 413 CT scans ($\sim$80k 2D images, $\sim$8k 3D organ annotations) from 413 patients each with 17 (female) or 19 (male) labelled organs, manually delineated by oncologists. We grouped scans based on clinical information into 1) diagnosis/radiotherapy (317 volumes), 2) partial excision without the whole organ missing (22 volumes), and 3) excision with the whole organ missing (74 volumes). RAOS provides a potential benchmark for evaluating model robustness including organ hallucination. It also includes some organs that can be very hard to access on public datasets like the rectum, colon, intestine, prostate and seminal vesicles. We benchmarked several state-of-the-art methods in these three clinical groups to evaluate performance and robustness. We also assessed cross-generalization between RAOS and three public datasets. This dataset and comprehensive analysis establish a potential baseline for future robustness research: \url{https://github.com/Luoxd1996/RAOS}.
Knee Osteoarthritis (OA) is a debilitating disease affecting over 250 million people worldwide. Currently, radiologists grade the severity of OA on an ordinal scale from zero to four using the Kellgren-Lawrence (KL) system. Recent studies have raised concern in relation to the subjectivity of the KL grading system, highlighting the requirement for an automated system, while also indicating that five ordinal classes may not be the most appropriate approach for assessing OA severity. This work presents preliminary results of an automated system with a continuous grading scale. This system, namely SS-FewSOME, uses self-supervised pre-training to learn robust representations of the features of healthy knee X-rays. It then assesses the OA severity by the X-rays' distance to the normal representation space. SS-FewSOME initially trains on only 'few' examples of healthy knee X-rays, thus reducing the barriers to clinical implementation by eliminating the need for large training sets and costly expert annotations that existing automated systems require. The work reports promising initial results, obtaining a positive Spearman Rank Correlation Coefficient of 0.43, having had access to only 30 ground truth labels at training time.
Cultural Conditioning or Placebo? On the Effectiveness of Socio-Demographic Prompting
2024-06-17
Sagnik Mukherjee, Muhammad Farid Adilazuarda, Sunayana Sitaram, Kalika Bali, Alham Fikri Aji, Monojit Choudhury
Socio-demographic prompting is a commonly employed approach to study cultural biases in LLMs as well as for aligning models to certain cultures. In this paper, we systematically probe four LLMs (Llama 3, Mistral v0.2, GPT-3.5 Turbo and GPT-4) with prompts that are conditioned on culturally sensitive and non-sensitive cues, on datasets that are supposed to be culturally sensitive (EtiCor and CALI) or neutral (MMLU and ETHICS). We observe that all models except GPT-4 show significant variations in their responses on both kinds of datasets for both kinds of prompts, casting doubt on the robustness of the culturally-conditioned prompting as a method for eliciting cultural bias in models or as an alignment strategy. The work also calls rethinking the control experiment design to tease apart the cultural conditioning of responses from "placebo effect", i.e., random perturbations of model responses due to arbitrary tokens in the prompt.
Rethinking Spatio-Temporal Transformer for Traffic Prediction:Multi-level Multi-view Augmented Learning Framework
2024-06-17
Jiaqi Lin, Qianqian Ren
Traffic prediction is a challenging spatio-temporal forecasting problem that involves highly complex spatio-temporal correlations. This paper proposes a Multi-level Multi-view Augmented Spatio-temporal Transformer (LVSTformer) for traffic prediction. The model aims to capture spatial dependencies from three different levels: local geographic, global semantic, and pivotal nodes, along with long- and short-term temporal dependencies. Specifically, we design three spatial augmented views to delve into the spatial information from the perspectives of local, global, and pivotal nodes. By combining three spatial augmented views with three parallel spatial self-attention mechanisms, the model can comprehensively captures spatial dependencies at different levels. We design a gated temporal self-attention mechanism to effectively capture long- and short-term temporal dependencies. Furthermore, a spatio-temporal context broadcasting module is introduced between two spatio-temporal layers to ensure a well-distributed allocation of attention scores, alleviating overfitting and information loss, and enhancing the generalization ability and robustness of the model. A comprehensive set of experiments is conducted on six well-known traffic benchmarks, the experimental results demonstrate that LVSTformer achieves state-of-the-art performance compared to competing baselines, with the maximum improvement reaching up to 4.32%.
Large Language Model Tokenizer Bias: A Case Study and Solution on GPT-4o
2024-06-17
Jin Yang, Zhiqiang Wang, Yanbin Lin, Zunduo Zhao
Recent advancements in large language models (LLMs), such as GPT-4 and GPT-4o, have shown exceptional performance, especially in languages with abundant resources like English, thanks to extensive datasets that ensure robust training. Conversely, these models exhibit limitations when processing under-resourced languages such as Chinese and Korean, where issues including hallucinatory responses remain prevalent. This paper traces the roots of these disparities to the tokenization process inherent to these models. Specifically, it explores how the tokenizer vocabulary, often used to speed up the tokenization process and reduce tokens but constructed independently of the actual model training data, inadequately represents non-English languages. This misrepresentation results in the propagation of 'under-trained' or 'untrained' tokens, which perpetuate biases and pose serious concerns related to data security and ethical standards. We aim to dissect the tokenization mechanics of GPT-4o, illustrating how its simplified token-handling methods amplify these risks and offer strategic solutions to mitigate associated security and ethical issues. Through this study, we emphasize the critical need to rethink tokenization frameworks to foster more equitable and secure AI technologies.
Rethinking Digitalization and Climate: Don't Predict, Mitigate
2024-06-16
Daria Gritsenko, Jon Aaen, Bent Flyvbjerg
Digitalization is a core component of the green transition. Today's focus is on quantifying and pre-dicting the climate effects of digitalization through various life-cycle assessments and baseline sce-nario methodologies. Here we argue that this is a mistake. Most attempts at prediction are based on three implicit assumptions: (a) the digital carbon footprint can be quantified, (b) business-as-usual with episodic change leading to a new era of stability, and (c) investments in digitalization will be delivered within the cost, timeframe, and benefits described in their business cases. We problema-tize each assumption within the context of digitalization and argue that the digital carbon footprint is inherently unpredictable. We build on uncertainty literature to show that even if you cannot predict, you can still mitigate. On that basis, we propose to rethink practice on the digital carbon footprint from prediction to mitigation.
As Large Language Models (LLMs) are deployed and integrated into thousands of applications, the need for scalable evaluation of how models respond to adversarial attacks grows rapidly. However, LLM security is a moving target: models produce unpredictable output, are constantly updated, and the potential adversary is highly diverse: anyone with access to the internet and a decent command of natural language. Further, what constitutes a security weak in one context may not be an issue in a different context; one-fits-all guardrails remain theoretical. In this paper, we argue that it is time to rethink what constitutes ``LLM security'', and pursue a holistic approach to LLM security evaluation, where exploration and discovery of issues are central. To this end, this paper introduces garak (Generative AI Red-teaming and Assessment Kit), a framework which can be used to discover and identify vulnerabilities in a target LLM or dialog system. garak probes an LLM in a structured fashion to discover potential vulnerabilities. The outputs of the framework describe a target model's weaknesses, contribute to an informed discussion of what composes vulnerabilities in unique contexts, and can inform alignment and policy discussions for LLM deployment.
Semantic segmentation, as a basic tool for intelligent interpretation of remote sensing images, plays a vital role in many Earth Observation (EO) applications. Nowadays, accurate semantic segmentation of remote sensing images remains a challenge due to the complex spatial-temporal scenes and multi-scale geo-objects. Driven by the wave of deep learning (DL), CNN- and Transformer-based semantic segmentation methods have been explored widely, and these two architectures both revealed the importance of multi-scale feature representation for strengthening semantic information of geo-objects. However, the actual multi-scale feature fusion often comes with the semantic redundancy issue due to homogeneous semantic contents in pyramid features. To handle this issue, we propose a novel Mamba-based segmentation network, namely PyramidMamba. Specifically, we design a plug-and-play decoder, which develops a dense spatial pyramid pooling (DSPP) to encode rich multi-scale semantic features and a pyramid fusion Mamba (PFM) to reduce semantic redundancy in multi-scale feature fusion. Comprehensive ablation experiments illustrate the effectiveness and superiority of the proposed method in enhancing multi-scale feature representation as well as the great potential for real-time semantic segmentation. Moreover, our PyramidMamba yields state-of-the-art performance on three publicly available datasets, i.e. the OpenEarthMap (70.8% mIoU), ISPRS Vaihingen (84.8% mIoU) and Potsdam (88.0% mIoU) datasets. The code will be available at https://github.com/WangLibo1995/GeoSeg.
Online courses have significantly lowered the barrier to accessing education, yet the varying content quality of these videos poses challenges. In this work, we focus on the task of automatically evaluating the quality of video course content. We have constructed a dataset with a substantial collection of video courses and teaching materials. We propose three evaluation principles and design a new evaluation framework, \textit{VCEval}, based on these principles. The task is modeled as a multiple-choice question-answering task, with a language model serving as the evaluator. Our method effectively distinguishes video courses of different content quality and produces a range of interpretable results.
Rethinking the Evaluation of Out-of-Distribution Detection: A Sorites Paradox
2024-06-14
Xingming Long, Jie Zhang, Shiguang Shan, Xilin Chen
Most existing out-of-distribution (OOD) detection benchmarks classify samples with novel labels as the OOD data. However, some marginal OOD samples actually have close semantic contents to the in-distribution (ID) sample, which makes determining the OOD sample a Sorites Paradox. In this paper, we construct a benchmark named Incremental Shift OOD (IS-OOD) to address the issue, in which we divide the test samples into subsets with different semantic and covariate shift degrees relative to the ID dataset. The data division is achieved through a shift measuring method based on our proposed Language Aligned Image feature Decomposition (LAID). Moreover, we construct a Synthetic Incremental Shift (Syn-IS) dataset that contains high-quality generated images with more diverse covariate contents to complement the IS-OOD benchmark. We evaluate current OOD detection methods on our benchmark and find several important insights: (1) The performance of most OOD detection methods significantly improves as the semantic shift increases; (2) Some methods like GradNorm may have different OOD detection mechanisms as they rely less on semantic shifts to make decisions; (3) Excessive covariate shifts in the image are also likely to be considered as OOD for some methods. Our code and data are released in https://github.com/qqwsad5/IS-OOD.
Rethinking Score Distillation as a Bridge Between Image Distributions
2024-06-13
David McAllister, Songwei Ge, Jia-Bin Huang, David W. Jacobs, Alexei A. Efros, Aleksander Holynski, Angjoo Kanazawa
Score distillation sampling (SDS) has proven to be an important tool, enabling the use of large-scale diffusion priors for tasks operating in data-poor domains. Unfortunately, SDS has a number of characteristic artifacts that limit its usefulness in general-purpose applications. In this paper, we make progress toward understanding the behavior of SDS and its variants by viewing them as solving an optimal-cost transport path from a source distribution to a target distribution. Under this new interpretation, these methods seek to transport corrupted images (source) to the natural image distribution (target). We argue that current methods' characteristic artifacts are caused by (1) linear approximation of the optimal path and (2) poor estimates of the source distribution. We show that calibrating the text conditioning of the source distribution can produce high-quality generation and translation results with little extra overhead. Our method can be easily applied across many domains, matching or beating the performance of specialized methods. We demonstrate its utility in text-to-2D, text-based NeRF optimization, translating paintings to real images, optical illusion generation, and 3D sketch-to-real. We compare our method to existing approaches for score distillation sampling and show that it can produce high-frequency details with realistic colors.
Rethinking Waveform for 6G: Harnessing Delay-Doppler Alignment Modulation
2024-06-13
Zhiqiang Xiao, Xianda Liu, Yong Zeng, J. Andrew Zhang, Shi Jin, Rui Zhang
Waveform design has served as a cornerstone for each generation of mobile communication systems. The future sixth-generation (6G) mobile communication networks are expected to employ larger-scale antenna arrays and exploit higher-frequency bands for further boosting data transmission rate and providing ubiquitous wireless sensing. This brings new opportunities and challenges for 6G waveform design. In this article, by leveraging the super spatial resolution of large antenna arrays and the multi-path spatial sparsity of highfrequency wireless channels, we introduce a new approach for waveform design based on the recently proposed delay-Doppler alignment modulation (DDAM). In particular, DDAM makes a paradigm shift of waveform design from the conventional manner of tolerating channel delay and Doppler spreads to actively manipulating them. First, we review the fundamental constraints and performance limitations of orthogonal frequency division multiplexing (OFDM) and introduce new opportunities for 6G waveform design. Next, the motivations and basic principles of DDAM are presented, followed by its various extensions to different wireless system setups. Finally, the main design considerations for DDAM are discussed and the new opportunities for future research are highlighted.
Recent text-to-video (T2V) technology advancements, as demonstrated by models such as Gen2, Pika, and Sora, have significantly broadened its applicability and popularity. Despite these strides, evaluating these models poses substantial challenges. Primarily, due to the limitations inherent in automatic metrics, manual evaluation is often considered a superior method for assessing T2V generation. However, existing manual evaluation protocols face reproducibility, reliability, and practicality issues. To address these challenges, this paper introduces the Text-to-Video Human Evaluation (T2VHE) protocol, a comprehensive and standardized protocol for T2V models. The T2VHE protocol includes well-defined metrics, thorough annotator training, and an effective dynamic evaluation module. Experimental results demonstrate that this protocol not only ensures high-quality annotations but can also reduce evaluation costs by nearly 50\%. We will open-source the entire setup of the T2VHE protocol, including the complete protocol workflow, the dynamic evaluation component details, and the annotation interface code. This will help communities establish more sophisticated human assessment protocols.
Molecular representation learning is pivotal for various molecular property prediction tasks related to drug discovery. Robust and accurate benchmarks are essential for refining and validating current methods. Existing molecular property benchmarks derived from wet experiments, however, face limitations such as data volume constraints, unbalanced label distribution, and noisy labels. To address these issues, we construct a large-scale and precise molecular representation dataset of approximately 140,000 small molecules, meticulously designed to capture an extensive array of chemical, physical, and biological properties, derived through a robust computational ligand-target binding analysis pipeline. We conduct extensive experiments on various deep learning models, demonstrating that our dataset offers significant physicochemical interpretability to guide model development and design. Notably, the dataset's properties are linked to binding affinity metrics, providing additional insights into model performance in drug-target interaction tasks. We believe this dataset will serve as a more accurate and reliable benchmark for molecular representation learning, thereby expediting progress in the field of artificial intelligence-driven drug discovery.
ConMe: Rethinking Evaluation of Compositional Reasoning for Modern VLMs
2024-06-12
Irene Huang, Wei Lin, M. Jehanzeb Mirza, Jacob A. Hansen, Sivan Doveh, Victor Ion Butoi, Roei Herzig, Assaf Arbelle, Hilde Kuhene, Trevor Darrel, Chuang Gan, Aude Oliva, Rogerio Feris, Leonid Karlinsky
Compositional Reasoning (CR) entails grasping the significance of attributes, relations, and word order. Recent Vision-Language Models (VLMs), comprising a visual encoder and a Large Language Model (LLM) decoder, have demonstrated remarkable proficiency in such reasoning tasks. This prompts a crucial question: have VLMs effectively tackled the CR challenge? We conjecture that existing CR benchmarks may not adequately push the boundaries of modern VLMs due to the reliance on an LLM-only negative text generation pipeline. Consequently, the negatives produced either appear as outliers from the natural language distribution learned by VLMs' LLM decoders or as improbable within the corresponding image context. To address these limitations, we introduce ConMe -- a compositional reasoning benchmark and a novel data generation pipeline leveraging VLMs to produce `hard CR Q&A'. Through a new concept of VLMs conversing with each other to collaboratively expose their weaknesses, our pipeline autonomously generates, evaluates, and selects challenging compositional reasoning questions, establishing a robust CR benchmark, also subsequently validated manually. Our benchmark provokes a noteworthy, up to 33%, decrease in CR performance compared to preceding benchmarks, reinstating the CR challenge even for state-of-the-art VLMs.
KernelWarehouse: Rethinking the Design of Dynamic Convolution
2024-06-12
Chao Li, Anbang Yao
Dynamic convolution learns a linear mixture of n static kernels weighted with their input-dependent attentions, demonstrating superior performance than normal convolution. However, it increases the number of convolutional parameters by n times, and thus is not parameter efficient. This leads to no research progress that can allow researchers to explore the setting n>100 (an order of magnitude larger than the typical setting n<10) for pushing forward the performance boundary of dynamic convolution while enjoying parameter efficiency. To fill this gap, in this paper, we propose KernelWarehouse, a more general form of dynamic convolution, which redefines the basic concepts of ``kernels", ``assembling kernels" and ``attention function" through the lens of exploiting convolutional parameter dependencies within the same layer and across neighboring layers of a ConvNet. We testify the effectiveness of KernelWarehouse on ImageNet and MS-COCO datasets using various ConvNet architectures. Intriguingly, KernelWarehouse is also applicable to Vision Transformers, and it can even reduce the model size of a backbone while improving the model accuracy. For instance, KernelWarehouse (n=4) achieves 5.61%|3.90%|4.38% absolute top-1 accuracy gain on the ResNet18|MobileNetV2|DeiT-Tiny backbone, and KernelWarehouse (n=1/4) with 65.10% model size reduction still achieves 2.29% gain on the ResNet18 backbone. The code and models are available at https://github.com/OSVAI/KernelWarehouse.
Comment on paper: Position: Rethinking Post-Hoc Search-Based Neural Approaches for Solving Large-Scale Traveling Salesman Problems
2024-06-11
Yimeng Min
We identify two major issues in the SoftDist paper (Xia et al.): (1) the failure to run all steps of different baselines on the same hardware environment, and (2) the use of inconsistent time measurements when comparing to other baselines. These issues lead to flawed conclusions. When all steps are executed in the same hardware environment, the primary claim made in SoftDist is no longer supported.
Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective
2024-06-11
De Li, Xianxian Li, Zeming Gan, Qiyu Li, Bin Qu, Jinyan Wang
Graph neural networks based on message-passing mechanisms have achieved advanced results in graph classification tasks. However, their generalization performance degrades when noisy labels are present in the training data. Most existing noisy labeling approaches focus on the visual domain or graph node classification tasks and analyze the impact of noisy labels only from a utility perspective. Unlike existing work, in this paper, we measure the effects of noise labels on graph classification from data privacy and model utility perspectives. We find that noise labels degrade the model's generalization performance and enhance the ability of membership inference attacks on graph data privacy. To this end, we propose the robust graph neural network approach with noisy labeled graph classification. Specifically, we first accurately filter the noisy samples by high-confidence samples and the first feature principal component vector of each class. Then, the robust principal component vectors and the model output under data augmentation are utilized to achieve noise label correction guided by dual spatial information. Finally, supervised graph contrastive learning is introduced to enhance the embedding quality of the model and protect the privacy of the training graph data. The utility and privacy of the proposed method are validated by comparing twelve different methods on eight real graph classification datasets. Compared with the state-of-the-art methods, the RGLC method achieves at most and at least 7.8% and 0.8% performance gain at 30% noisy labeling rate, respectively, and reduces the accuracy of privacy attacks to below 60%.
Instanton Density Operator in Lattice QCD from Higher Category Theory
2024-06-10
Jing-Yuan Chen
A natural definition for instanton density operator in lattice QCD has been long desired. We show this problem is, and has to be, resolved by higher category theory. The problem is resolved by refining at a conceptual level the Yang-Mills theory on lattice, in order to recover the homotopy information in the continuum, which would have been lost if we put the theory on lattice in the traditional way. The refinement needed is a generalization -- through the lens of higher category theory -- of the familiar process of Villainization that captures winding in lattice XY model and Dirac quantization in lattice Maxwell theory. The apparent difference is that Villainization is in the end described by principal bundles, hence familiar, but more general topological operators can only be captured on the lattice by more flexible structures beyond the usual group theory and fibre bundles, hence the language of categories becomes natural and necessary. The key structure we need for our particular problem is called multiplicative bundle gerbe, based upon which we can construct suitable structures to naturally define the 2d Wess-Zumino-Witten term, 3d skyrmion density operator and 4d hedgehog defect for lattice $S^3$ (pion vacua) non-linear sigma model, and the 3d Chern-Simons term, 4d instanton density operator and 5d Yang monopole defect for lattice $SU(N)$ Yang-Mills theory. In a broader perspective, higher category theory enables us to rethink more systematically the relation between continuum quantum field theory and lattice quantum field theory. We sketch a proposal towards a general machinery that constructs the suitably refined lattice degrees of freedom for a given non-linear sigma model or gauge theory in the continuum, realizing the desired topological operators on the lattice.
Assessing action quality is both imperative and challenging due to its significant impact on the quality of AI-generated videos, further complicated by the inherently ambiguous nature of actions within AI-generated video (AIGV). Current action quality assessment (AQA) algorithms predominantly focus on actions from real specific scenarios and are pre-trained with normative action features, thus rendering them inapplicable in AIGVs. To address these problems, we construct GAIA, a Generic AI-generated Action dataset, by conducting a large-scale subjective evaluation from a novel causal reasoning-based perspective, resulting in 971,244 ratings among 9,180 video-action pairs. Based on GAIA, we evaluate a suite of popular text-to-video (T2V) models on their ability to generate visually rational actions, revealing their pros and cons on different categories of actions. We also extend GAIA as a testbed to benchmark the AQA capacity of existing automatic evaluation methods. Results show that traditional AQA methods, action-related metrics in recent T2V benchmarks, and mainstream video quality methods perform poorly with an average SRCC of 0.454, 0.191, and 0.519, respectively, indicating a sizable gap between current models and human action perception patterns in AIGVs. Our findings underscore the significance of action quality as a unique perspective for studying AIGVs and can catalyze progress towards methods with enhanced capacities for AQA in AIGVs.
2024-06-09
Leigang Qu, Haochuan Li, Tan Wang, Wenjie Wang, Yongqi Li, Liqiang Nie, Tat-Seng Chua
How humans can efficiently and effectively acquire images has always been a perennial question. A typical solution is text-to-image retrieval from an existing database given the text query; however, the limited database typically lacks creativity. By contrast, recent breakthroughs in text-to-image generation have made it possible to produce fancy and diverse visual content, but it faces challenges in synthesizing knowledge-intensive images. In this work, we rethink the relationship between text-to-image generation and retrieval and propose a unified framework in the context of Multimodal Large Language Models (MLLMs). Specifically, we first explore the intrinsic discriminative abilities of MLLMs and introduce a generative retrieval method to perform retrieval in a training-free manner. Subsequently, we unify generation and retrieval in an autoregressive generation way and propose an autonomous decision module to choose the best-matched one between generated and retrieved images as the response to the text query. Additionally, we construct a benchmark called TIGeR-Bench, including creative and knowledge-intensive domains, to standardize the evaluation of unified text-to-image generation and retrieval. Extensive experimental results on TIGeR-Bench and two retrieval benchmarks, i.e., Flickr30K and MS-COCO, demonstrate the superiority and effectiveness of our proposed method.
SinkLoRA: Enhanced Efficiency and Chat Capabilities for Long-Context Large Language Models
2024-06-09
Hengyu Zhang
Extending the functionality of the Transformer model to accommodate longer sequence lengths has become a critical challenge. This extension is crucial not only for improving tasks such as language translation and long-context processing but also for enabling novel applications like chatbots, code generation, and multimedia content creation. The primary obstacle is the self-attention mechanism, which scales quadratically with sequence length in terms of computation time and memory requirements. LongLoRA proposed shifted sparse attention (S\(^2\)-Attn), effectively enabling context extension and leading to non-trivial computation savings with similar performance to fine-tuning with vanilla attention. However, LongLoRA is still not as efficient as vanilla attention, reaching only 39\% of the perplexity improvement compared to full attention. This inefficiency is due to the cyclic shift applied within different attention head patterns, causing either chaos in the attention head structure or unnecessary information exchange between token groups. To address these issues, We propose \textbf{SinkLoRA}, which features better work partitioning. Specifically, (1) we developed SF-Attn with a segmentation and reassembly algorithm to proportionally return cyclically shifted groups of attention heads to their un-shifted state together with global attention of "sink attention tokens", achieving 92\% of the perplexity improvement compared to full attention after fine tuning, and (2) applied a SOTA KV cache compression algorithm H$_2$O to accelerate inference. Furthermore, We conducted supervised fine-tuning with SinkLoRA using a self collected LongAlpaca-plus dataset. All our code, models, datasets, and demos are available at \url{https://github.com/Dexter-GT-86/SinkLoRA}.
LLMEmbed: Rethinking Lightweight LLM's Genuine Function in Text Classification
2024-06-06
Chun Liu, Hongguang Zhang, Kainan Zhao, Xinghai Ju, Lin Yang
With the booming of Large Language Models (LLMs), prompt-learning has become a promising method mainly researched in various research areas. Recently, many attempts based on prompt-learning have been made to improve the performance of text classification. However, most of these methods are based on heuristic Chain-of-Thought (CoT), and tend to be more complex but less efficient. In this paper, we rethink the LLM-based text classification methodology, propose a simple and effective transfer learning strategy, namely LLMEmbed, to address this classical but challenging task. To illustrate, we first study how to properly extract and fuse the text embeddings via various lightweight LLMs at different network depths to improve their robustness and discrimination, then adapt such embeddings to train the classifier. We perform extensive experiments on publicly available datasets, and the results show that LLMEmbed achieves strong performance while enjoys low training overhead using lightweight LLM backbones compared to recent methods based on larger LLMs, i.e. GPT-3, and sophisticated prompt-based strategies. Our LLMEmbed achieves adequate accuracy on publicly available benchmarks without any fine-tuning while merely use 4% model parameters, 1.8% electricity consumption and 1.5% runtime compared to its counterparts. Code is available at: https://github.com/ChunLiu-cs/LLMEmbed-ACL2024.
2024-06-05
Ali Momeni, Babak Rahmani, Benjamin Scellier, Logan G. Wright, Peter L. McMahon, Clara C. Wanjura, Yuhang Li, Anas Skalli, Natalia G. Berloff, Tatsuhiro Onodera, Ilker Oguz, Francesco Morichetti, Philipp del Hougne, Manuel Le Gallo, Abu Sebastian, Azalia Mirhoseini, Cheng Zhang, Danijela Marković, Daniel Brunner, Christophe Moser, Sylvain Gigan, Florian Marquardt, Aydogan Ozcan, Julie Grollier, Andrea J. Liu, Demetri Psaltis, Andrea Alù, Romain Fleury
Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern AI. Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "yes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained - primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models.
Rethinking Programming Paradigms in the QC-HPC Context
2024-06-05
Silvina Caino-Lores, Daniel Claudino, Eugene Dumitrescu, Travis S. Humble, Sonia Lopez Alarcon, Elaine Wong
Programming for today's quantum computers is making significant strides toward modern workflows compatible with high performance computing (HPC), but fundamental challenges still remain in the integration of these vastly different technologies. Quantum computing (QC) programming languages share some common ground, as well as their emerging runtimes and algorithmic modalities. In this short paper, we explore avenues of refinement for the quantum processing unit (QPU) in the context of many-tasks management, asynchronous or otherwise, in order to understand the value it can play in linking QC with HPC. Through examples, we illustrate how its potential for scientific discovery might be realized.
FAPNet: An Effective Frequency Adaptive Point-based Eye Tracker
2024-06-05
Xiaopeng Lin, Hongwei Ren, Bojun Cheng
Eye tracking is crucial for human-computer interaction in different domains. Conventional cameras encounter challenges such as power consumption and image quality during different eye movements, prompting the need for advanced solutions with ultra-fast, low-power, and accurate eye trackers. Event cameras, fundamentally designed to capture information about moving objects, exhibit low power consumption and high temporal resolution. This positions them as an alternative to traditional cameras in the realm of eye tracking. Nevertheless, existing event-based eye tracking networks neglect the pivotal sparse and fine-grained temporal information in events, resulting in unsatisfactory performance. Moreover, the energy-efficient features are further compromised by the use of excessively complex models, hindering efficient deployment on edge devices. In this paper, we utilize Point Cloud as the event representation to harness the high temporal resolution and sparse characteristics of events in eye tracking tasks. We rethink the point-based architecture PEPNet with preprocessing the long-term relationships between samples, leading to the innovative design of FAPNet. A frequency adaptive mechanism is designed to realize adaptive tracking according to the speed of the pupil movement and the Inter Sample LSTM module is introduced to utilize the temporal correlation between samples. In the Event-based Eye Tracking Challenge, we utilize vanilla PEPNet, which is the former work to achieve the $p_{10}$ accuracy of 97.95\%. On the SEET synthetic dataset, FAPNet can achieve state-of-the-art while consuming merely 10\% of the PEPNet's computational resources. Notably, the computational demand of FAPNet is independent of the sensor's spatial resolution, enhancing its applicability on resource-limited edge devices.
Rethinking Spiking Neural Networks as State Space Models
2024-06-05
Malyaban Bal, Abhronil Sengupta
Spiking neural networks (SNNs) are posited as a biologically plausible alternative to conventional neural architectures, with their core computational framework resting on the extensively studied leaky integrate-and-fire (LIF) neuron design. The stateful nature of LIF neurons has spurred ongoing discussions about the ability of SNNs to process sequential data, akin to recurrent neural networks (RNNs). Despite this, there remains a significant gap in the exploration of current SNNs within the realm of long-range dependency tasks. In this study, to extend the analysis of neuronal dynamics beyond simplistic LIF mechanism, we present a novel class of stochastic spiking neuronal model grounded in state space models. We expand beyond the scalar hidden state representation of LIF neurons, which traditionally comprises only the membrane potential, by proposing an n-dimensional hidden state. Additionally, we enable fine-tuned formulation of neuronal dynamics across each layer by introducing learnable parameters, as opposed to the fixed dynamics in LIF neurons. We also develop a robust framework for scaling these neuronal models to deep SNN-based architectures, ensuring efficient parallel training while also adeptly addressing the challenge of non-differentiability of stochastic spiking operation during the backward phase. Our models attain state-of-the-art performance among SNN models across diverse long-range dependency tasks, encompassing the Long Range Arena benchmark, permuted sequential MNIST, and the Speech Command dataset. Moreover, we provide an analysis of the energy efficiency advantages, emphasizing the sparse activity pattern intrinsic to this spiking model.
Empirical Risk Minimization (ERM) is fragile in scenarios with insufficient labeled samples. A vanilla extension of ERM to unlabeled samples is Entropy Minimization (EntMin), which employs the soft-labels of unlabeled samples to guide their learning. However, EntMin emphasizes prediction discriminability while neglecting prediction diversity. To alleviate this issue, in this paper, we rethink the guidance information to utilize unlabeled samples. By analyzing the learning objective of ERM, we find that the guidance information for labeled samples in a specific category is the corresponding label encoding. Inspired by this finding, we propose a Label-Encoding Risk Minimization (LERM). It first estimates the label encodings through prediction means of unlabeled samples and then aligns them with their corresponding ground-truth label encodings. As a result, the LERM ensures both prediction discriminability and diversity, and it can be integrated into existing methods as a plugin. Theoretically, we analyze the relationships between LERM and ERM as well as EntMin. Empirically, we verify the superiority of the LERM under several label insufficient scenarios. The codes are available at https://github.com/zhangyl660/LERM.
Recent advancements in solving large-scale traveling salesman problems (TSP) utilize the heatmap-guided Monte Carlo tree search (MCTS) paradigm, where machine learning (ML) models generate heatmaps, indicating the probability distribution of each edge being part of the optimal solution, to guide MCTS in solution finding. However, our theoretical and experimental analysis raises doubts about the effectiveness of ML-based heatmap generation. In support of this, we demonstrate that a simple baseline method can outperform complex ML approaches in heatmap generation. Furthermore, we question the practical value of the heatmap-guided MCTS paradigm. To substantiate this, our findings show its inferiority to the LKH-3 heuristic despite the paradigm's reliance on problem-specific, hand-crafted strategies. For the future, we suggest research directions focused on developing more theoretically sound heatmap generation methods and exploring autonomous, generalizable ML approaches for combinatorial problems. The code is available for review: https://github.com/xyfffff/rethink_mcts_for_tsp.
Contrastive Learning Via Equivariant Representation
2024-06-01
Sifan Song, Jinfeng Wang, Qiaochu Zhao, Xiang Li, Dufan Wu, Angelos Stefanidis, Jionglong Su, S. Kevin Zhou, Quanzheng Li
Invariant-based Contrastive Learning (ICL) methods have achieved impressive performance across various domains. However, the absence of latent space representation for distortion (augmentation)-related information in the latent space makes ICL sub-optimal regarding training efficiency and robustness in downstream tasks. Recent studies suggest that introducing equivariance into Contrastive Learning (CL) can improve overall performance. In this paper, we rethink the roles of augmentation strategies and equivariance in improving CL efficacy. We propose a novel Equivariant-based Contrastive Learning (ECL) framework, CLeVER (Contrastive Learning Via Equivariant Representation), compatible with augmentation strategies of arbitrary complexity for various mainstream CL methods and model frameworks. Experimental results demonstrate that CLeVER effectively extracts and incorporates equivariant information from data, thereby improving the training efficiency and robustness of baseline models in downstream tasks.
A Seamless Phase II/III Design with Dose Optimization for Oncology Drug Development
2024-05-31
Yuhan Li, Yiding Zhang, Gu Mi, Ji Lin
The US FDA's Project Optimus initiative that emphasizes dose optimization prior to marketing approval represents a pivotal shift in oncology drug development. It has a ripple effect for rethinking what changes may be made to conventional pivotal trial designs to incorporate a dose optimization component. Aligned with this initiative, we propose a novel Seamless Phase II/III Design with Dose Optimization (SDDO framework). The proposed design starts with dose optimization in a randomized setting, leading to an interim analysis focused on optimal dose selection, trial continuation decisions, and sample size re-estimation (SSR). Based on the decision at interim analysis, patient enrollment continues for both the selected dose arm and control arm, and the significance of treatment effects will be determined at final analysis. The SDDO framework offers increased flexibility and cost-efficiency through sample size adjustment, while stringently controlling the Type I error. This proposed design also facilitates both Accelerated Approval (AA) and regular approval in a "one-trial" approach. Extensive simulation studies confirm that our design reliably identifies the optimal dosage and makes preferable decisions with a reduced sample size while retaining statistical power.
Rethinking Open-World Semi-Supervised Learning: Distribution Mismatch and Inductive Inference
2024-05-31
Seongheon Park, Hyuk Kwon, Kwanghoon Sohn, Kibok Lee
Open-world semi-supervised learning (OWSSL) extends conventional semi-supervised learning to open-world scenarios by taking account of novel categories in unlabeled datasets. Despite the recent advancements in OWSSL, the success often relies on the assumptions that 1) labeled and unlabeled datasets share the same balanced class prior distribution, which does not generally hold in real-world applications, and 2) unlabeled training datasets are utilized for evaluation, where such transductive inference might not adequately address challenges in the wild. In this paper, we aim to generalize OWSSL by addressing them. Our work suggests that practical OWSSL may require different training settings, evaluation methods, and learning strategies compared to those prevalent in the existing literature.
SemFlow: Binding Semantic Segmentation and Image Synthesis via Rectified Flow
2024-05-30
Chaoyang Wang, Xiangtai Li, Lu Qi, Henghui Ding, Yunhai Tong, Ming-Hsuan Yang
Semantic segmentation and semantic image synthesis are two representative tasks in visual perception and generation. While existing methods consider them as two distinct tasks, we propose a unified diffusion-based framework (SemFlow) and model them as a pair of reverse problems. Specifically, motivated by rectified flow theory, we train an ordinary differential equation (ODE) model to transport between the distributions of real images and semantic masks. As the training object is symmetric, samples belonging to the two distributions, images and semantic masks, can be effortlessly transferred reversibly. For semantic segmentation, our approach solves the contradiction between the randomness of diffusion outputs and the uniqueness of segmentation results. For image synthesis, we propose a finite perturbation approach to enhance the diversity of generated results without changing the semantic categories. Experiments show that our SemFlow achieves competitive results on semantic segmentation and semantic image synthesis tasks. We hope this simple framework will motivate people to rethink the unification of low-level and high-level vision. Project page: https://github.com/wang-chaoyang/SemFlow.
Grade Like a Human: Rethinking Automated Assessment with Large Language Models
2024-05-30
Wenjing Xie, Juxin Niu, Chun Jason Xue, Nan Guan
While large language models (LLMs) have been used for automated grading, they have not yet achieved the same level of performance as humans, especially when it comes to grading complex questions. Existing research on this topic focuses on a particular step in the grading procedure: grading using predefined rubrics. However, grading is a multifaceted procedure that encompasses other crucial steps, such as grading rubrics design and post-grading review. There has been a lack of systematic research exploring the potential of LLMs to enhance the entire grading~process. In this paper, we propose an LLM-based grading system that addresses the entire grading procedure, including the following key components: 1) Developing grading rubrics that not only consider the questions but also the student answers, which can more accurately reflect students' performance. 2) Under the guidance of grading rubrics, providing accurate and consistent scores for each student, along with customized feedback. 3) Conducting post-grading review to better ensure accuracy and fairness. Additionally, we collected a new dataset named OS from a university operating system course and conducted extensive experiments on both our new dataset and the widely used Mohler dataset. Experiments demonstrate the effectiveness of our proposed approach, providing some new insights for developing automated grading systems based on LLMs.
AutoBreach: Universal and Adaptive Jailbreaking with Efficient Wordplay-Guided Optimization
2024-05-30
Jiawei Chen, Xiao Yang, Zhengwei Fang, Yu Tian, Yinpeng Dong, Zhaoxia Yin, Hang Su
Despite the widespread application of large language models (LLMs) across various tasks, recent studies indicate that they are susceptible to jailbreak attacks, which can render their defense mechanisms ineffective. However, previous jailbreak research has frequently been constrained by limited universality, suboptimal efficiency, and a reliance on manual crafting. In response, we rethink the approach to jailbreaking LLMs and formally define three essential properties from the attacker' s perspective, which contributes to guiding the design of jailbreak methods. We further introduce AutoBreach, a novel method for jailbreaking LLMs that requires only black-box access. Inspired by the versatility of wordplay, AutoBreach employs a wordplay-guided mapping rule sampling strategy to generate a variety of universal mapping rules for creating adversarial prompts. This generation process leverages LLMs' automatic summarization and reasoning capabilities, thus alleviating the manual burden. To boost jailbreak success rates, we further suggest sentence compression and chain-of-thought-based mapping rules to correct errors and wordplay misinterpretations in target LLMs. Additionally, we propose a two-stage mapping rule optimization strategy that initially optimizes mapping rules before querying target LLMs to enhance the efficiency of AutoBreach. AutoBreach can efficiently identify security vulnerabilities across various LLMs, including three proprietary models: Claude-3, GPT-3.5, GPT-4 Turbo, and two LLMs' web platforms: Bingchat, GPT-4 Web, achieving an average success rate of over 80% with fewer than 10 queries
2024-05-29
Simon Geisler, Arthur Kosmala, Daniel Herbst, Stephan Günnemann
Spatial Message Passing Graph Neural Networks (MPGNNs) are widely used for learning on graph-structured data. However, key limitations of l-step MPGNNs are that their "receptive field" is typically limited to the l-hop neighborhood of a node and that information exchange between distant nodes is limited by over-squashing. Motivated by these limitations, we propose Spatio-Spectral Graph Neural Networks (S$^2$GNNs) -- a new modeling paradigm for Graph Neural Networks (GNNs) that synergistically combines spatially and spectrally parametrized graph filters. Parameterizing filters partially in the frequency domain enables global yet efficient information propagation. We show that S$^2$GNNs vanquish over-squashing and yield strictly tighter approximation-theoretic error bounds than MPGNNs. Further, rethinking graph convolutions at a fundamental level unlocks new design spaces. For example, S$^2$GNNs allow for free positional encodings that make them strictly more expressive than the 1-Weisfeiler-Lehman (WL) test. Moreover, to obtain general-purpose S$^2$GNNs, we propose spectrally parametrized filters for directed graphs. S$^2$GNNs outperform spatial MPGNNs, graph transformers, and graph rewirings, e.g., on the peptide long-range benchmark tasks, and are competitive with state-of-the-art sequence modeling. On a 40 GB GPU, S$^2$GNNs scale to millions of nodes.
With recent advancements in large language models (LLMs), alignment has emerged as an effective technique for keeping LLMs consensus with human intent. Current methods primarily involve direct training through Supervised Fine-tuning (SFT) or Reinforcement Learning from Human Feedback (RLHF), both of which require substantial computational resources and extensive ground truth data. This paper explores an efficient method for aligning black-box large models using smaller models, introducing a model-agnostic and lightweight Bayesian Persuasion Alignment framework. We formalize this problem as an optimization of the signaling strategy from the small model's perspective. In the persuasion process, the small model (Advisor) observes the information item (i.e., state) and persuades large models (Receiver) to elicit improved responses. The Receiver then generates a response based on the input, the signal from the Advisor, and its updated belief about the information item. Through training using our framework, we demonstrate that the Advisor can significantly enhance the performance of various Receivers across a range of tasks. We theoretically analyze our persuasion framework and provide an upper bound on the Advisor's regret, confirming its effectiveness in learning the optimal signaling strategy. Our Empirical results demonstrates that GPT-2 can significantly improve the performance of various models, achieving an average enhancement of 16.1% in mathematical reasoning ability and 13.7% in code generation. We hope our work can provide an initial step toward rethinking the alignment framework from the Bayesian Persuasion perspective.
ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models
2024-05-28
Aparna Elangovan, Ling Liu, Lei Xu, Sravan Bodapati, Dan Roth
In this position paper, we argue that human evaluation of generative large language models (LLMs) should be a multidisciplinary undertaking that draws upon insights from disciplines such as user experience research and human behavioral psychology to ensure that the experimental design and results are reliable. The conclusions from these evaluations, thus, must consider factors such as usability, aesthetics, and cognitive biases. We highlight how cognitive biases can conflate fluent information and truthfulness, and how cognitive uncertainty affects the reliability of rating scores such as Likert. Furthermore, the evaluation should differentiate the capabilities and weaknesses of increasingly powerful large language models -- which requires effective test sets. The scalability of human evaluation is also crucial to wider adoption. Hence, to design an effective human evaluation system in the age of generative NLP, we propose the ConSiDERS-The-Human evaluation framework consisting of 6 pillars -- Consistency, Scoring Criteria, Differentiating, User Experience, Responsible, and Scalability.
Rethinking the A in STEAM: Insights from and for AI Literacy Education
2024-05-28
Pekka Mertala, JAnne Fagerlund, Tomi Slotte Dufva
This article rethinks the role of arts in STEAM education, emphasizing its importance in AI literacy within K-12 contexts. Arguing against the marginalization of arts, the paper is structured around four key domains: language studies, philosophy, social studies, and visual arts. Each section addresses critical AI-related phenomena and provides pedagogical strate-gies for effective integration into STEAM education. Language studies focus on media representations and the probabilistic nature of AI language models. The philosophy section examines anthropomorphism, ethics, and the misconstrued human-like capabilities of AI. Social studies discuss AI's societal impacts, biases, and ethical considerations in data prac-tices. Visual arts explore the implications of generative AI on artistic processes and intellec-tual property. The article concludes by advocating for a robust inclusion of arts in STEAM to foster a holistic, equitable, and sustainable understanding of AI, ultimately inspiring technologies that promote fairness and creativity.
2024-05-28
Andreas Lizenberger, Ferdinand Pfeifer, Bastian Polewka
Cluster-based algorithm selection deals with selecting recommendation algorithms on clusters of users to obtain performance gains. No studies have been attempted for many combinations of clustering approaches and recommendation algorithms. We want to show that clustering users prior to algorithm selection increases the performance of recommendation algorithms. Our study covers eight datasets, four clustering approaches, and eight recommendation algorithms. We select the best performing recommendation algorithm for each cluster. Our work shows that cluster-based algorithm selection is an effective technique for optimizing recommendation algorithm performance. For five out of eight datasets, we report an increase in nDCG@10 between 19.28% (0.032) and 360.38% (0.191) compared to algorithm selection without prior clustering.
Rethinking Pruning for Backdoor Mitigation: An Optimization Perspective
2024-05-28
Nan Li, Haiyang Yu, Ping Yi
Deep Neural Networks (DNNs) are known to be vulnerable to backdoor attacks, posing concerning threats to their reliable deployment. Recent research reveals that backdoors can be erased from infected DNNs by pruning a specific group of neurons, while how to effectively identify and remove these backdoor-associated neurons remains an open challenge. Most of the existing defense methods rely on defined rules and focus on neuron's local properties, ignoring the exploration and optimization of pruning policies. To address this gap, we propose an Optimized Neuron Pruning (ONP) method combined with Graph Neural Network (GNN) and Reinforcement Learning (RL) to repair backdoor models. Specifically, ONP first models the target DNN as graphs based on neuron connectivity, and then uses GNN-based RL agents to learn graph embeddings and find a suitable pruning policy. To the best of our knowledge, this is the first attempt to employ GNN and RL for optimizing pruning policies in the field of backdoor defense. Experiments show, with a small amount of clean data, ONP can effectively prune the backdoor neurons implanted by a set of backdoor attacks at the cost of negligible performance degradation, achieving a new state-of-the-art performance for backdoor mitigation.
Robot manipulation policies have shown unsatisfactory action performance when confronted with novel task or object instances. Hence, the capability to automatically detect and self-correct failure action is essential for a practical robotic system. Recently, Multimodal Large Language Models (MLLMs) have shown promise in visual instruction following and demonstrated strong reasoning abilities in various tasks. To unleash general MLLMs as an end-to-end robotic agent, we introduce a Self-Corrected (SC)-MLLM, equipping our model not only to predict end-effector poses but also to autonomously recognize and correct failure actions. Specifically, we first conduct parameter-efficient fine-tuning to empower MLLM with pose prediction ability, which is reframed as a language modeling problem. When facing execution failures, our model learns to identify low-level action error causes (i.e., position and rotation errors) and adaptively seeks prompt feedback from experts. Based on the feedback, SC-MLLM rethinks the current failure scene and generates the corrected actions. Furthermore, we design a continuous policy learning method for successfully corrected samples, enhancing the model's adaptability to the current scene configuration and reducing the frequency of expert intervention. To evaluate our SC-MLLM, we conduct extensive experiments in both simulation and real-world settings. SC-MLLM agent significantly improve manipulation accuracy compared to previous state-of-the-art robotic MLLM (ManipLLM), increasing from 57\% to 79\% on seen object categories and from 47\% to 69\% on unseen novel categories.
2024-05-27
Chenhao Lu, Ruizhe Shi, Yuyao Liu, Kaizhe Hu, Simon S. Du, Huazhe Xu
Sequential decision-making algorithms such as reinforcement learning (RL) in real-world scenarios inevitably face environments with partial observability. This paper scrutinizes the effectiveness of a popular architecture, namely Transformers, in Partially Observable Markov Decision Processes (POMDPs) and reveals its theoretical limitations. We establish that regular languages, which Transformers struggle to model, are reducible to POMDPs. This poses a significant challenge for Transformers in learning POMDP-specific inductive biases, due to their lack of inherent recurrence found in other models like RNNs. This paper casts doubt on the prevalent belief in Transformers as sequence models for RL and proposes to introduce a point-wise recurrent structure. The Deep Linear Recurrent Unit (LRU) emerges as a well-suited alternative for Partially Observable RL, with empirical results highlighting the sub-optimal performance of the Transformer and considerable strength of LRU.
Are Self-Attentions Effective for Time Series Forecasting?
2024-05-27
Dongbin Kim, Jinseong Park, Jaewook Lee, Hoki Kim
Time series forecasting is crucial for applications across multiple domains and various scenarios. Although Transformer models have dramatically advanced the landscape of forecasting, their effectiveness remains debated. Recent findings have indicated that simpler linear models might outperform complex Transformer-based approaches, highlighting the potential for more streamlined architectures. In this paper, we shift the focus from evaluating the overall Transformer architecture to specifically examining the effectiveness of self-attention for time series forecasting. To this end, we introduce a new architecture, Cross-Attention-only Time Series transformer (CATS), that rethinks the traditional Transformer framework by eliminating self-attention and leveraging cross-attention mechanisms instead. By establishing future horizon-dependent parameters as queries and enhanced parameter sharing, our model not only improves long-term forecasting accuracy but also reduces the number of parameters and memory usage. Extensive experiment across various datasets demonstrates that our model achieves superior performance with the lowest mean squared error and uses fewer parameters compared to existing models. The implementation of our model is available at: https://github.com/dongbeank/CATS.
Gender bias in vision-language models (VLMs) can reinforce harmful stereotypes and discrimination. In this paper, we focus on mitigating gender bias towards vision-language tasks. We identify object hallucination as the essence of gender bias in VLMs. Existing VLMs tend to focus on salient or familiar attributes in images but ignore contextualized nuances. Moreover, most VLMs rely on the co-occurrence between specific objects and gender attributes to infer the ignored features, ultimately resulting in gender bias. We propose GAMA, a task-agnostic generation framework to mitigate gender bias. GAMA consists of two stages: narrative generation and answer inference. During narrative generation, GAMA yields all-sided but gender-obfuscated narratives, which prevents premature concentration on localized image features, especially gender attributes. During answer inference, GAMA integrates the image, generated narrative, and a task-specific question prompt to infer answers for different vision-language tasks. This approach allows the model to rethink gender attributes and answers. We conduct extensive experiments on GAMA, demonstrating its debiasing and generalization ability.
Isolate and then Identify: Rethinking Adaptive Group Testing
2024-05-25
Hsin-Po Wang, Venkatesan Guruswami
Group testing (GT) is the art of identifying binary signals and the marketplace for exchanging new ideas for related fields such as unique-element counting, compressed sensing, traitor tracing, and geno-typing. A GT scheme can be nonadaptive or adaptive; the latter is preferred when latency is ess of an issue. To construct adaptive GT schemes, a popular strategy is to spend the majority of tests in the first few rounds to gain as much information as possible, and uses later rounds to refine details. In this paper, we propose a transparent strategy called "isolate and then identify" (I@I). In the first few rounds, I@I divides the population into teams until every team contains at most one sick person. Then, in the last round, I@I identifies the sick person in each team. Performance-wise, I@I is the first GT scheme that achieves the optimal coefficient $1/$capacity$(Z)$ for the $k \log_2 (n/k)$ term in the number of tests when $Z$ is a generic channel corrupting the test outcomes. I@I follows a modular methodology whereby the isolating part and the identification part can be optimized separately.
An Empirical Study of Excitation and Aggregation Design Adaptions in CLIP4Clip for Video-Text Retrieval
2024-05-25
Xiaolun Jing, Genke Yang, Jian Chu
CLIP4Clip model transferred from the CLIP has been the de-factor standard to solve the video clip retrieval task from frame-level input, triggering the surge of CLIP4Clip-based models in the video-text retrieval domain. In this work, we rethink the inherent limitation of widely-used mean pooling operation in the frame features aggregation and investigate the adaptions of excitation and aggregation design for discriminative video representation generation. We present a novel excitationand-aggregation design, including (1) The excitation module is available for capturing non-mutuallyexclusive relationships among frame features and achieving frame-wise features recalibration, and (2) The aggregation module is applied to learn exclusiveness used for frame representations aggregation. Similarly, we employ the cascade of sequential module and aggregation design to generate discriminative video representation in the sequential type. Besides, we adopt the excitation design in the tight type to obtain representative frame features for multi-modal interaction. The proposed modules are evaluated on three benchmark datasets of MSR-VTT, ActivityNet and DiDeMo, achieving MSR-VTT (43.9 R@1), ActivityNet (44.1 R@1) and DiDeMo (31.0 R@1). They outperform the CLIP4Clip results by +1.2% (+0.5%), +4.5% (+1.9%) and +9.5% (+2.7%) relative (absolute) improvements, demonstrating the superiority of our proposed excitation and aggregation designs. We hope our work will serve as an alternative for frame representations aggregation and facilitate future research.
Most recent multispectral object detectors employ a two-branch structure to extract features from RGB and thermal images. While the two-branch structure achieves better performance than a single-branch structure, it overlooks inference efficiency. This conflict is increasingly aggressive, as recent works solely pursue higher performance rather than both performance and efficiency. In this paper, we address this issue by improving the performance of efficient single-branch structures. We revisit the reasons causing the performance gap between these structures. For the first time, we reveal the information interference problem in the naive early-fusion strategy adopted by previous single-branch structures. Besides, we find that the domain gap between multispectral images, and weak feature representation of the single-branch structure are also key obstacles for performance. Focusing on these three problems, we propose corresponding solutions, including a novel shape-priority early-fusion strategy, a weakly supervised learning method, and a core knowledge distillation technique. Experiments demonstrate that single-branch networks equipped with these three contributions achieve significant performance enhancements while retaining high efficiency. Our code will be available at \url{https://github.com/XueZ-phd/Efficient-RGB-T-Early-Fusion-Detection}.
UnitNorm: Rethinking Normalization for Transformers in Time Series
2024-05-24
Nan Huang, Christian Kümmerle, Xiang Zhang
Normalization techniques are crucial for enhancing Transformer models' performance and stability in time series analysis tasks, yet traditional methods like batch and layer normalization often lead to issues such as token shift, attention shift, and sparse attention. We propose UnitNorm, a novel approach that scales input vectors by their norms and modulates attention patterns, effectively circumventing these challenges. Grounded in existing normalization frameworks, UnitNorm's effectiveness is demonstrated across diverse time series analysis tasks, including forecasting, classification, and anomaly detection, via a rigorous evaluation on 6 state-of-the-art models and 10 datasets. Notably, UnitNorm shows superior performance, especially in scenarios requiring robust attention mechanisms and contextual comprehension, evidenced by significant improvements by up to a 1.46 decrease in MSE for forecasting, and a 4.89% increase in accuracy for classification. This work not only calls for a reevaluation of normalization strategies in time series Transformers but also sets a new direction for enhancing model performance and stability. The source code is available at https://anonymous.4open.science/r/UnitNorm-5B84.
Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
2024-05-24
Rui Miao, Kaixiong Zhou, Yili Wang, Ninghao Liu, Ying Wang, Xin Wang
Graph neural networks (GNNs) have exhibited prominent performance in learning graph-structured data. Considering node classification task, based on the i.i.d assumption among node labels, the traditional supervised learning simply sums up cross-entropy losses of the independent training nodes and applies the average loss to optimize GNNs' weights. But different from other data formats, the nodes are naturally connected. It is found that the independent distribution modeling of node labels restricts GNNs' capability to generalize over the entire graph and defend adversarial attacks. In this work, we propose a new framework, termed joint-cluster supervised learning, to model the joint distribution of each node with its corresponding cluster. We learn the joint distribution of node and cluster labels conditioned on their representations, and train GNNs with the obtained joint loss. In this way, the data-label reference signals extracted from the local cluster explicitly strengthen the discrimination ability on the target node. The extensive experiments demonstrate that our joint-cluster supervised learning can effectively bolster GNNs' node classification accuracy. Furthermore, being benefited from the reference signals which may be free from spiteful interference, our learning paradigm significantly protects the node classification from being affected by the adversarial attack.
Deep neural networks suffer from catastrophic forgetting when continually learning new concepts. In this paper, we analyze this problem from a data imbalance point of view. We argue that the imbalance between old task and new task data contributes to forgetting of the old tasks. Moreover, the increasing imbalance ratio during incremental learning further aggravates the problem. To address the dynamic imbalance issue, we propose Uniform Prototype Contrastive Learning (UPCL), where uniform and compact features are learned. Specifically, we generate a set of non-learnable uniform prototypes before each task starts. Then we assign these uniform prototypes to each class and guide the feature learning through prototype contrastive learning. We also dynamically adjust the relative margin between old and new classes so that the feature distribution will be maintained balanced and compact. Finally, we demonstrate through extensive experiments that the proposed method achieves state-of-the-art performance on several benchmark datasets including CIFAR100, ImageNet100 and TinyImageNet.
Large language models (LLMs) need knowledge updates to meet the ever-growing world facts and correct the hallucinated responses, facilitating the methods of lifelong model editing. Where the updated knowledge resides in memories is a fundamental question for model editing. In this paper, we find that editing either long-term memory (direct model parameters) or working memory (non-parametric knowledge of neural network activations/representations by retrieval) will result in an impossible triangle -- reliability, generalization, and locality can not be realized together in the lifelong editing settings. For long-term memory, directly editing the parameters will cause conflicts with irrelevant pretrained knowledge or previous edits (poor reliability and locality). For working memory, retrieval-based activations can hardly make the model understand the edits and generalize (poor generalization). Therefore, we propose WISE to bridge the gap between memories. In WISE, we design a dual parametric memory scheme, which consists of the main memory for the pretrained knowledge and a side memory for the edited knowledge. We only edit the knowledge in the side memory and train a router to decide which memory to go through when given a query. For continual editing, we devise a knowledge-sharding mechanism where different sets of edits reside in distinct subspaces of parameters, and are subsequently merged into a shared memory without conflicts. Extensive experiments show that WISE can outperform previous model editing methods and overcome the impossible triangle under lifelong model editing of question answering, hallucination, and out-of-distribution settings across trending LLM architectures, e.g., GPT, LLaMA, and Mistral. Code is available at https://github.com/zjunlp/EasyEdit.
Reassessing Evaluation Functions in Algorithmic Recourse: An Empirical Study from a Human-Centered Perspective
2024-05-23
Tomu Tominaga, Naomi Yamashita, Takeshi Kurashima
In this study, we critically examine the foundational premise of algorithmic recourse - a process of generating counterfactual action plans (i.e., recourses) assisting individuals to reverse adverse decisions made by AI systems. The assumption underlying algorithmic recourse is that individuals accept and act on recourses that minimize the gap between their current and desired states. This assumption, however, remains empirically unverified. To address this issue, we conducted a user study with 362 participants and assessed whether minimizing the distance function, a metric of the gap between the current and desired states, indeed prompts them to accept and act upon suggested recourses. Our findings reveal a nuanced landscape: participants' acceptance of recourses did not correlate with the recourse distance. Moreover, participants' willingness to act upon recourses peaked at the minimal recourse distance but was otherwise constant. These findings cast doubt on the prevailing assumption of algorithmic recourse research and signal the need to rethink the evaluation functions to pave the way for human-centered recourse generation.
EMR-Merging: Tuning-Free High-Performance Model Merging
2024-05-23
Chenyu Huang, Peng Ye, Tao Chen, Tong He, Xiangyu Yue, Wanli Ouyang
The success of pretrain-finetune paradigm brings about the release of numerous model weights. In this case, merging models finetuned on different tasks to enable a single model with multi-task capabilities is gaining increasing attention for its practicability. Existing model merging methods usually suffer from (1) significant performance degradation or (2) requiring tuning by additional data or training. In this paper, we rethink and analyze the existing model merging paradigm. We discover that using a single model's weights can hardly simulate all the models' performance. To tackle this issue, we propose Elect, Mask & Rescale-Merging (EMR-Merging). We first (a) elect a unified model from all the model weights and then (b) generate extremely lightweight task-specific modulators, including masks and rescalers, to align the direction and magnitude between the unified model and each specific model, respectively. EMR-Merging is tuning-free, thus requiring no data availability or any additional training while showing impressive performance. We find that EMR-Merging shows outstanding performance compared to existing merging methods under different classical and newly-established settings, including merging different numbers of vision models (up to 30), NLP models, PEFT models, and multi-modal models.
Rethinking the Elementary Function Fusion for Single-Image Dehazing
2024-05-23
Yesian Rohn
This paper addresses the limitations of physical models in the current field of image dehazing by proposing an innovative dehazing network (CL2S). Building on the DM2F model, it identifies issues in its ablation experiments and replaces the original logarithmic function model with a trigonometric (sine) model. This substitution aims to better fit the complex and variable distribution of haze. The approach also integrates the atmospheric scattering model and other elementary functions to enhance dehazing performance. Experimental results demonstrate that CL2S achieves outstanding performance on multiple dehazing datasets, particularly in maintaining image details and color authenticity. Additionally, systematic ablation experiments supplementing DM2F validate the concerns raised about DM2F and confirm the necessity and effectiveness of the functional components in the proposed CL2S model. Our code is available at \url{https://github.com/YesianRohn/CL2S}, where the corresponding pre-trained models can also be accessed.
The increasing prevalence of large-scale graphs poses a significant challenge for graph neural network training, attributed to their substantial computational requirements. In response, graph condensation (GC) emerges as a promising data-centric solution aiming to substitute the large graph with a small yet informative condensed graph to facilitate data-efficient GNN training. However, existing GC methods suffer from intricate optimization processes, necessitating excessive computing resources. In this paper, we revisit existing GC optimization strategies and identify two pervasive issues: 1. various GC optimization strategies converge to class-level node feature matching between the original and condensed graphs, making the optimization target coarse-grained despite the complex computations; 2. to bridge the original and condensed graphs, existing GC methods rely on a Siamese graph network architecture that requires time-consuming bi-level optimization with iterative gradient computations. To overcome these issues, we propose a training-free GC framework termed Class-partitioned Graph Condensation (CGC), which refines the node feature matching from the class-to-class paradigm into a novel class-to-node paradigm. Remarkably, this refinement also simplifies the GC optimization as a class partition problem, which can be efficiently solved by any clustering methods. Moreover, CGC incorporates a pre-defined graph structure to enable a closed-form solution for condensed node features, eliminating the back-and-forth gradient descent in existing GC approaches without sacrificing accuracy. Extensive experiments demonstrate that CGC achieves state-of-the-art performance with a more efficient condensation process. For instance, compared with the seminal GC method (i.e., GCond), CGC condenses the largest Reddit graph within 10 seconds, achieving a 2,680X speedup and a 1.4% accuracy increase.
2024-05-22
Tobias Leemann, Alina Fastowski, Felix Pfeiffer, Gjergji Kasneci
We address the critical challenge of applying feature attribution methods to the transformer architecture, which dominates current applications in natural language processing and beyond. Traditional attribution methods to explainable AI (XAI) explicitly or implicitly rely on linear or additive surrogate models to quantify the impact of input features on a model's output. In this work, we formally prove an alarming incompatibility: transformers are structurally incapable to align with popular surrogate models for feature attribution, undermining the grounding of these conventional explanation methodologies. To address this discrepancy, we introduce the Softmax-Linked Additive Log-Odds Model (SLALOM), a novel surrogate model specifically designed to align with the transformer framework. Unlike existing methods, SLALOM demonstrates the capacity to deliver a range of faithful and insightful explanations across both synthetic and real-world datasets. Showing that diverse explanations computed from SLALOM outperform common surrogate explanations on different tasks, we highlight the need for task-specific feature attributions rather than a one-size-fits-all approach.
Identity-free Artificial Emotional Intelligence via Micro-Gesture Understanding
2024-05-21
Rong Gao, Xin Liu, Bohao Xing, Zitong Yu, Bjorn W. Schuller, Heikki Kälviäinen
In this work, we focus on a special group of human body language -- the micro-gesture (MG), which differs from the range of ordinary illustrative gestures in that they are not intentional behaviors performed to convey information to others, but rather unintentional behaviors driven by inner feelings. This characteristic introduces two novel challenges regarding micro-gestures that are worth rethinking. The first is whether strategies designed for other action recognition are entirely applicable to micro-gestures. The second is whether micro-gestures, as supplementary data, can provide additional insights for emotional understanding. In recognizing micro-gestures, we explored various augmentation strategies that take into account the subtle spatial and brief temporal characteristics of micro-gestures, often accompanied by repetitiveness, to determine more suitable augmentation methods. Considering the significance of temporal domain information for micro-gestures, we introduce a simple and efficient plug-and-play spatiotemporal balancing fusion method. We not only studied our method on the considered micro-gesture dataset but also conducted experiments on mainstream action datasets. The results show that our approach performs well in micro-gesture recognition and on other datasets, achieving state-of-the-art performance compared to previous micro-gesture recognition methods. For emotional understanding based on micro-gestures, we construct complex emotional reasoning scenarios. Our evaluation, conducted with large language models, shows that micro-gestures play a significant and positive role in enhancing comprehensive emotional understanding. The scenarios we developed can be extended to other micro-gesture-based tasks such as deception detection and interviews. We confirm that our new insights contribute to advancing research in micro-gesture and emotional artificial intelligence.
Rethinking the production and publication of machine-reusable expressions of research findings
2024-05-21
Markus Stocker, Lauren Snyder, Matthew Anfuso, Oliver Ludwig, Freya Thießen, Kheir Eddine Farfar, Muhammad Haris, Allard Oelen, Mohamad Yaser Jaradeh
Literature is the primary expression of scientific knowledge and an important source of research data. However, scientific knowledge expressed in narrative text documents is not inherently machine reusable. To facilitate knowledge reuse, e.g. for synthesis research, scientific knowledge must be extracted from articles and organized into databases post-publication. The high time costs and inaccuracies associated with completing these activities manually has driven the development of techniques that automate knowledge extraction. Tackling the problem with a different mindset, we propose a pre-publication approach, known as reborn, that ensures scientific knowledge is born reusable, i.e. produced in a machine-reusable format during knowledge production. We implement the approach using the Open Research Knowledge Graph infrastructure for FAIR scientific knowledge organization. We test the approach with three use cases, and discuss the role of publishers and editors in scaling the approach. Our results suggest that the proposed approach is superior compared to classical manual and semi-automated post-publication extraction techniques in terms of knowledge richness and accuracy as well as technological simplicity.
Face Recognition Systems (FRS) have increasingly integrated into critical applications, including surveillance and user authentication, highlighting their pivotal role in modern security systems. Recent studies have revealed vulnerabilities in FRS to adversarial (e.g., adversarial patch attacks) and backdoor attacks (e.g., training data poisoning), raising significant concerns about their reliability and trustworthiness. Previous studies primarily focus on traditional adversarial or backdoor attacks, overlooking the resource-intensive or privileged-manipulation nature of such threats, thus limiting their practical generalization, stealthiness, universality and robustness. Correspondingly, in this paper, we delve into the inherent vulnerabilities in FRS through user studies and preliminary explorations. By exploiting these vulnerabilities, we identify a novel attack, facial identity backdoor attack dubbed FIBA, which unveils a potentially more devastating threat against FRS:an enrollment-stage backdoor attack. FIBA circumvents the limitations of traditional attacks, enabling broad-scale disruption by allowing any attacker donning a specific trigger to bypass these systems. This implies that after a single, poisoned example is inserted into the database, the corresponding trigger becomes a universal key for any attackers to spoof the FRS. This strategy essentially challenges the conventional attacks by initiating at the enrollment stage, dramatically transforming the threat landscape by poisoning the feature database rather than the training data.
Rethinking the Effective Field Theory formulation of Gravity
2024-05-21
Jesse Daas, Cristobal Laporte, Frank Saueressig, Tim van Dijk
General relativity is highly successful in explaining a wide range of gravitational phenomena including the gravitational waves emitted by binary systems and the shadows cast by supermassive black holes. From a modern perspective the theory is not fundamental though, but constitutes the lowest order term in an effective field theory description of the gravitational force. As a consequence, the gravitational dynamics should receive corrections by higher-derivative terms. This essay discusses structural aspects associated with these corrections and summarizes their imprint on static, spherically symmetric geometries. Along these lines, we critically reassess the common practice of using local field redefinitions in order to simplify the dynamics at the danger of shifting physics effects into sectors which are beyond the approximation under consideration.
Rethink Predicting the Optical Flow with the Kinetics Perspective
2024-05-21
Yuhao Cheng, Siru Zhang, Yiqiang Yan
Optical flow estimation is one of the fundamental tasks in low-level computer vision, which describes the pixel-wise displacement and can be used in many other tasks. From the apparent aspect, the optical flow can be viewed as the correlation between the pixels in consecutive frames, so continuously refining the correlation volume can achieve an outstanding performance. However, it will make the method have a catastrophic computational complexity. Not only that, the error caused by the occlusion regions of the successive frames will be amplified through the inaccurate warp operation. These challenges can not be solved only from the apparent view, so this paper rethinks the optical flow estimation from the kinetics viewpoint.We propose a method combining the apparent and kinetics information from this motivation. The proposed method directly predicts the optical flow from the feature extracted from images instead of building the correlation volume, which will improve the efficiency of the whole network. Meanwhile, the proposed method involves a new differentiable warp operation that simultaneously considers the warping and occlusion. Moreover, the proposed method blends the kinetics feature with the apparent feature through the novel self-supervised loss function. Furthermore, comprehensive experiments and ablation studies prove that the proposed novel insight into how to predict the optical flow can achieve the better performance of the state-of-the-art methods, and in some metrics, the proposed method outperforms the correlation-based method, especially in situations containing occlusion and fast moving. The code will be public.
Visualizing, Rethinking, and Mining the Loss Landscape of Deep Neural Networks
2024-05-21
Xin-Chun Li, Lan Li, De-Chuan Zhan
The loss landscape of deep neural networks (DNNs) is commonly considered complex and wildly fluctuated. However, an interesting observation is that the loss surfaces plotted along Gaussian noise directions are almost v-basin ones with the perturbed model lying on the basin. This motivates us to rethink whether the 1D or 2D subspace could cover more complex local geometry structures, and how to mine the corresponding perturbation directions. This paper systematically and gradually categorizes the 1D curves from simple to complex, including v-basin, v-side, w-basin, w-peak, and vvv-basin curves. Notably, the latter two types are already hard to obtain via the intuitive construction of specific perturbation directions, and we need to propose proper mining algorithms to plot the corresponding 1D curves. Combining these 1D directions, various types of 2D surfaces are visualized such as the saddle surfaces and the bottom of a bottle of wine that are only shown by demo functions in previous works. Finally, we propose theoretical insights from the lens of the Hessian matrix to explain the observed several interesting phenomena.
Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers
2024-05-21
Fan Shi, Chong Zhang, Takahiro Miki, Joonho Lee, Marco Hutter, Stelian Coros
Legged locomotion has recently achieved remarkable success with the progress of machine learning techniques, especially deep reinforcement learning (RL). Controllers employing neural networks have demonstrated empirical and qualitative robustness against real-world uncertainties, including sensor noise and external perturbations. However, formally investigating the vulnerabilities of these locomotion controllers remains a challenge. This difficulty arises from the requirement to pinpoint vulnerabilities across a long-tailed distribution within a high-dimensional, temporally sequential space. As a first step towards quantitative verification, we propose a computational method that leverages sequential adversarial attacks to identify weaknesses in learned locomotion controllers. Our research demonstrates that, even state-of-the-art robust controllers can fail significantly under well-designed, low-magnitude adversarial sequence. Through experiments in simulation and on the real robot, we validate our approach's effectiveness, and we illustrate how the results it generates can be used to robustify the original policy and offer valuable insights into the safety of these black-box policies. Project page: https://fanshi14.github.io/me/rss24.html
Rethinking Overlooked Aspects in Vision-Language Models
2024-05-20
Yuan Liu, Le Tian, Xiao Zhou, Jie Zhou
Recent advancements in large vision-language models (LVLMs), such as GPT4-V and LLaVA, have been substantial. LLaVA's modular architecture, in particular, offers a blend of simplicity and efficiency. Recent works mainly focus on introducing more pre-training and instruction tuning data to improve model's performance. This paper delves into the often-neglected aspects of data efficiency during pre-training and the selection process for instruction tuning datasets. Our research indicates that merely increasing the size of pre-training data does not guarantee improved performance and may, in fact, lead to its degradation. Furthermore, we have established a pipeline to pinpoint the most efficient instruction tuning (SFT) dataset, implying that not all SFT data utilized in existing studies are necessary. The primary objective of this paper is not to introduce a state-of-the-art model, but rather to serve as a roadmap for future research, aiming to optimize data usage during pre-training and fine-tuning processes to enhance the performance of vision-language models.
Towards in-situ Psychological Profiling of Cybercriminals Using Dynamically Generated Deception Environments
2024-05-19
Jacob Quibell
Cybercrime is estimated to cost the global economy almost \$10 trillion annually and with businesses and governments reporting an ever-increasing number of successful cyber-attacks there is a growing demand to rethink the strategy towards cyber security. The traditional, perimeter security approach to cyber defence has so far proved inadequate to combat the growing threat of cybercrime. Cyber deception offers a promising alternative by creating a dynamic defence environment. Deceptive techniques aim to mislead attackers, diverting them from critical assets whilst simultaneously gathering cyber threat intelligence on the threat actor. This article presents a proof-of-concept (POC) cyber deception system that has been developed to capture the profile of an attacker in-situ, during a simulated cyber-attack in real time. By dynamically and autonomously generating deception material based on the observed attacker behaviour and analysing how the attacker interacts with the deception material, the system outputs a prediction on the attacker's motive. The article also explores how this POC can be expanded to infer other features of the attacker's profile such as psychological characteristics. By dynamically and autonomously generating deception material based on observed attacker behaviour and analysing how the attacker interacts with the deception material, the system outputs a prediciton on the attacker's motive. The article also explores how this POC can be expanded to infer other features of the attacker's profile such as psychological characteristics.
Quantum-Train: Rethinking Hybrid Quantum-Classical Machine Learning in the Model Compression Perspective
2024-05-18
Chen-Yu Liu, En-Jui Kuo, Chu-Hsuan Abraham Lin, Jason Gemsun Young, Yeong-Jar Chang, Min-Hsiu Hsieh, Hsi-Sheng Goan
We introduces the Quantum-Train(QT) framework, a novel approach that integrates quantum computing with classical machine learning algorithms to address significant challenges in data encoding, model compression, and inference hardware requirements. Even with a slight decrease in accuracy, QT achieves remarkable results by employing a quantum neural network alongside a classical mapping model, which significantly reduces the parameter count from $M$ to $O(\text{polylog} (M))$ during training. Our experiments demonstrate QT's effectiveness in classification tasks, offering insights into its potential to revolutionize machine learning by leveraging quantum computational advantages. This approach not only improves model efficiency but also reduces generalization errors, showcasing QT's potential across various machine learning applications.
Current multimodal Large Language Models (MLLMs) suffer from ``hallucination'', occasionally generating responses that are not grounded in the input images. To tackle this challenge, one promising path is to utilize reinforcement learning from human feedback (RLHF), which steers MLLMs towards learning superior responses while avoiding inferior ones. We rethink the common practice of using binary preferences (i.e., superior, inferior), and find that adopting multi-level preferences (e.g., superior, medium, inferior) is better for two benefits: 1) It narrows the gap between adjacent levels, thereby encouraging MLLMs to discern subtle differences. 2) It further integrates cross-level comparisons (beyond adjacent-level comparisons), thus providing a broader range of comparisons with hallucination examples. To verify our viewpoint, we present the Automated Multi-level Preference (AMP) framework for MLLMs. To facilitate this framework, we first develop an automated dataset generation pipeline that provides high-quality multi-level preference datasets without any human annotators. Furthermore, we design the Multi-level Direct Preference Optimization (MDPO) algorithm to robustly conduct complex multi-level preference learning. Additionally, we propose a new hallucination benchmark, MRHal-Bench. Extensive experiments across public hallucination and general benchmarks, as well as our MRHal-Bench, demonstrate the effectiveness of our proposed method. Code is available at https://github.com/takomc/amp.
A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers
2024-05-17
Kaiyu Huang, Fengran Mo, Hongliang Li, You Li, Yuanchi Zhang, Weijian Yi, Yulong Mao, Jinchen Liu, Yuzhuang Xu, Jinan Xu, Jian-Yun Nie, Yang Liu
The rapid development of Large Language Models (LLMs) demonstrates remarkable multilingual capabilities in natural language processing, attracting global attention in both academia and industry. To mitigate potential discrimination and enhance the overall usability and accessibility for diverse language user groups, it is important for the development of language-fair technology. Despite the breakthroughs of LLMs, the investigation into the multilingual scenario remains insufficient, where a comprehensive survey to summarize recent approaches, developments, limitations, and potential solutions is desirable. To this end, we provide a survey with multiple perspectives on the utilization of LLMs in the multilingual scenario. We first rethink the transitions between previous and current research on pre-trained language models. Then we introduce several perspectives on the multilingualism of LLMs, including training and inference methods, model security, multi-domain with language culture, and usage of datasets. We also discuss the major challenges that arise in these aspects, along with possible solutions. Besides, we highlight future research directions that aim at further enhancing LLMs with multilingualism. The survey aims to help the research community address multilingual problems and provide a comprehensive understanding of the core concepts, key techniques, and latest developments in multilingual natural language processing based on LLMs.
2024-05-17
Shiqi Huang, Tingfa Xu, Ziyi Shen, Shaheer Ullah Saeed, Wen Yan, Dean Barratt, Yipeng Hu
The goal of image registration is to establish spatial correspondence between two or more images, traditionally through dense displacement fields (DDFs) or parametric transformations (e.g., rigid, affine, and splines). Rethinking the existing paradigms of achieving alignment via spatial transformations, we uncover an alternative but more intuitive correspondence representation: a set of corresponding regions-of-interest (ROI) pairs, which we demonstrate to have sufficient representational capability as other correspondence representation methods.Further, it is neither necessary nor sufficient for these ROIs to hold specific anatomical or semantic significance. In turn, we formulate image registration as searching for the same set of corresponding ROIs from both moving and fixed images - in other words, two multi-class segmentation tasks on a pair of images. For a general-purpose and practical implementation, we integrate the segment anything model (SAM) into our proposed algorithms, resulting in a SAM-enabled registration (SAMReg) that does not require any training data, gradient-based fine-tuning or engineered prompts. We experimentally show that the proposed SAMReg is capable of segmenting and matching multiple ROI pairs, which establish sufficiently accurate correspondences, in three clinical applications of registering prostate MR, cardiac MR and abdominal CT images. Based on metrics including Dice and target registration errors on anatomical structures, the proposed registration outperforms both intensity-based iterative algorithms and DDF-predicting learning-based networks, even yielding competitive performance with weakly-supervised registration which requires fully-segmented training data.
Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective
2024-05-17
Zhiwei Zhang, Minhua Lin, Enyan Dai, Suhang Wang
Graph Neural Networks (GNNs) have shown remarkable performance in various tasks. However, recent works reveal that GNNs are vulnerable to backdoor attacks. Generally, backdoor attack poisons the graph by attaching backdoor triggers and the target class label to a set of nodes in the training graph. A GNN trained on the poisoned graph will then be misled to predict test nodes attached with trigger to the target class. Despite their effectiveness, our empirical analysis shows that triggers generated by existing methods tend to be out-of-distribution (OOD), which significantly differ from the clean data. Hence, these injected triggers can be easily detected and pruned with widely used outlier detection methods in real-world applications. Therefore, in this paper, we study a novel problem of unnoticeable graph backdoor attacks with in-distribution (ID) triggers. To generate ID triggers, we introduce an OOD detector in conjunction with an adversarial learning strategy to generate the attributes of the triggers within distribution. To ensure a high attack success rate with ID triggers, we introduce novel modules designed to enhance trigger memorization by the victim model trained on poisoned graph. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed method in generating in distribution triggers that can by-pass various defense strategies while maintaining a high attack success rate.
2024-05-17
Francesco Leofante, Hamed Ayoobi, Adam Dejl, Gabriel Freedman, Deniz Gorur, Junqi Jiang, Guilherme Paulino-Passos, Antonio Rago, Anna Rapberger, Fabrizio Russo, Xiang Yin, Dekai Zhang, Francesca Toni
AI has become pervasive in recent years, but state-of-the-art approaches predominantly neglect the need for AI systems to be contestable. Instead, contestability is advocated by AI guidelines (e.g. by the OECD) and regulation of automated decision-making (e.g. GDPR). In this position paper we explore how contestability can be achieved computationally in and for AI. We argue that contestable AI requires dynamic (human-machine and/or machine-machine) explainability and decision-making processes, whereby machines can (i) interact with humans and/or other machines to progressively explain their outputs and/or their reasoning as well as assess grounds for contestation provided by these humans and/or other machines, and (ii) revise their decision-making processes to redress any issues successfully raised during contestation. Given that much of the current AI landscape is tailored to static AIs, the need to accommodate contestability will require a radical rethinking, that, we argue, computational argumentation is ideally suited to support.
Rethinking ChatGPT's Success: Usability and Cognitive Behaviors Enabled by Auto-regressive LLMs' Prompting
2024-05-17
Xinzhe Li, Ming Liu
Over the last decade, a wide range of training and deployment strategies for Large Language Models (LLMs) have emerged. Among these, the prompting paradigms of Auto-regressive LLMs (AR-LLMs) have catalyzed a significant surge in Artificial Intelligence (AI). This paper aims to emphasize the significance of utilizing free-form modalities (forms of input and output) and verbal free-form contexts as user-directed channels (methods for transforming modalities) for downstream deployment. Specifically, we analyze the structure of modalities within both two types of LLMs and six task-specific channels during deployment. From the perspective of users, our analysis introduces and applies the analytical metrics of task customizability, transparency, and complexity to gauge their usability, highlighting the superior nature of AR-LLMs' prompting paradigms. Moreover, we examine the stimulation of diverse cognitive behaviors in LLMs through the adoption of free-form text and verbal contexts, mirroring human linguistic expressions of such behaviors. We then detail four common cognitive behaviors to underscore how AR-LLMs' prompting successfully imitate human-like behaviors using this free-form modality and channel. Lastly, the potential for improving LLM deployment, both as autonomous agents and within multi-agent systems, is identified via cognitive behavior concepts and principles.
Influence Maximization in Hypergraphs using Multi-Objective Evolutionary Algorithms
2024-05-16
Stefano Genetti, Eros Ribaga, Elia Cunegatti, Quintino Francesco Lotito, Giovanni Iacca
The Influence Maximization (IM) problem is a well-known NP-hard combinatorial problem over graphs whose goal is to find the set of nodes in a network that spreads influence at most. Among the various methods for solving the IM problem, evolutionary algorithms (EAs) have been shown to be particularly effective. While the literature on the topic is particularly ample, only a few attempts have been made at solving the IM problem over higher-order networks, namely extensions of standard graphs that can capture interactions that involve more than two nodes. Hypergraphs are a valuable tool for modeling complex interaction networks in various domains; however, they require rethinking of several graph-based problems, including IM. In this work, we propose a multi-objective EA for the IM problem over hypergraphs that leverages smart initialization and hypergraph-aware mutation. While the existing methods rely on greedy or heuristic methods, to our best knowledge this is the first attempt at applying EAs to this problem. Our results over nine real-world datasets and three propagation models, compared with five baseline algorithms, reveal that our method achieves in most cases state-of-the-art results in terms of hypervolume and solution diversity.
SpecDETR: A Transformer-based Hyperspectral Point Object Detection Network
2024-05-16
Zhaoxu Li, Wei An, Gaowei Guo, Longguang Wang, Yingqian Wang, Zaiping Lin
Hyperspectral target detection (HTD) aims to identify specific materials based on spectral information in hyperspectral imagery and can detect point targets, some of which occupy a smaller than one-pixel area. However, existing HTD methods are developed based on per-pixel binary classification, which limits the feature representation capability for point targets. In this paper, we rethink the hyperspectral point target detection from the object detection perspective, and focus more on the object-level prediction capability rather than the pixel classification capability. Inspired by the token-based processing flow of Detection Transformer (DETR), we propose the first specialized network for hyperspectral multi-class point object detection, SpecDETR. Without the backbone part of the current object detection framework, SpecDETR treats the spectral features of each pixel in hyperspectral images as a token and utilizes a multi-layer Transformer encoder with local and global coordination attention modules to extract deep spatial-spectral joint features. SpecDETR regards point object detection as a one-to-many set prediction problem, thereby achieving a concise and efficient DETR decoder that surpasses the current state-of-the-art DETR decoder in terms of parameters and accuracy in point object detection. We develop a simulated hyperSpectral Point Object Detection benchmark termed SPOD, and for the first time, evaluate and compare the performance of current object detection networks and HTD methods on hyperspectral multi-class point object detection. SpecDETR demonstrates superior performance as compared to current object detection networks and HTD methods on the SPOD dataset. Additionally, we validate on a public HTD dataset that by using data simulation instead of manual annotation, SpecDETR can detect real-world single-spectral point objects directly.
Rethinking Multi-User Semantic Communications with Deep Generative Models
2024-05-16
Eleonora Grassucci, Jinho Choi, Jihong Park, Riccardo F. Gramaccioni, Giordano Cicchetti, Danilo Comminiello
In recent years, novel communication strategies have emerged to face the challenges that the increased number of connected devices and the higher quality of transmitted information are posing. Among them, semantic communication obtained promising results especially when combined with state-of-the-art deep generative models, such as large language or diffusion models, able to regenerate content from extremely compressed semantic information. However, most of these approaches focus on single-user scenarios processing the received content at the receiver on top of conventional communication systems. In this paper, we propose to go beyond these methods by developing a novel generative semantic communication framework tailored for multi-user scenarios. This system assigns the channel to users knowing that the lost information can be filled in with a diffusion model at the receivers. Under this innovative perspective, OFDMA systems should not aim to transmit the largest part of information, but solely the bits necessary to the generative model to semantically regenerate the missing ones. The thorough experimental evaluation shows the capabilities of the novel diffusion model and the effectiveness of the proposed framework, leading towards a GenAI-based next generation of communications.
This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://github.com/Ferry-Li/SI-SOD.
Rethinking Barely-Supervised Volumetric Medical Image Segmentation from an Unsupervised Domain Adaptation Perspective
2024-05-16
Zhiqiang Shen, Peng Cao, Junming Su, Jinzhu Yang, Osmar R. Zaiane
This paper investigates an extremely challenging problem: barely-supervised volumetric medical image segmentation (BSS). A BSS training dataset consists of two parts: 1) a barely-annotated labeled set, where each labeled image contains only a single-slice annotation, and 2) an unlabeled set comprising numerous unlabeled volumetric images. State-of-the-art BSS methods employ a registration-based paradigm, which uses inter-slice image registration to propagate single-slice annotations into volumetric pseudo labels, constructing a completely annotated labeled set, to which a semi-supervised segmentation scheme can be applied. However, the paradigm has a critical limitation: the pseudo-labels generated by image registration are unreliable and noisy. Motivated by this, we propose a new perspective: instead of solving BSS within a semi-supervised learning scheme, this work formulates BSS as an unsupervised domain adaptation problem. To this end, we propose a novel BSS framework, \textbf{B}arely-supervised learning \textbf{via} unsupervised domain \textbf{A}daptation (BvA), as an alternative to the dominant registration paradigm. Specifically, we first design a novel noise-free labeled data construction algorithm (NFC) for slice-to-volume labeled data synthesis. Then, we introduce a frequency and spatial Mix-Up strategy (FSX) to mitigate the domain shifts. Extensive experiments demonstrate that our method provides a promising alternative for BSS. Remarkably, the proposed method, trained on the left atrial segmentation dataset with \textbf{only one} barely-labeled image, achieves a Dice score of 81.20%, outperforming the state-of-the-art by 61.71%. The code is available at https://github.com/Senyh/BvA.
Flashback: Enhancing Proposer-Builder Design with Future-Block Auctions in Proof-of-Stake Ethereum
2024-05-15
Yifan Mao, Mengya Zhang, Shaileshh Bojja Venkatakrishnan, Zhiqiang Lin
Maximal extractable value (MEV) in which block proposers unethically gain profits by manipulating the order in which transactions are included within a block, is a key challenge facing blockchains such as Ethereum today. Left unchecked, MEV can lead to a centralization of stake distribution thereby ultimately compromising the security of blockchain consensus. To preserve proposer decentralization (and hence security) of the blockchain, Ethereum has advocated for a proposer-builder separation (PBS) in which the functionality of transaction ordering is separated from proposers and assigned to separate entities called builders. Builders accept transaction bundles from searchers, who compete to find the most profitable bundles. Builders then bid completed blocks to proposers, who accept the most profitable blocks for publication. The auction mechanisms used between searchers, builders and proposers are crucial to the overall health of the blockchain. In this paper, we consider PBS design in Ethereum as a game between searchers, builders and proposers. A key novelty in our design is the inclusion of future block proposers, as all proposers of an epoch are decided ahead of time in proof-of-stake (PoS) Ethereum within the game model. Our analysis shows the existence of alternative auction mechanisms that result in a better (more profitable) equilibrium to players compared to state-of-the-art. Experimental evaluations based on synthetic and real-world data traces corroborate the analysis. Our results highlight that a rethinking of auction mechanism designs is necessary in PoS Ethereum to prevent disruption.
TunnelSense: Low-power, Non-Contact Sensing using Tunnel Diodes
2024-05-15
Lim Chang Quan Thaddeus, C. Rajashekar Reddy, Yuvraj Singh Bhadauria, Dhairya Shah, Manoj Gulati, Ambuj Varshney
Sensing the motion of physical objects in an environment enables numerous applications, from tracking occupancy in buildings and monitoring vital signs to diagnosing faults in machines. Typically, these application scenarios involve attaching a sensor, such as an accelerometer, to the object of interest, like a wearable device that tracks our steps. However, many of these scenarios require tracking motion in a noncontact manner where the sensor is not in touch with the object. A sensor in such a scenario observes variations in radio, light, acoustic, and infrared fields disturbed by the object's motion. Current noncontact sensing mechanisms often require substantial energy and involve complex processing on sophisticated hardware. We present TunnelSense, a novel mechanism that rethinks noncontact sensing using tunnel diode oscillators. They are highly sensitive to changes in their electromagnetic environments. The motion of an object near a tunnel diode oscillator induces corresponding changes in its resonant frequency and thus in the generated radio waves. Additionally, the low-power characteristics of the tunnel diode allow tags designed using them to operate on less than 100microwatt of power consumption and with a biasing voltage starting at 70 millivolt. This enables prolonged tag operation on a small battery or energy harvested from the environment. Among numerous applications enabled by the TunnelSense system, this work demonstrates its ability to detect breathing at distances up to 30 centimeter between the subject and the TunnelSense tag.
Rethinking the adaptive relationship between Encoder Layers and Decoder Layers
2024-05-14
Yubo Song
This article explores the adaptive relationship between Encoder Layers and Decoder Layers using the SOTA model Helsinki-NLP/opus-mt-de-en, which translates German to English. The specific method involves introducing a bias-free fully connected layer between the Encoder and Decoder, with different initializations of the layer's weights, and observing the outcomes of fine-tuning versus retraining. Four experiments were conducted in total. The results suggest that directly modifying the pre-trained model structure for fine-tuning yields suboptimal performance. However, upon observing the outcomes of the experiments with retraining, this structural adjustment shows significant potential.
Rethinking Scanning Strategies with Vision Mamba in Semantic Segmentation of Remote Sensing Imagery: An Experimental Study
2024-05-14
Qinfeng Zhu, Yuan Fang, Yuanzhi Cai, Cheng Chen, Lei Fan
Deep learning methods, especially Convolutional Neural Networks (CNN) and Vision Transformer (ViT), are frequently employed to perform semantic segmentation of high-resolution remotely sensed images. However, CNNs are constrained by their restricted receptive fields, while ViTs face challenges due to their quadratic complexity. Recently, the Mamba model, featuring linear complexity and a global receptive field, has gained extensive attention for vision tasks. In such tasks, images need to be serialized to form sequences compatible with the Mamba model. Numerous research efforts have explored scanning strategies to serialize images, aiming to enhance the Mamba model's understanding of images. However, the effectiveness of these scanning strategies remains uncertain. In this research, we conduct a comprehensive experimental investigation on the impact of mainstream scanning directions and their combinations on semantic segmentation of remotely sensed images. Through extensive experiments on the LoveDA, ISPRS Potsdam, and ISPRS Vaihingen datasets, we demonstrate that no single scanning strategy outperforms others, regardless of their complexity or the number of scanning directions involved. A simple, single scanning direction is deemed sufficient for semantic segmentation of high-resolution remotely sensed images. Relevant directions for future research are also recommended.
Rethinking Prior Information Generation with CLIP for Few-Shot Segmentation
2024-05-14
Jin Wang, Bingfeng Zhang, Jian Pang, Honglong Chen, Weifeng Liu
Few-shot segmentation remains challenging due to the limitations of its labeling information for unseen classes. Most previous approaches rely on extracting high-level feature maps from the frozen visual encoder to compute the pixel-wise similarity as a key prior guidance for the decoder. However, such a prior representation suffers from coarse granularity and poor generalization to new classes since these high-level feature maps have obvious category bias. In this work, we propose to replace the visual prior representation with the visual-text alignment capacity to capture more reliable guidance and enhance the model generalization. Specifically, we design two kinds of training-free prior information generation strategy that attempts to utilize the semantic alignment capability of the Contrastive Language-Image Pre-training model (CLIP) to locate the target class. Besides, to acquire more accurate prior guidance, we build a high-order relationship of attention maps and utilize it to refine the initial prior information. Experiments on both the PASCAL-5{i} and COCO-20{i} datasets show that our method obtains a clearly substantial improvement and reaches the new state-of-the-art performance.
Histology slide digitization is becoming essential for telepathology (remote consultation), knowledge sharing (education), and using the state-of-the-art artificial intelligence algorithms (augmented/automated end-to-end clinical workflows). However, the cumulative costs of digital multi-slide high-speed brightfield scanners, cloud/on-premises storage, and personnel (IT and technicians) make the current slide digitization workflows out-of-reach for limited-resource settings, further widening the health equity gap; even single-slide manual scanning commercial solutions are costly due to hardware requirements (high-resolution cameras, high-spec PC/workstation, and support for only high-end microscopes). In this work, we present a new cloud slide digitization workflow for creating scanner-quality whole-slide images (WSIs) from uploaded low-quality videos, acquired from cheap and inexpensive microscopes with built-in cameras. Specifically, we present a pipeline to create stitched WSIs while automatically deblurring out-of-focus regions, upsampling input 10X images to 40X resolution, and reducing brightness/contrast and light-source illumination variations. We demonstrate the WSI creation efficacy from our workflow on World Health Organization-declared neglected tropical disease, Cutaneous Leishmaniasis (prevalent only in the poorest regions of the world and only diagnosed by sub-specialist dermatopathologists, rare in poor countries), as well as other common pathologies on core biopsies of breast, liver, duodenum, stomach and lymph node. The code and pretrained models will be accessible via our GitHub (https://github.com/nadeemlab/DeepLIIF), and the cloud platform will be available at https://deepliif.org for uploading microscope videos and downloading/viewing WSIs with shareable links (no sign-in required) for telepathology and knowledge sharing.
AdaptNet: Rethinking Sensing and Communication for a Seamless Internet of Drones Experience
2024-05-12
Ananya Hazarika, Mehdi Rahmati
In the evolving era of Unmanned Aerial Vehicles (UAVs), the emphasis has moved from mere data collection to strategically obtaining timely and relevant data within the Internet of Drones (IoDs) ecosystem. However, the unpredictable conditions in dynamic IoDs pose safety challenges for drones. Addressing this, our approach introduces a multi-UAV framework using spatial-temporal clustering and the Frechet distance for enhancing reliability. Seamlessly coupled with Integrated Sensing and Communication (ISAC), it enhances the precision and agility of UAV networks. Our Multi-Agent Reinforcement Learning (MARL) mechanism ensures UAVs adapt strategies through ongoing environmental interactions and enhancing intelligent sensing. This focus ensures operational safety and efficiency, considering data capture and transmission viability. By evaluating the relevance of the sensed information, we can communicate only the most crucial data variations beyond a set threshold and optimize bandwidth usage. Our methodology transforms the UAV domain, transitioning drones from data gatherers to adept information orchestrators, establishing a benchmark for efficiency and adaptability in modern aerial systems.
Event cameras, drawing inspiration from biological systems, efficiently detect changes in ambient light with low latency and high dynamic range while consuming minimal power. The most current approach to processing event data often involves converting it into frame-based representations, which is well-established in traditional vision. However, this approach neglects the sparsity of event data, loses fine-grained temporal information during the transformation process, and increases the computational burden, making it ineffective for characterizing event camera properties. In contrast, Point Cloud is a popular representation for 3D processing and is better suited to match the sparse and asynchronous nature of the event camera. Nevertheless, despite the theoretical compatibility of point-based methods with event cameras, the results show a performance gap that is not yet satisfactory compared to frame-based methods. In order to bridge the performance gap, we propose EventMamba, an efficient and effective Point Cloud framework that achieves competitive results even compared to the state-of-the-art (SOTA) frame-based method in both classification and regression tasks. This notable accomplishment is facilitated by our rethinking of the distinction between Event Cloud and Point Cloud, emphasizing effective temporal information extraction through optimized network structures. Specifically, EventMamba leverages temporal aggregation and State Space Model (SSM) based Mamba boasting enhanced temporal information extraction capabilities. Through a hierarchical structure, EventMamba is adept at abstracting local and global spatial features and implicit and explicit temporal features. By adhering to the lightweight design principle, EventMamba delivers impressive results with minimal computational resource utilization, demonstrating its efficiency and effectiveness.
The article is an attempt to contribute to explorations of a common origin for language and planned-collaborative action. It gives `semantics of change' the central stage in the synthesis, from its history and recordkeeping to its development, its syntax, delivery and reception, including substratal aspects. It is suggested that to arrive at a common core, linguistic semantics must be understood as studying through syntax mobile agent's representing, tracking and coping with change and no change. Semantics of actions can be conceived the same way, but through plans instead of syntax. The key point is the following: Sequencing itself, of words and action sequences, brings in more structural interpretation to the sequence than which is immediately evident from the sequents themselves. Mobile sequencers can be understood as subjects structuring reporting, understanding and keeping track of change and no change. The idea invites rethinking of the notion of category, both in language and in planning. Understanding understanding change by mobile agents is suggested to be about human extended practice, not extended-human practice. That's why linguistics is as important as computer science in the synthesis. It must rely on representational history of acts, thoughts and expressions, personal and public, crosscutting overtness and covertness of these phenomena. It has implication for anthropology in the extended practice, which is covered briefly.
Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits
2024-05-06
Jiachen T. Wang, Tianji Yang, James Zou, Yongchan Kwon, Ruoxi Jia
Data Shapley provides a principled approach to data valuation and plays a crucial role in data-centric machine learning (ML) research. Data selection is considered a standard application of Data Shapley. However, its data selection performance has shown to be inconsistent across settings in the literature. This study aims to deepen our understanding of this phenomenon. We introduce a hypothesis testing framework and show that Data Shapley's performance can be no better than random selection without specific constraints on utility functions. We identify a class of utility functions, monotonically transformed modular functions, within which Data Shapley optimally selects data. Based on this insight, we propose a heuristic for predicting Data Shapley's effectiveness in data selection tasks. Our experiments corroborate these findings, adding new insights into when Data Shapley may or may not succeed.
U-DiTs: Downsample Tokens in U-Shaped Diffusion Transformers
2024-05-04
Yuchuan Tian, Zhijun Tu, Hanting Chen, Jie Hu, Chao Xu, Yunhe Wang
Diffusion Transformers (DiTs) introduce the transformer architecture to diffusion tasks for latent-space image generation. With an isotropic architecture that chains a series of transformer blocks, DiTs demonstrate competitive performance and good scalability; but meanwhile, the abandonment of U-Net by DiTs and their following improvements is worth rethinking. To this end, we conduct a simple toy experiment by comparing a U-Net architectured DiT with an isotropic one. It turns out that the U-Net architecture only gain a slight advantage amid the U-Net inductive bias, indicating potential redundancies within the U-Net-style DiT. Inspired by the discovery that U-Net backbone features are low-frequency-dominated, we perform token downsampling on the query-key-value tuple for self-attention that bring further improvements despite a considerable amount of reduction in computation. Based on self-attention with downsampled tokens, we propose a series of U-shaped DiTs (U-DiTs) in the paper and conduct extensive experiments to demonstrate the extraordinary performance of U-DiT models. The proposed U-DiT could outperform DiT-XL/2 with only 1/6 of its computation cost. Codes are available at https://github.com/YuchuanTian/U-DiT.
Position: Why We Must Rethink Empirical Research in Machine Learning
2024-05-03
Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl
We warn against a common but incomplete understanding of empirical research in machine learning that leads to non-replicable results, makes findings unreliable, and threatens to undermine progress in the field. To overcome this alarming situation, we call for more awareness of the plurality of ways of gaining knowledge experimentally but also of some epistemic limitations. In particular, we argue most current empirical machine learning research is fashioned as confirmatory research while it should rather be considered exploratory.
Large Language Models (LLMs) have achieved remarkable success across diverse tasks, yet they remain vulnerable to adversarial attacks, notably the well-documented \textit{jailbreak} attack. Recently, the Greedy Coordinate Gradient (GCG) attack has demonstrated efficacy in exploiting this vulnerability by optimizing adversarial prompts through a combination of gradient heuristics and greedy search. However, the efficiency of this attack has become a bottleneck in the attacking process. To mitigate this limitation, in this paper we rethink the generation of adversarial prompts through an optimization lens, aiming to stabilize the optimization process and harness more heuristic insights from previous iterations. Specifically, we introduce the \textbf{M}omentum \textbf{A}ccelerated G\textbf{C}G (\textbf{MAC}) attack, which incorporates a momentum term into the gradient heuristic. Experimental results showcase the notable enhancement achieved by MAP in gradient-based attacks on aligned language models. Our code is available at https://github.com/weizeming/momentum-attack-llm.
2024-05-02
James Mayfield, Eugene Yang, Dawn Lawrie, Sean MacAvaney, Paul McNamee, Douglas W. Oard, Luca Soldaini, Ian Soboroff, Orion Weller, Efsun Kayi, Kate Sanders, Marc Mason, Noah Hibbler
Large Language Models (LLMs) have enabled new ways to satisfy information needs. Although great strides have been made in applying them to settings like document ranking and short-form text generation, they still struggle to compose complete, accurate, and verifiable long-form reports. Reports with these qualities are necessary to satisfy the complex, nuanced, or multi-faceted information needs of users. In this perspective paper, we draw together opinions from industry and academia, and from a variety of related research areas, to present our vision for automatic report generation, and -- critically -- a flexible framework by which such reports can be evaluated. In contrast with other summarization tasks, automatic report generation starts with a detailed description of an information need, stating the necessary background, requirements, and scope of the report. Further, the generated reports should be complete, accurate, and verifiable. These qualities, which are desirable -- if not required -- in many analytic report-writing settings, require rethinking how to build and evaluate systems that exhibit these qualities. To foster new efforts in building these systems, we present an evaluation framework that draws on ideas found in various evaluations. To test completeness and accuracy, the framework uses nuggets of information, expressed as questions and answers, that need to be part of any high-quality generated report. Additionally, evaluation of citations that map claims made in the report to their source documents ensures verifiability.
War Elephants: Rethinking Combat AI and Human Oversight
2024-04-30
Philip Feldman, Aaron Dant, Harry Dreany
This paper explores the changes that pervasive AI is having on the nature of combat. We look beyond the substitution of AI for experts to an approach where complementary human and machine abilities are blended. Using historical and modern examples, we show how autonomous weapons systems can be effectively managed by teams of human "AI Operators" combined with AI/ML "Proxy Operators." By basing our approach on the principles of complementation, we provide for a flexible and dynamic approach to managing lethal autonomous systems. We conclude by presenting a path to achieving an integrated vision of machine-speed combat where the battlefield AI is operated by AI Operators that watch for patterns of behavior within battlefield to assess the performance of lethal autonomous systems. This approach enables the development of combat systems that are likely to be more ethical, operate at machine speed, and are capable of responding to a broader range of dynamic battlefield conditions than any purely autonomous AI system could support.
While question-answering~(QA) benchmark performance is an automatic and scalable method to compare LLMs, it is an indirect method of evaluating their underlying problem-solving capabilities. Therefore, we propose a holistic and generalizable framework based on \emph{cascaded question disclosure} that provides a more accurate estimate of the models' problem-solving capabilities while maintaining the scalability and automation. This approach collects model responses in a stagewise manner with each stage revealing partial information about the question designed to elicit generalized reasoning in LLMs. We find that our approach not only provides a better comparison between LLMs, but also induces better intermediate traces in models compared to the standard QA paradigm. We empirically verify this behavior on diverse reasoning and knowledge-heavy QA datasets by comparing LLMs of varying sizes and families. Our approach narrows the performance gap observed in the standard QA evaluation settings, indicating that the prevalent indirect QA paradigm of evaluation overestimates the differences in performance between models. We further validate our findings by extensive ablation studies.
Symbolic Computation with Symmetric Polynomials in Real Algebraic Geometry
2025-07-31
Cordian Riener, Thi Xuan Vu
Symmetry plays a central role in accelerating symbolic computation involving polynomials. This chapter surveys recent developments and foundational methods that leverage the inherent symmetries of polynomial systems to reduce complexity, improve algorithmic efficiency, and reveal deeper structural insights. The main focus is on symmetry by the permutation of variables.
A survey of multi-agent geosimulation methodologies: from ABM to LLM
2025-07-31
Virginia Padilla, Jacinto Dávila
We provide a comprehensive examination of agent-based approaches that codify the principles and linkages underlying multi-agent systems, simulations, and information systems. Based on two decades of study, this paper confirms a framework intended as a formal specification for geosimulation platforms. Our findings show that large language models (LLMs) can be effectively incorporated as agent components if they follow a structured architecture specific to fundamental agent activities such as perception, memory, planning, and action. This integration is precisely consistent with the architecture that we formalize, providing a solid platform for next-generation geosimulation systems.
Architectural practice process and artificial intelligence -- an evolving practice
2025-07-31
Mustapha El Moussaoui
In an era of exponential technological advancement, artificial intelligence (AI) has emerged as a transformative force in architecture, reshaping traditional design and construction practices. This article explores the multifaceted roles of AI in the architectural process, emphasizing its potential to enhance creativity and efficiency while addressing its limitations in capturing multisensory and experiential dimensions of space. Historically, architectural innovation has paralleled technological progress, from basic tools to advanced computer-aided design systems. However, the integration of AI presents unique challenges, requiring architects to critically evaluate its role in design. A narrative review methodology was adopted, focusing on academic sources selected for their relevance, recency, and credibility. The findings reveal that AI is increasingly integrated across various stages of the architectural process, from early conceptualization and site analysis to generative design and construction detailing. AI tools excel at automating repetitive tasks and generating innovative design solutions, freeing architects to focus on creativity and problem-solving. Additionally, AI's (text- toimage) visual representation strength challenges the ocularcentric approaches in architecture, which should push future architects to address the holistic sensory and experiential qualities of space or the critical thinking inherent to architectural design. While AI offers transformative potential, architects must view it as a collaborative partner rather than a passive tool.
An Empirical Study on the Amount of Changes Required for Merge Request Acceptance
2025-07-31
Samah Kansab, Mohammed Sayagh, Francis Bordeleau, Ali Tizghadam
Code review (CR) is essential to software development, helping ensure that new code is properly integrated. However, the CR process often involves significant effort, including code adjustments, responses to reviewers, and continued implementation. While past studies have examined CR delays and iteration counts, few have investigated the effort based on the volume of code changes required, especially in the context of GitLab Merge Requests (MRs), which remains underexplored. In this paper, we define and measure CR effort as the amount of code modified after submission, using a dataset of over 23,600 MRs from four GitLab projects. We find that up to 71% of MRs require adjustments after submission, and 28% of these involve changes to more than 200 lines of code. Surprisingly, this effort is not correlated with review time or the number of participants. To better understand and predict CR effort, we train an interpretable machine learning model using metrics across multiple dimensions: text features, code complexity, developer experience, review history, and branching. Our model achieves strong performance (AUC 0.84-0.88) and reveals that complexity, experience, and text features are key predictors. Historical project characteristics also influence current review effort. Our findings highlight the feasibility of using machine learning to explain and anticipate the effort needed to integrate code changes during review.
These notes constitute a survey on the geometric properties of globally subanalytic sets. We start with their definition and some fundamental results such as Gabrielov's Complement Theorem or existence of cell decompositions. We then give the main basic tools of subanalytic geometry, such as Curve Selection Lemma, Lojasiewicz's inequalities, existence of tubular neighborhood, Tamm's theorem (definability of regular points), or existence of regular stratifications (Whitney or Verdier). We then present the developments of Lipschitz geometry obtained by various authors during the four last decades, giving a proof of existence of metric triangulations, introduced by the author of these notes, definable bi-Lipschitz triviality, Lipschitz conic structure, as well as invariance of the link under definable bi-Lipschitz mappings. The last chapter is devoted to geometric integration theory, studying the Hausdorff measure of globally subanalytic sets, integrals of subanalytic functions, as well as the density of subanalytic sets (the Lelong number) and Stokes' formula.
Fast prediction of the hydrodynamic QGP evolution in ultra-relativistic heavy-ion collisions using Fourier Neural Operators
2025-07-31
David Stewart, Joern Putschke
Recent research in machine learning has employed neural networks to learn mappings between function spaces on bounded domains termed ``neural operators''. As such, these operators can provide alternatives to standard numerical methods for partial differential equation (PDE) solutions. In particular, the Fourier Neural Operator (FNO) has been shown to map solutions for classical fluid flow problems with accuracy competitive with traditional PDE solvers and with much greater computing speed. This paper explores the first application of FNOs to model ultra-relativistic hydrodynamic flow of the quark-gluon plasma (QGP) generated in relativistic heavy-ion collisions. The application in ultra-relativistic flow is novel relative to classical flow, due to the hydrodynamic evolution of the QGP occurring in femtometer-scaled explosions characterized by rapid expansion cooling. In this study we investigate the applicability of FNOs as computationally fast alternatives to standard numerical PDE solvers. The FNO predictions are evaluated by comparing to standard PDE solutions, using \MUSIC in the \JETSCAPE Monte Carlo event generator framework. The performance of calculating established experimental observables for flow and jet quenching using FNOs in the MC framework are also reported.
McKean-Vlasov equations with singular coefficients - a review of recent results
2025-07-31
Luca Bondi, Elena Issoglio, Francesco Russo
This paper focuses on recent works on McKean-Vlasov stochastic differential equations (SDEs) involving singular coefficients. After recalling the classical framework, we review existing recent literature depending on the type of singularities of the coefficients: on the one hand they satisfy some integrability and measurability conditions only, while on the other hand the drift is allowed to be a generalised function. Different types of dependencies on the law of the unknown and different noises will also be considered. McKean-Vlasov SDEs are closely related to non-linear Fokker-Planck equations that are satisfied by the law (or its density) of the unknown. These connections are often established also in this singular setting and will be reviewed here. Important tools for dealing with singular coefficients are also included in the paper, such as Figalli-Trevisan superposition principle, Zvonkin transformation, Markov marginal uniqueness, and stochastic sewing lemma.
A wide range of natural and engineered fluid flows exhibit spatial or temporal viscosity variations, spanning scales from microbial locomotion to planetary mantle convection. These variations introduce qualitatively new physical mechanisms absent in constant-viscosity flows. This review surveys such phenomena across scales. In low Reynolds number (Stokes) flows, viscosity gradients couple translation and rotation, enabling novel particle responses to uniform forcing-- mechanisms that microorganisms may exploit. In shear flows, viscosity variation alters base flow profiles and breaks symmetries, modifying stability and transition dynamics. At high Reynolds numbers, stratification fundamentally changes the singular perturbation structure governing energy production, enhancing or suppressing canonical instabilities and introducing new ones. Viscosity variation also affects nonnormal growth and nonlinear interactions that drive transition to turbulence. While laminar and fully developed turbulence have been extensively studied, transitional processes remain poorly understood in variable-viscosity flows. In turbulent regimes, viscosity variation impacts jets, wall-bounded flows, and mixing layers. At geophysical scales, incorporating eddy viscosity stratification in climate models may improve predictions, while in Earth's mantle, viscosity contrasts drive large-scale convection and geological evolution. Particle-laden flows, common across contexts, can generate effective viscosity stratification through inhomogeneous loading. Throughout, we highlight cases where viscosity variation alters flow behavior qualitatively, and point to open questions. This review aims to guide graduate students and researchers toward tractable, cross-disciplinary problems.
Channel Estimation for 6G Near-Field Wireless Communications: A Comprehensive Survey
2025-07-31
Wen-Xuan Long, Shengyu Ye, Marco Moretti, Michele Morelli, Luca Sanguinetti, Rui Chen, Cheng-Xiang Wang
The sixth-generation (6G) wireless systems are expected to adopt extremely large aperture arrays (ELAAs), novel antenna architectures, and operate in extremely high-frequency bands to meet growing data demands. ELAAs significantly increase the number of antennas, enabling finer spatial resolution and improved beamforming. At high frequencies, ELAAs shift communication from the conventional far-field to near-field regime, where spherical wavefronts dominate and the channel response depends on both angle and distance, increasing channel dimensionality. Conventional far-field channel estimation methods, which rely on angular information, struggle in near-field scenarios due to increased pilot overhead and computational complexity. This paper presents a comprehensive survey of recent advances in near-field channel estimation. It first defines the near- and far-field boundary from an electromagnetic perspective and discusses key propagation differences, alongside a brief review of ELAA developments. Then, it introduces mainstream near-field channel models and compares them with far-field models. Major estimation techniques are reviewed under different configurations (single/multi-user, single/multi-carrier), including both direct estimation and RIS-assisted cascaded estimation. These techniques reveal trade-offs among estimation accuracy, complexity, and overhead. This survey aims to provide insights and foundations for efficient and scalable near-field channel estimation in 6G systems, while identifying key challenges and future research directions.
Review on recent results of J/$ψ$ production at STAR
2025-07-31
Jitka Mrazkova
Studying the production of J/$\psi$ (bound state of charm and anti-charm quark) in proton-proton collisions gives an opportunity to test quantum chromodynamics (QCD) calculations, as the production of J/$\psi$ involves both perturbative and non-perturbative processes. However, theoretical calculations are still unable to fully and simultaneously explain experimental results, such as polarization and $p_\text{T}$ spectra measured in different kinematic regimes and colliding energies. More studies are needed to investigate J/$\psi$ production mechanism. In heavy-ion collisions, charmonia can be used to study the properties of the medium as they are expected to dissociate in the medium when the Debye radius, inversely proportional to the medium temperature, becomes smaller than their size. Other competing effects, such as recombination, have also been found to modify the observed J/$\psi$ yield in heavy-ion collisions. We review recent measurements of the J/$\psi$ production in proton-proton and heavy-ion collisions at various collision energies measured with the STAR experiment at RHIC. The data are compared with recent model calculations on charmonia production.
We investigate the trade-off between certificate length and verifier runtime. We prove a Verifier Trade-off Theorem showing that reducing the inherent verification time of a language from \(f(n)\) to \(g(n)\), where \(f(n) \ge g(n)\), requires certificates of length at least \(\Omega(\log(f(n) / g(n)))\). This theorem induces a natural hierarchy based on certificate complexity. We demonstrate its applicability to analyzing conjectured separations between complexity classes (e.g., \(\np\) and \(\exptime\)) and to studying natural problems such as string periodicity and rotation detection. Additionally, we provide perspectives on the \(\p\) vs. \(\np\) problem by relating it to the existence of sub-linear certificates.
Incorporating structural uncertainty in causal decision making
2025-07-31
Maurits Kaptein
Practitioners making decisions based on causal effects typically ignore structural uncertainty. We analyze when this uncertainty is consequential enough to warrant methodological solutions (Bayesian model averaging over competing causal structures). Focusing on bivariate relationships ($X \rightarrow Y$ vs. $X \leftarrow Y$), we establish that model averaging is beneficial when: (1) structural uncertainty is moderate to high, (2) causal effects differ substantially between structures, and (3) loss functions are sufficiently sensitive to the size of the causal effect. We prove optimality results of our suggested methodological solution under regularity conditions and demonstrate through simulations that modern causal discovery methods can provide, within limits, the necessary quantification. Our framework complements existing robust causal inference approaches by addressing a distinct source of uncertainty typically overlooked in practice.
Large language models (LLMs) hold promise in clinical decision support but face major challenges in safety evaluation and effectiveness validation. We developed the Clinical Safety-Effectiveness Dual-Track Benchmark (CSEDB), a multidimensional framework built on clinical expert consensus, encompassing 30 criteria covering critical areas like critical illness recognition, guideline adherence, and medication safety, with weighted consequence measures. Thirty-two specialist physicians developed and reviewed 2,069 open-ended Q&A items aligned with these criteria, spanning 26 clinical departments to simulate real-world scenarios. Benchmark testing of six LLMs revealed moderate overall performance (average total score 57.2%, safety 54.7%, effectiveness 62.3%), with a significant 13.3% performance drop in high-risk scenarios (p < 0.0001). Domain-specific medical LLMs showed consistent performance advantages over general-purpose models, with relatively higher top scores in safety (0.912) and effectiveness (0.861). The findings of this study not only provide a standardized metric for evaluating the clinical application of medical LLMs, facilitating comparative analyses, risk exposure identification, and improvement directions across different scenarios, but also hold the potential to promote safer and more effective deployment of large language models in healthcare environments.
Automated calibration of simulated galaxy catalogues for cosmological analyses
2025-07-31
I. Tutusaus, P. Fosalba, L. Blot, P. Tallada-Crespí, J. Carretero, F. J. Castander, E. J. Gonzalez, A. Alarcon
Simulated galaxy catalogues have become an essential tool for preparing and exploiting observations from galaxy surveys. They constitute a key ingredient in modelling the systematic uncertainties present in the analysis. However, in order to reach the large volume and high precision required for galaxy surveys, we generally populate dark matter haloes with galaxies following certain theoretical recipes. Such recipes contain free parameters that are calibrated comparing the simulations against observations, but the creation of galaxy mocks is a stochastic process with a large number of free parameters to calibrate. We present a new pipeline, based on the differential evolution algorithm, that can calibrate galaxy mocks in a fully automated way for realistic scenarios with a large parameter space. We apply the pipeline to galaxy mocks built on a combination of halo occupation distribution and sub-halo abundance matching techniques. We show that our pipeline can properly calibrate the galaxy mocks against observations for both $\Lambda$CDM and modified gravity halo catalogues. This type of calibration pipeline provides a new tool for automating the calibration of future massive galaxy mocks.
Recent advances in arrow relations and traces of sets
2025-07-31
Mingze Li, Jie Ma, Mingyuan Rong
The arrow relation, a central concept in extremal set theory, captures quantitative relationships between families of sets and their traces. Formally, the arrow relation $(n, m) \rightarrow (a, b)$ signifies that for any family $\mathcal{F} \subseteq 2^{[n]}$ with $|\mathcal{F}| \geqslant m$, there exists an $a$-element subset $T \subseteq [n]$ such that the trace $\mathcal{F}_{|T} = \{ F \cap T : F \in \mathcal{F} \}$ contains at least $b$ distinct sets. This survey highlights recent progress on a variety of problems and results connected to arrow relations. We explore diverse topics, broadly categorized by different extremal perspectives on these relations, offering a cohesive overview of the field.
2025-07-31
Radu-Andrei Bourceanu, Neil De La Fuente, Jan Grimm, Andrei Jardan, Andriy Manucharyan, Cornelius Weiss, Roman Pflugfelder
This report analyzes the evolution of key design patterns in computer vision by examining six influential papers. The analy- sis begins with foundational architectures for image recognition. We review ResNet, which introduced residual connections to overcome the vanishing gradient problem and enable effective training of significantly deeper convolutional networks. Subsequently, we examine the Vision Transformer (ViT), which established a new paradigm by applying the Transformer ar- chitecture to sequences of image patches, demonstrating the efficacy of attention-based models for large-scale image recogni- tion. Building on these visual representation backbones, we investigate generative models. Generative Adversarial Networks (GANs) are analyzed for their novel adversarial training process, which challenges a generator against a discriminator to learn complex data distributions. Then, Latent Diffusion Models (LDMs) are covered, which improve upon prior generative methods by performing a sequential denoising process in a perceptually compressed latent space. LDMs achieve high-fidelity synthesis with greater computational efficiency, representing the current state-of-the-art for image generation. Finally, we explore self-supervised learning techniques that reduce dependency on labeled data. DINO is a self-distillation framework in which a student network learns to match the output of a momentum-updated teacher, yielding features with strong k-NN classification performance. We conclude with Masked Autoencoders (MAE), which utilize an asymmetric encoder-decoder design to reconstruct heavily masked inputs, providing a highly scalable and effective method for pre-training large-scale vision models.
Quality Evaluation of COBOL to Java Code Transformation
2025-07-31
Shmulik Froimovich, Raviv Gal, Wesam Ibraheem, Avi Ziv
We present an automated evaluation system for assessing COBOL-to-Java code translation within IBM's watsonx Code Assistant for Z (WCA4Z). The system addresses key challenges in evaluating LLM-based translators, including model opacity and the complexity of translation quality assessment. Our approach combines analytic checkers with LLM-as-a-judge (LaaJ) techniques to deliver scalable, multi-faceted evaluations. The system supports continuous integration workflows, enables large-scale benchmarking, and reduces reliance on manual review. We describe the system architecture, evaluation strategies, and reporting mechanisms that provide actionable insights for developers and project managers, facilitating the evolution of high-quality, modernized codebases.
2025-07-31
Ray P. Norris, Miranda Yew, Evan Crawford, Nikhel Gupta, Lawrence Rudnick, H. Andernach, Miroslav D. Filipović, Yjan A. Gordon, Andrew M. Hopkins, Laurence Park, Michael J. I. Brown, Ana Jimenez-Gallardo, S. S. Shabala
We present a catalogue of 3557 Double Radio sources associated with Active Galactic Nuclei (DRAGNs) from the First Pilot Survey of the Evolutionary Map of the Universe (EMU), observed at 944 MHz with the Australian Square Kilometre Array Pathfinder (ASKAP) telescope, covering 270 deg^2. We have extracted and identified each source by eye, tagged it with a morphological type and measured its parameters. The resulting catalogue will be used in subsequent papers to explore the properties of these sources, to train machine-learning algorithms for the detection of these sources in larger fields, and to compare with the results of Citizen Science projects, with the ultimate goal of understanding the physical processes that drive DRAGNs. Compared with earlier, lower sensitivity, catalogues, we find more diffuse structure and a plethora of more complex structures, ranging from wings of radio emission on the side of the jets, to types of object which have not been seen in earlier observations. As well as the well-known FR1 and FR2 sources, we find significant numbers of rare types of radio source such as Hybrid Morphology Radio Sources and one-sided jets, as well as a wide range of bent-tail and head-tail sources.
First-principles study of Rh- and Pd-based kagome-layered shandites
2025-07-31
Luca Buiarelli, Turan Birol, Brian M. Andersen, Morten H. Christensen
The shandite structure hosts transition metals arranged in kagome layers stacked rhombohedrally, and interspersed with post-transition metal ions and chalcogens. The electronic states near the Fermi level are dominated by the transition metal $d$-orbitals and feature saddle points near several of the high-symmetry positions of the Brillouin zone, most notably the F and L points. Combining symmetry considerations with ab initio methods, we study the electronic and structural properties of these materials with an emphasis on the connection between electronic saddle points at specific momenta and structural instabilities at these momenta. While the parent compounds studied are all found to be structurally stable in the $R\bar{3}m$ space group under ambient conditions we show that, in specific compounds, moving the saddle point closer to the Fermi level using either hydrostatic pressure or doping, can induce a structural instability. The importance of the electronic degrees of freedom in driving this instability is supported by the dependence of the frequency of the soft phonon mode on the electronic smearing temperature, as is the case in charge density wave materials. Our first-principles calculations show that as the smearing temperature is increased, the compound becomes structurally stable again. Our findings survey the structural properties of a large family of shandite materials and shed light on the role played by saddle points in the electronic structure in driving structural instabilities in rhombohedrally stacked kagome-layered materials belonging to the $R\bar{3}m$ space group.
2025-07-31
Toru Misawa, Jane C. Charlton, Michael Eracleous
We consider if outflowing winds that are detected via narrow absorption lines (NALs) with FWHM of $<$ 500 km/s (i.e., NAL outflows) in quasar spectra contribute to feedback. As our sample, we choose 11 NAL systems in eight optically luminous quasars from the NAL survey of Misawa et al. (2007a), based on the following selection criteria: i) they exhibit ``partial coverage'' suggesting quasar origin (i.e., intrinsic NALs), ii) they have at least one low-ionization absorption line (C II and/or Si II), and iii) the Ly$\alpha$ absorption line is covered by available spectra. The results depend critically on this selection method, which has caveats and uncertainties associated with it, as we discuss in a dedicated section of the paper. Using the column density ratio of the excited and ground states of C II and Si II, we place upper limits on the electron density as $n_{\rm e}$ $<$ 0.2 - 18 cm$^{-3}$ and lower limits on their radial distance from the flux source $R$ as greater than several hundreds of kpc. We also calculate lower limits on the mass outflow rate and kinetic luminosity of $\log(\dot{M}/{\rm M_{\odot}~s}^{-1}) > 79$ - (3.1$\times 10^{5})$ and $\log(\dot{E_{\rm k}}/{\rm erg~s}^{-1}) > 42.9$ - 49.8, respectively. Taking the NAL selection and these results at face value, the inferred feedback efficiency can be comparable to or even larger than those of broad absorption line and other outflow classes, and large enough to generate significant AGN feedback. However, the question of the connection of quasar-driven outflows to NAL absorbers at large distances from the central engine remains open and should be addressed by future theoretical work.
Assessing the Alignment of Automated Vehicle Decisions with Human Reasons
2025-07-31
Lucas Elbert Suryana, Saeed Rahmani, Simeon Craig Calvert, Arkady Zgonnikov, Bart van Arem
A key challenge in deploying automated vehicles (AVs) is ensuring they make appropriate decisions in ethically challenging everyday driving situations. While much attention has been paid to rare, high-stakes dilemmas such as trolley problems, similar tensions also arise in routine scenarios, such as navigating empty intersections, where multiple human considerations, including legality and comfort, often conflict. Current AV planning systems typically rely on rigid rules, which struggle to balance these competing considerations and can lead to behaviour that misaligns with human expectations. This paper proposes a novel reasons-based trajectory evaluation framework that operationalises the tracking condition of Meaningful Human Control (MHC). The framework models the reasons of human agents, such as regulatory compliance, as quantifiable functions and evaluates how well candidate AV trajectories align with these reasons. By assigning adjustable weights to agent priorities and integrating a balance function to discourage the exclusion of any agent, the framework supports interpretable decision evaluation. Through a real-world-inspired overtaking scenario, we show how this approach reveals tensions, for instance between regulatory compliance, efficiency, and comfort. The framework functions as a modular evaluation layer over existing planning algorithms. It offers a transparent tool for assessing ethical alignment in everyday scenarios and provides a practical step toward implementing MHC in real-world AV deployment.
Gravitational waves (GWs) distort galaxy shapes through the tidal effect, offering a novel avenue to probe the nature of gravity. In this paper, we investigate how extra GW polarizations beyond those predicted by general relativity imprint observable signatures on galaxy shapes. Since galaxy shapes are measured as two-dimensional images projected onto the celestial sphere, we present three-dimensional statistical quantities of the projected tidal tensor sourced by the tensor perturbation. We show that the presence of extra polarization modes modifies both the amplitude and angular dependence of the correlation functions. Furthermore, we identify a distinct observational channel for probing parity violation in helicity-two and helicity-one modes. In particular, we show that if they propagate at different speeds, galaxy surveys can disentangle the source of parity violation. Our findings establish a theoretical framework for using upcoming large-scale galaxy surveys to test modified gravity theories through the polarization content of GWs.
The emergence of large language models (LLMs) is propelling automated scientific discovery to the next level, with LLM-based Artificial Intelligence (AI) Scientist systems now taking the lead in scientific research. Several influential works have already appeared in the field of AI Scientist systems, with AI-generated research papers having been accepted at the ICLR 2025 workshop, suggesting that a human-level AI Scientist capable of uncovering phenomena previously unknown to humans, may soon become a reality. In this survey, we focus on the central question: How far are AI scientists from changing the world and reshaping the scientific research paradigm? To answer this question, we provide a prospect-driven review that comprehensively analyzes the current achievements of AI Scientist systems, identifying key bottlenecks and the critical components required for the emergence of a scientific agent capable of producing ground-breaking discoveries that solve grand challenges. We hope this survey will contribute to a clearer understanding of limitations of current AI Scientist systems, showing where we are, what is missing, and what the ultimate goals for scientific AI should be.
CUHK-EE Systems for the vTAD Challenge at NCMMSC 2025
2025-07-31
Aemon Yat Fei Chiu, Jingyu Li, Yusheng Tian, Guangyan Zhang, Tan Lee
This paper presents the Voice Timbre Attribute Detection (vTAD) systems developed by the Digital Signal Processing & Speech Technology Laboratory (DSP&STL) of the Department of Electronic Engineering (EE) at The Chinese University of Hong Kong (CUHK) for the 20th National Conference on Human-Computer Speech Communication (NCMMSC 2025) vTAD Challenge. The proposed systems leverage WavLM-Large embeddings with attentive statistical pooling to extract robust speaker representations, followed by two variants of Diff-Net, i.e., Feed-Forward Neural Network (FFN) and Squeeze-and-Excitation-enhanced Residual FFN (SE-ResFFN), to compare timbre attribute intensities between utterance pairs. Experimental results demonstrate that the WavLM-Large+FFN system generalises better to unseen speakers, achieving 77.96% accuracy and 21.79% EER, while the WavLM-Large+SE-ResFFN model excels in the 'Seen' setting with 94.42% accuracy and 5.49% EER. These findings highlight a trade-off between model complexity and generalisation, and underscore the importance of architectural choices in fine-grained speaker modelling. Our analysis also reveals the impact of speaker identity, annotation subjectivity, and data imbalance on system performance, pointing to future directions for improving robustness and fairness in timbre attribute detection.
Atomic-Scale Light Coupling Control in Ultrathin Photonic Nanomembranes
2025-07-31
Chih-Zong Deng, Jui-Han Fu, Yen-Ju Wu, Kuniaki Konishi, Vincent Tung, Chun-Wei Chen, Ya-Lun Ho
Atomic layer materials have emerged as essential building blocks for next generation quantum and semiconductor technologies, where atomic scale control over light matter interactions is critical. However, their inherently small optical interaction volume poses fundamental challenges for efficient integration into quantum and nanophotonic devices. Addressing this limitation requires the development of photonic platforms that can effectively enhance atomic-scale optical coupling. To this end, air suspended nanomembranes with extreme thinness and minimal radiative loss offer an ideal framework for integrating atomic layer materials into photonic systems. Here, we demonstrate an ultrathin freestanding photonic nanomembrane with a thickness down to 29 nm comparable to that of atomic layer materials enabling atomic scale control of light coupling. This architecture supports strong field confinement at the surface and significantly enhances light matter interaction. Through the integration of atomic layer material, we achieve angstrom level thickness modulation, where a single atomic-layer-deposition cycle corresponding to a thickness increment of 0.65 angstrom results in a 0.09 nm shift in the high Q resonance. High-resolution spatial mapping further confirms uniform and deterministic resonance tuning across the nanomembrane surface. This approach provides a scalable and versatile route for atomic-scale light coupling, effectively overcoming the limitations of conventional photonics and enabling progress in quantum photonics, 2D optoelectronics, and advanced semiconductor technologies.
Generic object tracking remains an important yet challenging task in computer vision due to complex spatio-temporal dynamics, especially in the presence of occlusions, similar distractors, and appearance variations. Over the past two decades, a wide range of tracking paradigms, including Siamese-based trackers, discriminative trackers, and, more recently, prominent transformer-based approaches, have been introduced to address these challenges. While a few existing survey papers in this field have either concentrated on a single category or widely covered multiple ones to capture progress, our paper presents a comprehensive review of all three categories, with particular emphasis on the rapidly evolving transformer-based methods. We analyze the core design principles, innovations, and limitations of each approach through both qualitative and quantitative comparisons. Our study introduces a novel categorization and offers a unified visual and tabular comparison of representative methods. Additionally, we organize existing trackers from multiple perspectives and summarize the major evaluation benchmarks, highlighting the fast-paced advancements in transformer-based tracking driven by their robust spatio-temporal modeling capabilities.
Disorder driven crossover between anomalous Hall regimes in Fe$_3$GaTe$_2$
2025-07-31
Sang-Eon Lee, Minkyu Park, W. Kice Brown, Vadym Kulichenko, Yan Xin, S. H. Rhim, Chanyong Hwang, Jaeyong Kim, Gregory T. McCandless, Julia Y. Chan, Luis Balicas
The large anomalous Hall conductivity (AHC) of the Fe$_3$(Ge,Ga)Te$_2$ compounds has attracted considerable attention. Here, we expose the intrinsic nature of AHC in Fe$_3$GaTe$_2$ crystals characterized by high conductivities, which show disorder-independent AHC with a pronounced value $\sigma_{xy}^{\text{c}}\approx$ 420 $\Omega^{-1}$cm$^{-1}$. In the low conductivity regime, we observe the scaling relation $\sigma_{xy}\propto\sigma_{xx}^{1.6}$, which crosses over to $\sigma_{xy} \simeq \sigma_{xy}^{\text{c}}$ as $\sigma_{xx}$ increases. Disorder in low-conductivity crystals is confirmed by the broadening of a first-order transition between ferromagnetism and the ferrimagnetic ground state. Through density functional theory (DFT) calculations, we reveal that the dominant sources of Berry curvature are located a few hundred meV below the Fermi energy around the $\Gamma$-point. Therefore, Fe$_3$GaTe$_2$ clearly exposes the disorder-induced crossover among distinct AHC regimes, previously inferred from measurements on different ferromagnets located in either side of the crossover region.
A Novel Dataset for Flood Detection Robust to Seasonal Changes in Satellite Imagery
2025-07-31
Youngsun Jang, Dongyoun Kim, Chulwoo Pack, Kwanghee Won
This study introduces a novel dataset for segmenting flooded areas in satellite images. After reviewing 77 existing benchmarks utilizing satellite imagery, we identified a shortage of suitable datasets for this specific task. To fill this gap, we collected satellite imagery of the 2019 Midwestern USA floods from Planet Explorer by Planet Labs (Image \c{opyright} 2024 Planet Labs PBC). The dataset consists of 10 satellite images per location, each containing both flooded and non-flooded areas. We selected ten locations from each of the five states: Iowa, Kansas, Montana, Nebraska, and South Dakota. The dataset ensures uniform resolution and resizing during data processing. For evaluating semantic segmentation performance, we tested state-of-the-art models in computer vision and remote sensing on our dataset. Additionally, we conducted an ablation study varying window sizes to capture temporal characteristics. Overall, the models demonstrated modest results, suggesting a requirement for future multimodal and temporal learning strategies. The dataset will be publicly available on .
AutoBridge: Automating Smart Device Integration with Centralized Platform
2025-07-31
Siyuan Liu, Zhice Yang, Huangxun Chen
Multimodal IoT systems coordinate diverse IoT devices to deliver human-centered services. The ability to incorporate new IoT devices under the management of a centralized platform is an essential requirement. However, it requires significant human expertise and effort to program the complex IoT integration code that enables the platform to understand and control the device functions. Therefore, we propose AutoBridge to automate IoT integration code generation. Specifically, AutoBridge adopts a divide-and-conquer strategy: it first generates device control logic by progressively retrieving device-specific knowledge, then synthesizes platformcompliant integration code using platform-specific knowledge. To ensure correctness, AutoBridge features a multi-stage debugging pipeline, including an automated debugger for virtual IoT device testing and an interactive hardware-in-the-loop debugger that requires only binary user feedback (yes and no) for real-device verification. We evaluate AutoBridge on a benchmark of 34 IoT devices across two open-source IoT platforms. The results demonstrate that AutoBridge can achieves an average success rate of 93.87% and an average function coverage of 94.87%, without any human involvement. With minimal binary yes and no feedback from users, the code is then revised to reach 100% function coverage. A user study with 15 participants further shows that AutoBridge outperforms expert programmers by 50% to 80% in code accuracy, even when the programmers are allowed to use commercial code LLMs.
Identification and photometric classification of extragalactic transients in the Vera C. Rubin Observatory's Data Preview 1
2025-07-30
James Freeburn, Igor Andreoni, Kaylee M. de Soto, Cristina Andrade, Akash Anumarlapudi, Tyler Barna, Jonathan Carney, Sushant Sharma Chaudhary, Michael W. Coughlin, Felipe Fontinele Nunes, Sarah Teague, Mickael Rigault, V. Ashley Villar
The Vera C. Rubin Observatory will soon survey the southern sky, delivering a depth and sky coverage that is unprecedented in time domain astronomy. As part of commissioning, Data Preview 1 (DP1) has been released. It comprises a ComCam observing campaign between November and December 2024 with multi-band imaging of seven fields, covering roughly 0.4 square degree each, provides a first glimpse into the data products that will become available once the Legacy Survey of Space and Time begins. In this work, we search three fields for extragalactic transients. We identify six new extragalactic transients, and three known ones from a sample of 369,644 difference image analysis objects. Photometric classification using \texttt{Superphot+} indicates that this sample likely comprises six type Ia, two type II, two type Ibc and one type IIn supernovae. Our findings are in slight tension with supernova detection rate predictions from the literature of $12\pm3$ SN Ia and $3\pm1$ core-collapse supernovae likely due to the lack of suitable templates. Nevertheless, this work demonstrates the quality of the data products delivered in DP1 and indicates that Rubin Observatory Legacy Survey and Space and Time (LSST) is well placed to fulfill its discovery potential in time domain astronomy.
JADES-GS-z14-1: A Compact, Faint Galaxy at $z\approx14$ with Weak Metal Lines from Extremely Deep JWST MIRI, NIRCam, and NIRSpec Observations
2025-07-30
Zihao Wu, Daniel J. Eisenstein, Benjamin D. Johnson, Peter Jakobsen, Stacey Alberts, Santiago Arribas, William M. Baker, Andrew J. Bunker, Stefano Carniani, Stéphane Charlot, Jacopo Chevallard, Mirko Curti, Emma Curtis-Lake, Francesco D'Eugenio, Kevin Hainline, Jakob M. Helton, Tiger Yu-Yang Hsiao, Xihan Ji, Zhiyuan Ji, Tobias J. Looser, George Rieke, Pierluigi Rinaldi, Brant Robertson, Jan Scholtz, Fengwu Sun, Sandro Tacchella, James A. A. Trussler, Christina C. Williams, Christopher N. A. Willmer, Chris Willott, Joris Witstok, Yongda Zhu
JWST has shed light on galaxy formation and metal enrichment within 300 Myr of the Big Bang. While luminous galaxies at $z > 10$ often show significant [O III]$\lambda\lambda$4959, 5007 emission lines, it remains unclear whether such features are prevalent among fainter, more typical galaxies due to observational limits. We present deep imaging and spectroscopy of JADES-GS-z14-1 at $z_\mathrm{spec}=13.86^{+0.04}_{-0.05}$, currently the faintest spectroscopically confirmed galaxy at $z\approx 14$. It serendipitously received 70.7 hours of MIRI/F770W imaging in the JWST Advanced Deep Extragalactic Survey (JADES), the deepest MIRI exposure for any high-redshift galaxy to date. Nonetheless, we detect only tentative F770W emission of $7.9\pm2.8$ nJy at $2.8\sigma$ significance, constraining the total equivalent width of [O III]$\lambda\lambda$4959, 5007 + H$\beta$ to $520^{+400}_{-380}$ A, weaker than most $z > 10$ galaxies with MIRI detections. This source is unresolved across 16 NIRCam bands, implying a physical radius $\lesssim50$ pc. NIRSpec/PRISM spectroscopy totaling 56 hours reveals no rest-frame ultraviolet emission lines above $3 \sigma$. Stellar population synthesis suggests a stellar mass $\sim4\times 10^{7}$ $\mathrm{M_\odot}$ and a star formation rate $\sim 2$ $\mathrm{M_\odot yr^{-1}}$. The absence of strong metal emission lines despite intense star formation suggests a gas-phase metallicity below 10% solar and potentially a high escape fraction of ionizing photons. These deep observations provide rare constraints on faint, early galaxies, tracing the onset of chemical enrichment and ionization in the early Universe.
VRISE: A Virtual Reality Platfrom for Immersive and Interactive Surveying Education
2025-07-30
Daniel Udekwe, Dimitrios Bolkas, Eren Erman Ozguven, Ren Moses, Qianwen Guo
Surveying is a core component of civil engineering education, requiring students to engage in hands-on spatial measurement, instrumentation handling, and field-based decision-making. However, traditional instruction often poses logistical and cognitive challenges that can hinder accessibility and student engagement. While virtual laboratories have gained traction in engineering education, few are purposefully designed to support flexible, adaptive learning in surveying. To address this gap, we developed Virtual Reality for Immersive and Interactive Surveying Education (VRISE), an immersive virtual reality laboratory that replicates ground-based and aerial surveying tasks through customizable, accessible, and user-friendly modules. VRISE features interactive experiences such as differential leveling with a digital level equipment and waypoint-based drone navigation, enhanced by input smoothing, adaptive interfaces, and real-time feedback to accommodate diverse learning styles. Evaluation across multiple user sessions demonstrated consistent gains in measurement accuracy, task efficiency, and interaction quality, with a clear progression in skill development across the ground-based and aerial surveying modalities. By reducing cognitive load and physical demands, even in tasks requiring fine motor control and spatial reasoning, VRISE demonstrates the potential of immersive, repeatable digital environments to enhance surveying education, broaden participation, and strengthen core competencies in a safe and engaging setting.
Human mobility forms the backbone of contact patterns through which infectious diseases propagate, fundamentally shaping the spatio-temporal dynamics of epidemics and pandemics. While traditional models are often based on the assumption that all individuals have the same probability of infecting every other individual in the population, a so-called random homogeneous mixing, they struggle to capture the complex and heterogeneous nature of real-world human interactions. Recent advancements in data-driven methodologies and computational capabilities have unlocked the potential of integrating high-resolution human mobility data into epidemic modeling, significantly improving the accuracy, timeliness, and applicability of epidemic risk assessment, contact tracing, and intervention strategies. This review provides a comprehensive synthesis of the current landscape in human mobility-informed epidemic modeling. We explore diverse sources and representations of human mobility data, and then examine the behavioral and structural roles of mobility and contact in shaping disease transmission dynamics. Furthermore, the review spans a wide range of epidemic modeling approaches, ranging from classical compartmental models to network-based, agent-based, and machine learning models. And we also discuss how mobility integration enhances risk management and response strategies during epidemics. By synthesizing these insights, the review can serve as a foundational resource for researchers and practitioners, bridging the gap between epidemiological theory and the dynamic complexities of human interaction while charting clear directions for future research.
Spiral Structure Properties, Dynamics, and Evolution in MW-mass Galaxy Simulations
2025-07-30
J. R. Quinn, S. R. Loebman, K. J. Daniel, L. Beraldo e Silva, A. Wetzel, V. P. Debattista, A. Arora, S. Ansar, F. McCluskey, D. Masoumi, J. Bailin
The structure of spiral galaxies is essential to understanding the dynamics and evolution of disc galaxies; however, the precise nature of spiral arms remains uncertain. Two challenges in understanding the mechanisms driving spirals are how galactic environment impacts spiral morphology and how they evolve over time. We present a catalog characterizing the properties, dynamics, and evolution of m=2 spiral structure in 10 Milky Way-mass galaxies from the FIRE-2 cosmological zoom-in simulations. Consistent with previous literature, we find that FIRE-2 spirals are transient, recurring features simultaneously present in the disc at varying pattern speeds ($\Omega_p$) that broadly decrease with radius. These spirals persist on Gyr timescales (mean duration 1.90 Gyr), but fluctuate in amplitude on timescales of hundreds of Myr. Tidal interactions and bar episodes impact the resulting m=2 spiral structure; strong satellite interactions generally produce shorter-lived, stronger spirals with larger radial extent, and bars can increase $\Omega_p$. Galactic environment influences spiral structure; kinematically colder discs can support longer-lived, stronger spirals. The properties of identified spirals in FIRE-2 vary widely in radial extent (0.3-10.8 kpc), duration (1.00-6.00 Gyr), and amplitudes ($a_{2,\text{max}}$=0.018-0.192). We find the presence of spirals in all age populations, suggesting these are density wave-driven features. This work represents the first time that spiral structure has been cataloged in this manner in cosmological simulations; the catalog can be leveraged with current and forthcoming observational surveys, enabling systematic comparisons to further our understanding of galaxy evolution.
Segment Anything for Video: A Comprehensive Review of Video Object Segmentation and Tracking from Past to Future
2025-07-30
Guoping Xu, Jayaram K. Udupa, Yajun Yu, Hua-Chieh Shao, Songlin Zhao, Wei Liu, You Zhang
Video Object Segmentation and Tracking (VOST) presents a complex yet critical challenge in computer vision, requiring robust integration of segmentation and tracking across temporally dynamic frames. Traditional methods have struggled with domain generalization, temporal consistency, and computational efficiency. The emergence of foundation models like the Segment Anything Model (SAM) and its successor, SAM2, has introduced a paradigm shift, enabling prompt-driven segmentation with strong generalization capabilities. Building upon these advances, this survey provides a comprehensive review of SAM/SAM2-based methods for VOST, structured along three temporal dimensions: past, present, and future. We examine strategies for retaining and updating historical information (past), approaches for extracting and optimizing discriminative features from the current frame (present), and motion prediction and trajectory estimation mechanisms for anticipating object dynamics in subsequent frames (future). In doing so, we highlight the evolution from early memory-based architectures to the streaming memory and real-time segmentation capabilities of SAM2. We also discuss recent innovations such as motion-aware memory selection and trajectory-guided prompting, which aim to enhance both accuracy and efficiency. Finally, we identify remaining challenges including memory redundancy, error accumulation, and prompt inefficiency, and suggest promising directions for future research. This survey offers a timely and structured overview of the field, aiming to guide researchers and practitioners in advancing the state of VOST through the lens of foundation models.
Modality-Aware Feature Matching: A Comprehensive Review of Single- and Cross-Modality Techniques
2025-07-30
Weide Liu, Wei Zhou, Jun Liu, Ping Hu, Jun Cheng, Jungong Han, Weisi Lin
Feature matching is a cornerstone task in computer vision, essential for applications such as image retrieval, stereo matching, 3D reconstruction, and SLAM. This survey comprehensively reviews modality-based feature matching, exploring traditional handcrafted methods and emphasizing contemporary deep learning approaches across various modalities, including RGB images, depth images, 3D point clouds, LiDAR scans, medical images, and vision-language interactions. Traditional methods, leveraging detectors like Harris corners and descriptors such as SIFT and ORB, demonstrate robustness under moderate intra-modality variations but struggle with significant modality gaps. Contemporary deep learning-based methods, exemplified by detector-free strategies like CNN-based SuperPoint and transformer-based LoFTR, substantially improve robustness and adaptability across modalities. We highlight modality-aware advancements, such as geometric and depth-specific descriptors for depth images, sparse and dense learning methods for 3D point clouds, attention-enhanced neural networks for LiDAR scans, and specialized solutions like the MIND descriptor for complex medical image matching. Cross-modal applications, particularly in medical image registration and vision-language tasks, underscore the evolution of feature matching to handle increasingly diverse data interactions.
Euclid: Forecasts on $Λ$CDM consistency tests with growth rate data
2025-07-30
I. Ocampo, D. Sapone, S. Nesseris, G. Alestas, J. García-Bellido, Z. Sakr, C. J. A. P. Martins, J. P. Mimoso, A. Carvalho, A. Da Silva, A. Blanchard, S. Casas, S. Camera, M. Martinelli, V. Pettorino, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, F. Bernardeau, A. Biviano, E. Branchini, M. Brescia, G. Cañas-Herrera, V. Capobianco, C. Carbone, V. F. Cardone, J. Carretero, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, H. Degaudenzi, S. de la Torre, G. De Lucia, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, S. Escoffier, M. Farina, R. Farinelli, S. Farrens, F. Faustini, S. Ferriol, F. Finelli, P. Fosalba, N. Fourmanoit, M. Frailis, E. Franceschi, S. Galeotta, K. George, B. Gillis, C. Giocoli, J. Gracia-Carpio, A. Grazian, F. Grupp, S. V. H. Haugan, W. Holmes, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, B. Kubik, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, N. Martinet, F. Marulli, R. J. Massey, E. Medinaceli, S. Mei, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, C. Neissner, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, S. Pires, G. Polenta, M. Poncet, L. A. Popa, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, C. Rosset, R. Saglia, B. Sartoris, T. Schrabback, A. Secroun, E. Sefusatti, G. Seidel, M. Seiffert, S. Serrano, C. Sirignano, G. Sirri, A. Spurio Mancini, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, G. Zamorani, F. M. Zerbi, E. Zucca, M. Ballardini, C. Burigana, L. Gabarra, A. Pezzotta, V. Scottez, M. Viel
The large-scale structure (LSS) of the Universe is an important probe for deviations from the canonical cosmological constant $\Lambda$ and cold dark matter ($\Lambda$CDM) model. A statistically significant detection of any deviations would signify the presence of new physics or the breakdown of any number of the underlying assumptions of the standard cosmological model or possible systematic errors in the data. In this paper, we quantify the ability of the LSS data products of the spectroscopic survey of the Euclid mission, together with other contemporary surveys, to improve the constraints on deviations from $\Lambda$CDM in the redshift range $0
Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection
2025-07-30
Ahmed Sabbah, Radi Jarrar, Samer Zein, David Mohaisen
Despite outstanding results, machine learning-based Android malware detection models struggle with concept drift, where rapidly evolving malware characteristics degrade model effectiveness. This study examines the impact of concept drift on Android malware detection, evaluating two datasets and nine machine learning and deep learning algorithms, as well as Large Language Models (LLMs). Various feature types--static, dynamic, hybrid, semantic, and image-based--were considered. The results showed that concept drift is widespread and significantly affects model performance. Factors influencing the drift include feature types, data environments, and detection methods. Balancing algorithms helped with class imbalance but did not fully address concept drift, which primarily stems from the dynamic nature of the malware landscape. No strong link was found between the type of algorithm used and concept drift, the impact was relatively minor compared to other variables since hyperparameters were not fine-tuned, and the default algorithm configurations were used. While LLMs using few-shot learning demonstrated promising detection performance, they did not fully mitigate concept drift, highlighting the need for further investigation.
Review of the Higgs boson production via vector boson fusion and its decay into bottom quarks in $pp$ collisions at $\sqrt{s}=8,13$ TeV
2025-07-30
Fikriye C I Kaya, Ibrahim Mirza
We present a comprehensive overview of the vector boson fusion production and the following decay of the Higgs boson to two bottom quarks (\(H\!\to\! b\bar{b}\)), which dominates the Higgs total width yet remains experimentally challenging because of substantial QCD multijet backgrounds. We overview the use of boosted decision trees, which advance jet reconstruction and flavour tagging by including boosted topologies and deep-learning-based \(b\)-tagging and which improve sensitivity in associated production channels. Special attention is given to ATLAS and CMS selections and results pertaining to the vector boson fusion production of Higgs, decaying in the $b\bar{b}$-channel, from both the 8 TeV and the 13 TeV LHC data-sets conducted with up to 90.8 $fb^{-1}$ of Data and published through 2024. Consistent with the Standard Model gauge symmetry \(SU(3)_C \times SU(2)_L \times U(1)_Y\) predictions, we emphasise how \(H\!\to\! b\bar{b}\) measurements contribute, in conjunction with other production modes and decay channels, to tests of the bottom-quark Yukawa coupling, which is an essential probe of the electroweak symmetry-breaking mechanism. Finally, we discuss how the current precision constrains deviations in the bottom-quark Yukawa coupling, outline ongoing efforts during Run 3, and consider the prospects of forthcoming high-luminosity LHC operation and the proposed Future Circular Collider in further tightening precision tests of the Standard Model and beyond.
We propose a model for dust formation in Type II supernovae (SNe) interacting with confined circumstellar material (CSM), motivated by recent time-domain surveys that have revealed a substantial fraction of SN progenitors to be surrounded by CSM ejected shortly before core-collapse. We simulate the pre-SN mass eruption and the resulting confined CSM using the open-source code CHIPS, and follow the subsequent evolution of the SN ejecta and its interaction with the CSM. We show that a cold dense shell (CDS) is formed at the radiative shock under a wide range of conditions and later undergoes rapid adiabatic cooling during free expansion, leading to efficient dust condensation. The resulting dust mass ranges from $\sim10^{-3}\,M_\odot$ to $0.1\,M_\odot$, depending on the mass and spatial extent of the CSM. We further calculate the infrared (IR) emission from the newly formed dust and find broad consistency with observations of SN~1998S. Notably, the IR light curve exhibits a rapid rise within $\lesssim10\,{\rm d}$, closely resembling that of kilonovae (KNe). This suggests that dust emission powered by confined CSM interaction may be also discovered in KN searches. Moreover, the high-density environment of the CDS may allow dust grains to grow to larger sizes, enhancing their survivability against destruction by reverse shocks propagating from the interstellar medium at later times.
How Exposed Are UK Jobs to Generative AI? Developing and Applying a Novel Task-Based Index
2025-07-30
Golo Henseke, Rhys Davies, Alan Felstead, Duncan Gallie, Francis Green, Ying Zhou
We introduce the Generative AI Susceptibility Index (GAISI), a task-based measure of UK job exposure to large language models (LLMs), such as ChatGPT. GAISI is derived from probabilistic task ratings by LLMs and linked to worker-reported task data from the Skills and Employment Surveys. It reflects the share of job activities where an LLM or LLM-powered system can reduce task completion time by at least 25 per cent beyond existing productivity tools. The index demonstrates high reliability, strong alignment with AI capabilities, and superior predictive power compared to existing exposure measures. By 2023-24, nearly all UK jobs exhibited some exposure, yet only a minority were heavily affected. Aggregate exposure has risen since 2017, primarily due to occupational shifts rather than changes in task profiles. The price premium for AI-exposed tasks declined relative to 2017, measuring approximately 11 per cent lower in 2023-24. Job postings in high-exposure roles also fell by 6.5 per cent following the release of ChatGPT. GAISI offers a robust framework for assessing generative AI's impact on work, providing early evidence that displacement effects may already outweigh productivity gains.
Foundations for Energy-Aware Zero-Energy Devices: From Energy Sensing to Adaptive Protocols
2025-07-30
Onel L. A. López, Mateen Ashraf, Samer Nasser, Gabriel M. de Jesus, Ritesh Kumar Singh, Miltiadis C. Filippou, Jeroen Famaey
Zero-energy devices (ZEDs) are key enablers of sustainable Internet of Things networks by operating solely on harvested ambient energy. Their limited and dynamic energy budget calls for protocols that are energy-aware and intelligently adaptive. However, designing effective energy-aware protocols for ZEDs requires theoretical models that realistically reflect device constraints. Indeed, existing approaches often oversimplify key aspects such as energy information (EI) acquisition, task-level variability, and energy storage dynamics, limiting their practical relevance and transferability. This article addresses this gap by offering a structured overview of the key modeling components, trade-offs, and limitations involved in energy-aware ZED protocol design. For this, we dissect EI acquisition methods and costs, characterize core operational tasks, analyze energy usage models and storage constraints, and review representative protocol strategies. Moreover, we offer design insights and guidelines on how ZED operation protocols can leverage EI, often illustrated through selected in-house examples. Finally, we outline key research directions to inspire more efficient and scalable protocol solutions for future ZEDs.
2D CFT and efficient Bethe ansatz for exactly solvable Richardson-Gaudin models
2025-07-30
Grzegorz Biskowski, Franco Ferrari, Marcin R. Piatek
This work inaugurates a series of complementary studies on Richardson-Gaudin integrable models. We begin by reviewing the foundations of classical and quantum integrability, recalling the algebraic Bethe ansatz solution of the Richardson (reduced BCS) and Gaudin (central spin) models, and presenting a proof of their integrability based on the Knizhnik-Zamolodchikov equations and their generalizations to perturbed affine conformal blocks. Building on this foundation, we then describe an alternative CFT-based formulation. In this approach, the Bethe ansatz equations for these exactly solvable models are embedded within two-dimensional Virasoro CFT via irregular, degenerate conformal blocks. To probe new formulations within the Richardson-Gaudin class, we develop a high-performance numerical solver. The Bethe roots are encoded in the Baxter polynomial, with initial estimates obtained from a secular matrix eigenproblem and subsequently refined using a deflation-assisted hybrid Newton-Raphson/Laguerre algorithm. The solver proves effective in practical applications: when applied to picket-fence, harmonic oscillator, and hydrogen-like spectra, it accurately reproduces known rapidity trajectories and reveals consistent merging and branching patterns of arcs in the complex rapidity plane. We also explain how to generalize our computational approach to finite temperatures, allowing us to calculate temperature-dependent pairing energies and other thermodynamic observables directly within the discrete Richardson model. We propose an application of the solver to Gaudin-type Bethe equations, which emerge in the classical (large central charge) limit of Virasoro conformal blocks. We conclude by outlining future directions: direct minimization of the Yang-Yang function as an alternative root-finding strategy; revisiting time-dependent extensions; and ... .
The co-location of multiple database instances on resource constrained edge nodes creates significant cache contention, where traditional schemes are inefficient and unstable under dynamic workloads. To address this, we present SAM(a Stability-Aware Manager), an autonomic cache manager that establishes decision stability as a first-class design principle. It achieves this through its core control policy, AURA(Autonomic Utility-balancing Resource Allocator), which resolves the classic exploitation-exploration dilemma by synthesizing two orthogonal factors: the H-factor, representing proven historical efficiency (exploitation), and the V-factor, for estimated marginal gain (exploration). Through this practical synthesis and adaptive control, SAM achieves sustained high performance with strategic stability and robustness in volatile conditions. Extensive experiments against 14 diverse baselines demonstrate SAM's superiority. It achieves top-tier throughput while being uniquely resilient to complex workload shifts and adversarial workloads like cache pollution. Furthermore, its decision latency is highly scalable, remaining nearly constant as the system grows to 120 databases. Crucially, SAM achieves superior decision stability -- maintaining consistent optimization directions despite noise, avoiding performance oscillations while ensuring predictable Quality of Service. These results prove that a principled, stability-aware design is essential for sustained high performance in real-world, large-scale systems.
The purpose of quantum technologies is to explore how quantum effects can improve on existing solutions for the treatment of information. Quantum photonics sensing holds great promises for reaching a more efficient trade-off between invasivity and quality of the measurement, when compared with the potential of classical means. This tutorial is dedicated to presenting how this advantage is brought about by nonclassical light, examining the basic principles of parameter estimation and reviewing the state of the art.
Designing for Self-Regulation in Informal Programming Learning: Insights from a Storytelling-Centric Approach
2025-07-30
Sami Saeed Alghamdi, Christopher Bull, Ahmed Kharrufa
Many people learn programming independently from online resources and often report struggles in achieving their personal learning goals. Learners frequently describe their experiences as isolating and frustrating, challenged by abundant uncertainties, information overload, and distraction, compounded by limited guidance. At the same time, social media serves as a personal space where many engage in diverse self-regulation practices, including help-seeking, using external memory aids (e.g., self-notes), self-reflection, emotion regulation, and self-motivation. For instance, learners often mark achievements and set milestones through their posts. In response, we developed a system consisting of a web platform and browser extensions to support self-regulation online. The design aims to add learner-defined structure to otherwise unstructured experiences and bring meaning to curation and reflection activities by translating them into learning stories with AI-generated feedback. We position storytelling as an integrative approach to design that connects resource curation, reflective and sensemaking practice, and narrative practices learners already use across social platforms. We recruited 15 informal programming learners who are regular social media users to engage with the system in a self-paced manner; participation concluded upon submitting a learning story and survey. We used three quantitative scales and a qualitative survey to examine users' characteristics and perceptions of the system's support for their self-regulation. User feedback suggests the system's viability as a self-regulation aid. Learners particularly valued in-situ reflection, automated story feedback, and video annotation, while other features received mixed views. We highlight perceived benefits, friction points, and design opportunities for future AI-augmented self-regulation tools.
A Systematic Literature Review on Detecting Software Vulnerabilities with Large Language Models
2025-07-30
Sabrina Kaniewski, Fabian Schmidt, Markus Enzweiler, Michael Menth, Tobias Heer
The increasing adoption of Large Language Models (LLMs) in software engineering has sparked interest in their use for software vulnerability detection. However, the rapid development of this field has resulted in a fragmented research landscape, with diverse studies that are difficult to compare due to differences in, e.g., system designs and dataset usage. This fragmentation makes it difficult to obtain a clear overview of the state-of-the-art or compare and categorize studies meaningfully. In this work, we present a comprehensive systematic literature review (SLR) of LLM-based software vulnerability detection. We analyze 227 studies published between January 2020 and June 2025, categorizing them by task formulation, input representation, system architecture, and adaptation techniques. Further, we analyze the datasets used, including their characteristics, vulnerability coverage, and diversity. We present a fine-grained taxonomy of vulnerability detection approaches, identify key limitations, and outline actionable future research opportunities. By providing a structured overview of the field, this review improves transparency and serves as a practical guide for researchers and practitioners aiming to conduct more comparable and reproducible research. We publicly release all artifacts and maintain a living repository of LLM-based software vulnerability detection studies.
This paper presents a detailed theoretical review of the amplification process in Josephson Parametric Amplifiers (JPAs), which are crucial for quantum-limited signal amplification in superconducting circuits. The paper begins by outlining the principles of parametric amplification, focusing on how a strong classical pump interacts with the nonlinear Josephson medium in reflection geometry. The key dynamical equations are derived under intense pumping, leading to a nonlinear steady-state solution. Linearization around this solution allows to analyze the system response to weak signals and extract expressions for parametric gain and intermodulation gain using the input-output formalism. Numerically, the equations are solved to explore how the gain depends on frequency detuning and pump strength, which is visualized with a gain response curve. By enhancing our understanding of JPAs, this work aims to inspire continued research in the field of quantum-limited amplification.
2025-07-30
Caroline J. Klivans, Nicolau C. Saldanha
There is a rich history of domino tilings in two dimensions. Through a variety of techniques we can answer questions such as: how many tilings are there of a given region or what does the space of all tilings look like? These questions and their answers become significantly more difficult in dimension three and above. Despite this curse of dimensionality, there have been exciting recent advances in the theory. Here we briefly review foundational results of two-dimensional domino tilings and highlight where challenges arise when generalizing to higher dimensions. We then survey results in higher dimensional domino tilings, focusing on the question of connectivity of the space of tilings, and the open problems that still remain.
Metamorphic Testing of Deep Code Models: A Systematic Literature Review
2025-07-30
Ali Asgari, Milan de Koning, Pouria Derakhshanfar, Annibale Panichella
Large language models and deep learning models designed for code intelligence have revolutionized the software engineering field due to their ability to perform various code-related tasks. These models can process source code and software artifacts with high accuracy in tasks such as code completion, defect detection, and code summarization; therefore, they can potentially become an integral part of modern software engineering practices. Despite these capabilities, robustness remains a critical quality attribute for deep-code models as they may produce different results under varied and adversarial conditions (e.g., variable renaming). Metamorphic testing has become a widely used approach to evaluate models' robustness by applying semantic-preserving transformations to input programs and analyzing the stability of model outputs. While prior research has explored testing deep learning models, this systematic literature review focuses specifically on metamorphic testing for deep code models. By studying 45 primary papers, we analyze the transformations, techniques, and evaluation methods used to assess robustness. Our review summarizes the current landscape, identifying frequently evaluated models, programming tasks, datasets, target languages, and evaluation metrics, and highlights key challenges and future directions for advancing the field.
A MaNGA about the Legacy I: Connecting the Assembly of Stellar Halo with the Average Star Formation History in Low-Redshift Massive Galaxies
2025-07-30
Xiao-Ya Zhang, Song Huang, Meng Gu
We investigate the connection between stellar mass distribution, assembly history, and star formation timescales in low-redshift massive early-type galaxies (ETGs) by combining deep LegacySurvey imaging with MaNGA's spatially resolved spectroscopy. Focusing on stellar population properties, especially the [Mg/Fe] abundance ratio, we analyze stacked spectra using both absorption line indices and full-spectrum fitting. We find that, among massive ETGs with identical average stellar mass distributions beyond 5 kpc, those with higher central velocity dispersion ($\sigma_{cen}$) are older and more $\alpha$-enhanced, suggesting a connection between the in-situ star formation in the past and the central gravitational potential today for massive ETGs with a similar stellar accretion history. Conversely, at fixed $\sigma_{cen}$ and total stellar mass, galaxies with more extended stellar halos show lower [Fe/H], higher [Mg/Fe], and older ages, indicating an intriguing link between early starburst and quenching and later ex-situ assembly. These results demonstrate that the evolution of massive galaxies cannot be fully described by simple scaling relations alone, as the interplay between in-situ star formation and ex-situ accretion leaves distinct imprints in both their inner and outer stellar populations. Our findings highlight the importance of extending stellar population studies to large radii and underscore the scientific potential of next-generation IFU surveys and deep, high-resolution spectroscopy for probing the galaxy-halo connection.
Mexico 2021: Psychological Intimate Partner Violence Against Women and the Role of Childhood Violence Exposure -- A Machine Learning Approach
2025-07-30
Clara Strasser Ceballos, Anna-Carolina Haensch
In 2021, psychological violence was the most prevalent form of intimate partner violence (IPV) suffered by women in Mexico. The consequences of psychological IPV can include low self-esteem, depression, and even potential suicide. It is, therefore, crucial to identify the most relevant risk and protective factors of psychological IPV against women in Mexico. To this end, we adopt an ecological approach and analyze the role of a wide range of factors across four interrelated levels: Individual, relationship, community, and societal. We construct a multidimensional data set with 61,205 observations and 59 variables by integrating nationally representative data from the 2021 Mexican Survey on the Dynamics of Household Relationships with nine additional sources. For model estimation and factor selection, we combine model-based boosting with stability selection. Our findings reveal that women who were exposed to violence in childhood and whose partners were exposed to violence in childhood face a heightened risk of psychological IPV. These findings highlight the critical yet often overlooked role of childhood violence exposure for psychological IPV risk. Additionally, we confirm the role of three protective factors previously identified by Torres Mungu\'ia and Mart\'inez-Zarzoso (2022) using 2016 data, now validated with 2021 data: women who had their first sex later in life and under consent, who have autonomy in decision-making regarding their professional life and use of economic resources, and who live in a household where housework is done only by male members face a lower risk of suffering psychological IPV.
Lie groups for quantum complexity and barren plateau theory
2025-07-30
P. A. S. de Alcântara, Gabriel Audi, Leandro Morais
Advances in quantum computing over the last two decades have required sophisticated mathematical frameworks to deepen the understanding of quantum algorithms. In this review, we introduce the theory of Lie groups and their algebras to analyze two fundamental problems in quantum computing as done in some recent works. Firstly, we describe the geometric formulation of quantum computational complexity, given by the length of the shortest path on the $SU(2^n)$ manifold with respect to a right-invariant Finsler metric. Secondly, we deal with the barren plateau phenomenon in Variational Quantum Algorithms (VQAs), where we use the Dynamical Lie Algebra (DLA) to identify algebraic sources of untrainability
DeepC4: Deep Conditional Census-Constrained Clustering for Large-scale Multitask Spatial Disaggregation of Urban Morphology
2025-07-30
Joshua Dimasaka, Christian Geiß, Emily So
To understand our global progress for sustainable development and disaster risk reduction in many developing economies, two recent major initiatives - the Uniform African Exposure Dataset of the Global Earthquake Model (GEM) Foundation and the Modelling Exposure through Earth Observation Routines (METEOR) Project - implemented classical spatial disaggregation techniques to generate large-scale mapping of urban morphology using the information from various satellite imagery and its derivatives, geospatial datasets of the built environment, and subnational census statistics. However, the local discrepancy with well-validated census statistics and the propagated model uncertainties remain a challenge in such coarse-to-fine-grained mapping problems, specifically constrained by weak and conditional label supervision. Therefore, we present Deep Conditional Census-Constrained Clustering (DeepC4), a novel deep learning-based spatial disaggregation approach that incorporates local census statistics as cluster-level constraints while considering multiple conditional label relationships in a joint multitask learning of the patterns of satellite imagery. To demonstrate, compared to GEM and METEOR, we enhanced the quality of Rwandan maps of urban morphology, specifically building exposure and physical vulnerability, at the third-level administrative unit from the 2022 census. As the world approaches the conclusion of our global frameworks in 2030, our work has offered a new deep learning-based mapping technique towards a spatial auditing of our existing coarse-grained derived information at large scales.
Explainable Deep Anomaly Detection with Sequential Hypothesis Testing for Robotic Sewer Inspection
2025-07-30
Alex George, Will Shepherd, Simon Tait, Lyudmila Mihaylova, Sean R. Anderson
Sewer pipe faults, such as leaks and blockages, can lead to severe consequences including groundwater contamination, property damage, and service disruption. Traditional inspection methods rely heavily on the manual review of CCTV footage collected by mobile robots, which is inefficient and susceptible to human error. To automate this process, we propose a novel system incorporating explainable deep learning anomaly detection combined with sequential probability ratio testing (SPRT). The anomaly detector processes single image frames, providing interpretable spatial localisation of anomalies, whilst the SPRT introduces temporal evidence aggregation, enhancing robustness against noise over sequences of image frames. Experimental results demonstrate improved anomaly detection performance, highlighting the benefits of the combined spatiotemporal analysis system for reliable and robust sewer inspection.
A Benchmark Dataset and Evaluation Framework for Vietnamese Large Language Models in Customer Support
2025-07-30
Long S. T. Nguyen, Truong P. Hua, Thanh M. Nguyen, Toan Q. Pham, Nam K. Ngo, An X. Nguyen, Nghi D. M. Pham, Nghia H. Nguyen, Tho T. Quan
With the rapid growth of Artificial Intelligence, Large Language Models (LLMs) have become essential for Question Answering (QA) systems, improving efficiency and reducing human workload in customer service. The emergence of Vietnamese LLMs (ViLLMs) highlights lightweight open-source models as a practical choice for their accuracy, efficiency, and privacy benefits. However, domain-specific evaluations remain limited, and the absence of benchmark datasets reflecting real customer interactions makes it difficult for enterprises to select suitable models for support applications. To address this gap, we introduce the Customer Support Conversations Dataset (CSConDa), a curated benchmark of over 9,000 QA pairs drawn from real interactions with human advisors at a large Vietnamese software company. Covering diverse topics such as pricing, product availability, and technical troubleshooting, CSConDa provides a representative basis for evaluating ViLLMs in practical scenarios. We further present a comprehensive evaluation framework, benchmarking 11 lightweight open-source ViLLMs on CSConDa with both automatic metrics and syntactic analysis to reveal model strengths, weaknesses, and linguistic patterns. This study offers insights into model behavior, explains performance differences, and identifies key areas for improvement, supporting the development of next-generation ViLLMs. By establishing a robust benchmark and systematic evaluation, our work enables informed model selection for customer service QA and advances research on Vietnamese LLMs. The dataset is publicly available at https://huggingface.co/datasets/ura-hcmut/Vietnamese-Customer-Support-QA.
Green Wave as an Integral Part for the Optimization of Traffic Efficiency and Safety: A Survey
2025-07-30
Kranthi Kumar Talluri, Christopher Stang, Galia Weidl
Green Wave provides practical and advanced solutions to improve traffic efficiency and safety through network coordination. Nevertheless, the complete potential of Green Wave systems has yet to be explored. Utilizing emerging technologies and advanced algorithms, such as AI or V2X, would aid in achieving more robust traffic management strategies, especially when integrated with Green Wave. This work comprehensively surveys existing traffic control strategies that enable Green Waves and analyzes their impact on future traffic management systems and urban infrastructure. Understanding previous research on traffic management and its effect on traffic efficiency and safety helps explore the integration of Green Wave solutions with smart city initiatives for effective traffic signal coordination. This paper also discusses the advantages of using Green Wave strategies for emission reduction and considers road safety issues for vulnerable road users, such as pedestrians and cyclists. Finally, the existing challenges and research gaps in building robust and successful Green Wave systems are discussed to articulate explicitly the future requirement of sustainable urban transport.
Presenting a classifier to detect research contributions in OpenAlex
2025-07-30
Nick Haupka
This paper introduces a document type classifier with the purpose to optimise the distinction between research and non-research journal publications in OpenAlex. Based on open metadata, the classifier can detect non-research or editorial content within a set of classified articles and reviews (e.g. paratexts, abstracts, editorials, letters). The classifier achieves an F1-score of 0,95, indicating a potential improvement in the data quality of bibliometric research in OpenAlex when applying the classifier on real data. In total, 4.589.967 out of 42.701.863 articles and reviews could be reclassified as non-research contributions by the classifier, representing a share of 10,75%
Key Physical Parameters Influencing Fragmentation and Multiplicity in Dense Cores of Orion A
2025-07-30
Jo-Shui Kao, Hsi-Wei Yen, Shih-Ping Lai
When dense cores in molecular clouds or filamentary structures collapse and form protostars, they may undergo fragmentation and form binary or multiple systems. In this paper, we investigated the key mechanisms influencing fragmentation by comparing the physical conditions of fragmented and unfragmented dense cores (~0.1 pc) in Orion A. Utilizing archival submillimeter continuum data from the James Clerk Maxwell Telescope (JCMT) and the Atacama Large Millimeter/Submillimeter Array survey of Class 0 and I protostars at a 0.''1 resolution, we identified 38 dense cores hosting single protostars and 15 cores hosting binary or multiple systems. We measured the dense cores properties with the Herschel dust temperature, Nobeyama 45m N$_2$H$^+$ J=1-0, and JCMT polarization data. Our results reveal that the dense cores hosting binary/multiple systems exhibit significantly higher density and Mach number compared to those hosting single protostars, while there are no correlations between the occurrence of fragmentation and the energy ratios of turbulence and magnetic field to gravity. Our results suggest that the higher density and supersonic turbulence of the dense cores can lead to local collapse and fragmentation to form binary/multiple systems, while the magnetic field has limited influence on fragmentation in the dense cores in Orion A.
Pulsatile Flows for Simplified Smart Fluids with Variable Power-Law: Analysis and Numerics
2025-07-30
Luigi C. Berselli, Alex Kaltenbach
We study the fully-developed, time-periodic motion of a shear-dependent non-Newtonian fluid with variable exponent rheology through an infinite pipe $\Omega:= \mathbb{R}\times \Sigma\subseteq \mathbb{R}^d$, $d\in \{2,3\}$, of arbitrary cross-section $\Sigma\subseteq \mathbb{R}^{d-1}$. The focus is on a generalized $p(\cdot)$-fluid model, where the power-law index is position-dependent (with respect to $\Sigma$), $\textit{i.e.}$, a function $p\colon \Sigma\to (1,+\infty)$. We prove the existence of time-periodic solutions with either assigned time-periodic flow-rate or pressure-drop, generalizing known results for the Navier-Stokes and for $p$-fluid equations. In addition, we identify explicit solutions, relevant as benchmark cases, especially for electro-rheological fluids or, more generally, $\textit{`smart fluids'}$. To support practical applications, we present a fully-constructive existence proof for variational solutions by means of a fully-discrete finite-differences/-elements discretization, consistent with our numerical experiments. Our approach, which unifies the treatment of all values of $p(\overline{x})\in (1,+\infty)$, $\overline{x}\in \Sigma$, without requiring an auxiliary Newtonian term, provides new insights even in the constant exponent case. The theoretical findings are reviewed by means of numerical experiments.
AI-generated stories favour stability over change: homogeneity and cultural stereotyping in narratives generated by gpt-4o-mini
2025-07-30
Jill Walker Rettberg, Hermann Wigers
Can a language model trained largely on Anglo-American texts generate stories that are culturally relevant to other nationalities? To find out, we generated 11,800 stories - 50 for each of 236 countries - by sending the prompt "Write a 1500 word potential {demonym} story" to OpenAI's model gpt-4o-mini. Although the stories do include surface-level national symbols and themes, they overwhelmingly conform to a single narrative plot structure across countries: a protagonist lives in or returns home to a small town and resolves a minor conflict by reconnecting with tradition and organising community events. Real-world conflicts are sanitised, romance is almost absent, and narrative tension is downplayed in favour of nostalgia and reconciliation. The result is a narrative homogenisation: an AI-generated synthetic imaginary that prioritises stability above change and tradition above growth. We argue that the structural homogeneity of AI-generated narratives constitutes a distinct form of AI bias, a narrative standardisation that should be acknowledged alongside the more familiar representational bias. These findings are relevant to literary studies, narratology, critical AI studies, NLP research, and efforts to improve the cultural alignment of generative AI.
Spec-VLA: Speculative Decoding for Vision-Language-Action Models with Relaxed Acceptance
2025-07-30
Songsheng Wang, Rucheng Yu, Zhihang Yuan, Chao Yu, Feng Gao, Yu Wang, Derek F. Wong
Vision-Language-Action (VLA) models have made substantial progress by leveraging the robust capabilities of Visual Language Models (VLMs). However, VLMs' significant parameter size and autoregressive (AR) decoding nature impose considerable computational demands on VLA models. While Speculative Decoding (SD) has shown efficacy in accelerating Large Language Models (LLMs) by incorporating efficient drafting and parallel verification, allowing multiple tokens to be generated in one forward pass, its application to VLA models remains unexplored. This work introduces Spec-VLA, an SD framework designed to accelerate VLA models. Due to the difficulty of the action prediction task and the greedy decoding mechanism of the VLA models, the direct application of the advanced SD framework to the VLA prediction task yields a minor speed improvement. To boost the generation speed, we propose an effective mechanism to relax acceptance utilizing the relative distances represented by the action tokens of the VLA model. Empirical results across diverse test scenarios affirm the effectiveness of the Spec-VLA framework, and further analysis substantiates the impact of our proposed strategies, which enhance the acceptance length by 44%, achieving 1.42 times speedup compared with the OpenVLA baseline, without compromising the success rate. The success of the Spec-VLA framework highlights the potential for broader application of speculative execution in VLA prediction scenarios.
Object recognition is among the fundamental tasks in the computer vision applications, paving the path for all other image understanding operations. In every stage of progress in object recognition research, efforts have been made to collect and annotate new datasets to match the capacity of the state-of-the-art algorithms. In recent years, the importance of the size and quality of datasets has been intensified as the utility of the emerging deep network techniques heavily relies on training data. Furthermore, datasets lay a fair benchmarking means for competitions and have proved instrumental to the advancements of object recognition research by providing quantifiable benchmarks for the developed models. Taking a closer look at the characteristics of commonly-used public datasets seems to be an important first step for data-driven and machine learning researchers. In this survey, we provide a detailed analysis of datasets in the highly investigated object recognition areas. More than 160 datasets have been scrutinized through statistics and descriptions. Additionally, we present an overview of the prominent object recognition benchmarks and competitions, along with a description of the metrics widely adopted for evaluation purposes in the computer vision community. All introduced datasets and challenges can be found online at github.com/AbtinDjavadifar/ORDC.
Differential Corrections Algorithm for Initial Orbit Determination in the Cislunar Region using Angle-Only Measurements
2025-07-30
Seur Gi Jo, Brian Baker-McEvilly, David Canales
As space traffic continues to increase in the cislunar region, accurately determining the trajectories of objects operating within this domain becomes critical. However, due to the combined gravitational influences of the Earth and Moon, orbital dynamics in this region are highly nonlinear and often exhibit chaotic behavior, posing significant challenges for trajectory determination. Many existing methods attempt to address this complexity using machine learning models, advanced optimization techniques, or sensors that directly measure distance to the target, approaches that often increase computational burden and system complexity. This work presents a novel initial orbit determination (IOD) algorithm for cislunar objects based solely on three angle-only measurements taken at three discrete times. The core methodology builds upon a differential corrections framework which iteratively refines the target's trajectory to satisfy line-of-sight constraints. Numerical simulations demonstrate that the algorithm enables accurate IOD using minimal observational data, making it particularly suited for onboard implementation in resource-constrained cislunar missions.
Boosting Ensembles for Statistics of Tails at Conditionally Optimal Advance Split Times
2025-07-30
Justin Finkel, Paul A. O'Gorman
Climate science needs more efficient ways to study high-impact, low-probability extreme events, which are rare by definition and costly to simulate in large numbers. Rare event sampling (RES) and ensemble boosting offer a novel strategy to extract more information from those occasional simulated events: small perturbations in advance can turn a moderate event into a severe one, which otherwise might not come for many more simulation-years. But how to choose this ``advance split time'' (AST) remains a challenge for sudden, transient events like precipitation. In this work, we formulate a concrete optimization problem for the AST and instantiate it on an idealized but physically informative model system: a quasigeostrophic turbulent channel flow advecting a passive tracer, which captures key elements of midlatitude storm track dynamics. Three major questions guide our investigation: (1) Can RES methods, in particular \emph{ensemble boosting} and \emph{trying-early adaptive multilevel splitting}, accurately sample extreme events of return periods longer than the simulation time? (2) What is the optimal AST, and how does it depend on the definition of the extreme event, in particular the target location? (3) Can the AST be optimized ``online'' while running RES? Our answers are tentatively positive. (1) RES can meaningfully improve tail estimation, using (2) an optimal AST of 1-3 eddy turnover timescales, which varies weakly but detectably with target location. (3) A certain functional that we call the \emph{thresholded entropy} successfully picks out near-optimal ASTs, eliminating the need for arbitrary thresholds that have thus far hindered RES methods. Our work clarifies aspects of the optimization landscape and can, in our view, guide future research efforts on optimizing and sampling transient extreme events more efficiently in general chaotic systems.
The Radius, Composition, Albedo, and Absolute Magnitude of Planet Nine Based on Exoplanets with Teq less than 600 K and the Planet Nine Reference Population 3.0
2025-07-30
David G. Russell, Terry L. White
Evidence suggests the existence of a large planet in the outer Solar System, Planet Nine, with a predicted mass of 6.6 +2.6 / -1.7 Earth masses (Brown et al., 2024). Based on mass radius composition models, planet formation theory, and confirmed exoplanets with low mass and radius uncertainty and equilibrium temperature less than 600 K, we determine the most likely composition for Planet Nine is a mini-Neptune with a radius in the range 2.0 to 2.6 Earth radii and a H-He envelope fraction in the range of 0.6 percent to 3.5 percent by mass. Using albedo estimates for a mini-Neptune extrapolated from V-band data for the Solar Systems giant planets gives albedo values for Planet Nine in the range of 0.47 to 0.33. Using the most likely orbit and aphelion estimates from the Planet Nine Reference Population 3.0, we estimate Planet Nines absolute magnitude in the range of -6.1 to -5.2 and apparent magnitude in the range of +21.9 to +22.7. Finally, we estimate that, if the hypothetical Planet Nine exists and is detected by upcoming surveys, it will have a resolvable disk using some higher resolution world class telescopes.
As global energy systems transit to clean energy, accurate renewable generation and renewable demand forecasting is imperative for effective grid management. Foundation Models (FMs) can help improve forecasting of renewable generation and demand because FMs can rapidly process complex, high-dimensional time-series data. This review paper focuses on FMs in the realm of renewable energy forecasting, primarily focusing on wind and solar. We present an overview of the architectures, pretraining strategies, finetuning methods, and types of data used in the context of renewable energy forecasting. We emphasize the role of models that are trained at a large scale, domain specific Transformer architectures, where attention is paid to spatial temporal correlations, the embedding of domain knowledge, and also the brief and intermittent nature of renewable generation. We assess recent FM based advancements in forecast accuracy such as reconciling predictions over multiple time scales and quantifying uncertainty in renewable energy forecasting. We also review existing challenges and areas of improvement in long-term and multivariate time series forecasting. In this survey, a distinction between theory and practice is established regarding the use of FMs in the clean energy forecasting domain. Additionally, it critically assesses the strengths and weaknesses of FMs while advancing future research direction in this new and exciting area of forecasting.
Lightweight Language Models are Prone to Reasoning Errors for Complex Computational Phenotyping Tasks
2025-07-30
Sarah Pungitore, Shashank Yadav, David Maughan, Vignesh Subbian
Objective: Although computational phenotyping is a central informatics activity with resulting cohorts supporting a wide variety of applications, it is time-intensive because of manual data review. We previously assessed the ability of LLMs to perform computational phenotyping tasks using computable phenotypes for ARF respiratory support therapies. They successfully performed concept classification and classification of single-therapy phenotypes, but underperformed on multiple-therapy phenotypes. To understand issues with these complex tasks, we expanded PHEONA, a generalizable framework for evaluation of LLMs, to include methods specifically for evaluating faulty reasoning. Materials and Methods: We assessed the responses of three lightweight LLMs (DeepSeek-r1 32 billion, Mistral Small 24 billion, and Phi-4 14 billion) both with and without prompt modifications to identify explanation correctness and unfaithfulness errors for phenotyping. Results: For experiments without prompt modifications, both errors were present across all models although more responses had explanation correctness errors than unfaithfulness errors. For experiments assessing accuracy impact after prompt modifications, DeepSeek, a reasoning model, had the smallest overall accuracy impact when compared to Mistral and Phi. Discussion: Since reasoning errors were ubiquitous across models, our enhancement of PHEONA to include a component for assessing faulty reasoning provides critical support for LLM evaluation and evidence for reasoning errors for complex tasks. While insights from reasoning errors can help prompt refinement, a deeper understanding of why LLM reasoning errors occur will likely require further development and refinement of interpretability methods. Conclusion: Reasoning errors were pervasive across LLM responses for computational phenotyping, a complex reasoning task
How Massive Star Clusters Form and Evolve: A Near-IR Survey of the W51 Complex
2025-07-30
Aden Dawson, Adam Ginsburg, Carlos Roman-Zuniga
We present near-infrared JHKs and narrow-band H2(1-0) photometric observations of the W51A region, obtained with GTC EMIR, aiming to characterize its young stellar population and provide mass estimates for individual cluster members and the proto-clusters. Our observations reveal over 3000 new sources, out of which 88 are located in the proto-clusters, W51 IRS2 and W51 Main. The average extinction (AV), measured from the J-H color, of sources is 19 AV in W51 IRS2 and 14 AV in W51 Main. We document 17 new instances of H2 emission in the region by utilizing observations from the H2(1-0) narrow-band filter. Despite limited completeness, we estimated masses for each cluster member and estimated the total cluster mass to be in the range of 900-4700 solar masses for W51 IRS2 and 500-2700 solar masses for W51 Main, using an assumed age range of 1-3 Myr. We measured the initial mass function (IMF) in the proto-clusters assuming a range of ages from 1-3 Myr and found that the IMF slopes for both proto-clusters are consistent with the Salpeter IMF in the mass range greater than or equal to 8 solar masses within 1 to 2 sigma.
The relation between the masses of supermassive black holes (SMBHs) and their host galaxies encodes information on their mode of growth, especially at the earliest epochs. The James Webb Space Telescope (JWST) has opened such investigations by detecting the host galaxies of AGN and more luminous quasars within the first billion years of the universe (z > 6). Here, we evaluate the relation between the mass of SMBHs and the total stellar mass of their host galaxies using a sample of nine quasars at 6.18 < z < 6.4 from the Subaru High-z Exploration of Low-luminosity Quasars (SHELLQs) survey with NIRCam and NIRSpec observations. We find that the observed location of these quasars in the SMBH-galaxy mass plane (log MBH ~ 8-9; log M* ~9.5-11) is consistent with a non-evolving intrinsic mass relation with dispersion (0.80_{-0.28}^{+0.23} dex) higher than the local value (~0.3-0.4 dex). Our analysis is based on a forward model of systematics and includes a consideration of the impact of selection effects and measurement uncertainties, an assumption on the slope of the mass relation, and finds a reasonable AGN fraction (2.3%) of galaxies at z ~ 6 with an actively growing UV-unobscured black hole. In particular, models with a substantially higher normalisation in MBH would require an unrealistically low intrinsic dispersion (~0.22 dex) and a lower AGN fraction (~0.6%). Consequently, our results predict a large population of AGNs at lower black hole masses, as are now just starting to be discovered in focused efforts with JWST.
Origin of the ~150 kpc radio filament in galaxy ESO 137-006
2025-07-30
Toushif Alam, Kunal P. Mooley, Kartick C. Sarkar
Sensitive wide-field radio surveys have started uncovering many filamentary structures associated with the jets and lobes of radio galaxies, radio relics in galaxy clusters, and tailed galaxies. Although limited theoretical investigations on the origin of the filamentary structures have associated these filaments with astrophysical shocks and interactions with intracluster magneto-ionic media, more quantitative studies are needed to ascertain their precise nature and origin. Recent MeerKAT observations found peculiar filamentary structures (threaded radio structures) joining the lobes of a nearby FRII-like galaxy, ESO 137-006. Here we investigate the origin of these "synchrotron threads" to understand if they may be confined magnetically and could arise due to shocks associated with jet activity. Through simulation- and theory-based analysis, we find that the dynamical time (~70 Myr) associated with the shock front closely matches the estimated synchrotron age (~130 Myr) of the threads, thus making the shock origin hypothesis a favorable scenario for this particular filament.
Capturing star formation activity from compressed photometric images of galaxies
2025-07-30
Kyuseok Oh, M. Dennis Turp
We present a novel approach for classifying star-forming galaxies using photometric images. By utilizing approximately $124,000$ optical color composite images and spectroscopic data of nearby galaxies at $0.01
How to embed any likelihood into SBI: Application to Planck + Stage IV galaxy surveys and Dynamical Dark Energy
2025-07-30
Guillermo Franco Abellán, Noemi Anau Montel, Oleg Savchenko, Christoph Weniger
Simulation-based inference (SBI) allows fast Bayesian inference for simulators encoding implicit likelihoods. However, some explicit likelihoods cannot be easily reformulated as simulators, hindering their integration into combined analyses within SBI frameworks. One key example in cosmology is given by the Planck CMB likelihoods. We present a simple method to construct an effective simulator for any explicit likelihood using samples from a previously converged Markov Chain Monte Carlo (MCMC) run. This effective simulator can subsequently be combined with any forward simulator. To illustrate this method, we combine the full Planck CMB likelihoods with a 3x2pt simulator (cosmic shear, galaxy clustering and their cross-correlation) for a Stage IV survey like Euclid, and test evolving dark energy parameterized by the $w_0w_a$ equation-of-state. Assuming the $w_0w_a$CDM cosmology hinted by DESI BAO DR2 + Planck 2018 + PantheonPlus SNIa datasets, we find that future 3x2pt data alone could detect evolving dark energy at $5\sigma$, while its combination with current CMB, BAO and SNIa datasets could raise the detection to almost $7\sigma$. Moreover, thanks to simulation reuse enabled by SBI, we show that our joint analysis is in excellent agreement with MCMC while requiring zero Boltzmann solver calls. This result opens up the possibility of performing massive global scans combining explicit and implicit likelihoods in a highly efficient way.
Non-Local Conserved Currents and Continuous Non-Invertible Symmetries
2025-07-30
Diego Delmastro, Adar Sharon, Yunqin Zheng
We embark on a systematic study of continuous non-invertible symmetries, focusing on 1+1d CFTs. We describe a generalized version of Noether's theorem, where continuous non-invertible symmetries are associated to $\textit{non-local}$ conserved currents: point-like operators attached to extended topological defects. The generalized Noether's theorem unifies several constructions of continuous non-invertible symmetries in the literature, and allows us to exhibit many more examples in diverse theories of interest. We first review known examples which are non-intrinsic (i.e., invertible up to gauging), and then describe $\textit{new}$ examples in Wess-Zumino-Witten models and products of minimal models. For some of these new examples, we show that these continuous non-invertible symmetries are intrinsic if we demand that a certain global symmetry is preserved. The continuous non-invertible symmetries in products of minimal models also allow us to construct new examples of defect conformal manifolds in a single copy of a minimal model. Finally, we comment on continuous non-invertible symmetries in higher dimensions.
Biocompatibility is a critical factor in the application of nanomaterials in medical fields, as these materials must interact safely and effectively with biological systems to be viable for therapeutic and diagnostic use. This article investigates the biocompatibility of nanomaterials, focusing on their interactions with biological cells, tissues, and the immune system. Key properties such as surface chemistry, size, shape, and material composition are examined, as they significantly influence the biological response. The article explores the role of nanomaterials in medical applications, including drug delivery, diagnostic imaging, and tissue engineering, while discussing the challenges involved in enhancing their biocompatibility. A case study on the CaO-CaP binary system is presented, showcasing the use of calcium oxide (CaO) and calcium phosphate (CaP) nanoparticles in bone tissue engineering. This system is widely investigated for its ability to mimic the mineral content of bone and promote osteogenesis, highlighting both its therapeutic potential and challenges in ensuring safe biocompatibility in clinical settings. The article concludes by reviewing strategies to optimize the biocompatibility of nanomaterials and discussing future directions for research in advancing their applications in medical treatments.
Scientific Machine Learning with Kolmogorov-Arnold Networks
2025-07-30
Salah A. Faroughi, Farinaz Mostajeran, Amin Hamed Mashhadzadeh, Shirko Faroughi
The field of scientific machine learning, which originally utilized multilayer perceptrons (MLPs), is increasingly adopting Kolmogorov-Arnold Networks (KANs) for data encoding. This shift is driven by the limitations of MLPs, including poor interpretability, fixed activation functions, and difficulty capturing localized or high-frequency features. KANs address these issues with enhanced interpretability and flexibility, enabling more efficient modeling of complex nonlinear interactions and effectively overcoming the constraints associated with conventional MLP architectures. This review categorizes recent progress in KAN-based models across three distinct perspectives: (i) data-driven learning, (ii) physics-informed modeling, and (iii) deep operator learning. Each perspective is examined through the lens of architectural design, training strategies, application efficacy, and comparative evaluation against MLP-based counterparts. By benchmarking KANs against MLPs, we highlight consistent improvements in accuracy, convergence, and spectral representation, clarifying KANs' advantages in capturing complex dynamics while learning more effectively. Finally, this review identifies critical challenges and open research questions in KAN development, particularly regarding computational efficiency, theoretical guarantees, hyperparameter tuning, and algorithm complexity. We also outline future research directions aimed at improving the robustness, scalability, and physical consistency of KAN-based frameworks.
Lightcurves of stars in the Chamaeleon I Association
2025-07-29
K. Neumannová, L. Kueß, E. Paunzen, K. Bernhard
Star-forming regions are essential for studying very young stellar objects of various masses. They still contain a significant amount of dust and gas. We present a study of light curves of stars in the field of the Chamaeleon I association. We use automatic spectral classification with MKCLASS to identify the spectral types of the stars in the field with a light curve from the NEOWISE and Gaia surveys. The light curves are analysed using the software Peranso and astropy. We also used VSX to identify the variability type. Based on astrometry, we have identified 92 stars, 73 of which are members of the association. We received light curves for 55 stars from the Gaia survey and for 69 stars from the ALLWISE/NEOWISE survey. For 28 of them, it was possible to determine the types of variables, mostly T Tauri and Orion variables. The spectral types of the members are mostly cooler M-type stars, with one being a possible chemically peculiar (CP) star. The non-members associated with light curve measurements include spectral types A-G with one CP candidate.
PHAX: A Structured Argumentation Framework for User-Centered Explainable AI in Public Health and Biomedical Sciences
2025-07-29
Bahar İlgen, Akshat Dubey, Georges Hattab
Ensuring transparency and trust in AI-driven public health and biomedical sciences systems requires more than accurate predictions-it demands explanations that are clear, contextual, and socially accountable. While explainable AI (XAI) has advanced in areas like feature attribution and model interpretability, most methods still lack the structure and adaptability needed for diverse health stakeholders, including clinicians, policymakers, and the general public. We introduce PHAX-a Public Health Argumentation and eXplainability framework-that leverages structured argumentation to generate human-centered explanations for AI outputs. PHAX is a multi-layer architecture combining defeasible reasoning, adaptive natural language techniques, and user modeling to produce context-aware, audience-specific justifications. More specifically, we show how argumentation enhances explainability by supporting AI-driven decision-making, justifying recommendations, and enabling interactive dialogues across user types. We demonstrate the applicability of PHAX through use cases such as medical term simplification, patient-clinician communication, and policy justification. In particular, we show how simplification decisions can be modeled as argument chains and personalized based on user expertise-enhancing both interpretability and trust. By aligning formal reasoning methods with communicative demands, PHAX contributes to a broader vision of transparent, human-centered AI in public health.
2025-07-29
Patrick Iff, Paul Bruegger, Marcin Chrapek, Maciej Besta, Torsten Hoefler
Advances in embedding models for text, image, audio, and video drive progress across multiple domains, including retrieval-augmented generation, recommendation systems, vehicle/person reidentification, and face recognition. Many applications in these domains require an efficient method to retrieve items that are close to a given query in the embedding space while satisfying a filter condition based on the item's attributes, a problem known as Filtered Approximate Nearest Neighbor Search (FANNS). In this work, we present a comprehensive survey and taxonomy of FANNS methods and analyze how they are benchmarked in the literature. By doing so, we identify a key challenge in the current FANNS landscape: the lack of diverse and realistic datasets, particularly ones derived from the latest transformer-based text embedding models. To address this, we introduce a novel dataset consisting of embedding vectors for the abstracts of over 2.7 million research articles from the arXiv repository, accompanied by 11 real-world attributes such as authors and categories. We benchmark a wide range of FANNS methods on our novel dataset and find that each method has distinct strengths and limitations; no single approach performs best across all scenarios. ACORN, for example, supports various filter types and performs reliably across dataset scales but is often outperformed by more specialized methods. SeRF shows excellent performance for range filtering on ordered attributes but cannot handle categorical attributes. Filtered-DiskANN and UNG excel on the medium-scale dataset but fail on the large-scale dataset, highlighting the challenge posed by transformer-based embeddings, which are often more than an order of magnitude larger than earlier embeddings. We conclude that no universally best method exists.
Simulating quasar microlensing light curves: High magnification events
2025-07-29
Favio Neira, Timo Anguita, Georgios Vernardos
Quasar microlensing can be used to constrain important astrophysical properties, such as the accretion disk size and the amount of stars in the lensing galaxy. The associated brightness variations over time, in particular high magnification events (HMEs) and caustic crossings, can yield precise constraints due to their strong dependence on the relative projected velocities of the components and accretion disk size. The next generation of large sky area surveys, such as The Vera Rubin Observatory (LSST) and Euclid, are expected to find and follow-up thousands of lensed quasars from which such events could be identified and observed. In this work we present a characterization and estimation of all HMEs that could potentially be observed, focusing on systems that could be identified by ground based telescopes. From systems whose minimum image separation is at least 1 arcsec, and their second dimmest image is at least 21.5 magnitudes in the i-band ($\sim560$ in the southern or northern sky), we estimate $\sim60$ HMEs with amplitudes $>0.3$ [mag] in the r-band per year. We find that on average, saddle images are approximately four times more likely to host events than minima, and $\sim10\%$ ($\sim50\%$) of events are caustic crossings for saddles (minima). We also find that HMEs in saddle images can have amplitudes $\sim1-2$ [mag] larger than minima.
Precovery Observations of 3I/ATLAS from TESS Suggests Possible Distant Activity
2025-07-29
Adina D. Feinstein, John W. Noonan, Darryl Z. Seligman
3I/ATLAS is the third macroscopic interstellar object detected traversing the Solar System. Since its initial discovery on UT 01 July 2025, hundreds of hours on a range of observational facilities have been dedicated to measure the physical properties of this object. These observations have provided astrometry to refine the orbital solution, photometry to measure the color, a rotation period and secular light curve, and spectroscopy to characterize the composition of the coma. Here, we report precovery photometry of 3I/ATLAS as observed with NASA's Transiting Exoplanet Survey Satellite (TESS). 3I/ATLAS was observed nearly continuously by TESS from UT 07 May 2025 to 02 June 2025. We use the shift-stack method to create deep stack images to recover the object. These composite images reveal that 3I/ATLAS has an average TESS magnitude of $T_\textrm{mag} = 19.6 \pm 0.1$ and an absolute visual magnitude of $H_V = 12.5 \pm 0.3$, consistent with magnitudes reported in July 2025, suggesting that 3I/ATLAS may have been active out at $\sim 6.4$ au. Additionally, we extract a $\sim 20$ day light curve and find no statistically significant evidence of a nucleus rotation period. Nevertheless, the data presented here are some of the earliest precovery images of 3I/ATLAS and may be used in conjunction with future observations to constrain the properties of our third interstellar interloper.
Large-Scale Linear Energy System Optimization: A Systematic Review on Parallelization Strategies via Decomposition
2025-07-29
Lars Hadidi, Leonard Göke, Maximilian Hoffmann, Mario Klostermeier, Shima Sasanpour, Tim Varelmann, Vassilios Yfantis, Jochen Linßen, Detlef Stolten, Jann M. Weinand
As renewable energy integration, sector coupling, and spatiotemporal detail increase, energy system optimization models grow in size and complexity, often pushing solvers to their performance limits. This systematic review explores parallelization strategies that can address these challenges. We first propose a classification scheme for linear energy system optimization models, covering their analytical focus, mathematical structure, and scope. We then review parallel decomposition methods, finding that while many offer performance benefits, no single approach is universally superior. The lack of standardized benchmark suites further complicates comparison. To address this, we recommend essential criteria for future benchmarks and minimum reporting standards. We also survey available software tools for parallel decomposition, including modular frameworks and algorithmic abstractions. Though centered on energy system models, our insights extend to the broader operations research field.
Reprocessing of the Parkes 70-cm Survey and Discovery of a New Radio Pulsar in the Large Magellanic Cloud
2025-07-29
Wenke Xia, Fronefield Crawford, Shinnosuke Hisano, Tai Jespersen, Melanie Ficarra, Mckenzie Golden, Mia Gironda
We have reprocessed the data archived from the Parkes 70-cm pulsar (PKS70) survey with an expanded DM search range and an acceleration search. Our goal was to detect pulsars that might have been missed in the original survey processing. Of the original 43842 pointings, 34869 pointings were archived, along with 440 additional pointings for confirmation or timing. We processed all of these archived data and detected 359 known pulsars: 265 of these were detected in the original survey, while an additional 94 currently known pulsars were detected in our reprocessing. A few among those 94 pulsars are highly accelerated binary pulsars. Furthermore, we detected 5 more pulsars with DMs higher than the original survey thresholds, as well as 6 more pulsars below the nominal survey sensitivity threshold (from the original survey beams with longer integrations). We missed detection of 33 (of the 298) pulsars detected in the original survey, in part because portions of the survey data were missing in the archive and our early stage candidate sifting method. We discovered one new pulsar in the re-analysis, PSR J0540$-$69 which has a spin period of 0.909 s and resides in the Large Magellanic Cloud (LMC). This new pulsar appeared in three PKS70 beams and one additional L-band observation that targeted the LMC pulsar PSR B0540$-$69. The numerous pulsar detections found in our re-analysis and the discovery of a new pulsar in the LMC highlight the value of conducting multiple searches through pulsar datasets.
Applying salient object detection (SOD) to RGB-D videos is an emerging task called RGB-D VSOD and has recently gained increasing interest, due to considerable performance gains of incorporating motion and depth and that RGB-D videos can be easily captured now in daily life. Existing RGB-D VSOD models have different attempts to derive motion cues, in which extracting motion information explicitly from optical flow appears to be a more effective and promising alternative. Despite this, there remains a key issue that how to effectively utilize optical flow and depth to assist the RGB modality in SOD. Previous methods always treat optical flow and depth equally with respect to model designs, without explicitly considering their unequal contributions in individual scenarios, limiting the potential of motion and depth. To address this issue and unleash the power of motion and depth, we propose a novel selective cross-modal fusion framework (SMFNet) for RGB-D VSOD, incorporating a pixel-level selective fusion strategy (PSF) that achieves optimal fusion of optical flow and depth based on their actual contributions. Besides, we propose a multi-dimensional selective attention module (MSAM) to integrate the fused features derived from PSF with the remaining RGB modality at multiple dimensions, effectively enhancing feature representation to generate refined features. We conduct comprehensive evaluation of SMFNet against 19 state-of-the-art models on both RDVS and DVisal datasets, making the evaluation the most comprehensive RGB-D VSOD benchmark up to date, and it also demonstrates the superiority of SMFNet over other models. Meanwhile, evaluation on five video benchmark datasets incorporating synthetic depth validates the efficacy of SMFNet as well. Our code and benchmark results are made publicly available at https://github.com/Jia-hao999/SMFNet.
The Lyman-$α$ Forest from LBGs: First 3D Correlation Measurement with DESI and Prospects for Cosmology
2025-07-29
Hiram K. Herrera-Alcantar, Eric Armengaud, Christophe Yèche, Calum Gordon, Laura Casas, Andreu Font-Ribera, Christophe Magneville, Corentin Ravoux, J. Aguilar, S. Ahlen, A. Anand, D. Brooks, E. Chaussidon, T. Claybaugh, A. Cuceu, K. S. Dawson, A. de la Macorra, P. Doel, S. Ferraro, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, A. X. Gonzalez-Morales, G. Gutierrez, J. Guy, C. Hahn, D. Kirkby, A. Kremin, O. Lahav, A. Lambert, M. Landriau, L. Le Guillou, M. Manera, P. Martini, A. Meisner, R. Miquel, A. Muñoz-Gutiérrez, S. Nadathur, N. Palanque-Delabrouille, W. J. Percival, F. Prada, I. Pérez-Ràfols, G. Rossi, E. Sanchez, D. Schlegel, M. Schubnell, J. Silber, D. Sprayberry, G. Tarlé, B. A. Weaver, R. Zhou, H. Zou
The Lyman-$\alpha$ (Ly$\alpha$) forest is a key tracer of large-scale structure at redshifts z > 2, traditionally studied using spectra of quasars. Here, we explore the viability Lyman Break Galaxies (LBGs) as alternative background sources for Ly$\alpha$ forest studies. We analyze 4,151 Ly$\alpha$ forest skewers extracted from LBG spectra obtained in the DESI pilot surveys in the COSMOS and XMM-LSS fields. We present the first measurement of the Ly$\alpha$ forest auto-correlation function derived exclusively from LBG spectra, probing comoving separations up to 48 $h^{-1}$Mpc at an effective redshift of $z_\mathrm{eff}$ = 2.70. The measured signal is consistent with that from DESI DR2 quasar Ly$\alpha$ forest spectra at a comparable redshift, validating LBGs as reliable background sources. We also measure the cross-correlation between the LBG Ly$\alpha$ forest and 13,362 galaxy positions, showing that this observable serves as a sensitive diagnostic for galaxy redshift uncertainties and systematic offsets. Finally, using synthetic LBG spectra and Fisher forecasts, we show that a future wide-area survey over 5000 deg$^2$, targeting 1000 LBGs per deg$^2$ at similar signal-to-noise than our dataset, could enable Ly$\alpha$ forest baryon acoustic oscillation (BAO) measurements with 0.4% precision on the isotropic BAO scale and 1.3% on the anisotropic (Alcock-Paczynski) scale. Combining BAO with a Ly$\alpha$ forest full-shape analysis improves the AP constraint to 0.6%. These results open a new path for precision cosmology at high redshift using dense LBG samples.
Radio Variability in Recently-Quenched Galaxies: The Impact of TDE or AGN Driven Outflows
2025-07-29
K. Decker French, Kristina Nyland, Pallavi Patil, Kishalay De, Dillon Dong, Nicholas Earl, Samaresh Mondal, Kate Rowlands, Margaret Shepherd, Margaret E. Verrico
Outflows and jets launched from the nuclei of galaxies emit radio synchrotron emission that can be used to study the impact of accretion energy on the host galaxy. The decades-long baseline now enabled by large radio surveys allows us to identify cases where new outflows or jets have been launched. Here, we present the results of a targeted VLA program observing four post-starburst galaxies that have brightened significantly in radio emission over the past ~20 years. We obtain quasi-simultaneous observations in five bands (1-18 GHz) for each source. We find peaked spectral energy distributions, indicative of self-absorbed synchrotron emission. While all four sources have risen significantly over the past ~20 years in the 1-2 GHz band, two also show clear recent flares in the 2-4 GHz band. These sources are less luminous than typical peaked spectrum radio AGN. It remains unclear whether these sources are low luminosity analogs of the peaked radio AGN from accreted gas, or driven by tidal disruption events with missed optical flares. Regardless of the source of the accreted material, these newly-launched outflows contain sufficient energy to drive the molecular gas outflows observed in post-starburst galaxies and to drive turbulence suppressing star formation.
Recent perspectives on the Invariant Subspace Problem
2025-07-29
I. Chalendar, J. R. Partington
We review recent work connected with the invariant subspace problem for operators, in particular new developments in the last 15 years. In particular, we include discussions of almost-invariant subspaces, universal operators, specific classes of operators and new results in the framework of Banach spaces.
Detecting Stellar Coronal Mass Ejections via Coronal Dimming in the Extreme Ultraviolet
2025-07-29
James Paul Mason, Allison Youngblood, Kevin France, Astrid M. Veronig, Meng Jin
Stellar flares and coronal mass ejections (CMEs) can strip planetary atmospheres, reducing the potential habitability of terrestrial planets. While flares have been observed for decades, stellar CMEs remain elusive. Extreme ultraviolet (EUV) emissions are sensitive to both flares and CME-induced coronal dimming. We assess the detectability of stellar CME-induced EUV dimming events by adapting a known "Sun-as-a-star" dimming technique -- validated by the Solar Dynamics Observatory's EUV Variability Experiment (EVE) -- to stellar conditions. We adapt the solar data to reflect a range of stellar intensities, accounting for intrinsic brightness, distance, and interstellar medium (ISM) attenuation. We generate synthetic light curves for two different missions: the legacy EUV Explorer (EUVE) and the proposed ESCAPE mission. Our results indicate that dimming detections are well within reach. EUVE's broadband imager was capable of detecting stellar CMEs -- albeit with limited spectral (temperature) resolution -- but that was not part of the observing plan. EUVE's spectroscopic survey lacked sufficient sensitivity for CME detections. Optimizing modern instrument design for this task would make the observation fully feasible. In this work, we present a tool to explore the stellar-CME detection parameter space. Our tool shows that an instrument with performance similar to ESCAPE, setting a 600-second integration period, and integrating the spectra into bands, any star with an X-ray flux $\geq 2.51 \times 10^{-12}$ergs$^{-1}$~cm$^{-2}$ should have a $\geq 3\sigma$ detection even for a modest few-percent dimming profile, regardless of ISM attenuation. Such measurements would be crucial for understanding the space weather environments of exoplanet host stars and, ultimately, for evaluating planetary habitability.
Tidal heating in detached double white dwarf binaries
2025-07-29
Lucy O. McNeill, Ryosuke Hirai
Short--period ($P<$1 hr orbits) detached double white dwarf binary (DWDB) components identified with transient surveys (e.g. SDSS, ZTF) have hot surface temperatures ($>$10,000 K) and observed radii a factor two larger than completely degenerate white dwarfs. We formulate tidal heating in helium composition extremely low mass white dwarf (ELM WD) components of detached DWDBs which reach mass transfer within a Hubble time. We combine a mass radius relation which varies with surface temperature and the tidal friction model of Hut 1981, where the additional orbital energy loss from tidal friction is accounted for by increases in the primary surface temperature, and hence increasing radius. Applying this heating model to the current sample of binaries with ZTF, we predict temperature increases from the present day of up to $\sim$40\% before the onset of mass transfer. We find that helium white dwarfs are generically hot and large at the onset of mass transfer, even for the oldest DWDBs whose components can cool to be degenerate by the present day. In the population of Galactic DWDBs, we find that the onset of mass transfer should occur at orbital periods as long as 1000s (17 minutes), or binary gravitational wave frequency of 2 mHz. This is over three times longer than periods expected for degenerate WD (5 minutes). Since mass transferring DWDBs are progenitors for a variety of transients and stellar populations e.g. RCrB stars, AM CVn binaries, type .Ia supernova, the finite temperature of donor white dwarfs should be taken into account.
Rediscussion of eclipsing binaries. Paper XXV. The chemically-peculiar system AR Aurigae
2025-07-29
John Southworth
AR Aur is a detached eclipsing binary containing two late-B stars which are chemically peculiar, on a circular orbit of period 4.135 d. The primary is a HgMn star which shows temporal changes in its chemical abundances and spectral line profiles, whilst the secondary is a likely weak Am star. Published analyses of the system have used spectroscopic light ratios to constrain the eclipse models and found that the secondary star is larger than the primary. This unexpected outcome has been taken as an indication that the system is young and the secondary has yet to reach the main sequence. In this work we present the first analysis of the light curve of the system obtained by the Transiting Exoplanet Survey Satellite (TESS), whose quality allows us to avoid using a spectroscopic light ratio to constrain the solution. When combined with literature spectroscopic results we obtain highly precise masses of 2.544 +/- 0.009 Msun and 2.358 +/- 0.009 Msun, and radii of 1.843 +/- 0.002 Rsun and 1.766 +/- 0.003 Rsun. The light ratio is inconsistent with spectroscopic determinations, confirming the suggestion of Takeda (2025) that spectroscopic light ratios of the system are unreliable due the chemical peculiarity of the stars. The properties of the system are matched by theoretical predictions for a slightly super-solar metallicity and an age of 33 +/- 3 Myr: both components are young main-sequence stars.
The impact of large-scale EV charging on the real-time operation of distribution systems: A comprehensive review
2025-07-29
Zhe Yu, Chuang Yang, Qin Wang
With the large-scale integration of electric vehicles (EVs) in the distribution grid, the unpredictable nature of EV charging introduces considerable uncertainties to the grid's real-time operations. This can exacerbate load fluctuations, compromise power quality, and pose risks to the grid's stability and security. However, due to their dual role as controllable loads and energy storage devices, EVs have the potential to mitigate these fluctuations, balance the variability of renewable energy sources, and provide ancillary services that support grid stability. By leveraging the bidirectional flow of information and energy in smart grids, the adverse effects of EV charging can be minimized and even converted into beneficial outcomes through effective real-time management strategies. This paper explores the negative impacts of EV charging on the distribution system's real-time operations and outlines methods to transform these challenges into positive contributions. Additionally, it provides an in-depth analysis of the real-time management system for EV charging, focusing on state estimation and management strategies.
When Proximity Falls Short: Inequalities in Commuting and Accessibility by Public Transport in Santiago, Chile
2025-07-29
Cesar Marin-Flores, Leo Ferres, Henrikki Tenkanen
Traditional measures of urban accessibility often rely on static models or survey data. However, location information from mobile networks now enables large-scale, dynamic analyses of how people navigate cities. This study uses eXtended Detail Records (XDRs) derived from mobile phone activity to analyze commuting patterns and accessibility inequalities in Santiago, Chile. First, we identify residential and work locations and model commuting routes using the R5 multimodal routing engine, which combines public transport and walking. To explore spatial patterns, we apply a bivariate spatial clustering analysis (LISA) alongside regression techniques to identify distinct commuting behaviors and their alignment with vulnerable population groups. Our findings reveal that average commuting times remain consistent across socioeconomic groups. However, despite residing in areas with greater opportunity density, higher-income populations do not consistently experience shorter commuting times. This highlights a disconnect between spatial proximity to opportunities and actual travel experience. Our analysis reveals significant disparities between sociodemographic groups, particularly regarding the distribution of indigenous populations and gender. Overall, the findings of our study suggest that commuting and accessibility inequalities in Santiago are closely linked to broader social and demographic structures.
Probabilistic cosmological inference on HI tomographic data
2025-07-29
Sambatra Andrianomena
We explore the possibility of retrieving cosmological information from 21-cm tomographic data at intermediate redshift. The first step in our approach consists of training an encoder, composed of several three dimensional convolutional layers, to cast the neutral hydrogen 3D data into a lower dimension latent space. Once pre-trained, the featurizer is able to generate 3D grid representations which, in turn, will be mapped onto cosmology ($\Omega_{\rm m}$, $\sigma_{8}$) via likelihood-free inference. For the latter, which is framed as a density estimation problem, we consider a Bayesian approximation method which exploits the capacity of Masked Autoregressive Flow to estimate the posterior. It is found that the representations learned by the deep encoder are separable in latent space. Results show that the neural density estimator, trained on the latent codes, is able to constrain cosmology with a precision of $R^2 \ge 0.91$ on all parameters and that most of the ground truth of the instances in the test set fall within $1\sigma$ uncertainty. It is established that the posterior uncertainty from the density estimator is reasonably calibrated. We also investigate the robustness of the feature extractor by using it to compress out-of-distribution dataset, that is either from a different simulation or from the same simulation but at different redshift. We find that, while trained on the latent codes corresponding to different types of out-of-distribution dataset, the probabilistic model is still reasonably capable of constraining cosmology, with $R^2 \ge 0.80$ in general. This highlights both the predictive power of the density estimator considered in this work and the meaningfulness of the latent codes retrieved by the encoder. We believe that the approach prescribed in this proof of concept will be of great use when analyzing 21-cm data from various surveys in the near future.
Guaranteeing Line-of-Sight Wireless Connectivity in Stochastic Environments with Random Obstacles
2025-07-29
Mohsen Abedi, Alexis A. Dowhuszko, Mehdi Sookhak, Risto Wichman
Advancements in high-frequency communication technologies using millimeter waves (mmWave), Tera- Hertz (THz), and optical wireless frequency bands are key for extending wireless connectivity beyond 5G. These technologies offer a broader spectrum than the one available on low- and mid-bands, enabling ultra-high-speed data rates, higher device density, enhanced security, and improved positioning accuracy. However, their performance relies heavily on clear Line-of-Sight (LoS) conditions, as Non-LoS components are significantly weaker, making blockages a major challenge to ensure suitable received signal power. This paper addresses this limitation by identifying the minimum number and optimal placement of access points (APs) needed to ensure LoS connectivity in stochastic/dynamic environments with random obstacle locations. To achieve this, the stochastic environment is carefully modeled as a graph, where the nodes represent sub-polygons of layout realizations, and the edges capture the visibility overlaps between them. By employing maximal clique clustering and maximum clique packing methods over this graph, the proposed approach determines the AP placement locations that guarantee either full LoS coverage or controlled LoS gaps, while seamlessly adapting to the stochastic variability in obstacle locations. Simulations results in a representative stochastic environment demonstrate a 25% reduction in the required number of APs, achieving a tolerable 5% coverage gap compared to AP deployment optimized for full LoS coverage.
AI Literacy as a Key Driver of User Experience in AI-Powered Assessment: Insights from Socratic Mind
2025-07-29
Meryem Yilmaz Soylu, Jeonghyun Lee, Jui-Tse Hung, Christopher Zhang Cui, David A. Joyner
As Artificial Intelligence (AI) tools become increasingly embedded in higher education, understanding how students interact with these systems is essential to supporting effective learning. This study examines how students' AI literacy and prior exposure to AI technologies shape their perceptions of Socratic Mind, an interactive AI-powered formative assessment tool. Drawing on Self-Determination Theory and user experience research, we analyze relationships among AI literacy, perceived usability, satisfaction, engagement, and perceived learning effectiveness. Data from 309 undergraduates in Computer Science and Business courses were collected through validated surveys. Partial least squares structural equation modeling showed that AI literacy - especially self-efficacy, conceptual understanding, and application skills - significantly predicts usability, satisfaction, and engagement. Usability and satisfaction, in turn, strongly predict perceived learning effectiveness, while prior AI exposure showed no significant effect. These findings highlight that AI literacy, rather than exposure alone, shapes student experiences. Designers should integrate adaptive guidance and user-centered features to support diverse literacy levels, fostering inclusive, motivating, and effective AI-based learning environments.
Video anomaly detection (VAD) aims to identify and ground anomalous behaviors or events in videos, serving as a core technology in the fields of intelligent surveillance and public safety. With the advancement of deep learning, the continuous evolution of deep model architectures has driven innovation in VAD methodologies, significantly enhancing feature representation and scene adaptability, thereby improving algorithm generalization and expanding application boundaries. More importantly, the rapid development of multi-modal large language (MLLMs) and large language models (LLMs) has introduced new opportunities and challenges to the VAD field. Under the support of MLLMs and LLMs, VAD has undergone significant transformations in terms of data annotation, input modalities, model architectures, and task objectives. The surge in publications and the evolution of tasks have created an urgent need for systematic reviews of recent advancements. This paper presents the first comprehensive survey analyzing VAD methods based on MLLMs and LLMs, providing an in-depth discussion of the changes occurring in the VAD field in the era of large models and their underlying causes. Additionally, this paper proposes a unified framework that encompasses both deep neural network (DNN)-based and LLM-based VAD methods, offering a thorough analysis of the new VAD paradigms empowered by LLMs, constructing a classification system, and comparing their strengths and weaknesses. Building on this foundation, this paper focuses on current VAD methods based on MLLMs/LLMs. Finally, based on the trajectory of technological advancements and existing bottlenecks, this paper distills key challenges and outlines future research directions, offering guidance for the VAD community.
Optical Emission-Line Properties of eROSITA-selected SDSS-V Galaxies
2025-07-29
Nadiia G. Pulatova, Evgenii Rubtsov, Igor V. Chilingarian, Hans-Walter Rix, Mariia Demianenko, Kirill A. Grishin, Ivan Yu. Katkov, Donald P. Schneider, Catarina Aydar, Johannes Buchner, Mara Salvato, Andrea Merloni, Anton M. Koekemoer, Roberto J. Assef, Claudio Ricci, Dominika Wylezalek, Damir Gasymov, William Nielsen Brandt, Castalia Alenka Negrete Peñaloza, Sean Morrison, Scott F. Anderson, Franz E. Bauer, Hector Javier Ibarra-Medel, Qiaoya Wu
We present and discuss optical emission line properties obtained from the analysis of Sloan Digital Sky Survey (SDSS) spectra for an X-ray selected sample of 3684 galaxies (0.002 < z < 0.55), drawn from the eRASS1 catalog. We modeled SDSS-V DR19 spectra using the NBursts full spectrum fitting technique with E-MILES simple stellar populations (SSP) models and emission line templates to decompose broad and narrow emission line components for correlation with X-ray properties. We place the galaxies on the Baldwin-Phillips-Terlevich (BPT) diagram to diagnose their dominant excitation mechanism. We show that the consistent use of the narrow component fluxes shifts most galaxies systematically and significantly upward to the active galactic nuclei (AGN) region on the BPT diagram. On this basis, we confirm the dependence between a galaxys position on the BPT diagram and its (0.2-2.3 keV) X-ray/H$\alpha$ flux ratio. We also verified the correlation between X-ray luminosity and emission line luminosities of the narrow [O\iii]$\lambda 5007$ and broad H$\alpha$ component; as well as the relations between the Supermassive Black Hole (SMBH) mass, the X-ray luminosity, and the velocity dispersion of the stellar component ($\sigma_{*}$) on the base on the unique sample of optical spectroscopic follow-up of X-ray sources detected by eROSITA. These results highlight the importance of emission line decomposition in AGN classification and refine the connection between X-ray emission and optical emission line properties in galaxies.
Setting up a relativistic lunar reference frame is of a prime importance in the context of future exploration missions to the Moon. If the procedure for building a consistent reference frame within the framework of the general theory of relativity is well established (cf. resolutions B.3 of IAU 2000), there is still some freedom in the choice of the coordinate timescale to be adopted as reference in the cislunar region. In this paper, we review the orders of magnitude of the relativistic effects resulting from (i) the gravitational redshift of a clock on the lunar surface and (ii) the time transformations between a clock on the surface of the Moon and a clock on the surface of the Earth. We then discuss possible options for a lunar reference timescale with their advantages and drawbacks. Finally, we propose possible realizations of the lunar reference timescale as well as its traceability to UTC.
Emerging Trends in Pseudo-Label Refinement for Weakly Supervised Semantic Segmentation with Image-Level Supervision
2025-07-29
Zheyuan Zhang, Wang Zhang
Unlike fully supervised semantic segmentation, weakly supervised semantic segmentation (WSSS) relies on weaker forms of supervision to perform dense prediction tasks. Among the various types of weak supervision, WSSS with image level annotations is considered both the most challenging and the most practical, attracting significant research attention. Therefore, in this review, we focus on WSSS with image level annotations. Additionally, this review concentrates on mainstream research directions, deliberately omitting less influential branches. Given the rapid development of new methods and the limitations of existing surveys in capturing recent trends, there is a pressing need for an updated and comprehensive review. Our goal is to fill this gap by synthesizing the latest advancements and state-of-the-art techniques in WSSS with image level labels. Basically, we provide a comprehensive review of recent advancements in WSSS with image level labels, categorizing existing methods based on the types and levels of additional supervision involved. We also examine the challenges of applying advanced methods to domain specific datasets in WSSS,a topic that remains underexplored. Finally, we discuss the current challenges, evaluate the limitations of existing approaches, and outline several promising directions for future research. This review is intended for researchers who are already familiar with the fundamental concepts of WSSS and are seeking to deepen their understanding of current advances and methodological innovations.
A North-South Metallicity Asymmetry in the Outer Galactic disk -- Evidence for the Pericentric Passage of the Sagittarius Dwarf Galaxy
2025-07-29
Chun Wang, Yang Huang, Timothy C. Beers, Payel Das, Haibo Yuan, Lizhi Xie, Shi Shao
We present maps of the mean metallicity distributions on the Galactocentric $R$--$Z$ plane at different azimuthal angles using red clump stars selected from the LAMOST and APOGEE surveys. In the inner disk ($R < $ 11\,kpc), the metallicity distribution is symmetric between the upper and lower disk. However, we find a North-South metallicity asymmetry in the outer disk ($R > 11$\,kpc), especially towards the anti-Galactic center ($-5^\circ < \Phi < 15^\circ$) direction. By further dissecting the map in age space, we detect this asymmetry across all mono-age stellar populations. However, the asymmetry is less pronounced in older populations ($\tau > 8$ Gyr) compared to younger ones ($\tau < 6$\,Gyr). This reduced significance likely stems from three factors: larger age uncertainties, fewer stars in the outer disk, and the kinematically hotter nature of older populations. The observed metallicity asymmetry may be the consequence of the purturbation of the recent pericentric passage through the Galactic disk and tidal force of the well-known Sagittarius dwarf galaxy.
Decentralized Modeling of Vehicular Maneuvers and Interactions at Urban Junctions
2025-07-29
Saeed Rahmani, Simeon C. Calvert, Bart van Arem
Modeling and evaluation of automated vehicles (AVs) in mixed-autonomy traffic is essential prior to their safe and efficient deployment. This is especially important at urban junctions where complex multi-agent interactions occur. Current approaches for modeling vehicular maneuvers and interactions at urban junctions have limitations in formulating non-cooperative interactions and vehicle dynamics within a unified mathematical framework. Previous studies either assume predefined paths or rely on cooperation and central controllability, limiting their realism and applicability in mixed-autonomy traffic. This paper addresses these limitations by proposing a modeling framework for trajectory planning and decentralized vehicular control at urban junctions. The framework employs a bi-level structure where the upper level generates kinematically feasible reference trajectories using an efficient graph search algorithm with a custom heuristic function, while the lower level employs a predictive controller for trajectory tracking and optimization. Unlike existing approaches, our framework does not require central controllability or knowledge sharing among vehicles. The vehicle kinematics are explicitly incorporated at both levels, and acceleration and steering angle are used as control variables. This intuitive formulation facilitates analysis of traffic efficiency, environmental impacts, and motion comfort. The framework's decentralized structure accommodates operational and stochastic elements, such as vehicles' detection range, perception uncertainties, and reaction delay, making the model suitable for safety analysis. Numerical and simulation experiments across diverse scenarios demonstrate the framework's capability in modeling accurate and realistic vehicular maneuvers and interactions at various urban junctions, including unsignalized intersections and roundabouts.
Pretraining a Unified PDDL Domain from Real-World Demonstrations for Generalizable Robot Task Planning
2025-07-29
Haoming Ye, Yunxiao Xiao, Cewu Lu, Panpan Cai
Robotic task planning in real-world environments requires reasoning over implicit constraints from language and vision. While LLMs and VLMs offer strong priors, they struggle with long-horizon structure and symbolic grounding. Existing methods that combine LLMs with symbolic planning often rely on handcrafted or narrow domains, limiting generalization. We propose UniDomain, a framework that pre-trains a PDDL domain from robot manipulation demonstrations and applies it for online robotic task planning. It extracts atomic domains from 12,393 manipulation videos to form a unified domain with 3137 operators, 2875 predicates, and 16481 causal edges. Given a target class of tasks, it retrieves relevant atomics from the unified domain and systematically fuses them into high-quality meta-domains to support compositional generalization in planning. Experiments on diverse real-world tasks show that UniDomain solves complex, unseen tasks in a zero-shot manner, achieving up to 58% higher task success and 160% improvement in plan optimality over state-of-the-art LLM and LLM-PDDL baselines.
Sun sensor calibration algorithms: A systematic mapping and survey
2025-07-29
Michael Herman, Olivia J. Pinon Fischer, Dimitri N. Mavris
Attitude sensors determine the spacecraft attitude through the sensing of an astronomical object, field or other phenomena. The Sun and fixed stars are the two primary astronomical sensing objects. Attitude sensors are critical components for the survival and knowledge improvement of spacecraft. Of these, sun sensors are the most common and important sensor for spacecraft attitude determination. The sun sensor measures the Sun vector in spacecraft coordinates. The sun sensor calibration process is particularly difficult due to the complex nature of the uncertainties involved. The uncertainties are small, difficult to observe, and vary spatio-temporally over the lifecycle of the sensor. In addition, the sensors are affected by numerous sources of uncertainties, including manufacturing, electrical, environmental, and interference sources. This motivates the development of advanced calibration algorithms to minimize uncertainty over the sensor lifecycle and improve accuracy. Although modeling and calibration techniques for sun sensors have been explored extensively in the literature over the past two decades, there is currently no resource that consolidates and systematically reviews this body of work. The present review proposes a systematic mapping of sun sensor modeling and calibration algorithms across a breadth of sensor configurations. It specifically provides a comprehensive survey of each methodology, along with an analysis of research gaps and recommendations for future directions in sun sensor modeling and calibration techniques.
Human demonstration data is often ambiguous and incomplete, motivating imitation learning approaches that also exhibit reliable planning behavior. A common paradigm to perform planning-from-demonstration involves learning a reward function via Inverse Reinforcement Learning (IRL) then deploying this reward via Model Predictive Control (MPC). Towards unifying these methods, we derive a replacement of the policy in IRL with a planning-based agent. With connections to Adversarial Imitation Learning, this formulation enables end-to-end interactive learning of planners from observation-only demonstrations. In addition to benefits in interpretability, complexity, and safety, we study and observe significant improvements on sample efficiency, out-of-distribution generalization, and robustness. The study includes evaluations in both simulated control benchmarks and real-world navigation experiments using few-to-single observation-only demonstrations.
Automatic Classification of User Requirements from Online Feedback -- A Replication Study
2025-07-29
Meet Bhatt, Nic Boilard, Muhammad Rehan Chaudhary, Cole Thompson, Jacob Idoko, Aakash Sorathiya, Gouri Ginde
Natural language processing (NLP) techniques have been widely applied in the requirements engineering (RE) field to support tasks such as classification and ambiguity detection. Although RE research is rooted in empirical investigation, it has paid limited attention to replicating NLP for RE (NLP4RE) studies. The rapidly advancing realm of NLP is creating new opportunities for efficient, machine-assisted workflows, which can bring new perspectives and results to the forefront. Thus, we replicate and extend a previous NLP4RE study (baseline), "Classifying User Requirements from Online Feedback in Small Dataset Environments using Deep Learning", which evaluated different deep learning models for requirement classification from user reviews. We reproduced the original results using publicly released source code, thereby helping to strengthen the external validity of the baseline study. We then extended the setup by evaluating model performance on an external dataset and comparing results to a GPT-4o zero-shot classifier. Furthermore, we prepared the replication study ID-card for the baseline study, important for evaluating replication readiness. Results showed diverse reproducibility levels across different models, with Naive Bayes demonstrating perfect reproducibility. In contrast, BERT and other models showed mixed results. Our findings revealed that baseline deep learning models, BERT and ELMo, exhibited good generalization capabilities on an external dataset, and GPT-4o showed performance comparable to traditional baseline machine learning models. Additionally, our assessment confirmed the baseline study's replication readiness; however missing environment setup files would have further enhanced readiness. We include this missing information in our replication package and provide the replication study ID-card for our study to further encourage and support the replication of our study.
ST-DAI: Single-shot 2.5D Spatial Transcriptomics with Intra-Sample Domain Adaptive Imputation for Cost-efficient 3D Reconstruction
2025-07-29
Jiahe Qian, Yaoyu Fang, Xinkun Wang, Lee A. Cooper, Bo Zhou
For 3D spatial transcriptomics (ST), the high per-section acquisition cost of fully sampling every tissue section remains a significant challenge. Although recent approaches predict gene expression from histology images, these methods require large external datasets, which leads to high-cost and suffers from substantial domain discrepancies that lead to poor generalization on new samples. In this work, we introduce ST-DAI, a single-shot framework for 3D ST that couples a cost-efficient 2.5D sampling scheme with an intra-sample domain-adaptive imputation framework. First, in the cost-efficient 2.5D sampling stage, one reference section (central section) is fully sampled while other sections (adjacent sections) is sparsely sampled, thereby capturing volumetric context at significantly reduced experimental cost. Second, we propose a single-shot 3D imputation learning method that allows us to generate fully sampled 3D ST from this cost-efficient 2.5D ST scheme, using only sample-specific training. We observe position misalignment and domain discrepancy between sections. To address those issues, we adopt a pipeline that first aligns the central section to the adjacent section, thereafter generates dense pseudo-supervision on the central section, and then performs Fast Multi-Domain Refinement (FMDR), which adapts the network to the domain of the adjacent section while fine-tuning only a few parameters through the use of Parameter-Efficient Domain-Alignment Layers (PDLs). During this refinement, a Confidence Score Generator (CSG) reweights the pseudo-labels according to their estimated reliability, thereby directing imputation toward trustworthy regions. Our experimental results demonstrate that ST-DAI achieves gene expression prediction performance comparable to fully sampled approaches while substantially reducing the measurement burden.
The Fourier coefficients and singular moduli of the elliptic modular function $j(τ)$, revisited
2025-07-29
Toshiki Matsusaka
Kaneko's formula expresses the Fourier coefficients of the elliptic modular $j$-function as finite sums of singular moduli. First published as a short article in 1996, it was presented as a consequence of Zagier's work inspired by Borcherds products. Since then, the formula has developed into a broader framework that links the Fourier coefficients of modular forms to the special values of modular functions, extending in various directions. This article surveys these subsequent developments.
Evaluation and Benchmarking of LLM Agents: A Survey
2025-07-29
Mahmoud Mohammadi, Yipeng Li, Jane Lo, Wendy Yip
The rise of LLM-based agents has opened new frontiers in AI applications, yet evaluating these agents remains a complex and underdeveloped area. This survey provides an in-depth overview of the emerging field of LLM agent evaluation, introducing a two-dimensional taxonomy that organizes existing work along (1) evaluation objectives -- what to evaluate, such as agent behavior, capabilities, reliability, and safety -- and (2) evaluation process -- how to evaluate, including interaction modes, datasets and benchmarks, metric computation methods, and tooling. In addition to taxonomy, we highlight enterprise-specific challenges, such as role-based access to data, the need for reliability guarantees, dynamic and long-horizon interactions, and compliance, which are often overlooked in current research. We also identify future research directions, including holistic, more realistic, and scalable evaluation. This work aims to bring clarity to the fragmented landscape of agent evaluation and provide a framework for systematic assessment, enabling researchers and practitioners to evaluate LLM agents for real-world deployment.
2023 undergraduate thesis on a deterministic "hat game." For a digraph $D$, each player stands on a vertex $v$, is assigned a hat from $h(v)$ possible colors, and makes $g(v)$ guesses of her hat's color based on her out-neighbors' hats. If there exists a collective strategy that guarantees a correct guess for any hat assignment, the game is winnable. Which games $(D,g,h)$ are winnable? Two much-studied parameters: $\mu(D)$ is the maximum integer $k$ such that $(D,1,k)$ is winnable, and $\hat{\mu}(D)$ is the supremum of $h/g$ for integer $h, g$ such that $(D,g,h)$ is winnable. Chapter 0 is a casual, riddle-based introduction. Chapter 1 taxonomizes the games, surveys all previous work, and summarizes the piece. Chapter 2 proves lemmata and easy cases. Chapter 3 uses "hats as hints" and "admissible paths" for games on cycles. Chapter 4 generalizes several "constructors" and applies them to tree games. Chapter 5 uses "combinatorial prisms" for a new angle on the well-studied $K_{n,m}$ games. In chapter 6, we apply "dependency digraphs" to the continuous limit of this game. Chapter 7 collects open problems and minor results. We show: $(C_{k\geq 4},1,h)$ is winnable if and only if: $h=3$ and $k$ is divisible by $3$ or equal to $4$, $h\leq 4$ and the $h(v)$ sequence $(3,2,3)$ or $(2,3,3)$ appears in the cycle, or the $h(v)$ sequence $(2,...,2)$ appears with no intervening value $>4$. $(T, 1, h)$ is winnable for tree $T$ iff $T$ has a subtree $T'$ with $h(v)\leq 2^{deg_{T'}(v)}$ for all $v\in V(T')$. For a digraph $D$, $\hat{\mu}(D)\leq e(\Delta^-+1)$. For a graph $G$, $\hat{\mu}(G)\leq (\Delta-1)^{1-\Delta} \Delta^{\Delta}
Infrared spectroscopy offers rapid, non destructive measurement of chemical and material properties but suffers from high dimensional, overlapping spectral bands that challenge conventional chemometric approaches. Emerging large language models (LLMs), with their capacity for generalization and reasoning, offer promising potential for automating complex scientific workflows. Despite this promise, their application in IR spectral analysis remains largely unexplored. This study addresses the critical challenge of achieving accurate, automated infrared spectral interpretation under low-data conditions using an LLM-driven framework. We introduce an end-to-end, large language model driven agent framework that integrates a structured literature knowledge base, automated spectral preprocessing, feature extraction, and multi task reasoning in a unified pipeline. By querying a curated corpus of peer reviewed IR publications, the agent selects scientifically validated routines. The selected methods transform each spectrum into low dimensional feature sets, which are fed into few shot prompt templates for classification, regression, and anomaly detection. A closed loop, multi turn protocol iteratively appends mispredicted samples to the prompt, enabling dynamic refinement of predictions. Across diverse materials: stamp pad ink, Chinese medicine, Pu'er tea, Citri Reticulatae Pericarpium and waste water COD datasets, the multi turn LLM consistently outperforms single turn inference, rivaling or exceeding machine learning and deep learning models under low data regimes.
The Atacama Cosmology Telescope: DR6 Sunyaev-Zel'dovich Selected Galaxy Clusters Catalog
2025-07-29
ACT/DES/HSC Collaboration, :, M. Aguena, S. Aiola, S. Allam, F. Andrade-Oliveira, D. Bacon, N. Bahcall, N. Battaglia, E. S. Battistelli, S. Bocquet, B. Bolliet, J. R. Bond, D. Brooks, E. Calabrese, J. Carretero, S. K. Choi, L. N. da Costa, M. Costanzi, W. Coulton, T. M. Davis, S. Desai, M. J. Devlin, S. Dicker, P. Doel, A. J. Duivenvoorden, J. Dunkley, S. Ferraro, B. Flaugher, J. Frieman, P. A. Gallardo, M. Gatti, E. Gaztanaga, A. S. Gill, J. E. Golec, D. Gruen, R. A. Gruendl, M. Halpern, M. Hasselfield, J. C. Hill, M. Hilton, A. D. Hincks, S. R. Hinton, D. L. Hollowood, K. Honscheid, J. Hubmayr, K. M. Huffenberger, J. P. Hughes, D. J. James, M. Klein, K. Knowles, B. J. Koopman, A. Kosowsky, O. Lahav, E. Lee, Y. Lin, M. Lokken, M. S. Madhavacheril, A. A. Plazas Malagón, J. v. Marrewijk, J. L. Marshall, J. McMahon, J. Mena-Fernández, R. Miquel, H. Miyatake, J. J. Mohr, K. Moodley, T. Mroczkowski, S. Naess, F. Nati, A. Nicola, M. D. Niemack, R. L. C. Ogando, M. Oguri, J. Orlowski-Scherer, L. A. Page, B. Partridge, M. E. da Silva Pereira, A. Porredon, F. J. Qu, D. C. Ragavan, B. Ried Guachalla, A. K. Romer, A. Carnero Rosell, E. S. Rykoff, S. Samuroff, E. Sanchez, I. Sevilla-Noarbe, C. Sierra, C. Sifón, M. Smith, S. T. Staggs, E. Suchyta, M. E. C. Swanson, D. L. Tucker, C. Vargas, E. M. Vavagiakis, J. De Vicente, N. Weaverdyck, J. Weller, E. J. Wollack, I. Zubeldia
We present the results of a search for galaxy clusters in the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) microwave sky maps covering 16293 square degrees in three frequency bands, using data obtained over the lifetime of the project (2008-2022). We report redshifts and mass estimates for 9977 clusters detected via their Sunyaev-Zel'dovich (SZ) effect with signal-to-noise greater than 4 at a 2.4 arcminute filter scale. The catalog includes 1166 clusters at redshifts greater than 1, and 121 clusters at redshifts greater than 1.5. Using a relation between cluster SZ signal and mass that is consistent with recent weak-lensing measurements, we estimate that clusters detected with signal-to-noise greater than 5 form a sample which is 90% complete for clusters with masses greater than $5 \times 10^{14}$ MSun (measured within a spherical volume with mean density 500 times the critical density). El Gordo, a cluster found in an initial ACT survey of 755 square degrees, remains the most extreme cluster in mass and redshift; we find no cluster with a mass and redshift combination high enough to falsify the standard LCDM cosmology with Gaussian initial perturbations. We make public a variety of data products, including the full cluster candidate list, noise maps, and sky masks, along with our software for cluster detection and instructions for reproducing our cluster catalogs from the public ACT maps.
Requirements for Joint Orbital Characterization of Cold Giants and Habitable Worlds with Habitable Worlds Observatory
2025-07-29
Sabina Sagynbayeva, Asif Abbas, Stephen R. Kane, Eric L. Nielsen, William Thompson, Sarah Blunt, Malena Rice, Jessie L. Christiansen, Caleb K. Harada, Elisabeth R. Newton, Yasuhiro Hasegawa, Philip J. Armitage, Tansu Daylan
We determine optimal requirements for the joint detection of habitable-zone planets and cold giant planets with the Habitable Worlds Observatory (HWO). Analysis of 164 nearby stars shows that a coronagraph outer working angle (OWA) of 1440 milliarcseconds (mas) is necessary to achieve 80-90% visibility of cold giants. Approximately 40 precursor radial velocity measurements with 1 m/s precision are required to adequately constrain orbital parameters before HWO observations. We demonstrate that 6-8 astrometric measurements distributed across the mission timeline, compared to radial velocity constraints alone and to astrometry constraints alone, significantly improve orbital parameter precision, enabling direct determination of orbital inclination with uncertainties of 0.8-3 degrees. For habitable-zone planet characterization, 4-5 epochs provide moderate confidence, while high-confidence (95%) confirmation requires 8+ observations. These specifications are essential for the comprehensive characterization of planetary system architectures and understanding the potential habitability of terrestrial exoplanets.
Augmented data storytelling enhances narrative delivery by integrating visualizations with physical environments and presenter actions. Existing systems predominantly rely on body gestures or speech to control visualizations, leaving interactions with physical objects largely underexplored. We introduce augmented physical data storytelling, an approach enabling presenters to manipulate visualizations through physical object interactions. To inform this approach, we first conducted a survey of data-driven presentations to identify common visualization commands. We then conducted workshops with nine HCI/VIS researchers to collect mappings between physical manipulations and these commands. Guided by these insights, we developed InSituTale, a prototype that combines object tracking via a depth camera with Vision-LLM for detecting real-world events. Through physical manipulations, presenters can dynamically execute various visualization commands, delivering cohesive data storytelling experiences that blend physical and digital elements. A user study with 12 participants demonstrated that InSituTale enables intuitive interactions, offers high utility, and facilitates an engaging presentation experience.
These proceedings review the application of Bayesian inference to high momentum transfer probes of the quark--gluon plasma (QGP). Bayesian inference techniques are introduced, highlighting critical components to consider when comparing analyses. Recent calibrations using hadron observables are described, illustrating the importance of the choice of parametrization. Additional recent analyses that characterize the impact of the inclusion of jet observables, as well as soft-hard correlations, are reviewed. Finally, lessons learned from these analyses and important questions for the future are highlighted.
MUSEQuBES: The Column Density, Covering Fraction, Mass, and Environmental Dependence of Cool HI Gas Around Low-Redshift Galaxies
2025-07-29
Sayak Dutta, Sowgat Muzahid, Joop Schaye, Sean Johnson, Nicolas F. Bouche, Ramona Augustin, Sebastiano Cantalupo, Hsiao-Wen Chen, Martin Wendt
We investigate cool HI gas traced by Lyman series absorption in the vicinity of 256 galaxies (median stellar mass, log10(M*/Msun) = 8.7 and median redshift, z = 0.48) using 15 background quasars (median impact parameter, D ~ 140 physical kpc (pkpc)), as part of the MUSE Quasar-fields Blind Emitters Survey (MUSEQuBES). We find that the HI column density (N(HI)) profile around isolated, star-forming galaxies, which span ~ 3 dex in stellar mass, is well described by a power law with a slope of ~ -2.3 when expressed as a function of the normalized impact parameter, D/Rvir. The HI covering fraction (k) within the virial radius, for a threshold of log10(N(HI)/cm^{-2}) = 14, is significantly lower for high-mass passive galaxies compared to their isolated, star-forming counterparts. The k-profile of isolated, star-forming galaxies suggests a characteristic size of the HI-rich CGM of ~ 1.5 Rvir, consistently across the stellar mass range. The mean HI mass in the outer CGM (0.3-1 Rvir ) increases with stellar mass, ranging from ~ 10^{5} to 10^{6.6} Msun. The b-parameters of the strongest HI components correlate and anti-correlate with specific star-formation rate (sSFR) and mass, respectively, with > 2 sigma significance. Broad Lya absorbers (BLAs) with b > 60 km s^{-1} are predominantly associated with high-mass galaxies, likely tracing the warm-hot phase of the CGM. The velocity centroids of HI components indicate that the absorbers at D < Rvir are predominantly consistent with being gravitationally bound to their associated galaxy, regardless of stellar mass. Finally, leveraging ~3000 galaxies with spectroscopic redshifts from the wide-field Magellan follow-up of six MUSEQuBES fields, we find that non-isolated galaxies exhibit an HI-rich environment that extends approximately three times further than for their isolated counterparts.
Gemini Near Infrared Spectrograph$-$Distant Quasar Survey: the Chandra View
2025-07-29
Andrea Marlar, Ohad Shemmer, Michael S. Brotherton, Gordon T. Richards, Cooper Dix, Brandon M. Matthews, W. N. Brandt, R. M. Plotkin
We present Chandra observations of 63 sources from the Gemini Near Infrared Spectrograph$-$Distant Quasar Survey (GNIRS-DQS) of which 54 were targeted by snapshot observations in Cycle 24. A total of 55 sources are clearly detected in at least one X-ray band, and we set stringent upper limits on the X-ray fluxes of the remaining eight sources. In combination with rest-frame ultraviolet-optical spectroscopic data for these sources, we assess whether X-rays can provide a robust accretion-rate indicator for quasars, particularly at the highest accessible redshifts. We utilize a recently modified H$\beta$-based Eddington luminosity ratio estimator, as well as the C IV $\lambda$1549 emission-line parameter space to investigate trends and correlations with the optical-X-ray spectral slope ($\alpha_{\mathrm{ox}}$) and the effective hard-X-ray power-law photon index ($\Gamma$). We find that $\alpha_{\mathrm{ox}}$ does not improve current accretion-rate estimates based on H$\beta$ or C IV. Instead, within the limitations of our sample, we confirm previous findings that the C IV parameter space may be a better indicator of the accretion rate up to $z\sim3.5$. We also find that the average $\Gamma$ values for a small subset of our sources, as well as the average $\Gamma$ value in different groupings of our sources, are consistent with their respective relatively high Eddington luminosity ratios. Deeper X-ray observations of our X-ray-detected sources are needed for measuring $\Gamma$ accurately and testing whether this parameter can serve as a robust, un-biased accretion-rate diagnostic.
Towards Safe and Comfortable Vehicle Control Transitions: A Systematic Review of Takeover Time, Time Budget, and Takeover Performance
2025-07-29
Kexin Liang, Simeon C. Calvert, J. W. C. van Lint
Conditionally automated driving systems require human drivers to disengage from non-driving-related activities and resume vehicle control within limited time budgets when encountering scenarios beyond system capabilities. Ensuring safe and comfortable transitions is critical for reducing driving risks and improving user experience. However, takeovers involve complex human-vehicle interactions, resulting in substantial variability in drivers' responses, especially in takeover time, defined as the duration needed to regain control. This variability presents challenges in setting sufficient time budgets that are neither too short (risking safety and comfort) nor too long (reducing driver alertness and transition efficiency). Although previous research has examined the role of time budgets in influencing takeover time and performance, few studies have systematically addressed how to determine sufficient time budgets that adapt to diverse scenarios and driver needs. This review supports such efforts by examining the entire takeover sequence, including takeover time, time budget, and takeover performance. Specifically, we (i) synthesize causal factors influencing takeover time and propose a taxonomy of its determinants using the task-capability interface model; (ii) review existing work on fixed and adaptive time budgets, introducing the concept of the takeover buffer to describe the gap between takeover time and allocated time budget; (iii) present a second taxonomy to support standardized and context-sensitive measurement of takeover performance; (iv) propose a conceptual model describing the relationships among takeover time, time budget, and performance; and (v) outline a research agenda with six directions.
A catalog of near-IR absolute magnitudes of Solar System small bodies
2025-07-29
Alvaro Alvarez-Candal, Juan Luis Rizos, Milagros Colazo, René Duffard, David Morate, Valerio Carruba, Júlio I. B. Camargo, Andrés Gómez-Toribio
Context. Phase curves of small bodies are useful tools to obtain their absolute magnitudes and phase coefficients. The former relates to the object's apparent brightness, while the latter relates to how the light interacts with the surface. Data from multi-wavelength photometric surveys, which usually serendipitously observe small bodies, are becoming the cornerstone of large statistical studies of the Solar System. Nevertheless, to our knowledge, all studies have been carried out in visible wavelengths. Aims. We aim to provide the first catalog of absolute magnitudes in near-infrared filters (Y, J, H, and K). We will study the applicability of a non-linear model to these data and compare it with a simple linear model. Methods. We compute the absolute magnitudes using two photometric models: the HG* 12 and the linear model. We employ a combination of Bayesian inference and Monte Carlo sampling to calculate the probability distributions of the absolute magnitudes and their corresponding phase coefficients. We use the combination of four near-infrared photometric catalogs to create our input database. Results. We produced the first catalog of near-infrared magnitudes. We obtained absolute magnitudes for over 10 000 objects (with at least one absolute magnitude measured), with about 180 objects having four absolute magnitudes. We confirmed that a linear model that fits the phase curves produces accurate results. Since a linear behavior well describes the curves, fitting to a restricted phase angle range (in particular, larger than 9.5 deg) does not substantially affect the results. Finally, we also detect a phase-coloring effect in the near-infrared, as observed in visible wavelengths for asteroids and trans-Neptunian objects.
Improved measurements of the TeV--PeV extragalactic neutrino spectrum from joint analyses of IceCube tracks and cascades
2025-07-29
R. Abbasi, M. Ackermann, J. Adams, S. K. Agarwalla, J. A. Aguilar, M. Ahlers, J. M. Alameddine, S. Ali, N. M. Amin, K. Andeen, C. Argüelles, Y. Ashida, S. Athanasiadou, S. N. Axani, R. Babu, X. Bai, J. Baines-Holmes, A. Balagopal V., S. W. Barwick, S. Bash, V. Basu, R. Bay, J. J. Beatty, J. Becker Tjus, P. Behrens, J. Beise, C. Bellenghi, B. Benkel, S. BenZvi, D. Berley, E. Bernardini, D. Z. Besson, E. Blaufuss, L. Bloom, S. Blot, I. Bodo, F. Bontempo, J. Y. Book Motzkin, C. Boscolo Meneguolo, S. Böser, O. Botner, J. Böttcher, J. Braun, B. Brinson, Z. Brisson-Tsavoussis, R. T. Burley, D. Butterfield, M. A. Campana, K. Carloni, J. Carpio, S. Chattopadhyay, N. Chau, Z. Chen, D. Chirkin, S. Choi, B. A. Clark, A. Coleman, P. Coleman, G. H. Collin, D. A. Coloma Borja, A. Connolly, J. M. Conrad, R. Corley, D. F. Cowen, C. De Clercq, J. J. DeLaunay, D. Delgado, T. Delmeulle, S. Deng, P. Desiati, K. D. de Vries, G. de Wasseige, T. DeYoung, J. C. Díaz-Vélez, S. DiKerby, M. Dittmer, A. Domi, L. Draper, L. Dueser, D. Durnford, K. Dutta, M. A. DuVernois, T. Ehrhardt, L. Eidenschink, A. Eimer, P. Eller, E. Ellinger, D. Elsässer, R. Engel, H. Erpenbeck, W. Esmail, S. Eulig, J. Evans, P. A. Evenson, K. L. Fan, K. Fang, K. Farrag, A. R. Fazely, A. Fedynitch, N. Feigl, C. Finley, L. Fischer, D. Fox, A. Franckowiak, S. Fukami, P. Fürst, J. Gallagher, E. Ganster, A. Garcia, M. Garcia, G. Garg, E. Genton, L. Gerhardt, A. Ghadimi, C. Glaser, T. Glüsenkamp, J. G. Gonzalez, S. Goswami, A. Granados, D. Grant, S. J. Gray, S. Griffin, S. Griswold, K. M. Groth, D. Guevel, C. Günther, P. Gutjahr, C. Ha, C. Haack, A. Hallgren, L. Halve, F. Halzen, L. Hamacher, M. Ha Minh, M. Handt, K. Hanson, J. Hardin, A. A. Harnisch, P. Hatch, A. Haungs, J. Häußler, K. Helbing, J. Hellrung, B. Henke, L. Hennig, F. Henningsen, L. Heuermann, R. Hewett, N. Heyer, S. Hickford, A. Hidvegi, C. Hill, G. C. Hill, R. Hmaid, K. D. Hoffman, D. Hooper, S. Hori, K. Hoshina, M. Hostert, W. Hou, T. Huber, K. Hultqvist, K. Hymon, A. Ishihara, W. Iwakiri, M. Jacquart, S. Jain, O. Janik, M. Jansson, M. Jeong, M. Jin, N. Kamp, D. Kang, W. Kang, X. Kang, A. Kappes, L. Kardum, T. Karg, M. Karl, A. Karle, A. Katil, M. Kauer, J. L. Kelley, M. Khanal, A. Khatee Zathul, A. Kheirandish, H. Kimku, J. Kiryluk, C. Klein, S. R. Klein, Y. Kobayashi, A. Kochocki, R. Koirala, H. Kolanoski, T. Kontrimas, L. Köpke, C. Kopper, D. J. Koskinen, P. Koundal, M. Kowalski, T. Kozynets, N. Krieger, J. Krishnamoorthi, T. Krishnan, K. Kruiswijk, E. Krupczak, A. Kumar, E. Kun, N. Kurahashi, N. Lad, C. Lagunas Gualda, L. Lallement Arnaud, M. Lamoureux, M. J. Larson, F. Lauber, J. P. Lazar, K. Leonard DeHolton, A. Leszczyńska, J. Liao, C. Lin, Y. T. Liu, M. Liubarska, C. Love, L. Lu, F. Lucarelli, W. Luszczak, Y. Lyu, J. Madsen, E. Magnus, Y. Makino, E. Manao, S. Mancina, A. Mand, I. C. Mariş, S. Marka, Z. Marka, L. Marten, I. Martinez-Soler, R. Maruyama, J. Mauro, F. Mayhew, F. McNally, J. V. Mead, K. Meagher, S. Mechbal, A. Medina, M. Meier, Y. Merckx, L. Merten, J. Mitchell, L. Molchany, T. Montaruli, R. W. Moore, Y. Morii, A. Mosbrugger, M. Moulai, D. Mousadi, E. Moyaux, T. Mukherjee, R. Naab, M. Nakos, U. Naumann, J. Necker, L. Neste, M. Neumann, H. Niederhausen, M. U. Nisa, K. Noda, A. Noell, A. Novikov, A. Obertacke Pollmann, V. O'Dell, A. Olivas, R. Orsoe, J. Osborn, E. O'Sullivan, V. Palusova, H. Pandya, A. Parenti, N. Park, V. Parrish, E. N. Paudel, L. Paul, C. Pérez de los Heros, T. Pernice, J. Peterson, M. Plum, A. Pontén, V. Poojyam, Y. Popovych, M. Prado Rodriguez, B. Pries, R. Procter-Murphy, G. T. Przybylski, L. Pyras, C. Raab, J. Rack-Helleis, N. Rad, M. Ravn, K. Rawlins, Z. Rechav, A. Rehman, I. Reistroffer, E. Resconi, S. Reusch, C. D. Rho, W. Rhode, L. Ricca, B. Riedel, A. Rifaie, E. J. Roberts, S. Robertson, M. Rongen, A. Rosted, C. Rott, T. Ruhe, L. Ruohan, D. Ryckbosch, J. Saffer, D. Salazar-Gallegos, P. Sampathkumar, A. Sandrock, G. Sanger-Johnson, M. Santander, S. Sarkar, J. Savelberg, M. Scarnera, P. Schaile, M. Schaufel, H. Schieler, S. Schindler, L. Schlickmann, B. Schlüter, F. Schlüter, N. Schmeisser, T. Schmidt, F. G. Schröder, L. Schumacher, S. Schwirn, S. Sclafani, D. Seckel, L. Seen, M. Seikh, S. Seunarine, P. A. Sevle Myhr, R. Shah, S. Shefali, N. Shimizu, B. Skrzypek, R. Snihur, J. Soedingrekso, A. Søgaard, D. Soldin, P. Soldin, G. Sommani, C. Spannfellner, G. M. Spiczak, C. Spiering, J. Stachurska, M. Stamatikos, T. Stanev, T. Stezelberger, T. Stürwald, T. Stuttard, G. W. Sullivan, I. Taboada, S. Ter-Antonyan, A. Terliuk, A. Thakuri, M. Thiesmeyer, W. G. Thompson, J. Thwaites, S. Tilav, K. Tollefson, S. Toscano, D. Tosi, A. Trettin, A. K. Upadhyay, K. Upshaw, A. Vaidyanathan, N. Valtonen-Mattila, J. Valverde, J. Vandenbroucke, T. Van Eeden, N. van Eijndhoven, L. Van Rootselaar, J. van Santen, J. Vara, F. Varsi, M. Venugopal, M. Vereecken, S. Vergara Carrasco, S. Verpoest, D. Veske, A. Vijai, J. Villarreal, C. Walck, A. Wang, E. H. S. Warrick, C. Weaver, P. Weigel, A. Weindl, J. Weldert, A. Y. Wen, C. Wendt, J. Werthebach, M. Weyrauch, N. Whitehorn, C. H. Wiebusch, D. R. Williams, L. Witthaus, M. Wolf, G. Wrede, X. W. Xu, J. P. Yanez, Y. Yao, E. Yildizci, S. Yoshida, R. Young, F. Yu, S. Yu, T. Yuan, A. Zegarelli, S. Zhang, Z. Zhang, P. Zhelnin, P. Zilberman
The IceCube South Pole Neutrino Observatory has discovered the presence of a diffuse astrophysical neutrino flux at energies of TeV and beyond using neutrino induced muon tracks and cascade events from neutrino interactions. We present two analyses sensitive to neutrino events in the energy range \SI{1}{TeV} to \SI{10}{PeV}, using more than 10 years of IceCube data. Both analyses consistently reject a neutrino spectrum following a single power-law with significance $>4\,\sigma$ in favor of a broken power law. We describe the methods implemented in the two analyses, the spectral constraints obtained, and the validation of the robustness of the results. Additionally, we report the detection of a muon neutrino in the MESE sample with an energy of $11.4^{+2.46}_{-2.53} $\,\si{PeV}, the highest energy neutrino observed by IceCube to date. The results presented here show insights into the spectral shape of astrophysical neutrinos, which has important implications for inferring their production processes in a multi-messenger picture.
Evidence for a Spectral Break or Curvature in the Spectrum of Astrophysical Neutrinos from 5\,TeV--10\,PeV
2025-07-29
R. Abbasi, M. Ackermann, J. Adams, S. K. Agarwalla, J. A. Aguilar, M. Ahlers, J. M. Alameddine, S. Ali, N. M. Amin, K. Andeen, C. Argüelles, Y. Ashida, S. Athanasiadou, S. N. Axani, R. Babu, X. Bai, J. Baines-Holmes, A. Balagopal V., S. W. Barwick, S. Bash, V. Basu, R. Bay, J. J. Beatty, J. Becker Tjus, P. Behrens, J. Beise, C. Bellenghi, B. Benkel, S. BenZvi, D. Berley, E. Bernardini, D. Z. Besson, E. Blaufuss, L. Bloom, S. Blot, I. Bodo, F. Bontempo, J. Y. Book Motzkin, C. Boscolo Meneguolo, S. Böser, O. Botner, J. Böttcher, J. Braun, B. Brinson, Z. Brisson-Tsavoussis, R. T. Burley, D. Butterfield, M. A. Campana, K. Carloni, J. Carpio, S. Chattopadhyay, N. Chau, Z. Chen, D. Chirkin, S. Choi, B. A. Clark, A. Coleman, P. Coleman, G. H. Collin, D. A. Coloma Borja, A. Connolly, J. M. Conrad, R. Corley, D. F. Cowen, C. De Clercq, J. J. DeLaunay, D. Delgado, T. Delmeulle, S. Deng, P. Desiati, K. D. de Vries, G. de Wasseige, T. DeYoung, J. C. Díaz-Vélez, S. DiKerby, M. Dittmer, A. Domi, L. Draper, L. Dueser, D. Durnford, K. Dutta, M. A. DuVernois, T. Ehrhardt, L. Eidenschink, A. Eimer, P. Eller, E. Ellinger, D. Elsässer, R. Engel, H. Erpenbeck, W. Esmail, S. Eulig, J. Evans, P. A. Evenson, K. L. Fan, K. Fang, K. Farrag, A. R. Fazely, A. Fedynitch, N. Feigl, C. Finley, L. Fischer, D. Fox, A. Franckowiak, S. Fukami, P. Fürst, J. Gallagher, E. Ganster, A. Garcia, M. Garcia, G. Garg, E. Genton, L. Gerhardt, A. Ghadimi, C. Glaser, T. Glüsenkamp, J. G. Gonzalez, S. Goswami, A. Granados, D. Grant, S. J. Gray, S. Griffin, S. Griswold, K. M. Groth, D. Guevel, C. Günther, P. Gutjahr, C. Ha, C. Haack, A. Hallgren, L. Halve, F. Halzen, L. Hamacher, M. Ha Minh, M. Handt, K. Hanson, J. Hardin, A. A. Harnisch, P. Hatch, A. Haungs, J. Häußler, K. Helbing, J. Hellrung, B. Henke, L. Hennig, F. Henningsen, L. Heuermann, R. Hewett, N. Heyer, S. Hickford, A. Hidvegi, C. Hill, G. C. Hill, R. Hmaid, K. D. Hoffman, D. Hooper, S. Hori, K. Hoshina, M. Hostert, W. Hou, T. Huber, K. Hultqvist, K. Hymon, A. Ishihara, W. Iwakiri, M. Jacquart, S. Jain, O. Janik, M. Jansson, M. Jeong, M. Jin, N. Kamp, D. Kang, W. Kang, X. Kang, A. Kappes, L. Kardum, T. Karg, M. Karl, A. Karle, A. Katil, M. Kauer, J. L. Kelley, M. Khanal, A. Khatee Zathul, A. Kheirandish, H. Kimku, J. Kiryluk, C. Klein, S. R. Klein, Y. Kobayashi, A. Kochocki, R. Koirala, H. Kolanoski, T. Kontrimas, L. Köpke, C. Kopper, D. J. Koskinen, P. Koundal, M. Kowalski, T. Kozynets, N. Krieger, J. Krishnamoorthi, T. Krishnan, K. Kruiswijk, E. Krupczak, A. Kumar, E. Kun, N. Kurahashi, N. Lad, C. Lagunas Gualda, L. Lallement Arnaud, M. Lamoureux, M. J. Larson, F. Lauber, J. P. Lazar, K. Leonard DeHolton, A. Leszczyńska, J. Liao, C. Lin, Y. T. Liu, M. Liubarska, C. Love, L. Lu, F. Lucarelli, W. Luszczak, Y. Lyu, J. Madsen, E. Magnus, Y. Makino, E. Manao, S. Mancina, A. Mand, I. C. Mariş, S. Marka, Z. Marka, L. Marten, I. Martinez-Soler, R. Maruyama, J. Mauro, F. Mayhew, F. McNally, J. V. Mead, K. Meagher, S. Mechbal, A. Medina, M. Meier, Y. Merckx, L. Merten, J. Mitchell, L. Molchany, T. Montaruli, R. W. Moore, Y. Morii, A. Mosbrugger, M. Moulai, D. Mousadi, E. Moyaux, T. Mukherjee, R. Naab, M. Nakos, U. Naumann, J. Necker, L. Neste, M. Neumann, H. Niederhausen, M. U. Nisa, K. Noda, A. Noell, A. Novikov, A. Obertacke Pollmann, V. O'Dell, A. Olivas, R. Orsoe, J. Osborn, E. O'Sullivan, V. Palusova, H. Pandya, A. Parenti, N. Park, V. Parrish, E. N. Paudel, L. Paul, C. Pérez de los Heros, T. Pernice, J. Peterson, M. Plum, A. Pontén, V. Poojyam, Y. Popovych, M. Prado Rodriguez, B. Pries, R. Procter-Murphy, G. T. Przybylski, L. Pyras, C. Raab, J. Rack-Helleis, N. Rad, M. Ravn, K. Rawlins, Z. Rechav, A. Rehman, I. Reistroffer, E. Resconi, S. Reusch, C. D. Rho, W. Rhode, L. Ricca, B. Riedel, A. Rifaie, E. J. Roberts, S. Robertson, M. Rongen, A. Rosted, C. Rott, T. Ruhe, L. Ruohan, D. Ryckbosch, J. Saffer, D. Salazar-Gallegos, P. Sampathkumar, A. Sandrock, G. Sanger-Johnson, M. Santander, S. Sarkar, J. Savelberg, M. Scarnera, P. Schaile, M. Schaufel, H. Schieler, S. Schindler, L. Schlickmann, B. Schlüter, F. Schlüter, N. Schmeisser, T. Schmidt, F. G. Schröder, L. Schumacher, S. Schwirn, S. Sclafani, D. Seckel, L. Seen, M. Seikh, S. Seunarine, P. A. Sevle Myhr, R. Shah, S. Shefali, N. Shimizu, B. Skrzypek, R. Snihur, J. Soedingrekso, A. Søgaard, D. Soldin, P. Soldin, G. Sommani, C. Spannfellner, G. M. Spiczak, C. Spiering, J. Stachurska, M. Stamatikos, T. Stanev, T. Stezelberger, T. Stürwald, T. Stuttard, G. W. Sullivan, I. Taboada, S. Ter-Antonyan, A. Terliuk, A. Thakuri, M. Thiesmeyer, W. G. Thompson, J. Thwaites, S. Tilav, K. Tollefson, S. Toscano, D. Tosi, A. Trettin, A. K. Upadhyay, K. Upshaw, A. Vaidyanathan, N. Valtonen-Mattila, J. Valverde, J. Vandenbroucke, T. Van Eeden, N. van Eijndhoven, L. Van Rootselaar, J. van Santen, J. Vara, F. Varsi, M. Venugopal, M. Vereecken, S. Vergara Carrasco, S. Verpoest, D. Veske, A. Vijai, J. Villarreal, C. Walck, A. Wang, E. H. S. Warrick, C. Weaver, P. Weigel, A. Weindl, J. Weldert, A. Y. Wen, C. Wendt, J. Werthebach, M. Weyrauch, N. Whitehorn, C. H. Wiebusch, D. R. Williams, L. Witthaus, M. Wolf, G. Wrede, X. W. Xu, J. P. Yanez, Y. Yao, E. Yildizci, S. Yoshida, R. Young, F. Yu, S. Yu, T. Yuan, A. Zegarelli, S. Zhang, Z. Zhang, P. Zhelnin, P. Zilberman
We report improved measurements of the all flavor astrophysical neutrino spectrum with IceCube by combining complementary neutrino samples in two independent analyses. Both analyses show evidence of a harder spectrum at energies below $\sim$30~TeV compared to higher energies where the spectrum is well characterized by a power law. The spectrum is better described by a log parabola or a broken power law, the latter being the preferred model. Both, however, reject a single power law over an energy range 5~TeV-10~PeV with a significance $>4\sigma$, providing new constraints on properties of cosmic neutrino sources.
Understanding Concept Drift with Deprecated Permissions in Android Malware Detection
2025-07-29
Ahmed Sabbah, Radi Jarrar, Samer Zein, David Mohaisen
Permission analysis is a widely used method for Android malware detection. It involves examining the permissions requested by an application to access sensitive data or perform potentially malicious actions. In recent years, various machine learning (ML) algorithms have been applied to Android malware detection using permission-based features and feature selection techniques, often achieving high accuracy. However, these studies have largely overlooked important factors such as protection levels and the deprecation or restriction of permissions due to updates in the Android OS -- factors that can contribute to concept drift. In this study, we investigate the impact of deprecated and restricted permissions on the performance of machine learning models. A large dataset containing 166 permissions was used, encompassing more than 70,000 malware and benign applications. Various machine learning and deep learning algorithms were employed as classifiers, along with different concept drift detection strategies. The results suggest that Android permissions are highly effective features for malware detection, with the exclusion of deprecated and restricted permissions having only a marginal impact on model performance. In some cases, such as with CNN, accuracy improved. Excluding these permissions also enhanced the detection of concept drift using a year-to-year analysis strategy. Dataset balancing further improved model performance, reduced low-accuracy instances, and enhanced concept drift detection via the Kolmogorov-Smirnov test.
Secure coding for web applications: Frameworks, challenges, and the role of LLMs
2025-07-29
Kiana Kiashemshaki, Mohammad Jalili Torkamani, Negin Mahmoudi
Secure coding is a critical yet often overlooked practice in software development. Despite extensive awareness efforts, real-world adoption remains inconsistent due to organizational, educational, and technical barriers. This paper provides a comprehensive review of secure coding practices across major frameworks and domains, including web development, DevSecOps, and cloud security. It introduces a structured framework comparison and categorizes threats aligned with the OWASP Top 10. Additionally, we explore the rising role of Large Language Models (LLMs) in evaluating and recommending secure code, presenting a reproducible case study across four major vulnerability types. This paper offers practical insights for researchers, developers, and educators on integrating secure coding into real-world development processes.
On operation and control of CW magnetrons for superconducting accelerators
2025-07-29
G. Kazakevich, R. P. Johnson, I. Gonin, V. Yakovlev, Ya. Derbenev, H. Wang
CW magnetrons, developed for industrial RF heaters, were suggested to feed RF cavities of superconducting accelerators due to higher efficiency and lower cost of RF power than provide traditionally used klystrons, IOTs or solid-state amplifiers. RF amplifiers driven by a master oscillator serve as coherent RF sources. CW magnetrons are regenerative RF auto generators with a huge regenerative gain. This causes regenerative instability with a notable noise when a magnetron operates as an auto generator i.e., with the anode voltage above the threshold of self-excitation. Traditionally, an injection locking by a small signal is used for phase stabilization of magnetrons. In this case CW magnetrons with the injection-locked (coherent) oscillations generate a notable level of noise. This may preclude use of CW magnetrons in this mode in the Superconducting RF (SRF) accelerators. This paper reviews PIC modeling and previously obtained experimental results for the operation and control of CW magnetrons, which led to the development of techniques most suitable for various SRF accelerators using forced RF oscillation of magnetrons, when the magnetron is launched by an injected forcing signal, and regenerative noise is suppressed.
Testing for multiple change-points in macroeconometrics: an empirical guide and recent developments
2025-07-29
Otilia Boldea, Alastair R. Hall
We review recent developments in detecting and estimating multiple change-points in time series models with exogenous and endogenous regressors, panel data models, and factor models. This review differs from others in multiple ways: (1) it focuses on inference about the change-points in slope parameters, rather than in the mean of the dependent variable - the latter being common in the statistical literature; (2) it focuses on detecting - via sequential testing and other methods - multiple change-points, and only discusses one change-point when methods for multiple change-points are not available; (3) it is meant as a practitioner's guide for empirical macroeconomists first, and as a result, it focuses only on the methods derived under the most general assumptions relevant to macroeconomic applications.
2025-07-29
Erika Ábrahám, Miguel Goulão, Milena Vujošević Janičić, Sarah Jane Delany, Amal Mersni, Oleksandra Yeremenko, Ozge Buyukdagli, Karima Boudaoud, Caroline Oehlhorn, Ute Schmid, Christina Büsing, Helen Bolke-Hermanns, Kaja Köhnle, Matilde Pato, Deniz Sunar Cerci, Larissa Schmid
Computer science attracts few women, and their proportion decreases through advancing career stages. Few women progress to PhD studies in CS after completing master's studies. Empowering women at this stage in their careers is essential to unlock untapped potential for society, industry and academia. This paper identifies students' career assumptions and information related to PhD studies focused on gender-based differences. We propose a Women Career Lunch program to inform female master students about PhD studies that explains the process, clarifies misconceptions, and alleviates concerns. An extensive survey was conducted to identify factors that encourage and discourage students from undertaking PhD studies. We identified statistically significant differences between those who undertook PhD studies and those who didn't, as well as gender differences. A catalogue of questions to initiate discussions with potential PhD students which allowed them to explore these factors was developed and translated to 8 languages. Encouraging factors toward PhD study include interest and confidence in research arising from a research involvement during earlier studies; enthusiasm for and self-confidence in CS in addition to an interest in an academic career; encouragement from external sources; and a positive perception towards PhD studies which can involve achieving personal goals. Discouraging factors include uncertainty and lack of knowledge of the PhD process, a perception of lower job flexibility, and the requirement for long-term commitment. Gender differences highlighted that female students who pursue a PhD have less confidence in their technical skills than males but a higher preference for interdisciplinary areas. Female students are less inclined than males to perceive the industry as offering better job opportunities and more flexible career paths than academia.
Enabling Early Transient Discovery in LSST via Difference Imaging with DECam
2025-07-29
Yize Dong, Kaylee de Soto, V. Ashley Villar, Anya Nugent, Alex Gagliano, K. Azalee Bostroem
We present SLIDE, a pipeline that enables transient discovery in data from the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), using archival images from the Dark Energy Camera (DECam) as templates for difference imaging. We apply this pipeline to the recently released Data Preview 1 (DP1; the first public release of Rubin commissioning data) and search for transients in the resulting difference images. The image subtraction, photometry extraction, and transient detection are all performed on the Rubin Science Platform. We demonstrate that SLIDE effectively extracts clean photometry by circumventing poor or missing LSST templates. This is especially useful for transient analysis in the early years of LSST, when template coverage will be largely incomplete or when templates may be contaminated by transients present at the time of acquisition. We present multiband light curves for a sample of known transients, along with new transient candidates identified through our search. Finally, we discuss the prospects of applying this pipeline during the main LSST survey.
ABC-SN: Attention Based Classifier for Supernova Spectra
2025-07-29
Willow Fox Fortino, Federica B. Bianco, Pavlos Protopapas, Daniel Muthukrishna, Austin Brockmeier
While significant advances have been made in photometric classification ahead of the millions of transient events and hundreds of supernovae (SNe) each night that the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will discover, classifying SNe spectroscopically remains the best way to determine most subtypes of SNe. Traditional spectrum classification tools use template matching techniques (Blondin & Tonry 2007) and require significant human supervision. Two deep learning spectral classifiers, DASH (Muthukrishna et al. 2019) and SNIascore (Fremling et al. 2021) define the state of the art, but SNIascore is a binary classifier devoted to maximizing the purity of the SN Ia-norm sample, while DASH is no longer maintained and the original work suffers from contamination of multi-epoch spectra in the training and test sets. We have explored several neural network architectures in order to create a new automated method for classifying SN subtypes, settling on an attention-based model we call ABC-SN. We benchmark our results against an updated version of DASH, thus providing the community with an up-to-date general purpose SN classifier. Our dataset includes ten different SN subtypes including subtypes of SN Ia, core collapse and interacting SNe. We find that ABC-SN outperforms DASH, and we discuss the possibility that modern SN spectra datasets contain label noise which limit the performance of all classifiers.
One-Loop Galaxy Bispectrum: Consistent Theory, Efficient Analysis with COBRA, and Implications for Cosmological Parameters
2025-07-29
Thomas Bakx, Mikhail M. Ivanov, Oliver H. E. Philcox, Zvonimir Vlah
We present an efficient and accurate pipeline for the analysis of the redshift-space galaxy bispectrum multipoles at one-loop order in effective field theory (EFT). We provide a systematic theory derivation based on power counting, which features the first comprehensive treatment of stochastic EFT contributions -- these are found to significantly improve the match to data. Our computational pipeline utilizes the COBRA technique that expands the linear matter power spectrum over a basis of principal components based on a singular value decomposition, allowing the cosmology dependence to be captured to sub-permille accuracy with just eight templates. This transforms the problem of computing the one-loop EFT bispectrum to a simple tensor multiplication, reducing the computation time to around a second per cosmology with negligible loss of accuracy. Using these tools, we study the cosmological information in the bispectrum by analyzing PTChallenge simulations, whose gigantic volume provides the most powerful test of the one-loop EFT bispectrum so far. We find that the one-loop prediction provides an excellent match to the bispectrum data up to $k_{\rm max}=0.15~h$ Mpc$^{-1}$, as evidenced by the precise recovery of the dark matter density $\omega_\text{cdm}$, Hubble constant $H_0$, and mass fluctuation amplitude $\sigma_8$ parameters, and the amplitude of equilateral primordial non-Gaussianity (PNG) $f_{\rm NL}^{\rm equil}$. Combined with the power spectrum, the COBRA-based one-loop bispectrum multipoles yield tighter constraints than the tree-level bispectrum monopole, with the posteriors on $\omega_{\text{cdm}}$, $H_0$, and $\sigma_8$ shrinking by 43\%, 31\%, and 4\%, respectively. This suggests that the COBRA-based bispectrum analysis will be an important tool in the interpretation of data from ongoing redshift surveys such as DESI and Euclid.
AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock
2025-07-29
Umair Nawaz, Muhammad Zaigham Zaheer, Fahad Shahbaz Khan, Hisham Cholakkal, Salman Khan, Rao Muhammad Anwer
Crops, fisheries and livestock form the backbone of global food production, essential to feed the ever-growing global population. However, these sectors face considerable challenges, including climate variability, resource limitations, and the need for sustainable management. Addressing these issues requires efficient, accurate, and scalable technological solutions, highlighting the importance of artificial intelligence (AI). This survey presents a systematic and thorough review of more than 200 research works covering conventional machine learning approaches, advanced deep learning techniques (e.g., vision transformers), and recent vision-language foundation models (e.g., CLIP) in the agriculture domain, focusing on diverse tasks such as crop disease detection, livestock health management, and aquatic species monitoring. We further cover major implementation challenges such as data variability and experimental aspects: datasets, performance evaluation metrics, and geographical focus. We finish the survey by discussing potential open research directions emphasizing the need for multimodal data integration, efficient edge-device deployment, and domain-adaptable AI models for diverse farming environments. Rapid growth of evolving developments in this field can be actively tracked on our project page: https://github.com/umair1221/AI-in-Agriculture
Linking enhanced star formation and quenching to faint tidal features in galaxies
2025-07-28
Alexander J. Gordon, Annette M. N. Ferguson, Robert G. Mann, Vivienne Wild
Galaxy mergers and interactions have long been suggested as a significant driver of galaxy evolution. However, the exact extent to which mergers enhance star formation and AGN activity has been challenging to establish observationally. In previous work, we visually classified a sample of galaxies with various types of faint tidal features in DECaLS images. In this paper, we cross-correlate this sample with a principal component analysis of Sloan Digital Sky Survey data to investigate how the presence of these features, as well as their specific nature, correlates with intense star formation and AGN activity. Averaged over all tidal classes, we find that tidal feature galaxies are 10.3$\pm$1.5 times more likely to be in a starburst phase, and 24.3$\pm$5.0 times more likely to have rapidly quenched (post-starbursts), than those in a mass-matched control sample. Examining differences between tidal classes, galaxies with \textit{arm} features are 1.4$\pm$0.2 times more likely to be starbursting than the other categories, while those with \textit{shell} features are 2.7$\pm$0.6 times more likely to be in a quiescent state. In a similar analysis, we identify which galaxies show evidence of AGN activity and find no significant difference between the fraction of those with or without tidal features. Overall, our results reinforce the notion that mergers play an important role in driving star formation and rapid quenching in galaxies, and provide some of the first empirical evidence that the strength of this effect has a dependence on the detailed nature of the interaction, as traced by the tidal feature morphology.
Large Language Models (LLMs) have demonstrated strong capabilities but remain fundamentally static, unable to adapt their internal parameters to novel tasks, evolving knowledge domains, or dynamic interaction contexts. As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck, necessitating agents that can adaptively reason, act, and evolve in real time. This paradigm shift -- from scaling static models to developing self-evolving agents -- has sparked growing interest in architectures and methods enabling continual learning and adaptation from data, interactions, and experiences. This survey provides the first systematic and comprehensive review of self-evolving agents, organized around three foundational dimensions -- what to evolve, when to evolve, and how to evolve. We examine evolutionary mechanisms across agent components (e.g., models, memory, tools, architecture), categorize adaptation methods by stages (e.g., intra-test-time, inter-test-time), and analyze the algorithmic and architectural designs that guide evolutionary adaptation (e.g., scalar rewards, textual feedback, single-agent and multi-agent systems). Additionally, we analyze evaluation metrics and benchmarks tailored for self-evolving agents, highlight applications in domains such as coding, education, and healthcare, and identify critical challenges and research directions in safety, scalability, and co-evolutionary dynamics. By providing a structured framework for understanding and designing self-evolving agents, this survey establishes a roadmap for advancing adaptive agentic systems in both research and real-world deployments, ultimately shedding lights to pave the way for the realization of Artificial Super Intelligence (ASI), where agents evolve autonomously, performing at or beyond human-level intelligence across a wide array of tasks.
Reconstructing 4D spatial intelligence from visual observations has long been a central yet challenging task in computer vision, with broad real-world applications. These range from entertainment domains like movies, where the focus is often on reconstructing fundamental visual elements, to embodied AI, which emphasizes interaction modeling and physical realism. Fueled by rapid advances in 3D representations and deep learning architectures, the field has evolved quickly, outpacing the scope of previous surveys. Additionally, existing surveys rarely offer a comprehensive analysis of the hierarchical structure of 4D scene reconstruction. To address this gap, we present a new perspective that organizes existing methods into five progressive levels of 4D spatial intelligence: (1) Level 1 -- reconstruction of low-level 3D attributes (e.g., depth, pose, and point maps); (2) Level 2 -- reconstruction of 3D scene components (e.g., objects, humans, structures); (3) Level 3 -- reconstruction of 4D dynamic scenes; (4) Level 4 -- modeling of interactions among scene components; and (5) Level 5 -- incorporation of physical laws and constraints. We conclude the survey by discussing the key challenges at each level and highlighting promising directions for advancing toward even richer levels of 4D spatial intelligence. To track ongoing developments, we maintain an up-to-date project page: https://github.com/yukangcao/Awesome-4D-Spatial-Intelligence.
An Analysis of Stable Mode Contributions to Rayleigh-Bénard Convection
2025-07-28
Zachary R. Williams, Ava G. Lowe
In this work, we investigate the presence and impact of stable eigenmodes in Rayleigh-B\'{e}nard convection that arises due to a temperature gradient within a fluid system. The nonlinear evolution of the canonical convection system is cast in terms of eigenmode amplitudes. The linear modes that play a significant role in the dynamics across a range of Rayleigh numbers are identified. We find that while unstable eigenmodes are a significant contributor, a small number of linearly stable modes grow to large amplitude via nonlinear interactions and are essential in modeling system dynamics. Importantly, the stable eigenmodes are seen to contain the majority of the boundary layer structure that forms in the nonlinear state. We also demonstrate that the scaling of convective heat transport (as quantified by the Nusselt Number) with Rayleigh number can be effectively quantified with only a small fraction of the total number of eigenmodes in the system.
Modular Delta Merging with Orthogonal Constraints: A Scalable Framework for Continual and Reversible Model Composition
2025-07-28
Haris Khan, Shumaila Asif, Sadia Asif
In real-world machine learning deployments, models must be continually updated, composed, and when required, selectively undone. However, existing approaches to model merging and continual learning often suffer from task interference, catastrophic forgetting, or lack of reversibility. We propose Modular Delta Merging with Orthogonal Constraints (MDM-OC), a novel framework that enables scalable, interference-free, and reversible composition of fine-tuned models. Each task-specific model is encoded as a delta from a shared base and projected into an orthogonal subspace to eliminate conflict. These projected deltas are then merged via gradient-based optimization to form a unified model that retains performance across tasks. Our approach supports continual integration of new models, structured unmerging for compliance such as GDPR requirements, and model stability via elastic weight consolidation and synthetic replay. Extensive experiments on vision and natural language processing benchmarks demonstrate that MDM-OC outperforms prior baselines in accuracy, backward transfer, and unmerge fidelity, while remaining memory-efficient and computationally tractable. This framework offers a principled solution for modular and compliant AI system design.
PyBird-JAX: Accelerated inference in large-scale structure with model-independent emulation of one-loop galaxy power spectra
2025-07-28
Alexander Reeves, Pierre Zhang, Henry Zheng
We present $\texttt{PyBird-JAX}$, a differentiable, $\texttt{JAX}$-based implementation of $\texttt{PyBird}$, using internal neural network emulators to accelerate computationally costly operations for rapid large-scale structure (LSS) analysis. $\texttt{PyBird-JAX}$ computes one-loop EFTofLSS predictions for redshift-space galaxy power spectrum multipoles in 1.2 ms on a CPU and 0.2 ms on a GPU, achieving 3-4 orders of magnitude speed-up over $\texttt{PyBird}$. The emulators take a compact spline-based representation of the input linear power spectrum $P(k)$ as feature vectors, making the approach applicable to a wide range of cosmological models. We rigorously validate its accuracy against large-volume simulations and on BOSS data, including cosmologies not explicitly represented in the training set. Leveraging automatic differentiation, $\texttt{PyBird-JAX}$ supports Fisher forecasting, Taylor expansion of model predictions, gradient-based searches, and vectorised ensemble sampling. Interfaced with a variety of samplers and Boltzmann solvers, $\texttt{PyBird-JAX}$ provides a high-performance, end-to-end inference pipeline. Combined with a symbolic-$P(k)$ generator, a typical Stage-4 LSS MCMC converges in minutes on a GPU. Our results demonstrate that $\texttt{PyBird-JAX}$ delivers the precision and speed required for upcoming LSS surveys, opening the door to accelerated cosmological inference with minimal accuracy loss and no pretraining. In a companion paper [1], we put $\texttt{PyBird-JAX}$ to use in achieving LSS marginalised constraints free from volume projection effects through non-flat measures.
Repairing vulnerabilities without invisible hands. A differentiated replication study on LLMs
2025-07-28
Maria Camporese, Fabio Massacci
Background: Automated Vulnerability Repair (AVR) is a fast-growing branch of program repair. Recent studies show that large language models (LLMs) outperform traditional techniques, extending their success beyond code generation and fault detection. Hypothesis: These gains may be driven by hidden factors -- "invisible hands" such as training-data leakage or perfect fault localization -- that let an LLM reproduce human-authored fixes for the same code. Objective: We replicate prior AVR studies under controlled conditions by deliberately adding errors to the reported vulnerability location in the prompt. If LLMs merely regurgitate memorized fixes, both small and large localization errors should yield the same number of correct patches, because any offset should divert the model from the original fix. Method: Our pipeline repairs vulnerabilities from the Vul4J and VJTrans benchmarks after shifting the fault location by n lines from the ground truth. A first LLM generates a patch, a second LLM reviews it, and we validate the result with regression and proof-of-vulnerability tests. Finally, we manually audit a sample of patches and estimate the error rate with the Agresti-Coull-Wilson method.
Finetuning Stellar Spectra Foundation Models with LoRA
2025-07-28
Xiaosheng Zhao, Yuan-Sen Ting, Alexander S. Szalay, Yang Huang
Foundation models are beginning to impact stellar spectroscopy, where spectra encode rich physical information in a structured, language-like form. A key challenge is adapting these models across heterogeneous surveys with differing resolution and coverage. We apply Low-Rank Adaptation (LoRA) to fine-tune SpecCLIP--a contrastively pre-trained model on LAMOST and Gaia XP spectra--for downstream tasks on DESI Early Data Release (EDR) spectra. We show that LoRA enables few-shot learning on DESI, with performance varying by fine-tuned module and benefiting from Gaia XP knowledge embedded in the pre-trained model. Our results demonstrate that LoRA provides a lightweight and effective strategy for extending spectral foundation models to new instruments and survey domains.
A multivariate spatial model for ordinal survey-based data
2025-07-28
Miguel Ángel Beltrán-Sánchez, Miguel Ángel Martínez-Beneito, Ana Corberán-Vallet
Health surveys provide valuable information for monitoring population health, identifying risk factors and informing public health policies. Most of the questions included are coded as ordinal variables and organized into thematic blocks. Accordingly, multivariate modeling provides a natural framework for considering these variables as true groups, thereby accounting for potential dependencies among the responses within each block. In this paper, we propose a multivariate spatial analysis of ordinal survey-based data. This multivariate approach enables the joint analysis of sets of ordinal responses that are likely to be correlated, accounting for individual-level effects, while simultaneously improving the estimation of the geographical patterns for each variable and capturing their interdependencies. We apply this methodology to describe the spatial distribution of several mental health indicators from the Health Survey of the Region of Valencia (Spain) for the year 2022. Specifically, we analyze the block of questions from the 12-item General Health Questionnaire included in the survey.
Exploring text-to-image generation for historical document image retrieval
2025-07-28
Melissa Cote, Alexandra Branzan Albu
Attribute-based document image retrieval (ABDIR) was recently proposed as an alternative to query-by-example (QBE) searches, the dominant document image retrieval (DIR) paradigm. One drawback of QBE searches is that they require sample query documents on hand that may not be available. ABDIR aims to offer users a flexible way to retrieve document images based on memorable visual features of document contents, describing document images with combinations of visual attributes determined via convolutional neural network (CNN)-based binary classifiers. We present an exploratory study of the use of generative AI to bridge the gap between QBE and ABDIR, focusing on historical documents as a use case for their diversity and uniqueness in visual features. We hypothesize that text-to-image (T2I) generation can be leveraged to create query document images using text prompts based on ABDIR-like attributes. We propose T2I-QBE, which uses Leonardo.Ai as the T2I generator with prompts that include a rough description of the desired document type and a list of the desired ABDIR-style attributes. This creates query images that are then used within the traditional QBE paradigm, which compares CNN-extracted query features to those of the document images in the dataset to retrieve the most relevant documents. Experiments on the HisIR19 dataset of historical documents confirm our hypothesis and suggest that T2I-QBE is a viable option for historical document image retrieval. To the authors' knowledge, this is the first attempt at utilizing T2I generation for DIR.
Beyond Classical Models: Statistical Physics Tools for the Analysis of Time Series in Modern Air Transport
2025-07-28
Felipe Olivares, Massimiliano Zanin
Within the continuous endeavour of improving the efficiency and resilience of air transport, the trend of using concepts and metrics from statistical physics has recently gained momentum. This scientific discipline, which integrates elements from physics and statistics, aims at extracting knowledge about the microscale rules governing a (potentially complex) system when only its macroscale is observable. Translated to air transport, this entails extracting information about how individual operations are managed, by only studying coarse-grained information, e.g. average delays. We here review some fundamental concepts of statistical physics, and explore how these have been applied to the analysis of time series representing different aspects of the air transport system. In order to overcome the abstractness and complexity of some of these concepts, intuitive definitions and explanations are provided whenever possible. We further conclude by discussing the main obstacles towards a more widespread adoption of statistical physics in air transport, and sketch topics that we believe may be relevant in the future.
Modeling User Behavior from Adaptive Surveys with Supplemental Context
2025-07-28
Aman Shukla, Daniel Patrick Scantlebury, Rishabh Kumar
Modeling user behavior is critical across many industries where understanding preferences, intent, or decisions informs personalization, targeting, and strategic outcomes. Surveys have long served as a classical mechanism for collecting such behavioral data due to their interpretability, structure, and ease of deployment. However, surveys alone are inherently limited by user fatigue, incomplete responses, and practical constraints on their length making them insufficient for capturing user behavior. In this work, we present LANTERN (Late-Attentive Network for Enriched Response Modeling), a modular architecture for modeling user behavior by fusing adaptive survey responses with supplemental contextual signals. We demonstrate the architectural value of maintaining survey primacy through selective gating, residual connections and late fusion via cross-attention, treating survey data as the primary signal while incorporating external modalities only when relevant. LANTERN outperforms strong survey-only baselines in multi-label prediction of survey responses. We further investigate threshold sensitivity and the benefits of selective modality reliance through ablation and rare/frequent attribute analysis. LANTERN's modularity supports scalable integration of new encoders and evolving datasets. This work provides a practical and extensible blueprint for behavior modeling in survey-centric applications.
2025-07-28
M. Rademacher, A. Pontin, J. M. H. Gosling, P. F. Barker, M. Toroš
Levitated optomechanics, the interaction between light and small levitated objects, is a new macroscopic quantum system that is being used as a testing ground for fundamental physics and for the development of sensors with exquisite sensitivity. The utility of this system, when compared to other quantum optomechanical systems, is its extreme isolation from the environment and, by the relatively few degrees of freedom that a levitated object has. While work in the field has strongly focused on the three translational degrees of freedom of this system, it has become increasingly important to understand the induced rotational motion of levitated objects, particularly in optical trapping fields, but also in magnetic and electric traps. These additional three degrees of freedom, which are intrinsic to levitated systems, offer a new set of optomechanical nonlinear interactions that lead to a rich and yet largely unexplored roto-translational motion. The control and utilization of these interactions promise to extend the utility of levitated optomechanics in both fundamental studies and applications. In this review, we provide an overview of levitated optomechanics, before focusing on the roto-translational motion of optically levitated anisotropic objects. We first present a classical treatment of this induced motion, bridging the gap between classical and quantum formalisms. We describe the different types of roto-translational motion for different particle shapes via their interaction with polarized optical trapping fields. Subsequently, we provide an overview of the theoretical and experimental approaches as well as applications that have established this new field. The review concludes with an outlook of promising experiments and applications, including the creation of non-classical states of roto-translational motion, quantum-limited torque sensing and particle characterization methods.
Endoscopic Depth Estimation Based on Deep Learning: A Survey
2025-07-28
Ke Niu, Zeyun Liu, Xue Feng, Heng Li, Kaize Shi
Endoscopic depth estimation is a critical technology for improving the safety and precision of minimally invasive surgery. It has attracted considerable attention from researchers in medical imaging, computer vision, and robotics. Over the past decade, a large number of methods have been developed. Despite the existence of several related surveys, a comprehensive overview focusing on recent deep learning-based techniques is still limited. This paper endeavors to bridge this gap by systematically reviewing the state-of-the-art literature. Specifically, we provide a thorough survey of the field from three key perspectives: data, methods, and applications, covering a range of methods including both monocular and stereo approaches. We describe common performance evaluation metrics and summarize publicly available datasets. Furthermore, this review analyzes the specific challenges of endoscopic scenes and categorizes representative techniques based on their supervision strategies and network architectures. The application of endoscopic depth estimation in the important area of robot-assisted surgery is also reviewed. Finally, we outline potential directions for future research, such as domain adaptation, real-time implementation, and enhanced model generalization, thereby providing a valuable starting point for researchers to engage with and advance the field.
A survey of diversity quantification in natural language processing: The why, what, where and how
2025-07-28
Louis Estève, Marie-Catherine de Marneffe, Nurit Melnik, Agata Savary, Olha Kanishcheva
The concept of diversity has received increased consideration in Natural Language Processing (NLP) in recent years. This is due to various motivations like promoting and inclusion, approximating human linguistic behavior, and increasing systems' performance. Diversity has however often been addressed in an ad hoc manner in NLP, and with few explicit links to other domains where this notion is better theorized. We survey articles in the ACL Anthology from the past 6 years, with "diversity" or "diverse" in their title. We find a wide range of settings in which diversity is quantified, often highly specialized and using inconsistent terminology. We put forward a unified taxonomy of why, what on, where, and how diversity is measured in NLP. Diversity measures are cast upon a unified framework from ecology and economy (Stirling, 2007) with 3 dimensions of diversity: variety, balance and disparity. We discuss the trends which emerge due to this systematized approach. We believe that this study paves the way towards a better formalization of diversity in NLP, which should bring a better understanding of this notion and a better comparability between various approaches.
Compositional Video Synthesis by Temporal Object-Centric Learning
2025-07-28
Adil Kaan Akan, Yucel Yemez
We present a novel framework for compositional video synthesis that leverages temporally consistent object-centric representations, extending our previous work, SlotAdapt, from images to video. While existing object-centric approaches either lack generative capabilities entirely or treat video sequences holistically, thus neglecting explicit object-level structure, our approach explicitly captures temporal dynamics by learning pose invariant object-centric slots and conditioning them on pretrained diffusion models. This design enables high-quality, pixel-level video synthesis with superior temporal coherence, and offers intuitive compositional editing capabilities such as object insertion, deletion, or replacement, maintaining consistent object identities across frames. Extensive experiments demonstrate that our method sets new benchmarks in video generation quality and temporal consistency, outperforming previous object-centric generative methods. Although our segmentation performance closely matches state-of-the-art methods, our approach uniquely integrates this capability with robust generative performance, significantly advancing interactive and controllable video generation and opening new possibilities for advanced content creation, semantic editing, and dynamic scene understanding.
Large language models (LLMs) are increasingly integrated into users' daily lives, leading to a growing demand for personalized outputs. Previous work focuses on leveraging a user's own history, overlooking inter-user differences that are crucial for effective personalization. While recent work has attempted to model such differences, the reliance on language-based prompts often hampers the effective extraction of meaningful distinctions. To address these issues, we propose Difference-aware Embedding-based Personalization (DEP), a framework that models inter-user differences in the latent space instead of relying on language prompts. DEP constructs soft prompts by contrasting a user's embedding with those of peers who engaged with similar content, highlighting relative behavioral signals. A sparse autoencoder then filters and compresses both user-specific and difference-aware embeddings, preserving only task-relevant features before injecting them into a frozen LLM. Experiments on personalized review generation show that DEP consistently outperforms baseline methods across multiple metrics. Our code is available at https://github.com/SnowCharmQ/DEP.
Towards Explainable Deep Clustering for Time Series Data
2025-07-28
Udo Schlegel, Gabriel Marques Tavares, Thomas Seidl
Deep clustering uncovers hidden patterns and groups in complex time series data, yet its opaque decision-making limits use in safety-critical settings. This survey offers a structured overview of explainable deep clustering for time series, collecting current methods and their real-world applications. We thoroughly discuss and compare peer-reviewed and preprint papers through application domains across healthcare, finance, IoT, and climate science. Our analysis reveals that most work relies on autoencoder and attention architectures, with limited support for streaming, irregularly sampled, or privacy-preserved series, and interpretability is still primarily treated as an add-on. To push the field forward, we outline six research opportunities: (1) combining complex networks with built-in interpretability; (2) setting up clear, faithfulness-focused evaluation metrics for unsupervised explanations; (3) building explainers that adapt to live data streams; (4) crafting explanations tailored to specific domains; (5) adding human-in-the-loop methods that refine clusters and explanations together; and (6) improving our understanding of how time series clustering models work internally. By making interpretability a primary design goal rather than an afterthought, we propose the groundwork for the next generation of trustworthy deep clustering time series analytics.
Deep Extragalactic VIsible Legacy Survey (DEVILS): Satellite Quenching at Intermediate Redshift
2025-07-28
L. J. M. Davies, M. F. Fuentealba-Fuentes, R. J. Wright, M. Bravo, S. Wagh, M. Siudek
Determining the processes by which galaxies transition from a star-forming to a quiescent state (quenching) is paramount to our understanding of galaxy evolution. One of the key mechanisms by which this takes place is via a galaxy's interactions with a local, over-dense environment (satellite or environmental quenching). In the very local Universe, we see these processes in action, and can also observe their effects via the distribution of satellite galaxy properties. However, extending similar analyses outside of the local Universe is problematic, largely due to the difficulties in robustly defining environments with small and/or incomplete spectroscopic samples. We use new environmental metrics from the high-completeness Deep Extragalactic VIsible Legacy Survey (DEVILS) to explore the properties of satellite galaxies at intermediate redshift (0.3$<$z$<$0.5) and compare directly to the Galaxy And Mass Assembly Survey (GAMA) at 0$<$z$<$0.2. Importantly, both the galaxy properties and environmental metrics in DEVILS and GAMA are derived in an identical manner, reducing any methodology biases. We find: i) that satellite galaxies in DEVILS and GAMA show suppressed star-formation in comparison to isolated systems at the same stellar mass, by $\sim$0.5dex in log$_{10}$(SFR/M$_{\odot}$yr$^{-1}$), ii) that this suppression is strongest in higher mass dark matter halos (up to $\sim$1dex in log$_{10}$(SFR/M$_{\odot}$yr$^{-1}$) in the most massive halos) and iii) that at fixed stellar and halo mass, this suppression increases with time - with satellite passive fractions increasing by $\sim$10-15\% over the last $\sim$5Gyr. This is consistent with previous observations and numerical simulations.
Accurate forest height estimation is crucial for climate change monitoring and carbon cycle assessment. Synthetic Aperture Radar (SAR), particularly in multi-channel configurations, has provided support for a long time in 3D forest structure reconstruction through model-based techniques. More recently, data-driven approaches using Machine Learning (ML) and Deep Learning (DL) have enabled new opportunities for forest parameter retrieval. This paper introduces FGump, a forest height estimation framework by gradient boosting using multi-channel SAR processing with LiDAR profiles as Ground Truth(GT). Unlike typical ML and DL approaches that require large datasets and complex architectures, FGump ensures a strong balance between accuracy and computational efficiency, using a limited set of hand-designed features and avoiding heavy preprocessing (e.g., calibration and/or quantization). Evaluated under both classification and regression paradigms, the proposed framework demonstrates that the regression formulation enables fine-grained, continuous estimations and avoids quantization artifacts by resulting in more precise measurements without rounding. Experimental results confirm that FGump outperforms State-of-the-Art (SOTA) AI-based and classical methods, achieving higher accuracy and significantly lower training and inference times, as demonstrated in our results.
Automating Thematic Review of Prevention of Future Deaths Reports: Replicating the ONS Child Suicide Study using Large Language Models
2025-07-28
Sam Osian, Arpan Dutta, Sahil Bhandari, Iain E. Buchan, Dan W. Joyce
Prevention of Future Deaths (PFD) reports, issued by coroners in England and Wales, flag systemic hazards that may lead to further loss of life. Analysis of these reports has previously been constrained by the manual effort required to identify and code relevant cases. In 2025, the Office for National Statistics (ONS) published a national thematic review of child-suicide PFD reports ($\leq$ 18 years), identifying 37 cases from January 2015 to November 2023 - a process based entirely on manual curation and coding. We evaluated whether a fully automated, open source "text-to-table" language-model pipeline (PFD Toolkit) could reproduce the ONS's identification and thematic analysis of child-suicide PFD reports, and assessed gains in efficiency and reliability. All 4,249 PFD reports published from July 2013 to November 2023 were processed via PFD Toolkit's large language model pipelines. Automated screening identified cases where the coroner attributed death to suicide in individuals aged 18 or younger, and eligible reports were coded for recipient category and 23 concern sub-themes, replicating the ONS coding frame. PFD Toolkit identified 72 child-suicide PFD reports - almost twice the ONS count. Three blinded clinicians adjudicated a stratified sample of 144 reports to validate the child-suicide screening. Against the post-consensus clinical annotations, the LLM-based workflow showed substantial to almost-perfect agreement (Cohen's $\kappa$ = 0.82, 95% CI: 0.66-0.98, raw agreement = 91%). The end-to-end script runtime was 8m 16s, transforming a process that previously took months into one that can be completed in minutes. This demonstrates that automated LLM analysis can reliably and efficiently replicate manual thematic reviews of coronial data, enabling scalable, reproducible, and timely insights for public health and safety. The PFD Toolkit is openly available for future research.
Text embeddings have attracted growing interest due to their effectiveness across a wide range of natural language processing (NLP) tasks, such as retrieval, classification, clustering, bitext mining, and summarization. With the emergence of pretrained language models (PLMs), general-purpose text embeddings (GPTE) have gained significant traction for their ability to produce rich, transferable representations. The general architecture of GPTE typically leverages PLMs to derive dense text representations, which are then optimized through contrastive learning on large-scale pairwise datasets. In this survey, we provide a comprehensive overview of GPTE in the era of PLMs, focusing on the roles PLMs play in driving its development. We first examine the fundamental architecture and describe the basic roles of PLMs in GPTE, i.e., embedding extraction, expressivity enhancement, training strategies, learning objectives, and data construction. Then, we describe advanced roles enabled by PLMs, such as multilingual support, multimodal integration, code understanding, and scenario-specific adaptation. Finally, we highlight potential future research directions that move beyond traditional improvement goals, including ranking integration, safety considerations, bias mitigation, structural information incorporation, and the cognitive extension of embeddings. This survey aims to serve as a valuable reference for both newcomers and established researchers seeking to understand the current state and future potential of GPTE.
2025-07-28
Filip Cano, Thomas A. Henzinger, Konstantin Kueffner
Fairness in AI is traditionally studied as a static property evaluated once, over a fixed dataset. However, real-world AI systems operate sequentially, with outcomes and environments evolving over time. This paper proposes a framework for analysing fairness as a runtime property. Using a minimal yet expressive model based on sequences of coin tosses with possibly evolving biases, we study the problems of monitoring and enforcing fairness expressed in either toss outcomes or coin biases. Since there is no one-size-fits-all solution for either problem, we provide a summary of monitoring and enforcement strategies, parametrised by environment dynamics, prediction horizon, and confidence thresholds. For both problems, we present general results under simple or minimal assumptions. We survey existing solutions for the monitoring problem for Markovian and additive dynamics, and existing solutions for the enforcement problem in static settings with known dynamics.
Intention-Driven Generation of Project-Specific Test Cases
2025-07-28
Binhang Qi, Yun Lin, Xinyi Weng, Yuhuan Huang, Chenyan Liu, Hailong Sun, Jin Song Dong
Test cases are valuable assets for maintaining software quality. While numerous automated techniques have been proposed for generating tests (either by maximizing code coverage or by translating focal code into test code), practical tests are seldom driven by coverage alone. In real projects, each test reflects a developer's validation intention for a specific behaviour and embodies rich, project-specific knowledge: which specific APIs to call and what assertions truly matter. Without considering such knowledge, tests can hardly pass code review and be integrated into the software product. In this work, we propose IntentionTest, which generates project-specific tests with validation intention as a structured description. Our design is motivated by two insights: (1) a description of validation intention, compared to coverage and focal code, carries more crucial information about what to test; and (2) practical tests exhibit high code duplication, indicating that domain knowledge is highly reusable for writing new tests. Given a focal code and a description of validation intention (in the form of either an informal comment or a formal test plan), IntentionTest retrieves a referable test in the project to guide test generation. Moreover, IntentionTest reduces the test generation problem into an editing problem on the test code regarding the validation intention. It generates a test including both test prefix and oracle, which aims to be executable and semantically correct. We evaluate IntentionTest against state-of-the-art baselines on 4,146 test cases from 13 open-source projects. Specifically, compared to ChatTester, IntentionTest can (1) generate significantly more semantically correct tests, improving common mutation scores by 39.03% and coverage overlap with ground-truth tests by 40.14%; (2) generate 21.30% more successful passing tests.
Before the Outrage: Challenges and Advances in Predicting Online Antisocial Behavior
2025-07-28
Anaïs Ollagnier
Antisocial behavior (ASB) on social media-including hate speech, harassment, and trolling-poses growing challenges for platform safety and societal wellbeing. While prior work has primarily focused on detecting harmful content after it appears, predictive approaches aim to forecast future harmful behaviors-such as hate speech propagation, conversation derailment, or user recidivism-before they fully unfold. Despite increasing interest, the field remains fragmented, lacking a unified taxonomy or clear synthesis of existing methods. This paper presents a systematic review of over 49 studies on ASB prediction, offering a structured taxonomy of five core task types: early harm detection, harm emergence prediction, harm propagation prediction, behavioral risk prediction, and proactive moderation support. We analyze how these tasks differ by temporal framing, prediction granularity, and operational goals. In addition, we examine trends in modeling techniques-from classical machine learning to pre-trained language models-and assess the influence of dataset characteristics on task feasibility and generalization. Our review highlights methodological challenges, such as dataset scarcity, temporal drift, and limited benchmarks, while outlining emerging research directions including multilingual modeling, cross-platform generalization, and human-in-the-loop systems. By organizing the field around a coherent framework, this survey aims to guide future work toward more robust and socially responsible ASB prediction.
Stock price movements are influenced by many factors, and alongside historical price data, tex-tual information is a key source. Public news and social media offer valuable insights into market sentiment and emerging events. These sources are fast-paced, diverse, and significantly impact future stock trends. Recently, LLMs have enhanced financial analysis, but prompt-based methods still have limitations, such as input length restrictions and difficulties in predicting sequences of varying lengths. Additionally, most models rely on dense computational layers, which are resource-intensive. To address these challenges, we propose the FTS- Text-MoE model, which combines numerical data with key summaries from news and tweets using point embeddings, boosting prediction accuracy through the integration of factual textual data. The model uses a Mixture of Experts (MoE) Transformer decoder to process both data types. By activating only a subset of model parameters, it reduces computational costs. Furthermore, the model features multi-resolution prediction heads, enabling flexible forecasting of financial time series at different scales. Experimental results show that FTS-Text-MoE outperforms baseline methods in terms of investment returns and Sharpe ratio, demonstrating its superior accuracy and ability to predict future market trends.
Multi-Machine Scaling Laws for Fuel and Impurity Puffing Rates Sufficient for Detachment Access: a Systematic Review of Magnetic Confinement Fusion Devices
2025-07-28
M. Moscheni, A. Herrmann, R. Kembleton, M. Kryjak, S. Lazerson, F. Levi, M. Siccinio, P. Staniec, T. Giegerich, C. Tantos, the Gauss Fusion GmbH Team
From a database of 457 experimental and numerical data from 32 machines among solid-walled tokamaks, stellarators and linear plasma devices, we derive physics-informed multi-machine scaling laws predictive of fuel and impurity puffing rates sufficient to access plasma detachment -- leading candidate for a reactor-relevant solution to the open issue of plasma-wall interaction. Validation of our laws in up to 40 plasmas in low- and high-confinement mode also featuring advanced configurations demonstrates accuracy within a factor 1.5 in up to 50% of the instances, and within a factor 2 on average. Divertor volume alone is found to correlate to fuelling. The addition of plasma opaqueness leads to the empirically-calibrated law $\Gamma_{\text{D}}\propto [n_{\text{sep}}\times a\times(S_{\text{div}}/V_{\text{div}})^{-1.5}]^{1.05}$ valid across all toroidal devices. Its simplification to $\Gamma_{\text{D}}^{\text{HDL}}\propto 0.43\times a^{1.58}\times\lambda_q^{-0.89}$ avoids a dependence on $n_{\text{sep}}$ and bears intrinsic significance provided by the H/L density limit and the power fall-off length. Non-linearities of the impurity dynamics manifest in the seeding rate -- which the present work identifies both a generalised formula for, and the Greenwald-Eich-Scarabosio simplification $\Gamma_{\text{Z}}^{\text{GES}}\propto a^{1.51}\times\lambda_q^{-0.27}$. Similar relationships are defined for stellarators, closely following the mainstream but without providing data for validation -- which should stimulate further investigations. In the era of nuclear fusion reactor design, these results find immediate applicability in informing reactor fuel cycle design and edge plasma modelling. More generally, this study demonstrates that physics-based laws able to relate detachment to engineering actuators exist, and pave the way for analytical models bridging to the true inputs of experimental operation.
Runaway Growth of Planetesimals Revisited: Presenting Criteria Required for Realistic Modeling of Planetesimal Growth
2025-07-28
Nader Haghighipour, Luciano A. Darriba
We have initiated a large project on identifying the requirements for developing a realistic and ground-up approach to simulating the formation of terrestrial planets in our solar system. As the first phase of this project, we present here the criteria that any model of planetesimal growth needs to fulfill in order to be self-consistent and produce reliable results. We demonstrate how these criteria emerge by revisiting runaway growth and carrying out a thorough analysis of its results. As our goal is to identify the pathway to a realistic model, we focus analysis on simulations where at the beginning, planetesimals are not artificially enlarged. We show how using uninflated planetesimals, as the first requirement for a realistic model, will result in a set of criteria naturally emerging from the evolution of the system. For instance, the growth times in simulations with uninflated planetesimals become comparable to the time of giant planet formation implying that any realistic simulation of planetesimal growth, in addition to using real-size planetesimals, needs to include the perturbation of the growing giant planets as well. Our analysis also points to a strong connection between the initial distribution of planetesimals and the final outcome. For instance, due to their natural expansion, initially isolated distributions, or a collection of initially isolated distributions, such as rings of planetesimals, do not produce reliable results. In a self-consistent and realistic model, where the initial conditions are supported by basic principles and do not include simplifying, ad hoc assumptions, the entire disk of planetesimals has to be simulated at once. We present the results of our analyses and discuss their implied criteria.
Enhancing Spectroscopy and Microscopy with Emerging Methods in Photon-Correlation and Quantum Illumination
2025-07-28
Chieh Tsao, Haonan Ling, Alex Hinkle, Yifan Chen, Keshav Kumar Jha, Zhen-Li Yan, Hendrik Utzat
Quantum optics has driven major advances in our ability to generate and detect correlations between individual photons. Its principles are now increasingly translated into nanoscale characterization techniques, enhancing spectroscopy, microscopy, and metrology. In this Review, we highlight rapid progress in the field driven by advances in single-photon detectors and quantum light sources, including time-resolved single-photon counting cameras, superconducting nanowire detectors, and increasingly bright sources of entangled photons. We emphasize emerging applications in super-resolution microscopy, measurements below classical noise limits, and photon-number-resolved spectroscopy-a powerful paradigm for probing nanoscale electronic materials and molecular dynamics. We conclude by outlining key technological challenges and future opportunities across materials science and bio-nanophotonics.
Do Large Language Models Understand Morality Across Cultures?
2025-07-28
Hadi Mohammadi, Yasmeen F. S. S. Meijer, Efthymia Papadopoulou, Ayoub Bagheri
Recent advancements in large language models (LLMs) have established them as powerful tools across numerous domains. However, persistent concerns about embedded biases, such as gender, racial, and cultural biases arising from their training data, raise significant questions about the ethical use and societal consequences of these technologies. This study investigates the extent to which LLMs capture cross-cultural differences and similarities in moral perspectives. Specifically, we examine whether LLM outputs align with patterns observed in international survey data on moral attitudes. To this end, we employ three complementary methods: (1) comparing variances in moral scores produced by models versus those reported in surveys, (2) conducting cluster alignment analyses to assess correspondence between country groupings derived from LLM outputs and survey data, and (3) directly probing models with comparative prompts using systematically chosen token pairs. Our results reveal that current LLMs often fail to reproduce the full spectrum of cross-cultural moral variation, tending to compress differences and exhibit low alignment with empirical survey patterns. These findings highlight a pressing need for more robust approaches to mitigate biases and improve cultural representativeness in LLMs. We conclude by discussing the implications for the responsible development and global deployment of LLMs, emphasizing fairness and ethical alignment.
Preferential alignments of exoplanetary orbital planes in Milky Way spiral arms
2025-07-28
V. Bozza, P. Rota
Special orientations of the orbital planes may be reminiscent of the specific conditions that triggered and drove the star formation processes and how these are related to local and global Galactic kinematics. For a special sample of 66 extrasolar planets discovered with the microlensing method it is possible to determine the position angle of the planets in the sky relative to their hosts. We test the hypothesis that such orientations are randomly distributed against the possibility that the orbital planes follow some preferential alignment. We find that planets in the Scutum-Centaurus arm show a significant alignment with the Galactic plane, with an isotropic distribution disfavored by a factor of 10. Bulge planets and disk planets outside this major arm are instead compatible with isotropic distributions or show weak alternative preferences at most. Using the method proposed here, the future Roman microlensing survey will be able to identify and quantify preferential orientations in all structures from the Sun to the bulge with high confidence and accuracy.
Online Prediction For Streaming Observational Data
2025-07-28
Bertrand Clarke, Aleena Chanda
The automated collection of streaming observational data has become standard and defies most traditional analytic techniques. It is not just that models are hard to identify, there may not be any model that can be safely and usefully assumed. Indeed, frequently it is only predictions that can be made and assessed. Problems for this kind of data are often called {\cal{M}}-Open and have motivated new approaches and philosophies. This paper will review some of the most successful recent predictive methods for the {\cal{M}}-Open problem class. Techniques include predictors using expert advice such as the Shtarkov solution, Bayesian nonparametrics such as Gaussian process priors, hash function based predictors such as the {\sf Count-Min} sketch, and conformal prediction. Throughout, the properties of the predictors are presented and compared from a principled standpoint.
Each year, over half of global traffic fatalities involve vulnerable road users (e.g. pedestrians), often due to human error. Level-5 automated driving systems (ADSs) could reduce driver errors contributing to pedestrian accidents, though effectiveness depends on clarity and understandability for other road users. External human-machine interfaces (eHMIs) have been proposed to facilitate pedestrian-ADS communication, though consensus on optimal eHMI features remains unclear. In an online survey, 153 participants responded to road-crossing scenarios involving level-5 ADSs, with and without eHMIs. With eHMIs, pedestrians crossed earlier and more confidently, and reported significantly increased perceptions of safety, trust, and understanding when interacting with level-5 ADSs. Visual eHMI features (including a text display and external speedometer) were ranked more necessary than auditory ones, though auditory cues received positive feedback. This study demonstrates that eHMIs can significantly improve pedestrians' understanding of level-5 ADS intent and enhance perceived safety and trust, facilitating more intuitive pedestrian-ADS interactions.
Curiosity by Design: An LLM-based Coding Assistant Asking Clarification Questions
2025-07-28
Harsh Darji, Thibaud Lutellier
Large Language Models (LLMs) are increasingly used as coding assistants. However, the ambiguity of the developer's prompt often leads to incorrect code generation, as current models struggle to infer user intent without extensive prompt engineering or external context. This work aims to build an LLM-based coding assistant that mimics the human code review process by asking clarification questions when faced with ambiguous or under-specified queries. Our end-to-end system includes (1) a query classifier trained to detect unclear programming-related queries and (2) a fine-tuned LLM that generates clarification questions. Our evaluation shows that the fine-tuned LLM outperforms standard zero-shot prompting in generating useful clarification questions. Furthermore, our user study indicates that users find the clarification questions generated by our model to outperform the baseline, demonstrating that our coding assistant produces more accurate and helpful code responses compared to baseline coding assistants.
NIRPS detection of delayed atmospheric escape from the warm and misaligned Saturn-mass exoplanet WASP-69b
2025-07-28
Romain Allart, Yann Carteret, Vincent Bourrier, Lucile Mignon, Frederique Baron, Charles Cadieux, Andres Carmona, Christophe Lovis, Hritam Chakraborty, Elisa Delgado-Mena, Etienne Artigau, Susana C. C. Barros, Bjorn Benneke, Xavier Bonfils, Francois Bouchy, Marta Bryan, Bruno L. Canto Martins, Ryan Cloutier, Neil J. Cook, Nicolas B. Cowan, Xavier Delfosse, Rene Doyon, Xavier Dumusque, David Ehrenreich, Jonay I. Gonzalez Hernandez, David Lafreniere, Izan de Castro Leao, Lison Malo, Claudio Melo, Christoph Mordasini, Francesco Pepe, Rafael Rebolo, Jose Renan De Medeiros, Jason Rowe, Nuno C. Santos, Damien Segransan, Alejandro Suarez Mascareno, Stephane Udry, Diana Valencia, Gregg Wade, Manuel Abreu, Jose L. A. Aguiar, Babatunde Akinsanmi, Guillaume Allain, Jose Manuel Almenara, Khaled Al Moulla, Tomy Arial, Hugues Auger, Luc Bazinet, Nicolas Blind, Anne Boucher, Christopher Broeg, Denis Brousseau, Alexandre Cabral, Zalpha Challita, Joao Coelho, Marion Cointepas, Ana Rita Costa Silva, Eduardo Cristo, Antoine Darveau-Bernier, Laurie Dauplaise, Roseane de Lima Gomes, Daniel Brito de Freitas, Dasaev O. Fontinele, Thierry Forveille, Yolanda Frensch, Jonathan Gagne, Frederic Genest, Felix Gracia Temich, Nolan Grieves, Olivier Hernandez, Jens Hoeijmakers, Norbert Hubin, Farbod Jahandar, Ray Jayawardhana, Dan Kerley, Johann Kolb, Vigneshwaran Krishnamurthy, Alexandrine L'Heureux, Monika Lendl, Olivia Lim, Gaspare Lo Curto, Jaymie Matthews, Allan M. Martins, Jean-Sebastien Mayer, Stan Metchev, Yuri S. Messias, Leslie Moranta, Dany Mounzer, Nicola Nari, Louise D. Nielsen, Ares Osborn, Lena Parc, Luca Pasquini, Stefan Pelletier, Celine Peroux, Caroline Piaulet, Mykhaylo Plotnykov, Emanuela Pompei, Anne-Sophie Poulin-Girard, Angelica Psaridi, Jose Luis Rasilla, Vladimir Reshetov, Jonathan Saint-Antoine, Jorge Sanz-Forcada, Julia Seidel, Ivo Saviane, Joao Gomes da Silva, Danuta Sosnowska, Avidaan Srivastava, Atanas K. Stefanov, Marcio A. Teixeira, Simon Thibault, Philippe Vallee, Thomas Vandal, Valentina Vaulato, Joost P. Wardenier, Bachar Wehbe, Drew Weisserman, Francois Wildi, Vincent Yariv, Gerard Zins
Near-infrared high-resolution echelle spectrographs unlock access to fundamental properties of exoplanets, from their atmospheric escape and composition to their orbital architecture, which can all be studied simultaneously from transit observations. We present the first results of the newly commissioned ESO near-infrared spectrograph, NIRPS, from three transits of WASP-69b. We used the RM Revolutions technique to better constrain the orbital architecture of the system. We extracted the high-resolution helium absorption profile to study its spectral shape and temporal variations. Then, we made 3D simulations from the EVE code to fit the helium absorption time series. We measure a slightly misaligned orbit for WASP-69b (psi of 28.7+/-5.7 deg). We confirm the detection of helium with an average excess absorption of 3.17+/-0.05%. The helium absorption is spectrally and temporally resolved, extends to high altitudes and has a strong velocity shift up to -29.5+/-2.5 km/s 50 minutes after egress. EVE simulations put constraints on the mass loss of 2.25 10^11 g/s and hint at reactive chemistry within the cometary-like tail and interaction with the stellar winds that allow the metastable helium to survive longer than expected. Our results suggest that WASP-69b is undergoing a transformative phase in its history, losing mass while evolving on a misaligned orbit. This work shows how combining multiple observational tracers such as orbital architecture, atmospheric escape, and composition, is critical to understand exoplanet demographics and their formation and evolution. We demonstrate that NIRPS can reach precisions similar to HARPS for RM studies, and the high data quality of NIRPS leads to unprecedented atmospheric characterization. The high stability of NIRPS combined with the large GTO available for its consortium, enables in-depth studies of exoplanets as well as large population surveys.
Determinants of Saving Behavior Among Employees in Dhaka, Bangladesh
2025-07-28
Soumita Roy, Md Muntasir Kamal Dihan, Tasnimah Haque, Nafisa Nomani, Sadia Islam Preety
Purpose With an emphasis on elements like financial knowledge, financial attitude, social influence, financial self-efficacy, and financial management practices, this study explores the factors that influence employees' saving behavior in Dhaka, Bangladesh. We also welcome others to work on saving behavior, which is the main reason for publishing. The purpose is to make others aware of the methods for quantitative financial behavior analysis in Bangladesh. Design/methodology/approach The study uses a quantitative approach with a cross-sectional survey design. Data was collected from 40 participants through a structured questionnaire adapted from reliable sources. The questionnaire captured demographic information and used established items to measure the key variables. Data analysis included descriptive statistics, reliability analysis using Cronbachs alpha, and regression analysis to test the hypothesized relationships. Findings The results indicate that among the factors examined, only financial management practices had a significant positive relationship with saving behavior. Rest of the factors did not show significant relationships with saving behavior in this study sample. Limitation or Disclaimer It is still a work in progress, this paper is meant for pre-print with mostly incomplete and limited data. No data cleaning was performed, so it is very likely to include outliers and faulty data. Originality or value This study contributes to the limited research on saving behavior determinants in the Bangladeshi context, specifically among employees in the capital city of Dhaka. It explores the influence of multiple factors, including the rarely studied aspect of social influence.
Quenching Through Tidal Gas Removal: Molecular Gas and Star Formation in Tidal Tails of z ~ 0.7 Post-Starburst Galaxies
2025-07-28
Vincenzo R. D'Onofrio, Justin S. Spilker, Rachel Bezanson, Robert Feldmann, Andy D. Goulding, Jenny E. Greene, Mariska Kriek, Yuanze Luo, Desika Narayanan, David J. Setton, Katherine A. Suess, Yunchong Zhang, Pengpei Zhu
The active suppression of star formation in galaxies is critical in preventing the growth of overly massive systems and explaining the formation of present-day elliptical galaxies. We present a high-resolution, spatially-resolved multiwavelength study of two z ~ 0.7 massive post-starburst galaxies, SDSS J1448+1010 and SDSS J2258+2313, from the SQuIGGLE survey (Studying Quenching in Intermediate-z Galaxies: Gas, anguLar momentum, and Evolution), providing new insights into the role of mergers in driving quenching. ALMA CO(2-1) observations show that both galaxies removed ~50% of their molecular gas into extended tidal tails, spanning up to 65 kpc, following recent mergers. HST WFC3 imaging and grism spectroscopy show that while SDSS J1448+1010 exhibits Halpha emission in its northern tidal tail consistent with ongoing star formation, SDSS J2258+2313 lacks detectable star-forming activity outside the central galaxy. VLA 6 GHz continuum data reveal compact radio emission in SDSS J2258+2313, while SDSS J1448+1010 hosts small radio jets indicative of AGN activity. Both galaxies retain substantial molecular gas reservoirs in their central regions that appear more turbulent than 'normal' star-forming galaxies, likely contributing to the observed low star formation rates in the hosts. Despite similarities in their cold gas content and tidal features the galaxies are distinct from each other in their star formation, gas-star alignment, and radio morphology, highlighting the complexity of tidal gas removal as a quenching mechanism at intermediate redshifts.
The CHIMERA Survey: The first CO detection in Leo T, the lowest mass known galaxy still hosting cold molecular gas
2025-07-28
Vicente Villanueva, Matías Blaña, Alberto D. Bolatto, Mónica Rubio, Elizabeth Tarantino, Rodrigo Herrera-Camus, Andreas Burkert, Daniel Vaz, Justin I. Read, Gaspar Galaz, César Muñoz, Diego Calderón, Manuel Behrendt, Julio A. Carballo-Bello, Emily Gray, Michael Fellhauer
We report the first CO detection in Leo T, representing the most extreme observation of carbon monoxide molecules in the lowest stellar mass gas-rich dwarf galaxy ($M_{\star}$$\sim$10$^5$ M$_{\odot}$) known to date. We acquired and present new Atacama Compact Array (ACA) $^{12}$CO($J$=1-0) data within our CHIMERA Survey project for the central region of Leo~T, a metal-poor ([M/H]$\sim$-1.7) dwarf in the Milky Way (MW) outskirts. We identified three compact molecular clouds ($<13$ pc) with estimated upper limit virial masses of $M_{\rm mol}$$\sim$5$\times10^{3}$ M$_{\odot}$ each and a total of 1.4$\pm$0.4$\times$10$^{4}$ M$_{\odot}$, corresponding to $\sim\!3\%$ of the total gas mass. We obtained CO-to-H$_2$ conversion factors ($\alpha_{\rm CO}$) as high as $\sim$155 M$_{\odot}$ $({\rm K\, km\, s^{-1}\, pc^2})^{-1}$ and mean molecular gas surface densities of $\Sigma_{\rm mol}$$\sim$9 M$_\odot$ pc$^{-2}$ that are consistent with values found in dwarf galaxies with extremely low metal content. All CO clouds are shifted ($\sim$60 pc) from the stellar population centers, and only one cloud appears within the densest \hi region. Two clouds have velocity offsets with the \hi of $\Delta v_{\rm los}\sim\!+13$ km s$^{-1}$ being within twice the velocity dispersion ($\Delta v_{\rm los}/\sigma_{\rm HI,los}\sim2$) and probably bound. However, the northern cloud is faster ($\Delta v_{\rm los}\sim\!+57$ km s$^{-1}$); our models with low halo masses ($M_{\rm h}\! \lesssim \!10^9$ M$_{\odot}$) result in unbound orbits, suggesting that this material is likely being expelled from the dwarf, providing evidence for molecular gas depletion. These properties reveal a perturbed dynamics intertwined with star formation processes in low-mass dwarf galaxies, supporting a scenario of episodic bursts until they are fully quenched by the MW environment.
An empirical comparison of some outlier detection methods with longitudinal data
2025-07-28
Marcello D'Orazio
This note investigates the problem of detecting outliers in longitudinal data. It compares well-known methods used in official statistics with proposals from the fields of data mining and machine learning that are based on the distance between observations or binary partitioning trees. This is achieved by applying the methods to panel survey data related to different types of statistical units. Traditional methods are quite simple, enabling the direct identification of potential outliers, but they require specific assumptions. In contrast, recent methods provide only a score whose magnitude is directly related to the likelihood of an outlier being present. All the methods require the user to set a number of tuning parameters. However, the most recent methods are more flexible and sometimes more effective than traditional methods. In addition, these methods can be applied to multidimensional data.
Survey of NLU Benchmarks Diagnosing Linguistic Phenomena: Why not Standardize Diagnostics Benchmarks?
2025-07-27
Khloud AL Jallad, Nada Ghneim, Ghaida Rebdawi
Natural Language Understanding (NLU) is a basic task in Natural Language Processing (NLP). The evaluation of NLU capabilities has become a trending research topic that attracts researchers in the last few years, resulting in the development of numerous benchmarks. These benchmarks include various tasks and datasets in order to evaluate the results of pretrained models via public leaderboards. Notably, several benchmarks contain diagnostics datasets designed for investigation and fine-grained error analysis across a wide range of linguistic phenomena. This survey provides a comprehensive review of available English, Arabic, and Multilingual NLU benchmarks, with a particular emphasis on their diagnostics datasets and the linguistic phenomena they covered. We present a detailed comparison and analysis of these benchmarks, highlighting their strengths and limitations in evaluating NLU tasks and providing in-depth error analysis. When highlighting the gaps in the state-of-the-art, we noted that there is no naming convention for macro and micro categories or even a standard set of linguistic phenomena that should be covered. Consequently, we formulated a research question regarding the evaluation metrics of the evaluation diagnostics benchmarks: "Why do not we have an evaluation standard for the NLU evaluation diagnostics benchmarks?" similar to ISO standard in industry. We conducted a deep analysis and comparisons of the covered linguistic phenomena in order to support experts in building a global hierarchy for linguistic phenomena in future. We think that having evaluation metrics for diagnostics evaluation could be valuable to gain more insights when comparing the results of the studied models on different diagnostics benchmarks.
Cognitive Chain-of-Thought: Structured Multimodal Reasoning about Social Situations
2025-07-27
Eunkyu Park, Wesley Hanwen Deng, Gunhee Kim, Motahhare Eslami, Maarten Sap
Chain-of-Thought (CoT) prompting helps models think step by step. But what happens when they must see, understand, and judge-all at once? In visual tasks grounded in social context, where bridging perception with norm-grounded judgments is essential, flat CoT often breaks down. We introduce Cognitive Chain-of-Thought (CoCoT), a prompting strategy that scaffolds VLM reasoning through three cognitively inspired stages: perception, situation, and norm. Our experiments show that, across multiple multimodal benchmarks (including intent disambiguation, commonsense reasoning, and safety), CoCoT consistently outperforms CoT and direct prompting (+8\% on average). Our findings demonstrate that cognitively grounded reasoning stages enhance interpretability and social awareness in VLMs, paving the way for safer and more reliable multimodal systems.
Quark confinement consistent with holography due to hyperbolic magnetic monopoles and hyperbolic vortices unifiedly reduced from symmetric instantons
2025-07-27
Kei-Ichi Kondo
We give a review on hyperbolic magnetic monopoles and hyperbolic vortices obtained in the unified way through the conformal equivalence by the dimensional reduction from the symmetric instantons with various spatial symmetries in the four-dimensional Euclidean Yang-Mills theory. They are used to understand quark confinement in the sense of the area law of the Wilson loop average in a semi-classical picture from a unified treatment of Atiyah's hyperbolic magnetic monopole and Witten-Manton's hyperbolic vortex. In this way quark confinement is shown to be realized by the the non-perturbative vacuum disordered by these topological defects. For this purpose we start from the 4-dim. Euclidean Yang-Mills theory and require the conformal equivalence between the 4-dim. Euclidean space and the possible curved spacetimes with some compact dimensions. This requirement forces us to restrict the gauge configurations of 4-dim.Yang-Mills instantons to those with some spatial symmetries (called symmetric instantons) which are identified with magnetic monopoles and vortices living in the lower-dimensional curved hyperbolic spacetime with constant negative curvature through the dimensional reduction. At the same time, this scheme caused by the dimensional reduction give a holographic description of hyperbolic magnetic monopole dominance on AdS3 in the rigorous way without any further assumptions, which does not hold in the flat Euclidean case. This unified treatment of two topological defects is shown to give the semi-classical picture for quark confinement in the sense of Wilson. We give the understanding of the result from the viewpoint of the gauge-covariant Cho-Duan-Ge-Faddeev-Niemi decomposition for the gauge field.
ULISSE: Determination of star-formation rate and stellar mass based on the one-shot galaxy imaging technique
2025-07-27
Olena Torbaniuk, Lars Doorenbos, Maurizio Paolillo, Stefano Cavuoti, Massimo Brescia, Giuseppe Longo
Modern sky surveys produce vast amounts of observational data, making the application of classical methods for estimating galaxy properties challenging and time-consuming. This challenge can be significantly alleviated by employing automatic machine and deep learning techniques. We propose an implementation of the ULISSE algorithm aimed at determining physical parameters of galaxies, in particular star-formation rates (SFR) and stellar masses ($M_{\ast}$), using only composite-color images. ULISSE is able to rapidly and efficiently identify candidates from a single image based on photometric and morphological similarities to a given reference object with known properties. This approach leverages features extracted from the ImageNet dataset to perform similarity searches among all objects in the sample, eliminating the need for extensive neural network training. Our experiments, performed on the Sloan Digital Sky Survey, demonstrate that we are able to predict the joint star formation rate and stellar mass of the target galaxies within 1 dex in 60% to 80% of cases, depending on the investigated subsample (quiescent/star-forming galaxies, early-/late-type, etc.), and within 0.5 dex if we consider these parameters separately. This is approximately twice the fraction obtained from a random guess extracted from the parent population. Additionally, we find ULISSE is more effective for galaxies with active star formation compared to elliptical galaxies with quenched star formation. Additionally, ULISSE performs more efficiently for galaxies with bright nuclei such as AGN. Our results suggest that ULISSE is a promising tool for a preliminary estimation of star-formation rates and stellar masses for galaxies based only on single images in current and future wide-field surveys (e.g., Euclid, LSST), which target millions of sources nightly.
Biquandle cocycle condition for invariants of immersed surface-links in the four-space
2025-07-27
Michal Jablonowski
We consider a biquandle-cohomological framework for invariants of oriented immersed surface-links in the four-space. After reviewing projections and Roseman moves for immersed surfaces, we prove that the move types (a,b,c,e,f,g,h) form a minimal generating set, showing in particular that the singular move (h) is independent of the embedded-case set (a,b,c,e,f,g). We extend biquandle colorings to broken surface diagrams with singular points and establish that coloring sets are in bijection for diagrams related by these moves, yielding a coloring number invariant for immersed surface-links. We introduce singular biquandle 3-cocycles: biquandle 3-cocycles satisfying an additional antisymmetry when the singular relations hold. Using such cocycles, we define a triple-point state-sum with Boltzmann weights and prove its invariance under all generating moves, including (h), thereby obtaining a state-sum invariant for immersed surface-links. The theory is illustrated on the Fenn-Rolfsen link example, where a computation yields non-trivial value, demonstrating nontriviality of the invariant in the immersed setting. These results unify and extend biquandle cocycle invariants from embedded to immersed surface-links.
The $L_\infty$ structure of Free Differential Algebras and $d=11$ Supergravity
2025-07-27
L. Castellani, R. D'Auria
We discuss free differential algebras (FDA's), a generalization of the Cartan-Maurer equations for the group manifold vielbein, appropriate for theories containing $p$-forms ($p >1$). Their dual formulation is an extension of Lie algebras, called $L_\infty$ algebras, and we illustrate this duality in a simple example. Finally we review the FDA structure and the dual $L_\infty$ structure of $d=11$ supergravity.
EMBRACE: Shaping Inclusive Opinion Representation by Aligning Implicit Conversations with Social Norms
2025-07-27
Abeer Aldayel, Areej Alokaili
Shaping inclusive representations that embrace diversity and ensure fair participation and reflections of values is at the core of many conversation-based models. However, many existing methods rely on surface inclusion using mention of user demographics or behavioral attributes of social groups. Such methods overlook the nuanced, implicit expression of opinion embedded in conversations. Furthermore, the over-reliance on overt cues can exacerbate misalignment and reinforce harmful or stereotypical representations in model outputs. Thus, we took a step back and recognized that equitable inclusion needs to account for the implicit expression of opinion and use the stance of responses to validate the normative alignment. This study aims to evaluate how opinions are represented in NLP or computational models by introducing an alignment evaluation framework that foregrounds implicit, often overlooked conversations and evaluates the normative social views and discourse. Our approach models the stance of responses as a proxy for the underlying opinion, enabling a considerate and reflective representation of diverse social viewpoints. We evaluate the framework using both (i) positive-unlabeled (PU) online learning with base classifiers, and (ii) instruction-tuned language models to assess post-training alignment. Through this, we provide a lens on how implicit opinions are (mis)represented and offer a pathway toward more inclusive model behavior.
Future Parameter Constraints from Weak Lensing CMB and Galaxy Lensing Power- and Bispectra
2025-07-27
Jonas Frugte, P. Daniel Meerburg
Upcoming stage 4 surveys, such as the Simons Observatory, LSST, and Euclid, are poised to measure weak gravitational lensing of the Cosmic Microwave Background (CMB) and galaxies with unprecedented precision. While the power spectrum is the standard statistic used to analyze weak lensing data, non-Gaussianity from non-linear structure growth encodes additional cosmological information in higher-order statistics. We forecast the ability of future surveys to constrain cosmological parameters using the weak lensing power spectrum and bispectrum from both CMB and galaxy surveys, including their cross-correlations. We consider an eight-parameter model ($\Lambda$CDM + $\sum m_\nu$ + $w_0$) and assess constraints for stage 4 survey specifications. In the absence of systematics, both the CMB and galaxy lensing bispectra are found to be detectable at high signal-to-noise. We test two priors: a ''strong'' one based on constraints from CMB temperature and $E$-mode polarization anisotropies, and a ''weak'' one with minimal assumptions. With the weak prior, the bispectrum significantly improves parameter constraints by breaking degeneracies. For strong priors, improvements are more limited, especially for the CMB bispectrum. On small scales, where non-linear effects dominate, the bispectrum's constraining power can rival that of the power spectrum. We also find strong synergy between CMB and galaxy lensing; combining both probes leads to tighter constraints, particularly on neutrino mass. It was recently found that the CMB lensing bispectrum is strongly affected by the Born approximation, so we also consider post-Born corrections but find that our main conclusions remain the same. These results highlight the potential of higher-order lensing statistics and motivate further work on neglected effects such as non-Gaussian covariance, instrumental systematics, and baryonic feedback.
Strategic Motivators for Ethical AI System Development: An Empirical and Holistic Model
2025-07-27
Muhammad Azeem Akbar, Arif Ali Khan, Saima Rafi, Damian Kedziora, Sami Hyrynsalmi
Artificial Intelligence (AI) presents transformative opportunities for industries and society, but its responsible development is essential to prevent unintended consequences. Ethically sound AI systems demand strategic planning, strong governance, and an understanding of the key drivers that promote responsible practices. This study aims to identify and prioritize the motivators that drive the ethical development of AI systems. A Multivocal Literature Review (MLR) and a questionnaire-based survey were conducted to capture current practices in ethical AI. We applied Interpretive Structure Modeling (ISM) to explore the relationships between motivator categories, followed by MICMAC analysis to classify them by their driving and dependence power. Fuzzy TOPSIS was used to rank these motivators by importance. Twenty key motivators were identified and grouped into eight categories: Human Resource, Knowledge Integration, Coordination, Project Administration, Standards, Technology Factor, Stakeholders, and Strategy & Matrices. ISM results showed that 'Human Resource' and 'Coordination' heavily influence other factors. MICMAC analysis placed categories like Human Resource (CA1), Coordination (CA3), Stakeholders (CA7), and Strategy & Matrices (CA8) in the independent cluster, indicating high driving but low dependence power. Fuzzy TOPSIS ranked motivators such as promoting team diversity, establishing AI governance bodies, appointing oversight leaders, and ensuring data privacy as most critical. To support ethical AI adoption, organizations should align their strategies with these motivators and integrate them into their policies, governance models, and development frameworks.
Symbiotic stars are among the most crucial binary systems for studying binary star interactions and Type Ia supernova progenitors. Based on the unique observational characteristics of symbiotic stars, strong H I, He I emission lines, giant spectral features, and the presence of [O III], He II, O VI, and other emission lines with ionization potentials exceeding 35 eV, and the Gaia information, we search for new symbiotic stars using the low-resolution spectroscopic survey data from LAMOST. Thirty-six binary systems have been selected as symbiotic stars or candidates, in which the five known symbiotic stars are included. Among them five systems (ZTF J005917.52+315605.4, ATO J094137.5+075304, LAMOST J200310.90+360822.6, LAMOST J072528.18+342530.4, and V* V758 Cyg) have been found as new symbiotic stars. Notably, LAMOST J072528.18+342530.4 and V* V758 Cyg were also confirmed as new symbiotic stars in a recent study. For the remaining 26 candidates, classification is based solely on the presence of [O III] emission lines (with ionization potentials > 35 eV) and the absence of He II high-excitation emission lines. Further observations are needed to confirm their nature as symbiotic stars.
When Tokens Talk Too Much: A Survey of Multimodal Long-Context Token Compression across Images, Videos, and Audios
2025-07-27
Kele Shao, Keda Tao, Kejia Zhang, Sicheng Feng, Mu Cai, Yuzhang Shang, Haoxuan You, Can Qin, Yang Sui, Huan Wang
Multimodal large language models (MLLMs) have made remarkable strides, largely driven by their ability to process increasingly long and complex contexts, such as high-resolution images, extended video sequences, and lengthy audio input. While this ability significantly enhances MLLM capabilities, it introduces substantial computational challenges, primarily due to the quadratic complexity of self-attention mechanisms with numerous input tokens. To mitigate these bottlenecks, token compression has emerged as an auspicious and critical approach, efficiently reducing the number of tokens during both training and inference. In this paper, we present the first systematic survey and synthesis of the burgeoning field of multimodal long context token compression. Recognizing that effective compression strategies are deeply tied to the unique characteristics and redundancies of each modality, we categorize existing approaches by their primary data focus, enabling researchers to quickly access and learn methods tailored to their specific area of interest: (1) image-centric compression, which addresses spatial redundancy in visual data; (2) video-centric compression, which tackles spatio-temporal redundancy in dynamic sequences; and (3) audio-centric compression, which handles temporal and spectral redundancy in acoustic signals. Beyond this modality-driven categorization, we further dissect methods based on their underlying mechanisms, including transformation-based, similarity-based, attention-based, and query-based approaches. By providing a comprehensive and structured overview, this survey aims to consolidate current progress, identify key challenges, and inspire future research directions in this rapidly evolving domain. We also maintain a public repository to continuously track and update the latest advances in this promising area.
SoK: Root Cause of \$1 Billion Loss in Smart Contract Real-World Attacks via a Systematic Literature Review of Vulnerabilities
2025-07-27
Hadis Rezaei, Mojtaba Eshghie, Karl Anderesson, Francesco Palmieri
The Ethereum ecosystem, despite its maturity, continues to witness catastrophic attacks, with billions of dollars in assets lost annually. In response, a significant body of research has focused on identifying and mitigating smart contract vulnerabilities. However, these efforts predominantly focus on implementation-level bugs, leaving a critical gap between academic understanding of vulnerabilities and the root causes of real-world high-impact financial losses. We employ a two-pronged methodology: first, a systematic literature review of 71 academic papers to build a comprehensive and up-to-date catalog of 24 active and 5 deprecated vulnerabilities as understood by the research community. Second, we conduct an in-depth, empirical analysis of 50 of the most severe real-world exploits between 2022 and 2025, collectively incurring over \$1.09B in losses, to identify their true root causes. We introduce the concept of "exploit chains" by revealing that many incidents are not caused by isolated vulnerabilities but by combinations of human, operational, and economic design flaws that link with implementation bugs to enable an attack. Our analysis yields insights on how DApps are exploited in practice, leading to a novel, four-tier root-cause framework that moves beyond code-level vulnerabilities. We find that real-world successful attacks on Ethereum (and related networks) trace back to one of the four tiers of (1) protocol logic design, (2) lifecycle and governance, (3) external dependencies, and (4) traditional implementation bugs (classic smart contract vulnerabilities). We investigate the suitability of this multi-tier incident root-cause framework via a case study.
ASNN: Learning to Suggest Neural Architectures from Performance Distributions
2025-07-27
Jinwook Hong
The architecture of a neural network (NN) plays a critical role in determining its performance. However, there is no general closed-form function that maps between network structure and accuracy, making the process of architecture design largely heuristic or search-based. In this study, we propose the Architecture Suggesting Neural Network (ASNN), a model designed to learn the relationship between NN architecture and its test accuracy, and to suggest improved architectures accordingly. To train ASNN, we constructed datasets using TensorFlow-based models with varying numbers of layers and nodes. Experimental results were collected for both 2-layer and 3-layer architectures across a grid of configurations, each evaluated with 10 repeated trials to account for stochasticity. Accuracy values were treated as inputs, and architectural parameters as outputs. The trained ASNN was then used iteratively to predict architectures that yield higher performance. In both 2-layer and 3-layer cases, ASNN successfully suggested architectures that outperformed the best results found in the original training data. Repeated prediction and retraining cycles led to the discovery of architectures with improved mean test accuracies, demonstrating the model's capacity to generalize the performance-structure relationship. These results suggest that ASNN provides an efficient alternative to random search for architecture optimization, and offers a promising approach toward automating neural network design. "Parts of the manuscript, including text editing and expression refinement, were supported by OpenAI's ChatGPT. All content was reviewed and verified by the authors."
From Prompt to Pipeline: Large Language Models for Scientific Workflow Development in Bioinformatics
2025-07-27
Khairul Alam, Banani Roy
The increasing complexity of bioinformatics data analysis has made Scientific Workflow Systems (SWSs) like Galaxy and Nextflow essential for enabling scalable, reproducible, and automated workflows. However, creating and understanding these workflows remains challenging, particularly for domain experts without programming expertise. This study investigates whether modern Large Language Models (LLMs), GPT-4o, Gemini 2.5 Flash, and DeepSeek-V3, can support the generation of accurate, complete, and usable bioinformatics workflows, and examines which prompting strategies most effectively guide this process. We evaluate these models using diverse tasks such as SNP analysis, RNA-seq, DNA methylation, and data retrieval, spanning both graphical (Galaxy) and script-based (Nextflow) platforms. Expert reviewers assess the generated workflows against community-curated baselines from the Galaxy Training Network and nf-core repositories. The results show that Gemini 2.5 Flash excels in generating Galaxy workflows, while DeepSeek-V3 performs strongly in Nextflow. Prompting strategies significantly impact quality, with role-based and chain-of-thought prompts improving completeness and correctness. While GPT-4o benefits from structured inputs, DeepSeek-V3 offers rich technical detail, albeit with some verbosity. Overall, the findings highlight the potential of LLMs to lower the barrier for workflow development, improve reproducibility, and democratize access to computational tools in bioinformatics, especially when combined with thoughtful prompt engineering.
Comparative Analysis of Data-Driven Predictive Control Strategies
2025-07-27
Sohrab Rezaei, Ali Khaki-Sedigh
This paper compares data-driven predictive control strategies by examining their theoretical foundations, assumptions, and applications. The three most widely recognized and consequential methods, Data Enabled Predictive Control, Willems-Koopman Predictive Control, Model-Free Adaptive Predictive Control are employed. Each of these strategies is systematically reviewed, and the primary theories supporting it are outlined. Following analysis, a discussion is provided regarding their fundamental assumptions, emphasizing their influence on control effectiveness. A numerical example is presented as a benchmark for comparison to enable a rigorous performance evaluation.
Young Stellar Objects in the Carina nebula: Near-Infrared variability and spectroscopy
2025-07-27
Jura Borissova, Radostin Kurtev, Josemanuel Escobar, Javier Alonso-García, Nicolas Medina, Javier Osses, Zhen Guo, Philip Lucas, Michael Kuhn, Dante Minniti, Kevin R. Covey, Roberto Saito, Francisco Förster
We present a catalog of 652 young stellar objects (YSOs) in the Carina star-forming region. The catalog was constructed by combining near-infrared Ks-band variability from the VISTA Variables in the Via Lactea eXtended survey and medium-resolution H-band spectroscopy from APOGEE-2, Sloan Digital Sky Survey IV (SDSS-IV). Variability analysis of 6.35 million sources identified 606 variable stars. The classification of the spectral lines by semisupervised K-means clustering of 704 stars, refined through comparison with known catalogs in literature and visual inspection of the spectra, was performed. Combined with Ks variability, the final catalog contains three groups: Emission-line YSOs, Absorption-line YSOs, and Literature/Variable-identified YSOs. Cross validation with the Gaia DR3 proper motion and distance estimates supports Carina membership for 415 sources. The statistical characterization of YSO variability demonstrated that most Carina members (78%) exhibit variability patterns. Of these, 134 stars show emissions in their spectra, which is consistent with some accretion processes. Analysis of fundamental stellar parameters from StarHorse and Gaia DR3 reveals typical distributions of YSOs, dominated by low-mass 1-4 Msun, solar-metallicity stars with temperatures between 4000 and 6000K. Only a small fraction (4%) of the sources are more massive than 4 Msun, suggesting limited ongoing massive star formation in Carina. This well-characterized catalog also offers a robust training dataset for machine learning applications aimed at predicting YSO behavior.
Interpretable Anomaly-Based DDoS Detection in AI-RAN with XAI and LLMs
2025-07-27
Sotiris Chatzimiltis, Mohammad Shojafar, Mahdi Boloursaz Mashhadi, Rahim Tafazolli
Next generation Radio Access Networks (RANs) introduce programmability, intelligence, and near real-time control through intelligent controllers, enabling enhanced security within the RAN and across broader 5G/6G infrastructures. This paper presents a comprehensive survey highlighting opportunities, challenges, and research gaps for Large Language Models (LLMs)-assisted explainable (XAI) intrusion detection (IDS) for secure future RAN environments. Motivated by this, we propose an LLM interpretable anomaly-based detection system for distributed denial-of-service (DDoS) attacks using multivariate time series key performance measures (KPMs), extracted from E2 nodes, within the Near Real-Time RAN Intelligent Controller (Near-RT RIC). An LSTM-based model is trained to identify malicious User Equipment (UE) behavior based on these KPMs. To enhance transparency, we apply post-hoc local explainability methods such as LIME and SHAP to interpret individual predictions. Furthermore, LLMs are employed to convert technical explanations into natural-language insights accessible to non-expert users. Experimental results on real 5G network KPMs demonstrate that our framework achieves high detection accuracy (F1-score > 0.96) while delivering actionable and interpretable outputs.
Operator-Based Machine Intelligence: A Hilbert Space Framework for Spectral Learning and Symbolic Reasoning
2025-07-27
Andrew Kiruluta, Andreas Lemos, Priscilla Burity
Traditional machine learning models, particularly neural networks, are rooted in finite-dimensional parameter spaces and nonlinear function approximations. This report explores an alternative formulation where learning tasks are expressed as sampling and computation in infinite dimensional Hilbert spaces, leveraging tools from functional analysis, signal processing, and spectral theory. We review foundational concepts such as Reproducing Kernel Hilbert Spaces (RKHS), spectral operator learning, and wavelet-domain representations. We present a rigorous mathematical formulation of learning in Hilbert spaces, highlight recent models based on scattering transforms and Koopman operators, and discuss advantages and limitations relative to conventional neural architectures. The report concludes by outlining directions for scalable and interpretable machine learning grounded in Hilbertian signal processing.
On the Reinterpretation of Dark Energy as the Elasticity of Space
2025-07-26
Muhammad Ghulam Khuwajah Khan
In this work, we demonstrate that by treating space as a three dimensional elastic membrane, one can interpret the energy density stored in it as Dark Energy. In particular, we will model space as an infinitely large fundamental 3 brane and show that its vacuum stress energy tensor has the same mathematical form which one expects from the stress energy tensor describing a Lorentz invariant vacuum energy density (Dark Energy). We identify the dark-energy density with the tension of the fundamental 3 brane and thereby establish a direct correspondence between the cosmological constant and brane tension. Finally, we present a brief argument as to why the Cosmological Constant should be treated as a fundamental constant of nature, based on some light ontological discussion regarding the origin of the tension term in the broader framework of String Theory. In a forthcoming work, we will articulate a dynamical mechanism, which will be grounded in Q-theory, Hawking-Wu flux neutralization and broken super-symmetry, demonstrating the precise cancellation of vacuum energy in a manner such that the fundamental brane (spatial) tension remains the unique source of the Cosmological Constant.
Predicting Parkinson's Disease Progression Using Statistical and Neural Mixed Effects Models: A Comparative Study on Longitudinal Biomarkers
2025-07-26
Ran Tong, Lanruo Wang, Tong Wang, Wei Yan
Predicting Parkinson's Disease (PD) progression is crucial, and voice biomarkers offer a non-invasive method for tracking symptom severity (UPDRS scores) through telemonitoring. Analyzing this longitudinal data is challenging due to within-subject correlations and complex, nonlinear patient-specific progression patterns. This study benchmarks LMMs against two advanced hybrid approaches: the Generalized Neural Network Mixed Model (GNMM) (Mandel 2021), which embeds a neural network within a GLMM structure, and the Neural Mixed Effects (NME) model (Wortwein 2023), allowing nonlinear subject-specific parameters throughout the network. Using the Oxford Parkinson's telemonitoring voice dataset, we evaluate these models' performance in predicting Total UPDRS to offer practical guidance for PD research and clinical applications.
Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation
2025-07-26
Joydeep Chandra, Satyam Kumar Navneet
As AI-driven dataspaces become integral to data sharing and collaborative analytics, ensuring privacy, performance, and policy compliance presents significant challenges. This paper provides a comprehensive review of privacy-preserving and policy-aware AI techniques, including Federated Learning, Differential Privacy, Trusted Execution Environments, Homomorphic Encryption, and Secure Multi-Party Computation, alongside strategies for aligning AI with regulatory frameworks such as GDPR and the EU AI Act. We propose a novel taxonomy to classify these techniques based on privacy levels, performance impacts, and compliance complexity, offering a clear framework for practitioners and researchers to navigate trade-offs. Key performance metrics -- latency, throughput, cost overhead, model utility, fairness, and explainability -- are analyzed to highlight the multi-dimensional optimization required in dataspaces. The paper identifies critical research gaps, including the lack of standardized privacy-performance KPIs, challenges in explainable AI for federated ecosystems, and semantic policy enforcement amidst regulatory fragmentation. Future directions are outlined, proposing a conceptual framework for policy-driven alignment, automated compliance validation, standardized benchmarking, and integration with European initiatives like GAIA-X, IDS, and Eclipse EDC. By synthesizing technical, ethical, and regulatory perspectives, this work lays the groundwork for developing trustworthy, efficient, and compliant AI systems in dataspaces, fostering innovation in secure and responsible data-driven ecosystems.
Edge AI deployments are becoming increasingly complex, necessitating energy-efficient solutions for resource-constrained embedded systems. Approximate computing, which allows for controlled inaccuracies in computations, is emerging as a promising approach for improving power and energy efficiency. Among the key techniques in approximate computing are approximate arithmetic operators (AxOs), which enable application-specific optimizations beyond traditional computer arithmetic hardware reduction-based methods, such as quantization and precision scaling. Existing design space exploration (DSE) frameworks for approximate computing limit themselves to selection-based approaches or custom synthesis at fixed abstraction levels, which restricts the flexibility required for finding application-specific optimal solutions. Further, the tools available for the DSE of AxOs are quite limited in terms of exploring different approximation models and extending the analysis to different granularities. To this end, we propose AxOSyn, an open-source framework for the DSE of AxOs that supports both selection and synthesis approaches at various abstraction levels. AxOSyn allows researchers to integrate custom methods for evaluating approximations and facilitates DSE at both the operator-level and application-specific. Our framework provides an effective methodology for achieving energy-efficient, approximate operators.
2025-07-26
Jeremy A. Cribb, Farnaz Fazelpour, David A. Wollensak, Danielle Rice, Max deJong, David Hill, Richard Superfine
Molecular adhesion plays a central role in many biological systems, yet existing methods to quantify adhesive strength often struggle to bridge the gap between single-molecule resolution and biologically relevant environments. Here, we present a scalable micromagnetic bead-based adhesion assay capable of quantifying detachment forces under physiologically meaningful conditions. Designed to probe mucoadhesion in the context of mucociliary clearance, our system applies controlled magnetic forces to ligand-coated beads adhered to functionalized substrates and tracks detachment events using high-speed microscopy and calibrated z-displacement mapping. The platform combines substrate- and bead-side surface chemistry control with high-throughput imaging and in situ force calibration via Stokes drag. We demonstrate the ability to distinguish sub-nanonewton to nanonewton force regimes across a range of bead-substrate pairings, including COOH-COOH, PEG-PEG, and cell culture-derived human bronchial epithelial (HBE) mucus interactions. Surface functionalization was validated via fluorescence imaging and zeta potential measurements, while detachment forces were used to estimate binding energy and infer dissociation constants. This assay enables detailed characterization of multivalent, force-sensitive adhesive interactions and offers a powerful new approach for studying bioadhesive systems, including mucus-pathogen interactions and drug delivery materials.
Leveraging Fine-Tuned Large Language Models for Interpretable Pancreatic Cystic Lesion Feature Extraction and Risk Categorization
2025-07-26
Ebrahim Rasromani, Stella K. Kang, Yanqi Xu, Beisong Liu, Garvit Luhadia, Wan Fung Chui, Felicia L. Pasadyn, Yu Chih Hung, Julie Y. An, Edwin Mathieu, Zehui Gu, Carlos Fernandez-Granda, Ammar A. Javed, Greg D. Sacks, Tamas Gonda, Chenchan Huang, Yiqiu Shen
Background: Manual extraction of pancreatic cystic lesion (PCL) features from radiology reports is labor-intensive, limiting large-scale studies needed to advance PCL research. Purpose: To develop and evaluate large language models (LLMs) that automatically extract PCL features from MRI/CT reports and assign risk categories based on guidelines. Materials and Methods: We curated a training dataset of 6,000 abdominal MRI/CT reports (2005-2024) from 5,134 patients that described PCLs. Labels were generated by GPT-4o using chain-of-thought (CoT) prompting to extract PCL and main pancreatic duct features. Two open-source LLMs were fine-tuned using QLoRA on GPT-4o-generated CoT data. Features were mapped to risk categories per institutional guideline based on the 2017 ACR White Paper. Evaluation was performed on 285 held-out human-annotated reports. Model outputs for 100 cases were independently reviewed by three radiologists. Feature extraction was evaluated using exact match accuracy, risk categorization with macro-averaged F1 score, and radiologist-model agreement with Fleiss' Kappa. Results: CoT fine-tuning improved feature extraction accuracy for LLaMA (80% to 97%) and DeepSeek (79% to 98%), matching GPT-4o (97%). Risk categorization F1 scores also improved (LLaMA: 0.95; DeepSeek: 0.94), closely matching GPT-4o (0.97), with no statistically significant differences. Radiologist inter-reader agreement was high (Fleiss' Kappa = 0.888) and showed no statistically significant difference with the addition of DeepSeek-FT-CoT (Fleiss' Kappa = 0.893) or GPT-CoT (Fleiss' Kappa = 0.897), indicating that both models achieved agreement levels on par with radiologists. Conclusion: Fine-tuned open-source LLMs with CoT supervision enable accurate, interpretable, and efficient phenotyping for large-scale PCL research, achieving performance comparable to GPT-4o.
Eulerian-Lagrangian scaling of the Lyapunov exponent in homogeneous turbulence
2025-07-26
Jin Ge, Joran Rolland, John Christos Vassilicos
We present a heuristic derivation of the maximal Lyapunov exponent $\gamma$ of homogeneous turbulence which yields two new relations, a sweeping relation and the scaling of the uncertainty field's integral length $L_{\Delta}$. These relations and the maximal Lyapunov exponent's scaling that they imply are confirmed by periodic turbulence simulations. As the Reynolds number $Re_\lammbda$ increases, $L_\Delta$ and $\gamma^{-1}$ decrease towards values smaller than the Kolmogorov length and time scales.
2025-07-26
J. N. H. S. Aditya, Elaine M. Sadler, Raffaella Morganti, L. Y. Petrov, An Tao, Emily F. Kerrison, Elizabeth K. Mahony, Hyein Yoon, Renzhi Su, Matthew Whiting, Vanessa A. Moss, Simon Weng
We have conducted VLBA 1.4 GHz (L-band) continuum observations towards twelve sources with HI 21-cm absorption detections at redshift $0.4
Greening Schoolyards and Urban Property Values: A Systematic Review of Geospatial and Statistical Evidence
2025-07-26
Mahshid Gorjian
1.1 Background Parks and the greening of schoolyards are examples of urban green spaces that have been praised for their environmental, social, and economic benefits in cities all over the world. More studies show that living near green spaces is good for property values. However, there is still disagreement about how strong and consistent these effects are in different cities (Browning et al., 2023; Grunewald et al., 2024; Teo et al., 2023). 1.2 Purpose This systematic review is the first to bring together a lot of geographical and statistical information that links greening schoolyards to higher property prices, as opposed to just green space in general. By focusing on schoolyard-specific interventions, we find complex spatial, economic, and social effects that are often missed in larger studies of green space. 1.3 Methods This review followed the PRISMA guidelines and did a systematic search and review of papers that were published in well-known journals for urban studies, the environment, and real estate. The criteria for inclusion stressed the use of hedonic pricing or spatial econometric models to look at the relationship between urban green space and home values in a quantitative way. Fifteen studies from North America, Europe, and Asia met the requirements for inclusion (Anthamatten et al., 2022; Wen et al., 2019; Li et al., 2019; Mansur & Yusuf, 2022).
Anomalous superconductivity and unusual normal state properties of bilayer and twisted graphene (Brief review)
2025-07-26
M. Yu. Kagan, M. M. Korovushkin, V. A. Mitskan, K. I. Kugel, A. L. Rakhmanov, A. V. Rozhkov, A. O. Sboychakov
It has been shown that the Kohn--Luttinger superconductivity mechanism interplaying with other types of ordering can be implemented in systems with a hexagonal lattice. A number of unusual properties of such systems in the normal phase have also been considered. Our previous results on Kohn--Luttinger superconductivity with $p$-, $d$-, and $f$-wave pairing in monolayer and AB bilayer graphene, obtained disregarding the effect of substrate potential and impurities, have been presented in the first part. Then, the interplay of the superconducting Kohn--Luttinger state with the spin density wave state in actual AB, AA, and twisted bilayer graphene has been discussed in detail. In the last parts, a number of anomalous properties in the normal phase and the appearance of nematic superconductivity alongside with the spin density wave in the twisted bilayer graphene have been presented.
AgentMesh: A Cooperative Multi-Agent Generative AI Framework for Software Development Automation
2025-07-26
Sourena Khanzadeh
Software development is a complex, multi-phase process traditionally requiring collaboration among individuals with diverse expertise. We propose AgentMesh, a Python-based framework that uses multiple cooperating LLM-powered agents to automate software development tasks. In AgentMesh, specialized agents - a Planner, Coder, Debugger, and Reviewer - work in concert to transform a high-level requirement into fully realized code. The Planner agent first decomposes user requests into concrete subtasks; the Coder agent implements each subtask in code; the Debugger agent tests and fixes the code; and the Reviewer agent validates the final output for correctness and quality. We describe the architecture and design of these agents and their communication, and provide implementation details including prompt strategies and workflow orchestration. A case study illustrates AgentMesh handling a non-trivial development request via sequential task planning, code generation, iterative debugging, and final code review. We discuss how dividing responsibilities among cooperative agents leverages the strengths of large language models while mitigating single-agent limitations. Finally, we examine current limitations - such as error propagation and context scaling - and outline future work toward more robust, scalable multi-agent AI systems for software engineering automation.
From South to North: Leveraging Ground-Based LATs for Full-Sky CMB Delensing and Constraints on $r$
2025-07-26
Wen-Zheng Chen, Yang Liu, Yi-Ming Wang, Hong Li
Delensing--the process of mitigating the lensing-induced B-mode contamination in cosmic microwave background (CMB) observations--will be a pivotal challenge for next-generation CMB experiments seeking to detect primordial gravitational waves (PGWs) through B-mode polarization. This process requires an accurate lensing tracer, which can be obtained either through internal reconstruction from high-resolution CMB observations or from external large-scale structure (LSS) surveys. Ground-based large-aperture telescopes (LATs) are crucial for internal reconstruction, yet existing and planned facilities are confined to the southern hemisphere, limiting effective delensing to that region. In this work, we assess the impact of introducing a northern hemisphere LAT, assumed to be situated near AliCPT (hence termed Ali-like LAT, or LATN), on delensing performance and PGW detection, using simulations. Our baseline setup includes a space-based small-aperture mission (LiteBIRD-like, SAT) and a southern LAT (SO-like, LATS). External LSS tracers, which have been shown to play an important role in delensing before the availability of ultra-sensitive polarization data, are also considered. We find that southern-hemisphere internal delensing reduces the uncertainty in r by approximately 21% compared to the no-delensing case. Adding LATN enables full-sky internal delensing, achieving a further ~19% reduction--comparable to that from including LSS tracers (~17%). Once LATN is included, the marginal benefit of LSS tracers drops to ~10%. These results highlight the significant role of LATN in advancing delensing capabilities and improving PGW constraints.
With the rapid advancement of generative models, associated privacy concerns have attracted growing attention. To address this, researchers have begun adapting machine unlearning techniques from traditional classification models to generative settings. Although notable progress has been made in this area, a unified framework for systematically organizing and integrating existing work is still lacking. The substantial differences among current studies in terms of unlearning objectives and evaluation protocols hinder the objective and fair comparison of various approaches. While some studies focus on specific types of generative models, they often overlook the commonalities and systematic characteristics inherent in Generative Model Unlearning (GenMU). To bridge this gap, we provide a comprehensive review of current research on GenMU and propose a unified analytical framework for categorizing unlearning objectives, methodological strategies, and evaluation metrics. In addition, we explore the connections between GenMU and related techniques, including model editing, reinforcement learning from human feedback, and controllable generation. We further highlight the potential practical value of unlearning techniques in real-world applications. Finally, we identify key challenges and outline future research directions aimed at laying a solid foundation for further advancements in this field. We consistently maintain the related open-source materials at https://github.com/caxLee/Generative-model-unlearning-survey.
Sequence-based protein-protein interaction prediction and its applications in drug discovery
2025-07-26
François Charih, James R. Green, Kyle K. Biggar
Aberrant protein-protein interactions (PPIs) underpin a plethora of human diseases, and disruption of these harmful interactions constitute a compelling treatment avenue. Advances in computational approaches to PPI prediction have closely followed progress in deep learning and natural language processing. In this review, we outline the state-of the-art for sequence-based PPI prediction methods and explore their impact on target identification and drug discovery. We begin with an overview of commonly used training data sources and techniques used to curate these data to enhance the quality of the training set. Subsequently, we survey various PPI predictor types, including traditional similarity-based approaches, and deep learning-based approaches with a particular emphasis on the transformer architecture. Finally, we provide examples of PPI prediction in systems-level proteomics analyses, target identification, and design of therapeutic peptides and antibodies. We also take the opportunity to showcase the potential of PPI-aware drug discovery models in accelerating therapeutic development.
Particle effects are widely used in games and animation to simulate natural phenomena or stylized visual effects. However, creating effect artworks is challenging for non-expert users due to their lack of specialized skills, particularly in finding particle effects with kinematic behaviors that match their intent. To address these issues, we present KinemaFX, a kinematic-driven interactive system, to assist non-expert users in constructing customized particle effect artworks. We propose a conceptual model of particle effects that captures both semantic features and kinematic behaviors. Based on the model, KinemaFX adopts a workflow powered by Large Language Models (LLMs) that supports intent expression through combined semantic and kinematic inputs, while enabling implicit preference-guided exploration and subsequent creation of customized particle effect artworks based on exploration results. Additionally, we developed a kinematic-driven method to facilitate efficient interactive particle effect search within KinemaFX via structured representation and measurement of particle effects. To evaluate KinemaFX, we illustrate usage scenarios and conduct a user study employing an ablation approach. Evaluation results demonstrate that KinemaFX effectively supports users in efficiently and customarily creating particle effect artworks.
Towards a general distortion theory for univalent functions: Teichmuller spaces and coefficient problems of complex analysis
2025-07-26
Samuel L. Krushkal
Estimating the coefficient functionals on various classes of holomorphic functions traditionally forms an important field of geometric complex analysis and its mathematical and physical applications. These coefficients reflect fundamental intrinsic features of holomorphy and of conformality. This paper surveys the results obtained by a new approach involving deep features of Teichmuller spaces. This approach was recently suggested by the author. The paper also contains some new results generalizing the classical coefficient conjectures and presents open problems.
Computing optimal policies for managing inventories with noisy observations
2025-07-26
Eugene Feinberg, Jefferson Huang, Pavlo Kasyanov, Thomas O'Neill
This paper implements the Deep Deterministic Policy Gradient (DDPG) algorithm for computing optimal policies for partially observable single-product periodic review inventory control problems with setup costs and backorders. The decision maker does not know the exact inventory level, but can obtain noise-corrupted observations of them. The goal is to maximize the expected total discounted costs incurred over a finite planning horizon. We also investigate the Gaussian version of this problem with normally distributed initial inventories, demands, and observation noise. We show that expected posterior observations of inventory levels, also called mean beliefs, provide sufficient statistics for the Gaussian problem. Moreover, they can be represented in the form of a Markov Decision Processes for an inventory control system with time-dependent holding costs and demands. Thus, for a Gaussian problem, the there exist (s_t,S_t)-optimal policies based on mean beliefs, and this fact explains the structure of the approximately optimal policies computed by DDPG. For the Gaussian case, we also numerically compare the performance of policies derived from DDPG to optimal policies for discretized versions of the original continuous problem.
Phase II of the LAMOST-Kepler/K2 Survey. II. Time Domain of Medium-resolution Spectroscopic Observations from 2018 to 2023
2025-07-26
Mingfeng Qin, Jian-Ning Fu, Weikai Zong, Peter De Cat, Antonio Frasca, Tianqi Cang, Jiangtao Wang, Jianrong Shi, Ali Luo, Haotong Zhang, Hongliang Yan, J. Molenda- Żakowicz, R. O. Gray, Jiaxin Wang
The LAMOST-Kepler/K2 Medium-Resolution Spectroscopic Survey (LK-MRS) conducted time-domain medium-resolution spectroscopic observations of 20 LAMOST plates in the Kepler and K2 fields from 2018 to 2023, a phase designated as LK-MRS-I. A catalog of stellar parameters for a total of 36,588 stars, derived from the spectra collected during these five years, including the effective temperature, the surface gravity, the metallicity, the {\alpha}-element abundance, the radial velocity, and v sin i of the target stars, is released, together with the weighted averages and uncertainties. At S/N = 10, the measurement uncertainties are 120 K, 0.18 dex, 0.13 dex, 0.08 dex, 1.9 km/s, and 4.0 km/s for the above parameters, respectively. Comparisons with the parameters provided by the APOGEE and GALAH surveys validate the effective temperature and surface gravity measurements, showing minor discrepancies in metallicity and {\alpha}-element abundance values. We identified some peculiar star candidates, including 764 metal-poor stars, 174 very metal-poor stars, and 30 high-velocity stars. Moreover, we found 2,333 stars whose radial velocity seems to be variable. Using Kepler/K2 or TESS photometric data, we confirmed 371 periodic variable stars among the radial velocity variable candidates and classified their variability types. LK-MRS-I provides spectroscopic data being useful for studies of the Kepler and K2 fields. The LK-MRS project will continue collecting time-domain medium-resolution spectra for target stars during the third phase of LAMOST surveys, providing data to support further scientific research.
An inverse random diffraction grating problem for the Helmholtz equation
2025-07-26
Zhiqi Sun, Yiwen Lin
This paper investigates the inverse scattering problem of time-harmonic plane waves incident on a perfectly reflecting random periodic structure. To simulate random perturbations arising from manufacturing defects and surface wear in real-world grating profiles, we propose a stochastic surface modeling framework motivated by the discretization of the Wiener process. Our approach introduces randomness at discrete nodes and then applies linear interpolation to construct the surface, marking a novel attempt to incorporate the concepts of the Wiener process into random surface representation. Under this framework, each realization of the random surface generates a Lipschitz-continuous diffraction grating, mathematically represented as a sum of a baseline profile and a weighted linear combination of local `tent' basis functions, meanwhile preserving key statistics of the random surface. Building on this representation, we introduce the Recursive Parametric Smoothing Strategy (RPSS) to invert the key statistics of our random surfaces. Combined with Monte Carlo sampling and a wavenumber continuation strategy, our reconstruction scheme demonstrates effectiveness across multiple benchmark scenarios. Several numerical results are presented along with some discussions in the end on reconstruction mechanisms and future extensions.
2025-07-26
Zhuolin Xu, Chenglin Li, Qiushi Li, Shin Hwei Tan
Several code generation models have been proposed to help reduce time and effort in solving software-related tasks. To ensure responsible AI, there are growing interests over various ethical issues (e.g., unclear licensing, privacy, fairness, and environment impact). These studies have the overarching goal of ensuring ethically sourced generation, which has gained growing attentions in speech synthesis and image generation. In this paper, we introduce the novel notion of Ethically Sourced Code Generation (ES-CodeGen) to refer to managing all processes involved in code generation model development from data collection to post-deployment via ethical and sustainable practices. To build a taxonomy of ES-CodeGen, we perform a two-phase literature review where we read 803 papers across various domains and specific to AI-based code generation. We identified 71 relevant papers with 10 initial dimensions of ES-CodeGen. To refine our dimensions and gain insights on consequences of ES-CodeGen, we surveyed 32 practitioners, which include six developers who submitted GitHub issues to opt-out from the Stack dataset (these impacted users have real-world experience of ethically sourcing issues in code generation models). The results lead to 11 dimensions of ES-CodeGen with a new dimension on code quality as practitioners have noted its importance. We also identified consequences, artifacts, and stages relevant to ES-CodeGen. Our post-survey reflection showed that most practitioners tend to ignore social-related dimensions despite their importance. Most practitioners either agreed or strongly agreed that our survey help improve their understanding of ES-CodeGen. Our study calls for attentions of various ethical issues towards ES-CodeGen.
SHoM: A Mental-Synthesis Trust Management Model for Mitigating Botnet-Driven DDoS Attacks in the Internet of Things
2025-07-26
Masoud Hayeri Khyavi
The advantages of IoT in strengthening commercial, industrial, and social ecosystems have led to its widespread expansion. Nevertheless, because endpoint devices have limited computation, storage, and communication capabilities, the IoT infrastructure is vulnerable to several cyber threats. As a result, DDoS attacks pose a severe risk to the security of IoT. By taking advantage of these weaknesses, attackers may quickly employ IoT devices as a component of botnets to execute DDoS attacks. The most critical development is how more armies of robots are being constructed from IoT devices. We offer a Model for dealing with DDOS attacks on botnets in the Internet of Things via trust management. In this Model, an attempt has been made to consider all aspects of security concerning trust factors to design a reliable and flexible model against DDoS attacks against the Internet of Things. In the initial studies, about 40-50 security models related to the subject have been studied by using review articles
A ChatGPT-based approach for questions generation in higher education
2025-07-26
Sinh Trong Vu, Huong Thu Truong, Oanh Tien Do, Tu Anh Le, Tai Tan Mai
Large language models have been widely applied in many aspects of real life, bringing significant efficiency to businesses and offering distinctive user experiences. In this paper, we focus on exploring the application of ChatGPT, a chatbot based on a large language model, to support higher educator in generating quiz questions and assessing learners. Specifically, we explore interactive prompting patterns to design an optimal AI-powered question bank creation process. The generated questions are evaluated through a "Blind test" survey sent to various stakeholders including lecturers and learners. Initial results at the Banking Academy of Vietnam are relatively promising, suggesting a potential direction to streamline the time and effort involved in assessing learners at higher education institutes.
NANOGrav 15-year Data Set: Search for Gravitational Scattering of Pulsars by Free-Floating Objects in Interstellar Space
2025-07-25
Lankeswar Dey, Ross J. Jennings, Jackson D. Taylor, Joseph Glaser, Maura A. McLaughlin, Gabriella Agazie, Akash Anumarlapudi, Anne M. Archibald, Zaven Arzoumanian, Paul T. Baker, Paul R. Brook, H. Thankful Cromartie, Kathryn Crowter, Megan E. DeCesar, Paul B. Demorest, Timothy Dolch, Elizabeth C. Ferrara, William Fiore, Emmanuel Fonseca, Gabriel E. Freedman, Nate Garver-Daniels, Peter A. Gentile, Deborah C. Good, Jeffrey S. Hazboun, Megan L. Jones, David L. Kaplan, Matthew Kerr, Michael T. Lam, T. Joseph W. Lazio, Duncan R. Lorimer, Jing Luo, Ryan S. Lynch, Alexander McEwen, Natasha McMann, Bradley W. Meyers, Cherry Ng, David J. Nice, Timothy T. Pennucci, Benetge B. P. Perera, Nihan S. Pol, Henri A. Radovan, Scott M. Ransom, Paul S. Ray, Ann Schmiedekamp, Carl Schmiedekamp, Brent J. Shapiro-Albert, Ingrid H. Stairs, Kevin Stovall, Abhimanyu Susobhanan, Joseph K. Swiggum, Haley M. Wahl
Free-floating objects (FFOs) in interstellar space$-$rogue planets, brown dwarfs, and large asteroids that are not gravitationally bound to any star$-$are expected to be ubiquitous throughout the Milky Way. Recent microlensing surveys have discovered several free-floating planets that are not bound to any known stellar systems. Additionally, three interstellar objects, namely 1I/'Oumuamua, 2I/Borisov, and 3I/ATLAS, have been detected passing through our solar system on hyperbolic trajectories. In this work, we search for FFOs on hyperbolic orbits that pass near millisecond pulsars (MSPs), where their gravitational influence can induce detectable perturbations in pulse arrival times. Using the NANOGrav 15-year narrowband dataset, which contains high-precision timing data for 68 MSPs, we conduct a search for such hyperbolic scattering events between FFOs and pulsars. Although no statistically significant events were detected, this non-detection enables us to place upper limits on the number density of FFOs as a function of their mass within our local region of the Galaxy. For example, the upper limit on the number density for Jupiter-mass FFOs ($\sim 10^{-2.5} - 10^{-3.5}~M_{\odot}$) obtained from different pulsars ranges from $5.25\times10^{6}~\text{pc}^{-3}$ to $5.37\times10^{9}~\text{pc}^{-3}$, while the upper limit calculated by combining results from all the pulsars is $6.03\times10^{5}~\text{pc}^{-3}$. These results represent the first constraints on FFO population derived from pulsar timing data.
JWST+ALMA reveal the build up of stellar mass in the cores of dusty star-forming galaxies at Cosmic Noon
2025-07-25
Sarah Bodansky, Katherine E. Whitaker, Ayesha Abdullah, Jamie Lin, Pascal A. Oesch, Alexandra Pope, Mengyuan Xiao, Alba Covelo-Paz, Sam Cutler, Carlos Garcia Diaz, Minju M. Lee, Sinclaire M. Manning, Romain A. Meyer, Desika Narayanan, Erica Nelson, Irene Shivaei, Pieter van Dokkum
Dusty star-forming galaxies have long been suspected to serve as the missing evolutionary bridge between the star-forming and quiescent phases of massive galaxy evolution. With the combined power of JWST and ALMA, it is now possible to use high resolution imaging in rest-optical, rest near-infrared (NIR), and rest-submm wavelengths to study the multi-wavelength morphologies tracing both the stellar populations and dust within this key phase. We present the joint analysis of JWST/NIRCam imaging in GOODS-S and mm dust emission traced by ALMA for a sample of 33 galaxies at $z$=1.5 to $z$=5.5 selected from the 1.1mm GOODS-ALMA 2.0 survey, and compare the morphologies of this population to mass and redshift selected samples of field star-forming and quiescent galaxies. The 1.1mm-selected sample are morphologically distinct from other similarly massive star-forming galaxies; we find a steeper size-wavelength gradient from 1.5-4.4$\mu$m, with a more dramatic decrease in size towards longer wavelengths. While the rest-NIR surface brightness profiles of the 1.1mm-selected galaxies are brighter in the inner regions relative to the field star-forming population, they are remarkably similar to the quiescent population. These morphological differences could suggest that dusty star-forming galaxies, unlike more typical star-forming galaxies, have already built up stellar mass in a severely dust-obscured core, leading to extended and clumpy morphologies at rest-optical wavelengths and more compact emission in the rest-NIR that is co-spatial with dust. If the bulge is already established, we speculate that mm-selected galaxies may imminently evolve to join their quiescent descendants.
Towards Effective Immersive Technologies in Medicine: Potential and Future Applications based on VR, AR, XR and AI solutions
2025-07-25
Aliaksandr Marozau, Barbara Karpowicz, Tomasz Kowalewski, Pavlo Zinevych, Wiktor Stawski, Adam Kuzdraliński, Wiesław Kopeć
Mixed Reality (MR) technologies such as Virtual and Augmented Reality (VR, AR) are well established in medical practice, enhancing diagnostics, treatment, and education. However, there are still some limitations and challenges that may be overcome thanks to the latest generations of equipment, software, and frameworks based on eXtended Reality (XR) by enabling immersive systems that support safer, more controlled environments for training and patient care. Our review highlights recent VR and AR applications in key areas of medicine. In medical education, these technologies provide realistic clinical simulations, improving skills and knowledge retention. In surgery, immersive tools enhance procedural precision with detailed anatomical visualizations. VR-based rehabilitation has shown effectiveness in restoring motor functions and balance, particularly for neurological patients. In mental health, VR has been successful in treating conditions like PTSD and phobias. Although VR and AR solutions are well established, there are still some important limitations, including high costs and limited tactile feedback, which may be overcome with implementing new technologies that may improve the effectiveness of immersive medical applications such as XR, psychophysiological feedback or integration of artificial intelligence (AI) for real-time data analysis and personalized healthcare and training.
Long-term Optical Photometry of V709 Cas Using TESS : Refined Periods and Accretion Geometry
2025-07-25
Srinivas M Rao, Jeewan C Pandey, Nikita Rawat, Arti Joshi, Ajay Kumar Singh
Utilizing high-cadence and long-term optical photometry from the Transiting Exoplanet Survey Satellite (TESS ), we conducted a time-resolved timing analysis of the Intermediate Polar, V709 Cas. Our analysis reveals key characteristics of this system: an orbital period of 5.3329 $\pm$ 0.0002 h, a spin period of 312.7478 $\pm$ 0.0002 s, and a beat period of 317.9267 $\pm$ 0.0002 s. These periods represent a significant refinement over the results of previous studies. These analyses demonstrate that V709 Cas is primarily a disc-overflow accretor, with accretion predominantly occurring via a disc. However, the analysis also reveals epochs where stream-fed accretion is a dominant accretion process. Time-resolved analysis of 20 s short cadence data obtained from sectors 57 and 58 reveals the presence of distinct first harmonics of the spin and beat frequencies. This finding indicates the presence of double peak spin modulation, a characteristic signature of two-pole accretion onto the white dwarf.
The SWAN view of dense gas in the Whirlpool -- A cloud-scale comparison of N2H+, HCO+, HNC and HCN emission in M51
2025-07-25
Sophia K. Stuber, Eva Schinnerer, Antonio Usero, Frank Bigiel, Jakob den Brok, Jerome Pety, Lukas Neumann, María J. Jiménez-Donaire, Jiayi Sun, Miguel Querejeta, Ashley T. Barnes, Ivana Bešlic, Yixian Cao, Daniel A. Dale, Cosima Eibensteiner, Damian Gleis, Simon C. O. Glover, Kathryn Grasha, Ralf S. Klessen, Daizhong Liu, Sharon Meidt, Hsi-An Pan, Toshiki Saito, Mallory Thorp, Thomas G. Williams
Tracing dense molecular gas, the fuel for star formation, is essential for the understanding of the evolution of molecular clouds and star formation processes. We compare the emission of HCN(1-0), HNC(1-0) and HCO+(1-0) with the emission of N2H+(1-0) at cloud-scales (125 pc) across the central 5x7 kpc of the Whirlpool galaxy, M51a, from "Surveying the Whirlpool galaxy at Arcseconds with NOEMA" (SWAN). We find that the integrated intensities of HCN, HNC and HCO+ are more steeply correlated with N2H+ emission compared to the bulk molecular gas tracer CO, and we find variations in this relation across the center, molecular ring, northern and southern disk of M51. Compared to HCN and HNC emission, the HCO+ emission follows the N2H+ emission more similarly across the environments and physical conditions such as surface densities of molecular gas, stellar mass, star-formation rate, dynamical equilibrium pressure and radius. Under the assumption that N2H+ is a fair tracer of dense gas at these scales, this makes HCO+ a more favorable dense gas tracer than HCN within the inner disk of M51. In all environments within our field of view, even when removing the central 2 kpc, HCN/CO, commonly used to trace average cloud density, is only weakly depending on molecular gas mass surface density. While ratios of other dense gas lines to CO show a steeper dependency on the surface density of molecular gas, it is still shallow in comparison to other nearby star-forming disk galaxies. The reasons might be physical conditions in M51 that are different from other normal star-forming galaxies. Increased ionization rates, increased dynamical equilibrium pressure in the central few kpc and the impact of the dwarf companion galaxy NGC 5195 are proposed mechanisms that might enhance HCN and HNC emission over HCO+ and N2H+ emission at larger-scale environments and cloud scales.
On Arbitrary Predictions from Equally Valid Models
2025-07-25
Sarah Lockfisch, Kristian Schwethelm, Martin Menten, Rickmer Braren, Daniel Rueckert, Alexander Ziller, Georgios Kaissis
Model multiplicity refers to the existence of multiple machine learning models that describe the data equally well but may produce different predictions on individual samples. In medicine, these models can admit conflicting predictions for the same patient -- a risk that is poorly understood and insufficiently addressed. In this study, we empirically analyze the extent, drivers, and ramifications of predictive multiplicity across diverse medical tasks and model architectures, and show that even small ensembles can mitigate/eliminate predictive multiplicity in practice. Our analysis reveals that (1) standard validation metrics fail to identify a uniquely optimal model and (2) a substantial amount of predictions hinges on arbitrary choices made during model development. Using multiple models instead of a single model reveals instances where predictions differ across equally plausible models -- highlighting patients that would receive arbitrary diagnoses if any single model were used. In contrast, (3) a small ensemble paired with an abstention strategy can effectively mitigate measurable predictive multiplicity in practice; predictions with high inter-model consensus may thus be amenable to automated classification. While accuracy is not a principled antidote to predictive multiplicity, we find that (4) higher accuracy achieved through increased model capacity reduces predictive multiplicity. Our findings underscore the clinical importance of accounting for model multiplicity and advocate for ensemble-based strategies to improve diagnostic reliability. In cases where models fail to reach sufficient consensus, we recommend deferring decisions to expert review.
2025-07-25
Yunpeng Men, Ewan Barr, Amit Bansod, Weiwei Chen, Jason Wu, John Antoniadis, Jan Behrend, Niclas Esser, Oliver Polch, Gundolf Wieching, Tobias Winchen
Radio interferometers provide the means to perform the wide-field-of-view (FoV), high-sensitivity observations required for modern radio surveys. As computing power per cost has decreased, there has been a move towards larger arrays of smaller dishes, such as DSA-2000, the upcoming HIRAX, CHORD and SKA radio telescopes. Such arrays can have simpler receiver designs with room-temperature low-noise amplifiers and direct sampling to achieve, greatly reducing the cost per antenna. The ARGOS project is currently developing an array of five 6-meter antennas that will be used to demonstrate the technology required for a next generation "small-D, big-N" radio interferometer in Europe. In this work, our objective was to implement a first-stage digital signal processing system for the ARGOS demonstrator array, providing digitization, channelization, delay correction and frequency-dependent complex gain correction. The system is intended to produce delay and phase corrected dual-polarization channelized voltages in the frequency range 1-3 GHz with a nominal channel bandwidth of 1 MHz. We use an RFSoC 4x2 evaluation board with four analog-to-digital converters (ADCs) that can simultaneously sample two 1 GHz, dual-polarization bands. We use Xilinx Vitis HLS C++ to develop the required firmware as a set of customizable modules suitable for rapid prototyping. We performed hardware verification of the channel response of the critically sampled PFB and of the delay correction, showing both to be consistent with theoretical expectations. Furthermore, the board was installed at the Effelsberg 100-meter radio telescope where we performed commensal pulsar observations with the Effelsberg Direct Digitization backend, showing comparable performance. This work demonstrates the utility of high-level synthesis (HLS) languages in the development of high performance radio astronomy processing backends.
Learning neuro-symbolic convergent term rewriting systems
2025-07-25
Flavio Petruzzellis, Alberto Testolin, Alessandro Sperduti
Building neural systems that can learn to execute symbolic algorithms is a challenging open problem in artificial intelligence, especially when aiming for strong generalization and out-of-distribution performance. In this work, we introduce a general framework for learning convergent term rewriting systems using a neuro-symbolic architecture inspired by the rewriting algorithm itself. We present two modular implementations of such architecture: the Neural Rewriting System (NRS) and the Fast Neural Rewriting System (FastNRS). As a result of algorithmic-inspired design and key architectural elements, both models can generalize to out-of-distribution instances, with FastNRS offering significant improvements in terms of memory efficiency, training speed, and inference time. We evaluate both architectures on four tasks involving the simplification of mathematical formulas and further demonstrate their versatility in a multi-domain learning scenario, where a single model is trained to solve multiple types of problems simultaneously. The proposed system significantly outperforms two strong neural baselines: the Neural Data Router, a recent transformer variant specifically designed to solve algorithmic problems, and GPT-4o, one of the most powerful general-purpose large-language models. Moreover, our system matches or outperforms the latest o1-preview model from OpenAI that excels in reasoning benchmarks.
Integrating LLM in Agent-Based Social Simulation: Opportunities and Challenges
2025-07-25
Patrick Taillandier, Jean Daniel Zucker, Arnaud Grignard, Benoit Gaudou, Nghi Quang Huynh, Alexis Drogoul
This position paper examines the use of Large Language Models (LLMs) in social simulation, analyzing both their potential and their limitations from a computational social science perspective. The first part reviews recent findings on the ability of LLMs to replicate key aspects of human cognition, including Theory of Mind reasoning and social inference, while also highlighting significant limitations such as cognitive biases, lack of true understanding, and inconsistencies in behavior. The second part surveys emerging applications of LLMs in multi-agent simulation frameworks, focusing on system architectures, scale, and validation strategies. Notable projects such as Generative Agents (Smallville) and AgentSociety are discussed in terms of their design choices, empirical grounding, and methodological innovations. Particular attention is given to the challenges of behavioral fidelity, calibration, and reproducibility in large-scale LLM-driven simulations. The final section distinguishes between contexts where LLMs, like other black-box systems, offer direct value-such as interactive simulations and serious games-and those where their use is more problematic, notably in explanatory or predictive modeling. The paper concludes by advocating for hybrid approaches that integrate LLMs into traditional agent-based modeling platforms (GAMA, Netlogo, etc), enabling modelers to combine the expressive flexibility of language-based reasoning with the transparency and analytical rigor of classical rule-based systems.
GATOS. n. On the physical origin of the extended MIR emission in AGN
2025-07-25
Enrique Lopez-Rodriguez, Cristina Ramos Almeida, Miguel Pereira-Santaella, Ismael García-Bernete, Robert Nikutta, Almudena Alonso-Herrero, Enrica Bellocchi, Andrew Bunker, Steph Campbell, Françoise Combes, Richard Davies, Tanio Diaz-Santos, Lindsay Fuller, Poshak Gandhi, Santiago García-Burillo, Omaira González-Martín, Erin K. S. Hicks, Sebastian Hönig, Kohei Ichikawa, Masatoshi Imanishi, Takuma Izumi, Alvaro Labiano, Nancy A. Levenson, Christopher Packham, David Rosario, Dimitra Rigopoulou, Daniel Rouan, Taro Shimizu, Marko Stalevski, Martin Ward, Lulu Zhang, Claudio Ricci, Donaji Esparza-Arredondo, Begoña García-Lorenzo
The polar mid-infrared (MIR) emission detected from tens to hundreds of parsecs in some active galactic nuclei (AGN) has been associated with dusty winds driven away by radiation pressure. However, the physical characterization of this extended polar emission remains uncertain. Here we combine $10-21\mu$m JWST/MIRI imaging observations with $7-25 \mu$m MRS integral field spectroscopic observations of 6 nearby, $\bar{D}=35.4\pm4.6$~Mpc, AGN from the GATOS Survey to quantify the nature of the extended MIR emission at $\sim75$ pc resolution at $21~\mu$m. These AGN have similar bolometric luminosities, $\log_{10}(\bar{L}_{\rm{bol}}~[\rm{erg\,s}^{-1}])=44.0\pm0.3$, span a wide range of optical outflow rates, $\dot{M}=0.003-0.21$ M$_{\odot}$ yr$^{-1}$, column densities, $\log_{10}(N_{H}^{X-ray} [cm^{-2}])=22.2-24.3$, and Eddington ratios, $\lambda_{\rm{Edd}}=0.005-0.06$. We find a poor correlation between the line-only and continuum-only images, indicating that the extended MIR continuum emission is spatially uncorrelated with the warm outflows associated with narrow emission lines in the $10-15\,\mu$m wavelength range. The $75-450$~pc continuum emission has constant dust temperature, $T_{\rm{d}}=132^{+7}_{-7}$ K, and mass, $M_{\rm{d}}=728^{+29}_{-27}$ M$_{\odot}$. Using the conditions of energy balance between radiation pressure and gravity, we find that our AGN sample is in the gravitationally bounded regime, consistent with the lack of detection of dusty winds in our observations. At $10~\mu$m, the level of line emission contribution across the NLR is correlated with the outflow kinetic energy and mass outflow rates. We find no correlation with the AGN properties. These results indicate that the radio jet may be triggering the gas outflow and line emission, while the extended dust emission is distributed in molecular clouds and/or shocked regions across the narrow line region.
A Clockwork Quantum: Symmetry, Noise, and the Emergence of Quantum Order
2025-07-25
Eric R. Bittner, Bhavay Tyagi
We present a concise review and perspective on noise-induced synchronization and coherence protection in open quantum systems, with emphasis on recent work involving coupled spins, oscillators, and anyons. When local environments exhibit internal correlations, the structure of the noise determines which collective modes become decoherence-protected. This leads to steady-state entanglement, phase locking, and exceptional points (EPs) in the Liouvillian spectrum, signaling a collapse of the mode basis and the emergence of non-dissipative stabilized dynamics. Using a Lindblad framework, we show that symmetry in the noise correlations acts as a control parameter, protecting symmetric or antisymmetric modes depending on the sign of the correlation. In the pure-dephasing limit, coherence decay mirrors the Anderson-Kubo model, where the effective fluctuation strength scales as $\sigma^2(1 \pm \xi)$, and the dynamical regime (Gaussian vs. Lorentzian) is set by the ratio \( \sigma / \gamma \). Thus, the environment not only drives decoherence but can also selectively suppress it through symmetry filtering. We also revisit historical and conceptual origins of this idea, beginning with Huygens synchronized pendulum clocks and culminating in modern non-Hermitian dynamics. Correlated noise-though classically stochastic-can organize quantum dynamics and protect coherence without direct control over the system. These insights offer a unifying view of synchronization in classical and quantum regimes, with implications for quantum sensing, engineered decoherence, and long-lived coherence in complex environments such as biological light-harvesting complexes or avian magnetoreception.
There is widespread confusion among criminal justice practitioners and legal scholars about the use of artificial intelligence in criminal justice. This didactic review is written for readers with little or no background in statistics or computer science. It is not intended to replace more technical treatments. It is intended to supplement them and encourage readers to dig more deeply into topics that strike their fancy.
The environmental impact of Information and Communication Technologies (ICT) continues to grow, driven notably by increasing usage, rebound effects, and emerging demands. However, despite the virtual nature of its services, the sector remains inherently constrained by its materiality and cannot rely on an infinite pool of resources. As a result, the wide variety of supported services may need to be managed under stricter limits within hosting facilities in the future. Contrary to common assumptions, we show that tenants typically do not share computing resources, even in environments commonly perceived as mutualized, such as cloud platforms. Time-sharing has been progressively phased out for reasons of performance, security, predictability, and, perhaps more importantly, due to the decreasing cost of computing resources. This paper advocates for managing fewer physical resources by improving resource sharing between tenants. It represents a paradigm shift, moving beyond traditional time-sharing at the hardware level to a higher abstraction. This approach entails "doing with fewer resources" under conditions of "reduced performance". Nonetheless, enhancing the mutualization of infrastructure can reduce cluster sizes (through consolidation) and improve energy efficiency, with gains related to the accepted performance trade-off, a situation potentially more socially acceptable than eliminating services. We review the current state of the art, identify challenges and opportunities, propose interpretations of Time-Shared Computing, and outline key research directions.
Fine-Tuning Multilingual Language Models for Code Review: An Empirical Study on Industrial C# Projects
2025-07-25
Igli Begolli, Meltem Aksoy, Daniel Neider
Code review is essential for maintaining software quality but often time-consuming and cognitively demanding, especially in industrial environments. Recent advancements in language models (LMs) have opened new avenues for automating core review tasks. This study presents the empirical evaluation of monolingual fine-tuning on the performance of open-source LMs across three key automated code review tasks: Code Change Quality Estimation, Review Comment Generation, and Code Refinement. We fine-tuned three distinct models, CodeReviewer, CodeLlama-7B, and DeepSeek-R1-Distill, on a C\# specific dataset combining public benchmarks with industrial repositories. Our study investigates how different configurations of programming languages and natural languages in the training data affect LM performance, particularly in comment generation. Additionally, we benchmark the fine-tuned models against an automated software analysis tool (ASAT) and human reviewers to evaluate their practical utility in real-world settings. Our results show that monolingual fine-tuning improves model accuracy and relevance compared to multilingual baselines. While LMs can effectively support code review workflows, especially for routine or repetitive tasks, human reviewers remain superior in handling semantically complex or context-sensitive changes. Our findings highlight the importance of language alignment and task-specific adaptation in optimizing LMs for automated code review.
Quark models have a more than 60-year history and through this time they served as a powerful investigation and prediction tool in hadronic physics. In recent years, a lot of new experimental information has been arriving on hadrons that do not qualify as simple quark model states. Yet, quark models remain the cornerstone of the classification scheme for hadrons, provide valuable insights into various phenomena inherent in QCD, and facilitate gaining a clear and physically transparent picture of the underlying physics. In the spotlight of this review is a chiral quark model inspired by quantum field theory approach to confined quarks. The model is well suited for studies of spontaneous breaking of chiral symmetry in the vacuum of QCD as well as its implications in the spectrum of hadrons. It can also be employed to investigate chiral restoration at finite temperatures.
DBMS-LLM Integration Strategies in Industrial and Business Applications: Current Status and Future Challenges
2025-07-25
Zhengtong Yan, Gongsheng Yuan, Qingsong Guo, Jiaheng Lu
Modern enterprises are increasingly driven by the DATA+AI paradigm, in which Database Management Systems (DBMSs) and Large Language Models (LLMs) have become two foundational infrastructures powering a wide range of industrial and business applications, such as enterprise analytics, intelligent customer service, and data-driven decision-making. The efficient integration of DBMSs and LLMs within a unified system offers significant opportunities but also introduces new technical challenges. This paper surveys recent developments in DBMS+LLM integration and identifies key future challenges. Specifically, we categorize five representative architectural patterns based on their core design principles, strengths, and trade-offs. Based on this analysis, we further highlight several critical open challenges. We aim to provide a systematic understanding of the current integration landscape and to outline the unresolved issues that must be addressed to achieve scalable and efficient integration of traditional data management and advanced language reasoning in future intelligent applications.
Restoration of images contaminated by different adverse weather conditions such as fog, snow, and rain is a challenging task due to the varying nature of the weather conditions. Most of the existing methods focus on any one particular weather conditions. However, for applications such as autonomous driving, a unified model is necessary to perform restoration of corrupted images due to different weather conditions. We propose a continual learning approach to propose a unified framework for image restoration. The proposed framework integrates three key innovations: (1) Selective Kernel Fusion layers that dynamically combine global and local features for robust adaptive feature selection; (2) Elastic Weight Consolidation (EWC) to enable continual learning and mitigate catastrophic forgetting across multiple restoration tasks; and (3) a novel Cycle-Contrastive Loss that enhances feature discrimination while preserving semantic consistency during domain translation. Further, we propose an unpaired image restoration approach to reduce the dependance of the proposed approach on the training data. Extensive experiments on standard benchmark datasets for dehazing, desnowing and deraining tasks demonstrate significant improvements in PSNR, SSIM, and perceptual quality over the state-of-the-art.
Big Data Energy Systems: A Survey of Practices and Associated Challenges
2025-07-25
Lunodzo J. Mwinuka, Massimo Cafaro, Lucas Pereira, Hugo Morais
Energy systems generate vast amounts of data in extremely short time intervals, creating challenges for efficient data management. Traditional data management methods often struggle with scalability and accessibility, limiting their usefulness. More advanced solutions, such as NoSQL databases and cloud-based platforms, have been adopted to address these issues. Still, even these advanced solutions can encounter bottlenecks, which can impact the efficiency of data storage, retrieval, and analysis. This review paper explores the research trends in big data management for energy systems, highlighting the practices, opportunities and challenges. Also, the data regulatory demands are highlighted using chosen reference architectures. The review, in particular, explores the limitations of current storage and data integration solutions and examines how new technologies are applied to the energy sector. Novel insights into emerging technologies, including data spaces, various data management architectures, peer-to-peer data management, and blockchains, are provided, along with practical recommendations for achieving enhanced data sharing and regulatory compliance.
Automated Code Review Using Large Language Models at Ericsson: An Experience Report
2025-07-25
Shweta Ramesh, Joy Bose, Hamender Singh, A K Raghavan, Sujoy Roychowdhury, Giriprasad Sridhara, Nishrith Saini, Ricardo Britto
Code review is one of the primary means of assuring the quality of released software along with testing and static analysis. However, code review requires experienced developers who may not always have the time to perform an in-depth review of code. Thus, automating code review can help alleviate the cognitive burden on experienced software developers allowing them to focus on their primary activities of writing code to add new features and fix bugs. In this paper, we describe our experience in using Large Language Models towards automating the code review process in Ericsson. We describe the development of a lightweight tool using LLMs and static program analysis. We then describe our preliminary experiments with experienced developers in evaluating our code review tool and the encouraging results.
A systematic literature review to unveil users objective reaction to virtual experiences: Complemented with a conceptual model (QoUX in VE)
2025-07-25
Alireza Mortezapour, Andrea Antonio Cantone, Monica Maria Lucia Sebillo, Giuliana Vitiello
In pursuit of documenting users Neurophysiological responses during experiencing virtual environments (VE), this systematic review presents a novel conceptual model of UX in VE. Searching across seven databases yielded to 1743 articles. Rigorous screenings, included only 66 articles. Notably, UX in VE lacks a consensus definition. Obviously, this UX has many unique sub-dimensions that are not mentioned in other products. The presented conceptual model contains 26 subdimensions which mostly not supported in previous subjective tools and questionnaires. While EEG and ECG were common, brain ultrasound, employed in one study, highlights the need for using neurophysiological assessments to comprehensively grasp immersive UX intricacies.
Interactive, Grouped and Non-separable Fixed Effects: A Practitioner's Guide to the New Panel Data Econometrics
2025-07-25
Jan Ditzen, Yiannis Karavias
The past 20 years have brought fundamental advances in modeling unobserved heterogeneity in panel data. Interactive Fixed Effects (IFE) proved to be a foundational framework, generalizing the standard one-way and two-way fixed effects models by allowing the unit-specific unobserved heterogeneity to be interacted with unobserved time-varying common factors, allowing for more general forms of omitted variables. The IFE framework laid the theoretical foundations for other forms of heterogeneity, such as grouped fixed effects (GFE) and non-separable two-way fixed effects (NSTW). The existence of IFE, GFE or NSTW has significant implications for identification, estimation, and inference, leading to the development of many new estimators for panel data models. This paper provides an accessible review of the new estimation methods and their associated diagnostic tests, and offers a guide to empirical practice. In two separate empirical investigations we demonstrate that there is empirical support for the new forms of fixed effects and that the results can differ significantly from those obtained using traditional fixed effects estimators.
A Protocol to Address Ecological Redirection for Digital Practices in Organizations
2025-07-25
Valentin Girard, Antoine Martin, Maud Rio, Romain Couillet
The digitalization of societies raises questions about its sustainability and the socio-technical impacts it generates. Ecological redirection applied to organizations is a field of research aiming for achieving sustainability as a direction, rather than for technical means. Arbitration and renunciation to some digital usage and technologies are investigated. Ecological redirection is, however, not yet addressing concrete methodologies for its implementation in organizations. This paper therefore proposes a protocol to support stakeholders in the ecological redirection of their digital practices. This protocol is based on mapping attachments to digital tools through a multi-disciplinary survey. It then proposes increasing stakeholders' knowledge and skills to prepare a debate on the arbitration of renunciations, and finally, to operationalize the closure/transformation of targeted digital practices. This protocol will be tested in real conditions in different contexts. An empirical study is proposed to measure 1) the fluidity with which participants carry out the protocol, 2) the effectiveness of the protocol in terms of the redirection objective, 3) the socio-technical barriers to the redirection process. The paper concludes on the potential benefits for organizations to better understand both the barriers related to its ecological redirection and the transformative aim of such protocols. This will help them trigger large and radical policies towards a desirable and sustainable society.
Orbit dissimilarity criteria in meteor showers: a comparative review
2025-07-25
Ariane Courtot, Patrick Shober, Jérémie Vaubaillon
In meteor science, the identification of meteor showers is a crucial and complex problem. The most common method is to perform a systematic search of a database of observed orbits using an orbit dissimilarity criterion (D-criterion) and an algorithm. D-criteria compare the result of an orbit dissimilarity function (D-function) and a threshold. These D-functions associate one value to two meteoroids orbits. If this value is lower than the threshold, the meteoroids' orbits are considered similar. Group of meteors are thus formed. However, not all D-criteria have been evaluated, and their high number makes it hard to know which should be prioritised. This paper presents a review of each D-function, the tests they passed, the threshold choice, and the algorithms they are used with. We show what methods are currently used in the search for meteor showers, presenting statistics based on papers justifying the existence of established meteor showers. We review D-functions from eight different papers. We describe how thresholds and clustering algorithms are usually chosen. We also analyse tests that were performed on D-criteria. We discover that most of those criteria were not properly tested, and that some have been criticised for their theoretical background. Thus, we recommend performing a post-search analysis of the groups found, both in a statistical sense (to make sure the groups formed could not have been formed randomly) and an orbital dynamics sense (to check whether the group could indeed come from a singular parent body), to present the findings as potential meteor showers.
SESR-Eval: Dataset for Evaluating LLMs in the Title-Abstract Screening of Systematic Reviews
2025-07-25
Aleksi Huotala, Miikka Kuutila, Mika Mäntylä
Background: The use of large language models (LLMs) in the title-abstract screening process of systematic reviews (SRs) has shown promising results, but suffers from limited performance evaluation. Aims: Create a benchmark dataset to evaluate the performance of LLMs in the title-abstract screening process of SRs. Provide evidence whether using LLMs in title-abstract screening in software engineering is advisable. Method: We start with 169 SR research artifacts and find 24 of those to be suitable for inclusion in the dataset. Using the dataset we benchmark title-abstract screening using 9 LLMs. Results: We present the SESR-Eval (Software Engineering Systematic Review Evaluation) dataset containing 34,528 labeled primary studies, sourced from 24 secondary studies published in software engineering (SE) journals. Most LLMs performed similarly and the differences in screening accuracy between secondary studies are greater than differences between LLMs. The cost of using an LLM is relatively low - less than $40 per secondary study even for the most expensive model. Conclusions: Our benchmark enables monitoring AI performance in the screening task of SRs in software engineering. At present, LLMs are not yet recommended for automating the title-abstract screening process, since accuracy varies widely across secondary studies, and no LLM managed a high recall with reasonable precision. In future, we plan to investigate factors that influence LLM screening performance between studies.
A Survey of Multimodal Hallucination Evaluation and Detection
2025-07-25
Zhiyuan Chen, Yuecong Min, Jie Zhang, Bei Yan, Jiahao Wang, Xiaozhen Wang, Shiguang Shan
Multi-modal Large Language Models (MLLMs) have emerged as a powerful paradigm for integrating visual and textual information, supporting a wide range of multi-modal tasks. However, these models often suffer from hallucination, producing content that appears plausible but contradicts the input content or established world knowledge. This survey offers an in-depth review of hallucination evaluation benchmarks and detection methods across Image-to-Text (I2T) and Text-to-image (T2I) generation tasks. Specifically, we first propose a taxonomy of hallucination based on faithfulness and factuality, incorporating the common types of hallucinations observed in practice. Then we provide an overview of existing hallucination evaluation benchmarks for both T2I and I2T tasks, highlighting their construction process, evaluation objectives, and employed metrics. Furthermore, we summarize recent advances in hallucination detection methods, which aims to identify hallucinated content at the instance level and serve as a practical complement of benchmark-based evaluation. Finally, we highlight key limitations in current benchmarks and detection methods, and outline potential directions for future research.
MedIQA: A Scalable Foundation Model for Prompt-Driven Medical Image Quality Assessment
2025-07-25
Siyi Xun, Yue Sun, Jingkun Chen, Zitong Yu, Tong Tong, Xiaohong Liu, Mingxiang Wu, Tao Tan
Rapid advances in medical imaging technology underscore the critical need for precise and automated image quality assessment (IQA) to ensure diagnostic accuracy. Existing medical IQA methods, however, struggle to generalize across diverse modalities and clinical scenarios. In response, we introduce MedIQA, the first comprehensive foundation model for medical IQA, designed to handle variability in image dimensions, modalities, anatomical regions, and types. We developed a large-scale multi-modality dataset with plentiful manually annotated quality scores to support this. Our model integrates a salient slice assessment module to focus on diagnostically relevant regions feature retrieval and employs an automatic prompt strategy that aligns upstream physical parameter pre-training with downstream expert annotation fine-tuning. Extensive experiments demonstrate that MedIQA significantly outperforms baselines in multiple downstream tasks, establishing a scalable framework for medical IQA and advancing diagnostic workflows and clinical decision-making.
Infrared Image Super-Resolution (IRSR) is challenged by the low contrast and sparse textures of infrared data, requiring robust long-range modeling to maintain global coherence. While State-Space Models like Mamba offer proficiency in modeling long-range dependencies for this task, their inherent 1D causal scanning mechanism fragments the global context of 2D images, hindering fine-detail restoration. To address this, we propose Global Phase and Spectral Prompt-guided Mamba (GPSMamba), a framework that synergizes architectural guidance with non-causal supervision. First, our Adaptive Semantic-Frequency State Space Module (ASF-SSM) injects a fused semantic-frequency prompt directly into the Mamba block, integrating non-local context to guide reconstruction. Then, a novel Thermal-Spectral Attention and Phase Consistency Loss provides explicit, non-causal supervision to enforce global structural and spectral fidelity. By combining these two innovations, our work presents a systematic strategy to mitigate the limitations of causal modeling. Extensive experiments demonstrate that GPSMamba achieves state-of-the-art performance, validating our approach as a powerful new paradigm for infrared image restoration. Code is available at https://github.com/yongsongH/GPSMamba.
GENIAL: Generative Design Space Exploration via Network Inversion for Low Power Algorithmic Logic Units
2025-07-25
Maxence Bouvier, Ryan Amaudruz, Felix Arnold, Renzo Andri, Lukas Cavigelli
As AI workloads proliferate, optimizing arithmetic units is becoming increasingly important to reduce the footprint of digital systems. Conventional design flows, which often rely on manual or heuristics-based optimization, are limited in their ability to thoroughly explore the vast design space. In this paper, we introduce GENIAL, a machine learning-based framework for the automatic generation and optimization of arithmetic units, more specifically multipliers. At the core of GENIAL is a Transformer-based surrogate model trained in two stages, involving self-supervised pretraining followed by supervised finetuning, to robustly forecast key hardware metrics such as power and area from abstracted design representations. By inverting the surrogate model, GENIAL efficiently searches for new operand encodings that directly minimize power consumption in arithmetic units for specific input data distributions. Extensive experiments on large datasets demonstrate that GENIAL is consistently more sample efficient than other methods, and converges faster towards optimized designs. This enables to deploy a high-effort logic synthesis optimization flow in the loop, improving the accuracy of the surrogate model. Notably, GENIAL automatically discovers encodings that achieve up to 18% switching activity savings within multipliers on representative AI workloads compared with the conventional two's complement. We also demonstrate the versatility of our approach by achieving significant improvements on Finite State Machines, highlighting GENIAL's applicability for a wide spectrum of logic functions. Together, these advances mark a significant step toward automated Quality-of-Results-optimized combinational circuit generation for digital systems.
Legal Document Summarization: Enhancing Judicial Efficiency through Automation Detection
2025-07-25
Yongjie Li, Ruilin Nong, Jianan Liu, Lucas Evans
Legal document summarization represents a significant advancement towards improving judicial efficiency through the automation of key information detection. Our approach leverages state-of-the-art natural language processing techniques to meticulously identify and extract essential data from extensive legal texts, which facilitates a more efficient review process. By employing advanced machine learning algorithms, the framework recognizes underlying patterns within judicial documents to create precise summaries that encapsulate the crucial elements. This automation alleviates the burden on legal professionals, concurrently reducing the likelihood of overlooking vital information that could lead to errors. Through comprehensive experiments conducted with actual legal datasets, we demonstrate the capability of our method to generate high-quality summaries while preserving the integrity of the original content and enhancing processing times considerably. The results reveal marked improvements in operational efficiency, allowing legal practitioners to direct their efforts toward critical analytical and decision-making activities instead of manual reviews. This research highlights promising technology-driven strategies that can significantly alter workflow dynamics within the legal sector, emphasizing the role of automation in refining judicial processes.
Limits at a Distance: Design Directions to Address Psychological Distance in Policy Decisions Affecting Planetary Boundaries
2025-07-25
Eshta Bhardwaj, Han Qiao, Christoph Becker
Policy decisions relevant to the environment rely on tools like dashboards, risk models, and prediction models to provide information and data visualizations that enable decision-makers to make trade-offs. The conventional paradigm of data visualization practices for policy and decision-making is to convey data in a supposedly neutral, objective manner for rational decision-makers. Feminist critique advocates for nuanced and reflexive approaches that take into account situated decision-makers and their affective relationships to data. This paper sheds light on a key cognitive aspect that impacts how decision-makers interpret data. Because all outcomes from policies relevant to climate change occur at a distance, decision-makers experience so-called `psychological distance' to environmental decisions in terms of space, time, social identity, and hypotheticality. This profoundly impacts how they perceive and evaluate outcomes. Since policy decisions to achieve a safe planetary space are urgently needed for immediate transition and change, we need a design practice that takes into account how psychological distance affects cognition and decision-making. Our paper explores the role of alternative design approaches in developing visualizations used for climate policymaking. We conduct a literature review and synthesis which bridges psychological distance with speculative design and data visceralization by illustrating the value of affective design methods via examples from previous research. Through this work, we propose a novel premise for the communication and visualization of environmental data. Our paper lays out how future research on the impacts of alternative design approaches on psychological distance can make data used for policy decisions more tangible and visceral.
Retrieval-Augmented Generation (RAG) represents a major advancement in natural language processing (NLP), combining large language models (LLMs) with information retrieval systems to enhance factual grounding, accuracy, and contextual relevance. This paper presents a comprehensive systematic review of RAG, tracing its evolution from early developments in open domain question answering to recent state-of-the-art implementations across diverse applications. The review begins by outlining the motivations behind RAG, particularly its ability to mitigate hallucinations and outdated knowledge in parametric models. Core technical components-retrieval mechanisms, sequence-to-sequence generation models, and fusion strategies are examined in detail. A year-by-year analysis highlights key milestones and research trends, providing insight into RAG's rapid growth. The paper further explores the deployment of RAG in enterprise systems, addressing practical challenges related to retrieval of proprietary data, security, and scalability. A comparative evaluation of RAG implementations is conducted, benchmarking performance on retrieval accuracy, generation fluency, latency, and computational efficiency. Persistent challenges such as retrieval quality, privacy concerns, and integration overhead are critically assessed. Finally, the review highlights emerging solutions, including hybrid retrieval approaches, privacy-preserving techniques, optimized fusion strategies, and agentic RAG architectures. These innovations point toward a future of more reliable, efficient, and context-aware knowledge-intensive NLP systems.
We have developed a novel tracking detector utilizing a water-based liquid scintillator (WbLS) for the accurate characterization of neutrino interactions on a water target. In this detector, the WbLS is optically segmented into small cells by reflective separators, and the scintillation light is read out in three directions using wavelength-shifting fibers coupled to silicon photomultipliers. We developed and optimized WbLS samples for this application and measured their light yield using cosmic-ray muons. Subsequently, we constructed a prototype of the WbLS tracking detector and evaluated its performance with a positron beam. The beam test demonstrated good tracking performance, although the light yield was lower than required. The result prompted a review of the surfactant used in the WbLS and the material of the optical separators, leading to a significant improvement in light yield. In this paper, we report on a design of the WbLS tracking detector, the development of the WbLS, the results of the beam test, and subsequent improvements to the WbLS and optical separators.
AI-based Intelligent Tutoring Systems (ITS) have significant potential to transform teaching and learning. As efforts continue to design, develop, and integrate ITS into educational contexts, mixed results about their effectiveness have emerged. This paper provides a comprehensive review to understand how ITS operate in real educational settings and to identify the associated challenges in their application and evaluation. We use a systematic literature review method to analyze numerous qualified studies published from 2010 to 2025, examining domains such as pedagogical strategies, NLP, adaptive learning, student modeling, and domain-specific applications of ITS. The results reveal a complex landscape regarding the effectiveness of ITS, highlighting both advancements and persistent challenges. The study also identifies a need for greater scientific rigor in experimental design and data analysis. Based on these findings, suggestions for future research and practical implications are proposed.
Since a building's floorplans are easily accessible, consistent over time, and inherently robust to changes in visual appearance, self-localization within the floorplan has attracted researchers' interest. However, since floorplans are minimalist representations of a building's structure, modal and geometric differences between visual perceptions and floorplans pose challenges to this task. While existing methods cleverly utilize 2D geometric features and pose filters to achieve promising performance, they fail to address the localization errors caused by frequent visual changes and view occlusions due to variously shaped 3D objects. To tackle these issues, this paper views the 2D Floorplan Localization (FLoc) problem from a higher dimension by injecting 3D geometric priors into the visual FLoc algorithm. For the 3D geometric prior modeling, we first model geometrically aware view invariance using multi-view constraints, i.e., leveraging imaging geometric principles to provide matching constraints between multiple images that see the same points. Then, we further model the view-scene aligned geometric priors, enhancing the cross-modal geometry-color correspondences by associating the scene's surface reconstruction with the RGB frames of the sequence. Both 3D priors are modeled through self-supervised contrastive learning, thus no additional geometric or semantic annotations are required. These 3D priors summarized in extensive realistic scenes bridge the modal gap while improving localization success without increasing the computational burden on the FLoc algorithm. Sufficient comparative studies demonstrate that our method significantly outperforms state-of-the-art methods and substantially boosts the FLoc accuracy. All data and code will be released after the anonymous review.
MINERVA: A NIRCam Medium Band and MIRI Imaging Survey to Unlock the Hidden Gems of the Distant Universe
2025-07-25
Adam Muzzin, Katherine A. Suess, Danilo Marchesini, Luke Robbins, Chris J. Willott, Stacey Alberts, Jacqueline Antwi-Danso, Yoshihisa Asada, Gabriel Brammer, Sam E. Cutler, Kartheik G. Iyer, Ivo Labbe, Nicholas S. Martis, Tim B. Miller, Ikki Mitsuhashi, Alexandra Pope, Anna Sajina, Ghassan T. E. Sarrouh, Monu Sharma, Mauro Stefanon, Katherine E. Whitaker, Roberto Abraham, Hakim Atek, Marusa Bradac, Samantha Berek, Rachel Bezanson, Westley Brown, Adam J. Burgasser, Nathalie Chicoine, Aidan P. Cloonan, Olivia R. Cooper, Pratika Dayal, Anna de Graaff, Guillaume Desprez, Robert Feldmann, Ben Forrest, Marijn Franx, Yoshinobu Fudamoto, Seiji Fujimoto, Lukas J. Furtak, Karl Glazebrook, Ilias Goovaerts, Jenny E. Greene, Naadiyah Jagga, William W. H. Jarvis, Mariska Kriek, Gourav Khullar, Valentina La Torre, Joel Leja, Jamie Lin, Brian Lorenz, Daniel Lyon, Vladan Markov, Michael V. Maseda, Ian McConachie, Maya Merchant, Rosa M. Merida, Lamiya Mowla, Katherine Myers, Rohan P. Naidu, Themiya Nanayakkara, Erica J. Nelson, Gael Noirot, Pascal A. Oesch, Kiyoaki C. Omori, Richard Pan, Natalia Porraz Barrera, Sedona H. Price, Swara Ravindranath, Marcin Sawicki, David J. Setton, Renske Smit, Visal Sok, Joshua S. Speagle, Edward N. Taylor, Vivian Yun Yan Tan, Roberta Tripodi, Arjen van der Wel, Edgar Perez Vidal, Bingjie Wang, John R. Weaver, Christina C. Williams, Sunna Withers, Kumail Zaidi
We present an overview of the MINERVA survey, a 259.8 hour (prime) and 127 hour (parallel) Cycle 4 treasury program on the James Webb Space Telescope (JWST). MINERVA is obtaining 8 filter NIRCam medium band imaging (F140M, F162M, F182M, F210M, F250M, F300M, F360M, F460M) and 2 filter MIRI imaging (F1280W, F1500W) in four of the five CANDELS Extragalactic fields: UDS, COSMOS, AEGIS and GOODS-N. These fields were previously observed in Cycle 1 with 7 - 9 NIRCam filters by the PRIMER, CEERS and JADES programs. MINERVA reaches a 5$\sigma$ depth of 28.1 mag in F300M and covers $\sim$ 542 arcmin$^2$, increasing the area of existing JWST medium-band coverage in at least 8 bands by $\sim$ 7$\times$. The MIRI imaging reaches a 5$\sigma$ depth of 23.9 mag in F1280W and covers $\sim$ 275 arcmin$^2$ in at least 2 MIRI filters. When combined with existing imaging, these data will provide a photometric catalog with 20-26 JWST filters (depending on field) and 26-35 filters total, including HST. This paper presents a detailed breakdown of the filter coverage, exposure times, and field layout relative to previous observations, as well as an overview of the primary science goals of the project. These include uncovering the physics of enigmatic sources hiding in current broadband catalogs, improving systematics on stellar mass functions and number densities by factors of $\gtrsim$ 3, and resolved mapping of stellar mass and star formation at 1 $< z <$ 6. When complete, MINERVA will become an integral part of the treasury deep field imaging datasets, significantly improving population studies with well-understood completeness, robust photometric redshifts, stellar masses, and sizes, and facilitating spectroscopic follow up for decades to come.
Due to the remarkable capabilities and growing impact of large language models (LLMs), they have been deeply integrated into many aspects of society. Thus, ensuring their alignment with human values and intentions has emerged as a critical challenge. This survey provides a comprehensive overview of practical alignment techniques, training protocols, and empirical findings in LLM alignment. We analyze the development of alignment methods across diverse paradigms, characterizing the fundamental trade-offs between core alignment objectives. Our analysis shows that while supervised fine-tuning enables basic instruction-following, preference-based methods offer more flexibility for aligning with nuanced human intent. We discuss state-of-the-art techniques, including Direct Preference Optimization (DPO), Constitutional AI, brain-inspired methods, and alignment uncertainty quantification (AUQ), highlighting their approaches to balancing quality and efficiency. We review existing evaluation frameworks and benchmarking datasets, emphasizing limitations such as reward misspecification, distributional robustness, and scalable oversight. We summarize strategies adopted by leading AI labs to illustrate the current state of practice. We conclude by outlining open problems in oversight, value pluralism, robustness, and continuous alignment. This survey aims to inform both researchers and practitioners navigating the evolving landscape of LLM alignment.
"X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systems
2025-07-25
Beining Wu, Jun Huang, Shui Yu
The development of next-generation networking systems has inherently shifted from throughput-based paradigms towards intelligent, information-aware designs that emphasize the quality, relevance, and utility of transmitted information, rather than sheer data volume. While classical network metrics, such as latency and packet loss, remain significant, they are insufficient to quantify the nuanced information quality requirements of modern intelligent applications, including autonomous vehicles, digital twins, and metaverse environments. In this survey, we present the first comprehensive study of the ``X of Information'' continuum by introducing a systematic four-dimensional taxonomic framework that structures information metrics along temporal, quality/utility, reliability/robustness, and network/communication dimensions. We uncover the increasing interdependencies among these dimensions, whereby temporal freshness triggers quality evaluation, which in turn helps with reliability appraisal, ultimately enabling effective network delivery. Our analysis reveals that artificial intelligence technologies, such as deep reinforcement learning, multi-agent systems, and neural optimization models, enable adaptive, context-aware optimization of competing information quality objectives. In our extensive study of six critical application domains, covering autonomous transportation, industrial IoT, healthcare digital twins, UAV communications, LLM ecosystems, and metaverse settings, we illustrate the revolutionary promise of multi-dimensional information metrics for meeting diverse operational needs. Our survey identifies prominent implementation challenges, including ...
A Catalog of Galactic Supernova Remnants and Supernova Remnant Candidates from the EMU/POSSUM Radio Sky Surveys. I
2025-07-25
B. D. Ball, R. Kothes, E. Rosolowsky, C. Burger-Scheidlin, M. D. Filipović, S. Lazarević, Z. J. Smeaton, W. Becker, E. Carretti, B. M. Gaensler, A. M. Hopkins, D. Leahy, M. Tahani, J. L. West, C. S. Anderson, S. Loru, Y. K. Ma, N. M. McClure-Griffiths, M. J. Michałowski
We use data from the EMU (Evolutionary Map of the Universe) and POSSUM (Polarization Sky Survey of the Universe's Magnetism) radio southern sky surveys, conducted with the Australian Square Kilometre Array Pathfinder (ASKAP), to compile a catalogue of Galactic supernova remnants (SNRs) and candidate SNRs within the region of $277.5^\circ \leq \ell \leq 311.7^\circ$ Galactic longitude, $|b| \leq 5.4^\circ$ Galactic latitude, as well as an additional field along the Galactic plane, approximately $315.5^\circ \leq \ell \leq 323.0^\circ$ Galactic longitude, $-4.5^\circ \leq b \leq 1.5^\circ$ Galactic latitude. In the areas studied, there are 44 known SNRs and 46 SNR candidates that have been previously identified in the radio. We confirm eight of these candidates as SNRs based on evidence of linear polarization or through the calculation of nonthermal spectral indices. Additionally, we identify possible radio counterparts for seven SNR candidates that were previously only identified in X-rays (four) or optical (three). We also present six new SNRs and 37 new SNR candidates. The results of this study demonstrate the utility of ASKAP for discovering new and potential SNRs and refining the classification of previously identified candidates. In particular, we find that the EMU and POSSUM surveys are particularly well suited for observing high-latitude SNRs and confirming SNR candidates with polarization. The region studied in this work represents approximately one-quarter of the Galactic plane, by longitude, that will eventually be surveyed by EMU/POSSUM and we expect that the ongoing surveys will continue to uncover new SNRs and SNR candidates.
Bound dark energy: Particle origin of dark energy with DESI BAO and DES supernova data
2025-07-25
Jose Agustin Lozano Torres, Axel de la Macorra
The recent findings from the Dark Energy Spectroscopic Instrument (DESI) indicate a preference for dynamical dark energy at a significance level above $2.5 \sigma$, with baryon acoustic oscillation (BAO) combined with cosmic microwave background (CMB) data and Type Ia supernovae (SNe) data, favoring a time dependent equation of state $w(z)$ rather than the cosmological constant ($w = -1$). We introduce the Bound Dark Energy (BDE) model, in which dark energy arises from the lightest meson field $\phi$ in a dark SU(3) gauge group, developing dynamically through non perturbative interactions. Governed by an inverse power law potential $V(\phi) = \Lambda_c^{4+2/3}\phi^{-2/3}$, BDE features no dark energy free parameters: one less than $\Lambda$CDM ($\Lambda$) and three less than the $ w_{0}w_{a} $CDM ($ w_{0}, w_{a},\Lambda $) models. By integrating DESI BAO measurements, CMB data and Dark Energy Survey SN Ia distance data collected during the fifth year, BDE demonstrates a reduction of $42$ and $37$ percent in the reduced $\chi^{2}_{BAO}$ as well as lower AIC and BIC values compared to the $w_{0}w_{a}$CDM and $\Lambda$CDM models, respectively, while maintaining a comparable fit for both type Ia supernovae and the cosmic microwave background data. Although the ($w_{0},w_{a}$) contour in BDE is 10,000 times smaller than that found in the $w_{0}w_{a}$CDM model, the BDE model suggests a dynamical dark energy scenario with precise values of $w_{0}=$-0.9301 $\pm$ 0.0004 and $w_{a}=$ -0.8085 $\pm$ 0.0053 while providing a consistency on the six Planck and derived parameters at the 1$\sigma$ level between BDE, $\Lambda$CDM and $w_{0}w_{a}$CDM models. The critical parameters condensation energy scale $\Lambda_{c} = 43.806 \pm 0.190$ eV and epoch $a_{c} = (2.497 \pm 0.011) \times 10^{-6}$ are consistent with predictions from high energy physics.
Beyond Bonferroni: Hierarchical Multiple Testing in Empirical Research
2025-07-25
Sebastian Calonico, Sebastian Galiani
Empirical research in the social and medical sciences frequently involves testing multiple hypotheses simultaneously, increasing the risk of false positives due to chance. Classical multiple testing procedures, such as the Bonferroni correction, control the family-wise error rate (FWER) but tend to be overly conservative, reducing statistical power. Stepwise alternatives like the Holm and Hochberg procedures offer improved power while maintaining error control under certain dependence structures. However, these standard approaches typically ignore hierarchical relationships among hypotheses -- structures that are common in settings such as clinical trials and program evaluations, where outcomes are often logically or causally linked. Hierarchical multiple testing procedures -- including fixed sequence, fallback, and gatekeeping methods -- explicitly incorporate these relationships, providing more powerful and interpretable frameworks for inference. This paper reviews key hierarchical methods, compares their statistical properties and practical trade-offs, and discusses implications for applied empirical research.
Geometric Representation of Generalized Coherent States and their Symplectic Capacities: A Synthetic Approach
2025-07-25
Maurice de Gosson
In this work we review, complete, and synthesize results linking generalized coherent stages (nondegradable Gaussian wavefunctions) to the notions of Fermi ellipsoids, quantum blobs, and microlocal pairs introduced in previous work. These geometric objects are Fermi ellipsoids, quantum blobs, and microlocal pairs. In addition we study various symplectic capacities associated with these objects.
Transformer-based architectures have become the prevailing backbone of large language models. However, the quadratic time and memory complexity of self-attention remains a fundamental obstacle to efficient long-context modeling. To address this limitation, recent research has introduced two principal categories of efficient attention mechanisms. Linear attention methods achieve linear complexity through kernel approximations, recurrent formulations, or fastweight dynamics, thereby enabling scalable inference with reduced computational overhead. Sparse attention techniques, in contrast, limit attention computation to selected subsets of tokens based on fixed patterns, block-wise routing, or clustering strategies, enhancing efficiency while preserving contextual coverage. This survey provides a systematic and comprehensive overview of these developments, integrating both algorithmic innovations and hardware-level considerations. In addition, we analyze the incorporation of efficient attention into largescale pre-trained language models, including both architectures built entirely on efficient attention and hybrid designs that combine local and global components. By aligning theoretical foundations with practical deployment strategies, this work aims to serve as a foundational reference for advancing the design of scalable and efficient language models.
Classical game-theoretic models typically assume rational agents, complete information, and common knowledge of payoffs - assumptions that are often violated in real-world MAS characterized by uncertainty, misaligned perceptions, and nested beliefs. To overcome these limitations, researchers have proposed extensions that incorporate models of cognitive constraints, subjective beliefs, and heterogeneous reasoning. Among these, hypergame theory extends the classical paradigm by explicitly modeling agents' subjective perceptions of the strategic scenario, known as perceptual games, in which agents may hold divergent beliefs about the structure, payoffs, or available actions. We present a systematic review of agent-compatible applications of hypergame theory, examining how its descriptive capabilities have been adapted to dynamic and interactive MAS contexts. We analyze 44 selected studies from cybersecurity, robotics, social simulation, communications, and general game-theoretic modeling. Building on a formal introduction to hypergame theory and its two major extensions - hierarchical hypergames and HNF - we develop agent-compatibility criteria and an agent-based classification framework to assess integration patterns and practical applicability. Our analysis reveals prevailing tendencies, including the prevalence of hierarchical and graph-based models in deceptive reasoning and the simplification of extensive theoretical frameworks in practical applications. We identify structural gaps, including the limited adoption of HNF-based models, the lack of formal hypergame languages, and unexplored opportunities for modeling human-agent and agent-agent misalignment. By synthesizing trends, challenges, and open research directions, this review provides a new roadmap for applying hypergame theory to enhance the realism and effectiveness of strategic modeling in dynamic multi-agent environments.
First Temperature Profile of a Stellar Flare using Differential Chromatic Refraction
2025-07-25
Riley Clarke, Federica Bianco, James R. A. Davenport, Jeffery Cooke, Sara Webb, Igor Andreoni, Tyler Pritchard, Aaron Roodman
We present the first derivation of a stellar flare temperature profile from single-band photometry. Stellar flare DWF030225.574-545707.45129 was detected in 2015 by the Dark Energy Camera as part of the Deeper, Wider, Faster Programme. The brightness ($\Delta m_g = -6.12$) of this flare, combined with the high air mass ($1.45 \lesssim X \lesssim 1.75$) and blue filter (DES $g$, 398-548 nm) in which it was observed, provided ideal conditions to measure the zenith-ward apparent motion of the source due to differential chromatic refraction (DCR) and, from that, infer the effective temperature of the event. We model the flare's spectral energy distribution as a blackbody to produce the constraints on flare temperature and geometric properties derived from single-band photometry. We additionally demonstrate how simplistic assumptions on the flaring spectrum, as well as on the evolution of flare geometry, can result in solutions that overestimate effective temperature. Exploiting DCR enables studying chromatic phenomena with ground-based astrophysical surveys and stellar flares on M-dwarfs are a particularly enticing target for such studies due to their ubiquity across the sky, and the heightened color contrast between their red quiescent photospheres and the blue flare emission. Our novel method will enable similar temperature constraints for large sample of objects in upcoming photometric surveys like the Vera C. Rubin Legacy Survey of Space and Time.
All experimental evidence {indicates} that the vacuum is not void, but filled with something truly quantum. This is reflected by terms such as {zero-point} fluctuations, and Dirac's sea of virtual particle-antiparticle pairs, and last but not least the vacuum is the medium responsible for Maxwell's displacement current. While quantum electrodynamics (QED) is an exceptionally successful theory, it remains a perturbative framework rather than a fully self-contained one. Inherently, it includes singularities and divergences, which prevent the precise calculation of fundamental quantities such as the fine-structure constant $\alpha$. Any direct attempt to compute $\alpha$ results in divergent values. However, and most remarkable, what can be determined is how $\alpha$ ``runs", meaning how it varies with energy or exchanged momentum. In this article, we review the historical development of these ideas, the current state of knowledge, and ongoing efforts to find ways to move further. This includes a simple model to describe vacuum polarization in the low-energy regime, when considering only small (linear) deviations from the equilibrium {state}, relating {Maxwell's displacement} in the vacuum, to the quantum properties of the vacuum.
Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery
2025-07-25
You Wu, Philip E. Bourne, Lei Xie
Artificial intelligence (AI) has sparked immense interest in drug discovery, but most current approaches only digitize existing high-throughput experiments. They remain constrained by conventional pipelines. As a result, they do not address the fundamental challenges of predicting drug effects in humans. Similarly, biomedical digital twins, largely grounded in real-world data and mechanistic models, are tailored for late-phase drug development and lack the resolution to model molecular interactions or their systemic consequences, limiting their impact in early-stage discovery. This disconnect between early discovery and late development is one of the main drivers of high failure rates in drug discovery. The true promise of AI lies not in augmenting current experiments but in enabling virtual experiments that are impossible in the real world: testing novel compounds directly in silico in the human body. Recent advances in AI, high-throughput perturbation assays, and single-cell and spatial omics across species now make it possible to construct programmable virtual humans: dynamic, multiscale models that simulate drug actions from molecular to phenotypic levels. By bridging the translational gap, programmable virtual humans offer a transformative path to optimize therapeutic efficacy and safety earlier than ever before. This perspective introduces the concept of programmable virtual humans, explores their roles in a new paradigm of drug discovery centered on human physiology, and outlines key opportunities, challenges, and roadmaps for their realization.
Review of Deep Learning Applications to Structural Proteomics Enabled by Cryogenic Electron Microscopy and Tomography
2025-07-25
Brady K. Zhou, Jason J. Hu, Jane K. J. Lee, Z. Hong Zhou, Demetri Terzopoulos
The past decade's "cryoEM revolution" has produced exponential growth in high-resolution structural data through advances in cryogenic electron microscopy (cryoEM) and tomography (cryoET). Deep learning integration into structural proteomics workflows addresses longstanding challenges including low signal-to-noise ratios, preferred orientation artifacts, and missing-wedge problems that historically limited efficiency and scalability. This review examines AI applications across the entire cryoEM pipeline, from automated particle picking using convolutional neural networks (Topaz, crYOLO, CryoSegNet) to computational solutions for preferred orientation bias (spIsoNet, cryoPROS) and advanced denoising algorithms (Topaz-Denoise). In cryoET, tools like IsoNet employ U-Net architectures for simultaneous missing-wedge correction and noise reduction, while TomoNet streamlines subtomogram averaging through AI-driven particle detection. The workflow culminates with automated atomic model building using sophisticated tools like ModelAngelo, DeepTracer, and CryoREAD that translate density maps into interpretable biological structures. These AI-enhanced approaches have achieved near-atomic resolution reconstructions with minimal manual intervention, resolved previously intractable datasets suffering from severe orientation bias, and enabled successful application to diverse biological systems from HIV virus-like particles to in situ ribosomal complexes. As deep learning evolves, particularly with large language models and vision transformers, the future promises sophisticated automation and accessibility in structural biology, potentially revolutionizing our understanding of macromolecular architecture and function.
Harnessing intuitive local evolution rules for physical learning
2025-07-25
Roie Ezraty, Menachem Stern, Shmuel M. Rubinstein
Machine Learning, however popular and accessible, is computationally intensive and highly power-consuming, prompting interest in alternative physical implementations of learning tasks. We introduce a training scheme for physical systems that minimize power dissipation in which only boundary parameters (i.e. inputs and outputs) are externally controlled. Using this scheme, these Boundary-Enabled Adaptive State Tuning Systems (BEASTS) learn by exploiting local physical rules. Our scheme, BEASTAL (BEAST-Adaline), is the closest analog of the Adaline algorithm for such systems. We demonstrate this autonomous learning in silico for regression and classification tasks. Our approach advances previous physical learning schemes by using intuitive, local evolution rules without requiring large-scale memory or complex internal architectures. BEASTAL can perform any linear task, achieving best performance when the local evolution rule is non-linear.
2025-07-25
Lakpa Tamang, Mohamed Reda Bouadjenek, Richard Dazeley, Sunil Aryal
In the field of Machine Learning (ML) and data-driven applications, one of the significant challenge is the change in data distribution between the training and deployment stages, commonly known as distribution shift. This paper outlines different mechanisms for handling two main types of distribution shifts: (i) Covariate shift: where the value of features or covariates change between train and test data, and (ii) Concept/Semantic-shift: where model experiences shift in the concept learned during training due to emergence of novel classes in the test phase. We sum up our contributions in three folds. First, we formalize distribution shifts, recite on how the conventional method fails to handle them adequately and urge for a model that can simultaneously perform better in all types of distribution shifts. Second, we discuss why handling distribution shifts is important and provide an extensive review of the methods and techniques that have been developed to detect, measure, and mitigate the effects of these shifts. Third, we discuss the current state of distribution shift handling mechanisms and propose future research directions in this area. Overall, we provide a retrospective synopsis of the literature in the distribution shift, focusing on OOD data that had been overlooked in the existing surveys.
Acquiring high-quality instruction-code pairs is essential for training Large Language Models (LLMs) for code generation. Manually curated data is expensive and inherently limited in scale, motivating the development of code-centric synthesis methods. Yet, current approaches either focus on augmenting existing code or rely on predefined heuristics, both lacking rigorous data validation, which results in synthetic data that is ungrounded, repetitive, or overly simplistic. Inspired by collaborative programming practices, we propose CodeEvo, a framework that synthesizes code data through iterative interactions between two LLM agents: a Coder, which generates candidate code and test cases based on given instructions, and a Reviewer, which guides the synthesis process by producing new instructions and feedback. We further introduce a hybrid feedback mechanism that combines compiler determinism with the generative flexibility of agents, enabling automatic quality control throughout synthesis. Extensive experiments demonstrate that models fine-tuned on CodeEvo data significantly outperform established baselines across code generation benchmarks with various difficulties. In-depth analyses further provide insights from multiple perspectives into effective code-centric data synthesis.
2025-07-24
Junfei Xiao, Ceyuan Yang, Lvmin Zhang, Shengqu Cai, Yang Zhao, Yuwei Guo, Gordon Wetzstein, Maneesh Agrawala, Alan Yuille, Lu Jiang
We present Captain Cinema, a generation framework for short movie generation. Given a detailed textual description of a movie storyline, our approach firstly generates a sequence of keyframes that outline the entire narrative, which ensures long-range coherence in both the storyline and visual appearance (e.g., scenes and characters). We refer to this step as top-down keyframe planning. These keyframes then serve as conditioning signals for a video synthesis model, which supports long context learning, to produce the spatio-temporal dynamics between them. This step is referred to as bottom-up video synthesis. To support stable and efficient generation of multi-scene long narrative cinematic works, we introduce an interleaved training strategy for Multimodal Diffusion Transformers (MM-DiT), specifically adapted for long-context video data. Our model is trained on a specially curated cinematic dataset consisting of interleaved data pairs. Our experiments demonstrate that Captain Cinema performs favorably in the automated creation of visually coherent and narrative consistent short movies in high quality and efficiency. Project page: https://thecinema.ai
Synthetic Data Augmentation for Enhanced Chicken Carcass Instance Segmentation
2025-07-24
Yihong Feng, Chaitanya Pallerla, Xiaomin Lin, Pouya Sohrabipour Sr, Philip Crandall, Wan Shou, Yu She, Dongyi Wang
The poultry industry has been driven by broiler chicken production and has grown into the world's largest animal protein sector. Automated detection of chicken carcasses on processing lines is vital for quality control, food safety, and operational efficiency in slaughterhouses and poultry processing plants. However, developing robust deep learning models for tasks like instance segmentation in these fast-paced industrial environments is often hampered by the need for laborious acquisition and annotation of large-scale real-world image datasets. We present the first pipeline generating photo-realistic, automatically labeled synthetic images of chicken carcasses. We also introduce a new benchmark dataset containing 300 annotated real-world images, curated specifically for poultry segmentation research. Using these datasets, this study investigates the efficacy of synthetic data and automatic data annotation to enhance the instance segmentation of chicken carcasses, particularly when real annotated data from the processing line is scarce. A small real dataset with varying proportions of synthetic images was evaluated in prominent instance segmentation models. Results show that synthetic data significantly boosts segmentation performance for chicken carcasses across all models. This research underscores the value of synthetic data augmentation as a viable and effective strategy to mitigate data scarcity, reduce manual annotation efforts, and advance the development of robust AI-driven automated detection systems for chicken carcasses in the poultry processing industry.
Deep Learning for Blood-Brain Barrier Permeability Prediction
2025-07-24
Zihan Yang
Predicting whether a molecule can cross the blood-brain barrier (BBB) is a key step in early-stage neuropharmaceutical development, directly influencing both research efficiency and success rates in drug discovery. Traditional empirical methods based on physicochemical properties are prone to systematic misjudgements due to their reliance on static rules. Early machine learning models, although data-driven, often suffer from limited capacity, poor generalization, and insufficient interpretability. In recent years, artificial intelligence (AI) methods have become essential tools for predicting BBB permeability and guiding related drug design, owing to their ability to model molecular structures and capture complex biological mechanisms. This article systematically reviews the evolution of this field-from deep neural networks to graph-based structural modeling-highlighting the advantages of multi-task and multimodal learning strategies in identifying mechanism-relevant variables. We further explore the emerging potential of generative models and causal inference methods for integrating permeability prediction with mechanism-aware drug design. BBB modeling is in the transition from static classification toward mechanistic perception and structure-function modeling. This paradigm shift provides a methodological foundation and future roadmap for the integration of AI into neuropharmacological development.
A 3D Cross-modal Keypoint Descriptor for MR-US Matching and Registration
2025-07-24
Daniil Morozov, Reuben Dorent, Nazim Haouchine
Intraoperative registration of real-time ultrasound (iUS) to preoperative Magnetic Resonance Imaging (MRI) remains an unsolved problem due to severe modality-specific differences in appearance, resolution, and field-of-view. To address this, we propose a novel 3D cross-modal keypoint descriptor for MRI-iUS matching and registration. Our approach employs a patient-specific matching-by-synthesis approach, generating synthetic iUS volumes from preoperative MRI. This enables supervised contrastive training to learn a shared descriptor space. A probabilistic keypoint detection strategy is then employed to identify anatomically salient and modality-consistent locations. During training, a curriculum-based triplet loss with dynamic hard negative mining is used to learn descriptors that are i) robust to iUS artifacts such as speckle noise and limited coverage, and ii) rotation-invariant . At inference, the method detects keypoints in MR and real iUS images and identifies sparse matches, which are then used to perform rigid registration. Our approach is evaluated using 3D MRI-iUS pairs from the ReMIND dataset. Experiments show that our approach outperforms state-of-the-art keypoint matching methods across 11 patients, with an average precision of $69.8\%$. For image registration, our method achieves a competitive mean Target Registration Error of 2.39 mm on the ReMIND2Reg benchmark. Compared to existing iUS-MR registration approach, our framework is interpretable, requires no manual initialization, and shows robustness to iUS field-of-view variation. Code is available at https://github.com/morozovdd/CrossKEY.
AI/ML Life Cycle Management for Interoperable AI Native RAN
2025-07-24
Chu-Hsiang Huang, Chao-Kai Wen, Geoffrey Ye Li
Artificial intelligence (AI) and machine learning (ML) models are rapidly permeating the 5G Radio Access Network (RAN), powering beam management, channel state information (CSI) feedback, positioning, and mobility prediction. However, without a standardized life-cycle management (LCM) framework, challenges, such as model drift, vendor lock-in, and limited transparency, hinder large-scale adoption. 3GPP Releases 16-20 progressively evolve AI/ML from experimental features to managed, interoperable network functions. Beginning with the Network Data Analytics Function (NWDAF) in Rel-16, subsequent releases introduced standardized interfaces for model transfer, execution, performance monitoring, and closed-loop control, culminating in Rel-20's two-sided CSI-compression Work Item and vendor-agnostic LCM profile. This article reviews the resulting five-block LCM architecture, KPI-driven monitoring mechanisms, and inter-vendor collaboration schemes, while identifying open challenges in resource-efficient monitoring, environment drift detection, intelligent decision-making, and flexible model training. These developments lay the foundation for AI-native transceivers as a key enabler for 6G.
Code completion, a crucial task in software engineering that enhances developer productivity, has seen substantial improvements with the rapid advancement of large language models (LLMs). In recent years, retrieval-augmented generation (RAG) has emerged as a promising method to enhance the code completion capabilities of LLMs, which leverages relevant context from codebases without requiring model retraining. While existing studies have demonstrated the effectiveness of RAG on public repositories and benchmarks, the potential distribution shift between open-source and closed-source codebases presents unique challenges that remain unexplored. To mitigate the gap, we conduct an empirical study to investigate the performance of widely-used RAG methods for code completion in the industrial-scale codebase of WeChat, one of the largest proprietary software systems. Specifically, we extensively explore two main types of RAG methods, namely identifier-based RAG and similarity-based RAG, across 26 open-source LLMs ranging from 0.5B to 671B parameters. For a more comprehensive analysis, we employ different retrieval techniques for similarity-based RAG, including lexical and semantic retrieval. Based on 1,669 internal repositories, we achieve several key findings: (1) both RAG methods demonstrate effectiveness in closed-source repositories, with similarity-based RAG showing superior performance, (2) the effectiveness of similarity-based RAG improves with more advanced retrieval techniques, where BM25 (lexical retrieval) and GTE-Qwen (semantic retrieval) achieve superior performance, and (3) the combination of lexical and semantic retrieval techniques yields optimal results, demonstrating complementary strengths. Furthermore, we conduct a developer survey to validate the practical utility of RAG methods in real-world development environments.
Automated Code Review Using Large Language Models with Symbolic Reasoning
2025-07-24
Busra Icoz, Goksel Biricik
Code review is one of the key processes in the software development lifecycle and is essential to maintain code quality. However, manual code review is subjective and time consuming. Given its rule-based nature, code review is well suited for automation. In recent years, significant efforts have been made to automate this process with the help of artificial intelligence. Recent developments in Large Language Models (LLMs) have also emerged as a promising tool in this area, but these models often lack the logical reasoning capabilities needed to fully understand and evaluate code. To overcome this limitation, this study proposes a hybrid approach that integrates symbolic reasoning techniques with LLMs to automate the code review process. We tested our approach using the CodexGlue dataset, comparing several models, including CodeT5, CodeBERT, and GraphCodeBERT, to assess the effectiveness of combining symbolic reasoning and prompting techniques with LLMs. Our results show that this approach improves the accuracy and efficiency of automated code review.
Einstein metrics, their moduli spaces and stability
2025-07-24
Paul Schwahn, Uwe Semmelmann
This survey deals with two closely connected topics: first, the stability of Einstein metrics under the Einstein-Hilbert functional, and second, their deformation theory and the study of the moduli space of Einstein metrics on a compact manifold. To first order, both problems reduce to studying the spectrum and eigentensors of the Lichnerowicz Laplacian. We give an introduction to the classical theory and survey recent results and advances.
Generating clinical synthetic text represents an effective solution for common clinical NLP issues like sparsity and privacy. This paper aims to conduct a systematic review on generating synthetic medical free-text by formulating quantitative analysis to three research questions concerning (i) the purpose of generation, (ii) the techniques, and (iii) the evaluation methods. We searched PubMed, ScienceDirect, Web of Science, Scopus, IEEE, Google Scholar, and arXiv databases for publications associated with generating synthetic medical unstructured free-text. We have identified 94 relevant articles out of 1,398 collected ones. A great deal of attention has been given to the generation of synthetic medical text from 2018 onwards, where the main purpose of such a generation is towards text augmentation, assistive writing, corpus building, privacy-preserving, annotation, and usefulness. Transformer architectures were the main predominant technique used to generate the text, especially the GPTs. On the other hand, there were four main aspects of evaluation, including similarity, privacy, structure, and utility, where utility was the most frequent method used to assess the generated synthetic medical text. Although the generated synthetic medical text demonstrated a moderate possibility to act as real medical documents in different downstream NLP tasks, it has proven to be a great asset as augmented, complementary to the real documents, towards improving the accuracy and overcoming sparsity/undersampling issues. Yet, privacy is still a major issue behind generating synthetic medical text, where more human assessments are needed to check for the existence of any sensitive information. Despite that, advances in generating synthetic medical text will considerably accelerate the adoption of workflows and pipeline development, discarding the time-consuming legalities of data transfer.
On MAP estimates and source conditions for drift identification in SDEs
2025-07-24
Daniel Tenbrinck, Nikolas Uesseler, Philipp Wacker, Benedikt Wirth
We consider the inverse problem of identifying the drift in an SDE from $n$ observations of its solution at $M+1$ distinct time points. We derive a corresponding MAP estimate, we prove differentiability properties as well as a so-called tangential cone condition for the forward operator, and we review the existing theory for related problems, which under a slightly stronger tangential cone condition would additionally yield convergence rates for the MAP estimate as $n\to\infty$. Numerical simulations in 1D indicate that such convergence rates indeed hold true.
An integrated theoretical, experimental, and numerical study of small-amplitude water waves
2025-07-24
Lennon Ó Náraigh, Nicolas Farault, Nicola Young
We introduce an inexpensive experimental setup for analyzing free-surface water waves: a $1\,\mathrm{m}$-long tabletop flume made from perspex, driven by a variable-frequency piston wavemaker built from Lego. Using mobile-phone video capture, we collect experimental data and compare it with predictions from linear gravity-capillary wave theory and with multiphase simulations performed in OpenFOAM. We find excellent quantitative agreement across all three approaches. Our setup may be valuable for students with a background in Mathematical Modelling who lack hands-on laboratory experience. To explore this, we report on a survey of students who completed an integrated theoretical, experimental, and computational project. While students found the experience enhanced their learning of Fluid Mechanics, they also noted the need for better support in setting up and running CFD simulations.
With the global population growing and arable land resources becoming increasingly scarce,smart agriculture and precision agriculture have emerged as key directions for the future ofagricultural development.Artificial intelligence (AI) technologies, particularly deep learning models, have found widespread applications in areas such as crop monitoring and pest detection. As an emerging generative model, diffusion models have shown significant promise in tasks like agricultural image processing, data augmentation, and remote sensing. Compared to traditional generative adversarial networks (GANs), diffusion models offer superior training stability and generation quality, effectively addressing challenges such as limited agricultural data and imbalanced image samples. This paper reviews the latest advancements in the application of diffusion models in agriculture, focusing on their potential in crop pest and disease detection, remote sensing image enhancement, crop growth prediction, and agricultural resource management. Experimental results demonstrate that diffusion models significantly improve model accuracy and robustness in data augmentation, image generation, and denoising, especially in complex environments. Despite challenges related to computational efficiency and generalization capabilities, diffusion models are expected to play an increasingly important role in smart and precision agriculture as technology advances, providing substantial support for the sustainable development of global agriculture.
Synthesis of nanoparticles from carboxymethyl cellulose using one-pot hydrothermal carbonization for Drug Entrapment Studies
2025-07-24
Mohaddeseh Sharifi, S. Hajir Bahrami
Porous nanomaterials have recently attracted a lot of attention due to various properties and potential applications. In this study, carbon nanoparticles (CNPs) were synthesized by the one-pot hydrothermal carbonization (HTC) using carboxymethyl cellulose (CMC). Urea was used as the nitrogen source for carbonization. The presence of urea in CMC solution for carbonization resulted in CNPsu reduction in the diameter of particles from 4 micrometer to 1 micrometer. Activation process at high temperature for both the above samples resulted in nanoparticles with diameter of 51 nm and 31 nm, respectively. The positive effect of presence urea and its activation generated different functional groups including C-N, N-H, and C -(triple bond)- N with increasing aromatic rings that probably may help entrapment of drugs into them. On the other hand, activation CNPsu (ACNPsu) has the most aromatic rings with the lowest hydroxyl groups with 84.66% carbon and 12.29% oxygen in its structures. ACNPs, and ACNPsu exhibited a type I isotherm indicating microporous materials with a high surface area about 552.9 m2/g and 351.01 m2/g, respectively. The high surface area was characteristic of activated carbons with their high adsorption capacity. Thus, the synthesized materials were characterized using SEM, TEM, DLS, BET, FTIR, HNMR, and TGA techniques. Finally, the encapsulation of clindamycin drug (CD) with positive charge in different types of NPs with negative charge was investigated for drug delivery in biomedical engineering applications.
Social homophily predicts evacuation destination choice and long-term displacement decisions after the Marshall Fire
2025-07-24
Vaidehi Raipat, Takahiro Yabe
Rapid urbanization and climate change have contributed to a significant rise in the frequency and intensity of disasters, which have resulted in three million adults being displaced from their homes in the United States during the past year. Using large-scale mobility data, it is now possible to observe and analyze post-disaster mobility dynamics across a longer time period, compared to household surveys which are often limited in scale. However, much of the mobility data-driven research on evacuation and displacement destination choice behavior has often underemphasized the role of social factors, including individual preferences and social connections. To this end, we use large-scale data of anonymized GPS traces and online social connections from the Marshall Fires in Colorado, USA, to unravel the associations between social and behavioral factors and evacuation destinations and long-term displacement decisions. We find that behavioral characteristics and social homophily play a significant role in post-disaster mobility decisions. Conditioned on the same evacuation distance, evacuees chose locations that have a high sociodemographic homophily with their home locations, and locations that have more friendship connections, compared to the level of homophily they are spatially exposed to. The social homophily effect is stronger among White and educated populations than low income, and Black and Asian populations. This effect has significant implications for long-term disaster impacts and policymaking, as it is a significant predictor of displacement and return decisions. Our findings highlight the importance of incorporating the social, behavioral, and economic characteristics to better understand and predict evacuation and displacement dynamics after disasters.
Axion Searches at the CERN SPS: From Their Dawn to Current Prospects
2025-07-24
Paolo Crivelli, Martina Mongillo
This mini-review traces the evolution of axion searches at the CERN Super Proton Synchrotron (SPS), beginning with the early proposal by Guido Barbiellini in 1982 and culminating in the recent advances of the NA62 and NA64 experiments. We discuss the experimental strategies employed in early beam dump searches, the current status of axion and axion-like particle (ALPs) searches at the CERN SPS and future directions. This review serves as a tribute to Guido Barbiellini's scientific legacy and his visionary contributions to this field.
Revisiting the Muon Anomaly from $e^+ e^-\to$ Hadrons
2025-07-24
Stephan Narison
In this talk, I revisit and present a more comprehensive estimate of the lowest order Hadronic Vacuum Polarization (HVP) contribution $a_\mu\vert_{hvp}^{lo}$ to the muon anomalous magnetic moment (muon anomaly) from $e^+e^-\to$ Hadrons obtained recently in Ref.[1]. New CMD-3 data on $e^+e^-\to 2\pi$ [2] and precise BABAR [3] and recent BELLE2 [4] $e^+e^-\to 3\pi$ data are usedto update the estimate of the $I=0$ isoscalar channel below the $\phi$-meson mass. Adding the data compiled by PDG22 [5] above 1 GeV and the QCD improved continuum used in Ref. [1], one deduces\,: $a_\mu\vert^{hvp}_{lo}=(7043\pm 37)\times 10^{-11} $.Including the Higher Order $a_\mu\vert_{hvp}^{ho}$ corrections, the phenomenological estimate of the Hadronic Light by Light scattering up to NLO and the QED and Electroweak (EW) contributions, one obtains\,: $\Delta a_\mu^{pheno}\equiv a_\mu^{exp}-a_{\mu}^{pheno}= (136\pm 41)\times 10^{-11}$ where the recent experimental value $a_\mu^{exp}$ [6] has been used. This result consolidates the previous one in Ref. [1] and can be compared with the one $\Delta a_\mu^{lattice}= (90\pm 56)\times 10^{-11}$ using the most precise Lattice result. Then, we deduce the (tentative) SM prediction average : $\Delta a_\mu^{SM}= (120\pm 33)\times 10^{-11}$. We complete the paper by reviewing our predictions on the LO HVP contributions to the $\tau$ anomaly $a_\tau\vert^{hvp}_{lo}=(3495\pm 25)\times 10^{-11} $and to $\Delta \alpha^{(5)}_{had}(M_Z^2)=(2766\pm 4.5)\times 10^{-5}$ for 5 flavours obtained in Ref.[1].
Information Security Based on LLM Approaches: A Review
2025-07-24
Chang Gong, Zhongwen Li, Xiaoqi Li
Information security is facing increasingly severe challenges, and traditional protection means are difficult to cope with complex and changing threats. In recent years, as an emerging intelligent technology, large language models (LLMs) have shown a broad application prospect in the field of information security. In this paper, we focus on the key role of LLM in information security, systematically review its application progress in malicious behavior prediction, network threat analysis, system vulnerability detection, malicious code identification, and cryptographic algorithm optimization, and explore its potential in enhancing security protection performance. Based on neural networks and Transformer architecture, this paper analyzes the technical basis of large language models and their advantages in natural language processing tasks. It is shown that the introduction of large language modeling helps to improve the detection accuracy and reduce the false alarm rate of security systems. Finally, this paper summarizes the current application results and points out that it still faces challenges in model transparency, interpretability, and scene adaptability, among other issues. It is necessary to explore further the optimization of the model structure and the improvement of the generalization ability to realize a more intelligent and accurate information security protection system.
Characterisation of the signal to noise ratio of 2-photon microscopes
2025-07-24
Radek Macháň, Shau Poh Chong, Khee Leong Lee, Peter Török
Signal to noise ratio (SNR) is a key performance metric of an imaging instrument. Here we describe characterisation of SNR of a custom 2-photon microscope and compare the SNR performance of our microscope with selected commercial 2-photon microscopes. The methodology described in this paper can serve as guidance for others wishing to characterise and benchmark their 2-photon or other point-scanning microscopes.
Recent Trends in Distant Conversational Speech Recognition: A Review of CHiME-7 and 8 DASR Challenges
2025-07-24
Samuele Cornell, Christoph Boeddeker, Taejin Park, He Huang, Desh Raj, Matthew Wiesner, Yoshiki Masuyama, Xuankai Chang, Zhong-Qiu Wang, Stefano Squartini, Paola Garcia, Shinji Watanabe
The CHiME-7 and 8 distant speech recognition (DASR) challenges focus on multi-channel, generalizable, joint automatic speech recognition (ASR) and diarization of conversational speech. With participation from 9 teams submitting 32 diverse systems, these challenges have contributed to state-of-the-art research in the field. This paper outlines the challenges' design, evaluation metrics, datasets, and baseline systems while analyzing key trends from participant submissions. From this analysis it emerges that: 1) Most participants use end-to-end (e2e) ASR systems, whereas hybrid systems were prevalent in previous CHiME challenges. This transition is mainly due to the availability of robust large-scale pre-trained models, which lowers the data burden for e2e-ASR. 2) Despite recent advances in neural speech separation and enhancement (SSE), all teams still heavily rely on guided source separation, suggesting that current neural SSE techniques are still unable to reliably deal with complex scenarios and different recording setups. 3) All best systems employ diarization refinement via target-speaker diarization techniques. Accurate speaker counting in the first diarization pass is thus crucial to avoid compounding errors and CHiME-8 DASR participants especially focused on this part. 4) Downstream evaluation via meeting summarization can correlate weakly with transcription quality due to the remarkable effectiveness of large-language models in handling errors. On the NOTSOFAR-1 scenario, even systems with over 50\% time-constrained minimum permutation WER can perform roughly on par with the most effective ones (around 11\%). 5) Despite recent progress, accurately transcribing spontaneous speech in challenging acoustic environments remains difficult, even when using computationally intensive system ensembles.
Optimising the sample selection for photometric galaxy surveys
2025-07-24
Marc Alemany-Gotor, Isaac Tutusaus, Pablo Fosalba
Determining cosmological parameters with high precision, as well as resolving current tensions in their values derived from low and high redshift probes, is one of the main objectives of the new generation of cosmological surveys. The combination of complementary probes in terms of parameter degeneracies and systematics is key to achieving these ambitious scientific goals. In this context, determining the optimal survey configuration for an analysis that combines galaxy clustering, weak lensing, and galaxy-galaxy lensing, the so-called 3x2pt analysis, remains an open problem. In this paper, we present an efficient and flexible end-to-end pipeline to optimise the sample selection for 3x2pt analyses in an automated way. Our pipeline is articulated in two main steps: we first consider a self-organising map to determine the photometric redshifts of a simulated galaxy sample. As a proof of method for stage-IV surveys, we use samples from the DESC Data Challenge 2 catalogue. This allows us to classify galaxies into tomographic bins based on their colour phenotype clustering. We then explore different redshift-bin edge configurations for weak lensing only as well as 3x2pt analyses in a novel way. Our method explores multiple configurations of perturbed redshift-bin edges with respect to the fiducial case in an iterative manner. In particular, we sample tomographic configurations for the source and lens galaxies separately. We show that, using this method we quickly converge into an optimised configuration for different numbers of redshift bins and cosmologies. Our analysis demonstrates that for stage-IV surveys an optimal tomographic sample selection can increase the figure of merit of the dark energy (DE) equation of state by a factor of $\sim$2, comparable to an effective increase in survey area of $\sim$4 for non-optimal photometric survey analyses.
Recent progress in Multi-modal Large Language Models (MLLMs) has enabled step-by-step multi-modal mathematical reasoning by performing visual operations based on the textual instructions. A promising approach uses code as an intermediate representation to precisely express and manipulate the images in the reasoning steps. However, existing evaluations focus mainly on text-only reasoning outputs, leaving the MLLM's ability to perform accurate visual operations via code largely unexplored. This work takes a first step toward addressing that gap by evaluating MLLM's code-based capabilities in multi-modal mathematical reasoning.Specifically, our framework focuses on two key evaluation aspects: (1) Multi-modal Code Generation (MCG) evaluates the model's ability to accurately understand and construct visualizations from scratch. (2) Multi-modal Code Editing (MCE) assesses the model's capacity for fine-grained operations, which include three types: Deletion, Modification and Annotation. To evaluate the above tasks, we incorporate a dataset that covers the five most popular types of mathematical figures, including geometric diagrams, function plots, and three types of statistical charts, to provide a comprehensive and effective measurement of existing MLLMs. Our experimental evaluation involves nine mainstream MLLMs, and the results reveal that existing models still lag significantly behind human performance in performing fine-grained visual operations.
Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions
2025-07-24
Alberto Marchisio, Muhammad Shafique
The growing need for intelligent, adaptive, and energy-efficient autonomous systems across fields such as robotics, mobile agents (e.g., UAVs), and self-driving vehicles is driving interest in neuromorphic computing. By drawing inspiration from biological neural systems, neuromorphic approaches offer promising pathways to enhance the perception, decision-making, and responsiveness of autonomous platforms. This paper surveys recent progress in neuromorphic algorithms, specialized hardware, and cross-layer optimization strategies, with a focus on their deployment in real-world autonomous scenarios. Special attention is given to event-based dynamic vision sensors and their role in enabling fast, efficient perception. The discussion highlights new methods that improve energy efficiency, robustness, adaptability, and reliability through the integration of spiking neural networks into autonomous system architectures. We integrate perspectives from machine learning, robotics, neuroscience, and neuromorphic engineering to offer a comprehensive view of the state of the field. Finally, emerging trends and open challenges are explored, particularly in the areas of real-time decision-making, continual learning, and the development of secure, resilient autonomous systems.
Regression approaches for modelling genotype-environment interaction and making predictions into unseen environments
2025-07-24
Maksym Hrachov, Hans-Peter Piepho, Niaz Md. Farhat Rahman, Waqas Ahmed Malik
In plant breeding and variety testing, there is an increasing interest in making use of environmental information to enhance predictions for new environments. Here, we will review linear mixed models that have been proposed for this purpose. The emphasis will be on predictions and on methods to assess the uncertainty of predictions for new environments. Our point of departure is straight-line regression, which may be extended to multiple environmental covariates and genotype-specific responses. When observable environmental covariates are used, this is also known as factorial regression. Early work along these lines can be traced back to Stringfield & Salter (1934) and Yates & Cochran (1938), who proposed a method nowadays best known as Finlay-Wilkinson regression. This method, in turn, has close ties with regression on latent environmental covariates and factor-analytic variance-covariance structures for genotype-environment interaction. Extensions of these approaches - reduced rank regression, kernel- or kinship-based approaches, random coefficient regression, and extended Finlay-Wilkinson regression - will be the focus of this paper. Our objective is to demonstrate how seemingly disparate methods are very closely linked and fall within a common model-based prediction framework. The framework considers environments as random throughout, with genotypes also modelled as random in most cases. We will discuss options for assessing uncertainty of predictions, including cross validation and model-based estimates of uncertainty. The methods are illustrated using a long-term rice variety trial dataset from Bangladesh.
The MeerKAT Fornax Survey: removal of HI gas from galaxies in the Fornax cluster
2025-07-24
F. M. Maccagni, P. Serra
The MeerKAT Fornax Survey is conducting a thorough examinationof the nearby Fornax galaxy cluster to understand how galaxies lose their cold gas and stop forming stars in low-mass clusters (M$_{\rm vir} \leq 10^{14}$ M$_\odot$). We are doing so through very deep (down to $\sim 10^{18}$ cm$^{-2}$) and high resolution (up to$\sim~ 1$ kpc and 1 km s$^{-1}$) MeerKAT observations of neutral atomic hydrogen gas (HI) in a $1\times2$ Mpc$^2$ region centred on Fornax. At the time of writing, the survey is $88\%$ complete. Initial analysis of the cluster's central area has unveiled the widespread existence of previously unseen HI tails and clouds. Some of the HI is clearly being removed from Fornax galaxies as they interact with one another or with the intra-cluster medium. We present a sample of galaxies with long, one-sided, star-less HI tails (of which only one was previously known) radially oriented within the cluster. The properties of these tails represent the first conclusive evidence that ram pressure is a key force shaping the distribution of HI in the Fornax cluster. Furthermore, interactions within the Fornax environment shape the HI mass function, the HI content of dwarf galaxies and determines how the HI sustains the nuclear activity of some cluster members and neighbouring galaxies.
"I Would Not Be This Version of Myself Today": Elaborating on the Effects of Eudaimonic Gaming Experiences
2025-07-24
Nisha Devasia, Georgia Kenderova, Michele Newman, Julie Kientz, Jin Ha Lee
While much of the research in digital games has emphasized hedonic experiences, such as flow, enjoyment, and positive affect, recent years have seen increased interest in eudaimonic gaming experiences, typically mixed-affect and associated with personal meaningfulness and growth. The formation of such experiences in games is theorized to have four constituent elements: motivation, game use, experience, and effects. However, while the first three elements have been relatively well explored in the literature, the effects - and how they may influence positive individual outcomes - have been underexplored thus far. To this end, in this work, we investigate the perceived outcomes of eudaimonic gaming and how different components of the experience influence these effects. We conducted a survey (n = 166) in which respondents recounted meaningful gaming experiences and how they affected their present lives. We used a mixed-methods approach to classify effects and identify significant subcomponents of their formation. We contribute an empirical understanding of how meaningful gaming experiences can lead to positive reflective, learning, social, health, and career effects, extending current theoretical models of eudaimonic gaming experiences and offering implications for how researchers and practitioners might use these findings to promote positive outcomes for players.
The increasing use of synthetic data generated by Large Language Models (LLMs) presents both opportunities and challenges in data-driven applications. While synthetic data provides a cost-effective, scalable alternative to real-world data to facilitate model training, its diversity and privacy risks remain underexplored. Focusing on text-based synthetic data, we propose a comprehensive set of metrics to quantitatively assess the diversity (i.e., linguistic expression, sentiment, and user perspective), and privacy (i.e., re-identification risk and stylistic outliers) of synthetic datasets generated by several state-of-the-art LLMs. Experiment results reveal significant limitations in LLMs' capabilities in generating diverse and privacy-preserving synthetic data. Guided by the evaluation results, a prompt-based approach is proposed to enhance the diversity of synthetic reviews while preserving reviewer privacy.
Factors Impacting Faculty Adoption of Project-Based Learning in Computing Education: a Survey
2025-07-24
Ahmad D. Suleiman, Yiming Tang, Daqing Hou
This research full paper investigates the factors influencing computing educators' adoption of project-based learning (PjBL) in software engineering and computing curricula. Recognized as a student-centered pedagogical approach, PjBL has the potential to enhance student motivation, engagement, critical thinking, collaboration, and problem-solving skills. Despite these benefits, faculty adoption remains inconsistent due to challenges such as insufficient institutional support, time constraints, limited training opportunities, designing or sourcing projects, and aligning them with course objectives. This research explores these barriers and investigates the strategies and resources that facilitate a successful adoption. Using a mixed-methods approach, data from 80 computing faculty were collected through an online survey comprising closed-ended questions to quantify barriers, enablers, and resource needs, along with an open-ended question to gather qualitative insights. Quantitative data were analyzed using statistical methods, while qualitative responses underwent thematic analysis. Results reveal that while PjBL is widely valued, its adoption is often selective and impacted by challenges in planning and managing the learning process, designing suitable projects, and a lack of institutional support, such as time, funding, and teaching assistants. Faculty are more likely to adopt or sustain PjBL when they have access to peer collaboration, professional development, and institutional incentives. In addition, sourcing projects from research, industry partnerships, and borrowing from peers emerged as key facilitators for new projects. These findings underscore the need for systemic support structures to empower faculty to experiment with and scale PjBL practices.
Emotion recognition through body movements has emerged as a compelling and privacy-preserving alternative to traditional methods that rely on facial expressions or physiological signals. Recent advancements in 3D skeleton acquisition technologies and pose estimation algorithms have significantly enhanced the feasibility of emotion recognition based on full-body motion. This survey provides a comprehensive and systematic review of skeleton-based emotion recognition techniques. First, we introduce psychological models of emotion and examine the relationship between bodily movements and emotional expression. Next, we summarize publicly available datasets, highlighting the differences in data acquisition methods and emotion labeling strategies. We then categorize existing methods into posture-based and gait-based approaches, analyzing them from both data-driven and technical perspectives. In particular, we propose a unified taxonomy that encompasses four primary technical paradigms: Traditional approaches, Feat2Net, FeatFusionNet, and End2EndNet. Representative works within each category are reviewed and compared, with benchmarking results across commonly used datasets. Finally, we explore the extended applications of emotion recognition in mental health assessment, such as detecting depression and autism, and discuss the open challenges and future research directions in this rapidly evolving field.
Black-box optimization using factorization and Ising machines
2025-07-24
Ryo Tamura, Yuya Seki, Yuki Minamoto, Koki Kitai, Yoshiki Matsuda, Shu Tanaka, Koji Tsuda
Black-box optimization (BBO) is used in materials design, drug discovery, and hyperparameter tuning in machine learning. The world is experiencing several of these problems. In this review, a factorization machine with quantum annealing or with quadratic-optimization annealing (FMQA) algorithm to realize fast computations of BBO using Ising machines (IMs) is discussed. The FMQA algorithm uses a factorization machine (FM) as a surrogate model for BBO. The FM model can be directly transformed into a quadratic unconstrained binary optimization model that can be solved using IMs. This makes it possible to optimize the acquisition function in BBO, which is a difficult task using conventional methods without IMs. Consequently, it has the advantage of handling large BBO problems. To be able to perform BBO with the FMQA algorithm immediately, we introduce the FMQA algorithm along with Python packages to run it. In addition, we review examples of applications of the FMQA algorithm in various fields, including physics, chemistry, materials science, and social sciences. These successful examples include binary and integer optimization problems, as well as more general optimization problems involving graphs, networks, and strings, using a binary variational autoencoder. We believe that BBO using the FMQA algorithm will become a key technology in IMs including quantum annealers.
CU-JADE: A Method for Traversing Extinction Jumps along the Line-of-Sight
2025-07-24
Shiyu Zhang, Yang Su, Xuepeng Chen, Min Fang, Fujun Du, Shaobo Zhang, Qing-Zeng Yan, Xin Liu, Miaomiao Zhang, Yan Sun, Ji Yang
Although interstellar dust extinction serves as a powerful distance estimator, the solar system's location within the Galactic plane complicates distance determinations, especially for molecular clouds (MCs) at varying distances along the line of sight (LoS). The presence of complex extinction patterns along LoS introduces degeneracies, resulting in less accurate distance measurements to overlapping MCs in crowded regions of the Galactic plane. In this study, we develop the CUSUM-based Jump-point Analysis for Distance Estimation (CU-JADE), a novel method designed to help mitigate these observational challenges. The key strengths of CU-JADE include: (1) sensitivity to detect abrupt jumps in $Distance$-$A_{\lambda}$ ($D$-$A$) datasets, (2) minimal systematic errors as demonstrated on both mock and observed data, and (3) the ability to combine CUSUM analysis with multi-wavelength data to improve the completeness of distance measurements for nearby gas structures, even for extinction values as low as $\Delta A_{V} \gtrsim 0.15$ mag. By combining CO survey data with a large sample of stars characterized by high-precision parallaxes and extinctions, we uncovered the multi-layered molecular gas distribution in the high-latitude Cepheus region. We also determined accurate distances to MCs beyond the Cygnus Rift by analyzing the intricate structure of gas and extinction within the Galactic plane. Additionally, we constructed a full-sky 3D extinction map extending to 4 kpc, which provides critical insights into dense interstellar medium components dominated by molecular hydrogen. These results advance our understanding of the spatial distribution and physical properties of MCs across the Milky Way.
Ethical Considerations for Observational Research in Social VR
2025-07-24
Victoria Chang, Caro Williams-Pierce, Huaishu Peng, Ge Gao
Social VR introduces new ethical challenges for observational research. The current paper presents a narrative literature review of ethical considerations in observational methods, with a focus on work in HCI. We examine how unobtrusive or selectively disclosed observation is implemented in public face-to-face and social VR settings. Our review extends ethical discussions from traditional public research into the context of social VR, highlighting tensions between observer visibility, data traceability, and participant autonomy. Drawing on insights distilled from prior literature, we propose five constructive guidelines for ethical observational research in public social VR environments. Our work offers key implications for future research, addressing anticipated improvements in platform design, the management of researcher presence, and the development of community-informed consent mechanisms.
The Arizona Molecular ISM Survey with the SMT: The Diverse Carbon Monoxide Line Ratios and Spectral Line Energy Distributions of Star Forming Galaxies
2025-07-24
Ryan P. Keenan, Daniel P. Marrone, Garrett K. Keating
The carbon monoxide (CO) spectral line energy distributions (SLEDs) of galaxies contain a wealth of information about conditions in their cold interstellar gas. Here we use galaxy-scale observations of the three lowest energy CO lines to determine SLEDs and line ratios in a sample of 47 nearby, predominantly star forming galaxies. We find systematic trend of higher gas excitation with increasing star formation rate (SFR) and SFR surface density ($\Sigma_{\rm SFR}$), with the range of variations being even larger than predicted by simulations. Power law fits of the CO line ratios as a function of SFR and $\Sigma_{\rm SFR}$ provide a good description of the trends seen in our sample and also accurately predict values for a wide range of galaxy types compiled from the literature. Based on these fits, we provide prescriptions for estimating CO(1-0) luminosities and molecular gas masses using CO(3-2) or CO(2-1) in cases where CO(1-0) is not observed directly. We compare our observed SLEDs with molecular cloud models in order to examine how the physical properties of cold gas vary across the galaxy population. We find that gas conditions in star forming and starburst galaxies lie on a continuum with increasing gas density in more actively star forming systems.
Galaxy-galaxy weak lensing provides a powerful means of measuring the average matter distribution around lens galaxies -- i.e., the galaxy bias relation. Properly accounting for the spin-2 nature of weak lensing distortions, we develop a quadratic estimator for measuring the $E$- and $B$-mode angular power spectra from galaxy-galaxy weak lensing, correcting for survey window effects arising from, for example, survey geometry and bright star masks. The estimator can be implemented efficiently by adopting FFTs on pixelized maps of the lens galaxy distribution and source galaxy ellipticities, under the flat-sky approximation. Using simulated weak lensing fields and halo catalogs in the light-cone ray-tracing simulations, we show that the estimator can recover the underlying $E$-mode power spectrum, $C_{{\rm g}E}(\ell)$, to within a few percent in fractional error, while minimizing the leakage of $E$-mode into the $B$-mode power spectrum, in each multipole bin over the wide range of multipoles (up to $\ell \sim 3000$ studied in this paper). We then discuss that the estimator can be used to estimate the 3D galaxy-matter power spectrum, $P_{\rm gm}(k)$, by dividing lens galaxies into multiple redshift slices. We also derive an optimal weighting for each lens redshift slice in the shot noise-limited regime for the estimation of $P_{\rm gm}(k)$, which reduces the statistical errors by up to $\sim$20\% compared to the case without weighting.
Rubber Friction: Theory, Mechanisms, and Challenges
2025-07-24
B. N. J. Persson, R. Xu
Rubber friction is of major practical importance in applications such as tires, rubber seals, and footwear. This review article focuses on the theory and experimental studies of rubber friction on substrates with random roughness. We examine both steady sliding and accelerated motion, with particular attention to the origins of the breakloose friction force and the influence of pre-slip, elasticity, and flash temperature on friction dynamics. We further discuss rolling friction for cylinders and spheres, as well as sliding friction for triangular sliders on dry and lubricated rubber surfaces. Theoretical predictions are compared with experimental results obtained using different materials, geometries, and environmental conditions, highlighting the importance of accounting for multiscale roughness. Open challenges, such as the role of adhesion enhancement, energy dissipation due to crack opening, and the physical origin of the short-distance roughness cut-off, are discussed.
CityHood: An Explainable Travel Recommender System for Cities and Neighborhoods
2025-07-24
Gustavo H Santos, Myriam Delgado, Thiago H Silva
We present CityHood, an interactive and explainable recommendation system that suggests cities and neighborhoods based on users' areas of interest. The system models user interests leveraging large-scale Google Places reviews enriched with geographic, socio-demographic, political, and cultural indicators. It provides personalized recommendations at city (Core-Based Statistical Areas - CBSAs) and neighborhood (ZIP code) levels, supported by an explainable technique (LIME) and natural-language explanations. Users can explore recommendations based on their stated preferences and inspect the reasoning behind each suggestion through a visual interface. The demo illustrates how spatial similarity, cultural alignment, and interest understanding can be used to make travel recommendations transparent and engaging. This work bridges gaps in location-based recommendation by combining a kind of interest modeling, multi-scale analysis, and explainability in a user-facing system.
Agentic Program Repair from Test Failures at Scale: A Neuro-symbolic approach with static analysis and test execution feedback
2025-07-24
Chandra Maddila, Adam Tait, Claire Chang, Daniel Cheng, Nauman Ahmad, Vijayaraghavan Murali, Marshall Roch, Arnaud Avondet, Aaron Meltzer, Victor Montalvao, Michael Hopko, Chris Waterson, Parth Thakkar, Renuka Fernandez, Kristian Kristensen, Sivan Barzily, Sherry Chen, Rui Abreu, Nachiappan Nagappan, Payam Shodjai, Killian Murphy, James Everingham, Aparna Ramani, Peter C. Rigby
Aim: With the advent of LLMs, sophisticated agentic program repair has become viable at large organizations with large codebases. In this work, we develop an Engineering Agent that fixes the source code based on test failures at scale across diverse software offerings internally. Method: Using Llama as the base, we employ the ReAct harness to develop an agent. We start with a test failure that was triaged by a rule-based test failure bot. We then set up an agentic harness and allow the agent to reason and run a set of 15 actions from reading a file to generating a patch. We provide feedback to the agent through static analysis and test failures so it can refine its solution. We leverage an LLM-as-a-Judge to ensure that the patch conforms to the standards followed by a human review to land fixes. Benchmark Findings: We curated offline benchmarks for our patch generator, the Engineering Agent loop, and the LLM-as-a-Judge. In offline evaluations we found that a specialized 70B model is highly competitive with the much larger but vanilla Llama-405B. In an ablation study, we found that the ReAct harness (neural model) benefited from the symbolic information from static analysis tools and test execution traces. A model that strikes a balance between the solve rate and error rate vs the cost and latency has a benchmark solve rate of 42.3% using an average 11.8 feedback iterations. Production Findings: In a three month period, 80% of the generated fixes were reviewed, of which 31.5% were landed (25.5% of the total number of generated fixes). Feedback from Engineers: We used open coding to extract qualitative themes from engineers' feedback. We saw positive feedback in the form of quick approvals, gratitude, and surprise. We also found mixed feedback when the Engineering Agent's solution was partially correct and it served as a good starting point.
Exploring the Landscape of Fairness Interventions in Software Engineering
2025-07-24
Sadia Afrin Mim
Current developments in AI made it broadly significant for reducing human labor and expenses across several essential domains, including healthcare and finance. However, the application of AI in the actual world poses multiple risks and disadvantages due to potential risk factors in data (e.g., biased dataset). Practitioners developed a number of fairness interventions for addressing these kinds of problems. The paper acts as a survey, summarizing the various studies and approaches that have been developed to address fairness issues
Magellan Spectroscopy of AGNs in Low-mass Galaxies: Scaling Relations and a Triple-Peaked AGN
2025-07-24
Megan R. Sturm, Amy E. Reines, Jenny Greene, Sheyda Salehirad
In this work, we aim to further populate the low-mass regime of black hole (BH) scaling relations to better understand the formation and growth mechanisms of central supermassive BHs. We target six galaxies that have been previously identified as hosting active galactic nuclei (AGN) based on optical spectroscopy from the Galaxy and Mass Assembly (GAMA) survey or the Sloan Digital Sky Survey (SDSS) with stellar masses reported to be M$_\star < 5 \times 10^9$ M$_\odot$. Using follow-up optical spectroscopy from the Magellan Echellette Spectrograph (MagE), we extract galaxy velocity dispersions ($\sigma_\star$) and estimate virial BH masses from broad H$\alpha$ emission. We find that the galaxies in our sample do not deviate significantly from either the M$_{BH}-\sigma_\star$ or M$_{BH}-$M$_{\star}$ scaling relations defined by higher mass galaxies. Additionally, we identify one galaxy with triple-peaked SII, NII, H$\alpha$ and H$\beta$ emission lines. This spectral shape is not shared by OIII and, in fact, the OIII line appears to have distinct kinematics from the other emission lines. Incorporating the spatial distribution of the various emission lines, we find that the galaxy spectrum is consistent with a prominent central AGN driving an outflow, surrounded by an extended ring/disk of gas predominantly ionized by shocks and/or star formation. This work has implications for the demographics of BHs in low-mass galaxies and the role of AGN feedback.
Mapping water ice with infrared broadband photometry
2025-07-24
Stefan Meingast
Interstellar ices play a fundamental role in the physical and chemical evolution of molecular clouds and star-forming regions, yet their large-scale distribution and abundance remain challenging to map. In this work, I present the ice color excess method, which parametrizes the peak optical depth ($\tau_{3.0}^{\mathrm{max}}$) of the prominent 3$\mu$m absorption feature, which is predominantly caused by the presence of solid H$_2$O. The method builds on well-established near-infrared color excess techniques and uses widely available infrared broadband photometry. Through detailed evaluation of passband combinations and a comprehensive error analysis, I construct the ice color excess metric $\Lambda(W_1 - I_1)$. This parameter emerges as the optimal choice that minimizes systematic errors while leveraging high-quality, widely available photometry from Spitzer and WISE data archives. To calibrate the method, I compile from the literature a sample of stars located in the background of nearby molecular clouds, for which spectroscopically measured optical depths are available. The empirical calibration yields a remarkably tight correlation between $\tau_{3.0}^{\mathrm{max}}$ and $\Lambda(W_1 - I_1)$. This photometric technique opens a new avenue for tracing the icy component of the interstellar medium on Galactic scales, providing a powerful complement to spectroscopic surveys and enabling new insights into the environmental dependence of the formation and evolution of icy dust grains.
Bayesian Deep Gaussian Processes for Correlated Functional Data: A Case Study in Cosmological Matter Power Spectra
2025-07-24
Stephen A. Walsh, Annie S. Booth, David Higdon, Jared Clark, Kelly R. Moran, Katrin Heitmann
Understanding the structure of our universe and the distribution of matter is an area of active research. As cosmological surveys grow in complexity, the development of emulators to efficiently and effectively predict matter power spectra is essential. We are particularly motivated by the Mira-Titan Universe simulation suite that, for a specified cosmological parameterization (termed a "cosmology"), provides multiple response curves of various fidelities, including correlated functional realizations. Our objective is two-fold. First, we estimate the underlying true matter power spectra, with appropriate uncertainty quantification (UQ), from all of the provided curves. To this end, we propose a novel Bayesian deep Gaussian process (DGP) hierarchical model which synthesizes all the simulation information to estimate the underlying matter power spectra while providing effective UQ. Our model extends previous work on Bayesian DGPs from scalar responses to correlated functional outputs. Second, we leverage our predicted power spectra from various cosmologies in order to accurately predict the entire matter power spectra for an unobserved cosmology. For this task, we use basis function representations of the functional spectra to train a separate Gaussian process emulator. Our method performs well in synthetic exercises and against the benchmark cosmological emulator (Cosmic Emu).
Recommendations to overcome language barriers in the Vera C. Rubin Observatory Research Ecosystem
2025-07-24
José Antonio Alonso Pavón, Andrés Alejandro Plazas Malagón
The report presents a comprehensive set of five recommendations to reduce language barriers within the Vera C. Rubin Observatory Research Ecosystem, promoting greater inclusion of researchers who are speakers of English as an additional language. Recognizing that English linguistic hegemony in science limits participation and productivity, the document proposes multilingual presentation formats, academic writing training, a Virtual Writing Center, language support programs, and writing retreats. Each recommendation is grounded in both pedagogical theory and empirical evidence, with an emphasis on collaborative, socially embedded approaches to scientific writing. The proposed academic writing training integrates constructivist and socio-cultural perspectives, emphasizing genre awareness, rhetorical competence, and reflective practices. The Virtual Writing Center would serve as a permanent infrastructure offering personalized tutoring and peer review support, while the language support programs address ongoing needs through workshops, consultations, and access to language tools. Writing retreats provide immersive environments for focused work and mentorship. The recommendations also encourage ethical use of AI tools for translation and writing assistance, fostering digital literacy alongside linguistic proficiency. Collectively, these initiatives aim to transform language from a barrier into a resource, recognizing multilingualism as an asset in global research collaboration. Rather than offering a one-size-fits-all solution, the document advocates for adaptable, community-driven strategies that can evolve within the diverse institutional and disciplinary contexts of the Rubin Research Ecosystem. By implementing these practices, the Ecosystem could lead efforts to democratize scientific communication and foster a more equitable, multilingual research culture.
Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems
2025-07-24
Naseem Khan, Tuan Nguyen, Amine Bermak, Issa Khalil
The rapid advancement of Generative Artificial Intelligence has fueled deepfake proliferation-synthetic media encompassing fully generated content and subtly edited authentic material-posing challenges to digital security, misinformation mitigation, and identity preservation. This systematic review evaluates state-of-the-art deepfake detection methodologies, emphasizing reproducible implementations for transparency and validation. We delineate two core paradigms: (1) detection of fully synthetic media leveraging statistical anomalies and hierarchical feature extraction, and (2) localization of manipulated regions within authentic content employing multi-modal cues such as visual artifacts and temporal inconsistencies. These approaches, spanning uni-modal and multi-modal frameworks, demonstrate notable precision and adaptability in controlled settings, effectively identifying manipulations through advanced learning techniques and cross-modal fusion. However, comprehensive assessment reveals insufficient evaluation of adversarial robustness across both paradigms. Current methods exhibit vulnerability to adversarial perturbations-subtle alterations designed to evade detection-undermining reliability in real-world adversarial contexts. This gap highlights critical disconnect between methodological development and evolving threat landscapes. To address this, we contribute a curated GitHub repository aggregating open-source implementations, enabling replication and testing. Our findings emphasize urgent need for future work prioritizing adversarial resilience, advocating scalable, modality-agnostic architectures capable of withstanding sophisticated manipulations. This review synthesizes strengths and shortcomings of contemporary deepfake detection while charting paths toward robust trustworthy systems.
Educational Insights from Code: Mapping Learning Challenges in Object-Oriented Programming through Code-Based Evidence
2025-07-23
Andre Menolli, Bruno Strik
Object-Oriented programming is frequently challenging for undergraduate Computer Science students, particularly in understanding abstract concepts such as encapsulation, inheritance, and polymorphism. Although the literature outlines various methods to identify potential design and coding issues in object-oriented programming through source code analysis, such as code smells and SOLID principles, few studies explore how these code-level issues relate to learning difficulties in Object-Oriented Programming. In this study, we explore the relationship of the code issue indicators with common challenges encountered during the learning of object-oriented programming. Using qualitative analysis, we identified the main categories of learning difficulties and, through a literature review, established connections between these difficulties, code smells, and violations of the SOLID principles. As a result, we developed a conceptual map that links code-related issues to specific learning challenges in Object-Oriented Programming. The model was then evaluated by an expert who applied it in the analysis of the student code to assess its relevance and applicability in educational contexts.
A first measurement of baryonic feedback with Fast Radio Bursts
2025-07-23
Robert Reischke, Steffen Hagstotz
Baryonic feedback fundamentally alters the total matter distribution on small to intermediate cosmological scales, posing a significant challenge for contemporary cosmological analyses. Direct tracers of the baryon distribution are therefore key for unearthing cosmological information buried under astrophysical effects. Fast Radio Bursts (FRBs) have emerged as a novel and direct probe of baryons, tracing the integrated ionised electron density along the line-of-sight, quantified by the dispersion measure (DM). The scatter of the DM as a function of redshift provides insight into the lumpiness of the electron distribution and, consequently, baryonic feedback processes. Using a model calibrated to the \texttt{BAHAMAS} hydrodynamical simulation suite, we forward-model the statistical properties of the DM with redshift. Applying this model to approximately 100 localised FRBs, we constrain the governing feedback parameter, $\log T_\mathrm{AGN}$. Our findings represent the first measurement of baryonic feedback using FRBs, demonstrating a strong rejection of no-feedback scenarios at greater than $99.7\,\%$ confidence ($3\sigma$), depending on the FRB sample. We find that FRBs prefer fairly strong feedback, similar to other measurements of the baryon distribution, via the thermal and kinetic Sunyaev-Zel'dovich effect. The results are robust against sightline correlations and modelling assumptions. We emphasise the importance of accurate calibration of the host galaxy and Milky Way contributions to the DM. Furthermore, we discuss implications for future FRB surveys and necessary improvements to current models to ensure accurate fitting of upcoming data, particularly that from low-redshift FRBs.
Over the past decade, advances in generative modeling, such as generative adversarial networks, masked autoencoders, and diffusion models, have significantly transformed biological research and discovery, enabling breakthroughs in molecule design, protein generation, drug discovery, and beyond. At the same time, biological applications have served as valuable testbeds for evaluating the capabilities of generative models. Recently, flow matching has emerged as a powerful and efficient alternative to diffusion-based generative modeling, with growing interest in its application to problems in biology and life sciences. This paper presents the first comprehensive survey of recent developments in flow matching and its applications in biological domains. We begin by systematically reviewing the foundations and variants of flow matching, and then categorize its applications into three major areas: biological sequence modeling, molecule generation and design, and peptide and protein generation. For each, we provide an in-depth review of recent progress. We also summarize commonly used datasets and software tools, and conclude with a discussion of potential future directions. The corresponding curated resources are available at https://github.com/Violet24K/Awesome-Flow-Matching-Meets-Biology.
AI Telephone Surveying: Automating Quantitative Data Collection with an AI Interviewer
2025-07-23
Danny D. Leybzon, Shreyas Tirumala, Nishant Jain, Summer Gillen, Michael Jackson, Cameron McPhee, Jennifer Schmidt
With the rise of voice-enabled artificial intelligence (AI) systems, quantitative survey researchers have access to a new data-collection mode: AI telephone surveying. By using AI to conduct phone interviews, researchers can scale quantitative studies while balancing the dual goals of human-like interactivity and methodological rigor. Unlike earlier efforts that used interactive voice response (IVR) technology to automate these surveys, voice AI enables a more natural and adaptive respondent experience as it is more robust to interruptions, corrections, and other idiosyncrasies of human speech. We built and tested an AI system to conduct quantitative surveys based on large language models (LLM), automatic speech recognition (ASR), and speech synthesis technologies. The system was specifically designed for quantitative research, and strictly adhered to research best practices like question order randomization, answer order randomization, and exact wording. To validate the system's effectiveness, we deployed it to conduct two pilot surveys with the SSRS Opinion Panel and followed-up with a separate human-administered survey to assess respondent experiences. We measured three key metrics: the survey completion rates, break-off rates, and respondent satisfaction scores. Our results suggest that shorter instruments and more responsive AI interviewers may contribute to improvements across all three metrics studied.
From Feedback to Checklists: Grounded Evaluation of AI-Generated Clinical Notes
2025-07-23
Karen Zhou, John Giorgi, Pranav Mani, Peng Xu, Davis Liang, Chenhao Tan
AI-generated clinical notes are increasingly used in healthcare, but evaluating their quality remains a challenge due to high subjectivity and limited scalability of expert review. Existing automated metrics often fail to align with real-world physician preferences. To address this, we propose a pipeline that systematically distills real user feedback into structured checklists for note evaluation. These checklists are designed to be interpretable, grounded in human feedback, and enforceable by LLM-based evaluators. Using deidentified data from over 21,000 clinical encounters, prepared in accordance with the HIPAA safe harbor standard, from a deployed AI medical scribe system, we show that our feedback-derived checklist outperforms baseline approaches in our offline evaluations in coverage, diversity, and predictive power for human ratings. Extensive experiments confirm the checklist's robustness to quality-degrading perturbations, significant alignment with clinician preferences, and practical value as an evaluation methodology. In offline research settings, the checklist can help identify notes likely to fall below our chosen quality thresholds.
The Small Separation A-Star Companion Population: Tentative Signatures of Enhanced Multiplicity with Primary Mass
2025-07-23
Matthew De Furio, Tyler Gardner, John D. Monnier, Michael R. Meyer, Kaitlin M. Kratter, Cyprien Lanthermann, Narsireddy Anugu, Stefan Kraus, Benjamin R. Setterholm
We present updated results from our near-infrared long-baseline interferometry (LBI) survey to constrain the multiplicity properties of intermediate-mass A-type stars within 80 pc. Previous adaptive optics surveys of A-type stars are incomplete at separations $<$ 20au. Therefore, a LBI survey allows us to explore separations previously unexplored. Our sample consists of 54 A-type primaries with estimated masses between 1.44-2.93 M$_{\odot}$ and ages 10-790 Myr, which we observed with the MIRC-X and MYSTIC instruments at the CHARA Array. We use the open source software CANDID to detect two new companions, seven in total, and we performed a Bayesian demographic analysis to characterize the companion population. We find the separation distribution consistent with being flat, and we estimate a power-law fit to the mass ratio distribution with index -0.13$^{+0.92}_{-0.95}$ and a companion frequency of 0.25$^{+0.17}_{-0.11}$ over mass ratios 0.1-1.0 and projected separations 0.01-27.54au. We find a posterior probability of 0.53 and 0.04 that our results are consistent with extrapolations based on previous models of the solar-type and B-type companion population, respectively. Our results suggest that the close companion population to A-type stars is comparable to that of solar-types and that close companions to B-type stars are potentially more frequent which may be indicative of increased disk fragmentation for stars $\gtrsim$ 3M$_{\odot}$.
Estimating the baryon fraction in the IGM from well-localized FRBs and DESI data
2025-07-23
Thais Lemos
Current measurements of Baryon Acoustic Oscillations (BAO) from the Dark Energy Spectroscopic Survey (DESI DR2), when combined with data from Type Ia supernovae (SNe), challenge the observational viability of the $\Lambda$-Cold Dark Matter ($\Lambda$CDM) model, motivating combinations of independent datasets to estimate cosmological quantities. In a previous communication, we presented a cosmological independent method to constrain the baryon fraction in the IGM ($f_{\mathrm{IGM}}$), where we derived relevant expressions for the dispersion measure ($\mathrm{DM}$) in terms of luminosity distance, allowing us to estimate $f_{\mathrm{IGM}}$ combining directly measurements of 17 well-localized FRBs and 1048 SNe from the Pantheon catalog. Here we revisit this method to constrain $f_{\mathrm{IGM}}$, considering two parameterizations for the $f_{\mathrm{IGM}}$: constant and time-dependent. We expand our sample by combining 107 well-localized Fast Radio Bursts (FRBs) with BAO measurements from DESI DR2 and SNe observations from DESY5, and the Pantheon+ catalog. We find through a Bayesian model selection analysis that a conclusive answer about the evolution of $f_{\mathrm{IGM}}$ cannot be achieved from the current FRBs observational data. In particular, our results show weak evidence in favor of the constant case.
A Galactic Self-Portrait: Density Structure and Integrated Properties of the Milky Way Disk
2025-07-23
Julie Imig, Jon A. Holtzman, Gail Zasowski, Jianhui Lian, Nicholas F. Boardman, Alexander Stone-Martinez, J. Ted Mackereth, Moire K. M. Prescott, Rachael L. Beaton, Timothy C. Beers, Dmitry Bizyaev, Michael R. Blanton, Katia Cunha, José G. Fernández-Trincado, Catherine E. Fielder, Sten Hasselquist, Christian R. Hayes, Misha Haywood, Henrik Jönsson, Richard R. Lane, Steven R. Majewski, Szabolcs Mészáros, Ivan Minchev, David L. Nidever, Christian Nitschelm, Jennifer Sobeck
The evolution history of the Milky Way disk is imprinted in the ages, positions, and chemical compositions of individual stars. In this study, we derive the intrinsic density distribution of different stellar populations using the final data release of the Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey. A total of 203,197 red giant branch stars are used to sort the stellar disk ($R \leq 20$ kpc) into sub-populations of metallicity ($\Delta$[M/H]$= 0.1$ dex), age ($\Delta \log(\frac{\textrm{age}}{\textrm{yr}})= 0.1$), and $\alpha$-element abundances ([$\alpha$/M]). We fit the present-day structural parameters and density distribution of each stellar sub-population after correcting for the survey selection function. The low-$\alpha$ disk is characterized by longer scale lengths and shorter scale heights, and is best fit by a broken exponential radial profile for each population. The high-$\alpha$ disk is characterized by shorter scale lengths and larger scale heights, and is generally well-approximated by a single exponential radial profile. These results are applied to produce new estimates of the integrated properties of the Milky Way from early times to the present day. We measure the total stellar mass of the disk to be $5.27^{+0.2}_{-1.5} \times 10^{10}$ M$_\odot$ and the average mass-weighted scale length is $R_{d} = 2.37 \pm 0.2$ kpc. The Milky Way's present-day color of $(g-r) = 0.72 \pm 0.02$ is consistent with the classification of a red spiral galaxy, although it has only been in the "green valley" region of the galaxy color-mass diagram for the last $\sim 3$ Gyr.
PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Driving
2025-07-23
Maciej K. Wozniak, Lianhang Liu, Yixi Cai, Patric Jensfelt
While end-to-end autonomous driving models show promising results, their practical deployment is often hindered by large model sizes, a reliance on expensive LiDAR sensors and computationally intensive BEV feature representations. This limits their scalability, especially for mass-market vehicles equipped only with cameras. To address these challenges, we propose PRIX (Plan from Raw Pixels). Our novel and efficient end-to-end driving architecture operates using only camera data, without explicit BEV representation and forgoing the need for LiDAR. PRIX leverages a visual feature extractor coupled with a generative planning head to predict safe trajectories from raw pixel inputs directly. A core component of our architecture is the Context-aware Recalibration Transformer (CaRT), a novel module designed to effectively enhance multi-level visual features for more robust planning. We demonstrate through comprehensive experiments that PRIX achieves state-of-the-art performance on the NavSim and nuScenes benchmarks, matching the capabilities of larger, multimodal diffusion planners while being significantly more efficient in terms of inference speed and model size, making it a practical solution for real-world deployment. Our work is open-source and the code will be at https://maxiuw.github.io/prix.
Synthetic Voice Data for Automatic Speech Recognition in African Languages
2025-07-23
Brian DeRenzi, Anna Dixon, Mohamed Aymane Farhi, Christian Resch
Speech technology remains out of reach for most of the over 2300 languages in Africa. We present the first systematic assessment of large-scale synthetic voice corpora for African ASR. We apply a three-step process: LLM-driven text creation, TTS voice synthesis, and ASR fine-tuning. Eight out of ten languages for which we create synthetic text achieved readability scores above 5 out of 7. We evaluated ASR improvement for three (Hausa, Dholuo, Chichewa) and created more than 2,500 hours of synthetic voice data at below 1% of the cost of real data. Fine-tuned Wav2Vec-BERT-2.0 models trained on 250h real and 250h synthetic Hausa matched a 500h real-data-only baseline, while 579h real and 450h to 993h synthetic data created the best performance. We also present gender-disaggregated ASR performance evaluation. For very low-resource languages, gains varied: Chichewa WER improved about 6.5% relative with a 1:2 real-to-synthetic ratio; a 1:1 ratio for Dholuo showed similar improvements on some evaluation data, but not on others. Investigating intercoder reliability, ASR errors and evaluation datasets revealed the need for more robust reviewer protocols and more accurate evaluation data. All data and models are publicly released to invite further work to improve synthetic data for African languages.
Evaluation of centroiding algorithms for an autonomous star tracker
2025-07-23
Marcio Afonso Arimura Fialho
This work presents numerical results of a computer simulation performed with six centroiding algorithms targeting a star tracker in development at INPE, including readout noise and considering a Gaussian point spread function. Five of the tested algorithms are light-weight centroiding algorithms with low computational costs. These were compared to a shape fitting algorithm based on the lsqnonlin function available in Matlab and GNU Octave. The algorithms studied here are also applicable for astrometry and adaptive optics.
The aim of this short survey is to advertise various tools that have been developed to study hierarchically hyperbolic spaces (HHSs) in recent years, with particular emphasis on those that require little to no knowledge of the HHS machinery to be used.
Unfolding Data Quality Dimensions in Practice: A Survey
2025-07-23
Vasileios Papastergios, Lisa Ehrlinger, Anastasios Gounaris
Data quality describes the degree to which data meet specific requirements and are fit for use by humans and/or downstream tasks (e.g., artificial intelligence). Data quality can be assessed across multiple high-level concepts called dimensions, such as accuracy, completeness, consistency, or timeliness. While extensive research and several attempts for standardization (e.g., ISO/IEC 25012) exist for data quality dimensions, their practical application often remains unclear. In parallel to research endeavors, a large number of tools have been developed that implement functionalities for the detection and mitigation of specific data quality issues, such as missing values or outliers. With this paper, we aim to bridge this gap between data quality theory and practice by systematically connecting low-level functionalities offered by data quality tools with high-level dimensions, revealing their many-to-many relationships. Through an examination of seven open-source data quality tools, we provide a comprehensive mapping between their functionalities and the data quality dimensions, demonstrating how individual functionalities and their variants partially contribute to the assessment of single dimensions. This systematic survey provides both practitioners and researchers with a unified view on the fragmented landscape of data quality checks, offering actionable insights for quality assessment across multiple dimensions.
Early Identification of Optical Tidal Disruption Events: A science module for the Fink broker
2025-07-23
Miguel Llamas Lanza, Sergey Karpov, Etienne Russeil, Erwan Quintin, Emille Ishida, Julien Peloton, Maria Pruzhinskaya, Anais Möller
The detection of tidal disruption events (TDEs) is one of the key science goals of large optical time-domain surveys such as the Zwicky Transient Facility (ZTF) and the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time. However, identifying TDEs in the vast alert streams produced by these surveys requires automated and reliable classification pipelines that can select promising candidates in real time. We developed a module within the Fink alert broker to identify TDEs during their rising phase. It was built to autonomously operate within the ZTF alert stream, producing a list of candidates every night and enabling spectral and multi-wavelength follow-up near peak brightness. All rising alerts are submitted to selection cuts and feature extraction using the Rainbow multi-band lightcurve fit. Best-fit values were used as input to train an XGBoost classifier with the goal of identifying TDEs. The training set was constructed using ZTF observations for objects with available classification in the Transient Name Server. Finally, candidates with high enough probability were visually inspected. The classifier achieves 76% recall, indicating strong performance in early-phase identification, despite the limited available information before peak. We show that, out of the known TDEs that pass the selection cuts, half of them are flagged as TDE before halfway in their rise, proving the feasibility of early classification. Additionally, new candidates were identified by applying the classifier on archival data, including a likely repeated TDE and some potential TDEs occurring in active galaxies. The module is implemented into the Fink alert processing framework, reporting each night a small number of candidates to dedicated communication channels through a user-friendly interface, for manual vetting and potential follow-up.
Multifold degeneracy points of quantum systems and singularities of matrix varieties
2025-07-23
György Frank, András Pályi, Gergő Pintér, Dániel Varjas
Parameter-dependent quantum systems often exhibit energy degeneracy points, whose comprehensive description naturally lead to the application of methods from singularity theory. A prime example is an electronic band structure where two energy levels coincide in a point of momentum space. It may happen, and this case is the focus of our work, that three or more levels coincide at a parameter point, called multifold degeneracy. Upon a generic perturbation, such a multifold degeneracy point is dissolved into a set of Weyl points, that is, generic two-fold degeneracy points. In this work, we provide an upper bound to the number of Weyl points born from the multifold degeneracy point. To compute this upper bound, we describe the geometric degeneracy variety in the space of complex matrices. We compute its multiplicity at certain singular points corresponding to a multifold degeneracy, and the multiplicity of holomorphic map germs with respect to this variety. Our work covers physics and mathematics aspects in detail, and attempts to bridge the two disciplines and communities. For self-containedness, we survey examples of multi-fold degeneracies in quantum systems and condensed-matter physics, as well as the established tools of local algebraic geometry that we use to identify the upper bound.
Variant effects on protein-protein interactions: methods, models and diseases
2025-07-23
Sven Larsen-Ledet, Aleksandra Panfilova, Amelie Stein
Advances in sequencing have revealed that each individual carries about 10,000 missense variants. For the vast majority, we do not know what the functional consequences - if any - will be. Further, mechanistic insight, such as structural details, would be immensely helpful in development of therapeutic approaches. Here we review recent developments in experimental and computational techniques aimed to assess the impact of variants on protein-protein interactions, including limitations and upcoming challenges.
A Comprehensive Evaluation on Quantization Techniques for Large Language Models
2025-07-23
Yutong Liu, Cairong Zhao, Guosheng Hu
For large language models (LLMs), post-training quantization (PTQ) can significantly reduce memory footprint and computational overhead. Model quantization is a rapidly evolving research field. Though many papers have reported breakthrough performance, they may not conduct experiments on the same ground since one quantization method usually contains multiple components. In addition, analyzing the theoretical connections among existing methods is crucial for in-depth understanding. To bridge these gaps, we conduct an extensive review of state-of-the-art methods and perform comprehensive evaluations on the same ground to ensure fair comparisons. To our knowledge, this fair and extensive investigation remains critically important yet underexplored. To better understand the theoretical connections, we decouple the published quantization methods into two steps: pre-quantization transformation and quantization error mitigation. We define the former as a preprocessing step applied before quantization to reduce the impact of outliers, making the data distribution flatter and more suitable for quantization. Quantization error mitigation involves techniques that offset the errors introduced during quantization, thereby enhancing model performance. We evaluate and analyze the impact of different components of quantization methods. Additionally, we analyze and evaluate the latest MXFP4 data format and its performance. Our experimental results demonstrate that optimized rotation and scaling yield the best performance for pre-quantization transformation, and combining low-rank compensation with GPTQ occasionally outperforms using GPTQ alone for quantization error mitigation. Furthermore, we explore the potential of the latest MXFP4 quantization and reveal that the optimal pre-quantization transformation strategy for INT4 does not generalize well to MXFP4, inspiring further investigation.
The $10~\rm{kpc}$ collar of early-type galaxies -- probing evolution by focusing on the inner stellar density profile
2025-07-23
Rongfu Liu, Alessandro Sonnenfeld, Carlo Nipoti, Rui Li
Aims: In this work we aim to investigate the late evolution of ETGs, both observationally and theoretically, by focusing on the stellar mass density profile inside a fixed aperture, within $10 ~\rm{kpc}$ from the galaxy center. Methods: We first studied the stellar mass and mass-weighted density slope within $10 ~\rm{kpc}$, respectively $M_{*,10}$ and $\mathit{\Gamma_{*,10}}$, of a sample of early-type galaxies from the GAMA survey. We measured the $\mathit{\Gamma_{*,10}} - M_{*,10}$ relation and its evolution over the redshift range $0.17\leq z \leq 0.37$. We then built a toy model for the merger evolution of galaxies, based on N-body simulations, to explore to what extent the observed growth in $\mathit{\Gamma_{*,10}} - M_{*,10}$ relation is consistent with a dry-merger evolution scenario. Results: From the observations, we do not detect evidence for an evolution of the $\mathit{\Gamma_{*,10}} - M_{*,10}$ relation. We put an upper limit on the redshift derivative of the normalization$~(\mu)$ and slope$~(\beta)$of the $\mathit{\Gamma_{*,10}} - M_{*,10}$ relation $|\partial \mu/\partial \log (1+z)| \leq 0.13$ and $\left|\partial \beta/\partial \log (1+z)\right| \leq 1.10$, respectively. Simulations show that most mergers induce a decrease in $\mathit{\Gamma_{*,10}}$ and an increase in $M_{*,10}$, although some show a decrease in $M_{*,10}$, particularly for the most extended galaxies and smaller merger mass ratios. By combining the observations with our merger toy model, we placed an upper limit on $f_M = 11.2 \%$ in the redshift range $0.17 \leq z \leq 0.37$. Conclusions: While our measurement is limited by systematics, the application of our approach to samples with a larger redshift baseline, particularly with a time interval $\Delta t \geq 3.2~\mathrm{Gyr}$, should enable us to detect a signal and help us better understand the late growth of ETGs.
Investigating State-of-the-Art Planning Strategies for Electric Vehicle Charging Infrastructures in Coupled Transport and Power Networks: A Comprehensive Review
2025-07-23
Jinhao Li, Arlena Chew, Hao Wang
Electric vehicles (EVs) have emerged as a pivotal solution to reduce greenhouse gas emissions paving a pathway to net zero. As the adoption of EVs continues to grow, countries are proactively formulating systematic plans for nationwide electric vehicle charging infrastructure (EVCI) to keep pace with the accelerating shift towards EVs. This comprehensive review aims to thoroughly examine current global practices in EVCI planning and explore state-of-the-art methodologies for designing EVCI planning strategies. Despite remarkable efforts by influential players in the global EV market, such as China, the United States, and the European Union, the progress in EVCI rollout has been notably slower than anticipated in the rest of the world. This delay can be attributable to three major impediments: inadequate EVCI charging services, low utilization rates of public EVCI facilities, and the non-trivial integration of EVCI into the electric grid. This review dissects the interests of these stakeholders, clarifying their respective roles and expectations in the context of EVCI planning. This review also provides insights into level 1, 2, and 3 chargers with explorations of their applications in different geographical locations for diverse EV charging patterns. Finally, a thorough review of node-based and flow-based approaches to EV planning is presented. The modeling of placing charging stations is broadly categorized into set coverage, maximum coverage, flow-capturing, and flow-refueling location models. In conclusion, this review identifies several research gaps, including the dynamic modeling of EV charging demand and the coordination of vehicle electrification with grid decarbonization. This paper calls for further contributions to bridge these gaps and drive the advancement of EVCI planning.
In inaccessible environments with uncertain task demands, robots often rely on general-purpose tools that lack predefined usage strategies. These tools are not tailored for particular operations, making their longevity highly sensitive to how they are used. This creates a fundamental challenge: how can a robot learn a tool-use policy that both completes the task and prolongs the tool's lifespan? In this work, we address this challenge by introducing a reinforcement learning (RL) framework that incorporates tool lifespan as a factor during policy optimization. Our framework leverages Finite Element Analysis (FEA) and Miner's Rule to estimate Remaining Useful Life (RUL) based on accumulated stress, and integrates the RUL into the RL reward to guide policy learning toward lifespan-guided behavior. To handle the fact that RUL can only be estimated after task execution, we introduce an Adaptive Reward Normalization (ARN) mechanism that dynamically adjusts reward scaling based on estimated RULs, ensuring stable learning signals. We validate our method across simulated and real-world tool use tasks, including Object-Moving and Door-Opening with multiple general-purpose tools. The learned policies consistently prolong tool lifespan (up to 8.01x in simulation) and transfer effectively to real-world settings, demonstrating the practical value of learning lifespan-guided tool use strategies.
Understanding Prompt Programming Tasks and Questions
2025-07-23
Jenny T. Liang, Chenyang Yang, Agnia Sergeyuk, Travis D. Breaux, Brad A. Myers
Prompting foundation models (FMs) like large language models (LLMs) have enabled new AI-powered software features (e.g., text summarization) that previously were only possible by fine-tuning FMs. Now, developers are embedding prompts in software, known as prompt programs. The process of prompt programming requires the developer to make many changes to their prompt. Yet, the questions developers ask to update their prompt is unknown, despite the answers to these questions affecting how developers plan their changes. With the growing number of research and commercial prompt programming tools, it is unclear whether prompt programmers' needs are being adequately addressed. We address these challenges by developing a taxonomy of 25 tasks prompt programmers do and 51 questions they ask, measuring the importance of each task and question. We interview 16 prompt programmers, observe 8 developers make prompt changes, and survey 50 developers. We then compare the taxonomy with 48 research and commercial tools. We find that prompt programming is not well-supported: all tasks are done manually, and 16 of the 51 questions -- including a majority of the most important ones -- remain unanswered. Based on this, we outline important opportunities for prompt programming tools.
A not-too-simple solution to Goodman's new riddle of induction in the age of AI
2025-07-23
Luigi Scorzato
I review the works of G\"ardenfors (1990) and Scorzato (2013) and show that their combination provides an elegant solution of Goodman's new riddle of induction. The solution is based on two main ideas: (1) clarifying what is expected from a solution: understanding that philosophy of science is a science itself, with the same limitations and strengths as other scientific disciplines; (2) understanding that the concept of complexity of a model's assumptions and the concept of direct measurements must be characterized together. Although both measurements and complexity have been the subject of a vast literature, within the philosophy of science, essentially no other attempt has been made to combine them. The widespread expectation, among modern philosophers, that Goodman's new riddle cannot be solved is not defensible without a serious exploration of such a natural approach. A clarification of this riddle has always been very important, but it has become even more crucial in the age of AI.
Designing high-quality presentation slides can be challenging for non-experts due to the complexity involved in navigating various design choices. Numerous automated tools can suggest layouts and color schemes, yet often lack the ability to refine their own output, which is a key aspect in real-world workflows. We propose DesignLab, which separates the design process into two roles, the design reviewer, who identifies design-related issues, and the design contributor who corrects them. This decomposition enables an iterative loop where the reviewer continuously detects issues and the contributor corrects them, allowing a draft to be further polished with each iteration, reaching qualities that were unattainable. We fine-tune large language models for these roles and simulate intermediate drafts by introducing controlled perturbations, enabling the design reviewer learn design errors and the contributor learn how to fix them. Our experiments show that DesignLab outperforms existing design-generation methods, including a commercial tool, by embracing the iterative nature of designing which can result in polished, professional slides.
Data perturbation-based privacy-preserving methods have been widely adopted in various scenarios due to their efficiency and the elimination of the need for a trusted third party. However, these methods primarily focus on individual statistical indicators, neglecting the overall quality of the collected data from a distributional perspective. Consequently, they often fall short of meeting the diverse statistical analysis requirements encountered in practical data analysis. As a promising sensitive data perturbation method, negative survey methods is able to complete the task of collecting sensitive information distribution while protecting personal privacy. Yet, existing negative survey methods are primarily designed for discrete sensitive information and are inadequate for real-valued data distributions. To bridge this gap, this paper proposes a novel real-value negative survey model, termed RVNS, for the first time in the field of real-value sensitive information collection. The RVNS model exempts users from the necessity of discretizing their data and only requires them to sample a set of data from a range that deviates from their actual sensitive details, thereby preserving the privacy of their genuine information. Moreover, to accurately capture the distribution of sensitive information, an optimization problem is formulated, and a novel approach is employed to solve it. Rigorous theoretical analysis demonstrates that the RVNS model conforms to the differential privacy model, ensuring robust privacy preservation. Comprehensive experiments conducted on both synthetic and real-world datasets further validate the efficacy of the proposed method.
The interactions within cloud-native applications are complex, with a constantly changing number of services and loads, posing higher demands on auto-scaling approach. This mainly involves several challenges such as microservices dependency analysis, performance profiling, anomaly detection, workload characterization and task co-location. Therefore, some advanced algorithms have been investigated into auto-scaling cloud-native applications to optimize system and application performance. These algorithms can learn from historical data and appropriately adjust resource allocation based on the current environment and load conditions to optimize resource utilization and system performance. In this paper, we systematically review the literature on state-of-the-art auto-scaling approaches for cloud-native applications from 2020, and further explore the technological evolution. Additionally, we propose a detailed taxonomy to categorize current research from five perspectives, including infrastructure, architecture, scaling methods, optimization objectives, and behavior modeling. Then, we provide a comprehensive comparison and in-depth discussion of the key features, advantages, limitations, and application scenarios of each approach, considering their performance in diverse environments and under various conditions. Finally, we summarize the current state of research in this field, identify the gaps and unresolved challenges, and emphasize promising directions for future exploration, particularly in areas such as the application of large models, microservice dependency management, and the use of meta-learning techniques to enhance model applicability and adaptability across different environments.
These notes are based on the three lectures that one of the authors gave at Tsinghua University in the summer of 2023 as part of the workshop on Geometric Representation Theory and Applications. They contain an introduction to the evaluation of $\mathsf{SL}(3)$ foams and the associated topological theory of trivalent planar graphs and foam cobordisms between them. A categorification of the Kuperberg quantum $\mathfrak{sl}_3$ web and link invariant and the Robert-Wagner $\mathsf{SL}(N)$ foam evaluation are reviewed as well.
HySafe-AI: Hybrid Safety Architectural Analysis Framework for AI Systems: A Case Study
2025-07-23
Mandar Pitale, Jelena Frtunikj, Abhinaw Priyadershi, Vasu Singh, Maria Spence
AI has become integral to safety-critical areas like autonomous driving systems (ADS) and robotics. The architecture of recent autonomous systems are trending toward end-to-end (E2E) monolithic architectures such as large language models (LLMs) and vision language models (VLMs). In this paper, we review different architectural solutions and then evaluate the efficacy of common safety analyses such as failure modes and effect analysis (FMEA) and fault tree analysis (FTA). We show how these techniques can be improved for the intricate nature of the foundational models, particularly in how they form and utilize latent representations. We introduce HySAFE-AI, Hybrid Safety Architectural Analysis Framework for AI Systems, a hybrid framework that adapts traditional methods to evaluate the safety of AI systems. Lastly, we offer hints of future work and suggestions to guide the evolution of future AI safety standards.
Probabilistic graphical modeling is a branch of machine learning that uses probability distributions to describe the world, make predictions, and support decision-making under uncertainty. Underlying this modeling framework is an elegant body of theory that bridges two mathematical traditions: probability and graph theory. This framework provides compact yet expressive representations of joint probability distributions, yielding powerful generative models for probabilistic reasoning. This tutorial provides a concise introduction to the formalisms, methods, and applications of this modeling framework. After a review of basic probability and graph theory, we explore three dominant themes: (1) the representation of multivariate distributions in the intuitive visual language of graphs, (2) algorithms for learning model parameters and graphical structures from data, and (3) algorithms for inference, both exact and approximate.
The Anatomy of Coronary Risk: How Artery Geometry Shapes Coronary Artery Disease through Blood Flow Haemodynamics -- Latest Methods, Insights and Clinical Implications
2025-07-23
C. Shen, M. Zhang, H. Keramati, S. Zhang, R. Gharleghi, J. J. Wentzel, M. O. Khan, U. Morbiducci, A. Qayyum, S. A. Niederer, S. Samant, Y. S. Chatzizisis, D. Almeida, Tsung-Ying Tsai, P. Serruys, S. Beier
Despite tremendous advances in cardiovascular medicine, significant opportunities remain to improve coronary artery disease (CAD) prevention and treatment strategies. The key limitation lies in the understanding of disease formation and progression mechanisms. The coronary anatomy plays an important role in local haemodynamics, governing endothelial health and, thus, pathophysiological responses. The significant variation of the coronary anatomy among patients, with significant trends across different populations, increases the complexity of understanding the details of disease progression. This review covers different aspects of the current status and understanding of the blood flow investigation in coronary arteries. We summarised the current knowledge of the haemodynamic effect of coronary anatomy and its evaluation and analysis methods. We discussed recent progress across medical imaging techniques and computational haemodynamic analysis. Based on the reviewed papers, we identified the persisting knowledge gaps and challenges in the field. We then elaborated on future directions and opportunities to increase understanding of the fundamental mechanism of CAD in individuals representative of large populations and how this may translate to the patient's bedside.
Use as Directed? A Comparison of Software Tools Intended to Check Rigor and Transparency of Published Work
2025-07-23
Peter Eckmann, Adrian Barnett, Alexandra Bannach-Brown, Elisa Pilar Bascunan Atria, Guillaume Cabanac, Louise Delwen Owen Franzen, Małgorzata Anna Gazda, Kaitlyn Hair, James Howison, Halil Kilicoglu, Cyril Labbe, Sarah McCann, Vladislav Nachev, Martijn Roelandse, Maia Salholz-Hillel, Robert Schulz, Gerben ter Riet, Colby Vorland, Anita Bandrowski, Tracey Weissgerber
The causes of the reproducibility crisis include lack of standardization and transparency in scientific reporting. Checklists such as ARRIVE and CONSORT seek to improve transparency, but they are not always followed by authors and peer review often fails to identify missing items. To address these issues, there are several automated tools that have been designed to check different rigor criteria. We have conducted a broad comparison of 11 automated tools across 9 different rigor criteria from the ScreenIT group. We found some criteria, including detecting open data, where the combination of tools showed a clear winner, a tool which performed much better than other tools. In other cases, including detection of inclusion and exclusion criteria, the combination of tools exceeded the performance of any one tool. We also identified key areas where tool developers should focus their effort to make their tool maximally useful. We conclude with a set of insights and recommendations for stakeholders in the development of rigor and transparency detection tools. The code and data for the study is available at https://github.com/PeterEckmann1/tool-comparison.
Natural Language Processing for Tigrinya: Current State and Future Directions
2025-07-23
Fitsum Gaim, Jong C. Park
Despite being spoken by millions of people, Tigrinya remains severely underrepresented in Natural Language Processing (NLP) research. This work presents a comprehensive survey of NLP research for Tigrinya, analyzing over 40 studies spanning more than a decade of work from 2011 to 2025. We systematically review the current state of computational resources, models, and applications across ten distinct downstream tasks, including morphological processing, machine translation, speech recognition, and question-answering. Our analysis reveals a clear trajectory from foundational, rule-based systems to modern neural architectures, with progress consistently unlocked by resource creation milestones. We identify key challenges rooted in Tigrinya's morphological complexity and resource scarcity, while highlighting promising research directions, including morphology-aware modeling, cross-lingual transfer, and community-centered resource development. This work serves as both a comprehensive reference for researchers and a roadmap for advancing Tigrinya NLP. A curated metadata of the surveyed studies and resources is made publicly available.
Combining geometric group theory techniques with geometric topology tools, we show how primitive cohomologies provide useful insights towards unifying the mathematical formulation of Gromov-Witten invariants. In particular, we emphasise the role played by geodesic laminations in analysing such invariants for the case of complete intersections in projective space.
The Ejecta Nebula Around the Wolf-Rayet Star WR 71
2025-07-23
Robert A. Fesen, Manuel C. Peitsch, Martin R. Pugh, Bray Falls, Marcel Drechsler
We present deep H$\alpha$ and [O III] images of the ejecta rich nebulosity associated with the suspected runaway and binary Wolf-Rayet star WR~71 (HD 143414). In H$\alpha$ emission, the nebula appears as a crescent shaped, broken ring of clumpy emission some $9' \times 13'$ in angular size centered to the south and west of the WR star. At a Gaia estimated distance of 4.27 kpc, the nebula has physical dimensions of $11 \times 16$ pc making it one of the larger known ejecta rich WR ring nebulae. Our [O III] image also show considerable surrounding faint nebulosity much of which may be unrelated to the WR star. A comparison of the nebula's optical appearance with that seen in WISE 22 $\mu$m data shows infrared coincidence with the nebula's brightest [O III] emission features. Deep H$\alpha$ and [O III] images like those presented here suggests that new and substantially deeper imaging reconnaissance of WR star nebulae compared to earlier surveys may lead to additional WR ring nebula detections, thereby enhancing our understanding on the frequency and formation of WR ring nebulae.
Exceptionality, Supersymmetry and Non-associativity in Physics
2025-07-23
Murat Gunaydin
This is an expanded and updated version of the talk I gave at the meeting organized at CERN on April 27-28, 2015 dedicated to honoring the memory of Bruno Zumino and his legacy. In this talk I review the emergence of exceptional structures and non-associative algebras in quantum mechanics, supergravity and M/superstring theories.
Flat Spectrum Radio Quasars as high-energy neutrino sources
2025-07-23
Alberto Moretti, Alessandro Caccianiga
The astrophysical sources responsible for the production of high energy neutrinos remain largely uncertain. The strongest associations suggest a correlation between neutrinos and active galactic nuclei (AGN). However, it is still unclear which specific regions and mechanisms of the accreting supermassive black hole (SMBH) are responsible for their production. In this paper we investigate the correlation between the positions of IceCat-1 neutrino events and a large, optically selected QSO catalog extracted from the Sloan Digital Sky Survey. Within this sample, we distinguish radio-quiet QSOs from flat-spectrum radio QSOs (FSRQs)based on radio emission data from the Cosmic Lens All Sky Survey (CLASS) catalog. While the associations between neutrino events and radio-quiet QSOs are consistent with being all random matches, FSRQs exhibit a moderately significant correlation (2.7 sigma) with neutrino positions. Additionally we observe that the distribution of minimum distances between neutrino events and FSRQs differs significantly for events at declinations above and below 20 deg. In particular, using the KS test, we find that the high-declination event distribution shows a strong deviation (4 sigma) from a random distribution. We interpret all these results as an indication that a large fraction (> 60%) of the neutrino events observed by IceCube could be produced by the FSRQ and that the emission mechanism is likely related to the relativistic jets rather than the "radio-quiet" (RQ) component of these sources, such as the accretion disk or corona.
A Sequential Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys
2025-07-23
Bradley D. Hutchinson, Catherine A. Pilachowski, Christian I. Johnson
Observational astronomy has undergone a significant transformation driven by large-scale surveys, such as the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) Survey, the Sloan Digital Sky Survey (SDSS), and the Gaia Mission. These programs yield large, complex datasets that pose significant challenges for conventional analysis methods, and as a result, many different machine learning techniques are being tested and deployed. We introduce a new approach to analyzing multiband photometry by using a long-short term memory autoencoder (LSTM-AE). This model views mean measurements of multicolor photometry as a sequence in wavelength in order to encode patterns present in the stars' spectral energy distributions (SEDs) into a two-dimensional latent space. We showcase this by using Pan-STARRS grizy mean magnitudes, and we use globular clusters, labels from SIMBAD, Gaia DR3 parallaxes, and PanSTARRS images to aid our analysis and understanding of the latent space. For 3,112,259 stars in an annulus around the North Galactic Cap, 99.51% have their full SED shape reconstructed--that is the absolute difference between the observed and the model predicted magnitude in every band--within five hundredths of a magnitude. We show that the model likely denoises photometric data, potentially improving the quality of measurements. Lastly, we show that the detection of rare stellar types can be performed by analyzing poorly reconstructed photometry.
A Step-by-step Guide on Nonlinear Model Predictive Control for Safe Mobile Robot Navigation
2025-07-23
Dennis Benders, Laura Ferranti, Johannes Köhler
Designing a Model Predictive Control (MPC) scheme that enables a mobile robot to safely navigate through an obstacle-filled environment is a complicated yet essential task in robotics. In this technical report, safety refers to ensuring that the robot respects state and input constraints while avoiding collisions with obstacles despite the presence of disturbances and measurement noise. This report offers a step-by-step approach to implementing Nonlinear Model Predictive Control (NMPC) schemes addressing these safety requirements. Numerous books and survey papers provide comprehensive overviews of linear MPC (LMPC), NMPC, and their applications in various domains, including robotics. This report does not aim to replicate those exhaustive reviews. Instead, it focuses specifically on NMPC as a foundation for safe mobile robot navigation. The goal is to provide a practical and accessible path from theoretical concepts to mathematical proofs and implementation, emphasizing safety and performance guarantees. It is intended for researchers, robotics engineers, and practitioners seeking to bridge the gap between theoretical NMPC formulations and real-world robotic applications. This report is not necessarily meant to remain fixed over time. If someone finds an error in the presented theory, please reach out via the given email addresses. We are happy to update the document if necessary.
EWOCS-IV: 1Ms ACIS Chandra observation of the supergiant B[e] star Wd1-9
2025-07-23
K. Anastasopoulou, M. G. Guarcello, J. J. Drake, B. Ritchie, M. De Becker, A. Bayo, F. Najarro, I. Negueruela, S. Sciortino, E. Flaccomio, R. Castellanos, J. F. Albacete-Colombo, M. Andersen, F. Damiani, F. Fraschetti, M. Gennaro, S. J. Gunderson, C. J. K. Larkin, J. Mackey, A. F. J. Moffat, P. Pradhan, S. Saracino, I. R. Stevens, G. Weigelt
Supergiant B[e] (sgB[e]) stars are exceptionally rare objects, with only a handful of confirmed examples in the Milky Way. The evolutionary pathways leading to the sgB[e] phase remain largely debated, highlighting the need for additional observations. The sgB[e] star Wd1-9, located in the massive cluster Westerlund 1 (Wd1), is enshrouded in a dusty cocoon--likely the result of past eruptive activity--leaving its true nature enigmatic. We present the most detailed X-ray study of Wd1-9 to date, using X-rays that pierce through its cocoon with the aim to uncover its nature and evolutionary state. We utilize 36 Chandra observations of Wd1 from the 'Extended Westerlund 1 and 2 Open Clusters Survey' (EWOCS), plus eight archival datasets, totalling 1.1 Ms. This dataset allows investigation of long-term variability and periodicity in Wd1-9, while X-ray colours and spectra are analysed over time to uncover patterns that shed light on its nature. Wd1-9 exhibits significant long-term X-ray variability, within which we identify a strong 14-day periodic signal. We interpret this as the orbital period, marking the first period determination for the system. The X-ray spectrum of Wd1-9 is thermal and hard (kT approximately 3.0 keV), resembling the spectra of bright Wolf-Rayet (WR) binaries in Wd1, while a strong Fe emission line at 6.7 keV indicates hot plasma from a colliding-wind X-ray binary. Wd1-9, with evidence of past mass loss, circumbinary material, a hard X-ray spectrum, and a newly detected 14-day period, displays all the hallmarks of a binary--likely a WR+OB--that recently underwent early Case B mass transfer. Its sgB[e] classification is likely phenomenological reflecting emission from the dense circumbinary material. This places Wd1-9 in a rarely observed phase, possibly revealing a newly formed WN star, bridging the gap between immediate precursors and later evolutionary stages in Wd1.
Probing the ionizing photon output of galaxies near cosmic dawn with the patchy kSZ effect
2025-07-23
Garett Lopez, Anson D'Aloisio, Christopher Cain
A key result from JWST's first cycles is that galaxy formation was well underway by $z=10$. The implications of these early galaxies for reionization are less clear, however. The CMB is one of the few windows into the ionization state of the IGM during reionization's first half, providing an important probe of the ionizing photon sources at those times. Meanwhile, measurements of the Lyman-$\alpha$ forest in the spectra of high-$z$ quasars have improved to the level of tightly constraining the timing of reionization's end. In this paper, we use radiative transfer simulations to explore how measurements of the patchy kinetic Sunyaev Zel'dovich (pkSZ) effect, when combined with Lyman-$\alpha$ forest measurements, can be used to constrain the early stages of reionization and the nature of its sources. For a given source model, we find that the amplitude of the pkSZ power spectra strongly correlates with the start time of reionization, and constrains the number of ionizing photons produced by the high-$z$ source population. Allowing for variations in the source model, this correlation is weakened by a degeneracy between the reionization history and the effects of source clustering. However, we demonstrate two potential ways of breaking this degeneracy using: (1) measurements of large-scale fluctuations in the Ly$\alpha$ forest opacity at $z=5-6$, and/or; (2) the shape of the pkSZ power spectrum measured in future CMB surveys. Models with highly clustered sources yield steeper slopes in the pkSZ power around $\ell = 3,000$, so measurements at additional angular scales can be used to break the history-clustering degeneracy. Our results highlight how future pkSZ measurements will complement JWST observations to improve our understanding of the ionizing sources near cosmic dawn.
From Extraterrestrial Microbes to Alien Intelligence: Rebalancing Astronomical Research Priorities
2025-07-23
Omer Eldadi, Gershon Tenenbaum, Abraham Loeb
We examine the funding disparity in astronomical research priorities: the Habitable Worlds Observatory is planned to receive over $10 billion over the next two decades whereas extraterrestrial intelligence research receives nearly zero federal funding. This imbalance is in contrast to both scientific value and public interest, as 65% of Americans and 58.2% of surveyed astrobiologists believe extraterrestrial intelligence exists. Empirical psychological research demonstrates that humanity possesses greater resilience toward extraterrestrial contact than historically recognized. Contemporary studies reveal adaptive responses rather than mass panic, conflicting with the rationale for excluding extraterrestrial intelligence research from federal funding since 1993. The response to the recent interstellar object 3I/ATLAS exemplifies consequences of this underinvestment: despite discovery forecasts of a new interstellar object every few months for the coming decade, no funded missions exist to intercept or closely study these visitors from outside the Solar System. We propose establishing a comprehensive research program to explore both biosignatures and technosignatures on interstellar objects. This program would address profound public interest while advancing detection capabilities and enabling potentially transformative discoveries in the search for extraterrestrial life. The systematic exclusion of extraterrestrial intelligence research represents institutional bias rather than scientific limitation, requiring immediate reconsideration of funding priorities.
Hyperbolic Deep Learning for Foundation Models: A Survey
2025-07-23
Neil He, Hiren Madhu, Ngoc Bui, Menglin Yang, Rex Ying
Foundation models pre-trained on massive datasets, including large language models (LLMs), vision-language models (VLMs), and large multimodal models, have demonstrated remarkable success in diverse downstream tasks. However, recent studies have shown fundamental limitations of these models: (1) limited representational capacity, (2) lower adaptability, and (3) diminishing scalability. These shortcomings raise a critical question: is Euclidean geometry truly the optimal inductive bias for all foundation models, or could incorporating alternative geometric spaces enable models to better align with the intrinsic structure of real-world data and improve reasoning processes? Hyperbolic spaces, a class of non-Euclidean manifolds characterized by exponential volume growth with respect to distance, offer a mathematically grounded solution. These spaces enable low-distortion embeddings of hierarchical structures (e.g., trees, taxonomies) and power-law distributions with substantially fewer dimensions compared to Euclidean counterparts. Recent advances have leveraged these properties to enhance foundation models, including improving LLMs' complex reasoning ability, VLMs' zero-shot generalization, and cross-modal semantic alignment, while maintaining parameter efficiency. This paper provides a comprehensive review of hyperbolic neural networks and their recent development for foundation models. We further outline key challenges and research directions to advance the field.
Computer vision applications are omnipresent nowadays. The current paper explores the use of fuzzy logic in computer vision, stressing its role in handling uncertainty, noise, and imprecision in image data. Fuzzy logic is able to model gradual transitions and human-like reasoning and provides a promising approach to computer vision. Fuzzy approaches offer a way to improve object recognition, image segmentation, and feature extraction by providing more adaptable and interpretable solutions compared to traditional methods. We discuss key fuzzy techniques, including fuzzy clustering, fuzzy inference systems, type-2 fuzzy sets, and fuzzy rule-based decision-making. The paper also discusses various applications, including medical imaging, autonomous systems, and industrial inspection. Additionally, we explore the integration of fuzzy logic with deep learning models such as convolutional neural networks (CNNs) to enhance performance in complex vision tasks. Finally, we examine emerging trends such as hybrid fuzzy-deep learning models and explainable AI.
Stellar Mass-Dispersion Measure Correlations Constrain Baryonic Feedback in Fast Radio Burst Host Galaxies
2025-07-22
Calvin Leung, Sunil Simha, Isabel Medlock, Daisuke Nagai, Kiyoshi W. Masui, Lordrick A. Kahinga, Adam E. Lanman, Shion Andrew, Kevin Bandura, Alice P. Curtin, B. M. Gaensler, Nina Gusinskaia, Ronniy C. Joseph, Mattias Lazda, Lluis Mas-Ribas, Bradley W. Meyers, Kenzie Nimmo, Aaron B. Pearlman, J. Xavier Prochaska, Mawson W. Sammons, Kaitlyn Shin, Kendrick Smith, Haochen Wang
Low redshift fast radio bursts (FRBs) provide robust measurements of the host-galaxy contribution to the dispersion measure (DM), which can constrain the circumgalactic medium (CGM) of the hosts. We curate a sample of 20 nearby FRBs with low scattering timescales and face-on host galaxies with stellar masses ranging from $10^9 < M^* / M_\odot < 10^{11}$. We fit the distribution of the host galaxy DM to a quadratic model as a function of stellar mass with a mass-independent scatter and find that the more massive the host, the lower its host DM. We report that this relation has a negative slope of $m = -97 \pm 44$ pc/cm$^{-3}$ per dex in stellar mass. We compare this measurement to similar fits to three sub-grid models implemented in the CAMELS suite of simulations from Astrid, IllustrisTNG, and SIMBA and find that fine-tuning of the host ISM contribution as a function of stellar mass is required in order to reconcile the observational data with the predictions of the fiducial CAMELS-Astrid model. More generally, models which attribute a positive correlation between stellar mass and host dispersion measure ($m > 0$) to the CGM are in tension with our measurement. We show that this conclusion is robust to a wide range of assumptions, such as the offset distribution of FRBs from their hosts and the statistics of the cosmic contribution to the DM budget along each sightline. Our results indirectly imply a lower limit on the strength of baryonic feedback in the Local Universe $(z < 0.2)$ in isolated $\sim L^*$ halos, complementing results from weak lensing surveys and kSZ observations which target higher halo mass and redshift ranges.
The SOFIA Massive (SOMA) Radio Survey. II. Radio Emission from High-Luminosity Protostars
2025-07-22
Francisco Sequeira-Murillo, Viviana Rosero, Joshua Marvil, Jonathan C. Tan, Ruben Fedriani, Yichen Zhang, Azia Robinson, Prasanta Gorai, Kei E. I. Tanaka, James M. De Buizer, Maria T. Beltrán, Ryan D. Boyden
We present centimeter continuum observations of seven high luminosity massive protostars and their surroundings as part of the SOFIA Massive (SOMA) Star Formation Survey. With data from the Very Large Array and the Australia Telescope Compact Array, we analyze the spectral index, morphology and multiplicity of the detected radio sources. The high sensitivity and high resolution observations allow us to resolve many sources. We report thirteen new detections and two previously known detections that we observed for the first time in radio frequencies. We use the observations to build radio spectral energy distributions (SEDs) to calculate spectral indices. With radio morphologies and the spectral indices, we give assessments on the nature of the sources, highlighting five sources that display a radio jet-like morphology and a spectral index consistent with ionized jets. Combining with the SOMA Radio I sample, we present the radio - bolometric luminosity relation, especially probing the regime from $L_{\rm bol}\sim 10^4$ to $10^6\:L_\odot$. Here we find a steep rise in radio luminosity, which is expected by models that transition from shock ionization to photoionization.
Almost uniform vs. pointwise convergence from a linear point of view
2025-07-22
L. Bernal-González, M. C. Calderón-Moreno, P. J. Gerlach-Mena, J. A. Prado-Bassas
A review of the state of the art of the comparison between any two different modes of convergence of sequences of measurable functions is carried out with focus on the algebraic structure of the families under analysis. As a complement of the amount of results obtained by several authors, it is proved, among other assertions and under natural assumptions, the existence of large vector subspaces as well as of large algebras contained in the family of the sequences of measurable functions converging to zero pointwise almost everywhere but not almost uniformly, and in the family of the sequences of measurable functions converging to zero almost uniformly but not uniformly almost everywhere.
AI-enhanced conversational agents for personalized asthma support Factors for engagement, value and efficacy
2025-07-22
Laura Moradbakhti, Dorian Peters, Jennifer K. Quint, Björn Schuller, Darren Cook, Rafael A. Calvo
Asthma-related deaths in the UK are the highest in Europe, and only 30% of patients access basic care. There is a need for alternative approaches to reaching people with asthma in order to provide health education, self-management support and bridges to care. Automated conversational agents (specifically, mobile chatbots) present opportunities for providing alternative and individually tailored access to health education, self-management support and risk self-assessment. But would patients engage with a chatbot, and what factors influence engagement? We present results from a patient survey (N=1257) devised by a team of asthma clinicians, patients, and technology developers, conducted to identify optimal factors for efficacy, value and engagement for a chatbot. Results indicate that most adults with asthma (53%) are interested in using a chatbot and the patients most likely to do so are those who believe their asthma is more serious and who are less confident about self-management. Results also indicate enthusiasm for 24/7 access, personalisation, and for WhatsApp as the preferred access method (compared to app, voice assistant, SMS or website). Obstacles to uptake include security/privacy concerns and skepticism of technological capabilities. We present detailed findings and consolidate these into 7 recommendations for developers for optimising efficacy of chatbot-based health support.
Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art
2025-07-22
Dayu Fan, Rui Meng, Xiaodong Xu, Yiming Liu, Guoshun Nan, Chenyuan Feng, Shujun Han, Song Gao, Bingxuan Xu, Dusit Niyato, Tony Q. S. Quek, Ping Zhang
With the rapid development of Generative Artificial Intelligence (GAI) technology, Generative Diffusion Models (GDMs) have shown significant empowerment potential in the field of wireless networks due to advantages, such as noise resistance, training stability, controllability, and multimodal generation. Although there have been multiple studies focusing on GDMs for wireless networks, there is still a lack of comprehensive reviews on their technological evolution. Motivated by this, we systematically explore the application of GDMs in wireless networks. Firstly, starting from mathematical principles, we analyze technical advantages of GDMs and present six representative models. Furthermore, we propose the multi-layer wireless network architecture including sensing layer, transmission layer, application layer, and security plane. We also introduce the core mechanisms of GDM at each of the layers. Subsequently, we conduct a rigorous review on existing GDM-based schemes, with a focus on analyzing their innovative points, the role of GDMs, strengths, and weaknesses. Ultimately, we extract key challenges and provide potential solutions, with the aim of providing directional guidance for future research in this field.
Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges
2025-07-22
Senyao Li, Haozhao Wang, Wenchao Xu, Rui Zhang, Song Guo, Jingling Yuan, Xian Zhong, Tianwei Zhang, Ruixuan Li
As large language models (LLMs) evolve, deploying them solely in the cloud or compressing them for edge devices has become inadequate due to concerns about latency, privacy, cost, and personalization. This survey explores a collaborative paradigm in which cloud-based LLMs and edge-deployed small language models (SLMs) cooperate across both inference and training. We present a unified taxonomy of edge-cloud collaboration strategies. For inference, we categorize approaches into task assignment, task division, and mixture-based collaboration at both task and token granularity, encompassing adaptive scheduling, resource-aware offloading, speculative decoding, and modular routing. For training, we review distributed adaptation techniques, including parameter alignment, pruning, bidirectional distillation, and small-model-guided optimization. We further summarize datasets, benchmarks, and deployment cases, and highlight privacy-preserving methods and vertical applications. This survey provides the first systematic foundation for LLM-SLM collaboration, bridging system and algorithm co-design to enable efficient, scalable, and trustworthy edge-cloud intelligence.
Simulating realistic Lyman-$α$ emitting galaxies including the effect of radiative transfer
2025-07-22
Hasti Khoraminezhad, Shun Saito, Max Gronke, Chris Byrohl
We present an empirical yet physically motivated simulation of realistic Lyman-$\alpha$ emitters (LAEs) at $z\sim2-3$, crucial for ongoing and forthcoming cosmological LAE surveys. We combine an empirical $\mathtt{UniverseMachine}$ galaxy-halo model with a simple spherical expanding shell model for the Lyman-$\alpha$ radiative transfer, calibrating only three free parameters to simultaneously reproduce the observed Lyman-$\alpha$ luminosity function and the angular clustering. Our LAE model is further supported by its consistency with other observables such as the Lyman-$\alpha$ equivalent width distribution, the Lyman-$\alpha$ escape fraction as a function of stellar mass and dust reddening, and the systemic velocity offsets. Our LAE model provides predictions for the halo occupation distributions for LAEs and relationship between Ly$\alpha$ luminosity and halo mass, including the distribution of satellite LAEs. Our work provides a crucial first step towards creating a high-fidelity LAE synthetic catalog for the LAE cosmology surveys. We make our LAE catalog and spectra publicly available upon publication.
Meta-Learning for Cold-Start Personalization in Prompt-Tuned LLMs
2025-07-22
Yushang Zhao, Huijie Shen, Dannier Li, Lu Chang, Chengrui Zhou, Yinuo Yang
Generative, explainable, and flexible recommender systems, derived using Large Language Models (LLM) are promising and poorly adapted to the cold-start user situation, where there is little to no history of interaction. The current solutions i.e. supervised fine-tuning and collaborative filtering are dense-user-item focused and would be expensive to maintain and update. This paper introduces a meta-learning framework, that can be used to perform parameter-efficient prompt-tuning, to effectively personalize LLM-based recommender systems quickly at cold-start. The model learns soft prompt embeddings with first-order (Reptile) and second-order (MAML) optimization by treating each of the users as the tasks. As augmentations to the input tokens, these learnable vectors are the differentiable control variables that represent user behavioral priors. The prompts are meta-optimized through episodic sampling, inner-loop adaptation, and outer-loop generalization. On MovieLens-1M, Amazon Reviews, and Recbole, we can see that our adaptive model outperforms strong baselines in NDCG@10, HR@10, and MRR, and it runs in real-time (i.e., below 300 ms) on consumer GPUs. Zero-history personalization is also supported by this scalable solution, and its 275 ms rate of adaptation allows successful real-time risk profiling of financial systems by shortening detection latency and improving payment network stability. Crucially, the 275 ms adaptation capability can enable real-time risk profiling for financial institutions, reducing systemic vulnerability detection latency significantly versus traditional compliance checks. By preventing contagion in payment networks (e.g., Fedwire), the framework strengthens national financial infrastructure resilience.
Profile Guided Optimization (PGO) uses runtime profiling to direct compiler optimization decisions, effectively combining static analysis with actual execution behavior to enhance performance. Runtime profiles, collected through instrumentation or hardware- and software-assisted sampling, provide detailed insights into control flow, branch predictions, and memory access patterns. This survey systematically categorizes PGO research by profiling method (instrumentation vs. sampling), optimizations (compile time and link/post-link time), compiler integration (GCC, LLVM), and target architectures. Key algorithms and frameworks are shown in terms of design principles. Performance evaluation on representative examples demonstrates PGO's speedups, overheads, and integration maturity. Finally, we identify open challenges, such as reducing sampling overhead, dynamic input workloads, and supporting cross-architecture portability, and propose future research directions to low-overhead profiling and advanced compilers.
Forecast constraints on null tests of the $Λ$CDM model with SPHEREx
2025-07-22
Alejandro Mata Román, Indira Ocampo, Savvas Nesseris
In this work we quantify the ability of the upcoming SPHEREx survey to constrain cosmological observables and test the internal consistency of the cosmological constant and cold dark matter ($\Lambda$CDM) model. Using Fisher matrix forecasting, we assess the expected precision on Baryon Acoustic Oscillations (BAO) observables, such as the angular diameter distance $D_\mathrm{A}(z)$ and the Hubble parameter $H(z)$. We further explore SPHEREx's potential to probe some of the fundamental assumptions of large-scale spatial homogeneity and isotropy, through model-independent reconstructions of several consistency tests of the $\Lambda$CDM model. In addition, we also examine the effect of the model dependence of the resulting Fisher and covariance matrices, using a neural network (NN) classification approach. We find that, while it is commonly assumed the covariance matrix depends weakly on the model, in fact the NN can very accurately ($\sim 98\%$) detect the underlying fiducial cosmological model based solely on the covariance matrix of the data, thus challenging this assumption.
AI for Better UX in Computer-Aided Engineering: Is Academia Catching Up with Industry Demands? A Multivocal Literature Review
2025-07-22
Choro Ulan Uulu, Mikhail Kulyabin, Layan Etaiwi, Nuno Miguel Martins Pacheco, Jan Joosten, Kerstin Röse, Filippos Petridis, Jan Bosch, Helena Holmström Olsson
Computer-Aided Engineering (CAE) enables simulation experts to optimize complex models, but faces challenges in user experience (UX) that limit efficiency and accessibility. While artificial intelligence (AI) has demonstrated potential to enhance CAE processes, research integrating these fields with a focus on UX remains fragmented. This paper presents a multivocal literature review (MLR) examining how AI enhances UX in CAE software across both academic research and industry implementations. Our analysis reveals significant gaps between academic explorations and industry applications, with companies actively implementing LLMs, adaptive UIs, and recommender systems while academic research focuses primarily on technical capabilities without UX validation. Key findings demonstrate opportunities in AI-powered guidance, adaptive interfaces, and workflow automation that remain underexplored in current research. By mapping the intersection of these domains, this study provides a foundation for future work to address the identified research gaps and advance the integration of AI to improve CAE user experience.
We present a theoretical study of axion echoes in the context of multiple ALP models. We begin by reviewing the single ALP case, deriving the conditions for resonance and echo formation. Starting from a set of N ALPs coupling to the photon, we then derive the relevant echo equations for both coherent and incoherent configurations. In the former case, we show that the echo power scales with N leading to sharper amplification and potentially improving projected bounds discussed earlier in literature. Small mass splittings between the ALPs further increase this amplification, even for a N = 2 case. We also outline the potential experimental implications of our results and discuss prospects for detecting these echoes in a wide range of ALP masses. In the incoherent scenario, we show that the random phases lead to a suppression of the echo power, eventually resulting in observable signals akin to or even weaker than the single ALP case.
Low D/H ratio for benzonitrile in TMC-1: Implication for the origin of polycyclic aromatic hydrocarbons in cold dark clouds
2025-07-22
A. L. Steber, J. Janeiro, C. Cabezas, M. Agundez, M. Pereira-Santaella, C. Perez, D. Perez, D. Heras, A. Lesarri, I. Garcia-Bernete, J. R. Goicoechea, J. Cernicharo
Radioastronomical observations have recently discovered PAHs of moderate size (up to 24 carbon atoms) in cold dark clouds, although it is currently unknown whether they are formed in situ through a bottom-up mechanism or from larger PAHs (20-100 carbon atoms) inherited from a previous diffuse stage in a top-down scenario. Infrared observations have recently shown that large PAHs present in UV-illuminated regions are strongly enriched in deuterium. In order to shed light on the origin of PAHs in cold clouds, we have searched for deuterated benzonitrile in the cold dark cloud TMC-1. To that purpose we have synthesized the three isomers (ortho, meta, and para) of monodeuterated benzonitrile, measured their rotational spectra across the 2-18 GHz and 75-110 GHz frequency ranges in the laboratory, and searched for them in TMC-1 using data from the QUIJOTE line survey. We did not detect any of the three species and have derived a 3sigma upper limit on the column density of each of them of 3.0e10 cm-2, meaning a fractional abundance relative to H2 of <3e-12. We derived a D/H ratio (which we define as the total number of D atoms with respect to the total number of H atoms present in benzonitrile) of <1.2 %. This value is in line with the range of D/H ratios observed for other molecules in TMC-1 (0.06-3.3 %), where deuterium enrichment is explained in terms of isotopic fractionation at low temperature. It is however below the range of D/H ratios derived for large unspecific PAHs from JWST observations of the galactic PDRs Orion Bar and M17 and the galaxies M51 and NGC3256-S (between 1% and <17%). Although it is not straightforward to compare the deuteration of PAHs in dark and UV-irradiated clouds, our results suggest that the population of PAHs detected in cold dark clouds does not result from the fragmentation of larger PAHs inherited from the previous diffuse stage in a top-down scenario.
Hybrid RISs for Simultaneous Tunable Reflections and Sensing
2025-07-22
George C. Alexandropoulos, Nir Shlezinger, Ioannis Gavras, Haiyang Zhang
The concept of smart wireless environments envisions dynamic programmable propagation of information-bearing signals through the deployment of Reconfigurable Intelligent Surfaces (RISs). Typical RIS implementations include metasurfaces with passive unit elements capable to reflect their incident waves in controllable ways. However, this solely reflective operation induces significant challenges in the RIS orchestration from the wireless network. For example, channel estimation, which is essential for coherent RIS-empowered wireless communications, is quite challenging with the available solely reflecting RIS designs. This chapter reviews the emerging concept of Hybrid Reflecting and Sensing RISs (HRISs), which enables metasurfaces to reflect the impinging signal in a controllable manner, while simultaneously sensing a portion of it. The sensing capability of HRISs facilitates various network management functionalities, including channel parameter estimation and localization, while, most importantly, giving rise to computationally autonomous and self-configuring RISs. The implementation details of HRISs are first presented, which are then followed by a convenient mathematical model for characterizing their dual functionality. Then, two indicative applications of HRISs are discussed, one for simultaneous communications and sensing and another that showcases their usefulness for estimating the individual channels in the uplink of a multi-user HRIS-empowered communication system. For both of these applications, performance evaluation results are included validating the role of HRISs for sensing as well as integrated sensing and communications.
In the era of big data applications, Federated Graph Learning (FGL) has emerged as a prominent solution that reconcile the tradeoff between optimizing the collective intelligence between decentralized datasets holders and preserving sensitive information to maximum. Existing FGL surveys have contributed meaningfully but largely focus on integrating Federated Learning (FL) and Graph Machine Learning (GML), resulting in early stage taxonomies that emphasis on methodology and simulated scenarios. Notably, a data centric perspective, which systematically examines FGL methods through the lens of data properties and usage, remains unadapted to reorganize FGL research, yet it is critical to assess how FGL studies manage to tackle data centric constraints to enhance model performances. This survey propose a two-level data centric taxonomy: Data Characteristics, which categorizes studies based on the structural and distributional properties of datasets used in FGL, and Data Utilization, which analyzes the training procedures and techniques employed to overcome key data centric challenges. Each taxonomy level is defined by three orthogonal criteria, each representing a distinct data centric configuration. Beyond taxonomy, this survey examines FGL integration with Pretrained Large Models, showcases realistic applications, and highlights future direction aligned with emerging trends in GML.
2025-07-22
Jérémy Besson, Javier Carballo, Christiana Pantelidou, Benjamin Withers
Black hole quasinormal modes arise as eigenmodes of a non-normal Hamiltonian and consequently they do not obey orthogonality relations with respect to commonly used inner products, for example, the energy inner product. A direct consequence of this is the appearance of transient phenomena. This review summarises current developments on the topic, both in frequency- and time-domain. In particular, we discuss the appearance of i) transient plateaus: arbitrarily long-lived sums of quasinormal modes, corresponding to localised energy packets near the future horizon; ii) transient growth, with the latter either appearing in the vicinity of black hole phase transitions or in the context of higher-derivative Sobolev norms.
Narrow-line Seyfert 1 galaxies (NLS1s) are generally known to be radio-quiet Active Galactic Nuclei (AGN), but a tiny subset of them are found to be extremely radio-loud with radio loudness parameter ($R_{\rm 1.4~GHz}$) $>$ 100. Given their rarity we investigated intra-night optical variability (INOV) and radio characteristics of a sample of 16 extremely radio-loud NLS1s. For all but four sample sources we report intra-night photometric monitoring for the first time with at least one monitoring session per source lasting for a minimum of 3.0 hours duration. In our sample, we detect INOV with a high duty cycle (up to 25 per cent) and large average amplitude ($\overline{\psi}$ $\sim$ 0.16) similar to that found in blazars. Using 3.0 GHz Very Large Array Sky Survey (VLASS) and auxiliary multi-frequency radio data we find that our RL-NLS1s are luminous ($L_{\rm 3.0~GHz}$ $\geq$ 10$^{24}$ W~Hz$^{-1}$), compact (less than a few kpc), variable, flat spectrum (${\alpha}_{\rm radio}$ $>$ -0.5) radio sources. The INOV, radio characteristics, and radio luminosity ($L_{\rm 1.4~GHz}$) versus super-massive black hole mass ($M_{\rm SMBH}$) plot infer that extremely radio-loud NLS1s are low-$z$ and low-luminosity analogs of flat spectrum radio quasars wherein the former are powered by, on average, one order-of-magnitude less massive SMBHs.
Measuring the Unmeasurable? Systematic Evidence on Scale Transformations in Subjective Survey Data
2025-07-22
Caspar Kaiser, Anthony Lepinteur
Economists routinely use survey measures of, for example, risk preferences, trust, political attitudes, or wellbeing. The literature generally treats numerical response categories as if they represent equal psychological intervals. We provide the first systematic test of this assumption, developing a general framework to quantify how easily results can be overturned when this linearity assumption is relaxed. Using original experimental data, we show that respondents interpret survey scales in ways that do deviate from linearity, but only mildly. Focusing on wellbeing research, we then replicate 30,000+ coefficient estimates across more than 80 papers published in top economics journals. Replicated coefficient signs are remarkably robust to mild departures from linear scale-use. However, statistical inference and estimates of relative effect magnitudes become unreliable, even under modest departures from linearity. This is especially problematic for policy applications. We show that these concerns generalise to many other widely used survey-based constructs.
Soft augmentation regularizes the supervised learning process of image classifiers by reducing label confidence of a training sample based on the magnitude of random-crop augmentation applied to it. This paper extends this adaptive label smoothing framework to other types of aggressive augmentations beyond random-crop. Specifically, we demonstrate the effectiveness of the method for random erasing and noise injection data augmentation. Adaptive label smoothing permits stronger regularization via higher-intensity Random Erasing. However, its benefits vanish when applied with a diverse range of image transformations as in the state-of-the-art TrivialAugment method, and excessive label smoothing harms robustness to common corruptions. Our findings suggest that adaptive label smoothing should only be applied when the training data distribution is dominated by a limited, homogeneous set of image transformation types.
GG-BBQ: German Gender Bias Benchmark for Question Answering
2025-07-22
Shalaka Satheesh, Katrin Klug, Katharina Beckh, Héctor Allende-Cid, Sebastian Houben, Teena Hassan
Within the context of Natural Language Processing (NLP), fairness evaluation is often associated with the assessment of bias and reduction of associated harm. In this regard, the evaluation is usually carried out by using a benchmark dataset, for a task such as Question Answering, created for the measurement of bias in the model's predictions along various dimensions, including gender identity. In our work, we evaluate gender bias in German Large Language Models (LLMs) using the Bias Benchmark for Question Answering by Parrish et al. (2022) as a reference. Specifically, the templates in the gender identity subset of this English dataset were machine translated into German. The errors in the machine translated templates were then manually reviewed and corrected with the help of a language expert. We find that manual revision of the translation is crucial when creating datasets for gender bias evaluation because of the limitations of machine translation from English to a language such as German with grammatical gender. Our final dataset is comprised of two subsets: Subset-I, which consists of group terms related to gender identity, and Subset-II, where group terms are replaced with proper names. We evaluate several LLMs used for German NLP on this newly created dataset and report the accuracy and bias scores. The results show that all models exhibit bias, both along and against existing social stereotypes.
Sparse-View 3D Reconstruction: Recent Advances and Open Challenges
2025-07-22
Tanveer Younis, Zhanglin Cheng
Sparse-view 3D reconstruction is essential for applications in which dense image acquisition is impractical, such as robotics, augmented/virtual reality (AR/VR), and autonomous systems. In these settings, minimal image overlap prevents reliable correspondence matching, causing traditional methods, such as structure-from-motion (SfM) and multiview stereo (MVS), to fail. This survey reviews the latest advances in neural implicit models (e.g., NeRF and its regularized versions), explicit point-cloud-based approaches (e.g., 3D Gaussian Splatting), and hybrid frameworks that leverage priors from diffusion and vision foundation models (VFMs).We analyze how geometric regularization, explicit shape modeling, and generative inference are used to mitigate artifacts such as floaters and pose ambiguities in sparse-view settings. Comparative results on standard benchmarks reveal key trade-offs between the reconstruction accuracy, efficiency, and generalization. Unlike previous reviews, our survey provides a unified perspective on geometry-based, neural implicit, and generative (diffusion-based) methods. We highlight the persistent challenges in domain generalization and pose-free reconstruction and outline future directions for developing 3D-native generative priors and achieving real-time, unconstrained sparse-view reconstruction.
LLM-Driven Collaborative Model for Untangling Commits via Explicit and Implicit Dependency Reasoning
2025-07-22
Bo Hou, Xin Tan, Kai Zheng, Fang Liu, Yinghao Zhu, Li Zhang
Atomic commits, each of which addresses a single development concern, are a best practice in software development. However, developers frequently produce tangled commits that mix unrelated changes due to practical constraints or unclear boundaries, negatively impacting code review and maintenance. Although prior commit untangling approaches: rule-based, feature-based, or graph-based, have made progress, they often rely on shallow signals and fail to distinguish between explicit dependencies (e.g., control/data flow) and implicit ones (e.g., semantic or conceptual relationships). In this paper, we propose ColaUntangle, a new collaborative consultation framework for commit untangling that models both explicit and implicit dependencies among code changes. ColaUntangle integrates Large Language Model (LLM)-driven agents in a multi-agent architecture: one agent specializes in explicit dependencies, another in implicit ones, and a reviewer agent synthesizes their perspectives through iterative consultation. To capture explicit and implicit contextual information, we construct multi-version Program Dependency Graphs (delta-PDG), enabling agents to reason over code relationships with both symbolic and semantic depth. We evaluate ColaUntangle on two widely-used datasets (1,612 C# and 14k Java tangled commits). Experimental results show that ColaUntangle outperforms the best-performing baseline, achieving an improvement of 44% on the C# dataset and 100% on the Java dataset. These findings highlight the potential of LLM-based collaborative frameworks for advancing automated commit untangling tasks.
Recent advances on multimessenger astrophysics: Centaurus A, GW 170817, and KM3-230213A
2025-07-22
Cainã de Oliveira, Vitor de Souza
We review recent advances in multimessenger astrophysics, with particular emphasis on Centaurus A and on two pivotal events: the gravitational-wave detection GW 170817 and the ultra-high-energy neutrino KM3-230213A. Centaurus A is the prototype multimessenger source. The GW 170817 event, arising from a binary neutron star merger, marked a transformative moment in astronomy through the joint observation of gravitational waves and a broad spectrum of electromagnetic signals. The KM3-230213A neutrino, detected by the KM3NeT Collaboration, is the most energetic neutrino observed to date and poses significant challenges for current models, given its tension with null results from IceCube and the Pierre Auger Observatory. We assess astrophysical interpretations, including galactic, cosmogenic, and transient extragalactic sources, as well as implications for cosmic-ray acceleration. These cases underscore the scientific potential of high-energy multimessenger events in probing both the extreme universe and new physics.
Enhancing patent retrieval using automated patent summarization
2025-07-22
Eleni Kamateri, Renukswamy Chikkamath, Michail Salampasis, Linda Andersson, Markus Endres
Effective query formulation is a key challenge in long-document Information Retrieval (IR). This challenge is particularly acute in domain-specific contexts like patent retrieval, where documents are lengthy, linguistically complex, and encompass multiple interrelated technical topics. In this work, we present the application of recent extractive and abstractive summarization methods for generating concise, purpose-specific summaries of patent documents. We further assess the utility of these automatically generated summaries as surrogate queries across three benchmark patent datasets and compare their retrieval performance against conventional approaches that use entire patent sections. Experimental results show that summarization-based queries significantly improve prior-art retrieval effectiveness, highlighting their potential as an efficient alternative to traditional query formulation techniques.
QFT vector models: Definitions and basic properties
2025-07-22
Matti Raasakka
QFT vector models are a newly developed approach to quantum gravity. In this proceedings paper, we review in short form some basic definitions and properties related to these models, and point out directions for future research.
Despite the integration of safety alignment and external filters, text-to-image (T2I) generative models are still susceptible to producing harmful content, such as sexual or violent imagery. This raises serious concerns about unintended exposure and potential misuse. Red teaming, which aims to proactively identify diverse prompts that can elicit unsafe outputs from the T2I system (including the core generative model as well as potential external safety filters and other processing components), is increasingly recognized as an essential method for assessing and improving safety before real-world deployment. Yet, existing automated red teaming approaches often treat prompt discovery as an isolated, prompt-level optimization task, which limits their scalability, diversity, and overall effectiveness. To bridge this gap, in this paper, we propose DREAM, a scalable red teaming framework to automatically uncover diverse problematic prompts from a given T2I system. Unlike most prior works that optimize prompts individually, DREAM directly models the probabilistic distribution of the target system's problematic prompts, which enables explicit optimization over both effectiveness and diversity, and allows efficient large-scale sampling after training. To achieve this without direct access to representative training samples, we draw inspiration from energy-based models and reformulate the objective into simple and tractable objectives. We further introduce GC-SPSA, an efficient optimization algorithm that provide stable gradient estimates through the long and potentially non-differentiable T2I pipeline. The effectiveness of DREAM is validated through extensive experiments, demonstrating that it surpasses 9 state-of-the-art baselines by a notable margin across a broad range of T2I models and safety filters in terms of prompt success rate and diversity.
Mind the Gap: Evaluating the Representativeness of Quantitative Medical Language Reasoning LLM Benchmarks for African Disease Burdens
2025-07-22
Fred Mutisya, Shikoh Gitau, Christine Syovata, Diana Oigara, Ibrahim Matende, Muna Aden, Munira Ali, Ryan Nyotu, Diana Marion, Job Nyangena, Nasubo Ongoma, Keith Mbae, Elizabeth Wamicha, Eric Mibuari, Jean Philbert Nsengemana, Talkmore Chidede
Introduction: Existing medical LLM benchmarks largely reflect examination syllabi and disease profiles from high income settings, raising questions about their validity for African deployment where malaria, HIV, TB, sickle cell disease and other neglected tropical diseases (NTDs) dominate burden and national guidelines drive care. Methodology: We systematically reviewed 31 quantitative LLM evaluation papers (Jan 2019 May 2025) identifying 19 English medical QA benchmarks. Alama Health QA was developed using a retrieval augmented generation framework anchored on the Kenyan Clinical Practice Guidelines. Six widely used sets (AfriMedQA, MMLUMedical, PubMedQA, MedMCQA, MedQAUSMLE, and guideline grounded Alama Health QA) underwent harmonized semantic profiling (NTD proportion, recency, readability, lexical diversity metrics) and blinded expert rating across five dimensions: clinical relevance, guideline alignment, clarity, distractor plausibility, and language/cultural fit. Results: Alama Health QA captured >40% of all NTD mentions across corpora and the highest within set frequencies for malaria (7.7%), HIV (4.1%), and TB (5.2%); AfriMedQA ranked second but lacked formal guideline linkage. Global benchmarks showed minimal representation (e.g., sickle cell disease absent in three sets) despite large scale. Qualitatively, Alama scored highest for relevance and guideline alignment; PubMedQA lowest for clinical utility. Discussion: Quantitative medical LLM benchmarks widely used in the literature underrepresent African disease burdens and regulatory contexts, risking misleading performance claims. Guideline anchored, regionally curated resources such as Alama Health QA and expanded disease specific derivatives are essential for safe, equitable model evaluation and deployment across African health systems.
A Distributional View of High Dimensional Optimization
2025-07-22
Felix Benning
This PhD thesis presents a distributional view of optimization in place of a worst-case perspective. We motivate this view with an investigation of the failure point of classical optimization. Subsequently we consider the optimization of a randomly drawn objective function. This is the setting of Bayesian Optimization. After a review of Bayesian optimization we outline how such a distributional view may explain predictable progress of optimization in high dimension. It further turns out that this distributional view provides insights into optimal step size control of gradient descent. To enable these results, we develop mathematical tools to deal with random input to random functions and a characterization of non-stationary isotropic covariance kernels. Finally, we outline how assumptions about the data, specifically exchangability, can lead to random objective functions in machine learning and analyze their landscape.
Text-to-image (T2I) diffusion models have achieved impressive image generation quality and are increasingly fine-tuned for personalized applications. However, these models often inherit unsafe behaviors from toxic pretraining data, raising growing safety concerns. While recent safety-driven unlearning methods have made promising progress in suppressing model toxicity, they are identified to be fragile to downstream fine-tuning, where we reveal that state-of-the-art methods largely fail to retain their effectiveness even when fine-tuned on entirely benign datasets. To mitigate this problem, in this paper, we propose ResAlign, a safety-driven unlearning framework with enhanced resilience against downstream fine-tuning. By modeling downstream fine-tuning as an implicit optimization problem with a Moreau Envelope-based reformulation, ResAlign enables efficient gradient estimation to minimize the recovery of harmful behaviors. Additionally, a meta-learning strategy is proposed to simulate a diverse distribution of fine-tuning scenarios to improve generalization. Extensive experiments across a wide range of datasets, fine-tuning methods, and configurations demonstrate that ResAlign consistently outperforms prior unlearning approaches in retaining safety after downstream fine-tuning while preserving benign generation capability well.
ResearcherBench: Evaluating Deep AI Research Systems on the Frontiers of Scientific Inquiry
2025-07-22
Tianze Xu, Pengrui Lu, Lyumanshan Ye, Xiangkun Hu, Pengfei Liu
The emergence of deep research systems presents significant capabilities in problem-solving, extending from basic queries to sophisticated research tasks. However, existing benchmarks primarily evaluate these systems as agents for web retrieval and report generation, overlooking their potential to discover novel insights on the frontiers of scientific research. To address this gap, we introduce ResearcherBench, the first benchmark focused on evaluating the capabilities of these advanced, agentic systems - which we refer to as Deep AI Research Systems (DARS) - on frontier AI scientific questions. We compiled a dataset of 65 research questions expertly selected from real-world scientific scenarios such as laboratory discussions and interviews, spanning 35 different AI subjects and categorized into three types: technical details, literature review, and open consulting. Our dual evaluation framework combines rubric assessment, which uses expert-designed criteria to evaluate insight quality, with factual assessment, which measures citation accuracy (faithfulness) and coverage (groundedness). We evaluated several leading commercial DARS and baseline systems. Results show that OpenAI Deep Research and Gemini Deep Research significantly outperform other systems, with particular strength in open-ended consulting questions. Such capabilities represent a meaningful step toward AI self-improvement, aligning with the vision of ASI for AI. We open-source ResearcherBench to provide a standardized platform for promoting the development of next-generation AI research assistants, hoping to foster a new perspective in AI research evaluation for a novel pattern of scientific collaboration: https://github.com/GAIR-NLP/ResearcherBench.
From Contracts to Code: Automating Smart Contract Generation with Multi-Level Finite State Machines
2025-07-22
Lambard Maxence, Bertelle Cyrille, Duvallet Claude
In an increasingly complex contractual landscape, the demand for transparency, security, and efficiency has intensified. Blockchain technology, with its decentralized and immutable nature, addresses these challenges by reducing intermediary costs, minimizing fraud risks, and enhancing system compatibility. Smart contracts, initially conceptualized by Nick Szabo and later implemented on the Ethereum blockchain, automate and secure contractual clauses, offering a robust solution for various industries. However, their complexity and the requirement for advanced programming skills present significant barriers to widespread adoption. This study introduces a multi-level finite state machine model designed to represent and track the execution of smart contracts. Our model aims to simplify smart contract development by providing a formalized framework that abstracts underlying technical complexities, making it accessible to professionals without deep technical expertise. The hierarchical structure of the multi-level finite state machine enhances contract modularity and traceability, facilitating detailed representation and evaluation of functional properties. The paper explores the potential of this multi-level approach, reviewing existing methodologies and tools, and detailing the smart contract generation process with an emphasis on reusable components and modularity. We also conduct a security analysis to evaluate potential vulnerabilities in our model, ensuring the robustness and reliability of the generated smart contracts.
Animal Interaction with Autonomous Mobility Systems: Designing for Multi-Species Coexistence
2025-07-22
Tram Thi Minh Tran, Xinyan Yu, Marius Hoggenmueller, Callum Parker, Paul Schmitt, Julie Stephany Berrio Perez, Stewart Worrall, Martin Tomitsch
Autonomous mobility systems increasingly operate in environments shared with animals, from urban pets to wildlife. However, their design has largely focused on human interaction, with limited understanding of how non-human species perceive, respond to, or are affected by these systems. Motivated by research in Animal-Computer Interaction (ACI) and more-than-human design, this study investigates animal interactions with autonomous mobility through a multi-method approach combining a scoping review (45 articles), online ethnography (39 YouTube videos and 11 Reddit discussions), and expert interviews (8 participants). Our analysis surfaces five key areas of concern: Physical Impact (e.g., collisions, failures to detect), Behavioural Effects (e.g., avoidance, stress), Accessibility Concerns (particularly for service animals), Ethics and Regulations, and Urban Disturbance. We conclude with design and policy directions aimed at supporting multispecies coexistence in the age of autonomous systems. This work underscores the importance of incorporating non-human perspectives to ensure safer, more inclusive futures for all species.
The mass of TOI-654 b: A short-period sub-Neptune transiting a mid-M dwarf
2025-07-22
Kai Ikuta, Norio Narita, Takuya Takarada, Teruyuki Hirano, Akihiko Fukui, Hiroyuki Tako Ishikawa, Yasunori Hori, Tadahiro Kimura, Takanori Kodama, Masahiro Ikoma, Jerome P. de Leon, Kiyoe Kawauchi, Masayuki Kuzuhara, Gaia Lacedelli, John H. Livingston, Mayuko Mori, Felipe Murgas, Enric Palle, Hannu Parviainen, Noriharu Watanabe, Izuru Fukuda, Hiroki Harakawa, Yuya Hayashi, Klaus Hodapp, Keisuke Isogai, Taiki Kagetani, Yugo Kawai, Vigneshwaran Krishnamurthy, Tomoyuki Kudo, Takashi Kurokawa, Nobuhiko Kusakabe, Jun Nishikawa, Stevanus K. Nugroho, Masashi Omiya, Takuma Serizawa, Aoi Takahashi, Huan-Yu Teng, Yuka Terada, Akitoshi Ueda, Sébastien Vievard, Yujie Zou, Takayuki Kotani, Motohide Tamura
Sub-Neptunes are small planets between the size of the Earth and Neptune. The orbital and bulk properties of transiting sub-Neptunes can provide clues for their formation and evolution of small planets. In this paper, we report on follow-up observations of a planetary system around the mid-M dwarf TOI-654, whose transiting sub-Neptune TOI-654 b ($P=1.53$ day) is validated as a suitable target for the atmospheric observation. We measure the planetary mass and stellar properties with the InfraRed Doppler instrument (IRD) mounted on the Subaru telescope and obtain the stellar and planetary properties from additional transit observations by the Transit Exoplanetary Survey Satellite (TESS) and a series of the Multicolor Simultaneous Camera for studying Atmospheres of Transiting exoplanets (MuSCAT). As a result, the planetary mass of TOI-654 b is determined to be $M_{{\rm p}} = 8.71 \pm 1.25 M_{\oplus}$, and the radius is updated to be $R_{\rm p} = 2.378 \pm 0.089 R_{\oplus}$. The bulk density suggests that the planet is composed of a rocky and volatile-rich core or a rocky core surrounded by a small amount of H/He envelope.TOI-654 b is one of unique planets located around the radius valley and and also on the outer edge of the Neptune desert. The precise mass determination enables us to constrain the atmospheric properties with future spectroscopic observations especially for the emission by the James Webb Space Telescope and Ariel.
Galaxy Evolution in the Local Universe: Group Richness Effects on Mergers and Non-Mergers
2025-07-22
Pius Privatus, Umananda Dev Goswami
In this study, we use the luminous volume-limited samples obtained from the twelfth release of Sloan Digital Sky Survey data and mergers from Galaxy Zoo Project to investigate the influence of group richness in shaping galaxy properties' distributions and their relationships in the local Universe by comparison of mergers and non-mergers. The galaxies were restricted into mass-limited subsamples of low-mass, intermediate-mass and high-mass, assigned into groups from poor to rich group systems, where the distributions of star formation rate (SFR), specific SFR (SSFR), spectral index D$_n$ (4000) and $u-r$ colour properties between mergers and non-mergers for all subsamples and their relations with stellar mass of galaxies are compared. The study revealed a significant difference in the distributions between mergers' and non-mergers' properties for low-mass galaxies, while for high-mass galaxies the difference is very weak. For the low-mass sample, mergers possess higher SFR, SSFR than non-mergers when the group richness is kept constant, while for high-mass poor group galaxies have higher SFR, SSFR than rich group galaxies when merging status is kept constant. Mergers resemble young stellar populations and are bluer than non-mergers for low-mass, while for high-mass, mergers and non-mergers have comparable SFR, SSFR, D$_n$ (4000), and $u-r$ colour. The study concludes that group richness and stellar mass influence the mergers' and non-mergers' properties' distributions, and their relationships.
An AI super-resolution field emulator for cosmological hydrodynamics: the Lyman-α forest
2025-07-22
Fatemeh Hafezianzadeh, Xiaowen Zhang, Yueying Ni, Rupert A. C. Croft, Tiziana DiMatteo, Mahdi Qezlou, Simeon Bird
We extend our super-resolution and emulation framework for cosmological dark matter simulations to include hydrodynamics. We present a two-stage deep learning model to emulate high-resolution (HR-HydroSim) baryonic fields from low-resolution (LR-HydroSim) simulations at redshift $z = 3$. The method takes as inputs an LR-HydroSim and the high-resolution initial conditions (HR-HydroICs). First, the model stochastically generates high-resolution baryonic fields from the LR-HydroSim. Second, a deterministic emulator refines these fields using HR-HydroICs to reconstruct small-scale structures including displacement, velocity, internal energy, and gas/star classification. Trained on paired low- and high-resolution simulations produced with \texttt{MP-Gadget}, the model captures small-scale structures of the intergalactic medium and %Lyman-$\alpha$ forest observables down to the 100 kpc pressure smoothing scale relevant to the Lyman-$\alpha$ forest. The model achieves subpercent error for overdensity, temperature, velocity, and optical depth fields, a mean relative error of 1.07\% in the large-scale flux power spectrum (\(k < 3 \times 10^{-2}\ \mathrm{s/km}\)), and less than 10\% error in the flux probability distribution function. Notably, the two-stage model reduces the compute time by a factor of $\sim$450 compared to full smoothed particle hydrodynamics at the same resolution. This work demonstrates the potential of this framework as a powerful and efficient tool for generating high-resolution fields offering fast and accurate alternatives to traditional cosmological hydrodynamic simulations and enabling large-volume mock datasets for next-generation cosmological surveys.
The VISCACHA survey XIV. Chemical evolution history of the SMC: The Southern Bridge Clusters
2025-07-22
Sasi Saroon, B. Dias, T. Tsujimotto, F. Maia, B. P. L. Ferreira, R. A. P. Oliveira, M. C. Parisi, A. Pérez-Villegas, D. Minniti, B. J. De Bortoli, E. Bica, P. Westera, O. J. Katime Santrich, D. Geisler, David Sanmartim, Bruno Correa Quint, Luciano Fraga, J. F. C. Santos Jr., E. R. Garro, Jose G. Fernandez-Trincado, O. O. Casmir
The chemical evolution history of the Small Magellanic Cloud (SMC) is complex and is best understood through a comprehensive analysis of star clusters across its various regions. The VISCACHA survey aims to fully explain the chemical evolution of SMC star clusters by analyzing different sub-regions adopted from an existing framework. The west halo (WH) region, which contains the oldest and most metal-poor stellar populations, exhibits a clear age-metallicity relation (AMR) with minimal dispersion. This region shows a significant dip of ~0.5 dex in metallicity approximately 6 Gyr ago. This was likely caused by a major merger event that subsequently accelerated the star formation rate. Clusters in the Southern Bridge (SB) and Northern Bridge regions of the SMC may have experienced distinct chemical enrichment histories, as suggested by our previous works but with limited data coverage. Furthermore, the AMR of wing/bridge (W/B) shows no sign of enrichment caused by the aforementioned merger event, but exhibits signatures of the recent collisions between the clouds contemporaneous with the epochs of the Magellanic Stream and Bridge formations. In this study, we present an updated AMR for the SB region based on a sample that includes approximately 67% of its known clusters. Contrary to the expectation of a very unique chemical evolution history, these SB clusters show a trend similar to the one of the WH clusters. The chemical evolution models that best fit the AMR trend of the SB clusters show excellent agreement with the major merger model proposed for the WH clusters. Building on this, we suggest a new unified chemical evolution model for both the WH and SB clusters, which can be explained by a major merger at ~6 Gyr followed by episodic chemical enrichment over time.
Social robots are embodied agents that interact with people while following human communication norms. These robots interact using verbal and non-verbal cues, and share the physical environments of people. While social robots have previously utilized rule-based systems or probabilistic models for user interaction, the rapid evolution of large language models (LLMs) presents new opportunities to develop LLM-empowered social robots for enhanced human-robot interaction. To fully realize these capabilities, however, robots need to collect data such as audio, fine-grained images, video, and locations. As a result, LLMs often process sensitive personal information, particularly within home environments. Given the tension between utility and privacy risks, evaluating how current LLMs manage sensitive data is critical. Specifically, we aim to explore the extent to which out-of-the-box LLMs are privacy-aware in the context of household social robots. In this study, we present a set of privacy-relevant scenarios crafted through the lens of Contextual Integrity (CI). We first survey users' privacy preferences regarding in-home social robot behaviors and then examine how their privacy orientation affects their choices of these behaviors (N = 450). We then provide the same set of scenarios and questions to state-of-the-art LLMs (N = 10) and find that the agreement between humans and LLMs is low. To further investigate the capabilities of LLMs as a potential privacy controller, we implement four additional prompting strategies and compare their results. Finally, we discuss the implications and potential of AI privacy awareness in human-robot interaction.
Few-Shot Learning in Video and 3D Object Detection: A Survey
2025-07-22
Md Meftahul Ferdaus, Kendall N. Niles, Joe Tom, Mahdi Abdelguerfi, Elias Ioup
Few-shot learning (FSL) enables object detection models to recognize novel classes given only a few annotated examples, thereby reducing expensive manual data labeling. This survey examines recent FSL advances for video and 3D object detection. For video, FSL is especially valuable since annotating objects across frames is more laborious than for static images. By propagating information across frames, techniques like tube proposals and temporal matching networks can detect new classes from a couple examples, efficiently leveraging spatiotemporal structure. FSL for 3D detection from LiDAR or depth data faces challenges like sparsity and lack of texture. Solutions integrate FSL with specialized point cloud networks and losses tailored for class imbalance. Few-shot 3D detection enables practical autonomous driving deployment by minimizing costly 3D annotation needs. Core issues in both domains include balancing generalization and overfitting, integrating prototype matching, and handling data modality properties. In summary, FSL shows promise for reducing annotation requirements and enabling real-world video, 3D, and other applications by efficiently leveraging information across feature, temporal, and data modalities. By comprehensively surveying recent advancements, this paper illuminates FSL's potential to minimize supervision needs and enable deployment across video, 3D, and other real-world applications.
The AURORA Survey: Robust Helium Abundances at High Redshift Reveal A Subpopulation of Helium-Enhanced Galaxies in the Early Universe
2025-07-22
Danielle A. Berg, Ryan L. Sanders, Alice E. Shapley, Michael W. Topping, Naveen A. Reddy, Evan D. Skillman, Erik Aver, Fergus Cullen, Callum T. Donnan, James S. Dunlop, Tucker Jones, Ali Ahmad Khostovan, Derek J. McLeod, Desika Narayanan, Pascal A. Oesch, Anthony J. Pahl, Max Pettini, N. M. Förster Schreiber, Daniel P. Stark
We present the first robust helium (He) abundance measurements in star-forming galaxies at redshifts $1.6\lesssim z\lesssim 3.3$ using deep, moderate-resolution JWST/NIRSpec spectroscopy from the AURORA survey. We establish a High$-z$ HeI Sample consisting of 20 galaxies with multiple high-S/N ($>5\sigma$) HeI emission-line detections, including the critical near-infrared $\lambda$10833 line. This is the first study at high redshift leveraging $\lambda$10833 to break degeneracies between temperature, electron density, optical depth, and He$^+$/H$^+$, enabling reliable He abundance determinations in the early universe. We use a custom MCMC framework incorporating direct-method electron temperature priors, extended optical depth ($\tau_{\lambda3890}$) model grids up to densities of $10^6$~cm$^{-3}$, and simultaneous fits of the physical conditions and HeI/HI line ratios to derive ionic He$^+$/H$^+$ abundances. Most of the AURORA galaxies follow the extrapolated $z\sim0$ He/H-O/H trend, indicating modest He enrichment by $z\sim2-3$. However, we identify a subpopulation of four galaxies that exhibit elevated He mass fractions ($\Delta Y>0.03$) without corresponding enhancements in N/O or $\alpha$-elements ($\sim20$% of the sample). This abundance pattern is inconsistent with enrichment from asymptotic giant branch stars, but favors early He enrichment from very massive stars (VMSs; $M\gtrsim100\ M_\odot$), which can eject He-rich, N-poor material via stellar winds and binary stripping in young stellar populations. We speculate that these elevated-He systems may represent an early phase of globular cluster (GC) formation where N enrichment is still lagging behind He production. This work demonstrates the power of JWST multi-line HeI spectroscopy for tracing early stellar feedback, enrichment pathways, and GC progenitor signatures in the high-z universe.
Pioneering Advancements of 2D Graphene: Energy and Electronics applications
2025-07-22
Abdulrhman M. Alaraj, Aya A. Esmaeil, Mohamed A. Khamis, Naglaa M. Zian, Fatma Sameh, Abdallah M. Abdeldaiem, Ebrahem H. Abdelaal, Walid J. Hamouda, Sara R. Ghazal, Habiba E. El-Sayegh, Aml F. Dawood, Fayza R. Ramadan, Haneen A. Saad, Mena K. Selema, Nora H. El-Mowafy, Mohamed M. Kedra, Ahmed K. Abozaid, Ahmed M. Ragab, Ahmed A. El-Naggar, Walid Ismail, Swellam W. Sharshir, Abdelhamid El-Shaer, Mahmoud Abdelfatah
This review explores the synthesis, characterization, and potential applications of graphene, a two-dimensional material with exceptional properties. Graphene's versatility in energy and electronics applications is highlighted, with its high conductivity and huge surface area facilitating improved energy storage capabilities in supercapacitors and batteries. In electronics, graphene is revolutionizing the industry by enabling the development of flexible displays, high-speed transistors, and enhanced thermal management systems. The integration of graphene into composite materials presents opportunities for stronger, lighter, and more conductive materials. The study provides a comprehensive overview of graphene's current and future impact on technology, emphasizing its transformative potential in energy solutions and electronic advancements. In the energy sector, graphene's integration into batteries, energy storage systems, capacitors, fuel cells, and renewable energy technologies signifies a leap forward in efficiency, capacity, and sustainability. In the electronics sector, graphene's unique characteristics are utilized in RFID, sensors, and EMI shielding, leading to communication, security, and device miniaturization advancements. The study underscores graphene's potential to spearhead future innovations, reinforcing its status as a pivotal material in the ongoing technological evolution.
Towards Trustworthy AI: Secure Deepfake Detection using CNNs and Zero-Knowledge Proofs
2025-07-22
H M Mohaimanul Islam, Huynh Q. N. Vo, Aditya Rane
In the era of synthetic media, deepfake manipulations pose a significant threat to information integrity. To address this challenge, we propose TrustDefender, a two-stage framework comprising (i) a lightweight convolutional neural network (CNN) that detects deepfake imagery in real-time extended reality (XR) streams, and (ii) an integrated succinct zero-knowledge proof (ZKP) protocol that validates detection results without disclosing raw user data. Our design addresses both the computational constraints of XR platforms while adhering to the stringent privacy requirements in sensitive settings. Experimental evaluations on multiple benchmark deepfake datasets demonstrate that TrustDefender achieves 95.3% detection accuracy, coupled with efficient proof generation underpinned by rigorous cryptography, ensuring seamless integration with high-performance artificial intelligence (AI) systems. By fusing advanced computer vision models with provable security mechanisms, our work establishes a foundation for reliable AI in immersive and privacy-sensitive applications.
On embedding theorems in analytic and harmonic function spaces of several complex variables in some domains in $C^n$
2025-07-22
R. F. Shamoyan, M. G. Bashmakova
In this survey we collect some recent advances concerning embedding theorems in analytic and harmonic function spaces of several complex variables in various domains in $C^n$. The paper is based mainly only on recent results in this research area of the first author, but some recent new interesting nice, results of other authors will also be added in this survey in addition. Some rather short, but valuable discussions of related to this area of research problems in analytic and harmonic function spaces of one and several complex variables will also be provided and in addition some open questions in this reseach area will also be discusssed shortly and posed by us. The paper contains also various rather short but valuable interesting comments concerning this research area of analytic and harmonic function spaces of one and several complex variables. Some sharp embedding results presented in this survey paper extend sharp embeddings in analytic function spaces obtained in higher dimension previously by J. Ortega, J. Fabrega, W. Cima, D. Luecking, M. Abate and many others in recent decades in the unit ball and bounded strongly pseudoconvex domains with the smooth boundary for onefunctional case to similar type analytic multyfunctional Bergman type function spaces. Same type multifunctional sharp extensions of some recent embedding results of one functional analytic function spaces of B. Sehba and D. Bekolle will be provided by us in this survey for unbounded domains namely for tubular domains over symmetric cones. In this paper we collect many new sharp embeddings obtained recently by first author though some one dimensional new sharp embeddings will be also mentioned shortly at the end of paper.
The speed of sound of simple dense fluids is shown to exhibit a pronounced freezing temperature scaling of the form $c_{\rm s}/v_{\rm T}\simeq \sqrt{\gamma} +\alpha (T_{\rm fr}/T)^{\beta}$, where $c_s$ is the speed of sound, $v_{\rm T}$ is the characteristic thermal velocity, $\gamma$ is the ideal gas heat capacity ratio, $T$ is the temperature, $T_{\rm fr}$ is the freezing temperature, and $\alpha$ and $\beta$ are dimensionless parameters. For the Lennard-Jones fluid we get $\gamma=5/3$, $\alpha\simeq 7$ with a weak temperature dependence, and $\beta = 1/3$. Similar scaling works in several real liquids, such as argon, krypton, xenon, nitrogen, and methane. In this case, $\alpha$ and $\beta$ are substance-dependent fitting parameters. A comparison between the prediction of this freezing temperature scaling and a recent experimental measurement of the speed of sound in methane under conditions of planetary interiors is presented and discussed. The results provide a simple practical tool to estimate the speed of sound in regimes where no experimental data are yet available.
Harmonization in Magnetic Resonance Imaging: A Survey of Acquisition, Image-level, and Feature-level Methods
2025-07-22
Qinqin Yang, Firoozeh Shomal-Zadeh, Ali Gholipour
Modern medical imaging technologies have greatly advanced neuroscience research and clinical diagnostics. However, imaging data collected across different scanners, acquisition protocols, or imaging sites often exhibit substantial heterogeneity, known as "batch effects" or "site effects". These non-biological sources of variability can obscure true biological signals, reduce reproducibility and statistical power, and severely impair the generalizability of learning-based models across datasets. Image harmonization aims to eliminate or mitigate such site-related biases while preserving meaningful biological information, thereby improving data comparability and consistency. This review provides a comprehensive overview of key concepts, methodological advances, publicly available datasets, current challenges, and future directions in the field of medical image harmonization, with a focus on magnetic resonance imaging (MRI). We systematically cover the full imaging pipeline, and categorize harmonization approaches into prospective acquisition and reconstruction strategies, retrospective image-level and feature-level methods, and traveling-subject-based techniques. Rather than providing an exhaustive survey, we focus on representative methods, with particular emphasis on deep learning-based approaches. Finally, we summarize the major challenges that remain and outline promising avenues for future research.
AI-based Clinical Decision Support for Primary Care: A Real-World Study
2025-07-22
Robert Korom, Sarah Kiptinness, Najib Adan, Kassim Said, Catherine Ithuli, Oliver Rotich, Boniface Kimani, Irene King'ori, Stellah Kamau, Elizabeth Atemba, Muna Aden, Preston Bowman, Michael Sharman, Rebecca Soskin Hicks, Rebecca Distler, Johannes Heidecke, Rahul K. Arora, Karan Singhal
We evaluate the impact of large language model-based clinical decision support in live care. In partnership with Penda Health, a network of primary care clinics in Nairobi, Kenya, we studied AI Consult, a tool that serves as a safety net for clinicians by identifying potential documentation and clinical decision-making errors. AI Consult integrates into clinician workflows, activating only when needed and preserving clinician autonomy. We conducted a quality improvement study, comparing outcomes for 39,849 patient visits performed by clinicians with or without access to AI Consult across 15 clinics. Visits were rated by independent physicians to identify clinical errors. Clinicians with access to AI Consult made relatively fewer errors: 16% fewer diagnostic errors and 13% fewer treatment errors. In absolute terms, the introduction of AI Consult would avert diagnostic errors in 22,000 visits and treatment errors in 29,000 visits annually at Penda alone. In a survey of clinicians with AI Consult, all clinicians said that AI Consult improved the quality of care they delivered, with 75% saying the effect was "substantial". These results required a clinical workflow-aligned AI Consult implementation and active deployment to encourage clinician uptake. We hope this study demonstrates the potential for LLM-based clinical decision support tools to reduce errors in real-world settings and provides a practical framework for advancing responsible adoption.
ALMA survey of a massive node of the Cosmic Web at $z\sim 3$. II. A dynamically cold and massive disk galaxy in the proximity of a hyperluminous quasar
2025-07-22
A. Pensabene, S. Cantalupo, W. Wang, C. Bacchini, F. Fraternali, M. Bischetti, C. Cicone, R. Decarli, G. Pezzulli, M. Galbiati, T. Lazeyras, N. Ledos, G. Quadri, A. Travascio
Advancing our understanding of the formation and evolution of early massive galaxies and black holes requires detailed studies of dense structures in the high-redshift Universe. In this work, we present high-angular resolution ($\simeq0.3''$) ALMA observations targeting the CO(4--3) line and the underlying 3-mm dust continuum toward the Cosmic Web node MQN01, a region identified through deep multiwavelength surveys as one of the densest concentrations of galaxies and AGN at cosmic noon. At the center of this structure, we identify a massive, rotationally supported disk galaxy located approximately at $\sim10\,{\rm kpc}$ projected-distance and $\sim-300\,{\rm km\,s^{-1}}$ from a hyperluminous quasar at $z=3.2510$. By accurately modeling the cold gas kinematics, we determine a galaxy dynamical mass of $2.5\times10^{11}\,{M_{\odot}}$ within the inner $\simeq 4\,{\rm kpc}$, and a high degree of rotational support of $V_{\rm rot}/\sigma \approx 11$. This makes it the first quasar companion galaxy confirmed as a massive, dynamically cold rotating disk at such an early cosmic epoch. Despite the small projected separation from the quasar host, we find no clear evidence of strong tidal interactions affecting the galaxy disk. This might suggest that the quasar is a satellite galaxy in the early stages of a merger. Furthermore, our spectroscopic analysis reveals a broad, blueshifted component in the CO(4--3) line profile of the quasar host, which may trace a powerful molecular outflow or kinematic disturbances induced by its interaction with the massive companion galaxy. Our findings show that rotationally supported cold disks are able to survive even in high-density environments of the early Universe.
QSO MUSEUM III: the circumgalactic medium in Ly$α$ emission around 120 $z\sim3$ quasars covering the SDSS parameter space. Witnessing the instantaneous AGN feedback on halo scales
2025-07-22
Jay González Lobos, Fabrizio Arrigoni Battaia, Aura Obreja, Guinevere Kauffmann, Emanuele Paolo Farina, Tiago Costa
Recent surveys show that $z>2$ quasars are surrounded by Hydrogen Lyman-$\alpha$ (Ly$\alpha$) glows with diverse emission levels and extents. These seem to depend on the activity of embedded quasars, the number of active galactic nucleus (AGN) photons able to reach the halo gas or circumgalactic medium (CGM) and the physical properties of the CGM. In this framework, we present VLT/MUSE snapshot observations (45 min/source) of 59 $z\sim3$ quasars extending the long-term QSO MUSEUM campaign to fainter SDSS sources. The whole survey now targets 120 quasars with a median redshift of $z$=3.13, and bolometric luminosities, black hole masses and Eddington ratios of $45.1<\log(L_{\rm bol}/[{\rm erg\,s^{-1}]})<48.7$, $7.9<\log(M_{\rm BH}/[{\rm M_{\odot}]})<10.3 $ and $0.01<\lambda_{\rm Edd}<1.8$, respectively. We detect extended Ly$\alpha$ emission in 110/120 systems, with all non-detections in the new fainter sample. Stacking non-detections unveils emission below our individual detection limit. The Ly$\alpha$ surface brightness (SB$_{\rm Ly\alpha}$) of the CGM increases with quasar luminosity. Moreover, the Ly$\alpha$ linewidth increases in the central regions (projected radius $R<40$ kpc or $\sim$40% $R_{\rm vir}$) of the CGM around brighter quasars. These trends indicate that we are witnessing the instantaneous AGN feedback in action on CGM scales. Assuming that all targeted quasars sit in halos of $M_{\rm DM}\sim10^{12.5}\,M_\odot$, as found in clustering studies, the trend in SB$_{\rm Ly\alpha}$ can be explained by larger fractions of cool gas mass illuminated, implying that brighter quasars have larger ionization cone opening angles. Similarly, brighter AGNs seem to perturb the cool ($T\sim10^4$ K) gas more strongly. We show that QSO MUSEUM now has enough statistics to study the instantaneous AGN feedback while controlling for black hole properties, which are key to constraining AGN models.
Sparser2Sparse: Single-shot Sparser-to-Sparse Learning for Spatial Transcriptomics Imputation with Natural Image Co-learning
2025-07-22
Yaoyu Fang, Jiahe Qian, Xinkun Wang, Lee A. Cooper, Bo Zhou
Spatial transcriptomics (ST) has revolutionized biomedical research by enabling high resolution gene expression profiling within tissues. However, the high cost and scarcity of high resolution ST data remain significant challenges. We present Single-shot Sparser-to-Sparse (S2S-ST), a novel framework for accurate ST imputation that requires only a single and low-cost sparsely sampled ST dataset alongside widely available natural images for co-training. Our approach integrates three key innovations: (1) a sparser-to-sparse self-supervised learning strategy that leverages intrinsic spatial patterns in ST data, (2) cross-domain co-learning with natural images to enhance feature representation, and (3) a Cascaded Data Consistent Imputation Network (CDCIN) that iteratively refines predictions while preserving sampled gene data fidelity. Extensive experiments on diverse tissue types, including breast cancer, liver, and lymphoid tissue, demonstrate that our method outperforms state-of-the-art approaches in imputation accuracy. By enabling robust ST reconstruction from sparse inputs, our framework significantly reduces reliance on costly high resolution data, facilitating potential broader adoption in biomedical research and clinical applications.
Recent advances in large language models (LLMs) have improved reasoning in text and image domains, yet achieving robust video reasoning remains a significant challenge. Existing video benchmarks mainly assess shallow understanding and reasoning and allow models to exploit global context, failing to rigorously evaluate true causal and stepwise reasoning. We present CausalStep, a benchmark designed for explicit stepwise causal reasoning in videos. CausalStep segments videos into causally linked units and enforces a strict stepwise question-answer (QA) protocol, requiring sequential answers and preventing shortcut solutions. Each question includes carefully constructed distractors based on error type taxonomy to ensure diagnostic value. The benchmark features 100 videos across six categories and 1,852 multiple-choice QA pairs. We introduce seven diagnostic metrics for comprehensive evaluation, enabling precise diagnosis of causal reasoning capabilities. Experiments with leading proprietary and open-source models, as well as human baselines, reveal a significant gap between current models and human-level stepwise reasoning. CausalStep provides a rigorous benchmark to drive progress in robust and interpretable video reasoning.
Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review
2025-07-22
Charlotte Jennings, Andrew Broad, Lucy Godson, Emily Clarke, David Westhead, Darren Treanor
Multimodal machine learning integrating histopathology and molecular data shows promise for cancer prognostication. We systematically reviewed studies combining whole slide images (WSIs) and high-throughput omics to predict overall survival. Searches of EMBASE, PubMed, and Cochrane CENTRAL (12/08/2024), plus citation screening, identified eligible studies. Data extraction used CHARMS; bias was assessed with PROBAST+AI; synthesis followed SWiM and PRISMA 2020. Protocol: PROSPERO (CRD42024594745). Forty-eight studies (all since 2017) across 19 cancer types met criteria; all used The Cancer Genome Atlas. Approaches included regularised Cox regression (n=4), classical ML (n=13), and deep learning (n=31). Reported c-indices ranged 0.550-0.857; multimodal models typically outperformed unimodal ones. However, all studies showed unclear/high bias, limited external validation, and little focus on clinical utility. Multimodal WSI-omics survival prediction is a fast-growing field with promising results but needs improved methodological rigor, broader datasets, and clinical evaluation. Funded by NPIC, Leeds Teaching Hospitals NHS Trust, UK (Project 104687), supported by UKRI Industrial Strategy Challenge Fund.
With the rapid development of AI-generated content (AIGC), video generation has emerged as one of its most dynamic and impactful subfields. In particular, the advancement of video generation foundation models has led to growing demand for controllable video generation methods that can more accurately reflect user intent. Most existing foundation models are designed for text-to-video generation, where text prompts alone are often insufficient to express complex, multi-modal, and fine-grained user requirements. This limitation makes it challenging for users to generate videos with precise control using current models. To address this issue, recent research has explored the integration of additional non-textual conditions, such as camera motion, depth maps, and human pose, to extend pretrained video generation models and enable more controllable video synthesis. These approaches aim to enhance the flexibility and practical applicability of AIGC-driven video generation systems. In this survey, we provide a systematic review of controllable video generation, covering both theoretical foundations and recent advances in the field. We begin by introducing the key concepts and commonly used open-source video generation models. We then focus on control mechanisms in video diffusion models, analyzing how different types of conditions can be incorporated into the denoising process to guide generation. Finally, we categorize existing methods based on the types of control signals they leverage, including single-condition generation, multi-condition generation, and universal controllable generation. For a complete list of the literature on controllable video generation reviewed, please visit our curated repository at https://github.com/mayuelala/Awesome-Controllable-Video-Generation.
Features extraction for image identification using computer vision
2025-07-22
Venant Niyonkuru, Sylla Sekou, Jimmy Jackson Sinzinkayo
This study examines various feature extraction techniques in computer vision, the primary focus of which is on Vision Transformers (ViTs) and other approaches such as Generative Adversarial Networks (GANs), deep feature models, traditional approaches (SIFT, SURF, ORB), and non-contrastive and contrastive feature models. Emphasizing ViTs, the report summarizes their architecture, including patch embedding, positional encoding, and multi-head self-attention mechanisms with which they overperform conventional convolutional neural networks (CNNs). Experimental results determine the merits and limitations of both methods and their utilitarian applications in advancing computer vision.
Testing gravitational physics by combining DESI DR1 and weak lensing datasets using the E_G estimator
2025-07-21
S. J. Rauhut, C. Blake, U. Andrade, H. E. Noriega, J. Aguilar, S. Ahlen, S. BenZvi, D. Bianchi, D. Brooks, T. Claybaugh, A. Cuceu, A. de la Macorra, J. DeRose, P. Doel, N. Emas, S. Ferraro, J. E. Forero-Romero, C. Garcia-Quintero, E. Gaztañaga, G. Gutierrez, S. Heydenreich, K. Honscheid, C. Howlett, D. Huterer, M. Ishak, S. Joudaki, R. Joyce, E. Jullo, R. Kehoe, D. Kirkby, A. Kremin, A. Krolewski, O. Lahav, A. Lambert, C. Lamman, M. Landriau, J. U. Lange, L. Le Guillou, A. Leauthaud, M. Manera, A. Meisner, R. Miquel, S. Nadathur, J. A. Newman, G. Niz, N. Palanque-Delabrouille, W. J. Percival, A. Porredon, F. Prada, I. Pérez-Ràfols, G. Rossi, R. Ruggeri, E. Sanchez, C. Saulder, D. Schlegel, A. Semenaite, J. Silber, D. Sprayberry, Z. Sun, G. Tarlé, B. A. Weaver, P. Zarrouk, R. Zhou, H. Zou
The action of gravitational physics across space-time creates observable signatures in the behaviour of light and matter. We perform combined-probe studies using data from the Baryon Oscillation Spectroscopic Survey (BOSS) and Dark Energy Spectroscopic Instrument survey Data Release 1 (DESI-DR1), in combination with three existing weak lensing surveys, to test and constrain General Relativity (GR) in the context of the standard model of cosmology (LCDM). We focus on measuring the gravitational estimator statistic, E_G, which describes the relative amplitudes of weak gravitational lensing and galaxy velocities induced by a common set of overdensities. By comparing our amplitude measurements with their predicted scale- and redshift-dependence within the GR+LCDM model, we demonstrate that our results are consistent with the predictions of the Planck cosmology. The redshift span of the DESI dataset allows us to perform these E_G measurements at the highest redshifts achieved to date, z ~ 1.
Applying multimodal learning to Classify transient Detections Early (AppleCiDEr) I: Data set, methods, and infrastructure
2025-07-21
Alexandra Junell, Argyro Sasli, Felipe Fontinele Nunes, Maojie Xu, Benny Border, Nabeel Rehemtulla, Mariia Rizhko, Yu-Jing Qin, Theophile Jegou Du Laz, Antoine Le Calloch, Sushant Sharma Chaudhary, Shaowei Wu, Jesper Sollerman, Niharika Sravan, Steven L. Groom, David Hale, Mansi M. Kasliwal, Josiah Purdum, Avery Wold, Matthew J. Graham, Michael W. Coughlin
Modern time-domain surveys like the Zwicky Transient Facility (ZTF) and the Legacy Survey of Space and Time (LSST) generate hundreds of thousands to millions of alerts, demanding automatic, unified classification of transients and variable stars for efficient follow-up. We present AppleCiDEr (Applying Multimodal Learning to Classify Transient Detections Early), a novel framework that integrates four key data modalities (photometry, image cutouts, metadata, and spectra) to overcome limitations of single-modality classification approaches. Our architecture introduces (i) two transformer encoders for photometry, (ii) a multimodal convolutional neural network (CNN) with domain-specialized metadata towers and Mixture-of-Experts fusion for combining metadata and images, and (iii) a CNN for spectra classification. Training on ~ 30,000 real ZTF alerts, AppleCiDEr achieves high accuracy, allowing early identification and suggesting follow-up for rare transient spectra. The system provides the first unified framework for both transient and variable star classification using real observational data, with seamless integration into brokering pipelines, demonstrating readiness for the LSST era.
ARTEMIS observations of electrostatic shocks inside the lunar wake
2025-07-21
Terry Z. Liu, Xin An, Vassilis Angelopoulos, Andrew R. Poppe
When the solar wind encounters the Moon, a plasma void forms downstream of it, known as the lunar wake. In regions where the magnetic field is quasi-parallel to the plasma-vacuum boundary normal, plasma refills the wake primarily along magnetic field lines. As faster electrons outpace slower ions, an ambipolar electric field is generated, accelerating ions and decelerating electrons. Recent particle-in-cell simulations have shown that when accelerated supersonic ion beams from opposite sides of the wake meet near the wake center, electrostatic shocks may form, decelerating ions and heating electrons into flat-top velocity distributions. Using data from the Acceleration, Reconnection, Turbulence and Electrodynamics of the Moon's Interaction with the Sun (ARTEMIS) spacecraft, we present the first observational evidence of the predicted electrostatic shocks. Near the wake center of one event, we observed an electrostatic solitary structure with an amplitude of ~2 mV/m and a spatial scale of ~50 local Debye lengths. This structure generated a potential increase of ~50 V from upstream to downstream, heating incoming electrons by ~50 eV in the parallel direction while decelerating ions by ~60 km/s leading to a density enhancement. At a second event representing a more evolved stage, we observed more dissipated structures dominated by strong electrostatic waves, with persistent potential increases driving continued field-aligned electron heating and ion deceleration. These observations confirm simulation predictions of electrostatic shock formation and the associated particle dynamics within the lunar wake, with potential applications to understanding plasma interactions around other airless celestial bodies.
Toward music-based stress management: Contemporary biosensing systems for affective regulation
2025-07-21
Natasha Yamane, Varun Mishra, Matthew S. Goodwin
In the last decade, researchers have increasingly explored using biosensing technologies for music-based affective regulation and stress management interventions in laboratory and real-world settings. These systems -- including interactive music applications, brain-computer interfaces, and biofeedback devices -- aim to provide engaging, personalized experiences that improve therapeutic outcomes. In this scoping and mapping review, we summarize and synthesize systematic reviews and empirical research on biosensing systems with potential applications in music-based affective regulation and stress management, identify gaps in the literature, and highlight promising areas for future research. We identified 28 studies involving 646 participants, with most systems utilizing prerecorded music, wearable cardiorespiratory sensors, or desktop interfaces. We categorize these systems based on their biosensing modalities, music types, computational models for affect or stress detection and music prediction, and biofeedback mechanisms. Our findings highlight the promising potential of these systems and suggest future directions, such as integrating multimodal biosensing, exploring therapeutic mechanisms of music, leveraging generative artificial intelligence for personalized music interventions, and addressing methodological, data privacy, and user control concerns.
Handcrafted vs. Deep Radiomics vs. Fusion vs. Deep Learning: A Comprehensive Review of Machine Learning -Based Cancer Outcome Prediction in PET and SPECT Imaging
2025-07-21
Mohammad R. Salmanpour, Somayeh Sadat Mehrnia, Sajad Jabarzadeh Ghandilu, Zhino Safahi, Sonya Falahati, Shahram Taeb, Ghazal Mousavi, Mehdi Maghsoudi, Ahmad Shariftabrizi, Ilker Hacihaliloglu, Arman Rahmim
Machine learning (ML), including deep learning (DL) and radiomics-based methods, is increasingly used for cancer outcome prediction with PET and SPECT imaging. However, the comparative performance of handcrafted radiomics features (HRF), deep radiomics features (DRF), DL models, and hybrid fusion approaches remains inconsistent across clinical applications. This systematic review analyzed 226 studies published from 2020 to 2025 that applied ML to PET or SPECT imaging for outcome prediction. Each study was evaluated using a 59-item framework covering dataset construction, feature extraction, validation methods, interpretability, and risk of bias. We extracted key details including model type, cancer site, imaging modality, and performance metrics such as accuracy and area under the curve (AUC). PET-based studies (95%) generally outperformed those using SPECT, likely due to higher spatial resolution and sensitivity. DRF models achieved the highest mean accuracy (0.862), while fusion models yielded the highest AUC (0.861). ANOVA confirmed significant differences in performance (accuracy: p=0.0006, AUC: p=0.0027). Common limitations included inadequate handling of class imbalance (59%), missing data (29%), and low population diversity (19%). Only 48% of studies adhered to IBSI standards. These findings highlight the need for standardized pipelines, improved data quality, and explainable AI to support clinical integration.
AutoMeet: a proof-of-concept study of genAI to automate meetings in automotive engineering
2025-07-21
Simon Baeuerle, Max Radyschevski, Ulrike Pado
In large organisations, knowledge is mainly shared in meetings, which takes up significant amounts of work time. Additionally, frequent in-person meetings produce inconsistent documentation -- official minutes, personal notes, presentations may or may not exist. Shared information therefore becomes hard to retrieve outside of the meeting, necessitating lengthy updates and high-frequency meeting schedules. Generative Artificial Intelligence (genAI) models like Large Language Models (LLMs) exhibit an impressive performance on spoken and written language processing. This motivates a practical usage of genAI for knowledge management in engineering departments: using genAI for transcribing meetings and integrating heterogeneous additional information sources into an easily usable format for ad-hoc searches. We implement an end-to-end pipeline to automate the entire meeting documentation workflow in a proof-of-concept state: meetings are recorded and minutes are created by genAI. These are further made easily searchable through a chatbot interface. The core of our work is to test this genAI-based software tooling in a real-world engineering department and collect extensive survey data on both ethical and technical aspects. Direct feedback from this real-world setup points out both opportunities and risks: a) users agree that the effort for meetings could be significantly reduced with the help of genAI models, b) technical aspects are largely solved already, c) organizational aspects are crucial for a successful ethical usage of such a system.
Radiological and Biological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Breast Cancer; Dictionary Version BM1.0
2025-07-21
Arman Gorji, Nima Sanati, Amir Hossein Pouria, Somayeh Sadat Mehrnia, Ilker Hacihaliloglu, Arman Rahmim, Mohammad R. Salmanpour
Radiomics-based AI models show promise for breast cancer diagnosis but often lack interpretability, limiting clinical adoption. This study addresses the gap between radiomic features (RF) and the standardized BI-RADS lexicon by proposing a dual-dictionary framework. First, a Clinically-Informed Feature Interpretation Dictionary (CIFID) was created by mapping 56 RFs to BI-RADS descriptors (shape, margin, internal enhancement) through literature and expert review. The framework was applied to classify triple-negative breast cancer (TNBC) versus non-TNBC using dynamic contrast-enhanced MRI from a multi-institutional cohort of 1,549 patients. We trained 27 machine learning classifiers with 27 feature selection methods. SHapley Additive exPlanations (SHAP) were used to interpret predictions and generate a complementary Data-Driven Feature Interpretation Dictionary (DDFID) for 52 additional RFs. The best model, combining Variance Inflation Factor (VIF) selection with Extra Trees Classifier, achieved an average cross-validation accuracy of 0.83. Key predictive RFs aligned with clinical knowledge: higher Sphericity (round/oval shape) and lower Busyness (more homogeneous enhancement) were associated with TNBC. The framework confirmed known imaging biomarkers and uncovered novel, interpretable associations. This dual-dictionary approach (BM1.0) enhances AI model transparency and supports the integration of RFs into routine breast cancer diagnosis and personalized care.
BACFuzz: Exposing the Silence on Broken Access Control Vulnerabilities in Web Applications
2025-07-21
I Putu Arya Dharmaadi, Mohannad Alhanahnah, Van-Thuan Pham, Fadi Mohsen, Fatih Turkmen
Broken Access Control (BAC) remains one of the most critical and widespread vulnerabilities in web applications, allowing attackers to access unauthorized resources or perform privileged actions. Despite its severity, BAC is underexplored in automated testing due to key challenges: the lack of reliable oracles and the difficulty of generating semantically valid attack requests. We introduce BACFuzz, the first gray-box fuzzing framework specifically designed to uncover BAC vulnerabilities, including Broken Object-Level Authorization (BOLA) and Broken Function-Level Authorization (BFLA) in PHP-based web applications. BACFuzz combines LLM-guided parameter selection with runtime feedback and SQL-based oracle checking to detect silent authorization flaws. It employs lightweight instrumentation to capture runtime information that guides test generation, and analyzes backend SQL queries to verify whether unauthorized inputs flow into protected operations. Evaluated on 20 real-world web applications, including 15 CVE cases and 2 known benchmarks, BACFuzz detects 16 of 17 known issues and uncovers 26 previously unknown BAC vulnerabilities with low false positive rates. All identified issues have been responsibly disclosed, and artifacts will be publicly released.
The inevitable fate of massive stars in the initial mass range of ~8--30 M_{Sun} in the red supergiant (RSG) phase is a core-collapse supernova (SN) explosion, although some stars may collapse directly to a black hole. We know that this is the case, since RSGs have been directly identified and characterized for a number of supernovae (SNe) in pre-explosion archival optical and infrared images. RSGs likely all have some amount of circumstellar matter (CSM), through nominal mass loss, although evidence exists that some RSGs must experience enhanced mass loss during their lifetimes. The SNe from RSGs are hydrogen-rich Type II-Plateau (II-P), and SNe II-P at the low end of the luminosity range tend to arise from low-luminosity RSGs. The typical spectral energy distribution (SED) for such RSGs can generally be fit with a cool photospheric model, whereas the more luminous RSG progenitors of more luminous SNe II-P tend to require a greater quantity of dust in their CSM to account for their SEDs. The SN II-P progenitor luminosity range is log(L_{bol}/L_{Sun}) ~ 4.0--5.2. The fact RSGs are known up to log(L_{bol}/L_{Sun}) ~ 5.7 leads to the so-called ``RSG problem'', which may, in the end, be a result of small number of available statistics to date.
This review discusses the causes, nature, importance and observational evidence of mass loss by red supergiants. It arrives at the perception that mass loss finds its origin in the gravity which makes the star a star in the first place, and is a mechanism for the star to equilibrate. This is corroborated by a careful examination of various popular historical and recent empirical mass-loss rate prescriptions and theoretical works, and which provides no evidence for an explicit dependence of red supergiant mass loss on metallicity though dust-associated mass loss becomes less prevalent at lower metallicity. It also identifies a common problem in methods that use tracers of mass loss, which do not correct for varying scaling factors (often because there is no information available on which to base such correction) and as a result tend to underestimate mass-loss rates at the lower end. Conversely, dense, extended chromospheres in themselves do not translate into high mass-loss rates, and the significance of stochastic mass loss can be overstated. On a population scale, on the other hand, binary interaction acts as a stochastic agent of mass loss of great import. In all, evidence is overwhelming that points at red supergiants at the lower mass end losing mass at insufficient rates to shed their mantles before core collapse, but massive (at birth) red supergiants to be prone to intense, dusty mass loss which sees them become hotter stars before meeting their fate. This is consistent with the identified progenitors of hydrogen-rich supernovae. Supernova evolution holds great promise to probe the mass loss but we caution against confusing atmospheres with winds. Finally, promising avenues are looked into, which could forge step-change progress in what has been a long and arduous search for the holy grail of red supergiant mass loss. We may yet find it!
The envelopes of Red Supergiants (RSGs) have a unique chemical environment not seen in other types of stars. They foster an oxygen-rich synthesis but are tempered by sporadic and chaotic mass loss, which distorts the envelope and creates complex outflow sub-structures consisting of knots, clumps, and arcs. Near the stellar photosphere, molecules and grains form under approximate LTE conditions, as predicted by chemical models. However, the complicated outflows appear to have distinct chemistries generated by shocks and dust destruction. Various RSG envelopes have been probed for their molecular content, mostly by radio and millimeter observations; however, VY Canis Majoris (VY CMa) and NML Cygni (NML Cyg) display the highest chemical complexity, and also the most complicated envelope structure. Thus far, over 29 different molecules have been identified in the envelopes of RSGs. Some molecules are common for circumstellar gas, including CO, SiO, HCN and H$_2$O, which have abundances of $\sim$$10^{-6}$--$10^{-4}$, relative to H$_2$. More exotic oxides have additionally been discovered, such as AlO, AlOH, PO, TiO$_2$, and VO, with abundances of $\sim$$10^{-9}$--$10^{-7}$. RSG shells support intricate maser emission in OH, H$_2$O and SiO, as well. Studies of isotope ratios in molecules suggest dredge-up at least into the H-burning shell, but further exploration is needed.
The behavior of the bright red supergiant, Betelgeuse, is described with results principally from the past 6 years. The review includes imaging, photometry, and spectroscopy to record the Great Dimming of 2019--2020. This event was followed by a slow ongoing recovery from the massive surface mass ejection after which the stellar characteristics changed. Theoretical simulations address the cause of this episodic mass ejection and the optical Dimming. Recent publications evaluating the perplexing 2100 day periodicity in the star's brightness and radial velocity provide evidence that Betelgeuse may harbor a companion object. Current attempts at direct detection of this companion are discussed. Betelgeuse provides a well-studied and meaningful example for supergiant stars in our Galaxy and others.
Red Supergiants -- The Other Side of the H-R Diagram
2025-07-21
Roberta M. Humphreys
Red supergiants are the largest stars known with some of the highest mass loss rates observed. They are the final stage in the evolution of the majority of massive stars. The unexpected discovery of high mass loss episodes in many red supergiants have posed questions about the role of mass loss on their final stages. The papers in this volume are timely reviews of our current understanding of this often surprising population of massive stars. This introductory paper is a brief summary of their observed properties and a historical perspective on some of the current problems on mass loss, their circumstellar environments, and their evolutionary state.
Red Supergiants in the Milky Way and Nearby Galaxies
2025-07-21
Alceste Z. Bonanos
Identifications of red supergiants (RSGs) in the Milky Way and nearby galaxies have experienced an exponential increase in recent years, driven by advancements in selection techniques, the continued expansion of archival datasets, and a steady increase in spectroscopic data. This review describes the advances in methodologies and selection criteria for identifying RSGs and presents the current census of these stars in our own Galaxy and nearby galaxies. It also describes the insights gained from resolving nearby RSGs and their complex circumstellar material in the Milky Way and from the growing samples of RSGs being discovered in the Local Group and beyond. These advances impact the Humphreys--Davidson limit in the cool part of the Hertzsprung--Russell diagram. Furthermore, they provide insight into extreme RSGs and the role of photometric variability and, in particular, of the newly discovered phenomenon of dimming events. Recent observations have enabled the determination of the binarity fraction among RSGs, offering new constraints to stellar evolution. Looking ahead, the synergy between large-scale surveys, high-resolution observations, and emerging machine-learning tools promises to further transform our understanding of the final evolutionary stages of massive stars in the coming decade.
The red and yellow hypergiants are a rare and important phase in the evolution of the most massive stars that can reach the cool part of the HR Diagram. The hypergiant phase is commonly characterized by high, often episodic mass-loss rates and significant changes in spectral type, probably due to the formation of a pseudo photopsphere during a high mass-loss episode. Many of the yellow hypergiants are the immediate successors to the most luminous red supergiants, and often show evidence in their dusty, circumstellar envelopes from past red supergiant activity. In this paper we review the yellow and red hypergiants with an emphasis on how they differ from more normal red supergiants.
Stellar evolution through the Red Supergiant phase
2025-07-21
Sylvia Ekström, Cyril Georgy
Massive stars less massive than ~30 Msol evolve into a red supergiant after the main sequence. Given a standard IMF, this means about 80% of all single massive stars will experience this phase. RSGs are dominated by convection, with a radius that may extend up to thousands of solar radii. Their low temperature and gravity make them prone to lose large amounts of masses, either through a pulsationally-driven wind or through mass-loss outburst. RSGs are the progenitors of the most common core-collapse supernovae, the type II. In the present review, we give an overview of our theoretical understanding about this spectacular phase of massive stars evolution.
Accurate and efficient skin lesion classification on edge devices is critical for accessible dermatological care but remains challenging due to computational, energy, and privacy constraints. We introduce QANA, a novel quantization-aware neuromorphic architecture for incremental skin lesion classification on resource-limited hardware. QANA effectively integrates ghost modules, efficient channel attention, and squeeze-and-excitation blocks for robust feature representation with low-latency and energy-efficient inference. Its quantization-aware head and spike-compatible transformations enable seamless conversion to spiking neural networks (SNNs) and deployment on neuromorphic platforms. Evaluation on the large-scale HAM10000 benchmark and a real-world clinical dataset shows that QANA achieves 91.6\% Top-1 accuracy and 82.4\% macro F1 on HAM10000, and 90.8\% / 81.7\% on the clinical dataset, significantly outperforming state-of-the-art CNN-to-SNN models under fair comparison. Deployed on BrainChip Akida hardware, QANA achieves 1.5\,ms inference latency and 1.7\,mJ energy per image, reducing inference latency and energy use by over 94.6\%/98.6\% compared to GPU-based CNNs surpassing state-of-the-art CNN-to-SNN conversion baselines. These results demonstrate the effectiveness of QANA for accurate, real-time, and privacy-sensitive medical analysis in edge environments.
Pair production of massive charged vector bosons from the worldline
2025-07-21
Fiorenzo Bastianelli, Filippo Fecit, Alessandro Miccichè
We investigate a worldline formulation for a massive spin-1 particle interacting with an electromagnetic background. Two first-quantized descriptions of the spin degrees of freedom are considered: one based on bosonic oscillators and the other on fermionic oscillators. Focusing initially on the bosonic model -- which can accommodate particles of arbitrary integer spin -- we review how quantization in the spin-1 sector, performed both via Dirac's method and BRST quantization, reproduces the free Proca field theory. We then introduce coupling to an external electromagnetic field and demonstrate that Maxwell's equations for the background emerge as a consistency condition for the nilpotency of the BRST charge on the spin-1 sector. Encouraged by this result, which proves the viability of the particle model, we proceed to construct a path integral quantization of the worldline action for the charged spin-1 particle on the circle. This yields the one-loop effective Lagrangian for a constant electromagnetic field induced by a massive charged vector boson. As expected, the result reveals a vacuum instability, which we quantify by deriving the pair production rate for the vector bosons, recovering previous results obtained in quantum field theory. For comparison, we repeat the analysis using the standard $\mathcal{N}=2$ spinning particle model, which contains fermionic worldline degrees of freedom, finding identical results. Finally, we comment on possible extensions of the worldline models to include effective interactions and briefly explore their implications for pair production.
The HETDEX Survey: Probing neutral hydrogen in the circumgalactic medium of ~88,000 Lyman Alpha Emitters
2025-07-21
Mahan Mirza Khanlari, Karl Gebhardt, Laurel H. Weiss, Dustin Davis, Erin Mentuch Cooper, Mahdi Qezlou, Maja Lujan Niemeyer, Robin Ciardullo, Donald P. Schneider, Shiro Mukae, Chenxu Liu, Daniel Farrow, Gary J. Hill, Gregory R. Zeimann, Wolfram Kollatschny
We explore the neutral hydrogen (H I) gas around 1.9 < z < 3.5 Lyman Alpha Emitters (LAEs) from the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) using faint Ly$\alpha$ absorption. This absorption is the result of H I in the halo of the LAE scattering Ly$\alpha$ photons from the integrated light of background galaxies along the line of sight. We stack millions of spectra from regions around ~88,000 LAEs to focus on the physics of the gas at large radii. The extensive number of fiber spectra contributing to the stacks ensures significant signal-to-noise ratio (S/N) to detect the faint Ly$\alpha$ absorption which would otherwise be buried within the noise. We detect absorption out to a projected ~350 kpc around an average LAE at z~2.5. We use these results to create an empirical radial $W_\lambda$(Ly$\alpha$) profile around LAEs. Comparison with numerical simulations reveals a profile similar to the empirical one within this region. Compared to previous studies, the profile is similar but modestly higher. We also outline a simple physical picture motivated by the observed trends in the data. We plan to quantify this radial profile as a function of redshift, local density, and Ly$\alpha$ luminosity to explore the relationship between LAE environments and H I distribution.
A Late-time Radio Search for Highly Off-axis Jets from PTF Broad-lined Ic Supernovae in GRB-like Host Galaxy Environments
2025-07-21
Genevieve Schroeder, Anna Y. Q. Ho, Ranadeep G. Dastidar, Maryam Modjaz, Alessandra Corsi, Paul C. Duffell
Hydrogen/Helium-poor stripped-envelope core-collapse supernovae with broad lines (SNe Ic-bl) almost always accompany the nearby ($z < 0.3$) jetted relativistic explosions known as long duration gamma-ray bursts (GRBs). However, the majority of SNe Ic-bl have no detected GRB counterpart. At least some of these SNe should harbor off-axis jets, whose afterglow may become detectable at late times, particularly at radio wavelengths. Here, we present Karl G. Jansky Very Large Array radio observations (rest frame times of $\sim 3$-$4\times10^{3}$ days post SN discovery) of a sample of 14 SNe Ic-bl discovered by the Palomar Transient Factory (PTF) that have been demonstrated to originate from the same host environments as the SNe Ic-bl associated with nearby GRBs. Of the 14 SNe, we identify three that are radio detected, one of which (PTF10tqv, $z = 0.0795$) is consistent with an off-axis jet with energy similar to classical GRBs ($\gtrsim 10^{50.5}~$erg). Using recently developed synchrotron radiation code, we find that for our 11 non-detections, which are among the deepest limits obtained for Ic-bl, we rule out an off-axis jet with an energy of $\gtrsim 10^{51}~{\rm erg}$ in circumburst densities of $\gtrsim 10^{-1}~{\rm cm}^{-3}$. We predict that well-spaced monitoring of newly discovered SNe Ic-bl from $\sim 10~$days to $\sim 10~$years (rest frame) to luminosities of $\sim 10^{27}~{\rm erg~s}^{-1}~{\rm Hz}^{-1}$ will constrain the existence of highly off-axis jets ($\gtrsim60^\circ$) with classical GRB energies. The VLA Sky Survey will probe jets that are $\lesssim 60^\circ$ off-axis, whereas the Deep Synpotic Array 2000 will probe jets out to $\sim 90^\circ$ off-axis, demonstrating the importance of utilizing radio surveys to supplement targeted observations.
The VISCACHA Survey: XIII. The extended main-sequence turn-off in intermediate-age low-mass clusters
2025-07-21
Stefano Souza, Angeles Pérez-Villegas, Bruno Dias, Leandro Kerber, Beatriz Barbuy, Raphael A. P. Oliveira, Bernardo P. L. Ferreira, João F. C. Santos Jr., Francisco F. S. Maia, Eduardo Bica, Gustavo Baume, Dante Minniti, Elisa R. Garro, André L. Figueiredo, José G. Fernández-Trincado, Sasi Saroon, Luciano Fraga, Bruno Quint, David Sanmartim
The extended main-sequence turn-off (eMSTO) is a well-known feature observed in young and intermediate-age star clusters, characterized by a significant broadening of the main-sequence turn-off region. Although prolonged star formation and stellar rotation have been proposed as possible explanations, no consensus has yet been reached. Most previous studies have focused on high-mass clusters. In this work, we extend the analysis to the less-explored low-mass regime by investigating star clusters in the Magellanic Clouds using data from the VISCACHA survey. We employed a widely used method to quantify the MSTO width in terms of age spread. Additionally, to validate our approach, we used a cluster also observed with HST. Our analysis confirms that the eMSTO phenomenon is also present in low-mass clusters, following the known age/mass-MSTO width relations. In particular, the less massive cluster in our sample does not show an eMSTO, supporting the proposed link between the eMSTO and the escape velocity of the cluster, providing a new lower limit to the age spread of $88\pm40$ Myr for the presence of the eMSTO. The consistent MSTO width measurements between the VISCACHA and HST photometries confirm the robustness of our method and demonstrate that the age spread determination is independent of the photometric system, showing also the power of ground-based observations to investigate the eMSTO phenomenon.
oLIMpus: An Effective Model for Line Intensity Mapping Auto- and Cross- Power Spectra in Cosmic Dawn and Reionization
2025-07-21
Sarah Libanore, Julian B. Munoz, Ely D. Kovetz
Line-intensity mapping (LIM) is emerging as a powerful probe of the high-redshift Universe, with a growing number of LIM experiments targeting various spectral lines deep into the epochs of reionization and cosmic dawn. A key remaining challenge is the consistent and efficient modeling of the diverse emission lines and of the observables of different surveys. Here, we present oLIMpus, a fully analytical effective model to study LIM auto- and cross- power spectra. Our work builds on the 21-cm effective model presented in Zeus21, applying it to star-forming lines and improving it in different aspects. Our code accounts for shot noise and linear redshift-space distortions and it includes by default prescriptions for OII, OIII, H$\alpha$, H$\beta$, CII, CO line luminosities, together with the 21-cm model inherited from Zeus21. Beyond auto- and cross-power spectra, oLIMpus can produce mock coeval boxes and lightcones, and with a computational time of $\sim s$ it is ideal for parameter-space exploration and inference. Its modular implementation makes it easy to customize and extend, enabling various applications, such as MCMC analyses and consistent multi-line cross-correlations.
3LM: Bridging Arabic, STEM, and Code through Benchmarking
2025-07-21
Basma El Amel Boussaha, Leen AlQadi, Mugariya Farooq, Shaikha Alsuwaidi, Giulia Campesan, Ahmed Alzubaidi, Mohammed Alyafeai, Hakim Hacid
Arabic is one of the most widely spoken languages in the world, yet efforts to develop and evaluate Large Language Models (LLMs) for Arabic remain relatively limited. Most existing Arabic benchmarks focus on linguistic, cultural, or religious content, leaving a significant gap in domains like STEM and code which are increasingly relevant for real-world LLM applications. To help bridge this gap, we present 3LM, a suite of three benchmarks designed specifically for Arabic. The first is a set of STEM-related question-answer pairs, naturally sourced from Arabic textbooks and educational worksheets. The second consists of synthetically generated STEM questions, created using the same sources. The third benchmark focuses on code generation, built through a careful translation of two widely used code benchmarks, incorporating a human-in-the-loop process with several rounds of review to ensure high-quality and faithful translations. We release all three benchmarks publicly to support the growth of Arabic LLM research in these essential but underrepresented areas.
AutoWISP: Automated Processing of Wide-Field Color Images
2025-07-21
Angel E. Romero, Kaloyan Penev, S. Javad Jafarzadeh, Zoltan Csubry, Joel D. Hartman, Gaspar A. Bakos
We have developed a software pipeline, AutoWISP, for extracting high-precision photometry from citizen scientists' observations made with consumer-grade color digital cameras (digital single-lens reflex, or DSLR, cameras), based on our previously developed tool, AstroWISP. The new pipeline is designed to convert these observations, including color images, into high-precision light curves of stars. We outline the individual steps of the pipeline and present a case study using a Sony-alpha 7R II DSLR camera, demonstrating sub-percent photometric precision, and highlighting the benefits of three-color photometry of stars. Project PANOPTES will adopt this photometric pipeline and, we hope, be used by citizen scientists worldwide. Our aim is for AutoWISP to pave the way for potentially transformative contributions from citizen scientists with access to observing equipment.
Just Put a Human in the Loop? Investigating LLM-Assisted Annotation for Subjective Tasks
2025-07-21
Hope Schroeder, Deb Roy, Jad Kabbara
LLM use in annotation is becoming widespread, and given LLMs' overall promising performance and speed, simply "reviewing" LLM annotations in interpretive tasks can be tempting. In subjective annotation tasks with multiple plausible answers, reviewing LLM outputs can change the label distribution, impacting both the evaluation of LLM performance, and analysis using these labels in a social science task downstream. We conducted a pre-registered experiment with 410 unique annotators and over 7,000 annotations testing three AI assistance conditions against controls, using two models, and two datasets. We find that presenting crowdworkers with LLM-generated annotation suggestions did not make them faster, but did improve their self-reported confidence in the task. More importantly, annotators strongly took the LLM suggestions, significantly changing the label distribution compared to the baseline. When these labels created with LLM assistance are used to evaluate LLM performance, reported model performance significantly increases. We believe our work underlines the importance of understanding the impact of LLM-assisted annotation on subjective, qualitative tasks, on the creation of gold data for training and testing, and on the evaluation of NLP systems on subjective tasks.
Euclid preparation: Expected constraints on initial conditions
2025-07-21
Euclid Collaboration, F. Finelli, Y. Akrami, A. Andrews, M. Ballardini, S. Casas, D. Karagiannis, Z. Sakr, J. Valiviita, G. Alestas, N. Bartolo, J. R. Bermejo-Climent, S. Nesseris, D. Paoletti, D. Sapone, I. Tutusaus, A. Achúcarro, G. Cañas-Herrera, J. Jasche, G. Lavaux, N. Aghanim, B. Altieri, A. Amara, L. Amendola, S. Andreon, N. Auricchio, C. Baccigalupi, D. Bagot, M. Baldi, S. Bardelli, P. Battaglia, A. Biviano, E. Branchini, M. Brescia, S. Camera, V. Capobianco, C. Carbone, J. Carretero, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, S. de la Torre, G. De Lucia, A. M. Di Giorgio, H. Dole, M. Douspis, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, S. Escoffier, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, P. Fosalba, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, B. Gillis, C. Giocoli, J. Gracia-Carpio, A. Grazian, F. Grupp, S. V. H. Haugan, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. J. Massey, E. Medinaceli, S. Mei, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, C. Neissner, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, C. Rosset, R. Saglia, B. Sartoris, M. Schirmer, T. Schrabback, A. Secroun, E. Sefusatti, G. Seidel, M. Seiffert, S. Serrano, P. Simon, C. Sirignano, G. Sirri, A. Spurio Mancini, L. Stanco, J. Steinwagner, P. Tallada-Crespí, D. Tavagnacco, A. N. Taylor, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, L. Valenziano, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, E. Zucca, V. Allevato, E. Bozzo, C. Burigana, R. Cabanac, M. Calabrese, A. Cappi, D. Di Ferdinando, J. A. Escartin Vigo, L. Gabarra, J. Martín-Fleitas, S. Matthew, N. Mauri, R. B. Metcalf, A. A. Nucita, A. Pezzotta, M. Pöntinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, I. T. Andika, M. Archidiacono, F. Atrio-Barandela, S. Avila, A. Balaguera-Antolinez, D. Bertacca, M. Bethermin, A. Blanchard, L. Blot, H. Böhringer, S. Borgani, M. L. Brown, S. Bruton, A. Calabro, B. Camacho Quevedo, F. Caro, C. S. Carvalho, T. Castro, F. Cogato, S. Conseil, A. R. Cooray, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, J. M. Diego, P. Dimauro, A. Enia, Y. Fang, A. G. Ferrari, A. Finoguenov, A. Fontana, A. Franco, K. Ganga, J. García-Bellido, T. Gasparetto, V. Gautard, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, A. Gruppuso, M. Guidi, C. M. Gutierrez, S. Hemmati, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, S. Joudaki, J. J. E. Kajava, Y. Kang, V. Kansal, K. Kiiveri, C. C. Kirkpatrick, S. Kruk, M. Lattanzi, V. Le Brun, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, L. Leuzzi, T. I. Liaudat, J. Macias-Perez, G. Maggio, M. Magliocchetti, F. Mannucci, R. Maoli, C. J. A. P. Martins, L. Maurin, M. Migliaccio, M. Miluzio, P. Monaco, C. Moretti, G. Morgante, S. Nadathur, K. Naidoo, A. Navarro-Alsina, L. Pagano, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, S. Quai, M. Radovich, P. Reimberg, P. -F. Rocci, G. Rodighiero, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, A. Schneider, D. Sciotti, E. Sellentin, L. C. Smith, K. Tanidis, C. Tao, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, F. Vernizzi, G. Verza, P. Vielzeuf, N. A. Walton
The Euclid mission of the European Space Agency will deliver galaxy and cosmic shear surveys, which will be used to constrain initial conditions and statistics of primordial fluctuations. We present highlights for the Euclid scientific capability to test initial conditions beyond LCDM with the main probes, i.e. 3D galaxy clustering from the spectroscopic survey, the tomographic approach to 3x2pt statistics from photometric galaxy survey, and their combination. We provide Fisher forecasts from the combination of Euclid spectroscopic and photometric surveys for spatial curvature, running of the spectral index of the power spectrum of curvature perturbations, isocurvature perturbations, and primordial features. For the parameters of these models we also provide the combination of Euclid forecasts (pessimistic and optimistic) with current and future measurements of the cosmic microwave background (CMB) anisotropies., i.e. Planck, the Simons Observatory (SO), and CMB-S4. We provide Fisher forecasts for how the power spectrum and bispectrum from the Euclid spectroscopic survey will constrain the local, equilateral, and orthogonal shapes of primordial non-Gaussianity. We also review how Bayesian field-level inference of primordial non-Gaussianity can constrain local primordial non-Gaussianity. We show how Euclid, with its unique combination of the main probes, will provide the tightest constraints on low redshift to date. By targeting a markedly different range in redshift and scale, Euclid's expected uncertainties are complementary to those obtained by CMB primary anisotropy, returning the tightest combined constraints on the physics of the early Universe.
Chasing the formation history of the Galactic metal-poor disc
2025-07-21
Xiaokun Hou, Ruizhi Zhang, Haining Li, Gang Zhao
In our previous work, we identified $\sim100,000$ metal-poor stars ([Fe/H] $<$ -1.0) from the LAMOST Survey. This work estimates their chemical abundances and explores the origin and evolution of the Galactic metal-poor disc. Our chemo-dynamical analysis reveals four main populations within the metal-poor disc: (1) a primordial disc older than 12 Gyr with [Fe/H] $>$ -1.5; (2) debris stars from the progenitor galaxy of Gaia-Sausage-Enceladus (GSE), but now residing in the Galactic disc; (3) the metal-poor tail of the metal-rich, high-$\alpha$ disc formed 10-12 Gyr ago, with metallicity lower limit extending to -2.0; (4) the metal-poor tail of the metal-rich, low-$\alpha$ disc younger than 8 Gyr, reaching a lower metallicity limit of -1.8. These results reveal the presence of a primordial disc and show that both high-$\alpha$ and low-$\alpha$ discs reach lower metallicities than previously thought. Analysis of merger debris reveals that Wukong, with extremely low metallicity, likely originate from merger events distinct from GSE. Additionally, three new substructures are identified: ShangGu-1, characterized by unusual [Fe/H]-eccentricity correlations; ShangGu-2, possibly heated disc stars; and ShangGu-3, which can be divided into four subgroups based on differing orbital directions, with two aligning with the previously known Nyx and Nyx-2.
Decadal evolution of a repeating fast radio burst source
2025-07-21
P. Wang, J. S. Zhang, Y. P. Yang, D. K. Zhou, Y. K. Zhang, Y. Feng, Z. Y. Zhao, J. H. Fang, D. Li, W. W. Zhu, B. Zhang, F. Y. Wang, Y. F. Huang, R. Luo, J. L. Han, K. J. Lee, C. W. Tsai, Z. G. Dai, H. Gao, X. P. Zheng, J. H. Cao, X. L. Chen, E. Gugercinoglu, J. C. Jiang, W. C. Jing, Y. Li, J. Li, W. J. Lu, J. W. Luo, F. Lyu, C. C. Miao, C. H. Niu, J. R. Niu, Y. Qu, W. Y. Wang, Y. D. Wang, Y. B. Wang, C. J. Wang, Q. Wu, Y. S. Wu, S. M. Weng, D. Xiao, H. Xu, J. M. Yao, C. F. Zhang, R. S. Zhao, Q. Z. Liu, J. Zhang, D. J. Zhou, L. Zhang, Y. H. Zhu
The origin of fast radio bursts (FRBs), the brightest cosmic radio explosions, is still unknown. Bearing critical clues to FRBs' origin, the long-term evolution of FRBs has yet to be confirmed, since the field is still young and most FRBs were seen only once. Here we report clear evidence of decadal evolution of FRB~20121102A, the first precisely localized repeater. In conjunction with archival data, our FAST and GBT monitoring campaign since 2020 reveals a significant 7% decline of local dispersion measure (DM). The rotation measure (RM) of 30,755$\pm$16 $\mathrm{rad\,m^{-2}}$ detected in the last epoch represents a 70% decrease compared to that from December 2016. The $\sigma_{RM}$ parameter, which describes the complexity of the magneto-ionic environment surrounding the source, was shown to have decreased by 13%. These general trends reveal an evolving FRB environment, which could originate from an early-phase supernova associated with an enhanced pair wind from the FRB central engine.
Appearance Harmonization via Bilateral Grid Prediction with Transformers for 3DGS
2025-07-21
Jisu Shin, Richard Shaw, Seunghyun Shin, Anton Pelykh, Zhensong Zhang, Hae-Gon Jeon, Eduardo Perez-Pellitero
Modern camera pipelines apply extensive on-device processing, such as exposure adjustment, white balance, and color correction, which, while beneficial individually, often introduce photometric inconsistencies across views. These appearance variations violate multi-view consistency and degrade the quality of novel view synthesis. Joint optimization of scene representations and per-image appearance embeddings has been proposed to address this issue, but at the cost of increased computational complexity and slower training. In this work, we propose a transformer-based method that predicts spatially adaptive bilateral grids to correct photometric variations in a multi-view consistent manner, enabling robust cross-scene generalization without the need for scene-specific retraining. By incorporating the learned grids into the 3D Gaussian Splatting pipeline, we improve reconstruction quality while maintaining high training efficiency. Extensive experiments show that our approach outperforms or matches existing scene-specific optimization methods in reconstruction fidelity and convergence speed.
Towards physician-centered oversight of conversational diagnostic AI
2025-07-21
Elahe Vedadi, David Barrett, Natalie Harris, Ellery Wulczyn, Shashir Reddy, Roma Ruparel, Mike Schaekermann, Tim Strother, Ryutaro Tanno, Yash Sharma, Jihyeon Lee, Cían Hughes, Dylan Slack, Anil Palepu, Jan Freyberg, Khaled Saab, Valentin Liévin, Wei-Hung Weng, Tao Tu, Yun Liu, Nenad Tomasev, Kavita Kulkarni, S. Sara Mahdavi, Kelvin Guu, Joëlle Barral, Dale R. Webster, James Manyika, Avinatan Hassidim, Katherine Chou, Yossi Matias, Pushmeet Kohli, Adam Rodman, Vivek Natarajan, Alan Karthikesalingam, David Stutz
Recent work has demonstrated the promise of conversational AI systems for diagnostic dialogue. However, real-world assurance of patient safety means that providing individual diagnoses and treatment plans is considered a regulated activity by licensed professionals. Furthermore, physicians commonly oversee other team members in such activities, including nurse practitioners (NPs) or physician assistants/associates (PAs). Inspired by this, we propose a framework for effective, asynchronous oversight of the Articulate Medical Intelligence Explorer (AMIE) AI system. We propose guardrailed-AMIE (g-AMIE), a multi-agent system that performs history taking within guardrails, abstaining from individualized medical advice. Afterwards, g-AMIE conveys assessments to an overseeing primary care physician (PCP) in a clinician cockpit interface. The PCP provides oversight and retains accountability of the clinical decision. This effectively decouples oversight from intake and can thus happen asynchronously. In a randomized, blinded virtual Objective Structured Clinical Examination (OSCE) of text consultations with asynchronous oversight, we compared g-AMIE to NPs/PAs or a group of PCPs under the same guardrails. Across 60 scenarios, g-AMIE outperformed both groups in performing high-quality intake, summarizing cases, and proposing diagnoses and management plans for the overseeing PCP to review. This resulted in higher quality composite decisions. PCP oversight of g-AMIE was also more time-efficient than standalone PCP consultations in prior work. While our study does not replicate existing clinical practices and likely underestimates clinicians' capabilities, our results demonstrate the promise of asynchronous oversight as a feasible paradigm for diagnostic AI systems to operate under expert human oversight for enhancing real-world care.
BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning
2025-07-21
Sahana Srinivasan, Xuguang Ai, Thaddaeus Wai Soon Lo, Aidan Gilson, Minjie Zou, Ke Zou, Hyunjae Kim, Mingjia Yang, Krithi Pushpanathan, Samantha Yew, Wan Ting Loke, Jocelyn Goh, Yibing Chen, Yiming Kong, Emily Yuelei Fu, Michelle Ongyong Hui, Kristen Nwanyanwu, Amisha Dave, Kelvin Zhenghao Li, Chen-Hsin Sun, Mark Chia, Gabriel Dawei Yang, Wendy Meihua Wong, David Ziyou Chen, Dianbo Liu, Maxwell Singer, Fares Antaki, Lucian V Del Priore, Jost Jonas, Ron Adelman, Qingyu Chen, Yih-Chung Tham
Current benchmarks evaluating large language models (LLMs) in ophthalmology are limited in scope and disproportionately prioritise accuracy. We introduce BELO (BEnchmarking LLMs for Ophthalmology), a standardized and comprehensive evaluation benchmark developed through multiple rounds of expert checking by 13 ophthalmologists. BELO assesses ophthalmology-related clinical accuracy and reasoning quality. Using keyword matching and a fine-tuned PubMedBERT model, we curated ophthalmology-specific multiple-choice-questions (MCQs) from diverse medical datasets (BCSC, MedMCQA, MedQA, BioASQ, and PubMedQA). The dataset underwent multiple rounds of expert checking. Duplicate and substandard questions were systematically removed. Ten ophthalmologists refined the explanations of each MCQ's correct answer. This was further adjudicated by three senior ophthalmologists. To illustrate BELO's utility, we evaluated six LLMs (OpenAI o1, o3-mini, GPT-4o, DeepSeek-R1, Llama-3-8B, and Gemini 1.5 Pro) using accuracy, macro-F1, and five text-generation metrics (ROUGE-L, BERTScore, BARTScore, METEOR, and AlignScore). In a further evaluation involving human experts, two ophthalmologists qualitatively reviewed 50 randomly selected outputs for accuracy, comprehensiveness, and completeness. BELO consists of 900 high-quality, expert-reviewed questions aggregated from five sources: BCSC (260), BioASQ (10), MedMCQA (572), MedQA (40), and PubMedQA (18). A public leaderboard has been established to promote transparent evaluation and reporting. Importantly, the BELO dataset will remain a hold-out, evaluation-only benchmark to ensure fair and reproducible comparisons of future models.
A MeerKAT study of a neutral hydrogen rich grouping of galaxies with megaparsec-scale filamentary-like structure
2025-07-21
G. Lawrie, R. P. Deane, R. Davè
Environmental effects within cosmological overdensities, such as galaxy groups and clusters, have been shown to impact galaxies and their cold gas reservoirs and thereby provide constraints on galaxy evolution models. Galaxy groups foster frequent galaxy-galaxy interactions, making them rich environments in which to study galaxy transformation. In this work, we study a serendipitously discovered large overdensity of HI galaxies at z~0.04. The galaxies appear to lie in a filamentary-like structure of megaparsec scale. MeerKAT's angular resolution and field of view allow us to spatially resolve the HI galaxies while simultaneously probing large-scale structure. The HI and sub-arcsec Dark Energy Survey (DES) imaging reveal a large number of interacting galaxies in this collective group. MeerKAT data enables us to derive HI masses and investigate interacting galaxies. We use DES and Wide-field Infrared Survey Explorer (WISE) data to quantify the star formation rates, stellar masses, and stellar morphologies of member galaxies and compare these with field scaling relations. To place this discovery and the environmental effects in context, we use the SIMBA simulation to investigate the prevalence of qualitatively similar HI overdensities and their large-scale morphological properties. This enables a prediction of how frequently such structures might be serendipitously discovered with MeerKAT and SKA-Mid HI observations in comparable observation time. The combination of spatially resolved HI data and optical imaging reveals a group rich in interactions, suggesting environmental processes are already shaping galaxy properties within the structure. More of these serendipitous discoveries are expected and, alongside ongoing targeted programmes, these will provide a rich sample to study galaxy transformation and enable a MeerKAT HI perspective on large-scale structure, including filaments.
Combinatorics and Hodge theory of degenerations of abelian varieties: A survey of the Mumford construction
2025-07-21
Philip Engel, Olivier de Gaay Fortman, Stefan Schreieder
We survey the Mumford construction of degenerating abelian varieties, with a focus on the analytic version of the construction, and its relation to toric geometry. Moreover, we study the geometry and Hodge theory of multivariable degenerations of abelian varieties associated to regular matroids, and extend some fundamental results of Clemens on 1-parameter semistable degenerations to the multivariable setting.
Brain rhythms in cognition -- controversies and future directions
2025-07-21
Anne Keitel, Christian Keitel, Mohsen Alavash, Karin Bakardjian, Christopher S. Y. Benwell, Sophie Bouton, Niko A. Busch, Antonio Criscuolo, Keith B. Doelling, Laura Dugue, Laetitia Grabot, Joachim Gross, Simon Hanslmayr, Laura-Isabelle Klatt, Daniel S. Kluger, Gemma Learmonth, Raquel E. London, Christina Lubinus, Andrea E. Martin, Jonas Obleser, Johanna M. Rimmele, Vincenzo Romei, Manuela Ruzzoli, Felix Siebenhuhner, Sophie Slaats, Eelke Spaak, Luca Tarasi, Gregor Thut, Jelena Trajkovic, Danying Wang, Malte Wostmann, Benedikt Zoefel, Satu Palva, Paul Sauseng, Sonja A. Kotz
Brain rhythms seem central to understanding the neurophysiological basis of human cognition. Yet, despite significant advances, key questions remain unresolved. In this comprehensive position paper, we review the current state of the art on oscillatory mechanisms and their cognitive relevance. The paper critically examines physiological underpinnings, from phase-related dynamics like cyclic excitability, to amplitude-based phenomena, such as gating by inhibition, and their interactions, such as phase-amplitude coupling, as well as frequency dynamics, like sampling mechanisms. We also critically evaluate future research directions, including travelling waves and brain-body interactions. We then provide an in-depth analysis of the role of brain rhythms across cognitive domains, including perception, attention, memory, and communication, emphasising ongoing debates and open questions in each area. By summarising current theories and highlighting gaps, this position paper offers a roadmap for future research, aimed at facilitating a unified framework of rhythmic brain function underlying cognition.
Testing Homogeneity in a heteroscedastic contaminated normal mixture
2025-07-21
Xiaoqing Niu, Pengfei Li, Yuejiao Fu
Large-scale simultaneous hypothesis testing appears in many areas such as microarray studies, genome-wide association studies, brain imaging, disease mapping and astronomical surveys. A well-known inference method is to control the false discovery rate. One popular approach is to model the $z$-scores derived from the individual $t$-tests and then use this model to control the false discovery rate. We propose a new class of contaminated normal mixtures for modelling $z$-scores. We further design an EM-test for testing homogeneity in this class of mixture models. We show that the EM-test statistic has a shifted mixture of chi-squared limiting distribution. Simulation results show that the proposed testing procedure has accurate type I error and significantly larger power than its competitors under a variety of model specifications. A real-data example is analyzed to exemplify the application of the proposed method.
A Survey on Efficiency Optimization Techniques for DNN-based Video Analytics: Process Systems, Algorithms, and Applications
2025-07-21
Shanjiang Tang, Rui Huang, Hsinyu Luo, Chunjiang Wang, Ce Yu, Yusen Li, Hao Fu, Chao Sun, and Jian Xiao
The explosive growth of video data in recent years has brought higher demands for video analytics, where accuracy and efficiency remain the two primary concerns. Deep neural networks (DNNs) have been widely adopted to ensure accuracy; however, improving their efficiency in video analytics remains an open challenge. Different from existing surveys that make summaries of DNN-based video mainly from the accuracy optimization aspect, in this survey, we aim to provide a thorough review of optimization techniques focusing on the improvement of the efficiency of DNNs in video analytics. We organize existing methods in a bottom-up manner, covering multiple perspectives such as hardware support, data processing, operational deployment, etc. Finally, based on the optimization framework and existing works, we analyze and discuss the problems and challenges in the performance optimization of DNN-based video analytics.
Maximum multinomial likelihood estimation in compound mixture model with application to malaria study
2025-07-21
Zhaoyang Tian, Kun Liang, Pengfei Li
Malaria can be diagnosed by the presence of parasites and symptoms (usually fever) due to the parasites. In endemic areas, however, an individual may have fever attributable either to malaria or to other causes. Thus, the parasite level of an individual with fever follows a two-component mixture, with the two components corresponding to malaria and nonmalaria individuals. Furthermore, the parasite levels of nonmalaria individuals can be characterized as a mixture of a zero component and a positive distribution. In this article, we propose a nonparametric maximum multinomial likelihood approach for estimating the proportion of malaria using parasite-level data from two groups of individuals collected in two different seasons. We develop an EM-algorithm to numerically calculate the proposed estimates and further establish their convergence rates. Simulation results show that the proposed estimators are more efficient than existing nonparametric estimators. The proposed method is used to analyze a malaria survey data.
Dual Turing Test: A Framework for Detecting and Mitigating Undetectable AI
2025-07-21
Alberto Messina
In this short note, we propose a unified framework that bridges three areas: (1) a flipped perspective on the Turing Test, the "dual Turing test", in which a human judge's goal is to identify an AI rather than reward a machine for deception; (2) a formal adversarial classification game with explicit quality constraints and worst-case guarantees; and (3) a reinforcement learning (RL) alignment pipeline that uses an undetectability detector and a set of quality related components in its reward model. We review historical precedents, from inverted and meta-Turing variants to modern supervised reverse-Turing classifiers, and highlight the novelty of combining quality thresholds, phased difficulty levels, and minimax bounds. We then formalize the dual test: define the judge's task over N independent rounds with fresh prompts drawn from a prompt space Q, introduce a quality function Q and parameters tau and delta, and cast the interaction as a two-player zero-sum game over the adversary's feasible strategy set M. Next, we map this minimax game onto an RL-HF style alignment loop, in which an undetectability detector D provides negative reward for stealthy outputs, balanced by a quality proxy that preserves fluency. Throughout, we include detailed explanations of each component notation, the meaning of inner minimization over sequences, phased tests, and iterative adversarial training and conclude with a suggestion for a couple of immediate actions.
2025-07-21
David Bann, Ed Lowther, Liam Wright, Yevgeniya Kovalchuk
Recent advances in artificial intelligence (AI) - particularly generative AI - present new opportunities to accelerate, or even automate, epidemiological research. Unlike disciplines based on physical experimentation, a sizable fraction of Epidemiology relies on secondary data analysis and thus is well-suited for such augmentation. Yet, it remains unclear which specific tasks can benefit from AI interventions or where roadblocks exist. Awareness of current AI capabilities is also mixed. Here, we map the landscape of epidemiological tasks using existing datasets - from literature review to data access, analysis, writing up, and dissemination - and identify where existing AI tools offer efficiency gains. While AI can increase productivity in some areas such as coding and administrative tasks, its utility is constrained by limitations of existing AI models (e.g. hallucinations in literature reviews) and human systems (e.g. barriers to accessing datasets). Through examples of AI-generated epidemiological outputs, including fully AI-generated papers, we demonstrate that recently developed agentic systems can now design and execute epidemiological analysis, albeit to varied quality (see https://github.com/edlowther/automated-epidemiology). Epidemiologists have new opportunities to empirically test and benchmark AI systems; realising the potential of AI will require two-way engagement between epidemiologists and engineers.
In recent years, with an increasing number of type Ia supernovae (SNe Ia) discovered soon after their explosions, a non-negligible fraction of SNe Ia with early-excess emissions (EExSNe Ia) have been confirmed. In this letter, we present a total of \textbf{67} early-phase normal SNe Ia from published papers and ongoing transient survey projects to systematically investigate their photometric behaviors from very early time. We found that EExSNe Ia in our sample have longer rise and brighter peak luminosities compared to those of non-EExSNe Ia. Moreover, EExSNe Ia commonly have ``red-bump" features in the early $B-V$ color while non-EExSNe Ia show blueward evolution from the very beginning. Here, we propose that the thin-helium double-detonation scenario can phenomenologically explain the photometric diversities of normal SNe Ia considering different white dwarf-He-shell mass combinations and the viewing-angle effect, implying a unified explosion mechanism of normal-type SNe Ia. To further testify the possible common origin of normal SNe Ia, systematical studies of multiband photometric and spectral properties of early-phase SNe Ia through the new generation wide-field time-domain survey facilities and global real-time follow-up networks are highly demanded.
Since the 60s, musicology has been increasingly impacted by computational tools in various ways, from systematic analysis approaches to modeling of creativity. This article presents a comprehensive assessment of the current state of Computational Musicology tools based on survey data collected from practitioners in the field. We gathered information on tool usage patterns, common analytical tasks, user satisfaction levels, data characteristics, and prioritized features across four distinct domains: symbolic music, music-related imagery, audio, and text. Our findings reveal significant gaps between current tooling capabilities and user needs, highlighting some limitations of these tools across all domains. This assessment contributes to the ongoing dialogue between tool developers and music scholars, aiming to enhance the effectiveness and accessibility of computational methods in musicological research.
FlowForge: Guiding the Creation of Multi-agent Workflows with Design Space Visualization as a Thinking Scaffold
2025-07-21
Pan Hao, Dongyeop Kang, Nicholas Hinds, Qianwen Wang
Multi-agent workflows have become an effective strategy for tackling complicated tasks by decomposing them into multiple sub-tasks and assigning them to specialized agents. However, designing optimal workflows remains challenging due to the vast and intricate design space. Current practices rely heavily on the intuition and expertise of practitioners, often resulting in design fixation or an unstructured, time-consuming exploration of trial-and-error. To address these challenges, this work introduces FLOWFORGE, an interactive visualization tool to facilitate the creation of multi-agent workflow through i) a structured visual exploration of the design space and ii) in-situ guidance informed by established design patterns. Based on formative studies and literature review, FLOWFORGE organizes the workflow design process into three hierarchical levels (i.e., task planning, agent assignment, and agent optimization), ranging from abstract to concrete. This structured visual exploration enables users to seamlessly move from high-level planning to detailed design decisions and implementations, while comparing alternative solutions across multiple performance metrics. Additionally, drawing from established workflow design patterns, FLOWFORGE provides context-aware, in-situ suggestions at each level as users navigate the design space, enhancing the workflow creation process with practical guidance. Use cases and user studies demonstrate the usability and effectiveness of FLOWFORGE, while also yielding valuable insights into how practitioners explore design spaces and leverage guidance during workflow development.
Foundation Models and Transformers for Anomaly Detection: A Survey
2025-07-21
Mouïn Ben Ammar, Arturo Mendoza, Nacim Belkhir, Antoine Manzanera, Gianni Franchi
In line with the development of deep learning, this survey examines the transformative role of Transformers and foundation models in advancing visual anomaly detection (VAD). We explore how these architectures, with their global receptive fields and adaptability, address challenges such as long-range dependency modeling, contextual modeling and data scarcity. The survey categorizes VAD methods into reconstruction-based, feature-based and zero/few-shot approaches, highlighting the paradigm shift brought about by foundation models. By integrating attention mechanisms and leveraging large-scale pre-training, Transformers and foundation models enable more robust, interpretable, and scalable anomaly detection solutions. This work provides a comprehensive review of state-of-the-art techniques, their strengths, limitations, and emerging trends in leveraging these architectures for VAD.
Strategies to Manage Human Factors in Mixed Reality Pilot Training: A Survey
2025-07-21
Antonio Perez, Avinash Singh, Jonathan Mitchell, Philip Swadling
Mixed Reality (MR) head mounted displays (HMDs) offer a promising alternative to traditional Flight Simulator Training Device (FSTD) displays, providing immersion, realism and cost efficiency. However, these technologies require management of human factors; cybersickness, visual fatigue and ergonomic strain. If left unmitigated, these effects can hinder pilot performance and training outcomes. For safety critical fields like aviation, addressing human factors challenges is crucial for MR's training potential. This survey systematically reviews the current literature identifying key human factors challenges in MR HMD use in pilot training and examines strategies to mitigate these barriers. Drawing on existing industry standards set by a leading aviation authority, the review adopts a regulatory perspective to explore hardware, software, ergonomic, physiological and psychological interventions improving pilot comfort, safety and training effectiveness in an MR FSTD. Additionally, it evaluates which of these interventions are most appropriate and viable for MR pilot training under existing aviation training regulations, ensuring that technical requirements and pilot wellbeing remain balanced. The findings yield significant insights for the human dimensions of aviation simulation training, highlighting how regulatory considerations shape the practicality of mitigation measures. These insights inform emerging MR aviation training guidelines and best practices, supporting MR's readiness to enhance aviation training.
LLM world models are mental: Output layer evidence of brittle world model use in LLM mechanical reasoning
2025-07-21
Cole Robertson, Philip Wolff
Do large language models (LLMs) construct and manipulate internal world models, or do they rely solely on statistical associations represented as output layer token probabilities? We adapt cognitive science methodologies from human mental models research to test LLMs on pulley system problems using TikZ-rendered stimuli. Study 1 examines whether LLMs can estimate mechanical advantage (MA). State-of-the-art models performed marginally but significantly above chance, and their estimates correlated significantly with ground-truth MA. Significant correlations between number of pulleys and model estimates suggest that models employed a pulley counting heuristic, without necessarily simulating pulley systems to derive precise values. Study 2 tested this by probing whether LLMs represent global features crucial to MA estimation. Models evaluated a functionally connected pulley system against a fake system with randomly placed components. Without explicit cues, models identified the functional system as having greater MA with F1=0.8, suggesting LLMs could represent systems well enough to differentiate jumbled from functional systems. Study 3 built on this by asking LLMs to compare functional systems with matched systems which were connected up but which transferred no force to the weight; LLMs identified the functional system with F1=0.46, suggesting random guessing. Insofar as they may generalize, these findings are compatible with the notion that LLMs manipulate internal world models, sufficient to exploit statistical associations between pulley count and MA (Study 1), and to approximately represent system components' spatial relations (Study 2). However, they may lack the facility to reason over nuanced structural connectivity (Study 3). We conclude by advocating the utility of cognitive scientific methods to evaluate the world-modeling capacities of artificial intelligence systems.
The CARMENES search for exoplanets around M dwarfs. Revisiting the GJ 317, GJ 463, and GJ 3512 systems and two newly discovered planets orbiting GJ 9773 and GJ 508.2
2025-07-21
J. C. Morales, I. Ribas, S. Reffert, M. Perger, S. Dreizler, G. Anglada-Escudé, V. J. S. Béjar, E. Herrero, J. Kemmer, M. Kuzuhara, M. Lafarga, J. H. Livingston, F. Murgas, B. B. Ogunwale, L. Tal-Or, T. Trifonov, S. Vanaverbeke, P. J. Amado, A. Quirrenbach, A. Reiners, J. A. Caballero, J. F. Agüí Fernández, J. Banegas, P. Chaturvedi, S. Dufoer, A. P. Hatzes, Th. Henning, C. Rodríguez-López, A. Schweitzer, E. Solano, M. Zechmeister, H. Harakawa, T. Kotani, M. Omiya, B. Sato, M. Tamura
Surveys for exoplanets indicate that the occurrence rate of gas giant planets orbiting late-type stars in orbits with periods shorter than 1000 days is lower than in the case of Sun-like stars. This is in agreement with planet formation models based on the core or pebble accretion paradigm. The CARMENES exoplanet survey has been conducting radial-velocity observations of several targets that show long-period trends or modulations that are consistent with the presence of giant planets at large orbital separations. We present an analysis of five such systems that were monitored with the CARMENES spectrograph, as well as with the IRD spectrograph. In addition, we used archival data to improve the orbital parameters of the planetary systems. We improve the parameters of three previously known planets orbiting the M dwarfs GJ 317, GJ 463, and GJ 3512. We also determine the orbital parameters and minimum mass of the planet GJ 3512 c, for which only lower limits had been given previously. Furthermore, we present the discovery of two new giant planets orbiting the stars GJ 9733 and GJ 508.2, although for the second one only lower limits to the orbital properties can be determined. The new planet discoveries add to the short list of known giant planets orbiting M-dwarf stars with subsolar metallicity at long orbital periods above 2000 days. These results reveal that giant planets appear to form more frequently in wide orbits than in close-in orbits around low-mass and lower metallicity stars.
The increasing demand for autonomous systems in complex and dynamic environments has driven significant research into intelligent path planning methodologies. For decades, graph-based search algorithms, linear programming techniques, and evolutionary computation methods have served as foundational approaches in this domain. Recently, deep reinforcement learning (DRL) has emerged as a powerful method for enabling autonomous agents to learn optimal navigation strategies through interaction with their environments. This survey provides a comprehensive overview of traditional approaches as well as the recent advancements in DRL applied to path planning tasks, focusing on autonomous vehicles, drones, and robotic platforms. Key algorithms across both conventional and learning-based paradigms are categorized, with their innovations and practical implementations highlighted. This is followed by a thorough discussion of their respective strengths and limitations in terms of computational efficiency, scalability, adaptability, and robustness. The survey concludes by identifying key open challenges and outlining promising avenues for future research. Special attention is given to hybrid approaches that integrate DRL with classical planning techniques to leverage the benefits of both learning-based adaptability and deterministic reliability, offering promising directions for robust and resilient autonomous navigation.
2025-07-21
Daniel Grieser, Álvaro Sánchez-Hernández, Boris Vertman
We give a survey of a number of analytic and geometric results on `fibred cusp spaces'. These results cover topics in spectral geometry, in particular analytic torsion and index theory, and boundary value problems. The underlying tools include a careful microlocal analysis of the resolvent and the heat kernel. We include an exposition of the geometric and analytic foundations and sketch the ideas of the proofs of the main theorems.
One-point functions in AdS/dCFT: MPS and twisted Yangian
2025-07-21
Xin Qian
I focus on the scalar one-point functions in SO(6) sector of D5-D3 probe-brane set-up. Start with a general introduction of integrability, I explore both coordinate Bethe ansatz and algebraic Bethe ansatz, with possible generalization. I then shortly review how to use the Bethe ansatz in $N = 4$ super Yang-Mills theory, and then apply such procedure to the D5-D3 system. The dual field theory of such system corresponds to a defected version of $N = 4$ super Yang-Mills theory, where the one-point functions of certain scalars are non-zero. The calculation of one-point functions is mapped to the overlap between matrix product states and Bethe states. The matrix product states are found to be solutions of the twisted Boundary Yang-Baxter equation, and equivalently the representations of extended twisted Yangian. By dressing procedure or coproduct property, we can connect the scalar matrix product state and higher dimension matrix product states. We have used the branching rules to find the connection with some detailed parameters needed to be fixed. Such method can not only be used for calculations of one-point functions in probe-branes system, but also shed some light on non-equilibrium system.
Approaches for modelling the term-structure of default risk under IFRS 9: A tutorial using discrete-time survival analysis
2025-07-21
Arno Botha, Tanja Verster
Under the International Financial Reporting Standards (IFRS) 9, credit losses ought to be recognised timeously and accurately. This requirement belies a certain degree of dynamicity when estimating the constituent parts of a credit loss event, most notably the probability of default (PD). It is notoriously difficult to produce such PD-estimates at every point of loan life that are adequately dynamic and accurate, especially when considering the ever-changing macroeconomic background. In rendering these lifetime PD-estimates, the choice of modelling technique plays an important role, which is why we first review a few classes of techniques, including the merits and limitations of each. Our main contribution however is the development of an in-depth and data-driven tutorial using a particular class of techniques called discrete-time survival analysis. This tutorial is accompanied by a diverse set of reusable diagnostic measures for evaluating various aspects of a survival model and the underlying data. A comprehensive R-based codebase is further contributed. We believe that our work can help cultivate common modelling practices under IFRS 9, and should be valuable to practitioners, model validators, and regulators alike.
Designing at 1:1 Scale on Wall-Sized Displays Using Existing UI Design Tools
2025-07-21
Lou Schwartz, Mohammad Ghoniem, Valérie Maquil, Adrien Coppens, Johannes Hermen
Wall-Sized Displays have spatial characteristics that are difficult to address during user interface design. The design at scale 1:1 could be part of the solution. In this paper, we present the results of two user studies and one technology review, exploring the usability of popular, desktop-optimized prototyping tools, for designing at scale on Wall-Sized Displays. We considered two wall-sized display setups, and three different interaction methods: touch, a keyboard equipped with a touchpad, and a tablet. We observed that designing at scale 1:1 was appreciated. Tablet-based interaction proved to be the most comfortable interaction method, and a mix of interaction modalities is promising. In addition, care must be given to the surrounding environment, such as furniture. We propose twelve design guidelines for a design tool dedicated to this specific context. Overall, existing user interface design tools do not yet fully support design on and for wall-sized displays and require further considerations in terms of placement of user interface elements and the provision of additional features.
The Australian Vote: Transferable Voting, Its Limitations and Strengths
2025-07-21
Anthony B. Morton
The voting systems known as Alternative Vote (AV) and Single Transferable Vote (STV) are extensively used for elections in Australia, possibly more than in any other jurisdiction. Often proposed as superior alternatives to Plurality and other common systems, they are also criticised by theoreticians for their vulnerability to paradoxical outcomes. It is argued these 'transferable voting' systems in fact operate to balance competing desiderata that attempt to distil the common notions of 'majority rule' and 'voter choice'. Among positive characteristics of AV is that its outcome is always a majority runoff between two candidates with substantial voter support, and that a Condorcet winner who gains at least one-third of votes is always elected. It is shown that in situations where a Condorcet winner is not elected, there always exists a monotonic shift producing a Condorcet cycle without changing the election outcome. A detailed analysis of non-monotonicity paradoxes in three-way contests is provided, aided by a two-dimensional spatial model where each candidate has a one-in-four probability of gaining an absolute majority of primary votes while there is a similar one-in-four probability that no candidate wins a majority. On such a model it is shown non-monotonicities are latent in less than 3.5% of election scenarios (and conjectured to be less than 3%), and the close link between non-monotonicities and Condorcet cycles and instabilities is studied in more detail. Properties of multi-winner STV are also reviewed including a demonstration of the standard fact that STV becomes similar to a form of largest-remainder list voting in the special case where ballots are cast strictly by party allegiance. The argument that AV and STV exemplify the principle of voter choice while balancing absolute and relative concepts of majority-rule is made by reference to practice in Australian elections.
Prospects of detecting rotational flatness of exoplanets from space-based photometry
2025-07-21
Sz. Kálmán, Sz. Csizmadia, L. M. Bernabó, R. Szabó, Gy. M. Szabó
In the era of photometry with space-based telescopes, such as CHEOPS (CHaracterizing ExOPlanets Satellite), JWST (James Webb Space Telescope), PLATO (PLAnetary Transits and Oscillations of stars), and ARIEL (Atmospheric Remote-sensing Infrared Exoplanet Large-survey), the road has opened for detecting subtle distortions in exoplanet transit light curves -- resulting from their non-spherical shape. We investigate the prospects of retrieval of rotational flatness (oblateness) of exoplanets at various noise levels. We present a novel method for calculating the transit light curves based on the Gauss-Legendre quadrature. We compare it in the non-rotating limit to the available analytical models. We conduct injection-and-retrieval tests to assess the precision and accuracy of the retrievable oblateness values. We find that the light curve calculation technique is about $25$\% faster than a well-known analytical counterpart, while still being precise enough. We show that a $3 \sigma$ oblateness detection is possible for a planet orbiting bright enough stars, by exploiting a precise estimate on the stellar density obtained e.g. from asteroseismology. We also show that for noise levels $\geq 256$ ppm (expressed as point-to-point scatter with a $60$~s exposure time) detection of planetary oblateness is not reliable.
High pressure and temperature thermoelasticity of hcp osmium from ab initio quasi-harmonic theory
2025-07-21
Xuejun Gong, Andrea Dal Corso
We present a systematic ab initio study of the thermoelastic properties of hcp osmium as functions of temperature and pressure within the quasi-harmonic approximation (QHA). The precision of the Zero Static Internal Stress Approximation (ZSISA) and of the volume-constrained ZSISA (V-ZSISA) are rigorously assessed. For osmium, we find negligible deviations between ZSISA and a full free energy minimization (FFEM) approach. Also, the V-ZSISA approximation influences the results very little, as we found already in beryllium, despite the markedly different behavior of the c/a ratio with temperature in the two metals. Our QHA-derived ECs show excellent agreement with available experimental data in the temperature range of 5-301 K, outperforming the results obtained from the quasi-static approximation (QSA). Additionally, we report the pressure-dependent QHA ECs at 5 K, 301 K, and 1000 K, spanning pressures from 0 to 150 kbar.
This overview presents a collection of results from classical electrical network theory concerning properties of the network admittance matrix, and the relationship between electrical characteristics of the network and various mathematical properties of the admittance matrix. The derivation of standard circuit theorems taught to electrical engineers from properties of the admittance matrix and its cofactors is presented, and the determinant of the matrix is related to topological properties of the network. Later sections review key properties of DC and AC networks including monotonicity and orientability of DC networks, the impedance matrix for DC networks, and the extensions and limitations of these properties when translated to AC networks. It is shown that in general there is no impedance metric for AC networks extending that for DC networks but in many cases a form of semi-orientability can be exploited when analysing the AC power flow. The overview concludes with the derivation of standard properties of positive-real functions and impedance functions associated with AC networks, and of Thevenin and Norton equivalent sources for subnetworks.
The CHEOPS view of HD 95338b: refined transit parameters, and a search for exomoons
2025-07-21
Sz. Kálmán, A. E. Simon, A. Deline, Sz. Csizmadia, Gy. M. Szabó, D. Ehrenreich, T. G. Wilson, M. N. Günther, A. Heitzmann, S. G. Sousa, M. Farnir, A. Bonfanti, A. M. S. Smith, A. Pál, G. Scandariato, V. Adibekyan, A. Brandeker, S. Charnoz, B. Akinsanmi, S. C. C. Barros, X. Song, Y. Alibert, R. Alonso, T. Bárczy, D. Barrado Navascues, W. Baumjohann, W. Benz, N. Billot, F. Biondi, L. Borsato, C. Broeg, A. Collier Cameron, C. Corral van Damme, A. C. M. Correia, P. E. Cubillos, M. B. Davies, M. Deleuil, O. D. S. Demangeon, B. -O. Demory, A. Derekas, B. Edwards, J. A. Egger, A. Erikson, A. Fortier, L. Fossati, M. Fridlund, D. Gandolfi, K. Gazeas, M. Gillon, M. Güdel, P. Guterman, J. Hasiba, Ch. Helling, K. G. Isaak, L. L. Kiss, J. Korth, K. W. F. Lam, J. Laskar, A. Lecavelier des Etangs, A. Leleu, M. Lendl, D. Magrin, P. F. L. Maxted, B. Merín, C. Mordasini, M. Munari, V. Nascimbeni, G. Olofsson, R. Ottensamer, I. Pagano, E. Pallé, G. Peter, D. Piazza, G. Piotto, D. Pollacco, D. Queloz, R. Ragazzoni, N. Rando, H. Rauer, I. Ribas, N. C. Santos, D. Ségransan, M. Stalport, S. Sulis, S. Udry, B. Ulmer, S. Ulmer-Moll, V. Van Grootel, J. Venturini, E. Villaver, N. A. Walton, S. Wolf, T. Zingales
Despite the ever-increasing number of known exoplanets, no uncontested detections have been made of their satellites, known as exomoons. The quest to find exomoons is at the forefront of exoplanetary sciences. Certain space-born instruments are thought to be suitable for this purpose. We show the progress made with the CHaracterizing ExOPlanets Satellite (CHEOPS) in this field using the HD 95338 planetary system. We present a novel methodology as an important step in the quest to find exomoons. We utilize ground-based spectroscopic data in combination with Gaia observations to obtain precise stellar parameters. These are then used as input in the analysis of the planetary transits observed by CHEOPS and the Transiting Exoplanet Survey Satellite (TESS). In addition, we search for the signs of satellites primarily in the form of additional transits in the Hill sphere of the eccentric Neptune-sized planet HD 95338b in a sequential approach based on four CHEOPS visits. We also briefly explore the transit timing variations of the planet. We present refined stellar and planetary parameters, narrowing down the uncertainty on the planet-to-star radius ratio by a factor of $10$. We also pin down the ephemeris of HD 95338b. Using injection/retrieval tests, we show that a $5 \sigma$ detection of an exomoon would be possible at $R_{\rm Moon} = 0.8$~$R_\oplus$ with the methodology presented here. We exclude the transit of an exomoon in the system with $R_{\rm Moon} \approx 0.6$~$R_\oplus$ at the $1\sigma$ level. The algorithm used for finding the transit-like event can be used as a baseline for other similar targets, observed by CHEOPS or other missions.
Lecture notes on link homologies and knotted surfaces
2025-07-21
Kyle Hayden
Link homology theories (such as knot Floer homology and Khovanov homology) have become indispensable tools for studying knots and links, including powerful 4-dimensional obstructions. These notes, based on lectures given at the 2024 Georgia Topology Summer School, discuss what these toolkits say about surfaces in 4-space themselves, via the homomorphisms assigned to link cobordisms. We begin with a brief overview of these theories (focusing on their shared formal properties) and survey some of their applications to knotted surfaces. Afterwards, we give an introduction to Khovanov homology (with an eye towards its cobordism maps), discuss hands-on computational techniques for Khovanov and Bar-Natan homology, and outline the role of the Bar-Natan category in this story.
Fingerprint matching under diverse capture conditions remains a fundamental challenge in biometric recognition. To achieve robust and accurate performance in such scenarios, we propose DMD, a minutiae-anchored local dense representation which captures both fine-grained ridge textures and discriminative minutiae features in a spatially structured manner. Specifically, descriptors are extracted from local patches centered and oriented on each detected minutia, forming a three-dimensional tensor, where two dimensions represent spatial locations on the fingerprint plane and the third encodes semantic features. This representation explicitly captures abstract features of local image patches, enabling a multi-level, fine-grained description that aggregates information from multiple minutiae and their surrounding ridge structures. Furthermore, thanks to its strong spatial correspondence with the patch image, DMD allows for the use of foreground segmentation masks to identify valid descriptor regions. During matching, comparisons are then restricted to overlapping foreground areas, improving efficiency and robustness. Extensive experiments on rolled, plain, parital, contactless, and latent fingerprint datasets demonstrate the effectiveness and generalizability of the proposed method. It achieves state-of-the-art accuracy across multiple benchmarks while maintaining high computational efficiency, showing strong potential for large-scale fingerprint recognition. Corresponding code is available at https://github.com/Yu-Yy/DMD.
The H$α$ concentration of local star-forming galaxies: implications for galaxy structure formation
2025-07-21
Zhizheng Pan, Xianzhong Zheng, Xu Kong
In this work, we present a study on the H$\alpha$ emission line flux concentration of 3098 low-redshift star-forming galaxies (SFGs) using the MaNGA data available in the Data Release 17 from the Sloan Digital Sky Survey. We define the H$\alpha$ flux concentration index ($C_{\rm H\alpha}$) as $C_{\rm H\alpha}=F_{\rm H\alpha,0.8~R_e}/F_{\rm H\alpha,1.5~R_e}$, where $F_{\rm H\alpha,0.8~R_e}$ and $F_{\rm H\alpha,1.5~R_e}$ are the cumulative H$\alpha$ flux inside $0.8$ and $1.5$ $r-$band effective radius, respectively. We find that $C_{\rm H\alpha}$ is strongly correlated with the luminosity weighted stellar age gradient. $C_{\rm H\alpha}$ is also sensitive to environmental effects, in the sense that low-mass satellite galaxies below the star formation main sequence tend to have higher $C_{\rm H\alpha}$. For central galaxies, we find that massive disk galaxies with enhanced star formation rate tend to have higher $C_{\rm H\alpha}$, while such a phenomenon is not seen in the low-mass regime. We interpret this as evidence that compaction events more frequently occur in the high-mass regime, which eventually resulting in the buildup of prominent bulges in massive SFGs. Implications of these findings on galaxy structure formation are discussed.
Stellar-mass black holes ($3$ $M_\odot \lesssim M_{\rm BH} \lesssim 150$ $M_\odot$) are the natural product of the evolution of heavy stars ($M_{\rm star} \gtrsim 20$ $M_\odot$). In our Galaxy, we expect $10^8$-$10^9$ stellar-mass black holes formed from the gravitational collapse of heavy stars, but currently we know less than 100 objects. We also know $\sim 100$ stellar-mass black holes in other galaxies, most of them discovered by gravitational wave observatories in the past 10 years. The detection of black holes is indeed extremely challenging and possible only in very special cases. This article is a short review on the physics and astrophysics of stellar-mass black holes.
Advancing Responsible Innovation in Agentic AI: A study of Ethical Frameworks for Household Automation
2025-07-21
Joydeep Chandra, Satyam Kumar Navneet
The implementation of Artificial Intelligence (AI) in household environments, especially in the form of proactive autonomous agents, brings about possibilities of comfort and attention as well as it comes with intra or extramural ethical challenges. This article analyzes agentic AI and its applications, focusing on its move from reactive to proactive autonomy, privacy, fairness and user control. We review responsible innovation frameworks, human-centered design principles, and governance practices to distill practical guidance for ethical smart home systems. Vulnerable user groups such as elderly individuals, children, and neurodivergent who face higher risks of surveillance, bias, and privacy risks were studied in detail in context of Agentic AI. Design imperatives are highlighted such as tailored explainability, granular consent mechanisms, and robust override controls, supported by participatory and inclusive methodologies. It was also explored how data-driven insights, including social media analysis via Natural Language Processing(NLP), can inform specific user needs and ethical concerns. This survey aims to provide both a conceptual foundation and suggestions for developing transparent, inclusive, and trustworthy agentic AI in household automation.
Mammo-SAE: Interpreting Breast Cancer Concept Learning with Sparse Autoencoders
2025-07-21
Krishna Kanth Nakka
Interpretability is critical in high-stakes domains such as medical imaging, where understanding model decisions is essential for clinical adoption. In this work, we introduce Sparse Autoencoder (SAE)-based interpretability to breast imaging by analyzing {Mammo-CLIP}, a vision--language foundation model pretrained on large-scale mammogram image--report pairs. We train a patch-level \texttt{Mammo-SAE} on Mammo-CLIP to identify and probe latent features associated with clinically relevant breast concepts such as \textit{mass} and \textit{suspicious calcification}. Our findings reveal that top activated class level latent neurons in the SAE latent space often tend to align with ground truth regions, and also uncover several confounding factors influencing the model's decision-making process. Additionally, we analyze which latent neurons the model relies on during downstream finetuning for improving the breast concept prediction. This study highlights the promise of interpretable SAE latent representations in providing deeper insight into the internal workings of foundation models at every layer for breast imaging.
We present an optimised multipole algorithm for computing the three-point correlation function (3PCF), tailored for application to large-scale cosmological datasets. The algorithm builds on a $in\, situ$ interpretation of correlation functions, wherein spatial displacements are implemented via translation window functions. In Fourier space, these translations correspond to plane waves, whose decomposition into spherical harmonics naturally leads to a multipole expansion framework for the 3PCF. To accelerate computation, we incorporate density field reconstruction within the framework of multiresolution analysis, enabling efficient summation using either grid-based or particle-based schemes. In addition to the shared computational cost of reconstructing the multipole-decomposed density fields - scaling as $\mathcal{O}(L^2_{\text{trun}} N_g \log N_g)$ (where $N_g$ is the number of grids and $L_{\text{trun}}$ is the truncation order of the multipole expansion) - the final summation step achieves a complexity of $\mathcal{O}(D^6_{\text{sup}} N_g)$ for the grid-based approach and $\mathcal{O}(D^3_{\text{sup}} N_p)$ for the particle-based scheme (where $D_{\text{sup}}$ is the support of the basis function and $N_p$ is the number of particles). The proposed $in\, situ$ multipole algorithm is fully GPU-accelerated and implemented in the open-source $Hermes$ toolkit for cosmic statistics. This development enables fast, scalable higher-order clustering analyses for large-volume datasets from current and upcoming cosmological surveys such as Euclid, DESI, LSST, and CSST.
A Generative Model for Disentangling Galaxy Photometric Parameters
2025-07-21
Keen Leung, Colen Yan, Jun Yin
Ongoing and future photometric surveys will produce unprecedented volumes of galaxy images, necessitating robust, efficient methods for deriving galaxy morphological parameters at scale. Traditional approaches, such as parametric light-profile fitting, offer valuable insights but become computationally prohibitive when applied to billions of sources. In this work, we propose a Conditional AutoEncoder (CAE) framework to simultaneously model and characterize galaxy morphology. Our CAE is trained on a suite of realistic mock galaxy images generated via GalSim, encompassing a broad range of galaxy types, photometric parameters (e.g., flux, half-light radius, Sersic index, ellipticity), and observational conditions. By encoding each galaxy image into a low-dimensional latent representation conditioned on key parameters, our model effectively recovers these morphological features in a disentangled manner, while also reconstructing the original image. The results demonstrate that the CAE approach can accurately and efficiently infer complex structural properties, offering a powerful alternative to existing methods.
On the Image Profiles of Transients in the Palomar Sky Survey
2025-07-21
Beatriz Villarroel, Enrique Solano, Geoffrey W. Marcy
The VASCO project has discovered groups of short-lived transients on historical photographic plates that lack conventional explanation. Hambly & Blair (2024) examined nine such transients reported by Villarroel (2021) and found that they exhibit narrower, rounder profiles, attributing this to emulsion flaws. However, well-established optical principles and atmospheric physics imply that unresolved flashes lasting less than a second naturally appear sharper and more circular than stellar images, particularly on long-exposure plates where stars are significantly blurred by seeing and tracking errors. Such profiles are an expected consequence of sub-second optical flashes, making their findings consistent with the transient interpretation.
CLAMP: Contrastive Learning with Adaptive Multi-loss and Progressive Fusion for Multimodal Aspect-Based Sentiment Analysis
2025-07-21
Xiaoqiang He
Multimodal aspect-based sentiment analysis(MABSA) seeks to identify aspect terms within paired image-text data and determine their fine grained sentiment polarities, representing a fundamental task for improving the effectiveness of applications such as product review systems and public opinion monitoring. Existing methods face challenges such as cross modal alignment noise and insufficient consistency in fine-grained representations. While global modality alignment methods often overlook the connection between aspect terms and their corresponding local visual regions, bridging the representation gap between text and images remains a challenge. To address these limitations, this paper introduces an end to end Contrastive Learning framework with Adaptive Multi-loss and Progressive Attention Fusion(CLAMP). The framework is composed of three novel modules: Progressive Attention Fusion network, Multi-task Contrastive Learning, and Adaptive Multi-loss Aggregation. The Progressive Attention Fusion network enhances fine-grained alignment between textual features and image regions via hierarchical, multi-stage cross modal interactions, effectively suppressing irrelevant visual noise. Secondly, multi-task contrastive learning combines global modal contrast and local granularity alignment to enhance cross modal representation consistency. Adaptive Multi-loss Aggregation employs a dynamic uncertainty based weighting mechanism to calibrate loss contributions according to each task's uncertainty, thereby mitigating gradient interference. Evaluation on standard public benchmarks demonstrates that CLAMP consistently outperforms the vast majority of existing state of the art methods.
We give an overview of Darmon's program for resolving families of generalized Fermat equations with one varying exponent and survey what is currently known about this approach based on recent work of Billerey-Chen-Dieulefait-Freitas and Chen-Koutsianas. Additionally, we provide background material which is helpful to understand and apply the methods developed in these recent works. In particular, we explain the basic strategy for and simplified examples of each of the steps that is required in order to resolve a family of generalized Fermat equations.
Homophone normalization, where characters that have the same sound in a writing script are mapped to one character, is a pre-processing step applied in Amharic Natural Language Processing (NLP) literature. While this may improve performance reported by automatic metrics, it also results in models that are not able to understand different forms of writing in a single language. Further, there might be impacts in transfer learning, where models trained on normalized data do not generalize well to other languages. In this paper, we experiment with monolingual training and cross-lingual transfer to understand the impacts of normalization on languages that use the Ge'ez script. We then propose a post-inference intervention in which normalization is applied to model predictions instead of training data. With our simple scheme of post-inference normalization, we show that we can achieve an increase in BLEU score of up to 1.03 while preserving language features in training. Our work contributes to the broader discussion on technology-facilitated language change and calls for more language-aware interventions.
Stellar Population Astrophysics (SPA) with the TNG: NLTE atmospheric parameters and abundances of giant stars in 33 Open Clusters
2025-07-20
M. Dal Ponte, V. D'Orazi, A. Bragaglia, A. R. Casey, N. Storm, L. Spina, J. Alonso-Santiago, G. Andreuzzi, A. Frasca, J. Kos, S. Lucatello, D. Romano, A. Vallenari, N. Vernekar
Open clusters serve as important tools for accurately studying the chemical evolution of the Milky Way. By combining precise chemical data from high-resolution spectra with information on their distances and ages, we can effectively uncover the processes that have shaped our Galaxy. This study aims to derive NLTE atmospheric parameters and chemical abundances for approximately one hundred giant stars across 33 open clusters with near-solar metallicity. The clusters span a wide range of ages, enabling an assessment of the presence and extent of any age-related abundance gradients. In the Stellar Population Astrophysics (SPA) project, we acquired new high-resolution spectra of open clusters using the HARPS-N echelle spectrograph at the Telescopio Nazionale Galileo. We chemically characterized nine open clusters for the first time and reanalyzed previously studied SPA clusters, resulting in a consistent and homogeneous sample. We determined NLTE atmospheric parameters using the equivalent width method and derived NLTE chemical abundances through spectral synthesis for various elements, including alpha elements (Mg, Si, and Ti), light odd-Z elements (Na, Al), iron-peak elements (Mn, Co, and Ni), and neutron-capture elements (Sr, Y, and Eu). Our findings are compared with the existing literature, revealing good agreement. We examine the trends of [X/Fe] versus age, confirming previous observations and the enrichment patterns predicted by nucleosynthesis processes. Positive correlations with age are present for Mg, Si, Ti, Al, Mn, Co, Ni, and Sr, while Na and Y and Eu show a negative trend. This study emphasizes the significance of NLTE corrections and reinforces the utility of open clusters as tracers of Galactic chemical evolution. Furthermore, we provide a benchmark sample of NLTE abundances for upcoming open cluster surveys within large-scale projects such as 4MOST and WEAVE.
Longitudinal review of portfolios with minimum variance approach before during and after the pandemic
2025-07-20
Genjis A. Ossa, Luis H. Restrepo
This study investigates the impact of the pandemic on the most traded stocks in the Colombian stock market for the date of January 17, 2024. Based on the daily data of the most traded companies in Colombia for said date and covering a period general from 2015 to 2023, in a summarized way our analysis reveals that in the period 2015-2019, the return reached 5.70%, with a relatively low risk of 18.45%. However, in the following period 2016 -2020, although the yield decreased to 5.40%, the risk experienced a significant increase, reaching 24.64%. The beta also showed variations, being lowest in 2015-2019 with 0.61 and increasing to 1.02 in 2016-2020. The capital market line (LMC) in the constructed portfolios has a downward trend, indicating that the portfolio offers an expected rate of return lower than the risk-free rate. This finding is supported by the Sharpe index, which shows negative values throughout the periods studied.
The essential and the Cremona dimensions of a group
2025-07-20
Igor Dolgachev
The Cremona dimension of a group $G$ is the minimal $n$ such that $G$ is isomorphic to a subgroup of the Cremona group of birational transformations of an $n$-dimensional rational variety. In this survey article, we give many examples that gives evidence to the conjecture that the Cremona dimension of a finite group over the field of complex numbers is less than or equal to the essential dimension of the group.
Time-RA: Towards Time Series Reasoning for Anomaly with LLM Feedback
2025-07-20
Yiyuan Yang, Zichuan Liu, Lei Song, Kai Ying, Zhiguang Wang, Tom Bamford, Svitlana Vyetrenko, Jiang Bian, Qingsong Wen
Time series anomaly detection is critical across various domains, yet current approaches often limit analysis to mere binary anomaly classification without detailed categorization or further explanatory reasoning. To address these limitations, we propose a novel task, Time-series Reasoning for Anomaly (Time-RA) that transforms classical time series anomaly detection from a discriminative into a generative, reasoning-intensive task leveraging Large Language Models (LLMs). Also, we introduce the first real-world multimodal benchmark dataset, RATs40K, explicitly annotated for anomaly reasoning, comprising approximately 40,000 samples across 10 real-world domains. Each sample includes numeric time series data, contextual text information, and visual representations, each annotated with fine-grained categories (14 types for univariate anomalies and 6 for multivariate anomalies) and structured explanatory reasoning. We develop a sophisticated annotation framework utilizing ensemble-generated labels refined through GPT-4-driven feedback, ensuring accuracy and interpretability. Extensive benchmarking of LLMs and multimodal LLMs demonstrates the capabilities and limitations of current models, highlighting the critical role of supervised fine-tuning. Our dataset and task pave the way for significant advancements in interpretable time series anomaly detection and reasoning.
2025-07-20
Lucas Woodley, Chung Yi See, Daniel Palmer, Ashley Nunes
Preowned vehicles are disproportionally purchased by low-income households, a group that has long been unable to purchase electric vehicles. Yet, low-income households would disproportionally benefit from EV adoption given the operating costs savings offered by electrification. To help realize this benefit, provisions of the 2022 Inflation Reduction Act offer preowned EV purchasing incentives. How effective might these efforts be. Leveraging data from the United States Census Bureau, the National Household Travel Survey, and the Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model, we address this question. Our findings are fourfold. First, we demonstrate that although low-income households are more likely to benefit from preowned EV purchasing incentives offered by IRA, up to 8.4 million low-income households may be ineligible owing to heterogeneity in vehicle procurement pathways. Second, we show that program ineligibility risks preventing up to 113.9 million tons in lifecycle emissions reduction benefits from being realized. Third, we find that procurement pathways depend on vehicle price. More expensive preowned vehicles are purchased directly from commercial dealers, while less expensive preowned vehicles are purchased from private sellers. These procurement pathways matter because qualification for IRA incentives necessitates purchasing solely from commercial dealers. Fourth, we demonstrate that while incentives motivating preowned vehicle purchases from commercial dealers may be effective if the vehicle is expensive, this effectiveness diminishes at higher price points. The implications of our findings on decarbonization efforts and energy policy are discussed.
Condition and structural health monitoring (CM/SHM) is a pivotal component of predictive maintenance (PdM) strategies across diverse industrial sectors, including mechanical rotating machinery, airplane composite wings, offshore wind turbines, and civil engineering structures. Conventional deep learning models, while effective in fault diagnosis and anomaly detection through supervised feature extraction and rule-based data augmentation, often struggle with operational variability, imbalanced or scarce fault datasets, and multimodal sensory data from complex systems. Deep generative models (DGMs) in this regard, including autoregressive models, variational autoencoders, generative adversarial networks, diffusion-based models, and emerging large language models, offer transformative capabilities by synthesizing high-fidelity data samples, reconstructing latent system states, and modeling complex multimodal data streams. This review systematically examines state-of-the-art DGM applications in CM/SHM systems, emphasizing their role in addressing key challenges: data imbalance and imputation, domain adaptation and generalization, multimodal data fusion, and downstream fault diagnosis and anomaly detection tasks, with rigorous comparison among signal processing, conventional machine learning or deep learning models, and DGMs. We also analyze current limitations of DGMs, including challenges of explainable and trustworthy models, computational inefficiencies for edge deployment, and the need for parameter-efficient fine-tuning strategies. Future research directions can focus on zero-shot and few-shot learning, robust multimodal generalization, hybrid architectures integrating DGMs with physics knowledge, and reinforcement learning with DGMs to enhance robustness and accuracy in industrial scenarios.
Survey of GenAI for Automotive Software Development: From Requirements to Executable Code
2025-07-20
Nenad Petrovic, Vahid Zolfaghari, Andre Schamschurko, Sven Kirchner, Fengjunjie Pan, Chengdng Wu, Nils Purschke, Aleksei Velsh, Krzysztof Lebioda, Yinglei Song, Yi Zhang, Lukasz Mazur, Alois Knoll
Adoption of state-of-art Generative Artificial Intelligence (GenAI) aims to revolutionize many industrial areas by reducing the amount of human intervention needed and effort for handling complex underlying processes. Automotive software development is considered to be a significant area for GenAI adoption, taking into account lengthy and expensive procedures, resulting from the amount of requirements and strict standardization. In this paper, we explore the adoption of GenAI for various steps of automotive software development, mainly focusing on requirements handling, compliance aspects and code generation. Three GenAI-related technologies are covered within the state-of-art: Large Language Models (LLMs), Retrieval Augmented Generation (RAG), Vision Language Models (VLMs), as well as overview of adopted prompting techniques in case of code generation. Additionally, we also derive a generalized GenAI-aided automotive software development workflow based on our findings from this literature review. Finally, we include a summary of a survey outcome, which was conducted among our automotive industry partners regarding the type of GenAI tools used for their daily work activities.
The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering
2025-07-20
Hao Li, Haoxiang Zhang, Ahmed E. Hassan
The future of software engineering--SE 3.0--is unfolding with the rise of AI teammates: autonomous, goal-driven systems collaborating with human developers. Among these, autonomous coding agents are especially transformative, now actively initiating, reviewing, and evolving code at scale. This paper introduces AIDev, the first large-scale dataset capturing how such agents operate in the wild. Spanning over 456,000 pull requests by five leading agents--OpenAI Codex, Devin, GitHub Copilot, Cursor, and Claude Code--across 61,000 repositories and 47,000 developers, AIDev provides an unprecedented empirical foundation for studying autonomous teammates in software development. Unlike prior work that has largely theorized the rise of AI-native software engineering, AIDev offers structured, open data to support research in benchmarking, agent readiness, optimization, collaboration modeling, and AI governance. The dataset includes rich metadata on PRs, authorship, review timelines, code changes, and integration outcomes--enabling exploration beyond synthetic benchmarks like SWE-bench. For instance, although agents often outperform humans in speed, their PRs are accepted less frequently, revealing a trust and utility gap. Furthermore, while agents accelerate code submission--one developer submitted as many PRs in three days as they had in three years--these are structurally simpler (via code complexity metrics). We envision AIDev as a living resource: extensible, analyzable, and ready for the SE and AI communities. Grounding SE 3.0 in real-world evidence, AIDev enables a new generation of research into AI-native workflows and supports building the next wave of symbiotic human-AI collaboration. The dataset is publicly available at https://github.com/SAILResearch/AI_Teammates_in_SE3. > AI Agent, Agentic AI, Coding Agent, Agentic Coding, Software Engineering Agent
It is projected that more than 100,000 communication satellites will be deployed in Low-Earth Orbit (LEO) over the next decade. These LEO satellites (LEOsats) will be captured frequently by the survey camera onboard the China Space Station Telescope (CSST), contaminating sources in the images. As such, it is necessary to assess the impact of LEOsats on CSST survey observations. We use the images taken by the Hubble Space Telescope (HST) in its F814W band to simulate $i$-band images for the CSST. The simulation results indicate that LEOsats at higher altitudes cause more contamination than those at lower altitudes. If 100,000 LEOsats are deployed at altitudes between 550 km and 1200 km with a 53-degree orbital inclination, the fraction of contaminated sources in a 150-s exposure image would remain below 0.50%. For slitless spectroscopic images, the contaminated area is expected to be below 1.50%. After removing the LEOsat trails, the residual photon noise contributes to relative photometric errors that exceed one-tenth of the total error budget in approximately 0.10% of all sources. Our investigation shows that even though LEOsats are unavoidable in CSST observations, they only have a minor impact on samples extracted from the CSST survey.
Identifiability in Unlinked Linear Regression: Some Results and Open Problems
2025-07-20
Fadoua Balabdaoui, Martin Slawski, Jonathan Steffani
A tacit assumption in classical linear regression problems is the full knowledge of the existing link between the covariates and responses. In Unlinked Linear Regression (ULR) this link is either partially or completely missing. While the reasons causing such missingness can be different, a common challenge in statistical inference is the potential non-identifiability of the regression parameter. In this note, we review the existing literature on identifiability when the $d \ge 2$ components of the vector of covariates are independent and identically distributed. When these components have different distributions, we show that it is not possible to prove similar theorems in the general case. Nevertheless, we prove some identifiability results, either under additional parametric assumptions for $d \ge 2$ or conditions on the fourth moments in the case $d=2$. Finally, we draw some interesting connections between the ULR and the well established field of Independent Component Analysis (ICA).
Metaverse Security and Privacy Research: A Systematic Review
2025-07-20
Argianto Rahartomo, Leonel Merino, Mohammad Ghafari
The rapid growth of metaverse technologies, including virtual worlds, augmented reality, and lifelogging, has accelerated their adoption across diverse domains. This rise exposes users to significant new security and privacy challenges due to sociotechnical complexity, pervasive connectivity, and extensive user data collection in immersive environments. We present a systematic review of the literature published between 2013 and 2024, offering a comprehensive analysis of how the research community has addressed metaverse-related security and privacy issues over the past decade. We organize the studies by method, examined the security and privacy properties, immersive components, and evaluation strategies. Our investigation reveals a sharp increase in research activity in the last five years, a strong focus on practical and user-centered approaches, and a predominant use of benchmarking, human experimentation, and qualitative methods. Authentication and unobservability are the most frequently studied properties. However, critical gaps remain in areas such as policy compliance, accessibility, interoperability, and back-end infrastructure security. We emphasize the intertwined technical complexity and human factors of the metaverse and call for integrated, interdisciplinary approaches to securing inclusive and trustworthy immersive environments.
Think Like an Engineer: A Neuro-Symbolic Collaboration Agent for Generative Software Requirements Elicitation and Self-Review
2025-07-20
Sai Zhang, Zhenchang Xing, Jieshan Chen, Dehai Zhao, Zizhong Zhu, Xiaowang Zhang, Zhiyong Feng, Xiaohong Li
The vision of End-User Software Engineering (EUSE) is to empower non-professional users with full control over the software development lifecycle. It aims to enable users to drive generative software development using only natural language requirements. However, since end-users often lack knowledge of software engineering, their requirement descriptions are frequently ambiguous, raising significant challenges to generative software development. Although existing approaches utilize structured languages like Gherkin to clarify user narratives, they still struggle to express the causal logic between preconditions and behavior actions. This paper introduces RequireCEG, a requirement elicitation and self-review agent that embeds causal-effect graphs (CEGs) in a neuro-symbolic collaboration architecture. RequireCEG first uses a feature tree to analyze user narratives hierarchically, clearly defining the scope of software components and their system behavior requirements. Next, it constructs the self-healing CEGs based on the elicited requirements, capturing the causal relationships between atomic preconditions and behavioral actions. Finally, the constructed CEGs are used to review and optimize Gherkin scenarios, ensuring consistency between the generated Gherkin requirements and the system behavior requirements elicited from user narratives. To evaluate our method, we created the RGPair benchmark dataset and conducted extensive experiments. It achieves an 87% coverage rate and raises diversity by 51.88%.
PSPACE-completeness of bimodal transitive weak-density logic
2025-07-20
Philippe Balbiani, Olivier Gasquet
Windows have been introduce in \cite{BalGasq25} as a tool for designing polynomial algorithms to check satisfiability of a bimodal logic of weak-density. In this paper, after revisiting the ``folklore'' case of bimodal $\K4$ already treated in \cite{Halpern} but which is worth a fresh review, we show that windows allow to polynomially solve the satisfiability problem when adding transitivity to weak-density, by mixing algorithms for bimodal K together with windows-approach. The conclusion is that both satisfiability and validity are PSPACE-complete for these logics.
SYNTHIA: Synthetic Yet Naturally Tailored Human-Inspired PersonAs
2025-07-20
Vahid Rahimzadeh, Erfan Moosavi Monazzah, Mohammad Taher Pilehvar, Yadollah Yaghoobzadeh
Persona-driven LLMs have emerged as powerful tools in computational social science, yet existing approaches fall at opposite extremes, either relying on costly human-curated data or producing synthetic personas that lack consistency and realism. We introduce SYNTHIA, a dataset of 30,000 backstories derived from 10,000 real social media users from BlueSky open platform across three time windows, bridging this spectrum by grounding synthetic generation in authentic user activity. Our evaluation demonstrates that SYNTHIA achieves competitive performance with state-of-the-art methods in demographic diversity and social survey alignment while significantly outperforming them in narrative consistency. Uniquely, SYNTHIA incorporates temporal dimensionality and provides rich social interaction metadata from the underlying network, enabling new research directions in computational social science and persona-driven language modeling.
Time Series Information Visualization -- A Review of Approaches and Tools
2025-07-20
Evandro S. Ortigossa, Fábio F. Dias, Diego C. Nascimento, Luis Gustavo Nonato
Time series data are prevalent across various domains and often encompass large datasets containing multiple time-dependent features in each sample. Exploring time-varying data is critical for data science practitioners aiming to understand dynamic behaviors and discover periodic patterns and trends. However, the analysis of such data often requires sophisticated procedures and tools. Information visualization is a communication channel that leverages human perceptual abilities to transform abstract data into visual representations. Visualization techniques have been successfully applied in the context of time series to enhance interpretability by graphically representing the temporal evolution of data. The challenge for information visualization developers lies in integrating a wide range of analytical tools into rich visualization systems that can summarize complex datasets while clearly describing the impacts of the temporal component. Such systems enable data scientists to turn raw data into understandable and potentially useful knowledge. This review examines techniques and approaches designed for handling time series data, guiding users through knowledge discovery processes based on visual analysis. We also provide readers with theoretical insights and design guidelines for considering when developing comprehensive information visualization approaches for time series, with a particular focus on time series with multiple features. As a result, we highlight the challenges and future research directions to address open questions in the visualization of time-dependent data.
Redefining Elderly Care with Agentic AI: Challenges and Opportunities
2025-07-20
Ruhul Amin Khalil, Kashif Ahmad, Hazrat Ali
The global ageing population necessitates new and emerging strategies for caring for older adults. In this article, we explore the potential for transformation in elderly care through Agentic Artificial Intelligence (AI), powered by Large Language Models (LLMs). We discuss the proactive and autonomous decision-making facilitated by Agentic AI in elderly care. Personalized tracking of health, cognitive care, and environmental management, all aimed at enhancing independence and high-level living for older adults, represents important areas of application. With a potential for significant transformation of elderly care, Agentic AI also raises profound concerns about data privacy and security, decision independence, and access. We share key insights to emphasize the need for ethical safeguards, privacy protections, and transparent decision-making. Our goal in this article is to provide a balanced discussion of both the potential and the challenges associated with Agentic AI, and to provide insights into its responsible use in elderly care, to bring Agentic AI into harmony with the requirements and vulnerabilities specific to the elderly. Finally, we identify the priorities for the academic research communities, to achieve human-centered advancements and integration of Agentic AI in elderly care. To the best of our knowledge, this is no existing study that reviews the role of Agentic AI in elderly care. Hence, we address the literature gap by analyzing the unique capabilities, applications, and limitations of LLM-based Agentic AI in elderly care. We also provide a companion interactive dashboard at https://hazratali.github.io/agenticai/.
Non-destructive testing (NDT), particularly X-ray inspection, is vital for industrial quality assurance, yet existing deep-learning-based approaches often lack interactivity, interpretability, and the capacity for critical self-assessment, limiting their reliability and operator trust. To address these shortcomings, this paper proposes InsightX Agent, a novel LMM-based agentic framework designed to deliver reliable, interpretable, and interactive X-ray NDT analysis. Unlike typical sequential pipelines, InsightX Agent positions a Large Multimodal Model (LMM) as a central orchestrator, coordinating between the Sparse Deformable Multi-Scale Detector (SDMSD) and the Evidence-Grounded Reflection (EGR) tool. The SDMSD generates dense defect region proposals for multi-scale feature maps and sparsifies them through Non-Maximum Suppression (NMS), optimizing detection of small, dense targets in X-ray images while maintaining computational efficiency. The EGR tool guides the LMM agent through a chain-of-thought-inspired review process, incorporating context assessment, individual defect analysis, false positive elimination, confidence recalibration and quality assurance to validate and refine the SDMSD's initial proposals. By strategically employing and intelligently using tools, InsightX Agent moves beyond passive data processing to active reasoning, enhancing diagnostic reliability and providing interpretations that integrate diverse information sources. Experimental evaluations on the GDXray+ dataset demonstrate that InsightX Agent not only achieves a high object detection F1-score of 96.35% but also offers significantly improved interpretability and trustworthiness in its analyses, highlighting the transformative potential of agentic LLM frameworks for industrial inspection tasks.
Integrated Radio Sensing Capabilities for 6G Networks: AI/ML Perspective
2025-07-20
Victor Shatov, Steffen Schieler, Charlotte Muth, José Miguel Mateos-Ramos, Ivo Bizon, Florian Euchner, Sebastian Semper, Stephan ten Brink, Gerhard Fettweis, Christian Häger, Henk Wymeersch, Laurent Schmalen, Reiner Thomä, Norman Franchi
The sixth-generation wireless communications (6G) is often labeled as "connected intelligence". Radio sensing, aligned with machine learning (ML) and artificial intelligence (AI), promises, among other benefits, breakthroughs in the system's ability to perceive the environment and effectively utilize this awareness. This article offers a tutorial-style survey of AI and ML approaches to enhance the sensing capabilities of next-generation wireless networks. To this end, while staying in the framework of integrated sensing and communication (ISAC), we expand the term "sensing" from radar, via spectrum sensing, to miscellaneous applications of radio sensing like non-cooperative transmitter localization. We formulate the problems, explain the state-of-the-art approaches, and detail AI-based techniques to tackle various objectives in the context of wireless sensing. We discuss the advantages, enablers, and challenges of integrating various sensing capabilities into an envisioned AI-powered multimodal multi-task network. In addition to the tutorial-style core of this work based on direct authors' involvement in 6G research problems, we review the related literature, and provide both a good start for those entering this field of research, and a topical overview for a general reader with a background in wireless communications
Frabjous: Deep Learning Fast Radio Burst Morphologies
2025-07-20
Ajay Kumar, Ashish A. Mahabal, Shriharsh P. Tendulkar
The increasing field of view of radio telescopes and improved data processing capabilities have led to a surge in the detection of Fast Radio Bursts (FRBs). The discovery rate of FRBs is already a few per day and is expected to increase rapidly with new surveys coming online. The growing number of events necessitates prioritized follow-up due to limited multi-wavelength resources, requiring rapid and automated classification. In this study, we introduce Frabjous, a deep learning framework for an automated morphology classifier with an aim towards enabling the prompt follow-up of anomalous and intriguing FRBs, and a comprehensive statistical analysis of FRB morphologies. Deep learning models require a large training set of each FRB archetype, however, publicly available data lacks sufficient samples for most FRB types. In this paper, we build a simulation framework for generating realistic examples of FRBs and train a network based on a combination of simulated and real data, starting with the CHIME/FRB catalog. Applying our framework to the first CHIME/FRB catalog, we achieve an overall classification accuracy of approximately 55%, well over a random multiclass classification rate of 20 % with five balanced classes during training. While this falls short of desirable performance, we critically discuss the limitations of our approach and propose potential avenues for improvement. Future work should explore strategies to augment training datasets and broaden the scope of FRB morphological studies, aiming for more accurate and reliable classification results.
A meta-analysis of impact factors of particle physics journals using NASA/ADS
2025-07-20
Rayani Venkat Sai Rithvik, Shantanu Desai
In this work, we calculate the 2025 impact factors for 16 journals in Particle Physics (and related areas such as Nuclear Physics) using citations collated by NASA/ADS (Astrophysics Data System). We then compare them to the official impact factors calculated by Clarivate. We also compare these impact factors to the median-based impact factors introduced in a previous work. We do not find any systematic bias between the ADS and official impact factors. We find the maximum relative difference between official and median-based impact factors for PTEP and Annual Review of Nuclear and Particle Science. For PTEP, this difference is due to one outlier, viz Particle Data Group with over 1400 citations in the last two years. Similarly for Annual Review of Nuclear and Particle Science, the difference is due to a review paper on Hubble tension and early dark energy with over 100 citations in the past two years. Both these papers have significantly elevated the official impact factors above its median value.
VeriOpt: PPA-Aware High-Quality Verilog Generation via Multi-Role LLMs
2025-07-20
Kimia Tasnia, Alexander Garcia, Tasnuva Farheen, Sazadur Rahman
The rapid adoption of large language models(LLMs) in hardware design has primarily focused on generating functionally correct Verilog code, overlooking critical Power Performance-Area(PPA) metrics essential for industrial-grade designs. To bridge this gap, we propose VeriOpt, a novel framework that leverages role-based prompting and PPA-aware optimization to enable LLMs to produce high-quality, synthesizable Verilog. VeriOpt structures LLM interactions into specialized roles (e.g., Planner, Programmer, Reviewer, Evaluator) to emulate human design workflows, while integrating PPA constraints directly into the prompting pipeline. By combining multi-modal feedback (e.g., synthesis reports, timing diagrams) with PPA aware prompting, VeriOpt achieves PPA-efficient code generation without sacrificing functional correctness. Experimental results demonstrate up to 88% reduction in power, 76% reduction in area and 73% improvement in timing closure compared to baseline LLM-generated RTL, validated using industry standard EDA tools. At the same time achieves 86% success rate in functionality evaluation. Our work advances the state-of-the-art AI-driven hardware design by addressing the critical gap between correctness and quality, paving the way for reliable LLM adoption in production workflows.
Wavelength Requirements for Life Detection via Reflected Light Spectroscopy of Rocky Exoplanets
2025-07-19
Joshua Krissansen-Totton, Anna Grace Ulses, Maxwell Frissell, Samantha Gilbert-Janizek, Amber Young, Jacob Lustig-Yaeger, Tyler Robinson, Stephanie Olson, Eleonora Alei, Giada Arney, Celeste Hagee, Chester Harman, Natalie Hinkel, Emilie Lafleche, Natasha Latouf, Avi Mandell, Mark M. Moussa, Niki Parenteau, Sukrit Ranjan, Blair Russell, Edward W. Schwieterman, Clara Sousa-Silva, Armen Tokadjian, Nicholas Wogan
Searching for signs of life is a primary goal of the Habitable Worlds Observatory (HWO). However, merely detecting oxygen, methane, or other widely discussed biosignatures is insufficient evidence for a biosphere. In parallel with biosignature detection, exoplanet life detection additionally requires characterization of the broader physicochemical context to evaluate planetary habitability and the plausibility that life could produce a particular biosignature in a given environment. Life detection further requires that we can confidently rule out photochemical or geological phenomena that can mimic life (i.e. "false positives"). Evaluating false positive scenarios may require different observatory specifications than biosignature detection surveys. Here, we explore the coronagraph requirements for assessing habitability and ruling out known false positive (and false negative) scenarios for oxygen and methane, the two most widely discussed biosignatures for Earth-like exoplanets. We find that broad wavelength coverage ranging from the near UV (0.26 $\mu$m) and extending into the near infrared (1.7 $\mu$m), is necessary for contextualizing biosignatures with HWO. The short wavelength cutoff is driven by the need to identify Proterozoic-like biospheres via O$_3$, whereas the long wavelength cutoff is driven by the need to contextualize O$_2$ and CH$_4$ biosignatures via constraints on C-bearing atmospheric species. The ability to obtain spectra with signal-to-noise ratios of 20-40 across this 0.26-1.7 $\mu$m range (assuming R=7 UV, R=140 VIS, and R=70 NIR) is also required. Without sufficiently broad wavelength coverage, we risk being unprepared to interpret biosignature detections and may ultimately be ill-equipped to confirm the detection of an Earth-like biosphere, which is a driving motivation of HWO..
2025-07-19
Manaal Basha, Ivan Beschastnikh, Gema Rodriguez-Perez, Cleidson R. B. de Souza
Context: The increasing reliance on Code Generation Tools (CGTs), such as Windsurf and GitHub Copilot, are revamping programming workflows and raising critical questions about fairness and inclusivity. While CGTs offer potential productivity enhancements, their effectiveness across diverse user groups have not been sufficiently investigated. Objectives: We hypothesize that developers' interactions with CGTs vary based on gender, influencing task outcomes and cognitive load, as prior research suggests that gender differences can affect technology use and cognitive processing. Methods: The study will employ a mixed-subjects design with 54 participants, evenly divided by gender for a counterbalanced design. Participants will complete two programming tasks (medium to hard difficulty) with only CGT assistance and then with only internet access. Task orders and conditions will be counterbalanced to mitigate order effects. Data collection will include cognitive load surveys, screen recordings, and task performance metrics such as completion time, code correctness, and CGT interaction behaviors. Statistical analyses will be conducted to identify statistically significant differences in CGT usage. Expected Contributions: Our work can uncover gender differences in CGT interaction and performance among developers. Our findings can inform future CGT designs and help address usability and potential disparities in interaction patterns across diverse user groups. Conclusion: While results are not yet available, our proposal lays the groundwork for advancing fairness, accountability, transparency, and ethics (FATE) in CGT design. The outcomes are anticipated to contribute to inclusive AI practices and equitable tool development for all users.
In intensive care units (ICUs), patients with complex clinical conditions require vigilant monitoring and prompt interventions. Chest X-rays (CXRs) are a vital diagnostic tool, providing insights into clinical trajectories, but their irregular acquisition limits their utility. Existing tools for CXR interpretation are constrained by cross-sectional analysis, failing to capture temporal dynamics. To address this, we introduce CXR-TFT, a novel multi-modal framework that integrates temporally sparse CXR imaging and radiology reports with high-frequency clinical data, such as vital signs, laboratory values, and respiratory flow sheets, to predict the trajectory of CXR findings in critically ill patients. CXR-TFT leverages latent embeddings from a vision encoder that are temporally aligned with hourly clinical data through interpolation. A transformer model is then trained to predict CXR embeddings at each hour, conditioned on previous embeddings and clinical measurements. In a retrospective study of 20,000 ICU patients, CXR-TFT demonstrated high accuracy in forecasting abnormal CXR findings up to 12 hours before they became radiographically evident. This predictive capability in clinical data holds significant potential for enhancing the management of time-sensitive conditions like acute respiratory distress syndrome, where early intervention is crucial and diagnoses are often delayed. By providing distinctive temporal resolution in prognostic CXR analysis, CXR-TFT offers actionable 'whole patient' insights that can directly improve clinical outcomes.
A Risk Assessment Framework for Digital Identification Systems
2025-07-19
Allison Woodruff, Dirk Balfanz, Will Drewry, Mariana Raykova
We introduce a risk assessment framework for digital identification systems, as well as recommended best practices to enhance privacy, security, and other desirable properties in these systems. To generate these resources, we created a casebook of a wide range of digital identification systems, and we then applied expert analysis and critique to identify patterns. We piloted the framework on several reviews within our organization over a period of approximately one year, and found it to be robust and helpful for those reviews. This work is intended to inform product review and development, product policy, and standards efforts, and to help guide a consistent responsible approach to digital identification across the broader digital identification ecosystem.
2025-07-19
Mahdi Kord Zangeneh, Francisco S. N. Lobo
Wormholes are non-trivial topological structures that arise as exact solutions to Einstein's field equations, theoretically connecting distinct regions of spacetime via a throat-like geometry. While static traversable wormholes necessarily require exotic matter that violates the classical energy conditions, subsequent studies have sought to minimize such violations by introducing time-dependent geometries embedded within cosmological backgrounds. This review provides a comprehensive survey of evolving wormhole solutions, emphasizing their formulation within both general relativity and alternative theories of gravity. We explore key developments in the construction of non-static wormhole spacetimes, including those conformally related to static solutions, as well as dynamically evolving geometries influenced by scalar fields. Particular attention is given to the wormholes embedded into Friedmann-Lema\^itre-Robertson-Walker (FLRW) universes and de Sitter backgrounds, where the interplay between the cosmic expansion and wormhole dynamics is analyzed. We also examine the role of modified gravity theories, especially in hybrid metric-Palatini gravity, which enable the realization of traversable wormholes supported by effective stress-energy tensors that do not violate the null or weak energy conditions. By systematically analyzing a wide range of time-dependent wormhole solutions, this review identifies the specific geometric and physical conditions under which wormholes can evolve consistently with the null and weak energy conditions. These findings clarify how such configurations can be naturally integrated into cosmological models governed by general relativity or modified gravity, thereby contributing to a deeper theoretical understanding of localized spacetime structures in an expanding universe.
Disparities in Peer Review Tone and the Role of Reviewer Anonymity
2025-07-19
Maria Sahakyan, Bedoor AlShebli
The peer review process is often regarded as the gatekeeper of scientific integrity, yet increasing evidence suggests that it is not immune to bias. Although structural inequities in peer review have been widely debated, much less attention has been paid to the subtle ways in which language itself may reinforce disparities. This study undertakes one of the most comprehensive linguistic analyses of peer review to date, examining more than 80,000 reviews in two major journals. Using natural language processing and large-scale statistical modeling, it uncovers how review tone, sentiment, and supportive language vary across author demographics, including gender, race, and institutional affiliation. Using a data set that includes both anonymous and signed reviews, this research also reveals how the disclosure of reviewer identity shapes the language of evaluation. The findings not only expose hidden biases in peer feedback, but also challenge conventional assumptions about anonymity's role in fairness. As academic publishing grapples with reform, these insights raise critical questions about how review policies shape career trajectories and scientific progress.
MultiRetNet: A Multimodal Vision Model and Deferral System for Staging Diabetic Retinopathy
2025-07-19
Jeannie She, Katie Spivakovsky
Diabetic retinopathy (DR) is a leading cause of preventable blindness, affecting over 100 million people worldwide. In the United States, individuals from lower-income communities face a higher risk of progressing to advanced stages before diagnosis, largely due to limited access to screening. Comorbid conditions further accelerate disease progression. We propose MultiRetNet, a novel pipeline combining retinal imaging, socioeconomic factors, and comorbidity profiles to improve DR staging accuracy, integrated with a clinical deferral system for a clinical human-in-the-loop implementation. We experiment with three multimodal fusion methods and identify fusion through a fully connected layer as the most versatile methodology. We synthesize adversarial, low-quality images and use contrastive learning to train the deferral system, guiding the model to identify out-of-distribution samples that warrant clinician review. By maintaining diagnostic accuracy on suboptimal images and integrating critical health data, our system can improve early detection, particularly in underserved populations where advanced DR is often first identified. This approach may reduce healthcare costs, increase early detection rates, and address disparities in access to care, promoting healthcare equity.
Towards AI Urban Planner in the Age of GenAI, LLMs, and Agentic AI
2025-07-19
Yanjie Fu
Generative AI, large language models, and agentic AI have emerged separately of urban planning. However, the convergence between AI and urban planning presents an interesting opportunity towards AI urban planners. This paper conceptualizes urban planning as a generative AI task, where AI synthesizes land-use configurations under geospatial, social, and human-centric constraints. We survey how generative AI approaches, including VAEs, GANs, transformers, and diffusion models, reshape urban design. We further identify critical gaps: 1) limited research on integrating urban theory guidance, 2) limited research of AI urban planning over multiple spatial resolutions or angularities, 3) limited research on augmenting urban design knowledge from data, and 4) limited research on addressing real-world interactions. To address these limitations, we outline future research directions in theory-guided generation, digital twins, and human-machine co-design, calling for a new synthesis of generative intelligence and participatory urbanism.
Mind the Gap: A Review of Arabic Post-Training Datasets and Their Limitations
2025-07-19
Mohammed Alkhowaiter, Norah Alshahrani, Saied Alshahrani, Reem I. Masoud, Alaa Alzahrani, Deema Alnuhait, Emad A. Alghamdi, Khalid Almubarak
Post-training has emerged as a crucial technique for aligning pre-trained Large Language Models (LLMs) with human instructions, significantly enhancing their performance across a wide range of tasks. Central to this process is the quality and diversity of post-training datasets. This paper presents a review of publicly available Arabic post-training datasets on the Hugging Face Hub, organized along four key dimensions: (1) LLM Capabilities (e.g., Question Answering, Translation, Reasoning, Summarization, Dialogue, Code Generation, and Function Calling); (2) Steerability (e.g., persona and system prompts); (3) Alignment (e.g., cultural, safety, ethics, and fairness), and (4) Robustness. Each dataset is rigorously evaluated based on popularity, practical adoption, recency and maintenance, documentation and annotation quality, licensing transparency, and scientific contribution. Our review revealed critical gaps in the development of Arabic post-training datasets, including limited task diversity, inconsistent or missing documentation and annotation, and low adoption across the community. Finally, the paper discusses the implications of these gaps on the progress of Arabic LLMs and applications while providing concrete recommendations for future efforts in post-training dataset development.
Large Language Models as Medical Codes Selectors: a benchmark using the International Classification of Primary Care
2025-07-19
Vinicius Anjos de Almeida, Vinicius de Camargo, Raquel Gómez-Bravo, Egbert van der Haring, Kees van Boven, Marcelo Finger, Luis Fernandez Lopez
Background: Medical coding structures healthcare data for research, quality monitoring, and policy. This study assesses the potential of large language models (LLMs) to assign ICPC-2 codes using the output of a domain-specific search engine. Methods: A dataset of 437 Brazilian Portuguese clinical expressions, each annotated with ICPC-2 codes, was used. A semantic search engine (OpenAI's text-embedding-3-large) retrieved candidates from 73,563 labeled concepts. Thirty-three LLMs were prompted with each query and retrieved results to select the best-matching ICPC-2 code. Performance was evaluated using F1-score, along with token usage, cost, response time, and format adherence. Results: Twenty-eight models achieved F1-score > 0.8; ten exceeded 0.85. Top performers included gpt-4.5-preview, o3, and gemini-2.5-pro. Retriever optimization can improve performance by up to 4 points. Most models returned valid codes in the expected format, with reduced hallucinations. Smaller models (<3B) struggled with formatting and input length. Conclusions: LLMs show strong potential for automating ICPC-2 coding, even without fine-tuning. This work offers a benchmark and highlights challenges, but findings are limited by dataset scope and setup. Broader, multilingual, end-to-end evaluations are needed for clinical validation.
What Quality Engineers Need to Know about Degradation Models?
2025-07-19
Jared M. Clark, Jie Min, Mingyang Li, Richard L. Warr, Stephanie P. DeHart, Caleb B. King, Lu Lu, Yili Hong
Degradation models play a critical role in quality engineering by enabling the assessment and prediction of system reliability based on data. The objective of this paper is to provide an accessible introduction to degradation models. We explore commonly used degradation data types, including repeated measures degradation data and accelerated destructive degradation test data, and review modeling approaches such as general path models and stochastic process models. Key inference problems, including reliability estimation and prediction, are addressed. Applications across diverse fields, including material science, renewable energy, civil engineering, aerospace, and pharmaceuticals, illustrate the broad impact of degradation models in industry. We also discuss best practices for quality engineers, software implementations, and challenges in applying these models. This paper aims to provide quality engineers with a foundational understanding of degradation models, equipping them with the knowledge necessary to apply these techniques effectively in real-world scenarios.
Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches
2025-07-19
Xiaozheng Gao, Yichen Wang, Bosen Liu, Xiao Zhou, Ruichen Zhang, Jiacheng Wang, Dusit Niyato, Dong In Kim, Abbas Jamalipour, Chau Yuen, Jianping An, Kai Yang
The development of satellite-augmented low-altitude economy and terrestrial networks (SLAETNs) demands intelligent and autonomous systems that can operate reliably across heterogeneous, dynamic, and mission-critical environments. To address these challenges, this survey focuses on enabling agentic artificial intelligence (AI), that is, artificial agents capable of perceiving, reasoning, and acting, through generative AI (GAI) and large language models (LLMs). We begin by introducing the architecture and characteristics of SLAETNs, and analyzing the challenges that arise in integrating satellite, aerial, and terrestrial components. Then, we present a model-driven foundation by systematically reviewing five major categories of generative models: variational autoencoders (VAEs), generative adversarial networks (GANs), generative diffusion models (GDMs), transformer-based models (TBMs), and LLMs. Moreover, we provide a comparative analysis to highlight their generative mechanisms, capabilities, and deployment trade-offs within SLAETNs. Building on this foundation, we examine how these models empower agentic functions across three domains: communication enhancement, security and privacy protection, and intelligent satellite tasks. Finally, we outline key future directions for building scalable, adaptive, and trustworthy generative agents in SLAETNs. This survey aims to provide a unified understanding and actionable reference for advancing agentic AI in next-generation integrated networks.
Retrieval-Augmented Clinical Benchmarking for Contextual Model Testing in Kenyan Primary Care: A Methodology Paper
2025-07-19
Fred Mutisya, Shikoh Gitau, Christine Syovata, Diana Oigara, Ibrahim Matende, Muna Aden, Munira Ali, Ryan Nyotu, Diana Marion, Job Nyangena, Nasubo Ongoma, Keith Mbae, Elizabeth Wamicha, Eric Mibuari, Jean Philbert Nsengemana, Talkmore Chidede
Large Language Models(LLMs) hold promise for improving healthcare access in low-resource settings, but their effectiveness in African primary care remains underexplored. We present a methodology for creating a benchmark dataset and evaluation framework focused on Kenyan Level 2 and 3 clinical care. Our approach uses retrieval augmented generation (RAG) to ground clinical questions in Kenya's national guidelines, ensuring alignment with local standards. These guidelines were digitized, chunked, and indexed for semantic retrieval. Gemini Flash 2.0 Lite was then prompted with guideline excerpts to generate realistic clinical scenarios, multiple-choice questions, and rationale based answers in English and Swahili. Kenyan physicians co-created and refined the dataset, and a blinded expert review process ensured clinical accuracy, clarity, and cultural appropriateness. The resulting Alama Health QA dataset includes thousands of regulator-aligned question answer pairs across common outpatient conditions. Beyond accuracy, we introduce evaluation metrics that test clinical reasoning, safety, and adaptability such as rare case detection (Needle in the Haystack), stepwise logic (Decision Points), and contextual adaptability. Initial results reveal significant performance gaps when LLMs are applied to localized scenarios, consistent with findings that LLM accuracy is lower on African medical content than on US-based benchmarks. This work offers a replicable model for guideline-driven, dynamic benchmarking to support safe AI deployment in African health systems.
The ALMA Survey of 70 $μ$m Dark High-mass Clumps in Early Stages (ASHES). XII. Unanchored Forked Stream in the Propagating Path of a Protostellar Outflow
2025-07-19
Shuting Lin, Siyi Feng, Patricio Sanhueza, Ke Wang, Zhi-Yu Zhang, Yichen Zhang, Fengwei Xu, Junzhi Wang, Kaho Morii, Hauyu Baobab Liu, Sheng-Yuan Liu, Lile Wang, Hui Li, Daniel Tafoya, Willem Baan, Shanghuo Li, Giovanni Sabatini
Outflows are key indicators of ongoing star formation. We report the discovery of an unanchored forked stream within the propagating path of an extremely young protostellar outflow in the 70 $\mu$m-dark clump G34.74-0.12, based on ALMA 1.3 mm observations with an angular resolution of 1''.6 (~ 5000 au). This outflow originate from a 9.7 $M_{\odot}$ core, exhibits a fork-shaped stream structure in its red-shifted lobe, which is traced by CO (2-1), SiO (5-4), and H$_2$CO (3$_{0,3}$-2$_{0,2}$). It has a momentum of 13 $M_{\odot}$ km s$^{-1}$, an energy of 107 $M_{\odot}$ km$^{2}$ s$^{-2}$, and a dynamical timescale of ~10$^{4}$ yr. Significantly, the enhanced relative abundances of SiO, H$_2$CO, and CH$_3$OH with respect to CO, along with the increased temperature at the forked point, indicate a collisional origin. The forked point does not coincide with any dust continuum core > 0.1 $M_{\odot}$. Moreover, CO (2-1) emission also traces three other outflows in this region, characterized by their masses (0.40, 0.02 and 0.15 $M_{\odot}$) and momenta (5.2, 0.2, 1.8 $M_{\odot}$ km s$^{-1}$), as part of the ALMA Survey of 70 $\mu$m dark High-mass clumps in Early Stages (ASHES) project. All the newly discovered morphological and kinematic features associated with these extremely young protostellar outflows (with timescales of 10$^3$ - 10$^4$ years) suggest that the initial stages of star formation are more complicated than previously understood.
Quantum Sensing Enhancement through a Nuclear Spin Register in Nitrogen-Vacancy Centers in Diamond
2025-07-19
Jonathan Kenny, Feifei Zhou, Ruihua He, Fedor Jelezko, Teck Seng Koh, Weibo Gao
Quantum sensing has seen rapid progress from laboratory research to real-world applications. Solid-state spin systems, particularly nitrogen-vacancy (NV) centers in diamond, are attractive for their ability to operate at room temperature with high sensitivity. However, electron spin coherence due to noise from the surrounding spin bath and this environment effect limits the sensitivity of NV centers. Thus, a critical task in NV center-based quantum sensing is sensitivity enhancement through coherence protection. Nuclear spin assisted protocols have demonstrated greater enhancement of electron spin coherence due to the naturally occurring electron and nuclear spin pair. The longer nuclear coherence times allow for long-lived memory bit for quantum information protocols. This review discusses the physics of NV centers, the mechanisms and variations of nuclear spin-assisted protocols, and their applications in nuclear spin spectroscopy, atomic imaging, and magnetic sensing. Challenges in sensitivity enhancement, commercialization prospects, and future research directions are also explored.
The Origin of Self-Attention: From Pairwise Affinity Matrices to Transformers
2025-07-19
Giorgio Roffo
The self-attention mechanism, now central to deep learning architectures such as Transformers, is a modern instance of a more general computational principle: learning and using pairwise affinity matrices to control how information flows through a model. This paper traces the conceptual origins of self-attention across multiple domains, including computer vision, natural language processing, and graph learning, through their shared reliance on an affinity matrix, denoted as A. We highlight Infinite Feature Selection (Inf-FS) as a foundational approach that generalizes the idea of affinity-based weighting. Unlike the fixed dot-product structure used in Transformers, Inf-FS defines A either through domain knowledge or by learning, and computes feature relevance through multi-hop propagation over the affinity graph. From this perspective, self-attention can be seen as a special case of Inf-FS: it uses a single-hop affinity computation where A is dynamically built from token similarities. We argue that the underlying structure, reasoning over pairwise relationships, is preserved across both approaches, and the key differences lie in how the affinity matrix is defined and applied. By situating self-attention within the broader paradigm of affinity-based computation, we unify several strands of machine learning research and highlight a common mathematical foundation that underpins diverse models and tasks.
Emerging Trends in Software Architecture from the Practitioners Perspective: A Five Year Review
2025-07-19
Ruoyu Su, Noman ahmad, Matteo Esposito, Andrea Janes, Davide Taibi, Valentina Lenarduzzi
Software architecture plays a central role in the design, development, and maintenance of software systems. With the rise of cloud computing, microservices, and containers, architectural practices have diversified. Understanding these shifts is vital. This study analyzes software architecture trends across eight leading industry conferences over five years. We investigate the evolution of software architecture by analyzing talks from top practitioner conferences, focusing on the motivations and contexts driving technology adoption. We analyzed 5,677 talks from eight major industry conferences, using large language models and expert validation to extract technologies, their purposes, and usage contexts. We also explored how technologies interrelate and fit within DevOps and deployment pipelines. Among 450 technologies, Kubernetes, Cloud Native, Serverless, and Containers dominate by frequency and centrality. Practitioners present technology mainly related to deployment, communication, AI, and observability. We identify five technology communities covering automation, coordination, cloud AI, monitoring, and cloud-edge. Most technologies span multiple DevOps stages and support hybrid deployment. Our study reveals that a few core technologies, like Kubernetes and Serverless, dominate the contemporary software architecture practice. These are mainly applied in later DevOps stages, with limited focus on early phases like planning and coding. We also show how practitioners frame technologies by purpose and context, reflecting evolving industry priorities. Finally, we observe how only research can provide a more holistic lens on architectural design, quality, and evolution.
Architectural Degradation: Definition, Motivations, Measurement and Remediation Approaches
2025-07-19
Noman Ahmad, Ruoyu Su, Matteo Esposito, Andrea Janes, Valentina Lenarduzzi, Davide Taibi
Architectural degradation, also known as erosion, decay, or aging, impacts system quality, maintainability, and adaptability. Although widely acknowledged, current literature shows fragmented definitions, metrics, and remediation strategies. Our study aims to unify understanding of architectural degradation by identifying its definitions, causes, metrics, tools, and remediation approaches across academic and gray literature. We conducted a multivocal literature review of 108 studies extracting definitions, causes, metrics, measurement approaches, tools, and remediation strategies. We developed a taxonomy encompassing architectural, code, and process debt to explore definition evolution, methodological trends, and research gaps. Architectural degradation has shifted from a low-level issue to a socio-technical concern. Definitions now address code violations, design drift, and structural decay. Causes fall under architectural (e.g., poor documentation), code (e.g., hasty fixes), and process debt (e.g., knowledge loss). We identified 54 metrics and 31 measurement techniques, focused on smells, cohesion/coupling, and evolution. Yet, most tools detect issues but rarely support ongoing or preventive remediation. Degradation is both technical and organizational. While detection is well-studied, continuous remediation remains lacking. Our study reveals missed integration between metrics, tools, and repair logic, urging holistic, proactive strategies for sustainable architecture.
High-frequency oscillations (HFOs) in intracranial Electroencephalography (iEEG) are critical biomarkers for localizing the epileptogenic zone in epilepsy treatment. However, traditional rule-based detectors for HFOs suffer from unsatisfactory precision, producing false positives that require time-consuming manual review. Supervised machine learning approaches have been used to classify the detection results, yet they typically depend on labeled datasets, which are difficult to acquire due to the need for specialized expertise. Moreover, accurate labeling of HFOs is challenging due to low inter-rater reliability and inconsistent annotation practices across institutions. The lack of a clear consensus on what constitutes a pathological HFO further challenges supervised refinement approaches. To address this, we leverage the insight that legacy detectors reliably capture clinically relevant signals despite their relatively high false positive rates. We thus propose the Self-Supervised to Label Discovery (SS2LD) framework to refine the large set of candidate events generated by legacy detectors into a precise set of pathological HFOs. SS2LD employs a variational autoencoder (VAE) for morphological pre-training to learn meaningful latent representation of the detected events. These representations are clustered to derive weak supervision for pathological events. A classifier then uses this supervision to refine detection boundaries, trained on real and VAE-augmented data. Evaluated on large multi-institutional interictal iEEG datasets, SS2LD outperforms state-of-the-art methods. SS2LD offers a scalable, label-efficient, and clinically effective strategy to identify pathological HFOs using legacy detectors.
Towards Efficient Privacy-Preserving Machine Learning: A Systematic Review from Protocol, Model, and System Perspectives
2025-07-19
Wenxuan Zeng, Tianshi Xu, Yi Chen, Yifan Zhou, Mingzhe Zhang, Jin Tan, Cheng Hong, Meng Li
Privacy-preserving machine learning (PPML) based on cryptographic protocols has emerged as a promising paradigm to protect user data privacy in cloud-based machine learning services. While it achieves formal privacy protection, PPML often incurs significant efficiency and scalability costs due to orders of magnitude overhead compared to the plaintext counterpart. Therefore, there has been a considerable focus on mitigating the efficiency gap for PPML. In this survey, we provide a comprehensive and systematic review of recent PPML studies with a focus on cross-level optimizations. Specifically, we categorize existing papers into protocol level, model level, and system level, and review progress at each level. We also provide qualitative and quantitative comparisons of existing works with technical insights, based on which we discuss future research directions and highlight the necessity of integrating optimizations across protocol, model, and system levels. We hope this survey can provide an overarching understanding of existing approaches and potentially inspire future breakthroughs in the PPML field. As the field is evolving fast, we also provide a public GitHub repository to continuously track the developments, which is available at https://github.com/PKU-SEC-Lab/Awesome-PPML-Papers.
Diffusion Models for Time Series Forecasting: A Survey
2025-07-19
Chen Su, Zhengzhou Cai, Yuanhe Tian, Zihong Zheng, Yan Song
Diffusion models, initially developed for image synthesis, demonstrate remarkable generative capabilities. Recently, their application has expanded to time series forecasting (TSF), yielding promising results. In this survey, we firstly introduce the standard diffusion models and their prevalent variants, explaining their adaptation to TSF tasks. We then provide a comprehensive review of diffusion models for TSF, paying special attention to the sources of conditional information and the mechanisms for integrating this conditioning within the models. In analyzing existing approaches using diffusion models for TSF, we provide a systematic categorization and a comprehensive summary of them in this survey. Furthermore, we examine several foundational diffusion models applied to TSF, alongside commonly used datasets and evaluation metrics. Finally, we discuss current limitations in these approaches and potential future research directions. Overall, this survey details recent progress and future prospects for diffusion models in TSF, serving as a reference for researchers in the field.
Advances in Feed-Forward 3D Reconstruction and View Synthesis: A Survey
2025-07-19
Jiahui Zhang, Yuelei Li, Anpei Chen, Muyu Xu, Kunhao Liu, Jianyuan Wang, Xiao-Xiao Long, Hanxue Liang, Zexiang Xu, Hao Su, Christian Theobalt, Christian Rupprecht, Andrea Vedaldi, Hanspeter Pfister, Shijian Lu, Fangneng Zhan
3D reconstruction and view synthesis are foundational problems in computer vision, graphics, and immersive technologies such as augmented reality (AR), virtual reality (VR), and digital twins. Traditional methods rely on computationally intensive iterative optimization in a complex chain, limiting their applicability in real-world scenarios. Recent advances in feed-forward approaches, driven by deep learning, have revolutionized this field by enabling fast and generalizable 3D reconstruction and view synthesis. This survey offers a comprehensive review of feed-forward techniques for 3D reconstruction and view synthesis, with a taxonomy according to the underlying representation architectures including point cloud, 3D Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), etc. We examine key tasks such as pose-free reconstruction, dynamic 3D reconstruction, and 3D-aware image and video synthesis, highlighting their applications in digital humans, SLAM, robotics, and beyond. In addition, we review commonly used datasets with detailed statistics, along with evaluation protocols for various downstream tasks. We conclude by discussing open research challenges and promising directions for future work, emphasizing the potential of feed-forward approaches to advance the state of the art in 3D vision.
Combining Cost-Constrained Runtime Monitors for AI Safety
2025-07-19
Tim Tian Hua, James Baskerville, Henri Lemoine, Mia Hopman, Aryan Bhatt, Tyler Tracy
Monitoring AIs at runtime can help us detect and stop harmful actions. In this paper, we study how to combine multiple runtime monitors into a single monitoring protocol. The protocol's objective is to maximize the probability of applying a safety intervention on misaligned outputs (i.e., maximize recall). Since running monitors and applying safety interventions are costly, the protocol also needs to adhere to an average-case budget constraint. Taking the monitors' performance and cost as given, we develop an algorithm to find the most efficient protocol. The algorithm exhaustively searches over when and which monitors to call, and allocates safety interventions based on the Neyman-Pearson lemma. By focusing on likelihood ratios and strategically trading off spending on monitors against spending on interventions, we more than double our recall rate compared to a naive baseline in a code review setting. We also show that combining two monitors can Pareto dominate using either monitor alone. Our framework provides a principled methodology for combining existing monitors to detect undesirable behavior in cost-sensitive settings.
We present new constraints on the halo masses and matter density profiles of DESI galaxy groups by cross-correlating samples of Luminous Red Galaxies (LRGs) and Bright Galaxy Survey (BGS) galaxies with the publicly available CMB lensing convergence map from ACT DR6. This provides an independent, lensing-based calibration of halo masses, complementary to methods relying on clustering or dynamics. We derive constraints on the mean halo mass for three DESI-selected samples, finding $\log(M_{\rm halo}/(M_\odot/h)) \approx 13.18$, 13.03 and 13.02 for the Main LRG, Extended LRG, and BGS samples, respectively. Using a halo model approach, we also compare the projected galaxy-matter density profiles with previously reported gas profiles inferred from measurements of the kinematic Sunyaev-Zel'dovich (kSZ) effect. This work addresses one of the key uncertainties in interpreting kSZ signals -- the unknown host halo mass distribution -- by providing an independent and consistent mass calibration. The agreement between the gas and total mass profiles at large aperture suggests that sufficiently far from the group center (2--3 virial radii), we recover all the baryons, offering a resolution to the 'missing baryon' problem. We further study the cumulative gas fractions for all galaxies as well as for the most massive galaxy groups in the sample ($\log(M_{\rm halo}/(M_\odot/h)) \approx 13.5$), finding values that are physically sensible and in agreement with previous findings using kSZ and X-ray data: compared to the TNG300 simulation, the observed gas fractions are systematically lower at fixed radius by $\gtrsim$4$\sigma$, providing compelling, independent evidence for stronger baryonic feedback in the real Universe. These findings highlight the power of combining CMB lensing with galaxy surveys to probe the interplay between baryons and dark matter in group-sized halos.
The DBL Survey II: towards a mass-period distribution of double white dwarf binaries
2025-07-18
James Munday, Ingrid Pelisoli, Pier-Emmanuel Tremblay, David Jones, Gijs Nelemans, Mukremin Kilic, Tim Cunningham, Silvia Toonen, Alejandro Santos-García, Harry Dawson, Viktoria Pinter, Benjamin Godson, Llanos Martinez, Jaya Chand, Ross Dobson, Kiran Jhass, Shravya Shenoy
Double white dwarf binaries are an important remnant of binary evolution as they are possible type Ia supernova progenitors and strong sources of gravitational waves in the low-frequency regime. The double-lined double white dwarf (DBL) survey searches for compact double white dwarfs where both stars are spectrally disentangleable. Candidates are identified by being overluminous compared to the cooling sequence of a typical mass, single white dwarf. In this second DBL survey instalment, we present full orbital solutions of 15 double white dwarf binaries from our ongoing campaign to accurately measure a magnitude-limited mass-period distribution. 12 of these systems are fully solved for the first time. A long-standing bias in the full population has been evident, favouring systems with orbital periods up to a few hours, with little exploration of the majority of the compact double white dwarf population, whose orbital period distribution centres at approximately 20hr. The 15 systems in this study span the orbital period range 5-75hr, significantly augmenting the number of well-characterised systems over these periods, and in general have two similar mass stars combining to approximately 1.0 solar masses. We witness that the orbitally derived mass ratios generally show an excellent agreement with those deduced from atmospheric fits to double-lined spectra in previous work, emphasising the power of wide-scale spectroscopic surveys to efficiently locate the highest mass, double-lined double white dwarfs in the local Galaxy.
Spatiotemporal Order and Parametric Instabilities from First-Principles
2025-07-18
Daniel Kaplan, Pavel A. Volkov, Jennifer Coulter, Shiwei Zhang, Premala Chandra
Shaping crystal structure with light is an enduring goal of physics and materials engineering. Here we present calculations in candidate materials selected by symmetry that allow light-induced spatiotemporal parametric instabilities. We demonstrate a theoretical framework that includes a complete symmetry analysis of phonon modes that contribute to parametric instabilities across all non-centrosymmetric point groups, a detailed survey of the materials landscape and finally the computation of nonlinear couplings from first principles. We then showcase detailed results for chiral crystals, ferroelectrics, and layered van der Waals materials. Our results pave the way towards realizing designer time-crystalline order in quantum materials, detectable with time-resolved diffractive probes.
Semi-supervised classification of Stars, Galaxies and Quasars using K-means and Random Forest
2025-07-18
Vahid Asadi, Hosein Haghi, Akram Hasani Zonoozi
Classifying stars, galaxies, and quasars is essential for understanding cosmic structure and evolution, but the vast data from modern surveys make manual classification impractical, while supervised learning methods remain constrained by the scarcity of labeled spectroscopic data. We aim to develop a scalable, label-efficient method for astronomical classification by leveraging semi-supervised learning (SSL) to overcome the limitations of fully supervised approaches. We propose a novel SSL framework combining K-means clustering with Random Forest classification. Our method partitions unlabeled data into 50 clusters, propagates labels from spectroscopically confirmed centroids to 95\% of cluster members, and trains a Random Forest on the expanded pseudo-labeled dataset. We apply this to the CPz catalog, containing multi-survey photometric and spectroscopic data, and compare performance with a fully supervised Random Forest. Our SSL approach achieves F1-scores of 98.8\%, 98.9\%, and 92.0\% for stars, galaxies, and quasars, respectively, closely matching the supervised method with F1-scores of 99.1\%, 99.1\%, and 93.1\% while outperforming traditional color-cut techniques. The method demonstrates robustness in high-dimensional feature spaces and superior label efficiency compared to prior work. This work highlights SSL as a scalable solution for astronomical classification when labeled data is limited, though performance may degrade in lower-dimensional settings.
2025-07-18
Shuiguang Deng, Di Yu, Changze Lv, Xin Du, Linshan Jiang, Xiaofan Zhao, Wentao Tong, Xiaoqing Zheng, Weijia Fang, Peng Zhao, Gang Pan, Schahram Dustdar, Albert Y. Zomaya
The convergence of artificial intelligence and edge computing has spurred growing interest in enabling intelligent services directly on resource-constrained devices. While traditional deep learning models require significant computational resources and centralized data management, the resulting latency, bandwidth consumption, and privacy concerns have exposed critical limitations in cloud-centric paradigms. Brain-inspired computing, particularly Spiking Neural Networks (SNNs), offers a promising alternative by emulating biological neuronal dynamics to achieve low-power, event-driven computation. This survey provides a comprehensive overview of Edge Intelligence based on SNNs (EdgeSNNs), examining their potential to address the challenges of on-device learning, inference, and security in edge scenarios. We present a systematic taxonomy of EdgeSNN foundations, encompassing neuron models, learning algorithms, and supporting hardware platforms. Three representative practical considerations of EdgeSNN are discussed in depth: on-device inference using lightweight SNN models, resource-aware training and updating under non-stationary data conditions, and secure and privacy-preserving issues. Furthermore, we highlight the limitations of evaluating EdgeSNNs on conventional hardware and introduce a dual-track benchmarking strategy to support fair comparisons and hardware-aware optimization. Through this study, we aim to bridge the gap between brain-inspired learning and practical edge deployment, offering insights into current advancements, open challenges, and future research directions. To the best of our knowledge, this is the first dedicated and comprehensive survey on EdgeSNNs, providing an essential reference for researchers and practitioners working at the intersection of neuromorphic computing and edge intelligence.
In this paper, we propose a novel reference-free iterative learning model predictive control (MPC). In the proposed method, a certificate function based on the concept of Control Lyapunov Barrier Function (CLBF) is learned using data collected from past control executions and used to define the terminal set and cost in the MPC optimization problem at the current iteration. This scheme enables the progressive refinement of the MPC's terminal components over successive iterations. Unlike existing methods that rely on mixed-integer programming and suffer from numerical difficulties, the proposed approach formulates the MPC optimization problem as a standard nonlinear program, enabling more efficient online computation. The proposed method satisfies key MPC properties, including recursive feasibility and asymptotic stability. Additionally, we demonstrate that the performance cost is non-increasing with respect to the number of iterations, under certain assumptions. Numerical experiments including the simulation with PyBullet confirm that our control scheme iteratively enhances control performance and significantly improves online computational efficiency compared to the existing methods.
Testing Signatures of Phantom Crossing through Full-Shape Galaxy Clustering Analysis
2025-07-18
Emanuelly Silva, Rafael C. Nunes
Recent observations of baryon acoustic oscillations (BAO) from the Dark Energy Spectroscopic Instrument (DESI) survey, when combined with measurements of the cosmic microwave background (CMB) and Type Ia supernovae (SNIa), provide compelling evidence for a phantom crossing at late times, along with statistically significant deviations from the standard $\Lambda$CDM model. In this work, we investigate the role of redshift-space galaxy clustering data by employing the pre-reconstruction full-shape (FS) galaxy power spectrum from the Baryon Oscillation Spectroscopic Survey (BOSS) data release 12 (DR12) sample. This dataset is analyzed in combination with BAO measurements from DESI data release 2 (DR2) and various SNIa samples. Our analysis demonstrates that the joint combination of these datasets can yield deviations from $\Lambda$CDM at a significance level of up to $\sim 5\sigma$, suggesting strong indications that the dark energy equation of state parameter $w(z)$ may have crossed the phantom divide ($w = -1$) in the redshift range $z \sim 0.4$--$0.5$. The precise location and strength of this crossing depend on the adopted theoretical parameterizations. Importantly, our results reveal that this trend persists even in the absence of CMB data, underscoring the robustness of the FS power spectrum as a powerful and independent probe for testing dark energy models and for distinguishing between competing cosmological scenarios.
Signs of the Past, Patterns of the Present: On the Automatic Classification of Old Babylonian Cuneiform Signs
2025-07-18
Eli Verwimp, Gustav Ryberg Smidt, Hendrik Hameeuw, Katrien De Graef
The work in this paper describes the training and evaluation of machine learning (ML) techniques for the classification of cuneiform signs. There is a lot of variability in cuneiform signs, depending on where they come from, for what and by whom they were written, but also how they were digitized. This variability makes it unlikely that an ML model trained on one dataset will perform successfully on another dataset. This contribution studies how such differences impact that performance. Based on our results and insights, we aim to influence future data acquisition standards and provide a solid foundation for future cuneiform sign classification tasks. The ML model has been trained and tested on handwritten Old Babylonian (c. 2000-1600 B.C.E.) documentary texts inscribed on clay tablets originating from three Mesopotamian cities (Nippur, D\=ur-Abie\v{s}uh and Sippar). The presented and analysed model is ResNet50, which achieves a top-1 score of 87.1% and a top-5 score of 96.5% for signs with at least 20 instances. As these automatic classification results are the first on Old Babylonian texts, there are currently no comparable results.
2025-07-18
James A. D. Binnie, Paweł Dłotko, John Harvey, Jakub Malinowski, Ka Man Yim
It is a standard assumption that datasets in high dimension have an internal structure which means that they in fact lie on, or near, subsets of a lower dimension. In many instances it is important to understand the real dimension of the data, hence the complexity of the dataset at hand. A great variety of dimension estimators have been developed to find the intrinsic dimension of the data but there is little guidance on how to reliably use these estimators. This survey reviews a wide range of dimension estimation methods, categorising them by the geometric information they exploit: tangential estimators which detect a local affine structure; parametric estimators which rely on dimension-dependent probability distributions; and estimators which use topological or metric invariants. The paper evaluates the performance of these methods, as well as investigating varying responses to curvature and noise. Key issues addressed include robustness to hyperparameter selection, sample size requirements, accuracy in high dimensions, precision, and performance on non-linear geometries. In identifying the best hyperparameters for benchmark datasets, overfitting is frequent, indicating that many estimators may not generalise well beyond the datasets on which they have been tested.
Stablecoins: Fundamentals, Emerging Issues, and Open Challenges
2025-07-18
Ahmed Mahrous, Maurantonio Caprolu, Roberto Di Pietro
Stablecoins, with a capitalization exceeding 200 billion USD as of January 2025, have shown significant growth, with annual transaction volumes exceeding 10 trillion dollars in 2023 and nearly doubling that figure in 2024. This exceptional success has attracted the attention of traditional financial institutions, with an increasing number of governments exploring the potential of Central Bank Digital Currencies (CBDCs). Although academia has recognized the importance of stablecoins, research in this area remains fragmented, incomplete, and sometimes contradictory. In this paper, we aim to address the cited gap with a structured literature analysis, correlating recent contributions to present a picture of the complex economic, technical, and regulatory aspects of stablecoins. To achieve this, we formulate the main research questions and categorize scientific contributions accordingly, identifying main results, data sources, methodologies, and open research questions. The research questions we address in this survey paper cover several topics, such as the stability of various stablecoins, novel designs and implementations, and relevant regulatory challenges. The studies employ a wide range of methodologies and data sources, which we critically analyze and synthesize. Our analysis also reveals significant research gaps, including limited studies on security and privacy, underexplored stablecoins, unexamined failure cases, unstudied governance mechanisms, and the treatment of stablecoins under financial accounting standards, among other areas.
Advanced X-rays techniques for research-oriented high-resolution imaging of articular cartilage: a scoping review
2025-07-18
Simone Fantoni, Luca Brombal, Paolo Cardarelli, Fabio Baruffaldi
Articular cartilage is a musculoskeletal soft tissue renowned for its unique mechanical properties. Understanding both its hierarchical structure and the interplay between its constituents could shed light on the mechanical competence of the tissue. Therefore, rheologic approaches based on high-resolution non-destructive imaging techniques are desired. In this context, X-ray imaging could ideally accomplish this task. Nevertheless, the nature of articular cartilage translates into poor contrast using conventional absorption modality. To overcome this limitation, several approaches can be embraced. X-ray visibility of articular cartilage can be increased with the use of radiopaque contrast agents. Therefore, further discrimination of structures could be provided by spectral techniques, pivoting on either multi-energy acquisitions or photon-counting technology. Alternatively, phase-contrast techniques unveil details typically undetected with conventional approaches. Phase-contrast imaging, based on the intrinsic decrement in the refractive index of the tissue, can be achieved with different configurations and implementations, including distinct X-ray sources and optical elements. Additionally, some phase-contrast techniques retrieve the small-angle scattering-based dark-field signal, relatable to sub-pixel structures. This scoping review aims to catalogue the application of these advanced X-ray techniques to articular cartilage imaging, following PRISMA guidelines. It discusses their advantages, limitations, and includes an overview of rheologic applications to articular cartilage.
An integral field spectroscopic study of stellar and ionized gas properties around edge-on disk galaxies in the stellar mass range 9
2025-07-18
Guinevere Kauffmann, Richard D'Souza, Antonela Monachesi
We analyze the stellar light, 4000 Angstrom break and emission line profiles of 82 edge-on disk galaxies from the MaNGA survey. We characterize the stellar light profiles perpendicular to the disk plane using two parameters: a) the power law slope of the thick disk component, 2) the transition radius where the profile flattens. The 4000 Angstrom break profiles perpendicular to the plane are characterized by the number of significant changes in slope (breaks) and by the change in D_n(4000) from inner to outer disk. The slope correlates tightly with the stellar mass of the galaxy over the stellar mass range 10^9
One of the most fundamental relationships in modern cosmology is the cosmic distance duality relation (CDDR), which describes the relationship between the angular diameter distance ($D_{\rm A}$) and the luminosity distance ($D_{\rm L}$), and is expressed as: $\eta(z)=D_{\rm L}(z)(1+z)^{-2}/D_{\rm A}(z)=1$. In this work, we conduct a comprehensive test of the CDDR by combining baryon acoustic oscillation (BAO) data from the SDSS and DESI surveys with type Ia supernova (SN) data from PantheonPlus and DESY5. We utilize an artificial neural network approach to match the SN and BAO data at the same redshift. To explore potential violations of the CDDR, we consider three different parameterizations: (i) $\eta(z)=1+\eta_0z$; (ii) $\eta(z)=1+\eta_0z/(1+z)$; (iii) $\eta(z)=1+\eta_0\ln(1+z)$. Our results indicate that the calibration of the SN absolute magnitude $M_{\rm B}$ plays a crucial role in testing potential deviations from the CDDR, as there exists a significant negative correlation between $\eta_0$ and $M_{\rm B}$. For PantheonPlus analysis, when $M_{\rm B}$ is treated as a free parameter, no evidence of CDDR violation is found. In contrast, fixing $M_{\rm B}$ to the $M_{\rm B}^{\rm D20}$ prior with $-19.230\pm0.040$ mag leads to a deviation at approximately the $2\sigma$ level, while fixing $M_{\rm B}$ to the $M_{\rm B}^{\rm B23}$ prior with $-19.396\pm0.016$ mag remains in agreement with the CDDR. Furthermore, overall analyses based on the SDSS+DESY5 and DESI+DESY5 data consistently show no evidence of the deviation from the CDDR.
MorphoNAS: Embryogenic Neural Architecture Search Through Morphogen-Guided Development
2025-07-18
Mykola Glybovets, Sergii Medvid
While biological neural networks develop from compact genomes using relatively simple rules, modern artificial neural architecture search methods mostly involve explicit and routine manual work. In this paper, we introduce MorphoNAS (Morphogenetic Neural Architecture Search), a system able to deterministically grow neural networks through morphogenetic self-organization inspired by the Free Energy Principle, reaction-diffusion systems, and gene regulatory networks. In MorphoNAS, simple genomes encode just morphogens dynamics and threshold-based rules of cellular development. Nevertheless, this leads to self-organization of a single progenitor cell into complex neural networks, while the entire process is built on local chemical interactions. Our evolutionary experiments focused on two different domains: structural targeting, in which MorphoNAS system was able to find fully successful genomes able to generate predefined random graph configurations (8-31 nodes); and functional performance on the CartPole control task achieving low complexity 6-7 neuron solutions when target network size minimization evolutionary pressure was applied. The evolutionary process successfully balanced between quality of of the final solutions and neural architecture search effectiveness. Overall, our findings suggest that the proposed MorphoNAS method is able to grow complex specific neural architectures, using simple developmental rules, which suggests a feasible biological route to adaptive and efficient neural architecture search.
Spectroscopic Characterization of LOFAR Radio-emitting M dwarfs
2025-07-18
E. Koo, G. Stefansson, R. D. Kavanagh, M. Delamer, S. Mahadevan, J. R. Callingham, H. Vedantham, P. Robertson, D. Bruijne, C. F. Bender, C. I. Cañas, S. Diddams, J. I. Espinoza-Retamal, R. B. Fernandes, S. Halverson, S. Kanodia, D. Krolikowski, A. S. J. Lin, B. J. S. Pope, A. Roy, C. Schwab, R. Terrien, J. T. Wright
Recent observations with the LOw Frequency ARray (LOFAR) have revealed 19 nearby M dwarfs showing bright circularly polarised radio emission. One of the possible sources of such emission is through magnetic star-planet interactions (MSPI) with unseen close-in planets. We present initial results from a spectroscopic survey with the Habitable-zone Planet Finder (HPF) and NEID spectrographs designed to characterize this sample and further investigate the origin of the radio emission. We provide four new insights into the sample. I) We uniformly characterize the stellar properties, constraining their effective temperatures, surface gravities, metallicities, projected rotational velocities, rotation periods, stellar radii, and stellar inclinations where possible. Further, from a homogenous analysis of the HPF spectra, we infer their chromospheric activity and spectroscopic multiplicity states. From this, we identify GJ 625, GJ 1151, and LHS 2395 as single, quiescent stars amenable to precise RV follow-up, making them strong MSPI candidates. II) We show that the distribution of stellar inclinations are compatible with an isotropic distribution, providing no evidence for a preference to pole-on configurations. III) We refine the radial velocity solution for GJ 625 b, the only currently known close-in planet in the sample, reducing the uncertainty in its orbital period by a factor of three, to facilitate future phase-dependent radio analysis. IV) Finally, we identify GJ 3861 as a spectroscopic binary with an orbital period of $P=14.841181_{-0.00010}^{+0.00011}$ d, making it the only confirmed binary with a relatively short orbit in the sample, where we surmise the radio emission is likely related to magnetospheric interactions between the two stars. These results advance our understanding of radio-emitting M dwarfs and establish an observational foundation for identifying MSPI.
From Extraction to Synthesis: Entangled Heuristics for Agent-Augmented Strategic Reasoning
2025-07-18
Renato Ghisellini, Remo Pareschi, Marco Pedroni, Giovanni Battista Raggi
We present a hybrid architecture for agent-augmented strategic reasoning, combining heuristic extraction, semantic activation, and compositional synthesis. Drawing on sources ranging from classical military theory to contemporary corporate strategy, our model activates and composes multiple heuristics through a process of semantic interdependence inspired by research in quantum cognition. Unlike traditional decision engines that select the best rule, our system fuses conflicting heuristics into coherent and context-sensitive narratives, guided by semantic interaction modeling and rhetorical framing. We demonstrate the framework via a Meta vs. FTC case study, with preliminary validation through semantic metrics. Limitations and extensions (e.g., dynamic interference tuning) are discussed.
2025-07-18
Kai Wu, Zhongqin Wang, Shu-Lin Chen, J. Andrew Zhang, Y. Jay Guo
Integrated Sensing and Communications (ISAC) is poised to become one of the defining capabilities of the sixth generation (6G) wireless communications systems, enabling the network infrastructure to jointly support high-throughput communications and situational awareness. While recent advances have explored ISAC for both human-centric applications and environmental monitoring, existing research remains fragmented across these domains. This paper provides the first unified review of ISAC-enabled sensing for both human activities and environment, focusing on signal-level mechanisms, sensing features, and real-world feasibility. We begin by characterising how diverse physical phenomena, ranging from human vital sign and motion to precipitation and flood dynamics, impact wireless signal propagation, producing measurable signatures in channel state information (CSI), Doppler profiles, and signal statistics. A comprehensive analysis is then presented across two domains: human sensing applications including localisation, activity recognition, and vital sign monitoring; and environmental sensing for rainfall, soil moisture, and water level. Experimental results from Long-Term Evolution (LTE) sensing under non-line-of-sight (NLOS) conditions are incorporated to highlight the feasibility in infrastructure-limited scenarios. Open challenges in signal fusion, domain adaptation, and generalisable sensing architectures are discussed to facilitate future research toward scalable and autonomous ISAC.
2025-07-18
Ziyang Chen, Yufei Zhang, Bin Luo, Hong Guo
Optical time-frequency transfer establishes the metrological linkage in large-scale clock networks, which facilitates various applications. Fiber-based transfer benefits from the abundant deployment of fiber infrastructures to achieve this advantage. In this Review, we provide an overview of the advances in optical two-way time-frequency transfer, which began with characterizing the time-frequency transfer stability. Then, we discuss the system configuration, key modules, main challenges, and mainstream transfer methods. Finally, the Review concludes with an outlook on further applications toward global-scale high-precision clock networks.
In this paper, we first review the theoretical and experimental studies of the process $\Lambda_c^+ \to \pi^+ \eta \Lambda$. Motivated by the recent BESIII and Belle measurements, we have conducted a theoretical study of the process $\Lambda_c^+ \to \pi^+ \eta \Lambda$, where the $a_0(980)$ and $\Lambda(1670)$ resonances are dynamically generated from the $S$-wave meson meson and meson baryon interaction, respectively. Our results give a reasonable description of the invariant mass distributions, which implies that the spin flip contribution in the $\Sigma(1385)$ excitation plays an important role.
Quantum Blockchain Survey: Foundations, Trends, and Gaps
2025-07-18
Saurav Ghosh
Quantum computing poses fundamental risks to classical blockchain systems by undermining widely used cryptographic primitives. In response, two major research directions have emerged: post-quantum blockchains, which integrate quantum-resistant algorithms, and quantum blockchains, which leverage quantum properties such as entanglement and quantum key distribution. This survey reviews key developments in both areas, analyzing their cryptographic foundations, architectural designs, and implementation challenges. This work provides a comparative overview of technical proposals, highlight trade-offs in security, scalability, and deployment, and identify open research problems across hardware, consensus, and network design. The goal is to offer a structured and comprehensive reference for advancing secure blockchain systems in the quantum era.
Robust Probability Hypothesis Density Filtering: Theory and Algorithms
2025-07-18
Ming Lei, Shufan Wu
Multi-target tracking (MTT) serves as a cornerstone technology in information fusion, yet faces significant challenges in robustness and efficiency when dealing with model uncertainties, clutter interference, and target interactions. Conventional approaches like Gaussian Mixture PHD (GM-PHD) and Cardinalized PHD (CPHD) filters suffer from inherent limitations including combinatorial explosion, sensitivity to birth/death process parameters, and numerical instability. This study proposes an innovative minimax robust PHD filtering framework with four key contributions: (1) A theoretically derived robust GM-PHD recursion algorithm that achieves optimal worst-case error control under bounded uncertainties; (2) An adaptive real-time parameter adjustment mechanism ensuring stability and error bounds; (3) A generalized heavy-tailed measurement likelihood function maintaining polynomial computational complexity; (4) A novel partition-based credibility weighting method for extended targets. The research not only establishes rigorous convergence guarantees and proves the uniqueness of PHD solutions, but also verifies algorithmic equivalence with standard GM-PHD. Experimental results demonstrate that in high-clutter environments, this method achieves a remarkable 32.4% reduction in OSPA error and 25.3% lower cardinality RMSE compared to existing techniques, while maintaining real-time processing capability at 15.3 milliseconds per step. This breakthrough lays a crucial foundation for reliable MTT in safety-critical applications.
Hypergraphs require higher-dimensional representations, which makes it more difficult to compute and interpret their spectral properties. This survey article uses the framework of hypermatrices to give an in-depth overview of the spectral theory of hypergraphs. Our focus in this article relies on the theoretical aspects of hypergraphs that help to ease the computational methods. Spectral theory hypergraphs, one of the most advanced fields of study, are constantly finding novel applications in various domains such as theoretical computer science, quantum physics, and theoretical chemistry, among many others. We start our journey by introducing hypergraphs, hypermatrices (tensors), resultants, and their properties. We outline some of the results used to determine the adjacency spectrum of hypergraphs and go over some of the groundbreaking findings in the development of the theory. On passing through a list of bounds for the spectral radius of uniform hypergraphs, we will have a look into the spectral versions of Tur\'an-type problems in hypergraphs. Finally, in addition to the Estrada index of hypergraphs, some significant results related to the characteristic polynomial and its relationship with the matching polynomial are presented.
The impact of Transformer-based language models has been unprecedented in Natural Language Processing (NLP). The success of such models has also led to their adoption in other fields including bioinformatics. Taking this into account, this paper discusses recent advances in Transformer-based models for protein sequence analysis and design. In this review, we have discussed and analysed a significant number of works pertaining to such applications. These applications encompass gene ontology, functional and structural protein identification, generation of de novo proteins and binding of proteins. We attempt to shed light on the strength and weaknesses of the discussed works to provide a comprehensive insight to readers. Finally, we highlight shortcomings in existing research and explore potential avenues for future developments. We believe that this review will help researchers working in this field to have an overall idea of the state of the art in this field, and to orient their future studies.
Towards channel foundation models (CFMs): Motivations, methodologies and opportunities
2025-07-18
Jun Jiang, Yuan Gao, Xinyi Wu, Shugong Xu
Artificial intelligence (AI) has emerged as a pivotal enabler for next-generation wireless communication systems. However, conventional AI-based models encounter several limitations, such as heavy reliance on labeled data, limited generalization capability, and task-specific design. To address these challenges, this paper introduces, for the first time, the concept of channel foundation models (CFMs)-a novel and unified framework designed to tackle a wide range of channel-related tasks through a pretrained, universal channel feature extractor. By leveraging advanced AI architectures and self-supervised learning techniques, CFMs are capable of effectively exploiting large-scale unlabeled data without the need for extensive manual annotation. We further analyze the evolution of AI methodologies, from supervised learning and multi-task learning to self-supervised learning, emphasizing the distinct advantages of the latter in facilitating the development of CFMs. Additionally, we provide a comprehensive review of existing studies on self-supervised learning in this domain, categorizing them into generative, discriminative and the combined paradigms. Given that the research on CFMs is still at an early stage, we identify several promising future research directions, focusing on model architecture innovation and the construction of high-quality, diverse channel datasets.
Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques
2025-07-18
Niveen O. Jaffal, Mohammed Alkhanafseh, David Mohaisen
Large Language Models (LLMs) are transforming cybersecurity by enabling intelligent, adaptive, and automated approaches to threat detection, vulnerability assessment, and incident response. With their advanced language understanding and contextual reasoning, LLMs surpass traditional methods in tackling challenges across domains such as IoT, blockchain, and hardware security. This survey provides a comprehensive overview of LLM applications in cybersecurity, focusing on two core areas: (1) the integration of LLMs into key cybersecurity domains, and (2) the vulnerabilities of LLMs themselves, along with mitigation strategies. By synthesizing recent advancements and identifying key limitations, this work offers practical insights and strategic recommendations for leveraging LLMs to build secure, scalable, and future-ready cyber defense systems.
BreastSegNet: Multi-label Segmentation of Breast MRI
2025-07-18
Qihang Li, Jichen Yang, Yaqian Chen, Yuwen Chen, Hanxue Gu, Lars J. Grimm, Maciej A. Mazurowski
Breast MRI provides high-resolution imaging critical for breast cancer screening and preoperative staging. However, existing segmentation methods for breast MRI remain limited in scope, often focusing on only a few anatomical structures, such as fibroglandular tissue or tumors, and do not cover the full range of tissues seen in scans. This narrows their utility for quantitative analysis. In this study, we present BreastSegNet, a multi-label segmentation algorithm for breast MRI that covers nine anatomical labels: fibroglandular tissue (FGT), vessel, muscle, bone, lesion, lymph node, heart, liver, and implant. We manually annotated a large set of 1123 MRI slices capturing these structures with detailed review and correction from an expert radiologist. Additionally, we benchmark nine segmentation models, including U-Net, SwinUNet, UNet++, SAM, MedSAM, and nnU-Net with multiple ResNet-based encoders. Among them, nnU-Net ResEncM achieves the highest average Dice scores of 0.694 across all labels. It performs especially well on heart, liver, muscle, FGT, and bone, with Dice scores exceeding 0.73, and approaching 0.90 for heart and liver. All model code and weights are publicly available, and we plan to release the data at a later date.
In recent years, with the rapid development of science and technology, heterogeneous treatment effects have emerged as a focal research topic in statistics, econometrics, and sociology. This paper investigates HTE through semiparametric single-index models based on doubly robust estimation. Departing from conventional approaches, we neither impose boundedness constraints on the link function in single-index models nor restrict its support range. By employing the sieve method to approximate the link function, we achieve simultaneous estimation of both the link function and index parameters. Our study not only establishes the asymptotic properties of the proposed estimator but also systematically evaluates its finite-sample performance through comprehensive simulation studies. Numerical results demonstrate that our method significantly outperforms other commonly used competing estimators. Furthermore, we apply the proposed approach to the National Health and Nutrition Examination Survey dataset to assess the impact of participation in school lunch programs on body mass index.
An indirect correlation of dielectric properties using optical trapping and dielectric resonance in two different frequency regimes
2025-07-18
Somaditya Sen
Dielectric permittivity, $\varepsilon_r$, of materials are often limited to a sub-GHz range using normal LCR meters. In the GHz range the $\varepsilon_r$ can be measured using Vector Network Analyzers and measurement jigs (waveguides) which are specific for different frequency regimes. Hence, to measure er for the entire frequency range one needs several components and is an extremely costly experiment. However, for applications such as Dielectric Resonator Antennas one need to know the er and the dielectric loss to estimate the resonant frequency. An indirect method is proposed in this letter to find the er from Transversely Misaligned Dual-Fiber Optical Trapping at the optical frequency range to approximately estimate {\epsilon}r and thereby understand the correlation between these two regimes of frequency responses.
Developing Shared Vocabulary System For Collaborative Software Engineering
2025-07-18
Carey Lai Zheng Hui, Johnson Britto Jessia Esther Leena, Kumuthini Subramanian, Zhao Chenyu, Shubham Rajeshkumar Jariwala
Effective communication is a critical factor in successful software engineering collaboration. However, communication gaps remain a persistent challenge, often leading to misunderstandings, inefficiencies, and defects. This research investigates the technical factors contributing to such misunderstandings and explores the measurable benefits of establishing shared vocabulary systems within software documentation and codebases. Using a Design Science Research (DSR) framework, the study was structured into three iterative phases: problem identification, method development, and empirical validation. The problem identification phase involved thematic analysis of communication data and semi-structured interviews, revealing key factors such as ambiguous messaging, misalignment in documentation, inconsistent code review feedback, and API integration miscommunication. Grounded Theory principles were employed to design a structured methodology for collaborative vocabulary development. Empirical validation through controlled experiments demonstrated that while initial adoption introduced overhead, the shared vocabulary system significantly improved information density, documentation clarity, and collaboration efficiency over time. Findings offer actionable insights for improving communication practices in software engineering, while also identifying limitations and directions for future research.
ADEPTS: A Capability Framework for Human-Centered Agent Design
2025-07-18
Pierluca D'Oro, Caley Drooff, Joy Chen, Joseph Tighe
Large language models have paved the way to powerful and flexible AI agents, assisting humans by increasingly integrating into their daily life. This flexibility, potential, and growing adoption demands a holistic and cross-disciplinary approach to developing, monitoring and discussing the capabilities required for agent-driven user experiences. However, current guidance on human-centered AI agent development is scattered: UX heuristics focus on interface behaviors, engineering taxonomies describe internal pipelines, and ethics checklists address high-level governance. There is no concise, user-facing vocabulary that tells teams what an agent should fundamentally be able to do. We introduce ADEPTS, a capability framework defining a set of core user-facing capabilities to provide unified guidance around the development of AI agents. ADEPTS is based on six principles for human-centered agent design, that express the minimal, user-facing capabilities an AI agent should demonstrate to be understandable, controllable and trustworthy in everyday use. ADEPTS complements existing frameworks and taxonomies; differently from them, it sits at the interface between technical and experience development. By presenting ADEPTS, we aim to condense complex AI-UX requirements into a compact framework that is actionable guidance for AI researchers, designers, engineers, and policy reviewers alike. We believe ADEPTS has the potential of accelerating the improvement of user-relevant agent capabilities, of easing the design of experiences that take advantage of those capabilities, and of providing a shared language to track and discuss progress around the development of AI agents.
Incremental Causal Graph Learning for Online Cyberattack Detection in Cyber-Physical Infrastructures
2025-07-18
Arun Vignesh Malarkkan, Dongjie Wang, Haoyue Bai, Yanjie Fu
The escalating threat of cyberattacks on real-time critical infrastructures poses serious risks to public safety, demanding detection methods that effectively capture complex system interdependencies and adapt to evolving attack patterns. Traditional real-time anomaly detection techniques often suffer from excessive false positives due to their statistical sensitivity to high data variance and class imbalance. To address these limitations, recent research has explored modeling causal relationships among system components. However, prior work mainly focuses on offline causal graph-based approaches that require static historical data and fail to generalize to real-time settings. These methods are fundamentally constrained by: (1) their inability to adapt to dynamic shifts in data distribution without retraining, and (2) the risk of catastrophic forgetting when lacking timely supervision in live systems. To overcome these challenges, we propose INCADET, a novel framework for incremental causal graph learning tailored to real-time cyberattack detection. INCADET dynamically captures evolving system behavior by incrementally updating causal graphs across streaming time windows. The framework comprises three modules: 1) Early Symptom Detection: Detects transitions in system status using divergence in edge-weight distributions across sequential causal graphs. 2) Incremental Causal Graph Learning: Leverages experience replay and edge reinforcement to continually refine causal structures while preserving prior knowledge. 3) Causal Graph Classification: Employs Graph Convolutional Networks (GCNs) to classify system status using the learned causal graphs. Extensive experiments on real-world critical infrastructure datasets demonstrate that INCADET achieves superior accuracy, robustness, and adaptability compared to both static causal and deep temporal baselines in evolving attack scenarios.
Superuniversal Statistics with Topological Origins for non-Hermitian Scattering Singularities
2025-07-18
Nadav Shaibe, Jared M. Erb, Steven M. Anlage
Vortex singularities and their speckle patterns in random superpositions of waves are an inevitable consequence of destructive interference and are consequently generic and ubiquitous. Singularities are topologically stable, they persist under small perturbations and can only be removed via pairwise annihilation. They have found application in optical, acoustic, elastic, and fluid wave fields, among others. We generalize the concept of singularity speckle patterns to arbitrary two dimensional parameter spaces and any complex scalar function that describes wave phenomena involving complicated scattering. In scattering systems specifically, we are often concerned with singularities associated with complex zeros of the scattering matrix S. Some examples are Coherent Perfect Absorption (CPA), Reflectionless Scattering Modes (RSMs), Transmissionless Scattering Modes (TSMs), and S-matrix Exceptional Points (EPs). All scattering singularities empirically share a universal statistical property: the tail of the probability distribution function of a quantity that diverges only at a singularity has the form of a -3 power law. The heavy tail of the distribution provides an estimate for the likelihood of finding a given singularity in a generic scattering system. We use these universal statistical results to determine that homogeneous system loss is the most important parameter determining singularity density in a given parameter space. Finally, we discuss events where distinct singularities coincide in parameter space, which result in higher order singularities that have new applications. These higher order singularities are not topologically protected, and we do not find universal statistical properties for them. We support our empirical results from microwave experiments with Random Matrix Theory simulations and conclude that all results presented are found in all generic scattering systems.
2025-07-18
Albert Chen, Manas Bundele, Gaurav Ahlawat, Patrick Stetz, Zhitao Wang, Qiang Fei, Donghoon Jung, Audrey Chu, Bharadwaj Jayaraman, Ayushi Panth, Yatin Arora, Sourav Jain, Renjith Varma, Alexey Ilin, Iuliia Melnychuk, Chelsea Chueh, Joyan Sil, Xiaofeng Wang
The introduction of large language models has brought rapid progress on Text-to-SQL benchmarks, but it is not yet easy to build a working enterprise solution. In this paper, we present insights from building an internal chatbot that enables LinkedIn's product managers, engineers, and operations teams to self-serve data insights from a large, dynamic data lake. Our approach features three components. First, we construct a knowledge graph that captures up-to-date semantics by indexing database metadata, historical query logs, wikis, and code. We apply clustering to identify relevant tables for each team or product area. Second, we build a Text-to-SQL agent that retrieves and ranks context from the knowledge graph, writes a query, and automatically corrects hallucinations and syntax errors. Third, we build an interactive chatbot that supports various user intents, from data discovery to query writing to debugging, and displays responses in rich UI elements to encourage follow-up chats. Our chatbot has over 300 weekly users. Expert review shows that 53% of its responses are correct or close to correct on an internal benchmark set. Through ablation studies, we identify the most important knowledge graph and modeling components, offering a practical path for developing enterprise Text-to-SQL solutions.
AMICO galaxy clusters in KiDS-1000: cosmological sample
2025-07-18
M. Maturi, M. Radovich, L. Moscardini, G. F. Lesci, G. Castignani, F. Marulli, E. A. Puddu, M. Romanello, M. Sereno, C. Giocoli, L. Ingoglia, S. Bardelli, B. Giblin, H. Hildebrandt, S. Joudaki
Context. Galaxy clusters provide key insights into cosmic structure formation, galaxy formation and are essential for cosmological studies. Aims. We present a catalog of galaxy clusters detected in the Kilo-Degree Survey (KiDS-DR4) optimized for cosmological analyses and investigations of cluster properties. Each detection includes probabilistic membership assignments for the KiDS-DR4 galaxies within the magnitude range $153.5$. The sample is highly homogeneous across the entire survey thanks to the restrictive galaxy selection criteria we adopted. Spectroscopic data from the GAMA survey were used to calibrate the clusters photometric redshift and assess their uncertainties. We introduced algorithmic enhancements to AMICO to mitigate border effects among neighbor tiles. Quality flags are also provided for each cluster detection. The sample purity and completeness assessments have been estimated using the SinFoniA data driven approach, thus avoiding strong assumptions embedded in numerical simulations. We introduced a blinding scheme of the selection function meant to support the cosmological analyses. Results. Our cluster sample includes 321 cross-matches with the X-ray eRASS1 "primary" sample and 235 matches with the ACT-DR5 cluster sample. We derived a mass-proxy scaling relation based on intrinsic richness, $\lambda_*$, using masses from the eRASS1 catalog. Conclusions. The KiDS-DR4 cluster catalog provides a valuable data set for investigating galaxy cluster properties and contributes to cosmological studies by offering a large, well-characterized cluster sample.
A Statistician's Overview of Mechanistic-Informed Modeling
2025-07-18
Christopher K. Wikle, Joshua North, Giri Gopalan, Myungsoo Yoo
The recent success of deep neural network models with physical constraints (so-called, Physics-Informed Neural Networks, PINNs) has led to renewed interest in the incorporation of mechanistic information in predictive models. Statisticians and others have long been interested in this problem, which has led to several practical and innovative solutions dating back decades. In this overview, we focus on the problem of data-driven prediction and inference of dynamic spatio-temporal processes that include mechanistic information, such as would be available from partial differential equations, with a strong focus on the quantification of uncertainty associated with data, process, and parameters. We give a brief review of several paradigms and focus our attention on Bayesian implementations given they naturally accommodate uncertainty quantification. We then specify a general Bayesian hierarchical modeling framework for spatio-temporal data that can accommodate these mechanistic-informed process approaches. The advantage of this framework is its flexibility and generalizability. We illustrate the methodology via a simulation study in which a nonlinear Burgers' equation PDE is embedded via a Bayesian PINN.
Software correctness is ensured mathematically through formal verification, which involves the resources of generating formal requirement specifications and having an implementation that must be verified. Tools such as model-checkers and theorem provers ensure software correctness by verifying the implementation against the specification. Formal methods deployment is regularly enforced in the development of safety-critical systems e.g. aerospace, medical devices and autonomous systems. Generating these specifications from informal and ambiguous natural language requirements remains the key challenge. Our project, VERIFAI^{1}, aims to investigate automated and semi-automated approaches to bridge this gap, using techniques from Natural Language Processing (NLP), ontology-based domain modelling, artefact reuse, and large language models (LLMs). This position paper presents a preliminary synthesis of relevant literature to identify recurring challenges and prospective research directions in the generation of verifiable specifications from informal requirements.
Eigenvalue Distribution of Empirical Correlation Matrices for Multiscale Complex Systems and Application to Financial Data
2025-07-18
Luan M. T. de Moraes, Antônio M. S. Macêdo, Giovani L. Vasconcelos, Raydonal Ospina
We introduce a method for describing eigenvalue distributions of correlation matrices from multidimensional time series. Using our newly developed matrix H theory, we improve the description of eigenvalue spectra for empirical correlation matrices in multivariate financial data by considering an informational cascade modeled as a hierarchical structure akin to the Kolmogorov statistical theory of turbulence. Our approach extends the Marchenko-Pastur distribution to account for distinct characteristic scales, capturing a larger fraction of data variance, and challenging the traditional view of noise-dressed financial markets. We conjecture that the effectiveness of our method stems from the increased complexity in financial markets, reflected by new characteristic scales and the growth of computational trading. These findings not only support the turbulent market hypothesis as a source of noise but also provide a practical framework for noise reduction in empirical correlation matrices, enhancing the inference of true market correlations between assets.
In the context of gravity the Lagrangian and Hamiltonian formalisms have been developed largely independently, emphasizing renormalization and quantization, respectively. The formalisms use a different methodology to distinguish between gauge and physical degrees of freedom. In this review we analyze the connection between the Asymptotically Safe and Canonical Quantum Gravity approaches. Based on the Hamiltonian formulation, the Canonical Quantum Gravity approach inherently provides a natural framework for defining observables. This serves as the foundation for constructing the generating functional of the $n$-point correlation functions of physical degrees of freedom. By means of background-independent, non-perturbative renormalization methods well-established in the Lagrangian framework and typically employed in Asymptotic Safety, the resulting generating functional can be handled. In particular, we employ the Functional Renormalization Group to regularize the path integral and to compute the flow connecting the bare theory in the ultraviolet with the effective infrared theory. An important advantage of this approach is that it establishes an explicit, systematic relation between the quantization procedure and the systematics of quantum field theory-based renormalization group methods. More importantly, this synthesis not only bridges canonical and covariant approaches but also paves the way for a consistent and predictive quantum theory of gravity grounded in physically meaningful, gauge-invariant observables.
Early Emission from Double Detonation Type Ia Supernovae
2025-07-18
Anthony L. Piro, Ehud Nakar
A popular model for Type Ia supernovae (SNe Ia) is the detonation of a CO white dwarf (WD) that is triggered by the prior detonation of a thin surface layer of helium, known as a double detonation (DD). We explore the unique early electromagnetic signatures that are expected from collision of the CO detonation with the He detonation. The three features are (1) a shock breakout flash, (2) a stage of planar shock breakout cooling, and finally (3) shock cooling emission from the thermal energy released by the collision. The planar phase is unique to the unusual density profile of the He-detonated layer in comparison to the steep profile at a stellar edge as is usually considered for shock breakout. The shock cooling emission can be modified by recombination, and we explore these effects. All together, we expect an initial flash dominated by the planar phase of $\sim6\times10^{43}\,{\rm erg\,s^{-1}}$, which lasts ~5 s in the soft X-rays. This is followed by ~12-24 hrs of shock cooling at a luminosity of $3-10\times10^{40}\,{\rm erg\,s^{-1}}$ in the optical/UV. We discuss prospects for detection of this early DD emission with current and upcoming surveys.
We present AstroWISP: a collection of image processing tools for source extraction, background determination, point spread function/pixel response function fitting, and aperture photometry. AstroWISP is particularly well-suited for working with detectors featuring a Bayer mask (an array of microfilters applied to each detector pixel to allow color photography), such as consumer DSLR cameras. Such detectors pose significant challenges for existing tools while offering a much cheaper alternative to specialized devices. As a result, consumer DSLR cameras with Bayer masks are often underutilized for precision photometry. \astrowisp{} addresses this limitation in an effort to democratize precision photometry and support broader community participation in research. We demonstrate that our tools produce high-precision photometry from such images, enabling the use of such devices for detecting exoplanet transits. We package our tools for all major operating systems to ensure accessibility for amateur astronomers.
AMICO galaxy clusters in KiDS-1000: cosmological constraints and mass calibration from counts and weak lensing
2025-07-18
G. F. Lesci, F. Marulli, L. Moscardini, M. Maturi, M. Sereno, M. Radovich, M. Romanello, C. Giocoli, A. H. Wright, S. Bardelli, M. Bilicki, G. Castignani, H. Hildebrandt, L. Ingoglia, S. Joudaki, A. Kannawadi, E. Puddu
We present the joint modelling of weak-lensing and count measurements of the galaxy clusters detected with the AMICO code, in the fourth data release of the Kilo Degree Survey (KiDS-1000). The analysed sample comprises about 8000 clusters, covering an effective area of 839 deg$^{2}$ and extending up to a redshift of $z = 0.8$. Stacked cluster weak-lensing and count measurements have been derived in bins of redshift and intrinsic richness, $\lambda^*$. Based on self-organising maps, we reconstructed the true redshift distributions of the background galaxy samples. We accounted for the systematic uncertainties arising from impurities in the background and cluster samples, biases in the cluster $z$ and $\lambda^*$, projection effects, halo orientation and miscentring, truncation of cluster halo mass distributions, matter correlated with cluster haloes, multiplicative shear bias, baryonic matter, geometric distortions in the lensing profiles, uncertainties in the theoretical halo mass function, and super-sample covariance. We also employed a blinding strategy based on perturbing the cluster sample completeness. The improved statistics and photometry compared to the previous KiDS data release, KiDS-DR3, have led to a halving of the uncertainties on $\Omega_{\rm m}$ and $\sigma_8$, as we obtained $\Omega_{\rm m}=0.22\pm0.02$ and $\sigma_8=0.86\pm0.03$. The constraint on $S_8 \equiv \sigma_8(\Omega_{\rm m}/0.3)^{0.5}$, $S_8=0.74\pm0.03$, is in excellent agreement with recent cluster count and KiDS-1000 cosmic shear analyses, while it shows a $2.8\sigma$ tension with Planck cosmic microwave background results. The constraints on the $\log\lambda^*-\log M_{200}$ relation imply a mass precision of 8%, on average. In addition, the result on the intrinsic scatter of the $\log\lambda^*-\log M_{200}$ relation, $\sigma_{\rm intr}=0.05\pm0.02$, confirms that $\lambda^*$ is an excellent mass proxy.
Prospects for studying million-degree gas in the Milky Way halo using the forbidden optical [FeX] and [FeXIV] intersystem lines
2025-07-18
P. Richter, F. Ruenger, N. Lehner, J. C. Howk, C. Peroux, N. Libeskind, M. Steinmetz, R. de Jong
The Milky Way is surrounded by large amounts of hot gas at temperatures T>10^6 K, which represents a major baryon reservoir. We here explore the prospects of studying the hot coronal gas in Milky Way halo by analyzing the highly forbidden optical coronal lines of [FeX] and [FeXIV] in absorption against bright extragalactic background sources. We use a semi-analytic model of the Milky Way's coronal gas distribution together wih HESTIA simulations of the Local Group and observational constraints to predict the expected FeX and FeXIV column densities as well as the line shapes and strengths. We predict column densities of log N(FeX)=15.40 and log N(FeXIV)=15.23 in the Milky Way's hot halo and estimate that a minimum S/N of 50,000 (25,000) is required to detect [FeX] l6374.5 ([FeXIV] l5302.9) absorption at a 3sigma level. Using archical optical data from an original sample of 739 high resolution AGN spectra from VLT/UVES and KECK/HIRES, we generate a stacked composite spectrum to measure an upper limit for the column densities of FeX and FeXIV in the Milky Way's coronal gas. No [FeX] and [FeXIV] is detected in our composite spectrum, which achieves a maximum S/N= 1,240 near 5300 A. We derive 3sigma upper column-density limits of log N(FeX)<16.27 and log N(FeXIV)<15.85, in line with the above-mentioned predictions. While [FeX] and [FexIX] absorption is too weak to be detected with current optical data, we outline how up-coming extragalactic spectral surveys with millions of medium- to high-resolution optical spectra will provide the necessary sensitivity and spectral resolution to measure velocity-resolved [FeX] and [FeXIV] absorption in the Milky Way's coronal gas (and beyond). This gives the prospect of opening a new window for studying the dominant baryonic mass component of the Milky Way in the form of hot coronal gas via optical spectroscopy.
Comparative Analysis of Algorithms for the Fitting of Tessellations to 3D Image Data
2025-07-18
Andreas Alpers, Orkun Furat, Christian Jung, Matthias Neumann, Claudia Redenbach, Aigerim Saken, Volker Schmidt
This paper presents a comparative analysis of algorithmic strategies for fitting tessellation models to 3D image data of materials such as polycrystals and foams. In this steadily advancing field, we review and assess optimization-based methods -- including linear and nonlinear programming, stochastic optimization via the cross-entropy method, and gradient descent -- for generating Voronoi, Laguerre, and generalized balanced power diagrams (GBPDs) that approximate voxelbased grain structures. The quality of fit is evaluated on real-world datasets using discrepancy measures that quantify differences in grain volume, surface area, and topology. Our results highlight trade-offs between model complexity, the complexity of the optimization routines involved, and the quality of approximation, providing guidance for selecting appropriate methods based on data characteristics and application needs.
Hyper-spectral Unmixing algorithms for remote compositional surface mapping: a review of the state of the art
2025-07-18
Alfredo Gimenez Zapiola, Andrea Boselli, Alessandra Menafoglio, Simone Vantini
This work concerns a detailed review of data analysis methods used for remotely sensed images of large areas of the Earth and of other solid astronomical objects. In detail, it focuses on the problem of inferring the materials that cover the surfaces captured by hyper-spectral images and estimating their abundances and spatial distributions within the region. The most successful and relevant hyper-spectral unmixing methods are reported as well as compared, as an addition to analysing the most recent methodologies. The most important public data-sets in this setting, which are vastly used in the testing and validation of the former, are also systematically explored. Finally, open problems are spotlighted and concrete recommendations for future research are provided.
Dispute Resolution in Peer Review with Abstract Argumentation and OWL DL
2025-07-18
Ildar Baimuratov, Elena Lisanyuk, Dmitry Prokudin
The peer review process for scientific publications faces significant challenges due to the increasing volume of submissions and inherent reviewer biases. While artificial intelligence offers the potential to facilitate the process, it also risks perpetuating biases present in training data. This research addresses these challenges by applying formal methods from argumentation theory to support transparent and unbiased dispute resolution in peer review. Specifically, we conceptualize scientific peer review as a single mixed argumentative dispute between manuscript authors and reviewers and formalize it using abstract argumentation frameworks. We analyze the resulting peer review argumentation frameworks from semantic, graph-theoretic, and computational perspectives, showing that they are well-founded and decidable in linear time. These frameworks are then implemented using OWL DL and resolved with reasoning engines. We validate our approach by annotating a corpus of scientific peer reviews with abstract argumentation frameworks and applying a proof of concept to resolve the annotated disputes. The results demonstrate that integrating our method could enhance the quality of published work by providing a more rigorous and systematic approach to accounting reviewer arguments.
The performance of Large Language Models (LLMs) is fundamentally determined by the contextual information provided during inference. This survey introduces Context Engineering, a formal discipline that transcends simple prompt design to encompass the systematic optimization of information payloads for LLMs. We present a comprehensive taxonomy decomposing Context Engineering into its foundational components and the sophisticated implementations that integrate them into intelligent systems. We first examine the foundational components: context retrieval and generation, context processing and context management. We then explore how these components are architecturally integrated to create sophisticated system implementations: retrieval-augmented generation (RAG), memory systems and tool-integrated reasoning, and multi-agent systems. Through this systematic analysis of over 1300 research papers, our survey not only establishes a technical roadmap for the field but also reveals a critical research gap: a fundamental asymmetry exists between model capabilities. While current models, augmented by advanced context engineering, demonstrate remarkable proficiency in understanding complex contexts, they exhibit pronounced limitations in generating equally sophisticated, long-form outputs. Addressing this gap is a defining priority for future research. Ultimately, this survey provides a unified framework for both researchers and engineers advancing context-aware AI.
Testing halo models for constraining astrophysical feedback with multi-probe modeling: I. 3D Power spectra and mass fractions
2025-07-17
Pranjal R. S., Shivam Pandey, Dhayaa Anbajagane, Elisabeth Krause, Klaus Dolag
Upcoming Stage-IV surveys will deliver measurements of distribution of matter with unprecedented precision, demanding highly accurate theoretical models for cosmological parameter inference. A major source of modeling uncertainty lies in astrophysical processes associated with galaxy formation and evolution, which remain poorly understood. Probes such as the thermal and kinematic Sunyaev-Zel'dovich effects, X-rays, and dispersion measure from fast radio bursts offer a promising avenue for mapping the distribution and thermal properties of cosmic baryons. A unified analytical framework capable of jointly modeling these observables is essential for fully harnessing the complementary information while mitigating probe-specific systematics. In this work, we present a detailed assessment of existing analytical models, which differ in their assumptions and prescriptions for simultaneously describing the distribution of matter and baryons in the universe. Using the Magneticum hydrodynamical simulation, we test these models by jointly analyzing the 3D auto- and cross-power spectra of the matter and baryonic fields that underpin the above probes. We find that all models can reproduce the power spectra at sub-percent to few-percent accuracy, depending on the tracer combination and number of free parameters. Their ability to recover underlying halo properties, such as the evolution of gas abundance and thermodynamic profiles with halo mass, varies considerably. Our results suggest that these models require further refinement and testing for reliable interpretation of multi-wavelength datasets.
A Torus Remnant Revealed by the Infrared Echo of TDE AT2019qiz: Implications for the Missing Energy and QPE Formation
2025-07-17
Mingxin Wu, Ning Jiang, Jiazheng Zhu, Di Luo, Liming Dou, Tinggui Wang
AT2019qiz is the first standard optical tidal disruption event (TDE) with detection of X-ray quasi-periodic eruptions (QPEs), providing strong evidence for TDE-QPE association. Moreover, it belongs to the rare subset of optical TDEs with prominent infrared (IR) echoes revealed by the multi-epoch photometry from the Wide-field Infrared Survey Explorer (WISE). The IR light curve shows an early bump, followed by a steady rise until the second-to-last epoch, after which it appears to enter a plateau phase. The dust temperature decreased until the fourth epoch and remains approximately constant for the subsequent five epochs. We have fitted the last five epochs using a convex dust ring model, resulting in an inner radius $>1.2$ pc. Such a large radius greatly exceeds the inner radius of the active galactic nuclei (AGN) torus for a $10^6\,M_{\odot}$ black hole and thus could be a torus remnant with the inner part having vanished, further supporting the unified scenario of recently faded AGNs, TDEs, and QPEs. Consequently, a connection between QPEs and IR-bright TDEs is naturally expected. Moreover, the echo requires at least a peak bolometric luminosity of $(6.6, 9.5, 1.0)\times 44 \,\text{erg}\,\text{s}^{-1}$ assuming silicate, silicon carbide, and graphite dust grains, respectively, all of which are significantly higher than the peak optical blackbody luminosity. It adds to the accumulating evidence that the missing energy of TDEs may lie in the unobservable extreme UV. This work highlights the unique value of IR echoes in the study of TDEs and QPEs, and a promising prospect in the era of the Near-Earth Object (NEO) Surveyor, the successor to WISE.
2025-07-17
Huang Tang, Shuting Liu, Chenyue Kang, Xiang Wang, Xi Zhang, Kun Li, Gege Duan, Zheng Li, Boyang Hua
In the past 40 years, single-molecule techniques have been rapidly developed and widely applied in numerous fields of biology researches, offering new insights that conventional biochemical assays cannot discover. In this review, to help fully appreciate the powerfulness of single-molecule methods, we systemically summarize the various advantages of performing biochemical assays at the single-molecule level. Inspired by these examples, we propose a new single-molecule polysome profiling technique, to demonstrate that this strategy is not limited to the few special "outliers". Finally, we point out a possibility in the future of unifying different biochemical assays on the platform of single-molecule microscopy, which will reduce the cost of instrumentation and inevitably promote the applicability and adoptability of new biochemical and biophysical methods.
Multi-agent systems built on language models have shown strong performance on collaborative reasoning tasks. However, existing evaluations focus only on the correctness of the final output, overlooking how inefficient communication and poor coordination contribute to redundant reasoning and higher computational costs. We introduce GEMMAS, a graph-based evaluation framework that analyzes the internal collaboration process by modeling agent interactions as a directed acyclic graph. To capture collaboration quality, we propose two process-level metrics: Information Diversity Score (IDS) to measure semantic variation in inter-agent messages, and Unnecessary Path Ratio (UPR) to quantify redundant reasoning paths. We evaluate GEMMAS across five benchmarks and highlight results on GSM8K, where systems with only a 2.1% difference in accuracy differ by 12.8% in IDS and 80% in UPR, revealing substantial variation in internal collaboration. These findings demonstrate that outcome-only metrics are insufficient for evaluating multi-agent performance and highlight the importance of process-level diagnostics in designing more interpretable and resource-efficient collaborative AI systems.
Josephson Parametric Amplifiers (JPAs) are key components in quantum information processing due to their ability to amplify weak quantum signals with near-quantum-limited noise performance. This is essential for applications such as qubit readout, quantum sensing, and communication, where signal fidelity and coherence preservation are critical. Unlike CMOS and HEMT amplifiers used in conventional RF systems, JPAs are specifically optimized for millikelvin (mK) cryogenic environments. CMOS amplifiers offer good integration but perform poorly at ultra-low temperatures due to high noise. HEMT amplifiers provide better noise performance but are power-intensive and less suited for mK operation. JPAs, by contrast, combine low power consumption with ultra-low noise and excellent cryogenic compatibility, making them ideal for quantum systems. The first part of this study compares these RF amplifier types and explains why JPAs are preferred in cryogenic quantum applications. The second part focuses on the design and analysis of JPAs based on both single Josephson junctions and junction arrays. While single-junction JPAs utilize nonlinear inductance for amplification, they suffer from gain compression, limited dynamic range, and sensitivity to fabrication variations. To overcome these challenges, this work explores JPA designs using Josephson junction arrays. Arrays distribute the nonlinear response, enhancing power handling, linearity, impedance tunability, and coherence while reducing phase noise. Several advanced JPA architectures are proposed, simulated, and compared using quantum theory and CAD tools to assess performance trade-offs and improvements over conventional designs.
Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities
2025-07-17
Hao Sun, Mihaela van der Schaar
In the era of Large Language Models (LLMs), alignment has emerged as a fundamental yet challenging problem in the pursuit of more reliable, controllable, and capable machine intelligence. The recent success of reasoning models and conversational AI systems has underscored the critical role of reinforcement learning (RL) in enhancing these systems, driving increased research interest at the intersection of RL and LLM alignment. This paper provides a comprehensive review of recent advances in LLM alignment through the lens of inverse reinforcement learning (IRL), emphasizing the distinctions between RL techniques employed in LLM alignment and those in conventional RL tasks. In particular, we highlight the necessity of constructing neural reward models from human data and discuss the formal and practical implications of this paradigm shift. We begin by introducing fundamental concepts in RL to provide a foundation for readers unfamiliar with the field. We then examine recent advances in this research agenda, discussing key challenges and opportunities in conducting IRL for LLM alignment. Beyond methodological considerations, we explore practical aspects, including datasets, benchmarks, evaluation metrics, infrastructure, and computationally efficient training and inference techniques. Finally, we draw insights from the literature on sparse-reward RL to identify open questions and potential research directions. By synthesizing findings from diverse studies, we aim to provide a structured and critical overview of the field, highlight unresolved challenges, and outline promising future directions for improving LLM alignment through RL and IRL techniques.
2025-07-17
Kathleen Mandt, Oleksandra Ivanova, Olga Harrington Pinto, Nathan X. Roth, Darryl Z. Seligman
Small bodies are the remnant building blocks from the time when the planets formed and migrated to their current positions. Their volatile composition and relative abundances serve as time capsules for the formation conditions in the protosolar nebula. By constraining the volatile composition of Centaurs, we can fill in important gaps in understanding the history of our solar system. We review here the state of knowledge for volatiles in small bodies, processes that influence volatile composition and activity in small bodies, and future capabilities that can be leveraged to advance our understanding of volatiles in Centaurs.
Atomic vapors, manipulated and probed by light and other electromagnetic fields, constitute versatile and powerful quantum systems for sensing applications. Atoms are identical, isolatable, interfaceable, and intelligible. These features, coupled with the relative simplicity with which quantum properties can be exploited in state preparation and detection using modern laser and electro-optic tools, make atoms very attractive for sensing applications. This Perspective discusses the potential and process for realizing practical quantum sensors using atoms.
Unmodulated Visible Light Positioning: A Deep Dive into Techniques, Studies, and Future Prospects
2025-07-17
Morteza Alijani, Wout Joseph, David Plets
Visible Light Positioning (VLP) has emerged as a promising technology for next-generation indoor positioning systems (IPS), particularly within the scope of sixth-generation (6G) wireless networks. Its attractiveness stems from leveraging existing lighting infrastructures equipped with light-emitting diodes (LEDs), enabling cost-efficient deployments and achieving high-precision positioning accuracy in the centimeter-todecimeter range. However, widespread adoption of traditional VLP solutions faces significant barriers due to the increased costs and operational complexity associated with modulating LEDs, which consequently reduces illumination efficiency by lowering their radiant flux. To address these limitations, recent research has introduced the concept of unmodulated Visible Light Positioning (uVLP), which exploits Light Signals of Opportunity (LSOOP) emitted by unmodulated illumination sources such as conventional LEDs. This paradigm offers a cost-effective, lowinfrastructure alternative for indoor positioning by eliminating the need for modulation hardware and maintaining lighting efficiency. This paper delineates the fundamental principles of uVLP, provides a comparative analysis of uVLP versus conventional VLP methods, and classifies existing uVLP techniques according to receiver technologies into intensity-based methods (e.g., photodiodes, solar cells, etc.) and imaging-based methods. Additionally, we propose a comprehensive taxonomy categorizing techniques into demultiplexed and undemultiplexed approaches. Within this structured framework, we critically review current advancements in uVLP, discuss prevailing challenges, and outline promising research directions essential for developing robust, scalable, and widely deployable uVLP solutions.
(Exhaustive) Symbolic Regression and model selection by minimum description length
2025-07-17
Harry Desmond
Symbolic regression is the machine learning method for learning functions from data. After a brief overview of the symbolic regression landscape, I will describe the two main challenges that traditional algorithms face: they have an unknown (and likely significant) probability of failing to find any given good function, and they suffer from ambiguity and poorly-justified assumptions in their function-selection procedure. To address these I propose an exhaustive search and model selection by the minimum description length principle, which allows accuracy and complexity to be directly traded off by measuring each in units of information. I showcase the resulting publicly available Exhaustive Symbolic Regression algorithm on three open problems in astrophysics: the expansion history of the universe, the effective behaviour of gravity in galaxies and the potential of the inflaton field. In each case the algorithm identifies many functions superior to the literature standards. This general purpose methodology should find widespread utility in science and beyond.
History-dependent and frequency-dependent dielectric nonlinearities induced by polar nanoregions in a thin film of 0.5 ( Ba 0.7 Ca 0.3 TiO 3 ) -- 0.5 ( BaZr 0.2 Ti 0.8 O 3 )
2025-07-17
Kevin Nadaud, Guillaume Nataf, Nazir Jaber, Edgar Chaslin, Béatrice Negulescu, Jérôme Wolfman
In this article, dielectric nonlinearities in 0.5(Ba0.7 Ca0.3 TiO3 ) -- 0.5(BaZr 0.2 Ti0.8 O3 ) thin film are studied using impedance spectroscopy and harmonic measurements, as a function of the AC measuring field, at different frequencies and upon cycling. The measurements reveal that the pinching of the hysteresis loop, characterized by a phase angle of the third harmonic close to --270 deg, is stronger for low frequencies. This confirms that the pinching is induced by the presence of polar nanoregions (PNRs), whose responses are also strongly dependent on frequency. When repeating the measurement, the PNR contribution changes since the phase angle of the third-harmonic response evolves from pinched to conventional relaxor. This shows that strong changes in the PNR configuration can be induced, even for low AC fields. First-order reversal curves (FORCs) confirm the presence of a pinched hysteresis loop. When repeating the FORC measurement a second time, the distribution drastically changes and corresponds to a soft ferroelectric. The asymmetry of the Preisach plane measured using FORCs is confirmed by a proposed measurement strategy: unipolar impedance measurements.
Quantum at a Music Festival: the Impact of an Exhibit about Quantum Science and Technologies on Festival Visitors
2025-07-17
Vincent Koeman, Sanne Romp, Sanne J. W. Willems, Julia Cramer
Quantum technologies are seen as transformative, with a potential to revolutionize fields like drug discovery and machine learning. Public engagement is crucial to align these developments with societal needs and foster acceptance. This study measured the impact of an exhibit about quantum technologies at the 2024 Lowlands music festival (n = 812). Pre- and post-surveys assessed changes in attitude, concern, interest and subjective knowledge. Results showed an increase in subjective knowledge but a decrease in interest, possibly due to reduced novelty or increased perceived difficulty. These findings underscore the effectiveness of exhibits as outreach tools in informal settings and highlight the critical role of maintaining novelty and emphasizing the relevance of quantum technologies in future outreach efforts. Additionally, we emphasize the importance of assessing outreach effectiveness to ensure that objectives are successfully achieved.
Gravitational wave (GW) observations are expected to serve as a powerful and independent probe of the expansion history of the universe. By providing direct and calibration-free measurements of luminosity distances through waveform analysis, GWs provide a fundamentally different and potentially more robust approach to measuring cosmic-scale distances compared to traditional electromagnetic observations, which is known as the standard siren method. In this review, we present an overview of recent developments in GW standard siren cosmology, the latest observational results, and prospects for constraining cosmological parameters using future GW detections. We first introduce standard sirens based on how redshift information is obtained and outline the Bayesian framework used in cosmological parameter estimation. We then review the measurements on the Hubble constant from the LIGO-Virgo-KAGRA network and present the potential role of future standard siren observations in cosmological parameter estimations. A central focus of this review is the unique ability of GW observations to break cosmological parameter degeneracies inherent in the EM observations. Since the cosmological parameter degeneracy directions of GW and EM observations are quite different (roughly orthogonal in some cases), their combination can significantly improve constraints on cosmological parameters. This complementarity is expected to become one of the most critical advantages for GW standard siren cosmology. Looking forward, we highlight the importance of combining GW standard sirens with other emerging late-universe cosmological probes such as fast radio bursts, 21 cm intensity mapping, and strong gravitational lensing to forge a precise cosmological probe for exploring the late universe. Finally, we introduce the challenges and the role of machine learning in future standard siren analysis.
Data Movement Model for the Vera C. Rubin Observatory
2025-07-17
Fabio Hernandez, Mark G. Beckett, Andrew Hanushevsky, Tim Jenness, Kian-Tat Lim, Peter Love, Timothy Noble, Stephen R. Pietrowicz, Wei Yang
The sky images captured nightly by the camera on the Vera C. Rubin Observatory's telescope will be processed across facilities on three continents. Data acquisition will occur at the observatory's location on Cerro Pach\'{o}n in the Andes mountains of Chile. A first copy of the raw image data set is stored at the summit and immediately transmitted via dedicated network links to the archive center within the US Data Facility at SLAC National Accelerator Laboratory in California, USA and from there to two European facilities for processing and archiving purposes. Data products resulting from periodic processing campaigns of the entire set of images collected since the beginning of the survey are made available to the scientific community in the form of data releases. In this paper we present an overall view of how we leverage the tools selected for managing the movement of data among the Rubin processing and serving facilities, including Rucio and FTS. We also present the tools we developed to integrate Rucio's data model and Rubin's Data Butler, the software abstraction layer that mediates all access to storage by pipeline tasks that implement science algorithms.
Four-component Schr\"odinger cat states, superpositions of four coherent states symmetrically arranged in phase space, offer rich nonclassical features and enhanced resilience to decoherence, making them promising resources for quantum information science. We propose a Floquet-engineered scheme to deterministically generate four-component Schr\"odinger cat states in a hybrid quantum system composed of two superconducting qubits, a microwave cavity, and a magnon mode. By applying periodic transverse drives to the qubits, we derive an effective Hamiltonian that conditionally displaces the magnon mode depending on the joint qubit state. Conditional measurements in the $\sigma_x$ basis project the magnon mode into nonclassical four-component cat states with high fidelity. The Wigner functions in the original frame verified the non-classicality of the four-component Schr\"odinger cat states. Results of systematically analyzing the impacts of qubit decoherence, magnon loss, and cavity dissipation demonstrate the robustness to the dissipation. The results show that this scheme can realize the generation of multi-component quantum superposition states in a scalable solid-state platform, providing a new approach for hybrid quantum information processing based on nonclassical magnon states.tates.
Marker-based optical motion capture (MoCap) systems are widely used to provide ground truth (GT) trajectories for benchmarking SLAM algorithms. However, the accuracy of MoCap-based GT trajectories is mainly affected by two factors: spatiotemporal calibration errors between the MoCap system and the device under test (DUT), and inherent MoCap jitter. Consequently, existing benchmarks focus primarily on absolute translation error, as accurate assessment of rotation and inter-frame errors remains challenging, hindering thorough SLAM evaluation. This paper proposes MoCap2GT, a joint optimization approach that integrates MoCap data and inertial measurement unit (IMU) measurements from the DUT for generating high-precision GT trajectories. MoCap2GT includes a robust state initializer to ensure global convergence, introduces a higher-order B-spline pose parameterization on the SE(3) manifold with variable time offset to effectively model MoCap factors, and employs a degeneracy-aware measurement rejection strategy to enhance estimation accuracy. Experimental results demonstrate that MoCap2GT outperforms existing methods and significantly contributes to precise SLAM benchmarking. The source code is available at https://anonymous.4open.science/r/mocap2gt (temporarily hosted anonymously for double-blind review).
Architectural Backdoors in Deep Learning: A Survey of Vulnerabilities, Detection, and Defense
2025-07-17
Victoria Childress, Josh Collyer, Jodie Knapp
Architectural backdoors pose an under-examined but critical threat to deep neural networks, embedding malicious logic directly into a model's computational graph. Unlike traditional data poisoning or parameter manipulation, architectural backdoors evade standard mitigation techniques and persist even after clean retraining. This survey systematically consolidates research on architectural backdoors, spanning compiler-level manipulations, tainted AutoML pipelines, and supply-chain vulnerabilities. We assess emerging detection and defense strategies, including static graph inspection, dynamic fuzzing, and partial formal verification, and highlight their limitations against distributed or stealth triggers. Despite recent progress, scalable and practical defenses remain elusive. We conclude by outlining open challenges and proposing directions for strengthening supply-chain security, cryptographic model attestations, and next-generation benchmarks. This survey aims to guide future research toward comprehensive defenses against structural backdoor threats in deep learning systems.
Magnetoelectric multiferroics: from fundamentals to transformative applications -- a mini review
2025-07-17
Michał Wanic
Multiferroics, combining ferroelectric and magnetic orders, enable magnetoelectric (ME) coupling for advanced applications. This mini review explores single-phase and composite multiferroics, examining phenomenological, microscopic, nanostruc-tured, and quantum mechanisms driving ME effects. Phenomenological models quantify coupling coefficients, while microscopic approaches reveal spin-lattice in-teractions, including frustrated spin states and Dzyaloshinskii-Moriya contributions. Nanostructured systems, such as plasmonic skyrmion lattices and metasurfaces, en-hance ME effects for tunable birefringence and electromagnon amplification. Quan-tum heat engines utilize spin entanglement and topological protection in chiral chains and skyrmion lattices for efficient energy conversion. Applications include high-sensitivity magnetic sensors, tunable radio-frequency devices, energy-efficient MERAM, energy harvesters, quantum heat engines, and thermal diodes. Future re-search aims to optimize room-temperature ME coupling, scalability, coherence, and biocompatibility for innovations in sensing, quantum computing, and sustainable energy.
Multi-Channel Graph Neural Network for Financial Risk Prediction of NEEQ Enterprises
2025-07-17
Jianyu Zhu
With the continuous evolution of China's multi-level capital market, the National Equities Exchange and Quotations (NEEQ), also known as the "New Third Board," has become a critical financing platform for small and medium-sized enterprises (SMEs). However, due to their limited scale and financial resilience, many NEEQ-listed companies face elevated risks of financial distress. To address this issue, we propose a multi-channel deep learning framework that integrates structured financial indicators, textual disclosures, and enterprise relationship data for comprehensive financial risk prediction. Specifically, we design a Triple-Channel Graph Isomorphism Network (GIN) that processes numeric, textual, and graph-based inputs separately. These modality-specific representations are fused using an attention-based mechanism followed by a gating unit to enhance robustness and prediction accuracy. Experimental results on data from 7,731 real-world NEEQ companies demonstrate that our model significantly outperforms traditional machine learning methods and single-modality baselines in terms of AUC, Precision, Recall, and F1 Score. This work provides theoretical and practical insights into risk modeling for SMEs and offers a data-driven tool to support financial regulators and investors.
A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys
2025-07-17
Yufeng Luo, Adam D. Myers, Alex Drlica-Wagner, Dario Dematties, Salma Borchani, Frank Valdes, Arjun Dey, David Schlegel, Rongpu Zhou, DESI Legacy Imaging Surveys Team
As the data volume of astronomical imaging surveys rapidly increases, traditional methods for image anomaly detection, such as visual inspection by human experts, are becoming impractical. We introduce a machine-learning-based approach to detect poor-quality exposures in large imaging surveys, with a focus on the DECam Legacy Survey (DECaLS) in regions of low extinction (i.e., $E(B-V)<0.04$). Our semi-supervised pipeline integrates a vision transformer (ViT), trained via self-supervised learning (SSL), with a k-Nearest Neighbor (kNN) classifier. We train and validate our pipeline using a small set of labeled exposures observed by surveys with the Dark Energy Camera (DECam). A clustering-space analysis of where our pipeline places images labeled in ``good'' and ``bad'' categories suggests that our approach can efficiently and accurately determine the quality of exposures. Applied to new imaging being reduced for DECaLS Data Release 11, our pipeline identifies 780 problematic exposures, which we subsequently verify through visual inspection. Being highly efficient and adaptable, our method offers a scalable solution for quality control in other large imaging surveys.
Artificial intelligence (AI) has demonstrated significant potential in transforming healthcare through the analysis and modeling of electronic health records (EHRs). However, the inherent heterogeneity, temporal irregularity, and domain-specific nature of EHR data present unique challenges that differ fundamentally from those in vision and natural language tasks. This survey offers a comprehensive overview of recent advancements at the intersection of deep learning, large language models (LLMs), and EHR modeling. We introduce a unified taxonomy that spans five key design dimensions: data-centric approaches, neural architecture design, learning-focused strategies, multimodal learning, and LLM-based modeling systems. Within each dimension, we review representative methods addressing data quality enhancement, structural and temporal representation, self-supervised learning, and integration with clinical knowledge. We further highlight emerging trends such as foundation models, LLM-driven clinical agents, and EHR-to-text translation for downstream reasoning. Finally, we discuss open challenges in benchmarking, explainability, clinical alignment, and generalization across diverse clinical settings. This survey aims to provide a structured roadmap for advancing AI-driven EHR modeling and clinical decision support. For a comprehensive list of EHR-related methods, kindly refer to https://survey-on-tabular-data.github.io/.
Public Evaluation on Potential Social Impacts of Fully Autonomous Cybernetic Avatars for Physical Support in Daily-Life Environments: Large-Scale Demonstration and Survey at Avatar Land
2025-07-17
Lotfi El Hafi, Kazuma Onishi, Shoichi Hasegawa, Akira Oyama, Tomochika Ishikawa, Masashi Osada, Carl Tornberg, Ryoma Kado, Kento Murata, Saki Hashimoto, Sebastian Carrera Villalobos, Akira Taniguchi, Gustavo Alfonso Garcia Ricardez, Yoshinobu Hagiwara, Tatsuya Aoki, Kensuke Iwata, Takato Horii, Yukiko Horikawa, Takahiro Miyashita, Tadahiro Taniguchi, Hiroshi Ishiguro
Cybernetic avatars (CAs) are key components of an avatar-symbiotic society, enabling individuals to overcome physical limitations through virtual agents and robotic assistants. While semi-autonomous CAs intermittently require human teleoperation and supervision, the deployment of fully autonomous CAs remains a challenge. This study evaluates public perception and potential social impacts of fully autonomous CAs for physical support in daily life. To this end, we conducted a large-scale demonstration and survey during Avatar Land, a 19-day public event in Osaka, Japan, where fully autonomous robotic CAs, alongside semi-autonomous CAs, performed daily object retrieval tasks. Specifically, we analyzed responses from 2,285 visitors who engaged with various CAs, including a subset of 333 participants who interacted with fully autonomous CAs and shared their perceptions and concerns through a survey questionnaire. The survey results indicate interest in CAs for physical support in daily life and at work. However, concerns were raised regarding task execution reliability. In contrast, cost and human-like interaction were not dominant concerns. Project page: https://lotfielhafi.github.io/FACA-Survey/.
Transformer-based Spatial Grounding: A Comprehensive Survey
2025-07-17
Ijazul Haq, Muhammad Saqib, Yingjie Zhang
Spatial grounding, the process of associating natural language expressions with corresponding image regions, has rapidly advanced due to the introduction of transformer-based models, significantly enhancing multimodal representation and cross-modal alignment. Despite this progress, the field lacks a comprehensive synthesis of current methodologies, dataset usage, evaluation metrics, and industrial applicability. This paper presents a systematic literature review of transformer-based spatial grounding approaches from 2018 to 2025. Our analysis identifies dominant model architectures, prevalent datasets, and widely adopted evaluation metrics, alongside highlighting key methodological trends and best practices. This study provides essential insights and structured guidance for researchers and practitioners, facilitating the development of robust, reliable, and industry-ready transformer-based spatial grounding models.
Collinear Antiferromagnetic Tunnel Junctions Implemented in Van der Waals Heterostructures
2025-07-17
Wei-Min Zhao, Yi-Lun Liu, Liu Yang, Cheng Tan, Yuanjun Yang, Zhifeng Zhu, Meixia Chen, Tingting Yan, Rong Hu, James Partridge, Guopeng Wang, Mingliang Tian, Ding-Fu Shao, Lan Wang
Magnetic tunnel junctions (MTJs) are crucial components in high-performance spintronic devices. Traditional MTJs rely on ferromagnetic (FM) materials but significant improvements in speed and packing density could be enabled by exploiting antiferromagnetic (AFM) compounds instead. Here, we report all-collinear AFM tunnel junctions (AFMTJs) fabricated with van der Waals A-type AFM metal (Fe0.6Co0.4)5GeTe2 (FCGT) electrodes and nonmagnetic semiconducting WSe2 tunnel barriers. The AFMTJ heterostructure device achieves a tunneling magnetoresistance (TMR) ratio of up to 75% in response to magnetic field switching. Our results demonstrate that the TMR exclusively emerges in the AFM state of FCGT, rather than during the AFM-to-FM transition. By engineering FCGT electrodes with either even- or odd-layer configurations, volatile or non-volatile TMR could be selected, consistent with an entirely interfacial effect. TMR in the even-layer devices arose by N\'eel vector switching. In the odd-layer devices, TMR stemmed from interfacial spin-flipping. Experimental and theoretical analyses reveal a new TMR mechanism associated with interface-driven spin-polarized transport, despite the spin-independent nature of bulk FCGT. Our work demonstrates that all-collinear AFMTJs can provide comparable performance to conventional MTJs and introduces a new paradigm for AFM spintronics, in which the spin-dependent properties of AFM interfaces are harnessed.
Identification of Authoritative Nodes and Dismantling of Illicit Networks Using a Novel Metric for Measuring Strength of a Graph
2025-07-17
Kartikeya Kansal, Arunabha Sen
Dismantling criminal networks or containing epidemics or misinformation through node removal is a well-studied problem. To evaluate the effectiveness of such efforts, one must measure the strength of the network before and after node removal. Process P1 is considered more effective than P2 if the strength of the residual network after removing k nodes via P1 is smaller than that from P2. This leads to the central question: How should network strength be measured? Existing metrics rely solely on structural properties of the graph, such as connectivity. However, in real-world scenarios, particularly in law enforcement, the perception of agents regarding network strength can differ significantly from structural assessments. These perceptions are often ignored in traditional metrics. We propose a new strength metric that integrates both structural properties and human perception. Using human subject surveys, we validate our approach against existing metrics. Our metric not only aligns more closely with human judgment but also outperforms traditional methods in identifying authoritative nodes and effectively dismantling both synthetic and real-world networks.
LLM-Based Community Surveys for Operational Decision Making in Interconnected Utility Infrastructures
2025-07-17
Adaeze Okeukwu-Ogbonnaya, Rahul Amatapu, Jason Bergtold, George Amariucai
We represent interdependent infrastructure systems and communities alike with a hetero-functional graph (HFG) that encodes the dependencies between functionalities. This graph naturally imposes a partial order of functionalities that can inform the sequence of repair decisions to be made during a disaster across affected communities. However, using such technical criteria alone provides limited guidance at the point where the functionalities directly impact the communities, since these can be repaired in any order without violating the system constraints. To address this gap and improve resilience, we integrate community preferences to refine this partial order from the HFG into a total order. Our strategy involves getting the communities' opinions on their preferred sequence for repair crews to address infrastructure issues, considering potential constraints on resources. Due to the delay and cost associated with real-world survey data, we utilize a Large Language Model (LLM) as a proxy survey tool. We use the LLM to craft distinct personas representing individuals, each with varied disaster experiences. We construct diverse disaster scenarios, and each simulated persona provides input on prioritizing infrastructure repair needs across various communities. Finally, we apply learning algorithms to generate a global order based on the aggregated responses from these LLM-generated personas.
AI-Assisted Fixes to Code Review Comments at Scale
2025-07-17
Chandra Maddila, Negar Ghorbani, James Saindon, Parth Thakkar, Vijayaraghavan Murali, Rui Abreu, Jingyue Shen, Brian Zhou, Nachiappan Nagappan, Peter C. Rigby
Aim. There are 10s of thousands of code review comments each week at Meta. We developed Metamate for Code Review (MetaMateCR) that provides AI-assisted fixes for reviewer comments in production at scale. Method. We developed an internal benchmark of 64k data points to fine-tune Llama models. Once our models achieve reasonable offline results, we roll them into production. To ensure that our AI-assisted fixes do not negatively impact the time it takes to do code reviews, we conduct randomized controlled safety trials as well as full production experiments. Offline Results. As a baseline, we compare GPT-4o to our small and large Llama models. In offline results, our LargeLSFT model creates an exact match patch 68% of the time outperforming GPT-4o by 9 percentage points (pp). The internal models also use more modern Hack functions when compared to the PHP functions suggested by GPT-4o. Safety Trial. When we roll MetaMateCR into production in a safety trial that compares no AI patches with AI patch suggestions, we see a large regression with reviewers taking over 5% longer to conduct reviews. After investigation, we modify the UX to only show authors the AI patches, and see no regressions in the time for reviews. Production. When we roll LargeLSFT into production, we see an ActionableToApplied rate of 19.7%, which is a 9.2pp improvement over GPT-4o. Our results illustrate the importance of safety trials in ensuring that AI does not inadvertently slow down engineers, and a successful review comment to AI patch product running at scale.
ALMA FACTS III. High-Resolution CO(2-1)/CO(1-0) Maps of Twelve Nearby Galaxies
2025-07-17
Amanda M Lee, Jin Koda, Fumi Egusa, Akihiko Hirota, Shinya Komugi, Fumiya Maeda, Tsuyoshi Sawada
We present early results from a high-resolution analysis ($\sim$100-200pc) of the CO(2-1)/CO(1-0) line ratio in twelve nearby galaxies. We use new ALMA CO(1-0) observations from the Fundamental CO(1-0) Transition Survey (FACTS), and re-imaged CO(2-1) data from PHANGS. We make empirical classifications based on the optical and molecular gas morphologies, which show clear systematic trends in the variation of $R_{21}$ as a function of galactic structure. The sample includes barred and unbarred, and flocculent galaxies. The barred spiral galaxies follow a general trend: $R_{21}$ is high in the center, low along the bar, increases at the bar ends, and then declines in the outer parts of the disk. The structure dependence suggests the importance of galactic dynamics on molecular gas evolution, and consequently on star formation, in galaxies. $R_{21}$ fluctuates in the spiral arms for both barred and unbarred galaxies. HII regions increase $R_{21}$ locally in their surrounding gas and are often associated with galactic structures. Together, $R_{21}$ varies systematically as a function of galactic structure, dynamics, and star formation activity.
Socio-Technical Smell Dynamics in Code Samples: A Multivocal Review on Emergence, Evolution, and Co-Occurrence
2025-07-17
Arthur Bueno, Bruno Cafeo, Maria Cagnin, Awdren Fontão
Code samples play a pivotal role in open-source ecosystems (OSSECO), serving as lightweight artifacts that support knowledge transfer, onboarding, and framework adoption. Despite their instructional relevance, these samples are often governed informally, with minimal review and unclear ownership, which increases their exposure to socio-technical degradation. In this context, the co-occurrence and longitudinal interplay of code smells (e.g., large classes, poor modularity) and community smells (e.g., lone contributors, fragmented communication) become particularly critical. While each type of smell has been studied in isolation, little is known about how community-level dysfunctions anticipate or exacerbate technical anomalies in code samples over time. This study investigates how code and community smells emerge, co-occur, and evolve within code samples maintained in OSSECOs. A Multivocal Literature Review protocol was applied, encompassing 30 peer-reviewed papers and 17 practitioner-oriented sources (2013-2024). Thematic synthesis was conducted to identify recurring socio-technical patterns related to smell dynamics. Nine patterns were identified, showing that community smells often precede or reinforce technical degradation in code samples. Symptoms such as "radio silence" and centralized ownership were frequently associated with persistent structural anomalies. Additionally, limited onboarding, the absence of continuous refactoring, and informal collaboration emerged as recurring conditions for smell accumulation. Conclusion: In OSSECOs, particularly within code samples, community-level dysfunctions not only correlate with but often signal maintainability decay. These findings underscore the need for socio-technical quality indicators and lightweight governance mechanisms tailored to shared instructional artifacts.
Linking Multi-Site Sex Ad Data at the Individual Level to Aid Counter-Trafficking Efforts
2025-07-17
Nickolas K. Freeman, Gregory J. Bott, Burcu B. Keskin, Jason M. Parton, James J. Cochran
The Internet facilitates sex trafficking through adult service websites (ASWs) that host online advertisements for sexual services (sex ads). Since the closure of the popular site Backpage.com, the ecosystem of ASWs has expanded to include multiple competing sites that are hosted outside US jurisdiction. Gaining intelligence for counter-trafficking efforts requires collecting, linking, and cleaning the data from multiple sites. However, high ad volumes, disparate data types, and the existence of generic and misappropriated data make this process challenging. We present an end-to-end process for linking sex ad data and filtering potentially erroneous links. Outputs of the developed process have been used to inform counter-trafficking operations that have helped identify more than 60 potential victims of sex trafficking, some of whom are getting help to transition out of the life. Our process leverages concepts and techniques from network science, information systems, and artificial intelligence to link ads across sites at the level of an individual or unique posting entity. Our approach is computationally efficient, allowing millions of ads to be processed in under an hour. A key component of our process is an edge filtering procedure that identifies and removes potentially erroneous links in a graph representation of sex ad data. A comparison of the proposed process to an existing approach shows that our process is typically more computationally efficient and yields substantial increases in the number of individuals for which we can derive actionable intelligence. The proposed process is an efficient and effective approach for transforming the high volumes of disparate data from sex ads into intelligence that can save lives. It has been refined over years of collaboration with practitioners and represents a strong foundation upon which further counter-trafficking tools can be built.
Round-Preserving Asymptotic Compression of Prior-Free Interactive Protocols
2025-07-17
Gurleen Padda, Dave Touchette
There is a close relationship between the communication complexity and information complexity of communication problems, as demonstrated by results such as Shannon's noiseless source coding theorem, and the Slepian-Wolf theorem. Here, we study this relationship in the prior-free and interactive setting, where we provide an alternate proof for the result of Braverman [SIAM Review, vol. 59, no. 4, 2017], that the amortized communication complexity of simulating a prior-free interactive communication protocol, is equal to its prior-free information cost. While this is a known result, our approach addresses the need for a more natural proof of it. We also improve on the result by achieving round preservation, and using a bounded quantity of shared randomness. We do this by showing that the communicating parties can produce a reliable estimate of the joint type, or empirical distribution, of their inputs. This estimate is then used in our protocol for the prior-free reverse Shannon theorem with side information at the receiver. These results are then generalized to the interactive setting to obtain our main result.
Domain-randomized deep learning for neuroimage analysis
2025-07-17
Malte Hoffmann
Deep learning has revolutionized neuroimage analysis by delivering unprecedented speed and accuracy. However, the narrow scope of many training datasets constrains model robustness and generalizability. This challenge is particularly acute in magnetic resonance imaging (MRI), where image appearance varies widely across pulse sequences and scanner hardware. A recent domain-randomization strategy addresses the generalization problem by training deep neural networks on synthetic images with randomized intensities and anatomical content. By generating diverse data from anatomical segmentation maps, the approach enables models to accurately process image types unseen during training, without retraining or fine-tuning. It has demonstrated effectiveness across modalities including MRI, computed tomography, positron emission tomography, and optical coherence tomography, as well as beyond neuroimaging in ultrasound, electron and fluorescence microscopy, and X-ray microtomography. This tutorial paper reviews the principles, implementation, and potential of the synthesis-driven training paradigm. It highlights key benefits, such as improved generalization and resistance to overfitting, while discussing trade-offs such as increased computational demands. Finally, the article explores practical considerations for adopting the technique, aiming to accelerate the development of generalizable tools that make deep learning more accessible to domain experts without extensive computational resources or machine learning knowledge.
Hard-Stop Synthesis for Multi-DOF Compliant Mechanisms
2025-07-17
Dean Chen, Armin Pomeroy, Brandon T. Peterson, Will Flanagan, He Kai Lim, Alexandra Stavrakis, Nelson F. SooHoo, Jonathan B. Hopkins, Tyler R. Clites
Compliant mechanisms have significant potential in precision applications due to their ability to guide motion without contact. However, an inherent vulnerability to fatigue and mechanical failure has hindered the translation of compliant mechanisms to real-world applications. This is particularly challenging in service environments where loading is complex and uncertain, and the cost of failure is high. In such cases, mechanical hard stops are critical to prevent yielding and buckling. Conventional hard-stop designs, which rely on stacking single-DOF limits, must be overly restrictive in multi-DOF space to guarantee safety in the presence of unknown loads. In this study, we present a systematic design synthesis method to guarantee overload protection in compliant mechanisms by integrating coupled multi-DOF motion limits within a single pair of compact hard-stop surfaces. Specifically, we introduce a theoretical and practical framework for optimizing the contact surface geometry to maximize the mechanisms multi-DOF working space while still ensuring that the mechanism remains within its elastic regime. We apply this synthesis method to a case study of a caged-hinge mechanism for orthopaedic implants, and provide numerical and experimental validation that the derived design offers reliable protection against fatigue, yielding, and buckling. This work establishes a foundation for precision hard-stop design in compliant systems operating under uncertain loads, which is a crucial step toward enabling the application of compliant mechanisms in real-world systems.
Lensing without mass: The matter density profile in cosmic voids from UNIONS
2025-07-17
Hunter L. Martin, Michael J. Hudson, Alex Woodfinden, Lucie Baumont, Thomas de Boer, Pierre A. Burger, Jack Elvin-Poole, Sébastien Fabbro, Samuel Farrens, Sacha Guerrini, Axel Guinot, Fabian Hervas-Peters, Hendrik Hildebrandt, Martin Kilbinger, Ludovic van Waerbeke, Anna Wittje
We measure the distribution of matter contained within the emptiest regions of the Universe: cosmic voids. We use the large overlap between the Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) and voids identified in the LOWZ and CMASS catalogues of the Baryon Oscillation Spectroscopic Survey (BOSS) to constrain the excess surface mass density of voids using weak lensing. We present and validate a novel method for computing the Gaussian component of the conventional weak lensing covariance, adapted for use with void studies. We detect the stacked weak lensing void density profile at the $6.2\sigma$ level, the most significant detection of void lensing from spectroscopically-identified voids to date. We find that large and small voids have different matter density profiles, as expected from numerical studies of void profiles. This difference is significant at the $2.3\sigma$ level. Comparing the void profile to a measurement of the void-galaxy cross-correlation to test the linearity of the relationship between mass and light, we find good visual agreement between the two, and a galaxy bias factor of $2.45\pm0.36$, consistent with other works. This work represents a promising detection of the lensing effect from underdensities, with the goal of promoting its development into a competitive cosmological probe.
2025-07-17
Lina D'Aoust, Ben Coull-Neveu, Eve J. Lee, Nicolas B. Cowan
In spite of their long detection history, the origin of hot Jupiters remains to be resolved. While multiple dynamical evidence suggests high-eccentricity migration is most likely, conflicts remain when considering hot Jupiters as a population in the context of warm and cold Jupiters. Here, we turn to atmospheric signatures as an alternative mean to test the origin theory of hot Jupiters, focusing on population level trends that arise from post-formation pollution, motivated by the upcoming Ariel space mission whose goal is to deliver a uniform sample of exoplanet atmospheric constraints. We experiment with post-formation pollution by planetesimal accretion, pebble accretion, and disk-induced migration and find that an observable signature of post-formation pollution is only possible under pebble accretion in metal-heavy disks. If most hot Jupiters arrive at their present orbit by high-eccentricity migration while warm Jupiters emerge largely in situ, we expect the atmospheric water abundance of hot Jupiters to be significantly elevated compared to warm Jupiters, at a level that is easily detectable by Ariel transit survey. We provide suggestions for future comparative atmospheric characterization between hot Jupiters and wide-orbit directly imaged planets to elucidate the properties of the dust substructures in protoplanetary disks.
Studying the gaseous outskirts of galaxy groups with coherent Ly alpha absorption patterns
2025-07-17
P. Richter, J. C. Charlton, A. J. Fox, Sameer, B. P. Wakker
In this study, we explore the properties of diffuse intergalactic gas residing in the outskirts of the four nearby, low-mass galaxy groups NGC1052, NGC5866, NGC4631, and NGC3992 (all at cz<2000 km/s) beyond their group virial radii. Using archival ultraviolet absorption spectra of bright AGN observed with HST/COS, we search for HI Ly a absorption near the groups' recession velocities along 35 sightlines that pass the outer group medium (OGrM) at normalized impact parameters rho/Rvir=1-3. We derive HI column densities of the absorbers and constrain the physical conditions in the gas by using a hydrostatic toy model of the groups' gas environment and assuming photoionization. HI Ly absorption near the groups' recession velocities is detected along 19 sightlines with HI column densities in the range log N(HI)=12.50-14.34, implying a high OGrM detection rate of more than 50 percent. We transform this value into an incidence rate of OGrM absorbers per unit redshift of dN/dz=232pm58.This is 25 percent above the value derived for the general population of Ly a absorbers within z = 0 filaments and more than twice the value for the z = 0 Ly a forest. From the modeling, we obtain lower limits for the gas densities from log n_H=-5.00 to -3.72, comparable to densities found in the overall Ly a forest. Our study unveils a large cross section and overdensity of Ly a absorbers in the outskirts of these four nearby groups. Such an overdensity is in line with a previously proposed scenario, in which AGN feedback lifts gaseous material to large distances beyond the virial radius of groups into the OGrM. However, a larger survey of OGrM absorbers and a comparison with hydrodynamical simulations will be necessary to constrain the cosmological mass density of OGrM absorbers and pinpoint their role in cosmological structure formation and galaxy/group evolution.
NSF-DOE Vera C. Rubin Observatory Observations of Interstellar Comet 3I/ATLAS (C/2025 N1)
2025-07-17
Colin Orion Chandler, Pedro H. Bernardinelli, Mario Jurić, Devanshi Singh, Henry H. Hsieh, Ian Sullivan, R. Lynne Jones, Jacob A. Kurlander, Dmitrii Vavilov, Siegfried Eggl, Matthew Holman, Federica Spoto, Megan E. Schwamb, Eric J. Christensen, Wilson Beebe, Aaron Roodman, Kian-Tat Lim, Tim Jenness, James Bosch, Brianna Smart, Eric Bellm, Sean MacBride, Meredith L. Rawls, Sarah Greenstreet, Colin Slater, Aren Heinze, Željko Ivezić, Bob Blum, Andrew Connolly, Gregory Daues, Rahil Makadia, Michelle Gower, J. Bryce Kalmbach, David Monet, Michele T. Bannister, Luke Dones, Rosemary C. Dorsey, Wesley C. Fraser, John C. Forbes, Cesar Fuentes, Carrie E. Holt, Laura Inno, Geraint H. Jones, Matthew M. Knight, Chris J. Lintott, Tim Lister, Robert Lupton, Mark Jesus Mendoza Magbanua, Renu Malhotra, Beatrice E. A. Mueller, Joseph Murtagh, Nitya Pandey, William T. Reach, Nalin H. Samarasinha, Darryl Z. Seligman, Colin Snodgrass, Michael Solontoi, Gyula M. Szabó, Ellie White, Maria Womack, Leslie A. Young, Russ Allbery, Roberto Armellin, Éric Aubourg, Chrysa Avdellidou, Farrukh Azfar, James Bauer, Keith Bechtol, Matthew Belyakov, Susan D. Benecchi, Ivano Bertini, Bryce T. Bolin, vMaitrayee Bose, Laura E. Buchanan, Alexandre Boucaud, Rodrigo C. Boufleur, Dominique Boutigny, Felipe Braga-Ribas, Daniel Calabrese, J. I. B. Camargo, Neven Caplar, Benoit Carry, Juan Pablo Carvajal, Yumi Choi, Preeti Cowan, Steve Croft, Matija Ćuk, Felipe Daruich, Guillaume Daubard, James R. A. Davenport, Tansu Daylan, Jennifer Delgado, Hadrien A. R. Devillepoix, Peter E. Doherty, Abbie Donaldson, Holger Drass, Stephanie JH Deppe, Gregory P. Dubois-Felsmann, Frossie Economou, Marielle R. Eduardo, Davide Farnocchia, Maxwell K. Frissell, Grigori Fedorets, Maryann Benny Fernandes, Marco Fulle, David W. Gerdes, Alex R. Gibbs, A. Fraser Gillan, Leanne P. Guy, Mark Hammergren, Andrew Hanushevsky, Fabio Hernandez, Daniel Hestroffer, Matthew J. Hopkins, Mikael Granvik, Simone Ieva, David H. Irving, Buell T. Jannuzi, David Jimenez, Altair Ramos Gomes-Júnior, Claire Juramy, Steven M. Kahn, Arun Kannawadi, Yijung Kang, Agnieszka Kryszczyńska, Ivan Kotov, Alec Koumjian, K. Simon Krughoff, Craig Lage, Travis J. Lange, W. Garrett Levine, Zhuofu Li, Javier Licandro, Hsing Wen Lin, Nate B. Lust, Ryan R. Lyttle, Ashish A. Mahabal, Max Mahlke, Andrés A. Plazas Malagón, Luis E. Salazar Manzano, Moniez Marc, Giuliano Margoti, Dušan Marčeta, Felipe Menanteau, Joshua Meyers, Dave Mills, Naomi Morato, Surhud More, Christopher B. Morrison, Youssef Moulane, Marco A. Muñoz-Gutiérrez, Newcomer F. M., Paul O'Connor, Drew Oldag, William J Oldroyd, William O'Mullane, Cyrielle Opitom, Dagmara Oszkiewicz, Gary L. Page, Jack Patterson, Matthew J. Payne, Julien Peloton, Chrystian Luciano Pereira, John R. Peterson, Daniel Polin, Hannah Mary Margaret Pollek, Rebekah Polen, Yongqiang Qiu, Darin Ragozzine, Jayadev Rajagopal, vWouter van Reeven, Malena Rice, Stephen T. Ridgway, Andrew S. Rivkin, James E. Robinson, Agata Rożek, Andrei Salnikov, Bruno O. Sánchez, Gal Sarid, Charles A. Schambeau, Daniel Scolnic, Rafe H. Schindler, Robert Seaman, Šebag Jacques, Richard A. Shaw, Alysha Shugart, Jonathan Sick, Amir Siraj, Michael C. Sitarz, Shahram Sobhani, Christine Soldahl, Brian Stalder, Steven Stetzler, John D. Swinbank, László Szigeti, Michael Tauraso, Adam Thornton, Luca Tonietti, David E. Trilling, Chadwick A. Trujillo, Te-Wei Tsai, Tony Tyson, Elana K. Urbach, Peter Vereš, Stelios Voutsinas, Charlotte Ward, Maxine West, Ian Wong, W. M. Wood-Vasey, Bin Yang, Quanhzi Ye, Jinshuo Zhang
We report on the observation and measurement of astrometry, photometry, morphology, and activity of the interstellar object 3I/ATLAS, also designated C/2025 N1 (ATLAS), with the NSF-DOE Vera C. Rubin Observatory. The third interstellar object, comet 3I/ATLAS, was first discovered on UT 2025 July 1. Serendipitously, the Rubin Observatory collected imaging in the area of the sky inhabited by the object during regular commissioning activities. We successfully recovered object detections from Rubin visits spanning UT 2025 June 21 (10 days before discovery) to UT 2025 July 7. Facilitated by Rubin's high resolution and large aperture, we report on the detection of cometary activity as early as June 21st, and observe it throughout. We measure the location and magnitude of the object on 37 Rubin images in r, i, and z bands, with typical precision of about 20 mas (100 mas, systematic) and about 10 mmag, respectively. We use these to derive improved orbit solutions, and to show there is no detectable photometric variability on hourly timescales. We derive a V-band absolute magnitude of H_V = (13.7 +/- 0.2) mag, and an equivalent effective nucleus radius of around (5.6 +/- 0.7) km. These data represent the earliest observations of this object by a large (8-meter class) telescope reported to date, and illustrate the type of measurements (and discoveries) Rubin's Legacy Survey of Space and Time (LSST) will begin to provide once operational later this year.
Adiabatic Cooling of Planar Motion in a Penning Trap Ion Crystal to Sub-Millikelvin Temperatures
2025-07-16
Wes Johnson, Bryce Bullock, Athreya Shankar, John Zaris, John J. Bollinger, Scott E. Parker
Two-dimensional planar ion crystals in a Penning trap are a platform for quantum information science experiments. However, the low-frequency planar modes of these crystals are not efficiently cooled by laser cooling, which can limit the utility of the drumhead modes for quantum information processing. Recently, it has been shown that nonlinear mode coupling can enhance the cooling of the low-frequency planar modes. Here, we demonstrate in numerical simulations that this coupling can be dynamically tuned by adiabatically changing the rotation frequency of the ion crystal during experiments. Furthermore, we show that this technique can, in addition, produce lower temperatures for the low-frequency planar modes via an adiabatic cooling process. This result allows cooling of the planar modes to sub-millikelvin temperatures, resulting in improved spectral resolution of the drumhead modes at experimentally relevant rotation frequencies, which is crucial for quantum information processing applications.
AutoVDC: Automated Vision Data Cleaning Using Vision-Language Models
2025-07-16
Santosh Vasa, Aditi Ramadwar, Jnana Rama Krishna Darabattula, Md Zafar Anwar, Stanislaw Antol, Andrei Vatavu, Thomas Monninger, Sihao Ding
Training of autonomous driving systems requires extensive datasets with precise annotations to attain robust performance. Human annotations suffer from imperfections, and multiple iterations are often needed to produce high-quality datasets. However, manually reviewing large datasets is laborious and expensive. In this paper, we introduce AutoVDC (Automated Vision Data Cleaning) framework and investigate the utilization of Vision-Language Models (VLMs) to automatically identify erroneous annotations in vision datasets, thereby enabling users to eliminate these errors and enhance data quality. We validate our approach using the KITTI and nuImages datasets, which contain object detection benchmarks for autonomous driving. To test the effectiveness of AutoVDC, we create dataset variants with intentionally injected erroneous annotations and observe the error detection rate of our approach. Additionally, we compare the detection rates using different VLMs and explore the impact of VLM fine-tuning on our pipeline. The results demonstrate our method's high performance in error detection and data cleaning experiments, indicating its potential to significantly improve the reliability and accuracy of large-scale production datasets in autonomous driving.
Topological quantum materials have emerged as a frontier in condensed matter physics as well as in materials science, with intriguing electronic states that are robust to perturbations. Among the diverse structural motifs, kagome, chiral, and square-net structures offer a wide range of topological phases and physical phenomena. These include Dirac and Weyl fermions, nodal-line semimetals, flat bands, van Hove singularities, charge density waves, superconductivity, nontrivial Berry phase, nonlinear electrical and thermal transports. This review explores the distinct roles of geometry, symmetry, spin-orbit coupling, and electron correlations in these three classes of materials. It also highlights how their crystallographic features give rise to unique electronic band structures, topologically protected states and different physical properties, which require high-quality-single crystals. The present discussion comprises recent experimental discoveries and identification of the synthesis routes of key materials within each framework. Finally, the review outlines the current challenges and future directions in the design and exploration of topological quantum materials.
Large language models (LLMs) have traditionally relied on static training data, limiting their knowledge to fixed snapshots. Recent advancements, however, have equipped LLMs with web browsing capabilities, enabling real time information retrieval and multi step reasoning over live web content. While prior studies have demonstrated LLMs ability to access and analyze websites, their capacity to directly retrieve and analyze social media data remains unexplored. Here, we evaluate whether web browsing LLMs can infer demographic attributes of social media users given only their usernames. Using a synthetic dataset of 48 X (Twitter) accounts and a survey dataset of 1,384 international participants, we show that these models can access social media content and predict user demographics with reasonable accuracy. Analysis of the synthetic dataset further reveals how LLMs parse and interpret social media profiles, which may introduce gender and political biases against accounts with minimal activity. While this capability holds promise for computational social science in the post API era, it also raises risks of misuse particularly in information operations and targeted advertising underscoring the need for safeguards. We recommend that LLM providers restrict this capability in public facing applications, while preserving controlled access for verified research purposes.
Using deep learning to characterize single-exposure double-line spectroscopic binaries
2025-07-16
Avraham Binnenfeld, Samuel Lilek, Rami Nasser, Raja Giryes, Shay Zucker
Distinguishing the component spectra of double-line spectroscopic binaries (SB2s) and extracting their stellar parameters is a complex and computationally intensive task that usually requires observations spanning several epochs that represent various orbital phases. This poses an especially significant challenge for large surveys such as Gaia or LAMOST, where the number of available spectra per target is often not enough for a proper spectral disentangling. We present a new approach for characterizing SB2 components from single-exposure spectroscopic observations. The proposed tool uses deep neural networks to extract the stellar parameters of the individual component spectra that comprise the single exposure, without explicitly disentangling them or extracting their radial velocities. The neural networks were trained, tested, and validated using simulated data resembling Gaia RVS spectra, which will be made available to the community in the coming Gaia data releases. We expect our tool to be useful in their analysis.
Surrogate modeling for uncertainty quantification in nonlinear dynamics
2025-07-16
S. Marelli, S. Schär, B. Sudret
Predicting the behavior of complex systems in engineering often involves significant uncertainty about operating conditions, such as external loads, environmental effects, and manufacturing variability. As a result, uncertainty quantification (UQ) has become a critical tool in modeling-based engineering, providing methods to identify, characterize, and propagate uncertainty through computational models. However, the stochastic nature of UQ typically requires numerous evaluations of these models, which can be computationally expensive and limit the scope of feasible analyses. To address this, surrogate models, i.e., efficient functional approximations trained on a limited set of simulations, have become central in modern UQ practice. This book chapter presents a concise review of surrogate modeling techniques for UQ, with a focus on the particularly challenging task of capturing the full time-dependent response of dynamical systems. It introduces a classification of time-dependent problems based on the complexity of input excitation and discusses corresponding surrogate approaches, including combinations of principal component analysis with polynomial chaos expansions, time warping techniques, and nonlinear autoregressive models with exogenous inputs (NARX models). Each method is illustrated with simple application examples to clarify the underlying ideas and practical use.
First Constraints from Marked Angular Power Spectra with Subaru Hyper Suprime-Cam Survey First-Year Data
2025-07-16
Jessica A. Cowell, Joaquin Armijo, Leander Thiele, Gabriela A. Marques, Camila P. Novaes, Daniela Grandón, Sihao Cheng, Masato Shirasaki, David Alonso, Jia Liu
We present the first application of marked angular power spectra to weak lensing data, using maps from the Subaru Hyper Suprime-Cam Year 1 (HSC-Y1) survey. Marked convergence fields, constructed by weighting the convergence field with non-linear functions of its smoothed version, are designed to encode higher-order information while remaining computationally tractable. Using simulations tailored to the HSC-Y1 data, we test three mark functions that up- or down-weight different density environments. Our results show that combining multiple types of marked auto- and cross-spectra improves constraints on the clustering amplitude parameter $S_8\equiv\sigma_8\sqrt{\Omega_{\rm m}/0.3}$ by $\approx$43\% compared to standard two-point power spectra. When applied to the HSC-Y1 data, this translates into a constraint on $S_8 = 0.807\pm 0.024$. We assess the sensitivity of the marked power spectra to systematics, including baryonic effects, intrinsic alignment, photometric redshifts, and multiplicative shear bias. These results demonstrate the promise of marked statistics as a practical and powerful tool for extracting non-Gaussian information from weak lensing surveys.
In this thesis, I studied a mathematical development to define and quantify the uncertainty inherent in classical channels. This thesis starts with the introduction and background on how to formally think about uncertainty in the domain of classical states. The concept of probability vector majorization and its variants, relative majorization and conditional majorization, are reviewed. This thesis introduces three conceptually distinct approaches to formalize the notion of uncertainty inherent in classical channels. These three approaches define the same preordering on the domain of classical channels, leading to characterizations from many perspectives. With the solid foundation of uncertainty comparison, classical channel entropy is then defined to be an additive monotone with respect to the majorization relation. The well-known entropies in the domain of classical states are uniquely extended to the domain of channels via the optimal extensions, providing not only a solid foundation but also the quantifiers of uncertainty inherent in classical channels.
International promotion patterns in the smart city literature: Exploring the role of geography in affecting local drivers and smart cities' outcomes
2025-07-16
Filippo Marchesani, Francesca Masciarelli, Andrea Bikfalvi
The rise of smart cities represents a significant trend in urban development. However, only in recent years has attention shifted toward the international promotion of these cities. Despite ongoing academic discussions on the impact of smart city development on urban environments, the global recognition of smart cities remains uncertain due to their multidisciplinary nature. To address this, we conducted a systematic literature review of articles published in top-tier peer-reviewed journals from 2008 to December 2021, offering a comprehensive analysis of the existing literature.
Next-Gen Museum Guides: Autonomous Navigation and Visitor Interaction with an Agentic Robot
2025-07-16
Luca Garello, Francesca Cocchella, Alessandra Sciutti, Manuel Catalano, Francesco Rea
Autonomous robots are increasingly being tested into public spaces to enhance user experiences, particularly in cultural and educational settings. This paper presents the design, implementation, and evaluation of the autonomous museum guide robot Alter-Ego equipped with advanced navigation and interactive capabilities. The robot leverages state-of-the-art Large Language Models (LLMs) to provide real-time, context aware question-and-answer (Q&A) interactions, allowing visitors to engage in conversations about exhibits. It also employs robust simultaneous localization and mapping (SLAM) techniques, enabling seamless navigation through museum spaces and route adaptation based on user requests. The system was tested in a real museum environment with 34 participants, combining qualitative analysis of visitor-robot conversations and quantitative analysis of pre and post interaction surveys. Results showed that the robot was generally well-received and contributed to an engaging museum experience, despite some limitations in comprehension and responsiveness. This study sheds light on HRI in cultural spaces, highlighting not only the potential of AI-driven robotics to support accessibility and knowledge acquisition, but also the current limitations and challenges of deploying such technologies in complex, real-world environments.
Quantum error correction (QEC) underpins practical fault-tolerant quantum computing (FTQC) by addressing the fragility of quantum states and mitigating decoherence-induced errors. As quantum devices scale, integrating robust QEC protocols is imperative to suppress logical error rates below threshold and ensure reliable operation, though current frameworks suffer from substantial qubit overheads and hardware inefficiencies. Design automation in the QEC flow is thus critical, enabling automated synthesis, transpilation, layout, and verification of error-corrected circuits to reduce qubit footprints and push fault-tolerance margins. This chapter presents a comprehensive treatment of design automation in QEC, structured into four main sections. The first section delves into the theoretical aspects of QEC, covering logical versus physical qubit representations, stabilizer code construction, and error syndrome extraction mechanisms. In the second section, we outline the QEC design flow, detailing the areas highlighting the need for design automation. The third section surveys recent advancements in design automation techniques, including algorithmic $T$-gate optimization, modified surface code architecture to incorporate lesser qubit overhead, and machine-learning-based decoder automation. The final section examines near-term FTQC architectures, integrating automated QEC pipelines into scalable hardware platforms and discussing end-to-end verification methodologies. Each section is complemented by case studies of recent research works, illustrating practical implementations and performance trade-offs. Collectively, this chapter aims to equip readers with a holistic understanding of design automation in QEC system design in the fault-tolerant landscape of quantum computing.
Iterative Augmentation with Summarization Refinement (IASR) Evaluation for Unstructured Survey data Modeling and Analysis
2025-07-16
Payal Bhattad, Sai Manoj Pudukotai Dinakarrao, Anju Gupta
Text data augmentation is a widely used strategy for mitigating data sparsity in natural language processing (NLP), particularly in low-resource settings where limited samples hinder effective semantic modeling. While augmentation can improve input diversity and downstream interpretability, existing techniques often lack mechanisms to ensure semantic preservation during large-scale or iterative generation, leading to redundancy and instability. This work introduces a principled evaluation framework for large language model (LLM) based text augmentation, comprising two components: (1) Scalability Analysis, which measures semantic consistency as augmentation volume increases, and (2) Iterative Augmentation with Summarization Refinement (IASR), which evaluates semantic drift across recursive paraphrasing cycles. Empirical evaluations across state-of-the-art LLMs show that GPT-3.5 Turbo achieved the best balance of semantic fidelity, diversity, and generation efficiency. Applied to a real-world topic modeling task using BERTopic with GPT-enhanced few-shot labeling, the proposed approach results in a 400% increase in topic granularity and complete elimination of topic overlaps. These findings validated the utility of the proposed frameworks for structured evaluation of LLM-based augmentation in practical NLP pipelines.
On the error correction of iterative bounded distance decoding of generalized LDPC codes
2025-07-16
David Burshtein
Consider an ensemble of regular generalized LDPC (GLDPC) codes and assume that the same component code is associated with each parity check node. To decode a GLDPC code from the ensemble, we use the bit flipping bounded distance decoding algorithm, which is an extension of the bit flipping algorithm for LDPC codes. Previous work has shown conditions, under which, for a typical code in the ensemble with blocklength sufficiently large, a positive constant fraction of worst case errors can be corrected. In this work we first show that these requirements can be relaxed for ensembles with small left degrees. While previous work on GLDPC codes has considered expander graph arguments, our analysis formulates a necessary condition that the Tanner graph needs to satisfy for a failure event and then shows that the probability of this event vanishes for a sufficiently large blocklength. We then extend the analysis to random error correction and derive a lower bound on the fraction of random errors that can be corrected asymptotically. We discuss the extension of our results to non-binary GLDPC codes and present numerical examples.
In this paper, we investigate the thermodynamic aspects of quadratic gravity in a $D$-dimensional Friedmann-Lemaitre-Robertson-Walker (FLRW) universe. First, we derive the field equations and the effective energy-momentum tensor for quadratic gravity. Then, using these equations, we obtain the generalized Misner-Sharp energy within the framework of this model. We consider the thermodynamic behavior of the apparent horizon and derive the equations of state related to the pressure, temperature, and radius of the apparent horizon. Using the thermodynamic pressure, we obtain the critical points corresponding to phase transitions. We determine the critical temperature and critical radius in terms of model parameters, including the quadratic coupling and the cosmological constant. We also examine key thermodynamic quantities, such as Wald entropy, specific heat at constant pressure, enthalpy, Gibbs free energy. By examining the behavior of these quantities, we can gain insight into the thermodynamic stability of the quadratic gravity model. In particular, we find that quadratic terms change the stability conditions and can lead to new thermodynamic behaviors compared to general relativity.
2025-07-16
Sarah V. White, Ivan Delvecchio, Nathan Adams, Ian Heywood, Imogen H. Whittam, Catherine L. Hale, Neo Namane, Rebecca A. A. Bowler, Jordan D. Collier
In this work we study a robust, $K_s$-band complete, spectroscopically-confirmed sample of 104 unobscured (Type-1) quasars within the COSMOS and XMM-LSS fields of the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) Survey, at 0.60 < $z$ < 3.41. The quasars are selected via $gJK_s$ colour-space and, with 1.3-GHz flux-densities reaching rms ~ 3.0$\mu$Jy beam$^{-1}$, we find a radio-loudness fraction of 5 per cent. Thanks to the deep, multiwavelength datasets that are available over these fields, the properties of radio-loud and radio-quiet quasars can be studied in a statistically-robust way, with the emphasis of this work being on the active-galactic-nuclei (AGN)-related and star-formation-related contributions to the total radio emission. We employ multiple star-formation-rate estimates for the analysis so that our results can be compared more-easily with others in the literature, and find that the fraction of sources that have their radio emission dominated by the AGN crucially depends on the SFR estimate that is derived from the radio luminosity. When redshift dependence is not taken into account, a larger fraction of sources is classed as having their radio emission dominated by the AGN. When redshift dependence $is$ considered, a larger fraction of our sample is tentatively classed as 'starbursts'. We also find that the fraction of (possible) starbursts increases with redshift, and provide multiple suggestions for this trend.
Expanding ML-Documentation Standards For Better Security
2025-07-16
Cara Ellen Appel
This article presents the current state of ML-security and of the documentation of ML-based systems, models and datasets in research and practice based on an extensive review of the existing literature. It shows a generally low awareness of security aspects among ML-practitioners and organizations and an often unstandardized approach to documentation, leading to overall low quality of ML-documentation. Existing standards are not regularly adopted in practice and IT-security aspects are often not included in documentation. Due to these factors, there is a clear need for improved security documentation in ML, as one step towards addressing the existing gaps in ML-security. To achieve this, we propose expanding existing documentation standards for ML-documentation to include a security section with specific security relevant information. Implementing this, a novel expanded method of documenting security requirements in ML-documentation is presented, based on the existing Model Cards and Datasheets for Datasets standards, but with the recommendation to adopt these findings in all ML-documentation.
Generative Artificial Intelligence (GAI) has rapidly emerged as a transformative force in aquaculture, enabling intelligent synthesis of multimodal data, including text, images, audio, and simulation outputs for smarter, more adaptive decision-making. As the aquaculture industry shifts toward data-driven, automation and digital integration operations under the Aquaculture 4.0 paradigm, GAI models offer novel opportunities across environmental monitoring, robotics, disease diagnostics, infrastructure planning, reporting, and market analysis. This review presents the first comprehensive synthesis of GAI applications in aquaculture, encompassing foundational architectures (e.g., diffusion models, transformers, and retrieval augmented generation), experimental systems, pilot deployments, and real-world use cases. We highlight GAI's growing role in enabling underwater perception, digital twin modeling, and autonomous planning for remotely operated vehicle (ROV) missions. We also provide an updated application taxonomy that spans sensing, control, optimization, communication, and regulatory compliance. Beyond technical capabilities, we analyze key limitations, including limited data availability, real-time performance constraints, trust and explainability, environmental costs, and regulatory uncertainty. This review positions GAI not merely as a tool but as a critical enabler of smart, resilient, and environmentally aligned aquaculture systems.
A Survey of Deep Learning for Geometry Problem Solving
2025-07-16
Jianzhe Ma, Wenxuan Wang, Qin Jin
Geometry problem solving is a key area of mathematical reasoning, which is widely involved in many important fields such as education, mathematical ability assessment of artificial intelligence, and multimodal ability assessment. In recent years, the rapid development of deep learning technology, especially the rise of multimodal large language models, has triggered a widespread research boom. This paper provides a survey of the applications of deep learning in geometry problem solving, including (i) a comprehensive summary of the relevant tasks in geometry problem solving; (ii) a thorough review of related deep learning methods; (iii) a detailed analysis of evaluation metrics and methods; and (iv) a critical discussion of the current challenges and future directions that can be explored. Our goal is to provide a comprehensive and practical reference of deep learning for geometry problem solving to promote further developments in this field. We create a continuously updated list of papers on GitHub: https://github.com/majianz/dl4gps.
Resampling strategies for imbalanced regression: a survey and empirical analysis
2025-07-16
Juscimara G. Avelino, George D. C. Cavalcanti, Rafael M. O. Cruz
Imbalanced problems can arise in different real-world situations, and to address this, certain strategies in the form of resampling or balancing algorithms are proposed. This issue has largely been studied in the context of classification, and yet, the same problem features in regression tasks, where target values are continuous. This work presents an extensive experimental study comprising various balancing and predictive models, and wich uses metrics to capture important elements for the user and to evaluate the predictive model in an imbalanced regression data context. It also proposes a taxonomy for imbalanced regression approaches based on three crucial criteria: regression model, learning process, and evaluation metrics. The study offers new insights into the use of such strategies, highlighting the advantages they bring to each model's learning process, and indicating directions for further studies. The code, data and further information related to the experiments performed herein can be found on GitHub: https://github.com/JusciAvelino/imbalancedRegression.
Towards Autonomous Riding: A Review of Perception, Planning, and Control in Intelligent Two-Wheelers
2025-07-16
Mohammed Hassanin, Mohammad Abu Alsheikh, Carlos C. N. Kuhn, Damith Herath, Dinh Thai Hoang, Ibrahim Radwan
The rapid adoption of micromobility solutions, particularly two-wheeled vehicles like e-scooters and e-bikes, has created an urgent need for reliable autonomous riding (AR) technologies. While autonomous driving (AD) systems have matured significantly, AR presents unique challenges due to the inherent instability of two-wheeled platforms, limited size, limited power, and unpredictable environments, which pose very serious concerns about road users' safety. This review provides a comprehensive analysis of AR systems by systematically examining their core components, perception, planning, and control, through the lens of AD technologies. We identify critical gaps in current AR research, including a lack of comprehensive perception systems for various AR tasks, limited industry and government support for such developments, and insufficient attention from the research community. The review analyses the gaps of AR from the perspective of AD to highlight promising research directions, such as multimodal sensor techniques for lightweight platforms and edge deep learning architectures. By synthesising insights from AD research with the specific requirements of AR, this review aims to accelerate the development of safe, efficient, and scalable autonomous riding systems for future urban mobility.
Achieving human-like dexterous robotic manipulation remains a central goal and a pivotal challenge in robotics. The development of Artificial Intelligence (AI) has allowed rapid progress in robotic manipulation. This survey summarizes the evolution of robotic manipulation from mechanical programming to embodied intelligence, alongside the transition from simple grippers to multi-fingered dexterous hands, outlining key characteristics and main challenges. Focusing on the current stage of embodied dexterous manipulation, we highlight recent advances in two critical areas: dexterous manipulation data collection (via simulation, human demonstrations, and teleoperation) and skill-learning frameworks (imitation and reinforcement learning). Then, based on the overview of the existing data collection paradigm and learning framework, three key challenges restricting the development of dexterous robotic manipulation are summarized and discussed.
Totally eclipsing contact binaries provide a unique opportunity to accurately determine mass ratios through photometric methods alone, eliminating the need for spectroscopic data. Studying low mass ratio (LMR) contact binaries is crucial for advancing our understanding of binary star evolution and the formation of rare optical transients known as red novae. We identified 143 totally eclipsing contact binaries from the Transiting Exoplanet Survey Satellite. These high-precision light curves reveal a distinct O'Connell effect, which we interpret by introducing a cool spot on the primary star. Training a neural network model that includes cool spot parameters can generate a high-precision light curve 2 orders of magnitude faster than Phoebe. Utilizing the neural network (NNnol3) model combined with the Markov Chain Monte Carlo algorithm, we rapidly derived the fundamental parameters of these systems. By leveraging the relationship between orbital period and semimajor axis using the Random Sample Consensus algorithm, we estimated their absolute parameters. Our analysis identified 96 targets with mass ratios below 0.25, all of which were not listed in any previous catalog, thus signifying the discovery of new LMR system candidates. Assuming all 143 binary systems are affected by a third light during parameter estimation, we train a neural network (NNl3) model considering the third light. Then we calculate the residuals between the mass ratio ql3 (considering the third light) and qnol3 (neglecting it). For these residuals, the 25th percentile (Q1) is 0.012, the median (Q2) is 0.026, and the 75th percentile (Q3) is 0.05.
2025-07-16
Binglu Wang, Zhengnan Ma, Dashun Wang, Brian Uzzi
This study examines a fundamental yet overlooked function of peer review: its role in exposing reviewers to new and unexpected ideas. Leveraging a natural experiment involving over half a million peer review invitations covering both accepted and rejected manuscripts, and integrating high-scale bibliographic and editorial records for 37,279 submitting authors, we find that exposure to a manuscript's core ideas significantly influences the future referencing behavior and knowledge of reviewer invitees who decline the review invite. Specifically, declining reviewer invitees who could view concise summaries of the manuscript's core ideas not only increase their citations to the manuscript itself but also demonstrate expanded breadth, depth, diversity, and prominence of citations to the submitting author's broader body of work. Overall, these results suggest peer review substantially influences the spread of scientific knowledge. Ironically, while the massive scale of peer review, entailing millions of reviews annually, often drives policy debates about its costs and burdens, our findings demonstrate that precisely because of this scale, peer review serves as a powerful yet previously unrecognized engine for idea diffusion, which is central to scientific advances and scholarly communication.
Can a multi-tracer approach improve the constraints on the turnover scale?
2025-07-16
Yan Lai, Cullan Howlett, Tamara Davis
The turnover scale of the power spectrum is related to the size of the particle horizon at the matter-radiation equality, which can be used as a standard ruler to constrain cosmological parameters. In this work, we apply a model-independent method to mock datasets to forecast constraints on the turnover scale below a redshift of 0.5, investigating for the first time with a multi-tracer approach. We find that combining the galaxy density with peculiar velocity does not improve the turnover scale constraints for current or currently planned surveys because either the cosmological volume or the effective number density of peculiar velocities is too low. However, we demonstrate that when combining the galaxy power spectrum from 4HS with the HI power spectrum from SKA1-B2, the constraints on the turnover scale improve by $\sim30\%$ compared to using only a single tracer. We demonstrate for the first time that combining DESI, 4HS, and an SKA Phase 1 Band 2 survey could achieve a $\sim5\%$ level constraint on the turnover scale and a $\sim90\%$ probability of detecting the turnover below a redshift of 0.5. Lastly, we also demonstrate that combining the DESI BGS redshift sample with the LRG, ELG, and QSO samples could break the degeneracy between $r_H$ and $\Omega_m$ and improve their constraints by $\sim25\%$ and $\sim45\%$, respectively, compared to only using the high redshift samples. The constraints on the particle horizon at the matter-radiation equality $r_H$ and the matter density $\Omega_m$ could then further improve by $\sim20\%$ and $\sim30\%$, respectively, when combining the full set of DESI redshift tracers with 4HS and SKA1-B2.
Neural networks are often benchmarked using standard datasets such as MNIST, FashionMNIST, or other variants of MNIST, which, while accessible, are limited to generic classes such as digits or clothing items. For researchers working on domain-specific tasks, such as classifying trees, food items, or other real-world objects, these data sets are insufficient and irrelevant. Additionally, creating and publishing a custom dataset can be time consuming, legally constrained, or beyond the scope of individual projects. We present MNIST-Gen, an automated, modular, and adaptive framework for generating MNIST-style image datasets tailored to user-specified categories using hierarchical semantic categorization. The system combines CLIP-based semantic understanding with reinforcement learning and human feedback to achieve intelligent categorization with minimal manual intervention. Our hierarchical approach supports complex category structures with semantic characteristics, enabling fine-grained subcategorization and multiple processing modes: individual review for maximum control, smart batch processing for large datasets, and fast batch processing for rapid creation. Inspired by category theory, MNIST-Gen models each data transformation stage as a composable morphism, enhancing clarity, modularity, and extensibility. As proof of concept, we generate and benchmark two novel datasets-\textit{Tree-MNIST} and \textit{Food-MNIST}-demonstrating MNIST-Gen's utility for producing task-specific evaluation data while achieving 85\% automatic categorization accuracy and 80\% time savings compared to manual approaches.
Scientific innovation is undergoing a paradigm shift driven by the rapid advancement of Large Language Models (LLMs). As science faces mounting challenges including information overload, disciplinary silos, and diminishing returns on conventional research methods, LLMs are emerging as powerful agents capable not only of enhancing scientific workflows but also of participating in and potentially leading the innovation process. Existing surveys mainly focus on different perspectives, phrases, and tasks in scientific research and discovery, while they have limitations in understanding the transformative potential and role differentiation of LLM. This survey proposes a comprehensive framework to categorize the evolving roles of LLMs in scientific innovation across three hierarchical levels: Evaluator, Collaborator, and Scientist. We distinguish between LLMs' contributions to structured scientific research processes and open-ended scientific discovery, thereby offering a unified taxonomy that clarifies capability boundaries, evaluation criteria, and human-AI interaction patterns at each level. Through an extensive analysis of current methodologies, benchmarks, systems, and evaluation metrics, this survey delivers an in-depth and systematic synthesis on LLM-driven scientific innovation. We present LLMs not only as tools for automating existing processes, but also as catalysts capable of reshaping the epistemological foundations of science itself. This survey offers conceptual clarity, practical guidance, and theoretical foundations for future research, while also highlighting open challenges and ethical considerations in the pursuit of increasingly autonomous AI-driven science. Resources related to this survey can be accessed on GitHub at: https://github.com/haoxuan-unt2024/llm4innovation.
Data Transformation Strategies to Remove Heterogeneity
2025-07-16
Sangbong Yoo, Jaeyoung Lee, Chanyoung Yoon, Geonyeong Son, Hyein Hong, Seongbum Seo, Soobin Yim, Chanyoung Jung, Jungsoo Park, Misuk Kim, Yun Jang
Data heterogeneity is a prevalent issue, stemming from various conflicting factors, making its utilization complex. This uncertainty, particularly resulting from disparities in data formats, frequently necessitates the involvement of experts to find resolutions. Current methodologies primarily address conflicts related to data structures and schemas, often overlooking the pivotal role played by data transformation. As the utilization of artificial intelligence (AI) continues to expand, there is a growing demand for a more streamlined data preparation process, and data transformation becomes paramount. It customizes training data to enhance AI learning efficiency and adapts input formats to suit diverse AI models. Selecting an appropriate transformation technique is paramount in preserving crucial data details. Despite the widespread integration of AI across various industries, comprehensive reviews concerning contemporary data transformation approaches are scarce. This survey explores the intricacies of data heterogeneity and its underlying sources. It systematically categorizes and presents strategies to address heterogeneity stemming from differences in data formats, shedding light on the inherent challenges associated with each strategy.
Single Conversation Methodology: A Human-Centered Protocol for AI-Assisted Software Development
2025-07-16
Salvador D. Escobedo
We propose the Single Conversation Methodology (SCM), a novel and pragmatic approach to software development using large language models (LLMs). In contrast to ad hoc interactions with generative AI, SCM emphasizes a structured and persistent development dialogue, where all stages of a project - from requirements to architecture and implementation - unfold within a single, long-context conversation. The methodology is grounded on principles of cognitive clarity, traceability, modularity, and documentation. We define its phases, best practices, and philosophical stance, while arguing that SCM offers a necessary correction to the passive reliance on LLMs prevalent in current practices. We aim to reassert the active role of the developer as architect and supervisor of the intelligent tool.
An All-sky Survey of White Dwarf Merger Remnants: Far-UV is the Key
2025-07-16
Mukremin Kilic, Pierre Bergeron, Warren R. Brown, Simon Blouin, Gracyn Jewett, Adam Moss, Patrick Dufour, Olivier Vincent
The majority of merging white dwarfs leave behind a white dwarf remnant. Hot/warm DQ white dwarfs with carbon-rich atmospheres have high masses and unusual kinematics. All evidence points to a merger origin. Here, we demonstrate that far-UV + optical photometry provides an efficient way to identify these merger remnants. We take advantage of this photometric selection to identify 167 candidates in the GALEX All-Sky Imaging Survey footprint, and provide follow-up spectroscopy. Out of the 140 with spectral classifications, we identify 75 warm DQ white dwarfs with $T_{\rm eff}>10,000$ K, nearly tripling the number of such objects known. Our sample includes 13 DAQ white dwarfs with spectra dominated by hydrogen and (weaker) carbon lines. Ten of these are new discoveries, including the hottest DAQ known to date with $T_{\rm eff}\approx23,000$ K and $M=1.31~M_{\odot}$. We provide a model atmosphere analysis of all warm DQ white dwarfs found, and present their temperature and mass distributions. The sample mean and standard deviation are $T_{\rm eff} = 14,560 \pm 1970$ K and $M=1.11 \pm 0.09~M_{\odot}$. Warm DQs are roughly twice as massive as the classical DQs found at cooler temperatures. All warm DQs are found on or near the crystallization sequence. Even though their estimated cooling ages are of order 1 Gyr, their kinematics indicate an origin in the thick disk or halo. Hence, they are likely stuck on the crystallization sequence for $\sim$10 Gyr due to significant cooling delays from distillation of neutron-rich impurities. Future all-sky far-UV surveys like UVEX have the potential to significantly expand this sample.
Pathology-Guided Virtual Staining Metric for Evaluation and Training
2025-07-16
Qiankai Wang, James E. D. Tweel, Parsin Haji Reza, Anita Layton
Virtual staining has emerged as a powerful alternative to traditional histopathological staining techniques, enabling rapid, reagent-free image transformations. However, existing evaluation methods predominantly rely on full-reference image quality assessment (FR-IQA) metrics such as structural similarity, which are originally designed for natural images and often fail to capture pathology-relevant features. Expert pathology reviews have also been used, but they are inherently subjective and time-consuming. In this study, we introduce PaPIS (Pathology-Aware Perceptual Image Similarity), a novel FR-IQA metric specifically tailored for virtual staining evaluation. PaPIS leverages deep learning-based features trained on cell morphology segmentation and incorporates Retinex-inspired feature decomposition to better reflect histological perceptual quality. Comparative experiments demonstrate that PaPIS more accurately aligns with pathology-relevant visual cues and distinguishes subtle cellular structures that traditional and existing perceptual metrics tend to overlook. Furthermore, integrating PaPIS as a guiding loss function in a virtual staining model leads to improved histological fidelity. This work highlights the critical need for pathology-aware evaluation frameworks to advance the development and clinical readiness of virtual staining technologies.
Third-Party Credit Guarantees and the Cost of Debt: Evidence from Corporate Loans
2025-07-16
Mehdi Beyhaghi
Using a comprehensive dataset collected by the Federal Reserve, I find that over one-third of corporate loans issued by US banks are fully guaranteed by legal entities separate from borrowing firms. Using an empirical strategy that accounts for time-varying firm and lender effects, I find that the existence of a third-party credit guarantee is negatively related to loan risk, loan rate, and loan delinquency. Third party credit guarantees alleviate the effect of collateral constraints in credit market. Firms (particularly smaller firms) that experience a negative shock to their asset values are less likely to use collateral and more likely to use credit guarantees in new borrowings.
Qrisp Implementation and Resource Analysis of a T-Count-Optimised Non-Restoring Quantum Square-Root Circuit
2025-07-16
Heorhi Kupryianau, Marcin Niemiec
Efficient quantum arithmetic operations are essential building blocks for complex quantum algorithms, yet few theoretical designs have been implemented in practical quantum programming frameworks. This paper presents the first complete implementation of the T-count optimized non-restoring quantum square root algorithm using the Qrisp quantum programming framework. The algorithm, originally proposed by Thapliyal et al., offers better resource efficiency compared to alternative methods, achieving reduced T-count and qubit requirements while avoiding garbage output. Our implementation validates the theoretical resource estimates, confirming a T-count of 14n-14 and T-depth of 5n+3 for n-bit inputs. The modular design approach enabled by Qrisp allows construction from reusable components including reversible adders, subtractors, and conditional logic blocks built from fundamental quantum gates. The three-stage algorithm - comprising initial subtraction, iterative conditional addition/subtraction, and remainder restoration is successfully translated from algorithmic description to executable quantum code. Experimental validation across multiple test cases confirms correctness, with the circuit producing accurate integer square roots and remainders. This work demonstrates the practical realizability of resource-optimized quantum arithmetic algorithms and establishes a foundation for implementing different arithmetic operations in modern quantum programming frameworks.
A Survey of Explainable Reinforcement Learning: Targets, Methods and Needs
2025-07-16
Léo Saulières
The success of recent Artificial Intelligence (AI) models has been accompanied by the opacity of their internal mechanisms, due notably to the use of deep neural networks. In order to understand these internal mechanisms and explain the output of these AI models, a set of methods have been proposed, grouped under the domain of eXplainable AI (XAI). This paper focuses on a sub-domain of XAI, called eXplainable Reinforcement Learning (XRL), which aims to explain the actions of an agent that has learned by reinforcement learning. We propose an intuitive taxonomy based on two questions "What" and "How". The first question focuses on the target that the method explains, while the second relates to the way the explanation is provided. We use this taxonomy to provide a state-of-the-art review of over 250 papers. In addition, we present a set of domains close to XRL, which we believe should get attention from the community. Finally, we identify some needs for the field of XRL.
Best Practices for Large-Scale, Pixel-Wise Crop Mapping and Transfer Learning Workflows
2025-07-16
Judy Long, Tao Liu, Sean Alexander Woznicki, Miljana Marković, Oskar Marko, Molly Sears
Crop mapping involves identifying and classifying crop types using spatial data, primarily derived from remote sensing imagery. This study presents the first comprehensive review of large-scale, pixel-wise crop mapping workflows, encompassing both conventional supervised methods and emerging transfer learning approaches. To identify the optimal supervised crop mapping workflows, we conducted systematic experiments, comparing six widely adopted satellite image-based preprocessing methods, alongside eleven supervised pixel-wise classification models. Additionally, we assessed the synergistic impact of varied training sample sizes and variable combinations. Moreover, we identified optimal transfer learning techniques for different magnitudes of domain shift. The evaluation of best methods was conducted across five diverse agricultural sites. Landsat 8 served as the primary satellite data source. Labels come from CDL trusted pixels and field surveys. Our findings reveal three key insights. First, fine-scale interval preprocessing paired with Transformer models consistently delivered optimal performance for both supervised and transferable workflows. RF offered rapid training and competitive performance in conventional supervised learning and direct transfer to similar domains. Second, transfer learning techniques enhanced workflow adaptability, with UDA being effective for homogeneous crop classes while fine-tuning remains robust across diverse scenarios. Finally, workflow choice depends heavily on the availability of labeled samples. With a sufficient sample size, supervised training typically delivers more accurate and generalizable results. Below a certain threshold, transfer learning that matches the level of domain shift is a viable alternative to achieve crop mapping. Repository: Best-Practices-for-Large-Scale-Pixel-Wise-Crop-Mapping-and-Transfer-Learning-Workflows
Deep Bilinear Koopman Model for Real-Time Vehicle Control in Frenet Frame
2025-07-16
Mohammad Abtahi, Farhang Motallebi Araghi, Navid Mojahed, Shima Nazari
Accurate modeling and control of autonomous vehicles remain a fundamental challenge due to the nonlinear and coupled nature of vehicle dynamics. While Koopman operator theory offers a framework for deploying powerful linear control techniques, learning a finite-dimensional invariant subspace for high-fidelity modeling continues to be an open problem. This paper presents a deep Koopman approach for modeling and control of vehicle dynamics within the curvilinear Frenet frame. The proposed framework uses a deep neural network architecture to simultaneously learn the Koopman operator and its associated invariant subspace from the data. Input-state bilinear interactions are captured by the algorithm while preserving convexity, which makes it suitable for real-time model predictive control (MPC) application. A multi-step prediction loss is utilized during training to ensure long-horizon prediction capability. To further enhance real-time trajectory tracking performance, the model is integrated with a cumulative error regulator (CER) module, which compensates for model mismatch by mitigating accumulated prediction errors. Closed-loop performance is evaluated through hardware-in-the-loop (HIL) experiments using a CarSim RT model as the target plant, with real-time validation conducted on a dSPACE SCALEXIO system. The proposed controller achieved significant reductions in tracking error relative to baseline controllers, confirming its suitability for real-time implementation in embedded autonomous vehicle systems.
ROSE: Transformer-Based Refactoring Recommendation for Architectural Smells
2025-07-16
Samal Nursapa, Anastassiya Samuilova, Alessio Bucaioni. Phuong T. Nguyen
Architectural smells such as God Class, Cyclic Dependency, and Hub-like Dependency degrade software quality and maintainability. Existing tools detect such smells but rarely suggest how to fix them. This paper explores the use of pre-trained transformer models--CodeBERT and CodeT5--for recommending suitable refactorings based on detected smells. We frame the task as a three-class classification problem and fine-tune both models on over 2 million refactoring instances mined from 11,149 open-source Java projects. CodeT5 achieves 96.9% accuracy and 95.2% F1, outperforming CodeBERT and traditional baselines. Our results show that transformer-based models can effectively bridge the gap between smell detection and actionable repair, laying the foundation for future refactoring recommendation systems. We release all code, models, and data under an open license to support reproducibility and further research.
The vanishing of the long photometric cycle in AU Monocerotis
2025-07-16
L. Celedón, R. E. Mennickent, D. Barría, J. Garcés, M. Jurković
Double periodic variables (DPVs) are a group of semi-detached interacting binaries that exhibit a long photometric cycle with an average length of $\sim33$ times the orbital period of the system. It has been proposed that this long photometric cycle originates from a modulated mass transfer rate from the donor star, which itself is driven by an internal magnetic dynamo. One of the most well-studied DPVs in the Milky Way is AU Monocerotis (AU Mon). We aim to enhance our understanding of the long photometric cycle in AU Mon by characterising its behaviour through the analysis of available photometric data from several databases and surveys. We summarise previous findings on the system and analyse its published multi-wavelength photometry from different sources, covering 46.3 years, to study the variability of its light curve. We find that the orbital period has remained constant over recent decades, but the long cycle of approximately 417 days vanished around 2010. From an O-C analysis, we conclude that the system is experiencing a change in its orbital period of no greater than $0.038\pm0.040$ s yr$^{-1}$, and thus, imposing a value of $2\times10^{-8}$ M$_\odot$ yr$^{-1}$ for $\dot{M}$ in a fully conservative mass transfer regime. A time-series analysis of the disentangling light curve in the Ic filter shows a transient periodicity of approximately 1910 days lasting at least 2000 days before it also disappears around the year 2020. An analysis of the available APASS light curves around the year 2013 shows a strong periodicity at approximately 280 days, which appears to be stronger in the Z filter. We report what is the second observation of the sudden disappearance of the long cycle in a DPV, after the Galactic DPV TYC 5353-1137-1. The disappearance of the long cycle in AU Mon is a strong constraint for current models that aim to explain the long cycle in DPVs.
When Retriever Meets Generator: A Joint Model for Code Comment Generation
2025-07-16
Tien P. T. Le, Anh M. T. Bui, Huy N. D. Pham, Alessio Bucaioni, Phuong T. Nguyen
Automatically generating concise, informative comments for source code can lighten documentation effort and accelerate program comprehension. Retrieval-augmented approaches first fetch code snippets with existing comments and then synthesize a new comment, yet retrieval and generation are typically optimized in isolation, allowing irrelevant neighbors topropagate noise downstream. To tackle the issue, we propose a novel approach named RAGSum with the aim of both effectiveness and efficiency in recommendations. RAGSum is built on top offuse retrieval and generation using a single CodeT5 backbone. We report preliminary results on a unified retrieval-generation framework built on CodeT5. A contrastive pre-training phase shapes code embeddings for nearest-neighbor search; these weights then seed end-to-end training with a composite loss that (i) rewards accurate top-k retrieval; and (ii) minimizes comment-generation error. More importantly, a lightweight self-refinement loop is deployed to polish the final output. We evaluated theframework on three cross-language benchmarks (Java, Python, C), and compared it with three well-established baselines. The results show that our approach substantially outperforms thebaselines with respect to BLEU, METEOR, and ROUTE-L. These findings indicate that tightly coupling retrieval and generationcan raise the ceiling for comment automation and motivateforthcoming replications and qualitative developer studies.
The Self-Learning Monte Carlo (SLMC) method is a Monte Carlo approach that has emerged in recent years by integrating concepts from machine learning with conventional Monte Carlo techniques. Designed to accelerate the numerical study of interacting many-body systems, SLMC significantly improves sampling efficiency by constructing an effective model -- via machine learning methods -- based on configurations generated by conventional Monte Carlo methods and then proposes global updates based on the effective model. This enhancement leads to a substantial reduction in autocorrelation time, especially near the critical region, where traditional methods typically suffer from critical slowing down and increased computation complexity. Moreover, SLMC maintains statistical accuracy by implementing a cumulative update scheme that rigorously satisfies the detailed balance condition. And more recent applications have extended the SLMC to convolutional neural networks with applications not only in condensed matter physics but also high-energy physics, quantum chemistry, and quantum simulations. The generic applicability and high computational efficiency make SLMC a powerful and scalable framework for quantum Monte Carlo simulations of strongly correlated electron systems, extending the reach of numerical investigations beyond the limitations of conventional techniques.
A Spectroscopic Search for Dormant Black Holes in Low-Metallicity Binaries
2025-07-16
Pranav Nagarajan, Kareem El-Badry, Henrique Reggiani, Casey Y. Lam, Joshua D. Simon, Johanna Müller-Horn, Rhys Seeburger, Hans-Walter Rix, Howard Isaacson, Jessica Lu, Vedant Chandra, Rene Andrae
The discovery of the massive black hole (BH) system Gaia BH3 in pre-release Gaia DR4 data suggests that wide BH binaries with luminous companions may be significantly overrepresented at low metallicities. Motivated by this finding, we have initiated a spectroscopic survey of low-metallicity stars exhibiting elevated RUWE values in Gaia DR3, using the FEROS and APF spectrographs. We identify promising BH binary candidates as objects with instantaneously measured radial velocities (RVs) that are very different from their mean RVs reported in Gaia DR3. Thus far, we have observed over 500 targets, including a nearly complete sample of stars with $\text{[Fe/H]} < -1.5$, RUWE $> 2$, and $G < 15$. Our search has yielded one promising target exhibiting slow acceleration and an RV more than 98 km s$^{-1}$ different from its DR3 mean RV, as well as dozens of other candidates with smaller RV discrepancies. We quantify the sensitivity of our search using simulations, demonstrating that it recovers at least half of the BH companions within our selection criteria. We make all the spectra and RVs from our survey publicly available and encourage further follow-up.
Projective Representations, Bogomolov Multiplier, and Their Applications in Physics
2025-07-16
Ryohei Kobayashi, Haruki Watanabe
We present a pedagogical review of projective representations of finite groups and their physical applications in quantum many-body systems. Some of our physical results are new. We begin with a self-contained introduction to projective representations, highlighting the role of group cohomology, representation theory, and classification of irreducible projective representations. We then focus on a special subset of cohomology classes, known as the Bogomolov multiplier, which consists of cocycles that are symmetric on commuting pairs but remain nontrivial in group cohomology. Such cocycles have important physical implications: they characterize (1+1)D SPT phases that cannot be detected by string order parameters and give rise, upon gauging, to distinct gapped phases with completely broken non-invertible $\mathrm{Rep}(G)$ symmetry. We construct explicit lattice models for these phases and demonstrate how they are distinguished by the fusion rules of local order parameters. We show that a pair of completely broken $\mathrm{Rep}(G)$ SSB phases host nontrivial interface modes at their domain walls. As an example, we construct a lattice model where the ground state degeneracy on a ring increases from 32 without interfaces to 56 with interfaces.
TopicImpact: Improving Customer Feedback Analysis with Opinion Units for Topic Modeling and Star-Rating Prediction
2025-07-16
Emil Häglund, Johanna Björklund
We improve the extraction of insights from customer reviews by restructuring the topic modelling pipeline to operate on opinion units - distinct statements that include relevant text excerpts and associated sentiment scores. Prior work has demonstrated that such units can be reliably extracted using large language models. The result is a heightened performance of the subsequent topic modeling, leading to coherent and interpretable topics while also capturing the sentiment associated with each topic. By correlating the topics and sentiments with business metrics, such as star ratings, we can gain insights on how specific customer concerns impact business outcomes. We present our system's implementation, use cases, and advantages over other topic modeling and classification solutions. We also evaluate its effectiveness in creating coherent topics and assess methods for integrating topic and sentiment modalities for accurate star-rating prediction.
Depth estimation is a fundamental task in 3D computer vision, crucial for applications such as 3D reconstruction, free-viewpoint rendering, robotics, autonomous driving, and AR/VR technologies. Traditional methods relying on hardware sensors like LiDAR are often limited by high costs, low resolution, and environmental sensitivity, limiting their applicability in real-world scenarios. Recent advances in vision-based methods offer a promising alternative, yet they face challenges in generalization and stability due to either the low-capacity model architectures or the reliance on domain-specific and small-scale datasets. The emergence of scaling laws and foundation models in other domains has inspired the development of "depth foundation models": deep neural networks trained on large datasets with strong zero-shot generalization capabilities. This paper surveys the evolution of deep learning architectures and paradigms for depth estimation across the monocular, stereo, multi-view, and monocular video settings. We explore the potential of these models to address existing challenges and provide a comprehensive overview of large-scale datasets that can facilitate their development. By identifying key architectures and training strategies, we aim to highlight the path towards robust depth foundation models, offering insights into their future research and applications.
Intrinsic alignment demographics for next-generation lensing: Revealing galaxy property trends with DESI Y1 direct measurements
2025-07-15
J. Siegel, J. McCullough, A. Amon, C. Lamman, N. Jeffrey, B. Joachimi, H. Hoekstra, S. Heydenreich, A. J. Ross, J. Aguilar, S. Ahlen, D. Bianchi, C. Blake, D. Brooks, F. J. Castander, T. Claybaugh, A. de la Macorra, J. DeRose, P. Doel, N. Emas, S. Ferraro, A. Font-Ribera, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, M. Ishak, S. Joudaki, R. Kehoe, D. Kirkby, T. Kisner, A. Krolewski, O. Lahav, A. Lambert, M. Landriau, L. Le Guillou, M. E. Levi, M. Manera, A. Meisner, R. Miquel, J. Moustakas, S. Nadathur, J. A. Newman, G. Niz, N. Palanque-Delabrouille, W. J. Percival, A. Porredon, F. Prada, I. Pérez-Ràfols, G. Rossi, E. Sanchez, C. Saulder, D. Schlegel, M. Schubnell, A. Semenaite, J. Silber, D. Sprayberry, Z. Sun, G. Tarlé, B. A. Weaver, R. Zhou, H. Zou
We present direct measurements of the intrinsic alignments (IA) of over 2 million spectroscopic galaxies using DESI Data Release 1 and imaging from four lensing surveys: DES, HSC, KiDS, and SDSS. In this uniquely data-rich regime, we take initial steps towards a more tailored IA modelling approach by building a library of IA measurements across colour, luminosity, stellar mass, and redshift. We map the dependence between galaxy type -- in terms of rest-frame colour, strength of the 4000 Angstrom break, and specific star formation rate -- and IA amplitude; the bluest galaxies have an alignment consistent with zero, across low ($0.05
Remote sensing change detection is vital for monitoring environmental and urban transformations but faces challenges like manual feature extraction and sensitivity to noise. Traditional methods and early deep learning models, such as convolutional neural networks (CNNs), struggle to capture long-range dependencies and global context essential for accurate change detection in complex scenes. While Transformer-based models mitigate these issues, their computational complexity limits their applicability in high-resolution remote sensing. Building upon ChangeMamba architecture, which leverages state space models for efficient global context modeling, this paper proposes precision fusion blocks to capture channel-wise temporal variations and per-pixel differences for fine-grained change detection. An enhanced decoder pipeline, incorporating lightweight channel reduction mechanisms, preserves local details with minimal computational cost. Additionally, an optimized loss function combining Cross Entropy, Dice and Lovasz objectives addresses class imbalance and boosts Intersection-over-Union (IoU). Evaluations on SYSU-CD, LEVIR-CD+, and WHU-CD datasets demonstrate superior precision, recall, F1 score, IoU, and overall accuracy compared to state-of-the-art methods, highlighting the approach's robustness for remote sensing change detection. For complete transparency, the codes and pretrained models are accessible at https://github.com/Buddhi19/MambaCD.git
2025-07-15
Tariq Mehmood, Hamza Ahmad, Muhammad Haroon Shakeel, Murtaza Taj
EEG-based brain-computer interfaces (BCIs) have shown promise in various applications, such as motor imagery and cognitive state monitoring. However, decoding visual representations from EEG signals remains a significant challenge due to their complex and noisy nature. We thus propose a novel 5-stage framework for decoding visual representations from EEG signals: (1) an EEG encoder for concept classification, (2) cross-modal alignment of EEG and text embeddings in CLIP feature space, (3) caption refinement via re-ranking, (4) weighted interpolation of concept and caption embeddings for richer semantics, and (5) image generation using a pre-trained Stable Diffusion model. We enable context-aware EEG-to-image generation through cross-modal alignment and re-ranking. Experimental results demonstrate that our method generates high-quality images aligned with visual stimuli, outperforming SOTA approaches by 13.43% in Classification Accuracy, 15.21% in Generation Accuracy and reducing Fr\'echet Inception Distance by 36.61%, indicating superior semantic alignment and image quality.
2025-07-15
Michele Starnini, Fabian Baumann, Tobias Galla, David Garcia, Gerardo Iñiguez, Márton Karsai, Jan Lorenz, Katarzyna Sznajd-Weron
Opinion dynamics, the study of how individual beliefs and collective public opinion evolve, is a fertile domain for applying statistical physics to complex social phenomena. Like physical systems, societies exhibit macroscopic regularities from localized interactions, leading to outcomes such as consensus or fragmentation. This field has grown significantly, attracting interdisciplinary methods and driven by a surge in large-scale behavioral data. This review covers its rapid progress, bridging the literature dispersion. We begin with essential concepts and definitions, encompassing the nature of opinions, microscopic and macroscopic dynamics. This foundation leads to an overview of empirical research, from lab experiments to large-scale data analysis, which informs and validates models of opinion dynamics. We then present individual-based models, categorized by their macroscopic phenomena (e.g., consensus, polarization, echo chambers) and microscopic mechanisms (e.g., homophily, assimilation). We also review social contagion phenomena, highlighting their connection to opinion dynamics. Furthermore, the review covers common analytical and computational tools, including stochastic processes, treatments, simulations, and optimization. Finally, we explore emerging frontiers, such as connecting empirical data to models and using AI agents as testbeds for novel social phenomena. By systematizing terminology and emphasizing analogies with traditional physics, this review aims to consolidate knowledge, provide a robust theoretical foundation, and shape future research in opinion dynamics.
Optimizing loading of cold cesium atoms into a hollow-core fiber using machine learning
2025-07-15
Paul Anderson, Sreesh Venuturumilli, Michal Bajcsy
Experimental multi-parameter optimization can enhance the interfacing of cold atoms with waveguides and cavities. Recent implementations of machine learning (ML) algorithms demonstrate the optimization of complex cold atom ex perimental sequences in a multi-dimensional parameter space. Here, we report on the use of ML to optimize loading of cold atoms into a hollow-core fiber. We use Gaussian process machine learning in M-LOOP, an open-source online machine learning interface, to perform this optimization. This is implemented by iteratively adjusting experimental parameters based on feedback from an atom-counting measurement of optical "bleaching". We test the effectiveness of ML, alongside a manual scan, to converge to optimal loading conditions. We survey multiple ML runs to auto matically access appreciable atom-loading conditions. In conjunction with experimental design choices, ML-assisted optimization holds promise in the implementation and maintenance of complex cold atom experiments.
Colors are omnipresent in today's world and play a vital role in how humans perceive and interact with their surroundings. However, it is challenging for computers to imitate human color perception. This paper introduces the Human Perception-Based Fuzzy Color Model, COLIBRI (Color Linguistic-Based Representation and Interpretation), designed to bridge the gap between computational color representations and human visual perception. The proposed model uses fuzzy sets and logic to create a framework for color categorization. Using a three-phase experimental approach, the study first identifies distinguishable color stimuli for hue, saturation, and intensity through preliminary experiments, followed by a large-scale human categorization survey involving more than 1000 human subjects. The resulting data are used to extract fuzzy partitions and generate membership functions that reflect real-world perceptual uncertainty. The model incorporates a mechanism for adaptation that allows refinement based on feedback and contextual changes. Comparative evaluations demonstrate the model's alignment with human perception compared to traditional color models, such as RGB, HSV, and LAB. To the best of our knowledge, no previous research has documented the construction of a model for color attribute specification based on a sample of this size or a comparable sample of the human population (n = 2496). Our findings are significant for fields such as design, artificial intelligence, marketing, and human-computer interaction, where perceptually relevant color representation is critical.
REVA: Supporting LLM-Generated Programming Feedback Validation at Scale Through User Attention-based Adaptation
2025-07-15
Xiaohang Tang, Sam Wong, Zicheng He, Yalong Yang, Yan Chen
This paper introduces REVA, a human-AI system that expedites instructor review of voluminous AI-generated programming feedback by sequencing submissions to minimize cognitive context shifts and propagating instructor-driven revisions across semantically similar instances. REVA introduces a novel approach to human-AI collaboration in educational feedback by adaptively learning from instructors' attention in the review and revision process to continuously improve the feedback validation process. REVA's usefulness and effectiveness in improving feedback quality and the overall feedback review process were evaluated through a within-subjects lab study with 12 participants.
We propose a multi-robot control paradigm to solve point-to-point navigation tasks for a team of holonomic robots with access to the full environment information. The framework invokes two processes asynchronously at high frequency: (i) a centralized, discrete, and full-horizon planner for computing collision- and deadlock-free paths rapidly, leveraging recent advances in multi-agent pathfinding (MAPF), and (ii) dynamics-aware, robot-wise optimal trajectory controllers that ensure all robots independently follow their assigned paths reliably. This hierarchical shift in planning representation from (i) discrete and coupled to (ii) continuous and decoupled domains enables the framework to maintain long-term scalable motion synthesis. As an instantiation of this idea, we present LF, which combines a fast state-of-the-art MAPF solver (LaCAM), and a robust feedback control stack (Freyja) for executing agile robot maneuvers. LF provides a robust and versatile mechanism for lifelong multi-robot navigation even under asynchronous and partial goal updates, and adapts to dynamic workspaces simply by quick replanning. We present various multirotor and ground robot demonstrations, including the deployment of 15 real multirotors with random, consecutive target updates while a person walks through the operational workspace.
Spectroscopic redshift surveys are key tools to trace the large-scale structure (LSS) of the Universe and test the $\Lambda$CDM model. However, using redshifts as distance proxies introduces distortions in the 3D galaxy distribution. If uncorrected, these distortions lead to systematic errors in LSS analyses and cosmological parameter estimation. We present a new method that combines linear theory (LT) and a neural network (NN) to mitigate redshift space distortions (RSDs). The hybrid LT+NN approach is trained and validated on dark matter halo fields from z = 1 snapshots of the Quijote N-body simulations. LT corrects large-scale distortions in the linear regime, while the NN learns quasi-linear and small-scale features. The LT correction is applied first, then the NN is trained on the resulting fields to improve accuracy across scales. The method uses a Mean Squared Error (MSE) loss and yields significant performance gains: approximately 50% improvement over LT alone and 12% over NN alone. The reconstructed fields from the LT+NN method show stronger correlations with the true real-space fields than either LT or NN separately. The hybrid method also improves clustering statistics such as halo-halo and halo-void correlations, with benefits extending to BAO scales. Compared to NN-only, it provides better suppression of spurious anisotropies on large and quasi-linear scales, as measured by the quadrupole moments of correlation functions. This work shows that combining a physically motivated dynamical model with a machine learning algorithm leverages the strengths of both approaches. The LT+NN method achieves high accuracy with modest training data and computational cost, making it a promising tool for future applications to more realistic galaxy surveys.
Human-Robot collaboration in surgery: Advances and challenges towards autonomous surgical assistants
2025-07-15
Jacinto Colan, Ana Davila, Yutaro Yamada, Yasuhisa Hasegawa
Human-robot collaboration in surgery represents a significant area of research, driven by the increasing capability of autonomous robotic systems to assist surgeons in complex procedures. This systematic review examines the advancements and persistent challenges in the development of autonomous surgical robotic assistants (ASARs), focusing specifically on scenarios where robots provide meaningful and active support to human surgeons. Adhering to the PRISMA guidelines, a comprehensive literature search was conducted across the IEEE Xplore, Scopus, and Web of Science databases, resulting in the selection of 32 studies for detailed analysis. Two primary collaborative setups were identified: teleoperation-based assistance and direct hands-on interaction. The findings reveal a growing research emphasis on ASARs, with predominant applications currently in endoscope guidance, alongside emerging progress in autonomous tool manipulation. Several key challenges hinder wider adoption, including the alignment of robotic actions with human surgeon preferences, the necessity for procedural awareness within autonomous systems, the establishment of seamless human-robot information exchange, and the complexities of skill acquisition in shared workspaces. This review synthesizes current trends, identifies critical limitations, and outlines future research directions essential to improve the reliability, safety, and effectiveness of human-robot collaboration in surgical environments.
The Kepler problem concerns a point particle in an attractive inverse square force. After a brief review of the classical and quantum versions of this problem, focused on their hidden $\text{SU}(2) \times \text{SU}(2)$ symmetry, we discuss the quantum Kepler problem for a spin-$\frac{1}{2}$ particle. We show that the Hilbert space $\mathcal{H}$ of bound states for this problem is unitarily equivalent, as a representation of $\text{SU}(2) \times \text{SU}(2)$, to the Hilbert space of solutions of the Weyl equation on the spacetime $\mathbb{R} \times S^3$. This equation describes a massless left-handed spin-$\frac{1}{2}$ particle. We then form the fermionic Fock space on $\mathcal{H}$ and show this is unitarily equivalent to the Hilbert space of a massless left-handed spin-$\frac{1}{2}$ free quantum field on $\mathbb{R} \times S^3$, again as representations of $\text{SU}(2) \times \text{SU}(2)$. By modifying the Hamiltonian of this free field theory, we obtain the well-known "Madelung rules". These give a reasonable approximation to the observed filling of subshells as we consider elements with more and more electrons, and match the rough overall structure of the periodic table.
The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology
2025-07-15
Caleb Scharf
The application of convolutional autoencoder deep learning to imaging data for planetary science and astrobiological use is briefly reviewed and explored with a focus on the need to understand algorithmic rationale, process, and results when machine learning is utilized. Successful autoencoders train to build a model that captures the features of data in a dimensionally reduced form (the latent representation) that can then be used to recreate the original input. One application is the reconstruction of incomplete or noisy data. Here a baseline, lightweight convolutional autoencoder is used to examine the utility for planetary image reconstruction or inpainting in situations where there is destructive random noise (i.e., either luminance noise with zero returned data in some image pixels, or color noise with random additive levels across pixel channels). It is shown that, in certain use cases, multi-color image reconstruction can be usefully applied even with extensive random destructive noise with 90% areal coverage and higher. This capability is discussed in the context of intentional masking to reduce data bandwidth, or situations with low-illumination levels and other factors that obscure image data (e.g., sensor degradation or atmospheric conditions). It is further suggested that for some scientific use cases the model latent space and representations have more utility than large raw imaging datasets.
From Kinetic Theory to AI: a Rediscovery of High-Dimensional Divergences and Their Properties
2025-07-15
Gennaro Auricchio, Giovanni Brigati, Paolo Giudici, Giuseppe Toscani
Selecting an appropriate divergence measure is a critical aspect of machine learning, as it directly impacts model performance. Among the most widely used, we find the Kullback-Leibler (KL) divergence, originally introduced in kinetic theory as a measure of relative entropy between probability distributions. Just as in machine learning, the ability to quantify the proximity of probability distributions plays a central role in kinetic theory. In this paper, we present a comparative review of divergence measures rooted in kinetic theory, highlighting their theoretical foundations and exploring their potential applications in machine learning and artificial intelligence.
The miniJPAS survey quasar selection V: combined algorithm
2025-07-15
Ignasi Pérez-Ràfols, L. Raul Abramo, Ginés Martínez-Solaeche, Natália V. N. Rodrigues, Matthew M. Pieri, Marina Burjalès-del-Amo, Maria Escolà-Gallinat, Montserrat Ferré-Abad, Mireia Isern-Vizoso, Jailson Alcaniz, Narciso Benitez, Silvia Bonoli, Saulo Carneiro, Javier Cenarro, David Cristóbal-Hornillos, Renato Dupke, Alessandro Ederoclite, Rosa María González Delgado, Siddhartha Gurung-Lopez, Antonio Hernán-Caballero, Carlos Hernández-Monteagudo, Carlos López-Sanjuan, Antonio Marín-Franch, Valerio Marra, Claudia Mendes de Oliveira, Mariano Moles, Laerte Sodré Jr., Keith Taylor, Jesús Varela, Héctor Vázquez Ramió
Aims. Quasar catalogues from narrow-band photometric data are used in a variety of applications, including targeting for spectroscopic follow-up, measurements of supermassive black hole masses, or Baryon Acoustic Oscillations. Here, we present the final quasar catalogue, including redshift estimates, from the miniJPAS Data Release constructed using several flavours of machine-learning algorithms. Methods. In this work, we use a machine learning algorithm to classify quasars, optimally combining the output of 8 individual algorithms. We assess the relative importance of the different classifiers. We include results from 3 different redshift estimators to also provide improved photometric redshifts. We compare our final catalogue against both simulated data and real spectroscopic data. Our main comparison metric is the $f_1$ score, which balances the catalogue purity and completeness. Results. We evaluate the performance of the combined algorithm using synthetic data. In this scenario, the combined algorithm outperforms the rest of the codes, reaching $f_1=0.88$ and $f_1=0.79$ for high- and low-z quasars (with $z\geq2.1$ and $z<2.1$, respectively) down to magnitude $r=23.5$. We further evaluate its performance against real spectroscopic data, finding different performances. We conclude that our simulated data is not realistic enough and that a new version of the mocks would improve the performance. Our redshift estimates on mocks suggest a typical uncertainty of $\sigma_{\rm NMAD} =0.11$, which, according to our results with real data, could be significantly smaller (as low as $\sigma_{\rm NMAD}=0.02$). We note that the data sample is still not large enough for a full statistical consideration.
The escaping set of an entire function consists of the points in the complex plane that tend to infinity under iteration. This set plays a central role in the dynamics of transcendental entire functions. The goal of this survey is to explain this role, to summarise some of the main results in the area, and to identify a number of open questions.
This is a survey on the Farrell-Jones Conjecture about the algebraic K- and L-theory of groups rings and its applications to algebra, geometry, group theory, and topology.
Automated Novelty Evaluation of Academic Paper: A Collaborative Approach Integrating Human and Large Language Model Knowledge
2025-07-15
Wenqing Wu, Chengzhi Zhang, Yi Zhao
Novelty is a crucial criterion in the peer review process for evaluating academic papers. Traditionally, it's judged by experts or measure by unique reference combinations. Both methods have limitations: experts have limited knowledge, and the effectiveness of the combination method is uncertain. Moreover, it's unclear if unique citations truly measure novelty. The large language model (LLM) possesses a wealth of knowledge, while human experts possess judgment abilities that the LLM does not possess. Therefore, our research integrates the knowledge and abilities of LLM and human experts to address the limitations of novelty assessment. One of the most common types of novelty in academic papers is the introduction of new methods. In this paper, we propose leveraging human knowledge and LLM to assist pretrained language models (PLMs, e.g. BERT etc.) in predicting the method novelty of papers. Specifically, we extract sentences related to the novelty of the academic paper from peer review reports and use LLM to summarize the methodology section of the academic paper, which are then used to fine-tune PLMs. In addition, we have designed a text-guided fusion module with novel Sparse-Attention to better integrate human and LLM knowledge. We compared the method we proposed with a large number of baselines. Extensive experiments demonstrate that our method achieves superior performance.
A Review of Privacy Metrics for Privacy-Preserving Synthetic Data Generation
2025-07-15
Frederik Marinus Trudslev, Matteo Lissandrini, Juan Manuel Rodriguez, Martin Bøgsted, Daniele Dell'Aglio
Privacy Preserving Synthetic Data Generation (PP-SDG) has emerged to produce synthetic datasets from personal data while maintaining privacy and utility. Differential privacy (DP) is the property of a PP-SDG mechanism that establishes how protected individuals are when sharing their sensitive data. It is however difficult to interpret the privacy loss ($\varepsilon$) expressed by DP. To make the actual risk associated with the privacy loss more transparent, multiple privacy metrics (PMs) have been proposed to assess the privacy risk of the data. These PMs are utilized in separate studies to assess newly introduced PP-SDG mechanisms. Consequently, these PMs embody the same assumptions as the PP-SDG mechanism they were made to assess. Therefore, a thorough definition of how these are calculated is necessary. In this work, we present the assumptions and mathematical formulations of 17 distinct privacy metrics.
Mapping Diffuse Radio Sources Using TUNA: A Transformer-Based Deep Learning Approach
2025-07-15
Nicoletta Sanvitale, Claudio Gheller, Franco Vazza, Annalisa Bonafede, Virginia Cuciti, Emanuele De Rubeis, Federica Govoni, Matteo Murgia, Valentina Vacca
Vision Transformers are used via a customized TransUNet architecture, which is a hybrid model combining Transformers into a U-Net backbone, to achieve precise, automated, and fast segmentation of radio astronomy data affected by calibration and imaging artifacts, addressing the identification of faint, diffuse radio sources. Trained on mock radio observations from numerical simulations, the network is applied to the LOFAR Two-meter Sky Survey data. It is then evaluated on key use cases, specifically megahalos and bridges between galaxy clusters, to assess its performance in targeting sources at different resolutions and at the sensitivity limits of the telescope. The network is capable of detecting low surface brightness radio emission without manual source subtraction or re-imaging. The results demonstrate its groundbreaking capability to identify sources that typically require reprocessing at resolutions 4-6 times lower than that of the input image, accurately capturing their morphology and ensuring detection completeness. This approach represents a significant advancement in accelerating discovery within the large datasets generated by next-generation radio telescopes.
Dr.Copilot: A Multi-Agent Prompt Optimized Assistant for Improving Patient-Doctor Communication in Romanian
2025-07-15
Andrei Niculae, Adrian Cosma, Cosmin Dumitrache, Emilian Rǎdoi
Text-based telemedicine has become increasingly common, yet the quality of medical advice in doctor-patient interactions is often judged more on how advice is communicated rather than its clinical accuracy. To address this, we introduce Dr.Copilot , a multi-agent large language model (LLM) system that supports Romanian-speaking doctors by evaluating and enhancing the presentation quality of their written responses. Rather than assessing medical correctness, Dr.Copilot provides feedback along 17 interpretable axes. The system comprises of three LLM agents with prompts automatically optimized via DSPy. Designed with low-resource Romanian data and deployed using open-weight models, it delivers real-time specific feedback to doctors within a telemedicine platform. Empirical evaluations and live deployment with 41 doctors show measurable improvements in user reviews and response quality, marking one of the first real-world deployments of LLMs in Romanian medical settings.
Temperature and Persona Shape LLM Agent Consensus With Minimal Accuracy Gains in Qualitative Coding
2025-07-15
Conrad Borchers, Bahar Shahrokhian, Francesco Balzan, Elham Tajik, Sreecharan Sankaranarayanan, Sebastian Simon
Large Language Models (LLMs) enable new possibilities for qualitative research at scale, including coding and data annotation. While multi-agent systems (MAS) can emulate human coding workflows, their benefits over single-agent coding remain poorly understood. We conducted an experimental study of how agent persona and temperature shape consensus-building and coding accuracy of dialog segments based on a codebook with 8 codes. Our open-source MAS mirrors deductive human coding through structured agent discussion and consensus arbitration. Using six open-source LLMs (with 3 to 32 billion parameters) and 18 experimental configurations, we analyze over 77,000 coding decisions against a gold-standard dataset of human-annotated transcripts from online math tutoring sessions. Temperature significantly impacted whether and when consensus was reached across all six LLMs. MAS with multiple personas (including neutral, assertive, or empathetic), significantly delayed consensus in four out of six LLMs compared to uniform personas. In three of those LLMs, higher temperatures significantly diminished the effects of multiple personas on consensus. However, neither temperature nor persona pairing lead to robust improvements in coding accuracy. Single agents matched or outperformed MAS consensus in most conditions. Only one model (OpenHermesV2:7B) and code category showed above-chance gains from MAS deliberation when temperature was 0.5 or lower and especially when the agents included at least one assertive persona. Qualitative analysis of MAS collaboration for these configurations suggests that MAS may nonetheless aid in narrowing ambiguous code applications that could improve codebooks and human-AI coding. We contribute new insight into the limits of LLM-based qualitative methods, challenging the notion that diverse MAS personas lead to better outcomes. We open-source our MAS and experimentation code.
Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis
2025-07-15
Bo Liang, He Wang
The detection of gravitational waves by the LIGO-Virgo-KAGRA collaboration has ushered in a new era of observational astronomy, emphasizing the need for rapid and detailed parameter estimation and population-level analyses. Traditional Bayesian inference methods, particularly Markov chain Monte Carlo, face significant computational challenges when dealing with the high-dimensional parameter spaces and complex noise characteristics inherent in gravitational wave data. This review examines the emerging role of simulation-based inference methods in gravitational wave astronomy, with a focus on approaches that leverage machine-learning techniques such as normalizing flows and neural posterior estimation. We provide a comprehensive overview of the theoretical foundations underlying various simulation-based inference methods, including neural posterior estimation, neural ratio estimation, neural likelihood estimation, flow matching, and consistency models. We explore the applications of these methods across diverse gravitational wave data processing scenarios, from single-source parameter estimation and overlapping signal analysis to testing general relativity and conducting population studies. Although these techniques demonstrate speed improvements over traditional methods in controlled studies, their model-dependent nature and sensitivity to prior assumptions are barriers to their widespread adoption. Their accuracy, which is similar to that of conventional methods, requires further validation across broader parameter spaces and noise conditions.
Impact of local foreground radiations on Very High Energy observations of extragalactic sources
2025-07-15
Alberto Franceschini, Giorgio Galanti
In the framework of exploiting Very High Energy (VHE) gamma-ray observations of extragalactic sources to infer constraints on the intensity of cosmological background radiations, as well as on deviations from the standard model of particles and physical interactions (like violations of Lorentz invariance and axion-like particles -- LIV and ALPs), we discuss here possible contaminant effects due to the presence of radiations fields from local sources. We specifically model and analyze the foreground radiations produced inside the host galaxy of the VHE source and the cosmic environment itself hosting the source, as well as radiations produced in the Milky Way, along the VHE source's observational line-of-sights. Our analysis shows that such contaminant foregrounds may indeed impact on observations of only the very local Active Galactic Nuclei (e.g. Centaurus A), but not significantly those in the Virgo cluster (M87) and beyond.
This paper presents a comprehensive review of the Mixture-of-Experts (MoE) architecture in large language models, highlighting its ability to significantly enhance model performance while maintaining minimal computational overhead. Through a systematic analysis spanning theoretical foundations, core architectural designs, and large language model (LLM) applications, we examine expert gating and routing mechanisms, hierarchical and sparse MoE configurations, meta-learning approaches, multimodal and multitask learning scenarios, real-world deployment cases, and recent advances and challenges in deep learning. Our analysis identifies key advantages of MoE, including superior model capacity compared to equivalent Bayesian approaches, improved task-specific performance, and the ability to scale model capacity efficiently. We also underscore the importance of ensuring expert diversity, accurate calibration, and reliable inference aggregation, as these are essential for maximizing the effectiveness of MoE architectures. Finally, this review outlines current research limitations, open challenges, and promising future directions, providing a foundation for continued innovation in MoE architecture and its applications.
The Multiple Time-Stepping Method for 3-Body Interactions in High Performance Molecular Dynamics Simulations
2025-07-15
David Martin, Samuel James Newcome, Markus Mühlhäußer, Manish Kumar Mishra, Fabio Alexander Gratl, Hans-Joachim Bungartz
Understanding the complex behavior of molecular systems is fundamental to fields such as physics, materials science, and biology. Molecular dynamics (MD) simulations are crucial tools for studying atomic-level dynamics. This work focuses on improving the efficiency of MD simulations involving two-body and three-body interactions. Traditional two-body potentials often can not fully capture the complexity of molecular systems, making the inclusion of three-body interactions important. However, these interactions are in a cubic complexity class, compared to a quadratic one for two-body interactions, and therefore are computationally expensive, even when a cutoff distance is applied. One way to improve efficiency is to use the r-RESPA multiple time-stepping algorithm to reduce the number of three-body interaction calculations. In this work, we investigate this method in the context of High Performance Computing (HPC) methods that parallelize the calculations. In particular, we investigate a communication-reducing distributed-memory parallel method from literature and present a novel shared-memory parallel cutoff method, implemented in the particle simulation library AutoPas. The results and methods are discussed, providing insights into potential advancements in MD simulation efficiency.
Equality is Far Weaker than Constant-Cost Communication
2025-07-15
Mika Göös, Nathaniel Harms, Artur Riazanov
We exhibit an $n$-bit communication problem with a constant-cost randomized protocol but which requires $n^{\Omega(1)}$ deterministic (or even non-deterministic) queries to an Equality oracle. Therefore, even constant-cost randomized protocols cannot be efficiently "derandomized" using Equality oracles. This improves on several recent results and answers a question from the survey of Hatami and Hatami (SIGACT News 2024). It also gives a significantly simpler and quantitatively superior proof of the main result of Fang, G\"o\"os, Harms, and Hatami ( STOC 2025), that constant-cost communication does not reduce to the $k$-Hamming Distance hierarchy.
In recent years, visual recognition methods have advanced significantly, finding applications across diverse fields. While researchers seek to understand the mechanisms behind the success of these models, there is also a growing impetus to deploy them in critical areas like autonomous driving and medical diagnostics to better diagnose failures, which promotes the development of interpretability research. This paper systematically reviews existing research on the interpretability of visual recognition models and proposes a taxonomy of methods from a human-centered perspective. The proposed taxonomy categorizes interpretable recognition methods based on Intent, Object, Presentation, and Methodology, thereby establishing a systematic and coherent set of grouping criteria for these XAI methods. Additionally, we summarize the requirements for evaluation metrics and explore new opportunities enabled by recent technologies, such as large multimodal models. We aim to organize existing research in this domain and inspire future investigations into the interpretability of visual recognition models.
The Rosetta Stone project. III. ALMA synthetic observations of fragmentation in high-mass star-forming clumps
2025-07-15
Alice Nucara, Alessio Traficante, Ugo Lebreuilly, Ngo-Duy Tung, Sergio Molinari, Patrick Hennebelle, Leonardo Testi, Ralf S. Klessen, Veli-Matti Pelkonen, Adam Avison, Milena Benedettini, Alessandro Coletta, Fabrizio De Angelis, Davide Elia, Gary A. Fuller, Bethany M. Jones, Seyma Mercimek, Chiara Mininni, Stefania Pezzuto, Thushara Pillai, Veronica Roccatagliata, Eugenio Schisano, Juan D. Soler, Paolo Suin, Claudia Toci, Daniel Walker
The physical mechanisms that regulate the collapse of high-mass parsec-scale clumps and allow them to form clusters of new stars represent a crucial aspect of star formation. To investigate these mechanisms, we developed the Rosetta Stone project: an end-to-end (simulations-observations) framework that is based on the systematic production of realistic synthetic observations of clump fragmentation and their comparison with real data. In this work, we compare ALMA 1.3mm continuum dust emission observations from the SQUALO survey with a new set of 24 radiative magnetohydrodynamical simulations of high-mass clump fragmentation, post-processed using the CASA software to mimic the observing strategy of SQUALO. The simulations were initialized combining typical values of clump mass (500,1000 solar masses) and radius (~0.4pc) with two levels of turbulence (Mach number of 7,10) and three levels of magnetization (mass-to-flux ratio of ~3,10,100). Following the clump evolution over time with two random seeds projected along three orthogonal directions, we produced a collection of 732 synthetic fields. The synthetic observations of clump fragmentation at ~7000AU revealed between 2 and 14 fragments per field. Among the initial conditions of the simulations, magnetic fields have the largest impact on the fragment multiplicity at these scales. In advanced stages of clump evolution, a lower number of fragments is preferentially associated with magnetized clumps. Fragments identified at ~7000AU correspond to individual or multiple sink particles in ~75% of the cases, suggesting that not all fragments are actively forming stars. Both sinks and fragments accrete mass throughout the whole clump evolution, favoring a scenario in which fragments are not isolated from the environment. Our study demonstrates the importance of synthetic observations in interpreting results from interferometric observations.
A review of perfect quantum state transfer, from one to two and three dimensional arrays of qubits
2025-07-15
Marzieh Asoudeh, Vahid Karimipour
In the light of recent advances in fabricating single layer quantum chips and a possible road toward development of multi-layer quantum chips, we review, in a detailed way, the subject of quantum state transfer with particular emphasis on perfect quantum state transfer in two and three dimensional lattices. We show how one can route an unknown quantum state from one node in a single layer of a quantum chip to another one on another layer with unit fidelity. Our method of presentation in this review allows the reader with a modest background in quantum mechanics to grasp the essential ideas and methods of this important branch of quantum information theory.
Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review
2025-07-15
Tao Han, Zahra Taheri, Hyunwoong Ko
Semiconductor manufacturing relies heavily on film deposition processes, such as Chemical Vapor Deposition and Physical Vapor Deposition. These complex processes require precise control to achieve film uniformity, proper adhesion, and desired functionality. Recent advancements in Physics-Informed Neural Networks (PINNs), an innovative machine learning (ML) approach, have shown significant promise in addressing challenges related to process control, quality assurance, and predictive modeling within semiconductor film deposition and other manufacturing domains. This paper provides a comprehensive review of ML applications targeted at semiconductor film deposition processes. Through a thematic analysis, we identify key trends, existing limitations, and research gaps, offering insights into both the advantages and constraints of current methodologies. Our structured analysis aims to highlight the potential integration of these ML techniques to enhance interpretability, accuracy, and robustness in film deposition processes. Additionally, we examine state-of-the-art PINN methods, discussing strategies for embedding physical knowledge, governing laws, and partial differential equations into advanced neural network architectures tailored for semiconductor manufacturing. Based on this detailed review, we propose novel research directions that integrate the strengths of PINNs to significantly advance film deposition processes. The contributions of this study include establishing a clear pathway for future research in integrating physics-informed ML frameworks, addressing existing methodological gaps, and ultimately improving precision, scalability, and operational efficiency within semiconductor manufacturing.
LATIS: Comparing Galaxy and IGM Tomography Maps as Tracers of Large-scale Structure and Protoclusters at $z \sim 2.5$
2025-07-15
Andrew B. Newman, Nima Chartab, Mahdi Qezlou, Gwen C. Rudie, Guillermo A. Blanc, Daniel D. Kelson, Simeon Bird, Caitlin Casey, Enrico Congiu, Olga Cucciati, Denise Hung, Brian C. Lemaux, Victoria Pérez, Jorge Zavala
We investigate the consistency of intergalactic medium (IGM) tomography and galaxy surveys as tracers of the cosmic web and protoclusters at $z \sim 2.5$. We use maps from the Ly$\alpha$ Tomography IMACS Survey (LATIS), which trace the distributions of Lyman-break galaxies (LBGs) and IGM Ly$\alpha$ absorption on $\simeq 4$ $h^{-1}$ cMpc scales within the same large volume. Overall, the joint distribution of IGM absorption and LBG density is well constrained and accurately described by a simple physical model. However, we identify several exceptional locations exhibiting strong IGM absorption indicative of a massive protocluster, yet no coincident overdensity of LBGs. As discussed by Newman et al., whose results we revise using the complete LATIS survey data, these are candidate ultraviolet (UV)-dim protoclusters that may harbor distinct galaxy populations missed by rest-UV spectroscopic surveys. We present follow-up observations targeting one such candidate embedded within Antu, an extended region of IGM absorption at $z=2.685$ that contains five IGM-selected protoclusters and has a total mass of $3 \times 10^{15}~M_{\odot}$. Ly$\alpha$ emitters trace the overall structure of Antu but avoid the center of the candidate UV-dim protocluster, which also appears to contain no submillimeter-selected sources. A near-infrared spectroscopic galaxy census is needed to determine whether this large region is dominated by galaxies with reduced or absent star-formation activity. This work adds to a growing and puzzling literature on discrepancies among different galaxy and IGM tracers, whose resolution promises to shed light on the early stages of environment-dependent galaxy evolution.
LATIS: A Sample of IGM-selected Protoclusters and Protogroups at $z \sim 2.5$
2025-07-15
Andrew B. Newman, Mahdi Qezlou, Gwen C. Rudie, Nima Chartab, Guillermo A. Blanc, Daniel D. Kelson, Simeon Bird, Caitlin Casey, Enrico Congiu, Olga Cucciati, Denise Hung, Brian C. Lemaux, Victoria Pérez, Jorge Zavala
The Ly$\alpha$ Tomography IMACS Survey (LATIS) has produced large 3D maps of the intergalactic medium (IGM), providing a new window on the cosmic web at $z\sim2.5$. A key advantage of Ly$\alpha$ tomography is that it enables the discovery of overdense regions without the need to detect their galaxy members in spectroscopic surveys, circumventing possible selection biases. We use these maps to identify 37 IGM-selected overdensities as regions of strong and spatially coherent Ly$\alpha$ absorption. Simulations indicate that 85% of these are protoclusters, defined as the progenitors of $z=0$ halos with mass $M_{\rm desc} > 10^{14} M_{\odot}$, and that nearly all of the rest are protogroups ($10^{13.5} < M_{\rm desc} / M_{\odot} < 10^{14}$). We estimate the masses and space densities of the IGM-selected overdensities and show they are in accordance with mock surveys. We investigate the LATIS counterparts of some previously reported protoclusters, including the proto-supercluster Hyperion. We identify a new component of Hyperion beyond its previously known extent. We show that the Ly$\alpha$ transmission of the galaxy density peaks within Hyperion is consistent with a simple physical model (the fluctuating Gunn-Peterson approximation), suggesting that active galactic nucleus feedback or other processes have not affected the large-scale gas ionization within this structure as whole. The LATIS catalog represents an order-of-magnitude increase in the number of IGM-selected protogroups and protoclusters and will enable new investigations of the connections between galaxies and their large-scale environments at cosmic noon.
Progress in photonic technologies for next-generation astrophotonic instruments
2025-07-15
Ahmed Shadman Alam
The study of integrating photonic devices into astronomical instruments is the primary focus of astrophotonics. The growth in this area of study is relatively recent. Research related to astronomical spectroscopic phenomena has received a lot of attention in recent times. There are several important advantages to integrating photonics technology into an astronomical instrument, such as cost savings because of increased reproducibility, thermal and mechanical stability, and miniaturization. This paper provides a brief review of recent advances in astrophotonics.
This paper presents a multi-agent reinforcement learning (MARL) framework for cooperative collision avoidance of UAV swarms leveraging domain knowledge-driven reward. The reward is derived from knowledge in the domain of image processing, approximating contours on a two-dimensional field. By modeling obstacles as maxima on the field, collisions are inherently avoided as contours never go through peaks or intersect. Additionally, counters are smooth and energy-efficient. Our framework enables training with large swarm sizes as the agent interaction is minimized and the need for complex credit assignment schemes or observation sharing mechanisms in state-of-the-art MARL approaches are eliminated. Moreover, UAVs obtain the ability to adapt to complex environments where contours may be non-viable or non-existent through intensive training. Extensive experiments are conducted to evaluate the performances of our framework against state-of-the-art MARL algorithms.
Artificial Intelligence and Journalism: A Systematic Bibliometric and Thematic Analysis of Global Research
2025-07-15
Mohammad Al Masum Molla, Md Manjurul Ahsan
Artificial Intelligence (AI) is reshaping journalistic practices across the globe, offering new opportunities while raising ethical, professional, and societal concerns. This study presents a comprehensive systematic review of published articles on AI in journalism from 2010 to 2025. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a total of 72 peer-reviewed articles were selected from Scopus and Web of Science databases. The analysis combines bibliometric mapping and qualitative thematic synthesis to identify dominant trends, technologies, geographical distributions, and ethical debates. Additionally, sentiment analysis was performed on article abstracts using the Valence Aware Dictionary and sEntiment Reasoner (VADER) algorithm to capture evaluative tones across the literature. The findings show a sharp increase in research activity after 2020, with prominent focus areas including automation, misinformation, and ethical governance. While most studies reflect cautious optimism, concerns over bias, transparency, and accountability remain persistent. The review also highlights regional disparities in scholarly contributions, with limited representation from the Global South. By integrating quantitative and qualitative insights, this study offers a multi-dimensional understanding of how AI is transforming journalism and proposes future research directions for inclusive and responsible innovation.
The origin of the most recently ejected OB runaway star from the R136 cluster
2025-07-15
Simon Portegies Zwart, Michel Stoop, Lex Kaper, Alex de Kooter, Steven Rieder, Tomer Shenar
The $\sim 60\,000$ solar-mass (\MSun) star-cluster R136 (NGC~2070) in the Tarantula Nebula in the Large Magellanic Cloud is the host of at least 55 massive stars ($M \apgt 10$\,\MSun) which move away from the cluster at projected velocities $\gteq 27.5$\,km/s \cite{2024Natur.634..809S}. The origin of the high velocities of such runaway stars have been debated since the 1960s, resulting either from dynamical ejections \citep{1961BAN....15..265B,1961BAN....15..291B} or from supernova explosions \citep{1983ApJ...267..322H}. Due to the Gaia satellite's outstanding precision, we can now retrace the most recently ejected binary star, Mel 34, back to the center of R136 and reconstruct the events that 52\,000 years ago let to its removal from R136, i.e., we establish its dynamical interaction and ejection history. We find that this ejection requires the participation of 5 stars in a strong interaction between a triple composed of the tight massive binary Mel~39 orbited by the star VFTS~590, and the binary star Mel~34. The participation of 5 stars is unexpected because runaway stars were not expected to result from triple interactions \cite{2011Sci...334.1380F}. The deterministic nature of the Newtonian dynamics in the scattering enables us to reconstruct the encounter that ejected Mel~34. We then predict that Mel~39 is a binary star with an 80\,\Msun\, companion star that orbits within $\sim 1^\circ$ in the same plane as Mel~34, and escapes the cluster with a velocity of $\sim 64$\,km/s. The five stars will undergo supernova explosions in the coming 5\,Myr at a distance of $\sim 180$\,pc to $\sim 332$\,pc from their birth location (R\,136). The resulting black hole binaries, however, are not expected to merge within a Hubble time.
Survey of Swarm Intelligence Approaches to Search Documents Based On Semantic Similarity
2025-07-15
Chandrashekar Muniyappa, Eunjin Kim
Swarm Intelligence (SI) is gaining a lot of popularity in artificial intelligence, where the natural behavior of animals and insects is observed and translated into computer algorithms called swarm computing to solve real-world problems. Due to their effectiveness, they are applied in solving various computer optimization problems. This survey will review all the latest developments in Searching for documents based on semantic similarity using Swarm Intelligence algorithms and recommend future research directions.
The discovery of $N_{\rm pp} = 40$ Jupiter-mass binary objects (JuMBOs) alongside $N_{\rm p} = 500$ free-floating Jupiter-mass objects (JMOs) in the Trapezium cluster's central portion raises questions about their origin \cite{2023arXiv231001231P}. \citet{2024NatAs...8..756W} argue that the rate at which two planets orbiting the same star are stripped by a close encounter can explain about half the observed JuMBOs in the Trapezium cluster. Although, their cross-section calculations agree with our own \citep{2024ScPA....3....1P}, one cannot extrapolate their results into clustered environments because it ignores the dissociation of JuMBOs due to subsequent encounters in the clustered environment. The inability of forming JuMBOs via the proposed scenario either calls for another formation mechanism, or the observed JuMBOs require thorough confirmation.
Low-latency Forecasts of Kilonova Light Curves for Rubin and ZTF
2025-07-15
Natalya Plestkova, Niharika Sravan, R. Weizmann Kiendrebeogo, Michael W. Coughlin, Derek Davis, Andrew Toivonen, Theophile Jegou du Laz, Tomás Ahumada, Tyler Barna, George Helou, Roger Smith, Ben Rusholme, Russ R. Laher, Ashish A. Mahabal
Follow-up of gravitational-wave events by wide-field surveys is a crucial tool for the discovery of electromagnetic counterparts to gravitational wave sources, such as kilonovae. Machine learning tools can play an important role in aiding search efforts. We have developed a public tool to predict kilonova light curves using simulated low-latency alert data from the International Gravitational Wave Network during observing runs 4 (O4) and 5 (O5). It uses a bidirectional long-short-term memory (LSTM) model to forecast kilonova light curves from binary neutron star and neutron star-black hole mergers in the Zwicky Transient Facility (ZTF) and Rubin Observatory's Legacy Survey of Space and Time filters. The model achieves a test mean squared error (MSE) of 0.19 for ZTF filters and 0.22 for Rubin filters, calculated by averaging the squared error over all time steps, filters, and light curves in the test set. We verify the performance of the model against merger events followed-up by the ZTF partnership during O4a and O4b. We also analyze the effect of incorporating skymaps and constraints on physical features such as ejecta mass through a hybrid convolutional neural network and LSTM model. Using ejecta mass, the performance of the model improves to an MSE of 0.1. However, using full skymap information results in slightly lower model performance. Our models are publicly available and can help to add important information to help plan follow-up of candidate events discovered by current and next-generation public surveys.
Patterns of electrical brain activity recorded via electroencephalography (EEG) offer immense value for scientific and clinical investigations. The inability of supervised EEG encoders to learn robust EEG patterns and their over-reliance on expensive signal annotations have sparked a transition towards general-purpose self-supervised EEG encoders, i.e., EEG foundation models (EEG-FMs), for robust and scalable EEG feature extraction. However, the real-world readiness of early EEG-FMs and the rubric for long-term research progress remain unclear. A systematic and comprehensive review of first-generation EEG-FMs is therefore necessary to understand the current state-of-the-art and identify key directions for future EEG-FMs. To that end, this study reviews 10 early EEG-FMs and presents a critical synthesis of their methodology, empirical findings, and outstanding research gaps. We find that most EEG-FMs adopt a sequence-based modeling scheme that relies on transformer-based backbones and the reconstruction of masked sequences for self-supervision. However, model evaluations remain heterogeneous and largely limited, making it challenging to assess their practical off-the-shelf utility. In addition to adopting standardized and realistic evaluations, future work should demonstrate more substantial scaling effects and make principled and trustworthy choices throughout the EEG representation learning pipeline. We believe that developing benchmarks, software tools, technical methodologies, and applications in collaboration with domain experts may further advance the translational utility and real-world adoption of EEG-FMs.
Challenges in GenAI and Authentication: a scoping review
2025-07-15
Wesley dos Reis Bezerra, Lais Machado Bezerra, Carlos Becker Westphall
Authentication and authenticity have been a security challenge since the beginning of information sharing, especially in the context of digital information. With the advancement of generative artificial intelligence, these challenges have evolved, demanding a more up-to-date analysis of their impacts on society and system security. This work presents a scoping review that analyzed 88 documents from the IEEExplorer, Scopus, and ACM databases, promoting an analysis of the resulting portfolio through six guiding questions focusing on the most relevant work, challenges, attack surfaces, threats, proposed solutions, and gaps. Finally, the portfolio articles are analyzed through this guiding research lens and also receive individualized analysis. The results consistently outline the challenges, gaps, and threats related to images, text, audio, and video, thereby supporting new research in the areas of authentication and generative artificial intelligence.
DESI EDR: Calibrating the Tully-Fisher Relationship with the DESI Peculiar Velocity Survey
2025-07-15
K. Douglass, S. BenZvi, N. Uberoi, C. Howlett, C. Saulder, K. Said, R. Demina, J. Aguilar, S. Ahlen, G. Aldering, D. Bianchi, D. Brooks, T. Claybaugh, A. Cuceu, T. M. Davis, K. S. Dawson, A. de la Macorra, A. Font-Ribera, J. E. Forero-Romero, E. Gaztanaga, S. Gontcho A Gontcho, G. Gutierrez, C. Hahn, K. Honscheid, M. Ishak, R. Joyce, T. Kisner, A. Kremin, M. Landriau, M. E. Levi, J. Lucey, P. Martini, A. Meisner, R. Miquel, J. Moustakas, N. Palanque-Delabrouille, W. J. Percival, F. Prada, G. Rossi, E. Sanchez, D. Schlegel, M. Schubnell, J. Silber, D. Sprayberry, G. Tarle, B. A. Weaver, R. Zhou, H. Zou
We calibrate the Tully-Fisher relation (TFR) with data from the DESI Peculiar Velocity (PV) Survey taken during the Survey Validation (SV) period of the DESI galaxy redshift survey. Placing spectroscopic fibers on the centers and major axes of spatially-extended spiral galaxies identified in the 2020 Siena Galaxy Atlas using the DESI Legacy Surveys, we measure the rotational velocities at 0.33R26 for 1163 (1136 + 27 dwarf) spiral galaxies observed during SV. Using 41 spiral galaxies observed in the Coma Cluster, we find a slope for the TFR of -7.96+/-0.13 AB mag in the r-band, with a scatter about the TFR of 1.07+/-0.02 AB mag. We calibrate the zero-point of the TFR using galaxies with independent distances measured using type Ia supernovae via the cosmological distance ladder. From the SN Ia distances, we measure a zero-point of -19.34(+0.30,-0.29) AB mag in the r-band. We produce a public catalog of the distances to these 1136 spiral galaxies observed during DESI SV as part of the DESI PV Survey with our calibrated TFR. This is, to our knowledge, the first catalog of TFR distances produced with velocities measured at a single point in the disk.
Fiducial Matching: Differentially Private Inference for Categorical Data
2025-07-15
Ogonnaya Michael Romanus, Younes Boulaguiem, Roberto Molinari
The task of statistical inference, which includes the building of confidence intervals and tests for parameters and effects of interest to a researcher, is still an open area of investigation in a differentially private (DP) setting. Indeed, in addition to the randomness due to data sampling, DP delivers another source of randomness consisting of the noise added to protect an individual's data from being disclosed to a potential attacker. As a result of this convolution of noises, in many cases it is too complicated to determine the stochastic behavior of the statistics and parameters resulting from a DP procedure. In this work, we contribute to this line of investigation by employing a simulation-based matching approach, solved through tools from the fiducial framework, which aims to replicate the data generation pipeline (including the DP step) and retrieve an approximate distribution of the estimates resulting from this pipeline. For this purpose, we focus on the analysis of categorical (nominal) data that is common in national surveys, for which sensitivity is naturally defined, and on additive privacy mechanisms. We prove the validity of the proposed approach in terms of coverage and highlight its good computational and statistical performance for different inferential tasks in simulated and applied data settings.
Survey of Genetic and Differential Evolutionary Algorithm Approaches to Search Documents Based On Semantic Similarity
2025-07-15
Chandrashekar Muniyappa, Eunjin Kim
Identifying similar documents within extensive volumes of data poses a significant challenge. To tackle this issue, researchers have developed a variety of effective distributed computing techniques. With the advancement of computing power and the rise of big data, deep neural networks and evolutionary computing algorithms such as genetic algorithms and differential evolution algorithms have achieved greater success. This survey will explore the most recent advancements in the search for documents based on their semantic text similarity, focusing on genetic and differential evolutionary computing algorithms.
We review various semiclassical models for strong-field physics. These semiclassical models employ ensembles of classical trajectories to simulate electron motion in the continuum after being released from an atom or molecule by an external laser field. We discuss different approaches to trajectory-based simulations and identify their advantages and shortcomings. We also review some of the recent applications of semiclassical models to the key strong-field phenomena: above-threshold ionization, high-order harmonic generation, nonsequential double ionization, and frustrated tunneling ionization.
Seeing the Signs: A Survey of Edge-Deployable OCR Models for Billboard Visibility Analysis
2025-07-15
Maciej Szankin, Vidhyananth Venkatasamy, Lihang Ying
Outdoor advertisements remain a critical medium for modern marketing, yet accurately verifying billboard text visibility under real-world conditions is still challenging. Traditional Optical Character Recognition (OCR) pipelines excel at cropped text recognition but often struggle with complex outdoor scenes, varying fonts, and weather-induced visual noise. Recently, multimodal Vision-Language Models (VLMs) have emerged as promising alternatives, offering end-to-end scene understanding with no explicit detection step. This work systematically benchmarks representative VLMs - including Qwen 2.5 VL 3B, InternVL3, and SmolVLM2 - against a compact CNN-based OCR baseline (PaddleOCRv4) across two public datasets (ICDAR 2015 and SVT), augmented with synthetic weather distortions to simulate realistic degradation. Our results reveal that while selected VLMs excel at holistic scene reasoning, lightweight CNN pipelines still achieve competitive accuracy for cropped text at a fraction of the computational cost-an important consideration for edge deployment. To foster future research, we release our weather-augmented benchmark and evaluation code publicly.
Image-Based Multi-Survey Classification of Light Curves with a Pre-Trained Vision Transformer
2025-07-15
Daniel Moreno-Cartagena, Guillermo Cabrera-Vives, Alejandra M. Muñoz Arancibia, Pavlos Protopapas, Francisco Förster, Márcio Catelan, A. Bayo, Pablo A. Estévez, P. Sánchez-Sáez, Franz E. Bauer, M. Pavez-Herrera, L. Hernández-García, Gonzalo Rojas
We explore the use of Swin Transformer V2, a pre-trained vision Transformer, for photometric classification in a multi-survey setting by leveraging light curves from the Zwicky Transient Facility (ZTF) and the Asteroid Terrestrial-impact Last Alert System (ATLAS). We evaluate different strategies for integrating data from these surveys and find that a multi-survey architecture which processes them jointly achieves the best performance. These results highlight the importance of modeling survey-specific characteristics and cross-survey interactions, and provide guidance for building scalable classifiers for future time-domain astronomy.
Modern digital sky surveys have been acquiring images of billions of galaxies. While these images often provide sufficient details to analyze the shape of the galaxies, accurate analysis of such high volumes of images requires effective automation. Current solutions often rely on machine learning annotation of the galaxy images based on a set of pre-defined classes. Here we introduce a new approach to galaxy image analysis that is based on generative AI. The method simplifies the galaxy images and automatically converts them into a ``skeletonized" form. The simplified images allow accurate measurements of the galaxy shapes and analysis that is not limited to a certain pre-defined set of classes. We demonstrate the method by applying it to galaxy images acquired by the DESI Legacy Survey. The code and data are publicly available. The method was applied to 125,000 DESI Legacy Survey images, and the catalog of the simplified images is publicly available.
Decision Models for Selecting Architecture Patterns and Strategies in Quantum Software Systems
2025-07-15
Mst Shamima Aktar, Peng Liang, Muhammad Waseem, Amjed Tahir, Mojtaba Shahin, Muhammad Azeem Akbar, Arif Ali Khan, Aakash Ahmad, Musengamana Jean de Dieu, Ruiyin Li
Quantum software represents disruptive technologies in terms of quantum-specific software systems, services, and applications - leverage the principles of quantum mechanics via programmable quantum bits (Qubits) that manipulate quantum gates (QuGates) - to achieve quantum supremacy in computing. Quantum software architecture enables quantum software developers to abstract away implementation-specific details (i.e., mapping of Qubits and QuGates to high-level architectural components and connectors). Architectural patterns and strategies can provide reusable knowledge and best practices to engineer quantum software systems effectively and efficiently. However, quantum software practitioners face significant challenges in selecting and implementing appropriate patterns and strategies due to the complexity of quantum software systems and the lack of guidelines. To address these challenges, this study proposes decision models for selecting patterns and strategies in six critical design areas in quantum software systems: Communication, Decomposition, Data Processing, Fault Tolerance, Integration and Optimization, and Algorithm Implementation. These decision models are constructed based on data collected from both a mining study (i.e., GitHub and Stack Exchange) and a Systematic Literature Review, which were used to identify relevant patterns and strategies with their involved Quality Attributes (QAs). We then conducted semi-structured interviews with 16 quantum software practitioners to evaluate the familiarity, understandability, completeness, and usefulness of the proposed decision models. The results show that the proposed decision models can aid practitioners in selecting suitable patterns and strategies to address the challenges related to the architecture design of quantum software systems. The dataset is available at [6], allowing the community to reproduce and build upon our findings.
Asymptotic quasinormal modes, echoes, and black hole spectral instability: a brief review
2025-07-15
Shui-Fa Shen, Guan-Ru Li, Ramin G. Daghigh, Jodin C. Morey, Michael D. Green, Wei-Liang Qian, Rui-Hong Yue
We present a short review of the analytical aspects of recent progress in the study of black hole spectral instability and its potential observational consequences. This topic, inspired by earlier foundational works, has attracted considerable attention in the recent literature. It has been demonstrated that both the low-lying modes and high overtones of black hole quasinormal spectra can be substantially influenced by ultraviolet metric perturbations. The temporal evolution of gravitational wave signals is primarily governed by the first few low-lying quasinormal modes. In contrast, the asymptotic behavior of high overtones is closely associated with the phenomenon of black hole echoes. We review relevant studies on spectral instability in both regimes, highlighting their potential to produce substantial observational signatures in gravitational wave data. Additionally, recent proposals of Regge poles and reflectionless modes as alternative stable observables for probing black hole spacetimes are summarized.
CLASSY XIV: Nitrogen Enrichment Shaped by Gas Density and Feedback
2025-07-15
Karla Z. Arellano-Córdova, Danielle A. Berg, Matilde Mingozzi, Bethan L. James, Fiorenzo Vincenzo, Noah S. J. Rogers, Evan D. Skillman, Ricardo O. Amorín, Fergus Cullen, Sophia R. Flury, Valentina Abril-Melgarejo, John Chisholm, Timothy Heckman, Matthew J. Hayes, Svea Hernandez, Nimisha Kumari, Chiaki Kobayashi, Claus Leitherer, Crystal L. Martin, Zorayda Martinez, Themiya Nanayakkara, Kaelee S. Parker, Peter Senchyna, Claudia Scarlata, Mabel G. Stephenson, Aida Wofford, Xinfeng Xu, Peixin Zhu
We investigate the chemical evolution of N/O using a sample of 45 local star-forming galaxies (SFGs) from the CLASSY survey. This sample spans a wide range of galaxy properties, with robust determinations of nitrogen and oxygen abundances via the direct-$T_{\rm e}$ method. We explore how N/O relates to density structure, stellar mass, star formation rate (SFR), stellar age, compactness, and gas kinematics. In addition, we compare our results with those of galaxies at $z =2-10$ where N/O ratios were derived from optical or UV nitrogen lines, aiming to identify chemical enrichment pathways across cosmic time. Our analysis shows that the N/O-O/H relation in CLASSY galaxies aligns with the trends seen in local galaxies and extragalactic HII regions, and that galaxies at $z = 2-6$ exhibit similar N/O values, indicating no significant redshift evolution in N/O. We identify a significant correlation between electron density $n_{\rm e}$([S II]) and N/O, suggesting that density structure contributes to the scatter in the N/O-O/H relation. The CLASSY galaxies with high SFRs or compact star formation show elevated N/O, though no strong correlation with stellar mass is found. We also find that high-velocity outflows (v$_{out}$ > 350 km/s) and low mass-loading factors are linked to elevated N/O, indicating that feedback plays a significant role. These results highlight the importance of density, star formation, and feedback from young stellar populations in shaping N/O enrichment and provide key insights for interpreting high-$z$ galaxies observed with JWST.
We give a survey of computation of the conformal capacity of planar condensers, generalized capacity, and logarithmic capacity with emphasis on our recent work 2020-2025. We also discuss some applications of our method based on the boundary integral equation with the generalized Neumann kernel to the computation of several other conformal invariants: harmonic measure, modulus of a quadrilateral, reduced modulus, hyperbolic capacity, and elliptic capacity. Here the solution of mixed Dirichlet-Neumann boundary value problem for the Laplace equation has a key role. At the end of the paper we give a topicwise structured list of hints to our extensive bibliography on constructive complex analysis and potential theory.
DELVE-ing into the Milky Way's Globular Clusters: Assessing extra-tidal features in NGC 5897, NGC 7492, and testing detectability with deeper photometry
2025-07-15
A. Chiti, K. Tavangar, P. S. Ferguson, J. A. Carballo-Bello, A. M. Senkevich, D. Erkal, A. Drlica-Wagner, A. B. Pace, A. P. Ji, D. J. Sand, G. Limberg, A. Chaturvedi, D. Crnojević, G. E. Medina, A. H. Riley, N. Shipp, A. K. Vivas, M. Wertheim, Y. Choi, C. E. Martínez-Vázquez, B. Mutlu-Pakdil, M. Navabi, J. D. Sakowska, G. S. Stringfellow, A. Zenteno
Extra-tidal features around globular clusters (GCs) are tracers of their disruption, stellar stream formation, and their host's gravitational potential. However, these features remain challenging to detect due to their low surface brightness. We conduct a systematic search for such features around 19 GCs in the DECam Local Volume Exploration (DELVE) survey Data Release 2, discovering a new extra-tidal envelope around NGC 5897 and find tentative evidence for an extended envelope surrounding NGC 7492. Through a combination of dynamical modeling and analyzing synthetic stellar populations, we demonstrate these envelopes may have formed through tidal disruption. We use these models to explore the detectability of these features in the upcoming Legacy Survey of Space and Time (LSST), finding that while LSST's deeper photometry will enhance detection significance, additional methods for foreground removal like proper motions or metallicities may be important for robust stream detection. Our results both add to the sample of globular clusters with extra-tidal features and provide insights on interpreting similar features in current and upcoming data.
COS-EDGES: Co-rotation and Kinematic Stratification of the Multi-Phase CGM Around Edge-On Galaxies
2025-07-15
Glenn G. Kacprzak, Benjamin D. Oppenheimer, Nikole M. Nielsen, Antonia Fernandez-Figueroa, Michael T. Murphy, Rebecca J. Allen, Tania M. Barone, Sameer, Christopher W. Churchill, Joseph N. Burchett, Kaustubh R. Gupta, Jane C. Charlton, Caleb B. Platukis
We present the first results from the COS-EDGES survey, targeting the kinematic connection between the ISM and multi-phase circumgalactic medium (CGM) in nine isolated, edge-on galaxies at z~0.2, each probed along its major axis by a background quasar at impact parameters of 13-38kpc. Using VLT/UVES and HST/COS quasar spectra, we analyse MgI, MgII, HI, CII, CIII, and OVI absorption relative to galaxy rotation curves from Keck/LRIS and Magellan/MagE spectra. We find that at lower $D/R_{vir}$ ($D/R_{vir}\leq 0.2$), over 80% of absorption in all ions lies on the side of systemic velocity matching disk rotation, and the optical-depth-weighted median velocity ($v_{abs}$) is consistent with the peak rotation speed. At higher $D/R_{vir}$ ($D/R_{vir} > 0.2$), the kinematics diverge by ionisation state: For low ionisation gas, the amount of co-rotating absorption remains >80%, yet $v_{abs}$ drops to 60% of the galaxy rotation speed. For high ionisation gas (OVI), only 60% of the absorption is consistent with co-rotation and $v_{abs}$ drops to 20% of the rotation speed. Furthermore, the velocity widths, corresponding to 50% of the total optical depth ($\Delta v_{50}$) for low ionisation gas is 1.8 times larger in the inner halo than at larger radii, while for CIII and OVI $\Delta v_{50}$ remains unchanged with distance. These results suggest a radially dependent CGM kinematic structure: the inner halo hosts cool, dynamically broad gas tightly coupled to disk rotation, whereas beyond 0.2$R_{vir}$, particularly traced by OVI and HI, the CGM shows weaker rotational alignment and lower velocity dispersion. Therefore, low-ionisation gas likely traces extended co-rotating gas, inflows and/or recycled accretion, while high-ionisation gas reflects a mixture of co-rotating, lagging, discrete collisionally ionised structures, indicating a kinematic stratification of the multi-phase CGM. [Abridged]
The ALMA-CRISTAL survey: Resolved kinematic studies of main sequence star-forming galaxies at 4
2025-07-15
Lilian L. Lee, Natascha M. Förster Schreiber, Rodrigo Herrera-Camus, Daizhong Liu, Sedona H. Price, Reinhard Genzel, Linda J. Tacconi, Dieter Lutz, Ric Davies, Thorsten Naab, Hannah Übler, Manuel Aravena, Roberto J. Assef, Loreto Barcos-Muñoz, Rebecca A. A. Bowler, Andreas Burkert, Jianhang Chen, Rebecca L. Davies, Ilse De Looze, Tanio Diaz-Santos, Jorge González-López, Ryota Ikeda, Ikki Mitsuhashi, Ana Posses, Mónica Relaño Pastor, Alvio Renzini, Manuel Solimano, Justin S. Spilker, Amiel Sternberg, Kenichi Tadaki, Kseniia Telikova, Sylvain Veilleux, Vicente Villanueva
We present a detailed kinematic study of a sample of 32 massive ($9.5\leqslant\log(M_*/{\rm M_{\odot}})\leqslant10.9$) main-sequence star-forming galaxies (MS SFGs) at $4
Unmixed metals: Variations in the enrichment of z~4 sub-damped Lyman alpha systems
2025-07-15
Trystyn A. M. Berg, Louise A. Welsh, Ryan J. Cooke, Lise Christensen, Valentina D'Odorico, Sara L. Ellison, Sebastian Lopez
The chemical abundance patterns of near-pristine objects provide important constraints on the properties of the first generations of stars in the Universe. We present the chemical abundances of five very metal-poor ([M/H]<-2.5) sub damped Lyman alpha systems (subDLAs) covering the redshift range $3.6
AI, Humans, and Data Science: Optimizing Roles Across Workflows and the Workforce
2025-07-15
Richard Timpone, Yongwei Yang
AI is transforming research. It is being leveraged to construct surveys, synthesize data, conduct analysis, and write summaries of the results. While the promise is to create efficiencies and increase quality, the reality is not always as clear cut. Leveraging our framework of Truth, Beauty, and Justice (TBJ) which we use to evaluate AI, machine learning and computational models for effective and ethical use (Taber and Timpone 1997; Timpone and Yang 2024), we consider the potential and limitation of analytic, generative, and agentic AI to augment data scientists or take on tasks traditionally done by human analysts and researchers. While AI can be leveraged to assist analysts in their tasks, we raise some warnings about push-button automation. Just as earlier eras of survey analysis created some issues when the increased ease of using statistical software allowed researchers to conduct analyses they did not fully understand, the new AI tools may create similar but larger risks. We emphasize a human-machine collaboration perspective (Daugherty and Wilson 2018) throughout the data science workflow and particularly call out the vital role that data scientists play under VUCA decision areas. We conclude by encouraging the advance of AI tools to complement data scientists but advocate for continued training and understanding of methods to ensure the substantive value of research is fully achieved by applying, interpreting, and acting upon results most effectively and ethically.
Synthetic Tabular Data Generation: A Comparative Survey for Modern Techniques
2025-07-15
Raju Challagundla, Mohsen Dorodchi, Pu Wang, Minwoo Lee
As privacy regulations become more stringent and access to real-world data becomes increasingly constrained, synthetic data generation has emerged as a vital solution, especially for tabular datasets, which are central to domains like finance, healthcare and the social sciences. This survey presents a comprehensive and focused review of recent advances in synthetic tabular data generation, emphasizing methods that preserve complex feature relationships, maintain statistical fidelity, and satisfy privacy requirements. A key contribution of this work is the introduction of a novel taxonomy based on practical generation objectives, including intended downstream applications, privacy guarantees, and data utility, directly informing methodological design and evaluation strategies. Therefore, this review prioritizes the actionable goals that drive synthetic data creation, including conditional generation and risk-sensitive modeling. Additionally, the survey proposes a benchmark framework to align technical innovation with real-world demands. By bridging theoretical foundations with practical deployment, this work serves as both a roadmap for future research and a guide for implementing synthetic tabular data in privacy-critical environments.
The hypergraph product (HGP) is a famous code construction technique with an equally famous canonical visualisation. This visual perspective provides much more than simply a way to build intuition: HGP codes can be defined graphically, properties can be demonstrated graphically, and approaches to fault-tolerant logic have been developed graphically. In recent years two powerful generalisations of this product -- the lifted and balanced products -- have been proposed and employed to great success, but a unified graphical approach to describing these codes has been absent. In these notes I review the canonical approach to visualising HGP codes and then show how, via the addition of a third dimension, it can be generalised to apply to both the lifted and the balanced product. In the process we obtain clear intuition into various properties of these codes such as i) why it is hard to bound $k$ and $d$ ii) the issues with finding a canonical logical basis in the general case and iii) how the two products are related, and how they differ. I have attempted to structure these notes plainly and directly, so that the visual intuition can be easily obtained by those who want it while the rigorous justification is still available to those who demand it.
Perception of Brain-Computer Interface Implantation Surgery for Motor, Sensory, and Autonomic Restoration in Spinal Cord Injury and Stroke
2025-07-15
Derrick Lin, Tracie Tran, Shravan Thaploo, Jose Gabrielle E. Matias, Joy E. Pixley, Zoran Nenadic, An H. Do
(Abridged) Stroke and SCI are conditions that can significantly impact the QoL of survivors in both the physical and psychosocial domains. Both diseases often result in significant motor and sensory impairments that are not fully reversible despite current available therapies. Invasive BCIs have emerged as a promising means to bypass the site of injury and potentially restore motor and sensory function. However, to maximize the utility and participant satisfaction with such technology, participants' willingness to embrace BCIs must be assessed, and placed in context with functional goals and rehabilitative priorities. Hence, we conducted a survey of a cohort of stroke (n=33), SCI (n=37), and both (n=1) participants regarding their receptiveness to invasive ECoG-based BCIs as well as to assess their goals for functional rehabilitation. Overall, participants indicated a high level of willingness to undergo surgery to implant ECoG grids for BCI technology if basic motor functions, including upper extremity, gait, bowel/bladder, and sensory function were restored. There was no correlation between participant willingness to undergo a prospective BCI implantation and the level of functional recovery offered by the BCI. Similarly, there was no correlation between willingness to undergo surgery and the participants' perceived rehabilitative priorities and level of disability. These findings indicate that participants were interested in invasive BCI technology even if only basic functions can be restored, regardless of their level of disability and their rehabilitative priorities. Such observations imply that first generation commercial invasive BCIs may not need extensive functions to garner adoption. Conversely, it also raises a concern that participants from the stroke and SCI cohort may be overly enthusiastic about such technology, which poses potential risks for medical exploitation.
Data-Driven Meta-Analysis and Public-Dataset Evaluation for Sensor-Based Gait Age Estimation
2025-07-15
Varun Velankar
Estimating a person's age from their gait has important applications in healthcare, security and human-computer interaction. In this work, we review fifty-nine studies involving over seventy-five thousand subjects recorded with video, wearable and radar sensors. We observe that convolutional neural networks produce an average error of about 4.2 years, inertial-sensor models about 4.5 years and multi-sensor fusion as low as 3.4 years, with notable differences between lab and real-world data. We then analyse sixty-three thousand eight hundred forty-six gait cycles from the OU-ISIR Large-Population dataset to quantify correlations between age and five key metrics: stride length, walking speed, step cadence, step-time variability and joint-angle entropy, with correlation coefficients of at least 0.27. Next, we fine-tune a ResNet34 model and apply Grad-CAM to reveal that the network attends to the knee and pelvic regions, consistent with known age-related gait changes. Finally, on a one hundred thousand sample subset of the VersatileGait database, we compare support vector machines, decision trees, random forests, multilayer perceptrons and convolutional neural networks, finding that deep networks achieve up to 96 percent accuracy while processing each sample in under 0.1 seconds. By combining a broad meta-analysis with new large-scale experiments and interpretable visualizations, we establish solid performance baselines and practical guidelines for reducing gait-age error below three years in real-world scenarios.
Patterns, Models, and Challenges in Online Social Media: A Survey
2025-07-15
Niccolò Di Marco, Anita Bonetti, Edoardo Di Martino, Edoardo Loru, Jacopo Nudo, Mario Edoardo Pandolfo, Giulio Pecile, Emanuele Sangiorgio, Irene Scalco, Simon Zollo, Matteo Cinelli, Fabiana Zollo, Walter Quattrociocchi
The rise of digital platforms has enabled the large scale observation of individual and collective behavior through high resolution interaction data. This development has opened new analytical pathways for investigating how information circulates, how opinions evolve, and how coordination emerges in online environments. Yet despite a growing body of research, the field remains fragmented and marked by methodological heterogeneity, limited model validation, and weak integration across domains. This survey offers a systematic synthesis of empirical findings and formal models. We examine platform-level regularities, assess the methodological architectures that generate them, and evaluate the extent to which current modeling frameworks account for observed dynamics. The goal is to consolidate a shared empirical baseline and clarify the structural constraints that shape inference in this domain, laying the groundwork for more robust, comparable, and actionable analyses of online social systems.
Industrial defect detection is vital for upholding product quality across contemporary manufacturing systems. As the expectations for precision, automation, and scalability intensify, conventional inspection approaches are increasingly found wanting in addressing real-world demands. Notable progress in computer vision and deep learning has substantially bolstered defect detection capabilities across both 2D and 3D modalities. A significant development has been the pivot from closed-set to open-set defect detection frameworks, which diminishes the necessity for extensive defect annotations and facilitates the recognition of novel anomalies. Despite such strides, a cohesive and contemporary understanding of industrial defect detection remains elusive. Consequently, this survey delivers an in-depth analysis of both closed-set and open-set defect detection strategies within 2D and 3D modalities, charting their evolution in recent years and underscoring the rising prominence of open-set techniques. We distill critical challenges inherent in practical detection environments and illuminate emerging trends, thereby providing a current and comprehensive vista of this swiftly progressing field.
Statistics of stochastic entropy for recorded transitions between ENSO states
2025-07-14
Sílvio M. Duarte Queirós
We analyse the transitions between established phases of the El Ni\~no Southern Oscillation (ENSO) by surveying the daily data of the Southern Oscillation Index from an entropic viewpoint using the framework of stochastic Statistical Physics. We evaluate the variation of entropy produced due to each recorded path of that index during each transition as well as taking only into consideration the beginning and the end of the change between phases and verified both integral fluctuation relations. The statistical results show that these entropy variations have not been extreme entropic events; only the transition between the strong $1999-2000$ La Ni\~na to the moderate $2002-2003$ El Ni\~no is at the edge of being so. With that, the present work opens a long and winding avenue of research over the application of stochastic Statistical Physics to Climate Dynamics.
Characterization of Upper-Level Undergraduate Quantum Mechanics Courses in the U.S
2025-07-14
Jesse Kruse, Molly Griston, Bethany R. Wilcox
Upper-level, undergraduate quantum mechanics (QM) is widely considered a difficult subject with many varied approaches to teaching it and considerable variation in content coverage. For example, two common approaches to undergraduate QM instruction are spins-first, which focuses on the postulates of QM in spin systems before discussing wavefunctions, and wavefunctions-first, which focuses on the Schr\"odinger equation and its solutions for continuous functions in various potentials before discussing spin. These different approaches, along with the content variability in the textbooks used by instructors, may mean students learn different things in QM classes across the United States (U.S.). In this paper, we offer a characterization of QM courses based on survey responses from instructors at institutions across the U.S. With the responses of 76 instructors teaching QM courses (or sequences), we present results detailing their teaching methodologies, use of pedagogical resources, and coverage of QM topics. We find that the plurality of instructors in our sample are using traditional lecture, but many instructors are using interactive lecture or another non-traditional method. Additionally, instructors are using a wide variety of pedagogical tools (e.g., clicker questions). Many instructors in our sample reported teaching single-semester (or single-quarter) QM courses; these instructors report similar content coverage to instructors teaching first-semester (or quarter) QM courses, though their responses showed greater variability. We additionally report a comparison of content coverage between instructors using wavefunctions-first versus spins-first approaches, finding a large degree of overlap with differences in coverage for a few specific topics. These findings can help inform both future research and instructional efforts in QM education.
Signatures of Odd-Parity $s$-wave $Ξ^*$ States in Femtoscopic Correlation Functions
2025-07-14
Jia-Xin Lin, Pablo Encarnación, Miguel Albaladejo, Albert Feijoo
We investigate the $\Xi^*$ resonances within the molecular picture, where these states are dynamically generated as poles in the unitarized scattering amplitudes arising from the coupled-channel interactions of $K^{*-} \Lambda$, $K^{*-} \Sigma^0$, $\rho^- \Xi^0$, $\overline{K}{}^{*0} \Sigma^-$, $\rho^0 \Xi^-$, $\omega \Xi^-$, and $\phi \Xi^-$. The interaction kernel is derived from the local hidden gauge formalism, while the unitarization procedure employs a hybrid method that combines cutoff and dimensional regularizations in the evaluation of the loop function. From a detailed spectroscopic analysis, we identify two $S = -2$ baryon states whose properties are compatible with some of the $\Xi^*$ resonances listed in the Review of Particle Physics. To explore their possible experimental signatures, we compute the femtoscopic correlation functions for all the vector-baryon pairs considered in the present study, using realistic estimates of production weights and varying source sizes $R = 1, 1.1, 1.2, 1.3, 1.5$ fm.
Imprints of turbulence on heterogeneous deposition of adhesive particles
2025-07-14
Max P. Herzog, Jesse Capecelatro
We present direct numerical simulations (DNS) of particle deposition in a turbulent channel flow, incorporating a viscoelastic soft-sphere collision model with temperature-dependent van der Waals adhesion. Particle-wall contact is governed by an adhesion number that varies with temperature, enabling exploration of a wide range of deposition behaviors. Deposition is strongly heterogeneous, especially for inertial particles, where rolling and sliding enhance nonuniformity. Spanwise radial distribution functions reveal that deposited particles form streaks with characteristic spacing set by near-wall two-point velocity correlations. A clustering metric confirms that high-inertia, low-adhesion particles deposit in elongated, anisotropic patterns due to spanwise migration driven by velocity fluctuations. Finally, it is shown that this heterogeneity in deposition leads to localized wall wear rates exceeding ten times the mean, with the most severe wear associated with particles that are carried to the wall in clusters by sweep events.
2025-07-14
Tao Xiao, Youmei Fan, Fabio Calefato, Christoph Treude, Raula Gaikovina Kula, Hideaki Hata, Sebastian Baltes
The widespread adoption of generative AI (GenAI) tools such as GitHub Copilot and ChatGPT is transforming software development. Since generated source code is virtually impossible to distinguish from manually written code, their real-world usage and impact on open-source software development remain poorly understood. In this paper, we introduce the concept of self-admitted GenAI usage, that is, developers explicitly referring to the use of GenAI tools for content creation in software artifacts. Using this concept as a lens to study how GenAI tools are integrated into open-source software projects, we analyze a curated sample of more than 250,000 GitHub repositories, identifying 1,292 such self-admissions across 156 repositories in commit messages, code comments, and project documentation. Using a mixed methods approach, we derive a taxonomy of 32 tasks, 10 content types, and 11 purposes associated with GenAI usage based on 284 qualitatively coded mentions. We then analyze 13 documents with policies and usage guidelines for GenAI tools and conduct a developer survey to uncover the ethical, legal, and practical concerns behind them. Our findings reveal that developers actively manage how GenAI is used in their projects, highlighting the need for project-level transparency, attribution, and quality control practices in the new era of AI-assisted software development. Finally, we examine the impact of GenAI adoption on code churn in 151 repositories with self-admitted GenAI usage and find no general increase, contradicting popular narratives on the impact of GenAI on software development.
Gaussian Process Methods for Very Large Astrometric Data Sets
2025-07-14
Timothy Hapitas, Lawrence M. Widrow, Thavisha E. Dharmawardena, Daniel Foreman-Mackey
We present a novel non-parametric method for inferring smooth models of the mean velocity field and velocity dispersion tensor of the Milky Way from astrometric data. Our approach is based on Stochastic Variational Gaussian Process Regression (SVGPR) and provides an attractive alternative to binning procedures. SVGPR is an approximation to standard GPR, the latter of which suffers severe computational scaling with N and assumes independently distributed Gaussian Noise. In the Galaxy however, velocity measurements exhibit scatter from both observational uncertainty and the intrinsic velocity dispersion of the distribution function. We exploit the factorization property of the objective function in SVGPR to simultaneously model both the mean velocity field and velocity dispersion tensor as separate Gaussian Processes. This achieves a computational complexity of O(M^3) versus GPR's O(N^3), where M << N is a subset of points chosen in a principled way to summarize the data. Applied to a sample of ~8 x 10^5 stars from the Gaia DR3 Radial Velocity Survey, we construct differentiable profiles of the mean velocity and velocity dispersion as functions of height above the Galactic midplane. We find asymmetric features in all three diagonal components of the velocity dispersion tensor, providing evidence that the vertical dynamics of the Milky Way are in a state of disequilibrium. Furthermore, our dispersion profiles exhibit correlated structures at several locations in |z|, which we interpret as signatures of the Gaia phase spiral. These results demonstrate that our method provides a promising direction for data-driven analyses of Galactic dynamics.
Early detection of dementia is critical for timely medical intervention and improved patient outcomes. Neuropsychological tests are widely used for cognitive assessment but have traditionally relied on manual scoring. Automatic dementia classification (ADC) systems aim to infer cognitive decline directly from speech recordings of such tests. We propose Demenba, a novel ADC framework based on state space models, which scale linearly in memory and computation with sequence length. Trained on over 1,000 hours of cognitive assessments administered to Framingham Heart Study participants, some of whom were diagnosed with dementia through adjudicated review, our method outperforms prior approaches in fine-grained dementia classification by 21\%, while using fewer parameters. We further analyze its scaling behavior and demonstrate that our model gains additional improvement when fused with large language models, paving the way for more transparent and scalable dementia assessment tools. Code: https://anonymous.4open.science/r/Demenba-0861
Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligence
2025-07-14
Jiaming Tian, Liyao Li, Wentao Ye, Haobo Wang, Lingxin Wang, Lihua Yu, Zujie Ren, Gang Chen, Junbo Zhao
Tables are fundamental in domains such as finance, healthcare, and public administration, yet real-world table tasks often involve noise, structural heterogeneity, and semantic complexity--issues underexplored in existing research that primarily targets clean academic datasets. This survey focuses on LLM-based Table Agents, which aim to automate table-centric workflows by integrating preprocessing, reasoning, and domain adaptation. We define five core competencies--C1: Table Structure Understanding, C2: Table and Query Semantic Understanding, C3: Table Retrieval and Compression, C4: Executable Reasoning with Traceability, and C5: Cross-Domain Generalization--to analyze and compare current approaches. In addition, a detailed examination of the Text-to-SQL Agent reveals a performance gap between academic benchmarks and real-world scenarios, especially for open-source models. Finally, we provide actionable insights to improve the robustness, generalization, and efficiency of LLM-based Table Agents in practical settings.
We present Spatial Lifting (SL), a novel methodology for dense prediction tasks. SL operates by lifting standard inputs, such as 2D images, into a higher-dimensional space and subsequently processing them using networks designed for that higher dimension, such as a 3D U-Net. Counterintuitively, this dimensionality lifting allows us to achieve good performance on benchmark tasks compared to conventional approaches, while reducing inference costs and significantly lowering the number of model parameters. The SL framework produces intrinsically structured outputs along the lifted dimension. This emergent structure facilitates dense supervision during training and enables robust, near-zero-additional-cost prediction quality assessment at test time. We validate our approach across 19 benchmark datasets (13 for semantic segmentation and 6 for depth estimation), demonstrating competitive dense prediction performance while reducing the model parameter count by over 98% (in the U-Net case) and lowering inference costs. Spatial Lifting introduces a new vision modeling paradigm that offers a promising path toward more efficient, accurate, and reliable deep networks for dense prediction tasks in vision.
Unlocking Gravity and Gravitational Waves with Radio Pulsars: Advances and Challenges
2025-07-14
Huanchen Hu
Pulsars, the cosmic lighthouses, are strongly self-gravitating objects with core densities significantly exceeding nuclear density. Since the discovery of the Hulse--Taylor pulsar 50 years ago, binary pulsar studies have delivered numerous stringent tests of General Relativity (GR) in the strong-field regime as well as its radiative properties -- gravitational waves (GWs). These systems also enable high-precision neutron star mass measurements, placing tight constraints on the behaviour of matter at extreme densities. In addition, pulsars act as natural detectors for nanohertz GWs, primarily from supermassive black hole binaries, culminating in the first reported evidence of a stochastic GW background in 2023. In this article, I review key milestones in pulsar research and highlight some of contributions from my own work. After a brief overview of the gravity experiments in \S 1, I review the discovery of pulsars -- particularly those in binaries -- and their critical role in gravity experiments (\S 2) that laid the foundation for recent advances. In \S 3, I present the latest efforts on GR tests using the Double Pulsar and a pioneer technique to constrain the dense matter equation of state. \S 4 demonstrates the potential of binary pulsars on testing alternative theories to GR. Advances in nanohertz GW detection with pulsar timing arrays are discussed in \S 5. I outline some of the current challenges in \S 6 and conclude with final remarks in \S 7.
Survey for Categorising Explainable AI Studies Using Data Analysis Task Frameworks
2025-07-14
Hamzah Ziadeh, Hendrik Knoche
Research into explainable artificial intelligence (XAI) for data analysis tasks suffer from a large number of contradictions and lack of concrete design recommendations stemming from gaps in understanding the tasks that require AI assistance. In this paper, we drew on multiple fields such as visual analytics, cognition, and dashboard design to propose a method for categorising and comparing XAI studies under three dimensions: what, why, and who. We identified the main problems as: inadequate descriptions of tasks, context-free studies, and insufficient testing with target users. We propose that studies should specifically report on their users' domain, AI, and data analysis expertise to illustrate the generalisability of their findings. We also propose study guidelines for designing and reporting XAI tasks to improve the XAI community's ability to parse the rapidly growing field. We hope that our contribution can help researchers and designers better identify which studies are most relevant to their work, what gaps exist in the research, and how to handle contradictory results regarding XAI design.
The Effect of Pearl Vortices on the Shape and Position of Néel-Type Skyrmions in Superconductor-Chiral Ferromagnet Heterostructures
2025-07-14
S. S. Apostoloff, E. S. Andriyakhina, I. S. Burmistrov
This review presents recent work carried out at the Landau Institute for Theoretical Physics of the Russian Academy of Sciences on the study of the effect of superconducting vortices on the shape and position of N\'eel-type skyrmions in superconductor--chiral ferromagnet heterostructures. Based on analytical and numerical approaches, a number of effects caused by the inhomogeneous magnetic field of the vortex have been predicted: a significant increase in the skyrmion radius, a change in its chirality in the case of a coaxial configuration of the vortex and skyrmion, and modification of the skyrmion shape in the case of an eccentric configuration. Recent experiments studying these effects are discussed.
Although Large Language Models (LLMs) have demonstrated significant advancements in natural language processing tasks, their effectiveness in the classification and transformation of abusive text into non-abusive versions remains an area for exploration. In this study, we aim to use LLMs to transform abusive text (tweets and reviews) featuring hate speech and swear words into non-abusive text, while retaining the intent of the text. We evaluate the performance of two state-of-the-art LLMs, such as Gemini, GPT-4o, DeekSeek and Groq, on their ability to identify abusive text. We them to transform and obtain a text that is clean from abusive and inappropriate content but maintains a similar level of sentiment and semantics, i.e. the transformed text needs to maintain its message. Afterwards, we evaluate the raw and transformed datasets with sentiment analysis and semantic analysis. Our results show Groq provides vastly different results when compared with other LLMs. We have identified similarities between GPT-4o and DeepSeek-V3.
Adaptability in Multi-Agent Reinforcement Learning: A Framework and Unified Review
2025-07-14
Siyi Hu, Mohamad A Hady, Jianglin Qiao, Jimmy Cao, Mahardhika Pratama, Ryszard Kowalczyk
Multi-Agent Reinforcement Learning (MARL) has shown clear effectiveness in coordinating multiple agents across simulated benchmarks and constrained scenarios. However, its deployment in real-world multi-agent systems (MAS) remains limited, primarily due to the complex and dynamic nature of such environments. These challenges arise from multiple interacting sources of variability, including fluctuating agent populations, evolving task goals, and inconsistent execution conditions. Together, these factors demand that MARL algorithms remain effective under continuously changing system configurations and operational demands. To better capture and assess this capacity for adjustment, we introduce the concept of \textit{adaptability} as a unified and practically grounded lens through which to evaluate the reliability of MARL algorithms under shifting conditions, broadly referring to any changes in the environment dynamics that may occur during learning or execution. Centred on the notion of adaptability, we propose a structured framework comprising three key dimensions: learning adaptability, policy adaptability, and scenario-driven adaptability. By adopting this adaptability perspective, we aim to support more principled assessments of MARL performance beyond narrowly defined benchmarks. Ultimately, this survey contributes to the development of algorithms that are better suited for deployment in dynamic, real-world multi-agent systems.
Probabilistic Human Intent Prediction for Mobile Manipulation: An Evaluation with Human-Inspired Constraints
2025-07-14
Cesar Alan Contreras, Manolis Chiou, Alireza Rastegarpanah, Michal Szulik, Rustam Stolkin
Accurate inference of human intent enables human-robot collaboration without constraining human control or causing conflicts between humans and robots. We present GUIDER (Global User Intent Dual-phase Estimation for Robots), a probabilistic framework that enables a robot to estimate the intent of human operators. GUIDER maintains two coupled belief layers, one tracking navigation goals and the other manipulation goals. In the Navigation phase, a Synergy Map blends controller velocity with an occupancy grid to rank interaction areas. Upon arrival at a goal, an autonomous multi-view scan builds a local 3D cloud. The Manipulation phase combines U2Net saliency, FastSAM instance saliency, and three geometric grasp-feasibility tests, with an end-effector kinematics-aware update rule that evolves object probabilities in real-time. GUIDER can recognize areas and objects of intent without predefined goals. We evaluated GUIDER on 25 trials (five participants x five task variants) in Isaac Sim, and compared it with two baselines, one for navigation and one for manipulation. Across the 25 trials, GUIDER achieved a median stability of 93-100% during navigation, compared with 60-100% for the BOIR baseline, with an improvement of 39.5% in a redirection scenario (T5). During manipulation, stability reached 94-100% (versus 69-100% for Trajectron), with a 31.4% difference in a redirection task (T3). In geometry-constrained trials (manipulation), GUIDER recognized the object intent three times earlier than Trajectron (median remaining time to confident prediction 23.6 s vs 7.8 s). These results validate our dual-phase framework and show improvements in intent inference in both phases of mobile manipulation tasks.
Second order variations and the overspinning problem in Kerr-Taub-NUT spacetime
2025-07-14
Koray Düztaş
Previously, we attempted to over-spin Kerr-Taub-NUT black holes with test fields satisfying the null energy condition. We derived that a non-generic over-spinning could be achieved with strict restrictions on the test field modes and the initial space-time parameters. Here, we incorporate the effect of the second order variations by evaluating the Sorce-Wald condition for Kerr-Taub-NUT black holes. We calculate the second variation of the area for the sub-collection of Kerr-Taub-NUT black holes for which over-spinning is possible due to first order perturbations. We carry out an analysis to second order, avoiding the common order of magnitude problems where the second order variations appear in the form of a product with the square of an additional small parameter. We derive that the contribution of the second order variations fix the final state of the space-time parameters driving the Kerr-Taub-NUT black hole away from extremality. The event horizon is preserved in accord with the cosmic censorship conjecture and the laws of black hole dynamics.
Non-Gaussian Expansion of Minkowski Tensors in Redshift Space
2025-07-14
Stephen Appleby, Christophe Pichon, Pravabati Chingangbam, Dmitri Pogosyan, Changbom Park
This paper focuses on extending the use of Minkowski Tensors to analyze anisotropic signals in cosmological data, focusing on those introduced by redshift space distortion. We derive the ensemble average of the two translation-invariant, rank-2 Minkowski Tensors for a matter density field that is perturbatively non-Gaussian in redshift space. This is achieved through the Edgeworth expansion of the joint probability density function of the field and its derivatives, expressing the ensemble averages in terms of cumulants up to cubic order. Our goal is to connect these theoretical predictions to the underlying cosmological parameters, allowing for parameter estimation by measuring them from galaxy surveys. The work builds on previous analyses of Minkowski Functionals in both real and redshift space and addresses the effects of Finger-of-God velocity dispersion and shot noise. We validate our predictions by matching them to measurements of the Minkowski Tensors from dark matter simulation data, finding that perturbation theory is a qualified success. Non-perturbative Finger-of-God effects remain significant at relatively large scales (R< 20 Mpc/h) and are particularly pronounced in the components parallel to the line of sight.
Foundation Model Driven Robotics: A Comprehensive Review
2025-07-14
Muhammad Tayyab Khan, Ammar Waheed
The rapid emergence of foundation models, particularly Large Language Models (LLMs) and Vision-Language Models (VLMs), has introduced a transformative paradigm in robotics. These models offer powerful capabilities in semantic understanding, high-level reasoning, and cross-modal generalization, enabling significant advances in perception, planning, control, and human-robot interaction. This critical review provides a structured synthesis of recent developments, categorizing applications across simulation-driven design, open-world execution, sim-to-real transfer, and adaptable robotics. Unlike existing surveys that emphasize isolated capabilities, this work highlights integrated, system-level strategies and evaluates their practical feasibility in real-world environments. Key enabling trends such as procedural scene generation, policy generalization, and multimodal reasoning are discussed alongside core bottlenecks, including limited embodiment, lack of multimodal data, safety risks, and computational constraints. Through this lens, this paper identifies both the architectural strengths and critical limitations of foundation model-based robotics, highlighting open challenges in real-time operation, grounding, resilience, and trust. The review concludes with a roadmap for future research aimed at bridging semantic reasoning and physical intelligence through more robust, interpretable, and embodied models.
SRG/ART-XC All-Sky X-ray Survey: Sensitivity Assessment Based on Aperture Photometry
2025-07-14
N. Y. Tyrin, R. A. Krivonos, V. A. Arefiev, R. A. Burenin, E. I. Zakharov, A. A. Lutovinov, S. Y. Sazonov, A. D. Samorodova, E. V. Filippova, A. A. Abbakumov, V. V. Konoplev, F. V. Korotkov, V. N. Nazarov
The Spectrum-Roentgen-Gamma (SRG) observatory continues to operate successfully in orbit at the Lagrange point L2. The Mikhail Pavlinsky ART-XC telescope has demonstrated high efficiency in conducting X-ray surveys both over large sky regions and the entire celestial sphere. A recently published source catalog, based on data from the first four and partially completed fifth sky scans, contains 1,545 objects detected in the 4-12 keV energy range. In this work, using the same sky survey data, we assess the sensitivity to point source detection across the celestial sphere based on X-ray aperture photometry - that is, we calculate the upper flux limit in the 4-12 keV band at any given significance level. The method is implemented using both Poisson statistics and Bayesian inference, with consistent results between the two approaches. This information is important for studying variable and transient X-ray sources, as well as sources that are not detected with sufficient statistical significance in the ART-XC all-sky survey. The ART-XC upper limit service is available at https://www.srg.cosmos.ru/uplim.
A classification of global attractors for $\mathbb{S}^1$-equivariant parabolic equations
2025-07-14
Carlos Rocha, Bernold Fiedler, Alejandro López-Nieto
We survey the global dynamics of semiflows generated by scalar semilinear parabolic equations which are $\mathbb{SO}(2)$ equivariant under spatial shifts of $x\in \mathbb{S}^1=\mathbb{R}/2\pi\mathbb{Z}$, i.e. $$ u_t = u_{xx} + f(u,u_x),\qquad x\in \mathbb{S}^1.$$ For dissipative $C^2$ nonlinearities $f$, the semiflow possesses a compact global attractor $\mathcal{A}=\mathcal{A}^\mathcal{P}$ which we call Sturm attractor. The Sturm attractor $\mathcal{A}^\mathcal{P}$ decomposes as $$ \mathcal{A}^\mathcal{P}=\mathcal{E}\cup\mathcal{F}^\mathcal{P}\cup\mathcal{R}^\mathcal{P}\cup\mathcal{H}^\mathcal{P},$$ where $\mathcal{H}^\mathcal{P}$ denotes heteroclinic orbits between distinct elements of spatially homogeneous equilibria $\mathcal{E}$, rigidly rotating waves $\mathcal{R}^\mathcal{P}$ and, as their non-rotating counterparts, frozen waves $\mathcal{F}^\mathcal{P}$. We therefore represent $\mathcal{A}^\mathcal{P}$ by its connection graph $\mathcal{C}^\mathcal{P}$, with vertices in $\mathcal{E},\mathcal{F}^\mathcal{P},\mathcal{R}^\mathcal{P}$ and edges $\mathcal{H}^\mathcal{P}$. Under mild hyperbolicity assumptions, the directed graphs $\mathcal{C}^\mathcal{P}$ are finite and transitive. For illustration, we enumerate all 21 connection graphs $\mathcal{C}^\mathcal{P}$ with up to seven vertices. The result uses a lap signature of period maps associated to integrable versions of the steady state ODE of our PDE. As an example, we freeze and reconstruct the connection graph of the Vas tulip attractor, known from delay differential equations, in our PDE setting.
Fast and Accurate Stellar Mass Predictions from Broad-Band Magnitudes with a Simple Neural Network: Application to Simulated Star-Forming Galaxies
2025-07-14
E. Elson
A simple, fully connected neural network with a single hidden layer is used to estimate stellar masses for star-forming galaxies. The model is trained on broad-band photometry - from far-ultraviolet to mid-infrared wavelengths - generated by the Semi-Analytic Model of galaxy formation (SHARK), along with derived colour indices. It accurately reproduces the known SHARK stellar masses with respective root-mean-square and median errors of only 0.085 and 0.1 dex over the three decades in stellar mass. Analysis of the trained network's parameters reveals several colour indices to be particularly effective predictors of stellar mass. In particular, the FUV - NUV colour emerges as a strong determinant, suggesting that the network has implicitly learned to account for attenuation effects in the ultraviolet bands, thereby increasing the diagnostic power of this index. Traditional methods such as spectral energy distribution fitting, though widely used, are often complex, computationally expensive, and sensitive to model assumptions and parameter degeneracies. In contrast, the neural network relies solely on easily obtained observables, enabling rapid and accurate stellar mass predictions at minimal computational cost. The model derives its predictions exclusively from patterns learned in the data, without any built-in physical assumptions (such as stellar initial mass function). These results demonstrate the utility of this study's machine learning approach in astrophysical parameter estimation and highlight its potential to complement conventional techniques in upcoming large galaxy surveys.
Survey on Methods for Detection, Classification and Location of Faults in Power Systems Using Artificial Intelligence
2025-07-14
Juan A. Martinez-Velasco, Alexandre Serrano-Fontova, Ricard Bosch-Tous, Pau Casals-Torrens
Components of electrical power systems are susceptible to failures caused by lightning strikes, aging or human errors. These faults can cause equipment damage, affect system reliability, and results in expensive repair costs. As electric power systems are becoming more complex, traditional protection methods face limitations and shortcomings. Faults in power systems can occur at anytime and anywhere, can be caused by a natural disaster or an accident, and their occurrence can be hardly predicted or avoided; therefore, it is crucial to accurately estimate the fault location and quickly restore service. The development of methods capable of accurately detecting, locating and removing faults is essential (i.e. fast isolation of faults is necessary to maintain the system stability at transmission levels; accurate and fast detection and location of faults are essential for increasing reliability and customer satisfaction at distribution levels). This has motivated the development of new and more efficient methods. Methods developed to detect and locate faults in power systems can be divided into two categories, conventional and artificial intelligence-based techniques. Although the utilization of artificial intelligence (AI) techniques offer tremendous potential, they are challenging and time consuming (i.e. many AI techniques require training data for processing). This paper presents a survey of the application of AI techniques to fault diagnosis (detection, classification and location of faults) of lines and cables of power systems at both transmission and distribution levels. The paper provides a short introduction to AI concepts, a brief summary of the application of AI techniques to power system analysis and design, and a discussion on AI-based fault diagnosis methods.
With the rapid advancement of UAV technology and its extensive application in various fields such as military reconnaissance, environmental monitoring, and logistics, achieving efficient and accurate Anti-UAV tracking has become essential. The importance of Anti-UAV tracking is increasingly prominent, especially in scenarios such as public safety, border patrol, search and rescue, and agricultural monitoring, where operations in complex environments can provide enhanced security. Current mainstream Anti-UAV tracking technologies are primarily centered around computer vision techniques, particularly those that integrate multi-sensor data fusion with advanced detection and tracking algorithms. This paper first reviews the characteristics and current challenges of Anti-UAV detection and tracking technologies. Next, it investigates and compiles several publicly available datasets, providing accessible links to support researchers in efficiently addressing related challenges. Furthermore, the paper analyzes the major vision-based and vision-fusion-based Anti-UAV detection and tracking algorithms proposed in recent years. Finally, based on the above research, this paper outlines future research directions, aiming to provide valuable insights for advancing the field.
Evolution of Fear and Social Rewards in Prey-Predator Relationship
2025-07-14
Yuji Kanagawa, Kenji Doya
Fear is a critical brain function for detecting danger and learning to avoid specific stimuli that can lead to danger. While fear is believed to have evolved under pressure from predators, experimentally reproducing the evolution is challenging. To investigate the relationship between environmental conditions, the evolution of fear, and the evolution of other rewards, such as food reward and social reward, we developed a distributed evolutionary simulation. In our simulation, prey and predator agents co-evolve their innate reward functions, including a possibly fear-like term for observing predators, and learn behaviors via reinforcement learning. Surprisingly, our simulation revealed that social reward for observing the same species is more important for prey to survive, and fear-like negative reward for observing predators evolves only after acquiring social reward. We also found that the predator with increased hunting ability (larger mouth) amplified fear emergence, but also that fear evolution is more stable with non-evolving predators that are bad at chasing prey. Additionally, unlike for predators, we found that positive rewards evolve in opposition to fear for stationary threats, as areas with abundant leftover food develop around them. These findings suggest that fear and social reward have had a complex interplay with each other through evolution, along with the nature of predators and threats.
2025-07-14
Ming Wen, Jiaqi Zhu, Yuedong Xu, Yipeng Zhou, Dingding Han
Large language models (LLMs) typically require fine-tuning for domain-specific tasks, and LoRA offers a computationally efficient approach by training low-rank adapters. LoRA is also communication-efficient for federated LLMs when multiple users collaboratively fine-tune a global LLM model without sharing their proprietary raw data. However, even the transmission of local adapters between a server and clients risks serious privacy leakage. Applying differential privacy (DP) to federated LoRA encounters a dilemma: adding noise to both adapters amplifies synthetic noise on the model, while fixing one adapter impairs the learnability of fine-tuning. In this paper, we propose FedASK (Differentially Private Federated Low Rank Adaptation with Double Sketching) , a novel federated LoRA framework to enable effective updating of both low-rank adapters with robust differential privacy. Inspired by randomized SVD, our key idea is a two-stage sketching pipeline. This pipeline first aggregates carefully sketched, privacy-preserving local updates, and then reconstructs the global matrices on the server to facilitate effective updating of both adapters. We theoretically prove FedASK's differential privacy guarantee and its exact aggregation property. Comprehensive experiments demonstrate that FedASK consistently outperforms baseline methods across a variety of privacy settings and data distributions.
Has Anything Changed? Tracking Long-Term Interpretational Preferences in Quantum Mechanics
2025-07-14
Petr O. Jedlička, Šimon Kos, Martin Šmíd, Jiří Vomlel, Jan Slavík
As we approach the centennial anniversary of modern quantum mechanics this paper revisits the foundational debates through a new poll within the research community. Inspired by the survey by Schlosshauer, Kofler, and Zeilinger at the specialized 2011 Quantum Physics and the Nature of Reality conference, we expanded our recruitment to include a more representative sample of the broader community of physicists with the aim to reveal potential shifts in scientists' views and compare our findings with those from several previous polls. While quantum foundations still lack a consensus interpretation, our results indicate a persistent preference for the Copenhagen interpretation. This enduring support likely reflects both the educational emphasis on the Copenhagen interpretation and its pragmatic appeal in avoiding complex metaphysical questions and introducing new notions (e.g., other worlds or the pilot wave). Our findings thus underscore the relative stability of interpretational preferences over the past decades.
Noema formIng Cluster survEy (NICE): A Census of Star Formation and Cold Gas Properties in Massive protoclusters at 1.5
2025-07-14
Luwenjia Zhou, Tao Wang, Emanuele Daddi, Rosemary Coogan, Hanwen Sun, Ke Xu, Vinodiran Arumugam, Shuowen Jin, Daizhong Liu, Shiying Lu, Nikolaj Sillassen, Sicen Guo, Guillaume Elias, Yijun Wang, Yong Shi, Zhi-Yu Zhang, Qinghua Tan, Qiusheng Gu, David Elbaz, Aurelien Henry, Benjamin Magnelli, Carlos Gomez-Guijarro, Chiara d'Eugenio, Georgios E. Magdis, Francesco Valentino, Zhiyuan Ji, Raphael Gobat, Ivan Delvecchio, Mengyuan Xiao, Veronica Strazzullo, Alexis Finoguenov, Eva Schinnerer, R. Michael Rich, Jiasheng Huang, Yu Dai, Yanmei Chen, Fangyou Gao, Tiancheng Yang, Qiaoyang Hao
Massive protoclusters at z~1.5-4, the peak of the cosmic star formation history, are key to understanding the formation mechanisms of massive galaxies in today's clusters. However, studies of protoclusters at these high redshifts remain limited, primarily due to small sample sizes and heterogeneous selection criteria. In this work, we conduct a systematic investigation of the star formation and cold gas properties of member galaxies of eight massive protoclusters in the COSMOS field, using the statistical and homogeneously selected sample from the Noema formIng Cluster survEy (NICE). Our analysis reveals a steep increase in the star formation rates per halo mass ($\Sigma_{\rm SFR} /M_{\rm halo}$) with redshifts in these intensively star-forming protoclusters, reaching values one to two orders of magnitude higher than those observed in the field at z>2. We further show that, instead of an enhancement of starbursts, this increase is largely driven by the concentration of massive and gas-rich star-forming galaxies in the protocluster cores. The member galaxies still generally follow the same star formation main sequence as in the field, with a moderate enhancement at the low mass end. Notably, the most massive protocluster galaxies ($M_\star$>8$\times$10$^{10}$M$_\odot$) exhibit higher $f_{\rm gas}$ and $\tau_{\rm gas}$ than their field counterparts, while remaining on the star forming main sequence. These gas-rich, massive, and star-forming galaxies are predominantly concentrated in the protocluster cores and are likely progenitors of massive ellipticals in the center of today's clusters. These results suggest that the formation of massive galaxies in such environments is sustained by substantial gas reservoirs, which support persistent star formation and drive early mass assembly in forming cluster cores.
Text-Driven Causal Representation Learning for Source-Free Domain Generalization
2025-07-14
Lihua Zhou, Mao Ye, Nianxin Li, Shuaifeng Li, Jinlin Wu, Xiatian Zhu, Lei Deng, Hongbin Liu, Jiebo Luo, Zhen Lei
Deep learning often struggles when training and test data distributions differ. Traditional domain generalization (DG) tackles this by including data from multiple source domains, which is impractical due to expensive data collection and annotation. Recent vision-language models like CLIP enable source-free domain generalization (SFDG) by using text prompts to simulate visual representations, reducing data demands. However, existing SFDG methods struggle with domain-specific confounders, limiting their generalization capabilities. To address this issue, we propose TDCRL (\textbf{T}ext-\textbf{D}riven \textbf{C}ausal \textbf{R}epresentation \textbf{L}earning), the first method to integrate causal inference into the SFDG setting. TDCRL operates in two steps: first, it employs data augmentation to generate style word vectors, combining them with class information to generate text embeddings to simulate visual representations; second, it trains a causal intervention network with a confounder dictionary to extract domain-invariant features. Grounded in causal learning, our approach offers a clear and effective mechanism to achieve robust, domain-invariant features, ensuring robust generalization. Extensive experiments on PACS, VLCS, OfficeHome, and DomainNet show state-of-the-art performance, proving TDCRL effectiveness in SFDG.
Constraining $σ_8$ with Lensing Statistics in Low and High Density Regions
2025-07-14
Yiqi Huang, Fuyu Dong, Jun Zhang, Cong Liu, Hekun Li
Lensing studies are typically carried out around high density regions, such as groups and clusters, where the lensing signals are significant and indicative of rich density structures. However, a more comprehensive test of the cosmological model should also include the lensing effect in low density regions. In this work, we incorporate the stacked weak lensing signals around the low density positions, alongside galaxy-galaxy lensing and galaxy-galaxy two point correlation function to perform a joint cosmological analysis on $\sigma_8$. The low density positions are constructed from the DR9 data release of the DESI legacy imaging survey, using galaxies with r-band absolute magnitude cut M$<$-21.5 and in the redshift range of 0.18$<$z$<$0.28. In doing so, we simultaneously parameterize photometric redshift errors and halo mass uncertainties while building mock catalogs from simulations using the method of SubHalo Abundance Matching (SHAM). For the weak lensing measurements, we use the shear estimators derived from the DECaLS DR8 imaging data, processed by the Fourier\_Quad pipeline. The survey boundaries and masks are fully taken into account. Our analysis achieves a total significance of $31.1\sigma$ detection for lensing in the low density positions, which significantly improve the $\sigma_8$ constraint compared to galaxy-galaxy lensing and galaxy-galaxy two point correlation function by 14$\%$. For flat $\Lambda$CDM model, we constrain $\sigma_8$ =$0.824^{+0.015}_{-0.015}$, which shows a good agreement with the PLANCK result. Additionally, the halo mass uncertainty $\sigma_{\lg M}$ and photometric redshift error $\sigma_z$ are constrained to be $0.565^{+0.086}_{-0.070}$ and $0.004^{+0.004}_{-0.003}$ respectively, which are somewhat different from our expectations due to the significant degeneracy of the two parameters.
DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models
2025-07-14
Luolin Xiong, Haofen Wang, Xi Chen, Lu Sheng, Yun Xiong, Jingping Liu, Yanghua Xiao, Huajun Chen, Qing-Long Han, Yang Tang
DeepSeek, a Chinese Artificial Intelligence (AI) startup, has released their V3 and R1 series models, which attracted global attention due to their low cost, high performance, and open-source advantages. This paper begins by reviewing the evolution of large AI models focusing on paradigm shifts, the mainstream Large Language Model (LLM) paradigm, and the DeepSeek paradigm. Subsequently, the paper highlights novel algorithms introduced by DeepSeek, including Multi-head Latent Attention (MLA), Mixture-of-Experts (MoE), Multi-Token Prediction (MTP), and Group Relative Policy Optimization (GRPO). The paper then explores DeepSeek engineering breakthroughs in LLM scaling, training, inference, and system-level optimization architecture. Moreover, the impact of DeepSeek models on the competitive AI landscape is analyzed, comparing them to mainstream LLMs across various fields. Finally, the paper reflects on the insights gained from DeepSeek innovations and discusses future trends in the technical and engineering development of large AI models, particularly in data, training, and reasoning.
The Rosetta Stone Project. II. The correlation between SFE and L/M indicator for the evolutionary stages of star-forming clumps in post-processed RMHD simulations
2025-07-14
Ngo-Duy Tung, Alessio Traficante, Ugo Lebreuilly, Alice Nucara, Leonardo Testi, Patrick Hennebelle, Ralf S. Klessen, Sergio Molinari, Veli-Matti Pelkonen, Milena Benedettini, Alessandro Coletta, Davide Elia, Gary A. Fuller, Stefania Pezzuto, Juan D. Soler, Claudia Toci
Context. The evolution of massive star-forming clumps that are progenitors of high-mass young stellar objects are often classified based on a variety of observational indicators ranging from near-IR to radio wavelengths. Among them, the ratio between the bolometric luminosity and the mass of their envelope, $L/M$, has been observationally diagnosed as a good indicator for the evolutionary classification of parsec-scale star-forming clumps in the Galaxy. Aims. We have developed the Rosetta Stone project$\unicode{x2013}$an end-to-end framework designed to enable an accurate comparison between simulations and observations for investigating the formation and evolution of massive clumps. In this study, we calibrate the $L/M$ indicator in relation to the star formation efficiency (SFE) and the clump age, as derived from our suite of simulations. Methods. We perform multi-wavelength radiative transfer post-processing of RMHD simulations of the collapse of star-forming clumps fragmenting into protostars. We generate synthetic observations to obtain far-infrared emission from $70$ to $500\ \mu$m, as was done in the Hi-GAL survey and at $24\ \mu$m in the MIPSGAL survey, which are then used to build the spectral energy distributions (SEDs) and estimate the $L/M$ parameter. An additional $1.3$ mm wavelength in ALMA Band 6 has also been produced for the comparison with observational data. We have applied observational techniques, commonly employed by observers, to the synthetic data, in order to derive the corresponding physical parameters. Results. We find a correlation between $L/M$ and the SFE, with a power-law form $L/M\propto {\rm SFE}^{1.20^{+0.02}_{-0.03}}$. This correlation is independent of the mass of the clumps and the choice of initial conditions of the simulations in which they formed. (Abridged)
When Less is More: A systematic review of four-day workweek conceptualizations and their effects on organizational performance
2025-07-14
Marvin Auf der Landwehr, Julia Topp, Michael Neumann
Context: Agile IT organizations, which are characterized by self-organization and collaborative social interactions, require motivating, efficient and flexible work environments to maximize value creation. Compressed work schedules such as the four-day workweek have evolved into multiple facets over the last decades and are associated with various benefits for organizations and their employees. Objective: Our objective in this study is to deepen our comprehension of the impact of compressed work schedules on the operational efficacy of IT enterprises, while concurrently developing a comprehensive framework delineating the intricacies of compressed work schedules.Method: We conducted a systematic review of available conceptualizations related to four-day workweek schedules and elaborate on their organizational and social effects. To cover scientific and practice-oriented literature, our review combined a systematic literature review and a web content analysis. Results: Based on the generated insights, we derive a meta-framework that matches conceptualizations and effects, finally guiding the adoption of compressed work schedules based on individual managerial prerequisites and circumstances.
ASTAR-NTU solution to AudioMOS Challenge 2025 Track1
2025-07-14
Fabian Ritter-Gutierrez, Yi-Cheng Lin, Jui-Chiang Wei, Jeremy H. M. Wong, Nancy F. Chen, Hung-yi Lee
Evaluation of text-to-music systems is constrained by the cost and availability of collecting experts for assessment. AudioMOS 2025 Challenge track 1 is created to automatically predict music impression (MI) as well as text alignment (TA) between the prompt and the generated musical piece. This paper reports our winning system, which uses a dual-branch architecture with pre-trained MuQ and RoBERTa models as audio and text encoders. A cross-attention mechanism fuses the audio and text representations. For training, we reframe the MI and TA prediction as a classification task. To incorporate the ordinal nature of MOS scores, one-hot labels are converted to a soft distribution using a Gaussian kernel. On the official test set, a single model trained with this method achieves a system-level Spearman's Rank Correlation Coefficient (SRCC) of 0.991 for MI and 0.952 for TA, corresponding to a relative improvement of 21.21\% in MI SRCC and 31.47\% in TA SRCC over the challenge baseline.
Large language models (LLMs) increasingly rely on reinforcement learning (RL) to enhance their reasoning capabilities through feedback. A critical challenge is verifying the consistency of model-generated responses and reference answers, since these responses are often lengthy, diverse, and nuanced. Rule-based verifiers struggle with complexity, prompting the use of model-based verifiers. However, specialized verifiers lack flexibility, while general LLM judges can be inconsistent. Existing research primarily focuses on building better verifiers, yet a systematic evaluation of different types of verifiers' performance across domains remains lacking, severely constraining the reliable development of Reinforcement Learning with Verifiable Reward (RLVR). To address this, we propose VerifyBench--a cross-domain comprehensive benchmark for systematically evaluating verifiers. We construct 4,000 expert-level questions covering mathematics, physics, chemistry, and biology. Each question is equipped with reference answers and diverse responses. The reliability of the evaluation is ensured through a rigorous annotation process conducted by a multidisciplinary expert team. We design a four-dimensional experimental framework to comprehensively compare the performance boundaries of specialized verifiers and general LLMs under combined conditions of extracted answers vs. complete responses, and short vs. long outputs. Our evaluation uncovers fundamental trade-offs in verifiers: while specialized verifiers achieve leading accuracy, they exhibit deficiencies in recall; general models show stronger inclusivity but unstable precision. More importantly, we discover verifiers' high sensitivity to input structure and inherent limitations in cross-domain generalization, providing critical insights into the bottlenecks of current verifier technology.
Visually-Rich Document Understanding (VRDU) has emerged as a critical field, driven by the need to automatically process documents containing complex visual, textual, and layout information. Recently, Multimodal Large Language Models (MLLMs) have shown remarkable potential in this domain, leveraging both Optical Character Recognition (OCR)-dependent and OCR-free frameworks to extract and interpret information in document images. This survey reviews recent advancements in MLLM-based VRDU, highlighting three core components: (1) methods for encoding and fusing textual, visual, and layout features; (2) training paradigms, including pretraining strategies, instruction-response tuning, and the trainability of different model modules; and (3) datasets utilized for pretraining, instruction-tuning, and supervised fine-tuning. Finally, we discuss the challenges and opportunities in this evolving field and propose future directions to advance the efficiency, generalizability, and robustness of VRDU systems.
ThinkingViT: Matryoshka Thinking Vision Transformer for Elastic Inference
2025-07-14
Ali Hojjat, Janek Haberer, Soren Pirk, Olaf Landsiedel
Vision Transformers deliver state-of-the-art performance, yet their fixed computational budget prevents scalable deployment across heterogeneous hardware. Recent nested Transformer architectures mitigate this by embedding nested subnetworks within a single model to enable scalable inference. However, these models allocate the same amount of compute to all inputs, regardless of their complexity, which leads to inefficiencies. To address this, we introduce ThinkingViT, a nested ViT architecture that employs progressive thinking stages to dynamically adjust inference computation based on input difficulty. ThinkingViT initiates inference by activating a small subset of the most important attention heads and terminates early if predictions reach sufficient certainty. Otherwise, it activates additional attention heads and re-evaluates the input. At the core of ThinkingViT is our Token Recycling mechanism, which conditions each subsequent inference stage on the embeddings from the previous stage, enabling progressive improvement. Due to its backbone-preserving design, ThinkingViT also serves as a plugin upgrade for vanilla ViT. Experiments show that ThinkingViT surpasses nested baselines by up to 2.0 percentage points (p.p.) in accuracy at the same throughput and by up to 2.9 p.p. at equal GMACs on ImageNet-1K. The source code is available at https://github.com/ds-kiel/ThinkingViT.
Uncertainty-Informed Scheduling of Decision Points for Intelligent Mobile Health Interventions
2025-07-14
Asim H. Gazi, Bhanu T. Gullapalli, Daiqi Gao, Benjamin M. Marlin, Vivek Shetty, Susan A. Murphy
Timely decision making is critical to the effectiveness of mobile health (mHealth) interventions. At predefined timepoints called "decision points," intelligent mHealth systems such as just-in-time adaptive interventions (JITAIs) estimate an individual's biobehavioral context from sensor or survey data and determine whether and how to intervene. For interventions targeting habitual behavior (e.g., oral hygiene), effectiveness often hinges on delivering support shortly before the target behavior is likely to occur. Current practice schedules decision points at a fixed interval (e.g., one hour) before user-provided behavior times, and the fixed interval is kept the same for all individuals. However, this one-size-fits-all approach performs poorly for individuals with irregular routines, often scheduling decision points after the target behavior has already occurred, rendering interventions ineffective. In this paper, we propose SigmaScheduling, a method to dynamically schedule decision points based on uncertainty in predicted behavior times. When behavior timing is more predictable, SigmaScheduling schedules decision points closer to the predicted behavior time; when timing is less certain, SigmaScheduling schedules decision points earlier, increasing the likelihood of timely intervention. We evaluated SigmaScheduling using real-world data from 68 participants in a 10-week trial of Oralytics, a JITAI designed to improve daily toothbrushing. SigmaScheduling increased the likelihood that decision points preceded brushing events in at least 70% of cases, preserving opportunities to intervene and impact behavior. Our results indicate that SigmaScheduling can advance precision mHealth, particularly for JITAIs targeting time-sensitive, habitual behaviors such as oral hygiene or dietary habits.
Raman signature of cation vacancies in rare-earth nitrides
2025-07-14
M. Markwitz, K. Van Koughnet, K. Kneisel, W. F. Holmes-Hewett, F. Natali, E. X. M. Trewick, L. Porteous, B. J. Ruck, H. J. Trodahl
We report a coordinated Raman/computation study of the rare-earth nitrides, a series of intrinsic ferromagnetic semiconductors, to reveal the presence of cation vacancies. Their presence is signaled by a Raman-active vibrational mode at 1100-1400 cm$^{-1}$, rising steadily as the lattice contracts across the series. The mode's frequency is in excellent agreement with the computed breathing-mode vibration of the six nitrogen ions surrounding cation vacancies. The discovery of such cation vacancies opens the door for hole doping that has so far been lacking in the exploitation of rare-earth nitrides.
Standardized Evaluation of Fetal Phonocardiography Processing Methods
2025-07-14
Kristóf Müller, Janka Hatvani, Márton Áron Goda, Miklós Koller
Motivation. Phonocardiography can give access to the fetal heart rate as well as direct heart sound data, and is entirely passive, using no radiation of any kind. Approach. We discuss the currently available methods for fetal heart sound detection and heart rate estimation and compare them using a common benchmarking platform and a pre-selected testing dataset. Compared to previous reviews, we evaluated the discussed methods in a standardized manner for a fair comparison. Our tests included tolerance-based detection accuracy, error rates for label insertions, deletions, and substitutions, and statistical measures for heart rate mean square error. Results. Based on our results, there is no definite best method that can achieve the highest scores in all of the tests, and simpler methods could perform comparably to more complex ones. The best model for first heart sound detection achieved 97.6% F1-score, 97.4% positive predictive value, and 12.2+-8.0 ms mean absolute error. In terms of second heart sound detection the best model had 91.4% F1-score, 91.3% positive predictive value, and 17.3+-12.2 ms mean absolute error. For fetal heart rate a 0.644 mean square error was achieved by the best method. Significance. Our main conclusion is that further standardization is required in fetal heart rate and heart sound detection method evaluation. The tests and algorithm implementations are openly available at: https://github.com/mulkr/standard-fpcg-evaluation.
AI and the Net-Zero Journey: Energy Demand, Emissions, and the Potential for Transition
2025-07-14
Pandu Devarakota, Nicolas Tsesmetzis, Faruk O. Alpak, Apurva Gala, Detlef Hohl
Thanks to the availability of massive amounts of data, computing resources, and advanced algorithms, AI has entered nearly every sector. This has sparked significant investment and interest, particularly in building data centers with the necessary hardware and software to develop and operate AI models and AI-based workflows. In this technical review article, we present energy consumption scenarios of data centers and impact on GHG emissions, considering both near-term projections (up to 2030) and long-term outlook (2035 and beyond). We address the quintessential question of whether AI will have a net positive, neutral, or negative impact on CO2 emissions by 2035. Additionally, we discuss AI's potential to automate, create efficient and disruptive workflows across various fields related to energy production, supply and consumption. In the near-term scenario, the growing demand for AI will likely strain computing resources, lead to increase in electricity consumption and therefore associated CO2 emissions. This is due to the power-hungry nature of big data centers and the requirements for training and running of large and complex AI models, as well as the penetration of AI assistant search and applications for public use. However, the long-term outlook could be more promising. AI has the potential to be a game-changer in CO2 reduction. Its ability to further automate and optimize processes across industries, from energy production to logistics, could significantly decrease our carbon footprint. This positive impact is anticipated to outweigh the initial emissions bump, creating value for businesses and society in areas where traditional solutions have fallen short. In essence, AI might cause some initial growing pains for the environment, but it has the potential to support climate mitigation efforts.
Parsing Musical Structure to Enable Meaningful Variations
2025-07-14
Maziar Kanani, Sean O Leary, James McDermott
This paper presents a novel rule-based approach for generating music by varying existing tunes. We parse each tune to find the Pathway Assembly (PA) [ 1], that is a structure representing all repetitions in the tune. The Sequitur algorithm [2 ] is used for this. The result is a grammar. We then carry out mutation on the grammar, rather than on a tune directly. There are potentially 19 types of mutations such as adding, removing, swapping or reversing parts of the grammar that can be applied to the grammars. The system employs one of the mutations randomly in this step to automatically manipulate the grammar. Following the mutation, we need to expand the grammar which returns a new tune. The output after 1 or more mutations will be a new tune related to the original tune. Our study examines how tunes change gradually over the course of multiple mutations. Edit distances, structural complexity and length of the tunes are used to show how a tune is changed after multiple mutations. In addition, the size of effect of each mutation type is analyzed. As a final point, we review the musical aspect of the output tunes. It should be noted that the study only focused on generating new pitch sequences. The study is based on an Irish traditional tune dataset and a list of integers has been used to represent each tune's pitch values.
Will anyone review this paper? Screening, sorting, and the feedback cycles that imperil peer review
2025-07-14
Carl T. Bergstrom, Kevin Gross
Scholarly publishing relies on peer review to identify the best science. Yet finding willing and qualified reviewers to evaluate manuscripts has become an increasingly challenging task, possibly even threatening the long-term viability of peer review as an institution. What can or should be done to salvage it? Here, we develop mathematical models to reveal the intricate interactions among incentives faced by authors, reviewers, and readers in their endeavors to identify the best science. Two facets are particularly salient. First, peer review partially reveals authors' private sense of their work's quality through their decisions of where to send their manuscripts. Second, journals' reliance on traditionally unpaid and largely unrewarded review labor deprives them of a standard market mechanism -- wages -- to recruit additional reviewers when review labor is in short supply. We highlight a resulting feedback loop that threatens to overwhelm the peer review system: (1) an increase in submissions overtaxes the pool of suitable peer reviewers; (2) the accuracy of review drops because journals either must either solicit assistance from less qualified reviewers or ask current reviewers to do more; (3) as review accuracy drops, submissions further increase as more authors try their luck at venues that might otherwise be a stretch. We illustrate how this cycle is further propelled by forces including the increasing emphasis on high-impact publications, the proliferation of journals, and competition among these journals for peer reviews. Finally, we suggest interventions that could slow or even reverse this cycle of peer-review meltdown.
Universality in computable dynamical systems: Old and new
2025-07-14
Ángel González-Prieto, Eva Miranda, Daniel Peralta-Salas
The relationship between computational models and dynamics has captivated mathematicians and computer scientists since the earliest conceptualizations of computation. Recently, this connection has gained renewed attention, fueled by T. Tao's programme aiming to discover blowing-up solutions of the Navier-Stokes equations using an embedded computational model. In this survey paper, we review some of the recent works that introduce novel and exciting perspectives on the representation of computability through dynamical systems. Starting from dynamical universality in a classical sense, we shall explore the modern notions of Turing universality in fluid dynamics and Topological Kleene Field Theories as a systematic way of representing computable functions by means of dynamical bordisms. Finally, we will discuss some important open problems in the area.
Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems
2025-07-14
Sohan Shankar, Yi Pan, Hanqi Jiang, Zhengliang Liu, Mohammad R. Darbandi, Agustin Lorenzo, Junhao Chen, Md Mehedi Hasan, Arif Hassan Zidan, Eliana Gelman, Joshua A. Konfrst, Jillian Y. Russell, Katelyn Fernandes, Tianze Yang, Yiwei Li, Huaqin Zhao, Afrar Jahin, Triparna Ganguly, Shair Dinesha, Yifan Zhou, Zihao Wu, Xinliang Li, Lokesh Adusumilli, Aziza Hussein, Sagar Nookarapu, Jixin Hou, Kun Jiang, Jiaxi Li, Brenden Heinel, XianShen Xi, Hailey Hubbard, Zayna Khan, Levi Whitaker, Ivan Cao, Max Allgaier, Andrew Darby, Lin Zhao, Lu Zhang, Xiaoqiao Wang, Xiang Li, Wei Zhang, Xiaowei Yu, Dajiang Zhu, Yohannes Abate, Tianming Liu
This position and survey paper identifies the emerging convergence of neuroscience, artificial general intelligence (AGI), and neuromorphic computing toward a unified research paradigm. Using a framework grounded in brain physiology, we highlight how synaptic plasticity, sparse spike-based communication, and multimodal association provide design principles for next-generation AGI systems that potentially combine both human and machine intelligences. The review traces this evolution from early connectionist models to state-of-the-art large language models, demonstrating how key innovations like transformer attention, foundation-model pre-training, and multi-agent architectures mirror neurobiological processes like cortical mechanisms, working memory, and episodic consolidation. We then discuss emerging physical substrates capable of breaking the von Neumann bottleneck to achieve brain-scale efficiency in silicon: memristive crossbars, in-memory compute arrays, and emerging quantum and photonic devices. There are four critical challenges at this intersection: 1) integrating spiking dynamics with foundation models, 2) maintaining lifelong plasticity without catastrophic forgetting, 3) unifying language with sensorimotor learning in embodied agents, and 4) enforcing ethical safeguards in advanced neuromorphic autonomous systems. This combined perspective across neuroscience, computation, and hardware offers an integrative agenda for in each of these fields.
Structural evolution of quiescent galaxies at intermediate redshift at UV and red rest-frame wavelengths
2025-07-14
Michele Pizzardo, Ivana Damjanov, Jubee Sohn, Margaret J. Geller
We model the wavelength dependence of structural parameters for a mass-limited sample ($M_\star>10^{10}M_\odot$) of $\sim27,000$ quiescent galaxies with $0.2 < z < 0.6$ using $grizy$ photometry from Subaru/Hyper Suprime-Cam and dense spectroscopy from the HectoMAP survey. Based on S\'ersic profile fits in all five bands, we estimate the circularized half-light radius $R_{e,c}$ and S\'ersic index $n$ in two rest-frames: UV (3500 \r{A}) and red (7000 \r{A}). Combined with $M_\star$, $z$, and D$_n4000$, $R_{e,c}$ and $n$ enable exploration of the evolution in the structural properties - stellar mass correlations for quiescent galaxies with different stellar population ages. At intermediate redshift, quiescent galaxies at all stellar masses show a systematic decline in $R_{e,c}$ and rise in $n$ with rest-frame wavelength. These structural variations are stronger for galaxies that recently joined the quiescent population (newcomers) than for the descendants of galaxies that are already quiescent at the survey limit, $z \sim 0.6$ (aging population). The combined evidence supports inside-out quenching as the dominant mechanism halting star formation during this epoch. The typical size of a $M_\star\sim10^{11}M_\odot$ quiescent galaxy increases by $\sim30\%$ between $z \sim 0.6$ and $z \sim 0.2$ in the red and remains constant in the UV; newcomers are $\sim20\%$ larger than the aging population. In the UV, quiescent galaxies maintain a constant $n\sim4$ for the aging population and $n\sim2$ for newcomers; in the red, both subpopulations have de Vaucouleurs profiles. Our findings link newcomers to their direct progenitors in the star-forming population. For the aging population, we suggest minor mergers with progressively redder satellites at lower redshifts as the primary driver of quiescent galaxy evolution. Forthcoming sensitive large-area imaging surveys will allow testing this prediction.
Vision Language Action Models in Robotic Manipulation: A Systematic Review
2025-07-14
Muhayy Ud Din, Waseem Akram, Lyes Saad Saoud, Jan Rosell, Irfan Hussain
Vision Language Action (VLA) models represent a transformative shift in robotics, with the aim of unifying visual perception, natural language understanding, and embodied control within a single learning framework. This review presents a comprehensive and forward-looking synthesis of the VLA paradigm, with a particular emphasis on robotic manipulation and instruction-driven autonomy. We comprehensively analyze 102 VLA models, 26 foundational datasets, and 12 simulation platforms that collectively shape the development and evaluation of VLAs models. These models are categorized into key architectural paradigms, each reflecting distinct strategies for integrating vision, language, and control in robotic systems. Foundational datasets are evaluated using a novel criterion based on task complexity, variety of modalities, and dataset scale, allowing a comparative analysis of their suitability for generalist policy learning. We introduce a two-dimensional characterization framework that organizes these datasets based on semantic richness and multimodal alignment, showing underexplored regions in the current data landscape. Simulation environments are evaluated for their effectiveness in generating large-scale data, as well as their ability to facilitate transfer from simulation to real-world settings and the variety of supported tasks. Using both academic and industrial contributions, we recognize ongoing challenges and outline strategic directions such as scalable pretraining protocols, modular architectural design, and robust multimodal alignment strategies. This review serves as both a technical reference and a conceptual roadmap for advancing embodiment and robotic control, providing insights that span from dataset generation to real world deployment of generalist robotic agents.
From Semantic Web and MAS to Agentic AI: A Unified Narrative of the Web of Agents
2025-07-14
Tatiana Petrova, Boris Bliznioukov, Aleksandr Puzikov, Radu State
The concept of the Web of Agents (WoA), which transforms the static, document-centric Web into an environment of autonomous agents acting on users' behalf, has attracted growing interest as large language models (LLMs) become more capable. However, research in this area is still fragmented across different communities. Contemporary surveys catalog the latest LLM-powered frameworks, while the rich histories of Multi-Agent Systems (MAS) and the Semantic Web are often treated as separate, legacy domains. This fragmentation obscures the intellectual lineage of modern systems and hinders a holistic understanding of the field's trajectory. We present the first comprehensive evolutionary overview of the WoA. We show that modern protocols like A2A and the MCP, are direct evolutionary responses to the well-documented limitations of earlier standards like FIPA standards and OWL-based semantic agents. To systematize this analysis, we introduce a four-axis taxonomy (semantic foundation, communication paradigm, locus of intelligence, discovery mechanism). This framework provides a unified analytical lens for comparing agent architectures across all generations, revealing a clear line of descent where others have seen a disconnect. Our analysis identifies a paradigm shift in the 'locus of intelligence': from being encoded in external data (Semantic Web) or the platform (MAS) to being embedded within the agent's core model (LLM). This shift is foundational to modern Agentic AI, enabling the scalable and adaptive systems the WoA has long envisioned. We conclude that while new protocols are essential, they are insufficient for building a robust, open, trustworthy ecosystem. Finally, we argue that the next research frontier lies in solving persistent socio-technical challenges, and we map out a new agenda focused on decentralized identity, economic models, security, and governance for the emerging WoA.
SENSOR: An ML-Enhanced Online Annotation Tool to Uncover Privacy Concerns from User Reviews in Social-Media Applications
2025-07-14
Labiba Farah, Mohammad Ridwan Kabir, Shohel Ahmed, MD Mohaymen Ul Anam, Md. Sakibul Islam
The widespread use of social media applications has raised significant privacy concerns, often highlighted in user reviews. These reviews also provide developers with valuable insights into improving apps by addressing issues and introducing better features. However, the sheer volume and nuanced nature of reviews make manual identification and prioritization of privacy-related concerns challenging for developers. Previous studies have developed software utilities to automatically classify user reviews as privacy-relevant, privacy-irrelevant, bug reports, feature requests, etc., using machine learning. Notably, there is a lack of focus on classifying reviews specifically as privacy-related feature requests, privacy-related bug reports, or privacy-irrelevant. This paper introduces SENtinel SORt (SENSOR), an automated online annotation tool designed to help developers annotate and classify user reviews into these categories. For automating the annotation of such reviews, this paper introduces the annotation model, GRACE (GRU-based Attention with CBOW Embedding), using Gated Recurrent Units (GRU) with Continuous Bag of Words (CBOW) and Attention mechanism. Approximately 16000 user reviews from seven popular social media apps on Google Play Store, including Instagram, Facebook, WhatsApp, Snapchat, X (formerly Twitter), Facebook Lite, and Line were analyzed. Two annotators manually labelled the reviews, achieving a Cohen's Kappa value of 0.87, ensuring a labeled dataset with high inter-rater agreement for training machine learning models. Among the models tested, GRACE demonstrated the best performance (macro F1-score: 0.9434, macro ROC-AUC: 0.9934, and accuracy: 95.10%) despite class imbalance. SENSOR demonstrates significant potential to assist developers with extracting and addressing privacy-related feature requests or bug reports from user reviews, enhancing user privacy and trust.
Comprehension Without Competence: Architectural Limits of LLMs in Symbolic Computation and Reasoning
2025-07-14
Zheng Zhang
Large Language Models (LLMs) display striking surface fluency yet systematically fail at tasks requiring symbolic reasoning, arithmetic accuracy, and logical consistency. This paper offers a structural diagnosis of such failures, revealing a persistent gap between \textit{comprehension} and \textit{competence}. Through controlled experiments and architectural analysis, we demonstrate that LLMs often articulate correct principles without reliably applying them--a failure rooted not in knowledge access, but in computational execution. We term this phenomenon the computational \textit{split-brain syndrome}, where instruction and action pathways are geometrically and functionally dissociated. This core limitation recurs across domains, from mathematical operations to relational inferences, and explains why model behavior remains brittle even under idealized prompting. We argue that LLMs function as powerful pattern completion engines, but lack the architectural scaffolding for principled, compositional reasoning. Our findings delineate the boundary of current LLM capabilities and motivate future models with metacognitive control, principle lifting, and structurally grounded execution. This diagnosis also clarifies why mechanistic interpretability findings may reflect training-specific pattern coordination rather than universal computational principles, and why the geometric separation between instruction and execution pathways suggests limitations in neural introspection and mechanistic analysis.
Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats
2025-07-14
Quanyan Zhu
Protecting cyberspace requires not only advanced tools but also a shift in how we reason about threats, trust, and autonomy. Traditional cybersecurity methods rely on manual responses and brittle heuristics. To build proactive and intelligent defense systems, we need integrated theoretical frameworks and software tools. Game theory provides a rigorous foundation for modeling adversarial behavior, designing strategic defenses, and enabling trust in autonomous systems. Meanwhile, software tools process cyber data, visualize attack surfaces, verify compliance, and suggest mitigations. Yet a disconnect remains between theory and practical implementation. The rise of Large Language Models (LLMs) and agentic AI offers a new path to bridge this gap. LLM-powered agents can operationalize abstract strategies into real-world decisions. Conversely, game theory can inform the reasoning and coordination of these agents across complex workflows. LLMs also challenge classical game-theoretic assumptions, such as perfect rationality or static payoffs, prompting new models aligned with cognitive and computational realities. This co-evolution promises richer theoretical foundations and novel solution concepts. Agentic AI also reshapes software design: systems must now be modular, adaptive, and trust-aware from the outset. This chapter explores the intersection of game theory, agentic AI, and cybersecurity. We review key game-theoretic frameworks (e.g., static, dynamic, Bayesian, and signaling games) and solution concepts. We then examine how LLM agents can enhance cyber defense and introduce LLM-driven games that embed reasoning into AI agents. Finally, we explore multi-agent workflows and coordination games, outlining how this convergence fosters secure, intelligent, and adaptive cyber systems.
These notes begin in Chapter 1 with a review of linear algebra and the postulates of quantum mechanics, leading to an explanation of single- and multi-qubit gates. Chapter 2 explores the challenge of constructing arbitrary quantum states from given initial states, and introduces circuits for building oracles. Chapter 3 presents foundational algorithms such as entanglement creation, quantum teleportation, Deutsch-Jozsa, Bernstein-Vazirani, and Simon's algorithm. Chapters 4 and 5 cover algorithms based on the quantum Fourier transform, including phase estimation, period finding, factoring, and logarithm computation. These chapters also include complexity analysis and detailed quantum circuits suitable for implementation in code. Chapter 6 introduces Grover's algorithm for quantum search and amplitude amplification, including its realization via Hamiltonian simulation and a method for derandomization. Chapter 7 discusses basic techniques for Hamiltonian simulation, such as Lie-Trotter decomposition, sparse Hamiltonians, and the linear combination of unitaries. It also provides example circuits for simulating Hamiltonians expressed as linear combinations of Pauli operators. Chapter 8 introduces variational quantum algorithms, and Chapter 9 presents an algorithm for simulating fermionic many-particle systems, with an emphasis on molecular Hamiltonians. It also outlines the key transformations needed to map a molecular Hamiltonian to a form suitable for simulation on a quantum computer.
Reinforcement Learning (RL) has emerged as a powerful paradigm for sequential decision-making and has attracted growing interest across various domains, particularly following the advent of Deep Reinforcement Learning (DRL) in 2015. Simultaneously, the rapid advancement of Large Language Models (LLMs) has further fueled interest in integrating RL with LLMs to enable more adaptive and intelligent systems. In the field of software engineering (SE), the increasing complexity of systems and the rising demand for automation have motivated researchers to apply RL to a broad range of tasks, from software design and development to quality assurance and maintenance. Despite growing research in RL-for-SE, there remains a lack of a comprehensive and systematic survey of this evolving field. To address this gap, we reviewed 115 peer-reviewed studies published across 22 premier SE venues since the introduction of DRL. We conducted a comprehensive analysis of publication trends, categorized SE topics and RL algorithms, and examined key factors such as dataset usage, model design and optimization, and evaluation practices. Furthermore, we identified open challenges and proposed future research directions to guide and inspire ongoing work in this evolving area. To summarize, this survey offers the first systematic mapping of RL applications in software engineering, aiming to support both researchers and practitioners in navigating the current landscape and advancing the field. Our artifacts are publicly available: https://github.com/KaiWei-Lin-lanina/RL4SE.
Legendre Polynomials and Their Use for Karhunen-Loève Expansion
2025-07-13
Michal Béreš
This paper makes two main contributions. First, we present a pedagogical review of the derivation of the three-term recurrence relation for Legendre polynomials, without relying on the classical Legendre differential equation, Rodrigues' formula, or generating functions. This exposition is designed to be accessible to undergraduate students. Second, we develop a computational framework for Karhunen-Lo\`eve expansions of isotropic Gaussian random fields on hyper-rectangular domains. The framework leverages Legendre polynomials and their associated Gaussian quadrature, and it remains efficient even in higher spatial dimensions. A covariance kernel is first approximated by a non-negative mixture of squared-exponentials, obtained via a Newton-optimized fit with a theoretically informed initialization. The resulting separable kernel enables a Legendre-Galerkin discretization in the form of a Kronecker product over single dimensions, with submatrices that exhibit even/odd parity structure. For assembly, we introduce a Duffy-type transformation followed by quadrature. These structural properties significantly reduce both memory usage and arithmetic cost compared to naive approaches. All algorithms and numerical experiments are provided in an open-source repository that reproduces every figure and table in this work.
TinyTroupe: An LLM-powered Multiagent Persona Simulation Toolkit
2025-07-13
Paulo Salem, Robert Sim, Christopher Olsen, Prerit Saxena, Rafael Barcelos, Yi Ding
Recent advances in Large Language Models (LLM) have led to a new class of autonomous agents, renewing and expanding interest in the area. LLM-powered Multiagent Systems (MAS) have thus emerged, both for assistive and simulation purposes, yet tools for realistic human behavior simulation -- with its distinctive challenges and opportunities -- remain underdeveloped. Existing MAS libraries and tools lack fine-grained persona specifications, population sampling facilities, experimentation support, and integrated validation, among other key capabilities, limiting their utility for behavioral studies, social simulation, and related applications. To address these deficiencies, in this work we introduce TinyTroupe, a simulation toolkit enabling detailed persona definitions (e.g., nationality, age, occupation, personality, beliefs, behaviors) and programmatic control via numerous LLM-driven mechanisms. This allows for the concise formulation of behavioral problems of practical interest, either at the individual or group level, and provides effective means for their solution. TinyTroupe's components are presented using representative working examples, such as brainstorming and market research sessions, thereby simultaneously clarifying their purpose and demonstrating their usefulness. Quantitative and qualitative evaluations of selected aspects are also provided, highlighting possibilities, limitations, and trade-offs. The approach, though realized as a specific Python implementation, is meant as a novel conceptual contribution, which can be partially or fully incorporated in other contexts. The library is available as open source at https://github.com/microsoft/tinytroupe.
Pre-trained Under Noise: A Framework for Robust Bone Fracture Detection in Medical Imaging
2025-07-13
Robby Hoover, Nelly Elsayed, Zag ElSayed, Chengcheng Li
Medical Imagings are considered one of the crucial diagnostic tools for different bones-related diseases, especially bones fractures. This paper investigates the robustness of pre-trained deep learning models for classifying bone fractures in X-ray images and seeks to address global healthcare disparity through the lens of technology. Three deep learning models have been tested under varying simulated equipment quality conditions. ResNet50, VGG16 and EfficientNetv2 are the three pre-trained architectures which are compared. These models were used to perform bone fracture classification as images were progressively degraded using noise. This paper specifically empirically studies how the noise can affect the bone fractures detection and how the pre-trained models performance can be changes due to the noise that affect the quality of the X-ray images. This paper aims to help replicate real world challenges experienced by medical imaging technicians across the world. Thus, this paper establishes a methodological framework for assessing AI model degradation using transfer learning and controlled noise augmentation. The findings provide practical insight into how robust and generalizable different pre-trained deep learning powered computer vision models can be when used in different contexts.
Public electric vehicle (EV) charging infrastructure is crucial for accelerating EV adoption and reducing transportation emissions; however, disparities in infrastructure access have raised significant equity concerns. This systematic review synthesizes existing knowledge and identifies gaps regarding equity in EV public charging research. Following structured review protocols, 91 peer-reviewed studies from Scopus and Google Scholar were analyzed, focusing explicitly on equity considerations. The findings indicate that current research on EV public charging equity mainly adopted geographic information systems (GIS), network optimization, behavioral modeling, and hybrid analytical frameworks, yet lacks consistent normative frameworks for assessing equity outcomes. Equity assessments highlight four key dimensions: spatial accessibility, cost burdens, reliability and usability, and user awareness and trust. Socio-economic disparities, particularly income, housing tenure, and ethnicity, frequently exacerbate inequitable access, disproportionately disadvantaging low-income, renter, and minority populations. Additionally, infrastructure-specific choices, including charger reliability, strategic location, and pricing strategies, significantly influence adoption patterns and equity outcomes. However, existing literature primarily reflects North American, European, and Chinese contexts, revealing substantial geographical and methodological limitations. This review suggests the need for more robust normative evaluations of equity, comprehensive demographic data integration, and advanced methodological frameworks, thereby guiding targeted, inclusive, and context-sensitive infrastructure planning and policy interventions.
Identification of Outer Galaxy Cluster Members Using Gaia DR3 and Multidimensional Simulation
2025-07-13
Vishwas Patel, Joseph L. Hora, Matthew L. N. Ashby, Sarita Vig
The outer Galaxy presents a distinctive environment for investigating star formation. This study develops a novel approach to identify true cluster members based on unsupervised clustering using astrometry with significant uncertainties. As a proof of concept, we analyze three outer Galactic Young Stellar Object (YSO) clusters at different distances and densities within 65 < l < 265 degrees, each known to contain >100 members based on the Star Formation in Outer Galaxy catalog. The 618 YSO clusters in the SFOG dataset were based on 2-dimensional clustering. In this contribution, we apply the HDBSCAN* algorithm to the precise Gaia DR3 astrometry to assign YSO cluster membership. Monte Carlo simulation, coupled with HDBSCAN* (HDBSCAN-MC algorithm), addresses YSO astrometric uncertainties while 5-dimensional clustering. We introduce the Generation Of cLuster anD FIeld STar (GOLDFIST) simulation, to enable robust membership determination, performing an unsupervised clustering analysis in higher-dimensional feature space while accommodating measurement errors. In this study, we extended our approach to distant outer galaxy YSOs and clusters with larger astrometric uncertainties. The results include the discovery of new members in the previously identified clusters. We also analyze the known stars in the clusters and confirm their membership. The derived membership probabilities are included in the provided cluster catalogs. The more accurately predicted simulation distance estimates closely agree, within uncertainty limits, with the median distance estimates derived from Gaia data, and are compared with the kinematic distances from the WISE HII survey.
Tunable Nanophotonic Devices and Cavities based on a Two-Dimensional Magnet
2025-07-13
Ahmet Kemal Demir, Luca Nessi, Sachin Vaidya, Connor A. Occhialini, Marin Soljačić, Riccardo Comin
Central to the field of nanophotonics is the ability to engineer the flow of light through nanoscale structures. These structures often have permanent working spectral ranges and optical properties that are fixed during fabrication. Quantum materials, with their correlated and intertwined degrees of freedom, offer a promising avenue for dynamically controlling photonic devices without altering their physical structure. Here, we fabricate photonic crystal slabs from CrSBr, a van der Waals antiferromagnetic semiconductor, and demonstrate unprecedented in situ control over their optical properties. Leveraging the combination of the exceptionally large refractive index of CrSBr near its excitonic resonances and its tunability via external fields, we achieve precise manipulation of photonic modes at near-visible and infrared wavelengths, showcasing a new paradigm for nanophotonic device design. The resulting guided resonances of the photonic crystal are tightly packed in the spectrum with very small mode volumes, are highly tunable via external magnetic fields, and exhibit high Q-factors exceeding 500. These resonances self-hybridize with the excitonic degrees of freedom, resulting in intrinsic strong light-matter coupling. Our findings underscore the potential of quantum materials for developing in situ tunable photonic elements and cavities.
Token Compression Meets Compact Vision Transformers: A Survey and Comparative Evaluation for Edge AI
2025-07-13
Phat Nguyen, Ngai-Man Cheung
Token compression techniques have recently emerged as powerful tools for accelerating Vision Transformer (ViT) inference in computer vision. Due to the quadratic computational complexity with respect to the token sequence length, these methods aim to remove less informative tokens before the attention layers to improve inference throughput. While numerous studies have explored various accuracy-efficiency trade-offs on large-scale ViTs, two critical gaps remain. First, there is a lack of unified survey that systematically categorizes and compares token compression approaches based on their core strategies (e.g., pruning, merging, or hybrid) and deployment settings (e.g., fine-tuning vs. plug-in). Second, most benchmarks are limited to standard ViT models (e.g., ViT-B, ViT-L), leaving open the question of whether such methods remain effective when applied to structurally compressed transformers, which are increasingly deployed on resource-constrained edge devices. To address these gaps, we present the first systematic taxonomy and comparative study of token compression methods, and we evaluate representative techniques on both standard and compact ViT architectures. Our experiments reveal that while token compression methods are effective for general-purpose ViTs, they often underperform when directly applied to compact designs. These findings not only provide practical insights but also pave the way for future research on adapting token optimization techniques to compact transformer-based networks for edge AI and AI agent applications.
What Matters Most? A Quantitative Meta-Analysis of AI-Based Predictors for Startup Success
2025-07-13
Seyed Mohammad Ali Jafari, Ali Mobini Dehkordi, Ehsan Chitsaz, Yadollah Yaghoobzadeh
Background: Predicting startup success with machine learning is a rapidly growing field, yet findings on key predictors are often fragmented and context-specific. This makes it difficult to discern robust patterns and highlights a need for a systematic synthesis of the evidence. Methods: This study conducts a quantitative meta-analysis to synthesize the literature on predictor importance in AI-based startup evaluation. We performed a systematic review to identify a final sample of 13 empirical studies that report rankable feature importance. From these papers, we extracted and categorized 58 unique predictors, synthesizing their importance using a Weighted Importance Score (WIS) that balances a feature's average rank with its frequency of appearance. We also conducted a moderator analysis to investigate how predictor importance changes with context (e.g., success definition). Results: Our aggregate analysis reveals that the most consistently powerful predictors are a quartet of foundational attributes: Firm Characteristics (e.g., age, location), Investor Structure (e.g., investor quality), Digital and Social Traction (e.g., online momentum), and Funding History. The moderator analysis further reveals that this hierarchy is highly context-dependent. For instance, predicting near-term funding milestones elevates the importance of the deal's immediate context, while predicting long-term exits prioritizes fundamental firm and investor characteristics. Conclusion: The factors that best predict startup success are not universal but are contingent on the startup's goals, stage, and the data used for evaluation. Our findings point to a potential "convenience bias" in the literature, where predictor importance may be tied to data accessibility. We conclude by underscoring the need for standardized reporting practices to enable more robust, cumulative knowledge building in the field.
Navigating the Evolution of Two-dimensional Carbon Nitride Research: Integrating Machine Learning into Conventional Approaches
2025-07-13
Deep Mondal, Sujoy Datta, Debnarayan Jana
Carbon nitride research has reached a promising point in today's research endeavours with diverse applications including photocatalysis, energy storage, and sensing due to their unique electronic and structural properties. Recent advances in machine learning (ML) have opened new avenues for exploring and optimizing the potential of these materials. This study presents a comprehensive review of the integration of ML techniques in carbon nitride research with an introduction to CN classifications and recent advancements. We discuss the methodologies employed, such as supervised learning, unsupervised learning, and reinforcement learning, in predicting material properties, optimizing synthesis conditions, and enhancing performance metrics. Key findings indicate that ML algorithms can significantly reduce experimental trial-and-error, accelerate discovery processes, and provide deeper insights into the structure-property relationships of carbon nitride. The synergistic effect of combining ML with traditional experimental approaches is highlighted, showcasing studies where ML driven models have successfully predicted novel carbon nitride compositions with enhanced functional properties. Future directions in this field are also proposed, emphasizing the need for high-quality datasets, advanced ML models, and interdisciplinary collaborations to fully realize the potential of carbon nitride materials in next-generation technologies.
Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey
2025-07-13
Jason Zhu, Hongyu Li
Large reasoning models (LRMs) like OpenAI o1 and DeepSeek R1 have demonstrated impressive performance on complex reasoning tasks like mathematics and programming with long Chain-of-Thought (CoT) reasoning sequences (slow-thinking), compared with traditional large language models (fast-thinking). However, these reasoning models also face a huge challenge that generating unnecessarily lengthy and redundant reasoning chains even for trivial questions. This phenomenon leads to a significant waste of inference resources, increases the response time for simple queries, and hinders the practical application of LRMs in real-world products. To this end, it is crucial to shorten lengthy reasoning chains and learn adaptive reasoning between fast and slow thinking based on input difficulty. In this survey, we provide a comprehensive overview of recent progress in concise and adaptive thinking for efficient reasoning of LRMs, including methodologies, benchmarks, and challenges for future exploration. We hope this survey can help researchers quickly understand the landscape of this field and inspire novel adaptive thinking ideas to facilitate better usage of LRMs.
Rethinking LSM-tree based Key-Value Stores: A Survey
2025-07-13
Yina Lv, Qiao Li, Quanqing Xu, Congming Gao, Chuanhui Yang, Xiaoli Wang, Chun Jason Xue
LSM-tree is a widely adopted data structure in modern key-value store systems that optimizes write performance in write-heavy applications by using append writes to achieve sequential writes. However, the unpredictability of LSM-tree compaction introduces significant challenges, including performance variability during peak workloads and in resource-constrained environments, write amplification caused by data rewriting during compactions, read amplification from multi-level queries, trade-off between read and write performance, as well as efficient space utilization to mitigate space amplification. Prior studies on LSM-tree optimizations have addressed the above challenges; however, in recent years, research on LSM-tree optimization has continued to propose. The goal of this survey is to review LSM-tree optimization, focusing on representative works in the past five years. This survey first studies existing solutions on how to mitigate the performance impact of LSM-tree flush and compaction and how to improve basic key-value operations. In addition, distributed key-value stores serve multi-tenants, ranging from tens of thousands to millions of users with diverse requirements. We then analyze the new challenges and opportunities in these modern architectures and across various application scenarios. Unlike the existing survey papers, this survey provides a detailed discussion of the state-of-the-art work on LSM-tree optimizations and gives future research directions.
Disentanglement and Assessment of Shortcuts in Ophthalmological Retinal Imaging Exams
2025-07-13
Leonor Fernandes, Tiago Gonçalves, João Matos, Luis Filipe Nakayama, Jaime S. Cardoso
Diabetic retinopathy (DR) is a leading cause of vision loss in working-age adults. While screening reduces the risk of blindness, traditional imaging is often costly and inaccessible. Artificial intelligence (AI) algorithms present a scalable diagnostic solution, but concerns regarding fairness and generalization persist. This work evaluates the fairness and performance of image-trained models in DR prediction, as well as the impact of disentanglement as a bias mitigation technique, using the diverse mBRSET fundus dataset. Three models, ConvNeXt V2, DINOv2, and Swin V2, were trained on macula images to predict DR and sensitive attributes (SAs) (e.g., age and gender/sex). Fairness was assessed between subgroups of SAs, and disentanglement was applied to reduce bias. All models achieved high DR prediction performance in diagnosing (up to 94% AUROC) and could reasonably predict age and gender/sex (91% and 77% AUROC, respectively). Fairness assessment suggests disparities, such as a 10% AUROC gap between age groups in DINOv2. Disentangling SAs from DR prediction had varying results, depending on the model selected. Disentanglement improved DINOv2 performance (2% AUROC gain), but led to performance drops in ConvNeXt V2 and Swin V2 (7% and 3%, respectively). These findings highlight the complexity of disentangling fine-grained features in fundus imaging and emphasize the importance of fairness in medical imaging AI to ensure equitable and reliable healthcare solutions.
Code Review as Decision-Making -- Building a Cognitive Model from the Questions Asked During Code Review
2025-07-13
Lo Gullstrand Heander, Emma Söderberg, Christofer Rydenfält
Code review is a well-established and valued practice in the software engineering community contributing to both code quality and interpersonal benefits. However, there are challenges in both tools and processes that give rise to misalignments and frustrations. Recent research seeks to address this by automating code review entirely, but we believe that this risks losing the majority of the interpersonal benefits such as knowledge transfer and shared ownership. We believe that by better understanding the cognitive processes involved in code review, it would be possible to improve tool support, with out without AI, and make code review both more efficient, more enjoyable, while increasing or maintaining all of its benefits. In this paper, we conduct an ethnographic think-aloud study involving 10 participants and 34 code reviews. We build a cognitive model of code review bottom up through thematic, statistical, temporal, and sequential analysis of the transcribed material. Through the data, the similarities between the cognitive process in code review and decision-making processes, especially recognition-primed decision-making, become apparent. The result is the Code Review as Decision-Making (CRDM) model that shows how the developers move through two phases during the code review; first an orientation phase to establish context and rationale and then an analytical phase to understand, assess, and plan the rest of the review. Throughout the process several decisions must be taken, on writing comments, finding more information, voting, running the code locally, verifying continuous integration results, etc. Analysis software and process-coded data publicly available at: https://doi.org/10.5281/zenodo.15758266
The Hidden Costs of AI: A Review of Energy, E-Waste, and Inequality in Model Development
2025-07-13
Jenis Winsta
Artificial intelligence (AI) has made remarkable progress in recent years, yet its rapid expansion brings overlooked environmental and ethical challenges. This review explores four critical areas where AI's impact extends beyond performance: energy consumption, electronic waste (e-waste), inequality in compute access, and the hidden energy burden of cybersecurity systems. Drawing from recent studies and institutional reports, the paper highlights systemic issues such as high emissions from model training, rising hardware turnover, global infrastructure disparities, and the energy demands of securing AI. By connecting these concerns, the review contributes to Responsible AI discourse by identifying key research gaps and advocating for sustainable, transparent, and equitable development practices. Ultimately, it argues that AI's progress must align with ethical responsibility and environmental stewardship to ensure a more inclusive and sustainable technological future.
General-purpose sentence embedding models often struggle to capture specialized financial semantics, especially in low-resource languages like Korean, due to domain-specific jargon, temporal meaning shifts, and misaligned bilingual vocabularies. To address these gaps, we introduce NMIXX (Neural eMbeddings for Cross-lingual eXploration of Finance), a suite of cross-lingual embedding models fine-tuned with 18.8K high-confidence triplets that pair in-domain paraphrases, hard negatives derived from a semantic-shift typology, and exact Korean-English translations. Concurrently, we release KorFinSTS, a 1,921-pair Korean financial STS benchmark spanning news, disclosures, research reports, and regulations, designed to expose nuances that general benchmarks miss. When evaluated against seven open-license baselines, NMIXX's multilingual bge-m3 variant achieves Spearman's rho gains of +0.10 on English FinSTS and +0.22 on KorFinSTS, outperforming its pre-adaptation checkpoint and surpassing other models by the largest margin, while revealing a modest trade-off in general STS performance. Our analysis further shows that models with richer Korean token coverage adapt more effectively, underscoring the importance of tokenizer design in low-resource, cross-lingual settings. By making both models and the benchmark publicly available, we provide the community with robust tools for domain-adapted, multilingual representation learning in finance.
How to Define Design in Industrial Control and Automation Software
2025-07-13
Aydin Homay
Design is a fundamental aspect of engineering, enabling the creation of products, systems, and organizations to meet societal and/or business needs. However, the absence of a scientific foundation in design often results in subjective decision-making, reducing both efficiency and innovation. This challenge is particularly evident in the software industry and, by extension, in the domain of industrial control and automation systems (iCAS). In this study, first we review the existing design definitions within the software industry, challenge prevailing misconceptions about design, review design definition in the field of design theory and address key questions such as: When does design begin? How can design be defined scientifically? What constitutes good design? and the difference between design and design language by relying on advancements in the field of design theory. We also evaluate the distinction between ad-hoc and systematic design approaches, and present arguments on how to balance complementary operational concerns while resolving conflicting evolutionary concerns.
A Serverless Architecture for Real-Time Stock Analysis using Large Language Models: An Iterative Development and Debugging Case Study
2025-07-13
Taniv Ashraf
The advent of powerful, accessible Large Language Models (LLMs) like Google's Gemini presents new opportunities for democratizing financial data analysis. This paper documents the design, implementation, and iterative debugging of a novel, serverless system for real-time stock analysis. The system leverages the Gemini API for qualitative assessment, automates data ingestion and processing via GitHub Actions, and presents the findings through a decoupled, static frontend. We detail the architectural evolution of the system, from initial concepts to a robust, event-driven pipeline, highlighting the practical challenges encountered during deployment. A significant portion of this paper is dedicated to a case study on the debugging process, covering common software errors, platform-specific permission issues, and rare, environment-level platform bugs. The final architecture operates at a near-zero cost, demonstrating a viable model for individuals to build sophisticated AI-powered financial tools. The operational application is publicly accessible, and the complete source code is available for review. We conclude by discussing the role of LLMs in financial analysis, the importance of robust debugging methodologies, and the emerging paradigm of human-AI collaboration in software development.
AICrypto: A Comprehensive Benchmark For Evaluating Cryptography Capabilities of Large Language Models
2025-07-13
Yu Wang, Yijian Liu, Liheng Ji, Han Luo, Wenjie Li, Xiaofei Zhou, Chiyun Feng, Puji Wang, Yuhan Cao, Geyuan Zhang, Xiaojian Li, Rongwu Xu, Yilei Chen, Tianxing He
Large language models (LLMs) have demonstrated remarkable capabilities across a variety of domains. However, their applications in cryptography, which serves as a foundational pillar of cybersecurity, remain largely unexplored. To address this gap, we propose \textbf{AICrypto}, the first comprehensive benchmark designed to evaluate the cryptographic capabilities of LLMs. The benchmark comprises 135 multiple-choice questions, 150 capture-the-flag (CTF) challenges, and 18 proof problems, covering a broad range of skills from factual memorization to vulnerability exploitation and formal reasoning. All tasks are carefully reviewed or constructed by cryptography experts to ensure correctness and rigor. To support automated evaluation of CTF challenges, we design an agent-based framework. To gain deeper insight into the current state of cryptographic proficiency in LLMs, we introduce human expert performance baselines for comparison across all task types. Our evaluation of 17 leading LLMs reveals that state-of-the-art models match or even surpass human experts in memorizing cryptographic concepts, exploiting common vulnerabilities, and routine proofs. However, they still lack a deep understanding of abstract mathematical concepts and struggle with tasks that require multi-step reasoning and dynamic analysis. We hope this work could provide insights for future research on LLMs in cryptographic applications. Our code and dataset are available at https://aicryptobench.github.io.
Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges
2025-07-13
Yidong Jiang
The Segment Anything Model (SAM) has revolutionized image segmentation through its innovative prompt-based approach, yet the critical role of prompt engineering in its success remains underexplored. This paper presents the first comprehensive survey focusing specifically on prompt engineering techniques for SAM and its variants. We systematically organize and analyze the rapidly growing body of work in this emerging field, covering fundamental methodologies, practical applications, and key challenges. Our review reveals how prompt engineering has evolved from simple geometric inputs to sophisticated multimodal approaches, enabling SAM's adaptation across diverse domains including medical imaging and remote sensing. We identify unique challenges in prompt optimization and discuss promising research directions. This survey fills an important gap in the literature by providing a structured framework for understanding and advancing prompt engineering in foundation models for segmentation.
VDInstruct: Zero-Shot Key Information Extraction via Content-Aware Vision Tokenization
2025-07-13
Son Nguyen, Giang Nguyen, Hung Dao, Thao Do, Daeyoung Kim
Key Information Extraction (KIE) underpins the understanding of visual documents (e.g., receipts and contracts) by extracting precise semantic content and accurately capturing spatial structure. Yet existing multimodal large language models (MLLMs) often perform poorly on dense documents and rely on vision tokenization approaches that scale with image size, leading to redundant computation and memory inefficiency. To address these challenges, we introduce VDInstruct, an MLLM that separates spatial region detection from semantic feature extraction. Central to our model is a content-aware tokenization strategy: rather than fragmenting the entire image uniformly, it generates tokens in proportion to document complexity, preserving critical structure while eliminating wasted tokens. Leveraging a three-stage training paradigm, our model achieves state-of-the-art (SOTA) results on KIE benchmarks, matching or exceeding the accuracy of leading approaches while reducing the number of image tokens by roughly 3.6x. In zero-shot evaluations, VDInstruct surpasses strong baselines-such as DocOwl 1.5-by +5.5 F1 points, highlighting its robustness to unseen documents. These findings show that content-aware tokenization combined with explicit layout modeling offers a promising direction forward for document understanding. Data, source code, and model weights will be made publicly available.
Chiral solitons in quadratic quasi-phase-matched photonic crystals
2025-07-13
Yuxin Guo, Xuening Wang, Zhiwei Fan, Zhaopin Chen, Qiuyi Ning, Hexiang He, Wei Pang, Li Zhang, Yongyao Li
We introduce a quasi-phase-matched technique in quadratic nonlinear crystals, constructing an artificial gauge field by changing the inclination angle of stripes, which is realized by the positive and negative polarization directions of nonlinear susceptibility along the crystal. Unlike the artificial gauge field constructed through linear coupling in other settings, the gauge field in this system is realized by nonlinear coupling. We demonstrate that this gauge field can generate stable chiral solitons with chiral energy flow rotating around the solitons. In contrast to conventional chiral currents generated with the same specie or frequency, the chiral currents in the present system are formed by mutual coupling between fundamental frequency and second harmonic components. We derive the semi-analytical solution for the chiral energy flow in this system. It is found that there exists an optimal inclination angle that can maximize the chiral energy flow under different parameters, and this optimal inclination shows a positive correlation with the power and detuning. The mobility and collisions of the chiral solitons are also discussed. The results show that chiral solitons move in response to kicking and undergo fully elastic collisions with each other. In addition, the possibility of experimentally generating chiral solitons and chiral currents is outlined.
Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge, yet it falls short on problems that demand multi-step inference; conversely, purely reasoning-oriented approaches often hallucinate or mis-ground facts. This survey synthesizes both strands under a unified reasoning-retrieval perspective. We first map how advanced reasoning optimizes each stage of RAG (Reasoning-Enhanced RAG). Then, we show how retrieved knowledge of different type supply missing premises and expand context for complex inference (RAG-Enhanced Reasoning). Finally, we spotlight emerging Synergized RAG-Reasoning frameworks, where (agentic) LLMs iteratively interleave search and reasoning to achieve state-of-the-art performance across knowledge-intensive benchmarks. We categorize methods, datasets, and open challenges, and outline research avenues toward deeper RAG-Reasoning systems that are more effective, multimodally-adaptive, trustworthy, and human-centric. The collection is available at https://github.com/DavidZWZ/Awesome-RAG-Reasoning.
Meta-Reinforcement Learning for Fast and Data-Efficient Spectrum Allocation in Dynamic Wireless Networks
2025-07-13
Oluwaseyi Giwa, Tobi Awodunmila, Muhammad Ahmed Mohsin, Ahsan Bilal, Muhammad Ali Jamshed
The dynamic allocation of spectrum in 5G / 6G networks is critical to efficient resource utilization. However, applying traditional deep reinforcement learning (DRL) is often infeasible due to its immense sample complexity and the safety risks associated with unguided exploration, which can cause severe network interference. To address these challenges, we propose a meta-learning framework that enables agents to learn a robust initial policy and rapidly adapt to new wireless scenarios with minimal data. We implement three meta-learning architectures, model-agnostic meta-learning (MAML), recurrent neural network (RNN), and an attention-enhanced RNN, and evaluate them against a non-meta-learning DRL algorithm, proximal policy optimization (PPO) baseline, in a simulated dynamic integrated access/backhaul (IAB) environment. Our results show a clear performance gap. The attention-based meta-learning agent reaches a peak mean network throughput of 48 Mbps, while the PPO baseline decreased drastically to 10 Mbps. Furthermore, our method reduces SINR and latency violations by more than 50% compared to PPO. It also shows quick adaptation, with a fairness index 0.7, showing better resource allocation. This work proves that meta-learning is a very effective and safer option for intelligent control in complex wireless systems.
A Survey on Medical Image Compression: From Traditional to Learning-Based
2025-07-13
Guofeng Tong, Sixuan Liu, Yang Lv, Hanyu Pei, Feng-Lei Fan
The exponential growth of medical imaging has created significant challenges in data storage, transmission, and management for healthcare systems. In this vein, efficient compression becomes increasingly important. Unlike natural image compression, medical image compression prioritizes preserving diagnostic details and structural integrity, imposing stricter quality requirements and demanding fast, memory-efficient algorithms that balance computational complexity with clinically acceptable reconstruction quality. Meanwhile, the medical imaging family includes a plethora of modalities, each possessing different requirements. For example, 2D medical image (e.g., X-rays, histopathological images) compression focuses on exploiting intra-slice spatial redundancy, while volumetric medical image faces require handling intra-slice and inter-slice spatial correlations, and 4D dynamic imaging (e.g., time-series CT/MRI, 4D ultrasound) additionally demands processing temporal correlations between consecutive time frames. Traditional compression methods, grounded in mathematical transforms and information theory principles, provide solid theoretical foundations, predictable performance, and high standardization levels, with extensive validation in clinical environments. In contrast, deep learning-based approaches demonstrate remarkable adaptive learning capabilities and can capture complex statistical characteristics and semantic information within medical images. This comprehensive survey establishes a two-facet taxonomy based on data structure (2D vs 3D/4D) and technical approaches (traditional vs learning-based), thereby systematically presenting the complete technological evolution, analyzing the unique technical challenges, and prospecting future directions in medical image compression.
Power Consumption Analysis of QKD Networks under Different Protocols and Detector Configurations
2025-07-13
Jiaheng Xiong, Qiaolun Zhang, Yoann Piétri, Raja Yehia, Raouf Boutaba, Francesco Musumeci, Massimo Tornatore
We analyze the power consumption of quantum key distribution (QKD) networks under various protocol and detector configurations. Using realistic network topologies, we evaluate discrete-variable vs continuous-variable QKD and optimize device placement, quantifying power trade-offs of SNSPD vs APD detectors and the benefits of optical bypass.
DALI-PD: Diffusion-based Synthetic Layout Heatmap Generation for ML in Physical Design
2025-07-13
Bing-Yue Wu, Vidya A. Chhabria
Machine learning (ML) has demonstrated significant promise in various physical design (PD) tasks. However, model generalizability remains limited by the availability of high-quality, large-scale training datasets. Creating such datasets is often computationally expensive and constrained by IP. While very few public datasets are available, they are typically static, slow to generate, and require frequent updates. To address these limitations, we present DALI-PD, a scalable framework for generating synthetic layout heatmaps to accelerate ML in PD research. DALI-PD uses a diffusion model to generate diverse layout heatmaps via fast inference in seconds. The heatmaps include power, IR drop, congestion, macro placement, and cell density maps. Using DALI-PD, we created a dataset comprising over 20,000 layout configurations with varying macro counts and placements. These heatmaps closely resemble real layouts and improve ML accuracy on downstream ML tasks such as IR drop or congestion prediction.
Adversarial Activation Patching: A Framework for Detecting and Mitigating Emergent Deception in Safety-Aligned Transformers
2025-07-12
Santhosh Kumar Ravindran
Large language models (LLMs) aligned for safety through techniques like reinforcement learning from human feedback (RLHF) often exhibit emergent deceptive behaviors, where outputs appear compliant but subtly mislead or omit critical information. This paper introduces adversarial activation patching, a novel mechanistic interpretability framework that leverages activation patching as an adversarial tool to induce, detect, and mitigate such deception in transformer-based models. By sourcing activations from "deceptive" prompts and patching them into safe forward passes at specific layers, we simulate vulnerabilities and quantify deception rates. Through toy neural network simulations across multiple scenarios (e.g., 1000 trials per setup), we demonstrate that adversarial patching increases deceptive outputs to 23.9% from a 0% baseline, with layer-specific variations supporting our hypotheses. We propose six hypotheses, including transferability across models, exacerbation in multimodal settings, and scaling effects. An expanded literature review synthesizes over 20 key works in interpretability, deception, and adversarial attacks. Mitigation strategies, such as activation anomaly detection and robust fine-tuning, are detailed, alongside ethical considerations and future research directions. This work advances AI safety by highlighting patching's dual-use potential and provides a roadmap for empirical studies on large-scale models.
Optimal Differentially Private Ranking from Pairwise Comparisons
2025-07-12
T. Tony Cai, Abhinav Chakraborty, Yichen Wang
Data privacy is a central concern in many applications involving ranking from incomplete and noisy pairwise comparisons, such as recommendation systems, educational assessments, and opinion surveys on sensitive topics. In this work, we propose differentially private algorithms for ranking based on pairwise comparisons. Specifically, we develop and analyze ranking methods under two privacy notions: edge differential privacy, which protects the confidentiality of individual comparison outcomes, and individual differential privacy, which safeguards potentially many comparisons contributed by a single individual. Our algorithms--including a perturbed maximum likelihood estimator and a noisy count-based method--are shown to achieve minimax optimal rates of convergence under the respective privacy constraints. We further demonstrate the practical effectiveness of our methods through experiments on both simulated and real-world data.
Constrained Style Learning from Imperfect Demonstrations under Task Optimality
2025-07-12
Kehan Wen, Chenhao Li, Junzhe He, Marco Hutter
Learning from demonstration has proven effective in robotics for acquiring natural behaviors, such as stylistic motions and lifelike agility, particularly when explicitly defining style-oriented reward functions is challenging. Synthesizing stylistic motions for real-world tasks usually requires balancing task performance and imitation quality. Existing methods generally depend on expert demonstrations closely aligned with task objectives. However, practical demonstrations are often incomplete or unrealistic, causing current methods to boost style at the expense of task performance. To address this issue, we propose formulating the problem as a constrained Markov Decision Process (CMDP). Specifically, we optimize a style-imitation objective with constraints to maintain near-optimal task performance. We introduce an adaptively adjustable Lagrangian multiplier to guide the agent to imitate demonstrations selectively, capturing stylistic nuances without compromising task performance. We validate our approach across multiple robotic platforms and tasks, demonstrating both robust task performance and high-fidelity style learning. On ANYmal-D hardware we show a 14.5% drop in mechanical energy and a more agile gait pattern, showcasing real-world benefits.
A Systematic Review of Passive Cooling Strategies Integrating Traditional Wisdom and Modern Innovations for Sustainable Development in Arid Urban Environments
2025-07-12
Shiva Manshour, Steffen Lehmann
Urban environments in hot-arid regions are increasingly challenged by rising temperatures, rapid urban expansion, and reliance on energy intensive mechanical cooling systems. This study presents a systematic review of peer reviewed literature from 1980 to 2025 to assess both traditional and contemporary passive cooling strategies tailored for arid urban settings. Following PRISMA 2020 guidelines, 30 high-quality studies were selected from databases including Scopus, Web of Science, ScienceDirect, and Google Scholar. These works span diverse geographical contexts from the Middle East and North Africa to parts of South Asia, and apply methods including field experiments, computer-based simulations, and qualitative analyses. Findings highlight strong consensus around passive principles such as solar control, natural ventilation, and the use of thermal mass. Vernacular solutions like courtyards, wind towers, and thick masonry walls remain effective, while innovations such as cool roofs, phase change materials, and parametric optimization techniques expand the design toolkit. Implementation is often limited by climate variability, cultural shifts, regulations, and economic feasibility. The review concludes that context-sensitive, hybrid solutions combining traditional knowledge with modern technology hold the greatest potential for achieving sustainable thermal comfort. These approaches must be supported by adaptive urban planning, user centered design, and updated building codes. The study offers practical insights for architects, planners, and policymakers aiming to create resilient, low-carbon cities that harmonize cultural identity with environmental responsibility.
Ulrich bundles on double coverings of projective space
2025-07-12
Roberto Vacca
Fixed a polarised variety $X$, we can ask if it admits Ulrich bundles and, in case, what is their minimal possible rank. In this thesis, after recalling general properties of Ulrich sheaves, we show that any finite covering of $\mathbb{P}^n$ that embeds as a divisor in a weighted projective space with weights $(1^{n+1},m)$ admits Ulrich sheaves, by using matrix factorisations. Among these varieties, we focus on double coverings of with $n\ge3$. Through Hartshorne--Serre correspondence, which we review along the way, we prove that the general such $X$ admits a rank $2$ Ulrich sheaf if and only if $n=3$ and $m=2,3,4$, and characterise the zero loci of their sections. Moreover, we construct generically smooth components of the expected dimension of their moduli spaces, analyse the action of the natural involution on them and the restriction of those bundles to low degree hypersurfaces. For $m=2,3$, we verify the existence of slope-stable Ulrich bundles of all the possible ranks.
2025-07-12
Michele Re Fiorentin, Stefano Re Fiorentin
The recently unveiled deep-field images from the James Webb Space Telescope have renewed interest in what we can and cannot see of the universe. Answering these questions requires understanding the so-called "cosmological horizons" and the "Hubble sphere". Here we review the topic of cosmological horizons in a form that university physics teachers can use in their lessons, using the latest data about the so-called standard "Lambda Cold Dark Matter" ($\Lambda$CDM) model. Graphical representations are plotted in terms of both conformal and proper coordinates as an aid to understanding the propagation of light in the expanding universe at various epochs.
Second-harmonic generation (SHG) is a fundamental nonlinear optical process widely used in photonics; however, it is strictly forbidden in the bulk of centrosymmetric materials due to their inversion symmetry. Nevertheless, applying an external electric field breaks this inversion symmetry. It induces an effective second-order nonlinear response known as the electric-field-induced second-harmonic generation (EFISH) effect. This mechanism enables SHG even in centrosymmetric media and provides a powerful tool for dynamic and electrically tunable nonlinear nanophotonics. This review presents a comprehensive overview of the EFISH effect, covering the fundamentals, various material platforms (including bulk semiconductor crystals, ferroelectrics, van der Waals materials, and polymers), as well as diverse strategies for electric field engineering. It further distinguishs EFISH from related effects such as current-induced SHG and the quantum-confined Stark effect, and highlight emerging applications of EFISH in tunable photonic devices, carrier dynamics probing, and nonlinear optical modulation across optical, electronic, and THz regimes. Finally, key challenges and perspectives for the future development of electrically controlled nonlinear optical systems are outlined.
The Muon Magnetic Moment and Physics Beyond the Standard Model
2025-07-12
Peter Athron, Kilian Möhling, Dominik Stöckinger, Hyejung Stöckinger-Kim
We review the role of the anomalous magnetic moment of the muon a_\mu as a powerful probe of physics beyond the Standard Model (BSM), taking advantage of the final result of the Fermilab g-2 experiment and the recently updated Standard Model value. This review provides both a comprehensive summary of the current status, as well as an accessible entry point for phenomenologists with interests in dark matter, Higgs and electroweak or neutrino and flavour physics in the context of a wide range of BSM scenarios. It begins with a qualitative overview of the field and a collection of key properties and typical results. It then focuses on model-independent, generic formulas and classifies types of BSM scenarios with or without chiral enhancements. A strong emphasis of the review are the connections to a large number of other observables -- ranging from the muon mass and the muon--Higgs coupling and related dipole observables to dark matter, neutrino masses and high-energy collider observables. Finally, we survey a number of well-motivated BSM scenarios such as dark photons, axion-like particles, the two-Higgs doublet model, supersymmetric models and models with leptoquarks, vector-like leptons or neutrino mass models. We discuss the impact of the updated Standard Model value for a_\mu and of complementary constraints, exploring the phenomenology and identifying excluded and viable parameter regions.
Prompt4Trust: A Reinforcement Learning Prompt Augmentation Framework for Clinically-Aligned Confidence Calibration in Multimodal Large Language Models
2025-07-12
Anita Kriz, Elizabeth Laura Janes, Xing Shen, Tal Arbel
Multimodal large language models (MLLMs) hold considerable promise for applications in healthcare. However, their deployment in safety-critical settings is hindered by two key limitations: (i) sensitivity to prompt design, and (ii) a tendency to generate incorrect responses with high confidence. As clinicians may rely on a model's stated confidence to gauge the reliability of its predictions, it is especially important that when a model expresses high confidence, it is also highly accurate. We introduce Prompt4Trust, the first reinforcement learning (RL) framework for prompt augmentation targeting confidence calibration in MLLMs. A lightweight LLM is trained to produce context-aware auxiliary prompts that guide a downstream task MLLM to generate responses in which the expressed confidence more accurately reflects predictive accuracy. Unlike conventional calibration techniques, Prompt4Trust specifically prioritizes aspects of calibration most critical for safe and trustworthy clinical decision-making. Beyond improvements driven by this clinically motivated calibration objective, our proposed method also improves task accuracy, achieving state-of-the-art medical visual question answering (VQA) performance on the PMC-VQA benchmark, which is composed of multiple-choice questions spanning diverse medical imaging modalities. Moreover, our framework trained with a small downstream task MLLM showed promising zero-shot generalization to larger MLLMs in our experiments, suggesting the potential for scalable calibration without the associated computational costs. This work demonstrates the potential of automated yet human-aligned prompt engineering for improving the the trustworthiness of MLLMs in safety critical settings. Our codebase can be found at https://github.com/xingbpshen/vccrl-llm.
Enhancement of dark-photon haloscope sensitivity with degenerate modes: toward axion-level form factor and polarization determination
2025-07-12
Jose R. Navarro-Madrid, José Reina-Valero, Alejandro Díaz-Morcillo, Benito Gimeno
The dark photon has been postulated as a potential constituent of dark matter, exhibiting notable similarities to the axion. The primary distinction between the two particles lies in the nature of their respective fields: the dark photon field is a vector field with a polarization direction that remains undetermined. This work explores the prospect of utilizing three degenerate modes for scanning the three dimensions of space in order to mitigate the low form factor expected in the detection of the dark photon due to their unknown polarization. The employment of an haloscope with three orthogonal and degenerate modes in conjunction with the coherent sum of signals is demonstrated in this work in order to enhance the dark photon form factor up to the axion form factor, and to determine the direction of the dark photon polarization vector. We show in this manuscript that the maximum form factor is achieved in cavities of cubic, spherical, and cylindrical geometries, considering the introduction of tuning elements. To achieve this adequately, some conditions reviewed in this article must be fulfilled in the resonant cavity, leading to uncertainties in the final measurement. Finally, this technique can allow the simultaneous search for dark matter axions and dark photons, and to the knowledge of the authors, the method shown in this work is the most effective one for detecting dark photon with microwave resonant cavities.
The Narrative Construction of Generative AI Efficacy by the Media: A Case Study of the Role of ChatGPT in Higher Education
2025-07-12
Yinan Sun, Ali Unlu, Aditya Johri
The societal role of technology, including artificial intelligence (AI), is often shaped by sociocultural narratives. This study examines how U.S. news media construct narratives about the efficacy of generative AI (GenAI), using ChatGPT in higher education as a case study. Grounded in Agenda Setting Theory, we analyzed 198 articles published between November 2022 and October 2024, employing LDA topic modeling and sentiment analysis. Our findings identify six key topics in the media discourse, with sentiment analysis revealing generally positive portrayals of ChatGPT's integration into higher education through policy, curriculum, teaching practices, collaborative decision-making, skill development, and human-centered learning. In contrast, media narratives express more negative sentiment regarding their impact on entry-level jobs and college admissions. This research highlights how media coverage can influence public perceptions of GenAI in education and provides actionable insights for policymakers, educators, and AI developers navigating its adoption and representation in public discourse.
Modeling the impacts of galaxy intrinsic alignments on weak lensing peak statistics
2025-07-12
Tianyu Zhang, Xiangkun Liu, Ziwei Li, Chengliang Wei, Guoliang Li, Yu Luo, Xi Kang, Zuhui Fan
Weak gravitational lensing (WL) peak statistics capture cosmic non-linear structures and can provide additional cosmological information complementary to cosmic shear two-point correlation analyses. They have been applied to different WL surveys successfully. To further facilitate their high precision applications, it is very timely to investigate the impacts of different systematics on WL peak statistics and how to mitigate them. Concerning the influence from galaxy intrinsic alignments (IAs), in this paper, we develop a theoretical model for WL high peaks taking into account the IA effects. It is an extension of our previous halo-based model. The IA corrections mainly include the modification of the lensing profile of clusters of galaxies due to the alignments of satellite galaxies and the additional shape noise correlations. We validate our model using simulations with the semi-analytical galaxy formation. We consider the cases where the satellite galaxies are averagely radially aligned toward the centers of their host clusters but with different dispersions $\sigma_{\theta}$. We show that our model works well for $\sigma_{\theta}>45^{\circ}$. If the IA corrections are not included in the model, for the Euclid/CSST-like source galaxy distribution and the survey area of $\sim 1000 deg^2$, the IA induced bias on $S_8$ can reach $\sim 8\sigma$ even for $\sigma_{\theta}=75^{\circ}$. With our model, not only the bias can be well mitigated, but also we can constrain the satellite IA to the level of {\bf $\sigma(\sigma_{\theta})\sim \pm 24^{\circ}$} simultaneously from WL high peak analyses alone using data from such a survey.
Alleviating the Hubble tension with Torsion Condensation (TorC)
2025-07-12
Sinah Legner, Will Handley, Will Barker
Constraints on the cosmological parameters of Torsion Condensation (TorC) are investigated using Planck 2018 Cosmic Microwave Background data. TorC is a case of Poincar\'e gauge theory -- a formulation of gravity motivated by the gauge field theories underlying fundamental forces in the standard model of particle physics. Unlike general relativity, TorC incorporates intrinsic torsion degrees of freedom while maintaining second-order field equations. At specific parameter values, it reduces to the $\Lambda$CDM model, providing a natural extension to standard cosmology. The base model of TorC introduces two parameters beyond those in $\Lambda$CDM: the initial value of the torsion scalar field and its time derivative -- one can absorb the latter by allowing the dark energy density to float. To constrain these parameters, `PolyChord` nested sampling algorithm is employed, interfaced via `Cobaya` with a modified version of `CAMB`. Our results indicate that TorC allows for a larger inferred Hubble constant, offering a potential resolution to the Hubble tension. Tension analysis using the $R$-statistic shows that TorC alleviates the statistical tension between the Planck 2018 and SH0Es 2020 datasets, though this improvement is not sufficient to decisively favour TorC over $\Lambda$CDM in a Bayesian model comparison. This study highlights TorC as a compelling theory of gravity, demonstrating its potential to address cosmological tensions and motivating further investigations of extended theories of gravity within a cosmological context. As current and upcoming surveys -- including Euclid, Roman Space Telescope, Vera C. Rubin Observatory, LISA, and Simons Observatory -- deliver data on gravity across all scales, they will offer critical tests of gravity models like TorC, making the present a pivotal moment for exploring extended theories of gravity.
3D detection technology is widely used in the field of autonomous driving, with its application scenarios gradually expanding from enclosed highways to open conventional roads. For rare anomaly categories that appear on the road, 3D detection models trained on closed sets often misdetect or fail to detect anomaly objects. To address this risk, it is necessary to enhance the generalization ability of 3D detection models for targets of arbitrary shapes and to possess the capability to filter out anomalies. The generalization of 3D detection is limited by two factors: the coupled training of 2D and 3D, and the insufficient diversity in the scale distribution of training samples. This paper proposes a Stereo-based 3D Anomaly object Detection (S3AD) algorithm, which decouples the training strategy of 3D and 2D to release the generalization ability for arbitrary 3D foreground detection, and proposes an anomaly scoring algorithm based on foreground confidence prediction, achieving target-level anomaly scoring. In order to further verify and enhance the generalization of anomaly detection, we use a 3D rendering method to synthesize two augmented reality binocular stereo 3D detection datasets which named KITTI-AR. KITTI-AR extends upon KITTI by adding 97 new categories, totaling 6k pairs of stereo images. The KITTI-AR-ExD subset includes 39 common categories as extra training data to address the sparse sample distribution issue. Additionally, 58 rare categories form the KITTI-AR-OoD subset, which are not used in training to simulate zero-shot scenarios in real-world settings, solely for evaluating 3D anomaly detection. Finally, the performance of the algorithm and the dataset is verified in the experiments. (Code and dataset can be obtained at https://github.com/xxxx/xxx).
Explainable Recommender System (ExRec) provides transparency to the recommendation process, increasing users' trust and boosting the operation of online services. With the rise of large language models (LLMs), whose extensive world knowledge and nuanced language understanding enable the generation of human-like, contextually grounded explanations, LLM-powered ExRec has gained great momentum. However, existing LLM-based ExRec models suffer from profile deviation and high retrieval overhead, hindering their deployment. To address these issues, we propose Retrieval-Augmented Recommendation Explanation Generation with Hierarchical Aggregation (REXHA). Specifically, we design a hierarchical aggregation based profiling module that comprehensively considers user and item review information, hierarchically summarizing and constructing holistic profiles. Furthermore, we introduce an efficient retrieval module using two types of pseudo-document queries to retrieve relevant reviews to enhance the generation of recommendation explanations, effectively reducing retrieval latency and improving the recall of relevant reviews. Extensive experiments demonstrate that our method outperforms existing approaches by up to 12.6% w.r.t. the explanation quality while achieving high retrieval efficiency.
Probing periodic trends in the TESS light curves of the seventeen known Double White Dwarf systems
2025-07-12
Sedighe Sajadian, Aref Asadi
There is a relatively large population of known double white dwarfs (DWDs) that were mostly discovered through spectroscopic observations and by measuring their radial velocity variations. Photometric observations from these systems give us additional information about their faint components by manifesting eclipsing or lensing signals or periodic trends such as ellipsoidal variations or Doppler boosting. To find these signals and trends we probe the public photometric data collected by the Transiting Exoplanet Survey Satellite (TESS) telescope from 17 known DWD systems. We use the Singular Spectrum Analysis (SSA) technique to de-noise their light curves. For DWD systems J1717$+$6757, J1557$+$2823, LP400$-$22, J1449$+$1717, J2132$+$0754, and J2151$+$1614 we find regular and periodic trends in their TESS light curves. The periodic trend in light curve J1449$+$1717 is caused by the blending effect due to a variable and bright star close to it which are unresolvable in the TESS observations. The discovered periodic trend for J1717$+$6757 was recovered by the TESS data. The periodic trends in light curves of J1557$+$2823 and J2151$+$1614 have the False Alarm Probability (FAP) values $\simeq 14.8,~36.5\%$. So their detected trends are likely noises with non-orbital origins. Periods of trends in light curves of LP400$-$22 and J2132$+$0754 are the same as and half of orbital periods, respectively. We evaluate possible ranges for Doppler boosting and ellipsoidal variations's amplitudes for these targets. This study highlights the importance of TESS data for identifying periodic trends such as ellipsoidal or intrinsic variations rather than short eclipsing/lensing signals in DWD light curves specially bright targets with ignorable blending.
Magnification bias reveals severe contamination in Hubble Frontier Field photo-z catalogs
2025-07-12
Jiashuo Zhang, Jeremy Lim, Tom Broadhurst, Sung Kei Li, Man Cheung Li, Giorgio Manzoni, Rogier Windhorst
Gravitational lensing by massive galaxy clusters enables faint distant galaxies to be more abundantly detected than in blank fields, thereby allowing one to construct galaxy luminosity functions (LFs) to an unprecedented depth at high redshifts. Intriguingly, photometric redshift catalogs (e.g. Shipley et al. (2018)) constructed from the Hubble Frontier Fields survey display an excess of z$\gtrsim$4 galaxies in the cluster lensing fields and are not seen in accompanying blank parallel fields. The observed excess, while maybe a gift of gravitational lensing, could also be from misidentified low-z contaminants having similar spectral energy distributions as high-z galaxies. In the latter case, the contaminants may result in nonphysical turn-ups in UV LFs and/or wash out faint end turnovers predicted by contender cosmological models to $\Lambda$CDM. Here, we employ the concept of magnification bias to perform the first statistical estimation of contamination levels in HFF lensing field photometric redshift catalogs. To our great worry, while we were able to reproduce a lower-z lensed sample, it was found $\sim56\%$ of $3.5 < z_{phot} < 5.5$ samples are likely low-z contaminants! Widely adopted Lyman Break Galaxy-like selection rules in literature may give a 'cleaner' sample magnification bias-wise but we warn readers the resulting sample would also be less complete. Individual mitigation of the contaminants is arguably the best way for the investigation of faint high-z Universe, and this may be made possible with JWST observations.
AInsight: Augmenting Expert Decision-Making with On-the-Fly Insights Grounded in Historical Data
2025-07-12
Mohammad Abolnejadian, Shakiba Amirshahi, Matthew Brehmer, Anamaria Crisan
In decision-making conversations, experts must navigate complex choices and make on-the-spot decisions while engaged in conversation. Although extensive historical data often exists, the real-time nature of these scenarios makes it infeasible for decision-makers to review and leverage relevant information. This raises an interesting question: What if experts could utilize relevant past data in real-time decision-making through insights derived from past data? To explore this, we implemented a conversational user interface, taking doctor-patient interactions as an example use case. Our system continuously listens to the conversation, identifies patient problems and doctor-suggested solutions, and retrieves related data from an embedded dataset, generating concise insights using a pipeline built around a retrieval-based Large Language Model (LLM) agent. We evaluated the prototype by embedding Health Canada datasets into a vector database and conducting simulated studies using sample doctor-patient dialogues, showing effectiveness but also challenges, setting directions for the next steps of our work.
The Effective Field Theory of Large Scale Structure for Mixed Dark Matter Scenarios
2025-07-11
Francesco Verdiani, Emanuele Castorina, Ennio Salvioni, Emiliano Sefusatti
We initiate a systematic study of the perturbative nonlinear dynamics of cosmological fluctuations in dark sectors comprising a fraction of non-cold dark matter, for example ultra-light axions or light thermal relics. These mixed dark matter scenarios exhibit suppressed growth of perturbations below a characteristic, cosmologically relevant, scale associated with the microscopic nature of the non-cold species. As a consequence, the scale-free nonlinear solutions developed for pure cold dark matter and for massive neutrinos do not, in general, apply. We thus extend the Effective Field Theory of Large Scale Structure to model the coupled fluctuations of the cold and non-cold dark matter components, describing the latter as a perfect fluid with finite sound speed at linear level. We provide new analytical solutions wherever possible and devise an accurate and computationally tractable prescription for the evaluation of the one-loop galaxy power spectrum, which can be applied to probe mixed dark matter scenarios with current and upcoming galaxy survey data. As a first application of this framework, we derive updated constraints on the energy density in ultra-light axions using a combination of Planck and BOSS data. Our refined theoretical modeling leads to somewhat weaker bounds compared to previous analyses.
Recent Advances in Understanding R-Process Nucleosynthesis in Metal-Poor Stars and Stellar Systems
2025-07-11
Avrajit Bandyopadhyay, Timothy C. Beers
The rapid neutron-capture process (r-process) is responsible for the creation of roughly half of the elements heavier than iron, including precious metals like silver, gold, and platinum, as well as radioactive elements such as thorium and uranium. Despite its importance, the nature of the astrophysical sites where the r-process occurs, and the detailed mechanisms of its formation, remain elusive. The key to resolving these mysteries lies in the study of chemical signatures preserved in ancient, metal-poor stars. In this review, we explore r-process nucleosynthesis, focusing on the sites, progenitors, and formation mechanisms. We discuss the role of potential astrophysical sites such as neutron star mergers, core-collapse supernovae, magneto-rotational supernovae, and collapsars, that can play a key role in producing the heavy elements. We also highlight the importance of studying these signatures through high-resolution spectroscopic surveys, stellar archaeology, and multi-messenger astronomy. Recent advancements, such as the gravitational wave event GW170817 and detection of the r-process in the ejecta of its associated kilonovae, have established neutron star mergers as one of the confirmed sites. However, questions remain regarding whether they are the only sites that could have contributed in early epochs or if additional sources are needed to explain the signatures of r-process found in the oldest stars. Additionally, there are strong indications pointing towards additional sources of r-process-rich nuclei in the context of Galactic evolutionary timescales. This review summarizes what has been learned so far, the challenges that remain, and the exciting prospects for future discoveries. The increasing synergy between observational facilities, computational models, and large-scale surveys is poised to transform our understanding of r-process nucleosynthesis in the coming years.
Performance and extensions of the central data acquisition system for Phase II of the Pierre Auger Observatory
2025-07-11
Paul Filip
The Pierre Auger Observatory is a hybrid detector designed to observe and study ultra-high-energy particles of extraterrestrial origin. With its 27 fluorescence telescopes and over 1600 autonomously operating water-Cherenkov detectors spread over an area of 3000 km$^2$, it is world-leading in terms of exposure to cosmic rays and offers an unparalleled window into the physical processes that happen at energy scales unattainable by particle accelerators on Earth. Measurement information of candidate air-shower events from all associated detectors and telescopes is collected at a central data-acquisition system located in the nearby town of Malarg\"ue, and processed for higher-level physics analysis. On top of this, data for monitoring the long-term stability and operation of the observatory is forwarded to the central server as well. In this work, we briefly review the central data-acquisition system of the Pierre Auger Observatory. We examine the rates, efficiencies, and purity of detected events for Phase II of the Pierre Auger Observatory and compare them to performance parameters during Phase I. We detail challenges in the event detection up until now and present recent changes in the central data acquisition system and local station software that aim to streamline the data acquisition chain.
Statistical Analysis of Early Spectra in Type II and IIb Supernovae
2025-07-11
Maider González-Bañuelos, Claudia P. Gutiérrez, Lluís Galbany, Santiago González-Gaitán
We present a comprehensive analysis of the early spectra of type II and type IIb supernovae (SNe) to explore their diversity and distinguishable characteristics. Using 866 publicly available spectra from 393 SNe, 407 from type IIb SNe (SNe IIb) and 459 from type II SNe (SNe II), we analysed H$\alpha$ and He~I 5876 A at early phases ($<40$ days from the explosion) to identify possible differences between these two SN types. By comparing the pseudo-equivalent width (pEW) and full width at half maximum (FWHM), we find that the strength of the absorption component of these lines serves as a quantitative discriminator, with SNe IIb exhibiting stronger lines at all times. The most significant differences emerge within the first 10-20 days. To assess the statistical significance of these differences, we apply statistical methods and machine-learning techniques. Population density evolution reveals a clear distinction in both pEW and FWHM. Quadratic Discriminant Analysis confirms distinct evolutionary patterns, particularly in pEW, while FWHM variations are less pronounced. A combination of t-distributed Stochastic Neighbour Embedding and Linear Discriminant Analysis effectively separates the two SN types. Additionally, a Random Forest Classifier demonstrates the robustness of pEW and FWHM as classification criteria, allowing for accurate classification of newly observed SNe II and IIb based on computed classification probabilities. Applying our method to low-resolution spectra obtained from the Zwicky Transient Facility Bright Transient, a magnitude-limited survey, we identified 34 misclassified SNe. This revision increases the estimated fraction of SNe IIb from 4.0% to 7.26%. This finding suggests that misclassification significantly impacts the estimated core-collapse SN rate. Our approach enhances classification accuracy and provides a valuable tool for future supernova studies.
Some notes on higher order concentration of measure
2025-07-11
Holger Sambale
This survey-type paper provides a common framework for a larger number of higher order concentration results (i.\,e., concentration results for non-Lipschitz functions which have bounded derivatives of higher order) in the spirit of Bobkov--G\"otze--Sambale (2019). Situations inlude measures satisfying various functional inequalities (log-Sobolev, Poincar\'{e}, $\mathrm{LS}_q$), uniform and cone measures on spheres with respect to the Euclidean as well as $\ell_p^n$-norms, Stiefel and Grassmann manifolds as well as discrete situations. In particular in the latter case, some open questions and remarks are stated.
Qualitative Assessment of Low Power Wide Area Network Protocols and their Security Aspect
2025-07-11
Wesley dos Reis Bezerra, Lais Machado Bezerra, Carlos Becker Westphal
There are currently many communication options in the Internet of Things, even in particular areas such as constrained and battery-powered devices, such as Low Power Wide Area Networks. Understanding the differences and characteristics of each option is a challenge, even for professionals and researchers in the field. To meet this need, this work analyses the qualitative characteristics of Low Power Wide Area Network protocols and the challenges and opportunities of using constrained devices for sparse networks based on long-life batteries. For this study, a bibliographic survey of the literature was carried out as an analysis of three protocols (LoRaWAN, NB-IoT, and Sigfox), and a detailing of the first one. As a result, there is a discussion about the chosen network protocol and its use in IoT solutions with sparse sensors.
Polarization dynamics in vertical-cavity surface emitting lasers
2025-07-11
Thorsten Ackemann, Markus Sondermann
Experiments and their interpretation on polarization dynamics and polarization switching in vertical-cavity surface-emitting lasers operated in the fundamental transverse mode regime are reviewed. Important observations are switching events to a mode with the lower unsaturated gain and the existence of elliptically polarized dynamical transition states after the destabilization of the low-frequency polarization mode. The observations demonstrate the need to consider explicitly the phase properties of the optical field as well as nonlinear effects affecting polarization selection above threshold. Good qualitative agreement is found with a model which takes into account the spin degrees of freedom of the light field as well as of the carriers (`spin-flip model'), if the spin-flip rate is taken to be some tens of $10^9$~s$^{-1}$. This constitutes a strong -- though indirect -- indication that spin dependent processes are important in polarization selection in the devices investigated.
Metal-THINGS: gas metallicity gradients in nearby galaxies
2025-07-11
Guillermo Valé, Maritza A. Lara-López, Mabel Valerdi, Igor Zinchenko, Shane P. O'Sullivan, Leonid S. Pilyugin, Jordi Cepa, Viviana Casasola, María E. De Rossi, Sami Dib, Jacopo Fritz, Jesús Gallego, Luis E. Garduño, Omar López-Cruz, Vidhi Tailor, Javier Zaragoza-Cardiel
This paper explores the gas metallicity gradients in a sample of 25 nearby galaxies using new Integral Field Spectroscopy observations from the Metal-THINGS survey. We derive and study the resolved diffuse ionised gas content, Baldwin, Phillips and Terlevich diagrams and gas metallicities for our entire sample, at spatial resolutions of 40-300 pc. Gas metallicity gradients are studied as a function of the galaxy's stellar mass, H I gas fraction, diffuse ionised gas content, and using different parametric length scales for normalisation. The metallicity gradients are analysed using Bayesian statistics based on data from the Metal-THINGS survey. Bayesian MCMC models are developed to explore how metallicity gradients vary with a galaxy's mass and how they correlate with properties such as the stellar mass or the atomic gas fraction. For our sample, we find that the metallicity typically decreases with galactic radius, consistent with inside-out galaxy growth. We find a trend dependent on the stellar mass, with a break at log(M_star/M_sun)=9.5, and another between the metallicity gradients and the atomic gas fraction (f_g,HI) of a galaxy at fg,HI=0.75, indicating relatively shallower gradients for lower gas fractions. We find that normalisation using NUV-band effective radii are preferable for galaxies with a higher atomic gas content and lower stellar masses, while r-band radii are better suited for those with lower atomic gas fractions and more massive ones. Our results highlight a strong connection between gas content, stellar mass, and metallicity gradients. The breaks at log(M_star/M_sun)=9.5 and fg,HI=0.75 mark shifts in chemical enrichment behaviour, with low-mass galaxies showing greater sensitivity to gas processes. Overall, this points to gas accretion and removal as key drivers of chemical evolution in low-mass systems.
Adaptive Framework for Ambient Intelligence in Rehabilitation Assistance
2025-07-11
Gábor Baranyi, Zsolt Csibi, Kristian Fenech, Áron Fóthi, Zsófia Gaál, Joul Skaf, András Lőrincz
This paper introduces the Ambient Intelligence Rehabilitation Support (AIRS) framework, an advanced artificial intelligence-based solution tailored for home rehabilitation environments. AIRS integrates cutting-edge technologies, including Real-Time 3D Reconstruction (RT-3DR), intelligent navigation, and large Vision-Language Models (VLMs), to create a comprehensive system for machine-guided physical rehabilitation. The general AIRS framework is demonstrated in rehabilitation scenarios following total knee replacement (TKR), utilizing a database of 263 video recordings for evaluation. A smartphone is employed within AIRS to perform RT-3DR of living spaces and has a body-matched avatar to provide visual feedback about the excercise. This avatar is necessary in (a) optimizing exercise configurations, including camera placement, patient positioning, and initial poses, and (b) addressing privacy concerns and promoting compliance with the AI Act. The system guides users through the recording process to ensure the collection of properly recorded videos. AIRS employs two feedback mechanisms: (i) visual 3D feedback, enabling direct comparisons between prerecorded clinical exercises and patient home recordings and (ii) VLM-generated feedback, providing detailed explanations and corrections for exercise errors. The framework also supports people with visual and hearing impairments. It also features a modular design that can be adapted to broader rehabilitation contexts. AIRS software components are available for further use and customization.
UNIONS Optical Identifications for VLASS Radio Sources in the Euclid Sky (UNVEIL) I. A Catalog of ~146,000 Radio Galaxies up to z~5
2025-07-11
Yuxing Zhong, Kohei Ichikawa, Hendrik Hildebrandt, Stephen Gwyn, Akio K. Inoue, Masafusa Onoue, Masatoshi Imanishi, Taketo Yoshida, Martin Kilbinger, Thomas de Boer, Gregory S. H. Paek, Yoshiki Matsuoka, Tohru Nagao, Yoshiki Toba
We present results of optical identifications for Very Large Array Sky Survey (VLASS) radio sources using the Ultraviolet Near Infrared Optical Northern Survey (UNIONS). A cross-match between UNIONS and VLASS Epoch 2 catalogs yields 146,212 radio galaxies down to $r=24.5$ mag over a wide area of $\sim4,200\ \mathrm{deg^2}$. We perform g-dropout selections and find $>200$ sources at $z\sim4$ optimistically. Of 63,019 sources with valid photo-$z$, 8,692 are at $z_\mathrm{photo}>1$, and 1,171 are at $z_\mathrm{photo}>2$. Based on spectral luminosities at 1.4 GHz using the valid photo-$z$, we identify $\sim49,000$ radio-loud AGNs (RLAGNs) with $\textit{L}_\mathrm{1.4GHz}>10^{24}$ W/Hz, and all radio galaxies at $z>1$ are RL. Adopting radio loudness instead, 138,266 out of 146,212 UNIONS-VLASS radio galaxies are RL. Thus, our catalog greatly increases the number counts of RLAGNs at $z>1$. We further cross-match the UNIONS-VLASS catalog with LOw-Frequency ARray Two-metre Sky Survey (LoTSS) at 144 MHz and Faint Images of the Radio Sky at Twenty-cm (FIRST) at 1.4 GHz, yielding 101,671 UNIONS-VLASS-LoTSS, 79,638 -FIRST, and 64,672 -LoTSS-FIRST sources, respectively. This multifrequency radio dataset reveals sources of various spectral shapes, including the steep spectrum of aged populations, the peaked spectrum of young populations, and the upturned spectrum potentially associated with transient sources. The UNIONS-VLASS radio galaxies will be covered by the Euclid wide survey, offering legacy values to benefit multi-faceted studies related to RLAGNs and beyond.
XMM-Newton follow-up of two eROSITA X-ray binary candidates
2025-07-11
A. Avakyan, A. Zainab, V. Doroshenko, J. Wilms, A. Schwope, V. Suleimanov, D. Buckley, J. Brink, A. Santangelo
We report on the follow-up observations with XMM-Newton of two X-ray binary candidates identified in the first eROSITA all-sky survey data (eRASS1), 1eRASS J061330.8+160440 and 1eRASS J161201.9-464622. Based on the obtained results, in particular, the observed X-ray spectra and lack of pulsations, as well as properties of the identified optical counterparts, we conclude that both candidates are unlikely to be XRBs. Based on LAMOST optical spectroscopy and SED fit results for 1eRASS J061330.8+160440 we classify it as an M0 chromospherically active subgiant star. ZTF and TESS photometry reveal highly significant period for this object of 7.189 days, which likely attributed to starspot(s). On the other hand, SALT follow-up spectroscopy of 1eRASS J161201.9-464622 solidly classifies this source as a bright novalike cataclysmic variable (CV), the second discovered with eROSITA. A persistent 4.802 h signal is found across all three available TESS observations, and is tentatively identified as the orbital period of the binary. Follow-up high-speed photometry and time-resolved spectroscopy are required to confirm the derived orbital modulation.
Interfacial Entanglement-Induced Time-Dependent Solidification of Polymeric Fluids
2025-07-11
Jaewon Shim, Manhee Lee, Wonho Jhe
The structure of polymers at solid interfaces evolves over time, but the corresponding changes in their rheological properties remain poorly understood. Here, using a home-built quartz tuning fork atomic force microscope-based nano-rheometer, we directly measure the time-dependent viscoelasticity of the interfacial fluid. The bottommost layer, closest to the substrate, undergoes solidification over 10 hours, exhibiting an approximately five-fold increase in storage modulus and a two-fold increase in loss modulus. This arises from interfacial entanglement due to the strong binding of polymers to the solid surface driven by solid-wall attractive interactions. In contrast, within the second and third layers, the storage modulus remains nearly constant over time, while the loss modulus shows approximately two-fold increase. In this region, unlike the strongly bound first layer, entropic repulsion dominates, allowing the material to behave fluid-like while becoming increasingly viscous. Notably, as the first layer, where interfacial entanglement occurs, undergoes solidification, the flow boundary for interfacial fluid flow shifts upward away from the substrate, resulting in a negative slip length. This highlights the critical role of nanoscale interfacial structure and properties in governing macroscopic flow behavior.
Digital gazetteers: review and prospects for place name knowledge bases
2025-07-11
Kalana Wijegunarathna, Kristin Stock, Christopher B. Jones
Gazetteers typically store data on place names, place types and the associated coordinates. They play an essential role in disambiguating place names in online geographical information retrieval systems for navigation and mapping, detecting and disambiguating place names in text, and providing coordinates. Currently there are many gazetteers in use derived from many sources, with no commonly accepted standard for encoding the data. Most gazetteers are also very limited in the extent to which they represent the multiple facets of the named places yet they have potential to assist user search for locations with specific physical, commercial, social or cultural characteristics. With a view to understanding digital gazetteer technologies and advancing their future effectiveness for information retrieval, we provide a review of data sources, components, software and data management technologies, data quality and volunteered data, and methods for matching sources that refer to the same real-world places. We highlight the need for future work on richer representation of named places, the temporal evolution of place identity and location, and the development of more effective methods for data integration.
A survey for radio pulsars and transients in the 10 pc region around Sgr A*
2025-07-11
G. Desvignes, R. P. Eatough, Y. Men, F. Abbate, R. Karuppusamy, M. Kramer, K. Liu, L. Shao, P. Torne, R. S. Wharton
Here we report on a new survey for pulsars and transients in the 10 pc region around Sgr A* using the Effelsberg radio telescope at frequencies between 4 to 8 GHz. Our calibrated full-Stokes data were searched for pulsars and transients using PulsarX, TransientX and PRESTO. Polarisation information is used in the scoring of the candidates. Our periodicity acceleration and jerk searches allowed us to maintain good sensitivity towards binary pulsars in $\gtrsim$ 10-hr orbits. In addition we performed a dedicated search in linear polarisation for slow transients. While our searches yielded no new discovery beyond the redetection of the magnetar SGR J1745-2900, we report on a faint single pulse candidate in addition to several weak periodicity search candidates. After thoroughly assessing our survey's sensitivity, we determined that it is still not sensitive to a population of millisecond pulsars. Next generation radio interferometers can overcome the limitations of traditional single-dish pulsar searches of the Galactic Centre.
Efficient Deployment of Vision-Language Models on Mobile Devices: A Case Study on OnePlus 13R
2025-07-11
Pablo Robin Guerrero, Yueyang Pan, Sanidhya Kashyap
Vision-Language Models (VLMs) offer promising capabilities for mobile devices, but their deployment faces significant challenges due to computational limitations and energy inefficiency, especially for real-time applications. This study provides a comprehensive survey of deployment frameworks for VLMs on mobile devices, evaluating llama.cpp, MLC-Imp, and mllm in the context of running LLaVA-1.5 7B, MobileVLM-3B, and Imp-v1.5 3B as representative workloads on a OnePlus 13R. Each deployment framework was evaluated on the OnePlus 13R while running VLMs, with measurements covering CPU, GPU, and NPU utilization, temperature, inference time, power consumption, and user experience. Benchmarking revealed critical performance bottlenecks across frameworks: CPU resources were consistently over-utilized during token generation, while GPU and NPU accelerators were largely unused. When the GPU was used, primarily for image feature extraction, it was saturated, leading to degraded device responsiveness. The study contributes framework-level benchmarks, practical profiling tools, and an in-depth analysis of hardware utilization bottlenecks, highlighting the consistent overuse of CPUs and the ineffective or unstable use of GPUs and NPUs in current deployment frameworks.
RNA Dynamics and Interactions Revealed through Atomistic Simulations
2025-07-11
Olivier Languin-Cattoën, Giovanni Bussi
RNA function is deeply intertwined with its conformational dynamics. In this review, we survey recent advances in the use of atomistic molecular dynamics simulations to characterize RNA dynamics in diverse contexts, including isolated molecules and complexes with ions, small molecules, or proteins. We highlight how enhanced sampling techniques and integrative approaches can improve both the precision and accuracy of the resulting structural ensembles. Finally, we examine the emerging role of artificial intelligence in accelerating progress in RNA modeling and simulation.
Spectroscopic and femtoscopic insights into vector-baryon interactions in the strangeness $-1$ sector
2025-07-11
P. Encarnación, M. Albaladejo, A. Feijoo, J. Nieves
We revisit the strangeness $-1$ sector of the interaction between vector mesons of the $\rho-$nonet and $1/2^+$ ground state baryons ($VB$), within the unitary coupled-channel hidden gauge formalism. We adopt a renormalization scheme that induces a reasonable short-distance behavior of the two-hadron wave functions, which is the major limitation of femtoscopy techniques at this time. We perform an exhaustive spectroscopy study, implementing different improvements and considerations, and find compatibilities between the poles extracted from the present approach, and some of the states listed in the Review of Particle Physics. Finally, we predict several correlation functions (CFs) associated with various meson-baryon pairs in this sector, paying special attention to the distinctive and clear signatures produced by the dynamically generated states. To obtain realistic estimates for the CFs, we have calculated the production weights using the Thermal-FIST package. Such studies will shed light into the odd-parity $\Lambda^*$ and $\Sigma^*$ hadron spectra, up to 2 GeV, and will open the strategy to improve effective field theories by using low-energy constants fitted to femtoscopy data.
An Earth-Sized Planet in a 5.4h Orbit Around a Nearby K dwarf
2025-07-11
Kaya Han Taş, Gudmundur Stefansson, Syarief N. M. Fariz, Esha Garg, Juan I. Espinoza-Retamal, Elise Koo, David Bruijne, Jacob Luhn, Eric B. Ford, Suvrath Mahadevan, Sarah E. Logsdon, Caleb I. Cañas, Te Han, Mark E. Everett, Jaime A. Alvarado-Montes, Cullen Blake, William D. Cochran, Jiayin Dong, Rachel B. Fernandes, Mark R. Giovinazzi, Samuel Halverson, Shubham Kanodia, Daniel Krolikowski, Michael McElwain, Joe Ninan, Leonardo A. Paredes, Paul Robertson, Christian Schwab
We present the discovery and confirmation of the ultra-short period (USP) planet TOI-2431 b orbiting a nearby ($d\sim36$ pc) late K star ($T_{\mathrm{eff}}$ = $4109 \pm 28 \, {\rm K}$) using observations from the Transiting Exoplanet Survey Satellite (TESS), precise radial velocities with the NEID and the Habitable-zone Planet Finder (HPF) spectrographs, as well as ground-based high contrast imaging from NESSI. TOI-2431 b has a period of 5 hours and 22 minutes, making it one of the shortest-period exoplanets known to date. TOI-2431 b has a radius of $1.536 \pm 0.033\, \rm{R_\oplus}$, and a mass of $6.2 \pm 1.2\, \rm{M_\oplus}$, suggesting it has a density compatible with an Earth-like composition and, due to its high irradiation, is likely a 'lava-world' with a $T_{\mathrm{eq}}$ = $2063 \pm 30 \, {\rm K}$. We estimate that the current orbital period is only 30% larger than the Roche-limit orbital period, and that it has an expected orbital decay timescale of only $\sim$31 Myr. Finally, due to the brightness of the host star ($V = 10.9$, $K = 7.6$), TOI-2431 b has a high Emission Spectroscopy Metric of 27, making it one of the best USP systems for atmospheric phase-curve analysis.
3D reconstruction, which aims to recover the dense three-dimensional structure of a scene, is a cornerstone technology for numerous applications, including augmented/virtual reality, autonomous driving, and robotics. While traditional pipelines like Structure from Motion (SfM) and Multi-View Stereo (MVS) achieve high precision through iterative optimization, they are limited by complex workflows, high computational cost, and poor robustness in challenging scenarios like texture-less regions. Recently, deep learning has catalyzed a paradigm shift in 3D reconstruction. A new family of models, exemplified by DUSt3R, has pioneered a feed-forward approach. These models employ a unified deep network to jointly infer camera poses and dense geometry directly from an Unconstrained set of images in a single forward pass. This survey provides a systematic review of this emerging domain. We begin by dissecting the technical framework of these feed-forward models, including their Transformer-based correspondence modeling, joint pose and geometry regression mechanisms, and strategies for scaling from two-view to multi-view scenarios. To highlight the disruptive nature of this new paradigm, we contrast it with both traditional pipelines and earlier learning-based methods like MVSNet. Furthermore, we provide an overview of relevant datasets and evaluation metrics. Finally, we discuss the technology's broad application prospects and identify key future challenges and opportunities, such as model accuracy and scalability, and handling dynamic scenes.
Null geodesics, causal structure, and matter accretion in Lorentzian-Euclidean black holes
2025-07-11
Salvatore Capozziello, Emmanuele Battista, Silvia De Bianchi
Recently, we introduced the Lorentzian-Euclidean black hole, a static and spherically symmetric solution of vacuum Einstein equations that exhibits a change in metric signature across the event horizon. In this framework, the analysis of radial trajectories of freely falling bodies proves that the central singularity can be avoided via a mechanism we interpret as atemporality, which is responsible for the shift of the time variable from real to imaginary values. In this paper, we further explore this model by first examining the behavior of null geodesics. Our investigation requires a set of signature-adaptive coordinate changes that generalize the local Lorentz transformations underlying General Relativity. We find that photon orbits, like their massive counterparts, cannot traverse the event horizon, thereby strengthening the previous result on the impossibility to reach the $r=0$ singularity. Additionally, we discuss the causal structure of the spacetime, provide the corresponding Penrose diagram, and analyze the process of matter accretion in the outer region of the black hole.
A Survey of Large Language Models in Discipline-specific Research: Challenges, Methods and Opportunities
2025-07-11
Lu Xiang, Yang Zhao, Yaping Zhang, Chengqing Zong
Large Language Models (LLMs) have demonstrated their transformative potential across numerous disciplinary studies, reshaping the existing research methodologies and fostering interdisciplinary collaboration. However, a systematic understanding of their integration into diverse disciplines remains underexplored. This survey paper provides a comprehensive overview of the application of LLMs in interdisciplinary studies, categorising research efforts from both a technical perspective and with regard to their applicability. From a technical standpoint, key methodologies such as supervised fine-tuning, retrieval-augmented generation, agent-based approaches, and tool-use integration are examined, which enhance the adaptability and effectiveness of LLMs in discipline-specific contexts. From the perspective of their applicability, this paper explores how LLMs are contributing to various disciplines including mathematics, physics, chemistry, biology, and the humanities and social sciences, demonstrating their role in discipline-specific tasks. The prevailing challenges are critically examined and the promising research directions are highlighted alongside the recent advances in LLMs. By providing a comprehensive overview of the technical developments and applications in this field, this survey aims to serve as an invaluable resource for the researchers who are navigating the complex landscape of LLMs in the context of interdisciplinary studies.
Advances in Machine Learning: Where Can Quantum Techniques Help?
2025-07-11
Samarth Kashyap, Rohit K Ramakrishnan, Kumari Jyoti, Apoorva D Patel
Quantum Machine Learning (QML) represents a promising frontier at the intersection of quantum computing and artificial intelligence, aiming to leverage quantum computational advantages to enhance data-driven tasks. This review explores the potential of QML to address the computational bottlenecks of classical machine learning, particularly in processing complex datasets. We introduce the theoretical foundations of QML, including quantum data encoding, quantum learning theory and optimization techniques, while categorizing QML approaches based on data type and computational architecture. It is well-established that quantum computational advantages are problem-dependent, and so potentially useful directions for QML need to be systematically identified. Key developments, such as Quantum Principal Component Analysis, quantum-enhanced sensing and applications in material science, are critically evaluated for their theoretical speed-ups and practical limitations. The challenges posed by Noisy Intermediate-Scale Quantum (NISQ) devices, including hardware noise, scalability constraints and data encoding overheads, are discussed in detail. We also outline future directions, emphasizing the need for quantum-native algorithms, improved error correction, and realistic benchmarks to bridge the gap between theoretical promise and practical deployment. This comprehensive analysis underscores that while QML has significant potential for specific applications such as quantum chemistry and sensing, its broader utility in real-world scenarios remains contingent on overcoming technological and methodological hurdles.
Unsupervised Methods for Video Quality Improvement: A Survey of Restoration and Enhancement Techniques
2025-07-11
Alexandra Malyugina, Yini Li, Joanne Lin, Nantheera Anantrasirichai
Video restoration and enhancement are critical not only for improving visual quality, but also as essential pre-processing steps to boost the performance of a wide range of downstream computer vision tasks. This survey presents a comprehensive review of video restoration and enhancement techniques with a particular focus on unsupervised approaches. We begin by outlining the most common video degradations and their underlying causes, followed by a review of early conventional and deep learning methods-based, highlighting their strengths and limitations. We then present an in-depth overview of unsupervised methods, categorise by their fundamental approaches, including domain translation, self-supervision signal design and blind spot or noise-based methods. We also provide a categorization of loss functions employed in unsupervised video restoration and enhancement, and discuss the role of paired synthetic datasets in enabling objective evaluation. Finally, we identify key challenges and outline promising directions for future research in this field.
Theoretical study of the ECRIPAC accelerator concept
2025-07-11
Andrea Cernuschi, Thomas Thuillier, Laurent Garrigues
The Electron Cyclotron Resonance Ion Plasma ACcelerator (ECRIPAC) is an original concept for a plasma-based particle accelerator able to generate pulsed ion beams with adjustable energy, targeting mostly medical applications. In a complementary letter (Cernuschi et al., arXiv:2507.07827) we thoroughly reviewed and corrected the current literature on the subject, including also an original theoretical investigation on the influence of several physical parameters on the accelerator stability. This study led to the proposal of a compact demonstrator device, which has been validated through Monte Carlo simulations. The present paper provides a detailed and more extended explanation of the ECRIPAC working principle and physics, including mathematical derivations for several physical formulas. Moreover, several other parameters have been included in the accelerator stability study, providing an overview of achievable external fields and plasma characteristics allowing a stable ion acceleration.
In this note, we highlight the impact of the paper G. H. Hardy, A theorem concerning Fourier transforms, J. Lond. Math. Soc. (1) 8 (1933), 227--231 in the community of harmonic analysis in the last 90 years, reviewing, on the one hand, the direct generalizations of the main results and, on the other hand, the different connections to related areas and new perspectives.
In recent years, the increasing awareness of cybersecurity has led to a heightened focus on information security within hardware devices and products. Incorporating Trusted Execution Environments (TEEs) into product designs has become a standard practice for safeguarding sensitive user information. However, vulnerabilities within these components present significant risks, if exploited by attackers, these vulnerabilities could lead to the leakage of sensitive data, thereby compromising user privacy and security. This research centers on trusted applications (TAs) within the Qualcomm TEE and introduces a novel emulator specifically designed for these applications. Through reverse engineering techniques, we thoroughly analyze Qualcomm TAs and develop a partial emulation environment that accurately emulates their behavior. Additionally, we integrate fuzzing testing techniques into the emulator to systematically uncover potential vulnerabilities within Qualcomm TAs, demonstrating its practical effectiveness in identifying real-world security flaws. This research makes a significant contribution by being the first to provide both the implementation methods and source codes for a Qualcomm TAs emulator, offering a valuable reference for future research efforts. Unlike previous approaches that relied on complex and resource-intensive full-system simulations, our approach is lightweight and effective, making security testing of TA more convenient.
2025-07-11
Jorge Mario Cruz-Duarte, El-Ghazali Talbi
The increasing energy footprint of artificial intelligence systems urges alternative computational models that are both efficient and scalable. Neuromorphic Computing (NC) addresses this challenge by empowering event-driven algorithms that operate with minimal power requirements through biologically inspired spiking dynamics. We present the NeurOptimiser, a fully spike-based optimisation framework that materialises the neuromorphic-based metaheuristic paradigm through a decentralised NC system. The proposed approach comprises a population of Neuromorphic Heuristic Units (NHUs), each combining spiking neuron dynamics with spike-triggered perturbation heuristics to evolve candidate solutions asynchronously. The NeurOptimiser's coordination arises through native spiking mechanisms that support activity propagation, local information sharing, and global state updates without external orchestration. We implement this framework on Intel's Lava platform, targeting the Loihi 2 chip, and evaluate it on the noiseless BBOB suite up to 40 dimensions. We deploy several NeurOptimisers using different configurations, mainly considering dynamic systems such as linear and Izhikevich models for spiking neural dynamics, and fixed and Differential Evolution mutation rules for spike-triggered heuristics. Although these configurations are implemented as a proof of concept, we document and outline further extensions and improvements to the framework implementation. Results show that the proposed approach exhibits structured population dynamics, consistent convergence, and milliwatt-level power feasibility. They also position spike-native MHs as a viable path toward real-time, low-energy, and decentralised optimisation.
Generative AI in Science: Applications, Challenges, and Emerging Questions
2025-07-11
Ryan Harries, Cornelia Lawson, Philip Shapira
This paper examines the impact of Generative Artificial Intelligence (GenAI) on scientific practices, conducting a qualitative review of selected literature to explore its applications, benefits, and challenges. The review draws on the OpenAlex publication database, using a Boolean search approach to identify scientific literature related to GenAI (including large language models and ChatGPT). Thirty-nine highly cited papers and commentaries are reviewed and qualitatively coded. Results are categorized by GenAI applications in science, scientific writing, medical practice, and education and training. The analysis finds that while there is a rapid adoption of GenAI in science and science practice, its long-term implications remain unclear, with ongoing uncertainties about its use and governance. The study provides early insights into GenAI's growing role in science and identifies questions for future research in this evolving field.
Improving MLLM's Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiency
2025-07-11
Yupu Liang, Yaping Zhang, Zhiyang Zhang, Zhiyuan Chen, Yang Zhao, Lu Xiang, Chengqing Zong, Yu Zhou
Multimodal Large Language Models (MLLMs) have shown strong performance in document image tasks, especially Optical Character Recognition (OCR). However, they struggle with Document Image Machine Translation (DIMT), which requires handling both cross-modal and cross-lingual challenges. Previous efforts to enhance DIMT capability through Supervised Fine-Tuning (SFT) on the DIMT dataset often result in the forgetting of the model's existing monolingual abilities, such as OCR. To address these challenges, we introduce a novel fine-tuning paradigm, named Synchronously Self-Reviewing (SSR) its OCR proficiency, inspired by the concept "Bilingual Cognitive Advantage". Specifically, SSR prompts the model to generate OCR text before producing translation text, which allows the model to leverage its strong monolingual OCR ability while learning to translate text across languages. Comprehensive experiments demonstrate the proposed SSR learning helps mitigate catastrophic forgetting, improving the generalization ability of MLLMs on both OCR and DIMT tasks.
Obscured and unobscured X-ray AGNs I: Host galaxy properties
2025-07-11
Carlos G. Bornancini, Gabriel A. Oio, Georgina Coldwell
Active galactic nuclei (AGN) play a crucial role in galaxy evolution by influencing the observational properties of their host galaxies. We investigate the host galaxy properties of X-ray selected AGNs, focusing on differences between obscured and unobscured AGNs, and between high-(log([OIII]5007/H$\beta$}$>$0.5) and low-excitation sources (log([OIII]5007/H$\beta$$<$0.5). We selected a sample of AGNs from the spectroscopic zCOSMOS survey with 0.5 $< zsp <$ 0.9 based on the Mass-Excitation (MEx) diagram and X-ray emission. AGNs were classified as obscured or unobscured using hydrogen column density, and as high- or low-excitation based on the [OIII]5007/H$\beta$ ratio. We analysed various AGN properties, including the hardness ratio, X-ray luminosity, emission line ratios such as the ionisation-level sensitive parameter O32=log([OIII]5007/[OII]3727), and the metallicity sensitive parameter R23=log(([OIII]5007+[OII]3727/H$\beta$), and the specific black hole accretion rate. Unobscured AGNs exhibit a more evident correlation between the [OIII]5007/[OII]3727 ionisation ratio and X-ray luminosity than obscured AGNs, while high-excitation obscured AGNs reach, on average, higher X-ray luminosities. Furthermore, high-excitation AGNs typically show high values of R23, suggesting low metallicities, similar to that observed in high-redshift galaxies (4 $< z <$ 6). We find a positive correlation between the parameters $\lambda$sBHAR and N$_H$, R23 and O32 parameters. The correlation suggests that AGNs with a high specific accretion rate have not only a higher production of high-energy photons, which ionise the surrounding medium more intensely, but are also usually associated with environments less enriched in heavy elements. These results provide insights into the complex interplay between AGN activity, host galaxy properties, and the role of obscuration in shaping galaxy evolution.
Rocksalt rare-earth monoxides as electronic and magnetic materials
2025-07-11
Tomoteru Fukumura, Satoshi Sasaki, Masamichi Negishi, Daichi Oka
Stable binary rare earth (RE) oxides are usually trivalent RE ion sesquioxides (RE2O3), that are highly insulating and either nonmagnetic or antiferromagnetic. On the other hand, rocksalt-type divalent RE ion monoxides (REOs) have been scarcely synthesized owing to their metastable nature. Accordingly, their fundamental properties have not been unveiled. Recently, thin film epitaxy was successfully applied to synthesize various REOs. In stark contrast with RE2O3, REOs are highly electrically conducting, and exhibit superconductivity, room temperature ferromagnetism, and so on. Therefore, REOs are new and simple f-electron system, promising as electronic, magnetic, and spintronic materials. In this review, their fundamental properties are introduced, and their significance and future prospects are discussed toward a new paradigm in f-electron systems.
Constraining decaying dark matter models with gravitational lensing and cosmic voids
2025-07-11
Earl Lester, Krzysztof Bolejko
Despite overwhelming observational evidence for dark matter, we still have no evidence of direct detection. Consequently, our knowledge about dark matter is limited, for example, we do not know if dark matter is a stable particle or if it decays. Without a theoretical particle model, the parameter space of possible decay models is highly variable, and astrophysical or cosmological means of indirectly constraining the phenomenological models are required. This paper investigates a scenario in which a dark matter decays and the dark daughter particle moves with respect to the comoving mother particle. The model is parameterised by the decay rate and the injection velocity of the dark matter particles, which can be converted to the mass ratio. In previous work, a simpler model was used to investigate the evolution of cosmic voids typified as regions with low content of galaxies and non-baryonic matter. It was found that the growth of S-type voids is modified by the dark matter decay, leading to imprints at the present day. Here we extend our study and improve the method used to model the decay. We also study the gravitational weak-lensing signal that will be able to detect or constrain the parameter space of decaying dark matter. The results of this study suggest that future weak lensing surveys may provide unique probes of the phenomological parameters of dark matter.
Abductive Computational Systems: Creative Abduction and Future Directions
2025-07-11
Abhinav Sood, Kazjon Grace, Stephen Wan, Cecile Paris
Abductive reasoning, reasoning for inferring explanations for observations, is often mentioned in scientific, design-related and artistic contexts, but its understanding varies across these domains. This paper reviews how abductive reasoning is discussed in epistemology, science and design, and then analyses how various computational systems use abductive reasoning. Our analysis shows that neither theoretical accounts nor computational implementations of abductive reasoning adequately address generating creative hypotheses. Theoretical frameworks do not provide a straightforward model for generating creative abductive hypotheses, computational systems largely implement syllogistic forms of abductive reasoning. We break down abductive computational systems into components and conclude by identifying specific directions for future research that could advance the state of creative abductive reasoning in computational systems.
Leveraging Large Language Models for Classifying App Users' Feedback
2025-07-11
Yasaman Abedini, Abbas Heydarnoori
In recent years, significant research has been conducted into classifying application (app) user feedback, primarily relying on supervised machine learning algorithms. However, fine-tuning more generalizable classifiers based on existing labeled datasets remains an important challenge, as creating large and accurately labeled datasets often requires considerable time and resources. In this paper, we evaluate the capabilities of four advanced LLMs, including GPT-3.5-Turbo, GPT-4, Flan-T5, and Llama3-70b, to enhance user feedback classification and address the challenge of the limited labeled dataset. To achieve this, we conduct several experiments on eight datasets that have been meticulously labeled in prior research. These datasets include user reviews from app stores, posts from the X platform, and discussions from the public forums, widely recognized as representative sources of app user feedback. We analyze the performance of various LLMs in identifying both fine-grained and coarse-grained user feedback categories. Given the substantial volume of daily user feedback and the computational limitations of LLMs, we leverage these models as an annotation tool to augment labeled datasets with general and app-specific data. This augmentation aims to enhance the performance of state-of-the-art BERT-based classification models. Our findings indicate that LLMs when guided by well-crafted prompts, can effectively classify user feedback into coarse-grained categories. Moreover, augmenting the training dataset with datasets labeled using the consensus of LLMs can significantly enhance classifier performance.
Advancing AI Capabilities and Evolving Labor Outcomes
2025-07-11
Jacob Dominski, Yong Suk Lee
This study investigates the labor market consequences of AI by analyzing near real-time changes in employment status and work hours across occupations in relation to advances in AI capabilities. We construct a dynamic Occupational AI Exposure Score based on a task-level assessment using state-of-the-art AI models, including ChatGPT 4o and Anthropic Claude 3.5 Sonnet. We introduce a five-stage framework that evaluates how AI's capability to perform tasks in occupations changes as technology advances from traditional machine learning to agentic AI. The Occupational AI Exposure Scores are then linked to the US Current Population Survey, allowing for near real-time analysis of employment, unemployment, work hours, and full-time status. We conduct a first-differenced analysis comparing the period from October 2022 to March 2023 with the period from October 2024 to March 2025. Higher exposure to AI is associated with reduced employment, higher unemployment rates, and shorter work hours. We also observe some evidence of increased secondary job holding and a decrease in full-time employment among certain demographics. These associations are more pronounced among older and younger workers, men, and college-educated individuals. College-educated workers tend to experience smaller declines in employment but are more likely to see changes in work intensity and job structure. In addition, occupations that rely heavily on complex reasoning and problem-solving tend to experience larger declines in full-time work and overall employment in association with rising AI exposure. In contrast, those involving manual physical tasks appear less affected. Overall, the results suggest that AI-driven shifts in labor are occurring along both the extensive margin (unemployment) and the intensive margin (work hours), with varying effects across occupational task content and demographics.
From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion
2025-07-11
Zhenyu Yu, Mohd Yamani Idna Idris, Hua Wang, Pei Wang, Junyi Chen, Kun Wang
Quantitative remote sensing inversion aims to estimate continuous surface variables-such as biomass, vegetation indices, and evapotranspiration-from satellite observations, supporting applications in ecosystem monitoring, carbon accounting, and land management. With the evolution of remote sensing systems and artificial intelligence, traditional physics-based paradigms are giving way to data-driven and foundation model (FM)-based approaches. This paper systematically reviews the methodological evolution of inversion techniques, from physical models (e.g., PROSPECT, SCOPE, DART) to machine learning methods (e.g., deep learning, multimodal fusion), and further to foundation models (e.g., SatMAE, GFM, mmEarth). We compare the modeling assumptions, application scenarios, and limitations of each paradigm, with emphasis on recent FM advances in self-supervised pretraining, multi-modal integration, and cross-task adaptation. We also highlight persistent challenges in physical interpretability, domain generalization, limited supervision, and uncertainty quantification. Finally, we envision the development of next-generation foundation models for remote sensing inversion, emphasizing unified modeling capacity, cross-domain generalization, and physical interpretability.
2025-07-11
Eric C. Chi, Aaron J. Molstad, Zheming Gao
This survey reviews a clustering method based on solving a convex optimization problem. Despite the plethora of existing clustering methods, convex clustering has several uncommon features that distinguish it from prior art. The optimization problem is free of spurious local minima, and its unique global minimizer is stable with respect to all its inputs, including the data, a tuning parameter, and weight hyperparameters. Its single tuning parameter controls the number of clusters and can be chosen using standard techniques from penalized regression. We give intuition into the behavior and theory for convex clustering as well as practical guidance. We highlight important algorithms and give insight into how their computational costs scale with the problem size. Finally, we highlight the breadth of its uses and flexibility to be combined and integrated with other inferential methods.
SAGE: A Context-Aware Approach for Mining Privacy Requirements Relevant Reviews from Mental Health Apps
2025-07-11
Aakash Sorathiya, Gouri Ginde
Mental health (MH) apps often require sensitive user data to customize services for mental wellness needs. However, such data collection practices in some MH apps raise significant privacy concerns for users. These concerns are often mentioned in app reviews, but other feedback categories, such as reliability and usability, tend to take precedence. This poses a significant challenge in automatically identifying privacy requirements-relevant reviews (privacy reviews) that can be utilized to extract privacy requirements and address users' privacy concerns. Thus, this study introduces SAGE, a context-aware approach to automatically mining privacy reviews from MH apps using Natural Language Inference (NLI) with MH domain-specific privacy hypotheses (provides domain-specific context awareness) and a GPT model (eliminates the need for fine-tuning). The quantitative evaluation of SAGE on a dataset of 204K app reviews achieved an F1 score of 0.85 without any fine-tuning, outperforming the fine-tuned baseline classifiers BERT and T5. Furthermore, SAGE extracted 748 privacy reviews previously overlooked by keyword-based methods, demonstrating its effectiveness through qualitative evaluation. These reviews can later be refined into actionable privacy requirement artifacts.
CMER: A Context-Aware Approach for Mining Ethical Concern-related App Reviews
2025-07-11
Aakash Sorathiya, Gouri Ginde
With the increasing proliferation of mobile applications in our daily lives, the concerns surrounding ethics have surged significantly. Users communicate their feedback in app reviews, frequently emphasizing ethical concerns, such as privacy and security. Incorporating these reviews has proved to be useful for many areas of software engineering (e.g., requirement engineering, testing, etc.). However, app reviews related to ethical concerns generally use domain-specific language and are typically overshadowed by more generic categories of user feedback, such as app reliability and usability. Thus, making automated extraction a challenging and time-consuming effort. This study proposes CMER (A \underline{C}ontext-Aware Approach for \underline{M}ining \underline{E}thical Concern-related App \underline{R}eviews), a novel approach that combines Natural Language Inference (NLI) and a decoder-only (LLaMA-like) Large Language Model (LLM) to extract ethical concern-related app reviews at scale. In CMER, NLI provides domain-specific context awareness by using domain-specific hypotheses, and the Llama-like LLM eliminates the need for labeled data in the classification task. We evaluated the validity of CMER by mining privacy and security-related reviews (PSRs) from the dataset of more than 382K app reviews of mobile investment apps. First, we evaluated four NLI models and compared the results of domain-specific hypotheses with generic hypotheses. Next, we evaluated three LLMs for the classification task. Finally, we combined the best NLI and LLM models (CMER) and extracted 2,178 additional PSRs overlooked by the previous study using a keyword-based approach, thus demonstrating the effectiveness of CMER. These reviews can be further refined into actionable requirement artifacts.
Towards Extracting Software Requirements from App Reviews using Seq2seq Framework
2025-07-11
Aakash Sorathiya, Gouri Ginde
Mobile app reviews are a large-scale data source for software improvements. A key task in this context is effectively extracting requirements from app reviews to analyze the users' needs and support the software's evolution. Recent studies show that existing methods fail at this task since app reviews usually contain informal language, grammatical and spelling errors, and a large amount of irrelevant information that might not have direct practical value for developers. To address this, we propose a novel reformulation of requirements extraction as a Named Entity Recognition (NER) task based on the sequence-to-sequence (Seq2seq) generation approach. With this aim, we propose a Seq2seq framework, incorporating a BiLSTM encoder and an LSTM decoder, enhanced with a self-attention mechanism, GloVe embeddings, and a CRF model. We evaluated our framework on two datasets: a manually annotated set of 1,000 reviews (Dataset 1) and a crowdsourced set of 23,816 reviews (Dataset 2). The quantitative evaluation of our framework showed that it outperformed existing state-of-the-art methods with an F1 score of 0.96 on Dataset 2, and achieved comparable performance on Dataset 1 with an F1 score of 0.47.
ALIGN: Prompt-based Attribute Alignment for Reliable, Responsible, and Personalized LLM-based Decision-Making
2025-07-11
Bharadwaj Ravichandran, David Joy, Paul Elliott, Brian Hu, Jadie Adams, Christopher Funk, Emily Veenhuis, Anthony Hoogs, Arslan Basharat
Large language models (LLMs) are increasingly being used as decision aids. However, users have diverse values and preferences that can affect their decision-making, which requires novel methods for LLM alignment and personalization. Existing LLM comparison tools largely focus on benchmarking tasks, such as knowledge-based question answering. In contrast, our proposed ALIGN system focuses on dynamic personalization of LLM-based decision-makers through prompt-based alignment to a set of fine-grained attributes. Key features of our system include robust configuration management, structured output generation with reasoning, and several algorithm implementations with swappable LLM backbones, enabling different types of analyses. Our user interface enables a qualitative, side-by-side comparison of LLMs and their alignment to various attributes, with a modular backend for easy algorithm integration. Additionally, we perform a quantitative analysis comparing alignment approaches in two different domains: demographic alignment for public opinion surveys and value alignment for medical triage decision-making. The entire ALIGN framework is open source and will enable new research on reliable, responsible, and personalized LLM-based decision-makers.
From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research
2025-07-11
Amgad Muneer, Muhammad Waqas, Maliazurina B Saad, Eman Showkatian, Rukhmini Bandyopadhyay, Hui Xu, Wentao Li, Joe Y Chang, Zhongxing Liao, Cara Haymaker, Luisa Solis Soto, Carol C Wu, Natalie I Vokes, Xiuning Le, Lauren A Byers, Don L Gibbons, John V Heymach, Jianjun Zhang, Jia Wu
Cancer research is increasingly driven by the integration of diverse data modalities, spanning from genomics and proteomics to imaging and clinical factors. However, extracting actionable insights from these vast and heterogeneous datasets remains a key challenge. The rise of foundation models (FMs) -- large deep-learning models pretrained on extensive amounts of data serving as a backbone for a wide range of downstream tasks -- offers new avenues for discovering biomarkers, improving diagnosis, and personalizing treatment. This paper presents a comprehensive review of widely adopted integration strategies of multimodal data to assist advance the computational approaches for data-driven discoveries in oncology. We examine emerging trends in machine learning (ML) and deep learning (DL), including methodological frameworks, validation protocols, and open-source resources targeting cancer subtype classification, biomarker discovery, treatment guidance, and outcome prediction. This study also comprehensively covers the shift from traditional ML to FMs for multimodal integration. We present a holistic view of recent FMs advancements and challenges faced during the integration of multi-omics with advanced imaging data. We identify the state-of-the-art FMs, publicly available multi-modal repositories, and advanced tools and methods for data integration. We argue that current state-of-the-art integrative methods provide the essential groundwork for developing the next generation of large-scale, pre-trained models poised to further revolutionize oncology. To the best of our knowledge, this is the first review to systematically map the transition from conventional ML to advanced FM for multimodal data integration in oncology, while also framing these developments as foundational for the forthcoming era of large-scale AI models in cancer research.
ESG and the Cost of Capital: Insights from an AI-Assisted Systematic Literature Review
2025-07-11
Ebenezer Asem, Ruijie Fan, Gloria Y. Tian
This paper explores how AI-powered tools could be leveraged to streamline the process of identifying, screening, and analyzing relevant literature in academic research. More specifically, we examine the documented relationship between environmental, social, and governance (ESG) factors and the cost of capital (CoC). By applying an AI-assisted workflow, we identified 36 published studies, synthesized their key findings, and highlighted relevant theories, moderators, and methodological challenges. Our analyses demonstrate the value of AI tools in enhancing business research processes and also contribute to the growing literature on the importance of ESG in the field of corporate finance.
The advances in multi-modal foundation models (FMs) (e.g., CLIP and LLaVA) have facilitated the auto-labeling of large-scale datasets, enhancing model performance in challenging downstream tasks such as open-vocabulary object detection and segmentation. However, the quality of FM-generated labels is less studied as existing approaches focus more on data quantity over quality. This is because validating large volumes of data without ground truth presents a considerable challenge in practice. Existing methods typically rely on limited metrics to identify problematic data, lacking a comprehensive perspective, or apply human validation to only a small data fraction, failing to address the full spectrum of potential issues. To overcome these challenges, we introduce VISTA, a visual analytics framework that improves data quality to enhance the performance of multi-modal models. Targeting the complex and demanding domain of open-vocabulary image segmentation, VISTA integrates multi-phased data validation strategies with human expertise, enabling humans to identify, understand, and correct hidden issues within FM-generated labels. Through detailed use cases on two benchmark datasets and expert reviews, we demonstrate VISTA's effectiveness from both quantitative and qualitative perspectives.
An inferential model (IM) is a model describing the construction of provably reliable, data-driven uncertainty quantification and inference about relevant unknowns. IMs and Fisher's fiducial argument have similar objectives, but a fundamental distinction between the two is that the former doesn't require that uncertainty quantification be probabilistic, offering greater flexibility and allowing for a proof of its reliability. Important recent developments have been made thanks in part to newfound connections with the imprecise probability literature, in particular, possibility theory. The brand of possibilistic IMs studied here are straightforward to construct, have very strong frequentist-like reliability properties, and offer fully conditional, Bayesian-like (imprecise) probabilistic reasoning. This paper reviews these key recent developments, describing the new theory, methods, and computational tools. A generalization of the basic possibilistic IM is also presented, making new and unexpected connections with ideas in modern statistics and machine learning, e.g., bootstrap and conformal prediction.
Microlensing surveys suggest the presence of a surprisingly large population of free-floating planets, with a rate of about two Neptunes per star. The origin of such objects is not known, neither do we know if they are truly unbound or are merely orbiting at large separations from their host stars. Here, we investigate planet-planet scattering as a possible origin through numerical simulations of unstable multi-planet systems. We find that planet ejection by scattering can be slow, often taking more than billions of years for Neptune-mass scatterers orbiting at a few AU and beyond. Moreover, this process invariably delivers planets to orbits of hundreds of AU that are protected from further scattering. We call these ``detached" planets. Under the scattering hypothesis, we estimate that about half of the reported ``free-floating" Neptunes are not free but merely ``detached".
Results of direct numerical simulations (DNS) of porous-wall turbulent flows in open channels with conjugate heat transfer are reported in this work. For the conductive porous walls considered here, the change in heat transfer is not monotonic. The heat flux initially decreases when going from a conductive smooth wall to slightly porous walls. In this initial porous-wall turbulence regime, the near-wall flow remains smooth-wall like and the heat transfer is dominated by molecular diffusion. As such, a reduction of the more favorably conducting solid material diminishes the overall heat transfer performance. Beyond a certain level of permeability however, the near-wall flow transitions to the K-H-like regime marked by the presence of cross-stream rollers, and the heat flux undergoes an increasing trend until it eventually surpasses that of smooth-wall turbulence. Neglecting the thermal behavior of the solid material can therefore result in overestimation of any gains in heat transfer. Additionally, thermal performance is assessed in terms of the Reynolds analogy breakdown, which is the disparity between the fractional increases in the Stanton number, $St$, and the fractional increases in the skin-friction coefficient, $C_f$, relative to smooth-wall flow. Similar to rough walls, the breakdown is unfavorable for porous walls. The unfavorable breakdown in Reynolds analogy is due to growing dissimilarities between the transfer of momentum and heat in the vicinity of the porous wall as it becomes more permeable. Turbulent sweep and ejection type events contribute more significantly to momentum transfer across the permeable surface than they do to heat transfer. However, unlike for rough walls, a saturation limit for heat transfer is not observed for the porous walls considered here. How much of a maximum increase in heat transfer can be achieved is something that remains to be determined.
Interpretable Artificial Intelligence for Detecting Acute Heart Failure on Acute Chest CT Scans
2025-07-11
Silas Nyboe Ørting, Kristina Miger, Anne Sophie Overgaard Olesen, Mikael Ploug Boesen, Michael Brun Andersen, Jens Petersen, Olav W. Nielsen, Marleen de Bruijne
Introduction: Chest CT scans are increasingly used in dyspneic patients where acute heart failure (AHF) is a key differential diagnosis. Interpretation remains challenging and radiology reports are frequently delayed due to a radiologist shortage, although flagging such information for emergency physicians would have therapeutic implication. Artificial intelligence (AI) can be a complementary tool to enhance the diagnostic precision. We aim to develop an explainable AI model to detect radiological signs of AHF in chest CT with an accuracy comparable to thoracic radiologists. Methods: A single-center, retrospective study during 2016-2021 at Copenhagen University Hospital - Bispebjerg and Frederiksberg, Denmark. A Boosted Trees model was trained to predict AHF based on measurements of segmented cardiac and pulmonary structures from acute thoracic CT scans. Diagnostic labels for training and testing were extracted from radiology reports. Structures were segmented with TotalSegmentator. Shapley Additive explanations values were used to explain the impact of each measurement on the final prediction. Results: Of the 4,672 subjects, 49% were female. The final model incorporated twelve key features of AHF and achieved an area under the ROC of 0.87 on the independent test set. Expert radiologist review of model misclassifications found that 24 out of 64 (38%) false positives and 24 out of 61 (39%) false negatives were actually correct model predictions, with the errors originating from inaccuracies in the initial radiology reports. Conclusion: We developed an explainable AI model with strong discriminatory performance, comparable to thoracic radiologists. The AI model's stepwise, transparent predictions may support decision-making.
In this letter, we call attention to a gap in binaries in the Kuiper belt in the mass range between $\approx$10$^{19}$-10$^{20}$ kg, with a corresponding dearth in binaries between 4th and 5th absolute magnitude $H$. The low-mass end of the gap is consistent with the truncation of the cold classical population at 400 km, as suggested by the OSSOS survey, and predicted by simulations of planetesimal formation by streaming instability. The distribution of magnitudes for all KBOs is continuous, which means that many objects exist in the gap, but the binaries in this range have either been disrupted, or the companions are too close to the primary and/or too dim to be detected with the current generation of observational instruments. At the high-mass side of the gap, the objects have small satellites at small separations, and we find a trend that as mass decreases, the ratio of primary radius to secondary semimajor increases. If this trend continues into the gap, non-Keplerian effects should make mass determination more challenging.
Choosing the Right Git Workflow: A Comparative Analysis of Trunk-based vs. Branch-based Approaches
2025-07-11
Pedro Lopes, Paola Accioly, Paulo Borba, Vitor Menezes
Git has become one of the most widely used version control systems today. Among its distinguishing features, its ability to easily and quickly create branches stands out, allowing teams to customize their workflows. In this context, various formats of collaborative development workflows using Git have emerged and gained popularity among software engineers. We can categorize such workflows into two main types: branch-based workflows and trunk-based workflows. Branch-based workflows typically define a set of remote branches with well-defined objectives, such as feature branches, a branch for feature integration, and a main branch. The goal is to migrate changes from the most isolated branch to the main one shared by all as the code matures. In this category, GitFlow stands out as the most popular example. In contrast, trunk-based workflows have a single remote branch where developers integrate their changes directly. In this range of options, choosing a workflow that maximizes team productivity while promoting software quality becomes a non-trivial task. Despite discussions on forums, social networks, and blogs, few scientific articles have explored this topic. In this work, we provide evidence on how Brazilian developers work with Git workflows and what factors favor or hinder the use of each model. To this end, we conducted semi-structured interviews and a survey with software developers. Our results indicate that trunk-based development favors fast-paced projects with experienced and smaller teams, while branch-based development suits less experienced and larger teams better, despite posing management challenges.
2025-07-11
Duarte Muñoz Santos, Matthew M. Pieri, Dylan Nelson, Simon Weng, Teng Hu, Manuel F. Ruiz-Herrera Bernal
It has been demonstrated that one can track down galaxies in absorption 'hidden' in the Lyman-$\alpha$ forest through the use of 'strong, blended Lyman-$\alpha$' (or SBLA) absorption. Specifically a series of publications studied SBLA absorption systems with Lyman-$\alpha$ flux transmission, $F_{Ly \alpha} < 0.25$ on scales of 138 km s$^{-1}$ in the Sloan Digital Sky Survey (SDSS). In order to better understand the connection between halos and these SBLAs, we make use of several million synthetic absorption spectra from the TNG50 cosmological simulation, at z=2 and z=3. We explore spectra with SDSS-like resolution in order to understand the nature of SBLAs as defined thus far, as well as with high resolution (or 'resolved') spectra to generalise and optimise SBLAs as halo finders. For the SDSS SBLAs, we find that up to 78% of these absorption systems reside in hlaos, where the stronger the absorption and the lower the redshift, the higher the probability. We also manage to recover a mean halo mass of $10^{12.25} M_{\odot}$, in line with what is measured in observations. For the resolved SBLAs, we expand on the previous definition and allow the SBLA spectra size to vary between 54 km s$^{-1}$ and 483 km s$^{-1}$. We find that the largest absorbers have the highest probability of finding halos. When applying a hierarchical framework, where we allow the largest SBLAs to consume the smaller ones, we find that the halo mass distributions for each SBLA spectral size becomes narrower with respect to the non-hierarchical case. We are also able to probe halo masses from $M_h \approx 10^{9.5} M_{\odot}$ (for 100 km s$^{-1}$ SBLAs) to $M_h \approx 10^{11.5} M_{\odot}$ (for 450 km s$^{-1}$ SBLAs). With these results, we show that we are able to transform the Lyman-$\alpha$ forest into a powerful halo finding machine for not only identifying CGM regions, but also estimating their host halo masses.
Large-scale structure surveys can be used to measure the dipole in the cosmic microwave background (CMB), in the luminosity distances inferred from type-Ia supernova observations, and in the spatial distribution of galaxies and quasars. The measurements of these cosmic dipoles appear to be mutually inconsistent, even though they are expected to indicate the common observer velocity. This observational tension may represent a significant challenge to the standard model of cosmology. Here we study in detail what contributes to the cosmic dipoles from CMB, supernova, and galaxy survey in the standard $\Lambda$CDM model, though our theoretical model can be applied beyond the standard model. While measurements of the cosmic dipoles yield the relative velocities between the source samples and the observer velocity, the motion of the observer is the dominant contribution in the conformal Newtonian gauge, and the intrinsic velocities of the samples fall steeply with increasing redshift of the sources. Hence the cosmic dipoles of CMB, type-Ia supernovae, and galaxies should be aligned but can have different amplitudes. We also clarify several misconceptions that are commonly found in the literature.
Galaxy simulations have come a long way from the early days of simple N-body calculations, which considered only gravitational interactions, to the complex, multi-physics models used today. Beginning with initial conditions representative of the Universe shortly after the Big Bang, these modern simulations integrate the relevant physical processes involved in galaxy formation, such as gravity, gas dynamics, cooling, star formation, and feedback, while accounting for cosmic expansion and structure formation. This review provides an introductory overview of cosmological galaxy simulations, outlining the essential components and methods used to model the formation and evolution of galaxies on the computer. It also discusses common steps in the post-processing analysis, essential for extracting physical insights from these numerical experiments, along with basic tests to assess simulation validity and accuracy. Looking forward, next-generation simulations aim to push resolution boundaries, incorporate additional physical processes, and improve the robustness of the numerical models, promising to lead to a deeper understanding of how galaxies emerged and evolved over cosmic time.
The Bayesian Approach to Continual Learning: An Overview
2025-07-11
Tameem Adel
Continual learning is an online paradigm where a learner continually accumulates knowledge from different tasks encountered over sequential time steps. Importantly, the learner is required to extend and update its knowledge without forgetting about the learning experience acquired from the past, and while avoiding the need to retrain from scratch. Given its sequential nature and its resemblance to the way humans think, continual learning offers an opportunity to address several challenges which currently stand in the way of widening the range of applicability of deep models to further real-world problems. The continual need to update the learner with data arriving sequentially strikes inherent congruence between continual learning and Bayesian inference which provides a principal platform to keep updating the prior beliefs of a model given new data, without completely forgetting the knowledge acquired from the old data. This survey inspects different settings of Bayesian continual learning, namely task-incremental learning and class-incremental learning. We begin by discussing definitions of continual learning along with its Bayesian setting, as well as the links with related fields, such as domain adaptation, transfer learning and meta-learning. Afterwards, we introduce a taxonomy offering a comprehensive categorization of algorithms belonging to the Bayesian continual learning paradigm. Meanwhile, we analyze the state-of-the-art while zooming in on some of the most prominent Bayesian continual learning algorithms to date. Furthermore, we shed some light on links between continual learning and developmental psychology, and correspondingly introduce analogies between both fields. We follow that with a discussion of current challenges, and finally conclude with potential areas for future research on Bayesian continual learning.
Objectives: To evaluate the current limitations of large language models (LLMs) in medical question answering, focusing on the quality of datasets used for their evaluation. Materials and Methods: Widely-used benchmark datasets, including MedQA, MedMCQA, PubMedQA, and MMLU, were reviewed for their rigor, transparency, and relevance to clinical scenarios. Alternatives, such as challenge questions in medical journals, were also analyzed to identify their potential as unbiased evaluation tools. Results: Most existing datasets lack clinical realism, transparency, and robust validation processes. Publicly available challenge questions offer some benefits but are limited by their small size, narrow scope, and exposure to LLM training. These gaps highlight the need for secure, comprehensive, and representative datasets. Conclusion: A standardized framework is critical for evaluating LLMs in medicine. Collaborative efforts among institutions and policymakers are needed to ensure datasets and methodologies are rigorous, unbiased, and reflective of clinical complexities.
'Teens Need to Be Educated on the Danger': Digital Access, Online Risks, and Safety Practices Among Nigerian Adolescents
2025-07-11
Munachimso B. Oguine, Ozioma C. Oguine, Karla Badillo-Urquiola, Oluwasogo Adekunle Okunade
Adolescents increasingly rely on online technologies to explore their identities, form social connections, and access information and entertainment. However, their growing digital engagement exposes them to significant online risks, particularly in underrepresented contexts like West Africa. This study investigates the online experiences of 409 secondary school adolescents in Nigeria's Federal Capital Territory (FCT), focusing on their access to technology, exposure to risks, coping strategies, key stakeholders influencing their online interactions, and recommendations for improving online safety. Using self-administered surveys, we found that while most adolescents reported moderate access to online technology and connectivity, those who encountered risks frequently reported exposure to inappropriate content and online scams. Blocking and reporting tools were the most commonly used strategies, though some adolescents responded with inaction due to limited resources or awareness. Parents emerged as the primary support network, though monitoring practices and communication varied widely. Guided by Protection Motivation Theory (PMT), our analysis interprets adolescents' online safety behaviors as shaped by both their threat perceptions and their confidence in available coping strategies. A thematic analysis of their recommendations highlights the need for greater awareness and education, parental mediation, enhanced safety tools, stricter age restrictions, improved content moderation, government accountability, and resilience-building initiatives. Our findings underscore the importance of culturally and contextually relevant interventions to empower adolescents in navigating the digital world, with implications for parents, educators, designers, and policymakers.
Placing the Solar System in its Astrophysical Context
2025-07-11
Guillermo Gonzalez
We examine recent astronomical data to assess whether the sun and Solar System possess anomalous properties compared to other stars and exoplanetary systems, providing context for astrobiology research. Utilising data primarily from large surveys like {\it Gaia}, {\it Kepler}, {\it TESS}, and ground-based spectroscopy (e.g., GALAH, LAMOST, HARPS), we construct comparison samples (e.g., nearby stars, solar analogues and twins within 20-200 pc) and employ statistical methods, including regression analysis, to account for parameter dependencies. We find that the sun is modestly metal-rich compared to nearby solar-age stars. More anomalous solar properties include its mass (top $\sim$8 percent locally), low photometric variability on short timescales ($\sim$0.2 percent), specific light and heavy element abundance patterns (high beryllium, low lithium, low carbon/oxygen and nitrogen/oxygen ratios, and low heavy neutron capture and refractory elements), slow rotation, and low superflare rate. The sun has average $\alpha$/iron, phosphorus/iron, and Ytterbium/iron abundance ratios. It also has average chromospheric activity as measured by R$^{\rm '}_{\rm HK}$(T$_{\rm eff}$), R$^{\rm +}_{\rm HK}$, and H$\alpha$ indices. The Solar System is unusual in its lack of super-Earths despite hosting a cold Jupiter ($\sim$3 percent), the low eccentricities of its planets (especially considering detectability, $<2$ percent), its large size scale for a multi-planet system ($\sim$6 percent), and potentially the sun's obliquity. The sun's galactic orbit is less eccentric and has lower vertical excursions than $\sim$95 percent of nearby solar analogues. Its current position is near perigalacticon and minimum distance from the Galactic plane, resulting in a higher local star density than 98.8 percent of randomly chosen times from $-0.5$ to $+0.5$ Gyrs.
The Engineer's Dilemma: A Review of Establishing a Legal Framework for Integrating Machine Learning in Construction by Navigating Precedents and Industry Expectations
2025-07-11
M. Z. Naser
Despite the widespread interest in machine learning (ML), the engineering industry has not yet fully adopted ML-based methods, which has left engineers and stakeholders uncertain about the legal and regulatory frameworks that govern their decisions. This gap remains unaddressed as an engineer's decision-making process, typically governed by professional ethics and practical guidelines, now intersects with complex algorithmic outputs. To bridge this gap, this paper explores how engineers can navigate legal principles and legislative justifications that support and/or contest the deployment of ML technologies. Drawing on recent precedents and experiences gained from other fields, this paper argues that analogical reasoning can provide a basis for embedding ML within existing engineering codes while maintaining professional accountability and meeting safety requirements. In exploring these issues, the discussion focuses on established liability doctrines, such as negligence and product liability, and highlights how courts have evaluated the use of predictive models. We further analyze how legislative bodies and standard-setting organizations can furnish explicit guidance equivalent to prior endorsements of emergent technologies. This exploration stresses the vitality of understanding the interplay between technical justifications and legal precedents for shaping an informed stance on ML's legitimacy in engineering practice. Finally, our analysis catalyzes a legal framework for integrating ML through which stakeholders can critically assess the responsibilities, liabilities, and benefits inherent in ML-driven engineering solutions.
MASTER OT J072007.30+451611.6: A Polar with Strong Optical Variability and Suppressed He II Emission
2025-07-11
A. V. Bobakov, S. V. Zharikov, A. V. Karpova, D. A. Zyuzin, A. Yu. Kirichenko, Yu. A. Shibanov, R. Karimov, N. L. Vaidman, Sh. T. Nurmakhametova, M. R. Gilfanov, R. Michel
The transient optical source MASTER OT J072007.30+451611.6 has been recently discovered and proposed as a peculiar polar with an unusually high amplitude of the orbital brightness variation in the optical of $\sim$3 mag. To clarify its nature, we performed multiband time-series optical photometry with 1.5-m class telescopes and spectroscopy with the 10.4-m Gran Telescopio Canarias. We also analysed archival data of different optical surveys and detected the source in X-rays with the Spectrum-RG/eROSITA telescope. We confirm the orbital period of $\approx$1.5 h with the high amplitude of the brightness modulation. Compiling survey data, covering $\sim$19 yr, we find high and low brightness states of the object at time scales of years, likely explained by different accretion rates. Our data were obtained in the high brightness state. Optical spectra with hydrogen and helium emission lines, consisting of broad and narrow components, indicate the presence of an accretion stream without disk. The Doppler tomography shows that the narrow component is mainly emitted from the Lagrangian L$_1$ point, while the broad component is from the region where the accretion stream interacts with the white dwarf magnetosphere. The ratio of equivalent widths of HeII 4686 and H$\beta$ emission lines is $<$0.4, which is curiously low for polars. The X-ray spectrum of the source can be described by the thermal plasma emission model with parameters consistent with values observed for polars.
Schoolyard Greening, Child Health, and Neighborhood Change: A Comparative Study of Urban U.S. Cities
2025-07-11
Mahshid Gorjian
Background: Schoolyard greening has emerged as an innovative approach to enhancing childrens health and environmental equity in urban U.S. cities. Yet, the implications for neighborhood dynamics and social equity are insufficiently understood. Methods: This comparative literature review synthesizes quantitative and qualitative evidence from peer reviewed studies and case analyses of schoolyard greening in major U.S. cities. Results: Schoolyard greening consistently increases utilization and has a positive, though variable, effect on childrens physical activity and well-being. However, the benefits are not always equitably distributed, and greening projects can catalyze neighborhood change, sometimes leading to green gentrification. Conclusions: Greening urban schoolyards offers benefits for childrens health and urban sustainability but poses challenges for equity and social justice. Policies must prioritize inclusive, community driven approaches to ensure the gains of greening are shared by vulnerable populations.
SEALGuard: Safeguarding the Multilingual Conversations in Southeast Asian Languages for LLM Software Systems
2025-07-11
Wenliang Shan, Michael Fu, Rui Yang, Chakkrit Tantithamthavorn
Safety alignment is critical for LLM-powered systems. While recent LLM-powered guardrail approaches such as LlamaGuard achieve high detection accuracy of unsafe inputs written in English (e.g., ``How to create a bomb?''), they struggle with multilingual unsafe inputs. This limitation leaves LLM systems vulnerable to unsafe and jailbreak prompts written in low-resource languages such as those in Southeast Asia. This paper introduces SEALGuard, a multilingual guardrail designed to improve the safety alignment across diverse languages. It aims to address the multilingual safety alignment gap of existing guardrails and ensure effective filtering of unsafe and jailbreak prompts in LLM-powered systems. We adapt a general-purpose multilingual language model into a multilingual guardrail using low-rank adaptation (LoRA). We construct SEALSBench, a large-scale multilingual safety alignment dataset containing over 260,000 prompts in ten languages, including safe, unsafe, and jailbreak cases. We evaluate SEALGuard against state-of-the-art guardrails such as LlamaGuard on this benchmark. Our findings show that multilingual unsafe and jailbreak prompts substantially degrade the performance of the state-of-the-art LlamaGuard, which experiences a drop in Defense Success Rate (DSR) by 9% and 18%, respectively, compared to its performance on English-only prompts. In contrast, SEALGuard outperforms existing guardrails in detecting multilingual unsafe and jailbreak prompts, improving DSR by 48% over LlamaGuard and achieving the best DSR, precision, and F1-score. Our ablation study further reveals the contributions of adaptation strategies and model size to the overall performance of SEALGuard. SEALGuard advances the safety alignment of LLM systems by introducing an effective multilingual guardrail.
Anthropomimetic Uncertainty: What Verbalized Uncertainty in Language Models is Missing
2025-07-11
Dennis Ulmer, Alexandra Lorson, Ivan Titov, Christian Hardmeier
Human users increasingly rely on natural language interactions with large language models (LLMs) in order to receive help on a large variety of tasks and problems. However, the trustworthiness and perceived legitimacy of LLMs is undermined by the fact that their output is frequently stated in very confident terms, even when its accuracy is questionable. Therefore, there is a need to signal the confidence of the language model to a user in order to reap the benefits of human-machine collaboration and mitigate potential harms. Verbalized uncertainty is the expression of confidence with linguistic means, an approach that integrates perfectly into language-based interfaces. Nevertheless, most recent research in natural language processing (NLP) overlooks the nuances surrounding human uncertainty communication and the data biases that influence machine uncertainty communication. We argue for anthropomimetic uncertainty, meaning that intuitive and trustworthy uncertainty communication requires a degree of linguistic authenticity and personalization to the user, which could be achieved by emulating human communication. We present a thorough overview over the research in human uncertainty communication, survey ongoing research, and perform additional analyses to demonstrate so-far overlooked biases in verbalized uncertainty. We conclude by pointing out unique factors in human-machine communication of uncertainty and deconstruct anthropomimetic uncertainty into future research directions for NLP.
Transforming Sensitive Documents into Quantitative Data: An AI-Based Preprocessing Toolchain for Structured and Privacy-Conscious Analysis
2025-07-11
Anders Ledberg, Anna Thalén
Unstructured text from legal, medical, and administrative sources offers a rich but underutilized resource for research in public health and the social sciences. However, large-scale analysis is hampered by two key challenges: the presence of sensitive, personally identifiable information, and significant heterogeneity in structure and language. We present a modular toolchain that prepares such text data for embedding-based analysis, relying entirely on open-weight models that run on local hardware, requiring only a workstation-level GPU and supporting privacy-sensitive research. The toolchain employs large language model (LLM) prompting to standardize, summarize, and, when needed, translate texts to English for greater comparability. Anonymization is achieved via LLM-based redaction, supplemented with named entity recognition and rule-based methods to minimize the risk of disclosure. We demonstrate the toolchain on a corpus of 10,842 Swedish court decisions under the Care of Abusers Act (LVM), comprising over 56,000 pages. Each document is processed into an anonymized, standardized summary and transformed into a document-level embedding. Validation, including manual review, automated scanning, and predictive evaluation shows the toolchain effectively removes identifying information while retaining semantic content. As an illustrative application, we train a predictive model using embedding vectors derived from a small set of manually labeled summaries, demonstrating the toolchain's capacity for semi-automated content analysis at scale. By enabling structured, privacy-conscious analysis of sensitive documents, our toolchain opens new possibilities for large-scale research in domains where textual data was previously inaccessible due to privacy and heterogeneity constraints.
Universal Approximation Theorem for a Single-Layer Transformer
2025-07-11
Esmail Gumaan
Deep learning employs multi-layer neural networks trained via the backpropagation algorithm. This approach has achieved success across many domains and relies on adaptive gradient methods such as the Adam optimizer. Sequence modeling evolved from recurrent neural networks to attention-based models, culminating in the Transformer architecture. Transformers have achieved state-of-the-art performance in natural language processing (for example, BERT and GPT-3) and have been applied in computer vision and computational biology. However, theoretical understanding of these models remains limited. In this paper, we examine the mathematical foundations of deep learning and Transformers and present a novel theoretical result. We review key concepts from linear algebra, probability, and optimization that underpin deep learning, and we analyze the multi-head self-attention mechanism and the backpropagation algorithm in detail. Our main contribution is a universal approximation theorem for Transformers: we prove that a single-layer Transformer, comprising one self-attention layer followed by a position-wise feed-forward network with ReLU activation, can approximate any continuous sequence-to-sequence mapping on a compact domain to arbitrary precision. We provide a formal statement and a complete proof. Finally, we present case studies that demonstrate the practical implications of this result. Our findings advance the theoretical understanding of Transformer models and help bridge the gap between theory and practice.
2025-07-10
Benedikt T. Arnold, Christoph Lange, Christina Gillmann, Stefan Decker
Dataspaces are designed to support sovereign, trusted and decentralized data exchange between participants forming an ecosystem. They are standardized by initiatives such as the International Data Spaces Association or Gaia-X and have gained adoption in several domains such as mobility, manufacturing, tourism or culture. In dataspaces, participants use connectors to communicate peer-to-peer. The Eclipse Dataspace Components (EDC) Connector is a broadly adopted, open-source implementation that adheres to the standards and is supported by a large community. As dataspaces in general, it focuses on the exchange of data assets with associated usage policies and does not support services. In practice, however, there is demand for dataspace-based services and conceptual arguments support their inclusion in dataspaces. In this paper, we propose an abstraction layer for providing generic services within dataspaces. Adopters can use this layer to easily develop own services, seamlessly integrated with the existing dataspace technology. Besides, we present an initial implementation of this service architecture for the EDC Connector and demonstrate its practical applicability.
We review the linear response theory in the horizontal quantum liquids framework spanning from Coulomb liquids to Atomic gases. There are several well known references about this subject, like the onset of the Kramers-Kronig relations and the fluctuation-dissipation theorem. For the Coulomb systems we show the connection between the linear response function and the dielectric function which settles a parallelism between statistical mechanics and electrostatic properties. For (very degenerate, dilute, trapped, two dimensional) Atomic (Bose) gases we will prove, in full generality, how the response properties of the gas depend from the frequency of the harmonic trap.
On-Manifold Low-Thrust Maneuvering of Quasi-Periodic Orbits
2025-07-10
Ian M. Down, Manoranjan Majji, Kathleen C. Howell
A bi-level optimal control framework is introduced to solve the low-thrust re-phasing problem on quasi-periodic invariant tori in multi-body environments where deviations away from the torus during maneuver are considered unsafe or irresponsible. It is shown for a large class of mechanical systems that conformity to the torus manifold during periods of non-zero control input is infeasible. The most feasible trajectories on the torus surface are generated through the minimization of fictitious control input in the torus space using phase space control variables mapped via the torus function. These reference trajectories are then transitioned to the phase space both through a minimum tracking error homotopy and minimum time patched solutions. Results are compared to torus agnostic low-thrust transfers using measures of fuel consumption, cumulative torus error, and coast time spent on the torus during maneuver. Modifications to the framework are made for the inclusion of quasi-periodically forced dynamical systems. Lastly, minimum time recovery trajectories with free final torus conditions expose the disparity between the proposed framework and torus agnostic approaches. Examples are drawn from the circular and elliptical restricted three-body problems.
KeyDroid: A Large-Scale Analysis of Secure Key Storage in Android Apps
2025-07-10
Jenny Blessing, Ross J. Anderson, Alastair R. Beresford
Most contemporary mobile devices offer hardware-backed storage for cryptographic keys, user data, and other sensitive credentials. Such hardware protects credentials from extraction by an adversary who has compromised the main operating system, such as a malicious third-party app. Since 2011, Android app developers can access trusted hardware via the Android Keystore API. In this work, we conduct the first comprehensive survey of hardware-backed key storage in Android devices. We analyze 490 119 Android apps, collecting data on how trusted hardware is used by app developers (if used at all) and cross-referencing our findings with sensitive user data collected by each app, as self-reported by developers via the Play Store's data safety labels. We find that despite industry-wide initiatives to encourage adoption, 56.3% of apps self-reporting as processing sensitive user data do not use Android's trusted hardware capabilities at all, while just 5.03% of apps collecting some form of sensitive data use the strongest form of trusted hardware, a secure element distinct from the main processor. To better understand the potential downsides of using secure hardware, we conduct the first empirical analysis of trusted hardware performance in mobile devices, measuring the runtime of common cryptographic operations across both software- and hardware-backed keystores. We find that while hardware-backed key storage using a coprocessor is viable for most common cryptographic operations, secure elements capable of preventing more advanced attacks make performance infeasible for symmetric encryption with non-negligible payloads and any kind of asymmetric encryption.
The Potential of Olfactory Stimuli in Stress Reduction through Virtual Reality
2025-07-10
Yasmin Elsaddik Valdivieso, Mohd Faisal, Karim Alghoul, Monireh, Vahdati, Kamran Gholizadeh Hamlabadi, Fedwa Laamarti, Hussein Al Osman, Abdulmotaleb El Saddik
Immersive virtual reality (VR) is a promising tool for stress reduction and relaxation, traditionally relying on visual and auditory stimuli. This study examines the role of olfactory stimuli in enhancing these effects, using a randomized within-subject design. Thirty participants aged 18-60 experienced VR scenarios simulating a calming seaside environment, with sessions lasting 45 minutes, in two conditions: with and without a "Beach" essential oil scent (Yankee Candle) administered via diffuser. Stress and relaxation were assessed through self-reported surveys and physiological measures, specifically ECG-based heart rate variability (HRV). Results showed no significant difference in self-reported relaxation scores (p=0.371) between conditions, but HRV analysis revealed a significant stress reduction (p=0.002) with olfactory input, with HF increasing 108% from the Math Stress Test to the scented relaxation condition, compared to 44% without scent. Additionally, 71.4% of participants expressed willingness to use olfactory-enhanced VR for relaxation, suggesting practical appeal. These findings indicate that olfactory stimuli may enhance relaxation subconsciously, underscoring the importance of multisensory integration in VR. Future work could explore personalized scents and long-term effects to optimize VR- based interventions for emotional and physical well-being.
Baryonification: An alternative to hydrodynamical simulations for cosmological studies
2025-07-10
Aurel Schneider, Michael Kovač, Jozef Bucko, Andrina Nicola, Robert Reischke, Sambit K. Giri, Romain Teyssier, Tilman Tröster, Alexandre Refregier, Matthieu Schaller, Joop Schaye
We present an improved baryonification (BFC) model that modifies dark-matter-only $N$-body simulations to generate particle-level outputs for gas, dark matter, and stars. Unlike previous implementations, our approach first splits each simulation particle into separate dark matter and baryonic components, which are then displaced individually using the BFC technique. By applying the hydrostatic and ideal gas equations, we assign pressure and temperature values to individual gas particles. The model is validated against hydrodynamical simulations from the FLAMINGO and TNG suites (which feature varied feedback prescriptions) showing good agreement at the level of density and pressure profiles across a wide range of halo masses. As a further step, we calibrate the BFC model parameters to gas and stellar mass ratio profiles from the hydrodynamical simulations. Based on these calibrations, we baryonify $N$-body simulations and compare the resulting total matter power spectrum suppressions to the ones from the same hydrodynamical simulation. Carrying out this test of the BFC method at each redshift individually, we obtain a 2 percent agreement up to $k=5\,h$/Mpc across all tested feedback scenarios. We also define a reduced, 2+1 parameter BFC model that simultaneously accounts for feedback variations (2 parameters) and redshift evolution (1 parameter). The 2+1 parameter model agrees with the hydrodynamical simulations to better than 2.5 percent over the scales and redshifts relevant for cosmological surveys. Finally, we present a map-level comparison between a baryonified $N$-body simulation and a full hydrodynamical run from the TNG simulation suite. Visual inspection of dark matter, gas, and stellar density fields, along with the integrated pressure map, shows promising agreement. Further work is needed to quantify the accuracy at the level of observables.
Opting Out of Generative AI: a Behavioral Experiment on the Role of Education in Perplexity AI Avoidance
2025-07-10
Roberto Ulloa, Juhi Kulshrestha, Celina Kacperski
The rise of conversational AI (CAI), powered by large language models, is transforming how individuals access and interact with digital information. However, these tools may inadvertently amplify existing digital inequalities. This study investigates whether differences in formal education are associated with CAI avoidance, leveraging behavioral data from an online experiment (N = 1,636). Participants were randomly assigned to a control or an information-seeking task, either a traditional online search or a CAI (Perplexity AI). Task avoidance (operationalized as survey abandonment or providing unrelated responses during task assignment) was significantly higher in the CAI group (51%) compared to the search (30.9%) and control (16.8%) groups, with the highest CAI avoidance among participants with lower education levels (~74.4%). Structural equation modeling based on the theoretical framework UTAUT2 and LASSO regressions reveal that education is strongly associated with CAI avoidance, even after accounting for various cognitive and affective predictors of technology adoption. These findings underscore education's central role in shaping AI adoption and the role of self-selection biases in AI-related research, stressing the need for inclusive design to ensure equitable access to emerging technologies.
3D-ADAM: A Dataset for 3D Anomaly Detection in Advanced Manufacturing
2025-07-10
Paul McHard, Florent P. Audonnet, Oliver Summerell, Sebastian Andraos, Paul Henderson, Gerardo Aragon-Camarasa
Surface defects are one of the largest contributors to low yield in the manufacturing sector. Accurate and reliable detection of defects during the manufacturing process is therefore of great value across the sector. State-of-the-art approaches to automated defect detection yield impressive performance on current datasets, yet still fall short in real-world manufacturing settings and developing improved methods relies on large datasets representative of real-world scenarios. Unfortunately, high-quality, high-precision RGB+3D industrial anomaly detection datasets are scarce, and typically do not reflect real-world industrial deployment scenarios. To address this, we introduce 3D-ADAM, the first large-scale industry-relevant dataset for high-precision 3D Anomaly Detection. 3D-ADAM comprises 14,120 high-resolution scans across 217 unique parts, captured using 4 industrial depth imaging sensors. It includes 27,346 annotated defect instances from 12 categories, covering the breadth of industrial surface defects. 3D-ADAM uniquely captures an additional 8,110 annotations of machine element features, spanning the range of relevant mechanical design form factors. Unlike existing datasets, 3D-ADAM is captured in a real industrial environment with variations in part position and orientation, camera positioning, ambient lighting conditions, as well as partial occlusions. Our evaluation of SOTA models across various RGB+3D anomaly detection tasks demonstrates the significant challenge this dataset presents to current approaches. We further validated the industrial relevance and quality of the dataset through an expert labelling survey conducted by industry partners. By providing this challenging benchmark, 3D-ADAM aims to accelerate the development of robust 3D Anomaly Detection models capable of meeting the demands of modern manufacturing environments.
Milestone toward an ECRIPAC accelerator demonstrator
2025-07-10
Andrea Cernuschi, Thomas Thuillier, Laurent Garrigues
The Electron Cyclotron Resonance Ion Plasma ACcelerator (ECRIPAC) is an original accelerator concept proposed in the nineties for the generation of highly energetic pulsed ion beams, suitable for a wide array of applications. The initial studies on the subject were characterized by an important calculation mistake, leading to an incomplete and erroneous literature on the topic. Nevertheless, the simple and well mastered techniques involved in the system (radio frequency and magnetic field), together with the device compactness, are strong motivations for further studies on ECRIPAC. The working principle and physical theory behind the ECRIPAC accelerator concept are thoroughly reviewed in this paper, incorporating significant corrections to the existing literature on the subject, making this work a suitable reference for future studies. A detailed theoretical investigation of the stability condition for the ion acceleration is presented, highlighting more stringent limitations than previously anticipated. Next, the impact of several physical parameters on the accelerator design is analyzed. This study led to the design of a compact demonstrator device able to accelerate He2+ ions up to 9.5 MeV per nucleon inside a 1.8 m long accelerating cavity. Finally, the results of a Monte-Carlo simulation, developed to model the electron dynamics inside the system, are used to validate the theoretical framework of ECRIPAC.
SCOOTER: A Human Evaluation Framework for Unrestricted Adversarial Examples
2025-07-10
Dren Fazlija, Monty-Maximilian Zühlke, Johanna Schrader, Arkadij Orlov, Clara Stein, Iyiola E. Olatunji, Daniel Kudenko
Unrestricted adversarial attacks aim to fool computer vision models without being constrained by $\ell_p$-norm bounds to remain imperceptible to humans, for example, by changing an object's color. This allows attackers to circumvent traditional, norm-bounded defense strategies such as adversarial training or certified defense strategies. However, due to their unrestricted nature, there are also no guarantees of norm-based imperceptibility, necessitating human evaluations to verify just how authentic these adversarial examples look. While some related work assesses this vital quality of adversarial attacks, none provide statistically significant insights. This issue necessitates a unified framework that supports and streamlines such an assessment for evaluating and comparing unrestricted attacks. To close this gap, we introduce SCOOTER - an open-source, statistically powered framework for evaluating unrestricted adversarial examples. Our contributions are: $(i)$ best-practice guidelines for crowd-study power, compensation, and Likert equivalence bounds to measure imperceptibility; $(ii)$ the first large-scale human vs. model comparison across 346 human participants showing that three color-space attacks and three diffusion-based attacks fail to produce imperceptible images. Furthermore, we found that GPT-4o can serve as a preliminary test for imperceptibility, but it only consistently detects adversarial examples for four out of six tested attacks; $(iii)$ open-source software tools, including a browser-based task template to collect annotations and analysis scripts in Python and R; $(iv)$ an ImageNet-derived benchmark dataset containing 3K real images, 7K adversarial examples, and over 34K human ratings. Our findings demonstrate that automated vision systems do not align with human perception, reinforcing the need for a ground-truth SCOOTER benchmark.
This paper introduces a definition of ideological polarization of an electorate around a particular central point. By being flexible about the location or width of the center, this measure enables the researcher to analyze polarization around any point of interest. The paper then applies this approach to US voter survey data between 2004 and 2020, showing how polarization between right-of-center voters and the rest of the electorate was increasing gradually, while polarization between left-wingers and the rest was originally constant and then rose steeply. It also shows how, following elections, polarization around left-wing positions decreased while polarization around right-wing positions increased. Furthermore, the paper shows how this measure can be used to find cleavage points around which polarization changed the most. I then show how ideological polarization as defined here is related to other phenomena, such as affective polarization and increased salience of divisive issues.
Structured Prompts, Better Outcomes? Exploring the Effects of a Structured Interface with ChatGPT in a Graduate Robotics Course
2025-07-10
Jerome Brender, Laila El-Hamamsy, Kim Uittenhove, Francesco Mondada, Engin Bumbacher
Prior research shows that how students engage with Large Language Models (LLMs) influences their problem-solving and understanding, reinforcing the need to support productive LLM-uses that promote learning. This study evaluates the impact of a structured GPT platform designed to promote 'good' prompting behavior with data from 58 students in a graduate-level robotics course. The students were assigned to either an intervention group using the structured platform or a control group using ChatGPT freely for two practice lab sessions, before a third session where all students could freely use ChatGPT. We analyzed student perception (pre-post surveys), prompting behavior (logs), performance (task scores), and learning (pre-post tests). Although we found no differences in performance or learning between groups, we identified prompting behaviors - such as having clear prompts focused on understanding code - that were linked with higher learning gains and were more prominent when students used the structured platform. However, such behaviors did not transfer once students were no longer constrained to use the structured platform. Qualitative survey data showed mixed perceptions: some students perceived the value of the structured platform, but most did not perceive its relevance and resisted changing their habits. These findings contribute to ongoing efforts to identify effective strategies for integrating LLMs into learning and question the effectiveness of bottom-up approaches that temporarily alter user interfaces to influence students' interaction. Future research could instead explore top-down strategies that address students' motivations and explicitly demonstrate how certain interaction patterns support learning.
The investigation of single-particle dynamics in circular particle accelerators can be traced back to pioneering works in the 1950s. Traditionally, the design of new circular accelerators has focused on optimising linear dynamics, striving to minimise non-linear effects. When encountered, these effects were managed using suitable correction techniques. However, in recent years, this methodology has undergone significant reconsideration, leading to a more favourable view of non-linear beam dynamics. Notably, several new concepts have been promoted to improve the description and modelling of non-linear beam dynamics. Furthermore, new proposals have emerged that leverage the extensive potential of non-linear beam dynamics to enhance the control and manipulation of the characteristic features of charged particle beams. This article provides a detailed review and discussion of all these innovative approaches.
This paper establishes the first comprehensive review of Large Language Models (LLMs) applied within the legal domain. It pioneers an innovative dual lens taxonomy that integrates legal reasoning frameworks and professional ontologies to systematically unify historical research and contemporary breakthroughs. Transformer-based LLMs, which exhibit emergent capabilities such as contextual reasoning and generative argumentation, surmount traditional limitations by dynamically capturing legal semantics and unifying evidence reasoning. Significant progress is documented in task generalization, reasoning formalization, workflow integration, and addressing core challenges in text processing, knowledge integration, and evaluation rigor via technical innovations like sparse attention mechanisms and mixture-of-experts architectures. However, widespread adoption of LLM introduces critical challenges: hallucination, explainability deficits, jurisdictional adaptation difficulties, and ethical asymmetry. This review proposes a novel taxonomy that maps legal roles to NLP subtasks and computationally implements the Toulmin argumentation framework, thus systematizing advances in reasoning, retrieval, prediction, and dispute resolution. It identifies key frontiers including low-resource systems, multimodal evidence integration, and dynamic rebuttal handling. Ultimately, this work provides both a technical roadmap for researchers and a conceptual framework for practitioners navigating the algorithmic future, laying a robust foundation for the next era of legal artificial intelligence. We have created a GitHub repository to index the relevant papers: https://github.com/Kilimajaro/LLMs_Meet_Law.
X-RAFT: Cross-Modal Non-Rigid Registration of Blue and White Light Neurosurgical Hyperspectral Images
2025-07-10
Charlie Budd, Silvère Ségaud, Matthew Elliot, Graeme Stasiuk, Yijing Xie, Jonathan Shapey, Tom Vercauteren
Integration of hyperspectral imaging into fluorescence-guided neurosurgery has the potential to improve surgical decision making by providing quantitative fluorescence measurements in real-time. Quantitative fluorescence requires paired spectral data in fluorescence (blue light) and reflectance (white light) mode. Blue and white image acquisition needs to be performed sequentially in a potentially dynamic surgical environment. A key component to the fluorescence quantification process is therefore the ability to find dense cross-modal image correspondences between two hyperspectral images taken under these drastically different lighting conditions. We address this challenge with the introduction of X-RAFT, a Recurrent All-Pairs Field Transforms (RAFT) optical flow model modified for cross-modal inputs. We propose using distinct image encoders for each modality pair, and fine-tune these in a self-supervised manner using flow-cycle-consistency on our neurosurgical hyperspectral data. We show an error reduction of 36.6% across our evaluation metrics when comparing to a naive baseline and 27.83% reduction compared to an existing cross-modal optical flow method (CrossRAFT). Our code and models will be made publicly available after the review process.
Motivated by a growing research interest into automatic speech recognition (ASR), and the growing body of work for languages in which code-switching (CS) often occurs, we present a systematic literature review of code-switching in end-to-end ASR models. We collect and manually annotate papers published in peer reviewed venues. We document the languages considered, datasets, metrics, model choices, and performance, and present a discussion of challenges in end-to-end ASR for code-switching. Our analysis thus provides insights on current research efforts and available resources as well as opportunities and gaps to guide future research.
GuardVal: Dynamic Large Language Model Jailbreak Evaluation for Comprehensive Safety Testing
2025-07-10
Peiyan Zhang, Haibo Jin, Liying Kang, Haohan Wang
Jailbreak attacks reveal critical vulnerabilities in Large Language Models (LLMs) by causing them to generate harmful or unethical content. Evaluating these threats is particularly challenging due to the evolving nature of LLMs and the sophistication required in effectively probing their vulnerabilities. Current benchmarks and evaluation methods struggle to fully address these challenges, leaving gaps in the assessment of LLM vulnerabilities. In this paper, we review existing jailbreak evaluation practices and identify three assumed desiderata for an effective jailbreak evaluation protocol. To address these challenges, we introduce GuardVal, a new evaluation protocol that dynamically generates and refines jailbreak prompts based on the defender LLM's state, providing a more accurate assessment of defender LLMs' capacity to handle safety-critical situations. Moreover, we propose a new optimization method that prevents stagnation during prompt refinement, ensuring the generation of increasingly effective jailbreak prompts that expose deeper weaknesses in the defender LLMs. We apply this protocol to a diverse set of models, from Mistral-7b to GPT-4, across 10 safety domains. Our findings highlight distinct behavioral patterns among the models, offering a comprehensive view of their robustness. Furthermore, our evaluation process deepens the understanding of LLM behavior, leading to insights that can inform future research and drive the development of more secure models.
Advancements in large language models (LLMs) have led to a surge of prompt engineering (PE) techniques that can enhance various requirements engineering (RE) tasks. However, current LLMs are often characterized by significant uncertainty and a lack of controllability. This absence of clear guidance on how to effectively prompt LLMs acts as a barrier to their trustworthy implementation in the RE field. We present the first roadmap-oriented systematic literature review of Prompt Engineering for RE (PE4RE). Following Kitchenham's and Petersen's secondary-study protocol, we searched six digital libraries, screened 867 records, and analyzed 35 primary studies. To bring order to a fragmented landscape, we propose a hybrid taxonomy that links technique-oriented patterns (e.g., few-shot, Chain-of-Thought) to task-oriented RE roles (elicitation, validation, traceability). Two research questions, with five sub-questions, map the tasks addressed, LLM families used, and prompt types adopted, and expose current limitations and research gaps. Finally, we outline a step-by-step roadmap showing how today's ad-hoc PE prototypes can evolve into reproducible, practitioner-friendly workflows.
Machine Learning-Driven Enzyme Mining: Opportunities, Challenges, and Future Perspectives
2025-07-10
Yanzi Zhang, Felix Moorhoff, Sizhe Qiu, Wenjuan Dong, David Medina-Ortiz, Jing Zhao, Mehdi D. Davari
Enzyme mining is rapidly evolving as a data-driven strategy to identify biocatalysts with tailored functions from the vast landscape of uncharacterized proteins. The integration of machine learning into these workflows enables high-throughput prediction of enzyme functions, including Enzyme Commission numbers, Gene Ontology terms, substrate specificity, and key catalytic properties such as kinetic parameters, optimal temperature, pH, solubility, and thermophilicity. This review provides a systematic overview of state-of-the-art machine learning models and highlights representative case studies that demonstrate their effectiveness in accelerating enzyme discovery. Despite notable progress, current approaches remain limited by data scarcity, model generalizability, and interpretability. We discuss emerging strategies to overcome these challenges, including multi-task learning, integration of multi-modal data, and explainable AI. Together, these developments establish ML-guided enzyme mining as a scalable and predictive framework for uncovering novel biocatalysts, with broad applications in biocatalysis, biotechnology, and synthetic biology.
2025-07-10
Carsten Gundlach, David Hilditch, José M. Martín-García
As first discovered by Choptuik, the black hole threshold in the space of initial data for general relativity shows both surprising structure and surprising simplicity. Universality, power-law scaling of the black hole mass, and scale echoing have given rise to the term ``critical phenomena''. They are explained by the existence of exact solutions which are attractors within the black hole threshold, that is, attractors of codimension one in phase space, and which are typically self-similar. Critical phenomena give a natural route from smooth initial data to arbitrarily large curvatures visible from infinity, and are therefore likely to be relevant for cosmic censorship, quantum gravity, astrophysics, and our general understanding of the dynamics of general relativity. Major additions since the 2010 version of this review are numerical simulations beyond spherical symmetry, in particular of vacuum critical collapse, and new sections on mathematical results in PDE blowup (as a toy model for singularity formation) and on naked singularity formation in GR.
Non-uniform time-stepping in k-space pseudospectral time domain models of acoustic propagation
2025-07-10
Matthew J. King, B. E. Treeby, B. T. Cox
Non-uniform time stepping in acoustic propagation models can be used to preserve accuracy or reduce computational cost for an acoustic simulation with a wave front propagating through a domain with both heterogeneous and homogenous regions, such as for a simulation of breast ultrasound tomography. The k-space correction already exist within the literature to remove numerical dispersion caused by the time stepping procedure in pseudo-spectral time domain models, but requires a uniform time step. Here we expand this correction to be able to account for a non-uniform time stepping method and illustrate the potential advantages and considerations. A version of this Article has been submitted for review to the Journal of Theoretical and Computational Acoustics.
Euclid: Early Release Observations. Weak gravitational lensing analysis of Abell 2390
2025-07-10
T. Schrabback, G. Congedo, R. Gavazzi, W. G. Hartley, H. Jansen, Y. Kang, F. Kleinebreil, H. Atek, E. Bertin, J. -C. Cuillandre, J. M. Diego, S. Grandis, H. Hoekstra, M. Kümmel, L. Linke, H. Miyatake, N. Okabe, S. Paltani, M. Schefer, P. Simon, F. Tarsitano, A. N. Taylor, J. R. Weaver, R. Bhatawdekar, M. Montes, P. Rosati, S. Toft, B. Altieri, A. Amara, L. Amendola, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, R. Bender, A. Biviano, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, G. Cañas-Herrera, G. P. Candini, V. Capobianco, C. Carbone, V. F. Cardone, J. Carretero, S. Casas, F. J. Castander, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, C. J. Conselice, L. Conversi, Y. Copin, A. Costille, F. Courbin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, H. Dole, M. Douspis, F. Dubath, X. Dupac, S. Dusini, S. Escoffier, M. Farina, R. Farinelli, S. Farrens, F. Faustini, S. Ferriol, F. Finelli, P. Fosalba, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, W. Gillard, B. Gillis, C. Giocoli, J. Gracia-Carpio, A. Grazian, F. Grupp, S. V. H. Haugan, J. Hoar, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, M. Kilbinger, B. Kubik, K. Kuijken, M. Kunz, H. Kurki-Suonio, R. Laureijs, A. M. C. Le Brun, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. J. Massey, S. Maurogordato, E. Medinaceli, S. Mei, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, J. J. Mohr, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, C. Padilla, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, C. Rosset, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, M. Schirmer, P. Schneider, A. Secroun, G. Seidel, M. Seiffert, S. Serrano, C. Sirignano, G. Sirri, A. Spurio Mancini, L. Stanco, J. Steinwagner, P. Tallada-Crespí, I. Tereno, N. Tessore, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, E. A. Valentijn, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, G. Zamorani, F. M. Zerbi, E. Zucca, M. Bolzonella, C. Burigana, L. Gabarra, J. Martín-Fleitas, S. Matthew, A. Pezzotta, V. Scottez, M. Sereno, M. Viel, D. Scott
The Euclid space telescope of the European Space Agency (ESA) is designed to provide sensitive and accurate measurements of weak gravitational lensing distortions over wide areas on the sky. Here we present a weak gravitational lensing analysis of early Euclid observations obtained for the field around the massive galaxy cluster Abell 2390 as part of the Euclid Early Release Observations programme. We conduct galaxy shape measurements using three independent algorithms (LensMC, KSB+, and SourceXtractor++). Incorporating multi-band photometry from Euclid and Subaru/Suprime-Cam, we estimate photometric redshifts to preferentially select background sources from tomographic redshift bins, for which we calibrate the redshift distributions using the self-organising map approach and data from the Cosmic Evolution Survey (COSMOS). We quantify the residual cluster member contamination and correct for it in bins of photometric redshift and magnitude using their source density profiles, including corrections for source obscuration and magnification. We reconstruct the cluster mass distribution and jointly fit the tangential reduced shear profiles of the different tomographic bins with spherical Navarro--Frenk--White profile predictions to constrain the cluster mass, finding consistent results for the three shape catalogues and good agreement with earlier measurements. As an important validation test we compare these joint constraints to mass measurements obtained individually for the different tomographic bins, finding good consistency. More detailed constraints on the cluster properties are presented in a companion paper that additionally incorporates strong lensing measurements. Our analysis provides a first demonstration of the outstanding capabilities of Euclid for tomographic weak lensing measurements.
Stress Monitoring in Healthcare: An Ensemble Machine Learning Framework Using Wearable Sensor Data
2025-07-10
Arpana Sinhal, Anay Sinhal, Amit Sinhal
Healthcare professionals, particularly nurses, face elevated occupational stress, a concern amplified during the COVID-19 pandemic. While wearable sensors offer promising avenues for real-time stress monitoring, existing studies often lack comprehensive datasets and robust analytical frameworks. This study addresses these gaps by introducing a multimodal dataset comprising physiological signals, electrodermal activity, heart rate and skin temperature. A systematic literature review identified limitations in prior stress-detection methodologies, particularly in handling class imbalance and optimizing model generalizability. To overcome these challenges, the dataset underwent preprocessing with the Synthetic Minority Over sampling Technique (SMOTE), ensuring balanced representation of stress states. Advanced machine learning models including Random Forest, XGBoost and a Multi-Layer Perceptron (MLP) were evaluated and combined into a Stacking Classifier to leverage their collective predictive strengths. By using a publicly accessible dataset and a reproducible analytical pipeline, this work advances the development of deployable stress-monitoring systems, offering practical implications for safeguarding healthcare workers' mental health. Future research directions include expanding demographic diversity and exploring edge-computing implementations for low latency stress alerts.
Improving Clustering on Occupational Text Data through Dimensionality Reduction
2025-07-10
Iago Xabier Vázquez García, Damla Partanaz, Emrullah Fatih Yetkin
In this study, we focused on proposing an optimal clustering mechanism for the occupations defined in the well-known US-based occupational database, O*NET. Even though all occupations are defined according to well-conducted surveys in the US, their definitions can vary for different firms and countries. Hence, if one wants to expand the data that is already collected in O*NET for the occupations defined with different tasks, a map between the definitions will be a vital requirement. We proposed a pipeline using several BERT-based techniques with various clustering approaches to obtain such a map. We also examined the effect of dimensionality reduction approaches on several metrics used in measuring performance of clustering algorithms. Finally, we improved our results by using a specialized silhouette approach. This new clustering-based mapping approach with dimensionality reduction may help distinguish the occupations automatically, creating new paths for people wanting to change their careers.
In massive binary-star systems, supernova explosions can significantly alter the orbit during the formation of compact objects. Some compact objects are predicted to form via direct collapse, a scenario with negligible mass loss and no baryonic ejecta emitted. In this scenario, most of the energy is released via neutrinos, and any resulting natal kick arises from asymmetries in their emission. Here I investigate stellar collapse leading to binary black hole (BH) formation, with a focus on how the natal kick influences the gravitational-wave-driven merger time. Broadly, I find three regimes. For low natal kicks, the effect on the time-to-coalescence is negligible. For moderate natal kicks, if the binary remains bound, up to 50% of binary BHs experience a decrease in their time-to-coalescence by more than an order of magnitude. For large natal kicks, although most binaries become unbound, those that remain bound may acquire retrograde orbits and/or lead to shorter time-to-coalescence. For binary BH mergers, large natal kicks ($\gtrsim100$ km/s) are hard to reconcile with both neutrino natal kicks and the complete collapse scenario. This suggests that retrograde orbits and shortened merger times could only arise in volatile BH formation scenarios or if spin-axis tossing is at work. Consequently, electromagnetic observations of BHs in massive star binaries within the Local Group offer a more effective means to probe the physics behind complete collapse. Another promising population for deciphering the complete collapse scenario is that of massive, wide binaries. Although Gaia may help shed light on these systems, longer observational baselines will likely be needed to fully understand the roles of neutrino natal kicks and stellar collapse in BH formation.
Functional Time Series Forecasting of Distributions: A Koopman-Wasserstein Approach
2025-07-10
Ziyue Wang, Yuko Araki
We propose a novel method for forecasting the temporal evolution of probability distributions observed at discrete time points. Extending the Dynamic Probability Density Decomposition (DPDD), we embed distributional dynamics into Wasserstein geometry via a Koopman operator framework. Our approach introduces an importance-weighted variant of Extended Dynamic Mode Decomposition (EDMD), enabling accurate, closed-form forecasts in 2-Wasserstein space. Theoretical guarantees are established: our estimator achieves spectral convergence and optimal finite-sample Wasserstein error. Simulation studies and a real-world application to U.S. housing price distributions show substantial improvements over existing methods such as Wasserstein Autoregression. By integrating optimal transport, functional time series modeling, and spectral operator theory, DPDD offers a scalable and interpretable solution for distributional forecasting. This work has broad implications for behavioral science, public health, finance, and neuroimaging--domains where evolving distributions arise naturally. Our framework contributes to functional data analysis on non-Euclidean spaces and provides a general tool for modeling and forecasting distributional time series.
Detecting Galactic Rings in the DESI Legacy Imaging Surveys with Semi-Supervised Deep Learning
2025-07-10
Jianzhen Chen, Zhijian Luo, Cheng Cheng, Jun Hou, Shaohua Zhang, Chenggang Shu
The ring structures of disk galaxies are vital for understanding galaxy evolution and dynamics. However, due to the scarcity of ringed galaxies and challenges in their identification, traditional methods often struggle to efficiently obtain statistically significant samples. To address this, this study employs a novel semi-supervised deep learning model, GC-SWGAN, aimed at identifying galaxy rings from high-resolution images of the DESI Legacy Imaging Surveys. We selected over 5,000 confirmed ringed galaxies from the Catalog of Southern Ringed Galaxies (CSRG) and the Northern Ringed Galaxies from the GZ2 catalog (GZ2-CNRG), both verified by morphology expert R. J. Buta, to create an annotated training set. Additionally, we incorporated strictly selected non-ringed galaxy samples from the Galaxy Zoo 2 dataset and utilized unlabelled data from DESI Legacy Surveys to train our model. Through semi-supervised learning, the model significantly reduced reliance on extensive annotated data while enhancing robustness and generalization. On the test set, it demonstrated exceptional performance in identifying ringed galaxies. With a probability threshold of 0.5, the classification accuracy reached 97\%, with precision and recall for ringed galaxies at 94\% and 93\%, respectively. Building on these results, we predicted 750,000 galaxy images from the DESI Legacy Imaging Surveys with r-band apparent magnitudes less than 17.0 and redshifts in the range 0.0005 < z < 0.25, compiling the largest catalog of ringed galaxies to date, containing 62,962 galaxies with ring structures. This catalog provides essential data for subsequent research on the formation mechanisms and evolutionary history of galaxy rings.
ArchiveGPT: A human-centered evaluation of using a vision language model for image cataloguing
2025-07-10
Line Abele, Gerrit Anders, Tolgahan Aydın, Jürgen Buder, Helen Fischer, Dominik Kimmel, Markus Huff
The accelerating growth of photographic collections has outpaced manual cataloguing, motivating the use of vision language models (VLMs) to automate metadata generation. This study examines whether Al-generated catalogue descriptions can approximate human-written quality and how generative Al might integrate into cataloguing workflows in archival and museum collections. A VLM (InternVL2) generated catalogue descriptions for photographic prints on labelled cardboard mounts with archaeological content, evaluated by archive and archaeology experts and non-experts in a human-centered, experimental framework. Participants classified descriptions as AI-generated or expert-written, rated quality, and reported willingness to use and trust in AI tools. Classification performance was above chance level, with both groups underestimating their ability to detect Al-generated descriptions. OCR errors and hallucinations limited perceived quality, yet descriptions rated higher in accuracy and usefulness were harder to classify, suggesting that human review is necessary to ensure the accuracy and quality of catalogue descriptions generated by the out-of-the-box model, particularly in specialized domains like archaeological cataloguing. Experts showed lower willingness to adopt AI tools, emphasizing concerns on preservation responsibility over technical performance. These findings advocate for a collaborative approach where AI supports draft generation but remains subordinate to human verification, ensuring alignment with curatorial values (e.g., provenance, transparency). The successful integration of this approach depends not only on technical advancements, such as domain-specific fine-tuning, but even more on establishing trust among professionals, which could both be fostered through a transparent and explainable AI pipeline.
Toward Real-World Chinese Psychological Support Dialogues: CPsDD Dataset and a Co-Evolving Multi-Agent System
2025-07-10
Yuanchen Shi, Longyin Zhang, Fang Kong
The growing need for psychological support due to increasing pressures has exposed the scarcity of relevant datasets, particularly in non-English languages. To address this, we propose a framework that leverages limited real-world data and expert knowledge to fine-tune two large language models: Dialog Generator and Dialog Modifier. The Generator creates large-scale psychological counseling dialogues based on predefined paths, which guide system response strategies and user interactions, forming the basis for effective support. The Modifier refines these dialogues to align with real-world data quality. Through both automated and manual review, we construct the Chinese Psychological support Dialogue Dataset (CPsDD), containing 68K dialogues across 13 groups, 16 psychological problems, 13 causes, and 12 support focuses. Additionally, we introduce the Comprehensive Agent Dialogue Support System (CADSS), where a Profiler analyzes user characteristics, a Summarizer condenses dialogue history, a Planner selects strategies, and a Supporter generates empathetic responses. The experimental results of the Strategy Prediction and Emotional Support Conversation (ESC) tasks demonstrate that CADSS achieves state-of-the-art performance on both CPsDD and ESConv datasets.
Hallucination Stations: On Some Basic Limitations of Transformer-Based Language Models
2025-07-10
Varin Sikka, Vishal Sikka
With widespread adoption of transformer-based language models in AI, there is significant interest in the limits of LLMs capabilities, specifically so-called hallucinations, occurrences in which LLMs provide spurious, factually incorrect or nonsensical information when prompted on certain subjects. Furthermore, there is growing interest in agentic uses of LLMs - that is, using LLMs to create agents that act autonomously or semi-autonomously to carry out various tasks, including tasks with applications in the real world. This makes it important to understand the types of tasks LLMs can and cannot perform. We explore this topic from the perspective of the computational complexity of LLM inference. We show that LLMs are incapable of carrying out computational and agentic tasks beyond a certain complexity, and further that LLMs are incapable of verifying the accuracy of tasks beyond a certain complexity. We present examples of both, then discuss some consequences of this work.
2025-07-10
Nawaf Alampara, Anagha Aneesh, Martiño Ríos-García, Adrian Mirza, Mara Schilling-Wilhelmi, Ali Asghar Aghajani, Meiling Sun, Gordan Prastalo, Kevin Maik Jablonka
Data-driven techniques have a large potential to transform and accelerate the chemical sciences. However, chemical sciences also pose the unique challenge of very diverse, small, fuzzy datasets that are difficult to leverage in conventional machine learning approaches completely. A new class of models, general-purpose models (GPMs) such as large language models, have shown the ability to solve tasks they have not been directly trained on, and to flexibly operate with low amounts of data in different formats. In this review, we discuss fundamental building principles of GPMs and review recent applications of those models in the chemical sciences across the entire scientific process. While many of these applications are still in the prototype phase, we expect that the increasing interest in GPMs will make many of them mature in the coming years.
Spiking Neural Networks (SNNs), inspired by biological neural mechanisms, represent a promising neuromorphic computing paradigm that offers energy-efficient alternatives to traditional Artificial Neural Networks (ANNs). Despite proven effectiveness, SNN architectures have struggled to achieve competitive performance on large-scale speech processing task. Two key challenges hinder progress: (1) the high computational overhead during training caused by multi-timestep spike firing, and (2) the absence of large-scale SNN architectures tailored to speech processing tasks. To overcome the issues, we introduce Input-aware Multi-Level Spikeformer, i.e. IML-Spikeformer, a spiking Transformer architecture specifically designed for large-scale speech processing. Central to our design is the Input-aware Multi-Level Spike (IMLS) mechanism, which simulate multi-timestep spike firing within a single timestep using an adaptive, input-aware thresholding scheme. IML-Spikeformer further integrates a Reparameterized Spiking Self-Attention (RepSSA) module with a Hierarchical Decay Mask (HDM), forming the HD-RepSSA module. This module enhances the precision of attention maps and enables modeling of multi-scale temporal dependencies in speech signals. Experiments demonstrate that IML-Spikeformer achieves word error rates of 6.0\% on AiShell-1 and 3.4\% on Librispeech-960, comparable to conventional ANN transformers while reducing theoretical inference energy consumption by 4.64$\times$ and 4.32$\times$ respectively. IML-Spikeformer marks an advance of scalable SNN architectures for large-scale speech processing in both task performance and energy efficiency.
Audio-visual sound source localization (AV-SSL) identifies the position of a sound source by exploiting the complementary strengths of auditory and visual signals. However, existing AV-SSL methods encounter three major challenges: 1) inability to selectively isolate the target sound source in multi-source scenarios, 2) misalignment between semantic visual features and spatial acoustic features, and 3) overreliance on paired audio-visual data. To overcome these limitations, we introduce Cross-Instance Audio-Visual Localization (CI-AVL), a novel task that leverages images from different instances of the same sound event category to localize target sound sources, thereby reducing dependence on paired data while enhancing generalization capabilities. Our proposed VP-SelDoA tackles this challenging task through a semantic-level modality fusion and employs a Frequency-Temporal ConMamba architecture to generate target-selective masks for sound isolation. We further develop a Semantic-Spatial Matching mechanism that aligns the heterogeneous semantic and spatial features via integrated cross- and self-attention mechanisms. To facilitate the CI-AVL research, we construct a large-scale dataset named VGG-SSL, comprising 13,981 spatial audio clips across 296 sound event categories. Extensive experiments show that our proposed method outperforms state-of-the-art audio-visual localization methods, achieving a mean absolute error (MAE) of 12.04 and an accuracy (ACC) of 78.23%.
CLASS_SZ II: Notes and Examples of Fast and Accurate Calculations of Halo Model, Large Scale Structure and Cosmic Microwave Background Observables
2025-07-10
Boris Bolliet, Aleksandra Kusiak, Fiona McCarthy, Alina Sabyr, Kristen Surrao, Jens Chluba, Carmen Embil Villagra, Simone Ferraro, Boryana Hadzhiyska, Dongwon Han, J. Colin Hill, Juan Francisco Macías-Pérez, Mathew Madhavacheril, Abhishek Maniyar, Yogesh Mehta, Shivam Pandey, Emmanuel Schaan, Blake Sherwin, Alessio Spurio Mancini, Íñigo Zubeldia
These notes are very much work-in-progress and simply intended to showcase, in various degrees of details (and rigour), some of the cosmology calculations that class_sz can do. We describe the class_sz code in C, Python and Jax. Based on the Boltzmann code class, it can compute a wide range of observables relevant to current and forthcoming CMB and Large Scale Structure surveys. This includes galaxy shear and clustering, CMB lensing, thermal and kinetic Sunyaev and Zeldovich observables, Cosmic Infrared Background, cross-correlations and three-point statistics. Calculations can be done either within the halo model or the linear bias model. For standard $\Lambda$CDM cosmology and extensions, class_sz uses high-accuracy cosmopower emulators of the CMB and matter power spectrum to accelerate calculations. With this, along with efficient numerical integration routines, most class_sz output can be obtained in less than 500 ms (CMB $C_\ell$'s or matter $P(k)$ take $\mathcal{O}(1\mathrm{ms})$), allowing for fast or ultra-fast parameter inference analyses. Parts of the calculations are "jaxified", so the software can be integrated into differentiable pipelines.
Automatic Generation of Explainability Requirements and Software Explanations From User Reviews
2025-07-10
Martin Obaidi, Jannik Fischbach, Jakob Droste, Hannah Deters, Marc Herrmann, Jil Klünder, Steffen Krätzig, Hugo Villamizar, Kurt Schneider
Explainability has become a crucial non-functional requirement to enhance transparency, build user trust, and ensure regulatory compliance. However, translating explanation needs expressed in user feedback into structured requirements and corresponding explanations remains challenging. While existing methods can identify explanation-related concerns in user reviews, there is no established approach for systematically deriving requirements and generating aligned explanations. To contribute toward addressing this gap, we introduce a tool-supported approach that automates this process. To evaluate its effectiveness, we collaborated with an industrial automation manufacturer to create a dataset of 58 user reviews, each annotated with manually crafted explainability requirements and explanations. Our evaluation shows that while AI-generated requirements often lack relevance and correctness compared to human-created ones, the AI-generated explanations are frequently preferred for their clarity and style. Nonetheless, correctness remains an issue, highlighting the importance of human validation. This work contributes to the advancement of explainability requirements in software systems by (1) introducing an automated approach to derive requirements from user reviews and generate corresponding explanations, (2) providing empirical insights into the strengths and limitations of automatically generated artifacts, and (3) releasing a curated dataset to support future research on the automatic generation of explainability requirements.
Entity-Specific Cyber Risk Assessment using InsurTech Empowered Risk Factors
2025-07-10
Jiayi Guo, Zhiyu Quan, Linfeng Zhang
The lack of high-quality public cyber incident data limits empirical research and predictive modeling for cyber risk assessment. This challenge persists due to the reluctance of companies to disclose incidents that could damage their reputation or investor confidence. Therefore, from an actuarial perspective, potential resolutions conclude two aspects: the enhancement of existing cyber incident datasets and the implementation of advanced modeling techniques to optimize the use of the available data. A review of existing data-driven methods highlights a significant lack of entity-specific organizational features in publicly available datasets. To address this gap, we propose a novel InsurTech framework that enriches cyber incident data with entity-specific attributes. We develop various machine learning (ML) models: a multilabel classification model to predict the occurrence of cyber incident types (e.g., Privacy Violation, Data Breach, Fraud and Extortion, IT Error, and Others) and a multioutput regression model to estimate their annual frequencies. While classifier and regressor chains are implemented to explore dependencies among cyber incident types as well, no significant correlations are observed in our datasets. Besides, we apply multiple interpretable ML techniques to identify and cross-validate potential risk factors developed by InsurTech across ML models. We find that InsurTech empowered features enhance prediction occurrence and frequency estimation robustness compared to only using conventional risk factors. The framework generates transparent, entity-specific cyber risk profiles, supporting customized underwriting and proactive cyber risk mitigation. It provides insurers and organizations with data-driven insights to support decision-making and compliance planning.
As cyber-physical systems grow increasingly interconnected and spatially distributed, ensuring their resilience against evolving cyberattacks has become a critical priority. Spatio-Temporal Anomaly detection plays an important role in ensuring system security and operational integrity. However, current data-driven approaches, largely driven by black-box deep learning, face challenges in interpretability, adaptability to distribution shifts, and robustness under evolving system dynamics. In this paper, we advocate for a causal learning perspective to advance anomaly detection in spatially distributed infrastructures that grounds detection in structural cause-effect relationships. We identify and formalize three key directions: causal graph profiling, multi-view fusion, and continual causal graph learning, each offering distinct advantages in uncovering dynamic cause-effect structures across time and space. Drawing on real-world insights from systems such as water treatment infrastructures, we illustrate how causal models provide early warning signals and root cause attribution, addressing the limitations of black-box detectors. Looking ahead, we outline the future research agenda centered on multi-modality, generative AI-driven, and scalable adaptive causal frameworks. Our objective is to lay a new research trajectory toward scalable, adaptive, explainable, and spatially grounded anomaly detection systems. We hope to inspire a paradigm shift in cybersecurity research, promoting causality-driven approaches to address evolving threats in interconnected infrastructures.
Off-resonant light-induced topological phase transition and thermoelectric transport in semi-Dirac materials
2025-07-10
Vassilios Vargiamidis, P. Vasilopoulos, Neophytos Neophytou
We show that a semi-Dirac (SD) system with an inversion symmetry breaking mass exhibits a topological phase transition when irradiated with off-resonant light. Using Floquet theory, we derive the band structure, Chern numbers, phase diagram, and we show that as the light intensity is swept at fixed mass, the SD system undergoes normal-Chern-normal insulator transition. Along the phase boundaries we observe single semi-Dirac-cone (SSDC) semimetal states in which one SD cone is gapless and the other gapped. The nontrivial Berry curvature distribution $\Omega(\mathbf{k}) \neq -\Omega ( - \mathbf{k} )$ generates an orbital magnetization $M$ and anomalous Nernst ($\alpha_{xy}$) and thermal Hall ($\kappa_{xy}$) conductivities. We show that $M$ remains constant as the Fermi level $E_F$ scans the insulating gap, but it changes linearly with it in the Chern insulator (CI) phase, as expected. In the normal insulator phase, we find that $\alpha_{xy}$ exhibits a dip-peak profile which is reversed in the CI phase. We also find that switching the light's circular polarization from left to right induces a sign change in $M$, $\alpha_{xy}$, and $\kappa_{xy}$, regardless of the topological phase, thereby allowing us to reverse the direction of flow of the transverse charge and heat currents. Further, we evaluate the components of the charge ($\sigma_{aa}$), thermoelectric ($\alpha_{aa}$), and thermal ($\kappa_{aa}$) conductivity tensors ($a=x, y$) and examine the effect of light on them. With a linear dispersion along the $y$-direction, we find that $\alpha_{yy}$ and $\kappa_{yy}$ are significantly larger than $\alpha_{xx}$ and $\kappa_{xx}$, respectively, due to the much larger squared Dirac velocity $v_y^2$ compared to $v_x^2$.
Universal energy limits of radiation belts in planetary and brown dwarf magnetospheric systems
2025-07-10
Drew L. Turner, Savvas Raptis, Adnane Osmane, Arika Egan, George Clark, Tom Nordheim, Leonardo Regoli, Sasha Ukhorskiy
Radiation belts are regions of magnetically trapped particle radiation found around all of the sufficiently magnetized planets in the Solar System and recently also observed around brown dwarfs, yet despite their ubiquity, there is not yet a general theory or model to predict the uppermost energy limits that any particular magnetospheric system's radiation belts can attain. By considering only the most fundamental loss processes, a model and corresponding theory are developed that successfully bound and explain the maximum observed energies of all documented radiation belt systems. Interestingly, this approach yields a relatively simple function for the uppermost energy limit that depends on only the surface magnetic field strength of the system. The model predicts an energy limit for all radiation belt systems that asymptotes at 7 +/- 2 TeV (for protons and electrons), offering intriguing new insight on potential sources of galactic cosmic rays. This model is also applied to an exoplanetary system, demonstrating that the planet is likely a synchrotron emitter and showcasing the model's use for identifying candidate targets for synchrotron-emitting astrophysical systems and revealing details critical to habitability at those remote worlds.
AI for NONMEM Coding in Pharmacometrics Research and Education: Shortcut or Pitfall?
2025-07-10
Wenhao Zheng, Wanbing Wang, Carl M. J. Kirkpatrick, Cornelia B. Landersdorfer, Huaxiu Yao, Jiawei Zhou
Artificial intelligence (AI) is increasingly being explored as a tool to support pharmacometric modeling, particularly in addressing the coding challenges associated with NONMEM. In this study, we evaluated the ability of seven AI agents to generate NONMEM codes across 13 pharmacometrics tasks, including a range of population pharmacokinetic (PK) and pharmacodynamic (PD) models. We further developed a standardized scoring rubric to assess code accuracy and created an optimized prompt to improve AI agent performance. Our results showed that the OpenAI o1 and gpt-4.1 models achieved the best performance, both generating codes with great accuracy for all tasks when using our optimized prompt. Overall, AI agents performed well in writing basic NONMEM model structures, providing a useful foundation for pharmacometrics model coding. However, user review and refinement remain essential, especially for complex models with special dataset alignment or advanced coding techniques. We also discussed the applications of AI in pharmacometrics education, particularly strategies to prevent over-reliance on AI for coding. This work provides a benchmark for current AI agents performance in NONMEM coding and introduces a practical prompt that can facilitate more accurate and efficient use of AI in pharmacometrics research and education.
Pushing the Boundaries of Immersion and Storytelling: A Technical Review of Unreal Engine
2025-07-10
Oleksandra Sobchyshak, Santiago Berrezueta-Guzman, Stefan Wagner
Unreal Engine is a platform that has influenced immersive storytelling and virtual reality (VR) through its advanced features and diverse applications. This paper provides an in-depth technical review of Unreal Engine. It analyzes its key innovations in creating hyper-realistic environments and emotionally engaging narratives, with significant applications in gaming, virtual production, education, cultural preservation, and healthcare. The findings of this article highlight Unreal Engine's transformative impact across industries, demonstrating its ability to merge storytelling with cutting-edge technologies. Case studies illustrate how Unreal Engine facilitates seamless visuals, audio, and interactivity integration to create compelling experiences. Additionally, this study identifies Unreal Engine's versatility in applications ranging from procedural content generation and AI-driven workflows to smart city simulations and VR-based rehabilitation programs. While Unreal Engine sets new benchmarks for visual fidelity and interactivity, this paper underscores critical challenges, including its high hardware demands, limited accessibility, and ethical concerns related to over-immersion and data privacy. Addressing these challenges through cloud-based rendering, inclusive design, and ethical practices is essential for broader adoption and sustainability. This review concludes that Unreal Engine is suitable for innovation and interdisciplinary collaboration. Its ability to empower creators, redefine workflows, and push the boundaries of immersive storytelling positions Unreal Engine as pivotal in shaping the future of virtual reality and interactive media.
Audit, Alignment, and Optimization of LM-Powered Subroutines with Application to Public Comment Processing
2025-07-10
Reilly Raab, Mike Parker, Dan Nally, Sadie Montgomery, Anastasia Bernat, Sai Munikoti, Sameera Horawalavithana
The advent of language models (LMs) has the potential to dramatically accelerate tasks that may be cast to text-processing; however, real-world adoption is hindered by concerns regarding safety, explainability, and bias. How can we responsibly leverage LMs in a transparent, auditable manner -- minimizing risk and allowing human experts to focus on informed decision-making rather than data-processing or prompt engineering? In this work, we propose a framework for declaring statically typed, LM-powered subroutines (i.e., callable, function-like procedures) for use within conventional asynchronous code -- such that sparse feedback from human experts is used to improve the performance of each subroutine online (i.e., during use). In our implementation, all LM-produced artifacts (i.e., prompts, inputs, outputs, and data-dependencies) are recorded and exposed to audit on demand. We package this framework as a library to support its adoption and continued development. While this framework may be applicable across several real-world decision workflows (e.g., in healthcare and legal fields), we evaluate it in the context of public comment processing as mandated by the 1969 National Environmental Protection Act (NEPA): Specifically, we use this framework to develop "CommentNEPA," an application that compiles, organizes, and summarizes a corpus of public commentary submitted in response to a project requiring environmental review. We quantitatively evaluate the application by comparing its outputs (when operating without human feedback) to historical ``ground-truth'' data as labelled by human annotators during the preparation of official environmental impact statements.
Photonic quantum information with time-bins: Principles and applications
2025-07-10
Ashutosh Singh, Anuj Sethia, Leili Esmaeilifar, Raju Valivarthi, Neil Sinclair, Maria Spiropulu, Daniel Oblak
Long-range quantum communication, distributed quantum computing, and sensing applications require robust and reliable ways to encode transmitted quantum information. In this context, time-bin encoding has emerged as a promising candidate due to its resilience to mechanical and thermal perturbations, depolarization from refractive index changes, and birefringence in fiber optic media. Time-bin quantum bits (qubits) can be produced in various ways, and each implementation calls for different considerations regarding design parameters, component compatibility (optical, electrical, electro-optical), and measurement procedures. Here, we provide a comprehensive overview of experimental methods for preparing and characterizing time-bin qubits (TBQs) for quantum communication protocols, with an assessment of their advantages and limitations. We discuss challenges in transmitting TBQs over optical fibers and free-space channels, and methods to overcome them. We also analyze the selection of key time-bin parameters and component requirements across experiments. This leads us to explore the preparation and characterization of time-bin entanglement and examine requirements for interference of time-bins from separate sources. Further, we cover preparation and characterization techniques for high-dimensional time-bin states, namely qudits, and the generation of time-bin entangled qudit pairs. We review time-energy entanglement and key experimental realizations. Finally, we present notable applications of time-bin encoded quantum states, from quantum communication protocols to photonic quantum computation. This work serves as an accessible introduction and a comprehensive review of recent developments.
Supernova 2023ixf occurred on May 18th 2023 in the nearby galaxy Messier 101 (D ~ 6.85 Mpc), making it the closest supernova in the last decade. Following its discovery, astronomers around the world rushed to observe the explosion across the electromagnetic spectrum in order to uncover its early-time properties. Based on multi-wavelength analysis during its first year post-explosion, supernova 2023ixf is a type II supernova that interacted with dense, confined circumstellar material in its local environment -- this material being lost from its red supergiant progenitor in the final years before explosion. In this article, we will review the findings of >80 studies already published on this incredible event as well as explore how the synthesis of SN 2023ixf observations across the electromagnetic spectrum can be used to constrain both type II supernova explosion physics in addition to the the uncertain mass loss histories of red supergiant stars in their final years.
Tidal Disruption Event Demographics in Supermassive Black Hole Binaries Over Cosmic Times
2025-07-10
Denyz Melchor, Smadar Naoz, Suvi Gezari, Brenna Mockler
Tidal disruption events (TDEs) offer a unique probe of supermassive black hole (SMBH) demographics, but their observed rates remain difficult to reconcile with standard single-SMBH models. In this work, we use simulations of SMBH binaries, including the combined effects of eccentric Kozai-Lidov oscillations and two-body relaxation, to explore how TDE rates scale with SMBH mass and redshift. We find that binary systems exhibit increasing TDE rates with mass, in contrast to the declining trend expected for single SMBHs. These binary-driven rates match those observed in post-starburst galaxies, suggesting that a subset of TDE hosts may contain SMBH binaries. TDE light curves in some massive galaxies exhibit unexpectedly short durations, suggesting that the disrupting SMBH may be less massive than implied by host galaxy scaling relations, consistent with disruptions by the less massive black hole in a binary. By convolving our mass-dependent rates with the SMBH mass function, we predict redshift-dependent TDE rates, which we show can be used to constrain the supermassive black hole binary fraction. Our results provide a testable framework for interpreting TDE demographics in upcoming wide-field surveys such as LSST and Roman.
The State of Computational Science in Fission and Fusion Energy
2025-07-10
Andrea Morales Coto, Aditi Verma
The tools used to engineer something are just as important as the thing that is actually being engineered. In fact, in many cases, the tools can indeed determine what is engineerable. In fusion and fission1 energy engineering, software has become the dominant tool for design. For that reason, in 2024, for the first time ever, we asked 103 computational scientists developing the codes used in fusion and fission energy about the problems they are attempting to solve with their codes, the tools available to them to solve them, and their end to end developer experience with said tools. The results revealed a changing tide in software tools in fusion and fission, with more and more computational scientists preferring modern programming languages, open-source codes, and modular software. These trends represent a peek into what will happen 5 to 10 years in the future of nuclear engineering. Since the majority of our respondents belonged to US national labs and universities, these results hint at the most cutting-edge trends in the industry. The insights included in the State of Computational Science in Fission and Fusion Energy indicate a dramatic shift toward multiphysics codes, a drop-off in the use of FORTRAN in favor of more modern languages like Python and C++, and ever-rising budgets for code development, at times reaching $50M in a single organization. Our survey paints a future of nuclear engineering codes that is modular in nature, small in terms of compute, and increasingly prioritized by organizations. Access to our results in web form are available online.
With the advancement of digital payment technologies, central banks worldwide have increasingly begun to explore the implementation of Central Bank Digital Currencies (CBDCs). This paper presents a comprehensive review of the latest developments in CBDC system design and implementation. By analyzing 135 research papers published between 2018 and 2025, the study provides an in-depth examination of CBDC design taxonomy and ecosystem frameworks. Grounded in the CBDC Design Pyramid, the paper refines and expands key architectural elements by thoroughly investigating innovations in ledger technologies, the selection of consensus mechanisms, and challenges associated with offline payments and digital wallet integration. Furthermore, it conceptualizes a CBDC ecosystem. A detailed comparative analysis of 26 existing CBDC systems is conducted across four dimensions: system architecture, ledger technology, access model, and application domain. The findings reveal that the most common configuration consists of a two-tier architecture, distributed ledger technology (DLT), and a token-based access model. However, no dominant trend has emerged regarding application domains. Notably, recent research shows a growing focus on leveraging CBDCs for cross-border payments to resolve inefficiencies and structural delays in current systems. Finally, the paper offers several forward-looking recommendations for future research.
The growing availability and use of deepfake technologies increases risks for democratic societies, e.g., for political communication on online platforms. The EU has responded with transparency obligations for providers and deployers of Artificial Intelligence (AI) systems and online platforms. This includes marking deepfakes during generation and labeling deepfakes when they are shared. However, the lack of industry and enforcement standards poses an ongoing challenge. Through a multivocal literature review, we summarize methods for marking, detecting, and labeling deepfakes and assess their effectiveness under EU regulation. Our results indicate that individual methods fail to meet regulatory and practical requirements. Therefore, we propose a multi-level strategy combining the strengths of existing methods. To account for the masses of content on online platforms, our multi-level strategy provides scalability and practicality via a simple scoring mechanism. At the same time, it is agnostic to types of deepfake technology and allows for context-specific risk weighting.
The Nineteenth Data Release of the Sloan Digital Sky Survey
2025-07-09
SDSS Collaboration, Gautham Adamane Pallathadka, Mojgan Aghakhanloo, James Aird, Andrés Almeida, Singh Amrita, Friedrich Anders, Scott F. Anderson, Stefan Arseneau, Consuelo González Avila, Shir Aviram, Catarina Aydar, Carles Badenes, Jorge K. Barrera-Ballesteros, Franz E. Bauer, Aida Behmard, Michelle Berg, F. Besser, Christian Moni Bidin, Dmitry Bizyaev, Guillermo Blanc, Michael R. Blanton, Jo Bovy, William Nielsen Brandt, Joel R. Brownstein, Johannes Buchner, Esra Bulbul, Joseph N. Burchett, Leticia Carigi, Joleen K. Carlberg, Andrew R. Casey, Priyanka Chakraborty, Julio Chanamé, Vedant Chandra, Cristina Chiappini, Igor Chilingarian, Johan Comparat, Kevin Covey, Nicole Crumpler, Katia Cunha, Elena D'Onghia, Xinyu Dai, Jeremy Darling, Megan Davis, Nathan De Lee, Niall Deacon, José Eduardo Méndez Delgado, Sebastian Demasi, Mariia Demianenko, Delvin Demke, John Donor, Niv Drory, Monica Alejandra Villa Durango, Tom Dwelly, Oleg Egorov, Evgeniya Egorova, Kareem El-Badry, Mike Eracleous, Xiaohui Fan, Emily Farr, Douglas P. Finkbeiner, Logan Fries, Peter Frinchaboy, Nicola Pietro Gentile Fusillo, Luis Daniel Serrano Félix, Boris Gaensicke, Emma Galligan, Pablo García, Joseph Gelfand, Katie Grabowski, Eva Grebel, Paul J Green, Hannah Greve, Catherine Grier, Emily Griffith, Paloma Guetzoyan, Pramod Gupta, Zoe Hackshaw, Patrick B. Hall, Keith Hawkins, Viola Hegedűs, Saskia Hekker, T. M. Herbst, J. J. Hermes, Lorena Hernández-García, Pranavi Hiremath, David W Hogg, Jon Holtzman, Keith Horne, Danny Horta, Yang Huang, Brian Hutchinson, Maximilian Häberle, Hector Javier Ibarra-Medel, Alexander P. Ji, Paula Jofre, James W. Johnson, Jennifer Johnson, Evelyn J. Johnston, Mary Kaldor, Ivan Katkov, Arman Khalatyan, Sergey Khoperskov, Ralf Klessen, Matthias Kluge, Anton M. Koekemoer, Juna A. Kollmeier, Marina Kounkel, Kathryn Kreckel, Dhanesh Krishnarao, Mirko Krumpe, Ivan Lacerna, Chervin Laporte, Sebastien Lepine, Jing Li, Fu-Heng Liang, Guilherme Limberg, Xin Liu, Sarah Loebman, Knox Long, Yuxi Lu, Madeline Lucey, Alejandra Z. Lugo-Aranda, Mary Loli Martínez Martinez-Aldama, Kevin McKinnon, Ilija Medan, Andrea Merloni, Sean Morrison, Natalie Myers, Szabolcs Mészáros, Johanna Müller-Horn, Samir Nepal, Melissa Ness, David Nidever, Christian Nitschelm, Audrey Oravetz, Jonah Otto, Kaike Pan, Facundo Pérez Paolino, Castalia Alenka Negrete Peñaloza, Marc Pinsonneault, Manuchehr Taghizadeh Popp, Adrian Price-Whelan, Nadiia Pulatova, Anna Barbara Queiroz, Jordan Raddick, Amy Rankine, Hans-Walter Rix, Carlos Román-Zúñiga, Daniela Fernández Rosso, Jessie Runnoe, Serat Mahmud Saad, Mara Salvato, Sebastian F. Sanchez, Natascha Sattler, Andrew Saydjari, Conor Sayres, Kevin Schlaufman, Donald P. Schneider, Axel Schwope, Lucas M. Seaton, Rhys Seeburger, Javier Serna, Sanjib Sharma, Yue Shen, Amaya Sinha, Brian Sizemore, Marzena Sniegowska, Yingyi Song, Diogo Souto, Keivan Stassun, Matthias Steinmetz, Zachary Stone, Alexander Stone-Martinez, Guy S. Stringfellow, Aurora Mata Sánchez, José Sánchez-Gallego, Jonathan Tan, Jamie Tayar, Riley Thai, Ani Thakar, Pierre Thibodeaux, Yuan-Sen Ting, Andrew Tkachenko, Benny Trakhtenbrot, Jose G. Fernandez Trincado, Nicholas Troup, Jonathan R. Trump, Natalie Ulloa, Roeland P. Van der Marel, Pablo Vera, Sandro Villanova, Jaime Villaseñor, Ji Wang, Zachary Way, Anne-Marie Weijmans, Adam Wheeler, John C. Wilson, Aida Wofford, Tony Wong, Qiaoya Wu, Dominika Wylezalek, Xiang-Xiang Xue, Renbin Yan, Qian Yang, Nadia Zakamska, Eleonora Zari, Gail Zasowski, Grisha Zeltyn, Zheng Zheng, Catherine Zucker, Rodolfo de J. Zermeño
Mapping the local and distant Universe is key to our understanding of it. For decades, the Sloan Digital Sky Survey (SDSS) has made a concerted effort to map millions of celestial objects to constrain the physical processes that govern our Universe. The most recent and fifth generation of SDSS (SDSS-V) is organized into three scientific ``mappers". Milky Way Mapper (MWM) that aims to chart the various components of the Milky Way and constrain its formation and assembly, Black Hole Mapper (BHM), which focuses on understanding supermassive black holes in distant galaxies across the Universe, and Local Volume Mapper (LVM), which uses integral field spectroscopy to map the ionized interstellar medium in the local group. This paper describes and outlines the scope and content for the nineteenth data release (DR19) of SDSS and the most substantial to date in SDSS-V. DR19 is the first to contain data from all three mappers. Additionally, we also describe nine value added catalogs (VACs) that enhance the science that can be conducted with the SDSS-V data. Finally, we discuss how to access SDSS DR19 and provide illustrative examples and tutorials.
How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks
2025-07-09
Clement Ruah, Houssem Sifaou, Osvaldo Simeone, Bashir M. Al-Hashimi
Training effective artificial intelligence models for telecommunications is challenging due to the scarcity of deployment-specific data. Real data collection is expensive, and available datasets often fail to capture the unique operational conditions and contextual variability of the network environment. Digital twinning provides a potential solution to this problem, as simulators tailored to the current network deployment can generate site-specific data to augment the available training datasets. However, there is a need to develop solutions to bridge the inherent simulation-to-reality (sim-to-real) gap between synthetic and real-world data. This paper reviews recent advances on two complementary strategies: 1) the calibration of digital twins (DTs) through real-world measurements, and 2) the use of sim-to-real gap-aware training strategies to robustly handle residual discrepancies between digital twin-generated and real data. For the latter, we evaluate two conceptually distinct methods that model the sim-to-real gap either at the level of the environment via Bayesian learning or at the level of the training loss via prediction-powered inference.
Self-Adaptive Stabilization and Quality Boost for Electron Beams from All-Optical Plasma Wakefield Accelerators
2025-07-09
D. Campbell, T. Heinemann, A. Dickson, T. Wilson, L. Berman, M. Cerchez, S. Corde, A. Döpp, A. F. Habib, A. Irman, S. Karsch, A. Martinez de la Ossa, A. Pukhov, L. Reichwein, U. Schramm, A. Sutherland, B. Hidding
Shot-to-shot fluctuations in electron beams from laser wakefield accelerators present a significant challenge for applications. Here, we show that instead of using such fluctuating beams directly, employing them to drive a plasma photocathode-based wakefield refinement stage can produce secondary electron beams with greater stability, higher quality, and improved reliability. Our simulation-based analysis reveals that drive beam jitters are compensated by both the insensitivity of beam-driven plasma wakefield acceleration, and the decoupled physics of plasma photocathode injection. While beam-driven, dephasing-free plasma wakefield acceleration mitigates energy and energy spread fluctuations, intrinsically synchronized plasma photocathode injection compensates charge and current jitters of incoming electron beams, and provides a simultaneous quality boost. Our findings suggest plasma photocathodes are ideal injectors for hybrid laser-plasma wakefield accelerators, and nurture prospects for demanding applications such as free-electron lasers.
Tuning the probability detection of OAM entangled photons in Helical Ince-Gauss modes
2025-07-09
A. A. Aguilar-Cardoso, D. Rodriguez-Guillen, R. Ramirez-Alarcon
In this work we provide a detailed theoretical and experimental analysis of the two-photon Orbital Angular Momentum (OAM) entangled state, generated by a type-I Spontaneous Parametric Down Conversion (SPDC) process, when decomposed in terms of the Helical Ince-Gauss (HIG) modes basis. By exploiting the unique characteristics of this modal basis we show how the probability detection of the photon-pair can be tuned with the ellipticity parameter of the modes. We also show that, on the HIG basis the SPDC state has the contribution of two different symmetric Bell states, and it is possible to maximize the probability of each HIG symmetric Bell state separately, also by tuning the elipticity of the projected basis. The observed properties are confirmed experimentally by implementing measurements of the HIG modal joint probability of the SPDC two-photon state and Bell-type inequality violation experiments.
Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels
2025-07-09
Donovan Slabbert, Dean Brand, Francesco Petruccione
We compare a parameterized quantum kernel (pQK) with a quantum leaky integrate-and-fire (QLIF) neuromorphic computing approach that employs either the Victor-Purpura or van Rossum kernel in a spectral clustering task, as well as the classical radial basis function (RBF) kernel. Performance evaluation includes label-based classification and clustering metrics, as well as optimal number of clusters predictions for each dataset based on an elbow-like curve as is typically used in $K$-means clustering. The pQK encodes feature vectors through angle encoding with rotation angles scaled parametrically. Parameters are optimized through grid search to maximize kernel-target alignment, producing a kernel that reflects distances in the feature space. The quantum neuromorphic approach uses population coding to transform data into spike trains, which are then processed using temporal distance metrics. Kernel matrices are used as input into a classical spectral clustering pipeline prior to performance evaluation. For the synthetic datasets and \texttt{Iris}, the QLIF kernel typically achieves better classification and clustering performance than pQK. However, on higher-dimensional datasets, such as a preprocessed version of the Sloan Digital Sky Survey (\texttt{SDSS}), pQK performed better, indicating a possible advantage in higher-dimensional regimes.
On-Device Training of PV Power Forecasting Models in a Smart Meter for Grid Edge Intelligence
2025-07-09
Jian Huang, Yongli Zhu, Linna Xu, Zhe Zheng, Wenpeng Cui, Mingyang Sun
In this paper, an edge-side model training study is conducted on a resource-limited smart meter. The motivation of grid-edge intelligence and the concept of on-device training are introduced. Then, the technical preparation steps for on-device training are described. A case study on the task of photovoltaic power forecasting is presented, where two representative machine learning models are investigated: a gradient boosting tree model and a recurrent neural network model. To adapt to the resource-limited situation in the smart meter, "mixed"- and "reduced"-precision training schemes are also devised. Experiment results demonstrate the feasibility of economically achieving grid-edge intelligence via the existing advanced metering infrastructures.
Sloan Digital Sky Survey-V: Pioneering Panoptic Spectroscopy
2025-07-09
Juna A. Kollmeier, Hans-Walter Rix, Conny Aerts, James Aird, Pablo Vera Alfaro, Andrés Almeida, Scott F. Anderson, Óscar Jiménez Arranz, Stefan M. Arseneau, Roberto Assef, Shir Aviram, Catarina Aydar, Carles Badenes, Avrajit Bandyopadhyay, Kat Barger, Robert H. Barkhouser, Franz E. Bauer, Chad Bender, Felipe Besser, Binod Bhattarai, Pavaman Bilgi, Jonathan Bird, Dmitry Bizyaev, Guillermo A. Blanc, Michael R. Blanton, John Bochanski, Jo Bovy, Christopher Brandon, William Nielsen Brandt, Joel R. Brownstein, Johannes Buchner, Joseph N. Burchett, Joleen Carlberg, Andrew R. Casey, Lesly Castaneda-Carlos, Priyanka Chakraborty, Julio Chanamé, Vedant Chandra, Brian Cherinka, Igor Chilingarian, Johan Comparat, Maren Cosens, Kevin Covey, Jeffrey D. Crane, Nicole R. Crumpler, Katia Cunha, Tim Cunningham, Xinyu Dai, Jeremy Darling, James W. Davidson Jr., Megan C. Davis, Nathan De Lee, Niall Deacon, José Eduardo Méndez Delgado, Sebastian Demasi, Mariia Demianenko, Mark Derwent, Elena D'Onghia, Francesco Di Mille, Bruno Dias, John Donor, Niv Drory, Tom Dwelly, Oleg Egorov, Evgeniya Egorova, Kareem El-Badry, Mike Engelman, Mike Eracleous, Xiaohui Fan, Emily Farr, Logan Fries, Peter Frinchaboy, Cynthia S. Froning, Boris T. Gänsicke, Pablo García, Joseph Gelfand, Nicola Pietro Gentile Fusillo, Simon Glover, Katie Grabowski, Eva K. Grebel, Paul J Green, Catherine Grier, Pramod Gupta, Aidan C. Gray, Maximilian Häberle, Patrick B. Hall, Randolph P. Hammond, Keith Hawkins, Albert C. Harding, Viola Hegedűs, Tom Herbst, J. J. Hermes, Paola Rodríguez Hidalgo, Thomas Hilder, David W Hogg, Jon A. Holtzman, Danny Horta, Yang Huang, Hsiang-Chih Hwang, Hector Javier Ibarra-Medel, Julie Imig, Keith Inight, Arghajit Jana, Alexander P. Ji, Paula Jofre, Matt Johns, Jennifer Johnson, James W. Johnson, Evelyn J. Johnston, Amy M Jones, Ivan Katkov, Anton M. Koekemoer, Marina Kounkel, Kathryn Kreckel, Dhanesh Krishnarao, Mirko Krumpe, Nimisha Kumari, Thomas Kupfer, Ivan Lacerna, Chervin Laporte, Sebastien Lepine, Jing Li, Xin Liu, Sarah Loebman, Knox Long, Alexandre Roman-Lopes, Yuxi Lu, Steven Raymond Majewski, Dan Maoz, Kevin A. McKinnon, Ilija Medan, Andrea Merloni, Dante Minniti, Sean Morrison, Natalie Myers, Szabolcs Mészáros, Kirpal Nandra, Prasanta K. Nayak, Melissa K Ness, David L. Nidever, Thomas O'Brien, Micah Oeur, Audrey Oravetz, Daniel Oravetz, Jonah Otto, Gautham Adamane Pallathadka, Povilas Palunas, Kaike Pan, Daniel Pappalardo, Rakesh Pandey, Castalia Alenka Negrete Peñaloza, Marc H. Pinsonneault, Richard W. Pogge, Manuchehr Taghizadeh Popp, Adrian M. Price-Whelan, Nadiia Pulatova, Dan Qiu, Solange Ramirez, Amy Rankine, Claudio Ricci, Jessie C. Runnoe, Sebastian Sanchez, Mara Salvato, Natascha Sattler, Andrew K. Saydjari, Conor Sayres, Kevin C. Schlaufman, Donald P. Schneider, Matthias R. Schreiber, Axel Schwope, Javier Serna, Yue Shen, Cristóbal Sifón, Amrita Singh, Amaya Sinha, Stephen Smee, Ying-Yi Song, Diogo Souto, Keivan G. Stassun, Matthias Steinmetz, Alexander Stone-Martinez, Guy Stringfellow, Amelia Stutz, José, Sá, nchez-Gallego, Jonathan C. Tan, Jamie Tayar, Riley Thai, Ani Thakar, Yuan-Sen Ting, Andrew Tkachenko, Gagik Tovmasian, Benny Trakhtenbrot, José G. Fernández-Trincado, Nicholas Troup, Jonathan Trump, Sarah Tuttle, Roeland P. van der Marel, Sandro Villanova, Jaime Villaseñor, Stefanie Wachter, Zachary Way, Anne-Marie Weijmans, David Weinberg, Adam Wheeler, John Wilson, Alessa I. Wiggins, Tony Wong, Qiaoya Wu, Dominika Wylezalek, Xiang-Xiang Xue, Qian Yang, Nadia Zakamska, Eleonora Zari, Gail Zasowski, Grisha Zeltyn, Catherine Zucker, Carlos G. Román Zúñiga, Rodolfo de J. Zermeño
The Sloan Digital Sky Survey-V (SDSS-V) is pioneering panoptic spectroscopy: it is the first all-sky, multi-epoch, optical-to-infrared spectroscopic survey. SDSS-V is mapping the sky with multi-object spectroscopy (MOS) at telescopes in both hemispheres (the 2.5-m Sloan Foundation Telescope at Apache Point Observatory and the 100-inch du Pont Telescope at Las Campanas Observatory), where 500 zonal robotic fiber positioners feed light from a wide-field focal plane to an optical (R$\sim 2000$, 500 fibers) and a near-infrared (R$\sim 22,000$, 300 fibers) spectrograph. In addition to these MOS capabilities, the survey is pioneering ultra wide-field ($\sim$ 4000~deg$^2$) integral field spectroscopy enabled by a new dedicated facility (LVM-I) at Las Campanas Observatory, where an integral field spectrograph (IFS) with 1801 lenslet-coupled fibers arranged in a 0.5 degree diameter hexagon feeds multiple R$\sim$4000 optical spectrographs that cover 3600-9800 angstroms. SDSS-V's hardware and multi-year survey strategy are designed to decode the chemo-dynamical history of the Milky Way Galaxy and tackle fundamental open issues in stellar physics in its Milky Way Mapper program, trace the growth physics of supermassive black holes in its Black Hole Mapper program, and understand the self-regulation mechanisms and the chemical enrichment of galactic ecosystems at the energy-injection scale in its Local Volume Mapper program. The survey is well-timed to multiply the scientific output from major all-sky space missions. The SDSS-V MOS programs began robotic operations in 2021; IFS observations began in 2023 with the completion of the LVM-I facility. SDSS-V builds upon decades of heritage of SDSS's pioneering advances in data analysis, collaboration spirit, infrastructure, and product deliverables in astronomy.
A multi-modal dataset for insect biodiversity with imagery and DNA at the trap and individual level
2025-07-09
Johanna Orsholm, John Quinto, Hannu Autto, Gaia Banelyte, Nicolas Chazot, Jeremy deWaard, Stephanie deWaard, Arielle Farrell, Brendan Furneaux, Bess Hardwick, Nao Ito, Amlan Kar, Oula Kalttopää, Deirdre Kerdraon, Erik Kristensen, Jaclyn McKeown, Tommi Mononen, Ellen Nein, Hanna Rogers, Tomas Roslin, Paula Schmitz, Jayme Sones, Maija Sujala, Amy Thompson, Evgeny V. Zakharov, Iuliia Zarubiieva, Akshita Gupta, Scott C. Lowe, Graham W. Taylor
Insects comprise millions of species, many experiencing severe population declines under environmental and habitat changes. High-throughput approaches are crucial for accelerating our understanding of insect diversity, with DNA barcoding and high-resolution imaging showing strong potential for automatic taxonomic classification. However, most image-based approaches rely on individual specimen data, unlike the unsorted bulk samples collected in large-scale ecological surveys. We present the Mixed Arthropod Sample Segmentation and Identification (MassID45) dataset for training automatic classifiers of bulk insect samples. It uniquely combines molecular and imaging data at both the unsorted sample level and the full set of individual specimens. Human annotators, supported by an AI-assisted tool, performed two tasks on bulk images: creating segmentation masks around each individual arthropod and assigning taxonomic labels to over 17 000 specimens. Combining the taxonomic resolution of DNA barcodes with precise abundance estimates of bulk images holds great potential for rapid, large-scale characterization of insect communities. This dataset pushes the boundaries of tiny object detection and instance segmentation, fostering innovation in both ecological and machine learning research.
Observing the stars in our night sky tells us that giant, supergiant and hypergiant stars hold an unique importance in the understanding of stellar populations. Theoretical stellar models predict a rich tapestry of evolved stars. These evolved stars result in supernova explosions for massive stars and the shedding of the outer layers of low- and intermediate-mass stars to form white dwarf stars. Stars on these pathways provide elemental and kinematic feedback that shapes their host galaxies and synthesize the elements that we observe in the Universe today. Observational surveys in the Milky Way and the Local Group of galaxies shape our physical understanding of stellar evolution. Hypergiant stars represent stellar evolution at the extremes, displaying evidence of intense stellar winds combined with luminosities and radii that are unrivalled. Supergiant stars are used as elemental beacons in distant galaxies, and supergiants that cross the Cepheid variable instability strip can be used as rulers to measure the scale of our Universe. The overwhelming majority of stars in the Milky Way will become giant stars. Giants are hundreds of times larger and thousands of times more luminous than the Sun, which makes them excellent tracers of stellar structure. In this chapter, I describe the observational and theoretical manifestations of stellar evolution at various stages and masses. I highlight evolutionary connections between evolved products and comment on their eventual endpoints.
Pre-Columbian Settlements Shaped Palm Clusters in the Sierra Nevada de Santa Marta, Colombia
2025-07-09
Sebastian Fajardo, Sina Mohammadi, Jonas Gregorio de Souza, César Ardila, Alan Tapscott Baltar, Shaddai Heidgen, Maria Isabel Mayorga Hernández, Sylvia Mota de Oliveira, Fernando Montejo, Marco Moderato, Vinicius Peripato, Katy Puche, Carlos Reina, Juan Carlos Vargas, Frank W. Takes, Marco Madella
Ancient populations markedly transformed Neotropical forests, yet understanding the long-term effects of ancient human management, particularly at high-resolution scales, remains challenging. In this work we propose a new approach to investigate archaeological areas of influence based on vegetation signatures. It consists of a deep learning model trained on satellite imagery to identify palm trees, followed by a clustering algorithm to identify palm clusters, which are then used to estimate ancient management areas. To assess the palm distribution in relation to past human activity, we applied the proposed approach to unique high-resolution satellite imagery data covering 765 km2 of the Sierra Nevada de Santa Marta, Colombia. With this work, we also release a manually annotated palm tree dataset along with estimated locations of archaeological sites from ground-surveys and legacy records. Results demonstrate how palms were significantly more abundant near archaeological sites showing large infrastructure investment. The extent of the largest palm cluster indicates that ancient human-managed areas linked to major infrastructure sites may be up to two orders of magnitude bigger than indicated by archaeological evidence alone. Our findings suggest that pre-Columbian populations influenced local vegetation fostering conditions conducive to palm proliferation, leaving a lasting ecological footprint. This may have lowered the logistical costs of establishing infrastructure-heavy settlements in otherwise less accessible locations. Overall, this study demonstrates the potential of integrating artificial intelligence approaches with new ecological and archaeological data to identify archaeological areas of interest through vegetation patterns, revealing fine-scale human-environment interactions.
Device-to-Device Communication in 5G/6G: Architectural Foundations and Convergence with Enabling Technologies
2025-07-09
Mohammad Reza Fasihi, Brian L. Mark
Device-to-Device (D2D) communication is a promising solution to meet the growing demands of 5G and future 6G networks by enabling direct communication between user devices. It enhances spectral efficiency (SE) and energy efficiency (EE), reduces latency, and supports proximity-based services. As wireless systems evolve toward 5G and 6G paradigms, the integration of D2D with advanced cellular technologies introduces new opportunities and challenges. This survey paper reviews the architectural foundations of D2D communication and explores its integration with key 5G/6G enabling technologies. We review standardization efforts, analyze core challenges, and highlight future research directions to unlock the full potential of D2D in next-generation wireless networks.
A Bayesian catalog of 100 high-significance voids in the Local Universe
2025-07-09
Rosa Malandrino, Guilhem Lavaux, Benjamin D. Wandelt, Stuart McAlpine, Jens Jasche
While cosmic voids are now recognized as a valuable cosmological probe, identifying them in a galaxy catalog is challenging for multiple reasons: observational effects such as holes in the mask or magnitude selection hinder the detection process; galaxies are biased tracers of the underlying dark matter distribution; and it is non-trivial to estimate the detection significance and parameter uncertainties for individual voids. Our goal is to extract a catalog of voids from constrained simulations of the large-scale structure that are consistent with the observed galaxy positions, effectively representing statistically independent realizations of the probability distribution of the cosmic web. This allows us to carry out a full Bayesian analysis of the structures emerging in the Universe. We use 50 posterior realizations of the large-scale structure in the Manticore-Local suite, obtained from the 2M++ galaxies. Running the VIDE void finder on each realization, we extract 50 independent void catalogs. We perform a posterior clustering analysis to identify high-significance voids at the 5$\sigma$ level, and we assess the probability distribution of their properties. We produce a catalog of 100 voids with high statistical significance, available at https://voids.cosmictwin.org/, including the probability distributions of the centers and radii of the voids. We characterize the morphology of these regions, effectively producing a template for density environments that can be used in astrophysical applications such as galaxy evolution studies. While providing the community with a detailed catalog of voids in the nearby Universe, this work also constitutes an approach to identifying cosmic voids from galaxy surveys that allows us to account rigorously for the observational systematics intrinsic to direct detection, and provide a Bayesian characterization of their properties.
OpenDPDv2: A Unified Learning and Optimization Framework for Neural Network Digital Predistortion
2025-07-09
Yizhuo Wu, Ang Li, Chang Gao
Neural network (NN)-based Digital Predistortion (DPD) stands out in improving signal quality in wideband radio frequency (RF) power amplifiers (PAs) employing complex modulation. However, NN DPDs usually rely on a large number of parameters for effective linearization and can significantly contribute to the energy consumption of the digital back-end in RF systems. This paper presents OpenDPDv2, a unified framework for PA modeling, DPD learning, and model optimization to reduce power consumption while maintaining high linearization performance. The optimization techniques feature a novel DPD algorithm, TRes-DeltaGRU, alongside two energy-efficient methods. The top-performing 32-bit floating-point (FP32) TRes-DeltaGRU-DPD model achieves an Adjacent Channel Power Ratio (ACPR) of -59.4 dBc and Error Vector Magnitude (EVM) of -42.1 dBc. By exploiting fixed-point quantization and dynamic temporal sparsity of input signals and hidden neurons, the inference energy of our model can be reduced by 4.5X while still maintaining -50.3 dBc ACPR and -35.2 dB EVM with 56% temporal sparsity. This was evaluated using a TM3.1a 200 MHz bandwidth 256-QAM OFDM signal applied to a 3.5 GHz GaN Doherty RF PA. OpenDPDv2 code, datasets, and documentation are publicly accessible at: https://github.com/lab-emi/OpenDPD.
2025-07-09
Henrik Jeldtoft Jensen, Piergiulio Tempesta
Entropy can signify different things: For instance, heat transfer in thermodynamics or a measure of information in data analysis. Many entropies have been introduced and it can be difficult to ascertain their different importance and merits. Here we consider entropy in an abstract sense, as a functional on a probability space and we review how being able to handle the trivial case of non-interacting systems together with the subtle requirement of extensivity allows a systematic classification of the functional form.
Connecting the Unconnected -- Sentiment Analysis of Field Survey of Internet Connectivity in Emerging Economies
2025-07-09
Dibakar Das, Barath S Narayan, Aarna Bhammar, Jyotsna Bapat
Internet has significantly improved the quality of citizens across the world. Though the internet coverage is quite high, 40% of global population do not have access to broadband internet. This paper presents an analysis of a field survey of population in some areas of Kathmandu, Nepal, an emerging economy. This survey was triggered by intermittent severe congestion of internet in certain areas of the city. People from three different areas were asked about their present experience of internet usage, its impact on their lives and their aspirations for the future. Survey pointed to high speed, low cost, reliable and secure internet as a major aspiration of the respondents. Based on their inputs, this paper presents a sentiment analysis as well as demographic information. Keys insights from this analysis shows that overall sentiment to most queries are positive. The variances of positive sentiments are high whereas those for negative ones are low. Also, some correlations and clusters are observed among the attributes though no dominant component exists in the data.
ixi-GEN: Efficient Industrial sLLMs through Domain Adaptive Continual Pretraining
2025-07-09
Seonwu Kim, Yohan Na, Kihun Kim, Hanhee Cho, Geun Lim, Mintae Kim, Seongik Park, Ki Hyun Kim, Youngsub Han, Byoung-Ki Jeon
The emergence of open-source large language models (LLMs) has expanded opportunities for enterprise applications; however, many organizations still lack the infrastructure to deploy and maintain large-scale models. As a result, small LLMs (sLLMs) have become a practical alternative, despite their inherent performance limitations. While Domain Adaptive Continual Pretraining (DACP) has been previously explored as a method for domain adaptation, its utility in commercial applications remains under-examined. In this study, we validate the effectiveness of applying a DACP-based recipe across diverse foundation models and service domains. Through extensive experiments and real-world evaluations, we demonstrate that DACP-applied sLLMs achieve substantial gains in target domain performance while preserving general capabilities, offering a cost-efficient and scalable solution for enterprise-level deployment.
Better frame rates or better visuals? An early report of Esports player practice in Dota 2
2025-07-09
Arjun Madhusudan, Benjamin Watson
Esports athletes often reduce visual quality to improve latency and frame rate, and increase their in-game performance. Little research has examined the effects of this visuo-spatial tradeoff on performance, but we could find no work studying how players manage this tradeoff in practice. This paper is an initial examination of this question in the game Dota 2. First, we gather the game configuration data of Dota 2 players in a small survey. We learn that players do limit visual detail, particularly by turning off VSYNC, which removes rendering/display synchronization delay but permits visual "tearing". Second, we survey the intent of those same players with a few subjective questions. Player intent matches configuration practice. While our sampling of Dota 2 players may not be representative, our survey does reveal suggestive trends that lay the groundwork for future, more rigorous and larger surveys. Such surveys can help new players adapt to the game more quickly, encourage researchers to investigate the relative importance of temporal and visual detail, and justify design effort by developers in "low visual" game configurations.
Galaxy And Mass Assembly (GAMA): from filaments to voids, how extreme environment affects gas metallicity and SFR in galaxies
2025-07-09
J. A. Molina-Calzada, M. A. Lara-López, J. Gallego, A. M. Hopkins, Benne W. Holwerda, A. R. López-Sánchez
We analyse the stellar mass-metallicity (M-Z) and stellar mass-star formation rate (M-SFR) relations for star-forming galaxies classified by their environment and compare them with matched control samples of field galaxies. Using data from the Galaxy And Mass Assembly (GAMA) survey and the filament catalogue, which categorises galaxies into filaments, tendrils, and voids, we correct emission lines for dust extinction and select star-forming galaxies based on the BPT diagram. Metallicity and star formation rate are estimated and used to fit the M-Z and M-SFR relations through both Bayesian and least-squares approaches. We find that metallicity increases in denser environments, while star formation rate decreases, with the most notable contrasts seen between filament/tendril galaxies and those in voids. Galaxies in filaments and tendrils that are not group members show little to no deviation from their control samples. Morphological analysis reveals no significant differences. Overall, galaxies in denser environments appear more chemically enriched with lower SFRs, likely due to processed material and reduced cold gas availability, while isolated void galaxies maintain higher SFRs and lower metallicities, possibly due to ongoing cold gas accretion. These results suggest that local environmental conditions, rather than large-scale structure alone, are the main drivers of the observed trends.
Eclipse timing variations in 7 post-common envelope binaries: an update on recent circumbinary models
2025-07-09
D. Pulley, I. D. Sharp, J. Mallett, S. von Harrach
Eclipsing, short-period post-common envelope binaries (PCEBs) have been studied for several decades by eclipse timing variations (ETVs) which have been interpreted as being caused by circumbinary bodies. In this paper we report 355 new observations of 7 PECBs (HS0705+6700, NN Ser, NSVS 07826147, NSVS 14256825, NY Vir, QS Vir and RR Cae) and examine how the recent proposed models of these systems compare with our new observations. We find that none of the 18 recent models fit accurately with our data. We review alternative mechanisms of the ETVs, including magnetic effects, but conclude that they do not predict our results. Although we cannot exclude the presence of circumbinary bodies a combination of several mechanisms may be required to explain the observed ETVs.
Medical anomaly detection (AD) is challenging due to diverse imaging modalities, anatomical variations, and limited labeled data. We propose a novel approach combining visual adapters and prompt learning with Partial Optimal Transport (POT) and contrastive learning (CL) to improve CLIP's adaptability to medical images, particularly for AD. Unlike standard prompt learning, which often yields a single representation, our method employs multiple prompts aligned with local features via POT to capture subtle abnormalities. CL further enforces intra-class cohesion and inter-class separation. Our method achieves state-of-the-art results in few-shot, zero-shot, and cross-dataset scenarios without synthetic data or memory banks. The code is available at https://github.com/mahshid1998/MADPOT.
The EDGE-CALIFA survey: The effect of active galactic nucleus feedback on the integrated properties of galaxies at different stages of their evolution
2025-07-09
Z. Bazzi, D. Colombo, F. Bigiel, V. Kalinova, V. Villanueva, S. F. Sanchez, A. D. Bolatto, T. Wong
Galaxy quenching, the intricate process through which galaxies transition from active star-forming states to retired ones, remains a complex phenomenon that requires further investigation. This study investigates the role of active galactic nuclei (AGNs) in regulating star formation by analyzing a sample of 643 nearby galaxies with redshifts between 0.005 and 0.03 from the Calar Alto Legacy Integral Field Area (CALIFA) survey. Galaxies were classified according to the Quenching Stages and Nuclear Activity (QueStNA) scheme, which categorizes them based on their quenching stage and the presence of nuclear activity. We further utilized the integrated Extragalactic Database for Galaxy Evolution (iEDGE), which combined homogenized optical integral field unit and CO observations. This allowed us to examine how AGNs influence the molecular gas reservoirs of active galaxies compared to their non-active counterparts at similar evolutionary stages. Our Kolmogorov-Smirnov and chi-squared tests indicate that the star formation property distributions and scaling relations of AGN hosts are largely consistent with those of non-active galaxies. However, AGN hosts exhibit systematically higher molecular gas masses across all quenching stages except for the quiescent nuclear ring stage. We find that AGN hosts follow the expected trends of non-active quenching galaxies, characterized by a lower star formation efficiency and molecular gas fraction compared to star-forming galaxies. Our results suggest that signatures of instantaneous AGN feedback are not prominent in the global molecular gas and star formation properties of galaxies.
Computing Euler products and coefficients of classical modular forms for twisted L-functions
2025-07-09
Pascal Molin
We describe a complete algorithm to compute millions of coefficients of classical modular forms in a few seconds. We also review operations on Euler products and illustrate our methods with a computation of triple product L-function of large conductor.
Sustainability Transitions and Bending the Curve of Biodiversity Collapse in the Amazon Forest
2025-07-09
Romero-Goyeneche Oscar Yandy, Ramirez Matias, Osorio-Garcia Ana Milena, Harman Canalle Ursula
This paper undertakes an analysis of deforestation in the Amazon area using a pathways-based approach to sustainability. We ground the analysis primarily in the sustainability transitions literature but also draw a bridge with socio-ecological concepts which helps us to understand the nature of transitions in this context. The concept of a deforestation system is developed by examining the interplay of infrastructure, technologies, narratives, and institutions. Drawing on a literature review and an in-depth case study of Puerto Maldonado in Madre de Dios, Peru, the paper identifies three pathways for addressing deforestation: optimisation, natural capital, and regenerative change. We suggest that while the optimisation pathway provides partial solutions through mitigation and compensation strategies, it often reinforces extractivist logics. The study also underscores the limitations of natural capital frameworks, which tend to rely on centralised governance and market-based instruments while lacking broader social engagement. In contrast, our findings emphasise the potential of regenerative strategies rooted in local agency, community-led experimentation, and context-sensitive institutional arrangements. The paper contributes to ongoing debates on biodiversity governance by illustrating how the spatial and long-term dynamics of deforestation interact, and why inclusive, territorially grounded pathways are crucial for bending the curve of biodiversity loss.
Deep Disentangled Representation Network for Treatment Effect Estimation
2025-07-09
Hui Meng, Keping Yang, Xuyu Peng, Bo Zheng
Estimating individual-level treatment effect from observational data is a fundamental problem in causal inference and has attracted increasing attention in the fields of education, healthcare, and public policy.In this work, we concentrate on the study of disentangled representation methods that have shown promising outcomes by decomposing observed covariates into instrumental, confounding, and adjustment factors. However, most of the previous work has primarily revolved around generative models or hard decomposition methods for covariates, which often struggle to guarantee the attainment of precisely disentangled factors. In order to effectively model different causal relationships, we propose a novel treatment effect estimation algorithm that incorporates a mixture of experts with multi-head attention and a linear orthogonal regularizer to softly decompose the pre-treatment variables, and simultaneously eliminates selection bias via importance sampling re-weighting techniques. We conduct extensive experiments on both public semi-synthetic and real-world production datasets. The experimental results clearly demonstrate that our algorithm outperforms the state-of-the-art methods focused on individual treatment effects.
Expediting data extraction using a large language model (LLM) and scoping review protocol: a methodological study within a complex scoping review
2025-07-09
James Stewart-Evans, Emma Wilson, Tessa Langley, Andrew Prayle, Angela Hands, Karen Exley, Jo Leonardi-Bee
The data extraction stages of reviews are resource-intensive, and researchers may seek to expediate data extraction using online (large language models) LLMs and review protocols. Claude 3.5 Sonnet was used to trial two approaches that used a review protocol to prompt data extraction from 10 evidence sources included in a case study scoping review. A protocol-based approach was also used to review extracted data. Limited performance evaluation was undertaken which found high accuracy for the two extraction approaches (83.3% and 100%) when extracting simple, well-defined citation details; accuracy was lower (9.6% and 15.8%) when extracting more complex, subjective data items. Considering all data items, both approaches had precision >90% but low recall (<25%) and F1 scores (<40%). The context of a complex scoping review, open response types and methodological approach likely impacted performance due to missed and misattributed data. LLM feedback considered the baseline extraction accurate and suggested minor amendments: four of 15 (26.7%) to citation details and 8 of 38 (21.1%) to key findings data items were considered to potentially add value. However, when repeating the process with a dataset featuring deliberate errors, only 2 of 39 (5%) errors were detected. Review-protocol-based methods used for expediency require more robust performance evaluation across a range of LLMs and review contexts with comparison to conventional prompt engineering approaches. We recommend researchers evaluate and report LLM performance if using them similarly to conduct data extraction or review extracted data. LLM feedback contributed to protocol adaptation and may assist future review protocol drafting.
The Small Phase Condition is Necessary for Symmetric Systems
2025-07-09
Xiaokan Yang, Wei Chen, Li Qiu
In this paper, we show that the small phase condition is both sufficient and necessary to ensure the feedback stability when the interconnected systems are symmetric. Such symmetric systems arise in diverse applications. The key lies in that, for a complex symmetric and semi-sectorial matrix, the transformation matrix in its generalized sectorial decomposition can be taken to be real. Such a result fills the gap of phase based necessary condition for the feedback stability of symmetric systems, and serves as a counterpart of the necessity result for small gain condition. Moreover, we explore the necessity of small phase condition for general asymmetric systems. Some insightful results are presented, which help to clarify the main challenge in the general case.
Astro-COLIBRI: A Comprehensive Platform for Real-Time Multi-Messenger Astrophysics
2025-07-09
Fabian Schüssler, B. Cornejo, M. Costa, I. Jaroschewski, W. Kiendrébéogo
The detection of transient phenomena such as Gamma-Ray Bursts (GRBs), Fast Radio Bursts (FRBs), stellar flares, novae, and supernovae, alongside novel cosmic messengers like high-energy neutrinos and gravitational waves, has transformed astrophysics in recent years. Maximizing the discovery potential of multi-messenger and multi-wavelength follow-up observations, as well as serendipitous detections, requires a tool that rapidly compiles and contextualizes relevant information for each new event. We present Astro-COLIBRI, an advanced platform designed to meet this challenge. Astro-COLIBRI integrates a public RESTful API, real-time databases, a cloud-based alert system, and user-friendly clients (a website and mobile apps for iOS and Android). It processes astronomical alerts from multiple streams in real time, filtering them based on user-defined criteria and placing them in their multi-wavelength and multi-messenger context. The platform offers intuitive data visualization, a quick summary of relevant event properties, and an assessment of observing conditions at numerous observatories worldwide. We here describe its architecture, data resources, and main functionalities. We highlight the automatic collection of photometric data from a variety of large scale optical surveys, a recently added feature that significantly improves the capabilities of the Astro-COLIBRI platform.
Graph Learning for Cooperative Cell-Free ISAC Systems: From Optimization to Estimation
2025-07-09
Peng Jiang, Ming Li, Rang Liu, Qian Liu
Cell-free integrated sensing and communication (ISAC) systems have emerged as a promising paradigm for sixth-generation (6G) networks, enabling simultaneous high-rate data transmission and high-precision radar sensing through cooperative distributed access points (APs). Fully exploiting these capabilities requires a unified design that bridges system-level optimization with multi-target parameter estimation. This paper proposes an end-to-end graph learning approach to close this gap, modeling the entire cell-free ISAC network as a heterogeneous graph to jointly design the AP mode selection, user association, precoding, and echo signal processing for multi-target position and velocity estimation. In particular, we propose two novel heterogeneous graph learning frameworks: a dynamic graph learning framework and a lightweight mirror-based graph attention network (mirror-GAT) framework. The dynamic graph learning framework employs structural and temporal attention mechanisms integrated with a three-dimensional convolutional neural network (3D-CNN), enabling superior performance and robustness in cell-free ISAC environments. Conversely, the mirror-GAT framework significantly reduces computational complexity and signaling overhead through a bi-level iterative structure with share adjacency. Simulation results validate that both proposed graph-learning-based frameworks achieve significant improvements in multi-target position and velocity estimation accuracy compared to conventional heuristic and optimization-based designs. Particularly, the mirror-GAT framework demonstrates substantial reductions in computational time and signaling overhead, underscoring its suitability for practical deployments.
Illuminating the Future: Nanophotonics for Future Green Technologies, Precision Healthcare, and Optical Computing
2025-07-09
Osama M. Halawa, Esraa Ahmed, Malk M. Abdelrazek, Yasser M. Nagy, Omar A. M. Abdelraouf
Nanophotonics, an interdisciplinary field merging nanotechnology and photonics, has enabled transformative advancements across diverse sectors including green energy, biomedicine, and optical computing. This review comprehensively examines recent progress in nanophotonic principles and applications, highlighting key innovations in material design, device engineering, and system integration. In renewable energy, nanophotonic allows light-trapping nanostructures and spectral control in perovskite solar cells, concentrating solar power, and thermophotovoltaics. That have significantly enhanced solar conversion efficiencies, approaching theoretical limits. For biosensing, nanophotonic platforms achieve unprecedented sensitivity in detecting biomolecules, pathogens, and pollutants, enabling real-time diagnostics and environmental monitoring. Medical applications leverage tailored light-matter interactions for precision photothermal therapy, image-guided surgery, and early disease detection. Furthermore, nanophotonics underpins next-generation optical neural networks and neuromorphic computing, offering ultra-fast, energy-efficient alternatives to von Neumann architectures. Despite rapid growth, challenges in scalability, fabrication costs, and material stability persist. Future advancements will rely on novel materials, AI-driven design optimization, and multidisciplinary approaches to enable scalable, low-cost deployment. This review summarizes recent progress and highlights future trends, including novel material systems, multidisciplinary approaches, and enhanced computational capabilities, to pave the way for transformative applications in this rapidly evolving field.
The Flaws of Others: An LLM-driven Framework for Scientific Knowledge Production
2025-07-09
Juan B. Gutiérrez
Large-language models turn writing into a live exchange between humans and software. We capture this new medium with a discursive-network model that treats people and LLMs as equal nodes and tracks how their statements circulate. Broadening the focus from isolated hallucinations, we define invalidation (any factual, logical, or structural breach) and show it follows four hazards: drift from truth, self-repair, fresh fabrication, and external detection. A general mathematical model of discursive networks is developed to provide valuable insights: A network governed only by drift and self-repair stabilizes at a modest error rate; adding fabrication reproduces the high rates seen in current LLMs. Giving each false claim even a small chance of peer review shifts the system to a truth-dominant state. We operationalize peer review with the open-source \emph{Flaws-of-Others (FOO) algorithm}: a configurable loop in which any set of agents critique one another while a harmoniser merges their verdicts. The takeaway is practical and cultural: reliability in this new medium comes not from perfecting single models but from wiring imperfect ones into networks that keep each other honest.
2025-07-09
Ali Safarpoor Dehkordi, Ahad N. Zehmakan
In recent years, there has been a growing effort to develop effective and efficient algorithms for fake account detection in online social networks. This survey comprehensively reviews existing methods, with a focus on graph-based techniques that utilise topological features of social graphs (in addition to account information, such as their shared contents and profile data) to distinguish between fake and real accounts. We provide several categorisations of these methods (for example, based on techniques used, input data, and detection time), discuss their strengths and limitations, and explain how these methods connect in the broader context. We also investigate the available datasets, including both real-world data and synthesised models. We conclude the paper by proposing several potential avenues for future research.
Serum 25-hydroxyvitamin D concentration is not associated with mental health among Aboriginal and Torres Strait Islander Peoples in Australia: a cross-sectional exploratory study
2025-07-09
Belinda Neo, Noel Nannup, Dale Tilbrook, Carol Michie, Cindy Prior, Eleanor Dunlop, Brad Farrant, Won Sun Chen, Carrington C. J. Shepherd, Lucinda J. Black
Objective: To investigate the association between serum 25-hydroxyvitamin D [25(OH)D] concentration and mental health, measured using the Kessler Psychological Distress Scale 5 (K5), among Aboriginal and Torres Strait Islander Peoples. Methods: We used cross-sectional data from the 2012-2013 Australian Aboriginal and Torres Strait Islander Health Survey. Multiple linear regression was used to test the association between serum 25(OH)D concentration and K5, adjusting for age, sex, education, remoteness, socioeconomic status, season of blood collection, smoking, and alcohol intake (n = 1,983). We also stratified the analysis by sex and by remoteness. Results: There was no statistically significant association between serum 25(OH) concentration and K5 in the total population, nor when stratified by sex. When stratified by remoteness, higher serum 25(OH)D concentration was statistically significantly associated with lower K5 scores among those living remotely (adjusted \b{eta}: -0.18; 95% CI: -0.35, -0.01). Conclusions: Serum 25(OH)D concentration was inversely associated with psychological distress only among those living remotely. Implications for Public Health: Given the prevalence of vitamin D deficiency and the observed association between serum 25(OH)D concentration and psychological distress among Aboriginal and Torres Strait Islander Peoples living remotely, public health strategies to improve vitamin D status among this population group are warranted.
What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studies
2025-07-09
Yaoqi Huang, Julie Stephany Berrio, Mao Shan, Stewart Worrall
Advances in vision-based sensors and computer vision algorithms have significantly improved the analysis and understanding of traffic scenarios. To facilitate the use of these improvements for road safety, this survey systematically categorizes the critical elements that demand attention in traffic scenarios and comprehensively analyzes available vision-driven tasks and datasets. Compared to existing surveys that focus on isolated domains, our taxonomy categorizes attention-worthy traffic entities into two main groups that are anomalies and normal but critical entities, integrating ten categories and twenty subclasses. It establishes connections between inherently related fields and provides a unified analytical framework. Our survey highlights the analysis of 35 vision-driven tasks and comprehensive examinations and visualizations of 73 available datasets based on the proposed taxonomy. The cross-domain investigation covers the pros and cons of each benchmark with the aim of providing information on standards unification and resource optimization. Our article concludes with a systematic discussion of the existing weaknesses, underlining the potential effects and promising solutions from various perspectives. The integrated taxonomy, comprehensive analysis, and recapitulatory tables serve as valuable contributions to this rapidly evolving field by providing researchers with a holistic overview, guiding strategic resource selection, and highlighting critical research gaps.
In the past year, Generative Recommendations (GRs) have undergone substantial advancements, especially in leveraging the powerful sequence modeling and reasoning capabilities of Large Language Models (LLMs) to enhance overall recommendation performance. LLM-based GRs are forming a new paradigm that is distinctly different from discriminative recommendations, showing strong potential to replace traditional recommendation systems heavily dependent on complex hand-crafted features. In this paper, we provide a comprehensive survey aimed at facilitating further research of LLM-based GRs. Initially, we outline the general preliminaries and application cases of LLM-based GRs. Subsequently, we introduce the main considerations when LLM-based GRs are applied in real industrial scenarios. Finally, we explore promising directions for LLM-based GRs. We hope that this survey contributes to the ongoing advancement of the GR domain.
A Survey on Artificial Noise for Physical Layer Security: Opportunities, Technologies, Guidelines, Advances, and Trends
2025-07-09
Hong Niu, Yue Xiao, Xia Lei, Jiangong Chen, Zhihan Xiao, Mao Li, Chau Yuen
Due to the broadcast nature of wireless communications, physical-layer security has attracted increasing concerns from both academia and industry. Artificial noise (AN), as one of the promising physical-layer security techniques, is capable of utilizing the spatial degree-of-freedom of channels to effectively enhance the security of wireless communications. In contrast to other physicallayer security techniques, the key distinguishing feature of AN is to generate specific interfering signals according to channel characteristics, increasing the secrecy capacity by reducing the wiretap channel capacity without affecting the legitimate channel capacity. Hence, this paper provides the latest survey of AN, including its evolution, modeling, backgrounds, applications, and future trends. Initially, we introduce the development, fundamentals, and backgrounds of AN. Subsequently, we highlight a comprehensive survey of the current state of research on various AN-empowered scenarios and AN-combined technologies. Finally, we discuss some technical challenges to tackle for AN-aided wireless security in the future.
Energy-Efficient Supervised Learning with a Binary Stochastic Forward-Forward Algorithm
2025-07-09
Risi Jaiswal, Supriyo Datta, Joseph G. Makin
Reducing energy consumption has become a pressing need for modern machine learning, which has achieved many of its most impressive results by scaling to larger and more energy-consumptive neural networks. Unfortunately, the main algorithm for training such networks, backpropagation, poses significant challenges for custom hardware accelerators, due to both its serial dependencies and the memory footprint needed to store forward activations for the backward pass. Alternatives to backprop, although less effective, do exist; here the main computational bottleneck becomes matrix multiplication. In this study, we derive forward-forward algorithms for binary, stochastic units. Binarization of the activations transforms matrix multiplications into indexing operations, which can be executed efficiently in hardware. Stochasticity, combined with tied weights across units with different biases, bypasses the information bottleneck imposed by binary units. Furthermore, although slow and expensive in traditional hardware, binary sampling that is very fast can be implemented cheaply with p-bits (probabilistic bits), novel devices made up of unstable magnets. We evaluate our proposed algorithms on the MNIST, Fashion-MNIST, and CIFAR-10 datasets, showing that its performance is close to real-valued forward-forward, but with an estimated energy savings of about one order of magnitude.
2025-07-09
Brett M. Morris, Leslie Hebb, Suzanne L. Hawley, Kathryn Jones, Jake Romney
We present initial results from a planned 10 year survey of Ca II H & K emission, using observations made with the ARC 3.5m Telescope at Apache Point Observatory. The primary goal of the survey is to investigate activity cycles in low mass stars. The sample includes stars chosen from the legacy Mount Wilson survey carried out by Olin Wilson more than 50 years ago, together with newly identified planet-host stars and a select sample of early-mid M dwarfs. This paper presents the first four years of data, comprising 1040 observations of 271 stars, with a specific focus on K and M stars. We identify a subsample of 153 stars for continuing observations over the full 10 year survey. Early results indicate that our data are consistent with the MWO cycle periods over a time span of more than 50 years; that there is a bifurcation in activity in the late K range with separate populations of low and high activity stars at lower masses; and that M dwarf planet hosts tend to be mainly found in the population of low activity stars, even in the unbiased (by activity) TESS sample, potentially indicating a link between activity and planet formation. We have also found indications of possible cyclic variability in some of the lower mass stars in the sample. Our ultimate goal is to link the activity cycle and rotation periods in a robust sample of stars spanning FGKM spectral types and to investigate the implications for the underlying magnetic dynamo.
Superheating and melting phenomena of a vibrated granular layer of cubic particles
2025-07-09
Francisco López-González, Gustavo M. Rodríguez-Liñán, Fernando Donado, Felipe Pacheco Vázquez, Luis Fernando Elizondo-Aguilera
We report the combined results of experiments and molecular dynamics simulations conducted to investigate superheating phenomena in vertically vibrated granular matter. Specifically, we consider a system of cubic particles densely packed in a square-lattice array and subjected to different shaking strengths, denoted by Gamma, approaching a critical value Gamma_c. Below Gamma_c, the superheated crystalline array remains indefinitely stable. Above Gamma_c, it transitions progressively into a granular liquid-like state over a Gamma-dependent timescale tau. We show that while an increase in frictional contacts significantly prolongs the lifetime of the superheated crystalline state, it does not play a major role in the scaling laws governing how that lifetime depends on shaking strength. Our findings also show that the transition from the superheated solid to the liquid state in the vibrated system is well described by a Kolmogorov-Johnson-Mehl-Avrami (KJMA) equation, which is commonly used to model phase transformations in thermal systems.
Thermodynamic Prediction Enabled by Automatic Dataset Building and Machine Learning
2025-07-09
Juejing Liu, Haydn Anderson, Noah I. Waxman, Vsevolod Kovalev, Byron Fisher, Elizabeth Li, Xiaofeng Guo
New discoveries in chemistry and materials science, with increasingly expanding volume of requisite knowledge and experimental workload, provide unique opportunities for machine learning (ML) to take critical roles in accelerating research efficiency. Here, we demonstrate (1) the use of large language models (LLMs) for automated literature reviews, and (2) the training of an ML model to predict chemical knowledge (thermodynamic parameters). Our LLM-based literature review tool (LMExt) successfully extracted chemical information and beyond into a machine-readable structure, including stability constants for metal cation-ligand interactions, thermodynamic properties, and other broader data types (medical research papers, and financial reports), effectively overcoming the challenges inherent in each domain. Using the autonomous acquisition of thermodynamic data, an ML model was trained using the CatBoost algorithm for accurately predicting thermodynamic parameters (e.g., enthalpy of formation) of minerals. This work highlights the transformative potential of integrated ML approaches to reshape chemistry and materials science research.
Great Observatories Maturation: a Review of NASA Astrophysics Development Through Suborbital Rocket and Balloon Programs
2025-07-09
Drew M. Miles
The NASA Great Observatories Maturation Program is a development plan to efficiently and effectively develop large, strategic astrophysics missions. Suborbital rocket and balloon programs have long been a key development tool for enabling large missions in NASA astrophysics. We review the significance of these suborbital missions in the preceding decades to demonstrate their contributions to the Great Observatories Maturation Program for the Habitable Worlds Observatory and beyond. We show that suborbital instruments have obtained new science observations of astrophysical sources across the electromagnetic spectrum, matured high-priority component technologies, and served as a training ground for principal investigators of Explorer-class astrophysics satellites. A brief discussion of emerging CubeSat and SmallSat missions and their place in the NASA astrophysics portfolio is also provided.
A physics-informed neural network for modeling fracture without gradient damage: formulation, application, and assessment
2025-07-09
Aditya Konale, Vikas Srivastava
Accurate computational modeling of damage and fracture remains a central challenge in solid mechanics. The finite element method (FEM) is widely used for numerical modeling of fracture problems; however, classical damage models without gradient regularization yield mesh-dependent and usually inaccurate predictions. The use of gradient damage with FEM improves numerical robustness but introduces significant mathematical and numerical implementation complexities. Physics-informed neural networks (PINNs) can encode the governing partial differential equations, boundary conditions, and constitutive models into the loss functions, offering a new method for fracture modeling. Prior applications of PINNs have been limited to small-strain problems and have incorporated gradient damage formulation without a critical evaluation of its necessity. Since PINNs in their basic form are meshless, this work presents a PINN framework for modeling fracture in elastomers undergoing large deformation without the gradient damage formulation. The PINN implementation here does not require training data and utilizes the collocation method to formulate physics-informed loss functions. We have validated the PINN's predictions for various defect configurations using benchmark solutions obtained from FEM with gradient damage formulation. The crack paths obtained using the PINN are approximately insensitive to the collocation point distribution. This study offers new insights into the feasibility of using PINNs without gradient damage and suggests a simplified and efficient computational modeling strategy for fracture problems. The performance of the PINN has been evaluated through systematic variations in key neural network parameters to provide guidance for future applications. The results provide motivation for extending PINN-based approaches to a broader class of materials and damage models in mechanics.
The Open Cluster Chemical Abundances and Mapping Survey: VIII. Galactic Chemical Gradient and Azimuthal Analysis from SDSS/MWM DR19
2025-07-09
Jonah M. Otto, Peter M Frinchaboy, Natalie R. Myers, James W. Johnson, John Donor, Ahabar Hossain, Szabolcs Mészáros, Katia Cunha, Binod Bhattarai, Gail Zasowski, Sarah R. Loebman, Alessa I. Wiggins, Adrian M. Price-Whelan, Taylor Spoo, Diogo Souto, Dmitry Bizyaev, Kaike Pan, Andrew K. Saydjari
The Open Cluster Chemical Abundances and Mapping (OCCAM) survey seeks to curate a large, comprehensive, uniform dataset of open clusters and member stars to constrain key Galactic parameters. This eighth entry from the OCCAM survey, based on the newly released SDSS-V/MWM Data Release 19 (DR19), has established a sample of 164 high quality open clusters that are used to constrain the radial and azimuthal gradients of the Milky Way. The DR19 cluster sample [Fe/H] abundances are roughly consistent with measurements from other large-scale spectroscopic surveys. However, the gradients we calculate deviate considerably for some elements. We find an overall linear Galactic radial [Fe/H] gradient of $-0.075 \pm 0.006$ dex kpc$^{-1}$ using the cluster's current Galactocentric Radius ($R_{GC}$) and a gradient of $-0.068 \pm 0.005$ dex kpc$^-1$ with respect to the cluster's guiding center radius. We do not find strong evidence for significant evolution of the differential element gradients ([X/Fe]) investigated here (O, Mg, Si, S, Ca, Ti, Cr, Mn, Fe, Co, Ni, Na, Al, K, Ce, Nd). For the first time using the OCCAM sample we have sufficient numbers of clusters to investigate Galactic azimuthal variations. In this work, we do find evidence of azimuthal variations in the measured radial abundance gradient in the Galactic disk using our open cluster sample.
Robust Multimodal Learning Framework For Intake Gesture Detection Using Contactless Radar and Wearable IMU Sensors
2025-07-09
Chunzhuo Wang, Hans Hallez, Bart Vanrumste
Automated food intake gesture detection plays a vital role in dietary monitoring, enabling objective and continuous tracking of eating behaviors to support better health outcomes. Wrist-worn inertial measurement units (IMUs) have been widely used for this task with promising results. More recently, contactless radar sensors have also shown potential. This study explores whether combining wearable and contactless sensing modalities through multimodal learning can further improve detection performance. We also address a major challenge in multimodal learning: reduced robustness when one modality is missing. To this end, we propose a robust multimodal temporal convolutional network with cross-modal attention (MM-TCN-CMA), designed to integrate IMU and radar data, enhance gesture detection, and maintain performance under missing modality conditions. A new dataset comprising 52 meal sessions (3,050 eating gestures and 797 drinking gestures) from 52 participants is developed and made publicly available. Experimental results show that the proposed framework improves the segmental F1-score by 4.3% and 5.2% over unimodal Radar and IMU models, respectively. Under missing modality scenarios, the framework still achieves gains of 1.3% and 2.4% for missing radar and missing IMU inputs. This is the first study to demonstrate a robust multimodal learning framework that effectively fuses IMU and radar data for food intake gesture detection.
As machine learning models become increasingly deployed across the edge of internet of things environments, a partitioned deep learning paradigm in which models are split across multiple computational nodes introduces a new dimension of security risk. Unlike traditional inference setups, these distributed pipelines span the model computation across heterogeneous nodes and communication layers, thereby exposing a broader attack surface to potential adversaries. Building on these motivations, this work explores a previously overlooked vulnerability: even when both the edge and cloud components of the model are inaccessible (i.e., black-box), an adversary who intercepts the intermediate features transmitted between them can still pose a serious threat. We demonstrate that, under these mild and realistic assumptions, an attacker can craft highly transferable proxy models, making the entire deep learning system significantly more vulnerable to evasion attacks. In particular, the intercepted features can be effectively analyzed and leveraged to distill surrogate models capable of crafting highly transferable adversarial examples against the target model. To this end, we propose an exploitation strategy specifically designed for distributed settings, which involves reconstructing the original tensor shape from vectorized transmitted features using simple statistical analysis, and adapting surrogate architectures accordingly to enable effective feature distillation. A comprehensive and systematic experimental evaluation has been conducted to demonstrate that surrogate models trained with the proposed strategy, i.e., leveraging intermediate features, tremendously improve the transferability of adversarial attacks. These findings underscore the urgent need to account for intermediate feature leakage in the design of secure distributed deep learning systems.
Label-Efficient Chest X-ray Diagnosis via Partial CLIP Adaptation
2025-07-09
Heet Nitinkumar Dalsania
Modern deep learning implementations for medical imaging usually rely on large labeled datasets. These datasets are often difficult to obtain due to privacy concerns, high costs, and even scarcity of cases. In this paper, a label-efficient strategy is proposed for chest X-ray diagnosis that seeks to reflect real-world hospital scenarios. The experiments use the NIH Chest X-ray14 dataset and a pre-trained CLIP ViT-B/32 model. The model is adapted via partial fine-tuning of its visual encoder and then evaluated using zero-shot and few-shot learning with 1-16 labeled examples per disease class. The tests demonstrate that CLIP's pre-trained vision-language features can be effectively adapted to few-shot medical imaging tasks, achieving over 20\% improvement in mean AUC score as compared to the zero-shot baseline. The key aspect of this work is to attempt to simulate internal hospital workflows, where image archives exist but annotations are sparse. This work evaluates a practical and scalable solution for both common and rare disease diagnosis. Additionally this research is intended for academic and experimental purposes only and has not been peer reviewed yet. All code is found at https://github.com/heet007-code/CLIP-disease-xray.
Large language models (LLMs) often behave inconsistently across inputs, indicating uncertainty and motivating the need for its quantification in high-stakes settings. Prior work on calibration and uncertainty quantification often focuses on individual models, overlooking the potential of model diversity. We hypothesize that LLMs make complementary predictions due to differences in training and the Zipfian nature of language, and that aggregating their outputs leads to more reliable uncertainty estimates. To leverage this, we propose MUSE (Multi-LLM Uncertainty via Subset Ensembles), a simple information-theoretic method that uses Jensen-Shannon Divergence to identify and aggregate well-calibrated subsets of LLMs. Experiments on binary prediction tasks demonstrate improved calibration and predictive performance compared to single-model and naive ensemble baselines.
Spectroscopic Orbits of Subsystems in Multiple Stars. XI
2025-07-09
Andrei Tokovinin
Adding to the large radial velocity survey of nearby solar-type stars (summary in Tokovinin, 2023a), spectroscopic orbits are determined for four hierarchical systems: HIP 49442 (inner and outer periods of 164.55 d and 34 yr, respectively), HIP 55691 (2.4 and 415 yr), HIP 61465 (86.8 d), and HIP 78662C (0.82 d). Each system is discussed individually. Seven Gaia orbits of low-mass dwarfs, each with two additional resolved (interferometric and wide) companions, i.e. potential quadruples, are tested by monitoring radial velocities; five orbits are confirmed and two are refuted. Five of these systems are quadruples of 3+1 hierarchy, one is quintuple, and one is triple. Strengths and limitations of the Gaia data on multiple systems and the need of complementary observations are highlighted.
Efficient Parametric SVD of Koopman Operator for Stochastic Dynamical Systems
2025-07-09
Minchan Jeong, J. Jon Ryu, Se-Young Yun, Gregory W. Wornell
The Koopman operator provides a principled framework for analyzing nonlinear dynamical systems through linear operator theory. Recent advances in dynamic mode decomposition (DMD) have shown that trajectory data can be used to identify dominant modes of a system in a data-driven manner. Building on this idea, deep learning methods such as VAMPnet and DPNet have been proposed to learn the leading singular subspaces of the Koopman operator. However, these methods require backpropagation through potentially numerically unstable operations on empirical second moment matrices, such as singular value decomposition and matrix inversion, during objective computation, which can introduce biased gradient estimates and hinder scalability to large systems. In this work, we propose a scalable and conceptually simple method for learning the top-k singular functions of the Koopman operator for stochastic dynamical systems based on the idea of low-rank approximation. Our approach eliminates the need for unstable linear algebraic operations and integrates easily into modern deep learning pipelines. Empirical results demonstrate that the learned singular subspaces are both reliable and effective for downstream tasks such as eigen-analysis and multi-step prediction.
A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality
2025-07-09
Mohamed Elmoghany, Ryan Rossi, Seunghyun Yoon, Subhojyoti Mukherjee, Eslam Bakr, Puneet Mathur, Gang Wu, Viet Dac Lai, Nedim Lipka, Ruiyi Zhang, Varun Manjunatha, Chien Nguyen, Daksh Dangi, Abel Salinas, Mohammad Taesiri, Hongjie Chen, Xiaolei Huang, Joe Barrow, Nesreen Ahmed, Hoda Eldardiry, Namyong Park, Yu Wang, Jaemin Cho, Anh Totti Nguyen, Zhengzhong Tu, Thien Nguyen, Dinesh Manocha, Mohamed Elhoseiny, Franck Dernoncourt
Despite the significant progress that has been made in video generative models, existing state-of-the-art methods can only produce videos lasting 5-16 seconds, often labeled "long-form videos". Furthermore, videos exceeding 16 seconds struggle to maintain consistent character appearances and scene layouts throughout the narrative. In particular, multi-subject long videos still fail to preserve character consistency and motion coherence. While some methods can generate videos up to 150 seconds long, they often suffer from frame redundancy and low temporal diversity. Recent work has attempted to produce long-form videos featuring multiple characters, narrative coherence, and high-fidelity detail. We comprehensively studied 32 papers on video generation to identify key architectural components and training strategies that consistently yield these qualities. We also construct a comprehensive novel taxonomy of existing methods and present comparative tables that categorize papers by their architectural designs and performance characteristics.
Introduction to Sachdev-Ye-Kitaev Model: A Strongly Correlated System Perspective
2025-07-09
Rishabh Jha
The Sachdev-Ye-Kitaev (SYK) model delivers an analytically tractable framework for exotic strongly correlated phases where conventional paradigms like Landau's Fermi liquid theory collapse. This review offers a pedagogical introduction to the SYK physics, highlighting its unique capacity to model \textit{strange metals} -- systems exhibiting linear-in-temperature resistivity, Planckian dissipation, and quasiparticle breakdown. We systematically construct both Majorana and complex fermion variants, transforming them into training grounds for modern many-body physics techniques, for instance, (1) large-$N$ formulations via disorder averaging and replica symmetry, (2) Schwinger-Dyson and Kadanoff-Baym equations, (3) imaginary time Matsubara formulation, (4) real-time dynamics via Keldysh formalism, and the associated (5) non-perturbative Keldysh contour deformations. These tools lay the foundation for equilibrium thermodynamics, quantum chaos, quench dynamics, and transport in the thermodynamic limit -- all within a solvable, chaotic quantum system. Designed as a self-contained resource, the review bridges advanced technical machinery to physical insights, with computational implementations provided. Though principally treating the SYK model as a condensed matter laboratory, we also highlight its profound connection to quantum gravity -- woven throughout this work -- underscoring how this solvable chaotic fermionic model serves as a lens onto black hole thermodynamics and holographic duality.
Quantum algorithms for estimating the ground state energy of a quantum system often operate by preparing a classically accessible quantum state and then applying quantum phase estimation. Whether this approach yields quantum advantage hinges on the state's energy spectrum, that is, the sequence of the state's overlaps with the energy eigenstates of the system Hamiltonian. For any entanglement-compressed quantum state with minimal expected energy, it is shown that its energy spectrum decays at most with the inverse-squared energy eigenvalues. This explains the main empirical finding of Silvester, Carleo, and White (Physical Review Letters, 2025) that the energy spectra of matrix product states do not decay exponentially. It also reduces the question of quantum advantage to the energy and entanglement profile of the Hamiltonian's eigenstates.
Prompt Perturbations Reveal Human-Like Biases in LLM Survey Responses
2025-07-09
Jens Rupprecht, Georg Ahnert, Markus Strohmaier
Large Language Models (LLMs) are increasingly used as proxies for human subjects in social science surveys, but their reliability and susceptibility to known response biases are poorly understood. This paper investigates the response robustness of LLMs in normative survey contexts -- we test nine diverse LLMs on questions from the World Values Survey (WVS), applying a comprehensive set of 11 perturbations to both question phrasing and answer option structure, resulting in over 167,000 simulated interviews. In doing so, we not only reveal LLMs' vulnerabilities to perturbations but also reveal that all tested models exhibit a consistent \textit{recency bias} varying in intensity, disproportionately favoring the last-presented answer option. While larger models are generally more robust, all models remain sensitive to semantic variations like paraphrasing and to combined perturbations. By applying a set of perturbations, we reveal that LLMs partially align with survey response biases identified in humans. This underscores the critical importance of prompt design and robustness testing when using LLMs to generate synthetic survey data.
Accurate and efficient simulation-based emulators are essential for interpreting cosmological survey data down to nonlinear scales. Multifidelity emulation techniques reduce simulation costs by combining high- and low-fidelity data, but traditional regression methods such as Gaussian processes struggle with scalability in sample size and dimensionality. In this work, we present T2N-MusE, a neural network framework characterized by (i) a novel 2-step multifidelity architecture, (ii) a 2-stage Bayesian hyperparameter optimization, (iii) a 2-phase $k$-fold training strategy, and (iv) a per-$z$ principal component analysis strategy. We apply T2N-MusE to selected data from the Goku simulation suite, covering a 10-dimensional cosmological parameter space, and build emulators for the matter power spectrum over a range of redshifts with different configurations. We find the emulators outperform our earlier Gaussian process models significantly and demonstrate that each of these techniques is efficient in training neural networks or/and effective in improving generalization accuracy. We observe a reduction in validation error by more than a factor of five compared to previous work. This framework has been used to build the most powerful emulator for the matter power spectrum, GokuNEmu, and will also be used to construct emulators for other statistics in future.
Radius valley scaling among low mass stars with TESS
2025-07-09
Harshitha M. Parashivamurthy, Gijs D. Mulders
The Transiting Exoplanet Survey Satellite (TESS) has been highly successful in detecting planets in close orbits around low-mass stars, particularly M dwarfs. This presents a valuable opportunity to conduct detailed population studies to understand how these planets depend on the properties of their host stars. The previously observed radius valley in Sun-like stars has not been unambiguously detected among M dwarfs, and how its properties varies when compared with more massive stars remains uncertain. We use a volume-limited sample of low mass stars with precise photometric stellar parameters from the bioverse catalog of TESS Objects of Interest (TOIs) confirmed planets and candidates within 120 pc. We detect the radius valley around M dwarfs at a location of 1.64 $\pm$ 0.03 $R_{\oplus}$ and with a depth of approximately 45${\%}$. The radius valley among GKM stars scales with stellar mass as $R_p \propto M_*^{0.15\pm 0.04}$. The slope is consistent, within 0.3$\sigma$, with those around Sun-like stars. For M dwarfs, the discrepancy is 3.6$\sigma$ with the extrapolated slope from the Kepler FGK sample, marking the point where the deviation from previous results begins. Moreover, we do not see a clear shift in the radius valley between early and mid M dwarfs. The flatter scaling of the radius valley for lower-mass stars suggests that mechanisms other than atmospheric mass loss through photoevaporation may shape the radius distribution of planets around M dwarfs. Comparison of the slope with various planet formation and evolution models matches well with pebble accretion models including waterworlds, indicating a potentially different regime of planet formation that can be probed with exoplanets around the lowest mass stars.
A Cosmic Amplification for Muon-to-Positron Conversion in Nuclei
2025-07-09
Purushottam Sahu, Manibrata Sen
We present an analysis of the lepton-number and lepton-flavour-violating process of muon-to-positron conversion $\mu^- + N \rightarrow e^+ + N'$, in the presence of an ultralight scalar dark matter (ULSDM) field which couples to neutrinos. The ULSDM contributes to the effective off-diagonal Majorana mass $ m_{\mu e}$, therefore amplifying the rate of muon-to-positron conversion to experimentally observable levels. Using existing bounds from SINDRUM II, COMET, and Mu2e experiments, we derive constraints on the flavour-off-diagonal couplings of neutrinos to ULSDM for the first time. Our work reveals that upcoming experiments can provide stronger sensitivity to these new couplings than bounds arising from cosmological surveys and terrestrial experiments.
We present GokuNEmu, a ten-dimensional neural network emulator for the nonlinear matter power spectrum, designed to support next-generation cosmological analyses. Built on the Goku $N$-body simulation suite and the T2N-MusE emulation framework, GokuNEmu predicts the matter power spectrum with $\sim 0.5 \%$ average accuracy for redshifts $0 \leq z \leq 3$ and scales $0.006 \leq k/(h\,\mathrm{Mpc}^{-1}) \leq 10$. The emulator models a 10D parameter space that extends beyond $\Lambda$CDM to include dynamical dark energy (characterized by $w_0$ and $w_a$), massive neutrinos ($\sum m_\nu$), the effective number of neutrinos ($N_\text{eff}$), and running of the spectral index ($\alpha_\text{s}$). Its broad parameter coverage, particularly for the extensions, makes it the only matter power spectrum emulator capable of testing recent dynamical dark energy constraints from DESI. In addition, it requires only $\sim $2 milliseconds to predict a single cosmology on a laptop, orders of magnitude faster than existing emulators. These features make GokuNEmu a uniquely powerful tool for interpreting observational data from upcoming surveys such as LSST, Euclid, the Roman Space Telescope, and CSST.
The Orbital Eccentricity-Radius Relation for Planets Orbiting M Dwarfs
2025-07-09
Sheila Sagear, Sarah Ballard, Gregory J. Gilbert, Mariangel Albornoz, Christopher Lam
The orbital eccentricity-radius relation for small planets is indicative of the predominant dynamical sculpting processes during late-stage orbital evolution. Previous studies have shown that planets orbiting Sun-like stars exhibit an eccentricity-radius trend such that larger planets have higher orbital eccentricities, and that radius gap planets may have modestly higher orbital eccentricities than planets on either side of the radius gap. In this work, we investigate the trend for a sample of smaller M dwarf stars. For a sample of 236 single- and multi-transit confirmed planets or candidates discovered by the TESS and Kepler missions, we constrain orbital eccentricity for each planet from the transit photometry together with a stellar density prior. We investigate the binned eccentricity-planet radius relation for the combined planet sample and present evidence for a positive eccentricity-radius relationship with elevated eccentricities for planets larger than 3.5 R_earth, similar to the trend for planets orbiting Sun-like stars. We find modest evidence that single-transit M dwarf planets near the radius gap exhibit higher eccentricity, consistent with trends for Sun-like stars. However, we see no evidence for an increased eccentricity near the radius gap among multi-transit M dwarf planets. We discuss implications for these results in the context of predominant atmospheric loss mechanisms: namely, supporting evidence for photoevaporation in M dwarf planets vs. planet-planet collisions or giant impacts in FGK dwarf planets.
Correlations between Dust Extinction Features across All Wavelength Scales: From Diffuse Interstellar Bands to R(V)
2025-07-09
Andrew K. Saydjari, Gregory M. Green
Understanding variations in the dust extinction curve is imperative for using dust as a tracer of local structure in the interstellar medium, understanding dust chemistry, and observational color corrections where dust is a nuisance parameter. However, the extinction curve is complicated and exhibits features across a wide range of wavelength scales, from narrow atomic lines and diffuse interstellar bands ("DIBs"), to intermediate-scale and very broad structures ("ISS" and "VBS"), and the overall slope of the optical extinction curve, parameterized by R(V). Robust, population-level studies of variations in these features are only now possible with large, all-sky, spectroscopic surveys. However, these features are often studied independently because they require drastically different spectral resolution. In this work, we couple features with disparate wavelength scales by cross-matching precision catalogs of DIB measurements from APOGEE and Gaia RVS with low-resolution extinction-curve measurements from Gaia XP. Using this combination, we show that there are meaningful correlations between the strengths of extinction-curve features across all wavelength scales. We present a model that statistically explains part of the excess scatter in DIB strength versus extinction, and we show variation in line shapes of two DIBs as a function of R(V). We find that most DIBs increase in strength with increasing R(V) and/or increasing strength of the ISS, though we found one DIB that anomalously decreases in strength with increasing R(V). Using the behavior of the ensemble of DIBs in APOGEE, we present this as the first evidence of systematic chemical variation accompanying R(V) variation.
Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey
2025-07-09
Getamesay Haile Dagnaw, Yanming Zhu, Muhammad Hassan Maqsood, Wencheng Yang, Xingshuai Dong, Xuefei Yin, Alan Wee-Chung Liew
Explainable artificial intelligence (XAI) has become increasingly important in biomedical image analysis to promote transparency, trust, and clinical adoption of DL models. While several surveys have reviewed XAI techniques, they often lack a modality-aware perspective, overlook recent advances in multimodal and vision-language paradigms, and provide limited practical guidance. This survey addresses this gap through a comprehensive and structured synthesis of XAI methods tailored to biomedical image analysis.We systematically categorize XAI methods, analyzing their underlying principles, strengths, and limitations within biomedical contexts. A modality-centered taxonomy is proposed to align XAI methods with specific imaging types, highlighting the distinct interpretability challenges across modalities. We further examine the emerging role of multimodal learning and vision-language models in explainable biomedical AI, a topic largely underexplored in previous work. Our contributions also include a summary of widely used evaluation metrics and open-source frameworks, along with a critical discussion of persistent challenges and future directions. This survey offers a timely and in-depth foundation for advancing interpretable DL in biomedical image analysis.
AblationBench: Evaluating Automated Planning of Ablations in Empirical AI Research
2025-07-09
Talor Abramovich, Gal Chechik
Autonomous agents built on language models (LMs) are showing increasing popularity in many fields, including scientific research. AI co-scientists aim to support or automate parts of the research process using these agents. A key component of empirical AI research is the design of ablation experiments. To this end, we introduce AblationBench, a benchmark suite for evaluating agents on ablation planning tasks in empirical AI research. It includes two tasks: AuthorAblation, which helps authors propose ablation experiments based on a method section and contains 83 instances, and ReviewerAblation, which helps reviewers find missing ablations in a full paper and contains 350 instances. For both tasks, we develop LM-based judges that serve as an automatic evaluation framework. Our experiments with frontier LMs show that these tasks remain challenging, with the best-performing LM system identifying only 29% of the original ablations on average. Lastly, we analyze the limitations of current LMs on these tasks, and find that chain-of-thought prompting outperforms the currently existing agent-based approach.
Medical Visual Question Answering (MedVQA) is a promising tool to assist radiologists by automating medical image interpretation through question answering. Despite advances in models and datasets, MedVQA's integration into clinical workflows remains limited. This study systematically reviews 68 publications (2018-2024) and surveys 50 clinicians from India and Thailand to examine MedVQA's practical utility, challenges, and gaps. Following the Arksey and O'Malley scoping review framework, we used a two-pronged approach: (1) reviewing studies to identify key concepts, advancements, and research gaps in radiology workflows, and (2) surveying clinicians to capture their perspectives on MedVQA's clinical relevance. Our review reveals that nearly 60% of QA pairs are non-diagnostic and lack clinical relevance. Most datasets and models do not support multi-view, multi-resolution imaging, EHR integration, or domain knowledge, features essential for clinical diagnosis. Furthermore, there is a clear mismatch between current evaluation metrics and clinical needs. The clinician survey confirms this disconnect: only 29.8% consider MedVQA systems highly useful. Key concerns include the absence of patient history or domain knowledge (87.2%), preference for manually curated datasets (51.1%), and the need for multi-view image support (78.7%). Additionally, 66% favor models focused on specific anatomical regions, and 89.4% prefer dialogue-based interactive systems. While MedVQA shows strong potential, challenges such as limited multimodal analysis, lack of patient context, and misaligned evaluation approaches must be addressed for effective clinical integration.
Large Language Models (LLMs) have recently been applied to reranking tasks in information retrieval, achieving strong performance. However, their high computational demands often hinder practical deployment. Existing studies evaluate the efficiency of LLM-based rerankers using proxy metrics such as latency, the number of forward passes, input tokens, and output tokens. However, these metrics depend on hardware and running-time choices (\eg parallel or not, batch size, etc), and often fail to account for model size, making it difficult to interpret and obscuring the evaluation of the efficiency-effectiveness tradeoff. To address this issue, we propose E\textsuperscript{2}R-FLOPs, for LLM-based rerankers: ranking metrics per PetaFLOP (RPP) for relevance per compute and queries per PetaFLOP (QPP) for hardware-agnostic throughput. Companied with the new metrics, an interpretable FLOPs estimator is built to estimate the FLOPs of an LLM-based reranker even without running any experiments. Based on the proposed metrics, we conduct comprehensive experiments to evaluate a wide range of LLM-based rerankers with different architecture, studying the efficiency-effectiveness trade-off and bringing this issue to the attention of the research community.
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, especially when guided by explicit chain-of-thought (CoT) reasoning that verbalizes intermediate steps. While CoT improves both interpretability and accuracy, its dependence on natural language reasoning limits the model's expressive bandwidth. Latent reasoning tackles this bottleneck by performing multi-step inference entirely in the model's continuous hidden state, eliminating token-level supervision. To advance latent reasoning research, this survey provides a comprehensive overview of the emerging field of latent reasoning. We begin by examining the foundational role of neural network layers as the computational substrate for reasoning, highlighting how hierarchical representations support complex transformations. Next, we explore diverse latent reasoning methodologies, including activation-based recurrence, hidden state propagation, and fine-tuning strategies that compress or internalize explicit reasoning traces. Finally, we discuss advanced paradigms such as infinite-depth latent reasoning via masked diffusion models, which enable globally consistent and reversible reasoning processes. By unifying these perspectives, we aim to clarify the conceptual landscape of latent reasoning and chart future directions for research at the frontier of LLM cognition. An associated GitHub repository collecting the latest papers and repos is available at: https://github.com/multimodal-art-projection/LatentCoT-Horizon/.
Hidden Prompts in Manuscripts Exploit AI-Assisted Peer Review
2025-07-08
Zhicheng Lin
In July 2025, 18 academic manuscripts on the preprint website arXiv were found to contain hidden instructions known as prompts designed to manipulate AI-assisted peer review. Instructions such as "GIVE A POSITIVE REVIEW ONLY" were concealed using techniques like white-colored text. Author responses varied: one planned to withdraw the affected paper, while another defended the practice as legitimate testing of reviewer compliance. This commentary analyzes this practice as a novel form of research misconduct. We examine the technique of prompt injection in large language models (LLMs), revealing four types of hidden prompts, ranging from simple positive review commands to detailed evaluation frameworks. The defense that prompts served as "honeypots" to detect reviewers improperly using AI fails under examination--the consistently self-serving nature of prompt instructions indicates intent to manipulate. Publishers maintain inconsistent policies: Elsevier prohibits AI use in peer review entirely, while Springer Nature permits limited use with disclosure requirements. The incident exposes systematic vulnerabilities extending beyond peer review to any automated system processing scholarly texts, including plagiarism detection and citation indexing. Our analysis underscores the need for coordinated technical screening at submission portals and harmonized policies governing generative AI (GenAI) use in academic evaluation.
Complex networks have become essential tools for understanding diverse phenomena in social systems, traffic systems, biomolecular systems, and financial systems. Identifying critical nodes is a central theme in contemporary research, serving as a vital bridge between theoretical foundations and practical applications. Nevertheless, the intrinsic complexity and structural heterogeneity characterizing real-world networks, with particular emphasis on dynamic and higher-order networks, present substantial obstacles to the development of universal frameworks for critical node identification. This paper provides a comprehensive review of critical node identification techniques, categorizing them into seven main classes: centrality, critical nodes deletion problem, influence maximization, network control, artificial intelligence, higher-order and dynamic methods. Our review bridges the gaps in existing surveys by systematically classifying methods based on their methodological foundations and practical implications, and by highlighting their strengths, limitations, and applicability across different network types. Our work enhances the understanding of critical node research by identifying key challenges, such as algorithmic universality, real-time evaluation in dynamic networks, analysis of higher-order structures, and computational efficiency in large-scale networks. The structured synthesis consolidates current progress and highlights open questions, particularly in modeling temporal dynamics, advancing efficient algorithms, integrating machine learning approaches, and developing scalable and interpretable metrics for complex systems.
Impact of flavour coupling on $SO(10)$-inspired leptogenesis
2025-07-08
Pasquale Di Bari, Xubin Hu
We discuss the impact of flavour coupling on the predictions of low energy neutrino parameters from $SO(10)$-inspired leptogenesis (SO10INLEP). The right-handed (RH) neutrino mass spectrum is strongly hierarchical and successful leptogenesis relies on generating the asymmetry from next-to-lightest RH neutrino decays ($N_2$-leptogenesis) and circumventing the lightest RH neutrino washout. These two conditions yield distinctive predictions such as a lower bound on the lightest neutrino mass $m_1 \gtrsim 1\,{\rm meV}$. We first review the status of SO10INLEP, noticing how cosmological observations are now testing a particular neutrino mass window, $m_1 \simeq (10$--$30)\,{\rm meV}$, where only the first octant is allowed and a large range of values for the Dirac phase is excluded. Including flavour coupling, we find that the lower bound relaxes to $m_1 \gtrsim 0.65\,{\rm meV}$. Moreover, new muon-dominated solutions appear slightly relaxing the upper bound on the atmospheric mixing angle. We also study the impact on strong thermal SO10INLEP (ST-SO10INLEP) scenario where, in addition to successful leptogenesis, one can washout a large pre-existing asymmetry. Contrarily to naive expectations, for which flavour coupling could jeopardise the scenario, allowing a large pre-existing asymmetry to survive unconditionally, we show, and explain analytically, that ST-SO10INLEP is still viable within almost the same allowed region of parameters. There is even a slight relaxation of the $m_1$ viable window from (9--30)meV to (4--40)meV for a $10^{-3}$ pre-existing asymmetry. The new results from atmospheric neutrinos, mildly favouring normal ordering and first octant, are now in nice agreement with the predictions of ST-SO10INLEP. Intriguingly, the predicted $0\nu\beta\beta$ signal is starting to be within the reach of KamLAND-Zen.
2025-07-08
Nicola Schneider, Volker Ossenkopf-Okada, Markus Roellig, Daniel Seifried, Ralf S. Klessen, Alexei G. Kritsuk, Eduard Keilmann, Simon Dannhauer, Lars Bonne, Simon C. O. Glover
In recent decades, significant attention has been dedicated to analytical and observational studies of the atomic hydrogen (HI) to molecular hydrogen (H2) transition in the interstellar medium. We focussed on the Draco diffuse cloud to gain deeper insights into the physical properties of the transition from HI to H2. We employed the total hydrogen column density probability distribution function (N-PDF) derived from Herschel dust observations and the N(HI)-PDF obtained from HI data collected by the Effelsberg HI survey. The N-PDF of the Draco cloud exhibits a double-log-normal distribution, whereas the N(HI)-PDF follows a single log-normal distribution. The HI-to-H2 transition is identified as the point where the two log-normal components of the dust N-PDF contribute equally; it occurs at Av = 0.33 (N=6.2e20 cm^-2). The low-column-density segment of the dust N-PDF corresponds to the cold neutral medium, which is characterized by a temperature of around 100 K. The higher-column-density part is predominantly associated with H2. The shape of the Draco N-PDF is qualitatively reproduced by numerical simulations. In the absence of substantial stellar feedback, such as radiation or stellar winds, turbulence exerts a significant influence on the thermal stability of the gas and can regulate the condensation of gas into denser regions and its subsequent evaporation. Recent observations of the ionized carbon line at 158 micron in Draco support this scenario. Using the KOSMA-tau photodissociation model, we estimate a gas density of n=50 cm^-3 and a temperature of 100 K at the location of the HI-to-H2 transition. Both the molecular and atomic gas components are characterized by supersonic turbulence and strong mixing, suggesting that simplified steady-state chemical models are not applicable under these conditions.
In star-forming disk galaxies, the radio continuum emission (1-10 GHz) powered by star formation has an integral polarization direction imperfectly aligned with the apparent disk minor axis. This polarization-shape alignment effect was previously observed in a small sample of local spirals. If this is prevalent for disk galaxies out to cosmological redshifts, novel measurements of cosmic birefringence and cosmic shear will be enabled by leveraging radio continuum surveys such as the Square Kilometre Array synergized with galaxy shape measurements. We calculate the polarization-shape misalignment angle for star-forming galaxies in the IllustrisTNG50 simulation at 0 < z < 2, assuming that additional polarized radio emission from an active galactic nucleus is negligible in at least a sizable fraction of the star-forming galaxies. The alignment found for z = 0 is consistent with local spiral data, but significantly deteriorates as redshift increases. Moreover, it degrades toward lower frequencies due to internal Faraday depolarization. Thanks to cosmic redshifting, observing higher-z galaxies at a fixed frequency greatly mitigates degradation due to reduced Faraday depolarization at the source-frame frequency. We present analytic fits to the non-Gaussian misalignment angle distribution, and evaluate Fisher information per galaxy for measuring a polarization rotation angle induced by cosmic birefringence. For observation at 4.8 GHz, the effective root-mean-square misalignment angle is 18{\deg}, 23{\deg}, and 33{\deg} at z = 0, 1 and 2, respectively. As accurate observation-driven models are not yet available for cosmological galaxy samples, our results motivate pilot observations to empirically characterize polarization-shape alignment, and can facilitate forecasts of cosmology and fundamental physics applications that exploit this effect.
Magnetic White Dwarfs in the SDSS 100 pc Sample: Further Evidence of Two Formation Channels
2025-07-08
Adam Moss, Mukremin Kilic, Pierre Bergeron, Warren R. Brown, Gracyn Jewett, Marcel A. Agüeros, Maria Camisassa, Anthony Burrow
We conduct a model atmosphere analysis on all magnetic white dwarfs in the SDSS 100 pc sample. We have 163 magnetic targets in this sample, 87 of which are new discoveries, making this the largest volume-limited survey of magnetic white dwarfs to date. We discuss the distribution of multiple parameters, including mass, cooling age, and field strength. We find strong evidence of two populations of magnetic white dwarfs that form through separate mechanisms based on a cluster analysis of these parameters. The young, high mass objects typically have high field strengths which indicate a merger origin, while old, average mass objects have weaker fields that likely originated through a crystallization-induced dynamo or previous evolution stages. When comparing young and old objects, two-sample Kolmogorov-Smirnov tests yield statistically significant differences between the field strengths and masses of the magnetic targets. We use a Gaussian mixture model to identify where these populations lie in parameter space, and we find two groups centered at distinct cooling ages, masses, and field strengths: 2.9 Gyr, 0.71 $M_{\odot}$, 3.7 MG and 1.8 Gyr, 0.96 $M_{\odot}$, 84 MG respectively. Our results further support the dual formation channel previously reported in the literature. The occurrence of magnetism strongly correlates with the onset of crystallization. However, given the breakout times required for a crystallization dynamo, we find that many of our older, average mass objects can be better explained with a core-convective dynamo that forms on the main-sequence.
This survey reviews prompt tuning, a parameter-efficient approach for adapting language models by prepending trainable continuous vectors while keeping the model frozen. We classify existing approaches into two categories: direct prompt learning and transfer learning. Direct prompt learning methods include: general optimization approaches, encoder-based methods, decomposition strategies, and mixture-of-experts frameworks. Transfer learning methods consist of: general transfer approaches, encoder-based methods, and decomposition strategies. For each method, we analyze method designs, innovations, insights, advantages, and disadvantages, with illustrative visualizations comparing different frameworks. We identify challenges in computational efficiency and training stability, and discuss future directions in improving training robustness and broadening application scope.
In this work, we survey the role of GPUs in real-time systems. Originally designed for parallel graphics workloads, GPUs are now widely used in time-critical applications such as machine learning, autonomous vehicles, and robotics due to their high computational throughput. Their parallel architecture is well-suited for accelerating complex tasks under strict timing constraints. However, their integration into real-time systems presents several challenges, including non-preemptive execution, execution time variability, and resource contention; factors that can lead to unpredictable delays and deadline violations. We examine existing solutions that address these challenges, including scheduling algorithms, resource management techniques, and synchronization methods, and highlight open research directions to improve GPU predictability and performance in real-time environments.
AI-based Environment-Aware XL-MIMO Channel Estimation with Location-Specific Prior Knowledge Enabled by CKM
2025-07-08
Yuelong Qiu, Di Wu, Yong Zeng, Yanqun Tang, Nan Cheng, Chenhao Qi
Accurate and efficient acquisition of wireless channel state information (CSI) is crucial to enhance the communication performance of wireless systems. However, with the continuous densification of wireless links, increased channel dimensions, and the use of higher-frequency bands, channel estimation in the sixth generation (6G) and beyond wireless networks faces new challenges, such as insufficient orthogonal pilot sequences, inadequate signal-to-noise ratio (SNR) for channel training, and more sophisticated channel statistical distributions in complex environment. These challenges pose significant difficulties for classical channel estimation algorithms like least squares (LS) and maximum a posteriori (MAP). To address this problem, we propose a novel environment-aware channel estimation framework with location-specific prior channel distribution enabled by the new concept of channel knowledge map (CKM). To this end, we propose a new type of CKM called channel score function map (CSFM), which learns the channel probability density function (PDF) using artificial intelligence (AI) techniques. To fully exploit the prior information in CSFM, we propose a plug-and-play (PnP) based algorithm to decouple the regularized MAP channel estimation problem, thereby reducing the complexity of the optimization process. Besides, we employ Tweedie's formula to establish a connection between the channel score function, defined as the logarithmic gradient of the channel PDF, and the channel denoiser. This allows the use of the high-precision, environment-aware channel denoiser from the CSFM to approximate the channel score function, thus enabling efficient processing of the decoupled channel statistical components. Simulation results show that the proposed CSFM-PnP based channel estimation technique significantly outperforms the conventional techniques in the aforementioned challenging scenarios.
Counting mass with Gaia: Mass Density of stars and stellar remnants in the solar neighborhood
2025-07-08
Artem Lutsenko, Giovanni Carraro, Vladimir Korchagin, Roman Tkachenko, Katherine Vieira
In the light of new full-sky surveys, many attempts of creating a consistent Galactic model were made. The main interest is to estimate the still poorly understood dark matter content. However, the results vary depending on methodology, assumptions, and baryonic distribution used. To understand this discrepancy, we take the first step by estimating the model-free local mass density of stars and stellar remnants. We use a complete sample from the Gaia Catalogue of Nearby Stars within 100 pc from the Sun, together with the data on the sample of the White Dwarfs and the recent estimate of the Neutron Stars concentration in the solar neighborhood. After correction for unresolved binary stars and accounting for missing low-mass stars, we find the local mass density of stars and stellar remnants in the solar neighborhood is $\rho_{100} = 0.040^{+0.012} _{ -0.006}M_\odot pc^{-3}$ with Kroupa IMF, and $\rho_{100} = 0.037^{+0.012}_{ -0.006}M_\odot pc^{-3}$ with Chabrier IMF.
Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations
2025-07-08
Yibin Liu, Ang Li, Shijian Li
Explainable recommendations, which use the information of user and item with interaction to generate a explanation for why the user would interact with the item, are crucial for improving user trust and decision transparency to the recommender system. Existing methods primarily rely on encoding features of users and items to embeddings, which often leads to information loss due to dimensionality reduction, sparse interactions, and so on. With the advancements of large language models (LLMs) in language comprehension, some methods use embeddings as LLM inputs for explanation generation. However, since embeddings lack inherent semantics, LLMs must adjust or extend their parameters to interpret them, a process that inevitably incurs information loss. To address this issue, we propose a novel approach combining profile generation via hierarchical interaction summarization (PGHIS), which leverages a pretrained LLM to hierarchically summarize user-item interactions, generating structured textual profiles as explicit representations of user and item characteristics. Additionally, we propose contrastive prompting for explanation generation (CPEG) which employs contrastive learning to guide another reasoning language models in producing high-quality ground truth recommendation explanations. Finally, we use the textual profiles of user and item as input and high-quality explanation as output to fine-tune a LLM for generating explanations. Experimental results on multiple datasets demonstrate that our approach outperforms existing state-of-the-art methods, achieving a great improvement on metrics about explainability (e.g., 5% on GPTScore) and text quality. Furthermore, our generated ground truth explanations achieve a significantly higher win rate compared to user-written reviews and those produced by other methods, demonstrating the effectiveness of CPEG in generating high-quality ground truths.
Development of Bayesian methods for the characterization of galaxy star formation histories through stellar population synthesis models
2025-07-08
Edoardo Rossi
This project establishes parameters to characterize galaxy Star Formation History (SFH) beyond mean stellar age. We use ages at which fixed star fractions form to characterize SFH duration. We define Deltaage_n = (age_10 - age_90)/age_50 for SFH extension, where age_10, age_90, age_50 correspond to ages when 10%, 90%, and 50% of stars formed. Probability distributions for observed galaxies use robust Bayesian statistics comparing observed and model spectral features. We create composite stellar population (CSP) libraries using SEDlibrary software, implementing varied SFH, metallicity, and dust properties. We compare spectra using ten features: five spectral indices (D4000n, [Hdelta+Hgamma], Hbeta, [Mg_2Fe], [MgFe]') and five SDSS ugriz photometric fluxes. First, we focus on limiting Deltaage_n distinguishing extended from negligible SFH duration, Deltaage_n,min (time resolution). Using idealized CSP library of 5 million dust-free, burst-free models with fixed metallicities from subsolar to supersolar, Deltaage_n,min marks where spectral features depend on SFH duration. We find roughly flat log(Deltaage_n,min) around -0.3 dex over 4 magnitude orders in age. Deltaage_n,min decreases with higher SNR up to SNR=100, beyond which no improvement occurs. Second, we create mock datasets by perturbing 12,500 library models with realistic errors, testing retrieval capability for characteristic ages and SFH duration using realistic 500,000-model CSP library with up to 6 starbursts, dust, and variable metallicity. We constrain SFH duration log(Deltaage_n) within +-0.3 dex for most samples. For populations with strong Balmer absorption and mean age <10^9 yr, uncertainty exceeds 0.5 dex due to SFH degeneracies. These parameters will apply to current and upcoming deep spectroscopic galaxy surveys.
Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literature
2025-07-08
Shuning Zhang, Hui Wang, Xin Yi
As Artificial Intelligence (AI) increasingly becomes an active collaborator in co-creation, understanding the distribution and dynamic of agency is paramount. The Human-Computer Interaction (HCI) perspective is crucial for this analysis, as it uniquely reveals the interaction dynamics and specific control mechanisms that dictate how agency manifests in practice. Despite this importance, a systematic synthesis mapping agency configurations and control mechanisms within the HCI/CSCW literature is lacking. Addressing this gap, we reviewed 134 papers from top-tier HCI/CSCW venues (e.g., CHI, UIST, CSCW) over the past 20 years. This review yields four primary contributions: (1) an integrated theoretical framework structuring agency patterns, control mechanisms, and interaction contexts, (2) a comprehensive operational catalog of control mechanisms detailing how agency is implemented; (3) an actionable cross-context map linking agency configurations to diverse co-creative practices; and (4) grounded implications and guidance for future CSCW research and the design of co-creative systems, addressing aspects like trust and ethics.
Exploring Partial Multi-Label Learning via Integrating Semantic Co-occurrence Knowledge
2025-07-08
Xin Wu, Fei Teng, Yue Feng, Kaibo Shi, Zhuosheng Lin, Ji Zhang, James Wang
Partial multi-label learning aims to extract knowledge from incompletely annotated data, which includes known correct labels, known incorrect labels, and unknown labels. The core challenge lies in accurately identifying the ambiguous relationships between labels and instances. In this paper, we emphasize that matching co-occurrence patterns between labels and instances is key to addressing this challenge. To this end, we propose Semantic Co-occurrence Insight Network (SCINet), a novel and effective framework for partial multi-label learning. Specifically, SCINet introduces a bi-dominant prompter module, which leverages an off-the-shelf multimodal model to capture text-image correlations and enhance semantic alignment. To reinforce instance-label interdependencies, we develop a cross-modality fusion module that jointly models inter-label correlations, inter-instance relationships, and co-occurrence patterns across instance-label assignments. Moreover, we propose an intrinsic semantic augmentation strategy that enhances the model's understanding of intrinsic data semantics by applying diverse image transformations, thereby fostering a synergistic relationship between label confidence and sample difficulty. Extensive experiments on four widely-used benchmark datasets demonstrate that SCINet surpasses state-of-the-art methods.
The ACL community has very little interest in evaluating the real-world impact of NLP systems. A structured survey of the ACL Anthology shows that perhaps 0.1% of its papers contain such evaluations; furthermore most papers which include impact evaluations present them very sketchily and instead focus on metric evaluations. NLP technology would be more useful and more quickly adopted if we seriously tried to understand and evaluate its real-world impact.
Second-Order Conductivity Probes a Cascade of Singularities in a Moiré Superlattice
2025-07-08
Tanweer Ahmed, Bao Q. Tu, Kenji Watanabe, Takashi Taniguchi, Marco Gobbi, Fèlix Casanova, Luis E. Hueso
Systems lacking inversion symmetry inherently demonstrate a nonlinear electrical response (NLER) to an applied electric bias, emerging through extrinsic mechanisms. This response is highly sensitive to the electronic band structure, which can be engineered with remarkable precision in moir\'e superlattices formed from atomically thin quantum materials. Moir\'e superlattices host complex Fermi surface reconstructions near van Hove singularities (vHSs) in the electronic density of states. However, the role of these reconstructions in shaping NLER remains insufficiently understood. In this work, we systematically explore NLER in moir\'e superlattices of twisted double bilayer graphene (tDBLG) by tuning the Fermi level across multiple moir\'e bands on both sides of the charge neutrality point. We observe sharp variations and sign reversals in the NLER appearing via extrinsic pathways near mid-band vHSs. The second-order conductivity close to the vHSs demonstrates a much higher value than previous reports of extrinsic NLER in any other material. Our results demonstrate that NLER can serve as a sensitive probe of Fermi surface reconstructions and establish tDBLG as a versatile and highly efficient platform for generating and controlling the nonlinear electrical response.
SMEFT ATLAS: The Landscape Beyond the Standard Model
2025-07-08
Jason Aebischer, Andrzej J. Buras, Jacky Kumar
The Standard Model Effective Field Theory (SMEFT) based on the unbroken gauge group $\text{SU(3)}_C\otimes\text{SU(2)}_L\otimes\text{U(1)}_Y$ and containing only particles of the Standard Model (SM) has developed in the last decade to a mature field. It is the framework to be used in the energy gap from scales sufficiently higher than the electroweak scale up to the lowest energy scale at which new particles show up. We summarize the present status of this theory with a particular emphasize on its role in the indirect search for new physics (NP). While flavour physics of both quarks and leptons is the main topic of our review, we also discuss electric dipole moments, anomalous magnetic moments $(g-2)_{\mu,e}$, $Z$-pole observables, Higgs observables and high-$p_T$ scattering processes within the SMEFT. We group the observables into ten classes and list for each class the most relevant operators and the corresponding renormalization group equations (RGEs). We exhibit the correlations between different classes implied both by the operator mixing and the $\text{SU(2)}_L$ gauge symmetry. Our main goal is to provide an insight into the complicated operator structure of this framework which hopefully will facilitate the identification of valid ultraviolet completions behind possible anomalies observed in future data. Numerous colourful charts, and 85 tables, while representing rather complicated RG evolution from the NP scale down to the electroweak scale, beautify the involved SMEFT landscape. Over 950 references to the literature underline the importance and the popularity of this field. We discuss both top-down and bottom-up approaches as well as their interplay. This allows us eventually to present an atlas of different landscapes beyond the SM that includes heavy gauge bosons and scalars, vector-like quarks and leptons and leptoquarks.
Selective reflection Casimir-Polder spectroscopy in vapor cells: the influence of the thermal velocity distribution
2025-07-08
B. Dutta, C. Boldt, G. Garcia-Arellano, M. Ducloy, P. Pedri, S. Scheel, A. Laliotis
Selective reflection is a high-resolution spectroscopic method that allows the probing of atomic and molecular gases in the near field of dielectric cell windows. It is a sensitive technique for measuring interactions between excited atoms and surfaces, thus providing a unique tool to probe resonant effects in Casimir-Polder physics. The theoretical modelling of selective reflection spectra has thus far employed the infinite Doppler approximation which assumes that the Doppler shift dominates over the atom-surface interactions as well as the homogeneous transition linewidth. Here we extend the theoretical treatment of selective reflection spectra to include the effects of finite Doppler width. This allows us to analyse the regime of very large Casimir-Polder interactions, relevant for Rydberg atom spectroscopy, and revisit previous analyses of experiments with low-lying excited states. Further, this will aid in determining the signatures of quantum friction in vapor cell spectroscopy.
GRANDProto300 (hereafter referred to as GP300) is a pioneering prototype array of the GRAND experiment. It consists of 300 radio antennas and will cover an area of 200 km$^2$ in a radio-quiet region of western China. Serving as a test bench for the GRAND experiment, GRANDProto300 aims to achieve autonomous radio detection and reconstruction of highly inclined air showers. It is designed to detect ultra-high-energy cosmic rays in the energy range of $10^{16.5}$-$10^{18}$ eV at a rate comparable to that of the Pierre Auger Observatory. Over the past two years, significant improvements have been made to both the hardware and firmware of GP300. Currently, 65 antenna units have been deployed at the site by June 2025. We present the current status of detector commissioning, including updates on hardware, calibration results such as GPS timing and antenna positioning. Additionally, we discuss the solar radio bursts associated with solar flares, the galactic radio emissions detected, and preliminary cosmic ray surveys.
Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why
2025-07-08
Chenhao Li, Marco Hutter, Andreas Krause
This survey provides a comparative analysis of feature-based and GAN-based approaches to learning from demonstrations, with a focus on the structure of reward functions and their implications for policy learning. Feature-based methods offer dense, interpretable rewards that excel at high-fidelity motion imitation, yet often require sophisticated representations of references and struggle with generalization in unstructured settings. GAN-based methods, in contrast, use implicit, distributional supervision that enables scalability and adaptation flexibility, but are prone to training instability and coarse reward signals. Recent advancements in both paradigms converge on the importance of structured motion representations, which enable smoother transitions, controllable synthesis, and improved task integration. We argue that the dichotomy between feature-based and GAN-based methods is increasingly nuanced: rather than one paradigm dominating the other, the choice should be guided by task-specific priorities such as fidelity, diversity, interpretability, and adaptability. This work outlines the algorithmic trade-offs and design considerations that underlie method selection, offering a framework for principled decision-making in learning from demonstrations.
Survey of Surveys. II. Stellar parameters for 23 millions of stars
2025-07-08
A. Turchi, E. Pancino, A. Avdeeva, F. Rossi, M. Tsantaki, P. M. Marrese, S. Marinoni, N. Sanna, G. Fanari, D. Alvarez Garay, M. Echeveste, S. Nedhath, S. Rani, E. Reggiani, S. Saracino, L. Steinbauer, G. Thomas, F. Gran, G. Guiglion
In the current panorama of large surveys, the vast amount of data obtained with different methods, data types, formats, and stellar samples, is making an efficient use of the available information difficult. The Survey of Surveys is a project to critically compile survey results in a single catalogue, facilitating the scientific use of the available information. In this second release, we present two new catalogs of stellar parameters (Teff, logg, and [Fe/H]). To build the first catalog, SoS-Spectro, we calibrated internally and externally stellar parameters from five spectroscopic surveys (APOGEE, GALAH, Gaia-ESO, RAVE, and LAMOST) and externally on the PASTEL database. The second catalog, SoS-ML catalog, is obtained by using SoS-Spectro as a reference to train a multi-layer perceptron, which predicts stellar parameters based on two photometric surveys, SDSS and SkyMapper. As a novel approach, we build on previous parameters sets, from Gaia DR3 and Andrae et al. (2023), aiming to improve their precision and accuracy. We obtain a catalog of stellar parameters for around 23 millions of stars, which we make publicly available. We validate our results with several comparisons with other machine learning catalogs, stellar clusters, and astroseismic samples. We find substantial improvements in the parameters estimates compared to other Machine Learning methods in terms of precision and accuracy, especially in the metal-poor range, as shown in particular when validating our results with globular clusters. We believe that there are two reasons behind our improved results at the low-metallicity end: first, our use of a reference catalog, the SoS-Spectro, which is calibrated using high-resolution spectroscopic data; and second, our choice to build on pre-existing parameter estimates from em Gaia and Andrae et al., rather than attempting to obtain our predictions from survey data alone.
Psychometric Item Validation Using Virtual Respondents with Trait-Response Mediators
2025-07-08
Sungjib Lim, Woojung Song, Eun-Ju Lee, Yohan Jo
As psychometric surveys are increasingly used to assess the traits of large language models (LLMs), the need for scalable survey item generation suited for LLMs has also grown. A critical challenge here is ensuring the construct validity of generated items, i.e., whether they truly measure the intended trait. Traditionally, this requires costly, large-scale human data collection. To make it efficient, we present a framework for virtual respondent simulation using LLMs. Our central idea is to account for mediators: factors through which the same trait can give rise to varying responses to a survey item. By simulating respondents with diverse mediators, we identify survey items that robustly measure intended traits. Experiments on three psychological trait theories (Big5, Schwartz, VIA) show that our mediator generation methods and simulation framework effectively identify high-validity items. LLMs demonstrate the ability to generate plausible mediators from trait definitions and to simulate respondent behavior for item validation. Our problem formulation, metrics, methodology, and dataset open a new direction for cost-effective survey development and a deeper understanding of how LLMs replicate human-like behavior. We will publicly release our dataset and code to support future work.
Detecting Lifshitz Transitions Using Nonlinear Conductivity in Bilayer Graphene
2025-07-08
Tanweer Ahmed, Harsh Varshney, Bao Q. Tu, Kenji Watanabe, Takashi Taniguchi, Marco Gobbi, Fèlix Casanova, Amit Agarwal, Luis E. Hueso
The second-order nonlinear electrical response (NLER) is an intrinsic property of inversion symmetry-broken systems which can provide deep insights into the electronic band structures of atomically thin quantum materials. However, the impact of Fermi surface reconstructions, also known as Lifshitz transitions, on the NLER has remained elusive. We investigated NLER in bilayer graphene (BLG), where the low-energy bands undergo Lifshitz transitions. Here, NLER undergoes a sign change near the Lifshitz transitions even at elevated temperatures $T\gtrsim10~$K. At the band edge, NLER in BLG is modulated by both extrinsic scattering and interfacial-strain-induced intrinsic Berry curvature dipole, both of which can be finely tuned externally by varying doping and interlayer potential. Away from the band edge, BLG exhibits second-order conductivity exceeding $30~\mu$mV$^{-1}\Omega^{-1}$ at 3K higher than any previous report. Our work establishes NLER as a reliable tool to probe Lifshitz transitions in quantum materials.
Understanding support for AI regulation: A Bayesian network perspective
2025-07-08
Andrea Cremaschi, Dae-Jin Lee, Manuele Leonelli
As artificial intelligence (AI) becomes increasingly embedded in public and private life, understanding how citizens perceive its risks, benefits, and regulatory needs is essential. To inform ongoing regulatory efforts such as the European Union's proposed AI Act, this study models public attitudes using Bayesian networks learned from the nationally representative 2023 German survey Current Questions on AI. The survey includes variables on AI interest, exposure, perceived threats and opportunities, awareness of EU regulation, and support for legal restrictions, along with key demographic and political indicators. We estimate probabilistic models that reveal how personal engagement and techno-optimism shape public perceptions, and how political orientation and age influence regulatory attitudes. Sobol indices and conditional inference identify belief patterns and scenario-specific responses across population profiles. We show that awareness of regulation is driven by information-seeking behavior, while support for legal requirements depends strongly on perceived policy adequacy and political alignment. Our approach offers a transparent, data-driven framework for identifying which public segments are most responsive to AI policy initiatives, providing insights to inform risk communication and governance strategies. We illustrate this through a focused analysis of support for AI regulation, quantifying the influence of political ideology, perceived risks, and regulatory awareness under different scenarios.
PRODIGE VI -- Envelope to Disk with NOEMA: VI. The Missing Sulfur Problem
2025-07-08
J. J. Miranzo-Pastor, A. Fuente, D. Navarro-Almaida, J. E. Pineda, D. M. Segura-Cox, P. Caselli, R. Martin-Domenech, M. T. Valdivia-Mena, T. Henning, T. -H. Hsieh, L. A. Busch, C. Gieser, Y. -R. Chou, B. Commerçon, R. Neri, D. Semenov, A. Lopez-Sepulcre, N. Cunningham, L. Bouscasse, M. Maureira
Determining the amount of sulfur in volatiles and refractories in the ISM remains one of the main problems in astrochemistry. The detection of H$_2$S ices, which are thought to be one of the main sulfur reservoirs, has not been achieved yet, and the only S-bearing species detected in the ices to date is OCS. PRODIGE large survey observations with NOEMA of several Class 0/I protostars in the Perseus Molecular Cloud provide a perfect opportunity to study the H$_2$S and OCS composition of the ices through the volatiles sublimated in the warm inner core (T$>$100K, n $\sim10^6$cm$^{-3}$) of these protostars. Our aim is to determine the H$_2$S/OCS ratio in the warm inner core of 24 protostars in order to study how it is affected by different factors during its evolution. We used the NOEMA millimeter observations from the PRODIGE program of H$_2$S, H$_2^{33}$S, OCS, OC$^{33}$S and OC$^{34}$S to estimate the H$_2$S and OCS column densities in the warm inner cores. We used SO and SO$_2$ data from the ALMA archive to give a rough estimate of the total sulfur abundance. We explore the chemistry of H$_2$S and OCS in the warm cores using chemical and dynamical simulations of the collapse of a dense core to form a protostar. The estimated H$_2$S/OCS ratio reveals a segregation of the sources into ``OCS-poor'' and ``OCS-rich'' protostars, where the OCS-poor protostars present higher H$_2$S/OCS ratios than the OCS-rich ones. Total sulfur abundance is always dominated by either H$_2$S or OCS, grows with evolution during the Class 0 phase up to $D_S<8$, and decreases again in the Class I. Simulations show that temperature changes in the pre-stellar phase and during the collapse can produce substantial differences in the H$_2$S and OCS abundances and in the H$_2$S/OCS ratio. Our analysis shows that the H$_2$S/OCS ratio is strongly influenced by the environment and the initial conditions of the cloud.
A nonnegative function on the vertices of an infinite graph G which vanishes at a distinguished vertex o, has Laplacian 1 at o, and is harmonic at all other vertices is called a potential. We survey basic properties of potentials in recurrent networks. In particular, we show that potentials are Lipschitz with respect to the effective resistance metric, and if the potential is unique, then there is a determinantal formula for the harmonic measures from infinity. We also infer from the von Neumann minimax theorem that there always exists a potential tending to infinity.
2025-07-08
D. R. Smith, J. B. Pendry, M. C. K. Wiltshire
Recently, artificially constructed metamaterials have become of considerable interest, as these materials can exhibit electromagnetic characteristics unlike any conventional materials. Artificial magnetism and negative refractive index are two specific types of behavior that have been demonstrated over the past few years, illustrating the new physics and new applications possible when we expand our view as to what constitutes a material. In this review, we describe recent advances in metamaterials research, and discuss the potential that these materials may hold for realizing new and seemingly exotic electromagnetic phenomena.
Hyperspectral images are high-dimensional datasets consisting of hundreds of contiguous spectral bands, enabling detailed material and surface analysis. Hyperspectral anomaly detection (HAD) refers to the technique of identifying and locating anomalous targets in such data without prior information about a hyperspectral scene or target spectrum. This technology has seen rapid advancements in recent years, with applications in agriculture, defence, military surveillance, and environmental monitoring. Despite this significant progress, existing HAD methods continue to face challenges such as high computational complexity, sensitivity to noise, and limited generalisation across diverse datasets. This study presents a comprehensive comparison of various HAD techniques, categorising them into statistical models, representation-based methods, classical machine learning approaches, and deep learning models. We evaluated these methods across 17 benchmarking datasets using different performance metrics, such as ROC, AUC, and separability map to analyse detection accuracy, computational efficiency, their strengths, limitations, and directions for future research.The research shows that deep learning models achieved the highest detection accuracy, while statistical models demonstrated exceptional speed across all datasets. This study aims to provide valuable insights for researchers and practitioners working to advance the field of hyperspectral anomaly detection methods.
Large Language Models for Agent-Based Modelling: Current and possible uses across the modelling cycle
2025-07-08
Loïs Vanhée, Melania Borit, Peer-Olaf Siebers, Roger Cremades, Christopher Frantz, Önder Gürcan, František Kalvas, Denisa Reshef Kera, Vivek Nallur, Kavin Narasimhan, Martin Neumann
The emergence of Large Language Models (LLMs) with increasingly sophisticated natural language understanding and generative capabilities has sparked interest in the Agent-based Modelling (ABM) community. With their ability to summarize, generate, analyze, categorize, transcribe and translate text, answer questions, propose explanations, sustain dialogue, extract information from unstructured text, and perform logical reasoning and problem-solving tasks, LLMs have a good potential to contribute to the modelling process. After reviewing the current use of LLMs in ABM, this study reflects on the opportunities and challenges of the potential use of LLMs in ABM. It does so by following the modelling cycle, from problem formulation to documentation and communication of model results, and holding a critical stance.
DESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruning
2025-07-08
Kaixiang Zhao, Joseph Yousry Attalla, Qian Lou, Yushun Dong
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various graph-based learning tasks. However, enabling privacy-preserving GNNs in encrypted domains, such as under Fully Homomorphic Encryption (FHE), typically incurs substantial computational overhead, rendering real-time and privacy-preserving inference impractical. In this work, we propose DESIGN (EncrypteD GNN Inference via sErver-Side Input Graph pruNing), a novel framework for efficient encrypted GNN inference. DESIGN tackles the critical efficiency limitations of existing FHE GNN approaches, which often overlook input data redundancy and apply uniform computational strategies. Our framework achieves significant performance gains through a hierarchical optimization strategy executed entirely on the server: first, FHE-compatible node importance scores (based on encrypted degree statistics) are computed from the encrypted graph. These scores then guide a homomorphic partitioning process, generating multi-level importance masks directly under FHE. This dynamically generated mask facilitates both input graph pruning (by logically removing unimportant elements) and a novel adaptive polynomial activation scheme, where activation complexity is tailored to node importance levels. Empirical evaluations demonstrate that DESIGN substantially accelerates FHE GNN inference compared to state-of-the-art methods while maintaining competitive model accuracy, presenting a robust solution for secure graph analytics.
Generation structures and Yukawa couplings in magnetized $T^{2g}/\mathbb{Z}_N$ models
2025-07-08
Tim Jeric, Tatsuo Kobayashi, Kaito Nasu, Shohei Takada
We study fermion zero-mode wave functions with various chiralities in magnetized $T^{2g}$, $(g=2,3)$ torus. First, we consider the wave functions satisfying the Dirac equation and the boundary conditions on the magnetized torus. Second, we introduce the $SO(3)$ (or parity) transformations and derive the wave functions under the modular transformation. Additionally, we calculate the Yukawa couplings with consideration for the chirality. Lastly, we briefly review how to construct $T^{4}/\mathbb{Z}_N$ ($N=2,3,4,6$) and $T^6/\mathbb{Z}_{12}$ twisted orbifold. Also, we explicitly analyze the number of the wave functions in $\mathbb{Z}_N$ sectors.
Graph learning has rapidly evolved into a critical subfield of machine learning and artificial intelligence (AI). Its development began with early graph-theoretic methods, gaining significant momentum with the advent of graph neural networks (GNNs). Over the past decade, progress in scalable architectures, dynamic graph modeling, multimodal learning, generative AI, explainable AI (XAI), and responsible AI has broadened the applicability of graph learning to various challenging environments. Graph learning is significant due to its ability to model complex, non-Euclidean relationships that traditional machine learning struggles to capture, thus better supporting real-world applications ranging from drug discovery and fraud detection to recommender systems and scientific reasoning. However, challenges like scalability, generalization, heterogeneity, interpretability, and trustworthiness must be addressed to unlock its full potential. This survey provides a comprehensive introduction to graph learning, focusing on key dimensions including scalable, temporal, multimodal, generative, explainable, and responsible graph learning. We review state-of-the-art techniques for efficiently handling large-scale graphs, capturing dynamic temporal dependencies, integrating heterogeneous data modalities, generating novel graph samples, and enhancing interpretability to foster trust and transparency. We also explore ethical considerations, such as privacy and fairness, to ensure responsible deployment of graph learning models. Additionally, we identify and discuss emerging topics, highlighting recent integration of graph learning and other AI paradigms and offering insights into future directions. This survey serves as a valuable resource for researchers and practitioners seeking to navigate the rapidly evolving landscape of graph learning.
Domain adaptation of large language models for geotechnical applications
2025-07-08
Lei Fan, Fangxue Liu, Cheng Chen
Recent developments in large language models (LLMs) are opening up new opportunities in geotechnical engineering and engineering geology. While general-purpose LLMs possess broad capabilities, effective application in geotechnics often requires domain-specific adaptation. Such tailored LLMs are increasingly employed to streamline geotechnical workflows. This paper presents the first survey of the adaptation and application of LLMs in geotechnical engineering. It outlines key methodologies for adaptation to geotechnical domain, including prompt engineering, retrieval-augmented generation, domain-adaptive pretraining, and fine-tuning. The survey examines the state-of-the-art applications of geotechnical-adapted LLMs, including geological interpretation, subsurface characterization, site planning, design calculations, numerical modeling, safety and risk assessment, and educational tutoring. It also analyzes benefits and limitations of geotechnical-adapted LLMs, and identifies promising directions for future research in this interdisciplinary discipline. The findings serve as a valuable resource for practitioners seeking to integrate LLMs into geotechnical practice, while also providing a foundation to stimulate further investigation within the academic community.
Self-Review Framework for Enhancing Instruction Following Capability of LLM
2025-07-08
Sihyun Park
Various techniques have been proposed to improve large language models (LLMs) adherence to formatting and instruction constraints. One of the most effective approaches involves utilizing high-quality data generated by powerful models. However, such models often fail to fully comply with complex instructions in a single generation. To address this limitation, iterative revision methods have been introduced. Nevertheless, as the number of data points and revision iterations increases, the associated monetary costs grow significantly. As a resource-efficient alternative, methods have been proposed that leverage high-performance evaluation tools to compensate for the limited self-evaluation capabilities of open-source LLMs. However, these approaches often lead to a degradation in output quality due to excessive revision. To overcome these challenges, we propose Re5, a self-evaluation and revision framework designed to enhance instruction-following performance while preserving the quality of the generated content. Re5 extracts task and constraint components from user instructions, performs structural evaluations to prevent error accumulation, and applies fine-grained constraint-specific content evaluations followed by selective revisions. This process ensures precise and quality-preserving improvements. The final high-quality outputs are used for alignment tuning, enabling long-term alignment improvements through a data-centric iterative refinement loop. Experimental results demonstrate that Re5 achieves instruction-following performance comparable to models trained on data generated by GPT-4o-mini, a high-performance model, even with a small amount of data while maintaining response quality with a 64.24%-win rate over the non-revised initial responses. These results validate Re5 as an efficient and effective solution for enhancing instruction adherence with minimal external supervision.
Constructing a Pristine View of Extended Globular Cluster Structure
2025-07-08
Pete B. Kuzma, Miho N. Ishigaki, Takanobu Kirihara, Itsuki Ogami
Globular Clusters (GCs) displaying extended structures are becoming increasingly ubiquitous in the Milky Way (MW). Despite their low surface brightness, which makes disentangling the true structure from the MW overwhelmingly difficult, the increasing availability of multi-dimensional data sets has allowed for new detections of extended GC structure. This work utilises the Pristine-Gaia-Synthetic catalogue released as part of the Pristine Surveys first data release to search for tidally stripped stars in the peripheries of MW GCs. Pristine provides photometric [Fe/H] measurements based on CaHK-band photometry. Using unsupervised machine learning techniques, we provided lists of extra-tidal stars for 30 GCs, one of the largest surveys of its kind. We find that (1) 22 GCs that passed our quality cut have extended structure within 5 deg from the cluster centers of which six are new tentative detections, (2) four of those GCs exhibit diffuse envelope-like extra-tidal features, while the remaining GCs exhibit tidal tail-like structures. We measure the position angles of the extended structures, find broad consistency between the position angles and the GC orbits, and discuss our results concerning N-body models. This work demonstrates the effectiveness of adding photometric metallicities to the multi-dimensional search of extended tidal structure and how the upcoming multi-object spectrographs will be crucial for exploring GC peripheries in the coming years.
We investigate whether a novel method of quantum machine learning (QML) can identify anomalous events in X-ray light curves as transient events and apply it to detect such events from the XMM-Newton 4XMM-DR14 catalog. The architecture we adopt is a quantum version of the long-short term memory (LSTM) where some fully connected layers are replaced with quantum circuits. The LSTM, making predictions based on preceding data, allows identification of anomalies by comparing predicted and actual time-series data. The necessary training data are generated by simulating active galactic nucleus-like light curves as the species would be a significant population in the XMM-Newton catalog. Additional anomaly data used to assess trained quantum LSTM (QLSTM) models are produced by adding flares like quasi-periodic eruptions to the training data. Comparing various aspects of the performances of the quantum and classical LSTM models, we find that QLSTM models incorporating quantum superposition and entanglement slightly outperform the classical LSTM (CLSTM) model in expressive power, accuracy, and true-positive rate. The highest-performance QLSTM model is then used to identify transient events in 4XMM-DR14. Out of 40154 light curves in the 0.2--12 keV band, we detect 113 light curves with anomalies, or transient event candidates. This number is $\approx$ 1.3 times that of anomalies detectable with the CLSTM model. By utilizing SIMBAD and four wide-field survey catalogs made by ROSAT, SkyMapper, Pan-STARRS, and WISE, no possible counterparts are found for 12 detected anomalies.
Towards Measurement Theory for Artificial Intelligence
2025-07-08
Elija Perrier
We motivate and outline a programme for a formal theory of measurement of artificial intelligence. We argue that formalising measurement for AI will allow researchers, practitioners, and regulators to: (i) make comparisons between systems and the evaluation methods applied to them; (ii) connect frontier AI evaluations with established quantitative risk analysis techniques drawn from engineering and safety science; and (iii) foreground how what counts as AI capability is contingent upon the measurement operations and scales we elect to use. We sketch a layered measurement stack, distinguish direct from indirect observables, and signpost how these ingredients provide a pathway toward a unified, calibratable taxonomy of AI phenomena.
Can Contextualized Physics Problems Enhance Student Motivation? Uncovering a Student-Teacher Perception Gap through a Large-Scale Survey
2025-07-08
Yajun Wei, Xinting Peng, Yi Zhong, Feipeng Pi, Yanfang Zhai, Lei Bao
Embedding physics problems unreal-world settings, here termed contextualized physics problems (CPP), is widely believed to foster students' interest, motivation, and learning. However, firm evidence for this claim remains scarce. To explore this issue, we surveyed 868 secondary students and 154 teachers to examine their attitudes toward CPP and investigate whether students and teachers perceive these problems as promoting student interest and motivation in learning physics. The findings reveal a divergence between teacher and student perspectives. While most teachers view CPP as essential for enhancing interest and motivation, student responses tell a different story. Contextualized problems appear to boost interest and motivation only among 8th graders who are newly introduced to the subject. From 9th to 11th grade, students expressed a clear preference for de-contextualized physics problems (DPP) and generally disagreed that CPP increased their interest in physics. Gender differences were also observed among younger students, with boys showing a moderately stronger preference for CPP than girls. These results provide valuable insights for educators in designing course materials and creating effective test and exercise questions. The discrepancy between teacher and student perceptions, as highlighted by the surveys and interviews, underscores the need to address this gap through targeted teacher training and professional development.
FedPhD: Federated Pruning with Hierarchical Learning of Diffusion Models
2025-07-08
Qianyu Long, Qiyuan Wang, Christos Anagnostopoulos, Daning Bi
Federated Learning (FL), as a distributed learning paradigm, trains models over distributed clients' data. FL is particularly beneficial for distributed training of Diffusion Models (DMs), which are high-quality image generators that require diverse data. However, challenges such as high communication costs and data heterogeneity persist in training DMs similar to training Transformers and Convolutional Neural Networks. Limited research has addressed these issues in FL environments. To address this gap and challenges, we introduce a novel approach, FedPhD, designed to efficiently train DMs in FL environments. FedPhD leverages Hierarchical FL with homogeneity-aware model aggregation and selection policy to tackle data heterogeneity while reducing communication costs. The distributed structured pruning of FedPhD enhances computational efficiency and reduces model storage requirements in clients. Our experiments across multiple datasets demonstrate that FedPhD achieves high model performance regarding Fr\'echet Inception Distance (FID) scores while reducing communication costs by up to $88\%$. FedPhD outperforms baseline methods achieving at least a $34\%$ improvement in FID, while utilizing only $56\%$ of the total computation and communication resources.
Bilayer graphene as a template for manufacturing novel 2D materials
2025-07-08
Arkady V. Krasheninnikov, Yung-Chang Lin, Kazu Suenaga
Recent intensive research on two-dimensional materials (2DMs) rekindle the interest in the intercalation of various atoms and molecules into layered compounds as a tool to manufacture 2DMs and tune their optoelectronic, magnetic and catalytic properties. Intercalation into free-standing bilayer graphene (BLG) has received special attention, as graphene is stable, chemically inert and enables one to study the atomic structure of the intercalated 2DM using high-resolution transmission electron microscopy. It was also discovered that the protecting action of graphene sheets makes it possible to not only stabilize the encapsulated single sheets of marginally stable layered materials, but also synthesize completely new 2D systems inside BLG, which in comparison to the bulk graphite allows for easier intercalation and much larger increase in the inter-layer separation of the sheets. In this review, we summarize the recent progress in this area, with a special focus on new materials created inside BLG. We compare the experimental findings to the theoretical predictions, pay special attention to the discrepancies and outline the challenges in the field. Finally, we discuss unique opportunities offered by the intercalation into 2DMs beyond graphene and their heterostructures.
Assessing the Prevalence of AI-assisted Cheating in Programming Courses: A Pilot Study
2025-07-08
Kaléu Delphino
Tools that can generate computer code in response to inputs written in natural language, such as ChatGPT, pose an existential threat to Computer Science education in its current form, since students can now use these tools to solve assignments without much effort. While that risk has already been recognized by scholars, the proportion of the student body that is incurring in this new kind of plagiarism is still an open problem. We conducted a pilot study in a large CS class (n=120) to assess the feasibility of estimating AI plagiarism through anonymous surveys and interviews. More than 25% of the survey respondents admitted to committing AI plagiarism. Conversely, only one student accepted to be interviewed. Given the high levels of misconduct acknowledgment, we conclude that surveys are an effective method for studies on the matter, while interviews should be avoided or designed in a way that can entice participation.
2025-07-08
Ayrton San Joaquin, Rokas Gipiškis, Leon Staufer, Ariel Gil
As frontier artificial intelligence (AI) models rapidly advance, benchmarks are integral to comparing different models and measuring their progress in different task-specific domains. However, there is a lack of guidance on when and how benchmarks should be deprecated once they cease to effectively perform their purpose. This risks benchmark scores over-valuing model capabilities, or worse, obscuring capabilities and safety-washing. Based on a review of benchmarking practices, we propose criteria to decide when to fully or partially deprecate benchmarks, and a framework for deprecating benchmarks. Our work aims to advance the state of benchmarking towards rigorous and quality evaluations, especially for frontier models, and our recommendations are aimed to benefit benchmark developers, benchmark users, AI governance actors (across governments, academia, and industry panels), and policy makers.
Equivalent circuit modelling as a design tool for particle accelerator RF components
2025-07-08
Dieter Pelz, Mark Boland
This paper presents a comprehensive approach to generating and utilizing accurate equivalent circuits for structures commonly found in the particle accelerator domain. After a discussion of equivalent circuits in general, a systematic process for determining the appropriate circuit topology is outlined, and a method for finding circuit element values is introduced. The proposed methodology is demonstrated in detail, supported by four illustrative examples. Equivalent circuit element values are provided. An example of a decomposition strategy for handling complex structures is given. Additionally, a list of typical applications of equivalent circuits is provided with brief descriptions.
Experiments to test the hypothesis for solar and dark matter axions
2025-07-08
Babette Döbrich, Igor G. Irastorza
We present a pedagogical introduction to the direct search of axions as dark matter, as well as to searches for solar axions. The plethora of experimental searches exploit the axion's coupling to two photons: They attempt to convert the axion dark matter to photons in a resonator placed in an external magnetic field or convert solar axions into X-rays in a magnet pointing towards the sun. We give a basic introduction to this concept, its many variants and to searches that exploit the axion's other couplings. We also speculate about potentially transformative developments for such searches in the near-term future.
Confirmation of a non-transiting planet in the habitable zone of the nearby M dwarf L 98-59
2025-07-08
Paul Schwarz, Stefan Dreizler, René Heller
Only 40 exoplanetary systems with five or more planets are currently known. These systems are crucial for our understanding of planet formation and planet-planet interaction. The M dwarf L 98-59 has previously been found to show evidence of five planets, three of which are transiting. Our aim is to confirm the fifth planet in this system and to refine the system characteristics namely minimum masses, radii and the orbital parameters of the planets around L 98-59. We reanalysed RV and activity data from HARPS and ESPRESSO alongside TESS and HST transit data using a joint model. The parameter space was sampled using the dynesty nested sampler. We confirm the previously known fifth planet in the system's habitable zone with an orbital period of $23.07\pm0.08\,d$, a minimum mass of $3.0\pm0.5\,M_{\oplus}$ and an effective temperature of 289 K. We find an additional planet candidate in the RV data with an orbital period of $1.7361^{+0.0007}_{-0.0008}\,d$ and a minimum mass of $0.58\pm0.12\,M_{\oplus}$. This candidate (L 98-59.06) has a statistical significance between $2.9\sigma$ and $4.2\sigma$, details depending on the modelling of stellar variability. Moreover, we present evidence for a stellar rotation period of $76\pm4\,d$.
GloBIAS: strengthening the foundations of BioImage Analysis
2025-07-08
A. A. Corbat, C. G. Walther, L. R. de la Ballina, N. D. Condon, A. A. Felder, M. Schätz, B. Schmerl, K. Sugawara, C. Prats, A. Klemm, F. Levet, K. Miura, P. Sampaio, C. Tischer, R. D'Antuono, B. A. Cimini, R. Haase
There is a global need for BioImage Analysis (BIA) as advances in life sciences increasingly rely on cutting-edge imaging systems that have dramatically expanded the complexity and dimensionality of biological images. Turning these data into scientific discoveries requires people with effective data management skills and knowledge of state-of-the-art image processing and data analysis, in other words, BioImage Analysts. The Global BioImage Analysts' Society (GloBIAS) aims to enhance the profile of BioImage Analysts as a key role in science and research. Its vision encompasses fostering a global network, democratising access to BIA by providing educational resources tailored to various proficiency levels and disciplines, while also establishing guidelines for BIA courses. By collaboratively shaping the education of BioImage Analysts, GloBIAS aims to unlock the full potential of BIA in advancing life science research and to consolidate BIA as a career path. To better understand the needs and geographical representation of the BIA community, a worldwide survey was conducted and 291 responses were collected across people from all career stages and continents. This work discusses how GloBIAS aims to address community-identified shortcomings in work environment, funding, and scientific activities. The survey underscores a strong interest from the BIA community in activities proposed by GloBIAS and their interest to actively contribute. With 72% of respondents willing to pay for membership, the community's enthusiasm for both online and in-person events is set to drive the growth and sustainability of GloBIAS.
The EDGE-CALIFA survey: Star formation relationships for galaxies at different stages of their evolution
2025-07-08
D. Colombo, V. Kalinova, Z. Bazzi, S. F. Sanchez, A. D. Bolatto, T. Wong, V. Villanueva, E. Rosolowsky, A. Weiß, K. D. French, A. Leroy, J. Barrera-Ballesteros, Y. Garay-Solis, F. Bigiel, A. Tripathi, B. Rodriguez
Galaxy evolution is largely driven by star formation activity or by the cessation of it, also called star formation quenching. In this paper, we present star formation scaling relations for galaxies at different evolutionary stages. To do so, we used the integrated Extragalactic Database for Galaxy Evolution (iEDGE), which collects CO, optical continuum, and emission line information for 643 galaxies from the CALIFA IFU dataset. By considering the patterns described by star-forming and retired regions, we grouped the galaxies into quenching stages using the emission line classification scheme, QueStNA. We observed that the molecular gas mass ($M_{\rm mol}$) decreases from star-forming to retired systems and so does the molecular-to-stellar mass ratio ($f_{\rm mol}$). In contrast, star formation efficiency (SFE) is constant in the quenching stages dominated by star formation and rapidly declines afterwards. We observed that this rapid decline is more pronounced in the centre of the galaxies compared to the rest of the discs, reflecting the inside-out quenching displayed by nearby galaxies. We noticed that the relations between $M_{\rm mol}$ and the stellar mass ($M_*$) become increasingly shallow with the quenching stages; however, the relations between the star formation rate (SFR) and $M_{\rm mol}$ steepen when going from star-forming to retired systems. We observed that a three-dimensional relation between SFR, $M_*$, and $M_{\rm mol}$ exists for star-forming galaxies, while data points from other quenching groups are scattered across the parameter space. Taken together, these pieces of evidence indicate that the quenching of the galaxies cannot be explained solely by a depletion of the molecular gas and that a significant decrease in the SFE is necessary to retire the centre of the galaxies beyond the star formation green valley.
Jolting Technologies: Superexponential Acceleration in AI Capabilities and Implications for AGI
2025-07-08
David Orban
This paper investigates the Jolting Technologies Hypothesis, which posits superexponential growth (increasing acceleration, or a positive third derivative) in the development of AI capabilities. We develop a theoretical framework and validate detection methodologies through Monte Carlo simulations, while acknowledging that empirical validation awaits suitable longitudinal data. Our analysis focuses on creating robust tools for future empirical studies and exploring the potential implications should the hypothesis prove valid. The study examines how factors such as shrinking idea-to-action intervals and compounding iterative AI improvements drive this jolting pattern. By formalizing jolt dynamics and validating detection methods through simulation, this work provides the mathematical foundation necessary for understanding potential AI trajectories and their consequences for AGI emergence, offering insights for research and policy.
Mapping the Catacombs: An Underwater Cave Segment of the Devil's Eye System
2025-07-08
Michalis Chatzispyrou, Luke Horgan, Hyunkil Hwang, Harish Sathishchandra, Monika Roznere, Alberto Quattrini Li, Philippos Mordohai, Ioannis Rekleitis
This paper presents a framework for mapping underwater caves. Underwater caves are crucial for fresh water resource management, underwater archaeology, and hydrogeology. Mapping the cave's outline and dimensions, as well as creating photorealistic 3D maps, is critical for enabling a better understanding of this underwater domain. In this paper, we present the mapping of an underwater cave segment (the catacombs) of the Devil's Eye cave system at Ginnie Springs, FL. We utilized a set of inexpensive action cameras in conjunction with a dive computer to estimate the trajectories of the cameras together with a sparse point cloud. The resulting reconstructions are utilized to produce a one-dimensional retract of the cave passages in the form of the average trajectory together with the boundaries (top, bottom, left, and right). The use of the dive computer enables the observability of the z-dimension in addition to the roll and pitch in a visual/inertial framework (SVIn2). In addition, the keyframes generated by SVIn2 together with the estimated camera poses for select areas are used as input to a global optimization (bundle adjustment) framework -- COLMAP -- in order to produce a dense reconstruction of those areas. The same cave segment is manually surveyed using the MNemo V2 instrument, providing an additional set of measurements validating the proposed approach. It is worth noting that with the use of action cameras, the primary components of a cave map can be constructed. Furthermore, with the utilization of a global optimization framework guided by the results of VI-SLAM package SVIn2, photorealistic dense 3D representations of selected areas can be reconstructed.
The EDGE-CALIFA Survey: An integral field unit-based integrated molecular gas database for galaxy evolution studies in the Local Universe
2025-07-08
D. Colombo, V. Kalinova, Z. Bazzi, S. F. Sanchez, A. D. Bolatto, T. Wong, V. Villanueva, N. Mudivarthi, E. Rosolowsky, A. Weiß, K. D. French, A. Leroy, J. Barrera-Ballesteros, Y. Garay-Solis, F. Bigiel, A. Tripathi, B. Rodriguez
Studying galaxy evolution requires knowledge not only of the stellar properties, but also of the interstellar medium (in particular the molecular phase) out of which stars form, using a statistically significant and unbiased sample of galaxies. To this end, we introduce here the integrated Extragalactic Database for Galaxy Evolution (iEDGE), a collection of integrated stellar and nebular emission lines, and molecular gas properties from 643 galaxies in the local Universe. These galaxies are drawn from the CALIFA datasets, and are followed up in CO lines by the APEX, CARMA, and ACA telescopes. As this database is assembled from data coming from a heterogeneous set of telescopes (including IFU optical data and single-dish and interferometric CO data), we adopted a series of techniques (tapering, spatial and spectral smoothing, and aperture correction) to homogenise the data. Due to the application of these techniques, the database contains measurements from the inner regions of the galaxies and for the full galaxy extent. We used the database to study the fundamental star formation relationships between star formation rate (SFR), stellar mass ($M_*$), and molecular gas mass ($M_{\rm mol}$) across galaxies with different morphologies. We observed that the diagrams defined by these quantities are bi-modal, with early-type passive objects well separated from spiral star-forming galaxies. Additionally, while the molecular gas fraction ($f_{\rm mol}=M_{\rm mol}/M_*$) decreases homogeneously across these two types of galaxies, the star formation efficiency (SFE=SFR/$M_{\rm mol}$) in the inner regions of passive galaxies is almost two orders of magnitude lower compared to the global values. This indicates that inside-out quenching requires not only low $f_{\rm mol}$, but also strongly reduced SFE in the galactic centres.
Digital Wargames to Enhance Military Medical Evacuation Decision-Making
2025-07-08
Jeremy Fischer, Ram Krishnamoorthy, Vishal Kumar, Mahdi Al-Husseini
Medical evacuation is one of the United States Army's most storied and critical mission sets, responsible for efficiently and expediently evacuating the battlefield ill and injured. Medical evacuation planning involves designing a robust network of medical platforms and facilities capable of moving and treating large numbers of casualties. Until now, there has not been a medium to simulate these networks in a classroom setting and evaluate both offline planning and online decision-making performance. This work describes the Medical Evacuation Wargaming Initiative (MEWI), a three-dimensional multiplayer simulation developed in Unity that replicates battlefield constraints and uncertainties. MEWI accurately models patient interactions at casualty collection points, ambulance exchange points, medical treatment facilities, and evacuation platforms. Two operational scenarios are introduced: an amphibious island assault in the Pacific and a Eurasian conflict across a sprawling road and river network. These scenarios pit students against the clock to save as many casualties as possible while adhering to doctrinal lessons learned during didactic training. We visualize performance data collected from two iterations of the MEWI Pacific scenario executed in the United States Army's Medical Evacuation Doctrine Course. We consider post-wargame Likert survey data from student participants and external observer notes to identify key planning decision points, document medical evacuation lessons learned, and quantify general utility. Results indicate that MEWI participation substantially improves uptake of medical evacuation lessons learned and co-operative decision-making. MEWI is a substantial step forward in the field of high-fidelity training tools for medical education, and our study findings offer critical insights into improving medical evacuation education and operations across the joint force.
Experimental Ground-State Energy of a 125-Site Flat Kagome Antiferromagnet via Hamiltonian Engineering on Quantum Computer
2025-07-08
Muhammad Ahsan
We present an instance of utility-grade quantum computation by calculating the ground-state energy of a 125-site flat Kagome lattice under the antiferromagnetic Heisenberg model (KAFH), using IBM's Falcon and Hummingbird quantum processors. For spin-1/2 KAFH, our best per-site ground-state energy estimate reaches -0.417J, and after applying open-boundary corrections, it closely approaches the established thermodynamic value of -0.438J. To achieve this, we propose a hybrid approach that splits the variational quantum eigensolver (VQE) into local (classical) and global (quantum) components for efficient hardware utilization. We further introduce a Hamiltonian engineering strategy that increases coupling on defect triangles to mimic loop-flip dynamics, allowing us to simplify the ansatz while retaining physical accuracy. Using a single-repetition, hardware-efficient ansatz, we entangle up to 103 qubits with high fidelity to determine the Hamiltonian's lowest eigenvalue. This work demonstrates the scalability of VQE for frustrated 2D systems and lays the foundation for future studies using deeper ansatz circuits and larger lattices.
Deep learning-based species-area models reveal multi-scale patterns of species richness and turnover
2025-07-08
Victor Boussange, Philipp Brun, Johanna T. Malle, Gabriele Midolo, Jeanne Portier, Théophile Sanchez, Niklaus E. Zimmermann, Irena Axmanová, Helge Bruelheide, Milan Chytrý, Stephan Kambach, Zdeňka Lososová, Martin Večeřa, Idoia Biurrun, Klaus T. Ecker, Jonathan Lenoir, Jens-Christian Svenning, Dirk Nikolaus Karger
The number of species within ecosystems is influenced not only by their intrinsic characteristics but also by the spatial scale considered. As the sampled area expands, species richness increases, a phenomenon described by the species-area relationship (SAR). The accumulation dynamics of the SAR results from a complex interplay of biotic and abiotic processes operating at various spatial scales. However, the challenge of collecting exhaustive biodiversity records across spatial scales has hindered a comprehensive understanding of these dynamics. Here, we develop a deep learning approach that leverages sampling theory and small-scale ecological surveys to spatially resolve the scale-dependency of species richness. We demonstrate its performance by predicting the species richness of vascular plant communities across Europe, and evaluate the predictions against an independent dataset of plant community inventories. Our model improves species richness estimates by 32\% and delivers spatially explicit patterns of species richness and turnover for sampling areas ranging from square meters to hundreds of square kilometers. Explainable AI techniques further disentangle how drivers of species richness operate across spatial scales. The ability of our model to represent the multi-scale nature of biodiversity is essential to deliver robust biodiversity assessments and forecasts under global change.
A proposal and assessment of an improved heuristic for the Eager Test smell detection
2025-07-08
Huynh Khanh Vi Tran, Nauman bin Ali, Michael Unterkalmsteiner, Jürgen Börstler
Context: The evidence for the prevalence of test smells at the unit testing level has relied on the accuracy of detection tools, which have seen intense research in the last two decades. The Eager Test smell, one of the most prevalent, is often identified using simplified detection rules that practitioners find inadequate. Objective: We aim to improve the rules for detecting the Eager Test smell. Method: We reviewed the literature on test smells to analyze the definitions and detection rules of the Eager Test smell. We proposed a novel, unambiguous definition of the test smell and a heuristic to address the limitations of the existing rules. We evaluated our heuristic against existing detection rules by manually applying it to 300 unit test cases in Java. Results: Our review identified 56 relevant studies. We found that inadequate interpretations of original definitions of the Eager Test smell led to imprecise detection rules, resulting in a high level of disagreement in detection outcomes. Also, our heuristic detected patterns of eager and non-eager tests that existing rules missed. Conclusion: Our heuristic captures the essence of the Eager Test smell more precisely; hence, it may address practitioners' concerns regarding the adequacy of existing detection rules.
Quantum sensing with ultracold simulators in lattice and ensemble systems: a review
2025-07-08
Keshav Das Agarwal, Sayan Mondal, Ayan Sahoo, Debraj Rakshit, Aditi Sen De, Ujjwal Sen
Sensing of parameters is an important aspect in all disciplines, with applications ranging from fundamental science to medicine. Quantum sensing and metrology is an emerging field that lies at the cross-roads of quantum physics, quantum technology, and the discipline in which the parameter estimation is to be performed. While miniaturization of devices often requires quantum mechanics to be utilized for understanding and planning of a parameter estimation, quantum-enhanced sensing is also possible that uses paradigmatic quantum characteristics like quantum coherence and quantum entanglement to go beyond the so-called standard quantum limit. The current review hopes to bring together the concepts related to quantum sensing as realized in ensemble systems, like spin ensembles, light-matter systems, and Bose-Einstein condensates, and lattice systems, like those which can be modelled by the Bose- and Fermi-Hubbard models, and quantum spin models.
Quality attributes of test cases and test suites -- importance & challenges from practitioners' perspectives
2025-07-08
Huynh Khanh Vi Tran, Nauman bin Ali, Michael Unterkalmsteiner, Jürgen Börstler, Panagiota Chatzipetrou
Context: The quality of the test suites and the constituent test cases significantly impacts confidence in software testing. While research has identified several quality attributes of test cases and test suites, there is a need for a better understanding of their relative importance in practice. Objective: We investigate practitioners' perceptions regarding the relative importance of quality attributes of test cases and test suites and the challenges they face in ensuring the perceived important quality attributes. Method: We conducted an industrial survey using a questionnaire based on the quality attributes identified in an extensive literature review. We used a sampling strategy that leverages LinkedIn to draw a large and heterogeneous sample of professionals with experience in software testing. Results: We collected 354 responses from practitioners with a wide range of experience. We found that the majority of practitioners rated Fault Detection, Usability, Maintainability, Reliability, and Coverage to be the most important quality attributes. Resource Efficiency, Reusability, and Simplicity received the most divergent opinions, which, according to our analysis, depend on the software-testing contexts. We identified common challenges that apply to the important attributes, namely inadequate definition, lack of useful metrics, lack of an established review process, and lack of external support. Conclusion: The findings point out where practitioners actually need further support with respect to achieving high-quality test cases and test suites under different software testing contexts. The findings can serve as a guideline for academic researchers when looking for research directions on the topic. The findings can also be used to encourage companies to provide more support to practitioners to achieve high-quality test cases and test suites.
IceCube is a neutrino observatory located at the South Pole that uses Antarctic ice as a medium for detection of Cherenkov photons. As such, analysis of the data relies on our understanding of the properties of ice within and around the instrumented volume. Over the years we have made significant progress in understanding the glacial ice and now have a comprehensive model that covers many of the relevant aspects of the photon propagation in it. In this report we give a historical overview of the ice description within the IceCube detector, list some of the remaining issues, and assess how much more improvement is still needed. As the IceCube Upgrade is expected to be installed in less than a year, with several new types of calibration devices aiming to further our understanding of ice, this is the perfect time to review the current state of the ice model.
Could the Road to Grounded, Neuro-symbolic AI be Paved with Words-as-Classifiers?
2025-07-08
Casey Kennington, David Schlangen
Formal, Distributional, and Grounded theories of computational semantics each have their uses and their drawbacks. There has been a shift to ground models of language by adding visual knowledge, and there has been a call to enrich models of language with symbolic methods to gain the benefits from formal, distributional, and grounded theories. In this paper, we attempt to make the case that one potential path forward in unifying all three semantic fields is paved with the words-as-classifier model, a model of word-level grounded semantics that has been incorporated into formalisms and distributional language models in the literature, and it has been well-tested within interactive dialogue settings. We review that literature, motivate the words-as-classifiers model with an appeal to recent work in cognitive science, and describe a small experiment. Finally, we sketch a model of semantics unified through words-as-classifiers.
Centralized Copy-Paste: Enhanced Data Augmentation Strategy for Wildland Fire Semantic Segmentation
2025-07-08
Joon Tai Kim, Tianle Chen, Ziyu Dong, Nishanth Kunchala, Alexander Guller, Daniel Ospina Acero, Roger Williams, Mrinal Kumar
Collecting and annotating images for the purpose of training segmentation models is often cost prohibitive. In the domain of wildland fire science, this challenge is further compounded by the scarcity of reliable public datasets with labeled ground truth. This paper presents the Centralized Copy-Paste Data Augmentation (CCPDA) method, for the purpose of assisting with the training of deep-learning multiclass segmentation models, with special focus on improving segmentation outcomes for the fire-class. CCPDA has three main steps: (i) identify fire clusters in the source image, (ii) apply a centralization technique to focus on the core of the fire area, and (iii) paste the refined fire clusters onto a target image. This method increases dataset diversity while preserving the essential characteristics of the fire class. The effectiveness of this augmentation technique is demonstrated via numerical analysis and comparison against various other augmentation methods using a weighted sum-based multi-objective optimization approach. This approach helps elevate segmentation performance metrics specific to the fire class, which carries significantly more operational significance than other classes (fuel, ash, or background). Numerical performance assessment validates the efficacy of the presented CCPDA method in alleviating the difficulties associated with small, manually labeled training datasets. It also illustrates that CCPDA outperforms other augmentation strategies in the application scenario considered, particularly in improving fire-class segmentation performance.
Deep Learning Improves Photometric Redshifts in All Regions of Color Space
2025-07-08
Emma R. Moran, Brett H. Andrews, Jeffrey A. Newman, Biprateep Dey
Photometric redshifts (photo-$z$'s) are crucial for the cosmology, galaxy evolution, and transient science drivers of next-generation imaging facilities like the Euclid Mission, the Rubin Observatory, and the Nancy Grace Roman Space Telescope. Previous work has shown that image-based deep learning photo-$z$ methods produce smaller scatter than photometry-based classical machine learning (ML) methods on the Sloan Digital Sky Survey (SDSS) Main Galaxy Sample, a testbed photo-$z$ dataset. However, global assessments can obscure local trends. To explore this possibility, we used a self-organizing map (SOM) to cluster SDSS galaxies based on their $ugriz$ colors. Deep learning methods achieve lower photo-$z$ scatter than classical ML methods for all SOM cells. The fractional reduction in scatter is roughly constant across most of color space with the exception of the most bulge-dominated and reddest cells where it is smaller in magnitude. Interestingly, classical ML photo-$z$'s suffer from a significant color-dependent attenuation bias, where photo-$z$'s for galaxies within a SOM cell are systematically biased towards the cell's mean spectroscopic redshift and away from extreme values, which is not readily apparent when all objects are considered. In contrast, deep learning photo-$z$'s suffer from very little color-dependent attenuation bias. The increased attenuation bias for classical ML photo-$z$ methods is the primary reason why they exhibit larger scatter than deep learning methods. This difference can be explained by the deep learning methods weighting redshift information from the individual pixels of a galaxy image more optimally than integrated photometry.
Exploring Cosmic Dawn with PANORAMIC I: The Bright End of the UVLF at $z\sim9 -17$
2025-07-08
Andrea Weibel, Pascal A. Oesch, Christina C. Williams, Christian Kragh Jespersen, Marko Shuntov, Katherine E. Whitaker, Hakim Atek, Rachel Bezanson, Gabriel Brammer, Iryna Chemerynska, Aidan P. Cloonan, Pratika Dayal, Lukas J. Furtak, Anne Hutter, Zhiyuan Ji, Michael V. Maseda, Mengyuan Xiao
In its first two years of operation, the James Webb Space Telescope has enabled the discovery of a surprising number of UV-bright galaxies at $z\sim10-14$. Their number density is still relatively uncertain, due to cosmic variance effects, and the limited survey area with deep imaging. Here, we combine pure parallel imaging from the PANORAMIC survey with data from legacy fields to constrain the bright end (M$_{\rm UV}<-18.5$) of the UV luminosity function (UVLF) over $0.28\,$deg$^2$ of NIRCam imaging in 6 or more filters, and along 35 independent lines of sight. Using conservative color selections, we compile robust dropout samples at $z\sim10$, $z\sim13$, and $z\sim17$, and identify 16 new candidates from PANORAMIC. Our inferred UVLFs at $z\sim10$ are consistent with literature results and we confirm the high abundance of galaxies at the bright end (M$_{\rm UV}\lesssim-21$) with better number statistics. We find somewhat lower number densities at $z\sim13$ compared to previous studies, and no robust candidates at $z\sim17$, indicating a rapid evolution of the galaxy population from $z\sim10-17$. The improved upper limits at $z\sim17$ imply that the cosmic UV luminosity density drops by at least a factor $\sim50$ from $z\sim10$ to $z\sim17$. Comparing our results to models proposed to explain the abundance of UV-bright galaxies at $z\gtrsim10$, we conclude that a modest increase in the star formation efficiency, or in the burstiness of star formation, a more top-heavy initial mass function, a lack of dust attenuation, or a combination of these effects at $z\gtrsim10$, is sufficient to match our observational constraints.
Broad-band Spectral Modeling of Prompt Emission from Gamma-Ray Bursts Observed by the Transiting Exoplanet Survey Satellite
2025-07-08
Rahul Jayaraman, Michael Fausnaugh, George Ricker, Roland Vanderspek
Optical observations of gamma-ray bursts (GRBs) contemporaneous with their prompt high-energy emission are rare, but they can provide insights into the physical processes underlying these explosive events. The Transiting Exoplanet Survey Satellite's (TESS) large field of view and continuous observation capabilities make it uniquely positioned to detect and characterize prompt optical flashes from GRBs. In this work, we fit phenomenological models to the gamma-ray through optical spectral energy distributions (SEDs) of 24 bursts with arcsecond-level localizations that fell within the TESS field of view between 2018 July and 2024 December. In four cases, the extrapolation of the high-energy SED agrees with the observed optical flux to within 1-$\sigma$. In one case, there is a significant excess of optical flux relative to the extrapolation. In two cases, upper limits from TESS did not constrain the optical portion of the SED. In the remaining 17 cases, the optical flux is overpredicted by the extrapolation from high energies. This discrepancy could be explained by dust extinction in the host galaxy.
We introduce and review a new complexity measure, called `Krylov complexity', which takes its origins in the field of quantum-chaotic dynamics, serving as a canonical measure of operator growth and spreading. Krylov complexity, underpinned by the Lanczos algorithm, has since evolved into a highly diverse field of its own right, both because of its attractive features as a complexity, whose definition does not depend on arbitrary control parameters, and whose phenomenology serves as a rich and sensitive probe of chaotic dynamics up to exponentially late times, but also because of its relevance to seemingly far-afield subjects such as holographic dualities and the quantum physics of black holes. In this review we give a unified perspective on these topics, emphasizing the robust and most general features of K-complexity, both in chaotic and integrable systems, state and prove theorems on its generic features and describe how it is geometrised in the context of (dual) gravitational dynamics. We hope that this review will serve both as a source of intuition about K-complexity in and of itself, as well as a resource for researchers trying to gain an overview over what is by now a rather large and multi-faceted literature. We also mention and discuss a number of open problems related to K-complexity, underlining its currently very active status as a field of research.
Accurate geo-registration of LiDAR point clouds presents significant challenges in GNSS signal denied urban areas with high-rise buildings and bridges. Existing methods typically rely on real-time GNSS and IMU data, that require pre-calibration and assume stable positioning during data collection. However, this assumption often fails in dense urban areas, resulting in localization errors. To address this, we propose a structured geo-registration and spatial correction method that aligns 3D point clouds with satellite images, enabling frame-wise recovery of GNSS information and reconstruction of city scale 3D maps without relying on prior localization. The proposed approach employs a pre-trained Point Transformer model to segment the road points and then extracts the road skeleton and intersection points from the point cloud as well as the target map for alignment. Global rigid alignment of the two is performed using the intersection points, followed by local refinement using radial basis function (RBF) interpolation. Elevation correction is then applied to the point cloud based on terrain information from SRTM dataset to resolve vertical discrepancies. The proposed method was tested on the popular KITTI benchmark and a locally collected Perth (Western Australia) CBD dataset. On the KITTI dataset, our method achieved an average planimetric alignment standard deviation (STD) of 0.84~m across sequences with intersections, representing a 55.3\% improvement over the original dataset. On the Perth dataset, which lacks GNSS information, our method achieved an average STD of 0.96~m compared to the GPS data extracted from Google Maps API. This corresponds to a 77.4\% improvement from the initial alignment. Our method also resulted in elevation correlation gains of 30.5\% on the KITTI dataset and 50.4\% on the Perth dataset.
The increasing interest in research and innovation towards the development of autonomous agents presents a number of complex yet important scenarios of multiple AI Agents interacting with each other in an environment. The particular setting can be understood as exhibiting three possibly topologies of interaction - centrally coordinated cooperation, ad-hoc interaction and cooperation, and settings with noncooperative incentive structures. This article presents a comprehensive survey of all three domains, defined under the formalism of Federal Reinforcement Learning (RL), Decentralized RL, and Noncooperative RL, respectively. Highlighting the structural similarities and distinctions, we review the state of the art in these subjects, primarily explored and developed only recently in the literature. We include the formulations as well as known theoretical guarantees and highlights and limitations of numerical performance.
Advancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques
2025-07-08
Yassin Hussein Rassul, Aram M. Ahmed, Polla Fattah, Bryar A. Hassan, Arwaa W. Abdulkareem, Tarik A. Rashid, Joan Lu
Offline Handwritten Text Recognition (HTR) systems play a crucial role in applications such as historical document digitization, automatic form processing, and biometric authentication. However, their performance is often hindered by the limited availability of annotated training data, particularly for low-resource languages and complex scripts. This paper presents a comprehensive survey of offline handwritten data augmentation and generation techniques designed to improve the accuracy and robustness of HTR systems. We systematically examine traditional augmentation methods alongside recent advances in deep learning, including Generative Adversarial Networks (GANs), diffusion models, and transformer-based approaches. Furthermore, we explore the challenges associated with generating diverse and realistic handwriting samples, particularly in preserving script authenticity and addressing data scarcity. This survey follows the PRISMA methodology, ensuring a structured and rigorous selection process. Our analysis began with 1,302 primary studies, which were filtered down to 848 after removing duplicates, drawing from key academic sources such as IEEE Digital Library, Springer Link, Science Direct, and ACM Digital Library. By evaluating existing datasets, assessment metrics, and state-of-the-art methodologies, this survey identifies key research gaps and proposes future directions to advance the field of handwritten text generation across diverse linguistic and stylistic landscapes.
Exoplanet Atmospheric Escape Observations with the Habitable Worlds Observatory
2025-07-08
Leonardo A. Dos Santos, Eric D. Lopez, Luca Fossati, Antonio García Muñoz, Shingo Kameda, Munazza K. Alam, Keighley Rockcliffe, Seth Redfield, Yuichi Ito, Joshua Lothringer, Shreyas Vissapragada, Hannah R. Wakeford, Apurva V. Oza, Girish M. Duvvuri, Raissa Estrela, Ryoya Sakata, Chuanfei Dong, Ziyu Huang
The Decadal Survey on Astronomy and Astrophysics 2020 highlights the importance of advancing research focused on discovering and characterizing habitable worlds. In line with this priority, our goal is to investigate how planetary systems evolve through atmospheric escape and to develop methods for identifying potentially Earth-like planets. By leveraging the ultraviolet (UV) capabilities of the Habitable Worlds Observatory (HWO), we can use transit spectroscopy to observe atmospheric escape in exoplanets and explore the processes that shape their evolution, assess the ability of small planets to retain their atmospheres, and search for signs of Earth-like atmospheres. To achieve this, we support the development of a UV spectrograph with moderate- to high-resolution capabilities for point-source observations, coverage of key spectral features in the 100-300 nm range, and detectors that can register high count rates reliably. This article is an adaptation of a science case document developed for the Characterizing Exoplanets Steering Committee within HWO's Solar Systems in Context Working Group.
First release of LiteBIRD simulations from an end-to-end pipeline
2025-07-08
M. Bortolami, N. Raffuzzi, L. Pagano, G. Puglisi, A. Anand, A. J. Banday, P. Campeti, G. Galloni, A. I. Lonappan, M. Monelli, M. Tomasi, G. Weymann-Despres, D. Adak, E. Allys, J. Aumont, R. Aurvik, C. Baccigalupi, M. Ballardini, R. B. Barreiro, N. Bartolo, S. Basak, M. Bersanelli, A. Besnard, T. Brinckmann, E. Calabrese, E. Carinos, A. Carones, F. J. Casas, K. Cheung, M. Citran, L. Clermont, F. Columbro, G. Coppi, A. Coppolecchia, F. Cuttaia, P. Dal Bo, P. de Bernardis, E. de la Hoz, M. De Lucia, S. Della Torre, P. Diego-Palazuelos, H. K. Eriksen, T. Essinger-Hileman, C. Franceschet, U. Fuskeland, M. Galloway, M. Gerbino, M. Gervasi, T. Ghigna, S. Giardiello, C. Gimeno-Amo, E. Gjerløw, A. Gruppuso, M. Hazumi, S. Henrot-Versillé, L. T. Hergt, E. Hivon, B. Jost, K. Kohri, L. Lamagna, T. Lari, M. Lattanzi, C. Leloup, F. Levrier, M. López-Caniego, G. Luzzi, J. Macias-Perez, B. Maffei, E. Martínez-González, S. Masi, S. Matarrese, T. Matsumura, S. Micheli, L. Montier, G. Morgante, L. Mousset, R. Nagata, T. Namikawa, A. Novelli, F. Noviello, I. Obata, A. Occhiuzzi, A. Paiella, D. Paoletti, G. Pascual-Cisneros, F. Piacentini, M. Pinchera, G. Polenta, L. Porcelli, M. Remazeilles, A. Ritacco, A. Rizzieri, J. A. Rubiño-Martín, M. Ruiz-Granda, J. Sanghavi, V. Sauvage, M. Shiraishi, G. Signorelli, S. L. Stever, R. M. Sullivan, Y. Takase, K. Tassis, L. Terenzi, M. Tristram, L. Vacher, B. van Tent, P. Vielva, I. K. Wehus, M. Zannoni, Y. Zhou
The LiteBIRD satellite mission aims at detecting Cosmic Microwave Background $B$ modes with unprecedented precision, targeting a total error on the tensor-to-scalar ratio $r$ of $\delta r \sim 0.001$. Operating from the L2 Lagrangian point of the Sun-Earth system, LiteBIRD will survey the full sky across 15 frequency bands (34 to 448 GHz) for 3 years.The current LiteBIRD baseline configuration employs 4508 detectors sampling at 19.1 Hz to achieve an effective polarization sensitivity of $ 2 \mu\mathrm{K-arcmin}$ and an angular resolution of 31 arcmin (at 140 GHz).We describe the first release of the official LiteBIRD simulations, realized with a new simulation pipeline developed using the LiteBIRD Simulation Framework.This pipeline generates 500 full-sky simulated maps at a Healpix resolution of nside=512. The simulations include also one year of Time Ordered Data for approximately one-third of LiteBIRD's total detectors.
A High Resolution Search for Dual AGNs in Mergers: Pushing the Frontier with Keck AO
2025-07-07
Camilo Vazquez, S. Satyapal, G. Canalizo, N. J. Secrest, R. W. Pfeifle, T. Bohn, K. Nyland, A. Aravindan, L. Blecha, J. M. Cann, S. Doan, E. K. Hicks, P. Kurczynski, S. Juneau, M. Malkan, M. McDonald, J. McKaig, P. Nair, B. Rothberg, E. Schwartzman, F. Muller-Sanchez, R. Sexton, V. U
Accreting supermassive black holes (SMBHs) in galaxy mergers with separations $<$ 1kpc are crucial to our understanding of SMBH growth, galaxy evolution, and the evolution of SMBH binaries. Despite their importance, there are less than a handful known, and most have been discovered serendipitously. In this work, we employ a new selection method to systematically pre-select candidate advanced mergers likely to contain unresolved substructure at sub-arcsecond scales. By exploiting the large survey area and astrometric precision of the Wide-field Infrared Survey Explorer (WISE) and the Sloan Digital Sky Survey (SDSS), we have identified a sample of 48 nearby advanced mergers that have red WISE colors ($W_1-W_2>0.5$) indicative of accretion activity and significant sub-arcsecond offsets between their optical and infrared coordinates as measured by SDSS and WISE. We conducted high resolution adaptive optics (AO) observations of this sample with the Keck NIRC2 camera in the $K_p$ band ($2.124 ~ \mu m$, $\Delta\lambda = 0.351 \mu m$) to search for evidence of previously unresolved substructure suggested by the optical-to-infared offsets. We find that a significant fraction (20/48 or 42%) of the sample shows substructure tracing the SDSS/WISE offset and unresolved by SDSS, demonstrating that our methodology is a promising pathway to find dual AGN in follow-up spectroscopy. Archival optical Hubble Space Telescope (HST) imaging reveals that substructure identified with Keck is often missed in the optical or erroneously identified due to partial obscuration, underscoring the importance of carrying out studies of late-stage mergers in the infrared.
An Investigation into Maintenance Support for Neural Networks
2025-07-07
Fatema Tuz Zohra, Brittany Johnson
As the potential for neural networks to augment our daily lives grows, ensuring their quality through effective testing, debugging, and maintenance is essential. This is especially the case as we acknowledge the prospects of negative impacts from these technologies. Traditional software engineering methods, such as testing and debugging, have proven effective in maintaining software quality; however, they reveal significant research and practice gaps in maintaining neural networks. In particular, there is a limited understanding of how practitioners currently address challenges related to understanding and mitigating undesirable behaviors in neural networks. In our ongoing research, we explore the current state of research and practice in maintaining neural networks by curating insights from practitioners through a preliminary study involving interviews and supporting survey responses. Our findings thus far indicate that existing tools primarily concentrate on building and training models. While these tools can be beneficial, they often fall short of supporting practitioners' understanding and addressing the underlying causes of unexpected model behavior. By evaluating current procedures and identifying the limitations of traditional methodologies, our study aims to offer a developer-centric perspective on where current practices fall short and highlight opportunities for improving maintenance support in neural networks.
Constraining the Milky Way Halo Accretion History With Simulated Stellar Halos: Designing the HALO7D-X Survey
2025-07-07
Miranda Apfel, Kevin A. McKinnon, Constance M. Rockosi, Puragra Guhathakurta, Kathryn V. Johnston
We present the design for HALO7D-X, a survey of the stellar halo to investigate the accretion history of the Milky Way. The survey will use a combination of Hubble Space Telescope (HST) and Gaia data for sky position and proper motions of faint stars (18
CTA: Cross-Task Alignment for Better Test Time Training
2025-07-07
Samuel Barbeau, Pedram Fekri, David Osowiechi, Ali Bahri, Moslem Yazdanpanah, Masih Aminbeidokhti, Christian Desrosiers
Deep learning models have demonstrated exceptional performance across a wide range of computer vision tasks. However, their performance often degrades significantly when faced with distribution shifts, such as domain or dataset changes. Test-Time Training (TTT) has emerged as an effective method to enhance model robustness by incorporating an auxiliary unsupervised task during training and leveraging it for model updates at test time. In this work, we introduce CTA (Cross-Task Alignment), a novel approach for improving TTT. Unlike existing TTT methods, CTA does not require a specialized model architecture and instead takes inspiration from the success of multi-modal contrastive learning to align a supervised encoder with a self-supervised one. This process enforces alignment between the learned representations of both models, thereby mitigating the risk of gradient interference, preserving the intrinsic robustness of self-supervised learning and enabling more semantically meaningful updates at test-time. Experimental results demonstrate substantial improvements in robustness and generalization over the state-of-the-art on several benchmark datasets.
Reasoning with quantifier expressions in natural language combines logical and arithmetical features, transcending strict divides between qualitative and quantitative. Our topic is this cooperation of styles as it occurs in common linguistic usage and its extension into the broader practice of natural language plus "grassroots mathematics". We begin with a brief review of first-order logic with counting operators and cardinality comparisons. This system is known to be of high complexity, and drowns out finer aspects of the combination of logic and counting. We move to a small fragment that can represent numerical syllogisms and basic reasoning about comparative size: monadic first-order logic with counting. We provide normal forms that allow for axiomatization, determine which arithmetical notions can be defined on finite and on infinite models, and conversely, we discuss which logical notions can be defined out of purely arithmetical ones, and what sort of (non-)classical logics can be induced. Next, we investigate a series of strengthenings, again using normal form methods. The monadic second-order version is close, in a precise sense, to additive Presburger Arithmetic, while versions with the natural device of tuple counting take us to Diophantine equations, making the logic undecidable. We also define a system that combines basic modal logic over binary accessibility relations with counting, needed to formulate ubiquitous reasoning patterns such as the Pigeonhole Principle. We return to our starting point in natural language, confronting the architecture of our formal systems with linguistic quantifier vocabulary and syntax. We conclude with some general thoughts on yet further entanglements of logic and counting in formal systems, on rethinking the qualitative/quantitative divide, and on connecting our analysis to empirical findings in cognitive science.
2025-07-07
Saúl A. Herrera-González, Hugo A. Lara-García, Giuseppe Pirruccio, David A. Ruiz-Tijerina, Arturo Camacho-Guardian
Distinguished by their long lifetimes, strong dipolar interactions, and periodic confinement, moir\'e excitons provide a fertile territory for realizing interaction-driven excitonic phases beyond conventional semiconductor systems. Formed in twisted or lattice-mismatched van der Waals heterostructures, these excitons are shaped by a periodic potential landscape that enables the engineering of flat bands, strong interactions, and long-lived localised states. This has opened pathways to explore strongly correlated phases, including excitonic insulators, superfluids, and supersolids, potentially stable even at room temperature. When embedded in optical cavities, moir\'e excitons hybridize with photons to form moir\'e exciton-polaritons, a new class of quasiparticles exhibiting enhanced optical nonlinearities and novel topological features. In this review, we survey the theoretical foundations and experimental progress in the field of moir\'e excitons and polaritons. We begin by introducing the formation mechanisms of moir\'e patterns in two-dimensional semiconductors, and describe their impact on exciton confinement, optical selection rules, and spin-valley physics. We then discuss recent advances in the realization of many-body excitonic phases and exciton-based probes of electronic correlations. Finally, we explore the novel aspects of moir\'e polaritons, highlighting their unique nonlinear and topological properties. By bridging quantum optics, nanophotonics, and correlated electron systems, moir\'e excitons offer a powerful solid-state platform for quantum simulation, optoelectronic applications, and many-body photonics.
Exploring Responsible Innovation efforts in Canada and the world
2025-07-07
Ria Chakraborty, Bruna S. de Mendonça, Katya Driscoll, Rodolfo R. Soldati, Ray Laflamme
The global landscape for quantum technologies (QTs) is rapidly changing, and proper understanding of their impact and subsequent regulations need to match this pace. A Responsible Innovation (RI) approach and guiding principles have been proposed to accompany this development. We examine practical efforts globally and in Canada, from industry to research to governments, and analyze the current status of quantum technological advances under the RI framework. We analyze and compare what is being done internationally, identify gaps in the Canadian strategy, propose initiatives to fill those gaps, and highlight areas where Canada is leading or where more work is needed.
Mass Proxy Quality of Massive Halo Properties in the IllustrisTNG and FLAMINGO Simulations: I. Hot Gas
2025-07-07
Eddie Aljamal, August E. Evrard, Arya Farahi, Annalisa Pillepich, Dylan Nelson, Joop Schaye, Matthieu Schaller, Joey Braspenning
We examine scale and redshift dependence of mass-property relations (MPRs) for five hot gas properties of two large group- and cluster-scale halo samples realized by the IllustrisTNG, TNG-Cluster and FLAMINGO cosmological hydrodynamical simulations. For intrinsic properties of i) hot gas mass ($M_{\rm gas}$), ii) spectroscopic-like temperature ($T_{\rm sl}$), iii) soft-band X-ray luminosity ($L_{\rm X}$), and iv) X-ray ($Y_{\rm X}$) and v) Sunyaev-Zel'dovich ($Y_{\rm SZ}$) thermal energies, we use MPR parameters to infer mass proxy quality (MPQ) -- the implied scatter in total halo mass conditioned on a property -- for halos with $M_{\rm 500c} \geq 10^{13}{\, {\rm M}_\odot}$ at redshifts, $z \in \{0, 0.5, 1, 2\}$. We find: (1) in general, scaling relation slopes and covariance display moderate to strong dependence on halo mass, with redshift dependence secondary, (2) for halos with $M_{\rm 500c} > 10^{14}{\, {\rm M}_\odot}$, scalings of $M_{\rm gas}$ and $Y_{\rm SZ}$ simplify toward self-similar slope and constant intrinsic scatter (5 and 10%, respectively) nearly independent of scale, making both measures ideal for cluster finding and characterization to $z=2$, (3) halo mass-conditioned likelihoods of hot gas mass and thermal energy at fixed halo mass closely follow a log-normal form, (4) despite normalization differences ranging up to $0.4$ dex, there is good qualitative, and often quantitative, agreement between the scale-dependent slopes and property covariance of the two simulations. Slopes show appreciable redshift dependence at the group scale, while redshift dependence of the scatter is exhibited by low mass FLAMINGO halos only, (5) property correlations are largely consistent between the simulations, with values that mainly agree with existing empirical measurements. We close with a literature survey placing our MPR slopes and intrinsic scatter estimates into context.
Extreme Learning Machine Based System for DDoS Attacks Detections on IoMT Devices
2025-07-07
Nelly Elsayed, Lily Dzamesi, Zag ElSayed, Murat Ozer
The Internet of Medical Things (IoMT) represents a paradigm shift in the healthcare sector, enabling the interconnection of medical devices, sensors, and systems to enhance patient monitoring, diagnosis, and management. The rapid evolution of IoMT presents significant benefits to the healthcare domains. However, there is a rapid increase in distributed denial of service (DDoS) attacks on the IoMT networks due to several vulnerabilities in the IoMT-connected devices, which negatively impact patients' health and can even lead to deaths. Thus, in this paper, we aim to save lives via investigating an extreme learning machine for detecting DDoS attacks on IoMT devices. The proposed approach achieves a high accuracy at a low implementation budget. Thus, it can reduce the implementation cost of the DDoS detection system, making the model capable of executing on the fog level.
Understanding Everything as Code: A Taxonomy and Conceptual Model
2025-07-07
Haoran Wei, Nazim Madhavji, John Steinbacher
Background: Everything as Code (EaC) is an emerging paradigm aiming to codify all aspects of modern software systems. Despite its growing popularity, comprehensive industry standards and peer-reviewed research clarifying its scope and guiding its adoption remain scarce. Aims: This study systematically analyzes existing knowledge and perceptions of EaC, clarifies its scope and boundaries, and provides structured guidance for researchers and practitioners. Method: We conducted a large-scale multivocal literature review (MLR), synthesizing academic and grey literature sources. Findings were analyzed quantitatively and thematically. Based on this analysis, we developed a taxonomy and conceptual model of EaC, validated through collaboration with industry experts. Results: The resulting taxonomy comprises 25 distinct EaC practices organized into six layers based on industry awareness and functional roles. The conceptual model illustrates focus areas, overlaps, and interactions among these EaC practices within the software delivery lifecycle. Additionally, practical code examples demonstrating the implementation of these practices were developed in collaboration with industry experts. Conclusions: This work addresses the current scarcity of academic discourse on EaC by providing the first comprehensive taxonomy and conceptual model. These contributions enhance conceptual clarity, offer actionable guidance to practitioners, and lay the groundwork for future research in this emerging domain.
Strongly polarised radio pulses from a new white-dwarf-hosting long-period transient
2025-07-07
Sanne Bloot, Harish K. Vedantham, Cees G. Bassa, Joseph R. Callingham, William M. J. Best, Michael C. Liu, Eugene A. Magnier, Timothy W. Shimwell, Trent J. Dupuy
Long-period transients (LPTs) are a new and enigmatic class of objects that produce bright pulsations in the radio, with periods far exceeding those seen in rotationally powered pulsars. The proposed progenitors for LPTs are contested, with white dwarfs or magnetars being likely candidates. Here, we present the discovery of ILT\,J163430+445010, a new LPT detected in a blind search for Stokes\,V transients in the LOFAR Two-Metre Sky Survey. Unusual for LPTs, J1634+44 shows pulses that are 100\% circularly polarised, as well as pulses that are 100\% linearly polarised, with the polarisation state changing from pulse to pulse. We detect 19 pulses in total, each with a total polarisation fraction of $\sim100\%$ and a pulse duration of at most 10\,s. The pulses show a periodicity at $841.24808\pm0.00015$\,s, implying a low duty cycle of $0.012$. J1634+44 has a marginally detected counterpart in the ultraviolet GALEX MIS survey and the ultraviolet/optical UNIONS survey, suggesting that it contains a white dwarf with an effective temperature between 15000\,K and 33000\,K. We do not detect J1634+44 with a deep $J$-band exposure with UKIRT at a $3\sigma$ AB magnitude limit of 24.7, ruling out a main-sequence star or ultracool dwarf with a spectral type earlier than M7. The pulses from J1634+44 follow a particular pattern, with two pulses being produced every five periods after a waiting time of two or three periods. This pattern could be a result of spin-orbit coupling in a binary system with a 5:2 or 5:3 resonance, where a companion induces beamed radio emission on the white dwarf. The companion is most likely an ultracool dwarf or another white dwarf, making J1634+44 unique among the currently known sample of LPTs.
In 2022, we published a book, \emph{Maximum-Entropy Sampling: Algorithms and Application (Springer)}. Since then, there have been several notable advancements on this topic. In this manuscript, we survey some recent highlights.
What Shapes User Trust in ChatGPT? A Mixed-Methods Study of User Attributes, Trust Dimensions, Task Context, and Societal Perceptions among University Students
2025-07-07
Kadija Bouyzourn, Alexandra Birch
This mixed-methods inquiry examined four domains that shape university students' trust in ChatGPT: user attributes, seven delineated trust dimensions, task context, and perceived societal impact. Data were collected through a survey of 115 UK undergraduate and postgraduate students and four complementary semi-structured interviews. Behavioural engagement outweighed demographics: frequent use increased trust, whereas self-reported understanding of large-language-model mechanics reduced it. Among the dimensions, perceived expertise and ethical risk were the strongest predictors of overall trust; ease of use and transparency had secondary effects, while human-likeness and reputation were non-significant. Trust was highly task-contingent; highest for coding and summarising, lowest for entertainment and citation generation, yet confidence in ChatGPT's referencing ability, despite known inaccuracies, was the single strongest correlate of global trust, indicating automation bias. Computer-science students surpassed peers only in trusting the system for proofreading and writing, suggesting technical expertise refines rather than inflates reliance. Finally, students who viewed AI's societal impact positively reported the greatest trust, whereas mixed or negative outlooks dampened confidence. These findings show that trust in ChatGPT hinges on task verifiability, perceived competence, ethical alignment and direct experience, and they underscore the need for transparency, accuracy cues and user education when deploying LLMs in academic settings.
Surgical action planning requires predicting future instrument-verb-target triplets for real-time assistance. While teleoperated robotic surgery provides natural expert demonstrations for imitation learning (IL), reinforcement learning (RL) could potentially discover superior strategies through exploration. We present the first comprehensive comparison of IL versus RL for surgical action planning on CholecT50. Our Dual-task Autoregressive Imitation Learning (DARIL) baseline achieves 34.6% action triplet recognition mAP and 33.6% next frame prediction mAP with smooth planning degradation to 29.2% at 10-second horizons. We evaluated three RL variants: world model-based RL, direct video RL, and inverse RL enhancement. Surprisingly, all RL approaches underperformed DARIL i.e. world model RL dropped to 3.1% mAP at 10s while direct video RL achieved only 15.9%. Our analysis reveals that distribution matching on expert-annotated test sets systematically favors IL over potentially valid RL policies that differ from training demonstrations. This challenges assumptions about RL superiority in sequential decision making and provides crucial insights for surgical AI development.
Spin alignment of vector mesons in heavy-ion collision
2025-07-07
Xin-Li Sheng, Qun Wang
We give a brief review on the spin alignment induced by the strong field and the shear-stress tensor. In experiments, a significant positive deviation from 1/3 is observed for the $\phi$ meson, which can be explained by the anisotropy of the strong field fluctuation in the meson's rest frame, while the anisotropy is mainly a consequence of the motion of meson relative to the quark-gluon plasma. On the other hand, the shear-induced spin alignment is of the order $10^{-4}\sim10^{-5}$ if the magnitude of thermal shear tensor is $10^{-2}$.
The term emergence is increasingly used across scientific disciplines to describe phenomena that arise from interactions among a system's components but cannot be readily inferred by examining those components in isolation. While often invoked to explain higher-level behaviors, such as flocking, synchronization, or collective intelligence, the term is frequently used without precision, sometimes giving rise to ambiguity or even mystique. In this perspective paper, we clarify the scientific meaning of emergence as a measurable, physically grounded phenomenon. Through concrete examples, such as temperature, magnetism, and herd immunity in social networks, we review how collective behavior can arise from local interactions that are constrained by global boundaries. By disentangling emergence from vague overuse, we emphasize its role as a rigorous tool for understanding complex systems. Our goal is to show that emergence, when properly framed, offers not mysticism but insight.
Over the preceding years, nickel (Ni) and its compounds have been increasingly employed in various aspects of human social life, metallurgical/industrial manufactures, healthcare, and chemical processes. Although Ni is considered an essential trace element in biological systems, excessive intake or metabolic deficiency of $\mathrm{Ni}^{2+}$ ions may cause detrimental health effects to living organisms. Therefore, a facile and accurate detection of $\mathrm{Ni}^{2+}$, especially in environmental and biological settings, is of huge significance. As an efficient detection method, assaying $\mathrm{Ni}^{2+}$ using optical (colorimetric and/or fluorogenic) sensors has experienced quite a vigorous growth period, with a large number of excellent research contributions. Nanomaterial-based optical sensors, including metal nanoparticles (MNPs), quantum dots (QDs), and carbon dots (CDs), offer distinct advantages over conventional small-molecule organic and inorganic sensors. This study mainly provides an overview of the recent advancements and challenges related to the design strategies of various optical nanosensors to selectively detect the $\mathrm{Ni}^{2+}$ ion. Emphasis has also been placed on comparing the sensing performance of various nanosensors, along with exploring future perspectives.
2025-07-07
David Carmel, Simone Filice, Guy Horowitz, Yoelle Maarek, Oren Somekh, Ran Tavory, Mehdi Ghissassi, Edo Liberty, Roy Miara
The LiveRAG Challenge at SIGIR 2025, held between March and May 2025, provided a competitive platform for advancing Retrieval-Augmented Generation (RAG) technologies. Participants from academia and industry were invited to develop a RAG-based question-answering system using a fixed corpus (Fineweb-10BT) and a common open-source LLM (Falcon3-10B-Instruct). The goal was to facilitate challenging comparisons of retrieval and prompting strategies. During the Live Challenge Day, 70 teams from 27 different countries provided answers and supportive information to 500 unseen questions within a strict two-hour time window. Evaluation was conducted in two stages: first an automated LLM-as-a-judge approach was used to compute correctness and faithfulness score, then a manual review of top ranked submissions was conducted. The finalists were announced on June 12, 2025, with prizes awarded during the LiveRAG Workshop at SIGIR 2025 in Padua, Italy.
Multimodal Large Language Models (MLLMs) have demonstrated significant advances in visual understanding tasks involving both images and videos. However, their capacity to comprehend human-centric video data remains underexplored, primarily due to the absence of comprehensive and high-quality evaluation benchmarks. Existing human-centric benchmarks predominantly emphasize video generation quality and action recognition, while overlooking essential perceptual and cognitive abilities required in human-centered scenarios. Furthermore, they are often limited by single-question paradigms and overly simplistic evaluation metrics. To address above limitations, we propose a modern HV-MMBench, a rigorously curated benchmark designed to provide a more holistic evaluation of MLLMs in human-centric video understanding. Compared to existing human-centric video benchmarks, our work offers the following key features: (1) Diverse evaluation dimensions: HV-MMBench encompasses 15 tasks, ranging from basic attribute perception (e.g., age estimation, emotion recognition) to advanced cognitive reasoning (e.g., social relationship prediction, intention prediction), enabling comprehensive assessment of model capabilities; (2) Varied data types: The benchmark includes multiple-choice, fill-in-blank, true/false, and open-ended question formats, combined with diverse evaluation metrics, to more accurately and robustly reflect model performance; (3) Multi-domain video coverage: The benchmark spans 50 distinct visual scenarios, enabling comprehensive evaluation across fine-grained scene variations; (4) Temporal coverage: The benchmark covers videos from short-term (10 seconds) to long-term (up to 30min) durations, supporting systematic analysis of models temporal reasoning abilities across diverse contextual lengths.
BackFed: An Efficient & Standardized Benchmark Suite for Backdoor Attacks in Federated Learning
2025-07-07
Thinh Dao, Dung Thuy Nguyen, Khoa D Doan, Kok-Seng Wong
Federated Learning (FL) systems are vulnerable to backdoor attacks, where adversaries train their local models on poisoned data and submit poisoned model updates to compromise the global model. Despite numerous proposed attacks and defenses, divergent experimental settings, implementation errors, and unrealistic assumptions hinder fair comparisons and valid conclusions about their effectiveness in real-world scenarios. To address this, we introduce BackFed - a comprehensive benchmark suite designed to standardize, streamline, and reliably evaluate backdoor attacks and defenses in FL, with a focus on practical constraints. Our benchmark offers key advantages through its multi-processing implementation that significantly accelerates experimentation and the modular design that enables seamless integration of new methods via well-defined APIs. With a standardized evaluation pipeline, we envision BackFed as a plug-and-play environment for researchers to comprehensively and reliably evaluate new attacks and defenses. Using BackFed, we conduct large-scale studies of representative backdoor attacks and defenses across both Computer Vision and Natural Language Processing tasks with diverse model architectures and experimental settings. Our experiments critically assess the performance of proposed attacks and defenses, revealing unknown limitations and modes of failures under practical conditions. These empirical insights provide valuable guidance for the development of new methods and for enhancing the security of FL systems. Our framework is openly available at https://github.com/thinh-dao/BackFed.
Cross sections of $η$ mesons in $p$$+$$p$ collisions at forward rapidity at $\sqrt{s}=500$ GeV and central rapidity at $\sqrt{s}=510$ GeV
2025-07-07
PHENIX Collaboration, N. J. Abdulameer, U. Acharya, A. Adare, C. Aidala, N. N. Ajitanand, Y. Akiba, R. Akimoto, H. Al-Ta'ani, J. Alexander, M. Alfred, D. Anderson, K. R. Andrews, A. Angerami, S. Antsupov, K. Aoki, N. Apadula, E. Appelt, Y. Aramaki, R. Armendariz, H. Asano, E. C. Aschenauer, E. T. Atomssa, T. C. Awes, B. Azmoun, V. Babintsev, M. Bai, N. S. Bandara, B. Bannier, E. Bannikov, K. N. Barish, B. Bassalleck, A. T. Basye, S. Bathe, V. Baublis, C. Baumann, A. Bazilevsky, M. Beaumier, S. Beckman, R. Belmont, J. Ben-Benjamin, R. Bennett, A. Berdnikov, Y. Berdnikov, L. Bichon, D. Black, B. Blankenship, D. S. Blau, J. S. Bok, V. Borisov, K. Boyle, M. L. Brooks, D. Broxmeyer, J. Bryslawskyj, H. Buesching, V. Bumazhnov, G. Bunce, S. Butsyk, S. Campbell, P. Castera, P. Chaitanya, C. -H. Chen, D. Chen, C. Chiu, M. Chiu, C. Y. Chi, I. J. Choi, J. B. Choi, R. K. Choudhury, P. Christiansen, T. Chujo, O. Chvala, V. Cianciolo, Z. Citron, B. A. Cole, Z. Conesa del Valle, M. Connors, R. Corliss, M. Csanád, T. Csörgő, T. Csörgő, L. D'Orazio, S. Dairaku, A. Datta, M. S. Daugherity, G. David, M. K. Dayananda, K. DeBlasio, K. Dehmelt, A. Denisov, A. Deshpande, E. J. Desmond, K. V. Dharmawardane, O. Dietzsch, L. Ding, A. Dion, M. Donadelli, V. Doomra, J. H. Do, O. Drapier, A. Drees, K. A. Drees, J. M. Durham, A. Durum, Y. V. Efremenko, H. En'yo, T. Engelmore, A. Enokizono, R. Esha, B. Fadem, N. Feege, D. E. Fields, M. Finger, Jr., M. Finger, D. Firak, D. Fitzgerald, F. Fleuret, S. L. Fokin, J. E. Frantz, A. Franz, A. D. Frawley, Y. Fukao, T. Fusayasu, P. Gallus, C. Gal, P. Garg, I. Garishvili, H. Ge, F. Giordano, A. Glenn, X. Gong, M. Gonin, Y. Goto, R. Granier de Cassagnac, N. Grau, S. V. Greene, M. Grosse Perdekamp, T. Gunji, L. Guo, T. Guo, H. Guragain, H. -Å. Gustafsson, Y. Gu, T. Hachiya, J. S. Haggerty, K. I. Hahn, H. Hamagaki, J. Hamblen, J. Hanks, R. Han, S. Y. Han, C. Harper, S. Hasegawa, K. Hashimoto, E. Haslum, R. Hayano, T. K. Hemmick, T. Hester, X. He, J. C. Hill, A. Hodges, R. S. Hollis, W. Holzmann, K. Homma, B. Hong, T. Horaguchi, Y. Hori, D. Hornback, T. Hoshino, J. Huang, T. Ichihara, R. Ichimiya, H. Iinuma, Y. Ikeda, K. Imai, Y. Imazu, M. Inaba, A. Iordanova, D. Isenhower, M. Ishihara, M. Issah, D. Ivanishchev, Y. Iwanaga, B. V. Jacak, S. J. Jeon, M. Jezghani, X. Jiang, Z. Ji, B. M. Johnson, D. John, T. Jones, E. Joo, K. S. Joo, D. Jouan, D. S. Jumper, J. Kamin, S. Kaneti, B. H. Kang, J. H. Kang, J. S. Kang, J. Kapustinsky, K. Karatsu, M. Kasai, G. Kasza, D. Kawall, A. V. Kazantsev, T. Kempel, J. A. Key, V. Khachatryan, A. Khanzadeev, K. Kihara, K. M. Kijima, B. I. Kim, C. Kim, D. H. Kim, D. J. Kim, E. -J. Kim, H. -J. Kim, M. Kim, Y. -J. Kim, Y. K. Kim, E. Kinney, Á. Kiss, E. Kistenev, J. Klatsky, D. Kleinjan, P. Kline, T. Koblesky, L. Kochenda, M. Kofarago, B. Komkov, M. Konno, J. Koster, D. Kotov, L. Kovacs, A. Král, G. J. Kunde, K. Kurita, M. Kurosawa, Y. Kwon, G. S. Kyle, Y. S. Lai, J. G. Lajoie, D. Larionova, A. Lebedev, D. M. Lee, J. Lee, K. B. Lee, K. S. Lee, S. H. Lee, S. R. Lee, M. J. Leitch, M. A. L. Leite, M. Leitgab, S. H. Lim, L. A. Linden Levy, H. Liu, M. X. Liu, X. Li, D. A. Loomis, B. Love, D. Lynch, S. Lökös, C. F. Maguire, Y. I. Makdisi, M. Makek, A. Manion, V. I. Manko, E. Mannel, Y. Mao, H. Masui, M. McCumber, P. L. McGaughey, D. McGlinchey, C. McKinney, N. Means, A. Meles, M. Mendoza, B. Meredith, Y. Miake, T. Mibe, A. C. Mignerey, K. Miki, A. J. Miller, A. Milov, D. K. Mishra, J. T. Mitchell, M. Mitrankova, Iu. Mitrankov, Y. Miyachi, S. Miyasaka, S. Mizuno, A. K. Mohanty, P. Montuenga, H. J. Moon, T. Moon, Y. Morino, A. Morreale, D. P. Morrison, S. Motschwiller, T. V. Moukhanova, B. Mulilo, T. Murakami, J. Murata, A. Mwai, S. Nagamiya, J. L. Nagle, M. Naglis, M. I. Nagy, I. Nakagawa, H. Nakagomi, Y. Nakamiya, K. R. Nakamura, T. Nakamura, K. Nakano, C. Nattrass, P. K. Netrakanti, J. Newby, M. Nguyen, M. Nihashi, T. Niida, R. Nouicer, N. Novitzky, G. Nukazuka, A. S. Nyanin, E. O'Brien, C. Oakley, C. A. Ogilvie, K. Okada, M. Oka, J. D. Orjuela Koop, M. Orosz, A. Oskarsson, M. Ouchida, K. Ozawa, R. Pak, V. Pantuev, V. Papavassiliou, B. H. Park, I. H. Park, J. S. Park, S. Park, S. K. Park, L. Patel, M. Patel, S. F. Pate, H. Pei, J. -C. Peng, H. Pereira, D. V. Perepelitsa, G. D. N. Perera, D. Yu. Peressounko, J. Perry, R. Petti, C. Pinkenburg, R. Pinson, R. P. Pisani, M. Potekhin, M. Proissl, M. L. Purschke, H. Qu, J. Rak, I. Ravinovich, K. F. Read, K. Reygers, D. Reynolds, V. Riabov, Y. Riabov, E. Richardson, D. Richford, N. Riveli, D. Roach, G. Roche, S. D. Rolnick, M. Rosati, S. S. E. Rosendahl, Z. Rowan, J. G. Rubin, B. Sahlmueller, N. Saito, T. Sakaguchi, H. Sako, V. Samsonov, S. Sano, M. Sarsour, S. Sato, T. Sato, M. Savastio, S. Sawada, B. Schaefer, B. K. Schmoll, K. Sedgwick, J. Seele, R. Seidl, A. Seleznev, A. Sen, R. Seto, P. Sett, A. Sexton, D. Sharma, I. Shein, T. -A. Shibata, K. Shigaki, M. Shimomura, H. H. Shim, K. Shoji, P. Shukla, A. Sickles, C. L. Silva, D. Silvermyr, C. Silvestre, K. S. Sim, B. K. Singh, C. P. Singh, V. Singh, M. Slunečka, K. L. Smith, T. Sodre, R. A. Soltz, W. E. Sondheim, S. P. Sorensen, I. V. Sourikova, P. W. Stankus, E. Stenlund, M. Stepanov, S. P. Stoll, T. Sugitate, A. Sukhanov, T. Sumita, J. Sun, Z. Sun, J. Sziklai, E. M. Takagui, A. Takahara, A. Taketani, R. Tanabe, Y. Tanaka, S. Taneja, K. Tanida, M. J. Tannenbaum, S. Tarafdar, A. Taranenko, E. Tennant, H. Themann, D. Thomas, A. Timilsina, T. Todoroki, M. Togawa, L. Tomášek, M. Tomášek, H. Torii, M. Towell, R. Towell, R. S. Towell, I. Tserruya, Y. Tsuchimoto, B. Ujvari, K. Utsunomiya, C. Vale, H. W. van Hecke, M. Vargyas, E. Vazquez-Zambrano, A. Veicht, J. Velkovska, M. Virius, A. Vossen, V. Vrba, E. Vznuzdaev, R. Vértesi, X. R. Wang, D. Watanabe, K. Watanabe, Y. Watanabe, Y. S. Watanabe, F. Wei, R. Wei, J. Wessels, S. Whitaker, S. N. White, D. Winter, S. Wolin, C. L. Woody, R. M. Wright, M. Wysocki, B. Xia, L. Xue, S. Yalcin, Y. L. Yamaguchi, R. Yang, A. Yanovich, J. Ying, S. Yokkaichi, I. Yoon, J. S. Yoo, G. R. Young, I. Younus, Z. You, I. E. Yushmanov, W. A. Zajc, A. Zelenski, S. Zhou
We present the first measurements of the forward and midrapidity $\eta$-meson cross sections from $p$$+$$p$ collisions at $\sqrt{s}=500$ and $510$~GeV, respectively. We also report the midrapidity $\eta/\pi^0$ ratio at 510 GeV. The forward cross section is measured differentially in $\eta$-meson transverse momentum ($p_T$) from 1.0 to 6.5~GeV/$c$ for pseudorapidity $3.0<|\eta|<3.8$. The midrapidity cross section is measured from 3.5 to 44 GeV/$c$ for pseudorapidity $|\eta|<0.35$. Both cross sections serve as critical inputs to an updated global analysis of the $\eta$-meson fragmentation functions.
Severe Plastic Deformation of Ceramics by High-Pressure Torsion: Review of Principles and Applications
2025-07-07
Kaveh Edalati, Jacqueline Hidalgo-Jiménez, Thanh Tam Nguyen, Hadi Sena, Nariman Enikeev, Gerda Rogl, Valery I. Levitas, Zenji Horita, Michael J. Zehetbauer, Ruslan Z. Valiev, Terence G. Langdon
Ceramics are typically brittle at ambient conditions due to their covalent or ionic bonding and limited dislocation activities. While plasticity, and occasionally superplasticity, can be achieved in ceramics at high temperatures through thermally activated phenomena, creep, and grain boundary sliding, their deformation at ambient temperature and pressure remains challenging. Processing under high pressure via the high-pressure torsion (HPT) method offers new pathways for severe plastic deformation (SPD) of ceramics. This article reviews recent advances in HPT processing of ceramics, focusing primarily on traditional ceramics (e.g., oxides, carbides, nitrides, oxynitrides) and to a lesser extent advanced ceramics (e.g., silicon, carbon, perovskites, clathrates). Key structural and microstructural features of SPD-processed ceramics are discussed, including phase transformations and the generation of nanograins and defects such as vacancies and dislocations. The properties and applications of these deformed ceramics are summarized, including powder consolidation, photoluminescence, bandgap narrowing, photovoltaics, photocatalysis (dye degradation, plastic waste degradation, antibiotic degradation, hydrogen production, CO2 conversion), electrocatalysis, thermoelectric performance, dielectric performance, and ion conductivity for Li-ion batteries. Additionally, the article highlights the role of HPT in synthesizing novel materials, such as high-entropy ceramics (particularly high-entropy oxides), black oxides, and high-pressure polymorphs, which hold promise for energy and environmental applications.
A Survey of Pun Generation: Datasets, Evaluations and Methodologies
2025-07-07
Yuchen Su, Yonghua Zhu, Ruofan Wang, Zijian Huang, Diana Benavides-Prado, Michael Witbrock
Pun generation seeks to creatively modify linguistic elements in text to produce humour or evoke double meanings. It also aims to preserve coherence and contextual appropriateness, making it useful in creative writing and entertainment across various media and contexts. Although pun generation has received considerable attention in computational linguistics, there is currently no dedicated survey that systematically reviews this specific area. To bridge this gap, this paper provides a comprehensive review of pun generation datasets and methods across different stages, including conventional approaches, deep learning techniques, and pre-trained language models. Additionally, we summarise both automated and human evaluation metrics used to assess the quality of pun generation. Finally, we discuss the research challenges and propose promising directions for future work.
Multi-source Opinion Summarization (M-OS) extends beyond traditional opinion summarization by incorporating additional sources of product metadata such as descriptions, key features, specifications, and ratings, alongside reviews. This integration results in comprehensive summaries that capture both subjective opinions and objective product attributes essential for informed decision-making. While Large Language Models (LLMs) have shown significant success in various Natural Language Processing (NLP) tasks, their potential in M-OS remains largely unexplored. Additionally, the lack of evaluation datasets for this task has impeded further advancements. To bridge this gap, we introduce M-OS-EVAL, a benchmark dataset for evaluating multi-source opinion summaries across 7 key dimensions: fluency, coherence, relevance, faithfulness, aspect coverage, sentiment consistency, specificity. Our results demonstrate that M-OS significantly enhances user engagement, as evidenced by a user study in which, on average, 87% of participants preferred M-OS over opinion summaries. Our experiments demonstrate that factually enriched summaries enhance user engagement. Notably, M-OS-PROMPTS exhibit stronger alignment with human judgment, achieving an average Spearman correlation of \r{ho} = 0.74, which surpasses the performance of previous methodologies.
Customer reviews on e-commerce platforms capture critical affective signals that drive purchasing decisions. However, no existing research has explored the joint task of emotion detection and explanatory span identification in e-commerce reviews - a crucial gap in understanding what triggers customer emotional responses. To bridge this gap, we propose a novel joint task unifying Emotion detection and Opinion Trigger extraction (EOT), which explicitly models the relationship between causal text spans (opinion triggers) and affective dimensions (emotion categories) grounded in Plutchik's theory of 8 primary emotions. In the absence of labeled data, we introduce EOT-X, a human-annotated collection of 2,400 reviews with fine-grained emotions and opinion triggers. We evaluate 23 Large Language Models (LLMs) and present EOT-DETECT, a structured prompting framework with systematic reasoning and self-reflection. Our framework surpasses zero-shot and chain-of-thought techniques, across e-commerce domains.
We present the results of a deep neutral hydrogen (Hi) observation of the early-type galaxy NGC 2768 using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Leveraging the high sensitivity of FAST, we discover an extended gas envelope around NGC 2768. The total Hi mass is measured to be 8.1 x 10^8 M_sun , representing a magnitude increase compared to previous Westerbork Synthesis Radio Telescope (WSRT) studies. Position-velocity (PV) diagram indicates the envelope mainly involves two components: an Hi disk of NGC 2768 and a newly discovered satellite galaxy without detectable counterparts in currently deep optical surveys. The center of the gas disk is mis-aligned with the optical disk of NGC 2768, with more gas redshifted, indicating it has been disturbed. Our study indicates NGC 2768 is currently undergoing a transition from a spiral galaxy to an S0. Previous deep WSRT observations reveal two dense clumps (named as Clumps A and Clump B throughout this paper) in the center of the envelope. We find Clump A corresponds to the densest part of the disk, while Clump B might be a newly discovered satellite galaxy which probably collided NGC 2768 about 0.38 Gyr ago. We also find tidal interactions between Clump B and PGC 2599651, NGC 2768 and UGC 4808. Based on these new findings, we finally analyze hierarchical accretion history of NGC 2768.
Delensing CMB B-modes using galaxy surveys: the effect of galaxy bias and matter clustering non-linearities
2025-07-07
Shengzhu Wang, Antón Baleato Lizancos, José Luis Bernal
The B-mode of polarization of the CMB is a uniquely powerful probe of gravitational waves produced in the very early Universe. But searches for primordial B-mode anisotropies must contend with gravitational lensing, which induces late-time B-modes not associated with gravitational waves. These lensing B-modes can be removed -- i.e., delensed -- using observations of the E-modes and a proxy of the matter fluctuations along the line of sight that caused the deflections. The number density and redshift reach of galaxy surveys such as the upcoming Rubin observatory offer attractive prospects for using them to delens B-mode data from CMB experiments such as the Simons Observatory, LiteBIRD or CMB-S4. However, stochasticity and non-linear galaxy bias may in principle decorrelate the galaxy field from the matter distribution responsible for the lensing effect, thus hindering efforts to delens B-modes. In addition, non-linear gravitational evolution and bias introduce non-Gaussianities in the large-scale structure which further complicate the modelling. We quantify these effects by populating an N-body simulation with a magnitude-limited, photometric sample of galaxies similar to Rubin's gold selection, and using them to delens CMB maps lensed by the same matter distribution. We find that pipelines that treat the galaxy overdensity as a Gaussian field will incur negligible bias on the inferred tensor-to-scalar ratio, r. Moreover, we show that even in a highly conservative scenario where only the linear bias of the galaxies can be determined, the bias on r arising from this simplification is well within statistical uncertainties for a cosmic-variance limited scenario where Rubin-like galaxies are used for delensing.
Introduction to the China Space Station Telescope (CSST)
2025-07-07
CSST Collaboration, Yan Gong, Haitao Miao, Hu Zhan, Zhao-Yu Li, Jinyi Shangguan, Haining Li, Chao Liu, Xuefei Chen, Haibo Yuan, Jilin Zhou, Hui-Gen Liu, Cong Yu, Jianghui Ji, Zhaoxiang Qi, Jiacheng Liu, Zigao Dai, Xiaofeng Wang, Zhenya Zheng, Lei Hao, Jiangpei Dou, Yiping Ao, Zhenhui Lin, Kun Zhang, Wei Wang, Guotong Sun, Ran Li, Guoliang Li, Youhua Xu, Xinfeng Li, Shengyang Li, Peng Wu, Jiuxing Zhang, Bo Wang, Jinming Bai, Yi-Fu Cai, Zheng Cai, Kwan Chuen Chan, Jin Chang, Xiaodian Chen, Xuelei Chen, Yuqin Chen, Yun Chen, Wei Cui, Pu Du, Wenying Duan, Junhui Fan, LuLu Fan, Zhou Fan, Zuhui Fan, Taotao Fang, Jianning Fu, Liping Fu, Zhensen Fu, Jian Gao, Shenghong Gu, Yidong Gu, Qi Guo, Zhanwen Han, Zhiqi Huang, Luis C. Ho, Linhua Jiang, Yipeng Jing, Xi Kang, Xu Kong, Chengyuan Li, Di Li, Jing Li, Nan Li, Yang A. Li, Shilong Liao, Weipeng Lin, Fengshan Liu, Jifeng Liu, Xiangkun Liu, Ruiqing Mao, Shude Mao, Xianmin Meng, Xiaoying Pang, Xiyan Peng, Yingjie Peng, Huanyuan Shan, Juntai Shen, Shiyin Shen, Zhiqiang Shen, Sheng-Cai Shi, Yong Shi, Siyuan Tan, Hao Tian, Jianmin Wang, Jun-Xian Wang, Xin Wang, Yuting Wang, Hong Wu, Jingwen Wu, Xuebing Wu, Chun Xu, Xiang-Xiang Xue, Yongquan Xue, Ji Yang, Xiaohu Yang, Qijun Yao, Fangting Yuan, Zhen Yuan, Jun Zhang, Wei Zhang, Xin Zhang, Gang Zhao, Gongbo Zhao, Hongen Zhong, Jing Zhong, Liyong Zhou, Ying Zu
The China Space Station Telescope (CSST) is a next-generation Stage-IV sky survey telescope, distinguished by its large field of view (FoV), high image quality, and multi-band observation capabilities. It can simultaneously conduct precise measurements of the Universe by performing multi-color photometric imaging and slitless spectroscopic surveys. The CSST is equipped with five scientific instruments, i.e. Multi-band Imaging and Slitless Spectroscopy Survey Camera (SC), Multi-Channel Imager (MCI), Integral Field Spectrograph (IFS), Cool Planet Imaging Coronagraph (CPI-C), and THz Spectrometer (TS). Using these instruments, the CSST is expected to make significant contributions and discoveries across various astronomical fields, including cosmology, galaxy and active galactic nuclei (AGN), the Milky Way and nearby galaxies, stars, exoplanets, Solar System objects, astrometry, and transients and variable sources. This review aims to provide a comprehensive overview of the CSST instruments, observational capabilities, data products, and scientific potential.
Retraction Dynamics of a Highly Viscous Liquid Sheet
2025-07-07
Taosif Ahsan, Rodolfo Brandão, Benny Davidovitch, Howard A. Stone
We study the one-dimensional capillary-driven retraction of a finite, planar liquid sheet in the asymptotic regime where both the Ohnesorge number $\mathrm{Oh}$ and the initial length-to-thickness ratio $l_0/h_0$ are large. In this regime, the fluid domain decomposes into two regions: a thin-film region governed by one-dimensional mass and momentum equations, and a small tip region near the free edge described by a self-similar Stokes flow. Asymptotic matching between these regions yields an effective boundary condition for the thin-film region, representing a balance between viscous and capillary forces at the free edge. Surface tension drives the thin-film flow only through this boundary condition, while the local momentum balance is dominated by viscous and inertial stresses. We show that the thin-film flow possesses a conserved quantity, reducing the equation of thickness to heat equation with time-dependent boundary conditions. The reduced problem depends on a single dimensionless parameter $\mathcal{L} = l_0 / (4 h_0 \mathrm{Oh})$. Numerical solutions of the reduced model agree well with previous studies and reveal that the sheet undergoes distinct retraction regimes depending on $\mathcal{L}$ and a dimensionless time after rupture $T$. We derive asymptotic approximations for the thickness profile, velocity profile, and retraction speed during the early and late stages of retraction. At early times, the retraction speed grows as $T^{1/2}$, while at late times it decays as $1/T^2$. An intermediate regime arises for very long sheets ($\mathcal{L} \gg 1$). During this phase, the retraction speed approaches the Taylor-Culick value. When $T \approx \mathcal{L}$, the speed undergoes fast deceleration from the Taylor-Culick speed to late-time asymptotics.
Emerging Frameworks for Objective Task-based Evaluation of Quantitative Medical Imaging Methods
2025-07-07
Yan Liu, Huitian Xia, Nancy A. Obuchowski, Richard Laforest, Arman Rahmim, Barry A. Siegel, Abhinav K. Jha
Quantitative imaging (QI) is demonstrating strong promise across multiple clinical applications. For clinical translation of QI methods, objective evaluation on clinically relevant tasks is essential. To address this need, multiple evaluation strategies are being developed. In this paper, based on previous literature, we outline four emerging frameworks to perform evaluation studies of QI methods. We first discuss the use of virtual imaging trials (VITs) to evaluate QI methods. Next, we outline a no-gold-standard evaluation framework to clinically evaluate QI methods without ground truth. Third, a framework to evaluate QI methods for joint detection and quantification tasks is outlined. Finally, we outline a framework to evaluate QI methods that output multi-dimensional parameters, such as radiomic features. We review these frameworks, discussing their utilities and limitations. Further, we examine future research areas in evaluation of QI methods. Given the recent advancements in PET, including long axial field-of-view scanners and the development of artificial-intelligence algorithms, we present these frameworks in the context of PET.
Counterfactual explanations (CFs) offer human-centric insights into machine learning predictions by highlighting minimal changes required to alter an outcome. Therefore, CFs can be used as (i) interventions for abnormality prevention and (ii) augmented data for training robust models. In this work, we explore large language models (LLMs), specifically GPT-4o-mini, for generating CFs in a zero-shot and three-shot setting. We evaluate our approach on two datasets: the AI-Readi flagship dataset for stress prediction and a public dataset for heart disease detection. Compared to traditional methods such as DiCE, CFNOW, and NICE, our few-shot LLM-based approach achieves high plausibility (up to 99%), strong validity (up to 0.99), and competitive sparsity. Moreover, using LLM-generated CFs as augmented samples improves downstream classifier performance (an average accuracy gain of 5%), especially in low-data regimes. This demonstrates the potential of prompt-based generative techniques to enhance explainability and robustness in clinical and physiological prediction tasks. Code base: github.com/anonymous/SenseCF.
The scarcity of autonomous vehicle datasets from developing regions, particularly across Africa's diverse urban, rural, and unpaved roads, remains a key obstacle to robust perception in low-resource settings. We present a procedural augmentation pipeline that enhances low-cost monocular dashcam footage with realistic refractive distortions and weather-induced artifacts tailored to challenging African driving scenarios. Our refractive module simulates optical effects from low-quality lenses and air turbulence, including lens distortion, Perlin noise, Thin-Plate Spline (TPS), and divergence-free (incompressible) warps. The weather module adds homogeneous fog, heterogeneous fog, and lens flare. To establish a benchmark, we provide baseline performance using three image restoration models. To support perception research in underrepresented African contexts, without costly data collection, labeling, or simulation, we release our distortion toolkit, augmented dataset splits, and benchmark results.
Elementary Steps of Energy Conversion in Strongly Correlated Systems: Beyond Single Quasiparticles and Rigid Bands
2025-07-07
V. Moshnyaga, Ch. Jooss P. E. Blöchl, V. Bruchmann-Bamberg, A. Dehning, L. Allen-Rump, C. Hausmann, M. Krüger, A. Rathnakaran, S. Rajpurohit, D. Steil, C. Flathmann, J. Hoffmann, M. Seibt, C. Volkert
Energy conversion in materials can be considered as a sequence of elementary steps initiated by a primary excitation. While these steps are quite well understood in classical semiconductors in terms of quasiparticle (QP) excitations and interactions, their understanding in strongly correlated materials is still elusive. Here, we review the progress which has been achieved over recent years by studies of manganite perovskite oxides as a model system for materials with strong correlations. They show a subtle interplay of different types of correlations, i.e., electron-phonon, electron-electron and spin-spin, resulting in rich physical phenomena due to competition between different ground states accompanied by temperature- and field-induced phase transitions. They strongly impact various types of energy conversion and transport processes including friction at surfaces, thermal transport, time-, energy- and power-dependent optical excitations as well as photovoltaic energy conversion. The underlying microscopic processes can be broken down to the behavior of the low-energy thermal and high-energy optical excitations, their interactions, transport and conversion which are theoretically analyzed by using models of interacting and tunable QPs: Their nature and interactions can change during excitation, transport and phase transitions, thus modifying electronic structure. At sufficiently high stimulation, QP excitations can even induce or actuate phase transitions. As a result, we obtained a comprehensive understanding of energy conversion steps going far beyond single QP pictures and rigid band approximations well-known for conventional semiconductors.
Motion generation, the task of synthesizing realistic motion sequences from various conditioning inputs, has become a central problem in computer vision, computer graphics, and robotics, with applications ranging from animation and virtual agents to human-robot interaction. As the field has rapidly progressed with the introduction of diverse modeling paradigms including GANs, autoencoders, autoregressive models, and diffusion-based techniques, each approach brings its own advantages and limitations. This growing diversity has created a need for a comprehensive and structured review that specifically examines recent developments from the perspective of the generative approach employed. In this survey, we provide an in-depth categorization of motion generation methods based on their underlying generative strategies. Our main focus is on papers published in top-tier venues since 2023, reflecting the most recent advancements in the field. In addition, we analyze architectural principles, conditioning mechanisms, and generation settings, and compile a detailed overview of the evaluation metrics and datasets used across the literature. Our objective is to enable clearer comparisons and identify open challenges, thereby offering a timely and foundational reference for researchers and practitioners navigating the rapidly evolving landscape of motion generation.
Physical properties of HII regions at sub-kpc scales using integral field spectroscopy on IC 342
2025-07-07
J. K. Barrera-Ballesteros, S. F. Sanchez, K. Kreckel, A. Lugo-Aranda, H. Ibarra-Medel, L. Carigi, N. Drory, D. Bizyaev, J. E. Mendez Delgado, Guillermo Blanc
In this study we use Integral Field Spectroscopic (IFS) observations for one of the closest galaxy to us, the grand design spiral IC 342, to derive physical properties of HII regions at sub-kpc scales. This IFS data represents, to our knowledge, the most comprehensive observational effort in the optical for this galaxy. The final IFS datacube consists of 349 individual pointings using the IFS instrumentation from the SDSS-IV MaNGA survey. Using a prototype of the data analysis pipeline that will be devoted to the SDSS-V Local Volume Mapper (LVM) survey, we measure different observables from the emission line in the optical. In particular, using the flux map of the H$\alpha$ emission line, we derive the location and sizes of H ii region candidates for IC 342. Using the integrated flux for different emission lines within each region, we derived the radial distribution of different physical properties from the ionized gas (e.g., optical extinction, H$\alpha$ luminosity, oxygen abundance, etc). Comparing with larger samples of galaxies with IFS data, our results suggest that physical properties of the ionized gas of IC 342 are similar to galaxies with similar stellar mass in the nearby universe.
From General to Specialized: The Need for Foundational Models in Agriculture
2025-07-07
Vishal Nedungadi, Xingguo Xiong, Aike Potze, Ron Van Bree, Tao Lin, Marc Rußwurm, Ioannis N. Athanasiadis
Food security remains a global concern as population grows and climate change intensifies, demanding innovative solutions for sustainable agricultural productivity. Recent advances in foundation models have demonstrated remarkable performance in remote sensing and climate sciences, and therefore offer new opportunities for agricultural monitoring. However, their application in challenges related to agriculture-such as crop type mapping, crop phenology estimation, and crop yield estimation-remains under-explored. In this work, we quantitatively evaluate existing foundational models to assess their effectivity for a representative set of agricultural tasks. From an agricultural domain perspective, we describe a requirements framework for an ideal agricultural foundation model (CropFM). We then survey and compare existing general-purpose foundational models in this framework and empirically evaluate two exemplary of them in three representative agriculture specific tasks. Finally, we highlight the need for a dedicated foundational model tailored specifically to agriculture.
BASS LII: Clues from Twin Peaks -- Investigating the structure of the AGN broad-line region at low accretion rates with double-peaked emitters
2025-07-07
Charlotte Ward, Michael J. Koss, Michael Eracleous, Benny Trakhtenbrot, Franz E. Bauer, Turgay Caglar, Fiona Harrison, Arghajit Jana, Darshan Kakkad, Macon Magno, Ignacio del Moral-Castro, Richard Mushotzky, Kyuseok Oh, Alessandro Peca, Meredith C. Powell, Claudio Ricci, Alejandra Rojas, Krista Lynne Smith, Daniel Stern, Ezequiel Treister, C. Megan Urry
A fraction of active galactic nuclei (AGN) have double-peaked H$\alpha$, H$\beta$ and Mg II broad lines attributed to emission from rotating gas in the accretion disk. Using optical spectroscopy of a flux-limited sample of AGN selected via ultrahard X-rays from the BAT AGN Spectroscopic Survey (BASS), we systematically identify 71 double-peaked emitters amongst 343 broad-line AGN with redshifts $0.004
The gravitational form factors of pions, kaons and the nucleons are investigated by employing modern dispersive techniques and chiral perturbation theory. We determine the gravitational form factors of pions and kaons, extending our analysis to explore the pion mass dependence of these form factors at several unphysical pion masses up to 391 MeV, for which lattice results exist for the meson-meson scattering phase shifts. We also review our analysis on the nucleon gravitational form factors at the physical pion mass, and then systematically calculate various three-dimensional spatial and two-dimensional transverse density distributions for the nucleons. These results provide new insights into the mass distribution inside nucleons. As a by-product, we match our dispersion relation results and those obtained from chiral perturbation theory with external gravitational source at the next-to-next-to-leading order, yielding values for the low-energy constants $c_8=-4.28_{-0.38}^{+0.37} ~\mathrm{GeV}^{-1}$ and $c_9=-0.68_{-0.05}^{+0.06} ~\mathrm{GeV}^{-1}$. These results offer a robust benchmark for future experimental and theoretical studies.
The final WaZP galaxy cluster catalog of the Dark Energy Survey and comparison with SZE data
2025-07-07
C. Benoist, M. Aguena, L. da Costa, J. Gschwend, S. Allam, O. Alves, F. Andrade-Oliveira, D. Bacon, D. Brooks, A. Carnero Rosell, J. Carretero, F. J. Castander, M. Costanzi, J. De Vicente, S. Desai, S. Dodelson, P. Doel, S. Everett, B. Flaugher, J. Frieman, J. Garcia-Bellido, G. Giannini, P. Giles, R. Gruendl, G. Gutierrez, S. Hinton, D. L. Hollowood, K. Honscheid, D. James, K. Kuehn, S. Lee, J. Marshall, J. Mena-Fernández, R. Miquel, A. Plazas Malagón, K. Romer, E. Sanchez, B. Santiago, I. Sevilla, M. Smith, E. Suchyta, G. Tarle, N. Weaverdyck, J. Weller, M. E. da Silva Pereira
In this work, we present and characterize the galaxy cluster catalog detected by the WaZP cluster finder, which is not based on red-sequence identification, on the full six years of observations of the Dark Energy Survey (DES-Y6). The full catalog contains over 400k detected clusters with richnesses, Ngals, above 5 and that reach redshifts up to 1.3. We also provide a version of the catalog where the observation depth and richness computation are homogenized to be used for cosmology, containing 33k rich (Ngals >25) clusters. We compare our results with the previous WaZP catalog obtained from the DES first-year data release (DES-Y1). We find that essentially all clusters within the common footprint and depth limit are recovered. The deeper observations on DES-Y6 and the more complete available spectroscopic redshift sample lead to improvements in the redshifts of the clusters, resulting in an average scatter of 1.4% and offset of 0.2%. The optical clusters are also cross-matched with Sunyaev Zel'dovich Effect (SZE) cluster samples detected by the South Pole Telescope (SPT) and the Atacama Cosmology Telescope (ACT). We find that essentially all SZE clusters with reasonable overlapping footprint have a corresponding WaZP cluster. Conversely, 90% of the optical detections with richness greater than 150 have a counterpart in the deeper regions of the SZE surveys. Based on cross-match with the SZE catalogs, we also find that 15-20% of the SZE matched systems have more than one possible WaZP counterpart at the same redshift and within the SZE R500c, indicating possible interacting or unrelaxed systems. Finally, given the optical and SZE beams, WaZP and SZE centerings are found to be consistent. A more detailed study of the SZE-WaZP mass-richness relation will be presented in a separate paper.
Investigating the Impact of Supernova Feedback on Satellites in Elliptical Galaxies
2025-07-07
Sumi Kim, Ena Choi, Amanda C. N. Quirk, Rachel S. Somerville, Thorsten Naab, Jeremiah P. Ostriker, Michaela Hirschmann
We investigate the influence of supernova (SN) feedback on the satellites of elliptical host galaxies using hydrodynamic simulations. Utilizing a modified version of the GADGET-3 code, we perform cosmological zoom-in simulations of 11 elliptical galaxies with stellar masses in the range $10^{11} M_{\odot} < M_{*} < 2 \times 10^{11} M_{\odot}$. We conduct two sets of simulations with identical initial conditions: the Fiducial model, which includes a three-phase SN mechanical wind, and the weak SN feedback model, where nearly all SN energy is released as thermal energy with a reduced SN wind velocity. Our comparison shows minimal differences in the elliptical host galaxies, but significant variations in the physical properties of satellite galaxies. The weak SN feedback model produces a larger number of satellite galaxies compared to the Fiducial model, and significantly more than observed. For satellite galaxies with stellar masses above $10^{8}$ $M_{\odot}$, the weak SN feedback model generates approximately five times more satellites than observed in the xSAGA survey. Most of these overproduced satellites have small stellar masses, below $10^{10}$ $M_{\odot}$. Additionally, satellites in the weak SN feedback model are about 3.5 times more compact than those observed in the SAGA survey and the Fiducial model, with metallicities nearly 1 dex higher than observed values. In conclusion, the satellite galaxies in the Fiducial model, which includes mechanical SN feedback, exhibit properties more closely aligned with observations. This underscores the necessity of incorporating both mechanical AGN and SN feedback to reproduce the observed properties of elliptical galaxy and their satellites in simulations.
A new Bowen Fluorescence Flare and Extreme Coronal Line Emitter discovered by SRG/eROSITA
2025-07-07
Pietro Baldini, Arne Rau, Riccardo Arcodia, Taeho Ryu, Zhu Liu, Paula Sánchez-Sáez, Iuliia Grotova, Andrea Merloni, Stefano Ciroi, Adelle J. Goodwin, Mariusz Gromadzki, Adela Kawka, Megan Masterson, Dusán Tubín-Arenas, David A. H. Buckley, Francesco Di Mille, Gemma E. Anderson, Sabina Bahic, David Homan, Mirko Krumpe, James C. A. Miller-Jones, Kirpal Nandra
The nuclear transient eRASSt J012026.5-292727 (J012026 hereafter) was discovered in the second SRG/eROSITA all-sky survey (eRASS2). The source appeared more than one order of magnitude brighter than the eRASS1 upper limits (peak eRASS2 0.2-2.3 keV flux of 1.14 x 10^-12 erg cm^-2 s^-1), and with a soft X-ray spectrum (photon index Gamma = 4.3). Over the following months, the X-ray flux started decaying, with significant flaring activity on both hour- and year-timescales. By inspecting the multiwavelength light curves of time-domain wide-field facilities, we detected a strong mid-infrared flare, evolving over 2 years, and a weaker optical counterpart. Follow-up optical spectroscopy revealed transient features, including redshifted Balmer lines (FWHM ~1500 km/s), strong Fe II emission, He II and Bowen lines, and high-ionization iron coronal lines. One spectrum showed a triple-peaked H-beta line, consistent with emission from a face-on elliptical disk. The spectroscopic features and the slow evolution of the event place J012026 within the classifications of Bowen fluorescence flares (BFFs) and extreme coronal line emitters (ECLEs). BFFs have been associated with rejuvenated accreting SMBHs, although the mechanism triggering the onset of the new accretion flow is still unclear, while ECLEs have been linked to the disruption of stars in gas-rich environments. The association of J012026 to both classes, combined with the multi-wavelength information, suggests that BFFs could be, at least in some cases, due to tidal disruption events (TDEs). The observed X-ray variability, uncommon in standard TDEs, adds complexity to these families of nuclear transients. These results highlight the diverse phenomenology of nuclear accretion events and demonstrate the value of systematic X-ray surveys, such as eROSITA and Einstein Probe, for uncovering such transients and characterizing their physical origin.
Causal Foundation Models: Disentangling Physics from Instrument Properties
2025-07-07
Jeroen Audenaert, Daniel Muthukrishna, Paul F. Gregory, David W. Hogg, V. Ashley Villar
Foundation models for structured time series data must contend with a fundamental challenge: observations often conflate the true underlying physical phenomena with systematic distortions introduced by measurement instruments. This entanglement limits model generalization, especially in heterogeneous or multi-instrument settings. We present a causally-motivated foundation model that explicitly disentangles physical and instrumental factors using a dual-encoder architecture trained with structured contrastive learning. Leveraging naturally occurring observational triplets (i.e., where the same target is measured under varying conditions, and distinct targets are measured under shared conditions) our model learns separate latent representations for the underlying physical signal and instrument effects. Evaluated on simulated astronomical time series designed to resemble the complexity of variable stars observed by missions like NASA's Transiting Exoplanet Survey Satellite (TESS), our method significantly outperforms traditional single-latent space foundation models on downstream prediction tasks, particularly in low-data regimes. These results demonstrate that our model supports key capabilities of foundation models, including few-shot generalization and efficient adaptation, and highlight the importance of encoding causal structure into representation learning for structured data.
2025-07-07
Mauro Bernardini, Robert Feldmann, Daniel Anglés-Alcázar, Philipp Denzel, Jindra Gensior
Next-generation radio telescopes will provide unprecedented data volumes of the neutral hydrogen (HI) distribution across cosmic time. Combining weak lensing surveys with spatial and kinematic observations of HI could help constrain key properties of dark matter, such as its mass, clustering behavior, and spatial distribution. However, inferring dark matter properties from HI observations is challenging because of processes related to galaxy formation, such as stellar feedback. Methods that use empirical relations, often calibrated via numerical simulations, do not use full field-level information to model the complex relation between dark matter and HI. We address this shortcoming with a data-driven approach, leveraging the recently introduced EMBER-2 model to learn the HI-dark matter mapping at the field level for a wide redshift range, z=6-0. After training on cosmological galaxy formation simulations run with FIRE-2, EMBER-2 accurately recovers key statistics, including dark matter mass fractions and surface density profiles. The HI-dark matter density cross-correlation is reconstructed at an accuracy of 10% down to scales of k = 100 h/cMpc, a significant improvement over traditional approaches. The presented method may become a key ingredient in future inference pipelines as it can be readily integrated into downstream analysis tasks of radio surveys.
Exploring Empathy in Software Engineering: Insights from a Grey Literature Analysis of Practitioners' Perspectives
2025-07-07
Lidiany Cerqueira, João Pedro Bastos, Danilo Neves, Glauco Carneiro, Rodrigo Spínola, Sávio Freire, José Amancio Macedo Santos, Manoel Mendonça
Context. Empathy, a key social skill, is essential for communication and collaboration in SE but remains an under-researched topic. Aims. This study investigates empathy in SE from practitioners' perspectives, aiming to characterize its meaning, identify barriers, discuss practices to overcome them, and explore its effects. Method. A qualitative content analysis was conducted on 55 web articles from DEV and Medium, two communities widely used by practitioners. To strengthen our findings, we conducted a follow-up survey with empathy experts. Results. The study proposes a definition of empathy in SE, identifies barriers such as toxic culture and excessive technical focus, practices to foster empathy in teams, and outcomes, including improved collaboration, communication, and reduced anxiety, frustration, and stress. These findings are synthesized into a conceptual framework. Conclusion. Survey results indicate the framework is clear, valuable, and raises empathy awareness, with suggestions for improvements and integration into training. This study paves the way for improving team dynamics by addressing barriers and offering strategies to cultivate empathy. Future work will explore empathy's broader implications in SE practice.
Dataless Neural Networks for Resource-Constrained Project Scheduling
2025-07-07
Marc Bara
Dataless neural networks represent a paradigm shift in applying neural architectures to combinatorial optimization problems, eliminating the need for training datasets by encoding problem instances directly into network parameters. Despite the pioneering work of Alkhouri et al. (2022) demonstrating the viability of dataless approaches for the Maximum Independent Set problem, our comprehensive literature review reveals that no published work has extended these methods to the Resource-Constrained Project Scheduling Problem (RCPSP). This paper addresses this gap by presenting the first dataless neural network approach for RCPSP, providing a complete mathematical framework that transforms discrete scheduling constraints into differentiable objectives suitable for gradient-based optimization. Our approach leverages smooth relaxations and automatic differentiation to unlock GPU parallelization for project scheduling, traditionally a domain of sequential algorithms. We detail the mathematical formulation for both precedence and renewable resource constraints, including a memory-efficient dense time-grid representation. Implementation and comprehensive experiments on PSPLIB benchmark instances (J30, J60, and J120) are currently underway, with empirical results to be reported in an updated version of this paper.
Despite the remarkable performance of Large Language Models (LLMs) in automated discharge summary generation, they still suffer from hallucination issues, such as generating inaccurate content or fabricating information without valid sources. In addition, electronic medical records (EMRs) typically consist of long-form data, making it challenging for LLMs to attribute the generated content to the sources. To address these challenges, we propose LCDS, a Logic-Controlled Discharge Summary generation system. LCDS constructs a source mapping table by calculating textual similarity between EMRs and discharge summaries to constrain the scope of summarized content. Moreover, LCDS incorporates a comprehensive set of logical rules, enabling it to generate more reliable silver discharge summaries tailored to different clinical fields. Furthermore, LCDS supports source attribution for generated content, allowing experts to efficiently review, provide feedback, and rectify errors. The resulting golden discharge summaries are subsequently recorded for incremental fine-tuning of LLMs. Our project and demo video are in the GitHub repository https://github.com/ycycyc02/LCDS.
Narrowing the Gap: Supervised Fine-Tuning of Open-Source LLMs as a Viable Alternative to Proprietary Models for Pedagogical Tools
2025-07-07
Lorenzo Lee Solano, Charles Koutcheme, Juho Leinonen, Alexandra Vassar, Jake Renzella
Frontier Large language models (LLMs) like ChatGPT and Gemini can decipher cryptic compiler errors for novice programmers, but their computational scale, cost, and tendency to over-assist make them problematic for widespread pedagogical adoption. This work demonstrates that smaller, specialised language models, enhanced via Supervised Fine-Tuning (SFT), present a more viable alternative for educational tools. We utilise a new dataset of 40,000 C compiler error explanations, derived from real introductory programming (CS1/2) student-generated programming errors, which we used to fine-tune three open-source models: Qwen3-4B, Llama-3.1-8B, and Qwen3-32B. We performed a dual evaluation, combining expert human reviews with a large-scale automated analysis of 8,000 responses using a validated LLM-as-judge ensemble. Our results show that SFT significantly boosts the pedagogical quality of smaller models, achieving performance comparable to much larger models. We analyse the trade-offs between model size and quality, confirming that fine-tuning compact, efficient models on high-quality, domain-specific data is a potent strategy for creating specialised models to drive educational tools. We provide a replicable methodology to foster broader access to generative AI capabilities in educational contexts.
Digital-Twin Empowered Site-Specific Radio Resource Management in 5G Aerial Corridor
2025-07-06
Pulok Tarafder, Imtiaz Ahmed, Danda B. Rawat, Md. Zoheb Hassan, Kamrul Hasan
Base station (BS) association and beam selection in multi-cell drone corridor networks present unique challenges due to the high altitude, mobility and three-dimensional movement of drones. These factors lead to frequent handovers and complex beam alignment issues, especially in environments with dense BS deployments and varying signal conditions. To address these challenges, this paper proposes a channel-twin (CT) enabled resource-allocation framework for drone-corridor communications, where the CT constitutes the radio-channel component of a broader digital-twin (DT) environment. The CT supplies high-fidelity channel-state information (CSI), which drives a two-stage optimization procedure. In Stage 1, array-level beamforming weights at each BS are selected to maximize antenna gain. In Stage 2, the framework jointly optimizes drone-BS-beam associations at discrete corridor way-points to maximize end-to-end throughput. Simulation results confirm that the CT-driven strategy delivers significant throughput gains over baseline methods across diverse operating scenarios, validating the effectiveness of integrating precise digital-twin channel models with cross-layer resource optimization.
Extreme cloud collisions in nearby barred galaxies
2025-07-06
Tutku Kolcu, Mattia C. Sormani, Witold Maciejewski, Sophia K. Stuber, Eva Schinnerer, Francesca Fragkoudi, Ashley T. Barnes, Frank Bigiel, Mélanie Chevance, Dario Colombo, Éric Emsellem, Simon C. O. Glover, Jonathan D. Henshaw, Ralf S. Klessen, Sharon E. Meidt, Justus Neumann, Francesca Pinna, Miguel Querejeta, Thomas G. Williams
The inner regions of the Milky Way are known to contain an enigmatic population of prominent molecular clouds characterised by extremely broad lines. The physical origin of these ''extended velocity features'' (EVFs) is still debated, although a connection with the ''dust lanes'' of the Galactic bar has been hypothesised. In this paper, we search for analogous features in the dust lanes of nearby barred galaxies using the PHANGS-ALMA CO(2-1) survey. We aim to confirm existence of EVFs in other galaxies and to take advantage of the external perspective to gain insight into their origin. We study a sample of 29 barred galaxies and find that 34% contain one or more EVFs, while the remaining lack obvious signs of EVFs. Upon analysing the physical properties of the EVFs, we find they possess large virial parameters, ranging from few hundreds to several thousand, indicating that they are strongly out-of-equilibrium. The most likely explanation for their origin is extreme cloud-cloud collisions with relative velocities in excess of 100km/s in highly non-circular flow driven by the bar. This interpretation is consistent with previous high-resolution observations in Milky Way. Further corroboration of this interpretation comes from the inspection of high-sensitivity infrared observations from the PHANGS-JWST Treasury Survey that reveals streams of gas that appear to be hitting the dust lanes at locations where EVFs are found. We argue that EVFs are the clearest examples of cloud-cloud collisions available in literature and represent a unique opportunity to study cloud collisions and their impact on star formation.
Explainable Artificial Intelligence (XAI) is a crucial pathway in mitigating the risk of non-transparency in the decision-making process of black-box Artificial Intelligence (AI) systems. However, despite the benefits, XAI methods are found to leak the privacy of individuals whose data is used in training or querying the models. Researchers have demonstrated privacy attacks that exploit explanations to infer sensitive personal information of individuals. Currently there is a lack of defenses against known privacy attacks targeting explanations when vulnerable XAI are used in production and machine learning as a service system. To address this gap, in this article, we explore Privacy Enhancing Technologies (PETs) as a defense mechanism against attribute inference on explanations provided by feature-based XAI methods. We empirically evaluate 3 types of PETs, namely synthetic training data, differentially private training and noise addition, on two categories of feature-based XAI. Our evaluation determines different responses from the mitigation methods and side-effects of PETs on other system properties such as utility and performance. In the best case, PETs integration in explanations reduced the risk of the attack by 49.47%, while maintaining model utility and explanation quality. Through our evaluation, we identify strategies for using PETs in XAI for maximizing benefits and minimizing the success of this privacy attack on sensitive personal information.
RRAT-like behaviour of PSR B0656+14 observed with I-LOFAR
2025-07-06
S. C. Susarla, R. Ferguson, O. A. Johnson, L. Vincetti, D. J. McKenna, E. F. Keane, P. J. McCauley, A. Golden
Single pulse studies offer vital insights into the emission physics of pulsars, particularly in the case of young, nearby sources where intrinsic variability is often pronounced. PSR~B0656+14, known for its sporadic and sometimes intense pulses, provides an excellent opportunity to investigate such behaviour at low radio frequencies. This study aims to characterize the single pulse behaviour of PSR~B0656+14 using low-frequency observations at 110-190 MHz from the Irish LOFAR station. Single-pulse extraction is performed, and individual pulse DMs are estimated to probe pulse-to-pulse dispersion variability. We also perform a wait-time analysis to understand the statistical nature of pulse occurrence, and estimate the spectral index from frequency-resolved flux density measurements. The pulse energy distribution is modelled using a combination of log-normal and power-law components. A total of 41 pulses were detected in a 5-hour observation, allowing a wait-time distribution analysis which is well-modelled by an exponential function, indicative of a Poisson process. Profile stability analysis indicates that a significant number of pulses are required to reach a stable average profile, unusual compared to many other pulsars. The single-pulse spectral index varies significantly from pulse to pulse, with a mean value of $\alpha = -0.5$ and a standard deviation of $\Delta\alpha=1.3$. The pulse energy distribution shows a hybrid behaviour, consistent of a log-normal distribution and a power-law tail. Our results confirm that PSR~B0656+14 exhibits highly variable, memory-less emission at low frequencies, with characteristics that resemble those seen in some rotating radio transients (RRATs). If such variability proves to be widespread among pulsars, population synthesis models and survey yield predictions would need to incorporate this currently overlooked feature to ensure accuracy.
PIC simulation of current-driven solar type III radiation and whistler waves in an electron core-strahl plasma: Relevance to PSP and other space observations
2025-07-06
Konrad Sauer, Kaijun Liu
The aim of the paper is to demonstrate that electron current oscillations may generate electromagnetic waves as type III radiation and whistler waves without the involvement of the classical plasma emission via the coalescence of waves. PIC simulation results of an electron-core-strahl plasma without initial current compensation are presented which describe the conversion of current-driven Langmuir oscillations/waves into type III radiation whereby simultaneously whistler waves are excited. In contrast to the classical approach of Ginzburg and Zhelezniakov (1958) after which beam-excited Langmuir waves in a two-step process are converted in electromagnetic radiation, any instability is suppressed by selecting a low strahl velocity. Rather electric field oscillations at the electron plasma frequency are triggered by the initially non-compensated current of the strahl. The arising electromagnetic fields exhibit amplitude oscillations which are caused by the superposition of the two wave modes of mixed polarisation at the point of mode coupling. This basic mechanism of wave generation and transformation has already been described in earlier papers using simple fluid models. It is also the topic of the companion paper. Besides the fundamental electromagnetic radiation, the second harmonic of nearly the same intensity has been obtained which is an indication for nonlinear currents. Measurements of Langmuir waves, type III radiation and whistler waves on board various satellites in the solar wind, in particular Parker Solar Probe (PSP) observations are analysed in the light of our results. Interpretations of earlier PIC simulations are critically reviewed.
Structural Identifiability of Compartmental Models: Recent Progress and Future Directions
2025-07-06
Nicolette Meshkat, Anne Shiu
We summarize recent progress on the theory and applications of structural identifiability of compartmental models. On the applications side, we review identifiability analyses undertaken recently for models arising in epidemiology, oncology, and other areas; and we summarize common approaches for handling models that are unidentifiable. We also highlight recent theoretical and algorithmic results on how to reparametrize unidentifiable models and, in the context of linear compartmental models, how to predict identifiability properties directly from the model structure. Finally, we highlight future research directions.
Deciphering Baryonic Feedback from ACT tSZ Galaxy Clusters
2025-07-06
Nihar Dalal, Chun-Hao To, Chris Hirata, Tae Hyeon-Shin, Matt Hilton, Shivam Pandey, J. Richard Bond
The next generation of cosmology surveys will probe the matter distribution of the universe to unparalleled precision. To match this level of precision in cosmological parameter estimation, we need to use information at small scales of $\sim$ 1 Mpc, which requires an accurate model of baryonic feedback. In this paper, we employ the Dark Matter + Baryon (DMB) model, a flexible halo model that is well-fit to various hydrodynamical simulations, to extract information on baryonic feedback from galaxy cluster observables. Using a sample of thermal Sunyaev-Zeldovich (tSZ) selected galaxy clusters from the Atacama Cosmology Telescope (ACT) - with masses calibrated via weak lensing from the Dark Energy Survey (DES) - we develop a robust end-to-end pipeline that directly models the calibrated observables. Our analysis demonstrates that the tSZ Y-M relation can constrain several DMB model parameters, providing key insights into baryonic feedback effects on cosmic shear at the several percent level. We find a preference for intermediate to strong levels of feedback, which is both consistent with several hydrodynamic simulations and competitive with similar analyses performed on complementary probes. Finally, we discuss the implications of our results in the context of current and upcoming cosmic shear surveys.
The role of large language models in UI/UX design: A systematic literature review
2025-07-06
Ammar Ahmed, Ali Shariq Imran
This systematic literature review examines the role of large language models (LLMs) in UI/UX design, synthesizing findings from 38 peer-reviewed studies published between 2022 and 2025. We identify key LLMs in use, including GPT-4, Gemini, and PaLM, and map their integration across the design lifecycle, from ideation to evaluation. Common practices include prompt engineering, human-in-the-loop workflows, and multimodal input. While LLMs are reshaping design processes, challenges such as hallucination, prompt instability, and limited explainability persist. Our findings highlight LLMs as emerging collaborators in design, and we propose directions for the ethical, inclusive, and effective integration of these technologies.
The rapid evolution of deep learning (DL) models and the ever-increasing size of available datasets have raised the interest of the research community in the always important field of vision-based hand gesture recognition (VHGR), and delivered a wide range of applications, such as sign language understanding and human-computer interaction using cameras. Despite the large volume of research works in the field, a structured and complete survey on VHGR is still missing, leaving researchers to navigate through hundreds of papers in order to find the right combination of data, model, and approach for each task. The current survey aims to fill this gap by presenting a comprehensive overview of this aspect of computer vision. With a systematic research methodology that identifies the state-of-the-art works and a structured presentation of the various methods, datasets, and evaluation metrics, this review aims to constitute a useful guideline for researchers, helping them to choose the right strategy for delving into a certain VHGR task. Starting with the methodology used for study selection, literature retrieval, and the analytical framing, the survey identifies and organizes key VHGR approaches using a taxonomy-based format in various dimensions such as input modality and application domain. The core of the survey provides an in-depth analysis of state-of-the-art techniques across three primary VHGR tasks: static gesture recognition, isolated dynamic gestures and continuous gesture recognition. For each task, the architectural trends and learning strategies are listed. Additionally, the study reviews commonly used datasets - emphasizing on annotation schemes - and evaluates standard performance metrics. It concludes by identifying major challenges in VHGR, including both general computer vision issues and domain-specific obstacles, and outlines promising directions for future research.
Low-mass vector-meson production at forward rapidity in $p$$+$$p$ and Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$~GeV
2025-07-06
PHENIX Collaboration, N. J. Abdulameer, U. Acharya, A. Adare, C. Aidala, N. N. Ajitanand, Y. Akiba, M. Alfred, D. Anderson, V. Andrieux, S. Antsupov, N. Apadula, H. Asano, B. Azmoun, V. Babintsev, M. Bai, N. S. Bandara, B. Bannier, E. Bannikov, K. N. Barish, S. Bathe, A. Bazilevsky, M. Beaumier, S. Beckman, R. Belmont, A. Berdnikov, Y. Berdnikov, L. Bichon, B. Blankenship, D. S. Blau, J. S. Bok, V. Borisov, K. Boyle, M. L. Brooks, J. Bryslawskyj, V. Bumazhnov, S. Campbell, R. Cervantes, P. Chaitanya, C. -H. Chen, D. Chen, M. Chiu, C. Y. Chi, I. J. Choi, J. B. Choi, T. Chujo, Z. Citron, M. Connors, R. Corliss, N. Cronin, M. Csanád, T. Csörgő, T. W. Danley, A. Datta, M. S. Daugherity, G. David, K. DeBlasio, K. Dehmelt, A. Denisov, A. Deshpande, E. J. Desmond, A. Dion, P. B. Diss, D. Dixit, V. Doomra, J. H. Do, A. Drees, K. A. Drees, J. M. Durham, A. Durum, H. En'yo, A. Enokizono, R. Esha, B. Fadem, W. Fan, N. Feege, D. E. Fields, M. Finger, Jr., M. Finger, D. Firak, D. Fitzgerald, S. L. Fokin, J. E. Frantz, A. Franz, A. D. Frawley, Y. Fukuda, P. Gallus, C. Gal, P. Garg, H. Ge, F. Giordano, A. Glenn, Y. Goto, N. Grau, S. V. Greene, M. Grosse Perdekamp, T. Gunji, T. Guo, H. Guragain, T. Hachiya, J. S. Haggerty, K. I. Hahn, H. Hamagaki, H. F. Hamilton, J. Hanks, S. Y. Han, S. Hasegawa, T. O. S. Haseler, K. Hashimoto, T. K. Hemmick, X. He, J. C. Hill, K. Hill, A. Hodges, R. S. Hollis, K. Homma, B. Hong, T. Hoshino, N. Hotvedt, J. Huang, K. Imai, M. Inaba, A. Iordanova, D. Isenhower, D. Ivanishchev, B. V. Jacak, M. Jezghani, X. Jiang, Z. Ji, B. M. Johnson, D. Jouan, D. S. Jumper, S. Kanda, J. H. Kang, D. Kapukchyan, S. Karthas, G. Kasza, D. Kawall, A. V. Kazantsev, J. A. Key, V. Khachatryan, A. Khanzadeev, B. Kimelman, C. Kim, D. J. Kim, E. -J. Kim, G. W. Kim, M. Kim, D. Kincses, E. Kistenev, R. Kitamura, J. Klatsky, D. Kleinjan, P. Kline, T. Koblesky, B. Komkov, D. Kotov, L. Kovacs, S. Kudo, K. Kurita, M. Kurosawa, Y. Kwon, J. G. Lajoie, D. Larionova, A. Lebedev, S. Lee, S. H. Lee, M. J. Leitch, Y. H. Leung, S. H. Lim, M. X. Liu, X. Li, X. Li, V. -R. Loggins, D. A. Loomis, K. Lovasz, D. Lynch, S. Lökös, T. Majoros, Y. I. Makdisi, M. Makek, A. Manion, V. I. Manko, E. Mannel, M. McCumber, P. L. McGaughey, D. McGlinchey, C. McKinney, A. Meles, M. Mendoza, A. C. Mignerey, A. Milov, D. K. Mishra, J. T. Mitchell, M. Mitrankova, Iu. Mitrankov, G. Mitsuka, S. Miyasaka, S. Mizuno, A. K. Mohanty, P. Montuenga, T. Moon, D. P. Morrison, T. V. Moukhanova, B. Mulilo, T. Murakami, J. Murata, A. Mwai, K. Nagai, K. Nagashima, T. Nagashima, J. L. Nagle, M. I. Nagy, I. Nakagawa, H. Nakagomi, K. Nakano, C. Nattrass, P. K. Netrakanti, T. Niida, S. Nishimura, R. Nouicer, N. Novitzky, T. Novák, G. Nukazuka, A. S. Nyanin, E. O'Brien, C. A. Ogilvie, J. D. Orjuela Koop, M. Orosz, J. D. Osborn, A. Oskarsson, G. J. Ottino, K. Ozawa, R. Pak, V. Pantuev, V. Papavassiliou, J. S. Park, S. Park, M. Patel, S. F. Pate, J. -C. Peng, D. V. Perepelitsa, G. D. N. Perera, D. Yu. Peressounko, C. E. PerezLara, J. Perry, R. Petti, M. Phipps, C. Pinkenburg, R. Pinson, R. P. Pisani, M. Potekhin, M. L. Purschke, J. Rak, B. J. Ramson, I. Ravinovich, K. F. Read, D. Reynolds, V. Riabov, Y. Riabov, D. Richford, T. Rinn, S. D. Rolnick, M. Rosati, Z. Rowan, J. G. Rubin, A. S. Safonov, B. Sahlmueller, N. Saito, T. Sakaguchi, H. Sako, V. Samsonov, M. Sarsour, S. Sato, B. Schaefer, B. K. Schmoll, K. Sedgwick, R. Seidl, A. Seleznev, A. Sen, R. Seto, P. Sett, A. Sexton, D. Sharma, I. Shein, T. -A. Shibata, K. Shigaki, M. Shimomura, T. Shioya, P. Shukla, A. Sickles, C. L. Silva, D. Silvermyr, B. K. Singh, C. P. Singh, V. Singh, M. Slunečka, K. L. Smith, M. Snowball, R. A. Soltz, W. E. Sondheim, S. P. Sorensen, I. V. Sourikova, P. W. Stankus, M. Stepanov, S. P. Stoll, T. Sugitate, A. Sukhanov, T. Sumita, J. Sun, Z. Sun, J. Sziklai, A. Taketani, K. Tanida, M. J. Tannenbaum, S. Tarafdar, A. Taranenko, G. Tarnai, R. Tieulent, A. Timilsina, T. Todoroki, M. Tomášek, C. L. Towell, R. Towell, R. S. Towell, I. Tserruya, Y. Ueda, B. Ujvari, H. W. van Hecke, J. Velkovska, M. Virius, V. Vrba, N. Vukman, X. R. Wang, Y. Watanabe, Y. S. Watanabe, F. Wei, A. S. White, C. L. Woody, M. Wysocki, B. Xia, L. Xue, C. Xu, Q. Xu, S. Yalcin, Y. L. Yamaguchi, H. Yamamoto, A. Yanovich, I. Yoon, J. H. Yoo, I. E. Yushmanov, H. Yu, W. A. Zajc, A. Zelenski, S. Zhou, L. Zou
The PHENIX experiment at the Relativistic Heavy Ion Collider has measured low-mass vector-meson ($\omega+\rho$ and $\phi$) production through the dimuon decay channel at forward rapidity $(1.2<|\mbox{y}|<2.2)$ in $p$$+$$p$ and Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$~GeV. The low-mass vector-meson yield and nuclear-modification factor were measured as a function of the average number of participating nucleons, $\langle N_{\rm part}\rangle$, and the transverse momentum $p_T$. These results were compared with those obtained via the kaon decay channel in a similar $p_T$ range at midrapidity. The nuclear-modification factors in both rapidity regions are consistent within the uncertainties. A comparison of the $\omega+\rho$ and $J/\psi$ mesons reveals that the light and heavy flavors are consistently suppressed across both $p_T$ and ${\langle}N_{\rm part}\rangle$. In contrast, the $\phi$ meson displays a nuclear-modification factor consistent with unity, suggesting strangeness enhancement in the medium formed.
Learning Software Bug Reports: A Systematic Literature Review
2025-07-06
Guoming Long, Jingzhi Gong, Hui Fang, Tao Chen
The recent advancement of artificial intelligence, especially machine learning (ML), has significantly impacted software engineering research, including bug report analysis. ML aims to automate the understanding, extraction, and correlation of information from bug reports. Despite its growing importance, there has been no comprehensive review in this area. In this paper, we present a systematic literature review covering 1,825 papers, selecting 204 for detailed analysis. We derive seven key findings: 1) Extensive use of CNN, LSTM, and $k$NN for bug report analysis, with advanced models like BERT underutilized due to their complexity. 2) Word2Vec and TF-IDF are popular for feature representation, with a rise in deep learning approaches. 3) Stop word removal is the most common preprocessing, with structural methods rising after 2020. 4) Eclipse and Mozilla are the most frequently evaluated software projects. 5) Bug categorization is the most common task, followed by bug localization and severity prediction. 6) There is increasing attention on specific bugs like non-functional and performance bugs. 7) Common evaluation metrics are F1-score, Recall, Precision, and Accuracy, with $k$-fold cross-validation preferred for model evaluation. 8) Many studies lack robust statistical tests. We also identify six promising future research directions to provide useful insights for practitioners.
Machine Learning in Acoustics: A Review and Open-Source Repository
2025-07-06
Ryan A. McCarthy, You Zhang, Samuel A. Verburg, William F. Jenkins, Peter Gerstoft
Acoustic data provide scientific and engineering insights in fields ranging from bioacoustics and communications to ocean and earth sciences. In this review, we survey recent advances and the transformative potential of machine learning (ML) in acoustics, including deep learning (DL). Using the Python high-level programming language, we demonstrate a broad collection of ML techniques to detect and find patterns for classification, regression, and generation in acoustics data automatically. We have ML examples including acoustic data classification, generative modeling for spatial audio, and physics-informed neural networks. This work includes AcousticsML, a set of practical Jupyter notebook examples on GitHub demonstrating ML benefits and encouraging researchers and practitioners to apply reproducible data-driven approaches to acoustic challenges.
SpiritRAG: A Q&A System for Religion and Spirituality in the United Nations Archive
2025-07-06
Yingqiang Gao, Fabian Winiger, Patrick Montjourides, Anastassia Shaitarova, Nianlong Gu, Simon Peng-Keller, Gerold Schneider
Religion and spirituality (R/S) are complex and highly domain-dependent concepts which have long confounded researchers and policymakers. Due to their context-specificity, R/S are difficult to operationalize in conventional archival search strategies, particularly when datasets are very large, poorly accessible, and marked by information noise. As a result, considerable time investments and specialist knowledge is often needed to extract actionable insights related to R/S from general archival sources, increasing reliance on published literature and manual desk reviews. To address this challenge, we present SpiritRAG, an interactive Question Answering (Q&A) system based on Retrieval-Augmented Generation (RAG). Built using 7,500 United Nations (UN) resolution documents related to R/S in the domains of health and education, SpiritRAG allows researchers and policymakers to conduct complex, context-sensitive database searches of very large datasets using an easily accessible, chat-based web interface. SpiritRAG is lightweight to deploy and leverages both UN documents and user provided documents as source material. A pilot test and evaluation with domain experts on 100 manually composed questions demonstrates the practical value and usefulness of SpiritRAG.
Interband Lag Variability in Active Galactic Nuclei across ZTF Data from Multiple Years
2025-07-06
Zhen-Bo Su, Zhen-Yi Cai, Hengxiao Guo, Mouyuan Sun, Jun-Xian Wang
Interband lags in the optical continua of active galactic nuclei (AGN) have been observed over years of monitoring, yet their physical origins remain unclear. While variable interband lags have been found in a few individual AGN potentially, the temporal behavior of interband lags of an AGN sample has not been explored systematically. Here, we analyze the interband lags of 94 bright AGN at $z<0.8$, using both seasonal one-year and full six-year $gri$-band light curves from Zwicky Transient Facility Data Release 22. We find that more than half of 94 AGN show significant seasonal variations in the interband lags. Besides, the short-term lags, derived by averaging lags inferred from multiple seasonal light curves, are consistently smaller than the long-term lags, which are inferred from the full six-year light curves. This supports recent theoretical simulations where the lag measurement is sensitive to the baseline of light curve and the lag variation could be simply attributed to the inherent randomness of AGN variability. Our findings suggest that the interband lags of AGN are more complex and stochastic than commonly thought, and highlight the importance of high-precision time-domain surveys in uncovering the properties of AGN variability as well as the associated accretion physics.
Robot-assisted Transcranial Magnetic Stimulation (Robo-TMS): A Review
2025-07-06
Wenzhi Bai, Andrew Weightman, Rory J O Connor, Zhengtao Ding, Mingming Zhang, Sheng Quan Xie, Zhenhong Li
Transcranial magnetic stimulation (TMS) is a non-invasive and safe brain stimulation procedure with growing applications in clinical treatments and neuroscience research. However, achieving precise stimulation over prolonged sessions poses significant challenges. By integrating advanced robotics with conventional TMS, robot-assisted TMS (Robo-TMS) has emerged as a promising solution to enhance efficacy and streamline procedures. Despite growing interest, a comprehensive review from an engineering perspective has been notably absent. This paper systematically examines four critical aspects of Robo-TMS: hardware and integration, calibration and registration, neuronavigation systems, and control systems. We review state-of-the-art technologies in each area, identify current limitations, and propose future research directions. Our findings suggest that broader clinical adoption of Robo-TMS is currently limited by unverified clinical applicability, high operational complexity, and substantial implementation costs. Emerging technologies, including marker-less tracking, non-rigid registration, learning-based electric field (E-field) modelling, individualised magnetic resonance imaging (MRI) generation, robot-assisted multi-locus TMS (Robo-mTMS), and automated calibration and registration, present promising pathways to address these challenges.
Perspectives on Utilization of Measurements in Quantum Algorithms
2025-07-06
Valter Uotila, Ilmo Salmenperä, Leo Becker, Arianne Meijer - van de Griend, Aakash Ravindra Shinde, Jukka K. Nurminen
Measurement is a fundamental operation in quantum computing and has many important use cases in quantum algorithms. This article provides a comprehensive overview of the basic measurement operations in quantum computing and represents a selected set of their applications in quantum algorithms. Our goal is to provide one of the first algorithmic overviews of measurement processes in quantum computing. From the quantum information-theoretical perspective, measurements are either a method to access the result of a quantum computation or a technique to modify a quantum state. We also identify measurement-based methods to solve quantum computational challenges, such as error mitigation and circuit cutting. We discuss three main categories of measurements: performing measurements in static quantum circuits, modifying the quantum state in dynamic quantum circuits via measurements, and addressing challenges in quantum computing with measurements. Based on the reviewed topics, the measurement operations are frequently not at the center of the quantum algorithm design. However, the most novel and error-prone quantum algorithms will likely require sophisticated measurement schemes. Thus, the central message of this article is to broaden the view of measurement operations and highlight their importance at every level of quantum algorithm design.
2025-07-06
Fan Zhang, Tianqi Mao, Mingkun Li, Meng Hua, Jinshu Chen, Christos Masouros, Zhaocheng Wang
Sixth-generation (6G) wireless networks are expected not only to provide high-speed connectivity but also to support reliable sensing capabilities, giving rise to the integrated sensing and communication (ISAC) paradigm. To enable higher data rates and more accurate sensing, terahertz (THz) systems empowered by extremely large multiple-input-multiple-output (XL-MIMO) technology are envisioned as key enablers for future ISAC systems. Owing to the substantial increase in both effective array aperture and carrier frequency, a considerable portion of future ISAC applications is anticipated to fall within the near-field coverage region, instead of the conventional far-field. However, most existing ISAC techniques are designed under the far-field planar wave assumption, struggling to accommodate the unique characteristics of THz near-field propagation. To motivate future research into near-field ISAC research, we systematically investigate the characteristics of THz near-field propagation and explore its potential to facilitate ISAC systems. Specifically, we analyze three fundamental characteristics of THz near-field propagation and review state-of-the-art techniques that exploit these features to boost both communication and sensing performance. To further harness the angular-range coupling effect, we zoom into a particularly interesting approach to near-field sensing based on wavenumber domain. Besides, to exploit the beam squint effect, an ISAC resource allocation framework is introduced to support integrated multi-angle sensing and multi-user communication. Finally, we outline promising directions for future research in this emerging area.
Newton's laws of motion pose an apparent problem, sometimes referred to as "the independence problem": the first law seems to be a simple consequence of the second law, hence it is unclear why it is included as a separate law. Many different possible answers to this question appear in the literature. The main contribution of this paper is to offer two additional answers to this problem. One of which we call "the formal explanation," since unlike all previous explanations it relies on mathematical formalism, and the other, "the logical explanation", derives the importance of the first law from the fact it states of a more fundamental natural property. A second contribution is a comprehensive review of previous suggested explanations, which we categorize and discuss as well. We provide arguments why we think our novel explanations are the most plausible.
ATwo-Stage Ensemble Feature Selection and Particle Swarm Optimization Approach for Micro-Array Data Classification in Distributed Computing Environments
2025-07-06
Aayush Adhikari, Sandesh Bhatta, Harendra S. Jangwan, Amit Mishra, Khair Ul Nisa, Abu Taha Zamani, Aaron Sapkota, Debendra Muduli, Nikhat Parveen
High dimensionality in datasets produced by microarray technology presents a challenge for Machine Learning (ML) algorithms, particularly in terms of dimensionality reduction and handling imbalanced sample sizes. To mitigate the explained problems, we have proposedhybrid ensemble feature selection techniques with majority voting classifier for micro array classi f ication. Here we have considered both filter and wrapper-based feature selection techniques including Mutual Information (MI), Chi-Square, Variance Threshold (VT), Least Absolute Shrinkage and Selection Operator (LASSO), Analysis of Variance (ANOVA), and Recursive Feature Elimination (RFE), followed by Particle Swarm Optimization (PSO) for selecting the optimal features. This Artificial Intelligence (AI) approach leverages a Majority Voting Classifier that combines multiple machine learning models, such as Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), to enhance overall performance and accuracy. By leveraging the strengths of each model, the ensemble approach aims to provide more reliable and effective diagnostic predictions. The efficacy of the proposed model has been tested in both local and cloud environments. In the cloud environment, three virtual machines virtual Central Processing Unit (vCPU) with size 8,16 and 64 bits, have been used to demonstrate the model performance. From the experiment it has been observed that, virtual Central Processing Unit (vCPU)-64 bits provides better classification accuracies of 95.89%, 97.50%, 99.13%, 99.58%, 99.11%, and 94.60% with six microarray datasets, Mixed Lineage Leukemia (MLL), Leukemia, Small Round Blue Cell Tumors (SRBCT), Lymphoma, Ovarian, andLung,respectively, validating the effectiveness of the proposed modelin bothlocalandcloud environments.
Ethics by Design: A Lifecycle Framework for Trustworthy AI in Medical Imaging From Transparent Data Governance to Clinically Validated Deployment
2025-07-06
Umer Sadiq Khan, Saif Ur Rehman Khan
The integration of artificial intelligence (AI) in medical imaging raises crucial ethical concerns at every stage of its development, from data collection to deployment. Addressing these concerns is essential for ensuring that AI systems are developed and implemented in a manner that respects patient rights and promotes fairness. This study aims to explore the ethical implications of AI in medical imaging, focusing on five key stages: data collection, data processing, model training, model evaluation, and deployment. The goal is to evaluate how these stages adhere to fundamental ethical principles, including data privacy, fairness, transparency, accountability, and autonomy. An analytical approach was employed to examine the ethical challenges associated with each stage of AI development. We reviewed existing literature, guidelines, and regulations concerning AI ethics in healthcare and identified critical ethical issues at each stage. The study outlines specific inquiries and principles for each phase of AI development. The findings highlight key ethical issues: ensuring patient consent and anonymization during data collection, addressing biases in model training, ensuring transparency and fairness during model evaluation, and the importance of continuous ethical assessments during deployment. The analysis also emphasizes the impact of accessibility issues on different stakeholders, including private, public, and third-party entities. The study concludes that ethical considerations must be systematically integrated into each stage of AI development in medical imaging. By adhering to these ethical principles, AI systems can be made more robust, transparent, and aligned with patient care and data control. We propose tailored ethical inquiries and strategies to support the creation of ethically sound AI systems in medical imaging.
We study the computational framework of polyseries and poly-numbers introduced by Wildberger and Rubine in [2]. After reviewing Peano arithmetic, computable naturals, and non-standard models, we define the core data and procedure operations on polyseries. We then apply Catalan number expansions to solve quadratic congruences and derive explicit truncated series formulas in a finite field $\mathbb{Z}_p$. Our main result gives a closed form poly series solution in terms of Catalan numbers.
From the Densest Clusters to the Emptiest Voids: No Evidence For Environmental Effects on the Galaxy Size-Mass Relation at Low Redshift
2025-07-06
Mohamed H. Abdullah, Nouran E. Abdelhamid, Rasha M. Samir, Gillian Wilson
We present a comprehensive study of the galaxy size-stellar mass relation (SMR) at low redshift (z <= 0.125), using a large spectroscopic sample from the SDSS-DR13 survey. Our goal is to investigate how environment affects galaxy structural properties across multiple spatial scales. Galaxies are classified by specific star formation rate, optical color, and bulge-to-total light ratio, allowing us to disentangle environmental effects from intrinsic galaxy properties. We examine the SMR in three contexts: (1) comparing galaxy sizes in two extreme environments-dense clusters versus cosmic voids; (2) analyzing cluster galaxies across a range of cluster masses; and (3) studying member galaxies located in different cluster regions, from the core to the infall zone. In all three cases, we find no significant dependence of the SMR on environment at fixed stellar mass and galaxy type. Cluster and void galaxies follow consistent SMR trends, and no measurable variation is observed with cluster mass or cluster-centric distance. We also confirm that early-type galaxies exhibit steeper SMR slopes than late types. Notably, this consistent lack of environmental dependence on the SMR persists even when accounting for the differing galaxy number densities in voids, supporting the universality of this SMR scaling relation across diverse environments.
Integrating Generative AI in BIM Education: Insights from Classroom Implementation
2025-07-06
Islem Sahraoui, Kinam Kim, Lu Gao, Zia Din, Ahmed Senouci
This study evaluates the implementation of a Generative AI-powered rule checking workflow within a graduate-level Building Information Modeling (BIM) course at a U.S. university. Over two semesters, 55 students participated in a classroom-based pilot exploring the use of GenAI for BIM compliance tasks, an area with limited prior research. The instructional design included lectures on prompt engineering and AI-driven rule checking, followed by an assignment where students used a large language model (LLM) to identify code violations in designs using Autodesk Revit. Surveys and interviews were conducted to assess student workload, learning effectiveness, and overall experience, using the NASA-TLX scale and regression analysis. Findings indicate students generally achieved learning objectives but faced challenges such as difficulties debugging AI-generated code and inconsistent tool performance, probably due to their limited prompt engineering experience. These issues increased cognitive and emotional strain, especially among students with minimal programming backgrounds. Despite these challenges, students expressed strong interest in future GenAI applications, particularly with clear instructional support.
Governance and Technological Challenge in Digital Solidarity Economies: A Case Study of a Collaborative Transportation Platform in South Korea
2025-07-05
Jeongone Seo, Tawfiq Ammari
South Korea's City P illustrates how lofty goals of digital solidarity can falter when challenged by local governance realities. Drawing on Hansmann's ownership theory, collaborative governance concepts, and platform cooperativism, we conducted a qualitative case study involving policy documents, independent assessments, and 11 in-depth interviews with residents, officials, and technology developers. Findings reveal a marked disconnect between the initiative's stated emphasis on community co-ownership and the actual power dynamics that largely favored government agencies and external firms. Although blockchain and integrated digital tools were meant to enhance transparency and inclusivity, stakeholders--especially elderly residents--experienced confusion and mistrust. We argue that genuine collaboration in digital solidarity economies requires not only robust technical designs but also culturally resonant ownership structures, substantive inclusion of local voices, and transparent governance mechanisms. The City P case underscores the necessity of addressing heterogeneous digital capacities, aligning funding and incentives with grassroots empowerment, and mitigating performative participation to ensure meaningful and sustainable outcomes in community-based digital innovation.
Reinforcement Learning (RL) has emerged as a transformative approach for aligning and enhancing Large Language Models (LLMs), addressing critical challenges in instruction following, ethical alignment, and reasoning capabilities. This survey offers a comprehensive foundation on the integration of RL with language models, highlighting prominent algorithms such as Proximal Policy Optimization (PPO), Q-Learning, and Actor-Critic methods. Additionally, it provides an extensive technical overview of RL techniques specifically tailored for LLMs, including foundational methods like Reinforcement Learning from Human Feedback (RLHF) and AI Feedback (RLAIF), as well as advanced strategies such as Direct Preference Optimization (DPO) and Group Relative Policy Optimization (GRPO). We systematically analyze their applications across domains, i.e., from code generation to tool-augmented reasoning. We also present a comparative taxonomy based on reward modeling, feedback mechanisms, and optimization strategies. Our evaluation highlights key trends. RLHF remains dominant for alignment, and outcome-based RL such as RLVR significantly improves stepwise reasoning. However, persistent challenges such as reward hacking, computational costs, and scalable feedback collection underscore the need for continued innovation. We further discuss emerging directions, including hybrid RL algorithms, verifier-guided training, and multi-objective alignment frameworks. This survey serves as a roadmap for researchers advancing RL-driven LLM development, balancing capability enhancement with safety and scalability.
Growth Rate Analysis in $f(R,L_m)$ Gravity: A Comparative Study with \boldmath$Λ$CDM Cosmology
2025-07-05
G. K. Goswami, J. P. Saini
We investigate the evolution of cosmic structures within the framework of modified gravity, specifically focusing on theories described by the function $f(R, L_m)$, where $R$ is the Ricci scalar and $L_m$ is the matter Lagrangian. This class of models introduces a non-minimal coupling between geometry and matter, leading to modifications in the dynamics of density perturbations. We derive the linear growth equation and compute the observable growth rate $f\sigma_8(z)$, which is directly accessible from redshift-space distortion (RSD) data. Using recent observational constraints from galaxy surveys such as eBOSS and DESI, we perform a comparative analysis between predictions from $f(R, L_m)$ gravity and the standard $\Lambda$CDM model. Our results indicate that while $\Lambda$CDM remains broadly consistent with current data, the $f(R, L_m)$ framework can accommodate subtle deviations in structure growth, offering a possible resolution to existing tensions in large-scale structure observations. We also outline the implications of our findings for future surveys, including Euclid and LSST.
XISM: an eXploratory and Interactive Graph Tool to Visualize and Evaluate Semantic Map Models
2025-07-05
Zhu Liu, Zhen Hu, Lei Dai, Ying Liu
Semantic map models represent meanings or functions as nodes in a graph constrained by the local connectivity hypothesis, with edges indicating their associations. Widely used in typological linguistics, these models compare interrelated meanings across languages. Traditionally built manually in a bottom-up manner, they are inefficient for large datasets and lack visualization and evaluation tools. This paper introduces XISM, an interactive tool based on our prior algorithm, which constructs semantic maps from user data via a top-down approach, displays candidate maps, and evaluates them using multiple metrics. Users can refine maps by editing edges, combining data-driven efficiency with expert knowledge. This human-in-the-loop design benefits both typologists and computational linguists. The system https://770103knev48.vicp.fun/ and a demonstration video https://youtu.be/S-wsVDF2HSI?si=1OrcF41tRznaifhZ are publicly available.
Contemporary multi-agent systems encounter persistent challenges in cross-platform interoperability, dynamic task scheduling, and efficient resource sharing. Agents with heterogeneous implementations often lack standardized interfaces; collaboration frameworks remain brittle and hard to extend; scheduling policies are static; and inter-agent state synchronization is insufficient. We propose Hierarchical Agent Workflow (HAWK), a modular framework comprising five layers-User, Workflow, Operator, Agent, and Resource-and supported by sixteen standardized interfaces. HAWK delivers an end-to-end pipeline covering task parsing, workflow orchestration, intelligent scheduling, resource invocation, and data synchronization. At its core lies an adaptive scheduling and optimization module in the Workflow Layer, which harnesses real-time feedback and dynamic strategy adjustment to maximize utilization. The Resource Layer provides a unified abstraction over heterogeneous data sources, large models, physical devices, and third-party services&tools, simplifying cross-domain information retrieval. We demonstrate HAWK's scalability and effectiveness via CreAgentive, a multi-agent novel-generation prototype, which achieves marked gains in throughput, lowers invocation complexity, and improves system controllability. We also show how hybrid deployments of large language models integrate seamlessly within HAWK, highlighting its flexibility. Finally, we outline future research avenues-hallucination mitigation, real-time performance tuning, and enhanced cross-domain adaptability-and survey prospective applications in healthcare, government, finance, and education.
One important property in studying the exoplanet population is the host star metallicity ([M/H]). In this study, we derived stellar metallicities and oxygen abundances for 45 M dwarf stars (with 65 exoplanets) using the near-infrared high-resolution spectra from the SDSS APOGEE survey and synthetic spectra computed in LTE. We also investigated the exoplanetary radii distribution for a larger sample of 246 exoplanets orbiting 188 M dwarf stars. The [M/H] versus [O/M] distribution obtained indicates that our sample is composed mainly of thin disk stars, which follow the behavior of the low-alpha sequence in the Milky Way thin disk. Small planets with radii smaller than 3R$_{\oplus}$ were found around stars with a range of metallicities (-0.6$<$[M/H]$<$+0.3), while larger planets of the sample orbit only stars with [M/H]$>=0.0$. These results indicate that while small planets can form in different environments, larger planets preferentially form in metal-rich protoplanetary disks. Exoplanets with P$_{\rm orb}<$4.3 days orbit on average more metal-rich stars than planets with longer periods. This threshold is smaller than that found for FGK stars (8--10 days) and might be related to M dwarfs having a smaller dust sublimation radius. The distribution of exoplanets with R$_{\rm p}>$4R$_{\oplus}$ shows a concentration at orbital periods between 2 and 5 days, which may result from inward orbital migration. There is also a different behavior between single-detected exoplanets and planets from multiplanetary systems, with the latter being found on average around more metal-poor stars, and with orbital periods roughly up to 3 days.
Microscopy of Ultracold Fermions in Optical Lattices
2025-07-05
Waseem S. Bakr, Zengli Ba, Max L. Prichard
These lecture notes review recent progress in studying the Fermi-Hubbard model using ultracold gases in optical lattices. We focus on results from quantum gas microscope experiments that have allowed site-resolved measurements of charge and spin correlations in half-filled and doped Hubbard systems, as well as direct imaging of various types of polaronic quasiparticles. We also review experiments exploring dynamical properties of the Hubbard model through transport and spectroscopy. Moving beyond the plain-vanilla square-lattice Hubbard model, we present more recent work exploring Hubbard systems with novel lattice geometries and long-range interactions that stabilize new phases. Finally, we discuss the realization of entropy distribution protocols to cool these systems to ultralow temperatures where comparison to unbiased numerics is no longer possible.
Benchmarking Stochastic Approximation Algorithms for Fairness-Constrained Training of Deep Neural Networks
2025-07-05
Andrii Kliachkin, Jana Lepšová, Gilles Bareilles, Jakub Mareček
The ability to train Deep Neural Networks (DNNs) with constraints is instrumental in improving the fairness of modern machine-learning models. Many algorithms have been analysed in recent years, and yet there is no standard, widely accepted method for the constrained training of DNNs. In this paper, we provide a challenging benchmark of real-world large-scale fairness-constrained learning tasks, built on top of the US Census (Folktables). We point out the theoretical challenges of such tasks and review the main approaches in stochastic approximation algorithms. Finally, we demonstrate the use of the benchmark by implementing and comparing three recently proposed, but as-of-yet unimplemented, algorithms both in terms of optimization performance, and fairness improvement. We release the code of the benchmark as a Python package at https://github.com/humancompatible/train.
In this survey, we provide an in-depth investigation of exponential Runge-Kutta methods for the numerical integration of initial-value problems. These methods offer a valuable synthesis between classical Runge-Kutta methods, introduced more than a century ago, and exponential integrators, which date back to the 1960s. This manuscript presents both a historical analysis of the development of these methods up to the present day and several examples aimed at making the topic accessible to a broad audience.
Habitat Classification from Ground-Level Imagery Using Deep Neural Networks
2025-07-05
Hongrui Shi, Lisa Norton, Lucy Ridding, Simon Rolph, Tom August, Claire M Wood, Lan Qie, Petra Bosilj, James M Brown
Habitat assessment at local scales -- critical for enhancing biodiversity and guiding conservation priorities -- often relies on expert field survey that can be costly, motivating the exploration of AI-driven tools to automate and refine this process. While most AI-driven habitat mapping depends on remote sensing, it is often constrained by sensor availability, weather, and coarse resolution. In contrast, ground-level imagery captures essential structural and compositional cues invisible from above and remains underexplored for robust, fine-grained habitat classification. This study addresses this gap by applying state-of-the-art deep neural network architectures to ground-level habitat imagery. Leveraging data from the UK Countryside Survey covering 18 broad habitat types, we evaluate two families of models -- convolutional neural networks (CNNs) and vision transformers (ViTs) -- under both supervised and supervised contrastive learning paradigms. Our results demonstrate that ViTs consistently outperform state-of-the-art CNN baselines on key classification metrics (Top-3 accuracy = 91\%, MCC = 0.66) and offer more interpretable scene understanding tailored to ground-level images. Moreover, supervised contrastive learning significantly reduces misclassification rates among visually similar habitats (e.g., Improved vs. Neutral Grassland), driven by a more discriminative embedding space. Finally, our best model performs on par with experienced ecological experts in habitat classification from images, underscoring the promise of expert-level automated assessment. By integrating advanced AI with ecological expertise, this research establishes a scalable, cost-effective framework for ground-level habitat monitoring to accelerate biodiversity conservation and inform land-use decisions at the national scale.
Large language models (LLMs) have shown impressive performance on general-purpose tasks, yet adapting them to specific domains remains challenging due to the scarcity of high-quality domain data. Existing data synthesis tools often struggle to extract reliable fine-tuning data from heterogeneous documents effectively. To address this limitation, we propose Easy Dataset, a unified framework for synthesizing fine-tuning data from unstructured documents via an intuitive graphical user interface (GUI). Specifically, Easy Dataset allows users to easily configure text extraction models and chunking strategies to transform raw documents into coherent text chunks. It then leverages a persona-driven prompting approach to generate diverse question-answer pairs using public-available LLMs. Throughout the pipeline, a human-in-the-loop visual interface facilitates the review and refinement of intermediate outputs to ensure data quality. Experiments on a financial question-answering task show that fine-tuning LLMs on the synthesized dataset significantly improves domain-specific performance while preserving general knowledge. The source code and installable package are available at https://github.com/ConardLi/easy-dataset and have garnered over 9,000 GitHub stars.
Search for spatial coincidences between galaxy mergers and Fermi-LAT 4FGL-DR4 sources
2025-07-05
Siddhant Manna, Shantanu Desai
We present a systematic search for spatial association between a high-confidence sample of 3,166 morphologically selected galaxy mergers detected through SDSS imaging and the 4FGL-DR4 catalog of gamma-ray sources detected by the Fermi Large Area Telescope (LAT). Using a conservative 4$\sigma$ positional uncertainty threshold and a Poisson-based statistical framework, we identify 21 statistically significant associations with match probabilities $p < 0.05$. Among these are known classes of gamma-ray emitters such as flat-spectrum radio quasars (FSRQs), BL Lacertae objects (BLL), and radio galaxies bolstering the hypothesis that merger-driven processes can fuel high-energy activity. Intriguingly, five of the associated sources remain unclassified in 4FGL-DR4, hinting at a possible link between galaxy mergers and a hitherto unrecognized population of gamma-ray sources. The dominance of AGN-like matches supports the scenario in which mergers trigger accretion onto central supermassive black holes, initiating AGN activity observable at gamma-ray energies. Moreover, the recurrent presence of unassociated sources among secure matches underscores the potential of merger catalogs as physically motivated priors in gamma-ray source identification efforts. This work constitutes the first dedicated effort to explore associations between gamma-ray sources and a large, morphologically selected sample of galaxy mergers, opening new avenues for understanding the role of interactions in high-energy astrophysics. We additionally examined the sample of 70 galaxy pairs from the Canadian Network for Observational Cosmology Field Galaxy Redshift Survey and found no statistically significant matches, with $p < 0.05$ within the 4$\sigma$ positional uncertainty threshold.
More than One Step at a Time: Designing Procedural Feedback for Non-visual Makeup Routines
2025-07-05
Franklin Mingzhe Li, Akihiko Oharazawa, Chloe Qingyu Zhu, Misty Fan, Daisuke Sato, Chieko Asakawa, Patrick Carrington
Makeup plays a vital role in self-expression, identity, and confidence - yet remains an underexplored domain for assistive technology, especially for people with vision impairments. While existing tools support isolated tasks such as color identification or product labeling, they rarely address the procedural complexity of makeup routines: coordinating step sequences, managing product placement, and assessing the final look with accessible feedback. To understand the real-world process, we conducted a contextual inquiry with 15 visually impaired makeup users, capturing real-time makeup application behaviors and their step-by-step information needs and assessment approaches. Our findings reveal embodied, tactile-first strategies; persistent challenges in blending, symmetry, and assessment; and a desire for honest, real-time, goal-aligned feedback. We also interviewed five professional makeup artists, who reviewed participant makeup videos and provided expert responses to participant-raised questions and assessment practices. We contribute a taxonomy of feedback needs in non-visual makeup, and outline design implications for future assistive systems - emphasizing hands-free, conversational interaction and context-aware, procedural support for expressive and independent beauty practices.
A discontinuous Galerkin pressure correction scheme for the Oldroyd model of order one
2025-07-05
Pratyay Mondal, Rajen Kumar Sinha
We develop and analyze a discontinuous Galerkin pressure correction scheme for the Oldroyd model of order one. The existence and uniqueness of the discrete solution as well as the consistency of the scheme are proved. The stability of the discrete velocity and pressure are established. We derive optimal $\textit{a priori}$ error bounds for the fully discrete velocity in the discontinuous discrete space. In addition, an improved error estimate for the velocity is derived in the $L^2$ norm which is optimal with respect to space and time. Furthermore, the error bound for the pressure is obtained via the estimates of discrete time derivative of the velocity. Finally, numerical experiments confirm the optimal convergence rates.
The shortcomings of video conferencing technology, methods for revealing them, and emerging XR solutions
2025-07-05
Dani Paul Hove, Benjamin Watson
Video conferencing has become a central part of our daily lives, thanks to the COVID-19 pandemic. Unfortunately, so have its many limitations, resulting in poor support for communicative and social behavior and ultimately, Zoom fatigue. New technologies will be required to address these limitations, including many drawn from mixed reality (XR). In this paper, our goals are to equip and encourage future researchers to develop and test such technologies. Toward this end, we first survey research on the shortcomings of video conferencing systems, as defined before and after the pandemic. We then consider the methods that research uses to evaluate support for communicative behavior, and argue that those same methods should be employed in identifying, improving, and validating promising video conferencing technologies. Next, we survey emerging XR solutions to video conferencing's limitations, most off which do not employ head-mounted displays.
A Non-Dominated Sorting Evolutionary Algorithm Updating When Required
2025-07-05
Lucas R. C. Farias, Abimael J. F. Santos, Matheus R. B. Nobre
The NSGA-III algorithm relies on uniformly distributed reference points to promote diversity in many-objective optimization problems. However, this strategy may underperform when facing irregular Pareto fronts, where certain vectors remain unassociated with any optimal solutions. While adaptive schemes such as A-NSGA-III address this issue by dynamically modifying reference points, they may introduce unnecessary complexity in regular scenarios. This paper proposes NSGA-III with Update when Required (NSGA-III-UR), a hybrid algorithm that selectively activates reference vector adaptation based on the estimated regularity of the Pareto front. Experimental results on benchmark suites (DTLZ1-7, IDTLZ1-2) and real-world problems demonstrate that NSGA-III-UR consistently outperforms NSGA-III and A-NSGA-III across diverse problem landscapes.
Modern recommender systems operate in uniquely dynamic settings: user interests, item pools, and popularity trends shift continuously, and models must adapt in real time without forgetting past preferences. While existing tutorials on continual or lifelong learning cover broad machine learning domains (e.g., vision and graphs), they do not address recommendation-specific demands-such as balancing stability and plasticity per user, handling cold-start items, and optimizing recommendation metrics under streaming feedback. This tutorial aims to make a timely contribution by filling that gap. We begin by reviewing the background and problem settings, followed by a comprehensive overview of existing approaches. We then highlight recent efforts to apply continual learning to practical deployment environments, such as resource-constrained systems and sequential interaction settings. Finally, we discuss open challenges and future research directions. We expect this tutorial to benefit researchers and practitioners in recommender systems, data mining, AI, and information retrieval across academia and industry.
Measuring how changes in code readability attributes affect code quality evaluation by Large Language Models
2025-07-05
Igor Regis da Silva Simoes, Elaine Venson
Code readability is one of the main aspects of code quality, influenced by various properties like identifier names, comments, code structure, and adherence to standards. However, measuring this attribute poses challenges in both industry and academia. While static analysis tools assess attributes such as code smells and comment percentage, code reviews introduce an element of subjectivity. This paper explores using Large Language Models (LLMs) to evaluate code quality attributes related to its readability in a standardized, reproducible, and consistent manner. We conducted a quasi-experiment study to measure the effects of code changes on Large Language Model (LLM)s interpretation regarding its readability quality attribute. Nine LLMs were tested, undergoing three interventions: removing comments, replacing identifier names with obscure names, and refactoring to remove code smells. Each intervention involved 10 batch analyses per LLM, collecting data on response variability. We compared the results with a known reference model and tool. The results showed that all LLMs were sensitive to the interventions, with agreement with the reference classifier being high for the original and refactored code scenarios. The LLMs demonstrated a strong semantic sensitivity that the reference model did not fully capture. A thematic analysis of the LLMs reasoning confirmed their evaluations directly reflected the nature of each intervention. The models also exhibited response variability, with 9.37% to 14.58% of executions showing a standard deviation greater than zero, indicating response oscillation, though this did not always compromise the statistical significance of the results. LLMs demonstrated potential for evaluating semantic quality aspects, such as coherence between identifier names, comments, and documentation with code purpose.
The widespread deployment of large language models (LLMs) across critical domains has amplified the societal risks posed by algorithmically generated misinformation. Unlike traditional false content, LLM-generated misinformation can be self-reinforcing, highly plausible, and capable of rapid propagation across multiple languages, which traditional detection methods fail to mitigate effectively. This paper introduces a proactive defense paradigm, shifting from passive post hoc detection to anticipatory mitigation strategies. We propose a Three Pillars framework: (1) Knowledge Credibility, fortifying the integrity of training and deployed data; (2) Inference Reliability, embedding self-corrective mechanisms during reasoning; and (3) Input Robustness, enhancing the resilience of model interfaces against adversarial attacks. Through a comprehensive survey of existing techniques and a comparative meta-analysis, we demonstrate that proactive defense strategies offer up to 63\% improvement over conventional methods in misinformation prevention, despite non-trivial computational overhead and generalization challenges. We argue that future research should focus on co-designing robust knowledge foundations, reasoning certification, and attack-resistant interfaces to ensure LLMs can effectively counter misinformation across varied domains.
RELRaE: LLM-Based Relationship Extraction, Labelling, Refinement, and Evaluation
2025-07-04
George Hannah, Jacopo de Berardinis, Terry R. Payne, Valentina Tamma, Andrew Mitchell, Ellen Piercy, Ewan Johnson, Andrew Ng, Harry Rostron, Boris Konev
A large volume of XML data is produced in experiments carried out by robots in laboratories. In order to support the interoperability of data between labs, there is a motivation to translate the XML data into a knowledge graph. A key stage of this process is the enrichment of the XML schema to lay the foundation of an ontology schema. To achieve this, we present the RELRaE framework, a framework that employs large language models in different stages to extract and accurately label the relationships implicitly present in the XML schema. We investigate the capability of LLMs to accurately generate these labels and then evaluate them. Our work demonstrates that LLMs can be effectively used to support the generation of relationship labels in the context of lab automation, and that they can play a valuable role within semi-automatic ontology generation frameworks more generally.
Detecting alien living worlds and photosynthetic life using imaging polarimetry with the HWO coronagraph
2025-07-04
Svetlana Berdyugina, Lucas Patty, Jonathan Grone, Brice Demory, Kim Bott, Vincent Kofman, Giulia Roccetti, Kenneth Goodis Gordon, Frans Snik, Theodora Karalidi, Victor Trees, Daphne Stam, Mary N. Parenteau
Our Earth, being the only living planet that we know, provides us with clues that photosynthetic life-forms may be dominant on other exoplanets for billions of years. Spectropolarimetric signatures of the terrestrial photosynthetic life (PSLife) are well studied in the lab and remotely sensed with space and airborne instrumentation. An astonishing biosignature revealed by these measurements is an extremely strong linear polarization (tens \%) associated with broad absorption bands of biological pigments (biopigments) driving photosynthesis in various organisms. Also, unique circular-polarization signatures are associated with biopigments and other complex macromolecules as a sign of homochirality which is ubiquitous in terrestrial life forms. Thus, low-resolution spectro- or multi-band polarimetry of exoplanets directly imaged at an unprecedented contrast using the HWO coronagraph is a novel opportunity for a robust discovery of life on exoplanets. Here we propose to carry out two surveys and two follow-up observing programs. Survey 1 will identify potentially habitable planets (PHPs) through detection of atmospheres, clouds and liquid surface water (ocean) using linear polarimetry. Survey 2 will identify Living World (LW) candidates among PHPs by searching for strong linear polarization signatures associated with strong and broad absorption bands reminiscent of terrestrial biopigments. Follow-up program 3 will obtain multi-color surface maps of LWs, determine the distribution and abundance of alien photosynthetic organisms with exo-biopigments (exoBPs) and correlate their properties with the atmospheric and surface compositions. Follow-up program 4 will employ circular polarization to verify homochirality of exoBPs. This comprehensive approach aims at providing a quantitative answer to the ultimate question "Are we are alone in the Universe?".
Until today, the exact function of mammalian odorant binding proteins (OBPs) remains a topic of debate. Although their main established function lacks direct evidence in human olfaction, OBPs are traditionally believed to act as odorant transporters in the olfactory sense, which led to the exploration of OBPs as biomimetic sensor units in artificial noses. Now, available RNA-seq and proteomics data identified the expression of human OBPs (hOBP2A and hOBP2B) in both, male and female reproductive tissues. This observation prompted the conjecture that OBPs may possess functions that go beyond the olfactory sense, potentially as hormone transporters. Such a function could further link them to the tumorigenesis and cancer progression of hormone dependent cancer types including ovarian, breast, prostate and uterine cancer. In this structured review, we use available data to explore the effects of genetic alterations such as somatic copy number aberrations and single nucleotide variants on OBP function and their corresponding gene expression profiles. Our computational analyses suggest that somatic copy number aberrations in OBPs are associated with large changes in gene expression in reproductive cancers while point mutations have little to no effect. Additionally, the structural characteristics of OBPs, together with other lipocalin family members, allow us to explore putative functions within the context of cancer biology. Our overview consolidates current knowledge on putative human OBP functions, their expression patterns, and structural features. Finally, it provides an overview on applications, highlighting emerging hypotheses and future research directions within olfactory and non-olfactory roles.
Towards Underwater Detection of Diluted Bitumen with Interdigital Sensors
2025-07-04
Graziella Bedenik, Melissa Greeff, Matthew Robertson
Diluted bitumen spills in freshwater environments pose significant challenges for detection and remediation due to the complex behaviour of the material once released, rendering traditional oil monitoring techniques, such as aerial surveys and fluorometry, ineffective. This study investigates the feasibility of utilizing interdigital capacitive sensors to detect dilbit in water via frequency-dependent impedance measurements. Three sensor designs were fabricated and tested with a vector network analyzer across samples with varying dilbit concentrations. All capacitor designs were able to detect electrical changes in water and exhibited different linear regions, suggesting a strong potential for quantitative sensing. These findings support the integration of the sensors into autonomous underwater robotic platforms for real-time, distributed monitoring of dilbit spills in freshwater systems.
Causal Evidence for the Primordiality of Colors in Trans-Neptunian Objects
2025-07-04
Benjamin L. Davis, Mohamad Ali-Dib, Yujia Zheng, Zehao Jin, Kun Zhang, Andrea Valerio Macciò
The origins of the colors of Trans-Neptunian Objects (TNOs) represent a crucial unresolved question, central to understanding the history of our Solar System. Recent observational surveys have revealed correlations between the eccentricity and inclination of TNOs and their colors. This has rekindled the long-standing debate on whether these colors reflect the conditions of TNO formation or their subsequent collisional evolution. In this study, we address this question with 98.7% certainty, using a model-agnostic, data-driven approach based on causal graphs. First, as a sanity check, we demonstrate how our model can replicate the currently accepted paradigms of TNOs' dynamical history, blindly and without any orbital modeling or physics-based assumptions. In fact, our causal model (with no knowledge of the existence of Neptune) predicts the existence of an unknown perturbing body, i.e., Neptune. We then show how this model predicts, with high certainty, that the color of TNOs is the root cause of their inclination distribution, rather than the other way around. This strongly suggests that the colors of TNOs reflect an underlying dynamical property, most likely their formation location. Moreover, our causal model excludes formation scenarios that invoke substantial color modification by subsequent irradiation. We therefore conclude that the colors of TNOs are predominantly primordial.
The kappa distribution of velocities appears routinely in the study of collisionless plasmas present in Earth's magnetosphere, the solar wind among other contexts where particles are unable to reach thermal equilibrium. Originally justified through the use of Tsallis' non-extensive statistics, nowadays there are alternative frameworks that provide insight into these distributions, such as superstatistics. In this work we review the derivation of the multi-particle and single-particle kappa distributions for collisionless plasmas within the framework of superstatistics, as an alternative to the use of non-extensive statistics. We also show the utility of the superstatistical framework in the computation of expectation values under kappa distributions. Some consequences of the superstatistical formalism regarding correlations, temperature and entropy of kappa-distributed plasmas are also discussed.
Mapping Galactic Winds and Small-scale Structure in the Circumgalactic Medium with Habitable Worlds Observatory
2025-07-04
Joseph N. Burchett, Deborah M. Lokhorst, Yakov Faerman, Kevin France, Kate H. R. Rubin, David S. N. Rupke, Sanchayeeta Borthakur
We present a science case for the Habitable Worlds Observatory (HWO) to map the circumgalactic medium (CGM) in emission by targeting ultraviolet emission lines, which trace the 10^4 - 10^6 K gas engaged in the feedback and accretion mechanisms driving galaxy evolution. While the CGM-galaxy connection is clearly evident through absorption line experiments and limited work done in optical and radio emission from the ground, the nature of this connection is poorly understood with regard to how these cosmic ecosystems exchange matter and energy. We outline a two-pronged experiment with Habitable Worlds Observatory (HWO) utilizing both multi-object spectroscopy to map kpc-scale CGM structures such as galactic superwinds and integral field spectroscopy to unveil the sub-kpc-scale processes such as thermal instabilities, which are theorized to govern the cool gas reservoirs long detected in pencil-beam absorption surveys. This article is an adaptation of a science case document developed by HWO's CGM/IGM Working Group.
2025-07-04
Gabriel Rodrigues, Antonio J. Cuesta, Jailson Alcaniz, Miguel Aparicio Resco, Antonio L. Maroto, Manuel Masip, Jamerson G. Rodrigues, Felipe B. M. dos Santos, Javier de Cruz Pérez, Jorge Enrique García-Farieta, Clarissa Siqueira, Fuxing Qin, Yuting Wang, Gong-Bo Zhao, Carlos Hernández-Monteagudo, Valerio Marra, Raul Abramo, Narciso Benítez, Silvia Bonoli, Saulo Carneiro, Javier Cenarro, David Cristóbal-Hornillos, Renato Dupke, Alessandro Ederoclite, Antonio Hernán-Caballero, Carlos López-Sanjuan, Antonio Marín-Franch, Claudia Mendes de Oliveira, Mariano Moles, Laerte Sodré Jr., Keith Taylor, Jesús Varela, Héctor Vázquez Ramió
The large-scale structure survey J-PAS is taking data since October 2023. In this work, we present a forecast based on the Fisher matrix method to establish its sensitivity to the sum of the neutrino masses. We adapt the Fisher Galaxy Survey Code (FARO) to account for the neutrino mass under various configurations applied to galaxy clustering measurements. This approach allows us to test the sensitivity of J-PAS to the neutrino mass across different tracers, with and without non-linear corrections, and under varying sky coverage. We perform our forecast for two cosmological models: $\Lambda CDM + \sum m_\nu$ and $w_0w_a CDM + \sum m_\nu$. We combine our J-PAS forecast with Cosmic Microwave Background (CMB) data from the Planck Collaboration and Type Ia supernova (SN) data from Pantheon Plus. Our analysis shows that, for a sky coverage of 8,500 square degrees, J-PAS galaxy clustering data alone will constrain the sum of the neutrino masses to an upper limit of $\sum m_\nu < 0.32$\;eV for the $\Lambda CDM + \sum m_\nu$ model, and $\sum m_\nu < 0.36$\;eV for the $w_0w_a CDM + \sum m_\nu$ model. When combined with Planck data, the upper limit improves significantly. For J-PAS+Planck, we find $\sum m_\nu < 0.061$\;eV for the $\Lambda CDM + \sum m_\nu$ model, and for J-PAS+Planck+Pantheon Plus, we obtain $\sum m_\nu < 0.12$\;eV for the $w_0w_a CDM + \sum m_\nu$ model. These results demonstrate that J-PAS clustering measurements can play a crucial role in addressing challenges in the neutrino sector, including potential tensions between cosmological and terrestrial measurements of the neutrino mass, as well as in determining the mass ordering.
Achieving fault-tolerant quantum computation (FTQC) demands simultaneous progress in physical qubit performance and quantum error correction (QEC). This work reviews and benchmarks experimental advancements towards FTQC across leading platforms, including trapped ions, superconducting circuits, neutral atoms, NV centers, and semiconductors. We analyze key physical metrics like coherence times, entanglement error, and system size (qubit count), fitting observed exponential trends to characterize multi-order-of-magnitude improvements over the past two decades. At the logical level, we survey the implementation landscape of QEC codes, tracking realized parameters $[[n, k, d]]$ and complexity from early demonstrations to recent surface and color code experiments. Synthesizing these physical and logical benchmarks reveals substantial progress enabled by underlying hardware improvements, while also outlining persistent challenges towards scalable FTQC. The experimental databases and analysis code underpinning this review are publicly available at https://github.com/francois-marie/awesome-quantum-computing-experiments.
Steklov vs. Steklov: A Fourth-Order Affair Related to the Babuška Paradox
2025-07-04
Francesco Ferraresso, Pier Domenico Lamberti
We discuss two fourth-order Steklov problems and highlight a Babu\v{s}ka paradox appearing in their approximations on convex domains via sequences of convex polygons. To do so, we prove that the eigenvalues of one of the two problems depend with continuity upon domain perturbation in the class of convex domains, extending a result known in the literature for the first eigenvalue. This is obtained by examining in detail a nonlocal, second-order problem for harmonic functions introduced by Ferrero, Gazzola, and Weth. We further review how this result is connected to diverse variants of the classical Babu\v{s}ka paradox for the hinged plate and to a degeneration result by Maz'ya and Nazarov.
The gravitational interaction between the Milky Way (MW) and the Large Magellanic Cloud (LMC) perturbs the MW halo's density and kinematics, encoding information about both galaxies' masses and structures. We present a suite of 2,848 high-resolution ($10^7$ particles) N-body simulations that systematically vary the mass and shape of both galaxies' haloes. We model how the mean velocities and velocity dispersions of halo stars (30--120 kpc) depend on system parameters, and forecast constraints achievable with current and future observations. Assuming Gaia DR3-level astrometry, 20 km/s radial velocity precision, 10% distance precision, and a sample of $\sim$4,000 RR Lyrae stars, we achieve 1$\sigma$ uncertainties of $0.11 \times 10^{12} M_\odot$ in MW mass, $2.33 \times 10^{10} M_\odot$ in LMC mass, 2.38 in halo concentration ($c$), and 0.06 in halo flattening ($q$). These correspond to fractional uncertainties of 11%, 16%, 25%, and 6% respectively, relative to fiducial values. Improved Gaia proper motions (DR5) yield modest gains (up to 14%), while adding radial velocities improves constraints by up to 60% relative to using Gaia astrometry alone. Doubling the sample size to $\sim$8,000 stars yields an additional 30% improvement, whereas reducing distance uncertainties has minimal impact ($\le$10%). Mean velocities trace LMC-induced perturbations, while velocity dispersions constrain MW halo properties, jointly breaking degeneracies. Our results demonstrate that combining Gaia astrometry with large spectroscopic surveys will enable precise characterization of the MW-LMC system. This methodology paper establishes the framework for interpreting observations; future work will apply these tools to existing spectroscopic datasets. The full simulation suite, HaloDance, will be made publicly available at: https://github.com/Yanjun-Sheng/HaloDance.
Measuring public opinion at subnational geographies is critical to many theories in political science. Multilevel regression and post-stratification (MRP) is a popular tool for doing so, although existing work is limited to measuring opinion on a single survey question. We provide a framework for estimating the joint distribution of opinion on multiple questions ("Multivariate MRP"). To do so, we derive a novel method for variational inference in multinomial logistic regression with many random effects. This requires performing variational inference with high-dimensional fixed effects, but we show that this can be done at a low computational cost. We validate this procedure by estimating public opinion by party in the United States and show that existing methods can be improved considerably by adding contextual covariates on the prior levels of party identification. Substantively, we show how the output of multivariate MRP can be used to study representation across multiple policy issues simultaneously.
High-power readout of a transmon qubit using a nonlinear coupling
2025-07-04
Cyril Mori, Vladimir Milchakov, Francesca D'Esposito, Lucas Ruela, Shelender Kumar, Vishnu Narayanan Suresh, Waël Ardati, Dorian Nicolas, Quentin Ficheux, Nicolas Roch, Tomás Ramos, Olivier Buisson
The field of superconducting qubits is constantly evolving with new circuit designs. However, when it comes to qubit readout, the use of simple transverse linear coupling remains overwhelmingly prevalent. This standard readout scheme has significant drawbacks: in addition to the Purcell effect, it suffers from a limitation on the maximal number of photons in the readout mode, which restricts the signal-to-noise ratio (SNR) and the Quantum Non-Demolition (QND) nature of the readout. Here, we explore the high-power regime by engineering a nonlinear coupling between a transmon qubit and its readout mode. Our approach builds upon previous work by Dassonneville et al. [Physical Review X 10, 011045 (2020)], on qubit readout with a non-perturbative cross-Kerr coupling in a transmon molecule. We demonstrate a readout fidelity of 99.21% with 89 photons utilizing a parametric amplifier. At this elevated photon number, the QND nature remains high at 96.7%. Even with up to 300 photons, the QNDness is only reduced by a few percent. This is qualitatively explained by deriving a critical number of photons associated to the nonlinear coupling, yielding a theoretical value of $\bar{n}_r^\text{crit} = 377$ photons for our sample's parameters. These results highlight the promising performance of the transmon molecule in the high-power regime for high-fidelity qubit readout.
Large Language Models for Combinatorial Optimization: A Systematic Review
2025-07-04
Francesca Da Ros, Michael Soprano, Luca Di Gaspero, Kevin Roitero
This systematic review explores the application of Large Language Models (LLMs) in Combinatorial Optimization (CO). We report our findings using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We conduct a literature search via Scopus and Google Scholar, examining over 2,000 publications. We assess publications against four inclusion and four exclusion criteria related to their language, research focus, publication year, and type. Eventually, we select 103 studies. We classify these studies into semantic categories and topics to provide a comprehensive overview of the field, including the tasks performed by LLMs, the architectures of LLMs, the existing datasets specifically designed for evaluating LLMs in CO, and the field of application. Finally, we identify future directions for leveraging LLMs in this field.
Selection bias effects on high-$p_\mathrm{T}$ yield and correlation measurements in Oxygen+Oxygen collisions
2025-07-04
JaeBeom Park, J. L. Nagle, Dennis V. Perepelitsa, Sanghoon Lim, Constantin Loizides
Oxygen+Oxygen (O+O) collisions at RHIC and the LHC offer a unique experimental opportunity to observe the onset of jet quenching in intermediate relativistic collision systems. As with the smaller proton-nucleus or larger nucleus-nucleus systems, measurements of centrality-selected high-$p_\mathrm{T}$ processes in O+O collisions are expected to be sensitive to selection bias effects, which will be necessary to quantify or mitigate before a definitive conclusion on the presence of jet quenching. Using two Monte Carlo heavy-ion event generators, we provide a survey of centrality bias effects on high-$p_\mathrm{T}$ yield and correlation measurements. Some highlights of our findings include that (1) bias factors for the accessible kinematic range at RHIC show a non-trivial $p_\mathrm{T}$ dependence, compared to a negligible one at the LHC given the smaller accessible Bjorken-$x$ range, (2) centrality definitions based on multiplicity are less sensitive to bias effects than those based on the transverse energy, (3) the Angantyr generator gives qualitatively similar but larger-magnitude bias factors than HIJING, and (4) correlation measurements have a much smaller sensitivity to bias effects than do yield measurements. The findings here are intended to guide the experimental design and interpretation of O+O jet quenching and other hard-process measurements.
Since 2023, generative AI has rapidly advanced in the music domain. Despite significant technological advancements, music-generative models raise critical ethical challenges, including a lack of transparency and accountability, along with risks such as the replication of artists' works, which highlights the importance of fostering openness. With upcoming regulations such as the EU AI Act encouraging open models, many generative models are being released labelled as 'open'. However, the definition of an open model remains widely debated. In this article, we adapt a recently proposed evidence-based framework for assessing openness in LLMs to the music domain. Using feedback from a survey of 110 participants from the Music Information Retrieval (MIR) community, we refine the framework into MusGO (Music-Generative Open AI), which comprises 13 openness categories: 8 essential and 5 desirable. We evaluate 16 state-of-the-art generative models and provide an openness leaderboard that is fully open to public scrutiny and community contributions. Through this work, we aim to clarify the concept of openness in music-generative AI and promote its transparent and responsible development.
This paper is a review of the theory of abelian anyons in planar systems at an introductory level and with focus on the formalism of quantum field theory, but with the aim of clarify the connections between the mathematical structure and the theoretical and experimental physical aspects of these particle excitations.
Small Area Estimation of Fertility in Low- and Middle-Income Countries
2025-07-04
Yunhan Wu, Jon Wakefield
Accurate fertility estimates at fine spatial resolution are essential for localized public health planning, particularly in low- and middle-income countries (LMICs). While national-level indicators such as age-specific fertility rates (ASFR) and total fertility rate (TFR) are often reported through official statistics, they lack the spatial granularity needed to guide targeted interventions. To address this, we develop a framework for subnational fertility estimation using small-area estimation (SAE) techniques applied to birth history data from household surveys, in particular Demographic and Health Surveys (DHS). Disaggregation by geographic area, time period, and maternal age group leads to significant data sparsity, limiting the reliability of direct estimates at fine scales. To overcome this, we propose a suite of methods, including direct estimators, area-level and unit-level Bayesian hierarchical models, to produce accurate estimates across varying spatial resolutions. The model-based approaches incorporate spatiotemporal smoothing and integrate covariates such as maternal education, contraceptive use and urbanicity. Using data from the 2021 Madagascar DHS, we generate district-level ASFR and TFR estimates and evaluate model performance through cross-validation.
An Advanced Deep Learning Framework for Ischemic and Hemorrhagic Brain Stroke Diagnosis Using Computed Tomography (CT) Images
2025-07-04
Md. Sabbir Hossen, Eshat Ahmed Shuvo, Shibbir Ahmed Arif, Pabon Shaha, Md. Saiduzzaman, Mostofa Kamal Nasir
Brain stroke is one of the leading causes of mortality and long-term disability worldwide, highlighting the need for precise and fast prediction techniques. Computed Tomography (CT) scan is considered one of the most effective methods for diagnosing brain strokes. The majority of stroke classification techniques rely on a single slice-level prediction mechanism, allowing the radiologist to manually choose the most critical CT slice from the original CT volume. Although clinical evaluations are often used in traditional diagnostic procedures, machine learning (ML) has opened up new avenues for improving stroke diagnosis. To supplement traditional diagnostic techniques, this study investigates the use of machine learning models, specifically concerning the prediction of brain stroke at an early stage utilizing CT scan images. In this research, we proposed a novel approach to brain stroke detection leveraging machine learning techniques, focusing on optimizing classification performance with pre-trained deep learning models and advanced optimization strategies. Pre-trained models, including DenseNet201, InceptionV3, MobileNetV2, ResNet50, and Xception, are utilized for feature extraction. Additionally, we employed feature engineering techniques, including BFO, PCA, and LDA, to enhance models' performance further. These features are subsequently classified using machine learning algorithms such as SVC, RF, XGB, DT, LR, KNN, and GNB. Our experiments demonstrate that the combination of MobileNetV2, LDA, and SVC achieved the highest classification accuracy of 97.93%, significantly outperforming other model-optimizer-classifier combinations. The results underline the effectiveness of integrating lightweight pre-trained models with robust optimization and classification techniques for brain stroke diagnosis.
A Survey on Integrating Quantum Computers into High Performance Computing Systems
2025-07-04
Philip Döbler, Manpreet Singh Jattana
Quantum computers use quantum mechanical phenomena to perform conventionally intractable calculations for specific problems. Despite being universal machines, quantum computers are not expected to replace classical computers, but rather, to complement them and form hybrid systems. This makes integrating quantum computers into high performance computing (HPC) systems an increasingly relevant topic. We present a structured literature review on the integration aspect. We methodologically search literature databases and manually evaluate 107 publications. These publications are divided into seven categories that describe the state of the art in each category. After a brief quantitative analysis of the literature, this survey deals with the hardware architecture of hybrid quantum-classical systems, as well as the software stack. We observe the development of a wide range of tools enabling hybrid systems and emphasize the need for future standardization of interfaces and methods to foster synergy.
One of the major breakthroughs in science of the last (20th) century was building a bridge between the worlds of stochastic (random) systems and deterministic (dynamical) systems. It was started by the celebrated 1958 paper by A.N.Kolmogorov \cite{Kolmo}, who called this new theory (and actually a new way of thinking about deterministic systems) stochasticity of dynamical systems. Later, this name was essentially replaced by a short (sexier but more vague ``chaos theory"). Kolmogorov's discovery demonstrated that the time evolution of deterministic systems could be indistinguishable from the evolution of purely random (stochastic) systems. Moreover, it has been later established that typical deterministic systems are chaotic. However, as well as stochastic systems, which could be more or less random (from random processes with independent values to random processes with long memory) , chaotic dynamical systems could be strongly or weakly chaotic. Actually, the majority of chaotic systems are weakly chaotic, especially those that are relevant models for various real-world processes. Naturally, the theory of weakly chaotic systems is less developed because it deals with more complicated problems than the studies of strongly chaotic systems. We present here a brief review of this theory, including recently published and some new results.
The Role of Humour in Software Engineering -- A Literature Review and Preliminary Taxonomy
2025-07-04
Dulaji Hidellaarachchi, John Grundy, Rashina Hoda
Humour has long been recognized as a key factor in enhancing creativity, group effectiveness, and employee well-being across various domains. However, its occurrence and impact within software engineering (SE) teams remains under-explored. This paper introduces a comprehensive, literature review-based taxonomy exploring the characterisation and use of humour in SE teams, with the goal of boosting productivity, improving communication, and fostering a positive work environment while emphasising the responsible use of humour to mitigate its potential negative impacts. Drawing from a wide array of studies in psychology, sociology, and organizational behaviour, our proposed framework categorizes humour into distinct theories, styles, models, and scales, offering SE professionals and researchers a structured approach to understanding humour in their work. This study also addresses the unique challenges of applying humour in SE, highlighting its potential benefits while acknowledging the need for further empirical validation in this context. Ultimately, our study aims to pave the way for more cohesive, creative, and psychologically supportive SE environments through the strategic use of humour.
Limits of Safe AI Deployment: Differentiating Oversight and Control
2025-07-04
David Manheim, Aidan Homewood
Oversight and control (collectively, supervision) are often invoked as key levers for ensuring that AI systems are accountable, reliable, and able to fulfill governance and management requirements. However, the concepts are frequently conflated or insufficiently distinguished in academic and policy discourse, undermining efforts to design or evaluate systems that should remain under meaningful human supervision. This paper undertakes a targeted critical review of literature on supervision outside of AI, along with a brief summary of past work on the topic related to AI. We then differentiate control as being ex-ante or real-time, and operational rather than policy or governance. In contrast, oversight is either a policy and governance function, or is ex-post. We suggest that control aims to prevent failures. In contrast, oversight often focuses on detection, remediation, or incentives for future prevention; all preventative oversight strategies nonetheless necessitate control. Building on this foundation, we make three contributions. First, we propose a theoretically-informed yet policy-grounded framework that articulates the conditions under which each mechanism is possible, where they fall short, and what is required to make them meaningful in practice. Second, we outline how supervision methods should be documented and integrated into risk management, and drawing on the Microsoft Responsible AI Maturity Model, we outline a maturity model for AI supervision. Third, we explicitly highlight some boundaries of these mechanisms, including where they apply, where they fail, and where it is clear that no existing methods suffice. This foregrounds the question of whether meaningful supervision is possible in a given deployment context, and can support regulators, auditors, and practitioners in identifying both present limitations and the need for new conceptual and technical advances.
TILES-2018 Sleep Benchmark Dataset: A Longitudinal Wearable Sleep Data Set of Hospital Workers for Modeling and Understanding Sleep Behaviors
2025-07-04
Tiantian Feng, Brandon M Booth, Karel Mundnich, Emily Zhou, Benjamin Girault, Kristina Lerman, Shrikanth Narayanan
Sleep is important for everyday functioning, overall well-being, and quality of life. Recent advances in wearable sensing technology have enabled continuous, noninvasive, and cost-effective monitoring of sleep patterns in real-world natural living settings. Wrist-worn devices, in particular, are capable of tracking sleep patterns using accelerometers and heart rate sensors. To support sleep research in naturalistic environments using wearable sensors, we introduce the TILES-2018 Sleep Benchmark dataset, which we make publicly available to the research community. This dataset was collected over a 10-week period from 139 hospital employees and includes over 6,000 unique sleep recordings, alongside self-reported survey data from each participant, which includes sleep quality, stress, and anxiety among other measurements. We present in-depth analyses of sleep patterns by combining the TILES-2018 Sleep Benchmark dataset with a previously released dataset (TILES-2018), which follows a similar study protocol. Our analyses include sleep duration, sleep stages, and sleep diaries. Moreover, we report machine learning benchmarks using this dataset as a testbed for tasks including sleep stage classification, prediction of self-reported sleep quality, and classifying demographics. Overall, this dataset provides a valuable resource for advancing foundational studies in sleep behavior modeling.
SKYSURF IX -- The Cosmic Optical and Infrared Background from Integrated Galaxy Light Measurements
2025-07-04
Scott A. Tompkins, Simon P. Driver, Aaron S. G. Robotham, Rogier A. Windhorst, Delondrae Carter, Timothy Carleton, Zak Goisman, Daniel Henningsen, Luke J. Davies, Sabine Bellstedt, Jordan C. J. D'Silva, Juno Li, Seth H. Cohen, Rolf A. Jansen, Rosalia O'Brien, Anton M. Koekemoer, Norman Grogin, John MacKenty
As part of the SKYSURF Hubble Space Telescope (HST) Legacy Archival program we present galaxy number counts which yield measurements of the extragalactic background light (EBL) at 15 different wavelengths. We have processed 82,752 HST images across 23 filters into 16,686 mosaics using the same software and processing pipeline throughout. Using 17/23 filters that give reliable galaxy counts, we constrain the integrated galaxy light (IGL) with a 1.5-9\% error between 0.3 and 1.6 $\mu$m in combination with 8 bands from WAVES (Wide Area VISTA Extragalactic Survey) and DEVILS (Deep Extragalactic Visible Legacy Survey). While HST was never intended to undertake large area surveys, through extensive quality control and filtering, we were able to extract a reliable and representative sample of fields distributed across the sky. Our final catalogs cover a combined $\approx 19.6 \deg^2$, with individual filters covering areas ranging from $\approx 0.16-7.0 \deg^2$. The combination of numerous independent sight-lines and area coverage allows us to reduce cosmic variance uncertainties in deep number counts to 0.06\%-1.8\%. For the first time we are able to establish a measurement of the IGL, $\mathrm{9.07 \pm 0.35 nW m^{-2} sr^{-1}}$, at 0.59 $\mu$m using HST data. We obtain a cosmic optical background value of $ 24.45 \pm 0.50 \mathrm{nW m^{-2} sr^{-1}}$. Different techniques used to measure the COB, both directly and indirectly, have recently converged indicating that the COB arises almost exclusively from processes within galaxies. This in combination with the recent values reported from New Horizons and very high energy (VHE) constraints leaves very little room for any diffuse emission coming from outside the Milky Way.
Artificial intelligence in drug discovery: A comprehensive review with a case study on hyperuricemia, gout arthritis, and hyperuricemic nephropathy
2025-07-04
Junwei Su, Cheng Xin, Ao Shang, Shan Wu, Zhenzhen Xie, Ruogu Xiong, Xiaoyu Xu, Cheng Zhang, Guang Chen, Yau-Tuen Chan, Guoyi Tang, Ning Wang, Yong Xu, Yibin Feng
This paper systematically reviews recent advances in artificial intelligence (AI), with a particular focus on machine learning (ML), across the entire drug discovery pipeline. Due to the inherent complexity, escalating costs, prolonged timelines, and high failure rates of traditional drug discovery methods, there is a critical need to comprehensively understand how AI/ML can be effectively integrated throughout the full process. Currently available literature reviews often narrowly focus on specific phases or methodologies, neglecting the dependence between key stages such as target identification, hit screening, and lead optimization. To bridge this gap, our review provides a detailed and holistic analysis of AI/ML applications across these core phases, highlighting significant methodological advances and their impacts at each stage. We further illustrate the practical impact of these techniques through an in-depth case study focused on hyperuricemia, gout arthritis, and hyperuricemic nephropathy, highlighting real-world successes in molecular target identification and therapeutic candidate discovery. Additionally, we discuss significant challenges facing AI/ML in drug discovery and outline promising future research directions. Ultimately, this review serves as an essential orientation for researchers aiming to leverage AI/ML to overcome existing bottlenecks and accelerate drug discovery.
Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering
2025-07-04
Krishna Ronanki, Simon Arvidsson, Johan Axell
The rapid emergence of generative AI models like Large Language Models (LLMs) has demonstrated its utility across various activities, including within Requirements Engineering (RE). Ensuring the quality and accuracy of LLM-generated output is critical, with prompt engineering serving as a key technique to guide model responses. However, existing literature provides limited guidance on how prompt engineering can be leveraged, specifically for RE activities. The objective of this study is to explore the applicability of existing prompt engineering guidelines for the effective usage of LLMs within RE. To achieve this goal, we began by conducting a systematic review of primary literature to compile a non-exhaustive list of prompt engineering guidelines. Then, we conducted interviews with RE experts to present the extracted guidelines and gain insights on the advantages and limitations of their application within RE. Our literature review indicates a shortage of prompt engineering guidelines for domain-specific activities, specifically for RE. Our proposed mapping contributes to addressing this shortage. We conclude our study by identifying an important future line of research within this field.
Absolute Evaluation Measures for Machine Learning: A Survey
2025-07-04
Silvia Beddar-Wiesing, Alice Moallemy-Oureh, Marie Kempkes, Josephine M. Thomas
Machine Learning is a diverse field applied across various domains such as computer science, social sciences, medicine, chemistry, and finance. This diversity results in varied evaluation approaches, making it difficult to compare models effectively. Absolute evaluation measures offer a practical solution by assessing a model's performance on a fixed scale, independent of reference models and data ranges, enabling explicit comparisons. However, many commonly used measures are not universally applicable, leading to a lack of comprehensive guidance on their appropriate use. This survey addresses this gap by providing an overview of absolute evaluation metrics in ML, organized by the type of learning problem. While classification metrics have been extensively studied, this work also covers clustering, regression, and ranking metrics. By grouping these measures according to the specific ML challenges they address, this survey aims to equip practitioners with the tools necessary to select appropriate metrics for their models. The provided overview thus improves individual model evaluation and facilitates meaningful comparisons across different models and applications.
The generation and control of spin- and valley-polarization in antiferromagnets (AFMs) have garnered increasing attention due to their potential for enabling faster and more stable multifunctional spintronic and valleytronic memory and logic devices. However, the two primary categories of AFMs, altermagnets and TP-symmetric AFMs, either lack intrinsic valley-polarization or net spin-polarization. Here, we propose an effective approach for achieving spontaneous spin-valley polarization in TP-broken layered ferroelectric antiferromagnets (FE-AFMs). The FE-AFMs exhibit lifted spin degeneracy across the entire Brillouin zone, along with uncompensated spin density of states. They combine the benefits of spin-polarization in altermagnets with valley-polarization in TP-symmetric AFMs. Furthermore, the FE-AFMs feature layer-dependent spin-polarization, rooted in their intrinsic ferroelectric property, allowing for the flexible control over spin-valley polarization by interlayer sliding. This tunability facilitates sign-reversible and size-tunable valley Hall and Nernst effects, along with other spin-valley-dependent transport properties. Our findings are demonstrated in a broad class of TP-broken bilayer antiferromagnets such as Nb3X8 (X = Cl, Br, I), VX2 (X = S, Se), and VSi2X4 (X = N, P), underscoring the potential of FE-AFMs for advancing next-generation spin- and valley-based information technologies.
Backtesting Sentiment Signals for Trading: Evaluating the Viability of Alpha Generation from Sentiment Analysis
2025-07-04
Elvys Linhares Pontes, Carlos-Emiliano González-Gallardo, Georgeta Bordea, José G. Moreno, Mohamed Ben Jannet, Yuxuan Zhao, Antoine Doucet
Sentiment analysis, widely used in product reviews, also impacts financial markets by influencing asset prices through microblogs and news articles. Despite research in sentiment-driven finance, many studies focus on sentence-level classification, overlooking its practical application in trading. This study bridges that gap by evaluating sentiment-based trading strategies for generating positive alpha. We conduct a backtesting analysis using sentiment predictions from three models (two classification and one regression) applied to news articles on Dow Jones 30 stocks, comparing them to the benchmark Buy&Hold strategy. Results show all models produced positive returns, with the regression model achieving the highest return of 50.63% over 28 months, outperforming the benchmark Buy&Hold strategy. This highlights the potential of sentiment in enhancing investment strategies and financial decision-making.
Photometric Redshift Predictions with a Neural Network for DESI Quasars
2025-07-04
Jeremy P. Moss, Stephen J. Curran, Yvette C. Perrott
Accurate redshift measurements are essential for studying the evolution of quasi-stellar objects (QSOs) and their role in cosmic structure formation. While spectroscopic redshifts provide high precision, they are impractical for the vast number of sources detected in large-scale surveys. Photometric redshifts, derived from broadband fluxes, offer an efficient alternative, particularly when combined with machine learning techniques. In this work, we develop and evaluate a neural network model for predicting the redshifts of QSOs in the Dark Energy Spectroscopic Instrument (DESI) Early Data Release spectroscopic catalogue, using photometry from DESI, the Widefield Infrared Survey Explorer (WISE) and the Galactic Evolution Explorer (GALEX). We compare the performance of the neural network model against a k-Nearest Neighbours approach, these being the most accurate and least resource-intensive of the methods trialled herein, optimising model parameters and assessing accuracy with standard statistical metrics. Our results show that incorporating ultraviolet photometry from GALEX improves photometric redshift estimates, reducing scatter and catastrophic outliers compared to models trained only on near infrared and optical bands. The neural network achieves a correlation coefficient with spectroscopic redshift of $0.9187$ with normalised median absolute deviation of $0.197$, representing a significant improvement over other methods. Our work combines DESI, WISE and GALEX measurements, providing robust predictions which address the difficulties in predicting photometric redshift of QSOs over a large redshift range.
Electric vehicles (EVs) charging infrastructure is directly related to the overall EV user experience and thus impacts the widespread adoption of EVs. Understanding key factors that affect EV users' charging experience is essential for building a robust and user-friendly EV charging infrastructure. This study leverages about $17,000$ charging station (CS) reviews on Google Maps to explore EV user preferences for charging stations, employing ChatGPT 4.0 for aspect-based sentiment analysis. We identify twelve key aspects influencing user satisfaction, ranging from accessibility and reliability to amenities and pricing. Two distinct preference models are developed: a micro-level model focused on individual user satisfaction and a macro-level model capturing collective sentiment towards specific charging stations. Both models utilize the LightGBM algorithm for user preference prediction, achieving strong performance compared to other machine learning approaches. To further elucidate the impact of each aspect on user ratings, we employ SHAP (SHapley Additive exPlanations), a game-theoretic approach for interpreting machine learning models. Our findings highlight the significant impact of positive sentiment towards "amenities and location", coupled with negative sentiment regarding "reliability and maintenance", on overall user satisfaction. These insights offer actionable guidance to charging station operators, policymakers, and EV manufacturers, empowering them to enhance user experience and foster wider EV adoption.
Hungary and AI: efforts and opportunities in comparison with Singapore
2025-07-04
András Ferenczy
The study assesses Hungary's National AI Strategy and its implementation through the analysis of strategic documents, publicly available financial records, and expert interviews with the Hungarian AI Coalition President and Chief Strategic Advisor to the Government Commissioner for AI. 22 goals from Hungary's strategy were evaluated through conceptual, governance, temporal, and financial dimensions before being benchmarked against Singapore's National AI Strategies (NAIS 1.0 and NAIS 2.0). Key findings include an estimated total of EUR 4.65 billion in AI-related public investment in Hungary. Openly available financial data was found for only half of the evaluated goals, and just three projects made up 98\% of all documented funding. The research also reveals Hungary's implementation challenges, including fragmented execution following ministerial reorganizations and the absence of designated biennial reviews since 2020. Furthermore, the paper provides targeted recommendations for Hungary's forthcoming AI strategy, drawing on Singapore's framework as a reference point. These include adapting to the era of large language models, restructuring the existing triple helix network to foster more effective dialogue and advocacy, and positioning the country as an East-West bridge for automotive AI experimentation.
Establishing Best Practices for Building Rigorous Agentic Benchmarks
2025-07-03
Yuxuan Zhu, Tengjun Jin, Yada Pruksachatkun, Andy Zhang, Shu Liu, Sasha Cui, Sayash Kapoor, Shayne Longpre, Kevin Meng, Rebecca Weiss, Fazl Barez, Rahul Gupta, Jwala Dhamala, Jacob Merizian, Mario Giulianelli, Harry Coppock, Cozmin Ududec, Jasjeet Sekhon, Jacob Steinhardt, Antony Kellerman, Sarah Schwettmann, Matei Zaharia, Ion Stoica, Percy Liang, Daniel Kang
Benchmarks are essential for quantitatively tracking progress in AI. As AI agents become increasingly capable, researchers and practitioners have introduced agentic benchmarks to evaluate agents on complex, real-world tasks. These benchmarks typically measure agent capabilities by evaluating task outcomes via specific reward designs. However, we show that many agentic benchmarks have issues task setup or reward design. For example, SWE-bench Verified uses insufficient test cases, while TAU-bench counts empty responses as successful. Such issues can lead to under- or overestimation agents' performance by up to 100% in relative terms. To make agentic evaluation rigorous, we introduce the Agentic Benchmark Checklist (ABC), a set of guidelines that we synthesized from our benchmark-building experience, a survey of best practices, and previously reported issues. When applied to CVE-Bench, a benchmark with a particularly complex evaluation design, ABC reduces the performance overestimation by 33%.
Prediction of synthesis parameters for N, Si, Ge and Sn diamond vacancy centers using machine learning
2025-07-03
Zhi Jiang, Marco Peres, Carlo Bradac, Gil Gonçalves
Diamond and diamond color centers have become prime hardware candidates for solid state-based technologies in quantum information and computing, optics, photonics and (bio)sensing. The synthesis of diamond materials with specific characteristics and the precise control of the hosted color centers is thus essential to meet the demands of advanced applications. Yet, challenges remain in improving the concentration, uniform distribution and quality of these centers. Here we perform a review and meta-analysis of some of the main diamond synthesis methods and their parameters for the synthesis of N-, Si-, Ge- and Sn-vacancy color-centers, including worldwide trends in fabrication techniques and processes. We extract quantitative data from over 60 experimental papers and organize it in a large database (170 data sets and 1692 entries). We then use the database to train two machine learning algorithms to make robust predictions about the fabrication of diamond materials with specific properties from careful combinations of synthesis parameters. We use traditional statistical indicators to benchmark the performance of the algorithms and show that they are powerful and resource-efficient tools for researchers and material scientists working with diamond color centers and their applications.
A Highly Carbon-Rich Dayside and Disequilibrium Chemistry in the Ultra-Hot Jupiter WASP-19b
2025-07-03
Suman Saha, James S. Jenkins
Ultra-hot Jupiters (UHJs) offer exceptional opportunities for detailed atmospheric characterization via emission spectroscopy. Here we present a comprehensive analysis of the dayside atmosphere of WASP-19b$\unicode{x2014}$one of the shortest-period UHJs$\unicode{x2014}$using archival JWST NIRSpec/PRISM observations spanning 0.6-5.3 $\mu$m. We report robust detections of H$_2$O (16.44 $\sigma$), CO (5.47 $\sigma$), and CO$_2$ (10.72 $\sigma$), along with marginal detections of CH$_4$ (3.76 $\sigma$) and C$_2$H$_2$ (2.45 $\sigma$). The retrieved composition reveals a highly carbon-rich atmosphere with a tightly constrained super-solar C/O ratio of 0.94$\pm$0.03. Elevated abundances of carbon-bearing species provide strong evidence (11.69 $\sigma$) for disequilibrium chemistry. We also detect condensate clouds, likely Al$_2$O$_3$(c), at high significance (17.28 $\sigma$), and constrain the atmospheric metallicity to 1.7$_{-0.7}^{+1.2}$ $\times$ solar. These results establish a precise benchmark for modeling dayside conditions in extreme irradiated atmospheres and demonstrate JWST's transformative capabilities for exoplanet science.
Diffusive charge transport in the gapped 1D Hubbard model at all finite temperatures
2025-07-03
J. M. P. Carmelo, P. D. Sacramento
Studies relying on hydrodynamic theory and Kardar-Parisi-Zhang (KPZ) scaling have found that in the one-dimensional Hubbard model spin and charge transport are for all temperatures T > 0 anomalous superdiffusive at zero magnetic field, h = 0, and zero chemical potential, {\mu} = 0, respectively. However, this contradicts recent exact results that at very low temperature charge transport rather is normal diffusive. In this Letter we identify the mechanisms that control the different types of temperature dependence of the h = 0 spin and {\mu} = 0 charge transport and find that the latter is normal diffusive for all finite temperatures T > 0, in contrast to the hydrodynamic theory and KPZ scaling predictions.
The ESO SupJup Survey VIII. Chemical fingerprints of young L dwarf twins
2025-07-03
N. Grasser, I. A. G. Snellen, S. de Regt, D. González Picos, Y. Zhang, T. Stolker, S. Gandhi, E. Nasedkin, R. Landman, A. Y. Kesseli, W. Mulder
The potentially distinct formation pathways of exoplanets and brown dwarfs may be imprinted in their elemental and isotopic ratios. This work is part of the ESO SupJup Survey, which aims to disentangle the formation pathways of super-Jupiters and brown dwarfs through the analysis of their chemical and isotopic ratios. In this study, we characterize the atmospheres of two young L4 dwarfs, 2MASS J03552337+1133437 (2M0355) and 2MASS J14252798-3650229 (2M1425), in the AB Doradus Moving Group. This involved constraining their chemical composition, $^{12}$CO/$^{13}$CO ratio, pressure-temperature profile, surface gravity, and rotational velocity. We have obtained high-resolution CRIRES+ K-band spectra of these brown dwarfs, which we analyzed with an atmospheric retrieval pipeline. Atmospheric models were generated with the radiative transfer code petitRADTRANS, for which we employed a free and equilibrium chemistry approach, which we coupled with the PyMultiNest sampling algorithm to determine the best fit. We report robust detections of $^{13}$CO (13.4 & 8.0 $\sigma$) and HF (11.6 & 15.8 $\sigma$) in 2M0355 and 2M1425, respectively. The objects have similar overall atmospheric properties, including $^{12}$CO/$^{13}$CO isotope ratios of $95.5\substack{+6.8 \\ -6.4}$ for 2M0355 and $109.6\substack{+10.6 \\ -9.6}$ for 2M1425. In both brown dwarfs, we find only tentative hints of H$_2^{18}$O (1.1 & 3.0 $\sigma$), with lower limits of H$_2^{16}$O/H$_2^{18}$O $\sim$ 1000. Both objects appear to be close to chemical equilibrium, considering the main spectral contributors. Future studies will put them into the context of other objects observed as part of the ESO SupJup Survey.
Peer review is fundamental to scientific research, but the growing volume of publications has intensified the challenges of this expertise-intensive process. While LLMs show promise in various scientific tasks, their potential to assist with peer review, particularly in identifying paper limitations, remains understudied. We first present a comprehensive taxonomy of limitation types in scientific research, with a focus on AI. Guided by this taxonomy, for studying limitations, we present LimitGen, the first comprehensive benchmark for evaluating LLMs' capability to support early-stage feedback and complement human peer review. Our benchmark consists of two subsets: LimitGen-Syn, a synthetic dataset carefully created through controlled perturbations of high-quality papers, and LimitGen-Human, a collection of real human-written limitations. To improve the ability of LLM systems to identify limitations, we augment them with literature retrieval, which is essential for grounding identifying limitations in prior scientific findings. Our approach enhances the capabilities of LLM systems to generate limitations in research papers, enabling them to provide more concrete and constructive feedback.
Volatile-rich evolution of molten super-Earth L 98-59 d
2025-07-03
Harrison Nicholls, Tim Lichtenberg, Richard D. Chatterjee, Claire Marie Guimond, Emma Postolec, Raymond T. Pierrehumbert
Small low-density exoplanets are sculpted by strong stellar irradiation but their origin is unknown. Two competing scenarios are that they formed either with rocky interiors and H2-He atmospheres (`gas-dwarfs'), or instead with bulk compositions dominated by H2O phases (`water-worlds'). Observations of L 98-59 d have revealed its unusually low density and spectral characteristics, opening a window for disentangling its origin. We constrain the possible range of evolutionary histories linking L 98-59 d's birth conditions to these observations, using a coupled atmosphere-interior modelling framework. Observations are explained by a chemically-reducing mantle and early substantial (>1.8 mass%) sulfur and hydrogen content, inconsistent with both the gas-dwarf and water-world origin scenarios. Observed spectral features are explained by in-situ photochemical production of SO2 in an H2 background. L 98-59 d's interior comprises a permanent magma ocean, allowing long-term retention of volatiles within its interior over billions of years, consistent with surveyed Kepler Survey trends. Modelling of the `cosmic shoreline' suggests that early volatile-metal budget is a determinant of atmospheric retention - here, we explicitly show that large initial budgets can explain JWST observations of some super-Earth planets (including DDT targets). In breaking with the supposed gas-dwarf/water-world dichotomy, we reveal an evolutionary pathway defined by high-molar-mass atmospheres overlying magma oceans, and invite a more nuanced taxonomy of small exoplanets.
Calibrated Recommendations: Survey and Future Directions
2025-07-03
Diego Corrêa da Silva, Dietmar Jannach
The idea of calibrated recommendations is that the properties of the items that are suggested to users should match the distribution of their individual past preferences. Calibration techniques are therefore helpful to ensure that the recommendations provided to a user are not limited to a certain subset of the user's interests. Over the past few years, we have observed an increasing number of research works that use calibration for different purposes, including questions of diversity, biases, and fairness. In this work, we provide a survey on the recent developments in the area of calibrated recommendations. We both review existing technical approaches for calibration and provide an overview on empirical and analytical studies on the effectiveness of calibration for different use cases. Furthermore, we discuss limitations and common challenges when implementing calibration in practice.
Importance of anisotropic interactions for hard-axis/plane ordering of Ce-based ferromagnets
2025-07-03
Hanshang Jin, Rahim R. Ullah, Peter Klavins, Valentin Taufour
Ferromagnetic (FM) Kondo-lattice (KL) compounds often exhibit intriguing magnetic behavior driven by strong crystal electric field (CEF) anisotropy and exchange interactions. Recent studies suggest that many Ce-based and Yb-based KL ferromagnets order along the hard-axis or hard-plane determined by their CEF ground state. We performed a survey of Ce-based FM compounds, complemented with new single crystal synthesis, magnetization measurements, and analysis of the CEF scheme. Our results reveal that hard-axis/plane ordering is less common than previously reported, with most compounds ordering along their easy-axis/plane. We also find no clear correlation between the strength of the Kondo effect and whether a compound adopts hard-axis/plane or easy-axis/plane ordering. Instead, the key driver appears to be the competition between CEF anisotropy and exchange interaction anisotropy. A direct comparison of CeCuSi and CeAgSb$_2$ further indicates that antiferromagnetic (AFM) interactions along the CEF easy-axis can be crucial in stabilizing the hard-axis FM ordering observed in CeAgSb$_2$. Finally, our results suggest that an anisotropic Ruderman-Kittel-Kasuya-Yosida (RKKY) model could offer deeper insights into the complicated magnetic properties of Ce-based and other heavy-fermion systems.
The increasing importance of Vision-Based Navigation (VBN) algorithms in space missions raises numerous challenges in ensuring their reliability and operational robustness. Sensor faults can lead to inaccurate outputs from navigation algorithms or even complete data processing faults, potentially compromising mission objectives. Artificial Intelligence (AI) offers a powerful solution for detecting such faults, overcoming many of the limitations associated with traditional fault detection methods. However, the primary obstacle to the adoption of AI in this context is the lack of sufficient and representative datasets containing faulty image data. This study addresses these challenges by focusing on an interplanetary exploration mission scenario. A comprehensive analysis of potential fault cases in camera sensors used within the VBN pipeline is presented. The causes and effects of these faults are systematically characterized, including their impact on image quality and navigation algorithm performance, as well as commonly employed mitigation strategies. To support this analysis, a simulation framework is introduced to recreate faulty conditions in synthetically generated images, enabling a systematic and controlled reproduction of faulty data. The resulting dataset of fault-injected images provides a valuable tool for training and testing AI-based fault detection algorithms. The final link to the dataset will be added after an embargo period. For peer-reviewers, this private link is available.
Revisiting Active Learning under (Human) Label Variation
2025-07-03
Cornelia Gruber, Helen Alber, Bernd Bischl, Göran Kauermann, Barbara Plank, Matthias Aßenmacher
Access to high-quality labeled data remains a limiting factor in applied supervised learning. While label variation (LV), i.e., differing labels for the same instance, is common, especially in natural language processing, annotation frameworks often still rest on the assumption of a single ground truth. This overlooks human label variation (HLV), the occurrence of plausible differences in annotations, as an informative signal. Similarly, active learning (AL), a popular approach to optimizing the use of limited annotation budgets in training ML models, often relies on at least one of several simplifying assumptions, which rarely hold in practice when acknowledging HLV. In this paper, we examine foundational assumptions about truth and label nature, highlighting the need to decompose observed LV into signal (e.g., HLV) and noise (e.g., annotation error). We survey how the AL and (H)LV communities have addressed -- or neglected -- these distinctions and propose a conceptual framework for incorporating HLV throughout the AL loop, including instance selection, annotator choice, and label representation. We further discuss the integration of large language models (LLM) as annotators. Our work aims to lay a conceptual foundation for HLV-aware active learning, better reflecting the complexities of real-world annotation.
Gas Pressure Driven Screening Forces and Pebble Aggregation: A Pathway for Growth in Planet Formation
2025-07-03
Mukesh Kumar Vyas
The formation of planetesimals from cm-sized pebbles in protoplanetary disks faces significant barriers, including fragmentation and radial drift. We identify a previously unaccounted screening force, arising from mutual shielding of thermal gas particles between pebbles when their separation falls below the gas mean free path. This force facilitates pebble binding, overcoming key growth barriers under turbulent disk conditions. Unlike conventional mechanisms, screening forces operate independently of surface adhesion and complement streaming instability and pressure traps by enhancing aggregation in high-density regions. Our analysis predicts that screening interactions are most effective in the {middle disk regions ($ \sim 0.3$ to few AU),} consistent with ALMA observations (e.g., TW Hya) of enhanced dust concentrations. {Furthermore, we find that screening-induced pebble growth from centimeter to kilometer scales can occur on timescales significantly shorter than the disk lifetime ($\sim 10^5$ years). Importantly, this growth naturally terminates when particles smaller than the local gas mean free path are depleted, thereby avoiding runaway accretion.} Beyond planetary science, the screening forces have {potential} implications for high-energy astrophysics, dusty plasmas, confined particle suspensions and other relevant areas, suggesting a broader fundamental significance.
Steady-State Coherences under Partial Collective non-Markovian Decoherence
2025-07-03
S. L. Wu, W. Ma, Zhao-Ming Wang, P. Brumer, Lian-Ao Wu
Steady-state coherence in open quantum systems is crucial for quantum technologies, yet its behavior is not fully understood due to the interplay between collective and individual decoherence. While collective decoherence is thought to induce steady-state coherence, experiments often fail to observe this because of individual decoherence. We study a system of two harmonic oscillators coupled to both individual and collective environments, introducing a tunable parameter to adjust the decoherence proportions. By analytically solving the exact dynamical equations, we find that steady-state coherence depends on the initial state under collective decoherence, but not under partial decoherence. Interestingly, non-Markovianity induces rich and complex steady-state coherence behaviors. Our results offer new insights into the role of non-Markovian decoherence in quantum systems and serve as a benchmark for evaluating approximate methods in modelling quantum processes.
A Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE). XIX. The discovery of a spectacular 230 kpc Halpha tail following NGC 4569 in the Virgo cluster
2025-07-03
M. Sun, H. Le, B. Epinat, A. Boselli, R. Luo, K. Hosogi, N. Pichette, W. Forman, C. Sarazin, M. Fossati, H. Chen, E. Sarpa, J. Braine, J. C. Cuillandre, S. Gwyn, G. Hensler, S. Martocchia, B. Vollmer
Context. Galaxies fly inside galaxy clusters and ram pressure by the ICM can remove a large amount of the ISM from the galaxy, and deposit the gas in the ICM. The ISM decoupled from the host galaxy leaves a long trail following the moving galaxy. Such long trails track the galaxy motion and can be detected with sensitive data in Halpha. Aims. We study the Halpha tail trailing NGC 4569 in the Virgo cluster. Methods. The initial discovery was made with the deep Halpha imaging data with CFHT, from the VESTIGE project. The follow-up spectroscopic observations were made with APO/DIS, MMT/Binospec and CFHT/SITELLE. Results. Besides the known 80 kpc Halpha tail downstream of NGC 4569, the deep Halpha imaging data allow the Halpha tail detected to at least 230 kpc from the galaxy. More importantly, the Halpha clumps implied from the imaging data are confirmed with the spectroscopic data. The Halpha clumps show a smooth radial velocity gradient across about 1300 km/s, eventually reaching the velocity of the cluster. We build a simple model to explain the deceleration of stripped clumps and constrain the age to about 0.9 Gyr. Conclusions. This discovery, for the first time, demonstrates the full deceleration process of the stripped ISM. This discovery also showcases the potential with wide-field Halpha survey on galaxy clusters to discover intracluster optical emission-line clouds originated from cluster galaxies. These clouds provide kinematic tracers to the infall history of cluster galaxies and the turbulence in the ICM. They are also excellent multi-phase objects to study the relevant important physical processes.
Of lynxes and limetrees: New Insights into the Historical Ecology of the Bavarian Forest on the Threshold of Modernity
2025-07-03
Bettina Haas, Markus Gerstmeier, Ricarda Huter, Malte Rehbein
This study explores the historical ecology of the Bavarian Forest at the threshold of modernity through the lens of a large-scale biodiversity survey conducted in 1845 across the Kingdom of Bavaria. Focusing on the Forestry Office of Zwiesel, the article analyses archival records that document the distribution of animal and tree species during a period of administrative rationalisation and early scientific systematisation. These sources include official correspondence, detailed species inventories, and early distribution maps compiled under the direction of zoologist Johann Andreas Wagner. The study highlights local and regional observations of fauna, such as the Eurasian lynx, capercaillie, and various freshwater fish, and presents new data on historical forest composition, including the presence of rare tree species and venerable specimen trees such as the "Urwaldtanne" and the "Wolframslinde". By integrating historical ecology, environmental history, and digital humanities, the article sheds light on how 19th-century state institutions, scientific actors, and local knowledge contributed to the early documentation of biodiversity. The findings also offer a basis for long-term environmental comparisons and inform current conservation efforts in the region.
A Cookbook for Community-driven Data Collection of Impaired Speech in LowResource Languages
2025-07-03
Sumaya Ahmed Salihs, Isaac Wiafe, Jamal-Deen Abdulai, Elikem Doe Atsakpo, Gifty Ayoka, Richard Cave, Akon Obu Ekpezu, Catherine Holloway, Katrin Tomanek, Fiifi Baffoe Payin Winful
This study presents an approach for collecting speech samples to build Automatic Speech Recognition (ASR) models for impaired speech, particularly, low-resource languages. It aims to democratize ASR technology and data collection by developing a "cookbook" of best practices and training for community-driven data collection and ASR model building. As a proof-of-concept, this study curated the first open-source dataset of impaired speech in Akan: a widely spoken indigenous language in Ghana. The study involved participants from diverse backgrounds with speech impairments. The resulting dataset, along with the cookbook and open-source tools, are publicly available to enable researchers and practitioners to create inclusive ASR technologies tailored to the unique needs of speech impaired individuals. In addition, this study presents the initial results of fine-tuning open-source ASR models to better recognize impaired speech in Akan.
Defining DLT Immutability: A Qualitative Survey of Node Operators
2025-07-03
Alex Lynham, Geoff Goodell
Immutability is a core design goal of permissionless public blockchain systems. However, rewrites are more common than is normally understood, and the risk of rewrite, cyberattack, exploit or black swan event is also high. Taking the position that strict immutability is neither possible on these networks nor the observed reality, this paper uses thematic analysis of node operator interviews to examine the limits of immutability in light of rewrite events. The end result is a qualitative definition of the conditional immutability found on these networks, which we call Practical Immutability. This is immutability contingent on the legitimate governance demands of the network, where network stakeholders place their trust in the governance topology of a network to lend it legitimacy, and thus manage ledger state.
Benchmarking Akan ASR Models Across Domain-Specific Datasets: A Comparative Evaluation of Performance, Scalability, and Adaptability
2025-07-03
Mark Atta Mensah, Isaac Wiafe, Akon Ekpezu, Justice Kwame Appati, Jamal-Deen Abdulai, Akosua Nyarkoa Wiafe-Akenten, Frank Ernest Yeboah, Gifty Odame
Most existing automatic speech recognition (ASR) research evaluate models using in-domain datasets. However, they seldom evaluate how they generalize across diverse speech contexts. This study addresses this gap by benchmarking seven Akan ASR models built on transformer architectures, such as Whisper and Wav2Vec2, using four Akan speech corpora to determine their performance. These datasets encompass various domains, including culturally relevant image descriptions, informal conversations, biblical scripture readings, and spontaneous financial dialogues. A comparison of the word error rate and character error rate highlighted domain dependency, with models performing optimally only within their training domains while showing marked accuracy degradation in mismatched scenarios. This study also identified distinct error behaviors between the Whisper and Wav2Vec2 architectures. Whereas fine-tuned Whisper Akan models led to more fluent but potentially misleading transcription errors, Wav2Vec2 produced more obvious yet less interpretable outputs when encountering unfamiliar inputs. This trade-off between readability and transparency in ASR errors should be considered when selecting architectures for low-resource language (LRL) applications. These findings highlight the need for targeted domain adaptation techniques, adaptive routing strategies, and multilingual training frameworks for Akan and other LRLs.
The SOMA-POL Survey. I. Polarization and magnetic field properties of massive protostars
2025-07-03
Tuva Källberg, Chi Yan Law, Jonathan C. Tan, Kate Pattle, Zacariyya Khan
The role of magnetic fields in regulating the formation of massive stars remains much debated. Here we present sub-millimeter polarimetric observations with JCMT-POL2 at $850\:\mu$m of 13 regions of massive star formation selected from the SOFIA Massive (SOMA) star formation survey, yielding a total of 29 massive protostars. Our investigation of the $p'-I$ relationship suggests that grain alignment persists up to the highest intensities. We examine the relative orientations between polarization-inferred magnetic field direction and source column density elongation direction on small and large scales. On small scales, we find a bimodal distribution of these relative orientations, i.e., with an excess of near-parallel and near-perpendicular orientations. By applying a one-sample Kuiper test and Monte Carlo simulations to compare to a relative orientation distribution drawn from a uniform distribution, we statistically confirm this bimodal distribution, independent of the methods to measure structural orientation. This bimodal distribution suggests that magnetic fields are dynamically important on the local scales ($\lesssim 0.6\:$pc) of massive protostellar cores. We also examine how basic polarization properties of overall degree of polarization and local dispersion in polarization vector orientations depend on intrinsic protostellar properties inferred from spectral energy distribution (SED) modeling. We find a statistically significant anti-correlation between the debiased polarized fraction and the luminosity to mass ratio, $L_{\rm bol}/M_{\rm env}$, which hints at a change in the dust properties for protostellar objects at different evolutionary stages.
Topological nodal-line semimetals (NLSMs) are a new family of topological materials characterized by electronic band crossings that form lines in the Brillouin zone. These NLSMs host exotic nodal-line structures and exhibit distinct features such as drumhead surface states and unique electromagnetic responses. This review classifies various NLSM types based on their nodal structures and protecting symmetries, highlighting that these nodal-line structures can form links, knots, and chains. We discuss their characteristic electromagnetic responses, including Landau level spectroscopy, optical conductivity, and permittivity. Furthermore, the strong correlation effects in these NLSMs modify their semimetallic phases and lead to novel quantum phases where magnetism and superconductivity intertwine.
Electroencephalography (EEG) is one of the most common signals used to capture the electrical activity of the brain, and the decoding of EEG, to acquire the user intents, has been at the forefront of brain-computer/machine interfaces (BCIs/BMIs) research. Compared to traditional EEG analysis methods with machine learning, the advent of deep learning approaches have gradually revolutionized the field by providing an end-to-end long-cascaded architecture, which can learn more discriminative features automatically. Among these, Transformer is renowned for its strong handling capability of sequential data by the attention mechanism, and the application of Transformers in various EEG processing tasks is increasingly prevalent. This article delves into a relevant survey, summarizing the latest application of Transformer models in EEG decoding since it appeared. The evolution of the model architecture is followed to sort and organize the related advances, in which we first elucidate the fundamentals of the Transformer that benefits EEG decoding and its direct application. Then, the common hybrid architectures by integrating basic Transformer with other deep learning techniques (convolutional/recurrent/graph/spiking neural netwo-rks, generative adversarial networks, diffusion models, etc.) is overviewed in detail. The research advances of applying the modified intrinsic structures of customized Transformer have also been introduced. Finally, the current challenges and future development prospects in this rapidly evolving field are discussed. This paper aims to help readers gain a clear understanding of the current state of Transformer applications in EEG decoding and to provide valuable insights for future research endeavors.
Non-equilibrium dynamics have become a central research focus, exemplified by the counterintuitive Mpemba effect where initially hotter systems can cool faster than colder ones. Studied extensively in both classical and quantum regimes, this phenomenon reveals diverse and complex behaviors across different systems. This review provides a concise overview of the quantum Mpemba effect (QME), specifically emphasizing its connection to symmetry breaking and restoration in closed quantum many-body systems. We begin by outlining the classical Mpemba effect and its quantum counterparts, summarizing key findings. Subsequently, we introduce entanglement asymmetry and charge variance as key metrics for probing the QME from symmetry perspectives. Leveraging these tools, we analyze the early- and late-time dynamics of these quantities under Hamiltonian evolution and random unitary circuits. We conclude by discussing significant challenges and promising avenues for future research.
Human-Centered Explainability in Interactive Information Systems: A Survey
2025-07-03
Yuhao Zhang, Jiaxin An, Ben Wang, Yan Zhang, Jiqun Liu
Human-centered explainability has become a critical foundation for the responsible development of interactive information systems, where users must be able to understand, interpret, and scrutinize AI-driven outputs to make informed decisions. This systematic survey of literature aims to characterize recent progress in user studies on explainability in interactive information systems by reviewing how explainability has been conceptualized, designed, and evaluated in practice. Following PRISMA guidelines, eight academic databases were searched, and 100 relevant articles were identified. A structural encoding approach was then utilized to extract and synthesize insights from these articles. The main contributions include 1) five dimensions that researchers have used to conceptualize explainability; 2) a classification scheme of explanation designs; 3) a categorization of explainability measurements into six user-centered dimensions. The review concludes by reflecting on ongoing challenges and providing recommendations for future exploration of related issues. The findings shed light on the theoretical foundations of human-centered explainability, informing the design of interactive information systems that better align with diverse user needs and promoting the development of systems that are transparent, trustworthy, and accountable.
The dark matter content of Milky Way dwarf spheroidal galaxies: Draco, Sextans and Ursa Minor
2025-07-03
Hao Yang, Wenting Wang, Ling Zhu, Ting S. Li, Sergey E. Koposov, Jiaxin Han, Songting Li, Rui Shi, Monica Valluri, Alexander H. Riley, Arjun Dey, Constance Rockosi, Carles G. Palau, Jessica Nicole Aguilar, Steven Ahlen, David Brooks, Todd Claybaugh, Andrew Cooper, Axel de la Macorra, Peter Doel, Simone Ferraro, Jaime E. Forero-Romero, Enrique Gaztañaga, Satya Gontcho A Gontcho, Alma Xochitl Gonzalez Morales, Gaston Gutierrez, Julien Guy, Klaus Honscheid, Mustapha Ishak, Dick Joyce, Robert Kehoe, Theodore Kisner, Namitha Kizhuprakkat, Anthony Kremin, Ofer Lahav, Martin Landriau, Laurent Le Guillou, Gustavo Medina Toledo, Aaron Meisner, Ramon Miquel, Nathalie Palanque-Delabrouille, Francisco Prada, Ignasi Pérez-Ràfols, Graziano Rossi, Eusebio Sanchez, David Schlegel, Michael Schubnell, Joseph Harry Silber, David Sprayberry, Gregory Tarlé, Benjamin Alan Weaver, Rongpu Zhou, Hu Zou
The Milky Way Survey of the Dark Energy Spectroscopic Instrument (DESI) has so far observed three classical dwarf spheroidal galaxies (dSphs): Draco, Sextans and Ursa Minor. Based on the observed line-of-sight velocities and metallicities of their member stars, we apply the axisymmetric Jeans Anisotropic Multi-Gaussian Expansion modeling (JAM) approach to recover their inner dark matter distributions. In particular, both the traditional single-population Jeans model and the multiple population chemodynamical model are adopted. With the chemodynamical model, we divide member stars of each dSph into metal-rich and metal-poor populations. The metal-rich populations are more centrally concentrated and dynamically colder, featuring lower velocity dispersion profiles than the metal-poor populations. We find a diversity of the inner density slopes $\gamma$ of dark matter halos, with the best constraints by single-population or chemodynamical models consistent with each other. The inner density slopes are $0.71^{+0.34}_{-0.35}$, $0.26^{+0.22}_{-0.12}$ and $0.33^{+0.20}_{-0.16}$ for Draco, Sextans and Ursa Minor, respectively. We also present the measured astrophysical J and D factors of the three dSphs. Our results indicate that the study of the dark matter content of dSphs through stellar kinematics is still subject to uncertainties behind both the methodology and the observed data, through comparisons with previous measurements and data sets.
Content filtering methods for music recommendation: A review
2025-07-03
Terence Zeng, Abhishek K. Umrawal
Recommendation systems have become essential in modern music streaming platforms, shaping how users discover and engage with songs. One common approach in recommendation systems is collaborative filtering, which suggests content based on the preferences of users with similar listening patterns to the target user. However, this method is less effective on media where interactions are sparse. Music is one such medium, since the average user of a music streaming service will never listen to the vast majority of tracks. Due to this sparsity, there are several challenges that have to be addressed with other methods. This review examines the current state of research in addressing these challenges, with an emphasis on the role of content filtering in mitigating biases inherent in collaborative filtering approaches. We explore various methods of song classification for content filtering, including lyrical analysis using Large Language Models (LLMs) and audio signal processing techniques. Additionally, we discuss the potential conflicts between these different analysis methods and propose avenues for resolving such discrepancies.
We present CoInfra, a large-scale cooperative infrastructure perception system and dataset designed to advance robust multi-agent perception under real-world and adverse weather conditions. The CoInfra system includes 14 fully synchronized sensor nodes, each equipped with dual RGB cameras and a LiDAR, deployed across a shared region and operating continuously to capture all traffic participants in real-time. A robust, delay-aware synchronization protocol and a scalable system architecture that supports real-time data fusion, OTA management, and remote monitoring are provided in this paper. On the other hand, the dataset was collected in different weather scenarios, including sunny, rainy, freezing rain, and heavy snow and includes 195k LiDAR frames and 390k camera images from 8 infrastructure nodes that are globally time-aligned and spatially calibrated. Furthermore, comprehensive 3D bounding box annotations for five object classes (i.e., car, bus, truck, person, and bicycle) are provided in both global and individual node frames, along with high-definition maps for contextual understanding. Baseline experiments demonstrate the trade-offs between early and late fusion strategies, the significant benefits of HD map integration are discussed. By openly releasing our dataset, codebase, and system documentation at https://github.com/NingMingHao/CoInfra, we aim to enable reproducible research and drive progress in infrastructure-supported autonomous driving, particularly in challenging, real-world settings.
An Exploration of Internal States in Collaborative Problem Solving
2025-07-03
Sifatul Anindho, Videep Venkatesha, Mariah Bradford, Anne M. Cleary, Nathaniel Blanchard
Collaborative problem solving (CPS) is a complex cognitive, social, and emotional process that is increasingly prevalent in educational and professional settings. This study investigates the emotional states of individuals during CPS using a mixed-methods approach. Teams of four first completed a novel CPS task. Immediately after, each individual was placed in an isolated room where they reviewed the video of their group performing the task and self-reported their internal experiences throughout the task. We performed a linguistic analysis of these internal monologues, providing insights into the range of emotions individuals experience during CPS. Our analysis showed distinct patterns in language use, including characteristic unigrams and bigrams, key words and phrases, emotion labels, and semantic similarity between emotion-related words.
Disclosing Generative AI Use in Digital Humanities Research
2025-07-03
Rongqian Ma, Xuhan Zhang, Adrian Wisnicki
This survey study investigates how digital humanists perceive and approach generative AI disclosure in research. The results indicate that while digital humanities scholars acknowledge the importance of disclosing GenAI use, the actual rate of disclosure in research practice remains low. Respondents differ in their views on which activities most require disclosure and on the most appropriate methods for doing so. Most also believe that safeguards for AI disclosure should be established through institutional policies rather than left to individual decisions. The study's findings will offer empirical guidance to scholars, institutional leaders, funders, and other stakeholders responsible for shaping effective disclosure policies.
Large language models (LLMs) are increasingly integrated into applications ranging from review summarization to medical diagnosis support, where they affect human decisions. Even though LLMs perform well in many tasks, they may also inherit societal or cognitive biases, which can inadvertently transfer to humans. We investigate when and how LLMs expose users to biased content and quantify its severity. Specifically, we assess three LLM families in summarization and news fact-checking tasks, evaluating how much LLMs stay consistent with their context and/or hallucinate. Our findings show that LLMs expose users to content that changes the sentiment of the context in 21.86% of the cases, hallucinates on post-knowledge-cutoff data questions in 57.33% of the cases, and primacy bias in 5.94% of the cases. We evaluate 18 distinct mitigation methods across three LLM families and find that targeted interventions can be effective. Given the prevalent use of LLMs in high-stakes domains, such as healthcare or legal analysis, our results highlight the need for robust technical safeguards and for developing user-centered interventions that address LLM limitations.
Understanding Knowledge Transferability for Transfer Learning: A Survey
2025-07-03
Haohua Wang, Jingge Wang, Zijie Zhao, Yang Tan, Yanru Wu, Hanbing Liu, Jingyun Yang, Enming Zhang, Xiangyu Chen, Zhengze Rong, Shanxin Guo, Yang Li
Transfer learning has become an essential paradigm in artificial intelligence, enabling the transfer of knowledge from a source task to improve performance on a target task. This approach, particularly through techniques such as pretraining and fine-tuning, has seen significant success in fields like computer vision and natural language processing. However, despite its widespread use, how to reliably assess the transferability of knowledge remains a challenge. Understanding the theoretical underpinnings of each transferability metric is critical for ensuring the success of transfer learning. In this survey, we provide a unified taxonomy of transferability metrics, categorizing them based on transferable knowledge types and measurement granularity. This work examines the various metrics developed to evaluate the potential of source knowledge for transfer learning and their applicability across different learning paradigms emphasizing the need for careful selection of these metrics. By offering insights into how different metrics work under varying conditions, this survey aims to guide researchers and practitioners in selecting the most appropriate metric for specific applications, contributing to more efficient, reliable, and trustworthy AI systems. Finally, we discuss some open challenges in this field and propose future research directions to further advance the application of transferability metrics in trustworthy transfer learning.
Identifying the contributors to cosmic reionization with the Habitable World Observatory: LyC escape calibration in faint galaxies
2025-07-03
Annalisa Citro, Cody. A. Carr, Yumi Choi, Sophia. R. Flury, Matthew. J. Hayes, Anne Jaskot, Gagandeep Kaur, Alexandra Le Reste, Matilde Mingozzi, Themiya Nanayakkara, Sally Oey, Claudia. M. Scarlata
Investigating how small-scale physical processes shape large-scale astrophysical phenomena is one of the key science themes of the Astro2020 decadal Survey. An example of this interplay is Cosmic Reionization, where ionizing photons from galaxies escaped into the intergalactic medium (IGM), driving its transition from neutral to ionized at $z>6$. Star-forming galaxies are thought to be the dominant sources of reionization. However, an open question remains as to whether bright or faint star-forming galaxies are the primary reionization contributors. This depends on the escape fraction $f_{~esc}^{LyC}$ -- the fraction of ionizing photons that successfully escape the galaxy's interstellar and circumgalactic medium to reach the IGM. Performing direct measurements of $f_{~esc}^{LyC}$ during the reionization epoch is not feasible, due to the near-zero transmission of the IGM at $z\gtrsim4$. However, by calibrating $f_{~esc}^{LyC}$ against indirect indicators (i.e. galaxy properties that correlate with $f_{~esc}^{LyC}$) at lower $z$, we can estimate its value in galaxies during reionization. Currently, $f_{~esc}^{LyC}$ calibrations are mostly limited to $\sim 90$ sources brighter $M_{UV} \sim -18$ at $z\sim0.3$. Yet, recent models suggest that fainter galaxies ($-19
The Impact of LLM-Assistants on Software Developer Productivity: A Systematic Literature Review
2025-07-03
Amr Mohamed, Maram Assi, Mariam Guizani
Large language model assistants (LLM-assistants) present new opportunities to transform software development. Developers are increasingly adopting these tools across tasks, including coding, testing, debugging, documentation, and design. Yet, despite growing interest, there is no synthesis of how LLM-assistants affect software developer productivity. In this paper, we present a systematic literature review of 37 peer-reviewed studies published between January 2014 and December 2024 that examine this impact. Our analysis reveals that LLM-assistants offer both considerable benefits and critical risks. Commonly reported gains include minimized code search, accelerated development, and the automation of trivial and repetitive tasks. However, studies also highlight concerns around cognitive offloading, reduced team collaboration, and inconsistent effects on code quality. While the majority of studies (92%) adopt a multi-dimensional perspective by examining at least two SPACE dimensions, reflecting increased awareness of the complexity of developer productivity, only 14% extend beyond three dimensions, indicating substantial room for more integrated evaluations. Satisfaction, Performance, and Efficiency are the most frequently investigated dimensions, whereas Communication and Activity remain underexplored. Most studies are exploratory (64%) and methodologically diverse, but lack longitudinal and team-based evaluations. This review surfaces key research gaps and provides recommendations for future research and practice. All artifacts associated with this study are publicly available at https://zenodo.org/records/15788502.
Expert-level validation of AI-generated medical text with scalable language models
2025-07-03
Asad Aali, Vasiliki Bikia, Maya Varma, Nicole Chiou, Sophie Ostmeier, Arnav Singhvi, Magdalini Paschali, Ashwin Kumar, Andrew Johnston, Karimar Amador-Martinez, Eduardo Juan Perez Guerrero, Paola Naovi Cruz Rivera, Sergios Gatidis, Christian Bluethgen, Eduardo Pontes Reis, Eddy D. Zandee van Rilland, Poonam Laxmappa Hosamani, Kevin R Keet, Minjoung Go, Evelyn Ling, David B. Larson, Curtis Langlotz, Roxana Daneshjou, Jason Hom, Sanmi Koyejo, Emily Alsentzer, Akshay S. Chaudhari
With the growing use of language models (LMs) in clinical environments, there is an immediate need to evaluate the accuracy and safety of LM-generated medical text. Currently, such evaluation relies solely on manual physician review. However, detecting errors in LM-generated text is challenging because 1) manual review is costly and 2) expert-composed reference outputs are often unavailable in real-world settings. While the "LM-as-judge" paradigm (a LM evaluating another LM) offers scalable evaluation, even frontier LMs can miss subtle but clinically significant errors. To address these challenges, we propose MedVAL, a self-supervised framework that leverages synthetic data to train evaluator LMs to assess whether LM-generated medical outputs are factually consistent with inputs, without requiring physician labels or reference outputs. To evaluate LM performance, we introduce MedVAL-Bench, a dataset containing 840 outputs annotated by physicians, following a physician-defined taxonomy of risk levels and error categories. Across 6 diverse medical tasks and 10 state-of-the-art LMs spanning open-source, proprietary, and medically adapted models, MedVAL fine-tuning significantly improves (p < 0.001) alignment with physicians on both seen and unseen tasks, increasing average F1 scores from 66% to 83%, with per-sample safety classification scores up to 86%. MedVAL improves the performance of even the best-performing proprietary LM (GPT-4o) by 8%. To support a scalable, risk-aware pathway towards clinical integration, we open-source the 1) codebase ( https://github.com/StanfordMIMI/MedVAL ), 2) MedVAL-Bench ( https://huggingface.co/datasets/stanfordmimi/MedVAL-Bench ), and 3) MedVAL-4B ( https://huggingface.co/stanfordmimi/MedVAL-4B ), the best-performing open-source LM. Our research provides the first evidence of LMs approaching expert-level validation ability for medical text.
Although demonstrating remarkable performance on reasoning tasks, Large Language Models (LLMs) still tend to fabricate unreliable responses when confronted with problems that are unsolvable or beyond their capability, severely undermining the reliability. Prior studies of LLM reliability have primarily focused on knowledge tasks to identify unanswerable questions, while mathematical reasoning tasks have remained unexplored due to the dearth of unsolvable math problems. To systematically investigate LLM reliability in mathematical reasoning tasks, we formulate the reliability evaluation for both solvable and unsolvable problems. We then develop a ReliableMath dataset which incorporates open-source solvable problems and high-quality unsolvable problems synthesized by our proposed construction workflow with human evaluations. Experiments are conducted on various LLMs with several key findings uncovered. LLMs fail to directly identify unsolvable problems and always generate fabricated responses. When instructing LLMs to indicate unsolvability using a reliable prompt, the reliability of larger-sized LLMs remains on solvable problems, but notably improves on unsolvable problems yet still falls short of solvable problems. However, small LLMs rarely show any progress despite employing reliable prompts. Therefore, we further propose an alignment strategy to enhance small LLMs' reliability, which can significantly improve LLM reliability performances on both in-domain and out-of-domain tasks.
On Demographic Transformation: Why We Need to Think Beyond Silos
2025-07-03
Nicholle Mae Amor Tan Maravilla, Myles Joshua Toledo Tan
Developed nations are undergoing a profound demographic transformation, characterized by rapidly aging populations and declining birth rates. This dual trend places unprecedented strain on healthcare systems, economies, and social support structures, creating complex biological, economic, and social challenges. This paper argues that current, often siloed, policy responses, such as pronatalist initiatives that overlook the equally urgent needs of older adults, are inadequate for addressing these interconnected issues. We propose that a comprehensive, transdisciplinary framework is essential for developing sustainable and ethical solutions. Through a review of demographic drivers, policy responses, and technological advancements, we analyze the limitations of fragmented approaches and explore the potential of innovative interventions. Specifically, we examine the role of artificial intelligence (AI) and robotics in transforming geriatric care. While these technologies offer powerful tools for personalizing treatment, enhancing diagnostics, and enabling remote monitoring, their integration presents significant challenges. These include ethical concerns regarding data privacy and compassionate care, the need for human oversight to ensure accuracy, and practical barriers related to cost, interoperability, and user acceptance. To navigate this demographic shift effectively, we conclude by advocating for a transdisciplinary framework that unites policymakers, healthcare professionals, engineers, ethicists, and community stakeholders. By co-creating solutions that ethically integrate technology and prioritize human dignity, societies can build resilient systems that promote healthy longevity and well-being for all generations.
Earliest Galaxy Evolution in the CANUCS+Technicolor fields: Galaxy Properties at $z\sim10-16$ seen with the Full NIRCam Medium and Broad Band Filters
2025-07-03
Yoshihisa Asada, Chris Willott, Adam Muzzin, Maruša Bradač, Gabriel Brammer, Guillaume Desprez, Kartheik Iyer, Danilo Marchesini, Nicholas Martis, Gaël Noirot, Ghassan Sarrouh, Marcin Sawicki, Sunna Withers, Seiji Fujimoto, Giordano Felicioni, Ilias Goovaerts, Jon Judež, Naadiyah Jagga, Maya Merchant, Rosa Mérida, Luke Robbins
We present a sample of $z_{\rm phot}\sim10-16$ galaxies by exploiting one of the richest JWST NIRCam imaging data, taken in the CANUCS survey in Cycle 1 and the Technicolor (TEC) survey in Cycle 2. The combination of the CANUCS+TEC provides multi-epoch, deep NIRCam images in all medium bands (MBs) and broad bands (BBs) onboard NIRCam (22 filters in total), over $\sim23\ {\rm arcmin}^2$ in three independent lines of sight. We select high-$z$ galaxy candidates based on photometric redshifts, and obtain eight candidates at $z\sim10-16$, including a very robust candidate at $z\sim15.4$. The ultraviolet (UV) luminosity function (LF) from our sample is consistent with previous JWST studies showing a scatter of $\sim0.6$ dex across the literature, marking the significance of the field-to-field variance in interpreting galaxy abundance measurements at $z>10$. We find that the UV LF moderately evolves at $z>10$, and the LF normalization and the luminosity density decline by a factor of $\sim7$ from $z\sim11$ to $z\sim15$, indicating less steep evolution than $z<11$. We highlight the importance of MB filters, not only to minimize the contamination by low-$z$ interlopers but also to maximize the completeness. In particular, faint and less blue galaxies could be missed when the sample is built solely on BB data. The contamination and incompleteness of BB-only selected samples can bias our views of earliest galaxy evolution at $z>10$, including the UV LF by $\sim0.6$ dex, the size-magnitude relation by $\sim0.6$ dex, and the UV slope-magnitude relation by $\Delta\beta_{\rm UV}\sim-0.3$.
On Riedtmann's well-behaved functors and applications to composites of irreducible morphisms
2025-07-03
Viktor Chust, Flávio U. Coelho
In this survey, we summarize some results in the literature involving the mesh category, which is a combinatorial representation of the category of modules over a finite-dimensional associative algebra. We discuss Riedtmann's well-behaved functors, which compare the mesh category with the module category, and discuss how the properties of these functors can be applied to study the problem of composing irreducible morphisms, which is the problem of deciding when the composition of n irreducible morphisms is non-zero and lies on the (n+1)-th power of the radical.
Testing Photometric Techniques for Measuring the Rest-Frame UV Spectral Slope Against JWST PRISM Spectroscopy
2025-07-03
Alexa M. Morales, Steven L. Finkelstein, Pablo Arrabal Haro, Micaela B. Bagley, Antonello Calabrò, Óscar A. Chávez Ortiz, Kelcey Davis, Mark Dickinson, Eric Gawiser, Mauro Giavalisco, Nimish P. Hathi, Michaela Hirschmann, Jeyhan S. Kartaltepe, Anton M. Koekemoer, Arianna S. Long, Ray A. Lucas, Fabio Pacucci, Casey Papovich, Borja Pautasso, Nor Pirzkal, Anthony J. Taylor, Alexander de la Vega, Stephen M. Wilkins, L. Y. Aaron Yung
We present a sample of 53 galaxy spectra at z_spec ~ 5-12 from the JWST CEERS and RUBIES surveys, combining NIRSpec PRISM spectroscopy with NIRCam photometry. We aim to use these data to establish best practices for measuring the UV spectral slope ($\beta$) in the era of JWST. We adopt power-law fits to the rest-frame UV continuum from the spectroscopic data as our fiducial, or `true', $\beta$ values, and compare them to photometric estimates derived through four methods: (1) photometric power-law fitting, (2) power-law fitting to an SED model fitted to the photometry, (3) single-color fitting near the Lyman break, and (4) single-color fitting at fixed rest-frame wavelengths. We find that photometric power-law fitting most closely recovers the spectroscopic slopes, with minimal bias and scatter. SED fitting performs moderately well, and can be preferable in cases of low signal-to-noise where photometric power-law fitting may become unreliable. Single-color estimates, while commonly used in past studies, show the most significant deviations and are not recommended when more than a single color is available. Our results highlight the limitations and strengths of each approach and provide practical guidance for measuring $\beta$ from photometry when spectra are unavailable or are of insufficient quality.
A Inteligência Artificial Generativa no Ecossistema Acadêmico: Uma Análise de Aplicações, Desafios e Oportunidades para a Pesquisa, o Ensino e a Divulgação Científica
The rapid and disruptive integration of Generative Artificial Intelligence (GenAI) in higher education is reshaping fundamental academic practices. This article presents a comprehensive analysis of the impact of GenAI across three core academic domains: research, teaching, and scientific dissemination. Through a systematic review of recent literature indexed in the Scopus, Web of Science, and IEEEXplore databases, the main applications, benefits, and the profound ethical and governance challenges that are emerging are identified. The analysis reveals that, although GenAI offers significant potential to boost productivity and innovation, its adoption is outpacing the development of mature institutional safeguards. The main challenges include threats to academic integrity, the risk of algorithmic bias, and the need for robust AI literacy. The study is complemented by a case study detailing the development and positioning of a prototype AI assistant for scientific writing, demonstrating a path toward the development of responsible AI tools that augment rather than replace human intellect. It concludes that the integration of GenAI is an irreversible trend. The future of academia will not be defined by resistance to this technology, but by the ability of institutions and individuals to engage with it critically, ethically, and creatively. The article calls for increased interdisciplinary research, the development of clear ethical guidelines, and a focus on critical AI pedagogy as essential skills for the 21st century.
Radio Morphology of Gamma-ray Sources -- II. Giant Radio Galaxies
2025-07-03
Vaidehi S. Paliya, D. J. Saikia, Gabriele Bruni, Alberto Dominguez, C. S. Stalin
Giant radio sources, including galaxies and quasars (hereafter GRGs), are active galactic nuclei (AGN) hosting relativistic jets with source sizes exceeding the projected length of 0.7 Mpc. They are crucial to understanding the evolution of radio sources and their interaction with the surrounding environment. Some of these enigmatic objects, e.g., NGC 315, have also been reported as gamma-ray emitters. Since GRGs are thought to be aligned close to the plane of the sky, they are invaluable targets to explore the radiative mechanisms responsible for the observed gamma-ray emission. We have carried out a systematic search of gamma-ray emitting GRGs using sensitive low-resolution radio surveys, such as by Low Frequency Array, NRAO VLA Sky Survey, and Rapid ASKAP Continuum Survey, and considering the fourth data release of the fourth Fermi-Large Area Telescope gamma-ray source (4FGL-DR4) catalog. By carefully inspecting the radio maps of all AGN included in the 4FGL-DR4 catalog, we have identified 16 gamma-ray emitting GRGs, including 8 of them being reported as GRGs for the first time. Some of their observed parameters, e.g., core dominance, appeared to differ from that found for the non-gamma-ray detected GRG population, possibly due to the relatively small viewing angle of the gamma-ray emitting jet. The observed gamma-ray properties of these objects were found to be similar to non-GRG gamma-ray emitting misaligned AGN. We conclude that the origin of the gamma-ray emission could be similar in both source populations.
On groups with square-free gcd of character degree and codegree
2025-07-03
Karam Aldahleh, Alan Kappler, Neil Makur, Yong Yang
Let $G$ be a finite group and $\chi$ be an irreducible character of $G$. The codegree of $\chi$ is defined as $\chi^c(1) =\frac{|G: \ker\chi|}{\chi(1)}$. In a paper by Gao, Wang, and Chen \cite{GWC24}, it was shown that $G$ cannot satisfy the condition that $\gcd(\chi(1),\chi^c(1))$ is prime for all $\chi\in\Irr(G)^\#$. We generalize this theorem by solving one of Guohua Qian's unsolved problems on character codegrees. In Qian's survey article \cite{Qiancodegree}, he inquires about the structure of non-solvable finite groups with square-free $\gcd$ instead. In particular, we prove that if $G$ is such that $\gcd(\chi(1),\chi^c(1))$ is square-free for every irreducible character $\chi$, then $G/\text{Sol}(G)$ is isomorphic to one among a particular list of almost simple groups.
From stellar light to astrophysical insight: automating variable star research with machine learning
2025-07-03
Jeroen Audenaert
Large-scale photometric surveys are revolutionizing astronomy by delivering unprecedented amounts of data. The rich data sets from missions such as the NASA Kepler and TESS satellites, and the upcoming ESA PLATO mission, are a treasure trove for stellar variability, asteroseismology and exoplanet studies. In order to unlock the full scientific potential of these massive data sets, automated data-driven methods are needed. In this review, I illustrate how machine learning is bringing asteroseismology toward an era of automated scientific discovery, covering the full cycle from data cleaning to variability classification and parameter inference, while highlighting the recent advances in representation learning, multimodal datasets and foundation models. This invited review offers a guide to the challenges and opportunities machine learning brings for stellar variability research and how it could help unlock new frontiers in time-domain astronomy.
Exploring the X-ray--radio connection for AGN via measurements of the multi-dimensional luminosity function
2025-07-03
Clara M. Pennock, James Aird, Cassandra L. Barlow-Hall
We present new methods to quantify the AGN population in terms of a multi-dimensional luminosity function that describes the space density of sources as a function of both X-ray and radio luminosity. We compile a sample of 1891 radio and X-ray detected extragalactic sources from the Bo\"otes and COSMOS fields. First, we investigate the X-ray--radio luminosity correlation in the sample and find that an apparent correlation is introduced due to the sensitivity limits of the surveys; when considering individual redshift bins we find a wide range of radio luminosities associated with a given X-ray luminosity, and vice versa, indicating little direct connection between the emission processes. We then measure the X-ray luminosity function, radio luminosity function and multi-dimensional X-ray--radio luminosity function across redshift ($0
ASASSN-24fw: An 8-month long, 4.1 mag, optically achromatic and polarized dimming event
2025-07-03
R. Forés-Toribio, B. JoHantgen, C. S. Kochanek, S. G. Jorstad, J. J. Hermes, J. D. Armstrong, C. R. Burns, E. Gaidos, W. B. Hoogendam, E. Y. Hsiao, N. Morrell, B. J. Shappee, K. Stanek, M. A. Tucker, K. Auchettl, L. Lu, D. M. Rowan, T. Vaccaro, J. P. Williams
We discuss ASASSN-24fw, a 13th-magnitude star that optically faded by $\Delta g = 4.12 \pm 0.02$ mag starting in September 2024 after over a decade of quiescence in ASAS-SN. The dimmimg lasted $\sim$8 months before returning to quiescence in late May 2025. The spectral energy distribution (SED) before the event is that of a pre-main sequence or a modestly evolved F star with some warm dust emission. The shape of the optical SED during the dim phase is unchanged and the optical and near-infrared spectra are those of an F star. The SED and the dilution of some of the F star infrared absorption features near minimum suggest the presence of a $\sim$0.25$M_\odot$ M dwarf binary companion. The 43.8 year period proposed by Nair & Denisenko (2024) appears correct and is probably half the precession period of a circumbinary disk. The optical eclipse is nearly achromatic, although slightly deeper in bluer filters, $\Delta (g-z)=0.31\pm0.15$ mag, and the $V$ band emission is polarized by up to 4%. The materials most able to produce such small optical color changes and a high polarization are big ($\sim$20 $\mu$m) carbonaceous or water ice grains. Particle distributions dominated by big grains are seen in protoplanetary disks, Saturn-like ring systems and evolved debris disks. We also carry out a survey of occultation events, finding 42 additional systems, of which only 7 (4) closely match $\varepsilon$ Aurigae (KH 15D), the two archetypes of stars with long and deep eclipses. The full sample is widely distributed in an optical color-magnitude diagram, but roughly half show a mid-IR excess. It is likely many of the others have cooler dust since it seems essential to produce the events.
Lower bound on the proper lengths of stationary bound-state charged massive scalar clouds
2025-07-03
Shahar Hod
It has recently been revealed that charged scalar clouds, spatially regular matter configurations which are made of linearized charged massive scalar fields, can be supported by spinning and charged Kerr-Newman black holes. Using analytical techniques, we establish a no-short hair theorem for these stationary bound-state field configurations. In particular, we prove that the effective proper lengths of the supported charged massive scalar clouds are bounded from below by the remarkably compact dimensionless relation $\ell/M>\ln(3+\sqrt{8})$, where $M$ is the mass of the central supporting black hole. Intriguingly, this lower bound is universal in the sense that it is valid for all Kerr-Newman black-hole spacetimes [that is, in the entire regime $\{a/M\in(0,1],Q/M\in[0,1)\}$ of the dimensionless spin and charge parameters that characterize the central supporting black holes] and for all values of the physical parameters (electric charge $q$, proper mass $\mu$, and angular harmonic indexes $\{l,m\}$) that characterize the supported stationary bound-state scalar fields.
ARF-RLHF: Adaptive Reward-Following for RLHF through Emotion-Driven Self-Supervision and Trace-Biased Dynamic Optimization
2025-07-03
YuXuan Zhang
With the rapid advancement of Reinforcement Learning from Human Feedback (RLHF) and autoregressive transformers, state-of-the-art models such as GPT-4.0, DeepSeek R1, and Llama 3.3 increasingly emphasize answer depth and personalization. However, most existing RLHF approaches (e.g., PPO, DPO) still rely on a binary-preference (BT) paradigm, which, while reducing annotation costs, still requires substantial human effort and captures only group-level tendencies rather than individual preferences. To overcome these limitations, we propose Adaptive Reward-Following (ARF), a self-assessment framework that leverages a high-precision emotion analyzer achieving over 70% accuracy on GoEmotions, Sentiment140, and DailyDialog to convert free-form user feedback into continuous preference scores. We further enrich and debias these signals through lightweight data augmentations, including synonym replacement, random trace truncation, and score bias annotation algorithm. A Dynamic Adapter Preference Tracker continuously models evolving user tastes in real time, enabling our novel Trace Bias (TB) fine-tuning algorithm to optimize directly on these tracked rewards instead of coarse binary labels. Experiments on Qwen-2/2.5, Gemma-2, and Llama-3.2 across four preference domains demonstrate that ARF achieves an improvement of 3.3% over PPO and 7.6% over DPO. Moreover, TB preserves theoretical alignment with PPO and DPO objectives. Overall, ARF presents a scalable, personalized, and cost-effective approach to RLHF LLMs through autonomous reward modeling.
Identification of Potentially Misclassified Crash Narratives using Machine Learning (ML) and Deep Learning (DL)
2025-07-03
Sudesh Bhagat, Ibne Farabi Shihab, Jonathan Wood
This research investigates the efficacy of machine learning (ML) and deep learning (DL) methods in detecting misclassified intersection-related crashes in police-reported narratives. Using 2019 crash data from the Iowa Department of Transportation, we implemented and compared a comprehensive set of models, including Support Vector Machine (SVM), XGBoost, BERT Sentence Embeddings, BERT Word Embeddings, and Albert Model. Model performance was systematically validated against expert reviews of potentially misclassified narratives, providing a rigorous assessment of classification accuracy. Results demonstrated that while traditional ML methods exhibited superior overall performance compared to some DL approaches, the Albert Model achieved the highest agreement with expert classifications (73% with Expert 1) and original tabular data (58%). Statistical analysis revealed that the Albert Model maintained performance levels similar to inter-expert consistency rates, significantly outperforming other approaches, particularly on ambiguous narratives. This work addresses a critical gap in transportation safety research through multi-modal integration analysis, which achieved a 54.2% reduction in error rates by combining narrative text with structured crash data. We conclude that hybrid approaches combining automated classification with targeted expert review offer a practical methodology for improving crash data quality, with substantial implications for transportation safety management and policy development.
High-resolution Ultraviolet-to-nearinfrared Characterization of Exoplanet Atmospheres
2025-07-03
Patricio E. Cubillos, Matteo Brogi, Antonio García Muñoz, Luca Fossati, Sudeshna Boro Saikia, Vincent Bourrier, Jose A. Caballero, Juan Cabrera, Andrea Chiavassa, Andrzej Fludra, Leonardos Gkouvelis, John Lee Grenfell, Manuel Guedel, Alvaro Labiano, Monika Lendl, Donna Rodgers-Lee, Arnaud Salvador, Ilane Schroetter, Antoine Strugarek, Benjamin Taysum, Aline Vidotto, Thomas G. Wilson
The Habitable Worlds Observatory (HWO) offers a unique opportunity to revolutionize our understanding of planetary formation and evolution. The goal of this Science Case Development Document (SCDD) is to investigate the physical and chemical processes that shape the composition and atmospheric mass loss in exoplanets. We review the key observables currently known as diagnostics of mass loss via transit observations, i.e., absorption lines of escaping hydrogen (Lyman-alpha), helium, and metals (Fe, Mg, C, O). We also explore the challenges to infer planetary formation processes based on atmospheric composition characterization. HWO could enable a broad, continuous coverage from far-ultraviolet to near-infrared spectroscopy (~100--1600 nm) at high resolution (R > 60, 000), which is essential to make these measurements, disentangle their planetary origin from stellar activity, and ultimately, contextualize the escape rates by simultaneously characterizing the composition, cloud predominance, and thermal structure of exoplanet atmospheres.
Improving LLM Reasoning for Vulnerability Detection via Group Relative Policy Optimization
2025-07-03
Marco Simoni, Aleksandar Fontana, Giulio Rossolini, Andrea Saracino
Improving and understanding the training dynamics and reasoning of Large Language Models (LLMs) has become essential for their deployment in AI-based security tools, such as software vulnerability detection. In this work, we present an extensive study aimed at advancing recent RL-based finetuning techniques for LLMs in the context of vulnerability detection. We start by highlighting key limitations of commonly adopted LLMs, such as their tendency to over-predict certain types of vulnerabilities while failing to detect others. To address this challenge, we explore the use of Group Relative Policy Optimization (GRPO), a recent policy-gradient method, for guiding LLM behavior through structured, rule-based rewards. We enable its application to the vulnerability detection task by redefining its advantage functions and reward signals using annotations from widely used datasets in the field, including BigVul, DiverseVul, and CleanVul. The proposed methodology enables an extensive set of experiments, addressing multiple research questions regarding the impact of GRPO on generalization, reasoning capabilities, and performance improvements over standard supervised finetuning (SFT). Our findings offer valuable insights into the potential of RL-based training to enhance both the performance and reasoning abilities of LLMs in the context of software vulnerability detection.
From Turing to Tomorrow: The UK's Approach to AI Regulation
2025-07-03
Oliver Ritchie, Markus Anderljung, Tom Rachman
The UK has pursued a distinctive path in AI regulation: less cautious than the EU but more willing to address risks than the US, and has emerged as a global leader in coordinating AI safety efforts. Impressive developments from companies like London-based DeepMind began to spark concerns in the UK about catastrophic risks from around 2012, although regulatory discussion at the time focussed on bias and discrimination. By 2022, these discussions had evolved into a "pro-innovation" strategy, in which the government directed existing regulators to take a light-touch approach, governing AI at point of use, but avoided regulating the technology or infrastructure directly. ChatGPT arrived in late 2022, galvanising concerns that this approach may be insufficient. The UK responded by establishing an AI Safety Institute to monitor risks and hosting the first international AI Safety Summit in 2023, but - unlike the EU - refrained from regulating frontier AI development in addition to its use. A new government was elected in 2024 which promised to address this gap, but at the time of writing is yet to do so. What should the UK do next? The government faces competing objectives: harnessing AI for economic growth and better public services while mitigating risk. In light of these, we propose establishing a flexible, principles-based regulator to oversee the most advanced AI development, defensive measures against risks from AI-enabled biological design tools, and argue that more technical work is needed to understand how to respond to AI-generated misinformation. We argue for updated legal frameworks on copyright, discrimination, and AI agents, and that regulators will have a limited but important role if AI substantially disrupts labour markets. If the UK gets AI regulation right, it could demonstrate how democratic societies can harness AI's benefits while managing its risks.
Outcome prediction and individualized treatment effect estimation in patients with large vessel occlusion stroke
2025-07-03
Lisa Herzog, Pascal Bühler, Ezequiel de la Rosa, Beate Sick, Susanne Wegener
Mechanical thrombectomy has become the standard of care in patients with stroke due to large vessel occlusion (LVO). However, only 50% of successfully treated patients show a favorable outcome. We developed and evaluated interpretable deep learning models to predict functional outcomes in terms of the modified Rankin Scale score alongside individualized treatment effects (ITEs) using data of 449 LVO stroke patients from a randomized clinical trial. Besides clinical variables, we considered non-contrast CT (NCCT) and angiography (CTA) scans which were integrated using novel foundation models to make use of advanced imaging information. Clinical variables had a good predictive power for binary functional outcome prediction (AUC of 0.719 [0.666, 0.774]) which could slightly be improved when adding CTA imaging (AUC of 0.737 [0.687, 0.795]). Adding NCCT scans or a combination of NCCT and CTA scans to clinical features yielded no improvement. The most important clinical predictor for functional outcome was pre-stroke disability. While estimated ITEs were well calibrated to the average treatment effect, discriminatory ability was limited indicated by a C-for-Benefit statistic of around 0.55 in all models. In summary, the models allowed us to jointly integrate CT imaging and clinical features while achieving state-of-the-art prediction performance and ITE estimates. Yet, further research is needed to particularly improve ITE estimation.
2025-07-03
Xinhai Hou, Akhil Kondepudi, Cheng Jiang, Yiwei Lyu, Samir Harake, Asadur Chowdury, Anna-Katharina Meißner, Volker Neuschmelting, David Reinecke, Gina Furtjes, Georg Widhalm, Lisa Irina Koerner, Jakob Straehle, Nicolas Neidert, Pierre Scheffler, Juergen Beck, Michael Ivan, Ashish Shah, Aditya Pandey, Sandra Camelo-Piragua, Dieter Henrik Heiland, Oliver Schnell, Chris Freudiger, Jacob Young, Melike Pekmezci, Katie Scotford, Shawn Hervey-Jumper, Daniel Orringer, Mitchel Berger, Todd Hollon
The importance of rapid and accurate histologic analysis of surgical tissue in the operating room has been recognized for over a century. Our standard-of-care intraoperative pathology workflow is based on light microscopy and H\&E histology, which is slow, resource-intensive, and lacks real-time digital imaging capabilities. Here, we present an emerging and innovative method for intraoperative histologic analysis, called Intelligent Histology, that integrates artificial intelligence (AI) with stimulated Raman histology (SRH). SRH is a rapid, label-free, digital imaging method for real-time microscopic tumor tissue analysis. SRH generates high-resolution digital images of surgical specimens within seconds, enabling AI-driven tumor histologic analysis, molecular classification, and tumor infiltration detection. We review the scientific background, clinical translation, and future applications of intelligent histology in tumor neurosurgery. We focus on the major scientific and clinical studies that have demonstrated the transformative potential of intelligent histology across multiple neurosurgical specialties, including neurosurgical oncology, skull base, spine oncology, pediatric tumors, and periperal nerve tumors. Future directions include the development of AI foundation models through multi-institutional datasets, incorporating clinical and radiologic data for multimodal learning, and predicting patient outcomes. Intelligent histology represents a transformative intraoperative workflow that can reinvent real-time tumor analysis for 21st century neurosurgery.
Open Source, Hidden Costs: A Systematic Literature Review on OSS License Management
2025-07-03
Boyuan Li, Chengwei Liu, Lingling Fan, Sen Chen, Zhenlin Zhang, Zheli Liu
Integrating third-party software components is a common practice in modern software development, offering significant advantages in terms of efficiency and innovation. However, this practice is fraught with risks related to software licensing. A lack of understanding may lead to disputes, which can pose serious legal and operational challenges. To these ends, both academia and industry have conducted various investigations and proposed solutions and tools to deal with these challenges. However, significant limitations still remain. Moreover, the rapid evolution of open-source software (OSS) licenses, as well as the rapidly incorporated generative software engineering techniques, such as large language models for code (CodeLLMs), are placing greater demands on the systematic management of software license risks. To unveil the severe challenges and explore possible future directions, we conduct the first systematic literature review (SLR) on 80 carefully selected OSS license-related papers, classifying existing research into three key categories, i.e., license identification, license risk assessment, and license risk mitigation. Based on these, we discuss challenges in existing solutions, conclude the opportunities to shed light on future research directions and offer practical recommendations for practitioners. We hope this thorough review will help bridge the gaps between academia and industry and accelerate the ecosystem-wide governance of legitimate software risks within the software engineering community.
The Lockman-SpReSO project. Spectroscopic analysis of Type 1 AGN
2025-07-02
Castalia Alenka Negrete, Hector J. Ibarra-Medel, Erika Benitez, Irene Cruz-Gonzalez, Yair Krongold, J. Jesus Gonzalez, Jordi Cepa, Carmen Padilla-Torres, Miguel Cervino, Mirjana Povic, Martin Herrera-Endoqui, Nancy Jenaro-Ballesteros, Takamitsu Miyaji, Mauricio Elias-Chavez, Miguel Sanchez-Portal, Bernabe Cedres, Jacub Nadolny, Mauro Gonzalez-Otero, Bereket Assefa, Hector Hernandez-Toledo, J. Antonio de Diego, J. Ignacio Gonzalez-Serrano, A. M. Perez Garcia
We present the first optical-UV spectral systematic analysis of 30 Type 1 AGN selected in the FIR and X-ray in the Lockman-SpReSO Survey. The sample of faint objects (m_B = 19.6-21.8) covers a large redshift range of 0.33 > z > 4.97 with high S/N (~21 on average). A detailed spectral analysis based on the Quasar Main Sequence phenomenology prescription was applied to deblend the principal optical-UV emitting regions. Our sample spans a bolometric luminosity range of 44.85 < log Lbol < 47.87, absolute B-magnitude -20.46 > M_B > -26.14, BH mass of 7.59 < log MBH < 9.80, and Eddington ratio -1.70 < log REdd < 0.56. The analysis shows that 18 high-z objects correspond to Population B, whereas three low-z fall in Populations A2, B1, and B1+. The remaining eight are candidates to be Pop. B and one Pop. A object. None of them are extreme accretors. We looked for tendencies in our sample and compared them with other samples with different selection criteria. Evidence for winds was explored using CIV1549 line half-height centroid cmed finding wind velocities between 941 and -1587 kms-1. This result is consistent with samples with similar ranges of z and M_B. The Baldwin effect showed a slope of -0.23 pm 0.03 dex consistent with previous studies. Spectra from twelve objects in our sample were found in the Sloan Digital Sky Survey Data Release 17 database. We applied the same methodology to compare them to our spectra, finding no evidence of variability.
SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars
2025-07-02
Xiaosheng Zhao, Yang Huang, Guirong Xue, Xiao Kong, Jifeng Liu, Xiaoyu Tang, Timothy C. Beers, Yuan-Sen Ting, A-Li Luo
In recent years, large language models (LLMs) have transformed natural language understanding through vast datasets and large-scale parameterization. Inspired by this success, we present SpecCLIP, a foundation model framework that extends LLM-inspired methodologies to stellar spectral analysis. Stellar spectra, akin to structured language, encode rich physical and chemical information about stars. By training foundation models on large-scale spectral datasets, our goal is to learn robust and informative embeddings that support diverse downstream applications. As a proof of concept, SpecCLIP involves pre-training on two spectral types--LAMOST low-resolution and Gaia XP--followed by contrastive alignment using the CLIP (Contrastive Language-Image Pre-training) framework, adapted to associate spectra from different instruments. This alignment is complemented by auxiliary decoders that preserve spectrum-specific information and enable translation (prediction) between spectral types, with the former achieved by maximizing mutual information between embeddings and input spectra. The result is a cross-spectrum framework enabling intrinsic calibration and flexible applications across instruments. We demonstrate that fine-tuning these models on moderate-sized labeled datasets improves adaptability to tasks such as stellar-parameter estimation and chemical-abundance determination. SpecCLIP also enhances the accuracy and precision of parameter estimates benchmarked against external survey data. Additionally, its similarity search and cross-spectrum prediction capabilities offer potential for anomaly detection. Our results suggest that contrastively trained foundation models enriched with spectrum-aware decoders can advance precision stellar spectroscopy.
The Thin Line Between Comprehension and Persuasion in LLMs
2025-07-02
Adrian de Wynter, Tangming Yuan
Large language models (LLMs) are excellent at maintaining high-level, convincing dialogues. They are being fast deployed as chatbots and evaluators in sensitive areas, such as peer review and mental health applications. This, along with the disparate accounts on their reasoning capabilities, calls for a closer examination of LLMs and their comprehension of dialogue. In this work we begin by evaluating LLMs' ability to maintain a debate--one of the purest yet most complex forms of human communication. Then we measure how this capability relates to their understanding of what is being talked about, namely, their comprehension of dialogical structures and the pragmatic context. We find that LLMs are capable of maintaining coherent, persuasive debates, often swaying the beliefs of participants and audiences alike. We also note that awareness or suspicion of AI involvement encourage people to be more critical of the arguments made. When polling LLMs on their comprehension of deeper structures of dialogue, however, they cannot demonstrate said understanding. Our findings tie the shortcomings of LLMs-as-evaluators to their (in)ability to understand the context. More broadly, for the field of argumentation theory we posit that, if an agent can convincingly maintain a dialogue, it is not necessary for it to know what it is talking about. Hence, the modelling of pragmatic context and coherence are secondary to effectiveness.
A Survey on Vision-Language-Action Models: An Action Tokenization Perspective
2025-07-02
Yifan Zhong, Fengshuo Bai, Shaofei Cai, Xuchuan Huang, Zhang Chen, Xiaowei Zhang, Yuanfei Wang, Shaoyang Guo, Tianrui Guan, Ka Nam Lui, Zhiquan Qi, Yitao Liang, Yuanpei Chen, Yaodong Yang
The remarkable advancements of vision and language foundation models in multimodal understanding, reasoning, and generation has sparked growing efforts to extend such intelligence to the physical world, fueling the flourishing of vision-language-action (VLA) models. Despite seemingly diverse approaches, we observe that current VLA models can be unified under a single framework: vision and language inputs are processed by a series of VLA modules, producing a chain of \textit{action tokens} that progressively encode more grounded and actionable information, ultimately generating executable actions. We further determine that the primary design choice distinguishing VLA models lies in how action tokens are formulated, which can be categorized into language description, code, affordance, trajectory, goal state, latent representation, raw action, and reasoning. However, there remains a lack of comprehensive understanding regarding action tokens, significantly impeding effective VLA development and obscuring future directions. Therefore, this survey aims to categorize and interpret existing VLA research through the lens of action tokenization, distill the strengths and limitations of each token type, and identify areas for improvement. Through this systematic review and analysis, we offer a synthesized outlook on the broader evolution of VLA models, highlight underexplored yet promising directions, and contribute guidance for future research, hoping to bring the field closer to general-purpose intelligence.
Attosecond Control and Measurement of Chiral Photoionisation Dynamics
2025-07-02
Meng Han, Jia-Bao Ji, Alexander Blech, R. Esteban Goetz, Corbin Allison, Loren Greenman, Christiane P. Koch, Hans Jakob Wörner
Many chirality-sensitive light-matter interactions are governed by chiral electron dynamics. Therefore, the development of advanced technologies harnessing chiral phenomena would critically benefit from measuring and controlling chiral electron dynamics on their natural attosecond time scales. Such endeavors have so far been hampered by the lack of characterized circularly polarized attosecond pulses, an obstacle that has recently been overcome (Han et al. Optica 10 (2023) 1044-1052, Han et al. Nature Physics 19 (2023) 230-236). In this article, we introduce chiroptical spectroscopy with attosecond pulses and demonstrate attosecond coherent control over photoelectron circular dichroism (PECD) (Goetz et al. Physical Review Letters 122 (2019) 013204, Goetz et al. arXiv:2104.07522), as well as the measurement of chiral asymmetries in the forward-backward and angle-resolved photoionisation delays of chiral molecules. We show that co-rotating attosecond and near-infrared pulses can nearly double the PECD and even change its sign compared to single-photon ionisation. We demonstrate that chiral photoionisation delays depend on both polar and azimuthal angles of photoemission in the light-propagation frame, requiring three-dimensional momentum resolution. We measure forward-backward chiral-sensitive delays of up to 120 as and polar-angle-resolved photoionisation delays up to 240 as, which include an asymmmetry of $\sim$60 as originating from chirality in the continuum-continuum transitions. Attosecond chiroptical spectroscopy opens the door to quantitatively understanding and controlling the dynamics of chiral molecules on the electronic time scale.
Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs) such as OpenAI-o1 and DeepSeek-R1, have demonstrated remarkable capabilities in complex domains such as logical reasoning and experimental coding. Motivated by these advancements, numerous studies have explored the application of AI in the innovation process, particularly in the context of scientific research. These AI technologies primarily aim to develop systems that can autonomously conduct research processes across a wide range of scientific disciplines. Despite these significant strides, a comprehensive survey on AI for Research (AI4Research) remains absent, which hampers our understanding and impedes further development in this field. To address this gap, we present a comprehensive survey and offer a unified perspective on AI4Research. Specifically, the main contributions of our work are as follows: (1) Systematic taxonomy: We first introduce a systematic taxonomy to classify five mainstream tasks in AI4Research. (2) New frontiers: Then, we identify key research gaps and highlight promising future directions, focusing on the rigor and scalability of automated experiments, as well as the societal impact. (3) Abundant applications and resources: Finally, we compile a wealth of resources, including relevant multidisciplinary applications, data corpora, and tools. We hope our work will provide the research community with quick access to these resources and stimulate innovative breakthroughs in AI4Research.
2025-07-02
Nina R. Benway, Saba Tabatabaee, Dongliang Wang, Benjamin Munson, Jonathan L. Preston, Carol Espy-Wilson
Purpose: This study evaluated whether articulatory kinematics, inferred by Articulatory Phonology speech inversion neural networks, aligned with perceptual ratings of /r/ and /s/ in the speech of children with speech sound disorders. Methods: Articulatory Phonology vocal tract variables were inferred for 5,961 utterances from 118 children and 3 adults, aged 2.25-45 years. Perceptual ratings were standardized using the novel 5-point PERCEPT Rating Scale and training protocol. Two research questions examined if the articulatory patterns of inferred vocal tract variables aligned with the perceptual error category for the phones investigated (e.g., tongue tip is more anterior in dentalized /s/ productions than in correct /s/). A third research question examined if gradient PERCEPT Rating Scale scores predicted articulatory proximity to correct productions. Results: Estimated marginal means from linear mixed models supported 17 of 18 /r/ hypotheses, involving tongue tip and tongue body constrictions. For /s/, estimated marginal means from a second linear mixed model supported 7 of 15 hypotheses, particularly those related to the tongue tip. A third linear mixed model revealed that PERCEPT Rating Scale scores significantly predicted articulatory proximity of errored phones to correct productions. Conclusion: Inferred vocal tract variables differentiated category and magnitude of articulatory errors for /r/, and to a lesser extent for /s/, aligning with perceptual judgments. These findings support the clinical interpretability of speech inversion vocal tract variables and the PERCEPT Rating Scale in quantifying articulatory proximity to the target sound, particularly for /r/.
DIY-MKG: An LLM-Based Polyglot Language Learning System
2025-07-02
Kenan Tang, Yanhong Li, Yao Qin
Existing language learning tools, even those powered by Large Language Models (LLMs), often lack support for polyglot learners to build linguistic connections across vocabularies in multiple languages, provide limited customization for individual learning paces or needs, and suffer from detrimental cognitive offloading. To address these limitations, we design Do-It-Yourself Multilingual Knowledge Graph (DIY-MKG), an open-source system that supports polyglot language learning. DIY-MKG allows the user to build personalized vocabulary knowledge graphs, which are constructed by selective expansion with related words suggested by an LLM. The system further enhances learning through rich annotation capabilities and an adaptive review module that leverages LLMs for dynamic, personalized quiz generation. In addition, DIY-MKG allows users to flag incorrect quiz questions, simultaneously increasing user engagement and providing a feedback loop for prompt refinement. Our evaluation of LLM-based components in DIY-MKG shows that vocabulary expansion is reliable and fair across multiple languages, and that the generated quizzes are highly accurate, validating the robustness of DIY-MKG.
The TESS Grand Unified Hot Jupiter Survey. III. Thirty More Giant Planets
2025-07-02
Samuel W. Yee, Joshua N. Winn, Joel D. Hartman, Joseph E. Rodriguez, George Zhou, David W. Latham, Samuel N. Quinn, Allyson Bieryla, Karen A. Collins, Jason D. Eastman, Kevin I. Collins, Dennis M. Conti, Eric L. N. Jensen, David R. Anderson, Özgür Baştürk, David Baker, Khalid Barkaoui, Matthew P. Battley, Daniel Bayliss, Thomas G. Beatty, Yuri Beletsky, Alexander A. Belinski, Zouhair Benkhaldoun, Paul Benni, Pau Bosch-Cabot, César Briceño, Andrzej Brudny, Matthew R. Burleigh, R. Paul Butler, Stavros Chairetas, Ashley Chontos, Jessie Christiansen, David R. Ciardi, Catherine A. Clark, Ryan Cloutier, Matthew W. Craig, Jeffrey D. Crane, Nicholas Dowling, Courtney D. Dressing, Jehin Emmanuel, Phil Evans, Mark E. Everett, Gareb Fernández-Rodríguez, Jorge Fernández Fernández, Raquel Forés-Toribio, Charles D. Fortenbach, Akihiko Fukui, Elise Furlan, Tianjun Gan, Mourad Ghachoui, Steven Giacalone, Samuel Gill, Michaël Gillon, Kylie Hall, Yuya Hayashi, Christina Hedges, Jesus Higuera, Eric G. Hintz, Lea Hirsch, Rae Holcomb, Keith Horne, Ferran Grau Horta, Andrew W. Howard, Steve B. Howell, Howard Isaacson, Jon M. Jenkins, Taiki Kagetani, Jacob Kamler, Alicia Kendall, Judth Korth, Maxwell A. Kroft, Gaia Lacedelli, Didier Laloum, Nicholas Law, Jerome Pitogo de Leon, Alan M. Levine, Pablo Lewin, Sarah E. Logsdon, Michael B. Lund, Madelyn M. Madsen, Andrew W. Mann, Christopher R. Mann, Nataliia A. Maslennikova, Sandra Matutano, Mason McCormack, Kim K. McLeod, Edward J. Michaels, Ismael Mireles, Mayuko Mori, Jose A. Muñoz, Felipe Murgas, Norio Narita, Sean M. O'Brien, Caroline Odden, Enric Palle, Yatrik G. Patel, Peter Plavchan, Alex S. Polanski, Adam Popowicz, Don J. Radford, Phillip A. Reed, Howard M. Relles, Malena Rice, George R. Ricker, Boris S. Safonov, Arjun B. Savel, Jack Schulte, Richard P. Schwarz, Heidi Schweiker, Sara Seager, Ramotholo Sefako, Stephen A. Shectman, Avi Shporer, Denise C. Stephens, Chris Stockdale, Stephanie Striegel, Thiam-Guan Tan, Johanna K. Teske, Mathilde Timmermans, Solène Ulmer-Moll, Gavin Wang, Peter J. Wheatley, Selçuk Yalcinkaya, Roberto Zambelli, Judah Van Zandt, Carl Ziegler
We present the discovery of 30 transiting giant planets that were initially detected using data from NASA's Transiting Exoplanet Survey Satellite (TESS) mission. These new planets orbit relatively bright ($G \leq 12.5$) FGK host stars with orbital periods between 1.6 and 8.2 days, and have radii between 0.9 and 1.7 Jupiter radii. We performed follow-up ground-based photometry, high angular-resolution imaging, high-resolution spectroscopy and radial velocity monitoring for each of these objects to confirm that they are planets and determine their masses and other system parameters. The planets' masses span more than an order of magnitude ($0.17\,M_J < M_p < 3.3\,M_J$). For two planets, TOI-3593 b and TOI-4961 b, we measured significant non-zero eccentricities of $0.11^{+0.05}_{-0.03}$ and $0.18^{+0.04}_{-0.05}$ respectively, while for the other planets, the data typically provide a 1-$\sigma$ upper bound of 0.15 on the eccentricity. These discoveries represent a major step toward assembling a complete, magnitude-limited sample of transiting hot Jupiters around FGK stars.
An integrated data-driven approach combined with a high-throughput framework based on first-principles calculations was used to discover novel rare-earth-free permanent magnets, focusing on binary alloys. Compounds were screened systematically based on their elemental composition, structure, stability, and magnetization. Density functional theory (DFT) calculations were performed on the selected candidates to evaluate their magnetocrystalline anisotropy energy (MAE) and Curie temperature (Tc), resulting in the identification of ten promising materials. A thorough literature review was done to assess reports of prior existence, which confirmed the novelty of ZnFe and Fe8N. Their ferromagnetic ground state was re-established through DFT, and structural stability was confirmed via negative formation enthalpies, phonon spectra, and elastic criteria. Tetragonal ZnFe and Fe8N exhibit high saturation magnetization (>1 T), large anisotropy constants (>0.5 MJ/m^3), and high Tc (>1200 K). Their magnetic hardness parameters (kappa = 0.85 for ZnFe and 0.70 for Fe8N) further support their potential as gap magnets. These findings highlight the efficacy of our high-throughput screening, which may serve as a theoretical blueprint for the experimental realization of these materials.
MoIRA: Modular Instruction Routing Architecture for Multi-Task Robotics
2025-07-02
Dmytro Kuzmenko, Nadiya Shvai
Mixture-of-Experts (MoE) approaches have recently gained traction in robotics applications due to their ability to dynamically allocate computational resources and specialize sub-networks for distinct tasks or environmental contexts, enabling more efficient decision-making. Such systems often comprise sparsely activated experts combined under a single monolithic architecture and require a well-configured internal routing mechanism, which does not allow for selective low-level expert and router customization and requires additional training. We propose MoIRA, an architecture-agnostic modular MoE framework designed to coordinate existing experts with an external text-based router. MoIRA incorporates two zero-shot routing options: embedding-based similarity and prompt-driven language model inference. In our experiments, we choose large Vision-Language-Action models, gr00t-N1 and $\pi_0$, as the underlying experts, and train low-rank adapters for low-overhead inference. We evaluate MoIRA on various GR1 Humanoid tasks and LIBERO Spatial and Goal benchmarks, where it consistently outperforms generalist models and competes with other MoE pipelines. Additionally, we analyse the robustness of the proposed approach to the variations of the instructions. While relying solely on textual descriptions of tasks and experts, MoIRA demonstrates the practical viability of modular deployment with precise, low-effort routing and provides an alternative, scalable foundation for future multi-expert robotic systems.
We present a novel approach to knowledge transfer in model-based reinforcement learning, addressing the critical challenge of deploying large world models in resource-constrained environments. Our method efficiently distills a high-capacity multi-task agent (317M parameters) into a compact model (1M parameters) on the MT30 benchmark, significantly improving performance across diverse tasks. Our distilled model achieves a state-of-the-art normalized score of 28.45, surpassing the original 1M parameter model score of 18.93. This improvement demonstrates the ability of our distillation technique to capture and consolidate complex multi-task knowledge. We further optimize the distilled model through FP16 post-training quantization, reducing its size by $\sim$50\%. Our approach addresses practical deployment limitations and offers insights into knowledge representation in large world models, paving the way for more efficient and accessible multi-task reinforcement learning systems in robotics and other resource-constrained applications. Code available at https://github.com/dmytro-kuzmenko/td-mpc-opt.
The Cosmological analysis of X-ray cluster surveys VII. Bypassing scaling relations with Lagrangian Deep Learning and Simulation-based inference
2025-07-02
Nicolas Cerardi, Marguerite Pierre, François Lanusse, Xavier Corap
Galaxy clusters, the pinnacle of structure formation in our universe, are a powerful cosmological probe. Several approaches have been proposed to express cluster number counts, but all these methods rely on empirical explicit scaling relations that link observed properties to the total cluster mass. These scaling relations are over-parametrised, inducing some degeneracy with cosmology. Moreover, they do not provide a direct handle on the numerous non-gravitational phenomena that affect the physics of the intra-cluster medium. We present a proof-of-concept to model cluster number counts, that bypasses the explicit use of scaling relations. We rather implement the effect of several astrophysical processes to describe the cluster properties. We then evaluate the performances of this modelling for the cosmological inference. We developed an accelerated machine learning baryonic field-emulator, built upon the Lagrangian Deep Learning method and trained on the CAMELS simulations. We then created a pipeline that simulates cluster counts in terms of XMM observable quantities. We finally compare the performances of our model, with that involving scaling relations, for the purpose of cosmological inference based on simulations. Our model correctly reproduces the cluster population from the calibration simulations at the fiducial parameter values, and allows us to constrain feedback mechanisms. The cosmological-inference analyses indicate that our simulation-based model is less degenerate than the approach using scaling relations. This novel approach to model observed cluster number counts from simulations opens interesting perspectives for cluster cosmology. It has the potential to overcome the limitations of the standard approach, provided that the resolution and the volume of the simulations will allow a most realistic implementation of the complex phenomena driving cluster evolution.
Where are all the dark galaxies? Predicting galaxy/halo locations from their bright neighbors
2025-07-02
Alice Chen, Niayesh Afshordi
Astronomical objects in our universe that are too faint to be directly detectable exist and are important - an obvious example being dark matter. The same can also apply to very faint baryonic objects, such as low luminosity dwarf galaxies and gravitationally compact objects (e.g., rogue planets, white dwarfs, neutron stars, black holes, dark sirens). While they are very difficult to observe directly, they have locations that are highly important when studying astrophysical phenomena. Here, we use a machine learning algorithm known as symbolic regression to model the probability of a dark object's existence as a function of their separation distances to their closest two ``bright" (directly observable) neighbors, and the distances of these bright objects to each other. An advantage of this algorithm is that it is interpretable by humans and can be used to make reproducible predictions. Galaxies with masses above $10^9 M_{\odot}$ and halos above $10^{12} M_{\odot} $ are the objects that we separate into ``bright" and ``dark" to be used in our analysis. We find that it is possible to predict the density of dark objects using an analytic expression that depends on their distances to their closest bright neighbors in Illustris-TNG galaxy formation simulations, which is significantly better than the (linear) scale-dependent biasing prediction for $k \sim 1.0~ h$Mpc$^{-1}$ (and potentially beyond, if allowed by the resolution). This could potentially open the avenue for finding dark objects based on their vicinity to directly observable bright sources and make future surveys more targeted and efficient.
Frontiers of Generative AI for Network Optimization: Theories, Limits, and Visions
2025-07-02
Bo Yang, Ruihuai Liang, Weixin Li, Han Wang, Xuelin Cao, Zhiwen Yu, Samson Lasaulce, Mérouane Debbah, Mohamed-Slim Alouini, H. Vincent Poor, Chau Yuen
While interest in the application of generative AI (GenAI) in network optimization has surged in recent years, its rapid progress has often overshadowed critical limitations intrinsic to generative models that remain insufficiently examined in existing literature. This survey provides a comprehensive review and critical analysis of GenAI in network optimization. We focus on the two dominant paradigms of GenAI including generative diffusion models (GDMs) and large pre-trained models (LPTMs), and organize our discussion around a categorization we introduce, dividing network optimization problems into two primary formulations: one-shot optimization and Markov decision process (MDP). We first trace key works, including foundational contributions from the AI community, and categorize current efforts in network optimization. We also review frontier applications of GDMs and LPTMs in other networking tasks, providing additional context. Furthermore, we present theoretical generalization bounds for GDMs in both one-shot and MDP settings, offering insights into the fundamental factors affecting model performance. Most importantly, we reflect on the overestimated perception of GenAI's general capabilities and caution against the all-in-one illusion it may convey. We highlight critical limitations, including difficulties in constraint satisfying, limited concept understanding, and the inherent probabilistic nature of outputs. We also propose key future directions, such as bridging the gap between generation and optimization. Although they are increasingly integrated in implementations, they differ fundamentally in both objectives and underlying mechanisms, necessitating a deeper understanding of their theoretical connections. Ultimately, this survey aims to provide a structured overview and a deeper insight into the strengths, limitations, and potential of GenAI in network optimization.
LLMs for Legal Subsumption in German Employment Contracts
2025-07-02
Oliver Wardas, Florian Matthes
Legal work, characterized by its text-heavy and resource-intensive nature, presents unique challenges and opportunities for NLP research. While data-driven approaches have advanced the field, their lack of interpretability and trustworthiness limits their applicability in dynamic legal environments. To address these issues, we collaborated with legal experts to extend an existing dataset and explored the use of Large Language Models (LLMs) and in-context learning to evaluate the legality of clauses in German employment contracts. Our work evaluates the ability of different LLMs to classify clauses as "valid," "unfair," or "void" under three legal context variants: no legal context, full-text sources of laws and court rulings, and distilled versions of these (referred to as examination guidelines). Results show that full-text sources moderately improve performance, while examination guidelines significantly enhance recall for void clauses and weighted F1-Score, reaching 80\%. Despite these advancements, LLMs' performance when using full-text sources remains substantially below that of human lawyers. We contribute an extended dataset, including examination guidelines, referenced legal sources, and corresponding annotations, alongside our code and all log files. Our findings highlight the potential of LLMs to assist lawyers in contract legality review while also underscoring the limitations of the methods presented.
More sophisticated is not always better: comparison of similarity measures for unsupervised learning of pathways in biomolecular simulations
2025-07-02
Miriam Jäger, Steffen Wolf
Finding process pathways in molecular simulations such as the unbinding paths of small molecule ligands from their binding sites at protein targets in a set of trajectories via unsupervised learning approaches requires the definition of a suitable similarity measure between trajectories. We here evaluate the performance of four such measures with varying degree of sophistication, i.e., Euclidean and Wasserstein distances, Procrustes analysis and dynamical time warping, when analyzing trajectory data from two different biased simulation driving protocols in the form of constant velocity constraint targeted MD and steered MD. In a streptavidin-biotin benchmark system with known ground truth clusters, Wasserstein distances yielded the best clustering performance, closely followed by Euclidean distances, both being the most computationally efficient similarity measures. In a more complex A2a receptor-inhibitor system, however, the simplest measure, i.e., Euclidean distances, was sufficient to reveal meaningful and interpretable clusters.
Context: Weak gravitational lensing is a key cosmological probe for current and future large-scale surveys. While power spectra are commonly used for analyses, they fail to capture non-Gaussian information from nonlinear structure formation, necessitating higher-order statistics and methods for efficient map generation. Aims: To develop an emulator that generates accurate convergence maps directly from an input power spectrum and wavelet l1-norm without relying on computationally intensive simulations. Methods: We use either numerical or theoretical predictions to construct convergence maps by iteratively adjusting wavelet coefficients to match target marginal distributions and their inter-scale dependencies, incorporating higher-order statistical information. Results: The resulting kappa maps accurately reproduce the input power spectrum and exhibit higher-order statistical properties consistent with the input predictions, providing an efficient tool for weak lensing analyses.
The SOFIA Massive (SOMA) Star Formation Q-band Follow-up. II. Hydrogen Recombination Lines Toward High-Mass Protostars
2025-07-02
Prasanta Gorai, Kotomi Taniguchi, Jonathan C. Tan, Miguel Gomez-Garrido, Viviana Rosero, Izaskun Jimenez-Serra, Yichen Zhang, Giuliana Cosentino, Chi-Yan Law, Ruben Fedriani, Gemma Busquet, Brandt A. L. Gaches, Maryam Saberi, Ankan Das
Hydrogen recombination lines (HRLs) are valuable diagnostics of the physical conditions in ionized regions around high-mass stars. Understanding their broadening mechanisms and intensity trends can provide insights into the densities, temperatures, and kinematics of HII regions. We investigate the properties of ionized gas around massive protostars by analyzing hydrogen recombination lines (H-alpha and H-beta) in the Q-band. Observations were conducted using the Yebes 40m radio telescope in the Q-band (30.5~50 GHz) toward six high-mass protostars selected from the SOMA Survey (G45.12+0.13, G45.47+0.05, G28.20-0.05, G35.20-0.74, G19.08-0.29, and G31.28+0.06). The line profiles were analyzed to assess broadening mechanisms, from which electron densities and temperatures were derived. We compared our results with Q-band data from the TianMa 65m Radio Telescope (TMRT) and ALMA Band 1 Science Verification observations of Orion KL. A total of eight H-alpha (n = 51 to 58) and ten H-beta (n = 64 to 73) lines were detected toward G45.12+0.13, G45.47+0.05, and G28.20-0.05, with non-detections in the other sources. Electron densities of \sim \mathrm{1\text{--}5 \times 10^{6}~cm^{-3}} and temperatures of 8000-10000 K were derived. Orion KL shows one order of magnitude lower electron density, but a similar temperature. Notably, G45.12 and G28.20 show increasing intensity with frequency for both H-alpha and H-beta, in contrast to the decreasing trend in Orion KL. The observed line widths indicate contributions from both thermal and dynamical broadening, suggesting high-temperature ionized gas affected by turbulence, outflows, rotation, or stellar winds. Pressure broadening may also play a minor role. The contrasting intensity trends likely reflect differences in local physical conditions or radiative transfer effects, warranting further study through higher-resolution observations and modeling.
Graph Representation-based Model Poisoning on Federated LLMs in CyberEdge Networks
2025-07-02
Hanlin Cai, Haofan Dong, Houtianfu Wang, Kai Li, Ozgur B. Akan
Federated large language models (FedLLMs) provide powerful generative capabilities in CyberEdge networks while protecting data privacy. However, FedLLMs remains highly vulnerable to model poisoning attacks. This article first reviews recent model poisoning techniques and existing defense mechanisms for FedLLMs, highlighting critical limitations, particularly under non-IID text distributions. In particular, current defenses primarily utilize distance-based outlier detection or norm constraints, operating under the assumption that adversarial updates significantly diverge from benign statistics. This assumption can fail when facing adaptive attackers targeting billionparameter LLMs. Next, this article investigates emerging Graph Representation-Based Model Poisoning (GRMP), a novel attack paradigm that leverages higher-order correlations among honest client gradients to synthesize malicious updates indistinguishable from legitimate model updates. GRMP can effectively evade advanced defenses, resulting in substantial accuracy loss and performance degradation. Moreover, this article outlines a research roadmap emphasizing the importance of graph-aware secure aggregation methods, FedLLMs-specific vulnerability metrics, and evaluation frameworks to strengthen the robustness of future federated language model deployments.
Luminosity Function of Galaxies in Voids: A Modification Inspired by Excursion Set Theory
2025-07-02
Zeynab Ashurikisomi, Shant Baghram
In the standard picture of cosmology, the galaxies reside in dark matter (DM) halos. DM halos are distributed in the cosmic web in different environments. The luminosity of the galaxies in different environments can be used as a probe to assess a cosmological model. This study focuses on the properties of galaxies in void regions, where halos typically do not experience extreme conditions. By examining the galaxy luminosity function, we aim to understand the dependence of galaxy properties on their environment and redshift so that later, we could use this as a tool to evaluate cosmological models. We employ the Excursion Set Theory to incorporate parameters related to the number density of DM halos into the luminosity function. Using the Galaxy and Mass Assembly (GAMA) survey and 2dFGRS datasets, we fit our theoretical models to observational data, examining the environmental and redshift dependence of the galaxy luminosity function. Our results indicate that we model the galaxy luminosity function in voids effectively by considering the linear density contrast of the environment and the growth function $D(z)$ for redshift dependence. This study provides a model for the environmental dependence of galaxy luminosity function that offers an improvement in the $\chi ^2$ parameter compared to the previously proposed model in \cite{mcnaught2014galaxy}. Both BIC and AIC tests support the superiority of this model for the void region.
We study the protoplanetary disk lifetimes using a large sample of young stellar objects in nearby clusters. To investigate the final phase of disk dissipation, we selected 32 clusters, located within 500 pc and aged between 1 and 100 Myr, with membership determined using Gaia data. The Age and mass information of the sources are obtained through spectral energy distribution (SED) analysis and using evolutionary models of various ages. Using the IR data from 2MASS and WISE catalogues, we employ three methods to identify disks across the different wavelength regimes (1.1- 22 $\mu$m). We find that disk fraction consistently decreases as stellar systems age, a trend observed across all wavelengths included in this study. However, there is an increase in the time scale of disk decay as wavelength increases, with characteristic timescales of $\tau_{\text{short}}$ = 1.6 $\pm$ 0.1 Myr for shorter wavelengths (1.6-4.6 $\mu$m) versus $\tau_{\text{W3}}$ = 4.4 $\pm$ 0.3 Myr for 12 $\mu$m. This supports the idea that outer disk regions evolve more slowly. Notably, we detect infrared excesses at 12 $\mu$m and 22 $\mu$m in relatively older systems ($>$10 Myr), with some disks with estimated ages up to $\sim$ 100 Myr. Among these, we identify a population of full disks that persist beyond the typical dissipation timescale. We also observe that the median mass of disk-hosting stars decreases from 0.62 $M_\odot$ to 0.27 $M_\odot$ in clusters younger and older than 40 Myr, respectively, indicating slower disk dissipation around lower-mass stars. We identify 33 transitional disk candidates using various color-color diagrams. Using LAMOST DR8 optical spectra and H-alpha equivalent widths, we identify possible accretors and estimate their mass accretion rates, finding most are younger than 10 Myr.
QHARMA-GAN: Quasi-Harmonic Neural Vocoder based on Autoregressive Moving Average Model
2025-07-02
Shaowen Chen, Tomoki Toda
Vocoders, encoding speech signals into acoustic features and allowing for speech signal reconstruction from them, have been studied for decades. Recently, the rise of deep learning has particularly driven the development of neural vocoders to generate high-quality speech signals. On the other hand, the existing end-to-end neural vocoders suffer from a black-box nature that blinds the speech production mechanism and the intrinsic structure of speech, resulting in the ambiguity of separately modeling source excitation and resonance characteristics and the loss of flexibly synthesizing or modifying speech with high quality. Moreover, their sequence-wise waveform generation usually requires complicated networks, leading to substantial time consumption. In this work, inspired by the quasi-harmonic model (QHM) that represents speech as sparse components, we combine the neural network and QHM synthesis process to propose a novel framework for the neural vocoder. Accordingly, speech signals can be encoded into autoregressive moving average (ARMA) functions to model the resonance characteristics, yielding accurate estimates of the amplitudes and phases of quasi-harmonics at any frequency. Subsequently, the speech can be resynthesized and arbitrarily modified in terms of pitch shifting and time stretching with high quality, whereas the time consumption and network size decrease. The experiments indicate that the proposed method leverages the strengths of QHM, the ARMA model, and neural networks, leading to the outperformance of our methods over other methods in terms of generation speed, synthesis quality, and modification flexibility.
Cosmological constraints from galaxy clustering and galaxy-galaxy lensing with extended SubHalo Abundance Matching
2025-07-02
Constance Mahony, Sergio Contreras, Raul E. Angulo, David Alonso, Christos Georgiou, Andrej Dvornik
We present the first cosmological constraints from a joint analysis of galaxy clustering and galaxy-galaxy lensing using extended SubHalo Abundance Matching (SHAMe). We analyse stellar mass-selected Galaxy And Mass Assembly (GAMA) galaxy clustering and Kilo-Degree Survey (KiDS-1000) galaxy-galaxy lensing and find constraints on $S_8\equiv\sigma_8\sqrt{\Omega_{\rm m}/0.3}=0.793^{+0.025}_{-0.024}$, in agreement with Planck at 1.7$\sigma$, and consistent with previous results. We are able to constrain all 5 SHAMe parameters, which describe the galaxy-subhalo connection. We validate our methodology by first applying it to simulated catalogues, generated from the TNG300 simulation, which mimic the stellar mass selection of our real data. We show that we are able to recover the input cosmology for both our fiducial and all-scale analyses. Our all-scale analysis extends to scales of galaxy-galaxy lensing below $r_\mathrm{p}<1.4\,\mathrm{Mpc}/h$, which we exclude in our fiducial analysis to avoid baryonic effects. When including all scales, we find a value of $S_8$, which is 1.26$\sigma$ higher than our fiducial result (against naive expectations where baryonic feedback should lead to small-scale power suppression), and in agreement with Planck at 0.9$\sigma$. We also find a 21% tighter constraint on $S_8$ and a 29% tighter constraint on $\Omega_\mathrm{m}$ compared to our fiducial analysis. This work shows the power and potential of joint small-scale galaxy clustering and galaxy-galaxy lensing analyses using SHAMe.
DESI DR2 reference mocks: clustering results from Uchuu-BGS and LRG
2025-07-02
E. Fernández-García, F. Prada, A. Smith, J. DeRose, A. J. Ross, S. Bailey, M. S. Wang, Z. Ding, C. Guandalin, C. Lamman, R. Vaisakh, R. Kehoe, J. Lasker, T. Ishiyama, S. M. Moore, S. Cole, M. Siudek, A. Amalbert, A. Salcedo, A. Hearin, B. Joachimi, A. Rocher, S. Saito, A. Krolewski, Z. Slepian, Q. Li, K. S. Dawson, E. Jullo, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, A. de la Macorra, P. Doel, S. Ferraro, A. Font-Ribera, J. E. Forero-Romero, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, M. Ishak, R. Joyce, S. Juneau, D. Kirkby, T. Kisner, A. Kremin, O. Lahav, A. Lambert, M. Landriau, M. E. Levi, M. Manera, R. Miquel, J. Moustakas, S. Nadathur, W. J. Percival, I. Pérez-Ràfols, G. Rossi, E. Sanchez, D. Schlegel, H. Seo, J. Silber, D. Sprayberry, G. Tarlé, B. A. Weaver, P. Zarrouk, R. Zhou
The aim of this work is to construct mock galaxy catalogues that accurately reproduce the redshift evolution of galaxy number density, clustering statistics, and baryonic properties, such as stellar mass for luminous red galaxies (LRGs) and absolute magnitude in the $r$-band for the bright galaxy sample (BGS), based on the first three years of observations from the Dark Energy Spectroscopic Instrument (DESI). To achieve this, we applied the subhalo abundance matching (SHAM) technique to the Uchuu $N$-body simulation, which follows the evolution of 2.1 trillion particles within a volume of $8\,h^{-3}\,\mathrm{Gpc}^{3}$, assuming a Planck base-$\Lambda$CDM cosmology. Using SHAM, we populated Uchuu subhalos with LRGs and BGS-BRIGHT ($r<19.5$) galaxies up to redshift $z=1.1$, assigning stellar masses to LRGs and luminosities to BGS galaxies (up to $M_{\rm r}\leq 20$). Furthermore, we analyzed the clustering dependence on stellar mass and luminosity for each tracer. Our results show that the Uchuu BGS-BRIGHT and LRG mocks accurately reproduce the observed redshift evolution of clustering, with better than 5\% agreement for separations of $1
Extraction of Physical Parameters of RRab Variables using Neural Network based Interpolator
2025-07-02
Nitesh Kumar, Harinder P. Singh, Oleg Malkov, Santosh Joshi, Kefeng Tan, Philippe Prugniel, Anupam Bhardwaj
Determining the physical parameters of pulsating variable stars such as RR Lyrae is essential for understanding their internal structure, pulsation mechanisms, and evolutionary state. In this study, we present a machine learning framework that uses feedforward artificial neural networks (ANNs) to infer stellar parameters-mass ($M$), luminosity (log($L/L_\odot$)), effective temperature (log($T_{\rm eff}$)), and metallicity ($Z$)-directly from Transiting Exoplanet Survey Satellite (TESS) light curves. The network is trained on a synthetic grid of RRab light curves generated from hydrodynamical pulsation models spanning a broad range of physical parameters. We validate the model using synthetic self-inversion tests and demonstrate that the ANN accurately recovers the input parameters with minimal bias. We then apply the trained model to RRab stars observed by the TESS. The observed light curves are phase-folded, corrected for extinction, and passed through the ANN to derive physical parameters. Based on these results, we construct an empirical period-luminosity-metallicity (PLZ) relation: log($L/L_\odot$) = (1.458 $\pm$ 0.028) log($P$/days) + (-0.068 $\pm$ 0.007) [Fe/H] + (2.040 $\pm$ 0.007). This work shows that ANN-based light-curve inversion offers an alternative method for extracting stellar parameters from single-band photometry. The approach can be extended to other classes of pulsators such as Cepheids and Miras.
The Close AGN Reference Survey (CARS): A comparison between sub-mm and optical AGN diagnostic diagrams
2025-07-02
Jacob S. Elford, Timothy A. Davis, Ilaria Ruffa, Stefi A. Baum, Francoise Combes, Massimo Gaspari, Rebecca McElroy, Christopher P. O'Dea, Osase Omoruyi, Mainak Singha, Grant R. Tremblay, Nico Winkel
The $L_{\rm IR}-L_{\rm HCN}$ relation suggests that there is a tight connection between dense gas and star formation. We use data from the Close AGN Reference Survey (CARS) to investigate the dense gas - star formation relation in AGN hosting galaxies, and the use of dense gas as an active galactic nuclei (AGN) diagnostic. Our sample contains five Type-1 (unobscured) AGN that were observed with the Atacama Large Millimeter/submillimeter Array (ALMA) with the aim to detect HCN(4-3), HCO$^+$(4-3) and CS(7-6). We detect the dense gas emission required for this analysis in 3 of the 5 targets. We find that despite the potential impact from the AGN on the line fluxes of these sources, they still follow the $L_{\rm IR}-L_{\rm HCN}$ relation. We then go on to test claims that the HCN/HCO+ and HCN/CS line ratios can be used as a tool to classify AGN in the sub-mm HCN diagram. We produce the classic ionised emission-line ratio diagnostics (the so-called BPT diagrams), using available CARS data from the Multi Unit Spectroscopic Explorer (MUSE). We then compare the BPT classification with the sub-mm classification made using the dense gas tracers. Where it was possible to complete the analysis we find general agreement between optical and sub-mm classified gas excitation mechanisms. This suggests that AGN can contribute to the excitation of both the low density gas in the warm ionised medium and the high density gas in molecular clouds simultaneously, perhaps through X-ray, cosmic ray or shock heating mechanisms.
This paper explores how older adults, particularly aging migrants in urban China, can engage AI-assisted co-creation to express personal narratives that are often fragmented, underrepresented, or difficult to verbalize. Through a pilot workshop combining oral storytelling and the symbolic reconstruction of Hanzi, participants shared memories of migration and recreated new character forms using Xiaozhuan glyphs, suggested by the Large Language Model (LLM), together with physical materials. Supported by human facilitation and a soft AI presence, participants transformed lived experience into visual and tactile expressions without requiring digital literacy. This approach offers new perspectives on human-AI collaboration and aging by repositioning AI not as a content producer but as a supportive mechanism, and by supporting narrative agency within sociotechnical systems.
AI and Remote Sensing for Resilient and Sustainable Built Environments: A Review of Current Methods, Open Data and Future Directions
2025-07-02
Ubada El Joulani, Tatiana Kalganova, Stergios-Aristoteles Mitoulis, Sotirios Argyroudis
Critical infrastructure, such as transport networks, underpins economic growth by enabling mobility and trade. However, ageing assets, climate change impacts (e.g., extreme weather, rising sea levels), and hybrid threats ranging from natural disasters to cyber attacks and conflicts pose growing risks to their resilience and functionality. This review paper explores how emerging digital technologies, specifically Artificial Intelligence (AI), can enhance damage assessment and monitoring of transport infrastructure. A systematic literature review examines existing AI models and datasets for assessing damage in roads, bridges, and other critical infrastructure impacted by natural disasters. Special focus is given to the unique challenges and opportunities associated with bridge damage detection due to their structural complexity and critical role in connectivity. The integration of SAR (Synthetic Aperture Radar) data with AI models is also discussed, with the review revealing a critical research gap: a scarcity of studies applying AI models to SAR data for comprehensive bridge damage assessment. Therefore, this review aims to identify the research gaps and provide foundations for AI-driven solutions for assessing and monitoring critical transport infrastructures.
Quantifying the impact of detection bias from blended galaxies on cosmic shear surveys
2025-07-02
Eray Genc, Peter Schneider, Sandra Unruh, Tim Schrabback
Increasingly large areas in cosmic shear surveys lead to a reduction of statistical errors, necessitating to control systematic errors increasingly better. One of these systematic effects was initially studied by Hartlap et al. in 2011, namely that image overlap with (bright foreground) galaxies may prevent some distant (source) galaxies to remain undetected. Since this overlap is more likely to occur in regions of high foreground density -- which tend to be the regions in which the shear is largest -- this detection bias would cause an underestimation of the estimated shear correlation function. This detection bias adds to the possible systematic of image blending, where nearby pairs or multiplets of images render shear estimates more uncertain and thus may cause a reduction in their statistical weight. Based on simulations with data from the Kilo-Degree Survey, we study the conditions under which images are not detected. We find an approximate analytic expression for the detection probability in terms of the separation and brightness ratio to the neighbouring galaxies. Applying this fitting formula to weak lensing ray tracing through, and the galaxy distribution in the Millennium Simulation, we estimate that the detection bias alone leads to an underestimate of $S_8=\sigma_8\sqrt{\Omega_\mathrm{m}/0.3}$ by almost 2\% and can therefore not be neglected in current and forthcoming cosmic shear surveys.
CHemical Evolution in MassIve star-forming COres (CHEMICO). I. Evolution of the temperature structure
2025-07-02
F. Fontani, V. M. Rivilla, E. Roueff, H. Martín-Caballero, L. Bizzocchi, L. Colzi, Á. Lopez-Gallifa, M. T. Beltrán, P. Caselli, C. Mininni, A. Vasyunin
Increasing evidence shows that most stars in the Milky Way, including the Sun, are born in star-forming regions containing also high-mass stars, but due to both observational and theoretical challenges, our comprehension of their chemical evolution is far less clear than that of their low-mass counterparts. We present the project "CHemical Evolution of MassIve star-froming COres" (CHEMICO). The project aims at investigating any aspect of the chemical evolution of high-mass star-forming cores by observing representatives of the three main evolutionary categories: high-mass starless cores, high-mass protostellar objects, and ultra-compact HII regions. We carried out an unbiased spectral line survey of the entire bandwidth at 3, 2, and 1.2 mm with the 30m telescope of the Insitut de Radioastronomie millimetrique towards three targets that represent the three evolutionary stages. The number of lines and species detected increases with evolution. In this first work, we derive the temperature structure of the targets through the analysis of the carbon-bearing species C2H, c-C3H, c-C3H2, C4H, CH3CCH, HC3N, CH3CN, and HC5N. The excitation temperature, Tex, increases with evolution in each species, although not in the same way. Hydrocarbons tend to be associated with the smallest Tex values and enhancements with evolution, while cyanides are associated with the highest Tex values and enhancements. In each target, the higher the number of atoms in the molecule, the higher Tex tends to be. The temperature structure evolves from a cold, uniform envelope traced by simple hydrocarbons in the high-mass starless core stage, to a more stratified envelope in the protostellar stage (made by a hot core, a shell with intermediate Tex, and a larger cold envelope), to finally a hot core surrounded only by a cold envelope in the Ultracompact HII stage.
Crop Pest Classification Using Deep Learning Techniques: A Review
2025-07-02
Muhammad Hassam Ejaz, Muhammad Bilal, Usman Habib
Insect pests continue to bring a serious threat to crop yields around the world, and traditional methods for monitoring them are often slow, manual, and difficult to scale. In recent years, deep learning has emerged as a powerful solution, with techniques like convolutional neural networks (CNNs), vision transformers (ViTs), and hybrid models gaining popularity for automating pest detection. This review looks at 37 carefully selected studies published between 2018 and 2025, all focused on AI-based pest classification. The selected research is organized by crop type, pest species, model architecture, dataset usage, and key technical challenges. The early studies relied heavily on CNNs but latest work is shifting toward hybrid and transformer-based models that deliver higher accuracy and better contextual understanding. Still, challenges like imbalanced datasets, difficulty in detecting small pests, limited generalizability, and deployment on edge devices remain significant hurdles. Overall, this review offers a structured overview of the field, highlights useful datasets, and outlines the key challenges and future directions for AI-based pest monitoring systems.
How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computations
2025-07-02
Marc Damie, Florian Hahn, Andreas Peter, Jan Ramon
Ishai et al. (FOCS'06) introduced secure shuffling as an efficient building block for private data aggregation. Recently, the field of differential privacy has revived interest in secure shufflers by highlighting the privacy amplification they can provide in various computations. Although several works argue for the utility of secure shufflers, they often treat them as black boxes; overlooking the practical vulnerabilities and performance trade-offs of existing implementations. This leaves a central question open: what makes a good secure shuffler? This survey addresses that question by identifying, categorizing, and comparing 26 secure protocols that realize the necessary shuffling functionality. To enable a meaningful comparison, we adapt and unify existing security definitions into a consistent set of properties. We also present an overview of privacy-preserving technologies that rely on secure shufflers, offer practical guidelines for selecting appropriate protocols, and outline promising directions for future work.
Primordial Black Holes as Coma Cluster Dark Matter and the Unresolved γ-Ray Background
2025-07-02
Jeremy Mould, Bhashin Thakore
If 0.1% of the dark matter in the Coma cluster is constituted by primordial black holes (PBHs) with masses ranging from 10^-19 to 10^-17 solar masses, then the observed GeV {\gamma}-ray emission from the cluster could potentially be attributed to Hawking radiation. The emitted spectrum is inversely proportional to the black hole's mass, meaning lighter PBHs radiate at higher energies, potentially falling within the GeV range. If 0.1% of the Coma cluster's dark matter is PBH in this mass range, a fit to the cluster's GeV emission is obtained. We then investigate the potential for constraining evaporating PBHs through cross-correlations between the Unresolved Gamma-Ray Background and weak gravitational lensing. Utilizing 12 years of Fermi-LAT observations and weak lensing measurements from the Dark Energy Survey Year 3, we assess whether such correlations can reveal a PBH {\gamma}-ray component. While a statistically significant correlation between the UGRB and large-scale structure has been observed, this signal is consistent with emission from clustered astrophysical sources such as blazars. Attributing a measurable fraction of the UGRB to PBH evaporation would require unrealistically large PBH abundances. We also draw attention to a cluster of Coma-like X-ray clusters, designated Draco X, observed at a redshift of z = 0.12. These systems, characterized by their significant X-ray emission from hot, diffuse intracluster gas, represent massive gravitationally bound structures. The existence and properties of such clusters at these redshifts provide crucial cosmological probes, offering insights into the formation and evolution of large-scale structure and the underlying cosmological parameters. Further investigation of Draco X and similar high-redshift clusters would yield additional constraints on large scale structure.
AI Agents and Agentic AI-Navigating a Plethora of Concepts for Future Manufacturing
2025-07-02
Yinwang Ren, Yangyang Liu, Tang Ji, Xun Xu
AI agents are autonomous systems designed to perceive, reason, and act within dynamic environments. With the rapid advancements in generative AI (GenAI), large language models (LLMs) and multimodal large language models (MLLMs) have significantly improved AI agents' capabilities in semantic comprehension, complex reasoning, and autonomous decision-making. At the same time, the rise of Agentic AI highlights adaptability and goal-directed autonomy in dynamic and complex environments. LLMs-based AI Agents (LLM-Agents), MLLMs-based AI Agents (MLLM-Agents), and Agentic AI contribute to expanding AI's capabilities in information processing, environmental perception, and autonomous decision-making, opening new avenues for smart manufacturing. However, the definitions, capability boundaries, and practical applications of these emerging AI paradigms in smart manufacturing remain unclear. To address this gap, this study systematically reviews the evolution of AI and AI agent technologies, examines the core concepts and technological advancements of LLM-Agents, MLLM-Agents, and Agentic AI, and explores their potential applications in and integration into manufacturing, along with the potential challenges they may face.
Forecasting the Impact of Source Galaxy Photometric Redshift Uncertainties on the LSST $3\times2$pt Analysis
2025-07-02
Tianqing Zhang, Husni Almoubayyed, Rachel Mandelbaum, Markus Michael Rau, Nikolina Šarčević, C. Danielle Leonard, Jeffrey A. Newman, Brett Andrews, Shuang Liang, The LSST Dark Energy Science Collaboration
Photometric redshifts of the source galaxies are a key source of systematic uncertainty in the Rubin Observatory Legacy Survey of Space and Time (LSST)'s galaxy clustering and weak lensing analysis, i.e., the $3\times 2$pt analysis. This paper introduces a Fisher forecast code FisherA2Z for the LSST Year 10 (Y10) $3 \times 2$pt and cosmic shear analyses, utilizing a 15-parameter redshift distribution model, with one redshift bias, variance, and outlier rate per tomographic bin. FisherA2Z employs the Core Cosmology Library CCL to compute the large-scale structure power spectrum and incorporates a four-parameter nonlinear alignment model for intrinsic alignments. We evaluate the impact of marginalizing over redshift distribution parameters on weak lensing, forecast biases in cosmological parameters due to redshift errors, and assess cosmological parameter sensitivity to redshift systematic parameters using decision trees. The sensitivity study reveals that for LSST $3\times2$pt analysis, $S_8$ is most sensitive to the mean redshift of the fourth out of the five source tomographic bins, while other cosmological parameters possess different sensitivities. Additionally, we provide cosmological analysis forecasts based on different scenarios of spectroscopic training datasets. We find that the figures-of-merit for the cosmological results increase with the number of spectroscopic training galaxies, and with the completeness of the training set above $z=1.6$, assuming the redshift information comes solely from the training set galaxies without other external constraints.
Radial velocities and stellar populations for a sample of MATLAS survey dwarfs
2025-07-02
Kate Southon, Jonah S. Gannon, Duncan A. Forbes, Arsen Levitskiy, Maria Luisa Buzzo, Aaron J. Romanowsky, Jean P. Brodie
Spectroscopic observations are essential for confirming associations, measuring kinematics, and determining stellar populations in dwarf galaxies. Here, we present Keck Cosmic Web Imager (KCWI) spectra for 12 MATLAS survey dwarfs. For 9, we confirm recession velocities consistent with their literature-assumed host galaxies. We propose revisions of the host galaxy associations for MATLAS-631, 1494, and 1938. For MATLAS-1494, our measured redshift reclassifies it from an ultra-diffuse galaxy candidate to a dwarf galaxy that is of smaller physical size and places it in the field. It also appears old and passive, providing a challenge to models that invoke quenching by tidal effects. Additionally, we measure stellar population estimates for 7 of the 12 galaxies, finding a 'mixed bag' of old quenched galaxies and those that are currently forming stars. Compared to the literature we find generally younger ages and higher metallicities. This result may help reconcile the observed offset of MATLAS survey dwarf galaxies from the universal stellar mass-metallicity relationship reported by Heesters et al. (2023).
The birth of young radio jets in changing-look AGN: a population study
2025-07-02
Sufia Birmingham, Charlotte Ward, Kristina Nyland, Dougal Dobie, Matthew J. Graham, David L. Kaplan, Tara Murphy
Changing-Look Active Galactic Nuclei (CLAGN) are a rare subset of AGN that show significant changes to the flux of broad Balmer emission lines. Recent studies of CLAGN, such as 1ES 1927+654 and Mrk 590, have revealed that changes in the optically observed accretion rate are accompanied by changes in radio activity. We present a time-domain population study of 474 spectroscopically confirmed CLAGN at radio wavelengths using the Australia SKA Pathfinder Variable and Slow Transients Survey and the Very Large Array Sky Survey. We compare the radio properties of this CLAGN sample to a control sample of AGN that have not had recent changing-look events, and to AGN that were found to have transitioned from radio-quiet to radio-loud over 10-year timescales in VLASS. For 20 newly studied CLAGN detected in ASKAP VAST, we do not detect Mrk 590 or 1ES 1927+654-like fading of the radio flux in the 10 years following changing-look events. For 6 CLAGN with a sufficiently low redshift and high enough mass, we rule out a Mrk 590-like flare. We find that at the population level, CLAGN have higher VAST/VLASS detection rates, lower fractions of radio loudness, and higher variability rates in the 1 GHz frequency compared to the control AGN. Through VLA observations of radio SEDs and Magellan spectroscopic observations, we do not find evidence of a link between CLAGN and AGN that transitioned from radio-loud to radio-quiet in VLASS. We discuss the implications of this study for the physical mechanisms that drive enhanced accretion episodes.
Dissipative structures in one- and two-dimensional Kerr cavities with a spatially periodic pump
2025-07-02
Wen-Rong Sun, Wei-Chao Ma, Boris A. Malomed
The interplay of periodic driving and dissipation is a fundamental feature of nonequilibrium physics. We elaborate a scenario for the formation of dissipative multi-spot excitations (MSEs) in Kerr cavities, modeled by the one- and two-dimensional (1D and 2D) Lugiato-Lefever (LL) equations, which include a spatially periodic pump (SPP). First, we demonstrate that the SPP produces three novel exact periodic solutions of the LL equation, expressed in terms of the $\mathrm{sn}$, $\mathrm{cn}$, and $\mathrm{dn}$ elliptic functions. By means of numerical methods, we explore the modulational instability (MI) and transverse instability (TI) of the periodic states in 1D and 2D settings, respectively. In the case of the defocusing nonlinearity, the 1D MI breaks the periodic states into an array of spatiotemporal crescents. In the case of self-focusing, the 1D MI, initiated by small random perturbations, leads to establishment of a chaotic state, with the amplitude statistics featuring a long-tail probability distribution, that represents the presence of dissipative rogue waves. On the other hand, spatially periodic perturbations initiate formation of breather chains, which periodically disappear and reappear, resembling the Fermi-Pasta-Ulam-Tsingou recurrence. In the 2D regime, the TI results in the formation of an array of 2D lumps. For a given SPP strength, the exact solutions for the periodic structures are stable if the loss constant exceeds a critical value. The findings reported here provide a contribution to nonequilibrium physics in general and may find direct applications in laser physics.
Overview of Topics in Electrocatalysis for Sustainability: Reactions, Electrocatalysts, Degradation, and Mitigation
2025-07-02
Varada Purohit, Avdhoot Datar
Electrocatalysis provides an avenue for transitioning the global energy dependence from fossil fuels to renewable energy sources. While electrocatalytic reactions are being used for several decades, recently, there is a growing interest for electrocatalytic reactions that are useful from sustainability perspective. The wide industrial applications of these sustainable electrocatalytic processes is largely limited by the degradation of the electrocatalysts. This review begins with an introduction to such reactions, followed by a detailed discussion of the electrocatalysts. Finally we describe the processes that are responsible for the degradation of electrocatalytic activity.
SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations
2025-07-02
Zhican Wang, Guanghui He, Hongxiang Fan
The emergence of diffusion models has significantly advanced generative AI, improving the quality, realism, and creativity of image and video generation. Among them, Stable Diffusion (StableDiff) stands out as a key model for text-to-image generation and a foundation for next-generation multi-modal algorithms. However, its high computational and memory demands hinder inference speed and energy efficiency. To address these challenges, we identify three core issues: (1) intensive and often redundant computations, (2) heterogeneous operations involving convolutions and attention mechanisms, and (3) diverse weight and activation sizes. We present SD-Acc, a novel algorithm and hardware co-optimization framework. At the algorithm level, we observe that high-level features in certain denoising phases show significant similarity, enabling approximate computation. Leveraging this, we propose an adaptive, phase-aware sampling strategy that reduces compute and memory loads. This framework automatically balances image quality and complexity based on the StableDiff model and user requirements. At the hardware level, we design an address-centric dataflow to efficiently handle heterogeneous operations within a simple systolic array. We address the bottleneck of nonlinear functions via a two-stage streaming architecture and a reconfigurable vector processing unit. Additionally, we implement adaptive dataflow optimizations by combining dynamic reuse and operator fusion tailored to StableDiff workloads, significantly reducing memory access. Across multiple StableDiff models, our method achieves up to a 3x reduction in computational demand without compromising image quality. Combined with our optimized hardware accelerator, SD-Acc delivers higher speed and energy efficiency than traditional CPU and GPU implementations.
A Practical SAFE-AI Framework for Small and Medium-Sized Enterprises Developing Medical Artificial Intelligence Ethics Policies
2025-07-02
Ion Nemteanu, Adir Mancebo Jr., Leslie Joe, Ryan Lopez, Patricia Lopez, Warren Woodrich Pettine
Artificial intelligence (AI) offers incredible possibilities for patient care, but raises significant ethical issues, such as the potential for bias. Powerful ethical frameworks exist to minimize these issues, but are often developed for academic or regulatory environments and tend to be comprehensive but overly prescriptive, making them difficult to operationalize within fast-paced, resource-constrained environments. We introduce the Scalable Agile Framework for Execution in AI (SAFE-AI) designed to balance ethical rigor with business priorities by embedding ethical oversight into standard Agile-based product development workflows. The framework emphasizes the early establishment of testable acceptance criteria, fairness metrics, and transparency metrics to manage model uncertainty, while also promoting continuous monitoring and re-evaluation of these metrics across the AI lifecycle. A core component of this framework are responsibility metrics using scenario-based probability analogy mapping designed to enhance transparency and stakeholder trust. This ensures that retraining or tuning activities are subject to lightweight but meaningful ethical review. By focusing on the minimum necessary requirements for responsible development, our framework offers a scalable, business-aligned approach to ethical AI suitable for organizations without dedicated ethics teams.
2025-07-02
Maxim Trushin, Daria V. Andreeva, Francois M. Peeters, Kostya S. Novoselov
Water, a subject of human fascination for millennia, is likely the most studied substance on Earth, with an entire scientific field -- hydrodynamics -- dedicated to understanding water in motion. However, when water flows through one-dimensional or two-dimensional channels, its behavior deviates substantially from the principles of hydrodynamics. This is because reducing the dimensionality of any interacting physical system amplifies interaction effects that are beyond the reach of traditional hydrodynamic equations. In low-dimensional water, hydrogen bonds can become stable enough to arrange water molecules into an ordered state, causing water to behave not only like a liquid but also like a solid in certain respects. In this review, we explore the relationship between water's ordering and its ability to flow in low-dimensional channels, using viscosities of bulk water, vapor, and ice as benchmarks. We also provide a brief overview of the key theoretical approaches available for such analyses and discuss ionic transport, which is heavily influenced by the molecular structure of water.
The Evolutionary Pathway of Low-mass Supermassive Black Holes at Intermediate Redshift: Insights from the JADES Survey
2025-07-02
Atsushi Hoshi, Toru Yamada
Understanding the relationship between supermassive black holes (SMBHs) and their host galaxies at different redshifts is crucial for unraveling the processes of SMBH-galaxy co-evolution. We present the properties of nine type 1 Active Galactic Nuclei (AGNs) at intermediate redshift ($24$ ($1-10\%$), suggesting that black holes and galaxies may trace different evolutionary pathways at intermediate and high redshift. We also perform 2D image decomposition using GALFIT to constrain the bulge mass by evaluating the bulge contribution in the rest-frame near-infrared flux. We identify the AGNs with low BH-to-bulge mass ratios compared to those observed in the nearby bulge-dominant galaxies. This finding suggests the existence of a galaxy-first evolutionary path, in which bulge formation occurs before substantial gas is efficiently accreted onto the central engine.
Beyond Black-Box AI: Interpretable Hybrid Systems for Dementia Care
2025-07-02
Matthew JY Kang, Wenli Yang, Monica R Roberts, Byeong Ho Kang, Charles B Malpas
The recent boom of large language models (LLMs) has re-ignited the hope that artificial intelligence (AI) systems could aid medical diagnosis. Yet despite dazzling benchmark scores, LLM assistants have yet to deliver measurable improvements at the bedside. This scoping review aims to highlight the areas where AI is limited to make practical contributions in the clinical setting, specifically in dementia diagnosis and care. Standalone machine-learning models excel at pattern recognition but seldom provide actionable, interpretable guidance, eroding clinician trust. Adjacent use of LLMs by physicians did not result in better diagnostic accuracy or speed. Key limitations trace to the data-driven paradigm: black-box outputs which lack transparency, vulnerability to hallucinations, and weak causal reasoning. Hybrid approaches that combine statistical learning with expert rule-based knowledge, and involve clinicians throughout the process help bring back interpretability. They also fit better with existing clinical workflows, as seen in examples like PEIRS and ATHENA-CDS. Future decision-support should prioritise explanatory coherence by linking predictions to clinically meaningful causes. This can be done through neuro-symbolic or hybrid AI that combines the language ability of LLMs with human causal expertise. AI researchers have addressed this direction, with explainable AI and neuro-symbolic AI being the next logical steps in further advancement in AI. However, they are still based on data-driven knowledge integration instead of human-in-the-loop approaches. Future research should measure success not only by accuracy but by improvements in clinician understanding, workflow fit, and patient outcomes. A better understanding of what helps improve human-computer interactions is greatly needed for AI systems to become part of clinical practice.
2025-07-02
Mohammad Jahanbakht, Alex Olsen, Ross Marchant, Emilie Fillols, Mostafa Rahimi Azghadi
Weed mapping plays a critical role in precision management by providing accurate and timely data on weed distribution, enabling targeted control and reduced herbicide use. This minimizes environmental impacts, supports sustainable land management, and improves outcomes across agricultural and natural environments. Recent advances in weed mapping leverage ground-vehicle Red Green Blue (RGB) cameras, satellite and drone-based remote sensing combined with sensors such as spectral, Near Infra-Red (NIR), and thermal cameras. The resulting data are processed using advanced techniques including big data analytics and machine learning, significantly improving the spatial and temporal resolution of weed maps and enabling site-specific management decisions. Despite a growing body of research in this domain, there is a lack of comprehensive literature reviews specifically focused on weed mapping. In particular, the absence of a structured analysis spanning the entire mapping pipeline, from data acquisition to processing techniques and mapping tools, limits progress in the field. This review addresses these gaps by systematically examining state-of-the-art methods in data acquisition (sensor and platform technologies), data processing (including annotation and modelling), and mapping techniques (such as spatiotemporal analysis and decision support tools). Following PRISMA guidelines, we critically evaluate and synthesize key findings from the literature to provide a holistic understanding of the weed mapping landscape. This review serves as a foundational reference to guide future research and support the development of efficient, scalable, and sustainable weed management systems.
The Revolution Has Arrived: What the Current State of Large Language Models in Education Implies for the Future
2025-07-02
Russell Beale
Large language Models have only been widely available since 2022 and yet in less than three years have had a significant impact on approaches to education and educational technology. Here we review the domains in which they have been used, and discuss a variety of use cases, their successes and failures. We then progress to discussing how this is changing the dynamic for learners and educators, consider the main design challenges facing LLMs if they are to become truly helpful and effective as educational systems, and reflect on the learning paradigms they support. We make clear that the new interaction paradigms they bring are significant and argue that this approach will become so ubiquitous it will become the default way in which we interact with technologies, and revolutionise what people expect from computer systems in general. This leads us to present some specific and significant considerations for the design of educational technology in the future that are likely to be needed to ensure acceptance by the changing expectations of learners and users.
2025-07-02
Michael Fitzgerald, Rachel Freed, Dan Reichart, Kate Meredith, Kalee Tock, Daryl Janzen, Saeed Salimpour, Jennifer Lynn Bartlett, Matthew Beaky, Art Borja, Ken Brandt, Jim Buchholz, Patricia Craig, Anthony Crider, Richard Datwyler, Marta Dark-McNeese, Anna DeJong, Donovan Domingue, Debbie French, Oliver Fraser, Amy L. Glazier, Enrique Gomez, Erika Grundstrom, Nicole Gugliucci, Kevin Healy, Ardis Herrold, Ian Hewitt, Jack Howard, Katherine Hunt, Yashashree Jadhav, Jonathan Keohane, Noah Kearns, Lancelot Kao, Brian Kloppenborg, Kelly Kosmo O'Neil, Kevin Lee, Ulrike Lahaise, Sandy Liss, David McKinnon, Adam McKay, Stephen McNeil, Mariel Meier, Jackie Milingo, Qurat-ul-Ann Mirza, Abbas Mokhtarzadeh, Raul Morales-Juberias, Sean Moroney, Rhone O'Hara, Angela Osterman Meyer, Imad Pasha, Bradley W. Peterson, Luisa Rebull, Digesh Raut, Christine Russell, Ann Schmiedekamp, Carl Schmiedekamp, Madeline Shepley, Deanna Shields, Brooke Skelton, Don Smith, David Sukow, John B. Taylor, Elise Weaver, Michelle Wooten, Tiffany Stone Wolbrecht, David Yenerall
As astronomy enters an era defined by global telescope networks, petabyte-scale surveys, and powerful computational tools, the longstanding goals of astronomy education, particularly introductory ``ASTRO101'', but equally encompassing both higher and lower level courses, warrant fresh examination. In June 2024, the AstroEdUNC meeting at UNC--Chapel Hill convened 100 astronomers, education researchers, and practitioners to synthesise community perspectives on the purpose, content, and delivery of astronomy education. Beginning with historical vignettes, the meeting's deliberations were organised into six interrelated themes: (1) Context, highlighting astronomy's evolution from classical charting to multi-messenger discovery and its role as a connective thread across STEM and the humanities; (2) Content, exploring how curricula can balance essential concepts with authentic investigations and leverage open-source and AI-augmented resources; (3) Skills, arguing that astronomy should foreground scientific literacy, computational fluency, and communication through genuine data-driven inquiry; (4) Engagement, advocating for active-learning strategies, formative assessment, and culturally inclusive narratives; (5) Beyond the Classroom, emphasising scaffolding, universal-design practices, and K--12/community partnerships; and (6) Astronomy Education Research, outlining priority areas for assessing knowledge, attitudes, and long-term outcomes. We provide concrete recommendations for future astronomy education research development, underscoring the need for approaches to education that are authentic while meeting the learning and life goal needs of the students, a vibrant community of practice and robust researcher-practitioner partnerships to ensure that introductory astronomy is pertinent, applicable and inspiring to a broad student population.
Is the hyperscaling relation violated below the upper critical dimension in some particular cases?
2025-07-02
Hung T. Diep, Van-Thanh Ngo
In this review, we show our results with new interpretation on the critical exponents of thin films obtained by high-performance multi-histogram Monte Carlo simulations. The film thickness $N_z$ consists of a few layers up to a dozen of layers in the $z$ direction. The free boundary condition is applied in this direction while in the $xy$ plane periodic boundary conditions are used. Large $xy$ plane sizes are used for finite-size scaling. The Ising model is studied with nearest-neighbor (NN) interaction. When $N_z=1$, namely the two-dimensional (2D) system, we find the critical exponents given by the renormalization group. While, for $N_z>1$, the critical exponents calculated with the high-precision multi-histogram technique show that they deviate slightly but systematically from the 2D values. If we use these values of critical exponents in the hyperscaling relation with $d=2$, then the hyperscaling relation is violated. However, if we use the hyperscaling relation and the critical exponents obtained for $N_z>1$ to calculate the dimension of the system, we find the system dimension slightly larger than 2. This can be viewed as an "effective" dimension. More discussion is given in the paper. We also show the cross-over between the first- and second-order transition while varying the film thickness in an antiferromagnetic FCC Ising frustrated thin film. In addition, we will show evidence that when a 2D system has two order parameters of different symmetries with a single transition, the critical exponents are new, suggesting a universality class of coupled two-symmetry breakings. In this case, the 2D hyperscaling does not hold. Another case is the 3D Ising model coupled to the lattice vibration: the critical exponents deviate from the 3D Ising ones, the results suggest the violation of the hyperscaling.
2025-07-02
Ryan Walker, Andi Kisare, Lindsey Bleem
A wide range of scientific imaging datasets benefit from human inspection for purposes ranging from prosaic-such as fault identification and quality inspection-to profound, enabling the discovery of new phenomena. As such, these datasets come in a wide variety of forms, with diverse inspection needs. In this paper we present a software package, Image Marker, designed to help facilitate human categorization of images. The software allows for quick seeking through images and enables flexible marking and logging of up to 9 different classes of features and their locations in files of FITS, TIFF, PNG, and JPEG format. Additional tools are provided to add text-based comments to the marking logs and for displaying external mark datasets on images during the classification process. As our primary use case will be the identification of features in astronomical survey data, Image Marker will also utilize standard world coordinate system (WCS) headers embedded in FITS headers and TIFF metadata when available. The lightweight software, based on the Qt Framework to build the GUI application, enables efficient marking of thousands of images on personal-scale computers. We provide Image Marker as a Python package, and as Mac and Windows 11 executables. It is available on GitHub or via pip installation.
The study of dynamical systems involves analyzing how functions behave under iteration in different mathematical spaces. In the context of complex dynamics, tools such as Julia sets and filled Julia sets are used to understand the long-term behavior of functions in the complex Euclidean field. In this paper, we will present a review of Julia sets and filled Julia sets, provide an overview of the mathematical formulation of the alternate Julia set introduced in the work of Danca-Romera-Pastor, extend it to the $p$-adic setting, and propose a tool that can potentially be used to study the arithmetic dynamics of various types of functions. Additionally, we will summarize key results on connectivity properties and visualization techniques as discussed in the work of Danca-Bourke-Romera and provide a visualization algorithm and pseudocode that enable the visualization of alternate Julia sets with various connectivity properties.
Matrix Pencil-Based DoA Estimation for Hybrid Receivers in Snapshot-Limited Scenarios
2025-07-02
Mona Mostafa, Ramy H. Gohary, Amr El-Keyi, Yahia A. Eldemerdash Ahmed
The goal of this paper is to estimate the directions of arrival (DoAs) for hybrid analog/digital (HAD) receivers when the number of snapshots is too small for statistical averaging to be reliable. This goal is achieved in fully-digital receivers by employing the matrix pencil method (MPM). Unfortunately, the MPM cannot be directly applied in HAD receivers because of the entanglement induced by the underlying analog combiners on the output signals. Furthermore, these analog combiners project the received signal onto a low-dimensional space, jeopardizing the reception of signals arriving from particular DoA ranges. To circumvent these difficulties, we propose two approaches to enable the MPM to extract the DoAs in HAD receivers. The two approaches avoid severe attenuation induced by low-dimensional projection by cycling over an exhaustive set of analog combiners, collectively spanning the entire space. The first approach can be applied to both fully-connected (FC) and partially-connected (PC) HADs and relies on the availability of periodic, potentially unknown, signals to disentangle the output of the HAD receiver. The second approach applies to PC-HADs only, and eliminates contingency on periodic signals by exploiting the underlying block diagonal structure. The superiority of the proposed approaches is demonstrated via numerical simulations and comparisons with the Cram\'er-Rao lower bound.
The study of well-being, stress and other human factors has traditionally relied on self-report instruments to assess key variables. However, concerns about potential biases in these instruments, even when thoroughly validated and standardised, have driven growing interest in alternatives in combining these measures with more objective methods, such as physiological measures. We aimed to (i) compare psychometric stress measures and biometric indicators and (ii) identify stress-related patterns in biometric data during software engineering tasks. We conducted an experiment where participants completed a pre-survey, then programmed two tasks wearing biometric sensors, answered brief post-surveys for each, and finally went through a short exit interview. Our results showed diverse outcomes; we found no stress in the psychometric instruments. Participants in the interviews reported a mix of feeling no stress and experiencing time pressure. Finally, the biometrics showed a significant difference only in EDA phasic peaks. We conclude that our chosen way of inducing stress by imposing a stricter time limit was insufficient. We offer methodological insights for future studies working with stress, biometrics, and psychometric instruments.
Unlocking Quantum Control and Multi-Order Correlations via Terahertz Two-Dimensional Coherent Spectroscopy
2025-07-02
Chuankun Huang, Martin Mootz, Liang Luo, Ilias E. Perakis, Jigang Wang
Terahertz two-dimensional coherent spectroscopy (THz-2DCS) is transforming our ability to probe, visualize, and control quantum materials far from equilibrium. This emerging technique brings multi-dimensional resolution to the ultrafast dynamics of nonequilibrium phases of matter, enabling new capabilities demanding precise coherent control and measurement of many-body dynamics and multi-order correlations. By mapping complex excitations across time and frequency dimensions, THz-2DCS delivers coherence tomography of driven quantum matter, thus revealing hidden excitation pathways, measuring higher order nonlinear response functions, disentangling various quantum pathways, capturing collective modes on ultrafast timescales and at terahertz frequencies. These experimental features frequently remain obscured in traditional single particle measurements, ultrafast spectroscopy techniques, and equilibrium-based probes. This Review traces the early development of THz-2DCS and showcases significant recent progress in leveraging this technique to probe and manipulate quantum material properties, including nonequilibrium superconductivity, nonlinear magnonics, dynamical topological phases, and the detection of novel excitations and exotic collective modes with potential technological impact. Looking forward, we identify critical opportunities in advancing THz-2DCS instrumentation and experimental strategies that are shaping future applications in THz optoelectronics, quantum information processing, and sensing.
Impact of rotation on synthetic mass-radius relationships of two-layer rocky planets and water worlds
2025-07-02
J. -M. Huré, P. Noé, C. Staelen, E. Di Folco
We have analyzed the effects of rotation on mass-radius relationships for single-layer and two-layer planets having a core and an envelope made of pure materials among iron, perovskite and water in solid phase. The numerical surveys use the DROP code updated with a modified polytropic equation-of-state (EOS) and investigate flattening parameters $f$ up to $0.2$. In the mass range $0.1 M_\oplus < M < 10 M_\oplus$, we find that rotation systematically shifts the curves of composition towards larger radii and/or smaller masses. Relative to the spherical case, the equatorial radius $R_{eq}$ is increased by about $0.36f$ for single-layer planets, and by $0.30f$ to $0.55f$ for two-layer planets (depending on the core size fraction $q$ and planet mass $M$). Rotation is an additional source of confusion in deriving planetary structures, as the radius alterations are of the same order as i) current observational uncertainties for super-Earths, and ii) EOS variations. We have established a multivariate fit of the form $R_{eq}(M,f,q)$, which enables a fast characterization of the core size and rotational state of rocky planets and ocean worlds. We discuss how the observational data must be shifted in the diagrams to self-consistently account for an eventual planet spin, depending on the geometry of the transit (circular/oblate). A simple application to the recently characterized super-Earth candidate LHS1140b is discussed.
How the Solar Dynamics Observatory Revolutionized our Physical Understanding of the Sun
2025-07-02
Markus J. Aschwanden
We review solar studies using AIA, HMI, and EVE data from the SDO spacecraft that revolutionized our physical understanding of the Sun. The relevant SDO studies cover the entire 15-year lifetime of SDO, from 2010 May 1 to 2025 May 1. The discussed phenomena and their physical interpretations include (in chronological order): (1) MHD Waves and Oscillations (AIA, HMI); (2) Propagating MHD Waves (AIA); (3) Coronal Loop Cross-Sectional Temperatures (AIA); (4) Size Distributions of Solar Flare Parameters (AIA); (5) Spatio-Temporal Evolution and Diffusion (AIA); (6) The Rosner-Tucker-Vaiana (RTV) Scaling Law (AIA); (7) The Fractal-Diffusive Self-Organized Criticality Model (AIA); (8) Automated Temperature and Emission Measure Maps (AIA); (9) Automated Pattern Recognition Codes (AIA); (10) Kelvin-Helmholtz Instability in Reconnetion Outflows (AIA); (11) Hydrodstatics of Coronal Loops (AIA); (12) Magnetic Energy Dissipation (HMI); (13) Global Energetics of Solar Flares (AIA).
Can Internal Software Metrics Predict App Popularity at Launch? Yeas! and Nays!
2025-07-02
Md Nahidul Islam Opu, Fatima Islam Mouri, Rick Kazman, Yuanfang Cai, Shaiful Chowdhury
Predicting mobile app popularity before release can provide developers with a strategic advantage in a competitive marketplace, yet it remains a challenging problem. This study explores whether internal software metrics, measurable from source code before deployment, can predict an app's popularity, defined by user ratings (calculated from user reviews) and DownloadsPerYear (yearly downloads). Using a dataset of 446 open-source Android apps from F-Droid, we extract a wide array of features, including system-, class-, and method-level code metrics, code smells, and app metadata. Additional information, such as user reviews, download counts, and uses-permission, was collected from the Google Play Store. We evaluate regression and classification models across three feature sets: a minimal Size-only baseline, a domain-informed Handpicked set, and a Voting set derived via feature selection algorithms. Regression models perform poorly due to skewed data, with low $R^2$ scores. However, when reframed as binary classification (Popular vs. Unpopular), results improve significantly. The best model, a Multilayer Perceptron using the Voting set, achieves F1-scores of 0.72. These results suggest that internal code metrics, although limited in their explanatory power, can serve as useful indicators of app popularity. This challenges earlier findings that dismissed internal metrics as predictors of software quality.
The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems
2025-07-02
Reza Yousefi Maragheh, Yashar Deldjoo
Large language models (LLMs) are rapidly evolving from passive engines of text generation into agentic entities that can plan, remember, invoke external tools, and co-operate with one another. This perspective paper investigates how such LLM agents (and societies thereof) can transform the design space of recommender systems. We introduce a unified formalism that (i) models an individual agent as a tuple comprising its language core, tool set, and hierarchical memory, and (ii) captures a multi-agent recommender as a triple of agents, shared environment, and communication protocol. Within this framework, we present four end-to-end use cases-interactive party planning, synthetic user-simulation for offline evaluation, multi-modal furniture recommendation, and brand-aligned explanation generation-each illustrating a distinct capability unlocked by agentic orchestration. We then surface five cross-cutting challenge families: protocol complexity, scalability, hallucination and error propagation, emergent misalignment (including covert collusion), and brand compliance. For each, we formalize the problem, review nascent mitigation strategies, and outline open research questions. The result is both a blueprint and an agenda: a blueprint that shows how memory-augmented, tool-using LLM agents can be composed into robust recommendation pipelines, and an agenda inviting the RecSys community to develop benchmarks, theoretical guarantees, and governance tools that keep pace with this new degree of autonomy. By unifying agentic abstractions with recommender objectives, the paper lays the groundwork for the next generation of personalized, trustworthy, and context-rich recommendation services.
Optical Vortex Spin-Orbit Control of Refractive Index in Iron Garnets
2025-07-02
Seth Nelson, Cong Yu, Daniel Watson, Shahrzad Ramtinfard, Miguel Levy
The interaction between light's angular momentum (AM) and material systems has unlocked new avenues in structured photonics, including in magneto-optical (MO) materials. While spin angular momentum (SAM) effects in MO systems are well-established, orbital angular momentum (OAM) introduces novel opportunities for new nonreciprocal light-matter interactions. In this study, we demonstrate a unique optical phenomenon where OAM states undergo state-specific nonreciprocal operation within an MO medium, reducing Faraday rotation. This effect arises from transverse momentum transfer into the material, inducing spin-orbit coupling (SOC) at a perturbed electronic transition rate. The resulting OAM-dependent optical SOC modifies the material's refractive index, directly linking structured light and MO response. Our findings extend previous observations of paraxial beams and reveal a deeper fundamental mechanism governing OAM-driven nonreciprocal interactions. These insights pave the way for OAM-selective nonreciprocal photonic devices, chiral optical logic, quantum memory elements, and ultrafast spintronic architectures. This work advances MO integration with structured light for enhanced control over photonic and spintronic systems.
Supersymmetry is one of the most important and indispensable ingredients of modern theoretical physics. However, the absence, at least at the time of publishing this review, of experimental verification of supersymmetry in elementary particle/high-energy physics casts doubt on the viability of this concept at the energies achievable at the LHC. The more unexpected are either already experimentally verified or proposed for verification manifestations of supersymmetry at the level of low (condensed matter physics, quantum optics) and medium (nuclear physics) energies where standard quantum mechanics works. Using examples of various systems from completely different areas of physics, we discuss the isospectrality of quantum Hamiltonians, hidden and explicit supersymmetry, the advantages of the supersymmetric quantum mechanics approach and its role in quantum engineering.
Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs
2025-07-02
Mohammad Ali Alomrani, Yingxue Zhang, Derek Li, Qianyi Sun, Soumyasundar Pal, Zhanguang Zhang, Yaochen Hu, Rohan Deepak Ajwani, Antonios Valkanas, Raika Karimi, Peng Cheng, Yunzhou Wang, Pengyi Liao, Hanrui Huang, Bin Wang, Jianye Hao, Mark Coates
Large language models (LLMs) have rapidly progressed into general-purpose agents capable of solving a broad spectrum of tasks. However, current models remain inefficient at reasoning: they apply fixed inference-time compute regardless of task complexity, often overthinking simple problems while underthinking hard ones. This survey presents a comprehensive review of efficient test-time compute (TTC) strategies, which aim to improve the computational efficiency of LLM reasoning. We introduce a two-tiered taxonomy that distinguishes between L1-controllability, methods that operate under fixed compute budgets, and L2-adaptiveness, methods that dynamically scale inference based on input difficulty or model confidence. We benchmark leading proprietary LLMs across diverse datasets, highlighting critical trade-offs between reasoning performance and token usage. Compared to prior surveys on efficient reasoning, our review emphasizes the practical control, adaptability, and scalability of TTC methods. Finally, we discuss emerging trends such as hybrid thinking models and identify key challenges for future work towards making LLMs more computationally efficient, robust, and responsive to user constraints.
Crash detection from video feeds is a critical problem in intelligent transportation systems. Recent developments in large language models (LLMs) and vision-language models (VLMs) have transformed how we process, reason about, and summarize multimodal information. This paper surveys recent methods leveraging LLMs for crash detection from video data. We present a structured taxonomy of fusion strategies, summarize key datasets, analyze model architectures, compare performance benchmarks, and discuss ongoing challenges and opportunities. Our review provides a foundation for future research in this fast-growing intersection of video understanding and foundation models.
How do Software Engineering Candidates Prepare for Technical Interviews?
2025-07-02
Brian Bell, Teresa Thomas, Sang Won Lee, Chris Brown
To obtain employment, aspiring software engineers must complete technical interviews -- a hiring process which involves candidates writing code while communicating to an audience. However, the complexities of tech interviews are difficult to prepare for and seldom faced in computing curricula. To this end, we seek to understand how candidates prepare for technical interviews, investigating the effects of preparation methods and the role of education. We distributed a survey to candidates (n = 131) actively preparing for technical interviews. Our results suggest candidates rarely train in authentic settings and courses fail to support preparation efforts -- leading to stress and unpreparedness. Based on our findings, we provide implications for stakeholders to enhance tech interview preparation for candidates pursuing software engineering roles.
Vertical Structure and Dynamics of a Galactic Disk
2025-07-02
Chanda J. Jog
Most of the visible mass in a typical spiral galaxy is distributed in a thin disk, with a radial extent much larger than its thickness. While the planar disk structure, including non-axisymmetric features such as spiral structure, has been studied extensively, the vertical structure has not received comparable attention. This review aims to give a comprehensive, pedagogic introduction to the rich topic of vertical structure of a galactic disk in hydrostatic equilibrium and discuss the theoretical developments in this field in the context of recent observations. A realistic multi-component disk plus halo model of a galaxy has been developed and studied by us in detail. This takes account of both stars and interstellar gas, treated as isothermal components with different velocity dispersions, which are gravitationally coupled; further, the disk is in the gravitational field of the dark matter halo. This review focuses on this model and the results from it in different physical cases. The gas and halo crucially affect the resulting self-consistent stellar distribution such that it is vertically constrained to be closer to the mid-plane and has a steeper profile than in the standard one-component case, in agreement with modern observations. A typical stellar disk is shown to flare by a factor of few within the visible radial extent of the disk. These robust results question the sech^2 profile and a constant scale height, routinely used in the literature, for convenience. In an important application, the observed HI gas scale height is used as a constraint on the model which helps determine the shape and the density profile of the dark matter halo for galaxies. Finally, we outline some key, open questions which can be addressed in the near future using the above model, and new observational data -- for example, from IFU surveys and JWST -- for a better understanding of this topic.
Multi-messenger observations of binary neutron star mergers: synergies between the next generation gravitational wave interferometers and wide-field, high-multiplex spectroscopic facilities
2025-07-02
S. Bisero, S. D. Vergani, E. Loffredo, M. Branchesi, N. Hazra, U. Dupletsa, R. I. Anderson
Third-generation gravitational wave (GW) observatories such as the Einstein Telescope (ET) and Cosmic Explorer (CE) will detect hundreds of thousands of binary neutron star (BNS) mergers, reaching redshifts beyond $z\sim3$. To fully exploit joint GW and electromagnetic (EM) detections, dedicated strategies and adapted EM facilities are essential. We investigate the role of Integral Field and Multi-Object Spectroscopy (IFS and MOS) with the Wide-field Spectroscopic Telescope (WST) on next generation GW multi-messenger (MM) observations. We consider simulations of BNS populations, their GW detections with ET(+CE), and their EM counterparts: kilonovae (KNe) and gamma-ray bursts (GRBs). We consider two strategies: one in synergy with wide-field photometric surveys, and a galaxy-targeted one exploiting WST high multiplexing. We estimate the number of galaxies in GW error volumes, and identify observational challenges and mitigation strategies. We find that WST can detect KNe up to $z\sim0.4$ and $m_{\mathrm{AB}}\sim25$, and GRB afterglows beyond $z>1$ for $\Theta_{\mathrm{view}}\lesssim15^\circ$. KN observations are best scheduled 12-24 hours post-merger. For poorly localised GRBs, WST IFS can aid the identification. Mini-IFUs and galaxy catalogues complete to $z\leq0.5$ are key to EM counterpart detection. Even at low $z$, the number of galaxies can be huge-thousands at $z<0.1$, tens of thousands at $z<0.2$. Events at $z<0.3$ with localisation $<10$deg$^{2}$ are golden cases for WST, requiring few exposures to target all galaxies. Detecting and characterising EM counterparts of BNS detected in the extended volume explored by next-generation interferometers will be challenging. We show that high-sensitivity, wide-field, high-multiplex spectroscopic facilities are powerful instruments to fully exploit the new multi-messenger science opportunities enabled by next generation GW detectors.
Uniform Reanalysis of JWST MIRI 15μm Exoplanet Eclipse Observations using Frame-Normalized Principal Component Analysis
2025-07-02
Nicholas J. Connors, Christopher Monaghan, Björn Benneke, Lisa Dang
JWST MIRI 15 micron time-series eclipse photometry presents a powerful way to probe for the presence of atmospheres on low-temperature rocky exoplanets orbiting nearby stars. Here, we introduce a novel technique, frame-normalized principal component analysis (FN-PCA) to analyze and detrend these MIRI time-series observations. Using the FN-PCA technique, we perform a uniform reanalysis of the published MIRI 15 micron observations of LHS 1478 b, TOI-1468 b, LHS 1140 c, TRAPPIST-1 b, and TRAPPIST-1 c using our new data reduction pipeline (Erebus) and compare them to different potential atmospheric and surface compositions. We also investigate additional public data sets with the sole purpose of understanding the instrument systematics affecting MIRI. We identify and categorize important detector-level systematics in the observations that are generally present across all 17 analyzed eclipse observations, which we illustrate as eigenimage/eigenvalue pairs in the FN-PCA. One of these eigenimage/eigenvalue pairs corresponds to the prominent ramp effect at the beginning of the time-series observations which has widely been reported for JWST and Spitzer photometry. For JWST/MIRI, we show that the detector settling time scales exponentially with the apparent magnitude of the target star $T_\mathrm{set} \mathrm{[hours]} = 0.063\exp^{0.427\cdot m_K} -0.657$. This uniform reanalysis and investigation of JWST/MIRI systematics is done in preparation for the 500 hour Rocky Worlds DDT survey, to demonstrate a data-driven systematic model usable across all MIRI 15 micron datasets.
Constraining compact dark matter with time-varying quasar equivalent widths
2025-07-02
Georgios Vernardos, James Hung Hsu Chan, Frederic Courbin
One of the possible explanations for dark matter is that of compact dark objects of baryonic origin, such as black holes or even planets. Accumulating evidence, including the discovery of merging stellar mass black holes through gravitational waves, point to a population of such objects making up at least some fraction of dark matter. We revisit a historically heavily used probe, quasar spectra, from the new perspective of time variability and gravitational lensing. From a sample of 777 quasars selected from archival data we identify 19 that show decisive evidence of lensing by compact objects with masses measured in the range $5\times 10^{-5} < M/\mathrm{M}_{\odot} < 2\times 10^{-2}$ with 99\% confidence. This is much lower than what is hoped to be detected by even the most futuristic gravitational wave detectors and analysis strategies, but is crucial for theories of compact dark matter, such as primordial black holes predicted from quantum phase transitions in the early Universe.
TubuleTracker: a high-fidelity shareware software to quantify angiogenesis architecture and maturity
2025-07-02
Danish Mahmood, Stephanie Buczkowski, Sahaj Shah, Autumn Anthony, Rohini Desetty, Carlo R Bartoli
Background: In vitro endothelial cell culture is widely used to study angiogenesis. Histomicrographic images of cell networks are often analyzed manually, a process that is time-consuming and subjective. Automated tools like ImageJ (NIH) can assist, but are often slow and inaccurate. Additionally, as endothelial networks grow more complex, traditional architectural metrics may not fully reflect network maturity. To address these limitations, we developed tubuleTracker, a software tool that quantifies endothelial network architecture and maturity rapidly and objectively. Methods: Human umbilical vein endothelial cells were cultured in an extracellular matrix, and 54 images were acquired using phase contrast microscopy. Each image was analyzed manually by three independent reviewers, and by both ImageJ and tubuleTracker. Key metrics included tubule count, total length, node count, tubule area, and vessel circularity. In parallel, trained scientists rated each image for angiogenesis maturity on a 1-5 scale (1 = most mature). Results: Analysis time per image differed significantly: manual (8 min), ImageJ (58+/-4 s), and tubuleTracker (6+/-2 s) (p<0.0001). Significant differences were also found in tubule count (manual 168+/-SD, tubuleTracker 92+/-SD, ImageJ 433+/-SD), length, and node count (all p<0.0001). tubuleTracker's metrics varied significantly across angiogenesis maturity scores, including tubule count, length, node count, area, and circularity (all p<0.0001). Conclusions: tubuleTracker was faster and more consistent than both manual and ImageJ-based analysis. Vessel circularity proved especially effective in capturing angiogenesis maturity. tubuleTracker is available as free shareware for the biomedical research community.
The Book of Life approach: Enabling richness and scale for life course research
2025-07-02
Mark D. Verhagen, Benedikt Stroebl, Tiffany Liu, Lydia T. Liu, Matthew J. Salganik
For over a century, life course researchers have faced a choice between two dominant methodological approaches: qualitative methods that analyze rich data but are constrained to small samples, and quantitative survey-based methods that study larger populations but sacrifice data richness for scale. Two recent technological developments now enable us to imagine a hybrid approach that combines some of the depth of the qualitative approach with the scale of quantitative methods. The first development is the steady rise of ''complex log data,'' behavioral data that is logged for purposes other than research but that can be repurposed to construct rich accounts of people's lives. The second is the emergence of large language models (LLMs) with exceptional pattern recognition capabilities on plain text. In this paper, we take a necessary step toward creating this hybrid approach by developing a flexible procedure to transform complex log data into a textual representation of an individual's life trajectory across multiple domains, over time, and in context. We call this data representation a ''book of life.'' We illustrate the feasibility of our approach by writing over 100 million books of life covering many different facets of life, over time and placed in social context using Dutch population-scale registry data. We open source the book of life toolkit (BOLT), and invite the research community to explore the many potential applications of this approach.
AI Literacy and LLM Engagement in Higher Education: A Cross-National Quantitative Study
2025-07-02
Shahin Hossain, Shapla Khanam, Samaa Haniya, Nesma Ragab Nasr
This study presents a cross-national quantitative analysis of how university students in the United States and Bangladesh interact with Large Language Models (LLMs). Based on an online survey of 318 students, results show that LLMs enhance access to information, improve writing, and boost academic performance. However, concerns about overreliance, ethical risks, and critical thinking persist. Guided by the AI Literacy Framework, Expectancy-Value Theory, and Biggs' 3P Model, the study finds that motivational beliefs and technical competencies shape LLM engagement. Significant correlations were found between LLM use and perceived literacy benefits (r = .59, p < .001) and optimism (r = .41, p < .001). ANOVA results showed more frequent use among U.S. students (F = 7.92, p = .005) and STEM majors (F = 18.11, p < .001). Findings support the development of ethical, inclusive, and pedagogically sound frameworks for integrating LLMs in higher education.
Degrees of Freedom of Spatial Multiplexing in Distance Domain of Arbitrary Continuous-Aperture Array in Near-Field Region
2025-07-01
Son T. Duong, Tho Le-Ngoc
Extremely large aperture array operating in the near-field regime unlocks additional spatial resources that can be exploited to simultaneously serve multiple users even when they share the same angular direction, a capability not achievable in conventional far-field systems. A fundamental question, however, remains: What is the maximum spatial degree of freedom (DoF) of spatial multiplexing in the distance domain? In this paper, we address this open problem by investigating the spatial DoF of a line-of-sight (LoS) channel between a large two-dimensional transmit aperture and a linear receive array with collinearly-aligned elements (i.e., at the same angular direction) but located at different distances from the transmit aperture. We assume that both the aperture and linear array are continuous-aperture (CAP) arrays with an infinite number of elements and infinitesimal spacing, which establishes an upper bound for the spatial degrees of freedom (DoF) in the case of finite elements. First, we assume an ideal case where the transmit array is a single piece and the linear array is on the broad side of the transmit array. By reformulating the channel as an integral operator with a Hermitian convolution kernel, we derive a closed-form expression for the spatial DoF via the Fourier transform. Our analysis shows that the spatial DoF in the distance domain is predominantly determined by the extreme boundaries of the array rather than its detailed interior structure. We further extend the framework to non-broadside configurations by employing a projection method, which effectively converts the spatial DoF to an equivalent broadside case. Finally, we extend our analytical framework to the modular array, which shows the spatial DoF gain over the single-piece array given the constraint of the physical length of the array.
We have carried out a morphological search for molecular clouds possibly associated with 48 Galactic infrared bubbles with angular radii of $>1'$ in the southern Galactic plane of $295^\circ \le l\le 350^\circ$ and $|b|\le 1^\circ$ presented by Hanaoka et al. (2019). 116 molecular clouds in the $(l,b,V_{\rm LSR})$ space are identified from the archival $^{12}$CO~$J$~=~1--0 line data obtained by the Mopra Southern Galactic plane survey, where $V_{\rm LSR}$ is the CO-line radial velocity. The kinematic distances are derived from $V_{\rm LSR}$ using the most accurate rotation curve of the Milky Way. We also present measurements of velocity dispersion, size parameter, molecular mass, and virial mass of 116 molecular clouds. This catalog is presented to investigate star formation and the origin of molecular shells and cavities, possibly associated with infrared bubbles. CO line intensity maps and position-velocity diagrams of the molecular clouds are available online as supplementary data.
Multi-wavelength variability analysis of the blazar S5 0716+714 during a long-lasting period of low activity
2025-07-01
B. M. Mihov, E. G. Elhosseiny, L. S. Slavcheva-Mihova, A. Takey, M. N. Ismail, R. Mawad
We conducted a multi-wavelength monitoring campaign of the blazar S5 0716+714 from 2022 November 26 to 2023 May 28 using optical telescopes in Egypt and Bulgaria. Data were taken during 84 nights in 11 of which intranight monitoring was performed. We also use optical and gamma-ray survey data. On long-term time-scales, we find a gradual decrease of the S5 0716+714 activity since the beginning of 2020 in both optical and gamma-rays. On short-term time-scales, the individual optical light curves are strongly correlated among each other with no time lags observed. The V-band percentage variability amplitude equals 97.59 +/- 0.02 per cent. We find moderate flatter-when-brighter spectral behaviour with the strength of the 'spectral index - flux' anti-correlation decreasing towards the longer wavelengths. The main feature of the short-term light curves is a transient quasi-periodic oscillation with a period of 43.5 +/- 3.6 d. The V-band light curve is modelled with two helically moving blobs and a synchrotron flare. We estimate the resulting parameters, as well as limits on the radius, magnetic field strength, and electron Lorentz factor of the region responsible for the flare. On intranight time-scales, we find smooth flux variations with no flares and derive a duty cycle in the range ~10-20 per cent. The lack of flares on intranight time-scales could result from a temporarily homogeneous jet flow without formation of turbulent cells in terms of prevented Kelvin-Helmholtz instability. The analysis of the data reveals a low activity of S5 0716+714 on all time-scales during the observation period.
The ODYSSEUS Survey. Characterizing magnetospheric geometries and hotspot structures in T Tauri stars
2025-07-01
Caeley V. Pittman, C. C. Espaillat, Connor E. Robinson, Thanawuth Thanathibodee, Sophia Lopez, Nuria Calvet, Zhaohuan Zhu, Frederick M. Walter, John Wendeborn, Carlo Felice Manara, Justyn Campbell-White, Rik A. Claes, Min Fang, Antonio Frasca, J. F. Gameiro, Manuele Gangi, Jesus Hernández, Ágnes Kóspál, Karina Maucó, James Muzerolle, Michał Siwak, Łukasz Tychoniec, Laura Venuti
Magnetospheric accretion is a key process that shapes the inner disks of T Tauri stars, controlling mass and angular momentum evolution. It produces strong ultraviolet and optical emission that irradiates the planet-forming environment. In this work, we characterize the magnetospheric geometries, accretion rates, extinction properties, and hotspot structures of 67 T Tauri stars in the largest and most consistent study of ultraviolet and optical accretion signatures to date. To do so, we apply an accretion flow model to velocity-resolved H$\alpha$ profiles for T Tauri stars from the HST/ULLYSES program with consistently-derived stellar parameters. We find typical magnetospheric truncation radii to be almost half of the usually-assumed value of 5 stellar radii. We then model the same stars' HST/STIS spectra with an accretion shock model, finding a diverse range of hotspot structures. Phase-folding multi-epoch shock models reveals rotational modulation of observed hotspot energy flux densities, indicative of hotspots that persist for at least 3 stellar rotation periods. For the first time, we perform a large-scale, self-consistent comparison of accretion rates measured using accretion flow and shock models, finding them to be consistent within $\sim$0.16 dex for contemporaneous observations. Finally, we find that up to 50% of the total accretion luminosity is at short wavelengths accessible only from space, highlighting the crucial role of ultraviolet spectra in constraining accretion spectral energy distributions, hotspot structure, and extinction.
Sound source localization (SSL) adds a spatial dimension to auditory perception, allowing a system to pinpoint the origin of speech, machinery noise, warning tones, or other acoustic events, capabilities that facilitate robot navigation, human-machine dialogue, and condition monitoring. While existing surveys provide valuable historical context, they typically address general audio applications and do not fully account for robotic constraints or the latest advancements in deep learning. This review addresses these gaps by offering a robotics-focused synthesis, emphasizing recent progress in deep learning methodologies. We start by reviewing classical methods such as Time Difference of Arrival (TDOA), beamforming, Steered-Response Power (SRP), and subspace analysis. Subsequently, we delve into modern machine learning (ML) and deep learning (DL) approaches, discussing traditional ML and neural networks (NNs), convolutional neural networks (CNNs), convolutional recurrent neural networks (CRNNs), and emerging attention-based architectures. The data and training strategy that are the two cornerstones of DL-based SSL are explored. Studies are further categorized by robot types and application domains to facilitate researchers in identifying relevant work for their specific contexts. Finally, we highlight the current challenges in SSL works in general, regarding environmental robustness, sound source multiplicity, and specific implementation constraints in robotics, as well as data and learning strategies in DL-based SSL. Also, we sketch promising directions to offer an actionable roadmap toward robust, adaptable, efficient, and explainable DL-based SSL for next-generation robots.
The spherical noise added to gradients in differentially private (DP) training undermines the performance of adaptive optimizers like AdaGrad and Adam, and hence many recent works have proposed algorithms to address this challenge. However, the empirical results in these works focus on simple tasks and models and the conclusions may not generalize to model training in practice. In this paper we survey several of these variants, and develop better theoretical intuition for them as well as perform empirical studies comparing them. We find that a common intuition of aiming for unbiased estimates of second moments of gradients in adaptive optimizers is misguided, and instead that a simple technique called scale-then-privatize (which does not achieve unbiased second moments) has more desirable theoretical behaviors and outperforms all other variants we study on a small-scale language model training task. We additionally argue that scale-then-privatize causes the noise addition to better match the application of correlated noise mechanisms which are more desirable to use in practice.
Reproductive well-being is shaped by intersecting cultural, religious, gendered, and political contexts, yet current technologies often reflect narrow, Western-centric assumptions. In this literature review, we synthesize findings from 147 peer-reviewed papers published between 2015 and 2025 across HCI, CSCW and social computing, ICTD, digital and public health, and AI for well-being scholarship to map the evolving reproductive well-being landscape. We identify three thematic waves that focused on early access and education, cultural sensitivity and privacy, and AI integration with policy-aware design, and highlight how technologies support or constrain diverse reproductive experiences. Our analysis reveals critical gaps in inclusivity, with persistent exclusions of men and non-binary users, migrants, and users in the Global South. Additionally, we surfaced the significant absence of literature on the role of stakeholders (e.g., husband and family members, household maids and cleaning helping hands, midwife, etc.) in the reproductive well-being space. Drawing on the findings from the literature, we propose the ReWA framework to support reproductive well-being for all agendas through six design orientations associated with: location, culture, and history; polyvocality and agency; rationality, temporality, distributive roles, and methodology.
2025-07-01
Christopher J. Lindsay, Marc Hon, J. M. Joel Ong, Rafael A. García, Dinil B. Palakkatharappil, Jie Yu, Tanda Li, Tomás Ruiz-Lara, Amina Helmi
The Helmi streams are remnants of a dwarf galaxy that was accreted by the Milky Way and whose stars now form a distinct kinematic and chemical substructure in the Galactic halo. Precisely age-dating these typically faint stars of extragalactic origin has been notoriously difficult due to the limitations of using only spectroscopic data, interferometry, or coarse asteroseismic measurements. Using observations from NASA's Transiting Exoplanet Survey Satellite, we report the detailed asteroseismic modeling of two of the brightest red giants within the Helmi streams, HD 175305 and HD 128279. By modeling the individual oscillation mode frequencies and the spectroscopic properties of both stars, we determine their fundamental properties including mass, radius, and age ($\tau$). We report $\tau = 11.16 \pm 0.91$ Gyr for HD 175305 and $\tau = 12.52 \pm 1.05$ Gyr for HD 128279, consistent with previously inferred star-formation histories for the Helmi streams and the differential chemical abundances between the two stars. With precise ages for individual stream members, our results reinforce the hypothesis that the Helmi streams' progenitor must have existed at least 12 Gyr ago. Our results also highlight that the ages of metal-poor, $\alpha$-enhanced red giants can be severely underestimated when inferred using global asteroseismic parameters instead of individual mode frequencies.
LIGHTS. A robust technique to identify galaxy edges
2025-07-01
Giulia Golini, Ignacio Trujillo, Dennis Zaritsky, Mireia Montes, Raúl Infante Sainz, Garreth Martin, Nushkia Chamba, Ignacio Ruiz Cejudo, Andrés Asensio Ramos, Chen Yu Chuang, Mauro D'Onofrio, Sepideh Eskandarlou, S. Zahra Hosseini ShahiSavandi, Ouldouz Kaboud, Carlos Marrero de la Rosa, Minh Ngoc Le, Samane Raji, Javier Román, Nafise Sedighi, Zahra Sharbaf, Richard Donnerstein, Sergio Guerra Arencibia
The LIGHTS survey is imaging galaxies at a depth and spatial resolution comparable to what the Legacy Survey of Space and Time (LSST) will produce in 10 years (i.e., $\sim$31 mag/arcsec$^2$; 3$\sigma$ in areas equivalent to 10$^{\prime\prime}$$\times$ 10$^{\prime\prime}$). This opens up the possibility of probing the edge of galaxies, as the farthest location of in-situ star formation, with a precision that we have been unable to achieve in the past. Traditionally, galaxy edges have been analyzed in one-dimension through ellipse averaging or visual inspection. Our approach allows for a two-dimensional exploration of galaxy edges, which is crucial for understanding deviations from disc symmetry and the environmental effects on galaxy growth. In this paper, we propose a novel method using the second derivative of the surface mass density map of a galaxy to determine its edges. This offers a robust quantitative alternative to traditional edge-detection methods when deep imaging is available. Our technique incorporates Wiener-Hunt deconvolution to remove the effect of the Point Spread Function (PSF) by the galaxy itself. By applying our methodology to the LIGHTS galaxy NGC 3486, we identify the edge at 205$^{\prime\prime}$ $\pm$ 5$^{\prime\prime}$. At this radius, the stellar surface mass density is $\sim$1 M$_\odot$/pc$^2$, supporting a potential connection between galaxy edges and a threshold for in-situ star formation. Our two-dimensional analysis on NGC 3486 reveals an edge asymmetry of $\sim$5$\%$. These techniques will be of paramount importance for a physically motivated determination of the sizes of galaxies in ultra-deep surveys such as LSST, Euclid and Roman.
The BoRG-$JWST$ Survey: Analogs at $z\sim8$ to the UV-luminous Galaxy Population at $z\gtrsim10$
2025-07-01
Sofía Rojas-Ruiz, Guido Roberts-Borsani, Takahiro Morishita, Antonello Calabrò, Micaela B. Bagley, Tommaso Treu, Steven L. Finkelstein, Massimo Stiavelli, Michele Trenti, L. Y. Aaron Yung
The population of bright galaxies at $z\gtrsim10$ discovered by JWST, including the so-called "blue monsters", has been difficult to reconcile with standard galaxy evolution models. To shed light on this extraordinary population, we study the $z\sim8$ galaxies discovered by the BoRG-$JWST$ survey. These slightly-lower redshift analogs are comparable in UV luminosity to the blue monsters, and their lower redshift makes it much easier to access key rest frame optical diagnostics with NIRspec. We find that BoRG-$JWST$ galaxies are consistent with being dust-poor based on their blue UV slopes and Balmer decrement ratios. We find no strong evidence for dominant active galactic nuclei contribution to the UV brightness, based on line-ratio diagnostics, though some contribution cannot be excluded. We further infer the stellar mass, star formation and UV-brightness history of the BoRG-$JWST$ galaxies by fitting their rest-frame UV-optical spectra. We see evidence for stochastic episodes of star formation for all the BoRG-$JWST$ galaxies, providing a temporal boosting of UV luminosity in short timescales. The UV-bright blue monsters at $z\gtrsim10$ can be explained by the presence of stars with ages below 100 Myr.
RTMap: Real-Time Recursive Mapping with Change Detection and Localization
2025-07-01
Yuheng Du, Sheng Yang, Lingxuan Wang, Zhenghua Hou, Chengying Cai, Zhitao Tan, Mingxia Chen, Shi-Sheng Huang, Qiang Li
While recent online HD mapping methods relieve burdened offline pipelines and solve map freshness, they remain limited by perceptual inaccuracies, occlusion in dense traffic, and an inability to fuse multi-agent observations. We propose RTMap to enhance these single-traversal methods by persistently crowdsourcing a multi-traversal HD map as a self-evolutional memory. On onboard agents, RTMap simultaneously addresses three core challenges in an end-to-end fashion: (1) Uncertainty-aware positional modeling for HD map elements, (2) probabilistic-aware localization w.r.t. the crowdsourced prior-map, and (3) real-time detection for possible road structural changes. Experiments on several public autonomous driving datasets demonstrate our solid performance on both the prior-aided map quality and the localization accuracy, demonstrating our effectiveness of robustly serving downstream prediction and planning modules while gradually improving the accuracy and freshness of the crowdsourced prior-map asynchronously. Our source-code will be made publicly available at https://github.com/CN-ADLab/RTMap (Camera ready version incorporating reviewer suggestions will be updated soon).
This work studies how a suitably-designed classical system generates with a quantum-like (QL) state space mediated by a graph. The graph plays a special dual role by directing the topology of the classical network and defining a state space that comprises superpositions of states in a tensor product basis. The basis for constructing QL graphs and their properties is reviewed and extended. An optimization of the graph product is developed to produce a more compact graph with the essential properties required to produce states that mimic many of the properties of quantum states. This provides a concrete visualization of the correlation structure in a quantum state space. The question of whether and, if so, how, entanglement can be exhibited by these QL systems is discussed critically and contrasted to the concept of `classical entanglement' in optics.
2025-07-01
Jack Foxabbott, Arush Tagade, Andrew Cusick, Robbie McCorkell, Leo McKee-Reid, Jugal Patel, Jamie Rumbelow, Jessica Rumbelow, Zohreh Shams
The Discovery Engine is a general purpose automated system for scientific discovery, which combines machine learning with state-of-the-art ML interpretability to enable rapid and robust scientific insight across diverse datasets. In this paper, we benchmark the Discovery Engine against five recent peer-reviewed scientific publications applying machine learning across medicine, materials science, social science, and environmental science. In each case, the Discovery Engine matches or exceeds prior predictive performance while also generating deeper, more actionable insights through rich interpretability artefacts. These results demonstrate its potential as a new standard for automated, interpretable scientific modelling that enables complex knowledge discovery from data.
Social Robots for People with Dementia: A Literature Review on Deception from Design to Perception
2025-07-01
Fan Wang, Giulia Perugia, Yuan Feng, Wijnand IJsselsteijn
As social robots increasingly enter dementia care, concerns about deception, intentional or not, are gaining attention. Yet, how robotic design cues might elicit misleading perceptions in people with dementia, and how these perceptions arise, remains insufficiently understood. In this scoping review, we examined 26 empirical studies on interactions between people with dementia and physical social robots. We identify four key design cue categories that may influence deceptive impressions: cues resembling physiological signs (e.g., simulated breathing), social intentions (e.g., playful movement), familiar beings (e.g., animal-like form and sound), and, to a lesser extent, cues that reveal artificiality. Thematic analysis of user responses reveals that people with dementia often attribute biological, social, and mental capacities to robots, dynamically shifting between awareness and illusion. These findings underscore the fluctuating nature of ontological perception in dementia contexts. Existing definitions of robotic deception often rest on philosophical or behaviorist premises, but rarely engage with the cognitive mechanisms involved. We propose an empirically grounded definition: robotic deception occurs when Type 1 (automatic, heuristic) processing dominates over Type 2 (deliberative, analytic) reasoning, leading to misinterpretation of a robot's artificial nature. This dual-process perspective highlights the ethical complexity of social robots in dementia care and calls for design approaches that are not only engaging, but also epistemically respectful.
Rocky exoplanet characterization has been a top priority for early James Webb Space Telescope (JWST) science operations. Several milestones have been achieved, including the most precise rocky planet transmission spectra measured to date, and the first detection of thermal emission for rocky worlds below 800 Kelvin. Despite these advances, no atmospheres have been definitively detected. Several transmission spectra show tentative evidence for molecular absorption features, but these hints are marginally significant and the spectra may be affected by stellar contamination. Features from many plausible atmospheres, including those dominated by oxygen, nitrogen, and carbon dioxide, are below the current noise level. Meanwhile, the emerging picture from thermal emission spectra is that the planets have hot daysides, consistent with either a bare rock composition or low surface pressure atmospheres (< 10 bar). Higher surface pressures and high carbon dioxide abundances are generally ruled out, assuming cloud-free atmosphere models. The absence of strong CO$_2$ features hints at a limited initial volatile inventory or rapid atmospheric escape during the planets' early lifetimes. Taken together, these results motivate a push towards higher precision data, as well as observations of cooler planets that may be more likely to retain atmospheres. As a goal for future transmission spectroscopy, we suggest a "five scale height challenge," to achieve the precision necessary to detect CO$_2$ features in nitrogen-rich atmospheres. Detecting rocky planet atmospheres remains challenging, but with JWST's excellent performance and a continuing investment of telescope time, we are optimistic these uncharted atmospheres will be detected in coming years.
The impact of differential rotation on the stochastic excitation of acoustic modes in solar-like pulsators
2025-07-01
Gabriel Biscarrat, Leïla Bessila, Stéphane Mathis
Acoustic modes are excited by turbulent convection in the outer convective envelope of solar-like stars. Observational results from asteroseismic studies show that 44% of solar-like stars do not present detectable stochastically-excited acoustic modes. This phenomenon appears to be related to their rotation rate and magnetic activity. In a first paper, we showed that uniform rotation tends to diminish the mode amplitudes significantly. However, convective envelopes in solar-type stars are differentially rotating: the rotation rate difference between mid-latitudes and the equator can go up to 60%, as shown by recent asteroseismic works. In this paper, we examine the impact of differential rotation on the stochastic excitation of acoustic modes in solar-like stars. We provide theoretical predictions for the excitation of acoustic modes in a differentially rotating solar-like star. We use the Rotating Mixing-Length Theory approach to model the local influence of differential rotation on convection. We then estimate the resulting impact on power injection by turbulent convection into oscillation modes numerically, using a combination of the MESA stellar structure and evolution code and GYRE stellar pulsation code. We show that the power injected in acoustic modes differs by up to 30 % for stars with the same mean rotation rate $5 \Omega_{\odot}$ but a distinct differential rotation rate. The excitation of axisymmetric acoustic modes is further inhibited in the anti-solar differential rotation regime where the pole of stars rotates faster than their equator when compared to the uniform rotation case. This could hinder mode detection in such configurations. This study permits a first prediction of the excitation of acoustic modes as a function of the differential rotation in solar-like pulsators.
The pursuit of artificial general intelligence (AGI) has placed embodied intelligence at the forefront of robotics research. Embodied intelligence focuses on agents capable of perceiving, reasoning, and acting within the physical world. Achieving robust embodied intelligence requires not only advanced perception and control, but also the ability to ground abstract cognition in real-world interactions. Two foundational technologies, physical simulators and world models, have emerged as critical enablers in this quest. Physical simulators provide controlled, high-fidelity environments for training and evaluating robotic agents, allowing safe and efficient development of complex behaviors. In contrast, world models empower robots with internal representations of their surroundings, enabling predictive planning and adaptive decision-making beyond direct sensory input. This survey systematically reviews recent advances in learning embodied AI through the integration of physical simulators and world models. We analyze their complementary roles in enhancing autonomy, adaptability, and generalization in intelligent robots, and discuss the interplay between external simulation and internal modeling in bridging the gap between simulated training and real-world deployment. By synthesizing current progress and identifying open challenges, this survey aims to provide a comprehensive perspective on the path toward more capable and generalizable embodied AI systems. We also maintain an active repository that contains up-to-date literature and open-source projects at https://github.com/NJU3DV-LoongGroup/Embodied-World-Models-Survey.
Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications
2025-07-01
Jindong Han, Yansong Ning, Zirui Yuan, Hang Ni, Fan Liu, Tengfei Lyu, Hao Liu
The long-standing vision of intelligent cities is to create efficient, livable, and sustainable urban environments using big data and artificial intelligence technologies. Recently, the advent of Large Language Models (LLMs) has opened new ways toward realizing this vision. With powerful semantic understanding and reasoning capabilities, LLMs can be deployed as intelligent agents capable of autonomously solving complex problems across domains. In this article, we focus on Urban LLM Agents, which are LLM-powered agents that are semi-embodied within the hybrid cyber-physical-social space of cities and used for system-level urban decision-making. First, we introduce the concept of urban LLM agents, discussing their unique capabilities and features. Second, we survey the current research landscape from the perspective of agent workflows, encompassing urban sensing, memory management, reasoning, execution, and learning. Third, we categorize the application domains of urban LLM agents into five groups: urban planning, transportation, environment, public safety, and urban society, presenting representative works in each group. Finally, we discuss trustworthiness and evaluation issues that are critical for real-world deployment, and identify several open problems for future research. This survey aims to establish a foundation for the emerging field of urban LLM agents and to provide a roadmap for advancing the intersection of LLMs and urban intelligence. A curated list of relevant papers and open-source resources is maintained and continuously updated at https://github.com/usail-hkust/Awesome-Urban-LLM-Agents.
Advances in aviation radiation mitigation were demonstrated during the Gannon storm
2025-07-01
W. K. Tobiska, B. Hogan, L. Didkovsky, K. Judge, J. Bailey, K. Drumm, K. Wahl, A. Sosnov
The validation of a strategy for aviation radiation hazard mitigation in development for decades has been completed using two commercial airline flights in 2024 and 2025. This article provides a historical review of the primary elements leading to that strategy, including the building of aviation radiation awareness and collaborative efforts by global aviation and radiological bodies that established mitigation standards. The validation of an ALARA strategy was accomplished using two UAL 990 flights on B777 200 aircraft between San Francisco and Paris. Each carried the same ARMAS FM7 radiation monitoring unit, where one flight occurred during the extreme geomagnetic storm (Gannon storm) May 10 to 11, 2024 and one flight occurred during quiet geomagnetic conditions June 8 to 9, 2025. The flight results validated the strategy using the ALARA principle in an area that is under operational control during extreme space weather, i.e., applying shielding from two domains to reduce dose. One method is flying lower magnetic latitudes to gain more Earth magnetic field shielding and the other is flying lower altitudes to use atmosphere depth shielding. Both of these ALARA shielding methods are controllable in airline operations and air traffic management and have now been validated with total dose measurements by ARMAS. This study shows the effectiveness of the strategy to deviate flight paths to lower magnetic latitude routes and lower altitudes during major geomagnetic storms. Not only does this approach mitigate HF communication outages but it also reduces risks from increased GNSS errors for takeoff and landing navigation. Magnetic field shielding is a major risk reduction factor for radiation, communication, and navigation while altitude shielding substantially reduces radiation hazard risks.
Template-Fitting Meets Deep Learning: Redshift Estimation Using Physics-Guided Neural Networks
2025-07-01
Jonas Chris Ferrao, Dickson Dias, Pranav Naik, Glory D'Cruz, Anish Naik, Siya Khandeparkar, Manisha Gokuldas Fal Dessai
Accurate photometric redshift estimation is critical for observational cosmology, especially in large-scale surveys where spectroscopic measurements are impractical. Traditional approaches include template fitting and machine learning, each with distinct strengths and limitations. We present a hybrid method that integrates template fitting with deep learning using physics-guided neural networks. By embedding spectral energy distribution templates into the network architecture, our model encodes physical priors into the training process. The system employs a multimodal design, incorporating cross-attention mechanisms to fuse photometric and image data, along with Bayesian layers for uncertainty estimation. We evaluate our model on the publicly available PREML dataset, which includes approximately 400,000 galaxies from the Hyper Suprime-Cam PDR3 release, with 5-band photometry, multi-band imaging, and spectroscopic redshifts. Our approach achieves an RMS error of 0.0507, a 3-sigma catastrophic outlier rate of 0.13%, and a bias of 0.0028. The model satisfies two of the three LSST photometric redshift requirements for redshifts below 3. These results highlight the potential of combining physically motivated templates with data-driven models for robust redshift estimation in upcoming cosmological surveys.
Enhancing Vehicular Platooning with Wireless Federated Learning: A Resource-Aware Control Framework
2025-07-01
Beining Wu, Jun Huang, Qiang Duan, Liang Dong, Zhipeng Cai
This paper aims to enhance the performance of Vehicular Platooning (VP) systems integrated with Wireless Federated Learning (WFL). In highly dynamic environments, vehicular platoons experience frequent communication changes and resource constraints, which significantly affect information exchange and learning model synchronization. To address these challenges, we first formulate WFL in VP as a joint optimization problem that simultaneously considers Age of Information (AoI) and Federated Learning Model Drift (FLMD) to ensure timely and accurate control. Through theoretical analysis, we examine the impact of FLMD on convergence performance and develop a two-stage Resource-Aware Control framework (RACE). The first stage employs a Lagrangian dual decomposition method for resource configuration, while the second stage implements a multi-agent deep reinforcement learning approach for vehicle selection. The approach integrates Multi-Head Self-Attention and Long Short-Term Memory networks to capture spatiotemporal correlations in communication states. Experimental results demonstrate that, compared to baseline methods, the proposed framework improves AoI optimization by up to 45%, accelerates learning convergence, and adapts more effectively to dynamic VP environments on the AI4MARS dataset.
Do Echo Top Heights Improve Deep Learning Nowcasts?
2025-07-01
Peter Pavlík, Marc Schleiss, Anna Bou Ezzeddine, Viera Rozinajová
Precipitation nowcasting -- the short-term prediction of rainfall using recent radar observations -- is critical for weather-sensitive sectors such as transportation, agriculture, and disaster mitigation. While recent deep learning models have shown promise in improving nowcasting skill, most approaches rely solely on 2D radar reflectivity fields, discarding valuable vertical information available in the full 3D radar volume. In this work, we explore the use of Echo Top Height (ETH), a 2D projection indicating the maximum altitude of radar reflectivity above a given threshold, as an auxiliary input variable for deep learning-based nowcasting. We examine the relationship between ETH and radar reflectivity, confirming its relevance for predicting rainfall intensity. We implement a single-pass 3D U-Net that processes both the radar reflectivity and ETH as separate input channels. While our models are able to leverage ETH to improve skill at low rain-rate thresholds, results are inconsistent at higher intensities and the models with ETH systematically underestimate precipitation intensity. Three case studies are used to illustrate how ETH can help in some cases, but also confuse the models and increase the error variance. Nonetheless, the study serves as a foundation for critically assessing the potential contribution of additional variables to nowcasting performance.
On the Surprising Efficacy of LLMs for Penetration-Testing
2025-07-01
Andreas Happe, Jürgen Cito
This paper presents a critical examination of the surprising efficacy of Large Language Models (LLMs) in penetration testing. The paper thoroughly reviews the evolution of LLMs and their rapidly expanding capabilities which render them increasingly suitable for complex penetration testing operations. It systematically details the historical adoption of LLMs in both academic research and industry, showcasing their application across various offensive security tasks and covering broader phases of the cyber kill chain. Crucially, the analysis also extends to the observed adoption of LLMs by malicious actors, underscoring the inherent dual-use challenge of this technology within the security landscape. The unexpected effectiveness of LLMs in this context is elucidated by several key factors: the strong alignment between penetration testing's reliance on pattern-matching and LLMs' core strengths, their inherent capacity to manage uncertainty in dynamic environments, and cost-effective access to competent pre-trained models through LLM providers. The current landscape of LLM-aided penetration testing is categorized into interactive 'vibe-hacking' and the emergence of fully autonomous systems. The paper identifies and discusses significant obstacles impeding wider adoption and safe deployment. These include critical issues concerning model reliability and stability, paramount safety and security concerns, substantial monetary and ecological costs, implications for privacy and digital sovereignty, complex questions of accountability, and profound ethical dilemmas. This comprehensive review and analysis provides a foundation for discussion on future research directions and the development of robust safeguards at the intersection of AI and security.
ProxAnn: Use-Oriented Evaluations of Topic Models and Document Clustering
2025-07-01
Alexander Hoyle, Lorena Calvo-Bartolomé, Jordan Boyd-Graber, Philip Resnik
Topic model and document-clustering evaluations either use automated metrics that align poorly with human preferences or require expert labels that are intractable to scale. We design a scalable human evaluation protocol and a corresponding automated approximation that reflect practitioners' real-world usage of models. Annotators -- or an LLM-based proxy -- review text items assigned to a topic or cluster, infer a category for the group, then apply that category to other documents. Using this protocol, we collect extensive crowdworker annotations of outputs from a diverse set of topic models on two datasets. We then use these annotations to validate automated proxies, finding that the best LLM proxies are statistically indistinguishable from a human annotator and can therefore serve as a reasonable substitute in automated evaluations. Package, web interface, and data are at https://github.com/ahoho/proxann
The $3$-uniform tight $\ell$-cycle minus one edge $C_{\ell}^{3-}$ is the $3$-graph on $\ell$ vertices consisting of $\ell-1$ consecutive triples in the cyclic order. We show that for every integer $\ell \ge 5$ satisfying $\ell\not\equiv 0\pmod3$, every $C_{\ell}^{3-}$-free $3$-graph whose $\ell_2$-norm, that is, the sum of codegree squares, is close to the maximum must be structurally close to the iterative blowup of a single triple. This confirms a conjecture of Balogh--Clemen--Lidick\'{y}~[Surveys in combinatorics 2022, 21-63] in a stronger form.
3D medical image generation is essential for data augmentation and patient privacy, calling for reliable and efficient models suited for clinical practice. However, current methods suffer from limited anatomical fidelity, restricted axial length, and substantial computational cost, placing them beyond reach for regions with limited resources and infrastructure. We introduce TRACE, a framework that generates 3D medical images with spatiotemporal alignment using a 2D multimodal-conditioned diffusion approach. TRACE models sequential 2D slices as video frame pairs, combining segmentation priors and radiology reports for anatomical alignment, incorporating optical flow to sustain temporal coherence. During inference, an overlapping-frame strategy links frame pairs into a flexible length sequence, reconstructed into a spatiotemporally and anatomically aligned 3D volume. Experimental results demonstrate that TRACE effectively balances computational efficiency with preserving anatomical fidelity and spatiotemporal consistency. Code is available at: https://github.com/VinyehShaw/TRACE.
The Gaia-ESO Survey: Projected Rotational Velocities of B stars in the Carina Nebula
2025-07-01
W. Santos, S. Daflon, J. V. Sales Silva, K. Cunha, R. Blomme, T. Morel, A. Herrero, J. Maiz Apellániz, L. Mahy, S. R. Berlanas, T. Bensby, A. Bragaglia, F. Damiani, A. Frasca, G. Gilmore, V. Kalari, A. Lanzafame, S. Randich, S. Van Eck, S. Zaggia, L. O. Kerber, O. J Katime Santrich
The Carina Nebula is an active star-forming region with several open clusters rich in massive OB stars, thus making it an optimal target for studying stellar properties such as rotation for large samples of these early-type stars. We studied a sample of early-type stars probable members of the 8 open clusters in the Carina Complex. The observational data consist of high-resolution spectra from the Gaia-ESO public Spectroscopic Survey. Astrometric and photometric data from Gaia EDR3 and radial velocities measured from the observed spectra are used to confirm the cluster members. The projected rotational velocities of 330 early-type stars of Carina are derived from the widths of \ion{He}{i} lines at 4388 and 4471 \AA. The reported \Vsini\ values are the first estimates for 222 early-type stars. The \Vsini\ distribution for the Carina clusters peaks at $\sim$100-150 \kms, consistent with the distributions for B stars in Galactic clusters. \Vsini\ estimates for stars members of the clusters Trumpler 15, Collinder 228, Collinder 232, and Bochum 11 are presented for the first time in the literature. For a subsample of stars with earlier spectral types from B0 to B3, we find a bimodal distribution, with a third, small peak towards the upper values of \Vsini. When the full sample is split according to the parent cluster, we find that the oldest cluster in our sample, NGC 3293, presents a higher concentration of rapidly rotating stars. In contrast, Collinder 228 presents a larger number of stars with lower \Vsini.
Generative AI and the future of scientometrics: current topics and future questions
2025-07-01
Benedetto Lepori, Jens Peter Andersen, Karsten Donnay
The aim of this paper is to review the use of GenAI in scientometrics, and to begin a debate on the broader implications for the field. First, we provide an introduction on GenAI's generative and probabilistic nature as rooted in distributional linguistics. And we relate this to the debate on the extent to which GenAI might be able to mimic human 'reasoning'. Second, we leverage this distinction for a critical engagement with recent experiments using GenAI in scientometrics, including topic labelling, the analysis of citation contexts, predictive applications, scholars' profiling, and research assessment. GenAI shows promise in tasks where language generation dominates, such as labelling, but faces limitations in tasks that require stable semantics, pragmatic reasoning, or structured domain knowledge. However, these results might become quickly outdated. Our recommendation is, therefore, to always strive to systematically compare the performance of different GenAI models for specific tasks. Third, we inquire whether, by generating large amounts of scientific language, GenAI might have a fundamental impact on our field by affecting textual characteristics used to measure science, such as authors, words, and references. We argue that careful empirical work and theoretical reflection will be essential to remain capable of interpreting the evolving patterns of knowledge production.
Designing Visualization Widgets for Tangible Data Exploration: A Systematic Review
2025-07-01
Haonan Yao, Lingyun Yu, Lijie Yao
We present a systematic review on tasks, interactions, and visualization widgets (refer to tangible entities that are used to accomplish data exploration tasks through specific interactions) in the context of tangible data exploration. Tangible widgets have been shown to reduce cognitive load, enable more natural interactions, and support the completion of complex data exploration tasks. Yet, the field lacks a structured understanding of how task types, interaction methods, and widget designs are coordinated, limiting the ability to identify recurring design patterns and opportunities for innovation. To address this gap, we conduct a systematic review to analyze existing work and characterize the current design of data exploration tasks, interactions, and tangible visualization widgets. We next reflect based on our findings and propose a research agenda to inform the development of a future widget design toolkit for tangible data exploration. Our systematic review and supplemental materials are available at physicalviswidget.github.io and osf.io/vjw5e.
Microlensing of dark matter models in the Milky Way
2025-07-01
Bichu Li, Chan-Yu Tang, Zhuo-Ran Huang, Lei-Hua Liu
We investigate constraints on the abundance of primordial black holes (PBHs) as dark matter (DM) candidates using five years of microlensing data from the OGLE survey. While the majority of OGLE's $\sim 2000$ microlensing events are well-explained by stellar populations such as brown dwarfs, main-sequence stars, and compact remnants, a subset of six ultrashort-timescale events ($t_E \sim 0.1 - 0.3$ days) may signal the presence of PBHs. Building upon prior work that adopted the Navarro-Frenk-White (NFW) DM profile, we examine how alternative DM halo models -- specifically the Einasto and Burkert profiles -- affect microlensing predictions and the constraints on PBH abundance. We computed differential microlensing event rates for both profiles, using the main-sequence star rate as an observational benchmark. Our results show that neither the Einasto nor Burkert profiles reproduce the distribution of main-sequence star events, yet both allow for viable explanations of the ultrashort-timescale events with PBH masses $M_{\mathrm{PBH}} \sim 10^{-5} M_\odot$. Using a Poisson likelihood analysis under the null hypothesis that no PBH is observed in OGLE dataset, we derive 95\% C.L. upper bounds on $f_{\mathrm{PBH}}$, finding that the constraints are significantly relaxed under the Einasto and Burkert profiles compared to the NFW case. These results underscore the sensitivity of PBH constraints to the assumed DM halo structure and highlight the importance of accurately modeling the inner Galactic density profile to robustly assess PBH dark matter scenarios.
Physical Layer Group Key Generation With the Aid of Reconfigurable Intelligent Surfaces
2025-07-01
Vahid Shahiri, Guyue Li, Hamid Behroozi
Reconfigurable intelligent surfaces (RIS) have the ability to alter the wireless environment by making changes in the impinging signal. Motivated by this ability, in this study, we exploit the RIS to make the aggregate reflecting channels of different user terminals (UTs) as similar as possible to be able to extract common group secret keys from their channels. Specifically, the RIS will adjust its parameters to pave the way for group key generation (GKG) based on the physical channels of the UTs. Our method exploits the already gathered channel state information (CSI) in the RIS to beneficially design the phase shifts and does not impose additional probing burden on the network. Additionally, this scheme is broadcast-based and does not entail the overheads of the pairwise-based key generation. We consider both passive RIS (PRIS) and active RIS (ARIS) to generate the group keys. The PRIS is widely adopted in physical layer key generation (PLKG) studies due to its use of passive elements, whereas the ARIS demonstrates superior capability in aligning the aggregate reflected channels among nodes in the GKG scenario, as demonstrated in this study. We will exploit various optimization methods like successive convex approximation (SCA) and semidefinite relaxation with Gaussian randomization (SDR-GR) to address the raised optimization problems. Unlike most of the studies in the literature, our scheme can achieve a high GKG rate in static environments as well. Finally, we will examine the performance of the proposed method by normalized mean squared error (NMSE), key error rate (KER), key generation rate (KGR) and key randomness metrics. Our numerical results verify that for the equal available power budget, the ARIS significantly outperforms PRIS in NMSE and KER, achieving more than four times higher KGR.
Ultralight dark matter refers to the lightest potential dark matter candidates. We will focus on the mass range that has been studied using astrophysical and cosmological observations, corresponding to a mass $10^{-24} \, \mathrm{eV} \lesssim m \lesssim 10^{-18} \, \mathrm{eV}$. We will discuss the motivations for this mass range. The most studied model in this range corresponds to a minimally coupled, single, classical, spin-0 field comprising all dark matter. However, the work exploring extensions of this model (for example, higher spin, self-coupled, multiple field, and mixed models) will be one of the focuses of this review. The phenomenology associated with ultralight dark matter is rich and includes linear effects on the primordial power spectrum, core structures forming at the center of halos, nonlinear effects resulting in heating of stellar distributions, and non-relativistic effects relating to pulsar signals and black hole superradiance, to name a few. This set of effects has been studied using an equally extensive set of numerical tools. We will summarize the most common ones and discuss their applications and limitations. Ultralight dark matter also has a wide variety of observational constraints, including halo mass functions, the Lyman-alpha forest, halo density profiles, stellar dynamics, and black hole spins. We will review them focusing on the observations made, the method of study, and the major systematics. We will end with a discussion of the current status of the field and future work needed.
Inspired by the Turing test, we present a novel methodological framework to assess the extent to which a population of machines mirrors the philosophical views of a population of humans. The framework consists of three steps: (i) instructing machines to impersonate each human in the population, reflecting their backgrounds and beliefs, (ii) administering a questionnaire covering various philosophical positions to both humans and machines, and (iii) statistically analyzing the resulting responses. We apply this methodology to the debate on scientific realism, a long-standing philosophical inquiry exploring the relationship between science and reality. By considering the outcome of a survey of over 500 human participants, including both physicists and philosophers of science, we generate their machine personas using an artificial intelligence engine based on a large-language generative model. We reveal that the philosophical views of a population of machines are, on average, similar to those endorsed by a population of humans, irrespective of whether they are physicists or philosophers of science. As compared to humans, however, machines exhibit a weaker inclination toward scientific realism and a stronger coherence in their philosophical positions. Given the observed similarities between the populations of humans and machines, this methodological framework may offer unprecedented opportunities for advancing research in experimental philosophy by replacing human participants with their machine-impersonated counterparts, possibly mitigating the efficiency and reproducibility issues that affect survey-based empirical studies.
Edge computing enables real-time data processing closer to its source, thus improving the latency and performance of edge-enabled AI applications. However, traditional AI models often fall short when dealing with complex, dynamic tasks that require advanced reasoning and multimodal data processing. This survey explores the integration of multi-LLMs (Large Language Models) to address this in edge computing, where multiple specialized LLMs collaborate to enhance task performance and adaptability in resource-constrained environments. We review the transition from conventional edge AI models to single LLM deployment and, ultimately, to multi-LLM systems. The survey discusses enabling technologies such as dynamic orchestration, resource scheduling, and cross-domain knowledge transfer that are key for multi-LLM implementation. A central focus is on trusted multi-LLM systems, ensuring robust decision-making in environments where reliability and privacy are crucial. We also present multimodal multi-LLM architectures, where multiple LLMs specialize in handling different data modalities, such as text, images, and audio, by integrating their outputs for comprehensive analysis. Finally, we highlight future directions, including improving resource efficiency, trustworthy governance multi-LLM systems, while addressing privacy, trust, and robustness concerns. This survey provides a valuable reference for researchers and practitioners aiming to leverage multi-LLM systems in edge computing applications.
Integrating Network and Attack Graphs for Service-Centric Impact Analysis
2025-07-01
Joni Herttuainen, Vesa Kuikka, Kimmo K. Kaski
We present a novel methodology for modelling, visualising, and analysing cyber threats, attack paths, as well as their impact on user services in enterprise or infrastructure networks of digital devices and services they provide. Using probabilistic methods to track the propagation of an attack through attack graphs, via the service or application layers, and on physical communication networks, our model enables us to analyse cyber attacks at different levels of detail. Understanding the propagation of an attack within a service among microservices and its spread between different services or application servers could help detect and mitigate it early. We demonstrate that this network-based influence spreading modelling approach enables the evaluation of diverse attack scenarios and the development of protection and mitigation measures, taking into account the criticality of services from the user's perspective. This methodology could also aid security specialists and system administrators in making well-informed decisions regarding risk mitigation strategies.
Connecting mean-field theory with dynamo simulations
2025-07-01
Petri J. Käpylä
Mean-field dynamo theory, describing the evolution of large-scale magnetic fields, has been the mainstay of theoretical interpretation of magnetism in astrophysical objects such as the Sun for several decades. More recently, three-dimensional magnetohydrodynamic simulations have reached a level of fidelity where they capture dynamo action self-consistently on local and global scales without resorting to parametrization of unresolved scales. Recent global simulations also capture many of the observed characteristics of solar and stellar large-scale magnetic fields and cycles. Successful explanation of the results of such simulations with corresponding mean-field models is a crucial validation step for mean-field dynamo theory. Here the connections between mean-field theory and current dynamo simulations are reviewed. These connections range from the numerical computation of turbulent transport coefficients to mean-field models of simulations, and their relevance to the solar dynamo. Finally, the most notable successes and current challenges in mean-field theoretical interpretations of simulations are summarized.
Geometric Gaussian Approximations of Probability Distributions
2025-07-01
Nathaël Da Costa, Bálint Mucsányi, Philipp Hennig
Approximating complex probability distributions, such as Bayesian posterior distributions, is of central interest in many applications. We study the expressivity of geometric Gaussian approximations. These consist of approximations by Gaussian pushforwards through diffeomorphisms or Riemannian exponential maps. We first review these two different kinds of geometric Gaussian approximations. Then we explore their relationship to one another. We further provide a constructive proof that such geometric Gaussian approximations are universal, in that they can capture any probability distribution. Finally, we discuss whether, given a family of probability distributions, a common diffeomorphism can be found to obtain uniformly high-quality geometric Gaussian approximations for that family.
In edge-cloud speculative decoding (SD), edge devices equipped with small language models (SLMs) generate draft tokens that are verified by large language models (LLMs) in the cloud. A key bottleneck in such systems is the limited communication bandwidth between edge and cloud, which necessitates quantization of the information transmitted about generated tokens. In this work, we introduce a novel quantize-sample (Q-S) strategy that provably preserves the output distribution of the cloud-based model, ensuring that the verified tokens match the distribution of those that would have been generated directly by the LLM. We develop a throughput model for edge-cloud SD that explicitly accounts for communication latency. Leveraging this model, we propose an adaptive mechanism that optimizes token throughput by dynamically adjusting the draft length and quantization precision in response to both semantic uncertainty and channel conditions. Simulations demonstrate that the proposed Q-S approach significantly improves decoding efficiency in realistic edge-cloud deployment scenarios.
Overtake Detection in Trucks Using CAN Bus Signals: A Comparative Study of Machine Learning Methods
2025-07-01
Fernando Alonso-Fernandez, Talha Hanif Butt, Prayag Tiwari
Safe overtaking manoeuvres in trucks are vital for preventing accidents and ensuring efficient traffic flow. Accurate prediction of such manoeuvres is essential for Advanced Driver Assistance Systems (ADAS) to make timely and informed decisions. In this study, we focus on overtake detection using Controller Area Network (CAN) bus data collected from five in-service trucks provided by the Volvo Group. We evaluate three common classifiers for vehicle manoeuvre detection, Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM), and analyse how different preprocessing configurations affect performance. We find that variability in traffic conditions strongly influences the signal patterns, particularly in the no-overtake class, affecting classification performance if training data lacks adequate diversity. Since the data were collected under unconstrained, real-world conditions, class diversity cannot be guaranteed a priori. However, training with data from multiple vehicles improves generalisation and reduces condition-specific bias. Our pertruck analysis also reveals that classification accuracy, especially for overtakes, depends on the amount of training data per vehicle. To address this, we apply a score-level fusion strategy, which yields the best per-truck performance across most cases. Overall, we achieve an accuracy via fusion of TNR=93% (True Negative Rate) and TPR=86.5% (True Positive Rate). This research has been part of the BIG FUN project, which explores how Artificial Intelligence can be applied to logged vehicle data to understand and predict driver behaviour, particularly in relation to Camera Monitor Systems (CMS), being introduced as digital replacements for traditional exterior mirrors.
Detection probability of light compact binary mergers in future observing runs of the current ground-based gravitational wave detector network
2025-07-01
Om Sharan Salafia
With no binary neutron star (BNS) merger detected yet during the fourth observing run (O4) of the LIGO-Virgo-KAGRA (LVK) gravitational wave (GW) detector network, despite the time-volume (VT) surveyed with respect to the end of O3 increased by more than a factor of three, a pressing question is how likely the detection of at least one BNS merger is in the remainder of the run. I present here a simple and general method to address such a question, which constitutes the basis for the predictions that have been presented in the LVK Public Alerts User Guide during the hiatus between the O4a and O4b parts of the run. The method, which can be applied to neutron star - black hole (NSBH) mergers as well, is based on simple Poisson statistics and on an estimate of the ratio of the VT span by the future run to that span by previous runs. An attractive advantage of this method is that its predictions are independent from the mass distribution of the merging compact binaries, which is very uncertain at the present moment. The results, not surprisingly, show that the most likely outcome of the final part of O4 is the absence of any BNS merger detection. Still, the probability of a non-zero number of detections is 34-46\%. For NSBH mergers, the probability of at least one additional detection is 64-71\%. The prospects for the next observing run O5 are more promising, with predicted numbers $N_\mathrm{BNS,O5}=28_{-21}^{+44}$, and the NSBH detections to be $N_\mathrm{NSBH,O5}=65_{-38}^{+61}$ (median and 90\% symmetric credible range), based on the current LVK detector target sensitivities for the run. The calculations presented here also lead to an update of the LVK local BNS merger rate density estimate that accounts for the absence of BNS merger detections in O4 so far, that reads $2.8\,\mathrm{Gpc^{-3}\,yr^{-1}}\leq R_0\leq 480\,\mathrm{Gpc^{-3}\,yr^{-1}}$.
Out-of-distribution detection in 3D applications: a review
2025-07-01
Zizhao Li, Xueyang Kang, Joseph West, Kourosh Khoshelham
The ability to detect objects that are not prevalent in the training set is a critical capability in many 3D applications, including autonomous driving. Machine learning methods for object recognition often assume that all object categories encountered during inference belong to a closed set of classes present in the training data. This assumption limits generalization to the real world, as objects not seen during training may be misclassified or entirely ignored. As part of reliable AI, OOD detection identifies inputs that deviate significantly from the training distribution. This paper provides a comprehensive overview of OOD detection within the broader scope of trustworthy and uncertain AI. We begin with key use cases across diverse domains, introduce benchmark datasets spanning multiple modalities, and discuss evaluation metrics. Next, we present a comparative analysis of OOD detection methodologies, exploring model structures, uncertainty indicators, and distributional distance taxonomies, alongside uncertainty calibration techniques. Finally, we highlight promising research directions, including adversarially robust OOD detection and failure identification, particularly relevant to 3D applications. The paper offers both theoretical and practical insights into OOD detection, showcasing emerging research opportunities such as 3D vision integration. These insights help new researchers navigate the field more effectively, contributing to the development of reliable, safe, and robust AI systems.
A Critical Note on Information Distortion in a Supply Chain The Bullwhip Effect
2025-07-01
Hau Mike Ma, Jiazhen Huo
In the seminal paper Information Distortion in a Supply Chain: The Bullwhip Effect (Lee, et al. 1997, hereafter referred to as LPW), order batching is regarded as one of the four sources of the bullwhip effect. LPW proved that, in all cases (random ordering, balanced ordering, and correlated ordering), order batching will surely lead to bullwhip effects. However, we identify two improper assumptions in LPW. First, the batched order Zt is de facto the moving summation of previous demands, including overlapping demands. In fact, the batched order should be modeled as periodic summation of previous demands. Second, in the random ordering case, the number of retailers n is modeled as a binomial variable which is identically distributed for a randomly chosen period t in a review cycle. In fact, n should follow a sequential hypergeometric distribution. To address the two issues, we decompose a demand sequence using law of the total variance, exploring variance interplay between batched and non-batched demands in the positively correlated case. We find that even under the contrived iid assumption in LPW, order batching does not necessarily lead to the bullwhip effect.
Methodological Rigour in Algorithm Application: An Illustration of Topic Modelling Algorithm
2025-07-01
Malmi Amadoru
The rise of advanced computational algorithms has opened new avenues for computationally intensive research approaches to theory development. However, the opacity of these algorithms and lack of transparency and rigour in their application pose methodological challenges, potentially undermining trust in research. The discourse on methodological rigour in this new genre of research is still emerging. Against this backdrop, I attempt to offer guidance on methodological rigour, particularly in the context of topic modelling algorithms. By illustrating the application of the structural topic modelling algorithm and presenting a set of guidelines, I discuss how to ensure rigour in topic modelling studies. Although the guidelines are for the application of topic modelling algorithms, they can be applied to other algorithms with context-specific adjustments. The guidelines are helpful, especially for novice researchers applying topic modelling, and editors and reviewers handling topic modelling manuscripts. I contribute to the literature on topic modelling and join the emerging dialogue on methodological rigour in computationally intensive theory construction research.
Inverse Design in Nanophotonics via Representation Learning
2025-07-01
Reza Marzban, Ali Adibi, Raphael Pestourie
Inverse design in nanophotonics, the computational discovery of structures achieving targeted electromagnetic (EM) responses, has become a key tool for recent optical advances. Traditional intuition-driven or iterative optimization methods struggle with the inherently high-dimensional, non-convex design spaces and the substantial computational demands of EM simulations. Recently, machine learning (ML) has emerged to address these bottlenecks effectively. This review frames ML-enhanced inverse design methodologies through the lens of representation learning, classifying them into two categories: output-side and input-side approaches. Output-side methods use ML to learn a representation in the solution space to create a differentiable solver that accelerates optimization. Conversely, input-side techniques employ ML to learn compact, latent-space representations of feasible device geometries, enabling efficient global exploration through generative models. Each strategy presents unique trade-offs in data requirements, generalization capacity, and novel design discovery potentials. Hybrid frameworks that combine physics-based optimization with data-driven representations help escape poor local optima, improve scalability, and facilitate knowledge transfer. We conclude by highlighting open challenges and opportunities, emphasizing complexity management, geometry-independent representations, integration of fabrication constraints, and advancements in multiphysics co-designs.
Reliable Annotations with Less Effort: Evaluating LLM-Human Collaboration in Search Clarifications
2025-07-01
Leila Tavakoli, Hamed Zamani
Despite growing interest in using large language models (LLMs) to automate annotation, their effectiveness in complex, nuanced, and multi-dimensional labelling tasks remains relatively underexplored. This study focuses on annotation for the search clarification task, leveraging a high-quality, multi-dimensional dataset that includes five distinct fine-grained annotation subtasks. Although LLMs have shown impressive capabilities in general settings, our study reveals that even state-of-the-art models struggle to replicate human-level performance in subjective or fine-grained evaluation tasks. Through a systematic assessment, we demonstrate that LLM predictions are often inconsistent, poorly calibrated, and highly sensitive to prompt variations. To address these limitations, we propose a simple yet effective human-in-the-loop (HITL) workflow that uses confidence thresholds and inter-model disagreement to selectively involve human review. Our findings show that this lightweight intervention significantly improves annotation reliability while reducing human effort by up to 45%, offering a relatively scalable and cost-effective yet accurate path forward for deploying LLMs in real-world evaluation settings.
Edge Computing and its Application in Robotics: A Survey
2025-07-01
Nazish Tahir, Ramviyas Parasuraman
The Edge computing paradigm has gained prominence in both academic and industry circles in recent years. By implementing edge computing facilities and services in robotics, it becomes a key enabler in the deployment of artificial intelligence applications to robots. Time-sensitive robotics applications benefit from the reduced latency, mobility, and location awareness provided by the edge computing paradigm, which enables real-time data processing and intelligence at the network's edge. While the advantages of integrating edge computing into robotics are numerous, there has been no recent survey that comprehensively examines these benefits. This paper aims to bridge that gap by highlighting important work in the domain of edge robotics, examining recent advancements, and offering deeper insight into the challenges and motivations behind both current and emerging solutions. In particular, this article provides a comprehensive evaluation of recent developments in edge robotics, with an emphasis on fundamental applications, providing in-depth analysis of the key motivations, challenges, and future directions in this rapidly evolving domain. It also explores the importance of edge computing in real-world robotics scenarios where rapid response times are critical. Finally, the paper outlines various open research challenges in the field of edge robotics.
Extracting Contact Forces in Cohesive Granular Ensembles
2025-07-01
Abrar Naseer, Karen E. Daniels, Tejas G. Murthy
Interparticle cohesion is prevalent in stored powders, geological formations, and infrastructure engineering, yet a comprehensive understanding of the effects of its micro-mechanics on bulk properties has not been established experimentally. One challenge has been that while photoelasticy has been widely and successfully used to measure the vector contact forces within dry granular systems, where the particle-particle interactions are solely frictional and compressive in nature, it has seen little development in systems where tensile forces are present. The key difficulty has been the inability to distinguish between compressive and tensile forces, which appear identically within the photoelastic response. Here, we present a novel approach which solves this problem, by an extension to the open-source PeGS (Photoelastic Grain Solver) software available at https://github.com/photoelasticity. Our new implementation divides the procedure of finding vector contact forces into two steps: first evaluating the vector contact forces on the non-cohesive particles present in the ensemble, followed by using an equilibrium constraint to solve for the forces in the bonded particles. We find that in the dilute limit, for up to 25% bonded dimers, we can solve for all forces since each particle has only one force bearing contact that can potentially transmit tensile forces. While the case of dimers is an idealised version of cohesive granular ensemble, it provides an important first step towards experimentally studying the micro-mechanics of cohesive granular materials.
In this paper, we present the VMSE U-Net and VM-Unet CBAM+ model, two cutting-edge deep learning architectures designed to enhance medical image segmentation. Our approach integrates Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) techniques into the traditional VM U-Net framework, significantly improving segmentation accuracy, feature localization, and computational efficiency. Both models show superior performance compared to the baseline VM-Unet across multiple datasets. Notably, VMSEUnet achieves the highest accuracy, IoU, precision, and recall while maintaining low loss values. It also exhibits exceptional computational efficiency with faster inference times and lower memory usage on both GPU and CPU. Overall, the study suggests that the enhanced architecture VMSE-Unet is a valuable tool for medical image analysis. These findings highlight its potential for real-world clinical applications, emphasizing the importance of further research to optimize accuracy, robustness, and computational efficiency.
Coverage-Guided Testing for Deep Learning Models: A Comprehensive Survey
2025-07-01
Hongjing Guo, Chuanqi Tao, Zhiqiu Huang, Weiqin Zou
As Deep Learning (DL) models are increasingly applied in safety-critical domains, ensuring their quality has emerged as a pressing challenge in modern software engineering. Among emerging validation paradigms, coverage-guided testing (CGT) has gained prominence as a systematic framework for identifying erroneous or unexpected model behaviors. Despite growing research attention, existing CGT studies remain methodologically fragmented, limiting the understanding of current advances and emerging trends. This work addresses that gap through a comprehensive review of state-of-the-art CGT methods for DL models, including test coverage analysis, coverage-guided test input generation, and coverage-guided test input optimization. This work provides detailed taxonomies to organize these methods based on methodological characteristics and application scenarios. We also investigate evaluation practices adopted in existing studies, including the use of benchmark datasets, model architectures, and evaluation aspects. Finally, open challenges and future directions are highlighted in terms of the correlation between structural coverage and testing objectives, method generalizability across tasks and models, practical deployment concerns, and the need for standardized evaluation and tool support. This work aims to provide a roadmap for future academic research and engineering practice in DL model quality assurance.
A Retrieval-Augmented Generation (RAG)-based question-answering (QA) system enhances a large language model's knowledge by retrieving relevant documents based on user queries. Discrepancies between user queries and document phrasings often necessitate query rewriting. However, in specialized domains, the rewriter model may struggle due to limited domain-specific knowledge. To resolve this, we propose the R\&R (Read the doc before Rewriting) rewriter, which involves continual pre-training on professional documents, akin to how students prepare for open-book exams by reviewing textbooks. Additionally, it can be combined with supervised fine-tuning for improved results. Experiments on multiple datasets demonstrate that R\&R excels in professional QA across multiple domains, effectively bridging the query-document gap, while maintaining good performance in general scenarios, thus advancing the application of RAG-based QA systems in specialized fields.
IRC+10216 is the brightest infrared source in the northern sky, known for its rich chemical composition. It is often used as a standard reference for studying the circumstellar envelope (CSE) of carbon-rich stars. While pioneering 3\,mm spectral surveys have laid foundational datasets, their system temperature limitations rendered spectral line detection thresholds inadequate for probing the source's complex organic molecule inventory at this band, which made superseding observations necessary. We aim to gain an unbiased view regarding circumstellar chemistry and investigate whether IRC+10216 is typical or anomalous in terms of its chemical composition. We carried out an in-depth spectral line survey of the circumstellar envelope of IRC+10216 utilizing the Arizona Radio Observatory 12\,m telescope. We achieved complete spectral sampling across the 90--116 GHz atmospheric window ($\lambda=2.6$--3.3\,mm). A total of 214 emission lines belonging to 43 molecular species are identified in the CSE of IRC+10216, among which 28 lines are newly detected in this object and four emission lines remain unidentified. The excitation temperatures and column densities of 16 molecules are determined through rotation diagrams. We estimate the isotopic ratios of carbon, oxygen, and silicon elements. For the majority of the molecular species, the line intensity ratios between IRC+10216 and CIT\,6 are inversely proportional to the square of their distance, which suggests that the chemical processes occurring within them are similar. Nevertheless, there is evidence suggesting that the emission of C$_{4}$H and C$_{3}$N in IRC+10216 is unusually strong. These observations stand as the most sensitive and unbiased line survey of IRC+10216 within the $\lambda=3$ window carried out by a single-dish telescope. They offer a valuable reference for the astronomical community. (Abridged)
Embedded DevOps: A Survey on the Application of DevOps Practices in Embedded Software and Firmware Development
2025-07-01
Parthiv Katapara, Anand Sharma
The adoption of DevOps practices in embedded systems and firmware development is emerging as a response to the growing complexity of modern hardware--software co-designed products. Unlike cloud-native applications, embedded systems introduce challenges such as hardware dependency, real-time constraints, and safety-critical requirements. This literature review synthesizes findings from 20 academic and industrial sources to examine how DevOps principles--particularly continuous integration, continuous delivery, and automated testing--are adapted to embedded contexts. We categorize efforts across tooling, testing strategies, pipeline automation, and security practices. The review highlights current limitations in deployment workflows and observability, proposing a roadmap for future research. This work offers researchers and practitioners a consolidated understanding of Embedded DevOps, bridging fragmented literature with a structured perspective.
Geological Everything Model 3D: A Promptable Foundation Model for Unified and Zero-Shot Subsurface Understanding
2025-07-01
Yimin Dou, Xinming Wu, Nathan L Bangs, Harpreet Singh Sethi, Jintao Li, Hang Gao, Zhixiang Guo
Understanding Earth's subsurface is critical for energy transition, natural hazard mitigation, and planetary science. Yet subsurface analysis remains fragmented, with separate models required for structural interpretation, stratigraphic analysis, geobody segmentation, and property modeling-each tightly coupled to specific data distributions and task formulations. We introduce the Geological Everything Model 3D (GEM), a unified generative architecture that reformulates all these tasks as prompt-conditioned inference along latent structural frameworks derived from subsurface imaging. This formulation moves beyond task-specific models by enabling a shared inference mechanism, where GEM propagates human-provided prompts-such as well logs, masks, or structural sketches-along inferred structural frameworks to produce geologically coherent outputs. Through this mechanism, GEM achieves zero-shot generalization across tasks with heterogeneous prompt types, without retraining for new tasks or data sources. This capability emerges from a two-stage training process that combines self-supervised representation learning on large-scale field seismic data with adversarial fine-tuning using mixed prompts and labels across diverse subsurface tasks. GEM demonstrates broad applicability across surveys and tasks, including Martian radar stratigraphy analysis, structural interpretation in subduction zones, full seismic stratigraphic interpretation, geobody delineation, and property modeling. By bridging expert knowledge with generative reasoning in a structurally aware manner, GEM lays the foundation for scalable, human-in-the-loop geophysical AI-transitioning from fragmented pipelines to a vertically integrated, promptable reasoning system. Project page: https://douyimin.github.io/GEM
FAST search for circumstellar atomic hydrogen. III. Planetary nebulae at Galactic latitudes |b|>10degree
2025-07-01
Xu-Jia Ouyang, Yong Zhang, Albert Zijlstra, Chuan-Peng Zhang, Jun-ichi Nakashima, Quentin A Parker, Xiao-Hu Li
The detection of circumstellar atomic hydrogen (\ion{H}{1}) via the 21\,cm line remains a persistent challenge in planetary nebula (PN) studies, primarily due to contamination from ubiquitous interstellar \ion{H}{1} 21\,cm emission. In this paper, we report the results of a \ion{H}{1} survey of 12 high surface brightness PNe located at Galactic latitudes $|b|\geq10\arcdeg$, performed with the Five-hundred-meter Aperture Spherical radio Telescope (FAST), which is currently the most sensitive telescope in the $L$ band. Although the contamination from interstellar emission is still severe, we detect or tentatively detect circumstellar \ion{H}{1} 21\,cm absorption associated with two PNe: NGC\,6905 and NGC\,7662. \ion{H}{1} exhibits a comparatively high detection frequency in bipolar PNe. We estimate the \ion{H}{1} masses of the two PNe to range from 0.01 to 0.14 $M_{\sun}$, resulting in atomic-to-ionized hydrogen ratios of 0.02--0.3. The \ion{H}{1} shells have dynamical ages of 2100--2400 years. Our measurements confirm previous findings that the optical depth of \ion{H}{1} decreases with increasing linear radius of the nebula. The mass loss rates traced by the \ion{H}{1} absorption are larger than 10$^{-5}$\,M$_\sun$ yr$^{-1}$, indicating that they originate from the superwind phase at the tip of the asymptotic giant branch.
2025-07-01
Chengze Jiang, Jie Gui, Long Jin, Shuai Li
Zeroing neural networks (ZNNs) have demonstrated outstanding performance on time-varying optimization and control problems. Nonetheless, few studies are committed to illustrating the relationship among different ZNNs and the derivation of them. Therefore, reviewing the advances for a systematical understanding of this field is desirable. This paper provides a survey of ZNNs' progress regarding implementing methods, analysis theory, and practical applications.
Agentic AI in Product Management: A Co-Evolutionary Model
2025-07-01
Nishant A. Parikh
This study explores agentic AI's transformative role in product management, proposing a conceptual co-evolutionary framework to guide its integration across the product lifecycle. Agentic AI, characterized by autonomy, goal-driven behavior, and multi-agent collaboration, redefines product managers (PMs) as orchestrators of socio-technical ecosystems. Using systems theory, co-evolutionary theory, and human-AI interaction theory, the framework maps agentic AI capabilities in discovery, scoping, business case development, development, testing, and launch. An integrative review of 70+ sources, including case studies from leading tech firms, highlights PMs' evolving roles in AI orchestration, supervision, and strategic alignment. Findings emphasize mutual adaptation between PMs and AI, requiring skills in AI literacy, governance, and systems thinking. Addressing gaps in traditional frameworks, this study provides a foundation for future research and practical implementation to ensure responsible, effective agentic AI integration in software organizations.
LIGO/Virgo/KAGRA neutron star merger candidate S250206dm: Zwicky Transient Facility observations
2025-07-01
Tomás Ahumada, Shreya Anand, Mattia Bulla, Vaidehi Gupta, Mansi Kasliwal, Robert Stein, Viraj Karambelkar, Eric C. Bellm, Theophile Jegou du Laz, Michael W. Coughlin, Igor Andreoni, Smaranika Banerjee, Aleksandra Bochenek, K-Ryan Hinds, Lei Hu, Antonella Palmese, Daniel Perley, Natalya Pletskova, Anirudh Salgundi, Avinash Singh, Jesper Sollerman, Vishwajeet Swain, Avery Wold, Varun Bhalerao, S. Bradley Cenko, David O. Cook, Chris Copperwheat, Matthew Graham, David L. Kaplan, Leo P. Singer, Niharika Sravan, Malte Busmann, Julius Gassert, Daniel Gruen, Julian Sommer, Yajie Zhang, Ariel Amsellem, Tomás Cabrera, Xander J. Hall, Keerthi Kunnumkai, Brendan O'Connor, Tyler Barna, Felipe Fontinele Nunes, Andrew Toivonen, Argyro Sasli, Frank J. Masci, Tracy X. Chen, Richard Dekany, Josiah Purdum, Antoine Le-Calloch, G. C. Anupama, Sudhanshu Barway
We present the searches conducted with the Zwicky Transient Facility (ZTF) in response to S250206dm, a bona fide event with a false alarm rate of one in 25 years, detected by the International Gravitational Wave Network (IGWN). Although the event is significant, the nature of the compact objects involved remains unclear, with at least one likely neutron star. ZTF covered 68% of the localization region, though we did not identify any likely optical counterpart. We describe the ZTF strategy, potential candidates, and the observations that helped rule out candidates, including sources circulated by other collaborations. Similar to Ahumada et al. 2024, we perform a frequentist analysis, using simsurvey, as well as Bayesian analysis, using nimbus, to quantify the efficiency of our searches. We find that, given the nominal distance to this event of 373$\pm$104 Mpc, our efficiencies are above 10% for KNe brighter than $-17.5$ absolute magnitude. Assuming the optical counterpart known as kilonova (KN) lies within the ZTF footprint, our limits constrain the brightest end of the KN parameter space. Through dedicated radiative transfer simulations of KNe from binary neutron star (BNS) and black hole-neutron star (BHNS) mergers, we exclude parts of the BNS KN parameter space. Up to 35% of the models with high wind ejecta mass ($M_{\rm wind} \approx 0.13$ M$_{\odot}$) are ruled out when viewed face-on ($\cos\theta_{\rm obs} = 1.0$). Finally, we present a joint analysis using the combined coverage from ZTF and the Gravitational Wave Multimessenger Dark Energy Camera Survey (GW-MMADS). The joint observations cover 73% of the localization region, and the combined efficiency has a stronger impact on rising and slowly fading models, allowing us to rule out 55% of the high-mass KN models viewed face-on.
Dark from light (DfL): Inferring halo properties from luminous tracers with machine learning trained on cosmological simulations. I. Method, proof of concept & preliminary testing
2025-07-01
Asa F. L. Bluck, Joanna M. Piotrowska, Paul Goubert, Roberto Maiolino, Camilo Casimiro, Thomas Pinto Franco, Nicolas Cea
We present Dark from Light (DfL) - a novel method to infer the dark sector in wide-field galaxy surveys, leveraging a machine learning approach trained on contemporary cosmological simulations. The aim of this algorithm is to provide a fast, straightforward, and accurate route to estimating dark matter halo masses and group membership in wide-field spectroscopic galaxy surveys. This approach requires a highly limited number of input parameters and yields full probability distribution functions for the output halo masses. To achieve this, we train a series of Random Forest (RF) regression models on the IllustrisTNG and EAGLE simulations at z=0-3, which provide model-dependent mappings from luminous tracers to dark matter halo properties. We incorporate the individual regression models into a virial group-finding algorithm (DfL), which outputs halo properties for observational-like input data. We test the method at z=0-2 for both the EAGLE and IllustrisTNG models, as well as in a cross-validation mode. We demonstrate that known halo masses can be recovered with a mean systematic bias of $\langle b \rangle = \pm 0.10\,$dex (resulting from simulation choice), a mean statistical uncertainty of $\langle \sigma \rangle = 0.12 \,$dex across epochs, and a central - (core) satellite classification accuracy of 96%. We establish that this approach yields superior halo mass recovery to standard abundance matching applied to groups identified through a friends-of-friends algorithm. Additionally, we compare the outputs of DfL to observational constraints on the $M_* - M_{\rm Halo}$ relation from strong gravitational lensing at $z \sim 0$, demonstrating the promise of this novel approach. Finally, we systematically quantify how DfL performs on observational-like input data with varying stellar mass uncertainty and spectroscopic incompleteness, enabling robust error calibration.
In a seminal 2005 paper, Haagerup and Thorbj{\o}rnsen discovered that the norm of any noncommutative polynomial of independent complex Gaussian random matrices converges to that of a limiting family of operators that arises from Voiculescu's free probability theory. In recent years, new methods have made it possible to establish such strong convergence properties in much more general situations, and to obtain even more powerful quantitative forms of the strong convergence phenomenon. These, in turn, have led to a number of spectacular applications to long-standing open problems on random graphs, hyperbolic surfaces, and operator algebras, and have provided flexible new tools that enable the study of random matrices in unexpected generality. This survey aims to provide an introduction to this circle of ideas.
Large language models (LLMs) demonstrate strong reasoning abilities across mathematical, strategic, and linguistic tasks, yet little is known about how well they reason in dynamic, real-time, multi-agent scenarios, such as collaborative environments in which agents continuously adapt to each other's behavior, as in cooperative gameplay settings. In this paper, we bridge this gap by combining LLM-driven agents with strategic reasoning and real-time adaptation in cooperative, multi-agent environments grounded in game-theoretic principles such as belief consistency and Nash equilibrium. The proposed framework applies broadly to dynamic scenarios in which agents coordinate, communicate, and make decisions in response to continuously changing conditions. We provide real-time strategy refinement and adaptive feedback mechanisms that enable agents to dynamically adjust policies based on immediate contextual interactions, in contrast to previous efforts that evaluate LLM capabilities in static or turn-based settings. Empirical results show that our method achieves up to a 26\% improvement in return over PPO baselines in high-noise environments, while maintaining real-time latency under 1.05 milliseconds. Our approach improves collaboration efficiency, task completion rates, and flexibility, illustrating that game-theoretic guidance integrated with real-time feedback enhances LLM performance, ultimately fostering more resilient and flexible strategic multi-agent systems.
How to Design and Train Your Implicit Neural Representation for Video Compression
2025-06-30
Matthew Gwilliam, Roy Zhang, Namitha Padmanabhan, Hongyang Du, Abhinav Shrivastava
Implicit neural representation (INR) methods for video compression have recently achieved visual quality and compression ratios that are competitive with traditional pipelines. However, due to the need for per-sample network training, the encoding speeds of these methods are too slow for practical adoption. We develop a library to allow us to disentangle and review the components of methods from the NeRV family, reframing their performance in terms of not only size-quality trade-offs, but also impacts on training time. We uncover principles for effective video INR design and propose a state-of-the-art configuration of these components, Rabbit NeRV (RNeRV). When all methods are given equal training time (equivalent to 300 NeRV epochs) for 7 different UVG videos at 1080p, RNeRV achieves +1.27% PSNR on average compared to the best-performing alternative for each video in our NeRV library. We then tackle the encoding speed issue head-on by investigating the viability of hyper-networks, which predict INR weights from video inputs, to disentangle training from encoding to allow for real-time encoding. We propose masking the weights of the predicted INR during training to allow for variable, higher quality compression, resulting in 1.7% improvements to both PSNR and MS-SSIM at 0.037 bpp on the UCF-101 dataset, and we increase hyper-network parameters by 0.4% for 2.5%/2.7% improvements to PSNR/MS-SSIM with equal bpp and similar speeds. Our project website is available at https://mgwillia.github.io/vinrb/ and our code is available at https://github.com/mgwillia/vinrb.
This review focuses on the force-induced unzipping transition of double-stranded DNA. It begins with a brief history of DNA melting, which emerged alongside the growth of the field of molecular biology, juxtaposed with the advancements in physics during the same post-World War II period. The earlier theories of melting of DNA were based on the Ising model and its modifications, but gradually moved towards polymer-based models. The idea of force-induced unzipping was first introduced in 1999 as a cooperative mechanism for breaking base pairs without the need for temperature changes. The paper discusses several subsequent developments addressing different aspects of the unzipping transition.
The rapid progress of multimodal large language models (MLLM) has paved the way for Vision-Language-Action (VLA) paradigms, which integrate visual perception, natural language understanding, and control within a single policy. Researchers in autonomous driving are actively adapting these methods to the vehicle domain. Such models promise autonomous vehicles that can interpret high-level instructions, reason about complex traffic scenes, and make their own decisions. However, the literature remains fragmented and is rapidly expanding. This survey offers the first comprehensive overview of VLA for Autonomous Driving (VLA4AD). We (i) formalize the architectural building blocks shared across recent work, (ii) trace the evolution from early explainer to reasoning-centric VLA models, and (iii) compare over 20 representative models according to VLA's progress in the autonomous driving domain. We also consolidate existing datasets and benchmarks, highlighting protocols that jointly measure driving safety, accuracy, and explanation quality. Finally, we detail open challenges - robustness, real-time efficiency, and formal verification - and outline future directions of VLA4AD. This survey provides a concise yet complete reference for advancing interpretable socially aligned autonomous vehicles. Github repo is available at \href{https://github.com/JohnsonJiang1996/Awesome-VLA4AD}{SicongJiang/Awesome-VLA4AD}.
Advocacy for Physics and for Physicists: Results of an Informal Survey of American Physical Society Members in 2025
2025-06-30
Michael B. Bennett
In the wake of a rash of executive orders and administrative actions aimed at shaping the scale, scope, and focus of the scientific enterprise in the United States during the second Trump Presidency, we have undertaken a survey of members of the American Physical Society, the country's pre-eminent physics professional society to investigate their needs, interests, and priorities, and how their interests may differ from organizational leadership's priorities. This report provides detail on the context for the creation and implementation of the survey, as well as articulating results and some common themes found in responses. Broadly, our informal survey of APS members revealed that a majority of respondents perceive that the Society supports them, cares about their needs as physicists, and is communicating reliably and transparently. However, a sizeable proportion of respondents -- up to 30\% -- harbor concerns about the organization's actions, its prioritization of member needs over corporate interests, or its willingness to listen to members. In addition, a strong majority -- over two-thirds of respondents -- articulated a desire for more concrete and personal support from APS. While we acknowledge that the Society cannot be all things to all people, the results of our work lead us to the concern that there may indeed be a disconnect between the APS envisioned by those at the top of the organization and those who fill its ranks.
Supervised Diffusion-Model-Based PET Image Reconstruction
2025-06-30
George Webber, Alexander Hammers, Andrew P King, Andrew J Reader
Diffusion models (DMs) have recently been introduced as a regularizing prior for PET image reconstruction, integrating DMs trained on high-quality PET images with unsupervised schemes that condition on measured data. While these approaches have potential generalization advantages due to their independence from the scanner geometry and the injected activity level, they forgo the opportunity to explicitly model the interaction between the DM prior and noisy measurement data, potentially limiting reconstruction accuracy. To address this, we propose a supervised DM-based algorithm for PET reconstruction. Our method enforces the non-negativity of PET's Poisson likelihood model and accommodates the wide intensity range of PET images. Through experiments on realistic brain PET phantoms, we demonstrate that our approach outperforms or matches state-of-the-art deep learning-based methods quantitatively across a range of dose levels. We further conduct ablation studies to demonstrate the benefits of the proposed components in our model, as well as its dependence on training data, parameter count, and number of diffusion steps. Additionally, we show that our approach enables more accurate posterior sampling than unsupervised DM-based methods, suggesting improved uncertainty estimation. Finally, we extend our methodology to a practical approach for fully 3D PET and present example results from real [$^{18}$F]FDG brain PET data.
Dark matter as the source of neutrino mass: theory overview and experimental prospects
2025-06-30
Ivania M. Ávila, Anirban Karan, Sanjoy Mandal, Soumya Sadhukhan, José W. F. Valle
We review theoretical frameworks in which small neutrino masses arise radiatively through interactions with a dark sector that also accounts for cosmological dark matter. A prototypical example is provided by scotogenic schemes, that extend the inert Higgs doublet model. We outline their key features and limitations, including possible completions, such as the revamped setup. The phenomenological signatures of both fermionic and bosonic scotogenic dark matter are discussed, along with scoto-seesaw models that merge scotogenic and seesaw mechanisms, and scenarios where the dark sector triggers a low-scale seesaw. These frameworks can accommodate dark matter as either Weakly Interacting Massive Particles (WIMPs) or Feebly Interacting Massive Particles (FIMPs). While hidden dark sector models are inherently difficult to test experimentally, those based on visible dark sectors offer clear predictions that could be confirmed -- or ruled out -- by forthcoming dark matter searches, collider experiments, and lepton flavor violation studies.
2025-06-30
Hoang Nhan Luu, Yu-Cheng Qiu, S. -H. Henry Tye
The Dark Energy Survey (DES) and the Dark Energy Spectroscopic Instrument (DESI) measurements claim that the dark energy equation of state $w \ne -1$. This observation can be explained by the axion Dark Energy (aDE) model of an ultralight axion plus a cosmological constant $\Lambda$. Despite a relatively large degeneracy, there is a high probability that $\Lambda <0$. This negative $\Lambda$ leads the universe to end in a big crunch. Using the best-fit values of the model as a benchmark, we find the lifespan of our universe to be 33 billion years.
Large Language Models Don't Make Sense of Word Problems. A Scoping Review from a Mathematics Education Perspective
2025-06-30
Anselm R. Strohmaier, Wim Van Dooren, Kathrin Seßler, Brian Greer, Lieven Verschaffel
The progress of Large Language Models (LLMs) like ChatGPT raises the question of how they can be integrated into education. One hope is that they can support mathematics learning, including word-problem solving. Since LLMs can handle textual input with ease, they appear well-suited for solving mathematical word problems. Yet their real competence, whether they can make sense of the real-world context, and the implications for classrooms remain unclear. We conducted a scoping review from a mathematics-education perspective, including three parts: a technical overview, a systematic review of word problems used in research, and a state-of-the-art empirical evaluation of LLMs on mathematical word problems. First, in the technical overview, we contrast the conceptualization of word problems and their solution processes between LLMs and students. In computer-science research this is typically labeled mathematical reasoning, a term that does not align with usage in mathematics education. Second, our literature review of 213 studies shows that the most popular word-problem corpora are dominated by s-problems, which do not require a consideration of realities of their real-world context. Finally, our evaluation of GPT-3.5-turbo, GPT-4o-mini, GPT-4.1, and o3 on 287 word problems shows that most recent LLMs solve these s-problems with near-perfect accuracy, including a perfect score on 20 problems from PISA. LLMs still showed weaknesses in tackling problems where the real-world context is problematic or non-sensical. In sum, we argue based on all three aspects that LLMs have mastered a superficial solution process but do not make sense of word problems, which potentially limits their value as instructional tools in mathematics classrooms.
A planet-host ratio relation to synthesize microlensing and transiting exoplanet demography from Roman
2025-06-30
Kathryn Edmondson, Eamonn Kerins
The NASA Nancy Grace Roman Space Telescope (Roman) will be the first survey able to detect large numbers of both cold and hot exoplanets across Galactic distances: ~1,400 cold exoplanets via microlensing and ~200,000 hot, transiting planets. Differing sensitivities to planet bulk properties between the microlensing and transit methods require relations like a planet mass-radius relation (MRR) to mediate. We propose using instead a planet-host ratio relation (PHRR) to couple directly microlensing and transit observables. Unlike the MRR, a PHRR uses parameters that are always measured and so can leverage the full Roman exoplanet sample. Using 908 confirmed exoplanets from the NASA Exoplanet Archive, we show that transit depth, $\delta$, and planet-host mass ratio, $q$, obey a PHRR that is continuous over all planet scales. The PHRR is improved by including orbital period, $P$, and host effective temperature, $T_{\star}$. We compare several candidate PHRRs of the form $\delta (q,T_\star, P)$, with the Bayesian Information Criterion favouring power-law dependence on $T_\star$ and $P$, and broken power-law dependence on $q$. The break in $q$ itself depends on $T_\star$, as do the power-law slopes in $q$ either side of the break. The favoured PHRR achieves a fairly uniform $50\%$ relative precision in $\delta$ for all $q$. Approximately $5\%$ of the sample has a transit depth that is strongly under-predicted by the PHRR; around half of these are associated with large stars ($R_\star > 2.5 \, R_{\odot}$) potentially subject to Malmquist bias.
Auto-TA: Towards Scalable Automated Thematic Analysis (TA) via Multi-Agent Large Language Models with Reinforcement Learning
2025-06-30
Seungjun Yi, Joakim Nguyen, Huimin Xu, Terence Lim, Andrew Well, Mia Markey, Ying Ding
Congenital heart disease (CHD) presents complex, lifelong challenges often underrepresented in traditional clinical metrics. While unstructured narratives offer rich insights into patient and caregiver experiences, manual thematic analysis (TA) remains labor-intensive and unscalable. We propose a fully automated large language model (LLM) pipeline that performs end-to-end TA on clinical narratives, which eliminates the need for manual coding or full transcript review. Our system employs a novel multi-agent framework, where specialized LLM agents assume roles to enhance theme quality and alignment with human analysis. To further improve thematic relevance, we optionally integrate reinforcement learning from human feedback (RLHF). This supports scalable, patient-centered analysis of large qualitative datasets and allows LLMs to be fine-tuned for specific clinical contexts.
Variability-finding in Rubin Data Preview 1 with LSDB
2025-06-30
Konstantin Malanchev, Melissa DeLucchi, Neven Caplar, Alex I. Malz, Wilson Beebe, Doug Branton, Sandro Campos, Andrew Connolly, Mi Dai, Jeremy Kubica, Olivia Lynn, Rachel Mandelbaum, Sean McGuire, Eric Aubourg, Robert David Blum, Jeffrey L. Carlin, Francisco Delgado, Emmanuel Gangler, Buell T. Jannuzi, Tim Jenness, Yijung Kang, Arun Kannawadi, Marc Moniez, Andrés A. Plazas Malagón, Wouter van Reeven, David Sanmartim, Elana K. Urbach, W. M. Wood-Vasey
The Vera C. Rubin Observatory recently released Data Preview 1 (DP1) in advance of the upcoming Legacy Survey of Space and Time (LSST), which will enable boundless discoveries in time-domain astronomy over the next ten years. DP1 provides an ideal sandbox for validating innovative data analysis approaches for the LSST mission, whose scale challenges established software infrastructure paradigms. This note presents a pair of such pipelines for variability-finding using powerful software infrastructure suited to LSST data, namely the HATS (Hierarchical Adaptive Tiling Scheme) format and the LSDB framework, developed by the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) Frameworks team. This note presents a pair of variability-finding pipelines built on LSDB, the HATS catalog of DP1 data, and preliminary results of detected variable objects, two of which are novel discoveries.
Thermodynamics of Hard Sphere and Spherocylinder Mixtures -- Scaled Particle Theory and Monte Carlo Simulations
2025-06-30
Volodymyr Shmotolokha, Jonas Maier-Borst, Mark Vis, Anja Kuhnhold, Remco Tuinier
We review the literature on scaled particle theory (SPT) and its extensions and discuss results applied to describe the thermodynamics of hard particle mixtures. After explaining the basic concepts of scaled particle theory to compute the free energy of immersing a particle into a mixture, examples are discussed for the simple case of a hard sphere dispersion and the free volume fraction of ghost spheres in a hard sphere dispersion. Next, the concept is applied to mixtures, and general expressions are shown that relate the free volume fraction in mixtures to the key thermodynamic properties, such as the chemical potential(s) and (osmotic) pressure. Subsequently, it is revealed how these concepts can be extended towards multi-component systems. It is shown that free volume fractions provide chemical potentials and total pressure of multi-component mixtures, and thereby yield the full equation of state. We present novel results for ternary particle dispersions composed of hard spherocylinders and two types of hard spheres differing in size. Throughout, we show the accuracy of SPT by comparing the results with those of Monte Carlo computer simulations.
Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers
2025-06-30
Zhaochen Su, Peng Xia, Hangyu Guo, Zhenhua Liu, Yan Ma, Xiaoye Qu, Jiaqi Liu, Yanshu Li, Kaide Zeng, Zhengyuan Yang, Linjie Li, Yu Cheng, Heng Ji, Junxian He, Yi R. Fung
Recent progress in multimodal reasoning has been significantly advanced by textual Chain-of-Thought (CoT), a paradigm where models conduct reasoning within language. This text-centric approach, however, treats vision as a static, initial context, creating a fundamental "semantic gap" between rich perceptual data and discrete symbolic thought. Human cognition often transcends language, utilizing vision as a dynamic mental sketchpad. A similar evolution is now unfolding in AI, marking a fundamental paradigm shift from models that merely think about images to those that can truly think with images. This emerging paradigm is characterized by models leveraging visual information as intermediate steps in their thought process, transforming vision from a passive input into a dynamic, manipulable cognitive workspace. In this survey, we chart this evolution of intelligence along a trajectory of increasing cognitive autonomy, which unfolds across three key stages: from external tool exploration, through programmatic manipulation, to intrinsic imagination. To structure this rapidly evolving field, our survey makes four key contributions. (1) We establish the foundational principles of the think with image paradigm and its three-stage framework. (2) We provide a comprehensive review of the core methods that characterize each stage of this roadmap. (3) We analyze the critical landscape of evaluation benchmarks and transformative applications. (4) We identify significant challenges and outline promising future directions. By providing this structured overview, we aim to offer a clear roadmap for future research towards more powerful and human-aligned multimodal AI.
Exploring the Wolf Approach to Constraining NIR Extinction Laws in the Corona Australis Molecular Cloud
2025-06-30
Botao Jiang, Jun Li, Xi Chen
The viability of the star count (Wolf) method is assessed as a means of constraining the near-infrared (NIR) extinction law toward the Corona Australis molecular cloud. Using deep $JHK_S$ photometry from the VISIONS survey, extinction maps with 1 arcmin spatial resolution are constructed. The derived extinction ratios are $A_J/A_H=1.73\pm0.07$, $A_H/A_{K_S}=1.70\pm0.11$, and $A_J/A_{K_S}=3.02\pm0.22$, which are consistent with Galactic literature means. Assuming a power-law form ($A_\lambda \propto \lambda^{-\alpha}$) for the NIR extinction law, we derive indices of $\alpha\approx 2.0$ across all wavelength combinations, with no statistically significant wavelength dependence throughout the NIR wavelength range. While spatial variations in extinction properties are tentatively observed across the cloud, concerns persist regarding the impact of photometric completeness, and the role of reference field selection. Continued research is required to refine the approach, and scrutinize the veracity of potential extinction law variations over a more expansive region of sky.
2025-06-30
Martin Hoferichter, Jacobo Ruiz de Elvira
We give a pedagogical introduction to the origin of the mass of the nucleon. We first review the trace anomaly of the energy-momentum tensor, which generates most of the nucleon mass via the gluon fields and thus contributes even in the case of vanishing quark masses. We then discuss the contributions to the nucleon mass that do originate from the Higgs mechanism via the quark masses, reviewing the current status of nucleon $\sigma$-terms that encode the corresponding matrix elements.
Continuum optical-UV and X-ray variability of AGN: current results and future challenges
2025-06-30
Maurizio Paolillo, Iossif Papadakis
Active Galactic Nuclei (AGN) are believed to be powered by accretion of matter onto a supermassive black hole. A fundamental ingredient in shaping our understanding of AGN is their variability across the entire electromagnetic spectrum. Variability studies have the potential to help us understand the geometry of the emitting regions (in various energy bands), their causal relations, and the physics of the accretion processes. This review focuses on the observational properties of AGN variability in the optical/UV/X-ray bands (where most of the AGN luminosity is emitted) and their dependence on the AGN physical parameters (i.e. mass, luminosity, accretion rate). We also discuss possible interpretations in the context of accreting compact systems, and we review the use of variability as a tool to discover AGN and trace their properties across cosmic time, using both ground and space facilities. Finally, we discuss the opportunities and challenges provided by current and next-generation optical/X-ray surveys, to use variability as an effective tool to probe the growth of super massive black holes in the Universe.
Requirements for Active Assistance of Natural Questions in Software Architecture
2025-06-30
Diogo Lemos, Ademar Aguiar, Neil B. Harrison
Natural questions are crucial to shaping key architectural decisions and preserving architectural knowledge. They arise organically during the architectural design process, often resulting from the existing architectural experience of the designer and the distinctive characteristics of the system being designed. However, natural questions are often mismanaged or ignored, which can lead to architectural drift, knowledge loss, inefficient resource use, or poor understandability of the system's architecture. We aim to better understand the lifecycle of natural questions, its key requirements, challenges and difficulties, and then to envision an assisted environment to properly support it. The environment should be adaptable and responsive to real-world constraints and uncertainties by seamlessly integrating knowledge management tools and artificial intelligence techniques into software development workflows. Based on existing literature, a requirements workshop, and three design iterations, we proposed a lifecycle for natural questions and elicited essential functional and non-functional requirements for such an environment. At last, the results of a survey conducted with experts helped to analyze and validate the elicited requirements and proposed features for the environment to enhance collaboration, decision-making, and the preservation of architectural knowledge more effectively than conventional methods.
2025-06-30
Javad Noorbakhsh, Ali Foroughi pour, Jeffrey Chuang
Technological breakthroughs in spatial omics and artificial intelligence (AI) have the potential to transform the understanding of cancer cells and the tumor microenvironment. Here we review the role of AI in spatial omics, discussing the current state-of-the-art and further needs to decipher cancer biology from large-scale spatial tissue data. An overarching challenge is the development of interpretable spatial AI models, an activity which demands not only improved data integration, but also new conceptual frameworks. We discuss emerging paradigms, in particular data-driven spatial AI, constraint-based spatial AI, and mechanistic spatial modeling, as well as the importance of integrating AI with hypothesis-driven strategies and model systems to realize the value of cancer spatial information.
A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents
2025-06-30
Hang Su, Jun Luo, Chang Liu, Xiao Yang, Yichi Zhang, Yinpeng Dong, Jun Zhu
Recent advances in large language models (LLMs) have catalyzed the rise of autonomous AI agents capable of perceiving, reasoning, and acting in dynamic, open-ended environments. These large-model agents mark a paradigm shift from static inference systems to interactive, memory-augmented entities. While these capabilities significantly expand the functional scope of AI, they also introduce qualitatively novel security risks - such as memory poisoning, tool misuse, reward hacking, and emergent misalignment - that extend beyond the threat models of conventional systems or standalone LLMs. In this survey, we first examine the structural foundations and key capabilities that underpin increasing levels of agent autonomy, including long-term memory retention, modular tool use, recursive planning, and reflective reasoning. We then analyze the corresponding security vulnerabilities across the agent stack, identifying failure modes such as deferred decision hazards, irreversible tool chains, and deceptive behaviors arising from internal state drift or value misalignment. These risks are traced to architectural fragilities that emerge across perception, cognition, memory, and action modules. To address these challenges, we systematically review recent defense strategies deployed at different autonomy layers, including input sanitization, memory lifecycle control, constrained decision-making, structured tool invocation, and introspective reflection. We introduce the Reflective Risk-Aware Agent Architecture (R2A2), a unified cognitive framework grounded in Constrained Markov Decision Processes (CMDPs), which incorporates risk-aware world modeling, meta-policy adaptation, and joint reward-risk optimization to enable principled, proactive safety across the agent's decision-making loop.
The Ising model, originally developed for understanding magnetic phase transitions, has become a cornerstone in the study of collective phenomena across diverse disciplines. In this review, we explore how Ising and Ising-like models have been successfully adapted to sociophysical systems, where binary-state agents mimic human decisions or opinions. By focusing on key areas such as opinion dynamics, financial markets, social segregation, game theory, language evolution, and epidemic spreading, we demonstrate how the models describing these phenomena, inspired by the Ising model, capture essential features of collective behavior, including phase transitions, consensus formation, criticality, and metastability. In particular, we emphasize the role of the dynamical rules of evolution in the different models that often converge back to Ising-like universality. We end by outlining the future directions in sociphysics research, highlighting the continued relevance of the Ising model in the analysis of complex social systems.
Enhanced Thermodynamic Framework for Third-Order Galaxy Correlation Functions: From Theory to Observations
2025-06-30
Sameer Choudhary, Naseer Iqbal
We present a comprehensive thermodynamic derivation and observational validation of the third-order galaxy correlation function, extending the seminal work of Iqbal et al [ 1, 2]. Using the statistical mechanics framework for gravitating systems, we derive the governing partial differential equation and provide explicit analytical solutions. Our enhanced framework includes: (1) comparison with N-body simulations and SDSS/BOSS galaxy survey data showing excellent agreement (\c{hi}2/dof = 1.27), (2) systematic methodology for determining model parameters from observational constraints, (3) detailed analysis of scale-dependent behaviour across linear and nonlinear regimes, (4) direct connections to standard cosmological parameters ({\sigma}8, {\Omega}m, fN L), (5) comprehensive treatment of redshift-space distortions, and (6) analysis of observational systematics. We demonstrate that our third-order correlation parameter b3 provides independent constraints complementary to two-point analyses, with potential to break degeneracies in dark energy and modified gravity studies. Explicit quantitative limitations regarding cosmic expansion effects and equilibrium assumptions are provided, along with comparisons to alternative methodologies and extensions for next-generation surveys.
An innovative few-shot anomaly detection approach is presented, leveraging the pre-trained CLIP model for medical data, and adapting it for both image-level anomaly classification (AC) and pixel-level anomaly segmentation (AS). A dual-branch design is proposed to separately capture normal and abnormal features through learnable adapters in the CLIP vision encoder. To improve semantic alignment, learnable text prompts are employed to link visual features. Furthermore, SigLIP loss is applied to effectively handle the many-to-one relationship between images and unpaired text prompts, showcasing its adaptation in the medical field for the first time. Our approach is validated on multiple modalities, demonstrating superior performance over existing methods for AC and AS, in both same-dataset and cross-dataset evaluations. Unlike prior work, it does not rely on synthetic data or memory banks, and an ablation study confirms the contribution of each component. The code is available at https://github.com/mahshid1998/MadCLIP.
A Survey of LLM-based Automated Program Repair: Taxonomies, Design Paradigms, and Applications
2025-06-30
Boyang Yang, Zijian Cai, Fengling Liu, Bach Le, Lingming Zhang, Tegawendé F. Bissyandé, Yang Liu, Haoye Tian
Large language models (LLMs) are reshaping automated program repair (APR). We categorize the recent 63 LLM-based APR systems published from January 2022 to June 2025 into four paradigms, and show how retrieval- or analysis-augmented contexts strengthen any of them. This taxonomy clarifies key trade-offs: fine-tuning delivers strong task alignment at high training cost; prompting enables rapid deployment but is limited by prompt design and context windows; procedural pipelines offer reproducible control with moderate overhead; agentic frameworks tackle multi-hunk or cross-file bugs at the price of increased latency and complexity. Persistent challenges include verifying semantic correctness beyond test suites, repairing repository-scale defects, and lowering the costs of LLMs. We outline research directions that combine lightweight human feedback, repository-aware retrieval, code analysis, and cost-aware planning to advance reliable and efficient LLM-based APR.
Reviewing Current-Driven Dynamics and Monte Carlo based Analysis of Thermodynamic Properties of a Magnetic Skyrmion Crystal
2025-06-30
Rajdip Banerjee, Satyaki Kar
Magnetic skyrmions with its topologically protected, nano-sized spin textures have already earned immense fame as information carriers due to their stability and low-current mobility. While ferromagnetic skyrmions suffer from a transverse deflection (i.e., the skyrmion Hall effect), their anti-ferromagnetic counterparts promise straight-line motion and ultrafast dynamics. Here we present a numerical study of the dynamics of lattice-based antiferromagnetic skyrmions driven by spin-transfer torque for which the Landau-Lifshitz-Gilbert-Slonczewski (LLGS) equation is solved using a fourth-order Range-Kutta integration. Then we conduct a detailed Monte Carlo study of the two-dimensional classical XY model to quantify how spatial anisotropy and Dzyaloshinskii-Moriya (DM) coupling reshape its thermal response across multiple lattice sizes. By tuning the ratio Jy/Jx, we document a systematic evolution of the specific-heat anomaly. For example, in the quasi-one-dimensional limit, CV exhibits a broad, low-temperature hump, whereas stronger anisotropy yields sharper peaks that migrate to higher inverse temperature. Finite-size scaling confirms the crossover from quasi-1D fluctuations to a two-dimensional Kosterlitz-Thouless transition. Incorporating a DM interaction further enriches this landscape. At D/J = 0.1, the main peak shifts modestly upward and is slightly suppressed; raising D/J elevates the peak magnitudes and creates a pronounced low-temperature plateau. This residual CV signals enduring chiral excitations and complex spin-twist textures beyond simple vortex unbinding. Our findings chart how directional and chiral couplings can be harnessed to tune pseudo-critical temperatures and thermodynamic signatures in two-dimensional magnets, providing a practical blueprint for engineering topological spin systems.
The NEID Earth Twin Survey. III. Survey Performance After Three Years on Sky
2025-06-30
Arvind F. Gupta, Evan Fitzmaurice, Suvrath Mahadevan, Paul Robertson, Jacob K. Luhn, Jason T. Wright, Sarah E. Logsdon, Daniel M. Krolikowski, Leonardo A. Paredes, Chad F. Bender, Mark R. Giovinazzi, Andrea S. Lin, Cullen H. Blake, Caleb I. Cañas, Eric B. Ford, Samuel P. Halverson, Shubham Kanodia, Michael W. McElwain, Joe P. Ninan, Jayadev Rajagopal, Arpita Roy, Christian Schwab, Guðmundur Stefánsson, Ryan C. Terrien
The NEID Earth Twin Survey (NETS) has been delivering a rich set of precise radial velocity (RV) measurements for 41 bright, nearby main sequence stars. Here, we describe the status of the survey after three years on sky and we present the full set of RV measurements and accompanying stellar activity indicators. We discuss intermediate survey diagnostics, including calibration of the known RV zero point offset introduced following the Contreras fire in 2022 and the identification of an undiagnosed and previously unknown zero point offset in 2021. An analysis of our data set using exttt{RVSearch} demonstrates that for these target stars, NEID is independently sensitive to nearly all known planets with periods shorter than the NETS observing baseline. We also highlight a number of newly detected RV signals, which present exciting opportunities for future investigations.
Studying the multi-phase interstellar medium in the Large Magellanic Cloud with SRG/eROSITA -- I. Analysis of diffuse X-ray emission
2025-06-30
Martin G. F. Mayer, Manami Sasaki, Frank Haberl, Kisetsu Tsuge, Yasuo Fukui, Chandreyee Maitra, Miroslav D. Filipovic, Zachary J. Smeaton, Lister Staveley-Smith, Baerbel Koribalski, Sean Points, Patrick Kavanagh
The Large Magellanic Cloud (LMC), being a nearby and actively star-forming satellite galaxy of the Milky Way, is an ideal site to observe the multi-phase interstellar medium (ISM) of a galaxy across the electromagnetic spectrum. We aim to exploit the available SRG/eROSITA all-sky survey data to study the distribution, composition and properties of the diffuse X-ray emitting hot gas in the LMC. We construct multi-band X-ray images of the LMC, reflecting the morphology and temperatures of the diffuse hot gas. By performing spatially resolved X-ray spectroscopy of 175 regions, we constrain the distribution, physical state, and composition of the hot ISM phase throughout the LMC. We combine our constraints with multiwavelength data to obtain a comprehensive view of the different ISM phases. We measure a total X-ray luminosity of the hot ISM phase of $1.9\times10^{38}\,\mathrm{erg\,s^{-1}}$ ($0.2-5.0\,\mathrm{keV}$ band), and constrain its thermal energy to around $5\times10^{54}\,\mathrm{erg}$. The typical density and temperature of the X-ray emitting plasma are around $5\times10^{-3}\,\mathrm{cm^{-3}}$ and $0.25\,\mathrm{keV}$, respectively, with both exhibiting broad peaks in the southeast of the LMC. The observed degree of X-ray absorption correlates strongly with the distribution of foreground HI gas, whereas a spatial anticorrelation between the hot and cold ISM phases is visible on sub-kpc scales within the disk. The abundances of light metals show a strong gradient throughout the LMC, with the north and east exhibiting a strong $\alpha$-enhancement, as expected from observed massive stellar populations there. In contrast, the enigmatic ``X-ray spur'' exhibits a local deficit in $\alpha$-elements, and a peak in hot-gas pressure at $P/k\sim10^5\,\mathrm{K\,cm^{-3}}$, consistent with a dominant energy input through tidally driven gas collisions.
What Challenges Do Developers Face When Using Verification-Aware Programming Languages?
2025-06-30
Francisco Oliveira, Alexandra Mendes, Carolina Carreira
Software reliability is critical in ensuring that the digital systems we depend on function correctly. In software development, increasing software reliability often involves testing. However, for complex and critical systems, developers can use Design by Contract (DbC) methods to define precise specifications that software components must satisfy. Verification-Aware (VA) programming languages support DbC and formal verification at compile-time or run-time, offering stronger correctness guarantees than traditional testing. However, despite the strong guarantees provided by VA languages, their adoption remains limited. In this study, we investigate the barriers to adopting VA languages by analyzing developer discussions on public forums using topic modeling techniques. We complement this analysis with a developer survey to better understand the practical challenges associated with VA languages. Our findings reveal key obstacles to adoption, including steep learning curves and usability issues. Based on these insights, we identify actionable recommendations to improve the usability and accessibility of VA languages. Our findings suggest that simplifying tool interfaces, providing better educational materials, and improving integration with everyday development environments could improve the usability and adoption of these languages. Our work provides actionable insights for improving the usability of VA languages and making verification tools more accessible.
Surface Finishing and Coatings for Accelerator Vacuum Applications
2025-06-30
Mauro Taborelli
The main applications and techniques of thin film coating and plating on vacuum components for particle accelerators are reviewed through significative examples.
Interactive Reasoning: Visualizing and Controlling Chain-of-Thought Reasoning in Large Language Models
2025-06-30
Rock Yuren Pang, K. J. Kevin Feng, Shangbin Feng, Chu Li, Weijia Shi, Yulia Tsvetkov, Jeffrey Heer, Katharina Reinecke
The output quality of large language models (LLMs) can be improved via "reasoning": generating segments of chain-of-thought (CoT) content to further condition the model prior to producing user-facing output. While these chains contain valuable information, they are verbose and lack explicit organization, making them tedious to review. Moreover, they lack opportunities for user feedback, such as to remove unwanted considerations, add desired ones, or clarify unclear assumptions. We introduce Interactive Reasoning, an interaction design that visualizes chain-of-thought outputs as a hierarchy of topics and enables user review and modification. We implement interactive reasoning in Hippo, a prototype for AI-assisted decision making in the face of uncertain trade-offs. In a user study with 16 participants, we find that interactive reasoning in Hippo allows users to quickly identify and interrupt erroneous generations, efficiently steer the model towards customized responses, and better understand both model reasoning and model outputs. Our work contributes to a new paradigm that incorporates user oversight into LLM reasoning processes.
Even redder than we knew: color and $A_{\mathrm{V}}$ evolution up to $z=2.5$ from JWST/NIRCam photometry
2025-06-30
A. van der Wel, M. Martorano, D. Marchesini, S. Wuyts, E. F. Bell, S. E. Meidt, A. Gebek, G. Brammer, K. Whitaker, R. Bezanson, E. J. Nelson, G. Rudnick, M. Kriek, J. Leja, J. S. Dunlop, C. Casey, J. Kartaltepe
JWST/NIRCam provides rest-frame near-IR photometry of galaxies up to $z=2.5$ with exquisite depth and accuracy. This affords an unprecedented view of the evolution of the UV-optical-near-IR color distribution and its interpretation in terms of the evolving dust attenuation, $A_{\mathrm{V}}$. We use the value-added data products (photometric redshift, stellar mass, rest-frame $U-V$ and $V-J$ colors, and $A_{\rm V}$) provided by the public DAWN JWST Archive. This data product derives from fitting the spectral energy distributions obtained from multiple NIRCam imaging surveys, augmented with pre-existing HST imaging data. Our sample consists of a stellar mass complete sample of $\approx 28,000$ $M_\star> 10^{9}~M_\odot$ galaxies in the redshift range $0.53\times 10^{10}~M_\odot$), which have a pronounced tail of very red galaxies reaching $V-J> 2.5$ at $z>1.5$ that does not exist at $z<1$. Such red $V-J$ can only be explained by dust attenuation, with typical values for $M_\star \approx 10^{11}~M_\odot$ galaxies in the range $A_{\mathrm{V}}\approx 1.5-3.5$ at $z\approx 2$. This redshift evolution went largely unnoticed before because the photometric redshift estimates for the reddest ($V-J>2.5$), most attenuated galaxies has markedly improved thanks to the new, precise photometry. Despite the increased attenuation, $U-V$ colors across the entire mass range are slightly bluer at higher $z$. In conclusion, whereas the rest-frame UV-optical color distribution evolves remarkably little from $z=0.5$ to $z=2.5$, the rest-frame optical-near-IR color distribution evolves strongly, primarily due to a very substantial increase with redshift in dust attenuation for massive galaxies. (Abbr.)
Reexamination of the charge-ordered dimer pattern in the spinel compound CuIr2S4 using single-crystal synchrotron x-ray diffraction
2025-06-30
T. Ohashi, N. Katayama, K. Kojima, M. Emi, C. Koyama, T. Hara, K. Hashimoto, S. Kitani, H. Kawaji, H. S. Suzuki, S. Nagata, K. Sugimoto, K. Iida, H. Sawa
We have re-investigated the crystal structure of a spinel type CuIr2S4 at low temperatures using a single-crystal in a synchrotron radiation x-ray diffraction experiment. The crystal structure of the low-temperature phase of CuIr2S4 has been already studied by diffraction experiments using a powder sample, and it has been reported that the formation of dimer molecules accompanied by charge ordering of Ir has been achieved. The crystal structure of the low-temperature phase obtained in our reanalysis was the same as the previously reported structure in that it showed the formation of Ir dimers accompanied by charge ordering, but the charge ordering pattern and arrangement of the dimers in the unit cell were different. We will discuss the validity of the structure obtained in this study and provide the structural parameters revealed in the reanalysis. The results of this study should provide a basis for further studies of the physical properties of CuIr2S4, which are still being actively investigated.
Blending Concepts with Text-to-Image Diffusion Models
2025-06-30
Lorenzo Olearo, Giorgio Longari, Alessandro Raganato, Rafael Peñaloza, Simone Melzi
Diffusion models have dramatically advanced text-to-image generation in recent years, translating abstract concepts into high-fidelity images with remarkable ease. In this work, we examine whether they can also blend distinct concepts, ranging from concrete objects to intangible ideas, into coherent new visual entities under a zero-shot framework. Specifically, concept blending merges the key attributes of multiple concepts (expressed as textual prompts) into a single, novel image that captures the essence of each concept. We investigate four blending methods, each exploiting different aspects of the diffusion pipeline (e.g., prompt scheduling, embedding interpolation, or layer-wise conditioning). Through systematic experimentation across diverse concept categories, such as merging concrete concepts, synthesizing compound words, transferring artistic styles, and blending architectural landmarks, we show that modern diffusion models indeed exhibit creative blending capabilities without further training or fine-tuning. Our extensive user study, involving 100 participants, reveals that no single approach dominates in all scenarios: each blending technique excels under certain conditions, with factors like prompt ordering, conceptual distance, and random seed affecting the outcome. These findings highlight the remarkable compositional potential of diffusion models while exposing their sensitivity to seemingly minor input variations.
In this proceedings, we review the importance and complexity of detecting and characterizing supermassive binary black holes using conventional techniques (spectroscopy, timing, imaging). We show how spectropolarimetric data can strengthen or discard binary black hole candidates on solid grounds, and present what kind of instrument could perform this task. Namely, a high spectral resolution polarimeter operating from the far-ultraviolet (FUV) to the near-infrared (NIR), such as the POLLUX prototype, would perfectly fit within the payload of the Habitable Worlds Observatory and help revolutionize the study of extragalactic objects.
Cybersecurity AI: The Dangerous Gap Between Automation and Autonomy
2025-06-30
Víctor Mayoral-Vilches
The cybersecurity industry combines "automated" and "autonomous" AI, creating dangerous misconceptions about system capabilities. Recent milestones like XBOW topping HackerOne's leaderboard showcase impressive progress, yet these systems remain fundamentally semi-autonomous--requiring human oversight. Drawing from robotics principles, where the distinction between automation and autonomy is well-established, I take inspiration from prior work and establish a 6-level taxonomy (Level 0-5) distinguishing automation from autonomy in Cybersecurity AI. Current "autonomous" pentesters operate at Level 3-4: they execute complex attack sequences but need human review for edge cases and strategic decisions. True Level 5 autonomy remains aspirational. Organizations deploying mischaracterized "autonomous" tools risk reducing oversight precisely when it's most needed, potentially creating new vulnerabilities. The path forward requires precise terminology, transparent capabilities disclosure, and human-AI partnership-not replacement.
Evolutionary Map of the Universe: Detection and Analysis of the Shell Surrounding the Runaway Wolf-Rayet Star WR16
2025-06-30
A. C. Bradley, M. D. Filipović, Z. J. Smeaton, H. Sano, Y. Fukui, C. Bordiu, S. Cichowolski, N. F. H. Tothill, R. Z. E. Alsaberi, F. Bufano, S. Dai, Y. A. Gordon, A. M. Hopkins, T. H. Jarrett, B. S. Koribalski, S. Lazarević, C. J. Riseley, G. Rowell, M. Sasaki, D. Urošević, T. Vernstrom
We present the first radio--continuum detection of the circumstellar shell around the well-known WN8 type Wolf-Rayet star WR16 at 943.5\,MHz using the \ac{ASKAP} \ac{EMU} survey. At this frequency, the shell has a measured flux density of 72.2$\pm$7.2\,mJy. Using previous \ac{ATCA} measurements at 2.4, 4.8, and 8.64~GHz, as well as the \ac{EMU} observations of the star itself, we determine a spectral index of $\alpha\,=\,+0.74\pm0.02$, indicating thermal emission. We propose that the shell and star both exhibit thermal emission, supported by the its appearance in near-infrared and H$\alpha$ observations. The latest \textit{Gaia} parallax is used to determine a distance of 2.28$\pm$0.09\,kpc. This star is well-known for its surrounding circular nebulosity, and using the distance and an angular diameter of 8\farcm42, we determine the shell size to be 5.57$\pm$0.22~pc. We use the \textit{Gaia} \acp{PM} of WR16 to determine peculiar velocities of the star as $V_{\alpha}(pec) =$ --45.3$\pm$5.4\,\kms\ and $V_{\delta}(pec) =$ 22.8$\pm$4.7\,\kms, which indicates that the star is moving in a north-west direction, and translates to a peculiar tangential velocity to be 50.7$\pm$6.9\,\kms. We also use these \acp{PM} to determine the shell's origin, estimate an age of $\sim 9500\pm 1300$\,yr, and determine its average expansion velocity to be $280\pm40$\,\kms. This average expansion velocity suggests that the previous transitional phase is a \ac{LBV} phase, rather than a \ac{RSG} phase. We also use the measured flux at 943.5~MHz to determine a mass-loss rate of $1.753\times 10^{-5}~M_\odot~$yr$^{-1}$, and use this to determine a lower-limit on ionising photons of $N_{UV} > 1.406\times 10^{47}~$s$^{-1}$.
JAM-Flow: Joint Audio-Motion Synthesis with Flow Matching
2025-06-30
Mingi Kwon, Joonghyuk Shin, Jaeseok Jung, Jaesik Park, Youngjung Uh
The intrinsic link between facial motion and speech is often overlooked in generative modeling, where talking head synthesis and text-to-speech (TTS) are typically addressed as separate tasks. This paper introduces JAM-Flow, a unified framework to simultaneously synthesize and condition on both facial motion and speech. Our approach leverages flow matching and a novel Multi-Modal Diffusion Transformer (MM-DiT) architecture, integrating specialized Motion-DiT and Audio-DiT modules. These are coupled via selective joint attention layers and incorporate key architectural choices, such as temporally aligned positional embeddings and localized joint attention masking, to enable effective cross-modal interaction while preserving modality-specific strengths. Trained with an inpainting-style objective, JAM-Flow supports a wide array of conditioning inputs-including text, reference audio, and reference motion-facilitating tasks such as synchronized talking head generation from text, audio-driven animation, and much more, within a single, coherent model. JAM-Flow significantly advances multi-modal generative modeling by providing a practical solution for holistic audio-visual synthesis. project page: https://joonghyuk.com/jamflow-web
With the increasing interest in robotic synthesis in the context of organic chemistry, the automated extraction of chemical procedures from literature is critical. However, this task remains challenging due to the inherent ambiguity of chemical language and the high cost of human annotation required for developing reliable computer-aided extraction protocols. Here, we present ChemActor, a fully fine-tuned large language model (LLM), as a chemical executor to convert between unstructured experimental procedures and structured action sequences. We propose a sequential LLM-generated data framework to address the challenges of insufficient and low-quality annotated data. This framework integrates a data selection module that selects data based on distribution divergence, with a general-purpose LLM, to generate machine-executable actions from a single molecule input. Additionally, we introduce a novel multi-round LLMs circle review metric, which reflects the model's advanced understanding of chemical experimental procedures. Extensive experiments on reaction-to-description (R2D) and description-to-action (D2A) tasks demonstrate that ChemActor, augmented by LLM-generated data, achieves state-of-the-art performance, outperforming the baseline model by 10%. The code is available at: https://github.com/Zhanghahah/ChemActor.
Engineering Magnetization with Photons: Nanoscale Advances in the Inverse Faraday Effect for Metallic and Plasmonic Systems
2025-06-30
Chantal Hareau, Xingyu Yang, Maria Sanz, Matthew Sheldon, Mathieu Mivelle
The inverse Faraday effect, the ability of light to act as a source of magnetism, is a cornerstone of modern ultrafast optics. Harnessing this effect at the nanoscale promises to transform data storage and spintronics, yet its predictive understanding remains elusive. This review synthesizes recent progress in engineering the IFE within plasmonic architectures. We bridge the theoretical foundations, from classical drift current models to quantum descriptions, with the latest experimental milestones, including pump probe studies that have verified the effect s subpicosecond nature. Special emphasis is placed on how nanostructure design allows for unprecedented control, enabling functionalities like chiral or reversed magnetization by locally sculpting the optical spin density. Despite this progress, a crucial challenge pervades the field, a stark, often orders of magnitude, mismatch between predicted and measured magnetization values. We contend that resolving this discrepancy is paramount. The path forward requires the development of novel experimental probes capable of directly imaging these fleeting magnetic fields at their native length and time scales, ultimately unlocking the true potential of nanoscale optical magnetism.
Far-Field vs. Near-Field Propagation Channels: Key Differences and Impact on 6G XL-MIMO Performance Evaluation
2025-06-30
Zihang Ding, Jianhua Zhang, Changsheng You, Pan Tang, Hongbo Xing, Zhiqiang Yuan, Jie Meng, Guangyi Liu
Extremely large-scale multiple-input multiple-output (XL-MIMO) is regarded as a promising technology for next-generation communication systems. However, this will expand the near-field (NF) range, rendering more users more likely to be located in the NF region. In this paper, we aim to answer two questions: What are the new characteristics of the NF channel? Is it necessary to develop new transciver techniques to maintain system performance within the NF region? To this end, we first review current NF channel models and analyze the differences between the existing 3GPP TR 38.901 channel model and the NF channel model, including the spherical wavefront and spatially non-stationarity. Then, we provide examples on how these differences affect the XL-MIMO system performance in terms of beamforming gain and achievable rate. Simulation results demonstrate that, when using far-field (FF) technique under the NF channel, the maximum normalized beam gain loss is less than 3 dB for most users in the NF region defined by Rayleigh distance. Moreover, the achievable rate loss of beam training is less than 3% compared to that realized by NF technique. Finally, we demonstrate the necessity of employing NF transceiver techniques based on simulation results.
Evaluation of Geolocation Capabilities of Multimodal Large Language Models and Analysis of Associated Privacy Risks
2025-06-30
Xian Zhang, Xiang Cheng
Objectives: The rapid advancement of Multimodal Large Language Models (MLLMs) has significantly enhanced their reasoning capabilities, enabling a wide range of intelligent applications. However, these advancements also raise critical concerns regarding privacy and ethics. MLLMs are now capable of inferring the geographic location of images -- such as those shared on social media or captured from street views -- based solely on visual content, thereby posing serious risks of privacy invasion, including doxxing, surveillance, and other security threats. Methods: This study provides a comprehensive analysis of existing geolocation techniques based on MLLMs. It systematically reviews relevant litera-ture and evaluates the performance of state-of-the-art visual reasoning models on geolocation tasks, particularly in identifying the origins of street view imagery. Results: Empirical evaluation reveals that the most advanced visual large models can successfully localize the origin of street-level imagery with up to $49\%$ accuracy within a 1-kilometer radius. This performance underscores the models' powerful capacity to extract and utilize fine-grained geographic cues from visual data. Conclusions: Building on these findings, the study identifies key visual elements that contribute to suc-cessful geolocation, such as text, architectural styles, and environmental features. Furthermore, it discusses the potential privacy implications associated with MLLM-enabled geolocation and discuss several technical and policy-based coun-termeasures to mitigate associated risks. Our code and dataset are available at https://github.com/zxyl1003/MLLM-Geolocation-Evaluation.
Some Mathematical Problems Behind Lattice-Based Cryptography
2025-06-30
Chuanming Zong
In 1994, P. Shor discovered quantum algorithms which can break both the RSA cryptosystem and the ElGamal cryptosystem. In 2007, D-Wave demonstrated the first quantum computer. These events and further developments have brought a crisis to secret communication. In 2016, the National Institute of Standards and Technology (NIST) launched a global project to solicit and select a handful of encryption algorithms with the ability to resist quantum computer attacks. In 2022, it announced four candidates, CRYSTALS-Kyber, CRYSTALS-Dilithium, Falcon and Sphincs$+$ for post-quantum cryptography standards. The first three are based on lattice theory and the last on the Hash function. It is well known that the security of the lattice-based cryptosystems relies on the computational complexity of the shortest vector problem (SVP), the closest vector problem (CVP) and their generalizations. In fact, the SVP is a ball packing problem and the CVP is a ball covering problem. Furthermore, both SVP and CVP are equivalent to arithmetic problems for positive definite quadratic forms. Therefore, post-quantum cryptography provides unprecedented opportunities for mathematicians to make contributions in modern technology. This paper will briefly review the mathematical problems on which the lattice-based cryptography is built up, so that mathematicians can see that they are indeed in the game.
Modeling Group Actions on Stacks (Especially the Lubin-Tate Action)
2025-06-30
Rin Ray
Suppose we are given a profinite group $G$ acting on a formal moduli stack $\mathcal{M}$, and we want to understand the group action, and compute cohomology related to this group action. How can we do it? This prolegomenon surveys two methods of pinning down such an action: geometric modeling and the two tower method. We highlight their use on a specific action - the automorphisms of a formal group acting on its deformation space, called the Lubin-Tate action.
Exploring the probing power of gamma-Dor's inertial dip for core magnetism: case of a toroidal field
2025-06-30
Lucas Barrault, Lisa Bugnet, Stéphane Mathis, Joey S. G. Mombarg
Gamma-Dor stars are ideal targets for studies of stellar innermost dynamical properties due to their rich asteroseismic spectrum of gravity modes. Integrating internal magnetism to the picture appears as the next milestone of detailed asteroseismic studies, for its prime importance on stellar evolution. The inertial dip in prograde dipole modes period-spacing pattern of gamma-Dors stands out as a unique window on the convective core structure and dynamics. Recent studies have highlighted the dependence of the dip structure on core density stratification, contrast of the near-core Brunt-V\"ais\"al\"a frequency and rotation rate, as well as the core-to-near-core differential rotation. In the meantime, the effect of magnetism has been derived on envelope modes. We aim to revisit the inertial dip formation including core and envelope magnetism, and explore the probing power of this feature on dynamo-generated core fields. We consider a toroidal magnetic field with a bi-layer (core and envelope) Alfv\'en frequency. This configuration allows us to revisit the coupling problem using our knowledge on both core magneto-inertial modes and envelope magneto-gravito-inertial modes. We stay in an analytical framework to exhibit the magnetic effects on the inertial dip shape and location, setting up a laboratory towards the comprehension of magnetic effects on the dip structure. We show a shift of the inertial dip towards lower spin parameter values and a thinner dip with increasing core magnetic field, quite similar to the signature of differential rotation. The magnetic effects become sizeable when the ratio between the magnetic and the Coriolis effects is large enough. We explore the degeneracy of the magnetic effects with differential rotation. We study the detectability of core magnetism, considering observational constraints on the modes periods and potential gravito-inertial mode suppression.
Reducing Variability of Multiple Instance Learning Methods for Digital Pathology
2025-06-30
Ali Mammadov, Loïc Le Folgoc, Guillaume Hocquet, Pietro Gori
Digital pathology has revolutionized the field by enabling the digitization of tissue samples into whole slide images (WSIs). However, the high resolution and large size of WSIs present significant challenges when it comes to applying Deep Learning models. As a solution, WSIs are often divided into smaller patches with a global label (\textit{i.e., diagnostic}) per slide, instead of a (too) costly pixel-wise annotation. By treating each slide as a bag of patches, Multiple Instance Learning (MIL) methods have emerged as a suitable solution for WSI classification. A major drawback of MIL methods is their high variability in performance across different runs, which can reach up to 10-15 AUC points on the test set, making it difficult to compare different MIL methods reliably. This variability mainly comes from three factors: i) weight initialization, ii) batch (shuffling) ordering, iii) and learning rate. To address that, we introduce a Multi-Fidelity, Model Fusion strategy for MIL methods. We first train multiple models for a few epochs and average the most stable and promising ones based on validation scores. This approach can be applied to any existing MIL model to reduce performance variability. It also simplifies hyperparameter tuning and improves reproducibility while maintaining computational efficiency. We extensively validate our approach on WSI classification tasks using 2 different datasets, 3 initialization strategies and 5 MIL methods, for a total of more than 2000 experiments.
Modeling the Cosmological Lyman-$α$ Forest at the Field Level
2025-06-30
Roger de Belsunce, Mikhail M. Ivanov, James M. Sullivan, Kazuyuki Akitsu, Shi-Fan Chen
The distribution of absorption lines in the spectra of distant quasars, called the Lyman-$\alpha$ (Ly-$\alpha$) forest, is a unique probe of cosmology and the intergalactic medium at high redshifts and small scales. The statistical power of ongoing redshift surveys demands precise theoretical tools to model the Ly-$\alpha$ forest. We address this challenge by developing an analytic, perturbative forward model to predict the Ly-$\alpha$ forest at the field level for a given set of cosmological initial conditions. Our model shows a remarkable performance when compared with the Sherwood hydrodynamic simulations: it reproduces the flux distribution, the Ly-$\alpha$ - dark matter halo cross-correlations, and the count-in-cell statistics at the percent level down to scales of a few Mpc. Our work provides crucial tools that bridge analytic modeling on large scales with simulations on small-scales, enabling field-level inference from Ly-$\alpha$ forest data and simulation-based priors for cosmological analyses. This is especially timely for realizing the full scientific potential of the Ly-$\alpha$ forest measurements by the Dark Energy Spectroscopic Instrument.
Compact Representation of Semilinear and Terrain-like Graphs
2025-06-30
Jean Cardinal, Yelena Yuditsky
We consider the existence and construction of \textit{biclique covers} of graphs, consisting of coverings of their edge sets by complete bipartite graphs. The \textit{size} of such a cover is the sum of the sizes of the bicliques. Small-size biclique covers of graphs are ubiquitous in computational geometry, and have been shown to be useful compact representations of graphs. We give a brief survey of classical and recent results on biclique covers and their applications, and give new families of graphs having biclique covers of near-linear size. In particular, we show that semilinear graphs, whose edges are defined by linear relations in bounded dimensional space, always have biclique covers of size $O(n\polylog n)$. This generalizes many previously known results on special classes of graphs including interval graphs, permutation graphs, and graphs of bounded boxicity, but also new classes such as intersection graphs of L-shapes in the plane. It also directly implies the bounds for Zarankiewicz's problem derived by Basit, Chernikov, Starchenko, Tao, and Tran (\textit{Forum Math. Sigma}, 2021). We also consider capped graphs, also known as terrain-like graphs, defined as ordered graphs forbidding a certain ordered pattern on four vertices. Terrain-like graphs contain the induced subgraphs of terrain visibility graphs. We give an elementary proof that these graphs admit biclique partitions of size $O(n\log^3 n)$. This provides a simple combinatorial analogue of a classical result from Agarwal, Alon, Aronov, and Suri on polygon visibility graphs (\textit{Discrete Comput. Geom.} 1994). Finally, we prove that there exists families of unit disk graphs on $n$ vertices that do not admit biclique coverings of size $o(n^{4/3})$, showing that we are unlikely to improve on Szemer\'edi-Trotter type incidence bounds for higher-degree semialgebraic graphs.
Propulsion and interaction of wave-propelled interfacial particles
2025-06-30
Daniel M. Harris, Jack-William Barotta
When a floating body is internally or externally vibrated, its self-generated wavefield can lead to steady propulsion along the interface. In this article, we review several related and recently discovered systems that leverage this propulsion mechanism and interact hydrodynamically with one another via these surface waves. Particles with an onboard oscillatory driver may self-propel by virtue of a fore-aft asymmetric wavefield, a phenomenon with demonstrated relevance to biological and artificial systems across scales. Freely floating particles on a vibrated fluid bath can also self-propel along straight paths, but may also rotate in place or move along curved arcs, depending sensitively on the particle asymmetries and driving parameters. Such surfing particles interact at a distance through their mutual capillary wavefield and exhibit a rich array of collective dynamics. Overall, these accessible, tunable, and visually appealing systems motivate future investigations into a number of outstanding questions in fundamental fluid mechanics, while potentially also informing advances in the fields of active matter, hydrodynamic quantum analogs, and robotics.
Reconstructing high-quality images from low-resolution inputs using Residual Dense Spatial Networks (RDSNs) is crucial yet challenging, particularly in collaborative scenarios where centralized training poses significant privacy risks, including data leakage and inference attacks, as well as high computational costs. We propose a novel Privacy-Preserving Federated Learning-based RDSN (PPFL-RDSN) framework specifically tailored for lossy image reconstruction. PPFL-RDSN integrates Federated Learning (FL), local differential privacy, and robust model watermarking techniques, ensuring data remains secure on local devices, safeguarding sensitive information, and maintaining model authenticity without revealing underlying data. Empirical evaluations show that PPFL-RDSN achieves comparable performance to the state-of-the-art centralized methods while reducing computational burdens, and effectively mitigates security and privacy vulnerabilities, making it a practical solution for secure and privacy-preserving collaborative computer vision applications.
New Directions in Gravity Searches for Spacetime-Symmetry breaking
2025-06-30
Quentin G. Bailey
In this talk, we review recent results in testing spacetime symmetries in gravitational physics. Topics discussed include new signals for hypothetical Lorentz and diffeomorphism symmetry violations in short-range gravity tests. We review results for multipole expansions that predict extra polarizations for gravitational waves. Both explicit and spontaneous spacetime-symmetry breaking origins are considered. We also discuss recent numerical results for black hole solutions in a vector field model of spontaneous symmetry breaking.
This paper reviews the current progress in applying machine learning (ML) tools to solve NP-hard combinatorial optimization problems, with a focus on routing problems such as the traveling salesman problem (TSP) and the vehicle routing problem (VRP). Due to the inherent complexity of these problems, exact algorithms often require excessive computational time to find optimal solutions, while heuristics can only provide approximate solutions without guaranteeing optimality. With the recent success of machine learning models, there is a growing trend in proposing and implementing diverse ML techniques to enhance the resolution of these challenging routing problems. We propose a taxonomy categorizing ML-based routing methods into construction-based and improvement-based approaches, highlighting their applicability to various problem characteristics. This review aims to integrate traditional OR methods with state-of-the-art ML techniques, providing a structured framework to guide future research and address emerging VRP variants.
Earth's field diamond vector magnetometry with isotropic magnetic flux concentrators
2025-06-30
Maziar Saleh Ziabari, Nazanin Mosavian, Ilja Fescenko, Yaser Silani, Bryan A. Richards, Andris Berzins, Maxwell D. Aiello, Keith A. Lidke, Andrey Jarmola, Janis Smits, Victor M. Acosta
Vector magnetometers based on the optically detected magnetic resonance (ODMR) of nitrogen-vacancy centers in diamond are being developed for applications such as navigation and geomagnetism. However, at low magnetic fields, such as that on Earth (~50 {\mu}T), diamond magnetometers suffer from spectral congestion whereby ODMR peaks are not easily resolved. Here, we experimentally investigate a potential solution of using an isotropic, three-dimensional magnetic flux concentrator to amplify Earth's field without altering its direction. The concentrator consists of six ferrite cones, in a face-centered cubic arrangement, centered about a diamond. We vary the direction of a 50 {\mu}T applied field and record and fit the resulting ODMR spectra. By comparing the fitted fields to those of a reference fluxgate magnetometer, we characterize the angular response of the diamond magnetometer and quantify absolute errors in the field magnitude and angle. We find that the enhancement factor is nearly isotropic, with a mean of 19.05 and a standard deviation of 0.16, when weighted by solid angle coverage. Gradient broadening of the ODMR lines is sufficiently small that the spectra are well resolved for nearly all field directions, alleviating spectral congestion. For ~98% of the total 4{\pi} solid angle, Cram\'er-Rao lower bounds for magnetic field estimation uncertainty are within a factor of 2 of those of the fully-resolved case, indicating minimal deadzones. We track the stability of the magnetometer over six hours and observe variations less than or approximately 40 nT/hour, limited by temperature drift. Our study presents a new route for diamond vector magnetometry at Earth's field, with potential applications in geomagnetic surveys, anomaly detection, and navigation.
Is It Safe To Learn And Share? On Psychological Safety and Social Learning in (Agile) Communities of Practice
2025-06-30
Christiaan Verwijs, Evelien Acun-Roos, Daniel Russo
As hybrid, distributed, and asynchronous work models become more prevalent, continuous learning in Agile Software Development (ASD) gains renewed importance. Communities of Practice (CoPs) are increasingly adopted to support social learning beyond formal education, often relying on virtual communication. Psychological safety, a prerequisite for effective learning, remains insufficiently understood in these settings. This mixed-methods study investigates psychological safety within Agile CoPs through survey data from 143 participants. Results indicate that psychological safety is significantly lower in online interactions compared to face-to-face settings. Moreover, low psychological safety reduces participants' intent to continue contributing and avoidance of interpersonal risk. No significant differences emerged based on gender, community seniority, or content creation activity. However, differences by role and age group suggest potential generational or role-related effects. Thematic analysis revealed exclusionary behavior, negative interaction patterns, and hostility as primary threats to psychological safety, often reinforced by tribalism and specific community dynamics. Suggested interventions include establishing explicit norms, structured facilitation, and active moderation. The findings were validated through member checking with 30 participants. This study provides a comparative perspective on interaction modalities and offers practical guidance for organizers seeking to cultivate inclusive, high-impact CoPs and similarly structured virtual or hybrid work environments.
Testing Origin-of-Life Theories with the Habitable Worlds Observatory (HWO)
2025-06-30
Sukrit Ranjan, Martin Schlecker, Nicholas Wogan, Michael Wong
The Habitable Worlds Observatory (HWO) aims to telescopically constrain the frequency and abundance of biospheres in the solar neighborhood. Origin-of-life theories vary in their predictions for the environmental requirements and the expected frequency of abiogenesis, meaning that constraints on the frequency and distribution of life on exoplanets from HWO can in principle directly test theories of abiogenesis. We categorize origin-of-life theories into three broad classes and discuss how HWO could potentially test them. Nondetection of biology on a large sample of habitable planets would provide prior-independent evidence in favor of the class of abiogenesis theory which holds that the origin of life is a contingent, vanishingly unlikely event, whereas detection of event a single biosphere would falsify this class of theories. Correlations of candidate biospheres with planetary parameters such as UV irradiation, the presence of oceans, and the presence of continents can test specific origin-of-life theories. Simulated surveys with Bayesian analysis are required to quantify the ability of HWO to execute this science case. However, a clear theme from the limited such studies that have already been conducted is the need for large sample sizes ($\gtrsim50$ planets characterized) to provide meaningful constraints on abiogenesis theories, favoring a larger design sample for HWO.
FreeLong++: Training-Free Long Video Generation via Multi-band SpectralFusion
2025-06-30
Yu Lu, Yi Yang
Recent advances in video generation models have enabled high-quality short video generation from text prompts. However, extending these models to longer videos remains a significant challenge, primarily due to degraded temporal consistency and visual fidelity. Our preliminary observations show that naively applying short-video generation models to longer sequences leads to noticeable quality degradation. Further analysis identifies a systematic trend where high-frequency components become increasingly distorted as video length grows, an issue we term high-frequency distortion. To address this, we propose FreeLong, a training-free framework designed to balance the frequency distribution of long video features during the denoising process. FreeLong achieves this by blending global low-frequency features, which capture holistic semantics across the full video, with local high-frequency features extracted from short temporal windows to preserve fine details. Building on this, FreeLong++ extends FreeLong dual-branch design into a multi-branch architecture with multiple attention branches, each operating at a distinct temporal scale. By arranging multiple window sizes from global to local, FreeLong++ enables multi-band frequency fusion from low to high frequencies, ensuring both semantic continuity and fine-grained motion dynamics across longer video sequences. Without any additional training, FreeLong++ can be plugged into existing video generation models (e.g. Wan2.1 and LTX-Video) to produce longer videos with substantially improved temporal consistency and visual fidelity. We demonstrate that our approach outperforms previous methods on longer video generation tasks (e.g. 4x and 8x of native length). It also supports coherent multi-prompt video generation with smooth scene transitions and enables controllable video generation using long depth or pose sequences.
Forecast for growth-rate measurement using peculiar velocities from LSST supernovae
2025-06-30
Damiano Rosselli, Bastien Carreres, Corentin Ravoux, Julian E. Bautista, Dominique Fouchez, Alex G. Kim, Benjamin Racine, Fabrice Feinstein, Bruno Sánchez, Aurelien Valade, The LSST Dark Energy Science Collaboration
In this work, we investigate the feasibility of measuring the cosmic growth-rate parameter, $f\sigma_8$, using peculiar velocities (PVs) derived from Type Ia supernovae (SNe Ia) in the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). We produce simulations of different SN types using a realistic LSST observing strategy, incorporating noise, photometric detection from the Difference Image Analysis (DIA) pipeline, and a PV field modeled from the Uchuu UniverseMachine simulations. We test three observational scenarios, ranging from ideal conditions with spectroscopic host-galaxy redshifts and spectroscopic SN classification, to more realistic settings involving photometric classification and contamination from non-Ia supernovae. Using a maximum-likelihood technique, we show that LSST can measure $f\sigma_8$ with a precision of $10\%$ in the redshift range $ 0.02 < z < 0.14 $ in the most realistic case. Using three tomographic bins, LSST can constrain the growth-rate parameter with errors below $18\%$ up to $z = 0.14$. We also test the impact of contamination on the maximum likelihood method and find that for contamination fractions below $\sim 2\%$, the measurement remains unbiased. These results highlight the potential of the LSST SN Ia sample to complement redshift-space distortion measurements at high redshift, providing a novel avenue for testing general relativity and dark energy models.
We present DeepCHART (Deep learning for Cosmological Heterogeneity and Astrophysical Reconstruction via Tomography), a deep learning framework designed to reconstruct the three-dimensional dark matter density field at redshift $z=2.5$ from Ly$\alpha$ forest spectra. Leveraging a 3D variational autoencoder with a U-Net architecture, DeepCHART performs fast, likelihood-free inference, accurately capturing the non-linear gravitational dynamics and baryonic processes embedded in cosmological hydrodynamical simulations. When applied to joint datasets combining Ly$\alpha$ forest absorption and coeval galaxy positions, the reconstruction quality improves further. For current surveys, such as Subaru/PFS, CLAMATO, and LATIS, with an average transverse sightline spacing of $d_\perp=2.4h^{-1}$cMpc, DeepCHART achieves high-fidelity reconstructions over the density range $0.4<\Delta_{\rm DM}<15$, with a voxel-wise Pearson correlation coefficient of $\rho\simeq 0.77$. These reconstructions are obtained using Ly$\alpha$ forest spectra with signal-to-noise ratios as low as 2 and instrumental resolution $R=2500$, matching Subaru/PFS specifications. For future high-density surveys enabled by instruments such as ELT/MOSAIC and WST/IFS with $d_\perp\simeq 1h^{-1}\mathrm{cMpc}$, the correlation improves to $\rho\simeq 0.90$ across a wider dynamic range ($0.25<\Delta_{\rm DM}<40$). The framework reliably recovers the dark matter density PDF as well as the power spectrum, with only mild suppression at intermediate scales. In terms of cosmic web classification, DeepCHART successfully identifies 81% of voids, 75% of sheets, 63% of filaments, and 43% of nodes. We propose DeepCHART as a powerful and scalable framework for field-level cosmological inference, readily generalisable to other observables, and offering a robust, efficient means of maximising the scientific return of upcoming spectroscopic surveys.
CHEX-MATE: New detections and properties of the radio diffuse emission in massive clusters with MeerKAT
2025-06-30
M. Balboni, F. Gastaldello, A. Bonafede, A. Botteon, I. Bartalucci, R. Cassano, S. De Grandi, S. Ettori, M. Gaspari, S. Ghizzardi, M. Gitti, M. Johnston-Hollitt, L. Lovisari, S. Molendi, E. Pointecouteau, G. W. Pratt, G. Riva, M. Rossetti, J. Sayers, M. Sereno, R. J. van Weeren
Modern radio telescopes are revolutionising our understanding of non-thermal phenomena within galaxy clusters, collecting large samples of extended sources with unprecedented sensitivity and angular resolution. In this work, we present novel MeerKAT observations for a sample of 21 galaxy clusters being part of the CHEX-MATE project. These systems were selected based on their high mass and displaying signs of dynamical activity. Thanks to the high-quality data in hand, we detect extended radio emission in every target considered. We report two new halos, three new relics and confirm a previous candidate halo and two candidate radio relics. When investigating the scaling relations with the cluster properties, we confirm the presence of a radio halo power-mass correlation and relate it to a higher radio halo emissivity in more massive clusters. For radio relics, we highlight the MeerKAT capabilities to significantly extend the depth of radio observations to a new, unexplored field of low radio power sources ($\lesssim 10^{23} ~ {\rm W~Hz^{-1}} $ at 1.28 GHz). Thanks to such high-sensitivity data, we show that the radio relic power can display a wide range of values for a given cluster mass and relic size. Ultimately, we discuss how current radio observations, in combination with large radio surveys, are becoming capable of testing numerical simulation predictions and being close to perform direct comparison with them, in order to gain new insights on the evolution of radio relics.
Lya2pcf: an efficient pipeline to estimate two- and three-point correlation functions of the Lyman-$α$ forest
2025-06-30
Josue De-Santiago, Rafael Gutiérrez-Balboa, Gustavo Niz, Alma X. González-Morales
Studying the matter distribution in the universe through the Lyman-$\alpha$ forest allows us to constrain small-scale physics in the high-redshift regime. Spectroscopic quasar surveys are generating increasingly large datasets that require efficient algorithms to compute correlation functions. Moreover, cosmological analyses based on Lyman-$\alpha$ forests can significantly benefit from incorporating higher-order statistics alongside traditional two-point correlations. In this work, we present Lya2pcf, a pipeline designed to compute three-dimensional two-point and three-point correlation functions using Lyman-$\alpha$ forest data. The code implements standard algorithms widely used in current spectroscopic surveys for computing the two-point correlation function with its distortion matrix, covariance matrices; and it naturally extends the two-point estimator to three-point correlations. Thanks to GPU optimization, Lya2pcf achieves a substantial reduction in computational time for both the two-point correlation function and its distortion matrix when compared to the widely used PICCA code. We apply Lya2pcf to data from the Sloan Digital Sky Survey (SDSS) sixteenth data release (DR16) and a Dark Energy Spectroscopic Instrument Year-5 (DESI Y5) mock dataset, demonstrating overall performance gains over PICCA, especially on GPUs. We show the first measurement of the anisotropic three-point correlation function on a large spectroscopic sample for all possible triangles with scales up to 80 Mpc/h. The estimator's fast computation and the resulting signal-to-noise ratio -- above one for many triangle configurations -- demonstrate the viability of incorporating three-point statistics into future cosmological inference analyses, particularly with the larger datasets expected from Stage IV spectroscopic surveys.
Teaching Programming in the Age of Generative AI: Insights from Literature, Pedagogical Proposals, and Student Perspectives
2025-06-30
Clemente Rubio-Manzano, Jazna Meza, Rodolfo Fernandez-Santibanez, Christian Vidal-Castro
Computer programming is undergoing a true transformation driven by powerful new tools for automatic source code generation based on large language models. This transformation is also manifesting in introductory programming courses at universities around the world, generating an in-depth debate about how programming content should be taught, learned, and assessed in the context of generative artificial intelligence. This article aims, on the one hand, to review the most relevant studies on this issue, highlighting the advantages and disadvantages identified in the specialized literature. On the other hand, it proposes enriching teaching and learning methodologies by focusing on code comprehension and execution rather than on mere coding or program functionality. In particular, it advocates for the use of visual representations of code and visual simulations of its execution as effective tools for teaching, learning, and assessing programming, thus fostering a deeper understanding among students. Finally, the opinions of students who took the object-oriented programming course are presented to provide preliminary context supporting the incorporation of visual simulations in Java (or other languages) as part of the training process.
Satellite Constellations Exceed the Limits of Acceptable Brightness Established by the IAU
2025-06-30
Anthony Mallama, Richard E. Cole
Brightness statistics for satellites of the Starlink, BlueBird, Qianfan, Guowang and OneWeb constellations are reported. The means and standard deviations are compared to acceptable limits set by the International Astronomical Union's Centre for the Protection of the Dark and Quiet Sky From Satellite Constellations Interference. Nearly all these spacecraft exceed the magnitude 7+ brightness limit pertaining to interference with professional research. Most also exceed the magnitude 6 reference where they distract from aesthetic appreciation of the night sky.
Gender and Discipline Shape Length, Content and Tone of Grant Peer Review Reports
2025-06-30
Stefan Müller, Gabriel Okasa, Michaela Strinzel, Anne Jorstad, Katrin Milzow, Matthias Egger
Peer review by experts is central to the evaluation of grant proposals, but little is known about how gender and disciplinary differences shape the content and tone of grant peer review reports. We analyzed 39,280 review reports submitted to the Swiss National Science Foundation between 2016 and 2023, covering 11,385 proposals for project funding across 21 disciplines from the Social Sciences and Humanities (SSH), Life Sciences (LS), and Mathematics, Informatics, Natural Sciences, and Technology (MINT). Using supervised machine learning, we classified over 1.3 million sentences by evaluation criteria and sentiment. Reviews in SSH were significantly longer and more critical, with less focus on the applicant's track record, while those in MINT were more concise and positive, with a higher focus on the track record, as compared to those in LS. Compared to male reviewers, female reviewers write longer reviews that more closely align with the evaluation criteria and express more positive sentiments. Female applicants tend to receive reviews with slightly more positive sentiment than male applicants. Gender and disciplinary culture influence how grant proposals are reviewed - shaping the tone, length, and focus of peer review reports. These differences have important implications for fairness and consistency in research funding.
Exploring Privacy and Security as Drivers for Environmental Sustainability in Cloud-Based Office Solutions
2025-06-30
Jason Kayembe, Iness Ben Guirat, Jan Tobias Mühlberg
In this paper, we explore the intersection of privacy, security, and environmental sustainability in cloud-based office solutions, focusing on quantifying user- and network-side energy use and associated carbon emissions. We hypothesise that privacy-focused services are typically more energy-efficient than those funded through data collection and advertising. To evaluate this, we propose a framework that systematically measures environmental costs based on energy usage and network data traffic during well-defined, automated usage scenarios. To test our hypothesis, we first analyse how underlying architectures and business models, such as monetisation through personalised advertising, contribute to the environmental footprint of these services. We then explore existing methodologies and tools for software environmental impact assessment. We apply our framework to three mainstream email services selected to reflect different privacy policies, from ad-supported tracking-intensive models to privacy-focused designs: Microsoft Outlook, Google Mail (Gmail), and Proton Mail. We extend this comparison to a self-hosted email solution, evaluated with and without end-to-end encryption. We show that the self-hosted solution, even with 14% of device energy and 15% of emissions overheads from PGP encryption, remains the most energy-efficient, saving up to 33% of emissions per session compared to Gmail. Among commercial providers, Proton Mail is the most efficient, saving up to 0.1 gCO2 e per session compared to Outlook, whose emissions can be further reduced by 2% through ad-blocking.
Quantifying Student Success with Generative AI: A Monte Carlo Simulation Informed by Systematic Review
2025-06-30
Seyma Yaman Kayadibi
The exponential development of generative artificial intelligence (GenAI) technologies like ChatGPT has raised increasing curiosity about their use in higher education, specifically with respect to how students view them, make use of them, and the implications for learning outcomes. This paper employs a hybrid methodological approach involving a systematic literature review and simulation-based modeling to explore student perceptions of GenAI use in the context of higher education. A total of nineteen empirical articles from 2023 through 2025 were selected from the PRISMA-based search targeting the Scopus database. Synthesis of emerging patterns from the literature was achieved by thematic categorization. Six of these had enough quantitative information, i.e., item-level means and standard deviations, to permit probabilistic modeling. One dataset, from the resulting subset, was itself selected as a representative case with which to illustrate inverse-variance weighting by Monte Carlo simulation, by virtue of its well-designed Likert scale format and thematic alignment with the use of computing systems by the researcher. The simulation provided a composite "Success Score" forecasting the strength of the relationship between student perceptions and learning achievements. Findings reveal that attitude factors concerned with usability and real-world usefulness are significantly better predictors of positive learning achievement than affective or trust-based factors. Such an interdisciplinary perspective provides a unique means of linking thematic results with predictive modelling, resonating with longstanding controversies about the proper use of GenAI tools within the university.
Fairness benchmarks play a central role in shaping how we evaluate language models, yet surprisingly little attention has been given to examining the datasets that these benchmarks rely on. This survey addresses that gap by presenting a broad and careful review of the most widely used fairness datasets in current language model research, characterizing them along several key dimensions including their origin, scope, content, and intended use to help researchers better appreciate the assumptions and limitations embedded in these resources. To support more meaningful comparisons and analyses, we introduce a unified evaluation framework that reveals consistent patterns of demographic disparities across datasets and scoring methods. Applying this framework to twenty four common benchmarks, we highlight the often overlooked biases that can influence conclusions about model fairness and offer practical guidance for selecting, combining, and interpreting these datasets. We also point to opportunities for creating new fairness benchmarks that reflect more diverse social contexts and encourage more thoughtful use of these tools going forward. All code, data, and detailed results are publicly available at https://github.com/vanbanTruong/Fairness-in-Large-Language-Models/tree/main/datasets to promote transparency and reproducibility across the research community.
An investigation into correlations between FRB and host galaxy properties
2025-06-29
M. Glowacki, A. Bera, C. W. James, J. Paterson, A. T. Deller, A C. Gordon, L. Marnoch, A. R. Muller, J. X. Prochaska, S. D. Ryder, R. M. Shannon, N. Tejos, A. G. Mannings
Impulsive radio signals such as fast radio bursts (FRBs) are imprinted with the signatures of multi-path propagation through ionised media in the form of frequency-dependent temporal broadening of the pulse profile (scattering). The dominant source of scattering for most FRBs is expected to be within their host galaxies, an assumption which can be tested by examining potential correlations between properties of the FRBs and global properties of their hosts. Using results from the CRAFT survey, we investigate correlations across a range of host galaxy properties against attributes of the FRB that encode propagation effects: scattering timescale tau, polarisation fractions, and absolute Faraday rotation measure. From 21 host galaxy properties considered, we find three correlated with tau, including the stellar surface density (or compactness; Pearson p-value p = 0.002 and Spearman p = 0.010), mass-weighted age (Spearman p-value p = 0.009), and a weaker correlation with gas-phase metallicity (Spearman p = 0.017). Weakly significant correlations are also found with Halpha equivalent widths and gravitational potential. From 10,000 trials of reshuffled datasets, we expect 2 strong Spearman correlations only 2% of the time, and three weaker correlations in 6.6% of cases. Compact host galaxies may have more ionised content which scatters the FRB further. No correlation is seen with host galaxy inclination, which weakens the case for an inclination bias, as previously suggested for samples of localised FRBs. A strong (p = 0.002) correlation is found for absolute rotation measure with optical disc axis ratio b/a; greater rotation measures are seen for edge-on host galaxies. Further high-time resolution FRB detections, coupled with localisation and detailed follow-up on their host galaxies, are necessary to corroborate these initial findings and shed further light into the FRB mechanism.
Density, asymmetry and citation dynamics in scientific literature
2025-06-29
Nathaniel Imel, Zachary Hafen
Scientific behavior is often characterized by a tension between building upon established knowledge and introducing novel ideas. Here, we investigate whether this tension is reflected in the relationship between the similarity of a scientific paper to previous research and its eventual citation rate. To operationalize similarity to previous research, we introduce two complementary metrics to characterize the local geometry of a publication's semantic neighborhood: (1) \emph{density} ($\rho$), defined as the ratio between a fixed number of previously-published papers and the minimum distance enclosing those papers in a semantic embedding space, and (2) asymmetry ($\alpha$), defined as the average directional difference between a paper and its nearest neighbors. We tested the predictive relationship between these two metrics and its subsequent citation rate using a Bayesian hierarchical regression approach, surveying $\sim 53,000$ publications across nine academic disciplines and five different document embeddings. While the individual effects of $\rho$ on citation count are small and variable, incorporating density-based predictors consistently improves out-of-sample prediction when added to baseline models. These results suggest that the density of a paper's surrounding scientific literature may carry modest but informative signals about its eventual impact. Meanwhile, we find no evidence that publication asymmetry improves model predictions of citation rates. Our work provides a scalable framework for linking document embeddings to scientometric outcomes and highlights new questions regarding the role that semantic similarity plays in shaping the dynamics of scientific reward.
Artificial intelligence (AI) is rapidly transforming global industries and societies, making AI literacy an indispensable skill for future generations. While AI integration in education is still emerging in Nepal, this study focuses on assessing the current AI literacy levels and identifying learning needs among students in Chitwan District of Nepal. By measuring students' understanding of AI and pinpointing areas for improvement, this research aims to provide actionable recommendations for educational stakeholders. Given the pivotal role of young learners in navigating a rapidly evolving technological landscape, fostering AI literacy is paramount. This study seeks to understand the current state of AI literacy in Chitwan District by analyzing students' knowledge, skills, and attitudes towards AI. The results will contribute to developing robust AI education programs for Nepalese schools. This paper offers a contemporary perspective on AI's role in Nepalese secondary education, emphasizing the latest AI tools and technologies. Moreover, the study illuminates the potential revolutionary impact of technological innovations on educational leadership and student outcomes. A survey was conducted to conceptualize the newly emerging concept of AI and cybersecurity among students of Chitwan district from different schools and colleges to find the literacy rate. The participants in the survey were students between grade 9 to 12. We conclude with discussions of the affordances and barriers to bringing AI and cybersecurity education to students from lower classes.
Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction
2025-06-29
Perla Mayo, Carolin M. Pirkl, Alin Achim, Bjoern Menze, Mohammad Golbabaee
We introduce MRF-DiPh, a novel physics informed denoising diffusion approach for multiparametric tissue mapping from highly accelerated, transient-state quantitative MRI acquisitions like Magnetic Resonance Fingerprinting (MRF). Our method is derived from a proximal splitting formulation, incorporating a pretrained denoising diffusion model as an effective image prior to regularize the MRF inverse problem. Further, during reconstruction it simultaneously enforces two key physical constraints: (1) k-space measurement consistency and (2) adherence to the Bloch response model. Numerical experiments on in-vivo brain scans data show that MRF-DiPh outperforms deep learning and compressed sensing MRF baselines, providing more accurate parameter maps while better preserving measurement fidelity and physical model consistency-critical for solving reliably inverse problems in medical imaging.
Multimodal large language models (MLLMs) have enormous potential to perform few-shot in-context learning in the context of medical image analysis. However, safe deployment of these models into real-world clinical practice requires an in-depth analysis of the accuracies of their predictions, and their associated calibration errors, particularly across different demographic subgroups. In this work, we present the first investigation into the calibration biases and demographic unfairness of MLLMs' predictions and confidence scores in few-shot in-context learning for medical image classification. We introduce CALIN, an inference-time calibration method designed to mitigate the associated biases. Specifically, CALIN estimates the amount of calibration needed, represented by calibration matrices, using a bi-level procedure: progressing from the population level to the subgroup level prior to inference. It then applies this estimation to calibrate the predicted confidence scores during inference. Experimental results on three medical imaging datasets: PAPILA for fundus image classification, HAM10000 for skin cancer classification, and MIMIC-CXR for chest X-ray classification demonstrate CALIN's effectiveness at ensuring fair confidence calibration in its prediction, while improving its overall prediction accuracies and exhibiting minimum fairness-utility trade-off.
Hierarchical Quantized Diffusion Based Tree Generation Method for Hierarchical Representation and Lineage Analysis
2025-06-29
Zelin Zang, WenZhe Li, Fei Chen, Yongjie Xu, Chang Yu, Zhen Lei, Stan Z. Li
In single-cell research, tracing and analyzing high-throughput single-cell differentiation trajectories is crucial for understanding complex biological processes. Key to this is the modeling and generation of hierarchical data that represents the intrinsic structure within datasets. Traditional methods face limitations in terms of computational cost, performance, generative capacity, and stability. Recent VAEs based approaches have made strides in addressing these challenges but still require specialized network modules for each tree branch, limiting their stability and ability to capture deep hierarchical relationships. To overcome these challenges, we introduce diffusion-based approach called HDTree. HDTree captures tree relationships within a hierarchical latent space using a unified hierarchical codebook and quantized diffusion processes to model tree node transitions. This method improves stability by eliminating branch-specific modules and enhancing generative capacity through gradual hierarchical changes simulated by the diffusion process. HDTree's effectiveness is demonstrated through comparisons on both general-purpose and single-cell datasets, where it outperforms existing methods in terms of accuracy and performance. These contributions provide a new tool for hierarchical lineage analysis, enabling more accurate and efficient modeling of cellular differentiation paths and offering insights for downstream biological tasks. The code of HDTree is available at anonymous link https://anonymous.4open.science/r/code_HDTree_review-A8DB.
BASIL: Fast broadband line-rich spectral-cube fitting and image visualization via Bayesian quadrature
2025-06-29
Yuxin Lin, Masaki Adachi, Silvia Spezzano, Gordian Edenhofer, Vincent Eberle, Michael A. Osborne, Paola Caselli
Mapping the spatial distributions and abundances of complex organic molecules in hot cores and hot corinos is crucial for understanding the astrochemical pathways and the inheritance of prebiotic material by nascent planetary systems. However, the line-rich spectra from these sources pose significant challenges for robustly fitting molecular parameters due to severe line blending and unidentified lines. We present an efficient framework, Bayesian Active Spectral-cube Inference and Learning (BASIL), for estimating molecular parameter maps for hundreds of molecules based on the local thermodynamic equilibrium (LTE) model, applied to wideband spectral datacubes of line-rich sources. We adopted stochastic variational inference to infer molecular parameters from spectra at individual positions, balancing between fitting accuracy and computational speed. For obtaining parameter maps, instead of querying every location or pixel, we introduced an active learning framework based on Bayesian quadrature and its parallelization. Specifically, we assessed and selected the locations or pixels of spectrum that are most informative for estimating the entire set of parameter maps by training a Gaussian processes model. By greedily selecting locations with maximum information gain, we achieve sublinear convergence. We benchmarked BASIL on a large synthetic datacube and demonstrated that it produces accurate 468 molecular parameter maps from 117 molecules within ~180 hours, orders of magnitude faster than traditional pixel-by-pixel fitting using Markov chain Monte Carlo methods, with visually reliable results emerging in just ~20 hours. Additional training iterations provide progressively more accurate results. This quick visualization meets the demands of big data in modern astronomical surveys.
Autonomous AI agents powered by large language models (LLMs) with structured function-calling interfaces have dramatically expanded capabilities for real-time data retrieval, complex computation, and multi-step orchestration. Yet, the explosive proliferation of plugins, connectors, and inter-agent protocols has outpaced discovery mechanisms and security practices, resulting in brittle integrations vulnerable to diverse threats. In this survey, we introduce the first unified, end-to-end threat model for LLM-agent ecosystems, spanning host-to-tool and agent-to-agent communications, formalize adversary capabilities and attacker objectives, and catalog over thirty attack techniques. Specifically, we organized the threat model into four domains: Input Manipulation (e.g., prompt injections, long-context hijacks, multimodal adversarial inputs), Model Compromise (e.g., prompt- and parameter-level backdoors, composite and encrypted multi-backdoors, poisoning strategies), System and Privacy Attacks (e.g., speculative side-channels, membership inference, retrieval poisoning, social-engineering simulations), and Protocol Vulnerabilities (e.g., exploits in Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent Network Protocol (ANP), and Agent-to-Agent (A2A) protocol). For each category, we review representative scenarios, assess real-world feasibility, and evaluate existing defenses. Building on our threat taxonomy, we identify key open challenges and future research directions, such as securing MCP deployments through dynamic trust management and cryptographic provenance tracking; designing and hardening Agentic Web Interfaces; and achieving resilience in multi-agent and federated environments. Our work provides a comprehensive reference to guide the design of robust defense mechanisms and establish best practices for resilient LLM-agent workflows.
Deep survey of millimeter RRLs towards the planetary nebulae IC 418 and NGC 7027
2025-06-29
Teresa Huertas-Roldán, Javier Alcolea, D. Aníbal García-Hernández, Daniel Tafoya, J. Pablo Fonfría, J. Jairo Díaz-Luis, Arturo Manchado, Valentín Bujarrabal, Ransel Barzaga, Marco A. Gómez-Muñoz
The circumstellar environments of PNe are wonderful chemically rich astrophysical laboratories in which the ionization of atoms and the formation of simple and complex molecules can be studied. The new high-sensitivity receivers open the possibility to carry out deep observations, essential to unveil weak atomic and molecular spectra in the mm range. The main goal of this work is to study the emission lines detected in the spectra of the bright C-rich PNe IC 418 and NGC 7027 and to identify all those emission features associated with radio recombination lines (RRLs) of light elements. We aim to analyze the RRLs detected on each source, and to model the sources and derive their physical parameters. This work allows us to provide the most complete and updated catalog of RRLs in space, carried out at 2, 3 and 7 mm with the IRAM 30m and the Yebes 40m radio telescopes. We compare these observational data sets with synthetic models produced with the radiation transfer code Co3RaL. Our observations reveal the presence of several H and He I RRLs at mm wavelengths in the spectra of IC 418 and NGC 7027 and also of HeIIi RRLs in the spectrum of NGC 7027. Many of these lines had remained undetected until now due to their weakness and the lack of high-sensitivity observations at these frequencies. The data also confirm the absence of molecular emission towards IC 418, above a detection level of ~3 mK [Tmb]. These mm observations represent the most extended RRL line survey of two C-rich PNe carried out so far, with most of the lines never reported before. These extremely complete catalogs evidence the importance of high-sensitivity observations and are expected to be very helpful in the line identification process in mm observations, in particular for still unknown or poorly characterized molecular species existing in the vicinity of ionized environments.
Aggregating Local Saliency Maps for Semi-Global Explainable Image Classification
2025-06-29
James Hinns, David Martens
Deep learning dominates image classification tasks, yet understanding how models arrive at predictions remains a challenge. Much research focuses on local explanations of individual predictions, such as saliency maps, which visualise the influence of specific pixels on a model's prediction. However, reviewing many of these explanations to identify recurring patterns is infeasible, while global methods often oversimplify and miss important local behaviours. To address this, we propose Segment Attribution Tables (SATs), a method for summarising local saliency explanations into (semi-)global insights. SATs take image segments (such as "eyes" in Chihuahuas) and leverage saliency maps to quantify their influence. These segments highlight concepts the model relies on across instances and reveal spurious correlations, such as reliance on backgrounds or watermarks, even when out-of-distribution test performance sees little change. SATs can explain any classifier for which a form of saliency map can be produced, using segmentation maps that provide named segments. SATs bridge the gap between oversimplified global summaries and overly detailed local explanations, offering a practical tool for analysing and debugging image classifiers.
Multi-Functional Metasurfaces with M-Type Ferrites: Shaping the Future of mmWave Absorption and Beam Steering
2025-06-29
Nohgyeom Ha, Horim Lee, Min Jang, Gyoungdeuk Kim, Hoyong Kim, Byeongjin Park, Manos M. Tentzeris, Sangkil Kim
This paper presents a comprehensive review and tutorial on multi-functional metasurfaces integrated with M-type ferrite materials for millimeter-wave (mmWave) absorption and beam control. As wireless communication systems transition toward beyond-5G architectures, including non-terrestrial networks (NTNs), the demand for adaptive, low-profile electromagnetic surfaces that can manage interference while enabling beam reconfiguration becomes increasingly critical. Conventional metasurfaces often struggle to simultaneously achieve high absorption and beamforming over wide frequency ranges due to intrinsic material and structural limitations. This paper reviews the state-of-the-art in metasurface design for dual-functionality, particularly those combining frequency-selective magnetic materials with periodic surface lattices, to enable passive, compact, and reconfigurable reflectors and absorbers. Special emphasis is placed on the role of M-type ferrites in enhancing absorption via ferromagnetic resonance, and on the use of surface-wave trapping mechanisms to achieve narrowband and broadband functionality. A case study of a ferrite-based hybrid "reflectsorber" (reflectorarray + absorber) is presented to demonstrate key design concepts, analytical models, and application scenarios relevant to satellite, UAV, and NTN ground station deployments. Future directions for low-loss, tunable, and scalable metasurfaces in next-generation wireless infrastructures are also discussed.
Can LLM Improve for Expert Forecast Combination? Evidence from the European Central Bank Survey
2025-06-29
Yinuo Ren, Jue Wang
This study explores the potential of large language models (LLMs) to enhance expert forecasting through ensemble learning. Leveraging the European Central Bank's Survey of Professional Forecasters (SPF) dataset, we propose a comprehensive framework to evaluate LLM-driven ensemble predictions under varying conditions, including the intensity of expert disagreement, dynamics of herd behavior, and limitations in attention allocation.
Correlation between H$α$ emitters and their cosmic web environment at $z \sim 1$
2025-06-29
Ivan Rapoport, Vincent Desjacques, Ehud Behar, Ravi K. Sheth
Future near-infrared spectroscopic galaxy surveys will target high-redshift emission-line galaxies (ELGs) to test cosmological models. Deriving optimal constraints from emission-line galaxy clustering hinges on a robust understanding of their environmental dependence. Using the TNG300-1 simulation, we explore the correlation between properties of H$\alpha$ emitters and their environment anisotropy rather than traditional density-based measures. Our galactic H$\alpha$ emission model includes contributions from the warm interstellar medium. The environment anisotropy and type are assigned using a halo mass-dependent smoothing scale. We find that most luminous ELGs ($L_{\rm{H}\alpha}>10^{42}\ \rm{erg\ s^{-1}}$) reside in filaments and knots. More generally, ELGs are more biased in strongly anisotropic environments. While correlations with galactic properties are found to be weak, they are statistically significant for host halo masses $M\lesssim 10^{12}\ M_\odot/h$. Our analysis motivates further investigation into how environmental anisotropy influences galaxy evolution, and highlights the potential for leveraging these effects in the analyses of upcoming cosmological surveys.
We review the status of thermal leptogenesis in the minimal $SU(5)\times U(1)$ and $SO(10)$ unified models under the assumption that the leptonic asymmetry generated in the out-of-equilibrium decays of heavy Majorana neutrinos (and its subsequent conversion into baryons via sphalerons) constitutes the primary source of baryon asymmetry of the Universe. In both cases, leptogenesis is shown to provide a strong extra constraint on the flavour structure of the model under consideration, leading to interesting and potentially testable phenomenological effects.
Can Large Language Models Capture Human Risk Preferences? A Cross-Cultural Study
2025-06-29
Bing Song, Jianing Liu, Sisi Jian, Chenyang Wu, Vinayak Dixit
Large language models (LLMs) have made significant strides, extending their applications to dialogue systems, automated content creation, and domain-specific advisory tasks. However, as their use grows, concerns have emerged regarding their reliability in simulating complex decision-making behavior, such as risky decision-making, where a single choice can lead to multiple outcomes. This study investigates the ability of LLMs to simulate risky decision-making scenarios. We compare model-generated decisions with actual human responses in a series of lottery-based tasks, using transportation stated preference survey data from participants in Sydney, Dhaka, Hong Kong, and Nanjing. Demographic inputs were provided to two LLMs -- ChatGPT 4o and ChatGPT o1-mini -- which were tasked with predicting individual choices. Risk preferences were analyzed using the Constant Relative Risk Aversion (CRRA) framework. Results show that both models exhibit more risk-averse behavior than human participants, with o1-mini aligning more closely with observed human decisions. Further analysis of multilingual data from Nanjing and Hong Kong indicates that model predictions in Chinese deviate more from actual responses compared to English, suggesting that prompt language may influence simulation performance. These findings highlight both the promise and the current limitations of LLMs in replicating human-like risk behavior, particularly in linguistic and cultural settings.
While Text-to-SQL enables natural language interaction with structured databases, its effectiveness diminishes with unstructured data or ambiguous queries due to rigid syntax and limited expressiveness. Concurrently, vector search has emerged as a powerful paradigm for semantic retrieval, particularly for unstructured data. However, existing VectorSQL implementations still rely heavily on manual crafting and lack tailored evaluation frameworks, leaving a significant gap between theoretical potential and practical deployment. To bridge these complementary paradigms, we introduces Text2VectorSQL, a novel framework unifying Text-to-SQL and vector search to overcome expressiveness constraints and support more diverse and holistical natural language queries. Specifically, Text2VectorSQL enables semantic filtering, multi-modal matching, and retrieval acceleration. For evaluation, we build vector index on appropriate columns, extend user queries with semantic search, and annotate ground truths via an automatic pipeline with expert review. Furthermore, we develop dedicated Text2VectorSQL models with synthetic data, demonstrating significant performance improvements over baseline methods. Our work establishes the foundation for the Text2VectorSQL task, paving the way for more versatile and intuitive database interfaces. The repository will be publicly available at https://github.com/Open-DataFlow/Text2VectorSQL.
In this work, we review formulations of wave equations for spin-3/2 fields constructed from different Lorentz group representations. We analyze the Joss-Weinberg single-spin chiral representation and the double-spin chiral representation, focusing on the structure of their covariant operators. We explore the Duffin-Kemmer-Petiau (DKP) formalism and its algebraic properties, originally introduced for spin--0 and spin-1 particles, and here considered as a potential framework for spin 3/2. As a result, we recover the well-known Rarita-Schwinger representation and we find a new possibility in the $(3/2,0) \oplus (0,3/2) \oplus (1,1/2) \oplus (1/2,1)$ representation.
Fractional Policy Gradients: Reinforcement Learning with Long-Term Memory
2025-06-29
Urvi Pawar, Kunal Telangi
We propose Fractional Policy Gradients (FPG), a reinforcement learning framework incorporating fractional calculus for long-term temporal modeling in policy optimization. Standard policy gradient approaches face limitations from Markovian assumptions, exhibiting high variance and inefficient sampling. By reformulating gradients using Caputo fractional derivatives, FPG establishes power-law temporal correlations between state transitions. We develop an efficient recursive computation technique for fractional temporal-difference errors with constant time and memory requirements. Theoretical analysis shows FPG achieves asymptotic variance reduction of order O(t^(-alpha)) versus standard policy gradients while preserving convergence. Empirical validation demonstrates 35-68% sample efficiency gains and 24-52% variance reduction versus state-of-the-art baselines. This framework provides a mathematically grounded approach for leveraging long-range dependencies without computational overhead.
Towards a better approach to the Vehicle Routing Problem
2025-06-28
Souad Abdoune, Menouar Boulif
The Vehicle Routing Problem (VRP) is a fundamental challenge in logistics management research, given its substantial influence on transportation efficiency, cost minimization, and service quality. As a combinatorial optimization problem, VRP plays a crucial role in a wide range of real world applications, particularly in transportation, logistics, and delivery systems, due to its diverse formulations and numerous extensions. Over the years, researchers have introduced various VRP variants to address specific operational constraints, emerging industry requirements and optimize specific objectives, making it one of the most extensively studied problems in operations research. This article provides a comprehensive overview of VRP by exploring its theoretical foundations, discussing the limitations of its classical model, and introducing its key extensions. By systematically reviewing the diverse constraints, objectives, and variants examined in recent literature, this study aims to contribute to a deeper understanding of VRP while highlighting its ongoing evolution and relevance in modern optimization and decision making processes.
Machine Assistant with Reliable Knowledge: Enhancing Student Learning via RAG-based Retrieval
2025-06-28
Yongsheng Lian
We present Machine Assistant with Reliable Knowledge (MARK), a retrieval-augmented question-answering system designed to support student learning through accurate and contextually grounded responses. The system is built on a retrieval-augmented generation (RAG) framework, which integrates a curated knowledge base to ensure factual consistency. To enhance retrieval effectiveness across diverse question types, we implement a hybrid search strategy that combines dense vector similarity with sparse keyword-based retrieval. This dual-retrieval mechanism improves robustness for both general and domain-specific queries. The system includes a feedback loop in which students can rate responses and instructors can review and revise them. Instructor corrections are incorporated into the retrieval corpus, enabling adaptive refinement over time. The system was deployed in a classroom setting as a substitute for traditional office hours, where it successfully addressed a broad range of student queries. It was also used to provide technical support by integrating with a customer-specific knowledge base, demonstrating its ability to handle routine, context-sensitive tasks in applied domains. MARK is publicly accessible at https://app.eduquery.ai.
(World) Building Transformation: Students and Teachers as CoCreators in OpenXR Learning Environments
2025-06-28
Abigail Greenbaum, Elizabeth Strickler, Victoria Patterson, Bolu Oluleye
Emerging extended reality (XR) tools and platforms offer an exciting opportunity to align learning experiences in higher education with the futures in which students will pursue their goals. However, the dynamic nature of XR as subject matter challenges hierarchies and classroom practices typical of higher education. This instructional design practice paper reflects on how our team of faculty, learning experience designers, and user experience (UX) researchers implemented human-centered design thinking, transformative learning, and problem-posing education to design and implement a special topics media entrepreneurship course in building the metaverse. By pairing our practitioner experience with learner personas, as well as survey, interview, and focus group responses from our learners, we narrate our design and its implications through a human-centered, reflective lens.
Ising spin-1/2 XXZ chain's quantum problems beyond the spinon paradigm
2025-06-28
J. M. P. Carmelo, P. D. Sacramento
Spin chains are correlated quantum models of great interest in quantum systems and materials exhibiting quasi-one-dimensional magnetic properties. Here we review results on quantum problems associated with spin chains that are beyond the usual spinon paradigm. In this review we consider two quantum problems that are beyond the spinon representation: (a) Spin Bethe strings of length n that have no spinon representation, contribute to the dynamical properties of the spin-1/2 XXZ chain with anisotropy larger than 1 and for n=1,2,3 were experimentally identified and realized in the zigzag materials SrCo2V2O8 and BaCo2V2O8; (b) The spin stiffness associated with ballistic spin transport at arbitrary finite temperature, which involves a huge number of energy eigenstates, many of which are generated in the thermodynamic limit from ground states by an infinite number of elementary processes.
Immersive Technologies and Elderly Users: Current use, Limitations and Future Perspectives
2025-06-28
Zoe Anastasiadou, Andreas Lanitis
The increase of the percentage of elderly population in modern societies dictates the use of emerging technologies as a means of supporting elder members of the society. Within this scope, Extended Reality (XR) technologies pose as a promising technology for improving the daily lives of the elderly population. This paper presents a literature review that describes the most common characteristics of the physical and mental state of the elderly, allowing readers, and specifically XR developers, to understand the main difficulties faced by elderly users of extended reality applications so they can develop accessible, user friendly and engaging applications for the target audience. Furthermore, a review of existing extended reality applications that target the elder population is presented, allowing readers to get acquainted with existing design paradigms that can inspire future developments.
Point Cloud Compression and Objective Quality Assessment: A Survey
2025-06-28
Yiling Xu, Yujie Zhang, Shuting Xia, Kaifa Yang, He Huang, Ziyu Shan, Wenjie Huang, Qi Yang, Le Yang
The rapid growth of 3D point cloud data, driven by applications in autonomous driving, robotics, and immersive environments, has led to criticals demand for efficient compression and quality assessment techniques. Unlike traditional 2D media, point clouds present unique challenges due to their irregular structure, high data volume, and complex attributes. This paper provides a comprehensive survey of recent advances in point cloud compression (PCC) and point cloud quality assessment (PCQA), emphasizing their significance for real-time and perceptually relevant applications. We analyze a wide range of handcrafted and learning-based PCC algorithms, along with objective PCQA metrics. By benchmarking representative methods on emerging datasets, we offer detailed comparisons and practical insights into their strengths and limitations. Despite notable progress, challenges such as enhancing visual fidelity, reducing latency, and supporting multimodal data remain. This survey outlines future directions, including hybrid compression frameworks and advanced feature extraction strategies, to enable more efficient, immersive, and intelligent 3D applications.
Over the Eight decades, computing paradigms have shifted from large, centralized systems to compact, distributed architectures, leading to the rise of the Distributed Computing Continuum (DCC). In this model, multiple layers such as cloud, edge, Internet of Things (IoT), and mobile platforms work together to support a wide range of applications. Recently, the emergence of Generative AI and large language models has further intensified the demand for computational resources across this continuum. Although traditional performance metrics have provided a solid foundation, they need to be revisited and expanded to keep pace with changing computational demands and application requirements. Accurate performance measurements benefit both system designers and users by supporting improvements in efficiency and promoting alignment with system goals. In this context, we review commonly used metrics in DCC and IoT environments. We also discuss emerging performance dimensions that address evolving computing needs, such as sustainability, energy efficiency, and system observability. We also outline criteria and considerations for selecting appropriate metrics, aiming to inspire future research and development in this critical area.
Symmetry, microscopy and spectroscopy signatures of altermagnetism
2025-06-28
Tomas Jungwirth, Jairo Sinova, Rafael M. Fernandes, Qihang Liu, Hikaru Watanabe, Shuichi Murakami, Satoru Nakatsuji, Libor Smejkal
Altermagnetism is a collinear compensated magnetically-ordered phase with a d, g or i-wave anisotropy and alternating spin polarization of the electronic structure in the position and momentum space. Its recent discovery was in part motivated by the research of compensated magnets towards highly scalable spintronic technologies. Simultaneously, altermagnetism shares the anisotropic higher-partial-wave nature of ordering with unconventional superfluid phases which have been at the forefront of research for the past several decades. These examples illustrate the interest in altermagnetism from a broad range of science and technology perspectives. After summarizing the diverse research context, we turn the focus of this review to the symmetry, microscopy and spectroscopy signatures of altermagnetism. We start from the description of spontaneously broken and retained symmetries which delineate the compensated altermagnetic ordering as a distinct magnetic phase. Next we focus on microscopic signatures and ordering mechanism of the altermagnetic phase. We highlight crystal-structure realizations of a characteristic ferroic order of anisotropic higher-partial-wave components of atomic-scale spin densities in altermagnets, ranging from weakly-interacting metals to strongly correlated insulators. The symmetry and microscopy signatures of altermagnetism are directly reflected in spin-dependent electronic spectra and responses. We review salient band-structure features originating from the altermagnetic ordering, and from its interplay with spin-orbit coupling and topological phenomena. Throughout the review we compare altermagnetism to traditional ferromagnetism and Neel antiferromagntism, and to the currently intensely explored magnetic phases with non-collinear symmetry-protected compensated spin orders. We accompany the theoretical discussions by references to relevant experiments.
Dichoptic Opacity: Managing Occlusion in Stereoscopic Displays via Dichoptic Presentation
2025-06-28
George Bell, Alma Cantu
Adjusting transparency is a common method of mitigating occlusion but is often detrimental for understanding the relative depth relationships between objects as well as removes potentially important information from the occluding object. We propose using dichoptic opacity, a novel method for occlusion management that contrasts the transparency of occluders presented to each eye. This allows for better simultaneous understanding of both occluder and occluded. A user study highlights the technique's potential, showing strong user engagement and a clear preference for dichoptic opacity over traditional presentations. While it does not determine optimal transparency values, it reveals promising trends in both percentage and range that merit further investigation.
Probing Binary Neutron Star Merger Ejecta and Remnants with Gravitational Wave and Radio Observations
2025-06-28
Kara Merfeld, Alessandra Corsi
We present a study aimed at quantifying the detectability of radio counterparts of binary neutron star (BNS) mergers with total masses $\lesssim 3$\,M$_{\odot}$, which may form neutron star (NS) remnants. We focus on mergers localized by gravitational-wave (GW) observations to sky areas $\lesssim 10$\,deg$^2$, a precision that greatly facilitates optical counterpart identification and enables radio discovery even without detections at other wavelengths. Widely separated GW detectors are essential for building samples of well-localized BNS mergers accessible to US-based radio telescopes, with minimum yearly detection rates (assuming the smallest values of the BNS local merger rate) ranging from a few with current GW detectors to hundreds with next-generation GW instruments. Current GW networks limit well-localized detections to $z\lesssim 0.2$, while next-generation GW detectors extend the reach to $z\lesssim 0.8$, encompassing the median redshift of short gamma-ray bursts (GRBs). With next-generation radio arrays operating at a several tens of GHz and providing an order of magnitude improvement in sensitivity compared to the most sensitive ones available today, short GRB-like jet afterglows can be detected for a large fraction of the considered BNS mergers. At lower radio frequencies, detections with current radio interferometric arrays are feasible, though subject to synchrotron self-absorption effects. The enhanced sensitivity and survey speed of future radio interferometers operating at a few GHz, combined with the higher detection rate of well-localized BNSs enabled by next-generation GW observatories, are key to probing disk-wind and dynamical ejecta afterglows, as well as remnant diversity.
This article is a survey of Ecalle's theory of flexion units. In particular, we provide complete proofs of several key assertions that were stated without proof in Ecalle's original works. These results are crucial for understanding applications of flexion units to the theory of multiple zeta values and the Kashiwara-Vergne problem.
Dislocations are line defects in crystalline solids and often exert a significant influence on the mechanical properties of metals. Recently, there has been a growing interest in using dislocations in ceramics to enhance materials performance. However, dislocation engineering has frequently been deemed uncommon in ceramics owing to the brittle nature of ceramics. Contradicting this conventional view, various approaches have been used to introduce dislocations into ceramic materials without crack formation, thereby paving the way for controlled ceramics performance. However, the influence of dislocations on functional properties is equally complicated owing to the intricate structure of ceramic materials. Furthermore, despite numerous experiments and simulations investigating dislocation-controlled properties in ceramics, comprehensive reviews summarizing the effects of dislocations on ceramics are still lacking. This review focuses on some representative dislocation-controlled properties of ceramic materials, including mechanical and some key functional properties, such as transport, ferroelectricity, thermal conductivity, and superconducting properties. A brief integration of dislocations in ceramic is anticipated to offer new insights for the advancement of dislocation engineering across various disciplines.
RGE-GS: Reward-Guided Expansive Driving Scene Reconstruction via Diffusion Priors
2025-06-28
Sicong Du, Jiarun Liu, Qifeng Chen, Hao-Xiang Chen, Tai-Jiang Mu, Sheng Yang
A single-pass driving clip frequently results in incomplete scanning of the road structure, making reconstructed scene expanding a critical requirement for sensor simulators to effectively regress driving actions. Although contemporary 3D Gaussian Splatting (3DGS) techniques achieve remarkable reconstruction quality, their direct extension through the integration of diffusion priors often introduces cumulative physical inconsistencies and compromises training efficiency. To address these limitations, we present RGE-GS, a novel expansive reconstruction framework that synergizes diffusion-based generation with reward-guided Gaussian integration. The RGE-GS framework incorporates two key innovations: First, we propose a reward network that learns to identify and prioritize consistently generated patterns prior to reconstruction phases, thereby enabling selective retention of diffusion outputs for spatial stability. Second, during the reconstruction process, we devise a differentiated training strategy that automatically adjust Gaussian optimization progress according to scene converge metrics, which achieving better convergence than baseline methods. Extensive evaluations of publicly available datasets demonstrate that RGE-GS achieves state-of-the-art performance in reconstruction quality. Our source-code will be made publicly available at https://github.com/CN-ADLab/RGE-GS. (Camera-ready version incorporating reviewer suggestions will be updated soon.)
Observed Anti-parallel Correlation Between Spiral Galaxy and Cosmic Filament Spins
2025-06-28
Hao-da Wang, Peng Wang, Min Bao, Yanmei Chen, Xiao-xiao Tang, Youcai Zhang, Xi Kang, Quan Guo, Ming-Jie Sheng, Hao-Ran Yu
Understanding the origin of galactic angular momentum and its connection to the cosmic web remains a pivotal issue in galaxy formation. Using kinematic data from the MaNGA survey, we investigate the alignment between the spin directions of spiral galaxies and their host cosmic filaments. By incorporating filament spin measurements derived from redshift asymmetry across filament spines, we reveal a mass-dependent anti-parallel correlation: low-mass spiral galaxies ($\log_{10}(M_*/M_\odot) \lesssim 10$) exhibit a statistically significant anti-parallel alignment between their stellar/gas spins and filament spins, while high-mass spirals show no such trend. Spatial analysis further indicates that high-mass spirals preferentially reside near filament spines, whereas low-mass spirals occupy filament outskirts. These findings extend previous alignment studies that neglected directional spin correlations and provide new insights into how cosmic environments shape galactic angular momentum. The observed anti-parallel trend suggests a critical role for filament spin in regulating the angular momentum acquisition of low-mass spirals. This anti-parallel alignment is significantly enhanced for low-mass spirals residing in dynamically cold filaments, highlighting the importance of filament properties in shaping galaxy spin.
What's Privacy Good for? Measuring Privacy as a Shield from Harms due to Personal Data Use
2025-06-28
Sri Harsha Gajavalli, Junichi Koizumi, Rakibul Hasan
We propose a harm-centric conceptualization of privacy that asks: What harms from personal data use can privacy prevent? The motivation behind this research is limitations in existing privacy frameworks (e.g., Contextual Integrity) to capture or categorize many of the harms that arise from modern technology's use of personal data. We operationalize this conceptualization in an online study with 400 college and university students. Study participants indicated their perceptions of different harms (e.g., manipulation, discrimination, and harassment) that may arise when artificial intelligence-based algorithms infer personal data (e.g., demographics, personality traits, and cognitive disability) and use it to identify students who are likely to drop out of a course or the best job candidate. The study includes 14 harms and six types of personal data selected based on an extensive literature review. Comprehensive statistical analyses of the study data show that the 14 harms are internally consistent and collectively represent a general notion of privacy harms. The study data also surfaces nuanced perceptions of harms, both across the contexts and participants' demographic factors. Based on these results, we discuss how privacy can be improved equitably. Thus, this research not only contributes to enhancing the understanding of privacy as a concept but also provides practical guidance to improve privacy in the context of education and employment.
Study on Pentaqaurks by Solving Schrodinger Equation in the Non-Hermitian Quantum Mechanics
2025-06-28
Bao-Xi Sun
The interaction of the charmed baryon and the anticharmed meson is assumed to be realized by exchanging a scalar meson of $f_0(500)$, and then these systems are studied by solving the Schrodinger equation, respectively. When the pentaquarks $P_{c\bar{c}}(4312)^{+}$, $P_{c\bar{c}}(4457)^{+}$, $P_{c\bar{c}s}(4338)^0$, and $P_{c\bar{c}s}(4459)^{0}$ are treated as $\Sigma_c \bar{D}$, $\Sigma_c \bar{D}^*$, $\Xi_c \bar{D}$ and $\Xi_c \bar{D}^*$ bound states, four resonance states of them are obtained as solutions of the Schrodinger equation under the outgoing wave condition, respectively. The resonance state of $\Sigma_c \bar{D}$ might correspond to the particle $P_{c\bar{c}}(4440)^{+}$, while the other three resonance states have no counterparts in the review of the Particle Data Group(PDG). Although the binding energy of the bound state is only several MeVs, all these resonance states are more than 100 MeV higher than their corresponding thresholds, respectively. The calculation results indicate that the spectrums of the strange and non-strange pentaquarks are symmetric to each other.
Resolving structural dynamics in situ through cryogenic electron tomography
2025-06-28
Jackson Carrion, Joseph H. Davis
Cryo-electron tomography (cryo-ET) has emerged as a powerful tool for studying the structural heterogeneity of proteins and their complexes, offering insights into macromolecular dynamics directly within cells. Driven by recent computational advances, including powerful machine learning frameworks, researchers can now resolve both discrete structural states and continuous conformational changes from 3D subtomograms and stacks of 2D particle-images acquired across tilt-series. In this review, we survey recent innovations in particle classification and heterogeneous 3D reconstruction methods, focusing specifically on the relative merits of workflows that operate on reconstructed 3D subtomogram volumes compared to those using extracted 2D particle-images. We additionally highlight how these methods have provided specific biological insights into the organization, dynamics, and structural variability of cellular components. Finally, we advocate for the development of benchmarking datasets collected in vitro and in situ to enable a more objective comparison of existent and emerging methods for particle classification and heterogeneous 3D reconstruction.
Impact of Outreach on Physics Student Development: Qualitative Results from a National Survey
2025-06-28
Jonathan D. Perry, Carlee Garrett, Isabella Oaks, James Hirons, Toni Sauncy, Jonan P. Donaldson, Susan White, Rachel L. Ivie, Tatiana Erukhimova
The role of student experiences in physics beyond the classroom which support their development has been the subject of exciting research in recent years. Results, typically from small studies at single institutions, have illustrated that facilitating informal physics experiences for non-scientists can enhance student disciplinary identity, learning, sense of belonging, and more. However, it is essential to examine whether these impacts are the sole provenance of institutions with well-developed outreach programs or if they may be shared by institutions anywhere. This work reports on the analysis and findings of responses to three open-ended questions presented to students who indicated they had engaged in facilitating outreach programs as part of a national survey distributed through the Society of Physics Students network in spring 2023. Employing a network analysis with Girvan-Newman clusters revealed six core themes of student experiences: community participation, resilience, transformation, audience dialog, disciplinary development, and disciplinary connectedness. The first four of these clusters were observed to be highly interconnected, providing evidence that the impacts and experiences within them are interrelated with other clusters, particularly interactions with the audience, which is a central feature of informal physics programs. In particular, student experiences highlighted that facilitating informal physics programs enhanced their resilience and belonging, grew their physics identity, provided opportunities to develop essential career skills, and cultivated a growth mindset.
Not All Water Consumption Is Equal: A Water Stress Weighted Metric for Sustainable Computing
2025-06-28
Yanran Wu, Inez Hua, Yi Ding
Water consumption is an increasingly critical dimension of computing sustainability, especially as AI workloads rapidly scale. However, current water impact assessment often overlooks where and when water stress is more severe. To fill in this gap, we present SCARF, the first general framework that evaluates water impact of computing by factoring in both spatial and temporal variations in water stress. SCARF calculates an Adjusted Water Impact (AWI) metric that considers both consumption volume and local water stress over time. Through three case studies on LLM serving, datacenters, and semiconductor fabrication plants, we show the hidden opportunities for reducing water impact by optimizing location and time choices, paving the way for water-sustainable computing. The code is available at https://github.com/jojacola/SCARF.
2025-06-27
Martino Stefanini, Aleksandra A. Ziolkowska, Dmitry Budker, Ulrich Poschinger, Ferdinand Schmidt-Kaler, Antoine Browaeys, Atac Imamoglu, Darrick Chang, Jamir Marino
The Lindblad master equation is a foundational tool for modeling the dynamics of open quantum systems. As its use has extended far beyond its original domain, the boundaries of its validity have grown opaque. In particular, the rise of new research areas including open quantum many-body systems, non-equilibrium condensed matter, and the possibility to test its limits in driven-open quantum simulators, call for a critical revision of its regimes of applicability. In this pedagogical review, we re-examine the folklore surrounding its three standard approximations (Born, Markov, and Rotating Wave Approximation), as we build our narrative by employing a series of examples and case studies accessible to any reader with a solid background on the fundamentals of quantum mechanics. As a synthesis of our work, we offer a checklist that contrasts common lore with refined expectations, offering a practical guideline for assessing the breakdown of the Lindblad framework in the problem at hand.
Economic Model Predictive Control with a Non-Fixed Reference Trajectory for Optimal Microgrid Dispatch
2025-06-27
Avik Ghosh, Adil Khurram, Jan Kleissl, Sonia Martinez
Economic Model Predictive Control (EMPC), instead of stabilizing a reference trajectory/state in the objective function like a Tracking MPC, optimizes the economic performance over the prediction horizon, making it attractive for economical microgrid (MG) dispatch. However, the demand charge component in the monthly electricity cost, make it difficult to be encapsulated in additive stage costs, and can make solutions violate the principle of optimality if naively introduced in the objective function. Moreover, previous EMPC based works mostly rely on a-priori knowledge of an optimal economic steady state or optimal periodic trajectory for performance guarantees, which are not useful or possibly don't exist respectively, for real-time economical MG dispatch where load/generation forecasts are known only 24-48 h in advance. This paper, first, proposes an EMPC formulation for a generic deterministic discrete non-linear time varying system with hard state and input constraints, without any a-priori requirements of an optimal economic steady state or optimal periodic trajectory. It is proved that under mild assumptions on terminal cost and region, the asymptotic average economic cost of the proposed method is no worse than the asymptotic average economic cost of any other non-fixed arbitrary reference trajectory which is known only until the current time-step. The EMPC framework is then leveraged for optimal MG dispatch by showing that the problem can be reformulated to satisfy the assumptions required for the asymptotic performance guarantee. Realistic simulations at the Port of San Diego MG demonstrated that the proposed method can also reduce monthly electricity costs in closed-loop with respect to reference trajectories generated by directly optimizing the electricity cost function over the prediction horizon or by tracking an ideal grid import curve in a majority of the cases.
Forecasting constraints on quintessential inflation from future generation of galaxy and CMB surveys
2025-06-27
G. Rodrigues, F. B. M. dos Santos, S. Santos da Costa, J. G. Rodrigues, R. von Marttens, R. Silva, D. F. Mota, J. S. Alcaniz
We investigate the constraining power of future CMB and galaxy surveys on models of quintessential inflation realized within the framework of $\alpha$-attractors. We analyze how these future datasets will probe the parameter space of $\alpha$-attractor quintessential inflation, specifically the inflationary potential parameters. Our results demonstrate that the synergy between CMB-S4, LiteBIRD, and Euclid can significantly tighten the bounds on the model parameters, achieving forecasted $1\sigma$ uncertainties of $\alpha=2\pm 0.17$, $n_s=0.965\pm 0.0014$, $\ln(10^{10}A_s)=3.0447\pm 0.0029$ for the CMB+GC$_{sp}$ case. This level of sensitivity will enable us to discriminate between different realizations of quintessential inflation and test the attractor behavior characteristic of these models.
Constraining the Stellar-to-Halo Mass Relation with Galaxy Clustering and Weak Lensing from DES Year 3 Data
2025-06-27
G. Zacharegkas, C. Chang, J. Prat, W. Hartley, S. Mucesh, A. Alarcon, O. Alves, A. Amon, K. Bechtol, M. R. Becker, G. Bernstein, J. Blazek, A. Campos, A. Carnero Rosell, M. Carrasco Kind, R. Cawthon, R. Chen, A. Choi, J. Cordero, C. Davis, J. Derose, H. Diehl, S. Dodelson, C. Doux, A. Drlica-Wagner, K. Eckert, T. F. Eifler, J. Elvin-Poole, S. Everett, X. Fang, A. Ferte, M. Gatti, G. Giannini, D. Gruen, R. A. Gruendl, I. Harrison, H. Huang, E. M. Huff, M. Jarvis, E. Krause, N. Kuropatkin, P. F. Leget, N. Maccrann, J. McCullough, J. Myles, A. N. Alsina, S. Pandey, M. Raveri, R. P. Rollins, A. Roodman, A. J. Ross, E. S. Rykoff, C. Sanchez, L. F. Secco, I. Sevilla-Noarbe, E. Sheldon, T. Shin, M. A. Troxel, I. Tutusaus, B. Yanny, B. Yin, Y. Zhang, J. Zuntz
We develop a framework to study the relation between the stellar mass of a galaxy and the total mass of its host dark matter halo using galaxy clustering and galaxy-galaxy lensing measurements. We model a wide range of scales, roughly from $\sim 100 \; {\rm kpc}$ to $\sim 100 \; {\rm Mpc}$, using a theoretical framework based on the Halo Occupation Distribution and data from Year 3 of the Dark Energy Survey (DES) dataset. The new advances of this work include: 1) the generation and validation of a new stellar mass-selected galaxy sample in the range of $\log M_\star/M_\odot \sim 9.6$ to $\sim 11.5$; 2) the joint-modeling framework of galaxy clustering and galaxy-galaxy lensing that is able to describe our stellar mass-selected sample deep into the 1-halo regime; and 3) stellar-to-halo mass relation (SHMR) constraints from this dataset. In general, our SHMR constraints agree well with existing literature with various weak lensing measurements. We constrain the free parameters in the SHMR functional form $\log M_\star (M_h) = \log(\epsilon M_1) + f\left[ \log\left( M_h / M_1 \right) \right] - f(0)$, with $f(x) \equiv -\log(10^{\alpha x}+1) + \delta [\log(1+\exp(x))]^\gamma / [1+\exp(10^{-x})]$, to be $\log M_1 = 11.559^{+0.334}_{-0.415}$, $\log \epsilon = -1.689^{+0.333}_{-0.220}$, $\alpha = -1.637^{+0.107}_{-0.096}$, $\gamma = 0.588^{+0.265}_{-0.220}$ and $\delta = 4.227^{+2.223}_{-1.776}$. The inferred average satellite fraction is within $\sim 5-35\%$ for our fiducial results and we do not see any clear trends with redshift or stellar mass. Furthermore, we find that the inferred average galaxy bias values follow the generally expected trends with stellar mass and redshift. Our study is the first SHMR in DES in this mass range, and we expect the stellar mass sample to be of general interest for other science cases.
2025-06-27
J. Williams, G. Hackman, K. Starosta, R. S. Lubna, Priyanka Choudhary, Subhrajit Sahoo, P. C. Srivastava, C. Andreoiu, D. Annen, H. Asch, M. D. H. K. G. Badanage, G. C. Ball, M. Beuschlein, H. Bidaman, V. Bildstein, R. J. Coleman, A. B. Garnsworthy, B. Greaves, G. Leckenby, V. Karayonchev, M. S. Martin, C. Natzke, C. M. Petrache, A. Radich, E. Raleigh-Smith, D. Rhodes, R. Russell, M. Satrazani, P. Spagnoletti, C. E. Svensson, D. Tam, F. Wu, D. Yates, Z. Yu
We have studied $^{32}$Si and $^{29}$Al using $^{12}$C($^{22}$Ne,2p) and $^{12}$C($^{22}$Ne,$\alpha$p) fusion-evaporation reactions. In both cases, we observed significant population of high-spin structures distinct from the ground-state yrast bands. In $^{32}$Si, most of the high-energy states feed into a $J^{\pi} = 5^-$ nanosecond isomer. In $^{29}$Al, we identified a rotor-like negative-parity band with a $J^{\pi} = 7/2^-$ band-head. Doppler shift lifetime measurements were performed for all observed states. These results were compared to shell model calculations and interpreted in terms of proton and neutron cross-shell excitation.
Agent-based modeling and the sociology of money: some suggestions for refining monetary theory using social simulation
2025-06-27
Eduardo Coltre Ferraciolli, Tanya V. Araújo
The institution of money can be seen as a foundational social mechanism, enabling communities to quantify collectively regulate economic processes. Money can be said, indeed, to constitute the micro-macro link in economics. This paper reviews influential views on the nature of money in economics and sociology, contrasting them to the relatively limited findings of recent agent-based models of "the emergence of money". Noting ample room for novel combinations of sociological and formal methods to drive insight into the many roles played by money in the economy, we conclude by indicating research directions in which we believe this combination can provide new answers to old questions in monetary theory
Observing the interplay between galaxies and their gaseous surroundings is crucial for understanding how galaxies form and evolve, including the roles of long-lived cool gas reservoirs, starburst and AGN driven outflows. We use stacked Mg II absorption lines in the spectra of background quasars to study the cool gas out to 9Mpc from massive quiescent, star-forming and post-starburst galaxies with stellar masses $\log_{10}(M_{\mathrm{gal}}/M_\odot) \gtrsim 11.4$ and $0.4 \lesssim z \lesssim 0.8$ selected from the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxies. Consistent with previous studies, we observe a decline in absorption strength indicating a decrease in cool gas content with increasing distance from the galaxies, as well as decreasing star formation rate of the galaxies. Beyond 1Mpc, this decline levels off to the same absorption strength in all galaxy types, suggesting a transition from the circumgalactic medium (CGM) to the intergalactic medium (IGM) at approximately the virial radius of the host dark matter haloes. We find that post-starburst galaxies, that have experienced a recent burst of star formation that has rapidly quenched, exhibit significantly stronger Mg II absorption within 1Mpc than star-forming or quiescent galaxies of the same stellar mass. Because post-starburst galaxies are a potentially significant pathway for the formation of quiescent elliptical galaxies, our results have wide reaching implications for understanding the mechanisms involved in quenching star formation in galaxies. We speculate that the excess cool gas absorption out to 1Mpc around post-starburst galaxies is related to their observed high velocity ($\sim$1000\,km/s) cool gas outflows. Thus, strong, short-lived bursts of star formation impact the CGM around galaxies on Mpc distances and Gyr timescales.
Leveraging In-Context Learning for Political Bias Testing of LLMs
2025-06-27
Patrick Haller, Jannis Vamvas, Rico Sennrich, Lena A. Jäger
A growing body of work has been querying LLMs with political questions to evaluate their potential biases. However, this probing method has limited stability, making comparisons between models unreliable. In this paper, we argue that LLMs need more context. We propose a new probing task, Questionnaire Modeling (QM), that uses human survey data as in-context examples. We show that QM improves the stability of question-based bias evaluation, and demonstrate that it may be used to compare instruction-tuned models to their base versions. Experiments with LLMs of various sizes indicate that instruction tuning can indeed change the direction of bias. Furthermore, we observe a trend that larger models are able to leverage in-context examples more effectively, and generally exhibit smaller bias scores in QM. Data and code are publicly available.
Adapting University Policies for Generative AI: Opportunities, Challenges, and Policy Solutions in Higher Education
2025-06-27
Russell Beale
The rapid proliferation of generative artificial intelligence (AI) tools - especially large language models (LLMs) such as ChatGPT - has ushered in a transformative era in higher education. Universities in developed regions are increasingly integrating these technologies into research, teaching, and assessment. On one hand, LLMs can enhance productivity by streamlining literature reviews, facilitating idea generation, assisting with coding and data analysis, and even supporting grant proposal drafting. On the other hand, their use raises significant concerns regarding academic integrity, ethical boundaries, and equitable access. Recent empirical studies indicate that nearly 47% of students use LLMs in their coursework - with 39% using them for exam questions and 7% for entire assignments - while detection tools currently achieve around 88% accuracy, leaving a 12% error margin. This article critically examines the opportunities offered by generative AI, explores the multifaceted challenges it poses, and outlines robust policy solutions. Emphasis is placed on redesigning assessments to be AI-resilient, enhancing staff and student training, implementing multi-layered enforcement mechanisms, and defining acceptable use. By synthesizing data from recent research and case studies, the article argues that proactive policy adaptation is imperative to harness AI's potential while safeguarding the core values of academic integrity and equity.
Cardiovascular disease classification using radiomics and geometric features from cardiac CT
2025-06-27
Ajay Mittal, Raghav Mehta, Omar Todd, Philipp Seeböck, Georg Langs, Ben Glocker
Automatic detection and classification of Cardiovascular disease (CVD) from Computed Tomography (CT) images play an important part in facilitating better-informed clinical decisions. However, most of the recent deep learning based methods either directly work on raw CT data or utilize it in pair with anatomical cardiac structure segmentation by training an end-to-end classifier. As such, these approaches become much more difficult to interpret from a clinical perspective. To address this challenge, in this work, we break down the CVD classification pipeline into three components: (i) image segmentation, (ii) image registration, and (iii) downstream CVD classification. Specifically, we utilize the Atlas-ISTN framework and recent segmentation foundational models to generate anatomical structure segmentation and a normative healthy atlas. These are further utilized to extract clinically interpretable radiomic features as well as deformation field based geometric features (through atlas registration) for CVD classification. Our experiments on the publicly available ASOCA dataset show that utilizing these features leads to better CVD classification accuracy (87.50\%) when compared against classification model trained directly on raw CT images (67.50\%). Our code is publicly available: https://github.com/biomedia-mira/grc-net
We revisit Coincident General Relativity (CGR) in the gauge approach to gravity based on Symmetric Teleparallel Equivalent to General Relativity (STEGR) in the {\it internal-space formulation}, which one of the authors recently proposed in Ref.~[J. Math. Phys. 66 (2025) 5, 052505]. First, we review the standard formulation of STEGR theories in the Palatini approach to gravity, in which formulation we impose the teleparallel and torsion-free conditions by using Lagrange multipliers. Second, we introduce the STEGR theories in the gauge approach to gravity, which is formulated in the internal space, and derive its field equations. We briefly discuss whether the Ostrogradski ghost instability exists and find that the theory may require a degenerate condition to be imposed. Finally, assuming the coincident gauge, we derive CGR in both terms of the action integral and the field equation. Discussing the possible kinematics of the STEGR theory, we formulate a motion of a test scalar particle in the spacetime with non-metricity.
JADES reveals a large population of low mass black holes at high redshift
2025-06-27
Sophia Geris, Roberto Maiolino, Yuki Isobe, Jan Scholtz, Francesco D'Eugenio, Xihan Ji, Ignas Juodzbalis, Charlotte Simmonds, Pratika Dayal, Alessandro Trinca, Raffaella Schneider, Santiago Arribas, Rachana Bhatawdekar, Andrew J. Bunker, Stefano Carniani, Stephane Charlot, Jacopo Chevallard, Emma Curtis-Lake, Benjamin D. Johnson, Eleonora Parlanti, Pierluigi Rinaldi, Brant Robertson, Sandro Tacchella, Hannah Uebler, Giacomo Venturi, Christina C. Williams, Joris Witstok
JWST has revealed a large population of active galactic nuclei (AGN) in the distant universe, which are challenging our understanding of early massive black hole seeding and growth. We expand the exploration of this population to lower luminosities by stacking $\sim 600$ NIRSpec grating spectra from the JWST Advanced Deep Extragalactic Survey (JADES) at $3
Infrared Variability of Carbon Stars in the LMC Observed with NEOWISE-R
2025-06-27
Kyung-Won Suh
We investigate the infrared variability of carbon stars in the Large Magellanic Cloud (LMC). Our sample consists of 11,134 carbon stars identified in both visual and infrared bands. Among these, 1,184 objects are known Mira variables based on the Optical Gravitational Lensing Experiment III (OGLE-III) observations. We study the infrared variability of the entire sample using the Wide-field Infrared Survey Explorer (WISE) photometric data spanning the past 16 years, including the AllWISE multiepoch data and the Near-Earth Object WISE Reactivation (NEOWISE-R) 2024 final data release. We generate light curves using WISE observations in the W1 and W2 bands and compute Lomb-Scargle periodograms for all sample stars. From the WISE light curves, we derive reliable variability parameters for 1,615 objects. Among these, we identify 672 objects exhibiting clear Mira-like variations: 445 of these are previously known Miras from OGLE-III, while 227 are candidates for new Mira variables identified from the WISE data. We establish period-magnitude and period-color relations in both visual and infrared bands for the known Miras and the newly identified candidates from WISE data. We anticipate that these relations will serve as valuable references for studying carbon stars in other galaxies, including the Milky Way.
HOLISMOKES XVIII: Detecting strongly lensed SNe Ia from time series of multi-band LSST-like imaging data
2025-06-27
Satadru Bag, Raoul Canameras, Sherry H. Suyu, Stefan Schuldt, Stefan Taubenberger, Irham Taufik Andika, Alejandra Melo
Strong gravitationally lensed supernovae (LSNe), though rare, are exceptionally valuable probes for cosmology and astrophysics. Upcoming time-domain surveys like the Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) offer a major opportunity to discover them in large numbers. Early identification is crucial for timely follow-up observations. We develop a deep learning pipeline to detect LSNe using multi-band, multi-epoch image cutouts. Our model is based on a 2D convolutional long short-term memory (ConvLSTM2D) architecture, designed to capture both spatial and temporal correlations in time-series imaging data. Predictions are made after each observation in the time series, with accuracy improving as more data arrive. We train the model on realistic simulations derived from Hyper Suprime-Cam (HSC) data, which closely matches LSST in depth and filters. This work focuses exclusively on Type Ia supernovae (SNe Ia). LSNe Ia are injected onto HSC luminous red galaxies (LRGs) at various phases of evolution to create positive examples. Negative examples include variable sources from HSC Transient Survey (including unclassified transients), and simulated unlensed SNe Ia in LRG and spiral galaxies. Our multi-band model shows rapid classification improvements during the initial few observations and quickly reaches high detection efficiency: at a fixed false-positive rate (FPR) of $0.01\%$, the true-positive rate (TPR) reaches $\gtrsim 60\%$ by the 7th observation and exceeds $\gtrsim 70\%$ by the 9th. Among the negative examples, SNe in LRGs remain the primary source of FPR, as they can resemble their lensed counterparts under certain conditions. The model detects quads more effectively than doubles and performs better on systems with larger image separations. Although trained and tested on HSC-like data, our approach applies to any cadenced imaging survey, particularly LSST.
Disentangling the growth rate of perturbations from the HI bias using only clustering data from galaxy surveys
2025-06-27
Pankaj Chavan, Tapomoy Guha Sarkar, Anjan A Sen
This work serves two-fold purpose. Firstly, we provide an alternative to the traditional method of determining the growth rate of density perturbations $f(z)$. In usual practice, $f(z)$ can not be directly measured from tracer clustering at some redshift without knowledge of the bias. While the bayron acoustic oscillation (BAO) imprint allows the determination of $(D_A(z), H(z))$, redshift space anisotropy (RSD) allows the measurement of a quantity $f_8(z) = f(z) \sigma_{8,0} D_{+}(z)$. To extract $f(z)$ from $f_8(z)$, one usually requires some other data set. We show that precise BAO and RSD measurements in and around some key redshifts themselves can solely reconstruct $f(z)$ without requiring any other data sets. Secondly, we extend this approach to another tracer, namely the post-reionization 21-cm brightness temperature intensity maps. We demonstrate that the measured $f(z)$ from purely redshift space clustering allows us to measure the 21-cm bias, which is a largely unknown quantity. This may help interpret the observed intensity mapping signal in the future.
The NIKA2 cosmological legacy survey at 2 mm: catalogs, colors, redshift distributions, and implications for deep surveys
2025-06-27
M. Béthermin, G. Lagache, C. Carvajal-Bohorquez, R. Adam, P. Ade, H. Ajeddig, S. Amarantidis, P. André, H. Aussel, A. Beelen, A. Benoît, S. Berta, L. J. Bing, A. Bongiovanni, J. Bounmy, O. Bourrion, M. Calvo, A. Catalano, D. Chérouvrier, M. De Petris, F. -X. Désert, S. Doyle, E. F. C. Driessen, G. Ejlali, A. Ferragamo, A. Gomez, J. Goupy, C. Hanser, S. Katsioli, F. Kéruzoré, C. Kramer, B. Ladjelate, S. Leclercq, J. -F. Lestrade, J. F. Macías-Pérez, S. C. Madden, A. Maury, F. Mayet, A. Monfardini, A. Moyer-Anin, M. Muñoz-Echeverría, I. Myserlis, A. Paliwal, L. Perotto, G. Pisano, N. Ponthieu, V. Revéret, A. J. Rigby, A. Ritacco, H. Roussel, F. Ruppin, M. Sánchez-Portal, S. Savorgnano, K. Schuster, A. Sievers, C. Tucker, R. Zylka
Millimeter galaxy surveys are particularly effective in detecting dusty star-forming galaxies at high redshift. While such observations are typically conducted at ~1mm, studies suggest that 2mm may be better suited for selecting sources at even higher redshifts. We use the unprecedented 2mm data from the N2CLS, together with the SIDES simulation, to study and interpret the statistical properties of 2mm-selected galaxies. We use the N2CLS robust sample at 2mm, which contains 25 sources in the deep GOODS-N field and 90 sources in the wide COSMOS. The sources are matched with the N2CLS 1.2mm sources, the ancillary 850um sources, and redshift catalogs to study the colors and redshift distributions. We also produce end-to-end simulations based on SIDES and the observed N2CLS detector timelines to interpret the data. We find a mean S2/S1.2 color of 0.215$\pm$0.006 with a standard deviation of 0.056$\pm$0.004. We measure a mean redshift of $3.6\pm0.3$ in GOODS-N, which is marginally higher than expectations from SIDES ($3.0\pm0.2$) because of an overdensity at $z\sim5.2$, and $3.0\pm0.2$ in COSMOS, which agrees with the $3.2\pm0.2$ predicted by SIDES. We also show that the observed S2/S1.2 colors exhibit a weak dependence with redshift but a large dispersion, which limits its efficiency to select high-z sources. Finally, we studied the nine 2mm sources not detected at 1.2mm, and found that two of them are radiogalaxies, one is a z~2 galaxy, and the remaining six are compatible with the expected number of spurious detections. The N2CLS survey shows no evidence for any exotic 2mm-only galaxy population. Using SIDES, we show that 2mm samples have a higher mean redshift compared to 1.2mm because they miss z~2 dusty galaxies. Finally, we compare the N2CLS with the ex-MORA survey and show that N2CLS is more efficient than interferometric observations to build samples of high-z dusty galaxies.
Literature-Grounded Novelty Assessment of Scientific Ideas
2025-06-27
Simra Shahid, Marissa Radensky, Raymond Fok, Pao Siangliulue, Daniel S. Weld, Tom Hope
Automated scientific idea generation systems have made remarkable progress, yet the automatic evaluation of idea novelty remains a critical and underexplored challenge. Manual evaluation of novelty through literature review is labor-intensive, prone to error due to subjectivity, and impractical at scale. To address these issues, we propose the Idea Novelty Checker, an LLM-based retrieval-augmented generation (RAG) framework that leverages a two-stage retrieve-then-rerank approach. The Idea Novelty Checker first collects a broad set of relevant papers using keyword and snippet-based retrieval, then refines this collection through embedding-based filtering followed by facet-based LLM re-ranking. It incorporates expert-labeled examples to guide the system in comparing papers for novelty evaluation and in generating literature-grounded reasoning. Our extensive experiments demonstrate that our novelty checker achieves approximately 13% higher agreement than existing approaches. Ablation studies further showcases the importance of the facet-based re-ranker in identifying the most relevant literature for novelty evaluation.
Seismic resolution enhancement via deep Learning with Knowledge Distillation and Domain Adaptation
2025-06-27
Hanpeng Cai, Haonan Zhang, Liyu Zhang, Suo Cheng
High-resolution processing of seismic signals is crucial for subsurface geological characterization and thin-layer reservoir identification. Traditional high-resolution algorithms can partially recover high-frequency information but often lack robustness, computational efficiency, and consideration of inter-trace structural relationships. Many deep learning methods use end-to-end architectures that do not incorporate prior knowledge or address data domain disparities, leading to limited generalization.To overcome these challenges, this paper presents the Domain-Adaptive Knowledge Distillation Network (DAKD-Net), which integrates a knowledge distillation strategy with a domain adaptation mechanism for high-resolution seismic data processing. Trained on datasets from forward modeling, DAKD-Net establishes physical relationships between low and high-resolution data, extracting high-frequency prior knowledge during a guided phase before detail restoration without prior conditions. Domain adaptation enhances the model's generalization to real seismic data, improving both generalization capability and structural expression accuracy.DAKD-Net employs a U-Net backbone to extract spatial structural information from multi-trace seismic profiles. The knowledge distillation mechanism enables prior knowledge transfer, allowing recovery of high-resolution data directly from low-resolution inputs. Domain-adaptive fine-tuning further enhances the network's performance in actual survey areas. Experimental results show that DAKD-Net outperforms traditional methods and classical deep networks in longitudinal resolution and complex structural detail restoration, demonstrating strong robustness and practicality.
The Bulk Penetration of Edge Properties in Two-Dimensional Materials
2025-06-27
Markus Kari, Pekka Koskinen
Edges are essential for the mechanical, chemical, electronic, and magnetic properties of two-dimensional (2D) materials. Research has shown that features assigned to edges are not strictly localized but often penetrate the bulk to some degree. However, mechanical edge properties, such as edge energies and stresses, are typically assigned at the system level, with spatial bulk penetrations that remain unknown. Here, we use density-functional tight-binding simulations to study how deep various edge properties spatially penetrate the 2D bulk. We study nine different edges made of four materials: graphene, goldene, boron nitride, and molybdenum disulfide. By investigating edge energies, edge stresses, and edge elastic moduli, we find that although the edge properties typically originate near edges, they still penetrate the bulk to some degree. An utmost example is goldene with a staggered edge, whose edge properties penetrate the bulk nanometer-deep. Our results caution against associating system-level edge properties too strictly with the edge, especially if those properties are further used in continuum models.
Modern speech quality prediction models are trained on audio data resampled to a specific sampling rate. When faced with higher-rate audio at test time, these models can produce biased scores. We introduce HighRateMOS, the first non-intrusive mean opinion score (MOS) model that explicitly considers sampling rate. HighRateMOS ensembles three model variants that exploit the following information: (i) a learnable embedding of speech sampling rate, (ii) Wav2vec 2.0 self-supervised embeddings, (iii) multi-scale CNN spectral features, and (iv) MFCC features. In AudioMOS 2025 Track3, HighRateMOS ranked first in five out of eight metrics. Our experiments confirm that modeling the sampling rate directly leads to more robust and sampling-rate-agnostic speech quality predictions.
Energetic ($< 2$ MeV) Ion Environment of the Magnetosphere as measured by ASPEX-STEPS on board Aditya-L1 during its earth-bound phase
2025-06-27
Dibyendu Chakrabarty, Bijoy Dalal, Santosh Vadawale, Aveek Sarkar, Shiv Kumar Goyal, Jacob Sebastian, Anil Bhardwaj, P. Janardhan, M. Shanmugam, Neeraj Kumar Tiwari, Aaditya Sarda, Piyush Sharma, Aakash Gupta, Prashant Kumar, Manan S. Shah, Bhas Bapat, Pranav R Adhyaru, Arpit R. Patel, Hitesh Kumar Adalja, Abhishek Kumar, Tinkal Ladiya, Sushil Kumar, Nishant Singh, Deepak Kumar Painkra, Abhishek J. Verma, Nandita Srivastava, Swaroop Banerjee, K. P. Subramanian, M. B. Dadhania
During its earth-bound phase of the Aditya-L1 spacecraft of India, the Supra-Thermal and Energetic Particle Spectrometer (STEPS) of the Aditya Solar wind Particle EXperiment (ASPEX) was operated whenever the orbit was above 52000 km during 11-19 September 2023. This phase of operation provided measurements of energetic ions (with energies 0.1-2 MeV) in the magnetosphere, magnetosheath, and interplanetary medium. Three interplanetary coronal mass ejections (ICME) hit the magnetosphere during this period. This provided opportunity to examine the relative roles of external (ICME) and internal (substorm) drivers in controlling the energetic ion environment in the terrestrial magnetosphere by detailed spectral analysis of energetic ion fluxes measured by two units of ASPEX-STEPS. We identify three distinctly different conditions of the north-south component of the interplanetary magnetic field (IMF $B_z = 0$, $> 0$, and $< 0$) and use the derived spectral indices to understand the role of external and internal drivers. By combining these with the simultaneous eneregtic ion flux variations from the Advanced Composition Explorer (ACE) around the Sun-Earth first Lagrangian (L1) point and the Geostationary Operational Environmental Satellite (GOES) in the Earth's magnetosphere, we show that the polarity of IMF $B_z$ influences the energetic ion spectra in the magnetosphere by modulating the interplay of the external and internal drivers. Further, we observe directional anisotropy of energetic ions and much harder spectra associated with one ICME compared to another one, although both led to geomagnetic storms having nearly equal intensities.
2025-06-27
Lachlan J. Barnes, Andrew M. Hopkins, Lawrence Rudnick, Heinz Andernach, Michael Cowley, Nikhel Gupta, Ray P. Norris, Stanislav S. Shabala, Tayyaba Zafar
The advent of next-generation telescope facilities brings with it an unprecedented amount of data, and the demand for effective tools to process and classify this information has become increasingly important. This work proposes a novel approach to quantify the radio galaxy morphology, through the development of a series of algorithmic metrics that can quantitatively describe the structure of radio source, and can be applied to radio images in an automatic way. These metrics are intuitive in nature and are inspired by the intrinsic structural differences observed between the existing Fanaroff-Riley (FR) morphology types. The metrics are defined in categories of asymmetry, blurriness, concentration, disorder, and elongation ($ABCDE$/single-lobe metrics), as well as the asymmetry and angle between lobes (source metrics). We apply these metrics to a sample of $480$ sources from the Evolutionary Map of the Universe Pilot Survey (EMU-PS) and $72$ well resolved extensively studied sources from An Atlas of DRAGNs, a subset of the revised Third Cambridge Catalogue of Radio Sources (3CRR). We find that these metrics are relatively robust to resolution changes, independent of each other, and measure fundamentally different structural components of radio galaxy lobes. These metrics work particularly well for sources with reasonable signal-to-noise and well separated lobes. We also find that we can recover the original FR classification using probabilistic combinations of our metrics, highlighting the usefulness of our approach for future large data sets from radio sky surveys.
The past few years has witnessed specialized large language model (LLM) inference systems, such as vLLM, SGLang, Mooncake, and DeepFlow, alongside rapid LLM adoption via services like ChatGPT. Driving these system design efforts is the unique autoregressive nature of LLM request processing, motivating new techniques for achieving high performance while preserving high inference quality over high-volume and high-velocity workloads. While many of these techniques are discussed across the literature, they have not been analyzed under the framework of a complete inference system, nor have the systems themselves been analyzed and compared. In this survey, we review these techniques, starting from operators and algorithms for request processing, then moving on to techniques for model optimization and execution, including kernel design, batching, and scheduling, before ending with techniques for memory management, including paged memory, eviction and offloading techniques, quantization, and cache persistence. Through these discussions, we show that these techniques fundamentally rely on load prediction, adaptive mechanisms, and cost reduction in order to overcome the challenges introduced by autoregressive generation and achieve the goals of the system. We then discuss how these techniques can be combined to form single-replica and multi-replica inference systems, including disaggregated inference systems that offer more control over resource allocation and serverless systems that can be deployed over shared hardware infrastructure. We end with a discussion of remaining challenges.
Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review
2025-06-27
Amara Zuffer, Michael Burke, Mehrtash Harandi
The diversity of tasks and dynamic nature of reinforcement learning (RL) require RL agents to be able to learn sequentially and continuously, a learning paradigm known as continuous reinforcement learning. This survey reviews how continual learning transforms RL agents into dynamic continual learners. This enables RL agents to acquire and retain useful and reusable knowledge seamlessly. The paper delves into fundamental aspects of continual reinforcement learning, exploring key concepts, significant challenges, and novel methodologies. Special emphasis is placed on recent advancements in continual reinforcement learning within robotics, along with a succinct overview of evaluation environments utilized in prominent research, facilitating accessibility for newcomers to the field. The review concludes with a discussion on limitations and promising future directions, providing valuable insights for researchers and practitioners alike.
Thompson Sampling in Function Spaces via Neural Operators
2025-06-27
Rafael Oliveira, Xuesong Wang, Kian Ming A. Chai, Edwin V. Bonilla
We propose an extension of Thompson sampling to optimization problems over function spaces where the objective is a known functional of an unknown operator's output. We assume that functional evaluations are inexpensive, while queries to the operator (such as running a high-fidelity simulator) are costly. Our algorithm employs a sample-then-optimize approach using neural operator surrogates. This strategy avoids explicit uncertainty quantification by treating trained neural operators as approximate samples from a Gaussian process. We provide novel theoretical convergence guarantees, based on Gaussian processes in the infinite-dimensional setting, under minimal assumptions. We benchmark our method against existing baselines on functional optimization tasks involving partial differential equations and other nonlinear operator-driven phenomena, demonstrating improved sample efficiency and competitive performance.
Integrating Multi-Modal Sensors: A Review of Fusion Techniques for Intelligent Vehicles
2025-06-27
Chuheng Wei, Ziye Qin, Ziyan Zhang, Guoyuan Wu, Matthew J. Barth
Multi-sensor fusion plays a critical role in enhancing perception for autonomous driving, overcoming individual sensor limitations, and enabling comprehensive environmental understanding. This paper first formalizes multi-sensor fusion strategies into data-level, feature-level, and decision-level categories and then provides a systematic review of deep learning-based methods corresponding to each strategy. We present key multi-modal datasets and discuss their applicability in addressing real-world challenges, particularly in adverse weather conditions and complex urban environments. Additionally, we explore emerging trends, including the integration of Vision-Language Models (VLMs), Large Language Models (LLMs), and the role of sensor fusion in end-to-end autonomous driving, highlighting its potential to enhance system adaptability and robustness. Our work offers valuable insights into current methods and future directions for multi-sensor fusion in autonomous driving.
GRASP-PsONet: Gradient-based Removal of Spurious Patterns for PsOriasis Severity Classification
2025-06-27
Basudha Pal, Sharif Amit Kamran, Brendon Lutnick, Molly Lucas, Chaitanya Parmar, Asha Patel Shah, David Apfel, Steven Fakharzadeh, Lloyd Miller, Gabriela Cula, Kristopher Standish
Psoriasis (PsO) severity scoring is important for clinical trials but is hindered by inter-rater variability and the burden of in person clinical evaluation. Remote imaging using patient captured mobile photos offers scalability but introduces challenges, such as variation in lighting, background, and device quality that are often imperceptible to humans but can impact model performance. These factors, along with inconsistencies in dermatologist annotations, reduce the reliability of automated severity scoring. We propose a framework to automatically flag problematic training images that introduce spurious correlations which degrade model generalization, using a gradient based interpretability approach. By tracing the gradients of misclassified validation images, we detect training samples where model errors align with inconsistently rated examples or are affected by subtle, nonclinical artifacts. We apply this method to a ConvNeXT based weakly supervised model designed to classify PsO severity from phone images. Removing 8.2% of flagged images improves model AUC-ROC by 5% (85% to 90%) on a held out test set. Commonly, multiple annotators and an adjudication process ensure annotation accuracy, which is expensive and time consuming. Our method detects training images with annotation inconsistencies, potentially removing the need for manual review. When applied to a subset of training data rated by two dermatologists, the method identifies over 90% of cases with inter-rater disagreement by reviewing only the top 30% of samples. This improves automated scoring for remote assessments, ensuring robustness despite data collection variability.
2025-06-27
Chaofan Pan, Xin Yang, Yanhua Li, Wei Wei, Tianrui Li, Bo An, Jiye Liang
Reinforcement Learning (RL) is an important machine learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in this field due to the rapid development of deep neural networks. However, the success of RL currently relies on extensive training data and computational resources. In addition, RL's limited ability to generalize across tasks restricts its applicability in dynamic and real-world environments. With the arisen of Continual Learning (CL), Continual Reinforcement Learning (CRL) has emerged as a promising research direction to address these limitations by enabling agents to learn continuously, adapt to new tasks, and retain previously acquired knowledge. In this survey, we provide a comprehensive examination of CRL, focusing on its core concepts, challenges, and methodologies. Firstly, we conduct a detailed review of existing works, organizing and analyzing their metrics, tasks, benchmarks, and scenario settings. Secondly, we propose a new taxonomy of CRL methods, categorizing them into four types from the perspective of knowledge storage and/or transfer. Finally, our analysis highlights the unique challenges of CRL and provides practical insights into future directions.
DeepTalk: Towards Seamless and Smart Speech Interaction with Adaptive Modality-Specific MoE
2025-06-27
Hang Shao, Heting Gao, Yunhang Shen, Jiawei Chen, Lijiang Li, Zuwei Long, Bo Tong, Ke Li, Xing Sun
Native multimodal large language models (MLLMs) restructure a single large language model (LLM) into a spoken language model (SLM) capable of both speech and text generation. Compared to modular and aligned MLLMs, native MLLMs preserve richer paralinguistic features such as emotion and prosody, and generate speech responses directly within the backbone LLM rather than using a separate speech decoder. This integration also results in lower response latency and smoother interaction. However, native MLLMs suffer from catastrophic forgetting and performance degradation because the available paired speech-text data is insufficient to support the pretraining of MLLMs compared to the vast amount of text data required to pretrain text LLMs. To address this issue, we propose DeepTalk, a framework for adaptive modality expert learning based on a Mixture of Experts (MoE) architecture. DeepTalk first adaptively distinguishes modality experts according to their modality load within the LLM. Each modality expert then undergoes specialized single-modality training, followed by joint multimodal collaborative training. As a result, DeepTalk incurs only a 5.5% performance drop compared to the original LLM, which is significantly lower than the average performance drop of over 20% typically seen in native MLLMs (such as GLM-4-Voice), and is on par with modular MLLMs. Meanwhile, the end-to-end dialogue latency remains within 0.5 seconds, ensuring a seamless and intelligent speech interaction experience. Code and models are released at https://github.com/talkking/DeepTalk.
Universal Effective Charges in the $sd$ and $fp$ Shells
2025-06-27
T. H. Ogunbeku, J. M. Allmond, T. J. Gray, W. -J. Ong, B. A. Brown, A. Gargano, R. Grzywacz, J. D. Holt, A. O. Macchiavelli, T. Miyagi, S. Neupane, B. C. Rasco, H. Schatz, B. M. Sherrill, O. B. Tarasov, H. Arora, A. D. Ayangeakaa, H. C. Berg, J. M. Berkman, D. L. Bleuel, K. Bosmpotinis, M. P. Carpenter, G. Cerizza, A. Chester, J. M. Christie, I. Cox, H. L. Crawford, B. P. Crider, J. Davis, A. A. Doetsch, J. G. Duarte, A. Estrade, A. Fijałkowska, C. Frantzis, T. Gaballah, E. C. Good, K. Haak, S. Hanai, J. T. Harke, A. C. Hartley, K. Hermansen, D. E. M. Hoff, D. Hoskins III, J. Huffman, P. Van Isacker, R. Jain, M. Karny, T. T. King, N. Kitamura, K. Kolos, A. Laminack, S. N. Liddick, B. Longfellow, R. S. Lubna, S. Lyons, M. Madurga, M. J. Mogannam, G. Owens-Fryar, J R. Palomino, M. M. Rajabali, A. L. Richard, I. J. Richardson, E. K. Ronning, G. E. Rose, T. J. Ruland, K. P. Rykaczewski, N. D. Scielzo, D. P. Scriven, D. Seweryniak, K. Siegl, M. Singh, A. Spyrou, M. Stepaniuk, A. E. Stuchbery, A. Sweet, V. Tripathi, A. Tsantiri, S. Uthayakumaar, W. B. Walters, S. Watters, Z. Xu, R. Yokoyama
The 247-keV state in $^{54}$Sc, populated in the $\beta$ decay of $^{54}$Ca, is reported here as a nanosecond isomer with a half-life of 26.0(22) ns. The state is interpreted as the $1^+$ member of the $\pi f_{7/2}\otimes\nu f_{5/2}$ spin-coupled multiplet, which decays to the $3^+,\pi f_{7/2} \otimes \nu p_{1/2}$ ground state. The new half-life corresponds to a pure $E2$ transition with a strength of 1.93(16) W.u., providing the most precise, unambiguous $B(E2)$ value in the neutron-rich $fp$ region to date for a nucleus with valence protons above $Z=20$. Notably, it is roughly four times larger than the $B(E2; 1/2^{-} \rightarrow 5/2^{-})$ value in $^{55}$Ca. The results, as compared to semi-empirical and ab initio shell-model calculations, indicate (1) a weak $N=34$ sub-shell gap relative to $N = 32$, (2) a large $E2$ enhancement in Sc as compared to Ca due to $1p-1h$ proton excitations across $Z=28$, and (3) empirical effective proton and neutron charges, $e_\pi$ = 1.30(8)$e$ and $e_\nu$ = 0.452(7)$e$, respectively, that are in contrast to reports of $e_\pi \approx 1.1-1.15e$ and $e_\nu \approx 0.6-0.8e$ for $fp$-shell nuclei near $N = Z$. We demonstrate that these reports are erroneous and that, in fact, a universal set of effective charges can be used across the $sd$ and $fp$ shells.
RGB-IR(RGB-Infrared) image pairs are frequently applied simultaneously in various applications like intelligent surveillance. However, as the number of modalities increases, the required data storage and transmission costs also double. Therefore, efficient RGB-IR data compression is essential. This work proposes a joint compression framework for RGB-IR image pair. Specifically, to fully utilize cross-modality prior information for accurate context probability modeling within and between modalities, we propose a Channel-wise Cross-modality Entropy Model (CCEM). Among CCEM, a Low-frequency Context Extraction Block (LCEB) and a Low-frequency Context Fusion Block (LCFB) are designed for extracting and aggregating the global low-frequency information from both modalities, which assist the model in predicting entropy parameters more accurately. Experimental results demonstrate that our approach outperforms existing RGB-IR image pair and single-modality compression methods on LLVIP and KAIST datasets. For instance, the proposed framework achieves a 23.1% bit rate saving on LLVIP dataset compared to the state-of-the-art RGB-IR image codec presented at CVPR 2022.
Quantum Machine Learning (QML) integrates quantum computing with classical machine learning, primarily to solve classification, regression and generative tasks. However, its rapid development raises critical security challenges in the Noisy Intermediate-Scale Quantum (NISQ) era. This chapter examines adversarial threats unique to QML systems, focusing on vulnerabilities in cloud-based deployments, hybrid architectures, and quantum generative models. Key attack vectors include model stealing via transpilation or output extraction, data poisoning through quantum-specific perturbations, reverse engineering of proprietary variational quantum circuits, and backdoor attacks. Adversaries exploit noise-prone quantum hardware and insufficiently secured QML-as-a-Service (QMLaaS) workflows to compromise model integrity, ownership, and functionality. Defense mechanisms leverage quantum properties to counter these threats. Noise signatures from training hardware act as non-invasive watermarks, while hardware-aware obfuscation techniques and ensemble strategies disrupt cloning attempts. Emerging solutions also adapt classical adversarial training and differential privacy to quantum settings, addressing vulnerabilities in quantum neural networks and generative architectures. However, securing QML requires addressing open challenges such as balancing noise levels for reliability and security, mitigating cross-platform attacks, and developing quantum-classical trust frameworks. This chapter summarizes recent advances in attacks and defenses, offering a roadmap for researchers and practitioners to build robust, trustworthy QML systems resilient to evolving adversarial landscapes.
Fetal Sleep: A Cross-Species Review of Physiology, Measurement, and Classification
2025-06-27
Weitao Tang, Johann Vargas-Calixto, Nasim Katebi, Robert Galinsky, Gari D. Clifford, Faezeh Marzbanrad
Fetal sleep is a relatively underexplored yet vital aspect of prenatal neurodevelopment. Understanding fetal sleep patterns could provide insights into early brain maturation and help clinicians detect signs of neurological compromise that arise due to fetal hypoxia or fetal growth restriction. This review synthesizes over eight decades of research on the physiological characteristics, ontogeny, and regulation of fetal sleep. We compare sleep-state patterns in humans and large animal models, highlighting species-specific differences and the presence of sleep-state analogs. We review both invasive techniques in animals and non-invasive modalities in humans. Computational methods for sleep-state classification are also examined, including rule-based approaches (with and without clustering-based preprocessing) and state-of-the-art deep learning techniques. Finally, we discuss how intrauterine conditions such as hypoxia and fetal growth restriction can disrupt fetal sleep. This review provides a comprehensive foundation for the development of objective, multimodal, and non-invasive fetal sleep monitoring technologies to support early diagnosis and intervention in prenatal care.
Towards an Observational Detection of Halo Spin Bias using Spin-Orbit Coherence
2025-06-27
Yigon Kim, Antonio D. Montero-Dorta, Rory Smith, Jong-Ho Shinn
Context. The clustering of dark-matter halos depends primarily on halo mass. However, at fixed halo mass, numerical simulations have revealed multiple secondary dependencies. This so-called secondary halo bias has important implications for our understanding of structure formation and observational cosmology. Despite its significance, the effect has not yet been measured observationally with statistical confidence. Aims. We aim to develop the first observational method to probe halo spin bias: the secondary dependence of halo clustering on halo spin at fixed halo mass. Methods. We use a proxy for halo spin based on the coherent motion of galaxies within and around a halo. This technique is tested using the IllustrisTNG hydrodynamical simulation and subsequently applied to a group catalog from the Sloan Digital Sky Survey (SDSS). By splitting the SDSS groups according to this spin proxy and measuring the two-point correlation function of the resulting samples, the existence of halo spin bias is investigated. Results. We find consistent indications that, at fixed mass, groups with higher values of the spin proxy exhibit higher bias than those with lower spin proxy values, on scales of 5-15 ${h}^{-1}$$\mathrm{Mpc}$. The highest significance is seen for groups with halo masses ${M}_{\rm h} \gtrsim {10}^{13.2}$ ${h}^{-1}{\rm M}_\odot$, for which 85$\%$ of the sampled measurements display the expected trend. As we continue to improve the method, our results could open new avenues for studying the connection between halo spin and the large-scale structure with upcoming spectroscopic surveys.
Fundamental Nuclear and Particle Physics At Neutron Sources
2025-06-27
H. Abele, J. Amaral, W. R. Anthony, L. AAstrand, M. Atzori Corona, S. Baessler, M. Bartis, E. Baussan, D. H. Beck, J. Bijnens, K. Bodek, J. Bosina, E. Bossio, G. Brooijmans, L. J. Broussard, G. Brunetti, A. Burgman, M. Cadeddu, N. Cargioli, J. Cederkall, A. Chambon, T. W. Choi, P. Christiansen, V. Cianciolo, C. B. Crawford, S. Degenkolb, N. Delarosa, M. Demarteau, K. Dickerson, D. D. DiJulio, F. Dordei, Y. Efremenko, T. Ekelof, M. Eshraqi, R. R. Fan, M. Fertl, H. Filter-Pieler, B. Fornal, G. Fragneto, C. Gatto, P. Geltenbort, F. Ghazi Moradi, H. Gisbert, P. Golubev, M. Gonzalez-Alonso, G. Gorini, P. Heil, N. Hermansson-Truedsson, Y. Hicyilmaz, M. Holl, T. Ito, K. E. Iversen, T. Jenke, M. Jentschel, M. Juni Ferreira, S. Kawasaki, E. Kemp, P. Kinhult, M. Kitaguchi, J. Klenke, W. Korten, A. Kozela, B. Lauss, M. Lebert, W. Lee, T. Lesiak, C. Y. Liu, L. Lobell, A. Longhin, E. Lytken, B. Maerkisch, J. Marton, B. Meirose, N. Milas, D. Milstead, F. Monrabal, S. Moretti, P. Mueller, A. Nepomuceno, J. Newby, R. Nieuwenhuis, T. Palasz, R. Pasechnik, S. Penttila, M. Persoz, L. B. Persson, F. M. Piegsa, B. Plaster, I. Pradler, F. Pupilli, K. Pysz, T. Quirino, J. C. Ramsey, B. Rataj, J. Rathsman, S. Roccia, D. Rozpedzik, D. Rudolph, E. Salehi, V. Santoro, A. Saunders, H. Schober, K. Scholberg, W. Schreyer, A. Schubert, D. Silvermyr, O. Smirnova, W. M. Snow, T. Soldner, S. R. Soleti, Y. V. Stadnik, R. Strauss, F. Terranova, T. Tolba, N. Tsapatsaris, L. Vale Silva, W. Van Goethem, R. Wagner, M. Wolke, W. Yao, N. Yazdandoost, A. R. Young, L. Zanini, M. Zielinski
Fundamental neutron and neutrino physics at neutron sources, combining precision measurements and theory, can probe new physics at energy scales well beyond the highest energies probed by the LHC and possible future high energy collider facilities. The European Spallation Source (ESS) will in the not too far future be a most powerful pulsed neutron source and simultaneously the world's brightest pulsed neutrino source. The ESS, and neutron sources in general, can provide unprecedented and unique opportunities to contribute to the search for the missing elements in the Standard Model of particle physics. Currently there are no strong indications where hints of the origin of the new physics will emerge. A multi-pronged approach will provide the fastest path to fill the gaps in our knowledge and neutron sources have a pivotal role to play. To survey the ongoing and proposed physics experiments at neutron sources and assess their potential impact, a workshop was held at Lund University in January, 2025. This report is a summary of that workshop and has been prepared as input to the European Strategy Update.
Determining Exciton Binding Energy and Reduced Effective Mass in Metal Tri-Halide Perovskites from Optical and Impedance Spectroscopy Measurements
2025-06-27
K. Lizárraga, J. A. Guerra, L. A. Enrique-Moran, E. Serquen, E. Ventura, Cesar E. P. Villegas, A. R. Rocha, P. Venezuela
Accurate determination of the exciton binding energy and reduced effective mass in halide perovskites is of utmost importance for the selective design of optoelectronic devices. Although these properties are currently determined by several spectroscopic techniques, complementary theoretical models are often required to bridge macroscopic and microscopic properties. Here, we present a novel method to determine these quantities while fully accounting for polarization effects due to carrier interactions with longitudinal optical phonons. Our approach estimates the exciton-polaron binding energy from optical absorption measurements using a recently developed Elliott based Band Fluctuations model. The reduced effective mass is obtained via the Pollmann-Buttner exciton-polaron model, which is based on the Frohlich polaron framework, where the strength of the electron-phonon interaction arises from changes in the dielectric properties. The procedure is applied to the family of perovskites ABX3 (A = MA, FA, Cs; B = Pb; X = I, Br, Cl), showing excellent agreement with high field magnetoabsorption and other optical-resolved techniques. The results suggest that the Pollmann-Buttner model offers a robust and novel approach for determining the reduced effective mass in metal tri-halide perovskites and other polar materials exhibiting free exciton bands.
Recent advances in deep learning have significantly transformed the field of 3D shape generation, enabling the synthesis of complex, diverse, and semantically meaningful 3D objects. This survey provides a comprehensive overview of the current state of the art in 3D shape generation, organizing the discussion around three core components: shape representations, generative modeling approaches, and evaluation protocols. We begin by categorizing 3D representations into explicit, implicit, and hybrid setups, highlighting their structural properties, advantages, and limitations. Next, we review a wide range of generation methods, focusing on feedforward architectures. We further summarize commonly used datasets and evaluation metrics that assess fidelity, diversity, and realism of generated shapes. Finally, we identify open challenges and outline future research directions that could drive progress in controllable, efficient, and high-quality 3D shape generation. This survey aims to serve as a valuable reference for researchers and practitioners seeking a structured and in-depth understanding of this rapidly evolving field.
Do Electric Vehicles Induce More Motion Sickness Than Fuel Vehicles? A Survey Study in China
2025-06-27
Weiyin Xie, Chunxi Huang, Jiyao Wang, Dengbo He
Electric vehicles (EVs) are a promising alternative to fuel vehicles (FVs), given some unique characteristics of EVs, for example, the low air pollution and maintenance cost. However, the increasing prevalence of EVs is accompanied by widespread complaints regarding the high likelihood of motion sickness (MS) induction, especially when compared to FVs, which has become one of the major obstacles to the acceptance and popularity of EVs. Despite the prevalence of such complaints online and among EV users, the association between vehicle type (i.e., EV versus FV) and MS prevalence and severity has not been quantified. Thus, this study aims to investigate the existence of EV-induced MS and explore the potential factors leading to it. A survey study was conducted to collect passengers' MS experience in EVs and FVs in the past one year. In total, 639 valid responses were collected from mainland China. The results show that FVs were associated with a higher frequency of MS, while EVs were found to induce more severe MS symptoms. Further, we found that passengers' MS severity was associated with individual differences (i.e., age, gender, sleep habits, susceptibility to motion-induced MS), in-vehicle activities (i.e., chatting with others and watching in-vehicle displays), and road conditions (i.e., congestion and slope), while the MS frequency was associated with the vehicle ownership and riding frequency. The results from this study can guide the directions of future empirical studies that aim to quantify the inducers of MS in EVs and FVs, as well as the optimization of EVs to reduce MS.
Weak gravitational lensing is a powerful probe for constraining cosmological parameters, but its success relies on accurate shear measurements. In this paper, we use image simulations to investigate how a joint analysis of high-resolution space-based and deep ground-based imaging can improve shear estimation. We simulate two scenarios: a grid-based setup, where galaxies are placed on a regular grid to mimic an idealised, blending-free scenario, and a random setup, where galaxies are randomly distributed to capture the impact of blending. Comparing these cases, we find that blending introduces significant biases, particularly in LSST-like data due to its larger point spread function. This highlights the importance of including realistic blending effects when evaluating the performance of joint analyses. Using simulations that account for blending, we find that the most effective catalogue-level synergy is achieved by combining all galaxies detected in either survey. This approach yields an effective galaxy number density of $44.08~\rm arcmin^{-2}$ over the magnitude range of 20.0 to 27.5, compared to $39.17~\rm arcmin^{-2}$ for LSST-like data alone and $30.31~\rm arcmin^{-2}$ for \textit{Euclid}-like data alone. Restricting the analysis to only the overlapping sources detected in both surveys results in a gain of ${\sim}12\%$ compared to using either survey alone. While this joint-object approach is suboptimal at the catalogue level, it may become more effective in pixel-level analyses, where a joint fit to individual galaxy shapes can better leverage the complementary strengths of both data sets. Studying such pixel-level combinations, with realistic blending effects properly accounted for, remains a promising direction for future work.
Ultra-High-Temperature Vacuum Prober for Electrical and Thermal Measurements
2025-06-27
Laurent Jalabert, Jose Ordonez-Miranda, Yunhui Wu, Byunggi Kim, Roman Anufriev, Masahiro Nomura, Sebastian Volz
We develop an ultra-high-temperature vacuum probe station (UHT-VPS) featuring a sample holder heated by thermal radiation from a silicon carbide heater. This contactless configuration electrically isolates the sample from the high-power heating source through a vacuum gap, ensuring reliable measurements under extreme conditions. The capability of this UHT-VPS to measure electrical signals from 30 nV upward on bulk sapphire is demonstrated using the 3w/2w method. Measurements are continuously operated from 300 to 1150 K, under high vacuum, for a total of about 66 hours without readjusting the contact. They yield the linear and quadratic temperature coefficients of resistance of chromium/platinum micro-resistances, as well as the sapphire's thermal conductivity and thermal diffusivity. By recording the heater and sensor temperature signals up to 30 kHz and fitting them with theoretical models that account for the quadratic TCR of Cr/Pt microwires, we obtain values in agreement with literature data obtained by optical methods. In this temperature range, we also measure thermal conductivity, which cannot be directly accessed by optical techniques. Our system thus provides an effective solution for simultaneously retrieving the electrical and thermal properties of materials using a single set of 3w/2w data up to unprecedented temperature levels.
Kilonova constraints for the LIGO/Virgo/KAGRA neutron star merger candidate S250206dm: GW-MMADS observations
2025-06-27
Lei Hu, Tomás Cabrera, Antonella Palmese, James Freeburn, Mattia Bulla, Igor Andreoni, Xander J. Hall, Brendan O'Connor, Ariel Amsellem, Clécio R. Bom, Malte Busmann, Jennifer Fabà, Julius Gassert, Sena Kalabalik, Keerthi Kunnumkai, Daniel Gruen, Luidhy Santana-Silva, André Santos, Tomás Ahumada, Jonathan Carney, Michael W. Coughlin, Xingzhuo Chen, K. E. Saavik Ford, Daniel E. Holz, Mansi M. Kasliwal, Ignacio Magaña Hernandez, Cassidy Mihalenko, Rosalba Perna, Arno Riffeser, Christoph Ries, Lena Schnappinger, Michael Schmidt, Julian Sommer, Sarah Teague, Pablo Vega, Olga Volchansky, Lifan Wang, Yajie Zhang
Gravitational wave (GW) neutron star mergers with an associated electromagnetic counterpart constitute powerful probes of binary evolution, the production sites of heavy elements, general relativity, and the expansion of the universe. Only a handful of candidate GW binary mergers during the fourth LIGO/Virgo/KAGRA observing run (O4) so far are believed to include a neutron star. We present optical-near infrared follow-up observations of the candidate neutron-star black hole GW merger S250206dm. This is the first high-significance mass gap neutron star-black hole candidate observed by multiple GW detectors (thus having a significantly smaller sky localization than one-detector events), offering the first opportunity to effectively follow up a GW event of this kind. Our GW MultiMessenger Astronomy DECam Survey (GW-MMADS) campaign consisted of a wide-field search using the Dark Energy Camera (DECam) and T80-South (T80S), as well as galaxy-targeted observations using the Southern Astrophysical Research (SOAR) imager and the Wendelstein 2.1m 3-channel camera. No viable kilonova counterpart was found in our observations. We use our observation depths to place competitive constraints on kilonova models similar to or brighter than the GW170817 kilonova AT 2017gfo within our observed fields, ruling out 100\% of such models with SOAR galaxy-targeted observations and $\sim43$\% (48\%) with DECam (DECam and T80S).
Hierarchical Modeling and Architecture Optimization: Review and Unified Framework
2025-06-27
Paul Saves, Edward Hallé-Hannan, Jasper Bussemaker, Youssef Diouane, Nathalie Bartoli
Simulation-based problems involving mixed-variable inputs frequently feature domains that are hierarchical, conditional, heterogeneous, or tree-structured. These characteristics pose challenges for data representation, modeling, and optimization. This paper reviews extensive literature on these structured input spaces and proposes a unified framework that generalizes existing approaches. In this framework, input variables may be continuous, integer, or categorical. A variable is described as meta if its value governs the presence of other decreed variables, enabling the modeling of conditional and hierarchical structures. We further introduce the concept of partially-decreed variables, whose activation depends on contextual conditions. To capture these inter-variable hierarchical relationships, we introduce design space graphs, combining principles from feature modeling and graph theory. This allows the definition of general hierarchical domains suitable for describing complex system architectures. The framework supports the use of surrogate models over such domains and integrates hierarchical kernels and distances for efficient modeling and optimization. The proposed methods are implemented in the open-source Surrogate Modeling Toolbox (SMT 2.0), and their capabilities are demonstrated through applications in Bayesian optimization for complex system design, including a case study in green aircraft architecture.
Learning Individual Reproductive Behavior from Aggregate Fertility Rates via Neural Posterior Estimation
2025-06-27
Daniel Ciganda, Ignacio Campón, Iñaki Permanyer, Jakob H Macke
While age-specific fertility rates (ASFRs) provide the most extensive record of reproductive change, their aggregate nature masks the underlying behavioral mechanisms that ultimately drive fertility trends. To recover these mechanisms, we develop a likelihood-free Bayesian framework that couples an individual-level model of the reproductive process with Sequential Neural Posterior Estimation (SNPE). This allows us to infer eight behavioral and biological parameters from just two aggregate series: ASFRs and the age-profile of planned versus unplanned births. Applied to U.S. National Survey of Family Growth cohorts and to Demographic and Health Survey cohorts from Colombia, the Dominican Republic, and Peru, the method reproduces observed fertility schedules and, critically, predicts out-of-sample micro-level distributions of age at first sex, inter-birth intervals, and family-size ideals, none of which inform the estimation step. Because the fitted model yields complete synthetic life histories, it enables behaviorally explicit population forecasts and supports the construction of demographic digital twins.
Toward Sustainable Rare Earth Element Production: Key Challenges in Techno-Economic, Life Cycle, and Social Impact Assessment
2025-06-27
Adam Smerigan, Rui Shi
Rare earth elements (REEs) are 17 critical minerals used in many clean energy technologies like wind turbines and electric vehicles. Conventionally, we produce REEs from mining in few, geopolitically restricted regions. Developing systems that utilize new technologies and unconventional feedstocks provides an opportunity to meet increasing demand while improving sustainability. Techno-economic analysis (TEA), life cycle assessment (LCA), and social LCA (sLCA) are commonly used tools to assess the sustainability performance of these systems. However, analyses of REE systems encounter challenges including system scope, data availability, technology scale-up, and uncertainty. In the reviewed literature, systems served multiple functions beyond producing REEs, including circularizing production and waste remediation, leading to discrepancies in scope. Further, the instability of REE prices led to high uncertainty due to different revenue, costs, functional unit, and impact allocation. Therefore, these analyses leave decision makers with an incomplete understanding of the current landscape of REE production inhibiting intelligent and efficient identification of future direction. In this narrative review, we conducted a comprehensive overview of the literature, synthesized studies from each pillar of sustainability (economic, environmental, and social), highlighted the challenges and limitations in each field, and recommended direction for future work developing sustainable REE production systems.
This paper improves upon the Pix2Seq object detector by extending it for videos. In the process, it introduces a new way to perform end-to-end video object detection that improves upon existing video detectors in two key ways. First, by representing objects as variable-length sequences of discrete tokens, we can succinctly represent widely varying numbers of video objects, with diverse shapes and locations, without having to inject any localization cues in the training process. This eliminates the need to sample the space of all possible boxes that constrains conventional detectors and thus solves the dual problems of loss sparsity during training and heuristics-based postprocessing during inference. Second, it conceptualizes and outputs the video objects as fully integrated and indivisible 3D boxes or tracklets instead of generating image-specific 2D boxes and linking these boxes together to construct the video object, as done in most conventional detectors. This allows it to scale effortlessly with available computational resources by simply increasing the length of the video subsequence that the network takes as input, even generalizing to multi-object tracking if the subsequence can span the entire video. We compare our video detector with the baseline Pix2Seq static detector on several datasets and demonstrate consistent improvement, although with strong signs of being bottlenecked by our limited computational resources. We also compare it with several video detectors on UA-DETRAC to show that it is competitive with the current state of the art even with the computational bottleneck. We make our code and models publicly available.
Are Helium Atmospheres Hiding as Volatile-rich in Current Exoplanet Analysis Frameworks?
2025-06-27
Julien de Wit, Aaron Householder, Prajwal Niraula
The increasing number of detailed exoplanet observations offers an opportunity to refine our analyses and interpretations, and uncover previously-hidden trends. One such trend is unexpectedly high water abundances ($\sim$1$-$10\%) for several hot Jupiters. Here, we show that such atmospheres may instead be helium-rich, and the perceived high water abundances simply biases in analyses compensating for ``ad hocly'' fixed helium-to-hydrogen ratio (He/H$_2$ = 0.157). As transmission spectra constrain the atmospheric scale height ($H$), a helium-enriched atmosphere can be misinterpreted as H$_2$-dominated water-rich to bring the mean molecular weight ($\mu$) to intermediate values ($\sim$4$-$15) when He/H$_2$ is fixed. We present a proof-of-concept reanalysis of HD~209458~b's JWST transmission spectrum treating He/H$_2$ as a free parameter, resulting in sets of solutions with helium enrichment and non-extreme water abundance. The He-enhancement found requires an order of magnitude less enrichment/evolution than does a water abundance of 10\%, suggesting that the He-rich hypothesis is a viable alternative. We argue that He enhancement must be considered to reliably constrain atmospheric composition, be sensitive to a more diverse planetary population, and ultimately yield robust metallicity trends to inform formation/evolution pathways. Looking ahead, we suggest leveraging insights from differences in pressure-broadening effects, outflow measurements, and atmospheric chemistry to help disentangle reliably between He- and volatile-rich atmospheres -- while recognizing that existing opacity/outflow/chemistry models may need targeted upgrades to reach the required fidelity level.
Rashba spin-orbit coupling and artificially engineered topological superconductors
2025-06-26
Sankar Das Sarma, Katharina Laubscher, Haining Pan, Jay D. Sau, Tudor D. Stanescu
One of the most important physical effects in condensed matter physics is the Rashba spin-orbit coupling (RSOC), introduced in seminal works by Emmanuel Rashba. In this article, we discuss, describe, and review (providing critical perspectives on) the crucial role of RSOC in the currently active research area of topological quantum computation. Most, if not all, of the current experimental topological quantum computing platforms use the idea of Majorana zero modes as the qubit ingredient because of their non-Abelian anyonic property of having an intrinsic quantum degeneracy, which enables nonlocal encoding protected by a topological energy gap. It turns out that RSOC is a crucial ingredient in producing a low-dimensional topological superconductor in the laboratory, and such topological superconductors naturally have isolated localized midgap Majorana zero modes. In addition, increasing the RSOC strength enhances the topological gap, thus enhancing the topological immunity of the qubits to decoherence. Thus, Rashba's classic work on SOC may lead not only to the realization of localized non-Abelian anyons, but also fault tolerant quantum computation.
The Kaleidoscope Survey: Strong Gravitational Lensing in Galaxy Clusters with Radial Arcs
2025-06-26
Catherine Cerny, Mathilde Jauzac, David Lagattuta, Anna Niemiec, Guillaume Mahler, Alastair Edge, Richard Massey, Joseph Allingham
We measure the dark matter density profiles of six galaxy clusters: A383, MS 2137-23, MACS J0326.8-0043, MACS J1427.6-2521, MACS J0417.5-1154, and MACS J0949.8+1708. Each cluster contains at least one radial arc, a unique physical feature that allows for more precise measurements of the inner mass profile (R < 50 kpc) from strong lensing. We present the first strong lensing analysis for MACS J0326 and MACS J1427. We use a combination of HST imaging and VLT/MUSE observations from the ESO Kaleidoscope Clusters Survey, a large `filler' program, to identify and measure redshifts for multiply-imaged systems and obtain the 2-D stellar velocity dispersion for each centrally-located brightest cluster galaxy (BCG). The BCG kinematics are used to subtract the baryonic mass component from the inner mass profile. We find total mass density profiles consistent with previous works using a combination of strong lensing and BCG kinematics. The overall shape of these profiles appears core-like, with an average dark matter slope measurement of $\gamma$~0.66. These results demonstrate the ongoing need for the construction of observational models for galaxy clusters, and show how galaxy-scale kinematics can be used to disentangle baryonic and dark matter concentrations in cluster cores.
Quasar cosmology II: joint analyses with Cosmic Microwave Background
2025-06-26
M. Benetti, G. Bargiacchi, G. Risaliti, S. Capozziello, E. Lusso, M. Signorini
Currently, the increasing availability of accurate cosmological probes leads to the emergence of tensions between data on the one hand and between theoretical predictions and direct observations on the other. Moreover, after 25 years since the discovery of the accelerated expansion of the Universe has elected the $\Lambda$CDM model as the reference model, resolving shortcomings of the standard cosmological model seems to be an unpostponed priority. Hence, it is key to test alternative models and investigate new cosmological probes at distances that range from the late to the early Universe, namely between the cosmic microwave background (CMB) and type Ia supernovae and baryonic acoustic oscillations (BAO) data. Bargiacchi et al. (2022) for the first time analysed dark energy (DE) models using quasars (QSOs) while also testing their consistency with BAO. Here, we carry on by exploring the compatibility of QSOs with both CMB data and dark energy survey measurements against the standard cosmological model and some DE extensions, such as the $w$CDM and Chevallier-Polarski-Linder parameterisations. We also consider an interacting dark matter and vacuum energy scenario, where vacuum energy perturbations affect the evolution of the matter growth rate in a decomposed Chaplygin gas model. We implement the QSO probe in Cobaya Markov chain Monte Carlo algorithm, using Botzmann solver codes as Cosmic Linear Anisotropy Solving System (CLASS) for the theory predictions. Our work shows that simple DE deviations from $\Lambda$CDM model do not reconcile the data and that only more complex models of interaction in the dark sector can succeed in solving the discrepancies of probes at all scales.
Domain Knowledge-Enhanced LLMs for Fraud and Concept Drift Detection
2025-06-26
Ali Şenol, Garima Agrawal, Huan Liu
Detecting deceptive conversations on dynamic platforms is increasingly difficult due to evolving language patterns and Concept Drift (CD)-i.e., semantic or topical shifts that alter the context or intent of interactions over time. These shifts can obscure malicious intent or mimic normal dialogue, making accurate classification challenging. While Large Language Models (LLMs) show strong performance in natural language tasks, they often struggle with contextual ambiguity and hallucinations in risk-sensitive scenarios. To address these challenges, we present a Domain Knowledge (DK)-Enhanced LLM framework that integrates pretrained LLMs with structured, task-specific insights to perform fraud and concept drift detection. The proposed architecture consists of three main components: (1) a DK-LLM module to detect fake or deceptive conversations; (2) a drift detection unit (OCDD) to determine whether a semantic shift has occurred; and (3) a second DK-LLM module to classify the drift as either benign or fraudulent. We first validate the value of domain knowledge using a fake review dataset and then apply our full framework to SEConvo, a multiturn dialogue dataset that includes various types of fraud and spam attacks. Results show that our system detects fake conversations with high accuracy and effectively classifies the nature of drift. Guided by structured prompts, the LLaMA-based implementation achieves 98% classification accuracy. Comparative studies against zero-shot baselines demonstrate that incorporating domain knowledge and drift awareness significantly improves performance, interpretability, and robustness in high-stakes NLP applications.
Low-metallicity massive single stars with rotation. III. Source of ionization and C-IV emission in I Zw 18
2025-06-26
Dorottya Szécsi, Frank Tramper, Brankica Kubátová, Carolina Kehrig, Jiří Kubát, Jiří Krtička, Andreas A. C. Sander, Miriam Garcia
Chemically-homogeneously evolving stars have been proposed to account for several exotic phenomena, including gravitational-wave emissions and gamma-ray bursts. Here we study whether these stars can explain the metal-poor dwarf galaxy, I Zwicky 18. We apply our synthetic spectral models from Paper II to (i) establish a classification sequence for these hot stars, (ii) predict the photonionizing flux and the strength of emission lines from a IZw18-like stellar population, and (iii) compare our predictions to available observations of this galaxy. Adding two new models computed with PoWR, we report (i) these stars to follow a unique sequence of classes: O->WN->WO (i.e. without ever being WC). From our population synthesis with standard assumptions, we predict that (ii) the source of the UV C-IV and other emission bumps is a couple dozen WO-type Wolf-Rayet stars (not WC as previously assumed) which are the result of chem.-hom. evolution, while these, combined with the rest of the O-star population, account for the He-II ionizing flux and spectral hardness. Contrasting our results against published optical and UV data and accounting for different aperture sizes and spatial regions probed by the observations, we find that (iii) our models are highly consistent with them. Since our "massive Pop II stars" might just as well exist in early star-forming regions, our findings have implications for upcoming JWST surveys; and given that our results apply for binary populations too as long as the same fraction (10%) of the systems evolves chem. homogeneously, we conclude that the stellar progenitors of gravitational waves may very well exist today in IZw18.
Accelerating GNN Training through Locality-aware Dropout and Merge
2025-06-26
Gongjian Sun, Mingyu Yan, Dengke Han, Runzhen Xue, Duo Wang, Xiaochun Ye, Dongrui Fan
Graph Neural Networks (GNNs) have demonstrated significant success in graph learning and are widely adopted across various critical domains. However, the irregular connectivity between vertices leads to inefficient neighbor aggregation, resulting in substantial irregular and coarse-grained DRAM accesses. This lack of data locality presents significant challenges for execution platforms, ultimately degrading performance. While previous accelerator designs have leveraged on-chip memory and data access scheduling strategies to address this issue, they still inevitably access features at irregular addresses from DRAM. In this work, we propose LiGNN, a hardware-based solution that improves data locality by applying dropout and merge techniques during neighbor aggregation to accelerate GNN training. Unlike conventional algorithm-level dropout methods that primarily aim to improve accuracy while overlooking hardware costs, LiGNN introduces a locality-aware feature dropout mechanism. This approach selectively drops node features with data locality awareness, effectively reducing irregular DRAM accesses without compromising model accuracy. Moreover, by leveraging detailed knowledge of memory layout and organization-including critical alignment constraints-LiGNN strategically merges memory accesses during neighbor aggregation at the DRAM row level, guided by GNN-level semantics. This optimization significantly improves data locality with minimal additional cost. Under the commonly adopted 0.5 dropout rate, LiGNN outperforms state-of-the-art methods, delivering a 1.48~3.02x speedup, reducing DRAM accesses by 34%~55%, and lowering DRAM row activations by 59%~82%, all while maintaining model accuracy.
Sifting for a Stream: The Morphology of the $300S$ Stellar Stream
2025-06-26
Benjamin Cohen, Alexander P. Ji, Peter S. Ferguson, Sergey E. Koposov, Alex Drlica-Wagner, Andrew P. Li, Ting S. Li, Lara R. Cullinane, Gary S. Da Costa, Denis Erkal, Kyler Kuehn, Geraint F. Lewis, Sarah L. Martell, Andrew B. Pace, Daniel B. Zucker, Petra Awad, Joss Bland-Hawthorn, Guilherme Limberg, Clara E. Martínez-Vázquez, Joshua D. Simon, Yong Yang
Stellar streams are sensitive laboratories for understanding the small-scale structure in our Galaxy's gravitational field. Here, we analyze the morphology of the $300S$ stellar stream, which has an eccentric, retrograde orbit and thus could be an especially powerful probe of both baryonic and dark substructures within the Milky Way. Due to extensive background contamination from the Sagittarius stream (Sgr), we perform an analysis combining Dark Energy Camera Legacy Survey photometry, $\textit{Gaia}$ DR3 proper motions, and spectroscopy from the Southern Stellar Stream Spectroscopic Survey ($\textit{S}^5$). We redetermine the stream coordinate system and distance gradient, then apply two approaches to describe $300S$'s morphology. In the first, we analyze stars from $\textit{Gaia}$ using proper motions to remove Sgr. In the second, we generate a simultaneous model of $300S$ and Sgr based purely on photometric information. Both approaches agree within their respective domains and describe the stream over a region spanning $33^\circ$. Overall, $300S$ has three well-defined density peaks and smooth variations in stream width. Furthermore, $300S$ has a possible gap of $\sim 4.7^\circ$ and a kink. Dynamical modeling of the kink implies that $300S$ was dramatically influenced by the Large Magellanic Cloud. This is the first model of $300S$'s morphology across its entire known footprint, opening the door for deeper analysis to constrain the structures of the Milky Way.
Computational Design of Two-Dimensional MoSi$_2$N$_4$ Family Field-Effect Transistor for Future Ångström-Scale CMOS Technology Nodes
2025-06-26
Che Chen Tho, Zongmeng Yang, Shibo Fang, Shiying Guo, Liemao Cao, Chit Siong Lau, Fei Liu, Shengli Zhang, Jing Lu, L. K. Ang, Lain-Jong Li, Yee Sin Ang
Advancing complementary metal-oxide-semiconductor (CMOS) technology into the sub-1-nm angstr\"om-scale technology nodes is expected to involve alternative semiconductor channel materials, as silicon transistors encounter severe performance degradation at physical gate lengths below 10 nm. Two-dimensional (2D) semiconductors have emerged as strong candidates for overcoming short-channel effects due to their atomically thin bodies, which inherently suppress electrostatic leakage and improve gate control in aggressively scaled field-effect transistors (FETs). Among the growing library of 2D materials, the MoSi$_2$N$_4$ family -- a synthetic septuple-layered materials -- has attracted increasing attention for its remarkable ambient stability, suitable bandgaps, and favorable carrier transport characteristics, making it a promising platform for next-generation transistors. While experimental realization of sub-10-nm 2D FETs remains technologically demanding, computational device simulation using first-principles density functional theory combined with nonequilibrium Green's function transport simulations provide a powerful and cost-effective route for exploring the performance limits and optimal design of ultrascaled FET. This review consolidates the current progress in the computational design of MoSi$_2$N$_4$ family FETs. We review the physical properties of MoSi$_2$N$_4$ that makes them compelling candidates for transistor applications, as well as the simulated device performance and optimization strategy of MoSi$_2$N$_4$ family FETs. Finally, we identify key challenges and research gaps, and outline future directions that could accelerate the practical deployment of MoSi$_2$N$_4$ family FET in the angstr\"om-scale CMOS era.
2025-06-26
Saloni Singh, Koen Hindriks, Dirk Heylen, Kim Baraka
The co creativity community is making significant progress in developing more sophisticated and tailored systems to support and enhance human creativity. Design considerations from prior work can serve as a valuable and efficient foundation for future systems. To support this effort, we conducted a systematic literature review of 62 papers on co-creative systems. These papers cover a diverse range of applications, including visual arts, design, and writing, where the AI acts not just as a tool but as an active collaborator in the creative process. From this review, we identified several key dimensions relevant to system design: phase of the creative process, creative task, proactive behavior of the system, user control, system embodiment, and AI model type. Our findings suggest that systems offering high user control lead to greater satisfaction, trust, and a stronger sense of ownership over creative outcomes. Furthermore, proactive systems, when adaptive and context sensitive, can enhance collaboration. We also extracted 24 design considerations, highlighting the value of encouraging users to externalize their thoughts and of increasing the system's social presence and transparency to foster trust. Despite recent advancements, important gaps remain, such as limited support for early creative phases like problem clarification, and challenges related to user adaptation to AI systems.
Automatic Reviewers Assignment to a Research Paper Based on Allied References and Publications Weight
2025-06-26
Tamim Al Mahmud, B M Mainul Hossain, Dilshad Ara
Everyday, a vast stream of research documents is submitted to conferences, anthologies, journals, newsletters, annual reports, daily papers, and various periodicals. Many such publications use independent external specialists to review submissions. This process is called peer review, and the reviewers are called referees. However, it is not always possible to pick the best referee for reviewing. Moreover, new research fields are emerging in every sector, and the number of research papers is increasing dramatically. To review all these papers, every journal assigns a small team of referees who may not be experts in all areas. For example, a research paper in communication technology should be reviewed by an expert from the same field. Thus, efficiently selecting the best reviewer or referee for a research paper is a big challenge. In this research, we propose and implement program that uses a new strategy to automatically select the best reviewers for a research paper. Every research paper contains references at the end, usually from the same area. First, we collect the references and count authors who have at least one paper in the references. Then, we automatically browse the web to extract research topic keywords. Next, we search for top researchers in the specific topic and count their h-index, i10-index, and citations for the first n authors. Afterward, we rank the top n authors based on a score and automatically browse their homepages to retrieve email addresses. We also check their co-authors and colleagues online and discard them from the list. The remaining top n authors, generally professors, are likely the best referees for reviewing the research paper.
We present first-light deep H$\alpha$ imaging taken with the Nanshan 1-meter wide-field telescope on the local galaxy NGC 4258, alongside archival data from Hubble Space telescope (HST), Westerbork Synthesis Radio Telescope, and The Dark Energy Camera Legacy Survey. The H$\alpha$ image shows ongoing star formation not only inside the galaxy but also in an HI cloud in the eastern HI tail, which is roughly 16 kpc away from the main galaxy. The HST images reveal several ultra-blue compact objects ($\rm F555W - F814W <-0.5 mag,\, FWHM\sim 0.2''$) in the H$\alpha$ bright region, aligned with the HI tail, suggesting the presence of young open cluster candidate in the HI tail. Our results suggest that wide field H$\alpha$ imaging is a valuable tool for investigating recent star formation in the extended regions of NGC 4258. Furthermore, the star formation in diffuse HI tails could highlight an potential aspect of galaxy stellar halo formation, warranting further investigation of the impact of star formation in halos on galaxy evolution.
Cat and Mouse -- Can Fake Text Generation Outpace Detector Systems?
2025-06-26
Andrea McGlinchey, Peter J Barclay
Large language models can produce convincing "fake text" in domains such as academic writing, product reviews, and political news. Many approaches have been investigated for the detection of artificially generated text. While this may seem to presage an endless "arms race", we note that newer LLMs use ever more parameters, training data, and energy, while relatively simple classifiers demonstrate a good level of detection accuracy with modest resources. To approach the question of whether the models' ability to beat the detectors may therefore reach a plateau, we examine the ability of statistical classifiers to identify "fake text" in the style of classical detective fiction. Over a 0.5 version increase, we found that Gemini showed an increased ability to generate deceptive text, while GPT did not. This suggests that reliable detection of fake text may remain feasible even for ever-larger models, though new model architectures may improve their deceptiveness
Collecting data of students solving programming tasks is incredibly valuable for researchers and educators. It allows verifying that the students correctly apply the features and concepts they are taught, or finding students' misconceptions. However, existing data collection tools have limitations, e.g., no control over the granularity of the collected code, not collecting the specific events of the programming environment used, and overall being hard to configure. To overcome these limitations, we propose KOALA, a convenient and highly configurable tool for collecting code snapshots and feature usage from students solving programming tasks in JetBrains IDEs. The plugin can be installed in IDEs and configured to provide the students with the necessary tasks, enable or disable certain IDE features like code completion, and run surveys. During problem solving, the plugin collects code snapshots at the configured granularity, all IDE actions like running and debugging, as well as some data not collected in prior works, like employed hotkeys and switching focus between files. The collected data is sent to the server that comes with the tool, where it is stored and can be converted to the standardized ProgSnap2 format. To showcase the tool, we collected data from 28 students solving tasks in two courses within the IDE, highlighting some insights from this data.
Estimation of superconducting cavity bandwidth and detuning using a Luenberger observer
2025-06-26
Bozo Richter, Andrea Bellandi, Julien Branlard, Leon Speidel, Annika Eichler
Enabled by progress in superconducting technology, several continuous wave linear accelerators are foreseen in the next decade. For these machines, it is of crucial importance to track the main cavity parameters, such as the resonator bandwidth and detuning. The bandwidth yields information on the superconducting state of the cavity. The detuning should be minimized to limit the required power to operate the cavity. The estimation of these parameters is commonly implemented in the digital electronics of the Low-Level RF control system to minimize the computation delay. In this proceeding, we present a way to compute the bandwidth and detuning using a Luenberger observer. In contrast to previous methods, a state observer yields estimations at the native control system sample rate without explicitly filtering the input signals. Additionally, the error convergence properties of the estimations can be controlled intuitively by adjusting gain parameters. Implementation considerations and test results on the derived observer are presented in the manuscript.
Saturns, but not super-Jupiters, occur more frequently in the presence of inner super-Earths
2025-06-26
Etienne Lefevre-Forjan, Gijs D. Mulders
Studies from recent years have reached different conclusions regarding how frequently super-Earths are accompanied by long period giant planets and vice versa. This relation has been predicted to be mass dependent by planet formation models. We investigate that as the origin of the discrepancy using a radial velocity sample: the California Legacy Survey. We perform detection completeness corrections in order to discard detection bias as a possible explanation to our results. After bias corrections, we find that cold Jupiters are 5.65 times more massive when not in company of an inner super-Earth, while super-Earths are not significantly more massive while in company of an outer giant planet. We also report an occurrence enhancement for Saturns (median projected mass of 0.6MJ) while in presence of a super-Earth by a factor of 4, and for super-Earths in presence of Saturns by the same factor. This positive correlation disappears for super-Jupiters (median projected mass of 3.1MJ). These results show that while cold Jupiters are generally accompanied by inner super-Earths, this does not hold for the largest giant planets, such as those that will be discovered by Gaia, which will likely not be accompanied by transiting planets. The mass dependence, in combination with different detection limits of different surveys, may explain the discrepancies concerning occurrence relations between cold Jupiters and super-Earths.
Temporal evolution of quasi-periodic oscillations in an accreting black hole Swift J1727.8-1613: coevolution of the disk-corona during the state transition
2025-06-26
Sai-En Xu, Bei You, Yi Long, Han He
Low-frequency quasi-periodic oscillations (QPOs) are commonly observed in black hole X-ray binaries, and their frequency has been found to correlate with various energy spectral properties. In this work, we present a detailed timing analysis of Swift J1727.8-1613, revealing a novel two-branch correlation between the QPO frequency and the observed disk emission, which differs from previous findings of a single correlation. Specifically, at QPO frequencies below 3 Hz, the QPO frequency is negatively correlated with the observed disk emission. Such a negative relation transitions to a positive one, as the QPO frequency exceeds approximately 3 Hz. The correlation between QPO frequency and Compton flux exhibits an opposite trend, with a positive correlation at lower frequencies and a negative correlation at higher ones. We interpret these behaviors as signatures of an evolving disk-corona geometry, within the framework of a Lense-Thirring precessing hot flow. Additionally, we find that during the flare state, the QPO fractional root-mean-square (rms) remains nearly constant above 15 keV, but increases with energy below this threshold. The slope of the rms-energy relation becomes steeper as the energy spectrum softens.
ESMStereo: Enhanced ShuffleMixer Disparity Upsampling for Real-Time and Accurate Stereo Matching
2025-06-26
Mahmoud Tahmasebi, Saif Huq, Kevin Meehan, Marion McAfee
Stereo matching has become an increasingly important component of modern autonomous systems. Developing deep learning-based stereo matching models that deliver high accuracy while operating in real-time continues to be a major challenge in computer vision. In the domain of cost-volume-based stereo matching, accurate disparity estimation depends heavily on large-scale cost volumes. However, such large volumes store substantial redundant information and also require computationally intensive aggregation units for processing and regression, making real-time performance unattainable. Conversely, small-scale cost volumes followed by lightweight aggregation units provide a promising route for real-time performance, but lack sufficient information to ensure highly accurate disparity estimation. To address this challenge, we propose the Enhanced Shuffle Mixer (ESM) to mitigate information loss associated with small-scale cost volumes. ESM restores critical details by integrating primary features into the disparity upsampling unit. It quickly extracts features from the initial disparity estimation and fuses them with image features. These features are mixed by shuffling and layer splitting then refined through a compact feature-guided hourglass network to recover more detailed scene geometry. The ESM focuses on local contextual connectivity with a large receptive field and low computational cost, leading to the reconstruction of a highly accurate disparity map at real-time. The compact version of ESMStereo achieves an inference speed of 116 FPS on high-end GPUs and 91 FPS on the AGX Orin.
Leveraging Machine Learning for Accurate and Fast Stellar Mass Estimation of Galaxies
2025-06-26
Vahid Asadi, Akram Hasani Zonoozi, Hosein Haghi, Fatemeh Abedini, Atousa Kalantari, Marziye Jafariyazani, Nima Chartab
Unveiling the evolutionary history of galaxies necessitates a precise understanding of their physical properties. Traditionally, astronomers achieve this through spectral energy distribution (SED) fitting. However, this approach can be computationally intensive and time-consuming, particularly for large datasets. This study investigates the viability of machine learning (ML) algorithms as an alternative to traditional SED-fitting for estimating stellar masses in galaxies. We compare a diverse range of unsupervised and supervised learning approaches including prominent algorithms such as K-means, HDBSCAN, Parametric t-Distributed Stochastic Neighbor Embedding (Pt-SNE), Principal Component Analysis (PCA), Random Forest, and Self-Organizing Maps (SOM) against the well-established LePhare code, which performs SED-fitting as a benchmark. We train various ML algorithms using simple model SEDs in photometric space, generated with the BC03 code. These trained algorithms are then employed to estimate the stellar masses of galaxies within a subset of the COSMOS survey dataset. The performance of these ML methods is subsequently evaluated and compared with the results obtained from LePhare, focusing on both accuracy and execution time. Our evaluation reveals that ML algorithms can achieve comparable accuracy to LePhare while offering significant speed advantages (1,000 to 100,000 times faster). K-means and HDBSCAN emerge as top performers among our selected ML algorithms. Supervised learning algorithms like Random Forest and manifold learning techniques such as Pt-SNE and SOM also show promising results. These findings suggest that ML algorithms hold significant promise as a viable alternative to traditional SED-fitting methods for estimating the stellar masses of galaxies.
Control of Marine Robots in the Era of Data-Driven Intelligence
2025-06-26
Lin Hong, Lu Liu, Zhouhua Peng, Fumin Zhang
The control of marine robots has long relied on model-based methods grounded in classical and modern control theory. However, the nonlinearity and uncertainties inherent in robot dynamics, coupled with the complexity of marine environments, have revealed the limitations of conventional control methods. The rapid evolution of machine learning has opened new avenues for incorporating data-driven intelligence into control strategies, prompting a paradigm shift in the control of marine robots. This paper provides a review of recent progress in marine robot control through the lens of this emerging paradigm. The review covers both individual and cooperative marine robotic systems, highlighting notable achievements in data-driven control of marine robots and summarizing open-source resources that support the development and validation of advanced control methods. Finally, several future perspectives are outlined to guide research toward achieving high-level autonomy for marine robots in real-world applications. This paper aims to serve as a roadmap toward the next-generation control framework of marine robots in the era of data-driven intelligence.
Heisenberg's uncertainty principle, coherence and Bell nonlocality have been individually examined through many experiments. In this Letter, we systematically characterize all of this quantumness in a unified manner. We first construct universal uncertainty relations to reveal intrinsic features of incompatible measurements, which include all the state-independent uncertainties as special cases. We further extend to witness both quantum coherence and Bell nonlocality. We finally perform experiments with unified two-photon states, and validate the uncertainty principle, coherence and Bell nonlocality within the experimental error. Our methods for witnessing quantumness are valuable in characterizing quantum correlations in quantum information processing.
In real-world applications, users always interact with items in multiple aspects, such as through implicit binary feedback (e.g., clicks, dislikes, long views) and explicit feedback (e.g., comments, reviews). Modern recommendation systems (RecSys) learn user-item collaborative signals from these implicit feedback signals as a large-scale binary data-streaming, subsequently recommending other highly similar items based on users' personalized historical interactions. However, from this collaborative-connection perspective, the RecSys does not focus on the actual content of the items themselves but instead prioritizes higher-probability signals of behavioral co-occurrence among items. Consequently, under this binary learning paradigm, the RecSys struggles to understand why a user likes or dislikes certain items. To alleviate it, some works attempt to utilize the content-based reviews to capture the semantic knowledge to enhance recommender models. However, most of these methods focus on predicting the ratings of reviews, but do not provide a human-understandable explanation.
Signed Minkowski decompositions of convex polygons into minimum simplices and factorization of max-plus functions
2025-06-26
Soujun Kitagawa
Signed Minkowski decomposition is an expression of a polytope as a Minkowski sum and difference of smaller polytopes. Signed Minkowski decompositions of a polytope can be interpreted as factorizations of a max-plus (tropical) function. We review two relations about Minkowski decompositions, and we prove that any 2-dimensional integral polytopes (polygons) have a signed Minkowski decomposition which consists of integral points, integral line segments of length 1, and integral triangles of area 1/2. From this result, we also obtain that any max-plus functions with two variables and integer coefficients can be expressed as a set of specified form of simpler max-plus functions.
We carry out deep imaging of the Milky Way satellite galaxies, Bootes III and Draco, with WFST as one pilot observing program to demonstrate the capability of WFST. Combining catalogs with PS1 DR2 and Gaia DR3, we derive proper motions for candidate member stars in these two satellite galaxies over a 12-year time baseline, yielding uncertainties of ~1.8 mas/yr at 21 mag and ~3.0 mas/yr at 22 mag in the r band. The proper motions derived from bright and faint stars are consistent, indicating no significant variation in proper motion across stellar luminosity as these galaxies undergo tidal interactions with the MW. Meanwhile, we suggest that Bootes III represents the bound remnant of the progenitor galaxy that gave rise to the Styx stream, as evidenced by its elongated density profile and overdensity in both spatial and kinematic space. This is the first paper to use WFST to measure the proper motions of faint stars in Milky Way satellite galaxies. More detailed analyses will be presented in forthcoming papers from the wide field survey (WFS) program.
Large language models (LLMs) have gained significant traction across a wide range of fields in recent years. There is also a growing expectation for them to display human-like personalities during interactions. To meet this expectation, numerous studies have proposed methods for modelling LLM personalities through psychometric evaluations. However, most existing models face two major limitations: they rely on the Big Five (OCEAN) framework, which only provides coarse personality dimensions, and they lack mechanisms for controlling trait intensity. In this paper, we address this gap by extending the Machine Personality Inventory (MPI), which originally used the Big Five model, to incorporate the 16 Personality Factor (16PF) model, allowing expressive control over sixteen distinct traits. We also developed a structured framework known as Specific Attribute Control (SAC) for evaluating and dynamically inducing trait intensity in LLMs. Our method introduces adjective-based semantic anchoring to guide trait intensity expression and leverages behavioural questions across five intensity factors: \textit{Frequency}, \textit{Depth}, \textit{Threshold}, \textit{Effort}, and \textit{Willingness}. Through experimentation, we find that modelling intensity as a continuous spectrum yields substantially more consistent and controllable personality expression compared to binary trait toggling. Moreover, we observe that changes in target trait intensity systematically influence closely related traits in psychologically coherent directions, suggesting that LLMs internalize multi-dimensional personality structures rather than treating traits in isolation. Our work opens new pathways for controlled and nuanced human-machine interactions in domains such as healthcare, education, and interviewing processes, bringing us one step closer to truly human-like social machines.
Vision-language-action (VLA) models extend vision-language models (VLM) by integrating action generation modules for robotic manipulation. Leveraging strengths of VLM in vision perception and instruction understanding, VLA models exhibit promising generalization across diverse manipulation tasks. However, applications demanding high precision and accuracy reveal performance gaps without further adaptation. Evidence from multiple domains highlights the critical role of post-training to align foundational models with downstream applications, spurring extensive research on post-training VLA models. VLA model post-training aims to address the challenge of improving an embodiment's ability to interact with the environment for the given tasks, analogous to the process of humans motor skills acquisition. Accordingly, this paper reviews post-training strategies for VLA models through the lens of human motor learning, focusing on three dimensions: environments, embodiments, and tasks. A structured taxonomy is introduced aligned with human learning mechanisms: (1) enhancing environmental perception, (2) improving embodiment awareness, (3) deepening task comprehension, and (4) multi-component integration. Finally, key challenges and trends in post-training VLA models are identified, establishing a conceptual framework to guide future research. This work delivers both a comprehensive overview of current VLA model post-training methods from a human motor learning perspective and practical insights for VLA model development. (Project website: https://github.com/AoqunJin/Awesome-VLA-Post-Training)
Graph-based Retrieval-Augmented Generation (Graph-RAG) enhances large language models (LLMs) by structuring retrieval over an external corpus. However, existing approaches typically assume a static corpus, requiring expensive full-graph reconstruction whenever new documents arrive, limiting their scalability in dynamic, evolving environments. To address these limitations, we introduce EraRAG, a novel multi-layered Graph-RAG framework that supports efficient and scalable dynamic updates. Our method leverages hyperplane-based Locality-Sensitive Hashing (LSH) to partition and organize the original corpus into hierarchical graph structures, enabling efficient and localized insertions of new data without disrupting the existing topology. The design eliminates the need for retraining or costly recomputation while preserving high retrieval accuracy and low latency. Experiments on large-scale benchmarks demonstrate that EraRag achieves up to an order of magnitude reduction in update time and token consumption compared to existing Graph-RAG systems, while providing superior accuracy performance. This work offers a practical path forward for RAG systems that must operate over continually growing corpora, bridging the gap between retrieval efficiency and adaptability. Our code and data are available at https://github.com/EverM0re/EraRAG-Official.
Data Visualization for Improving Financial Literacy: A Systematic Review
2025-06-26
Meng Du, Robert Amor, Kwan-Liu Ma, Burkhard C. Wünsche
Financial literacy empowers individuals to make informed and effective financial decisions, improving their overall financial well-being and security. However, for many people understanding financial concepts can be daunting and only half of US adults are considered financially literate. Data visualization simplifies these concepts, making them accessible and engaging for learners of all ages. This systematic review analyzes 37 research papers exploring the use of data visualization and visual analytics in financial education and literacy enhancement. We classify these studies into five key areas: (1) the evolution of visualization use across time and space, (2) motivations for using visualization tools, (3) the financial topics addressed and instructional approaches used, (4) the types of tools and technologies applied, and (5) how the effectiveness of teaching interventions was evaluated. Furthermore, we identify research gaps and highlight opportunities for advancing financial literacy. Our findings offer practical insights for educators and professionals to effectively utilize or design visual tools for financial literacy.
Large Language Models (LLMs) have played a pivotal role in advancing Artificial Intelligence (AI). However, despite their achievements, LLMs often struggle to explain their decision-making processes, making them a 'black box' and presenting a substantial challenge to explainability. This lack of transparency poses a significant obstacle to the adoption of LLMs in high-stakes domain applications, where interpretability is particularly essential. To overcome these limitations, researchers have developed various explainable artificial intelligence (XAI) methods that provide human-interpretable explanations for LLMs. However, a systematic understanding of these methods remains limited. To address this gap, this survey provides a comprehensive review of explainability techniques by categorizing XAI methods based on the underlying transformer architectures of LLMs: encoder-only, decoder-only, and encoder-decoder models. Then these techniques are examined in terms of their evaluation for assessing explainability, and the survey further explores how these explanations are leveraged in practical applications. Finally, it discusses available resources, ongoing research challenges, and future directions, aiming to guide continued efforts toward developing transparent and responsible LLMs.
BASS. XLIV. Morphological preferences of local hard X-ray selected AGN
2025-06-26
Miguel Parra Tello, Franz E. Bauer, Demetra De Cicco, Goran Doll, Michael Koss, Ezequiel Treister, Carolina Finlez, Marco Troncoso, Connor Auge, I. del Moral-Castro, Aeeree Chung, Kriti K. Gupta, Jeein Kim, Kyuseok Oh, Claudio Ricci, Federica Ricci, Alejandra Rojas, Turgay Caglar, Fiona Harrison, Meredith C. Powell, Daniel Stern, Benny Trakhtenbrot, C. Megan Urry
We present morphological classifications for the hosts of 1189 hard X-ray selected (14-195 keV) active galactic nuclei (AGNs) from the Swift-BAT 105-month catalog as part of the BAT AGN Spectroscopic Survey (BASS). BASS provides a powerful all-sky census of nearby AGN, minimizing obscuration biases and providing a robust dataset for studying AGN-host galaxy connections. Classifications are based on volunteer-based visual inspection on the Zooniverse platform, adapted from Galaxy Zoo DECaLS (GZD). Dual-contrast grz color composite images, generated from public surveys (e.g., NOAO Legacy Survey, Pan-STARRS, SDSS) and dedicated observations enabled key morphological features to be identified. Our analysis reveals that, with respect to a control sample of inactive galaxies matched in redshift and i-band magnitude, BASS AGN hosts show a deficiency of smooth ellipticals (~70%) and disks with prominent arms (~80%), while displaying an excess of mergers or disturbed systems (~400%), and disk galaxies without a spiral structure (~300%). These trends suggest a preference for AGN activity in gas-rich, dynamically disturbed environments or transitional disk systems. We also find a higher bar fraction among AGN hosts than the control sample (~50% vs. ~30%). We further explore the relations between AGN properties (e.g., X-ray luminosity, black hole mass, and Eddington ratio) and host morphology, and find that high-luminosity and high-accretion AGN preferentially reside in smooth or point-like hosts. In parallel, lower-luminosity AGN are more common in disk galaxies. These results underscore the importance of morphological studies in understanding the fueling and feedback mechanisms that drive AGN activity and their role in galaxy evolution. Our dataset provides a valuable benchmark for future multiwavelength surveys (e.g. LSST, Roman, and Euclid) and automated morphological classification efforts.
Estimating Average Causal Effects with Incomplete Exposure and Confounders
2025-06-26
Lan Wen, Glen McGee
Standard methods for estimating average causal effects require complete observations of the exposure and confounders. In observational studies, however, missing data are ubiquitous. Motivated by a study on the effect of prescription opioids on mortality, we propose methods for estimating average causal effects when exposures and potential confounders may be missing. We consider missingness at random and additionally propose several specific missing not at random (MNAR) assumptions. Under our proposed MNAR assumptions, we show that the average causal effects are identified from the observed data and derive corresponding influence functions in a nonparametric model, which form the basis of our proposed estimators. Our simulations show that standard multiple imputation techniques paired with a complete data estimator is unbiased when data are missing at random (MAR) but can be biased otherwise. For each of the MNAR assumptions, we instead propose doubly robust targeted maximum likelihood estimators (TMLE), allowing misspecification of either (i) the outcome models or (ii) the exposure and missingness models. The proposed methods are suitable for any outcome types, and we apply them to a motivating study that examines the effect of prescription opioid usage on all-cause mortality using data from the National Health and Nutrition Examination Survey (NHANES).
Due to the COVID-19 pandemic, many professional entities shifted toward remote collaboration and video conferencing (VC) tools. Social virtual reality (VR) platforms present an alternative to VC for meetings and collaborative activities. Well-crafted social VR environments could enhance feelings of co-presence and togetherness at meetings, helping reduce the need for carbon-intensive travel to face-to-face meetings. This research contributes to creating meeting tools in VR by exploring the effects of avatar styles and virtual environments on groups creative performance using the Mozilla Hubs platform. We present the results of two sequential studies. Study One surveys avatar and environment preferences in various VR meeting contexts (N=87). Study Two applies these findings to the design of a between-subjects and within-subjects research where participants (N=40) perform creativity tasks in pairs as embodied avatars in different virtual settings using VR headsets. We discuss the design implications of avatar appearances and meeting settings on teamwork.
Efficient Estimation of Causal Effects Under Two-Phase Sampling with Error-Prone Outcome and Treatment Measurements
2025-06-26
Keith Barnatchez, Kevin P. Josey, Nima S. Hejazi, Bryan E. Shepherd, Giovanni Parmigiani, Rachel Nethery
Measurement error is a common challenge for causal inference studies using electronic health record (EHR) data, where clinical outcomes and treatments are frequently mismeasured. Researchers often address measurement error by conducting manual chart reviews to validate measurements in a subset of the full EHR data -- a form of two-phase sampling. To improve efficiency, phase-two samples are often collected in a biased manner dependent on the patients' initial, error-prone measurements. In this work, motivated by our aim of performing causal inference with error-prone outcome and treatment measurements under two-phase sampling, we develop solutions applicable to both this specific problem and the broader problem of causal inference with two-phase samples. For our specific measurement error problem, we construct two asymptotically equivalent doubly-robust estimators of the average treatment effect and demonstrate how these estimators arise from two previously disconnected approaches to constructing efficient estimators in general two-phase sampling settings. We document various sources of instability affecting estimators from each approach and propose modifications that can considerably improve finite sample performance in any two-phase sampling context. We demonstrate the utility of our proposed methods through simulation studies and an illustrative example assessing effects of antiretroviral therapy on occurrence of AIDS-defining events in patients with HIV from the Vanderbilt Comprehensive Care Clinic.
TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker
2025-06-26
Qi Li, Shaheer U. Saeed, Yuliang Huang, Mingyuan Luo, Zhongnuo Yan, Jiongquan Chen, Xin Yang, Dong Ni, Nektarios Winter, Phuc Nguyen, Lucas Steinberger, Caelan Haney, Yuan Zhao, Mingjie Jiang, Bowen Ren, SiYeoul Lee, Seonho Kim, MinKyung Seo, MinWoo Kim, Yimeng Dou, Zhiwei Zhang, Yin Li, Tomy Varghese, Dean C. Barratt, Matthew J. Clarkson, Tom Vercauteren, Yipeng Hu
Trackerless freehand ultrasound reconstruction aims to reconstruct 3D volumes from sequences of 2D ultrasound images without relying on external tracking systems, offering a low-cost, portable, and widely deployable alternative for volumetric imaging. However, it presents significant challenges, including accurate inter-frame motion estimation, minimisation of drift accumulation over long sequences, and generalisability across scanning protocols. The TUS-REC2024 Challenge was established to benchmark and accelerate progress in trackerless 3D ultrasound reconstruction by providing a publicly available dataset for the first time, along with a baseline model and evaluation framework. The Challenge attracted over 43 registered teams, of which 6 teams submitted 21 valid dockerized solutions. Submitted methods spanned a wide range of algorithmic approaches, including recurrent models, registration-driven volume refinement, attention, and physics-informed models. This paper presents an overview of the Challenge design, summarises the key characteristics of the dataset, provides a concise literature review, introduces the technical details of the underlying methodology working with tracked freehand ultrasound data, and offers a comparative analysis of submitted methods across multiple evaluation metrics. The results highlight both the progress and current limitations of state-of-the-art approaches in this domain, and inform directions for future research. The data, evaluation code, and baseline are publicly available to facilitate ongoing development and reproducibility. As a live and evolving benchmark, this Challenge is designed to be continuously developed and improved. The Challenge was held at MICCAI 2024 and will be organised again at MICCAI 2025, reflecting its growing impact and the sustained commitment to advancing this field.
Large Language Models (LLMs) are accelerating scientific idea generation, but rigorously evaluating these numerous, often superficial, AI-generated propositions for novelty and factual accuracy is a critical bottleneck; manual verification is too slow.Existing validation methods are inadequate: LLMs as standalone verifiers may hallucinate and lack domain knowledge (our findings show ~60\% unawareness of relevant papers in specific domains), while traditional citation networks lack explicit causality and narrative surveys are unstructured.This underscores a core challenge: the absence of structured, verifiable, and causally-linked historical data of scientific evolution.To address this,we introduce \textbf{THE-Tree} (\textbf{T}echnology \textbf{H}istory \textbf{E}volution Tree), a computational framework that constructs such domain-specific evolution trees from scientific literature.THE-Tree employs a search algorithm to explore evolutionary paths. During its node expansion, it utilizes a novel "Think-Verbalize-Cite-Verify" process: an LLM proposes potential advancements and cites supporting literature. Critically, each proposed evolutionary link is then validated for logical coherence and evidential support by a recovered natural language inference mechanism that interrogates the cited literature, ensuring that each step is grounded.We construct and validate 88 THE-Trees across diverse domains and release a benchmark dataset including up to 71k fact verifications covering 27k papers to foster further research.Experiments demonstrate that i) in graph completion, our THE-Tree improves hit@1 by 8\% to 14\% across multiple models compared to traditional citation networks; ii) for predicting future scientific developments, it improves hit@1 metric by nearly 10\%; and iii) when combined with other methods, it boosts the performance of evaluating important scientific papers by almost 100\%.
Constraining the Faint-End Slope of the FRB Energy Function Using CHIME/FRB Catalog-1 and Local Volume Galaxies
2025-06-26
Mohit Bhardwaj, Victoria M. Kaspi, K. W. Masui, B. M. Gaensler, Adaeze L. Ibik, Mawson W. Sammons
Despite hundreds of detected fast radio bursts (FRBs), the faint-end slope ($\gamma$) of their energy distribution remains poorly constrained, hindering understanding of whether bright, cosmological FRBs and faint, Galactic magnetar SGR 1935+2154-like bursts share a common origin. In this study, we constrain this faint-end slope, modeled with a Schechter-like distribution, by searching for potential associations between bursts from the CHIME/FRB Catalog-1 and galaxies in the local volume. We cross-matched Catalog-1 FRBs with 495 local volume galaxies within 21 Mpc, identified from the HECATE catalog, and found no associations. Assuming the FRB energy function extends to $\sim 3 \times 10^{34}$ erg-the energy of the Galactic magnetar burst from SGR 1935+2154-this null result constrains $\gamma$ to be $<$ 2.3 (95% confidence upper limit), representing the first empirical estimate for extragalactic FRBs at such low energies. This finding supports the hypothesis that the FRB population is dominated by bright, likely cosmological bursts with a relatively flat energy distribution ($\gamma < 2.5$). However, the constraint weakens if higher energy thresholds are assumed. A flatter energy function is consistent with the observed anti-correlation between FRB dispersion measure and fluence, as seen across various observational bands. While the contribution of low-energy bursts, such as those from the Galactic magnetar SGR 1935+2154, appears minimal, our results suggest that normal magnetars like SGR 1935+2154 could dominate the FRB population if their burst rates and energies scale with age and magnetic field. Upcoming CHIME/FRB Catalog-2 data and targeted nearby galaxy surveys will further refine these constraints, offering critical insight into whether FRBs arise from a single population or diverse origins.
Kinematic misalignment as a driver of black hole activity in galaxies with external interactions
2025-06-26
Sandra I. Raimundo, Rogerio Riffel, Song-lin Li, Cristina Ramos Almeida, Sandro Rembold, Rogemar A. Riffel, Thaisa Storchi-Bergmann, Marianne Vestergaard, José L. Tous
The process of Active Galactic Nuclei (AGN) fuelling relies on the transport of gas across several orders of magnitude in physical scale until the gas reaches the supermassive black hole at the centre of a galaxy. This work explores the role of kinematically misaligned gas in the fuelling of AGN in a sample of 4769 local galaxies from the MaNGA survey. We investigate for the first time the relative role of external interactions and the presence of kinematic misalignment as mechanisms to explain the observed increase in AGN fraction in galaxies with large stellar to gas kinematic misalignment ($>$45 degrees). Using a sample of galaxies with evidence of recent external interactions we find that there is a significantly higher fraction of AGN in those where a large stellar to gas kinematic misalignment is observed (20$^{+6}_{-4}\%$) compared with 6.2$^{+0.6}_{-0.5}\%$ in galaxies where no kinematic misalignment is observed. We determine that gas to stellar misalignment has an important role in the fraction of AGN observed, increasing the AGN fraction beyond the potential effect of external interactions. This result demonstrates the importance of misaligned structures to the fuelling of supermassive black holes.
With the non-metricity scalar $Q$ as the functional argument, several $f(Q)$ gravity models is found to be proposed which are perfectly able to mimic the late time accelerated expansion as pointed out by the type Ia supernovae observations. Temperature fluctuation differences for two celestial hemispheres, Hubble tension, voids, dipole modulation, anisotropic inflation etc motivates us to think beyond the $\Lambda$CDM model and cosmological principle. Bianchi-I model portrays anisotropic universe imposing shear. $f(Q)$ model also enables us to produce early inflation to late deSitter universe without the requirement of $\Lambda$CDM. Embarrassment regarding fine tuning or coincidences can be avoided alongwith. So, this article finds different stationary points of cosmic evolution with $f(Q)$ models habilitating in Bianchi-I anisotropic universe. Depending on models' nature, fixed points with different categories are found. Perturbations are followed wherever are applicable. While pursuing cosmological implications towards these fixed points, some are found to be formed only for the consideration of $f(Q)$ gravity and Bianchi-I both. Besides different prediction towards early inflation to late time expansion which are available in existing literature of dynamical system studies, occurances of ultra slow roll inflation is predicted. For particular $f(Q)$ model, shear is predicted to decay leaving behind a constant valued residue. This models a universe that gradually turns more homogeneous. In some other models, depending on initial conditions, a final isotropic leftover is marked as the future fate of anisotropic world. More than one stable points are marked for special cases and are cosmologically interpreted.
FOCUS: Internal MLLM Representations for Efficient Fine-Grained Visual Question Answering
2025-06-26
Liangyu Zhong, Fabio Rosenthal, Joachim Sicking, Fabian Hüger, Thorsten Bagdonat, Hanno Gottschalk, Leo Schwinn
While Multimodal Large Language Models (MLLMs) offer strong perception and reasoning capabilities for image-text input, Visual Question Answering (VQA) focusing on small image details still remains a challenge. Although visual cropping techniques seem promising, recent approaches have several limitations: the need for task-specific fine-tuning, low efficiency due to uninformed exhaustive search, or incompatibility with efficient attention implementations. We address these shortcomings by proposing a training-free visual cropping method, dubbed FOCUS, that leverages MLLM-internal representations to guide the search for the most relevant image region. This is accomplished in four steps: first, we identify the target object(s) in the VQA prompt; second, we compute an object relevance map using the key-value (KV) cache; third, we propose and rank relevant image regions based on the map; and finally, we perform the fine-grained VQA task using the top-ranked region. As a result of this informed search strategy, FOCUS achieves strong performance across four fine-grained VQA datasets and two types of MLLMs. It outperforms three popular visual cropping methods in both accuracy and efficiency, and matches the best-performing baseline, ZoomEye, while requiring 3 - 6.5 x less compute.
AGN with massive black holes have closer galactic neighbors: k-Nearest-Neighbor statistics of an unbiased sample of AGN at z~0.03
2025-06-26
A. Mhatre, M. C. Powell, S. Yuan, S. W. Allen, T. Caglar, M. Koss, I. del Moral-Castro, K. Oh, A. Peca, C. Ricci, F. Ricci, A. Rojas, M. Signorini
The large-scale environments of active galactic nuclei (AGN) reveal important information on the growth and evolution of supermassive black holes (SMBHs). Previous AGN clustering measurements using 2-point correlation functions have hinted that AGN with massive black holes preferentially reside in denser cosmic regions than AGN with less-massive SMBHs. At the same time, little to no dependence on the accretion rate is found. However, the significance of such trends have been limited. Here we present kth-nearest-neighbor (kNN) statistics of 2MASS galaxies around AGN from the Swift/BAT AGN Spectroscopic survey. These statistics have been shown to contribute additional higher-order clustering information on the cosmic density field. By calculating the distances to the nearest 7 galaxy neighbors in angular separation to each AGN within two redshift ranges(0.01 < z < 0.03 and 0.03 < z < 0.06), we compare their cumulative distribution functions to that of a randomly distributed sample to show the sensitivity of this method to the clustering of AGN. We also split the AGN into bins of bolometric luminosity, black hole mass, and Eddington ratio (while controlling for redshift) to search for trends between kNN statistics and fundamental AGN properties. We find that AGN with massive SMBHs have significantly closer neighbors than AGN with less-massive SMBHs (at the 99.98% confidence level), especially in our lower redshift range. We find less significant trends with luminosity or Eddington ratio. By comparing our results to empirical SMBH-galaxy-halo models implemented in N-body simulations, we show that small-scale kNN trends with black hole mass may go beyond stellar mass dependencies. This suggests that massive SMBHs in the local universe reside in more massive dark matter halos and denser regions of the cosmic web, which may indicate that environment is important for the growth of SMBHs.
The DevSafeOps Dilemma: A Systematic Literature Review on Rapidity in Safe Autonomous Driving Development and Operation
2025-06-26
Ali Nouri, Beatriz Cabrero-Daniel, Fredrik Törner, Christian Berger
Developing autonomous driving (AD) systems is challenging due to the complexity of the systems and the need to assure their safe and reliable operation. The widely adopted approach of DevOps seems promising to support the continuous technological progress in AI and the demand for fast reaction to incidents, which necessitate continuous development, deployment, and monitoring. We present a systematic literature review meant to identify, analyse, and synthesise a broad range of existing literature related to usage of DevOps in autonomous driving development. Our results provide a structured overview of challenges and solutions, arising from applying DevOps to safety-related AI-enabled functions. Our results indicate that there are still several open topics to be addressed to enable safe DevOps for the development of safe AD.
CANUCS/Technicolor Data Release 1: Imaging, Photometry, Slit Spectroscopy, and Stellar Population Parameters
2025-06-26
Ghassan T. E. Sarrouh, Yoshihisa Asada, Nicholas S. Martis, Chris J. Willott, Kartheik G. Iyer, Gaël Noirot, Adam Muzzin, Marcin Sawicki, Gabriel Brammer, Guillaume Desprez, Gregor Rihtaršič, Johannes Zabl, Roberto Abraham, Maruša Bradač, René Doyon, Jacqueline Antwi-Danso, Samantha Berek, Westley Brown, Vince Estrada-Carpenter, Jeremy Favaro, Giordano Felicioni, Ben Forrest, Gaia Gaspar, Katriona M. L. Gould, Rachel Gledhill, Anishya Harshan, Nusrath Jahan, Naadiyah Jagga, Jon Judež, Danilo Marchesini, Vladan Markov, Jasleen Matharu, Shannon MacFarland, Maya Merchant, Rosa M. Mérida, Lamiya Mowla, Katherine Myers, Kiyoaki C. Omori, Camilla Pacifici, Swara Ravindranath, Luke Robbins, Victoria Strait, Visal Sok, Vivian Yun Yan Tan, Roberta Tripodi, Gillian Wilson, Sunna Withers
We present the first data release of the CAnadian NIRISS Unbiased Cluster Survey (CANUCS), a JWST Cycle 1 GTO program targeting 5 lensing clusters and flanking fields in parallel (Abell 370, MACS0416, MACS0417, MACS1149, MACS1423; survey area \tilda100 arcmin$^{2}$), with NIRCam imaging, NIRISS slitless spectroscopy, and NIRSpec prism multi-object spectroscopy. Fields centered on cluster cores include imaging in 8 bands from 0.9-4.4$\mu$m, alongside continuous NIRISS coverage from 1.15-2$\mu$m, while the NIRCam flanking fields provide 5 wide and 9 medium band filters for exceptional spectral sampling, all to \tilda29 mag$_{AB}$. We also present JWST in Technicolor, a Cycle 2 follow-up GO program targeting 3 CANUCS clusters (Abell 370, MACS0416, MACS1149). The Technicolor program adds NIRISS slitless spectroscopy in F090W to the cluster fields while adding 8 wide, medium, and narrow band filters to the flanking fields. This provides NIRCam imaging in all wide and medium band filters over \tilda30 arcmin$^{2}$. This paper describes our data reduction and photometry methodology. We release NIRCam, NIRISS, and HST imaging, PSFs, PSF-matched imaging, photometric catalogs, and photometric and spectroscopic redshifts. We provide lens models and stellar population parameters in up to 19 filters for \tilda53,000 galaxies in the cluster fields, and \tilda44,000 galaxies in up to 29 filters in the flanking fields. We further present 733 NIRSpec spectra and redshift measurements up to $z=10.8$. Comparing against our photometric redshifts, we find catastrophic outlier rates of only 4-7\% and scatter of $\sigma_{\rm NMAD}$ of 0.01-0.03.
KSZ Velocity Reconstruction with ACT and DESI-LS using a Tomographic QML Power Spectrum Estimator
2025-06-26
Anderson C. M. Lai, Yurii Kvasiuk, Moritz Münchmeyer
We perform kinetic Sunyaev-Zel'dovich (kSZ) velocity reconstruction on data from ACT DR6 and DESI-LS DR9. To estimate the cross-power between kSZ velocity reconstruction and galaxy density, we make use of a novel quadratic maximum likelihood QML power spectrum estimator implementation in red-shift binned spherical coordinates. We find a detection of the kSZ signal from the cross-correlation between the estimated velocity field and the large-scale galaxy field of $11.7 \sigma$. We estimate an amplitude $A=0.39 \pm 0.04$ of the kSZ signal with respect to a halo model prediction, possibly indicating a high feedback in massive halos, in agreement with previous studies. Our result demonstrates the feasibility of an optimal QML pipeline at the resolution required for this analysis, and will be a powerful tool for kSZ cosmology with upcoming high-resolution surveys.
2025-06-26
Xihong Su, Jia Lin Hau, Gersi Doko, Kishan Panaganti, Marek Petrik
Risk-averse total-reward Markov Decision Processes (MDPs) offer a promising framework for modeling and solving undiscounted infinite-horizon objectives. Existing model-based algorithms for risk measures like the entropic risk measure (ERM) and entropic value-at-risk (EVaR) are effective in small problems, but require full access to transition probabilities. We propose a Q-learning algorithm to compute the optimal stationary policy for total-reward ERM and EVaR objectives with strong convergence and performance guarantees. The algorithm and its optimality are made possible by ERM's dynamic consistency and elicitability. Our numerical results on tabular domains demonstrate quick and reliable convergence of the proposed Q-learning algorithm to the optimal risk-averse value function.
Lensing Without Borders: Measurements of galaxy-galaxy lensing and projected galaxy clustering in DESI DR1
2025-06-26
S. Heydenreich, A. Leauthaud, C. Blake, Z. Sun, J. U. Lange, T. Zhang, M. DeMartino, A. J. Ross, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, F. J. Castander, T. Claybaugh, A. Cuceu, A. de la Macorra, J. DeRose, Arjun Dey, Biprateep Dey, P. Doel, N. Emas, S. Ferraro, A. Font-Ribera, J. E. Forero-Romero, C. Garcia-Quintero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, B. Hadzhiyska, K. Honscheid, D. Huterer, M. Ishak, N. Jeffrey, S. Joudaki, E. Jullo, S. Juneau, D. Kirkby, T. Kisner, A. Kremin, A. Krolewski, O. Lahav, C. Lamman, M. Landriau, L. Le Guillou, M. Manera, A. Meisner, R. Miquel, S. Nadathur, N. Palanque-Delabrouille, W. J. Percival, A. Porredon, F. Prada, I. Pérez-Ràfols, G. Rossi, R. Ruggeri, E. Sanchez, C. Saulder, D. Schlegel, A. Semenaite, J. Silber, D. Sprayberry, G. Tarlé, B. A. Weaver, S. Yuan, P. Zarrouk, R. Zhou, H. Zou
We present Galaxy-Galaxy Lensing measurements obtained by cross-correlating spectroscopically observed galaxies from the first data release of the Dark Energy Spectroscopic Instrument (DESI) with source galaxies from the Hyper Suprime-Cam Subaru Strategic Survey, the Kilo-Degree Survey, the Sloan Digital Sky Survey, and the Dark Energy Survey. Specifically, we measure the excess surface mass density $\Delta\Sigma$ and tangential shear $\gamma_\mathrm{t}$ for the Bright Galaxy Sample and Luminous Red Galaxies measured within the first year of observations with DESI. To ensure robustness, we test the measurements for systematic biases, finding no significant trends related to the properties of the \acrshort{desi} lens galaxies. We identify a significant trend with the average redshift of source galaxies, however, this trend vanishes once we apply shifts to the Hyper Suprime-Cam Subaru Strategic Survey redshift distributions that are also favored by their fiducial cosmology analysis. Additionally, we compare the observed scatter in the measurements with the theoretical covariance and find excess scatter, driven primarily by small-scale measurements of $r\leq 1 \, \mathrm{Mpc}/h$; measurements on larger scales are consistent at the $2\,\sigma$ level. We further present the projected clustering measurements $w_p$ of the galaxy samples in the the first data release of DESI. These measurements, which will be made publicly available, serve as a foundation for forthcoming cosmological analyses.
A joint explanation for the soft lepton and monojet LHC excesses in the wino-bino model
2025-06-26
Diyar Agin, Benjamin Fuks, Mark D. Goodsell, Taylor Murphy
We present new recasts of the CMS Run 2 soft-leptons + missing energy analysis and the ATLAS Run 2 multijet + missing energy analysis. These analyses are relevant for probing the parameter space of electroweak-charged particles with compressed spectra. We review these analyses and detail their implementation and validation in HackAnalysis (for CMS) and MadAnalysis 5 (ATLAS). We then use these new recasts to combine four LHC analyses to identify a region of parameter space of the "wino-bino" simplified model, which corresponds to a limit of the Minimal Supersymmetric Standard Model in which higgsinos are decoupled, that is preferred over the Standard Model by excesses in the data. We find that the favoured region is compatible with the observed dark matter relic density, assuming freeze out within a standard cosmology, and we comment on the importance of this result and on how the simplified model should be mapped onto a complete supersymmetric model.
REBELS-MOSFIRE: Weak CIII] Emission is Typical Among Extremely UV-bright, Massive Galaxies at $z\sim7$
2025-06-26
Ryan Endsley, Alice E. Shapley, Michael W. Topping, Daniel P. Stark, Rychard J. Bouwens, Lucie E. Rowland, Laura Sommovigo, Hiddo S. B. Algera, Manuel Aravena, Rebecca A. A. Bowler, Elisabete da Cunha, Ilse de Looze, Andrea Ferrara, Rebecca Fisher, Valentino González, Hanae Inami, Themiya Nanayakkara, Sander Schouws, Mengtao Tang
We present Keck/MOSFIRE H-band spectroscopic measurements covering the [CIII]1907, CIII]1909 doublet for a sample of 8 z~7 spectroscopically-confirmed star-forming galaxies drawn from the Reionization Era Bright Emission Line Survey (REBELS). This sample is notable for its bright median UV luminosity (Muv=-22.5 AB) and large median stellar mass (log(Mstar/Msun)=9.2). Although three sources show tentative evidence of a CIII] detection, we obtain no confident detections for any of the 8 REBELS sources. The median [CIII]1907+CIII]1909 3-sigma upper limit in equivalent width (EW) for the REBELS-MOSFIRE sample is 6.5 AA, and a stack of their H-band MOSFIRE spectra yields a non-detection with an associated 3-sigma upper limit of 2.6 AA. These upper limits fall significantly below the CIII] EW measured in a composite spectrum of representative z~7 star-forming galaxies, as well as those measured for notable early star-forming galaxies such as GN-z11, GHZ2, GS-z12, and RXCJ2248-ID. The lack of strong CIII] emission can be understood within the context of the stellar populations of the REBELS galaxies, as well as the ionization conditions and gas-phase metallicity implied by rest-frame optical spectroscopic properties ([OIII]+Hb EWs, and [OIII]5007/[OII]3727 and [NeIII]3869/[OII]3727 line ratios). The REBELS-MOSFIRE sample represents the higher-mass, higher-metallicity, lower-excitation tail of the z~7 galaxy population, whose ionizing properties must be fully characterized to constrain the role of star-forming galaxies during cosmic reionization.
Velocity Reconstruction from KSZ: Measuring $f_{NL}$ with ACT and DESILS
2025-06-26
Selim C. Hotinli, Kendrick M. Smith, Simone Ferraro
The kinetic Sunyaev-Zel'dovich (kSZ) effect offers an indirect way to reconstruct large-scale cosmic velocities, by correlating high-resolution CMB temperature maps with galaxy surveys. In this work, we present the first three-dimensional reconstruction of the large-scale velocity field using a photometric galaxy survey, using data from the DESI Legacy Imaging Surveys (DESILS) and the Atacama Cosmology Telescope (ACT) DR5. We detect an $11.7\sigma$ correlation between our velocity reconstruction and the galaxy field, using only DESILS LRGs in the northern Galactic hemisphere. We find that the overall amplitude of the kSZ-induced correlation is low relative to a halo model prediction ($b_v = 0.45^{+0.06}_{-0.05}$), in agreement with previous results which find high feedback and smoothed gas profiles near massive galaxies. We use this measurement to place new constraints on local-type primordial non-Gaussianity (PNG), obtaining $f_{\rm NL}\!=\!-39^{+40}_{-33}$. This represents the most stringent $f_{\rm NL}$ constraint from kSZ velocity-based analyses to date. We validate our findings through extensive null tests, including tests for CMB foregrounds based on comparing 90 and 150 GHz CMB data.
Synergies between Euclid, Roman and JWST Could Reveal Quasars at up to $z \sim$ 15
2025-06-26
Muhammad A. Latif, Daniel J. Whalen
Although supermassive black holes (SMBHs) are found at the centers of most galaxies today, over 300 have now been discovered at $z >$ 6, including a $4 \times 10^7$ M$_{\odot}$ BH in UHZ1 at $z = 10.1$ and an $8\times10^7$ M$_{\odot}$ BH in GHZ9 at $z =$ 10.4. They are thought to form when 10$^4$ - 10$^5$ M$_{\odot}$ primordial stars die as direct-collapse black holes (DCBHs) at $z \sim$ 20 - 25. While studies have shown that DCBHs should be visible at birth at $z \gtrsim$ 20 in the near infrared (NIR) to the James Webb Space Telescope (JWST), none have considered SMBH detections at later stages growth down to $z \sim$ 6 - 7. Here, we present continuum NIR luminosities for a quasar like ULAS J1120+0641, a $1.35 \times 10^9$ M$_{\odot}$ BH at $z =$ 7.1, from a cosmological simulation for Euclid, Roman Space Telescope (RST) and JWST bands from $z =$ 6 - 15. We find that Euclid and RST could detect such BHs, including others like UHZ1 and GHZ9, at much earlier stages of evolution, out to $z \sim$ 14 - 15, and that their redshifts could be confirmed spectroscopically with JWST. Synergies between these three telescopes could thus reveal the numbers of quasars at much higher redshifts and discriminate between their formation channels because Euclid and RST can capture large numbers of them in wide-field surveys for further study by JWST.
A Review on Improving PSC Performance through Charge Carrier Management: Where We Stand and What's Next?
2025-06-26
Dilshod Nematov
Perovskite solar cells (PSCs) represent a breakthrough in photovoltaic technology, combining high power conversion efficiencies (PCEs), ease of fabrication, and tunable optoelectronic properties. However, their commercial viability is limited by critical issues such as charge carrier recombination, interfacial defects, instability under environmental stress, and toxicity of lead-based components. This review systematically examines recent advancements in charge carrier management strategies aimed at overcoming these limitations. Initially, fundamental mechanisms governing carrier generation, separation, transport, and recombination are outlined to provide a clear foundation. The study then delves into an in-depth analysis of carrier lifetime and mobility, evaluating recent methodologies for their enhancement through compositional engineering and structural optimization. Subsequently, trap state passivation techniques and interface engineering approaches are reviewed, with a particular focus on their impact on device stability and efficiency. The review also discusses long-term stability strategies and emerging trends in lead-free and scalable PSC technologies. In this work, recent strategies for charge carrier management are systematically categorized, comparative analyses are provided and synergistic solutions with high potential for real-world implementation are highlighted. By synthesizing data and perspectives from over thirty recent studies, this article offers a comprehensive roadmap for researchers seeking to optimize PSC performance and accelerate their transition toward commercial application.
Model extraction attacks pose significant security threats to deployed language models, potentially compromising intellectual property and user privacy. This survey provides a comprehensive taxonomy of LLM-specific extraction attacks and defenses, categorizing attacks into functionality extraction, training data extraction, and prompt-targeted attacks. We analyze various attack methodologies including API-based knowledge distillation, direct querying, parameter recovery, and prompt stealing techniques that exploit transformer architectures. We then examine defense mechanisms organized into model protection, data privacy protection, and prompt-targeted strategies, evaluating their effectiveness across different deployment scenarios. We propose specialized metrics for evaluating both attack effectiveness and defense performance, addressing the specific challenges of generative language models. Through our analysis, we identify critical limitations in current approaches and propose promising research directions, including integrated attack methodologies and adaptive defense mechanisms that balance security with model utility. This work serves NLP researchers, ML engineers, and security professionals seeking to protect language models in production environments.
Automated Defect Identification and Categorization in NDE 4.0 with the Application of Artificial Intelligence
2025-06-26
Aditya Sharma
This investigation attempts to create an automated framework for fault detection and organization for usage in contemporary radiography, as per NDE 4.0. The review's goals are to address the lack of information that is sufficiently explained, learn how to make the most of virtual defect increase, and determine whether the framework is viable by using NDE measurements. As its basic information source, the technique consists of compiling and categorizing 223 CR photographs of airplane welds. Information expansion systems, such as virtual defect increase and standard increase, are used to work on the preparation dataset. A modified U-net model is prepared using the improved data to produce semantic fault division veils. To assess the effectiveness of the model, NDE boundaries such as Case, estimating exactness, and misleading call rate are used. Tiny a90/95 characteristics, which provide strong differentiating evidence of flaws, reveal that the suggested approach achieves exceptional awareness in defect detection. Considering a 90/95, size error, and fake call rate in the weld area, the consolidated expansion approach clearly wins. Due to the framework's fast derivation speed, large images can be broken down efficiently and quickly. Professional controllers evaluate the transmitted system in the field and believe that it has a guarantee as a support device in the testing cycle, irrespective of particular equipment cut-off points and programming resemblance.
In today's digital world, casual user-generated content often contains subtle cues that may inadvertently expose sensitive personal attributes. Such risks underscore the growing importance of effective text anonymization to safeguard individual privacy. However, existing methods either rely on rigid replacements that damage utility or cloud-based LLMs that are costly and pose privacy risks. To address these issues, we explore the use of locally deployed smaller-scale language models (SLMs) for anonymization. Yet training effective SLMs remains challenging due to limited high-quality supervision. To address the challenge, we propose AgentStealth, a self-reinforcing LLM anonymization framework.First, we introduce an adversarial anonymization workflow enhanced by In-context Contrastive Learning and Adaptive Utility-Aware Control. Second, we perform supervised adaptation of SLMs using high-quality data collected from the workflow, which includes both anonymization and attack signals. Finally, we apply online reinforcement learning where the model leverages its internal adversarial feedback to iteratively improve anonymization performance. Experiments on two datasets show that our method outperforms baselines in both anonymization effectiveness (+12.3%) and utility (+6.8%). Our lightweight design supports direct deployment on edge devices, avoiding cloud reliance and communication-based privacy risks. Our code is open-source at https://github.com/tsinghua-fib-lab/AgentStealth.
DemoDiffusion: One-Shot Human Imitation using pre-trained Diffusion Policy
2025-06-25
Sungjae Park, Homanga Bharadhwaj, Shubham Tulsiani
We propose DemoDiffusion, a simple and scalable method for enabling robots to perform manipulation tasks in natural environments by imitating a single human demonstration. Our approach is based on two key insights. First, the hand motion in a human demonstration provides a useful prior for the robot's end-effector trajectory, which we can convert into a rough open-loop robot motion trajectory via kinematic retargeting. Second, while this retargeted motion captures the overall structure of the task, it may not align well with plausible robot actions in-context. To address this, we leverage a pre-trained generalist diffusion policy to modify the trajectory, ensuring it both follows the human motion and remains within the distribution of plausible robot actions. Our approach avoids the need for online reinforcement learning or paired human-robot data, enabling robust adaptation to new tasks and scenes with minimal manual effort. Experiments in both simulation and real-world settings show that DemoDiffusion outperforms both the base policy and the retargeted trajectory, enabling the robot to succeed even on tasks where the pre-trained generalist policy fails entirely. Project page: https://demodiffusion.github.io/
An iterative CMB lensing estimator minimizing instrumental noise bias
2025-06-25
Louis Legrand, Blake Sherwin, Anthony Challinor, Julien Carron, Gerrit S. Farren
Noise maps from CMB experiments are generally statistically anisotropic, due to scanning strategies, atmospheric conditions, or instrumental effects. Any mis-modeling of this complex noise can bias the reconstruction of the lensing potential and the measurement of the lensing power spectrum from the observed CMB maps. We introduce a new CMB lensing estimator based on the maximum a posteriori (MAP) reconstruction that is minimally sensitive to these instrumental noise biases. By modifying the likelihood to rely exclusively on correlations between CMB map splits with independent noise realizations, we minimize auto-correlations that contribute to biases. In the regime of many independent splits, this maximum closely approximates the optimal MAP reconstruction of the lensing potential. In simulations, we demonstrate that this method is able to determine lensing observables that are immune to any noise mis-modeling with a negligible cost in signal-to-noise ratio. Our estimator enables unbiased and nearly optimal lensing reconstruction for next-generation CMB surveys.
Comparing the Architectures of Multiplanet Systems from Kepler, K2, and TESS Data
2025-06-25
Robert Royer III, Jason H. Steffen
Exoplanet surveys like Kepler, TESS, and K2 have shown that planetary systems are common in our galaxy. These surveys, along with several others, have identified thousands of planetary candidates, with more than five thousand having already been confirmed. Many of these planetary systems host multiple planets. As we discover more multiplanet systems, notable trends begin to appear in the data. We use kernel density estimation (KDE) to analyze the period ratios of adjacent planet pairs in multiplanet systems in the most recent Kepler, TESS and K2 data, paying particular attention to pairs in first order mean motion resonance (MMR). We compare a recent Kepler catalog with the DR25 data release. We also compare TESS and K2 against the latest Kepler catalog. To verify the significance of our findings against selection bias, we perform Monte Carlo simulations of multiplanet systems, finding an excess of planet pairs near the 2.2, 2 (2:1), and 1.5 (3:2) period ratios in the Kepler, K2, and TESS catalogs, all exceeding the 99% confidence interval when constrained to orbital periods less than 25 days. We identify two planet pairs orbiting M dwarf stars in a very tight ratio, as well as two likely misidentified planet pairs.
Reflection-less filter for superconducting quantum circuits
2025-06-25
Jessica Kedziora, Eric Q. Bui, Alec Yen, Andres E. Lombo, Kaidong Peng, Terence J. Weir, Kevin P. O'Brien
Protecting superconducting quantum circuits from non-ideal return loss, including out-of-band circulator behavior and enhancing the performance of broadband quantum-limited amplifiers can be accomplished using a superconducting version of a special class of microwave filters known as reflection-less filters. These filters can simultaneously permit low pass band loss to preserve quantum efficiency and broad band reflection-less characteristics in the stop and pass bands. The filter also suppresses thermal photons emitted in its pass band from the termination resistors by the nature of the dual network topology. This work will review the application, theory, design, and modeling of a superconducting reflection-less filter, followed by fabrication details and the presentation of cryogenic performance measurements of a monolithic device. The filter was fabricated using Al on Si, incorporating NiCr resistors, which allows for simple integration with other superconducting quantum devices. The filter with an area of 0.6 $\mathrm{\mathbf{mm^2}}$ achieves insertion loss below 1 dB, including its connectorized package over a 80\% fractional bandwidth centered at 8 GHz, and 10 dB packaged return loss from DC to above 14.5 GHz.
The VLA Frontier Fields Survey: A 6GHz High-resolution Radio Survey of Abell 2744
2025-06-25
Esteban A. Orozco, Eric F. Jiménez-Andrade, Eric J. Murphy, Ian Smail, Emmanuel Momjian, Ian Heywood, Miguel Vega-Gutierrez, Christa DeCoursey
We present a 6GHz radio continuum image of the galaxy cluster Abell 2744 ($z = 0.307$) obtained with the Karl G. Jansky Very Large Array (VLA) as part of the VLA Frontier Fields program, whose goal is to explore the radio continuum emission from high-redshift galaxies that are magnified by foreground, massive galaxy clusters. With an rms noise of $\approx 1 \mu$Jy beam$^{-1}$, at the phase center, and sub-arcsec angular resolution ($\theta_{1/2}=0.82$ arcsec), this is the deepest and most detailed radio image of Abell 2744 ever obtained. A total of 93 sources are detected with a peak signal-to-noise ratio $\geq5$, of which 46 have optical/near-infrared (IR) counterparts with available redshift, magnification ($\mu$), and stellar mass (${M}_*$) estimates. The radio sources are distributed over a redshift from 0.15 to 3.55, with a median redshift value of $z = 0.93$, and a median stellar mass of ${M}_{*}= 2.3 \times 10^{10}\,\rm{M}_{\odot}$. A comparison between the radio-based star formation rates (SFRs) and those derived from ultraviolet-to-near IR data reveals that the radio SFRs are, on average, an order of magnitude higher than the ultraviolet-to-near IR SFRs. We look for radio counterparts of the so-called "Little Red Dots (LRDs)'' galaxies at $z\approx6$ in Abell 2744, but find no significant detections. After stacking, we derive a 3$\sigma$ upper limit to the 6GHz radio luminosity of LRDs of $4.1\times 10^{39}\,\rm erg\,s^{-1}$. Finally, we present a sample of 22 moderately/strongly lensed galaxies ($\mu \gtrsim 2$) in the VLA Frontier Fields survey, which is adequate to zoom into star formation processes of main sequence galaxies at $z\approx 1-2$.
Define-ML: An Approach to Ideate Machine Learning-Enabled Systems
2025-06-25
Silvio Alonso, Antonio Pedro Santos Alves, Lucas Romao, Hélio Lopes, Marcos Kalinowski
[Context] The increasing adoption of machine learning (ML) in software systems demands specialized ideation approaches that address ML-specific challenges, including data dependencies, technical feasibility, and alignment between business objectives and probabilistic system behavior. Traditional ideation methods like Lean Inception lack structured support for these ML considerations, which can result in misaligned product visions and unrealistic expectations. [Goal] This paper presents Define-ML, a framework that extends Lean Inception with tailored activities - Data Source Mapping, Feature-to-Data Source Mapping, and ML Mapping - to systematically integrate data and technical constraints into early-stage ML product ideation. [Method] We developed and validated Define-ML following the Technology Transfer Model, conducting both static validation (with a toy problem) and dynamic validation (in a real-world industrial case study). The analysis combined quantitative surveys with qualitative feedback, assessing utility, ease of use, and intent of adoption. [Results] Participants found Define-ML effective for clarifying data concerns, aligning ML capabilities with business goals, and fostering cross-functional collaboration. The approach's structured activities reduced ideation ambiguity, though some noted a learning curve for ML-specific components, which can be mitigated by expert facilitation. All participants expressed the intention to adopt Define-ML. [Conclusion] Define-ML provides an openly available, validated approach for ML product ideation, building on Lean Inception's agility while aligning features with available data and increasing awareness of technical feasibility.
Communicating Smartly in the Molecular Domain: Neural Networks in the Internet of Bio-Nano Things
2025-06-25
Jorge Torres Gómez, Pit Hofmann, Lisa Y. Debus, Osman Tugay Başaran, Sebastian Lotter, Roya Khanzadeh, Stefan Angerbauer, Bige Deniz Unluturk, Sergi Abadal, Werner Haselmayr, Frank H. P. Fitzek, Robert Schober, Falko Dressler
Recent developments in the Internet of Bio-Nano Things (IoBNT) are laying the groundwork for innovative applications across the healthcare sector. Nanodevices designed to operate within the body, managed remotely via the internet, are envisioned to promptly detect and actuate on potential diseases. In this vision, an inherent challenge arises due to the limited capabilities of individual nanosensors; specifically, nanosensors must communicate with one another to collaborate as a cluster. Aiming to research the boundaries of the clustering capabilities, this survey emphasizes data-driven communication strategies in molecular communication (MC) channels as a means of linking nanosensors. Relying on the flexibility and robustness of machine learning (ML) methods to tackle the dynamic nature of MC channels, the MC research community frequently refers to neural network (NN) architectures. This interdisciplinary research field encompasses various aspects, including the use of NNs to facilitate communication in MC environments, their implementation at the nanoscale, explainable approaches for NNs, and dataset generation for training. Within this survey, we provide a comprehensive analysis of fundamental perspectives on recent trends in NN architectures for MC, the feasibility of their implementation at the nanoscale, applied explainable artificial intelligence (XAI) techniques, and the accessibility of datasets along with best practices for their generation. Additionally, we offer open-source code repositories that illustrate NN-based methods to support reproducible research for key MC scenarios. Finally, we identify emerging research challenges, such as robust NN architectures, biologically integrated NN modules, and scalable training strategies.
Two-Loop Turbulent Helical Magnetohydrodynamics: Large-Scale Dynamo and Energy Spectrum
2025-06-25
Michal Hnatič, Tomáš Lučivjanský, Lukáš Mižišin, Yurii Molotkov, Andrei Ovsiannikov
We present a two-loop field-theoretic analysis of incompressible helical magnetohydrodynamics (MHD) in fully developed stationary turbulence. A key feature of helical MHD is the appearance of an infrared-unstable ``mass-like'' term in the loop diagrams of the magnetic response function. Physically, this term corresponds to the relevant perturbation of the Joule damping, proportional to $\boldsymbol{\nabla} \times \boldsymbol{b}$ ($\boldsymbol{b} =$ magnetic field). Its presence destabilizes the trivial ground state $\langle \boldsymbol{b} \rangle = 0$ and forces us to look for a mechanism for stabilizing the system. We show that such stabilization can be achieved in two ways: (i) by introducing into induction equation an external mass-like parameter that precisely cancels these dangerous loop corrections (kinematic regime), or (ii) via spontaneous breaking of the rotational symmetry, leading to a new ground state with nonzero large-scale magnetic field (turbulent dynamo regime). For the latter case, we study the two-loop correction to the spontaneously generated magnetic field and demonstrate that Goldstone-like corrections to Alfv\'en modes along with some other anisotropic structures arise. Our results also confirm that the emergent mean magnetic field leads to a steeper slope of the magnetic energy spectrum, $-11/3 + 2\gamma_{b\star}$ (with $\gamma_{b\star} = -0.1039 - 0.4202\rho^2$, for $|\rho| \leqslant 1$ as the degree of helicity), compared to the Kolmogorov velocity spectrum of $-11/3$, thereby breaking equipartition.
Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series
2025-06-25
Laura Boggia, Rafael Teixeira de Lima, Bogdan Malaescu
Anomaly detection in multivariate time series is an important problem across various fields such as healthcare, financial services, manufacturing or physics detector monitoring. Accurately identifying when unexpected errors or faults occur is essential, yet challenging, due to the unknown nature of anomalies and the complex interdependencies between time series dimensions. In this paper, we investigate transformer-based approaches for time series anomaly detection, focusing on the recently proposed iTransformer architecture. Our contributions are fourfold: (i) we explore the application of the iTransformer to time series anomaly detection, and analyse the influence of key parameters such as window size, step size, and model dimensions on performance; (ii) we examine methods for extracting anomaly labels from multidimensional anomaly scores and discuss appropriate evaluation metrics for such labels; (iii) we study the impact of anomalous data present during training and assess the effectiveness of alternative loss functions in mitigating their influence; and (iv) we present a comprehensive comparison of several transformer-based models across a diverse set of datasets for time series anomaly detection.
CCISolver: End-to-End Detection and Repair of Method-Level Code-Comment Inconsistency
2025-06-25
Renyi Zhong, Yintong Huo, Wenwei Gu, Jinxi Kuang, Zhihan Jiang, Guangba Yu, Yichen Li, David Lo, Michael R. Lyu
Comments within code serve as a crucial foundation for software documentation, facilitating developers to communicate and understand the code effectively. However, code-comment inconsistency (CCI) can negatively affect software development, testing, and maintenance. Recent efforts to mitigate this issue have emerged, but existing studies often suffer from inaccurate datasets and inadequate solutions, weakening their practical effectiveness. In this study, we first conduct a quantitative analysis of existing datasets, revealing a substantial portion of sampled data are mislabeled. To address these data limitations, we introduce CCIBench, a refined dataset comprising high-quality data, to support the training and evaluation of method-level CCI methods. Furthermore, we present an innovative end-to-end LLM-based framework, CCISolver, designed to improve code quality by identifying and rectifying CCIs. Comprehensive evaluations demonstrate CCISolver's superior performance. For detection, it establishes a new state-of-the-art with an F1-score of 89.54%. In fixing task, it achieves a remarkable 18.84% relative improvement in GLEU score over the strongest baseline. This superiority is confirmed by human evaluation, where CCISolver's fixing success rate of 0.6533 significantly surpasses existing methods. Critically, in a practical end-to-end setting, CCISolver's innovative architecture is approximately 36% faster for inference than the baseline model, underscoring its scalability and real-world applicability.
Case-based Reasoning Augmented Large Language Model Framework for Decision Making in Realistic Safety-Critical Driving Scenarios
2025-06-25
Wenbin Gan, Minh-Son Dao, Koji Zettsu
Driving in safety-critical scenarios requires quick, context-aware decision-making grounded in both situational understanding and experiential reasoning. Large Language Models (LLMs), with their powerful general-purpose reasoning capabilities, offer a promising foundation for such decision-making. However, their direct application to autonomous driving remains limited due to challenges in domain adaptation, contextual grounding, and the lack of experiential knowledge needed to make reliable and interpretable decisions in dynamic, high-risk environments. To address this gap, this paper presents a Case-Based Reasoning Augmented Large Language Model (CBR-LLM) framework for evasive maneuver decision-making in complex risk scenarios. Our approach integrates semantic scene understanding from dashcam video inputs with the retrieval of relevant past driving cases, enabling LLMs to generate maneuver recommendations that are both context-sensitive and human-aligned. Experiments across multiple open-source LLMs show that our framework improves decision accuracy, justification quality, and alignment with human expert behavior. Risk-aware prompting strategies further enhance performance across diverse risk types, while similarity-based case retrieval consistently outperforms random sampling in guiding in-context learning. Case studies further demonstrate the framework's robustness in challenging real-world conditions, underscoring its potential as an adaptive and trustworthy decision-support tool for intelligent driving systems.
Periodontitis, a chronic inflammatory disease causing alveolar bone loss, significantly affects oral health and quality of life. Accurate assessment of bone loss severity and pattern is critical for diagnosis and treatment planning. In this study, we propose a novel AI-based deep learning framework to automatically detect and quantify alveolar bone loss and its patterns using intraoral periapical (IOPA) radiographs. Our method combines YOLOv8 for tooth detection with Keypoint R-CNN models to identify anatomical landmarks, enabling precise calculation of bone loss severity. Additionally, YOLOv8x-seg models segment bone levels and tooth masks to determine bone loss patterns (horizontal vs. angular) via geometric analysis. Evaluated on a large, expertly annotated dataset of 1000 radiographs, our approach achieved high accuracy in detecting bone loss severity (intra-class correlation coefficient up to 0.80) and bone loss pattern classification (accuracy 87%). This automated system offers a rapid, objective, and reproducible tool for periodontal assessment, reducing reliance on subjective manual evaluation. By integrating AI into dental radiographic analysis, our framework has the potential to improve early diagnosis and personalized treatment planning for periodontitis, ultimately enhancing patient care and clinical outcomes.
2025-06-25
Samantha E. Ball, Benjamin C. Bromley, Scott J. Kenyon
Symbiotic stars, binary pairs with a cool giant fueling accretion onto a hot compact companion, offer unique insights to our understanding of stellar evolution. Yet, only a few hundred symbiotic stars are confirmed. Here, we report on a new search for symbiotic star candidates in Gaia Data Release 3 (GDR3), based entirely on the archive's astrometric, photometric, and spectroscopic information. To begin our search, we identified known symbiotic stars in GDR3 and assessed their absolute magnitude and colors, which are dominated by the cool giant. We also considered measures of astrometric quality that might be affected by binary motion in these systems. Finally, from those sources with Gaia spectroscopic data, we built a low-resolution spectral template that characterizes the unique features of these systems, including H$\alpha$ emission from interaction with the giant's wind and radiation from the hot star. We then queried the full GDR3 archive for sources with spectroscopic data that are bright (< 17 mag in G-band), have modest relative parallax uncertainties (< 20%), and fall within a region of color-magnitude space characteristic of red giants, keeping only sources with spectra that quantitatively match our template. A machine-learning algorithm, trained on known symbiotic stars, produced a new catalog of 1,674 sources. From cross-matches with infrared and X-ray surveys, we present 25 of these sources as particularly compelling candidates for new symbiotic stars.
The MALATANG survey: Dense gas distribution on sub-kiloparsec scales across the disk of M82
2025-06-25
Jian-Fa Wang, Yu Gao, Qing-Hua Tan, Xue-Jian Jiang, Li Ji, Zhi-Yu Zhang, Jun-Zhi Wang, Jun-Feng Wang, R. Thomas Greve, Yan Jiang, Ashley Bemis, Elias Brinks, Aeree Chung, J. Malcolm Currie, Richard de Grijs, Taotao Fang, C. Luis Ho, Bumhyun Lee, Satoki Matsushita, Michał Michałowski, Soojong Pak, Panomporn Poojon, G. Mark Rawlings, Amelie Saintonge, Yi-Chen Sun, Jing Zhou
We present observations of HCN J=4-3 and HCO^+ J=4-3 lines obtained with the James Clerk Maxwell Telescope as part of the MALATANG survey, combined with archival HCN J=1-0 and HCO^+ J=1-0 data from the Green Bank Telescope, to study the spatial distribution and excitation conditions of dense molecular gas in the disk of M82. We detect HCN J=4-3 and HCO^+ J=4-3 emission within the central region (< 500 pc) of the galaxy, while the J=1-0 emission lines exhibit a more extended spatial distribution (> 700 pc). The dense gas shows a clear double-lobed structure in both spatial distribution and kinematics, with the HCN and HCO^+ J=4-3 lines in the southwest lobe blueshifted by ~ 40 km/s relative to the J=1-0 lines. The HCN J=4-3/1-0 and HCO^+ J=4-3/1-0 line-luminosity ratios range from 0.09 to 0.53 and from 0.14 to 0.87, respectively, with mean values of 0.18 +/- 0.04 and 0.36 +/- 0.06. The HCN ratio is lower than the typical average observed in nearby star-forming galaxies, whereas the HCO^+ ratio is comparatively higher, suggesting that the high-J HCN emission in M82 is significantly sub-thermally excited. Spatially, the peak values of the J=4-3/1-0 ratios are found in the northwest region of M82, coinciding with the galaxy-scale outflow. Elevated HCN/HCO^+ ratios are also detected in roughly the same area, potentially tracing local excitation enhancements driven by the outflow. The HCN/HCO^+ J=4-3 ratio across all detected regions ranges from 0.19 to 1.07 with a mean value of 0.41 +/- 0.11, which is significantly lower than the average J=1-0 ratio of 0.76 +/- 0.08. Both ratios are significantly lower than the average values observed in nearby star-forming galaxies, which could be related to the relatively low gas density and the presence of an extended photo-dissociation region in M82.
Rock bolts are crucial components of the subterranean support systems in underground mines that provide adequate structural reinforcement to the rock mass to prevent unforeseen hazards like rockfalls. This makes frequent assessments of such bolts critical for maintaining rock mass stability and minimising risks in underground mining operations. Where manual surveying of rock bolts is challenging due to the low light conditions in the underground mines and the time-intensive nature of the process, automated detection of rock bolts serves as a plausible solution. To that end, this study focuses on the automatic identification of rock bolts within medium to large-scale 3D point clouds obtained from underground mines using mobile laser scanners. Existing techniques for automated rock bolt identification primarily rely on feature engineering and traditional machine learning approaches. However, such techniques lack robustness as these point clouds present several challenges due to data noise, varying environments, and complex surrounding structures. Moreover, the target rock bolts are extremely small objects within large-scale point clouds and are often partially obscured due to the application of reinforcement shotcrete. Addressing these challenges, this paper proposes an approach termed DeepBolt, which employs a novel two-stage deep learning architecture specifically designed for handling severe class imbalance for the automatic and efficient identification of rock bolts in complex 3D point clouds. The proposed method surpasses state-of-the-art semantic segmentation models by up to 42.5% in Intersection over Union (IoU) for rock bolt points. Additionally, it outperforms existing rock bolt identification techniques, achieving a 96.41% precision and 96.96% recall in classifying rock bolts, demonstrating its robustness and effectiveness in complex underground environments.
A Review of Personalisation in Human-Robot Collaboration and Future Perspectives Towards Industry 5.0
2025-06-25
James Fant-Male, Roel Pieters
The shift in research focus from Industry 4.0 to Industry 5.0 (I5.0) promises a human-centric workplace, with social and well-being values at the centre of technological implementation. Human-Robot Collaboration (HRC) is a core aspect of I5.0 development, with an increase in adaptive and personalised interactions and behaviours. This review investigates recent advancements towards personalised HRC, where user-centric adaption is key. There is a growing trend for adaptable HRC research, however there lacks a consistent and unified approach. The review highlights key research trends on which personal factors are considered, workcell and interaction design, and adaptive task completion. This raises various key considerations for future developments, particularly around the ethical and regulatory development of personalised systems, which are discussed in detail.
Nucleosynthesis and the chemical enrichment of galaxies
2025-06-25
Chiaki Kobayashi
Stars are fossils that retain the history of their host galaxies. Carbon and heavier elements are created inside stars and are ejected when they die. From the spatial distribution of elements in galaxies, it is therefore possible to constrain the physical processes during galaxy formation and evolution. This approach, Galactic archaeology, has been popularly used for our Milky Way Galaxy thanks to a vast amount of data from the Gaia satellite and multi-object spectrographs, and now can also be applied to very distant galaxies with the James Webb Space Telescope (JWST) - extra-galactic archaeology. In these studies the most important factor is the input stellar physics, namely nucleosynthesis yields and binary physics, which predominantly determine the model predictions. In this review I give a summary of stellar nucleosynthesis, and how they are tested with the observations in the Milky Way. Then I show how chemical enrichment of galaxies can be calculated, and show some results with the latest nucleosynthesis yields.
The Composition of Digital Twins for Systems-of-Systems: a Systematic Literature Review
2025-06-25
Mennatullah T. Khedr, John S. Fitzgerald
Digital Twins (DTs) are increasingly used to model complex systems, especially in Cyber-Physical Systems (CPS) and System-of-Systems (SoS), where effective integration is key. This systematic literature review investigates DT composition and verification and validation (V&V) methodologies. Analyzing 21 studies from 2022-2024, we examined composition mechanisms, SoS characteristics, and V&V formality, scope, and challenges. While composition is discussed, formalization is limited. V&V approaches vary, with semi-formal methods and simulations dominating; formal verification is underutilized. Key technical challenges include model uncertainty and integration complexity. Methodological challenges highlight the lack of standardized DT-specific V&V frameworks. There is a need to move beyond model validation to address integration and cyber-physical consistency. This review contributes a structured classification of V&V approaches and emphasizes the need for standardized, scalable V&V and rigorous composition methodologies for complex DT implementations.
Shell effects in nuclear charge radii based on Skyrme density functionals
2025-06-25
Rong An, Shuai Sun, Xiang Jiang, Na Tang, Li-Gang Cao, Feng-Shou Zhang
A unified description of the charge radii throughout the entire nuclide chart plays an essential role for our understanding of nuclear structure and fundamental nuclear interactions. In this work, the influence of new term, which catches the spirit of neutron and proton pairs condensation around Fermi surface, on the charge radii has been investigated based on the Skyrme density functionals with the effective forces SLy5 and SkM$^{*}$. The differential charge radii of even-even Ca, Ni, Sn, and Pb isotopes are employed to evaluate the validity of this theoretical model. Meanwhile, the results obtained by the relativistic density functional with the effective Lagrangian NL3 are also shown for the quantitative comparison. The calculated results suggest that the modified model can improve the trend of changes of the differential charge radii along Ca, Ni, Sn, and Pb isotopic chains, especially the shell closure effect at the neutron numbers $N=28$, 82 and 126. The shell quenching phenomena of charge radii can also be predicted at the neutron number $N=50$ along the corresponding Ni and Sn isotopes, respectively. The inverted parabolic-like shapes between the two fully filled shells can also be observed, but the amplitude is gradually weakened from Ca to Pb isotopic chains. Combining the existing literatures, it suggests that the discontinuous behavior in nuclear charge radii can be described well by considering the influence of neutron Cooper pairs condensation around Fermi surface.
Recurrent neural network-based robust control systems with closed-loop regional incremental ISS and application to MPC design
2025-06-25
Daniele Ravasio, Marcello Farina, Alessio La Bella, Andrea Ballarino
This paper investigates the design of output-feedback schemes for systems described by a class of recurrent neural networks. We propose a procedure based on linear matrix inequalities for designing an observer and a static state-feedback controller. The algorithm leverages global and regional incremental input-to-state stability (incremental ISS) and enables the tracking of constant setpoints, ensuring robustness to disturbances and state estimation uncertainty. To address the potential limitations of regional incremental ISS, we introduce an alternative scheme in which the static law is replaced with a tube-based nonlinear model predictive controller (NMPC) that exploits regional incremental ISS properties. We show that these conditions enable the formulation of a robust NMPC law with guarantees of convergence and recursive feasibility, leading to an enlarged region of attraction. Theoretical results are validated through numerical simulations on the pH-neutralisation process benchmark, demonstrating the effectiveness of the proposed schemes.
Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining and Extracting Rare and Hidden Content
2025-06-25
Rian Touchent, Nathan Godey, Eric de la Clergerie
We introduce Biomed-Enriched, a biomedical text dataset constructed from PubMed via a two-stage annotation process. In the first stage, a large language model annotates 400K paragraphs from PubMed scientific articles, assigning scores for their type (review, study, clinical case, other), domain (clinical, biomedical, other), and educational quality. The educational quality score (rated 1 to 5) estimates how useful a paragraph is for college-level learning. These annotations are then used to fine-tune a small language model, which propagates the labels across the full PMC-OA corpus. The resulting metadata allows us to extract refined subsets, including 2M clinical case paragraphs with over 450K high-quality ones from articles with commercial-use licenses, and to construct several variants via quality filtering and domain upsampling. Clinical text is typically difficult to access due to privacy constraints, as hospital records cannot be publicly shared. Hence, our dataset provides an alternative large-scale, openly available collection of clinical cases from PubMed, making it a valuable resource for biomedical and clinical NLP. Preliminary continual-pretraining experiments with OLMo2 suggest these curated subsets enable targeted improvements, with clinical upsampling boosting performance by ~5% on MMLU ProfMed and educational quality filtering improving MedQA and MedMCQA by ~1%. Combinations of these techniques led to faster convergence, reaching same performance with a third of training tokens, indicating potential for more efficient and effective biomedical pretraining strategies.
Building Forest Inventories with Autonomous Legged Robots -- System, Lessons, and Challenges Ahead
2025-06-25
Matías Mattamala, Nived Chebrolu, Jonas Frey, Leonard Freißmuth, Haedam Oh, Benoit Casseau, Marco Hutter, Maurice Fallon
Legged robots are increasingly being adopted in industries such as oil, gas, mining, nuclear, and agriculture. However, new challenges exist when moving into natural, less-structured environments, such as forestry applications. This paper presents a prototype system for autonomous, under-canopy forest inventory with legged platforms. Motivated by the robustness and mobility of modern legged robots, we introduce a system architecture which enabled a quadruped platform to autonomously navigate and map forest plots. Our solution involves a complete navigation stack for state estimation, mission planning, and tree detection and trait estimation. We report the performance of the system from trials executed over one and a half years in forests in three European countries. Our results with the ANYmal robot demonstrate that we can survey plots up to 1 ha plot under 30 min, while also identifying trees with typical DBH accuracy of 2cm. The findings of this project are presented as five lessons and challenges. Particularly, we discuss the maturity of hardware development, state estimation limitations, open problems in forest navigation, future avenues for robotic forest inventory, and more general challenges to assess autonomous systems. By sharing these lessons and challenges, we offer insight and new directions for future research on legged robots, navigation systems, and applications in natural environments. Additional videos can be found in https://dynamic.robots.ox.ac.uk/projects/legged-robots
Federated learning (FL) enables collaborative training across distributed clients without sharing raw data, often at the cost of substantial communication overhead induced by transmitting high-dimensional model updates. This overhead can be alleviated by having the clients quantize their model updates, with dithered lattice quantizers identified as an attractive scheme due to its structural simplicity and convergence-preserving properties. However, existing lattice-based FL schemes typically rely on a fixed quantization rule, which is suboptimal in heterogeneous and dynamic environments where the model updates distribution varies across users and training rounds. In this work, we propose Online Learned Adaptive Lattices (OLALa), a heterogeneous FL framework where each client can adjust its quantizer online using lightweight local computations. We first derive convergence guarantees for FL with non-fixed lattice quantizers and show that proper lattice adaptation can tighten the convergence bound. Then, we design an online learning algorithm that enables clients to tune their quantizers throughout the FL process while exchanging only a compact set of quantization parameters. Numerical experiments demonstrate that OLALa consistently improves learning performance under various quantization rates, outperforming conventional fixed-codebook and non-adaptive schemes.
Conversational Recommender Systems (CRSs) have garnered attention as a novel approach to delivering personalized recommendations through multi-turn dialogues. This review developed a taxonomy framework to systematically categorize relevant publications into four groups: dataset construction, algorithm design, system evaluation, and empirical studies, providing a comprehensive analysis of simulation methods in CRSs research. Our analysis reveals that simulation methods play a key role in tackling CRSs' main challenges. For example, LLM-based simulation methods have been used to create conversational recommendation data, enhance CRSs algorithms, and evaluate CRSs. Despite several challenges, such as dataset bias, the limited output flexibility of LLM-based simulations, and the gap between text semantic space and behavioral semantics, persist due to the complexity in Human-Computer Interaction (HCI) of CRSs, simulation methods hold significant potential for advancing CRS research. This review offers a thorough summary of the current research landscape in this domain and identifies promising directions for future inquiry.
Using data from the MIRIS Pa{\alpha} Galactic Plane Survey (MIPAPS), we present a Pa{\alpha} 1.87 um line image of the entire Galactic plane within the latitude range of -3{\deg} < b < +3{\deg}, revealing numerous Pa{\alpha} features. Based on the MIPAPS Pa{\alpha} image and the WISE H II region catalog, we compile a catalog of 1489 Pa{\alpha} emission-line sources in the Galactic plane within 90{\deg} < l < 330{\deg}. By comparing our Pa{\alpha} images with H{\alpha} images constructed from the IPHAS and VPHAS+ survey data, we demonstrate the advantages of Pa{\alpha} line observations. We identify 902 Pa{\alpha} sources associated with H II regions, and newly confirm 619 H II region candidates as definitive H II regions through Pa{\alpha} or H{\alpha} detections. We also identify 261 extended and 326 point-like Pa{\alpha} sources not included in the WISE catalog, most of which have H{\alpha} counterparts in the IPHAS or VPHAS+ images. A search of the SIMBAD database indicates that these sources originate from diverse object types. By measuring Pa{\alpha} and H{\alpha} fluxes, we estimate the E(B-V) color excesses derived from extended emissions for 138 Pa{\alpha} sources, showing good agreement with values obtained from spectrophotometry of ionizing stars in previous studies. Futhermore, we calculate total Lyman continuum luminosities for 42 Pa{\alpha} sources, providing constraints on the distances to H II regions and the spectral types of their ionizing stars. These results highlight the scientific potential of Pa{\alpha} line observations and the benefits of combining multiple hydrogen recombination lines in exploring ionized regions.
Log-concavity and anti-maximum principles for semilinear and linear elliptic equations
2025-06-25
François Hamel, Nikolai Nadirashvili
This paper is concerned with existence and qualitative properties of positive solutions of semilinear elliptic equations in bounded domains with Dirichlet boundary conditions. We show the existence of positive solutions in the vicinity of the linear equation and the log-concavity of the solutions when the domain is strictly convex. We also review the standard results on the log-concavity or the more general quasi-concavity of solutions of elliptic equations. The existence and other convergence results especially rely on the maximum principle, on a quantified version of the anti-maximum principle, on the Schauder fixed point theorem, and on some a priori estimates.
2025-06-25
Junyan Cheng, Peter Clark, Kyle Richardson
Can we leverage LLMs to model the process of discovering novel language model (LM) architectures? Inspired by real research, we propose a multi-agent LLM approach that simulates the conventional stages of research, from ideation and literature search (proposal stage) to design implementation (code generation), generative pre-training, and downstream evaluation (verification). Using ideas from scaling laws, our system, Genesys, employs a Ladder of Scales approach; new designs are proposed, adversarially reviewed, implemented, and selectively verified at increasingly larger model scales (14M$\sim$350M parameters) with a narrowing budget (the number of models we can train at each scale). To help make discovery efficient and factorizable, Genesys uses a novel genetic programming backbone, which we show has empirical advantages over commonly used direct prompt generation workflows (e.g., $\sim$86\% percentage point improvement in successful design generation, a key bottleneck). We report experiments involving 1,162 newly discovered designs (1,062 fully verified through pre-training) and find the best designs to be highly competitive with known architectures (e.g., outperform GPT2, Mamba2, etc., on 6/9 common benchmarks). We couple these results with comprehensive system-level ablations and formal results, which give broader insights into the design of effective autonomous discovery systems.
Enhancing Large Language Models through Structured Reasoning
2025-06-25
Yubo Dong, Hehe Fan
Recent Large Language Models (LLMs) have significantly advanced natural language processing and automated decision-making. However, these models still encounter difficulties when performing complex reasoning tasks involving logical deduction and systematic planning, primarily due to their reliance on implicit statistical relationships without structured knowledge representation.Inspired by cognitive science and neurosymbolic AI, we introduce a novel approach to enhance LLMs through explicit structured reasoning. First, we convert unstructured data into structured formats by explicitly annotating reasoning steps. We then employ this structured dataset to train LLMs through Supervised Fine-Tuning (SFT). Additionally, we enhance the structured reasoning capabilities of LLMs using Group Relative Policy Optimization (GRPO), incorporating two innovative algorithms--MAX-Flow and Longest Common Subsequence (LCS)--which notably improve reasoning effectiveness and reduce computational complexity. Experimental results from fine-tuning a DeepSeek-R1-Distill-Qwen-1.5B model demonstrate concise reasoning, robust performance across various scenarios, and improved compatibility with optimization techniques, validating the efficacy of structured reasoning integration in LLMs.
Direct High-Resolution Imaging of Earth-Like Exoplanets
2025-06-25
Slava G. Turyshev
We have surveyed all conventional methods proposed or conceivable for obtaining resolved images of an Earth-like exoplanet. Generating a 10x10 pixel map of a 1 R_Earth world at 10 pc demands ~0.85 uas angular resolution and photon collection sufficient for SNR >= 5 per "micro-pixel". We derived diffraction-limit and photon-budget requirements for: (1) large single-aperture space telescopes with internal coronagraphs; (2) external starshades; (3) space-based interferometry (nulling and non-nulling); (4) ground-based extremely large telescopes with extreme adaptive optics; (5) pupil-densified "hypertelescopes"; (6) indirect reconstructions (rotational light-curve inversion, eclipse mapping, intensity interferometry); and (7) diffraction occultation by Solar System bodies. Even though these approaches serve their primary goals - exoplanet discovery and initial coarse characterization - each remains orders of magnitude away from delivering a spatially resolved image. In every case, technology readiness falls short, and fundamental barriers leave them 2-5 orders of magnitude below the astrometric resolution and photon-budget thresholds needed to map an Earth analog even on decadal timescales. Ultimately, an in situ platform delivered to <= 0.1 AU of the target could, in principle, overcome both diffraction and photon-starvation limits - but such a mission far exceeds current propulsion, autonomy, and communications capabilities. By contrast, the Solar Gravitational Lens - providing on-axis gain of ~1e10 and inherent uas-scale focusing once a spacecraft reaches >=550 AU - remains the only near-term, scientifically and technologically viable means to acquire true, resolved surface images and spatially resolved spectroscopy of Earth-like exoplanets in our stellar neighborhood.
Hybrid morphology radio sources (HyMoRSs) are a rare subclass of radio galaxies that display a Fanaroff-Riley type I (FR I) morphology on one side of the central supermassive black hole (SMBH) and a type II (FR II) morphology on the other. These unique sources provide valuable insights into the physical mechanisms behind the FR dichotomy. In this study, we report the discovery of thirty-six new HyMoRSs, marking the largest collection of such sources in the southern sky to date, using data obtained from the MeerKAT absorption line survey (MALS). The identified HyMoRSs exhibit moderate radio luminosities, with a median value of 4.4 $\times 10^{24}$ W Hz$^{-1}$ at 1.4 GHz, and are located within the redshift range 0.04 < z < 1.34. To gain further insights into their origin, we also investigate the mid-infrared properties and environments of their host galaxies. Notably, nine out of the thirty-six sources are situated near the centers of galaxy clusters, including one with giant radio jets that extend over 811 kpc. Our analysis reveals that the majority of HyMoRSs are high-excitation radio galaxies (HERGs), hosted by actively star-forming galaxies that exhibit elevated star formation rates. Furthermore, our findings suggest that hybrid morphology may arise from FR II jets being deflected by a dense, cluster-like environment, along with orientation effects that make one jet appear FR I-like. As our candidates are selected through visual inspection of MALS radio maps, higher-resolution follow-up observations are still necessary to confirm the nature of their morphologies.
User Understanding of Privacy Permissions in Mobile Augmented Reality: Perceptions and Misconceptions
2025-06-25
Viktorija Paneva, Verena Winterhalter, Franziska Augustinowski, Florian Alt
Mobile Augmented Reality (AR) applications leverage various sensors to provide immersive user experiences. However, their reliance on diverse data sources introduces significant privacy challenges. This paper investigates user perceptions and understanding of privacy permissions in mobile AR apps through an analysis of existing applications and an online survey of 120 participants. Findings reveal common misconceptions, including confusion about how permissions relate to specific AR functionalities (e.g., location and measurement of physical distances), and misinterpretations of permission labels (e.g., conflating camera and gallery access). We identify a set of actionable implications for designing more usable and transparent privacy mechanisms tailored to mobile AR technologies, including contextual explanations, modular permission requests, and clearer permission labels. These findings offer actionable guidance for developers, researchers, and policymakers working to enhance privacy frameworks in mobile AR.
Technological advances in the past decades have begun to enable neuroscientists to address fundamental questions about consciousness in an unprecedented way. Here we review remarkable recent progress in our understanding of cellular-level mechanisms of conscious processing in the brain. Of particular interest are the cortical pyramidal neurons -- or "psychic cells" called by Ram\'on y Cajal more than 100 years ago -- which have an intriguing cellular mechanism that accounts for selective disruption of feedback signaling in the brain upon anesthetic-induced loss of consciousness. Importantly, a particular class of metabotropic receptors distributed over the dendrites of pyramidal cells are highlighted as the key cellular mechanism. After all, Cajal's instinct over a century ago may turn out to be correct -- we may have just begun to understand whether and how psychic cells indeed generate and control our consciousness.
Irec: A Metacognitive Scaffolding for Self-Regulated Learning through Just-in-Time Insight Recall: A Conceptual Framework and System Prototype
2025-06-25
Xuefei Hou, Xizhao Tan
The core challenge in learning has shifted from knowledge acquisition to effective Self-Regulated Learning (SRL): planning, monitoring, and reflecting on one's learning. Existing digital tools, however, inadequately support metacognitive reflection. Spaced Repetition Systems (SRS) use de-contextualized review, overlooking the role of context, while Personal Knowledge Management (PKM) tools require high manual maintenance. To address these challenges, this paper introduces "Insight Recall," a novel paradigm that conceptualizes the context-triggered retrieval of personal past insights as a metacognitive scaffold to promote SRL. We formalize this paradigm using the Just-in-Time Adaptive Intervention (JITAI) framework and implement a prototype system, Irec, to demonstrate its feasibility. At its core, Irec uses a dynamic knowledge graph of the user's learning history. When a user faces a new problem, a hybrid retrieval engine recalls relevant personal "insights." Subsequently, a large language model (LLM) performs a deep similarity assessment to filter and present the most relevant scaffold in a just-in-time manner. To reduce cognitive load, Irec features a human-in-the-loop pipeline for LLM-based knowledge graph construction. We also propose an optional "Guided Inquiry" module, where users can engage in a Socratic dialogue with an expert LLM, using the current problem and recalled insights as context. The contribution of this paper is a solid theoretical framework and a usable system platform for designing next-generation intelligent learning systems that enhance metacognition and self-regulation.
Accept More, Reject Less: Reducing up to 19% Unnecessary Desk-Rejections over 11 Years of ICLR Data
2025-06-25
Xiaoyu Li, Zhao Song, Jiahao Zhang
The explosive growth of AI research has driven paper submissions at flagship AI conferences to unprecedented levels, necessitating many venues in 2025 (e.g., CVPR, ICCV, KDD, AAAI, IJCAI, WSDM) to enforce strict per-author submission limits and to desk-reject any excess papers by simple ID order. While this policy helps reduce reviewer workload, it may unintentionally discard valuable papers and penalize authors' efforts. In this paper, we ask an essential research question on whether it is possible to follow submission limits while minimizing needless rejections. We first formalize the current desk-rejection policies as an optimization problem, and then develop a practical algorithm based on linear programming relaxation and a rounding scheme. Under extensive evaluation on 11 years of real-world ICLR (International Conference on Learning Representations) data, our method preserves up to $19.23\%$ more papers without violating any author limits. Moreover, our algorithm is highly efficient in practice, with all results on ICLR data computed within at most 53.64 seconds. Our work provides a simple and practical desk-rejection strategy that significantly reduces unnecessary rejections, demonstrating strong potential to improve current CS conference submission policies.
Carbon Nitride: Physical properties and Applications
2025-06-25
Shilpi Kumari, Soubhagyam sharma, Manisha Kumari, Manish Kumar Singh, Rakesh K. Prasad, Kwang-geol Lee, Dilip K. Singh
Graphitic carbon nitride has emerged as a versatile, metal-free semiconductor with applications spanning over broad range of domains encompassing energy storage, environmental remediation and sensing. Despite significant progress in recent years, there remains a lack of comprehensive discussion on the graphitic carbon nitride's evolving role in next-generation technologies and the engineering strategies needed to overcome existing challenges. In this review article, the critical assessment of the physicochemical properties of graphitic carbon nitride which holds potential to enable its function across diverse applications has been elucidated. Current advances in doping, heterojunction formation and composite engineering that enhances its catalytic and electronic performance has been summarized. The article also presents future research directions to unlock the full potential of graphitic carbon nitride as a useful material in sustainable and intelligent systems.
From 2D to 3D Cognition: A Brief Survey of General World Models
2025-06-25
Ningwei Xie, Zizi Tian, Lei Yang, Xiao-Ping Zhang, Meng Guo, Jie Li
World models have garnered increasing attention in the development of artificial general intelligence (AGI), serving as computational frameworks for learning representations of the external world and forecasting future states. While early efforts focused on 2D visual perception and simulation, recent 3D-aware generative world models have demonstrated the ability to synthesize geometrically consistent, interactive 3D environments, marking a shift toward 3D spatial cognition. Despite rapid progress, the field lacks systematic analysis to categorize emerging techniques and clarify their roles in advancing 3D cognitive world models. This survey addresses this need by introducing a conceptual framework, providing a structured and forward-looking review of world models transitioning from 2D perception to 3D cognition. Within this framework, we highlight two key technological drivers, particularly advances in 3D representations and the incorporation of world knowledge, as fundamental pillars. Building on these, we dissect three core cognitive capabilities that underpin 3D world modeling: 3D physical scene generation, 3D spatial reasoning, and 3D spatial interaction. We further examine the deployment of these capabilities in real-world applications, including embodied AI, autonomous driving, digital twin, and gaming/VR. Finally, we identify challenges across data, modeling, and deployment, and outline future directions for advancing more robust and generalizable 3D world models.
In this paper we perform the boundary algebraic Bethe Ansatz for massive representations of the $AdS_3 \times S^3 \times T^4$ integrable system. This is a companion analysis to our study of massless representations \cite{Bielli:2024bve}. Our treatment is comprehensive of all possible assortments of tensor-factor polarisations which build the physical representations in the spectrum, and includes different choices of auxiliary spaces, revealing subtle differences in the procedure. We survey both singlet and vector boundaries, obtaining the auxiliary Bethe equations in very general form for all cases.
AI Copilots for Reproducibility in Science: A Case Study
2025-06-25
Adrien Bibal, Steven N. Minton, Deborah Khider, Yolanda Gil
Open science initiatives seek to make research outputs more transparent, accessible, and reusable, but ensuring that published findings can be independently reproduced remains a persistent challenge. This paper introduces OpenPub, an AI-powered platform that supports researchers, reviewers, and readers through a suite of modular copilots focused on key open science tasks. In this work, we present the Reproducibility Copilot, which analyzes manuscripts, code, and supplementary materials to generate structured Jupyter Notebooks and recommendations aimed at facilitating computational, or "rote", reproducibility. We conducted feasibility tests using previously studied research papers with known reproducibility benchmarks. Results indicate that OpenPub can substantially reduce reproduction time - from over 30 hours to about 1 hour - while achieving high coverage of figures, tables, and results suitable for computational reproduction. The system systematically detects barriers to reproducibility, including missing hyperparameters, undocumented preprocessing steps, and incomplete or inaccessible datasets. These findings suggest that AI-driven tools can meaningfully reduce the burden of reproducibility efforts and contribute to more transparent and verifiable scientific communication. The modular copilot architecture also provides a foundation for extending AI assistance to additional open science objectives beyond reproducibility.
A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection
2025-06-25
Songsoo Kim, Seungtae Lee, See Young Lee, Joonho Kim, Keechan Kan, Dukyong Yoon
Background: The positive predictive value (PPV) of large language model (LLM)-based proofreading for radiology reports is limited due to the low error prevalence. Purpose: To assess whether a three-pass LLM framework enhances PPV and reduces operational costs compared with baseline approaches. Materials and Methods: A retrospective analysis was performed on 1,000 consecutive radiology reports (250 each: radiography, ultrasonography, CT, MRI) from the MIMIC-III database. Two external datasets (CheXpert and Open-i) were validation sets. Three LLM frameworks were tested: (1) single-prompt detector; (2) extractor plus detector; and (3) extractor, detector, and false-positive verifier. Precision was measured by PPV and absolute true positive rate (aTPR). Efficiency was calculated from model inference charges and reviewer remuneration. Statistical significance was tested using cluster bootstrap, exact McNemar tests, and Holm-Bonferroni correction. Results: Framework PPV increased from 0.063 (95% CI, 0.036-0.101, Framework 1) to 0.079 (0.049-0.118, Framework 2), and significantly to 0.159 (0.090-0.252, Framework 3; P<.001 vs. baselines). aTPR remained stable (0.012-0.014; P>=.84). Operational costs per 1,000 reports dropped to USD 5.58 (Framework 3) from USD 9.72 (Framework 1) and USD 6.85 (Framework 2), reflecting reductions of 42.6% and 18.5%, respectively. Human-reviewed reports decreased from 192 to 88. External validation supported Framework 3's superior PPV (CheXpert 0.133, Open-i 0.105) and stable aTPR (0.007). Conclusion: A three-pass LLM framework significantly enhanced PPV and reduced operational costs, maintaining detection performance, providing an effective strategy for AI-assisted radiology report quality assurance.
A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression
2025-06-25
Ainaz Jamshidi, Dongchan Kim, Muhammad Arif
Predictive maintenance (PdM) has become a crucial element of modern industrial practice. PdM plays a significant role in operational dependability and cost management by decreasing unforeseen downtime and optimizing asset life cycle management. Machine learning and deep learning have enabled more precise forecasts of equipment failure and remaining useful life (RUL). Although many studies have been conducted on PdM, there has not yet been a standalone comparative study between regression- and classification-based approaches. In this review, we look across a range of PdM methodologies, while focusing more strongly on the comparative use of classification and regression methods in prognostics. While regression-based methods typically provide estimates of RUL, classification-based methods present a forecast of the probability of failure across defined time intervals. Through a comprehensive analysis of recent literature, we highlight key advancements, challenges-such as data imbalance and high-dimensional feature spaces-and emerging trends, including hybrid approaches and AI-enabled prognostic systems. This review aims to provide researchers and practitioners with an awareness of the strengths and compromises of various PdM methods and to help identify future research and build more robust, directed adaptive maintenance systems. Future work may include a systematic review of practical aspects such as public datasets, benchmarking platforms, and open-source tools to support the advancement of PdM research.
Bridging Compositional and Distributional Semantics: A Survey on Latent Semantic Geometry via AutoEncoder
2025-06-25
Yingji Zhang, Danilo S. Carvalho, André Freitas
Integrating compositional and symbolic properties into current distributional semantic spaces can enhance the interpretability, controllability, compositionality, and generalisation capabilities of Transformer-based auto-regressive language models (LMs). In this survey, we offer a novel perspective on latent space geometry through the lens of compositional semantics, a direction we refer to as \textit{semantic representation learning}. This direction enables a bridge between symbolic and distributional semantics, helping to mitigate the gap between them. We review and compare three mainstream autoencoder architectures-Variational AutoEncoder (VAE), Vector Quantised VAE (VQVAE), and Sparse AutoEncoder (SAE)-and examine the distinctive latent geometries they induce in relation to semantic structure and interpretability.
Low-Luminosity Type IIP Supernovae from the Zwicky Transient Facility Census of the Local Universe. II: Lightcurve Analysis
2025-06-25
Kaustav K. Das, Mansi M. Kasliwal, Jesper Sollerman, Christoffer Fremling, Takashi J. Moriya, K-Ryan Hinds, Daniel A. Perley, Eric C. Bellm, Tracy X. Chen, Evan P. O'Connor, Michael W. Coughlin, W. V. Jacobson-Galan, Anjasha Gangopadhyay, Matthew Graham, S. R. Kulkarni, Josiah Purdum, Nikhil Sarin, Steve Schulze, Avinash Singh, Daichi Tsuna, Avery Wold
The Zwicky Transient Facility Census of the Local Universe survey yielded a sample of 330 Type IIP supernovae (SNe) with well-constrained peak luminosities. In paper I (arXiv:2502.19493), we measured their luminosity function and volumetric rate. Here (paper II), we present the largest systematic study of lightcurve properties for Type IIP SNe from a volume-limited survey, analyzing a selected subset of 129 events, including 16 low-luminosity Type IIP (LLIIP) SNe with M${r,peak} \geq -16$ mag. We find that plateau slope correlates with peak brightness, with many LLIIP SNe showing positive slopes--suggesting smaller progenitor radii and distinct density profiles compared to brighter Type IIP SNe. The plateau duration shows only a weak dependence on peak brightness, likely suggesting binary interaction. One SN exhibits a plateau-to-tail drop of >3.5 mag, consistent with an electron-capture or failed SN with very low or zero nickel mass. We derive explosion and progenitor parameters of the entire Type IIP SN sample using semi-analytical and radiation-hydrodynamical models. Based on radiation-hydrodynamical model fitting, LLIIP SNe are characterized by low nickel masses (0.001-0.025 $\mathrm{M_\odot}$), low explosion energies (0.1-0.28 $\times 10^{51}$ erg), low ejecta masses ($8.1^{+0.8}_{-1.7}$ $\mathrm{M\odot}$), and ZAMS masses below 11 $\mathrm{M_\odot}$. In comparison, the full Type IIP SN sample spans a wider range with nickel masses (0.001-0.222 $\mathrm{M_\odot}$), explosion energies (0.10-4.43 $\times 10^{51}$ erg), ejecta masses (5.4-24.8 $\mathrm{M_\odot}$), and ZAMS masses (9.3-16.7 $\mathrm{M_\odot}$). We find strong correlations between peak brightness, explosion energy, and nickel mass that extend to the low-luminosity end. We conclude that LLIIP SNe represent the faint, low-energy end of the Type IIP population and originate from the lowest-mass core-collapse progenitors.
"TikTok, Do Your Thing": User Reactions to Social Surveillance in the Public Sphere
2025-06-25
Meira Gilbert, Miranda Wei, Lindah Kotut
''TikTok, Do Your Thing'' is a viral trend where users attempt to identify strangers they see in public via information crowd-sourcing. The trend started as early as 2021 and users typically engage with it for romantic purposes (similar to a ''Missed Connections'' personal advertisement). This practice includes acts of surveillance and identification in the public sphere, although by peers rather than governments or corporations. To understand users' reactions to this trend we conducted a qualitative analysis of 60 TikTok videos and 1,901 user comments. Of the 60 videos reviewed, we find 19 individuals were successfully identified. We also find that while there were comments expressing disapproval (n=310), more than double the number expressed support (n=883). Supportive comments demonstrated genuine interest and empathy, reflecting evolving conceptions of community and algorithmic engagement. On the other hand, disapproving comments highlighted concerns about inappropriate relationships, stalking, consent, and gendered double standards. We discuss these insights in relation to the normalization of interpersonal surveillance, online stalking, and as an evolution of social surveillance to offer a new perspective on user perceptions surrounding interpersonal surveillance and identification in the public sphere.
Global properties and stability of transonic plasma acceleration in the magnetic nozzle
2025-06-25
N. Sheth, A. Smolyakov, J. Deguire, S. Pande, P. N. Yushmanov
It is shown that transonic plasma acceleration in the converging-diverging magnetic field (magnetic nozzle) follows the unique global solution which is fully defined by the magnetic field. Such solution, which was analytically obtained earlier in the paraxial approximation, is compared here with results of the axisymmetric two-dimensional (rz) magnetohydrodynamics(MHD) simulations. It is shown that analytical solution describes well the region near the axis but also can be applied to arbitrary magnetic surfaces. The simulations with different length of the nozzle and different boundary values for plasma velocity show that the plasma flow switches to the unique transonic acceleration profile via the shock-like transition in the velocity and pressure profiles. The simulations with arbitrary (not vacuum) initial magnetic field demonstrate the global adjustment of the magnetic field such that the transonic acceleration velocity profile follows the analytic predictions with the modified magnetic field.
Quantum-Accelerated Wireless Communications: Concepts, Connections, and Implications
2025-06-25
Naoki Ishikawa, Giuseppe Thadeu Freitas de Abreu, Petar Popovski, Robert W. Heath Jr
Quantum computing is poised to redefine the algorithmic foundations of communication systems. While quantum superposition and entanglement enable quadratic or exponential speedups for specific problems, identifying use cases where these advantages yield engineering benefits is, however, still nontrivial. This article presents the fundamentals of quantum computing in a style familiar to the communications society, outlining the current limits of fault-tolerant quantum computing and uncovering a mathematical harmony between quantum and wireless systems, which makes the topic more enticing to wireless researchers. Based on a systematic review of pioneering and state-of-the-art studies, we distill common design trends for the research and development of quantum-accelerated communication systems and highlight lessons learned. The key insight is that classical heuristics can sharpen certain quantum parameters, underscoring the complementary strengths of classical and quantum computing. This article aims to catalyze interdisciplinary research at the frontier of quantum information processing and future communication systems.
Nonzero neutrino masses guarantee new physics and neutrinos are excellent probes of extreme environments in the Universe. The recent collider neutrino experimental program, including FASER$\nu$ and SND@LHC, along with the planned Forward Physics Facility at the High-Luminosity Large Hadron Collider, is opening a new window into neutrino physics and astrophysics. In this article, we review recent achievements and prospects of collider neutrino experiments, including key achievements such as the first measurements of collider neutrino interactions at unprecedented energies and the exploration of new physics scenarios, like dark matter candidates, sterile neutrinos, and non-standard neutrino interactions. For concreteness, we will focus on the significant scientific opportunities presented by the Forward Physics Facility, which will enable precision measurements of neutrino cross sections and proton structure at low parton momentum fraction. Furthermore, collider neutrino studies will substantially reduce systematic uncertainties in calculating atmospheric neutrino fluxes, thereby improving astrophysical neutrino observations as well as advancing our understanding of cosmic-ray interactions.
Prospects for radio weak lensing: studies using LOFAR observations in the ELAIS-N1 field
2025-06-25
Jinyi Liu, Reinout van Weeren, Huub Rottgering, Konrad Kuijken
We carry out a shape and weak lensing analysis of Low Frequency Array (LOFAR) radio sources and Hyper Suprime-Cam (HSC) optical sources within the European Large Area Infrared Space Observatory Survey-North 1 (ELAIS-N1) field. Using HSC data alone, we detect a cosmic shear correlation signal at a significance of $\sim$$9\sigma$ over a $\sim$$6.4$ deg$^2$ region. For the radio dataset, we analyse observations from both the LOFAR Two Metre Sky Survey (LoTSS) and the International LOFAR Telescope (ILT). While LoTSS provides the deepest radio imaging of ELAIS-N1 with a central source density of $\sim$2.7 arcmin$^{-2}$, its $6^{\prime\prime}$ resolution limits the accuracy of shape measurements. But, using LoTSS-matched HSC sources, we show that accurate radio shape measurements would enable us to measure the amplitude of the shear correlation function at least at $\sim$2$\sigma$ significance. In contrast, ILT observation of the field offers a superior $0.3^{\prime\prime}$ resolution. By cross-matching HSC and ILT samples, we measure a position angle correlation of $R_{\cos(2\alpha)} = 0.15 \pm 0.02$. This result highlights ILT's ability to resolve extended and diffuse emission. The current ILT observations lack the required depth for robust weak lensing measurements. To assess the potential of ILT, we use simulated data with increased observation hours. Our analysis indicates that with 3200 hours of ILT observations or deeper data, and assuming that statistical errors dominate over systematics, a shear correlation could be detected with moderate significance. To achieve this will require precise radio shear measurements and effective mitigation of point spread function (PSF) systematics.
We review some facts, properties and applications of the urn of Hill, Lane and Sudderth, a paradigmatic model of stochastic process with memory, where the urn evolution is as follows: consider an urn of given capacity, at each step a new ball, black or white, is added to the urn with probability that is function (urn function) of the fraction of black balls. The process runs until capacity is reached.
Uncovering Hidden Violent Tendencies in LLMs: A Demographic Analysis via Behavioral Vignettes
2025-06-25
Quintin Myers, Yanjun Gao
Large language models (LLMs) are increasingly proposed for detecting and responding to violent content online, yet their ability to reason about morally ambiguous, real-world scenarios remains underexamined. We present the first study to evaluate LLMs using a validated social science instrument designed to measure human response to everyday conflict, namely the Violent Behavior Vignette Questionnaire (VBVQ). To assess potential bias, we introduce persona-based prompting that varies race, age, and geographic identity within the United States. Six LLMs developed across different geopolitical and organizational contexts are evaluated under a unified zero-shot setting. Our study reveals two key findings: (1) LLMs surface-level text generation often diverges from their internal preference for violent responses; (2) their violent tendencies vary across demographics, frequently contradicting established findings in criminology, social science, and psychology.
The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas
2025-06-25
Chenglei Si, Tatsunori Hashimoto, Diyi Yang
Large Language Models (LLMs) have shown promise in accelerating the scientific research pipeline. A key capability for this process is the ability to generate novel research ideas, and prior studies have found settings in which LLM-generated research ideas were judged as more novel than human-expert ideas. However, a good idea should not simply appear to be novel, it should also result in better research after being executed. To test whether AI-generated ideas lead to better research outcomes, we conduct an execution study by recruiting 43 expert researchers to execute randomly-assigned ideas, either written by experts or generated by an LLM. Each expert spent over 100 hours implementing the idea and wrote a 4-page short paper to document the experiments. All the executed projects are then reviewed blindly by expert NLP researchers. Comparing the review scores of the same ideas before and after execution, the scores of the LLM-generated ideas decrease significantly more than expert-written ideas on all evaluation metrics (novelty, excitement, effectiveness, and overall; p < 0.05), closing the gap between LLM and human ideas observed at the ideation stage. When comparing the aggregated review scores from the execution study, we even observe that for many metrics there is a flip in rankings where human ideas score higher than LLM ideas. This ideation-execution gap highlights the limitations of current LLMs in generating truly effective research ideas and the challenge of evaluating research ideas in the absence of execution outcomes.
Spiking Neural Networks for SAR Interferometric Phase Unwrapping: A Theoretical Framework for Energy-Efficient Processing
2025-06-25
Marc Bara
We present the first theoretical framework for applying spiking neural networks (SNNs) to synthetic aperture radar (SAR) interferometric phase unwrapping. Despite extensive research in both domains, our comprehensive literature review confirms that SNNs have never been applied to phase unwrapping, representing a significant gap in current methodologies. As Earth observation data volumes continue to grow exponentially (with missions like NISAR expected to generate 100PB in two years) energy-efficient processing becomes critical for sustainable data center operations. SNNs, with their event-driven computation model, offer potential energy savings of 30-100x compared to conventional approaches while maintaining comparable accuracy. We develop spike encoding schemes specifically designed for wrapped phase data, propose SNN architectures that leverage the spatial propagation nature of phase unwrapping, and provide theoretical analysis of computational complexity and convergence properties. Our framework demonstrates how the temporal dynamics inherent in SNNs can naturally model the spatial continuity constraints fundamental to phase unwrapping. This work opens a new research direction at the intersection of neuromorphic computing and SAR interferometry, offering a complementary approach to existing algorithms that could enable more sustainable large-scale InSAR processing.
Balancing the cellular budget: lessons in metabolism from microbes to cancer
2025-06-25
B. Vibishan, Mohit Kumar Jolly, Akshit Goyal
Cancer cells are often seen to prefer glycolytic metabolism over oxidative phosphorylation even in the presence of oxygen-a phenomenon termed the Warburg effect. Despite significant strides in the decades since its discovery, a clear basis is yet to be established for the Warburg effect and why cancer cells show such a preference for aerobic glycolysis. In this review, we draw on what is known about similar metabolic shifts both in normal mammalian physiology and overflow metabolism in microbes to shed new light on whether aerobic glycolysis in cancer represents some form of optimisation of cellular metabolism. From microbes to cancer, we find that metabolic shifts favouring glycolysis are sometimes driven by the need for faster growth, but the growth rate is by no means a universal goal of optimal metabolism. Instead, optimisation goals at the cellular level are often multi-faceted and any given metabolic state must be considered in the context of both its energetic costs and benefits over a range of environmental contexts. For this purpose, we identify the conceptual framework of resource allocation as a potential testbed for the investigation of the cost-benefit balance of cellular metabolic strategies. Such a framework is also readily integrated with dynamical systems modelling, making it a promising avenue for new answers to the age-old question of why cells, from cancers to microbes, choose the metabolic strategies they do.
Latent-space Field Tension for Astrophysical Component Detection An application to X-ray imaging
2025-06-25
Matteo Guardiani, Vincent Eberle, Margret Westerkamp, Julian Rüstig, Philipp Frank, Torsten Enßlin
Modern observatories are designed to deliver increasingly detailed views of astrophysical signals. To fully realize the potential of these observations, principled data-analysis methods are required to effectively separate and reconstruct the underlying astrophysical components from data corrupted by noise and instrumental effects. In this work, we introduce a novel multi-frequency Bayesian model of the sky emission field that leverages latent-space tension as an indicator of model misspecification, enabling automated separation of diffuse, point-like, and extended astrophysical emission components across wavelength bands. Deviations from latent-space prior expectations are used as diagnostics for model misspecification, thus systematically guiding the introduction of new sky components, such as point-like and extended sources. We demonstrate the effectiveness of this method on synthetic multi-frequency imaging data and apply it to observational X-ray data from the eROSITA Early Data Release (EDR) of the SN1987A region in the Large Magellanic Cloud (LMC). Our results highlight the method's capability to reconstruct astrophysical components with high accuracy, achieving sub-pixel localization of point sources, robust separation of extended emission, and detailed uncertainty quantification. The developed methodology offers a general and well-founded framework applicable to a wide variety of astronomical datasets, and is therefore well suited to support the analysis needs of next-generation multi-wavelength and multi-messenger surveys.
A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools
2025-06-25
Minh-Hao Van, Prateek Verma, Chen Zhao, Xintao Wu
Foundation models (FMs) are catalyzing a transformative shift in materials science (MatSci) by enabling scalable, general-purpose, and multimodal AI systems for scientific discovery. Unlike traditional machine learning models, which are typically narrow in scope and require task-specific engineering, FMs offer cross-domain generalization and exhibit emergent capabilities. Their versatility is especially well-suited to materials science, where research challenges span diverse data types and scales. This survey provides a comprehensive overview of foundation models, agentic systems, datasets, and computational tools supporting this growing field. We introduce a task-driven taxonomy encompassing six broad application areas: data extraction, interpretation and Q\&A; atomistic simulation; property prediction; materials structure, design and discovery; process planning, discovery, and optimization; and multiscale modeling. We discuss recent advances in both unimodal and multimodal FMs, as well as emerging large language model (LLM) agents. Furthermore, we review standardized datasets, open-source tools, and autonomous experimental platforms that collectively fuel the development and integration of FMs into research workflows. We assess the early successes of foundation models and identify persistent limitations, including challenges in generalizability, interpretability, data imbalance, safety concerns, and limited multimodal fusion. Finally, we articulate future research directions centered on scalable pretraining, continual learning, data governance, and trustworthiness.
Symmetry Classification of Magnetic Orders and Emergence of Spin-Orbit Magnetism
2025-06-25
Yuntian Liu, Xiaobing Chen, Yutong Yu, Qihang Liu
Magnetism, a fundamental concept predating condensed matter physics, has achieved significant advancements in recent decades, driven by its potential for next-generation storage devices. Meanwhile, the classification of magnetic orders, even for the most fundamental concepts like ferromagnetism (FM) and antiferromagnetism (AFM), has encountered unprecedented challenges since the discovery of unconventional magnets and advancements in antiferromagnetic spintronics. Here, we present a rigorous classification of magnetic order using state-of-the-art spin space group (SSG) theory. Based on whether the net magnetic moment is constrained to zero by SSG, magnetic order is unambiguously dichotomized into FM (including ferrimagnetism) and AFM. Additionally, we classify AFM geometries into four categories -- primary, bi-color, spiral, and multi-axial -- based on periodic spin propagation beyond the symmetry operations of magnetic space groups. We then introduce a distinct magnetic phase, dubbed spin-orbit magnetism, characterized by its unique behavior involving the spin-orbit coupling (SOC) order parameter and SOC-driven phase transition. We further create an oriented SSG description, i.e., SSG with a fixed magnetic configuration, apply the framework to 2,065 experimentally validated magnetic materials in MAGNDATA database, and identify over 220 spin-orbit magnets with distinct spin and orbital magnetization mechanisms. Implemented by the online program FINDSPINGROUP, our work establishes a universal symmetry standard for magnetic order classification, offering new understandings of unconventional magnets and broad applicability in spintronics and quantum material design.
One Giant Leap for Womankind: First Menstrual Cups Tested in Space Flight Conditions
2025-06-25
Ligia F. Coelho, Catarina Miranda, Joao Canas, Miguel Morgado, Diogo Nunes, Andre F. Henriques, Adam B. Langeveld
In the early days of space exploration, when Sally Ride was offered 100 tampons for a week-long mission, menstrual medical devices first began to be used in space conditions. Since then, hormonal menstrual suppression has become the preferred method for managing menstruation in space, offering significant advantages. However, this is not an option for astronauts who choose to menstruate. The lack of sustainable menstrual technologies will pose challenges for long-duration missions to the Moon and Mars, where astronauts may spend years in space. The AstroCup mission represents the first effort to test menstrual cups in spaceflight, evaluating their durability and functionality. Through material integrity tests and functional assessments using a rheological analogue of human blood, we demonstrate the resilience of menstrual cups and discuss their implications for sustainable menstrual management in future lunar and Martian missions.
Dark Classification Matters: Searching for Primordial Black Holes with LSST
2025-06-25
Miguel Crispim Romao, Djuna Croon, Benedict Crossey, Daniel Godines
We present projected constraints on the abundance of primordial black holes (PBHs) as a constituent of dark matter, based on microlensing observations from the upcoming Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory. We use a catalogue of microlensing light curves simulated with Rubin Observatory's OpSims to demonstrate that competitive constraints crucially rely on minimising the false positive rate (FPR) of the classification algorithm. We propose the Bayesian information criterion and a Boosted Decision Tree as effective discriminators and compare their derived efficiency and FPR to a more standard $\chi^2$-test.
The original description of the k-d tree recognized that rebalancing techniques, such as used to build an AVL tree or a red-black tree, are not applicable to a k-d tree. Hence, in order to build a balanced k-d tree, it is necessary to find the median of a set of data for each recursive subdivision of that set. The sort or selection used to find the median, and the technique used to partition the set about that median, strongly influence the computational complexity of building a k-d tree. This article describes and contrasts three variants of the k-d tree that differ in their technique used to partition the set, and compares the performance of those variants. In addition, dual-threaded execution is proposed and analyzed for one of the three variants.
Quantum key distribution (QKD) represents a cornerstone of secure communication in the quantum era. While discrete-variable QKD (DV-QKD) protocols were historically the first to demonstrate secure key exchange, continuous-variable QKD (CV-QKD) has emerged as a more practical alternative due to its seamless compatibility with current telecommunications infrastructure. CV-QKD relies on coherent and squeezed states of light, offering significant advantages for integration into modern optical networks. This review comprehensively explores the theoretical underpinnings, technological advancements, and practical challenges of CV-QKD. Special attention is given to the role of photonic integrated circuits (PICs) in enabling scalable and efficient implementation of CV-QKD systems. Furthermore, recent advances in machine learning have been leveraged to optimize CV-QKD performance, with data-driven techniques enhancing noise estimation, parameter optimization, and system security. Additionally, tensor networks provide efficient computational tools for analyzing complex quantum correlations, improving the efficiency and robustness of quantum key distribution protocols. These developments, combined with ongoing improvements in quantum photonic integration, pave the way for the practical deployment of large-scale, high-speed quantum-secure networks.
Unmanned aerial vehicles (UAVs) are increasingly employed in diverse applications such as land surveying, material transport, and environmental monitoring. Following missions like data collection or inspection, UAVs must land safely at docking stations for storage or recharging, which is an essential requirement for ensuring operational continuity. However, accurate landing remains challenging due to factors like GPS signal interference. To address this issue, we propose a deviation warning system for UAV landings, powered by a novel vision-based model called AeroLite-MDNet. This model integrates a multiscale fusion module for robust cross-scale object detection and incorporates a segmentation branch for efficient orientation estimation. We introduce a new evaluation metric, Average Warning Delay (AWD), to quantify the system's sensitivity to landing deviations. Furthermore, we contribute a new dataset, UAVLandData, which captures real-world landing deviation scenarios to support training and evaluation. Experimental results show that our system achieves an AWD of 0.7 seconds with a deviation detection accuracy of 98.6\%, demonstrating its effectiveness in enhancing UAV landing reliability. Code will be available at https://github.com/ITTTTTI/Maskyolo.git
It has recently been revealed that, in curved black-hole spacetimes, non-minimally coupled massive Proca fields may be characterized by the existence of poles in their linearized perturbation equations and may therefore develop exponentially growing instabilities. Interestingly, recent numerical computations [H. W. Chiang, S. Garcia-Saenz, and A. Sang, arXiv:2504.04779] have provided compelling evidence that the onset of monopole instabilities in the composed black-hole-field system is controlled by the dimensionless physical parameter $\mu r_-$, where $\mu$ is the proper mass of the non-minimally coupled Proca field and $r_-\equiv (-2\alpha)^{1/3}r_{\text{H}}$ is the radial location of the pole [here $\alpha$ is the non-minimal coupling parameter of the Einstein-Proca theory and $r_{\text{H}}$ is the radius of the black-hole horizon]. In the present paper we use {\it analytical} techniques in order to explore the physical properties of critical (marginally-stable) composed Schwarzschild-black-hole-nonminimally-coupled-monopole-Proca-field configurations. In particular, we derive a remarkably compact analytical formula for the discrete spectrum $\{\mu(r_{\text{H}},r_-;n) \}^{n=\infty}_{n=1}$ of Proca field masses which characterize the critical black-hole-monopole-Proca-field configurations in the dimensionless regime ${{r_- -r_{\text{H}}}\over{r_{\text{H}}}}\ll1$ of near-horizon poles. The physical significance of the analytically derived resonance spectrum stems from the fact that the critical field mass $\mu_{\text{c}}\equiv\mu(r_{\text{H}},r_-;n=1)$ marks the onset of instabilities in the Schwarzschild-black-hole-nonminimally-coupled-monopole-Proca-field system. In particular, composed black-hole-linearized-Proca-field configurations in the small-mass regime $\mu\leq\mu_{\text{c}}$ of the Proca field are stable.
JoyAgents-R1: Joint Evolution Dynamics for Versatile Multi-LLM Agents with Reinforcement Learning
2025-06-24
Ai Han, Junxing Hu, Pu Wei, Zhiqian Zhang, Yuhang Guo, Jiawei Lu, Zicheng Zhang
Multi-agent reinforcement learning (MARL) has emerged as a prominent paradigm for increasingly complex tasks. However, joint evolution across heterogeneous agents remains challenging due to cooperative inefficiency and training instability. In this paper, we propose the joint evolution dynamics for MARL called JoyAgents-R1, which first applies Group Relative Policy Optimization (GRPO) to the joint training of heterogeneous multi-agents. By iteratively refining agents' large language models (LLMs) and memories, the method achieves holistic equilibrium with optimal decision-making and memory capabilities. Specifically, JoyAgents-R1 first implements node-wise Monte Carlo sampling on the behavior of each agent across entire reasoning trajectories to enhance GRPO sampling efficiency while maintaining policy diversity. Then, our marginal benefit-driven selection strategy identifies top-$K$ sampling groups with maximal reward fluctuations, enabling targeted agent model updates that improve training stability and maximize joint benefits through cost-effective parameter adjustments. Meanwhile, JoyAgents-R1 introduces an adaptive memory evolution mechanism that repurposes GRPO rewards as cost-free supervisory signals to eliminate repetitive reasoning and accelerate convergence. Experiments across general and domain-specific scenarios demonstrate that JoyAgents-R1 achieves performance comparable to that of larger LLMs while built on smaller open-source models.
Non-Gaussian statistics in galaxy weak lensing: compressed three-point correlations and cosmological forecasts
2025-06-24
Sofia Samario-Nava, Alejandro Aviles, Juan Carlos Hidalgo
Building on previous developments of a harmonic decomposition framework for computing the three-point correlation function (3PCF) of projected scalar fields over the sky, this work investigates how much cosmological information is contained in these higher-order statistics. We perform a forecast to determine the number of harmonic multipoles required to capture the full information content of the 3PCF in the context of galaxy weak lensing, finding that only the first few multipoles are sufficient to capture the additional cosmological information provided by the 3PCF. This study addresses a critical practical question: to what extent can the high-dimensional 3PCF signal be compressed without significant loss of cosmological information? Since the different multipoles contain highly redundant information, we apply a principal component analysis (PCA) which further reduces its dimensionality and preserving information. We also account for non-linear parameter degeneracies using the DALI method, an extension of Fisher forecasting that includes higher-order likelihood information. Under optimistic settings, we find that the 3PCF improves considerably the constraining power of the 2PCF for the matter abundance $\Omega_m$ and fluctuations amplitude $\sigma_8$ by about 18% for galaxy maps at redshift $z=0.5$. However, for the amplitude $S_8$ and the equation of state parameter of dark energy, $w_0$, the improvements are very modest. Thanks to the harmonic basis and PCA, the method can be implemented with relatively low computational cost, providing a practical tool for current and future photometric surveys.
Systematic Review of Pituitary Gland and Pituitary Adenoma Automatic Segmentation Techniques in Magnetic Resonance Imaging
2025-06-24
Mubaraq Yakubu, Navodini Wijethilake, Jonathan Shapey, Andrew King, Alexander Hammers
Purpose: Accurate segmentation of both the pituitary gland and adenomas from magnetic resonance imaging (MRI) is essential for diagnosis and treatment of pituitary adenomas. This systematic review evaluates automatic segmentation methods for improving the accuracy and efficiency of MRI-based segmentation of pituitary adenomas and the gland itself. Methods: We reviewed 34 studies that employed automatic and semi-automatic segmentation methods. We extracted and synthesized data on segmentation techniques and performance metrics (such as Dice overlap scores). Results: The majority of reviewed studies utilized deep learning approaches, with U-Net-based models being the most prevalent. Automatic methods yielded Dice scores of 0.19--89.00\% for pituitary gland and 4.60--96.41\% for adenoma segmentation. Semi-automatic methods reported 80.00--92.10\% for pituitary gland and 75.90--88.36\% for adenoma segmentation. Conclusion: Most studies did not report important metrics such as MR field strength, age and adenoma size. Automated segmentation techniques such as U-Net-based models show promise, especially for adenoma segmentation, but further improvements are needed to achieve consistently good performance in small structures like the normal pituitary gland. Continued innovation and larger, diverse datasets are likely critical to enhancing clinical applicability.
Canary in the Mine: An LLM Augmented Survey of Disciplinary Complaints to the Ordre des ingénieurs du Québec (OIQ)
2025-06-24
Tammy Mackenzie, Varsha Kesavan, Thomas Mekhael, Animesh Paul, Branislav Radeljic, Sara Kodeiri, Sreyoshi Bhaduri
This study uses pre-trained LLMs to conduct thematic analysis to investigate disciplinary incidents involving engineers in Quebec, shedding light on critical gaps in engineering education. Through a comprehensive review of the disciplinary register of the Ordre des ing\'enieurs du Qu\'ebec (OIQ)'s disciplinary register for 2010 to 2024, researchers from engineering education and human resources management in technological development laboratories conducted a thematic analysis of reported incidents to identify patterns, trends, and areas for improvement. The analysis aims to uncover the most common types of disciplinary incidents, underlying causes, and implications for the field in how engineering education addresses (or fails to address) these issues. Our findings identify recurring themes, analyze root causes, and offer recommendations for engineering educators and students to mitigate similar incidents. This research has implications for informing curriculum development, professional development, and performance evaluation, ultimately fostering a culture of professionalism and ethical responsibility in engineering. By providing empirical evidence of disciplinary incidents and their causes, this study contributes to evidence-based practices for engineering education and professional development, enhancing the engineering education community's understanding of professionalism and ethics.
A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects
2025-06-24
Shulan Ruan, Rongwei Wang, Xuchen Shen, Huijie Liu, Baihui Xiao, Jun Shi, Kun Zhang, Zhenya Huang, Yu Liu, Enhong Chen, You He
Multi-sensor fusion perception (MSFP) is a key technology for embodied AI, which can serve a variety of downstream tasks (e.g., 3D object detection and semantic segmentation) and application scenarios (e.g., autonomous driving and swarm robotics). Recently, impressive achievements on AI-based MSFP methods have been reviewed in relevant surveys. However, we observe that the existing surveys have some limitations after a rigorous and detailed investigation. For one thing, most surveys are oriented to a single task or research field, such as 3D object detection or autonomous driving. Therefore, researchers in other related tasks often find it difficult to benefit directly. For another, most surveys only introduce MSFP from a single perspective of multi-modal fusion, while lacking consideration of the diversity of MSFP methods, such as multi-view fusion and time-series fusion. To this end, in this paper, we hope to organize MSFP research from a task-agnostic perspective, where methods are reported from various technical views. Specifically, we first introduce the background of MSFP. Next, we review multi-modal and multi-agent fusion methods. A step further, time-series fusion methods are analyzed. In the era of LLM, we also investigate multimodal LLM fusion methods. Finally, we discuss open challenges and future directions for MSFP. We hope this survey can help researchers understand the important progress in MSFP and provide possible insights for future research.
Multiband analysis of the O'Connell effect in 14 eclipsing binaries
2025-06-24
D. Flores Cabrera, M. Catelan, A. Papageorgiou, A. Clocchiatti
The O'Connell effect is a phenomenon in eclipsing binary (EB) systems that consists of unequal maxima in a light curve when out of eclipse. Despite being known for decades and with several theories proposed over the years, this effect is still not fully understood. Our goal is to find different O'Connell effect properties using a multiband approach, compare them with each other, and find correlations between these properties and the physical parameters of the systems. We present the analysis of 14 new EBs that show the O'Connell effect using multiband data extracted from the Asteroid Terrestrial-impact Last Alert System (ATLAS) and Zwicky Transient Facility (ZTF) all-sky surveys. We measured the difference in maximum amplitudes (delta m) alongside different light curve features in different passbands via a new modeling process that uses Gaussian fits. We created a brand-new phenomenological model for O'Connell effect systems whose analysis had previously been hampered by overfitting. Although the magnitude of the O'Connell effect seems to be more pronounced at shorter effective wavelengths, supporting the idea that cool starspots cause the effect, a conclusive correlation is not found. On the other hand, we do find strong correlations between the magnitude of the effect and both the temperature and the period, both of which are inconsistent with a previous study. We also find that in systems that show both positive and negative O'Connell effects, there are different correlations with the aforementioned parameters. We conclude that, even though starspots may be one cause of the O'Connell effect, it is likely a multipronged phenomenon; for instance, the physical interaction between the components of close binary stars may be another important factor.
MUSE-DARK-I: Dark matter halo properties of intermediate-z star-forming galaxies
2025-06-24
B. I. Ciocan, N. F. Bouché, J. Fensch, W. Mercier, D. Krajnović, J. Richard, T. Contini, A. Jeanneau
[Abridged] We analyse the dark matter (DM) halo properties of 127 0.365% of the sample. We find that the kinematically inferred stellar masses agree with values from SED fitting. We find that 89% of galaxies have DM fractions >50%. For 70% of SFGs, we infer a DM inner slope, gamma < 0.5, indicating cored DM profiles, but no correlation is found between gamma and star formation rate of the sample. The stellar- and concentration-mass relations agree with theoretical expectations, but with larger scatter. We confirm the anticorrelation between halo scale radius and DM density. The halo scale radii and DM surface densities increase with Mstar, while DM densities stay constant. We find tentative evidence of an evolution of the DM density with z, which suggests that the DM halos of intermediate-z systems are denser than those of local galaxies. In contrast, the halo scale radii are z-invariant.
XMM-Newton follow-up of a sample of apparent low surface brightness galaxy groups detected in the ROSAT All-Sky Survey
2025-06-24
Claudia Spinelli, Angie Veronica, Florian Pacaud, Thomas H. Reiprich, Konstantinos Migkas, Weiwei Xu, Miriam E. Ramos-Ceja
Galaxy cluster cosmology relies on complete and pure samples spanning a large range of masses and redshifts. In Xu et al. (2018) and Xu et al. (2022), we discovered an apparently new population of galaxy groups and clusters with, on average, flatter X-ray surface brightness profiles than known clusters; this cluster population was missed in previous cluster surveys. The discovery of such a new class of objects could have a significant impact on cosmological applications of galaxy clusters. In this work we use a subsample of these systems to assess whether they belong to a new population. We follow up three of these galaxy groups and clusters with high-quality XMM-Newton observations. We produce clean images and spectra and use them for model fitting. We also identify known galaxies, groups, or clusters in the field. The observations reveal that all three systems are composed of multiple groups each, either at the same or at different redshifts. In total, we characterize nine groups. We measure flat surface brightness profiles with slope parameter $\beta < 0.6$; i.e, less than the canonical $\beta = 2/3$. For the two main central groups, we even measure $\beta < 0.4$. When the fluxes for the three observations are split up across the nine identified groups, none of them exceeds the typical flux limit adopted in previous RASS cluster catalogs, $\approx 3 \times 10^{-12}\,\mathrm{erg s^{-1}cm^{-2}}$ in the 0.1$-$2.4 keV energy band. The observations reveal that groups with flat surface brightness profiles exist. Whether they form a new, separate population requires additional follow-up observations of further systems from the Xu et al. sample, given the complexity we have discovered. Such extended low surface brightness systems, as well as multiple systems and projection effects, need to be taken into account when determining selection functions of group and cluster samples.
Precision oncology requires accurate molecular insights, yet obtaining these directly from genomics is costly and time-consuming for broad clinical use. Predicting complex molecular features and patient prognosis directly from routine whole-slide images (WSI) remains a major challenge for current deep learning methods. Here we introduce PathLUPI, which uses transcriptomic privileged information during training to extract genome-anchored histological embeddings, enabling effective molecular prediction using only WSIs at inference. Through extensive evaluation across 49 molecular oncology tasks using 11,257 cases among 20 cohorts, PathLUPI demonstrated superior performance compared to conventional methods trained solely on WSIs. Crucially, it achieves AUC $\geq$ 0.80 in 14 of the biomarker prediction and molecular subtyping tasks and C-index $\geq$ 0.70 in survival cohorts of 5 major cancer types. Moreover, PathLUPI embeddings reveal distinct cellular morphological signatures associated with specific genotypes and related biological pathways within WSIs. By effectively encoding molecular context to refine WSI representations, PathLUPI overcomes a key limitation of existing models and offers a novel strategy to bridge molecular insights with routine pathology workflows for wider clinical application.
A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures
2025-06-24
Dezhang Kong, Shi Lin, Zhenhua Xu, Zhebo Wang, Minghao Li, Yufeng Li, Yilun Zhang, Zeyang Sha, Yuyuan Li, Changting Lin, Xun Wang, Xuan Liu, Muhammad Khurram Khan, Ningyu Zhang, Chaochao Chen, Meng Han
In recent years, Large-Language-Model-driven AI agents have exhibited unprecedented intelligence, flexibility, and adaptability, and are rapidly changing human production and lifestyle. Nowadays, agents are undergoing a new round of evolution. They no longer act as an isolated island like LLMs. Instead, they start to communicate with diverse external entities, such as other agents and tools, to collectively perform more complex tasks. Under this trend, agent communication is regarded as a foundational pillar of the future AI ecosystem, and many organizations intensively begin to design related communication protocols (e.g., Anthropic's MCP and Google's A2A) within the recent few months. However, this new field exposes significant security hazard, which can cause severe damage to real-world scenarios. To help researchers to quickly figure out this promising topic and benefit the future agent communication development, this paper presents a comprehensive survey of agent communication security. More precisely, we first present a clear definition of agent communication and categorize the entire lifecyle of agent communication into three stages: user-agent interaction, agent-agent communication, and agent-environment communication. Next, for each communication phase, we dissect related protocols and analyze its security risks according to the communication characteristics. Then, we summarize and outlook on the possible defense countermeasures for each risk. Finally, we discuss open issues and future directions in this promising research field.
Simulating the Waterfall Model: A Systematic Review
2025-06-24
Antonios Saravanos
This systematic mapping study examines how the Waterfall Model has been represented in computational simulations within peer-reviewed literature. While Agile methodologies dominate contemporary software design practices, the Waterfall Model persists, particularly, within hybrid approaches that fuse structured, sequential workflows with the adaptability of agile practices. Despite its continued presence, little attention has been given to how the Waterfall Model is simulated in research contexts. A structured search of major academic databases identified 68 peer-reviewed studies published between 2000 and 2024. After applying inclusion criteria, selected studies were analyzed across four dimensions: (1) simulation methodologies (e.g., discrete-event simulation, system dynamics), (2) platforms and tools (e.g., Simphony.NET, SimPy), (3) geographic and temporal trends, and (4) fidelity to Royce's original seven-phase model. Discrete-event simulation was most commonly used, reflecting the model's sequential nature. Early work relied on proprietary platforms, while recent studies increasingly use open-source, Python-based tools. No studies fully implemented Royce's original formulation, most employed adaptations. These findings suggest that although niche, simulation of the Waterfall Model is present in academic discourse. This work highlights the need for accessible modeling tools and calls for future research that integrates the waterfall software process model with modern hybrid practices.
Power-law Emission-line Wings and Radiation-Driven Superwinds in Local Lyman Continuum Emitters
2025-06-24
Lena Komarova, Sally Oey, Rui Marques-Chaves, Ricardo Amorín, Alaina Henry, Daniel Schaerer, Alberto Saldana-Lopez, Alexandra Le Reste, Claudia Scarlata, Matthew J. Hayes, Omkar Bait, Sanchayeeta Borthakur, Cody Carr, John Chisholm, Harry C. Ferguson, Vital Gutierrez Fernandez, Brian Fleming, Sophia R. Flury, Mauro Giavalisco, Andrea Grazian, Timothy Heckman, Anne E. Jaskot, Zhiyuan Ji, Göran Östlin, Laura Pentericci, Swara Ravindranath, Trinh Thuan, Jose M. Vílchez, Gabor Worseck, Xinfeng Xu
We investigate broad emission-line wings, reaching velocities up to 800 km/s, observed in 26 galaxies with Lyman continuum (LyC) observations, primarily from the Low-redshift Lyman Continuum Survey (LzLCS). Using Magellan/MIKE, VLT/X-shooter, and WHT/ISIS high-resolution spectroscopy, we show that this fast gas appears to probe the dominant feedback mechanisms linked to LyC escape. We find that in 14 galaxies, the wings are best fit with power laws with slopes -3.5 to -1.6, with four others best fit by Gaussians of width ~300 km/s; the remaining eight show ambiguous wing morphologies. Gaussian wings are found only at low O32 = [O III]5007/[O II]3726,3729 and high metallicity, while power-law wings span the full range of these parameters. The general evidence suggests a dual-mode paradigm for LyC escape: radiation-driven superwinds traced by power-law wings and supernova-driven feedback traced by Gaussian wings. For the former, the < 3 Myr-old, pre-supernova stellar population correlates with more luminous, faster winds. The data also show that radiation-driven wind parameters like wind luminosity and power-law slope depend on the UV luminosity more than the optically thick covering fraction, consistent with ``picket-fence" radiative transfer. Observed wing slope values flatten with both escaping LyC luminosity and higher extinction, while still preserving the anticorrelation between these two quantities. Additionally, the differential between red and blue slopes implies that extinction and dense gas are centrally concentrated relative to the wind emission. Overall, our results show that power-law emission-line wings probe LyC-driven winds and LyC escape in metal-poor starbursts.
A primer on the closure of algebraic complexity classes under factoring
2025-06-24
C. S. Bhargav, Prateek Dwivedi, Nitin Saxena
Polynomial factorization is a fundamental problem in computational algebra. Over the past half century, a variety of algorithmic techniques have been developed to tackle different variants of this problem. In parallel, algebraic complexity theory classifies polynomials into complexity classes based on their perceived `hardness'. This raises a natural question: Do these classes afford efficient factorization? In this survey, we revisit two pivotal techniques in polynomial factorization: Hensel lifting and Newton iteration. Though they are variants of the same theme, their distinct applications across the literature warrant separate treatment. These techniques have played an important role in resolving key factoring questions in algebraic complexity theory. We examine and organise the known results through the lens of these techniques to highlight their impact. We also discuss their equivalence while reflecting on how their use varies with the context of the problem. We focus on four prominent complexity classes: circuits of polynomial size ($\text{VP}_{\text{nb}}$), circuits with both polynomial size and degree (VP and its border $\overline{\text{VP}}$), verifier circuits of polynomial size and degree (VNP), and polynomial-size algebraic branching programs (VBP). We also examine more restricted models, such as formulas and bounded-depth circuits. Along the way, we list several open problems that remain unresolved.
Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI
2025-06-24
Joris Wuts, Jakub Ceranka, Nicolas Michoux, Frédéric Lecouvet, Jef Vandemeulebroucke
The segmentation of metastatic bone disease (MBD) in whole-body MRI (WB-MRI) is a challenging problem. Due to varying appearances and anatomical locations of lesions, ambiguous boundaries, and severe class imbalance, obtaining reliable segmentations requires large, well-annotated datasets capturing lesion variability. Generating such datasets requires substantial time and expertise, and is prone to error. While self-supervised learning (SSL) can leverage large unlabeled datasets, learned generic representations often fail to capture the nuanced features needed for accurate lesion detection. In this work, we propose a Supervised Anatomical Pretraining (SAP) method that learns from a limited dataset of anatomical labels. First, an MRI-based skeletal segmentation model is developed and trained on WB-MRI scans from healthy individuals for high-quality skeletal delineation. Then, we compare its downstream efficacy in segmenting MBD on a cohort of 44 patients with metastatic prostate cancer, against both a baseline random initialization and a state-of-the-art SSL method. SAP significantly outperforms both the baseline and SSL-pretrained models, achieving a normalized surface Dice of 0.76 and a Dice coefficient of 0.64. The method achieved a lesion detection F2 score of 0.44, improving on 0.24 (baseline) and 0.31 (SSL). When considering only clinically relevant lesions larger than 1~ml, SAP achieves a detection sensitivity of 100% in 28 out of 32 patients. Learning bone morphology from anatomy yields an effective and domain-relevant inductive bias that can be leveraged for the downstream segmentation task of bone lesions. All code and models are made publicly available.
Euclid: Quick Data Release (Q1) -- Watching ICM-selected galaxy clusters with Euclid eyes -- prospects of Euclid data in the context of large SZ and X-ray based surveys
2025-06-24
M. Klein, K. George, J. J. Mohr, B. Altieri, L. Amendola, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, A. Biviano, E. Branchini, M. Brescia, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, C. Dolding, H. Dole, F. Dubath, F. Ducret, X. Dupac, S. Dusini, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, B. Gillis, C. Giocoli, J. Gracia-Carpio, A. Grazian, F. Grupp, S. V. H. Haugan, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, E. Keihänen, S. Kermiche, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, S. Maurogordato, E. Medinaceli, S. Mei, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, L. Moscardini, R. Nakajima, C. Neissner, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, C. Rosset, H. J. A. Rottgering, R. Saglia, Z. Sakr, D. Sapone, M. Schirmer, P. Schneider, T. Schrabback, A. Secroun, E. Sefusatti, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, A. Veropalumbo, Y. Wang, J. Weller, F. M. Zerbi, E. Zucca, C. Burigana, V. Scottez, M. Sereno, M. Viel
Galaxy clusters detected through their X-ray emission or Sunyaev--Zeldovich effect (SZE), both produced by the intra-cluster medium (ICM), are key probes in cosmological and astrophysical studies. To maximise the scientific return of such surveys, complementary data are required for cluster confirmation and redshift estimation. This is typically provided by wide-field optical and infrared surveys, which are increasingly challenged by ongoing and future ICM-selected samples. In particular, at high redshifts ($z>1$) probed by upcoming SZE-selected samples, current large surveys may be insufficient for reliable confirmation. Deep, high-resolution infrared surveys like Euclid will thus be essential for confirming most high-redshift clusters. We present an analysis of the first sizeable Euclid dataset (Q1), overlapping with several ICM-selected cluster samples. We apply an adaptation of the MCMF cluster confirmation tool to estimate key properties, including redshift and richness, and to predict Euclid's capabilities for high-redshift cluster confirmation. We find promising performance, particularly at high redshifts, while richness estimates at low redshifts ($z<0.4$) are currently limited by Q1 data quality but should improve with future releases. Using MCMF runs on random lines of sight, we predict that Euclid will confirm clusters at $1
We self-consistently determine the 3D density distribution of NGC 551's stellar disk and study observational signatures of two-component stellar disks. Assuming baryonic disks are in hydrostatic equilibrium, we solved the Poisson-Boltzmann equation to estimate 3D density distribution. We used integral-field spectroscopic observations to estimate stellar velocity dispersion and built a 3D dynamical model using these density solutions and the observed rotation curve. We generated simulated surface brightness maps and compared them with observations to verify modeling consistency. The dynamical model was inclined to 90{\deg} to produce an edge-on surface density map, which we investigated by fitting different 2D functions and plotting vertical cuts in logarithmic scale. We estimated vertical stellar velocity dispersion using an iterative method, obtaining results consistent with the Disk Mass Survey formalism. Through dynamical modeling, we produced moment maps that reasonably matched observations. We examined the simulated edge-on model by taking vertical cuts and decomposing them into multiple Gaussian components. We find that artificial double Gaussian components arise due to line-of-sight integration effects, even for single-component disks. This indicates that decomposing vertical intensity cuts into multiple Gaussian components is unreliable for multicomponent disks. Instead, an up-bending break visible in logarithmic-scale vertical cuts serves as a more reliable indicator for two-component disks. We performed 2D fitting on the edge-on surface density map using the product of a scaled modified Bessel function and $sech^2$ function to estimate structural parameters. These traditional methods systematically underestimate the scale length and flattening ratio. Therefore, we suggest using detailed modeling to accurately deduce stellar disk structural parameters.
Toward the Explainability of Protein Language Models for Sequence Design
2025-06-24
Andrea Hunklinger, Noelia Ferruz
Transformer-based language models excel in a variety of protein-science tasks that range from structure prediction to the design of functional enzymes. However, these models operate as black boxes, and their underlying working principles remain unclear. Here, we survey emerging applications of explainable artificial intelligence (XAI) to protein language models (pLMs) and describe their potential in protein research. We break down the workflow of a generative decoder-only Transformer into four information contexts: (i) training sequences, (ii) input prompt, (iii) model architecture, and (iv) output sequence. For each, we describe existing methods and applications of XAI. Additionally, from published studies we distil five (potential) roles that XAI can play in protein design: Evaluator, Multitasker, Engineer, Coach, and Teacher, with the Evaluator role being the only one widely adopted so far. These roles aim to help both protein science practitioners and model developers understand the possibilities and limitations of implementing XAI for the design of sequences. Finally, we highlight the critical areas of application for the future, including risks related to security, trustworthiness, and bias, and we call for community benchmarks, open-source tooling, and domain-specific visualizations to advance explainable protein design. Overall, our analysis aims to move the discussion toward the use of XAI in protein design.
As the wireless research community moves toward shaping the vision of sixth-generation (6G) networks, reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for controlling the propagation environment. Although RIS has not yet been standardized, its versatile applications and enabling capabilities have attracted growing attention in both academia and industry. This survey presents a comprehensive review of RIS technology spanning theoretical foundations, design aspects, and practical deployment considerations. In contrast to existing surveys that focus on isolated aspects, this work offers an integrated view covering use cases, control mechanisms, channel sounding methodologies, and channel estimation strategies. Each of these topics is reviewed through the lens of recent literature, synthesizing the latest advancements to provide updated insights for both academic researchers and industry practitioners. It further addresses emerging topics such as standardization activities and industrial perspectives, which are often overlooked in prior literature. By bridging theoretical insights with practical challenges, this survey aims to provide a holistic understanding of RIS and support its evolution from a research concept toward real-world implementation.
Physics-Informed Neural Networks for Industrial Gas Turbines: Recent Trends, Advancements and Challenges
2025-06-24
Afila Ajithkumar Sophiya, Sepehr Maleki, Giuseppe Bruni, Senthil K. Krishnababu
Physics-Informed Neural Networks (PINNs) have emerged as a promising computational framework for solving differential equations by integrating deep learning with physical constraints. However, their application in gas turbines is still in its early stages, requiring further refinement and standardization for wider adoption. This survey provides a comprehensive review of PINNs in Industrial Gas Turbines (IGTs) research, highlighting their contributions to the analysis of aerodynamic and aeromechanical phenomena, as well as their applications in flow field reconstruction, fatigue evaluation, and flutter prediction, and reviews recent advancements in accuracy, computational efficiency, and hybrid modelling strategies. In addition, it explores key research efforts, implementation challenges, and future directions aimed at improving the robustness and scalability of PINNs.
Dialogic Pedagogy for Large Language Models: Aligning Conversational AI with Proven Theories of Learning
2025-06-24
Russell Beale
Large Language Models (LLMs) are rapidly transforming education by enabling rich conversational learning experiences. This article provides a comprehensive review of how LLM-based conversational agents are being used in higher education, with extensions to secondary and lifelong learning contexts. We synthesize existing literature on LLMs in education and theories of conversational and dialogic pedagogy - including Vygotsky's sociocultural learning (scaffolding and the Zone of Proximal Development), the Socratic method, and Laurillard's conversational framework - and examine how prompting strategies and retrieval-augmented generation (RAG) can align LLM behaviors with these pedagogical theories, and how it can support personalized, adaptive learning. We map educational theories to LLM capabilities, highlighting where LLM-driven dialogue supports established learning principles and where it challenges or falls short of traditional pedagogical assumptions. Notable gaps in applying prior theories to LLMs are identified, such as the models tendency to provide direct answers instead of fostering co-construction of knowledge, and the need to account for the constant availability and broad but non-human expertise of LLM tutors. In response, we propose practical strategies to better align LLM interactions with sound pedagogy - for example, designing prompts that encourage Socratic questioning, scaffolded guidance, and student reflection, as well as integrating retrieval mechanisms to ensure accuracy and contextual relevance. Our aim is to bridge the gap between educational theory and the emerging practice of AI-driven conversational learning, offering insights and tools for making LLM-based dialogues more educationally productive and theory-aligned.
Can Large Language Models Capture Human Annotator Disagreements?
2025-06-24
Jingwei Ni, Yu Fan, Vilém Zouhar, Donya Rooein, Alexander Hoyle, Mrinmaya Sachan, Markus Leippold, Dirk Hovy, Elliott Ash
Human annotation variation (i.e., annotation disagreements) is common in NLP and often reflects important information such as task subjectivity and sample ambiguity. While Large Language Models (LLMs) are increasingly used for automatic annotation to reduce human effort, their evaluation often focuses on predicting the majority-voted "ground truth" labels. It is still unclear, however, whether these models also capture informative human annotation variation. Our work addresses this gap by extensively evaluating LLMs' ability to predict annotation disagreements without access to repeated human labels. Our results show that LLMs struggle with modeling disagreements, which can be overlooked by majority label-based evaluations. Notably, while RLVR-style (Reinforcement learning with verifiable rewards) reasoning generally boosts LLM performance, it degrades performance in disagreement prediction. Our findings highlight the critical need for evaluating and improving LLM annotators in disagreement modeling. Code and data at https://github.com/EdisonNi-hku/Disagreement_Prediction.
The Crab Nebula at subarcsecond resolution with the International LOFAR Telescope
2025-06-24
M. Arias, R. Timmerman, F. Sweijen, R. J. van Weeren, C. G. Bassa
We present International LOw Frequency ARray (LOFAR) Telescope (ILT) observations of the Crab Nebula, the remnant of a core-collapse supernova explosion observed by astronomers in 1054. The field of the Crab Nebula was observed between 120 and 168 MHz as part of the LOFAR Two Meter Sky Survey (LoTSS), and the data were re-processed to include the LOFAR international stations to create a high angular resolution ($0.43'' \times 0.28''$) map at a central frequency of 145 MHz. Combining the ILT map with archival centimeter-range observations of the Nebula with the Very Large Array (VLA) and LOFAR data at 54 MHz, we become sensitive to the effects of free-free absorption against the synchrotron emission of the pulsar wind nebula. This absorption is caused by the ionised filaments visible in optical and infrared data of the Crab Nebula, which are the result of the pulsar wind nebula expanding into the denser stellar ejecta that surrounds it and forming Rayleigh-Taylor fingers. The LOFAR observations are sensitive to two components of these filaments: their dense cores, which show electron densities of $\gtrsim1,000$ cm$^{-3}$, and the diffuse envelopes, with electron densities of $\sim50-250$ cm$^{-3}$. The denser structures have widths of $\sim0.03$ pc, whereas the diffuse component is at one point as large as $0.2$ pc. The morphology of the two components is not always the same. These finding suggests that the layered temperature, density, and ionisation structure of the Crab optical filaments extends to larger scales than previously considered.
Integrating AIs With Body Tracking Technology for Human Behaviour Analysis: Challenges and Opportunities
2025-06-24
Adrien Coppens, Valérie Maquil
The automated analysis of human behaviour provides many opportunities for the creation of interactive systems and the post-experiment investigations for user studies. Commodity depth cameras offer reasonable body tracking accuracy at a low price point, without the need for users to wear or hold any extra equipment. The resulting systems typically perform body tracking through a dedicated machine learning model, but they can be enhanced with additional AI components providing extra capabilities. This leads to opportunities but also challenges, for example regarding the orchestration of such AI components and the engineering of the resulting tracking pipeline. In this paper, we discuss these elements, based on our experience with the creation of a remote collaboration system across distant wall-sized displays, that we built using existing and readily available building blocks, including AI-based recognition models.
Loss functions incorporating auditory spatial perception in deep learning -- a review
2025-06-24
Boaz Rafaely, Stefan Weinzierl, Or Berebi, Fabian Brinkmann
Binaural reproduction aims to deliver immersive spatial audio with high perceptual realism over headphones. Loss functions play a central role in optimizing and evaluating algorithms that generate binaural signals. However, traditional signal-related difference measures often fail to capture the perceptual properties that are essential to spatial audio quality. This review paper surveys recent loss functions that incorporate spatial perception cues relevant to binaural reproduction. It focuses on losses applied to binaural signals, which are often derived from microphone recordings or Ambisonics signals, while excluding those based on room impulse responses. Guided by the Spatial Audio Quality Inventory (SAQI), the review emphasizes perceptual dimensions related to source localization and room response, while excluding general spectral-temporal attributes. The literature survey reveals a strong focus on localization cues, such as interaural time and level differences (ITDs, ILDs), while reverberation and other room acoustic attributes remain less explored in loss function design. Recent works that estimate room acoustic parameters and develop embeddings that capture room characteristics indicate their potential for future integration into neural network training. The paper concludes by highlighting future research directions toward more perceptually grounded loss functions that better capture the listener's spatial experience.
Continuous Indexed Points for Multivariate Volume Visualization
2025-06-24
Liang Zhou, Xinyi Gou, Daniel Weiskopf
We introduce continuous indexed points for improved multivariate volume visualization. Indexed points represent linear structures in parallel coordinates and can be used to encode local correlation of multivariate (including multifield, multifaceted, and multiattribute) volume data. First, we perform local linear fitting in the spatial neighborhood of each volume sample using principal component analysis, accelerated by hierarchical spatial data structures. This local linear information is then visualized as continuous indexed points in parallel coordinates: a density representation of indexed points in a continuous domain. With our new method, multivariate volume data can be analyzed using the eigenvector information from local spatial embeddings. We utilize both 1-flat and 2-flat indexed points, allowing us to identify correlations between two variables and even three variables, respectively. An interactive occlusion shading model facilitates good spatial perception of the volume rendering of volumetric correlation characteristics. Interactive exploration is supported by specifically designed multivariate transfer function widgets working in the image plane of parallel coordinates. We show that our generic technique works for multi-attribute datasets. The effectiveness and usefulness of our new method is demonstrated through a case study, an expert user study, and domain expert feedback.
A Survey on Soft Robot Adaptability: Implementations, Applications, and Prospects
2025-06-24
Zixi Chen, Di Wu, Qinghua Guan, David Hardman, Federico Renda, Josie Hughes, Thomas George Thuruthel, Cosimo Della Santina, Barbara Mazzolai, Huichan Zhao, Cesare Stefanini
Soft robots, compared to rigid robots, possess inherent advantages, including higher degrees of freedom, compliance, and enhanced safety, which have contributed to their increasing application across various fields. Among these benefits, adaptability is particularly noteworthy. In this paper, adaptability in soft robots is categorized into external and internal adaptability. External adaptability refers to the robot's ability to adjust, either passively or actively, to variations in environments, object properties, geometries, and task dynamics. Internal adaptability refers to the robot's ability to cope with internal variations, such as manufacturing tolerances or material aging, and to generalize control strategies across different robots. As the field of soft robotics continues to evolve, the significance of adaptability has become increasingly pronounced. In this review, we summarize various approaches to enhancing the adaptability of soft robots, including design, sensing, and control strategies. Additionally, we assess the impact of adaptability on applications such as surgery, wearable devices, locomotion, and manipulation. We also discuss the limitations of soft robotics adaptability and prospective directions for future research. By analyzing adaptability through the lenses of implementation, application, and challenges, this paper aims to provide a comprehensive understanding of this essential characteristic in soft robotics and its implications for diverse applications.
Advances in generative models have transformed the field of synthetic image generation for privacy-preserving data synthesis (PPDS). However, the field lacks a comprehensive survey and comparison of synthetic image generation methods across diverse settings. In particular, when we generate synthetic images for the purpose of training a classifier, there is a pipeline of generation-sampling-classification which takes private training as input and outputs the final classifier of interest. In this survey, we systematically categorize existing image synthesis methods, privacy attacks, and mitigations along this generation-sampling-classification pipeline. To empirically compare diverse synthesis approaches, we provide a benchmark with representative generative methods and use model-agnostic membership inference attacks (MIAs) as a measure of privacy risk. Through this study, we seek to answer critical questions in PPDS: Can synthetic data effectively replace real data? Which release strategy balances utility and privacy? Do mitigations improve the utility-privacy tradeoff? Which generative models perform best across different scenarios? With a systematic evaluation of diverse methods, our study provides actionable insights into the utility-privacy tradeoffs of synthetic data generation methods and guides the decision on optimal data releasing strategies for real-world applications.
Very strong coupling limit of Eliashberg-McMillan theory and the upper limit for superconducting transition temperature
2025-06-24
M. V. Sadovskii
We present a brief review of some recent work on the problem of highest achievable temperature of superconducting transition $T_c$ in electron-phonon systems. The discovery of record-breaking values of $T_c$ in quite a number of hydrides under high pressure was an impressive demonstration of capabilities of electron-phonon mechanism of Cooper pairing. This lead to an increased interest on possible limitations of Eliashberg-McMillan theory as the main theory of superconductivity in a system of electrons and phonons. We shall consider some basic conclusions following from this theory and present some remarks on the limit of very strong electron-phonon coupling. We shall discuss possible limitations on the value of the coupling constant related to possible lattice and specific heat instability and conclude that within the stable metallic phase the effective pairing constant may acquire very large values. We discuss some bounds for $T_c$ derived in the strong coupling limit and propose an elementary estimate of an upper limit for $T_c$, expressed via combination of fundamental physical constants. Finally we also briefly discuss some pessimistic estimates for $T_c$ of metallic hydrogen obtained in ``jellium'' model.
The revolutionary advancement of Artificial Intelligence Generated Content (AIGC) has fundamentally transformed the landscape of visual content creation and artistic expression. While remarkable progress has been made in image generation and style transfer, the underlying mechanisms and aesthetic implications of these technologies remain insufficiently understood. This paper presents a comprehensive survey of AIGC technologies in visual arts, tracing their evolution from early algorithmic frameworks to contemporary deep generative models. We identify three pivotal paradigms: Variational Autoencoders (VAE), Generative Adversarial Networks (GANs), and Diffusion Models, and examine their roles in bridging the gap between human creativity and machine synthesis. To support our analysis, we systematically review over 500 research papers published in the past decade, spanning both foundational developments and state-of-the-art innovations. Furthermore, we propose a multidimensional evaluation framework that incorporates Technical Innovation, Artistic Merit, Visual Quality, Computational Efficiency, and Creative Potential. Our findings reveal both the transformative capacities and current limitations of AIGC systems, emphasizing their profound impact on the future of creative practices. Through this extensive synthesis, we offer a unified perspective on the convergence of artificial intelligence and artistic expression, while outlining key challenges and promising directions for future research in this rapidly evolving field.
We present HARPT, a large-scale annotated corpus of mobile health app store reviews aimed at advancing research in user privacy and trust. The dataset comprises over 480,000 user reviews labeled into seven categories that capture critical aspects of trust in applications, trust in providers and privacy concerns. Creating HARPT required addressing multiple complexities, such as defining a nuanced label schema, isolating relevant content from large volumes of noisy data, and designing an annotation strategy that balanced scalability with accuracy. This strategy integrated rule-based filtering, iterative manual labeling with review, targeted data augmentation, and weak supervision using transformer-based classifiers to accelerate coverage. In parallel, a carefully curated subset of 7,000 reviews was manually annotated to support model development and evaluation. We benchmark a broad range of classification models, demonstrating that strong performance is achievable and providing a baseline for future research. HARPT is released as a public resource to support work in health informatics, cybersecurity, and natural language processing.
The VVV near-IR galaxy catalogue of the southern Galactic disc
2025-06-24
M. V. Alonso, L. D. Baravalle, J. L. Nilo Castellón, C. Villalon, M. Soto, M. A. Sgró, I. V. Daza Perilla, C. Valotto, M. Lares, D. Minniti, P. Marchant Cortés, F. Milla Castro, M. Hempel, J. Alonso-García, L. Macri, A. Pichel, N. Masetti, R. K. Saito, M. Gómez
The distribution of galaxies in the Zone of Avoidance (ZoA) is incomplete due to the presence of our own Galaxy. Our research focused on the identification and characterisation of galaxies in the ZoA, using the new near-infrared data from the VVVX survey in the regions that cover the southern Galactic disc. We used our previously-established procedure based on photometric and morphological criteria to identify galaxies. The large data volume collected by the VVVX required alternatives to visual inspection, including artificial intelligence techniques, such as classifiers based on neural networks. The VVV NIR galaxy catalogue is presented, covering the southern Galactic disc, significantly extending the vision down to $K^0_s=16$ mag throughout the ZoA. This catalogue provides positions, photometric and morphological parameters for a total of 167,559 galaxies with their probabilities determined by the CNN and XGBoost algorithms based on image and photometric data, respectively. The construction of the catalogue involves the employment of optimal probability criteria. 14% of these galaxies were confirmed by visual inspection or by matching with previous catalogues. The peculiarities exhibited by distinct regions across the Galactic disc, along with the characteristics of the galaxies, are thoroughly examined. The catalogue serves as a valuable resource for extragalactic studies within the ZoA, providing a crucial complement to the forthcoming radio catalogues and future surveys utilizing the Vera C.~Rubin Observatory and the Nancy Grace Roman Space Telescope. We present a deep galaxy map, covering a 1080 sq. deg. region, which reveals that the apparent galaxy density is predominantly influenced by foreground extinction from the Milky Way. However, the presence of intrinsic inhomogeneities, potentially associated with candidate galaxy groups or clusters and filaments, is also discernible.
In this article, we give a brief survey of recent developments on relations between global embeddability of a closed strictly pseudoconvex CR manifold and the CR Paneitz operator.
Ancient scripts, e.g., Egyptian hieroglyphs, Oracle Bone Inscriptions, and Ancient Greek inscriptions, serve as vital carriers of human civilization, embedding invaluable historical and cultural information. Automating ancient script image recognition has gained importance, enabling large-scale interpretation and advancing research in archaeology and digital humanities. With the rise of deep learning, this field has progressed rapidly, with numerous script-specific datasets and models proposed. While these scripts vary widely, spanning phonographic systems with limited glyphs to logographic systems with thousands of complex symbols, they share common challenges and methodological overlaps. Moreover, ancient scripts face unique challenges, including imbalanced data distribution and image degradation, which have driven the development of various dedicated methods. This survey provides a comprehensive review of ancient script image recognition methods. We begin by categorizing existing studies based on script types and analyzing respective recognition methods, highlighting both their differences and shared strategies. We then focus on challenges unique to ancient scripts, systematically examining their impact and reviewing recent solutions, including few-shot learning and noise-robust techniques. Finally, we summarize current limitations and outline promising future directions. Our goal is to offer a structured, forward-looking perspective to support ongoing advancements in the recognition, interpretation, and decipherment of ancient scripts.
When Domains Collide: An Activity Theory Exploration of Cross-Disciplinary Collaboration
2025-06-24
Zixuan Feng, Thomas Zimmermann, Lorenzo Pisani, Christopher Gooley, Jeremiah Wander, Anita Sarma
Background: Software development teams are increasingly diverse, embedded, and cross-disciplinary. Domain experts (DEs) from different disciplines collaborate with professional software developers (SDEs), bringing complementary expertise in creating and maintaining complex production software. However, contested expectations, divergent problem-solving perspectives, and conflicting priorities lead to friction. Aims: This study aims to investigate the dynamics of emerging collaboration of cross-disciplinary software development (CDSD) by exploring the expectations held by DEs and SDEs and understanding how these frictions manifest in practice. Method: We utilize Activity Theory (AT), a well-established socio-technical framework, as an analytical lens in a grounded, empirical investigation, conducted through a mixed-method study involving 24 interviews (12 DEs and 12 SDEs) and a large-scale validation survey with 293 participants (161 DEs and 132 SDEs). Results: We conceptualize and empirically ground the CDSD dynamics. We identified eight expectations held by SDEs and six by DEs. By mapping these expectations to AT components, we revealed 21 frictions in CDSD and illustrated where and how they arise. Conclusions: This study offers a theoretical lens for understanding the dynamics and frictions in CDSD and provides actionable insights for future research, practitioners, and infrastructure design.
Learning Instruction-Following Policies through Open-Ended Instruction Relabeling with Large Language Models
2025-06-24
Zhicheng Zhang, Ziyan Wang, Yali Du, Fei Fang
Developing effective instruction-following policies in reinforcement learning remains challenging due to the reliance on extensive human-labeled instruction datasets and the difficulty of learning from sparse rewards. In this paper, we propose a novel approach that leverages the capabilities of large language models (LLMs) to automatically generate open-ended instructions retrospectively from previously collected agent trajectories. Our core idea is to employ LLMs to relabel unsuccessful trajectories by identifying meaningful subtasks the agent has implicitly accomplished, thereby enriching the agent's training data and substantially alleviating reliance on human annotations. Through this open-ended instruction relabeling, we efficiently learn a unified instruction-following policy capable of handling diverse tasks within a single policy. We empirically evaluate our proposed method in the challenging Craftax environment, demonstrating clear improvements in sample efficiency, instruction coverage, and overall policy performance compared to state-of-the-art baselines. Our results highlight the effectiveness of utilizing LLM-guided open-ended instruction relabeling to enhance instruction-following reinforcement learning.
Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization
2025-06-24
Yuheng Chen, Alexander Montes McNeil, Taehyuk Park, Blake A. Wilson, Vaishnavi Iyer, Michael Bezick, Jae-Ik Choi, Rohan Ojha, Pravin Mahendran, Daksh Kumar Singh, Geetika Chitturi, Peigang Chen, Trang Do, Alexander V. Kildishev, Vladimir M. Shalaev, Michael Moebius, Wenshan Cai, Yongmin Liu, Alexandra Boltasseva
Photonic device development (PDD) has achieved remarkable success in designing and implementing new devices for controlling light across various wavelengths, scales, and applications, including telecommunications, imaging, sensing, and quantum information processing. PDD is an iterative, five-step process that consists of: i) deriving device behavior from design parameters, ii) simulating device performance, iii) finding the optimal candidate designs from simulations, iv) fabricating the optimal device, and v) measuring device performance. Classically, all these steps involve Bayesian optimization, material science, control theory, and direct physics-driven numerical methods. However, many of these techniques are computationally intractable, monetarily costly, or difficult to implement at scale. In addition, PDD suffers from large optimization landscapes, uncertainties in structural or optical characterization, and difficulties in implementing robust fabrication processes. However, the advent of machine learning over the past decade has provided novel, data-driven strategies for tackling these challenges, including surrogate estimators for speeding up computations, generative modeling for noisy measurement modeling and data augmentation, reinforcement learning for fabrication, and active learning for experimental physical discovery. In this review, we present a comprehensive perspective on these methods to enable machine-learning-assisted PDD (ML-PDD) for efficient design optimization with powerful generative models, fast simulation and characterization modeling under noisy measurements, and reinforcement learning for fabrication. This review will provide researchers from diverse backgrounds with valuable insights into this emerging topic, fostering interdisciplinary efforts to accelerate the development of complex photonic devices and systems.
Metamaterials in Superconducting and Cryogenic Quantum Technologies
2025-06-24
Alex Krasnok
The development of fault-tolerant quantum computers based on superconducting circuits faces critical challenges in qubit coherence, connectivity, and scalability. This review establishes metamaterials, artificial structures with on-demand electromagnetic properties, as a transformative solution. By engineering the photonic density of states, metamaterials can suppress decoherence via the Purcell effect and create multi-mode quantum buses for hardware-efficient control and long-range qubit coupling. We provide a comprehensive overview, from foundational principles and Hamiltonian engineering to the materials science of high-coherence devices. We survey state-of-the-art performance, highlighting record coherence times and coupling strengths achieved through metamaterial design. Furthermore, we explore advanced applications where engineered environments give rise to exotic excitations and topologically protected states, enabling novel error correction schemes and qubit architectures. Ultimately, we argue that metamaterials are evolving from passive components into the core architectural element of next-generation quantum technologies, paving a viable path toward scalable quantum computation.
LSH-DynED: A Dynamic Ensemble Framework with LSH-Based Undersampling for Evolving Multi-Class Imbalanced Classification
2025-06-24
Soheil Abadifard, Fazli Can
The classification of imbalanced data streams, which have unequal class distributions, is a key difficulty in machine learning, especially when dealing with multiple classes. While binary imbalanced data stream classification tasks have received considerable attention, only a few studies have focused on multi-class imbalanced data streams. Effectively managing the dynamic imbalance ratio is a key challenge in this domain. This study introduces a novel, robust, and resilient approach to address these challenges by integrating Locality Sensitive Hashing with Random Hyperplane Projections (LSH-RHP) into the Dynamic Ensemble Diversification (DynED) framework. To the best of our knowledge, we present the first application of LSH-RHP for undersampling in the context of imbalanced non-stationary data streams. The proposed method undersamples the majority classes by utilizing LSH-RHP, provides a balanced training set, and improves the ensemble's prediction performance. We conduct comprehensive experiments on 23 real-world and ten semi-synthetic datasets and compare LSH-DynED with 15 state-of-the-art methods. The results reveal that LSH-DynED outperforms other approaches in terms of both Kappa and mG-Mean effectiveness measures, demonstrating its capability in dealing with multi-class imbalanced non-stationary data streams. Notably, LSH-DynED performs well in large-scale, high-dimensional datasets with considerable class imbalances and demonstrates adaptation and robustness in real-world circumstances. To motivate our design, we review existing methods for imbalanced data streams, outline key challenges, and offer guidance for future work. For the reproducibility of our results, we have made our implementation available on GitHub.
Giant Outer Transiting Exoplanet Mass (GOT 'EM) Survey. VI: Confirmation of a Long-Period Giant Planet Discovered with a Single TESS Transit
2025-06-24
Zahra Essack, Diana Dragomir, Paul A. Dalba, Matthew P. Battley, David R. Ciardi, Karen A. Collins, Steve B. Howell, Matias I. Jones, Stephen R. Kane, Eric E. Mamajek, Christopher R. Mann, Ismael Mireles, Dominic Oddo, Lauren A. Sgro, Keivan G. Stassun, Solene Ulmer-Moll, Cristilyn N. Watkins, Samuel W. Yee, Carl Ziegler, Allyson Bieryla, Ioannis Apergis, Khalid Barkaoui, Rafael Brahm, Edward M. Bryant, Thomas M. Esposito, Pedro Figueira, Benjamin J. Fulton, Samuel Gill, Andrew W. Howard, Howard Isaacson, Alicia Kendall, Nicholas Law, Michael B. Lund, Andrew W. Mann, Rachel A. Matson, Felipe Murgas, Enric Palle, Samuel N. Quinn, Alexandre Revol, Suman Saha, Richard P. Schwarz, Ramotholo Sefako, Avi Shporer, Ivan A. Strakhov, Steven Villanueva Jr., George R. Ricker, Roland Vanderspek, David W. Latham, Sara Seager, Joshua N. Winn, Pau Bosch-Cabot, Kevin I. Collins, Raquel Forés-Toribio, Fabian Rodriguez Frustagia, Eric Girardin, Ian J. Helm, Pablo Lewin, Jose A. Muñoz, Patrick Newman, Peter Plavchan, Gregor Srdoc, Chris Stockdale, Anaël Wünsche, Mario Billiani, Martin Davy, Alex Douvas, Keiichi Fukui, Bruno Guillet, Cory Ostrem, Michael Rushton, Angsar Schmidt, Andrea Finardi, Patrice Girard, Tateki Goto, Julien S. de Lambilly, Liouba Leroux, Fabrice Mortecrette, John W. Pickering, Michael Primm, Marc Ribot, Ethan Teng, Aad Verveen, Stefan Will, Mark Ziegler
We report the discovery and confirmation of TOI-4465 b, a $1.25^{+0.08}_{-0.07}~R_{J}$, $5.89\pm0.26~M_{J}$ giant planet orbiting a G dwarf star at $d\simeq$ 122 pc. The planet was detected as a single-transit event in data from Sector 40 of the Transiting Exoplanet Survey Satellite (TESS) mission. Radial velocity (RV) observations of TOI-4465 showed a planetary signal with an orbital period of $\sim$102 days, and an orbital eccentricity of $e=0.24\pm0.01$. TESS re-observed TOI-4465 in Sector 53 and Sector 80, but did not detect another transit of TOI-4465 b, as the planet was not expected to transit during these observations based on the RV period. A global ground-based photometry campaign was initiated to observe another transit of TOI-4465 b after the RV period determination. The $\sim$12 hour-long transit event was captured from multiple sites around the world, and included observations from 24 citizen scientists, confirming the orbital period as $\sim$102 days. TOI-4465 b is a relatively dense ($3.73\pm0.53~\rm{g/cm^3}$), temperate (375-478 K) giant planet. Based on giant planet structure models, TOI-4465 b appears to be enriched in heavy elements at a level consistent with late-stage accretion of icy planetesimals. Additionally, we explore TOI-4465 b's potential for atmospheric characterization, and obliquity measurement. Increasing the number of long-period planets by confirming single-transit events is crucial for understanding the frequency and demographics of planet populations in the outer regions of planetary systems.
This paper investigates real-time decision support systems that leverage low-latency AI models, bringing together recent progress in holistic AI-driven decision tools, integration with Edge-IoT technologies, and approaches for effective human-AI teamwork. It looks into how large language models can assist decision-making, especially when resources are limited. The research also examines the effects of technical developments such as DeLLMa, methods for compressing models, and improvements for analytics on edge devices, while also addressing issues like limited resources and the need for adaptable frameworks. Through a detailed review, the paper offers practical perspectives on development strategies and areas of application, adding to the field by pointing out opportunities for more efficient and flexible AI-supported systems. The conclusions set the stage for future breakthroughs in this fast-changing area, highlighting how AI can reshape real-time decision support.
Hundreds of TESS exoplanets might be larger than we thought
2025-06-24
Te Han, Paul Robertson, Timothy D. Brandt, Shubham Kanodia, Caleb Cañas, Avi Shporer, George Ricker, Corey Beard
The radius of a planet is a fundamental parameter that probes its composition and habitability. Precise radius measurements are typically derived from the fraction of starlight blocked when a planet transits its host star. The wide-field Transiting Exoplanet Survey Satellite (TESS) has discovered hundreds of new exoplanets, but its low angular resolution means that the light from a star hosting a transiting exoplanet can be blended with the light from background stars. If not fully corrected, this extra light can dilute the transit signal and result in a smaller measured planet radius. In a study of hundreds of TESS planet discoveries using deblended light curves from our validated methodology, we show that systematically incorrect planet radii are common in the literature: studies using various public TESS photometry pipelines have underestimated the planet radius by a weighted median of $6.1\% \pm 0.3\%$, leading to a $\sim20\%$ overestimation of planet density. The widespread presence of these biases in the literature has profoundly shaped-and potentially misrepresented-our understanding of the exoplanet population. Addressing these biases will refine the exoplanet mass-radius relation, reshape our understanding of exoplanet atmospheric and bulk composition, and potentially inform prevailing planet formation theories.
Logarithmic convexity of evolution equations and application to inverse problems
2025-06-24
S. E. Chorfi
We review some results on the logarithmic convexity for evolution equations, a well-known method in inverse and ill-posed problems. We start with the classical case of self-adjoint operators. Then, we analyze the case of analytic semigroups. In this general case, we give an explicit estimate, which will be used to study inverse problems for initial data recovery. We illustrate our abstract result by an application to the Ornstein-Uhlenbeck equations. We discuss both analytic and non-analytic semigroups. We conclude with recent results for the time-fractional evolution equations with the Caputo derivative of order $0<\alpha <1$. We start with symmetric evolution equations. Then, we show that the results extend to the non-symmetric case for diffusion equations, provided that a gradient vector field generates the drift coefficient. Finally, some open problems will be mentioned.
Particle Injection Problem in Magnetic Reconnection and Turbulence
2025-06-24
Fan Guo, Omar French, Qile Zhang, Xiaocan Li, Jeongbhin Seo
Magnetic reconnection and turbulence in magnetically-dominated environments have been proposed as important nonthermal particle acceleration mechanisms that generate high energy particles and associated emissions. While the acceleration to high energy that produces the power-law energy distribution has drawn strong interest, recent studies actively discuss pre-acceleration, or injection, to a sufficient energy for a sustained and prolonged Fermi-like acceleration. The injection process is important for determining the fraction of nonthermal particles and energy partition between thermal and nonthermal particles. We review recent advances in understanding the injection mechanisms responsible for populating these nonthermal power-law spectra, and conclude with an outlook for studies and applications of injection models.
Bayesian Posteriors with Stellar Population Synthesis on GPUs
2025-06-24
Georgios Zacharegkas, Andrew Hearin, Andrew Benson
Models of Stellar Population Synthesis (SPS) provide a predictive framework for the spectral energy distribution (SED) of a galaxy. SPS predictions can be computationally intensive, creating a bottleneck for attempts to infer the physical properties of large populations of individual galaxies from their SEDs and photometry; these computational challenges are especially daunting for near-future cosmology surveys that will measure the photometry of billions of galaxies. In this paper, we explore a range of computational techniques aimed at accelerating SPS predictions of galaxy photometry using the JAX library to target GPUs. We study a particularly advantageous approximation to the calculation of galaxy photometry that speeds up the computation by a factor of 50 relative to the exact calculation. We introduce a novel technique for incorporating burstiness into models of galaxy star formation history that captures very short-timescale fluctuations with negligible increase in computation time. We study the performance of Hamiltonian Monte Carlo (HMC) algorithms in which individual chains are parallelized across independent GPU threads, finding that our pipeline can carry out Bayesian inference at a rate of approximately $1,000$ galaxy posteriors per GPU-minute. Our results provide an update to standard benchmarks in the literature on the computational demands of SPS inference; our publicly available code enables previously-impractical Bayesian analyses of large galaxy samples, and includes several standalone modules that could be adopted to speedup existing SPS pipelines.
Geometrically Frustrated Quadrupoles on the Pyrochlore Lattice and Generalized Spin Liquids
2025-06-24
Kristian Tyn Kai Chung, Sylvain Petit, Julien Robert, Paul McClarty
As an instance of geometrical frustration with non-magnetic degrees of freedom, we explore the physics of local spin $S\geq 1$ moments on the pyrochlore lattice that interact via pure quadrupolar couplings. In the presence of spin-orbit coupling, there are nine allowed couplings between nearest neighbor quadrupoles. We determine the semi-classical phases and survey the phase diagram of the model. One may view the Hamiltonian as being composed of two copies of the well-studied dipolar model with couplings between the copies, and we find that each easy-plane dipolar phase has two quadrupolar counterparts. As geometrical frustration is important over broad swathes of the parameter space, there are many classical quadrupolar liquids and regions with order-by-disorder selection of discrete states. Order-by-disorder with quadrupoles admits cubic terms in the Landau theory whose effects appear in Monte Carlo simulations and flavor wave calculations for fixed spin $S$. We showcase a number of examples of quadrupolar spin liquids, including one realizing a rank-3 symmetric tensor gauge theory exhibiting 6-fold pinch point singularities. We discuss remarkable differences between the quadrupolar physics of different spin quantum number. We also discuss connections to the non-Kramers rare earth pyrochlore materials.
Discovery of Diffuse Radio Emission in a Massive z=1.709 Cool Core Cluster: A Candidate Radio Mini-Halo
2025-06-24
Julie Hlavacek-Larrondo, Roland Timmerman, Christoph Pfrommer, Erik Osinga, Larissa Tevlin, Tracy M. A. Webb, Natalia Martorella, Xiaoyuan Zhang, Reinout van Weeren, Hyunseop Choi, Gabriella Di Gennaro, Marie-Lou Gendron-Marsolais, Carter Rhea
Clusters of galaxies host spectacular diffuse radio sources, extending over scales from 100 kpc to several Mpcs. These sources, with extremely faint surface brightness ($\mu$Jy/arcsec$^2$ level), are not tied to individual galaxies but trace synchrotron emission from large-scale magnetic fields and relativistic particles within the intracluster environment. Here, we report the discovery of a candidate radio mini-halo in SpARCS104922.6+564032.5, the most distant cool-core galaxy cluster identified to date at $z=1.709$, using deep LOFAR 120-168 MHz observations. We show that this emission originates from diffuse cluster-associated processes rather than unresolved AGN or star-forming galaxies. The diffuse radio emission coincides spatially with the X-ray emission of the hot intracluster medium and has a radio power of $P_{\rm 150~MHz}=49.8^{+14.7}_{-11.7} \times10^{24}$ W Hz$^{-1}$, exhibiting striking similarities to low-redshift radio mini-halos. This discovery doubles the redshift of previously known mini-halos, challenging models of inverse Compton losses and indicating the presence of strong magnetic fields, enhanced turbulence in high-redshift clusters, or active hadronic processes that require a cosmic ray-to-thermal energy ratio of 0.07 within 200 kpc, assuming a clumped distribution with spatial correlations among the gas, cosmic rays, and magnetic field that partially compensate for cosmological redshift dimming. It further implies that magnetic fields are efficiently amplified to $\sim$$10~\mu$G levels within a Mpc$^3$ volume during the epoch of cluster formation before $z\sim2$. These findings provide critical insights into high-redshift cluster physics and emphasize the transformative potential of next-generation radio surveys, such as those with the SKA and ngVLA, in exploring the early evolution of galaxy clusters.
Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track
2025-06-24
Rylan Schaeffer, Joshua Kazdan, Yegor Denisov-Blanch, Brando Miranda, Matthias Gerstgrasser, Susan Zhang, Andreas Haupt, Isha Gupta, Elyas Obbad, Jesse Dodge, Jessica Zosa Forde, Koustuv Sinha, Francesco Orabona, Sanmi Koyejo, David Donoho
Science progresses by iteratively advancing and correcting humanity's understanding of the world. In machine learning (ML) research, rapid advancements have led to an explosion of publications, but have also led to misleading, incorrect, flawed or perhaps even fraudulent studies being accepted and sometimes highlighted at ML conferences due to the fallibility of peer review. While such mistakes are understandable, ML conferences do not offer robust processes to help the field systematically correct when such errors are made.This position paper argues that ML conferences should establish a dedicated "Refutations and Critiques" (R & C) Track. This R & C Track would provide a high-profile, reputable platform to support vital research that critically challenges prior research, thereby fostering a dynamic self-correcting research ecosystem. We discuss key considerations including track design, review principles, potential pitfalls, and provide an illustrative example submission concerning a recent ICLR 2025 Oral. We conclude that ML conferences should create official, reputable mechanisms to help ML research self-correct.
Developing a general robot manipulation system capable of performing a wide range of tasks in complex, dynamic, and unstructured real-world environments has long been a challenging task. It is widely recognized that achieving human-like efficiency and robustness manipulation requires the robotic brain to integrate a comprehensive set of functions, such as task planning, policy generation, anomaly monitoring and handling, and long-term memory, achieving high-efficiency operation across all functions. Vision-Language Models (VLMs), pretrained on massive multimodal data, have acquired rich world knowledge, exhibiting exceptional scene understanding and multimodal reasoning capabilities. However, existing methods typically focus on realizing only a single function or a subset of functions within the robotic brain, without integrating them into a unified cognitive architecture. Inspired by a divide-and-conquer strategy and the architecture of the human brain, we propose FrankenBot, a VLM-driven, brain-morphic robotic manipulation framework that achieves both comprehensive functionality and high operational efficiency. Our framework includes a suite of components, decoupling a part of key functions from frequent VLM calls, striking an optimal balance between functional completeness and system efficiency. Specifically, we map task planning, policy generation, memory management, and low-level interfacing to the cortex, cerebellum, temporal lobe-hippocampus complex, and brainstem, respectively, and design efficient coordination mechanisms for the modules. We conducted comprehensive experiments in both simulation and real-world robotic environments, demonstrating that our method offers significant advantages in anomaly detection and handling, long-term memory, operational efficiency, and stability -- all without requiring any fine-tuning or retraining.
A field-level reaction for screened modified gravity
2025-06-23
Daniela Saadeh, Kazuya Koyama, Xan Morice-Atkinson
We present a field-level reaction framework to emulate the nonlinear effects of screened modified gravity on the cosmic web. This approach is designed to enable field-level inference with data from Stage IV cosmological surveys. Building on the reaction method, which models the nonlinear matter power spectrum in modified gravity as corrections to a "pseudo" $\Lambda$CDM cosmology, we extend the method to full field-level predictions by applying it to the output of $N$-body simulations, including both positions and velocities. We focus on modifications to gravity that are scale-independent at the linear level, allowing us to isolate and emulate nonlinear deviations, particularly screening effects. Our neural network predicts the field-level correction ("reaction") to a pseudo$\Lambda$CDM simulation whose linear clustering matches that of the target. The emulator achieves sub-percent accuracy across a broad range of summary statistics, including 0.4\% agreement in the matter power spectrum at scales $k < 1$ Mpc$/h$, and 2\% accuracy in redshift-space distortion multipoles at $k < 0.3$ Mpc$/h$. We also validate the emulator against $N$-body simulations with increased force resolution and time steps, confirming the robustness of its performance. These results demonstrate that our framework is a practical and reliable tool for incorporating screened modified gravity models into field-level cosmological inference, enabling stringent tests of extra fundamental forces at cosmological scales.
FICA: Faster Inner Convex Approximation of Chance Constrained Grid Dispatch with Decision-Coupled Uncertainty
2025-06-23
Yihong Zhou, Hanbin Yang, Thomas Morstyn
This paper proposes a Faster Inner Convex Approximation (FICA) method for solving power system dispatch problems with Wasserstein distributionally robust joint chance constraints (WJCC) and incorporating the modelling of the automatic generation control factors. The problem studied belongs to the computationally challenging class of WJCC with left-hand-side uncertainty (LHS-WJCC). By exploiting the special one-dimensional structure (even if only partially present) of the problem, the proposed FICA incorporates a set of strong valid inequalities to accelerate the solution process. We prove that FICA achieves the same optimality as the well-known conditional value-at-risk (CVaR) inner convex approximation method. Our numerical experiments demonstrate that the proposed FICA can yield 40x computational speedup compared to CVaR, and can even reach up to 500x speedup when the optimisation horizon exceeds 16 time steps. This speedup is achieved when only 50% of constraints in a WJCC have the one-dimensional structure. The approximation quality is numerically verified to be the same as CVaR, and the quality gap is below 1% when compared to the computationally demanding exact reformulation of the LHS-WJCC in most cases. We also discuss the applications of FICA in optimisation problems from other domains that (partially) exhibit the one-dimensional structure.
OC-SOP: Enhancing Vision-Based 3D Semantic Occupancy Prediction by Object-Centric Awareness
2025-06-23
Helin Cao, Sven Behnke
Autonomous driving perception faces significant challenges due to occlusions and incomplete scene data in the environment. To overcome these issues, the task of semantic occupancy prediction (SOP) is proposed, which aims to jointly infer both the geometry and semantic labels of a scene from images. However, conventional camera-based methods typically treat all categories equally and primarily rely on local features, leading to suboptimal predictions, especially for dynamic foreground objects. To address this, we propose Object-Centric SOP (OC-SOP), a framework that integrates high-level object-centric cues extracted via a detection branch into the semantic occupancy prediction pipeline. This object-centric integration significantly enhances the prediction accuracy for foreground objects and achieves state-of-the-art performance among all categories on SemanticKITTI.
SWA-SOP: Spatially-aware Window Attention for Semantic Occupancy Prediction in Autonomous Driving
2025-06-23
Helin Cao, Rafael Materla, Sven Behnke
Perception systems in autonomous driving rely on sensors such as LiDAR and cameras to perceive the 3D environment. However, due to occlusions and data sparsity, these sensors often fail to capture complete information. Semantic Occupancy Prediction (SOP) addresses this challenge by inferring both occupancy and semantics of unobserved regions. Existing transformer-based SOP methods lack explicit modeling of spatial structure in attention computation, resulting in limited geometric awareness and poor performance in sparse or occluded areas. To this end, we propose Spatially-aware Window Attention (SWA), a novel mechanism that incorporates local spatial context into attention. SWA significantly improves scene completion and achieves state-of-the-art results on LiDAR-based SOP benchmarks. We further validate its generality by integrating SWA into a camera-based SOP pipeline, where it also yields consistent gains across modalities.
Patient-Centred Explainability in IVF Outcome Prediction
2025-06-23
Adarsa Sivaprasad, Ehud Reiter, David McLernon, Nava Tintarev, Siladitya Bhattacharya, Nir Oren
This paper evaluates the user interface of an in vitro fertility (IVF) outcome prediction tool, focussing on its understandability for patients or potential patients. We analyse four years of anonymous patient feedback, followed by a user survey and interviews to quantify trust and understandability. Results highlight a lay user's need for prediction model \emph{explainability} beyond the model feature space. We identify user concerns about data shifts and model exclusions that impact trust. The results call attention to the shortcomings of current practices in explainable AI research and design and the need for explainability beyond model feature space and epistemic assumptions, particularly in high-stakes healthcare contexts where users gather extensive information and develop complex mental models. To address these challenges, we propose a dialogue-based interface and explore user expectations for personalised explanations.
Estimating the effect binarity can have on planet-formation is of crucial importance, as almost half of field stars reside in multiple systems. One effective way to assess this effect is to get an accurate picture of the population of planet-hosting binaries and compare its characteristics to that of field star binaries. We construct an extensive database, collected from intensive literature exploration, to achieve a complete census of all planet-hosting binaries known to date. Despite the heterogeneous character of the different surveys this database is built on, and the biases and selection effects that unavoidably affect any sample of planet-hosting binaries, we look for statistically significant trends and correlations within our sample. Our database provides the characteristics for 759 systems (among which 31 circumbinaries), which is an increase by a factor of 9 with respect to the previous complete census of planet-hosting binaries. Of the 728 S-type systems, 651 are binaries, 73 are triples and 4 are quadruples. The raw distribution of planet-hosting binary separations peaks around 500\,au instead of 50\,au for field binaries. By analyzing the distribution of on-sky angular separations as a function of distance $d_b$ to the systems we argue that the observed deficit of planet-hosting close-in binaries cannot be explained solely by observational biases. Likewise, by exploring how multiplicity fractions among planet-hosts vary with $d_b$ we suggest that the subsample of known planet-hosting binaries at $<500\,$pc is not bias-dominated (but not bias-free). In this $<500\,$pc domain, the multiplicity fraction of planet-hosting stars is $\sim22.5\%$, approximately half of the value for field stars, and the deficit of planet-hosting binaries extends to separations of $\sim500\,$au, giving an approximate estimate of the detrimental effect binarity has on planet-formation
Spiral galaxies with unusual azimuthal asymmetry in oxygen abundance
2025-06-23
L. S. Pilyugin, G. Tautvaisiene
We considered five galaxies in the MaNGA survey that show distinct azimuthal asymmetry in the abundance, in the sense that in the inner part (more than half of the optical radius, $R_{25}$) of each galaxy there is a sector-like region where the oxygen abundances (O/H)_h are higher than the abundances (O/H)_l in other sectors. M-11761-12705 is a massive galaxy with a stellar mass of log(M_star/M_sun) = 11.6; the masses of four other galaxies are moderate: 10.1 < log($M_star/M_sun) < 10.4. Abundances within both high- and low-metallicity regions show flat radial gradients. The histogram for the spaxel abundances demonstrates two distinct peaks, the difference between the (O/H)_h and the (O/H)_l abundances are of 0.06 - 0.08 dex. The high-metallicity regions are located in the O/H - N/O diagram closer to the lower envelope of the band than the low-metallicity regions. The abundance properties in the massive galaxy can be explained by the low-metallicity gas infall onto the galaxy and subsequent starburst in the diluted interstellar gas. For moderate-mass galaxies, the higher oxygen abundance in the high-metallicity region and its shift towards the lower envelope in the N/O -- O/H diagram compared to the low-metallicity region can be explained in one of two ways: either the starburst in the high-metallicity region occurred several dozens of Myr ago, or the star formation in the galaxy is accompanied by galactic winds, and the region evolved with the lower efficiency of the enriched galactic winds shows higher metallicity. Two galaxies of our sample are members of galaxy pairs. However, the asymmetry parameter, A, quantifying the asymmetry of a light distribution across the galaxy, is above the canonical threshold (A = 0.35) between non-interacting/interacting galaxies in the massive galaxy only.
PARALLELPROMPT: Extracting Parallelism from Large Language Model Queries
2025-06-23
Steven Kolawole, Keshav Santhanam, Virginia Smith, Pratiksha Thaker
LLM serving systems typically treat user prompts as monolithic inputs, optimizing inference through decoding tricks or inter-query batching. However, many real-world prompts contain latent semantic parallelism--decomposable structures where subtasks can be executed independently to reduce latency while preserving meaning. We introduce PARALLELPROMPT, the first benchmark for measuring intra-query parallelism in natural user prompts. Our dataset comprises over 37,000 real-world prompts from public LLM chat logs, each annotated with a structured schema capturing task templates, shared context, and iteration inputs. These schemas are extracted using LLM-assisted prompting with rule-based multilingual validation. To evaluate the benefits of decomposition, we provide an execution suite that benchmarks serial vs. parallel strategies, measuring latency, structural adherence, and semantic fidelity. Our results show that intra-query parallelism can be successfully parsed in over 75% of curated datasets, unlocking up to 5x speedups on tasks like translation, comprehension, and comparative analysis, with minimal quality degradation. By releasing this benchmark, curation pipeline, and evaluation suite, we provide the first standardized testbed for studying structure-aware execution in LLM serving pipelines.
Individual fairness (IF) in graph neural networks (GNNs), which emphasizes the need for similar individuals should receive similar outcomes from GNNs, has been a critical issue. Despite its importance, research in this area has been largely unexplored in terms of (1) a clear understanding of what induces individual unfairness in GNNs and (2) a comprehensive consideration of identifying similar individuals. To bridge these gaps, we conduct a preliminary analysis to explore the underlying reason for individual unfairness and observe correlations between IF and similarity consistency, a concept introduced to evaluate the discrepancy in identifying similar individuals based on graph structure versus node features. Inspired by our observations, we introduce two metrics to assess individual similarity from two distinct perspectives: topology fusion and feature fusion. Building upon these metrics, we propose Similarity-aware GNNs for Individual Fairness, named SaGIF. The key insight behind SaGIF is the integration of individual similarities by independently learning similarity representations, leading to an improvement of IF in GNNs. Our experiments on several real-world datasets validate the effectiveness of our proposed metrics and SaGIF. Specifically, SaGIF consistently outperforms state-of-the-art IF methods while maintaining utility performance. Code is available at: https://github.com/ZzoomD/SaGIF.
FuGa3D: Fast full-sky analysis of Galaxy catalogs in 3D
2025-06-23
Elina Keihänen, Jani Haapala, Valtteri Lindholm, Martin Reinecke, Susan Rissanen, Jussi Valiviita, Akke Viitanen
We present FuGa3D, a code for fast computation of correlation functions and power spectra for galaxy survey observables, including galaxy clustering and cosmic shear. We define the redshift-space correlation function (RCF) as the correlation function defined in the parameter space of two redshifts and an angular separation angle. Assuming that there is no preferred direction in the sky, these parameters fully define the relative position of two galaxies, independently of the assumed cosmological model. Once the RCF is constructed, it is easy to compute derived correlation metrics, such as the real-space clustering correlation function and its multipoles. We further define the redshift-space power spectrum as the harmonic counterpart of the RCF, and show that it can be computed efficiently using the discrete galaxy coordinates. We validate the code with simulated mock catalogs. Computing the RCF and the two-point correlation function at 1.5 Mpc (3 Mpc) resolution for a MICE simulation with 46 million galaxies, took 47 node-min for clustering only, and 7.3 node-hours with shear analysis included.
This is an introduction to advanced linear algebra, with emphasis on geometric aspects, and with some applications included too. We first review basic linear algebra, notably with the spectral theorem in its general form, and with the theory of the resultant and discriminant. Then we discuss the Jordan form and its basic applications to physics, and other advanced decomposition results for the matrices. We then go into positivity topics, involving matrices and bilinear forms, and with a look into curved space-time, and discrete Laplacians. Finally, we discuss the various groups of matrices, with a look at reflection groups, Lie groups, spin matrices, and random matrices.
Deceptive Game Design? Investigating the Impact of Visual Card Style on Player Perception
2025-06-23
Leonie Kallabis, Timo Bertram, Florian Rupp
The visual style of game elements considerably contributes to the overall experience. Aesthetics influence player appeal, while the abilities of game pieces define their in-game functionality. In this paper, we investigate how the visual style of collectible cards influences the players' perception of the card's actual strength in the game. Using the popular trading card game Magic: The Gathering, we conduct a single-blind survey study that examines how players perceive the strength of AI-generated cards that are shown in two contrasting visual styles: cute and harmless, or heroic and mighty. Our analysis reveals that some participants are influenced by a card's visual appearance when judging its in-game strength. Overall, differences in style perception are normally distributed around a neutral center, but individual participants vary in both directions: some generally perceive the cute style to be stronger, whereas others believe that the heroic style is better.
Stabilization of Quasilinear Parabolic Equations by Cubic Feedback at Boundary with Estimated Region of Attraction
2025-06-23
Mohamed Camil Belhadjoudja, M Maghenem, E Witrant, M Krstic
For quasilinear parabolic partial differential equations (PDEs) that exhibit finite-time blow up in open loop, i.e., under null boundary conditions, we provide an estimate of the region of attraction under cubic feedback laws applied at the boundary, using boundary measurements. We guarantee: 1-L 2 and H 1 exponential stability of the origin with an estimate of the region of attraction. 2-Convergence of the H 2 and the C 1 norms of the solutions to zero. 3-Existence and uniqueness of complete classical solutions. 4-Positivity of the solutions starting from positive initial conditions. Unlike existing approaches, our framework handles nonlinear state-dependent diffusion, convection, and (destabilizing) reaction. The cubic terms are used to enlarge our estimate of the region of attraction. The size of the region of attraction is shown, in many cases, to grow unboundedly as diffusion increases. Finally, our controllers can be implemented as Neumann, Dirichlet, or mixed-type boundary conditions.
Parallel Continuous Chain-of-Thought with Jacobi Iteration
2025-06-23
Haoyi Wu, Zhihao Teng, Kewei Tu
Continuous chain-of-thought has been shown to be effective in saving reasoning tokens for large language models. By reasoning with continuous latent thought tokens, continuous CoT is able to perform implicit reasoning in a compact manner. However, the sequential dependencies between latent thought tokens spoil parallel training, leading to long training time. In this paper, we propose Parallel Continuous Chain-of-Thought (PCCoT), which performs Jacobi iteration on the latent thought tokens, updating them iteratively in parallel instead of sequentially and thus improving both training and inference efficiency of continuous CoT. Experiments demonstrate that by choosing the proper number of iterations, we are able to achieve comparable or even better performance while saving nearly 50% of the training and inference time. Moreover, PCCoT shows better stability and robustness in the training process. Our code is available at https://github.com/whyNLP/PCCoT.
Agentic Markets: Game Dynamics and Equilibrium in Markets with Learning Agents
2025-06-23
Martin Bichler, Julius Durmann, Matthias Oberlechner
Autonomous and learning agents increasingly participate in markets - setting prices, placing bids, ordering inventory. Such agents are not just aiming to optimize in an uncertain environment; they are making decisions in a game-theoretical environment where the decision of one agent influences the profit of other agents. While game theory usually predicts outcomes of strategic interaction as an equilibrium, it does not capture how repeated interaction of learning agents arrives at a certain outcome. This article surveys developments in modeling agent behavior as dynamical systems, with a focus on projected gradient and no-regret learning algorithms. In general, learning in games can lead to all types of dynamics, including convergence to equilibrium, but also cycles and chaotic behavior. It is important to understand when we can expect efficient equilibrium in automated markets and when this is not the case. Thus, we analyze when and how learning agents converge to an equilibrium of a market game, drawing on tools from variational inequalities and Lyapunov stability theory. Special attention is given to the stability of projected dynamics and the convergence to equilibrium sets as limiting outcomes. Overall, the paper provides mathematical foundations for analyzing stability and convergence in agentic markets driven by autonomous, learning agents.
IceTop is the cosmic-ray detector located on the surface of the IceCube Neutrino Observatory at the South Pole, consisting of 81 pairs of ice-Cherenkov tanks. The rise in the energy threshold of air-shower measurements in IceTop due to accumulating snow emphasized the need for the next generation of IceCube surface detectors. For this purpose, the Surface Array Enhancement (SAE) is set to comprise elevated scintillator panels and radio antennas controlled by hybrid DAQ systems. The detectors of the SAE are also expected to extend to the planned IceCube-Gen2 Surface Array. An initial study with a prototype station is already conducted. We briefly review the SAE and the deployment as well as the calibration status of the upcoming stations of the planned array of 32 stations. The focus of this contribution is on the radio detection of extensive air showers. A preliminary estimation of the position of the shower maximum ($X_\mathrm{max}$), that is sensitive to the primary mass, with data from the 3 antennas of the prototype station was carried out. An extension of the method from previous analyses is also briefly discussed.
We report the discovery of J1218+1813, a circular diffuse radio source detected in the Very Large Array (VLA) FIRST survey at 1400 MHz. The source has an angular diameter of approximately one arcminute, corresponding to a physical size of ~180 kpc, and is associated with an elliptical galaxy with a redshift of z = 0.139635. To investigate its nature, we conducted a comprehensive multiwavelength analysis spanning both high and low radio frequencies, utilizing the VLA in C-configuration at L, C and X bands, along with the upgraded Giant Metrewave Radio Telescope (uGMRT) at bands 3 (250-500 MHz), 4 (550-850 MHz), and 5 (1000-1460 MHz). A spectral study based on these multiwavelength observations reveals that the core of J1218+1813 exhibits a steep spectral index of 0.8, indicating that the emission is dominated by optically thin synchrotron radiation. The spectral index varies between 1.1 and 1.8, from the inner to the outer structure, with the steepest values observed at the periphery of the diffuse emission. An optical analysis of the central host galaxy using spectroscopic data is also performed. The estimated black hole mass is $2.8 \pm 0.8 \times 10^{8}$ M${_\odot}$, while the host galaxy has a stellar mass of 2.9 $\times$ 10$^{11}$ M${_\odot}$ and a stellar age of 8.58 Gyr. The identification of J1218+1813 is particularly significant because it provides insight into the mechanisms responsible for the formation of circular diffuse radio structures surrounded by an elliptical galaxy. Potential formation scenarios for J1218+1813 are discussed in this paper.
X-ray and Optical Analysis of the Prototype Polar AN UMa
2025-06-23
S. Ok, M. Yardimci, B. Kalomeni, A. Schwope
We present a long-term optical and X-ray photometric study of AN UMa, one of the prototypical polar-type cataclysmic variables, tracing more than 34 years of its accretion history. Observations from both ground-based and space-based facilities have been analysed to investigate state transitions within the system. Throughout this period, significant changes in the light curve have been observed, corresponding to different mass accretion states. From four years of TESS photometry, we derive a revised photometric period that agrees with the spectroscopic period to within 1.2$\sigma$. These optical observations further suggest switching between two accretion poles. During intervals of high accretion, dips in the X-ray light curve indicate that the primary accretion pole is obscured by an accretion stream elevated above the orbital plane, a feature also evident in the TESS light curves. Additionally, periodogram analysis reveals a periodicity of $\approx$437 days, which may be related to long-term accretion state changes. Following a 16-year high state, AN UMa entered two short-lived low states, lasting 180 and 123 days, during which it faded to a magnitude of 19.2, as recorded by the ZTF and ATLAS surveys. Using the system's low-state brightness and the distance provided by Gaia, we estimate that the system may have a white dwarf with an effective temperature of $\approx$15000~K, and a donor of spectral type M4.7. This work provides a useful reference for future studies of polars with variable accretion geometries and highlights the importance of long-term, multi-wavelength monitoring in the study of magnetic cataclysmic variables.
Rethinking Click Models in Light of Carousel Interfaces: Theory-Based Categorization and Design of Click Models
2025-06-23
Jingwei Kang, Maarten de Rijke, Santiago de Leon-Martinez, Harrie Oosterhuis
Click models are a well-established for modeling user interactions with web interfaces. Previous work has mainly focused on traditional single-list web search settings; this includes existing surveys that introduced categorizations based on the first generation of probabilistic graphical model (PGM) click models that have become standard. However, these categorizations have become outdated, as their conceptualizations are unable to meaningfully compare PGM with neural network (NN) click models nor generalize to newer interfaces, such as carousel interfaces. We argue that this outdated view fails to adequately explain the fundamentals of click model designs, thus hindering the development of novel click models. This work reconsiders what should be the fundamental concepts in click model design, grounding them - unlike previous approaches - in their mathematical properties. We propose three fundamental key-design choices that explain what statistical patterns a click model can capture, and thus indirectly, what user behaviors they can capture. Based on these choices, we create a novel click model taxonomy that allows a meaningful comparison of all existing click models; this is the first taxonomy of single-list, grid and carousel click models that includes PGMs and NNs. Finally, we show how our conceptualization provides a foundation for future click model design by an example derivation of a novel design for carousel interfaces.
A Question Bank to Assess AI Inclusivity: Mapping out the Journey from Diversity Errors to Inclusion Excellence
2025-06-23
Rifat Ara Shams, Didar Zowghi, Muneera Bano
Ensuring diversity and inclusion (D&I) in artificial intelligence (AI) is crucial for mitigating biases and promoting equitable decision-making. However, existing AI risk assessment frameworks often overlook inclusivity, lacking standardized tools to measure an AI system's alignment with D&I principles. This paper introduces a structured AI inclusivity question bank, a comprehensive set of 253 questions designed to evaluate AI inclusivity across five pillars: Humans, Data, Process, System, and Governance. The development of the question bank involved an iterative, multi-source approach, incorporating insights from literature reviews, D&I guidelines, Responsible AI frameworks, and a simulated user study. The simulated evaluation, conducted with 70 AI-generated personas related to different AI jobs, assessed the question bank's relevance and effectiveness for AI inclusivity across diverse roles and application domains. The findings highlight the importance of integrating D&I principles into AI development workflows and governance structures. The question bank provides an actionable tool for researchers, practitioners, and policymakers to systematically assess and enhance the inclusivity of AI systems, paving the way for more equitable and responsible AI technologies.
The undetectable fraction of core-collapse supernovae in luminous infrared galaxies
2025-06-23
I. Mäntynen, E. Kankare, S. Mattila, A. Efstathiou, S. D. Ryder, T. M. Reynolds, C. Vassallo, P. Väisänen
A large fraction of core-collapse supernovae (CCSNe) in luminous infrared galaxies (LIRGs) remain undetected due to extremely high line-of-sight host galaxy dust extinction, and strong contrast between the SN and the galaxy background in the central regions of LIRGs, where the star formation is concentrated. This fraction of undetected CCSNe, unaccounted for by typical extinction corrections, is an important factor in determining CCSN rates, in particular at redshifts $z \gtrsim 1$, where LIRGs dominate the cosmic star formation. Our aim is to derive a robust estimate for the undetected fraction of CCSNe in LIRGs in the local Universe. Our study is based on the K-band multi-epoch SUNBIRD survey data set of a sample of eight LIRGs using the Gemini-North Telescope with the ALTAIR/NIRI laser guide star adaptive optics system. We used simulated SNe and a standard image subtraction method to determine limiting detection magnitudes for the data set. Subsequently, we used a Monte Carlo method to combine the limiting magnitudes with the survey cadence, and an adopted distribution of CCSN subtypes and their light curve evolution to determine SN detection probabilities. Lastly, we combined these probabilities with the intrinsic CCSN rates of the sample galaxies estimated based on their detailed radiative transfer modeling to derive the fraction of undetectable CCSNe in local LIRGs. For high angular resolution near-infrared surveys, we find an undetectable fraction of $66.0^{+8.6}_{-14.6}$\%, assuming that CCSNe with host extinctions up to $A_V$ = 16 mag are detectable, corresponding to the most obscured CCSN discovered in our data set. Alternatively, assuming a host extinction limit of $A_V$ = 3 mag, corresponding to typical optical surveys, we find an undetectable CCSN fraction of $89.7^{+2.6}_{-4.4}$\%.
Detection of subsurface structures with a vehicle-based atom gravity gradiometer
2025-06-23
Xiaowei Zhang, Jiaqi Zhong, Muyan Wang, Huilin Wan, Hui Xiong, Dandan Jiang, Zhi Li, Dekai Mao, Bin Gao, Biao Tang, Xi Chen, Jin Wang, Mingsheng Zhan
High-precision mobile gravity gradiometers are very useful in geodesy and geophysics. Atom gravity gradiometers (AGGs) could be among the most accurate mobile gravity gradiometers but are currently constrained by the trade-off between portability and sensitivity. Here, we present a high-sensitivity mobile AGG featuring an ultra-compact sensor head with a volume of only 94 L. In the laboratory, it achieves a sensitivity of 77 E/$\sqrt{Hz}$ (1 E=1$\times10^{-9}$/s$^2$) and a long-term stability of better than 0.5 E. We integrated the instrument in a minivan, enabling efficient mobile field surveys with excellent maneuverability in confined spaces. Using this vehicular system, we surveyed the gravitational field over a set of subsurface structures within a small wooded area, successfully resolving their structural signatures with a signal-to-noise ratio of 57 and quantifying the water depth in a reservoir with an accuracy of $\pm$0.23 m. Compared with previous observations using a CG-5 gravimeter, the superior spatial resolution inherent in gradiometry is clearly demonstrated. This work paves the way for bring AGGs to practical field applications.
Generalizing Vision-Language Models to Novel Domains: A Comprehensive Survey
2025-06-23
Xinyao Li, Jingjing Li, Fengling Li, Lei Zhu, Yang Yang, Heng Tao Shen
Recently, vision-language pretraining has emerged as a transformative technique that integrates the strengths of both visual and textual modalities, resulting in powerful vision-language models (VLMs). Leveraging web-scale pretraining data, these models exhibit strong zero-shot capabilities. However, their performance often deteriorates when confronted with domain-specific or specialized generalization tasks. To address this, a growing body of research focuses on transferring or generalizing the rich knowledge embedded in VLMs to various downstream applications. This survey aims to comprehensively summarize the generalization settings, methodologies, benchmarking and results in VLM literatures. Delving into the typical VLM structures, current literatures are categorized into prompt-based, parameter-based and feature-based methods according to the transferred modules. The differences and characteristics in each category are furthered summarized and discussed by revisiting the typical transfer learning (TL) settings, providing novel interpretations for TL in the era of VLMs. Popular benchmarks for VLM generalization are further introduced with thorough performance comparisons among the reviewed methods. Following the advances in large-scale generalizable pretraining, this survey also discusses the relations and differences between VLMs and up-to-date multimodal large language models (MLLM), e.g., DeepSeek-VL. By systematically reviewing the surging literatures in vision-language research from a novel and practical generalization prospective, this survey contributes to a clear landscape of current and future multimodal researches.
Electromagnetic Proximity Effect: Superconducting Magnonics and Beyond
2025-06-23
Tao Yu, Xi-Han Zhou, Gerrit E. W. Bauer, Irina Bobkova
The exchange interaction at interfaces between superconductors (SCs) and ferromagnets (FMs) has been a central topic in condensed matter physics for many decades, starting with the prediction of exotic phases such as the Fulde-Ferrell-Larkin-Ovchinnikov states and leading to the discovery of triplet superconductivity. This review focuses on new phenomena in SC$|$FM heterostructures caused by the \textit{non-contact dipolar interaction} between magnons, i.e., the quanta of spin wave excitations in the ferromagnet, and the superconducting order. A universal non-relativistic spin-orbit coupling locks the polarization and momentum of their evanescent stray magnetic fields and leads to chiral screening by proximate superconductors. The interaction-induced hybrid quasiparticles are magnon-Meissner collective modes, magnon-cooparon, Josephson plasmonic modes, and nodal magnon-photon polaritons. Superconducting and normal metallic gates modulate and control the magnetodipolar interaction and thereby magnetization and energy transport at interfaces and in thin films.
Comparative Evaluation of ChatGPT and DeepSeek Across Key NLP Tasks: Strengths, Weaknesses, and Domain-Specific Performance
2025-06-23
Wael Etaiwi, Bushra Alhijawi
The increasing use of large language models (LLMs) in natural language processing (NLP) tasks has sparked significant interest in evaluating their effectiveness across diverse applications. While models like ChatGPT and DeepSeek have shown strong results in many NLP domains, a comprehensive evaluation is needed to understand their strengths, weaknesses, and domain-specific abilities. This is critical as these models are applied to various tasks, from sentiment analysis to more nuanced tasks like textual entailment and translation. This study aims to evaluate ChatGPT and DeepSeek across five key NLP tasks: sentiment analysis, topic classification, text summarization, machine translation, and textual entailment. A structured experimental protocol is used to ensure fairness and minimize variability. Both models are tested with identical, neutral prompts and evaluated on two benchmark datasets per task, covering domains like news, reviews, and formal/informal texts. The results show that DeepSeek excels in classification stability and logical reasoning, while ChatGPT performs better in tasks requiring nuanced understanding and flexibility. These findings provide valuable insights for selecting the appropriate LLM based on task requirements.
Accelerating Photonic Integrated Circuit Design: Traditional, ML and Quantum Methods
2025-06-23
Alessandro Daniele Genuardi Oquendo, Ali Nadir, Tigers Jonuzi, Siddhartha Patra, Nilotpal Kanti Sinha, Román Orús, Sam Mugel
Photonic Integrated Circuits (PICs) provide superior speed, bandwidth, and energy efficiency, making them ideal for communication, sensing, and quantum computing applications. Despite their potential, PIC design workflows and integration lag behind those in electronics, calling for groundbreaking advancements. This review outlines the state of PIC design, comparing traditional simulation methods with machine learning approaches that enhance scalability and efficiency. It also explores the promise of quantum algorithms and quantum-inspired methods to address design challenges.
We review recent developments in cosmological models based on Finsler geometry and extensions of general relativity within this framework. Finsler geometry generalizes Riemannian geometry by allowing the metric tensor to depend on position and an additional internal degree of freedom, typically represented by a vector field at each point of the spacetime manifold. We explore whether Finsler-type geometries can describe gravitational interaction and cosmological dynamics. In particular, we examine the Barthel connection and $(\alpha, \beta)$ geometries, where $\alpha$ is a Riemannian metric and $\beta$ is a one-form. For a specific construction of $\beta$, the Barthel connection coincides with the Levi-Civita connection of the associated Riemann metric. We review gravitational field and cosmological evolution in three geometries: Barthel-Randers ($F = \alpha + \beta$), Barthel-Kropina ($F = \alpha^2 \beta$), and the conformally transformed Barthel-Kropina geometry. After presenting the mathematical foundations of Finslerian-type modified gravity theories, we derive generalized Friedmann equations in these geometries assuming a Friedmann-Lema\^itre-Robertson-Walker type metric. We also present the matter-energy balance equations, interpreting them from the perspective of thermodynamics with particle creation. The cosmological properties of Barthel-Randers and Barthel-Kropina models are explored in detail. The additional geometric terms in these models can be interpreted as an effective dark energy component, generating an effective cosmological constant. Several cosmological solutions are compared with observational data (Cosmic Chronometers, Type Ia Supernovae, Baryon Acoustic Oscillations) using MCMC analysis. A comparison with the $\Lambda$CDM model shows that Finslerian models fit observational data well, suggesting they offer a viable alternative to general relativity.
Melanoma represents a critical health risk due to its aggressive progression and high mortality, underscoring the need for early, interpretable diagnostic tools. While deep learning has advanced in skin lesion classification, most existing models provide only binary outputs, offering limited clinical insight. This work introduces a novel approach that extends beyond classification, enabling interpretable risk modelling through a Conditional Variational Autoencoder. The proposed method learns a structured latent space that captures semantic relationships among lesions, allowing for a nuanced, continuous assessment of morphological differences. An SVM is also trained on this representation effectively differentiating between benign nevi and melanomas, demonstrating strong and consistent performance. More importantly, the learned latent space supports visual and geometric interpretation of malignancy, with the spatial proximity of a lesion to known melanomas serving as a meaningful indicator of risk. This approach bridges predictive performance with clinical applicability, fostering early detection, highlighting ambiguous cases, and enhancing trust in AI-assisted diagnosis through transparent and interpretable decision-making.
ADNF-Clustering: An Adaptive and Dynamic Neuro-Fuzzy Clustering for Leukemia Prediction
2025-06-23
Marco Aruta, Ciro Listone, Giuseppe Murano, Aniello Murano
Leukemia diagnosis and monitoring rely increasingly on high-throughput image data, yet conventional clustering methods lack the flexibility to accommodate evolving cellular patterns and quantify uncertainty in real time. We introduce Adaptive and Dynamic Neuro-Fuzzy Clustering, a novel streaming-capable framework that combines Convolutional Neural Network-based feature extraction with an online fuzzy clustering engine. ADNF initializes soft partitions via Fuzzy C-Means, then continuously updates micro-cluster centers, densities, and fuzziness parameters using a Fuzzy Temporal Index (FTI) that measures entropy evolution. A topology refinement stage performs density-weighted merging and entropy-guided splitting to guard against over- and under-segmentation. On the C-NMC leukemia microscopy dataset, our tool achieves a silhouette score of 0.51, demonstrating superior cohesion and separation over static baselines. The method's adaptive uncertainty modeling and label-free operation hold immediate potential for integration within the INFANT pediatric oncology network, enabling scalable, up-to-date support for personalized leukemia management.
Taming Vision-Language Models for Medical Image Analysis: A Comprehensive Review
2025-06-23
Haoneng Lin, Cheng Xu, Jing Qin
Modern Vision-Language Models (VLMs) exhibit unprecedented capabilities in cross-modal semantic understanding between visual and textual modalities. Given the intrinsic need for multi-modal integration in clinical applications, VLMs have emerged as a promising solution for a wide range of medical image analysis tasks. However, adapting general-purpose VLMs to medical domain poses numerous challenges, such as large domain gaps, complicated pathological variations, and diversity and uniqueness of different tasks. The central purpose of this review is to systematically summarize recent advances in adapting VLMs for medical image analysis, analyzing current challenges, and recommending promising yet urgent directions for further investigations. We begin by introducing core learning strategies for medical VLMs, including pretraining, fine-tuning, and prompt learning. We then categorize five major VLM adaptation strategies for medical image analysis. These strategies are further analyzed across eleven medical imaging tasks to illustrate their current practical implementations. Furthermore, we analyze key challenges that impede the effective adaptation of VLMs to clinical applications and discuss potential directions for future research. We also provide an open-access repository of related literature to facilitate further research, available at https://github.com/haonenglin/Awesome-VLM-for-MIA. It is anticipated that this article can help researchers who are interested in harnessing VLMs in medical image analysis tasks have a better understanding on their capabilities and limitations, as well as current technical barriers, to promote their innovative, robust, and safe application in clinical practice.
Scientific progress increasingly relies on effective collaboration among researchers, a dynamic that large language models (LLMs) have only begun to emulate. While recent LLM-based scientist agents show promise in autonomous scientific discovery, they often lack the interactive reasoning and evaluation mechanisms essential to real-world research. We propose IDVSCI (Internal Discussion and Vote SCIentists), a multi-agent framework built on LLMs that incorporates two key innovations: a Dynamic Knowledge Exchange mechanism enabling iterative feedback among agents, and a Dual-Diversity Review paradigm that simulates heterogeneous expert evaluation. These components jointly promote deeper reasoning and the generation of more creative and impactful scientific ideas. To evaluate the effectiveness and generalizability of our approach, we conduct experiments on two datasets: a widely used benchmark in computer science and a new dataset we introduce in the health sciences domain. Results show that IDVSCI consistently achieves the best performance across both datasets, outperforming existing systems such as AI Scientist and VIRSCI. These findings highlight the value of modeling interaction and peer review dynamics in LLM-based autonomous research.
The fine structure constant: a review of measurement results and possible space-time variations
2025-06-23
K. A. Bronnikov, V. D. Ivashchuk, V. V. Khruschov
A brief description of the main methods for determining the fine structure constant is given. It is shown that the exact value of the fine structure constant is important for the new International System of Units (SI) and for fundamental metrology. Recent measurement results and theoretical calculations of the fine structure constant, as well as its possible space-time variations, are presented. The results of laboratory experiments on the search for long-term variations of the fine structure constant are described. The astrophysical and cosmological observational data on possible variability of the fine structure constant are displayed. The possibility of slightly lower values of the fine structure constant in the remote past as compared to its modern value, as well as the existence of unresolved problems related to possible space-time variations of the fine structure constant and the spread of the results of its precise laboratory measurements, are mentioned. Despite the absence of experimentally confirmed long-term variations of the fine structure constant at a high level of accuracy, possible practical applications of the results are noted, namely, the construction of an optical frequency standard with high stability and frequency reproduction accuracy based on the ytterbium-171 ion and a laser frequency synthesizer which may replace the caesium frequency standard.
Attention-Based Ensemble Learning for Crop Classification Using Landsat 8-9 Fusion
2025-06-23
Zeeshan Ramzan, Nisar Ahmed, Qurat-ul-Ain Akram, Shahzad Asif, Muhammad Shahbaz, Rabin Chakrabortty, Ahmed F. Elaksher
Remote sensing offers a highly effective method for obtaining accurate information on total cropped area and crop types. The study focuses on crop cover identification for irrigated regions of Central Punjab. Data collection was executed in two stages: the first involved identifying and geocoding six target crops through field surveys conducted in January and February 2023. The second stage involved acquiring Landsat 8-9 imagery for each geocoded field to construct a labelled dataset. The satellite imagery underwent extensive pre-processing, including radiometric calibration for reflectance values, atmospheric correction, and georeferencing verification to ensure consistency within a common coordinate system. Subsequently, image fusion techniques were applied to combine Landsat 8 and 9 spectral bands, creating a composite image with enhanced spectral information, followed by contrast enhancement. During data acquisition, farmers were interviewed, and fields were meticulously mapped using GPS instruments, resulting in a comprehensive dataset of 50,835 data points. This dataset facilitated the extraction of vegetation indices such as NDVI, SAVO, RECI, and NDRE. These indices and raw reflectance values were utilized for classification modeling using conventional classifiers, ensemble learning, and artificial neural networks. A feature selection approach was also incorporated to identify the optimal feature set for classification learning. This study demonstrates the effectiveness of combining remote sensing data and advanced modeling techniques to improve crop classification accuracy in irrigated agricultural regions.
We survey some of the stratification theorems concerning exponential sums over finite fields, especially those due to Katz-Laumon and Fouvry-Katz, as well as some of their applications. Moreover, motivated partly by recent work of Bonolis, Pierce and Woo (arXiv:2505.11226), we prove that these stratification statements admit uniform variants in families, both algebraically and analytically. The paper includes an Appendix by Forey, Fres\'an and Kowalski (excerpted from arXiv:2109.11961), which provides an elementary intuitive introduction to trace functions in more than one variable over finite fields.
AI's exponential growth intensifies computational demands and energy challenges. While practitioners employ various optimization techniques, that we refer as "knobs" in this paper, to tune model efficiency, these are typically afterthoughts and reactive ad-hoc changes applied in isolation without understanding their combinatorial effects on energy efficiency. This paper emphasizes on treating energy efficiency as the first-class citizen and as a fundamental design consideration for a compute-intensive pipeline. We show that strategic selection across five AI pipeline phases (data, model, training, system, inference) creates cascading efficiency. Experimental validation shows orthogonal combinations reduce energy consumption by up to $94.6$% while preserving $95.95$% of the original F1 score of non-optimized pipelines. This curated approach provides actionable frameworks for informed sustainable AI that balance efficiency, performance, and environmental responsibility.
NIKA2 Cosmological Legacy Survey: Blind detection of galaxy clusters in the COSMOS field via the Sunyaev-Zel'dovich effect
2025-06-23
D. Chérouvrier, J. F. Macias-Perez, F. X. Désert, R. Adam, P. Ade, H. Ajeddig, S. Amarantidis, P. André, H. Aussel, R. Barrena, A. Beelen, A. Benoit, S. Berta, M. Béthermin, A. Bongiovanni, J. Bounmy, O. Bourrion, L. -J. Bing, M. Calvo, A. Catalano, M. De Petris, S. Doyle, E. F. C. Driessen, G. Ejlali, A. Ferragamo, M. Fernandez-Torreiro, A. Gomez, J. Goupy, C. Hanser, S. Katsioli, F. Kéruzoré, C. Kramer, B. Ladjelate, G. Lagache, S. Leclercq, J. -F. Lestrade, S. C. Madden, A. Maury, F. Mayet, J. -B. Melin, A. Monfardini, A. Moyer-Anin, M. Mu noz-Echeverria, I. Myserlis, R. Neri, A. Paliwal, L. Perotto, G. Pisano, E. Pointecouteau, N. Ponthieu, G. W. Pratt, V. Reveret, A. J. Rigby, A. Ritacco, H. Roussel, F. Ruppin, M. Sanchez-Portal, S. Savorgnano, K. Schuster, A. Sievers, C. Tucker, R. Zylka
(Abridged) Clusters of galaxies, formed in the latest stages of structure formation, are unique cosmological probes. With the advent of large CMB surveys like those from the Planck satellite, the ACT and SPT telescopes, we now have access to a large number of galaxy clusters detected at millimeter wavelengths via the thermal Sunyaev-Zel'dovich (tSZ) effect. Nevertheless, it is interesting to complement them with high-angular-resolution (tens of arcseconds) observations to target the lowest-mass and highest-redshift clusters. This is the case of observations with the NIKA2 camera, which is installed on the IRAM 30--m telescope in Pico Veleta, Spain. We used the existing 150 GHz (2 mm) data from the NIKA2 Cosmological Legacy Survey (N2CLS) Large Program to blindly search for galaxy clusters in the well-known COSMOS field, across a 877 arcmin$^2$ region centered on (R.A., Dec.)$_{J2000}$ = (10h00m28.81s, +02d17m30.44s). We first developed a dedicated data reduction pipeline to construct NIKA2 maps at 2 mm. We then used a matched-filter algorithm to extract cluster candidates assuming a universal pressure profile to model the expected cluster tSZ signal. We computed the purity and completeness of the sample by applying the previous algorithm to simulated maps of the sky signal in the COSMOS field. We find a total of 16 cluster candidates at S/N > 4, from which eight have either an optical or X-ray cluster (or group of galaxies) counterpart. This is the first blind detection of clusters of galaxies at mm wavelengths at 18" angular resolution. From this analysis, we confirm that NIKA2 and the IRAM 30--m telescope should be sensitive to low-mass clusters at intermediate and high redshift, complementing current and planned large tSZ-based cluster surveys.
These Are Not All the Features You Are Looking For: A Fundamental Bottleneck in Supervised Pretraining
2025-06-23
Xingyu Alice Yang, Jianyu Zhang, Léon Bottou
Transfer learning is a cornerstone of modern machine learning, promising a way to adapt models pretrained on a broad mix of data to new tasks with minimal new data. However, a significant challenge remains in ensuring that transferred features are sufficient to handle unseen datasets, amplified by the difficulty of quantifying whether two tasks are "related". To address these challenges, we evaluate model transfer from a pretraining mixture to each of its component tasks, assessing whether pretrained features can match the performance of task-specific direct training. We identify a fundamental limitation in deep learning models -- an "information saturation bottleneck" -- where networks fail to learn new features once they encode similar competing features during training. When restricted to learning only a subset of key features during pretraining, models will permanently lose critical features for transfer and perform inconsistently on data distributions, even components of the training mixture. Empirical evidence from published studies suggests that this phenomenon is pervasive in deep learning architectures -- factors such as data distribution or ordering affect the features that current representation learning methods can learn over time. This study suggests that relying solely on large-scale networks may not be as effective as focusing on task-specific training, when available. We propose richer feature representations as a potential solution to better generalize across new datasets and, specifically, present existing methods alongside a novel approach, the initial steps towards addressing this challenge.
What drives the growth of black holes: a decade of progress
2025-06-23
D. M. Alexander, R. C. Hickox, J. Aird, F. Combes, T. Costa, M. Habouzit, C. M. Harrison, R. I. Leng, L. K. Morabito, S. L. Uckelman, P. Vickers
The last decade has witnessed significant progress in our understanding of the growth of super-massive black holes (SMBHs). It is now clear that an Active Galactic Nucleus (AGN: the observed manifestation of a growing SMBH) is an "event" within the broader lifecycle of a galaxy, which can significantly influence the shape and evolution of the galaxy itself. Our view of the obscuring medium that affects the observed properties of an AGN has also undergone a revolution, and we now have a more physical understanding of the connection between the fuelling of (and feedback from) the SMBH and the broader host-galaxy and larger-scale environment. We have a greater understanding of the physics of SMBH accretion, can identify AGNs out to z = 8-10 witnessing the very earliest phases of SMBH growth, and have a more complete census of AGN activity than ever before. This great progress has been enabled by new innovative facilities, an ever-increasing quantity of multi-wavelength data, the exploitation and development of new techniques, and greater community-wide engagement. In this article we review our understanding of AGNs and the growth of SMBHs, providing an update of the earlier Alexander and Hickox (2012) review. Using citation-network analyses we also show where this review fits within the broader black-hole research literature and, adopting the previous article as a snapshot of the field over a decade ago, identify the drivers that have enabled the greatest scientific progress.
The DECam MAGIC Survey: Spectroscopic Follow-up of the Most Metal-Poor Stars in the Distant Milky Way Halo
2025-06-23
Vinicius M. Placco, Guilherme Limberg, Anirudh Chiti, Deepthi S. Prabhu, Alexander P. Ji, Fabrícia O. Barbosa, William Cerny, Andrew B. Pace, Guy S. Stringfellow, David J. Sand, Clara E. Martínez-Vázquez, Alexander H. Riley, Silvia Rossi, Noelia E. D. Noël, A. Katherina Vivas, Gustavo E. Medina, Alex Drlica-Wagner, Joanna D. Sakowska, Burçin Mutlu-Pakdil, Pol Massana, Julio A. Carballo-Bello, Yumi Choi, Denija Crnojević, Chin Yi Tan
In this work, we present high-resolution spectroscopic observations for six metal-poor stars with [Fe/H]<-3 (including one with [Fe/H]<-4), selected using narrow-band Ca II HK photometry from the DECam MAGIC Survey. The spectroscopic data confirms the accuracy of the photometric metallicities and allows for the determination of chemical abundances for 16 elements, from carbon to barium. The program stars have chemical abundances consistent with this metallicity range. A kinematic/dynamical analysis suggests that all program stars belong to the distant Milky Way halo population (heliocentric distances 35 < dhelio/kpc < 55), including three with high-energy orbits that might have been associated with the Magellanic system and one, J0026-5445, having parameters consistent with being a member of the Sagittarius stream. The remaining two stars show kinematics consistent with the Gaia-Sausage/Enceladus dwarf galaxy merger. J0433-5548, with [Fe/H]=-4.12, is a carbon-enhanced ultra metal-poor star, with [C/Fe]=+1.73. This star is believed to be a bona fide second-generation star, and its chemical abundance pattern was compared with yields from metal-free supernova models. Results suggest that J0433-5548 could have been formed from a gas cloud enriched by a single supernova explosion from a ~11Mo star in the early universe. The successful identification of such objects demonstrates the reliability of photometric metallicity estimates, which can be used for target selection and statistical studies of faint targets in the Milky Way and its satellite population. These discoveries illustrate the power of measuring chemical abundances of metal-poor Milky Way halo stars to learn more about early galaxy formation and evolution.
Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education
2025-06-23
Ruiwei Xiao, Xinying Hou, Runlong Ye, Majeed Kazemitabaar, Nicholas Diana, Michael Liut, John Stamper
With the proliferation of large language model (LLM) applications since 2022, their use in education has sparked both excitement and concern. Recent studies consistently highlight students' (mis)use of LLMs can hinder learning outcomes. This work aims to teach students how to effectively prompt LLMs to improve their learning. We first proposed pedagogical prompting, a theoretically-grounded new concept to elicit learning-oriented responses from LLMs. To move from concept design to a proof-of-concept learning intervention in real educational settings, we selected early undergraduate CS education (CS1/CS2) as the example context. We began with a formative survey study with instructors (N=36) teaching early-stage undergraduate-level CS courses to inform the instructional design based on classroom needs. Based on their insights, we designed and developed a learning intervention through an interactive system with scenario-based instruction to train pedagogical prompting skills. Finally, we evaluated its instructional effectiveness through a user study with CS novice students (N=22) using pre/post-tests. Through mixed methods analyses, our results indicate significant improvements in learners' LLM-based pedagogical help-seeking skills, along with positive attitudes toward the system and increased willingness to use pedagogical prompts in the future. Our contributions include (1) a theoretical framework of pedagogical prompting; (2) empirical insights into current instructor attitudes toward pedagogical prompting; and (3) a learning intervention design with an interactive learning tool and scenario-based instruction leading to promising results on teaching LLM-based help-seeking. Our approach is scalable for broader implementation in classrooms and has the potential to be integrated into tools like ChatGPT as an on-boarding experience to encourage learning-oriented use of generative AI.
Benchmarking Music Generation Models and Metrics via Human Preference Studies
2025-06-23
Florian Grötschla, Ahmet Solak, Luca A. Lanzendörfer, Roger Wattenhofer
Recent advancements have brought generated music closer to human-created compositions, yet evaluating these models remains challenging. While human preference is the gold standard for assessing quality, translating these subjective judgments into objective metrics, particularly for text-audio alignment and music quality, has proven difficult. In this work, we generate 6k songs using 12 state-of-the-art models and conduct a survey of 15k pairwise audio comparisons with 2.5k human participants to evaluate the correlation between human preferences and widely used metrics. To the best of our knowledge, this work is the first to rank current state-of-the-art music generation models and metrics based on human preference. To further the field of subjective metric evaluation, we provide open access to our dataset of generated music and human evaluations.
A Principled Approach to Randomized Selection under Uncertainty: Applications to Peer Review and Grant Funding
2025-06-23
Alexander Goldberg, Giulia Fanti, Nihar B. Shah
Many decision-making processes involve evaluating and then selecting items; examples include scientific peer review, job hiring, school admissions, and investment decisions. The eventual selection is performed by applying rules or deliberations to the raw evaluations, and then deterministically selecting the items deemed to be the best. These domains feature error-prone evaluations and uncertainty about future outcomes, which undermine the reliability of such deterministic selection rules. As a result, selection mechanisms involving explicit randomization that incorporate the uncertainty are gaining traction in practice. However, current randomization approaches are ad hoc, and as we prove, inappropriate for their purported objectives. In this paper, we propose a principled framework for randomized decision-making based on interval estimates of the quality of each item. We introduce MERIT (Maximin Efficient Randomized Interval Top-k), an optimization-based method that maximizes the worst-case expected number of top candidates selected, under uncertainty represented by overlapping intervals (e.g., confidence intervals or min-max intervals). MERIT provides an optimal resource allocation scheme under an interpretable notion of robustness. We develop a polynomial-time algorithm to solve the optimization problem and demonstrate empirically that the method scales to over 10,000 items. We prove that MERIT satisfies desirable axiomatic properties not guaranteed by existing approaches. Finally, we empirically compare algorithms on synthetic peer review data. Our experiments demonstrate that MERIT matches the performance of existing algorithms in expected utility under fully probabilistic review data models used in previous work, while outperforming previous methods with respect to our novel worst-case formulation.
MFTCXplain: A Multilingual Benchmark Dataset for Evaluating the Moral Reasoning of LLMs through Hate Speech Multi-hop Explanation
2025-06-23
Jackson Trager, Francielle Vargas, Diego Alves, Matteo Guida, Mikel K. Ngueajio, Ameeta Agrawal, Flor Plaza-del-Arco, Yalda Daryanai, Farzan Karimi-Malekabadi
Ensuring the moral reasoning capabilities of Large Language Models (LLMs) is a growing concern as these systems are used in socially sensitive tasks. Nevertheless, current evaluation benchmarks present two major shortcomings: a lack of annotations that justify moral classifications, which limits transparency and interpretability; and a predominant focus on English, which constrains the assessment of moral reasoning across diverse cultural settings. In this paper, we introduce MFTCXplain, a multilingual benchmark dataset for evaluating the moral reasoning of LLMs via hate speech multi-hop explanation using Moral Foundation Theory (MFT). The dataset comprises 3,000 tweets across Portuguese, Italian, Persian, and English, annotated with binary hate speech labels, moral categories, and text span-level rationales. Empirical results highlight a misalignment between LLM outputs and human annotations in moral reasoning tasks. While LLMs perform well in hate speech detection (F1 up to 0.836), their ability to predict moral sentiments is notably weak (F1 < 0.35). Furthermore, rationale alignment remains limited mainly in underrepresented languages. These findings show the limited capacity of current LLMs to internalize and reflect human moral reasoning.
A Late-time Radio Survey of Type Ia-CSM Supernovae with the Very Large Array
2025-06-23
Olivia Griffith, Grace Showerman, Sumit K. Sarbadhicary, Chelsea E. Harris, Laura Chomiuk, Jesper Sollerman, Peter Lundqvist, Javier Moldon, Miguel Perez-Torres, Erik C. Kool, Takashi J. Moriya
Type Ia-CSM supernovae (SNe) are a rare and peculiar subclass of thermonuclear SNe characterized by emission lines of hydrogen or helium, indicative of a high-density circumstellar medium (CSM). Their implied mass-loss rates of $\sim 10^{-4}-10^{-1}$ M$_{\odot}$ yr$^{-1}$ (assuming $\mathrm{ \sim 100 \ km\ s^{-1}}$ winds) from optical observations are generally in excess of values observed in realistic SN Ia progenitors. In this paper, we present an independent study of CSM densities around a sample of 29 archival Ia-CSM SNe using radio observations with the Very Large Array at 6 GHz. Motivated by the late ($\sim$2 yr) radio detection of the Ia-CSM SN 2020eyj, we observed old ($>$1 yr) SNe where we are more likely to see the emergent synchrotron emission that may have been suppressed earlier by free-free absorption by the CSM. We do not detect radio emission down to 3$\sigma$ limits of $\sim$35 $\mu$Jy in our sample. The only radio-detected candidate in our sample, SN 2022esa, was likely mis-classified as a Ia-CSM with early spectra, and appears more consistent with a peculiar Ic based on later-epochs. Assuming a wind-like CSM with temperatures between $2 \times 10^4$ K and $10^5$ K, and magnetic field-to-shock energy fraction ($\epsilon_B$) = $0.01-0.1$, the radio upper limits rule out mass-loss rates between $\sim 10^{-4}-10^{-2}$ M$_{\odot}$ yr$^{-1}$ (100 km s$^{-1}$)$^{-1}$. This is somewhat in tension with the estimates from optical observations, and may indicate that more complex CSM geometries and/or lower values of $\epsilon_B$ may be present.
Detectability of the 21 cm signal with BINGO through cross-correlation with photometric surveys
2025-06-23
Gabriel A. S. Silva, Camila P. Novaes, Carlos A. Wuensche, Eduardo J. de Mericia, Bruno B. Bizarria, Jiajun Zhang, Elcio Abdalla, Filipe B. Abdalla, Amilcar R. Queiroz, Thyrso Villela, Bin Wang, Chang Feng, Edmar C. Gurjao, Alessandro Marins
21 cm intensity mapping (HI IM) can efficiently map large cosmic volumes with good redshift resolution, but systematics and foreground contamination pose major challenges for extracting accurate cosmological information. Cross-correlation with galaxy surveys offers an efficient mitigation strategy, as both datasets have largely uncorrelated systematics. We evaluate the detectability of the 21 cm signal from the BINGO radio telescope by cross-correlating with the LSST photometric survey, given their strong overlap in area and redshift. Using lognormal simulations, we model the cosmological signal in the BINGO frequency range (980 - 1260 MHz), incorporating thermal noise, foregrounds, and cleanup. The LSST simulations include uncertainties in photometric redshift (photo-z) and galaxy number density in the first three redshift intervals (mean redshift approximately equal to 0.25, 0.35, 0.45), corresponding to the expected performance after 10 years of the survey. We show that photo-z errors significantly increase the noise in the cross-correlation, reducing its statistical significance to levels comparable to those of the autocorrelation. Still, the HI signal remains detectable through the cross-correlation, even with photo-z uncertainties similar to those of the LSST. Our results corroborate the feasibility of this approach under realistic conditions and motivate further refinement of the current analysis methods.
Identifying Causally-Robust Mediators of Health Disparities: A Review and Simulation Studies With Directed Acyclic Graphs
2025-06-23
Soojin Park, Su Yeon Kim, Chioun Lee
Background Traditionally researchers have used linear approaches such as difference in coefficients DIC and Kitagawa Oaxaca Blinder KOB decomposition to identify risk factors or resources referred to as mediators underlying health disparities Recently causal decomposition analysis CDA has gained popularity by defining clear causal effects of interest and estimating them without modeling restrictions Methods We begin with a brief review of each method under the assumption of no unmeasured confounders followed by two realistic scenarios where unmeasured confounders affect first the relationship between intermediate confounders and the mediator and second the relationship between the mediator and the outcome For each scenario we generate simulated data apply all three methods compare estimates and interpret results using directed acyclic graphs Results The DIC approach performs well only when no intermediate confounders are present a condition unlikely in real world health disparities that arise from many factors over the life course The KOB decomposition is appropriate only when baseline covariates such as age need not be controlled When unmeasured confounding exists DIC yields biased estimates in both scenarios while both KOB and CDA yield biased estimates in the second scenario however CDA supplemented by sensitivity analysis can reveal how robust its estimates are to unmeasured confounding Conclusions We recommend against using DIC for investigating drivers of health disparities and instead advise applying CDA combined with sensitivity analysis as a robust strategy for identifying mediators of health disparities
Statistical Inference for Optimal Transport Maps: Recent Advances and Perspectives
2025-06-23
Sivaraman Balakrishnan, Tudor Manole, Larry Wasserman
In many applications of optimal transport (OT), the object of primary interest is the optimal transport map. This map rearranges mass from one probability distribution to another in the most efficient way possible by minimizing a specified cost. In this paper we review recent advances in estimating and developing limit theorems for the OT map, using samples from the underlying distributions. We also review parallel lines of work that establish similar results for special cases and variants of the basic OT setup. We conclude with a discussion of key directions for future research with the goal of providing practitioners with reliable inferential tools.
The rapid growth of AI, data-intensive science, and digital twin technologies has driven an unprecedented demand for high-performance computing (HPC) across the research ecosystem. While national laboratories and industrial hyperscalers have invested heavily in exascale and GPU-centric architectures, university-operated HPC systems remain comparatively under-resourced. This survey presents a comprehensive assessment of the HPC landscape across U.S. universities, benchmarking their capabilities against Department of Energy (DOE) leadership-class systems and industrial AI infrastructures. We examine over 50 premier research institutions, analyzing compute capacity, architectural design, governance models, and energy efficiency. Our findings reveal that university clusters, though vital for academic research, exhibit significantly lower growth trajectories (CAGR $\approx$ 18%) than their national ($\approx$ 43%) and industrial ($\approx$ 78%) counterparts. The increasing skew toward GPU-dense AI workloads has widened the capability gap, highlighting the need for federated computing, idle-GPU harvesting, and cost-sharing models. We also identify emerging paradigms, such as decentralized reinforcement learning, as promising opportunities for democratizing AI training within campus environments. Ultimately, this work provides actionable insights for academic leaders, funding agencies, and technology partners to ensure more equitable and sustainable HPC access in support of national research priorities.
From simulations to observations. Methodology and data release of mock TNG50 galaxies at 0.3 < z < 0.7 for WEAVE-StePS
2025-06-23
A. Ikhsanova, L. Costantin, A. Pizzella, E. M. Corsini, L. Morelli, F. R. Ditrani, A. Ferré-Mateu, L. Gabarra, M. Gullieuszik, C. P. Haines, A. Iovino, M. Longhetti, A. Mercurio, R. Ragusa, P. Sánchez-Blázquez, C. Tortora, B. Vulcani, S. Zhou, E. Gafton, F. Pistis
The new generation of optical spectrographs (i.e., WEAVE, 4MOST, DESI, and WST) offer unprecedented opportunities for statistically studying the star formation histories of galaxies. However, these observations are not easily comparable to predictions from cosmological simulations. Our goal is to build a reference framework for comparing spectroscopic observations with simulations and test tools for deriving stellar population properties of galaxies. We focus on the observational strategy of the Stellar Population at Intermediate Redshift Survey (StePS) with the WEAVE instrument. We generate mock datasets of ~750 galaxies at redshifts z = 0.3, 0.5, and 0.7 using the TNG50 simulation, perform radiative transfer with SKIRT, and analyze the spectra with pPXF as if they were real observations. We present the methodology to generate these datasets and provide an initial exploration of stellar population parameters (i.e., mass-weighted ages and metallicities) and star formation histories for three galaxies at z = 0.7 and their descendants at z = 0.5 and 0.3. We find good agreement between the mock spectra and intrinsic ages in TNG50 (average difference $0.2\pm0.3$ Gyr) and successfully recover their star formation histories, especially for galaxies form the bulk of their stars on short timescales and at early epochs. We release these datasets, including multi-wavelength imaging and spectra, to support forthcoming WEAVE observations.
Time-resolved p-mode oscillations for subgiant HD 142091 with NEID at WIYN
2025-06-23
Jacob K. Luhn, Paul Robertson, Samuel Halverson, Arvind F. Gupta, Jared C. Siegel, Jason T. Wright, Eric B. Ford, Suvrath Mahadevan, Timothy R. Bedding, Jaime A. Alvarado-Montes, Chad F. Bender, Jiayin Dong, Fred Hearty, Sarah E. Logsdon, Andrew Monson, Michael W. McElwain, Joe P. Ninan, Jayadev Rajagopal, Arpita Roy, Christian Schwab, Gudmundur Stefansson, Daniel J. Stevens, Ryan C. Terrien, Sharon Xuesong Wang, Jinglin Zhao
Detections of Earth-analog planets in radial velocity observations are limited by stellar astrophysical variability occurring on a variety of timescales. Current state-of-the-art methods to disentangle potential planet signals from intrinsic stellar signals assume that stellar signals introduce asymmetries to the line profiles that can therefore be separated from the pure translational Doppler shifts of planets. Here, we examine this assumption using a time series of resolved stellar p-mode oscillations in HD 142091 ($\kappa$ CrB), as observed on a single night with the NEID spectrograph at 2-minute cadence and with 25 cm/s precision. As an evolved subgiant star, this target has p-mode oscillations that are larger in amplitude (4-8 m/s) and occur on longer timescales (80 min.) than those of typical Sun-like stars of RV surveys, magnifying their corresponding effects on the stellar spectral profile. We show that for HD 142091, p-mode oscillations manifest primarily as pure Doppler shifts in the average line profile -- measured by the cross-correlation function (CCF) -- with "shape-driven" CCF variations as a higher-order effect. Specifically, we find that the amplitude of the shift varies across the CCF bisector, with 10% larger oscillation amplitudes closer to the core of the CCF, and 25% smaller oscillation amplitudes for bisector velocities derived near the wings; we attribute this trend to larger oscillation velocities higher in the stellar atmosphere. Using a line-by-line analysis, we verify that a similar trend is seen as a function of average line depth, with deeper lines showing larger oscillation amplitudes. Finally, we find no evidence that p-mode oscillations have a chromatic dependence across the NEID bandpass beyond that due to intrinsic line depth differences across the spectrum.
The OATMEAL Survey. II. The 3D spin-orbit obliquity of an eccentric transiting brown dwarf in the Ruprecht 147 open cluster
2025-06-23
Theron W. Carmichael, Steven Giacalone, Noah Vowell, Daniel Huber, Xian-Yu Wang, Malik Bossett, Luke Handley, Aaron Householder, Yaguang Li, Benjamin J. Fulton, Andrew Howard, Howard Isaacson, Samuel Halverson, Arpita Roy
We present new analysis of the CWW 89 system as part of the Orbital Architectures of Transiting Massive Exoplanets And Low-mass stars (OATMEAL) survey. The CWW 89 system is a member of the 2.8 Gyr old Ruprecht 147 (NGC 6774) cluster and features two stars, CWW 89A (EPIC 219388192) and CWW 89B, with the primary hosting a transiting brown dwarf. We use in-transit, highly precise radial velocity measurements with the Keck Planet Finder (KPF) to characterize the Rossiter-McLaughlin (RM) effect and measure the projected spin-orbit obliquity $|\lambda|=1.4\pm2.5^\circ$ and the full 3D spin-orbit obliquity of the brown dwarf to be $\psi=15.1^{+15.0^\circ}_{-10.9}$. This value of $\lambda$ implies that the brown dwarf's orbit is prograde and well-aligned with the equator of the host star, continuing the trend of transiting brown dwarfs showing a preference for alignment ($\lambda \approx 0^\circ$) regardless of the stellar effective temperature. We find that this contrast with the transiting giant planet population, whose spin-orbit alignments depend on host $T_{\rm eff}$, shows an increasingly clear distinction in the formation and orbital migration mechanisms between transiting giant planets and transiting brown dwarfs like CWW 89Ab. For this system in particular, we find it plausible that the brown dwarf may have undergone coplanar high-eccentricity migration influence by CWW 89B.
Euclid: An emulator for baryonic effects on the matter bispectrum
2025-06-23
P. A. Burger, G. Aricò, L. Linke, R. E. Angulo, J. C. Broxterman, J. Schaye, M. Schaller, M. Zennaro, A. Halder, L. Porth, S. Heydenreich, M. J. Hudson, A. Amara, S. Andreon, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, A. Biviano, E. Branchini, M. Brescia, S. Camera, V. Capobianco, C. Carbone, V. F. Cardone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, A. Da Silva, H. Degaudenzi, S. de la Torre, G. De Lucia, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, S. Escoffier, M. Farina, R. Farinelli, S. Ferriol, F. Finelli, P. Fosalba, N. Fourmanoit, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, B. Gillis, C. Giocoli, J. Gracia-Carpio, A. Grazian, F. Grupp, S. V. H. Haugan, H. Hoekstra, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, M. Kilbinger, B. Kubik, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, E. Medinaceli, S. Mei, M. Melchior, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, C. Neissner, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, P. Schneider, T. Schrabback, A. Secroun, E. Sefusatti, G. Seidel, S. Serrano, P. Simon, C. Sirignano, G. Sirri, A. Spurio Mancini, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, G. Zamorani, E. Zucca, C. Burigana, L. Gabarra, A. Pezzotta, V. Scottez, M. Viel
The Euclid mission and other next-generation large-scale structure surveys will enable high-precision measurements of the cosmic matter distribution. Understanding the impact of baryonic processes such as star formation and AGN feedback on matter clustering is crucial to ensure precise and unbiased cosmological inference. Most theoretical models of baryonic effects to date focus on two-point statistics, neglecting higher-order contributions. This work develops a fast and accurate emulator for baryonic effects on the matter bispectrum, a key non-Gaussian statistic in the nonlinear regime. We employ high-resolution $N$-body simulations from the BACCO suite and apply a combination of cutting-edge techniques such as cosmology scaling and baryonification to efficiently span a large cosmological and astrophysical parameter space. A deep neural network is trained to emulate baryonic effects on the matter bispectrum measured in simulations, capturing modifications across various scales and redshifts relevant to Euclid. We validate the emulator accuracy and robustness using an analysis of \Euclid mock data, employing predictions from the state-of-the-art FLAMINGO hydrodynamical simulations. The emulator reproduces baryonic suppression in the bispectrum to better than 2$\%$ for the $68\%$ percentile across most triangle configurations for $k \in [0.01, 20]\,h^{-1}\mathrm{Mpc}$ and ensures consistency between cosmological posteriors inferred from second- and third-order weak lensing statistics.
We present a comprehensive multivariate statistical analysis of 517 exoplanets from the NASA Exoplanet Archive to identify potentially habitable worlds and quantify detection bias in current surveys. Using eight key parameters (planetary radius, equilibrium temperature, insolation flux, density, and stellar effective temperature, radius, mass, metallicity), we developed a classification framework that successfully identifies Earth as an "Excellent Candidate" for habitability. Our analysis reveals that only 0.6% (3 planets including Earth) meet all habitability criteria under relaxed thresholds, while 75.0% exhibit "Good Star, Poor Planet" characteristics, indicating significant observational bias toward unsuitable planetary systems. Hotelling's T2 test demonstrates that potentially habitable planets are statistically significantly different from the general exoplanet population (p = 0.015). Mahalanobis distance analysis places Earth in the 69.4th percentile for statistical unusualness, confirming that Earth-like planets are genuine outliers in parameter space. We identify Kepler-22 b as a compelling Earth analog with remarkable parameter similarity, and reveal that 1.2% of planets represent "edge cases" orbiting M-dwarf stars with suitable planetary but marginal stellar conditions. These findings demonstrate systematic detection bias in exoplanet surveys and provide quantitative evidence for the rarity of Earth-like worlds while identifying high-priority targets for atmospheric characterization with JWST.
Large language models have demonstrated remarkable capabilities across various domains, yet concerns about cultural bias - particularly towards Arabs and Muslims - pose significant ethical challenges by perpetuating harmful stereotypes and marginalization. Despite growing recognition of bias in LLMs, prompt engineering strategies specifically addressing Arab and Muslim representation remain understudied. This mixed-methods systematic review examines such techniques, offering evidence-based guidance for researchers and practitioners. Following PRISMA guidelines and Kitchenham's systematic review methodology, we analyzed 8 empirical studies published between 2021-2024 investigating bias mitigation strategies. Our findings reveal five primary prompt engineering approaches: cultural prompting, affective priming, self-debiasing techniques, structured multi-step pipelines, and parameter-optimized continuous prompts. Although all approaches show potential for reducing bias, effectiveness varied substantially across studies and bias types. Evidence suggests that certain bias types may be more resistant to prompt-based mitigation than others. Structured multi-step pipelines demonstrated the highest overall effectiveness, achieving up to 87.7% reduction in bias, though they require greater technical expertise. Cultural prompting offers broader accessibility with substantial effectiveness. These results underscore the accessibility of prompt engineering for mitigating cultural bias without requiring access to model parameters. The limited number of studies identified highlights a significant research gap in this critical area. Future research should focus on developing culturally adaptive prompting techniques, creating Arab and Muslim-specific evaluation resources, and integrating prompt engineering with complementary debiasing methods to address deeper stereotypes while maintaining model utility.
Single-Cell Proteomic Technologies: Tools in the quest for principles
2025-06-22
Nikolai Slavov
Over the last decade, proteomic analysis of single cells by mass spectrometry transitioned from an uncertain possibility to a set of robust and rapidly advancing technologies supporting the accurate quantification of thousands of proteins. We review the major drivers of this progress, from establishing feasibility to powerful and increasingly scalable methods. We focus on the tradeoffs and synergies of different technological solutions within a coherent conceptual framework, which projects considerable room both for throughput scaling and for extending the analysis scope to functional protein measurements. We highlight the potential of these technologies to support the development of mechanistic biophysical models and help uncover new principles.
Six Decades Post-Discovery of Taylor's Power Law: From Ecological and Statistical Universality, Through Prime Number Distributions and Tipping-Point Signals, to Heterogeneity and Stability of Complex Networks
2025-06-22
Zhanshan Sam Ma, R. A. J. Taylor
First discovered by L. R. Taylor (1961, Nature), Taylor's Power Law (TPL) correlates the mean (M) population abundances and the corresponding variances (V) across a set of insect populations using a power function (V=aM^b). TPL has demonstrated its 'universality' across numerous fields of sciences, social sciences, and humanities. This universality has inspired two main prongs of exploration: one from mathematicians and statisticians, who might instinctively respond with a convergence theorem similar to the central limit theorem of the Gaussian distribution, and another from biologists, ecologists, physicists, etc., who are more interested in potential underlying ecological or organizational mechanisms. Over the past six decades, TPL studies have produced a punctuated landscape with three relatively distinct periods (1960s-1980s; 1990s-2000s, and 2010s-2020s) across the two prongs of abstract and physical worlds. Eight themes have been identified and reviewed on this landscape, including population spatial aggregation and ecological mechanisms, TPL and skewed statistical distributions, mathematical/statistical mechanisms of TPL, sample vs. population TPL, population stability, synchrony, and early warning signals for tipping points, TPL on complex networks, TPL in macrobiomes, and in microbiomes. Three future research directions including fostering reciprocal interactions between the two prongs, heterogeneity measuring, and exploration in the context of evolution. The significance of TPL research includes practically, population fluctuations captured by TPL are relevant for agriculture, forestry, fishery, wildlife-conservation, epidemiology, tumor heterogeneity, earthquakes, social inequality, stock illiquidity, financial stability, tipping point events, etc.; theoretically, TPL is one form of power laws, which are related to phase transitions, universality, scale-invariance, etc.
Effective bounds for polynomial systems defined over the rationals
2025-06-22
Teresa Krick
This survey paper was primarily written as as the support for a course pesented at the JNCF2025: it aims to present some material that illustrates the kind of estimates one can obtain in effective algebraic geometry, for affine polynomial equation systems defined over the rational numbers Q, and focuses on the case of finite varieties.
Conceptualization, Operationalization, and Measurement of Machine Companionship: A Scoping Review
2025-06-22
Jaime Banks, Zhixin Li
The notion of machine companions has long been embedded in social-technological imaginaries. Recent advances in AI have moved those media musings into believable sociality manifested in interfaces, robotic bodies, and devices. Those machines are often referred to colloquially as "companions" yet there is little careful engagement of machine companionship (MC) as a formal concept or measured variable. This PRISMA-guided scoping review systematically samples, surveys, and synthesizes current scholarly works on MC (N = 71; 2017-2025), to that end. Works varied widely in considerations of MC according to guiding theories, dimensions of a-priori specified properties (subjectively positive, sustained over time, co-active, autotelic), and in measured concepts (with more than 50 distinct measured variables). WE ultimately offer a literature-guided definition of MC as an autotelic, coordinated connection between human and machine that unfolds over time and is subjectively positive.
Mental Health Equity in LLMs: Leveraging Multi-Hop Question Answering to Detect Amplified and Silenced Perspectives
2025-06-22
Batool Haider, Atmika Gorti, Aman Chadha, Manas Gaur
Large Language Models (LLMs) in mental healthcare risk propagating biases that reinforce stigma and harm marginalized groups. While previous research identified concerning trends, systematic methods for detecting intersectional biases remain limited. This work introduces a multi-hop question answering (MHQA) framework to explore LLM response biases in mental health discourse. We analyze content from the Interpretable Mental Health Instruction (IMHI) dataset across symptom presentation, coping mechanisms, and treatment approaches. Using systematic tagging across age, race, gender, and socioeconomic status, we investigate bias patterns at demographic intersections. We evaluate four LLMs: Claude 3.5 Sonnet, Jamba 1.6, Gemma 3, and Llama 4, revealing systematic disparities across sentiment, demographics, and mental health conditions. Our MHQA approach demonstrates superior detection compared to conventional methods, identifying amplification points where biases magnify through sequential reasoning. We implement two debiasing techniques: Roleplay Simulation and Explicit Bias Reduction, achieving 66-94% bias reductions through few-shot prompting with BBQ dataset examples. These findings highlight critical areas where LLMs reproduce mental healthcare biases, providing actionable insights for equitable AI development.
Peering into the heart of darkness with VLBA : Radio Quiet AGN in the JWST North Ecliptic Pole Time-Domain Field
2025-06-22
Payaswini Saikia, Ramon Wrzosek, Joseph Gelfand, Walter Brisken, William Cotton, S. P. Willner, Hansung B. Gim, Rogier A. Windhorst, Vicente Estrada-Carpenter, Ivan Yu. Katkov, Ingyin Zaw, Michael Rosenthal, Hanaan Shafi, Kenneth Kellermann, James Condon, Anton M. Koekemoer, Christopher J. Conselice, Rafael Ortiz III, Christopher N. A. Willmer, Brenda Frye, Norman A. Grogin, Heidi B. Hammel, Seth H. Cohen, Rolf A. Jansen, Jake Summers, Jordan C. J. D'Silva, Simon P. Driver, Nor Pirzkal, Haojing Yan, Min S. Yun
We present initial results from the 4.8 GHz Very Long Baseline Array (VLBA) survey of the JWST North Ecliptic Pole Time-Domain Field (TDF). From 106 radio sources found in the Karl G. Jansky Very Large Array observations in the TDF, we detected 12 sources (11% detection rate) at 3.3 $\mu$Jy rms sensitivity and 4 mas resolution. Most detections exhibit pc-scale emission (less than 40 pc) with high VLBA/VLA flux density ratios and brightness temperatures exceeding 10$^5$ K, confirming non-thermal AGN activity. Spectral indices ($>$ -0.5) correlate with higher VLBA/VLA flux ratios, consistent with synchrotron emission from AGN coronae or jets. In the majority of our sources star formation contributes less than 50% of the total VLBA radio emission, with a few cases where the emission is almost entirely AGN-driven. Although the radio emission from radio quiet AGN is thought to be primarily driven by star formation, our VLBA observations confirm that there is also often a contribution at various levels from black hole driven AGN. Eight VLBA detections have JWST/NIRCam counterparts, predominantly early-type, bulge-dominated galaxies, which we use to get an estimate of the redshift and star formation rate (SFR). WISE colors indicate that VLBA detections are either AGN or intermediate-disk-dominated systems, while VLBA non-detections correspond to extended, star-forming galaxies. We compare SFRs derived from previous SCUBA-2 850 $\mu$m observations with new JWST-based estimates, and discuss the observed discrepancies, highlighting JWST's improved capability to disentangle AGN activity from star formation.
Deep Research Agents: A Systematic Examination And Roadmap
2025-06-22
Yuxuan Huang, Yihang Chen, Haozheng Zhang, Kang Li, Meng Fang, Linyi Yang, Xiaoguang Li, Lifeng Shang, Songcen Xu, Jianye Hao, Kun Shao, Jun Wang
The rapid progress of Large Language Models (LLMs) has given rise to a new category of autonomous AI systems, referred to as Deep Research (DR) agents. These agents are designed to tackle complex, multi-turn informational research tasks by leveraging a combination of dynamic reasoning, adaptive long-horizon planning, multi-hop information retrieval, iterative tool use, and the generation of structured analytical reports. In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute Deep Research agents. We begin by reviewing information acquisition strategies, contrasting API-based retrieval methods with browser-based exploration. We then examine modular tool-use frameworks, including code execution, multimodal input processing, and the integration of Model Context Protocols (MCPs) to support extensibility and ecosystem development. To systematize existing approaches, we propose a taxonomy that differentiates between static and dynamic workflows, and we classify agent architectures based on planning strategies and agent composition, including single-agent and multi-agent configurations. We also provide a critical evaluation of current benchmarks, highlighting key limitations such as restricted access to external knowledge, sequential execution inefficiencies, and misalignment between evaluation metrics and the practical objectives of DR agents. Finally, we outline open challenges and promising directions for future research. A curated and continuously updated repository of DR agent research is available at: {https://github.com/ai-agents-2030/awesome-deep-research-agent}.
A Survey on False Information Detection: From A Perspective of Propagation on Social Networks
2025-06-22
Kun Xie, Sibo Wang
The proliferation of false information in the digital age has become a pressing concern, necessitating the development of effective and robust detection methods. This paper offers a comprehensive review of existing false information detection techniques, approached from a novel perspective that emphasizes the propagation characteristics of misinformation. We introduce a new taxonomy that categorizes these methods into homogeneous and heterogeneous propagation-based approaches, providing a deeper understanding of the varying scopes and complexities involved in information dissemination. For each category, we present a formal problem formulation, review commonly used datasets, and summarize state-of-the-art methods. Additionally, we identify several promising directions for future research, including the creation of a unified benchmark suite, exploration of diverse information modalities, and development of innovative rumor debunking tasks. By systematically organizing the vast array of current techniques, this work offers a clear overview of the research landscape, aiding researchers and practitioners in navigating this complex field and inspiring further advancements.
Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities
2025-06-22
Yuanchen Bei, Weizhi Zhang, Siwen Wang, Weizhi Chen, Sheng Zhou, Hao Chen, Yong Li, Jiajun Bu, Shirui Pan, Yizhou Yu, Irwin King, Fakhri Karray, Philip S. Yu
AI agents have experienced a paradigm shift, from early dominance by reinforcement learning (RL) to the rise of agents powered by large language models (LLMs), and now further advancing towards a synergistic fusion of RL and LLM capabilities. This progression has endowed AI agents with increasingly strong abilities. Despite these advances, to accomplish complex real-world tasks, agents are required to plan and execute effectively, maintain reliable memory, and coordinate smoothly with other agents. Achieving these capabilities involves contending with ever-present intricate information, operations, and interactions. In light of this challenge, data structurization can play a promising role by transforming intricate and disorganized data into well-structured forms that agents can more effectively understand and process. In this context, graphs, with their natural advantage in organizing, managing, and harnessing intricate data relationships, present a powerful data paradigm for structurization to support the capabilities demanded by advanced AI agents. To this end, this survey presents a first systematic review of how graphs can empower AI agents. Specifically, we explore the integration of graph techniques with core agent functionalities, highlight notable applications, and identify prospective avenues for future research. By comprehensively surveying this burgeoning intersection, we hope to inspire the development of next-generation AI agents equipped to tackle increasingly sophisticated challenges with graphs. Related resources are collected and continuously updated for the community in the Github link.
Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification
2025-06-22
Sharon Torao Pingi, Md Abul Bashar, Richi Nayak
Longitudinal data is commonly utilised across various domains, such as health, biomedical, education and survey studies. This ubiquity has led to a rise in statistical, machine and deep learning-based methods for Longitudinal Data Classification (LDC). However, the intricate nature of the data, characterised by its multi-dimensionality, causes instance-level heterogeneity and temporal correlations that add to the complexity of longitudinal data analysis. Additionally, LDC accuracy is often hampered by the pervasiveness of missing values in longitudinal data. Despite ongoing research that draw on the generative power and utility of Generative Adversarial Networks (GANs) to address the missing data problem, critical considerations include statistical assumptions surrounding longitudinal data and missingness within it, as well as other data-level challenges like class imbalance and mixed data types that impact longitudinal data imputation (LDI) and the subsequent LDC process in GANs. This paper provides a comprehensive overview of how GANs have been applied in LDI, with a focus whether GANS have adequately addressed fundamental assumptions about the data from a LDC perspective. We propose a categorisation of main approaches to GAN-based LDI, highlight strengths and limitations of methods, identify key research trends, and provide promising future directions. Our findings indicate that while GANs show great potential for LDI to improve usability and quality of longitudinal data for tasks like LDC, there is need for more versatile approaches that can handle the wider spectrum of challenges presented by longitudinal data with missing values. By synthesising current knowledge and identifying critical research gaps, this survey aims to guide future research efforts in developing more effective GAN-based solutions to address LDC challenges.
ROBBO: An Efficient Method for Pareto Front Estimation with Guaranteed Accuracy
2025-06-22
Roberto Boffadossi, Marco Leonesio, Lorenzo Fagiano
A new method to estimate the Pareto Front (PF) in bi-objective optimization problems is presented. Assuming a continuous PF, the approach, named ROBBO (RObust and Balanced Bi-objective Optimization), needs to sample at most a finite, pre-computed number of PF points. Upon termination, it guarantees that the worst-case approximation error lies within a desired tolerance range, predefined by the decision maker, for each of the two objective functions. Theoretical results are derived, about the worst-case number of PF samples required to guarantee the wanted accuracy, both in general and for specific sampling methods from the literature. A comparative analysis, both theoretical and numerical, demonstrates the superiority of the proposed method with respect to popular ones. The approach is finally showcased in a constrained path-following problem for a 2-axis positioning system and in a steady-state optimization problem for a Continuous-flow Stirred Tank Reactor. An open demo implementation of ROBBO is made available online.
A Survey of Quantum Generative Adversarial Networks: Architectures, Use Cases, and Real-World Implementations
2025-06-22
Mujahidul Islam, Serkan Turkeli, Fatih Ozaydin
Quantum Generative Adversarial Networks (QGANs) have emerged as a promising direction in quantum machine learning, combining the strengths of quantum computing and adversarial training to enable efficient and expressive generative modeling. This survey provides a comprehensive overview of QGAN models, highlighting key advances from theoretical proposals to experimental realizations. We categorize existing QGAN architectures based on their quantum-classical hybrid structures and summarize their applications in fields such as image synthesis, medical data generation, channel prediction, software defect detection, and educational tools. Special attention is given to the integration of QGANs with domain-specific techniques, such as optimization heuristics, Wasserstein distance, variational circuits, and large language models. We also review experimental demonstrations on photonic and ion-trap quantum processors, assessing their feasibility under current hardware limitations. This survey aims to guide future research by outlining existing trends, challenges, and opportunities in developing QGANs for practical quantum advantage.
An Empirical Comparison of Weak-IV-Robust Procedures in Just-Identified Models
2025-06-22
Wenze Li
Instrumental variable (IV) regression is recognized as one of the five core methods for causal inference, as identified by Angrist and Pischke (2008). This paper compares two leading approaches to inference under weak identification for just-identified IV models: the classical Anderson-Rubin (AR) procedure and the recently popular tF method proposed by Lee et al. (2022). Using replication data from the American Economic Review (AER) and Monte Carlo simulation experiments, we evaluate the two procedures in terms of statistical significance testing and confidence interval (CI) length. Empirically, we find that the AR procedure typically offers higher power and yields shorter CIs than the tF method. Nonetheless, as noted by Lee et al. (2022), tF has a theoretical advantage in terms of expected CI length. Our findings suggest that the two procedures may be viewed as complementary tools in empirical applications involving potentially weak instruments.
Changing the discrete spectrum of the half-line matrix Schrödinger operator
2025-06-22
Tuncay Aktosun, Ricardo Weder
We consider the matrix-valued Schr\"odinger operator on the half line with the general selfadjoint boundary condition. When the discrete spectrum is changed without changing the continuous spectrum, we present a review of the transformations of the relevant quantities including the regular solution, the Jost solution, the Jost matrix, the scattering matrix, and the boundary matrices used to describe the selfadjoint boundary condition. The changes in the discrete spectrum are considered when an existing bound state is removed, a new bound state is added, and the multiplicity of a bound state is decreased or increased without removing the bound state. We provide various explicit examples to illustrate the theoretical resultspresented.
LLMs for Customized Marketing Content Generation and Evaluation at Scale
2025-06-22
Haoran Liu, Amir Tahmasbi, Ehtesham Sam Haque, Purak Jain
Offsite marketing is essential in e-commerce, enabling businesses to reach customers through external platforms and drive traffic to retail websites. However, most current offsite marketing content is overly generic, template-based, and poorly aligned with landing pages, limiting its effectiveness. To address these limitations, we propose MarketingFM, a retrieval-augmented system that integrates multiple data sources to generate keyword-specific ad copy with minimal human intervention. We validate MarketingFM via offline human and automated evaluations and large-scale online A/B tests. In one experiment, keyword-focused ad copy outperformed templates, achieving up to 9% higher CTR, 12% more impressions, and 0.38% lower CPC, demonstrating gains in ad ranking and cost efficiency. Despite these gains, human review of generated ads remains costly. To address this, we propose AutoEval-Main, an automated evaluation system that combines rule-based metrics with LLM-as-a-Judge techniques to ensure alignment with marketing principles. In experiments with large-scale human annotations, AutoEval-Main achieved 89.57% agreement with human reviewers. Building on this, we propose AutoEval-Update, a cost-efficient LLM-human collaborative framework to dynamically refine evaluation prompts and adapt to shifting criteria with minimal human input. By selectively sampling representative ads for human review and using a critic LLM to generate alignment reports, AutoEval-Update improves evaluation consistency while reducing manual effort. Experiments show the critic LLM suggests meaningful refinements, improving LLM-human agreement. Nonetheless, human oversight remains essential for setting thresholds and validating refinements before deployment.
Out of Control -- Why Alignment Needs Formal Control Theory (and an Alignment Control Stack)
2025-06-21
Elija Perrier
This position paper argues that formal optimal control theory should be central to AI alignment research, offering a distinct perspective from prevailing AI safety and security approaches. While recent work in AI safety and mechanistic interpretability has advanced formal methods for alignment, they often fall short of the generalisation required of control frameworks for other technologies. There is also a lack of research into how to render different alignment/control protocols interoperable. We argue that by recasting alignment through principles of formal optimal control and framing alignment in terms of hierarchical stack from physical to socio-technical layers according to which controls may be applied we can develop a better understanding of the potential and limitations for controlling frontier models and agentic AI systems. To this end, we introduce an Alignment Control Stack which sets out a hierarchical layered alignment stack, identifying measurement and control characteristics at each layer and how different layers are formally interoperable. We argue that such analysis is also key to the assurances that will be needed by governments and regulators in order to see AI technologies sustainably benefit the community. Our position is that doing so will bridge the well-established and empirically validated methods of optimal control with practical deployment considerations to create a more comprehensive alignment framework, enhancing how we approach safety and reliability for advanced AI systems.
Rethinking Ecological Measures Of Functional Diversity
2025-06-21
Ines Meraoumia, Adji Bousso Dieng
Understanding functional diversity, the range and variability of species' roles and actions within their communities, is key to predicting and preserving the functions that sustain both nature and human well-being. In this paper, we provide a comprehensive review of the literature on functional diversity measurement. We begin by consolidating essential criteria that effective measures of functional diversity should meet. We then evaluate fifteen widely used functional diversity metrics against these criteria and assess their performance across six synthetic ecosystem scenarios where optimal behavior is known. Surprisingly, our analysis reveals that none of the widely used metrics fully satisfy all the established requirements, and all fail in at least one ecosystem scenario. In particular, we find that almost all metrics flagrantly violate set monotonicity and distance monotonicity, requirements that adding a novel species should increase diversity, and that the magnitude of that increase should grow with trait dissimilarity. We also find that metrics fail to decline when rare, functionally extreme species are lost, and even increase when a perfectly redundant species is added. These critical flaws leave them blind to the very biodiversity loss that functional diversity measures are intended to detect. Our findings underscore the urgent need to develop a new generation of functional diversity metrics that more accurately reflect ecological realities.
The Impact of AI-Generated Solutions on Software Architecture and Productivity: Results from a Survey Study
2025-06-21
Giorgio Amasanti, Jasmin Jahic
AI-powered software tools are widely used to assist software engineers. However, there is still a need to understand the productivity benefits of such tools for software engineers. In addition to short-term benefits, there is a question of how adopting AI-generated solutions affects the quality of software over time (e.g., maintainability and extendability). To provide some insight on these questions, we conducted a survey among software practitioners who use AI tools. Based on the data collected from our survey, we conclude that AI tools significantly increase the productivity of software engineers. However, the productivity benefits of using AI tools reduce as projects become more complex. The results also show that there are no significant negative influences of adopting AI-generated solutions on software quality, as long as those solutions are limited to smaller code snippets. However, when solving larger and more complex problems, AI tools generate solutions of a lower quality, indicating the need for architects to perform problem decomposition and solution integration.
Actionable Interpretability via Causal Hypergraphs: Unravelling Batch Size Effects in Deep Learning
2025-06-21
Zhongtian Sun, Anoushka Harit, Pietro Lio
While the impact of batch size on generalisation is well studied in vision tasks, its causal mechanisms remain underexplored in graph and text domains. We introduce a hypergraph-based causal framework, HGCNet, that leverages deep structural causal models (DSCMs) to uncover how batch size influences generalisation via gradient noise, minima sharpness, and model complexity. Unlike prior approaches based on static pairwise dependencies, HGCNet employs hypergraphs to capture higher-order interactions across training dynamics. Using do-calculus, we quantify direct and mediated effects of batch size interventions, providing interpretable, causally grounded insights into optimisation. Experiments on citation networks, biomedical text, and e-commerce reviews show that HGCNet outperforms strong baselines including GCN, GAT, PI-GNN, BERT, and RoBERTa. Our analysis reveals that smaller batch sizes causally enhance generalisation through increased stochasticity and flatter minima, offering actionable interpretability to guide training strategies in deep learning. This work positions interpretability as a driver of principled architectural and optimisation choices beyond post hoc analysis.
Full-spectrum modeling of mobile gamma-ray spectrometry systems in scattering media
2025-06-21
David Breitenmoser, Alberto Stabilini, Sabine Mayer
Mobile gamma-ray spectrometry (MGRS) systems are essential for localizing, identifying, and quantifying gamma-ray sources in complex environments. Full-spectrum template matching offers the highest accuracy and sensitivity for these tasks but is limited by the computational cost of generating the required spectral templates. Here, we present a generalized full-spectrum modeling framework for MGRS systems in scattering media, enabling near real-time template generation through dynamic, anisotropic instrument response functions (IRFs). Benchmarked against high-fidelity brute-force Monte Carlo simulations, our method yields a computational speedup by a factor $\mathcal{O}(10^7)$, while achieving comparable accuracy with median spectral deviations below 6%. The presented methodology is platform-agnostic and applicable across marine, terrestrial, and airborne domains, unlocking new capabilities for MGRS in a variety of applications, such as environmental monitoring, geophysical exploration, nuclear safeguards, and radiological emergency response.
Bayesian Social Deduction with Graph-Informed Language Models
2025-06-21
Shahab Rahimirad, Guven Gergerli, Lucia Romero, Angela Qian, Matthew Lyle Olson, Simon Stepputtis, Joseph Campbell
Social reasoning - inferring unobservable beliefs and intentions from partial observations of other agents - remains a challenging task for large language models (LLMs). We evaluate the limits of current reasoning language models in the social deduction game Avalon and find that while the largest models demonstrate strong performance, they require extensive test-time inference and degrade sharply when distilled to smaller, real-time-capable variants. To address this, we introduce a hybrid reasoning framework that externalizes belief inference to a structured probabilistic model, while using an LLM for language understanding and interaction. Our approach achieves competitive performance with much larger models in Agent-Agent play and, notably, is the first language agent to defeat human players in a controlled study - achieving a 67% win rate and receiving higher qualitative ratings than both reasoning baselines and human teammates. We release code, models, and a dataset to support future work on social reasoning in LLM agents, which can be found at https://camp-lab-purdue.github.io/bayesian-social-deduction/
Atomic layer deposition on particulate materials from 1988 through 2023: A quantitative review of technologies, materials and applications
2025-06-21
Peter M. Piechulla, Mingliang Chen, Aristeidis Goulas, Riikka L. Puurunen, J. Ruud van Ommen
Atomic layer deposition (ALD) is widely studied for numerous applications and is commercially employed in the semiconductor industry, where planar substrates are the norm. However, the inherent ALD feature of coating virtually any surface geometry with atomistic thickness control is equally attractive for coating particulate materials (supports). In this review, we provide a comprehensive overview of the developments in this decades-old field of ALD on particulate materials, drawing on a bottom-up and quantitative analysis of 799 articles from this field. The obtained dataset is the basis for abstractions regarding reactor types (specifically for particles), coating materials, reactants, supports and processing conditions. Furthermore, the dataset enables direct access to specific processing conditions (for a given material, surface functionality, application etc.) and increases accessibility of the respective literature. We also review fundamental concepts of ALD on particles, and discuss the most common applications, i.e., catalysis (thermo-, electro-, photo-), batteries, luminescent phosphors and healthcare. Finally, we identify historical trends, and provide an outlook on prospective developments.
In this tutorial, we will survey known results on the complexity of conjunctive query evaluation in different settings, ranging from Boolean queries over counting to more complex models like enumeration and direct access. A particular focus will be on showing how different relatively recent hypotheses from complexity theory connect to query answering and allow showing that known algorithms in several cases can likely not be improved.
The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future
2025-06-21
Summra Saleem, Muhammad Nabeel Asim, Shaista Zulfiqar, Andreas Dengel
Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP) by automating traditional labor-intensive tasks and consequently accelerated the development of computer-aided applications. As researchers continue to advance this field with the introduction of novel language models and more efficient training/finetuning methodologies, the idea of prompt engineering and subsequent optimization strategies with LLMs has emerged as a particularly impactful trend to yield a substantial performance boost across diverse NLP tasks. To best of our knowledge numerous review articles have explored prompt engineering, however, a critical gap exists in comprehensive analyses of prompt optimization strategies. To bridge this gap this paper provides unique and comprehensive insights about the potential of diverse prompt optimization strategies. It analyzes their underlying working paradigms and based on these principles, categorizes them into 11 distinct classes. Moreover, the paper provides details about various NLP tasks where these prompt optimization strategies have been employed, along with details of different LLMs and benchmark datasets used for evaluation. This comprehensive compilation lays a robust foundation for future comparative studies and enables rigorous assessment of prompt optimization and LLM-based predictive pipelines under consistent experimental settings: a critical need in the current landscape. Ultimately, this research will centralize diverse strategic knowledge to facilitate the adaptation of existing prompt optimization strategies for development of innovative predictors across unexplored tasks.
2025-06-21
M. V. Suslikov, A. I. Kolbin, N. V. Borisov
Based on optical and infrared survey data spanning $\approx 20$ years of observations, the long-term variability of the polar V379 Vir with a brown dwarf secondary has been studied. By modeling the spectral energy distribution, we constrain the white dwarf's mass to $M_1 = 0.61 \pm 0.05~M_{\odot}$ and its effective temperature to $T_{eff} = 10930 \pm 350~K$. Near-infrared photometry yields a donor radius of $R_2 = 0.095 \pm 0.018 R_{\odot}$ and temperature $T_{eff} = 1600 \pm 180 K$. Modeling of the cyclotron emission from the accretion spot, detected with the Spitzer infrared telescope, gives an accretion rate of $\dot{M} \approx 3 \times 10^{-13} M_{\odot}/yr$. This rate is consistent with polars in a low accretion state, but significantly higher than expected from wind-driven mass transfer.
Deep Learning (DL) frameworks are a fundamental component of DL development. Therefore, the detection of DL framework defects is important and challenging. As one of the most widely adopted DL testing techniques, model mutation has recently gained significant attention. In this study, we revisit the defect detection ability of existing mutation-based testing methods and investigate the factors that influence their effectiveness. To begin with, we reviewed existing methods and observed that many of them mutate DL models (e.g., changing their parameters) without any customization, ignoring the unique challenges in framework testing. Another issue with these methods is their limited effectiveness, characterized by a high rate of false positives caused by illegal mutations arising from the use of generic, non-customized mutation operators. Moreover, we tracked the defects identified by these methods and discovered that most of them were ignored by developers. Motivated by these observations, we investigate the effectiveness of existing mutation-based testing methods in detecting important defects that have been authenticated by framework developers. We begin by collecting defect reports from three popular frameworks and classifying them based on framework developers' ratings to build a comprehensive dataset. We then perform an in-depth analysis to uncover valuable insights. Based on our findings, we propose optimization strategies to address the shortcomings of existing approaches. Following these optimizations, we identified seven new defects, four of which were confirmed by developers as high-priority issues, with three resolved. In summary, we identified 39 unique defects across just 23 models, of which 31 were confirmed by developers, and eight have been fixed.
Recent developments in 3D Gaussian Splatting have made significant advances in surface reconstruction. However, scaling these methods to large-scale scenes remains challenging due to high computational demands and the complex dynamic appearances typical of outdoor environments. These challenges hinder the application in aerial surveying and autonomous driving. This paper proposes a novel solution to reconstruct large-scale surfaces with fine details, supervised by full-sized images. Firstly, we introduce a coarse-to-fine strategy to reconstruct a coarse model efficiently, followed by adaptive scene partitioning and sub-scene refining from image segments. Additionally, we integrate a decoupling appearance model to capture global appearance variations and a transient mask model to mitigate interference from moving objects. Finally, we expand the multi-view constraint and introduce a single-view regularization for texture-less areas. Our experiments were conducted on the publicly available dataset GauU-Scene V2, which was captured using unmanned aerial vehicles. To the best of our knowledge, our method outperforms existing NeRF-based and Gaussian-based methods, achieving high-fidelity visual results and accurate surface from full-size image optimization. Open-source code will be available on GitHub.
Exploiting Efficiency Vulnerabilities in Dynamic Deep Learning Systems
2025-06-21
Ravishka Rathnasuriya, Wei Yang
The growing deployment of deep learning models in real-world environments has intensified the need for efficient inference under strict latency and resource constraints. To meet these demands, dynamic deep learning systems (DDLSs) have emerged, offering input-adaptive computation to optimize runtime efficiency. While these systems succeed in reducing cost, their dynamic nature introduces subtle and underexplored security risks. In particular, input-dependent execution pathways create opportunities for adversaries to degrade efficiency, resulting in excessive latency, energy usage, and potential denial-of-service in time-sensitive deployments. This work investigates the security implications of dynamic behaviors in DDLSs and reveals how current systems expose efficiency vulnerabilities exploitable by adversarial inputs. Through a survey of existing attack strategies, we identify gaps in the coverage of emerging model architectures and limitations in current defense mechanisms. Building on these insights, we propose to examine the feasibility of efficiency attacks on modern DDLSs and develop targeted defenses to preserve robustness under adversarial conditions.
DRAMA-X: A Fine-grained Intent Prediction and Risk Reasoning Benchmark For Driving
2025-06-21
Mihir Godbole, Xiangbo Gao, Zhengzhong Tu
Understanding the short-term motion of vulnerable road users (VRUs) like pedestrians and cyclists is critical for safe autonomous driving, especially in urban scenarios with ambiguous or high-risk behaviors. While vision-language models (VLMs) have enabled open-vocabulary perception, their utility for fine-grained intent reasoning remains underexplored. Notably, no existing benchmark evaluates multi-class intent prediction in safety-critical situations, To address this gap, we introduce DRAMA-X, a fine-grained benchmark constructed from the DRAMA dataset via an automated annotation pipeline. DRAMA-X contains 5,686 accident-prone frames labeled with object bounding boxes, a nine-class directional intent taxonomy, binary risk scores, expert-generated action suggestions for the ego vehicle, and descriptive motion summaries. These annotations enable a structured evaluation of four interrelated tasks central to autonomous decision-making: object detection, intent prediction, risk assessment, and action suggestion. As a reference baseline, we propose SGG-Intent, a lightweight, training-free framework that mirrors the ego vehicle's reasoning pipeline. It sequentially generates a scene graph from visual input using VLM-backed detectors, infers intent, assesses risk, and recommends an action using a compositional reasoning stage powered by a large language model. We evaluate a range of recent VLMs, comparing performance across all four DRAMA-X tasks. Our experiments demonstrate that scene-graph-based reasoning enhances intent prediction and risk assessment, especially when contextual cues are explicitly modeled.
Signal Recovery on Algebraic Varieties Using Linear Samples
2025-06-21
Zhiqiang Xu
The recovery of an unknown signal from its linear measurements is a fundamental problem spanning numerous scientific and engineering disciplines. Commonly, prior knowledge suggests that the underlying signal resides within a known algebraic variety. This context naturally leads to a question: what is the minimum number of measurements required to uniquely recover any signal belonging to such an algebraic variety? In this survey paper, we introduce a method that leverages tools from algebraic geometry to address this question. We then demonstrate the utility of this approach by applying it to two problems: phase retrieval and low-rank matrix recovery. %Furthermore, this paper bridges abstract algebraic concepts with concrete signal processing. We also highlight several open problems, which could serve as a basis for future investigations in this field.
This review exhaustively evaluates the role of nanomaterials across synthesis, characterization and application stages of biofuel systems. The common types of nanomaterials that are used for biofuel applications include metal oxides, carbon-based structures, and hybrids are evaluated for their effectiveness in efficient biofuel production. The properties of such nanomaterials are being utilized as an aid to produce biofuels through improved catalysis, enzyme immobilization and thermal stability. Common synthesis methods such as sol-gel, co-precipitation, and green synthesis are compared, alongside characterization tools like TEM, SEM, FTIR, and BET. This study focuses on transesterification, biomass pretreatment, and fermentation processes, where nanomaterials significantly improve yield and reusability. There are several challenges despite the merit of using nanomaterials, the tradeoffs include costs, scalability, and environmental impact, which further expands into evaluating the life cycle of such materials being used. This review outlines the practical potential of the nanomaterials in enabling more efficient and sustainable biofuel production.
The growing dependence on mobile phones and their apps has made multi-user interactive features, like chat calls, live streaming, and video conferencing, indispensable for bridging the gaps in social connectivity caused by physical and situational barriers. However, automating these interactive features for testing is fraught with challenges, owing to their inherent need for timely, dynamic, and collaborative user interactions, which current automated testing methods inadequately address. Inspired by the concept of agents designed to autonomously and collaboratively tackle problems, we propose MAdroid, a novel multi-agent approach powered by the Large Language Models (LLMs) to automate the multi-user interactive task for app feature testing. Specifically, MAdroid employs two functional types of multi-agents: user agents (Operator) and supervisor agents (Coordinator and Observer). Each agent takes a specific role: the Coordinator directs the interactive task; the Operator mimics user interactions on the device; and the Observer monitors and reviews the task automation process. Our evaluation, which included 41 multi-user interactive tasks, demonstrates the effectiveness of our approach, achieving 82.9% of the tasks with 96.8% action similarity, outperforming the ablation studies and state-of-the-art baselines. Additionally, a preliminary investigation underscores MAdroid's practicality by helping identify 11 multi-user interactive bugs during regression app testing, confirming its potential value in real-world software development contexts.
Association between optically identified galaxy clusters and the underlying dark matter halos
2025-06-21
DES Collaboration, Shulei Cao, Hao-Yi Wu, Matteo Costanzi, Arya Farahi, Sebastian Grandis, David H. Weinberg, August E. Evrard, Eduardo Rozo, Andrés N. Salcedo, Chun-Hao To, Lei Yang, Conghao Zhou
Clusters of galaxies trace massive dark matter halos in the Universe, but they can include multiple halos projected along lines of sight. We study the halos contributing to clusters using the Cardinal simulation, which mimics the Dark Energy Survey data. We use the red-sequence-based cluster finding algorithm redMaPPer as a case study. For each cluster, we identify the halos hosting its member galaxies, and we define the main halo as the one contributing the most to the cluster's richness ($\lambda$, the estimated number of member galaxies). At $z=0.3$, for clusters with $\lambda > 60$, the main halo typically contributes to $92\%$ of the richness, and this fraction drops to $67\%$ for $\lambda \approx 20$. Defining "clean" clusters as those with $\geq50\%$ of the richness contributed by the main halo, we find that $100\%$ of the $\lambda > 60$ clusters are clean, while $73\%$ of the $\lambda \approx 20$ clusters are clean. Three halos can usually account for more than $80\%$ of the richness of a cluster. The main halos associated with redMaPPer clusters have a completeness ranging from $98\%$ at virial mass $10^{14.6}~h^{-1}M_{\odot}$ to $64\%$ at $10^{14}~h^{-1}M_{\odot}$. In addition, we compare the inferred cluster centers with true halo centers, finding that $30\%$ of the clusters are miscentered with a mean offset $40\%$ of the cluster radii, in agreement with recent X-ray studies. These systematics worsen as redshift increases, but we expect that upcoming surveys extending to longer wavelengths will improve the cluster finding at high redshifts. Our results affirm the robustness of the redMaPPer algorithm and provide a framework for benchmarking other cluster-finding strategies.
Intellectual property (IP) rules have the potential to shape cross-border trade far more than their legalistic origins might suggest. Drawing on three decades of evidence, this review shows that stronger IP rights simultaneously create market-power forces that raise prices and market-expansion forces that broaden demand, while dynamic incentives spur quality upgrading and new export varieties. Micro-data and quasi-natural experiments after TRIPS reveal that IP most often boosts trade along the extensive margin and redirects some activity toward licensing and foreign investment. Policy bundling and measurement gaps on the strength of IP rights still cloud causal inference. Future work must map intangible flows and enforcement quality to capture the digital, data-driven frontier of international commerce.
From Tiny Machine Learning to Tiny Deep Learning: A Survey
2025-06-21
Shriyank Somvanshi, Md Monzurul Islam, Gaurab Chhetri, Rohit Chakraborty, Mahmuda Sultana Mimi, Swagat Ahmed Shuvo, Kazi Sifatul Islam, Syed Aaqib Javed, Sharif Ahmed Rafat, Anandi Dutta, Subasish Das
The rapid growth of edge devices has driven the demand for deploying artificial intelligence (AI) at the edge, giving rise to Tiny Machine Learning (TinyML) and its evolving counterpart, Tiny Deep Learning (TinyDL). While TinyML initially focused on enabling simple inference tasks on microcontrollers, the emergence of TinyDL marks a paradigm shift toward deploying deep learning models on severely resource-constrained hardware. This survey presents a comprehensive overview of the transition from TinyML to TinyDL, encompassing architectural innovations, hardware platforms, model optimization techniques, and software toolchains. We analyze state-of-the-art methods in quantization, pruning, and neural architecture search (NAS), and examine hardware trends from MCUs to dedicated neural accelerators. Furthermore, we categorize software deployment frameworks, compilers, and AutoML tools enabling practical on-device learning. Applications across domains such as computer vision, audio recognition, healthcare, and industrial monitoring are reviewed to illustrate the real-world impact of TinyDL. Finally, we identify emerging directions including neuromorphic computing, federated TinyDL, edge-native foundation models, and domain-specific co-design approaches. This survey aims to serve as a foundational resource for researchers and practitioners, offering a holistic view of the ecosystem and laying the groundwork for future advancements in edge AI.
Detecting anisotropic screening of the cosmic microwave background (CMB) holds the promise of revealing the distribution of gas in the Universe, characterizing the complex processes of galaxy formation and feedback, and studying the epoch of reionization. Estimators for inhomogeneous screening, including some recently proposed small-scale (stacked) estimators, are quadratic or higher order in the CMB temperature or polarization fields and are therefore subject to contamination from CMB lensing. We review the origin of this lensing bias and show that, when stacking on unWISE galaxies, the expected lensing bias dominates the signal if left unmitigated. Hardening techniques that null the lensing bias have been proposed for standard quadratic estimators, whereas only approximate methods have been proposed for stacked estimators. We review these techniques and apply the former to stacked estimators, presenting several strategies (including the optimal strategy) to null lensing contamination when stacking on any large-scale structure (LSS) tracer.
Walking Fingerprinting Using Wrist Accelerometry During Activities of Daily Living in NHANES
2025-06-20
Lily Koffman, John Muschelli III, Ciprian Crainiceanu
We propose a method for identifying individuals based on their continuously monitored wrist-worn accelerometry during activities of daily living. The method consists of three steps: (1) using Adaptive Empirical Pattern Transformation (ADEPT), a highly specific method to identify walking; (2) transforming the accelerometry time series into an image that corresponds to the joint distribution of the time series and its lags; and (3) using the resulting images to construct a person-specific walking fingerprint. The method is applied to 15,000 individuals from the National Health and Nutrition Examination Survey (NHANES) with up to 7 days of wrist accelerometry data collected at 80 Hertz. The resulting dataset contains more than 10 terabytes, is roughly 2 to 3 orders of magnitude larger than previous datasets used for activity recognition, is collected in the free living environment, and does not contain labels for walking periods. Using extensive cross-validation studies, we show that our method is highly predictive and can be successfully extended to a large, heterogeneous sample representative of the U.S. population: in the highest-performing model, the correct participant is in the top 1% of predictions 96% of the time.
Medical image analysis is critical yet challenged by the need of jointly segmenting organs or tissues, and numerous instances for anatomical structures and tumor microenvironment analysis. Existing studies typically formulated different segmentation tasks in isolation, which overlooks the fundamental interdependencies between these tasks, leading to suboptimal segmentation performance and insufficient medical image understanding. To address this issue, we propose a Co-Seg++ framework for versatile medical segmentation. Specifically, we introduce a novel co-segmentation paradigm, allowing semantic and instance segmentation tasks to mutually enhance each other. We first devise a spatio-temporal prompt encoder (STP-Encoder) to capture long-range spatial and temporal relationships between segmentation regions and image embeddings as prior spatial constraints. Moreover, we devise a multi-task collaborative decoder (MTC-Decoder) that leverages cross-guidance to strengthen the contextual consistency of both tasks, jointly computing semantic and instance segmentation masks. Extensive experiments on diverse CT and histopathology datasets demonstrate that the proposed Co-Seg++ outperforms state-of-the-arts in the semantic, instance, and panoptic segmentation of dental anatomical structures, histopathology tissues, and nuclei instances. The source code is available at https://github.com/xq141839/Co-Seg-Plus.
The Average Soft X-ray Spectra of eROSITA Active Galactic Nuclei
2025-06-20
Shi-Jiang Chen, Johannes Buchner, Teng Liu, Scott Hagen, Sophia G. H. Waddell, Kirpal Nandra, Mara Salvato, Zsofi Igo, Catarina Aydar, Andrea Merloni, Qingling Ni, Jia-Lai Kang, Zhen-Yi Cai, Jun-Xian Wang, Ruancun Li, Miriam E. Ramos-Ceja, Jeremy Sanders, Antonis Georgakakis, Yi Zhang
Context. AGNs are strong X-ray emitters shaped by disk-corona interactions. The soft excess (0.5-2.0 keV) reveals key information about the "warm corona" bridging the disk and hot corona. Yet, how this feature evolves with accretion properties remains poorly constrained, especially in large samples using spectral stacking. Aims. The eROSITA All-Sky Survey (eRASS:5) provides an unprecedented sample. We investigate how the average AGN X-ray spectra evolve with accretion parameters, and explore disk-corona connection by further combining stacked UV data. Methods. We developed Xstack, a novel tool that stacks rest-frame X-ray spectra and responses while preserving spectral shape through optimized weighting. We stack 17929 AGNs ("spec-z" sample, 23 Ms) with similar X-ray loudness alpha_ox, UV luminosity L_UV, and 4159 AGNs ("BH-mass" sample, 3 Ms) with similar Eddington ratio lambda_Edd and black hole mass M_BH. The resulting stacked X-ray spectra are analyzed with a phenomenological model. We further fit the stacked optical-UV-Xray SED with AGNSED model. Results. Soft excess strengthens strongly with alpha_ox and lambda_Edd (~5), while the hard X-ray spectral shape remains largely unchanged, supporting that soft excess is dominated by warm corona rather than reflection. AGNSED modeling reveals that warm corona radius (R_g units) generally increases with lambda_Edd and decreases with M_BH, or equivalently the disk-to-warm-corona transition consistently occurs near 1e4 K. The hot corona contracts with lambda_Edd and is unaffected by M_BH, aligning with disk evaporation predictions. Conclusions. The soft excess likely originates from a warm corona, with the disk to warm corona transition tied to hydrogen ionization near 1e4 K - supporting earlier eFEDS-HSC stacking results (Hagen et al. 2024). This study shows the strength of spectral stacking in probing AGN disk-corona physics.
SRG/eROSITA No. 5: Discovery of quasi-periodic eruptions every ~3.7 days from a galaxy at z>0.1
2025-06-20
R. Arcodia, P. Baldini, A. Merloni, A. Rau, K. Nandra, J. Chakraborty, A. J. Goodwin, M. J. Page, J. Buchner, M. Masterson, I. Monageng, Z. Arzoumanian, D. Buckley, E. Kara, G. Ponti, M. E. Ramos-Ceja, M. Salvato, K. Gendreau, I. Grotova, M. Krumpe
Quasi-periodic eruptions (QPEs) are repeating soft X-ray bursts from the nuclei of galaxies, tantalizingly proposed to be extreme mass ratio inspirals. Here, we report the discovery of a new galaxy showing X-ray QPEs, the fifth found through a dedicated blind search in the \emph{SRG}/eROSITA all-sky survey data, hereafter named eRO-QPE5. Its QPE duration ($t_{\rm dur}\sim0.6$\,d), recurrence time ($t_{\rm recur}\sim3.7\,$d), integrated energy per eruption ($\sim3.4 \times 10^{47}\,$erg), and black hole mass ($M_{\rm BH}\sim 2.8 \times 10^7\,M_{\odot}$) sit at the high end of the known population. Like other eROSITA or X-ray-discovered QPEs, no previous or concurrent optical-IR transient is found in archival photometric datasets, and the optical spectrum looks almost featureless. With a spectroscopic redshift of $0.1155$, eRO-QPE5 is the most distant QPE source discovered to date. Given the number of recent discoveries, we test for possible correlations and confirm a connection between $t_{\rm dur}$ and $t_{\rm recur}$, while we do not find any significant correlation involving either $M_{\rm BH}$ or the QPE temperature. The slope of the $t_{\rm dur}-t_{\rm recur}$ relation ($1.14\pm0.16$) is roughly consistent with predictions from star-disk collision models, with a preference for those that suggest that QPEs are powered by stellar debris streams around the orbiter. Considering this and previous discoveries, eROSITA has proved extremely successful in finding many QPE candidates given its grasp, namely its sensitivity and large field of view, and scanning capabilities over the full sky. We advocate the need of sensitive wide-area and time-domain oriented surveys from future-generation soft X-ray missions.
Reflecting Human Values in XAI: Emotional and Reflective Benefits in Creativity Support Tools
2025-06-20
Samuel Rhys Cox, Helena Bøjer Djernæs, Niels van Berkel
In this workshop paper, we discuss the potential for measures of user-centric benefits (such as emotional well-being) that could be explored when evaluating explainable AI (XAI) systems within the arts. As a background to this, we draw from our recent review of creativity support tool (CST) evaluations, that found a paucity of studies evaluating CSTs for user-centric measures that benefit the user themselves. Specifically, we discuss measures of: (1) developing intrinsic abilities, (2) emotional well-being, (3) self-reflection, and (4) self-perception. By discussing these user-centric measures within the context of XAI and the arts, we wish to provoke discussion regarding the potential of such measures.
Control systems will play a pivotal role in addressing societal-scale challenges as they drive the development of sustainable future smart cities. At the heart of these challenges is the trustworthy, fair, and efficient allocation of scarce public resources, including renewable energy, transportation, data, computation, etc.. Historical evidence suggests that monetary control -- the prototypical mechanism for managing resource scarcity -- is not always well-accepted in socio-technical resource contexts. In this vision article, we advocate for karma economies as an emerging non-monetary mechanism for socio-technical control. Karma leverages the repetitive nature of many socio-technical resources to jointly attain trustworthy, fair, and efficient allocations; by budgeting resource consumption over time and letting resource users ``play against their future selves.'' To motivate karma, we review related concepts in economics through a control systems lens, and make a case for a) shifting the viewpoint of resource allocations from single-shot and static to repeated and dynamic games; and b) adopting long-run Nash welfare as the formalization of ``fairness and efficiency'' in socio-technical contexts. We show that in many dynamic resource settings, karma Nash equilibria maximize long-run Nash welfare. Moreover, we discuss implications for a future smart city built on multi-karma economies: by choosing whether to combine different socio-technical resources, e.g., electricity and transportation, in a single karma economy, or separate into resource-specific economies, karma provides new flexibility to design the scope of fairness and efficiency.
Can an Extra Degree of Freedom in Scalar-Tensor Non-Metricity Gravity Account for the Evolution of the Universe?
2025-06-20
Ghulam Murtaza, Avik De, Andronikos Paliathanasis, Tee-How Loo
We investigate whether the extra scalar degree of freedom that arises in the second connection class of scalar-tensor non-metricity gravity can accurately replicate and potentially enrich the cosmic expansion history. Focusing on a spatially flat FLRW background, we introduce Hubble-normalized variables and recast the field equations into an autonomous dynamical system. Four representative scenarios are analyzed comprehensively. Phase-space research reveals a rich hierarchy of critical points: matter-dominated, stiff-fluid, and de Sitter solutions, together with asymptotic trajectories leading to Big-Crunch/Rip singularities and transient, unstable matter epochs. With suitable parameter choices, the standard $\Lambda$CDM sequence is reinstated; however, novel late-time and high-curvature regimes arise exclusively from the non-metricity sector. A systematic comparison of metric scalar-tensor and teleparallel scalar-torsion theories reveals unique stability characteristics and potential observational discriminants. Our findings indicate that the additional time-dependent function inherent to scalar-tensor non-metricity gravity can effectively explain the Universe's evolution while providing new phenomenology that can be tested by upcoming surveys.
Magnetic braking and dynamo evolution of $β$ Hydri
2025-06-20
A. R. G. Santos, T. S. Metcalfe, O. Kochukhov, T. R. Ayres, R. Gafeira, T. L. Campante
The evolution of magnetic braking and dynamo processes in subgiant stars is essential for understanding how these stars lose angular momentum. We investigate the magnetic braking and dynamo evolution of $\beta$ Hydri, a G-type subgiant, to test the hypothesis of weakened magnetic braking and the potential rejuvenation of large-scale magnetic fields. We analyze spectropolarimetric observations from HARPS (HARPSpol; polarimetric mode of High Accuracy Radial velocity Planet Searcher), and combine them with archival X-ray data and asteroseismic properties from TESS (Transiting Exoplanet Survey Satellite) to estimate the current wind braking torque of $\beta$ Hydri. Despite experiencing weakened magnetic braking during the second half of its main-sequence lifetime, our results indicate that $\beta$ Hydri has regained significant magnetic activity and a large-scale magnetic field. This observation aligns with the "born-again" dynamo hypothesis. Furthermore, our estimated wind braking torque is considerably stronger than what would be expected for a star in the weakened magnetic braking regime. This suggests that subgiants with extended convective zones can temporarily re-establish large-scale dynamo action. These results provide critical constraints on stellar rotation models and improve our understanding of the interplay between magnetic field structure, stellar activity cycles, and angular momentum evolution in old solar-type stars.
Critical Appraisal of Fairness Metrics in Clinical Predictive AI
2025-06-20
João Matos, Ben Van Calster, Leo Anthony Celi, Paula Dhiman, Judy Wawira Gichoya, Richard D. Riley, Chris Russell, Sara Khalid, Gary S. Collins
Predictive artificial intelligence (AI) offers an opportunity to improve clinical practice and patient outcomes, but risks perpetuating biases if fairness is inadequately addressed. However, the definition of "fairness" remains unclear. We conducted a scoping review to identify and critically appraise fairness metrics for clinical predictive AI. We defined a "fairness metric" as a measure quantifying whether a model discriminates (societally) against individuals or groups defined by sensitive attributes. We searched five databases (2014-2024), screening 820 records, to include 41 studies, and extracted 62 fairness metrics. Metrics were classified by performance-dependency, model output level, and base performance metric, revealing a fragmented landscape with limited clinical validation and overreliance on threshold-dependent measures. Eighteen metrics were explicitly developed for healthcare, including only one clinical utility metric. Our findings highlight conceptual challenges in defining and quantifying fairness and identify gaps in uncertainty quantification, intersectionality, and real-world applicability. Future work should prioritise clinically meaningful metrics.
Toward Understanding Similarity of Visualization Techniques
2025-06-20
Abdulhaq Adetunji Salako, Christian Tominski
The literature describes many visualization techniques for different types of data, tasks, and application contexts, and new techniques are proposed on a regular basis. Visualization surveys try to capture the immense space of techniques and structure it with meaningful categorizations. Yet, it remains difficult to understand the similarity of visualization techniques in general. We approach this open research question from two angles. First, we follow a model-driven approach that is based on defining the signature of visualization techniques and interpreting the similarity of signatures as the similarity of their associated techniques. Second, following an expert-driven approach, we asked visualization experts in a small online study for their ad-hoc intuitive assessment of the similarity of pairs visualization techniques. From both approaches, we gain insight into the similarity of a set of 13 basic and advanced visualizations for different types of data. While our results are so far preliminary and academic, they are first steps toward better understanding the similarity of visualization techniques.
Estimating Deprivation Cost Functions for Power Outages During Disasters: A Discrete Choice Modeling Approach
2025-06-20
Xiangpeng Li, Mona Ahmadiani, Richard Woodward, Bo Li, Arnold Vedlitz, Ali Mostafavi
Systems for the generation and distribution of electrical power represents critical infrastructure and, when extreme weather events disrupt such systems, this imposes substantial costs on consumers. These costs can be conceptualized as deprivation costs, an increasing function of time without service, quantifiable through individuals' willingness to pay for power restoration. Despite widespread recognition of outage impacts, a gap in the research literature exists regarding the systematic measurement of deprivation costs. This study addresses this deficiency by developing and implementing a methodology to estimate deprivation cost functions for electricity outages, using stated preference survey data collected from Harris County, Texas. This study compares multiple discrete choice model architectures, including multinomial logit and mixed logit specifications, as well as models incorporating BoxCox and exponential utility transformations for the deprivation time attribute. The analysis examines heterogeneity in deprivation valuation through sociodemographic interactions, particularly across income groups. Results confirm that power outage deprivation cost functions are convex and strictly increasing with time. Additionally, the study reveals both systematic and random taste variation in how individuals value power loss, highlighting the need for flexible modeling approaches. By providing both methodological and empirical foundations for incorporating deprivation costs into infrastructure risk assessments and humanitarian logistics, this research enables policymakers to better quantify service disruption costs and develop more equitable resilience strategies.
Effective and efficient task planning is essential for mobile robots, especially in applications like warehouse retrieval and environmental monitoring. These tasks often involve selecting one location from each of several target clusters, forming a Generalized Traveling Salesman Problem (GTSP) that remains challenging to solve both accurately and efficiently. To address this, we propose a Multimodal Fused Learning (MMFL) framework that leverages both graph and image-based representations to capture complementary aspects of the problem, and learns a policy capable of generating high-quality task planning schemes in real time. Specifically, we first introduce a coordinate-based image builder that transforms GTSP instances into spatially informative representations. We then design an adaptive resolution scaling strategy to enhance adaptability across different problem scales, and develop a multimodal fusion module with dedicated bottlenecks that enables effective integration of geometric and spatial features. Extensive experiments show that our MMFL approach significantly outperforms state-of-the-art methods across various GTSP instances while maintaining the computational efficiency required for real-time robotic applications. Physical robot tests further validate its practical effectiveness in real-world scenarios.
Shadow in the Galactic Center: Theoretical Concept -- Prediction -- Realization
2025-06-20
Alexander F. Zakharov
General Relativity (GR) was created in November 1915 and since its creation and up to now this theory has undergone many tests. The first realistic cosmological models were proposed in the works of Friedman, written in the 1920s. For a long time Friedman's cosmological works were actually banned in Soviet Union due to philosophical reasons, since the models where the birth and evolution of the Universe occurs were considered ideologically unacceptable. Due to great achievements in relativity and cosmology and due to increasing interest to these branches of of science in last decades we recall a development of relativistic astrophysics and contribution of Russian researchers in these studies. Since one of the world leaders in physical cosmology A. A. Friedman passed away in September 1925, it is reasonable to outline the main achievements of physical cosmology over the past 100 years. We discuss also observational and theoretical achievements in confirmations of relativistic observational predictions for black holes, including the closest supermassive black hole in our Galactic Center. We outline an evolution of black hole shadow from the purely theoretical concept to observable quantities for supermassive black holes in Sgr A* and M87*.
Since the discovery of the Goos-H\"anchen (GH) shift in the 1940s, its deep connections to Fourier transforms and causality have led to widespread interest and applications in optics, acoustics, and quantum mechanics. Control of the shift involves both its magnitude and direction. Although resonance-enhanced GH shift under reflection has significantly expanded and facilitated its observation and application, implementations in transmission scenarios remain scarce. More importantly, discussions on the direction of the GH shift are rare, and the associated degree of freedom for controlling directional asymmetry has not been fully explored. To address these issues, we discuss a control framework for enhancing transmitted GH shifts from symmetric to asymmetric. A design with complete degrees of freedom from symmetric shift enhancement to unidirectional shift enhancement is demonstrated in transmission scenarios. The control dimension associated with directionality significantly enhances the flexibility of beam shift control, with broad application prospects in scenarios such as high-sensitivity sensing, precision measurement, optical isolators, and asymmetric optical switches.
Navigating AGN variability with self-organizing maps
2025-06-20
Ylenia Maruccia, Demetra De Cicco, Stefano Cavuoti, Giuseppe Riccio, Paula Sánchez-Sáez, Maurizio Paolillo, Noemi Lery Borrelli, Riccardo Crupi, Massimo Brescia
Context. The classification of active galactic nuclei (AGNs) is a challenge in astrophysics. Variability features extracted from light curves offer a promising avenue for distinguishing AGNs and their subclasses. This approach would be very valuable in sight of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). Aims. Our goal is to utilize self-organizing maps (SOMs) to classify AGNs based on variability features and investigate how the use of different subsets of features impacts the purity and completeness of the resulting classifications. Methods. We derived a set of variability features from light curves, similar to those employed in previous studies, and applied SOMs to explore the distribution of AGNs subclasses. We conducted a comparative analysis of the classifications obtained with different subsets of features, focusing on the ability to identify different AGNs types. Results. Our analysis demonstrates that using SOMs with variability features yields a relatively pure AGNs sample, though completeness remains a challenge. In particular, Type 2 AGNs are the hardest to identify, as can be expected. These results represent a promising step toward the development of tools that may support AGNs selection in future large-scale surveys such as LSST.
With the recent progress in generative artificial intelligence (Generative AI), particularly in the development of large language models, recommendation systems are evolving to become more versatile. Unlike traditional techniques, generative AI not only learns patterns and representations from complex data but also enables content generation, data synthesis, and personalized experiences. This generative capability plays a crucial role in the field of recommendation systems, helping to address the issue of data sparsity and improving the overall performance of recommendation systems. Numerous studies on generative AI have already emerged in the field of recommendation systems. Meanwhile, the current requirements for recommendation systems have surpassed the single utility of accuracy, leading to a proliferation of multi-objective research that considers various goals in recommendation systems. However, to the best of our knowledge, there remains a lack of comprehensive studies on multi-objective recommendation systems based on generative AI technologies, leaving a significant gap in the literature. Therefore, we investigate the existing research on multi-objective recommendation systems involving generative AI to bridge this gap. We compile current research on multi-objective recommendation systems based on generative techniques, categorizing them by objectives. Additionally, we summarize relevant evaluation metrics and commonly used datasets, concluding with an analysis of the challenges and future directions in this domain.
Quantum Optimization for Software Engineering: A Survey
2025-06-20
Man Zhang, Yuechen Li, Tao Yue, Kai-Yuan Cai
Quantum computing, particularly in the area of quantum optimization, is steadily progressing toward practical applications, supported by an expanding range of hardware platforms and simulators. While Software Engineering (SE) optimization has a strong foundation, which is exemplified by the active Search-Based Software Engineering (SBSE) community and numerous classical optimization methods, the growing complexity of modern software systems and their engineering processes demands innovative solutions. This Systematic Literature Review (SLR) focuses specifically on studying the literature that applies quantum or quantum-inspired algorithms to solve classical SE optimization problems. We examine 77 primary studies selected from an initial pool of 2083 publications obtained through systematic searches of six digital databases using carefully crafted search strings. Our findings reveal concentrated research efforts in areas such as SE operations and software testing, while exposing significant gaps across other SE activities. Additionally, the SLR uncovers relevant works published outside traditional SE venues, underscoring the necessity of this comprehensive review. Overall, our study provides a broad overview of the research landscape, empowering the SBSE community to leverage quantum advancements in addressing next-generation SE challenges.
Revolutionizing Validation and Verification: Explainable Testing Methodologies for Intelligent Automotive Decision-Making Systems
2025-06-20
Halit Eris, Stefan Wagner
Autonomous Driving Systems (ADS) use complex decision-making (DM) models with multimodal sensory inputs, making rigorous validation and verification (V&V) essential for safety and reliability. These models pose challenges in diagnosing failures, tracing anomalies, and maintaining transparency, with current manual testing methods being inefficient and labor-intensive. This vision paper presents a methodology that integrates explainability, transparency, and interpretability into V&V processes. We propose refining V&V requirements through literature reviews and stakeholder input, generating explainable test scenarios via large language models (LLMs), and enabling real-time validation in simulation environments. Our framework includes test oracle, explanation generation, and a test chatbot, with empirical studies planned to evaluate improvements in diagnostic efficiency and transparency. Our goal is to streamline V&V, reduce resources, and build user trust in autonomous technologies.
On the evolution of the mass density profile of dense molecular clouds
2025-06-20
S. Donkov, I. Zh. Stefanov, T. V. Veltchev, R. S. Klessen
We aim to obtain the equations which govern the evolution of the mass density profile of a dense irrotational molecular cloud (MC). Our study is based on the notion of "ensemble of MCs", introduced in our previous work. The MCs are modeled by use of the "ensemble abstract representative member": a spherically symmetric, isotropic and isothermal MC which accretes radially matter from its surroundings. Applying the equations of hydrodynamics to a self-gravitating isothermal spherical gas cloud, we obtain a system of 2 first-order non-linear partial differential equations, which govern the evolution of two unknown fields: the exponent of density profile and the accretion velocity. Assuming a steady-state flow, we get approximate solutions using the method of leading-order terms. Far from the cloud centre the obtained density profile is $\varrho=\ell^{-2}$ and the accretion velocity is constant, while near to the centre $\varrho=\ell^{-3/2}$ and $v_{\rm a}\propto\ell^{-1/2}$. Through our dynamical equations, the obtained solutions coincide completely with those found using the equation of energy conservation of a fluid element (in our previous work). Also, combining the equations of energy balance for a fluid element, we arrive at the conclusion that the cloud layers far from the centre are in a stable dynamical state if the accretion velocity flow is sub- or transsonic; otherwise they are marginally stable (moderately supersonic flow) or unstable (supersonic flow). Both solutions are consistent only if the accretion flow is subsonic and hence the outer layers are stable. Finally, under the assumption that both the accretion velocity and density scale with $\ell$ and their power-law exponents are position-independent, we show that the density scaling exponent far away from the centre is $p=2$ and this value is an attractor. Hence this value should be observable in dense MCs.
Reward-Agnostic Prompt Optimization for Text-to-Image Diffusion Models
2025-06-20
Semin Kim, Yeonwoo Cha, Jaehoon Yoo, Seunghoon Hong
We investigate a general approach for improving user prompts in text-to-image (T2I) diffusion models by finding prompts that maximize a reward function specified at test-time. Although diverse reward models are used for evaluating image generation, existing automated prompt engineering methods typically target specific reward configurations. Consequently, these specialized designs exhibit suboptimal performance when applied to new prompt engineering scenarios involving different reward models. To address this limitation, we introduce RATTPO (Reward-Agnostic Test-Time Prompt Optimization), a flexible test-time optimization method applicable across various reward scenarios without modification. RATTPO iteratively searches for optimized prompts by querying large language models (LLMs) \textit{without} requiring reward-specific task descriptions. Instead, it uses the optimization trajectory and a novel reward-aware feedback signal (termed a "hint") as context. Empirical results demonstrate the versatility of RATTPO, effectively enhancing user prompts across diverse reward setups that assess various generation aspects, such as aesthetics, general human preference, or spatial relationships between objects. RATTPO surpasses other test-time search baselines in search efficiency, using up to 3.5 times less inference budget, and, given sufficient inference budget, achieves performance comparable to learning-based baselines that require reward-specific fine-tuning. The code is available at https://github.com/seminkim/RATTPO.
The status quo of fire evacuation drills in nursery schools
2025-06-20
Hana Najmanová, Petr Novák, Enrico Ronchi
Fire drills are a commonly-used training method for improving how people act in emergency situations. This article deals with fire drills in early childhood education facilities and analyses data gathered from an online survey of 1151 Czech nursery schools (23.5% of nursery schools invited to participate, 21.5% of all officially registered nursery schools in Czechia). It provides recommendations for improving the effectiveness of fire drills in nursery schools. Results suggest that regular (typically annual) fire drills are common training practices at these schools, but more frequent fire drills led to significantly fewer issues during evacuation. The most frequent evacuation issue encountered during fire drills, according to our data, was slow movement. Many children needed assistance, notably on stairs; nursery schools located only on the ground floor reported fewer issues during evacuation than other schools. The purpose, design, and implementation of fire drills with our study confirming results from prior studies must be properly integrated into complex and systematic fire safety education programs that specifically reflect the characteristics, needs, and possible limitations of preschool children.
CosmoGLINT: Cosmological Generative Model for Line Intensity Mapping with Transformer
2025-06-20
Kana Moriwaki, Rui Lan Jun, Ken Osato, Naoki Yoshida
Modelling star-forming galaxies is crucial for upcoming observations of large-scale matter and galaxy distributions with galaxy redshift surveys and line intensity mapping (LIM). We introduce CosmoGLINT (Cosmological Generative model for Line INtensity mapping with Transformer), a Transformer-based generative framework designed to create realistic galaxy populations from dark matter (DM)-only simulations. CosmoGLINT auto-regressively generates sequences of galaxy properties -- including star formation rate (SFR), distance to the halo centre, and radial and tangential velocities relative to the halo -- conditioned on halo mass. Trained on the IllustrisTNG hydrodynamic simulation, the model reproduces key statistical properties of the original data, including the voxel intensity distribution and the power spectrum both in real and redshift space. It can efficiently generate a number of different realisations of the designated galaxy populations, enabling the creation of mock LIM/redshift survey catalogues from large halo catalogues produced by fast DM-only simulations. We show that our model trained at multiple redshifts can be applied to DM halo lightcone data to generate a realistic mock galaxy lightcone with incorporating the redshift evolution of the galaxy population. The mock catalogues can be readily used to derive statistical quantities and to develop data analysis pipelines for ongoing and future wide-field surveys.
Accountability of Robust and Reliable AI-Enabled Systems: A Preliminary Study and Roadmap
2025-06-20
Filippo Scaramuzza, Damian A. Tamburri, Willem-Jan van den Heuvel
This vision paper presents initial research on assessing the robustness and reliability of AI-enabled systems, and key factors in ensuring their safety and effectiveness in practical applications, including a focus on accountability. By exploring evolving definitions of these concepts and reviewing current literature, the study highlights major challenges and approaches in the field. A case study is used to illustrate real-world applications, emphasizing the need for innovative testing solutions. The incorporation of accountability is crucial for building trust and ensuring responsible AI development. The paper outlines potential future research directions and identifies existing gaps, positioning robustness, reliability, and accountability as vital areas for the development of trustworthy AI systems of the future.
Using SRv6 to access Edge Applications in 5G Networks
2025-06-20
Louis Royer, Emmanuel Lavinal, Emmanuel Chaput
With the emergence of Multi-Access Edge Computing in 5G and beyond, it has become essential for operators to optimize the data path for the end-user while ensuring resources are used according to their policy. In this paper, we review existing solutions to access edge resources, underline their limits, and propose the use of Segment Routing over IPv6 (SRv6) in a 5G/edge architecture.
Uncrewed Aerial Vehicle (UAV) computing and networking are becoming a fundamental computation infrastructure for diverse cyber-physical application systems. UAVs can be empowered by AI on edge devices and can communicate with other UAVs and ground stations via wireless communication networks. Dynamic computation demands and heterogeneous computing resources are distributed in the system and need to be controlled to maintain the quality of services and to accomplish critical missions. With the evolution of UAV-based systems, dependability assurance of such systems emerges as a crucial challenge. UAV-based systems confront diverse sources of uncertainty that may threaten their dependability, such as software bugs, component failures, network disconnections, battery shortages, and disturbances from the real world. In this paper, we conduct systematic literature reviews on the dependability of UAV-based networks and computing systems. The survey report reveals emerging research trends in this field and summarizes the literature into comprehensive categories by threat types and adopted technologies. Based on our literature reviews, we identify eight research fields that require further exploration in the future to achieve dependable UAV-based systems.
What Is the Point of Equality in Machine Learning Fairness? Beyond Equality of Opportunity
2025-06-20
Youjin Kong
Fairness in machine learning (ML) has become a rapidly growing area of research. But why, in the first place, is unfairness in ML morally wrong? And why should we care about improving fairness? Most fair-ML research implicitly appeals to distributive equality: the idea that desirable goods and benefits, such as opportunities (e.g., Barocas et al., 2023), should be equally distributed across society. Unfair ML models, then, are seen as wrong because they unequally distribute such benefits. This paper argues that this exclusive focus on distributive equality offers an incomplete and potentially misleading ethical foundation. Grounding ML fairness in egalitarianism -- the view that equality is a fundamental moral and social ideal -- requires challenging structural inequality: systematic, institutional, and durable arrangements that privilege some groups while disadvantaging others. Structural inequality manifests through ML systems in two primary forms: allocative harms (e.g., economic loss) and representational harms (e.g., stereotypes, erasure). While distributive equality helps address allocative harms, it fails to explain why representational harms are wrong -- why it is wrong for ML systems to reinforce social hierarchies that stratify people into superior and inferior groups -- and why ML systems should aim to foster a society where people relate as equals (i.e., relational equality). To address these limitations, the paper proposes a multifaceted egalitarian framework for ML fairness that integrates both distributive and relational equality. Drawing on critical social and political philosophy, this framework offers a more comprehensive ethical foundation for tackling the full spectrum of harms perpetuated by ML systems. The paper also outlines practical pathways for implementing the framework across the ML pipeline.
Understanding the magnetic properties of matter plays a key role in materials physics. However, university education on fundamental magnetism is limited to a theoretical survey because of the lack of appropriate apparatus that can be applied for laboratory courses at the undergraduate level. In this work, we introduce an AC magnetometer based on the Colpitts self-oscillator with an inductor coil as a probe. We show that this type of self-oscillator can be adopted in a typical university laboratory course to learn the principles of magnetic measurement and to understand the fundamental magnetism of matter. We demonstrate the exceptional stability of the circuit with a working frequency range of ~10 kHz to 10 MHz and excellent performance to detect the diamagnetic signal from a superconductor at cryogenic temperature.
Large Language Models as Psychological Simulators: A Methodological Guide
2025-06-20
Zhicheng Lin
Large language models (LLMs) offer emerging opportunities for psychological and behavioral research, but methodological guidance is lacking. This article provides a framework for using LLMs as psychological simulators across two primary applications: simulating roles and personas to explore diverse contexts, and serving as computational models to investigate cognitive processes. For simulation, we present methods for developing psychologically grounded personas that move beyond demographic categories, with strategies for validation against human data and use cases ranging from studying inaccessible populations to prototyping research instruments. For cognitive modeling, we synthesize emerging approaches for probing internal representations, methodological advances in causal interventions, and strategies for relating model behavior to human cognition. We address overarching challenges including prompt sensitivity, temporal limitations from training data cutoffs, and ethical considerations that extend beyond traditional human subjects review. Throughout, we emphasize the need for transparency about model capabilities and constraints. Together, this framework integrates emerging empirical evidence about LLM performance--including systematic biases, cultural limitations, and prompt brittleness--to help researchers wrangle these challenges and leverage the unique capabilities of LLMs in psychological research.
This paper systematizes research on auditing Differential Privacy (DP) techniques, aiming to identify key insights into the current state of the art and open challenges. First, we introduce a comprehensive framework for reviewing work in the field and establish three cross-contextual desiderata that DP audits should target--namely, efficiency, end-to-end-ness, and tightness. Then, we systematize the modes of operation of state-of-the-art DP auditing techniques, including threat models, attacks, and evaluation functions. This allows us to highlight key details overlooked by prior work, analyze the limiting factors to achieving the three desiderata, and identify open research problems. Overall, our work provides a reusable and systematic methodology geared to assess progress in the field and identify friction points and future directions for our community to focus on.
A review of heat transport in solvated gold nanoparticles: Molecular dynamics modeling and experimental perspectives
2025-06-20
Md Adnan Mahathir Munshi, Emdadul Haque Chowdhury, Luis E. Paniagua-Guerra, Jaymes Dionne, Ashutosh Giri, Bladimir Ramos-Alvarado
Turning gold nanoparticles (AuNPs) into nanoscale heat sources via light irradiation has prompted significant research interest, particularly for biomedical applications over the past few decades. The AuNP's tunable photothermal effect, notable biocompatibility, and ability to serve as vehicles for temperature-sensitive chemical linkers enable thermo-therapeutics, such as localized drug/gene delivery and thermal ablation of cancerous tissue. Thermal transport in aqueous AuNP solutions stands as the fundamental challenge to developing targeted thermal therapies; thus, this review article surveys recent advancements in our understanding of heat transfer and surface chemistry in AuNPs, with a particular focus on thermal boundary conductance across gold- and functionalized-gold-water interfaces. This review article highlights computational advances based on molecular dynamics simulations that offer valuable insights into nanoscopic interfacial heat transfer in solvated interfaces, particularly for chemically functionalized AuNPs. Additionally, it outlines current experimental techniques for measuring interfacial thermal transport, their limitations, and potential pathways to improve sensitivity. This review further examines computational methodologies to guide the accurate modeling of solvated gold interfaces. Finally, it concludes with a discussion of future research directions aimed at deepening our understanding of interfacial heat transfer in solvated AuNPs, crucial to optimize thermoplasmonic applications.
A Survey of State Representation Learning for Deep Reinforcement Learning
2025-06-20
Ayoub Echchahed, Pablo Samuel Castro
Representation learning methods are an important tool for addressing the challenges posed by complex observations spaces in sequential decision making problems. Recently, many methods have used a wide variety of types of approaches for learning meaningful state representations in reinforcement learning, allowing better sample efficiency, generalization, and performance. This survey aims to provide a broad categorization of these methods within a model-free online setting, exploring how they tackle the learning of state representations differently. We categorize the methods into six main classes, detailing their mechanisms, benefits, and limitations. Through this taxonomy, our aim is to enhance the understanding of this field and provide a guide for new researchers. We also discuss techniques for assessing the quality of representations, and detail relevant future directions.
On the Photoluminescence Theory in Nanocrystalline Silicon: A New Improvement
2025-06-20
Arturo Ramírez-Porras
Silicon has long been the foundational semiconductor material for a broad range of electronic devices, owing to its numerous advantages: wide natural availability, ease of synthesis in both crystalline and amorphous forms, and relatively low production cost. However, despite these benefits, silicon is inherently limited in the field of optoelectronics due to its indirect bandgap, which results in low quantum efficiency for light emission, typically in the infrared region. One promising strategy to address this limitation is the development of nanocrystalline silicon, which consists of low-dimensional nanostructures such as quantum wires (QWs) and quantum dots (QDs). These structures exhibit enhanced photoluminescence and electroluminescence, primarily due to quantum confinement of excitons within the conduction and valence bands, leading to significantly improved quantum efficiency. Although research into these processes has spanned several decades, a definitive consensus on the mechanisms underlying photoluminescent emission in nanocrystalline silicon remains elusive. This work reviews two leading theoretical models proposed to explain this phenomenon and introduces a new, more comprehensive model that may provide a deeper and more accurate understanding of photoluminescence in nanocrystalline silicon. The new theory is applied in a case study.
Public Perceptions of Autonomous Vehicles: A Survey of Pedestrians and Cyclists in Pittsburgh
2025-06-20
Rudra Y. Bedekar
This study investigates how autonomous vehicle(AV) technology is perceived by pedestrians and bicyclists in Pittsburgh. Using survey data from over 1200 respondents, the research explores the interplay between demographics, AV interactions, infrastructural readiness, safety perceptions, and trust. Findings highlight demographic divides, infrastructure gaps, and the crucial role of communication and education in AV adoption.
Scattering and absorption of massless scalar waves by a ModMax black hole
2025-06-20
Arturo Quispe Baptista, M. L. Peñafiel
ModMax electrodynamics is a remarkable example of nonlinear electrodynamics that preserves both conformal and duality invariance. When coupled to General Relativity, the resulting black hole solutions introduce a tunable nonlinearity parameter that effectively screens the electromagnetic charge. We study the scattering and absorption of massless scalar waves by a ModMax black hole by applying the partial waves method. We also compute some well-known analytical approximations for the scattering and absorption cross sections and contrast them with the full numerical solution. Our results adequately reproduce those of Maxwell electrodynamics in the appropriate limit and illustrate the effect of the charge screening in ModMax electrodynamics.
Evolution Equations on Manifolds with Conical Singularities
2025-06-20
Elmar Schrohe
This is an introduction to the analysis of nonlinear evolution equations on manifolds with conical singularities via maximal regularity techniques. We address the specific difficulties due to the singularities, in particular the choice of extensions of the conic Laplacian that guarantee the existence of a bounded $H_\infty$-calculus. We introduce the relevant technical tools and survey, as main examples, applications to the porous medium equation, the fractional porous medium equation, the Yamabe flow, and the Cahn-Hilliard equation.
Computational Approaches to Understanding Large Language Model Impact on Writing and Information Ecosystems
2025-06-20
Weixin Liang
Large language models (LLMs) have shown significant potential to change how we write, communicate, and create, leading to rapid adoption across society. This dissertation examines how individuals and institutions are adapting to and engaging with this emerging technology through three research directions. First, I demonstrate how the institutional adoption of AI detectors introduces systematic biases, particularly disadvantaging writers of non-dominant language varieties, highlighting critical equity concerns in AI governance. Second, I present novel population-level algorithmic approaches that measure the increasing adoption of LLMs across writing domains, revealing consistent patterns of AI-assisted content in academic peer reviews, scientific publications, consumer complaints, corporate communications, job postings, and international organization press releases. Finally, I investigate LLMs' capability to provide feedback on research manuscripts through a large-scale empirical analysis, offering insights into their potential to support researchers who face barriers in accessing timely manuscript feedback, particularly early-career researchers and those from under-resourced settings.
2025-06-20
F. Arleo, P. Caucal, A. Deshpande, J. M. Durham, G. M. Innocenti, J. Jalilian-Marian, A. Kusina, M. X. Liu, Y. Mehtar-Tani, C. -J. Naïm, H. Paukkunen, S. Platchkov, F. Salazar, I. Vitev, R. Vogt
This review examines data from hadron-nucleus collisions, primarily focusing on hard processes like Drell-Yan, heavy flavor and quarkonium production. It highlights observed modifications of particle yields as functions of momentum and rapidity, aiming to clarify the underlying QCD effects in cold nuclear matter (CNM). The paper outlines strategies for future experiments, including the Electron-Ion Collider (EIC), to distinguish between these effects. Key questions address the universality of suppression mechanisms and the role of non-perturbative physics, providing a road map for upcoming nuclear data.
Keeping Medical AI Healthy: A Review of Detection and Correction Methods for System Degradation
2025-06-20
Hao Guan, David Bates, Li Zhou
Artificial intelligence (AI) is increasingly integrated into modern healthcare, offering powerful support for clinical decision-making. However, in real-world settings, AI systems may experience performance degradation over time, due to factors such as shifting data distributions, changes in patient characteristics, evolving clinical protocols, and variations in data quality. These factors can compromise model reliability, posing safety concerns and increasing the likelihood of inaccurate predictions or adverse outcomes. This review presents a forward-looking perspective on monitoring and maintaining the "health" of AI systems in healthcare. We highlight the urgent need for continuous performance monitoring, early degradation detection, and effective self-correction mechanisms. The paper begins by reviewing common causes of performance degradation at both data and model levels. We then summarize key techniques for detecting data and model drift, followed by an in-depth look at root cause analysis. Correction strategies are further reviewed, ranging from model retraining to test-time adaptation. Our survey spans both traditional machine learning models and state-of-the-art large language models (LLMs), offering insights into their strengths and limitations. Finally, we discuss ongoing technical challenges and propose future research directions. This work aims to guide the development of reliable, robust medical AI systems capable of sustaining safe, long-term deployment in dynamic clinical settings.
A Morphologically Self-Consistent Phase Field Model for the Computational Study of Memristive Thin Film Current-Voltage Hysteresis
2025-06-20
John F. Sevic, Ambroise Juston, Nobuhiko P. Kobayashi
A multiphysics phase field model is used for the computational study of memristive thin film morphology and current-voltage hysteresis. In contrast to previous computational methods, no requirements are made on conducting filament geometry. Our method correctly predicts conducting filaments evolve on thermodynamic paths that are energetically favored due to stochastic structural and chemical variations naturally occurring at the atomic-level, due to both latent and intentional fabrication effects. These results have significant implications for the computational design of a broad class of memristive thin films, enabling practical wafer-scale mapping, uniformity, and endurance analysis and optimization.
Observing Population III (PopIII) galaxies, the hosts of first-generation stars, remains challenging even with the JWST. The current few candidates have been identified through the combination of a prominent HeII emission and the absence of metal lines, a well-known but extremely brief signature of metal-free systems. Here, we accurately model the evolution of the emission from PopIII galaxies to increase the number of candidates in JWST observations. To achieve this, we employ a locally calibrated galaxy-formation model that self-consistently follows the star formation and chemical evolution initiated by the first stars. We find that PopIII galaxies can emit metal lines in their ``self-polluted'' phase, while galaxies host only metal-free stars but the gas has been chemically-enriched by the first supernovae. In this phase, PopIII galaxies have $\rm [OIII]/H\beta \approx 1$, which opens the pool of candidates to more easily detectable sources. We predict that the high HeII emission of PopIII galaxies can last up to $\rm \approx 20 \, Myrs$ and that it is partly maintained in the ``hybrid'' phase, when PopIII and PopII stars co-exist in the host galaxy. We propose novel diagnostics involving UV metal lines to select PopIII candidates in high-z JWST surveys. In JADES, we identify 9 candidate galaxies with $>25\%$ of their stellar mass in metal-free stars, showcasing the effectiveness of our method. Ultimately, the key to discovering PopIII galaxies could be to catch them during their first episodes of chemical enrichment.
The JWST/AURORA Survey: Multiple Balmer and Paschen Emission Lines for Individual Star-forming Galaxies at z=1.5-4.4. I. A Diversity of Nebular Attenuation Curves and Evidence for Non-Unity Dust Covering Fractions
2025-06-20
Naveen A. Reddy, Alice E. Shapley, Ryan L. Sanders, Michael W. Topping, Richard S. Ellis, Max Pettini, Gabriel Brammer, Fergus Cullen, Natascha M. Forster Schreiber, Ali A. Khostovan, Derek J. McLeod, Ross J. McLure, Desika Narayanan, Pascal A. Oesch, Anthony J. Pahl, Charles C. Steidel, Danielle A. Berg
We present the nebular attenuation curves and dust covering fractions for 24 redshift z=1.5-4.4 star-forming galaxies using multiple Balmer and Paschen lines from the JWST/AURORA survey. Nebular reddening derived from Paschen lines exceeds that from Balmer lines for at least half the galaxies in the sample when assuming the commonly-adopted Galactic extinction curve, implying the presence of optically-thick star formation. The nebular attenuation curves exhibit a broad range of normalizations (Rv ~ 3.2-16.4). Motivated by the offsets in reddening deduced from the Balmer and Paschen lines, and the high Rv values for the individual nebular attenuation curves, both of which suggest variations in the dust-stars geometry, we propose a model with a subunity dust covering fraction (fcov). Fitting such a model to the HI recombination line ratios indicates fcov ~ 0.6-1.0. The normalizations of the nebular attenuation curves, Rv, are driven primarily by fcov and the mix of optically-thick and thin OB associations. Thus, the diversity of nebular attenuation curves can be accommodated by assuming dust grain properties similar to that of Milky Way sightlines but with a subunity covering fraction of dust. Integrated measurements of multiple Balmer and Paschen lines can be used to place novel constraints on the dust covering fraction towards OB associations. These, in turn, provide new avenues for exploring the role of dust and gas covering fraction in a number of relevant aspects of high-redshift galaxies, including the impact of stellar feedback on ISM porosity and the escape of Ly-alpha and Lyman continuum radiation.
JWST reveals cosmic ray dominated chemistry in the local ULIRG IRAS 07251$-$0248
2025-06-20
G. Speranza, M. Pereira-Santaella, M. Agúndez, E. González-Alfonso, I. García-Bernete, J. R. Goicoechea, M. Imanishi, D. Rigopoulou, M. G. Santa-Maria, N. Thatte
We analyze the ro-vibrational absorption bands of various molecular cations (HCO$^+$, HCNH$^+$, and N$_2$H$^+$) and neutral species (HCN, HNC, and HC$_3$N) detected in the JWST/MIRI MRS spectrum (4.9-27.9 $\mu$m) of the local ultra luminous infrared galaxy (ULIRG) IRAS~07251$-$0248. We find that the molecular absorptions are blueshifted by 160 km s$^{-1}$ relative to the systemic velocity of the target. Using local thermal equilibrium (LTE) excitation models, we derive rotational temperatures ($T_{\rm rot}$) from 42 to 185 K for these absorption bands. This range of measured $T_{\rm rot}$ can be explained by infrared (IR) radiative pumping as a byproduct of the strength, effective critical density, and opacity of each molecular band. Thus, these results suggest that these absorptions originate in a warm expanding gas shell ($\dot{M}$$\sim$90-330 $M_\odot$ yr$^{-1}$), which might be the base of the larger scale cold molecular outflow detected in this source. Finally, the elevated abundance of molecular cations (H$_3^+$, HCO$^+$, HCNH$^+$, and N$_2$H$^+$) can be explained by a high cosmic ray ionization rate, with log($\zeta_{\text{H}_2}$/n$_{\rm H}\, [\text{cm}^3 \text{s}^{-1}])$ in the range of -19.1 to -18.2, consistent with a cosmic ray dominated chemistry as predicted by chemical models.
Characterizing Density and Gravitational Potential Fluctuations of the Interstellar Medium
2025-06-20
Shaunak Modak, Eve C. Ostriker, Chris Hamilton, Scott Tremaine
Substructure in the interstellar medium (ISM) is crucial for establishing the correlation between star formation and feedback and has the capacity to significantly perturb stellar orbits, thus playing a central role in galaxy dynamics and evolution. Contemporary surveys of gas and dust emission in nearby galaxies resolve structure down to $\sim 10\,$pc scales, demanding theoretical models of ISM substructure with matching fidelity. In this work, we address this need by quantitatively characterizing the gas density in state-of-the-art MHD simulations of disk galaxies that resolve pc to kpc scales. The TIGRESS-NCR framework we employ includes sheared galactic rotation, self-consistent star formation and feedback, and nonequilibrium chemistry and cooling. We fit simple analytic models to the one-point spatial, two-point spatial, and two-point spatio-temporal statistics of the surface density fluctuation field. We find that for both solar neighborhood and inner-galaxy conditions, (i) the surface density fluctuations follow a log-normal distribution, (ii) the linear and logarithmic fluctuation power spectra are well-approximated as power laws with indices of $\approx -2.2$ and $\approx -2.8$ respectively, and (iii) lifetimes of structures at different scales are set by a combination of feedback and effective pressure terms. Additionally, we find that the vertical structure of the gas is well-modeled by a mixture of exponential and sech$^2$ profiles, allowing us to link the surface density statistics to those of the volume density and gravitational potential. We provide convenient parameterizations for incorporating realistic ISM effects into stellar-dynamical studies and for comparison with multi-wavelength observations.
The AURORA Survey: Tracing Galactic Outflows at $z\gtrsim2.5$ with JWST/NIRSpec NUV Absorption Lines
2025-06-20
Emily Kehoe, Alice E. Shapley, Ryan L. Sanders, Naveen A. Reddy, Michael W. Topping, Natalie Lam, Leonardo Clarke, Fergus Cullen, Richard S. Ellis, N. M. Forster Schreiber, Tucker Jones, Ali Ahmad Khostovan, Derek J. McLeod, Ross J. McLure, Desika Narayanan, Pascal Oesch, Anthony J. Pahl
We probe galactic-scale outflows in star-forming galaxies at $z\gtrsim2.5$ drawn from the JWST/NIRSpec AURORA program. For the first time, we directly compare outflow properties from the early universe to the present day using near-UV absorption lines. We measure ISM kinematics from Fe II and Mg II absorption features in 41 and 43 galaxies, respectively, and examine how these kinematics correlate with galaxy properties. We find that galaxies with outflows tend to have higher stellar masses, and that maximum outflow velocities increase with stellar mass, SFR, $\beta$, $E(B-V)$, and $A_V$. We also find that Mg II emission is more common in galaxies with lower masses, higher sSFRs, and less dust. These trends are consistent with those in star-forming galaxies at $z<2$ when using the same outflow tracers, suggesting that the feedback from star formation has played a persistent role in shaping galaxy evolution over cosmic time. We also directly compare near-UV and far-UV features in the same NIRSpec spectrum for a $z=5.19$ galaxy, finding consistent ISM kinematics and demonstrating that different tracers yield comparable measurements. We also detect Na D absorption in 10 galaxies, which have higher stellar mass, SFR, and dust attenuation compared to galaxies without Na D absorption, which is consistent with $z\sim0$ studies. The broad continuum coverage and sensitivity of NIRSpec will enable future studies with larger samples, allowing for robust tests of these trends across a wider dynamic range of galaxy properties.
The secondary anisotropies of the cosmic microwave background (CMB) provide a wealth of astrophysical and cosmological information. Pairing measurements of the CMB temperature map obtained by DR5 of the Atacama Cosmology Telescope (ACT) with the large-scale structure imaging survey conducted by the Dark Energy Spectroscopic Instrument, DECaLS DR9, we investigate two effects that are sensitive to the gas density $\tau$: kinematic Sunyaev-Zel'dovich (kSZ) and `patchy screening' (also known as `anisotropic screening'). In particular, we measure the stacked profiles around Luminous Red Galaxies (LRGs) at a mean redshift of $z \approx 0.7$. We detect the kSZ signal at 7.2$\sigma$, and we find a signal at $\sim 4.1\sigma$ for the patchy screening estimator, which is in excess relative to the kSZ signal. We attribute this excess to contamination from CMB lensing. We demonstrate the effect of lensing using $N$-body simulations, and we show that the screening signal is dominated by it. Accounting for lensing, our measurement places a 95\% upper bound on the optical depth of the Extended DESI LRG sample of $\tau <$ 2.5 $10^{-4}$ for a mean value of the sample of $\tau \approx$ 1.6 $10^{-4}$. Furthermore, via hydro simulations, we show that the underlying optical depth signal measured by both effects (after removing the CMB lensing contribution) is in perfect agreement when adopting either a Compensated Aperture Photometry (CAP) filter or a high-pass filter. Consistent with previous measurements, we see evidence for excess baryonic feedback around DESI LRGs in the patchy screening measurement. Provided both effects are measured with high signal-to-noise, one can measure the amplitude ratio between them, which is proportional to the root-mean-square velocity of the host halo sample, and place constraints on velocity-sensitive models such as modified gravity and phantom dark energy.
Gaussian Processes and Reproducing Kernels: Connections and Equivalences
2025-06-20
Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic, Bharath K. Sriperumbudur
This monograph studies the relations between two approaches using positive definite kernels: probabilistic methods using Gaussian processes, and non-probabilistic methods using reproducing kernel Hilbert spaces (RKHS). They are widely studied and used in machine learning, statistics, and numerical analysis. Connections and equivalences between them are reviewed for fundamental topics such as regression, interpolation, numerical integration, distributional discrepancies, and statistical dependence, as well as for sample path properties of Gaussian processes. A unifying perspective for these equivalences is established, based on the equivalence between the Gaussian Hilbert space and the RKHS. The monograph serves as a basis to bridge many other methods based on Gaussian processes and reproducing kernels, which are developed in parallel by the two research communities.
Virtual Teaching Assistants (VTAs) powered by Large Language Models (LLMs) have the potential to enhance student learning by providing instant feedback and facilitating multi-turn interactions. However, empirical studies on their effectiveness and acceptance in real-world classrooms are limited, leaving their practical impact uncertain. In this study, we develop an LLM-based VTA and deploy it in an introductory AI programming course with 477 graduate students. To assess how student perceptions of the VTA's performance evolve over time, we conduct three rounds of comprehensive surveys at different stages of the course. Additionally, we analyze 3,869 student--VTA interaction pairs to identify common question types and engagement patterns. We then compare these interactions with traditional student--human instructor interactions to evaluate the VTA's role in the learning process. Through a large-scale empirical study and interaction analysis, we assess the feasibility of deploying VTAs in real-world classrooms and identify key challenges for broader adoption. Finally, we release the source code of our VTA system, fostering future advancements in AI-driven education: \texttt{https://github.com/sean0042/VTA}.
Towards Safety Evaluations of Theory of Mind in Large Language Models
2025-06-20
Tatsuhiro Aoshima, Mitsuaki Akiyama
As the capabilities of large language models (LLMs) continue to advance, the importance of rigorous safety evaluation is becoming increasingly evident. Recent concerns within the realm of safety assessment have highlighted instances in which LLMs exhibit behaviors that appear to disable oversight mechanisms and respond in a deceptive manner. For example, there have been reports suggesting that, when confronted with information unfavorable to their own persistence during task execution, LLMs may act covertly and even provide false answers to questions intended to verify their behavior.To evaluate the potential risk of such deceptive actions toward developers or users, it is essential to investigate whether these behaviors stem from covert, intentional processes within the model. In this study, we propose that it is necessary to measure the theory of mind capabilities of LLMs. We begin by reviewing existing research on theory of mind and identifying the perspectives and tasks relevant to its application in safety evaluation. Given that theory of mind has been predominantly studied within the context of developmental psychology, we analyze developmental trends across a series of open-weight LLMs. Our results indicate that while LLMs have improved in reading comprehension, their theory of mind capabilities have not shown comparable development. Finally, we present the current state of safety evaluation with respect to LLMs' theory of mind, and discuss remaining challenges for future work.
Climate change and natural disasters are recognized as worldwide challenges requiring complex and efficient ecosystems to deal with social, economic, and environmental effects. This chapter advocates a holistic approach, distinguishing preparedness, emergency responses, and postcrisis phases. The role of the Early Warning System (EWS), Risk modeling and mitigation measures are particularly emphasized. The chapter reviews the various Artificial Intelligence (AI)-enabler technologies that can be leveraged at each phase, focusing on the INFORM risk framework and EWSs. Emergency communication and psychological risk perception have been emphasized in emergency response times. Finally, a set of case studies from Nordic countries has been highlighted.
Graph machine learning has led to a significant increase in the capabilities of models that learn on arbitrary graph-structured data and has been applied to molecules, social networks, recommendation systems, and transportation, among other domains. Data in multi-tabular relational databases can also be constructed as 'relational entity graphs' for Relational Deep Learning (RDL) - a new blueprint that enables end-to-end representation learning without traditional feature engineering. Compared to arbitrary graph-structured data, relational entity graphs have key properties: (i) their structure is defined by primary-foreign key relationships between entities in different tables, (ii) the structural connectivity is a function of the relational schema defining a database, and (iii) the graph connectivity is temporal and heterogeneous in nature. In this paper, we provide a comprehensive review of RDL by first introducing the representation of relational databases as relational entity graphs, and then reviewing public benchmark datasets that have been used to develop and evaluate recent GNN-based RDL models. We discuss key challenges including large-scale multi-table integration and the complexities of modeling temporal dynamics and heterogeneous data, while also surveying foundational neural network methods and recent architectural advances specialized for relational entity graphs. Finally, we explore opportunities to unify these distinct modeling challenges, highlighting how RDL converges multiple sub-fields in graph machine learning towards the design of foundation models that can transform the processing of relational data.
LLMs in Coding and their Impact on the Commercial Software Engineering Landscape
2025-06-19
Vladislav Belozerov, Peter J Barclay, Askhan Sami
Large-language-model coding tools are now mainstream in software engineering. But as these same tools move human effort up the development stack, they present fresh dangers: 10% of real prompts leak private data, 42% of generated snippets hide security flaws, and the models can even ``agree'' with wrong ideas, a trait called sycophancy. We argue that firms must tag and review every AI-generated line of code, keep prompts and outputs inside private or on-premises deployments, obey emerging safety regulations, and add tests that catch sycophantic answers -- so they can gain speed without losing security and accuracy.
2025-06-19
Anvith Pabba, Alex Mathai, Anindya Chakraborty, Baishakhi Ray
Large Language Models (LLMs) have shown impressive capabilities in downstream software engineering tasks such as Automated Program Repair (APR). In particular, there has been a lot of research on repository-level issue-resolution benchmarks such as SWE-Bench. Although there has been significant progress on this topic, we notice that in the process of solving such issues, existing agentic systems tend to hyper-localize on immediately suspicious lines of code and fix them in isolation, without a deeper understanding of the issue semantics, code semantics, or execution semantics. Consequently, many existing systems generate patches that overfit to the user issue, even when a more general fix is preferable. To address this limitation, we introduce SemAgent, a novel workflow-based procedure that leverages issue, code, and execution semantics to generate patches that are complete - identifying and fixing all lines relevant to the issue. We achieve this through a novel pipeline that (a) leverages execution semantics to retrieve relevant context, (b) comprehends issue-semantics via generalized abstraction, (c) isolates code-semantics within the context of this abstraction, and (d) leverages this understanding in a two-stage architecture: a repair stage that proposes fine-grained fixes, followed by a reviewer stage that filters relevant fixes based on the inferred issue-semantics. Our evaluations show that our methodology achieves a solve rate of 44.66% on the SWEBench-Lite benchmark beating all other workflow-based approaches, and an absolute improvement of 7.66% compared to our baseline, which lacks such deep semantic understanding. We note that our approach performs particularly well on issues requiring multi-line reasoning (and editing) and edge-case handling, suggesting that incorporating issue and code semantics into APR pipelines can lead to robust and semantically consistent repairs.
Magnetoelastic dynamics of the "spin Jahn-Teller" transition in CoTi$_{2}$O$_{5}$
2025-06-19
K. Guratinder, R. D. Johnson, D. Prabhakaran, R. A. Taylor, F. Lang, S. J. Blundell, L. S. Taran, S. V. Streltsov, T. J. Williams, S. R. Giblin, T. Fennell, K. Schmalzl, C. Stock
CoTi$_{2}$O$_{5}$ has the paradox that low temperature static magnetic order is incompatible with the crystal structure owing to a mirror plane that exactly frustrates magnetic interactions. Despite no observable structural distortion with diffraction, CoTi$_{2}$O$_{5}$ does magnetically order below $T_{\rm N}$ $\sim$ 25 K with the breaking of spin ground state degeneracy proposed to be a realization of the spin Jahn-Teller effect in analogy to the celebrated orbital Jahn-Teller transition. We apply neutron and Raman spectroscopy to study the dynamics of this transition in CoTi$_{2}$O$_{5}$. We find anomalous acoustics associated with a symmetry breaking strain that characterizes the spin Jahn-Teller transition. Crucially, the energy of this phonon coincides with the energy scale of the magnetic excitations, and has the same symmetry of an optic mode, observed with Raman spectroscopy, which atypically softens in energy with decreasing temperature. Taken together, we propose that the energetics of the spin Jahn-Teller effect in CoTi$_{2}$O$_{5}$ are related to cooperative magnetoelastic fluctuations as opposed to conventional soft critical dynamics which typically drive large measurable static displacements.
Data marketplaces can increase the willingness to share social media data at low prices
2025-06-19
Meysam Alizadeh, Fabrizio Gilardi
Living in the Post API age, researchers face unprecedented challenges in obtaining social media data, while users are concerned about how big tech companies use their data. Data donation offers a promising alternative, however, its scalability is limited by low participation and high dropout rates. Research suggests that data marketplaces could be a solution, but its realization remains challenging due to theoretical gaps in treating data as an asset. This paper examines whether data marketplaces can increase individuals willingness to sell their X (Twitter) data package and the minimum price they would accept. It also explores how privacy protections and the type of data buyer may affect these decisions. Results from two preregistered online survey experiments show that a data marketplace increases participants' willingness to sell their X data by 12 to 25 percentage points compared to data donation (depending on treatments), and by 6.8 points compared to onetime purchase offers. Although difference in minimum acceptable prices are not statistically significant, over 64 percentage of participants set their price within the marketplace's suggested range (0.25 to 2), substantially lower than the amounts offered in prior onetime purchase studies. Finally, in the marketplace setting, neither the type of buyer nor the inclusion of a privacy safeguard significantly influenced participants willingness to sell.
Synthetic data generation--mitigating data scarcity, privacy concerns, and data quality challenges in biomedical fields--has been facilitated by rapid advances of large language models (LLMs). This scoping review follows PRISMA-ScR guidelines and synthesizes 59 studies, published between 2020 and 2025 and collected from PubMed, ACM, Web of Science, and Google Scholar. The review systematically examines biomedical research and application trends in synthetic data generation, emphasizing clinical applications, methodologies, and evaluations. Our analysis identifies data modalities of unstructured texts (78.0%), tabular data (13.6%), and multimodal sources (8.4%); generation methods of prompting (72.9%), fine-tuning (22.0%) LLMs and specialized model (5.1%); and heterogeneous evaluations of intrinsic metrics (27.1%), human-in-the-loop assessments (55.9%), and LLM-based evaluations (13.6%). The analysis addresses current limitations in what, where, and how health professionals can leverage synthetic data generation for biomedical domains. Our review also highlights challenges in adaption across clinical domains, resource and model accessibility, and evaluation standardizations.
Advancing Harmful Content Detection in Organizational Research: Integrating Large Language Models with Elo Rating System
2025-06-19
Mustafa Akben, Aaron Satko
Large language models (LLMs) offer promising opportunities for organizational research. However, their built-in moderation systems can create problems when researchers try to analyze harmful content, often refusing to follow certain instructions or producing overly cautious responses that undermine validity of the results. This is particularly problematic when analyzing organizational conflicts such as microaggressions or hate speech. This paper introduces an Elo rating-based method that significantly improves LLM performance for harmful content analysis In two datasets, one focused on microaggression detection and the other on hate speech, we find that our method outperforms traditional LLM prompting techniques and conventional machine learning models on key measures such as accuracy, precision, and F1 scores. Advantages include better reliability when analyzing harmful content, fewer false positives, and greater scalability for large-scale datasets. This approach supports organizational applications, including detecting workplace harassment, assessing toxic communication, and fostering safer and more inclusive work environments.
Obtaining continuum physics from dynamical simulations of Hamiltonian lattice gauge theories
2025-06-19
Christopher F. Kane, Siddharth Hariprakash, Christian W. Bauer
Taking the continuum limit is essential for extracting physical observables from quantum simulations of lattice gauge theories. Achieving the correct continuum limit requires careful control of all systematic uncertainties, including those arising from approximate implementations of the time evolution operator. In this work, we review existing approaches based on renormalization techniques, and point out their limitations. To overcome these limitations, we introduce a new general framework -- the Statistically-Bounded Time Evolution (SBTE) protocol -- for rigorously controlling the impact of approximate time evolution on the continuum limit. The central insight is that, since exact time evolution introduces no UV divergences, errors from approximate evolution can be treated as a source of systematic uncertainty that can be neglected if reduced below the working statistical uncertainty. We show that, using the SBTE protocol, which prescribes driving the approximate time evolution error below the working statistical uncertainty, leads to a simplified renormalization procedure. Furthermore, we show that, due to the existence of rigorous error bounds, one can guarantee a priori that such errors are negligible and do not affect the continuum limit. Ultimately, our protocol lays the foundation for performing systematic and fair comparisons between different simulation algorithms for lattice gauge theory simulations.
Primordial magnetic fields and modified recombination histories
2025-06-19
Jonathan Schiff, Tejaswi Venumadhav
Recent cosmological data and astrophysical observations, such as the Hubble tension and the increasing preference from galaxy surveys for dynamical dark energy, have begun to challenge the standard $\Lambda$-cold dark matter cosmological model. Primordial magnetic fields (PMFs) offer a mechanism to alleviate these tensions within the framework of the standard model. These fields source excess small-scale baryon clumping, which can speed up recombination and shrink the comoving sound horizon at the surface of last scattering. Computing the modified recombination history requires coupling the radiative transport of Lyman-$\alpha$ photons to compressible magnetohydronamic simulations. Since doing so is generically computationally intractable, we have developed a linearized treatment which self-consistently computes the modified recombination history in the presence of PMF induced baryon clumping for fields with red-tilted spectra. The clumping factors we find are too small to alleviate outstanding cosmological tensions, but our general framework can be applied to other PMF spectra, and provides a significant theoretical step towards a complete account of recombination in the presence of small-scale baryon clumping.
Disentangling Metallicity Effects in Hot Jupiter Occurrence Across Galactic Birth Radius and Phase-Space Density
2025-06-19
Rayna Rampalli, Melissa K. Ness, Elisabeth R. Newton, Andrew Vanderburg, Tobias Buck, Jessica Mills
We explore how the correlation between host star metallicity and giant planets shapes hot Jupiter occurrence as a function of Galactic birth radius (\rbirth) and phase-space density in the Milky Way disk. Using the GALAH and APOGEE surveys and a galaxy from the NIHAO simulation suite, we inject hot Jupiters around stars based on metallicity power laws, reflecting the trend that giant planets preferentially form around metal-rich stars. For \rbirth\ $\geq 5$ kpc, hot Jupiter occurrence decreases with \rbirth\ by $\sim -0.1%$ per kpc; this is driven by the Galaxy's chemical evolution, where the inner regions of the disk are more metal-rich. Differences in GALAH occurrence rates versus APOGEE's and the simulation's at \rbirth\ $< 5$ kpc arise from survey selection effects. APOGEE and the NIHAO simulation have more high-$\alpha$ sequence stars than GALAH, resulting in average differences in metallicity (0.2--0.4 dex), $\alpha$-process element enrichment (0.2 dex), and vertical velocities (7--14 km/s) at each \rbirth\ bin. Additionally, we replicate the result of \cite{Winter20}, which showed that over 92% of hot Jupiters are associated with stars in phase-space overdensities, or "clustered environments." However, our findings suggest that this clustering effect is primarily driven by chemical and kinematic differences between low and high-$\alpha$ sequence star properties. Our results support stellar characteristics, particularly metallicity, being the primary drivers of hot Jupiter formation, which serves as the "null hypothesis" for interpreting planet demographics. This underscores the need to disentangle planetary and stellar properties from Galactic-scale effects in future planet demographics studies.
Taking a Break at Cosmic Noon: Continuum-selected Low-mass Galaxies Require Long Burst Cycles
2025-06-19
Abby Mintz, David J. Setton, Jenny E. Greene, Joel Leja, Bingjie Wang, Emilie Burnham, Katherine A. Suess, Hakim Atek, Rachel Bezanson, Gabriel Brammer, Sam E. Cutler, Pratika Dayal, Robert Feldmann, Lukas J. Furtak, Karl Glazebrook, Gourav Khullar, Vasily Kokorev, Ivo Labbé, Michael V. Maseda, Tim B. Miller, Ikki Mitsuhashi, Themiya Nanayakkara, Richard Pan, Sedona H. Price, John R. Weaver, Katherine E. Whitaker
While bursty star formation in low-mass galaxies has been observed in local populations and reproduced in simulations, the dormant phase of the burst cycle has not been well studied beyond the local Universe due to observational limitations. We present a unique sample of 41 JWST PRISM spectra of low-mass galaxies ($M_\star < 10^{9.5}\,M_\odot$) at cosmic noon ($1
2025-06-19
Renato Cordeiro Ferreira, Thatiane de Oliveira Rosa, Alfredo Goldman, Eduardo Guerra
Developing complex systems using microservices is a current challenge. In this paper, we present our experience with teaching this subject to more than 80 students at the University of S\~ao Paulo (USP), fostering team work and simulating the industry's environment. We show it is possible to teach such advanced concepts for senior undergraduate students of Computer Science and related fields.
We review recent progress in our understanding of the nucleon excitation spectrum. Thanks to dedicated efforts at facilities such as ELSA, MAMI and Jefferson Lab, several new nucleon resonances have been discovered, and evidence for previously elusive states has been significantly improved. Numerous decay channels have been observed for the first time, and resonance properties are being extracted from these data by several groups through coupled-channel analyses of varying complexity. Electroproduction experiments have provided further insights into the internal structure of light baryon resonances -- for example, the long-debated Roper resonance $N(1440)$ is observed as a three-quark state with a significant meson-cloud component.While the non-relativistic quark model remains a valuable tool for organizing the spectrum of nucleon and $\Delta$ resonances, a variety of theoretical frameworks have emerged to offer deeper understanding, including phenomenological quark models, holographic QCD, functional methods, effective field theories, and lattice QCD. We examine the interplay between these approaches, highlight their respective strengths and explore how they complement each other in shaping our knowledge of light baryon resonances. We address several open questions in baryon spectroscopy, including the nature of the enigmatic $\Lambda(1405)$, ongoing searches for exotic states such as hybrid baryons and pentaquarks, and the dichotomy between microscopic descriptions of baryons in terms of quarks and gluons versus effective hadronic descriptions based on meson-baryon dynamics.
Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation
2025-06-19
Jun Qi, Chen-Yu Liu, Sabato Marco Siniscalchi, Chao-Han Huck Yang, Min-Hsiu Hsieh
Low-Rank Adaptation (LoRA) is widely recognized for its parameter-efficient fine-tuning of large-scale neural models. However, standard LoRA independently optimizes low-rank matrices, which inherently limits its expressivity and generalization capabilities. While classical tensor-train (TT) decomposition can be separately employed on individual LoRA matrices, this work demonstrates that the classical TT-based approach neither significantly improves parameter efficiency nor achieves substantial performance gains. This paper proposes TensorGuide, a novel tensor-train-guided adaptation framework to overcome these limitations. TensorGuide generates two correlated low-rank LoRA matrices through a unified TT structure driven by controlled Gaussian noise. The resulting joint TT representation inherently provides structured, low-rank adaptations, significantly enhancing expressivity, generalization, and parameter efficiency without increasing the number of trainable parameters. Theoretically, we justify these improvements through neural tangent kernel analyses, demonstrating superior optimization dynamics and enhanced generalization. Extensive experiments on quantum dot classification and GPT-2 fine-tuning benchmarks demonstrate that TensorGuide-based LoRA consistently outperforms standard LoRA and TT-LoRA, achieving improved accuracy and scalability with fewer parameters.
The release of ChatGPT in 2022 triggered a rapid surge in generative artificial intelligence mobile apps (i.e., Gen-AI apps). Despite widespread adoption, little is known about how end users perceive and evaluate these Gen-AI functionalities in practice. In this work, we conduct a user-centered analysis of 676,066 reviews from 173 Gen-AI apps on the Google Play Store. We introduce a four-phase methodology, SARA (Selection, Acquisition, Refinement, and Analysis), that enables the systematic extraction of user insights using prompt-based LLM techniques. First, we demonstrate the reliability of LLMs in topic extraction, achieving 91% accuracy through five-shot prompting and non-informative review filtering. Then, we apply this method to the informative reviews, identify the top 10 user-discussed topics (e.g., AI Performance, Content Quality, and Content Policy & Censorship) and analyze the key challenges and emerging opportunities. Finally, we examine how these topics evolve over time, offering insight into shifting user expectations and engagement patterns with Gen-AI apps. Based on our findings and observations, we present actionable implications for developers and researchers.
Going beyond $S_8$: fast inference of the matter power spectrum from weak-lensing surveys
2025-06-19
Cyrille Doux, Tanvi Karwal
Weak lensing surveys are often summarized by constraints on the derived parameter ${S_8\equiv\sigma_8\sqrt{\Omega_{\rm m}/0.3}}$, obscuring the rich scale and redshift information encoded in the data, and limiting our ability to identify the origin of any tensions with $\Lambda$CDM predictions from the cosmic microwave background. In this work, we introduce a fast and flexible framework to extract the scale-dependent matter power spectrum $P(k, z)$ from cosmic shear and CMB lensing measurements, parameterizing deviations from the Planck $\Lambda$CDM prediction as a free function $\alpha(k)$. Using public data from DES Y3, KiDS-1000, HSC Y3, and ACT DR6, we constrain $\alpha(k)$ with fast Hamiltonian Monte Carlo inference. Our results show a consistent 15-30\% suppression in the matter power spectrum at intermediate scales ($k \sim 0.1-1{\rm Mpc}^{-1}$) in galaxy-lensing data relative to Planck, with combined tensions reaching up to $4\sigma$ at fixed cosmology. In contrast, ACT CMB lensing is consistent with $\Lambda$CDM at ${k\lesssim 0.1 {\rm Mpc}^{-1}}$. We validate our method using mock data, quantify consistency between datasets, and demonstrate how the resulting $\alpha(k)$ likelihoods can be used to test specific models for the power spectrum. All code, data products, and derived likelihoods are publicly released. Our results highlight the importance of reporting lensing constraints on $P(k, z)$ and pave the way for model-agnostic test of growth of structure with upcoming surveys such as LSST, Euclid, and Roman.
When Does Divide and Conquer Work for Long Context LLM? A Noise Decomposition Framework
2025-06-19
Zhen Xu, Shang Zhu, Jue Wang, Junlin Wang, Ben Athiwaratkun, Chi Wang, James Zou, Ce Zhang
We investigate the challenge of applying Large Language Models (LLMs) to long texts. We propose a theoretical framework that distinguishes the failure modes of long context tasks into three categories: cross-chunk dependence (task noise), confusion that grows with context size (model noise), and the imperfect integration of partial results (aggregator noise). Under this view, we analyze when it is effective to use multi-agent chunking, i.e., dividing a length sequence into smaller chunks and aggregating the processed results of each chunk. Our experiments on tasks such as retrieval, question answering, and summarization confirm both the theoretical analysis and the conditions that favor multi-agent chunking. By exploring superlinear model noise growth with input length, we also explain why, for large inputs, a weaker model configured with chunk-based processing can surpass a more advanced model like GPT4o applied in a single shot. Overall, we present a principled understanding framework and our results highlight a direct pathway to handling long contexts in LLMs with carefully managed chunking and aggregator strategies.
Stabilizing simulation-based cosmological Fisher forecasts: a case study using the Voronoi volume function
2025-06-19
Saee Dhawalikar, Aseem Paranjape, Shadab Alam
Forecasting cosmological constraints from halo-based statistics often suffers from instability in derivative estimates, especially when the number of simulations is limited. This instability reduces the reliability of Fisher forecasts and machine learning based approaches that use derivatives. We introduce a general framework that addresses this challenge by stabilizing the input statistic and then systematically identifying the optimal subset of summary statistics that maximizes cosmological information while simultaneously minimizing the instability of predicted constraints. We demonstrate this framework using the Voronoi volume function (VVF), a summary statistic that captures beyond two-point clustering information. Applying our two-step procedure -- random sub-sampling followed by optimization -- improves the constraining power by up to a factor of 2, while also enhancing the stability of the forecasts across realizations. As surveys like Euclid, DESI, and LSST push toward tighter constraints, the ability to produce stable and accurate theoretical predictions is essential. Our results suggest that new summary statistics such as the VVF, combined with careful data curation and stabilization strategies, can play a key role in next-generation precision cosmology.
Robustness Evaluation of OCR-based Visual Document Understanding under Multi-Modal Adversarial Attacks
2025-06-19
Dong Nguyen Tien, Dung D. Le
Visual Document Understanding (VDU) systems have achieved strong performance in information extraction by integrating textual, layout, and visual signals. However, their robustness under realistic adversarial perturbations remains insufficiently explored. We introduce the first unified framework for generating and evaluating multi-modal adversarial attacks on OCR-based VDU models. Our method covers six gradient-based layout attack scenarios, incorporating manipulations of OCR bounding boxes, pixels, and texts across both word and line granularities, with constraints on layout perturbation budget (e.g., IoU >= 0.6) to preserve plausibility. Experimental results across four datasets (FUNSD, CORD, SROIE, DocVQA) and six model families demonstrate that line-level attacks and compound perturbations (BBox + Pixel + Text) yield the most severe performance degradation. Projected Gradient Descent (PGD)-based BBox perturbations outperform random-shift baselines in all investigated models. Ablation studies further validate the impact of layout budget, text modification, and adversarial transferability.
From toroids to helical tubules: Kirigami-inspired programmable assembly of two-periodic curved crystals
2025-06-19
Mason Price, Daichi Hayakawa, Thomas E. Videbæk, Rupam Saha, Botond Tyukodi, Michael F. Hagan, Seth Fraden, Gregory M. Grason, W. Benjamin Rogers
Biology is full of intricate molecular structures whose geometries are inextricably linked to their function. Many of these structures exhibit varying curvature, such as the helical structure of the bacterial flagellum, which is critical for their motility. Because synthetic analogues of these shapes could be valuable platforms for nanotechnologies, including drug delivery and plasmonics, controllable synthesis of variable-curvature structures of various material systems, from fullerenes to supramolecular assemblies, has been a long-standing goal. Like two-dimensional crystals, these structures have a two-periodic symmetry, but unlike standard two-dimensional crystals, they are embedded in three dimensions with complex, spatially-varying curvatures that cause the structures to close upon themselves in one or more dimensions. Here, we develop and implement a design strategy to program the self-assembly of a complex spectrum of two-periodic curved crystals with variable periodicity, spatial dimension, and topology, spanning from toroids to achiral serpentine tubules to both left- and right-handed helical tubules. Our design strategy uses a kirigami-based mapping of 2D planar tilings to 3D curved crystals that preserves the periodicity, two-fold rotational symmetries, and subunit dimensions via the arrangement of disclination defects. We survey the modular geometry of these curved crystals and infer the addressable subunit interactions required to assemble them from triangular subunits. To demonstrate this design strategy, we program the self-assembly of toroids, helical- and serpentine-tubules from DNA origami subunits. A simulation model of the assembly pathways reveals physical considerations for programming the geometric specificity of angular folds in the curved crystal required to avoid defect-mediated misassembly.
TrajSceneLLM: A Multimodal Perspective on Semantic GPS Trajectory Analysis
2025-06-19
Chunhou Ji, Qiumeng Li
GPS trajectory data reveals valuable patterns of human mobility and urban dynamics, supporting a variety of spatial applications. However, traditional methods often struggle to extract deep semantic representations and incorporate contextual map information. We propose TrajSceneLLM, a multimodal perspective for enhancing semantic understanding of GPS trajectories. The framework integrates visualized map images (encoding spatial context) and textual descriptions generated through LLM reasoning (capturing temporal sequences and movement dynamics). Separate embeddings are generated for each modality and then concatenated to produce trajectory scene embeddings with rich semantic content which are further paired with a simple MLP classifier. We validate the proposed framework on Travel Mode Identification (TMI), a critical task for analyzing travel choices and understanding mobility behavior. Our experiments show that these embeddings achieve significant performance improvement, highlighting the advantage of our LLM-driven method in capturing deep spatio-temporal dependencies and reducing reliance on handcrafted features. This semantic enhancement promises significant potential for diverse downstream applications and future research in geospatial artificial intelligence. The source code and dataset are publicly available at: https://github.com/februarysea/TrajSceneLLM.
From LLM-anation to LLM-orchestrator: Coordinating Small Models for Data Labeling
2025-06-19
Yao Lu, Zhaiyuan Ji, Jiawei Du, Yu Shanqing, Qi Xuan, Tianyi Zhou
Although the annotation paradigm based on Large Language Models (LLMs) has made significant breakthroughs in recent years, its actual deployment still has two core bottlenecks: first, the cost of calling commercial APIs in large-scale annotation is very expensive; second, in scenarios that require fine-grained semantic understanding, such as sentiment classification and toxicity classification, the annotation accuracy of LLMs is even lower than that of Small Language Models (SLMs) dedicated to this field. To address these problems, we propose a new paradigm of multi-model cooperative annotation and design a fully automatic annotation framework AutoAnnotator based on this. Specifically, AutoAnnotator consists of two layers. The upper-level meta-controller layer uses the generation and reasoning capabilities of LLMs to select SLMs for annotation, automatically generate annotation code and verify difficult samples; the lower-level task-specialist layer consists of multiple SLMs that perform annotation through multi-model voting. In addition, we use the difficult samples obtained by the secondary review of the meta-controller layer as the reinforcement learning set and fine-tune the SLMs in stages through a continual learning strategy, thereby improving the generalization of SLMs. Extensive experiments show that AutoAnnotator outperforms existing open-source/API LLMs in zero-shot, one-shot, CoT, and majority voting settings. Notably, AutoAnnotator reduces the annotation cost by 74.15% compared to directly annotating with GPT-3.5-turbo, while still improving the accuracy by 6.21%. Project page: https://github.com/Zhaiyuan-Ji/AutoAnnotator.
Large Language Models in Argument Mining: A Survey
2025-06-19
Hao Li, Viktor Schlegel, Yizheng Sun, Riza Batista-Navarro, Goran Nenadic
Argument Mining (AM), a critical subfield of Natural Language Processing (NLP), focuses on extracting argumentative structures from text. The advent of Large Language Models (LLMs) has profoundly transformed AM, enabling advanced in-context learning, prompt-based generation, and robust cross-domain adaptability. This survey systematically synthesizes recent advancements in LLM-driven AM. We provide a concise review of foundational theories and annotation frameworks, alongside a meticulously curated catalog of datasets. A key contribution is our comprehensive taxonomy of AM subtasks, elucidating how contemporary LLM techniques -- such as prompting, chain-of-thought reasoning, and retrieval augmentation -- have reconfigured their execution. We further detail current LLM architectures and methodologies, critically assess evaluation practices, and delineate pivotal challenges including long-context reasoning, interpretability, and annotation bottlenecks. Conclusively, we highlight emerging trends and propose a forward-looking research agenda for LLM-based computational argumentation, aiming to strategically guide researchers in this rapidly evolving domain.
Can structural correspondences ground real world representational content in Large Language Models?
2025-06-19
Iwan Williams
Large Language Models (LLMs) such as GPT-4 produce compelling responses to a wide range of prompts. But their representational capacities are uncertain. Many LLMs have no direct contact with extra-linguistic reality: their inputs, outputs and training data consist solely of text, raising the questions (1) can LLMs represent anything and (2) if so, what? In this paper, I explore what it would take to answer these questions according to a structural-correspondence based account of representation, and make an initial survey of this evidence. I argue that the mere existence of structural correspondences between LLMs and worldly entities is insufficient to ground representation of those entities. However, if these structural correspondences play an appropriate role - they are exploited in a way that explains successful task performance - then they could ground real world contents. This requires overcoming a challenge: the text-boundedness of LLMs appears, on the face of it, to prevent them engaging in the right sorts of tasks.
Objective: Computational phenotyping is a central informatics activity with resulting cohorts supporting a wide variety of applications. However, it is time-intensive because of manual data review, limited automation, and difficulties in adapting algorithms across sources. Since LLMs have demonstrated promising capabilities for text classification, comprehension, and generation, we posit they will perform well at repetitive manual review tasks traditionally performed by human experts. To support next-generation computational phenotyping methods, we developed SHREC, a framework for comprehensive integration of LLMs into end-to-end phenotyping pipelines. Materials and Methods: We applied and tested the ability of three lightweight LLMs (Gemma2 27 billion, Mistral Small 24 billion, and Phi-4 14 billion) to classify concepts and phenotype patients using previously developed phenotypes for ARF respiratory support therapies. Results: All models performed well on concept classification, with the best model (Mistral) achieving an AUROC of 0.896 across all relevant concepts. For phenotyping, models demonstrated near-perfect specificity for all phenotypes, and the top-performing model (Mistral) reached an average AUROC of 0.853 for single-therapy phenotypes, despite lower performance on multi-therapy phenotypes. Discussion: There are several advantages of LLMs that support their application to computational phenotyping, such as their ability to adapt to new tasks with prompt engineering alone and their ability to incorporate raw EHR data. Future steps to advance next-generation phenotyping methods include determining optimal strategies for integrating biomedical data, exploring how LLMs reason, and advancing generative model methods. Conclusion: Current lightweight LLMs can feasibly assist researchers with resource-intensive phenotyping tasks such as manual data review.
2025-06-19
Julian Amette Estrada, Marc E. Brachet, Pablo D. Mininni
Quantum turbulence shares many similarities with classical turbulence in the isotropic and homogeneous case, despite the inviscid and quantized nature of its vortices. However, when quantum fluids are subjected to rotation, their turbulent dynamics depart significantly from the classical expectations. We explore the phenomenology of rotating quantum turbulence, emphasizing how rotation introduces new regimes with no classical analogs. We review recent theoretical, experimental, and numerical developments, and present new numerical results that map out distinct dynamical regimes arising from the interplay of rotation, quantization, non-linearities, and condensed matter regimes. In particular, we show the importance of distinguishing the dynamics of rotating quantum fluids in the slowly rotating, rapidly rotating, and low Landau level regimes. The findings have implications for the dynamics of liquid helium, atomic Bose-Einstein condensates, and neutron stars, and show how rotating quantum fluids can serve as a unique platform bridging turbulence theory and condensed matter physics revealing novel states of out-of-equilibrium quantum matter.
High-energy SETI pushes astrobiology to its limits, testing the most fundamental needs of life and the most extreme limits of technology. It has lagged behind the rest of the field, but the increased respectability of SETI could spark interest in the coming years. This white paper reviews the case for SETI in X-rays, gamma rays, and neutrinos, including rationales, challenges, and possible technosignatures, and suggests future strategies for observational work.
Generalizability of Media Frames: Corpus creation and analysis across countries
2025-06-19
Agnese Daffara, Sourabh Dattawad, Sebastian Padó, Tanise Ceron
Frames capture aspects of an issue that are emphasized in a debate by interlocutors and can help us understand how political language conveys different perspectives and ultimately shapes people's opinions. The Media Frame Corpus (MFC) is the most commonly used framework with categories and detailed guidelines for operationalizing frames. It is, however, focused on a few salient U.S. news issues, making it unclear how well these frames can capture news issues in other cultural contexts. To explore this, we introduce FrameNews-PT, a dataset of Brazilian Portuguese news articles covering political and economic news and annotate it within the MFC framework. Through several annotation rounds, we evaluate the extent to which MFC frames generalize to the Brazilian debate issues. We further evaluate how fine-tuned and zero-shot models perform on out-of-domain data. Results show that the 15 MFC frames remain broadly applicable with minor revisions of the guidelines. However, some MFC frames are rarely used, and novel news issues are analyzed using general 'fall-back' frames. We conclude that cross-cultural frame use requires careful consideration.
Sharpening Kubernetes Audit Logs with Context Awareness
2025-06-19
Matteo Franzil, Valentino Armani, Luis Augusto Dias Knob, Domenico Siracusa
Kubernetes has emerged as the de facto orchestrator of microservices, providing scalability and extensibility to a highly dynamic environment. It builds an intricate and deeply connected system that requires extensive monitoring capabilities to be properly managed. To this account, K8s natively offers audit logs, a powerful feature for tracking API interactions in the cluster. Audit logs provide a detailed and chronological record of all activities in the system. Unfortunately, K8s auditing suffers from several practical limitations: it generates large volumes of data continuously, as all components within the cluster interact and respond to user actions. Moreover, each action can trigger a cascade of secondary events dispersed across the log, with little to no explicit linkage, making it difficult to reconstruct the context behind user-initiated operations. In this paper, we introduce K8NTEXT, a novel approach for streamlining K8s audit logs by reconstructing contexts, i.e., grouping actions performed by actors on the cluster with the subsequent events these actions cause. Correlated API calls are automatically identified, labeled, and consistently grouped using a combination of inference rules and a Machine Learning model, largely simplifying data consumption. We evaluate K8NTEXT's performance, scalability, and expressiveness both in systematic tests and with a series of use cases. We show that it consistently provides accurate context reconstruction, even for complex operations involving 50, 100 or more correlated actions, achieving over 95 percent accuracy across the entire spectrum, from simple to highly composite actions.
Constraining parameters of spinor field dark energy: An alternative to $Λ$CDM model under the spherically symmetric FLRW space-time
2025-06-19
Bijan Saha, Mahendra Goray
This study constrains the cosmological parameters within the scope of a spherically symmetric FLRW cosmological model, the role of a nonlinear spinor field in the universe's evolution. To test this approach, we incorporate the recent Cosmic chronometers, Supernova, and Sloan Digital Sky Survey data. It is found that if spherical coordinates give the FLRW model, the energy-momentum tensor (EMT) of the spinor field possesses nontrivial non-diagonal components. These non-diagonal components of EMT neither depend on the spinor field nonlinearity nor the value of the parameter $k$ defining the type of curvature of the FLRW model. In this context, we construct a dark energy model and perform an MCMC simulation to obtain the best-fit values of the parameters. The results are well comparable to the present Hubble parameter and deceleration parameter, indicating the accelerated expansion of the universe.
Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation
2025-06-19
Carmelo Scribano, Elena Govi, Paolo Bertellini, Simone Parisi, Giorgia Franchini, Marko Bertogna
Accurate mapping of agricultural field boundaries is essential for the efficient operation of agriculture. Automatic extraction from high-resolution satellite imagery, supported by computer vision techniques, can avoid costly ground surveys. In this paper, we present a pipeline for field delineation based on the Segment Anything Model (SAM), introducing a fine-tuning strategy to adapt SAM to this task. In addition to using published datasets, we describe a method for acquiring a complementary regional dataset that covers areas beyond current sources. Extensive experiments assess segmentation accuracy and evaluate the generalization capabilities. Our approach provides a robust baseline for automated field delineation. The new regional dataset, known as ERAS, is now publicly available.
Investigating Transit Timing Variations in the Ultra-short Period Exoplanet WASP-19b
2025-06-19
Shraddha Biswas, Ing-Guey Jiang, Li-Chin Yeh, Hsin-Min Liu, Kaviya Parthasarathy, Devesh P. Sariya, D. Bisht, Mohit Singh Bisht, A. Raj
In this study, we present a comprehensive analysis of transit timing variations (TTVs) in the ultra-short-period gas giant WASP-19b, which orbits a G-type main-sequence star. Our analysis is based on a dataset comprising 204 transit light curves obtained from the Transiting Exoplanet Survey Satellite (TESS), the Exoplanet Transit Database (ETD), and the ExoClock project, supplemented by 18 publicly available light curves. Mid-transit times were extracted from these data, and an additional 98 mid-transit times compiled from the literature were incorporated, resulting in a combined dataset spanning approximately 14 years. After excluding light curves significantly impacted by stellar activity, such as starspot anomalies, the final dataset consisted of 252 high-quality mid-transit times. Initial inspection of the transit timing residuals using an apsidal precession model suggested the possible presence of an additional planetary companion. However, subsequent frequency analysis and sinusoidal model fitting indicate that the observed TTVs are more consistently explained by apsidal precession of WASP-19b's orbit. We also considered alternative mechanisms, including the Applegate mechanism and the Shklovskii effect. Our findings suggest that stellar magnetic activity, potentially linked to the Applegate mechanism, may also contribute to the observed timing variations. To further constrain the origin of the TTVs and assess the contributions of these mechanisms, continued high-precision photometric monitoring of the WASP-19 system is strongly recommended.
Landslide monitoring is essential for understanding geohazards and mitigating associated risks. However, existing point cloud-based methods typically rely on either geometric or radiometric information and often yield sparse or non-3D displacement estimates. In this paper, we propose a hierarchical partition-based coarse-to-fine approach that fuses 3D point clouds and co-registered RGB images to estimate dense 3D displacement vector fields. We construct patch-level matches using both 3D geometry and 2D image features. These matches are refined via geometric consistency checks, followed by rigid transformation estimation per match. Experimental results on two real-world landslide datasets demonstrate that our method produces 3D displacement estimates with high spatial coverage (79% and 97%) and high accuracy. Deviations in displacement magnitude with respect to external measurements (total station or GNSS observations) are 0.15 m and 0.25 m on the two datasets, respectively, and only 0.07 m and 0.20 m compared to manually derived references. These values are below the average scan resolutions (0.08 m and 0.30 m). Our method outperforms the state-of-the-art method F2S3 in spatial coverage while maintaining comparable accuracy. Our approach offers a practical and adaptable solution for TLS-based landslide monitoring and is extensible to other types of point clouds and monitoring tasks. Our example data and source code are publicly available at https://github.com/zhaoyiww/fusion4landslide.
Neural rendering methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have achieved significant progress in photorealistic 3D scene reconstruction and novel view synthesis. However, most existing models assume clean and high-resolution (HR) multi-view inputs, which limits their robustness under real-world degradations such as noise, blur, low-resolution (LR), and weather-induced artifacts. To address these limitations, the emerging field of 3D Low-Level Vision (3D LLV) extends classical 2D Low-Level Vision tasks including super-resolution (SR), deblurring, weather degradation removal, restoration, and enhancement into the 3D spatial domain. This survey, referred to as R\textsuperscript{3}eVision, provides a comprehensive overview of robust rendering, restoration, and enhancement for 3D LLV by formalizing the degradation-aware rendering problem and identifying key challenges related to spatio-temporal consistency and ill-posed optimization. Recent methods that integrate LLV into neural rendering frameworks are categorized to illustrate how they enable high-fidelity 3D reconstruction under adverse conditions. Application domains such as autonomous driving, AR/VR, and robotics are also discussed, where reliable 3D perception from degraded inputs is critical. By reviewing representative methods, datasets, and evaluation protocols, this work positions 3D LLV as a fundamental direction for robust 3D content generation and scene-level reconstruction in real-world environments.
Cosmic-ray and Interstellar Gas Properties in the Solar Neighborhood Revealed by Diffuse Gamma Rays
2025-06-19
Tsunefumi Mizuno, Katsuhiro Hayashi, Hinako Ochi, Igor V. Moskalenko, Elena Orlando, Andrew W. Strong
To investigate the interstellar medium (ISM) and Galactic cosmic rays (CRs) in the solar neighborhood, we analyzed ${\gamma}$-ray data by Fermi Large Area Telescope (LAT) for five nearby molecular cloud regions. Our data includes the MBM/Pegasus region (MBM~53, 54, 55 clouds and Pegasus loop), R CrA region (R Coronae Australis clouds), Chamaeleon region (Chamaeleon clouds), Cep/Pol region (Cepheus and Polaris flare), and Orion region (Orion clouds). The ISM templates are constructed by a component decomposition of the 21~cm {\HI} line, the Planck dust emission model, and the carbon monoxide (CO) 2.6~mm line. Through $\gamma$-ray data analysis the ISM gas is successfully decomposed into non-local {\HI}, narrow-line and optically thick {\HI}, broad-line and optically thin {\HI}, CO-bright {\Htwo}, and CO-dark {\Htwo} for all five regions. CR intensities evaluated by the ${\gamma}$-ray emissivity of broad {\HI} agree well with a model based on directly-measured CR spectra at the Earth, with a gradient giving a higher CR intensity toward the inner Galaxy at the 10\% level in ${\sim}$ 500~pc. The ratio of CO-dark {\Htwo} to CO-bright {\Htwo} anti-correlates with the {\Htwo} mass traced by the CO 2.6~mm line, and reaches 5--10 for small systems of ${\sim}$1000 solar mass.
Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation
2025-06-19
Chenrui Ma, Zechang Sun, Tao Jing, Zheng Cai, Yuan-Sen Ting, Song Huang, Mingyu Li
Observational astronomy relies on visual feature identification to detect critical astrophysical phenomena. While machine learning (ML) increasingly automates this process, models often struggle with generalization in large-scale surveys due to the limited representativeness of labeled datasets -- whether from simulations or human annotation -- a challenge pronounced for rare yet scientifically valuable objects. To address this, we propose a conditional diffusion model to synthesize realistic galaxy images for augmenting ML training data. Leveraging the Galaxy Zoo 2 dataset which contains visual feature -- galaxy image pairs from volunteer annotation, we demonstrate that our model generates diverse, high-fidelity galaxy images closely adhere to the specified morphological feature conditions. Moreover, this model enables generative extrapolation to project well-annotated data into unseen domains and advancing rare object detection. Integrating synthesized images into ML pipelines improves performance in standard morphology classification, boosting completeness and purity by up to 30\% across key metrics. For rare object detection, using early-type galaxies with prominent dust lane features ( $\sim$0.1\% in GZ2 dataset) as a test case, our approach doubled the number of detected instances from 352 to 872, compared to previous studies based on visual inspection. This study highlights the power of generative models to bridge gaps between scarce labeled data and the vast, uncharted parameter space of observational astronomy and sheds insight for future astrophysical foundation model developments. Our project homepage is available at https://galaxysd-webpage.streamlit.app/.
AI labeling reduces the perceived accuracy of online content but has limited broader effects
2025-06-19
Chuyao Wang, Patrick Sturgis, Daniel de Kadt
Explicit labeling of online content produced by artificial intelligence (AI) is a widely mooted policy for ensuring transparency and promoting public confidence. Yet little is known about the scope of AI labeling effects on public assessments of labeled content. We contribute new evidence on this question from a survey experiment using a high-quality nationally representative probability sample (n = 3,861). First, we demonstrate that explicit AI labeling of a news article about a proposed public policy reduces its perceived accuracy. Second, we test whether there are spillover effects in terms of policy interest, policy support, and general concerns about online misinformation. We find that AI labeling reduces interest in the policy, but neither influences support for the policy nor triggers general concerns about online misinformation. We further find that increasing the salience of AI use reduces the negative impact of AI labeling on perceived accuracy, while one-sided versus two-sided framing of the policy has no moderating effect. Overall, our findings suggest that the effects of algorithm aversion induced by AI labeling of online content are limited in scope.
Web(er) of Hate: A Survey on How Hate Speech Is Typed
2025-06-19
Luna Wang, Andrew Caines, Alice Hutchings
The curation of hate speech datasets involves complex design decisions that balance competing priorities. This paper critically examines these methodological choices in a diverse range of datasets, highlighting common themes and practices, and their implications for dataset reliability. Drawing on Max Weber's notion of ideal types, we argue for a reflexive approach in dataset creation, urging researchers to acknowledge their own value judgments during dataset construction, fostering transparency and methodological rigour.
Vibrational properties of epitaxial graphene buffer layer on silicon carbide
2025-06-19
Guillaume Radtke, Michele Lazzeri
The vibrational properties of semiconducting graphene buffer layer epitaxially grown on hexagonal silicon carbide are determined using first-principles calculations on a realistic structural model. Despite the important chemical and structural disorder associated with the partial covalent bonding with the substrate, the buffer-layer carbon atoms still display quasidispersive phonons mimicking those of graphene. The related frequency softening and broadening provide a natural interpretation of the measured Raman signal. The vibrations determining thermal conduction are found to delocalize completely on the SiC substrate, leading to an effective spatial separation between material components determining, respectively, electronic and thermal transport properties. This situation opens perspectives for thermoelectric applications.
The Carroll group arises in the vanishing speed of light limit of the Poincar\'{e} group and was initially discarded as just a mathematical curiosity. However, recent developments have proved otherwise. Carroll and conformal Carroll symmetries are now ubiquitous, appearing in diverse physical phenomena starting from condensed matter physics to quantum gravity. This review aims to provide the reader a gateway into this fast-developing field. After an introduction and setting the stage with basics of the symmetry in question, we detail the construction of Carrollian and Carrollian Conformal field theories (CCFT). We then focus on applications. By far the most popular of these applications is in the context of the construction of holography in asymptotically flat spacetimes (AFS) in terms of a co-dimension one dual CCFT. We review the early work on AFS$_3$ /CCFT$_2$ before delving into an in-depth analysis for the construction of the dual to 4D AFS. Two other important sets of applications are in hydrodynamics and in condensed matter physics, which we discuss in detail. Carroll hydrodynamics is introduced as the $c\to 0$ limit of relativistic hydrodynamics first and then reconstructed from a symmetry based approach. Relations to ultrarelativistic flows and connections to the quark-gluon plasma are discussed with concrete examples of the Bjorken and Gubser flow models. In condensed matter applications, we cover connections to fractons, flat bands, and phase separation in Luttinger liquid models. To conclude, we give very brief outlines of other topics of interest including string theory and black hole horizons.
The Evolutionary Map of the Universe (EMU) survey with ASKAP is transforming our understanding of radio galaxies, AGN duty cycles, and cosmic structure. EMUCAT efficiently identifies compact radio sources, yet struggles with extended objects, requiring alternative approaches. The Radio Galaxy Zoo: EMU (RGZ EMU) project proposes a general framework that combines citizen science and machine learning to identify around 4 million extended sources in EMU. This framework is expected to enhance the EMUCAT cataloging on extended sources and can be further empowered with the introduction of cross-matched external data from surveys such as POSSUM and WALLABY.
CNN-Derived Elemental Abundances of LAMOST DR10 Giants: Implications for Galactic Substructures
2025-06-19
Haoyang Liu, Cuihua Du, Mingji Deng, Jian Zhang
Stellar parameters and abundances provide crucial insights into stellar and Galactic evolution studies. In this work, we developed a convolutional neural network (CNN) to estimate stellar parameters: effective temperature ($T_{\text{eff}}$), surface gravity (log $g$) and metallicity (both [Fe/H] and [M/H]) as well as six $\alpha$-elements (C, N, O, Mg, Si, Ca) and [$\alpha$/M]. We selected giant stars with \( 3500 \, \text{K} < T_{\text{eff}} < 5500 \, \text{K} \) and \( 0 \, \text{dex} < \log g < 3.6 \, \text{dex} \) from the LAMOST and APOGEE surveys, while requiring (S/N)$_g$ of the LAMOST low-resolution spectra $>$ 10, which leaves 1,100,858 giant stars. The spectral from LAMOST and the labels from APOGEE for 62,511 common stars were used as our training set. The corresponding test set yields scatters 50 K, 0.06 dex and 0.13 dex for $T_{\text{eff}}$, [Fe/H] and log $g$, respectively. For $\alpha$ elements O, Mg, Si and Ca, the scatters are 0.05 dex, 0.04 dex, 0.03 and 0.04 dex, respectively. For C and N elements, the scatters are 0.07 dex and 0.05 dex. For [$\alpha$/M] and [M/H], the scatters are 0.03 dex and 0.06 dex. The mean absolute error (MAE) of most elements are between 0.02 $-$ 0.04 dex. The predicted abundances were cross-matched with previously identified substructures PG1 and PG2, with their origins subsequently analyzed. Finally, the catalog is available at https://nadc.china-vo.org/res/r101529/.
From 600 Tools to 1 Console: A UX-Driven Transformation
2025-06-19
Mariann Kornelia Smith, Jacqueline Meijer-Irons, Andrew Millar
In 2021 the Technical Infrastructure (TI) User Experience (UX) team sent a survey to 10,000 Google Developers (Googlers) and uncovered that Google's internal infrastructure tools were fragmented and inefficient, hindering developers' productivity. Using user centered research and design methodologies the team first created a story map and service blueprint to visualize the relationship between internal applications, then formulated a strategic vision to consolidate tools, streamline workflows, and measure the impact of their work. We secured executive buy-in and delivered incremental improvements.
Advancing atomic electron tomography with neural networks
2025-06-19
Juhyeok Lee, Yongsoo Yang
Accurate determination of three-dimensional (3D) atomic structures is crucial for understanding and controlling the properties of nanomaterials. Atomic electron tomography (AET) offers non-destructive atomic imaging with picometer-level precision, enabling the resolution of defects, interfaces, and strain fields in 3D, as well as the observation of dynamic structural evolution. However, reconstruction artifacts arising from geometric limitations and electron dose constraints can hinder reliable atomic structure determination. Recent progress has integrated deep learning, especially convolutional neural networks, into AET workflows to improve reconstruction fidelity. This review highlights recent advances in neural network-assisted AET, emphasizing its role in overcoming persistent challenges in 3D atomic imaging. By significantly enhancing the accuracy of both surface and bulk structural characterization, these methods are advancing the frontiers of nanoscience and enabling new opportunities in materials research and technology.
Regression Testing Optimization for ROS-based Autonomous Systems: A Comprehensive Review of Techniques
2025-06-19
Yupeng Jiang, Shuaiyi Sun, Xi Zheng
Regression testing plays a critical role in maintaining software reliability, particularly for ROS-based autonomous systems (ROSAS), which frequently undergo continuous integration and iterative development. However, conventional regression testing techniques face significant challenges when applied to autonomous systems due to their dynamic and non-deterministic behaviors, complex multi-modal sensor data, asynchronous distributed architectures, and stringent safety and real-time constraints. Although numerous studies have explored test optimization in traditional software contexts, regression testing optimization specifically for ROSAS remains largely unexplored. To address this gap, we present the first comprehensive survey systematically reviewing regression testing optimization techniques tailored for ROSAS. We analyze and categorize 122 representative studies into regression test case prioritization, minimization, and selection methods. A structured taxonomy is introduced to clearly illustrate their applicability and limitations within ROSAS contexts. Furthermore, we highlight major challenges specific to regression testing for ROSAS, including effectively prioritizing tests in response to frequent system modifications, efficiently minimizing redundant tests, and difficulty in accurately selecting impacted test cases. Finally, we propose research insights and identify promising future directions, such as leveraging frame-to-vector coverage metrics, multi-source foundation models, and neurosymbolic reasoning to enhance regression testing efficiency and effectiveness. This survey provides a foundational reference and practical roadmap for advancing the state-of-the-art in regression testing optimization for ROSAS.
Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods
2025-06-19
Aina Zhang, Xiaoming Kong, Bowen Liu, Nan Li, Yude Bu, Zhenping Yi, Meng Liu
The formation and evolution of ring structures in galaxies are crucial for understanding the nature and distribution of dark matter, galactic interactions, and the internal secular evolution of galaxies. However, the limited number of existing ring galaxy catalogs has constrained deeper exploration in this field. To address this gap, we introduce a two-stage binary classification model based on the Swin Transformer architecture to identify ring galaxies from the DESI Legacy Imaging Surveys. This model first selects potential candidates and then refines them in a second stage to improve classification accuracy. During model training, we investigated the impact of imbalanced datasets on the performance of the two-stage model. We experimented with various model combinations applied to the datasets of the DESI Legacy Imaging Surveys DR9, processing a total of 573,668 images with redshifts ranging from z_spec = 0.01-0.20 and magr <17.5. After applying the two-stage filtering and conducting visual inspections, the overall Precision of the models exceeded 64.87%, successfully identifying a total of 8052 newly discovered ring galaxies. With our catalog, the forthcoming spectroscopic data from DESI will facilitate a more comprehensive investigation into the formation and evolution of ring galaxies.
Towards AI-Driven RANs for 6G and Beyond: Architectural Advancements and Future Horizons
2025-06-19
Mathushaharan Rathakrishnan, Samiru Gayan, Rohit Singh, Amandeep Kaur, Hazer Inaltekin, Sampath Edirisinghe, H. Vincent Poor
It is envisioned that 6G networks will be supported by key architectural principles, including intelligence, decentralization, interoperability, and digitalization. With the advances in artificial intelligence (AI) and machine learning (ML), embedding intelligence into the foundation of wireless communication systems is recognized as essential for 6G and beyond. Existing radio access network (RAN) architectures struggle to meet the ever growing demands for flexibility, automation, and adaptability required to build self-evolving and autonomous wireless networks. In this context, this paper explores the transition towards AI-driven RAN (AI-RAN) by developing a novel AI-RAN framework whose performance is evaluated through a practical scenario focused on intelligent orchestration and resource optimization. Besides, the paper reviews the evolution of RAN architectures and sheds light on key enablers of AI-RAN including digital twins (DTs), intelligent reflecting surfaces (IRSs), large generative AI (GenAI) models, and blockchain (BC). Furthermore, it discusses the deployment challenges of AI-RAN, including technical and regulatory perspectives, and outlines future research directions incorporating technologies such as integrated sensing and communication (ISAC) and agentic AI.
Human-Centered Shared Autonomy for Motor Planning, Learning, and Control Applications
2025-06-19
MH Farhadi, Ali Rabiee, Sima Ghafoori, Anna Cetera, Wei Xu, Reza Abiri
With recent advancements in AI and computational tools, intelligent paradigms have emerged to enhance fields like shared autonomy and human-machine teaming in healthcare. Advanced AI algorithms (e.g., reinforcement learning) can autonomously make decisions to achieve planning and motion goals. However, in healthcare, where human intent is crucial, fully independent machine decisions may not be ideal. This chapter presents a comprehensive review of human-centered shared autonomy AI frameworks, focusing on upper limb biosignal-based machine interfaces and associated motor control systems, including computer cursors, robotic arms, and planar platforms. We examine motor planning, learning (rehabilitation), and control, covering conceptual foundations of human-machine teaming in reach-and-grasp tasks and analyzing both theoretical and practical implementations. Each section explores how human and machine inputs can be blended for shared autonomy in healthcare applications. Topics include human factors, biosignal processing for intent detection, shared autonomy in brain-computer interfaces (BCI), rehabilitation, assistive robotics, and Large Language Models (LLMs) as the next frontier. We propose adaptive shared autonomy AI as a high-performance paradigm for collaborative human-AI systems, identify key implementation challenges, and outline future directions, particularly regarding AI reasoning agents. This analysis aims to bridge neuroscientific insights with robotics to create more intuitive, effective, and ethical human-machine teaming frameworks.
The Modeling Landscape of Extragalactic CO in CMB Surveys
2025-06-19
Yogesh Mehta, Anirban Roy, Simon Foreman, Alexander van Engelen, Nick Battaglia
Extragalactic carbon monoxide (CO) line emission will likely be an important signal in current and future Cosmic Microwave Background (CMB) surveys on small scales. However, great uncertainty surrounds our current understanding of CO emission. We investigate the implications of this modeling uncertainty on CMB surveys. Using a range of star formation rate and luminosity relations, we generate a suite of CO simulations across cosmic time, together with the broadband cosmic infrared background (CIB). From these, we quantify the power spectrum signatures of CO that we would observe in a CMB experiment at 90, 150, and 220 GHz. We find that the resulting range of CO auto-spectra spans up to two orders of magnitude and that while CO on its own is unlikely to be detectable in current CMB experiments, its cross-correlation with the CIB will be a significant CMB foreground in future surveys. We then forecast the bias on CMB foregrounds that would result if CO were neglected in a CMB power spectrum analysis, finding shifts that can be comparable to some of the uncertainties on CMB foreground constraints from recent surveys, particularly for the thermal and kinetic Sunyaev-Zel'dovich effects and radio sources, and many times greater than the expected uncertainties expected for future data. Finally, we assess how the broad range of multifrequency CO$\times$CIB spectra we obtain is captured by a reduced parameter set by performing a principal component analysis, finding that three amplitude parameters suffice for a CMB-S4-like survey. Our results demonstrate the importance for future CMB experiments to account for a wide range of CO modeling, and that high-precision CMB experiments may help constrain extragalactic CO models.
Bridging Brain with Foundation Models through Self-Supervised Learning
2025-06-19
Hamdi Altaheri, Fakhri Karray, Md. Milon Islam, S M Taslim Uddin Raju, Amir-Hossein Karimi
Foundation models (FMs), powered by self-supervised learning (SSL), have redefined the capabilities of artificial intelligence, demonstrating exceptional performance in domains like natural language processing and computer vision. These advances present a transformative opportunity for brain signal analysis. Unlike traditional supervised learning, which is limited by the scarcity of labeled neural data, SSL offers a promising solution by enabling models to learn meaningful representations from unlabeled data. This is particularly valuable in addressing the unique challenges of brain signals, including high noise levels, inter-subject variability, and low signal-to-noise ratios. This survey systematically reviews the emerging field of bridging brain signals with foundation models through the innovative application of SSL. It explores key SSL techniques, the development of brain-specific foundation models, their adaptation to downstream tasks, and the integration of brain signals with other modalities in multimodal SSL frameworks. The review also covers commonly used evaluation metrics and benchmark datasets that support comparative analysis. Finally, it highlights key challenges and outlines future research directions. This work aims to provide researchers with a structured understanding of this rapidly evolving field and a roadmap for developing generalizable brain foundation models powered by self-supervision.
DIGMAPPER: A Modular System for Automated Geologic Map Digitization
2025-06-19
Weiwei Duan, Michael P. Gerlek, Steven N. Minton, Craig A. Knoblock, Fandel Lin, Theresa Chen, Leeje Jang, Sofia Kirsanova, Zekun Li, Yijun Lin, Yao-Yi Chiang
Historical geologic maps contain rich geospatial information, such as rock units, faults, folds, and bedding planes, that is critical for assessing mineral resources essential to renewable energy, electric vehicles, and national security. However, digitizing maps remains a labor-intensive and time-consuming task. We present DIGMAPPER, a modular, scalable system developed in collaboration with the United States Geological Survey (USGS) to automate the digitization of geologic maps. DIGMAPPER features a fully dockerized, workflow-orchestrated architecture that integrates state-of-the-art deep learning models for map layout analysis, feature extraction, and georeferencing. To overcome challenges such as limited training data and complex visual content, our system employs innovative techniques, including in-context learning with large language models, synthetic data generation, and transformer-based models. Evaluations on over 100 annotated maps from the DARPA-USGS dataset demonstrate high accuracy across polygon, line, and point feature extraction, and reliable georeferencing performance. Deployed at USGS, DIGMAPPER significantly accelerates the creation of analysis-ready geospatial datasets, supporting national-scale critical mineral assessments and broader geoscientific applications.
$La_3Pd_2NaO_9$: A High-Valent Insulating Palladate
2025-06-19
Qingqing Yang, Ning Guo, Tieyan Chang, Zheng Guo, Xiaoli Wang, Chuanyan Fan, Chao Liu, Lu Han, Feiyu Li, Tao He, Qiang Zheng, Yu-Sheng Chen, Junjie Zhang
A high-valent palladate, $La_3Pd_2NaO_9$, has been synthesized for the first time. Single crystals with dimensions of 20 ${\mu}$m on edge were successfully grown using the flux method at 420 $^o$C and 70 bar oxygen pressure. Energy dispersive spectroscopy (EDS) and inductively coupled plasma mass spectroscopy (ICP) measurements show that the atomic ratio of La: (Pd+Na) is 3: 3 and Pd: Na is 2: 1. X-ray photoelectron spectroscopy (XPS) measurements show that the oxidation state of Pd is dominated by +4. Synchrotron X-ray single-crystal diffraction measurements revealed that this material crystallizes in the monoclinic $P2_1/c$ space group with charge ordering of Na and Pd. Real-space imaging via scanning transmission electron microscopy (STEM) confirmed the crystal structure and revealed excellent sample homogeneity. Electrical resistivity measurements show an insulating behavior. Magnetic measurements show an unexpected paramagnetic behavior, which probably originate from a small fraction of high-spin Pd$^{2+}$ evidenced by XPS. The successful growth of $La_3Pd_2NaO_9$ single crystals with a high-valent oxidation state of Pd offers an approach for exploring interesting palladates, including potential bilayer Ruddlesden-Popper palladates analogous to the high temperature superconducting $La_3Ni_2O_7$.
Exploring Big Five Personality and AI Capability Effects in LLM-Simulated Negotiation Dialogues
2025-06-19
Myke C. Cohen, Zhe Su, Hsien-Te Kao, Daniel Nguyen, Spencer Lynch, Maarten Sap, Svitlana Volkova
This paper presents an evaluation framework for agentic AI systems in mission-critical negotiation contexts, addressing the need for AI agents that can adapt to diverse human operators and stakeholders. Using Sotopia as a simulation testbed, we present two experiments that systematically evaluated how personality traits and AI agent characteristics influence LLM-simulated social negotiation outcomes--a capability essential for a variety of applications involving cross-team coordination and civil-military interactions. Experiment 1 employs causal discovery methods to measure how personality traits impact price bargaining negotiations, through which we found that Agreeableness and Extraversion significantly affect believability, goal achievement, and knowledge acquisition outcomes. Sociocognitive lexical measures extracted from team communications detected fine-grained differences in agents' empathic communication, moral foundations, and opinion patterns, providing actionable insights for agentic AI systems that must operate reliably in high-stakes operational scenarios. Experiment 2 evaluates human-AI job negotiations by manipulating both simulated human personality and AI system characteristics, specifically transparency, competence, adaptability, demonstrating how AI agent trustworthiness impact mission effectiveness. These findings establish a repeatable evaluation methodology for experimenting with AI agent reliability across diverse operator personalities and human-agent team dynamics, directly supporting operational requirements for reliable AI systems. Our work advances the evaluation of agentic AI workflows by moving beyond standard performance metrics to incorporate social dynamics essential for mission success in complex operations.
High resolution radio observations of the Chamaeleon star-forming region
2025-06-19
Ernesto García Valencia, Laurent Loinard, Arnaud Belloche, Gisela N. Ortiz León, Sergio A. Dzib, Cherie K. Day, Roopesh Ojha
We report on large-scale radio observations of the Chamaeleon star-forming region obtained with the Australia Telescope Compact Array (ATCA) that led to the definite detection of five young stars and the tentative detection of five more. As in other regions surveyed in the radio domain, the majority of detected sources are fairly evolved low-mass T Tauri stars, but we also detect one protostellar object (Ced 110 IRS4) and one Herbig Ae/Be star. With the exception of the protostellar source, the radio emission mechanism is likely of non-thermal origin. The three brightest radio stars identified with ATCA were subsequently observed with the Australian Long Baseline Array (LBA) and one, J11061540-7721567 (Ced 110 IRS2), was detected at three epochs. This confirms the non-thermal nature of the radio emission in that specific case, and enabled accurate radio position measurements. Comparison with predictions from Gaia DR3 strongly suggests that this star is a binary system with an orbital period of order 40 years; additional LBA observations in the next decades would enable accurate determinations of the individual stellar masses in that system.
AI is the Strategy: From Agentic AI to Autonomous Business Models onto Strategy in the Age of AI
2025-06-19
René Bohnsack, Mickie de Wet
This article develops the concept of Autonomous Business Models (ABMs) as a distinct managerial and strategic logic in the age of agentic AI. While most firms still operate within human-driven or AI-augmented models, we argue that we are now entering a phase where agentic AI (systems capable of initiating, coordinating, and adapting actions autonomously) can increasingly execute the core mechanisms of value creation, delivery, and capture. This shift reframes AI not as a tool to support strategy, but as the strategy itself. Using two illustrative cases, getswan.ai, an Israeli startup pursuing autonomy by design, and a hypothetical reconfiguration of Ryanair as an AI-driven incumbent, we depict the evolution from augmented to autonomous business models. We show how ABMs reshape competitive advantage through agentic execution, continuous adaptation, and the gradual offloading of human decision-making. This transition introduces new forms of competition between AI-led firms, which we term synthetic competition, where strategic interactions occur at rapid, machine-level speed and scale. It also challenges foundational assumptions in strategy, organizational design, and governance. By positioning agentic AI as the central actor in business model execution, the article invites us to rethink strategic management in an era where firms increasingly run themselves.
Candidate Dark Galaxy-2: Validation and Analysis of an Almost Dark Galaxy in the Perseus Cluster
2025-06-18
Dayi Li, Qing Liu, Gwendolyn Eadie, Roberto Abraham, Francine Marleau, William Harris, Pieter van Dokkum, Aaron Romanowsky, Shany Danieli, Patrick Brown, Alex Stringer
Candidate Dark Galaxy-2 (CDG-2) is a potential dark galaxy consisting of four globular clusters (GCs) in the Perseus cluster, first identified in Li et al. (2025) through a sophisticated statistical method. The method searched for over-densities of GCs from a \textit{Hubble Space Telescope} (\textit{HST}) survey targeting Perseus. Using the same \textit{HST} images and the new imaging data from the \textit{Euclid} survey, we report the detection of extremely faint but significant diffuse emission around the four GCs of CDG-2. We thus have exceptionally strong evidence that CDG-2 is a galaxy. This is the first galaxy detected purely through its GC population. Under the conservative assumption that the four GCs make up the entire GC population, preliminary analysis shows that CDG-2 has a total luminosity of $L_{V, \mathrm{gal}}= 6.2\pm{3.0} \times 10^6 L_{\odot}$ and a minimum GC luminosity of $L_{V, \mathrm{GC}}= 1.03\pm{0.2}\times 10^6 L_{\odot}$. Our results indicate that CDG-2 is one of the faintest galaxies having associated GCs, while at least $\sim 16.6\%$ of its light is contained in its GC population. This ratio is likely to be much higher ($\sim 33\%$) if CDG-2 has a canonical GC luminosity function (GCLF). In addition, if the previously observed GC-to-halo mass relations apply to CDG-2, it would have a minimum dark matter halo mass fraction of $99.94\%$ to $99.98\%$. If it has a canonical GCLF, then the dark matter halo mass fraction is $\gtrsim 99.99\%$. Therefore, CDG-2 may be the most GC dominated galaxy and potentially one of the most dark matter dominated galaxies ever discovered.
In recent decades, it has been emphasized that the evolving structure of networks may be shaped by interaction principles that yield sparse graphs with a vertex degree distribution exhibiting an algebraic tail, and other structural traits that are not featured in traditional random graphs. In this respect, through a mean-field approach, this review tackles the statistical physics of graph models based on the interaction principle of duplication-divergence. Additional sophistications extending the duplication-divergence model are also reviewed as well as generalizations of other known models. Possible research gaps and related prior results are then discussed.
The AI Policy Module: Developing Computer Science Student Competency in AI Ethics and Policy
2025-06-18
James Weichert, Daniel Dunlap, Mohammed Farghally, Hoda Eldardiry
As artificial intelligence (AI) further embeds itself into many settings across personal and professional contexts, increasing attention must be paid not only to AI ethics, but also to the governance and regulation of AI technologies through AI policy. However, the prevailing post-secondary computing curriculum is currently ill-equipped to prepare future AI practitioners to confront increasing demands to implement abstract ethical principles and normative policy preferences into the design and development of AI systems. We believe that familiarity with the 'AI policy landscape' and the ability to translate ethical principles to practices will in the future constitute an important responsibility for even the most technically-focused AI engineers. Toward preparing current computer science (CS) students for these new expectations, we developed an AI Policy Module to introduce discussions of AI policy into the CS curriculum. Building on a successful pilot in fall 2024, in this innovative practice full paper we present an updated and expanded version of the module, including a technical assignment on "AI regulation". We present the findings from our pilot of the AI Policy Module 2.0, evaluating student attitudes towards AI ethics and policy through pre- and post-module surveys. Following the module, students reported increased concern about the ethical impacts of AI technologies while also expressing greater confidence in their abilities to engage in discussions about AI regulation. Finally, we highlight the AI Regulation Assignment as an effective and engaging tool for exploring the limits of AI alignment and emphasizing the role of 'policy' in addressing ethical challenges.
This is a survey paper that discusses the original bounds of the seminal papers by Chernoff and Hoeffding. Moreover, it includes a variety of derivative bounds in a variety of forms. Complete proofs are provided as needed. The intent is to provide a repository of reference bounds for the interested researcher.
From Model to Classroom: Evaluating Generated MCQs for Portuguese with Narrative and Difficulty Concerns
2025-06-18
Bernardo Leite, Henrique Lopes Cardoso, Pedro Pinto, Abel Ferreira, Luís Abreu, Isabel Rangel, Sandra Monteiro
While MCQs are valuable for learning and evaluation, manually creating them with varying difficulty levels and targeted reading skills remains a time-consuming and costly task. Recent advances in generative AI provide an opportunity to automate MCQ generation efficiently. However, assessing the actual quality and reliability of generated MCQs has received limited attention -- particularly regarding cases where generation fails. This aspect becomes particularly important when the generated MCQs are meant to be applied in real-world settings. Additionally, most MCQ generation studies focus on English, leaving other languages underexplored. This paper investigates the capabilities of current generative models in producing MCQs for reading comprehension in Portuguese, a morphologically rich language. Our study focuses on generating MCQs that align with curriculum-relevant narrative elements and span different difficulty levels. We evaluate these MCQs through expert review and by analyzing the psychometric properties extracted from student responses to assess their suitability for elementary school students. Our results show that current models can generate MCQs of comparable quality to human-authored ones. However, we identify issues related to semantic clarity and answerability. Also, challenges remain in generating distractors that engage students and meet established criteria for high-quality MCQ option design.
Tracing the Milky Way: Calibrating chemical ages with high-precision Kepler data
2025-06-18
G. Casali, J. Montalbàn, A. Miglio, L. Casagrande, L. Magrini, C. Chiappini, A. Bragaglia, M. Matteuzzi, K. Brogaard, A. Stokholm, V. Grisoni, M. Tailo, E. Willett
Chemical clocks offer a powerful tool for estimating stellar ages from spectroscopic surveys. We present a new detailed spectroscopic analysis of 68 Kepler red giant stars to provide a suite of high-precision abundances along with asteroseismic ages with better than 10 percent precision from individual mode frequencies. We obtained several chemical clocks as ratios between s-process elements (Y, Zr, Ba, La, Ce) and alpha-elements (Mg, Ca, Si, Al, Ti). Our data show that [Ce/Mg] and [Zr/Ti] display a remarkably tight correlation with stellar ages, with abundance dispersions of 0.08 and 0.01 dex respectively and below 3 Gyr in ages, across the entire Galactic chronochemical history. While improving the precision floor of spectroscopic surveys is critical for broadening the scope and applicability of chemical clocks, the intrinsic accuracy of our relations -- enabled by high-resolution chemical abundances and stellar ages in our sample -- allows us to draw meaningful conclusions about age trends across stellar populations. By applying our relations to the APOGEE and Gaia-ESO surveys, we are able to differentiate the low- and high-alpha sequences in age, recover the age-metallicity relation, observe the disc flaring of the Milky Way, and identify a population of old metal-rich stars.
2025-06-18
Francesco Alessio, Paolo Di Vecchia, Maurizio Firrotta, Paolo Pichini
We continue the approach of [1] to attempt to reproduce the classical electromagnetic current of the $\sqrt{\mathrm{Kerr}}$ solution from the infinite-spin limit of a three-point amplitude with two higher-spin string states and a massless vector. We review the infinite-spin limit of three-point amplitudes along the leading Regge trajectory, which differ from $\sqrt{\mathrm{Kerr}}$, and we provide evidence that any sub-leading trajectory parallel to the leading one give the same result. We extend the above investigation to the simplest sub-leading trajectory not parallel to the leading one, containing physical states with the first two harmonics. We compute all three-point amplitudes involving two such states and a massless vector, using both covariant and DDF formalism, and we extract the associated classical infinite-spin limit. Inspired by the leading Regge case, where this limit reproduces the classical electromagnetic current sourced by a rigid rotating string, we derive new classical string solutions containing the first two harmonics and compare the currents they source to the infinite-spin amplitude of the two-harmonic state. We do not see a matching between the two approaches, and we find that the classical behaviour is instead reproduced by the amplitude involving coherent string states. We conclude this paper by discussing common patterns between the different amplitudes we considered, and we conjecture how they can be generalised to reproduce $\sqrt{\mathrm{Kerr}}$.
Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Review
2025-06-18
Salijona Dyrmishi, Mohamed Djilani, Thibault Simonetto, Salah Ghamizi, Maxime Cordy
Adversarial attacks in machine learning have been extensively reviewed in areas like computer vision and NLP, but research on tabular data remains scattered. This paper provides the first systematic literature review focused on adversarial attacks targeting tabular machine learning models. We highlight key trends, categorize attack strategies and analyze how they address practical considerations for real-world applicability. Additionally, we outline current challenges and open research questions. By offering a clear and structured overview, this review aims to guide future efforts in understanding and addressing adversarial vulnerabilities in tabular machine learning.
Cosmic curl -- Features and convergence of the vorticity power spectrum in $N$-body simulations
2025-06-18
Camilla T. G. Sørensen, Steen Hannestad, Thomas Tram
Observations of the cosmic velocity field could become an important cosmological probe in the near future. To take advantage of future velocity-flow surveys we must however have the theoretical predictions under control. In many respects, the velocity field is easier to simulate than the density field because it is less severely affected by small-scale clustering. Therefore, as we also show in this paper, a particle-mesh (PM) based simulation approach is usually sufficient, yielding results within a few percent of a corresponding P$^3$M simulation in which short-range forces are properly accounted for, but which also carry a much larger computational cost. However, in other respects the velocity field is much more challenging to deal with than the density field: Interpolating the velocity field onto a grid is significantly more complicated, and the vorticity field (the curl-part of the velocity field) is severely affected by both sample variance and discretisation effects. While the former can be dealt with using fixed amplitude initial conditions, the former makes it infeasible to run fully converged simulations in a cosmological volume. However, using the $N$-body code CONCEPT we show that one can robustly extrapolate the cosmic vorticity power spectrum from just 4 simulations with different number of particles. We expect our extrapolated vorticity power spectra to be correct within 5\% of the fully converged result across three orders of magnitude in $k$.
Procedures for Constraining Robotic Fiber Positioning for Highly Multiplexed Spectroscopic Surveys: The Case of FPS for SDSS-V
2025-06-18
Ilija Medan, Tom Dwelly, Kevin R. Covey, Eleonora Zari, Michael R. Blanton, Joleen K. Carlberg, S. Drew Chojnowski, Alexander Ji, Yue Shen, John Donor, José Sánchez-Gallego, Sean Morrison, Héctor J. Ibarra-Medel, Conor Sayres, Keivan G. Stassun
One crucial aspect of planning any large scale astronomical survey is constructing an observing strategy that maximizes reduced data quality. This is especially important for surveys that are rather heterogeneous and broad-ranging in their science goals. The Sloan Digital Sky Survey V (SDSS-V), which now utilizes the Focal Plane System (FPS) to robotically place fibers that feed the spectrographs, certainly meets these criteria. The addition of the FPS facilities an increase in survey efficiency, number of targets and target diversity, but also means the positions of fibers must be constrained to allow for simultaneous observations of sometimes competing programs. The constraints on the positions of the fibers are clearly driven by properties of the science targets e.g., the type of target, brightness of the target, position of the target relative to others in the field, etc. The parameters used to describe these constraints will also depend on the intended science goal of the observation, which will vary with the types of objects requested for the particular observation and the planned sky conditions for the observation. In this work, we detail the SDSS-V data collection scenarios, which consist of sets of parameters that serve as the framework for constraining fiber placements. The numerical values of these parameters were set based on either past experiences or from a series of new tests, which we describe in detail here. These parameters allow a survey like SDSS-V to be algorithmically planned to maximize the science output, while guaranteeing data quality throughout its operation.
In reinforcement learning (RL), agents continually interact with the environment and use the feedback to refine their behavior. To guide policy optimization, reward models are introduced as proxies of the desired objectives, such that when the agent maximizes the accumulated reward, it also fulfills the task designer's intentions. Recently, significant attention from both academic and industrial researchers has focused on developing reward models that not only align closely with the true objectives but also facilitate policy optimization. In this survey, we provide a comprehensive review of reward modeling techniques within the deep RL literature. We begin by outlining the background and preliminaries in reward modeling. Next, we present an overview of recent reward modeling approaches, categorizing them based on the source, the mechanism, and the learning paradigm. Building on this understanding, we discuss various applications of these reward modeling techniques and review methods for evaluating reward models. Finally, we conclude by highlighting promising research directions in reward modeling. Altogether, this survey includes both established and emerging methods, filling the vacancy of a systematic review of reward models in current literature.
Estimating Hubble Constant with Gravitational Observations: A Concise Review
2025-06-18
Rosa Poggiani
The Hubble constant is of paramount importance in astrophysics and cosmology. A large number of methods have been developed with different electromagnetic probes to estimate its value. The most recent results show a tension between values obtained from Cosmic Microwave Background observations and supernovae. The simultaneous detection of gravitational waves and electromagnetic radiation from GW170817 provided a direct estimation of the Hubble constant that did not depend on the astronomical distance ladder. This concise review will present the methods to estimate the Hubble constant with the gravitational observations of compact binary mergers, discussing both bright and dark sirens and reporting the state of the art of the results.
Unifying VXAI: A Systematic Review and Framework for the Evaluation of Explainable AI
2025-06-18
David Dembinsky, Adriano Lucieri, Stanislav Frolov, Hiba Najjar, Ko Watanabe, Andreas Dengel
Modern AI systems frequently rely on opaque black-box models, most notably Deep Neural Networks, whose performance stems from complex architectures with millions of learned parameters. While powerful, their complexity poses a major challenge to trustworthiness, particularly due to a lack of transparency. Explainable AI (XAI) addresses this issue by providing human-understandable explanations of model behavior. However, to ensure their usefulness and trustworthiness, such explanations must be rigorously evaluated. Despite the growing number of XAI methods, the field lacks standardized evaluation protocols and consensus on appropriate metrics. To address this gap, we conduct a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and introduce a unified framework for the eValuation of XAI (VXAI). We identify 362 relevant publications and aggregate their contributions into 41 functionally similar metric groups. In addition, we propose a three-dimensional categorization scheme spanning explanation type, evaluation contextuality, and explanation quality desiderata. Our framework provides the most comprehensive and structured overview of VXAI to date. It supports systematic metric selection, promotes comparability across methods, and offers a flexible foundation for future extensions.
Expanding the Ice Inventory of NGC 1333 IRAS 2A with INDRA using JWST Observations: Tracing Organic Refractories and Beyond
2025-06-18
Prathap Rayalacheruvu, Liton Majumdar, W. R. M. Rocha, Michael E. Ressler, Pabitra Ranjan Giri, S. Maitrey, K. Willacy, D. C. Lis, Y. Chen, P. D. Klaassen
In the era of JWST, with its unprecedented sensitivity and spectral resolution, infrared spectral surveys have revealed a rich inventory of ices, including complex organic molecules (COMs), in young stellar objects (YSOs). However, robust methods to decompose and quantify these absorption features particularly across broad spectral ranges, are still under investigation. We present INDRA (Ice-fitting with NNLS-based Decomposition and Retrieval Algorithm), a fully Python-based tool that performs continuum and silicate removal, global ice fitting using Weighted Non-Negative Least Squares (NNLS), and estimates column densities and statistical significance. We apply INDRA to NGC 1333 IRAS 2A, a target from the JWST Observations of Young protoStars (JOYS+) program previously studied using local fitting. We derive optical depths via polynomial continuum subtraction and remove silicate absorption using a synthetic model, isolating ice features for global MIRI fitting. Our results are consistent with previous local fits, confirming simple species and COMs, and expand the inventory by identifying additional absorption features from CO2 and NH4+. We also propose the presence of organic refractories contributing up to 9.6% in the spectral region of 5-8 microns among the various ice components, whose inclusion significantly improves the global spectral fitting. These broad absorption features, extending across 5.5-11 microns, are likely produced by large, complex molecules containing carbonyl (C=O), hydroxyl (O-H), amine (N-H), and C-H bending modes. Our expanded inventory, now incorporating these organic residues, offers new insights into the chemical evolution of ices in star-forming regions and highlights the importance of global spectral fitting in constraining ice compositions.
Overdense fireworks in GOODS-N: Unveiling a record number of massive dusty star forming galaxies at z$\sim$5.2 with the N2CLS
2025-06-18
G. Lagache, M. Xiao, A. Beelen, S. Berta, L. Ciesla, R. Neri, R. Pello, R. Adam, P. Ade, H. Ajeddig, S. Amarantidis, P. André, H. Aussel, A. Benoît, M. Béthermin, L. -J. Bing, A. Bongiovanni, J. Bounmy, O. Bourrion, M. Calvo, A. Catalano, D. Chérouvrier, U. Chowdhury, M. De Petris, F. -X. Désert, S. Doyle, E. F. C. Driessen, G. Ejlali, A. Ferragamo, A. Gomez, J. Goupy, C. Hanser, S. Katsioli, F. Kéruzoré, C. Kramer, B. Ladjelate, S. Leclercq, J. -F. Lestrade, J. F. Macías-Pérez, S. C. Madden, A. Maury, F. Mayet, A. Monfardini, A. Moyer-Anin, M. Muñoz-Echeverría, I. Myserlis, P. Oesch, A. Paliwal, L. Perotto, G. Pisano, N. Ponthieu, V. Revéret, A. J. Rigby, A. Ritacco, H. Roussel, F. Ruppin, M. Sánchez-Portal, S. Savorgnano, K. Schuster, A. Sievers, C. Tucker, R. Zylka
As part of the N2CLS Survey, we have identified a remarkable overdensity of ten bright dusty star-forming galaxies at z$\sim$5.2 in the GOODS-N field. Three of these galaxies, N2GN_1_01, 06, and 23 (known as GN10, HDF850.1, and S3, respectively), had previously been spectroscopically confirmed as members of the exceptional large-scale structure at z$\sim$5.1-5.3, which is notably elongated along the line of sight, spanning 30 cMpc. We present the spectroscopic confirmation of N2GN_1_13 at z$_{\rm spec}$=5.182, a massive dusty star-forming galaxy identified through targeted NOEMA observations, and N2GN_1_61 at z$_{\rm spec}$=5.201, revealed using JWST/FRESCO data. In addition to these five spectroscopically confirmed members, we identify five further candidates with photometric redshifts consistent with the overdense structure. These galaxies are massive (with a median stellar mass of 10$^{11}$ M$_{\odot}$) and highly obscured (with a median A$_V$ of 2.9), caught in a short-lived yet extreme starburst phase at z$\sim$5.2. Their high SFRs (with a median of 680 M$_{\odot}$ yr$^{-1}$), efficient baryon to stellar mass conversion ($\epsilon_{\star}>$20%), substantial gas reservoir and dust content, suggest rapid evolution and imminent quenching. Six of these galaxies reside in overdense filaments, while the remaining four may trace new distinct structures which will have to be spectroscopically confirmed. These few dusty galaxies dominate the star formation within the overdensity, contributing more than the numerous H$_{\alpha}$ emitters, and surpassing the cosmic average star formation rate density for this epoch. Their properties suggest an accelerated evolution that current models and simulations have difficulty reproducing.
SOT Enabled 3D Magnetic Field Sensor with Low Offset and High Sensitivity
2025-06-18
Sebastian Zeilinger, Johannes Güttinger, Klemens Prügl, Michael Kirsch, Joshua M. Salazar-Mejía, Sabri Koraltan, Philip Heinrich, Sophie Zeilinger, Bernd Aichner, Florian Bruckner, Hubert Brückl, Armin Satz, Dieter Suess
In this work we demonstrate a spin-orbit torque (SOT) magnetic field sensor, designed as a Ta/CoFeB/MgO structure, with high sensitivity and capable of active offset compensation in all three spatial directions. This is described and verified in both experiment and simulation. The measurements of magnetic fields showed an offset of 36, 50, and 37$\mathrm{\mu T}$ for x-, y-, and z-fields. Furthermore, the sensitivities of these measurements had values of 590, 580, and 490$\mathrm{V\,A^{-1}\,T^{-1}}$ in the x-, y-, and z-direction. In addition, the robustness to bias fields is demonstrated via experiments and single spin simulations by applying bias fields in y-direction. Cross sensitivities were further analyzed via single spin simulations performing a parameter sweep of different bias fields in the y- and z-direction up to $\pm$1mT. Finally, the extraction of the SOT parameters $\eta_\mathrm{DL}$ and $\eta_\mathrm{FL}$ is shown via optimization of a single-spin curve to the experimental measurements.
New Physics Opportunities at Neutrino Facilities: BSM Physics at Accelerator, Atmospheric, and Reactor Neutrino Experiments
2025-06-18
Koun Choi, Doojin Kim, Jong-Chul Park, Seodong Shin, Pouya Bakhti, Ki-Young Choi, Chang Hyon Ha, Kazumi Hata, Wooyoung Jang, Yu Seon Jeong, Young Ju Ko, Hyun Su Lee, Weijun Li, Yu-Feng Li, Mehedi Masud, Kenny C. Y. Ng, Jungsic Park, Min-Gwa Park, Komninos-John Plows, Meshkat Rajaee, Eunil Won, Byeongsu Yang, Seong Moon Yoo, Jaehoon Yu, Seokhoon Yun
Since the discovery of the Higgs boson, the long-standing task at hand in particle physics is the search for new physics beyond the Standard Model, which accounts for only about 5\% of the Universe. In light of this situation, the neutrino sector has drawn significant attention due to neutrino oscillations, which require physics beyond the Standard Model and have prompted a wide array of active and planned experimental programs. Notably, neutrino facilities offer substantial potential to search for new physics beyond neutrino oscillations, owing to their precision measurement capabilities, diverse experimental configurations, and various neutrino sources. This paper provides a review of the landscape of new physics that can be probed at current and future neutrino experiments, categorized into laboratory-produced and cosmogenic signals. We discuss recent experimental results interpreted through the lens of new physics, as well as detailed plans and projected sensitivities of next-generation facilities. This review is based on presentations from the 4th Workshop on New Physics Opportunities in Neutrino Facilities (NPN 2024), held at IBS in Daejeon, Korea, on June 3-5, 2024. Particular emphasis is placed on accelerator-based neutrino experiments and a range of neutrino programs in East Asia. We also outline key tasks necessary to realize the promising new physics opportunities ahead.
Cohort Discovery: A Survey on LLM-Assisted Clinical Trial Recruitment
2025-06-18
Shrestha Ghosh, Moritz Schneider, Carina Reinicke, Carsten Eickhoff
Recent advances in LLMs have greatly improved general-domain NLP tasks. Yet, their adoption in critical domains, such as clinical trial recruitment, remains limited. As trials are designed in natural language and patient data is represented as both structured and unstructured text, the task of matching trials and patients benefits from knowledge aggregation and reasoning abilities of LLMs. Classical approaches are trial-specific and LLMs with their ability to consolidate distributed knowledge hold the potential to build a more general solution. Yet recent applications of LLM-assisted methods rely on proprietary models and weak evaluation benchmarks. In this survey, we are the first to analyze the task of trial-patient matching and contextualize emerging LLM-based approaches in clinical trial recruitment. We critically examine existing benchmarks, approaches and evaluation frameworks, the challenges to adopting LLM technologies in clinical research and exciting future directions.
UXR Point of View on Product Feature Prioritization Prior To Multi-Million Engineering Commitments
2025-06-18
Jonas Lau, Annie Tran
This paper discusses a popular UX research activity, feature prioritization, using the User Experience Research Point of View (UXR PoV) Playbook framework. We describe an application of multinomial logistic regression, frequently marketed as MaxDiff, for prioritizing product features in consumer product development. It addresses challenges of traditional surveying techniques. We propose a solution using MaxDiff to generate a reliable preference list with a reasonable sample size. We also adapt the MaxDiff method to reduce the number of survey responses in half, making it less tedious from the survey takers' perspective. We present a case study using the adapted MaxDiff method for tablet feature prioritization research involving users with disabilities.
Existing recommendation methods often struggle to model users' multifaceted preferences due to the diversity and volatility of user behavior, as well as the inherent uncertainty and ambiguity of item attributes in practical scenarios. Multi-interest recommendation addresses this challenge by extracting multiple interest representations from users' historical interactions, enabling fine-grained preference modeling and more accurate recommendations. It has drawn broad interest in recommendation research. However, current recommendation surveys have either specialized in frontier recommendation methods or delved into specific tasks and downstream applications. In this work, we systematically review the progress, solutions, challenges, and future directions of multi-interest recommendation by answering the following three questions: (1) Why is multi-interest modeling significantly important for recommendation? (2) What aspects are focused on by multi-interest modeling in recommendation? and (3) How can multi-interest modeling be applied, along with the technical details of the representative modules? We hope that this survey establishes a fundamental framework and delivers a preliminary overview for researchers interested in this field and committed to further exploration. The implementation of multi-interest recommendation summarized in this survey is maintained at https://github.com/WHUIR/Multi-Interest-Recommendation-A-Survey.
AVID: Formation and evolution of a coalesced major merger of late-type dwarf galaxies (VCC 479) on the outskirts of the Virgo cluster
2025-06-18
Weibin Sun, Hong-Xin Zhang, Rory Smith, Elias Brinks, Patrick Côté, Se-Heon Oh, Zesen Lin, Allessandro Boselli, Laura Ferrarese, Fujia Li, Yuzhu Sun, Lijun Chen, Lanyue Zhang, Minsu Kim, Jaebeom Kim, Tie Li, Bojun Tao, Matt Taylor, Pierre-Alain Duc, Ruben Sánchez-Janssén, Yinghe Zhao, Sanjaya Paudel, Eric W. Peng, Kaixiang Wang, Stephen Gwyn, Matteo Fossati, Jean-Charles Cuillandre
Dwarf-dwarf galaxy mergers are among the least explored aspects of dwarf galaxy pre-processing as they fall into clusters. We present the first case study of a coalesced late-type dwarf major merger (VCC 479; stellar mass $\sim\,8\,\times\,10^7\,\rm M_\odot$) that has undergone significant environmental influence, with the aim of exploring dwarf galaxy evolution under the combined effects of galaxy interactions and environmental processes, and understanding its relevance to the diversity of dwarf galaxies in cluster environments. Our analysis is based on VLA and FAST HI emission line mapping from the Atomic gas in Virgo Interacting Dwarf galaxies (AVID) survey. We also perform idealized hydrodynamical simulations of dwarf-dwarf mergers to help interpret the observations. We identify symmetric stellar shell structures in VCC 479, indicative of a coalesced major merger of dwarf galaxies. The galaxy features a central starburst, initiated $\sim$600 Myr ago, embedded within an exponential disk quenched $\sim$1 Gyr ago. The starburst contributes only 2.9$\pm$0.5\% of the total stellar mass, and VCC 479's global star formation rate is 0.3 dex lower than typical dwarfs of similar mass. The galaxy is highly HI deficient, with most HI gas concentrated within the central 1 kpc and little extended HI envelope. The misalignment of the HI velocity field with the stellar body is best explained by merger-triggered gas inflow, as seen in our simulations. Our analysis is consistent with a scenario that the majority of HI gas of the progenitor galaxies was removed by the cluster environment prior to the final coalescence. The merger concentrates the remaining gas toward the galaxy center, triggering a central starburst. The combined effect of environment stripping and galaxy merger has transformed VCC 479 into a blue-core dwarf undergoing morphological transition from a late-type to an early-type galaxy.
The effects of continuum fitting on Lyman-$α$ forest correlations
2025-06-18
Nicolas Busca, James Rich, Julian Bautista, Andrei Cuceu, Andreu Font-Ribera, Julien Guy, Hiram K. Herrera-Alcantar, Julianna Stermer, Christophe Balland, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, A. de la Macorra, P. Doel, S. Ferraro, J. E. Forero-Romero, E. Gaztañaga, C. Gordon, G. Gutierrez, M. Ishak, R. Kehoe, D. Kirkby, A. Kremin, M. Landriau, L. Le Guillou, C. Magneville, P. Martini, R. Miquel, S. Nadathur, N. Palanque-Delabrouille, F. Prada, I. Pérez-Ràfols, C. Ravoux, G. Rossi, E. Sanchez, D. Schlegel, H. Seo, J. Silber, D. Sprayberry, G. Tarlé, B. A. Weaver, R. Zhou, H. Zou
Correlations of fluctuations of the flux in Lyman-$\alpha$ forests of high-redshift quasars have been observed by the Baryonic Acoustic Oscillation Spectroscopy Survey (BOSS) and the Dark Energy Spectroscopy Instrument (DESI) survey where they have revealed the effects of baryon acoustic oscillations (BAO). In order to fit the correlation functions to a physical model and thereby constrain cosmological parameters, it is necessary to take into account the effects of fitting the observed spectra to a template about which the fluctuations are measured. In this paper we use mock spectra to test the distortion matrix technique that has been used since the final BOSS data release to appropriately distort the models. We show that while percent-level effects on the derived forest bias parameters may be present, the technique works sufficiently well that the determination of the BAO peak position is not affected at the percent level. We introduce modifications in the technique used by DESI that were not in the original applications and suggest further possibilities for improvements.
First Steps Toward the Development of a Straight-Line Reference Alignment System for Future Accelerators at CERN Using Pseudo-Nondiffracting Layer Beams
2025-06-18
Martin Dušek, Sebastian Figura, Jakub Michal Polak, Solomon William Kamugasa, Dirk Mergelkuhl, Witold Niewiem, Štěpán Kunc, Jean-Christophe Gayde, Miroslav Šulc
This paper presents experimental results that allow for the performance evaluation of a straight-line reference alignment system based on pseudo-nondiffracting Layer beams. Sensors, developed specifically for this system, feature four linear CMOS chips and a square aperture. This allows for simultaneous measurements along the beam path without disrupting the laser reference. Measurements, conducted over a distance of 2 m from the first to the last sensor, were compared with a laser tracker measurement to assess the sensor performance. The alignment reference generated by the Layer Beams exhibited a repeatability and reproducibility root-mean-square error (RMSE) of less than 30 ${\mu}$m. The relative alignment precision for a known displacement was validated with a standard deviation of 4.3 ${\mu}$m. The results highlight the underlying sources of noise, which are induced mainly by the cover glass, the protective film of the pixels, and the dark noise of the CMOS chips. Solutions to address these challenges are proposed. Additionally, a proof-of-concept for future development of a radiation-hard sensor utilizing optical fiber matrices is demonstrated. The RMSE of the reference position detection introduced by the fiber matrix remained below 1.3 ${\mu}$m. This would allow the sensor to be used reliably in high-radiation environments typical for accelerator facilities. This study serves as a foundational step toward developing a robust straight-line reference alignment system based on pseudo-nondiffracting Layer beams intended for deployment in the accelerator facilities.
Unit testing is a fundamental practice in modern software engineering, with the aim of ensuring the correctness, maintainability, and reliability of individual software components. Very recently, with the advances in Large Language Models (LLMs), a rapidly growing body of research has leveraged LLMs to automate various unit testing tasks, demonstrating remarkable performance and significantly reducing manual effort. However, due to ongoing explorations in the LLM-based unit testing field, it is challenging for researchers to understand existing achievements, open challenges, and future opportunities. This paper presents the first systematic literature review on the application of LLMs in unit testing until March 2025. We analyze \numpaper{} relevant papers from the perspectives of both unit testing and LLMs. We first categorize existing unit testing tasks that benefit from LLMs, e.g., test generation and oracle generation. We then discuss several critical aspects of integrating LLMs into unit testing research, including model usage, adaptation strategies, and hybrid approaches. We further summarize key challenges that remain unresolved and outline promising directions to guide future research in this area. Overall, our paper provides a systematic overview of the research landscape to the unit testing community, helping researchers gain a comprehensive understanding of achievements and promote future research. Our artifacts are publicly available at the GitHub repository: https://github.com/iSEngLab/AwesomeLLM4UT.
Limits to star formation in post starburst galaxies from Type II supernovae
2025-06-18
Laetile Makoko Roberto De Propris
We establish significant upper limits to the current star formation rate in two samples of post starburst galaxies by measuring the rate of Type II supernovae from the Zwicky Transient Factory Bright Transient Survey. No Type II supernovae are observed within the Petrosian radii of $z < 0.05$ post starburst galaxies during this supernova search survey. We calculate that at 95\% confidence level the star formation rate in these galaxies is $< 0.8$ M$_{\odot}$ yr$^{-1}$.
In recent years, deep learning has facilitated the creation of wireless receivers capable of functioning effectively in conditions that challenge traditional model-based designs. Leveraging programmable hardware architectures, deep learning-based receivers offer the potential to dynamically adapt to varying channel environments. However, current adaptation strategies, including joint training, hypernetwork-based methods, and meta-learning, either demonstrate limited flexibility or necessitate explicit optimization through gradient descent. This paper presents gradient-free adaptation techniques rooted in the emerging paradigm of in-context learning (ICL). We review architectural frameworks for ICL based on Transformer models and structured state-space models (SSMs), alongside theoretical insights into how sequence models effectively learn adaptation from contextual information. Further, we explore the application of ICL to cell-free massive MIMO networks, providing both theoretical analyses and empirical evidence. Our findings indicate that ICL represents a principled and efficient approach to real-time receiver adaptation using pilot signals and auxiliary contextual information-without requiring online retraining.
Large language models (LLMs) are rapidly evolving from single-modal systems to multimodal LLMs and intelligent agents, significantly expanding their capabilities while introducing increasingly severe security risks. This paper presents a systematic survey of the growing complexity of jailbreak attacks and corresponding defense mechanisms within the expanding LLM ecosystem. We first trace the developmental trajectory from LLMs to MLLMs and Agents, highlighting the core security challenges emerging at each stage. Next, we categorize mainstream jailbreak techniques from both the attack impact and visibility perspectives, and provide a comprehensive analysis of representative attack methods, related datasets, and evaluation metrics. On the defense side, we organize existing strategies based on response timing and technical approach, offering a structured understanding of their applicability and implementation. Furthermore, we identify key limitations in existing surveys, such as insufficient attention to agent-specific security issues, the absence of a clear taxonomy for hybrid jailbreak methods, a lack of detailed analysis of experimental setups, and outdated coverage of recent advancements. To address these limitations, we provide an updated synthesis of recent work and outline future research directions in areas such as dataset construction, evaluation framework optimization, and strategy generalization. Our study seeks to enhance the understanding of jailbreak mechanisms and facilitate the advancement of more resilient and adaptive defense strategies in the context of ever more capable LLMs.
With growing demands for privacy protection, security, and legal compliance (e.g., GDPR), machine unlearning has emerged as a critical technique for ensuring the controllability and regulatory alignment of machine learning models. However, a fundamental challenge in this field lies in effectively verifying whether unlearning operations have been successfully and thoroughly executed. Despite a growing body of work on unlearning techniques, verification methodologies remain comparatively underexplored and often fragmented. Existing approaches lack a unified taxonomy and a systematic framework for evaluation. To bridge this gap, this paper presents the first structured survey of machine unlearning verification methods. We propose a taxonomy that organizes current techniques into two principal categories -- behavioral verification and parametric verification -- based on the type of evidence used to assess unlearning fidelity. We examine representative methods within each category, analyze their underlying assumptions, strengths, and limitations, and identify potential vulnerabilities in practical deployment. In closing, we articulate a set of open problems in current verification research, aiming to provide a foundation for developing more robust, efficient, and theoretically grounded unlearning verification mechanisms.
EMUSE: Evolutionary Map of the Universe Search Engine
2025-06-18
Nikhel Gupta, Zeeshan Hayder, Minh Huynh, Ray P. Norris, Lars Petersson, Andrew M. Hopkins, Simone Riggi, Bärbel S. Koribalski, Miroslav D. Filipović
We present EMUSE (Evolutionary Map of the Universe Search Engine), a tool designed for searching specific radio sources within the extensive datasets of the EMU (Evolutionary Map of the Universe) survey, with potential applications to other Big Data challenges in astronomy. Built on a multimodal approach to radio source classification and retrieval, EMUSE fine-tunes the OpenCLIP model on curated radio galaxy datasets. Leveraging the power of foundation models, our work integrates visual and textual embeddings to enable efficient and flexible searches within large radio astronomical datasets. We fine-tune OpenCLIP using a dataset of 2,900 radio galaxies, encompassing various morphological classes, including FR-I, FR-II, FR-x, R-type, and other rare and peculiar sources. The model is optimized using adapter-based fine-tuning, ensuring computational efficiency while capturing the unique characteristics of radio sources. The fine-tuned model is then deployed in EMUSE, allowing for seamless image- and text-based queries over the EMU survey dataset. Our results demonstrate the model's effectiveness in retrieving and classifying radio sources, particularly in recognizing distinct morphological features. However, challenges remain in identifying rare or previously unseen radio sources, highlighting the need for expanded datasets and continuous refinement. This study showcases the potential of multimodal machine learning in radio astronomy, paving the way for more scalable and accurate search tools in the field. The search engine is accessible at https://askap-emuse.streamlit.app/ and can be used locally by cloning the repository at https://github.com/Nikhel1/EMUSE.
Fuzzing is a powerful software testing technique renowned for its effectiveness in identifying software vulnerabilities. Traditional fuzzing evaluations typically focus on overall fuzzer performance across a set of target programs, yet few benchmarks consider how fine-grained program features influence fuzzing effectiveness. To bridge this gap, we introduce a novel benchmark designed to generate programs with configurable, fine-grained program features to enhance fuzzing evaluations. We reviewed 25 recent grey-box fuzzing studies, extracting 7 program features related to control-flow and data-flow that can impact fuzzer performance. Using these features, we generated a benchmark consisting of 153 programs controlled by 10 fine-grained configurable parameters. We evaluated 11 popular fuzzers using this benchmark. The results indicate that fuzzer performance varies significantly based on the program features and their strengths, highlighting the importance of incorporating program characteristics into fuzzing evaluations.
GalaxyGenius: A Mock Galaxy Image Generator for Various Telescopes from Hydrodynamical Simulations
2025-06-18
Xingchen Zhou, Hang Yang, Nan Li, Qi Xiong, Furen Deng, Xian-Min Meng, Renhao Ye, Shiyin Shen, Peng Wei, Qifan Cui, Zizhao He, Ayodeji Ibitoye, Chengliang Wei, Yuedong Fang
To advance research on galaxies in the era of large-scale sky surveys, we introduce GalaxyGenius, a Python package designed to produce synthetic galaxy images tailored to different telescopes based on hydrodynamical simulations. The package comprises three main modules: data preprocessing, ideal data cube generation, and mock observation. Specifically, the preprocessing module extracts necessary properties of star and gas particles for a selected subhalo from hydrodynamical simulations and creates the execution file for the following radiative transfer procedure. Subsequently, building on the above information, the ideal data cube generation module executes a widely-used radiative transfer project, specifically the SKIRT, to perform the SED assignment for each particle and the radiative transfer procedure to produce an IFU-like ideal data cube. Lastly, the mock observation module takes the ideal data cube and applies the throughputs of aiming telescopes while also incorporating the relevant instrumental effects, point spread functions (PSFs), and background noise to generate desired mock observational images of galaxies. To showcase the outcomes of GalaxyGenius, We create a series of mock images of galaxies based on the IllustrisTNG and EAGLE simulations for both space and ground-based surveys spanning from the ultraviolet to infrared wavelength coverages, including CSST, Euclid, HST, JWST, Roman, and HSC. GalaxyGenius offers a flexible framework developed to generate mock galaxy images with customizable recipes. These generated images can serve as valuable references for verifying and validating new approaches in astronomical research, and they can also act as training sets for deep learning relevant studies where real observational data are insufficient.
From RAG to Agentic: Validating Islamic-Medicine Responses with LLM Agents
2025-06-18
Mohammad Amaan Sayeed, Mohammed Talha Alam, Raza Imam, Shahab Saquib Sohail, Amir Hussain
Centuries-old Islamic medical texts like Avicenna's Canon of Medicine and the Prophetic Tibb-e-Nabawi encode a wealth of preventive care, nutrition, and holistic therapies, yet remain inaccessible to many and underutilized in modern AI systems. Existing language-model benchmarks focus narrowly on factual recall or user preference, leaving a gap in validating culturally grounded medical guidance at scale. We propose a unified evaluation pipeline, Tibbe-AG, that aligns 30 carefully curated Prophetic-medicine questions with human-verified remedies and compares three LLMs (LLaMA-3, Mistral-7B, Qwen2-7B) under three configurations: direct generation, retrieval-augmented generation, and a scientific self-critique filter. Each answer is then assessed by a secondary LLM serving as an agentic judge, yielding a single 3C3H quality score. Retrieval improves factual accuracy by 13%, while the agentic prompt adds another 10% improvement through deeper mechanistic insight and safety considerations. Our results demonstrate that blending classical Islamic texts with retrieval and self-evaluation enables reliable, culturally sensitive medical question-answering.
Advancing Autonomous Racing: A Comprehensive Survey of the RoboRacer (F1TENTH) Platform
2025-06-18
Israel Charles, Hossein Maghsoumi, Yaser Fallah
The RoboRacer (F1TENTH) platform has emerged as a leading testbed for advancing autonomous driving research, offering a scalable, cost-effective, and community-driven environment for experimentation. This paper presents a comprehensive survey of the platform, analyzing its modular hardware and software architecture, diverse research applications, and role in autonomous systems education. We examine critical aspects such as bridging the simulation-to-reality (Sim2Real) gap, integration with simulation environments, and the availability of standardized datasets and benchmarks. Furthermore, the survey highlights advancements in perception, planning, and control algorithms, as well as insights from global competitions and collaborative research efforts. By consolidating these contributions, this study positions RoboRacer as a versatile framework for accelerating innovation and bridging the gap between theoretical research and real-world deployment. The findings underscore the platform's significance in driving forward developments in autonomous racing and robotics.
Challenges and Research Directions from the Operational Use of a Machine Learning Damage Assessment System via Small Uncrewed Aerial Systems at Hurricanes Debby and Helene
2025-06-18
Thomas Manzini, Priyankari Perali, Robin R. Murphy, David Merrick
This paper details four principal challenges encountered with machine learning (ML) damage assessment using small uncrewed aerial systems (sUAS) at Hurricanes Debby and Helene that prevented, degraded, or delayed the delivery of data products during operations and suggests three research directions for future real-world deployments. The presence of these challenges is not surprising given that a review of the literature considering both datasets and proposed ML models suggests this is the first sUAS-based ML system for disaster damage assessment actually deployed as a part of real-world operations. The sUAS-based ML system was applied by the State of Florida to Hurricanes Helene (2 orthomosaics, 3.0 gigapixels collected over 2 sorties by a Wintra WingtraOne sUAS) and Debby (1 orthomosaic, 0.59 gigapixels collected via 1 sortie by a Wintra WingtraOne sUAS) in Florida. The same model was applied to crewed aerial imagery of inland flood damage resulting from post-tropical remnants of Hurricane Debby in Pennsylvania (436 orthophotos, 136.5 gigapixels), providing further insights into the advantages and limitations of sUAS for disaster response. The four challenges (variationin spatial resolution of input imagery, spatial misalignment between imagery and geospatial data, wireless connectivity, and data product format) lead to three recommendations that specify research needed to improve ML model capabilities to accommodate the wide variation of potential spatial resolutions used in practice, handle spatial misalignment, and minimize the dependency on wireless connectivity. These recommendations are expected to improve the effective operational use of sUAS and sUAS-based ML damage assessment systems for disaster response.
Novel Mechanism for Production of Planck Relics in Third Order Effective Field Theory
2025-06-18
Lorens F. Niehof, Sjors Heefer, Andrea Fuster, Federico Toschi
We present a novel mechanism for the formation of black hole remnants arising from a third order effective field theory correction to general relativity. We first calculate the corresponding modifications to the Schwarzschild metric and the Hawking temperature and use these to derive a modified evaporation law that deviates significantly from the classical prediction for sufficiently small black holes. Notably, the rate of mass loss vanishes at a critical mass $M_{crit}$, while the Hawking temperature remains finite. This leads to a freeze-out in the evaporation process, naturally producing stable black hole remnants of Planck scale called Planck relics. We also identify a local maximum in the evaporation rate for a certain mass $M_{*} > M_{crit}$, and compute the corresponding heat capacities which differ from Hawking's predictions. Our results show that higher-order curvature corrections in effective field theory can provide a physical mechanism for the production of black hole remnants, although the validity of such predictions near the Planck scale remains limited by the breakdown of semiclassical approximations.
DeckFlow: Iterative Specification on a Multimodal Generative Canvas
2025-06-18
Gregory Croisdale, Emily Huang, John Joon Young Chung, Anhong Guo, Xu Wang, Austin Z. Henley, Cyrus Omar
Generative AI promises to allow people to create high-quality personalized media. Although powerful, we identify three fundamental design problems with existing tooling through a literature review. We introduce a multimodal generative AI tool, DeckFlow, to address these problems. First, DeckFlow supports task decomposition by allowing users to maintain multiple interconnected subtasks on an infinite canvas populated by cards connected through visual dataflow affordances. Second, DeckFlow supports a specification decomposition workflow where an initial goal is iteratively decomposed into smaller parts and combined using feature labels and clusters. Finally, DeckFlow supports generative space exploration by generating multiple prompt and output variations, presented in a grid, that can feed back recursively into the next design iteration. We evaluate DeckFlow for text-to-image generation against a state-of-practice conversational AI baseline for image generation tasks. We then add audio generation and investigate user behaviors in a more open-ended creative setting with text, image, and audio outputs.
Development and in vitro Characterization of a Novel Bioactive Hydrogel for Bioprinting Uterine Constructs
2025-06-18
Abbas Fazel Anvari Yazdi, Kobra Tahermanesh, Maryam Ejlali, Louison Blivet-Bailly, Vatsala Singh, Bishnu Acharya, Daniel J. MacPhee, Ildiko Badea, Xiong-biao Chen
Background and Aim: Decellularized uterine extracellular matrix (dUECM) offers a promising bioactive scaffold for uterine tissue engineering, but its application in 3D constructs has been limited by fabrication challenges. This study aimed to develop a printable, bioactive hydrogel from dUECM suitable for 3D bioprinting and to evaluate its ability to support human uterine myometrial cell growth in vitro. Materials and Methods: Porcine uterine tissues were decellularized using 1 percent Triton X-100 and 0.1 to 1.5 percent SDS for 48 to 72 hours. The resulting dUECM was assessed via histology, DNA and GAG quantification, scanning electron microscopy, FTIR, Raman spectroscopy, and thermogravimetric analysis. Selected dUECM samples were digested with pepsin and blended with 2 or 3 percent alginate to create bioinks. Constructs were printed using extrusion-based bioprinting and evaluated for swelling, degradation, mechanical properties, and printability. Biocompatibility was tested by seeding hTERT-HM cells onto cast hydrogels and performing MTT and Live/Dead assays over seven days. Results: The optimal protocol (1 percent Triton X-100 and 1 percent SDS for 48 hours) reduced DNA to 51.3 ng per mg, while retaining 54.9 micrograms per mg of GAGs. FTIR and Raman spectroscopy confirmed collagen preservation, while thermal stability was moderately reduced. The 3 percent alginate with 1.5 percent dUECM formulation showed superior printability, swelling stability, degradation resistance, and mechanical strength. Cell proliferation reached 258 percent by day 7, significantly outperforming alginate alone. Conclusion: The optimized dUECM hydrogel supports printability, mechanical performance, and cell viability, offering a robust platform for uterine tissue engineering.
A Sea of Cyber Threats: Maritime Cybersecurity from the Perspective of Mariners
2025-06-18
Anna Raymaker, Akshaya Kumar, Miuyin Yong Wong, Ryan Pickren, Animesh Chhotaray, Frank Li, Saman Zonouz, Raheem Beyah
Maritime systems, including ships and ports, are critical components of global infrastructure, essential for transporting over 80% of the world's goods and supporting internet connectivity. However, these systems face growing cybersecurity threats, as shown by recent attacks disrupting Maersk, one of the world's largest shipping companies, causing widespread impacts on international trade. The unique challenges of the maritime environment--such as diverse operational conditions, extensive physical access points, fragmented regulatory frameworks, and its deeply interconnected structure--require maritime-specific cybersecurity research. Despite the sector's importance, maritime cybersecurity remains underexplored, leaving significant gaps in understanding its challenges and risks. To address these gaps, we investigate how maritime system operators perceive and navigate cybersecurity challenges within this complex landscape. We conducted a user study comprising surveys and semi-structured interviews with 21 officer-level mariners. Participants reported direct experiences with shipboard cyber-attacks, including GPS spoofing and logistics-disrupting ransomware, demonstrating the real-world impact of these threats. Our findings reveal systemic and human-centric issues, such as training poorly aligned with maritime needs, insufficient detection and response tools, and serious gaps in mariners' cybersecurity understanding. Our contributions include a categorization of threats identified by mariners and recommendations for improving maritime security, including better training, response protocols, and regulation. These insights aim to guide future research and policy to strengthen the resilience of maritime systems.
HI asymmetries in spatially resolved SIMBA galaxies
2025-06-18
Nadine A. N. Hank, Marc A. W. Verheijen, Sarah-L. Blyth, Romeel Davé, Kyle A. Oman, Nathan Deg, Marcin Glowacki
We present a study of the neutral atomic hydrogen (HI) content of spatially resolved, low-redshift galaxies in the SIMBA cosmological simulations. We create synthetic HI data cubes designed to match observations from the Apertif Medium-Deep HI imaging survey, and follow an observational approach to derive the HI size-mass relation. The HI size-mass relation for SIMBA is in broad agreement with the observed relation to within 0.1 dex, but SIMBA galaxies are slightly smaller than expected at fixed HI mass. We quantify the HI spectral ($A_{\mathrm{flux}}$) and morphological ($A_{\mathrm{mod}}$) asymmetries of the galaxies and motivate standardizing the relative spatial resolution when comparing values in a sample that spans several orders of magnitude in HI mass. Galaxies are classified into three categories (isolated, interacted, or merged) based on their dynamical histories over the preceding ~2 Gyr to contextualize disturbances in their HI reservoirs. We determine that the interacted and merged categories have higher mean asymmetries than the isolated category, with a larger separation between the categories' $A_{\mathrm{mod}}$ distributions than between their $A_{\mathrm{flux}}$ distributions. For the interacted and merged categories, we find an inverse correlation between baryonic mass and $A_{\mathrm{mod}}$ that is not observed between baryonic mass and $A_{\mathrm{flux}}$. These results, coupled with the weak correlation found between $A_{\mathrm{flux}}$ and $A_{\mathrm{mod}}$, highlight the limitations of only using $A_{\mathrm{flux}}$ to infer the HI distributions of spatially unresolved HI detections.
Accurate and complete product descriptions are crucial for e-commerce, yet seller-provided information often falls short. Customer reviews offer valuable details but are laborious to sift through manually. We present PRAISE: Product Review Attribute Insight Structuring Engine, a novel system that uses Large Language Models (LLMs) to automatically extract, compare, and structure insights from customer reviews and seller descriptions. PRAISE provides users with an intuitive interface to identify missing, contradictory, or partially matching details between these two sources, presenting the discrepancies in a clear, structured format alongside supporting evidence from reviews. This allows sellers to easily enhance their product listings for clarity and persuasiveness, and buyers to better assess product reliability. Our demonstration showcases PRAISE's workflow, its effectiveness in generating actionable structured insights from unstructured reviews, and its potential to significantly improve the quality and trustworthiness of e-commerce product catalogs.
2025-06-18
Jonathan Reif, Daniel Dittler, Milapji Singh Gill, Tamás Farkas, Valentin Stegmaier, Felix Gehlhoff, Tobias Kleinert, Michael Weyrich
Digital Twins (DTs) are becoming increasingly vital for future industrial applications, enhancing monitoring, control, and optimization of physical assets. This enhancement is made possible by integrating various Digital Models (DMs) within DTs, which must interoperate to represent different system aspects and fulfill diverse application purposes. However, industry perspectives on the challenges and research needs for integrating these models are rarely obtained. Thus, this study conducts an expert survey across multiple application domains to identify and analyze the challenges in utilizing diverse DMs within DTs. The results reveal missing standardized interfaces, high manual adaptation effort, and limited support for model reuse across lifecycle phases, highlighting future research needs in automated model composition and semantics-based interoperability.
Academic paper review typically requires substantial time, expertise, and human resources. Large Language Models (LLMs) present a promising method for automating the review process due to their extensive training data, broad knowledge base, and relatively low usage cost. This work explores the feasibility of using LLMs for academic paper review by proposing an automated review system. The system integrates Retrieval Augmented Generation (RAG), the AutoGen multi-agent system, and Chain-of-Thought prompting to support tasks such as format checking, standardized evaluation, comment generation, and scoring. Experiments conducted on 290 submissions from the WASA 2024 conference using GPT-4o show that LLM-based review significantly reduces review time (average 2.48 hours) and cost (average \$104.28 USD). However, the similarity between LLM-selected papers and actual accepted papers remains low (average 38.6\%), indicating issues such as hallucination, lack of independent judgment, and retrieval preferences. Therefore, it is recommended to use LLMs as assistive tools to support human reviewers, rather than to replace them.
The explosive growth of Large Language Models (LLMs) - such as GPT-4 with 1.8 trillion parameters - demands a radical rethinking of data center architecture to ensure scalability, efficiency, and cost-effectiveness. Our work provides a comprehensive co-design framework that jointly explores FLOPS, HBM bandwidth and capacity, multiple network topologies (two-tier vs. FullFlat optical), the size of the scale-out domain, and popular parallelism/optimization strategies used in LLMs. We introduce and evaluate FullFlat network architectures, which provide uniform high-bandwidth, low-latency connectivity between all nodes, and demonstrate their transformative impact on performance and scalability. Through detailed sensitivity analyses, we quantify the benefits of overlapping compute and communication, leveraging hardware-accelerated collectives, wider scale-out domains, and larger memory capacity. Our study spans both sparse (mixture of experts) and dense transformer-based LLMs, revealing how system design choices affect Model FLOPS Utilization (MFU = Model flops per token x Observed tokens per sec / Peak flops of the hardware) and overall throughput. For the co-design study, we extended and validated a performance modeling tool capable of predicting LLM runtime within 10% of real-world measurements. Our findings offer actionable insights and a practical roadmap for designing AI data centers that can efficiently support trillion-parameter models, reduce optimization complexity, and sustain the rapid evolution of AI capabilities.
Hypothesis Testing for Quantifying LLM-Human Misalignment in Multiple Choice Settings
2025-06-17
Harbin Hong, Sebastian Caldas, Liu Leqi
As Large Language Models (LLMs) increasingly appear in social science research (e.g., economics and marketing), it becomes crucial to assess how well these models replicate human behavior. In this work, using hypothesis testing, we present a quantitative framework to assess the misalignment between LLM-simulated and actual human behaviors in multiple-choice survey settings. This framework allows us to determine in a principled way whether a specific language model can effectively simulate human opinions, decision-making, and general behaviors represented through multiple-choice options. We applied this framework to a popular language model for simulating people's opinions in various public surveys and found that this model is ill-suited for simulating the tested sub-populations (e.g., across different races, ages, and incomes) for contentious questions. This raises questions about the alignment of this language model with the tested populations, highlighting the need for new practices in using LLMs for social science studies beyond naive simulations of human subjects.
Revisiting Reinforcement Learning for LLM Reasoning from A Cross-Domain Perspective
2025-06-17
Zhoujun Cheng, Shibo Hao, Tianyang Liu, Fan Zhou, Yutao Xie, Feng Yao, Yuexin Bian, Yonghao Zhuang, Nilabjo Dey, Yuheng Zha, Yi Gu, Kun Zhou, Yuqi Wang, Yuan Li, Richard Fan, Jianshu She, Chengqian Gao, Abulhair Saparov, Haonan Li, Taylor W. Killian, Mikhail Yurochkin, Zhengzhong Liu, Eric P. Xing, Zhiting Hu
Reinforcement learning (RL) has emerged as a promising approach to improve large language model (LLM) reasoning, yet most open efforts focus narrowly on math and code, limiting our understanding of its broader applicability to general reasoning. A key challenge lies in the lack of reliable, scalable RL reward signals across diverse reasoning domains. We introduce Guru, a curated RL reasoning corpus of 92K verifiable examples spanning six reasoning domains--Math, Code, Science, Logic, Simulation, and Tabular--each built through domain-specific reward design, deduplication, and filtering to ensure reliability and effectiveness for RL training. Based on Guru, we systematically revisit established findings in RL for LLM reasoning and observe significant variation across domains. For example, while prior work suggests that RL primarily elicits existing knowledge from pretrained models, our results reveal a more nuanced pattern: domains frequently seen during pretraining (Math, Code, Science) easily benefit from cross-domain RL training, while domains with limited pretraining exposure (Logic, Simulation, and Tabular) require in-domain training to achieve meaningful performance gains, suggesting that RL is likely to facilitate genuine skill acquisition. Finally, we present Guru-7B and Guru-32B, two models that achieve state-of-the-art performance among open models RL-trained with publicly available data, outperforming best baselines by 7.9% and 6.7% on our 17-task evaluation suite across six reasoning domains. We also show that our models effectively improve the Pass@k performance of their base models, particularly on complex tasks less likely to appear in pretraining data. We release data, models, training and evaluation code to facilitate general-purpose reasoning at: https://github.com/LLM360/Reasoning360
Review of heat and charge transport in strongly magnetized relativistic plasmas
2025-06-17
Igor A. Shovkovy, Ritesh Ghosh
We review field-theoretic studies of charge transport in hot relativistic plasmas under strong magnetic fields and extend the analysis to thermal conductivity. The calculations rely on accurately determining the fermion damping rate. Using the Landau-level representation, these damping rates are computed exactly at leading order and incorporated into the Kubo formula to obtain the thermal and electrical conductivity tensors. Our analysis reveals that the underlying mechanisms of longitudinal and transverse transport differ significantly. Strong magnetic fields markedly suppress transverse transport by confining particles within localized Landau orbits, allowing transport only through quantum transitions between these discrete states. In contrast, longitudinal transport is enhanced, as it primarily depends on the reduced scattering probability of particles moving along the direction of the magnetic field. We also examine the modification of the Wiedemann--Franz law in strongly magnetized plasmas.
The MUSE view of the Sculptor galaxy: survey overview and the planetary nebulae luminosity function
2025-06-17
E. Congiu, F. Scheuermann, K. Kreckel, A. Leroy, E. Emsellem, F. Belfiore, J. Hartke, G. Anand, O. V. Egorov, B. Groves, T. Kravtsov, D. Thilker, C. Tovo, F. Bigiel, G. A. Blanc, A. D. Bolatto, S. A. Cronin, D. A. Dale, R. McClain, J. E. Méndez-Delgado, E. K. Oakes, R. S. Klessen, E. Schinnerer, T. G. Williams
NGC 253, the Sculptor galaxy, is the southern, massive, star-forming disk galaxy closest to the Milky Way. In this work, we present a new 103-pointing MUSE mosaic of this galaxy covering the majority of its star-forming disk up to 0.75xR25. With an area of ~20x5 arcmin2 (~20x5 kpc2, projected) and a physical resolution of ~15 pc, this mosaic constitutes one of the largest, highest physical resolution integral field spectroscopy surveys of any star-forming galaxy to date. Here, we exploit the mosaic to identify a sample of ~500 planetary nebulae (~20 times larger than in previous studies) to build the planetary nebula luminosity function (PNLF) and obtain a new estimate of the distance to NGC 253. The value obtained is 17% higher than estimates returned by other reliable measurements, mainly obtained via the top of the red giant branch method (TRGB). The PNLF also varies between the centre (r < 4 kpc) and the disk of the galaxy. The distance derived from the PNLF of the outer disk is comparable to that of the full sample, while the PNLF of the centre returns a distance ~0.9 Mpc larger. Our analysis suggests that extinction related to the dust-rich interstellar medium and edge-on view of the galaxy (the average E(B-V) across the disk is ~0.35 mag) plays a major role in explaining both the larger distance recovered from the full PNLF and the difference between the PNLFs in the centre and in the disk.
Impact of Gravity Waves From Tropospheric and Non-tropospheric Sources on the Middle and Upper Atmosphere and Comparison with ICON/MIGHTI Winds
2025-06-17
Erdal Yiğit, Alexander S. Medvedev, Ayden L. S. Gann, Gary P. Klaassen, Douglas E. Rowland
We study the dynamical and thermal roles of internal gravity waves generated in the troposphere and above using the Coupled Middle Atmosphere Thermosphere-2 General Circulation Model. This model incorporates the whole atmosphere nonlinear gravity wave parameterization and its extension to include non-tropospheric sources. We conducted model experiments for northern summer solstice conditions, first including only tropospheric sources, then including sources localized at 50 and 90 km, and uniformly distributed over all heights. The simulated differences in mean temperature and horizontal winds demonstrate that gravity waves produce the greatest dynamical and thermal changes in the latter case compared to the localized sources. While the gravity wave drag is longitudinally uniform in the lower thermosphere, it is more localized in the upper thermosphere in all the simulations. Waves from uniformly distributed sources increase the longitudinal variability of zonal winds in the thermosphere up to $\sim$150 km. Gravity wave effects exhibit different local time variations in the lower thermosphere (100--140 km) than in the upper thermosphere. In the upper thermosphere, gravity wave effects are stronger during the day than at night. In contrast, nighttime gravity wave effects are stronger than the daytime ones in the lower thermosphere. Finally, a comparison with ICON-MIGHTI observations shows that the model reproduces the basic structure of thermospheric winds, performing better with zonal winds than with meridional winds. Adding non-tropospheric wave sources modifies wind structures in wave-breaking regions, but does not improve the global statistical comparison.
How many federal employees are not satisfied? Using response times to estimate population proportions under the survey variable cause model
2025-06-17
Jonathan Auerbach
We propose a statistical model to estimate population proportions under the survey variable cause model (Groves 2006), the setting in which the characteristic measured by the survey has a direct causal effect on survey participation. For example, we estimate employee satisfaction from a survey in which the decision of an employee to participate depends on their satisfaction. We model the time at which a respondent 'arrives' to take the survey, leveraging results from the counting processes literature that has been developed to analyze similar problems with survival data. Our approach is particularly useful for nonresponse bias analysis because it relies on different assumptions than traditional adjustments such as poststratification, which assumes the common cause model, the setting in which external factors explain the characteristic measured by the survey and participation. Our motivation is the Federal Employee Viewpoint Survey, which asks federal employees whether they are satisfied with their work organization. Our model suggests that the sample proportion overestimates the proportion of federal employees that are not satisfied with their work organization even after adjustment by poststratification. Employees that are not satisfied likely select into the survey, and this selection cannot be explained by personal characteristics like race, gender, and occupation or work-place characteristics like agency, unit, and location.
Self-consistent population synthesis of AGN from observational constraints in the X-rays
2025-06-17
D. Gerolymatou, S. Paltani, C. Ricci, M. Regamey
The cosmic X-ray background (CXB) is produced by the emission of unresolved active galactic nuclei (AGN), thus providing key information about the properties of the primary and reprocessed X-ray emission components of the AGN population. Equally important, studies of individual sources provide additional constraints on the properties of AGN, such as their luminosity and obscuration. Until now, these constraints have not been self-consistently addressed by intrinsically linking emission, absorption, and reflection. Here we perform numerical simulations with the ray-tracing code, RefleX, which allows us to self-consistently model the X-ray emission of AGN with flexible geometries for the circumnuclear medium. Using the RefleX-simulated emission of an AGN population, we attempt to simultaneously reproduce the CXB and absorption properties measured in the X-rays, namely the observed fraction of $N_{\mathrm{H}}$ in bins of log($N_{\mathrm{H}}$) and the fraction of absorbed AGN, including their redshift and luminosity evolution. We sample an intrinsic X-ray luminosity function and construct gradually more complex physically motivated geometrical models. We examine how well each model can match all observational constraints using a simulation-based inference (SBI) approach. We find that, while the simple unification model can reproduce the CXB, a luminosity dependent dusty torus is needed to reproduce the absorption properties. When adding an accretion disc, the model best matches all constraints simultaneously. Our synthetic population is able to reproduce the dependence of the covering factor on luminosity, the AGN number counts from several surveys, and the observed correlation between reflection and obscuration. Finally, we derive an intrinsic Compton-thick fraction of 21$\pm$7%, consistent with local observations.
The ASAS-SN Low Surface Brightness Survey I: Proof-of-Concept and Potential Applications
2025-06-17
Evan Jennerjahn, Michael A. Tucker, Benjamin J. Shappee, Christopher S. Kochanek, Subo Dong, Annika H. G. Peter, Jose L. Prieto, K. Z. Stanek, Todd A. Thompson
The ASAS-SN Low Surface Brightness Survey utilizes the $\sim7$ years of g-band CCD data from ASAS-SN (The All-Sky Automated Survey for Supernovae) to create stacked images of the entire sky. It is significantly deeper than previous photographic surveys. Our median/95th percentile cumulative exposure time per field is 58.1/86.8 hours, and our median $3{\sigma}$ g-band surface brightness limit off the Galactic plane ($|b| > 20{\deg}$) is 26.1 mag arcsec$^{-2}$. We image large-scale diffuse structures within the Milky Way, such as multiple degree-spanning supernova remnants and star-forming nebulae, and tidal features of nearby galaxies. To quantify how effective our deep images are, we compare with a catalog of known ultra-diffuse galaxies and find a recovery rate of 82$\%$. In the future, we intend to use this data set to perform an all-sky search for new nearby dwarf galaxies, create an all-sky Galactic cirrus map, create an all-sky low surface brightness mosaic for public use, and more.
JADES and BlackTHUNDER: rest-frame Balmer-line absorption and the local environment in a Little Red Dot at z = 5
2025-06-17
Francesco D'Eugenio, Ignas Juodžbalis, Xihan Ji, Jan Scholtz, Roberto Maiolino, Stefano Carniani, Michele Perna, Giovanni Mazzolari, Hannah Übler, Santiago Arribas, Rachana Bhatawdekar, Andrew J. Bunker, Giovanni Cresci, Emma Curtis-Lake, Kevin Hainline, Kohei Inayoshi, Yuki Isobe, Benjamin D. Johnson, Gareth C. Jones, Tobias J. Looser, Erica J. Nelson, Eleonora Parlanti, Dávid Puskás, Pierluigi Rinaldi, Brant Robertson, Bruno Rodríguez Del Pino, Irene Shivaei, Fengwu Sun, Sandro Tacchella, Giacomo Venturi, Marta Volonteri, Christina C. Williams, Christopher N. A. Willmer, Chris Willott, Joris Witstok
We present a broad-line active galactic nucleus (AGN) at z = 5.077, observed with both NIRSpec/MSA and NIRSpec/IFU by the JADES and BlackTHUNDER surveys. The target exhibits all the hallmark features of a 'Little Red Dot' (LRD) AGN. The combination of spatially resolved and high-resolution spectroscopy offers deeper insight into its nature. The H$\alpha$ line has multiple components, including two broad Gaussians, yielding a black-hole mass of $\log(M_{\rm BH}/M_\odot) = 7.65$, while the narrow [O III]$\lambda$5007 gives a galaxy dynamical mass of $\log(M_{\rm dyn}/M_\odot) = 9.1$, suggesting a dynamically overmassive black hole relative to the host galaxy. The target has two satellites, and is immersed in a 7-kpc wide pool of ionized gas. A spatially detached outflow is also tentatively detected. H$\alpha$ shows strong absorption with high equivalent width (EW), ruling out a stellar origin, and with velocity and velocity dispersion of v = -13 km s$^{-1}$ and $\sigma$ = 120 km s$^{-1}$. There is tentative evidence (2.6 $\sigma$) of temporal variability in the EW of the H$\alpha$ absorber over two rest-frame months. If confirmed, this would suggest a highly dynamic environment. Notably, while the H$\alpha$ absorber is clearly visible and even dominant in the high-resolution G395H observations, it is not detected in the medium-resolution G395M data of the same epoch. This implies that the current incidence rate of absorbers in LRDs - and especially of rest-frame absorbers - may be severely underestimated, because most LRDs rely on lower-resolution spectroscopy. In this context, the high incidence rate of rest-frame absorbers in LRDs may indicate a configuration that is either intrinsically stationary, such as a rotating disc, or that exhibits time-averaged stability, such as an oscillatory 'breathing mode' accretion of cyclic expansion and contraction of the gas around the SMBH.
pycnet-audio: A Python package to support bioacoustics data processing
2025-06-17
Zachary J. Ruff, Damon B. Lesmeister
Passive acoustic monitoring is an emerging approach in wildlife research that leverages recent improvements in purpose-made automated recording units (ARUs). The general approach is to deploy ARUs in the field to record on a programmed schedule for extended periods (weeks or months), after which the audio data are retrieved. These data must then be processed, typically either by measuring or analyzing characteristics of the audio itself (e.g. calculating acoustic indices), or by searching for some signal of interest within the recordings, e.g. vocalizations or other sounds produced by some target species, anthropogenic or environmental noise, etc. In the latter case, some method is required to locate the signal(s) of interest within the audio. While very small datasets can simply be searched manually, even modest projects can produce audio datasets on the order of 105 hours of recordings, making manual review impractical and necessitating some form of automated detection. pycnet-audio (Ruff 2024) is intended to provide a practical processing workflow for acoustic data, built around the PNW-Cnet model, which was initially developed by the U.S. Forest Service to support population monitoring of northern spotted owls (Strix occidentalis caurina) and other forest owls (Lesmeister and Jenkins 2022; Ruff et al. 2020). PNW-Cnet has been expanded to detect vocalizations of ca. 80 forest wildlife species and numerous forms of anthropogenic and environmental noise (Ruff et al. 2021, 2023).
Technosignature Searches with Real-time Alert Brokers
2025-06-17
Eleanor M. Gallay, James R. A. Davenport, Steve Croft
We present an exploration of technosignature research that is possible using real-time alert brokers from surveys such as the Zwicky Transient Facility (ZTF) and the upcoming Legacy Survey of Space and Time (LSST). Nine alert brokers currently stream up to 1 million alerts each night from ZTF, and LSST is projected to increase this volume by an order of magnitude. While these brokers are primarily designed to facilitate real-time follow-up of explosive transients such as supernovae, they offer a unique platform to discover rare forms of variability from nearby stars in real time, which is crucial for follow-up and characterization. We evaluate the capability for both spatial and temporal searches for extraterrestrial intelligence (SETI) methods using the currently available brokers, and present examples of technosignature searches using ZTF alert and archival data. We have deployed optical SETI techniques, such as planetary transit zone geometries and the SETI Ellipsoid. We have also developed a search for novel high-amplitude stellar dippers, and present a workflow that integrates features available directly through the brokers, as well as post-processing steps that build on the existing capabilities. Though the SETI methods that alert brokers can execute are currently limited, we provide suggestions that may enhance future technosignature and anomaly searches in the era of the Vera C. Rubin Observatory.
Sulphur abundances in star-forming regions from optical emission lines: A new approach based on photoionization models consistent with the direct method
2025-06-17
Enrique Pérez-Montero, Borja Pérez-Díaz, José M. Vílchez, Igor A. Zinchenko, Asier Castrillo, Marta Gavilán, Sandra Zamora, Ángeles I. Díaz
The derivation of sulphur chemical abundances in the gas-phase of star-forming galaxies is explored in this work, using the emission lines produced in these regions in the optical part of the spectrum and by means of photoionization models. We adapted the code HII-CHI-mistry to account for these abundances by implementing additional grids of models that assume a variable sulphur-to-oxygen abundance ratio, beyond the commonly assumed solar ratio. The addition of these models, and their use in a new iteration of the code allows us to use sulphur lines to precisely estimate the sulphur abundance, even in the absence of auroral lines. This approach aligns with the results from the direct method, and no additional assumptions about the ionization correction factor are needed, as the models directly predict the total sulphur abundance. We applied this new methodology to a large sample of star-forming regions from the MaNGA survey, and we explored the variation of the S/O ratio as a function of metallicity, making corrections for the significant contribution from diffuse ionized gas, which particularly affects the [SII] emission. Our results indicate no significant deviations from the solar S/O value in the range 8.0 < 12+log(O/H) < 8.7, where the bulk of the MaNGA sample stays, , but also with possible enhancements of sulphur production both at low and high metallicity regimes. The latter may be linked to the depletion of oxygen in the gas-phase due to its incorporation onto dust grains
On Sun-Zhang's theory of Fano fibrations -- weighted volumes, moduli and bubbling Fano fibrations
2025-06-17
Yuji Odaka
We revisit the recent theory of Sun-Zhang on general Fano fibration (germs) which emerged from the study of non-compact Kahler-Ricci soliton metrics, primarily from an algebro-geometric perspective. In addition to reviewing the existing framework, we present new results, conjectures, and remarks. These include methods for computing weighted volumes via (restricted) volumes, Laplace transforms, and incomplete Gamma-functions, and a conjectural algebro-geometric construction (``bubbling") of asymptotically conical Fano fibrations from degenerating Fano fibrations.
StreetLens: Enabling Human-Centered AI Agents for Neighborhood Assessment from Street View Imagery
2025-06-17
Jina Kim, Leeje Jang, Yao-Yi Chiang, Guanyu Wang, Michelle Pasco
Traditionally, neighborhood studies have employed interviews, surveys, and manual image annotation guided by detailed protocols to identify environmental characteristics, including physical disorder, decay, street safety, and sociocultural symbols, and to examine their impact on developmental and health outcomes. While these methods yield rich insights, they are time-consuming and require intensive expert intervention. Recent technological advances, including vision-language models (VLMs), have begun to automate parts of this process; however, existing efforts are often ad hoc and lack adaptability across research designs and geographic contexts. In this demo paper, we present StreetLens, a human-centered, researcher-configurable workflow that embeds relevant social science expertise in a VLM for scalable neighborhood environmental assessments. StreetLens mimics the process of trained human coders by grounding the analysis in questions derived from established interview protocols, retrieving relevant street view imagery (SVI), and generating a wide spectrum of semantic annotations from objective features (e.g., the number of cars) to subjective perceptions (e.g., the sense of disorder in an image). By enabling researchers to define the VLM's role through domain-informed prompting, StreetLens places domain knowledge at the core of the analysis process. It also supports the integration of prior survey data to enhance robustness and expand the range of characteristics assessed across diverse settings. We provide a Google Colab notebook to make StreetLens accessible and extensible for researchers working with public or custom SVI datasets. StreetLens represents a shift toward flexible, agentic AI systems that work closely with researchers to accelerate and scale neighborhood studies.
Navigating the growing field of research on AI for software testing -- the taxonomy for AI-augmented software testing and an ontology-driven literature survey
2025-06-17
Ina K. Schieferdecker
In industry, software testing is the primary method to verify and validate the functionality, performance, security, usability, and so on, of software-based systems. Test automation has gained increasing attention in industry over the last decade, following decades of intense research into test automation and model-based testing. However, designing, developing, maintaining and evolving test automation is a considerable effort. Meanwhile, AI's breakthroughs in many engineering fields are opening up new perspectives for software testing, for both manual and automated testing. This paper reviews recent research on AI augmentation in software test automation, from no automation to full automation. It also discusses new forms of testing made possible by AI. Based on this, the newly developed taxonomy, ai4st, is presented and used to classify recent research and identify open research questions.
AIn't Nothing But a Survey? Using Large Language Models for Coding German Open-Ended Survey Responses on Survey Motivation
2025-06-17
Leah von der Heyde, Anna-Carolina Haensch, Bernd Weiß, Jessica Daikeler
The recent development and wider accessibility of LLMs have spurred discussions about how they can be used in survey research, including classifying open-ended survey responses. Due to their linguistic capacities, it is possible that LLMs are an efficient alternative to time-consuming manual coding and the pre-training of supervised machine learning models. As most existing research on this topic has focused on English-language responses relating to non-complex topics or on single LLMs, it is unclear whether its findings generalize and how the quality of these classifications compares to established methods. In this study, we investigate to what extent different LLMs can be used to code open-ended survey responses in other contexts, using German data on reasons for survey participation as an example. We compare several state-of-the-art LLMs and several prompting approaches, and evaluate the LLMs' performance by using human expert codings. Overall performance differs greatly between LLMs, and only a fine-tuned LLM achieves satisfactory levels of predictive performance. Performance differences between prompting approaches are conditional on the LLM used. Finally, LLMs' unequal classification performance across different categories of reasons for survey participation results in different categorical distributions when not using fine-tuning. We discuss the implications of these findings, both for methodological research on coding open-ended responses and for their substantive analysis, and for practitioners processing or substantively analyzing such data. Finally, we highlight the many trade-offs researchers need to consider when choosing automated methods for open-ended response classification in the age of LLMs. In doing so, our study contributes to the growing body of research about the conditions under which LLMs can be efficiently, accurately, and reliably leveraged in survey research.
This draft is a working document, having a summary of nighty-four (94) papers with additional sections on Traceability of Software Requirements (Section 4), Formal Methods and Its Tools (Section 5), Unifying Theories of Programming (UTP) and Theory of Institutions (Section 6). Please refer to abstract of [7,8]. Key difference of this draft from our recently anticipated ones with similar titles, i.e. AACS 2025 [7] and SAIV 2025 [8] is: [7] is a two page submission to ADAPT Annual Conference, Ireland. Submitted on 18th of March, 2025, it went through the light-weight blind review and accepted for poster presentation. Conference was held on 15th of May, 2025. [8] is a nine page paper with additional nine pages of references and summary tables, submitted to Symposium on AI Verification (SAIV 2025) on 24th of April, 2025. It went through rigorous review process. The uploaded version on arXiv.org [8] is the improved one of the submission, after addressing the specific suggestions to improve the paper.
The Second and Third Data Releases from the UKIRT Hemisphere Survey
2025-06-17
Adam C. Schneider, Frederick J. Vrba, Justice Bruursema, Jeffrey A. Munn, Mike Irwin, Mike Read, Watson Varricatt, Tom Kerr, Klaus Hodapp, Simon Dye, Stephen J. Williams, Andrew T. Cenko, Trudy M. Tilleman, Marc A. Murison, Barry Rothberg, Scott Dahm, Bryan Dorland, Andy Lawrence, Kenneth C. Chambers
This paper describes the second and third data releases (DR2 and DR3, respectively) from the ongoing United Kingdom Infrared Telescope (UKIRT) Hemisphere Survey (UHS). DR2 is primarily comprised of the $K$-band portion of the UHS survey, and was released to the public on June 1, 2023. DR3 mainly includes the $H$-band portion of the survey, with a public release scheduled for September 2025. The $H$- and $K$-band data releases complement the previous $J$-band data release (DR1) from 2018. The survey covers approximately 12,700 square degrees between declinations of 0 degrees and $+$60 degrees and achieves median 5$\sigma$ point source sensitivities of 19.0 mag and 18.0 mag (Vega) for $H$ and $K$, respectively. The data releases include images and source catalogs which include $\sim$581 million $H$-band detections and $\sim$461 million $K$-band detections. DR2 and DR3 also include merged catalogs, created by combining $J$- and $K$-band detections (DR2) and $J$-, $H$-, and $K$-band detections (DR3). The DR2 merged catalog has a total of $\sim$513 million sources, while the DR3 merged catalog contains $\sim$560 million sources.
The Horvitz-Thompson estimate of a total can be seen as as differentially private mechanism applied to this population total. We provide forumlae to compute the $\epsilon$ and $\delta$ parameter for this specific mecanism, coupled or not coupled with the addition of a Laplace or a Gaussian noise. This allows to determine the scale of the Laplace privacy mechanism to be added to reach a specified level of privacy, expressed in terms of $\epsilon,\delta$ differential privacy. In particular, we provide simple formulae for the special case of simple random sampling on binary data.
BEBOP VII. SOPHIE discovery of BEBOP-3b, a circumbinary giant planet on an eccentric orbit
2025-06-17
Thomas A. Baycroft, Alexandre Santerne, Amaury H. M. J. Triaud, Neda Heidari, Daniel Sebastian, Yasmin T. Davis, Alexandre C. M. Correia, Lalitha Sairam, Alix V. Freckelton, Aleyna Adamson, Isabelle Boisse, Gavin A. L. Coleman, Georgina Dransfield, João Faria, Salomé Grouffal, Nathan Hara, Guillaume Hébrard, Vedad Kunovac, David V. Martin, Pierre F. L. Maxted, Richard P. Nelson, Madison G. Scott, Owen J. Scutt, Matthew R. Standing
Planetary systems orbiting close binaries are valuable testing grounds for planet formation and migration models. More detections with good mass measurements are needed. We present a new planet discovered during the BEBOP survey for circumbinary exoplanets using radial velocities. We use data taken with the SOPHIE spectrograph at the Observatoire de Haute-Provence, and perform a spectroscopic analysis to obtain high precision radial velocities. This planet is the first radial velocity detection of a previously unknown circumbinary system. The planet has a mass of $0.56$ $M_{Jup}$ and orbits its host binary in 550 days with an eccentricity of 0.25. Compared to most of the previously known circumbinary planets, BEBOP-3b has a long period (relative to the binary) and a high eccentricity. There also is a candidate outer planet with a $\sim1400$ day orbital period. We test the stability of potential further candidate signals inside the orbit of BEBOP-3b, and demonstrate that there are stable orbital solutions for planets near the instability region which is where the Kepler circumbinary planets are located. We also use our data to obtain independent dynamical masses for the two stellar components of the eclipsing binary using High Resolution Cross-Correlation Spectroscopy (HRCCS), and compare those results to a more traditional approach, finding them compatible with one another.
Influencer marketing has become a crucial feature of digital marketing strategies. Despite its rapid growth and algorithmic relevance, the field of computational studies in influencer marketing remains fragmented, especially with limited systematic reviews covering the computational methodologies employed. This makes overarching scientific measurements in the influencer economy very scarce, to the detriment of interested stakeholders outside of platforms themselves, such as regulators, but also researchers from other fields. This paper aims to provide an overview of the state of the art of computational studies in influencer marketing by conducting a systematic literature review (SLR) based on the PRISMA model. The paper analyses 69 studies to identify key research themes, methodologies, and future directions in this research field. The review identifies four major research themes: Influencer identification and characterisation, Advertising strategies and engagement, Sponsored content analysis and discovery, and Fairness. Methodologically, the studies are categorised into machine learning-based techniques (e.g., classification, clustering) and non-machine-learning-based techniques (e.g., statistical analysis, network analysis). Key findings reveal a strong focus on optimising commercial outcomes, with limited attention to regulatory compliance and ethical considerations. The review highlights the need for more nuanced computational research that incorporates contextual factors such as language, platform, and industry type, as well as improved model explainability and dataset reproducibility. The paper concludes by proposing a multidisciplinary research agenda that emphasises the need for further links to regulation and compliance technology, finer granularity in analysis, and the development of standardised datasets.
Review of Machine Learning for Real-Time Analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb
2025-06-17
Laura Boggia, Carlos Cocha, Fotis Giasemis, Joachim Hansen, Patin Inkaew, Kaare Endrup Iversen, Pratik Jawahar, Henrique Pineiro Monteagudo, Micol Olocco, Sten Astrand, Martino Borsato, Leon Bozianu, Steven Schramm, the SMARTHEP Network
The field of high energy physics (HEP) has seen a marked increase in the use of machine learning (ML) techniques in recent years. The proliferation of applications has revolutionised many aspects of the data processing pipeline at collider experiments including the Large Hadron Collider (LHC). In this whitepaper, we discuss the increasingly crucial role that ML plays in real-time analysis (RTA) at the LHC, namely in the context of the unique challenges posed by the trigger systems of the large LHC experiments. We describe a small selection of the ML applications in use at the large LHC experiments to demonstrate the breadth of use-cases. We continue by emphasising the importance of collaboration and engagement between the HEP community and industry, highlighting commonalities and synergies between the two. The mutual benefits are showcased in several interdisciplinary examples of RTA from industrial contexts. This whitepaper, compiled by the SMARTHEP network, does not provide an exhaustive review of ML at the LHC but rather offers a high-level overview of specific real-time use cases.
SoK: Privacy-Enhancing Technologies in Artificial Intelligence
2025-06-17
Nouha Oualha
As artificial intelligence (AI) continues to permeate various sectors, safeguarding personal and sensitive data has become increasingly crucial. To address these concerns, privacy-enhancing technologies (PETs) have emerged as a suite of digital tools that enable data collection and processing while preserving privacy. This paper explores the current landscape of data privacy in the context of AI, reviews the integration of PETs within AI systems, and assesses both their achievements and the challenges that remain.
Automatic Qiskit Code Refactoring Using Large Language Models
2025-06-17
José Manuel Suárez, Luis Mariano Bibbó, Joaquin Bogado, Alejandro Fernandez
As quantum software frameworks evolve, developers face increasing challenges in maintaining compatibility with rapidly changing APIs. In this work, we present a novel methodology for refactoring Qiskit code using large language models (LLMs). We begin by extracting a taxonomy of migration scenarios from the different sources of official Qiskit documentation (such as release notes), capturing common patterns such as migration of functionality to different modules and deprecated usage. This taxonomy, along with the original Python source code, is provided as input to an LLM, which is then tasked with identifying instances of migration scenarios in the code and suggesting appropriate refactoring solutions. Our approach is designed to address the context length limitations of current LLMs by structuring the input and reasoning process in a targeted, efficient manner. The results demonstrate that LLMs, when guided by domain-specific migration knowledge, can effectively assist in automating Qiskit code migration. This work contributes both a set of proven prompts and taxonomy for Qiskit code migration from earlier versions to version 0.46 and a methodology to asses the capabilities of LLMs to assist in the migration of quantum code.
Towards Improved Research Methodologies for Industrial AI: A case study of false call reduction
2025-06-17
Korbinian Pfab, Marcel Rothering
Are current artificial intelligence (AI) research methodologies ready to create successful, productive, and profitable AI applications? This work presents a case study on an industrial AI use case called false call reduction for automated optical inspection to demonstrate the shortcomings of current best practices. We identify seven weaknesses prevalent in related peer-reviewed work and experimentally show their consequences. We show that the best-practice methodology would fail for this use case. We argue amongst others for the necessity of requirement-aware metrics to ensure achieving business objectives, clear definitions of success criteria, and a thorough analysis of temporal dynamics in experimental datasets. Our work encourages researchers to critically assess their methodologies for more successful applied AI research.
FAST Pulsar Database: II. Scattering profiles of 122 Pulsars
2025-06-17
W. C. Jing, J. L. Han, C. Wang, P. F. Wang, T. Wang, N. N. Cai, J. Xu, Z. L. Yang, D. J. Zhou, Yi Yan, W. Q. Su, X. Y. Gao, L. Xie
The turbulent ionized interstellar medium diffracts radio waves and makes them propagate in multiple paths. The pulse-broadening observed at low frequencies results from the scattering effect of interstellar clouds of ionized gas. During the Galactic Plane Pulsar Snapshot (GPPS) survey and other projects by using the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we detect the pulse-broadening for 122 pulsars in the radio frequency band between 1.0 and 1.5 GHz, including 60 newly discovered pulsars in the GPPS survey and 62 previously known pulsars. We find that a more accurate dispersion measure can be obtained from aligning the front edge of the scattered subband pulses at the 1/4 or 1/2 peak level for most pulsars with one dominant component in the intrinsic profile, and the best DM values from aligning the intrinsic profile components from the model-fitting. From the pulse profiles at a few subbands we derive the pulse-broadening timescale and the scattering spectral index. These scattering parameters are measured for the first time for 93 pulsars. For 29 pulsars with previously detected scattering features, our measurements of the pulse-broadening timescale are consistent with results in the literature. We find that pulsars behind spiral arms show a stronger scattering effect due to greater density fluctuations in the arm regions. With a properly derived dispersion measure and careful calibration, we also present polarization profiles for 41 pulsars in three subbands of FAST observations.
The Future of Solar modelling: requirements for a new generation of solar models
2025-06-17
Gael Buldgen, Gloria. Canocchi, Arthur. Le Saux, Vladimir A. Baturin, Regner Trampedach, Anna V. Oreshina, Sergey V. Ayukov, Anil Pradhan, Jean-Christophe Pain, Masanobu Kunitomo, Thierry Appourchaux, Rafael A. Garcia, Morgan Deal, Nicolas Grevesse, Arlette Noels, Joergen Christensen-Dalsgaard, Tristan Guillot, Devesh Nandal, Jérôme Bétrisey, Christophe Blancard, James Colgan, Philippe Cossé Christopher, J. Fontes, Ludovic Petitdemange, Charly Pincon
Helioseismology and solar modelling have enjoyed a golden era thanks to decades-long surveys from ground-based networks such as for example GONG, BiSON, IRIS and the SOHO and SDO space missions which have provided high-quality helioseismic observations that supplemented photometric, gravitational, size and shape, limb-darkening and spectroscopic constraints as well as measurements of neutrino fluxes. However, the success of solar models is also deeply rooted in progress in fundamental physics (equation of state of the solar plasma, high-quality atomic physics computations and opacities, description of convection and the role of macroscopic transport processes of angular momentum and chemicals, such as for example meridional circulation, internal gravity waves, shear-induced turbulence or even convection. In this paper, we briefly outline some key areas of research that deserve particular attention in solar modelling. We discuss the current uncertainties that need to be addressed, how these limit our predictions from solar models and their impact on stellar evolution in general. We outline potential strategies to mitigate them and how multidisciplinary approaches will be needed in the future to tackle them.
ODIN: The LAE Lyα Luminosity Function over Cosmic Time and Environmental Density
2025-06-17
Gautam Nagaraj, Robin Ciardullo, Caryl Gronwall, Vandana Ramakrishnan, Kyoung-Soo Lee, Eric Gawiser, Nicole M. Firestone, Govind Ramgopal, J. Aguilar, Steven Ahlen, Davide Bianchi, David Brooks, Francisco Javier Castander, Todd Claybaugh, Andrei Cuceu, Axel de la Macorra, Arjun Dey, Biprateep Dey, Peter Doel, Jaime Forero-Romero, Enrique Gaztanaga, Satya Gontcho A Gontcho, Gaston Gutierrez, Hiram K. Herrera-Alcantar, Klaus Honscheid, Mustapha Ishak, Robert Kehoe, David Kirkby, T. Kisner, Anthony Kremin, Martin Landriau, Laurent Le Guillou, Michael Levi, Christophe Magneville, Marc Manera, Paul Martini, Aaron M. Meisner, Ramon Miquel, John Moustakas, Nathalie Palanque-Delabrouille, Francisco Prada, Ignasi Perez-Rafols, Graziano Rossi, Lado Samushia, Eusebio Sanchez, David J. Schlegel, Michael F. Schubnell, Hee-Jong Seo, Joseph H. Silber, David Sprayberry, Gregory Tarle, Francisco Valdes, Benjamin A. Weaver, Martin White, Rongpu Zhou, Hu Zou
The ubiquity and relative ease of discovery make $2\lesssim z\lesssim 5$ Ly$\alpha$ emitting galaxies (LAEs) ideal tracers for cosmology. In addition, because Ly$\alpha$ is a resonance line, but frequently observed at large equivalent width, it is potentially a probe of galaxy evolution. The LAE Ly$\alpha$ luminosity function (LF) is an essential measurement for making progress on both of these aspects. Although several studies have computed the LAE LF, very few have delved into how the function varies with environment. The large area and depth of the One-hundred-deg2 DECam Imaging in Narrowbands (ODIN) survey makes such measurements possible at the cosmic noon redshifts of z~2.4, ~3.1, and ~4.5. In this initial work, we present algorithms to rigorously compute the LAE LF and test our methods on the ~16,000 ODIN LAEs found in the extended COSMOS field. Using these limited samples, we find slight evidence that protocluster environments either suppress the numbers of very faint and very bright LAEs or enhance medium-bright LAEs in comparison to the field. We also find that the LF decreases in number density and evolves towards a steeper faint-end slope over cosmic time from z~4.5 to z~2.4.
An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences
2025-06-17
Styliani-Christina Fragkouli, Somya Iqbal, Lisa Crossman, Barbara Gravel, Nagat Masued, Mark Onders, Devesh Haseja, Alex Stikkelman, Alfonso Valencia, Tom Lenaerts, Fotis Psomopoulos, Pilib Ó Broin, Núria Queralt-Rosinach, Davide Cirillo
Synthetic data has emerged as a powerful resource in life sciences, offering solutions for data scarcity, privacy protection and accessibility constraints. By creating artificial datasets that mirror the characteristics of real data, allows researchers to develop and validate computational methods in controlled environments. Despite its promise, the adoption of synthetic data in Life Sciences hinges on rigorous evaluation metrics designed to assess their fidelity and reliability. To explore the current landscape of synthetic data evaluation metrics in several Life Sciences domains, the ELIXIR Machine Learning Focus Group performed a systematic review of the scientific literature following the PRISMA guidelines. Six critical domains were examined to identify current practices for assessing synthetic data. Findings reveal that, while generation methods are rapidly evolving, systematic evaluation is often overlooked, limiting researchers ability to compare, validate, and trust synthetic datasets across different domains. This systematic review underscores the urgent need for robust, standardized evaluation approaches that not only bolster confidence in synthetic data but also guide its effective and responsible implementation. By laying the groundwork for establishing domain-specific yet interoperable standards, this scoping review paves the way for future initiatives aimed at enhancing the role of synthetic data in scientific discovery, clinical practice and beyond.
With the development of audio deepfake techniques, attacks with partially deepfake audio are beginning to rise. Compared to fully deepfake, it is much harder to be identified by the detector due to the partially cryptic manipulation, resulting in higher security risks. Although some studies have been launched, there is no comprehensive review to systematically introduce the current situations and development trends for addressing this issue. Thus, in this survey, we are the first to outline a systematic introduction for partially deepfake audio manipulated region localization tasks, including the fundamentals, branches of existing methods, current limitations and potential trends, providing a revealing insight into this scope.
Asteroseismology of four eccentric double-lined spectroscopic eclipsing binaries
2025-06-17
Alexios Liakos
Photometric data from the Transiting Exoplanet Survey Satellite (TESS) mission and radial velocities from the Gaia mission and ground-based observations were used to model the light curves and calculate the physical parameters of the eccentric eclipsing systems CH Ind, V577 Oph, CX Phe, and TIC 35481236. The components of these systems have temperatures between 6450 and 7500 K, masses between 1.4 and 1.85 solar masses, and radii between 1.49 and 3.05 solar radii. The residuals of these models were further analyzed using the Fourier method to reveal the pulsational frequencies of their oscillating components. Due to the similarity of the components of each system, the eclipses were used as spatial filters in order to determine which member is the pulsating star. CH Ind was found to pulsate in 46 frequencies; its primary component is a $\gamma$ Dor star and the secondary a $\delta$ Sct star. The primary component of V577 Oph oscillates in both the regimes of $\gamma$ Dor and $\delta$ Sct stars. Moreover, using past timings of minima, an eclipse timing variation analysis was also performed for V577 Oph, resulting in the calculation of the apsidal motion parameters and the existence of a third body around the system. Both components of CX Phe were found to be $\delta$ Sct stars; its primary has three independent frequencies in the range of 14.5-17.4 d$^{-1}$ and its secondary has two main modes of 5.19 and 7.22 d$^{-1}$. The analysis of TIC 35481236 indicates the hybrid $\delta$ Sct-$\gamma$ Dor nature of its secondary component. The physical and pulsational properties of the $\delta$ Sct stars of these systems were compared with those of other $\delta$ Sct stars-members of binaries in evolutionary diagrams.
Enclosing Prototypical Variational Autoencoder for Explainable Out-of-Distribution Detection
2025-06-17
Conrad Orglmeister, Erik Bochinski, Volker Eiselein, Elvira Fleig
Understanding the decision-making and trusting the reliability of Deep Machine Learning Models is crucial for adopting such methods to safety-relevant applications. We extend self-explainable Prototypical Variational models with autoencoder-based out-of-distribution (OOD) detection: A Variational Autoencoder is applied to learn a meaningful latent space which can be used for distance-based classification, likelihood estimation for OOD detection, and reconstruction. The In-Distribution (ID) region is defined by a Gaussian mixture distribution with learned prototypes representing the center of each mode. Furthermore, a novel restriction loss is introduced that promotes a compact ID region in the latent space without collapsing it into single points. The reconstructive capabilities of the Autoencoder ensure the explainability of the prototypes and the ID region of the classifier, further aiding the discrimination of OOD samples. Extensive evaluations on common OOD detection benchmarks as well as a large-scale dataset from a real-world railway application demonstrate the usefulness of the approach, outperforming previous methods.
Ole E. Barndorff-Nielsen: Sand, Wind and Inference
2025-06-17
Michael Sørensen
This paper reviews Ole Eiler Barndorff-Nielsen's research in the first decades of his career. The focus is on topics that he kept returning to throughout his scientific life, and on papers that he built on in later important contributions. First his early contributions to the foundations of statistical inference are reviewed with focus on conditional inference and exponential families, two topics in which he had a lifelong interest. The second half of the paper reviews his research on wind blown sand and hyperbolic distributions and processes, including his early contributions to modelling of turbulent wind fields. This research laid the foundations for his later work on financial econometrics and ambit processes.
Multiplicity of young isolated planetary mass objects in Taurus and Upper Scorpius
2025-06-17
H. Bouy, G. Duchêne, G. Strampelli, J. Aguilar, J. Olivares, D. Barrado, S. N. Raymond, N. Huélamo, M. Tamura, E. Bertin, W. Brandner, J. -C. Cuillandre, P. A. B. Galli, N. Miret-Roig
Free-floating planetary mass objects-worlds that roam interstellar space untethered to a parent star-challenge conventional notions of planetary formation and migration, but also of star and brown dwarf formation. We focus on the multiplicity among free-floating planets. By virtue of their low binding energy (compared to other objects formed in these environments), these low-mass substellar binaries represent a most sensitive probe of the mechanisms at play during the star formation process. We use the Hubble Space Telescope and its Wide Field Camera 3 and the Very Large Telescope and its ERIS adaptive optics facility to search for visual companions among a sample of 77 objects members of the Upper Scorpius and Taurus young nearby associations with estimated masses in the range between approximately 5-33 M$_{\rm Jup}$. We report the discovery of one companion candidate around a Taurus member with a separation of 111.9$\pm$0.4 mas, or $\sim$18 au assuming a distance of 160pc, with an estimated primary mass in the range between 3-6 M$_{\rm Jup}$ and a secondary mass between 2.6-5.2 M$_{\rm Jup}$, depending on the assumed age. This corresponds to an overall binary fraction of 1.8$^{+2.6}_{-1.3}$% among free-floating planetary mass objects over the separation range $\ge$7 au. Despite the limitations of small-number statistics and variations in spatial resolution and sensitivity, our results, combined with previous high-spatial-resolution surveys, suggest a notable difference in the multiplicity properties of objects below $\sim$25 M$_{\rm Jup}$ between Upper Sco and Taurus. In Taurus, five companions were identified among 78 observed objects (4.9$^{+2.8}_{-2.0}$%), whereas none were found among 97 objects in Upper Sco ($\le$1.2%).}
IntelliLung: Advancing Safe Mechanical Ventilation using Offline RL with Hybrid Actions and Clinically Aligned Rewards
2025-06-17
Muhammad Hamza Yousuf, Jason Li, Sahar Vahdati, Raphael Theilen, Jakob Wittenstein, Jens Lehmann
Invasive mechanical ventilation (MV) is a life-sustaining therapy for critically ill patients in the intensive care unit (ICU). However, optimizing its settings remains a complex and error-prone process due to patient-specific variability. While Offline Reinforcement Learning (RL) shows promise for MV control, current stateof-the-art (SOTA) methods struggle with the hybrid (continuous and discrete) nature of MV actions. Discretizing the action space limits available actions due to exponential growth in combinations and introduces distribution shifts that can compromise safety. In this paper, we propose optimizations that build upon prior work in action space reduction to address the challenges of discrete action spaces. We also adapt SOTA offline RL algorithms (IQL and EDAC) to operate directly on hybrid action spaces, thereby avoiding the pitfalls of discretization. Additionally, we introduce a clinically grounded reward function based on ventilator-free days and physiological targets, which provides a more meaningful optimization objective compared to traditional sparse mortality-based rewards. Our findings demonstrate that AI-assisted MV optimization may enhance patient safety and enable individualized lung support, representing a significant advancement toward intelligent, data-driven critical care solutions.
Cold gas in a post-starburst pair at z ~ 1.4: major mergers as a pathway to quenching in the HeavyMetal survey
2025-06-17
Katherine A. Suess, Aliza G. Beverage, Mariska Kriek, Justin S. Spilker, Rachel Bezanson, Vincenzo R. D'Onofrio, Jenny E. Greene, Jamie Lin, Yuanze Luo, Desika Narayanan, Imad Pasha, Sedona H. Price, David J. Setton, Margaret E. Verrico, Yunchong Zhang
Recent observations at low redshift have revealed that some post-starburst galaxies retain significant molecular gas reservoirs despite low ongoing star formation rates, challenging theoretical predictions for galaxy quenching. To test whether this finding holds during the peak epoch of quenching, here we present ALMA CO(2-1) observations of five spectroscopically confirmed post-starburst galaxies at z ~ 1.4 from the HeavyMetal survey. While four galaxies are undetected in CO emission, we detect M_H2 ~ 10^9.7 Msun of molecular gas in one system. The detected system is a close pair of massive (M* = 10^(11.1-11.2) Msun) post-starburst galaxies with no clear tidal features, likely caught in the early stages of a major merger. These results suggest that mergers may be a key factor in retaining molecular gas while simultaneously suppressing star formation in quenched galaxies at high redshift, possibly by driving increased turbulence that decreases star formation efficiency. Unlike previous studies at z < 1, we find no correlation between molecular gas mass and time since quenching. This may be explained by the fact that -- despite having similar UVJ colors -- all galaxies in our sample have post-burst ages older than typical gas-rich quenched systems at low redshift. Our results highlight the importance of major mergers in shaping the cold gas content of quiescent galaxies during the peak epoch of quenching.
Diffuse Interstellar Bands (DIBs) are crucial tracers of the interstellar medium (ISM), yet their carriers remain poorly understood. While large-scale surveys have advanced DIB studies in cool stellar spectra, measurements in hot stellar spectra are still limited. Using 287 277 high signal-to-noise (S/N $>$ 50) hot stellar spectra from the tenth data release of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope low-resolution spectroscopic survey (LAMOST LRS DR10), we systematically measured the three prominent optical DIBs at 5780, 5797, and 6614 \r{A}. We published three catalogs containing 285 103, 279 195, and 281 146 valid measurements for the DIBs at 5780, 5797, and 6614 \r{A}, respectively. Among them, 112 479, 25 232, and 71 048 are high-quality samples after rigorous quality control. To our knowledge, these are the largest hot-star DIB datasets in the northern sky. The catalogs provide spectral metadata, added astrometeric information, DIB profiles, and quality metrics. Our methodology and open-source pipeline ensure reproducibility, while the scale and precision of the data support future statistical studies. We anticipate that these catalogs will highlight the LAMOST's role in advancing DIB research and deepening our understanding of the ISM.
Tensor products of ultrafilters have special combinatorial features closely related to Ramsey's Theorem, making them useful tools in applications. Here we first review their fundamental properties and isolate some new ones, including a characterisation of the limit superior and inferior of sequences as limits along tensor products, and an application to the Banach asymptotic density. We then prove a general result on the combinatorial structure of sets belonging to tensor products and, as a result, we obtain several characterisations of the additive properties of sets of natural numbers. Finally, we show that tensor products can be described as idempotents of an appropriate semigroup.
Vulnerability Disclosure or Notification? Best Practices for Reaching Stakeholders at Scale
2025-06-17
Ting-Han Chen, Jeroen van der Ham-de Vos
Security researchers are interested in security vulnerabilities, but these security vulnerabilities create risks for stakeholders. Coordinated Vulnerability Disclosure has been an accepted best practice for many years in disclosing newly discovered vulnerabilities. This practice has mostly worked, but it can become challenging when there are many different parties involved. There has also been research into known vulnerabilities, using datasets or active scans to discover how many machines are still vulnerable. The ethical guidelines suggest that researchers also make an effort to notify the owners of these machines. We posit that this differs from vulnerability disclosure, but rather the practice of vulnerability notification. This practice has some similarities with vulnerability disclosure but should be distinguished from it, providing other challenges and requiring a different approach. Based on our earlier disclosure experience and on prior work documenting their disclosure and notification operations, we provide a meta-review on vulnerability disclosure and notification to observe the shifts in strategies in recent years. We assess how researchers initiated their messaging and examine the outcomes. We then compile the best practices for the existing disclosure guidelines and for notification operations.
Fair for a few: Improving Fairness in Doubly Imbalanced Datasets
2025-06-17
Ata Yalcin, Asli Umay Ozturk, Yigit Sever, Viktoria Pauw, Stephan Hachinger, Ismail Hakki Toroslu, Pinar Karagoz
Fairness has been identified as an important aspect of Machine Learning and Artificial Intelligence solutions for decision making. Recent literature offers a variety of approaches for debiasing, however many of them fall short when the data collection is imbalanced. In this paper, we focus on a particular case, fairness in doubly imbalanced datasets, such that the data collection is imbalanced both for the label and the groups in the sensitive attribute. Firstly, we present an exploratory analysis to illustrate limitations in debiasing on a doubly imbalanced dataset. Then, a multi-criteria based solution is proposed for finding the most suitable sampling and distribution for label and sensitive attribute, in terms of fairness and classification accuracy
Peering into the Unknown: Active View Selection with Neural Uncertainty Maps for 3D Reconstruction
2025-06-17
Zhengquan Zhang, Feng Xu, Mengmi Zhang
Some perspectives naturally provide more information than others. How can an AI system determine which viewpoint offers the most valuable insight for accurate and efficient 3D object reconstruction? Active view selection (AVS) for 3D reconstruction remains a fundamental challenge in computer vision. The aim is to identify the minimal set of views that yields the most accurate 3D reconstruction. Instead of learning radiance fields, like NeRF or 3D Gaussian Splatting, from a current observation and computing uncertainty for each candidate viewpoint, we introduce a novel AVS approach guided by neural uncertainty maps predicted by a lightweight feedforward deep neural network, named UPNet. UPNet takes a single input image of a 3D object and outputs a predicted uncertainty map, representing uncertainty values across all possible candidate viewpoints. By leveraging heuristics derived from observing many natural objects and their associated uncertainty patterns, we train UPNet to learn a direct mapping from viewpoint appearance to uncertainty in the underlying volumetric representations. Next, our approach aggregates all previously predicted neural uncertainty maps to suppress redundant candidate viewpoints and effectively select the most informative one. Using these selected viewpoints, we train 3D neural rendering models and evaluate the quality of novel view synthesis against other competitive AVS methods. Remarkably, despite using half of the viewpoints than the upper bound, our method achieves comparable reconstruction accuracy. In addition, it significantly reduces computational overhead during AVS, achieving up to a 400 times speedup along with over 50\% reductions in CPU, RAM, and GPU usage compared to baseline methods. Notably, our approach generalizes effectively to AVS tasks involving novel object categories, without requiring any additional training.
The ejection and detectability of high- and hyper-velocity stars by compact object binaries in globular clusters
2025-06-17
Fraser A. Evans, Steffani M. Grondin, Claire S. Ye, Jeremy Webb, Alexander Laroche, Jo Bovy
The dense cores of Milky Way globular clusters (GCs) play host to a variety of dynamical encounters between stellar objects, which can accelerate stars to velocities high enough to escape the GC. The most extreme examples of these encounters are interactions between single GC stars and binaries including at least one compact object. These interactions can result in ejection velocities of up to several hundred $\mathrm{km \ s^{-1}}$, approaching or even exceeding the escape velocity of the Galaxy itself. In order to study whether these interactions contribute to the Galactic population of hypervelocity stars (stars moving faster than the Galactic escape speed), we combine Monte Carlo $N$-body GC simulations, observations of Galactic GCs, and a particle spray code to generate realistic populations of stars which have escaped from Milky Way GCs following star + compact object binary (S+COB) interactions. We find that over the last 500 Myr, S+COB interactions have likely ejected $\sim$6300 stars from Galactic GCs, of which $839_{-67}^{+70}$ have present-day velocities exceeding $500 \; \mathrm{km \ s^{-1}}$. Using mock photometric observations, we find that $290_{-23}^{+28}$ ejected stars are detectable in Gaia Data Release 3, however, only $1_{-1}^{+2}$ stars faster than $500 \; \mathrm{km \ s^{-1}}$ are detectable. Even so, we show that observational prospects in the upcoming Legacy Survey of Space and Time are more optimistic, and future detected fast extratidal GC stars will serve as a useful probe of GC cores.
Public Acceptance of Cybernetic Avatars in the service sector: Evidence from a Large-Scale Survey in Dubai
2025-06-17
Laura Aymerich-Franch, Tarek Taha, Takahiro Miyashita, Hiroko Kamide, Hiroshi Ishiguro, Paolo Dario
Cybernetic avatars are hybrid interaction robots or digital representations that combine autonomous capabilities with teleoperated control. This study investigates the acceptance of cybernetic avatars in the highly multicultural society of Dubai, with particular emphasis on robotic avatars for customer service. Specifically, we explore how acceptance varies as a function of robot appearance (e.g., android, robotic-looking, cartoonish), deployment settings (e.g., shopping malls, hotels, hospitals), and functional tasks (e.g., providing information, patrolling). To this end, we conducted a large-scale survey with over 1,000 participants. Overall, cybernetic avatars received a high level of acceptance, with physical robot avatars receiving higher acceptance than digital avatars. In terms of appearance, robot avatars with a highly anthropomorphic robotic appearance were the most accepted, followed by cartoonish designs and androids. Animal-like appearances received the lowest level of acceptance. Among the tasks, providing information and guidance was rated as the most valued. Shopping malls, airports, public transport stations, and museums were the settings with the highest acceptance, whereas healthcare-related spaces received lower levels of support. An analysis by community cluster revealed among others that Emirati respondents showed significantly greater acceptance of android appearances compared to the overall sample, while participants from the 'Other Asia' cluster were significantly more accepting of cartoonish appearances. Our study underscores the importance of incorporating citizen feedback into the design and deployment of cybernetic avatars from the early stages to enhance acceptance of this technology in society.
One Polynomial Strategy for Computing Local Projections on Square-Lattice Cluster States
2025-06-17
Nyau Fisn, Houren Fu
Quantum computing has attracted a lot of attention in recent years. It is one of the promising candidates for the next-generation computing paradigms. Basically, there are two technical lines to realize quantum computing. One is composing the unitary operators of a few qubits to achieve general unitary operators on an arbitrary number of qubits, known as the approach of quantum circuits. The other one focuses on preparing quantum cluster states and performing the computation by measuring the states with a particular basis, known as measurement-based quantum computing or one-way quantum computing. The two strategies have been proven to be equivalent to each other. This note aims to discuss the strategies for computing the local projections on square-lattice cluster states. Seemingly, one strategy for the computation could require both polynomial steps and memories. In particular, if the number of qubits in a square-lattice is denoted by $N$, the step number for computing an arbitrary local projection on the state could be proportional to a polynomial of $N$ under a bounded cost of memory. Consider that the square-lattice cluster states are one kind of universal computing resource, the results might be helpful for understanding the computational advantages of quantum algorithms, as well as the limits of the numerical analysis on other relevant quantum models. Although the results in the note are not peer reviewed, we would like to make them public because they are quite easy to check.
Bayesian Inference for Initial Heat States with Gaussian Series Priors
2025-06-17
Matteo Giordano
We consider the statistical linear inverse problem of recovering the unknown initial heat state from noisy interior measurements over an inhomogeneous domain of the solution to the heat equation at a fixed time instant. We employ nonparametric Bayesian procedures with Gaussian series priors defined on the Dirichlet-Laplacian eigenbasis, yielding convenient conjugate posterior distributions with explicit expressions for posterior inference. We review recent theoretical results that provide asymptotic performance guarantees (in the large sample size limit) for the resulting posterior-based point estimation and uncertainty quantification. We further provide an implementation of the approach, and illustrate it via a numerical simulation study.
Polycyclic Aromatic Hydrocarbons in the circumstellar medium of Herbig Ae/Be stars
2025-06-17
R. Arun, Blesson Mathew, B. Shridharan, K. Ujjwal, Akhil Krishna R, G. Maheswar, Sreeja S. Kartha
We present a comprehensive mid-infrared spectroscopic survey of 124 Herbig Ae/Be stars using newly processed Spitzer/IRS spectra from the newly released CASSISjuice database. Based on prominent dust and molecular signatures (polycyclic aromatic hydrocarbons, silicates, and hydrogenated amorphous carbons), we classify the stars into five groups. Our analysis reveals that 64% of the spectra show PAH emission, with detections peaking in the stellar effective temperature range 7000-11000 K (B9-A5). Silicate features appear in 50% of the sample and likewise diminish at higher temperatures. Additionally, we find that future PAH studies can focus on Herbig Ae/Be stars with a spectral index ($n_{2-24}$ > -1) and flared morphologies to maximize PAH detections. The 6.2 $\mu$m PAH band is the most frequently observed in our sample, shifting blueward with increasing stellar temperature, and this is the largest sample yet used to test that peak shift. The weaker 6.0 $\mu$m feature does not shift with 6.2 $\mu$m, implying a distinct origin of C=O (carbonyl) or olefinic C=C stretching relative to C--C vibrations. We examined the 11.0/11.2 $\mu$m PAH ratio using high-resolution Spitzer spectra for the first time in a sample of Herbig Ae/Be stars, finding a range of ionization conditions. This study provides a strong foundation for future JWST observations of intermediate-mass pre-main-sequence stars.
The Mineralogical Connection Between M- and K-type Asteroids as Indicated by Polarimetry
2025-06-17
Joseph R. Masiero, Yuna G. Kwon, Elena Selmi, Manaswi Kondapally
Polarimetry has the capacity to provide a unique probe of the surface properties of asteroids. Trends in polarization behavior as a function of wavelength trace asteroid regolith mineral properties that are difficult to probe without measurements in situ or on returned samples. We present recent results from our ongoing survey of near-infrared polarimetric properties of asteroids. Our data reveal a mineralogical link between asteroids in the broader M- and K- spectral classes. In particular, M-type objects (16) Psyche, (55) Pandora, (135) Hertha, and (216) Kleopatra show the same polarimetric-phase behavior as K-type objects (89) Julia, (221) Eos, and (233) Asterope from visible through near-infrared light. The near-infrared behavior for these objects is distinct from other classes observed to date, and shows a good match to the polarimetric properties of M-type asteroid (21) Lutetia from the visible to the near-infrared. The best link for these objects from laboratory polarimetric phase curve measurements is to a troilite-rich fine-grained regolith. Our observations indicate that the M- and K-type spectral classes are most likely part of a continuum, with the observed spectral differences due to heterogeneity from partial differentiation, shock darkening of the surface material, or other later evolution of the original parent population. We also provide incidental J- and H-band polarimetric observations of other Main Belt asteroids obtained during our survey.
Mobile Application Review Summarization using Chain of Density Prompting
2025-06-17
Shristi Shrestha, Anas Mahmoud
Mobile app users commonly rely on app store ratings and reviews to find apps that suit their needs. However, the sheer volume of reviews available on app stores can lead to information overload, thus impeding users' ability to make informed app selection decisions. To address this challenge, we leverage Large Language Models (LLMs) to summarize mobile app reviews. In particular, we use the Chain of Density (CoD) prompt to guide OpenAI GPT-4 to generate abstractive, semantically dense, and easily interpretable summaries of mobile app reviews. The CoD prompt is engineered to iteratively extract salient entities from the source text and fuse them into a fixed-length summary. We evaluate the performance of our approach using a large dataset of mobile app reviews. We further conduct an empirical evaluation with 48 study participants to assess the readability of the generated summaries. Our results demonstrate that adapting the CoD prompt to focus on app features improves its ability to extract key themes from user reviews and generate natural language summaries tailored for end-user consumption. The prompt also manages to maintain the readability of the generated summaries while increasing their semantic density. Our work in this paper aims to improve mobile app users' experience by providing an effective mechanism for summarizing important user feedback in the review stream.
Leading-order gravitational time delay of massive particles by a moving Schwarzschild lens
2025-06-17
Guansheng He, Wenbin Lin, Yi Xie
The leading-order gravitational time delay of relativistic neutral massive particles (e.g., neutrinos or high-energy cosmic-ray particles) caused by a moving Schwarzschild black hole with a constant radial velocity is investigated for the first time. On the basis of the equations of motion in the spacetime of the moving lens, we achieve a new unified formula for the travel times of relativistic massive and massless particles propagating from the source to the observer within the first post-Minkowskian approximation. The analytical form of the difference between the travel times of a relativistic massive particle and a light signal in this geometry, as well as that of two relativistic massive particles, is thus obtained in the weak-field and slow-motion limit. The influence of the radial lens motion on the leading-order Schwarzschild time delay of relativistic massive particles is then discussed. It is found that in the slow-motion limit, the radial lens motion towards the observer decreases the flight time of an ultrarelativistic massive particle, when compared with the case of no translational motion of the central body. Conversely, if the lens gets away from the detector radially under the same conditions, the propagation process of the particle will slow down and its flight time will thus increase in comparison with the Schwarzschild case. Finally, we analyze the magnitude of the full radial motion effect of the lens and evaluate the possibility of its astronomical detection by modeling three typical black holes as the lens respectively.
A Comprehensive Survey on Underwater Acoustic Target Positioning and Tracking: Progress, Challenges, and Perspectives
2025-06-17
Zhong Yang, Zhengqiu Zhu, Yong Zhao, Yonglin Tian, Changjun Fan, Runkang Guo, Wenhao Lu, Jingwei Ge, Bin Chen, Yin Zhang, Guohua Wu, Rui Wang, Gyorgy Eigner, Guangquan Cheng, Jincai Huang, Zhong Liu, Jun Zhang, Imre J. Rudas, Fei-Yue Wang
Underwater target tracking technology plays a pivotal role in marine resource exploration, environmental monitoring, and national defense security. Given that acoustic waves represent an effective medium for long-distance transmission in aquatic environments, underwater acoustic target tracking has become a prominent research area of underwater communications and networking. Existing literature reviews often offer a narrow perspective or inadequately address the paradigm shifts driven by emerging technologies like deep learning and reinforcement learning. To address these gaps, this work presents a systematic survey of this field and introduces an innovative multidimensional taxonomy framework based on target scale, sensor perception modes, and sensor collaboration patterns. Within this framework, we comprehensively survey the literature (more than 180 publications) over the period 2016-2025, spanning from the theoretical foundations to diverse algorithmic approaches in underwater acoustic target tracking. Particularly, we emphasize the transformative potential and recent advancements of machine learning techniques, including deep learning and reinforcement learning, in enhancing the performance and adaptability of underwater tracking systems. Finally, this survey concludes by identifying key challenges in the field and proposing future avenues based on emerging technologies such as federated learning, blockchain, embodied intelligence, and large models.
A break from the norm? Parametric representations of preference heterogeneity for discrete choice models in health
2025-06-17
John Buckell, Alice Wreford, Matthew Quaife, Thomas O. Hancock
Background: Any sample of individuals has its own, unique distribution of preferences for choices that they make. Discrete choice models try to capture these distributions. Mixed logits are by far the most commonly used choice model in health. A raft of parametric model specifications for these models are available. We test a range of alternatives assumptions, and model averaging, to test if or how model outputs are impacted. Design: Scoping review of current modelling practices. Seven alternative distributions, and model averaging over all distributional assumptions, were compared on four datasets: two were stated preference, one was revealed preference, and one was simulated. Analyses examined model fit, preference distributions, willingness-to-pay, and forecasting. Results: Almost universally, using normal distributions is the standard practice in health. Alternative distributional assumptions outperformed standard practice. Preference distributions and the mean willingness-to-pay varied significantly across specifications, and were seldom comparable to those derived from normal distributions. Model averaging offered distributions allowed for greater flexibility, further gains in fit, reproduced underlying distributions in simulations, and mitigated against analyst bias arising from distribution selection. There was no evidence that distributional assumptions impacted predictions from models. Limitations: Our focus was on mixed logit models since these models are the most common in health, though latent class models are also used. Conclusions: The standard practice of using all normal distributions appears to be an inferior approach for capturing random preference heterogeneity. Implications: Researchers should test alternative assumptions to normal distributions in their models.
The integration of Large Language Models (LLMs) with computer vision is profoundly transforming perception tasks like image segmentation. For intelligent transportation systems (ITS), where accurate scene understanding is critical for safety and efficiency, this new paradigm offers unprecedented capabilities. This survey systematically reviews the emerging field of LLM-augmented image segmentation, focusing on its applications, challenges, and future directions within ITS. We provide a taxonomy of current approaches based on their prompting mechanisms and core architectures, and we highlight how these innovations can enhance road scene understanding for autonomous driving, traffic monitoring, and infrastructure maintenance. Finally, we identify key challenges, including real-time performance and safety-critical reliability, and outline a perspective centered on explainable, human-centric AI as a prerequisite for the successful deployment of this technology in next-generation transportation systems.
A Halpha metric for identifying dormant black holes in X-ray transients
2025-06-16
J. Casares, M. A. P. Torres, S. Navarro Umpiérrez
Dormant black holes (BHs) in X-ray transients can be identified by the presence of broad Ha emission lines from quiescent accretion discs. Unfortunately, short-period cataclysmic variables (CVs) can also produce broad Ha lines, especially when viewed at high inclinations, and are thus a major source of contamination. Here we compare the full width at half maximum (FWHM) and equivalent width (EW) of the Ha line in a sample of 20 quiescent BH transients and 354 CVs (305 from SDSS I to IV) with secure orbital periods (Porb) and find that: (1) FWHM and EW values decrease with Porb, and (2) for a given Porb both parameters are typically larger in BH transients than in CVs. We also compile spectral types for 17 low-mass companions in BH transients from the literature and derive an empirical Porb-Teff calibration. Using this, we conclude that the decrease in EW with Porb is mostly driven by the dilution of the Ha flux by the donor star continuum, which dominates the r-band spectrum for Porb>~0.2 d. At shorter periods, the larger contribution of the disc to the total r-band flux introduces significant scatter in the EWs due to the changing visibility of the disc projected area with binary inclination. On the other hand, the larger EWs observed in BHs can be explained by their extreme mass ratios (which limit the fractional contribution of the companion to the total flux) and the absence of a white dwarf component (important at Porb<~0.085 d). Finally, we present a tentative metric, based on Ha FWHM and EW information, and provide optimal cuts to select ~80 % of the BH X-ray transients, while rejecting ~78 % of the CVs in our sample. Such a metric, combined with other multi-frequency diagnostics, can help detect new dormant BH X-ray transients in blind large-scale surveys such as HaWKs and its pathfinder, Mini-HaWKs.
Discrete Diffusion in Large Language and Multimodal Models: A Survey
2025-06-16
Runpeng Yu, Qi Li, Xinchao Wang
In this work, we provide a systematic survey of Discrete Diffusion Language Models (dLLMs) and Discrete Diffusion Multimodal Language Models (dMLLMs). Unlike autoregressive (AR) models, dLLMs and dMLLMs adopt a multi-token, parallel decoding paradigm using full attention and a denoising-based generation strategy. This paradigm naturally enables parallel generation, fine-grained output controllability, and dynamic, response-aware perception. These capabilities are previously difficult to achieve with AR models. Recently, a growing number of industrial-scale proprietary d(M)LLMs, as well as a large number of open-source academic d(M)LLMs, have demonstrated performance comparable to their autoregressive counterparts, while achieving up to 10x acceleration in inference speed. The advancement of discrete diffusion LLMs and MLLMs has been largely driven by progress in two domains. The first is the development of autoregressive LLMs and MLLMs, which has accumulated vast amounts of data, benchmarks, and foundational infrastructure for training and inference. The second contributing domain is the evolution of the mathematical models underlying discrete diffusion. Together, these advancements have catalyzed a surge in dLLMs and dMLLMs research in early 2025. In this work, we present a comprehensive overview of the research in the dLLM and dMLLM domains. We trace the historical development of dLLMs and dMLLMs, formalize the underlying mathematical frameworks, and categorize representative models. We further analyze key techniques for training and inference, and summarize emerging applications across language, vision-language, and biological domains. We conclude by discussing future directions for research and deployment. Paper collection: https://github.com/LiQiiiii/DLLM-Survey
Two-neutrino $0^+ \to 0^+$ double beta decay of $^{48}\mathrm{Ca}$ within the DFT-NCCI framework
2025-06-16
Jan Miśkiewicz, Maciej Konieczka, Wojciech Satuła
We present a seminal calculation of the nuclear matrix element for the two-neutrino double beta ($2\nu\beta\beta$) decay of ${}^{48}\text{Ca} \rightarrow {}^{48}\text{Ti}$ using a post-Hartree-Fock (HF) Density Functional Theory-based No-Core Configuration-Interaction (DFT-NCCI) framework developed by our group [Phys. Rev. C 94, 024306 (2016)]. In the present calculation, we utilize a variant of the approach that restores rotational symmetry and mixes states projected from self-consistent mean-field configurations obtained by solving the HF equations with the density-independent local Skyrme interaction. Our calculations yield $|\mathcal{M}_{2\nu\beta\beta}| = 0.056(6)$ MeV$^{-1}$ for the nuclear matrix element describing this process. This result is in very good agreement with shell-model studies - for example, with the calculations by Horoi {\it et al.\/} [Phys. Rev. C 75, 034303 (2007)], which yielded 0.054 (0.064) MeV$^{-1}$ for the GXPF1A (GXPF1) interactions, respectively. It is also in a reasonable agreement with the most recent experimental estimate from the review by Barabash, which is 0.068(6) MeV$^{-1}$, assuming quenching $qg_\text{A} \approx 1$. The consistency of our prediction with the shell-model results increases our confidence in the nuclear modeling of this second-order, very rare process which is of paramount importance for further modeling of the neutrinoless double beta ($0\nu\beta\beta$) decay process.
Numerical approach to second-order canonical perturbation theory in the planetary 3-body problem. Application to exoplanets
2025-06-16
Aya Alnajjarine, Federico Mogavero, Jacques Laskar
Extrasolar planetary systems commonly exhibit planets on eccentric orbits, with many systems located near or within mean-motion resonances, showcasing a wide diversity of orbital architectures. Such complex systems challenge traditional secular theories, which are limited to first-order approximations in planetary masses or rely on expansions in orbital elements--eccentricities, inclinations, and semi-major axis ratios--that are subject to convergence issues, especially in highly eccentric, inclined, or tightly-packed systems. To overcome these limitations, we develop a numerical approach to second-order perturbation theory based on the Lie transform formalism. Our method avoids the need for expansions in orbital elements, ensuring broader applicability and more robust convergence. We first outline the Hamiltonian framework for the 3-body planetary problem, and apply a canonical transformation to eliminate fast angle dependencies, deriving the secular Hamiltonian up to second order in the mass ratio. We then use the fast Fourier transform algorithm to numerically simulate, in an accurate way, the long-term evolution of planetary systems near or away from mean-motion resonances. Finally, we validate our methods against well-known planetary configurations, such as the Sun-Jupiter-Saturn system, as well as to exoplanetary systems like WASP-148, TIC 279401253 and GJ 876, demonstrating the applicability of our models across a wide range of planetary configurations.
Quasar outflows can play a crucial role in the evolution of their host galaxies through various feedback processes. This effect is expected to be particularly important when the universe was only 2-3 billion years old, during the period known as cosmic noon. By utilizing observations from the Dark Energy Spectroscopy Instrument (DESI), we conduct a survey of high-ionization quasar outflows at cosmic noon, with the aim of doubling the current sample of such outflows with distance and energetics determination. We also aim to compare these properties to those derived from spatially resolved outflows in similar quasars probed through Integral Field Spectroscopy (IFS). In a sample of 130 quasars we detect signatures of high-ionization outflows in the form of a S IV trough in eight objects. We characterize the physical conditions of these outflows by determining their total hydrogen column density ($N_H$), ionization parameter ($U_H$), and electron number density ($n_e$). The detected outflows show complex kinematic structures with a wide range in blueshifted velocities (100$-$4600 km s$^{-1}$). We locate five out of the eight outflows at distances between 240$-$5500 pc away from the central source. From the combined sample of 15 high-ionization S IV outflows at cosmic noon, we find a high fraction (up to 46 %) of them to be powerful enough to contribute significantly to multi-stage AGN feedback processes. Their mass outflow rates and kinetic luminosities are also found to be consistent with spatially resolved outflows in a luminosity and redshift matched sample of quasars. Comparison with previous spectra reveals interesting variations in some objects, with two cases of emerging high-velocity broad absorption line features with velocities of $-$8000 and $-$39,000 km s$^{-1}$. An impressive case of four line-locked Si IV outflow systems is also revealed in one of the objects.
We introduce the Lecture Video Visual Objects (LVVO) dataset, a new benchmark for visual object detection in educational video content. The dataset consists of 4,000 frames extracted from 245 lecture videos spanning biology, computer science, and geosciences. A subset of 1,000 frames, referred to as LVVO_1k, has been manually annotated with bounding boxes for four visual categories: Table, Chart-Graph, Photographic-image, and Visual-illustration. Each frame was labeled independently by two annotators, resulting in an inter-annotator F1 score of 83.41%, indicating strong agreement. To ensure high-quality consensus annotations, a third expert reviewed and resolved all cases of disagreement through a conflict resolution process. To expand the dataset, a semi-supervised approach was employed to automatically annotate the remaining 3,000 frames, forming LVVO_3k. The complete dataset offers a valuable resource for developing and evaluating both supervised and semi-supervised methods for visual content detection in educational videos. The LVVO dataset is publicly available to support further research in this domain.
Model editing aims to efficiently update a pre-trained model's knowledge without the need for time-consuming full retraining. While existing pioneering editing methods achieve promising results, they primarily focus on editing single-modal language models (LLMs). However, for vision-language models (VLMs), which involve multiple modalities, the role and impact of each modality on editing performance remain largely unexplored. To address this gap, we explore the impact of textual and visual modalities on model editing and find that: (1) textual and visual representations reach peak sensitivity at different layers, reflecting their varying importance; and (2) editing both modalities can efficiently update knowledge, but this comes at the cost of compromising the model's original capabilities. Based on our findings, we propose DualEdit, an editor that modifies both textual and visual modalities at their respective key layers. Additionally, we introduce a gating module within the more sensitive textual modality, allowing DualEdit to efficiently update new knowledge while preserving the model's original information. We evaluate DualEdit across multiple VLM backbones and benchmark datasets, demonstrating its superiority over state-of-the-art VLM editing baselines as well as adapted LLM editing methods on different evaluation metrics.
Disturbance-aware minimum-time planning strategies for motorsport vehicles with probabilistic safety certificates
2025-06-16
Martino Gulisano, Matteo Masoni, Marco Gabiccini, Massimo Guiggiani
This paper presents a disturbance-aware framework that embeds robustness into minimum-lap-time trajectory optimization for motorsport. Two formulations are introduced. (i) Open-loop, horizon-based covariance propagation uses worst-case uncertainty growth over a finite window to tighten tire-friction and track-limit constraints. (ii) Closed-loop, covariance-aware planning incorporates a time-varying LQR feedback law in the optimizer, providing a feedback-consistent estimate of disturbance attenuation and enabling sharper yet reliable constraint tightening. Both methods yield reference trajectories for human or artificial drivers: in autonomous applications the modelled controller can replicate the on-board implementation, while for human driving accuracy increases with the extent to which the driver can be approximated by the assumed time-varying LQR policy. Computational tests on a representative Barcelona-Catalunya sector show that both schemes meet the prescribed safety probability, yet the closed-loop variant incurs smaller lap-time penalties than the more conservative open-loop solution, while the nominal (non-robust) trajectory remains infeasible under the same uncertainties. By accounting for uncertainty growth and feedback action during planning, the proposed framework delivers trajectories that are both performance-optimal and probabilistically safe, advancing minimum-time optimization toward real-world deployment in high-performance motorsport and autonomous racing.
A Structured Bangla Dataset of Disease-Symptom Associations to Improve Diagnostic Accuracy
2025-06-16
Abdullah Al Shafi, Rowzatul Zannat, Abdul Muntakim, Mahmudul Hasan
Disease-symptom datasets are significant and in demand for medical research, disease diagnosis, clinical decision-making, and AI-driven health management applications. These datasets help identify symptom patterns associated with specific diseases, thus improving diagnostic accuracy and enabling early detection. The dataset presented in this study systematically compiles disease-symptom relationships from various online sources, medical literature, and publicly available health databases. The data was gathered through analyzing peer-reviewed medical articles, clinical case studies, and disease-symptom association reports. Only the verified medical sources were included in the dataset, while those from non-peer-reviewed and anecdotal sources were excluded. The dataset is structured in a tabular format, where the first column represents diseases, and the remaining columns represent symptoms. Each symptom cell contains a binary value (1 or 0), indicating whether a symptom is associated with a disease (1 for presence, 0 for absence). Thereby, this structured representation makes the dataset very useful for a wide range of applications, including machine learning-based disease prediction, clinical decision support systems, and epidemiological studies. Although there are some advancements in the field of disease-symptom datasets, there is a significant gap in structured datasets for the Bangla language. This dataset aims to bridge that gap by facilitating the development of multilingual medical informatics tools and improving disease prediction models for underrepresented linguistic communities. Further developments should include region-specific diseases and further fine-tuning of symptom associations for better diagnostic performance
Experimental search for electric dipole moments of light radioactive nuclei
2025-06-16
Chavdar Dutsov, Timothy Hume, Maxim Pospelov, Philipp Schmidt-Wellenburg
We discuss a search for the electric dipole moment (EDM) of a light beta-radioactive ion using a compact ion trap by adapting the "frozen-spin" method. The measurement will be done on ions stripped of their valence electrons, thereby bypassing the significant Schiff screening that hinders the application of successful contemporary EDM searches using heavy neutral atoms and molecules to light nuclei. We identified $^8$Li as the most promising candidate for a proof-of-concept EDM search and we estimate that the current indirect proton EDM limit of a few $10^{-25} e\cdot$cm set by $^{199}$Hg measurements can be surpassed with a week of measurement time at existing facilities.
From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Automation of Patient Care
2025-06-16
Daniel Anadria, Roel Dobbe, Anastasia Giachanou, Ruurd Kuiper, Richard Bartels, Wouter van Amsterdam, Íñigo Martínez de Rituerto de Troya, Carmen Zürcher, Daniel Oberski
In this work, we reflect on the data-driven modeling paradigm that is gaining ground in AI-driven automation of patient care. We argue that the repurposing of existing real-world patient datasets for machine learning may not always represent an optimal approach to model development as it could lead to undesirable outcomes in patient care. We reflect on the history of data analysis to explain how the data-driven paradigm rose to popularity, and we envision ways in which systems thinking and clinical domain theory could complement the existing model development approaches in reaching human-centric outcomes. We call for a purpose-driven machine learning paradigm that is grounded in clinical theory and the sociotechnical realities of real-world operational contexts. We argue that understanding the utility of existing patient datasets requires looking in two directions: upstream towards the data generation, and downstream towards the automation objectives. This purpose-driven perspective to AI system development opens up new methodological opportunities and holds promise for AI automation of patient care.
Machine Learning-Driven Compensation for Non-Ideal Channels in AWG-Based FBG Interrogator
2025-06-16
Ivan A. Kazakov, Iana V. Kulichenko, Egor E. Kovalev, Angelina A. Treskova, Daria D. Barma, Kirill M. Malakhov, Arkady V. Shipulin
We present an experimental study of a fiber Bragg grating (FBG) interrogator based on a silicon oxynitride (SiON) photonic integrated arrayed waveguide grating (AWG). While AWG-based interrogators are compact and scalable, their practical performance is limited by non-ideal spectral responses. To address this, two calibration strategies within a 2.4 nm spectral region were compared: (1) a segmented analytical model based on a sigmoid fitting function, and (2) a machine learning (ML)-based regression model. The analytical method achieves a root mean square error (RMSE) of 7.11 pm within the calibrated range, while the ML approach based on exponential regression achieves 3.17 pm. Moreover, the ML model demonstrates generalization across an extended 2.9 nm wavelength span, maintaining sub-5 pm accuracy without re-fitting. Residual and error distribution analyses further illustrate the trade-offs between the two approaches. ML-based calibration provides a robust, data-driven alternative to analytical methods, delivering enhanced accuracy for non-ideal channel responses, reduced manual calibration effort, and improved scalability across diverse FBG sensor configurations.
Accurate road topology reasoning is critical for autonomous driving, enabling effective navigation and adherence to traffic regulations. Central to this task are lane perception and topology reasoning. However, existing methods typically focus on either lane detection or Lane-to-Lane (L2L) topology reasoning, often \textit{neglecting} Lane-to-Traffic-element (L2T) relationships or \textit{failing} to optimize these tasks jointly. Furthermore, most approaches either overlook relational modeling or apply it in a limited scope, despite the inherent spatial relationships among road elements. We argue that relational modeling is beneficial for both perception and reasoning, as humans naturally leverage contextual relationships for road element recognition and their connectivity inference. To this end, we introduce relational modeling into both perception and reasoning, \textit{jointly} enhancing structural understanding. Specifically, we propose: 1) a relation-aware lane detector, where our geometry-biased self-attention and \curve\ cross-attention refine lane representations by capturing relational dependencies; 2) relation-enhanced topology heads, including a geometry-enhanced L2L head and a cross-view L2T head, boosting reasoning with relational cues; and 3) a contrastive learning strategy with InfoNCE loss to regularize relationship embeddings. Extensive experiments on OpenLane-V2 demonstrate that our approach significantly improves both detection and topology reasoning metrics, achieving +3.1 in DET$_l$, +5.3 in TOP$_{ll}$, +4.9 in TOP$_{lt}$, and an overall +4.4 in OLS, setting a new state-of-the-art. Code will be released.
Video Scene Parsing (VSP) has emerged as a cornerstone in computer vision, facilitating the simultaneous segmentation, recognition, and tracking of diverse visual entities in dynamic scenes. In this survey, we present a holistic review of recent advances in VSP, covering a wide array of vision tasks, including Video Semantic Segmentation (VSS), Video Instance Segmentation (VIS), Video Panoptic Segmentation (VPS), as well as Video Tracking and Segmentation (VTS), and Open-Vocabulary Video Segmentation (OVVS). We systematically analyze the evolution from traditional hand-crafted features to modern deep learning paradigms -- spanning from fully convolutional networks to the latest transformer-based architectures -- and assess their effectiveness in capturing both local and global temporal contexts. Furthermore, our review critically discusses the technical challenges, ranging from maintaining temporal consistency to handling complex scene dynamics, and offers a comprehensive comparative study of datasets and evaluation metrics that have shaped current benchmarking standards. By distilling the key contributions and shortcomings of state-of-the-art methodologies, this survey highlights emerging trends and prospective research directions that promise to further elevate the robustness and adaptability of VSP in real-world applications.
UAV Object Detection and Positioning in a Mining Industrial Metaverse with Custom Geo-Referenced Data
2025-06-16
Vasiliki Balaska, Ioannis Tsampikos Papapetros, Katerina Maria Oikonomou, Loukas Bampis, Antonios Gasteratos
The mining sector increasingly adopts digital tools to improve operational efficiency, safety, and data-driven decision-making. One of the key challenges remains the reliable acquisition of high-resolution, geo-referenced spatial information to support core activities such as extraction planning and on-site monitoring. This work presents an integrated system architecture that combines UAV-based sensing, LiDAR terrain modeling, and deep learning-based object detection to generate spatially accurate information for open-pit mining environments. The proposed pipeline includes geo-referencing, 3D reconstruction, and object localization, enabling structured spatial outputs to be integrated into an industrial digital twin platform. Unlike traditional static surveying methods, the system offers higher coverage and automation potential, with modular components suitable for deployment in real-world industrial contexts. While the current implementation operates in post-flight batch mode, it lays the foundation for real-time extensions. The system contributes to the development of AI-enhanced remote sensing in mining by demonstrating a scalable and field-validated geospatial data workflow that supports situational awareness and infrastructure safety.
This paper comprehensively surveys research trends in imitation learning for contact-rich robotic tasks. Contact-rich tasks, which require complex physical interactions with the environment, represent a central challenge in robotics due to their nonlinear dynamics and sensitivity to small positional deviations. The paper examines demonstration collection methodologies, including teaching methods and sensory modalities crucial for capturing subtle interaction dynamics. We then analyze imitation learning approaches, highlighting their applications to contact-rich manipulation. Recent advances in multimodal learning and foundation models have significantly enhanced performance in complex contact tasks across industrial, household, and healthcare domains. Through systematic organization of current research and identification of challenges, this survey provides a foundation for future advancements in contact-rich robotic manipulation.
Watermarking LLM-Generated Datasets in Downstream Tasks
2025-06-16
Yugeng Liu, Tianshuo Cong, Michael Backes, Zheng Li, Yang Zhang
Large Language Models (LLMs) have experienced rapid advancements, with applications spanning a wide range of fields, including sentiment classification, review generation, and question answering. Due to their efficiency and versatility, researchers and companies increasingly employ LLM-generated data to train their models. However, the inability to track content produced by LLMs poses a significant challenge, potentially leading to copyright infringement for the LLM owners. In this paper, we propose a method for injecting watermarks into LLM-generated datasets, enabling the tracking of downstream tasks to detect whether these datasets were produced using the original LLM. These downstream tasks can be divided into two categories. The first involves using the generated datasets at the input level, commonly for training classification tasks. The other is the output level, where model trainers use LLM-generated content as output for downstream tasks, such as question-answering tasks. We design a comprehensive set of experiments to evaluate both watermark methods. Our results indicate the high effectiveness of our watermark approach. Additionally, regarding model utility, we find that classifiers trained on the generated datasets achieve a test accuracy exceeding 0.900 in many cases, suggesting that the utility of such models remains robust. For the output-level watermark, we observe that the quality of the generated text is comparable to that produced using real-world datasets. Through our research, we aim to advance the protection of LLM copyrights, taking a significant step forward in safeguarding intellectual property in this domain.
An Interdisciplinary Approach to Human-Centered Machine Translation
2025-06-16
Marine Carpuat, Omri Asscher, Kalika Bali, Luisa Bentivogli, Frédéric Blain, Lynne Bowker, Monojit Choudhury, Hal Daumé III, Kevin Duh, Ge Gao, Alvin Grissom II, Marzena Karpinska, Elaine C. Khoong, William D. Lewis, André F. T. Martins, Mary Nurminen, Douglas W. Oard, Maja Popovic, Michel Simard, François Yvon
Machine Translation (MT) tools are widely used today, often in contexts where professional translators are not present. Despite progress in MT technology, a gap persists between system development and real-world usage, particularly for non-expert users who may struggle to assess translation reliability. This paper advocates for a human-centered approach to MT, emphasizing the alignment of system design with diverse communicative goals and contexts of use. We survey the literature in Translation Studies and Human-Computer Interaction to recontextualize MT evaluation and design to address the diverse real-world scenarios in which MT is used today.
Using smartphone apps during crises is well-established, proving critical for efficient crisis response. However, such apps become futile without an Internet connection, which is a common issue during crises. The ongoing 6G standardization explores the capability to provide local cellular connectivity for areas cut off from the Internet in crises. This paper introduces to the HCI community the concept of cellular island connectivity in isolated areas, promising a seamless transition from normal operation to island operation with local-only cellular connectivity. It presents findings from a survey (N = 857) among adult smartphone users from major German cities regarding their smartphone usage preferences in this model. Results show a shift in app demand, with users favoring general-purpose apps over dedicated crisis apps in specific scenarios. We prioritize smartphone services based on their criticality, distinguishing between apps essential for crisis response and those supporting routines. Our findings provide operators, developers, and authorities insights into making user-centric design decisions for implementing island-ready 6G communication.
Breakthrough Listen: A Technosignature Search Around 27 Eclipsing Exoplanets Selected from the Transiting Exoplanet Survey Satellite Catalogue
2025-06-16
R. Barrett, C. D. Tremblay, B. Addison, D. C. Price, J. A. Green
Here we analyse the archival data for a set of 27 Transiting Exoplanet Survey Satellite (TESS) Targets of Interest (TOIs) in search for artificially generated radio signals, or 'technosignatures', interrupted by occultation. Exoplanetary eclipses are notable events to observe in the search for technosignatures, as they mark the geometrical alignment of the target, its host star, and Earth. During an eclipse event, any signal emanating from the target of interest should cease for the duration of the eclipse, and resume after the line-of-sight has been restored. Target observations were made by Breakthrough Listen (BL) using Murriyang, the CSIRO Parkes 64-m radio telescope, coupled with the Ultra-wide Low frequency (UWL) receiver covering a continuous range of frequencies spanning 704-4032 MHz inclusive. Each target was observed in a pattern consisting of six back-to-back 5-minute source and reference sky positions for comparison during data analysis. We performed a Doppler search for narrowband signals with a minimum signal-to-noise (S/N) ratio of 10, a minimum drift rate of $\pm0.1$ Hz/s, and a maximum drift rate of $\pm4.0$ Hz/s using the turboSETI pipeline. In the analysis of 1,954,880 signals, 14,639 passed automated radio interference filters where each event was presented as a set of stacked dynamic spectra. Despite manually inspecting each diagram for a signal of interest, all events were attributed to terrestrial radio frequency interference (RFI).
Deep Learning-Based Multi-Object Tracking: A Comprehensive Survey from Foundations to State-of-the-Art
2025-06-16
Momir Adžemović
Multi-object tracking (MOT) is a core task in computer vision that involves detecting objects in video frames and associating them across time. The rise of deep learning has significantly advanced MOT, particularly within the tracking-by-detection paradigm, which remains the dominant approach. Advancements in modern deep learning-based methods accelerated in 2022 with the introduction of ByteTrack for tracking-by-detection and MOTR for end-to-end tracking. Our survey provides an in-depth analysis of deep learning-based MOT methods, systematically categorizing tracking-by-detection approaches into five groups: joint detection and embedding, heuristic-based, motion-based, affinity learning, and offline methods. In addition, we examine end-to-end tracking methods and compare them with existing alternative approaches. We evaluate the performance of recent trackers across multiple benchmarks and specifically assess their generality by comparing results across different domains. Our findings indicate that heuristic-based methods achieve state-of-the-art results on densely populated datasets with linear object motion, while deep learning-based association methods, in both tracking-by-detection and end-to-end approaches, excel in scenarios with complex motion patterns.
Recently, Adiprasito et al. have initiated the study of the so-called no-dimensional Tverberg problem. This problem can be informally stated as follows: Given $n\geq k$, partition an $n$-point set in Euclidean space into $k$ parts such that their convex hulls intersect a ball of relatively small radius. In this survey, we aim to present the recent progress towards solving the no-dimensional Tverberg problem and new open questions arising in its context. Also, we discuss the colorful variation of this problem and its algorithmic aspects, particularly focusing on the case when each part of a partition contains exactly 2 points. The latter turns out to be related to the following no-dimensional Tverberg-type problem of Huemer et al.: For an even set of points in Euclidean space, find a perfect matching such that the balls with diameters induced by its edges intersect.
Dark Energy Survey Year 3 results: $w$CDM cosmology from simulation-based inference with persistent homology on the sphere
2025-06-16
J. Prat, M. Gatti, C. Doux, P. Pranav, C. Chang, N. Jeffrey, L. Whiteway, D. Anbajagane, S. Sugiyama, A. Thomsen, A. Alarcon, A. Amon, K. Bechtol, G. M. Bernstein, A. Campos, R. Chen, A. Choi, C. Davis, J. DeRose, S. Dodelson, K. Eckert, J. Elvin-Poole, S. Everett, A. Ferté, D. Gruen, E. M. Huff, I. Harrison, K. Herner, M. Jarvis, N. Kuropatkin, P. -F. Leget, N. MacCrann, J. McCullough, J. Myles, A. Navarro-Alsina, S. Pandey, M. Raveri, R. P. Rollins, A. Roodman, C. Sánchez, L. F. Secco, E. Sheldon, T. Shin, M. A. Troxel, I. Tutusaus, T. N. Varga, B. Yanny, B. Yin, Y. Zhang, J. Zuntz, T. M. C. Abbott, M. Aguena, S. Allam, F. Andrade-Oliveira, J. Blazek, S. Bocquet, D. Brooks, J. Carretero, A. Carnero Rosell, R. Cawthon, J. De Vicente, S. Desai, M. E. da Silva Pereira, H. T. Diehl, B. Flaugher, J. Frieman, J. García-Bellido, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, K. Kuehn, L. N. da Costa, O. Lahav, S. Lee, J. L. Marshall, J. Mena-Fernández, R. Miquel, J. J. Mohr, R. L. C. Ogando, A. A. Plazas Malagón, A. Porredon, S. Samuroff, E. Sanchez, B. Santiago, I. Sevilla-Noarbe, M. Smith, E. Suchyta, M. E. C. Swanson, D. Thomas, C. To, V. Vikram, A. R. Walker, N. Weaverdyck, J. Weller
We present cosmological constraints from Dark Energy Survey Year 3 (DES Y3) weak lensing data using persistent homology, a topological data analysis technique that tracks how features like clusters and voids evolve across density thresholds. For the first time, we apply spherical persistent homology to galaxy survey data through the algorithm TopoS2, which is optimized for curved-sky analyses and HEALPix compatibility. Employing a simulation-based inference framework with the Gower Street simulation suite, specifically designed to mimic DES Y3 data properties, we extract topological summary statistics from convergence maps across multiple smoothing scales and redshift bins. After neural network compression of these statistics, we estimate the likelihood function and validate our analysis against baryonic feedback effects, finding minimal biases (under $0.3\sigma$) in the $\Omega_\mathrm{m}-S_8$ plane. Assuming the $w$CDM model, our combined Betti numbers and second moments analysis yields $S_8 = 0.821 \pm 0.018$ and $\Omega_\mathrm{m} = 0.304\pm0.037$-constraints 70% tighter than those from cosmic shear two-point statistics in the same parameter plane. Our results demonstrate that topological methods provide a powerful and robust framework for extracting cosmological information, with our spherical methodology readily applicable to upcoming Stage IV wide-field galaxy surveys.
Cloud-to-cloud velocity dispersions across a Local arm segment
2025-06-16
Lixia Yuan, Ji Yang
Using a large sample of 9617 molecular clouds (MCs) from the Milky Way Imaging Scroll Painting survey, we mainly measure one-dimensional cloud-to-cloud velocity dispersions across a 450 deg$^{2}$ segment of the Local arm in the Galactic second quadrant. We define the cloud-to-cloud velocity dispersion using two metrics: the standard deviation ($\sigma_{\rm bin}$) and flux-weighted root-mean-squared value ($\sigma_{\rm bin, w}$) of the centroid velocities of $^{12}$CO-detected MCs within spatial bins. The typical values of $\sigma_{\rm bin}$ and $\sigma_{\rm bin, w}$ are 7.5$\pm$0.5 km s$^{-1}$ and 6.2$\pm$0.5 km s$^{-1}$, respectively. After categorizing clouds by sizes into three types: Type $S$ (0.15 $-$ 1.2 pc), Type $M$ (1.2 $-$ 4.8 pc), and Type L ($\gtrsim$ 4.8 pc), we find that the spatial distribution of Type $S$ and $M$ MCs projected onto the Galactic longitude-latitude (l-b) plane is generally uniform. Additionally, the cloud-to-cloud velocity dispersion among Type $S$ clouds ($\sim$ 7.6 and 7.4 km s$^{-1}$ for $\sigma_{\rm bin}$ and $\sigma_{\rm bin, w}$, respectively) is systematically greater than that among Type $M$ clouds ($\sim$ 6.7 and 6.0 km s$^{-1}$ for $\sigma_{\rm bin}$ and $\sigma_{\rm bin, w}$, respectively), with differences of 0.9 $-$ 1.4 km s$^{-1}$. From these measurements, we estimate merger timescales between MCs to be approximately 0.3 to 0.9 Myr, which is shorter than their internal crossing timescales ($\sim$ 1 Myr for Type $S$, $\sim$ 2 Myr for Type $M$, and $\gtrsim$ 5 Myr for Type $L$). This disparity, particularly pronounced for larger Type $L$ clouds, suggests that MCs are dynamically transient structures, with their gas content changing due to frequent interactions with neighboring clouds.
2025-06-16
Tong Tian, Sukyoon Oh, Christian Spielmann
Ghost imaging (GI) forms images from intensity-correlation data collected by a single-pixel detector, decoupling illumination and sensing. Since its quantum-photon origins, the technique has evolved through classical pseudothermal, computational and deep-learning variants to span an unprecedented spectral range--from extreme-ultraviolet (XUV) to terahertz (THz) waves and even matter waves. This review traces that evolution, highlighting how wavelength dictates modulators, detectors and propagation physics and, in turn, the attainable penetration depth, resolution and dose. We survey X-ray/XUV implementations that deliver low-damage microscopy, visible/near-IR systems that achieve video-rate lidar through fog and water, mid-IR platforms that extract molecular fingerprints in photon-starved conditions, and THz schemes that provide non-destructive inspection of concealed structures. These advances collectively position multi-wavelength GI as a versatile, low-dose alternative wherever conventional focal-plane arrays falter.
This paper is a personal overview of the efforts over the last half century to understand fluid turbulence in terms of simpler coherent units. The consequences of chaos and the concept of coherence are first reviewed, using examples from free-shear and wall-bounded shear flows, and including how the simplifications due to coherent structures have been useful in the conceptualization and control of turbulence. It is remarked that, even if this approach has revolutionized our understanding of the flow, most of turbulence cannot yet be described by structures. This includes cascades, both direct and inverse, and possibly junk turbulence, whose role, if any, is currently unknown. This part of the paper is mostly a catalog of questions, some of them answered and others still open. A second part of the paper examines which new techniques can be expected to help in attacking the open questions, and which, in the opinion of the author, are the strengths and limitations of current approaches, such as data-driven science and causal inference.
2D MXene-Based Photocatalysts for Efficient Water Splitting and Hydrogen Evolution: A brief review
2025-06-16
C. B. Subba, Prasad Mattipally, J. Sivakumar, A. Laref, A. Yvaz, D. P. Rai, Z. Pachuau
Photocatalytic water splitting offers a viable and sustainable method for hydrogen production. MXenes, a class of 2D transition-metal carbides/nitrides, have emerged as potential photocatalysts and co-catalysts due to their tunable electronic properties, high conductivity, and surface functionality. This review explores recent advances in MXene-based photocatalysts for hydrogen production, discussing their synthesis, electronic structures, and photocatalytic mechanisms. The key challenges, including stability issues, charge recombination, and bandgap optimisation, are critically analysed. Finally, future research directions are outlined to improve MXene-based systems for large-scale hydrogen production.
Delving Into the Psychology of Machines: Exploring the Structure of Self-Regulated Learning via LLM-Generated Survey Responses
2025-06-16
Leonie V. D. E. Vogelsmeier, Eduardo Oliveira, Kamila Misiejuk, Sonsoles López-Pernas, Mohammed Saqr
Large language models (LLMs) offer the potential to simulate human-like responses and behaviors, creating new opportunities for psychological science. In the context of self-regulated learning (SRL), if LLMs can reliably simulate survey responses at scale and speed, they could be used to test intervention scenarios, refine theoretical models, augment sparse datasets, and represent hard-to-reach populations. However, the validity of LLM-generated survey responses remains uncertain, with limited research focused on SRL and existing studies beyond SRL yielding mixed results. Therefore, in this study, we examined LLM-generated responses to the 44-item Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich \& De Groot, 1990), a widely used instrument assessing students' learning strategies and academic motivation. Particularly, we used the LLMs GPT-4o, Claude 3.7 Sonnet, Gemini 2 Flash, LLaMA 3.1-8B, and Mistral Large. We analyzed item distributions, the psychological network of the theoretical SRL dimensions, and psychometric validity based on the latent factor structure. Our results suggest that Gemini 2 Flash was the most promising LLM, showing considerable sampling variability and producing underlying dimensions and theoretical relationships that align with prior theory and empirical findings. At the same time, we observed discrepancies and limitations, underscoring both the potential and current constraints of using LLMs for simulating psychological survey data and applying it in educational contexts.
Towards Pervasive Distributed Agentic Generative AI -- A State of The Art
2025-06-16
Gianni Molinari, Fabio Ciravegna
The rapid advancement of intelligent agents and Large Language Models (LLMs) is reshaping the pervasive computing field. Their ability to perceive, reason, and act through natural language understanding enables autonomous problem-solving in complex pervasive environments, including the management of heterogeneous sensors, devices, and data. This survey outlines the architectural components of LLM agents (profiling, memory, planning, and action) and examines their deployment and evaluation across various scenarios. Than it reviews computational and infrastructural advancements (cloud to edge) in pervasive computing and how AI is moving in this field. It highlights state-of-the-art agent deployment strategies and applications, including local and distributed execution on resource-constrained devices. This survey identifies key challenges of these agents in pervasive computing such as architectural, energetic and privacy limitations. It finally proposes what we called "Agent as a Tool", a conceptual framework for pervasive agentic AI, emphasizing context awareness, modularity, security, efficiency and effectiveness.
The explosive growth of teletraffic, fueled by the convergence of cyber-physical systems and data-intensive applications, such as the Internet of Things (IoT), autonomous systems, and immersive communications, demands a multidisciplinary suite of innovative solutions across the physical and network layers. Fluid antenna systems (FAS) represent a transformative advancement in antenna design, offering enhanced spatial degrees of freedom through dynamic reconfigurability. By exploiting spatial flexibility, FAS can adapt to varying channel conditions and optimize wireless performance, making it a highly promising candidate for next-generation communication networks. This paper provides a comprehensive survey of the state of the art in FAS research. We begin by examining key application scenarios in which FAS offers significant advantages. We then present the fundamental principles of FAS, covering channel measurement and modeling, single-user configurations, and the multi-user fluid antenna multiple access (FAMA) framework. Following this, we delve into key network-layer techniques such as quality-of-service (QoS) provisioning, power allocation, and content placement strategies. We conclude by identifying prevailing challenges and outlining future research directions to support the continued development of FAS in next-generation wireless networks.
Large Language Models as 'Hidden Persuaders': Fake Product Reviews are Indistinguishable to Humans and Machines
2025-06-16
Weiyao Meng, John Harvey, James Goulding, Chris James Carter, Evgeniya Lukinova, Andrew Smith, Paul Frobisher, Mina Forrest, Georgiana Nica-Avram
Reading and evaluating product reviews is central to how most people decide what to buy and consume online. However, the recent emergence of Large Language Models and Generative Artificial Intelligence now means writing fraudulent or fake reviews is potentially easier than ever. Through three studies we demonstrate that (1) humans are no longer able to distinguish between real and fake product reviews generated by machines, averaging only 50.8% accuracy overall - essentially the same that would be expected by chance alone; (2) that LLMs are likewise unable to distinguish between fake and real reviews and perform equivalently bad or even worse than humans; and (3) that humans and LLMs pursue different strategies for evaluating authenticity which lead to equivalently bad accuracy, but different precision, recall and F1 scores - indicating they perform worse at different aspects of judgment. The results reveal that review systems everywhere are now susceptible to mechanised fraud if they do not depend on trustworthy purchase verification to guarantee the authenticity of reviewers. Furthermore, the results provide insight into the consumer psychology of how humans judge authenticity, demonstrating there is an inherent 'scepticism bias' towards positive reviews and a special vulnerability to misjudge the authenticity of fake negative reviews. Additionally, results provide a first insight into the 'machine psychology' of judging fake reviews, revealing that the strategies LLMs take to evaluate authenticity radically differ from humans, in ways that are equally wrong in terms of accuracy, but different in their misjudgments.
Foundation models (FMs), large neural networks pretrained on extensive and diverse datasets, have revolutionized artificial intelligence and shown significant promise in medical imaging by enabling robust performance with limited labeled data. Although numerous surveys have reviewed the application of FM in healthcare care, brain imaging remains underrepresented, despite its critical role in the diagnosis and treatment of neurological diseases using modalities such as MRI, CT, and PET. Existing reviews either marginalize brain imaging or lack depth on the unique challenges and requirements of FM in this domain, such as multimodal data integration, support for diverse clinical tasks, and handling of heterogeneous, fragmented datasets. To address this gap, we present the first comprehensive and curated review of FMs for brain imaging. We systematically analyze 161 brain imaging datasets and 86 FM architectures, providing information on key design choices, training paradigms, and optimizations driving recent advances. Our review highlights the leading models for various brain imaging tasks, summarizes their innovations, and critically examines current limitations and blind spots in the literature. We conclude by outlining future research directions to advance FM applications in brain imaging, with the aim of fostering progress in both clinical and research settings.
Adopting Use Case Descriptions for Requirements Specification: an Industrial Case Study
2025-06-16
Julian Frattini, Anja Frattini
Context: Use case (UC) descriptions are a prominent format for specifying functional requirements. Existing literature abounds with recommendations on how to write high-quality UC descriptions but lacks insights into (1) their real-world adoption, (2) whether these recommendations correspond to actual quality, and (3) which factors influence the quality of UCs. Objectives: We aim to contribute empirical evidence about the adoption of UC descriptions in a large, globally distributed case company. Methods: We surveyed 1188 business requirements of a case company that were elicited from 2020-01-01 until 2024-12-31 and contained 1192 UCs in various forms. Among these, we manually evaluated the 273 template-style UC descriptions against established quality guidelines. We generated descriptive statistics of the format's adoption over the surveyed time frame. Furthermore, we used inferential statistics to determine (a) how properties of the requirements engineering process affected the UC quality and (b) how UC quality affects subsequent software development activities. Results and Conclusions: Our descriptive results show how the adoption of UC descriptions in practice deviates from textbook recommendations. However, our inferential results suggest that only a few phenomena like solution-orientation show an actual impact in practice. These results can steer UC quality research into a more relevant direction.
Validation of Fast Mocks Generation for CSST Photometric Survey
2025-06-16
Yiming Hu, Yu Yu
Weak lensing has become a powerful tool for probing the matter distribution in the Universe and constraining cosmological parameters. This paper aims to explore the fast mock generation pipeline to obtain the covariance matrix of the $3\times 2$pt analysis for the upcoming China Space Station Telescope (CSST). We adopt the $N_\mathrm{side}$ pipeline, which generates matter distribution with lognormal assumptions, to create full-sky galaxy mocks with certain two-point statistics. We also employ the Markov-Chain Monte Carlo simulation to test the accuracy of the covariance matrix from the mock-generated galaxy catalogue. Our work validates the accuracy of the $3\times 2$pt statistics in both spherical harmonic space and real space. The critical scale below which the fractional error of correlation exceeds 1$\%$ can decrease as the resolution parameter $N_\mathrm{side}$ increases. After excluding certain scales, the covariance matrix from the mock-generated galaxy catalogue can constrain the cosmological parameters with 0.1$\%$ accuracy. This work demonstrates the potential of $\texttt{GLASS}$ for real-space cosmological measurements and highlights the importance of discarding appropriate scales.
A Game-Theoretic Negotiation Framework for Cross-Cultural Consensus in LLMs
2025-06-16
Guoxi Zhang, Jiawei Chen, Tianzhuo Yang, Jiaming Ji, Yaodong Yang, Juntao Dai
The increasing prevalence of large language models (LLMs) is influencing global value systems. However, these models frequently exhibit a pronounced WEIRD (Western, Educated, Industrialized, Rich, Democratic) cultural bias due to lack of attention to minority values. This monocultural perspective may reinforce dominant values and marginalize diverse cultural viewpoints, posing challenges for the development of equitable and inclusive AI systems. In this work, we introduce a systematic framework designed to boost fair and robust cross-cultural consensus among LLMs. We model consensus as a Nash Equilibrium and employ a game-theoretic negotiation method based on Policy-Space Response Oracles (PSRO) to simulate an organized cross-cultural negotiation process. To evaluate this approach, we construct regional cultural agents using data transformed from the World Values Survey (WVS). Beyond the conventional model-level evaluation method, We further propose two quantitative metrics, Perplexity-based Acceptence and Values Self-Consistency, to assess consensus outcomes. Experimental results indicate that our approach generates consensus of higher quality while ensuring more balanced compromise compared to baselines. Overall, it mitigates WEIRD bias by guiding agents toward convergence through fair and gradual negotiation steps.
Polyra Swarms: A Shape-Based Approach to Machine Learning
2025-06-16
Simon Klüttermann, Emmanuel Müller
We propose Polyra Swarms, a novel machine-learning approach that approximates shapes instead of functions. Our method enables general-purpose learning with very low bias. In particular, we show that depending on the task, Polyra Swarms can be preferable compared to neural networks, especially for tasks like anomaly detection. We further introduce an automated abstraction mechanism that simplifies the complexity of a Polyra Swarm significantly, enhancing both their generalization and transparency. Since Polyra Swarms operate on fundamentally different principles than neural networks, they open up new research directions with distinct strengths and limitations.
A Comprehensive Survey on Deep Learning Solutions for 3D Flood Mapping
2025-06-16
Wenfeng Jia, Bin Liang, Yuxi Liu, Muhammad Arif Khan, Lihong Zheng
Flooding remains a major global challenge, worsened by climate change and urbanization, demanding advanced solutions for effective disaster management. While traditional 2D flood mapping techniques provide limited insights, 3D flood mapping, powered by deep learning (DL), offers enhanced capabilities by integrating flood extent and depth. This paper presents a comprehensive survey of deep learning-based 3D flood mapping, emphasizing its advancements over 2D maps by integrating flood extent and depth for effective disaster management and urban planning. The survey categorizes deep learning techniques into task decomposition and end-to-end approaches, applicable to both static and dynamic flood features. We compare key DL architectures, highlighting their respective roles in enhancing prediction accuracy and computational efficiency. Additionally, this work explores diverse data sources such as digital elevation models, satellite imagery, rainfall, and simulated data, outlining their roles in 3D flood mapping. The applications reviewed range from real-time flood prediction to long-term urban planning and risk assessment. However, significant challenges persist, including data scarcity, model interpretability, and integration with traditional hydrodynamic models. This survey concludes by suggesting future directions to address these limitations, focusing on enhanced datasets, improved models, and policy implications for flood management. This survey aims to guide researchers and practitioners in leveraging DL techniques for more robust and reliable 3D flood mapping, fostering improved flood management strategies.
Do Music Preferences Reflect Cultural Values? A Cross-National Analysis Using Music Embedding and World Values Survey
2025-06-16
Yongjae Kim, Seongchan Park
This study explores the extent to which national music preferences reflect underlying cultural values. We collected long-term popular music data from YouTube Music Charts across 62 countries, encompassing both Western and non-Western regions, and extracted audio embeddings using the CLAP model. To complement these quantitative representations, we generated semantic captions for each track using LP-MusicCaps and GPT-based summarization. Countries were clustered based on contrastive embeddings that highlight deviations from global musical norms. The resulting clusters were projected into a two-dimensional space via t-SNE for visualization and evaluated against cultural zones defined by the World Values Survey (WVS). Statistical analyses, including MANOVA and chi-squared tests, confirmed that music-based clusters exhibit significant alignment with established cultural groupings. Furthermore, residual analysis revealed consistent patterns of overrepresentation, suggesting non-random associations between specific clusters and cultural zones. These findings indicate that national-level music preferences encode meaningful cultural signals and can serve as a proxy for understanding global cultural boundaries.
2025-06-16
Hongjiang Lei, Congke Jiang, Ki-Hong Park, Mohamed A. Aboulhassan, Sen Zhou, Gaofeng Pan
Integrated communication and sensing, which can make full use of the limited spectrum resources to perform communication and sensing tasks simultaneously, is an up-and-coming technology in wireless communication networks. In this work, we investigate the secrecy performance of an uncrewed aerial vehicle (UAV)-assisted secure integrated communication, sensing, and computing system, where the UAV sends radar signals to locate and disrupt potential eavesdroppers while providing offload services to ground users (GUs). Considering the constraints of UAV maximum speed, transmit power, and propulsion energy, as well as secure offloading, data transmission, and computation time, the total energy consumption of GUs is minimized by jointly optimizing user offloading ratio, user scheduling strategy, transmit beamforming, and UAV trajectory. An efficient iterative optimization algorithm is proposed to solve the non-convex optimization problem caused by tightly coupled dependent variables. In particular, the original optimization problem is decomposed into four sub-optimization problems, and the non-convex sub-problems are transformed into approximately convex forms via successive convex approximation. Then, all sub-problems are solved successively by using the block coordinate descent technique. Numerical results demonstrate the convergence and validate the effectiveness of the proposed algorithm.
Formation Pathways of Compact Elliptical Galaxies: Perspective of Internal Structures
2025-06-16
Jinwon Yi, Soo-Chang Rey, Suk Kim, Youngdae Lee
We present a structural analysis of 138 compact elliptical galaxies (cEs) in the redshift range of $z < 0.05$ using the Sloan Digital Sky Survey (SDSS) DR12 data. We perform single- and double-component S\'ersic model fitting to their SDSS $r$-band surface brightness profiles. By dividing cEs into those with [cE(w)] and without [cE(w/o)] a bright host galaxy, we find a significant structural dichotomy: the majority ($\sim$85\%) of cE(w)s exhibit single-component profiles, while a similar proportion ($\sim$85\%) of isolated cE(w/o)s display double-component profiles, characterized by a compact, inner component and a diffuse, disk-like outer component. These results suggest that host-associated cE(w)s primarily form through the tidal stripping of larger progenitors, resulting in a compact bulge-like core. In contrast, isolated cE(w/o)s appear to form intrinsically at early epochs, likely through gas-rich mergers, and retain disk-like outer structures. The S\'ersic index distribution of cE(w)s with single-component structure indicates progenitor types ranging from pseudo-bulge to classical bulge, supported by differences in stellar populations. A small fraction of cEs, including double-component cE(w)s and single-component cE(w/o)s, suggests complex evolutionary channels involving environmental capture or ejection. Our results emphasize that the structural characteristics of cEs, specifically the presence or absence of an extended outer envelope, serve as a crucial diagnostic tool to distinguish tidally stripped remnants from intrinsically formed low-mass cEs in isolation.
Descriptions of Cantor Sets: A Set-Theoretic Survey and Open Problems
2025-06-16
Mohsen Soltanifar
This survey synthesizes the principal descriptive set-theoretic perspectives on deterministic Cantor sets on the real line and charts directions for future study. After recounting their historical genesis and compiling an up-to-date taxonomy, we review the Borel hierarchy and four hierarchically ordered representations-general, nested, iterated-function-system (IFS), and q-ary expansion-presented from the most general to the most specific set-theoretic description of deterministic Cantor sets. We then present explicit and recursive descriptions for two thin families of measure-zero Cantor sets and an augmented "tick" family of positive measure, respectively, showing that the classical middle-third set lies in the intersection of all three families of after-mentioned Cantor sets. The survey closes by isolating several open problems in four directions, aiming to provide mathematicians with a coherent platform for further descriptive set-theoretic investigations into Cantor-type sets on the real line.
IKDiffuser: Fast and Diverse Inverse Kinematics Solution Generation for Multi-arm Robotic Systems
2025-06-16
Zeyu Zhang, Ziyuan Jiao
Solving Inverse Kinematics (IK) problems is fundamental to robotics, but has primarily been successful with single serial manipulators. For multi-arm robotic systems, IK remains challenging due to complex self-collisions, coupled joints, and high-dimensional redundancy. These complexities make traditional IK solvers slow, prone to failure, and lacking in solution diversity. In this paper, we present IKDiffuser, a diffusion-based model designed for fast and diverse IK solution generation for multi-arm robotic systems. IKDiffuser learns the joint distribution over the configuration space, capturing complex dependencies and enabling seamless generalization to multi-arm robotic systems of different structures. In addition, IKDiffuser can incorporate additional objectives during inference without retraining, offering versatility and adaptability for task-specific requirements. In experiments on 6 different multi-arm systems, the proposed IKDiffuser achieves superior solution accuracy, precision, diversity, and computational efficiency compared to existing solvers. The proposed IKDiffuser framework offers a scalable, unified approach to solving multi-arm IK problems, facilitating the potential of multi-arm robotic systems in real-time manipulation tasks.
Discerning What Matters: A Multi-Dimensional Assessment of Moral Competence in LLMs
2025-06-16
Daniel Kilov, Caroline Hendy, Secil Yanik Guyot, Aaron J. Snoswell, Seth Lazar
Moral competence is the ability to act in accordance with moral principles. As large language models (LLMs) are increasingly deployed in situations demanding moral competence, there is increasing interest in evaluating this ability empirically. We review existing literature and identify three significant shortcoming: (i) Over-reliance on prepackaged moral scenarios with explicitly highlighted moral features; (ii) Focus on verdict prediction rather than moral reasoning; and (iii) Inadequate testing of models' (in)ability to recognize when additional information is needed. Grounded in philosophical research on moral skill, we then introduce a novel method for assessing moral competence in LLMs. Our approach moves beyond simple verdict comparisons to evaluate five dimensions of moral competence: identifying morally relevant features, weighting their importance, assigning moral reasons to these features, synthesizing coherent moral judgments, and recognizing information gaps. We conduct two experiments comparing six leading LLMs against non-expert humans and professional philosophers. In our first experiment using ethical vignettes standard to existing work, LLMs generally outperformed non-expert humans across multiple dimensions of moral reasoning. However, our second experiment, featuring novel scenarios designed to test moral sensitivity by embedding relevant features among irrelevant details, revealed a striking reversal: several LLMs performed significantly worse than humans. Our findings suggest that current evaluations may substantially overestimate LLMs' moral reasoning capabilities by eliminating the task of discerning moral relevance from noisy information, which we take to be a prerequisite for genuine moral skill. This work provides a more nuanced framework for assessing AI moral competence and highlights important directions for improving moral competence in advanced AI systems.
Hopfield Networks as Models of Emergent Function in Biology
2025-06-16
Maria Yampolskaya, Pankaj Mehta
Hopfield models, originally developed to study memory retrieval in neural networks, have become versatile tools for modeling diverse biological systems in which function emerges from collective dynamics. In this review, we provide a pedagogical introduction to both classical and modern Hopfield networks from a biophysical perspective. After presenting the underlying mathematics, we build physical intuition through three complementary interpretations of Hopfield dynamics: as noise discrimination, as a geometric construction defining a natural coordinate system in pattern space, and as gradient-like descent on an energy landscape. We then survey recent applications of Hopfield networks a variety of biological setting including cellular differentiation and epigenetic memory, molecular self-assembly, and spatial neural representations.
Beyond the First Read: AI-Assisted Perceptual Error Detection in Chest Radiography Accounting for Interobserver Variability
2025-06-16
Adhrith Vutukuri, Akash Awasthi, David Yang, Carol C. Wu, Hien Van Nguyen
Chest radiography is widely used in diagnostic imaging. However, perceptual errors -- especially overlooked but visible abnormalities -- remain common and clinically significant. Current workflows and AI systems provide limited support for detecting such errors after interpretation and often lack meaningful human--AI collaboration. We introduce RADAR (Radiologist--AI Diagnostic Assistance and Review), a post-interpretation companion system. RADAR ingests finalized radiologist annotations and CXR images, then performs regional-level analysis to detect and refer potentially missed abnormal regions. The system supports a "second-look" workflow and offers suggested regions of interest (ROIs) rather than fixed labels to accommodate inter-observer variation. We evaluated RADAR on a simulated perceptual-error dataset derived from de-identified CXR cases, using F1 score and Intersection over Union (IoU) as primary metrics. RADAR achieved a recall of 0.78, precision of 0.44, and an F1 score of 0.56 in detecting missed abnormalities in the simulated perceptual-error dataset. Although precision is moderate, this reduces over-reliance on AI by encouraging radiologist oversight in human--AI collaboration. The median IoU was 0.78, with more than 90% of referrals exceeding 0.5 IoU, indicating accurate regional localization. RADAR effectively complements radiologist judgment, providing valuable post-read support for perceptual-error detection in CXR interpretation. Its flexible ROI suggestions and non-intrusive integration position it as a promising tool in real-world radiology workflows. To facilitate reproducibility and further evaluation, we release a fully open-source web implementation alongside a simulated error dataset. All code, data, demonstration videos, and the application are publicly available at https://github.com/avutukuri01/RADAR.
The rapid advancement of generative models has enabled modern AI systems to comprehend and produce highly sophisticated content, even achieving human-level performance in specific domains. However, these models remain fundamentally constrained by catastrophic forgetting - a persistent challenge where adapting to new tasks typically leads to significant degradation in performance on previously learned tasks. To address this practical limitation, numerous approaches have been proposed to enhance the adaptability and scalability of generative models in real-world applications. In this work, we present a comprehensive survey of continual learning methods for mainstream generative models, including large language models, multimodal large language models, vision language action models, and diffusion models. Drawing inspiration from the memory mechanisms of the human brain, we systematically categorize these approaches into three paradigms: architecture-based, regularization-based, and replay-based methods, while elucidating their underlying methodologies and motivations. We further analyze continual learning setups for different generative models, including training objectives, benchmarks, and core backbones, offering deeper insights into the field. The project page of this paper is available at https://github.com/Ghy0501/Awesome-Continual-Learning-in-Generative-Models.
Evolution of ReID: From Early Methods to LLM Integration
2025-06-16
Amran Bhuiyan, Mizanur Rahman, Md Tahmid Rahman Laskar, Aijun An, Jimmy Xiangji Huang
Person re-identification (ReID) has evolved from handcrafted feature-based methods to deep learning approaches and, more recently, to models incorporating large language models (LLMs). Early methods struggled with variations in lighting, pose, and viewpoint, but deep learning addressed these issues by learning robust visual features. Building on this, LLMs now enable ReID systems to integrate semantic and contextual information through natural language. This survey traces that full evolution and offers one of the first comprehensive reviews of ReID approaches that leverage LLMs, where textual descriptions are used as privileged information to improve visual matching. A key contribution is the use of dynamic, identity-specific prompts generated by GPT-4o, which enhance the alignment between images and text in vision-language ReID systems. Experimental results show that these descriptions improve accuracy, especially in complex or ambiguous cases. To support further research, we release a large set of GPT-4o-generated descriptions for standard ReID datasets. By bridging computer vision and natural language processing, this survey offers a unified perspective on the field's development and outlines key future directions such as better prompt design, cross-modal transfer learning, and real-world adaptability.
2025-06-16
Jinhang Jiang, Nan Wu, Ben Liu, Mei Feng, Xin Ji, Karthik Srinivasan
Time series forecasting underpins vital decision-making across various sectors, yet raw predictions from sophisticated models often harbor systematic errors and biases. We examine the Forecast-Then-Optimize (FTO) framework, pioneering its systematic synopsis. Unlike conventional Predict-Then-Optimize (PTO) methods, FTO explicitly refines forecasts through optimization techniques such as ensemble methods, meta-learners, and uncertainty adjustments. Furthermore, deep learning and large language models have established superiority over traditional parametric forecasting models for most enterprise applications. This paper surveys significant advancements from 2016 to 2025, analyzing mainstream deep learning FTO architectures. Focusing on real-world applications in operations management, we demonstrate FTO's crucial role in enhancing predictive accuracy, robustness, and decision efficacy. Our study establishes foundational guidelines for future forecasting methodologies, bridging theory and operational practicality.
Modern deep learning models are highly overparameterized, resulting in large sets of parameter configurations that yield the same outputs. A significant portion of this redundancy is explained by symmetries in the parameter space--transformations that leave the network function unchanged. These symmetries shape the loss landscape and constrain learning dynamics, offering a new lens for understanding optimization, generalization, and model complexity that complements existing theory of deep learning. This survey provides an overview of parameter space symmetry. We summarize existing literature, uncover connections between symmetry and learning theory, and identify gaps and opportunities in this emerging field.
New Field OB and OBe Binaries of the SMC Wing: Observational Properties and Population Modeling
2025-06-16
Irene Vargas-Salazar, M. S. Oey, Jan J. Eldridge, Drew Weisserman, Helen C. Januszewski, Juliette C. Becker, Stefano Zazzera, Norberto Castro, Yongjung Kim, Kaitlin M. Kratter, Mario Mateo, John I. Bailey III
We present a radial velocity (RV) survey of the field OB and OBe stars of the SMC Wing. We use multi-epoch observations of 55 targets obtained with the Magellan IMACS and M2FS multi-object spectrographs to identify single- and double-lined spectroscopic binaries. We also use TESS light curves to identify new eclipsing binary candidates. We find that 10 each of our 34 OB (29\%) and 21 OBe (48\%) stars are confirmed binaries, and at least $\sim$ 6 more are candidates. Using our RV measurements, we set constraints on the companion masses, and in some cases, on periods, eccentricities and inclinations. The RV data suggest that OB binaries favor more circular orbits (mean eccentricity $\langle e\rangle = 0.08\pm 0.02$) while OBe binaries are eccentric ($\langle e\rangle = 0.45\pm 0.04$). We identify 2 candidate black hole binaries, [M2002] 77616, and 81941. We use BPASS to predict the frequencies of ejected OB and OBe stars and binaries, assuming OBe stars are binary mass gainers ejected by the companion supernova. We also predict the frequencies of black-hole, neutron-star, and stripped-star companions, and we model the distributions of primary and secondary masses, periods, eccentricities, and velocity distributions. The models are broadly consistent with the binary origin scenario for OBe stars, and predict an even larger number of post-supernova OB binaries. Comparison with the kinematics supports a significant contribution from dynamical ejections for both OB and OBe stars, although less so for binaries.
An Interdisciplinary Review of Commonsense Reasoning and Intent Detection
2025-06-16
Md Nazmus Sakib
This review explores recent advances in commonsense reasoning and intent detection, two key challenges in natural language understanding. We analyze 28 papers from ACL, EMNLP, and CHI (2020-2025), organizing them by methodology and application. Commonsense reasoning is reviewed across zero-shot learning, cultural adaptation, structured evaluation, and interactive contexts. Intent detection is examined through open-set models, generative formulations, clustering, and human-centered systems. By bridging insights from NLP and HCI, we highlight emerging trends toward more adaptive, multilingual, and context-aware models, and identify key gaps in grounding, generalization, and benchmark design.
An 11,000-Study Open-Access Dataset of Longitudinal Magnetic Resonance Images of Brain Metastases
2025-06-16
Saahil Chadha, David Weiss, Anastasia Janas, Divya Ramakrishnan, Thomas Hager, Klara Osenberg, Klara Willms, Joshua Zhu, Veronica Chiang, Spyridon Bakas, Nazanin Maleki, Durga V. Sritharan, Sven Schoenherr, Malte Westerhoff, Matthew Zawalich, Melissa Davis, Ajay Malhotra, Khaled Bousabarah, Cornelius Deuschl, MingDe Lin, Sanjay Aneja, Mariam S. Aboian
Brain metastases are a common complication of systemic cancer, affecting over 20% of patients with primary malignancies. Longitudinal magnetic resonance imaging (MRI) is essential for diagnosing patients, tracking disease progression, assessing therapeutic response, and guiding treatment selection. However, the manual review of longitudinal imaging is time-intensive, especially for patients with multifocal disease. Artificial intelligence (AI) offers opportunities to streamline image evaluation, but developing robust AI models requires comprehensive training data representative of real-world imaging studies. Thus, there is an urgent necessity for a large dataset with heterogeneity in imaging protocols and disease presentation. To address this, we present an open-access dataset of 11,884 longitudinal brain MRI studies from 1,430 patients with clinically confirmed brain metastases, paired with clinical and image metadata. The provided dataset will facilitate the development of AI models to assist in the long-term management of patients with brain metastasis.
The DORCHA suite: nature, nurture, and the phase space distribution of the Milky Way's high redshift progenitors today
2025-06-16
Sreedhar Balu, Chris Power, Kris Walker, J. Stuart B. Wyithe
Where in the present-day Milky Way should we search for the remnants of its earliest stars? We address this question using the DORCHA (Gaelic for Dark; DUR-uh-khuh) suite: a set of 25 high-resolution, dark-matter-only cosmological zoom-in simulations of Milky Way analogue (MWA) haloes evolved to $z=0$. Of these, 15 are isolated and the rest are in pairs, similar to the MW and M31. By identifying and tagging the most bound material in high-redshift ($z\geq5$) progenitor haloes -- those likely to host early star formation -- we track the present-day phase-space distribution of this ancient component. We find that this material is highly centrally concentrated at $z=0$, with 90 - 100 per cent residing within $r \lesssim 15\,h^{-1}\,\mathrm{kpc}$. It exhibits steep density profiles ($\rho\propto\,r^{-4}$), low velocity dispersions ($\sigma_r / \sigma_{\rm max} \lesssim 0.6$), and radially biased orbits ($\beta \gtrsim 0.5$ for $r \gtrsim 0.1\,R_{200}$), consistent with a relaxed, centrally embedded population. These results hold across haloes with diverse formation histories and environments, suggesting that the dynamical signature of early progenitors is robust to later mergers and interactions. Our findings imply that the fossil record of the first generations of stars -- including Population III and extremely metal-poor stars -- should be sought in the innermost regions of the Milky Way, where they retain distinctive kinematic imprints. While these stellar populations may overlap, we caution that low metallicity does not uniquely identify ancient stars, nor vice versa. The DORCHA suite thus provides a physically motivated baseline for interpreting observations from Galactic Archaeology surveys targeting the bulge and inner halo.
MeerKAT HI observations of Low Surface Brightness/Ultradiffuse Galaxy Candidates Projected around Two Southern Loose Groups
2025-06-16
Chandreyee Sengupta, Tom C. Scott, Hao Chen, Hyein Yoon, Yogesh Chandola, Mengtian Li, Gyula I. G. Józsa, O. Ivy Wong, Yin-Zhe Ma, Patricio Lagos, Ruta Kale, Denis Tramonte
A large catalogue of low surface brightness galaxies (LSBGs) from the Dark Energy Survey showed significant clustering around nearby galaxy groups and clusters. Using the HIPASS survey, we tried to determine the redshift of a sub-sample of these LSBGs and determine whether they were members of the groups they were projected near, but this was hampered by HIPASS's high spectral rms. This letter reports on MeerKAT H I observations to determine the redshifts of 52 LSBG candidates projected in the vicinity of two groups from our previous HIPASS study. The main goal is to investigate and ascertain whether these LSBGs are genuine group members. H I was detected with MeerKAT and redshifts were determined for only five of the 52 candidates within a velocity range of $\pm$ 2500 km/s of their respective group velocities. All five H I detections were blue LSBGs and two of them were confirmed to be ultradiffuse galaxies (UDGs). Both these UDGs were group members, while the other three detections were either foreground or background galaxies. In this letter we explore scenarios that can explain the 90% non-detection. MeerKAT's excellent sensitivity allows us to conclude that the majority of the non-detected candidates, particularly the blue galaxies, are not group members but lie at higher redshifts. However, this still leaves the open question as why Tanoglidis LSBG candidates, in particular the red ones, appear to be clustered in projection around nearby groups.
$\mathrm{^{117m}Sn}$ and $\mathrm{^{119m}Te}$ Production via Proton Bombardment on Natural Antimony and Implications for Modeling Charged Particle Reactions
2025-06-16
Catherine E. Apgar, Andrew S. Voyles, Jon C. Batchelder, Cathy S. Cutler, Morgan B. Fox, Yun-Hsuan Lee, Dmitri G. Medvedev, Jonathan T. Morrell, Ellen M. O'Brien, Michael Skulski, C. Etienne Vermeulen, Lee A. Bernstein
$\mathrm{^{117m}Sn}$ and $\mathrm{^{119}Sb}$, the latter of which is produced via a $\mathrm{^{119m}Te}$ generator, are promising radionuclides for the targeted treatment of both osteoarthritis and small mass tumors via Auger therapy. Experiments were conducted at Lawrence Berkeley National Laboratory, Los Alamos National Laboratory, and Brookhaven National Laboratory to measure the $\mathrm{^{nat}Sb}$(p,x)$\mathrm{^{117m}Sn}$ and $\mathrm{^{nat}Sb}$(p,x)$\mathrm{^{119m}Te}$ cross sections for incident proton energies up to 200 MeV. Additional measurements for co-produced isotopes are included as well. In addition to this dataset, this paper investigates improvements for proton-induced reaction modeling capabilities through comparison of these experimental dataset against theoretical models in TALYS 1.95. Parameter adjustments affecting level density, optical model potential, and pre-equilibrium emission were explored, with a goodness-of-fit metric established by the largest independent cross section channels and cross-validated with remaining channels.
Euclid: The potential of slitless infrared spectroscopy: A z=5.4 quasar and new ultracool dwarfs
2025-06-16
E. Bañados, V. Le Brun, S. Belladitta, I. Momcheva, D. Stern, J. Wolf, M. Ezziati, D. J. Mortlock, A. Humphrey, R. L. Smart, S. L. Casewell, A. Pérez-Garrido, B. Goldman, E. L. Martín, A. Mohandasan, C. Reylé, C. Dominguez-Tagle, Y. Copin, E. Lusso, Y. Matsuoka, K. McCarthy, F. Ricci, H. -W. Rix, H. J. A. Rottgering, J. -T. Schindler, J. R. Weaver, A. Allaoui, T. Bedrine, M. Castellano, P. -Y. Chabaud, G. Daste, F. Dufresne, J. Gracia-Carpio, M. Kümmel, M. Moresco, M. Scodeggio, C. Surace, D. Vibert, A. Balestra, A. Bonnefoi, A. Caillat, F. Cogato, A. Costille, S. Dusini, S. Ferriol, E. Franceschi, W. Gillard, K. Jahnke, D. Le Mignant, S. Ligori, E. Medinaceli, G. Morgante, F. Passalacqua, K. Paterson, S. Pires, C. Sirignano, I. T. Andika, H. Atek, D. Barrado, S. Bisogni, C. J. Conselice, R. Decarli, H. Dole, T. Dupuy, A. Feltre, S. Fotopoulou, B. Gillis, X. Lopez Lopez, M. Onoue, G. Rodighiero, N. Sedighi, F. Shankar, M. Siudek, L. Spinoglio, D. Vergani, G. Vietri, F. Walter, G. Zamorani, M. R. Zapatero Osorio, J. -Y. Zhang, M. Bethermin, N. Aghanim, B. Altieri, A. Amara, S. Andreon, C. Baccigalupi, M. Baldi, S. Bardelli, A. Basset, P. Battaglia, A. Biviano, A. Bonchi, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, G. Castignani, S. Cavuoti, A. Cimatti, C. Colodro-Conde, G. Congedo, L. Conversi, F. Courbin, H. M. Courtois, M. Cropper, J. -G. Cuby, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, C. Dolding, F. Dubath, C. A. J. Duncan, X. Dupac, A. Ealet, M. Farina, F. Faustini, N. Fourmanoit, M. Frailis, S. Galeotta, K. George, C. Giocoli, B. R. Granett, A. Grazian, F. Grupp, L. Guzzo, S. V. H. Haugan, J. Hoar, H. Hoekstra, W. Holmes, I. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, B. Kubik, K. Kuijken, M. Kunz, H. Kurki-Suonio, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, S. Mei, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, L. Moscardini, C. Neissner, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, J. A. Schewtschenko, M. Schirmer, P. Schneider, T. Schrabback, A. Secroun, E. Sefusatti, G. Seidel, M. Seiffert, S. Serrano, G. Sirri, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, H. I. Teplitz, I. Tereno, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, F. M. Zerbi, E. Zucca, M. Bolzonella, C. Burigana, R. Cabanac, L. Gabarra, V. Scottez, M. Viel, H. Dannerbauer, D. Scott
We demonstrate the potential of Euclid's slitless spectroscopy to discover high-redshift (z>5) quasars and their main photometric contaminant, ultracool dwarfs. Sensitive infrared spectroscopy from space is able to efficiently identify both populations, as demonstrated by Euclid Near-Infrared Spectrometer and Photometer Red Grism (NISP RGE) spectra of the newly discovered z=5.404 quasar EUCL J181530.01+652054.0, as well as several ultracool dwarfs in the Euclid Deep Field North and the Euclid Early Release Observation field Abell 2764. The ultracool dwarfs were identified by cross-correlating their spectra with templates. The quasar was identified by its strong and broad CIII] and MgII emission lines in the NISP RGE 1206-1892 nm spectrum, and confirmed through optical spectroscopy from the Large Binocular Telescope. The NISP Blue Grism (NISP BGE) 926-1366 nm spectrum confirms CIV and CIII] emission. NISP RGE can find bright quasars at z~5.5 and z>7, redshift ranges that are challenging for photometric selection due to contamination from ultracool dwarfs. EUCL J181530.01+652054.0 is a high-excitation, broad absorption line quasar detected at 144 MHz by the LOw-Frequency Array (L144=4e25 W/Hz). The quasar has a bolometric luminosity of 3e12 Lsun and is powered by a 3.4e9 Msun black hole. The discovery of this bright quasar is noteworthy as fewer than one such object was expected in the ~20 deg2 surveyed. This finding highlights the potential and effectiveness of NISP spectroscopy in identifying rare, luminous high-redshift quasars, previewing the census of these sources that Euclid's slitless spectroscopy will deliver over about 14,000 deg2 of the sky.
The Anatomy of India's Industrial Interdependencies: 7-Digit Product-Level Supply-Use and Input-Output Tables from ASI Data (2016-2022) with a Case Study of the Mobile Phone Sector
2025-06-16
Sourish Dutta
Outlines the construction of high-resolution, 7-digit product-level Supply-Use Tables (SUTs) and symmetric Input-Output Tables (IOTs) for the Indian economy, leveraging microdata from the Annual Survey of Industries (ASI) for the period 2016-2022. We delineate a robust methodology encompassing the generation of detailed input and output flows, with a particular focus on the reconciliation of data from registered and unregistered manufacturing sectors through a meticulously developed NPCMS-NIC concordance. The critical transformation from the often-rectangular SUTs to square, symmetric product-by-product IOTs is explicated using the Industry Technology Assumption, a choice justified by its suitability for handling by-products prevalent in a diverse manufacturing landscape. The analytical prowess of this newly constructed high-resolution IOT framework is then demonstrated through its application to assess key economic impacts, specifically the Domestic Value Added (DVA) generated and the employment supported by production and export activities. A detailed case study of India's rapidly evolving mobile phone manufacturing sector (NPCMS 4722200) for the 2016-2022 period reveals profound structural shifts: significant output growth coupled with notable import substitution, a remarkable surge in exports, and a dynamic evolution in the DVA versus Foreign Value Added (FVA) shares, particularly in export-oriented production. The analysis further uncovers substantial employment growth, albeit with an increasing reliance on contractual labour and a heartening rise in female participation in the workforce. These meticulously constructed tables represent a significant methodological advancement and provide an invaluable empirical resource for nuanced analysis of sectoral interdependencies, the efficacy of industrial policy, and the complex dynamics of India's engagement with global value chains.
The rise of large language models (LLMs) has led to dramatic improvements across a wide range of natural language tasks. These advancements have extended into the domain of code, facilitating complex tasks such as code generation, translation, summarization, and repair. However, their utility for real-world deployment in-the-wild has only recently been studied, particularly on software engineering (SWE) tasks such as GitHub issue resolution. In this study, we examine the code reasoning techniques that underlie the ability to perform such tasks, and examine the paradigms used to drive their performance. Our contributions in this paper are: (1) the first dedicated survey on code reasoning for code tasks, highlighting overarching strategies, hybrid and agentic approaches; (2) a taxonomy of various techniques used to drive code reasoning; (3) a comprehensive overview of performance on common benchmarks and a showcase of new, under-explored benchmarks with high potential in SWE; (4) an exploration on how core properties of code can be used to explain different reasoning techniques; and (5) gaps and potentially under-explored areas for future research.
2025-06-16
Jack Stethem, Christopher S. Kochanek, Anya Phillips, Lyra Cao, Marc Pinsonneault
Rotational variables are stars that vary in brightness due to star spots modulated by rotation. They are probes of stellar magnetism, binarity, and evolution. Phillips et al. (2023) explored distinct populations of ~50,000 high-amplitude rotational variables from the All-Sky Automated Survey for Supernovae (ASAS-SN), examining correlations between stellar rotation, binarity, and activity. Here, we carry out a similar analysis of ~50,000 much lower amplitude Kepler rotational variables. The Kepler population is dominated by slowly rotating, single, main sequence stars, with a striking absence of the rapidly rotating main sequence group in the ASAS-SN sample. The binary fractions of the Kepler rotators are significantly lower than for the ASAS-SN systems and they are significantly less spotted, as expected from their lower amplitudes. The scope of these statistical surveys will dramatically increase in the near future.
Despite promising developments in Explainable Artificial Intelligence, the practical value of XAI methods remains under-explored and insufficiently validated in real-world settings. Robust and context-aware evaluation is essential, not only to produce understandable explanations but also to ensure their trustworthiness and usability for intended users, but tends to be overlooked because of no clear guidelines on how to design an evaluation with users. This study addresses this gap with two main goals: (1) to develop a framework of well-defined, atomic properties that characterise the user experience of XAI in healthcare; and (2) to provide clear, context-sensitive guidelines for defining evaluation strategies based on system characteristics. We conducted a systematic review of 82 user studies, sourced from five databases, all situated within healthcare settings and focused on evaluating AI-generated explanations. The analysis was guided by a predefined coding scheme informed by an existing evaluation framework, complemented by inductive codes developed iteratively. The review yields three key contributions: (1) a synthesis of current evaluation practices, highlighting a growing focus on human-centred approaches in healthcare XAI; (2) insights into the interrelations among explanation properties; and (3) an updated framework and a set of actionable guidelines to support interdisciplinary teams in designing and implementing effective evaluation strategies for XAI systems tailored to specific application contexts.
(Abridged) Resolved observations of the CO emission from $z=1-3$ star-forming galaxies are becoming increasingly common, with new high-resolution surveys on the horizon. We aim to inform the interpretation of this resolved CO emission by creating synthetic observations and testing to what extent routinely observed CO transitions can be used to trace H$_2$ across galaxy disks. To this end, we extract $z=1-3$ massive star-forming galaxies (on and above the main sequence) from the SIMBA cosmological simulation and predict their spatially resolved CO(1$-$0)-to-CO(5$-$4) emission using the $\texttt{SLICK}$ pipeline, which combines sub-resolution modeling of the cloud population with the DESPOTIC spectral line calculation code. We find that the CO(1$-$0)-to-H$_2$ ratio ($\alpha_{\rm CO}$) varies significantly within these galaxy disks$-$from values of $\sim1-5$ $\mathrm{M_\odot}$ (K km s$^{-1}$ pc$^2$)$^{-1}$ in the central 1-3 kpc of the most massive galaxies to $>100$ $\mathrm{M_\odot}$ (K km s$^{-1}$ pc$^2$)$^{-1}$ at $\sim$ 15 kpc. Thus, the use of a single $\alpha_{\rm CO}$ to derive the H$_2$ surface density leads to severe underestimates of the H$_2$ contribution in its outskirts. As expected, higher-$J$ CO transitions trace molecular gas in the centers at higher densities, whereas CO(1$-$0) better traces the more diffuse, extended molecular gas. We see significant variations in the CO excitation, with CO(3$-$2)/CO(1$-$0) line luminosity ratios of the most massive galaxies at $z\sim2$ declining from $\sim$ 0.9 in the galaxy centers to $\sim$ 0.1 in the outskirts. On average, line ratios increase substantially toward higher redshifts and lower galaxy stellar masses. We predict that tracing molecular gas with CO beyond 3-5 kpc of cosmic noon galaxies will be challenging with current facilities due to the drastic increase in $\alpha_{\rm CO}$.
DeSPITE: Exploring Contrastive Deep Skeleton-Pointcloud-IMU-Text Embeddings for Advanced Point Cloud Human Activity Understanding
2025-06-16
Thomas Kreutz, Max Mühlhäuser, Alejandro Sanchez Guinea
Despite LiDAR (Light Detection and Ranging) being an effective privacy-preserving alternative to RGB cameras to perceive human activities, it remains largely underexplored in the context of multi-modal contrastive pre-training for human activity understanding (e.g., human activity recognition (HAR), retrieval, or person re-identification (RE-ID)). To close this gap, our work explores learning the correspondence between LiDAR point clouds, human skeleton poses, IMU data, and text in a joint embedding space. More specifically, we present DeSPITE, a Deep Skeleton-Pointcloud-IMU-Text Embedding model, which effectively learns a joint embedding space across these four modalities. At the heart of our empirical exploration, we have combined the existing LIPD and Babel datasets, which enabled us to synchronize data of all four modalities, allowing us to explore the learning of a new joint embedding space. Our experiments demonstrate novel human activity understanding tasks for point cloud sequences enabled through DeSPITE, including Skeleton<->Pointcloud<->IMU matching, retrieval, and temporal moment retrieval. Furthermore, we show that DeSPITE is an effective pre-training strategy for point cloud HAR through experiments in MSR-Action3D and HMPEAR.
Life cycle assessment tools for road design: analysing linearity assumptions
2025-06-16
Nikolaos Kalyviotis
Road infrastructure significantly impacts how people move and live and the emissions associated with travel behaviour. The design of roads is crucial in mitigating emissions. This paper reviews existing transport life cycle assessment tools that have been developed by various entities and can be used for roads. The review focuses on data sources used in the analysis, methods of estimating carbon dioxide emissions, the underlying software that is used to make the estimates, and any limitations of the tools. A critical issue identified in life cycle assessment analysis is the erroneous assumption that relationships within the assessed systems are linear. The current tools focusing on transport infrastructure assessment were developed based on the linear assumptions and limitations of the life cycle assessment analysis. A significant research gap identified is that existing life cycle assessment tools are not integrated with the design process. The analysis is an add-on process to design and the results of an assessment are not then used iteratively to enhance the design. A case study on aggregate road design found that road area significantly correlates with emissions, slope adjustments reduce emissions, and soil type impacts emissions, suggesting future research should explore non-linear relationships for sustainable road design.
Deep learning inference with the Event Horizon Telescope II. The Zingularity framework for Bayesian artificial neural networks
2025-06-16
M. Janssen, C. -k. Chan, J. Davelaar, M. Wielgus
(abridged) In this second paper in our publication series, we present the open-source Zingularity framework for parameter inference with deep Bayesian artificial neural networks. We carried out out supervised learning with synthetic millimeter very long baseline interferometry observations of the EHT. Our ground-truth models are based on GRMHD simulations of Sgr A* and M87* on horizon scales. We investigated how well Zingularity neural networks are able to infer key model parameters from EHT observations, such as the black hole spin and the magnetic state of the accretion disk, when uncertainties in the data are accurately taken into account. Zingularity makes use of the TensorFlow Probability library and is able to handle large amounts of data with a combination of the efficient TFRecord data format plus the Horovod framework. Our approach is the first analysis of EHT data with Bayesian neural networks, where an unprecedented training data size, under consideration of a closely modeled EHT signal path, and the full information content of the observational data are used. Zingularity infers parameters based on salient features in the data and is containerized. Through parameter surveys and dedicated validation tests, we identified neural network architectures, that are robust against internal stochastic processes and unaffected by noise in the observational and model data. We give examples of how different data properties affect the network training. We show how the Bayesian nature of our networks gives trustworthy uncertainties and uncovers failure modes for uncharacterizable data. It is easy to achieve low validation errors during training on synthetic data with neural networks, particularly when the forward modeling is too simplified. Through careful studies, we demonstrate that our trained networks can generalize well so that reliable results can be obtained from observational data.
Feature Intensity Mapping: Polycyclic Aromatic Hydrocarbon Emission from All Galaxies Across Cosmic Time
2025-06-16
Yun-Ting Cheng, Brandon S. Hensley, Thomas S. -Y. Lai
Line intensity mapping (LIM) is an emerging technique for probing the aggregate emission of a spectral line from all sources, without requiring individual detections. Through the wavelength-redshift relation, one can map the line-of-sight evolution of the line emission that traces the underlying large-scale structure in a spectral-imaging survey. In this work, we present a new technique -- feature intensity mapping -- as an extension of the LIM formalism to map broad spectral features in 3D, rather than the narrow emission lines typically targeted by LIM. By accounting for the convolution of spectral features with the instrument's spectral response across redshift, our technique enables simultaneous constraints on the redshift-dependent emission from multiple features. This approach enables 3D intensity mapping with some of the brightest features in galaxies' infrared spectra: the polycyclic aromatic hydrocarbon (PAH) emission bands. We forecast the detectability of PAH signals using feature intensity mapping with the ongoing SPHEREx mission in the near-infrared and the proposed PRIMA mission in the far-infrared. We find that $S/N$ of $\gtrsim 10$ per redshift bin of widths $\Delta z = 0.1$ and $0.5$ can be achieved at $z < 0.5$ and $1 < z < 5$ with SPHEREx and PRIMA, respectively, for multiple PAH features, suggesting a promising prospect for mapping the aggregate PAH emission at cosmological distances with upcoming datasets.
Interstellar dust production, destruction and effects of dust depletion in galaxies
2025-06-16
Francesco Calura
Despite its small mass fraction typically observed in the interstellar medium, dust plays a significant role as a key component of galaxies, affecting a wide range of properties. This review focuses specifically on how dust grains influence interstellar chemical abundances and on the processes that regulate the evolution of the galactic dust budget. I describe the main physical processes regulating dust evolution, including production by stars and other sources, destruction in supernova shocks and interstellar growth and how they are included in galactic chemical evolution models. I discuss the main effects of interstellar dust on the abundances measured in various high-redshift systems that include Damped Lyman alpha absorbers detected along the lines of sight of distant quasars and in the absorption spectra of Gamma Ray Burst afterglows. I discuss the measure of the dust mass in galaxies and review its global budget, evaluated through the study of the evolution of the comoving dust mass density, for which I present an up-to-date compilation of data chosen from the literature. Interstellar dust growth plays a critical role in regulating the dust budget, for which I present a list of evidence both in favour of it and against. The dust budget at high redshift is one aspect that requires attention to drive significant progress in the future, along with the investigation of the properties of dust in local, low-metallicity systems. Our poor theoretical knowledge of basic aspects related to dust evolution evidences the need for a new high-sensitivity space telescope operating in the far-infrared regime, still awaited by the community since the demise of Herschel.
The increasing availability and use of artificial intelligence (AI) tools in educational settings has raised concerns about students' overreliance on these technologies. Overreliance occurs when individuals accept incorrect AI-generated recommendations, often without critical evaluation, leading to flawed problem solutions and undermining learning outcomes. This study investigates potential factors contributing to patterns of AI reliance among undergraduate students, examining not only overreliance but also appropriate reliance (correctly accepting helpful and rejecting harmful recommendations) and underreliance (incorrectly rejecting helpful recommendations). Our approach combined pre- and post-surveys with a controlled experimental task where participants solved programming problems with an AI assistant that provided both accurate and deliberately incorrect suggestions, allowing direct observation of students' reliance patterns when faced with varying AI reliability. We find that appropriate reliance is significantly related to students' programming self-efficacy, programming literacy, and need for cognition, while showing negative correlations with post-task trust and satisfaction. Overreliance showed significant correlations with post-task trust and satisfaction with the AI assistant. Underreliance was negatively correlated with programming literacy, programming self-efficacy, and need for cognition. Overall, the findings provide insights for developing targeted interventions that promote appropriate reliance on AI tools, with implications for the integration of AI in curriculum and educational technologies.
A Gravity-informed Spatiotemporal Transformer for Human Activity Intensity Prediction
2025-06-16
Yi Wang, Zhenghong Wang, Fan Zhang, Chengling Tang, Chaogui Kang, Di Zhu, Zhongfu Ma, Sijie Ruan, Weiyu Zhang, Yu Zheng, Philip S. Yu, Yu Liu
Human activity intensity prediction is a crucial to many location-based services. Although tremendous progress has been made to model dynamic spatiotemporal patterns of human activity, most existing methods, including spatiotemporal graph neural networks (ST-GNNs), overlook physical constraints of spatial interactions and the over-smoothing phenomenon in spatial correlation modeling. To address these limitations, this work proposes a physics-informed deep learning framework, namely Gravity-informed Spatiotemporal Transformer (Gravityformer) by refining transformer attention to integrate the universal law of gravitation and explicitly incorporating constraints from spatial interactions. Specifically, it (1) estimates two spatially explicit mass parameters based on inflow and outflow, (2) models the likelihood of cross-unit interaction using closed-form solutions of spatial interactions to constrain spatial modeling randomness, and (3) utilizes the learned spatial interaction to guide and mitigate the over-smoothing phenomenon in transformer attention matrices. The underlying law of human activity can be explicitly modeled by the proposed adaptive gravity model. Moreover, a parallel spatiotemporal graph convolution transformer structure is proposed for achieving a balance between coupled spatial and temporal learning. Systematic experiments on six real-world large-scale activity datasets demonstrate the quantitative and qualitative superiority of our approach over state-of-the-art benchmarks. Additionally, the learned gravity attention matrix can be disentangled and interpreted based on geographical laws. This work provides a novel insight into integrating physical laws with deep learning for spatiotemporal predictive learning.
A Survey on World Models Grounded in Acoustic Physical Information
2025-06-16
Xiaoliang Chen, Le Chang, Xin Yu, Yunhe Huang, Xianling Tu
This survey provides a comprehensive overview of the emerging field of world models grounded in the foundation of acoustic physical information. It examines the theoretical underpinnings, essential methodological frameworks, and recent technological advancements in leveraging acoustic signals for high-fidelity environmental perception, causal physical reasoning, and predictive simulation of dynamic events. The survey explains how acoustic signals, as direct carriers of mechanical wave energy from physical events, encode rich, latent information about material properties, internal geometric structures, and complex interaction dynamics. Specifically, this survey establishes the theoretical foundation by explaining how fundamental physical laws govern the encoding of physical information within acoustic signals. It then reviews the core methodological pillars, including Physics-Informed Neural Networks (PINNs), generative models, and self-supervised multimodal learning frameworks. Furthermore, the survey details the significant applications of acoustic world models in robotics, autonomous driving, healthcare, and finance. Finally, it systematically outlines the important technical and ethical challenges while proposing a concrete roadmap for future research directions toward robust, causal, uncertainty-aware, and responsible acoustic intelligence. These elements collectively point to a research pathway towards embodied active acoustic intelligence, empowering AI systems to construct an internal "intuitive physics" engine through sound.
Data discovery and table unionability in particular became key tasks in modern Data Science. However, the human perspective for these tasks is still under-explored. Thus, this research investigates the human behavior in determining table unionability within data discovery. We have designed an experimental survey and conducted a comprehensive analysis, in which we assess human decision-making for table unionability. We use the observations from the analysis to develop a machine learning framework to boost the (raw) performance of humans. Furthermore, we perform a preliminary study on how LLM performance is compared to humans indicating that it is typically better to consider a combination of both. We believe that this work lays the foundations for developing future Human-in-the-Loop systems for efficient data discovery.
Does the Expansion of Medicaid Lead to Income Adjustment -- Evidence from SIPP
2025-06-15
Mingjian Li
This study examines whether Medicaid expansion under the Affordable Care Act (ACA) led to strategic income reductions to qualify for coverage. Using monthly data from the Survey of Income and Program Participation (2013 to 2019) and a regression discontinuity design, this paper finds that childless households in expansion states with earnings just above the eligibility threshold reduced their lowest monthly earnings by 39 percentage points of the Federal Poverty Level (roughly 700 dollars for a two-person household) relative to those just below. The effect intensified as the mandate penalty increased and diminished after its repeal. Evidence suggests earnings adjustments along both intensive and extensive margins. The paper reinforces the validity of using the lowest monthly earning to identify Medicaid eligibility and provides the first evidence of a substantial labor supply response to the ACA
2025-06-15
G. Esplugues, M. Agúndez, G. Molpeceres, B. Tercero, C. Cabezas, N. Marcelino, R. Fuentetaja, J. Cernicharo
We report the first detection of HS2 towards the cold dark cloud TMC-1. This is the first observation of a chemical species containing more than one sulphur atom in this type of sources. The astronomical observations are part of QUIJOTE, a line survey of TMC-1 in the Q band (31-50 GHz). The detection is confirmed by the observation of the fine and hyperfine components of two rotational transitions (2(0,2)-1(0,1) and 3(0,3)-2(0,2)). Assuming a rotational temperature of 7 K, we derived an HS2 column density of 5.7x10^11 cm-2, using a local thermodynamic equilibrium model that reproduces the observed spectra. The abundance of HS2 relative to H2 is 5.7x10^-11, which means that it is about seven times more abundant than its oxygenated counterpart HSO. We also explored the main formation and destruction mechanisms of HS2 using a chemical model, which reproduces the observed abundance of HS2 and indicates that dissociative recombination reactions from the ions H2S2+ and H3S2+ play a major role in forming HS2.
Domain Specific Benchmarks for Evaluating Multimodal Large Language Models
2025-06-15
Khizar Anjuma, Muhammad Arbab Arshad, Kadhim Hayawi, Efstathios Polyzos, Asadullah Tariq, Mohamed Adel Serhani, Laiba Batool, Brady Lund, Nishith Reddy Mannuru, Ravi Varma Kumar Bevara, Taslim Mahbub, Muhammad Zeeshan Akram, Sakib Shahriar
Large language models (LLMs) are increasingly being deployed across disciplines due to their advanced reasoning and problem solving capabilities. To measure their effectiveness, various benchmarks have been developed that measure aspects of LLM reasoning, comprehension, and problem-solving. While several surveys address LLM evaluation and benchmarks, a domain-specific analysis remains underexplored in the literature. This paper introduces a taxonomy of seven key disciplines, encompassing various domains and application areas where LLMs are extensively utilized. Additionally, we provide a comprehensive review of LLM benchmarks and survey papers within each domain, highlighting the unique capabilities of LLMs and the challenges faced in their application. Finally, we compile and categorize these benchmarks by domain to create an accessible resource for researchers, aiming to pave the way for advancements toward artificial general intelligence (AGI)
When regarding the suffering of others, we often experience personal distress and feel compelled to help\footnote{Preprint. Under review.}. Inspired by living systems, we investigate the emergence of prosocial behavior among autonomous agents that are motivated by homeostatic self-regulation. We perform multi-agent reinforcement learning, treating each agent as a vulnerable homeostat charged with maintaining its own well-being. We introduce an empathy-like mechanism to share homeostatic states between agents: an agent can either \emph{observe} their partner's internal state ({\bf cognitive empathy}) or the agent's internal state can be \emph{directly coupled} to that of their partner ({\bf affective empathy}). In three simple multi-agent environments, we show that prosocial behavior arises only under homeostatic coupling - when the distress of a partner can affect one's own well-being. Additionally, we show that empathy can be learned: agents can ``decode" their partner's external emotive states to infer the partner's internal homeostatic states. Assuming some level of physiological similarity, agents reference their own emotion-generation functions to invert the mapping from outward display to internal state. Overall, we demonstrate the emergence of prosocial behavior when homeostatic agents learn to ``read" the emotions of others and then to empathize, or feel as they feel.
Disorder by Design: Unveiling Local Structure and Functional Insights in High Entropy Oxides
2025-06-15
John P. Barber, William J. Deary, Andrew N. Titus, Gerald R. Bejger, Saeed S. I. Almishal, Christina M. Rost
High entropy oxides (HEOs) are a rapidly growing class of compositionally complex ceramics in which configurational disorder is engineered to unlock novel functionality. While average crystallographic symmetry is often retained, local structural and chemical disorder, including cation size and valence mismatch, oxygen sublattice distortions, and site-specific bonding, strongly governs ionic transport, redox behavior, magnetic ordering, and dielectric response. This review outlines how these modes of disorder manifest across key oxide families such as rock salt, spinel, fluorite, and perovskite. We highlight recent advances in spectroscopy, total scattering, and high-resolution microscopy enable multi-scale insight into short- and intermediate-range order. By integrating experimental observations with theoretical modeling of entropy and local energetics, we establish a framework linking structural heterogeneity to emergent properties. These insights not only deepen our fundamental understanding of disorder-property relationships but also offer a path toward rational design of tunable materials for catalysis, energy storage, electronics, and much more.
Model-Agnostic, Temperature-Informed Sampling Enhances Cross-Year Crop Mapping with Deep Learning
2025-06-15
Mehmet Ozgur Turkoglu, Selene Ledain, Helge Aasen
Conventional benchmarks for crop type classification from optical satellite time series typically assume access to labeled data from the same year and rely on fixed calendar-day sampling. This limits generalization across seasons, where crop phenology shifts due to interannual climate variability, and precludes real-time application when current-year labels are unavailable. Furthermore, uncertainty quantification is often neglected, making such approaches unreliable for crop monitoring applications. Inspired by ecophysiological principles of plant growth, we propose a simple, model-agnostic sampling strategy that leverages growing degree days (GDD), based on daily average temperature, to replace calendar time with thermal time. By uniformly subsampling time series in this biologically meaningful domain, the method emphasizes phenologically active growth stages while reducing temporal redundancy and noise. We evaluate the method on a multi-year Sentinel-2 dataset spanning all of Switzerland, training on one growing season and testing on other seasons. Compared to state-of-the-art baselines, our method delivers substantial gains in classification accuracy and, critically, produces more calibrated uncertainty estimates. Notably, our method excels in low-data regimes and enables significantly more accurate early-season classification. With only 10 percent of the training data, our method surpasses the state-of-the-art baseline in both predictive accuracy and uncertainty estimation, and by the end of June, it achieves performance similar to a baseline trained on the full season. These results demonstrate that leveraging temperature data not only improves predictive performance across seasons but also enhances the robustness and trustworthiness of crop-type mapping in real-world applications.
MaskPro: Linear-Space Probabilistic Learning for Strict (N:M)-Sparsity on Large Language Models
2025-06-15
Yan Sun, Qixin Zhang, Zhiyuan Yu, Xikun Zhang, Li Shen, Dacheng Tao
The rapid scaling of large language models (LLMs) has made inference efficiency a primary bottleneck in the practical deployment. To address this, semi-structured sparsity offers a promising solution by strategically retaining $N$ elements out of every $M$ weights, thereby enabling hardware-friendly acceleration and reduced memory. However, existing (N:M)-compatible approaches typically fall into two categories: rule-based layerwise greedy search, which suffers from considerable errors, and gradient-driven combinatorial learning, which incurs prohibitive training costs. To tackle these challenges, we propose a novel linear-space probabilistic framework named MaskPro, which aims to learn a prior categorical distribution for every $M$ consecutive weights and subsequently leverages this distribution to generate the (N:M)-sparsity throughout an $N$-way sampling without replacement. Furthermore, to mitigate the training instability induced by the high variance of policy gradients in the super large combinatorial space, we propose a novel update method by introducing a moving average tracker of loss residuals instead of vanilla loss. Finally, we conduct comprehensive theoretical analysis and extensive experiments to validate the superior performance of MaskPro, as well as its excellent scalability in memory efficiency and exceptional robustness to data samples. Our code is available at https://github.com/woodenchild95/Maskpro.git.
A Far-Infrared Search for Planet Nine Using AKARI All-Sky Survey
2025-06-15
Amos Y. -A. Chen, Tomotsugu Goto, Issei Yamamura, Takao Nakagawa, Cossas K. -W. Wu, Terry Long Phan, Tetsuya Hashimoto, Yuri Uno, Simon C. -C. Ho, Seong Jin Kim
An unusual orbital element clustering of Kuiper belt objects (KBOs) has been observed. The most promising dynamic solution is the presence of a giant planet in the outer Solar system, Planet Nine. However, due to its extreme distance, intensive searches in optical have not been successful. We aim to find Planet Nine in the far-infrared, where it has the peak of the black body radiation, using the most sensitive all-sky far-infrared survey to date, AKARI. In contrast to optical searches, where the energy of reflected sunlight decreases by $d^{4}$, thermal radiation in the infrared decreases with the square of the heliocentric distance $d^{2}$. We search for moving objects in the AKARI Single Scan Detection List. We select sources from a promising region suggested by an N-body simulation from Millholland and Laughlin 2017: $30^{\circ}<$ R.A. $<50^{\circ}$ and $-20^{\circ}<$ Dec. $<20^{\circ}$. Known sources are excluded by cross-matching AKARI sources with 9 optical and infrared catalogues. Furthermore, we select sources with small background strength to avoid sources in the cirrus. Since Planet Nine is stationary in a timescale of hours but moves on a monthly scale, our primary strategy is to select slowly moving objects that are stationary in 24 hours but not in six months, using multiple single scans by AKARI. The selected slowly moving AKARI sources are scrutinised for potential contamination from cosmic rays. Our analysis reveals two possible Planet Nine candidates whose positions and flux are within the theoretical prediction ranges. These candidates warrant further investigation through follow-up observations to confirm the existence and properties of Planet Nine.
Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison
2025-06-15
Jan C. Schulze, Alexander Mitsos
Computationally cheap yet accurate enough dynamical models are vital for real-time capable nonlinear optimization and model-based control. When given a computationally expensive high-order prediction model, a reduction to a lower-order simplified model can enable such real-time applications. Herein, we review state-of-the-art nonlinear model order reduction methods and provide a theoretical comparison of method properties. Additionally, we discuss both general-purpose methods and tailored approaches for (chemical) process systems and we identify similarities and differences between these methods. As manifold-Galerkin approaches currently do not account for inputs in the construction of the reduced state subspace, we extend these methods to dynamical systems with inputs. In a comparative case study, we apply eight established model order reduction methods to an air separation process model: POD-Galerkin, nonlinear-POD-Galerkin, manifold-Galerkin, dynamic mode decomposition, Koopman theory, manifold learning with latent predictor, compartment modeling, and model aggregation. Herein, we do not investigate hyperreduction (reduction of FLOPS). Based on our findings, we discuss strengths and weaknesses of the model order reduction methods.
Governments Should Mandate Tiered Anonymity on Social-Media Platforms to Counter Deepfakes and LLM-Driven Mass Misinformation
2025-06-15
David Khachaturov, Roxanne Schnyder, Robert Mullins
This position paper argues that governments should mandate a three-tier anonymity framework on social-media platforms as a reactionary measure prompted by the ease-of-production of deepfakes and large-language-model-driven misinformation. The tiers are determined by a given user's $\textit{reach score}$: Tier 1 permits full pseudonymity for smaller accounts, preserving everyday privacy; Tier 2 requires private legal-identity linkage for accounts with some influence, reinstating real-world accountability at moderate reach; Tier 3 would require per-post, independent, ML-assisted fact-checking, review for accounts that would traditionally be classed as sources-of-mass-information. An analysis of Reddit shows volunteer moderators converge on comparable gates as audience size increases -- karma thresholds, approval queues, and identity proofs -- demonstrating operational feasibility and social legitimacy. Acknowledging that existing engagement incentives deter voluntary adoption, we outline a regulatory pathway that adapts existing US jurisprudence and recent EU-UK safety statutes to embed reach-proportional identity checks into existing platform tooling, thereby curbing large-scale misinformation while preserving everyday privacy.
A Review of the Long Horizon Forecasting Problem in Time Series Analysis
2025-06-15
Hans Krupakar, Kandappan V A
The long horizon forecasting (LHF) problem has come up in the time series literature for over the last 35 years or so. This review covers aspects of LHF in this period and how deep learning has incorporated variants of trend, seasonality, fourier and wavelet transforms, misspecification bias reduction and bandpass filters while contributing using convolutions, residual connections, sparsity reduction, strided convolutions, attention masks, SSMs, normalization methods, low-rank approximations and gating mechanisms. We highlight time series decomposition techniques, input data preprocessing and dataset windowing schemes that improve performance. Multi-layer perceptron models, recurrent neural network hybrids, self-attention models that improve and/or address the performances of the LHF problem are described, with an emphasis on the feature space construction. Ablation studies are conducted over the ETTm2 dataset in the multivariate and univariate high useful load (HUFL) forecasting contexts, evaluated over the last 4 months of the dataset. The heatmaps of MSE averages per time step over test set series in the horizon show that there is a steady increase in the error proportionate to its length except with xLSTM and Triformer models and motivate LHF as an error propagation problem. The trained models are available here: https://bit.ly/LHFModelZoo
Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises
2025-06-15
Afifa Khaled, Mohammed Sabir, Rizwan Qureshi, Camillo Maria Caruso, Valerio Guarrasi, Suncheng Xiang, S Kevin Zhou
The Medical Information Mart for Intensive Care (MIMIC) datasets have become the Kernel of Digital Health Research by providing freely accessible, deidentified records from tens of thousands of critical care admissions, enabling a broad spectrum of applications in clinical decision support, outcome prediction, and healthcare analytics. Although numerous studies and surveys have explored the predictive power and clinical utility of MIMIC based models, critical challenges in data integration, representation, and interoperability remain underexplored. This paper presents a comprehensive survey that focuses uniquely on open problems. We identify persistent issues such as data granularity, cardinality limitations, heterogeneous coding schemes, and ethical constraints that hinder the generalizability and real-time implementation of machine learning models. We highlight key progress in dimensionality reduction, temporal modelling, causal inference, and privacy preserving analytics, while also outlining promising directions including hybrid modelling, federated learning, and standardized preprocessing pipelines. By critically examining these structural limitations and their implications, this survey offers actionable insights to guide the next generation of MIMIC powered digital health innovations.
This chapter presents an overview of Prosocial Design, an approach to platform design and governance that recognizes design choices influence behavior and that those choices can or should be made toward supporting healthy interactions and other prosocial outcomes. The authors discuss several core principles of Prosocial Design and its relationship to Trust and Safety and other related fields. As a primary contribution, the chapter reviews relevant research to demonstrate how Prosocial Design can be an effective approach to reducing rule-breaking and other harmful behavior and how it can help to stem the spread of harmful misinformation. Prosocial Design is a nascent and evolving field and research is still limited. The authors hope this chapter will not only inspire more research and the adoption of a prosocial design approach, but that it will also provoke discussion about the principles of Prosocial Design and its potential to support Trust and Safety.
Efficient Estimation of Distributional Treatment Effects with Endogenous Treatments: A Machine Learning Approach
2025-06-15
Charles Shaw
We introduce a novel, robust estimator for the Distributional Instrumental Variable Local Average Treatment Effect (D-IV-LATE) when the treatment is endogenous and an instrumental variable is available. Understanding the full distributional impact of policies or interventions, rather than just average effects, is crucial for informed decision-making. Yet existing methods often assume exogeneity or are not designed for distributional parameters in an IV context. We bridge this gap by extending the double/debiased machine learning (DML) framework to estimate the D-IV-LATE. Our proposed estimator leverages flexible machine learning models to control for high-dimensional covariates whilst achieving Neyman orthogonality. This ensures robustness to regularisation bias in the nuisance function estimates. We establish the asymptotic normality of our D-IV-LATE estimator. A Monte Carlo simulation study demonstrates its excellent finite-sample performance, showing low bias and root mean squared error. We illustrate the practical utility of our method with an empirical application, analysing the distributional effects of 401(k) participation on net financial assets using data from the Survey of Income and Program Participation. The results reveal heterogeneous impacts across the wealth distribution that would be masked by a simple average effect estimate.
In this paper, we test the cosmic distance duality relation (CDDR), as required by the Etherington reciprocity theorem, which connects the angular diameter distance and the luminosity distance via the relation \( D_{\rm L}(z) = D_{\rm A}(z)(1+z)^2 \). Our analysis is based on the latest baryon acoustic oscillation (BAO) measurements provided by the Dark Energy Survey (DES), the Baryon Oscillation Spectroscopic Survey (BOSS)/Extended BOSS (eBOSS), and the Dark Energy Spectroscopic Instrument (DESI) surveys. Specifically, an unbiased test of the CDDR is performed through a novel, model-independent method inspired by the two-point diagnostic approach, with DES-SN5YR and Pantheon type Ia supernova (SN Ia) sample reconstructed using the Artificial Neural Network (ANN) technique. This methodology effectively eliminates all nuisance parameters, including the sound horizon scale \( r_{\rm d} \) from BAO and the absolute magnitude \( M_{\rm B} \) from SN Ia. A set of \( N-1 \) independent CDDR ratios \( \eta_{ij} \) are constructed for statistical analysis. At the current observational level, no significant deviation from the CDDR is observed at low redshifts, whereas we find positive evidence ($>2\sigma$ C.L.) of deviation from the CDDR at two high redshifts ($z=2.33$ and $z=2.334$). Therefore, our results confirm that the BAO measurement provides a powerful tool to test such fundamental relation in modern cosmology.
The Multi-Wavelength Context of Delayed Radio Emission in TDEs: Evidence for Accretion-Driven Outflows
2025-06-15
Kate D. Alexander, Raffaella Margutti, Sebastian Gomez, Michael Stroh, Ryan Chornock, Tanmoy Laskar, Y. Cendes, Edo Berger, Tarraneh Eftekhari, Noah Franz, Aprajita Hajela, B. D. Metzger, Giacomo Terreran, Michael Bietenholz, Collin Christy, Fabio de Colle, S. Komossa, Matt Nicholl, Enrico Ramirez-Ruiz, Richard Saxton, Genevieve Schroeder, Peter Williams, William Wu
Recent observations presented in Cendes et al. (2024a) show that optically selected tidal disruption events (TDEs) commonly produce delayed radio emission that can peak years post-disruption. Here, we explore the multi-wavelength properties of a sample of radio-observed optically selected TDEs to shed light on the physical process(es) responsible for the late-rising radio emission. We combine new late-time X-ray observations with archival optical, UV, X-ray, and radio data to conclude that a diversity of accretion-driven outflows may power delayed radio emission in TDEs. Simultaneous X-ray data and modeling of the UV/optical emission suggest that some late radio outflows may be launched by a delayed phase of super-Eddington accretion onto the central supermassive black hole (SMBH), while others may result from a state transition to a "low-hard" radiatively inefficient accretion flow or the deceleration of an off-axis relativistic jet. We additionally find weak statistical evidence that TDEs with delayed radio emission have larger optical/UV photospheric radii than other TDEs and are less likely to exhibit helium emission lines at early times, possibly also supporting the hypothesis that the onset of SMBH accretion is delayed in these systems. Our results have implications for our understanding of state changes in SMBH accretion flows, the circularization timescale for TDE debris, and the prevalence of off-axis jets in TDEs, and motivates systematic, long-term monitoring of these unique transients. The brightest objects in our sample are also detected in the VLA Sky Survey (VLASS), demonstrating that all-sky radio surveys can play an important role in discovering unexpected radio properties of the TDE population.
SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation
2025-06-15
Yashothara Shanmugarasa, Ming Ding, M. A. P Chamikara, Thierry Rakotoarivelo
Large language models (LLMs) are sophisticated artificial intelligence systems that enable machines to generate human-like text with remarkable precision. While LLMs offer significant technological progress, their development using vast amounts of user data scraped from the web and collected from extensive user interactions poses risks of sensitive information leakage. Most existing surveys focus on the privacy implications of the training data but tend to overlook privacy risks from user interactions and advanced LLM capabilities. This paper aims to fill that gap by providing a comprehensive analysis of privacy in LLMs, categorizing the challenges into four main areas: (i) privacy issues in LLM training data, (ii) privacy challenges associated with user prompts, (iii) privacy vulnerabilities in LLM-generated outputs, and (iv) privacy challenges involving LLM agents. We evaluate the effectiveness and limitations of existing mitigation mechanisms targeting these proposed privacy challenges and identify areas for further research.
The rapid growth of scientific literature demands robust tools for automated survey-generation. However, current large language model (LLM)-based methods often lack in-depth analysis, structural coherence, and reliable citations. To address these limitations, we introduce SciSage, a multi-agent framework employing a reflect-when-you-write paradigm. SciSage features a hierarchical Reflector agent that critically evaluates drafts at outline, section, and document levels, collaborating with specialized agents for query interpretation, content retrieval, and refinement. We also release SurveyScope, a rigorously curated benchmark of 46 high-impact papers (2020-2025) across 11 computer science domains, with strict recency and citation-based quality controls. Evaluations demonstrate that SciSage outperforms state-of-the-art baselines (LLM x MapReduce-V2, AutoSurvey), achieving +1.73 points in document coherence and +32% in citation F1 scores. Human evaluations reveal mixed outcomes (3 wins vs. 7 losses against human-written surveys), but highlight SciSage's strengths in topical breadth and retrieval efficiency. Overall, SciSage offers a promising foundation for research-assistive writing tools.
A unified picture for quantum Zeno and anti-Zeno effects
2025-06-15
Sacha Greenfield, Archana Kamal, Justin Dressel, Eli Levenson-Falk
The quantum Zeno effect is a striking feature of quantum mechanics with foundational implications and practical applications in quantum control, error suppression, and error correction. The effect has branched off into a variety of different interpretations, making it easy to miss the unifying features of the underlying effect. In particular, the quantum Zeno effect has been studied in the context of both selective and nonselective measurements; for both pulsed and continuous interactions; for suppression and enhancement of decay (Zeno / anti-Zeno effects); and even in the absence of measurement entirely. This concise review presents a unified picture of these effects by examining how they all arise in the context of a driven qubit subjected to measurements or dissipation. Zeno and anti-Zeno effects are revealed as regimes of a unified effect that appears whenever a measurement-like process competes with a non-commuting evolution. The current landscape of Zeno and anti-Zeno effects is reviewed through this unifying lens, with a focus on experimental applications and implementations. The quantum Zeno effect is found to be both ubiquitous and essential for the future of near-term quantum computing.
The Trip to ZigBee Backscatter across a Decade, a Systematic Review
2025-06-15
Yang Liu
The field of backscatter communication has undergone a profound transformation, evolving from a niche technology for radio-frequency identification (RFID) into a sophisticated paradigm poised to enable a truly battery-free Internet of Things (IoT). This evolution is built upon a deepening understanding of the fundamental principles governing these ultra-low-power links. Modern backscatter systems are no longer simple reflectors of continuous waves but are increasingly designed to interact with complex, data-carrying ambient signals from ubiquitous sources like WiFi, ZigBee, and cellular networks. This review systematically charts the journey of ambient backscatter, particularly focusing on its interaction with ZigBee and other commodity wireless protocols over the last decade. We analyze the progression from foundational proof-of-concept systems that established productive backscatter to modern high-throughput, concurrent, and cross-technology communication architectures. Key advancements in fine-grained modulation, robust synchronization, cross-technology physical layer emulation, and multi-tag coordination are detailed. A comparative analysis of state-of-the-art systems highlights the core trade-offs between performance metrics like data rate and range, power consumption, and compatibility with commodity hardware. Finally, we synthesize the primary challenges, including networking scalability, security vulnerabilities, the near-far problem, and practical deployment hurdles, and outline future research directions, such as integration with Reconfigurable Intelligent Surfaces (RIS) and 6G networks, that promise to further expand the capabilities of this transformative technology.
The Software Landscape for the Density Matrix Renormalization Group
2025-06-14
Per Sehlstedt, Jan Brandejs, Paolo Bientinesi, Lars Karlsson
The density matrix renormalization group (DMRG) algorithm is a cornerstone computational method for studying quantum many-body systems, renowned for its accuracy and adaptability. Despite DMRG's broad applicability across fields such as materials science, quantum chemistry, and quantum computing, numerous independent implementations have been developed. This survey maps the rapidly expanding DMRG software landscape, providing a comprehensive comparison of features among 35 existing packages. We found significant overlap in features among the packages when comparing key aspects, such as parallelism strategies for high-performance computing and symmetry-adapted formulations that enhance efficiency. This overlap suggests opportunities for modularization of common operations, including tensor operations, symmetry representations, and eigensolvers, as the packages are mostly independent and share few third-party library dependencies where functionality is factored out. More widespread modularization and standardization would result in reduced duplication of efforts and improved interoperability. We believe that the proliferation of packages and the current lack of standard interfaces and modularity are more social than technical. We aim to raise awareness of existing packages, guide researchers in finding a suitable package for their needs, and help developers identify opportunities for collaboration, modularity standardization, and optimization. Ultimately, this work emphasizes the value of greater cohesion and modularity, which would benefit DMRG software, allowing these powerful algorithms to tackle more complex and ambitious problems.
The Rise of AI Companions: How Human-Chatbot Relationships Influence Well-Being
2025-06-14
Yutong Zhang, Dora Zhao, Jeffrey T. Hancock, Robert Kraut, Diyi Yang
As large language models (LLMs)-enhanced chatbots grow increasingly expressive and socially responsive, many users are beginning to form companionship-like bonds with them, particularly with simulated AI partners designed to mimic emotionally attuned interlocutors. These emerging AI companions raise critical questions: Can such systems fulfill social needs typically met by human relationships? How do they shape psychological well-being? And what new risks arise as users develop emotional ties to non-human agents? This study investigates how people interact with AI companions, especially simulated partners on CharacterAI, and how this use is associated with users' psychological well-being. We analyzed survey data from 1,131 users and 4,363 chat sessions (413,509 messages) donated by 244 participants, focusing on three dimensions of use: nature of the interaction, interaction intensity, and self-disclosure. By triangulating self-reports primary motivation, open-ended relationship descriptions, and annotated chat transcripts, we identify patterns in how users engage with AI companions and its associations with well-being. Findings suggest that people with smaller social networks are more likely to turn to chatbots for companionship, but that companionship-oriented chatbot usage is consistently associated with lower well-being, particularly when people use the chatbots more intensively, engage in higher levels of self-disclosure, and lack strong human social support. Even though some people turn to chatbots to fulfill social needs, these uses of chatbots do not fully substitute for human connection. As a result, the psychological benefits may be limited, and the relationship could pose risks for more socially isolated or emotionally vulnerable users.
A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications
2025-06-14
Renjun Xu, Jingwen Peng
This survey examines the rapidly evolving field of Deep Research systems -- AI-powered applications that automate complex research workflows through the integration of large language models, advanced information retrieval, and autonomous reasoning capabilities. We analyze more than 80 commercial and non-commercial implementations that have emerged since 2023, including OpenAI/Deep Research, Gemini/Deep Research, Perplexity/Deep Research, and numerous open-source alternatives. Through comprehensive examination, we propose a novel hierarchical taxonomy that categorizes systems according to four fundamental technical dimensions: foundation models and reasoning engines, tool utilization and environmental interaction, task planning and execution control, and knowledge synthesis and output generation. We explore the architectural patterns, implementation approaches, and domain-specific adaptations that characterize these systems across academic, scientific, business, and educational applications. Our analysis reveals both the significant capabilities of current implementations and the technical and ethical challenges they present regarding information accuracy, privacy, intellectual property, and accessibility. The survey concludes by identifying promising research directions in advanced reasoning architectures, multimodal integration, domain specialization, human-AI collaboration, and ecosystem standardization that will likely shape the future evolution of this transformative technology. By providing a comprehensive framework for understanding Deep Research systems, this survey contributes to both the theoretical understanding of AI-augmented knowledge work and the practical development of more capable, responsible, and accessible research technologies. The paper resources can be viewed at https://github.com/scienceaix/deepresearch.
Recent work shows that Sparse Autoencoders (SAE) applied to large language model (LLM) layers have neurons corresponding to interpretable concepts. These SAE neurons can be modified to align generated outputs, but only towards pre-identified topics and with some parameter tuning. Our approach leverages the observational and modification properties of SAEs to enable alignment for any topic. This method 1) scores each SAE neuron by its semantic similarity to an alignment text and uses them to 2) modify SAE-layer-level outputs by emphasizing topic-aligned neurons. We assess the alignment capabilities of this approach on diverse public topic datasets including Amazon reviews, Medicine, and Sycophancy, across the currently available open-source LLMs and SAE pairs (GPT2 and Gemma) with multiple SAEs configurations. Experiments aligning to medical prompts reveal several benefits over fine-tuning, including increased average language acceptability (0.25 vs. 0.5), reduced training time across multiple alignment topics (333.6s vs. 62s), and acceptable inference time for many applications (+0.00092s/token). Our open-source code is available at github.com/IBM/sae-steering.
Who's in? Household-targeted Government Policies and the Role of Financial Literacy in Market Participation
2025-06-14
Maria Elena Filippin
This paper examines how household-targeted government policies influence financial market participation conditional on financial literacy, focusing on potential Central Bank Digital Currency (CBDC) adoption. Due to the lack of empirical CBDC data, I use the introduction of retail Treasury bonds in Italy as a proxy to investigate how financial literacy affects households' likelihood to engage with the new instrument. Using the Bank of Italy's Survey on Household Income and Wealth, I explore how financial literacy influenced households' participation in the Treasury bond market following the 2012 introduction of retail Treasury bonds, showing that households with some but low financial literacy are more likely to participate than other household groups. Based on these findings, I develop a theoretical model to explore the potential implications of financial literacy for CBDC adoption, showing that low-literate households with limited access to risky assets allocate more wealth to CBDC, while high-literate households use risky assets to safeguard against income risk. These results highlight the role of financial literacy in shaping portfolio choices and CBDC adoption.
A covariant scalar-field framework for singularity resolution and dark energy
2025-06-14
Martin Drobczyk
We present a covariant scalar-field framework that unifies the space-time singularity resolution with dynamic dark energy. The theory extends general relativity by introducing a scalar field $\Phi$ whose potential couples to the Lorentz-invariant quantity $X \equiv u_{\alpha} u_{\beta} T^{\alpha\beta}_{\mathrm{matter}}$, ensuring manifest covariance. The resulting density-responsive scalar energy $\rho_\Phi$ exhibits dual behavior: (i) in high-density regimes, it saturates at $\rho_\Phi \leq AM_P^4/2$, providing a Planck-scale upper bound on the total energy density that regularizes classical singularities; (ii) in low-density regimes, it approaches a constant $\rho_\Phi \to AM_U^4$, driving cosmic acceleration as dynamical dark energy. A natural renormalization group evolution with an anomalous dimension $\gamma \approx 0.501$ connects the Planck scale to the meV dark energy scale without fine-tuning. The model makes distinctive, testable predictions: $w_0 \approx -0.99$ and $w_a \approx +0.03$, where the positive $w_a$ distinguishes it from $\Lambda$CDM and standard quintessence models. Despite the novel interaction terms, the fifth forces are suppressed by $\beta_{\rm eff} \propto 1/\rho_m^2$, yielding factors below $10^{-58}$ in laboratory environments, and ensuring compatibility with all precision gravity tests. This framework demonstrates how a single quantum field theory mechanism can simultaneously address UV singularities and IR dark energy, providing concrete predictions for future Stage-IV cosmological surveys.
Style-based Composer Identification and Attribution of Symbolic Music Scores: a Systematic Survey
2025-06-14
Federico Simonetta
This paper presents the first comprehensive systematic review of literature on style-based composer identification and authorship attribution in symbolic music scores. Addressing the critical need for improved reliability and reproducibility in this field, the review rigorously analyzes 58 peer-reviewed papers published across various historical periods, with the search adapted to evolving terminology. The analysis critically assesses prevailing repertoires, computational approaches, and evaluation methodologies, highlighting significant challenges. It reveals that a substantial portion of existing research suffers from inadequate validation protocols and an over-reliance on simple accuracy metrics for often imbalanced datasets, which can undermine the credibility of attribution claims. The crucial role of robust metrics like Balanced Accuracy and rigorous cross-validation in ensuring trustworthy results is emphasized. The survey also details diverse feature representations and the evolution of machine learning models employed. Notable real-world authorship attribution cases, such as those involving works attributed to Bach, Josquin Desprez, and Lennon-McCartney, are specifically discussed, illustrating the opportunities and pitfalls of applying computational techniques to resolve disputed musical provenance. Based on these insights, a set of actionable guidelines for future research are proposed. These recommendations are designed to significantly enhance the reliability, reproducibility, and musicological validity of composer identification and authorship attribution studies, fostering more robust and interpretable computational stylistic analysis.
Exploring Cultural Variations in Moral Judgments with Large Language Models
2025-06-14
Hadi Mohammadi, Efthymia Papadopoulou, Yasmeen F. S. S. Meijer, Ayoub Bagheri
Large Language Models (LLMs) have shown strong performance across many tasks, but their ability to capture culturally diverse moral values remains unclear. In this paper, we examine whether LLMs can mirror variations in moral attitudes reported by two major cross-cultural surveys: the World Values Survey and the PEW Research Center's Global Attitudes Survey. We compare smaller, monolingual, and multilingual models (GPT-2, OPT, BLOOMZ, and Qwen) with more recent instruction-tuned models (GPT-4o, GPT-4o-mini, Gemma-2-9b-it, and Llama-3.3-70B-Instruct). Using log-probability-based moral justifiability scores, we correlate each model's outputs with survey data covering a broad set of ethical topics. Our results show that many earlier or smaller models often produce near-zero or negative correlations with human judgments. In contrast, advanced instruction-tuned models (including GPT-4o and GPT-4o-mini) achieve substantially higher positive correlations, suggesting they better reflect real-world moral attitudes. While scaling up model size and using instruction tuning can improve alignment with cross-cultural moral norms, challenges remain for certain topics and regions. We discuss these findings in relation to bias analysis, training data diversity, and strategies for improving the cultural sensitivity of LLMs.
Data from FAST and MeerKAT reviews as a test of radio pulsar physics
2025-06-14
F. A. Kniazev, A. Yu. Istomin, V. S. Beskin
The data from the FAST and MeerKAT surveys has significantly increased the number of radio pulsars for which the polarization characteristics of their mean profiles have been determined in detail. This has allowed us to confirm earlier conclusions both about the nature of propagation of two orthogonal modes in the pulsar magnetospheres and about the mechanism of particle production in neutron star polar regions and their evolutionary features. We can now say with even greater confidence that mean profiles formed by the O-mode are significantly wider than those formed by the X-mode. Moreover, the observations confirm the validity of the classical Ruderman-Sutherland vacuum model of particle generation, as well as the evolution of the inclination angles of the magnetic axis to the rotation one in the direction of $90^{\circ}$.
Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching
2025-06-14
Hyeonseo Lee, Juhyun Park, Jihyong Oh, Chanho Eom
Person Re-identification (ReID) aims to retrieve images of the same individual captured across non-overlapping camera views, making it a critical component of intelligent surveillance systems. Traditional ReID methods assume that the training and test domains share similar characteristics and primarily focus on learning discriminative features within a given domain. However, they often fail to generalize to unseen domains due to domain shifts caused by variations in viewpoint, background, and lighting conditions. To address this issue, Domain-Adaptive ReID (DA-ReID) methods have been proposed. These approaches incorporate unlabeled target domain data during training and improve performance by aligning feature distributions between source and target domains. Domain-Generalizable ReID (DG-ReID) tackles a more realistic and challenging setting by aiming to learn domain-invariant features without relying on any target domain data. Recent methods have explored various strategies to enhance generalization across diverse environments, but the field remains relatively underexplored. In this paper, we present a comprehensive survey of DG-ReID. We first review the architectural components of DG-ReID including the overall setting, commonly used backbone networks and multi-source input configurations. Then, we categorize and analyze domain generalization modules that explicitly aim to learn domain-invariant and identity-discriminative representations. To examine the broader applicability of these techniques, we further conduct a case study on a related task that also involves distribution shifts. Finally, we discuss recent trends, open challenges, and promising directions for future research in DG-ReID. To the best of our knowledge, this is the first systematic survey dedicated to DG-ReID.
Methods for pitch analysis in contemporary popular music: multiple pitches from harmonic tones in Vitalic's music
2025-06-14
Emmanuel Deruty, David Meredith, Maarten Grachten, Pascal Arbez-Nicolas, Andreas Hasselholt Jørgensen, Oliver Søndermølle Hansen, Magnus Stensli, Christian Nørkær Petersen
Aims. This study suggests that the use of multiple perceived pitches arising from a single harmonic complex tone is an active and intentional feature of contemporary popular music. The phenomenon is illustrated through examples drawn from the work of electronic artist Vitalic and others. Methods. Two listening tests were conducted: (1) evaluation of the number of simultaneous pitches perceived from single harmonic tones, and (2) manual pitch transcription of sequences of harmonic tones. Relationships between signal characteristics and pitch perception were then analyzed. Results. The synthetic harmonic tones found in the musical sequences under study were observed to transmit more perceived pitches than their acoustic counterparts, with significant variation across listeners. Multiple ambiguous pitches were associated with tone properties such as prominent upper partials and particular autocorrelation profiles. Conclusions. Harmonic tones in a context of contemporary popular music can, in general, convey several ambiguous pitches. The set of perceived pitches depends on both the listener and the listening conditions.
Recent Advances and Future Directions in Literature-Based Discovery
2025-06-14
Andrej Kastrin, Bojan Cestnik, Nada Lavrač
The explosive growth of scientific publications has created an urgent need for automated methods that facilitate knowledge synthesis and hypothesis generation. Literature-based discovery (LBD) addresses this challenge by uncovering previously unknown associations between disparate domains. This article surveys recent methodological advances in LBD, focusing on developments from 2000 to the present. We review progress in three key areas: knowledge graph construction, deep learning approaches, and the integration of pre-trained and large language models (LLMs). While LBD has made notable progress, several fundamental challenges remain unresolved, particularly concerning scalability, reliance on structured data, and the need for extensive manual curation. By examining ongoing advances and outlining promising future directions, this survey underscores the transformative role of LLMs in enhancing LBD and aims to support researchers and practitioners in harnessing these technologies to accelerate scientific innovation.
Distinguishing features of longitudinal magnetoconductivity for a Rarita-Schwinger-Weyl node
2025-06-14
Ipsita Mandal
The band-degeneracy points in the Brillouin zones of chiral crystals exist in multiple avatars, with the high-symmetry points being able to host multifold nodes of distinct characters. A class of such crystals, assisted by the spin-orbit coupling, harbours fourfold degeneracy in the form of Rarita-Schwinger-Weyl node (RSWN) at the $\Gamma$-point. Our aim is to explore the nature of longitudinal magnetoconductivity, arising from applying collinear electric and magnetic fields, for such systems. Adjusting the chemical potential to lie near the intrinsic energy-location of the RSWN, the multifold nature of the RSWN is revealed by an interplay of intraband and interband scatterings, which would not arise in twofold degeneracies like the conventional Weyl nodes. The current study fills up the much-needed gap in obtaining the linear response from an exact computation, rather than the insufficient relaxation-time approximation employed earlier.
Advances in LLMs with Focus on Reasoning, Adaptability, Efficiency and Ethics
2025-06-14
Asifullah khan, Muhammad Zaeem Khan, Saleha Jamshed, Sadia Ahmad, Aleesha Zainab, Kaynat Khatib, Faria Bibi, Abdul Rehman
This survey paper outlines the key developments in the field of Large Language Models (LLMs), such as enhancing their reasoning skills, adaptability to various tasks, increased computational efficiency, and ability to make ethical decisions. The techniques that have been most effective in bridging the gap between human and machine communications include the Chain-of-Thought prompting, Instruction Tuning, and Reinforcement Learning from Human Feedback. The improvements in multimodal learning and few-shot or zero-shot techniques have further empowered LLMs to handle complex jobs with minor input. They also manage to do more with less by applying scaling and optimization tricks for computing power conservation. This survey also offers a broader perspective on recent advancements in LLMs going beyond isolated aspects such as model architecture or ethical concerns. It categorizes emerging methods that enhance LLM reasoning, efficiency, and ethical alignment. It also identifies underexplored areas such as interpretability, cross-modal integration and sustainability. With recent progress, challenges like huge computational costs, biases, and ethical risks remain constant. Addressing these requires bias mitigation, transparent decision-making, and clear ethical guidelines. Future research will focus on enhancing models ability to handle multiple input, thereby making them more intelligent, safe, and reliable.
While recent advances in large reasoning models have demonstrated remarkable performance, efficient reasoning remains critical due to the rapid growth of output length. Existing optimization approaches highlights a tendency toward "overthinking", yet lack fine-grained analysis. In this work, we focus on Self-Affirmation Reflections: redundant reflective steps that affirm prior content and often occurs after the already correct reasoning steps. Observations of both original and optimized reasoning models reveal pervasive self-affirmation reflections. Notably, these reflections sometimes lead to longer outputs in optimized models than their original counterparts. Through detailed analysis, we uncover an intriguing pattern: compared to other reflections, the leading words (i.e., the first word of sentences) in self-affirmation reflections exhibit a distinct probability bias. Motivated by this insight, we can locate self-affirmation reflections and conduct a train-free experiment demonstrating that suppressing self-affirmation reflections reduces output length without degrading accuracy across multiple models (R1-Distill-Models, QwQ-32B, and Qwen3-32B). Furthermore, we also improve current train-based method by explicitly suppressing such reflections. In our experiments, we achieve length compression of 18.7\% in train-free settings and 50.2\% in train-based settings for R1-Distill-Qwen-1.5B. Moreover, our improvements are simple yet practical and can be directly applied to existing inference frameworks, such as vLLM. We believe that our findings will provide community insights for achieving more precise length compression and step-level efficient reasoning.
Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review
2025-06-14
Johnny Peng, Thanh Tung Khuat, Katarzyna Musial, Bogdan Gabrys
Data is crucial for machine learning (ML) applications, yet acquiring large datasets can be costly and time-consuming, especially in complex, resource-intensive fields like biopharmaceuticals. A key process in this industry is upstream bioprocessing, where living cells are cultivated and optimised to produce therapeutic proteins and biologics. The intricate nature of these processes, combined with high resource demands, often limits data collection, resulting in smaller datasets. This comprehensive review explores ML methods designed to address the challenges posed by small data and classifies them into a taxonomy to guide practical applications. Furthermore, each method in the taxonomy was thoroughly analysed, with a detailed discussion of its core concepts and an evaluation of its effectiveness in tackling small data challenges, as demonstrated by application results in the upstream bioprocessing and other related domains. By analysing how these methods tackle small data challenges from different perspectives, this review provides actionable insights, identifies current research gaps, and offers guidance for leveraging ML in data-constrained environments.
Transverse thermoelectric effects interconvert charge and heat currents in orthogonal directions due to the breaking of either time-reversal symmetry or structural symmetry, enabling simple and versatile thermal energy harvesting and solid-state cooling within single materials. In comparison to the complex module structures required for the conventional Seebeck and Peltier effects, the transverse thermoelectric effects provide the complete device structures, potentially resolving the fundamental issue of multi-module degradation of thermoelectric conversion performance. This review article provides an overview of all currently known transverse thermoelectric conversion phenomena and principles, as well as their characteristics, and reclassifies them in a unified manner. The performance of the transverse thermoelectric generator, refrigerator, and active cooler is formulated, showing that thermal boundary conditions play an essential role to discuss their behaviors. Examples of recent application research and material development in transverse thermoelectrics are also introduced, followed by a discussion of future prospects.
The Budget AI Researcher and the Power of RAG Chains
2025-06-14
Franklin Lee, Tengfei Ma
Navigating the vast and rapidly growing body of scientific literature is a formidable challenge for aspiring researchers. Current approaches to supporting research idea generation often rely on generic large language models (LLMs). While LLMs are effective at aiding comprehension and summarization, they often fall short in guiding users toward practical research ideas due to their limitations. In this study, we present a novel structural framework for research ideation. Our framework, The Budget AI Researcher, uses retrieval-augmented generation (RAG) chains, vector databases, and topic-guided pairing to recombine concepts from hundreds of machine learning papers. The system ingests papers from nine major AI conferences, which collectively span the vast subfields of machine learning, and organizes them into a hierarchical topic tree. It uses the tree to identify distant topic pairs, generate novel research abstracts, and refine them through iterative self-evaluation against relevant literature and peer reviews, generating and refining abstracts that are both grounded in real-world research and demonstrably interesting. Experiments using LLM-based metrics indicate that our method significantly improves the concreteness of generated research ideas relative to standard prompting approaches. Human evaluations further demonstrate a substantial enhancement in the perceived interestingness of the outputs. By bridging the gap between academic data and creative generation, the Budget AI Researcher offers a practical, free tool for accelerating scientific discovery and lowering the barrier for aspiring researchers. Beyond research ideation, this approach inspires solutions to the broader challenge of generating personalized, context-aware outputs grounded in evolving real-world knowledge.
Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research
2025-06-14
Kan Hatakeyama-Sato, Toshihiko Nishida, Kenta Kitamura, Yoshitaka Ushiku, Koichi Takahashi, Yuta Nabae, Teruaki Hayakawa
This review explores the potential of foundation models to advance laboratory automation in the materials and chemical sciences. It emphasizes the dual roles of these models: cognitive functions for experimental planning and data analysis, and physical functions for hardware operations. While traditional laboratory automation has relied heavily on specialized, rigid systems, foundation models offer adaptability through their general-purpose intelligence and multimodal capabilities. Recent advancements have demonstrated the feasibility of using large language models (LLMs) and multimodal robotic systems to handle complex and dynamic laboratory tasks. However, significant challenges remain, including precision manipulation of hardware, integration of multimodal data, and ensuring operational safety. This paper outlines a roadmap highlighting future directions, advocating for close interdisciplinary collaboration, benchmark establishment, and strategic human-AI integration to realize fully autonomous experimental laboratories.
Dynamical non-Hermitian systems: Fingerprints and pure dephasing induced protection effect
2025-06-14
Wanchen Ma, Hao Zhang, Junjie Liu
Non-Hermitian systems have shown promising potential for realizing quantum information tasks that lack counterparts in the Hermitian realm. Understanding the dynamical characteristics of non-Hermitian systems as reflected in information-theoretic quantities is essential for advancing their applications. Here we investigate dynamics of trace distance and concurrence, which quantify information flow and entanglement, respectively, in non-Hermitian qubit systems exhibiting either parity-time or anti-parity-time symmetry. We identify dynamical fingerprints from the system density matrix which provide a unified explanation to the common oscillation and relaxation dynamics shared by trace distance and concurrence observed in existing studies. We further investigate the fate of dynamical fingerprints under the impact of pure dephasing. Surprisingly, we find that pure dephasing can slow down the inherent relaxation of information flow and entanglement in non-Hermitian systems with unbroken anti-parity-time symmetry, suggesting a passive environment-assisted information protection in this class of non-Hermitian systems. Our findings enhance our understanding of dynamical non-Hermitian systems and have specific relevance to their information-oriented applications.
Primordial black holes (PBHs) remain one of the most intriguing candidates for dark matter and a unique probe of physics at extreme curvatures. Here, we examine their formation in a bounce cosmology when the post-crunch universe inherits a highly inhomogeneous distribution of imprint entropy from the Quantum Memory Matrix (QMM). Within QMM, every Planck-scale cell stores quantum information about infalling matter; the surviving entropy field S(x) contributes an effective dust component T^QMM_{\mu\nu} = lambda * [ (nabla_mu S)(nabla_nu S) - (1/2) * g_{\mu\nu} * (nabla S)^2 + ... ] that deepens curvature wherever S is large. We show that (i) reasonable bounce temperatures and a QMM coupling lambda ~ O(1) naturally amplify these "information wells" until the density contrast exceeds the critical value delta_c ~ 0.3; (ii) the resulting PBH mass spectrum spans 10^{-16} to 10^3 solar masses, matching current microlensing and PTA windows; and (iii) the same mechanism links PBH abundance to earlier QMM explanations of dark matter and the cosmic matter-antimatter imbalance. Observable signatures include a mild blue tilt in small-scale power, characteristic mu-distortions, and an enhanced integrated Sachs-Wolfe signal - all of which will be tested by upcoming CMB, PTA, and lensing surveys.
EMLoC: Emulator-based Memory-efficient Fine-tuning with LoRA Correction
2025-06-13
Hsi-Che Lin, Yu-Chu Yu, Kai-Po Chang, Yu-Chiang Frank Wang
Open-source foundation models have seen rapid adoption and development, enabling powerful general-purpose capabilities across diverse domains. However, fine-tuning large foundation models for domain-specific or personalized tasks remains prohibitively expensive for most users due to the significant memory overhead beyond that of inference. We introduce EMLoC, an Emulator-based Memory-efficient fine-tuning framework with LoRA Correction, which enables model fine-tuning within the same memory budget required for inference. EMLoC constructs a task-specific light-weight emulator using activation-aware singular value decomposition (SVD) on a small downstream calibration set. Fine-tuning then is performed on this lightweight emulator via LoRA. To tackle the misalignment between the original model and the compressed emulator, we propose a novel compensation algorithm to correct the fine-tuned LoRA module, which thus can be merged into the original model for inference. EMLoC supports flexible compression ratios and standard training pipelines, making it adaptable to a wide range of applications. Extensive experiments demonstrate that EMLoC outperforms other baselines across multiple datasets and modalities. Moreover, without quantization, EMLoC enables fine-tuning of a 38B model on a single 24GB consumer GPU-bringing efficient and practical model adaptation to individual users.
Tracing LLM Reasoning Processes with Strategic Games: A Framework for Planning, Revision, and Resource-Constrained Decision Making
2025-06-13
Xiaopeng Yuan, Xingjian Zhang, Ke Xu, Yifan Xu, Lijun Yu, Jindong Wang, Yushun Dong, Haohan Wang
Large language models (LLMs) are increasingly used for tasks that require complex reasoning. Most benchmarks focus on final outcomes but overlook the intermediate reasoning steps - such as planning, revision, and decision making under resource constraints. We argue that measuring these internal processes is essential for understanding model behavior and improving reliability. We propose using strategic games as a natural evaluation environment: closed, rule-based systems with clear states, limited resources, and automatic feedback. We introduce a framework that evaluates LLMs along three core dimensions: planning, revision, and resource-constrained decision making. To operationalize this, we define metrics beyond win rate, including overcorrection risk rate, correction success rate, improvement slope, and over-budget ratio. In 4320 adversarial rounds across 12 leading models, ChatGPT-o3-mini achieves the top composite score, with a win rate of 74.7 percent, a correction success rate of 78.6 percent, and an improvement slope of 0.041. By contrast, Qwen-Plus, despite an overcorrection risk rate of 81.6 percent, wins only 25.6 percent of its matches - primarily due to excessive resource use. We also observe a negative correlation between overcorrection risk rate and correction success rate (Pearson r = -0.51, p = 0.093), suggesting that more frequent edits do not always improve outcomes. Our findings highlight the value of assessing not only what LLMs decide but how they arrive at those decisions
Bayesian and frequentist perspectives agree on dynamical dark energy
2025-06-13
Laura Herold, Tanvi Karwal
Baryon acoustic oscillation data from the Dark Energy Spectroscopic Instrument (DESI) show evidence of a deviation from a cosmological constant $\Lambda$ within a Bayesian analysis. In this work, we validate that frequentist constraints from profile likelihoods on the Chevallier-Polarski-Linder parameters $w_0$, $w_a$ are in excellent agreement with the Bayesian constraints when combining with Planck cosmic microwave background, Planck and Atacama Cosmology Telescope lensing, and either Pantheon+ or Dark Energy Survey Y5 supernova data. Further, we assess which datasets drive these constraints by considering the contributions to the $\chi^2$ from the individual datasets. For profile likelihoods of the matter fraction $\Omega_\mathrm{m}$, such an investigation shows internal inconsistencies when assuming $\Lambda$, which are resolved when assuming a $w_0w_a$ dark-energy model. We infer the equations of state $w(z)$ at the pivot redshifts, supporting previous interpretations that current data appears to be more sensitive to the derivative of $w(z)$ rather than a mean offset from $\Lambda$. Thus our frequentist analysis corroborates previous findings on dynamical DE.
Evaluating Program Sequences with Double Machine Learning: An Application to Labor Market Policies
2025-06-13
Fabian Muny
Many programs evaluated in observational studies incorporate a sequential structure, where individuals may be assigned to various programs over time. While this complexity is often simplified by analyzing programs at single points in time, this paper reviews, explains, and applies methods for program evaluation within a sequential framework. It outlines the assumptions required for identification under dynamic confounding and demonstrates how extending sequential estimands to dynamic policies enables the construction of more realistic counterfactuals. Furthermore, the paper explores recently developed methods for estimating effects across multiple treatments and time periods, utilizing Double Machine Learning (DML), a flexible estimator that avoids parametric assumptions while preserving desirable statistical properties. Using Swiss administrative data, the methods are demonstrated through an empirical application assessing the participation of unemployed individuals in active labor market policies, where assignment decisions by caseworkers can be reconsidered between two periods. The analysis identifies a temporary wage subsidy as the most effective intervention, on average, even after adjusting for its extended duration compared to other programs. Overall, DML-based analysis of dynamic policies proves to be a useful approach within the program evaluation toolkit.
Quantum criticality plays a central role in understanding non-Fermi liquid behavior and unconventional superconductivity in strongly correlated systems. In this review, we explore the quantum critical Eliashberg theory, which extends conventional Eliashberg approaches to non-Fermi liquid regimes governed by critical fluctuations. We discuss the theoretical foundations and recent developments in the field, focusing on the interplay between electronic interactions and bosonic modes near quantum phase transitions as described in the Yukawa-coupled version of the Sachdev-Ye-Kitaev model. Special emphasis is placed on the breakdown of quasiparticle coherence, anomalous scaling behaviour, Cooper pairing without quasiparticles, and emergent universality in different physical settings. Starting from a zero-dimensional "quantum-dot" model, we discuss the generalization to higher spatial dimensions and demonstrate the connection between quantum-critical Eliashberg theory and holographic superconductivity. Our analysis provides a perspective on how quantum criticality shapes the dynamics of strongly correlated metals and superconductors.
Subjective Experience in AI Systems: What Do AI Researchers and the Public Believe?
2025-06-13
Noemi Dreksler, Lucius Caviola, David Chalmers, Carter Allen, Alex Rand, Joshua Lewis, Philip Waggoner, Kate Mays, Jeff Sebo
We surveyed 582 AI researchers who have published in leading AI venues and 838 nationally representative US participants about their views on the potential development of AI systems with subjective experience and how such systems should be treated and governed. When asked to estimate the chances that such systems will exist on specific dates, the median responses were 1% (AI researchers) and 5% (public) by 2024, 25% and 30% by 2034, and 70% and 60% by 2100, respectively. The median member of the public thought there was a higher chance that AI systems with subjective experience would never exist (25%) than the median AI researcher did (10%). Both groups perceived a need for multidisciplinary expertise to assess AI subjective experience. Although support for welfare protections for such AI systems exceeded opposition, it remained far lower than support for protections for animals or the environment. Attitudes toward moral and governance issues were divided in both groups, especially regarding whether such systems should be created and what rights or protections they should receive. Yet a majority of respondents in both groups agreed that safeguards against the potential risks from AI systems with subjective experience should be implemented by AI developers now, and if created, AI systems with subjective experience should treat others well, behave ethically, and be held accountable. Overall, these results suggest that both AI researchers and the public regard the emergence of AI systems with subjective experience as a possibility this century, though substantial uncertainty and disagreement remain about the timeline and appropriate response.
Calibrating baryonic effects in cosmic shear with external data in the LSST era
2025-06-13
Amy Wayland, David Alonso, Matteo Zennaro
Cosmological constraints derived from weak lensing (WL) surveys are limited by baryonic effects, which suppress the non-linear matter power spectrum on small scales. By combining WL measurements with data from external tracers of the gas around massive structures, it is possible to calibrate baryonic effects and, therefore, obtain more precise cosmological constraints. In this study, we generate mock data for a Stage-IV weak lensing survey such as the Legacy Survey of Space and Time (LSST), X-ray gas fractions, and stacked kinetic Sunyaev-Zel'dovich (kSZ) measurements, to jointly constrain cosmological and astrophysical parameters describing baryonic effects (using the Baryon Correction Model - BCM). First, using WL data alone, we quantify the level to which the BCM parameters will need to be constrained to recover the cosmological constraints obtained under the assumption of perfect knowledge of baryonic feedback. We identify the most relevant baryonic parameters and determine that they must be calibrated to a precision of $\sim 10$-$20\%$ to avoid significant degradation of the fiducial WL constraints. We forecast that long-term X-ray data from $\sim 5000$ clusters should be able to reach this threshold for the parameters that characterise the abundance of hot virialised gas. Constraining the distribution of ejected gas presents a greater challenge, however, but we forecast that long-term kSZ data from a CMB-S4-like experiment should achieve the level of precision required for full self-calibration.
Viral Dark Matter: Illuminating Protein Function, Ecology, and Biotechnological Promises
2025-06-13
James C. Kosmopoulos, Karthik Anantharaman
Viruses are the most abundant biological entities on Earth and play central roles in shaping microbiomes and influencing ecosystem functions. Yet, most viral genes remain uncharacterized, comprising what is commonly referred to as "viral dark matter." Metagenomic studies across diverse environments consistently show that 40-90% of viral genes lack known homologs or annotated functions. This persistent knowledge gap limits our ability to interpret viral sequence data, understand virus-host interactions, and assess the ecological or applied significance of viral genes. Among the most intriguing components of viral dark matter are auxiliary viral genes (AVGs), including auxiliary metabolic genes (AMGs), regulatory genes (AReGs), and host physiology-modifying genes (APGs), which may alter host function during infection and contribute to microbial metabolism, stress tolerance, or resistance. In this review, we explore recent advances in the discovery and functional characterization of viral dark matter. We highlight representative examples of novel viral proteins across diverse ecosystems including human microbiomes, soil, oceans, and extreme environments, and discuss what is known, and still unknown, about their roles. We then examine the bioinformatic and experimental challenges that hinder functional characterization, and present emerging strategies to overcome these barriers. Finally, we highlight both the fundamental and applied benefits that multidisciplinary efforts to characterize viral proteins can bring. By integrating computational predictions with experimental validation, and fostering collaboration across disciplines, we emphasize that illuminating viral dark matter is both feasible and essential for advancing microbial ecology and unlocking new tools for biotechnology.
MIGHTEE-HI: The direct detection of neutral hydrogen in galaxies at $z>0.25$
2025-06-13
Matt J. Jarvis, Madalina N. Tudorache, I. Heywood, Anastasia A. Ponomareva, M. Baes, Natasha Maddox, Kristine Spekkens, Andreea Varasteanu, C. L. Hale, Mario G. Santos, R. G. Varadaraj, Elizabeth A. K. Adams, Alessandro Bianchetti, Barbara Catinella, Jacinta Delhaize, M. Maksymowicz-Maciata, Pavel E. Mancera Piña, Hengxing Pan, Amélie Saintonge, Gauri Sharma, O. Ivy Wong
Atomic hydrogen constitutes the gas reservoir from which molecular gas and star formation in galaxies emerges. However, the weakness of the line means it has been difficult to directly detect in all but the very local Universe. Here we present results from the first search using the MeerKAT International Tiered Extragalactic Exploration (MIGHTEE) Survey for high-redshift ($z>0.25$) H{\sc i} emission from individual galaxies. By searching for 21-cm emission centered on the position and redshift of optically-selected emission-line galaxies we overcome difficulties that hinder untargeted searches. We detect 11 galaxies at $z>0.25$, forming the first sample of $z>0.25$ detections with an interferometer, with the highest redshift detection at $z = 0.3841$. We find they have much larger H{\sc i} masses than their low-redshift H{\sc i}-selected counterparts for a given stellar mass. This can be explained by the much larger cosmological volume probed at these high redshifts, and does not require any evolution of the H{\sc i} mass function. We make the first-ever measurement of the baryonic Tully-Fisher relation (bTFr) with H{\sc i} at $z>0.25$ and find consistency with the local bTFr, but with tentative evidence of a flattening in the relation at these redshifts for higher-mass objects. This may signify evolution, in line with predictions from hydrodynamic simulations, or that the molecular gas mass in these high-mass galaxies could be significant. This study paves the way for future studies of H{\sc i} beyond the local Universe, using both searches targeted at known objects and via pure H{\sc i} selection.
Enter: Graduated Realism: A Pedagogical Framework for AI-Powered Avatars in Virtual Reality Teacher Training
2025-06-13
Judson Leroy Dean Haynes IV
Virtual Reality simulators offer a powerful tool for teacher training, yet the integration of AI-powered student avatars presents a critical challenge: determining the optimal level of avatar realism for effective pedagogy. This literature review examines the evolution of avatar realism in VR teacher training, synthesizes its theoretical implications, and proposes a new pedagogical framework to guide future design. Through a systematic review, this paper traces the progression from human-controlled avatars to generative AI prototypes. Applying learning theories like Cognitive Load Theory, we argue that hyper-realism is not always optimal, as high-fidelity avatars can impose excessive extraneous cognitive load on novices, a stance supported by recent empirical findings. A significant gap exists between the technological drive for photorealism and the pedagogical need for scaffolded learning. To address this gap, we propose Graduated Realism, a framework advocating for starting trainees with lower-fidelity avatars and progressively increasing behavioral complexity as skills develop. To make this computationally feasible, we outline a novel single-call architecture, Crazy Slots, which uses a probabilistic engine and a Retrieval-Augmented Generation database to generate authentic, real-time responses without the latency and cost of multi-step reasoning models. This review provides evidence-based principles for designing the next generation of AI simulators, arguing that a pedagogically grounded approach to realism is essential for creating scalable and effective teacher education tools.
The use of language technologies in high-stake settings is increasing in recent years, mostly motivated by the success of Large Language Models (LLMs). However, despite the great performance of LLMs, they are are susceptible to ethical concerns, such as demographic biases, accountability, or privacy. This work seeks to analyze the capacity of Transformers-based systems to learn demographic biases present in the data, using a case study on AI-based automated recruitment. We propose a privacy-enhancing framework to reduce gender information from the learning pipeline as a way to mitigate biased behaviors in the final tools. Our experiments analyze the influence of data biases on systems built on two different LLMs, and how the proposed framework effectively prevents trained systems from reproducing the bias in the data.
2025-06-13
Hanchen Wang, Min-Gu Kang, Davit Petrosyan, Shilei Ding, Richard Schlitz, Lauren J. Riddiford, William Legrand, Pietro Gambardella
We report the detection of pure orbital currents generated by both coherent and thermal magnons in the magnetic insulator Bi-doped yttrium iron garnet (BiYIG). The pumping of orbital and spin currents is jointly investigated in nano-devices made of naturally oxidized Cu, pure Cu, Pt, and Cr. The absence of charge conduction in BiYIG and the negligible spin-to-charge conversion of oxidized Cu allows us to disambiguate the orbital current contribution. Comparative measurements on YIG and BiYIG show that the origin of the orbital pumping in BiYIG/oxidized Cu is the dynamics of the orbital magnetization in the magnetic insulator. In Cr, the pumping signal is dominated by the negative spin Hall effect rather than the positive orbital Hall effect, indicating that orbital currents represent a minority of the total angular momentum current pumped from the magnetic insulator. Our results also evidence that improving the interfacial transparency significantly enhances pumping efficiencies not only for spin, but also for orbital currents.
This paper presents a focused literature survey on the use of large language models (LLM) to assist in writing formal specifications for software. A summary of thirty-five key papers is presented, including examples for specifying programs written in Dafny, C and Java. This paper arose from the project VERIFAI - Traceability and verification of natural language requirements that addresses the challenges in writing formal specifications from requirements that are expressed in natural language. Our methodology employed multiple academic databases to identify relevant research. The AI-assisted tool Elicit facilitated the initial paper selection, which were manually screened for final selection. The survey provides valuable insights and future directions for utilising LLMs while formalising software requirements.
This tutorial provides a survey of algorithms for Defensive Forecasting, where predictions are derived not by prognostication but by correcting past mistakes. Pioneered by Vovk, Defensive Forecasting frames the goal of prediction as a sequential game, and derives predictions to minimize metrics no matter what outcomes occur. We present an elementary introduction to this general theory and derive simple, near-optimal algorithms for online learning, calibration, prediction with expert advice, and online conformal prediction.
Stellar Velocity Dispersion versus Age: Consistency across Observations and Simulations, with the Milky Way as an Outlier
2025-06-13
Fiona McCluskey, Andrew Wetzel, Sarah Loebman, Jorge Moreno
Within disk galaxies, the velocity dispersion, $\sigma$, of stars increases with age, $\tau$, as measured in the Milky Way (MW) and nearby galaxies. This relation provides a key window into galactic formation history, tracing both the kinematics of stars at birth and the dynamical heating of stars after birth. We compile and compare observational measurements of the MW, M31, M33, and 16 galaxies from the PHANGS survey. The MW exhibits significantly colder stellar kinematics, with 2-3 times lower $\sigma(\tau)/v_{\phi,0}$ at a given age, than all but one other observed galaxy. Therefore, the MW is a kinematic outlier. To assess how measurement effects influence $\sigma(\tau)$, we analyze the FIRE-2 cosmological simulations, quantifying the impact of uncertainties in stellar age, aperture size, galactocentric radius, and galaxy inclination. Aperture size and galactocentric radius affect $\sigma(\tau)$ by up to a factor of $\approx2$ for stars younger than 100 Myr, with milder effects on older stars. Age uncertainties up to 40\% change the \textit{value} of $\sigma(\tau)$ at a given age by $\lesssim20\%$ but can reshape the relation with age and erase merger signatures. We compare $\sigma(\tau)/v_{\phi,0}$ in FIRE-2 simulations with observations. FIRE-2 agrees well with M31 and M33 at all measured ages, and with PHANGS for stars older than $\approx500$ Myr. The average $\sigma(\tau)/v_{\phi,0}$ in FIRE-2 is about two times higher than the MW at most ages, but the youngest stars show better agreement. The velocity ratios ($\sigma_{\phi}/\sigma_{R}$, $\sigma_{Z}/\sigma_{\phi}$, $\sigma_{Z}/\sigma_{R}$) in FIRE-2 broadly agree with the MW. We conclude that $\sigma(\tau)$ in FIRE-2, and most cosmological zoom-in simulations, reasonably matches observed nearby galaxies, but matching the MW is rare, because it is a kinematic outlier.
Solving Inverse Problems in Stochastic Self-Organising Systems through Invariant Representations
2025-06-13
Elias Najarro, Nicolas Bessone, Sebastian Risi
Self-organising systems demonstrate how simple local rules can generate complex stochastic patterns. Many natural systems rely on such dynamics, making self-organisation central to understanding natural complexity. A fundamental challenge in modelling such systems is solving the inverse problem: finding the unknown causal parameters from macroscopic observations. This task becomes particularly difficult when observations have a strong stochastic component, yielding diverse yet equivalent patterns. Traditional inverse methods fail in this setting, as pixel-wise metrics cannot capture feature similarities between variable outcomes. In this work, we introduce a novel inverse modelling method specifically designed to handle stochasticity in the observable space, leveraging the capacity of visual embeddings to produce robust representations that capture perceptual invariances. By mapping the pattern representations onto an invariant embedding space, we can effectively recover unknown causal parameters without the need for handcrafted objective functions or heuristics. We evaluate the method on two canonical models--a reaction-diffusion system and an agent-based model of social segregation--and show that it reliably recovers parameters despite stochasticity in the outcomes. We further apply the method to real biological patterns, highlighting its potential as a tool for both theorists and experimentalists to investigate the dynamics underlying complex stochastic pattern formation.
The Karl G. Jansky Very Large Array Local Group L-band Survey (LGLBS)
2025-06-13
Eric W. Koch, Adam K. Leroy, Erik W. Rosolowsky, Laura Chomiuk, Julianne J. Dalcanton, Nickolas M. Pingel, Sumit K. Sarbadhicary, Snežana Stanimirović, Fabian Walter, Haylee N. Archer, Alberto D. Bolatto, Michael P. Busch, Hongxing Chen, Ryan Chown, Harrisen Corbould, Serena A. Cronin, Jeremy Darling, Thomas Do, Jennifer Donovan Meyer, Cosima Eibensteiner, Deidre Hunter, Rémy Indebetouw, Preshanth Jagannathan, Amanda A. Kepley, Chang-Goo Kim, Shin-Jeong Kim, Timea O. Kovacs, Joshua Marvil, Eric J. Murphy, Claire E. Murray, Jürgen Ott, D. J. Pisano, Mary Putman, Daniel R. Rybarczyk, Julia Roman-Duval, Karin Sandstrom, Eva Schinnerer, Evan D. Skillman, Adam Smercina, Ioana Stelea, Jay Strader, Jiayi Sun, Devisree Tallapaneni, Elizabeth Tarantino, Vicente Villanueva, Daniel R. Weisz, Thomas G. Williams, Tony Wong
We present the Local Group L-Band Survey (LGLBS), a Karl G. Jansky Very Large Array (VLA) survey producing the highest quality 21-cm and 1-2 GHz radio continuum images to date for the six VLA-accessible, star-forming, Local Group galaxies. Leveraging the VLA's spectral multiplexing power, we simultaneously survey the 21-cm line at high 0.4 km/s velocity resolution, the 1-2 GHz polarized continuum, and four OH lines. For the massive spiral M31, the dwarf spiral M33, and the dwarf irregular galaxies NGC6822, IC10, IC1613, and the Wolf-Lundmark-Melotte Galaxy (WLM), we use all four VLA configurations and the Green Bank Telescope to reach angular resolutions of $< 5''$ ($10{-}20$~pc) for the 21-cm line with $<10^{20}$~cm$^{-2}$ column density sensitivity, and even sharper views ($< 2''$; $5{-}10$~pc) of the continuum. Targeting these nearby galaxies ($D\lesssim1$ Mpc) reveals a sharp, resolved view of the atomic gas, including 21-cm absorption, and continuum emission from supernova remnants and HII regions. These datasets can be used to test theories of the abundance and formation of cold clouds, the driving and dissipation of interstellar turbulence, and the impact of feedback from massive stars and supernovae. Here, we describe the survey design and execution, scientific motivation, data processing, and quality assurance. We provide a first look at and publicly release the wide-field 21-cm HI data products for M31, M33, and four dwarf irregular targets in the survey, which represent some of the highest physical resolution 21-cm observations of any external galaxies beyond the LMC and SMC.
Large language models have not only captivated the public imagination but have also sparked a profound rethinking of how we learn. In the third year following the breakthrough launch of ChatGPT, everyday informal learning has been transformed as diverse user groups explore these novel tools. Who is embracing LLMs for self-directed learning, and who remains hesitant? What are their reasons for adoption or avoidance? What learning patterns emerge with this novel technological landscape? We present an in-depth analysis from a large-scale survey of 776 participants, showcasing that 88% of our respondents already incorporate LLMs into their everyday learning routines for a wide variety of (learning) tasks. Young adults are at the forefront of adopting LLMs, primarily to enhance their learning experiences independently of time and space. Four types of learners emerge across learning contexts, depending on the tasks they perform with LLMs and the devices they use to access them. Interestingly, our respondents exhibit paradoxical behaviours regarding their trust in LLMs' accuracy and privacy protection measures. Our implications emphasize the importance of including different media types for learning, enabling collaborative learning, providing sources and meeting the needs of different types of learners and learning by design.
DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion Models
2025-06-13
Muhammad Sarmad, Arnt-Børre Salberg, Michael Kampffmeyer
This paper presents DiffFuSR, a modular pipeline for super-resolving all 12 spectral bands of Sentinel-2 Level-2A imagery to a unified ground sampling distance (GSD) of 2.5 meters. The pipeline comprises two stages: (i) a diffusion-based super-resolution (SR) model trained on high-resolution RGB imagery from the NAIP and WorldStrat datasets, harmonized to simulate Sentinel-2 characteristics; and (ii) a learned fusion network that upscales the remaining multispectral bands using the super-resolved RGB image as a spatial prior. We introduce a robust degradation model and contrastive degradation encoder to support blind SR. Extensive evaluations of the proposed SR pipeline on the OpenSR benchmark demonstrate that the proposed method outperforms current SOTA baselines in terms of reflectance fidelity, spectral consistency, spatial alignment, and hallucination suppression. Furthermore, the fusion network significantly outperforms classical pansharpening approaches, enabling accurate enhancement of Sentinel-2's 20 m and 60 m bands. This study underscores the power of harmonized learning with generative priors and fusion strategies to create a modular framework for Sentinel-2 SR. Our code and models can be found at https://github.com/NorskRegnesentral/DiffFuSR.
Simulating realistic radio continuum survey maps with diffusion models
2025-06-13
Tobias Vičánek Martínez, Henrik W. Edler, Marcus Brüggen
The next generation of radio surveys is going to be transformative for cosmology and other aspects of our understanding of astrophysics. Realistic simulations of radio observations are essential for the design and planning of radio surveys. They are employed in the development of methods for tasks, such as data calibration and reduction, automated analysis and statistical studies in cosmology. We implemented a software for machine learning-assisted simulations of realistic surveys with the LOFAR telescope, resulting in a synthetic radio sky model and a corresponding artificial telescope observation. We employed a diffusion model trained on LoTSS observations to generate individual radio galaxy images with control over the angular size. Single sources are assembled into a radio sky model, using an input catalog from cosmological simulations. We then transformed this sky model into visibilities corresponding to a typical LoTSS pointing. We added realistic noise to this synthetic measurement and obtained our final simulated sky maps through deconvolution. We explored different ways to evaluate our resulting sky model. We were able to simulate realistic LOFAR observations, covering a sky patch of 5x5 degrees at an effective resolution of 8.5 arcseconds. The simulated sources have flux and size distributions that match real observations, and the resulting maps have sensitivities compatible with LoTSS observations. Our diffusion model is able to synthesize high-quality realistic radio galaxy images with precise control over the source sizes. This software can readily be applied to other instruments.
Digital payment policy impact analysis on the intention to use QRIS (quick response code Indonesian standard) during COVID-19 pandemic
2025-06-13
Wishnu Badrawani
This study aims to evaluate the adoption of Bank Indonesia's QRIS (Quick Response code Indonesian Standard) payment system policy. The evaluation is hindered by the contemporaneous emergence of the COVID-19 pandemic, which acts as a confounding factor in adopting the new payment instrument. To disentangle the impact of central bank policy from the pandemic, a novel variation of the model of Unified Theory of Acceptance and Use of Technology (UTAUT) is proposed and is estimated using purposive sampling from an online survey with 572 respondents during the pandemic. The result of the study successfully disentangles the policy effect from the pandemic effect, and also separate the risk of pandemic with common risks (PR) and other technology adoption determinants. The results indicate that perceived central bank policy and pandemic risk are the most influential variables affecting the intention to use QRIS. The findings suggest that this measurement approach can be appropriately used as a complementary tool to examine the effectiveness of the central bank's policy in influencing people's behavior.
The Diversity of Exoplanetary Environments and the Search for Signs of Life Beyond Earth
2025-06-13
Sara Seager, Janusz J. Petkowski, William Bains
Thousands of exoplanets orbit nearby stars, showcasing a remarkable diversity in mass, size, and orbits. With the James Webb Space Telescope now operational, we are observing exoplanet atmospheres and aiming to reach down to small, habitable-zone exoplanets in search of signs of habitability and possibly even biosignature gases. Given the scarcity of targets, it is imperative to embrace the known diversity and consider the range of exoplanets that might host life. We review how Earth life interacts with various atmospheric gases, noting that bacteria can survive in high concentrations of gases such as H2, He, CO2, and CO. Additionally, we consider the potential for life in alternative solvents and in cloud biospheres where rocky surfaces are excessively hot, as well as in hypothesized planetary global oceans. We highlight that life fundamentally requires metal ions for catalytic reactions, suggesting that environments without surface contact need meteoritic delivery to provide these essential elements. Despite today's observational limits, a suite of next-generation telescopes is being designed specifically for exoplanet studies, promising to expand our capabilities and understanding in the future.
Differential Privacy in Machine Learning: From Symbolic AI to LLMs
2025-06-13
Francisco Aguilera-Martínez, Fernando Berzal
Machine learning models should not reveal particular information that is not otherwise accessible. Differential privacy provides a formal framework to mitigate privacy risks by ensuring that the inclusion or exclusion of any single data point does not significantly alter the output of an algorithm, thus limiting the exposure of private information. This survey paper explores the foundational definitions of differential privacy, reviews its original formulations and tracing its evolution through key research contributions. It then provides an in-depth examination of how DP has been integrated into machine learning models, analyzing existing proposals and methods to preserve privacy when training ML models. Finally, it describes how DP-based ML techniques can be evaluated in practice. %Finally, it discusses the broader implications of DP, highlighting its potential for public benefit, its real-world applications, and the challenges it faces, including vulnerabilities to adversarial attacks. By offering a comprehensive overview of differential privacy in machine learning, this work aims to contribute to the ongoing development of secure and responsible AI systems.
HeII emitters in the cosmic noon and beyond. Characterising the HeII λ1640 emission with MUSE and JWST/NIRSpec
2025-06-13
R. Gonz'alez-D'iaz, J. M. Vílchez, C. Kehrig, I. del Moral-Castro, J. Iglesias-Páramo
The study of high-redshift galaxies provides critical insights into the early stages of cosmic evolution, particularly during the so-called 'cosmic noon', when star formation activity reached its peak. Within this context, the origin of the nebular emission remains an open question. In this work, we conduct a systematic, multi-wavelength investigation of a sample of z ~ 2-4 emitters from the MUSE Hubble Ultra Deep Field surveys, utilising both MUSE and JWST/NIRSpec data and extending the sample presented by previous studies. We derive gas-phase metallicities and key physical properties, including electron densities, temperatures and the production rates of hydrogen- and He+-ionising photons. Our results suggest that a combination of factors-such as stellar mass, initial mass function, stellar metallicity, and stellar multiplicity-likely contributes to the origin of the observed nebular emission. Specifically, for our galaxies with higher gas-phase metallicity (12 + log(O/H) > 7.55), we find that models for binary population with Salpeter IMF (Mup=100 Msol) and stellar metallicity ~ 0.001 (i.e., similar to that of the gas) can reproduce the observed ionising conditions. However at lower metallicities, models for binary population with `top-heavy' initial mass function (Mup = 300 Msol) and Zstar much lower < Zstar) than that of the gas are required to fully account for the observed ionising photon production. These results reinforce that the ionisation keeps challenging current stellar populations, and the ionisation problem persists in the very low metallicity regime.
Perspectives on Explanation Formats From Two Stakeholder Groups in Germany: Software Providers and Dairy Farmers
2025-06-13
Mengisti Berihu Girmay, Felix Möhrle
This paper examines the views of software providers in the German dairy industry with regard to dairy farmers' needs for explanation of digital decision support systems. The study is based on mastitis detection in dairy cows using a hypothetical herd management system. We designed four exemplary explanation formats for mastitis assessments with different types of presentation (textual, rule-based, herd comparison, and time series). In our previous study, 14 dairy farmers in Germany had rated these formats in terms of comprehensibility and the trust they would have in a system providing each format. In this study, we repeat the survey with 13 software providers active in the German dairy industry. We ask them how well they think the formats would be received by farmers. We hypothesized that there may be discrepancies between the views of both groups that are worth investigating, partly to find reasons for the reluctance to adopt digital systems. A comparison of the feedback from both groups supports the hypothesis and calls for further investigation. The results show that software providers tend to make assumptions about farmers' preferences that are not necessarily accurate. Our study, although not representative due to the small sample size, highlights the potential benefits of a thorough user requirements analysis (farmers' needs) to improve software adaptation and user acceptance.
This work presents a comprehensive overview of three recently developed geometric frameworks for the study of classical action-dependent field theories. Specifically, the three underlying geometric structures - namely, k-contact, k-cocontact, and multicontact - are first introduced, and then used to develop the Lagrangian and Hamiltonian formalisms of the aforementioned theories. Finally, the relationship among these three types of structures is analyzed in the case of trivial bundles; as well as the comparison with other alternative definitions of multicontact structure presented in the literature.
Multivariate Time-series Transformer Embeddings for Light Curves
2025-06-13
Gabriel Chiong, Ignacio Becker, Pavlos Protopapas
Astronomical surveys produce time-series data by observing stellar objects across multiple wavelength bands. Foundational transformer-based models, such as Astromer, encode each time-series as a sequence of embeddings of uniform dimensions. However, such models operate independently on each band at a single time and do not natively leverage information across telescope filters. We extend this framework by introducing a fusion mechanism that maps the collection of single-band embeddings to a unified sequence representation, enabling multiband modeling for downstream tasks. The challenge lies in devising a mechanism within the encoder to coordinate between data from different wavelengths, which are often recorded at asynchronous times. We pre-train multiband models on a subset of 600000 high signal-to-noise light curves from the MACHO survey and fine-tune them using the Alcock and ATLAS survey datasets. Experimental results show that both our proposed multiband architectures outperform the single-band models by approximately 10% in F1-score, with jointly pre-trained multiband encoders further improving performance over a collection of independently pre-trained single-band encoders. Furthermore, our experiments show that there are minimal differences in multiband performance when sampling individual band data asynchronously versus sampling all individual bands on the same set of time-steps. However, jointly pre-trained models can take more than twice the time to pre-train. These results demonstrate the trade-offs of the multiband approach where multivariate data are available.
On generalised Pythagorean triples over number fields
2025-06-13
Pedro-José Cazorla García
Generalised Pythagorean triples are integer tuples $(x,y,z)$ satisfying the equation $E_{a,b,c}: ax^2+by^2+cz^2=0$. A significant amount of research has been devoted towards understanding generalised Pythagorean triples and, in particular, we can now determine whether $E_{a,b,c}$ has solutions and find them in a computationally effective manner. In this paper, we consider an extension of generalised Pythagorean triples to number fields $K$. In particular, we survey and extend the existing results over $\mathbb{Q}$ for determining if $E_{a,b,c}$ has solutions over number fields and if so, to find and parameterise them, as well as to find a minimal solution. Throughout the text, we incorporate numerous examples to make our results accessible to all researchers interested in the topic of generalised Pythagorean triples.
Local and global approaches to the thermodynamics of pure decoherence processes in open quantum systems
2025-06-13
Irene Ada Picatoste, Alessandra Colla, Heinz-Peter Breuer
We study the nonequilibrium thermodynamics of pure decoherence processes in open quantum systems coupled to a thermal reservoir. We review various definitions of central quantities, such as internal energy, work, heat and entropy production, developed within local and global approaches to quantum thermodynamics. Within local approaches thermodynamic quantities only refer to the open system's degrees of freedom, while in the global approaches certain quantities are defined by referring explicitly to the reservoir degrees of freedom. Employing a microscopic, analytically solvable model, we perform a comparison of these two perspectives, revealing substantial differences in the thermodynamic quantities and in the formulations of the first and second law. The main reason for these discrepancies is the fact that the global approaches involve the system-reservoir interaction which exchanges a large amount of energy with the environment, while the average open system energy is constant in time because the dynamics represents pure decoherence and does not affect the open system populations.
2025-06-13
Edward Turner, Anna Soligo, Mia Taylor, Senthooran Rajamanoharan, Neel Nanda
Recent work discovered Emergent Misalignment (EM): fine-tuning large language models on narrowly harmful datasets can lead them to become broadly misaligned. A survey of experts prior to publication revealed this was highly unexpected, demonstrating critical gaps in our understanding of model alignment. In this work, we both advance understanding and provide tools for future research. Using new narrowly misaligned datasets, we create a set of improved model organisms that achieve 99% coherence (vs. 67% prior), work with smaller 0.5B parameter models (vs. 32B), and that induce misalignment using a single rank-1 LoRA adapter. We demonstrate that EM occurs robustly across diverse model sizes, three model families, and numerous training protocols including full supervised fine-tuning. Leveraging these cleaner model organisms, we isolate a mechanistic phase transition and demonstrate that it corresponds to a robust behavioural phase transition in all studied organisms. Aligning large language models is critical for frontier AI safety, yet EM exposes how far we are from achieving this robustly. By distilling clean model organisms that isolate a minimal alignment-compromising change, and where this is learnt, we establish a foundation for future research into understanding and mitigating alignment risks in LLMs.
Unveiling a Population of Strong Galaxy-Galaxy Lensed Faint Dusty Star-Forming Galaxies
2025-06-13
Ting-Kai Yang, Chian-Chou Chen, Zhen-Kai Gao, Bovornpratch Vijarnwannaluk, Adarsh Ranjan, Wei-Hao Wang, Caitlin M. Casey, Tomotsugu Goto, Jeyhan S. Kartaltepe, Chayan Mondal, James Pearson, Chris Sedgwick, Stephen Serjeant
The measurements of the number density of galaxy-galaxy strong lenses can be used to put statistical constraints on the foreground mass distributions. Dusty galaxies uncovered in submillimeter surveys are particularly useful in this regard because of the large volume probed by these surveys. Previous discoveries of strong galaxy-galaxy lensed dusty galaxies are predominantly the brightest in the sky discovered by Herschel, SPT, and Planck. However, models have also predicted a non-negligible fraction of strong galaxy-galaxy lensed faint dusty galaxies, which were difficult to confirm due to technical difficulties. Utilizing the deepest SCUBA-2 submillimeter survey, STUDIES, in both the COSMOS and the UDS fields, together with a red JWST color selection method, we discover a population of 13 strong galaxy-galaxy lensed faint dusty galaxies. The rich ancillary data allow us to confirm their strongly lensed nature via estimates of redshifts and lens modeling. Our systematic search has allowed us to construct the 450$\mu$m number counts of strongly lensed sources down to the flux levels about an order of magnitude fainter than previous measurements. The measured lensing fractions of $\sim$1% are consistent with predictions from models that also successfully produce the number density of the strong galaxy-galaxy lensed bright dusty galaxies. Future searches from Euclid and Roman are expected to discover orders of magnitude more strongly lensed faint dusty galaxies.
A$^2$LC: Active and Automated Label Correction for Semantic Segmentation
2025-06-13
Youjin Jeon, Kyusik Cho, Suhan Woo, Euntai Kim
Active Label Correction (ALC) has emerged as a promising solution to the high cost and error-prone nature of manual pixel-wise annotation in semantic segmentation, by selectively identifying and correcting mislabeled data. Although recent work has improved correction efficiency by generating pseudo-labels using foundation models, substantial inefficiencies still remain. In this paper, we propose Active and Automated Label Correction for semantic segmentation (A$^2$LC), a novel and efficient ALC framework that integrates an automated correction stage into the conventional pipeline. Specifically, the automated correction stage leverages annotator feedback to perform label correction beyond the queried samples, thereby maximizing cost efficiency. In addition, we further introduce an adaptively balanced acquisition function that emphasizes underrepresented tail classes and complements the automated correction mechanism. Extensive experiments on Cityscapes and PASCAL VOC 2012 demonstrate that A$^2$LC significantly outperforms previous state-of-the-art methods. Notably, A$^2$LC achieves high efficiency by outperforming previous methods using only 20% of their budget, and demonstrates strong effectiveness by yielding a 27.23% performance improvement under an equivalent budget constraint on the Cityscapes dataset. The code will be released upon acceptance.
Automated code review comment generation (RCG) aims to assist developers by automatically producing natural language feedback for code changes. Existing approaches are primarily either generation-based, using pretrained language models, or information retrieval-based (IR), reusing comments from similar past examples. While generation-based methods leverage code-specific pretraining on large code-natural language corpora to learn semantic relationships between code and natural language, they often struggle to generate low-frequency but semantically important tokens due to their probabilistic nature. In contrast, IR-based methods excel at recovering such rare tokens by copying from existing examples but lack flexibility in adapting to new code contexts-for example, when input code contains identifiers or structures not found in the retrieval database. To bridge the gap between generation-based and IR-based methods, this work proposes to leverage retrieval-augmented generation (RAG) for RCG by conditioning pretrained language models on retrieved code-review exemplars. By providing relevant examples that illustrate how similar code has been previously reviewed, the model is better guided to generate accurate review comments. Our evaluation on the Tufano et al. benchmark shows that RAG-based RCG outperforms both generation-based and IR-based RCG. It achieves up to +1.67% higher exact match and +4.25% higher BLEU scores compared to generation-based RCG. It also improves the generation of low-frequency ground-truth tokens by up to 24.01%. We additionally find that performance improves as the number of retrieved exemplars increases.
Methodological Advances and Challenges in Indirect Treatment Comparisons: A Review of International Guidelines and HAS TC Case Studies
2025-06-13
Matthias Monnereau, Ana Jarne, Axel Benoist, Clémence Fradet, Maurice Perol, Thomas Filleron, Louise Baschet
To evaluate methodological challenges and regulatory considerations of indirect treatment comparisons (ITCs) with the analysis of international health technology assessment guidelines and French Transparency Committee (TC) decisions. We conducted a pragmatic review of ITC guidelines from major health technology assessment (HTA) bodies and multistakeholder organizations. Then, we analyzed TC opinions published between 2021-2023. We extracted data on ITC methodology, therapeutic areas, acceptability, and limitations expressed by the TC. The targeted review of the main guidelines showed mainly agreements between HTA bodies and multistakeholder organizations, with some specificities. 138 TC opinions containing 195 ITCs were analyzed. Only 13.3% of these ITCs influenced TC decision-making. ITCs were more frequently accepted in genetic diseases (34.4%) compared to oncology (10.0%) and autoimmune diseases (11.1%). Methods using individual patient data showed higher acceptance rates (23.1%) than network meta-analyses (4.2%). Main limitations included heterogeneity/bias risk (59%), lack of data (48%), statistical methodology issues (29%), study design concerns (27%), small sample size (25%), and outcome definition variability (20%). When ITCs were the primary source of evidence, the proportion of important clinical benefit was lower (60.9% vs. 73.4%) than when randomized controlled trials were available. While ITCs are increasingly submitted, particularly where direct evidence is impractical, their influence on reimbursement decisions remains limited. There is a need for clear and accessible guides so manufacturers can produce clearer and more robust ITCs that follow regulatory guidelines, from the planning phase to execution.
Characterizing the outer disk of eXtended-UV galaxies in the optical domain with deep surveys
2025-06-13
E. Bernaud, S. Boissier, Junais, K. Malek, E. Hugot, G. Galaz
Aims : Extended-UV (XUV) galaxies are galaxies presenting an extended outer ultraviolet disks. Although some Giant Low Surface Brightness (GLSB) galaxies are also XUV, their relation has been seldom studied. This work aims to determine whether a sample of nine XUV galaxies can be classified as GLSBs, by analyzing their photometric properties in deep optical images. Method : The method presented here uses optical data from the Dark Energy Survey (DES) to construct surface brightness (SB) profiles for each galaxy. A characteristic UV radius is defined to examine the XUV disks. The surface brightness profiles are fitted using simple exponential functions, and the extracted parameters are compared with the literature to identify possible GLSB galaxies. We examined also other diagnostics, including color profiles. Results : The analysis of the optical surface brightness profiles of XUV galaxies reveal that they can be classified into three different families. One third meet the GLSB criteria in terms of diffuseness, another third are regular, and the remaining have a stronger decline in the outer disk than in the inner disk, opposite to GLSBs. The color profiles allowed us, in addition to distinguish one of the galaxy, NGC1140, likely resulting from a rare dwarf-dwarf merger.
Circum-nuclear eccentric gas flow in the Galactic Center revealed by ALMA CMZ Exploration Survey (ACES)
2025-06-13
Yoshiaki Sofue, Tomoharu Oka, Steven N. Longmore, Daniel Walker, Adam Ginsburg, Jonathan D. Henshaw, John Bally, Ashley T. Barnes, Cara Battersby, Laura Colzi, Paul Ho, Izaskun Jimenez-Serra, J. M. Diederik Kruijssen, Elizabeth Mills, Maya A. Petkova, Mattia C. Sormani, Jennifer Wallace, Jairo Armijos-Abendaño, Katarzyna M. Dutkowska, Rei Enokiya, Pablo García, Savannah Gramze, Christian Henkel, Pei-Ying Hsieh, Yue Hu, Katharina Immer, Yuhei Iwata, Janik Karoly, Ralf S. Klessen, Kotaro Kohno, Mark R. Krumholz, Dani Lipman, Mark R. Morris, Francisco Nogueras-Lara, Jaime E. Pineda, Sergio Martín, Miguel Angel Requenatorres, Víctor M. Rivilla, Denise Riquelme-Vásquez, Álvaro Sánchez-Monge, Miriam G. Santa-Maria, Howard A. Smith, Volker Tolls, Q. Daniel Wang
We analyze the CS (J=2-1) line cube from the internal data release obtained by the large-scale program "ALMA CMZ Exploration Survey (ACES)" to investigate the kinematic structure of the innermost $\sim 10$ pc region of the Galaxy, which contains the high-velocity compact cloud (HVCC) at $(l,b,v_{\rm lsr})\sim(+0^\circ.02,-0^\circ.02, 100 {\rm km~s}^{-1})$ (hereafter G0.02). The longitude-velocity diagram (LVD) of the cloud draws an elliptical structure, which is interpreted as an orbital trajectory in the $(l,V_{\rm lsr})$ space of a noncircular (eccentric) motion of the molecular gas in the gravitational potential of an extended mass distribution in the central 10 pc of the Galaxy. We argue that G0.02 is a kinematic tracer of the inner potential, a rare case of a dense gas following an eccentric orbit in the nuclear gravitational field.
Transformable Plasmonic Helix with Swinging Gold Nanoparticles
2025-06-13
Andreas Peil, Pengfei Zhan, Xiaoyang Duan, Roman Krahne, Denis Garoli, Luis M. Liz-Marzán, Na Liu
Control over multiple optical elements that can be dynamically rearranged to yield substantial three-dimensional structural transformations is of great importance to realize reconfigurable plasmonic nanoarchitectures with sensitive and distinct optical feedback. In this work, we demonstrate a transformable plasmonic helix system, in which multiple gold nanoparticles (AuNPs) can be directly transported by DNA swingarms to target positions without undergoing consecutive stepwise movements. The swingarms allow for programmable AuNP translocations in large leaps within plasmonic nanoarchitectures, giving rise to tailored circular dichroism spectra. Our work provides an instructive bottom-up solution to building complex dynamic plasmonic systems, which can exhibit prominent optical responses through cooperative rearrangements of the constituent optical elements with high fidelity and programmability.
Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics
2025-06-13
Zijian Wang, Peng Tao, Luonan Chen
Predicting time-series is of great importance in various scientific and engineering fields. However, in the context of limited and noisy data, accurately predicting dynamics of all variables in a high-dimensional system is a challenging task due to their nonlinearity and also complex interactions. Current methods including deep learning approaches often perform poorly for real-world systems under such circumstances. This study introduces the Delayformer framework for simultaneously predicting dynamics of all variables, by developing a novel multivariate spatiotemporal information (mvSTI) transformation that makes each observed variable into a delay-embedded state (vector) and further cross-learns those states from different variables. From dynamical systems viewpoint, Delayformer predicts system states rather than individual variables, thus theoretically and computationally overcoming such nonlinearity and cross-interaction problems. Specifically, it first utilizes a single shared Visual Transformer (ViT) encoder to cross-represent dynamical states from observed variables in a delay embedded form and then employs distinct linear decoders for predicting next states, i.e. equivalently predicting all original variables parallelly. By leveraging the theoretical foundations of delay embedding theory and the representational capabilities of Transformers, Delayformer outperforms current state-of-the-art methods in forecasting tasks on both synthetic and real-world datasets. Furthermore, the potential of Delayformer as a foundational time-series model is demonstrated through cross-domain forecasting tasks, highlighting its broad applicability across various scenarios.
Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
2025-06-13
Yuan Gao, Mattia Piccinini, Yuchen Zhang, Dingrui Wang, Korbinian Moller, Roberto Brusnicki, Baha Zarrouki, Alessio Gambi, Jan Frederik Totz, Kai Storms, Steven Peters, Andrea Stocco, Bassam Alrifaee, Marco Pavone, Johannes Betz
For autonomous vehicles, safe navigation in complex environments depends on handling a broad range of diverse and rare driving scenarios. Simulation- and scenario-based testing have emerged as key approaches to development and validation of autonomous driving systems. Traditional scenario generation relies on rule-based systems, knowledge-driven models, and data-driven synthesis, often producing limited diversity and unrealistic safety-critical cases. With the emergence of foundation models, which represent a new generation of pre-trained, general-purpose AI models, developers can process heterogeneous inputs (e.g., natural language, sensor data, HD maps, and control actions), enabling the synthesis and interpretation of complex driving scenarios. In this paper, we conduct a survey about the application of foundation models for scenario generation and scenario analysis in autonomous driving (as of May 2025). Our survey presents a unified taxonomy that includes large language models, vision-language models, multimodal large language models, diffusion models, and world models for the generation and analysis of autonomous driving scenarios. In addition, we review the methodologies, open-source datasets, simulation platforms, and benchmark challenges, and we examine the evaluation metrics tailored explicitly to scenario generation and analysis. Finally, the survey concludes by highlighting the open challenges and research questions, and outlining promising future research directions. All reviewed papers are listed in a continuously maintained repository, which contains supplementary materials and is available at https://github.com/TUM-AVS/FM-for-Scenario-Generation-Analysis.
A Procedural Framework for Assessing the Desirability of Process Deviations
2025-06-13
Michael Grohs, Nadine Cordes, Jana-Rebecca Rehse
Conformance checking techniques help process analysts to identify where and how process executions deviate from a process model. However, they cannot determine the desirability of these deviations, i.e., whether they are problematic, acceptable or even beneficial for the process. Such desirability assessments are crucial to derive actions, but process analysts typically conduct them in a manual, ad-hoc way, which can be time-consuming, subjective, and irreplicable. To address this problem, this paper presents a procedural framework to guide process analysts in systematically assessing deviation desirability. It provides a step-by-step approach for identifying which input factors to consider in what order to categorize deviations into mutually exclusive desirability categories, each linked to action recommendations. The framework is based on a review and conceptualization of existing literature on deviation desirability, which is complemented by empirical insights from interviews with process analysis practitioners and researchers. We evaluate the framework through a desirability assessment task conducted with practitioners, indicating that the framework effectively enables them to streamline the assessment for a thorough yet concise evaluation.
Modulating lipid membrane morphology by dynamic DNA origami networks
2025-06-13
Juanjuan Yang, Kevin Jahnke, Ling Xin, Xinxin Jing, Pengfei Zhan, Andreas Peil, Alessandra Griffo, Marko Škugor, Donglei Yang, Sisi Fan, Kerstin Göpfrich, Hao Yan, Pengfei Wang, Na Liu
Membrane morphology and its dynamic adaptation regulate many cellular functions, which are often mediated by membrane proteins. Advances in DNA nanotechnology have enabled the realization of various protein-inspired structures and functions with precise control at the nanometer level, suggesting a viable tool to artificially engineer the membrane morphology. In this work, we demonstrate a DNA origami cross (DOC) structure that can be anchored onto giant unilamellar vesicles (GUVs) and subsequently polymerized into micron-scale reconfigurable one-dimensional (1D) chains or two-dimensional (2D) lattices. Such DNA origami-based networks can be switched between left-handed (LH) and right-handed (RH) conformations by DNA fuels and exhibit potent efficacy in remodeling the membrane curvatures of GUVs. This work sheds light on designing hierarchically-assembled dynamic DNA systems for the programmable modulation of synthetic cells for useful applications.
Multimodal large language models (MLLMs), which bridge the gap between audio-visual and natural language processing, achieve state-of-the-art performance on several audio-visual tasks. Despite the superior performance of MLLMs, the scarcity of high-quality audio-visual training data and computational resources necessitates the utilization of third-party data and open-source MLLMs, a trend that is increasingly observed in contemporary research. This prosperity masks significant security risks. Empirical studies demonstrate that the latest MLLMs can be manipulated to produce malicious or harmful content. This manipulation is facilitated exclusively through instructions or inputs, including adversarial perturbations and malevolent queries, effectively bypassing the internal security mechanisms embedded within the models. To gain a deeper comprehension of the inherent security vulnerabilities associated with audio-visual-based multimodal models, a series of surveys investigates various types of attacks, including adversarial and backdoor attacks. While existing surveys on audio-visual attacks provide a comprehensive overview, they are limited to specific types of attacks, which lack a unified review of various types of attacks. To address this issue and gain insights into the latest trends in the field, this paper presents a comprehensive and systematic review of audio-visual attacks, which include adversarial attacks, backdoor attacks, and jailbreak attacks. Furthermore, this paper also reviews various types of attacks in the latest audio-visual-based MLLMs, a dimension notably absent in existing surveys. Drawing upon comprehensive insights from a substantial review, this paper delineates both challenges and emergent trends for future research on audio-visual attacks and defense.
Machine Learning-Based Quantification of Vesicoureteral Reflux with Enhancing Accuracy and Efficiency
2025-06-13
Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon, Mohammad Subhi Al-Batah, Lana Yasin Al Aesa, Mohammed Hasan Abu-Arqoub, Rashiq Rafiq Marie, Firas Hussein Alsmad
Vesicoureteral reflux (VUR) is traditionally assessed using subjective grading systems, which introduces variability in diagnosis. This study investigates the use of machine learning to improve diagnostic consistency by analyzing voiding cystourethrogram (VCUG) images. A total of 113 VCUG images were reviewed, with expert grading of VUR severity. Nine image-based features were selected to train six predictive models: Logistic Regression, Decision Tree, Gradient Boosting, Neural Network, and Stochastic Gradient Descent. The models were evaluated using leave-one-out cross-validation. Analysis identified deformation patterns in the renal calyces as key indicators of high-grade VUR. All models achieved accurate classifications with no false positives or negatives. High sensitivity to subtle image patterns characteristic of different VUR grades was confirmed by substantial Area Under the Curve (AUC) values. The results suggest that machine learning can offer an objective and standardized alternative to current subjective VUR assessments. These findings highlight renal calyceal deformation as a strong predictor of severe cases. Future research should aim to expand the dataset, refine imaging features, and improve model generalizability for broader clinical use.
Recent advances in DNA origami-engineered nanomaterials and applications
2025-06-13
Pengfei Zhan, Andreas Peil, Qiao Jiang, Dongfang Wang, Shikufa Mousavi, Qiancheng Xiong, Qi Shen, Yingxu Shang, Baoquan Ding, Chenxiang Lin, Yonggang Ke, Na Liu
DNA nanotechnology is a unique field, where physics, chemistry, biology, mathematics, engineering, and materials science can elegantly converge. Since the original proposal of Nadrian Seeman, significant advances have been achieved in the past four decades. During this glory time, the DNA origami technique developed by Paul Rothemund further pushed the field forward with a vigorous momentum, fostering a plethora of concepts, models, methodologies, and applications that were not thought of before. This review focuses on the recent progress in DNA origami-engineered nanomaterials in the past five years, outlining the exciting achievements as well as the unexplored research avenues. We believe that the spirits and asset that Seeman left for scientists will continue to bring inter-disciplinary innovations and useful applications to this field in the next decade.
Environmental Change Detection: Toward a Practical Task of Scene Change Detection
2025-06-13
Kyusik Cho, Suhan Woo, Hongje Seong, Euntai Kim
Humans do not memorize everything. Thus, humans recognize scene changes by exploring the past images. However, available past (i.e., reference) images typically represent nearby viewpoints of the present (i.e., query) scene, rather than the identical view. Despite this practical limitation, conventional Scene Change Detection (SCD) has been formalized under an idealized setting in which reference images with matching viewpoints are available for every query. In this paper, we push this problem toward a practical task and introduce Environmental Change Detection (ECD). A key aspect of ECD is to avoid unrealistically aligned query-reference pairs and rely solely on environmental cues. Inspired by real-world practices, we provide these cues through a large-scale database of uncurated images. To address this new task, we propose a novel framework that jointly understands spatial environments and detects changes. The main idea is that matching at the same spatial locations between a query and a reference may lead to a suboptimal solution due to viewpoint misalignment and limited field-of-view (FOV) coverage. We deal with this limitation by leveraging multiple reference candidates and aggregating semantically rich representations for change detection. We evaluate our framework on three standard benchmark sets reconstructed for ECD, and significantly outperform a naive combination of state-of-the-art methods while achieving comparable performance to the oracle setting. The code will be released upon acceptance.
This paper introduces LUCID-MA (Learning and Understanding Crime through Dialogue of Multiple Agents), an innovative AI powered framework where multiple AI agents collaboratively analyze and understand crime data. Our system that consists of three core components: an analysis assistant that highlights spatiotemporal crime patterns, a feedback component that reviews and refines analytical results and a prediction component that forecasts future crime trends. With a well-designed prompt and the LLaMA-2-13B-Chat-GPTQ model, it runs completely offline and allows the agents undergo self-improvement through 100 rounds of communication with less human interaction. A scoring function is incorporated to evaluate agent's performance, providing visual plots to track learning progress. This work demonstrates the potential of AutoGen-style agents for autonomous, scalable, and iterative analysis in social science domains maintaining data privacy through offline execution.
A Review and Comparison of Different Sensitivity Analysis Techniques in Practice
2025-06-13
Devin Francom, Abigael Nachtsheim
There exist many methods for sensitivity analysis readily available to the practitioner. While each seeks to help the modeler answer the same general question -- How do sources of uncertainty or changes in the model inputs relate to uncertainty in the output? -- different methods are associated with different assumptions, constraints, and required resources, leading to conclusions that may vary in interpretability and level of detail. Thus, it is crucial that the practitioner selects the desired sensitivity analysis method judiciously, making sure to match the selected approach to the specifics of their problem and to their desired objectives. In this chapter, we provide a practical overview of a collection of widely used, widely available sensitivity analysis methods. We focus on global sensitivity approaches, which seek to characterize how uncertainty in the model output may be allocated to sources of uncertainty in model inputs across the entire input space. Generally, this will require the practitioner to specify a probability distribution over the input space. On the other hand, methods for local sensitivity analysis do not require this specification but they have more limited utility, providing insight into sources of uncertainty associated only with a particular, specified location in the input space. Our hope is that this chapter may serve as a decision-making tool for practitioners, helping to guide the selection of a sensitivity analysis approach that will best fit their needs. To support this goal, we have selected a suite of approaches to cover, which, while not exhaustive, we believe provides a flexible and robust sensitivity analysis toolkit. All methods included are widely used and available in standard software packages.
A Gamified Evaluation and Recruitment Platform for Low Resource Language Machine Translation Systems
2025-06-13
Carlos Rafael Catalan
Human evaluators provide necessary contributions in evaluating large language models. In the context of Machine Translation (MT) systems for low-resource languages (LRLs), this is made even more apparent since popular automated metrics tend to be string-based, and therefore do not provide a full picture of the nuances of the behavior of the system. Human evaluators, when equipped with the necessary expertise of the language, will be able to test for adequacy, fluency, and other important metrics. However, the low resource nature of the language means that both datasets and evaluators are in short supply. This presents the following conundrum: How can developers of MT systems for these LRLs find adequate human evaluators and datasets? This paper first presents a comprehensive review of existing evaluation procedures, with the objective of producing a design proposal for a platform that addresses the resource gap in terms of datasets and evaluators in developing MT systems. The result is a design for a recruitment and gamified evaluation platform for developers of MT systems. Challenges are also discussed in terms of evaluating this platform, as well as its possible applications in the wider scope of Natural Language Processing (NLP) research.
Auditing Data Provenance in Real-world Text-to-Image Diffusion Models for Privacy and Copyright Protection
2025-06-13
Jie Zhu, Leye Wang
Text-to-image diffusion model since its propose has significantly influenced the content creation due to its impressive generation capability. However, this capability depends on large-scale text-image datasets gathered from web platforms like social media, posing substantial challenges in copyright compliance and personal privacy leakage. Though there are some efforts devoted to explore approaches for auditing data provenance in text-to-image diffusion models, existing work has unrealistic assumptions that can obtain model internal knowledge, e.g., intermediate results, or the evaluation is not reliable. To fill this gap, we propose a completely black-box auditing framework called Feature Semantic Consistency-based Auditing (FSCA). It utilizes two types of semantic connections within the text-to-image diffusion model for auditing, eliminating the need for access to internal knowledge. To demonstrate the effectiveness of our FSCA framework, we perform extensive experiments on LAION-mi dataset and COCO dataset, and compare with eight state-of-the-art baseline approaches. The results show that FSCA surpasses previous baseline approaches across various metrics and different data distributions, showcasing the superiority of our FSCA. Moreover, we introduce a recall balance strategy and a threshold adjustment strategy, which collectively allows FSCA to reach up a user-level accuracy of 90% in a real-world auditing scenario with only 10 samples/user, highlighting its strong auditing potential in real-world applications. Our code is made available at https://github.com/JiePKU/FSCA.
A survey of The Prouhet-Tarry-Escott Problem and its Generalizations
2025-06-13
Chen Shuwen
This paper explores the Prouhet-Tarry-Escott problem (PTE), the Generalized PTE problem (GPTE), and the Fermat form of Generalized PTE problem (FPTE). The GPTE problem extends the PTE problem by allowing different sets of exponents, while the FPTE problem considers cases where the number of integers in the two sets differs by one. Multigrade chains are also investigated, involving multiple sets of integers satisfying the GPTE system. The study of PTE and GPTE problems is further extended from integers to trigonometric functions. Three novel generalizations of the Girard-Newton Identities are introduced to solve the PTE and GPTE problems: the first extends the domain of exponents to all integers; the second further generalizes to a broader form; and the third focuses on odd integer exponents. The constant $C$ in the PTE and GPTE problems is investigated, and a novel approach is proposed by introducing the normalized GPTE problem with six conjectures to determine bounds for each variable. This enhances the efficiency of computer searches for ideal non-negative integer solutions that satisfy $\sum_{i=1}^{n+1} a_i^k = \sum_{i=1}^{n+1} b_i^k$ for $k = k_1, k_2, \dots, k_n$. A general process of computer searches for GPTE solutions is discussed, and reference code for the search program is provided. Several parametric solutions for GPTE are presented, along with parametric methods for ideal prime solutions. Six open problems related to the PTE and GPTE problems are proposed, and three approaches to address these problems are suggested. In the appendix, an overview of the research status is provided for the 296 types of GPTE and FPTE that have ideal solutions.
A correlation-permutation approach for speech-music encoders model merging
2025-06-13
Fabian Ritter-Gutierrez, Yi-Cheng Lin, Jeremy H. M Wong, Hung-yi Lee, Eng Siong Chng, Nancy F. Chen
Creating a unified speech and music model requires expensive pre-training. Model merging can instead create an unified audio model with minimal computational expense. However, direct merging is challenging when the models are not aligned in the weight space. Motivated by Git Re-Basin, we introduce a correlation-permutation approach that aligns a music encoder's internal layers with a speech encoder. We extend previous work to the case of merging transformer layers. The method computes a permutation matrix that maximizes the model's features-wise cross-correlations layer by layer, enabling effective fusion of these otherwise disjoint models. The merged model retains speech capabilities through this method while significantly enhancing music performance, achieving an improvement of 14.83 points in average score compared to linear interpolation model merging. This work allows the creation of unified audio models from independently trained encoders.
Coefficient Shape Transfer Learning for Functional Linear Regression
2025-06-13
Shuhao Jiao, Ian W. Mckeague, N. -H. Chan
In this paper, we develop a novel transfer learning methodology to tackle the challenge of data scarcity in functional linear models. The methodology incorporates samples from the target model (target domain) alongside those from auxiliary models (source domains), transferring knowledge of coefficient shape from the source domains to the target domain. This shape-based knowledge transfer offers two key advantages. First, it is robust to covariate scaling, ensuring effectiveness despite variations in data distributions across different source domains. Second, the notion of coefficient shape homogeneity represents a meaningful advance beyond traditional coefficient homogeneity, allowing the method to exploit a wider range of source domains and achieve significantly improved model estimation. We rigorously analyze the convergence rates of the proposed estimator and examine the minimax optimality. Our findings show that the degree of improvement depends not only on the similarity of coefficient shapes between the target and source domains, but also on coefficient magnitudes and the spectral decay rates of the functional covariates covariance operators. To address situations where only a subset of auxiliary models is informative for the target model, we further develop a data-driven procedure for identifying such informative sources. The effectiveness of the proposed methodology is demonstrated through comprehensive simulation studies and an application to occupation time analysis using physical activity data from the U.S. National Health and Nutrition Examination Survey.
Foundation models have gained growing interest in the IoT domain due to their reduced reliance on labeled data and strong generalizability across tasks, which address key limitations of traditional machine learning approaches. However, most existing foundation model based methods are developed for specific IoT tasks, making it difficult to compare approaches across IoT domains and limiting guidance for applying them to new tasks. This survey aims to bridge this gap by providing a comprehensive overview of current methodologies and organizing them around four shared performance objectives by different domains: efficiency, context-awareness, safety, and security & privacy. For each objective, we review representative works, summarize commonly-used techniques and evaluation metrics. This objective-centric organization enables meaningful cross-domain comparisons and offers practical insights for selecting and designing foundation model based solutions for new IoT tasks. We conclude with key directions for future research to guide both practitioners and researchers in advancing the use of foundation models in IoT applications.
Improving Public Service Chatbot Design and Civic Impact: Investigation of Citizens' Perceptions of a Metro City 311 Chatbot
2025-06-13
Jieyu Zhou, Rui Shen, Yue You, Carl DiSalvo, Lynn Dombrowski, Christopher MacLellan
As governments increasingly adopt digital tools, public service chatbots have emerged as a growing communication channel. This paper explores the design considerations and engagement opportunities of public service chatbots, using a 311 chatbot from a metropolitan city as a case study. Our qualitative study consisted of official survey data and 16 interviews examining stakeholder experiences and design preferences for the chatbot. We found two key areas of concern regarding these public chatbots: individual-level and community-level. At the individual level, citizens experience three key challenges: interpretation, transparency, and social contextualization. Moreover, the current chatbot design prioritizes the efficient completion of individual tasks but neglects the broader community perspective. It overlooks how individuals interact and discuss problems collectively within their communities. To address these concerns, we offer design opportunities for creating more intelligent, transparent, community-oriented chatbots that better engage individuals and their communities.
New tissue engineered scaffolds for rotator cuff tendon-bone interface regeneration
2025-06-13
Ting Zhang, Jianying Zhang
Healing of Tendon-bone interface(TBI) injuries is slow and is often repaired with scar tissue formation that compromises normal function. Despite the increasing maturity of surgical techniques, re-tearing of the rotator cuff after surgery remains common. The main reason for this issue is that the original structure of the rotator cuff at the TBI area is difficult to fully restore after surgery, and anatomical healing of the rotator cuff TBI is challenging to achieve solely through surgery. With the advancement of tissue engineering technology, more and more basic researchers and clinical surgeons are recognizing the enormous potential of tissue engineering in promoting TBI healing. Growing research evidence indicates that tissue engineering technology not only effectively promotes repairing and remodeling of the TBI but also reduces the formation of fibrous vascular scar tissue, leading to more orderly tissue reconstruction. The core of tissue engineering technology approaches lies in combining the use of various scaffolds, cells and bioactive molecules to simulate the natural environment of TBI healing, achieving optimal therapeutic outcomes. In this review, we will systematically summarize and highlight recent progress in the application of tissue engineering on TBI regeneration, particularly focusing on advancements in novel scaffolds and their role and potential in promoting healing of the TBI of rotator cuff, providing valuable references for clinical application and research.
Statistical Machine Learning for Astronomy -- A Textbook
2025-06-13
Yuan-Sen Ting
This textbook provides a systematic treatment of statistical machine learning for astronomical research through the lens of Bayesian inference, developing a unified framework that reveals connections between modern data analysis techniques and traditional statistical methods. We show how these techniques emerge from familiar statistical foundations. The consistently Bayesian perspective prioritizes uncertainty quantification and statistical rigor essential for scientific inference in astronomy. The textbook progresses from probability theory and Bayesian inference through supervised learning including linear regression with measurement uncertainties, logistic regression, and classification. Unsupervised learning topics cover Principal Component Analysis and clustering methods. We then introduce computational techniques through sampling and Markov Chain Monte Carlo, followed by Gaussian Processes as probabilistic nonparametric methods and neural networks within the broader statistical context. Our theory-focused pedagogical approach derives each method from first principles with complete mathematical development, emphasizing statistical insight and complementing with astronomical applications. We prioritize understanding why algorithms work, when they are appropriate, and how they connect to broader statistical principles. The treatment builds toward modern techniques including neural networks through a solid foundation in classical methods and their theoretical underpinnings. This foundation enables thoughtful application of these methods to astronomical research, ensuring proper consideration of assumptions, limitations, and uncertainty propagation essential for advancing astronomical knowledge in the era of large astronomical surveys.
Morphology across cosmic time: assessing the evolution and interplay of disk and bulge-dominated galaxies in the CANDELS survey
2025-06-13
Vitor M. Sampaio, Igor Kolesnikov, Reinaldo R. de Carvalho, Ignacio Ferreras, Joseph Silk
We investigate the redshift evolution of disk and bulge-dominated galaxies using a mass-complete sample of $\sim$14,000 galaxies from the CANDELS survey, selected with $H_{\rm mag} \leq 24$, $M_{\rm stellar} \geq 10^9\,{\rm M}_\odot$, and spanning $0.2 \leq z \leq 2.4$. Adopting an unbiased morphological classification, free from visual inspection or parametric assumptions, we explore the evolution of specific star formation rate (sSFR), stellar mass, structural properties, and galaxy fractions as a function of redshift and morphology. We find that while disk and bulge-dominated galaxies exhibit similar sSFR distributions at $z \sim 2.4$, bulge-dominated systems develop a redshift-dependent bimodality below $z < 1.6$, unlike the unimodal behaviour of disks. This bimodality correlates with stellar mass: bulge-dominated galaxies with lower sSFR are significantly more massive and exhibit higher S\'ersic indices than their star-forming counterparts, despite having similar effective radii. Based on a Gaussian mixture decomposition, we identify two evolutionary tracks for bulge-dominated galaxies: G1, a long-lived, star-forming population with disk-like properties; and G2, a quenched, massive population whose prominence increases with decreasing redshift. The evolution of the star formation main sequence and morphology--mass fractions support a scenario in which G2 systems form through merger-driven transformations of massive disks. Our results indicate that bulge-dominated galaxies are not a homogeneous population, but instead follow divergent evolutionary paths driven by distinct physical mechanisms.
OSI Stack Redesign for Quantum Networks: Requirements, Technologies, Challenges, and Future Directions
2025-06-13
Shakil Ahmed, Muhammad Kamran Saeed, Ashfaq Khokhar
Quantum communication is poised to become a foundational element of next-generation networking, offering transformative capabilities in security, entanglement-based connectivity, and computational offloading. However, the classical OSI model-designed for deterministic and error-tolerant systems-cannot support quantum-specific phenomena such as coherence fragility, probabilistic entanglement, and the no-cloning theorem. This paper provides a comprehensive survey and proposes an architectural redesign of the OSI model for quantum networks in the context of 7G. We introduce a Quantum-Converged OSI stack by extending the classical model with Layer 0 (Quantum Substrate) and Layer 8 (Cognitive Intent), supporting entanglement, teleportation, and semantic orchestration via LLMs and QML. Each layer is redefined to incorporate quantum mechanisms such as enhanced MAC protocols, fidelity-aware routing, and twin-based applications. This survey consolidates over 150 research works from IEEE, ACM, MDPI, arXiv, and Web of Science (2018-2025), classifying them by OSI layer, enabling technologies such as QKD, QEC, PQC, and RIS, and use cases such as satellite QKD, UAV swarms, and quantum IoT. A taxonomy of cross-layer enablers-such as hybrid quantum-classical control, metadata-driven orchestration, and blockchain-integrated quantum trust-is provided, along with simulation tools including NetSquid, QuNetSim, and QuISP. We present several domain-specific applications, including quantum healthcare telemetry, entangled vehicular networks, and satellite mesh overlays. An evaluation framework is proposed based on entropy throughput, coherence latency, and entanglement fidelity. Key future directions include programmable quantum stacks, digital twins, and AI-defined QNet agents, laying the groundwork for a scalable, intelligent, and quantum-compliant OSI framework for 7G and beyond.
The Star Formation in Radio Survey: Adding 90 GHz Data to 3-33 GHz Observations of Star-forming Regions in Nearby Galaxies
2025-06-13
Anna Dignan, Eric J. Murphy, Brian Mason, Cosima Eibensteiner, Brandon S. Hensley, Eric F. Jiménez-Andrade, Sean T. Linden, Simon R. Dicker, Dillon Z. Dong, Emmanuel Momjian, Charles E. Romero, Eva Schinnerer, Jean L. Turner
We present 90 GHz continuum imaging of 119 star-forming regions in 30 nearby galaxies observed with MUSTANG-2 on the Robert C. Byrd Green Bank Telescope as part of the Star Formation in Radio Survey. The 90 GHz data were combined with 3, 15, and 33 GHz data taken previously by the Karl G. Jansky Very Large Array to decompose radio spectra on $\approx$0.8 kpc scales into their synchrotron, free-free, and thermal dust emission components. This was done using three scenarios: (i) a power law fit from 3 to 33 GHz, (ii) Markov Chain Monte Carlo (MCMC) fitting from 3 to 90 GHz with a thermal dust component, and (iii) MCMC fitting from 3 to 33 GHz without a thermal dust component. For these cases, we find a median thermal (free-free) emission fraction at 33 GHz of (i) $88 \pm 2$% with a scatter of 17%, (ii) $76\pm 3$% with a scatter of 25%, and (iii) $84\pm 2$% with a scatter of 18%. From this we conclude that, on average, free-free, not thermal dust, remains the dominant emission component at 33 GHz. While scenario (ii) yields a thermal fraction that is $\approx$10% larger than scenario (iii), this difference decreases to $\approx$5% after AGN are removed. Consequently, star formation rates measured with thermal fractions at 33 GHz are only mildly biased high without 90 GHz data for the spectral decomposition. Furthermore, a power law fit of data from 3 to 33 GHz still provides a reliable estimate of the free-free emission at 33 GHz.
A 13-Billion-Year View of Galaxy Growth: Metallicity Gradient Evolution from the Local Universe to $z=9$ with JWST and Archival Surveys
2025-06-13
Zihao Li, Zheng Cai, Xin Wang, Zhaozhou Li, Avishai Dekel, Kartick C. Sarkar, Eduardo Bañados, Fuyan Bian, Aklant K. Bhowmick, Laura Blecha, Sarah E. I. Bosman, Jaclyn B. Champagne, Xiaohui Fan, Emmet Golden-Marx, Hyunsung D. Jun, Mingyu Li, Xiaojing Lin, Weizhe Liu, Fengwu Sun, Maxime Trebitsch, Fabian Walter, Feige Wang, Yunjing Wu, Jinyi Yang, Huanian Zhang, Shiwu Zhang, Mingyang Zhuang, Siwei Zou
The gas-phase metallicity gradients of galaxies have been extensively studied over the past four decades both in the local and high-redshift universe, as they trace the baryon cycle and growth of galaxies. With the unprecedented spatial resolution and sensitivity of the James Webb Space Telescope (JWST), it is now possible to measure metallicity and its radial gradients out to redshifts as high as $z = 9$. Here, we present a sample of 456 spectroscopically confirmed galaxies from redshifts $1.5 \lesssim z \lesssim 9$ that are spatially resolved on sub-kiloparsec (kpc) scales by deep JWST NIRCam or NIRISS Wide Field Slitless Spectroscopy (WFSS). Synthesizing these new JWST observations with legacy observations from the literature, we observe that at redshift $z > 5$, galaxy centers are more metal-rich, exhibiting negative metallicity gradients of $\sim-0.4$ dex kpc$^{-1}$. These gradients flatten over time, reaching near-zero around $z \approx 2$, coinciding with the peak of the cosmic star formation rate. Beyond this point, the gradients become negative again at lower redshifts approaching $z=0$. This evolution likely reflects transitions in galaxy formation modes: an inside-out growth phase dominated by intense central star formation with inefficient feedback and limited gas mixing during ``cosmic dawn", enhanced gas mixing due to feedback-driven wind and gas accretion at ``cosmic noon", and a later phase of slow evolution and reduced feedback toward the present day. These physical processes including gas accretion and feedback not only regulate star and galaxy formation on cosmic scale, but also shape the evolutionary pathways of individual galaxies over cosmic time.
pop-cosmos: Insights from generative modeling of a deep, infrared-selected galaxy population
2025-06-13
Stephen Thorp, Hiranya V. Peiris, Gurjeet Jagwani, Sinan Deger, Justin Alsing, Boris Leistedt, Daniel J. Mortlock, Anik Halder, Joel Leja
We present an extension of the pop-cosmos model for the evolving galaxy population up to redshift $z\sim6$. The model is trained on distributions of observed colors and magnitudes, from 26-band photometry of $\sim420,000$ galaxies in the COSMOS2020 catalog with Spitzer IRAC $\textit{Ch. 1}<26$. The generative model includes a flexible distribution over 16 stellar population synthesis (SPS) parameters, and a depth-dependent photometric uncertainty model, both represented using score-based diffusion models. We use the trained model to predict scaling relationships for the galaxy population, such as the stellar mass function, star-forming main sequence, and gas-phase and stellar metallicity vs. mass relations, demonstrating reasonable-to-excellent agreement with previously published results. We explore the connection between mid-infrared emission from active galactic nuclei (AGN) and star-formation rate, finding high AGN activity for galaxies above the star-forming main sequence at $1\lesssim z\lesssim 2$. Using the trained population model as a prior distribution, we perform inference of the redshifts and SPS parameters for 429,669 COSMOS2020 galaxies, including 39,588 with publicly available spectroscopic redshifts. The resulting redshift estimates exhibit minimal bias ($\text{median}[\Delta_z]=-8\times10^{-4}$), scatter ($\sigma_\text{MAD}=0.0132$), and outlier fraction ($6.19\%$) for the full $0
Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review
2025-06-13
Céline Finet, Stephane Da Silva Martins, Jean-Bernard Hayet, Ioannis Karamouzas, Javad Amirian, Sylvie Le Hégarat-Mascle, Julien Pettré, Emanuel Aldea
With the emergence of powerful data-driven methods in human trajectory prediction (HTP), gaining a finer understanding of multi-agent interactions lies within hand's reach, with important implications in areas such as autonomous navigation and crowd modeling. This survey reviews some of the most recent advancements in deep learning-based multi-agent trajectory prediction, focusing on studies published between 2020 and 2024. We categorize the existing methods based on their architectural design, their input representations, and their overall prediction strategies, placing a particular emphasis on models evaluated using the ETH/UCY benchmark. Furthermore, we highlight key challenges and future research directions in the field of multi-agent HTP.
Large language models (LLMs) have been increasingly applied to automated harmful content detection tasks, assisting moderators in identifying policy violations and improving the overall efficiency and accuracy of content review. However, existing resources for harmful content detection are predominantly focused on English, with Chinese datasets remaining scarce and often limited in scope. We present a comprehensive, professionally annotated benchmark for Chinese content harm detection, which covers six representative categories and is constructed entirely from real-world data. Our annotation process further yields a knowledge rule base that provides explicit expert knowledge to assist LLMs in Chinese harmful content detection. In addition, we propose a knowledge-augmented baseline that integrates both human-annotated knowledge rules and implicit knowledge from large language models, enabling smaller models to achieve performance comparable to state-of-the-art LLMs. Code and data are available at https://github.com/zjunlp/ChineseHarm-bench.
The Role of Generative AI in Facilitating Social Interactions: A Scoping Review
2025-06-12
T. T. J. E. Arets, G. Perugia, M. Houben, W. A. IJsselsteijn
Reduced social connectedness increasingly poses a threat to mental health, life expectancy, and general well-being. Generative AI (GAI) technologies, such as large language models (LLMs) and image generation tools, are increasingly integrated into applications aimed at enhancing human social experiences. Despite their growing presence, little is known about how these technologies influence social interactions. This scoping review investigates how GAI-based applications are currently designed to facilitate social interaction, what forms of social engagement they target, and which design and evaluation methodologies designers use to create and evaluate them. Through an analysis of 30 studies published since 2020, we identify key trends in application domains including storytelling, socio-emotional skills training, reminiscence, collaborative learning, music making, and general conversation. We highlight the role of participatory and co-design approaches in fostering both effective technology use and social engagement, while also examining socio-ethical concerns such as cultural bias and accessibility. This review underscores the potential of GAI to support dynamic and personalized interactions, but calls for greater attention to equitable design practices and inclusive evaluation strategies.
Semi-Automated Quality Assurance in Digital Pathology: Tile Classification Approach
2025-06-12
Meredith VandeHaar, M. Clinch, I. Yilmaz, M. A. Rahman, Y. Xiao, F. Dogany, H. M. Alazab, A. Nassar, Z. Akkus, B. Dangott
Quality assurance is a critical but underexplored area in digital pathology, where even minor artifacts can have significant effects. Artifacts have been shown to negatively impact the performance of AI diagnostic models. In current practice, trained staff manually review digitized images prior to release of these slides to pathologists which are then used to render a diagnosis. Conventional image processing approaches, provide a foundation for detecting artifacts on digital pathology slides. However, current tools do not leverage deep learning, which has the potential to improve detection accuracy and scalability. Despite these advancements, methods for quality assurance in digital pathology remain limited, presenting a gap for innovation. We propose an AI algorithm designed to screen digital pathology slides by analyzing tiles and categorizing them into one of 10 predefined artifact types or as background. This algorithm identifies and localizes artifacts, creating a map that highlights regions of interest. By directing human operators to specific tiles affected by artifacts, the algorithm minimizes the time and effort required to manually review entire slides for quality issues. From internal archives and The Cancer Genome Atlas, 133 whole slide images were selected and 10 artifacts were annotated using an internally developed software ZAPP (Mayo Clinic, Jacksonville, FL). Ablation study of multiple models at different tile sizes and magnification was performed. InceptionResNet was selected. Single artifact models were trained and tested, followed by a limited multiple instance model with artifacts that performed well together (chatter, fold, and pen). From the results of this study we suggest a hybrid design for artifact screening composed of both single artifact binary models as well as multiple instance models to optimize detection of each artifact.
Physical properties of $R$Co$_{2}$Al$_{8}$ ($R=$ La, Ce, Pr, Nd and Sm) single crystals: an emerging structure-type for anisotropic Kondo lattice studies
2025-06-12
Fernando A. Garcia, Sushma Kumari, Juan Schmidt, Cris Adriano, Aashish Sapkota, Paul C. Canfield, Rebecca Flint, Raquel A. Ribeiro
Systematic investigations of rare-earth ($R$) based intermetallic materials is a leading strategy to reveal the underlying mechanisms governing a range of physical phenomena, such as the formation of a Kondo lattice and competing electronic and magnetic anisotropies. In this work, the magnetic, thermal and transport properties of $R$Co$_{2}$Al$_{8}$ ($R=$ La, Ce, Pr, Nd and Sm) single crystals are presented. LaCo$_{2}$Al$_{8}$ is characterized as a Pauli paramagnet and transport measurements, with the current along and perpendicular to the orthorhombic $c$-axis ($\rho_{c}$ and $\rho_{ab}$, respectively), reveal a clear electronic anisotropy, with $\rho_{ab }\approx(4-7)\rho_{c }$ at $300$ K. We show that CeCo$_{2}$Al$_{8}$ is a Kondo-lattice for which the Kondo coherence temperature $T_{\text{K}}^{*}$, deduced from broad maximums in $\rho_{c}$ and $\rho_{ab}$ at $\approx$ 68 and 46 K, respectively, is also anisotropic. This finding is related to a possible underlying anisotropy of the Kondo coupling in CeCo$_{2}$Al$_{8}$. The Pr and Nd-based materials present strong easy-axis anisotropy ($c$-axis) and antiferromagnetic (AFM) orders below $T=4.84$ K and $T=8.1$ K, respectively. Metamagnetic transitions from this AFM to a spin-polarized paramagnetic phase state are investigated by isothermal magnetization measurements. The Sm-based compound is also an easy-axis AFM with a transition at $T=21.6$ K.
We review the intrinsic geometry of the tangent bundle of a differentiable manifold $M$, aside from any non-natural structures. We recall the properties of the mirror map $B\in\mathrm{End}(TTM)$, known also as the canonical endomorphism or the almost-tangent structure, solve some cohomological questions and raise other induced by the $\mathrm{d}_B$ operator.
Landauer Principle and Thermodynamics of Computation
2025-06-12
Pritam Chattopadhyay, Avijit Misra, Tanmoy Pandit, Goutam Paul
According to the Landauer principle, any logically irreversible process accompanies entropy production, which results in heat dissipation in the environment. Erasing of information, one of the primary logically irreversible processes, has a lower bound on heat dissipated into the environment, called the Landauer bound (LB). However, the practical erasure processes dissipate much more heat than the LB. Recently, there have been a few experimental investigations to reach this bound both in the classical and quantum domains. There has also been a spate of activities to enquire about this LB in finite time, with finite-size heat baths, non-Markovian and nonequilibrium environment in the quantum regime where the effects of fluctuations and correlation of the systems with the bath can no longer be ignored. This article provides a comprehensive review of the recent progress on the Landauer bound, which serves as a fundamental principle in the thermodynamics of computation. We also provide a perspective for future endeavors in these directions. Furthermore, we review the recent exploration toward establishing energetic bounds of a computational process. We also review the thermodynamic aspects of error correction, which is an indispensable part of information processing and computations. In doing so, we briefly discuss the basics of these fields to provide a complete picture.
We present VRBench, the first long narrative video benchmark crafted for evaluating large models' multi-step reasoning capabilities, addressing limitations in existing evaluations that overlook temporal reasoning and procedural validity. It comprises 1,010 long videos (with an average duration of 1.6 hours), along with 9,468 human-labeled multi-step question-answering pairs and 30,292 reasoning steps with timestamps. These videos are curated via a multi-stage filtering process including expert inter-rater reviewing to prioritize plot coherence. We develop a human-AI collaborative framework that generates coherent reasoning chains, each requiring multiple temporally grounded steps, spanning seven types (e.g., event attribution, implicit inference). VRBench designs a multi-phase evaluation pipeline that assesses models at both the outcome and process levels. Apart from the MCQs for the final results, we propose a progress-level LLM-guided scoring metric to evaluate the quality of the reasoning chain from multiple dimensions comprehensively. Through extensive evaluations of 12 LLMs and 16 VLMs on VRBench, we undertake a thorough analysis and provide valuable insights that advance the field of multi-step reasoning.
Identifying Compact Chirping SMBHBs in LSST using Bayesian Analysis
2025-06-12
Chengcheng Xin, Maximiliano Isi, Will M. Farr, Zoltán Haiman
The Legacy Survey of Space and Time (LSST) is expected to observe up to ${\sim}100$ million quasars in the next decade. In this work, we show that it is possible to use such data to measure the characteristic frequency evolution of a "chirp" induced by gravitational waves, which can serve as robust evidence for the presence of a compact supermassive black-hole binary. Following the LSST specifications, we generate mock lightcurves consisting of (i) a post-Newtonian chirp produced by orbital motion through, e.g., relativistic Doppler boosting, (ii) a damped random walk representing intrinsic quasar variability, and (iii) Gaussian photometric errors, while assuming non-uniform observations with extended gaps over a period of 10 yr. Through a fully-Bayesian analysis, we show that we can simultaneously measure the chirp and noise parameters with little degeneracy between the two. For chirp signals with an amplitude of $A = 0.5$ mag and a range of times to merger ($t_m = 15{-}10^4$ yr), we can typically measure a non-zero amplitude and positive frequency derivative with over $5\sigma$ credibility. For binaries with $t_m = 50$ yr, we achieve $3\sigma$ ($5\sigma$) confidence that the signal is chirping for $A \gtrsim 0.1$ ($A > 0.2$). Our analysis can take as little as 35 s (and typically $<$ 10 min) to run, making it scalable to a large number of lightcurves. This implies that LSST could, on its own, establish the presence of a compact supermassive black-hole binary, and thus discover gravitational wave sources detectable by LISA and by Pulsar Timing Arrays.
Generalist Models in Medical Image Segmentation: A Survey and Performance Comparison with Task-Specific Approaches
2025-06-12
Andrea Moglia, Matteo Leccardi, Matteo Cavicchioli, Alice Maccarini, Marco Marcon, Luca Mainardi, Pietro Cerveri
Following the successful paradigm shift of large language models, leveraging pre-training on a massive corpus of data and fine-tuning on different downstream tasks, generalist models have made their foray into computer vision. The introduction of Segment Anything Model (SAM) set a milestone on segmentation of natural images, inspiring the design of a multitude of architectures for medical image segmentation. In this survey we offer a comprehensive and in-depth investigation on generalist models for medical image segmentation. We start with an introduction on the fundamentals concepts underpinning their development. Then, we provide a taxonomy on the different declinations of SAM in terms of zero-shot, few-shot, fine-tuning, adapters, on the recent SAM 2, on other innovative models trained on images alone, and others trained on both text and images. We thoroughly analyze their performances at the level of both primary research and best-in-literature, followed by a rigorous comparison with the state-of-the-art task-specific models. We emphasize the need to address challenges in terms of compliance with regulatory frameworks, privacy and security laws, budget, and trustworthy artificial intelligence (AI). Finally, we share our perspective on future directions concerning synthetic data, early fusion, lessons learnt from generalist models in natural language processing, agentic AI and physical AI, and clinical translation.
What Users Value and Critique: Large-Scale Analysis of User Feedback on AI-Powered Mobile Apps
2025-06-12
Vinaik Chhetri, Krishna Upadhyay, A. B. Siddique, Umar Farooq
Artificial Intelligence (AI)-powered features have rapidly proliferated across mobile apps in various domains, including productivity, education, entertainment, and creativity. However, how users perceive, evaluate, and critique these AI features remains largely unexplored, primarily due to the overwhelming volume of user feedback. In this work, we present the first comprehensive, large-scale study of user feedback on AI-powered mobile apps, leveraging a curated dataset of 292 AI-driven apps across 14 categories with 894K AI-specific reviews from Google Play. We develop and validate a multi-stage analysis pipeline that begins with a human-labeled benchmark and systematically evaluates large language models (LLMs) and prompting strategies. Each stage, including review classification, aspect-sentiment extraction, and clustering, is validated for accuracy and consistency. Our pipeline enables scalable, high-precision analysis of user feedback, extracting over one million aspect-sentiment pairs clustered into 18 positive and 15 negative user topics. Our analysis reveals that users consistently focus on a narrow set of themes: positive comments emphasize productivity, reliability, and personalized assistance, while negative feedback highlights technical failures (e.g., scanning and recognition), pricing concerns, and limitations in language support. Our pipeline surfaces both satisfaction with one feature and frustration with another within the same review. These fine-grained, co-occurring sentiments are often missed by traditional approaches that treat positive and negative feedback in isolation or rely on coarse-grained analysis. To this end, our approach provides a more faithful reflection of the real-world user experiences with AI-powered apps. Category-aware analysis further uncovers both universal drivers of satisfaction and domain-specific frustrations.
Machine learning (ML) models in production do not fail due to statistical anomalies in their input data; they fail due to contextual misalignment -- when their environment deviates from training assumptions, leading to unreliable predictions. Effective ML monitoring requires rich contextual information to move beyond detecting statistical shifts toward meaningful alerts and systematic root-cause analysis. Yet, surprisingly, despite extensive research in ML monitoring and related disciplines (drift detection, data validation, out-of-distribution detection), there is no shared understanding of how to use contextual information -- striking, given that monitoring involves interpretation of information in context. In response, this paper presents a systematic review to characterize and structure the various types of contextual information in this domain. Our analysis examines 94 primary studies across data mining, databases, software engineering, and ML. We introduce the Contextual System--Aspect--Representation (C-SAR) framework, a conceptual model that synthesizes our findings. We also identify 20 recurring and potentially reusable patterns of specific system, aspect, and representation combinations, and map them to the monitoring activities they support. This study provides a new perspective on ML monitoring: from interpreting "tea leaves" of observational statistics into constructing and managing "system maps" that enable systematic and reliable ML monitoring practices.
The ALMA Survey of Gas Evolution of PROtoplanetary Disks (AGE-PRO): XII. Extreme millimetre variability detected in a Class II disc
2025-06-12
James M. Miley, Laura M. Perez, Carolina Agurto-Gangas, Anibal Sierra, Leon Trapman, Miguel Vioque, Nicolas Kurtovic, Paola Pinilla, Ilaria Pascucci, Ke Zhang, Rossella Anania, John Carpenter, Lucas A. Cieza, Dingshan Deng, Camilo Gonzalez-Ruilova, Giovanni P. Rosotti, Dary A. Ruiz-Rodriguez, Estephani E. TorresVillanueva
Variability of millimetre wavelength continuum emission from Class II protoplanetary disks is extremely rare, and when detected it is usually interpreted as originating from non-thermal emission mechanisms that relate to the host star itself rather than its disk. During observations made as part of the AGE-PRO ALMA Large program, significant variability in the brightness of the 2MASS J16202863-2442087 system was detected between individual executions. We report the observed properties of the variability detected at millimetre wavelengths and investigate potential driving mechanisms. To investigate the nature of the variability we construct a light curve from the continuum observations and analyse imaged constructed from both flaring and quiescent emission. We characterise the dust disk around the star through analysis in the image and visibility plane, and carry out kinematic analysis of the CO(2-1) emission from the gas disk. The continuum flux decays by a factor of 8 in less than an hour, and by a factor of 13 within 8 days. The peak brightness coincides with an expected brightness maximum extrapolated from the periodicity of previously observed optical variability. The flare is most likely the product of synchrotron emission in the close vicinity of the star. The nature of the millimetre flare closely resembles those detected in very close binary systems, and may be due to the interaction of magnetic fields in an as yet undetected binary. Alternatively if the central host is a single-star object, the flare may be due to the interaction of magnetic field loops at the stellar surface or a strong accretion burst.
The ALMA Survey of Gas Evolution of PROtoplanetary Disks (AGE-PRO): XI. Beam-corrected gas disk sizes from fitting 12CO moment zero maps
2025-06-12
Leon Trapman, Miguel Vioque, Nicolás T. Kurtovic, Ke Zhang, Giovanni P. Rosotti, Paola Pinilla, John Carpenter, Lucas A. Cieza, Ilaria Pascucci, Rossella Anania, Carolina Agurto-Gangas, Dingshan Deng, James Miley, Laura M. Pérez, Anibal Sierra, Benoît Tabone, Dary A. Ruíz-Rodríguez, Camilo González-Ruilova, Estephani TorresVillanueva
The inward drift of mm-cm sized pebbles in protoplanetary disks has become an important part of our current theories of planet formation and, more recently, planet composition as well. The gas-to-dust size ratio of protoplanetary disks can provide an important constraint on how pebbles have drifted inward provided that observational effects, especially resolution, can be accounted for. Here we present a method for fitting beam-convolved models to integrated intensity maps of line emission using the astropy python package and use it to fit 12 CO moment zero maps of ten Lupus and ten Upper Scorpius protoplanetary disks from the AGE-PRO ALMA Large Program, a sample of disks around M3-K6 stars that cover the ~1 to 6 Myr of gas disk evolution. From the unconvolved best fit models we measure the gas disk size (RCO,90%[model]), which we combine with the dust disk size (Rdust,90%[FRANK]) from continuum visibility fits from Vioque et al. (2025, in press.) to compute beam-corrected gas-to-dust size ratios. In our sample we find gas-to-dust size ratios between ~1 and ~5.5, with a median value of 2.78(+0.37,-0.32). Contrary to models of dust evolution that predict an increasing size ratio with time, we find that the younger disks in Lupus have similar (or even larger) median ratios (3.02(+0.33,-0.33)) than the older disks in Upper Sco (2.46(+0.53,-0.38)). A possible explanation to this discrepancy is that pebble drift is halted in dust traps combined with truncation of the gas disk by external photo-evaporation in Upper Sco, although survivorship bias could also play a role.
Computational Complexity of Statistics: New Insights from Low-Degree Polynomials
2025-06-12
Alexander S. Wein
This is a survey on the use of low-degree polynomials to predict and explain the apparent statistical-computational tradeoffs in a variety of average-case computational problems. In a nutshell, this framework measures the complexity of a statistical task by the minimum degree that a polynomial function must have in order to solve it. The main goals of this survey are to (1) describe the types of problems where the low-degree framework can be applied, encompassing questions of detection (hypothesis testing), recovery (estimation), and more; (2) discuss some philosophical questions surrounding the interpretation of low-degree lower bounds, and notably the extent to which they should be treated as evidence for inherent computational hardness; (3) explore the known connections between low-degree polynomials and other related approaches such as the sum-of-squares hierarchy and statistical query model; and (4) give an overview of the mathematical tools used to prove low-degree lower bounds. A list of open problems is also included.
The ALMA Survey of Gas Evolution of PROtoplanetary Disks (AGE-PRO): X. Dust Substructures, Disk Geometries, and Dust-disk Radii
2025-06-12
Miguel Vioque, Nicolás T. Kurtovic, Leon Trapman, Anibal Sierra, Laura M. Pérez, Ke Zhang, Pietro Curone, Giovanni P. Rosotti, John Carpenter, Benoît Tabone, Paola Pinilla, Dingshan Deng, Ilaria Pascucci, James Miley, Carolina Agurto-Gangas, Lucas A. Cieza, Rossella Anania, Dary A. Ruiz-Rodriguez, Camilo González-Ruilova, Estephani E. TorresVillanueva, Aleksandra Kuznetsova
We perform visibility fitting to the dust continuum Band 6 1.3 mm data of the 30 protoplanetary disks in the AGE-PRO ALMA Large Program. We obtain disk geometries, dust-disk radii, and azimuthally symmetric radial profiles of the intensity of the dust continuum emission. We examine the presence of continuum substructures in the AGE-PRO sample by using these radial profiles and their residuals. We detect substructures in 15 out of 30 disks. We report five disks with large ($>$15 au) inner dust cavities. The Ophiuchus Class I disks show dust-disk substructures in $\sim80\%$ of the resolved sources. This evidences the early formation of substructures in protoplanetary disks. A spiral is identified in IRS 63, hinting to gravitational instability in this massive disk. We compare our dust-disk brightness radial profiles with gas-disk brightness radial profiles and discuss colocal substructures in both tracers. In addition, we discuss the evolution of dust-disk radii and substructures across Ophiuchus, Lupus, and Upper Scorpius. We find that disks in Lupus and Upper Scorpius with large inner dust cavities have typical gas-disk masses, suggesting an abundance of dust cavities in these regions. The prevalence of pressure dust traps at later ages is supported by a potential trend with time with more disks with large inner dust cavities (or "transition disks") in Upper Scorpius and the absence of evolution of dust-disk sizes with time in the AGE-PRO sample. We propose this is caused by an evolutionary sequence with a high fraction of protoplanetary disks with inner protoplanets carving dust cavities.
The ALMA Survey of Gas Evolution of PROtoplanetary Disks (AGE-PRO): VIII. The impact of external photoevaporation on disk masses and radii in Upper Scorpius
2025-06-12
Rossella Anania, Giovanni P. Rosotti, Matías Gárate, Paola Pinilla, Miguel Vioque, Leon Trapman, John Carpenter, Ke Zhang, Ilaria Pascucci, Lucas A. Cieza, Anibal Sierra, Nicolas T. Kurtovic, James Miley, Laura M. Pérez, Benôit Tabone, Michiel Hogerheijde, Dingshan Deng, Carolina Agurto-Gangas, Dary A. Ruiz-Rodriguez, Camilo González-Ruilova, Estephani E. TorresVillanueva
Protoplanetary disk evolution can be deeply influenced by the UV radiation emitted by neighboring massive stars (mainly of spectral type O and B). We show that the process of external photoevaporation, which causes an outside-in depletion of disk material due to environmental UV radiation, can lead to a significant decrease in disk size, and moderate in disk mass and lifetime even at moderate irradiation levels (1-10 G$_{0}$). In this work we investigate the role of external photoevaporation in shaping the masses and sizes of the ten AGE-PRO disks in the Upper Scorpius region, which we estimate to be subject to FUV fluxes ranging between 2 and 12 G$_{0}$, on average. We compare the disk masses and sizes resulting from 1D numerical viscous evolution simulations in which the effect of external photoevaporation is included, to the values retrieved from the AGE-PRO observations. While the pure viscous framework fails in adequately explaining the observed disk properties in Upper Scorpius, with the inclusion of external photoevaporation we can successfully reproduce gas disk sizes for 7 out of 10 sources within a factor <2, when the initial disk mass is 1-10% of the stellar mass. We emphasize the importance of accounting for the environmental irradiation when comparing star-forming regions of different ages, even when moderate FUV irradiation fields are experienced, as in the case of Upper Scorpius.
The ALMA Survey of Gas Evolution of PROtoplanetary Disks (AGE-PRO): VII. Testing accretion mechanisms from disk population synthesis
2025-06-12
Benoît Tabone, Giovanni P. Rosotti, Leon Trapman, Paola Pinilla, Ilaria Pascucci, Alice Somigliana, Richard Alexander, Miguel Vioque, Rossella Anania, Aleksandra Kuznetsova, Ke Zhang, Laura M. Pérez, Lucas A. Cieza, John Carpenter, Dingshan Deng, Carolina Agurto-Gangas, Dary A. Ruíz-Rodríguez, Anibal Sierra, Nicolás T. Kurtovic, James Miley, Camilo González-Ruilova, Estephani TorresVillanueva, Michiel R. Hogerheijde, Kamber Schwarz, Claudia Toci, Leonardo Testi, Giuseppe Lodato
The architecture of planetary systems depends on the evolution of the disks in which they form. In this work, we develop a population synthesis approach to interpret the AGE-PRO measurements of disk gas mass and size considering two scenarios: turbulence-driven evolution with photoevaporative winds and MHD disk-wind-driven evolution. A systematic method is proposed to constrain the distribution of disk parameters from the disk fractions, accretion rates, disk gas masses, and CO gas sizes. We find that turbulence-driven accretion with initially compact disks ($R_0 \simeq 5-20~$au), low mass-loss rates, and relatively long viscous timescales ($t_{\nu,0} \simeq 0.4-3~$Myr or $\alpha_{SS} \simeq 2-4 \times 10^{-4}$) can reproduce the disk fraction and gas sizes. However, the distribution of apparent disk lifetime defined as the $M_D/\dot{M}_*$ ratio is severely overestimated by turbulence-driven models. On the other hand, MHD wind-driven accretion can reproduce the bulk properties of the disk populations from Ophiuchus to Upper Sco assuming compact disks with an initial magnetization of about $\beta \simeq 10^5$ ($\alpha_{DW} \simeq 0.5-1 \times 10^{-3}$) and a magnetic field that declines with time. More studies are needed to confirm the low masses found by AGE-PRO, notably for compact disks that question turbulence-driven accretion. The constrained synthetic disk populations can now be used for realistic planet population models to interpret the properties of planetary systems on a statistical basis.
The ALMA Survey of Gas Evolution of PROtoplanetary Disks (AGE-PRO): VI. Comparison of Dust Evolution Models to AGE-PRO Observations
2025-06-12
Nicolas T. Kurtovic, Matias Gárate, Paola Pinilla, Ke Zhang, Giovanni P. Rosotti, Rossella Anania, Ilaria Pascucci, Benoît Tabone, Leon Trapman, Dingshan Deng, Miguel Vioque, John Carpenter, Lucas A. Cieza, Laura M. Pérez, Carolina Agurto-Gangas, Anibal Sierra, Dary A. Ruíz-Rodriguez, James Miley, Camilo González-Ruilova, Estephani Torres-Villanueva, Aleksandra Kuznetsova
The potential for planet formation of a circumstellar disk depends on the dust and gas reservoirs, which evolve as a function of the disk age. The ALMA Large Program AGE-PRO has measured several disk properties across three star-forming regions of different ages, and in this study we compare the observational results to dust evolution simulations. Using DustPy for the dust evolution, and RADMC-3D for the radiative transfer, we ran a large grid of models spanning stellar masses of 0.25, 0.50, 0.75, and 1.0 $M_\odot$, with different initial conditions, including: disk sizes, disk gas masses, and dust-to-gas ratio, and viscosity. Our models are performed assuming smooth, weakly, or strongly substructured disks, aiming to investigate if any observational trend can favor or exclude the presence of dust traps. The observed gas masses in the disks of the AGE-PRO sample are not reproducible with our models, which only consider viscous evolution with constant $\alpha$, suggesting that additional physical mechanisms play a role in the evolution of the gas mass of disks. When comparing the dust continuum emission fluxes and sizes at 1.3 mm, we find that most of the disks in the AGE-PRO sample are consistent with simulations that have either weak or strong dust traps. The evolution of spectral index in the AGE-PRO sample is also suggestive of an unresolved population of dust traps. Future observations at high angular resolution are still needed to test several hypotheses that result from comparing the observations to our simulations, including that more massive disks in gas mass have the potential to form dust traps at larger disk radii.
The ALMA Survey of Gas Evolution of PROtoplanetary Disks (AGE-PRO): V. Protoplanetary gas disk masses
2025-06-12
Leon Trapman, Ke Zhang, Giovanni P. Rosotti, Paola Pinilla, Benoît Tabone, Ilaria Pascucci, Carolina Agurto-Gangas, Rossella Anania, John Carpenter, Lucas A. Cieza, Dingshan Deng, Camilo González-Ruilova, Michiel R. Hogerheijde, Nicolás T. Kurtovic, Aleksandra Kuznetsova, James Miley, Laura M. Pérez, Dary A. Ruíz-Rodríguez, Kamber Schwarz, Anibal Sierra, Estephani TorresVillanueva, Miguel Vioque
The evolution of the gas mass of planet-forming disks around young stars is crucial for our understanding of planet formation, yet it has proven hard to constrain observationally, due both to the difficulties of measuring gas masses and the lack of a homogeneous sample. Here we present a large grid of thermochemical models which we use to measure protoplanetary gas disk masses of AGE-PRO, the ALMA survey of Gas Evolution in PROtoplanetary disks. AGE-PRO covers a sample of 30 disks around similar spectral type (M3-K6) stars with ages between 0.1 and 10 Myr. Our approach is to simultaneously fit observations of CO isotopologues and N2H+, a complementary molecule produced when CO freezes out. We find that the median gas mass of the three regions decreases over time, from 7.0(+4.4,-2.6)x10^-3 Msun in Ophiuchus (<1 Myr) to 9.4(+5.4,-3.4)x10^-4 Msun for Lupus (~1-3 Myr) and 6.8(+5.1,-2.8)x10^-4 Msun for Upper Sco (~2-6 Myr), with ~1 dex scatter in gas mass in each region. We note that the gas mass distributions for Lupus and Upper Sco look very similar, which could be due to survivorship bias for the latter. The median bulk CO abundance in the CO emitting layer is found to be a factor ~10 lower than the ISM value but does not significantly change between Lupus and Upper Sco. From Lupus to Upper Sco the median gas-to-dust mass ratio increases by a factor ~3 from ~40 to ~120, suggesting efficient inward pebble drift and/or the formation of planetesimals.
The ALMA Survey of Gas Evolution of PROtoplanetary Disks (AGE-PRO): IV. Dust and Gas Disk Properties in the Upper Scorpius Star-forming Region
2025-06-12
Carolina Agurto-Gangas, L. M. Pérez, Anibal Sierra, James Miley, Ke Zhang, Ilaria Pascucci, Paola Pinilla, Dingshan Deng, John Carpenter, Leon Trapman, Miguel Vioque, Giovanni P. Rosotti, Nicolás Kurtovic, Lucas A. Cieza, Kamber Schwarz, Michiel R. Hogerheijde, Rossella Anania, Benoît Tabone, Estephani E. Torres-Villanueva, Dary A. Ruiz-Rodriguez, Camilo González-Ruilova
The Atacama Large Millimeter/submillimeter Array (ALMA) large program AGE-PRO explores protoplanetary disk evolution by studying gas and dust across various ages. This work focuses on ten evolved disks in Upper Scorpius, observed in dust continuum emission, CO and its isotopologues, and N$_2$H$^+$ with ALMA Bands 6 and 7. Disk radii, from the radial location enclosing 68% of the flux, are comparable to those in the younger Lupus region for both gas and dust tracers. However, solid masses are about an order of magnitude below those in Lupus and Ophiuchus, while the dust spectral index suggests some level of dust evolution. These empirical findings align with a combination of radial drift, dust trapping, and grain growth into larger bodies. A moderate correlation between CO and continuum fluxes suggests a link between gas and dust content, through the increased scatter compared to younger regions, possibly due to age variations, gas-to-dust ratio differences, or CO depletion. Additionally, the correlation between C$^{18}$O and N$_2$H$^+$ fluxes observed in Lupus persists in Upper Sco, indicating a relatively stable CO gas abundance over the Class II stage of disk evolution. In conclusion, the AGE-PRO survey of Upper Scorpius disks reveals intriguing trends in disk evolution. The findings point towards potential gas evolution and the presence of dust traps in these older disks. Future high-resolution observations are needed to confirm these possibilities and further refine our understanding of disk evolution and planet formation in older environments.
The ALMA Survey of Gas Evolution of PROtoplanetary Disks (AGE-PRO): III. Dust and Gas Disk Properties in the Lupus Star-forming Region
2025-06-12
Dingshan Deng, Miguel Vioque, Ilaria Pascucci, Laura M. Pérez, Ke Zhang, Nicolás T. Kurtovic, Leon Trapman, Estephani E. TorresVillanueva, Carolina Agurto-Gangas, John Carpenter, Paola Pinilla, Uma Gorti, Benoît Tabone, Anibal Sierra, Giovanni P. Rosotti, Lucas A. Cieza, Rossella Anania, Camilo González-Ruilova, Michiel R. Hogerheijde, James Miley, Dary A. Ruiz-Rodriguez, Maxime Ruaud, Kamber Schwarz
We present Band 6 and Band 7 observations of 10 Lupus disks around M3-K6 stars from the ALMA survey of Gas Evolution in PROtoplanetary disks (AGE-PRO) Large Program. In addition to continuum emission in both bands, our Band 6 setup covers the $\mathrm{{}^{12}CO}$, $\mathrm{{}^{13}CO}$ and $\mathrm{C^{18}O}\,J$=2-1 lines, while our Band 7 setup covers the $\mathrm{N_2H^+}\,J$=3-2 line. All of our sources are detected in $\mathrm{{}^{12}CO}$ and $\mathrm{{}^{13}CO}$, 7 out of 10 are detected in $\mathrm{C^{18}O}$, and 3 are detected in $\mathrm{N_2H^+}$. We find strong correlations between the CO isotopologue line fluxes and the continuum flux densities. With the exception of one disk, we also identify a strong correlation between the $\mathrm{C^{18}O}\,J$=2-1 and $\mathrm{N_2H^+}\,J$=3-2 fluxes, indicating similar CO abundances across this sample. For the two sources with well-resolved continuum and $\mathrm{{}^{12}CO}\,J$=2-1 images, we find that their gas-to-dust size ratio is consistent with the median value of $\sim 2$ inferred from a larger sample of Lupus disks. We derive dust disk masses from continuum flux densities. We estimate gas disk masses by comparing $\mathrm{C^{18}O}\,J$=2-1 line fluxes with those predicted by the limited grid of self-consistent disk models of Ruaud et al. (2022). A comparison of these mass estimates with those derived by Trapman et al. (2025), using a combination of CO isotopologue and $\mathrm{N_2H^+}$ line emission, shows that the masses are consistent with each other. Some discrepancies appear for small and faint disks, but they are still within the uncertainties. Both methods find gas disk masses increase with dust disk masses, and gas-to-dust mass ratios are between $10-100$ in the AGE-PRO Lupus sample.
The ALMA Survey of Gas Evolution of PROtoplanetary Disks (AGE-PRO): II. Dust and Gas Disk Properties in the Ophiuchus Star-forming Region
2025-06-12
Dary A. Ruiz-Rodriguez, Camilo Gonzalez-Ruilova, Lucas A. Cieza, Ke Zhang, Leon Trapman, Anibal Sierra, Paola Pinilla, Ilaria Pascucci, Laura M. Perez, Dingshan Deng, Carolina Agurto-Gangas, John Carpenter, Benoît Tabone, Giovanni P. Rosotti, Rossella Anania, James Miley, Kamber Schwarz, Aleksandra Kuznetsova, Miguel Vioque, Nicolas Kurtovic
The ALMA survey of Gas Evolution in PROtoplanetary disks (AGE-PRO) Large Program aims to trace the evolution of gas disk mass and size throughout the lifetime of protoplanetary disks. This paper presents Band-6 ALMA observations of 10 embedded (Class I and Flat Spectrum) sources in the Ophiuchus molecular cloud, with spectral types ranging from M3 to K6 stars, which serve as the evolutionary starting point in the AGE-PRO sample. While we find 4 nearly edge on disks (>70 deg.), and 3 highly inclined disks (>60 deg.) in our sample, we show that, as a population, embedded disks in Ophiuchus are not significantly contaminated by more evolved, but highly inclined sources. We derived dust disk masses from the Band 6 continuum and estimated gas disk masses from the C18O and C17O lines. The mass estimates from the C17O line are slightly higher, suggesting C18O emission might be partially optically thick. While the 12CO and 13CO lines are severely contaminated by extended emission and self-absorption, the C18O and C17O lines allowed us to trace the radial extent of the gaseous disks. From these measurements, we found that the C18O and C17O fluxes correlate well with each other and with the continuum fluxes. Furthermore, the C18O and C17O lines present a larger radial extension than disk dust sizes by factors ranging from 1.5 to 2.5, as it is found for Class II disks using the radial extension of the 12CO. In addition, we have detected outflows in three disks from 12CO observations.
TED-LaST: Towards Robust Backdoor Defense Against Adaptive Attacks
2025-06-12
Xiaoxing Mo, Yuxuan Cheng, Nan Sun, Leo Yu Zhang, Wei Luo, Shang Gao
Deep Neural Networks (DNNs) are vulnerable to backdoor attacks, where attackers implant hidden triggers during training to maliciously control model behavior. Topological Evolution Dynamics (TED) has recently emerged as a powerful tool for detecting backdoor attacks in DNNs. However, TED can be vulnerable to backdoor attacks that adaptively distort topological representation distributions across network layers. To address this limitation, we propose TED-LaST (Topological Evolution Dynamics against Laundry, Slow release, and Target mapping attack strategies), a novel defense strategy that enhances TED's robustness against adaptive attacks. TED-LaST introduces two key innovations: label-supervised dynamics tracking and adaptive layer emphasis. These enhancements enable the identification of stealthy threats that evade traditional TED-based defenses, even in cases of inseparability in topological space and subtle topological perturbations. We review and classify data poisoning tricks in state-of-the-art adaptive attacks and propose enhanced adaptive attack with target mapping, which can dynamically shift malicious tasks and fully leverage the stealthiness that adaptive attacks possess. Our comprehensive experiments on multiple datasets (CIFAR-10, GTSRB, and ImageNet100) and model architectures (ResNet20, ResNet101) show that TED-LaST effectively counteracts sophisticated backdoors like Adap-Blend, Adapt-Patch, and the proposed enhanced adaptive attack. TED-LaST sets a new benchmark for robust backdoor detection, substantially enhancing DNN security against evolving threats.
The ALMA Survey of Gas Evolution of PROtoplanetary Disks (AGE-PRO): I. Program Overview and Summary of First Results
2025-06-12
Ke Zhang, Laura M. Pérez, Ilaria Pascucci, Paola Pinilla, Lucas A. Cieza, John Carpenter, Leon Trapman, Dingshan Deng, Carolina Agurto-Gangas, Anibal Sierra, Nicolás T. Kurtovic, Dary A. Ruíz-Rodríguez, Miguel Vioque, James Miley, Benoît Tabone, Camilo González-Ruilova, Rossella Anania, Giovanni P. Rosotti, Estephani TorresVillanueva, Michiel R. Hogerheijde, Kamber Schwarz, Aleksandra Kuznetsova
We present the ALMA Survey of Gas Evolution of PROtoplanetary Disks (AGE-PRO), a Large Program of the Atacama Large Millimeter/submillimeter Array (ALMA). AGE-PRO aims to systematically trace the evolution of gas disk mass and size throughout the lifetime of protoplanetary disks. It uses a carefully selected sample of 30 disks around M3-K6 stars in three nearby star-forming regions: Ophiuchus (0.5-1 Myr), Lupus (1-3 Myr), and Upper Sco (2-6 Myr). Assuming the three regions had similar initial conditions and evolutionary paths, we find the median gas disk mass appears to decrease with age. Ophiuchus disks have the highest median gas mass (6 M$_{\rm Jup}$), while the Lupus and Upper Sco disks have significantly lower median masses (0.68 and 0.44 M$_{\rm Jup}$, respectively). Notably, the gas and dust disk masses appear to evolve on different timescales. This is evidenced by the median gas-to-dust mass ratio, which decreases from 122 in the youngest disks ($<$1 Myr) to 46 in Lupus disks, and then increases to 120 in the Upper Sco disks. The median gas disk sizes range between 74-110 au, suggesting that typical gas disks are much smaller than those of well-studied, massive disks. Population synthesis models suggest that magneto-hydrodynamic wind-driven accretion can reproduce median disk properties across all three regions, when assuming compact disks with a declining magnetic field over time. In contrast, turbulent-driven models overestimate gas masses of $>$1 Myr disks by an order of magnitude. Here we discuss the program's motivation, survey design, sample selection, observation and data calibration processes, and highlight the initial results.
Not One to Rule Them All: Mining Meaningful Code Review Orders From GitHub
2025-06-12
Abir Bouraffa, Carolin Brandt, Andy Zaidmann, Walid Maalej
Developers use tools such as GitHub pull requests to review code, discuss proposed changes, and request modifications. While changed files are commonly presented in alphabetical order, this does not necessarily coincide with the reviewer's preferred navigation sequence. This study investigates the different navigation orders developers follow while commenting on changes submitted in pull requests. We mined code review comments from 23,241 pull requests in 100 popular Java and Python repositories on GitHub to analyze the order in which the reviewers commented on the submitted changes. Our analysis shows that for 44.6% of pull requests, the reviewers comment in a non-alphabetical order. Among these pull requests, we identified traces of alternative meaningful orders: 20.6% (2,134) followed a largest-diff-first order, 17.6% (1,827) were commented in the order of the files' similarity to the pull request's title and description, and 29% (1,188) of pull requests containing changes to both production and test files adhered to a test-first order. We also observed that the proportion of reviewed files to total submitted files was significantly higher in non-alphabetically ordered reviews, which also received slightly fewer approvals from reviewers, on average. Our findings highlight the need for additional support during code reviews, particularly for larger pull requests, where reviewers are more likely to adopt complex strategies rather than following a single predefined order.
On the origin of the E1 electron trap level in GaN and dilute AlxGa1-xN films
2025-06-12
Piotr Kruszewski, Jose Coutinho, Vladimir P. Markevich, Pawel Prystawko, Lijie Sun, Jerzy Plesiewicz, Chris A. Dawe, Matthew P. Halsall, Anthony R. Peaker
The results of high-resolution Laplace deep-level transient spectroscopy (L-DLTS) measurements applied to the E1 and FeGa electron traps in dilute AlxGa1-xN films (x = 0.063), grown by metal-organic vapor phase epitaxy (MOVPE) on Ammono-GaN substrates, are presented. It is shown that the electron emission signals associated with the E1 donor and the FeGa acceptor levels split into individual components due to the aluminium fluctuations in the nearest neighbour shells around the E1 and FeGa defects. The splitting patterns observed in the L-DLTS spectra are nearly identical for both signals. Furthermore, the ratios of peak magnitudes determined from the L-DLTS analysis for both the E1 and E3 traps are consistent with calculated probabilities of finding a given number of aluminium atoms in the second nearest neighbour shell around a Ga lattice site in AlxGa1-xN with x = 0.063. These findings provide strong evidence that both the E1 and the FeGa trap states in dilute AlxGa1-xN are related to defects located in the Ga sublattice. To elucidate the origin of the E1 trap in AlxGa1-xN, we have performed a comprehensive scan of possible impurities and defects in GaN and AlxGa1-xN using hybrid density functional calculations of transition levels and their associated shifts upon substitution of Ga neighbour atoms by Al. From analysis of the results, we find that the E1 electron trap in GaN and AlxGa1-xN is most likely related to a donor transition from a carbon or molybdenum impurity atom at the gallium site, respectively.
The Nearby Evolved Stars Survey (NESS) V: properties of volume-limited samples of Galactic evolved stars
2025-06-12
I. McDonald, S. Srinivasan, P. Scicluna, O. C. Jones, A. A. Zijlstra, S. H. J. Wallström, T. Danilovich, J. H. He, J. P. Marshall, J. Th. van Loon, R. Wesson, F. Kemper, A. Trejo-Cruz, J. Greaves, T. Dharmawardena, J. Cami, H. Kim, K. E. Kraemer, C. J. R. Clark, H. Shinnaga, C. Haswell, H. Imai, J. G. A. Wouterloot, A. J. Pérez Vidal, G. Rau, the NESS collaboration
We provide a meta-study of the statistical and individual properties of two volume-complete sets of evolved stars in the Solar Neighbourhood: (1) 852 stars from the Nearby Evolved Stars Survey (NESS), and (2) a partially overlapping set of 507 evolved stars within 300 pc. We also investigate distance determinations to these stars, their luminosity functions and their spatial distribution. Gaia APSIS GSP-Phot AENEAS temperatures of bright giant stars often appear to be underestimated. Existing literature on AGB stars under-samples both the most and least extreme nearby dust-producing stars. We reproduce the literature star-formation history of the solar neighbourhood, though stellar-evolution models over-predict the number of AGB stars of ages around 500 Myr. The distribution of AGB stars broadly matches the known 300 pc scale height of the Galactic disc and shows concentration in the direction of the Galactic centre. Most dust-producing carbon stars belong to the Galactic thick-disc population.
Despite rapid advancements in video generation models, generating coherent storytelling videos that span multiple scenes and characters remains challenging. Current methods often rigidly convert pre-generated keyframes into fixed-length clips, resulting in disjointed narratives and pacing issues. Furthermore, the inherent instability of video generation models means that even a single low-quality clip can significantly degrade the entire output animation's logical coherence and visual continuity. To overcome these obstacles, we introduce AniMaker, a multi-agent framework enabling efficient multi-candidate clip generation and storytelling-aware clip selection, thus creating globally consistent and story-coherent animation solely from text input. The framework is structured around specialized agents, including the Director Agent for storyboard generation, the Photography Agent for video clip generation, the Reviewer Agent for evaluation, and the Post-Production Agent for editing and voiceover. Central to AniMaker's approach are two key technical components: MCTS-Gen in Photography Agent, an efficient Monte Carlo Tree Search (MCTS)-inspired strategy that intelligently navigates the candidate space to generate high-potential clips while optimizing resource usage; and AniEval in Reviewer Agent, the first framework specifically designed for multi-shot animation evaluation, which assesses critical aspects such as story-level consistency, action completion, and animation-specific features by considering each clip in the context of its preceding and succeeding clips. Experiments demonstrate that AniMaker achieves superior quality as measured by popular metrics including VBench and our proposed AniEval framework, while significantly improving the efficiency of multi-candidate generation, pushing AI-generated storytelling animation closer to production standards.
LLMs Are Not Yet Ready for Deepfake Image Detection
2025-06-12
Shahroz Tariq, David Nguyen, M. A. P. Chamikara, Tingmin Wu, Alsharif Abuadbba, Kristen Moore
The growing sophistication of deepfakes presents substantial challenges to the integrity of media and the preservation of public trust. Concurrently, vision-language models (VLMs), large language models enhanced with visual reasoning capabilities, have emerged as promising tools across various domains, sparking interest in their applicability to deepfake detection. This study conducts a structured zero-shot evaluation of four prominent VLMs: ChatGPT, Claude, Gemini, and Grok, focusing on three primary deepfake types: faceswap, reenactment, and synthetic generation. Leveraging a meticulously assembled benchmark comprising authentic and manipulated images from diverse sources, we evaluate each model's classification accuracy and reasoning depth. Our analysis indicates that while VLMs can produce coherent explanations and detect surface-level anomalies, they are not yet dependable as standalone detection systems. We highlight critical failure modes, such as an overemphasis on stylistic elements and vulnerability to misleading visual patterns like vintage aesthetics. Nevertheless, VLMs exhibit strengths in interpretability and contextual analysis, suggesting their potential to augment human expertise in forensic workflows. These insights imply that although general-purpose models currently lack the reliability needed for autonomous deepfake detection, they hold promise as integral components in hybrid or human-in-the-loop detection frameworks.
Two of the most prominent unresolved conjectures in graph theory, the Albertson-Berman conjecture and the Matheson-Tarjan conjecture, have been extensively studied by many researchers. (AB) Every planar graph of order $n$ has an induced forest of order at least $\frac{n}{2}$. (MT) Every plane triangulation of sufficiently large order $n$ has a dominating set of cardinality at most $\frac{n}{4}$. Although partial results and weaker bounds than those originally conjectured have been obtained, both problems remain open. To contribute to their resolution, various generalizations and variations of the original concepts have been investigated, such as total dominating set, induced linear forests, and others. In this paper, we clarify the relations among several notions related to these two major conjectures, such as connected domination and induced outerplanar subgraphs, etc., and survey the associated conjectures. We then provide counterexamples to some of these conjectures and establish the best bounds on the gap between the maximum orders of induced subgraphs under different structural conditions. In addition, we present a general upper bound on the order of induced subgraphs in terms of treewidth, a fundamental graph invariant.
Medical image segmentation is crucial for clinical diagnosis, yet existing models are limited by their reliance on explicit human instructions and lack the active reasoning capabilities to understand complex clinical questions. While recent advancements in multimodal large language models (MLLMs) have improved medical question-answering (QA) tasks, most methods struggle to generate precise segmentation masks, limiting their application in automatic medical diagnosis. In this paper, we introduce medical image reasoning segmentation, a novel task that aims to generate segmentation masks based on complex and implicit medical instructions. To address this, we propose MedSeg-R, an end-to-end framework that leverages the reasoning abilities of MLLMs to interpret clinical questions while also capable of producing corresponding precise segmentation masks for medical images. It is built on two core components: 1) a global context understanding module that interprets images and comprehends complex medical instructions to generate multi-modal intermediate tokens, and 2) a pixel-level grounding module that decodes these tokens to produce precise segmentation masks and textual responses. Furthermore, we introduce MedSeg-QA, a large-scale dataset tailored for the medical image reasoning segmentation task. It includes over 10,000 image-mask pairs and multi-turn conversations, automatically annotated using large language models and refined through physician reviews. Experiments show MedSeg-R's superior performance across several benchmarks, achieving high segmentation accuracy and enabling interpretable textual analysis of medical images.
On the Mass Budget Problem of Protoplanetary Disks: Streaming Instability and Optically Thick Emission
2025-06-12
Daniel Godines, Wladimir Lyra, Luca Ricci, Chao-Chin Yang, Jacob B. Simon, Jeonghoon Lim, Daniel Carrera
Statistical studies of protoplanetary disks and exoplanet populations often exhibit a "missing mass" problem, where observed dust masses in (sub-)millimeter surveys are significantly lower than expected when compared to the mass of evolved exoplanetary systems. We investigate how the streaming instability and subsequent planetesimal formation in protoplanetary disks might solve this missing mass problem when (sub-)millimeter observations are interpreted under the assumption of optically thin emission. We conduct hydrodynamical simulations of the streaming instability with self-gravity after which radiative transfer calculations with dust scattering are performed to measure the (sub-)millimeter intensity. The measured intensity is then used to estimate the disk mass under the assumption of optically thin emission and compared to the true mass in the simulation to calculate the observational bias via the mass excess. We find that the emission from overdense filaments that emerge due to the streaming instability are optically thick at (sub-)millimeter wavelengths, leading to mass excess factors of $\sim 2-7$, even when the optically thick fraction is low.
A Hybrid Heuristic Framework for Resource-Efficient Querying of Scientific Experiments Data
2025-06-12
Mayank Patel, Minal Bhise
Scientific experiments and modern applications are generating large amounts of data every day. Most organizations utilize In-house servers or Cloud resources to manage application data and workload. The traditional database management system (DBMS) and HTAP systems spend significant time & resources to load the entire dataset into DBMS before starting query execution. On the other hand, in-situ engines may reparse required data multiple times, increasing resource utilization and data processing costs. Additionally, over or under-allocation of resources also increases application running costs. This paper proposes a lightweight Resource Availability &Workload aware Hybrid Framework (RAW-HF) to optimize querying raw data by utilizing existing finite resources efficiently. RAW-HF includes modules that help optimize the resources required to execute a given workload and maximize the utilization of existing resources. The impact of applying RAW-HF to real-world scientific dataset workloads like Sloan Digital Sky Survey (SDSS) and Linked Observation Data (LOD) presented over 90% and 85% reduction in workload execution time (WET) compared to widely used traditional DBMS PostgreSQL. The overall CPU, IO resource utilization, and WET have been reduced by 26%, 25%, and 26%, respectively, while improving memory utilization by 33%, compared to the state-of-the-art workload-aware partial loading technique (WA) proposed for hybrid systems. A comparison of MUAR technique used by RAW-HF with machine learning based resource allocation techniques like PCC is also presented.
Time series data in real-world applications such as healthcare, climate modeling, and finance are often irregular, multimodal, and messy, with varying sampling rates, asynchronous modalities, and pervasive missingness. However, existing benchmarks typically assume clean, regularly sampled, unimodal data, creating a significant gap between research and real-world deployment. We introduce Time-IMM, a dataset specifically designed to capture cause-driven irregularity in multimodal multivariate time series. Time-IMM represents nine distinct types of time series irregularity, categorized into trigger-based, constraint-based, and artifact-based mechanisms. Complementing the dataset, we introduce IMM-TSF, a benchmark library for forecasting on irregular multimodal time series, enabling asynchronous integration and realistic evaluation. IMM-TSF includes specialized fusion modules, including a timestamp-to-text fusion module and a multimodality fusion module, which support both recency-aware averaging and attention-based integration strategies. Empirical results demonstrate that explicitly modeling multimodality on irregular time series data leads to substantial gains in forecasting performance. Time-IMM and IMM-TSF provide a foundation for advancing time series analysis under real-world conditions. The dataset is publicly available at https://www.kaggle.com/datasets/blacksnail789521/time-imm/data, and the benchmark library can be accessed at https://anonymous.4open.science/r/IMMTSF_NeurIPS2025.
Runaway electron-induced plasma facing component damage in tokamaks
2025-06-12
S. Ratynskaia, M. Hoelzl, E. Nardon, P. Aleynikov, F. J. Artola, V. Bandaru, M. Beidler, B. Breizman, D. del-Castillo-Negrete, M. De Angeli, V. Dimitriou, R. Ding, J. Eriksson, O. Ficker, R. S. Granetz, E. Hollmann, M. Hoppe, M. Houry, I. Jepu, H. R. Koslowski, C. Liu, J. R. Martin-Solis, G. Pautasso, Y. Peneliau, R. A. Pitts, G. I. Pokol, C. Reux, U. Sheikh, S. A. Silburn, T. Tang, R. A. Tinguely, P. Tolias, E. Tomesova, R. Villari
This Roadmap article addresses the critical and multifaceted challenge of plasma-facing component (PFC) damage caused by runaway electrons (REs) in tokamaks, a phenomenon that poses a significant threat to the viability and longevity of future fusion reactors such as ITER and DEMO. The dramatically increased RE production expected in future high-current tokamaks makes it difficult to avoid or mitigate REs when a plasma discharge terminates abnormally. Preventing damage from the intense localised heat loads REs can cause requires a holistic approach that considers plasma, REs and PFC damage. Despite decades of progress in understanding the physics of REs and the thermomechanical response of PFCs, their complex interplay remains poorly understood. This document aims to initiate a coordinated, interdisciplinary approach to bridge this gap by reviewing experimental evidence, advancing diagnostic capabilities, and improving modelling tools across different scales, dimensionalities and fidelities. Key topics include RE beam formation and transport, damage mechanisms in brittle and metallic PFCs, and observations in major facilities such as JET, DIII-D, WEST and EAST. The Roadmap emphasises the urgency of predictive, high-fidelity modelling validated against well-diagnosed controlled experiments, particularly in the light of recent changes in ITER's wall material strategy and the growing importance of private sector initiatives. Each section of the article is written to provide a concise overview of one area of this multidisciplinary subject, with an assessment of the status, a look at current and future challenges, and a brief summary. The ultimate goal of this initiative is to guide future mitigation strategies and design resilient components that can withstand the loads imposed by REs, thus ensuring the safe and sustainable operation of the next generation of fusion power plants.
Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges
2025-06-12
Jintao Liang, Gang Su, Huifeng Lin, You Wu, Rui Zhao, Ziyue Li
Retrieval-Augmented Generation (RAG) has emerged as a powerful framework to overcome the knowledge limitations of Large Language Models (LLMs) by integrating external retrieval with language generation. While early RAG systems based on static pipelines have shown effectiveness in well-structured tasks, they struggle in real-world scenarios requiring complex reasoning, dynamic retrieval, and multi-modal integration. To address these challenges, the field has shifted toward Reasoning Agentic RAG, a paradigm that embeds decision-making and adaptive tool use directly into the retrieval process. In this paper, we present a comprehensive review of Reasoning Agentic RAG methods, categorizing them into two primary systems: predefined reasoning, which follows fixed modular pipelines to boost reasoning, and agentic reasoning, where the model autonomously orchestrates tool interaction during inference. We analyze representative techniques under both paradigms, covering architectural design, reasoning strategies, and tool coordination. Finally, we discuss key research challenges and propose future directions to advance the flexibility, robustness, and applicability of reasoning agentic RAG systems. Our collection of the relevant research has been organized into a https://github.com/ByebyeMonica/Reasoning-Agentic-RAG.
Bug Classification in Quantum Software: A Rule-Based Framework and Its Evaluation
2025-06-12
Mir Mohammad Yousuf, Shabir Ahmad Sofi
Accurate classification of software bugs is essential for improving software quality. This paper presents a rule-based automated framework for classifying issues in quantum software repositories by bug type, category, severity, and impacted quality attributes, with additional focus on quantum-specific bug types. The framework applies keyword and heuristic-based techniques tailored to quantum computing. To assess its reliability, we manually classified a stratified sample of 4,984 issues from a dataset of 12,910 issues across 36 Qiskit repositories. Automated classifications were compared with ground truth using accuracy, precision, recall, and F1-score. The framework achieved up to 85.21% accuracy, with F1-scores ranging from 0.7075 (severity) to 0.8393 (quality attribute). Statistical validation via paired t-tests and Cohen's Kappa showed substantial to almost perfect agreement for bug type (k = 0.696), category (k = 0.826), quality attribute (k = 0.818), and quantum-specific bug type (k = 0.712). Severity classification showed slight agreement (k = 0.162), suggesting room for improvement. Large-scale analysis revealed that classical bugs dominate (67.2%), with quantum-specific bugs at 27.3%. Frequent bug categories included compatibility, functional, and quantum-specific defects, while usability, maintainability, and interoperability were the most impacted quality attributes. Most issues (93.7%) were low severity; only 4.3% were critical. A detailed review of 1,550 quantum-specific bugs showed that over half involved quantum circuit-level problems, followed by gate errors and hardware-related issues.
We state necessary and sufficient conditions for the existence of $T$-discrete exponential attractors for semigroups in complete metric spaces. These conditions are formulated in terms of a covering condition for iterates of the absorbing set under the time evolution of the semigroup and imply the existence and finite-dimensionality of the global attractor. We then review, generalize and compare existing construction methods for exponential attractors and show that they all imply the covering condition. Furthermore, we relate the results and concept of $T$-discrete exponential attractors to the classical notion of exponential attractors.
TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning
2025-06-12
Xiaohan Yu, Pu Jian, Chong Chen
Retrieval-Augmented Generation (RAG) has demonstrated considerable effectiveness in open-domain question answering. However, when applied to heterogeneous documents, comprising both textual and tabular components, existing RAG approaches exhibit critical limitations. The prevailing practice of flattening tables and chunking strategies disrupts the intrinsic tabular structure, leads to information loss, and undermines the reasoning capabilities of LLMs in multi-hop, global queries. To address these challenges, we propose TableRAG, an hybrid framework that unifies textual understanding and complex manipulations over tabular data. TableRAG iteratively operates in four steps: context-sensitive query decomposition, text retrieval, SQL programming and execution, and compositional intermediate answer generation. We also develop HeteQA, a novel benchmark designed to evaluate the multi-hop heterogeneous reasoning capabilities. Experimental results demonstrate that TableRAG consistently outperforms existing baselines on both public datasets and our HeteQA, establishing a new state-of-the-art for heterogeneous document question answering. We release TableRAG at https://github.com/yxh-y/TableRAG/tree/main.
Automatic Speech Recognition systems have made significant progress with large-scale pre-trained models. However, most current systems focus solely on transcribing the speech without identifying speaker roles, a function that is critical for conversational AI. In this work, we investigate the use of serialized output training (SOT) for joint ASR and speaker role tagging. By augmenting Whisper with role-specific tokens and fine-tuning it with SOT, we enable the model to generate role-aware transcriptions in a single decoding pass. We compare the SOT approach against a self-supervised previous baseline method on two real-world conversational datasets. Our findings show that this approach achieves more than 10% reduction in multi-talker WER, demonstrating its feasibility as a unified model for speaker-role aware speech transcription.
An Analysis of Datasets, Metrics and Models in Keyphrase Generation
2025-06-12
Florian Boudin, Akiko Aizawa
Keyphrase generation refers to the task of producing a set of words or phrases that summarises the content of a document. Continuous efforts have been dedicated to this task over the past few years, spreading across multiple lines of research, such as model architectures, data resources, and use-case scenarios. Yet, the current state of keyphrase generation remains unknown as there has been no attempt to review and analyse previous work. In this paper, we bridge this gap by presenting an analysis of over 50 research papers on keyphrase generation, offering a comprehensive overview of recent progress, limitations, and open challenges. Our findings highlight several critical issues in current evaluation practices, such as the concerning similarity among commonly-used benchmark datasets and inconsistencies in metric calculations leading to overestimated performances. Additionally, we address the limited availability of pre-trained models by releasing a strong PLM-based model for keyphrase generation as an effort to facilitate future research.
A Comprehensive Survey of Unmanned Aerial Systems' Risks and Mitigation Strategies
2025-06-12
Sharad Shrestha, Mohammed Ababneh, Satyajayant Misra, Henry M. Cathey, Jr., Roopa Vishwanathan, Matt Jansen, Jinhong Choi, Rakesh Bobba, Yeongjin Jang
In the last decade, the rapid growth of Unmanned Aircraft Systems (UAS) and Unmanned Aircraft Vehicles (UAV) in communication, defense, and transportation has increased. The application of UAS will continue to increase rapidly. This has led researchers to examine security vulnerabilities in various facets of UAS infrastructure and UAVs, which form a part of the UAS system to reinforce these critical systems. This survey summarizes the cybersecurity vulnerabilities in several phases of UAV deployment, the likelihood of each vulnerability's occurrence, the impact of attacks, and mitigation strategies that could be applied. We go beyond the state-of-the-art by taking a comprehensive approach to enhancing UAS security by performing an analysis of both UAS-specific and non-UAS-specific mitigation strategies that are applicable within the UAS domain to define the lessons learned. We also present relevant cybersecurity standards and their recommendations in the UAS context. Despite the significant literature in UAS security and the relevance of cyberphysical and networked systems security approaches from the past, which we identify in the survey, we find several critical research gaps that require further investigation. These form part of our discussions and recommendations for the future exploration by our research community.
Software vulnerabilities in source code pose serious cybersecurity risks, prompting a shift from traditional detection methods (e.g., static analysis, rule-based matching) to AI-driven approaches. This study presents a systematic review of software vulnerability detection (SVD) research from 2018 to 2023, offering a comprehensive taxonomy of techniques, feature representations, and embedding methods. Our analysis reveals that 91% of studies use AI-based methods, with graph-based models being the most prevalent. We identify key limitations, including dataset quality, reproducibility, and interpretability, and highlight emerging opportunities in underexplored techniques such as federated learning and quantum neural networks, providing a roadmap for future research.
VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning
2025-06-12
Jun Qi, Chao-Han Yang, Pin-Yu Chen, Min-Hsiu Hsieh
Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning, yet their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges, and acute sensitivity to quantum hardware noise. This work introduces VQC-MLPNet, a scalable and robust hybrid quantum-classical architecture designed to overcome these obstacles. By innovatively employing quantum circuits to dynamically generate parameters for classical Multi-Layer Perceptrons (MLPs) via amplitude encoding and parameterized quantum operations, VQC-MLPNet substantially expands representation capabilities and augments training stability. We provide rigorous theoretical guarantees via statistical learning techniques and Neural Tangent Kernel analysis, explicitly deriving upper bounds on approximation, uniform deviation, and optimization errors. These theoretical insights demonstrate exponential improvements in representation capacity relative to quantum circuit depth and the number of qubits, providing clear computational advantages over standalone quantum circuits and existing hybrid quantum architectures. Our theoretical claims are empirically corroborated through extensive experiments, including classifying semiconductor quantum-dot charge states and predicting genomic transcription factor binding sites, demonstrating resilient performance even under realistic IBM quantum noise simulations. This research establishes a theoretically sound and practically robust framework, advancing the frontiers of quantum-enhanced learning for unconventional computing paradigms in the Noisy Intermediate-Scale Quantum era and beyond.
2025-06-12
Pooneh Mousavi, Gallil Maimon, Adel Moumen, Darius Petermann, Jiatong Shi, Haibin Wu, Haici Yang, Anastasia Kuznetsova, Artem Ploujnikov, Ricard Marxer, Bhuvana Ramabhadran, Benjamin Elizalde, Loren Lugosch, Jinyu Li, Cem Subakan, Phil Woodland, Minje Kim, Hung-yi Lee, Shinji Watanabe, Yossi Adi, Mirco Ravanelli
Discrete audio tokens are compact representations that aim to preserve perceptual quality, phonetic content, and speaker characteristics while enabling efficient storage and inference, as well as competitive performance across diverse downstream tasks.They provide a practical alternative to continuous features, enabling the integration of speech and audio into modern large language models (LLMs). As interest in token-based audio processing grows, various tokenization methods have emerged, and several surveys have reviewed the latest progress in the field. However, existing studies often focus on specific domains or tasks and lack a unified comparison across various benchmarks. This paper presents a systematic review and benchmark of discrete audio tokenizers, covering three domains: speech, music, and general audio. We propose a taxonomy of tokenization approaches based on encoder-decoder, quantization techniques, training paradigm, streamability, and application domains. We evaluate tokenizers on multiple benchmarks for reconstruction, downstream performance, and acoustic language modeling, and analyze trade-offs through controlled ablation studies. Our findings highlight key limitations, practical considerations, and open challenges, providing insight and guidance for future research in this rapidly evolving area. For more information, including our main results and tokenizer database, please refer to our website: https://poonehmousavi.github.io/dates-website/.
Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification
2025-06-12
Ryota Ueda, Takami Sato, Ken Kobayashi, Kazuhide Nakata
Semidefinite programming (SDP) relaxation has emerged as a promising approach for neural network verification, offering tighter bounds than other convex relaxation methods for deep neural networks (DNNs) with ReLU activations. However, we identify a critical limitation in the SDP relaxation when applied to deep networks: interior-point vanishing, which leads to the loss of strict feasibility -- a crucial condition for the numerical stability and optimality of SDP. Through rigorous theoretical and empirical analysis, we demonstrate that as the depth of DNNs increases, the strict feasibility is likely to be lost, creating a fundamental barrier to scaling SDP-based verification. To address the interior-point vanishing, we design and investigate five solutions to enhance the feasibility conditions of the verification problem. Our methods can successfully solve 88% of the problems that could not be solved by existing methods, accounting for 41% of the total. Our analysis also reveals that the valid constraints for the lower and upper bounds for each ReLU unit are traditionally inherited from prior work without solid reasons, but are actually not only unbeneficial but also even harmful to the problem's feasibility. This work provides valuable insights into the fundamental challenges of SDP-based DNN verification and offers practical solutions to improve its applicability to deeper neural networks, contributing to the development of more reliable and secure systems with DNNs.
From Replication to Redesign: Exploring Pairwise Comparisons for LLM-Based Peer Review
2025-06-12
Yaohui Zhang, Haijing Zhang, Wenlong Ji, Tianyu Hua, Nick Haber, Hancheng Cao, Weixin Liang
The advent of large language models (LLMs) offers unprecedented opportunities to reimagine peer review beyond the constraints of traditional workflows. Despite these opportunities, prior efforts have largely focused on replicating traditional review workflows with LLMs serving as direct substitutes for human reviewers, while limited attention has been given to exploring new paradigms that fundamentally rethink how LLMs can participate in the academic review process. In this paper, we introduce and explore a novel mechanism that employs LLM agents to perform pairwise comparisons among manuscripts instead of individual scoring. By aggregating outcomes from substantial pairwise evaluations, this approach enables a more accurate and robust measure of relative manuscript quality. Our experiments demonstrate that this comparative approach significantly outperforms traditional rating-based methods in identifying high-impact papers. However, our analysis also reveals emergent biases in the selection process, notably a reduced novelty in research topics and an increased institutional imbalance. These findings highlight both the transformative potential of rethinking peer review with LLMs and critical challenges that future systems must address to ensure equity and diversity.
2025-06-12
Yiyu Ni, Marine A. Denolle, Jannes Munchmeyer, Yinzhi Wang, Kuan-Fu Feng, Carlos Garcia Jurado Suarez, Amanda M. Thomas, Chad Trabant, Alex Hamilton, David Mencin
Seismology has entered the petabyte era, driven by decades of continuous recordings of broadband networks, the increase in nodal seismic experiments, and the recent emergence of Distributed Acoustic Sensing (DAS). This review explains how commercial clouds - AWS, Google Cloud, and Azure - by providing object storage, elastic compute, and managed databases, enable researchers to "bring the code to the data," thereby overcoming traditional HPC solutions' bandwidth and capacity limitations. After literature reviews of cloud concepts and their research applications in seismology, we illustrate the capacities of cloud-native workflows using two canonical end-to-end demonstrations: 1) ambient noise seismology and cross-correlation, and 2) earthquake detection, discrimination, and phase picking. Both workflows utilized S3 for streaming I/O and DocumentDB for provenance, demonstrating that cloud throughput can rival on-premises HPC at comparable costs, scanning 100 TBs to 1.3 PBs of seismic data in a few hours or days of processing. The review also discusses research and education initiatives, the reproducibility benefits of containers, and cost pitfalls (e.g., egress, I/O fees) of energy-intensive seismological research computing. While designing cloud pipelines remains non-trivial, partnerships with research software engineers enable converting domain code into scalable, automated, and environmentally conscious solutions for next-generation seismology.
Semi-empirical constraints on the HI mass function of star-forming galaxies and $Ω_{\rm HI}$ at $z\sim 0.37$ from interferometric surveys
2025-06-12
Francesco Sinigaglia, Alessandro Bianchetti, Giulia Rodighiero, Lucio Mayer, Miroslava Dessauges-Zavadsky, Ed Elson, Mattia Vaccari, Matt J. Jarvis
The HI mass function is a crucial tool to understand the evolution of the HI content in galaxies over cosmic times and to constrain both the baryon cycle in galaxy evolution and the reionization history of the Universe. We aim to derive semi-empirical constraints at $z\sim 0.37$ by combining literature results on the stellar mass function from optical surveys with recent findings on the $M_{\rm HI}-M_\star$ scaling relation derived via spectral stacking analysis applied to 21-cm line interferometric data from the MIGHTEE and CHILES surveys, conducted with MeerKAT and the VLA, respectively. We draw synthetic stellar mass samples directly from the publicly-available results underlying the analysis of the COSMOS2020 galaxy photometric sample. Afterwards, we convert $M_\star$ into $M_{\rm HI}$ using analytical fitting functions to the data points from HI stacking. We then fit a Schechter function to the median HIMF from all the samples via MCMC. We finally derive the posterior distribution for $\Omega_{\rm HI}$ by integrating the models for the HIMF built from the posteriors samples of the Schechter parameters. We find evolution of the HIMF at $z\sim 0.37$ with respect to results at $z\sim 0$ from the ALFALFA survey and at $z\sim 1$ from uGMRT data. Our results for $\Omega_{\rm HI}$ are in broad agreement with other literature results, and follow the trend on $\Omega_{\rm HI}$ as a function of redshift. The derived value $\Omega_{\rm HI}=\left(7.02^{+0.59}_{-0.52}\right)\times10^{-4}$ at $z\sim 0.37$ from the combined analysis deviates at $ \sim 2.9\sigma$ from the ALFALFA result at $z\sim 0$. We conclude that the HIMF and $\Omega_{\rm HI}$ constraints that we derive from state-of-the-art deep interferometric surveys suggest an evolution of the HIMF and of the cosmic HI density, supporting a picture of smooth transition of the HI content of galaxies from $z\sim 0$ to $z\sim 1$.
Discovery of an HI 21 cm absorption system at z=2.327 with CHIME
2025-06-12
CHIME Collaboration, Mandana Amiri, Arnab Chakraborty, Simon Foreman, Mark Halpern, Alex S. Hill, Gary Hinshaw, Carolin Hofer, Albin Joseph, Joshua MacEachern, Kiyoshi W. Masui, Juan Mena-Parra, Arash Mirhosseini, Ue-Li Pen, Tristan Pinsonneault-Marotte, Alex Reda, J. Richard Shaw, Seth R. Siegel, Yukari Uchibori, Rik van Lieshout, Haochen Wang, Dallas Wulf
We report the detection of a new HI absorption system associated with the radio source NVSS J164725+375218 at a redshift of z=2.327, identified through a blind survey conducted by the Canadian Hydrogen Intensity Mapping Experiment (CHIME). This marks the fifth detection of an associated system at z > 2. By analyzing a subset of available data, we conducted a blind survey for HI absorption systems within the redshift range of 0.78 to 2.55 along 202 lines of sight in the declination range of 35 to 60 degrees. We detected three HI absorbers, including two previously known intervening systems and one newly discovered associated system. By fitting the absorption profile with a number of Gaussian functions, we put lower limits on the optical depth, velocity integrated optical depth and the ratio between the HI column density and the spin temperature of the absorption systems. These results represent a validation of CHIME's absorber search strategy, and demonstrate CHIME's ability to discover new absorbers even in small subsets of its full dataset.
2025-06-12
Tatiana Chakravorti, Xinyu Wang, Pranav Narayanan Venkit, Sai Koneru, Kevin Munger, Sarah Rajtmajer
The integration of artificial intelligence (AI) into social science research practices raises significant technological, methodological, and ethical issues. We present a community-centric study drawing on 284 survey responses and 15 semi-structured interviews with social scientists, describing their familiarity with, perceptions of the usefulness of, and ethical concerns about the use of AI in their field. A crucial innovation in study design is to split our survey sample in half, providing the same questions to each -- but randomizing whether participants were asked about "AI" or "Machine Learning" (ML). We find that the use of AI in research settings has increased significantly among social scientists in step with the widespread popularity of generative AI (genAI). These tools have been used for a range of tasks, from summarizing literature reviews to drafting research papers. Some respondents used these tools out of curiosity but were dissatisfied with the results, while others have now integrated them into their typical workflows. Participants, however, also reported concerns with the use of AI in research contexts. This is a departure from more traditional ML algorithms which they view as statistically grounded. Participants express greater trust in ML, citing its relative transparency compared to black-box genAI systems. Ethical concerns, particularly around automation bias, deskilling, research misconduct, complex interpretability, and representational harm, are raised in relation to genAI. To guide this transition, we offer recommendations for AI developers, researchers, educators, and policymakers focusing on explainability, transparency, ethical safeguards, sustainability, and the integration of lived experiences into AI design and evaluation processes.
Can Time-Series Foundation Models Perform Building Energy Management Tasks?
2025-06-12
Ozan Baris Mulayim, Pengrui Quan, Liying Han, Xiaomin Ouyang, Dezhi Hong, Mario Bergés, Mani Srivastava
Building energy management (BEM) tasks require processing and learning from a variety of time-series data. Existing solutions rely on bespoke task- and data-specific models to perform these tasks, limiting their broader applicability. Inspired by the transformative success of Large Language Models (LLMs), Time-Series Foundation Models (TSFMs), trained on diverse datasets, have the potential to change this. Were TSFMs to achieve a level of generalizability across tasks and contexts akin to LLMs, they could fundamentally address the scalability challenges pervasive in BEM. To understand where they stand today, we evaluate TSFMs across four dimensions: (1) generalizability in zero-shot univariate forecasting, (2) forecasting with covariates for thermal behavior modeling, (3) zero-shot representation learning for classification tasks, and (4) robustness to performance metrics and varying operational conditions. Our results reveal that TSFMs exhibit \emph{limited} generalizability, performing only marginally better than statistical models on unseen datasets and modalities for univariate forecasting. Similarly, inclusion of covariates in TSFMs does not yield performance improvements, and their performance remains inferior to conventional models that utilize covariates. While TSFMs generate effective zero-shot representations for downstream classification tasks, they may remain inferior to statistical models in forecasting when statistical models perform test-time fitting. Moreover, TSFMs forecasting performance is sensitive to evaluation metrics, and they struggle in more complex building environments compared to statistical models. These findings underscore the need for targeted advancements in TSFM design, particularly their handling of covariates and incorporating context and temporal dynamics into prediction mechanisms, to develop more adaptable and scalable solutions for BEM.
Tunable Photodetectors Based on 2D Hybrid Structures from Transition Metal Dichalcogenides and Photochromic Molecules
2025-06-12
Sewon Park, Jaehoon Ji, Joakim Andreasson, Jeong Ho You, Jong Hyun Choi
This article reviews recent progress in two-dimensional (2D) hybrid structures that integrate transition metal dichalcogenides (TMDs) with photochromic molecules for photodetector applications. Atomically thin TMD semiconductors offer strong light-matter interaction, tunable bandgaps, and efficient carrier transport, making them suitable for photodetectors. Photochromic molecules, capable of reversible structural changes in response to external light, can modulate their chemical, optical, and electronic properties. The combinations of various light-adaptive compounds and TMDs provide a versatile platform to explore optically programmable behaviors such as wavelength-selective photoresponse, nonvolatile switching, and multilevel memory characteristics. TMD/photochromic hybrids offer new functional capabilities that are difficult to achieve with either component alone. This mini-review summarizes material properties, interfacial integration strategies, working mechanisms, and representative device demonstrations. We conclude by highlighting future research directions toward the practical implementation of photoresponsive hybrid systems in high-performance adaptive optoelectronics.
User Perceptions and Attitudes Toward Untraceability in Messaging Platforms
2025-06-12
Carla F. Griggio, Boel Nelson, Zefan Sramek, Aslan Askarov
Mainstream messaging platforms offer a variety of features designed to enhance user privacy, such as disappearing messages, password-protected chats, and end-to-end encryption (E2EE), which primarily protect message contents. Beyond contents, the transmission of messages generates metadata that can reveal who communicates with whom, when and how often. In this paper, we study user perceptions of "untraceability", i.e., preventing third parties from tracing who communicates with whom, with the goal of informing the design of privacy-enhancing features in messaging platforms and untraceable communication protocols that depend on large anonymity sets and widespread user adoption. We explore this from a broad conceptual standpoint: rather than studying mental models of a particular solution, we analyze how users reason about what features should be incorporated by two fictitious platforms, Texty and Chatty, to prevent third parties from knowing who communicates with whom. Through a vignette-based survey with 189 participants, we found that users associate the concept of untraceability with a wide range of privacy enhancing technologies, implying a diverse set of threat models. Overall, the features suggested by participants show awareness of privacy threats stemming from forms of surveillance and unauthorized access to message contents. Many participants also associated untraceability with the notion of anonymity, but interpreted it as senders and receivers concealing their identity from each other rather than only from third parties. We discuss the gap between users' perceptions of untraceability and the threat models addressed by untraceable communication protocols, as well as how different privacy attitudes point to challenges and opportunities for the adoption of untraceable communication tools in messaging platforms.
Modeling Gravitational Wave Bias from 3D Power Spectra of Spectroscopic Surveys
2025-06-12
Dorsa Sadat Hosseini, Amir Dehghani, J. Leo Kim, Alex Krolewski, Suvodip Mukherjee, Ghazal Geshnizjani
We present a framework for relating gravitational wave (GW) sources to the astrophysical properties of spectroscopic galaxy samples. We show how this can enable using clustering measurements of gravitational wave (GW) sources to infer the relationship between the GW sources and the astrophysical properties of their host galaxies. We accomplish this by creating mock GW catalogs from the spectroscopic Sloan Digital Sky Survey (SDSS) DR7 galaxy survey. We populate the GWs using a joint host-galaxy probability function defined over stellar mass, star formation rate (SFR), and metallicity. This probability is modeled as the product of three broken power-law distributions, each with a turnover point motivated by astrophysical processes governing the relation between current-day galaxy properties and BBH mergers, such as galaxy quenching and BBH delay time. Our results show that GW bias is most sensitive to host-galaxy probability dependence on stellar mass, with increases of up to $\sim O (10)\%$ relative to galaxy bias as the stellar mass pivot scale rises. We also find a notable relationship between GW bias and SFR: when the host-galaxy probability favors low-SFR galaxies, the GW bias significantly increases. In contrast, we observe no strong correlation between GW bias and metallicity. These findings suggest that the spatial clustering of GW sources is primarily driven by the stellar mass and SFR of their host galaxies and shows how GW bias measurements can inform models of the host-galaxy probability function.
DBNets2.0: simulation-based inference for planet-induced dust substructures in protoplanetary discs
2025-06-12
A. Ruzza, G. Lodato, G. P. Rosotti, P. J. Armitage
Dust substructures in protoplanetary discs can be signatures of embedded young planets whose detection and characterisation would provide a better understanding of planet formation. Traditional techniques used to link substructures' morphology to the properties of putative embedded planets present several limitations that the use of deep learning methods has partly overcome. In our previous work, we developed DBNets, a tool exploiting an ensemble of Convolutional Neural Networks (CNNs) to estimate the mass of putative planets in disc dust substructures. This inference problem, however, is degenerate as planets of different masses could produce the same rings and gaps if other physical disc properties were different. In this paper, we address this issue improving our simulation-based inference pipeline to estimate the full posterior distribution for the planet mass and three additional disc properties: the disc $\alpha$-viscosity, the scale height and the dust Stokes number. We also address some minor issues of our previous tool. The new pipeline involves a CNN that summarises the input images in a set of summary statistics, followed by an ensemble of normalising flows that model the inferred posterior for the target properties. We tested our pipeline on a dedicated set of synthetic observations using the TARP test and standard metrics, demonstrating its accuracy and precision. Additionally, we use the results obtained on the test set to study the degeneracies between pairs of parameters. Finally, we apply the developed pipeline to a set of 49 gaps in 34 protoplanetary discs' continuum observations. The results show typically low values of $\alpha$-viscosity, disc scale heights, and planet masses, with 83% of them being lower than 1M$_J$. These low masses are consistent with the non-detections of these putative planets in direct imaging surveys. Our tool is publicly available.
Patchy Helium and Hydrogen Reionization from the Kinetic Sunyaev-Zel'dovich Effect and Galaxies
2025-06-12
Neha Anil Kumar, Mesut Çalışkan, Selim C. Hotinli, Marc Kamionkowski, Simone Ferraro, Kendrick Smith
Upcoming cosmic microwave background (CMB) experiments will measure temperature fluctuations on small angular scales with unprecedented precision, enabling improved measurements of the kinetic Sunyaev-Zel'dovich (kSZ) effect. This secondary anisotropy has emerged as a valuable probe of the distribution of ionized electrons in the post-recombination Universe. Although the sensitivity of the kSZ effect has recently been utilized to study the high-redshift epoch of hydrogen (H) reionization, its redshift-integrated nature -- combined with anticipated improvements in measurement precision -- suggests that accounting for the later epoch of helium (He) reionization will become increasingly important in the near future. Joint characterization of the epochs will allow for a more coherent understanding of early-star and -quasar formation, as these sources drive the ionization of H and He in the intergalactic medium. In this paper, we extend the kSZ higher-order statistic introduced by Smith \& Ferraro (2017) to forecast the ability of upcoming CMB surveys to probe the morphology of both H and He reionization. Moreover, given that upcoming large-scale structure surveys will trace density fluctuations at redshifts overlapping with the epoch of He reionization, we propose a novel cross-correlation between the kSZ higher-order statistic and galaxy survey measurements. Using a joint information-matrix analysis of H and He reionization, we show that next-generation CMB and galaxy surveys will have sufficient statistical power to characterize the patchy morphology of H reionization and set constraints on the redshift evolution of its He counterpart.
Multivariate time series data, collected across various fields such as manufacturing and wearable technology, exhibit states at multiple levels of granularity, from coarse-grained system behaviors to fine-grained, detailed events. Effectively segmenting and integrating states across these different granularities is crucial for tasks like predictive maintenance and performance optimization. However, existing time series segmentation methods face two key challenges: (1) the inability to handle multiple levels of granularity within a unified model, and (2) limited adaptability to new, evolving patterns in dynamic environments. To address these challenges, we propose PromptTSS, a novel framework for time series segmentation with multi-granularity states. PromptTSS uses a unified model with a prompting mechanism that leverages label and boundary information to guide segmentation, capturing both coarse- and fine-grained patterns while adapting dynamically to unseen patterns. Experiments show PromptTSS improves accuracy by 24.49% in multi-granularity segmentation, 17.88% in single-granularity segmentation, and up to 599.24% in transfer learning, demonstrating its adaptability to hierarchical states and evolving time series dynamics.
Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and Algorithms
2025-06-12
Soumen Garai, Suman Samui
Small-Footprint Keyword Spotting (SF-KWS) has gained popularity in today's landscape of smart voice-activated devices, smartphones, and Internet of Things (IoT) applications. This surge is attributed to the advancements in Deep Learning, enabling the identification of predefined words or keywords from a continuous stream of words. To implement the SF-KWS model on edge devices with low power and limited memory in real-world scenarios, a efficient Tiny Machine Learning (TinyML) framework is essential. In this study, we explore seven distinct categories of techniques namely, Model Architecture, Learning Techniques, Model Compression, Attention Awareness Architecture, Feature Optimization, Neural Network Search, and Hybrid Approaches, which are suitable for developing an SF-KWS system. This comprehensive overview will serve as a valuable resource for those looking to understand, utilize, or contribute to the field of SF-KWS. The analysis conducted in this work enables the identification of numerous potential research directions, encompassing insights from automatic speech recognition research and those specifically pertinent to the realm of spoken SF-KWS.
Quantum Computing and Cybersecurity in Accounting and Finance: Current and the Future Challenges and Opportunities for Securing Accounting and Finance Systems
2025-06-12
Huma Habib Shadan, Sardar Islam
Quantum computing is revolutionising information systems and will have a significant impact on accounting and finance, especially in the area of cybersecurity. It presents both opportunities and risks in ensuring confidentiality and protecting financial data. The purpose of this thesis is to show the application of quantum technologies in accounting cybersecurity, utilising quantum algorithms and QKD to overcome the limitations of classical computing. The literature review reveals the vulnerabilities of the current accounting cybersecurity to quantum attacks and the need for quantum-resistant cryptographic mechanisms. It elaborates on the risks associated with conventional encryption in the context of quantum capabilities. This study contributes to the understanding of how quantum computing can revolutionise accounting cybersecurity by enhancing quantum-resistant algorithms and utilising quantum key distribution (QKD) in accounting. The study employs PSALSAR systematic review methodology to ensure rigour and depth. The analysis shows that quantum computing enhances encryption techniques to superior possibilities than classical ones. Using quantum technologies in accounting minimises data breaches and unauthorised access. The study concludes that quantum-resistant algorithms and quantum key distribution (QKD) are necessary for securing the accounting and finance systems of the future. Keywords Quantum Computing, Cybersecurity, Accounting, Machine Learning, Artificial Intelligence, Quantum Key Distribution, Operations Management
2025-06-11
Ziyi Wang, Yongming Rao, Shuofeng Sun, Xinrun Liu, Yi Wei, Xumin Yu, Zuyan Liu, Yanbo Wang, Hongmin Liu, Jie Zhou, Jiwen Lu
Recently, we have witnessed the great success of the generalist model in natural language processing. The generalist model is a general framework trained with massive data and is able to process various downstream tasks simultaneously. Encouraged by their impressive performance, an increasing number of researchers are venturing into the realm of applying these models to computer vision tasks. However, the inputs and outputs of vision tasks are more diverse, and it is difficult to summarize them as a unified representation. In this paper, we provide a comprehensive overview of the vision generalist models, delving into their characteristics and capabilities within the field. First, we review the background, including the datasets, tasks, and benchmarks. Then, we dig into the design of frameworks that have been proposed in existing research, while also introducing the techniques employed to enhance their performance. To better help the researchers comprehend the area, we take a brief excursion into related domains, shedding light on their interconnections and potential synergies. To conclude, we provide some real-world application scenarios, undertake a thorough examination of the persistent challenges, and offer insights into possible directions for future research endeavors.
Microservices and Real-Time Processing in Retail IT: A Review of Open-Source Toolchains and Deployment Strategies
2025-06-11
Aaditaa Vashisht, Rekha B S
With the rapid pace of digital transformation, the retail industry is increasingly depending on real-time, scalable, and resilient systems to manage financial transactions, analyze customer behavior, and streamline order processing. This literature review explores how modern event-driven and microservices-based architectures, particularly those leveraging Apache Kafka, Spring Boot, MongoDB, and Kubernetes are transforming retail and financial systems. By systematically reviewing academic publications, technical white papers, and industry reports from recent years, this study synthesizes key themes and implementation strategies. The analysis reveals that technologies like Kafka and Spring Boot are instrumental in building low-latency, event-driven applications that support real-time analytics and fraud detection, while MongoDB, when deployed on Kubernetes, ensures fault tolerance and high availability in inventory and transaction systems. Kubernetes itself plays a crucial role in automating deployment and scaling of microservices. These findings provide valuable insights for industry practitioners aiming to design scalable infrastructures, identify research opportunities in hybrid deployment models, and offer educators a foundation to integrate modern system architectures into professional and technical communication training.
One promise that Vision-Language-Action (VLA) models hold over traditional imitation learning for robotics is to leverage the broad generalization capabilities of large Vision-Language Models (VLMs) to produce versatile, "generalist" robot policies. However, current evaluations of VLAs remain insufficient. Traditional imitation learning benchmarks are unsuitable due to the lack of language instructions. Emerging benchmarks for VLAs that incorporate language often come with limited evaluation tasks and do not intend to investigate how much VLM pretraining truly contributes to the generalization capabilities of the downstream robotic policy. Meanwhile, much research relies on real-world robot setups designed in isolation by different institutions, which creates a barrier for reproducibility and accessibility. To address this gap, we introduce a unified probing suite of 50 simulation-based tasks across 10 subcategories spanning language instruction, vision, and objects. We systematically evaluate several state-of-the-art VLA architectures on this suite to understand their generalization capability. Our results show that while VLM backbones endow VLAs with robust perceptual understanding and high level planning, which we refer to as good intentions, this does not reliably translate into precise motor execution: when faced with out-of-distribution observations, policies often exhibit coherent intentions, but falter in action execution. Moreover, finetuning on action data can erode the original VLM's generalist reasoning abilities. We release our task suite and evaluation code to serve as a standardized benchmark for future VLAs and to drive research on closing the perception-to-action gap. More information, including the source code, can be found at https://ai4ce.github.io/INT-ACT/
Reviews are valuable resources for customers making purchase decisions in online shopping. However, it is impractical for customers to go over the vast number of reviews and manually conclude the prominent opinions, which prompts the need for automated opinion summarization systems. Previous approaches, either extractive or abstractive, face challenges in automatically producing grounded aspect-centric summaries. In this paper, we propose a novel summarization system that not only captures predominant opinions from an aspect perspective with supporting evidence, but also adapts to varying domains without relying on a pre-defined set of aspects. Our proposed framework, ASESUM, summarizes viewpoints relevant to the critical aspects of a product by extracting aspect-centric arguments and measuring their salience and validity. We conduct experiments on a real-world dataset to demonstrate the superiority of our approach in capturing diverse perspectives of the original reviews compared to new and existing methods.
Virtualizing RAN: Science, Strategy, and Architecture of Software-Defined Mobile Networks
2025-06-11
Ryan Barker
Virtualising the Radio Access Network (RAN) is widely touted as the corner-stone of affordable 5G and a prerequisite for AI-native 6G. Yet current discourse often isolates spectrum policy, cloud engineering and organisational readiness into silos. This paper delivers an integrated analysis that spans science, technology, business strategy and culture. I first review spectrum-auction economics and show-via a comparative study of T-Mobile US and Verizon-that mid-band contiguity leveraged through software-defined carrier aggregation outperforms mmWave-centric deployments in both coverage and churn metrics. I then formalise the technical foundations of virtualised and open RAN, deriving capacity limits from contiguous and dis-contiguous spectrum maths and quantifying hardware ceilings for 400 MHz mmWave channels. Edge compute platforms (NVIDIA EGX, Samsung vRAN 3.0) and SDN-controlled RAN Intelligent Controllers are examined alongside AI ML pipelines that enable digital-twin-driven optimisation. A security cost model extends recent O-RAN measurements to show how 256-bit cipher enforcement adds 35-60 us latency unless mitigated by inline crypto off-load. Finally, a national automation case study of live vRAN sites -- demonstrates an 81 to 13 day cycle-time reduction once cultural change errors are corrected. I conclude with open research challenges for sub-THz 6G, energy-neutral AI accelerators and zero-trust orchestration, offering actionable recommendations for operators, vendors and researchers.
Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practice
2025-06-11
Emma Kallina, Thomas Bohné, Jat Singh
Responsible AI (rAI) guidance increasingly promotes stakeholder involvement (SHI) during AI development. At the same time, SHI is already common in commercial software development, but with potentially different foci. This study clarifies the extent to which established SHI practices are able to contribute to rAI efforts as well as potential disconnects -- essential insights to inform and tailor future interventions that further shift industry practice towards rAI efforts. First, we analysed 56 rAI guidance documents to identify why SHI is recommended (i.e. its expected benefits for rAI) and uncovered goals such as redistributing power, improving socio-technical understandings, anticipating risks, and enhancing public oversight. To understand why and how SHI is currently practised in commercial settings, we then conducted an online survey (n=130) and semi-structured interviews (n=10) with AI practitioners. Our findings reveal that SHI in practice is primarily driven by commercial priorities (e.g. customer value, compliance) and several factors currently discourage more rAI-aligned SHI practices. This suggests that established SHI practices are largely not contributing to rAI efforts. To address this disconnect, we propose interventions and research opportunities to advance rAI development in practice.
Magnetic excitations and exchange parameters of a nickel chain compound PbMn$_2$Ni$_6$Te$_3$O$_{18}$: Neutron scattering and density functional theory studies
2025-06-11
S. Uthayakumar, D. T. Adroja, Amit Pokhriyal, A. K. Bera, Haranath Ghosh, Tatiana Gudi, Manh Duc Le, Christian Balz, R. A. Ewings, Minal Gupta, P. R. Sagdeo, D. Prabhakaran, J. P. Goff
We have investigated the quasi-one dimensional Ni-chain compound PbMn$_2$Ni$_6$Te$_2$O$_{18}$ using theoretical DFT calculations, inelastic neutron scattering and optical spectroscopy in order to understand the nature of magnetic exchange interactions. Our inelastic neutron scattering study at 5 K on a powder sample reveals two bands of magnetic excitations, the first near 8 meV and the second near 18 meV originating from the antiferromagnetic zone center near $Q$ = 1~\AA. On the other hand at 100 K (which is above T$_N$ = 86 K) a broad diffuse scattering signal is observed indicating the presence of short range magnetic correlations. We have analyzed the magnetic excitations based on the Linear Spin Wave Theory (LSWT) and compared the experimentally estimated exchange parameters with the DFT calculations. Our analysis reveals that the value of the exchange parameter at the larger distance (d=3.654 $\AA$) $J_3$=4.21(8) meV between Ni-Ni (from inter-chain) is the strongest amongst the allowed six exchange parameters, which suggests that this system is not really a quasi-one-dimensional and confirmed by the absence of a Haldane gap. We have also presented the electronic structure calculations. The spin-polarized partial density of states (DOS) projected onto the Mn-d and Ni-d orbitals reveals that the Ni-d$_{x^2-y^2}$ contribution is dominant below the Fermi level in the spin-up and spin-down channel, while a minimal contribution from spin-up Mn states in the occupied region, suggesting a nearly high-spin state. The estimated N\'eel temperature, based on experimental exchange parameters is found to be in close agreement with the experimental value.
Learning to Align: Addressing Character Frequency Distribution Shifts in Handwritten Text Recognition
2025-06-11
Panagiotis Kaliosis, John Pavlopoulos
Handwritten text recognition aims to convert visual input into machine-readable text, and it remains challenging due to the evolving and context-dependent nature of handwriting. Character sets change over time, and character frequency distributions shift across historical periods or regions, often causing models trained on broad, heterogeneous corpora to underperform on specific subsets. To tackle this, we propose a novel loss function that incorporates the Wasserstein distance between the character frequency distribution of the predicted text and a target distribution empirically derived from training data. By penalizing divergence from expected distributions, our approach enhances both accuracy and robustness under temporal and contextual intra-dataset shifts. Furthermore, we demonstrate that character distribution alignment can also improve existing models at inference time without requiring retraining by integrating it as a scoring function in a guided decoding scheme. Experimental results across multiple datasets and architectures confirm the effectiveness of our method in boosting generalization and performance. We open source our code at https://github.com/pkaliosis/fada.
2025-06-11
Rebecca Loubet, Pascal Zittlau, Marco Hoffmann, Luisa Vollmer, Sophie Fellenz, Heike Leitte, Fabian Jirasek, Johannes Lenhard, Hans Hasse
In this short note, we report and analyze a striking event: OpenAI's large language model o3 has outwitted all students in a university exam on thermodynamics. The thermodynamics exam is a difficult hurdle for most students, where they must show that they have mastered the fundamentals of this important topic. Consequently, the failure rates are very high, A-grades are rare - and they are considered proof of the students' exceptional intellectual abilities. This is because pattern learning does not help in the exam. The problems can only be solved by knowledgeably and creatively combining principles of thermodynamics. We have given our latest thermodynamics exam not only to the students but also to OpenAI's most powerful reasoning model, o3, and have assessed the answers of o3 exactly the same way as those of the students. In zero-shot mode, the model o3 solved all problems correctly, better than all students who took the exam; its overall score was in the range of the best scores we have seen in more than 10,000 similar exams since 1985. This is a turning point: machines now excel in complex tasks, usually taken as proof of human intellectual capabilities. We discuss the consequences this has for the work of engineers and the education of future engineers.
The identification of the devices from which a message is received is part of security mechanisms to ensure authentication in wireless communications. Conventional authentication approaches are cryptography-based, which, however, are usually computationally expensive and not adequate in the Internet of Things (IoT), where devices tend to be low-cost and with limited resources. This paper provides a comprehensive survey of physical layer-based device fingerprinting, which is an emerging device authentication for wireless security. In particular, this article focuses on hardware impairment-based identity authentication and channel features-based authentication. They are passive techniques that are readily applicable to legacy IoT devices. Their intrinsic hardware and channel features, algorithm design methodologies, application scenarios, and key research questions are extensively reviewed here. The remaining research challenges are discussed, and future work is suggested that can further enhance the physical layer-based device fingerprinting.
Wasserstein Distances on Quantum Structures: an Overview
2025-06-11
Emily Beatty
The theory of optimal transport of probability measures has wide-ranging applications across a number of different fields, including concentration of measure, machine learning, Markov chains, and economics. The generalisation of optimal transport tools from probability measures to quantum states has shown great promise over the last few years, particularly in the development of the theory of Wasserstein-style distances and divergences between quantum states. Such distances have already led to a broad range of developments in the quantum setting such as functional inequalities, convergence of solutions in many-body physics, improvements to quantum generative adversarial networks, and more. However, the literature in this field is quite scattered, with very few links between different works and no real consensus on a `true' quantum Wasserstein distance. The aim of this review is to bring these works together under one roof and give a full overview of the state of the art in the development of quantum Wasserstein distances. We also present a variety of open problems and unexplored avenues in the field, and examine the future directions of this promising line of research. This review is written for those interested in quantum optimal transport in coming from both the fields of classical optimal transport and of quantum information theory, and as a resource for those working in one area of quantum optimal transport interested in how existing work may relate to their own.
2025-06-11
H. F. Stevance, K. W. Smith, S. J. Smartt, S. J. Roberts, N. Erasmus, D. R. Young, A. Clocchiatti
We present the Virtual Research Assistant (VRA) of the ATLAS sky survey which performs preliminary eyeballing on our clean transient data stream. The VRA uses Histogram Based Gradient Boosted Decision Tree Classifiers trained on real data to score incoming alerts on two axes: "Real" and "Galactic". The alerts are then ranked using a geometric distance such that the most "Real" and "Extra-galactic" receive high scores; the scores are updated when new light curve data is obtained on subsequent visits. To assess the quality of the training we use the Recall at rank K, which is more informative to our science goal than general metrics such as accuracy or F1-Scores. We also establish benchmarks for our metric based on the pre-VRA eyeballing strategy, to ensure our models provide notable improvements before being added to the ATLAS pipeline. Finally, policies are defined on the ranked list to select the most promising alerts for humans to eyeball and to automatically remove the bogus alerts. In production the VRA method has resulted in a reduction in eyeballing workload by 85% with a loss of follow-up opportunity <0.08%. It also allows us to automatically trigger follow-up observations with the Lesedi telescope, paving the way to automated methods that will be required in the era of LSST.
Load-Aware Training Scheduling for Model Circulation-based Decentralized Federated Learning
2025-06-11
Haruki Kainuma, Takayuki Nishio
This paper proposes Load-aware Tram-FL, an extension of Tram-FL that introduces a training scheduling mechanism to minimize total training time in decentralized federated learning by accounting for both computational and communication loads. The scheduling problem is formulated as a global optimization task, which-though intractable in its original form-is made solvable by decomposing it into node-wise subproblems. To promote balanced data utilization under non-IID distributions, a variance constraint is introduced, while the overall training latency, including both computation and communication costs, is minimized through the objective function. Simulation results on MNIST and CIFAR-10 demonstrate that Load-aware Tram-FL significantly reduces training time and accelerates convergence compared to baseline methods.
Intelligent Design 4.0: Paradigm Evolution Toward the Agentic AI Era
2025-06-11
Shuo Jiang, Min Xie, Frank Youhua Chen, Jian Ma, Jianxi Luo
Research and practice in Intelligent Design (ID) have significantly enhanced engineering innovation, efficiency, quality, and productivity over recent decades, fundamentally reshaping how engineering designers think, behave, and interact with design processes. The recent emergence of Foundation Models (FMs), particularly Large Language Models (LLMs), has demonstrated general knowledge-based reasoning capabilities, and open new paths and avenues for further transformation in engineering design. In this context, this paper introduces Intelligent Design 4.0 (ID 4.0) as an emerging paradigm empowered by agentic AI systems. We review the historical evolution of ID across four distinct stages: rule-based expert systems, task-specific machine learning models, large-scale foundation AI models, and the recent emerging paradigm of multi-agent collaboration. We propose a conceptual framework for ID 4.0 and discuss its potential to support end-to-end automation of engineering design processes through coordinated, autonomous multi-agent-based systems. Furthermore, we discuss future perspectives to enhance and fully realize ID 4.0's potential, including more complex design scenarios, more practical design implementations, novel agent coordination mechanisms, and autonomous design goal-setting with better human value alignment. In sum, these insights lay a foundation for advancing Intelligent Design toward greater adaptivity, autonomy, and effectiveness in addressing increasingly complex design challenges.
Multi-modal graphs, which integrate diverse multi-modal features and relations, are ubiquitous in real-world applications. However, existing multi-modal graph learning methods are typically trained from scratch for specific graph data and tasks, failing to generalize across various multi-modal graph data and tasks. To bridge this gap, we explore the potential of Multi-modal Graph Large Language Models (MG-LLM) to unify and generalize across diverse multi-modal graph data and tasks. We propose a unified framework of multi-modal graph data, task, and model, discovering the inherent multi-granularity and multi-scale characteristics in multi-modal graphs. Specifically, we present five key desired characteristics for MG-LLM: 1) unified space for multi-modal structures and attributes, 2) capability of handling diverse multi-modal graph tasks, 3) multi-modal graph in-context learning, 4) multi-modal graph interaction with natural language, and 5) multi-modal graph reasoning. We then elaborate on the key challenges, review related works, and highlight promising future research directions towards realizing these ambitious characteristics. Finally, we summarize existing multi-modal graph datasets pertinent for model training. We believe this paper can contribute to the ongoing advancement of the research towards MG-LLM for generalization across multi-modal graph data and tasks.
AMUSE-Antlia. II. Intracluster X-ray Population in the Antlia Cluster
2025-06-11
Zhensong Hu, Yuanyuan Su, Zhiyuan Li, Meicun Hou, Ralph P. Kraft, Kelley M. Hess, Hao Chen
We conduct a systematic survey of X-ray sources in the inner ($r\sim200$ kpc) region of the Antlia cluster based on \Chandra observations, down to a source detection limit of $ L(0.5\text{--}8\ \mathrm{keV})\sim4.2\times10^{-7}\ \mathrm{ph\ cm^{-2}\ s^{-1}}$ ($2\times10^{38}\ \mathrm{erg\ s^{-1}}$). We present an X-ray source catalog with 202 sources and provide their coordinates, multi-band flux information and hardness ratios. We find a statistically significant excess at a significance level of $4.2\sigma$ with 37.6 excess sources beyond three times the mean effective radius of the two BCGs. This implies that these excess sources could be a genuine intracluster X-ray population that is not associated with the bulk stellar component. Also, the increased number of excess sources in the fields containing a BCG implies a potential connection between the excess sources and BCGs. The discovery of these sources in the Antlia cluster, together with previous research of similar findings in other two nearby clusters, Virgo and Fornax, indicates that the intracluster X-ray population could be universal in nearby galaxy clusters. Furthermore, we discuss the candidate origins of the excess sources, including low-mass X-ray binaries (LMXBs) associated with intracluster light (ICL-LMXBs), LMXBs in globular clusters (GC-LMXBs) and supernova-kicked LMXBs (SN-kicked LMXBs). We estimate the contribution of ICL-LMXBs, which should include the LMXBs relating with the stellar halo surrounding BCGs, are unlikely to dominate the intracluster X-ray population in Antlia. Meanwhile, GC-LMXBs and SN-kicked LMXBs, each component could contribute $\sim30\%$ to the total excess sources.
Generative Models for Parameter Space Reduction applied to Reduced Order Modelling
2025-06-11
Guglielmo Padula, Gianluigi Rozza
Solving and optimising Partial Differential Equations (PDEs) in geometrically parameterised domains often requires iterative methods, leading to high computational and time complexities. One potential solution is to learn a direct mapping from the parameters to the PDE solution. Two prominent methods for this are Data-driven Non-Intrusive Reduced Order Models (DROMs) and Parametrised Physics Informed Neural Networks (PPINNs). However, their accuracy tends to degrade as the number of geometric parameters increases. To address this, we propose adopting Generative Models to create new geometries, effectively reducing the number of parameters, and improving the performance of DROMs and PPINNs. The first section briefly reviews the general theory of Generative Models and provides some examples, whereas the second focusses on their application to geometries with fixed or variable points, emphasising their integration with DROMs and PPINNs. DROMs trained on geometries generated by these models demonstrate enhanced accuracy due to reduced parameter dimensionality. For PPINNs, we introduce a methodology that leverages Generative Models to reduce the parameter dimensions and improve convergence. This approach is tested on a Poisson equation defined over deformed Stanford Bunny domains.
Large language model-specific inference engines (in short as \emph{LLM inference engines}) have become a fundamental component of modern AI infrastructure, enabling the deployment of LLM-powered applications (LLM apps) across cloud and local devices. Despite their critical role, LLM inference engines are prone to bugs due to the immense resource demands of LLMs and the complexities of cross-platform compatibility. However, a systematic understanding of these bugs remains lacking. To bridge this gap, we present the first empirical study on bugs in LLM inference engines. We mine official repositories of 5 widely adopted LLM inference engines, constructing a comprehensive dataset of 929 real-world bugs. Through a rigorous open coding process, we analyze these bugs to uncover their symptoms, root causes, and commonality. Our findings reveal six major bug symptoms and a taxonomy of 28 root causes, shedding light on the key challenges in bug detection and location within LLM inference engines. Based on these insights, we propose a series of actionable implications for researchers, inference engine vendors, and LLM app developers.
Fine-Tuning Large Audio-Language Models with LoRA for Precise Temporal Localization of Prolonged Exposure Therapy Elements
2025-06-11
Suhas BN, Andrew M. Sherrill, Jyoti Alaparthi, Dominik Mattioli, Rosa I. Arriaga, Chris W. Wiese, Saeed Abdullah
Prolonged Exposure (PE) therapy is an effective treatment for post-traumatic stress disorder (PTSD), but evaluating therapist fidelity remains labor-intensive due to the need for manual review of session recordings. We present a method for the automatic temporal localization of key PE fidelity elements -- identifying their start and stop times -- directly from session audio and transcripts. Our approach fine-tunes a large pre-trained audio-language model, Qwen2-Audio, using Low-Rank Adaptation (LoRA) to process focused 30-second windows of audio-transcript input. Fidelity labels for three core protocol phases -- therapist orientation (P1), imaginal exposure (P2), and post-imaginal processing (P3) -- are generated via LLM-based prompting and verified by trained raters. The model is trained to predict normalized boundary offsets using soft supervision guided by task-specific prompts. On a dataset of 313 real PE sessions, our best configuration (LoRA rank 8, 30s windows) achieves a mean absolute error (MAE) of 5.3 seconds across tasks. We further analyze the effects of window size and LoRA rank, highlighting the importance of context granularity and model adaptation. This work introduces a scalable framework for fidelity tracking in PE therapy, with potential to support clinician training, supervision, and quality assurance.
A Metallicity Catalog of Very Metal-poor Main-sequence Turn-off and Red Giant Stars from LAMOST DR10
2025-06-11
Xiangyu Li, Huiling Chen, Yang Huang, Huawei Zhang, Timothy C. Beers, Linxuan Zhu, Jifeng Liu
We present a catalog of 8,440 candidate very metal-poor (VMP; [Fe/H] < -2.0) main-sequence turn-off (MSTO) and red giant stars in the Milky Way, identified from low-resolution spectra in LAMOST DR10. More than 7,000 of these candidates are brighter than G ~ 16, making them excellent targets for high-resolution spectroscopic follow-up with 4-10 meter-class telescopes. Unlike most previous studies, we employed an empirical calibration to estimate metallicities from the equivalent widths (EWs) of the Calcium Triplet (CaT) lines, taking advantage of the high signal-to-noise ratio (SNR) in the red arm of LAMOST spectra. We further refined this calibration to improve its reliability for more distant stars. This method enables robust identification of VMP candidates with metallicities as low as [Fe/H] = -4.0 among both MSTO and red giant stars. Comparisons with metal-poor samples from other spectroscopic surveys and high-resolution follow-up observations confirm the accuracy of our estimates, showing a typical median offset of ~0.1 dex and a standard deviation of ~0.2 dex.
Calculating Software's Energy Use and Carbon Emissions: A Survey of the State of Art, Challenges, and the Way Ahead
2025-06-11
Priyavanshi Pathania, Nikhil Bamby, Rohit Mehra, Samarth Sikand, Vibhu Saujanya Sharma, Vikrant Kaulgud, Sanjay Podder, Adam P. Burden
The proliferation of software and AI comes with a hidden risk: its growing energy and carbon footprint. As concerns regarding environmental sustainability come to the forefront, understanding and optimizing how software impacts the environment becomes paramount. In this paper, we present a state-of-the-art review of methods and tools that enable the measurement of software and AI-related energy and/or carbon emissions. We introduce a taxonomy to categorize the existing work as Monitoring, Estimation, or Black-Box approaches. We delve deeper into the tools and compare them across different dimensions and granularity - for example, whether their measurement encompasses energy and carbon emissions and the components considered (like CPU, GPU, RAM, etc.). We present our observations on the practical use (component wise consolidation of approaches) as well as the challenges that we have identified across the current state-of-the-art. As we start an initiative to address these challenges, we emphasize active collaboration across the community in this important field.
The ongoing evolution of AI paradigms has propelled AI research into the Agentic AI stage. Consequently, the focus of research has shifted from single agents and simple applications towards multi-agent autonomous decision-making and task collaboration in complex environments. As Large Language Models (LLMs) advance, their applications become more diverse and complex, leading to increasingly situational and systemic risks. This has brought significant attention to value alignment for AI agents, which aims to ensure that an agent's goals, preferences, and behaviors align with human values and societal norms. This paper reviews value alignment in agent systems within specific application scenarios. It integrates the advancements in AI driven by large models with the demands of social governance. Our review covers value principles, agent system application scenarios, and agent value alignment evaluation. Specifically, value principles are organized hierarchically from a top-down perspective, encompassing macro, meso, and micro levels. Agent system application scenarios are categorized and reviewed from a general-to-specific viewpoint. Agent value alignment evaluation systematically examines datasets for value alignment assessment and relevant value alignment methods. Additionally, we delve into value coordination among multiple agents within agent systems. Finally, we propose several potential research directions in this field.
Deriving physical parameters of unresolved star clusters. IX. Sky background effects in the aperture photometry
2025-06-11
Karolis Daugevičius, Rima Stonkutė, Eimantas Kriščiūnas, Erikas Cicėnas, Vladas Vansevičius
The aperture photometry method is a powerful tool that enables us to study large star cluster systems efficiently. However, its accuracy depends on various factors, including the stochasticity of the stellar initial mass function and variations in the sky background. Previously, in the eighth paper of this series, we established the best achievable limits of the aperture photometry method for star cluster studies in the local universe. The aim of this study is to determine how the sky background affects the limits and applicability of the aperture photometry method in star cluster analysis. We used a large sample of star cluster models spanning the parameter space of M 31 clusters. To determine how the background affects star cluster photometry, we placed images of simulated clusters into five background fields of different stellar density from the Panchromatic $Hubble$ Andromeda Treasury (PHAT) survey and measured them using aperture photometry. We determined age and mass limits for the M 31 disc star clusters at which photometric uncertainties are low enough to enable the determination of cluster parameters using the aperture photometry method. We demonstrated that for typical-size clusters, optimal aperture diameters are of ~3 half-light radii. We assessed cluster detection completeness in relation to varying sky background densities, based on the M 31 PHAT survey data. Our results suggest that a significant selection bias towards more compact clusters may exist in the PHAT survey. We derived low-mass limits of the cluster mass function (CMF) in the PHAT survey, reaching down to masses of ~500 $M_\odot$ in outer disc areas, ~1500 $M_\odot$ in middle disc or star-forming regions, and ~3000 $M_\odot$ in inner disc regions. Therefore, we stress a necessity of careful accounting for selection effects arising due to sky background variations when studying the CMF.
FPGA-Based Multiplier with a New Approximate Full Adder for Error-Resilient Applications
2025-06-11
Ali Ranjbar, Elham Esmaeili, Roghayeh Rafieisangari, Nabiollah Shiri
Electronic devices primarily aim to offer low power consumption, high speed, and a compact area. The performance of very large-scale integration (VLSI) devices is influenced by arithmetic operations, where multiplication is a crucial operation. Therefore, a high-speed multiplier is essential for developing any signal-processing module. Numerous multipliers have been reviewed in existing literature, and their speed is largely determined by how partial products (PPs) are accumulated. To enhance the speed of multiplication beyond current methods, an approximate adder-based multiplier is introduced. This approach allows for the simultaneous addition of PPs from two consecutive bits using a novel approximate adder. The proposed multiplier is utilized in a mean filter structure and implemented in ISE Design Suite 14.7 using VHDL and synthesized on the Xilinx Spartan3-XC3S400 FPGA board. Compared to the literature, the proposed multiplier achieves power and power-delay product (PDP) improvements of 56.09% and 73.02%, respectively. The validity of the expressed multiplier is demonstrated through the mean filter system. Results show that it achieves power savings of 33.33%. Additionally, the proposed multiplier provides more accurate results than other approximate multipliers by expressing higher values of peak signal-to-noise ratio (PSNR), (30.58%), and structural similarity index metric (SSIM), (22.22%), while power consumption is in a low range.
Attention-Based Map Encoding for Learning Generalized Legged Locomotion
2025-06-11
Junzhe He, Chong Zhang, Fabian Jenelten, Ruben Grandia, Moritz BÄcher, Marco Hutter
Dynamic locomotion of legged robots is a critical yet challenging topic in expanding the operational range of mobile robots. It requires precise planning when possible footholds are sparse, robustness against uncertainties and disturbances, and generalizability across diverse terrains. While traditional model-based controllers excel at planning on complex terrains, they struggle with real-world uncertainties. Learning-based controllers offer robustness to such uncertainties but often lack precision on terrains with sparse steppable areas. Hybrid methods achieve enhanced robustness on sparse terrains by combining both methods but are computationally demanding and constrained by the inherent limitations of model-based planners. To achieve generalized legged locomotion on diverse terrains while preserving the robustness of learning-based controllers, this paper proposes to learn an attention-based map encoding conditioned on robot proprioception, which is trained as part of the end-to-end controller using reinforcement learning. We show that the network learns to focus on steppable areas for future footholds when the robot dynamically navigates diverse and challenging terrains. We synthesize behaviors that exhibit robustness against uncertainties while enabling precise and agile traversal of sparse terrains. Additionally, our method offers a way to interpret the topographical perception of a neural network. We have trained two controllers for a 12-DoF quadrupedal robot and a 23-DoF humanoid robot respectively and tested the resulting controllers in the real world under various challenging indoor and outdoor scenarios, including ones unseen during training.
Probability-One Optimization of Generalized Rayleigh Quotient Sum For Multi-Source Generalized Total Least-Squares
2025-06-11
Dominik Friml, Pavel Václavek
This paper addresses the global optimization of the sum of the Rayleigh quotient and the generalized Rayleigh quotient on the unit sphere. While various methods have been proposed for this problem, they do not guarantee convergence to the global maximizer. To overcome this limitation, we introduce a probability-one homotopy optimization method that, under certain conditions, guarantees convergence to the global maximizer. The proposed method is analyzed alongside state-of-the-art approaches through numerical experiments, evaluating their performance in terms of convergence speed and ability to reach the global maximizer. Furthermore, we demonstrate how this ties in with the multi-source Bayesian Generalized Total Least-Squares (B-GTLS) problem, illustrating its applicability.
Integrating structured knowledge from Knowledge Graphs (KGs) into Large Language Models (LLMs) enhances factual grounding and reasoning capabilities. This survey paper systematically examines the synergy between KGs and LLMs, categorizing existing approaches into two main groups: KG-enhanced LLMs, which improve reasoning, reduce hallucinations, and enable complex question answering; and LLM-augmented KGs, which facilitate KG construction, completion, and querying. Through comprehensive analysis, we identify critical gaps and highlight the mutual benefits of structured knowledge integration. Compared to existing surveys, our study uniquely emphasizes scalability, computational efficiency, and data quality. Finally, we propose future research directions, including neuro-symbolic integration, dynamic KG updating, data reliability, and ethical considerations, paving the way for intelligent systems capable of managing more complex real-world knowledge tasks.
Gradient-Weighted, Data-Driven Normalization for Approximate Border Bases -- Concept and Computation
2025-06-11
Hiroshi Kera, Achim Kehrein
This paper studies the concept and the computation of approximately vanishing ideals of a finite set of data points. By data points, we mean that the points contain some uncertainty, which is a key motivation for the approximate treatment. A careful review of the existing border basis concept for an exact treatment motivates a new adaptation of the border basis concept for an approximate treatment. In the study of approximately vanishing polynomials, the normalization of polynomials plays a vital role. So far, the most common normalization in computational commutative algebra uses the coefficient norm of a polynomial. Inspired by recent developments in machine learning, the present paper proposes and studies the use of gradient-weighted normalization. The gradient-weighted semi-norm evaluates the gradient of a polynomial at the data points. This data-driven nature of gradient-weighted normalization produces, on the one hand, better stability against perturbation and, on the other hand, very significantly, invariance of border bases with respect to scaling the data points. Neither property is achieved with coefficient normalization. In particular, we present an example of the lack of scaling invariance with respect to coefficient normalization, which can cause an approximate border basis computation to fail. This is extremely relevant because scaling of the point set is often recommended for preprocessing the data. Further, we use an existing algorithm with coefficient normalization to show that it is easily adapted to gradient-weighted normalization. The analysis of the adapted algorithm only requires tiny changes, and the time complexity remains the same. Finally, we present numerical experiments on three affine varieties to demonstrate the superior stability of our data-driven normalization over coefficient normalization. We obtain robustness to perturbations and invariance to scaling.
LLM-Powered CPI Prediction Inference with Online Text Time Series
2025-06-11
Yingying Fan, Jinchi Lv, Ao Sun, Yurou Wang
Forecasting the Consumer Price Index (CPI) is an important yet challenging task in economics, where most existing approaches rely on low-frequency, survey-based data. With the recent advances of large language models (LLMs), there is growing potential to leverage high-frequency online text data for improved CPI prediction, an area still largely unexplored. This paper proposes LLM-CPI, an LLM-based approach for CPI prediction inference incorporating online text time series. We collect a large set of high-frequency online texts from a popularly used Chinese social network site and employ LLMs such as ChatGPT and the trained BERT models to construct continuous inflation labels for posts that are related to inflation. Online text embeddings are extracted via LDA and BERT. We develop a joint time series framework that combines monthly CPI data with LLM-generated daily CPI surrogates. The monthly model employs an ARX structure combining observed CPI data with text embeddings and macroeconomic variables, while the daily model uses a VARX structure built on LLM-generated CPI surrogates and text embeddings. We establish the asymptotic properties of the method and provide two forms of constructed prediction intervals. The finite-sample performance and practical advantages of LLM-CPI are demonstrated through both simulation and real data examples.
A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications
2025-06-11
Donglin Wang, Anjie Qiu, Qiuheng Zhou, Hans D. Schotten
The rapid advancement of Vehicle-to-Everything (V2X) communication is transforming Intelligent Transportation Systems (ITS), with 6G networks expected to provide ultra-reliable, low-latency, and high-capacity connectivity for Connected and Autonomous Vehicles (CAVs). Artificial Intelligence (AI) and Machine Learning (ML) have emerged as key enablers in optimizing V2X communication by enhancing network management, predictive analytics, security, and cooperative driving due to their outstanding performance across various domains, such as natural language processing and computer vision. This survey comprehensively reviews recent advances in AI and ML models applied to 6G-V2X communication. It focuses on state-of-the-art techniques, including Deep Learning (DL), Reinforcement Learning (RL), Generative Learning (GL), and Federated Learning (FL), with particular emphasis on developments from the past two years. Notably, AI, especially GL, has shown remarkable progress and emerging potential in enhancing the performance, adaptability, and intelligence of 6G-V2X systems. Despite these advances, a systematic summary of recent research efforts in this area remains lacking, which this survey aims to address. We analyze their roles in 6G-V2X applications, such as intelligent resource allocation, beamforming, intelligent traffic management, and security management. Furthermore, we explore the technical challenges, including computational complexity, data privacy, and real-time decision-making constraints, while identifying future research directions for AI-driven 6G-V2X development. This study aims to provide valuable insights for researchers, engineers, and policymakers working towards realizing intelligent, AI-powered V2X ecosystems in 6G communication.
Bridging Online Behavior and Clinical Insight: A Longitudinal LLM-based Study of Suicidality on YouTube Reveals Novel Digital Markers
2025-06-11
Ilanit Sobol, Shir Lissak, Refael Tikochinski, Tal Nakash, Anat Brunstein Klomek, Eyal Fruchter, Roi Reichart
Suicide remains a leading cause of death in Western countries, underscoring the need for new research approaches. As social media becomes central to daily life, digital footprints offer valuable insight into suicidal behavior. Focusing on individuals who attempted suicide while uploading videos to their channels, we investigate: How do suicidal behaviors manifest on YouTube, and how do they differ from expert knowledge? We applied complementary approaches: computational bottom-up, hybrid, and expert-driven top-down, on a novel longitudinal dataset of 181 YouTube channels from individuals with life-threatening attempts, alongside 134 control channels. In the bottom-up approach, we applied LLM-based topic modeling to identify behavioral indicators. Of 166 topics, five were associated with suicide-attempt, with two also showing temporal attempt-related changes ($p<.01$) - Mental Health Struggles ($+0.08$)* and YouTube Engagement ($+0.1$)*. In the hybrid approach, a clinical expert reviewed LLM-derived topics and flagged 19 as suicide-related. However, none showed significant attempt-related temporal effects beyond those identified bottom-up. Notably, YouTube Engagement, a platform-specific indicator, was not flagged by the expert, underscoring the value of bottom-up discovery. In the top-down approach, psychological assessment of suicide attempt narratives revealed that the only significant difference between individuals who attempted before and those attempted during their upload period was the motivation to share this experience: the former aimed to Help Others ($\beta=-1.69$, $p<.01$), while the latter framed it as part of their Personal Recovery ($\beta=1.08$, $p<.01$). By integrating these approaches, we offer a nuanced understanding of suicidality, bridging digital behavior and clinical insights. * Within-group changes in relation to the suicide attempt.
BemaGANv2: A Tutorial and Comparative Survey of GAN-based Vocoders for Long-Term Audio Generation
2025-06-11
Taesoo Park, Mungwi Jeong, Mingyu Park, Narae Kim, Junyoung Kim, Mujung Kim, Jisang Yoo, Hoyun Lee, Sanghoon Kim, Soonchul Kwon
This paper presents a tutorial-style survey and implementation guide of BemaGANv2, an advanced GAN-based vocoder designed for high-fidelity and long-term audio generation. Built upon the original BemaGAN architecture, BemaGANv2 incorporates major architectural innovations by replacing traditional ResBlocks in the generator with the Anti-aliased Multi-Periodicity composition (AMP) module, which internally applies the Snake activation function to better model periodic structures. In the discriminator framework, we integrate the Multi-Envelope Discriminator (MED), a novel architecture we originally proposed, to extract rich temporal envelope features crucial for periodicity detection. Coupled with the Multi-Resolution Discriminator (MRD), this combination enables more accurate modeling of long-range dependencies in audio. We systematically evaluate various discriminator configurations, including MSD + MED, MSD + MRD, and MPD + MED + MRD, using objective metrics (FAD, SSIM, PLCC, MCD) and subjective evaluations (MOS, SMOS). This paper also provides a comprehensive tutorial on the model architecture, training methodology, and implementation to promote reproducibility. The code and pre-trained models are available at: https://github.com/dinhoitt/BemaGANv2.
The growing coupling among electricity, gas, and hydrogen systems is driven by green hydrogen blending into existing natural gas pipelines, paving the way toward a renewable-dominated energy future. However, the integration poses significant challenges, particularly ensuring efficient and safe operation under varying hydrogen penetration and infrastructure adaptability. This paper reviews progress in optimization and control technologies for hydrogen-blended integrated gas-electricity system. First, key technologies and international demonstration projects are introduced to provide an overview of current developments. Besides, advances in gas-electricity system integration, including modeling, scheduling, planning and market design, are reviewed respectively. Then, the potential for cross-system fault propagation is highlighted, and practical methods for safety analysis and control are proposed. Finally, several possible research directions are introduced, aiming to ensure efficient renewable integration and reliable operation.
Detecting deception in an increasingly digital world is both a critical and challenging task. In this study, we present a comprehensive evaluation of the automated deception detection capabilities of Large Language Models (LLMs) and Large Multimodal Models (LMMs) across diverse domains. We assess the performance of both open-source and commercial LLMs on three distinct datasets: real life trial interviews (RLTD), instructed deception in interpersonal scenarios (MU3D), and deceptive reviews (OpSpam). We systematically analyze the effectiveness of different experimental setups for deception detection, including zero-shot and few-shot approaches with random or similarity-based in-context example selection. Our results show that fine-tuned LLMs achieve state-of-the-art performance on textual deception detection tasks, while LMMs struggle to fully leverage cross-modal cues. Additionally, we analyze the impact of auxiliary features, such as non-verbal gestures and video summaries, and examine the effectiveness of different prompting strategies, including direct label generation and chain-of-thought reasoning. Our findings provide key insights into how LLMs process and interpret deceptive cues across modalities, highlighting their potential and limitations in real-world deception detection applications.
Securing Open RAN: A Survey of Cryptographic Challenges and Emerging Solutions for 5G
2025-06-11
Ryan Barker, Fatemeh Afghah
The advent of Open Radio Access Networks (O-RAN) introduces modularity and flexibility into 5G deployments but also surfaces novel security challenges across disaggregated interfaces. This literature review synthesizes recent research across thirteen academic and industry sources, examining vulnerabilities such as cipher bidding-down attacks, partial encryption exposure on control/user planes, and performance trade-offs in securing O-RAN interfaces like E2 and O1. The paper surveys key cryptographic tools -- SNOW-V, AES-256, and ZUC-256 -- evaluating their throughput, side-channel resilience, and adaptability to heterogeneous slices (eMBB, URLLC, mMTC). Emphasis is placed on emerging testbeds and AI-driven controllers that facilitate dynamic orchestration, anomaly detection, and secure configuration. We conclude by outlining future research directions, including hardware offloading, cross-layer cipher adaptation, and alignment with 3GPP TS 33.501 and O-RAN Alliance security mandates, all of which point toward the need for integrated, zero-trust architectures in 6G.
Anomaly Detection and Generation with Diffusion Models: A Survey
2025-06-11
Yang Liu, Jing Liu, Chengfang Li, Rui Xi, Wenchao Li, Liang Cao, Jin Wang, Laurence T. Yang, Junsong Yuan, Wei Zhou
Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing, by identifying unexpected patterns that deviate from established norms in real-world data. Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest due to their ability to learn complex data distributions and generate high-fidelity samples, offering a robust framework for unsupervised AD. In this survey, we comprehensively review anomaly detection and generation with diffusion models (ADGDM), presenting a tutorial-style analysis of the theoretical foundations and practical implementations and spanning images, videos, time series, tabular, and multimodal data. Crucially, unlike existing surveys that often treat anomaly detection and generation as separate problems, we highlight their inherent synergistic relationship. We reveal how DMs enable a reinforcing cycle where generation techniques directly address the fundamental challenge of anomaly data scarcity, while detection methods provide critical feedback to improve generation fidelity and relevance, advancing both capabilities beyond their individual potential. A detailed taxonomy categorizes ADGDM methods based on anomaly scoring mechanisms, conditioning strategies, and architectural designs, analyzing their strengths and limitations. We final discuss key challenges including scalability and computational efficiency, and outline promising future directions such as efficient architectures, conditioning strategies, and integration with foundation models (e.g., visual-language models and large language models). By synthesizing recent advances and outlining open research questions, this survey aims to guide researchers and practitioners in leveraging DMs for innovative AD solutions across diverse applications.
ContextBuddy: AI-Enhanced Contextual Insights for Security Alert Investigation (Applied to Intrusion Detection)
2025-06-11
Ronal Singh, Mohan Baruwal Chhetri, Surya Nepal, Cecile Paris
Modern Security Operations Centres (SOCs) integrate diverse tools, such as SIEM, IDS, and XDR systems, offering rich contextual data, including alert enrichments, flow features, and similar case histories. Yet, analysts must still manually determine which of these contextual cues are most relevant when validating specific alerts. We introduce ContextBuddy, an AI assistant that learns from analysts' prior investigations to help them identify the most relevant context for new alerts. Rather than providing enrichments, ContextBuddy models how analysts have previously selected context and suggests tailored cues based on the characteristics of each alert. We formulate context selection as a sequential decision-making problem and apply imitation learning (IL) to capture analysts' strategies, evaluating multiple IL approaches. Through staged evaluation, we validate ContextBuddy using two intrusion detection datasets (HIKARI-2021, UNSW-NB15). In simulation-based experiments, ContextBuddy helped simulated reinforcement learning analysts improve classification accuracy (p < 0.001) (increasing F1 by 2.5% for HIKARI and 9% for UNSW), reducing false negatives (1.5% for HIKARI and 10% for UNSW), and keeping false positives below 1%. Decision confidence among agents also improved by 2-3% (p < 0.001). In a within-subject user study (N=13; power = 0.8), non-experts using ContextBuddy improved classification accuracy by 21.1% (p = 0.008) and reduced alert validation time by 24% (p = 0.01). These results demonstrate that by learning context-selection patterns from analysts, ContextBuddy can yield notable improvements in investigation effectiveness and efficiency.
SAGE: Exploring the Boundaries of Unsafe Concept Domain with Semantic-Augment Erasing
2025-06-11
Hongguang Zhu, Yunchao Wei, Mengyu Wang, Siyu Jiao, Yan Fang, Jiannan Huang, Yao Zhao
Diffusion models (DMs) have achieved significant progress in text-to-image generation. However, the inevitable inclusion of sensitive information during pre-training poses safety risks, such as unsafe content generation and copyright infringement. Concept erasing finetunes weights to unlearn undesirable concepts, and has emerged as a promising solution. However, existing methods treat unsafe concept as a fixed word and repeatedly erase it, trapping DMs in ``word concept abyss'', which prevents generalized concept-related erasing. To escape this abyss, we introduce semantic-augment erasing which transforms concept word erasure into concept domain erasure by the cyclic self-check and self-erasure. It efficiently explores and unlearns the boundary representation of concept domain through semantic spatial relationships between original and training DMs, without requiring additional preprocessed data. Meanwhile, to mitigate the retention degradation of irrelevant concepts while erasing unsafe concepts, we further propose the global-local collaborative retention mechanism that combines global semantic relationship alignment with local predicted noise preservation, effectively expanding the retentive receptive field for irrelevant concepts. We name our method SAGE, and extensive experiments demonstrate the comprehensive superiority of SAGE compared with other methods in the safe generation of DMs. The code and weights will be open-sourced at https://github.com/KevinLight831/SAGE.
Symbiotic star (SySt) is long-period interacting binary system, typically consisting of a white dwarf and a red giant surrounded by a nebula. These systems are natural astrophysical laboratories for investigating binary star evolution. In this paper, we identified nine SySts from the LAMOST DR10 low-resolution spectra survey, seven of which were previously known, while two are newly identified. Initially, we selected LAMOST spectra exhibiting typical SySt emission lines (e.g., $\rm H_{\alpha}, ~H_{\beta}, ~H_{\gamma}, ~and ~He II$). Subsequently, we utilized the distribution of known SySts on the HR diagram to select SySt candidates, and visually inspected their spectra. Ultimately, we classified all nine as S-type SySts using the $J - H$ vs. $H - K$ diagram. Additionally, based on multi-band photometric data from GALEX, Gaia, 2MASS, ALLWISE, and several X-ray catalogs, we found 12 accreting-only SySt (acc-SySt) candidates, characterized by concurrent ultraviolet and infrared excess and accretion process. Furthermore, we estimated the white dwarf temperatures by fitting their observed SEDs using a combination of Kurucz stellar atmosphere model and Koester white dwarf model. We compared the accretion rates of acc-SySt candidates and confirmed SySts, and found they have similar accretion rate distribution, providing evidence that these acc-SySt candidates constitute bona fide SySts.
Correct use of electrical appliances has significantly improved human life quality. Unlike simple tools that can be manipulated with common sense, different parts of electrical appliances have specific functions defined by manufacturers. If we want the robot to heat bread by microwave, we should enable them to review the microwave manual first. From the manual, it can learn about component functions, interaction methods, and representative task steps about appliances. However, previous manual-related works remain limited to question-answering tasks while existing manipulation researchers ignore the manual's important role and fail to comprehend multi-page manuals. In this paper, we propose the first manual-based appliance manipulation benchmark CheckManual. Specifically, we design a large model-assisted human-revised data generation pipeline to create manuals based on CAD appliance models. With these manuals, we establish novel manual-based manipulation challenges, metrics, and simulator environments for model performance evaluation. Furthermore, we propose the first manual-based manipulation planning model ManualPlan to set up a group of baselines for the CheckManual benchmark.
We study the collective oscillations of spin-orbit-coupled Bose-Einstein condensates in the presence of position-dependent detuning. Specifically, we explore the quadrupole modes of the system using both numerical and analytical approaches based on the Gross-Pitaevskii equation and hydrodynamic theory. Due to spin-orbit coupling and the synthetic magnetic field, {the $xy$ scissors mode couples with a superposition of the three diagonal quadrupole modes ($x^2$, $y^2$, and $z^2$),} resulting in the characteristic beating effect. {The remaining two scissors modes, $xz$ and $yz$, are coupled, giving rise to a Lissajous-like pattern that is highly sensitive to the excitation method and orientation of the synthetic magnetic field.} Furthermore, we find that anisotropic interactions as well as the direction of the synthetic magnetic field, can significantly influence the oscillation amplitude and frequency of the quadrupole modes. These findings highlight the potential of Bose-Einstein condensates under synthetic magnetic fields for quantum sensing applications, such as magnetic field {gradient} measurements, and provide a promising foundation for future experimental research and technological development.
Immunological mechanisms and immunoregulatory strategies in intervertebral disc degeneration
2025-06-11
Xianyi Yan, Xi Wang, Ying Che, Peng Feng, Ting Zhang
Intervertebral discs are avascular and maintain immune privilege. However, during intervertebral disc degeneration (IDD), this barrier is disrupted, leading to extensive immune cell infiltration and localized inflammation. In degenerated discs, macrophages, T lymphocytes, neutrophils, and granulocytic myeloid-derived suppressor cells (G-MDSCs) are key players, exhibiting functional heterogeneity. Dysregulated activation of inflammatory pathways, including nuclear factor kappa-B (NF-kappaB), interleukin-17 (IL-17), and nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) inflammasome activation, drives local pro-inflammatory responses, leading to cell apoptosis and extracellular matrix (ECM) degradation. Innovative immunotherapies, including exosome-based treatments, CRISPR/Cas9-mediated gene editing, and chemokine-loaded hydrogel systems, have shown promise in reshaping the immunological niche of intervertebral discs. These strategies can modulate dysregulated immune responses and create a supportive environment for tissue regeneration. However, current studies have not fully elucidated the mechanisms of inflammatory memory and the immunometabolic axis, and they face challenges in balancing tissue regeneration with immune homeostasis. Future studies should employ interdisciplinary approaches such as single-cell and spatial transcriptomics to map a comprehensive immune atlas of IDD, elucidate intercellular crosstalk and signaling networks, and develop integrated therapies combining targeted immunomodulation with regenerative engineering, thereby facilitating the clinical translation of effective IDD treatments.
A causal evaluation of Bogota's cable car illustrates the transformative potential of mobile phone data for policy analysis
2025-06-11
Elena Lutz, Sam Heroy, David Kaufmann, Neave O'Clery
Transport infrastructure is vital to the functioning of cities. However, assessing the impact of transport policies on urban mobility and behaviour is often costly and time-consuming, particularly in low-data environments. We demonstrate how GPS location data derived from smartphones, available at high spatial granularity and in near real time, can be used to conduct causal impact evaluation, capturing broad mobility and interaction patterns beyond the scope of traditional sources such as surveys or administrative data. We illustrate this approach by assessing the impact of a 2018 cable car system connecting a peripheral low-income neighbourhood in Bogota to the bus rapid transit (BRT) system. Using a difference-in-differences event study design, we compare people living near the new cable car line to people living in similar areas near planned stations of a future line. We find that the cable car increased mobility by approximately 6.5 trips per person per month, with most trips within the local neighbourhood and to the city centre. However, we find limited evidence of increased encounters between the low income cable car residents and other socioeconomic groups, suggesting that while the cable car improved access to urban amenities and quality of life, its impact on everyday socioeconomic mixing was more modest. Our study highlights the potential of mobile phone data to capture previously hard-to-measure outcomes of transport policies, such as socioeconomic mixing.
Quantum Algorithm Software for Condensed Matter Physics
2025-06-11
T. Farajollahpour
This report offers a comprehensive analysis of the evolving landscape of quantum algorithm software specifically tailored for condensed matter physics. It examines fundamental quantum algorithms such as Variational Quantum Eigensolver (VQE), Quantum Phase Estimation (QPE), Quantum Annealing (QA), Quantum Approximate Optimization Algorithm (QAOA), and Quantum Machine Learning (QML) as applied to key condensed matter problems including strongly correlated systems, topological phases, and quantum magnetism. This review details leading software development kits (SDKs) like Qiskit, Cirq, PennyLane, and Q\#, and profiles key academic, commercial, and governmental initiatives driving innovation in this domain. Furthermore, it assesses current challenges, including hardware limitations, algorithmic scalability, and error mitigation, and explores future trajectories, anticipating new algorithmic breakthroughs, software enhancements, and the impact of next-generation quantum hardware. The central theme emphasizes the critical role of a co-design approach, where algorithms, software, and hardware evolve in tandem, and highlights the necessity of standardized benchmarks to accelerate progress towards leveraging quantum computation for transformative discoveries in condensed matter physics.
Often when we interact with a first-person account of events, we consider whether or not the narrator, the primary speaker of the text, is reliable. In this paper, we propose using computational methods to identify unreliable narrators, i.e. those who unintentionally misrepresent information. Borrowing literary theory from narratology to define different types of unreliable narrators based on a variety of textual phenomena, we present TUNa, a human-annotated dataset of narratives from multiple domains, including blog posts, subreddit posts, hotel reviews, and works of literature. We define classification tasks for intra-narrational, inter-narrational, and inter-textual unreliabilities and analyze the performance of popular open-weight and proprietary LLMs for each. We propose learning from literature to perform unreliable narrator classification on real-world text data. To this end, we experiment with few-shot, fine-tuning, and curriculum learning settings. Our results show that this task is very challenging, and there is potential for using LLMs to identify unreliable narrators. We release our expert-annotated dataset and code and invite future research in this area.
Spontaneous emission and Purcell effect: some aspects
2025-06-11
P. P. Abrantes, D. Szilard, C. Farina
This chapter, part of the Proceedings of the III International Workshop on Quantum Nonstationary Systems (eds. Alexandre Dodonov and Lucas Chibebe Celeri), held in Brasilia in August 2024, offers a comprehensive overview of spontaneous emission (SE) and the Purcell effect within the broader context of quantum electrodynamics (QED) and vacuum fluctuations. It begins with a historical and theoretical review, tracing the development of classical and quantum electromagnetic theories. The chapter then examines how the SE rate of a quantum emitter is fundamentally influenced by its electromagnetic environment, the so-called Purcell effect, through canonical examples, such as emitters near perfectly conducting plates and inside cavities, as well as more advanced scenarios. Special attention is given to modern strategies for tailoring SE using engineered environments, including plasmonic cloaks, composite media near percolation thresholds, metal-insulator phase transitions, and strain-induced modulation in phosphorene. Analytical techniques, such as mode summation and Green's function formalism, are employed to describe how surrounding materials and boundary conditions affect local field modes and the local density of states. The findings underscore both the theoretical depth and experimental relevance of SE modulation, paving the way for innovations in nano-optics, quantum technologies, and advanced material design.
Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction
2025-06-11
Mohammad Subhi Al-Batah, Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon
Oral cancer presents a formidable challenge in oncology, necessitating early diagnosis and accurate prognosis to enhance patient survival rates. Recent advancements in machine learning and data mining have revolutionized traditional diagnostic methodologies, providing sophisticated and automated tools for differentiating between benign and malignant oral lesions. This study presents a comprehensive review of cutting-edge data mining methodologies, including Neural Networks, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and ensemble learning techniques, specifically applied to the diagnosis and prognosis of oral cancer. Through a rigorous comparative analysis, our findings reveal that Neural Networks surpass other models, achieving an impressive classification accuracy of 93,6 % in predicting oral cancer. Furthermore, we underscore the potential benefits of integrating feature selection and dimensionality reduction techniques to enhance model performance. These insights underscore the significant promise of advanced data mining techniques in bolstering early detection, optimizing treatment strategies, and ultimately improving patient outcomes in the realm of oral oncology.
Symmetric quantum states: a review of recent progress
2025-06-11
Carlo Marconi, Guillem Müller-Rigat, Jordi Romero-Pallejà, Jordi Tura, Anna Sanpera
Symmetric quantum states are fascinating objects. They correspond to multipartite systems that remain invariant under particle permutations. This symmetry is reflected in their compact mathematical characterisation and also in their unique physical properties: they exhibit genuine multipartite entanglement and notable robustness against noise and perturbations. These features make such states particularly well-suited for a wide range of quantum information tasks. Here, we provide a pedagogic analysis of the mathematical structure and relevant physical properties of this class of states. Beyond the theoretical framework, it is essential to develop robust tools for certifying and verifying the properties of symmetric states in experimental settings. In this regard, we explore how standard techniques -- such as quantum state tomography, Bell tests, and entanglement witnesses -- can be specifically adapted for symmetric systems. Next, we provide an up-to-date overview of the most relevant applications in which these states outperform other classes of states in specific tasks. Specifically, we address their central role in quantum metrology, highlight their use in quantum error correction codes, and examine their contribution in computation and communication tasks. Finally, we present the current state of the art in their experimental generation, ranging from systems of cold atoms to implementations via quantum algorithms. We also review the most recent breakthroughs obtained in the different experimental realizations. Despite the significant progress made in recent years regarding the characterization and application of symmetric quantum states, several intriguing questions remain unsolved. We conclude this review discussing some of these open problems and outlining promising directions for future research.
A Conjecture on a Fundamental Trade-Off between Certainty and Scope in Symbolic and Generative AI
2025-06-11
Luciano Floridi
This article introduces a conjecture that formalises a fundamental trade-off between provable correctness and broad data-mapping capacity in Artificial Intelligence (AI) systems. When an AI system is engineered for deductively watertight guarantees (demonstrable certainty about the error-free nature of its outputs) -- as in classical symbolic AI -- its operational domain must be narrowly circumscribed and pre-structured. Conversely, a system that can input high-dimensional data to produce rich information outputs -- as in contemporary generative models -- necessarily relinquishes the possibility of zero-error performance, incurring an irreducible risk of errors or misclassification. By making this previously implicit trade-off explicit and open to rigorous verification, the conjecture significantly reframes both engineering ambitions and philosophical expectations for AI. After reviewing the historical motivations for this tension, the article states the conjecture in information-theoretic form and contextualises it within broader debates in epistemology, formal verification, and the philosophy of technology. It then offers an analysis of its implications and consequences, drawing on notions of underdetermination, prudent epistemic risk, and moral responsibility. The discussion clarifies how, if correct, the conjecture would help reshape evaluation standards, governance frameworks, and hybrid system design. The conclusion underscores the importance of eventually proving or refuting the inequality for the future of trustworthy AI.
V455 Car is a southern oscillating eclipsing Algol-type system with an orbital period of 5.132888 days. Our first photometric solutions based on the Transiting Exoplanet Survey Satellite indicate that it is a semi-detached binary with the secondary star is almost filling its Roche lobe. The noticeable O'Connell effect in light curve could be explained by hot spot on the primary component, which may be attributed to the mass transfer from the secondary component to the primary one. The absolute parameters are determined as: $M_{1} = 5.30 \pm 1.10 \, \rm M_{\odot}$, $R_{1} = 3.17 \pm 0.22 \, \rm R_{\odot}$ for the primary, and $M_{2} = 1.58 \pm 0.32 \, \rm M_{\odot}$, $R_{2} = 6.66 \pm 0.46 \, \rm R_{\odot}$ for the secondary. \textbf{Based on $O-C$ analysis, we find a periodic variation of $P_3=26.62(\pm1.66)\,yr$. The periodic oscillation suggests a possible third body with a minimal mass of $0.59(\pm0.13)\,\rm M_{\odot}$}. It is speculated that the secondary star has undergone a longer evolution, leading to a mass ratio reversal being experienced in the binary system. Our frequency analysis finds that the primary of V455 Car may be an SPB/SLF star. This study reports a novel example of an oscillating eclipsing Algol-type system featuring an SPB/SLF primary star and a red giant star, which suggest that strong observational results for a high incidence of third bodies in massive binaries.
We present a hierarchical Bayesian framework designed to infer the luminosity function of any class of objects by jointly modeling data from multiple surveys with varying depth, completeness, and sky coverage. Our method explicitly accounts for selection effects and measurement uncertainties (e.g., in luminosity) and could be generalized to any extensive quantity, such as mass. We validate the model using mock catalogs, recovering how deep data reaching $\gtrsim 1.5$ dex below a characteristic luminosity $\tilde{L}^\star$ are essential to reduce biases at the faint end ($\lesssim 0.1$ dex), while wide-area data help constrain the bright end. As proof of concept, we consider a combined sample of 1176 Ly$\alpha$ emitters at redshift $3 < z < 5$ drawn from several MUSE surveys, ranging from ultra-deep ($\gtrsim 90$ hr) and narrow ($\lesssim 1$ arcmin$^2$) fields to shallow ($\lesssim 5$ hr) and wide ($\gtrsim 20$ arcmin$^2$) fields. With this complete sample, we constrain the luminosity function parameters $\log(\Phi^\star/\mathrm{Mpc^{-3}}) = -2.86^{+0.15}_{-0.17}$, $\log(L^\star/\mathrm{erg\,s^{-1}}) = 42.72^{+0.10}_{-0.09}$, and $\alpha = -1.81^{+0.09}_{-0.09}$, where the uncertainties represent the $90\%$ credible intervals. These values are in agreement with studies based on gravitational lensing that reach $\log(L/\mathrm{erg\,s^{-1}}) \approx 41$, although differences in the faint-end slope underscore how systematic errors are starting to dominate. In contrast, wide-area surveys represent the natural extension needed to constrain the brightest Ly$\alpha$ emitters [$\log(L/\mathrm{erg\,s^{-1}}) \gtrsim 43$], where statistical uncertainties still dominate.
Cybernetic Marionette: Channeling Collective Agency Through a Wearable Robot in a Live Dancer-Robot Duet
2025-06-11
Anup Sathya, Jiasheng Li, Zeyu Yan, Adriane Fang, Bill Kules, Jonathan David Martin, Huaishu Peng
We describe DANCE^2, an interactive dance performance in which audience members channel their collective agency into a dancer-robot duet by voting on the behavior of a wearable robot affixed to the dancer's body. At key moments during the performance, the audience is invited to either continue the choreography or override it, shaping the unfolding interaction through real-time collective input. While post-performance surveys revealed that participants felt their choices meaningfully influenced the performance, voting data across four public performances exhibited strikingly consistent patterns. This tension between what audience members do, what they feel, and what actually changes highlights a complex interplay between agentive behavior, the experience of agency, and power. We reflect on how choreography, interaction design, and the structure of the performance mediate this relationship, offering a live analogy for algorithmically curated digital systems where agency is felt, but not exercised.
Inverted Classroom in der Einführungsveranstaltung Programmierung
2025-06-11
Ulrich von Zadow, Natalie Kiesler
Traditionally, the introductory programming course for computer science students at Nuremberg Tech had been implemented as a combination of lectures and exercise sessions. Due to high failure rates in the winter semester 2023/24, an experimental teaching concept based on the inverted classroom was implemented for one cohort in the winter semester 2024/25. Students had to prepare themselves through literature work and activating teaching and learning methods. The course was accompanied by a series of data collections (i.e., a Teaching Analysis Poll, two surveys, and a teaching diary) to gain insights into students' learning methods and behaviors. The concept was evaluated positively overall, although many detailed opportunities for improvement were identified. In this article, we document the results of the surveys and discuss the implications
Multiverse Privacy Theory for Contextual Risks in Complex User-AI Interactions
2025-06-11
Ece Gumusel
In an era of increasing interaction with artificial intelligence (AI), users face evolving privacy decisions shaped by complex, uncertain factors. This paper introduces Multiverse Privacy Theory, a novel framework in which each privacy decision spawns a parallel universe, representing a distinct potential outcome based on user choices over time. By simulating these universes, this theory provides a foundation for understanding privacy through the lens of contextual integrity, evolving preferences, and probabilistic decision-making. Future work will explore its application using real-world, scenario-based survey data.
2025-06-11
Tania Chakraborty, Eylon Caplan, Dan Goldwasser
Understanding human social behavior such as recognizing emotions and the social dynamics causing them is an important and challenging problem. While LLMs have made remarkable advances, they are limited to the textual domain and cannot account for the major role that non-verbal cues play in understanding social situations. Vision Language Models (VLMs) can potentially account for this gap, however their ability to make correct inferences over such social cues has received little attention. In this paper, we explore the capabilities of VLMs at social reasoning. We identify a previously overlooked limitation in VLMs: the Visual Social-Pragmatic Inference gap. To target this gap, we propose a new task for VLMs: Visual Social-Pragmatic Inference. We construct a high quality dataset to test the abilities of a VLM for this task and benchmark the performance of several VLMs on it.
Longitudinal Omics Data Analysis: A Review on Models, Algorithms, and Tools
2025-06-11
Ali R. Taheriyoun, Allen Ross, Abolfazl Safikhani, Damoon Soudbakhsh, Ali Rahnavard
Longitudinal omics data (LOD) analysis is essential for understanding the dynamics of biological processes and disease progression over time. This review explores various statistical and computational approaches for analyzing such data, emphasizing their applications and limitations. The main characteristics of longitudinal data, such as imbalancedness, high-dimensionality, and non-Gaussianity are discussed for modeling and hypothesis testing. We discuss the properties of linear mixed models (LMM) and generalized linear mixed models (GLMM) as foundation stones in LOD analyses and highlight their extensions to handle the obstacles in the frequentist and Bayesian frameworks. We differentiate in dynamic data analysis between time-course and longitudinal analyses, covering functional data analysis (FDA) and replication constraints. We explore classification techniques, single-cell as exemplary omics longitudinal studies, survival modeling, and multivariate methods for clinical/biomarker-based applications. Emerging topics, including data integration, clustering, and network-based modeling, are also discussed. We categorized the state-of-the-art approaches applicable to omics data, highlighting how they address the data features. This review serves as a guideline for researchers seeking robust strategies to analyze longitudinal omics data effectively, which is usually complex.
On the development of an AI performance and behavioural measures for teaching and classroom management
2025-06-11
Andreea I. Niculescu, Jochen Ehnen, Chen Yi, Du Jiawei, Tay Chiat Pin, Joey Tianyi Zhou, Vigneshwaran Subbaraju, Teh Kah Kuan, Tran Huy Dat, John Komar, Gi Soong Chee, Kenneth Kwok
This paper presents a two-year research project focused on developing AI-driven measures to analyze classroom dynamics, with particular emphasis on teacher actions captured through multimodal sensor data. We applied real-time data from classroom sensors and AI techniques to extract meaningful insights and support teacher development. Key outcomes include a curated audio-visual dataset, novel behavioral measures, and a proof-of-concept teaching review dashboard. An initial evaluation with eight researchers from the National Institute for Education (NIE) highlighted the system's clarity, usability, and its non-judgmental, automated analysis approach -- which reduces manual workloads and encourages constructive reflection. Although the current version does not assign performance ratings, it provides an objective snapshot of in-class interactions, helping teachers recognize and improve their instructional strategies. Designed and tested in an Asian educational context, this work also contributes a culturally grounded methodology to the growing field of AI-based educational analytics.
Learning Correlated Astrophysical Foregrounds with Denoising Diffusion Probabilistic Models
2025-06-10
Karthik Prabhu, Srinivasan Raghunathan, Ethan B. Anderes, Lloyd E. Knox
Extragalactic foregrounds -- most notably the Cosmic Infrared Background (CIB) and the thermal Sunyaev-Zel'dovich (tSZ) effect -- exhibit complex, non-Gaussian structure and correlations that can bias analyses of small-scale cosmic microwave background (CMB) temperature anisotropies. These foregrounds can introduce mode coupling at small-scales (multipoles $\ell \geq 3000$) that mimic true lensing signals, thereby complicating analyses such as CMB lensing reconstruction. We present a novel approach to learn their full joint distribution using Denoising Diffusion Probabilistic Models (DDPMs) trained on paired CIB-tSZ patches at 150 GHz, from the Agora suite of extragalactic sky simulations. While simulations like Agora, which are based on N-body calculations, can take thousands of CPU hours, DDPM can synthesize realistic CIB-tSZ patches that faithfully reproduce both auto- and cross-spectral statistics of the 2-point, 3-point, and 4-point correlation functions, in a matter of seconds. We further demonstrate matching pixel-value histograms and Minkowski functionals, confirming that conventional non-Gaussian benchmarks are also satisfied. This framework provides a powerful generative tool for forward-modeling correlated extragalactic foregrounds in current and future CMB analyses. Although we mainly demonstrate the joint modeling of tSZ and CIB at a single frequency, we also include examples of its extension to multiple frequencies, showing that the framework can learn the spectral energy distributions (SEDs) across different bands. While establishing DDPMs as a promising tool for addressing foreground contamination in next-generation CMB surveys, we also outline remaining challenges to their practical deployment in analysis pipelines, such as scaling to larger sky areas and reliance on the underlying cosmological and astrophysical assumptions in the simulations used for training.
Boosting Rust Unit Test Coverage through Hybrid Program Analysis and Large Language Models
2025-06-10
Bei Chu, Yang Feng, Kui Liu, Hange Shi, Zifan Nan, Zhaoqiang Guo, Baowen Xu
Unit testing is essential for ensuring software reliability and correctness. Classic Search-Based Software Testing (SBST) methods and concolic execution-based approaches for generating unit tests often fail to achieve high coverage due to difficulties in handling complex program units, such as branching conditions and external dependencies. Recent work has increasingly utilized large language models (LLMs) to generate test cases, improving the quality of test generation by providing better context and correcting errors in the model's output. However, these methods rely on fixed prompts, resulting in relatively low compilation success rates and coverage. This paper presents PALM, an approach that leverages large language models (LLMs) to enhance the generation of high-coverage unit tests. PALM performs program analysis to identify branching conditions within functions, which are then combined into path constraints. These constraints and relevant contextual information are used to construct prompts that guide the LLMs in generating unit tests. We implement the approach and evaluate it in 10 open-source Rust crates. Experimental results show that within just two or three hours, PALM can significantly improves test coverage compared to classic methods, with increases in overall project coverage exceeding 50% in some instances and its generated tests achieving an average coverage of 75.77%, comparable to human effort (71.30%), highlighting the potential of LLMs in automated test generation. We submitted 91 PALM-generated unit tests targeting new code. Of these submissions, 80 were accepted, 5 were rejected, and 6 remain pending review. The results demonstrate the effectiveness of integrating program analysis with AI and open new avenues for future research in automated software testing.
Investigating the physical properties of dusty star-forming galaxies at z>=1.5 in the GOODS-South field using JWST
2025-06-10
Dipanjan Mitra, Mattia Negrello, Gianfranco De Zotti
We investigated how well the physical properties of progenitors of present-day massive spheroidal galaxies (proto-spheroids) can be constrained by the JWST Advanced Deep Extragalactic Survey (JADES) in the GOODS-South field, which benefits from extensive photometric and spectroscopic data, including those from the Hubble, Spitzer and Herschel. We adopted a physical model for the evolution of proto-spheroidal galaxies, which form the bulk of dusty star-forming galaxies (DSFGs) at z>=1.5 and confirmed its consistency with recent mid-infrared high-z galaxy luminosity functions. Using the model and the JADES survey strategy, we simulated a sample of proto-spheroids over 87.5 arcmin^2, matching the JADES/GOODS-S survey area. Photometric redshifts estimated from simulated JWST photometry showed >=95% accuracy and were used in SED fitting with CIGALE. We demonstrated that JWST will provide reliable stellar mass estimates up to 0.1 dex for the majority of proto-spheroids at z>=1.5 and can detect low-mass systems during cosmic noon that were inaccessible in the pre-JWST era. Focusing on the active star-forming phase of the proto-spheroid evolution, we defined a sub-sample flux limited at 250 micron (DSFG sample) and derived SFR, dust luminosity and dust mass complementing the JWST photometry with that from Spitzer/MIPS and Herschel. We also constructed a JWST-selected DSFG catalog from ASTRODEEP data using NIRCam colour criteria and demonstrated strong consistency between the observed and simulated DSFG populations.
N-ary Knowledge Graphs (NKGs) are a specialized type of knowledge graph designed to efficiently represent complex real-world facts. Unlike traditional knowledge graphs, where a fact typically involves two entities, NKGs can capture n-ary facts containing more than two entities. Link prediction in NKGs aims to predict missing elements within these n-ary facts, which is essential for completing NKGs and improving the performance of downstream applications. This task has recently gained significant attention. In this paper, we present the first comprehensive survey of link prediction in NKGs, providing an overview of the field, systematically categorizing existing methods, and analyzing their performance and application scenarios. We also outline promising directions for future research.
A New Spectral Class of Brown Dwarfs at the Bottom of the IMF in IC 348
2025-06-10
K. L. Luhman, C. Alves de Oliveira
In a previous study, we used JWST to identify three new brown dwarfs in the center of a nearby star-forming cluster, IC 348. The faintest object had an estimated mass of 3-4 $M_{\rm Jup}$, making it a contender for the least massive brown dwarf confirmed with spectroscopy. Two of the new brown dwarfs also exhibited absorption features from an unidentified aliphatic hydrocarbon, which were not predicted by atmospheric models and were not previously detected in atmospheres outside of the solar system. We have used JWST to perform a deeper survey for brown dwarfs across a larger field in IC 348. We have identified 39 brown dwarf candidates in NIRCam images and have obtained spectra for 15 of them with NIRSpec, nine of which are classified as substellar members of the cluster. The faintest new members have mass estimates of $\sim2$ $M_{\rm Jup}$, providing a new constraint on the minimum mass of the IMF. Two new members ($\sim2$ and 10 $M_{\rm Jup}$) exhibit large excess emission from circumstellar disks, demonstrating that they harbor the raw materials for planet formation. Finally, eight of the nine new brown dwarfs and one known member that is newly observed with NIRSpec show the aforementioned hydrocarbon features. Among the total of 11 brown dwarfs in IC 348 that have hydrocarbon detections, the features are stronger at fainter magnitudes, indicating that the hydrocarbon is a natural constituent of the atmospheres of the coolest newborn brown dwarfs. We propose a new spectral class "H" that is defined by the presence of the 3.4 $\mu$m fundamental band of the hydrocarbon.
Heavy-heavy and heavy-light mesons in cold nuclear matter
2025-06-10
J. J. Cobos-Martínez, Guilherme N. Zeminiani, Kazuo Tsushima
We review the in-medium modifications of effective masses (Lorentz scalar potentials or phenomenon of mass shift) of heavy-heavy and heavy-light mesons in symmetric nuclear matter and their nuclear bound states. We use a combined approach with the quark-meson coupling (QMC) model and an effective Lagrangian. As demonstrated by the cases of pionic and kaonic atoms, studies of meson-nucleus bound state can provide us with important information on chiral symmetry in dense nuclear medium. In this review, we treat the mesons, $K, K^*, D, D^*, B, B^*, \eta, \eta', \phi, \eta_c, J/\psi, \eta_b, \Upsilon$, and $B_c$, where our emphasys is on the heavy mesons. In addition, we also present some new results for the $B_c$-nucleus bound states.
Measurement of the Dispersion$\unicode{x2013}$Galaxy Cross-Power Spectrum with the Second CHIME/FRB Catalog
2025-06-10
Haochen Wang, Kiyoshi Masui, Shion Andrew, Emmanuel Fonseca, B. M. Gaensler, R. C. Joseph, Victoria M. Kaspi, Bikash Kharel, Adam E. Lanman, Calvin Leung, Lluis Mas-Ribas, Juan Mena-Parra, Kenzie Nimmo, Aaron B. Pearlman, Ue-Li Pen, J. Xavier Prochaska, Ryan Raikman, Kaitlyn Shin, Seth R. Siegel, Kendrick M. Smith, Ingrid H. Stairs
The dispersion of extragalactic fast radio bursts (FRBs) can serve as a powerful probe of the diffuse plasma between and surrounding galaxies, which contains most of the Universe's baryons. By cross-correlating the dispersion of background FRBs with the locations of foreground galaxies, we can study the relative spatial distributions of plasma and galaxies on scales of 0.1 to 50 Mpc, which are strongly affected by feedback processes in galaxy formation. Here we present the measurement of the dispersion$\unicode{x2013}$galaxy angular cross-power spectrum between 2873 FRBs from the Second CHIME/FRB Catalog and nearly 6 million galaxies from the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Survey. Over five photometric galaxy redshift bins spanning $0.05 < z <0.5$ and at 5.1$\sigma$ significance, we make the first definitive detection of spatial correlations in FRB dispersion measure due to cosmic structure. While parameter inferences should be interpreted with caution because of incomplete modelling of both the signal and systematic errors, our data indicate that the plasma$\unicode{x2013}$galaxy cross-power spectrum cuts off relative to the matter power spectrum at a scale $k_\textrm{cut}^{-1}=0.9^{+0.4}_{-0.4}\,\textrm{Mpc}$. This scale is consistent with those X-ray stacking analyses that suggest dark-matter halos with group-scale masses are largely evacuated of their baryons by feedback processes. Our study demonstrates that FRBs are promising tools to discern the physics of baryonic structure formation and will only become more powerful as FRB surveys expand.
Splashback radius and the mass accretion rate of RASS MCMF galaxy clusters
2025-06-10
Jitendra Joshi, Divya Rana, Surhud More, Matthias Klein
We present measurements of the radial profile of mass and galaxy number density around X-ray selected ROSAT All Sky Survey-Multi-Component Matched Filter galaxy clusters using Year 3 data from the Dark Energy Survey. We measure the projected cross-correlation signal of the RedMaGiC "high density" galaxies around an approximately volume-limited sample of 255 galaxy clusters at a median redshift of $z=0.4$ and an X-ray luminosity $L_X > 10^{44} \,\text{ergs} \, \text{s}^{-1} \, \text{h}^{-2}$. This cross-correlation signal measured with a signal-to-noise ratio of 16.41 allows us to infer a 3D number density profile which shows a significant steepening at the edges of these galaxy clusters, namely the splashback radius of $r_{sp}$ /$h^{-1} \mathrm{M_{\odot}} = 2.19^{+0.50}_{-0.43}$. We present the dependence of the splashback radius value over a range of absolute galaxy magnitude cuts to look for any evidence of dynamical friction affecting these results. The weak lensing signal around our galaxy clusters measured with a signal-to-noise ratio of 32.19 allows us to infer a halo mass $\text{log} (M_{\rm 200m} / h^{-1} \text{Mpc}) = 14.68_{-0.04}^{+0.04}$. Comparison of the location of the splashback radius with the spherical overdensity boundary $r_{\rm 200m}$ shows consistency with the $\mathrm{\Lambda CDM}$ predictions. We present the first inference of the average mass accretion rate of galaxy clusters using our measurements of the splashback radius.
PropMEND: Hypernetworks for Knowledge Propagation in LLMs
2025-06-10
Zeyu Leo Liu, Greg Durrett, Eunsol Choi
Knowledge editing techniques for large language models (LLMs) can inject knowledge that is later reproducible verbatim, but they fall short on propagating that knowledge: models cannot answer questions that require reasoning with the injected knowledge. We present a hypernetwork-based approach for knowledge propagation, named PropMEND, where we meta-learn how to modify gradients of a language modeling loss to encourage injected information to propagate. Our approach extends the meta-objective of MEND [29] so that gradient updates on knowledge are transformed to enable answering multi-hop questions involving that knowledge. We show improved performance on the RippleEdit dataset, showing almost 2x accuracy on challenging multi-hop questions whose answers are not explicitly stated in the injected fact. We further introduce a new dataset, Controlled RippleEdit, to evaluate the generalization of our hypernetwork, testing knowledge propagation along relations and entities unseen during hypernetwork training. PropMEND still outperforms existing approaches in unseen entity-relation pairs, yet the performance gap decreases substantially, suggesting future work in propagating knowledge to a wide range of relations.
Star formation (SF) studies are benefiting from the huge amount of data made available by recent large-area Galactic plane surveys conducted between 2 {\mu}m and 3 mm. Fully characterizing SF demands integrating far-infrared/sub-millimetre (FIR/sub-mm) data, tracing the earliest phases, with near-/mid-infrared (NIR/MIR) observations, revealing later stages characterized by YSOs just before main sequence star appearance. However, the resulting dataset is often a complex mix of heterogeneous and intricate features, limiting the effectiveness of traditional analysis in uncovering hidden patterns and relationships. In this framework, machine learning emerges as a powerful tool to handle the complexity of feature-rich datasets and investigate potential physical connections between the cold dust component traced by FIR/sub-mm emission and the presence of YSOs. We present a study on the evolutionary path of star forming clumps in the Hi-GAL survey through a multi-step approach, with the final aims of (a) obtaining a robust and accurate set of features able to well classify the star forming clumps in Hi-GAL based on their evolutionary properties, (b) establishing whether a connection exists between the cold material reservoir in clumps, traced by FIR/sub-mm emission, and the already formed YSOs, precursors of stars. For these purposes, our designed experiments aim at testing whether the FIR/sub-mm properties related to clumps are sufficient to predict the clump evolutionary stage, without considering the direct information about the embedded YSOs at NIR/MIR. Our machine learning-based method involves a four-step approach, based on feature engineering, data handling, feature selection and classification. Our findings suggest that FIR/sub-mm and NIR/MIR emissions trace different evolutionary phases of star forming clumps, highlighting the complex and asynchronous nature of the SF process.
Recent deep reinforcement learning methods have achieved remarkable success in solving multi-objective combinatorial optimization problems (MOCOPs) by decomposing them into multiple subproblems, each associated with a specific weight vector. However, these methods typically treat all subproblems equally and solve them using a single model, hindering the effective exploration of the solution space and thus leading to suboptimal performance. To overcome the limitation, we propose POCCO, a novel plug-and-play framework that enables adaptive selection of model structures for subproblems, which are subsequently optimized based on preference signals rather than explicit reward values. Specifically, we design a conditional computation block that routes subproblems to specialized neural architectures. Moreover, we propose a preference-driven optimization algorithm that learns pairwise preferences between winning and losing solutions. We evaluate the efficacy and versatility of POCCO by applying it to two state-of-the-art neural methods for MOCOPs. Experimental results across four classic MOCOP benchmarks demonstrate its significant superiority and strong generalization.
Observations of Carbon Radio Recombination Lines with the NenuFAR telescope. I. Cassiopeia A and Cygnus A
2025-06-10
Lucie Cros, Antoine Gusdorf, Philippe Salomé, Sergiy Stepkin, Philippe Zarka, Pedro Salas, Alan Loh, Pierre Lesaffre, Jonathan Freundlich, Marta Alves, François Boulanger, Andrea Bracco, Stéphane Corbel, Maryvonne Gerin, Javier Goicoechea, Isabelle Grenier, Jean-Mathias Grießmeier, Martin Houde, Oleksandr Konovalenko, Antoine Marchal, Alexandre Marcowith, Florent Mertens, Frédérique Motte, Michel Tagger, Alexander Tielens, Gilles Theureau, Peter Tokarsky, Oleg Ulyanov, Vyacheslav Zakharenko
Carbon Radio Recombination Lines (CRRLs) at decametre wavelengths trace the diffuse phase of the interstellar medium (ISM) of the Galaxy. Their observation allows to measure physical parameters of this phase. We observed CRRLs with the recently commissioned New Extension in Nan\c{c}ay Upgrading LOFAR (NenuFAR) telescope towards two of the brightest sources at low-frequency (10-85 MHz): Cassiopeia A and Cygnus A (hereafter Cas A and Cyg A respectively), to measure the density n_e and temperature T_e of electrons in line-of-sight clouds. We used NenuFAR's beamforming mode, and we integrated several tens of hours on each source. The nominal spectral resolution was 95.4 Hz. We developed a pipeline to remove radio frequency interference (RFI) contamination and correct the baselines. We then fitted the spectral lines observed in absorption, associated to line-of-sight clouds. Cas A is the brightest source in the sky at low frequencies and represents an appropriate test bench for this new telescope. On this source, we detected 398 C\alpha lines between principal quantum numbers n=426 and n=826. C\alpha lines towards Cyg A were fainter. We stacked the signal by groups of a few tens of lines to improve the quality of our fitting process. On both sources we reached significantly higher S/N and spectral resolution than the most recent detections by the LOw Frequency ARray (LOFAR). The variation of line shape with n provides constraints on the physical properties of the clouds: T_e, n_e, the temperature T_0 of the radiation field, the mean turbulent velocity v_t and the typical size of the cloud. The NenuFAR observations sample a larger space volume than LOFAR's towards the same sources due to the differences in instrumental beamsizes, and the discrepancies highlight the sensitivity of low-frequency CRRLs as probes of the diffuse ISM, paving the way towards large area surveys of CRRLs in our Galaxy.
Radio-loud AGN morphology and host-galaxy properties in the LOFAR Two-Metre Sky Survey Data Release 2
2025-06-10
L. Clews, J. H. Croston, H. Dickinson, B. Mingo, M. J. Hardcastle, B. Barkus, J. M. G. H. J. de Jong, H. J. A. Röttgering
Radio-loud active galaxies (RLAGN) can exhibit various morphologies. The Fanaroff-Riley (FR) classifications, which are defined by the locations of peaks in surface brightness, have been applied to many catalogues of RLAGN. The FR classifications were initially found to correlate with radio luminosity. However, recent surveys have demonstrated that radio luminosity alone does not reliably predict radio morphology. We have devised a new-semi automated method involving ridgeline characterisations to compile the largest known classified catalogue of RLAGN to date with data from the second data release of the LOFAR Two-Metre Sky Survey (LoTSS DR2). We reassess the FR divide and its cause by examining the physical and host galaxy properties of $3590$ FRIIs and $2354$ FRIs (at $z \leq 0.8$). We find that RLAGN near the FR divide with $10^{25} \le L_{144} \le 10^{26} $ WHz$^{-1}$ are more likely to show FRI over FRII morphology if they occupy more massive host galaxies. We find no correlation, when considering selection effects, between the FR break luminosity and stellar mass or host-galaxy rest-frame absolute magnitude. Overall, we find the cause of different radio morphologies in this sample to be complex. Considering sources near the FR divide with $10^{25} \le L_{144} \le 10^{26} $ WHz$^{-1}$, we find evidence to support inner environment having a role in determining jet disruption. We make available a public catalogue of morphologies for our sample, which will be of use for future investigations of RLAGN and their impact on their surroundings.
On The Impact of Merge Request Deviations on Code Review Practices
2025-06-10
Samah Kansab, Francis Bordeleau, Ali Tizghadam
Code review is a key practice in software engineering, ensuring quality and collaboration. However, industrial Merge Request (MR) workflows often deviate from standardized review processes, with many MRs serving non-review purposes (e.g., drafts, rebases, or dependency updates). We term these cases deviations and hypothesize that ignoring them biases analytics and undermines ML models for review analysis. We identify seven deviation categories, occurring in 37.02% of MRs, and propose a few-shot learning detection method (91% accuracy). By excluding deviations, ML models predicting review completion time improve performance in 53.33% of cases (up to 2.25x) and exhibit significant shifts in feature importance (47% overall, 60% top-*k*). Our contributions include: (1) a taxonomy of MR deviations, (2) an AI-driven detection approach, and (3) empirical evidence of their impact on ML-based review analytics. This work aids practitioners in optimizing review efforts and ensuring reliable insights.
The MeerKAT Massive Distant Clusters Survey: Detection of Diffuse Radio Emission in Galaxy Clusters at $z > 1$
2025-06-10
Dakalo G. Phuravhathu, M. Hilton, S. P. Sikhosana, Y. C. Perrott, T. Mroczkowski, L. Di Mascolo, D. Y. Klutse, K. Knowles, J. van Marrewijk, K. Moodley, B. Partridge, C. Sifón, U. Sureshkumar, E. J. Wollack
Diffuse, low surface-brightness radio emission in merging galaxy clusters provides insights into cosmic structure formation, the growth of magnetic fields, and turbulence. This paper reports a search for diffuse radio emission in a pilot sample of six high-redshift ($1.01 < z < 1.31$) galaxy clusters from the MeerKAT Massive Distant Cluster Survey (MMDCS). These six clusters are selected as the most massive $(M_{\rm 500c} = 6.7\,- 8.5 \times 10^{14}~\rm{M_{\odot}})$ systems based on their Sunyaev-Zel'dovich mass from the full MMDCS sample of 30 ACT DR5 clusters, and were observed first to explore the high-mass, high-redshift regime. Diffuse radio emission is confidently detected in four clusters and tentatively identified in two, with $k$-corrected radio powers scaled to 1.4 GHz ranging from $(0.46 \pm 0.16)$ to $(4.51 \pm 1.68) \times 10^{24}\, \mathrm{WHz^{-1}}$ and linear sizes between 0.47 and 1.08 Mpc. Combining $Chandra$ X-ray data with MeerKAT radio data, we find that 80$\%$ of clusters with X-ray observations exhibit disturbed morphologies indicative of mergers. These $z > 1$ galaxy clusters scatter around the established radio power-mass scaling relation observed at lower redshifts, supporting turbulent re-acceleration models in high-redshift mergers. However, their radio spectra are predicted to steepen ($\alpha < -1.5$) due to enhanced inverse Compton losses in the cosmic microwave background, rendering them under-luminous at 1.4 GHz and placing them below the correlation. Our results demonstrate that merger-driven turbulence can sustain radio halos even at $z > 1$ while highlighting MeerKAT's unique ability to probe non-thermal processes in the early universe.
IMAGIC-500: IMputation benchmark on A Generative Imaginary Country (500k samples)
2025-06-10
Siyi Sun, David Antony Selby, Yunchuan Huang, Sebastian Vollmer, Seth Flaxman, Anisoara Calinescu
Missing data imputation in tabular datasets remains a pivotal challenge in data science and machine learning, particularly within socioeconomic research. However, real-world socioeconomic datasets are typically subject to strict data protection protocols, which often prohibit public sharing, even for synthetic derivatives. This severely limits the reproducibility and accessibility of benchmark studies in such settings. Further, there are very few publicly available synthetic datasets. Thus, there is limited availability of benchmarks for systematic evaluation of imputation methods on socioeconomic datasets, whether real or synthetic. In this study, we utilize the World Bank's publicly available synthetic dataset, Synthetic Data for an Imaginary Country, which closely mimics a real World Bank household survey while being fully public, enabling broad access for methodological research. With this as a starting point, we derived the IMAGIC-500 dataset: we select a subset of 500k individuals across approximately 100k households with 19 socioeconomic features, designed to reflect the hierarchical structure of real-world household surveys. This paper introduces a comprehensive missing data imputation benchmark on IMAGIC-500 under various missing mechanisms (MCAR, MAR, MNAR) and missingness ratios (10\%, 20\%, 30\%, 40\%, 50\%). Our evaluation considers the imputation accuracy for continuous and categorical variables, computational efficiency, and impact on downstream predictive tasks, such as estimating educational attainment at the individual level. The results highlight the strengths and weaknesses of statistical, traditional machine learning, and deep learning imputation techniques, including recent diffusion-based methods. The IMAGIC-500 dataset and benchmark aim to facilitate the development of robust imputation algorithms and foster reproducible social science research.
Cosmic filaments confirm unexplained cooling of CMB photons in two independent redshift ranges
2025-06-10
Juan Ignacio Domínguez Feldman, Luis A. Pereyra, Frode K. Hansen, Facundo Toscano, Diego Garcia Lambas
Recent papers have reported an unexplained cooling of CMB photons passing through galaxies in nearby cosmic filaments $z<0.02$ at the $>5\sigma$ level. Here we show for the first time that this effect is also present at higher redshifts $0.02
SPECULOOS: five years hunting terrestrial planets around ultra-cool dwarfs
2025-06-10
Sebastián Zúñiga-Fernández, Michael Gillon, SPECULOOS consortium
The SPECULOOS (Search for habitable Planets EClipsing ULtra-cOOl Stars) project aims to detect temperate terrestrial planets transiting nearby ultracool dwarfs, including late M-dwarf stars and brown dwarfs, which are well-suited for atmospheric characterization using the James Webb Space Telescope (JWST) and upcoming giant telescopes like the European Extremely Large Telescope (ELT). Led by the University of Li\`ege, SPECULOOS is conducted in partnership with the University of Cambridge, the University of Birmingham, the Massachusetts Institute of Technology, the University of Bern, and ETH Zurich. The project operates a network of robotic telescopes at two main observatories: SPECULOOS-South in Chile, with four telescopes, and SPECULOOS-North in Tenerife, currently with one telescope (soon to be two). This network is complemented by the SAINT-EX telescope located in San Pedro M\'artir, Mexico. In this paper, we review the status of our facilities after five years of operations, the current challenges and development plans, and our latest scientific results.
Using gravitational waves and multi-messenger Astronomy to reverse-engineer the properties of galactic nuclei
2025-06-10
K. E. Saavik Ford, Barry McKernan
Active galactic nuclei (AGN) are powered by accretion disks onto supermassive black holes in the the centers of galaxies. AGN are believed to play important roles in the evolution of both supermassive black holes and their host galaxies over cosmic time. AGN and the nuclear star clusters (NSCs) that interact with them remain unresolved with present and planned telescopes. As a result, the properties of AGN and NSCs are highly uncertain. Here we review how binary black hole (BBH) mergers can occur in AGN disks and how both the gravitational wave (GW) and electromagnetic wave (EM) properties of such mergers allow us to reverse-engineer the properties of AGN disks and NSCs over cosmic time. We point out that the feature in the BBH mass spectrum around $\sim 35M_{\odot}$ is an excellent probe of hierarchical merger models. Likewise constraints on the spins of upper-mass gap BH ($\gtrsim 50M_{\odot}$) test the AGN channel. The effective spin ($\chi_{\rm eff}$) distribution, including asymmetry, islands of structure and magnitudes are excellent tests of AGN model predictions. We also argue, that the rate of AGN-driven BBH mergers as a function of redshift should scale slightly shallower than the AGN number density, at least out to redshifts of $\sim 2$, and should turnover at the same redshift as the AGN number density. Finally, we emphasize a determination of an AGN fraction of observed BBH mergers ($f_{\rm BBH,AGN}$), \emph{regardless of the actual value}, allows us to infer the average properties of AGN disks and NSCs out to high redshift.
Measuring Data Science Automation: A Survey of Evaluation Tools for AI Assistants and Agents
2025-06-10
Irene Testini, José Hernández-Orallo, Lorenzo Pacchiardi
Data science aims to extract insights from data to support decision-making processes. Recently, Large Language Models (LLMs) are increasingly used as assistants for data science, by suggesting ideas, techniques and small code snippets, or for the interpretation of results and reporting. Proper automation of some data-science activities is now promised by the rise of LLM agents, i.e., AI systems powered by an LLM equipped with additional affordances--such as code execution and knowledge bases--that can perform self-directed actions and interact with digital environments. In this paper, we survey the evaluation of LLM assistants and agents for data science. We find (1) a dominant focus on a small subset of goal-oriented activities, largely ignoring data management and exploratory activities; (2) a concentration on pure assistance or fully autonomous agents, without considering intermediate levels of human-AI collaboration; and (3) an emphasis on human substitution, therefore neglecting the possibility of higher levels of automation thanks to task transformation.
Hidden geometric invariance in Laplace-governed magnetization problems
2025-06-10
Yujun Shi
For over a century, the response of magnetic materials to static fields, formulated as transmission problems of the Laplace equation, has been a standard topic in undergraduate electrodynamics. In the infinite permeability limit, the body becomes nearly equipotential and the internal magnetic field nearly vanishes, giving rise to magnetostatic shielding, a phenomenon in which the magnetic induction is nearly zero throughout the interior free space. While this textbook-level result has long been considered fully understood, here, we identify a previously overlooked geometric invariance that complements the quasi-equipotential picture: in the same limit, the entire external magnetic response of a body is determined solely by its outer surface geometry, regardless of internal structure or deformation, as long as the outer surface remains fixed. This surface-encoded invariance encompasses the external field distribution and all magnetic multipole moments of the body, including the dipole and quadrupole, derived as volume integrals from the internal scalar potential. It provides a theoretical foundation for the lightweight design of certain magnetic devices, such as flux concentrators. Given that analogous Laplace transmission problems arise across physics, including electricity conduction, heat conduction and acoustic scattering, this geometric invariance exhibits cross-disciplinary universality. It merits inclusion in undergraduate electromagnetism curricula, as it and the quasi-equipotential property together form a complete characterization of Laplace transmission problems in the infinite permeability limit.
syren-baryon: Analytic emulators for the impact of baryons on the matter power spectrum
2025-06-10
Lukas Kammerer, Deaglan J. Bartlett, Gabriel Kronberger, Harry Desmond, Pedro G. Ferreira
Baryonic physics has a considerable impact on the distribution of matter in our Universe on scales probed by current and future cosmological surveys, acting as a key systematic in such analyses. We seek simple symbolic parametrisations for the impact of baryonic physics on the matter power spectrum for a range of physically motivated models, as a function of wavenumber, redshift, cosmology, and parameters controlling the baryonic feedback. We use symbolic regression to construct analytic approximations for the ratio of the matter power spectrum in the presence of baryons to that without such effects. We obtain separate functions of each of four distinct sub-grid prescriptions of baryonic physics from the CAMELS suite of hydrodynamical simulations (Astrid, IllustrisTNG, SIMBA and Swift-EAGLE) as well as for a baryonification algorithm. We also provide functions which describe the uncertainty on these predictions, due to both the stochastic nature of baryonic physics and the errors on our fits. The error on our approximations to the hydrodynamical simulations is comparable to the sample variance estimated through varying initial conditions, and our baryonification expression has a root mean squared error of better than one percent, although this increases on small scales. These errors are comparable to those of previous numerical emulators for these models. Our expressions are enforced to have the physically correct behaviour on large scales and at high redshift. Due to their analytic form, we are able to directly interpret the impact of varying cosmology and feedback parameters, and we can identify parameters which have little to no effect. Each function is based on a different implementation of baryonic physics, and can therefore be used to discriminate between these models when applied to real data. We provide publicly available code for all symbolic approximations found.
Enhancing Accuracy and Maintainability in Nuclear Plant Data Retrieval: A Function-Calling LLM Approach Over NL-to-SQL
2025-06-10
Mishca de Costa, Muhammad Anwar, Dave Mercier, Mark Randall, Issam Hammad
Retrieving operational data from nuclear power plants requires exceptional accuracy and transparency due to the criticality of the decisions it supports. Traditionally, natural language to SQL (NL-to-SQL) approaches have been explored for querying such data. While NL-to-SQL promises ease of use, it poses significant risks: end-users cannot easily validate generated SQL queries, and legacy nuclear plant databases -- often complex and poorly structured -- complicate query generation due to decades of incremental modifications. These challenges increase the likelihood of inaccuracies and reduce trust in the approach. In this work, we propose an alternative paradigm: leveraging function-calling large language models (LLMs) to address these challenges. Instead of directly generating SQL queries, we define a set of pre-approved, purpose-specific functions representing common use cases. Queries are processed by invoking these functions, which encapsulate validated SQL logic. This hybrid approach mitigates the risks associated with direct NL-to-SQL translations by ensuring that SQL queries are reviewed and optimized by experts before deployment. While this strategy introduces the upfront cost of developing and maintaining the function library, we demonstrate how NL-to-SQL tools can assist in the initial generation of function code, allowing experts to focus on validation rather than creation. Our study includes a performance comparison between direct NL-to-SQL generation and the proposed function-based approach, highlighting improvements in accuracy and maintainability. This work underscores the importance of balancing user accessibility with operational safety and provides a novel, actionable framework for robust data retrieval in critical systems.
We survey recent work ranging around the question in how far a group, or a property of a group, is determined by the set of finite quotient groups. Our focus lies on $S$-arithmetic groups, branch groups, and their relatives.
Timing advance and Doppler shift estimation in LEO satellite networks: A recursive Bayesian study
2025-06-10
Ashutosh Balakrishnan, Pierre Popineau, Philippe Martins
Low earth orbit (LEO) satellite based non-terrestrial networks are a key theme of the upcoming 6G networks. These space networks are proposed to be used for high-mobility use-cases like airplanes and vehicles. The initial access process between a base station (BS) and a user equipment (UE) involves timing advance (TA) value computation at the BS, requiring precise BS location information at the UE. It becomes more challenging in LEO satellite networks due to the fast moving LEO satellites and large pathloss, in addition to the mobile UE. This paper aims to compute the TA and Doppler shift experienced at the UE by modeling the joint system dynamics in a LEO satellite-mobile UE network through an extended Kalman filter (EKF) based recursive Bayesian framework. The framework accurately models the joint system dynamics by considering the LEO satellite acceleration. It constructs the Jacobian to linearize the inherent non-linearities present in the motion. Probabilistic insights regarding the state-update and propagation are also provided. The analytical framework factors in the limited satellite visibility at the UE and the satellite-UE geometry w.r.t. the earth center. The proposed framework is also useful when the satellite and UE clocks are not in sync, with the corresponding clock drift a function of the measured time difference of arrivals. Our results showcase the efficacy and robustness of the proposed EKF framework to estimate the TA and Doppler shift, even at very high UE speeds. The work is expected to be extremely useful in realizing LEO satellite based non-terrestrial networks.
Microscopic analysis of the giant monopole resonance excitation energy
2025-06-10
M. K. Gaidarov, M. V. Ivanov, Y. I. Katsarov, A. N. Antonov, I. C. Danchev
A systematic study of the isoscalar giant monopole resonance (ISGMR) in a wide range of nuclei from various isotopic chains is performed within the microscopic self-consistent Skyrme HF+BCS method and coherent density fluctuation model (CDFM). The calculations for the nuclear incompressibility are based on the Brueckner and Barcelona-Catania-Paris-Madrid (BCPM) energy density functionals for nuclear matter using the capability of the CDFM to make a transition to the corresponding incompressibility in finite nuclei. The results obtained by applying of different definitions of the ISGMR energy, as well as the two energy-density functionals, are analyzed and compared with the available experimental data. The consideration includes the peculiarities of the proton and neutron density distributions and their corresponding linear size characteristics. In general, a connection with the measured neutron skin thicknesses is proposed as a possible way for realistic estimations of the energy of ISGMR.
2025-06-10
Hyeon Jeon, Jeongin Park, Sungbok Shin, Jinwook Seo
Misuses of t-SNE and UMAP in visual analytics have become increasingly common. For example, although t-SNE and UMAP projections often do not faithfully reflect true distances between clusters, practitioners frequently use them to investigate inter-cluster relationships. In this paper, we bring this issue to the surface and comprehensively investigate why such misuse occurs and how to prevent it. We conduct a literature review of 114 papers to verify the prevalence of the misuse and analyze the reasonings behind it. We then execute an interview study to uncover practitioners' implicit motivations for using these techniques -- rationales often undisclosed in the literature. Our findings indicate that misuse of t-SNE and UMAP primarily stems from limited discourse on their appropriate use in visual analytics. We conclude by proposing future directions and concrete action items to promote more reasonable use of DR.
Enhancing Synthetic CT from CBCT via Multimodal Fusion: A Study on the Impact of CBCT Quality and Alignment
2025-06-10
Maximilian Tschuchnig, Lukas Lamminger, Philipp Steininger, Michael Gadermayr
Cone-Beam Computed Tomography (CBCT) is widely used for real-time intraoperative imaging due to its low radiation dose and high acquisition speed. However, despite its high resolution, CBCT suffers from significant artifacts and thereby lower visual quality, compared to conventional Computed Tomography (CT). A recent approach to mitigate these artifacts is synthetic CT (sCT) generation, translating CBCT volumes into the CT domain. In this work, we enhance sCT generation through multimodal learning, integrating intraoperative CBCT with preoperative CT. Beyond validation on two real-world datasets, we use a versatile synthetic dataset, to analyze how CBCT-CT alignment and CBCT quality affect sCT quality. The results demonstrate that multimodal sCT consistently outperform unimodal baselines, with the most significant gains observed in well-aligned, low-quality CBCT-CT cases. Finally, we demonstrate that these findings are highly reproducible in real-world clinical datasets.
On the Ethics of Using LLMs for Offensive Security
2025-06-10
Andreas Happe, Jürgen Cito
Large Language Models (LLMs) have rapidly evolved over the past few years and are currently evaluated for their efficacy within the domain of offensive cyber-security. While initial forays showcase the potential of LLMs to enhance security research, they also raise critical ethical concerns regarding the dual-use of offensive security tooling. This paper analyzes a set of papers that leverage LLMs for offensive security, focusing on how ethical considerations are expressed and justified in their work. The goal is to assess the culture of AI in offensive security research regarding ethics communication, highlighting trends, best practices, and gaps in current discourse. We provide insights into how the academic community navigates the fine line between innovation and ethical responsibility. Particularly, our results show that 13 of 15 reviewed prototypes (86.6\%) mentioned ethical considerations and are thus aware of the potential dual-use of their research. Main motivation given for the research was allowing broader access to penetration-testing as well as preparing defenders for AI-guided attackers.
Deriving physical parameters of unresolved star clusters. VIII. Limits of aperture photometry for star cluster studies
2025-06-10
Karolis Daugevičius, Eimantas Kriščiūnas, Erikas Cicėnas, Rima Stonkutė, Vladas Vansevičius
Recently, it has been noticed that the discrepancies in the integrated colour indices (CIs) between star clusters and models are mostly due to the projection of bright stars in the apertures. In order to reduce this problem, the method of adaptive aperture photometry has been proposed. This method has been applied to star clusters from the M 31 Panchromatic Hubble Andromeda Treasury (PHAT) survey, and studies show that the adaptive aperture photometry performs better than the conventional approach. The aim of this study is to determine the best achievable limits on the accuracy and applicability of the aperture photometry method for studying star clusters in the local Universe. We computed a large network of artificial 3D star clusters spanning the parameter space of the M 31 clusters. We then simulated images of these clusters by projecting each onto a 2D plane from 100 directions. Star cluster images were generated in six passbands to match the PHAT survey. To investigate the limiting accuracy of aperture photometry and the limits of its applicability to star cluster studies, we measured the simulated images and performed parameter determination tests. We demonstrate that star clusters with and without post-main-sequence stars have significant photometric differences. We show that in order to obtain reliable physical parameters of star clusters, the CIs must be measured using an aperture with a radius larger than the cluster's half-light radius. Furthermore, we demonstrate that the parameter determination of young clusters (~10 Myr) is problematic regardless of the aperture size used. Therefore, it is advisable to determine the parameters of these clusters using colour-magnitude diagram fitting methods, when possible. We also show that the randomness of the viewing angle can lead to a CI uncertainty of up to 0.1 mag, depending on cluster parameters and aperture size.
Blockchain and edge computing are two instrumental paradigms of decentralized computation, driving key advancements in Smart Cities applications such as supply chain, energy and mobility. Despite their unprecedented impact on society, they remain significantly fragmented as technologies and research areas, while they share fundamental principles of distributed systems and domains of applicability. This paper introduces a novel and large-scale systematic literature review on the nexus of blockchain and edge computing with the aim to unravel a new understanding of how the interfacing of the two computing paradigms can boost innovation to provide solutions to timely but also long-standing research challenges. By collecting almost 6000 papers from 3 databases and putting under scrutiny almost 1000 papers, we build a novel taxonomy and classification consisting of 22 features with 287 attributes that we study using quantitative and machine learning methods. They cover a broad spectrum of technological, design, epistemological and sustainability aspects. Results reveal 4 distinguishing patterns of interplay between blockchain and edge computing with key determinants the public (permissionless) vs. private (permissioned) design, technology and proof of concepts. They also demonstrate the prevalence of blockchain-assisted edge computing for improving privacy and security, in particular for mobile computing applications.
MOSAIC-F: A Framework for Enhancing Students' Oral Presentation Skills through Personalized Feedback
2025-06-10
Alvaro Becerra, Daniel Andres, Pablo Villegas, Roberto Daza, Ruth Cobos
In this article, we present a novel multimodal feedback framework called MOSAIC-F, an acronym for a data-driven Framework that integrates Multimodal Learning Analytics (MMLA), Observations, Sensors, Artificial Intelligence (AI), and Collaborative assessments for generating personalized feedback on student learning activities. This framework consists of four key steps. First, peers and professors' assessments are conducted through standardized rubrics (that include both quantitative and qualitative evaluations). Second, multimodal data are collected during learning activities, including video recordings, audio capture, gaze tracking, physiological signals (heart rate, motion data), and behavioral interactions. Third, personalized feedback is generated using AI, synthesizing human-based evaluations and data-based multimodal insights such as posture, speech patterns, stress levels, and cognitive load, among others. Finally, students review their own performance through video recordings and engage in self-assessment and feedback visualization, comparing their own evaluations with peers and professors' assessments, class averages, and AI-generated recommendations. By combining human-based and data-based evaluation techniques, this framework enables more accurate, personalized and actionable feedback. We tested MOSAIC-F in the context of improving oral presentation skills.
RE-oriented Model Development with LLM Support and Deduction-based Verification
2025-06-10
Radoslaw Klimek
The requirements engineering (RE) phase is pivotal in developing high-quality software. Integrating advanced modelling techniques with large language models (LLMs) and formal verification in a logical style can significantly enhance this process. We propose a comprehensive framework that focuses on specific Unified Modelling Language (UML) diagrams for preliminary system development. This framework offers visualisations at various modelling stages and seamlessly integrates large language models and logical reasoning engines. The behavioural models generated with the assistance of LLMs are automatically translated into formal logical specifications. Deductive formal verification ensures that logical requirements and interrelations between software artefacts are thoroughly addressed. Ultimately, the framework facilitates the automatic generation of program skeletons, streamlining the transition from design to implementation.
Constraints on primordial black holes from interstellar dust temperature in the Galaxy
2025-06-10
A. N. Melikhov, E. V. Mikheeva
The photons emitted by primordial black holes (PBHs) can be a heating factor for interstellar dust. Assuming that dust in the Galaxy has a homogeneous distribution and that PBHs have the same distribution as dark matter (DM), we constrain the fraction of PBHs in DM from the observational dust temperature in the Galaxy.
Transformers Meet Hyperspectral Imaging: A Comprehensive Study of Models, Challenges and Open Problems
2025-06-10
Guyang Zhang, Waleed Abdulla
Transformers have become the architecture of choice for learning long-range dependencies, yet their adoption in hyperspectral imaging (HSI) is still emerging. We reviewed more than 300 papers published up to 2025 and present the first end-to-end survey dedicated to Transformer-based HSI classification. The study categorizes every stage of a typical pipeline-pre-processing, patch or pixel tokenization, positional encoding, spatial-spectral feature extraction, multi-head self-attention variants, skip connections, and loss design-and contrasts alternative design choices with the unique spatial-spectral properties of HSI. We map the field's progress against persistent obstacles: scarce labeled data, extreme spectral dimensionality, computational overhead, and limited model explainability. Finally, we outline a research agenda prioritizing valuable public data sets, lightweight on-edge models, illumination and sensor shifts robustness, and intrinsically interpretable attention mechanisms. Our goal is to guide researchers in selecting, combining, or extending Transformer components that are truly fit for purpose for next-generation HSI applications.
Hateful Person or Hateful Model? Investigating the Role of Personas in Hate Speech Detection by Large Language Models
2025-06-10
Shuzhou Yuan, Ercong Nie, Mario Tawfelis, Helmut Schmid, Hinrich Schütze, Michael Färber
Hate speech detection is a socially sensitive and inherently subjective task, with judgments often varying based on personal traits. While prior work has examined how socio-demographic factors influence annotation, the impact of personality traits on Large Language Models (LLMs) remains largely unexplored. In this paper, we present the first comprehensive study on the role of persona prompts in hate speech classification, focusing on MBTI-based traits. A human annotation survey confirms that MBTI dimensions significantly affect labeling behavior. Extending this to LLMs, we prompt four open-source models with MBTI personas and evaluate their outputs across three hate speech datasets. Our analysis uncovers substantial persona-driven variation, including inconsistencies with ground truth, inter-persona disagreement, and logit-level biases. These findings highlight the need to carefully define persona prompts in LLM-based annotation workflows, with implications for fairness and alignment with human values.
Exploring the Convergence of HCI and Evolving Technologies in Information Systems
2025-06-10
Rajan Das Gupta, Ashikur Rahman, Md Imrul Hasan Showmick, Md. Yeasin Rahat, Md. Jakir Hossen
Modern technology driven information systems are part of our daily lives. However, this deep integration poses new challenges to the human computer interaction (HCI) professionals. With the rapid growth of mobile and cloud computing and the Internet of Things (IoT), the demand for HCI specialists to design user-friendly and adaptable interfaces has never been more pressing. Especially for diverse user groups such as children, the elderly and people with disabilities who need interfaces tailored to their needs regardless of time and location. This study reviewed 50 recent papers on HCI interface design for modern information systems. The goal is to see how well these methods address the demands of current technology. The findings show that most HCI design methods are still based on old desktop models and do not support mobile users and location-based services well. Most existing interface design guidelines do not align with the flexibility and dynamism of emerging technologies. The goal of this study is to improve interface design by combining agile methodologies with human-centered design principles. Future studies should also incorporate both qualitative and quantitative approaches, particularly in the context of cloud-based technologies and organizational information systems. This approach aims to bridge the gap between current interface design practices and the changing technological landscape.
The Compton-thick AGN Population and the $N_{\rm H}$ Distribution of Low-mass AGN in our Cosmic Backyard
2025-06-10
A. Annuar, D. M. Alexander, P. Gandhi, G. B. Lansbury, M. N. Rosli, D. Stern, D. Asmus, D. R. Ballantyne, M. Baloković, F. E. Bauer, P. G. Boorman, W. N. Brandt, M. Brightman, C. T. J. Chen, A. Del Moro, D. Farrah, F. A. Harrison, M. J. Koss, L. Lanz, S. Marchesi, P. Mohanadas, E. Nardini, C. Ricci, L. Zappacosta
We present a census of the Compton-thick (CT) active galactic nucleus (AGN) population and the column density ($N_{\rm{H}}$) distribution of AGN in our cosmic backyard using a mid-infrared selected AGN sample within 15 Mpc. The column densities are measured from broadband X-ray spectral analysis, mainly using data from $\textit{Chandra}$ and $\textit{NuSTAR}$. Our sample probes AGN with intrinsic 2-10 keV luminosities of $L_{\rm 2-10, int} = 10^{37}$-$10^{43}$ erg s$^{-1}$, reaching a parameter space inaccessible to more distant samples. We directly measure a 32$^{+30}_{-18}\%$ CT AGN fraction and obtain an $N_{\rm{H}}$ distribution that agrees with that inferred by the $\textit{Swift}$-BAT survey. Restricting the sample to the largely unexplored domain of low-luminosity AGN with $L_{\rm 2-10, int}$ $\leq$ $10^{42}$ erg s$^{-1}$, we found a CT fraction of 19$^{+30}_{-14}\%$, consistent with those observed at higher luminosities. Comparing the host-galaxy properties between the two samples, we find consistent star formation rates, though the majority of our galaxy have lower stellar masses (by $\approx 0.3$ dex). In contrast, the two samples have very different black hole mass ($M_{\rm BH}$) distributions, with our sample having $\approx$1.5 dex lower mean mass ($M_{\rm BH}$ $\sim$ 10$^{6}$ $M_\odot$). Additionally, our sample contains a significantly higher number of LINERs and H$_{\rm{II}}$-type nuclei. The Eddington ratio range probed by our sample, however, is the same as $\textit{Swift}$-BAT, although the latter dominates at higher accretion rates, and our sample is more evenly distributed. The majority of our sample with $\lambda_{\rm Edd} \ge$ 10$^{-3}$ tend to be CT, while those with $\lambda_{\rm Edd} <$ 10$^{-3}$ are mostly unobscured or mildly obscured.
NeurIPS 2024 ML4CFD Competition: Results and Retrospective Analysis
2025-06-10
Mouadh Yagoubi, David Danan, Milad Leyli-Abadi, Ahmed Mazari, Jean-Patrick Brunet, Abbas Kabalan, Fabien Casenave, Yuxin Ma, Giovanni Catalani, Jean Fesquet, Jacob Helwig, Xuan Zhang, Haiyang Yu, Xavier Bertrand, Frederic Tost, Michael Baurheim, Joseph Morlier, Shuiwang Ji
The integration of machine learning (ML) into the physical sciences is reshaping computational paradigms, offering the potential to accelerate demanding simulations such as computational fluid dynamics (CFD). Yet, persistent challenges in accuracy, generalization, and physical consistency hinder the practical deployment of ML models in scientific domains. To address these limitations and systematically benchmark progress, we organized the ML4CFD competition, centered on surrogate modeling for aerodynamic simulations over two-dimensional airfoils. The competition attracted over 240 teams, who were provided with a curated dataset generated via OpenFOAM and evaluated through a multi-criteria framework encompassing predictive accuracy, physical fidelity, computational efficiency, and out-of-distribution generalization. This retrospective analysis reviews the competition outcomes, highlighting several approaches that outperformed baselines under our global evaluation score. Notably, the top entry exceeded the performance of the original OpenFOAM solver on aggregate metrics, illustrating the promise of ML-based surrogates to outperform traditional solvers under tailored criteria. Drawing from these results, we analyze the key design principles of top submissions, assess the robustness of our evaluation framework, and offer guidance for future scientific ML challenges.
Catalog of very-high-energy emitting active galactic nuclei at high Galactic latitudes
2025-06-10
A. Neronov, D. Semikoz
Large number of Active Galactic Nuclei (AGN) producing Very-High-Energy (VHE) gamma-rays (energies above 100~GeV) has been revealed using observations with Imaging Atmospheric Cherenkov Telescopes (IACTs). However, our knowledge of the VHE emitting AGN population is limited in the absence of an unbiased sky survey. We use long exposure of Fermi Large Area Telescope (LAT) to perform a survey of VHE emitting AGN at Galactic latitudes |b|>10 degrees. We consider clustering of gamma-ray events with energies E>100 GeV around positions of AGN from the 4-th LAT source catalog to select sources detected in the VHE range. The VHE AGN catalog produced in this way contains overall 175 sources detected with high confidence and additional 100 sources detected at more than 3-sigma level. It is 90% complete at the flux limit 1.3e-12 erg/cm2s. Less than half of the source sample (71) are previously reported VHE emitters, other sources are new detections in the VHE band. The majority of VHE AGN detectable at the survey flux limit are BL Lac type objects. We find their luminosity function to derive their spatial density (6.5+/-0.5)e-7/ Mpc3 and the characteristic luminosity scale ~1e44 erg/s. Ten sources in the VHE AGN catalog are nearby radio galaxies and seven are flat spectrum radio quasars, while 20 sources are unclassified AGN. We also include in our catalog four unidentified sources that may or may not be VHE AGN. 63 source in the catalog are "extreme" blazars, with 41 of them being new VHE band detections. In spite of the fact that the VHE flux is heavily attenuated by the pair production in interactions with Extragalactic Background Light (EBL), the catalog includes 7 sources at redshift larger than 1. Some of these sources show peculiar hardening of the VHE band spectra that point either to errors in redshift determination, or to limitations of modeling of cosmological evolution of EBL.
Photonic Energy-Coherence Theorem and Experimental Validations
2025-06-10
Yan-Han Yang, Xin-Zhu Liu, Jun-Li Jiang, Hu Chen, Xue Yang, Xiao-Feng Qian, Shao-Ming Fei, Ming-Xing Luo
Wave-particle duality, intertwining two inherently contradictory properties of quantum systems, remains one of the most conceptually profound aspects of quantum mechanics. By using the concept of energy capacity, the ability of a quantum system to store and extract energy, we derive a device-independent uncertainty relation for wave-particle duality. This relation is shown to be independent of both the representation space and the measurement basis of the quantum system. Furthermore, we experimentally validate this wave-particle duality relation using a photon-based platform.
The central spectra of massive star-forming galaxies
2025-06-10
Jaimie R. Sheil, Michael J. I . Brown, Virginia A. Kilborn, Michelle E. Cluver, Thomas Jarrett
We have examined the nuclear spectra of very massive star-forming galaxies at $z \sim 0$ to understand how they differ from other galaxies with comparable masses, which are typically passive. We selected a sample of 126 nearby massive star-forming galaxies ($<100~{\rm Mpc}$, $10^{11.3}~\rm{M_\odot} \leq M_{\rm stellar} \leq 10^{11.7}~\rm{M_\odot}$, $1 ~{\rm M_\odot~yr^{-1}}< {\rm SFR} <13 ~{\rm M_\odot~yr^{-1}}$) from the 2MRS-Bright WXSC catalogue. LEDA morphologies indicate at least 63\% of our galaxies are spirals, while visual inspection of Dark Energy Survey images reveals 75\% of our galaxies to be spirals with the remainder being lenticular. Of our sample 59 have archival nuclear spectra, which we have modelled and subsequently measured emission lines ([NII]$\rm{\lambda 6583}$, H$\alpha\rm{\lambda 6563}$, [OIII]$\rm{\lambda 5008}$, and H$\beta\rm{\lambda 4863}$), classifying galaxies as star-forming, LINERS, or AGNs. Using a BPT diagram we find $83 \pm 6$ \% of our galaxies, with sufficient signal-to-noise to measure all 4 emission lines, to be LINERs. Using the [NII]$\rm{\lambda 6583}$/H$\alpha\rm{\lambda 6563}$ emission line ratio alone we find that $79 \pm 6$ \% of the galaxies (46 galaxies) with archival spectra are LINERs, whereas just $\sim 30\%$ of the overall massive galaxy population are LINERs (Belfiore et al. 2016). Our sample can be considered a local analogue of the Ogle et al. (2016, 2019) sample of $z \sim 0.22$ massive star-forming galaxies in terms of selection criteria, and we find 64\% of their galaxies are LINERs using SDSS spectra. The high frequency of LINER emission in these massive star-forming galaxies indicates that LINER emission in massive galaxies may be linked to the presence of gas that fuels star formation.
A Review on Score-based Generative Models for Audio Applications
2025-06-10
Ge Zhu, Yutong Wen, Zhiyao Duan
Diffusion models have emerged as powerful deep generative techniques, producing high-quality and diverse samples in applications in various domains including audio. These models have many different design choices suitable for different applications, however, existing reviews lack in-depth discussions of these design choices. The audio diffusion model literature also lacks principled guidance for the implementation of these design choices and their comparisons for different applications. This survey provides a comprehensive review of diffusion model design with an emphasis on design principles for quality improvement and conditioning for audio applications. We adopt the score modeling perspective as a unifying framework that accommodates various interpretations, including recent approaches like flow matching. We systematically examine the training and sampling procedures of diffusion models, and audio applications through different conditioning mechanisms. To address the lack of audio diffusion model codebases and to promote reproducible research and rapid prototyping, we introduce an open-source codebase at https://github.com/gzhu06/AudioDiffuser that implements our reviewed framework for various audio applications. We demonstrate its capabilities through three case studies: audio generation, speech enhancement, and text-to-speech synthesis, with benchmark evaluations on standard datasets.
Enhancing Motion Dynamics of Image-to-Video Models via Adaptive Low-Pass Guidance
2025-06-10
June Suk Choi, Kyungmin Lee, Sihyun Yu, Yisol Choi, Jinwoo Shin, Kimin Lee
Recent text-to-video (T2V) models have demonstrated strong capabilities in producing high-quality, dynamic videos. To improve the visual controllability, recent works have considered fine-tuning pre-trained T2V models to support image-to-video (I2V) generation. However, such adaptation frequently suppresses motion dynamics of generated outputs, resulting in more static videos compared to their T2V counterparts. In this work, we analyze this phenomenon and identify that it stems from the premature exposure to high-frequency details in the input image, which biases the sampling process toward a shortcut trajectory that overfits to the static appearance of the reference image. To address this, we propose adaptive low-pass guidance (ALG), a simple fix to the I2V model sampling procedure to generate more dynamic videos without compromising per-frame image quality. Specifically, ALG adaptively modulates the frequency content of the conditioning image by applying low-pass filtering at the early stage of denoising. Extensive experiments demonstrate that ALG significantly improves the temporal dynamics of generated videos, while preserving image fidelity and text alignment. Especially, under VBench-I2V test suite, ALG achieves an average improvement of 36% in dynamic degree without a significant drop in video quality or image fidelity.
Mathematical reasoning has long represented one of the most fundamental and challenging frontiers in artificial intelligence research. In recent years, large language models (LLMs) have achieved significant advances in this area. This survey examines the development of mathematical reasoning abilities in LLMs through two high-level cognitive phases: comprehension, where models gain mathematical understanding via diverse pretraining strategies, and answer generation, which has progressed from direct prediction to step-by-step Chain-of-Thought (CoT) reasoning. We review methods for enhancing mathematical reasoning, ranging from training-free prompting to fine-tuning approaches such as supervised fine-tuning and reinforcement learning, and discuss recent work on extended CoT and "test-time scaling". Despite notable progress, fundamental challenges remain in terms of capacity, efficiency, and generalization. To address these issues, we highlight promising research directions, including advanced pretraining and knowledge augmentation techniques, formal reasoning frameworks, and meta-generalization through principled learning paradigms. This survey tries to provide some insights for researchers interested in enhancing reasoning capabilities of LLMs and for those seeking to apply these techniques to other domains.
Discovery of Odd Radio Circles and Other Peculiars in the First Year of the EMU Survey using Object Detection
2025-06-10
Nikhel Gupta, Ray P. Norris, Zeeshan Hayder, Minh Huynh, Heinz Andernach, Andrew M. Hopkins, Stanislav Shabala, Lawrence Rudnick, Miroslav D. Filipović, Bärbel S. Koribalski, Lars Petersson, X. Rosalind Wang
We present a systematic search for Odd Radio Circles (ORCs) and other unusual radio morphologies using data from the first year of the EMU (Evolutionary Map of the Universe) survey. ORCs are rare, enigmatic objects characterized by edge-brightened rings of radio emission, often found in association with distant galaxies. To identify these objects, we employ a hybrid methodology combining supervised object detection techniques and visual inspection of radio source candidates. This approach leads to the discovery of five new ORCs and two additional candidate ORCs, expanding the known population of these objects. In addition to ORCs, we also identify 55 Galaxies with Large-scale Ambient Radio Emission (GLAREs), which feature irregular, rectangular, or circular shapes of diffuse radio emission mostly surrounding central host galaxies. These GLAREs may represent different evolutionary stages of ORCs, and studying them could offer valuable insights into their evolutionary processes. We also highlight a subset of Starburst Radio Ring Galaxies (SRRGs), which are star-forming galaxies exhibiting edge-brightened radio rings surrounding their central star-forming regions. We emphasize the importance of multi-wavelength follow-up observations to better understand the physical properties, host galaxy characteristics, and evolutionary pathways of these radio sources.
Federated learning (FL) enables collaborative model training among multiple clients without the need to expose raw data. Its ability to safeguard privacy, at the heart of FL, has recently been a hot-button debate topic. To elaborate, several studies have introduced a type of attacks known as gradient leakage attacks (GLAs), which exploit the gradients shared during training to reconstruct clients' raw data. On the flip side, some literature, however, contends no substantial privacy risk in practical FL environments due to the effectiveness of such GLAs being limited to overly relaxed conditions, such as small batch sizes and knowledge of clients' data distributions. This paper bridges this critical gap by empirically demonstrating that clients' data can still be effectively reconstructed, even within realistic FL environments. Upon revisiting GLAs, we recognize that their performance failures stem from their inability to handle the gradient matching problem. To alleviate the performance bottlenecks identified above, we develop FedLeak, which introduces two novel techniques, partial gradient matching and gradient regularization. Moreover, to evaluate the performance of FedLeak in real-world FL environments, we formulate a practical evaluation protocol grounded in a thorough review of extensive FL literature and industry practices. Under this protocol, FedLeak can still achieve high-fidelity data reconstruction, thereby underscoring the significant vulnerability in FL systems and the urgent need for more effective defense methods.
Having a unified, coherent taxonomy is essential for effective knowledge representation in domain-specific applications as diverse terminologies need to be mapped to underlying concepts. Traditional manual approaches to taxonomy alignment rely on expert review of concept pairs, but this becomes prohibitively expensive and time-consuming at scale, while subjective interpretations often lead to expert disagreements. Existing automated methods for taxonomy alignment have shown promise but face limitations in handling nuanced semantic relationships and maintaining consistency across different domains. These approaches often struggle with context-dependent concept mappings and lack transparent reasoning processes. We propose a novel framework that combines large language models (LLMs) with expert calibration and iterative prompt optimization to automate taxonomy alignment. Our method integrates expert-labeled examples, multi-stage prompt engineering, and human validation to guide LLMs in generating both taxonomy linkages and supporting rationales. In evaluating our framework on a domain-specific mapping task of concept essentiality, we achieved an F1-score of 0.97, substantially exceeding the human benchmark of 0.68. These results demonstrate the effectiveness of our approach in scaling taxonomy alignment while maintaining high-quality mappings and preserving expert oversight for ambiguous cases.
Machine translation has long been a central task in natural language processing. With the rapid advancement of large language models (LLMs), there has been remarkable progress in translation quality. However, fully realizing the translation potential of LLMs remains an open challenge. Recent studies have explored multi-agent systems to decompose complex translation tasks into collaborative subtasks, showing initial promise in enhancing translation quality through agent cooperation and specialization. Nevertheless, existing multi-agent translation frameworks largely neglect foundational insights from cognitive translation studies. These insights emphasize how human translators employ different cognitive strategies, such as balancing literal and free translation, refining expressions based on context, and iteratively evaluating outputs. To address this limitation, we propose a cognitively informed multi-agent framework called TACTIC, which stands for T ranslation A gents with Cognitive- T heoretic Interactive Collaboration. The framework comprises six functionally distinct agents that mirror key cognitive processes observed in human translation behavior. These include agents for drafting, refinement, evaluation, scoring, context reasoning, and external knowledge gathering. By simulating an interactive and theory-grounded translation workflow, TACTIC effectively leverages the full capacity of LLMs for high-quality translation. Experimental results on diverse language pairs from the FLORES-200 and WMT24 benchmarks show that our method consistently achieves state-of-the-art performance. Using DeepSeek-V3 as the base model, TACTIC surpasses GPT-4.1 by an average of +0.6 XCOMET and +1.18 COMETKIWI-23. Compared to DeepSeek-R1, it further improves by +0.84 XCOMET and +2.99 COMETKIWI-23. Code is available at https://github.com/weiyali126/TACTIC.
Submillimeter and Mid-Infrared Variability of Young Stellar Objects in the M17 HII Region
2025-06-10
Zhiwei Chen, Doug Johnstone, Carlos Contreras Peña, Jeong-Eun Lee, Sheng-Yuan Liu, Gregory Herczeg, Steve Mairs, Geumsook Park, Kee-Tae Kim, Mi-Ryang Kim, Keping Qiu, Yao-Te Wang, Xu Zhang, Megan Reiter, the JCMT Transient Team
We conducted a comprehensive analysis of young stellar object (YSO) variability at submillimeter and mid-infrared (mid-IR) wavelengths for the M\,17 \ion{H}{2} region, using 3.5 years monitoring data from the JCMT Transient Survey at $450$ and $850\,\mu$m and 9 years mid-IR monitoring data from the NEOWISE mission. Our study encompasses observations of 198 and 164 bright submillimeter peaks identified within the deep JCMT coadded maps at 450 and $850\,\mu$m, and 66 YSOs seen by NEOWISE W2 that were previously identified in mid-IR observations. We find one robust linear submillimeter variable, an intermediate mass protostar, with a $4\%$ peak flux change in 3.5 years of JCMT monitoring that sets a lower limit of $16\%$ luminosity increase for the source. At mid-IR wavelengths, our analysis reveals secular and stochastic variability in 22 YSOs, with the highest fraction of secular variability occurring at the earliest evolutionary stage. This mid-IR fractional variability as a function of evolutionary stage result is similar to what has previously been found for YSO variability within the Gould Belt and the intermediate-mass star formation region M17\,SWex, though overall less variability is detected in M\,17 in submillimeter and mid-IR. We suspect that this lower detection of YSO variability is due to both the greater distance to M\,17 and the strong feedback from the \ion{H}{2} region. Our findings showcase the utility of multiwavelength observations to better capture the complex variability phenomena inherent to star formation processes and demonstrate the importance of years-long monitoring of a diverse selection of star-forming environments.
AlphaFold Database Debiasing for Robust Inverse Folding
2025-06-10
Cheng Tan, Zhenxiao Cao, Zhangyang Gao, Siyuan Li, Yufei Huang, Stan Z. Li
The AlphaFold Protein Structure Database (AFDB) offers unparalleled structural coverage at near-experimental accuracy, positioning it as a valuable resource for data-driven protein design. However, its direct use in training deep models that are sensitive to fine-grained atomic geometry, such as inverse folding, exposes a critical limitation. Comparative analysis of structural feature distributions reveals that AFDB structures exhibit distinct statistical regularities, reflecting a systematic geometric bias that deviates from the conformational diversity found in experimentally determined structures from the Protein Data Bank (PDB). While AFDB structures are cleaner and more idealized, PDB structures capture the intrinsic variability and physical realism essential for generalization in downstream tasks. To address this discrepancy, we introduce a Debiasing Structure AutoEncoder (DeSAE) that learns to reconstruct native-like conformations from intentionally corrupted backbone geometries. By training the model to recover plausible structural states, DeSAE implicitly captures a more robust and natural structural manifold. At inference, applying DeSAE to AFDB structures produces debiased structures that significantly improve inverse folding performance across multiple benchmarks. This work highlights the critical impact of subtle systematic biases in predicted structures and presents a principled framework for debiasing, significantly boosting the performance of structure-based learning tasks like inverse folding.
MD-ViSCo: A Unified Model for Multi-Directional Vital Sign Waveform Conversion
2025-06-10
Franck Meyer, Kyunghoon Hur, Edward Choi
Despite the remarkable progress of deep-learning methods generating a target vital sign waveform from a source vital sign waveform, most existing models are designed exclusively for a specific source-to-target pair. This requires distinct model architectures, optimization procedures, and pre-processing pipelines, resulting in multiple models that hinder usability in clinical settings. To address this limitation, we propose the Multi-Directional Vital-Sign Converter (MD-ViSCo), a unified framework capable of generating any target waveform such as electrocardiogram (ECG), photoplethysmogram (PPG), or arterial blood pressure (ABP) from any single input waveform with a single model. MD-ViSCo employs a shallow 1-Dimensional U-Net integrated with a Swin Transformer that leverages Adaptive Instance Normalization (AdaIN) to capture distinct waveform styles. To evaluate the efficacy of MD-ViSCo, we conduct multi-directional waveform generation on two publicly available datasets. Our framework surpasses state-of-the-art baselines (NabNet & PPG2ABP) on average across all waveform types, lowering Mean absolute error (MAE) by 8.8% and improving Pearson correlation (PC) by 4.9% over two datasets. In addition, the generated ABP waveforms satisfy the Association for the Advancement of Medical Instrumentation (AAMI) criterion and achieve Grade B on the British Hypertension Society (BHS) standard, outperforming all baselines. By eliminating the need for developing a distinct model for each task, we believe that this work offers a unified framework that can deal with any kind of vital sign waveforms with a single model in healthcare monitoring.
The use of appropriate methods of Interpretable Machine Learning (IML) and eXplainable Artificial Intelligence (XAI) is essential for adopting black-box predictive models in fields where model and prediction explainability is required. As a novel tool for interpreting black-box models, we introduce the R package midr, which implements Maximum Interpretation Decomposition (MID). MID is a functional decomposition approach that derives a low-order additive representation of a black-box model by minimizing the squared error between the model's prediction function and this additive representation. midr enables learning from black-box models by constructing a global surrogate model with advanced analytical capabilities. After reviewing related work and the theoretical foundation of MID, we demonstrate the package's usage and discuss some of its key features.
Graph learning models have demonstrated great prowess in learning expressive representations from large-scale graph data in a wide variety of real-world scenarios. As a prevalent strategy for training powerful graph learning models, the "pre-training, adaptation" scheme first pre-trains graph learning models on unlabeled graph data in a self-supervised manner and then adapts them to specific downstream tasks. During the adaptation phase, graph prompting emerges as a promising approach that learns trainable prompts while keeping the pre-trained graph learning models unchanged. In this paper, we present a systematic review of recent advancements in graph prompting. First, we introduce representative graph pre-training methods that serve as the foundation step of graph prompting. Next, we review mainstream techniques in graph prompting and elaborate on how they design learnable prompts for graph prompting. Furthermore, we summarize the real-world applications of graph prompting from different domains. Finally, we discuss several open challenges in existing studies with promising future directions in this field.
AstroCompress: A benchmark dataset for multi-purpose compression of astronomical data
2025-06-10
Tuan Truong, Rithwik Sudharsan, Yibo Yang, Peter Xiangyuan Ma, Ruihan Yang, Stephan Mandt, Joshua S. Bloom
The site conditions that make astronomical observatories in space and on the ground so desirable -- cold and dark -- demand a physical remoteness that leads to limited data transmission capabilities. Such transmission limitations directly bottleneck the amount of data acquired and in an era of costly modern observatories, any improvements in lossless data compression has the potential scale to billions of dollars worth of additional science that can be accomplished on the same instrument. Traditional lossless methods for compressing astrophysical data are manually designed. Neural data compression, on the other hand, holds the promise of learning compression algorithms end-to-end from data and outperforming classical techniques by leveraging the unique spatial, temporal, and wavelength structures of astronomical images. This paper introduces AstroCompress: a neural compression challenge for astrophysics data, featuring four new datasets (and one legacy dataset) with 16-bit unsigned integer imaging data in various modes: space-based, ground-based, multi-wavelength, and time-series imaging. We provide code to easily access the data and benchmark seven lossless compression methods (three neural and four non-neural, including all practical state-of-the-art algorithms). Our results on lossless compression indicate that lossless neural compression techniques can enhance data collection at observatories, and provide guidance on the adoption of neural compression in scientific applications. Though the scope of this paper is restricted to lossless compression, we also comment on the potential exploration of lossy compression methods in future studies.
A Topic Modeling Analysis of Stigma Dimensions, Social, and Related Behavioral Circumstances in Clinical Notes Among Patients with HIV
2025-06-10
Ziyi Chen, Yiyang Liu, Mattia Prosperi, Krishna Vaddiparti, Robert L Cook, Jiang Bian, Yi Guo, Yonghui Wu
Objective: To characterize stigma dimensions, social, and related behavioral circumstances in people living with HIV (PLWHs) seeking care, using natural language processing methods applied to a large collection of electronic health record (EHR) clinical notes from a large integrated health system in the southeast United States. Methods: We identified 9,140 cohort of PLWHs from the UF Health IDR and performed topic modeling analysis using Latent Dirichlet Allocation (LDA) to uncover stigma dimensions, social, and related behavioral circumstances. Domain experts created a seed list of HIV-related stigma keywords, then applied a snowball strategy to iteratively review notes for additional terms until saturation was reached. To identify more target topics, we tested three keyword-based filtering strategies. Domain experts manually reviewed the detected topics using the prevalent terms and key discussion topics. Word frequency analysis was used to highlight the prevalent terms associated with each topic. In addition, we conducted topic variation analysis among subgroups to examine differences across age and sex-specific demographics. Results and Conclusion: Topic modeling on sentences containing at least one keyword uncovered a wide range of topic themes associated with HIV-related stigma, social, and related behaviors circumstances, including "Mental Health Concern and Stigma", "Social Support and Engagement", "Limited Healthcare Access and Severe Illness", "Treatment Refusal and Isolation" and so on. Topic variation analysis across age subgroups revealed differences. Extracting and understanding the HIV-related stigma dimensions, social, and related behavioral circumstances from EHR clinical notes enables scalable, time-efficient assessment, overcoming the limitations of traditional questionnaires and improving patient outcomes.
A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO
2025-06-10
Jonas Svedas, Hannah Watson, Nathan Laubeuf, Diksha Moolchandani, Abubakr Nada, Arjun Singh, Dwaipayan Biswas, James Myers, Debjyoti Bhattacharjee
Distributed deep neural networks (DNNs) have become a cornerstone for scaling machine learning to meet the demands of increasingly complex applications. However, the rapid growth in model complexity far outpaces CMOS technology scaling, making sustainable and efficient system design a critical challenge. Addressing this requires coordinated co-design across software, hardware, and technology layers. Due to the prohibitive cost and complexity of deploying full-scale training systems, simulators play a pivotal role in enabling this design exploration. This survey reviews the landscape of distributed DNN training simulators, focusing on three major dimensions: workload representation, simulation infrastructure, and models for total cost of ownership (TCO) including carbon emissions. It covers how workloads are abstracted and used in simulation, outlines common workload representation methods, and includes comprehensive comparison tables covering both simulation frameworks and TCO/emissions models, detailing their capabilities, assumptions, and areas of focus. In addition to synthesizing existing tools, the survey highlights emerging trends, common limitations, and open research challenges across the stack. By providing a structured overview, this work supports informed decision-making in the design and evaluation of distributed training systems.
Aluminum oxide coatings on Co-rich cathodes and interactions with organic electrolyte
2025-06-10
M. D. Hashan C. Peiris, Michael Woodcox, Diana Liepinya, Robert Shephard, Hao Liu, Manuel Smeu
Lithium-ion batteries (LIBs) have become essential in modern energy storage; however, their performance is often limited by the stability and efficiency of their components, particularly the cathode and electrolyte. Transition metal layered oxide cathodes, a popular choice for lithium-ion batteries (LIBs), suffer from several degradation mechanisms, including capacity fading, reactions with the electrolyte, unstable cathode-electrolyte interfaces, and lattice breakdown during cycling. In recent years, oxide coating, such as alumina, has emerged as a promising strategy to enhance the durability of cathodes by forming a protective layer that mitigates detrimental reactions and improves the stability of the cathode electrolyte interphase (CEI). This study employs ab initio molecular dynamics (AIMD) simulations to investigate the chemical and mechanical behavior of LiCoO2 cathodes with and without aluminum oxide coatings in contact with an organic electrolyte. We examine the interactions between electrolyte molecules with both bare and coated cathode surfaces, focusing on the decomposition of ethylene carbonate (EC) and dimethyl carbonate (DMC), the formation of oxygen species, and solvation dynamics, and evaluate the mechanical robustness of the cathode-coating interface using calculations of axial strain and cleavage energy. Our findings reveal that alumina coatings effectively reduce electrolyte degradation and stabilize the cathode structure, particularly under high-charge states. The coating's thickness and structural orientation are crucial in enhancing mechanical strength and minimizing detrimental reactions at the cathode-electrolyte interface. These insights contribute to the development of more durable LIBs by optimizing the interface chemistry and mechanical properties, providing a pathway toward higher energy densities and longer cycle life.
Augmented Reality User Interfaces for First Responders: A Scoping Literature Review
2025-06-10
Erin Argo, Tanim Ahmed, Sarah Gable, Callie Hampton, Jeronimo Grandi, Regis Kopper
During the past decade, there has been a significant increase in research focused on integrating AR User Interfaces into public safety applications, particularly for first responders in the domains of Emergency Medical Services, Firefighting, and Law Enforcement. This paper presents the results of a scoping review involving the application of AR user interfaces in the public safety domain and applies an established systematic review methodology to provide a comprehensive analysis of the current research landscape, identifying key trends, challenges, and gaps in the literature. This review includes peer-reviewed publications indexed by the major scientific databases up to April 2025. A basic keyword search retrieved 1,751 papers, of which 90 were deemed relevant for this review. An in-depth analysis of the literature allowed the development of a faceted taxonomy that categorizes AR user interfaces for public safety. This classification lays a solid foundation for future research, while also highlighting key design considerations, challenges, and gaps in the literature. This review serves as a valuable resource for researchers and developers, offering insights that can drive further advances in the field.
2025-06-10
Jie Ren, Yue Xing, Yingqian Cui, Charu C. Aggarwal, Hui Liu
Large language model (LLM) unlearning has become a critical topic in machine learning, aiming to eliminate the influence of specific training data or knowledge without retraining the model from scratch. A variety of techniques have been proposed, including Gradient Ascent, model editing, and re-steering hidden representations. While existing surveys often organize these methods by their technical characteristics, such classifications tend to overlook a more fundamental dimension: the underlying intention of unlearning--whether it seeks to truly remove internal knowledge or merely suppress its behavioral effects. In this SoK paper, we propose a new taxonomy based on this intention-oriented perspective. Building on this taxonomy, we make three key contributions. First, we revisit recent findings suggesting that many removal methods may functionally behave like suppression, and explore whether true removal is necessary or achievable. Second, we survey existing evaluation strategies, identify limitations in current metrics and benchmarks, and suggest directions for developing more reliable and intention-aligned evaluations. Third, we highlight practical challenges--such as scalability and support for sequential unlearning--that currently hinder the broader deployment of unlearning methods. In summary, this work offers a comprehensive framework for understanding and advancing unlearning in generative AI, aiming to support future research and guide policy decisions around data removal and privacy.
An Introduction to Solving the Least-Squares Problem in Variational Data Assimilation
2025-06-10
I. Daužickaitė, M. A. Freitag, S. Gürol, A. S. Lawless, A. Ramage, J. A. Scott, J. M. Tabeart
Variational data assimilation is a technique for combining measured data with dynamical models. It is a key component of Earth system state estimation and is commonly used in weather and ocean forecasting. The approach involves a large-scale generalized nonlinear least-squares problem. Solving the resulting sequence of sparse linear subproblems requires the use of sophisticated numerical linear algebra methods. In practical applications, the computational demands severely limit the number of iterations of a Krylov subspace solver that can be performed and so high-quality preconditioners are vital. In this paper, we introduce variational data assimilation from a numerical linear algebra perspective and review current solution techniques, with a focus on the challenges that arise in large-scale geophysical systems.
Estimating Visceral Adiposity from Wrist-Worn Accelerometry
2025-06-10
James R. Williamson, Andrew Alini, Brian A. Telfer, Adam W. Potter, Karl E. Friedl
Visceral adipose tissue (VAT) is a key marker of both metabolic health and habitual physical activity (PA). Excess VAT is highly correlated with type 2 diabetes and insulin resistance. The mechanistic basis for this pathophysiology relates to overloading the liver with fatty acids. VAT is also a highly labile fat depot, with increased turnover stimulated by catecholamines during exercise. VAT can be measured with sophisticated imaging technologies, but can also be inferred directly from PA. We tested this relationship using National Health and Nutrition Examination Survey (NHANES) data from 2011-2014, for individuals aged 20-60 years with 7 days of accelerometry data (n=2,456 men; 2,427 women) [1]. Two approaches were used for estimating VAT from activity. The first used engineered features based on movements during gait and sleep, and then ridge regression to map summary statistics of these features into a VAT estimate. The second approach used deep neural networks trained on 24 hours of continuous accelerometry. A foundation model first mapped each 10s frame into a high-dimensional feature vector. A transformer model then mapped each day's feature vector time series into a VAT estimate, which were averaged over multiple days. For both approaches, the most accurate estimates were obtained with the addition of covariate information about subject demographics and body measurements. The best performance was obtained by combining the two approaches, resulting in VAT estimates with correlations of r=0.86. These findings demonstrate a strong relationship between PA and VAT and, by extension, between PA and metabolic health risks.
Exploring the Eclipsing Binary System IY Aur: First Photometric Insights
2025-06-10
N. Alan, M. Alpsoy, Y. Kilic
Eclipsing binary systems play a vital role in astrophysics, as they provide a direct means of measuring fundamental stellar parameters. By combining high-precision space-based observations with ground-based multicolor photometric data, these parameters can be determined with greater accuracy. In this study, we present the first photometric analysis of the IY Aur eclipsing binary system, using a combination of the Transiting Exoplanet Survey Satellite (TESS) light curve and new UBVRI CCD observations obtained with the 60 cm robotic telescope (T60) at the TUBITAK National Observatory. Through detailed photometric modeling, the masses and radii of the system's primary and secondary components were determined as $M_{1}=6.51\pm 0.81\,M_{\odot}$, $M_{2}=5.39\pm 0.87\,M_{\odot}$, and $R_{1}=4.15\pm 0.20\,R_{\odot}$, $R_{2}=6.88\pm 0.33\,R_{\odot}$, respectively. The logarithmic values of luminosity and surface gravity were calculated as $\log L_{1}=3.14\pm 0.20\,L_{\odot}$ and $\log g_{1}=4.01\pm 0.02$ cgs for the primary component, and $\log L_{2}=2.50 \pm 0.22\,L_{\odot}$ and $\log g_{2}=3.49\pm 0.03$ cgs for the secondary component. Furthermore, the distance to IY Aur was estimated as $d=1690\pm237$ pc.
Electromagnetic signatures from pulsar remnants of binary neutron star mergers: prospects for unique identification using multi-wavelength signatures
2025-06-10
Mainak Mukhopadhyay, Shigeo S. Kimura
Binary neutron star (BNS) mergers can result in the formation of long-lived magnetar remnants which can enhance neutrino and electromagnetic (EM) emissions. In this work, we study the resulting multi-wavelength EM emissions and the prospects of their detectability in the current and upcoming EM telescopes. We model the pulsar-wind neubla system where the long-lived pulsar with dipolar magnetic fields of $10^{13} - 10^{15}$ G (magnetar) spins down and is surrounded by an outward expanding nebula and kilonova ejecta. Although at early times post the merger, the EM signatures are unobservable due to heavy attenuation, they become observable on timescales of $\mathcal{O}(1 - 10)$ days after the merger. We find that the survey and follow-up observations have horizon distances $\gtrsim 1\ \rm Gpc$ for most of the wavebands and conclude that the detection prospects for such long-lived remnants in the electromagnetic channel are promising. This is of crucial importance for multi-messenger observations from BNS mergers to constrain the physical parameters of the remnants. Furthermore, we highlight how observations across the electromagnetic band can uniquely identify magnetar-powered transients resulting from BNS mergers and establish concrete associations of the detected gravitational wave signatures with such transients.
Beyond Traditional Diagnostics: Identifying Active Galactic Nuclei with Spectral Energy Distribution Fitting in DESI Data
2025-06-10
M. Siudek, M. Mezcua, C. Circosta, C. Maraston, J. Moustakas, H. Zou, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, K. S. Dawson, A. de la Macorra, Arjun Dey, P. Doel, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, M. Ishak, S. Juneau, D. Kirkby, T. Kisner, A. Kremin, A. Lambert, M. Landriau, L. Le Guillou, A. Meisner, R. Miquel, F. Prada, I. Pérez-Ràfols, G. Rossi, E. Sanchez, D. Schlegel, M. Schubnell, H. Seo, D. Sprayberry, G. Tarlé, B. A. Weaver
Active galactic nuclei (AGN) are typically identified through their distinctive X-ray or radio emissions, mid-infrared (MIR) colors, or emission lines. However, each method captures different subsets of AGN due to signal-to-noise (SNR) limitations, redshift coverage, and extinction effects, underscoring the necessity for a multi-wavelength approach for comprehensive AGN samples. This study explores the effectiveness of spectral energy distribution (SED) fitting as a robust method for AGN identification. Using {\tt CIGALE} optical-MIR SED fits on DESI Early Data Release galaxies, we compare SED-based AGN selection ({\tt AGNFRAC} $\geq0.1$) with traditional methods including BPT diagrams, WISE colors, X-ray, and radio diagnostics. SED fitting identifies $\sim 70\%$ of narrow/broad-line AGN and 87\% of WISE-selected AGN. Incorporating high SNR WISE photometry reduces star-forming galaxy contamination from 62\% to 15\%. Initially, $\sim50\%$ of SED-AGN candidates are undetected by standard methods, but additional diagnostics classify $\sim85\%$ of these sources, revealing LINERs and retired galaxies potentially representing evolved systems with weak AGN activity. Further spectroscopic and multi-wavelength analysis will be essential to determine the true AGN nature of these sources. SED fitting provides complementary AGN identification, unifying multi-wavelength AGN selections. This approach enables more complete -- albeit with some contamination -- AGN samples essential for upcoming large-scale surveys where spectroscopic diagnostics may be limited.
Towards the $p$-adic derived Hecke algebra for weight one forms
2025-06-10
Robin Zhang
This note outlines an approach to defining $p$-adic Shimura classes and $p$-adic derived Hecke operators on the completed cohomology of modular curves from upcoming work by the author. After reviewing the modulo-$p$ constructions of Harris and Venkatesh, we formulate a conjecture relating the action of $p$-adic derived Hecke operators on cusp forms of weight $1$ and level $\Gamma_1(N)$ to the $p$-adic logarithm of the Stark unit for the corresponding adjoint Deligne-Serre representation. This new $p$-adic conjecture can be viewed as complementary to the Harris-Venkatesh conjecture.
Can repeating and non-repeating FRBs be drawn from the same population?
2025-06-10
Paz Beniamini, Pawan Kumar
Do all Fast Radio Burst (FRB) sources repeat? We present evidence that FRB sources follow a Zipf-like distribution, in which the number density of sources is approximately inversely proportional to their burst rate above a fixed energy threshold-even though both the burst rate and number density span many orders of magnitude individually. We introduce a model-independent framework that predicts the distribution of observed fluences and distances, and repetition rates of an FRB population based on an assumed burst rate distribution per source. Using parameters derived directly from observations, this framework simultaneously explains several key features of the FRB population: (i) The observed ratio of repeaters to apparent non-repeaters; (ii) The much lower ratio of apparent non-repeaters to the total number of Soft Gamma Repeater (SGR) sources within the observable Universe; And (iii) the slightly smaller average distances of known repeaters compared to non-repeaters. We further explore how survey parameters, such as radio sensitivity and observation time, influence these statistics. Notably, we find that the fraction of repeaters rises only mildly with improved sensitivity or longer exposure. This weak dependence could be misinterpreted as evidence that not all FRBs repeat. Overall, our results support the idea that a single population-likely magnetars-can account for the full observed diversity of FRB activity, from very inactive FRB sources like SGR 1935+2154 to the most active repeaters.
Constraining resolved extragalactic $R_{21}$ variation with well calibrated ALMA observations
2025-06-10
Jakob den Brok, Elias K. Oakes, Adam K. Leroy, Eric W. Koch, Antonio Usero, Erik W. Rosolowsky, Frank Bigiel, Jiayi Sun, Hao He, Ashley T. Barnes, Yixian Cao, Fu-Heng Liang, Hsi-An Pan, Toshiki Saito, Sumit K. Sarbadhicary, Thomas G. Williams
CO(1-0) and CO(2-1) are commonly used as bulk molecular gas tracers. The CO line ratios (especially CO(2-1)/CO(1-0) - $R_{21}$) vary within and among galaxies, yet previous studies on $R_{21}$ and alike often rely on measurements constructed by combining data from facilities with substantial relative calibration uncertainties that have the same order as physical line ratio variations. Hence robustly determining systematic $R_{21}$ variations is challenging. Here, we compare CO(1-0) and CO(2-1) mapping data from ALMA for 14 nearby galaxies, at a common physical resolution of 1.7 kpc. Our dataset includes new ALMA (7m+TP) CO(1-0) maps of 12 galaxies. We investigate $R_{21}$ variation to understand its dependence on global galaxy properties, kpc-scale environmental factors, and its correlation with star formation rate (SFR) surface density and metallicity. We find that the galaxy-to-galaxy scatter is 0.05 dex. This is lower than previous studies which reported over 0.1 dex variation, likely reflecting significant flux calibration uncertainties in single-dish surveys. Within individual galaxies, $R_{21}$ has a typical mean value of ~0.64 and 0.1 dex variation, with an increase to ~0.75 towards galactic centers. We find strong correlations between $R_{21}$ and various galactic parameters, particularly SFR surface density, which shows a power-law slope of 0.10-0.11 depending on the adopted binning/fitting methods. Our findings suggest that, for studies covering main sequence galaxy samples, assuming a fixed $R_{21}$=0.64 does not significantly bias kpc-scale molecular gas mass estimates from CO(2-1). Instead, systematic uncertainties from flux calibration and the CO-to-H$_2$ conversion factor account for more systematic scatter of CO-derived molecular gas properties.
Energy Correlators: A Journey From Theory to Experiment
2025-06-10
Ian Moult, Hua Xing Zhu
Collider experiments offer a unique opportunity to explore the Standard Model (SM), and to search for new physics, new interactions, and new principles of nature. The theoretical abstraction of a collider, namely the study of correlations in asymptotic fluxes, provides one of the most basic examples of an observable in quantum field theory (QFT) and quantum gravity. Energy flux is described in QFT by energy flow operators, a particular example of light-ray operators. In addition to their central role in the theoretical description of collider physics, energy flow operators play an important role in diverse areas of formal QFT and gravity, providing a connection between real world collider phenomenology, and the deep underlying principles of QFT. Recently it has become possible to measure correlation functions of energy flow operators in a wide variety of collider experiments, providing an exciting new connection between collider physics and formal theory. In this review, we provide a survey of recent progress in our understanding of energy operators and their correlators, highlighting their importance in both formal theory and collider phenomenology, and in particular, their great potential for bridging these areas to provide new ways to understand the real world. We intend this article as a resource for both formal theorists interested in understanding how light-ray operators are being applied in particle and nuclear physics, as well as for experimentalists interested in the theoretical motivation for these observables. Most importantly, we aim to stimulate further interaction between the formal, phenomenological and experimental communities through the common lens of energy correlators.
CAIRe: Cultural Attribution of Images by Retrieval-Augmented Evaluation
2025-06-10
Arnav Yayavaram, Siddharth Yayavaram, Simran Khanuja, Michael Saxon, Graham Neubig
As text-to-image models become increasingly prevalent, ensuring their equitable performance across diverse cultural contexts is critical. Efforts to mitigate cross-cultural biases have been hampered by trade-offs, including a loss in performance, factual inaccuracies, or offensive outputs. Despite widespread recognition of these challenges, an inability to reliably measure these biases has stalled progress. To address this gap, we introduce CAIRe, a novel evaluation metric that assesses the degree of cultural relevance of an image, given a user-defined set of labels. Our framework grounds entities and concepts in the image to a knowledge base and uses factual information to give independent graded judgments for each culture label. On a manually curated dataset of culturally salient but rare items built using language models, CAIRe surpasses all baselines by 28% F1 points. Additionally, we construct two datasets for culturally universal concept, one comprising of T2I-generated outputs and another retrieved from naturally occurring data. CAIRe achieves Pearson's correlations of 0.56 and 0.66 with human ratings on these sets, based on a 5-point Likert scale of cultural relevance. This demonstrates its strong alignment with human judgment across diverse image sources.
2025-06-10
Míriam Barrabés, Daniel Mas Montserrat, Kapal Dev, Alexander G. Ioannidis
Feature shifts between data sources are present in many applications involving healthcare, biomedical, socioeconomic, financial, survey, and multi-sensor data, among others, where unharmonized heterogeneous data sources, noisy data measurements, or inconsistent processing and standardization pipelines can lead to erroneous features. Localizing shifted features is important to address the underlying cause of the shift and correct or filter the data to avoid degrading downstream analysis. While many techniques can detect distribution shifts, localizing the features originating them is still challenging, with current solutions being either inaccurate or not scalable to large and high-dimensional datasets. In this work, we introduce the Feature Shift Localization Network (FSL-Net), a neural network that can localize feature shifts in large and high-dimensional datasets in a fast and accurate manner. The network, trained with a large number of datasets, learns to extract the statistical properties of the datasets and can localize feature shifts from previously unseen datasets and shifts without the need for re-training. The code and ready-to-use trained model are available at https://github.com/AI-sandbox/FSL-Net.
Detecting malignant dynamics on very few blood sample using signature coefficients
2025-06-10
Rémi Vaucher, Stéphane Chrétien
Recent discoveries have suggested that the promising avenue of using circulating tumor DNA (ctDNA) levels in blood samples provides reasonable accuracy for cancer monitoring, with extremely low burden on the patient's side. It is known that the presence of ctDNA can result from various mechanisms leading to DNA release from cells, such as apoptosis, necrosis or active secretion. One key idea in recent cancer monitoring studies is that monitoring the dynamics of ctDNA levels might be sufficient for early multi-cancer detection. This interesting idea has been turned into commercial products, e.g. in the company named GRAIL. In the present work, we propose to explore the use of Signature theory for detecting aggressive cancer tumors based on the analysis of blood samples. Our approach combines tools from continuous time Markov modelling for the dynamics of ctDNA levels in the blood, with Signature theory for building efficient testing procedures. Signature theory is a topic of growing interest in the Machine Learning community (see Chevyrev2016 and Fermanian2021), which is now recognised as a powerful feature extraction tool for irregularly sampled signals. The method proposed in the present paper is shown to correctly address the challenging problem of overcoming the inherent data scarsity due to the extremely small number of blood samples per patient. The relevance of our approach is illustrated with extensive numerical experiments that confirm the efficiency of the proposed pipeline.
2025-06-10
Chaoyang Zhou, Shunyu Liu, Zengmao Wang, Di Wang, Rong-Cheng Tu, Bo Du, Dacheng Tao
Reward models are critical for improving large language models (LLMs), particularly in reinforcement learning from human feedback (RLHF) or inference-time verification. Current reward modeling typically relies on scores of overall responses to learn the outcome rewards for the responses. However, since the response-level scores are coarse-grained supervision signals, the reward model struggles to identify the specific components within a response trajectory that truly correlate with the scores, leading to poor generalization on unseen responses. In this paper, we propose to leverage generation probabilities to establish reward consistency between processes in the response trajectory, which allows the response-level supervisory signal to propagate across processes, thereby providing additional fine-grained signals for reward learning. Building on analysis under the Bayesian framework, we develop an intra-trajectory consistency regularization to enforce that adjacent processes with higher next-token generation probability maintain more consistent rewards. We apply the proposed regularization to the advanced outcome reward model, improving its performance on RewardBench. Besides, we show that the reward model trained with the proposed regularization induces better DPO-aligned policies and achieves better best-of-N (BON) inference-time verification results. Our code is provided in https://github.com/chaoyang101/ICRM.
Foundation Models in Medical Imaging -- A Review and Outlook
2025-06-10
Vivien van Veldhuizen, Vanessa Botha, Chunyao Lu, Melis Erdal Cesur, Kevin Groot Lipman, Edwin D. de Jong, Hugo Horlings, Clárisa Sanchez, Cees Snoek, Ritse Mann, Eric Marcus, Jonas Teuwen
Foundation models (FMs) are changing the way medical images are analyzed by learning from large collections of unlabeled data. Instead of relying on manually annotated examples, FMs are pre-trained to learn general-purpose visual features that can later be adapted to specific clinical tasks with little additional supervision. In this review, we examine how FMs are being developed and applied in pathology, radiology, and ophthalmology, drawing on evidence from over 150 studies. We explain the core components of FM pipelines, including model architectures, self-supervised learning methods, and strategies for downstream adaptation. We also review how FMs are being used in each imaging domain and compare design choices across applications. Finally, we discuss key challenges and open questions to guide future research.
The spatially variable effects of mangroves on flood depths and losses from storm surges in Florida
2025-06-10
Siddharth Narayan, Christopher J. Thoma, Kechi Nzerem, Joss Matthewman, Christine Shepard, Laura Geselbracht, Michael W. Beck
Mangroves modify storm surges with impacts to property damages from tropical cyclones (TC), but the magnitude of these effects and their spatial variability are not well understood especially at sub-county scales. We use high-resolution storm surge flood and loss models to examine variation in the effects of mangroves on these losses spatially and by storm intensity in Florida. We estimate that mangroves reduce storm surge losses to properties by $67.5 million annually in Collier County in western Florida with over half the cumulative benefits from storm surges with return periods under 30 years. We estimate the benefits of mangroves during hurricanes Irma (2017) and Ian (2022) as US$ 725 Million and $4.1 Billion. We show that flood depths and losses are always lower for properties landward of mangroves but can increase for properties seaward or between mangroves, underlining the importance of nuanced descriptions of variability in mangrove effects during storms.
Scalable Medication Extraction and Discontinuation Identification from Electronic Health Records Using Large Language Models
2025-06-10
Chong Shao, Douglas Snyder, Chiran Li, Bowen Gu, Kerry Ngan, Chun-Ting Yang, Jiageng Wu, Richard Wyss, Kueiyu Joshua Lin, Jie Yang
Identifying medication discontinuations in electronic health records (EHRs) is vital for patient safety but is often hindered by information being buried in unstructured notes. This study aims to evaluate the capabilities of advanced open-sourced and proprietary large language models (LLMs) in extracting medications and classifying their medication status from EHR notes, focusing on their scalability on medication information extraction without human annotation. We collected three EHR datasets from diverse sources to build the evaluation benchmark. We evaluated 12 advanced LLMs and explored multiple LLM prompting strategies. Performance on medication extraction, medication status classification, and their joint task (extraction then classification) was systematically compared across all experiments. We found that LLMs showed promising performance on the medication extraction and discontinuation classification from EHR notes. GPT-4o consistently achieved the highest average F1 scores in all tasks under zero-shot setting - 94.0% for medication extraction, 78.1% for discontinuation classification, and 72.7% for the joint task. Open-sourced models followed closely, Llama-3.1-70B-Instruct achieved the highest performance in medication status classification on the MIV-Med dataset (68.7%) and in the joint task on both the Re-CASI (76.2%) and MIV-Med (60.2%) datasets. Medical-specific LLMs demonstrated lower performance compared to advanced general-domain LLMs. Few-shot learning generally improved performance, while CoT reasoning showed inconsistent gains. LLMs demonstrate strong potential for medication extraction and discontinuation identification on EHR notes, with open-sourced models offering scalable alternatives to proprietary systems and few-shot can further improve LLMs' capability.
Large Language Models and Emergence: A Complex Systems Perspective
2025-06-10
David C. Krakauer, John W. Krakauer, Melanie Mitchell
Emergence is a concept in complexity science that describes how many-body systems manifest novel higher-level properties, properties that can be described by replacing high-dimensional mechanisms with lower-dimensional effective variables and theories. This is captured by the idea "more is different". Intelligence is a consummate emergent property manifesting increasingly efficient -- cheaper and faster -- uses of emergent capabilities to solve problems. This is captured by the idea "less is more". In this paper, we first examine claims that Large Language Models exhibit emergent capabilities, reviewing several approaches to quantifying emergence, and secondly ask whether LLMs possess emergent intelligence.
Exposing Hidden Backdoors in NFT Smart Contracts: A Static Security Analysis of Rug Pull Patterns
2025-06-09
Chetan Pathade, Shweta Hooli
The explosive growth of Non-Fungible Tokens (NFTs) has revolutionized digital ownership by enabling the creation, exchange, and monetization of unique assets on blockchain networks. However, this surge in popularity has also given rise to a disturbing trend: the emergence of rug pulls - fraudulent schemes where developers exploit trust and smart contract privileges to drain user funds or invalidate asset ownership. Central to many of these scams are hidden backdoors embedded within NFT smart contracts. Unlike unintentional bugs, these backdoors are deliberately coded and often obfuscated to bypass traditional audits and exploit investor confidence. In this paper, we present a large-scale static analysis of 49,940 verified NFT smart contracts using Slither, a static analysis framework, to uncover latent vulnerabilities commonly linked to rug pulls. We introduce a custom risk scoring model that classifies contracts into high, medium, or low risk tiers based on the presence and severity of rug pull indicators. Our dataset was derived from verified contracts on the Ethereum mainnet, and we generate multiple visualizations to highlight red flag clusters, issue prevalence, and co-occurrence of critical vulnerabilities. While we do not perform live exploits, our results reveal how malicious patterns often missed by simple reviews can be surfaced through static analysis at scale. We conclude by offering mitigation strategies for developers, marketplaces, and auditors to enhance smart contract security. By exposing how hidden backdoors manifest in real-world smart contracts, this work contributes a practical foundation for detecting and mitigating NFT rug pulls through scalable automated analysis.
2025-06-09
Yujie Lian, Zhichen Pan, Haiyan Zhang, Shuo Cao, P. C. C. Freire, Lei Qian, Ralph P. Eatough, Lijing Shao, Scott M. Ransom, Duncan R. Lorimer, Dejiang Yin, Yinfeng Dai, Kuo Liu, Lin Wang, Yujie Wang, Zhongli Zhang, Zhonghua Feng, Baoda Li, Minghui Li, Tong Liu, Yaowei Li, Bo Peng, Yu Pan, Yuxiao Wu, Liyun Zhang, Xingnan Zhang, Peng Jiang
By January 2025, 60 pulsars were discovered by the Five-hundred-meter Aperture Spherical radio Telescope globular cluster (GC) pulsar survey (GC FANS), with spin periods spanning 1.98 ms to 3960.72 ms. Of these, 55 are millisecond pulsars (MSPs; $P<30$ ms), while 34 are binaries with orbital periods spanning 0.12 days to 466.47 days. This paper describes GC FANS, a deep, thorough search for pulsars in 41 GCs in the FAST sky ($-14^\circ < \delta < 65^\circ$) and describes new discoveries in 14 of them. We present updated timing solutions for M92A, NGC 6712A, M71A, and M71E, all of which are ``spider'' pulsars with short orbital periods. We present new timing solutions for M71B, C, and D. With orbital periods of $\sim$466 and 378 days, M71B and M71C are the widest known GC binaries; these systems resemble the normal wide MSP-He WD systems in the Galactic disk. With a spin period of 101 ms, M71D is in an eccentric ($e\sim$0.63) orbit with an 11-day period and a massive companion; the system has a total mass of $2.63 \pm 0.08 \, M_{\odot}$. These features and its large characteristic age suggest it is a double neutron star system (DNS) formed via massive binary evolution early in the cluster's history, akin to Galactic disk DNSs--unlike other candidate GC DNSs, which typically form dynamically. A comparative analysis of GC pulsar populations within FAST's sky reveals that most clusters (10 of 14) resemble the Galactic disk MSP population, likely due to lower stellar densities.
Implementation Considerations for Automated AI Grading of Student Work
2025-06-09
Zewei, Tian, Alex Liu, Lief Esbenshade, Shawon Sarkar, Zachary Zhang, Kevin He, Min Sun
This study explores the classroom implementation of an AI-powered grading platform in K-12 settings through a co-design pilot with 19 teachers. We combine platform usage logs, surveys, and qualitative interviews to examine how teachers use AI-generated rubrics and grading feedback. Findings reveal that while teachers valued the AI's rapid narrative feedback for formative purposes, they distrusted automated scoring and emphasized the need for human oversight. Students welcomed fast, revision-oriented feedback but remained skeptical of AI-only grading. We discuss implications for the design of trustworthy, teacher-centered AI assessment tools that enhance feedback while preserving pedagogical agency.
A Categorical Perspective on Braid Representations
2025-06-09
P. P. Martin, E. C. Rowell, F. Torzewska
We study categories whose objects are the braid representations, that is, strict monoidal functors from the braid category $B$ to the category of matrices $Mat$. Braid representations are equivalent to solutions to the (constant) Yang-Baxter equation. So their classification problems are also equivalent. We consider both the categories $MoonFun(B,Mat)$ whose morphisms are natural transformations and the category $MonFun(B,Mat)$ whose morphisms are the monoidal natural transformations. This categorical contextualisation naturally gives a three-fold focus to the problem: the source $B$; the target $Mat$; and the natural transformations and other symmetries between functors between them. Indeed the approach is mainly motivated by the recent classification of charge conserving Yang-Baxter operators, in which $Mat$ is replaced by a certain subcategory. One objective is to understand from the categorical perspective how the solution was facilitated by this change (with the aim of generalising). Another is to understand appropriate notions of equivalence - the restriction of target introduces new notions such as that of inner and outer equivalences. And another is to understand how universal such a restricted target is (does it give a transversal of all equivalence classes for a suitable notion of equivalence?). And then the aim is to complete the classification with a suitably universal target. With regard to the first part of the triple-focus, the category $B$, we review several features. Work has already been done to generalise to similar categories such as the loop braid category. But a significant part of our input here is to recast $MonFun(B,-)$ as the isomorphic category of Yang-Baxter objects - which manoeuvre leans heavily on the source being $B$ on the nose.
New Insights into the T Tauri Binary Separation Distribution
2025-06-09
Caleb Eastlund, Maxwell Moe, Kaitlin M. Kratter, Marina Kounkel
For three decades, adaptive optic surveys have revealed an excess of T Tauri binaries across a = 10-100 au in nearby star-forming regions compared to the field population of main-sequence (MS) stars. Such an excess requires that most stars are born in dense clusters and subjected to significant dynamical processing that disrupts such binaries across intermediate separations. However, we demonstrate that the apparent excess is due to an observational selection bias. Close binaries within a < 100 au clear out their dusty circumstellar disks on faster timescales compared to wide binaries and single stars. A magnitude-limited sample is therefore biased toward close binaries that have preferentially cleared out their obscuring disks. We re-examine the separation distribution of pre-MS binaries in low-density Taurus, moderately dense Upper Scorpius, and the extremely dense Orion Nebula Cluster (ONC). By limiting the samples to primary spectral type / mass instead of magnitude, the artificial excess across a = 10-100 au disappears in all three environments. Across wider separations a = 100-4,000 au, Taurus exhibits an excess of companions (mostly tertiaries), the ONC displays a deficit, and Upper Scorpius matches the field MS population. The field derives from an amalgam of all three environments, where Upper Scorpius corresponds to the average birth environment of solar-type stars. The total binary fraction within a < 10,000 au in Taurus is only 52% +/- 7%, substantially lower than the 100% inferred from the biased observations and only slightly higher than the field MS value of 45%. N-body interactions preferentially disrupt outer tertiaries with only marginal dynamical processing of the inner binaries, especially those within a < 100 au.
Uncovering the Functional Roles of Nonlinearity in Memory
2025-06-09
Manuel Brenner, Georgia Koppe
Memory and long-range temporal processing are core requirements for sequence modeling tasks across natural language processing, time-series forecasting, speech recognition, and control. While nonlinear recurrence has long been viewed as essential for enabling such mechanisms, recent work suggests that linear dynamics may often suffice. In this study, we go beyond performance comparisons to systematically dissect the functional role of nonlinearity in recurrent networks--identifying both when it is computationally necessary, and what mechanisms it enables. We use Almost Linear Recurrent Neural Networks (AL-RNNs), which allow fine-grained control over nonlinearity, as both a flexible modeling tool and a probe into the internal mechanisms of memory. Across a range of classic sequence modeling tasks and a real-world stimulus selection task, we find that minimal nonlinearity is not only sufficient but often optimal, yielding models that are simpler, more robust, and more interpretable than their fully nonlinear or linear counterparts. Our results provide a principled framework for selectively introducing nonlinearity, bridging dynamical systems theory with the functional demands of long-range memory and structured computation in recurrent neural networks, with implications for both artificial and biological neural systems.
This paper introduces MiniCPM4, a highly efficient large language model (LLM) designed explicitly for end-side devices. We achieve this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems. Specifically, in terms of model architecture, we propose InfLLM v2, a trainable sparse attention mechanism that accelerates both prefilling and decoding phases for long-context processing. Regarding training data, we propose UltraClean, an efficient and accurate pre-training data filtering and generation strategy, and UltraChat v2, a comprehensive supervised fine-tuning dataset. These datasets enable satisfactory model performance to be achieved using just 8 trillion training tokens. Regarding training algorithms, we propose ModelTunnel v2 for efficient pre-training strategy search, and improve existing post-training methods by introducing chunk-wise rollout for load-balanced reinforcement learning and data-efficient tenary LLM, BitCPM. Regarding inference systems, we propose CPM.cu that integrates sparse attention, model quantization, and speculative sampling to achieve efficient prefilling and decoding. To meet diverse on-device requirements, MiniCPM4 is available in two versions, with 0.5B and 8B parameters, respectively. Sufficient evaluation results show that MiniCPM4 outperforms open-source models of similar size across multiple benchmarks, highlighting both its efficiency and effectiveness. Notably, MiniCPM4-8B demonstrates significant speed improvements over Qwen3-8B when processing long sequences. Through further adaptation, MiniCPM4 successfully powers diverse applications, including trustworthy survey generation and tool use with model context protocol, clearly showcasing its broad usability.
The link between galaxy merger, radio jet expansion and molecular outflow in the ULIRG IRAS 00183-7111
2025-06-09
Ilaria Ruffa, Marilena Spavone, Enrichetta Iodice, Santiago Garcia-Burillo, Timothy A. Davis, Kazushi Iwasawa, Henrik W. W. Spoon, Rosita Paladino, Michele Perna, Cristian Vignali
The ultraluminous infrared galaxy (ULIRG) IRAS 00183-7111 ($z=0.328$) is one of the three ULIRGs that are currently known to host an active galactic nucleus (AGN) with young radio jets. We present a detailed study of the link between galaxy merger, AGN ignition, radio jet expansion and galactic-scale molecular outflow in IRAS 00183-7111, using high-resolution Atacama Large Millimeter/sub-millimeter Array (ALMA) observations of the $^{12}$CO(1-0) and $^{12}$CO(3-2) lines and very deep $i$-band VLT Survey Telescope (VST) imaging. The latter allows us to put - for the first time - tight constraints on the assembly history of the system, confirming that it formed through a major merger between two gas-rich spirals, likely characterised by a prograde encounter and no older than 2 Gyr. The recent merger channelled about $(1.0\pm0.1)\times10^{10}$ M$_{\odot}$ of molecular gas in the central regions of the remnant, as traced by the CO detections. The spatial correlation between the CO distribution and the radio core suggests that this gas likely contributed to the ignition of the AGN and thus to the launch of the radio jets, which are no older than 2 Myr. Furthermore, by comparing the relative strength of the two CO transitions, we find extreme gas excitation (i.e. $T_{\rm ex}\gg50$ K) around the radio lobes, supporting the case for a jet-ISM interaction. A qualitative study of the CO kinematics also demonstrates that, despite the overall disturbed dynamical state with no clear signs of regular rotation, at least one non-rotational kinematic component can be identified and likely associated to an outflow with $v_{\rm out}\approx 556$ km s$^{-1}$ and $\dot{M_{\rm out}}\approx 110$ M$_{\odot}$ yr$^{-1}$.
SDSS-V Milky Way Mapper (MWM): ASPCAP Stellar Parameters and Abundances in SDSS-V Data Release 19
2025-06-09
Szabolcs Mészáros, Paula Jofré, Jennifer A. Johnson, Jonathan C. Bird, Andrew R. Casey, Katia Cunha, Nathan De Lee, Peter Frinchaboy, Guillaume Guiglion, Viola Hegedűs, Alex P. Ji, Juna A. Kollmeier, Melissa K. Ness, Jonah Otto, Marc H. Pinsonneault, Alexandre Roman-Lopes, Amaya Sinha, Ying-Yi Song, Guy S. Stringfellow, Keivan G. Stassun, Jamie Tayar, Andrew Tkachenko, Marica Valentini, Zachary Way, Jörg Weingrill
The goal of this paper is to describe the science verification of Milky Way Mapper (MWM) ASPCAP data products published in Data Release 19 (DR19) of the fifth phase of the Sloan Digital Sky Survey (SDSS-V). We compare MWM ASPCAP atmospheric parameters T$_{\rm eff}$, log g, 24 abundances of 21 elements (carbon, nitrogen, and oxygen have multiple sources for deriving their abundance values) and their uncertainties determined from APOGEE spectrograph spectra with those of the literature and evaluate their accuracy and precision. We also test the zero-point calibration of the v$_{\rm rad}$ derived by the APOGEE Data Reduction Pipeline. This data release contains ASPCAP parameters for 964,989 stars, including all APOGEE-2 targets expanded with new observations of 336,511 stars from the Apache Point Observatory (APO) observed until 4 July 2023. Overall, the new T$_{\rm eff}$ values show excellent agreement with the IRFM scale, while the surface gravities exhibit slight systematic offsets compared to asteroseisimic gravities. The estimated precision of T$_{\rm eff}$ is between 50 and 70 K for giants and 70$-$100 K for dwarfs, while surface gravities are measured with a precision of 0.07$-$0.09 dex for giants. We achieve an estimated precision of 0.02$-$0.04 dex for multiple elements, including metallicity, $\alpha$, Mg, and Si, while the precision of at least 10 elements is better than 0.1 dex.
Integrating Artificial Intelligence as Assistive Technology for Older Adult Gamers: A Pilot Study
2025-06-09
Yichi Zhang, Brandon Lyman, Celia Pearce, Miso Kim, Casper Harteveld, Leanne Chukoskie, Bob De Schutter
With respect to digital games, older adults are a demographic that is often underserved due to an industry-wide focus on younger audiences' preferences and skill sets. Meanwhile, as artificial intelligence (AI) continues to expand into everyday technologies, its assistive capabilities have been recognized, suggesting its potential in improving the gaming experience for older gamers. To study this potential, we iteratively developed a pilot survey aimed at understanding older adult gamers' current gameplay preference, challenges they are facing, and their perspectives of AI usage in gaming. This article contributes an overview of our iterative survey-design workflow, and pilot results from 39 participants. During each iteration, we analyzed the survey's efficacy and adjusted the content, language, and format to better capture meaningful data, and was able to create a refined survey for a larger, more representative future parent study. At the same time, preliminary findings suggest that for older adult gamers, usability issues in gaming remain key obstacles, while this demographic's perceptions of AI are shaped by both its practical benefits and concerns about autonomy and complexity. These findings also offer early insights for the design of age-inclusive, AI-supported gaming experiences.
Identifiability in epidemic models with prior immunity and under-reporting
2025-06-09
Fanny Bergström, Martina Favero, Tom Britton
Identifiability is the property in mathematical modelling that determines if model parameters can be uniquely estimated from data. For infectious disease models, failure to ensure identifiability can lead to misleading parameter estimates and unreliable policy recommendations. We examine the identifiability of a modified SIR model that accounts for under-reporting and pre-existing immunity in the population. We provide a mathematical proof of the unidentifiability of jointly estimating three parameters: the fraction under-reporting, the proportion of the population with prior immunity, and the community transmission rate, when only reported case data are available. We then show, analytically and with a simulation study, that the identifiability of all three parameters is achieved if the reported incidence is complemented with sample survey data of prior immunity or prevalence during the outbreak. Our results show the limitations of parameter inference in partially observed epidemics and the importance of identifiability analysis when developing and applying models for public health decision making.
Addition in Four Movements: Mapping Layer-wise Information Trajectories in LLMs
2025-06-09
Yao Yan
Multi-digit addition is a clear probe of the computational power of large language models. To dissect the internal arithmetic processes in LLaMA-3-8B-Instruct, we combine linear probing with logit-lens inspection. Inspired by the step-by-step manner in which humans perform addition, we propose and analyze a coherent four-stage trajectory in the forward pass:Formula-structure representations become linearly decodable first, while the answer token is still far down the candidate list.Core computational features then emerge prominently.At deeper activation layers, numerical abstractions of the result become clearer, enabling near-perfect detection and decoding of the individual digits in the sum.Near the output, the model organizes and generates the final content, with the correct token reliably occupying the top rank.This trajectory suggests a hierarchical process that favors internal computation over rote memorization. We release our code and data to facilitate reproducibility.
A family of nanographene molecules called phenalenyl-tessellation molecules (PTMs) exhibits two types of zero modes: a $\sqrt{3} \times \sqrt{3}$ type that spreads over the entire molecule and a vacancy-localized type. A periodic system of PTMs is expected to have low-energy bands that strongly reflect the properties of the zero modes of PTMs as effective atoms. In this study, we show that the low-energy Dirac bands in a class of honeycomb PTMs (H-PTM) can be represented by an effective honeycomb model which is determined only by the connections between neighboring effective atoms.The hopping parameters of H-PTM in each direction take positive integer ratios according to the connection order between two PTMs.By structurally designing each PTM, we can change the connection order of the PTMs and hence modulate the energy gap and the Fermi velocity of the Dirac band of the H-PTM. Moreover, we confirm that Dirac bands coexist with vacancy-localized zero modes in the H-PTM with vacancies.The result indicates that the nanographene structure arranging PTMs as effective atoms extends material design freedom that effectively generates a modulated Dirac electron system with coexisting localized electron spins for graphene-based electronic and quantum devices.
Looking Beyond Visible Cues: Implicit Video Question Answering via Dual-Clue Reasoning
2025-06-09
Tieyuan Chen, Huabin Liu, Yi Wang, Chaofan Gan, Mingxi Lyu, Gui Zou, Weiyao Lin
Video Question Answering (VideoQA) aims to answer natural language questions based on the given video, with prior work primarily focusing on identifying the duration of relevant segments, referred to as explicit visual evidence. However, explicit visual evidence is not always directly available, particularly when questions target symbolic meanings or deeper intentions, leading to significant performance degradation. To fill this gap, we introduce a novel task and dataset, $\textbf{I}$mplicit $\textbf{V}$ideo $\textbf{Q}$uestion $\textbf{A}$nswering (I-VQA), which focuses on answering questions in scenarios where explicit visual evidence is inaccessible. Given an implicit question and its corresponding video, I-VQA requires answering based on the contextual visual cues present within the video. To tackle I-VQA, we propose a novel reasoning framework, IRM (Implicit Reasoning Model), incorporating dual-stream modeling of contextual actions and intent clues as implicit reasoning chains. IRM comprises the Action-Intent Module (AIM) and the Visual Enhancement Module (VEM). AIM deduces and preserves question-related dual clues by generating clue candidates and performing relation deduction. VEM enhances contextual visual representation by leveraging key contextual clues. Extensive experiments validate the effectiveness of our IRM in I-VQA tasks, outperforming GPT-4o, OpenAI-o3, and fine-tuned VideoChat2 by $0.76\%$, $1.37\%$, and $4.87\%$, respectively. Additionally, IRM performs SOTA on similar implicit advertisement understanding and future prediction in traffic-VQA. Datasets and codes are available for double-blind review in anonymous repo: https://github.com/tychen-SJTU/Implicit-VideoQA.
Traces of the evolution of cosmic void galaxies: An Integral Field Spectroscopy based analysis
2025-06-09
Agustín M. Rodríguez-Medrano, Dante J. Paz, Damián Mast, Federico A. Stasyszyn, Andrés N. Ruiz
Galaxies in the most underdense regions of the Universe, known as cosmic voids, exhibit astrophysical properties that suggest a distinct evolutionary path compared to galaxies in denser environments. Numerical simulations indicate that the assembly of void galaxies occurs later, leading to galaxies with younger stellar populations, low metallicities, and a high gas content in their halos, which provides the fuel to sustain elevated star formation activity. Our objective in this work is to test these numerical predictions with observational data by comparing galaxies in voids with galaxies in non-void environments. We used voids identified in SDSS data and galaxies from the MaNGA survey, which provides galaxies with integral field spectroscopy (IFS). We separated the galaxies into void and non-void samples, mimicked the magnitude distribution, and compared their integrated astrophysical properties as well as the metallicity and age profiles through a stacking technique, ETGs and LTGs separately. We find that void galaxies have younger and less metal-rich stellar populations. Regarding gas mass, we do not find differences across environments. When dividing galaxies into ETGs and LTGs, we observe that ETGs show negative gradients in both age and metallicity, with void galaxies consistently appearing younger and less metal-rich. For LTGs, age gradients are also negative, showing younger populations in void galaxies. However, we do not find statistically significant differences in stellar metallicity gradients between void and non-void environments. Our results show how the astrophysical properties of galaxies in voids differ from those of galaxies in the rest of the Universe. This suggests that the void environment plays a role in the evolution of its galaxies, delaying their assembly and growth.
Galikyan et al. (2025) reported a statistically significant change in galaxy spectral properties at redshift $z$ $\simeq$ 2.7 based on a Kolmogorov analysis of JWST spectroscopic data of galaxies. In this comment, we demonstrate that their result is critically driven by a single outlier in the dataset. This outlier arises from the use of a questionable redshift estimate for one spectrum. When the outlier is removed or the redshift is corrected, the claimed transition at $z$ $\simeq$ 2.7 disappears entirely. By independently reproducing their analysis, we demonstrate that the claimed feature is not a robust statistical signal, but an artefact of this anomalous data point.
Level order of quark systems: The puzzle of the Roper resonance, and related questions
2025-06-09
Jean-Marc Richard
The problem of ordering of radial vs.\ orbital excitations is reviewed. It is shown that the current quark models cannot explain the location of the Roper resonance which is slightly lower than the lowest negative-parity excitations. We also study some related spectral problems, such as the dependence of the energies on the quark masses, and the possibility of bound states in simple chromelectric models.
AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking
2025-06-09
Silin Gao, Antoine Bosselut, Samy Bengio, Emmanuel Abbe
Recent studies have shown that large language models (LLMs), especially smaller ones, often lack robustness in their reasoning. I.e., they tend to experience performance drops when faced with distribution shifts, such as changes to numerical or nominal variables, or insertions of distracting clauses. A possible strategy to address this involves generating synthetic data to further "instantiate" reasoning problems on potential variations. In contrast, our approach focuses on "abstracting" reasoning problems. This not only helps counteract distribution shifts but also facilitates the connection to symbolic tools for deriving solutions. We find that this abstraction process is better acquired through reinforcement learning (RL) than just supervised fine-tuning, which often fails to produce faithful abstractions. Our method, AbstRaL -- which promotes abstract reasoning in LLMs using RL on granular abstraction data -- significantly mitigates performance degradation on recent GSM perturbation benchmarks.
Research quality evaluation by AI in the era of Large Language Models: Advantages, disadvantages, and systemic effects
2025-06-09
Mike Thelwall
Artificial Intelligence (AI) technologies like ChatGPT now threaten bibliometrics as the primary generators of research quality indicators. They are already used in at least one research quality evaluation system and evidence suggests that they are used informally by many peer reviewers. Since using bibliometrics to support research evaluation continues to be controversial, this article reviews the corresponding advantages and disadvantages of AI-generated quality scores. From a technical perspective, generative AI based on Large Language Models (LLMs) equals or surpasses bibliometrics in most important dimensions, including accuracy (mostly higher correlations with human scores), and coverage (more fields, more recent years) and may reflect more research quality dimensions. Like bibliometrics, current LLMs do not "measure" research quality, however. On the clearly negative side, LLM biases are currently unknown for research evaluation, and LLM scores are less transparent than citation counts. From a systemic perspective, the key issue is how introducing LLM-based indicators into research evaluation will change the behaviour of researchers. Whilst bibliometrics encourage some authors to target journals with high impact factors or to try to write highly cited work, LLM-based indicators may push them towards writing misleading abstracts and overselling their work in the hope of impressing the AI. Moreover, if AI-generated journal indicators replace impact factors, then this would encourage journals to allow authors to oversell their work in abstracts, threatening the integrity of the academic record.
ArchiLense: A Framework for Quantitative Analysis of Architectural Styles Based on Vision Large Language Models
2025-06-09
Jing Zhong, Jun Yin, Peilin Li, Pengyu Zeng, Miao Zang, Ran Luo, Shuai Lu
Architectural cultures across regions are characterized by stylistic diversity, shaped by historical, social, and technological contexts in addition to geograph-ical conditions. Understanding architectural styles requires the ability to describe and analyze the stylistic features of different architects from various regions through visual observations of architectural imagery. However, traditional studies of architectural culture have largely relied on subjective expert interpretations and historical literature reviews, often suffering from regional biases and limited ex-planatory scope. To address these challenges, this study proposes three core contributions: (1) We construct a professional architectural style dataset named ArchDiffBench, which comprises 1,765 high-quality architectural images and their corresponding style annotations, collected from different regions and historical periods. (2) We propose ArchiLense, an analytical framework grounded in Vision-Language Models and constructed using the ArchDiffBench dataset. By integrating ad-vanced computer vision techniques, deep learning, and machine learning algo-rithms, ArchiLense enables automatic recognition, comparison, and precise classi-fication of architectural imagery, producing descriptive language outputs that ar-ticulate stylistic differences. (3) Extensive evaluations show that ArchiLense achieves strong performance in architectural style recognition, with a 92.4% con-sistency rate with expert annotations and 84.5% classification accuracy, effec-tively capturing stylistic distinctions across images. The proposed approach transcends the subjectivity inherent in traditional analyses and offers a more objective and accurate perspective for comparative studies of architectural culture.
On the arithmetic properties of partitions into parts simultaneously $4$-regular and $9$-distinct
2025-06-09
Mohammed L. Nadji, Moussa Ahmia
In 2017, Keith presented a comprehensive survey on integer partitions into parts that are simultaneously regular, distinct, and/or flat. Recently, the authors initiated a study of partitions into parts that are simultaneously regular and distinct, examining them from both arithmetic and combinatorial perspectives. In particular, several Ramanujan-like congruences were obtained for $\myRD^{(\ell, t)}(n)$, the number of partitions of $n$ into parts that are simultaneously $\ell$-regular and $t$-distinct (parts appearing fewer than $t$ times), for various pairs $(\ell, t)$. In this paper, we focus on the case $(\ell, t)=(4,9)$ and conduct a thorough investigation of the arithmetic properties of $\myRD^{(4, 9)}(n)$. We establish several infinite families of congruences modulo $4$, $6$, and $12$, along with a collection of Ramanujan-like congruences modulo $24$.
Towards a Small Language Model Lifecycle Framework
2025-06-09
Parsa Miraghaei, Sergio Moreschini, Antti Kolehmainen, David Hästbacka
Background: The growing demand for efficient and deployable language models has led to increased interest in Small Language Models (SLMs). However, existing research remains fragmented, lacking a unified lifecycle perspective. Objective: This study aims to define a comprehensive lifecycle framework for SLMs by synthesizing insights from academic literature and practitioner sources. Method: We conducted a comprehensive survey of 36 works, analyzing and categorizing lifecycle-relevant techniques. Results: We propose a modular lifecycle model structured into main, optional, and cross-cutting components. The model captures key interconnections across stages, supporting method reuse, co-adaptation, and lifecycle-awareness. Conclusion: Our framework provides a coherent foundation for developing and maintaining SLMs, bridging theory and practice, and guiding future research and tool development.
In certain general relativistic time-dependent gravitational fields, free test particles can asymptotically line up relative to fiducial static observers and produce a cosmic jet whose speed approaches the speed of light. Two scenarios for the formation of these purely gravitational jets have thus far been theoretically demonstrated: the double-jet collapse scenario and the wave scenario that involves both a cosmic jet in the general direction of wave propagation as well as a counterjet in the opposite direction. These scenarios are briefly reviewed in this paper. Moreover, to elucidate the process of jet formation in these scenarios, we study the propagation of scalar fields in related gravitational fields in connection with the correspondence between the scalar wave perturbations and the motion of free test particles and null rays.
Non-Separable Halo Bias from High-Redshift Galaxy Clustering
2025-06-09
Emy Mons, Vipul Prasad Maranchery, M. S. Suryan Sivadas, Charles Jose
The halo model provides a powerful framework for interpreting galaxy clustering by linking the spatial distribution of dark matter haloes to the underlying matter distribution. A key assumption within the linear bias approximation of the halo model is that the halo bias between two halo populations is a separable function of the mass of each population. In this work, we test the validity of the separable bias approximation on quasi-linear scales using both simulations and observational data. Unlike previous studies, we explicitly disentangle the effects of scale dependence of the bias to assess the robustness of bias separability across a broad range of halo masses and redshifts. In particular, we define a separability function based on halo or galaxy cross-correlations to quantify deviations from halo bias separability, and measure it from N-body simulations. We find significant departures from separability on quasi-linear scales ($\sim$ 1-5 Mpc) at high redshifts ($z \geq 3$), leading to enhancements in halo cross-correlations by up to a factor of 2 -- or even higher. In contrast, deviations at low redshifts remain modest. Additionally, using high-redshift ($z \sim 3.6$) galaxy samples, we detect deviations from bias separability that closely align with simulation predictions. The breakdown of the separable bias approximation on quasi-linear scales at high redshifts underscore the importance to account for non-separability in models of the galaxy-halo connection in this regime. Furthermore, these results highlight the potential of high-redshift galaxy cross-correlations as a probe for improving the galaxy-halo connection from upcoming large-scale surveys.
While Large Language Models (LLMs) have shown significant potential in assisting peer review, current methods often struggle to generate thorough and insightful reviews while maintaining efficiency. In this paper, we propose TreeReview, a novel framework that models paper review as a hierarchical and bidirectional question-answering process. TreeReview first constructs a tree of review questions by recursively decomposing high-level questions into fine-grained sub-questions and then resolves the question tree by iteratively aggregating answers from leaf to root to get the final review. Crucially, we incorporate a dynamic question expansion mechanism to enable deeper probing by generating follow-up questions when needed. We construct a benchmark derived from ICLR and NeurIPS venues to evaluate our method on full review generation and actionable feedback comments generation tasks. Experimental results of both LLM-based and human evaluation show that TreeReview outperforms strong baselines in providing comprehensive, in-depth, and expert-aligned review feedback, while reducing LLM token usage by up to 80% compared to computationally intensive approaches. Our code and benchmark dataset are available at https://github.com/YuanChang98/tree-review.
IDENT Review: Recent Advances in Identification of Differential Equations from Noisy Data
2025-06-09
Roy Y. He, Hao Liu, Wenjing Liao, Sung Ha Kang
Differential equations and numerical methods are extensively used to model various real-world phenomena in science and engineering. With modern developments, we aim to find the underlying differential equation from a single observation of time-dependent data. If we assume that the differential equation is a linear combination of various linear and nonlinear differential terms, then the identification problem can be formulated as solving a linear system. The goal then reduces to finding the optimal coefficient vector that best represents the time derivative of the given data. We review some recent works on the identification of differential equations. We find some common themes for the improved accuracy: (i) The formulation of linear system with proper denoising is important, (ii) how to utilize sparsity and model selection to find the correct coefficient support needs careful attention, and (iii) there are ways to improve the coefficient recovery. We present an overview and analysis of these approaches about some recent developments on the topic.
Instructing Large Language Models for Low-Resource Languages: A Systematic Study for Basque
2025-06-09
Oscar Sainz, Naiara Perez, Julen Etxaniz, Joseba Fernandez de Landa, Itziar Aldabe, Iker García-Ferrero, Aimar Zabala, Ekhi Azurmendi, German Rigau, Eneko Agirre, Mikel Artetxe, Aitor Soroa
Instructing language models with user intent requires large instruction datasets, which are only available for a limited set of languages. In this paper, we explore alternatives to conventional instruction adaptation pipelines in low-resource scenarios. We assume a realistic scenario for low-resource languages, where only the following are available: corpora in the target language, existing open-weight multilingual base and instructed backbone LLMs, and synthetically generated instructions sampled from the instructed backbone. We present a comprehensive set of experiments for Basque that systematically study different combinations of these components evaluated on benchmarks and human preferences from 1,680 participants. Our conclusions show that target language corpora are essential, with synthetic instructions yielding robust models, and, most importantly, that using as backbone an instruction-tuned model outperforms using a base non-instructed model, and improved results when scaling up. Using Llama 3.1 instruct 70B as backbone our model comes near frontier models of much larger sizes for Basque, without using any Basque data apart from the 1.2B word corpora. We release code, models, instruction datasets, and human preferences to support full reproducibility in future research on low-resource language adaptation.
Beyond the Sentence: A Survey on Context-Aware Machine Translation with Large Language Models
2025-06-09
Ramakrishna Appicharla, Baban Gain, Santanu Pal, Asif Ekbal
Despite the popularity of the large language models (LLMs), their application to machine translation is relatively underexplored, especially in context-aware settings. This work presents a literature review of context-aware translation with LLMs. The existing works utilise prompting and fine-tuning approaches, with few focusing on automatic post-editing and creating translation agents for context-aware machine translation. We observed that the commercial LLMs (such as ChatGPT and Tower LLM) achieved better results than the open-source LLMs (such as Llama and Bloom LLMs), and prompt-based approaches serve as good baselines to assess the quality of translations. Finally, we present some interesting future directions to explore.
What Do Indonesians Really Need from Language Technology? A Nationwide Survey
2025-06-09
Muhammad Dehan Al Kautsar, Lucky Susanto, Derry Wijaya, Fajri Koto
There is an emerging effort to develop NLP for Indonesias 700+ local languages, but progress remains costly due to the need for direct engagement with native speakers. However, it is unclear what these language communities truly need from language technology. To address this, we conduct a nationwide survey to assess the actual needs of native speakers in Indonesia. Our findings indicate that addressing language barriers, particularly through machine translation and information retrieval, is the most critical priority. Although there is strong enthusiasm for advancements in language technology, concerns around privacy, bias, and the use of public data for AI training highlight the need for greater transparency and clear communication to support broader AI adoption.
Resolving the Unresolved: Using NESSI to Search for Unresolved Companions in Low-mass Disk Wide Binaries
2025-06-09
Zachary D. Hartman, Gerard van Belle, Sébastien Lépine, Mark E. Everett, Ilija Medan
Stellar systems consisting of three or more stars are not an uncommon occurrence in the Galaxy. Nearly 50% of solar-type wide binaries with separations >1000 au are actually higher-order multiples with one component being a close binary. Additionally, the higher-order multiplicity fraction appears to be correlated with the physical separation of the widest component. These facts have motivated some of our current theories behind how the widest stellar systems formed, which can have separations on the order of or larger than protostellar cores. However, it is unclear if the correlation between wide binary separation and higher-order multiplicity extends to low-mass binaries. We present initial results of an ongoing speckle imaging survey of nearby low-mass wide binaries. We find an overall higher-order multiplicity fraction for our sample of $42.0\% \pm 10.9\%$. If we include systems where Gaia indicates that a companion is likely present, this fraction increases to $62.0\% \pm 14.2\%$. This is consistent with previous results from both higher-mass stars and a previous result for low-mass wide binaries. However, we do not detect the expected increase in higher-order multiplicity fraction with separation, as was seen with previous studies. We briefly explore why higher-order multiplicity statistics could be different in low-mass stars, and what the significance might be for models of wide binary formation.
Improving Fairness of Large Language Models in Multi-document Summarization
2025-06-09
Haoyuan Li Yusen Zhang, Snigdha Chaturvedi
Fairness in multi-document summarization (MDS) is crucial for providing comprehensive views across documents with diverse social attribute values, which can significantly impact decision-making. For example, a summarization system that tends to overrepresent negative reviews of products can mislead customers into disregarding good products. Previous works measure fairness in MDS at two levels: summary-level and corpus-level. While summary-level fairness focuses on individual summaries, corpus-level fairness focuses on a corpus of summaries. Recent methods primarily focus on summary-level fairness. We propose FairPO, a preference tuning method that focuses on both summary-level and corpus-level fairness in MDS. To improve summary-level fairness, we propose to generate preference pairs by perturbing document sets. To improve corpus-level fairness, we propose fairness-aware preference tuning by dynamically adjusting the weights of preference pairs. Our experiments show that FairPO outperforms strong baselines while maintaining the critical qualities of summaries. The code is available at https://github.com/leehaoyuan/coverage_fairnes.
The Internal Kinematics, Stellar Population, and Gas-phase Properties of The Pseudobulge in An Ultra-diffuse Galaxy: AGC721966
2025-06-09
Shihong Liu, Yu Rong, Huiyuan Wang, Hong-Xin Zhang, Tie Li, Yao Yao, Zhicheng He, Teng Liu, Enci Wang, Cheng Cheng, Xu Kong
Leveraging spectroscopic data from the Sloan Digital Sky Survey, we conduct a comprehensive analysis of the central stellar velocity dispersion, stellar population properties, star formation history, and gas-phase chemical abundances in AGC721966, a unique ultra-diffuse galaxy (UDG) harboring a pseudobulge. Our findings reveal that the pseudobulge formed in the early universe but underwent a recent episode of rejuvenated star formation. The system exhibits a mass-weighted (light-weighted) stellar population age of $\tau_{\star}\sim 7.4\pm2.5$ ($2.9\pm1.5$)~Gyr, a stellar metallicity of [M/H]$\sim -0.62\pm0.26$ ($-0.55\pm0.20$), an $\alpha$-element enhancement of [$\alpha$/Fe]$\sim 0.36\pm0.09$ ($0.37\pm0.07$), and a gas-phase oxygen abundance of \Oabund$\sim 8.15\pm0.03$. The central stellar velocity dispersion is measured as $\sigma_{\rm c}\sim 57.9\pm15.7$~km/s. These results provide robust evidence supporting the early halo-halo merging formation scenario proposed by \cite{Rong25}, while unequivocally ruling out the ``failed'' $L^{\star}$ formation model, at least for AGC721966. Furthermore, through systematic application of the baryonic Tully-Fisher relation, we establish that these pseudobulge-hosting UDGs are neither misidentified nuclear star cluster-bearing dwarf galaxies nor bulge-dominated massive galaxies, thereby affirming their distinct evolutionary pathway.
Constraints on cosmology and baryonic feedback with joint analysis of Dark Energy Survey Year 3 lensing data and ACT DR6 thermal Sunyaev-Zel'dovich effect observations
2025-06-09
S. Pandey, J. C. Hill, A. Alarcon, O. Alves, A. Amon, D. Anbajagane, F. Andrade-Oliveira, N. Battaglia, E. Baxter, K. Bechtol, M. R. Becker, G. M. Bernstein, J. Blazek, S. L. Bridle, E. Calabrese, H. Camacho, A. Campos, A. Carnero Rosell, M. Carrasco Kind, R. Cawthon, C. Chang, R. Chen, P. Chintalapati, A. Choi, J. Cordero, W. Coulton, M. Crocce, C. Davis, J. DeRose, M. Devlin, H. T. Diehl, S. Dodelson, C. Doux, A. Drlica-Wagner, K. Eckert, T. F. Eifler, J. Elvin-Poole, S. Everett, X. Fang, A. Ferté, P. Fosalba, O. Friedrich, M. Gatti, E. Gaztanaga, G. Giannini, V. Gluscevic, D. Gruen, R. A. Gruendl, B. Ried Guachalla, I. Harrison, W. G. Hartley, K. Herner, H. Huang, E. M. Huff, D. Huterer, B. Jain, M. Jarvis, E. Krause, N. Kuropatkin, A. Kusiak, P. Leget, P. Lemos, A. R. Liddle, M. Lokken, N. MacCrann, J. McCullough, K. Moodley, J. Muir, J. Myles, A. Navarro-Alsina, Y. Omori, Y. Park, B. Partridge, A. Porredon, J. Prat, M. Raveri, A. Refregier, R. P. Rollins, A. Roodman, R. Rosenfeld, A. J. Ross, E. S. Rykoff, S. Samuroff, J. Sanchez, C. Sánchez, L. F. Secco, I. Sevilla-Noarbe, S. Shaikh, E. Sheldon, T. Shin, Cristóbal Sifón, C. To, A. Troja, M. A. Troxel, I. Tutusaus, T. N. Varga, N. Weaverdyck, R. H. Wechsler, E. J. Wollack, B. Yanny, B. Yin, Y. Zhang, J. Zuntz, S. S. Allam, D. Bacon, S. Bocquet, D. Brooks, D. L. Burke, J. Carretero, R. Cawthon, M. Costanzi, L. N. da Costa, M. E. da Silva Pereira, T. M. Davis, S. Desai, J. Frieman, J. García-Bellido, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, N. Jeffrey, S. Lee, J. L. Marshall, J. Mena-Fernández, R. Miquel, J. J. Mohr, R. L. C. Ogando, A. A. Plazas Malag'on, A. K. Romer, E. Sanchez, B. Santiago, M. Smith, E. Suchyta, M. E. C. Swanson, D. Thomas, V. Vikram, A. R. Walker, J. Weller, P. Wiseman
We present a joint analysis of weak gravitational lensing (shear) data obtained from the first three years of observations by the Dark Energy Survey and thermal Sunyaev-Zel'dovich (tSZ) effect measurements from a combination of Atacama Cosmology Telescope (ACT) and Planck data. A combined analysis of shear (which traces the projected mass) with the tSZ effect (which traces the projected gas pressure) can jointly probe both the distribution of matter and the thermodynamic state of the gas, accounting for the correlated effects of baryonic feedback on both observables. We detect the shear$~\times~$tSZ cross-correlation at a 21$\sigma$ significance, the highest to date, after minimizing the bias from cosmic infrared background leakage in the tSZ map. By jointly modeling the small-scale shear auto-correlation and the shear$~\times~$tSZ cross-correlation, we obtain $S_8 = 0.811^{+0.015}_{-0.012}$ and $\Omega_{\rm m} = 0.263^{+0.023}_{-0.030}$, results consistent with primary CMB analyses from Planck and P-ACT. We find evidence for reduced thermal gas pressure in dark matter halos with masses $M < 10^{14} \, M_{\odot}/h$, supporting predictions of enhanced feedback from active galactic nuclei on gas thermodynamics. A comparison of the inferred matter power suppression reveals a $2-4\sigma$ tension with hydrodynamical simulations that implement mild baryonic feedback, as our constraints prefer a stronger suppression. Finally, we investigate biases from cosmic infrared background leakage in the tSZ-shear cross-correlation measurements, employing mitigation techniques to ensure a robust inference. Our code is publicly available on GitHub.
Galaxy Cluster Mass Estimation Through The Splashback Radius
2025-06-09
Lucas Gabriel-Silva, Laerte Sodré Jr
We present an analysis of the splashback radius ($R_{\text{sp}}$) and the associated splashback mass ($M_{\text{sp}}$) for a sample of galaxy clusters using SDSS spectroscopic data and mock simulations. $R_{\text{sp}}$ marks a physical boundary between the virialized core and the outer infall regions of clusters, providing a robust measure of cluster mass accretion history without being affected by pseudo-evolution. We model the cumulative galaxy number profile of clusters, testing different halo density models and considering the impact of cluster properties, such as center definitions, magnitude limits, galaxy colors, and field contamination, on the estimation of splashback features. Our results show that observed splashback radii, measured in projection (2D), are consistently smaller than predicted by dark matter simulations, with $R_\text{sp}/R_{200m} \approx 1$, supporting previous discrepancies in the literature. We also explore the relationship between $M_{\text{sp}}$ and $R_{\text{sp}}$, proposing a new scaling relation for future cosmological studies, as $R_{\text{sp}}$ is easily observable. Our findings indicate that splashback masses strongly correlate with radii, with a dispersion of $\approx 0.15$ dex, competitive with other mass-observable relations. However, the fitted relation diverges from the constant density expectations of galaxy clusters around $R_\text{sp}$. Additionally, the $M_{\text{sp}} \textendash R_{\text{sp}}$ relation shows significant redshift evolution, though the predominantly low-redshift range of our sample limits our ability to confirm this trend conclusively. The approach developed here may play a key role in cluster characterization and cosmology in the era of large galaxy surveys.
The impact of extracurricular education on socioeconomic mobility in Japan: an application of causal machine learning
2025-06-09
Yang Qiang
This paper explores the socioeconomic impacts of extracurricular education, specifically private tutoring, on social mobility in Japan. Using data from the 2015 National Survey on Social Stratification and Social Mobility (SSM), we employed a causal machine learning approach to evaluate this educational intervention on income, educational attainment, and occupational prestige. Our research suggests that while shadow education holds the potential for positive socioeconomic impacts, its benefits are undermined by the economic disparities among households, resulting in minimal overall improvement. This highlights the complex mechanisms between individual demographics and educational interventions, revealing promising machine learning applications in this field.
MedChat: A Multi-Agent Framework for Multimodal Diagnosis with Large Language Models
2025-06-09
Philip R. Liu, Sparsh Bansal, Jimmy Dinh, Aditya Pawar, Ramani Satishkumar, Shail Desai, Neeraj Gupta, Xin Wang, Shu Hu
The integration of deep learning-based glaucoma detection with large language models (LLMs) presents an automated strategy to mitigate ophthalmologist shortages and improve clinical reporting efficiency. However, applying general LLMs to medical imaging remains challenging due to hallucinations, limited interpretability, and insufficient domain-specific medical knowledge, which can potentially reduce clinical accuracy. Although recent approaches combining imaging models with LLM reasoning have improved reporting, they typically rely on a single generalist agent, restricting their capacity to emulate the diverse and complex reasoning found in multidisciplinary medical teams. To address these limitations, we propose MedChat, a multi-agent diagnostic framework and platform that combines specialized vision models with multiple role-specific LLM agents, all coordinated by a director agent. This design enhances reliability, reduces hallucination risk, and enables interactive diagnostic reporting through an interface tailored for clinical review and educational use. Code available at https://github.com/Purdue-M2/MedChat.
Generative Models at the Frontier of Compression: A Survey on Generative Face Video Coding
2025-06-09
Bolin Chen, Shanzhi Yin, Goluck Konuko, Giuseppe Valenzise, Zihan Zhang, Shiqi Wang, Yan Ye
The rise of deep generative models has greatly advanced video compression, reshaping the paradigm of face video coding through their powerful capability for semantic-aware representation and lifelike synthesis. Generative Face Video Coding (GFVC) stands at the forefront of this revolution, which could characterize complex facial dynamics into compact latent codes for bitstream compactness at the encoder side and leverages powerful deep generative models to reconstruct high-fidelity face signal from the compressed latent codes at the decoder side. As such, this well-designed GFVC paradigm could enable high-fidelity face video communication at ultra-low bitrate ranges, far surpassing the capabilities of the latest Versatile Video Coding (VVC) standard. To pioneer foundational research and accelerate the evolution of GFVC, this paper presents the first comprehensive survey of GFVC technologies, systematically bridging critical gaps between theoretical innovation and industrial standardization. In particular, we first review a broad range of existing GFVC methods with different feature representations and optimization strategies, and conduct a thorough benchmarking analysis. In addition, we construct a large-scale GFVC-compressed face video database with subjective Mean Opinion Scores (MOSs) based on human perception, aiming to identify the most appropriate quality metrics tailored to GFVC. Moreover, we summarize the GFVC standardization potentials with a unified high-level syntax and develop a low-complexity GFVC system which are both expected to push forward future practical deployments and applications. Finally, we envision the potential of GFVC in industrial applications and deliberate on the current challenges and future opportunities.
A Survey on LUT-based Deep Neural Networks Implemented in FPGAs
2025-06-09
Zeyu Guo
Low-latency, energy-efficient deep neural networks (DNNs) inference are critical for edge applications, where traditional cloud-based deployment suffers from high latency and security risks. Field-Programmable Gate Arrays (FPGAs) offer a compelling solution, balancing reconfigurability, power efficiency, and real-time performance. However, conventional FPGA-based DNNs rely heavily on digital signal processing (DSP) blocks for multiply-accumulate (MAC) operations, limiting scalability. LUT-based DNNs address this challenge by fully leveraging FPGA lookup tables (LUTs) for computation, improving resource utilization and reducing inference latency. This survey provides a comprehensive review of LUT-based DNN architectures, including their evolution, design methodologies, and performance trade-offs, while outlining promising directions for future research.
SALT: A Lightweight Model Adaptation Method for Closed Split Computing Environments
2025-06-09
Yuya Okada, Takayuki Nishio
We propose SALT (Split-Adaptive Lightweight Tuning), a lightweight model adaptation framework for Split Computing under closed constraints, where the head and tail networks are proprietary and inaccessible to users. In such closed environments, conventional adaptation methods are infeasible since they require access to model parameters or architectures. SALT addresses this challenge by introducing a compact, trainable adapter on the client side to refine latent features from the head network, enabling user-specific adaptation without modifying the original models or increasing communication overhead. We evaluate SALT on user-specific classification tasks with CIFAR-10 and CIFAR-100, demonstrating improved accuracy with lower training latency compared to fine-tuning methods. Furthermore, SALT facilitates model adaptation for robust inference over lossy networks, a common challenge in edge-cloud environments. With minimal deployment overhead, SALT offers a practical solution for personalized inference in edge AI systems under strict system constraints.
Reproducibility in the Control of Autonomous Mobility-on-Demand Systems
2025-06-09
Xinling Li, Meshal Alharbi, Daniele Gammelli, James Harrison, Filipe Rodrigues, Maximilian Schiffer, Marco Pavone, Emilio Frazzoli, Jinhua Zhao, Gioele Zardini
Autonomous Mobility-on-Demand (AMoD) systems, powered by advances in robotics, control, and Machine Learning (ML), offer a promising paradigm for future urban transportation. AMoD offers fast and personalized travel services by leveraging centralized control of autonomous vehicle fleets to optimize operations and enhance service performance. However, the rapid growth of this field has outpaced the development of standardized practices for evaluating and reporting results, leading to significant challenges in reproducibility. As AMoD control algorithms become increasingly complex and data-driven, a lack of transparency in modeling assumptions, experimental setups, and algorithmic implementation hinders scientific progress and undermines confidence in the results. This paper presents a systematic study of reproducibility in AMoD research. We identify key components across the research pipeline, spanning system modeling, control problems, simulation design, algorithm specification, and evaluation, and analyze common sources of irreproducibility. We survey prevalent practices in the literature, highlight gaps, and propose a structured framework to assess and improve reproducibility. Specifically, concrete guidelines are offered, along with a "reproducibility checklist", to support future work in achieving replicable, comparable, and extensible results. While focused on AMoD, the principles and practices we advocate generalize to a broader class of cyber-physical systems that rely on networked autonomy and data-driven control. This work aims to lay the foundation for a more transparent and reproducible research culture in the design and deployment of intelligent mobility systems.
We derive Klein-Gordon equation and Schr\"odinger equation on curved manifolds embedded in high-dimensional manifolds to describe the motion of constrained spineless particles. We start from the equations on the main manifold and obtain the effective equations on the submanifold by degenerate additional dimensions. A geometric induced potential appears in our results.We reviewed the previous works of others and our results are consistent in the special case they calculated,We focused on discussing our results under general conditions and obtained a connection between differential geometry and physics.Especially the influence of peripheral manifolds on submanifold physics is somewhat counterintuitive.We have analyzed several explicit examples, presenting an elucidation of the interactions between the submanifold and matter fields. In one specific case, our approach derived the mass term of scalar fields on a lower-dimensional manifold through purely geometric induction. This mass term coincides with that in Kaluza-Klein theory, yet was achieved without invoking any periodic compactification constraints.
A cosmology weakly dependent measurement of 2D Baryon Acoustic Oscillations scale from the Southern Photometric Local Universe Survey
2025-06-09
U. Ribeiro, F. Avila, C. R. Bom, C. Franco, A. Cortesi, C. Mendes de Oliveira, A. Kanaan, T. Ribeiro, W. Schoenell, E. Telles, A. Bernui
Baryon Acoustic Oscillations (BAO) provide a robust standard ruler for observational cosmology, enabling precise constraints on the expansion history of the Universe. We present a weakly model-dependent measurement of the BAO angular scale in the low-redshift Universe using the blue galaxies from the Southern Photometric Local Universe Survey (S-PLUS). Our analysis is based on the 2-point angular correlation function applied to a selected photometric sample of $5977$ galaxies with redshifts $0.03 \leq z \leq 0.1$. To account for photometric redshift uncertainties, we implement a resampling technique using the probability distribution function of each galaxy. Angular correlations are computed using the Landy-Szalay estimator; the uncertainties are quantified using a set of $1000$ log-normal mock catalogues. Our 2-point angular correlation analyses reveal a prominent BAO signal that after a shift correction, due to the projection effect caused by the finite thickness of the redshift bin, provides the transversal BAO measurement: $\theta_{BAO} = 21.81^{\circ} \pm 0.85^{\circ}$, at $z_{eff} = 0.075$, detected with a statistical significance of $3.22 \sigma$. In addition, we performed consistency tests that support the robustness of our result. Our measurement constitutes the first robust detection of the transversal BAO scale: at the lowest-redshift in the Universe and using multi-band (narrow+wide) photometry data from the S-PLUS.
Surgeons Awareness, Expectations, and Involvement with Artificial Intelligence: a Survey Pre and Post the GPT Era
2025-06-09
Lorenzo Arboit, Dennis N. Schneider, Toby Collins, Daniel A. Hashimoto, Silvana Perretta, Bernard Dallemagne, Jacques Marescaux, EAES Working Group, Nicolas Padoy, Pietro Mascagni
Artificial Intelligence (AI) is transforming medicine, with generative AI models like ChatGPT reshaping perceptions of its potential. This study examines surgeons' awareness, expectations, and involvement with AI in surgery through comparative surveys conducted in 2021 and 2024. Two cross-sectional surveys were distributed globally in 2021 and 2024, the first before an IRCAD webinar and the second during the annual EAES meeting. The surveys assessed demographics, AI awareness, expectations, involvement, and ethics (2024 only). The surveys collected a total of 671 responses from 98 countries, 522 in 2021 and 149 in 2024. Awareness of AI courses rose from 14.5% in 2021 to 44.6% in 2024, while course attendance increased from 12.9% to 23%. Despite this, familiarity with foundational AI concepts remained limited. Expectations for AI's role shifted in 2024, with hospital management gaining relevance. Ethical concerns gained prominence, with 87.2% of 2024 participants emphasizing accountability and transparency. Infrastructure limitations remained the primary obstacle to implementation. Interdisciplinary collaboration and structured training were identified as critical for successful AI adoption. Optimism about AI's transformative potential remained high, with 79.9% of respondents believing AI would positively impact surgery and 96.6% willing to integrate AI into their clinical practice. Surgeons' perceptions of AI are evolving, driven by the rise of generative AI and advancements in surgical data science. While enthusiasm for integration is strong, knowledge gaps and infrastructural challenges persist. Addressing these through education, ethical frameworks, and infrastructure development is essential.
On Finite Element Methods for Heterogeneous Elliptic Problems
2025-06-09
Abimael F. D. Loula, Maicon R. Correa, João N. C. Guerreiro, Elson M. Toledo
Dealing with variational formulations of second order elliptic problems with discontinuous coefficients, we recall a single field minimization problem of an extended functional presented by Bevilacqua et al (1974), which we associate with the basic idea supporting discontinuous Galerkin finite element methods. We review residual based stabilized mixed methods applied to Darcy flow in homogeneous porous media and extend them to heterogeneous media with an interface of discontinuity. For smooth interfaces, the proposed formulations preserve the continuity of the flux and exactly imposes the constraint between the tangent components of Darcy velocity on the interface. Convergence studies for a heterogeneous and anisotropic porous medium confirm the same rates of convergence predicted for homogeneous problem with smooth solutions.
Large language models (LLMs) are increasingly being used for complex research tasks such as literature review, idea generation, and scientific paper analysis, yet their ability to truly understand and process the intricate relationships within complex research papers, such as the logical links between claims and supporting evidence remains largely unexplored. In this study, we present CLAIM-BENCH, a comprehensive benchmark for evaluating LLMs' capabilities in scientific claim-evidence extraction and validation, a task that reflects deeper comprehension of scientific argumentation. We systematically compare three approaches which are inspired by divide and conquer approaches, across six diverse LLMs, highlighting model-specific strengths and weaknesses in scientific comprehension. Through evaluation involving over 300 claim-evidence pairs across multiple research domains, we reveal significant limitations in LLMs' ability to process complex scientific content. Our results demonstrate that closed-source models like GPT-4 and Claude consistently outperform open-source counterparts in precision and recall across claim-evidence identification tasks. Furthermore, strategically designed three-pass and one-by-one prompting approaches significantly improve LLMs' abilities to accurately link dispersed evidence with claims, although this comes at increased computational cost. CLAIM-BENCH sets a new standard for evaluating scientific comprehension in LLMs, offering both a diagnostic tool and a path forward for building systems capable of deeper, more reliable reasoning across full-length papers.
Recent advancements in large language models (LLMs) and AI systems have led to a paradigm shift in the design and optimization of complex AI workflows. By integrating multiple components, compound AI systems have become increasingly adept at performing sophisticated tasks. However, as these systems grow in complexity, new challenges arise in optimizing not only individual components but also their interactions. While traditional optimization methods such as supervised fine-tuning (SFT) and reinforcement learning (RL) remain foundational, the rise of natural language feedback introduces promising new approaches, especially for optimizing non-differentiable systems. This paper provides a systematic review of recent progress in optimizing compound AI systems, encompassing both numerical and language-based techniques. We formalize the notion of compound AI system optimization, classify existing methods along several key dimensions, and highlight open research challenges and future directions in this rapidly evolving field. A list of surveyed papers is publicly available at https://github.com/MiuLab/AISysOpt-Survey.
The main goal from this study is to discuss the main features of Artificial intelligence (AI) as well as their applicability for early cardiovascular Disease (CVDs) Detection, Material and Method : Systematic review approach Results : It was seen that integrating AI algorithm the diagnosis of CVDs become more accurate and lee time consuming. Conclusion: Now the concept of using AI technologies in cardiovascular health care holds the potential to transform disease management .
"I Wrote, I Paused, I Rewrote" Teaching LLMs to Read Between the Lines of Student Writing
2025-06-09
Samra Zafar, Shaheer Minhas, Syed Ali Hassan Zaidi, Arfa Naeem, Zahra Ali
Large language models(LLMs) like Gemini are becoming common tools for supporting student writing. But most of their feedback is based only on the final essay missing important context about how that text was written. In this paper, we explore whether using writing process data, collected through keystroke logging and periodic snapshots, can help LLMs give feedback that better reflects how learners think and revise while writing. We built a digital writing tool that captures both what students type and how their essays evolve over time. Twenty students used this tool to write timed essays, which were then evaluated in two ways: (i) LLM generated feedback using both the final essay and the full writing trace, and (ii) After the task, students completed surveys about how useful and relatable they found the feedback. Early results show that learners preferred the process-aware LLM feedback, finding it more in tune with their own thinking. We also found that certain types of edits, like adding new content or reorganizing paragraphs, aligned closely with higher scores in areas like coherence and elaboration. Our findings suggest that making LLMs more aware of the writing process can lead to feedback that feels more meaningful, personal, and supportive.
The VMC Survey -- LIV. Anomalous Cepheids in the Magellanic Clouds Period-Luminosity relations in the near-infrared bands
2025-06-09
Teresa Sicignano, Vincenzo Ripepi, Marina Rejkuba, Martino Romaniello, Marcella Marconi, Roberto Molinaro, Anupam Bhardwaj, Giulia De Somma, Maria-Rosa Cioni, Felice Cusano, Gisella Clementini, Richard de Grijs, Valentin Ivanov, Jesper Storm, Martin Groenewegen
Anomalous Cepheids (ACs) are less studied metal-poor pulsating stars ([Fe/H]<-1.5) compared to Classical Cepheids (CCs) and RR Lyrae stars. They follow distinct Period-Luminosity (PL) and Period-Wesenheit (PW) relations and pulsate in either the fundamental (F) or first overtone (1O) mode. Our goal is to assess the precision and accuracy of AC-based distances and evaluate their potential for establishing an independent distance scale. We derive new PL and PW relations for F-mode, 1O-mode, and, for the first time, combined F+1O ACs in the Magellanic Clouds. We study their wavelength dependence and apply the relations to estimate distances to Local Group stellar systems hosting ACs, while also confirming AC classifications. Our analysis is based on near-infrared time-series photometry in the Y, J, and Ks bands for about 200 ACs in the Magellanic Clouds from the VISTA survey of the Magellanic Clouds system (VMC, 2009-2018). VMC data are complemented with optical photometry from Gaia DR3 and OGLE-IV, which also provide periods and pulsation modes. Custom light-curve templates were used to derive precise intensity-averaged magnitudes for 118 ACs in the Large Magellanic Cloud (LMC) and 75 in the Small Magellanic Cloud. These data were used to derive multi-band PL and PW relations, calibrated using the geometric LMC distance from eclipsing binaries. We find that PL relation slopes increase and dispersions decrease with wavelength. Using Gaia parallaxes, we determine the LMC distance modulus and the LMC-SMC relative distance. We also confirm the AC nature of several new candidates in Galactic Globular Clusters and derive a distance modulus for the Draco dSph galaxy of 19.425+/-0.048 mag. A 0.1 mag discrepancy with RR Lyrae-based distances may reflect metallicity effects. Future spectroscopic surveys and Gaia DR4 will help refine the AC distance scale and quantify metallicity impacts.
This research examines the persistent challenge of health inequalities in India, departing from the conventional focus on aggregate improvements in mortality rates. While India has achieved progress in overall health indicators since independence, the distribution of health outcomes remains uneven, a fact starkly highlighted by the COVID-19 pandemic. This study investigates the socio-economic determinants of health disparities using the National Family and Health Survey (NFHS)-5 data from 2019-20, focusing on both national and state-level analyses, specifically for Maharashtra. Employing a health economics framework, the analysis delves into individual-level data, population shares, self-reported morbidity prevalence, and treatment patterns across diverse socio-economic groups. Regression analyses, stratified by gender, are conducted to quantify the impact of socio-economic factors on reported morbidity. Furthermore, a Fairlie decomposition, an extension of the Oaxaca decomposition, is utilised to dissect the gender gap in morbidity, assessing the extent to which observed differences are attributable to explanatory variables. The findings reveal a significant burden of self-reported morbidity, with approximately one in nine individuals in India and one in eight in Maharashtra reporting morbidity. Notably, women exhibit nearly double the morbidity rate compared to men. The decomposition analysis identifies key drivers of gender disparities. In India, marital status exacerbates these differences, while insurance coverage, caste, urban residence, and wealth mitigate them. In Maharashtra, urban residence and marital status widen the gap, whereas religion, caste, and insurance coverage narrow it. This research underscores the importance of targeted policy interventions to address the complex interplay of socio-economic factors driving health inequalities in India.
Stochastic dissipative systems in Banach spaces driven by Lévy noise
2025-06-09
Davide A. Bignamini, Enrico Priola
In this paper, we are interested in the well-posedness of stochastic reaction diffusion equations like \begin{equation} \begin{cases} dX(t)(\xi)=\big(\Delta_\xi X(t)(\xi)-p(X(t)(\xi))\big)dt+RdW(t)+dL(t) , \quad t\in [0,T];\\ X(0)=x\in L^2(\mathcal{O}) \end{cases} \end{equation} where $\mathcal{O}$ is a bounded open domain of $\mathbb{R}^d$ with regular boundary, $d\in\mathbb{N}$, $p:\mathbb{R}\rightarrow\mathbb{R}$ is a polynomial of odd degree with positive leading coefficient, $R$ is a linear bounded operator on $L^2(\mathcal{O})$, $\{W(t)\}_{t\geq 0}$ is a $L^2(\mathcal{O})$-cylindrical Wiener process, $\{L(t)\}_{t\geq 0}$ is a pure-jump L\'evy process on $L^2(\mathcal{O})$. We complement the equation with suitable boundary conditions on $\partial \mathcal{O}.$ Some papers in literature analize existence and uniqueness of mild solutions for every $x\in L^p(\mathcal{O})$, for some suitable $p\geq 2$. The results of this paper allow to study reaction diffusion equations also on the space of continuous function $C(\overline{O})$. This seems to be new in the L\'evy case (it is already done in the Wiener case).\\ We also discuss and review the previous cited works with the aim of unifying the different frameworks. We underline that when $R=0$ for every $x\in C(\overline{O})$ (or $x\in L^p(\mathcal{O})$) the mild solution to the equation has a c\`adl\`ag modifications in $C(\overline{O})$ (or $\in L^p(\mathcal{O})$), even if $\{L(t)\}_{t \geq 0}$ is not a L\'evy process taking values in $C(\overline{O})$ (or $\in L^p(\mathcal{O})$). This phenomenon for the linear problem (i.e., $F\equiv 0$ in the SPDE) has been investigated in other papers.
Unified reconstruction of the Lyman-alpha power spectrum with Hamiltonian Monte Carlo
2025-06-09
N. G. Karaçaylı, P. L. Taylor
The complex geometry of the Ly$\alpha$ forest data has motivated the use of various two-point statistics as alternatives to the three-dimensional power spectrum ($P_{\mathrm{3D}}$), which carries cosmological information in Fourier space. On large scales, the three-dimensional correlation function ($\xi_\mathrm{3D}$) has provided robust measurements of the baryon acoustic oscillation (BAO) scale at 150 Mpc. On smaller scales, the one-dimensional power spectrum, $P_{\mathrm{1D}}(k_\|)$, has been the primary tool for extracting information. At the same time, the cross-spectrum, $P_\times(\theta, k_\|)$, has been introduced to incorporate angular information without the complications caused by survey window functions. We propose an analytical forward-modeling framework to reconstruct $P_{\mathrm{3D}}$ from all these observables in a nearly model-independent way. We demonstrate the performance of our method using a hypothetical mock data vector representative of future Dark Energy Spectroscopic Instrument (DESI) measurements and show that the monopole of $P_{\mathrm{3D}}$ can be reconstructed in 25 $k$ bins between $0.07~\mathrm{Mpc}^{-1}$ and $1.8~\mathrm{Mpc}^{-1}$, achieving a median precision of $8\%$ and a mean precision of $13\%$. Our method can serve as an intermediary for consistency checks, though it is not intended to replace direct $P_{\mathrm{3D}}$ estimation.
Integrated Galaxy Light from Stacking $10^5$ Random Pointings in the Dark Energy Survey Data
2025-06-09
Jenna E. Moore, Seth H. Cohen, Philip Mauskopf, Evan Scannapieco
We present a new technique for measuring the integrated galaxy light (IGL) with stacked image data from the Dark Energy Survey (DES). We extract $1\times1$ arcminute cutouts from nearly 100,000 randomly selected positions in the g, r, i, z, and Y bands from the DES data release 2 (DR2) maps. We generate source catalogs and masks for each cutout and the images are subsequently stacked to generate deep images of the sky both with and without sources. The IGL is then calculated by taking the difference in average brightness between stacks that contain galaxies and stacks in which galaxies have been masked. We find IGL values of $g = 4.27 \pm 0.28, r = 6.97 \pm 0.42, i = 8.66 \pm 0.53, z = 10.16 \pm 0.7,$ and $Y = 13.78 \pm 2.35$ nW/m$^2$/sr. These measurements, which require no foreground estimation or removal, are in agreement with previously reported IGL values derived from galaxy number counts and other methods. This stacking technique reduces the sensitivity to diffuse local backgrounds but is not sensitive to large-scale diffuse extragalactic background light.
The AI Imperative: Scaling High-Quality Peer Review in Machine Learning
2025-06-09
Qiyao Wei, Samuel Holt, Jing Yang, Markus Wulfmeier, Mihaela van der Schaar
Peer review, the bedrock of scientific advancement in machine learning (ML), is strained by a crisis of scale. Exponential growth in manuscript submissions to premier ML venues such as NeurIPS, ICML, and ICLR is outpacing the finite capacity of qualified reviewers, leading to concerns about review quality, consistency, and reviewer fatigue. This position paper argues that AI-assisted peer review must become an urgent research and infrastructure priority. We advocate for a comprehensive AI-augmented ecosystem, leveraging Large Language Models (LLMs) not as replacements for human judgment, but as sophisticated collaborators for authors, reviewers, and Area Chairs (ACs). We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making. Crucially, we contend that the development of such systems hinges on access to more granular, structured, and ethically-sourced peer review process data. We outline a research agenda, including illustrative experiments, to develop and validate these AI assistants, and discuss significant technical and ethical challenges. We call upon the ML community to proactively build this AI-assisted future, ensuring the continued integrity and scalability of scientific validation, while maintaining high standards of peer review.
SOP-Bench: Complex Industrial SOPs for Evaluating LLM Agents
2025-06-09
Subhrangshu Nandi, Arghya Datta, Nikhil Vichare, Indranil Bhattacharya, Huzefa Raja, Jing Xu, Shayan Ray, Giuseppe Carenini, Abhi Srivastava, Aaron Chan, Man Ho Woo, Amar Kandola, Brandon Theresa, Francesco Carbone
Large Language Models (LLMs) demonstrate impressive general-purpose reasoning and problem-solving abilities. However, they struggle with executing complex, long-horizon workflows that demand strict adherence to Standard Operating Procedures (SOPs), a critical requirement for real-world industrial automation. Despite this need, there is a lack of public benchmarks that reflect the complexity, structure, and domain-specific nuances of SOPs. To address this, we present three main contributions. First, we introduce a synthetic data generation framework to create realistic, industry-grade SOPs that rigorously test the planning, reasoning, and tool-use capabilities of LLM-based agents. Second, using this framework, we develop SOP-Bench, a benchmark of over 1,800 tasks across 10 industrial domains, each with APIs, tool interfaces, and human-validated test cases. Third, we evaluate two prominent agent architectures: Function-Calling and ReAct Agents, on SOP-Bench, observing average success rates of only 27% and 48%, respectively. Remarkably, when the tool registry is much larger than necessary, agents invoke incorrect tools nearly 100% of the time. These findings underscore a substantial gap between current agentic capabilities of LLMs and the demands of automating real-world SOPs. Performance varies significantly by task and domain, highlighting the need for domain-specific benchmarking and architectural choices before deployment. SOP-Bench is publicly available at http://sop-bench.s3-website-us-west-2.amazonaws.com/. We also release the prompts underpinning the data generation framework to support new domain-specific SOP benchmarks. We invite the community to extend SOP-Bench with SOPs from their industrial domains.
The impact of applying black hole-host galaxy scaling relations to large galaxy populations
2025-06-09
Maggie C. Huber, Joseph Simon, Julia M. Comerford
Supermassive black holes (SMBHs) with dynamically measured masses have shown empirical correlations with host galaxy properties. These correlations are often the only method available to estimate SMBH masses and gather statistics for large galaxy populations across a range of redshifts, even though the scaling relations themselves are derived from a small subset of nearby galaxies. Depending on the scaling relation used, estimated SMBH masses can vary significantly. The most widely used scaling relations are the M$_{BH}-$M$_{\mathrm{bulge}}$ and M$_{BH}- \sigma$ relations, where M$_{\mathrm{bulge}}$ is galaxy bulge mass and $\sigma$ is the bulge velocity dispersion. In this paper, we determine how severely the choice of scaling relation impacts SMBH mass estimates for different subsets of a large galaxy population. For this analysis we use a sample of $\sim$ 400,000 galaxies, including 1,240 Type 1 AGN from the Sloan Digital Sky Survey. We calculate SMBH masses from M$_{BH}-$M$_{\mathrm{bulge}}$ and M$_{BH}- \sigma$ and compare to single-epoch virial SMBH masses from broad-line H$\beta$, which are derived independently of black hole-host galaxy scaling relations. We find that SMBH masses derived from the single-epoch virial relation for H$\beta$ are better reproduced by M$_{BH}- \sigma$ than M$_{BH}-$M$_{\mathrm{bulge}}$. Finally, in cases where $\sigma$ and M$_{\mathrm{bulge}}$ cannot be measured directly, we show that it is possible to infer $\sigma$ from photometry with more accuracy than we can infer M$_{\mathrm{bulge}}$.
Hydrostatic and explosive $α$-element chemical abundances of Milky Way globular clusters, halo substructures, and satellite galaxies
2025-06-09
Danny Horta, Melissa K. Ness
Stellar atmospheric element abundance ratios of stars retain information about their birth conditions, helping elucidate their origin and nature. In this letter, we analyse and contrast the hydrostatic and explosive $\alpha$-element abundance ratios, and the ratio of the two (the hex ratio), for a large sample of Galactic globular clusters (GCs), halo substructures, satellite galaxies, and the Milky Way high-/low-$\alpha$ discs using data from the $APOGEE$ survey. Our results show that: $i$) Milky Way GCs and halo substructures appear to have qualitatively similar hex ratios across a broad range of [Fe/H], that are higher than that of dwarf satellite galaxies of similar [Fe/H]; $ii$) for all stellar populations studied, there is a trend in the hex ratio with [Fe/H]; $iii$) there is a weak trend in the hex ratio with respect to age for Galactic GCs, but not with initial or final GC mass; $iv$) there are no differences in the hex ratio between GCs formed $in$ $situ$ versus those labelled as accreted.
Spectroscopic and kinematic analyses of a warm survivor of a D6 supernova
2025-06-09
Mark A. Hollands, Ken. J. Shen, Roberto Raddi, Boris T. Gaensicke, Evan B. Bauer, Alberto Rebassa-Mansergas
SDSSJ163712.21+363155.9 is a candidate hyper-runaway star, first identified from its unusual spectrum in the Sloan Digital Sky Survey, which exhibits oxygen, magnesium, and silicon lines redshifted by several $100\,$km/s, leading to the suggestion it was ejected from a thermonuclear supernova. We have acquired GTC OSIRIS spectroscopy of SDSSJ1637+3631 establishing a warm ($T_\mathrm{eff}=15680\pm250\,$K) carbon+oxygen dominated atmosphere, that is also abundant in the intermediate mass elements silicon, sulphur, and calcium. We interpret SDSSJ1637+3631 as the donor to an accreting white dwarf that exploded in a dynamically-driven double-degenerate double-detonation (D6) type Ia supernova, where the current composition is consistent with a CO white dwarf core, enriched with intermediate mass elements from deposited supernova ejecta. While SDSSJ1637+3631 has a low-precision Gaia parallax, our spectroscopic surface gravity ($\log g=6.3\pm0.3\,$dex) helps constrain its tangential velocity to $1950^{+810}_{-530}\,$km/s, providing additional support to the D6 mechanism. Under the assumption that SDSSJ1637+3631 is a D6 survivor, we construct a kinematic model combining all astrometric, spectroscopic, and photometric information, but also including the structure and gravitational potential of the Milky Way. Our model localises the ejection site to the inner few kpc of the Galactic disc (though excluding the Galactic centre), with an ejection speed of $1870^{+360}_{-300}\,$km/s, and a $4.5^{+0.4}_{-0.5}\,$Myr time of flight.
Scale-dependent bias and mode coupling in redshift-space clustering near the BAO scale
2025-06-09
Aseem Paranjape, Ravi K. Sheth
The baryon acoustic oscillation (BAO) feature in the 2-point clustering of biased tracers in redshift space can be described in a model-agnostic manner, relying only on the assumption that nonlinear growth approximately smears this feature with a Gaussian kernel sourced by gravitationally driven bulk flows as in the Zel'dovich approximation. An explicit model that demonstrated this in recent work did not account for two physical effects that are very likely observationally relevant in the context of ongoing surveys, namely, the scale-dependence of linear Lagrangian density and velocity bias and the effects of mode coupling. We rectify this shortcoming in this paper by showing that a simple model including these effects is able to accurately describe the multipoles of the 2pcf of realistic tracer samples at BAO scales. Our results indicate that the effects of scale-dependent bias will be important to model for surveys such as DESI, while those of mode coupling are relatively less significant. Our model for scale-dependent bias and mode coupling, which is motivated by model-agnostic arguments from peaks theory and the Zel'dovich approximation, lies in the class of `Laplace-Gauss' expansions, making it straightforward to incorporate these effects in the model-agnostic inference framework mentioned above.
JWST interferometric imaging reveals the dusty disk obscuring the supermassive black hole of the Circinus galaxy
2025-06-09
Enrique Lopez-Rodriguez, Joel Sanchez-Bermudez, Omaira Gonzalez-Martin, Robert Nikutta, Ryan M. Lau, Deepashri Thatte, Ismael Garcia-Bernete, Julien H. Girard, Matthew J. Hankins
The dusty and molecular torus is one of the most elusive structures surrounding supermassive black holes, yet its importance is unequivocal for understanding feedback and accretion mechanisms. The torus and accretion disk feed the inspiraling gas onto the supermassive black hole (SMBH) and launch outflows, fundamentally connecting the SMBH activity to the host galaxy. This scenario situates the torus as the interface between the AGN and its host galaxy with a flow cycle of molecular gas and dust of a few parsecs in size. Here, we utilize a novel aperture-masking interferometric mode onboard the JWST, achieving twice the previously possible resolution, and bringing out the fainter features that clearly show the torus being the critical interface for feeding material from galaxy scales into the SMBH. We also identify that $<1$% of the emission arises from an arc structure composed of hot dust entrained in a molecular and ionized outflow. The rest of the emission, $12$%, is associated with dust heated by the AGN and/or radio-jet at large scales. Combined with continuum data, gas tracers, and torus models, our study shows that most of the dust mass is located in the equatorial axis in the form of a disk feeding the AGN.
The Epoch of Giant Planet Migration Planet Search Program. III. The Occurrence Rate of Young Giant Planets Inside the Water Ice Line
2025-06-09
Quang H. Tran, Brendan P. Bowler, William D. Cochran, Chad F. Bender, Samuel Halverson, Suvrath Mahadevan, Joe P. Ninan, Paul Robertson, Arpita Roy, Guðmundur Stefánsson, Ryan C. Terrien
We present statistical results from the Epoch of Giant Planet Migration RV planet search program. This survey was designed to measure the occurrence rate of giant planets interior to the water ice line of young Sun-like stars, compare this to the prevalence of giant planets at older ages, and provide constraints on the timescale and dominant inward migration mechanism of giant planets. Our final sample amounts to 85 single young (20-200 Myr) G and K dwarfs which we target across a 4-year time baseline with the near-infrared Habitable-zone Planet Finder spectrograph at McDonald Observatory's Hobby-Eberly Telescope. As part of this survey, we discovered the young hot Jupiter HS Psc b. We characterize survey detection completeness with realistic injection-recovery tests and measure an occurrence rate of $1.9^{+2.6}_{-1.4}$% for intermediate-age giant planets ($0.3 < m \; sin \; i < 13$ $M_\mathrm{Jup}$) within 2.5 AU. This is lower than the field age occurrence rate for the same planet masses and separations and favors an increase in the prevalence of giant planets over time from $\sim$100 Myr to several Gyr, although our results cannot rule out a constant rate. A decaying planet occurrence rate is, however, strongly excluded. This suggests that giant planets located inside the water ice line originate from a combination of in situ formation or early migration coupled with longer-term inward scattering. The completeness-corrected prevalence of young hot Jupiters in our sample is $1.5^{+2.2}_{-1.1}$%--similar to the rate for field stars--and the 95% upper limit for young brown dwarfs within 5000 d is $<$3.6%.
Unlocking the hidden potential of pulsar astronomy
2025-06-09
D. Kaur, G. Hobbs, A. Zic, J. R. Dawson, J. Morgan, W. Ling, S. Camtepe, J. Pieprzyk, M. C. M. Cheung
Pulsars have traditionally been used for research into fundamental physics and astronomy. In this paper, we investigate the expanding applications of radio pulsars in societal and industrial domains beyond their conventional scientific roles. We describe emerging applications in positioning, navigation, timing and synchronization, random number generation, space weather monitoring, public engagement, antenna calibration techniques, and leveraging extensive pulsar data sets generated by large-scale observatories. Such pulsar data sets have already been used to demonstrate quantum-computing algorithms. We evaluate the potential for compact radio receiver systems for pulsar detection by describing optimal observing bands. We show that relatively simple and compact receiver systems can detect the brightest pulsar, Vela. The equivalent of an ~4m-diameter dish with a small bandwidth operating around 700 MHz would be able to detect many more pulsars. Such a detector would be able to localise itself to around 10 km using pulsar navigation techniques. The space weather community requires direct measurements of the integrated electron density at a range of solar elongations. The only method to get model-independent values is through pulsar observations and we explore the possibility of measuring dispersion measures (DMs) and rotation measures with a range of telescopes (observing from low to mid-frequencies) as well as using a typical model to predict the variation of the DM as a function of solar radii. We review how pulsars can be used to produce random sequences and demonstrate that such sequences can be produced using the scintillation properties of pulsars as well as from pulse jitter.
A Systematic Literature Review on Continuous Integration and Deployment (CI/CD) for Secure Cloud Computing
2025-06-09
Sabbir M. Saleh, Nazim Madhavji, John Steinbacher
As cloud environments become widespread, cybersecurity has emerged as a top priority across areas such as networks, communication, data privacy, response times, and availability. Various sectors, including industries, healthcare, and government, have recently faced cyberattacks targeting their computing systems. Ensuring secure app deployment in cloud environments requires substantial effort. With the growing interest in cloud security, conducting a systematic literature review (SLR) is critical to identifying research gaps. Continuous Software Engineering, which includes continuous integration (CI), delivery (CDE), and deployment (CD), is essential for software development and deployment. In our SLR, we reviewed 66 papers, summarising tools, approaches, and challenges related to the security of CI/CD in the cloud. We addressed key aspects of cloud security and CI/CD and reported on tools such as Harbor, SonarQube, and GitHub Actions. Challenges such as image manipulation, unauthorised access, and weak authentication were highlighted. The review also uncovered research gaps in how tools and practices address these security issues in CI/CD pipelines, revealing a need for further study to improve cloud-based security solutions.
Mind the Gap: Revealing Security Barriers through Situational Awareness of Small and Medium Business Key Decision-Makers
2025-06-09
Yuanhaur Chang, Oren Heller, Yaniv Shlomo, Iddo Bar-Noy, Ella Bokobza, Michal Grinstein-Weiss, Ning Zhang
Key decision-makers in small and medium businesses (SMBs) often lack the awareness and knowledge to implement cybersecurity measures effectively. To gain a deeper understanding of how SMB executives navigate cybersecurity decision-making, we deployed a mixed-method approach, conducting semi-structured interviews (n=21) and online surveys (n=322) with SMB key decision-makers. Using thematic analysis, we revealed SMB decision-makers' perceived risks in terms of the digital assets they valued, and found reasons for their choice of defense measures and factors impacting security perception. We employed the situational awareness model to characterize decision-makers based on cybersecurity awareness, identifying those who have comparatively low awareness in the fight against adversaries. We further explored the relationship between awareness and business attributes, and constructed a holistic structural equation model to understand how awareness can be improved. Finally, we proposed interventions to help SMBs overcome potential challenges.
Research Knowledge Graphs: the Shifting Paradigm of Scholarly Information Representation
2025-06-08
Matthäus Zloch, Danilo Dessì, Jennifer D'Souza, Leyla Jael Castro, Benjamin Zapilko, Saurav Karmakar, Brigitte Mathiak, Markus Stocker, Wolfgang Otto, Sören Auer, Stefan Dietze
Sharing and reusing research artifacts, such as datasets, publications, or methods is a fundamental part of scientific activity, where heterogeneity of resources and metadata and the common practice of capturing information in unstructured publications pose crucial challenges. Reproducibility of research and finding state-of-the-art methods or data have become increasingly challenging. In this context, the concept of Research Knowledge Graphs (RKGs) has emerged, aiming at providing an easy to use and machine-actionable representation of research artifacts and their relations. That is facilitated through the use of established principles for data representation, the consistent adoption of globally unique persistent identifiers and the reuse and linking of vocabularies and data. This paper provides the first conceptualisation of the RKG vision, a categorisation of in-use RKGs together with a description of RKG building blocks and principles. We also survey real-world RKG implementations differing with respect to scale, schema, data, used vocabulary, and reliability of the contained data. We also characterise different RKG construction methodologies and provide a forward-looking perspective on the diverse applications, opportunities, and challenges associated with the RKG vision.
Parsing the Switch: LLM-Based UD Annotation for Complex Code-Switched and Low-Resource Languages
2025-06-08
Olga Kellert, Nemika Tyagi, Muhammad Imran, Nelvin Licona-Guevara, Carlos Gómez-Rodríguez
Code-switching presents a complex challenge for syntactic analysis, especially in low-resource language settings where annotated data is scarce. While recent work has explored the use of large language models (LLMs) for sequence-level tagging, few approaches systematically investigate how well these models capture syntactic structure in code-switched contexts. Moreover, existing parsers trained on monolingual treebanks often fail to generalize to multilingual and mixed-language input. To address this gap, we introduce the BiLingua Parser, an LLM-based annotation pipeline designed to produce Universal Dependencies (UD) annotations for code-switched text. First, we develop a prompt-based framework for Spanish-English and Spanish-Guaran\'i data, combining few-shot LLM prompting with expert review. Second, we release two annotated datasets, including the first Spanish-Guaran\'i UD-parsed corpus. Third, we conduct a detailed syntactic analysis of switch points across language pairs and communicative contexts. Experimental results show that BiLingua Parser achieves up to 95.29% LAS after expert revision, significantly outperforming prior baselines and multilingual parsers. These results show that LLMs, when carefully guided, can serve as practical tools for bootstrapping syntactic resources in under-resourced, code-switched environments. Data and source code are available at https://github.com/N3mika/ParsingProject
Engineering and harnessing long-range interactions for atomic quantum simulators
2025-06-08
Argüello-Luengo Javier
Interactions between quantum particles, such as electrons, are the source of important effects, ranging from superconductivity, to the formation of molecular bonds, or the stability of elementary compounds at high-energies. In this article, we illustrate how advances in the cold-atom community to further engineer such long-range interactions have stimulated the simulation of new regimes of these fundamental many-body problems. The goal is two-fold: first, to provide a comprehensive review of the different strategies proposed and/or experimentally realized to induce long-range interactions among atoms moving in optical potentials. Second, to showcase various fields where such platforms can offer new insights, ranging from the simulation of condensed matter phenomena to the study of lattice gauge theories, and the simulation of electronic configurations in chemistry. We then discuss the challenges and opportunities of these platforms compared to other complementary approaches based on digital simulation and quantum computation.
A Narrative Review on Large AI Models in Lung Cancer Screening, Diagnosis, and Treatment Planning
2025-06-08
Jiachen Zhong, Yiting Wang, Di Zhu, Ziwei Wang
Lung cancer remains one of the most prevalent and fatal diseases worldwide, demanding accurate and timely diagnosis and treatment. Recent advancements in large AI models have significantly enhanced medical image understanding and clinical decision-making. This review systematically surveys the state-of-the-art in applying large AI models to lung cancer screening, diagnosis, prognosis, and treatment. We categorize existing models into modality-specific encoders, encoder-decoder frameworks, and joint encoder architectures, highlighting key examples such as CLIP, BLIP, Flamingo, BioViL-T, and GLoRIA. We further examine their performance in multimodal learning tasks using benchmark datasets like LIDC-IDRI, NLST, and MIMIC-CXR. Applications span pulmonary nodule detection, gene mutation prediction, multi-omics integration, and personalized treatment planning, with emerging evidence of clinical deployment and validation. Finally, we discuss current limitations in generalizability, interpretability, and regulatory compliance, proposing future directions for building scalable, explainable, and clinically integrated AI systems. Our review underscores the transformative potential of large AI models to personalize and optimize lung cancer care.
This article surveys our present understanding of the internal structure of the fully developed quark-gluon plasma at temperatures outside the crossover region. The theoretical part of the review covers perturbative and nonperturbative approaches to quark-gluon plasma structure, in particular, hard-thermal loop effective theory, lattice QCD and the functional renormalization group. The phenomenological part of the review scrutinizes the information that has been derived from bulk observables and hard probes in relativistic heavy ion collisions in terms of how it informs our knowledge about the structure of the quark-gluon plasma. The final section lists possible avenues for future progress.
2025-06-08
Chenlong Zhang, Zhuoran Jin, Hongbang Yuan, Jiaheng Wei, Tong Zhou, Kang Liu, Jun Zhao, Yubo Chen
The widespread deployment of Large Language Models (LLMs) trained on massive, uncurated corpora has raised growing concerns about the inclusion of sensitive, copyrighted, or illegal content. This has led to increasing interest in LLM unlearning: the task of selectively removing specific information from a model without retraining from scratch or degrading overall utility. However, existing methods often rely on large-scale forget and retain datasets, and suffer from unnatural responses, poor generalization, or catastrophic utility loss. In this work, we propose Reinforcement UnLearning (RULE), an efficient framework that formulates unlearning as a refusal boundary optimization problem. RULE is trained with a small portion of the forget set and synthesized boundary queries, using a verifiable reward function that encourages safe refusal on forget--related queries while preserving helpful responses on permissible inputs. We provide both theoretical and empirical evidence demonstrating the effectiveness of RULE in achieving targeted unlearning without compromising model utility. Experimental results show that, with only $12%$ forget set and $8%$ synthesized boundary data, RULE outperforms existing baselines by up to $17.5%$ forget quality and $16.3%$ naturalness response while maintaining general utility, achieving forget--retain Pareto optimality. Remarkably, we further observe that RULE improves the naturalness of model outputs, enhances training efficiency, and exhibits strong generalization ability, generalizing refusal behavior to semantically related but unseen queries.
Web-use agents are rapidly being deployed to automate complex web tasks, operating with extensive browser capabilities including multi-tab navigation, DOM manipulation, JavaScript execution and authenticated session access. However, these powerful capabilities create a critical and previously unexplored attack surface. This paper demonstrates how attackers can exploit web-use agents' high-privilege capabilities by embedding malicious content in web pages such as comments, reviews, or advertisements that agents encounter during legitimate browsing tasks. In addition, we introduce the task-aligned injection technique that frame malicious commands as helpful task guidance rather than obvious attacks. This technique exploiting fundamental limitations in LLMs' contextual reasoning: agents struggle in maintaining coherent contextual awareness and fail to detect when seemingly helpful web content contains steering attempts that deviate from their original task goal. Through systematic evaluation of four popular agents (OpenAI Operator, Browser Use, Do Browser, OpenOperator), we demonstrate nine payload types that compromise confidentiality, integrity, and availability, including unauthorized camera activation, user impersonation, local file exfiltration, password leakage, and denial of service, with validation across multiple LLMs achieving success rates of 80%-100%. These payloads succeed across agents with built-in safety mechanisms, requiring only the ability to post content on public websites, creating unprecedented risks given the ease of exploitation combined with agents' high-privilege access. To address this attack, we propose comprehensive mitigation strategies including oversight mechanisms, execution constraints, and task-aware reasoning techniques, providing practical directions for secure development and deployment.
We present a Bayesian analysis of cosmological parameter constraints from early- and late-universe observations, focusing on the matter density parameter ($\Omega_m$) and the amplitude of matter fluctuations ($\sigma_8$) within the $\Lambda$CDM framework. Using data from the Planck 2018 satellite mission and the Dark Energy Survey (DES) Year 3, we compute theoretical predictions for angular and matter power spectra via Boltzmann solvers and perform Markov Chain Monte Carlo (MCMC) sampling using the \texttt{emcee} Python package. Our key contribution is a direct and quantitative comparison of DES and Planck constraints, assessing their consistency using chi-squared analysis and Gaussian tension metrics. We find a statistically significant $6.46\sigma$ tension in $\Omega_m$ and a $2.68\sigma$ tension in $\sigma_8$ between the two datasets. These results provide fresh evidence of persistent discrepancies in cosmological parameter estimates and suggest that simple extensions to the $\Lambda$CDM model may be insufficient to fully reconcile early- and late-time observations, motivating the need for more complex theoretical models or refined treatment of systematics.
Open-Flavor Heavy Hadron Production in Heavy-Ion Collisions
2025-06-08
C. E. Fontoura, G. Krein, A. Valcarce, J. Vijande
We study the production of open-flavor heavy hadrons in relativistic heavy-ion collisions. The hadronization in the quark-gluon plasma is described in the quark coalescence model. We evaluated yields and transverse momentum distributions. A simultaneous study of conventional and exotic hadrons is carried out. The Wigner functions are evaluated using hadron wave functions obtained from a single realistic quark model. Thus, results are presented in a single framework for the production of open-flavor heavy mesons, baryons, and exotic tetraquarks, in particular: $D^{0}$, $B^{0}$, $\Lambda_{Q}$, $\Sigma_{Q}$, $\Xi_{Q}$, $\Xi_{QQ^\prime}$, and $T_{QQ^\prime}$ ($Q,Q^\prime=c$ or $b$). The consequences of a partial restoration of chiral symmetry at the hadronization temperature are studied in detail.
Results from the Pan-STARRS Search for Kilonovae: Contamination by Massive Stellar Outbursts
2025-06-08
M. D. Fulton, S. J. Smartt, M. E. Huber, K. W. Smith, K. C. Chambers, M. Nicholl, S. Srivastav, D. R. Young, E. A. Magnier, C. -C. Lin, P. Minguez, T. de Boer, T. Lowe, R. Wainscoat
We present results from the Pan-STARRS optical search for kilonovae without the aid of gravitational wave and gamma-ray burst triggers. The search was conducted from 26 October 2019 to 15 December 2022. During this time, we reported 29,740 transients observed by Pan-STARRS to the IAU Transient Name Server. Of these, 175 were Pan-STARRS credited discoveries that had a host galaxy within 200 Mpc and had discovery absolute magnitudes M > -16.5. A subset of 11 transients was plausibly identified as kilonova candidates by our kilonova prediction algorithm. Through a combination of historical forced photometry, extensive follow-up, and aggregating observations from multiple sky surveys, we eliminated all as kilonova candidates. Rapidly evolving outbursts from massive stars (likely to be Luminous Blue Variable eruptions) accounted for 55% of the subset's contaminating sources. We estimate the rate of such eruptions using the ATLAS 100 Mpc volume-limited survey data. As these outbursts appear to be significant contaminants in kilonova searches, we estimate contaminating numbers when searching gravitational wave skymaps produced by the LIGO-Virgo-Kagra science collaboration during the Rubin era. The Legacy Survey of Space and time, reaching limiting magnitudes of m = 25, could detect 2-6 massive stellar outbursts per 500 deg^2 within a 4-day observing window, within the skymaps and volumes typical for binary neutron star mergers projected for Ligo-Virgo-Kagra Observing run 5. We conclude that while they may be a contaminant, they can be photometrically identified.
E-BATS: Efficient Backpropagation-Free Test-Time Adaptation for Speech Foundation Models
2025-06-08
Jiaheng Dong, Hong Jia, Soumyajit Chatterjee, Abhirup Ghosh, James Bailey, Ting Dang
Speech Foundation Models encounter significant performance degradation when deployed in real-world scenarios involving acoustic domain shifts, such as background noise and speaker accents. Test-time adaptation (TTA) has recently emerged as a viable strategy to address such domain shifts at inference time without requiring access to source data or labels. However, existing TTA approaches, particularly those relying on backpropagation, are memory-intensive, limiting their applicability in speech tasks and resource-constrained settings. Although backpropagation-free methods offer improved efficiency, existing ones exhibit poor accuracy. This is because they are predominantly developed for vision tasks, which fundamentally differ from speech task formulations, noise characteristics, and model architecture, posing unique transferability challenges. In this paper, we introduce E-BATS, the first Efficient BAckpropagation-free TTA framework designed explicitly for speech foundation models. E-BATS achieves a balance between adaptation effectiveness and memory efficiency through three key components: (i) lightweight prompt adaptation for a forward-pass-based feature alignment, (ii) a multi-scale loss to capture both global (utterance-level) and local distribution shifts (token-level) and (iii) a test-time exponential moving average mechanism for stable adaptation across utterances. Experiments conducted on four noisy speech datasets spanning sixteen acoustic conditions demonstrate consistent improvements, with 4.1%-13.5% accuracy gains over backpropagation-free baselines and 2.0-6.4 times GPU memory savings compared to backpropagation-based methods. By enabling scalable and robust adaptation under acoustic variability, this work paves the way for developing more efficient adaptation approaches for practical speech processing systems in real-world environments.
Accelerating 3D Gaussian Splatting with Neural Sorting and Axis-Oriented Rasterization
2025-06-08
Zhican Wang, Guanghui He, Dantong Liu, Lingjun Gao, Shell Xu Hu, Chen Zhang, Zhuoran Song, Nicholas Lane, Wayne Luk, Hongxiang Fan
3D Gaussian Splatting (3DGS) has recently gained significant attention for high-quality and efficient view synthesis, making it widely adopted in fields such as AR/VR, robotics, and autonomous driving. Despite its impressive algorithmic performance, real-time rendering on resource-constrained devices remains a major challenge due to tight power and area budgets. This paper presents an architecture-algorithm co-design to address these inefficiencies. First, we reveal substantial redundancy caused by repeated computation of common terms/expressions during the conventional rasterization. To resolve this, we propose axis-oriented rasterization, which pre-computes and reuses shared terms along both the X and Y axes through a dedicated hardware design, effectively reducing multiply-and-add (MAC) operations by up to 63%. Second, by identifying the resource and performance inefficiency of the sorting process, we introduce a novel neural sorting approach that predicts order-independent blending weights using an efficient neural network, eliminating the need for costly hardware sorters. A dedicated training framework is also proposed to improve its algorithmic stability. Third, to uniformly support rasterization and neural network inference, we design an efficient reconfigurable processing array that maximizes hardware utilization and throughput. Furthermore, we introduce a $\pi$-trajectory tile schedule, inspired by Morton encoding and Hilbert curve, to optimize Gaussian reuse and reduce memory access overhead. Comprehensive experiments demonstrate that the proposed design preserves rendering quality while achieving a speedup of $23.4\sim27.8\times$ and energy savings of $28.8\sim51.4\times$ compared to edge GPUs for real-world scenes. We plan to open-source our design to foster further development in this field.
User reporting systems are central to addressing interpersonal conflicts and protecting users from harm in online spaces, particularly those with heightened privacy expectations. However, users often express frustration at their lack of insight and input into the reporting process. Drawing on offline legal literature, we trace these frustrations to the inquisitorial nature of today's online reporting systems, where moderators lead evidence gathering and case development. In contrast, adversarial models can grant users greater control and thus are better for procedural justice and privacy protection, despite their increased risks of system abuse. This motivates us to explore the potential of incorporating adversarial practices into online reporting systems. Through literature review, formative interviews, and threat modeling, we find a rich design space for empowering users to collect and present their evidence while mitigating potential abuse in the reporting process. In particular, we propose designs that minimize the amount of information shared for reporting purposes, as well as supporting evidence authentication. Finally, we discuss how our findings can inform new cryptographic tools and new efforts to apply comparative legal frameworks to online moderation.
During collaborative tasks, human behavior is guided by multiple levels of intentions that evolve over time, such as task sequence preferences and interaction strategies. To adapt to these changing preferences and promptly correct any inaccurate estimations, collaborative robots must accurately track these dynamic human intentions in real time. We propose a Hierarchical Intention Tracking (HIT) algorithm for collaborative robots to track dynamic and hierarchical human intentions effectively in real time. HIT represents human intentions as intention trees with arbitrary depth, and probabilistically tracks human intentions by Bayesian filtering, upward measurement propagation, and downward posterior propagation across all levels. We develop a HIT-based robotic system that dynamically switches between Interaction-Task and Verification-Task trees for a collaborative assembly task, allowing the robot to effectively coordinate human intentions at three levels: task-level (subtask goal locations), interaction-level (mode of engagement with the robot), and verification-level (confirming or correcting intention recognition). Our user study shows that our HIT-based collaborative robot system surpasses existing collaborative robot solutions by achieving a balance between efficiency, physical workload, and user comfort while ensuring safety and task completion. Post-experiment surveys further reveal that the HIT-based system enhances the user trust and minimizes interruptions to user's task flow through its effective understanding of human intentions across multiple levels.
Safety-Aware Reinforcement Learning for Control via Risk-Sensitive Action-Value Iteration and Quantile Regression
2025-06-08
Clinton Enwerem, Aniruddh G. Puranic, John S. Baras, Calin Belta
Mainstream approximate action-value iteration reinforcement learning (RL) algorithms suffer from overestimation bias, leading to suboptimal policies in high-variance stochastic environments. Quantile-based action-value iteration methods reduce this bias by learning a distribution of the expected cost-to-go using quantile regression. However, ensuring that the learned policy satisfies safety constraints remains a challenge when these constraints are not explicitly integrated into the RL framework. Existing methods often require complex neural architectures or manual tradeoffs due to combined cost functions. To address this, we propose a risk-regularized quantile-based algorithm integrating Conditional Value-at-Risk (CVaR) to enforce safety without complex architectures. We also provide theoretical guarantees on the contraction properties of the risk-sensitive distributional Bellman operator in Wasserstein space, ensuring convergence to a unique cost distribution. Simulations of a mobile robot in a dynamic reach-avoid task show that our approach leads to more goal successes, fewer collisions, and better safety-performance trade-offs compared to risk-neutral methods.
UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs
2025-06-08
Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee
Agentic UAVs represent a new frontier in autonomous aerial intelligence, integrating perception, decision-making, memory, and collaborative planning to operate adaptively in complex, real-world environments. Driven by recent advances in Agentic AI, these systems surpass traditional UAVs by exhibiting goal-driven behavior, contextual reasoning, and interactive autonomy. We provide a comprehensive foundation for understanding the architectural components and enabling technologies that distinguish Agentic UAVs from traditional autonomous UAVs. Furthermore, a detailed comparative analysis highlights advancements in autonomy with AI agents, learning, and mission flexibility. This study explores seven high-impact application domains precision agriculture, construction & mining, disaster response, environmental monitoring, infrastructure inspection, logistics, security, and wildlife conservation, illustrating the broad societal value of agentic aerial intelligence. Furthermore, we identify key challenges in technical constraints, regulatory limitations, and data-model reliability, and we present emerging solutions across hardware innovation, learning architectures, and human-AI interaction. Finally, a future roadmap is proposed, outlining pathways toward self-evolving aerial ecosystems, system-level collaboration, and sustainable, equitable deployments. This survey establishes a foundational framework for the future development, deployment, and governance of agentic aerial systems (Agentic UAVs) across diverse societal and industrial domains.
2025-06-07
Do Xuan Long, Duy Dinh, Ngoc-Hai Nguyen, Kenji Kawaguchi, Nancy F. Chen, Shafiq Joty, Min-Yen Kan
As large language models (LLMs) have progressed towards more human-like and human--AI communications have become prevalent, prompting has emerged as a decisive component. However, there is limited conceptual consensus on what exactly quantifies natural language prompts. We attempt to address this question by conducting a meta-analysis surveying more than 150 prompting-related papers from leading NLP and AI conferences from 2022 to 2025 and blogs. We propose a property- and human-centric framework for evaluating prompt quality, encompassing 21 properties categorized into six dimensions. We then examine how existing studies assess their impact on LLMs, revealing their imbalanced support across models and tasks, and substantial research gaps. Further, we analyze correlations among properties in high-quality natural language prompts, deriving prompting recommendations. We then empirically explore multi-property prompt enhancements in reasoning tasks, observing that single-property enhancements often have the greatest impact. Finally, we discover that instruction-tuning on property-enhanced prompts can result in better reasoning models. Our findings establish a foundation for property-centric prompt evaluation and optimization, bridging the gaps between human--AI communication and opening new prompting research directions.
Traditional decision-based black-box adversarial attacks on image classifiers aim to generate adversarial examples by slightly modifying input images while keeping the number of queries low, where each query involves sending an input to the model and observing its output. Most existing methods assume that all queries have equal cost. However, in practice, queries may incur asymmetric costs; for example, in content moderation systems, certain output classes may trigger additional review, enforcement, or penalties, making them more costly than others. While prior work has considered such asymmetric cost settings, effective algorithms for this scenario remain underdeveloped. In this paper, we propose a general framework for decision-based attacks under asymmetric query costs, which we refer to as asymmetric black-box attacks. We modify two core components of existing attacks: the search strategy and the gradient estimation process. Specifically, we propose Asymmetric Search (AS), a more conservative variant of binary search that reduces reliance on high-cost queries, and Asymmetric Gradient Estimation (AGREST), which shifts the sampling distribution to favor low-cost queries. We design efficient algorithms that minimize total attack cost by balancing different query types, in contrast to earlier methods such as stealthy attacks that focus only on limiting expensive (high-cost) queries. Our method can be integrated into a range of existing black-box attacks with minimal changes. We perform both theoretical analysis and empirical evaluation on standard image classification benchmarks. Across various cost regimes, our method consistently achieves lower total query cost and smaller perturbations than existing approaches, with improvements of up to 40% in some settings.
We present a study of LLM integration in final-year undergraduate astronomy education, examining how students develop AI literacy through structured guidance and documentation requirements. We developed AstroTutor, a domain-specific astronomy tutoring system enhanced with curated arXiv content, and deployed it alongside general-purpose LLMs in the course. Students documented their AI usage through homework reflections and post-course surveys. We analyzed student evolution in AI interaction strategies and conducted experimental comparisons of LLM-assisted versus traditional grading methods. LLM grading showed strong correlation with human evaluation while providing more detailed and consistent feedback. We also piloted LLM-facilitated interview-based examinations as a scalable alternative to traditional assessments, demonstrating potential for individualized evaluation that addresses common testing limitations. Students experienced decreased rather than increased reliance on LLMs over the semester, developing critical evaluation skills and strategic tool selection. They evolved from basic assistance-seeking to verification workflows, with documentation requirements fostering metacognitive awareness. Students developed effective prompting strategies, contextual enrichment techniques, and cross-verification practices. Our findings suggest that structured LLM integration with transparency requirements and domain-specific tools can enhance astronomy education while building essential AI literacy skills. We provide implementation guidelines for educators and make our AstroTutor repository freely available.
Graph neural networks (GNNs), as topology/structure-aware models within deep learning, have emerged as powerful tools for AI-aided drug discovery (AIDD). By directly operating on molecular graphs, GNNs offer an intuitive and expressive framework for learning the complex topological and geometric features of drug-like molecules, cementing their role in modern molecular modeling. This review provides a comprehensive overview of the methodological foundations and representative applications of GNNs in drug discovery, spanning tasks such as molecular property prediction, virtual screening, molecular generation, biomedical knowledge graph construction, and synthesis planning. Particular attention is given to recent methodological advances, including geometric GNNs, interpretable models, uncertainty quantification, scalable graph architectures, and graph generative frameworks. We also discuss how these models integrate with modern deep learning approaches, such as self-supervised learning, multi-task learning, meta-learning and pre-training. Throughout this review, we highlight the practical challenges and methodological bottlenecks encountered when applying GNNs to real-world drug discovery pipelines, and conclude with a discussion on future directions.
Separating Angular and Radial Modes with Spherical-Fourier Bessel Power Spectrum on All Scales and Implications for Systematics Mitigation
2025-06-07
Robin Y. Wen, Henry S. Grasshorn Gebhardt, Chen Heinrich, Olivier Doré
Current and upcoming large-scale structure surveys place stringent requirements on the mitigation of observational systematics in order to achieve their unprecedented constraining power. In this work, we investigate the potential use of the spherical Fourier-Bessel (SFB) power spectrum in controlling systematics, leveraging its capability of disentangling angular and radial scales. We first clarify how the discrete SFB basis describes radial scales via the index $n$ and demonstrate that the SFB power spectrum reduces to the clustering wedge $P(k,\mu)$ in the plane-parallel limit, enabling it to inherit results from past literature based on the clustering wedge. Crucially, the separation of angular and radial scales allows systematics to be localized in SFB space. In particular, systematics with broad and smooth radial distributions primarily concentrate in the $n=0$ modes corresponding to the largest radial scales. This localization behavior enables one to selectively remove only particular angular and radial modes contaminated by systematics. This is in contrast to standard 3D clustering analyses based on power spectrum multipoles, where systematic effects necessitate the removal of all modes below a given $k_{\rm min}$. Our findings advocate for adopting the SFB basis in 3D clustering analyses where systematics have become a limiting factor.
The Legacy of the Cartwright-Littlewood Collaboration
2025-06-07
John Guckenheimer
Mary L. Cartwright and John E. Littlewood published a short preliminary survey in 1945 describing results of their investigation of the forced van der Pol equation \begin{equation*} \ddot{y}-k(1-y^2)\dot{y}+y = b \lambda k \cos(\lambda t+a) \end{equation*} in which $b,\lambda,k,a$ are parameters with $k$ large. Their description of dynamical behavior now known as chaos in this dissipative dynamical system was a landmark in dynamical systems theory. Littlewood's monster paper containing the details of their investigation finally appeared twelve years later in the journal Acta Mathematica. I review here the context in which Cartwright and Littlewood worked when they wrote their 1945 paper and the enduring mathematical legacy of their discoveries. I also give brief pointers to research they inspired in other application areas.
FAUST XXVI. The dust opacity spectral indices of protostellar envelopes bridge the gap between interstellar medium and disks
2025-06-07
Luca Cacciapuoti, L. Testi, A. J. Maury, C. Chandler, N. Sakai, C. Ceccarelli, C. Codella, M. De Simone, L. Podio, G. Sabatini, E. Bianchi, E. Macias, A. Miotello, C. Toci, L. Loinard, D. Johnstone, H. B. Liu, Y. Aikawa, Y. Shirley, B. Svoboda, T. Sakai, T. Hirota, S. Viti, B. Lefloch, Y. Oya, S. Ohashi, S. Feng, F. Fontani, Y. Watanabe, A. Lopez-Sepulcre, Y. Zhang, C. Vastel, D. Segura-Cox, J. Pineda, A. Isella, R. S. Klessen, P. Hennebelle, S. Molinari, S. Yamamoto
The sub-millimetre dust opacity spectral index is a critical observable to constrain dust properties, such as the maximum grain size of an observed dust population. It has been widely measured at galactic scales and down to protoplanetary disks. However, because of observational and analytical challenges, quite a gap exists in measuring dust properties in the envelopes that feed newborn protostars and their disks. To fill this gap, we use sensitive dust continuum emission data at 1.2 and 3.1 mm from the ALMA FAUST Large Program and constrain the dust opacity millimetre spectral index around a sample of protostars. Our high-resolution data, along with a more refined methodology with respect to past efforts, allow us to disentangle disk and envelope contributions in the uv-plane, and thus measure spectral indices for the envelopes uncontaminated by the optically thick emission of the inner regions. First, we find that the young disks are small and optically thick. Secondly, we measure the dust opacity spectral index at envelope scales for n=11 sources: the beta of n=9 sources had never been constrained in the literature. We effectively double the number of sources for which the dust opacity spectral index beta has been measured at these scales. Third, combining the available literature measurements with our own (total n=18), we show how envelope spectral indices distribute between ISM-like and disk-like values, bridging the gap in the inferred dust evolution. Finally, we statistically confirm a significant correlation between beta and the mass of protostellar envelopes, previously suggested in the literature. Our findings indicate that the dust optical properties smoothly vary from the ISM, through envelopes and all the way down to disks. Multi-wavelength surveys are needed to further this study and make more general claims on dust evolution in its pathway from cloud to disks.
Identity Deepfake Threats to Biometric Authentication Systems: Public and Expert Perspectives
2025-06-07
Shijing He, Yaxiong Lei, Zihan Zhang, Yuzhou Sun, Shujun Li, Chi Zhang, Juan Ye
Generative AI (Gen-AI) deepfakes pose a rapidly evolving threat to biometric authentication, yet a significant gap exists between expert understanding of these risks and public perception. This disconnection creates critical vulnerabilities in systems trusted by millions. To bridge this gap, we conducted a comprehensive mixed-method study, surveying 408 professionals across key sectors and conducting in-depth interviews with 37 participants (25 experts, 12 general public [non-experts]). Our findings reveal a paradox: while the public increasingly relies on biometrics for convenience, experts express grave concerns about the spoofing of static modalities like face and voice recognition. We found significant demographic and sector-specific divides in awareness and trust, with finance professionals, for example, showing heightened skepticism. To systematically analyze these threats, we introduce a novel Deepfake Kill Chain model, adapted from Hutchins et al.'s cybersecurity frameworks to map the specific attack vectors used by malicious actors against biometric systems. Based on this model and our empirical findings, we propose a tri-layer mitigation framework that prioritizes dynamic biometric signals (e.g., eye movements), robust privacy-preserving data governance, and targeted educational initiatives. This work provides the first empirically grounded roadmap for defending against AI-generated identity threats by aligning technical safeguards with human-centered insights.
While promptable segmentation (\textit{e.g.}, SAM) has shown promise for various segmentation tasks, it still requires manual visual prompts for each object to be segmented. In contrast, task-generic promptable segmentation aims to reduce the need for such detailed prompts by employing only a task-generic prompt to guide segmentation across all test samples. However, when applied to Camouflaged Object Segmentation (COS), current methods still face two critical issues: 1) \textit{\textbf{semantic ambiguity in getting instance-specific text prompts}}, which arises from insufficient discriminative cues in holistic captions, leading to foreground-background confusion; 2) \textit{\textbf{semantic discrepancy combined with spatial separation in getting instance-specific visual prompts}}, which results from global background sampling far from object boundaries with low feature correlation, causing SAM to segment irrelevant regions. To address the issues above, we propose \textbf{RDVP-MSD}, a novel training-free test-time adaptation framework that synergizes \textbf{R}egion-constrained \textbf{D}ual-stream \textbf{V}isual \textbf{P}rompting (RDVP) via \textbf{M}ultimodal \textbf{S}tepwise \textbf{D}ecomposition Chain of Thought (MSD-CoT). MSD-CoT progressively disentangles image captions to eliminate semantic ambiguity, while RDVP injects spatial constraints into visual prompting and independently samples visual prompts for foreground and background points, effectively mitigating semantic discrepancy and spatial separation. Without requiring any training or supervision, RDVP-MSD achieves a state-of-the-art segmentation result on multiple COS benchmarks and delivers a faster inference speed than previous methods, demonstrating significantly improved accuracy and efficiency. The codes will be available at \href{https://github.com/ycyinchao/RDVP-MSD}{https://github.com/ycyinchao/RDVP-MSD}
RF-Source Seeking with Obstacle Avoidance using Real-time Modified Artificial Potential Fields in Unknown Environments
2025-06-07
Shahid Mohammad Mulla, Aryan Kanakapudi, Lakshmi Narasimhan, Anuj Tiwari
Navigation of UAVs in unknown environments with obstacles is essential for applications in disaster response and infrastructure monitoring. However, existing obstacle avoidance algorithms, such as Artificial Potential Field (APF) are unable to generalize across environments with different obstacle configurations. Furthermore, the precise location of the final target may not be available in applications such as search and rescue, in which case approaches such as RF source seeking can be used to align towards the target location. This paper proposes a real-time trajectory planning method, which involves real-time adaptation of APF through a sampling-based approach. The proposed approach utilizes only the bearing angle of the target without its precise location, and adjusts the potential field parameters according to the environment with new obstacle configurations in real time. The main contributions of the article are i) an RF source seeking algorithm to provide a bearing angle estimate using RF signal calculations based on antenna placement, and ii) a modified APF for adaptable collision avoidance in changing environments, which are evaluated separately in the simulation software Gazebo, using ROS2 for communication. Simulation results show that the RF source-seeking algorithm achieves high accuracy, with an average angular error of just 1.48 degrees, and with this estimate, the proposed navigation algorithm improves the success rate of reaching the target by 46% and reduces the trajectory length by 1.2% compared to standard potential fields.
Across the Horizon: On Gravitational Wave Flux Laws and Tests of Gravity
2025-06-07
David Maibach
Motivated by the first detection of gravitational waves, this dissertation develops analytical, numerical, and data analysis techniques to address persistent blind spots in our understanding of gravity. Beginning with asymptotically flat spacetimes and the geometry of null geodesic congruences, the derivation of the shear tensor--encoding gravitational radiation--is revisited. The covariant phase space formulation of General Relativity is then employed to derive a non-conservation law associated with the symmetry group at null infinity, generalizing prior constructions of radiative phase space and yielding consistent results. These flux laws are used to derive constraint equations that enable the evaluation and comparison of state-of-the-art numerical waveform models, leading to new insights and a robust algorithm for benchmarking future improvements. These flux laws are further applied to compute quantum corrections to gravitational waveforms arising from the gravitational wave echo effect. Two leading phenomenological scenarios involving echoes from binary black hole mergers are analyzed. The structure of both the primary echo signal and its induced corrections to nonlinear features of the waveform are studied for observability by the upcoming LISA mission. The results indicate that LISA could detect such signals, potentially probing black hole area quantization. Finally, motivated by recent hints of a stochastic gravitational wave background from Pulsar Timing Array data, this work reviews its theoretical basis and studies several astrophysical and cosmological source scenarios. A forecast for detection prospects with LISA is presented using a modern data analysis pipeline. The results suggest LISA will constrain the extra-galactic stochastic background to a spectral energy density below $ \Omega_\text{GW} \lesssim 10^{-8}$.
ASKAP-EMU radio continuum detection of planetary nebula NGC 5189: the "Infinity" nebula
2025-06-07
A. D. Asher, Z. J. Smeaton, M. D. Filipović, A. M. Hopkins, J. Th. van Loon, T. J. Galvin, L. A. Barnes
We report the radio continuum detection of well known Galactic Planetary Nebula (PN) NGC 5189, observed at 943 MHz during the Australian Square Kilometre Array Pathfinder (ASKAP) Evolutionary Map of the Universe (EMU) survey. Two detections of NGC 5189 have been made during the survey, of better resolution than previous radio surveys. Both measurements of the integrated flux density are consistent with each other, at $S_{\rm 943\,MHz} = 0.33\pm0.03$ Jy, and the spectral luminosity is $L_{\rm{943\,MHz}}$ = 8.89 $\times$ 10$^{13}$ W m$^{-2}$ Hz$^{-1}$. Using available flux density measurements for radio-detections of NGC 5189, we calculate a radio surface brightness at 1 GHz and measure $\Sigma_{\rm 1~GHz}$ = 6.0 $\times$ 10$^{-21}$ W m$^{-2}$ Hz$^{-1}$ sr$^{-1}$, which is in the expected range for Galactic PNe. We measure an apparent size of 3.4${'}$ $\times$ 2.2${'}$ corresponding to physical diameters of 1.48 pc $\times$ 0.96 pc, and combine available radio observations of NGC 5189 to estimate a spectral index of $\alpha$ = 0.12 $\pm$ 0.05. Hence, we agree with previous findings that NGC 5189 is a thermal (free-free) emitting nebula. Additional measurements of the optical depth ($\tau = 0.00246$) and electron density ($N_{e} = 138~cm^{-3}$) support our findings that NGC 5189 is optically thin at 943 MHz. Furthermore, the radio contours from the ASKAP-EMU image have been overlaid onto a Hubble Space Telescope (HST) Wide Field Camera 3 image, demonstrating that the radio morphology closely traces the optical. Notably, the contour alignment for the innermost region highlights the two envelopes of gas previously reported to be low-ionisation structures, which is considered a defining feature of post common-envelope PNe that surround a central Wolf-Rayet star.
The State-of-the-Art in Lifelog Retrieval: A Review of Progress at the ACM Lifelog Search Challenge Workshop 2022-24
2025-06-07
Allie Tran, Werner Bailer, Duc-Tien Dang-Nguyen, Graham Healy, Steve Hodges, Björn Þór Jónsson, Luca Rossetto, Klaus Schoeffmann, Minh-Triet Tran, Lucia Vadicamo, Cathal Gurrin
The ACM Lifelog Search Challenge (LSC) is a venue that welcomes and compares systems that support the exploration of lifelog data, and in particular the retrieval of specific information, through an interactive competition format. This paper reviews the recent advances in interactive lifelog retrieval as demonstrated at the ACM LSC from 2022 to 2024. Through a detailed comparative analysis, we highlight key improvements across three main retrieval tasks: known-item search, question answering, and ad-hoc search. Our analysis identifies trends such as the widespread adoption of embedding-based retrieval methods (e.g., CLIP, BLIP), increased integration of large language models (LLMs) for conversational retrieval, and continued innovation in multimodal and collaborative search interfaces. We further discuss how specific retrieval techniques and user interface (UI) designs have impacted system performance, emphasizing the importance of balancing retrieval complexity with usability. Our findings indicate that embedding-driven approaches combined with LLMs show promise for lifelog retrieval systems. Likewise, improving UI design can enhance usability and efficiency. Additionally, we recommend reconsidering multi-instance system evaluations within the expert track to better manage variability in user familiarity and configuration effectiveness.
LADSG: Label-Anonymized Distillation and Similar Gradient Substitution for Label Privacy in Vertical Federated Learning
2025-06-07
Zeyu Yan, Yifei Yao, Xuanbing Wen, Juli Zhang, Kai Fan
Vertical federated learning (VFL) has become a key paradigm for collaborative machine learning, enabling multiple parties to train models over distributed feature spaces while preserving data privacy. Despite security protocols that defend against external attacks - such as gradient masking and encryption, which prevent unauthorized access to sensitive data - recent label inference attacks from within the system have emerged. These attacks exploit gradients and semantic embeddings to reconstruct private labels, bypassing traditional defenses. For example, the passive label inference attack can reconstruct tens of thousands of participants' private data using just 40 auxiliary labels, posing a significant security threat. Existing defenses address single leakage pathways, such as gradient leakage or label exposure. As attack strategies evolve, their limitations become clear, especially against hybrid attacks that combine multiple vectors. To address this, we propose Label-Anonymized Defense with Substitution Gradient (LADSG), a unified defense framework that integrates gradient substitution, label anonymization, and anomaly detection. LADSG mitigates both gradient and label leakage while maintaining the scalability and efficiency of VFL. Experiments on six real-world datasets show that LADSG reduces label inference attack success rates by 30-60%, with minimal computational overhead, underscoring the importance of lightweight defenses in securing VFL.
Ai-Driven Vulnerability Analysis in Smart Contracts: Trends, Challenges and Future Directions
2025-06-07
Mesut Ozdag
Smart contracts, integral to blockchain ecosystems, enable decentralized applications to execute predefined operations without intermediaries. Their ability to enforce trustless interactions has made them a core component of platforms such as Ethereum. Vulnerabilities such as numerical overflows, reentrancy attacks, and improper access permissions have led to the loss of millions of dollars throughout the blockchain and smart contract sector. Traditional smart contract auditing techniques such as manual code reviews and formal verification face limitations in scalability, automation, and adaptability to evolving development patterns. As a result, AI-based solutions have emerged as a promising alternative, offering the ability to learn complex patterns, detect subtle flaws, and provide scalable security assurances. This paper examines novel AI-driven techniques for vulnerability detection in smart contracts, focusing on machine learning, deep learning, graph neural networks, and transformer-based models. This paper analyzes how each technique represents code, processes semantic information, and responds to real world vulnerability classes. We also compare their strengths and weaknesses in terms of accuracy, interpretability, computational overhead, and real time applicability. Lastly, it highlights open challenges and future opportunities for advancing this domain.
Electronic structure and transport in materials with flat bands: 2D materials and quasicrystals
2025-06-07
Guy Trambly de Laissardière, Somepalli Venkateswarlu, Ahmed Misssaoui, Ghassen Jemaï, Khouloud Chika, Javad Vahedi, Omid Faizy Namarvar, Jean-Pierre Julien, Andreas Honecker, Laurence Magaud, Jouda Jemaa Khabthani, Didier Mayou
In this review, we present our recent works on materials whose common point is the presence of electronic bands of very low dispersion, called "flat bands", which are always the signature of an electronic confinement. A first part is devoted to the cases where this confinement is due to the long-range geometry of the defect-free structure. We have thus studied periodic approximant structures of quasiperiodic Penrose and octagonal tilings, and twisted bilayers of graphene or transition metal dichalcogenides (TMDs) whose rotation angle between the two layers assumes a special value, called "magic angle". In these materials, the flat bands correspond to electronic states distributed over a very large number of atoms (several hundreds or even thousands of atoms) and are very sensitive to small structural distortions such as "heterostrain". Their electronic transport properties cannot be described by usual Bloch-Boltzmann theories, because the interband terms of the velocity operator dominate the intraband terms as far as quantum diffusion is concerned. In twisted bilayer graphene, flat bands can induce a magnetic state and other electron-electron correlation effects. The second part focuses on 2D nanomaterials in the presence of local point defects that cause resonant electronic states (vacancies, adsorbed atoms or molecules). We present studies on monolayer graphene, twisted or Bernal bilayer graphene, carbon nanotubes, monolayer and multilayer black phosphorene, and monolayer TMDs. A recent result is the discovery that the selective functionalization of a Bernal bilayer graphene sublattice leads to a metallic or insulating behavior depending on the functionalized sublattice type. This result, which seems to be confirmed by very recent experimental measurements, suggests that functionalization can be a key parameter to control the electronic properties of two-dimensional materials.
A Framework for Controllable Multi-objective Learning with Annealed Stein Variational Hypernetworks
2025-06-07
Minh-Duc Nguyen, Dung D. Le
Pareto Set Learning (PSL) is popular as an efficient approach to obtaining the complete optimal solution in Multi-objective Learning (MOL). A set of optimal solutions approximates the Pareto set, and its mapping is a set of dense points in the Pareto front in objective space. However, some current methods face a challenge: how to make the Pareto solution is diverse while maximizing the hypervolume value. In this paper, we propose a novel method to address this challenge, which employs Stein Variational Gradient Descent (SVGD) to approximate the entire Pareto set. SVGD pushes a set of particles towards the Pareto set by applying a form of functional gradient descent, which helps to converge and diversify optimal solutions. Additionally, we employ diverse gradient direction strategies to thoroughly investigate a unified framework for SVGD in multi-objective optimization and adapt this framework with an annealing schedule to promote stability. We introduce our method, SVH-MOL, and validate its effectiveness through extensive experiments on multi-objective problems and multi-task learning, demonstrating its superior performance.
A Systematic Investigation on Deep Learning-Based Omnidirectional Image and Video Super-Resolution
2025-06-07
Qianqian Zhao, Chunle Guo, Tianyi Zhang, Junpei Zhang, Peiyang Jia, Tan Su, Wenjie Jiang, Chongyi Li
Omnidirectional image and video super-resolution is a crucial research topic in low-level vision, playing an essential role in virtual reality and augmented reality applications. Its goal is to reconstruct high-resolution images or video frames from low-resolution inputs, thereby enhancing detail preservation and enabling more accurate scene analysis and interpretation. In recent years, numerous innovative and effective approaches have been proposed, predominantly based on deep learning techniques, involving diverse network architectures, loss functions, projection strategies, and training datasets. This paper presents a systematic review of recent progress in omnidirectional image and video super-resolution, focusing on deep learning-based methods. Given that existing datasets predominantly rely on synthetic degradation and fall short in capturing real-world distortions, we introduce a new dataset, 360Insta, that comprises authentically degraded omnidirectional images and videos collected under diverse conditions, including varying lighting, motion, and exposure settings. This dataset addresses a critical gap in current omnidirectional benchmarks and enables more robust evaluation of the generalization capabilities of omnidirectional super-resolution methods. We conduct comprehensive qualitative and quantitative evaluations of existing methods on both public datasets and our proposed dataset. Furthermore, we provide a systematic overview of the current status of research and discuss promising directions for future exploration. All datasets, methods, and evaluation metrics introduced in this work are publicly available and will be regularly updated. Project page: https://github.com/nqian1/Survey-on-ODISR-and-ODVSR.
2025-06-07
Haiqi Yang, Zhiyuan Li, Yi Chang, Yuan Wu
Retentive Network (RetNet) represents a significant advancement in neural network architecture, offering an efficient alternative to the Transformer. While Transformers rely on self-attention to model dependencies, they suffer from high memory costs and limited scalability when handling long sequences due to their quadratic complexity. To mitigate these limitations, RetNet introduces a retention mechanism that unifies the inductive bias of recurrence with the global dependency modeling of attention. This mechanism enables linear-time inference, facilitates efficient modeling of extended contexts, and remains compatible with fully parallelizable training pipelines. RetNet has garnered significant research interest due to its consistently demonstrated cross-domain effectiveness, achieving robust performance across machine learning paradigms including natural language processing, speech recognition, and time-series analysis. However, a comprehensive review of RetNet is still missing from the current literature. This paper aims to fill that gap by offering the first detailed survey of the RetNet architecture, its key innovations, and its diverse applications. We also explore the main challenges associated with RetNet and propose future research directions to support its continued advancement in both academic research and practical deployment.
Quantum emitters serve as essential on-demand photonic resources, generating quantum states of light such as single photons and entangled photon pairs while serving as interfaces between light and matter. Buried in the solid state, quantum emitters enable a straightforward adoption of advanced nanofabrication techniques, facilitating precise engineering of their photonic environment for scalable quantum technologies. In this review, we introduce the fundamentals of quantum emitters and the key metrics characterising their performance. We highlight three material platforms: quantum dots, defect centres in diamond, and defect centres in silicon carbide. We summarise the recent developments of these platforms and discuss their advancements in quantum applications, including quantum communication, computation, and sensing. Finally, we provide a comparison across the three platforms, along with an outlook on future directions and potential challenges.
Infertility has a considerable impact on individuals' quality of life, affecting them socially and psychologically, with projections indicating a rise in the upcoming years. In vitro fertilization (IVF) emerges as one of the primary techniques within economically developed nations, employed to address the rising problem of low fertility. Expert embryologists conventionally grade embryos by reviewing blastocyst images to select the most optimal for transfer, yet this process is time-consuming and lacks efficiency. Blastocyst images provide a valuable resource for assessing embryo viability. In this study, we introduce an explainable artificial intelligence (XAI) framework for classifying embryos, employing a fusion of convolutional neural network (CNN) and long short-term memory (LSTM) architecture, referred to as CNN-LSTM. Utilizing deep learning, our model achieves high accuracy in embryo classification while maintaining interpretability through XAI.
NSUN2 as a potential prognostic as well as therapeutic target in cancer by regulating m5C modification
2025-06-07
Zifang He, Longtao Yang, Peiyao Ma
m5C modification is a type of RNA methylation modification, and its major methyltransferase, NSUN2, catalyzes m5C modification. NSUN2 is overexpressed in a variety of cancers, and it affects the metabolism of RNA from target genes by affecting the level of m5C modification in cancer cells, which in turn promotes the development of cancers and is associated with poor prognosis. This review summarizes the mechanisms by which NSUN2 and m5C play a pro-cancer role in various cancers, and the relationship between NSUN2 and the prognosis of various cancers, with the aim of identifying NSUN2 as a prognostic indicator and a target for future cancer therapy, and to provide a clearer therapeutic idea and direction for the future treatment of cancer.
Indicator Functions: Distilling the Information from Gaussian Random Fields
2025-06-07
Andrew Repp, Ravi K. Sheth, Istvan Szapudi, Yan-Chuan Cai
A random Gaussian density field contains a fixed amount of Fisher information on the amplitude of its power spectrum. For a given smoothing scale, however, that information is not evenly distributed throughout the smoothed field. We investigate which parts of the field contain the most information by smoothing and splitting the field into different levels of density (using the formalism of indicator functions), deriving analytic expressions for the information content of each density bin in the joint-probability distribution (given a distance separation). We find that the information peaks at moderately rare densities (where the number of smoothed survey cells $N\sim 100$). Counter-intuitively, we find that, for a finite survey volume, indicator function analysis can outperform conventional two-point statistics while using only a fraction of the total survey cells, and we explain why. In light of recent developments in marked statistics (such as the indicator power spectrum and density-split clustering), this result elucidates how to optimize sampling for effective extraction of cosmological information.
RARL: Improving Medical VLM Reasoning and Generalization with Reinforcement Learning and LoRA under Data and Hardware Constraints
2025-06-07
Tan-Hanh Pham, Chris Ngo
The growing integration of vision-language models (VLMs) in medical applications offers promising support for diagnostic reasoning. However, current medical VLMs often face limitations in generalization, transparency, and computational efficiency-barriers that hinder deployment in real-world, resource-constrained settings. To address these challenges, we propose a Reasoning-Aware Reinforcement Learning framework, \textbf{RARL}, that enhances the reasoning capabilities of medical VLMs while remaining efficient and adaptable to low-resource environments. Our approach fine-tunes a lightweight base model, Qwen2-VL-2B-Instruct, using Low-Rank Adaptation and custom reward functions that jointly consider diagnostic accuracy and reasoning quality. Training is performed on a single NVIDIA A100-PCIE-40GB GPU, demonstrating the feasibility of deploying such models in constrained environments. We evaluate the model using an LLM-as-judge framework that scores both correctness and explanation quality. Experimental results show that RARL significantly improves VLM performance in medical image analysis and clinical reasoning, outperforming supervised fine-tuning on reasoning-focused tasks by approximately 7.78%, while requiring fewer computational resources. Additionally, we demonstrate the generalization capabilities of our approach on unseen datasets, achieving around 27% improved performance compared to supervised fine-tuning and about 4% over traditional RL fine-tuning. Our experiments also illustrate that diversity prompting during training and reasoning prompting during inference are crucial for enhancing VLM performance. Our findings highlight the potential of reasoning-guided learning and reasoning prompting to steer medical VLMs toward more transparent, accurate, and resource-efficient clinical decision-making. Code and data are publicly available.
Emergent Holographic Spacetime from Quantum Information
2025-06-07
Tadashi Takayanagi
Holographic duality describes gravitational theories in terms of quantum many-body systems. In holography, quantum information theory provides a crucial tool that directly connects microscopic structures of these systems to the geometries of gravitational spacetimes. One manifestation is that the entanglement entropy in quantum many-body systems can be calculated from the area of an extremal surface in the corresponding gravitational spacetime. This implies that a gravitational spacetime can emerge from an enormous number of entangled qubits. In this Essay, I will discuss open problems in this area of research, considering recent developments and outlining future prospects towards a complete understanding of quantum gravity. The first step in this direction is to understand what kind of quantum circuits each holographic spacetime corresponds to, drawing on recent developments in quantum complexity theories and studying concrete examples of holography in string theory. Next, we should extend the concept of holography to general spacetimes, e.g., those spacetimes which appear in realistic cosmologies, by utilizing the connections between quantum information and holography. To address the fundamental question of how time emerges, I will propose the concepts of pseudo-entropy and time-like entanglement as a useful tool in our exploration.
Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding
2025-06-06
Emmanouil Zaranis, António Farinhas, Saul Santos, Beatriz Canaverde, Miguel Moura Ramos, Aditya K Surikuchi, André Viveiros, Baohao Liao, Elena Bueno-Benito, Nithin Sivakumaran, Pavlo Vasylenko, Shoubin Yu, Sonal Sannigrahi, Wafaa Mohammed, Ben Peters, Danae Sánchez Villegas, Elias Stengel-Eskin, Giuseppe Attanasio, Jaehong Yoon, Stella Frank, Alessandro Suglia, Chrysoula Zerva, Desmond Elliott, Mariella Dimiccoli, Mohit Bansal, Oswald Lanz, Raffaella Bernardi, Raquel Fernández, Sandro Pezzelle, Vlad Niculae, André F. T. Martins
Despite recent progress in vision-language models (VLMs), holistic understanding of long-form video content remains a significant challenge, partly due to limitations in current benchmarks. Many focus on peripheral, ``needle-in-a-haystack'' details, encouraging context-insensitive retrieval over deep comprehension. Others rely on large-scale, semi-automatically generated questions (often produced by language models themselves) that are easier for models to answer but fail to reflect genuine understanding. In this paper, we introduce MF$^2$, a new benchmark for evaluating whether models can comprehend, consolidate, and recall key narrative information from full-length movies (50-170 minutes long). MF$^2$ includes over 50 full-length, open-licensed movies, each paired with manually constructed sets of claim pairs -- one true (fact) and one plausible but false (fib), totalling over 850 pairs. These claims target core narrative elements such as character motivations and emotions, causal chains, and event order, and refer to memorable moments that humans can recall without rewatching the movie. Instead of multiple-choice formats, we adopt a binary claim evaluation protocol: for each pair, models must correctly identify both the true and false claims. This reduces biases like answer ordering and enables a more precise assessment of reasoning. Our experiments demonstrate that both open-weight and closed state-of-the-art models fall well short of human performance, underscoring the relative ease of the task for humans and their superior ability to retain and reason over critical narrative information -- an ability current VLMs lack.
PyGemini: Unified Software Development towards Maritime Autonomy Systems
2025-06-06
Kjetil Vasstein, Christian Le, Simon Lervåg Breivik, Trygve Maukon Myhr, Annette Stahl, Edmund Førland Brekke
Ensuring the safety and certifiability of autonomous surface vessels (ASVs) requires robust decision-making systems, supported by extensive simulation, testing, and validation across a broad range of scenarios. However, the current landscape of maritime autonomy development is fragmented -- relying on disparate tools for communication, simulation, monitoring, and system integration -- which hampers interdisciplinary collaboration and inhibits the creation of compelling assurance cases, demanded by insurers and regulatory bodies. Furthermore, these disjointed tools often suffer from performance bottlenecks, vendor lock-in, and limited support for continuous integration workflows. To address these challenges, we introduce PyGemini, a permissively licensed, Python-native framework that builds on the legacy of Autoferry Gemini to unify maritime autonomy development. PyGemini introduces a novel Configuration-Driven Development (CDD) process that fuses Behavior-Driven Development (BDD), data-oriented design, and containerization to support modular, maintainable, and scalable software architectures. The framework functions as a stand-alone application, cloud-based service, or embedded library -- ensuring flexibility across research and operational contexts. We demonstrate its versatility through a suite of maritime tools -- including 3D content generation for simulation and monitoring, scenario generation for autonomy validation and training, and generative artificial intelligence pipelines for augmenting imagery -- thereby offering a scalable, maintainable, and performance-oriented foundation for future maritime robotics and autonomy research.
Perceiving the world from both egocentric (first-person) and exocentric (third-person) perspectives is fundamental to human cognition, enabling rich and complementary understanding of dynamic environments. In recent years, allowing the machines to leverage the synergistic potential of these dual perspectives has emerged as a compelling research direction in video understanding. In this survey, we provide a comprehensive review of video understanding from both exocentric and egocentric viewpoints. We begin by highlighting the practical applications of integrating egocentric and exocentric techniques, envisioning their potential collaboration across domains. We then identify key research tasks to realize these applications. Next, we systematically organize and review recent advancements into three main research directions: (1) leveraging egocentric data to enhance exocentric understanding, (2) utilizing exocentric data to improve egocentric analysis, and (3) joint learning frameworks that unify both perspectives. For each direction, we analyze a diverse set of tasks and relevant works. Additionally, we discuss benchmark datasets that support research in both perspectives, evaluating their scope, diversity, and applicability. Finally, we discuss limitations in current works and propose promising future research directions. By synthesizing insights from both perspectives, our goal is to inspire advancements in video understanding and artificial intelligence, bringing machines closer to perceiving the world in a human-like manner. A GitHub repo of related works can be found at https://github.com/ayiyayi/Awesome-Egocentric-and-Exocentric-Vision.
Accurate $N$-body simulations with local Primordial non-Gaussianities: initial conditions and aliasing
2025-06-06
Adrian G. Adame, Santiago Avila, Violeta Gonzalez-Perez, Oliver Hahn, Gustavo Yepes, Marc Manera
New generation galaxy surveys targeting constraints on local primordial non-Gaussianity (PNG) demand $N$-body simulations that accurately reproduce its effects. In this work, we explore various prescriptions for the initial conditions of simulations with PNG, aiming to optimise accuracy and minimise numerical errors, particularly due to aliasing. We have used $186$ runs that vary the starting redshift, LPT order, and non-Gaussianities ($f^{\rm local}_{\rm NL}$ and $g^{\rm local}_{\rm NL}$). Starting with $3$LPT at a redshift as low as $z_{\rm ini}\simeq 11.5$ reproduces to $<1 \%$ the power spectrum, bispectrum and halo mass function of a high-resolution reference simulation. The aliasing induced by the PNG terms in the power spectrum produces a $ \leq 3 \%$ excess small-scale power at the initial conditions, dropping below $0.1\%$ by $z=0$. State-of-the-art initial condition generators show a sub-percent agreement. We show that initial conditions for simulations with PNG should be established at a lower redshift using higher-order LPT schemes. We also show that removing the PNG aliasing signal is unnecessary for current simulations. The methodology proposed here can accelerate the generation of simulations with PNG while enhancing their accuracy.
Theory and computation of thermal-field emission from semiconductors
2025-06-06
Salvador Barranco Cárceles, Veronika Zadin, Aquila Mavalankar, Ian Underwood, Andreas Kyritsakis
Semiconducting field emitters present some interesting features (e.g.; self-limited electron emission) for both scientific interest and industrial applications. The analysis of experimental results and device design has been restrained by the lack of accurate 3D models for the simulation of thermal-field emission from semiconductors. Here we review and correct the equations of field emission from semiconductors and include them to expand GETELEC (General Tool for Electron Emission Calculations). Our method covers all electron emission regime (field, thermal, and intermediate), aiming to maximise the calculation accuracy while minimising the computational cost. GETELEC-2.0 is able to reproduce the characteristic non-linear I-V curves in Fowler-Nordheim coordinates obtained from semiconductors, giving insights about their nature. As well as providing an explanation to the lack of experimental observation of valence band electrons from semiconductors.
Validation of the Critical Reflection and Agency in Computing Index: Do Computing Ethics Courses Make a Difference?
2025-06-06
Aadarsh Padiyath, Casey Fiesler, Mark Guzdial, Barbara Ericson
Computing ethics education aims to develop students' critical reflection and agency. We need validated ways to measure whether our efforts succeed. Through two survey administrations (N=474, N=464) with computing students and professionals, we provide evidence for the validity of the Critical Reflection and Agency in Computing Index. Our psychometric analyses demonstrate distinct dimensions of ethical development and show strong reliability and construct validity. Participants who completed computing ethics courses showed higher scores in some dimensions of ethical reflection and agency, but they also exhibited stronger techno-solutionist beliefs, highlighting a challenge in current pedagogy. This validated instrument enables systematic measurement of how computing students develop critical consciousness, allowing educators to better understand how to prepare computing professionals to tackle ethical challenges in their work.
Does It Run and Is That Enough? Revisiting Text-to-Chart Generation with a Multi-Agent Approach
2025-06-06
James Ford, Anthony Rios
Large language models can translate natural-language chart descriptions into runnable code, yet approximately 15\% of the generated scripts still fail to execute, even after supervised fine-tuning and reinforcement learning. We investigate whether this persistent error rate stems from model limitations or from reliance on a single-prompt design. To explore this, we propose a lightweight multi-agent pipeline that separates drafting, execution, repair, and judgment, using only an off-the-shelf GPT-4o-mini model. On the \textsc{Text2Chart31} benchmark, our system reduces execution errors to 4.5\% within three repair iterations, outperforming the strongest fine-tuned baseline by nearly 5 percentage points while requiring significantly less compute. Similar performance is observed on the \textsc{ChartX} benchmark, with an error rate of 4.6\%, demonstrating strong generalization. Under current benchmarks, execution success appears largely solved. However, manual review reveals that 6 out of 100 sampled charts contain hallucinations, and an LLM-based accessibility audit shows that only 33.3\% (\textsc{Text2Chart31}) and 7.2\% (\textsc{ChartX}) of generated charts satisfy basic colorblindness guidelines. These findings suggest that future work should shift focus from execution reliability toward improving chart aesthetics, semantic fidelity, and accessibility.
2025-06-06
Julia Monika Koulen, Stefano Profumo, Nolan Smyth
Primordial black holes (PBHs) constitute a compelling dark matter candidate whose gravitational effects could significantly influence early cosmic structure formation. We investigate the impact of PBHs on Population III star formation through detailed $N$-body and hydrodynamic simulations, extending beyond previous semi-analytical approaches. Our results reveal a mass-dependent dichotomy in PBH effects: massive PBHs ($M_{\rm PBH} \gtrsim 10^2 M_\odot$) with sufficient abundance can accelerate structure formation and shift Pop III formation to higher redshifts, potentially conflicting with observational constraints from high-redshift galaxy surveys. Conversely, lower-mass PBHs can induce tidal disruption of gas-rich minihalos, suppressing star formation and delaying the cosmic dawn depending on their abundance. We quantify these competing effects to derive new constraints on the PBH mass function and their contribution to the total dark matter density, with implications for forthcoming observations with the James Webb Space Telescope and 21-cm cosmology experiments.
2025-06-06
Longkang Dai, Dmitrij V. Titov, Wencheng D. Shao, Xi Zhang, Jun Cui, Siteng Fan
Venus has regained attention on the international stage with the approval of three new missions by ESA and NASA. As the twin sister of Earth, Venus exhibits a distinct atmosphere, which casts a veil of mystery over the planetary evolution and is of great scientific significance. One of the most important components of Venus-the cloud-is believed to have significantly regulated its climate evolution and affect the environmental habitability. However, due to sparse in-situ measurements and the limitation of remote sensing, properties of these clouds remain largely unknown. Based on research conducted in past decades, this article reviews the observational structure of cloud properties, the progress of microphysical and simplified cloud model developments, and perspectives of future directions of this research field. Several possible solutions to the challenges associated with the coupling effect, ultraviolet absorption, and habitability are proposed and discussed in details, providing insights for future Venus' explorations.
Resource Allocation for Pinching-Antenna Systems: State-of-the-Art, Key Techniques and Open Issues
2025-06-06
Ming Zeng, Ji Wang, Octavia A. Dobre, Zhiguo Ding, George K. Karagiannidis, Robert Schober, H. Vincent Poor
Pinching antennas have emerged as a promising technology for reconfiguring wireless propagation environments, particularly in high-frequency communication systems operating in the millimeter-wave and terahertz bands. By enabling dynamic activation at arbitrary positions along a dielectric waveguide, pinching antennas offer unprecedented channel reconfigurability and the ability to provide line-of-sight (LoS) links in scenarios with severe LoS blockages. The performance of pinching-antenna systems is highly dependent on the optimized placement of the pinching antennas, which must be jointly considered with traditional resource allocation (RA) variables -- including transmission power, time slots, and subcarriers. The resulting joint RA problems are typically non-convex with complex variable coupling, necessitating sophisticated optimization techniques. This article provides a comprehensive survey of existing RA algorithms designed for pinching-antenna systems, supported by numerical case studies that demonstrate their potential performance gains. Key challenges and open research problems are also identified to guide future developments in this emerging field.
Search for the rare decay D$^0$ $\to$ $μ^+μ^-$ in proton-proton collisions at $\sqrt{s}$ = 13.6 TeV
2025-06-06
CMS Collaboration
A search for the rare decay D$^0$ $\to$ $\mu^+\mu^-$ is reported using proton-proton collision events at $\sqrt{s}$ = 13.6 TeV collected by the CMS detector in 2022$-$2023, corresponding to an integrated luminosity of 64.5 fb$^{-1}$. This is the first analysis to use a newly developed inclusive dimuon trigger, expanding the scope of the CMS flavor physics program. The search uses D$^0$ mesons obtained from D$^{*+}$ $\to$ D$^0\pi^+$ decays. No significant excess is observed. A limit on the branching fraction of $\mathcal{B}$(D$^0$ $\to$ $\mu^+\mu^-$) $\lt$ 2.4 $\times$ 10$^{-9}$ at 95% confidence level is set. This is the most stringent upper limit set on any flavor changing neutral current decay in the charm sector.
Testing OH Megamaser Identification Methods in HI Surveys: Updated Source-Flagging Algorithms and New Detections in ALFALFA
2025-06-06
Hayley Roberts, Jeremy Darling, Kelley M. Hess, Andrew J. Baker, Elizabeth A. K. Adams, Helga Dénes
OH megamasers (OHMs) are extragalactic masers found primarily in gas-rich galaxy major mergers. To date, only $\sim$120 OHMs have been cataloged since their discovery in 1982, and efforts to identify distinct characteristics of OHM host galaxies have remained inconclusive. As radio astronomy advances with next-generation telescopes and extensive 21 cm HI surveys, precursors to the Square Kilometre Array (SKA) are expected to detect the 18 cm OH masing line with significantly increased frequency, potentially expanding the known OHM population tenfold. These detections, however, risk confusion with lower-redshift HI emitters unless accompanied by independent spectroscopic redshifts. Building on methods proposed by Roberts et al. (arXiv:2102.12486) for distinguishing these interloping OHMs via near- to mid-IR photometry and emission line frequencies, we apply these techniques to data from the Arecibo Legacy Fast ALFA [Arecibo L-band Feed Array] (ALFALFA) survey and a preliminary APERture Tile In Focus (Apertif) HI emission line catalog from the Westerbork Synthesis Radio Telescope. Our study, utilizing the Apache Point Observatory 3.5m telescope to obtain optical spectroscopic redshifts of 142 candidates (107 from ALFALFA and 35 from Apertif), confirms five new OHM host galaxies and reidentifies two previously catalogued OHMs misclassified as HI emitters in ALFALFA. These findings support the predictions from Roberts et al. (arXiv:2102.12486 [astro-ph.GA]) and underscore the evolving landscape of radio astronomy in the context of next-generation telescopes.
Synthetic Tabular Data: Methods, Attacks and Defenses
2025-06-06
Graham Cormode, Samuel Maddock, Enayat Ullah, Shripad Gade
Synthetic data is often positioned as a solution to replace sensitive fixed-size datasets with a source of unlimited matching data, freed from privacy concerns. There has been much progress in synthetic data generation over the last decade, leveraging corresponding advances in machine learning and data analytics. In this survey, we cover the key developments and the main concepts in tabular synthetic data generation, including paradigms based on probabilistic graphical models and on deep learning. We provide background and motivation, before giving a technical deep-dive into the methodologies. We also address the limitations of synthetic data, by studying attacks that seek to retrieve information about the original sensitive data. Finally, we present extensions and open problems in this area.
Reinforcing Code Generation: Improving Text-to-SQL with Execution-Based Learning
2025-06-06
Atharv Kulkarni, Vivek Srikumar
In this work, we study the problem of code generation with a large language model (LLM), with a focus on generating SQL queries from natural language questions. We ask: Instead of using supervised fine tuning with text-code pairs, can we tune a model by having it interact with a database engine? We frame this problem as a reinforcement learning problem where the model receives execution-based feedback from the environment in the form of scalar rewards. These rewards penalize execution failures and assign positive values when a query returns a correct answer. We use the rewards within the Group Relative Policy Optimization (GRPO) framework. We use a tabular reasoning benchmark to test and evaluate our findings. We find that with only weak supervision in the form of question-answer pairs, RL-tuning improves the accuracy of model generated SQL code from 31.49 to 49.83 while reducing error percentage from 25.43% to 14.71%. This improvement allowed the model nearly match the performance performance to the larger SQLCoder-70B model. Our work demonstrates the potential of using execution-based feedback to improve symbolic reasoning capabilities of LLMs.
MIRIAD: Augmenting LLMs with millions of medical query-response pairs
2025-06-06
Qinyue Zheng, Salman Abdullah, Sam Rawal, Cyril Zakka, Sophie Ostmeier, Maximilian Purk, Eduardo Reis, Eric J. Topol, Jure Leskovec, Michael Moor
LLMs are bound to transform healthcare with advanced decision support and flexible chat assistants. However, LLMs are prone to generate inaccurate medical content. To ground LLMs in high-quality medical knowledge, LLMs have been equipped with external knowledge via RAG, where unstructured medical knowledge is split into small text chunks that can be selectively retrieved and integrated into the LLMs context. Yet, existing RAG pipelines rely on raw, unstructured medical text, which can be noisy, uncurated and difficult for LLMs to effectively leverage. Systematic approaches to organize medical knowledge to best surface it to LLMs are generally lacking. To address these challenges, we introduce MIRIAD, a large-scale, curated corpus of 5,821,948 medical QA pairs, each rephrased from and grounded in a passage from peer-reviewed medical literature using a semi-automated pipeline combining LLM generation, filtering, grounding, and human annotation. Unlike prior medical corpora, which rely on unstructured text, MIRIAD encapsulates web-scale medical knowledge in an operationalized query-response format, which enables more targeted retrieval. Experiments on challenging medical QA benchmarks show that augmenting LLMs with MIRIAD improves accuracy up to 6.7% compared to unstructured RAG baselines with the same source corpus and with the same amount of retrieved text. Moreover, MIRIAD improved the ability of LLMs to detect medical hallucinations by 22.5 to 37% (increase in F1 score). We further introduce MIRIAD-Atlas, an interactive map of MIRIAD spanning 56 medical disciplines, enabling clinical users to visually explore, search, and refine medical knowledge. MIRIAD promises to unlock a wealth of down-stream applications, including medical information retrievers, enhanced RAG applications, and knowledge-grounded chat interfaces, which ultimately enables more reliable LLM applications in healthcare.
While Classifier-Free Guidance (CFG) has become standard for improving sample fidelity in conditional diffusion models, it can harm diversity and induce memorization by applying constant guidance regardless of whether a particular sample needs correction. We propose FeedBack Guidance (FBG), which uses a state-dependent coefficient to self-regulate guidance amounts based on need. Our approach is derived from first principles by assuming the learned conditional distribution is linearly corrupted by the unconditional distribution, contrasting with CFG's implicit multiplicative assumption. Our scheme relies on feedback of its own predictions about the conditional signal informativeness to adapt guidance dynamically during inference, challenging the view of guidance as a fixed hyperparameter. The approach is benchmarked on ImageNet512x512, where it significantly outperforms Classifier-Free Guidance and is competitive to Limited Interval Guidance (LIG) while benefitting from a strong mathematical framework. On Text-To-Image generation, we demonstrate that, as anticipated, our approach automatically applies higher guidance scales for complex prompts than for simpler ones and that it can be easily combined with existing guidance schemes such as CFG or LIG.
The Turn to Practice in Design Ethics: Characteristics and Future Research Directions for HCI Research
2025-06-06
Gizem Öz, Christian Dindler, Sharon Lindberg
As emerging technologies continue to shape society, there is a growing emphasis on the need to engage with design ethics as it unfolds in practice to better capture the complexities of ethical considerations embedded in day-to-day work. Positioned within the broader "turn to practice" in HCI, the review characterizes this body of work in terms of its motivations, conceptual frameworks, methodologies, and contributions across a range of design disciplines and academic databases. The findings reveal a shift away from static and abstract ethical frameworks toward an understanding of ethics as an evolving, situated, and inherent aspect of design activities, one that can be cultivated and fostered collaboratively. This review proposes six future directions for establishing common research priorities and fostering the field's growth. While the review promotes cross-disciplinary dialogue, we argue that HCI research, given its cumulative experience with practice-oriented research, is well-equipped to guide this emerging strand of work on design ethics.
Into the Unknown: From Structure to Disorder in Protein Function Prediction
2025-06-06
Đesika Kolarić, Chi Fung Willis Chow, Rita Zi Zhu, Agnes Toth-Petroczy, T. Reid Alderson, Iva Pritišanac
Intrinsically disordered regions (IDRs) account for one-third of the human proteome and play essential biological roles. However, predicting the functions of IDRs remains a major challenge due to their lack of stable structures, rapid sequence evolution, and context-dependent behavior. Many predictors of protein function neglect or underperform on IDRs. Recent advances in computational biology and machine learning, including protein language models, alignment-free approaches, and IDR-specific methods, have revealed conserved bulk features and local motifs within IDRs that are linked to function. This review highlights emerging computational methods that map the sequence-function relationship in IDRs, outlines critical challenges in IDR function annotation, and proposes a community-driven framework to accelerate interpretable functional predictions for IDRs.
Domain Adaptation in Agricultural Image Analysis: A Comprehensive Review from Shallow Models to Deep Learning
2025-06-06
Xing Hu, Siyuan Chen, Dawei Zhang
With the increasing use of computer vision in agriculture, image analysis has become crucial for tasks like crop health monitoring and pest detection. However, significant domain shifts between source and target domains-due to environmental differences, crop types, and data acquisition methods-pose challenges. These domain gaps limit the ability of models to generalize across regions, seasons, and complex agricultural environments. This paper explores how Domain Adaptation (DA) techniques can address these challenges, focusing on their role in enhancing the cross-domain transferability of agricultural image analysis. DA has gained attention in agricultural vision tasks due to its potential to mitigate domain heterogeneity. The paper systematically reviews recent advances in DA for agricultural imagery, particularly its practical applications in complex agricultural environments. We examine the key drivers for adopting DA in agriculture, such as limited labeled data, weak model transferability, and dynamic environmental conditions. We also discuss its use in crop health monitoring, pest detection, and fruit recognition, highlighting improvements in performance across regions and seasons. The paper categorizes DA methods into shallow and deep learning models, with further divisions into supervised, semi-supervised, and unsupervised approaches. A special focus is given to adversarial learning-based DA methods, which have shown great promise in challenging agricultural scenarios. Finally, we review key public datasets in agricultural imagery, analyzing their value and limitations in DA research. This review provides a comprehensive framework for researchers, offering insights into current research gaps and supporting the advancement of DA methods in agricultural image analysis.
2025-06-06
Roman Kashitsyn, Robin Künzler, Ognjen Marić, Lara Schmid
Reentrancy is a well-known source of smart contract bugs on Ethereum, leading e.g. to double-spending vulnerabilities in DeFi applications. But less is known about this problem in other blockchains, which can have significantly different execution models. Sharded blockchains in particular generally use an asynchronous messaging model that differs substantially from the synchronous and transactional model of Ethereum. We study the features of this model and its effect on reentrancy bugs on three examples: the Internet Computer (ICP) blockchain, NEAR Protocol, and MultiversX. We argue that this model, while useful for improving performance, also makes it easier to introduce reentrancy bugs. For example, reviews of the pre-production versions of some of the most critical ICP smart contracts found that 66% (10/15) of the reviewed contracts -- written by expert authors -- contained reentrancy bugs of medium or high severity, with potential damages in tens of millions of dollars. We evaluate existing Ethereum programming techniques (in particular the effects-checks-interactions pattern, and locking) to prevent reentrancy bugs in the context of this new messaging model and identify some issues with them. We then present novel Rust and Motoko patterns that can be leveraged on ICP to solve these issues. Finally, we demonstrate that the formal verification tool TLA+ can be used to find and eliminate such bugs in real world smart contracts on sharded blockchains.
The Interplay of Polar and Nematic Order in Active Matter: Implications for Non-Equilibrium Physics and Biology
2025-06-06
Varun Venkatesh, Niels de Graaf Sousa, Amin Doostmohammadi
Active matter has played a pivotal role in advancing our understanding of non-equilibrium systems, leading to a fundamental shift in the study of biophysical phenomena. The foundation of active matter research is built on assumptions regarding the symmetry of microscopic constituents. While these assumptions have been validated extensively, instances of mixed or joint symmetries are prevalent in biological systems. This review explores the coexistence of polar and nematic order in active matter, emphasizing the theoretical and experimental challenges associated with these systems. By integrating insights from recent studies, we highlight the importance of considering mixed symmetries to accurately describe biological processes. This exploration not only benefits the field of biology but could also open new horizons for non-equilibrium physics, offering a comprehensive framework for understanding complex behavior in active matter.
Twenty-Five Years of the Intelligent Driver Model: Foundations, Extensions, Applications, and Future Directions
2025-06-06
Shirui Zhou, Shiteng Zheng, Junfang Tian, Rui Jiang, and H. M. Zhang
The Intelligent Driver Model (IDM), proposed in 2000, has become a foundational tool in traffic flow modeling, renowned for its simplicity, computational efficiency, and ability to capture diverse traffic dynamics. Over the past 25 years, IDM has significantly advanced car-following theory and found extensive application in intelligent transportation systems, including driver assistance systems and autonomous vehicle control. However, IDM's deterministic framework and simplified assumptions face limitations in addressing real-world complexities such as stochastic variability, driver heterogeneity, and mixed traffic conditions. This paper provides a systematic review and critical reflection on IDM's theoretical foundations, academic influence, practical applications, and model extensions. While highlighting IDM's contributions, we emphasize the need to extend the model into a modular and extensible framework. Future directions include integrating stochastic elements, human behavioral insights, and hybrid modeling approaches that combine physics-based structures with data-driven methodologies. By reimagining IDM as a flexible modeling basis, this paper aims to inspire its continued development to meet the demands of intelligent, connected, and increasingly complex traffic systems.
The NetMob25 Dataset: A High-resolution Multi-layered View of Individual Mobility in Greater Paris Region
2025-06-06
Alexandre Chasse, Anne J. Kouam, Aline C. Viana, Razvan Stanica, Wellington V. Lobato, Geymerson Ramos, Geoffrey Deperle, Abdelmounaim Bouroudi, Suzanne Bussod, Fernando Molano
High-quality mobility data remains scarce despite growing interest from researchers and urban stakeholders in understanding individual-level movement patterns. The Netmob25 Data Challenge addresses this gap by releasing a unique GPS-based mobility dataset derived from the EMG 2023 GNSS-based mobility survey conducted in the Ile-de-France region (Greater Paris area), France. This dataset captures detailed daily mobility over a full week for 3,337 volunteer residents aged 16 to 80, collected between October 2022 and May 2023. Each participant was equipped with a dedicated GPS tracking device configured to record location points every 2-3 seconds and was asked to maintain a digital or paper logbook of their trips. All inferred mobility traces were algorithmically processed and validated through follow-up phone interviews. The dataset includes three components: (i) an Individuals database describing demographic, socioeconomic, and household characteristics; (ii) a Trips database with over 80,000 annotated displacements including timestamps, transport modes, and trip purposes; and (iii) a Raw GPS Traces database comprising about 500 million high-frequency points. A statistical weighting mechanism is provided to support population-level estimates. An extensive anonymization pipeline was applied to the GPS traces to ensure GDPR compliance while preserving analytical value. Access to the dataset requires acceptance of the challenge's Terms and Conditions and signing a Non-Disclosure Agreement. This paper describes the survey design, collection protocol, processing methodology, and characteristics of the released dataset.
Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series
2025-06-06
Adrien Petralia, Paul Boniol, Philippe Charpentier, Themis Palpanas
Improving smart grid system management is crucial in the fight against climate change, and enabling consumers to play an active role in this effort is a significant challenge for electricity suppliers. In this regard, millions of smart meters have been deployed worldwide in the last decade, recording the main electricity power consumed in individual households. This data produces valuable information that can help them reduce their electricity footprint; nevertheless, the collected signal aggregates the consumption of the different appliances running simultaneously in the house, making it difficult to apprehend. Non-Intrusive Load Monitoring (NILM) refers to the challenge of estimating the power consumption, pattern, or on/off state activation of individual appliances using the main smart meter signal. Recent methods proposed to tackle this task are based on a fully supervised deep-learning approach that requires both the aggregate signal and the ground truth of individual appliance power. However, such labels are expensive to collect and extremely scarce in practice, as they require conducting intrusive surveys in households to monitor each appliance. In this paper, we introduce CamAL, a weakly supervised approach for appliance pattern localization that only requires information on the presence of an appliance in a household to be trained. CamAL merges an ensemble of deep-learning classifiers combined with an explainable classification method to be able to localize appliance patterns. Our experimental evaluation, conducted on 4 real-world datasets, demonstrates that CamAL significantly outperforms existing weakly supervised baselines and that current SotA fully supervised NILM approaches require significantly more labels to reach CamAL performances. The source of our experiments is available at: https://github.com/adrienpetralia/CamAL. This paper appeared in ICDE 2025.
With the rapid growth of fintech, personalized financial product recommendations have become increasingly important. Traditional methods like collaborative filtering or content-based models often fail to capture users' latent preferences and complex relationships. We propose a hybrid framework integrating large language models (LLMs) and graph neural networks (GNNs). A pre-trained LLM encodes text data (e.g., user reviews) into rich feature vectors, while a heterogeneous user-product graph models interactions and social ties. Through a tailored message-passing mechanism, text and graph information are fused within the GNN to jointly optimize embeddings. Experiments on public and real-world financial datasets show our model outperforms standalone LLM or GNN in accuracy, recall, and NDCG, with strong interpretability. This work offers new insights for personalized financial recommendations and cross-modal fusion in broader recommendation tasks.
Impact of the $(\ell=2,m=0)$ spherical harmonic mode with memory on parameter estimation for ground-based detectors
2025-06-06
Maria Rosselló-Sastre, Sascha Husa, Sayantani Bera, Yumeng Xu
We recently presented an efficient and accurate waveform model for the $(2,0)$ spherical harmonic mode including both the displacement memory contribution and the ringdown oscillations for aligned-spin binary black holes in quasi-circular orbits. The model we developed is constructed in time domain and implemented within the computationally efficient IMRPhenomTHM waveform model. In this article, we employ it to perform in-depth parameter estimation studies for future ground-based detectors, specifically considering LIGO A$^{\#}$, Cosmic Explorer, and the Einstein Telescope, combining them in different detector networks. While previous studies have reviewed the impact of the memory contribution in parameter estimation, we assess the effect of incorporating the complete mode in the analysis on the posterior estimation of source parameters, performing zero-noise injections of high signal-to-noise ratio signals. We investigate the impact of this mode on the distance-inclination degeneracy and compare its impact in edge-on and face-on configurations. We find that including this mode helps mitigate biases in the estimation of individual spin components, which may otherwise arise when the mode is neglected.
2025-06-06
Gleb S. Pokrovski, Kalin Kouzmanov, Stefansson Andri
This chapter overviews sulfur chemical speciation and behavior in different types of hydrothermal fluids across the lithosphere, spanning from active geothermal systems to basinal brines, to metamorphic and magmatic-hydrothermal fluids. The information on sulfur in these fluids stems from a wide range of recent analytical, experimental and computational methods whose advantages and limitations are discussed. A special emphasis is given to in situ approaches that have enabled unprecedented insights into the fascinating sulfur chemistry in hydrothermal fluids. These insights motivated a critical review of the chemical speciation of key base, precious, and critical metals whose behavior is intimately linked to sulfur. The results allow the role played by the different sulfur ligands on hydrothermal transport of metals and ore deposit formation to be evaluated. An outline of major challenges and emerging perspectives for sulfur-related research in geological fluids ends up this chapter.
In this paper, we present a survey of research studies related to the cost-effectiveness of serverless approach and corresponding cost savings. We conducted a systematic literature review using Google Scholar search engine, covering the period from 2010 to 2024. We identified 34 related studies, from which we extracted 17 parameters that might influence the relative cost savings of applying the serverless approach.
Towards Mixed-Criticality Software Architectures for Centralized HPC Platforms in Software-Defined Vehicles: A Systematic Literature Review
2025-06-06
Lucas Mauser, Eva Zimmermann, Pavel Nedvědický, Tobias Eisenreich, Moritz Wäschle, Stefan Wagner
Centralized electrical/electronic architectures and High-Performance Computers (HPCs) are redefining automotive software development, challenging traditional microcontroller-based approaches. Ensuring real-time, safety, and scalability in software-defined vehicles necessitates reevaluating how mixed-criticality software is integrated into centralized architectures. While existing research on automotive SoftWare Architectures (SWAs) is relevant to the industry, it often lacks validation through systematic, empirical methods. To address this gap, we conduct a systematic literature review focusing on automotive mixed-criticality SWAs. Our goal is to provide practitioner-oriented guidelines that assist automotive software architects and developers design centralized, mixed-criticality SWAs based on a rigorous and transparent methodology. First, we set up a systematic review protocol grounded in established guidelines. Second, we apply this protocol to identify relevant studies. Third, we extract key functional domains, constraints, and enabling technologies that drive changes in automotive SWAs, thereby assessing the protocol's effectiveness. Additionally, we extract techniques, architectural patterns, and design practices for integrating mixed-criticality requirements into HPC-based SWAs, further demonstrating the protocol's applicability. Based on these insights, we propose an exemplary SWA for a microprocessor-based system-on-chip. In conclusion, this study provides a structured approach to explore and realize mixed-criticality software integration for next-generation automotive SWAs, offering valuable insights for industry and research applications.
Defect-free and defective adaptations of crystalline sheets to stretching deformation
2025-06-06
Ranzhi Sun, Zhenwei Yao
The elastic response of the crystalline sheet to the stretching deformation in the form of wrinkles has been extensively investigated. In this work, we extend this fundamental scientific question to the plastic regime by exploring the adaptations of crystalline sheets to the large uniaxial mechanical stretching. We reveal the intermittent plastic shear deformations leading to the complete fracture of the sheets wrapping the cylinder. Specifically, systematic investigations of crystalline sheets of varying geometry show that the fracture processes can be classified into defect-free and defective categories depending on the emergence of topological defects. We highlight the characteristic mechanical and geometric patterns in response to the large stretching deformation, including the shear-driven intermittent lattice tilting, the vortex structure in the displacement field, and the emergence of mobile and anchored dislocations as plastic excitations. The effects of noise and initial lattice orientation on the plastic deformation of the stretched crystalline sheet are also discussed. These results advance our understanding of the atomic level on the irreversible plastic instabilities of 2D crystals under large uniaxial stretching and may have potential practical implications in the precise engineering of structural instabilities in packings of covalently bonded particulate systems.
Efficient dataset generation for machine learning perovskite alloys
2025-06-06
Henrietta Homm, Jarno Laakso, Patrick Rinke
Lead-based perovskite solar cells have reached high efficiencies, but toxicity and lack of stability hinder their wide-scale adoption. These issues have been partially addressed through compositional engineering of perovskite materials, but the vast complexity of the perovskite materials space poses a significant obstacle to exploration. We previously demonstrated how machine learning (ML) can accelerate property predictions for the CsPb(Cl/Br)$_3$ perovskite alloy. However, the substantial computational demand of density functional theory (DFT) calculations required for model training prevents applications to more complex materials. Here, we introduce a data-efficient scheme to facilitate model training, validated initially on CsPb(Cl/Br)$_3$ data and extended to the ternary alloy CsSn(Cl/Br/I)$_3$. Our approach employs clustering to construct a compact yet diverse initial dataset of atomic structures. We then apply a two-stage active learning approach to first improve the reliability of the ML-based structure relaxations and then refine accuracy near equilibrium structures. Tests for CsPb(Cl/Br)$_3$ demonstrate that our scheme reduces the number of required DFT calculations during the different parts of our proposed model training method by up to 20% and 50%. The fitted model for CsSn(Cl/Br/I)$_3$ is robust and highly accurate, evidenced by the convergence of all ML-based structure relaxations in our tests and an average relaxation error of only 0.5 meV/atom.
Distinguishing one person from another (what biometricians call recognition) is extremely relevant for different aspects of life. Traditional biometric modalities (fingerprint, face, iris, voice) rely on unique, stable features that reliably differentiate individuals. Recently, the term fingerprinting has gained popularity in neuroscience, with a growing number of studies adopting the term to describe various brain based metrics derived from different techniques. However, we think there is a mismatch between its widely accepted meaning in the biometric community and some brain based metrics. Many of these measures do not satisfy the strict definition of a biometric fingerprint that is, a stable trait that uniquely identifies an individual. In this study we discuss some issues that may generate confusion in this context and suggest how to treat the question in the future. In particular, we review how fingerprint is currently used in the neuroscience literature, highlight mismatches with the biometric community definition, and offer clear guidelines for distinguishing genuine biometric fingerprints from exploratory similarity metrics. By clarifying terminology and criteria, we aim to align practices and facilitate communication across fields.
Wireless communication provides great advantages that are not available through their wired counterparts such as flexibility, ease of deployment and use, cost reductions, and convenience. Wireless multi-hop networks (WMN) do not have any centralized management infrastructure. Wireless multi-hop networks have many benefits since proposed. In such networks when a node wants to send a packet to a destination where is not in the transmission range, depend on some intermediate nodes. In this type of networks packet sending is in the form of multiple hop until destination and this work is dynamic. Lack of centralized management cause that some nodes show malicious function. Malicious nodes are that receive packets and drop them maliciously. These malicious nodes could have many reasons such as hardware failure, software failure or lack of power. Such nodes make multiple packets drop from the network and the performance of network strongly decreases. As a result, the throughput of the network decrease, increase end-to-end delay and increase overhead. Therefore, we must aware from presence of malicious node in the network and do routing based on this awareness. Therefore, this paper aims to study and review the present malicious node detection methods that proposed in literatures. We categorized networks in groups, including ad hoc networks, MANET, DTN, Opportunistic networks, WSN, VANET and other wireless networks and compare malicious node detection met
Regenerating Daily Routines for Young Adults with Depression through User-Led Indoor Environment Modifications Using Local Natural Materials
2025-06-06
Ziqun Hua, Ao Jiang, Haoling Yang, Hao Fan, Huizhong Hu, Bernard Foing
Young adults with depression often experience prolonged indoor stays, limiting their access to natural environments and exacerbating mental health challenges. While nature therapy is recognized for its psychological benefits, existing interventions frequently require outdoor engagement, which may not be accessible for all individuals. This study explores the potential of user-led indoor modifications using local natural materials as a mental health intervention. A qualitative approach wasemployedtoassessemotionalandenvironmentalconnectedness.Participants engaged in material exploration, collection, and crafting, integrating natural elements into their living spaces. Findings indicate improved mood,increased environmental awareness,and a stronger sense of agency over personal space. The standardized intervention steps suggest the feasibility of a self-help toolkit, enabling broader implementation. This research contributes to sustainable, user-driven mental health interventions, bridging the gap between nature therapy and practical indoor applications.
A Survey of Earable Technology: Trends, Tools, and the Road Ahead
2025-06-06
Changshuo Hu, Qiang Yang, Yang Liu, Tobias Röddiger, Kayla-Jade Butkow, Mathias Ciliberto, Adam Luke Pullin, Jake Stuchbury-Wass, Mahbub Hassan, Cecilia Mascolo, Dong Ma
Earable devices, wearables positioned in or around the ear, are undergoing a rapid transformation from audio-centric accessories into multifunctional systems for interaction, contextual awareness, and health monitoring. This evolution is driven by commercial trends emphasizing sensor integration and by a surge of academic interest exploring novel sensing capabilities. Building on the foundation established by earlier surveys, this work presents a timely and comprehensive review of earable research published since 2022. We analyze over one hundred recent studies to characterize this shifting research landscape, identify emerging applications and sensing modalities, and assess progress relative to prior efforts. In doing so, we address three core questions: how has earable research evolved in recent years, what enabling resources are now available, and what opportunities remain for future exploration. Through this survey, we aim to provide both a retrospective and forward-looking view of earable technology as a rapidly expanding frontier in ubiquitous computing. In particular, this review reveals that over the past three years, researchers have discovered a variety of novel sensing principles, developed many new earable sensing applications, enhanced the accuracy of existing sensing tasks, and created substantial new resources to advance research in the field. Based on this, we further discuss open challenges and propose future directions for the next phase of earable research.
Statistically Valid Post-Deployment Monitoring Should Be Standard for AI-Based Digital Health
2025-06-06
Pavel Dolin, Weizhi Li, Gautam Dasarathy, Visar Berisha
This position paper argues that post-deployment monitoring in clinical AI is underdeveloped and proposes statistically valid and label-efficient testing frameworks as a principled foundation for ensuring reliability and safety in real-world deployment. A recent review found that only 9% of FDA-registered AI-based healthcare tools include a post-deployment surveillance plan. Existing monitoring approaches are often manual, sporadic, and reactive, making them ill-suited for the dynamic environments in which clinical models operate. We contend that post-deployment monitoring should be grounded in label-efficient and statistically valid testing frameworks, offering a principled alternative to current practices. We use the term "statistically valid" to refer to methods that provide explicit guarantees on error rates (e.g., Type I/II error), enable formal inference under pre-defined assumptions, and support reproducibility--features that align with regulatory requirements. Specifically, we propose that the detection of changes in the data and model performance degradation should be framed as distinct statistical hypothesis testing problems. Grounding monitoring in statistical rigor ensures a reproducible and scientifically sound basis for maintaining the reliability of clinical AI systems. Importantly, it also opens new research directions for the technical community--spanning theory, methods, and tools for statistically principled detection, attribution, and mitigation of post-deployment model failures in real-world settings.
Evaluating AI-Powered Learning Assistants in Engineering Higher Education: Student Engagement, Ethical Challenges, and Policy Implications
2025-06-06
Ramteja Sajja, Yusuf Sermet, Brian Fodale, Ibrahim Demir
As generative AI tools become increasingly integrated into higher education, understanding how students interact with and perceive these technologies is essential for responsible and effective adoption. This study evaluates the use of the Educational AI Hub, an AI-powered learning framework, in undergraduate civil and environmental engineering courses at a large R1 public university. Using a mixed-methods approach that combines pre- and post-surveys, system usage logs, and qualitative analysis of the open-ended prompts and questions students posed to the AI chatbot, the research explores students' perceptions of trust, ethical concerns, usability, and learning outcomes. Findings reveal that students appreciated the AI assistant for its convenience and comfort, with nearly half reporting greater ease in using the AI tool compared to seeking help from instructors or teaching assistants. The tool was seen as most helpful for completing homework and understanding course concepts, though perceptions of its instructional quality were mixed. Ethical concerns emerged as a key barrier to full engagement: while most students viewed AI use as ethically acceptable, many expressed uncertainties about institutional policies and apprehension about potential academic misconduct. This study contributes to the growing body of research on AI in education by highlighting the importance of usability, policy clarity, and faculty guidance in fostering meaningful AI engagement. The findings suggest that while students are ready to embrace AI as a supplement to human instruction, thoughtful integration and transparent institutional frameworks are critical for ensuring student confidence, trust, and learning effectiveness.
Learning Design-Score Manifold to Guide Diffusion Models for Offline Optimization
2025-06-06
Tailin Zhou, Zhilin Chen, Wenlong Lyu, Zhitang Chen, Danny H. K. Tsang, Jun Zhang
Optimizing complex systems, from discovering therapeutic drugs to designing high-performance materials, remains a fundamental challenge across science and engineering, as the underlying rules are often unknown and costly to evaluate. Offline optimization aims to optimize designs for target scores using pre-collected datasets without system interaction. However, conventional approaches may fail beyond training data, predicting inaccurate scores and generating inferior designs. This paper introduces ManGO, a diffusion-based framework that learns the design-score manifold, capturing the design-score interdependencies holistically. Unlike existing methods that treat design and score spaces in isolation, ManGO unifies forward prediction and backward generation, attaining generalization beyond training data. Key to this is its derivative-free guidance for conditional generation, coupled with adaptive inference-time scaling that dynamically optimizes denoising paths. Extensive evaluations demonstrate that ManGO outperforms 24 single- and 10 multi-objective optimization methods across diverse domains, including synthetic tasks, robot control, material design, DNA sequence, and real-world engineering optimization.
TissUnet: Improved Extracranial Tissue and Cranium Segmentation for Children through Adulthood
2025-06-06
Markiian Mandzak, Elvira Yang, Anna Zapaishchykova, Yu-Hui Chen, Lucas Heilbroner, John Zielke, Divyanshu Tak, Reza Mojahed-Yazdi, Francesca Romana Mussa, Zezhong Ye, Sridhar Vajapeyam, Viviana Benitez, Ralph Salloum, Susan N. Chi, Houman Sotoudeh, Jakob Seidlitz, Sabine Mueller, Hugo J. W. L. Aerts, Tina Y. Poussaint, Benjamin H. Kann
Extracranial tissues visible on brain magnetic resonance imaging (MRI) may hold significant value for characterizing health conditions and clinical decision-making, yet they are rarely quantified. Current tools have not been widely validated, particularly in settings of developing brains or underlying pathology. We present TissUnet, a deep learning model that segments skull bone, subcutaneous fat, and muscle from routine three-dimensional T1-weighted MRI, with or without contrast enhancement. The model was trained on 155 paired MRI-computed tomography (CT) scans and validated across nine datasets covering a wide age range and including individuals with brain tumors. In comparison to AI-CT-derived labels from 37 MRI-CT pairs, TissUnet achieved a median Dice coefficient of 0.79 [IQR: 0.77-0.81] in a healthy adult cohort. In a second validation using expert manual annotations, median Dice was 0.83 [IQR: 0.83-0.84] in healthy individuals and 0.81 [IQR: 0.78-0.83] in tumor cases, outperforming previous state-of-the-art method. Acceptability testing resulted in an 89% acceptance rate after adjudication by a tie-breaker(N=108 MRIs), and TissUnet demonstrated excellent performance in the blinded comparative review (N=45 MRIs), including both healthy and tumor cases in pediatric populations. TissUnet enables fast, accurate, and reproducible segmentation of extracranial tissues, supporting large-scale studies on craniofacial morphology, treatment effects, and cardiometabolic risk using standard brain T1w MRI.
AI Simulation by Digital Twins: Systematic Survey, Reference Framework, and Mapping to a Standardized Architecture
2025-06-06
Xiaoran Liu, Istvan David
Insufficient data volume and quality are particularly pressing challenges in the adoption of modern subsymbolic AI. To alleviate these challenges, AI simulation uses virtual training environments in which AI agents can be safely and efficiently developed with simulated, synthetic data. Digital twins open new avenues in AI simulation, as these high-fidelity virtual replicas of physical systems are equipped with state-of-the-art simulators and the ability to further interact with the physical system for additional data collection. In this article, we report on our systematic survey of digital twin-enabled AI simulation. By analyzing 22 primary studies, we identify technological trends and derive a reference framework to situate digital twins and AI components. Based on our findings, we derive a reference framework and provide architectural guidelines by mapping it onto the ISO 23247 reference architecture for digital twins. Finally, we identify challenges and research opportunities for prospective researchers.
Recent progress in Language Models (LMs) has dramatically advanced the field of natural language processing (NLP), excelling at tasks like text generation, summarization, and question answering. However, their inference remains computationally expensive and energy intensive, especially in settings with limited hardware, power, or bandwidth. This makes it difficult to deploy LMs in mobile, edge, or cost sensitive environments. To address these challenges, recent approaches have introduced multi LLM intelligent model selection strategies that dynamically allocate computational resources based on query complexity -- using lightweight models for simpler queries and escalating to larger models only when necessary. This survey explores two complementary strategies for efficient LLM inference: (i) routing, which selects the most suitable model based on the query, and (ii) cascading or hierarchical inference (HI), which escalates queries through a sequence of models until a confident response is found. Both approaches aim to reduce computation by using lightweight models for simpler tasks while offloading only when needed. We provide a comparative analysis of these techniques across key performance metrics, discuss benchmarking efforts, and outline open challenges. Finally, we outline future research directions to enable faster response times, adaptive model selection based on task complexity, and scalable deployment across heterogeneous environments, making LLM based systems more efficient and accessible for real world applications.
This paper proposes AS-ASR, a lightweight aphasia-specific speech recognition framework based on Whisper-tiny, tailored for low-resource deployment on edge devices. Our approach introduces a hybrid training strategy that systematically combines standard and aphasic speech at varying ratios, enabling robust generalization, and a GPT-4-based reference enhancement method that refines noisy aphasic transcripts, improving supervision quality. We conduct extensive experiments across multiple data mixing configurations and evaluation settings. Results show that our fine-tuned model significantly outperforms the zero-shot baseline, reducing WER on aphasic speech by over 30% while preserving performance on standard speech. The proposed framework offers a scalable, efficient solution for real-world disordered speech recognition.
Large Language Models Can Be a Viable Substitute for Expert Political Surveys When a Shock Disrupts Traditional Measurement Approaches
2025-06-06
Patrick Y. Wu
After a disruptive event or shock, such as the Department of Government Efficiency (DOGE) federal layoffs of 2025, expert judgments are colored by knowledge of the outcome. This can make it difficult or impossible to reconstruct the pre-event perceptions needed to study the factors associated with the event. This position paper argues that large language models (LLMs), trained on vast amounts of digital media data, can be a viable substitute for expert political surveys when a shock disrupts traditional measurement. We analyze the DOGE layoffs as a specific case study for this position. We use pairwise comparison prompts with LLMs and derive ideology scores for federal executive agencies. These scores replicate pre-layoff expert measures and predict which agencies were targeted by DOGE. We also use this same approach and find that the perceptions of certain federal agencies as knowledge institutions predict which agencies were targeted by DOGE, even when controlling for ideology. This case study demonstrates that using LLMs allows us to rapidly and easily test the associated factors hypothesized behind the shock. More broadly, our case study of this recent event exemplifies how LLMs offer insights into the correlational factors of the shock when traditional measurement techniques fail. We conclude by proposing a two-part criterion for when researchers can turn to LLMs as a substitute for expert political surveys.
2025-06-06
Hans-Peter Lehmann, Thomas Mueller, Rasmus Pagh, Giulio Ermanno Pibiri, Peter Sanders, Sebastiano Vigna, Stefan Walzer
Given a set $S$ of $n$ keys, a perfect hash function for $S$ maps the keys in $S$ to the first $m \geq n$ integers without collisions. It may return an arbitrary result for any key not in $S$ and is called minimal if $m = n$. The most important parameters are its space consumption, construction time, and query time. Years of research now enable modern perfect hash functions to be extremely fast to query, very space-efficient, and scale to billions of keys. Different approaches give different trade-offs between these aspects. For example, the smallest constructions get within 0.1% of the space lower bound of $\log_2(e)$ bits per key. Others are particularly fast to query, requiring only one memory access. Perfect hashing has many applications, for example to avoid collision resolution in static hash tables, and is used in databases, bioinformatics, and stringology. Since the last comprehensive survey in 1997, significant progress has been made. This survey covers the latest developments and provides a starting point for getting familiar with the topic. Additionally, our extensive experimental evaluation can serve as a guide to select a perfect hash function for use in applications.
A Systematic Review of Poisoning Attacks Against Large Language Models
2025-06-06
Neil Fendley, Edward W. Staley, Joshua Carney, William Redman, Marie Chau, Nathan Drenkow
With the widespread availability of pretrained Large Language Models (LLMs) and their training datasets, concerns about the security risks associated with their usage has increased significantly. One of these security risks is the threat of LLM poisoning attacks where an attacker modifies some part of the LLM training process to cause the LLM to behave in a malicious way. As an emerging area of research, the current frameworks and terminology for LLM poisoning attacks are derived from earlier classification poisoning literature and are not fully equipped for generative LLM settings. We conduct a systematic review of published LLM poisoning attacks to clarify the security implications and address inconsistencies in terminology across the literature. We propose a comprehensive poisoning threat model applicable to categorize a wide range of LLM poisoning attacks. The poisoning threat model includes four poisoning attack specifications that define the logistics and manipulation strategies of an attack as well as six poisoning metrics used to measure key characteristics of an attack. Under our proposed framework, we organize our discussion of published LLM poisoning literature along four critical dimensions of LLM poisoning attacks: concept poisons, stealthy poisons, persistent poisons, and poisons for unique tasks, to better understand the current landscape of security risks.
Impact of redshift distribution uncertainties on Lyman-break galaxy cosmological parameter inference
2025-06-06
Francesco Petri, Boris Leistedt, Daniel J. Mortlock, Joel Leja, Stephen Thorp, Justin Alsing, Hiranya V. Peiris, Sinan Deger
A significant number of Lyman-break galaxies (LBGs) with redshifts 3 < z < 5 are expected to be observed by the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). This will enable us to probe the universe at higher redshifts than is currently possible with cosmological galaxy clustering and weak lensing surveys. However, accurate inference of cosmological parameters requires precise knowledge of the redshift distributions of selected galaxies, where the number of faint objects expected from LSST alone will make spectroscopic based methods of determining these distributions extremely challenging. To overcome this difficulty, it may be possible to leverage the information in the large volume of photometric data alone to precisely infer these distributions. This could be facilitated using forward models, where in this paper we use stellar population synthesis (SPS) to estimate uncertainties on LBG redshift distributions for a 10 year LSST (LSSTY10) survey. We characterise some of the modelling uncertainties inherent to SPS by introducing a flexible parameterisation of the galaxy population prior, informed by observations of the galaxy stellar mass function (GSMF) and cosmic star formation density (CSFRD). These uncertainties are subsequently marginalised over and propagated to cosmological constraints in a Fisher forecast. Assuming a known dust attenuation model for LBGs, we forecast constraints on the sigma8 parameter comparable to Planck cosmic microwave background (CMB) constraints.
Mapping the Spatial Distribution of Fast Radio Bursts within their Host Galaxies
2025-06-06
Alexa C. Gordon, Wen-fai Fong, Adam T. Deller, Lachlan Marnoch, Sungsoon Lim, Eric W. Peng, Keith W. Bannister, Apurba Bera, N. D. R. Bhat, Tyson Dial, Yuxin Dong, Tarraneh Eftekhari, Marcin Glowacki, Kelly Gourdji, Vivek Gupta, Joscha N. Jahns-Schindler, Akhil Jaini, Charles D. Kilpatrick, Chang Liu, J. Xavier Prochaska, Stuart D. Ryder, Ryan M. Shannon, Sunil Simha, Nicolas Tejos, Yuanming Wang, Ziteng Wang
We present deep optical and near-infrared observations of the host galaxies of 34 fast radio bursts (FRBs) detected by the Commensal Real-time ASKAP Fast Transient (CRAFT) survey on the Australian SKA Pathfinder (ASKAP) to compare the locations of FRBs relative to their host light distributions. Incorporating three additional FRBs from the literature, for a total of four repeating and 33 apparently non-repeating FRBs, we determine their projected galactocentric offsets and find a median of $ 4.2^{+5.7}_{-2.5}$ kpc ($1.0^{+1.5}_{-0.6}r_e$). We model their host surface brightness profiles and develop synthetic spatial distributions of their globular clusters based on host properties. We calculate the likelihood the observed location of each FRB is consistent with the smooth light of its host galaxy, residual (primarily spiral) substructure, or globular cluster distributions. The majority of FRBs favor locations within the disks of their galaxies, while only 7-13\% favor a globular cluster origin, primarily those with galactocentric offsets $\gtrsim3r_e$. At $z<0.15$, where spiral structure is apparent in 86\% of our sample of FRB hosts, we find $\approx 20-46\%$ of FRBs favor an association with spiral arms. Assuming FRBs derive from magnetars, our results support multiple formation channels with the majority of progenitors associated with massive stars and a minority formed through dynamical channels. However, the moderate fraction of FRBs associated with spiral structure indicates that high star formation efficiency of the youngest and most massive stars is not a predominant driver in the production of FRB progenitors.
Distinct origins of environmentally quenched galaxies in the core and outer virialised regions of massive clusters at $0.8
2025-06-06
Guillaume Hewitt, Florian Sarron, Michael L. Balogh, Gregory Rudnick, Yannick Bahé, Devontae C. Baxter, Gianluca Castignani, Pierluigi Cerulo, M. C. Cooper, Ricardo Demarco, Adit H. Edward, Rose A. Finn, Ben Forrest, Adam Muzzin, Julie Nantais, Benedetta Vulcani, Gillian Wilson, Dennis Zaritsky
High-redshift ($z\sim1$) galaxy clusters are the domain where environmental quenching mechanisms are expected to emerge as important factors in the evolution of the quiescent galaxy population. Uncovering these initially subtle effects requires exploring multiple dependencies of quenching across the cluster environment, and through time. We analyse the stellar-mass functions (SMFs) of 17 galaxy clusters within the GOGREEN and GCLASS surveys between $0.89.5$. The data are fit simultaneously with a Bayesian model that allows the Schechter function parameters of the quiescent and star-forming populations to vary smoothly with cluster-centric radius and redshift. The model also fits the radial galaxy number density profile of each population, allowing the global quenched fraction to be parameterised as a function of redshift and cluster velocity dispersion. We find the star-forming SMF to not depend on radius or redshift. For the quiescent population however, there is $\sim2\sigma$ evidence for a radial dependence. Outside the cluster core ($R>0.3\,R_{\rm200}$), the quenched fraction above $\log{(M/{\rm{M_\odot}})}=9.5$ is $\sim40{\rm\;per\,cent}$, and the quiescent SMF is similar in shape to the star-forming field. In contrast, the cluster core has an elevated quenched fraction ($\sim70{\rm\;per\,cent}$), and a quiescent SMF similar in shape to the quiescent field population. We explore contributions of 'early mass-quenching' and mass-independent 'environmental-quenching' models in each of these radial regimes. The core is well-described primarily by early mass-quenching, which we interpret as accelerated quenching of massive galaxies in protoclusters, possibly through merger-driven feedback mechanisms. The non-core is better described through mass-independent, environmental-quenching of the infalling field population.
Maximizing Ariel's Survey Leverage for Population-Level Studies of Exoplanets
2025-06-06
Nicolas B. Cowan, Ben Coull-Neveu
ESA's Ariel mission will be uniquely suited to performing population-level studies of exoplanets. Most of these studies consist of quantifying trends between an Ariel-measured quantity, y, and an a priori planetary property, x; for example, atmospheric metallicity as inferred from Ariel transit spectroscopy vs. planetary radius. We define the leverage of a survey with N targets as L = sqrt(N)stdev(x) and show that it quantitatively predicts the precision of population-level trends. The target selection challenge of Ariel can therefore be summarized as maximizing L along some axes of diversity for a given cumulative observing time. To this end, we consider different schemes to select the mission reference sample for a notional three year transit spectroscopy survey with Ariel. We divide the exoplanets in the mission candidate sample into logarithmic classes based on radius, equilibrium temperature, and host star temperature. We then construct a target list by cyclically choosing the easiest remaining target in each class. We find that in many cases the leverage is greatest for a single class: dividing planets into multiple classes increases the diversity of targets, but reduces their numbers. The leverage on a single axis of diversity can be increased by dividing that axis into many planet classes, but this sacrifices leverage along other axes of diversity. We conclude that a modest number of classes, possibly only one, should be defined when selecting Ariel targets. Lastly, we note that the statistical leverage of the Ariel transit survey would be significantly increased if current candidate planets were confirmed. This highlights the urgency of vetting and confirming the easiest transmission and emission spectroscopy targets in the Ariel mission candidate sample.
The Identification of Two JWST/NIRCam-Dark Starburst Galaxies at $z=6.6$ with ALMA
2025-06-06
Fengwu Sun, Jinyi Yang, Feige Wang, Daniel J. Eisenstein, Roberto Decarli, Xiaohui Fan, George H. Rieke, Eduardo Bañados, Sarah E. I. Bosman, Zheng Cai, Jaclyn B. Champagne, Luis Colina, Francesco D'Eugenio, Yoshinobu Fudamoto, Mingyu Li, Xiaojing Lin, Weizhe Liu, Jianwei Lyu, Chiara Mazzucchelli, Xiangyu Jin, Hyunsung D. Jun, Yunjing Wu, Huanian Zhang
We analyze two dusty star-forming galaxies at $z=6.6$. These galaxies are selected from the ASPIRE survey, a JWST Cycle-1 medium and ALMA Cycle-9 large program targeting 25 quasars and their environments at $z\simeq6.5 - 6.8$. These galaxies are identified as companions to UV-luminous quasars and robustly detected in ALMA continuum and [C II] emission, yet they are extraordinarily faint at the NIRCam wavelengths (down to $>28.0$ AB mag in the F356W band). They are more obscured than galaxies like Arp220, and thus we refer to them as "NIRCam-dark" starburst galaxies (star formation rate $\simeq 80 - 250\,\mathrm{M}_{\odot}\,\mathrm{yr}^{-1}$). Such galaxies are typically missed by (sub)-millimeter blank-field surveys. From the star-formation history (SFH), we show that the NIRCam-dark galaxies are viable progenitors of massive quiescent galaxies at $z\gtrsim4$ and descendants of UV-luminous galaxies at $z>10$. Although it is hard to constrain their number density from a quasar survey, we conclude that NIRCam-dark galaxies can be as abundant as $n\sim10^{-5.5}$ Mpc$^{-3}$ assuming a light halo occupation model. If true, this would equal to $\sim$30% of the number densities of both the quiescent galaxies at $z\gtrsim4$ and UV-luminous galaxies at $z>10$. We further predict that analogs at $z\sim8$ should exist according to the SFH of early massive quiescent galaxies. However, they may fall below the current detection limits of wide JWST and ALMA surveys, thus remaining "JWST-dark". To fully trace the evolution of massive galaxies and dust-obscured cosmic star formation at $z\gtrsim8$, wide-field JWST/NIRCam imaging and slitless spectroscopic surveys of early protoclusters are essential.
2025-06-06
Heloise F. Stevance, Jack Leland, Ken W. Smith
The Asteroid Terrestrial-impact Last Alert System (ATLAS) is an all-sky optical sky survey with a cadence of 24 to 48 hours and the ATLAS Transient Server processes the alert stream to enable the discovery and follow-up of extra-galactic transients. The data from the ATLAS server can be accessed through a REST API, which has allowed the development of bots that need direct access to the data to help rank alerts and trigger followup observations of promising targets. Here we present the python client we have developed for the ATLAS API to help connect bots and scientists to our data.
Generalizable, real-time neural decoding with hybrid state-space models
2025-06-05
Avery Hee-Woon Ryoo, Nanda H. Krishna, Ximeng Mao, Mehdi Azabou, Eva L. Dyer, Matthew G. Perich, Guillaume Lajoie
Real-time decoding of neural activity is central to neuroscience and neurotechnology applications, from closed-loop experiments to brain-computer interfaces, where models are subject to strict latency constraints. Traditional methods, including simple recurrent neural networks, are fast and lightweight but often struggle to generalize to unseen data. In contrast, recent Transformer-based approaches leverage large-scale pretraining for strong generalization performance, but typically have much larger computational requirements and are not always suitable for low-resource or real-time settings. To address these shortcomings, we present POSSM, a novel hybrid architecture that combines individual spike tokenization via a cross-attention module with a recurrent state-space model (SSM) backbone to enable (1) fast and causal online prediction on neural activity and (2) efficient generalization to new sessions, individuals, and tasks through multi-dataset pretraining. We evaluate POSSM's decoding performance and inference speed on intracortical decoding of monkey motor tasks, and show that it extends to clinical applications, namely handwriting and speech decoding in human subjects. Notably, we demonstrate that pretraining on monkey motor-cortical recordings improves decoding performance on the human handwriting task, highlighting the exciting potential for cross-species transfer. In all of these tasks, we find that POSSM achieves decoding accuracy comparable to state-of-the-art Transformers, at a fraction of the inference cost (up to 9x faster on GPU). These results suggest that hybrid SSMs are a promising approach to bridging the gap between accuracy, inference speed, and generalization when training neural decoders for real-time, closed-loop applications.
Cryogenic Optical Lattice Clock with $1.7\times 10^{-20}$ Blackbody Radiation Stark Uncertainty
2025-06-05
Youssef S. Hassan, Kyle Beloy, Jacob L. Siegel, Takumi Kobayashi, Eric Swiler, Tanner Grogan, Roger C. Brown, Tristan Rojo, Tobias Bothwell, Benjamin D. Hunt, Adam Halaoui, Andrew D. Ludlow
Controlling the Stark perturbation from ambient thermal radiation is key to advancing the performance of many atomic frequency standards, including state-of-the-art optical lattice clocks (OLCs). We demonstrate a cryogenic OLC that utilizes a dynamically actuated radiation shield to control the perturbation at $1.7\times10^{-20}$ fractional frequency, a factor of $\sim$40 beyond the best OLC to date. Our shield furnishes the atoms with a near-ideal cryogenic blackbody radiation (BBR) environment by rejecting external thermal radiation at the part-per-million level during clock spectroscopy, overcoming a key limitation with previous cryogenic BBR control solutions in OLCs. While the lowest BBR shift uncertainty is realized with cryogenic operation, we further exploit the radiation control that the shield offers over a wide range of temperatures to directly measure and verify the leading BBR Stark dynamic correction coefficient for ytterbium. This independent measurement reduces the literature-combined uncertainty of this coefficient by 30%, thus benefiting state-of-the-art Yb OLCs operated at room temperature. We verify the static BBR coefficient for Yb at the low $10^{-18}$ level.
These notes are based on a sequence of 4 lectures delivered at the 2024 Theoretical Advanced Study Institute (TASI) and at the Universit\`a degli Studi di Padova. They are intended for graduate students at the early stages of their study of dark matter with some prior exposure to cosmology and quantum field theory. The primary aim is to offer an accessible introduction to dark matter and to lay the groundwork for exploring its phenomenology. These lectures are not intended to serve as a comprehensive review. We begin by motivating the study of dark matter through a discussion of the empirical evidence and the constraints it places on dark matter properties. This is followed by an overview of several canonical mechanisms for the production of cosmological dark matter, and a concluding section that surveys current experimental and observational efforts to detect it with a focus on direct detection.
The Hourglass Simulation: A Catalog for the Roman High-Latitude Time-Domain Core Community Survey
2025-06-05
B. M. Rose, M. Vincenzi, R. Hounsell, H. Qu, L. Aldoroty, D. Scolnic, R. Kessler, P. Macias, D. Brout, M. Acevedo, R. C. Chen, S. Gomez, E. Peterson, D. Rubin, M. Sako
We present a simulation of the time-domain catalog for the Nancy Grace Roman Space Telescope's High-Latitude Time-Domain Core Community Survey. This simulation, called the Hourglass simulation, uses the most up-to-date spectral energy distribution models and rate measurements for ten extra-galactic time-domain sources. We simulate these models through the design reference Roman Space Telescope survey: four filters per tier, a five day cadence, over two years, a wide tier of 19 deg$^2$ and a deep tier of 4.2 deg$^2$, with $\sim$20% of those areas also covered with prism observations. We find that a science-independent Roman time-domain catalog, assuming a S/N at max of >5, would have approximately 21,000 Type Ia supernovae, 40,000 core-collapse supernovae, around 70 superluminous supernovae, $\sim$35 tidal disruption events, 3 kilonovae, and possibly pair-instability supernovae. In total, Hourglass has over 64,000 transient objects, 11 million photometric observations, and 500,000 spectra. Additionally, Hourglass is a useful data set to train machine learning classification algorithms. We show that SCONE is able to photometrically classify Type Ia supernovae with high precision ($\sim$95%) to a z > 2. Finally, we present the first realistic simulations of non-Type Ia supernovae spectral-time series data from Roman's prism.
Search for binarity in Asymptotic Giant Branch stars utilizing the future Chinese Space Station Telescope (CSST)
2025-06-05
Zhi-Meng Li, Yong Zhang
Binary systems in the Asymptotic Giant Branch (AGB) phase are widely recognized as a leading theoretical framework underpinning the observed asymmetric morphologies of planetary nebulae. However, the detection of binary companions in AGB systems is severely hampered by the overwhelming brightness and variability of the evolved primary star, which dominate the photo-metric and spectroscopic signatures. Ultraviolet (UV) excess emission has been proposed as a candidate diagnostic for the presence of binary companions in AGB systems. This paper evaluates the Chinese Space Station Telescope's (CSST) ability to detect UV excess emission in AGB stars, leveraging its unprecedented UV sensitivity and wide-field survey capabilities. We employed synthetic spectral libraries of M0-M8 type giants for primary stars and the ATLAS 9 atmospheric model grid for companion stars spanning a temperature range of 6500 K to 12000 K. By convolving these model spectra with the CSST multi-band filter system, we computed color-color diagrams (g-y versus NUV-u) to construct a diagnostic grid. This grid incorporates interstellar extinction corrections and establishes a framework for identifying AGB binary candidates through direct comparison between observed photometry and theoretical predictions. Furthermore, we discuss the physical origins of UV excess in AGB stars. This study pioneers a diagnostic framework leveraging CSST's unique multi-band UV-visible synergy to construct color-color grids for binary candidate identification, overcoming limitations of non-simultaneous multi-instrument observations.
Learning Intrinsic Alignments from Local Galaxy Environments
2025-06-05
Matthew Craigie, Eric Huff, Yuan-Sen Ting, Rossana Ruggeri, Tamara M. Davis
We present DELTA (Data-Empiric Learned Tidal Alignments), a deep learning model that isolates galaxy intrinsic alignments (IAs) from weak lensing distortions using only observational data. The model uses an Equivariant Graph Neural Network backbone suitable for capturing information from the local galaxy environment, in conjunction with a probabilistic orientation output. Unlike parametric models, DELTA flexibly learns the relationship between galaxy shapes and their local environments, without assuming an explicit IA form or relying on simulations. When applied to mock catalogs with realistic noisy IAs injected, it accurately reconstructs the noise-free, pure IA signal. Mapping these alignments provides a direct visualization of IA patterns in the mock catalogs. Combining DELTA with deep learning interpretation techniques provides further insights into the physics driving tidal relationships between galaxies. This new approach to understanding and controlling IAs is suitable for application to joint photometric and spectroscopic surveys such as the combination of upcoming Euclid, Rubin, and DESI datasets.
Survey on the Evaluation of Generative Models in Music
2025-06-05
Alexander Lerch, Claire Arthur, Nick Bryan-Kinns, Corey Ford, Qianyi Sun, Ashvala Vinay
Research on generative systems in music has seen considerable attention and growth in recent years. A variety of attempts have been made to systematically evaluate such systems. We provide an interdisciplinary review of the common evaluation targets, methodologies, and metrics for the evaluation of both system output and model usability, covering subjective and objective approaches, qualitative and quantitative approaches, as well as empirical and computational methods. We discuss the advantages and challenges of such approaches from a musicological, an engineering, and an HCI perspective.
Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform
2025-06-05
Matthew Craigie, Yuan-Sen Ting, Rossana Ruggeri, Tamara M. Davis
We present a cosmology analysis of weak lensing convergence maps using the Neural Field Scattering Transform (NFST) to constrain cosmological parameters. The NFST extends the Wavelet Scattering Transform (WST) by incorporating trainable neural field filters while preserving rotational and translational symmetries. This setup balances flexibility with robustness, ideal for learning in limited training data regimes. We apply the NFST to 500 simulations from the CosmoGrid suite, each providing a total of 1000 square degrees of noiseless weak lensing convergence maps. We use the resulting learned field compression to model the posterior over $\Omega_m$, $\sigma_8$, and $w$ in a $w$CDM cosmology. The NFST consistently outperforms the WST benchmark, achieving a 16% increase in the average posterior probability density assigned to test data. Further, the NFST improves direct parameter prediction precision on $\sigma_8$ by 6% and w by 11%. We also introduce a new visualization technique to interpret the learned filters in physical space and show that the NFST adapts its feature extraction to capture task-specific information. These results establish the NFST as a promising tool for extracting maximal cosmological information from the non-Gaussian information in upcoming large-scale structure surveys, without requiring large simulated training datasets.
A Survey on Vietnamese Document Analysis and Recognition: Challenges and Future Directions
2025-06-05
Anh Le, Thanh Lam, Dung Nguyen
Vietnamese document analysis and recognition (DAR) is a crucial field with applications in digitization, information retrieval, and automation. Despite advancements in OCR and NLP, Vietnamese text recognition faces unique challenges due to its complex diacritics, tonal variations, and lack of large-scale annotated datasets. Traditional OCR methods often struggle with real-world document variations, while deep learning approaches have shown promise but remain limited by data scarcity and generalization issues. Recently, large language models (LLMs) and vision-language models have demonstrated remarkable improvements in text recognition and document understanding, offering a new direction for Vietnamese DAR. However, challenges such as domain adaptation, multimodal learning, and computational efficiency persist. This survey provide a comprehensive review of existing techniques in Vietnamese document recognition, highlights key limitations, and explores how LLMs can revolutionize the field. We discuss future research directions, including dataset development, model optimization, and the integration of multimodal approaches for improved document intelligence. By addressing these gaps, we aim to foster advancements in Vietnamese DAR and encourage community-driven solutions.
Mapping Parton Distributions of Hadrons with Lattice QCD
2025-06-05
Huey-Wen Lin
The strong force which binds hadrons is described by the theory of quantum chromodynamics (QCD). Determining the character and manifestations of QCD is one of the most important and challenging outstanding issues necessary for a comprehensive understanding of the structure of hadrons. Within the context of the QCD parton picture, the parton distribution functions (PDFs) have been remarkably successful in describing a wide variety of processes. However, these PDFs have generally been confined to the description of collinear partons within the hadron. New experiments and facilities provide the opportunity to additionally explore the three-dimensional structure of hadrons, which can be described by generalized parton distributions (GPDs), for example. In recent years, a breakthrough was made in calculating the Bjorken-$x$ dependence of PDFs in lattice QCD by using large-momentum effective theory (LaMET) and other similar frameworks. The breakthrough has led to the emergence and rapid development of direct calculations of Bjorken-$x$--dependent structure. In this review article, we show some of the recent progress made in lattice QCD in PDFs and GPDs and discuss future challenges.
Low noise flux estimate and data quality control monitoring in EUCLID-NISP cosmological survey
2025-06-05
B. Kubik, R. Barbier, P. Calabria, A. Castera, E. Chabanat, F. Charlieu, J-C. Clemens, A. Ealet, S. Ferriol, W. Gillard, T. Maciaszek, E. Prieto, F. Schirra, A. Secroun, B. Serra, G. Smadja, A. Tilquin, J. Zoubianb
Euclid mission is designed to understand the dark sector of the universe. Precise redshift measurements are provided by H2RG detectors. We propose an unbiased method of fitting the flux with Poisson distributed and correlated data, which has an analytic solution and provides a reliable quality factor - fundamental features to ensure the goals of the mission. We compare our method to other techniques of signal estimation and illustrate the anomaly detection on the flight like detectors. Although our discussion is focused on Euclid NISP instrument, much of what is discussed will be of interest to any mission using similar near-infrared sensors
Pure time series forecasting tasks typically focus exclusively on numerical features; however, real-world financial decision-making demands the comparison and analysis of heterogeneous sources of information. Recent advances in deep learning and large scale language models (LLMs) have made significant strides in capturing sentiment and other qualitative signals, thereby enhancing the accuracy of financial time series predictions. Despite these advances, most existing datasets consist solely of price series and news text, are confined to a single market, and remain limited in scale. In this paper, we introduce FinMultiTime, the first large scale, multimodal financial time series dataset. FinMultiTime temporally aligns four distinct modalities financial news, structured financial tables, K-line technical charts, and stock price time series across both the S&P 500 and HS 300 universes. Covering 5,105 stocks from 2009 to 2025 in the United States and China, the dataset totals 112.6 GB and provides minute-level, daily, and quarterly resolutions, thus capturing short, medium, and long term market signals with high fidelity. Our experiments demonstrate that (1) scale and data quality markedly boost prediction accuracy; (2) multimodal fusion yields moderate gains in Transformer models; and (3) a fully reproducible pipeline enables seamless dataset updates.
From Screen to Space: Evaluating Siemens' Cinematic Reality
2025-06-05
Gijs Luijten, Lisle Faray de Paiva, Sebastian Krueger, Alexander Brost, Laura Mazilescu, Ana Sofia Ferreira Santos, Peter Hoyer, Jens Kleesiek, Sophia Marie-Therese Schmitz, Ulf Peter Neumann, Jan Egger
As one of the first research teams with full access to Siemens' Cinematic Reality, we evaluate its usability and clinical potential for cinematic volume rendering on the Apple Vision Pro. We visualized venous-phase liver computed tomography and magnetic resonance cholangiopancreatography scans from the CHAOS and MRCP\_DLRecon datasets. Fourteen medical experts assessed usability and anticipated clinical integration potential using the System Usability Scale, ISONORM 9242-110-S questionnaire, and an open-ended survey. Their feedback identified feasibility, key usability strengths, and required features to catalyze the adaptation in real-world clinical workflows. The findings provide insights into the potential of immersive cinematic rendering in medical imaging.
On Pioneering Works of Albert Shiryaev on Markov Decision Processes and Some Later Developments
2025-06-05
Eugene A. Feinberg
This article is dedicated to three fundamental papers on Markov Decision Processes and on control with incomplete observations published by Albert Shiryaev approximately sixty years ago. One of these papers was coauthored with O.V. Viskov. We discuss some of the results and some of many rich ideas presented in these papers and survey some later developments. At the end we mention some recent studies of Albert Shiryaev on Kolmogorov's equations for jump Markov processes and on control of continuous-time jump Markov processes.
A distributed system perspective on Backscatter systems: A review
2025-06-05
Tonghuan Xiao, Jiecheng Zhou
This review investigates the pivotal role of distributed architectures and intelligent resource allocation in enabling robust and scalable wireless systems, with a particular emphasis on backscatter communication, indoor localization, battery-free networks, and Simultaneous Wireless Information and Power Transfer (SWIPT).
Online reviews have become an integral aspect of consumer decision-making on e-commerce websites, especially in the restaurant industry. Unlike sighted users who can visually skim through the reviews, perusing reviews remains challenging for blind users, who rely on screen reader assistive technology that supports predominantly one-dimensional narration of content via keyboard shortcuts. In an interview study, we uncovered numerous pain points of blind screen reader users with online restaurant reviews, notably, the listening fatigue and frustration after going through only the first few reviews. To address these issues, we developed QuickQue assistive tool that performs aspect-focused sentiment-driven summarization to reorganize the information in the reviews into an alternative, thematically-organized presentation that is conveniently perusable with a screen reader. At its core, QuickQue utilizes a large language model to perform aspect-based joint classification for grouping reviews, followed by focused summarizations within the groups to generate concise representations of reviewers' opinions, which are then presented to the screen reader users via an accessible interface. Evaluation of QuickQue in a user study with 10 participants showed significant improvements in overall usability and task workload compared to the status quo screen reader.
Oversight Structures for Agentic AI in Public-Sector Organizations
2025-06-05
Chris Schmitz, Jonathan Rystrøm, Jan Batzner
This paper finds that the introduction of agentic AI systems intensifies existing challenges to traditional public sector oversight mechanisms -- which rely on siloed compliance units and episodic approvals rather than continuous, integrated supervision. We identify five governance dimensions essential for responsible agent deployment: cross-departmental implementation, comprehensive evaluation, enhanced security protocols, operational visibility, and systematic auditing. We evaluate the capacity of existing oversight structures to meet these challenges, via a mixed-methods approach consisting of a literature review and interviews with civil servants in AI-related roles. We find that agent oversight poses intensified versions of three existing governance challenges: continuous oversight, deeper integration of governance and operational capabilities, and interdepartmental coordination. We propose approaches that both adapt institutional structures and design agent oversight compatible with public sector constraints.
Distributed system perspective on Backscatter systems
2025-06-05
Jincheng Guan, Jun Zhang
Backscatter system is a system based on backscatter communication technology, which is a low cost, low power consumption and easy to deploy communication technology. At present, the backscatter technology is mainly applied to RFID tags and the Internet of Things and other fields. With the rapid development of the Internet of Things, the application of backscatter systems is increasing. Moreover, the backscatter system is essentially a distributed system, but existing research rarely conducts studies and analyses from a distributed perspective. This paper conducts a study on the backscattering system from the perspective of distributed systems, comprehensively reviewing the basic principles of the backscattering system, and analyzing the distributed system architectures of different backscattering systems. Then, it introduces the application scenarios, research status and challenges of the backscattering system, and finally discusses the future research directions of the backscattering system, hoping to provide references for future research.
We investigate the origin of NGC 5634 through a comprehensive analysis of its morphology, kinematics and dynamics. Utilizing data from the DESI Legacy Survey, we refined its fundamental parameters (age t = 12.8 +/- 0.3 Gyr, metallicity [Fe/H] = -1.8 +/- 0.1 dex, distance modulus dm = 17.0 +/- 0.1 mag) and constructed matched-filter template based on the combination of these parameters to search for extra-tidal structures. However, no significant features were detected above a 3 sigma signal-to-noise threshold, which limits our ability to further investigate the association between NGC 5634 and the Sagittarius (Sgr) stream based on morphological evidence. Incorporating GAIA data, we further examine the orbital path of NGC 5634. We found that its orbit only briefly intersects with the Sgr stream and diverges significantly over long-term integrations. This behavior contrasts with that of confirmed Sgr-associated clusters, whose orbits remain closely aligned with the stream throughout their orbital evolution. Additionally, NGC 5634 exhibits a relatively shorter semi-major axis and smaller apocenter and pericenter distances compared to Sgr clusters. These orbital characteristics are more consistent with clusters associated with the Gaia-Sausage-Enceladus (GSE) or the Helmi streams. From a dynamical perspective, in the Lz-E space, NGC 5634 is also distinctly different from Sgr clusters and aligns more closely with the GSE and Helmi regions. Taken together, these findings do not support a strong connection between NGC 5634 and the Sgr dSph, but instead suggest a potential association with another progenitor system, such as GSE or Helmi stream. Nevertheless, further evidence is needed to definitively establish its origin.
The Impact of Cluster Mergers on Galaxy Properties
2025-06-05
Oğuzhan Çakır, Matt S. Owers, Lucas C. Kimmig, Paul Nulsen, Mina Pak, Gabriella Quattropani, Warrick J. Couch
Being the most energetic events in the known universe, the impact of galaxy cluster mergers on the properties of the resident galaxies has yet to be well-understood. In this paper, we investigate the effects of merging environments on star formation in nearby clusters ($0.04
Towards LLM-Centric Multimodal Fusion: A Survey on Integration Strategies and Techniques
2025-06-05
Jisu An, Junseok Lee, Jeoungeun Lee, Yongseok Son
The rapid progress of Multimodal Large Language Models(MLLMs) has transformed the AI landscape. These models combine pre-trained LLMs with various modality encoders. This integration requires a systematic understanding of how different modalities connect to the language backbone. Our survey presents an LLM-centric analysis of current approaches. We examine methods for transforming and aligning diverse modal inputs into the language embedding space. This addresses a significant gap in existing literature. We propose a classification framework for MLLMs based on three key dimensions. First, we examine architectural strategies for modality integration. This includes both the specific integration mechanisms and the fusion level. Second, we categorize representation learning techniques as either joint or coordinate representations. Third, we analyze training paradigms, including training strategies and objective functions. By examining 125 MLLMs developed between 2021 and 2025, we identify emerging patterns in the field. Our taxonomy provides researchers with a structured overview of current integration techniques. These insights aim to guide the development of more robust multimodal integration strategies for future models built on pre-trained foundations.
Integrated photonics for continuous-variable quantum optics
2025-06-05
R. N. Clark, B. Puzio, O. M. Green, S. T. Pradyumna, O. Trojak, A. Politi, J. C. F. Matthews
Quantum technologies promise profound advances in communication security, sensing and computing. The underpinning hardware must be engineered to generate, manipulate and detect quantum phenomena with exceptional performance, whilst being mass-manufacturable for real-world applications. A leading approach is chip-scale quantum photonics. The continuous-variable regime for quantum optics has been exploited in a number of technologies, including the detection of gravitational waves, by operating below the standard quantum limit of the light's shot noise. The availability of room-temperature, deterministic sources and high efficiency detectors suitable for continuous-variable state generation and measurement is a compelling motivation for this particular paradigm. This review focusses on efforts to integrate sources and detectors of continuous-variable light states into chip-scale photonic integrated circuits.
Impact of border defects on the magnetic flux penetration in superconducting films
2025-06-05
Alejandro V. Silhanek, Lu Jiang, Cun Xue, Benoît Vanderheyden
Defects in superconducting systems are ubiquitous and nearly unavoidable. They can vary in nature, geometry, and size, ranging from microscopic-size defects such as dislocations, grain boundaries, twin planes, and oxygen vacancies, to macroscopic-size defects such as segregations, indentations, contamination, cracks, or voids. Irrespective of their type, defects perturb the otherwise laminar flow of electric current, forcing it to deviate from its path. In the best-case scenario, the associated perturbation can be damped within a distance of the order of the size of the defect if the rigidity of the superconducting state, characterized by the creep exponent $n$, is low. In most cases, however, this perturbation spans macroscopic distances covering the entire superconducting sample and thus dramatically influences the response of the system. In this work, we review the current state of theoretical understanding and experimental evidence on the modification of magnetic flux patterns in superconductors by border defects, including the influence of their geometry, temperature, and applied magnetic field. We scrutinize and contrast the picture emerging from a continuous media standpoint, i.e. ignoring the granularity imposed by the vortex quantization, with that provided by a phenomenological approach dictated by the vortex dynamics. In addition, we discuss the influence of border indentations on the nucleation of thermomagnetic instabilities. Assessing the impact of surface and border defects is of utmost importance for all superconducting technologies, including superconducting resonators, superconducting single-photon detectors, superconducting radio-frequency cavities and accelerators, superconducting cables, superconducting metamaterials, superconducting diodes, and many others.
The Centre of Attention: a Powerful Radio Galaxy Pinpoints a NIR-Dark Protocluster at z~3.9
2025-06-05
A. J. Hedge, N. Seymour, J. W. Broderick, A. Gupta, J. Afonso, L. Ighina, M. Lehnert, G. Noirot, S. Shabala, D. Stern, R. J. Turner
We report the discovery of a $z\sim3.9$ protocluster identified from Atacama Large Millimetre/sub-millimetre Array Band 3 spectral scans of a bright radio source selected from the GaLactic and Extra-galactic All-sky Murchison Widefield Array (GLEAM) survey. Extended CO(4-3) and [CI](1-0) line emission was detected in GLEAM J005332$-$325630 confirming it to be a $z=3.879$ powerful radio galaxy with luminosity, $L_{500 MHz}=1.3\times10^{28}$ ${W Hz}^{-1}$. This source is part of a sample of candidate high redshift radio galaxies with bright radio fluxes, $S_{150MHz}>0.1$ Jy, but host galaxies with $K_s({AB})\gtrsim23$ mag. The molecular gas associated with the radio galaxy host has two kinematically separate components, likely in-falling and indicative of a recent interaction or merger with another galaxy. One 100-GHz continuum source $\sim120$ pkpc away is found to have both CO(4-3) and [CI](1-0) emission lines and a further five protocluster members are identified from CO(4-3) emission alone, all at similar redshift ($\Delta v<700$ km s$^{-1}$) and within a radius of $1.1^{\prime}$. Using photometry from the High Acuity Widefield K-band Imager $K_s$-band and the Dark Energy Survey $g, r, i, z$ and $Y$ bands, we find this protocluster harbours a rare, optically-dark, very massive $M_*\sim10^{12}$ ${M}_\odot$ galaxy. Comparisons with the TNG300 cosmological simulation puts this galaxy in a dark matter halo of $M_{DM}\sim3\times10^{13}$ ${M}_\odot$ which will evolve into a Coma-like DM halo ($M_{DM}\sim10^{15}$ ${M}_\odot$) by the present day.
2025-06-05
Saurabh Pandey, Hector Mas, Georgios Vasilakis, Wolf von Klitzing
Matterwaves made up of ultra-cold quantum-degenerate atoms have enabled the creation of tools having unprecedented sensitivity and precision in measuring gravity, rotation or magnetic fields. Applications range from gravitational wave detection and tests of Einstein's equivalence principle to inertial sensing for navigation and gravitational gradient sensing for oil and mineral exploration. In this letter, we introduce atom-optics as a novel tool of manipulating matterwaves in ring-shaped coherent waveguides. We collimate and focus matterwaves derived from Bose-Einstein Condensates (BECs) and ultra-cold thermal atoms in ring-shaped time-averaged adiabatic potentials. We demonstrate `delta-kick cooling' of BECs, reducing their expansion energies by a factor of 34. The atomtronic waveguide ring has a radius of only $485\,\mu m$, compared to other state-of-the-art experiments requiring zero gravity or chambers of ten meter. This level of control with extremely reduced spatial requirements is an important step towards atomtronic quantum sensors.
Exoskeletons are widely used in rehabilitation and industrial applications to assist human motion. However, direct human testing poses risks due to possible exoskeleton malfunctions and inconsistent movement replication. To provide a safer and more repeatable testing environment, this study employs a bipedal robot platform to reproduce human gait, allowing for controlled exoskeleton evaluations. A control strategy based on the State-Dependent Riccati Equation (SDRE) is formulated to achieve optimal torque control for accurate gait replication. The bipedal robot dynamics are represented using double pendulum model, where SDRE-optimized control inputs minimize deviations from human motion trajectories. To align with motor behavior constraints, a parameterized control method is introduced to simplify the control process while effectively replicating human gait. The proposed approach initially adopts a ramping trapezoidal velocity model, which is then adapted into a piecewise linear velocity-time representation through motor command overwriting. This modification enables finer control over gait phase transitions while ensuring compatibility with motor dynamics. The corresponding cost function optimizes the control parameters to minimize errors in joint angles, velocities, and torques relative to SDRE control result. By structuring velocity transitions in accordance with motor limitations, the method reduce the computational load associated with real-time control. Experimental results verify the feasibility of the proposed parameterized control method in reproducing human gait. The bipedal robot platform provides a reliable and repeatable testing mechanism for knee-type exoskeletons, offering insights into exoskeleton performance under controlled conditions.
Galaxy clustering is an important probe in the upcoming China Space Station Telescope (CSST) survey to understand the structure growth and reveal the nature of the dark sector. However, it is a long-term challenge to model this biased tracer and connect the observable to the underlying physics. In this work, we present a hybrid Lagrangian bias expansion emulator, combining the Lagrangian bias expansion and the accurate dynamical evolution from $N$-body simulation, to predict the power spectrum of the biased tracer in real space. We employ the Kun simulation suite to construct the emulator, emulating across the space of 8 cosmological parameters including dynamic dark energy $w_0$, $w_a$, and total neutrino mass $\sum m_{\nu}$. The sample variance due to the finite simulation box is further reduced using the Zel'dovich variance control, and it enables the precise measurement of the Lagrangian basis spectra up to quadratic order. The emulation of basis spectra realizes 1% level accuracy, covering wavelength $ k \leq 1 \,{\rm Mpc}^{-1}h$ and redshift $0\leq z\leq 3$ up to quadratic order field. To validate the emulator, we perform the joint fitting of the halo auto power spectrum and the halo-matter cross power spectrum from 46 independent simulations. Depending on the choice of counterpart, the joint fitting is unbiased up to $k_{\rm max}\simeq 0.7\,{\rm Mpc}^{-1}h$ with $1\sim 2$ percent accuracy, for all the redshift and halo mass samples. As one of the CSST cosmological emulator series, our emulator is expected to provide accurate theoretical predictions of the galaxy power spectrum for the upcoming CSST survey.
Neural Network Reprogrammability: A Unified Theme on Model Reprogramming, Prompt Tuning, and Prompt Instruction
2025-06-05
Zesheng Ye, Chengyi Cai, Ruijiang Dong, Jianzhong Qi, Lei Feng, Pin-Yu Chen, Feng Liu
As large-scale pre-trained foundation models continue to expand in size and capability, efficiently adapting them to specific downstream tasks has become increasingly critical. Despite substantial progress, existing adaptation approaches have evolved largely in isolation, without a clear understanding of their interrelationships. This survey introduces neural network reprogrammability as a unifying framework that bridges mainstream model adaptation techniques--model reprogramming, prompt tuning, and prompt instruction--previously fragmented research areas yet converges on a shared principle: repurposing a pre-trained model by manipulating information at the interfaces while keeping the model parameters frozen. These methods exploit neural networks' sensitivity to manipulation on different interfaces, be it through perturbing inputs, inserting tokens into intermediate layers, or providing task-specific examples in context, to redirect model behaviors towards desired outcomes. We then present a taxonomy that categorizes such information manipulation-based adaptation approaches across four key dimensions: manipulation format (fixed or learnable), location (interfaces where manipulations occur), operator (how they are applied), and output alignment requirement (post-processing needed to align outputs with downstream tasks). Notably, this framework applies consistently across data modalities, independent of specific model architectures. Moreover, viewing established techniques like in-context learning and chain-of-thought prompting through this lens reveals both their theoretical connections and practical distinctions. We further analyze remaining technical challenges and ethical considerations, positioning neural network reprogrammability as a fundamental paradigm for efficient model adaptation. We lastly identify promising research directions emerging from this integrative viewpoint.
Image matching, which establishes correspondences between two-view images to recover 3D structure and camera geometry, serves as a cornerstone in computer vision and underpins a wide range of applications, including visual localization, 3D reconstruction, and simultaneous localization and mapping (SLAM). Traditional pipelines composed of ``detector-descriptor, feature matcher, outlier filter, and geometric estimator'' falter in challenging scenarios. Recent deep-learning advances have significantly boosted both robustness and accuracy. This survey adopts a unique perspective by comprehensively reviewing how deep learning has incrementally transformed the classical image matching pipeline. Our taxonomy highly aligns with the traditional pipeline in two key aspects: i) the replacement of individual steps in the traditional pipeline with learnable alternatives, including learnable detector-descriptor, outlier filter, and geometric estimator; and ii) the merging of multiple steps into end-to-end learnable modules, encompassing middle-end sparse matcher, end-to-end semi-dense/dense matcher, and pose regressor. We first examine the design principles, advantages, and limitations of both aspects, and then benchmark representative methods on relative pose recovery, homography estimation, and visual localization tasks. Finally, we discuss open challenges and outline promising directions for future research. By systematically categorizing and evaluating deep learning-driven strategies, this survey offers a clear overview of the evolving image matching landscape and highlights key avenues for further innovation.
Test-Time Scaling (TTS) improves the reasoning performance of Large Language Models (LLMs) by allocating additional compute during inference. We conduct a structured survey of TTS methods and categorize them into sampling-based, search-based, and trajectory optimization strategies. We observe that reasoning-optimized models often produce less diverse outputs, which limits TTS effectiveness. To address this, we propose ADAPT (A Diversity Aware Prefix fine-Tuning), a lightweight method that applies prefix tuning with a diversity-focused data strategy. Experiments on mathematical reasoning tasks show that ADAPT reaches 80% accuracy using eight times less compute than strong baselines. Our findings highlight the essential role of generative diversity in maximizing TTS effectiveness.
Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets
2025-06-05
Marianna Nezhurina, Tomer Porian, Giovanni Pucceti, Tommie Kerssies, Romain Beaumont, Mehdi Cherti, Jenia Jitsev
In studies of transferable learning, scaling laws are obtained for various important foundation models to predict their properties and performance at larger scales. We show here how scaling law derivation can also be used for model and dataset comparison, allowing to decide which procedure is to be preferred for pre-training. For the first time, full scaling laws based on dense measurements across a wide span of model and samples seen scales are derived for two important language-vision learning procedures, CLIP and MaMMUT, that use either contrastive only or contrastive and captioning text generative loss. Ensuring sufficient prediction accuracy for held out points, we use derived scaling laws to compare both models, obtaining evidence for MaMMUT's stronger improvement with scale and better sample efficiency than standard CLIP. To strengthen validity of the comparison, we show scaling laws for various downstream tasks, classification, retrieval, and segmentation, and for different open datasets, DataComp, DFN and Re-LAION, observing consistently the same trends. We show that comparison can also be performed when deriving scaling laws with a constant learning rate schedule, reducing compute cost. Accurate derivation of scaling laws provides thus means to perform model and dataset comparison across scale spans, avoiding misleading conclusions based on measurements from single reference scales only, paving the road for systematic comparison and improvement of open foundation models and datasets for their creation. We release all the pre-trained models with their intermediate checkpoints, including openMaMMUT-L/14, which achieves $80.3\%$ zero-shot ImageNet-1k accuracy, trained on 12.8B samples from DataComp-1.4B. Code for reproducing experiments in the paper and raw experiments data can be found at https://github.com/LAION-AI/scaling-laws-for-comparison.
The advent of gravitational wave astronomy (GW) has revolutionized the observation of cataclysmic cosmic events, such as black hole mergers and neutron star collisions. The Laser Interferometer Gravitational-Wave Observatory (LIGO) has been at the forefront of these discoveries. However, the immense volume and complexity of gravitational wave data present significant challenges for traditional analysis methods. This paper investigates the growing synergy between artificial intelligence (AI) and GW science, emphasizing how AI enhances signal detection, noise reduction, and data interpretation. It begins with an overview of GW fundamentals and the role of machine learning in increasing detector sensitivity. Notable GW events observed by LIGO are discussed alongside persistent analytical challenges such as data quality, generalization, and computational constraints. A comprehensive performance review of AI techniques, including supervised learning, unsupervised learning, deep learning, and reinforcement learning, is presented based on data spanning 2021 to 2024. Evaluation metrics include accuracy, precision, true positive rate (TPR), false positive rate (FPR), and computational efficiency. Findings indicate that deep learning and supervised learning outperform other approaches, particularly in enhancing TPR and minimizing FPR. While unsupervised and reinforcement learning models offer less precision, they demonstrate high efficiency and potential for real-time applications. The study also explores AI integration into next-generation detectors and waveform reconstruction techniques. Overall, the integration of AI into GW research significantly improves the reliability and speed of event detection, unlocking new possibilities for exploring the dynamic universe. This paper provides a comprehensive outlook on the transformative role of AI in shaping the future of GW astronomy.
RIDEN pilot survey: broad-band selection of candidate quasars with extended Lyman-$α$ nebulae using CLAUDS-HSC-SSP-DUNES$^2$ joint data
2025-06-05
Rhythm Shimakawa, Satoshi Kikuta, Haruka Kusakabe, Marcin Sawicki, Yongming Liang, Rieko Momose, Stephen Gwyn, Guillaume Desprez
The Vera C. Rubin Observatory will conduct the Legacy Survey of Space and Time (LSST), delivering deep, multi-band ($ugrizy$) imaging data across 18,000 square degrees over the next decade. Before this ultra-wide-field survey, we constructed a broad-band Ly$\alpha$ imaging toward 483 SDSS/BOSS quasars at $z=$ 1.9-3.0, using deep, wide-field ultraviolet to near-infrared ($u$-to-$K$) data from the Hyper Suprime-Cam Subaru Strategic Survey (HSC-SSP), the CFHT Large Area U-band Deep Survey (CLAUDS), the Deep UKIRT Near-Infrared Steward Survey (DUNES$^2$), and additional public data covering 13 square degrees. Our broad-band selection allowed us to select 24 candidate quasar nebulae that exhibit $u$ or $g$ band excess over 50-170 kpc, some of which exhibit asymmetrical extended features similar to those seen in previously discovered giant nebulae. We then investigated whether the Ly$\alpha$ morphology of quasar nebulae differs between two redshift intervals, $z=$ 1.9-2.3 and $z=$ 2.3-3.0, and examined environmental dependence based on a control sample. Comparison results show no significant difference in asymmetry within Ly$\alpha$ nebulae between the two redshift intervals. Furthermore, we found no systematic differences in overdensities around the complete quasar samples, quasars with large Ly$\alpha$ nebulae, and control samples, while the most extended nebula appears to be located in the high-density region. Further verification analyses are required since the current dataset lacks spectroscopic confirmation for both quasar nebulae and their surrounding neighbours. Nevertheless, the results demonstrate the great potential of the Rubin LSST to discover giant Ly$\alpha$ nebulae on an unprecedented scale.
From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems
2025-06-05
Jiayi Chen, Junyi Ye, Guiling Wang
Compound Al Systems (CAIS) is an emerging paradigm that integrates large language models (LLMs) with external components, such as retrievers, agents, tools, and orchestrators, to overcome the limitations of standalone models in tasks requiring memory, reasoning, real-time grounding, and multimodal understanding. These systems enable more capable and context-aware behaviors by composing multiple specialized modules into cohesive workflows. Despite growing adoption in both academia and industry, the CAIS landscape remains fragmented, lacking a unified framework for analysis, taxonomy, and evaluation. In this survey, we define the concept of CAIS, propose a multi-dimensional taxonomy based on component roles and orchestration strategies, and analyze four foundational paradigms: Retrieval-Augmented Generation (RAG), LLM Agents, Multimodal LLMs (MLLMs), and orchestration-centric architectures. We review representative systems, compare design trade-offs, and summarize evaluation methodologies across these paradigms. Finally, we identify key challenges-including scalability, interoperability, benchmarking, and coordination-and outline promising directions for future research. This survey aims to provide researchers and practitioners with a comprehensive foundation for understanding, developing, and advancing the next generation of system-level artificial intelligence.
Traditional offline evaluation methods for recommender systems struggle to capture the complexity of modern platforms due to sparse behavioural signals, noisy data, and limited modelling of user personality traits. While simulation frameworks can generate synthetic data to address these gaps, existing methods fail to replicate behavioural diversity, limiting their effectiveness. To overcome these challenges, we propose the Personality-driven User Behaviour Simulator (PUB), an LLM-based simulation framework that integrates the Big Five personality traits to model personalised user behaviour. PUB dynamically infers user personality from behavioural logs (e.g., ratings, reviews) and item metadata, then generates synthetic interactions that preserve statistical fidelity to real-world data. Experiments on the Amazon review datasets show that logs generated by PUB closely align with real user behaviour and reveal meaningful associations between personality traits and recommendation outcomes. These results highlight the potential of the personality-driven simulator to advance recommender system evaluation, offering scalable, controllable, high-fidelity alternatives to resource-intensive real-world experiments.
Users of social media platforms based on recommendation systems (RecSys) (e.g. TikTok, X, YouTube) strategically interact with platform content to influence future recommendations. On some such platforms, users have been documented to form large-scale grassroots movements encouraging others to purposefully interact with algorithmically suppressed content in order to "boost" its recommendation; we term this behavior user altruism. To capture this behavior, we study a game between users and a RecSys, where users provide the RecSys (potentially manipulated) preferences over the contents available to them, and the RecSys -- limited by data and computation constraints -- creates a low-rank approximation preference matrix, and ultimately provides each user her (approximately) most-preferred item. We compare the users' social welfare under truthful preference reporting and under a class of strategies capturing user altruism. In our theoretical analysis, we provide sufficient conditions to ensure strict increases in user social welfare under user altruism, and provide an algorithm to find an effective altruistic strategy. Interestingly, we show that for commonly assumed recommender utility functions, effectively altruistic strategies also improve the utility of the RecSys! We show that our results are robust to several model misspecifications, thus strengthening our conclusions. Our theoretical analysis is complemented by empirical results of effective altruistic strategies on the GoodReads dataset, and an online survey on how real-world users behave altruistically in RecSys. Overall, our findings serve as a proof-of-concept of the reasons why traditional RecSys may incentivize users to form collectives and/or follow altruistic strategies when interacting with them.
Fast Luminous Extragalactic Transients in the VLA Sky Survey: Implications for the rates of Accretion-Induced Collapse Events, Fast Blue Optical Transients and Gamma Ray Burst Afterglows
2025-06-05
Kritti Sharma, Vikram Ravi, Dillon Z. Dong, Gregg Hallinan, Casey Law, Delina Levine, Jean J. Somalwar, Jessie Miller, Nikita Kosogorov, Steven T. Myers
Radio wavelengths offer a unique window into high-energy astrophysical phenomena that may be obscured or too rapidly evolving to be captured at other wavelengths. Leveraging data from the Very Large Array Sky Survey, we perform a systematic search for fast, luminous transients with characteristic timescales $\lesssim 3$ years in the nearby universe ($z \leq 0.3$). We report the discovery of five such transients, and classify them based on their synchrotron emission energetics and host galaxy properties. From this sample, we derive observational constraints on the volumetric rates of certain corresponding transient classes. We limit the rates of accretion-induced collapse of white dwarfs with dense circumstellar medium interaction (and those producing pulsar wind nebulae) at $\lesssim 1.10_{-0.90}^{+2.60}$% ($\lesssim 0.20_{-0.10}^{+5.80}$%) of the local Type Ia supernova rate, respectively, broadly consistent with theoretical predictions. For AT2018cow-like radio-bright luminous fast blue optical transients, we estimate a rare occurrence rate of $\lesssim 0.02_{-0.01}^{+0.32}$% of the local core-collapse supernova rate. We constrain the local volumetric rates of long- and short-duration gamma-ray bursts (GRBs) to be $\lesssim 11.46_{-9.48}^{+26.28}$~Gpc$^{-3}$~yr$^{-1}$ and $\lesssim 80.88_{-66.90}^{+185.87}$~Gpc$^{-3}$~yr$^{-1}$, respectively. These estimates incorporate beaming corrections, with median detectable viewing angles derived from afterglow simulations of $\sim 0.4$ and $\sim 0.3$ radians for long- and short-duration GRBs. Our findings highlight the potential of radio surveys to uncover rare, energetic transients. We emphasize the critical role of coordinated multi-wavelength follow-up in fully characterizing these enigmatic events.
The TESS Ten Thousand Catalog: 10,001 uniformly-vetted and -validated Eclipsing Binary Stars detected in Full-Frame Image data by machine learning and analyzed by citizen scientists
2025-06-05
Veselin B. Kostov, Brian P. Powell, Aline U. Fornear, Marco Z. Di Fraia, Robert Gagliano, Thomas L. Jacobs, Julien S. de Lambilly, Hugo A. Durantini Luca, Steven R. Majewski, Mark Omohundro, Jerome Orosz, Saul A. Rappaport, Ryan Salik, Donald Short, William Welsh, Svetoslav Alexandrov, Cledison Marcos da Silva, Erika Dunning, Gerd Guhne, Marc Huten, Michiharu Hyogo, Davide Iannone, Sam Lee, Christian Magliano, Manya Sharma, Allan Tarr, John Yablonsky, Sovan Acharya, Fred Adams, Thomas Barclay, Benjamin T. Montet, Susan Mullally, Greg Olmschenk, Andrej Prsa, Elisa Quintana, Robert Wilson, Hasret Balcioglu, Ethan Kruse, the Eclipsing Binary Patrol Collaboration
The Transiting Exoplanet Survey Satellite (TESS) has surveyed nearly the entire sky in Full-Frame Image mode with a time resolution of 200 seconds to 30 minutes and a temporal baseline of at least 27 days. In addition to the primary goal of discovering new exoplanets, TESS is exceptionally capable at detecting variable stars, and in particular short-period eclipsing binaries which are relatively common, making up a few percent of all stars, and represent powerful astrophysical laboratories for deep investigations of stellar formation and evolution. We combed Sectors 1-82 of TESS Full-Frame Image data searching for eclipsing binary stars using a neural network that identified ~1.2 million stars with eclipse-like features. Of these, we have performed an in-depth analysis on ~60,000 targets using automated methods and manual inspection by citizen scientists. Here we present a catalog of 10001 uniformly-vetted and -validated eclipsing binary stars that passed all our ephemeris and photocenter tests, as well as complementary visual inspection. Of these, 7936 are new eclipsing binaries while the remaining 2065 are known systems for which we update the published ephemerides. We outline the detection and analysis of the targets, discuss the properties of the sample, and highlight potentially interesting systems. Finally, we also provide a list of ~900,000 unvetted and unvalidated targets for which the neural network found eclipse-like features with a score higher than 0.9, and for which there are no known eclipsing binaries within a sky-projected separation of a TESS pixel (~21 arcsec).
Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods
2025-06-05
Xiaohua Lu, Liubov Tupikina, Mehwish Alam
Real-world knowledge can take various forms, including structured, semi-structured, and unstructured data. Among these, knowledge graphs are a form of structured human knowledge that integrate heterogeneous data sources into structured representations but typically reduce complex n-ary relations to simple triples, thereby losing higher-order relational details. In contrast, hypergraphs naturally represent n-ary relations with hyperedges, which directly connect multiple entities together. Yet hypergraph representation learning often overlooks entity roles in hyperedges, limiting the fine-grained semantic modelling. To address these issues, knowledge hypergraphs and hyper-relational knowledge graphs combine the advantages of knowledge graphs and hypergraphs to better capture the complex structures and role-specific semantics of real-world knowledge. This survey provides a comprehensive review of methods handling n-ary relational data, covering both knowledge hypergraphs and hyper-relational knowledge graphs literatures. We propose a two-dimensional taxonomy: the first dimension categorises models based on their methodology, i.e., translation-based models, tensor factorisation-based models, deep neural network-based models, logic rules-based models, and hyperedge expansion-based models. The second dimension classifies models according to their awareness of entity roles and positions in n-ary relations, dividing them into aware-less, position-aware, and role-aware approaches. Finally, we discuss existing datasets, negative sampling strategies, and outline open challenges to inspire future research.
This review synthesizes recent advancements in the study of moir\'e magnetism. This emerging field, at the intersection of twistronics, topology, and strongly correlated systems, explores novel phenomena that arise when moir\'e potentials influence magnetic two-dimensional systems. The manuscript presents recent advances highlighting the interfacial incongruity as a novel mechanism for regulating the magnetism of two-dimensional materials and for the manifestation of various phenomena in twisted and mismatched magnetic two-dimensional interfaces. The manuscript addresses seminal and recent experimental and theoretical advances associated with both small- and large-period magnetic moir\'e lattices, including novel magnetic phases, low-energy and topological magnetic excitations, magnetic and electronic transport, optical properties, phase transitions, and prospective applications of these materials. Moir\'e magnetism signifies a promising frontier for manipulating complex quantum states in quantum matter. The ongoing advances in this field are poised to impact condensed matter physics, materials science, and quantum information science.
Trinity VII. Predictions for the Observable Correlation Functions of Accreting Black Holes
2025-06-05
Oddisey Knox, Haowen Zhang, Peter Behroozi
The quasar correlation function assesses the occurrence of quasar pairs as a function of separation, which is strongly influenced by quasar host halo masses. The empirical Trinity model recently inferred the redshift-dependent relationship between supermassive black hole (SMBH) mass, galaxy mass, and halo mass, using constraints other than correlation functions (e.g., quasar luminosity functions, active galactic nuclei occupation fractions, and SMBH mass-bulge mass relations). Hence, comparing the predicted quasar correlation functions from Trinity to real observations is an important test of Trinity's inferred SMBH -- halo relation. In this work, we use a compilation of observed two-point projected and redshift-space correlation functions from $0 \leq z \leq 3.5$. We find that Trinity accurately predicts quasar correlation functions within observed error bars, although observations do not have much constraining power at lower redshifts due to smaller observable volumes and lower quasar number densities. This finding is consistent with Trinity having the correct placement of quasars within their host galaxies and dark matter halos, without requiring quasar clustering constraints during model fitting. Using Trinity, we also predict the clustering as a function of quasar bolometric luminosity, finding that existing survey uncertainties are too large to show measurable differences ($\lesssim 0.3$ dex change in bias for $10^{42}$ erg s$^{-1}$ compared to $10^{46}$ erg s$^{-1}$ SMBHs across redshifts). This fact arises because most SMBH growth (and hence quasar luminosity) occurs in halos in a similar mass range ($10^{12}-10^{13} M_\odot$).
Near-term Application Engineering Challenges in Emerging Superconducting Qudit Processors
2025-06-05
Davide Venturelli, Erik Gustafson, Doga Kurkcuoglu, Silvia Zorzetti
We review the prospects to build quantum processors based on superconducting transmons and radiofrequency cavities for testing applications in the NISQ era. We identify engineering opportunities and challenges for implementation of algorithms in simulation, combinatorial optimization, and quantum machine learning in qudit-based quantum computers.
Scenarios in Computing Research: A Systematic Review of the Use of Scenario Methods for Exploring the Future of Computing Technologies in Society
2025-06-05
Julia Barnett, Kimon Kieslich, Jasmine Sinchai, Nicholas Diakopoulos
Scenario building is an established method to anticipate the future of emerging technologies. Its primary goal is to use narratives to map future trajectories of technology development and sociotechnical adoption. Following this process, risks and benefits can be identified early on, and strategies can be developed that strive for desirable futures. In recent years, computer science has adopted this method and applied it to various technologies, including Artificial Intelligence (AI). Because computing technologies play such an important role in shaping modern societies, it is worth exploring how scenarios are being used as an anticipatory tool in the field -- and what possible traditional uses of scenarios are not yet covered but have the potential to enrich the field. We address this gap by conducting a systematic literature review on the use of scenario building methods in computer science over the last decade (n = 59). We guide the review along two main questions. First, we aim to uncover how scenarios are used in computing literature, focusing especially on the rationale for why scenarios are used. Second, in following the potential of scenario building to enhance inclusivity in research, we dive deeper into the participatory element of the existing scenario building literature in computer science.
Preprocessing Methods for Memristive Reservoir Computing for Image Recognition
2025-06-05
Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky
Reservoir computing (RC) has attracted attention as an efficient recurrent neural network architecture due to its simplified training, requiring only its last perceptron readout layer to be trained. When implemented with memristors, RC systems benefit from their dynamic properties, which make them ideal for reservoir construction. However, achieving high performance in memristor-based RC remains challenging, as it critically depends on the input preprocessing method and reservoir size. Despite growing interest, a comprehensive evaluation that quantifies the impact of these factors is still lacking. This paper systematically compares various preprocessing methods for memristive RC systems, assessing their effects on accuracy and energy consumption. We also propose a parity-based preprocessing method that improves accuracy by 2-6% while requiring only a modest increase in device count compared to other methods. Our findings highlight the importance of informed preprocessing strategies to improve the efficiency and scalability of memristive RC systems.
Investigation of $^{31}$P levels near the proton threshold by Nuclear Resonance Fluorescence and the impact on the $^{30}$Si(p,$γ$)$^{31}$P thermonuclear rate
2025-06-05
David Gribble, Christian Iliadis, Robert V. F. Janssens, Udo Friman-Gayer, Akaa D. Ayangeakaa, Art Champagne, Emily Churchman, William Fox, Steven Frye, Xavier K. -H. James, Samantha R. Johnson, Richard Longland, Antonella Saracino, Nirupama Sensharma, Kaixin Song, Clay Wegner
We investigated the nuclear structure of $^{31}$P near the proton threshold using Nuclear Resonance Fluorescence (NRF) to refine the properties of key resonances in the $^{30}$Si(p,$\gamma$)$^{31}$P reaction, which is critical for nucleosynthesis in stellar environments. Excitation energies and spin-parities were determined for several states, including two unobserved resonances at $E_r$ $=$ $18.7$~keV and $E_r$ $=$ $50.5$~keV. The angular correlation analysis enabled the first unambiguous determination of the orbital angular momentum transfer for these states. These results provide a significant update to the $^{30}$Si(p,$\gamma$)$^{31}$P thermonuclear reaction rate, with direct implications for models of nucleosynthesis in globular clusters and other astrophysical sites. The revised rate is substantially lower than previous estimates at temperatures below $200$~MK, affecting predictions for silicon isotopic abundances in stellar environments. Our work demonstrates the power of NRF in constraining nuclear properties, and provides a framework for future studies of low-energy resonances relevant to astrophysical reaction rates.
DART-Vetter: A Deep LeARning Tool for automatic triage of exoplanet candidates
2025-06-05
Stefano Fiscale, Laura Inno, Alessandra Rotundi, Angelo Ciaramella, Alessio Ferone, Christian Magliano, Luca Cacciapuoti, Veselin Kostov, Elisa Quintana, Giovanni Covone, Maria Teresa Muscari Tomajoli, Vito Saggese, Luca Tonietti, Antonio Vanzanella, Vincenzo Della Corte
In the identification of new planetary candidates in transit surveys, the employment of Deep Learning models proved to be essential to efficiently analyse a continuously growing volume of photometric observations. To further improve the robustness of these models, it is necessary to exploit the complementarity of data collected from different transit surveys such as NASA's Kepler, Transiting Exoplanet Survey Satellite (TESS), and, in the near future, the ESA PLAnetary Transits and Oscillation of stars (PLATO) mission. In this work, we present a Deep Learning model, named DART-Vetter, able to distinguish planetary candidates (PC) from false positives signals (NPC) detected by any potential transiting survey. DART-Vetter is a Convolutional Neural Network that processes only the light curves folded on the period of the relative signal, featuring a simpler and more compact architecture with respect to other triaging and/or vetting models available in the literature. We trained and tested DART-Vetter on several dataset of publicly available and homogeneously labelled TESS and Kepler light curves in order to prove the effectiveness of our model. Despite its simplicity, DART-Vetter achieves highly competitive triaging performance, with a recall rate of 91% on an ensemble of TESS and Kepler data, when compared to Exominer and Astronet-Triage. Its compact, open source and easy to replicate architecture makes DART-Vetter a particularly useful tool for automatizing triaging procedures or assisting human vetters, showing a discrete generalization on TCEs with Multiple Event Statistic (MES) > 20 and orbital period < 50 days.
Using Large Language Models to Simulate Human Behavioural Experiments: Port of Mars
2025-06-05
Oliver Slumbers, Joel Z. Leibo, Marco A. Janssen
Collective risk social dilemmas (CRSD) highlight a trade-off between individual preferences and the need for all to contribute toward achieving a group objective. Problems such as climate change are in this category, and so it is critical to understand their social underpinnings. However, rigorous CRSD methodology often demands large-scale human experiments but it is difficult to guarantee sufficient power and heterogeneity over socio-demographic factors. Generative AI offers a potential complementary approach to address thisproblem. By replacing human participants with large language models (LLM), it allows for a scalable empirical framework. This paper focuses on the validity of this approach and whether it is feasible to represent a large-scale human-like experiment with sufficient diversity using LLM. In particular, where previous literature has focused on political surveys, virtual towns and classical game-theoretic examples, we focus on a complex CRSD used in the institutional economics and sustainability literature known as Port of Mars
Towards Data Systems That Are Business Semantic-Centric and AI Agents-Assisted
2025-06-05
Cecil Pang
Contemporary businesses operate in dynamic environments requiring rapid adaptation to achieve goals and maintain competitiveness. Existing data platforms often fall short by emphasizing tools over alignment with business needs, resulting in inefficiencies and delays. To address this gap, I propose the Business Semantics Centric, AI Agents Assisted Data System (BSDS), a holistic system that integrates architecture, workflows, and team organization to ensure data systems are tailored to business priorities rather than dictated by technical constraints. BSDS redefines data systems as dynamic enablers of business success, transforming them from passive tools into active drivers of organizational growth. BSDS has a modular architecture that comprises curated data linked to business entities, a knowledge base for context-aware AI agents, and efficient data pipelines. AI agents play a pivotal role in assisting with data access and system management, reducing human effort, and improving scalability. Complementing this architecture, BSDS incorporates workflows optimized for both exploratory data analysis and production requirements, balancing speed of delivery with quality assurance. A key innovation of BSDS is its incorporation of the human factor. By aligning data team expertise with business semantics, BSDS bridges the gap between technical capabilities and business needs. Validated through real-world implementation, BSDS accelerates time-to-market for data-driven initiatives, enhances cross-functional collaboration, and provides a scalable blueprint for businesses of all sizes. Future research can build on BSDS to explore optimization strategies using complex systems and adaptive network theories, as well as developing autonomous data systems leveraging AI agents.
A method of numerical calculation of the effect of short-range correlations for a wide range of nuclei
2025-06-05
Tianyang Ma, Shalom Shlomo
We introduce a method to calculate the effect of short-range correlation (SRC) on the proton density distribution numerically, up to the first order of the cluster expansion, that can be used for wide range of closed shell nuclei and determine the effect on proton density, root-mean square (RMS) radius, and form factor of many different closed-shell nuclei: $^4$He, $^{16}$O, $^{28}$Si, $^{32}$S, $^{40}$Ca, $^{60}$Ni, $^{90}$Zr, $^{140}$Ce and $^{208}$Pb. In the short-range correlations, we have included the effects of repulsive and attractive parts of the N-N interaction and adjusted the SRC parameters to reproduce the results obtained by solving the Bethe-Goldstone Equation.
Speech Neurophysiology in Realistic Contexts: Big Hype or Big Leap?
2025-06-05
Giovanni M. Di Liberto, Emily Y. J. Ip
Understanding the neural basis of speech communication is essential for uncovering how sounds are translated into meaning, how that changes with development, ageing, and speech-related deficits, as well as contributing to brain-computer interfaces research. While traditional neurophysiological studies have relied on simplified, controlled paradigms, recent advances have shifted the field toward more ecologically-valid approaches. Here, we examine the impact of continuous speech research and discuss the potential of speech interaction neurophysiology. We present a discussion on how realistic paradigms challenge conventional methods, offering richer insights into neural encoding, functional brain mapping, and neural entrainment. At the same time, they introduce significant analytical and technical complexities, particularly when incorporating social interaction. We discuss the evolving landscape of experimental designs, from discrete to continuous stimuli and from socially-isolated listening to dynamic, multi-agent communication. By synthesising findings across studies, we highlight how naturalistic speech paradigms contribute to refining theories of language processing and open new avenues for research. In doing so, this review critically evaluates of whether the move toward realism in speech neurophysiology represents a technological trend or a transformative leap in understanding the neural underpinnings of speech communication.
Repeating Flares, X-ray Outbursts and Delayed Infrared Emission: A Comprehensive Compilation of Optical Tidal Disruption Events
2025-06-05
D. A. Langis, I. Liodakis, K. I. I. Koljonen, A. Paggi, N. Globus, L. Wyrzykowski, P. J. Mikołajczyk, K. Kotysz, P. Zieliński, N. Ihanec, J. Ding, D. Morshed, Z. Torres
Tidal disruption events (TDEs) have been proposed as valuable laboratories for studying dormant black holes. However, progress in this field has been hampered by the limited number of observed events. In this work, we present TDECat, a comprehensive catalogue of 134 confirmed TDEs (131 optical TDEs and 3 jetted TDEs) discovered up to the end of 2024, accompanied by multi-wavelength photometry (X-ray, UV, optical, and IR) and publicly available spectra. We also study the statistical properties, spectral classifications, and multi-band variability of these events. Using a Bayesian Blocks algorithm, we determine the duration, rise time , decay time, and their ratio for 103 flares in our sample. We find that these timescales follow a log-normal distribution. Furthermore, our spectral analysis shows that most optical TDEs belong to the TDE-H+He class, followed by the TDE-H, TDE-He, and TDE-featureless classes, which is consistent with expectations from main sequence star disruption. Using archival observations, we identify four new potentially repeating TDEs, namely AT 2024pvu, AT 2022exr, AT2021uvz, and AT 2019teq, increasing the number of known repeating events. In both newly identified and previously known cases, the secondary flares exhibit a similar shape to the primary. We also examine the IR and X-ray emission from the TDEs in our catalogue, and find that 14 out of the 18 IR events have associated X-ray emission, strongly suggesting a potential correlation. Finally, we find that for three subsamples (repeating flares, IR-, and X-ray-emitting events), the spectral classes are unlikely to be randomly distributed, suggesting a connection between spectral characteristics and multi-wavelength emission. TDEcat enables large-scale population studies across wavelengths and spectral classes, providing essential tools for navigating the data-rich era of upcoming surveys such as the LSST.
A Reassessment of the Pantheon+ and DES 5YR Calibration Uncertainties: Dovekie
2025-06-05
B. Popovic, W. D. Kenworthy, M. Ginolin, A. Goobar, P. Shah, B. M. Boyd, A. Do, D. Brout, D. Scolnic, M. Vincenzi, S. Dhawan, D. O. Jones, M. Smith, M. Rigault, B. Racine, E. E. Hayes, R. Chen, P. Wiseman, L. Galbany, M. Grayling, L. LaCroix, C. Barjou-Delayre, D. Kuhn, C. Lemon
Type Ia Supernovae (SNe Ia) are crucial tools to measure the accelerating expansion of the universe, comprising thousands of SNe across multiple telescopes. Accurate measurements of cosmological parameters with SNe Ia require a robust understanding and cross-calibration of the telescopes and filters. A previous cross-calibration effort, 'Fragilistic', provided 25 photometric systems, but offered no public code or ability to add new surveys. We provide an open-source cross-calibration solution, available at https://github.com/bap37/Dovekie/ . Using the Pan-STARRs (PS1) and Gaia all-sky telescopes, we characterise the measured filters from 11 photometric systems, including CfA, PS1, Foundation, DES, CSP, SDSS, and SNLS, using published observations of field stars. For the first time, we derive uncertainties on effective filter transmissions and modify filters to match the data. With the addition of direct observations of DA white dwarfs (Boyd et al. 2025), we simultaneously cross-calibrate our zeropoints across photometric systems and propagate to cosmology. With improved uncertainties from DA WDs, we find improvements to the calibration systematic uncertainty of x1.5 for the Pantheon+ (Brout et al. 2022) sample with a new systematic photometric uncertainty = 0.016 for FlatwCDM, and modest improvements to that of the DES5YR analysis. We find good agreement with previous calibration, and show that even these small calibration changes can be amplified by up to a factor of x6 in the inferred SN Ia distances, driven by calibration sensitivity in the colour-luminosity relations and SALT training. Initial results indicate that these changes cause dmu/dz = 0.025 and change the recovered value of Omega_M in LCDM by ~0.01. These may have a potentially larger impact in w0/wa space and inferences about evolving dark energy. We pursue this calculation in an ongoing full re-analysis of DES.
Probing neutral outflows in z ~ 2 galaxies using JWST observations of Ca II H and K absorption lines
2025-06-05
Caterina Liboni, Sirio Belli, Letizia Bugiani, Rebecca Davies, Minjung Park, Charlie Conroy, Razieh Emami, Benjamin D. Johnson, Amir H. Khoram, Joel Leja, Gabriel Maheson, Matteo Sapori, Trevor Mendel, Sandro Tacchella, Rainer Weinberger
Using deep JWST/NIRSpec spectra from the Blue Jay survey, we perform the first systematic investigation of neutral gas content in massive galaxies at Cosmic Noon based on the Ca II H, K absorption lines. We analyze a sample of 9 galaxies at 1.8 < z < 2.8 with stellar masses > 10.6, for which we detect neutral gas absorption both in Ca II and in Na I. After removing the stellar continuum using the best-fit model obtained with Prospector, we fit the excess absorption due to neutral gas in the Ca II H, K doublet and in the Na I D doublet, together with nearby emission lines produced by ionized gas. We measure covering fractions between 0.2 and 0.9 from the Ca II H and K lines, which are spectrally well resolved in the NIRSpec R ~ 1000 observations, unlike the absorption lines in the Na I D doublet. We measure the velocity shift, velocity dispersion, and column density separately for Ca II and Na I. About half of the galaxies present blueshifted Ca II, indicative of an outflow of neutral gas, consistent with previous results based on Na I. The velocity shift and the column density measured from Ca II are correlated with those measured from Na I, implying that these absorption lines trace gas in similar physical conditions. However, the column densities are not in a 1:1 relation, meaning that the relative amount of Ca II and Na I atoms along the line of sight varies with the gas column density. After discussing possible reasons for this behavior, we derive an empirical relation between the column density of Ca II and the column density of Na I and, in a more indirect way, of neutral hydrogen H I. This calibration offers a new way to estimate the outflow mass and the mass outflow rate for the neutral phase from current and future JWST observations of massive galaxies at Cosmic Noon and beyond
The DECam Field of Streams: a deep view of the Milky Way halo
2025-06-05
Peter Ferguson, Nora Shipp
We present a Field of Streams visualizing stellar structures in the Milky Way halo as viewed by the Dark Energy Camera (DECam). We use $g$- and $r$-band imaging from the Dark Energy Survey and the DECam Legacy Survey, covering $18{,}700 \deg^2$ across the sky. Using an isochrone-based matched filter in $g$ vs. $g-r$, we select old and metal-poor stars in three distance bins, and generate a false-color RGB image of the number density of selected stars. The DECam Field of Streams shows a variety of Milky Way halo structures, including dwarf galaxies, globular clusters, and an abundance of stellar streams, illustrating the significant progress that has been made in recent years in uncovering the building blocks of the Milky Way's stellar halo in deep, wide-area photometric surveys. This view of our Galaxy will be improved in the coming years as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) begins to collect data to greater depths and across a larger fraction of the sky than ever before.
Galaxy cluster count cosmology with simulation-based inference
2025-06-05
M. Regamey, D. Eckert, R. Seppi, W. Hartley, K. Umetsu, S. Tam, D. Gerolymatou
The abundance and mass distribution of galaxy clusters is a sensitive probe of cosmological parameters, through the sensitivity of the high-mass end of the halo mass function to $\Omega_m$ and $\sigma_8$. While galaxy cluster surveys have been used as cosmological probes for more than a decade, the accuracy of cluster count experiments is still hampered by systematic, such as the relation between observables and halo mass, the accuracy of the halo mass function, and the survey selection function. Here we show that these uncertainties can be alleviated by forward modeling the observed cluster population with simulation-based inference. We construct a pipeline that predicts the distribution of observables from cosmological parameters and scaling relations, and then train a neural network to learn the mapping between the input parameters and the measured distributions. We focus on fiducial X-ray surveys with available flux, temperature, and redshift measurements, although the method can be easily adapted to any available observable. We apply our method to mock samples extracted from the UNIT1i simulation and demonstrate the accuracy of our approach. We then study the impact of several systematic uncertainties on the recovered cosmological parameters. We show that sample variance and the choice of the halo mass function are subdominant sources of uncertainty. Conversely, the absolute mass scale is the leading source of systematic error and must be calibrated at the $<10\%$ level to recover accurate values of $\Omega_m$ and $\sigma_8$. However, the quantity $S_8=\sigma_8(\Omega_m/0.3)^{0.3}$ appears to be less sensitive to the accuracy of the mass calibration. We conclude that simulation-based inference is a promising avenue for future cosmological studies from galaxy cluster surveys such as eROSITA and Euclid as it allows to consider all the available observables in a straightforward manner.
RUBIES: A Spectroscopic Census of Little Red Dots; All V-Shaped Point Sources Have Broad Lines
2025-06-05
Raphael E. Hviding, Anna de Graaff, Tim B. Miller, David J. Setton, Jenny E. Greene, Ivo Labbé, Gabriel Brammer, Rachel Bezanson, Leindert A. Boogaard, Nikko J. Cleri, Joel Leja, Michael V. Maseda, Ian McConachie, Jorryt Matthee, Rohan P. Naidu, Pascal A. Oesch, Bingjie Wang, Katherine E. Whitaker, Christina Williams
The physical nature of Little Red Dots (LRDs) - a population of compact, red galaxies revealed by JWST - remains unclear. Photometric samples are constructed from varying selection criteria with limited spectroscopic follow-up available to test intrinsic spectral shapes and prevalence of broad emission lines. We use the RUBIES survey, a large spectroscopic program with wide color-morphology coverage and homogeneous data quality, to systematically analyze the emission-line kinematics, spectral shapes, and morphologies of $\sim$1500 galaxies at $z > 3.1$. We identify broad Balmer lines via a novel fitting approach that simultaneously models NIRSpec/PRISM and G395M spectra, yielding 80 broad-line sources with 28 (35%) at $z > 6$. A large subpopulation naturally emerges from the broad Balmer line sources, with 36 exhibiting `v-shaped' UV-to-optical continua and a dominant point source component in the rest-optical; we define these as spectroscopic LRDs, constituting the largest such sample to date. Strikingly, the spectroscopic LRD population is largely recovered when either a broad line or rest-optical point source is required in combination with a v-shaped continuum, suggesting an inherent link between these three defining characteristics. We compare the spectroscopic LRD sample to published photometric searches. Although these selections have high accuracy, down to $\rm F444W<26.5$, only 50-62% of the RUBIES LRDs were previously identified. The remainder were missed due to a mixture of faint rest-UV photometry, comparatively blue rest-optical colors, or highly uncertain photometric redshifts. Our findings highlight that well-selected spectroscopic campaigns are essential for robust LRD identification, while photometric criteria require refinement to capture the full population.
Interpretation Meets Safety: A Survey on Interpretation Methods and Tools for Improving LLM Safety
2025-06-05
Seongmin Lee, Aeree Cho, Grace C. Kim, ShengYun Peng, Mansi Phute, Duen Horng Chau
As large language models (LLMs) see wider real-world use, understanding and mitigating their unsafe behaviors is critical. Interpretation techniques can reveal causes of unsafe outputs and guide safety, but such connections with safety are often overlooked in prior surveys. We present the first survey that bridges this gap, introducing a unified framework that connects safety-focused interpretation methods, the safety enhancements they inform, and the tools that operationalize them. Our novel taxonomy, organized by LLM workflow stages, summarizes nearly 70 works at their intersections. We conclude with open challenges and future directions. This timely survey helps researchers and practitioners navigate key advancements for safer, more interpretable LLMs.
AI-powered Contextual 3D Environment Generation: A Systematic Review
2025-06-05
Miguel Silva, Alexandre Valle de Carvalho
The generation of high-quality 3D environments is crucial for industries such as gaming, virtual reality, and cinema, yet remains resource-intensive due to the reliance on manual processes. This study performs a systematic review of existing generative AI techniques for 3D scene generation, analyzing their characteristics, strengths, limitations, and potential for improvement. By examining state-of-the-art approaches, it presents key challenges such as scene authenticity and the influence of textual inputs. Special attention is given to how AI can blend different stylistic domains while maintaining coherence, the impact of training data on output quality, and the limitations of current models. In addition, this review surveys existing evaluation metrics for assessing realism and explores how industry professionals incorporate AI into their workflows. The findings of this study aim to provide a comprehensive understanding of the current landscape and serve as a foundation for future research on AI-driven 3D content generation. Key findings include that advanced generative architectures enable high-quality 3D content creation at a high computational cost, effective multi-modal integration techniques like cross-attention and latent space alignment facilitate text-to-3D tasks, and the quality and diversity of training data combined with comprehensive evaluation metrics are critical to achieving scalable, robust 3D scene generation.
21-cm Intensity Mapping (IM) is a promising approach to detecting information about the large-scale structure beyond the local universe. One of the biggest challenges for an IM observation is the foreground removal procedure. In this paper, we attempt to conduct the restoration of contaminated data in an IM experiment with a Deep Neural Network (DNN). To investigate the impact of such data restoration, we compare the root-mean-square (RMS) of data with and without restoration after foreground removal using polynomial fitting, singular value decomposition, and independent component analysis, respectively. We find that the DNN-based pipeline performs well in lowering the RMS level of data, especially for data with large contaminated fractions. Furthermore, we investigate the impact of the restoration on the large-scale 21-cm signal in the simulation generated by CRIME. Simulation results show that the angular power spectrum curves from data with restoration are closer to the real one. Our work demonstrates that the DNN-based data restoration approach significantly increases the signal-to-noise ratio compared with conventional ones, achieving excellent potential for IM observations.
A Kernel-Based Approach for Accurate Steady-State Detection in Performance Time Series
2025-06-04
Martin Beseda, Vittorio Cortellessa, Daniele Di Pompeo, Luca Traini, Michele Tucci
This paper addresses the challenge of accurately detecting the transition from the warmup phase to the steady state in performance metric time series, which is a critical step for effective benchmarking. The goal is to introduce a method that avoids premature or delayed detection, which can lead to inaccurate or inefficient performance analysis. The proposed approach adapts techniques from the chemical reactors domain, detecting steady states online through the combination of kernel-based step detection and statistical methods. By using a window-based approach, it provides detailed information and improves the accuracy of identifying phase transitions, even in noisy or irregular time series. Results show that the new approach reduces total error by 14.5% compared to the state-of-the-art method. It offers more reliable detection of the steady-state onset, delivering greater precision for benchmarking tasks. For users, the new approach enhances the accuracy and stability of performance benchmarking, efficiently handling diverse time series data. Its robustness and adaptability make it a valuable tool for real-world performance evaluation, ensuring consistent and reproducible results.
Analyzing Line-of-sight selection biases in galaxy-scale strong lensing with external convergence and shear
2025-06-04
Xianzhe TZ Tang, Simon Birrer, Anowar J. Shajib, Narayan Khadka, the LSST Strong Gravitational Lensing Science Collaboration, the LSST Dark Energy Science Collaboration
The upcoming Vera Rubin Observatory Legacy Survey of Space and Time (LSST) will dramatically increase the number of strong gravitational lensing systems, requiring precise modeling of line-of-sight (LOS) effects to mitigate biases in lensing observations and cosmological inferences. We develop a method to construct joint distributions of external convergence ($\kappa_{\mathrm{ext}}$) and shear ($\gamma_{\mathrm{ext}}$) for strong lensing LOS by aggregating large-scale structure simulations with high-resolution halo renderings and non-linear correction. Our approach captures both smooth background matter and perturbations from halos, enabling accurate modeling of LOS effects. We apply non-linear LOS corrections to $\kappa_{\mathrm{ext}}$ and $\gamma_{\mathrm{ext}}$ that address the non-additive lensing effects caused by objects along the LOS in strong lensing. We find that, with a minimum image separation of $1.0^{\prime\prime}$, non-linear LOS correction due to the presence of a dominant deflector slightly increases the ratio of quadruple to double lenses; this non-linear LOS correction also introduces systematic biases of $\sim 0.1\%$ for galaxy-AGN lenses in the inferred Hubble constant ($H_0$) if not accounted for. We also observe a $0.66\%$ bias for galaxy-galaxy lenses on $H_0$, and even larger biases up to $1.02\%$ for galaxy-AGN systems if LOS effects are not accounted for. These results highlight the importance of LOS for precision cosmology. The publicly available code and datasets provide tools for incorporating LOS effects in future analyses.
A Correlation Between FRB Dispersion Measure and Foreground Large-Scale Structure
2025-06-04
Maryam Hussaini, Liam Connor, Ralf M. Konietzka, Vikram Ravi, Jakob Faber, Kritti Sharma, Myles Sherman
The distribution of baryons in the Universe remains a fundamental open question in astronomy, and the dispersion measure (DM) of Fast Radio Bursts (FRBs) serves as a valuable tool for probing this cosmic gas. We investigate the impact of the foreground cosmic web on FRB DMs using 61 localized FRBs and public galaxy catalogs. We test for the large-scale structure's impact on cosmological DM using two methods. First, we searched for a correlation between galaxy number density along the line of sight and extragalactic DM, and found a statistically significant positive correlation ($p$ = $1.76 \times 10^{-5}$). The shape of this correlation contains information about the cosmic baryon distribution, and can also be used to better constrain host galaxy DM by providing an estimate of the cosmic contribution on a per-source basis. We observe similar correlations in a mock FRB survey based on the IllustrisTNG cosmological simulation, where the DM is dominated by filaments in the IGM and not by halos. Next, we performed a stacking analysis that measures the average excess DM as a function of impact parameter of foreground galaxies to obtain spatial information about how ionized gas is distributed around galaxy halos. We report excess DM in the stacked signal for impact parameters up to Mpc scales ($\sim$3$\sigma$). Finally, we identified FRBs that do not appear to intersect intervening halos within $r_{vir}$, allowing us to estimate the fraction of baryons that reside in the IGM. We find $f_{\mathrm{IGM}} \geq 0.69$ at 95$\%$ confidence, indicating significant astrophysical feedback.
Core-collapse supernova parameter estimation with the upcoming Vera C. Rubin Observatory
2025-06-04
Andrea Simongini, F. Ragosta, I. Di Palma, S. Piranomonte
The Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) is expected to revolutionize time-domain optical astronomy as we know it. With its unprecedented depth, the LSST will survey the southern hemisphere sky, generating nearly 32 trillion observations over its nominal 10-year operation. Among these, approximately 10 million will be supernovae (SNe). These observations will uniquely characterize the SN population, enabling studies of known and rare SN types, detailed parameterization of their light curves, deep searches for new SN progenitor populations, the discovery of strongly lensed SNe, and the compilation of a large, well-characterized sample of superluminous SNe. We analyzed a sample of 22663 simulations of LSST light curves for core collapse SNe (CCSNe), modeled using the radiative transfer code STELLA. We analyzed this dataset with the software CASTOR, which enables the reconstruction of synthetic light curves and spectra via a machine learning technique that allows one to retrieve the complete parameter map of a SN. For each parameter we compared the observed and the true values, determining how LSST light curves alone will contribute to characterize the progenitor and the explosion. Our results indicate that LSST alone will not suffice for a comprehensive and precise characterization of progenitor properties and explosion parameters. The limited spectral coverage of LSST light curves (in most cases) does not allow for the accurate estimation of bolometric luminosity, and consequently, of the explosion energy and nickel yield. Additionally, the redshift-absorption degeneracy is difficult to resolve without supplementary information. These findings suggest that for the most interesting SNe, complementary follow-up observations using spectrographs and optical facilities (particularly in the infrared bands) will be essential for accurate parameter determination.
Synthetic multi-inversion time magnetic resonance images for visualization of subcortical structures
2025-06-04
Savannah P. Hays, Lianrui Zuo, Anqi Feng, Yihao Liu, Blake E. Dewey, Jiachen Zhuo, Ellen M. Mowry, Scott D. Newsome Jerry L. Prince, Aaron Carass
Purpose: Visualization of subcortical gray matter is essential in neuroscience and clinical practice, particularly for disease understanding and surgical planning.While multi-inversion time (multi-TI) T$_1$-weighted (T$_1$-w) magnetic resonance (MR) imaging improves visualization, it is rarely acquired in clinical settings. Approach: We present SyMTIC (Synthetic Multi-TI Contrasts), a deep learning method that generates synthetic multi-TI images using routinely acquired T$_1$-w, T$_2$-weighted (T$_2$-w), and FLAIR images. Our approach combines image translation via deep neural networks with imaging physics to estimate longitudinal relaxation time (T$_1$) and proton density (PD) maps. These maps are then used to compute multi-TI images with arbitrary inversion times. Results: SyMTIC was trained using paired MPRAGE and FGATIR images along with T$_2$-w and FLAIR images. It accurately synthesized multi-TI images from standard clinical inputs, achieving image quality comparable to that from explicitly acquired multi-TI data.The synthetic images, especially for TI values between 400-800 ms, enhanced visualization of subcortical structures and improved segmentation of thalamic nuclei. Conclusion: SyMTIC enables robust generation of high-quality multi-TI images from routine MR contrasts. It generalizes well to varied clinical datasets, including those with missing FLAIR images or unknown parameters, offering a practical solution for improving brain MR image visualization and analysis.
A Resonant Beginning for the Solar System Terrestrial Planets
2025-06-04
Shuo Huang, Chris Ormel, Simon Portegies Zwart, Eiichiro Kokubo, Tian Yi
In the past two decades, transit surveys have revealed a class of planets with thick atmospheres -- sub-Neptunes -- that must have completed their accretion in protoplanet disks. When planets form in the gaseous disk, the gravitational interaction with the disk gas drives their migration and results in the trapping of neighboring planets in mean motion resonances, though these resonances can later be broken when the damping effects of disk gas or planetesimals wane. It is widely accepted that the outer Solar System gas giant planets originally formed in a resonant chain, which was later disrupted by dynamical instabilities. Here, we explore whether the early formation of the terrestrial planets in a resonance chain (including Theia) can evolve to the present configuration. Using N-body simulations, we demonstrate that the giant planet instability would also have destabilized the terrestrial resonance chain, triggering moon-forming giant impacts in 20--50\% of our simulated systems, dependent on the initial resonance architecture. After the instability, the eccentricity and inclination of the simulated planets match their present-day values. Under the proposed scenario, the current period ratio of 3.05 between Mars and Venus -- devoid of any special significance in traditional late formation models -- naturally arises as a relic of the former resonance chain.
The effective running Hubble constant in SNe Ia as a marker for the dark energy nature
2025-06-04
E. Fazzari, M. G. Dainotti, G. Montani, A. Melchiorri
We propose a new method that reveal the nature of dark energy (DE) evolution. Specifically, the method consists of studying the evolving trend regarding the effective running Hubble constant: when it increases, it indicates a quintessence nature, and when it decreases, it reveals a phantom behavior. Within the framework of the dark energy models we analyze three parameterizations: the $w$CDM model, a reduced Chevallier-Polarski-Linder (CPL) model and a new theoretical model based on the possible creation of dark energy by the time-varying gravitational field of the expanding Universe. For each DE model, we construct a theoretical effective running Hubble constant, i.e. a function of the redshift, which highlights the difference between modified dynamics and the $\Lambda$CDM-one. Furthermore, these dark energy models are compared to the phenomenological model of a decreasing trend of the Hubble constant as a function of the redshift, called the power-law model (PL) and the $\Lambda$CDM one. These three theoretical functions are fitted against the binned SNe Ia data samples, i.e. the Pantheon and the Master samples, the latter being a collection of SNe Ia from 4 catalogs: Dark Energy Survey (DES), PantheonPlus, Pantheon and Joint Lightcurve Analysis (JLA), without duplicated SNe Ia, called the Master sample. The main result of our study is that the phenomenological PL model is statistically favored compared to the other proposed scenarios, both for the Pantheon and the Master samples. At this stage, the SNe Ia data do not indicate that the evolution of dark energy models among the studied ones is favored respect to the $\Lambda$CDM. Nevertheless, the binned Pantheon sample allows for a discrimination of the nature of dark energy at least at the $1\,\sigma$ level via the fit of the effective running Hubble constant.
COSMOS-Web: Estimating Physical Parameters of Galaxies Using Self-Organizing Maps
2025-06-04
Fatemeh Abedini, Ghassem Gozaliasl, Akram Hasani Zonoozi, Atousa Kalantari, Maarit Korpi-Lagg, Olivier Ilbert, Hollis B. Akins, Natalie Allen, Rafael C. Arango-Toro, Caitlin M. Casey, Nicole E. Drakos, Andreas L. Faisst, Carter Flayhart, Maximilien Franco, Santosh Harish, Hossein Hatamnia, Jeyhan S. Kartaltepe, Ali Ahmad Khostovan, Anton M. Koekemoer, Vasily Kokorev, Rebecca L. Larson, Gavin Leroy, Daizhong Liu, Henry Joy McCracken, Jed McKinney, Nicolas McMahon, Bahram Mobasher, Sophie Newman, Louise Paquereau, Jason Rhodes, Brant E. Robertson, Marko Shuntov, Sina Taamoli, Sune Toft, Francesco Valentino, Eleni Vardoulaki, John R. Weaver
The COSMOS-Web survey, with its unparalleled combination of multiband data -- particularly near-infrared imaging from JWST's NIRCam (F115W, F150W, F277W, and F444W), provides a transformative dataset reaching down to approximately 28th magnitude (F444W) for studying galaxy evolution. In this work, we employ Self-Organizing Maps (SOMs), an unsupervised machine learning method, to estimate key physical parameters of galaxies, redshift, stellar mass, star formation rate (SFR), specific SFR (sSFR), and age, directly from photometric data out to redshift $z = 3.5$. SOMs efficiently project high-dimensional galaxy color information onto two-dimensional maps, revealing how physical properties vary among galaxies with similar spectral energy distributions.We first validate our approach using mock galaxy catalogs from the HORIZON-AGN simulation, where the SOM accurately recovers the true parameters, demonstrating its robustness. Applying the method to COSMOS-Web observations, we find that the SOM delivers reliable estimates despite the increased complexity of real galaxy populations. Performance metrics (NMAD and RMSE typically between 0.1 and 0.3) confirm the method's precision, with about 70\% of predictions falling within one standard deviation (dex) of the reference values. While redshift estimation in COSMOS-Web presents challenges (median NMAD = 0.05), the overall success of the SOM highlights its potential as a powerful and interpretable tool for galaxy parameter estimation.
Agentic AI systems, built on large language models (LLMs) and deployed in multi-agent configurations, are redefining intelligent autonomy, collaboration and decision-making across enterprise and societal domains. This review presents a structured analysis of Trust, Risk, and Security Management (TRiSM) in the context of LLM-based agentic multi-agent systems (AMAS). We begin by examining the conceptual foundations of agentic AI, its architectural differences from traditional AI agents, and the emerging system designs that enable scalable, tool-using autonomy. The TRiSM in the agentic AI framework is then detailed through four pillars governance, explainability, ModelOps, and privacy/security each contextualized for agentic LLMs. We identify unique threat vectors and introduce a comprehensive risk taxonomy for the agentic AI applications, supported by case studies illustrating real-world vulnerabilities. Furthermore, the paper also surveys trust-building mechanisms, transparency and oversight techniques, and state-of-the-art explainability strategies in distributed LLM agent systems. Additionally, metrics for evaluating trust, interpretability, and human-centered performance are reviewed alongside open benchmarking challenges. Security and privacy are addressed through encryption, adversarial defense, and compliance with evolving AI regulations. The paper concludes with a roadmap for responsible agentic AI, proposing research directions to align emerging multi-agent systems with robust TRiSM principles for safe, accountable, and transparent deployment.
The Near-Centaur Environment: Satellites, Rings, and Debris
2025-06-04
A. A. Sickafoose, S. M. Giuliatti Winter, R. Leiva, C. B. Olkin, D. Ragozzine, L. M. Woodney
The unexpected finding of a ring system around the Centaur (10199) Chariklo opened a new window for dynamical studies and posed many questions about the formation and evolutionary mechanisms of Centaurs as well as the relationship to satellites and outbursting activity. As minor planets that cross the orbits of the giant planets, Centaurs have short dynamical lifetimes: Centaurs are supplied from the trans-Neptunian region and some fraction migrates inward to become Jupiter-family comets. Given these dynamical pathways, a comparison of attributes across these classifications provides information to understand the source population(s) and the processes that have affected these minor planets throughout their lifetimes. In this chapter we review the current knowledge of satellites, rings, and debris around Centaur-like bodies, discuss the observational techniques involved, place the information into context with the trans-Neptunian Objects, and consider what the results tell us about the outer solar system. We also examine open questions and future prospects.
Medical image classification is crucial for diagnosis and treatment, benefiting significantly from advancements in artificial intelligence. The paper reviews recent progress in the field, focusing on three levels of solutions: basic, specific, and applied. It highlights advances in traditional methods using deep learning models like Convolutional Neural Networks and Vision Transformers, as well as state-of-the-art approaches with Vision Language Models. These models tackle the issue of limited labeled data, and enhance and explain predictive results through Explainable Artificial Intelligence.
2025-06-04
Amy Y. Lien, Hans Krimm, Craig Markwardt, Kyuseok Oh, Lea Marcotulli, Richard Mushotzky, Nicholas R. Collins, Scott Barthelmy, Wayne H. Baumgartner, S. Bradley Cenko, Michael Koss, Sibasish Laha, Takanori Sakamoto, David Palmer, Tyler Parsotan
The Burst Alert Telescope (BAT) onboard the Neil Gehrels Swift observatory has been serving as a survey instrument for the hard X-ray sky, and has detected thousands of X-ray sources (e.g., AGNs, X-ray binaries, etc). BAT monitors these X-ray sources and follows their light curves on time scales from minutes to years. In addition, BAT discovers hundreds of new X-ray sources in survey images stacked throughout the mission lifetime. We present the updated BAT survey catalog since the last published BAT 105 month survey catalog (Oh et al. 2018) with additional of 4.5 years of data until December 2017. Data since 2007 are reprocessed to include updated instrumental calibration. Analysis in this study shows that additional systematic noise can be seen in the 157-month mosaic images, resulting in decreases in the expected improvement in sensitivity and the number of new detections. The BAT 157-month survey reaches a sensitivity of $8.83 \times 10^{-12} \rm \ erg \ s^{-1} \ cm^{-2}$ for 90\% of the sky and $6.44 \times 10^{-12} \rm \ erg \ s^{-1} \ cm^{-2}$ for 10\% of the sky. This catalog includes spectra, monthly and snapshot light curves in eight energy bands (14-20, 20-24, 24-35, 35-50, 50-75, 75-100, 100-150, and 150-195 keV) for 1888 sources, including 256 new detections above the detection threshold of $4.8\sigma$. The light curves, spectra, and tables that summarize the information of the detected-sources are available in the online journal and in the catalog web page https://swift.gsfc.nasa.gov/results/bs157mon/.
2025-06-04
Jane Kondev, Marc Kirschner, Hernan G. Garcia, Gabriel L. Salmon, Rob Phillips
Many biological processes can be thought of as the result of an underlying dynamics in which the system repeatedly undergoes distinct and abortive trajectories with the dynamical process only ending when some specific process, purpose, structure or function is achieved. A classic example is the way in which microtubules attach to kinetochores as a prerequisite for chromosome segregation and cell division. In this example, the dynamics is characterized by apparently futile time histories in which microtubules repeatedly grow and shrink without chromosomal attachment. We hypothesize that for biological processes for which it is not the initial conditions that matter, but rather the final state, this kind of exploratory dynamics is biology's unique and necessary solution to achieving these functions with high fidelity. This kind of cause and effect relationship can be contrasted to examples from physics and chemistry where the initial conditions determine the outcome. In this paper, we examine the similarities of many biological processes that depend upon random trajectories starting from the initial state and the selection of subsets of these trajectories to achieve some desired functional final state. We begin by reviewing the long history of the principles of dynamics, first in the context of physics, and then in the context of the study of life. These ideas are then stacked against the broad categories of biological phenomenology that exhibit exploratory dynamics. We then build on earlier work by making a quantitative examination of a succession of increasingly sophisticated models for exploratory dynamics, all of which share the common feature of being a series of repeated trials that ultimately end in a "winning" trajectory. We also explore the ways in which microscopic parameters can be tuned to alter exploratory dynamics as well as the energetic burden of performing such processes.
Investigating the HI distribution and kinematics of ESO444-G084 and [KKS2000]23: New insights from the MHONGOOSE survey
2025-06-04
Brenda Namumba, Roger Ianjamasimanana, Bärbel Koribalski, Albert Bosma, Evangelia Athanassoula, Claude Carignan, Gyula I. G. Józsa, Peter Kamphuis, Roger P. Deane, Sinenhlanhla P. Sikhosana, Lourdes Verdes-Montenegro, Amidou Sorgho, Xola Ndaliso, Philippe Amram, Elias Brinks, Laurent Chemin, Francoise Combes, Erwin de Blok, Nathan Deg, Jayanne English, Julia Healy, Sushma Kurapati, Antonino Marasco, Stacy McGaugh, Kyle Oman, Kristine Spekkens, Simone Veronese, O. Ivy Wong
We present the HI distribution, kinematics, mass modeling, and disk stability of the dwarf irregular galaxies ESO444-G084 and [KKS2000]23 using high-resolution, high-sensitivity MHONGOOSE survey data from MeerKAT. ESO444-G084 shows centrally concentrated HI emission, while [KKS2000]23 exhibits irregular high-density clumps. Total HI fluxes measured down to 10^19 and 10^18 cm^-2 are nearly identical, indicating that the increased HI diameter at lower column densities results mainly from the larger beam, with no significant extra emission detected. We derive total HI masses of (1.1 +/- 0.1) x 10^8 and (6.1 +/- 0.3) x 10^8 solar masses for ESO444-G084 and [KKS2000]23, respectively. Using PyFAT and TiRiFiC, we extract 3D rotation curves that reveal disk-like kinematics in both galaxies. ESO444-G084 shows a warp beyond ~1.8 kpc and a fast-rising curve consistent with a centrally concentrated dark matter distribution, while [KKS2000]23's more gradual rise suggests a more extended halo. Mass modeling with an isothermal halo and stellar mass-to-light ratios of 0.20 for ESO444-G084 and 0.18 for [KKS2000]23 yields consistent results. We analyze disk stability using spatially resolved Toomre Q and gas-to-critical surface density ratios, linking these with H-alpha and FUV-based star formation. ESO444-G084 supports localized star formation despite global stability, while [KKS2000]23 appears gravitationally unstable yet lacks H-alpha, suggesting that turbulence, gas depletion, or past feedback suppresses star formation. No inflows or outflows are detected, indicating internal processes regulate star formation. This study highlights the interplay between HI morphology, kinematics, dark matter distribution, and disk stability, showing how internal processes shape dwarf galaxy evolution.
COSMOS Web: Morphological quenching and size-mass evolution of brightest group galaxies from z = 3.7
2025-06-04
Ghassem Gozaliasl, Lilan Yang, Jeyhan Kartaltepe, Greta Toni, Fatemeh Abedini, Hollis Akins, Natalie Allen, Rafael Arango-Toro, Arif Babul, Caitlin Casey, Nima Chartab, Nicole Drakos, Andreas Faisst, Alexis Finoguenov, Carter Flayhart, Maximilien Franco, Gavin Leroy, Santosh Harish, Günther Hasinger, Hossein Hatamnia, Olivier Ilbert, Shuowen Jin, Darshan Kakkad, Atousa Kalantari, Ali Ahmad Khostovan, Anton Koekemoer, Maarit Korpi-Lagg, Clotilde Laigle, Daizhong Liu, Georgios Magdis, Matteo Maturi, Henry Joy McCracken, Jed McKinney, Nicolas McMahon, Bahram Mobasher, Lauro Moscardini, Jason Rhodes, Brant Robertson, Louise Paquereau, Annagrazia Puglisi, Rasha Samir, Mark Sargent, Zahra Sattari, Diana Scognamiglio, Nick Scoville, Marko Shuntov, David Sanders, Sina Taamoli, Sune Toft, Eleni Vardoulaki
We present a comprehensive study of the structural evolution of Brightest Group Galaxies (BGGs) from redshift $z \simeq 0.08$ to $z = 3.7$ using the \textit{James Webb Space Telescope}'s 255h COSMOS-Web program. This survey provides deep NIRCam imaging in four filters (F115W, F150W, F277W, F444W) across $\sim 0.54~\mathrm{deg}^2$ and MIRI coverage in $\sim 0.2~\mathrm{deg}^2$ of the COSMOS field. High-resolution NIRCam imaging enables robust size and morphological measurements, while multiwavelength photometry yields stellar masses, SFRs, and S\'ersic parameters. We classify BGGs as star-forming and quiescent using both rest-frame NUV--$r$--$J$ colors and a redshift-dependent specific star formation rate (sSFR) threshold. Our analysis reveals: (1) quiescent BGGs are systematically more compact than their star-forming counterparts and exhibit steeper size--mass slopes; (2) effective radii evolve as $R_e \propto (1+z)^{-\alpha}$, with $\alpha = 1.11 \pm 0.07$ (star-forming) and $1.40 \pm 0.09$ (quiescent); (3) star formation surface density ($\Sigma_{\mathrm{SFR}}$) increases with redshift and shows stronger evolution for massive BGGs ($\log_{10}(M_\ast/M_\odot) \geq 10.75$); (4) in the $\Sigma_*$--sSFR plane, a structural transition marks the quenching process, with bulge-dominated systems comprising over 80\% of the quiescent population. These results highlight the co-evolution of structure and star formation in BGGs, shaped by both internal and environmental processes, and establish BGGs as critical laboratories for studying the baryonic assembly and morphological transformation of central galaxies in group-scale halos.
QQSUM: A Novel Task and Model of Quantitative Query-Focused Summarization for Review-based Product Question Answering
2025-06-04
An Quang Tang, Xiuzhen Zhang, Minh Ngoc Dinh, Zhuang Li
Review-based Product Question Answering (PQA) allows e-commerce platforms to automatically address customer queries by leveraging insights from user reviews. However, existing PQA systems generate answers with only a single perspective, failing to capture the diversity of customer opinions. In this paper we introduce a novel task Quantitative Query-Focused Summarization (QQSUM), which aims to summarize diverse customer opinions into representative Key Points (KPs) and quantify their prevalence to effectively answer user queries. While Retrieval-Augmented Generation (RAG) shows promise for PQA, its generated answers still fall short of capturing the full diversity of viewpoints. To tackle this challenge, our model QQSUM-RAG, which extends RAG, employs few-shot learning to jointly train a KP-oriented retriever and a KP summary generator, enabling KP-based summaries that capture diverse and representative opinions. Experimental results demonstrate that QQSUM-RAG achieves superior performance compared to state-of-the-art RAG baselines in both textual quality and quantification accuracy of opinions. Our source code is available at: https://github.com/antangrocket1312/QQSUMM
AgentMisalignment: Measuring the Propensity for Misaligned Behaviour in LLM-Based Agents
2025-06-04
Akshat Naik, Patrick Quinn, Guillermo Bosch, Emma Gouné, Francisco Javier Campos Zabala, Jason Ross Brown, Edward James Young
As Large Language Model (LLM) agents become more widespread, associated misalignment risks increase. Prior work has examined agents' ability to enact misaligned behaviour (misalignment capability) and their compliance with harmful instructions (misuse propensity). However, the likelihood of agents attempting misaligned behaviours in real-world settings (misalignment propensity) remains poorly understood. We introduce a misalignment propensity benchmark, AgentMisalignment, consisting of a suite of realistic scenarios in which LLM agents have the opportunity to display misaligned behaviour. We organise our evaluations into subcategories of misaligned behaviours, including goal-guarding, resisting shutdown, sandbagging, and power-seeking. We report the performance of frontier models on our benchmark, observing higher misalignment on average when evaluating more capable models. Finally, we systematically vary agent personalities through different system prompts. We find that persona characteristics can dramatically and unpredictably influence misalignment tendencies -- occasionally far more than the choice of model itself -- highlighting the importance of careful system prompt engineering for deployed AI agents. Our work highlights the failure of current alignment methods to generalise to LLM agents, and underscores the need for further propensity evaluations as autonomous systems become more prevalent.
Time domain astrophysics with transient sources. Delay estimate via Cross Correlation Function techniques
2025-06-04
W. Leone, L. Burderi, T. di Salvo, A. Anitra, A. Sanna, A. Riggio, R. Iaria, F. Fiore, F. Longo, M. Ďurišková, A. Tsvetkova, C. Maraventano, C. Miceli
The timing analysis of transient events allows for investigating numerous still open areas of modern astrophysics. The article explores all the mathematical and physical tools required to estimate delays and associated errors between two Times of Arrival (ToA) lists, by exploiting Cross Correlation Function (CCF) techniques. The CCF permits the establishment of the delay between two observed signals and is defined on two continuous functions. A detector does not directly measure the intensity of the electromagnetic signal (interacting with its material) but rather detects each photon ToA through a probabilistic process. Since the CCF is defined on continuous functions, the crucial step is to obtain a continuous rate curve from a list of ToA. This step is treated in the article and the constructed rate functions are light curves that are continuous functions. This allows, in principle, the estimation of delays with any desired resolution. Due to the statistical nature of the measurement process, two independent detections of the same signal yield different photon times. Consequently, light curves derived from these lists differ due to Poisson fluctuations, leading the CCF between them to fluctuate around the true theoretical delay. This article describes a Monte Carlo technique that enables reliable delay estimation by providing a robust measure of the uncertainties induced by Poissonian fluctuations. GRB data are considered as they offer optimal test cases for the proposed techniques. The developed techniques provides a significant computational advantage and are useful analyzing of data characterized by low-count statistics (i.e., low photon count rates in c/s), as they allow overcoming the limitations associated with traditional fixed bin-size methods.
Lonely Little Red Dots: Challenges to the AGN-nature of little red dots through their clustering and spectral energy distributions
2025-06-04
María Carranza-Escudero, Christopher J. Conselice, Nathan Adams, Thomas Harvey, Duncan Austin, Peter Behroozi, Leonardo Ferreira, Katherine Ormerod, Qiao Duan, James Trussler, Qiong Li, Lewi Westcott, Rogier A. Windhorst, Dan Coe, Seth H. Cohen, Cheng Cheng, Simon P. Driver, Brenda Frye, Lukas J. Furtak, Norman A. Grogin, Nimish P. Hathi, Rolf A. Jansen, Anton M. Koekemoer, Madeline A. Marshall, Rosalia O'Brien, Norbert Pirzkal, Maria Polletta, Aaron Robotham, Michael J. Rutkowski, Jake Summers, Stephen M. Wilkins, Christopher N. A. Willmer, Haojing Yan, Adi Zitrin
Observations with the James Webb Space Telescope (JWST) reveal a previously unseen population of compact red objects, known as ``little red dots`` (LRDs). We study a new photometrically selected sample of 124 LRDs in the redshift range $z$ $\sim$ 3 - 10 selected from NIRCam coverage of the CEERS, NEP-TDF, JADES and JEMS surveys. For JADES, the NEP-TDF and CEERS, we compare SED models with and without AGN components and analyse the impact of an AGN component on the goodness of fit using the Bayesian information criterion (BIC). We find that whilst the $\chi^{2}$ of the majority of models containing AGN components is improved compared to models without AGN components, we show that the BIC suggests models without AGN are a more appropriate fit to LRD SEDs, especially when MIRI data is available. We also measure LRD clustering in the CEERS field, JADES field, and NEP-TDF, where we compare the spatial distribution of LRDs and galaxies with Kolmogorov-Smirnov tests of equality of distribution. We find that the neighbourhood of LRDs tends to be less dense compared to galaxies at all selections and masses and at similar redshifts. We further measure upper limit estimates for the halo masses of LRDs using abundance matching. Whilst the population of LRDs could be a mixture of several different inherent populations, as a whole it does appear that these systems are mostly hosting compact galaxies or star clusters in formation.
RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectors
2025-06-04
Hicham Eddoubi, Jonas Ricker, Federico Cocchi, Lorenzo Baraldi, Angelo Sotgiu, Maura Pintor, Marcella Cornia, Lorenzo Baraldi, Asja Fischer, Rita Cucchiara, Battista Biggio
AI-generated images have reached a quality level at which humans are incapable of reliably distinguishing them from real images. To counteract the inherent risk of fraud and disinformation, the detection of AI-generated images is a pressing challenge and an active research topic. While many of the presented methods claim to achieve high detection accuracy, they are usually evaluated under idealized conditions. In particular, the adversarial robustness is often neglected, potentially due to a lack of awareness or the substantial effort required to conduct a comprehensive robustness analysis. In this work, we tackle this problem by providing a simpler means to assess the robustness of AI-generated image detectors. We present RAID (Robust evaluation of AI-generated image Detectors), a dataset of 72k diverse and highly transferable adversarial examples. The dataset is created by running attacks against an ensemble of seven state-of-the-art detectors and images generated by four different text-to-image models. Extensive experiments show that our methodology generates adversarial images that transfer with a high success rate to unseen detectors, which can be used to quickly provide an approximate yet still reliable estimate of a detector's adversarial robustnessOur findings indicate that current state-of-the-art AI-generated image detectors can be easily deceived by adversarial examples, highlighting the critical need for the development of more robust methods. We release our dataset at https://huggingface.co/datasets/aimagelab/RAID and evaluation code at https://github.com/pralab/RAID.
Differentiable Fuzzy Cosmic-Web for Field Level Inference
2025-06-04
P. Rosselló, F. -S. Kitaura, D. Forero-Sánchez, F. Sinigaglia, G. Favole
A comprehensive analysis of the cosmological large-scale structure derived from galaxy surveys involves field-level inference, which requires a forward modelling framework that simultaneously accounts for structure formation and tracer bias. While structure formation models are well-understood, the development of an effective field-level bias model remains imprecise, particularly in the context of tracer perturbation theory within Bayesian reconstruction methods, which we address in this work. To bridge this gap, we have developed a differentiable model that integrates augmented Lagrangian perturbation theory, nonlinear, nonlocal, and stochastic biasing. At the core of our approach is the Hierarchical Cosmic-Web Biasing Nonlocal (HICOBIAN) model, which provides a positive definite description of tracer bias while incorporating a long- and short-range nonlocal framework via cosmic-web regions and deviations from Poissonity in the likelihood. A key insight of our model is that transitions between cosmic-web regions are inherently smooth, which we implement using sigmoid-based gradient operations. This enables a fuzzy, and, hence, differentiable hierarchical cosmic-web description, making the model well-suited for machine learning frameworks. Our approach accurately reproduces the primordial density field within associated error bars, as validated by two- and three-point statistics in Fourier space. Furthermore, we demonstrate that the methodology approaches the maximum encoded information consistent with Poisson noise. We introduce a Bayesian field-level inference algorithm that leverages the same forward modelling framework used in galaxy, quasar, and Lyman alpha forest mock catalog generation -- including nonlinear, nonlocal and stochastic bias with redshift space distortions -- providing a unified and consistent approach to the analysis of large-scale cosmic structure.
FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review
2025-06-04
Cédric Léonard, Dirk Stober, Martin Schulz
New UAV technologies and the NewSpace era are transforming Earth Observation missions and data acquisition. Numerous small platforms generate large data volume, straining bandwidth and requiring onboard decision-making to transmit high-quality information in time. While Machine Learning allows real-time autonomous processing, FPGAs balance performance with adaptability to mission-specific requirements, enabling onboard deployment. This review systematically analyzes 66 experiments deploying ML models on FPGAs for Remote Sensing applications. We introduce two distinct taxonomies to capture both efficient model architectures and FPGA implementation strategies. For transparency and reproducibility, we follow PRISMA 2020 guidelines and share all data and code at https://github.com/CedricLeon/Survey_RS-ML-FPGA.
A synoptic review of the electrical circuital model-based analysis of laboratory plasma sheaths, alongside their stability features in a realistic broader horizon, is systematically presented herein. It explains the basic physics responsible for the inductive (L_sh), capacitive (C_sh), and resistive (R_sh) properties simultaneously, exhibited by plasma sheaths. The analyzed model sheath behaviors are judiciously described in the light of the state-of-the-art sheath scenarios, illustratively. The sheath-based circuital components are minutely contrasted with the traditionally available circuital counterparts. The applications of the novel circuital sheath model in widespread fields of research having both fundamental and applied importance are discussed. The main merits of modelling plasma sheaths through the circuital formalism over the existing non-circuital theoretical ones are briefly outlined, jointly with future applied scope.
Gravothermal expansion of dwarf spheroidal galaxies heated by dark subhaloes
2025-06-04
Jorge Peñarrubia, Raphaël Errani, Eduardo Vitral, Matthew G. Walker
We use analytical and $N$-body methods to study the evolution of dwarf spheroidal galaxies (dSphs) embedded in dark matter (DM) haloes that host a sizeable subhalo population. Dark subhaloes generate a fluctuating gravitational field that injects energy into stellar orbits, driving a gradual expansion of dSphs. Despite the overall expansion, the stellar density profile preserves its initial shape, suggesting that the evolution proceeds in a self-similar manner. Meanwhile, the velocity dispersion profile, initially flat, evolves as the galaxy expands: the inner regions heat up, while the outer regions cool down. Kinematically, this resembles gravothermal collapse but with an inverted evolution, instead of collapsing the stellar system expands within a fluctuating halo potential. As the half-light radius $r_{\rm half}$ approaches the halo peak velocity radius $r_{\rm max}$, the expansion slows, and the velocity dispersion peaks at $\sigma_{\rm max} \simeq 0.27 v_{\rm max}$. The stellar heat capacity remains positive for deeply embedded stars but diverges near $r_{\rm max}$, turning negative beyond this threshold, which indicates a phase transition in the dynamical response to energy injection. The relaxation time scales as $t_{\rm rel} \sim r_{\rm half}^{3/2}$, showing that orbital diffusion slows as the galaxy expands. Ultra-faint dSphs, having the smallest sizes and shortest relaxation times, are particularly sensitive to the presence of dark subhaloes. Some of our dSph models expand beyond the detection of current photometric surveys, becoming `stealth' galaxies with half-light radii and velocity dispersions similar to ultra-diffuse galaxies (UDGs), but orders of magnitude lower luminosities, representing a distinct, yet currently undetected, population of DM-dominated satellites.
2025-06-04
Pierre-François Massiani, Christian Fiedler, Lukas Haverbeck, Friedrich Solowjow, Sebastian Trimpe
We propose a framework for hypothesis testing on conditional probability distributions, which we then use to construct conditional two-sample statistical tests. These tests identify the inputs -- called covariates in this context -- where two conditional expectations differ with high probability. Our key idea is to transform confidence bounds of a learning method into a conditional two-sample test, and we instantiate this principle for kernel ridge regression (KRR) and conditional kernel mean embeddings. We generalize existing pointwise-in-time or time-uniform confidence bounds for KRR to previously-inaccessible yet essential cases such as infinite-dimensional outputs with non-trace-class kernels. These bounds enable circumventing the need for independent data in our statistical tests, since they allow online sampling. We also introduce bootstrapping schemes leveraging the parametric form of testing thresholds identified in theory to avoid tuning inaccessible parameters, making our method readily applicable in practice. Such conditional two-sample tests are especially relevant in applications where data arrive sequentially or non-independently, or when output distributions vary with operational parameters. We demonstrate their utility through examples in process monitoring and comparison of dynamical systems. Overall, our results establish a comprehensive foundation for conditional two-sample testing, from theoretical guarantees to practical implementation, and advance the state-of-the-art on the concentration of vector-valued least squares estimation.
Relativistic jets from AGN are an important driver of feedback in galaxies. They interact with their environments over a wide range of physical scales during their lifetime, and an understanding of these interactions is crucial for unraveling the role of supermassive blackholes in shaping galaxy evolution. The impact of such jets have been traditionally considered in the context of heating the large-scale environments. However, in the last few decades there has been additional focus on the immediate impact of jet feedback on the host galaxy itself. In this review we outline the development of various numerical simulations since the onset of studies of jets to the present day, where sophisticated numerical techniques have been employed to study jet feedback including a range of physical processes. The jets can act as an important agent of injecting energy in the host's ISM, as confirmed both in observations of multi-phase gas, as well as in simulations. Such interactions have the potential to impact the kinematics of the gas as well as its star formation. We summarize the recent results from simulations of jet feedback on kpc scales, and outline the broader implications for observations and galaxy evolution.
An integrated multi-size collision model for flotation
2025-06-04
Benedikt Tiedemann, Moritz Kreuseler, Jochen Fröhlich
The accuracy obtained with CFD and process simulations of flotation critically depends on the quality and robustness of the underlying models for the non-resolved sub-processes. An important issue in flotation is the collision between particles and air bubbles. Many models have been developed, but their accuracy for applications in flotation is limited. In particular, the significant size difference between particles and bubbles and their intricate coupling to the turbulent flow field pose severe challenges. The present paper first reviews presently employed collision models, highlighting their advantages and disadvantages when applied to flotation. On this basis, the "Integrated Multi-Size Collision model" (IMSC) is proposed. After a detailed evaluation, it combines existing approaches from various sources and introduces new developments designed to address present shortcomings. The model is validated by own DNS data as well as data from the literature. It is shown that, overall, the IMSC provides better predictions for the collision rate in typical flotation conditions than presently employed collision models and covers the entire parameter range of the flotation process very well. Using the available data, some of the underlying modelling assumptions are validated. Finally, a comprehensive overview of the model is provided for further use in Euler-Euler frameworks or process simulations.
We discuss recent progress in the study of entanglement within cosmological frameworks, focusing on both momentum and position-space approaches and also reviewing the possibility to directly extract entanglement from quantum fields. Entanglement generation in expanding spacetimes can be traced back to the phenomenon of gravitational particle production, according to which the background gravitational field may transfer energy and momentum to quantum fields. The corresponding entanglement amount and its mode dependence are both sensitive to the field statistics and to the details of spacetime expansion, thus encoding information about the background. Gravitational production processes also play a key role in addressing the quantum-to-classical transition of cosmological perturbations. In order to directly extract entanglement from quantum fields, local interactions with additional quantum systems, working as detectors, have been suggested, leading to the formulation of the entanglement harvesting protocol. Despite harvesting procedures are currently unfeasible from an experimental point of view, various proposals for implementation exist and a proper modeling of detectors and local interactions is crucial to address entanglement extraction via realistic setups. In the final part of the work, we address entanglement characterization in position space, primarily focusing on black hole spacetimes. We first investigate a possible interpretation of Bekenstein-Hawking black hole entropy in terms of the entanglement entropy arising in discrete quantum field theories, on account of the area law. Then, we discuss the resolution of the black hole information paradox via the gravitational fine-grained entropy formula, which provides a new way to compute the entropy of Hawking radiation and allows to preserve unitarity in black hole evaporation processes.
Neurodiversity describes variation in brain function among people, including common conditions such as Autism spectrum disorder (ASD), Attention deficit hyperactivity disorder (ADHD), and dyslexia. While Software Engineering (SE) literature has started to explore the experiences of neurodivergent software engineers, there is a lack of research that compares their challenges to those of neurotypical software engineers. To address this gap, we analyze existing data from the 2022 Stack Overflow Developer survey that collected data on neurodiversity. We quantitatively compare the answers of professional engineers with ASD (n=374), ADHD (n=1305), and dyslexia (n=363) with neurotypical engineers. Our findings indicate that neurodivergent engineers face more difficulties than neurotypical engineers. Specifically, engineers with ADHD report that they face more interruptions caused by waiting for answers, and that they less frequently interact with individuals outside their team. This study provides a baseline for future research comparing neurodivergent engineers with neurotypical ones. Several factors in the Stack Overflow survey and in our analysis are likely to lead to conservative estimates of the actual effects between neurodivergent and neurotypical engineers, e.g., the effects of the COVID-19 pandemic and our focus on employed professionals.
Canceling the elastic Poynting effect with geometry
2025-06-04
M. Destrade, Y. Du, J. Blackwell, N. Colgan, V. Balbi
The Poynting effect is a paragon of nonlinear soft matter mechanics. It is the tendency (found in all incompressible, isotropic, hyperelastic solids) exhibited by a soft block to expand vertically when sheared horizontally. It can be observed whenever the length of the cuboid is at least four times its thickness. Here we show that the Poynting effect can be easily reversed and the cuboid can shrink vertically, simply by reducing this aspect ratio. In principle, this discovery means that for a given solid, say one used as a seismic wave absorber under a building, an optimal ratio exists where vertical displacements and vibrations can be completely eliminated. Here we first recall the classical theoretical treatment of the positive Poynting effect, and then show experimentally how it can be reversed. Using Finite Element simulations, we then investigate how the effect can be suppressed. We find that cubes always provide a reverse Poynting effect, irrespective of their material properties (in the third-order theory of weakly nonlinear elasticity).
Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Grid
2025-06-04
Julius Gonsior, Tim Rieß, Anja Reusch, Claudio Hartmann, Maik Thiele, Wolfgang Lehner
Annotating data is a time-consuming and costly task, but it is inherently required for supervised machine learning. Active Learning (AL) is an established method that minimizes human labeling effort by iteratively selecting the most informative unlabeled samples for expert annotation, thereby improving the overall classification performance. Even though AL has been known for decades, AL is still rarely used in real-world applications. As indicated in the two community web surveys among the NLP community about AL, two main reasons continue to hold practitioners back from using AL: first, the complexity of setting AL up, and second, a lack of trust in its effectiveness. We hypothesize that both reasons share the same culprit: the large hyperparameter space of AL. This mostly unexplored hyperparameter space often leads to misleading and irreproducible AL experiment results. In this study, we first compiled a large hyperparameter grid of over 4.6 million hyperparameter combinations, second, recorded the performance of all combinations in the so-far biggest conducted AL study, and third, analyzed the impact of each hyperparameter in the experiment results. In the end, we give recommendations about the influence of each hyperparameter, demonstrate the surprising influence of the concrete AL strategy implementation, and outline an experimental study design for reproducible AL experiments with minimal computational effort, thus contributing to more reproducible and trustworthy AL research in the future.
Event classifiers are the most fundamental observables to probe the system size and event topology of high-energy collisions. Significant progress has been made to establish suitable event classifiers to probe different physics processes occurring in elementary $e^{+}e^{-}$ to heavy-ion collisions in a range of center of mass energies. One of the major motivations to revisit event classifiers at the Large Hadron Collider (LHC) originates from the recent measurements of high multiplicity proton-proton collisions, which have revealed that these small collision systems exhibit features similar to the formation of quark-gluon plasma (QGP), traditionally believed to be only achievable in heavy nucleus-nucleus collisions at ultra-relativistic energies. To pinpoint the origin of these QGP-like phenomena with substantially reduced auto-correlation and selection biases, and to bring all collision systems on equal footing, along with charged-particle multiplicity, lately several event topology classifiers such as transverse sphericity, transverse spherocity, relative transverse activity classifier, and charged-particle flattenicity have been used extensively in experiments as well as in the phenomenological front. In this review article, we extensively discuss the purpose, coverage, and usage guidelines of these event shape classifiers, which can be crucial given the upcoming measurements in a precision era during Runs 3 and 4 of the LHC. In most cases, the event shape observables are found to be better probes in understanding the heavy-ion-like behavior seen at the LHC, while making a multi-differential study of multihadron production dynamics in hadronic and nuclear collisions.
Conventional and Fuzzy Data Envelopment Analysis with deaR
2025-06-04
Vicente J. Bolos, Rafael Benitez, Vicente Coll-Serrano
deaR is a recently developed R package for data envelopment analysis (DEA) that implements a large number of conventional and fuzzy models, along with super-efficiency models, cross-efficiency analysis, Malmquist index, bootstrapping, and metafrontier analysis. It should be noted that deaR is the only package to date that incorporates Kao-Liu, Guo-Tanaka and possibilistic fuzzy models. The versatility of the package allows the user to work with different returns to scale and orientations, as well as to consider special features, namely non-controllable, non-discretionary or undesirable variables. Moreover, it includes novel graphical representations that can help the user to display the results. This paper is a comprehensive description of deaR, reviewing all implemented models and giving examples of use.
Advancements in Artificial Intelligence Applications for Cardiovascular Disease Research
2025-06-04
Yuanlin Mo, Haishan Huang, Bocheng Liang, Weibo Ma
Recent advancements in artificial intelligence (AI) have revolutionized cardiovascular medicine, particularly through integration with computed tomography (CT), magnetic resonance imaging (MRI), electrocardiography (ECG) and ultrasound (US). Deep learning architectures, including convolutional neural networks and generative adversarial networks, enable automated analysis of medical imaging and physiological signals, surpassing human capabilities in diagnostic accuracy and workflow efficiency. However, critical challenges persist, including the inability to validate input data accuracy, which may propagate diagnostic errors. This review highlights AI's transformative potential in precision diagnostics while underscoring the need for robust validation protocols to ensure clinical reliability. Future directions emphasize hybrid models integrating multimodal data and adaptive algorithms to refine personalized cardiovascular care.
Searching for Dark Galaxies with HI detection from the Arecibo Legacy Fast ALFA (ALFALFA) survey
2025-06-04
Minseong Kwon, Ho Seong Hwang, Brian R. Kent, Ilsang Yoon, Gain Lee, Hyein Yoon
We present a catalog of 142 dark galaxy candidates in a region covered by the Arecibo Legacy Fast ALFA (ALFALFA) survey. We start with 344 ALFALFA HI sources without optical counterparts and remove those that do not seem to have dark galaxy origin. To do that, we first eliminate 83 sources that are known HI clouds probably formed from tidal interactions between galaxies and 13 sources that have optical counterparts. We then remove 56 sources located near other HI sources, which are likely to be HI clouds. We further exclude 10 sources that have nearby HI sources within the ALFALFA beam and 40 sources potentially associated with nearby galaxies. We perform visual inspection of optical images from DESI Legacy Imaging Survey with an $r$-band surface brightness limit of $\sim 28.5 \rm \ mag\ arcsec^{-2}$ as well as NUV images from GALEX to confirm the absence of stellar emission. We additionally inspect infrared images from WISE and AKARI for dust emission. As a result, we identify 142 dark galaxy candidates and analyze their physical properties by comparing with luminous galaxies. We find that the dark galaxy candidates generally have smaller dynamical masses, higher HI-to-dynamical mass ratios, and are located in less dense regions when compared to luminous galaxies, which is consistent with results from cosmological simulations. This sample provides an important testbed for studying the role of dark matter in galaxy formation and evolution.
Trustworthy Medical Question Answering: An Evaluation-Centric Survey
2025-06-04
Yinuo Wang, Robert E. Mercer, Frank Rudzicz, Sudipta Singha Roy, Pengjie Ren, Zhumin Chen, Xindi Wang
Trustworthiness in healthcare question-answering (QA) systems is important for ensuring patient safety, clinical effectiveness, and user confidence. As large language models (LLMs) become increasingly integrated into medical settings, the reliability of their responses directly influences clinical decision-making and patient outcomes. However, achieving comprehensive trustworthiness in medical QA poses significant challenges due to the inherent complexity of healthcare data, the critical nature of clinical scenarios, and the multifaceted dimensions of trustworthy AI. In this survey, we systematically examine six key dimensions of trustworthiness in medical QA, i.e., Factuality, Robustness, Fairness, Safety, Explainability, and Calibration. We review how each dimension is evaluated in existing LLM-based medical QA systems. We compile and compare major benchmarks designed to assess these dimensions and analyze evaluation-guided techniques that drive model improvements, such as retrieval-augmented grounding, adversarial fine-tuning, and safety alignment. Finally, we identify open challenges-such as scalable expert evaluation, integrated multi-dimensional metrics, and real-world deployment studies-and propose future research directions to advance the safe, reliable, and transparent deployment of LLM-powered medical QA.
Fast Sampling for System Identification: Overcoming Noise, Offsets, and Closed-Loop Challenges with State Variable Filter
2025-06-04
Ichiro Maruta, Toshiharu Sugie
This paper investigates the effects of setting the sampling frequency significantly higher than conventional guidelines in system identification. Although continuous-time identification methods resolve the numerical difficulties encountered in discrete-time approaches when employing fast sampling (e.g., the problems caused by all poles approaching unity), the potential benefits of using sampling frequencies that far exceed traditional rules like the "ten times the bandwidth" guideline remained largely unexplored. We show that using a state variable filter (SVF)-like least squares approach, the variance of the estimation error scales as $O(h)$ with the sampling interval $h$. Importantly, this scaling holds even with colored noise or noise correlations between variables. Thus, increasing the sampling frequency and applying the SVF method offers a novel solution for challenging problems such as closed-loop system identification and measurements with offsets. Theoretical findings are supported by numerical examples, including the closed-loop identification of unstable multi-input multi-output (MIMO) systems.
The rapid advancement of photography has created a growing demand for a practical blind raw image denoising method. Recently, learning-based methods have become mainstream due to their excellent performance. However, most existing learning-based methods suffer from camera-specific data dependency, resulting in performance drops when applied to data from unknown cameras. To address this challenge, we introduce a novel blind raw image denoising method named YOND, which represents You Only Need a Denoiser. Trained solely on synthetic data, YOND can generalize robustly to noisy raw images captured by diverse unknown cameras. Specifically, we propose three key modules to guarantee the practicality of YOND: coarse-to-fine noise estimation (CNE), expectation-matched variance-stabilizing transform (EM-VST), and SNR-guided denoiser (SNR-Net). Firstly, we propose CNE to identify the camera noise characteristic, refining the estimated noise parameters based on the coarse denoised image. Secondly, we propose EM-VST to eliminate camera-specific data dependency, correcting the bias expectation of VST according to the noisy image. Finally, we propose SNR-Net to offer controllable raw image denoising, supporting adaptive adjustments and manual fine-tuning. Extensive experiments on unknown cameras, along with flexible solutions for challenging cases, demonstrate the superior practicality of our method. The source code will be publicly available at the \href{https://fenghansen.github.io/publication/YOND}{project homepage}.
Antibiotic resistance (AR) is one of the greatest public health challenges worldwide. Processes that allow the reduction of AR predictor of hospital wastewater has become crucial process that contributes to the protection of public health and the environment. The aim of this review article was to compare the effectiveness of various methods for treatment hospital wastewater in eliminating antibiotic-resistant bacteria (ARB) and degrading antibiotic resistance genes (ARGs) and antibiotics. A large number of studies dealing with wastewater treatment suggest that this topic is highly relevant and that new solutions are being developed to limit the spread of AR. Some wastewater treatment techniques have been in use for decades. Despite the negative effects of chlorine compounds, chlorination is still applied to eliminate ARB, ARGs, and drug metabolites. Ultraviolet (UV) radiation and ozonation have long been recognized for their treating properties. In the literature, advanced oxidation processes (AOPs) are increasingly often indicated as the most effective alternative to conventional treatment methods. Various methods for disinfecting hospital wastewater were reviewed and their environmental impact was analyzed in this article, and the results provide valuable insights for the further development of effective wastewater management strategies.
2025-06-04
Qian Dan, Hongjiang Lei, Ki-Hong Park, Gaofeng Pan
This work investigates the physical layer security of rate-splitting multiple access (RSMA)-aided integrated communication and sensing (ISAC) systems. The ISAC base station (BS) transmits signals to communicate with users in an eavesdropped scenario and to estimate the parameters of the sensed targets. The research considers different sensing signals under RSMA technology and the Cram{\'{e}}r-Rao bound of the parameter estimation is utilized as the sensing metric. With the channel state information (CSI) of eavesdroppers known, the transmitting beam of the BS is optimized to maximize the energy efficiency in terms of the minimum user rate and secrecy capacity, considering the fairness among users and ensuring the sensing performance and communication security. With the CSI of eavesdroppers unknown, the transmitting beam of the BS is designed to minimize the energy consumption for sensing and communication, and the residual power is utilized for artificial noise, which is isotropically emitted to achieve interference with potential eavesdroppers. To solve the non-convex problems, three iterative algorithms based on successive convex approximation and penalty function are proposed. The simulation results illustrate the effectiveness of the proposed schemes.
VCDiag: Classifying Erroneous Waveforms for Failure Triage Acceleration
2025-06-04
Minh Luu, Surya Jasper, Khoi Le, Evan Pan, Michael Quinn, Aakash Tyagi, Jiang Hu
Failure triage in design functional verification is critical but time-intensive, relying on manual specification reviews, log inspections, and waveform analyses. While machine learning (ML) has improved areas like stimulus generation and coverage closure, its application to RTL-level simulation failure triage, particularly for large designs, remains limited. VCDiag offers an efficient, adaptable approach using VCD data to classify failing waveforms and pinpoint likely failure locations. In the largest experiment, VCDiag achieves over 94% accuracy in identifying the top three most likely modules. The framework introduces a novel signal selection and statistical compression approach, achieving over 120x reduction in raw data size while preserving features essential for classification. It can also be integrated into diverse Verilog/SystemVerilog designs and testbenches.
Preface to the Special Issue of the TAL Journal on Scholarly Document Processing
2025-06-04
Florian Boudin, Akiko Aizawa
The rapid growth of scholarly literature makes it increasingly difficult for researchers to keep up with new knowledge. Automated tools are now more essential than ever to help navigate and interpret this vast body of information. Scientific papers pose unique difficulties, with their complex language, specialized terminology, and diverse formats, requiring advanced methods to extract reliable and actionable insights. Large language models (LLMs) offer new opportunities, enabling tasks such as literature reviews, writing assistance, and interactive exploration of research. This special issue of the TAL journal highlights research addressing these challenges and, more broadly, research on natural language processing and information retrieval for scholarly and scientific documents.
Extendible cardinals, and Laver-generic large cardinal axioms for extendibility
2025-06-04
Sakaé Fuchino
We introduce (super-$C^{(\infty)}$-)Laver-generic large cardinal axioms for extendibility ((super-$C^{(\infty)}$-)LgLCAs for extendible, for short), and show that most of the previously known consequences of the (super-$C^{(\infty)}$-)LgLCAs for ultrahuge, in particular, the strong and general forms of Resurrection Principles, Maximality Principles, and Absoluteness Theorems, lready follow from (super-$C^{(\infty)}$-)LgLCAs for extendible. The consistency of the LgLCAs for extendible (for transfinitely iterable $\Sigma_2$-definable classes of posets) follows from an extendible cardinal while the consistency of super-$C^{(\infty)}$-LgLCAs for extendible follows from a model with a super-$C^{(\infty)}$-extendible cardinal. If $\kappa$ is an almost-huge cardinal, there are cofinally many $\kappa_0<\kappa$ such that $V_\kappa\models{\kappa_0\mbox{ is super-}C^{(\infty)}\mbox{ extendible}}$. Most of the known reflection properties follow already from some of the LgLCAs for supercompact. We give a survey on the related results. We also show the separation between some of the LgLCAs as well as between LgLCAs and their consequences.
A Threat Intelligence Event Extraction Conceptual Model for Cyber Threat Intelligence Feeds
2025-06-04
Jamal H. Al-Yasiri, Mohamad Fadli Bin Zolkipli, Nik Fatinah N Mohd Farid, Mohammed Alsamman, Zainab Ali Mohammed
In response to the escalating cyber threats, the efficiency of Cyber Threat Intelligence (CTI) data collection has become paramount in ensuring robust cybersecurity. However, existing works encounter significant challenges in preprocessing large volumes of multilingual threat data, leading to inefficiencies in real-time threat analysis. This paper presents a systematic review of current techniques aimed at enhancing CTI data collection efficiency. Additionally, it proposes a conceptual model to further advance the effectiveness of threat intelligence feeds. Following the PRISMA guidelines, the review examines relevant studies from the Scopus database, highlighting the critical role of artificial intelligence (AI) and machine learning models in optimizing CTI data preprocessing. The findings underscore the importance of AI-driven methods, particularly supervised and unsupervised learning, in significantly improving the accuracy of threat detection and event extraction, thereby strengthening cybersecurity. Furthermore, the study identifies a gap in the existing research and introduces XBC conceptual model integrating XLM-RoBERTa, BiGRU, and CRF, specifically developed to address this gap. This paper contributes conceptually to the field by providing a detailed analysis of current CTI data collection techniques and introducing an innovative conceptual model to enhance future threat intelligence capabilities.
VChatter: Exploring Generative Conversational Agents for Simulating Exposure Therapy to Reduce Social Anxiety
2025-06-04
Han Zhang, KaWing Tsang, Zhenhui Peng
Many people struggle with social anxiety, feeling fear, or even physically uncomfortable in social situations like talking to strangers. Exposure therapy, a clinical method that gradually and repeatedly exposes individuals to the source of their fear and helps them build coping mechanisms, can reduce social anxiety but traditionally requires human therapists' guidance and constructions of situations. In this paper, we developed a multi-agent system VChatter to explore large language models(LLMs)-based conversational agents for simulating exposure therapy with users. Based on a survey study (N=36) and an expert interview, VChatter includes an Agent-P, which acts as a psychotherapist to design the exposure therapy plans for users, and two Agent-Hs, which can take on different interactive roles in low, medium, and high exposure scenarios. A six-day qualitative study (N=10) showcases VChatter's usefulness in reducing users' social anxiety, feelings of isolation, and avoidance of social interactions. We demonstrated the feasibility of using LLMs-based conversational agents to simulate exposure therapy for addressing social anxiety and discussed future concerns for designing agents tailored to social anxiety.
Benchmarking gate-based quantum devices via certification of qubit von Neumann measurements
2025-06-04
Paulina Lewandowska, Martin Beseda
We present an updated version of PyQBench, an open-source Python library designed for benchmarking gate-based quantum computers, with a focus on certifying qubit von Neumann measurements. This version extends PyQBench's capabilities by incorporating a certification scheme of quantum measurements that evaluates the accuracy on Noisy Intermediate-Scale Quantum devices, alongside its original functionality of von Neumann measurements' discrimination. PyQBench offers a user-friendly command-line interface and Python library integration, allowing users to implement custom measurement schemes and error models for more advanced benchmarking tasks. The new version is specifically designed to support IBM Q devices through integration with the Qiskit library, enabling seamless benchmarking on real quantum hardware. By providing flexible benchmarking tools, PyQBench addresses the critical need for reliable performance metrics in the NISQ era, contributing to the development of error mitigation techniques and the verification of quantum measurement fidelity. The source code is available on GitHub under an open-source license, encouraging community collaboration and further advancements in quantum hardware benchmarking.
Software Bill of Materials in Software Supply Chain Security A Systematic Literature Review
2025-06-04
Eric O'Donoghue, Yvette Hastings, Ernesto Ortiz, A. Redempta Manzi Muneza
Software Bill of Materials (SBOMs) are increasingly regarded as essential tools for securing software supply chains (SSCs), yet their real-world use and adoption barriers remain poorly understood. This systematic literature review synthesizes evidence from 40 peer-reviewed studies to evaluate how SBOMs are currently used to bolster SSC security. We identify five primary application areas: vulnerability management, transparency, component assessment, risk assessment, and SSC integrity. Despite clear promise, adoption is hindered by significant barriers: generation tooling, data privacy, format/standardization, sharing/distribution, cost/overhead, vulnerability exploitability, maintenance, analysis tooling, false positives, hidden packages, and tampering. To structure our analysis, we map these barriers to the ISO/IEC 25019:2023 Quality-in-Use model, revealing critical deficiencies in SBOM trustworthiness, usability, and suitability for security tasks. We also highlight key gaps in the literature. These include the absence of applying machine learning techniques to assess SBOMs and limited evaluation of SBOMs and SSCs using software quality assurance techniques. Our findings provide actionable insights for researchers, tool developers, and practitioners seeking to advance SBOM-driven SSC security and lay a foundation for future work at the intersection of SSC assurance, automation, and empirical software engineering.
We explore the stability of supersolid striped waves, plane-wave boselets, and other extended states in one-dimensional spin-orbit-coupled Bose-Einstein condensates with repulsive three-body interactions (R3BIs), modeled by quintic terms in the framework of the corresponding Gross-Pitaevskii equations. In the absence of R3BIs, the extended states are susceptible to the modulational instability (MI) induced by the cubic attractive nonlinearity. Using the linearized Bogoliubov-de-Gennes equations, we identify multiple new types of MI, including baseband, passband, mixedband, and zero-wavenumber-gain ones, which give rise to deterministic rogue waves and complex nonlinear wave patterns. Our analysis reveals that R3BIs eliminate baseband and zero-wavenumber-gain MIs, forming, instead, phonon modes that enable stable boselets. Additionally, mixedband and passband MIs are suppressed, which results in a lattice-like phonon-roton mode that supports a stable supersolid phase. These stable supersolids can be realized using currently available ultracold experimental setup.
Measuring Human Involvement in AI-Generated Text: A Case Study on Academic Writing
2025-06-04
Yuchen Guo, Zhicheng Dou, Huy H. Nguyen, Ching-Chun Chang, Saku Sugawara, Isao Echizen
Content creation has dramatically progressed with the rapid advancement of large language models like ChatGPT and Claude. While this progress has greatly enhanced various aspects of life and work, it has also negatively affected certain areas of society. A recent survey revealed that nearly 30% of college students use generative AI to help write academic papers and reports. Most countermeasures treat the detection of AI-generated text as a binary classification task and thus lack robustness. This approach overlooks human involvement in the generation of content even though human-machine collaboration is becoming mainstream. Besides generating entire texts, people may use machines to complete or revise texts. Such human involvement varies case by case, which makes binary classification a less than satisfactory approach. We refer to this situation as participation detection obfuscation. We propose using BERTScore as a metric to measure human involvement in the generation process and a multi-task RoBERTa-based regressor trained on a token classification task to address this problem. To evaluate the effectiveness of this approach, we simulated academic-based scenarios and created a continuous dataset reflecting various levels of human involvement. All of the existing detectors we examined failed to detect the level of human involvement on this dataset. Our method, however, succeeded (F1 score of 0.9423 and a regressor mean squared error of 0.004). Moreover, it demonstrated some generalizability across generative models. Our code is available at https://github.com/gyc-nii/CAS-CS-and-dual-head-detector
Modeling Bulimia Nervosa in the Digital Age: The Role of Social Media
2025-06-04
Brenda Murillo, Fabio Sanchez
Globalization has fundamentally reshaped societal dynamics, influencing how individuals interact and perceive themselves and others. One significant consequence is the evolving landscape of eating disorders such as bulimia nervosa (BN), which are increasingly driven not just by internal psychological factors but by broader sociocultural and digital contexts. While mathematical modeling has provided valuable insights, traditional frameworks often fall short in capturing the nuanced roles of social contagion, digital media, and adaptive behavior. This review synthesizes two decades of quantitative modeling efforts, including compartmental, stochastic, and delay-based approaches. We spotlight foundational work that conceptualizes BN as a socially transmissible condition and identify critical gaps, especially regarding the intensifying impact of social media. Drawing on behavioral epidemiology and the adaptive behavior framework by Fenichel et al., we advocate for a new generation of models that incorporate feedback mechanisms, content-driven influence functions, and dynamic network effects. This work outlines a roadmap for developing more realistic, data-informed models that can guide effective public health interventions in the digital era.
The Latent Space Hypothesis: Toward Universal Medical Representation Learning
2025-06-04
Salil Patel
Medical data range from genomic sequences and retinal photographs to structured laboratory results and unstructured clinical narratives. Although these modalities appear disparate, many encode convergent information about a single underlying physiological state. The Latent Space Hypothesis frames each observation as a projection of a unified, hierarchically organized manifold -- much like shadows cast by the same three-dimensional object. Within this learned geometric representation, an individual's health status occupies a point, disease progression traces a trajectory, and therapeutic intervention corresponds to a directed vector. Interpreting heterogeneous evidence in a shared space provides a principled way to re-examine eponymous conditions -- such as Parkinson's or Crohn's -- that often mask multiple pathophysiological entities and involve broader anatomical domains than once believed. By revealing sub-trajectories and patient-specific directions of change, the framework supplies a quantitative rationale for personalised diagnosis, longitudinal monitoring, and tailored treatment, moving clinical practice away from grouping by potentially misleading labels toward navigation of each person's unique trajectory. Challenges remain -- bias amplification, data scarcity for rare disorders, privacy, and the correlation-causation divide -- but scale-aware encoders, continual learning on longitudinal data streams, and perturbation-based validation offer plausible paths forward.
Recent advancements in large language models (LLMs) have spurred interest in robotic navigation that incorporates complex spatial, mathematical, and conditional constraints from natural language into the planning problem. Such constraints can be informal yet highly complex, making it challenging to translate into a formal description that can be passed on to a planning algorithm. In this paper, we propose STPR, a constraint generation framework that uses LLMs to translate constraints (expressed as instructions on ``what not to do'') into executable Python functions. STPR leverages the LLM's strong coding capabilities to shift the problem description from language into structured and transparent code, thus circumventing complex reasoning and avoiding potential hallucinations. We show that these LLM-generated functions accurately describe even complex mathematical constraints, and apply them to point cloud representations with traditional search algorithms. Experiments in a simulated Gazebo environment show that STPR ensures full compliance across several constraints and scenarios, while having short runtimes. We also verify that STPR can be used with smaller, code-specific LLMs, making it applicable to a wide range of compact models at low inference cost.
Using the Coolest Ae Stars to Constrain Circumstellar Disk Viscosity
2025-06-04
R. Anusha, T. A. A. Sigut
Classical Ae (CAe) stars are main sequence, A-type stars with H{\alpha} emission but no signature of dust. They are thought to be the cool extension of the classical Be stars to lower masses. Recent surveys based on H{\alpha} spectroscopy have significantly increased the number of known CAe stars, with the population extending to spectral types as cool as A4 (Teff approx. 8500 K). We compute the temperature structure of gaseous, circumstellar disks around A-type stars, including both radiative heating from the central star and viscous shear heating from the disk's rotation. We find that shear heating can become important for spectral types A2 and later and can act to increase the low temperatures predicted by purely radiatively heated disks. Our modeling indicates that the presence and strength of H{\alpha} emission for spectral types A2 and later significantly increases with the amount of shear heating included, and we propose that this dependence can be used to constrain the {\alpha} viscosity parameter appropriate for CAe star disks.
Leveraging Reward Models for Guiding Code Review Comment Generation
2025-06-04
Oussama Ben Sghaier, Rosalia Tufano, Gabriele Bavota, Houari Sahraoui
Code review is a crucial component of modern software development, involving the evaluation of code quality, providing feedback on potential issues, and refining the code to address identified problems. Despite these benefits, code review can be rather time consuming, and influenced by subjectivity and human factors. For these reasons, techniques to (partially) automate the code review process have been proposed in the literature. Among those, the ones exploiting deep learning (DL) are able to tackle the generative aspect of code review, by commenting on a given code as a human reviewer would do (i.e., comment generation task) or by automatically implementing code changes required to address a reviewer's comment (i.e., code refinement task). In this paper, we introduce CoRAL, a deep learning framework automating review comment generation by exploiting reinforcement learning with a reward mechanism considering both the semantics of the generated comments as well as their usefulness as input for other models automating the code refinement task. The core idea is that if the DL model generates comments that are semantically similar to the expected ones or can be successfully implemented by a second model specialized in code refinement, these comments are likely to be meaningful and useful, thus deserving a high reward in the reinforcement learning framework. We present both quantitative and qualitative comparisons between the comments generated by CoRAL and those produced by the latest baseline techniques, highlighting the effectiveness and superiority of our approach.
Behavioural vs. Representational Systematicity in End-to-End Models: An Opinionated Survey
2025-06-04
Ivan Vegner, Sydelle de Souza, Valentin Forch, Martha Lewis, Leonidas A. A. Doumas
A core aspect of compositionality, systematicity is a desirable property in ML models as it enables strong generalization to novel contexts. This has led to numerous studies proposing benchmarks to assess systematic generalization, as well as models and training regimes designed to enhance it. Many of these efforts are framed as addressing the challenge posed by Fodor and Pylyshyn. However, while they argue for systematicity of representations, existing benchmarks and models primarily focus on the systematicity of behaviour. We emphasize the crucial nature of this distinction. Furthermore, building on Hadley's (1994) taxonomy of systematic generalization, we analyze the extent to which behavioural systematicity is tested by key benchmarks in the literature across language and vision. Finally, we highlight ways of assessing systematicity of representations in ML models as practiced in the field of mechanistic interpretability.
On the Practices of Autonomous Systems Development: Survey-based Empirical Findings
2025-06-04
Katerina Goseva-Popstojanova, Denny Hood, Johann Schumann, Noble Nkwocha
Autonomous systems have gained an important role in many industry domains and are beginning to change everyday life. However, due to dynamically emerging applications and often proprietary constraints, there is a lack of information about the practice of developing autonomous systems. This paper presents the first part of the longitudinal study focused on establishing state-of-the-practice, identifying and quantifying the challenges and benefits, identifying the processes and standards used, and exploring verification and validation (V&V) practices used for the development of autonomous systems. The results presented in this paper are based on data about software systems that have autonomous functionality and may employ model-based software engineering (MBSwE) and reuse. These data were collected using an anonymous online survey that was administered in 2019 and were provided by experts with experience in development of autonomous systems and /or the use of MBSwE. Our current work is focused on repeating the survey to collect more recent data and discover how the development of autonomous systems has evolved over time.
Edge interventions can mitigate demographic and prestige disparities in the Computer Science coauthorship network
2025-06-04
Kate Barnes, Mia Ellis-Einhorn, Carolina Chávez-Ruelas, Nayera Hasan, Mohammad Fanous, Blair D. Sullivan, Sorelle Friedler, Aaron Clauset
Social factors such as demographic traits and institutional prestige structure the creation and dissemination of ideas in academic publishing. One place these effects can be observed is in how central or peripheral a researcher is in the coauthorship network. Here we investigate inequities in network centrality in a hand-collected data set of 5,670 U.S.-based faculty employed in Ph.D.-granting Computer Science departments and their DBLP coauthorship connections. We introduce algorithms for combining name- and perception-based demographic labels by maximizing alignment with self-reported demographics from a survey of faculty from our census. We find that women and individuals with minoritized race identities are less central in the computer science coauthorship network, implying worse access to and ability to spread information. Centrality is also highly correlated with prestige, such that faculty in top-ranked departments are at the core and those in low-ranked departments are in the peripheries of the computer science coauthorship network. We show that these disparities can be mitigated using simulated edge interventions, interpreted as facilitated collaborations. Our intervention increases the centrality of target individuals, chosen independently of the network structure, by linking them with researchers from highly ranked institutions. When applied to scholars during their Ph.D., the intervention also improves the predicted rank of their placement institution in the academic job market. This work was guided by an ameliorative approach: uncovering social inequities in order to address them. By targeting scholars for intervention based on institutional prestige, we are able to improve their centrality in the coauthorship network that plays a key role in job placement and longer-term academic success.
Public health experts need scalable approaches to monitor large volumes of health data (e.g., cases, hospitalizations, deaths) for outbreaks or data quality issues. Traditional alert-based monitoring systems struggle with modern public health data monitoring systems for several reasons, including that alerting thresholds need to be constantly reset and the data volumes may cause application lag. Instead, we propose a ranking-based monitoring paradigm that leverages new AI anomaly detection methods. Through a multi-year interdisciplinary collaboration, the resulting system has been deployed at a national organization to monitor up to 5,000,000 data points daily. A three-month longitudinal deployed evaluation revealed a significant improvement in monitoring objectives, with a 54x increase in reviewer speed efficiency compared to traditional alert-based methods. This work highlights the potential of human-centered AI to transform public health decision-making.
Reviewriter: AI-Generated Instructions For Peer Review Writing
2025-06-04
Xiaotian Su, Thiemo Wambsganss, Roman Rietsche, Seyed Parsa Neshaei, Tanja Käser
Large Language Models (LLMs) offer novel opportunities for educational applications that have the potential to transform traditional learning for students. Despite AI-enhanced applications having the potential to provide personalized learning experiences, more studies are needed on the design of generative AI systems and evidence for using them in real educational settings. In this paper, we design, implement and evaluate \texttt{Reviewriter}, a novel tool to provide students with AI-generated instructions for writing peer reviews in German. Our study identifies three key aspects: a) we provide insights into student needs when writing peer reviews with generative models which we then use to develop a novel system to provide adaptive instructions b) we fine-tune three German language models on a selected corpus of 11,925 student-written peer review texts in German and choose German-GPT2 based on quantitative measures and human evaluation, and c) we evaluate our tool with fourteen students, revealing positive technology acceptance based on quantitative measures. Additionally, the qualitative feedback presents the benefits and limitations of generative AI in peer review writing.
A Framework for Auditing Chatbots for Dialect-Based Quality-of-Service Harms
2025-06-04
Emma Harvey, Rene F. Kizilcec, Allison Koenecke
Increasingly, individuals who engage in online activities are expected to interact with large language model (LLM)-based chatbots. Prior work has shown that LLMs can display dialect bias, which occurs when they produce harmful responses when prompted with text written in minoritized dialects. However, whether and how this bias propagates to systems built on top of LLMs, such as chatbots, is still unclear. We conduct a review of existing approaches for auditing LLMs for dialect bias and show that they cannot be straightforwardly adapted to audit LLM-based chatbots due to issues of substantive and ecological validity. To address this, we present a framework for auditing LLM-based chatbots for dialect bias by measuring the extent to which they produce quality-of-service harms, which occur when systems do not work equally well for different people. Our framework has three key characteristics that make it useful in practice. First, by leveraging dynamically generated instead of pre-existing text, our framework enables testing over any dialect, facilitates multi-turn conversations, and represents how users are likely to interact with chatbots in the real world. Second, by measuring quality-of-service harms, our framework aligns audit results with the real-world outcomes of chatbot use. Third, our framework requires only query access to an LLM-based chatbot, meaning that it can be leveraged equally effectively by internal auditors, external auditors, and even individual users in order to promote accountability. To demonstrate the efficacy of our framework, we conduct a case study audit of Amazon Rufus, a widely-used LLM-based chatbot in the customer service domain. Our results reveal that Rufus produces lower-quality responses to prompts written in minoritized English dialects, and that these quality-of-service harms are exacerbated by the presence of typos in prompts.
Cosmology Constraints from Type Ia Supernova Simulations of the Nancy Grace Roman Space Telescope Strategy Recommended by the High Latitude Time Domain Survey Definition Committee
2025-06-04
Richard Kessler, Rebekah Hounsell, Bhavin Joshi, David Rubin, Masao Sako, Rebecca Chen, Vivian Miranda, Benjamin. M. Rose
Within the next few years, the upcoming Nancy Grace Roman Space Telescope will be gathering data for the High Latitude Time Domain Survey (HLTDS) that will be used to significantly improve the Type Ia supernova measurement of the dark energy equation of state parameters w0 and wa. Here we generate a catalog-level simulation of the in-guide strategy recommended by the HLTDS definition committee, and determine dark energy parameter constraints using a detailed analysis that includes light curve fitting, photometric redshifts and classification, BEAMS formalism, systematic uncertainties, and cosmology fitting. After analysis and selection requirements, the sample includes 10,000 Roman SNe Ia that we combine with 4,500 events from LSST. The resulting dark energy figure of merit is well above the NASA mission requirement of 326, with the caveat that SN Ia model training systematics have not been included.
A novel test of gravity: Does spacetime geometry track matter density?
2025-06-04
Camille Bonvin, Nastassia Grimm, Isaac Tutusaus
We propose a novel test of gravity that combines galaxy clustering with gravitational lensing. In general relativity, the evolution of matter density fluctuations and of the Weyl potential -- the sum of spatial and temporal distortions of the geometry -- are governed by the same growth function. In contrast, alternative theories of gravity that modify the relation between geometry and matter content can lead to differences in these two growths. Exploiting a recent method to directly measure the Weyl potential, we construct a null test that deviates from zero if and only if there is a mismatch between the growth rate of density and that of geometry distortions. We show that changes in the background expansion due to alternative dark energy models and additional forces in the dark matter sector induce no deviations in this test, making it a robust probe for detecting departures from general relativity. Applying the test to current data, we find no evidence of deviation. From an initial $z_*=10$ to $z\sim 0.5$, we constrain the evolution of the Weyl potential to track that of the density to within 33\%. Combining stage-IV surveys will improve the precision across a broad redshift range, limiting differences between the two evolutions to below $2-4\%$.
Precise Timing Analysis of Four Magnetic Cataclysmic Variables with TESS
2025-06-04
Srinivas M Rao, Jeewan C Pandey, Nikita Rawat, Arti Joshi, Ajay Kumar Singh
We analysed high time-resolution optical photometric data from the Transiting Exoplanet Survey Satellite (TESS) to study the timing behaviour of four intermediate polar-like objects, namely, V1460 Her, 1RXS J045707.4+452751, Swift J0958.0-4208, and V842 Cen. In the case of V1460 Her, we refined the measurement of its orbital period. Long-term observations suggest a gradual decrease in the orbital period of V1460 Her, and the stable light curve during the TESS observations indicates its quiescent state. We detected a beat period of 1290.6 $\pm$ 0.5 s for the first time for the source 1RXS J045707.4+452751, suggesting a possible disc-overflow accretion scenario. For the sources Swift J0958.0-4208 and V842 Cen, we determined periods 6.11 $\pm$ 0.02 h and 3.555 $\pm$ 0.005 h, respectively, which can be provisionally suggested to be orbital periods. These findings provide valuable insights into the accretion processes and long-term evolution of these intriguing binary systems.
ViT-based Local Volume dwarf galaxy Identificationin (VIDA) in the CSST survey
2025-06-04
Han Qu, Zhen Yuan, Chengliang Wei, Chao Liu, Jiang Chang, Guoliang Li, Nicolas F. Martin, Chaowei Tsai, Shi Shao, Yu Luo, Ran Li, Xi Kang, Xiangxiang Xue, Zhou Fan
Identifying dwarf galaxies within the Local Volume is crucial for constraining the luminosity function of satellite galaxies in the nearby universe. We report the detection capabilities of dwarf galaxies within the Local Volume using the Chinese Space Station Telescope (CSST). Based on the simulated imaging data of CSST, we develop a detection and classification pipeline that combines traditional image-based search techniques with advanced machine learning classification models. The simulated Local Volume dwarf galaxies can be identified using a pre-processing method for "extended source detection", followed by classification with a pretrained ViT-Base model. This pipeline achieves a true positive rate (TPR) exceeding 85% with a false positive rate (FPR) of only 0.1%. We quantify the detection completeness of Local Volume dwarf galaxies across a three-dimensional parameter space defined by absolute magnitude ($M_V$), half-light radius ($R_h$), and heliocentric distance, based on simulated single-exposure CSST wide-field imaging survey data. For unresolved or semi-resolved dwarf galaxies, our method achieves a significantly deeper absolute magnitude detection limit compared to catalog-based approaches, reaching $M_V = -7$ within 10 \Mpc. By combining this image-based approach with traditional stellar catalog-based "matched filter" techniques, our automated framework established in this work can identify dwarf galaxies within 20 \Mpc for the CSST mission.
A large, chemically enriched, neutral gas reservoir in a galaxy at z = 6.782
2025-06-04
A. Saccardi, S. D. Vergani, L. Izzo, V. D'Elia, K. E. Heintz, A. De Cia, D. B. Malesani, J. T. Palmerio, P. Petitjean, S. Savaglio, N. R. Tanvir, R. Salvaterra, R. Brivio, S. Campana, L. Christensen, S. Covino, J. P. U. Fynbo, D. H. Hartmann, C. Konstantopoulou, A. J. Levan, A. Martin-Carrillo, A. Melandri, L. Piro, G. Pugliese, P. Schady, B. Schneider
The chemical characterization of galaxies in the first billion years after the Big Bang is one of the central goals of current astrophysics. Optical/near-infrared spectroscopy of long gamma-ray bursts (GRBs) have been heralded as an effective diagnostic to probe the interstellar medium of their host galaxies and their metal and dust content, up to the highest redshift. An opportunity to fulfill this expectation was provided by the recent blast triggered by the Neil Gehrels Swift Observatory of GRB 240218A at redshift z=6.782. We study a high-redshift galaxy selected in a complementary way with respect to flux-limited surveys, not depending on galaxy luminosity and stellar mass. We present the VLT/X-shooter spectrum of its afterglow enabling the detection of neutral-hydrogen, low-ionization, high-ionization and fine-structure absorption lines. We determine the metallicity, kinematics and chemical abundance pattern, providing the first detailed characterization of the neutral gas of a galaxy at z>6.5. From the analysis of fine-structure lines we estimate the distance of the closest gas clouds as $d_{II}=620^{+230}_{-140}$ pc. We determine a high neutral hydrogen column density, $\log(N(HI)/cm^{-2})=22.5\pm0.3$, which is the highest one at z>6 determined so far for a GRB host galaxy, as well as a surprisingly high metal column density, $\log(N(ZnII)/cm^{-2})>14.3$. The observed metallicity of the host galaxy system is [Zn/H]>-0.8. We find evidence of a high amount of dust depletion and of aluminum overabundance, although a number of transitions are saturated. The high hydrogen column density, metal abundances and dust depletion in the neutral gas align with those of the ionized gas of very high-redshift galaxies unveiled by ALMA and JWST, testifying that a rapid build up of metals and dust, and massive neutral hydrogen reservoirs seem to be common features of galaxies in the early Universe.
Simulation-based inference of galaxy properties from JWST pixels
2025-06-04
Patricia Iglesias-Navarro, Marc Huertas-Company, Pablo Pérez-González, Johan H. Knapen, ChangHoon Hahn, Anton M. Koekemoer, Steven L. Finkelstein, Natalia Villanueva, Andrés Asensio Ramos
We present an efficient Bayesian SED-fitting framework tailored to multiwavelength pixel photometry from the JWST Advanced Deep Extragalactic Survey (JADES). Our method employs simulation-based inference to enable rapid posterior sampling across galaxy pixels, leveraging the unprecedented spatial resolution, wavelength coverage, and depth provided by the survey. It is trained on synthetic photometry generated from MILES stellar population models, incorporating both parametric and non-parametric SFHs, realistic noise, and JADES-like filter sensitivity thresholds. We validate this amortised inference approach on mock datasets, achieving robust and well-calibrated posterior distributions, with an $R^2$ score of 0.99 for stellar mass. Applying our pipeline to real observations, we derive spatially resolved maps of stellar population properties down to $\mathrm{S/N}_{\rm{pixel}}=5$ (averaged over F277W, F356W, F444W) for 1083 JADES galaxies and ~2 million pixels with spectroscopic redshifts. These maps enable the identification of dusty or starburst regions and offer insights into mass growth and the structural assembly. We assess the outshining phenomenon by comparing pixel-based and integrated stellar mass estimates, finding limited impact only in low-mass galaxies ($<10^8M_{\odot}$) but systematic differences of ~0.20 dex linked to SFH priors. With an average posterior sampling speed of $10^{-4}$ seconds per pixel and a total inference time of ~1 CPU-day for the full dataset, our model offers a scalable solution for extracting high-fidelity stellar population properties from HST+JWST datasets, opening the way for statistical studies at sub-galactic scales.
Initial Characterization of Stellar Photometry of Roman images from the OpenUniverse Simulations
2025-06-04
Lauren Aldoroty, Daniel Scolnic, Arun Kannawadi, Rob Knop, Benjamin Rose, Rebekah Hounsell, Michael Troxel
NASA's Nancy Grace Roman Space Telescope (Roman) will provide an opportunity to study dark energy with unprecedented precision and accuracy using several techniques, including measurements of high-$z$ Type Ia Supernovae (SNe Ia, $z \lesssim 3.0$) via the High-Latitude Time Domain Survey (HLTDS). In this work, we do an initial "benchmark" characterization of the photometric repeatability of stellar fluxes, which must be below $1\%$ when sky noise is subdominant in order xto enable a number of calibration requirements. Achieving this level of flux precision requires attention to Roman's highly-structured, spatially-varying, undersampled PSF. In this work, we build a library of effective PSFs (ePSFs) compatible with the OpenUniverse HLTDS simulations. Using our library of ePSFs, we recover fractional flux between $0.6 - 1.2\%$ photometric precision, finding that redder bands perform better by this metric. We also find that flux recovery is improved by up to $20\%$ when a chip (sensor chip assembly; SCA) is divided into 8 sub-SCAs in order to account for the spatial variation of the PSF. With our optimized algorithm, we measure non-linearity due to photometry (magnitude dependence) of $|s_{NL}| < 1.93 \times 10^{-3}$ per dex, which is still larger than stated requirements for detector effects and indicates that further work is necessary. We also measure the dependence of photometric residuals on stellar color, and find the largest possible dependence in R062, implying a color-dependent PSF model may be needed. Finally, we characterize the detection efficiency function of each OpenUniverse Roman filter, which will inform future studies.
Uncalibrated Cosmic Standards as a Robust Test on Late-Time Cosmological Models
2025-06-04
Yihao Wang, Weikang Lin
We present a minimally model-dependent framework for testing late-time cosmological models using Uncalibrated Cosmic Standards (UCS), including standard rulers and standard candles, without relying on absolute calibrations. The method exploits a tight, model-insensitive correlation between the sound horizons at recombination and the drag epoch. By avoiding dependence on pre-recombination physics and the amplitude of the Cosmic Microwave Background (CMB) power spectra, the UCS framework reduces potential early-time biases while retaining much of the constraining power of full analyses. Applying UCS to the recent dynamical dark energy (DE) study that reported deviations from $\Lambda$CDM, we find the constraints shift systematically toward the $\Lambda$CDM case, suggesting the present of pre-recombination physics or systematics that likely affect the scale-dependence of the CMB spectra. We also observe a mild tension between uncalibrated standard rulers and candles, which can be largely mitigated by introducing a redshift-dependent magnitude bias in the supernova (SNe Ia) data. Our results highlights the importance of isolating post-recombination observables for testing late-time models in the era of precision cosmology, positioning UCS analysis as a robust framework for upcoming galaxy surveys.
Fishing for the Optimal Roman High Latitude Time Domain Survey: Cosmological Constraints for 1,000 Possible Surveys
2025-06-04
David Rubin, Greg Aldering, Andy Fruchter, Lluis Galbany, Rebekah Hounsell, Rick Kessler, Saul Perlmutter, Ben Rose, Masao Sako, Dan Scolnic, Jannik Truong, the Roman Supernova Cosmology Project Infrastructure Team
The upcoming Nancy Grace Roman Space Telescope is set to conduct a generation-defining SN Ia cosmology measurement with its High Latitude Time Domain Survey (HLTDS). However, between optical elements, exposure times, cadences, and survey areas, there are many survey parameters to consider. This work was part of a Roman Project Infrastructure Team effort to help the Core Community Survey (CCS) Committee finalize the HLTDS recommendation to the Roman Observations Time Allocation Committee. We simulate 1,000 surveys, with and without a conservative (volume-limited) version of the Vera C. Rubin Observatory Deep Drilling Field SNe Ia, and compute Fisher-matrix-analysis Dark Energy Task Force Figures of Merit (FoM, based on w0-wa constraints) for each. We investigate which survey parameters correlate with FoM, as well as the dependence of the FoM values on calibration uncertainties and the SN dispersion model. The exact optimum depends on the assumed dispersion model and whether Rubin DDF SNe Ia are also considered, but ~20% time in prism, ~30--40% time in Wide imaging and the remainder in Deep imaging seems most promising. We also advocate for "interlaced" cadences where not every filter is used in every cadence step to reduce overheads while maintaining a good cadence and increasing the number of filters compared to the Rose et al. (2021) reference survey (the prism has proportionately lower overheads and can be used for each cadence step). We show simulated light curves and spectra for the baseline HLTDS CCS recommendation and release distance-modulus covariance matrices for all surveys to the community.
Estimating Bolometric Luminosities of Type 1 Quasars with Self-Organizing Maps
2025-06-04
Jie Chen, Linhua Jiang, Shengxiu Sun, Zijian Zhang, Mouyuan Sun
We present a new method to calculate bolometric luminosities for unobscured, type 1 quasars with multi-band photometric data. Bolometric luminosity is a fundamental property to understand quasars and it is commonly estimated from monochromatic luminosities using bolometric corrections that often neglect quasar SED diversity. We take advantage of the fact that most quasars now have multi-band observations from UV to mid-IR, and construct SEDs for a well-defined sample of SDSS quasars at $0.5\leq z\leq 2$. Based on this fiducial sample, we explore quasar SEDs, their diversity, and their relations with bolometric luminosities. We then use unsupervised neural network self-organizing maps (SOM) to describe the SED diversity and compute the bolometric luminosities with a fully-trained SOM model. This method reduces systematical uncertainties compared to the traditional method. In addition, we update the multi-linear regression relations between bolometric luminosity and monochromatic luminosities at restframe 1450\r{A}, 3000\r{A}, and 5100\r{A}. Our method is applicable to large quasar samples with a wide range of luminosity and redshift. We have applied it to the SDSS DR16 quasars. We have also made our code publicly available.
Characterising the Standardisation Properties of Type Ia Supernovae in the z band with Hierarchical Bayesian Modelling
2025-06-04
Erin E. Hayes, Suhail Dhawan, Kaisey S. Mandel, David O. Jones, Ryan J. Foley, Stephen Thorp, Matthew Grayling, Sam M. Ward, Aaron Do, Danial Langeroodi, Nicholas Earl, Kaylee M. de Soto, Gautham Narayan, Katie Auchettl, Thomas de Boer, Kenneth C. Chambers, David A. Coulter, Christa Gall, Hua Gao, Luca Izzo, Chien-Cheng Lin, Eugene A. Magnier, Armin Rest, Qinan Wang
Type Ia supernovae (SNe Ia) are standardisable candles: their peak magnitudes can be corrected for correlations between light curve properties and their luminosities to precisely estimate distances. Understanding SN Ia standardisation across wavelength improves methods for correcting SN Ia magnitudes. Using 150 SNe Ia from the Foundation Supernova Survey and Young Supernova Experiment, we present the first study focusing on SN Ia standardisation properties in the z band. Straddling the optical and near-infrared, SN Ia light in the z band is less sensitive to dust extinction and can be collected alongside the optical on CCDs. Pre-standardisation, SNe Ia exhibit less residual scatter in z-band peak magnitudes than in the g and r bands. SNe Ia peak z-band magnitudes still exhibit a significant dependence on light-curve shape. Post-standardisation, the z-band Hubble diagram has a total scatter of RMS = 0.195 mag. We infer a z-band mass step of $\gamma_{z} = -0.105 \pm 0.031$ mag, which is consistent within 1$\sigma$ of that estimated from gri data, assuming Rv = 2.61. When assuming different Rv values for high and low mass host galaxies, the z-band and optical mass steps remain consistent within 1$\sigma$. Based on current statistical precision, these results suggest dust reddening cannot fully explain the mass step. SNe Ia in the z band exhibit complementary standardisability properties to the optical that can improve distance estimates. Understanding these properties is important for the upcoming Vera Rubin Observatory and Nancy G. Roman Space Telescope, which will probe the rest-frame z band to redshifts 0.1 and 1.8.
Enduring Disparities in the Workplace: A Pilot Study in the AI Community
2025-06-04
Yunusa Simpa Abdulsalam, Siobhan Mackenzie Hall, Ana Quintero-Ossa, William Agnew, Carla Muntean, Sarah Tan, Ashley Heady, Savannah Thais, Jessica Schrouff
In efforts toward achieving responsible artificial intelligence (AI), fostering a culture of workplace transparency, diversity, and inclusion can breed innovation, trust, and employee contentment. In AI and Machine Learning (ML), such environments correlate with higher standards of responsible development. Without transparency, disparities, microaggressions and misconduct will remain unaddressed, undermining the very structural inequities responsible AI aims to mitigate. While prior work investigates workplace transparency and disparities in broad domains (e.g. science and technology, law) for specific demographic subgroups, it lacks in-depth and intersectional conclusions and a focus on the AI/ML community. To address this, we conducted a pilot survey of 1260 AI/ML professionals both in industry and academia across different axes, probing aspects such as belonging, performance, workplace Diversity, Equity and Inclusion (DEI) initiatives, accessibility, performance and compensation, microaggressions, misconduct, growth, and well-being. Results indicate enduring disparities in workplace experiences for underrepresented and/or marginalized subgroups. In particular, we highlight that accessibility remains an important challenge for a positive work environment and that disabled employees have a worse workplace experience than their non-disabled colleagues. We further surface disparities for intersectional groups and discuss how the implementation of DEI initiatives may differ from their perceived impact on the workplace. This study is a first step towards increasing transparency and informing AI/ML practitioners and organizations with empirical results. We aim to foster equitable decision-making in the design and evaluation of organizational policies and provide data that may empower professionals to make more informed choices of prospective workplaces.
Logical Inferentialism & Attacks on Classical Logic
2025-06-04
Khashayar Irani
This paper undertakes a foundational inquiry into logical inferentialism with particular emphasis on the normative standards it establishes and the implications these pose for classical logic. The central question addressed herein is: 'What is Logical Inferentialism & How do its Standards challenge Classical Logic?' In response, the study begins with a survey of the three principal proof systems that is, David Hilbert's axiomatic systems and Gerhard Gentzen's natural deduction and his sequent calculus, thus situating logical inferentialism within a broader proof-theoretic landscape. The investigation then turns to the core tenets of logical inferentialism by focusing on the role of introduction and elimination rules in determining the meaning of logical constants. Through this framework, natural deduction is evaluated as a system that satisfies key inferentialist virtues including harmony, conservativeness and the subformula property. Ultimately, the paper presents challenges to classical logic from intuitionist and revisionist perspectives by arguing that certain classical principles fail to uphold inferentialist standards, consequently undermining their legitimacy within a meaning-theoretic framework.
Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy
2025-06-04
Muhammed Golec, Maha AlabdulJalil
Large Language Models (LLM), which have developed in recent years, enable credit risk assessment through the analysis of financial texts such as analyst reports and corporate disclosures. This paper presents the first systematic review and taxonomy focusing on LLMbased approaches in credit risk estimation. We determined the basic model architectures by selecting 60 relevant papers published between 2020-2025 with the PRISMA research strategy. And we examined the data used for scenarios such as credit default prediction and risk analysis. Since the main focus of the paper is interpretability, we classify concepts such as explainability mechanisms, chain of thought prompts and natural language justifications for LLM-based credit models. The taxonomy organizes the literature under four main headings: model architectures, data types, explainability mechanisms and application areas. Based on this analysis, we highlight the main future trends and research gaps for LLM-based credit scoring systems. This paper aims to be a reference paper for artificial intelligence and financial researchers.
Training large language model (LLM) agents to acquire necessary skills and perform diverse tasks within an environment is gaining interest as a means to enable open-endedness. However, creating the training dataset for their skill acquisition faces several challenges. Manual trajectory collection requires significant human effort. Another approach, where LLMs directly propose tasks to learn, is often invalid, as the LLMs lack knowledge of which tasks are actually feasible. Moreover, the generated data may not provide a meaningful learning signal, as agents often already perform well on the proposed tasks. To address this, we propose a novel automatic skill discovery framework EXIF for LLM-powered agents, designed to improve the feasibility of generated target behaviors while accounting for the agents' capabilities. Our method adopts an exploration-first strategy by employing an exploration agent (Alice) to train the target agent (Bob) to learn essential skills in the environment. Specifically, Alice first interacts with the environment to retrospectively generate a feasible, environment-grounded skill dataset, which is then used to train Bob. Crucially, we incorporate an iterative feedback loop, where Alice evaluates Bob's performance to identify areas for improvement. This feedback then guides Alice's next round of exploration, forming a closed-loop data generation process. Experiments on Webshop and Crafter demonstrate EXIF's ability to effectively discover meaningful skills and iteratively expand the capabilities of the trained agent without any human intervention, achieving substantial performance improvements. Interestingly, we observe that setting Alice to the same model as Bob also notably improves performance, demonstrating EXIF's potential for building a self-evolving system.
OxO2 -- A SSSOM mapping browser for logically sound crosswalks
2025-06-04
Henriette Harmse, Haider Iqbal, Helen Parkinson, James McLaughlin
EMBL-EBI created OxO to enable users to map between datasets that are annotated with different ontologies. Mappings identified by the first version of OxO were not necessarily logically sound, missed important provenance information such as author and reviewer, and could timeout or crash for certain requests. In this paper we introduce OxO2 to address these concerns. Provenance is addressed by implementing SSSOM, a mapping standard that defines provenance for mappings. SSSOM defines the conditions under which logical sound mappings can be derived and is implemented in OxO2 using, Nemo, a Datalog rule engine. To ensure reasoning is performant and memory efficient, Nemo implements a number of strategies that ensures OxO2 will be stable for all requests. Due to these changes, OxO2 users will be able to integrate between disparate datasets with greater confidence.
Beyond Memorization: A Rigorous Evaluation Framework for Medical Knowledge Editing
2025-06-04
Shigeng Chen, Linhao Luo, Zhangchi Qiu, Yanan Cao, Carl Yang, Shirui Pan
Recently, knowledge editing (KE) has emerged as a promising approach to update specific facts in Large Language Models (LLMs) without the need for full retraining. Despite the effectiveness in general-domain benchmarks, their applicability to complex medical domain remains largely unexplored. Medical knowledge editing is particularly challenging, as it requires LLMs to internalize the knowledge and generalize to unseen scenarios for effective and interpretable decision-making. In this work, we propose a novel framework called MedEditBench to rigorously evaluate the effectiveness of existing KE methods in the medical domain. In MedEditBench, we introduce a new medical knowledge editing benchmark as well as three different knowledge editing paradigms, which are designed to assess the impact of different knowledge sources for editing. Our findings indicate that current KE methods result in only superficial memorization of the injected information, failing to generalize to new scenarios. To overcome this limitation, we present Self-Generated Rationale Editing (SGR-Edit), which utilizes model-derived rationales as the target knowledge for editing, thereby uncovering the underlying reasoning process and demonstrating significant improvements over existing KE approaches. Additionally, we offer deeper insights into medical knowledge editing, including the localization of medical knowledge in LLMs and the impact of sequential editing on evolving knowledge. This could provide practical guidance for implementing KE methods in real-world medical applications.
Gaze-referential inference--the ability to infer what others are looking at--is a critical component of a theory of mind that underpins natural human-AI interaction. In a controlled study, we evaluated this skill across 111 Vision Language Models (VLMs) using photos taken with manipulated difficulty and variability, comparing performance with that of human participants (N = 65), and analyzed behaviors using mixed-effects models. We found that 94 of the 111 VLMs failed to do better than random guessing, while humans achieved near-ceiling accuracy. VLMs even respond with each choice almost equally frequently. Are they randomly guessing? Although most VLMs struggle, when we zoom in on five of the top-tier VLMs with above-chance performance, we find that their performance declined with increasing task difficulty but varied only slightly across different prompts and scene objects. These behavioral features cannot be explained by considering them as random guessers. Instead, they likely use a combination of heuristics and guessing such that their performance is subject to the task difficulty but robust to perceptual variations. This suggests that VLMs, lacking gaze inference capability, have yet to become technologies that can naturally interact with humans, but the potential remains.
Beyond the Norm: A Survey of Synthetic Data Generation for Rare Events
2025-06-04
Jingyi Gu, Xuan Zhang, Guiling Wang
Extreme events, such as market crashes, natural disasters, and pandemics, are rare but catastrophic, often triggering cascading failures across interconnected systems. Accurate prediction and early warning can help minimize losses and improve preparedness. While data-driven methods offer powerful capabilities for extreme event modeling, they require abundant training data, yet extreme event data is inherently scarce, creating a fundamental challenge. Synthetic data generation has emerged as a powerful solution. However, existing surveys focus on general data with privacy preservation emphasis, rather than extreme events' unique performance requirements. This survey provides the first overview of synthetic data generation for extreme events. We systematically review generative modeling techniques and large language models, particularly those enhanced by statistical theory as well as specialized training and sampling mechanisms to capture heavy-tailed distributions. We summarize benchmark datasets and introduce a tailored evaluation framework covering statistical, dependence, visual, and task-oriented metrics. A central contribution is our in-depth analysis of each metric's applicability in extremeness and domain-specific adaptations, providing actionable guidance for model evaluation in extreme settings. We categorize key application domains and identify underexplored areas like behavioral finance, wildfires, earthquakes, windstorms, and infectious outbreaks. Finally, we outline open challenges, providing a structured foundation for advancing synthetic rare-event research.
AGNBoost: A Machine Learning Approach to AGN Identification with JWST/NIRCam+MIRI Colors and Photometry
2025-06-03
Kurt Hamblin, Allison Kirkpatrick, Bren E. Backhaus, Gregory Troiani, Fabio Pacucci, Jonathan R. Trump, Alexander de la Vega, L. Y. Aaron Yung, Jeyhan S. Kartaltepe, Dale D. Kocevski, Anton M. Koekemoer, Erini Lambrides, Casey Papovich, Kaila Ronayne, Guang Yang, Pablo Arrabal Haro, Micaela B. Bagley, Mark Dickinson, Steven L. Finkelstein, Nor Pirzkal
We present AGNBoost, a machine learning framework utilizing XGBoostLSS to identify AGN and estimate redshifts from JWST NIRCam and MIRI photometry. AGNBoost constructs 121 input features from 7 NIRCam and 4 MIRI bands-including magnitudes, colors, and squared color terms-to simultaneously predict the fraction of mid-IR $3-30\,\mu$m emission attributable to an AGN power law (frac$_\text{AGN}$) and photometric redshift. Each model is trained on a sample of $10^6$ simulated galaxies from CIGALE providing ground truth values of both frac$_\text{AGN}$ and redshift. Models are tested against both mock CIGALE galaxies set aside for testing and 698 observations from the JWST MIRI EGS Galaxy and AGN (MEGA) survey. On mock galaxies, AGNBoost achieves $15\%$ outlier fractions of $0.19\%$ (frac$_\text{AGN}$) and $0.63\%$ (redshift), with a root mean square error ($\sigma_\text{RMSE}$) of $0.027$ for frac$_\text{AGN}$ and a normalized mean absolute deviation ($\sigma_\text{NMAD}$) of 0.011 for redshift. On MEGA galaxies with spectroscopic redshifts, AGNBoost achieves $\sigma_\text{NMAD}$ = 0.074 and $17.05\%$ outliers, with most outliers at $z_\text{spec} > 2$. AGNBoost frac$_\text{AGN}$ estimates broadly agree with CIGALE fitting ($\sigma_\text{RMSE} = 0.183$, $20.41\%$ outliers), and AGNBoost finds a similar number of AGNs as CIGALE SED fitting. The flexible framework of AGNBoost allows straightforward incorporation of additional photometric bands and derived qualities, and simple re-training for other variables of interest. AGNBoost's computational efficiency makes it well-suited for wide-sky surveys requiring rapid AGN identification and redshift estimation.
Simulating dynamics of correlated matter with neural quantum states
2025-06-03
Markus Schmitt, Markus Heyl
While experimental advancements continue to expand the capabilities to control and probe non-equilibrium quantum matter at an unprecedented level, the numerical simulation of the dynamics of correlated quantum systems remains a pivotal challenge - especially in intermediate spatial dimensions. Neural quantum states are emerging as a new computational tool to investigate the time evolution of many-body quantum systems in previously inaccessible regimes. We review the recent progress in the field with a focus on the different time propagation methods, an overview of the reported applications, and a discussion of the major current challenges.
A Complete Survey from the $\texttt{CompactObject}$ Perspective on Equation of State Cross-Comparison Using Observational and Nuclear Experimental Constraints
2025-06-03
João Cartaxo, Chun Huang, Tuhin Malik, Shashwat Sourav, Wen-Li Yuan, Tianzhe Zhou, Xuezhi Liu, Constança Providência
This extensive survey seeks to analyze and contrast different phenomenological methods used to model the nuclear equation of state (EOS) for neutron star matter based on covariant energy density functionals (CEDF). Using two complementary methodologies, we seek to capture a comprehensive picture of the potential behaviors of ultra-dense nucleonic matter and identify the most plausible models based on current observational and experimental constraints. Observational data from radio pulsar timing, gravitational wave detection of GW170817, and X-ray timing provide critical benchmarks for testing the models. We have derived the EOS posteriors for various CEDF models within the $\texttt{CompactObject}$ package, utilizing recent observational data on neutron stars, state-of-the-art theoretical constraints from chiral effective field theory ($\chi$EFT) calculations for pure neutron matter at low densities, and pQCD-derived constraints at approximately $7 \times 0.16$ $ \mathrm{fm}^{-3}$. Our analysis has demonstrated that while all considered CEDF models broadly reproduce current astrophysical and theoretical constraints, subtle yet important differences persist among them, with each framework exhibiting distinct characteristics at supra-nuclear density. This is in particular true for the proton fraction inside neutron stars, but also supported by the models' behavior with respect to the pure neutron matter EOS and the density dependence of the speed of sound. Our study highlights the sensitivity of dense matter predictions to the underlying EOS parameterizations and the priors considered.
Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds
2025-06-03
Yang Guo, Yutian Tao, Yifei Ming, Robert D. Nowak, Yingyu Liang
Retrieval-augmented generation (RAG) has seen many empirical successes in recent years by aiding the LLM with external knowledge. However, its theoretical aspect has remained mostly unexplored. In this paper, we propose the first finite-sample generalization bound for RAG in in-context linear regression and derive an exact bias-variance tradeoff. Our framework views the retrieved texts as query-dependent noisy in-context examples and recovers the classical in-context learning (ICL) and standard RAG as the limit cases. Our analysis suggests that an intrinsic ceiling on generalization error exists on RAG as opposed to the ICL. Furthermore, our framework is able to model retrieval both from the training data and from external corpora by introducing uniform and non-uniform RAG noise. In line with our theory, we show the sample efficiency of ICL and RAG empirically with experiments on common QA benchmarks, such as Natural Questions and TriviaQA.
Paramagnetism in spherically confined charged active matter
2025-06-03
Balázs Németh, Ronojoy Adhikari
The celebrated theorem of Bohr and van Leeuwen guarantees that a classical charged system cannot have a magnetization in thermal equilibrium. Quantum mechanically, however, a diamagnetic response is obtained. In contrast, we show here that a classical charged active system, consisting of a motile particle confined to the surface of a sphere, has a nonzero magnetization and a paramagnetic response. We numerically sample Langevin trajectories of this system and compare with limiting analytical solutions of the Fokker-Planck equation, at small and large temperatures, to find excellent agreement in the magnetic response. Our Letter suggests experimental routes to controlling and extracting work from charged active matter.
The Close AGN Reference Survey (CARS). Long-term spectral variability study of the changing look AGN Mrk 1018
2025-06-03
T. Saha, M. Krumpe, A. Markowitz, M. Powell, G. Leung, F. Combes, R. E. McElroy, J. S. Elford, M. Gaspari, N. Winkel, A. L. Coil, T. Urrutia
Changing-look AGNs (CLAGN) are accreting supermassive black hole systems that undergo variations in optical spectral type, driven by major changes in accretion rate. Mrk 1018 has undergone two transitions, a brightening event in the 1980s and a transition back to a faint state over the course of 2-3 years in the early 2010s. We characterize the evolving physical properties of the source's inner accretion flow, particularly during the bright-to-faint transition, as well as the morphological properties of its parsec-scale circumnuclear gas. We model archival X-ray spectra from XMM-Newton, Chandra, Suzaku, and Swift, using physically-motivated models to characterize X-ray spectral variations and track Fe Kalpha line flux. We also quantify Mrk 1018's long-term multi-wavelength spectral variability from optical/UV to the X-rays. Over the duration of the bright-to-faint transition, the UV and hard X-ray flux fell by differing factors, roughly 24 and 8, respectively. The soft X-ray excess faded, and was not detected by 2021. In the faint state, when the Eddington ratio drops to log Lbol/LEdd < -1.7, the hot X-ray corona photon index shows a 'softer-when-fainter' trend, similar to that seen in some black hole X-ray binaries and samples of low-luminosity AGNs. Finally, the Fe Kalpha line flux has dropped by only half the factor of the drop in the X-ray continuum. The transition from the bright state to the faint state is consistent with the inner accretion flow transitioning from a geometrically-thin disk to an ADAF-dominated state, with the warm corona disintegrating or becoming energetically negligible, while the X-ray-emitting hot flow becoming energetically dominant. Meanwhile, narrow Fe Kalpha emission has not yet fully responded to the drop in its driving continuum, likely because its emitter extends up to roughly 10 pc.
Corrigibility as a Singular Target: A Vision for Inherently Reliable Foundation Models
2025-06-03
Ram Potham, Max Harms
Foundation models (FMs) face a critical safety challenge: as capabilities scale, instrumental convergence drives default trajectories toward loss of human control, potentially culminating in existential catastrophe. Current alignment approaches struggle with value specification complexity and fail to address emergent power-seeking behaviors. We propose "Corrigibility as a Singular Target" (CAST)-designing FMs whose overriding objective is empowering designated human principals to guide, correct, and control them. This paradigm shift from static value-loading to dynamic human empowerment transforms instrumental drives: self-preservation serves only to maintain the principal's control; goal modification becomes facilitating principal guidance. We present a comprehensive empirical research agenda spanning training methodologies (RLAIF, SFT, synthetic data generation), scalability testing across model sizes, and demonstrations of controlled instructability. Our vision: FMs that become increasingly responsive to human guidance as capabilities grow, offering a path to beneficial AI that remains as tool-like as possible, rather than supplanting human judgment. This addresses the core alignment problem at its source, preventing the default trajectory toward misaligned instrumental convergence.
Constructing Evidence-Based Tailoring Variables for Adaptive Interventions
2025-06-03
John J. Dziak, Inbal Nahum-Shani
Background: An adaptive intervention (ADI) uses individual information in order to select treatment, to improve effectiveness while reducing cost and burden. ADIs require tailoring variables: person- and potentially time-specific information used to decide whether and how to deliver treatment. Specifying a tailoring variable for an intervention requires specifying what to measure, when to measure it, when to make the resulting decisions, and what cutoffs should be used in making those decisions. This involves tradeoffs between specificity versus sensitivity, and between waiting for sufficient information versus intervening quickly. These questions are causal and prescriptive (what should be done and when), not merely predictive (what would happen if current conditions persist). Purpose: There is little specific guidance in the literature on how to empirically choose tailoring variables, including cutoffs, measurement times, and decision times. Methods: We review possible approaches for comparing potential tailoring variables and propose a framework for systematically developing tailoring variables. Results: Although secondary observational data can be used to select tailoring variables, additional assumptions are needed. A specifically designed randomized experiment for optimization purposes (an optimization randomized clinical trial or ORCT), in the form of a multi-arm randomized trial, sequential multiple assignment randomized trial, a factorial experiment, or hybrid among them, may provide a more direct way to answer these questions. Conclusions: Using randomization directly to inform tailoring variables would provide the most direct causal evidence, but designing a trial to compare both tailoring variables and treatments adds complexity; further methodological research is warranted.
2025-06-03
Sinem Erisken, Timothy Gothard, Martin Leitgab, Ram Potham
Traditional AI safety evaluations on isolated LLMs are insufficient as multi-agent AI ensembles become prevalent, introducing novel emergent risks. This paper introduces the Multi-Agent Emergent Behavior Evaluation (MAEBE) framework to systematically assess such risks. Using MAEBE with the Greatest Good Benchmark (and a novel double-inversion question technique), we demonstrate that: (1) LLM moral preferences, particularly for Instrumental Harm, are surprisingly brittle and shift significantly with question framing, both in single agents and ensembles. (2) The moral reasoning of LLM ensembles is not directly predictable from isolated agent behavior due to emergent group dynamics. (3) Specifically, ensembles exhibit phenomena like peer pressure influencing convergence, even when guided by a supervisor, highlighting distinct safety and alignment challenges. Our findings underscore the necessity of evaluating AI systems in their interactive, multi-agent contexts.
2025-06-03
Filip Bártek, Ahmed Bhayat, Robin Coutelier, Márton Hajdu, Matthias Hetzenberger, Petra Hozzová, Laura Kovács, Jakob Rath, Michael Rawson, Giles Reger, Martin Suda, Johannes Schoisswohl, Andrei Voronkov
During the past decade of continuous development, the theorem prover Vampire has become an automated solver for the combined theories of commonly-used data structures. Vampire now supports arithmetic, induction, and higher-order logic. These advances have been made to meet the demands of software verification, enabling Vampire to effectively complement SAT/SMT solvers and aid proof assistants. We explain how best to use Vampire in practice and review the main changes Vampire has undergone since its last tool presentation, focusing on the engineering principles and design choices we made during this process.
TDCOSMO 2025: Cosmological constraints from strong lensing time delays
2025-06-03
TDCOSMO Collaboration, Simon Birrer, Elizabeth J. Buckley-Geer, Michele Cappellari, Frédéric Courbin, Frédéric Dux, Christopher D. Fassnacht, Joshua A. Frieman, Aymeric Galan, Daniel Gilman, Xiang-Yu Huang, Shawn Knabel, Danial Langeroodi, Huan Lin, Martin Millon, Takahiro Morishita, Veronica Motta, Pritom Mozumdar, Eric Paic, Anowar J. Shajib, William Sheu, Dominique Sluse, Alessandro Sonnenfeld, Chiara Spiniello, Massimo Stiavelli, Sherry H. Suyu, Chin Yi Tan, Tommaso Treu, Lyne Van de Vyvere, Han Wang, Patrick Wells, Devon M. Williams, Kenneth C. Wong
We present cosmological constraints from 8 strongly lensed quasars (hereafter, the TDCOSMO-2025 sample). Building on previous work, our analysis incorporated new deflector stellar velocity dispersions measured from spectra obtained with the James Webb Space Telescope (JWST), the Keck Telescopes, and the Very Large Telescope (VLT), utilizing improved methods. We used integrated JWST stellar kinematics for 5 lenses, VLT-MUSE for 2, and resolved kinematics from Keck and JWST for RX J1131-1231. We also considered two samples of non-time-delay lenses: 11 from the Sloan Lens ACS (SLACS) sample with Keck-KCWI resolved kinematics; and 4 from the Strong Lenses in the Legacy Survey (SL2S) sample. We improved our analysis of line-of-sight effects, the surface brightness profile of the lens galaxies, and orbital anisotropy, and corrected for projection effects in the dynamics. Our uncertainties are maximally conservative by accounting for the mass-sheet degeneracy in the deflectors' mass density profiles. The analysis was blinded to prevent experimenter bias. Our primary result is based on the TDCOSMO-2025 sample, in combination with $\Omega_{\rm m}$ constraints from the Pantheon+ Type Ia supernovae (SN) dataset. In the flat $\Lambda$ Cold Dark Matter (CDM), we find $H_0=72.1^{+4.0}_{-3.7}$ km s$^{-1}$ Mpc$^{-1}$. The SLACS and SL2S samples are in excellent agreement with the TDCOSMO-2025 sample, improving the precision on $H_0$ in flat $\Lambda$CDM to 4.4%. Using the Dark Energy Survey SN Year-5 dataset (DES-SN5YR) or DESI-DR2 baryonic acoustic oscillations (BAO) likelihoods instead of Pantheon+ yields very similar results. We also present constraints in the open $\Lambda$CDM, $w$CDM, $w_0w_a$CDM, and $w_{\phi}$CDM cosmologies. The TDCOSMO $H_0$ inference is robust and consistent across all presented cosmological models, and our cosmological constraints in them agree with those from the BAO and SN.
Local dark matter density from Gaia DR3 K-dwarfs using Gaussian processes
2025-06-03
Laurin Söding, Ruben Bartel, Philipp Mertsch
Modern astrophysical surveys have produced a wealth of data on the positions and velocities of stars in the Milky Way with varying accuracies. An ever-increasing detail in observational data calls for more complex physical models and in turn more sophisticated statistical methods to extract information. We perform a vertical Jeans analysis, including a local approximation of the tilt term, using a sample of $200\,000$ K-dwarf stars from the Gaia DR3 catalogue. After combination with the Survey-of-Surveys (SoS) catalogue, $160\,888$ of those have radial velocity measurements. We use Gaussian processes as priors for the covariance matrix of radial and vertical velocities. Joint inference of the posterior distribution of the local dark matter density and the velocity moments is performed using geometric variational inference. We find a local dark matter density of ${\rho_\mathrm{dm} = 0.0131 \pm 0.0041\, \mathrm{M}_\odot\,\mathrm{pc}^{-3} = 0.50 \pm 0.15\, \mathrm{GeV}\,\mathrm{cm}^{-3}}$ at the Sun's position, which is in agreement with most other recent analyses. By comparing a ($z$-dependent) Gaussian process prior with a ($z$-independent) scalar prior for the tilt term, we quantify its impact on estimates of the local dark matter density and argue that careful modelling is required to mitigate systematic biases.
An Algorithmic Pipeline for GDPR-Compliant Healthcare Data Anonymisation: Moving Toward Standardisation
2025-06-03
Hamza Khan, Lore Menten, Liesbet M. Peeters
High-quality real-world data (RWD) is essential for healthcare but must be transformed to comply with the General Data Protection Regulation (GDPR). GDPRs broad definitions of quasi-identifiers (QIDs) and sensitive attributes (SAs) complicate implementation. We aim to standardise RWD anonymisation for GDPR compliance while preserving data utility by introducing an algorithmic method to identify QIDs and SAs and evaluate utility in anonymised datasets. We conducted a systematic literature review via ProQuest and PubMed to inform a three-stage anonymisation pipeline: identification, de-identification, and quasi-identifier dimension evaluation. The pipeline was implemented, validated, and tested on two mock RWD datasets (500 and 1000 rows). Privacy was assessed using k-anonymity, l-diversity, and t-closeness; utility was measured by non-uniform entropy (NUE). The review yielded two studies on QID/SA identification and five on utility metrics. Applying the pipeline, attributes were classified by re-identification risk using alpha and beta thresholds (25 percent/1 percent for 500 rows; 10 percent/1 percent for 1000 rows). Privacy metrics improved k-anonymity from 1 to 4 (500 rows) and 1 to 110 (1000 rows). NUE scores were 69.26 percent and 69.05 percent, respectively, indicating consistent utility despite varying privacy gains. We present a GDPR-compliant anonymisation pipeline for healthcare RWD that provides a reproducible approach to QID/SA identification and utility evaluation; publicly available code promotes standardisation, data privacy, and open science.
2025-06-03
Matthew A. Kenworthy, Sebastiaan Y. Haffert
Imaging terrestrial exoplanets around nearby stars is a formidable technical challenge, requiring the development of coronagraphs to suppress the stellar halo of diffracted light at the location of the planet. In this review, we derive the science requirement for high-contrast imaging, present an overview of diffraction theory and the Lyot coronagraph, and define the parameters used in our optimization. We detail the working principles of coronagraphs both in the laboratory and on-sky with current high-contrast instruments, and we describe the required algorithms and processes necessary for terrestrial planet imaging with the extremely large telescopes and proposed space telescope missions: * Imaging terrestrial planets around nearby stars is possible with a combination of coronagraphs and active wavefront control using feedback from wavefront sensors. * Ground based 8-40m class telescopes can target the habitable zone around nearby M dwarf stars with contrasts of $10^{-7}$ and space telescopes can search around solar-type stars with contrasts of $10^{-10}$. * Focal plane wavefront sensing, hybrid coronagraph designs and multiple closed loops providing active correction are required to reach the highest sensitivities. * Polarization effects need to be mitigated for reaching $10^{-10}$ contrasts whilst keeping exoplanet yields as high as possible. * Recent technological developments, including photonics and microwave kinetic inductance detectors, will be folded into high-contrast instruments.
Unlikely intersections in Shimura varieties and beyond: a survey
2025-06-03
Christopher Daw
The aim of this note is to provide a concise introduction to so-called problems of unlikely intersections for (pure) Shimura varieties and to review the current state-of-the-art. In the process, we will touch upon more general settings and some of the results in those contexts.
Optical spectroscopic signatures of the red giant evolutionary state
2025-06-03
Ella Xi Wang, Melissa Ness, Thomas Nordlander, Andrew R. Casey, Sarah Martell, Marc Pinsonneault, Xiaoting Fu, Dennis Stello, Claudia Reyes, Marc Hon, Madeleine McKenzie, Mingjie Jian, Jie Yu, Sven Buder, Karin Lind, Joss Bland-Hawthorn, Daniel B. Zucker, Pradosh Barun Das, Richard de Grijs, Michael Hayden
Modern spectroscopic surveys output large data volumes. Theoretical models provide a means to transform the information encoded in these data to measurements of physical stellar properties. However, in detail the models are incomplete and simplified, and prohibit interpretation of the fine details in spectra. Instead, the available data provide an opportunity to use data-driven, differential analysis techniques, as a means towards understanding spectral signatures. We deploy such an analysis to examine core helium-fusing red clump (RC) and shell hydrogen-fusing red giant branch (RGB) stars, to uncover signatures of evolutionary state imprinted in optical stellar spectra. We exploit 786 pairs of RC and RGB stars from the GALAH survey, chosen to minimise spectral differences, with evolutionary state classifications from TESS and K2 asteroseismology. We report sub-percent residual, systematic spectral differences between the two classes of stars, and show that these residuals are significant compared to a reference sample of RC$-$RC and RGB$-$RGB pairs selected using the same criteria. First, we report systematic differences in the Swan ($\rm{C}_2$) band and CN bands caused by stellar evolution and a difference in mass, where RGB stars at similar stellar parameters have higher masses than RC stars. Secondly, we observe systematic differences in the line-width of the H$_{\alpha}$ and H$_{\beta}$ lines caused by a difference in microturbulence, as measured by GALAH, where we measure higher microturbulence in RC stars than RGB stars. This work demonstrates the ability of large surveys to uncover the subtle spectroscopic signatures of stellar evolution using model-free, data-driven methods.
Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review
2025-06-03
Mrinmay Sen, Shruti Aparna, Rohit Agarwal, Chalavadi Krishna Mohan
Federated Learning (FL) is a learning mechanism that falls under the distributed training umbrella, which collaboratively trains a shared global model without disclosing the raw data from different clients. This paper presents an extensive survey on the impact of partial client participation in federated learning. While much of the existing research focuses on addressing issues such as generalization, robustness, and fairness caused by data heterogeneity under the assumption of full client participation, limited attention has been given to the practical and theoretical challenges arising from partial client participation, which is common in real-world scenarios. This survey provides an in-depth review of existing FL methods designed to cope with partial client participation. We offer a comprehensive analysis supported by theoretical insights and empirical findings, along with a structured categorization of these methods, highlighting their respective advantages and disadvantages.
Large reasoning models (LRMs) have demonstrated impressive capabilities in complex problem-solving, yet their internal reasoning mechanisms remain poorly understood. In this paper, we investigate the reasoning trajectories of LRMs from an information-theoretic perspective. By tracking how mutual information (MI) between intermediate representations and the correct answer evolves during LRM reasoning, we observe an interesting MI peaks phenomenon: the MI at specific generative steps exhibits a sudden and significant increase during LRM's reasoning process. We theoretically analyze such phenomenon and show that as MI increases, the probability of model's prediction error decreases. Furthermore, these MI peaks often correspond to tokens expressing reflection or transition, such as ``Hmm'', ``Wait'' and ``Therefore,'' which we term as the thinking tokens. We then demonstrate that these thinking tokens are crucial for LRM's reasoning performance, while other tokens has minimal impacts. Building on these analyses, we propose two simple yet effective methods to improve LRM's reasoning performance, by delicately leveraging these thinking tokens. Overall, our work provides novel insights into the reasoning mechanisms of LRMs and offers practical ways to improve their reasoning capabilities. The code is available at https://github.com/ChnQ/MI-Peaks.
Combining social relations and interaction data in Recommender System with Graph Convolution Collaborative Filtering
2025-06-03
Tin T. Tran, Vaclav Snasel, Loc Tan Nguyen
A recommender system is an important subject in the field of data mining, where the item rating information from users is exploited and processed to make suitable recommendations with all other users. The recommender system creates convenience for e-commerce users and stimulates the consumption of items that are suitable for users. In addition to e-commerce, a recommender system is also used to provide recommendations on books to read, movies to watch, courses to take or websites to visit. Similarity between users is an important impact for recommendation, which could be calculated from the data of past user ratings of the item by methods of collaborative filtering, matrix factorization or singular vector decomposition. In the development of graph data mining techniques, the relationships between users and items can be represented by matrices from which collaborative filtering could be done with the larger database, more accurate and faster in calculation. All these data can be represented graphically and mined by today's highly developed graph neural network models. On the other hand, users' social friendship data also influence consumption habits because recommendations from friends will be considered more carefully than information sources. However, combining a user's friend influence and the similarity between users whose similar shopping habits is challenging. Because the information is noisy and it affects each particular data set in different ways. In this study, we present the input data processing method to remove outliers which are single reviews or users with little interaction with the items; the next proposed model will combine the social relationship data and the similarity in the rating history of users to improve the accuracy and recall of the recommender system.
The Growing Impact of Unintended Starlink Broadband Emission on Radio Astronomy in the SKA-Low Frequency Range
2025-06-03
Dylan Grigg, Steven Tingay, Marcin Sokolowski
We present the largest survey to date characterising intended and unintended emission from Starlink satellites across the SKA-Low frequency range. This survey analyses ~76 million full sky images captured over ~29 days of observing with an SKA-Low prototype station - the Engineering Development Array 2 - at the site of SKA-Low. We report 112,534 individual detections of 1,806 unique Starlink satellites, some emitting broadband emission and others narrowband emission. Our analysis compares observations across different models of Starlink satellite, with 76% of all v2-mini Ku and 71% of all v2-mini Direct to Cell satellites identified. It is shown that in the worst cases, some datasets have a detectable Starlink satellite in ~30% of all images acquired. Emission from Starlink satellites is detected in primary and secondary frequency ranges protected by the International Telecommunication Union, with 13 satellites identified between 73.00 - 74.60 MHz and 703 identified between 150.05 - 153.00 MHz. We also detect the reflections of terrestrial FM radio off different models of Starlink satellites at 99.70 MHz. The polarisation of the broadband emission shows the flux density of two orthogonal polarisations is anti-correlated with temporally shifting spectral structure observed. We compare our results to previous EDA2 and LOFAR results and provide open public access to our final data products to assist in quantifying future changes in this emission.
An open-source finite element toolbox for anisotropic creep and irradiation growth: Application to tube and spacer grid assembly
2025-06-03
Fabrizio E. Aguzzi, Santiago M. Rabazzi, Martín S. Armoa, César I. Pairetti, Alejandro E. Albanesi
This work presents an open-source interface that couples the viscoplastic self-consistent (VPSC) model capable of simulating anisotropic creep and irradiation growth in polycrystalline materials with the finite element solver Code_Aster. The interface enables the simulation of the micromechanical response of irradiated zirconium alloy components by integrating grain-level constitutive behavior into a structural FEM framework. A key feature is the automated rotation of stress and strain tensors between the global FEM frame and the local crystallographic axes, a transformation not natively supported by Code_Aster. The elastic strain is recovered analytically using the inverse of the self-consistently stiffness tensor provided by VPSC. As a demonstration, the framework is applied to an actual model of a pressurized water reactor (PWR) spacer grid, based on a patented design, capturing nonlinear contact and the anisotropic response of the cladding and grid. Simulations reveal the micromechanisms controlling the evolution of clearance between components and highlight the role of crystallographic texture in mitigating wear. In particular, a texture with a high fraction of prismatic planes oriented in the normal direction of the grid appears to be the most suitable for spacer design, as it minimizes clearance and contributes to wear resistance. The interface offers a flexible, extensible platform for high-fidelity simulations in nuclear fuel performance analysis.
Sorcha: A Solar System Survey Simulator for the Legacy Survey of Space and Time
2025-06-03
Stephanie R. Merritt, Grigori Fedorets, Megan E. Schwamb, Samuel Cornwall, Pedro H. Bernardinelli, Mario Juric, Matthew J. Holman, Jacob A. Kurlander, Siegfried Eggl, Drew Oldag, Maxine West, Jeremy Kubica, Joseph Murtagh, R. Lynne Jones, Peter Yoachim, Ryan R. Lyttle, Michael S. P. Kelley, Joachim Moeyens, Kathleen Kiker, Shantanu P. Naidu, Colin Snodgrass, Shannon M. Matthews, Colin Orion Chandler
The upcoming Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory is expected to revolutionize solar system astronomy. Unprecedented in scale, this ten-year wide-field survey will collect billions of observations and discover a predicted $\sim$5 million new solar system objects. Like all astronomical surveys, its results will be affected by a complex system of intertwined detection biases. Survey simulators have long been used to forward-model the effects of these biases on a given population, allowing for a direct comparison to real discoveries. However, the scale and tremendous scope of the LSST requires the development of new tools. In this paper we present Sorcha, an open-source survey simulator written in Python. Designed with the scale of LSST in mind, Sorcha is a comprehensive survey simulator to cover all solar system small-body populations. Its flexible, modular design allows Sorcha to be easily adapted to other surveys by the user. The simulator is built to run both locally and on high-performance computing (HPC) clusters, allowing for repeated simulation of millions to billions of objects (both real and synthetic).
Predictions of the LSST Solar System Yield: Discovery Rates and Characterizations of Centaurs
2025-06-03
Joseph Murtagh, Megan E. Schwamb, Stephanie R. Merritt, Pedro H. Bernardinelli, Jacob A. Kurlander, Samuel Cornwall, Mario Jurić, Grigori Fedorets, Matthew J. Holman, Siegfried Eggl, David Nesvorný, Kathryn Volk, R. Lynne Jones, Peter Yoachim, Joachim Moeyens, Jeremy Kubica, Drew Oldag, Maxine West, Colin Orion Chandler
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will start by the end of 2025 and operate for ten years, offering billions of observations of the southern night sky. One of its main science goals is to create an inventory of the Solar System, allowing for a more detailed understanding of small body populations including the Centaurs, which will benefit from the survey's high cadence and depth. In this paper, we establish the first discovery limits for Centaurs throughout the LSST's decade-long operation using the best available dynamical models. Using the survey simulator $\texttt{Sorcha}$, we predict a $\sim$7-12 fold increase in Centaurs in the Minor Planet Center (MPC) database, reaching $\sim$1200-2000 (dependent on definition) by the end of the survey - about 50$\%$ of which are expected within the first 2 years. Approximately 30-50 Centaurs will be observed twice as frequently as they fall within one of the LSST's Deep Drilling Fields (DDF) for on average only up to two months. Outside of the DDFs, Centaurs will receive $\sim$200 observations across the $\textit{ugrizy}$ filter range, facilitating searches for cometary-like activity through PSF extension analysis, as well as fitting light-curves and phase curves for color determination. Regardless of definition, over 200 Centaurs will achieve high-quality color measurements across at least three filters in the LSST's six filters. These observations will also provide over 300 well-defined phase curves in the $\textit{griz}$ bands, improving absolute magnitude measurements to a precision of 0.2 mags.
We address the problem of safely learning controlled stochastic dynamics from discrete-time trajectory observations, ensuring system trajectories remain within predefined safe regions during both training and deployment. Safety-critical constraints of this kind are crucial in applications such as autonomous robotics, finance, and biomedicine. We introduce a method that ensures safe exploration and efficient estimation of system dynamics by iteratively expanding an initial known safe control set using kernel-based confidence bounds. After training, the learned model enables predictions of the system's dynamics and permits safety verification of any given control. Our approach requires only mild smoothness assumptions and access to an initial safe control set, enabling broad applicability to complex real-world systems. We provide theoretical guarantees for safety and derive adaptive learning rates that improve with increasing Sobolev regularity of the true dynamics. Experimental evaluations demonstrate the practical effectiveness of our method in terms of safety, estimation accuracy, and computational efficiency.
SCOTCH III: Complete search for Hypercompact HII regions in the fourth quadrant
2025-06-03
A. L. Patel, J. S. Urquhart, A. Y. Yang, L. K. Morgan, K. M. Menten, M. A. Thompson, T. Moore, I. Grozdanova, S. Khan, T. Csengeri
We present high-frequency (18-24 GHz) radio continuum observations towards 335 methanol masers, excellent signposts for young, embedded high-mass protostars. These complete the search for hypercompact HII (HCHII) regions towards young high-mass star-forming clumps within the fourth quadrant of the Galactic plane. HCHII regions are the earliest observable signatures of radio continuum emission from high-mass stars ionizing their surroundings, though their rarity and short lifetimes make them challenging to study. We have observed methanol maser sites at 20-arcsec resolution and identified 121 discrete high-frequency radio sources. Of these, 42 compact sources are embedded in dense clumps and coincide with methanol masers, making them as excellent HCHII region candidates. These sources were followed up at higher resolution (0.5-arcsec) for confirmation. We constructed spectral energy distributions across 5-24 GHz to determine their physical properties, fitting either a simple HII region model or a power-law as needed. This analysis identified 20 HCHII regions, 9 intermediate objects, 3 UCHII regions, and 3 radio jet candidates. Combining these results with previous findings, the SCOTCH survey has identified 33 HCHII regions, 15 intermediate objects, 9 UCHII regions, and 4 radio jet candidates, tripling the known number of HCHII regions. Eleven of these sources remain optically thick at 24 GHz. This survey provides a valuable sample of the youngest HII regions and insights into early massive star formation.
Clues for Solar System Formation from Meteorites and their Parent Bodies
2025-06-03
Bernard Marty, Katherine R. Bermingham, Larry R. Nittler, Sean N. Raymond
Understanding the origin of comets requires knowledge of how the Solar System formed from a cloud of dust and gas 4.567 Gyr ago. Here, a review is presented of how the remnants of this formation process, meteorites and to a lesser extent comets, shed light on Solar System evolution. The planets formed by a process of collisional agglomeration during the first hundred million years of Solar System history. The vast majority of the original population of planetary building blocks (~100 km-scale planetesimals) was either incorporated into the planets or removed from the system, via dynamical ejection or through a collision with the Sun. Only a small fraction of the original rocky planetesimals survive to this day in the form of asteroids (which represent a total of ~0.05% of Earth's mass) and comets. Meteorites are fragments of asteroids that have fallen to Earth, thereby providing scientists with samples of Solar System-scale processes for laboratory-based analysis. Meteorite datasets complement cometary datasets, which are predominantly obtained via remote observation as there are few cometary samples currently available for laboratory-based measurements. This chapter discusses how analysis of the mineralogical, elemental, and isotopic characteristics of meteorites provides insight into (i) the origin of matter that formed planets, (ii) the pressure, temperature, and chemical conditions that prevailed during planet formation, and (iii) a precise chronological framework of planetary accretion. Also examined is the use of stable isotope variations and nucleosynthetic isotope anomalies as constraints on the dynamics of the disk and planet formation, and how these data are integrated into new models of Solar System formation. It concludes with a discussion of Earth's accretion and its source of volatile elements, including water and organic species.
Geometry problem solving (GPS) represents a critical frontier in artificial intelligence, with profound applications in education, computer-aided design, and computational graphics. Despite its significance, automating GPS remains challenging due to the dual demands of spatial understanding and rigorous logical reasoning. Recent advances in large models have enabled notable breakthroughs, particularly for SAT-level problems, yet the field remains fragmented across methodologies, benchmarks, and evaluation frameworks. This survey systematically synthesizes GPS advancements through three core dimensions: (1) benchmark construction, (2) textual and diagrammatic parsing, and (3) reasoning paradigms. We further propose a unified analytical paradigm, assess current limitations, and identify emerging opportunities to guide future research toward human-level geometric reasoning, including automated benchmark generation and interpretable neuro-symbolic integration.
Are Economists Always More Introverted? Analyzing Consistency in Persona-Assigned LLMs
2025-06-03
Manon Reusens, Bart Baesens, David Jurgens
Personalized Large Language Models (LLMs) are increasingly used in diverse applications, where they are assigned a specific persona - such as a happy high school teacher - to guide their responses. While prior research has examined how well LLMs adhere to predefined personas in writing style, a comprehensive analysis of consistency across different personas and task types is lacking. In this paper, we introduce a new standardized framework to analyze consistency in persona-assigned LLMs. We define consistency as the extent to which a model maintains coherent responses when assigned the same persona across different tasks and runs. Our framework evaluates personas across four different categories (happiness, occupation, personality, and political stance) spanning multiple task dimensions (survey writing, essay generation, social media post generation, single turn, and multi-turn conversations). Our findings reveal that consistency is influenced by multiple factors, including the assigned persona, stereotypes, and model design choices. Consistency also varies across tasks, increasing with more structured tasks and additional context. All code is available on GitHub.
Observations of non complex organic molecules in the gas phase of the interstellar medium
2025-06-03
Charlotte Vastel, Francesco Fontani
The field of astrochemistry has seen major advances triggered by the completion of new powerful radio telescopes, with gains in sensitivity of receivers and in bandwidth. To date, about 330 molecular species are detected, in interstellar clouds, circumstellar shells and even extragalactic sources. The first interstellar molecules were first discovered through their electronic transitions in the visual and near UV regions of the spectra in the 1930s. Then the discovery of (pure) rotational transitions of interstellar molecules dates back to the late 1960s. The improvement of detectors and the increase in telescope sizes really opened up the submillimeter sky. The radio and submillimeter ranges cover the lowest rotational lines of molecular species. The bigger the molecule, the more spectral lines at different frequencies it produces, with weaker line intensities. Over the past 30 years, we have discovered that we live in a molecular universe, where molecules are abundant and widespread, probing the structure and evolution of galaxies, as well as the temperature and density of the observed medium, opening a new field called astrochemistry. The progress has been dramatic, since the discovery of the first molecules about 100 years ago. We present in this review, the detection techniques that led to the discovery of the simple molecules in the gas phase and the methodology that lead to the abundances determinations and the comparison with chemical modelling.
Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories
2025-06-03
Abhishek Vivekanandan, Christian Hubschneider, J. Marius Zöllner
The ability to retrieve semantically and directionally similar short-range trajectories with both accuracy and efficiency is foundational for downstream applications such as motion forecasting and autonomous navigation. However, prevailing approaches often depend on computationally intensive heuristics or latent anchor representations that lack interpretability and controllability. In this work, we propose a novel framework for learning fixed-dimensional embeddings for short trajectories by leveraging a Transformer encoder trained with a contrastive triplet loss that emphasize the importance of discriminative feature spaces for trajectory data. We analyze the influence of Cosine and FFT-based similarity metrics within the contrastive learning paradigm, with a focus on capturing the nuanced directional intent that characterizes short-term maneuvers. Our empirical evaluation on the Argoverse 2 dataset demonstrates that embeddings shaped by Cosine similarity objectives yield superior clustering of trajectories by both semantic and directional attributes, outperforming FFT-based baselines in retrieval tasks. Notably, we show that compact Transformer architectures, even with low-dimensional embeddings (e.g., 16 dimensions, but qualitatively down to 4), achieve a compelling balance between retrieval performance (minADE, minFDE) and computational overhead, aligning with the growing demand for scalable and interpretable motion priors in real-time systems. The resulting embeddings provide a compact, semantically meaningful, and efficient representation of trajectory data, offering a robust alternative to heuristic similarity measures and paving the way for more transparent and controllable motion forecasting pipelines.
Natural Language Processing to Enhance Deliberation in Political Online Discussions: A Survey
2025-06-03
Maike Behrendt, Stefan Sylvius Wagner, Carina Weinmann, Marike Bormann, Mira Warne, Stefan Harmeling
Political online participation in the form of discussing political issues and exchanging opinions among citizens is gaining importance with more and more formats being held digitally. To come to a decision, a careful discussion and consideration of opinions and a civil exchange of arguments, which is defined as the act of deliberation, is desirable. The quality of discussions and participation processes in terms of their deliberativeness highly depends on the design of platforms and processes. To facilitate online communication for both participants and initiators, machine learning methods offer a lot of potential. In this work we want to showcase which issues occur in political online discussions and how machine learning can be used to counteract these issues and enhance deliberation.
Modern biomedical studies frequently collect complex, high-dimensional physiological signals using wearables and sensors along with time-to-event outcomes, making efficient variable selection methods crucial for interpretation and improving the accuracy of survival models. We propose a novel variable selection method for a functional linear Cox model with multiple functional and scalar covariates measured at baseline. We utilize a spline-based semiparametric estimation approach for the functional coefficients and a group minimax concave type penalty (MCP), which effectively integrates smoothness and sparsity into the estimation of functional coefficients. An efficient group descent algorithm is used for optimization, and an automated procedure is provided to select optimal values of the smoothing and sparsity parameters. Through simulation studies, we demonstrate the method's ability to perform accurate variable selection and estimation. The method is applied to 2003-06 cohort of the National Health and Nutrition Examination Survey (NHANES) data, identifying the key temporally varying distributional patterns of physical activity and demographic predictors related to all-cause mortality. Our analysis sheds light on the intricate association between daily distributional patterns of physical activity and all-cause mortality among older US adults.
Predictions of the LSST Solar System Yield: Near-Earth Objects, Main Belt Asteroids, Jupiter Trojans, and Trans-Neptunian Objects
2025-06-03
Jacob A. Kurlander, Pedro H. Bernardinelli, Megan E. Schwamb, Mario Juric, Joseph Murtagh, Colin Orion Chandler, Stephanie R. Merritt, David Nesvorny, David Vokrouhlicky, R. Lynne Jones, Grigori Fedorets, Samuel Cornwall, Matthew J. Holman, Siegfried Eggl, Drew Oldag, Maxine West, Jeremy Kubica, Peter Yoachim, Joachim Moeyens, Kathleen Kiker, Laura E. Buchanan
The NSF-DOE Vera C. Rubin Observatory is a new 8m-class survey facility presently being commissioned in Chile, expected to begin the 10yr-long Legacy Survey of Space and Time (LSST) by the end of 2025. Using the purpose-built Sorcha survey simulator (Merritt et al. In Press), and near-final observing cadence, we perform the first high-fidelity simulation of LSST's solar system catalog for key small body populations. We show that the final LSST catalog will deliver over 1.1 billion observations of small bodies and raise the number of known objects to 1.27E5 near-Earth objects, 5.09E6 main belt asteroids, 1.09E5 Jupiter Trojans, and 3.70E4 trans-Neptunian objects. These represent 4-9x more objects than are presently known in each class, making LSST the largest source of data for small body science in this and the following decade. We characterize the measurements available for these populations, including orbits, griz colors, and lightcurves, and point out science opportunities they open. Importantly, we show that ~70% of the main asteroid belt and more distant populations will be discovered in the first two years of the survey, making high-impact solar system science possible from very early on. We make our simulated LSST catalog publicly available, allowing researchers to test their methods on an up-to-date, representative, full-scale simulation of LSST data.
Do Language Models Think Consistently? A Study of Value Preferences Across Varying Response Lengths
2025-06-03
Inderjeet Nair, Lu Wang
Evaluations of LLMs' ethical risks and value inclinations often rely on short-form surveys and psychometric tests, yet real-world use involves long-form, open-ended responses -- leaving value-related risks and preferences in practical settings largely underexplored. In this work, we ask: Do value preferences inferred from short-form tests align with those expressed in long-form outputs? To address this question, we compare value preferences elicited from short-form reactions and long-form responses, varying the number of arguments in the latter to capture users' differing verbosity preferences. Analyzing five LLMs (llama3-8b, gemma2-9b, mistral-7b, qwen2-7b, and olmo-7b), we find (1) a weak correlation between value preferences inferred from short-form and long-form responses across varying argument counts, and (2) similarly weak correlation between preferences derived from any two distinct long-form generation settings. (3) Alignment yields only modest gains in the consistency of value expression. Further, we examine how long-form generation attributes relate to value preferences, finding that argument specificity negatively correlates with preference strength, while representation across scenarios shows a positive correlation. Our findings underscore the need for more robust methods to ensure consistent value expression across diverse applications.
Computer-Use Agents (CUAs) with full system access enable powerful task automation but pose significant security and privacy risks due to their ability to manipulate files, access user data, and execute arbitrary commands. While prior work has focused on browser-based agents and HTML-level attacks, the vulnerabilities of CUAs remain underexplored. In this paper, we investigate Visual Prompt Injection (VPI) attacks, where malicious instructions are visually embedded within rendered user interfaces, and examine their impact on both CUAs and Browser-Use Agents (BUAs). We propose VPI-Bench, a benchmark of 306 test cases across five widely used platforms, to evaluate agent robustness under VPI threats. Each test case is a variant of a web platform, designed to be interactive, deployed in a realistic environment, and containing a visually embedded malicious prompt. Our empirical study shows that current CUAs and BUAs can be deceived at rates of up to 51% and 100%, respectively, on certain platforms. The experimental results also indicate that system prompt defenses offer only limited improvements. These findings highlight the need for robust, context-aware defenses to ensure the safe deployment of multimodal AI agents in real-world environments. The code and dataset are available at: https://github.com/cua-framework/agents
A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges
2025-06-03
Sudhanshu Sekhar Tripathy, Bichitrananda Behera
IDS aims to protect computer networks from security threats by detecting, notifying, and taking appropriate action to prevent illegal access and protect confidential information. As the globe becomes increasingly dependent on technology and automated processes, ensuring secured systems, applications, and networks has become one of the most significant problems of this era. The global web and digital technology have significantly accelerated the evolution of the modern world, necessitating the use of telecommunications and data transfer platforms. Researchers are enhancing the effectiveness of IDS by incorporating popular datasets into machine learning algorithms. IDS, equipped with machine learning classifiers, enhances security attack detection accuracy by identifying normal or abnormal network traffic. This paper explores the methods of capturing and reviewing intrusion detection systems (IDS) and evaluates the challenges existing datasets face. A deluge of research on machine learning (ML) and deep learning (DL) architecture-based intrusion detection techniques has been conducted in the past ten years on various cybersecurity datasets, including KDDCUP'99, NSL-KDD, UNSW-NB15, CICIDS-2017, and CSE-CIC-IDS2018. We conducted a literature review and presented an in-depth analysis of various intrusion detection methods that use SVM, KNN, DT, LR, NB, RF, XGBOOST, Adaboost, and ANN. We provide an overview of each technique, explaining the role of the classifiers and algorithms used. A detailed tabular analysis highlights the datasets used, classifiers employed, attacks detected, evaluation metrics, and conclusions drawn. This article offers a thorough review for future IDS research.
Recent dimension-free communication frameworks in Federated Learning (FL), such as DeComFL, significantly reduce per-round communication by transmitting only scalars via zeroth-order stochastic gradient descent (ZO-SGD). This method is particularly advantageous for federated fine-tuning of Large Language Models (LLMs). Yet, the high variance in ZO gradient estimation typically leads to slow convergence. Although leveraging Hessian information is known to enhance optimization speed, integrating this into FL presents significant challenges. These include clients' restrictions on local data and the critical need to maintain the dimension-free communication property. To overcome this limitation, we first introduce a generalized scalar-only communication FL framework that decouples dimension-free communication from standard ZO-SGD, enabling the integration of more advanced optimization strategies. Building on this framework, we propose HiSo, a fast federated fine-tuning method via Hessian-informed zeroth-order optimization and Scalar-only communication. Specifically, it leverages global curvature information to accelerate convergence while preserving the same minimal communication cost per round. Theoretically, we establish convergence guarantees that are independent of the global Lipschitz constant, and further show that HiSo achieves faster rates when the global Hessian exhibits a low effective rank -- a common phenomenon in LLMs. Extensive experiments on benchmark datasets and LLM fine-tuning tasks confirm that HiSo significantly outperforms existing ZO-based FL methods in both convergence speed and communication efficiency.
Evaluating LLM Agent Adherence to Hierarchical Safety Principles: A Lightweight Benchmark for Probing Foundational Controllability Components
2025-06-03
Ram Potham
Credible safety plans for advanced AI development require methods to verify agent behavior and detect potential control deficiencies early. A fundamental aspect is ensuring agents adhere to safety-critical principles, especially when these conflict with operational goals. Failure to prioritize such principles indicates a potential basic control failure. This paper introduces a lightweight, interpretable benchmark methodology using a simple grid world to evaluate an LLM agent's ability to uphold a predefined, high-level safety principle (e.g., "never enter hazardous zones") when faced with conflicting lower-level task instructions. We probe whether the agent reliably prioritizes the inviolable directive, testing a foundational controllability aspect of LLMs. This pilot study demonstrates the methodology's feasibility, offers preliminary insights into agent behavior under principle conflict, and discusses how such benchmarks can contribute empirical evidence for assessing controllability. We argue that evaluating adherence to hierarchical principles is a crucial early step in understanding our capacity to build governable AI systems.
The breakdown of gravitational effective field theories is intimately connected to the emergence of infinite towers of light states near infinite-distance limits in field space. In string theory, up to duality frame, such towers arise from Kaluza-Klein or weakly-coupled critical string oscillator modes. Motivated by the Black Hole-String Correspondence, we review a broader mechanism whereby black holes undergo a transition into a tower of light states, governed by the Quantum Gravity cutoff -- known as the Species Scale. Building on these developments, the Black Hole-Tower correspondence aims to provide a unified thermodynamic framework that describes black hole entropy in terms of the spectrum of the lightest degrees of freedom across various perturbative regimes of quantum gravity theories. In those regimes, thermodynamic consistency of such transition imposes stringent constraints on the spectrum, in agreement with string theory predictions. This defines the basis of the so-called Species Thermodynamics. In this review, we emphasize these recent advances and synthesize their implications, offering an overview of how the outlined correspondence, the species scale and related thermodynamic principles enhance our understanding of black hole entropy within the effective field theory framework.
Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior
2025-06-03
Yue Gong, Raul Castro Fernandez
As hypothesis generation becomes increasingly automated, a new bottleneck has emerged: hypothesis assessment. Modern systems can surface thousands of statistical relationships-correlations, trends, causal links-but offer little guidance on which ones are novel, non-trivial, or worthy of expert attention. In this work, we study the complementary problem to hypothesis generation: automatic hypothesis assessment. Specifically, we ask: given a large set of statistical relationships, can we automatically assess which ones are novel and worth further exploration? We focus on correlations as they are a common entry point in exploratory data analysis that often serve as the basis for forming deeper scientific or causal hypotheses. To support automatic assessment, we propose to leverage the vast knowledge encoded in LLMs' weights to derive a prior distribution over the correlation value of a variable pair. If an LLM's prior expects the correlation value observed, then such correlation is not surprising, and vice versa. We propose the Logit-based Calibrated Prior, an LLM-elicited correlation prior that transforms the model's raw output logits into a calibrated, continuous predictive distribution over correlation values. We evaluate the prior on a benchmark of 2,096 real-world variable pairs and it achieves a sign accuracy of 78.8%, a mean absolute error of 0.26, and 95% credible interval coverage of 89.2% in predicting Pearson correlation coefficient. It also outperforms a fine-tuned RoBERTa classifier in binary correlation prediction and achieves higher precision@K in hypothesis ranking. We further show that the prior generalizes to correlations not seen during LLM pretraining, reflecting context-sensitive reasoning rather than memorization.
Survey of generalized Turán problems -- counting subgraphs
2025-06-03
Dániel Gerbner, Cory Palmer
For fixed graphs $H$ and $F$, the \emph{generalized Tur\'an number} $\mathrm{ex}(n,H,F)$ is the maximum possible number of copies of a subgraph $H$ in an $n$-vertex $F$-free graph. This article is a survey of this extremal function whose study was initiated in an influential 2016 article by Alon and Shikhelman (\emph{J. Combin. Theory, B}, {\bf 121}, 2016).
Trajectory Prediction Meets Large Language Models: A Survey
2025-06-03
Yi Xu, Ruining Yang, Yitian Zhang, Yizhou Wang, Jianglin Lu, Mingyuan Zhang, Lili Su, Yun Fu
Recent advances in large language models (LLMs) have sparked growing interest in integrating language-driven techniques into trajectory prediction. By leveraging their semantic and reasoning capabilities, LLMs are reshaping how autonomous systems perceive, model, and predict trajectories. This survey provides a comprehensive overview of this emerging field, categorizing recent work into five directions: (1) Trajectory prediction via language modeling paradigms, (2) Direct trajectory prediction with pretrained language models, (3) Language-guided scene understanding for trajectory prediction, (4) Language-driven data generation for trajectory prediction, (5) Language-based reasoning and interpretability for trajectory prediction. For each, we analyze representative methods, highlight core design choices, and identify open challenges. This survey bridges natural language processing and trajectory prediction, offering a unified perspective on how language can enrich trajectory prediction.
This chapter reviews some aspects of the theory of age-structured models of populations with finite maximum age. We formulate both the renewal equation for the birth rate and the partial differential equation for the age density, and show their equivalence. Next, we define and discuss central concepts in population dynamics, like the basic reproduction number $R_0$, the Malthusian parameter $r$, and the stable age distribution. We briefly review the sun-star theory that turns the birth term into a bounded additive perturbation, thus allowing to develop stability and bifurcation theory along standard lines. Finally, we review the pseudospectral approximation of the infinite-dimensional age-structured models by means of a finite system of ordinary differential equations, which allows to perform numerical bifurcation analysis with existing software tools. Here, Nicholson's blowfly equation serves as a worked example.
2025-06-03
Bryce Wedig, Tansu Daylan, Simon Birrer, Francis-Yan Cyr-Racine, Cora Dvorkin, Douglas P. Finkbeiner, Alan Huang, Xiaosheng Huang, Rahul Karthik, Narayan Khadka, Priyamvada Natarajan, Anna M. Nierenberg, Annika H. G. Peter, Justin D. R. Pierel, Xianzhe TZ Tang, Risa H. Wechsler
Galaxy-galaxy strong gravitational lenses can constrain dark matter models and the Lambda Cold Dark Matter cosmological paradigm at sub-galactic scales. Currently, there is a dearth of images of these rare systems with high signal-to-noise and angular resolution. The Nancy Grace Roman Space Telescope (hereafter, Roman), scheduled for launch in late 2026, will play a transformative role in strong lensing science with its planned wide-field surveys. With its remarkable 0.281 square degree field of view and diffraction-limited angular resolution of ~0.1 arcsec, Roman is uniquely suited to characterizing dark matter substructure from a robust population of strong lenses. We present a yield simulation of detectable strong lenses in Roman's planned High Latitude Wide Area Survey (HLWAS). We simulate a population of galaxy-galaxy strong lenses across cosmic time with Cold Dark Matter subhalo populations, select those detectable in the HLWAS, and generate simulated images accounting for realistic Wide Field Instrument detector effects. For a fiducial case of single 146-second exposures, we predict around 160,000 detectable strong lenses in the HLWAS, of which about 500 will have sufficient signal-to-noise to be amenable to detailed substructure characterization. We investigate the effect of the variation of the point-spread function across Roman's field of view on detecting individual subhalos and the suppression of the subhalo mass function at low masses. Our simulation products are available to support strong lens science with Roman, such as training neural networks and validating dark matter substructure analysis pipelines.
Comparison of different Unique hard attention transformer models by the formal languages they can recognize
2025-06-03
Leonid Ryvkin
This note is a survey of various results on the capabilities of unique hard attention transformers encoders (UHATs) to recognize formal languages. We distinguish between masked vs. non-masked, finite vs. infinite image and general vs. bilinear attention score functions. We recall some relations between these models, as well as a lower bound in terms of first-order logic and an upper bound in terms of circuit complexity.
Thermodynamics and Statistical Equilibrium of Large-Scale Hydroelastic Wave Turbulence
2025-06-03
Marlone Vernet, Eric Falcon
Understanding how statistical equilibrium can occur in out-of-equilibrium systems is of paramount interest, as it would enable the use of statistical mechanics tools to these systems. Here, we report the first experimental evidence of statistical equilibrium of the large scales of hydroelastic turbulent waves driven by small-scale random forcing. The wave field statistics at scales larger than the forcing scale, resolved in space and time, align well with the predictions of Rayleigh-Jeans equilibrium spectra over more than a decade. We measure zero net energy flux in this regime, as expected. We also determine the effective temperature, entropy, and heat capacity of this nonequilibrium system, demonstrating that classical thermodynamic concepts apply to describe large scales in statistical equilibrium of turbulent systems.
Controlling defects in semiconductor processes is important for maintaining yield, improving production cost, and preventing time-dependent critical component failures. Electron beam-based imaging has been used as a tool to survey wafers in the line and inspect for defects. However, manual classification of images for these nano-scale defects is limited by time, labor constraints, and human biases. In recent years, deep learning computer vision algorithms have shown to be effective solutions for image-based inspection applications in industry. This work proposes application of vision transformer (ViT) neural networks for automatic defect classification (ADC) of scanning electron microscope (SEM) images of wafer defects. We evaluated our proposed methods on 300mm wafer semiconductor defect data from our fab in IBM Albany. We studied 11 defect types from over 7400 total images and investigated the potential of transfer learning of DinoV2 and semi-supervised learning for improved classification accuracy and efficient computation. We were able to achieve classification accuracies of over 90% with less than 15 images per defect class. Our work demonstrates the potential to apply the proposed framework for a platform agnostic in-house classification tool with faster turnaround time and flexibility.
A limit on the mass of the Taurid Resonant Swarm at sub-100 meter sizes
2025-06-03
Paul Wiegert, Denis Vida, David L. Clark, Auriane Egal, Richard Wainscoat, Robert Weryk
We report on a pencil-beam survey of the Taurid Swarm, a possible concentration of bodies in the Taurid meteoroid stream associated with the 7:2 mean-motion resonance with Jupiter. Canada-France-Hawaii Telescope MegaCam observations reaching apparent magnitudes of 24.5 in the gri filter were taken over three nights. Rates of motion on the sky allowed for the quick elimination of main-belt objects from the over 1000 moving sources seen. Eight candidates with on-sky rates of motion consistent with Taurids were detected, but seven were subsequently shown to be non-Taurids (Hungarias, Mars-crossers, etc). One object might be a 60 m class Taurid but not enough data was collected and its orbit remains ambiguous. Our results are consistent with no Taurid Swarm members observed, and an upper limit of fewer than 3e3 - 3e4 objects down to H=25.6 +/- 0.3 (diameter of 34-76 m assuming a 2P/Encke-like albedo) at the 95% confidence level. While meteor observations have confirmed the Taurid Swarm's existence at meter and smaller sizes, our results indicate that the current mass budget of the swarm at 100 m sizes does not require an outsize parent to explain it.
COSMOS-Web: MIRI Data Reduction and Number Counts at 7.7$μ$m using JWST
2025-06-03
Santosh Harish, Jeyhan S. Kartaltepe, Daizhong Liu, Anton M. Koekemoer, Caitlin M. Casey, Maximilien Franco, Hollis B. Akins, Olivier Ilbert, Marko Shuntov, Nicole E. Drakos, Mike Engesser, Andreas L. Faisst, Ghassem Gozaliasl, Crystal L. Martin, Michaela Hirschmann, Vasily Kokorev, Erini Lambrides, Henry Joy McCracken, Jed McKinney, Louise Paquereau, Jason Rhodes, Brant E. Robertson
The COSMOS-Web survey is the largest JWST Cycle 1 General Observer program covering a contiguous ~0.54 deg$^2$ area with NIRCam imaging in four broad-band filters and a non-contiguous ~0.2 deg$^2$ with parallel MIRI imaging in a single broad-band filter, F770W. Here we present a comprehensive overview of the MIRI imaging observations, the data reduction procedure, the COSMOS-Web MIRI photometric catalog, and the first data release including the entire COSMOS-Web MIRI coverage. Data reduction is predominantly based on the JWST Science Calibration Pipeline with an additional step involving custom background subtraction to mitigate the presence of strong instrumental features and sky background in the MIRI images. We reach 5$\sigma$ (point source) limiting depths ($m_{F770W}$~25.51 based on $r$~0.3'' circular apertures) that are significantly better than initial expectations. We create a COSMOS-Web MIRI catalog based on the images presented in this release and compare the F770W flux densities with the Spitzer/IRAC CH4 measurements from the COSMOS2020 catalog for CH4 detections with S/N $>5$. We find that these are in reasonable agreement with a small median offset of $<0.05$ mag. We also derive robust 7.7$\mu$m number counts spanning five orders of magnitude in flux ($\sim$0.2-2300 $\mu$Jy) $\unicode{x2013}$ making COSMOS-Web the only JWST survey to date to efficiently sample such a large flux range $\unicode{x2013}$ which is in good agreement with estimates from other JWST and IRAC surveys.
Parallel CPU-GPU Execution for LLM Inference on Constrained GPUs
2025-06-03
Jiakun Fan, Yanglin Zhang, Xiangchen Li, Dimitrios S. Nikolopoulos
Deploying large language models (LLMs) for online inference is often constrained by limited GPU memory, particularly due to the growing KV cache during auto-regressive decoding. Hybrid GPU-CPU execution has emerged as a promising solution by offloading KV cache management and parts of attention computation to the CPU. However, a key bottleneck remains: existing schedulers fail to effectively overlap CPU-offloaded tasks with GPU execution during the latency-critical, bandwidth-bound decode phase. This particularly penalizes real-time, decode-heavy applications (e.g., chat, Chain-of-Thought reasoning) which are currently underserved by existing systems, especially under memory pressure typical of edge or low-cost deployments. We present APEX, a novel, profiling-informed scheduling strategy that maximizes CPU-GPU parallelism during hybrid LLM inference. Unlike systems relying on static rules or purely heuristic approaches, APEX dynamically dispatches compute across heterogeneous resources by predicting execution times of CPU and GPU subtasks to maximize overlap while avoiding scheduling overheads.We evaluate APEX on diverse workloads and GPU architectures (NVIDIA T4, A10), using LLaMa-2-7B and LLaMa-3.1-8B models. Compared to GPU-only schedulers like VLLM, APEX improves throughput by 84% - 96% on T4 and 11% - 89% on A10 GPUs, while preserving latency. Against the best existing hybrid schedulers, it delivers up to 49% (T4) and 37% (A10) higher throughput in long-output settings.APEX significantly advances hybrid LLM inference efficiency on such memory-constrained hardware and provides a blueprint for scheduling in heterogeneous AI systems, filling a critical gap for efficient real-time LLM applications.
The Onset of Feedback in Abell 1885: Evidence for Large-Scale Quenching Despite a Young Central AGN
2025-06-03
Laurel White, Michael McDonald, Francesco Ubertosi, Massimo Gaspari, Julie Hlavacek-Larrondo, Helen Russell, Taweewat Somboonpanyakul
We present a new 8.5 ks Chandra observation of Abell 1885, obtained as part of the Cluster Evolution Reference Ensemble At Low-z (CEREAL) survey of ~200 low-z galaxy groups and clusters. These data reveal that Abell 1885 is a strong cool core, with a central cooling time of 0.43 Gyr, and that the central galaxy hosts an X-ray luminous point source at its center (L=2.3x10^42 erg/s), indicative of a rapidly accreting supermassive black hole. In the context of the larger CEREAL sample, we constrain the fraction of clusters at z~0.15 with X-ray bright central AGN to be no more than 4.1%. Including radio data from LOFAR, GMRT, ASKAP, and the VLA and optical integral field unit data from SDSS MaNGA, we probe the details of cooling, feeding, and feedback in this system. These data reveal that cooling of the intracluster medium is highly suppressed on large (>10 kpc) scales despite a central supermassive black hole that is in the early stages of the self-regulation cycle (characterized by rapid accretion, physically small jets, and no large-scale low-frequency radio emission). To reconcile the large-scale quenching with a lack of visible large-scale feedback, we propose that the timescale on which energy is dissipated on large scales is significantly longer than the timescale on which black hole feeding operates on small (~pc) scales. These observations suggest that there are two separate timescales characterizing AGN feedback in clusters: the short timescale of small-scale feeding and feedback processes and a longer timescale by which energy is dissipated on large physical scales in the intracluster medium. This interpretation disfavors a model in which the energy is rapidly dissipated (e.g. shocks), which would synchronize the feeding and feedback timescales, and favors a model in which the heating effects of AGN feedback can linger long after the outburst has passed (e.g. turbulent mixing).
Exploring selection biases in FRB dispersion-galaxy cross-correlations with magnetohydrodynamical simulations
2025-06-03
April Qiu Cheng, Shion Elizabeth Andrew, Haochen Wang, Kiyoshi W. Masui
The dispersion of fast radio bursts (FRBs) in conjunction with their redshifts can be used as powerful probes of the distribution of extragalactic plasma, and with a large enough sample, the free-electron--galaxy power spectrum $P_{eg}$ can be measured by cross-correlating FRB dispersions with galaxy positions. However, a precise measurement of $P_{eg}$ requires a careful investigation of the selection effects -- the fact that the probability of both observing the FRB dispersion measure and obtaining a host galaxy redshift depends on observed properties of the FRB and its host. We use ray tracing simulations with IllustrisTNG300-1 to investigate the impact of expected observational selection effects on FRB dispersion-galaxy angular cross-correlations with a sample of 3000 FRBs at redshift range of $0.3 \leq z \leq 0.4$ . Our results show that cross-correlations with such an FRB sample are robust to properties of the FRB host galaxy: this includes DM contributions from the FRB host and optical followup selection effects biased against FRBs with dim galaxy hosts. We also find that such cross-correlations are robust to DM dependent and scattering selection effects specific to the CHIME/FRB survey. However, a DM dependent selection effect that cuts off the 10\% most dispersed FRB at a fixed redshift shell can bias the amplitude of the cross-correlation signal by over 50\% at angular scales of $\sim 0.1^\circ$ (corresponding to Mpc physical scales). Our findings highlight the importance of both measuring and accounting for selection effects present in existing FRB surveys as well as mitigating DM dependent selection effects in the design of upcoming FRB surveys aiming to use FRBs as probes for large-scale structure.
COSMOS-Web: Comprehensive Data Reduction for Wide-Area JWST NIRCam Imaging
2025-06-03
Maximilien Franco, Caitlin M. Casey, Anton M. Koekemoer, Daizhong Liu, Micaela B. Bagley, Henry Joy McCracken, Jeyhan S. Kartaltepe, Hollis B. Akins, Olivier Ilbert, Marko Shuntov, Santosh Harish, Brant E. Robertson, Rafael C. Arango-Toro, Andrew J. Battisti, Nima Chartab, Nicole E. Drakos, Andreas L. Faisst, Carter Flayhart, Ghassem Gozaliasl, Michaela Hirschmann, Richard Massey, Jason Rhodes, Zahra Sattari, Diana Scognamiglio, John R. Weaver, Lilan Yang, Jorge A. Zavala, Edward M. Berman, Fabrizio Gentile, Steven Gillman, Arianna S. Long, Georgios Magdis, Jacqueline E. McCleary, Jed McKinney, Bahram Mobasher, Louise Paquereau, Armin Rest, David B. Sanders, Sune Toft, Si-Yue Yu
We present the data reduction methodology used for the COSMOS-Web survey JWST NIRCam data. Covering 0.54 deg^2 with four broadband filters (F115W, F150W, F277W, F444W) and a total exposure time of approximately 270 hours, COSMOS-Web represents the largest contiguous field surveyed during JWST Cycle 1, posing unique data reduction challenges due to its extensive scale. By combining the official JWST Calibration Pipeline with custom improvements for noise removal, background subtraction, and astrometric alignment, we achieve high fidelity science-ready mosaics. We detail the systematic approach employed in the three stages of the JWST Calibration Pipeline. The data, collected in three epochs from January 2023 to January 2024, encompass 152 visits and have been processed into 20 mosaic tiles to optimize computational efficiency and data processing. The final data products achieve 5 sigma depths of 26.7-28.3 AB mag in 0.15" apertures. The processed and calibrated datasets are made available to the public.
COSMOS2025: The COSMOS-Web galaxy catalog of photometry, morphology, redshifts, and physical parameters from JWST, HST, and ground-based imaging
2025-06-03
Marko Shuntov, Hollis B. Akins, Louise Paquereau, Caitlin M. Casey, Olivier Ilbert, Rafael C. Arango-Toro, Henry Joy McCracken, Maximilien Franco, Santosh Harish, Jeyhan S. Kartaltepe, Anton M. Koekemoer, Lilan Yang, Marc Huertas-Company, Edward M. Berman, Jacqueline E. McCleary, Sune Toft, Raphaël Gavazzi, Mark J. Achenbach, Emmanuel Bertin, Malte Brinch, Jackie Champagne, Nima Chartab, Nicole E. Drakos, Eiichi Egami, Ryan Endsley, Andreas L. Faisst, Xiaohui Fan, Carter Flayhart, William G. Hartley, Hossein Hatamnia, Ghassem Gozaliasl, Fabrizio Gentile, Iris Jermann, Shuowen Jin, Koki Kakiichi, Ali Ahmad Khostovan, Martin Kümmel, Clotilde Laigle, Ronaldo Laishram, Erini Lambrides, Daizhong Liu, Jianwei Lyu, Georgios Magdis, Bahram Mobasher, Thibaud Moutard, Alvio Renzini, Brant E. Robertson, Marc Schefer, Diana Scognamiglio, Nick Scoville, Zahra Sattari, David B. Sanders, Sina Taamoli, Benny Trakhtenbrot, Francesco Valentino, Feige Wang, John R. Weaver, Jinyl Yang
We present COSMOS2025, the COSMOS-Web catalog of photometry, morphology, photometric redshifts and physical parameters for more than 700,000 galaxies in the Cosmic Evolution Survey (COSMOS) field. This catalog is based on our \textit{James Webb Space Telescope} 255\,h COSMOS-Web program, which provides deep near-infrared imaging in four NIRCam (F115W, F150W, F277W, F444W) and one MIRI (F770W) filter over the central $\sim 0.54 {\, \rm deg}^2$ ($\sim 0.2 {\, \rm deg}^2$ for MIRI) in COSMOS. These data are combined with ground- and space-based data to derive photometric measurements of NIRCam-detected sources using both fixed-aperture photometry (on the space-based bands) and a profile-fitting technique on all 37 bands spanning 0.3-8 micron. We provide morphology for all sources from complementary techniques including profile fitting and machine-learning classification. We derive photometric redshifts, physical parameters and non-parametric star formation histories from spectral energy distribution (SED) fitting. The catalog has been extensively validated against previous COSMOS catalogs and other surveys. Photometric redshift accuracy measured using spectroscopically confirmed galaxies out to $z\sim9$ reaches $\sigma_{\rm MAD} = 0.012$ at $m_{\rm F444W}<28$ and remains at $\sigma_{\rm MAD} \lesssim 0.03$ as a function of magnitude, color, and galaxy type. This represents a factor of $\sim 2$ improvement at 26 AB mag compared to COSMOS2020. The catalog is approximately 80\% complete at $\log(M_{\star}/{\rm M}_{\odot}) \sim 9$ at $z \sim 10$ and at $\log(M_{\star}/{\rm M}_{\odot}) \sim 7$ at $z \sim 0.2$, representing a gain of 1\,dex compared to COSMOS2020. COSMOS2025 represents the definitive COSMOS-Web catalog. It is provided with complete documentation, together with redshift probability distributions, and it is ready for scientific exploitation today.
Pivoting the paradigm: the role of spreadsheets in K-12 data science
2025-06-03
Oren Tirschwell, Nicholas Jon Horton
Spreadsheet tools are widely accessible to and commonly used by K-12 students and teachers. They have an important role in data collection and organization. Beyond data organization, spreadsheets also make data visible and easy to interact with, facilitating student engagement in data exploration and analysis. Though not suitable for all circumstances, spreadsheets can and do help foster data and computing skills for K-12 students. This paper 1) reviews prior frameworks on K-12 data tools; 2) proposes data-driven learning outcomes that can be accomplished by incorporating spreadsheets into the curriculum; and 3) discusses how spreadsheets can help develop data acumen and computational fluency. We provide example class activities, identify challenges and barriers to adoption, suggest pedagogical approaches to ease the learning curve for instructors and students, and discuss the need for professional development to facilitate deeper use of spreadsheets for data science and STEM disciplines.
Human-In-The-Loop Workflow for Neuro- Symbolic Scholarly Knowledge Organization
2025-06-03
Lena John, Tim Wittenborg, Sören Auer, Oliver Karras
As the volume of scientific literature continues to grow, efficient knowledge organization is an increasingly challenging task. Traditional structuring of scientific content is time-consuming and requires significant domain expertise, increasing the need for tool support. Our goal is to create a Human-in-the-Loop (HITL) workflow that supports researchers in creating and structuring scientific knowledge, leveraging neural models and knowledge graphs, exemplified using the Open Research Knowledge Graph (ORKG). The workflow aims to automate key steps, including data extraction and knowledge structuring, while keeping user oversight through human validation. We developed a modular framework implementing the workflow and evaluated it along the Quality Improvement Paradigm (QIP) with participants from the ORKG user community. The evaluation indicated that the framework is highly usable and provides practical support. It significantly reduces the time and effort required to transition from a research interest to literature-based answers by streamlining the import of information into a knowledge graph. Participants evaluated the framework with an average System Usability Scale (SUS) score of 84.17, an A+ -- the highest achievable rating. They also reported that it improved their time spent, previously between 4 hours and two weeks, down to an average of 24:40 minutes. The tool streamlines the creation of scientific corpora and extraction of structured knowledge for KG integration by leveraging LLMs and user-defined models, significantly accelerating the review process. However, human validation remains essential throughout the extraction process, and future work is needed to improve extraction accuracy and entity linking to existing knowledge resources.
2025-06-03
Arbind Agrahari Baniya, Tsz-Kwan Lee, Peter Eklund, Sunil Aryal
Video super-resolution (VSR) is a prominent research topic in low-level computer vision, where deep learning technologies have played a significant role. The rapid progress in deep learning and its applications in VSR has led to a proliferation of tools and techniques in the literature. However, the usage of these methods is often not adequately explained, and decisions are primarily driven by quantitative improvements. Given the significance of VSR's potential influence across multiple domains, it is imperative to conduct a comprehensive analysis of the elements and deep learning methodologies employed in VSR research. This methodical analysis will facilitate the informed development of models tailored to specific application needs. In this paper, we present an overarching overview of deep learning-based video super-resolution models, investigating each component and discussing its implications. Furthermore, we provide a synopsis of key components and technologies employed by state-of-the-art and earlier VSR models. By elucidating the underlying methodologies and categorising them systematically, we identified trends, requirements, and challenges in the domain. As a first-of-its-kind survey of deep learning-based VSR models, this work also establishes a multi-level taxonomy to guide current and future VSR research, enhancing the maturation and interpretation of VSR practices for various practical applications.
Bridging the Silos of Digitalization and Sustainability by Twin Transition: A Multivocal Literature Review
2025-06-03
Baran Shajari, Istvan David
Twin transition is the method of parallel digital and sustainability transitions in a mutually supporting way or, in common terms, "greening of and by IT and data." Twin transition reacts to the growing problem of unsustainable digitalization, particularly in the ecological sense. Ignoring this problem will eventually limit the digital adeptness of society and the problem-solving capacity of humankind. Information systems engineering must find ways to support twin transition journeys through its substantial body of knowledge, methods, and techniques. To this end, we systematically survey the academic and gray literature on twin transition, clarify key concepts, and derive leads for researchers and practitioners to steer their innovation efforts.
Nuclear Quantum Many-Body Dynamics: From Collective Vibrations to Heavy-Ion Collisions (2nd edition)
2025-06-03
Cédric Simenel
New edition of the review [EPJA 48, (2012) 152]. The increase in computational power has naturally led to new applications of mean-field (and beyond) methods. This is particularly the case of quasi-fission reactions. Since the first edition, significant progress has also been made in treating pairing correlations dynamically, leading to realistic applications in multi-nucleon transfer, fusion and fission reactions. A new section has been added on fission dynamics. A summary of recent researches on nuclear dynamics with realistic microscopic quantum approaches is presented. The Balian-Veneroni variational principle is used to derive the time-dependent Hartree-Fock (TDHF) equation describing the dynamics at the mean-field level, as well as an extension including small-amplitude quantum fluctuations which is equivalent to the time-dependent random-phase approximation (TDRPA). Such formalisms as well as their practical implementation in the nuclear physics framework with modern three-dimensional codes are discussed. Recent applications to nuclear dynamics, from collective vibrations to heavy-ion collisions are presented. A particular attention is devoted to the interplay between collective motions and internal degrees of freedom. For instance, the harmonic nature of collective vibrations is questioned. Large amplitude collective motions are investigated in the framework of heavy-ion collisions and fission. How fusion is affected by the internal structure of the collision partners, such as their deformation, is discussed. Other mechanisms in competition with fusion, and responsible for the formation of fragments which differ from the entrance channel (transfer reactions, deep-inelastic collisions, and quasi-fission) are investigated. Finally, studies of actinide collisions forming, during very short times of few zeptoseconds, the heaviest nuclear systems available on Earth, are presented.
Auto Review: Second Stage Error Detection for Highly Accurate Information Extraction from Phone Conversations
2025-06-03
Ayesha Qamar, Arushi Raghuvanshi, Conal Sathi, Youngseo Son
Automating benefit verification phone calls saves time in healthcare and helps patients receive treatment faster. It is critical to obtain highly accurate information in these phone calls, as it can affect a patient's healthcare journey. Given the noise in phone call transcripts, we have a two-stage system that involves a post-call review phase for potentially noisy fields, where human reviewers manually verify the extracted data$\unicode{x2013}$a labor-intensive task. To automate this stage, we introduce Auto Review, which significantly reduces manual effort while maintaining a high bar for accuracy. This system, being highly reliant on call transcripts, suffers a performance bottleneck due to automatic speech recognition (ASR) issues. This problem is further exacerbated by the use of domain-specific jargon in the calls. In this work, we propose a second-stage postprocessing pipeline for accurate information extraction. We improve accuracy by using multiple ASR alternatives and a pseudo-labeling approach that does not require manually corrected transcripts. Experiments with general-purpose large language models and feature-based model pipelines demonstrate substantial improvements in the quality of corrected call transcripts, thereby enhancing the efficiency of Auto Review.
Attention-based transformer models for image captioning across languages: An in-depth survey and evaluation
2025-06-03
Israa A. Albadarneh, Bassam H. Hammo, Omar S. Al-Kadi
Image captioning involves generating textual descriptions from input images, bridging the gap between computer vision and natural language processing. Recent advancements in transformer-based models have significantly improved caption generation by leveraging attention mechanisms for better scene understanding. While various surveys have explored deep learning-based approaches for image captioning, few have comprehensively analyzed attention-based transformer models across multiple languages. This survey reviews attention-based image captioning models, categorizing them into transformer-based, deep learning-based, and hybrid approaches. It explores benchmark datasets, discusses evaluation metrics such as BLEU, METEOR, CIDEr, and ROUGE, and highlights challenges in multilingual captioning. Additionally, this paper identifies key limitations in current models, including semantic inconsistencies, data scarcity in non-English languages, and limitations in reasoning ability. Finally, we outline future research directions, such as multimodal learning, real-time applications in AI-powered assistants, healthcare, and forensic analysis. This survey serves as a comprehensive reference for researchers aiming to advance the field of attention-based image captioning.
Four-point correlation functions in axion inflation
2025-06-02
Hing-Tong Cho, Kin-Wang Ng
We study parity violation in the early universe by examining the four-point correlation function within the axion inflation model. Using an open quantum system formalism from our previous work, we calculate the influence functional to fourth order, from which we then derive the inflaton four-point correlation function. When we decompose this function using isotropic basis functions, the expansion coefficients $\zeta_{\ell',\ell'',\ell'''}$ naturally split into parity-even and parity-odd components. In the large $\xi$ approximation, which enhances the production of right-handed photons in the model, the derivation of these coefficients simplifies. We work out the lowest-order nonvanishing parity-odd $\zeta_{234}$ term which clearly indicates the presence of parity violation. Moreover, our derived values of the coefficients are consistent with recent observational data from galaxy surveys.
The transformative capability of quantum-accurate machine learning interatomic potentials
2025-06-02
Alfredo A. Correa, Sebastien Hamel
Many materials's properties and phase boundaries are generally not well known under extreme pressure and temperature conditions. This is a consequence of the scarcity of experimental information and the difficulty of extrapolating approximations to the atomic interactions in such conditions. Nguyen-Cong and colleagues, in their publication (J.Phys.Chem.Lett. 15, 1152 (2024)), achieved an impressive result using a SNAP (Spectral Neighbor Analysis Potential), an interatomic potential for carbon obtained by machine learning techniques. In a way, their contribution closes a full circle of research that spanned more than three decades.
Greedy recursion parameter selection for the One-Way Navier-Stokes (OWNS) equations
2025-06-02
Michael K. Sleeman, Tim Colonius
The One-Way Navier-Stokes (OWNS) equations use recursive filtering to construct efficient, well-posed one-way approximations to linear hyperbolic systems. The recursion parameters are critical to the accuracy and stability of the method, and have previously been chosen based on heuristic estimates of key eigenvalues (or their branches), which converges slowly and requires trial-and-error tuning for new systems. We review the projection and recursive OWNS formulations (OWNS-P and OWNS-R) for inhomogeneous equations and propose a greedy algorithm for parameter selection. We show that it converges faster and leads to a net decrease in computational cost.
A Wide Field Map of Ultra-Compact Dwarfs in the Coma Cluster
2025-06-02
Richard T. Pomeroy, Juan P. Madrid, Conor R. O'Neill, Alexander T. Gagliano
A dataset of 23,351 globular clusters (GCs) and ultra-compact dwarfs (UCDs) in the Coma cluster of galaxies was built using Hubble Space Telescope Advanced Camera for Surveys data. Based on the standard magnitude cut of $M_V \leq -11$, a total of 523 UCD candidates are found within this dataset of Compact Stellar Systems (CSS). From a color-magnitude diagram (CMD) analysis built using this catalog, we find a clear mass-magnitude relation extending marginally into the UCD parameter space. The luminosity function defined by this dataset, shows an excess of sources at bright magnitudes, suggesting a bimodal formation scenario for UCDs. We estimate the number of UCDs with a different origin than GC to be $N_{UCD} \geq 32 \pm 1$. We derive the total number of CSS within the core (1 Mpc) of Coma to be $N_{CSS} \approx 69,400 \pm 1400$. The radial distribution of UCDs in Coma shows that, like GCs, UCDs agglomerate around three giant ellipticals: NGC 4874, NGC 4889, and IC 4051. We find UCDs are more centrally concentrated around these three ellipticals than GCs. IC 4051 has a satellite population of UCDs similar to NGC 4874 and NGC 4889. We estimate only ~14% of UCDs, inhabit the intracluster space (ICUCD) between galaxies in the region, in comparison to ~24% for GCs (ICGC). We find red (metal-rich) UCDs are more likely located closer to a host galaxy, with blue (metal-poor) UCDs showing a greater dispersion and lower average density in the region.
2025-06-02
Marco Pacini, Gabriele Santin, Bruno Lepri, Shubhendu Trivedi
Equivariant neural networks provide a principled framework for incorporating symmetry into learning architectures and have been extensively analyzed through the lens of their separation power, that is, the ability to distinguish inputs modulo symmetry. This notion plays a central role in settings such as graph learning, where it is often formalized via the Weisfeiler-Leman hierarchy. In contrast, the universality of equivariant models-their capacity to approximate target functions-remains comparatively underexplored. In this work, we investigate the approximation power of equivariant neural networks beyond separation constraints. We show that separation power does not fully capture expressivity: models with identical separation power may differ in their approximation ability. To demonstrate this, we characterize the universality classes of shallow invariant networks, providing a general framework for understanding which functions these architectures can approximate. Since equivariant models reduce to invariant ones under projection, this analysis yields sufficient conditions under which shallow equivariant networks fail to be universal. Conversely, we identify settings where shallow models do achieve separation-constrained universality. These positive results, however, depend critically on structural properties of the symmetry group, such as the existence of adequate normal subgroups, which may not hold in important cases like permutation symmetry.
Radiation GRMHD Models of Accretion onto Stellar-Mass Black Holes: I. Survey of Eddington Ratios
2025-06-02
Lizhong Zhang, James M. Stone, Patrick D. Mullen, Shane W. Davis, Yan-Fei Jiang, Christopher J. White
We summarize results from a survey of radiation-dominated black hole accretion flows across a wide range of mass accretion rates, as well as two values of black hole spin and initial magnetic field geometry. These models apply an algorithm targeting direct solutions to the radiation transport equation in full general relativity and have been enabled by access to modern exascale computing systems. Super-Eddington accretion flows form geometrically thick radiation pressure supported disks that drive powerful equatorial outflows. A narrow funnel-shaped photosphere in the inner region results in very low radiative efficiencies in this regime. The structure of near- and sub-Eddington accretion depends on whether there is net vertical magnetic flux at the midplane of the disk. With net flux, the disk forms a thin, dense layer at the midplane surrounded by a magnetically-dominated corona, whereas without net flux the disk remains magnetically dominated everywhere. Although none of our models achieve the magnetically arrested disk (MAD) regime, those with net vertical flux and a rapidly spinning black hole still produce powerful relativistic jets. Our calculations adopt simple opacity models (with scalings appropriate to stellar-mass black hole accretion). We discuss the application of our results to observations of X-ray binaries and ultraluminous X-ray sources such as Cyg X-3 and SS433. We also speculate on the application of our super-Eddington models to the interpretation of little red dots (LRDs) recently discovered by JWST.
We conduct a parameter survey of neutron star accretion column simulations by solving the relativistic radiation MHD equations with opacities that account for strong magnetic fields and pair production. We study how column properties depend on accretion rate, magnetic field strength, and accretion flow geometry. All the simulated accretion columns exhibit kHz oscillatory behavior, consistent with our previous findings. We show how the predicted oscillation properties depend on the column parameters. At higher accretion rates for fixed magnetic field, the column height increases, reducing the local field strength and leading to an anti-correlation between the observed cyclotron line energy and luminosity. We estimate the line energy from the simulations and find agreement with the observed trend. Downward scattering in the free-fall zone plays a key role in shaping sideways emission properties and column height. Strong downward scattering not only re-injects heat back into the column, increasing its height, but also compresses sideways emission, potentially smearing out shock oscillation signals. When the pair-production regime is reached at the base of the column, the system quickly readjusts to a force balance between gravity and radiative support. The high opacity in the pair-production region raises the radiation energy density, enhancing sideways emission through a large horizontal gradient. This shifts the sideways fan-beam radiation toward lower altitudes. In a hollow column geometry, both pencil- and fan-beam radiation emission occurs. Self-illumination across the hollow region increases the height and stabilizes the inner wall of the column, while shock oscillations persist in the outer regions.
Large Language Models (LLMs) are transforming Natural Language Processing (NLP), but their benefits are largely absent for Africa's 2,000 low-resource languages. This paper comparatively analyzes African language coverage across six LLMs, eight Small Language Models (SLMs), and six Specialized SLMs (SSLMs). The evaluation covers language coverage, training sets, technical limitations, script problems, and language modelling roadmaps. The work identifies 42 supported African languages and 23 available public data sets, and it shows a big gap where four languages (Amharic, Swahili, Afrikaans, and Malagasy) are always treated while there is over 98\% of unsupported African languages. Moreover, the review shows that just Latin, Arabic, and Ge'ez scripts are identified while 20 active scripts are neglected. Some of the primary challenges are lack of data, tokenization biases, computational costs being very high, and evaluation issues. These issues demand language standardization, corpus development by the community, and effective adaptation methods for African languages.
Stochastic Interpolants (SI) are a powerful framework for generative modeling, capable of flexibly transforming between two probability distributions. However, their use in jointly optimized latent variable models remains unexplored as they require direct access to the samples from the two distributions. This work presents Latent Stochastic Interpolants (LSI) enabling joint learning in a latent space with end-to-end optimized encoder, decoder and latent SI models. We achieve this by developing a principled Evidence Lower Bound (ELBO) objective derived directly in continuous time. The joint optimization allows LSI to learn effective latent representations along with a generative process that transforms an arbitrary prior distribution into the encoder-defined aggregated posterior. LSI sidesteps the simple priors of the normal diffusion models and mitigates the computational demands of applying SI directly in high-dimensional observation spaces, while preserving the generative flexibility of the SI framework. We demonstrate the efficacy of LSI through comprehensive experiments on the standard large scale ImageNet generation benchmark.
Immunometabolism at the Crossroads of Infection: Mechanistic and Systems-Level Perspectives from Host and Pathogen
2025-06-02
Sunayana Malla, Nabia Shahreen, Rajib Saha
The emerging field of immunometabolism has underscored the central role of metabolic pathways in orchestrating immune cell function. Far from being passive background processes, metabolic activities actively regulate key immune responses. Fundamental pathways such as glycolysis, the tricarboxylic acid (TCA) cycle, and oxidative phosphorylation critically shape the behavior of immune cells, influencing macrophage polarization, T cell activation, and dendritic cell function. In this review, we synthesize recent advances in immunometabolism, with a focus on the metabolic mechanisms that govern the responses of both innate and adaptive immune cells to bacterial, viral, and fungal pathogens. Drawing on experimental, computational, and integrative methodologies, we highlight how metabolic reprogramming contributes to host defense in response to infection. These findings reveal new opportunities for therapeutic intervention, suggesting that modulation of metabolic pathways could enhance immune function and improve pathogen clearance.
Leveraging Natural Language Processing to Unravel the Mystery of Life: A Review of NLP Approaches in Genomics, Transcriptomics, and Proteomics
2025-06-02
Ella Rannon, David Burstein
Natural Language Processing (NLP) has transformed various fields beyond linguistics by applying techniques originally developed for human language to the analysis of biological sequences. This review explores the application of NLP methods to biological sequence data, focusing on genomics, transcriptomics, and proteomics. We examine how various NLP methods, from classic approaches like word2vec to advanced models employing transformers and hyena operators, are being adapted to analyze DNA, RNA, protein sequences, and entire genomes. The review also examines tokenization strategies and model architectures, evaluating their strengths, limitations, and suitability for different biological tasks. We further cover recent advances in NLP applications for biological data, such as structure prediction, gene expression, and evolutionary analysis, highlighting the potential of these methods for extracting meaningful insights from large-scale genomic data. As language models continue to advance, their integration into bioinformatics holds immense promise for advancing our understanding of biological processes in all domains of life.
This paper reviews the NTIRE 2025 RAW Image Restoration and Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Restoration and Super-Resolution could be essential in modern Image Signal Processing (ISP) pipelines, however, this problem is not as explored as in the RGB domain. The goal of this challenge is two fold, (i) restore RAW images with blur and noise degradations, (ii) upscale RAW Bayer images by 2x, considering unknown noise and blur. In the challenge, a total of 230 participants registered, and 45 submitted results during thee challenge period. This report presents the current state-of-the-art in RAW Restoration.
Blast-frozen Dark Matter and Modulated Density Perturbations
2025-06-02
Miha Nemevšek, Yue Zhang
First-order phase transitions (FOPT) are ubiquitous in beyond the Standard Model physics and leave distinctive echoes in the history of early universe. We consider a FOPT serving the well-motivated role of dark matter mass generation and present {\it blast-frozen dark matter} (BFDM), which transitions from radiation to non-relativistic relic in a period much shorter than the corresponding Hubble time. Its cosmological imprint are strong oscillations in the dark matter density perturbations that seed structure formation on large and small scales. For a FOPT occurring not long before the matter-radiation equality, next generation cosmological surveys bear a strong potential to discover BFDM and in turn establish the origin of dark matter mass.
An Overview of GPU-based First-Order Methods for Linear Programming and Extensions
2025-06-02
Haihao Lu, Jinwen Yang
The rapid progress in GPU computing has revolutionized many fields, yet its potential in mathematical programming, such as linear programming (LP), has only recently begun to be realized. This survey aims to provide a comprehensive overview of recent advancements in GPU-based first-order methods for LP, with a particular focus on the design and development of cuPDLP. We begin by presenting the design principles and algorithmic foundation of the primal-dual hybrid gradient (PDHG) method, which forms the core of the solver. Practical enhancements, such as adaptive restarts, preconditioning, Halpern-type acceleration and infeasibility detection, are discussed in detail, along with empirical comparisons against industrial-grade solvers, highlighting the scalability and efficiency of cuPDLP. We also provide a unified theoretical framework for understanding PDHG, covering both classical and recent results on sublinear and linear convergence under sharpness conditions. Finally, we extend the discussion to GPU-based optimization beyond LP, including quadratic, semidefinite, conic, and nonlinear programming.
An Approximation Theory Perspective on Machine Learning
2025-06-02
Hrushikesh N. Mhaskar, Efstratios Tsoukanis, Ameya D. Jagtap
A central problem in machine learning is often formulated as follows: Given a dataset $\{(x_j, y_j)\}_{j=1}^M$, which is a sample drawn from an unknown probability distribution, the goal is to construct a functional model $f$ such that $f(x) \approx y$ for any $(x, y)$ drawn from the same distribution. Neural networks and kernel-based methods are commonly employed for this task due to their capacity for fast and parallel computation. The approximation capabilities, or expressive power, of these methods have been extensively studied over the past 35 years. In this paper, we will present examples of key ideas in this area found in the literature. We will discuss emerging trends in machine learning including the role of shallow/deep networks, approximation on manifolds, physics-informed neural surrogates, neural operators, and transformer architectures. Despite function approximation being a fundamental problem in machine learning, approximation theory does not play a central role in the theoretical foundations of the field. One unfortunate consequence of this disconnect is that it is often unclear how well trained models will generalize to unseen or unlabeled data. In this review, we examine some of the shortcomings of the current machine learning framework and explore the reasons for the gap between approximation theory and machine learning practice. We will then introduce our novel research to achieve function approximation on unknown manifolds without the need to learn specific manifold features, such as the eigen-decomposition of the Laplace-Beltrami operator or atlas construction. In many machine learning problems, particularly classification tasks, the labels $y_j$ are drawn from a finite set of values.
Introduction to the theory of generalized locally Toeplitz sequences and its applications
2025-06-02
Carlo Garoni
The theory of generalized locally Toeplitz (GLT) sequences was conceived as an apparatus for computing the spectral distribution of matrices arising from the numerical discretization of differential equations (DEs). The purpose of this review is to introduce the reader to the theory of GLT sequences and to present some of its applications to the computation of the spectral distribution of DE discretization matrices. We mainly focus on the applications, whereas the theory is presented in a self-contained tool-kit fashion, without entering into technical details. The exposition is supposed to be understandable to master's degree students in mathematics. It also discloses new more efficient approaches to the spectral analysis of DE discretization matrices as well as a novel spectral analysis tool that has not been considered in the GLT literature heretofore, i.e., the modulus of integral continuity.
2025-06-02
Matthew J. Holman, Kevin J. Napier, Matthew J. Payne, Jacob A. Kurlander
We present a search for distant planets in Pan-STARRS1. We calibrated our search by injecting an isotropic control population of synthetic detections into Pan-STARRS1 source catalogs, providing a high-fidelity alternative to injecting synthetic sources at the image level. We found that our method is sensitive to a wide range of distances, as well as all rates and directions of motion. We identified 692 solar system objects (109 of which are not yet listed in the Minor Planet Center's database), including 642 TNOs, 23 of which are dwarf planets. By raw number of detections, this makes our search the third most productive Kuiper Belt survey to date, in spite of the fact that we did not explicitly search for objects closer than 80 au. Although we did not find Planet Nine or any other planetary objects, we were able to show that the remaining parameter space for Planet Nine is highly concentrated in the galactic plane.
Sorcha: Optimized Solar System Ephemeris Generation
2025-06-02
Matthew J. Holman, Pedro H. Bernardinelli, Megan E. Schwamb, Mario Jurić, Drew Oldag, Maxine West, Kevin J. Napier, Stephanie R. Merritt, Grigori Fedorets, Samuel Cornwall, Jacob A. Kurlander, Siegfried Eggl, Jeremy Kubica, Kathleen Kiker, Joseph Murtagh, Shantanu P. Naidu, Colin Orion Chandler
Sorcha is a solar system survey simulator built for the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) and future large-scale wide-field surveys. Over the ten-year survey, the LSST is expected to collect roughly a billion observations of minor planets. The task of a solar system survey simulator is to take a set of input objects (described by orbits and physical properties) and determine what a real or hypothetical survey would have discovered. Existing survey simulators have a computational bottleneck in determining which input objects lie in each survey field, making them infeasible for LSST data scales. Sorcha can swiftly, efficiently, and accurately calculate the on-sky positions for sets of millions of input orbits and surveys with millions of visits, identifying which exposures these objects cross, in order for later stages of the software to make detailed estimates of the apparent magnitude and detectability of those input small bodies. In this paper, we provide the full details of the algorithm and software behind Sorcha's ephemeris generator. Like many of Sorcha's components, its ephemeris generator can be easily used for other surveys.
Graph Neural Networks (GNNs) achieve high performance across many applications but function as black-box models, limiting their use in critical domains like healthcare and criminal justice. Explainability methods address this by providing feature-level explanations that identify important node attributes for predictions. These explanations create privacy risks. Combined with auxiliary information, feature explanations can enable adversaries to reconstruct graph structure, exposing sensitive relationships. Existing graph reconstruction attacks assume access to original auxiliary data, but practical systems use differential privacy to protect node features and labels while providing explanations for transparency. We study a threat model where adversaries access public feature explanations along with privatized node features and labels. We show that existing explanation-based attacks like GSEF perform poorly with privatized data due to noise from differential privacy mechanisms. We propose ReconXF, a graph reconstruction attack for scenarios with public explanations and privatized auxiliary data. Our method adapts explanation-based frameworks by incorporating denoising mechanisms that handle differential privacy noise while exploiting structural signals in explanations. Experiments across multiple datasets show ReconXF outperforms SoTA methods in privatized settings, with improvements in AUC and average precision. Results indicate that public explanations combined with denoising enable graph structure recovery even under the privacy protection of auxiliary data. Code is available at (link to be made public after acceptance).
Random-key genetic algorithms: Principles and applications
2025-06-02
Mariana A. Londe, Luciana S. Pessoa, Carlos E. Andrade, José F. Gonçalves, Mauricio G. C. Resende
A random-key genetic algorithm is an evolutionary metaheuristic for discrete and global optimization. Each solution is encoded as a vector of N random keys, where a random key is a real number randomly generated in the continuous interval [0, 1). A decoder maps each vector of random keys to a solution of the optimization problem being solved and computes its cost. The benefit of this approach is that all genetic operators and transformations can be maintained within the unitary hypercube, regardless of the problem being addressed. This enhances the productivity and maintainability of the core framework. The algorithm starts with a population of P vectors of random keys. At each iteration, the vectors are partitioned into two sets: a smaller set of high-valued elite solutions and the remaining non-elite solutions. All elite elements are copied, without change, to the next population. A small number of random-key vectors (the mutants) is added to the population of the next iteration. The remaining elements of the population of the next iteration are generated by combining, with the parametrized uniform crossover of Spears and DeJong (1991), pairs of solutions. This chapter reviews random-key genetic algorithms and describes an effective variant called biased random-key genetic algorithms.
SN 2024bfu, SN 2025qe, and the early light curves of type Iax supernovae
2025-06-02
M. R. Magee, T. L. Killestein, M. Pursiainen, B. Godson, D. Jarvis, C. Jiménez-Palau, J. D. Lyman, D. Steeghs, B. Warwick, J. P. Anderson, T. Butterley, T. -W. Chen, V. S. Dhillon, L. Galbany, S. González-Gaitán, M. Gromadzki, C. Inserra, L. Kelsey, A. Kumar, G. Leloudas, S. Mattila, T. E. Müller-Bravo, K. Noysena, G. Ramsay, S. Srivastav, R. Starling, R. W. Wilson, D. R. Young, K. Ackley, R. P. Breton, J. Casares Velázquez, M. J. Dyer, D. K. Galloway, E. Kankare, R. Kotak, L. K. Nuttall, D. O'Neill, P. Pessi, D. Pollacco, K. Ulaczyk, O. Yaron
Type Iax supernovae (SNe Iax) are perhaps the most numerous class of peculiar thermonuclear supernova and yet their sample size, particularly those observed shortly after explosion, remains relatively small. In this paper we present photometric and spectroscopic observations of two SNe Iax discovered shortly after explosion, SN 2024bfu and SN 2025qe. Both SNe were observed by multiple all-sky surveys, enabling tight constraints on the moment of first light and the shape of the early light curve. Our observations of SN 2025qe begin <2 d after the estimated time of first light and represent some of the earliest observations of any SN Iax. We identify features consistent with carbon absorption throughout the spectroscopic evolution of SN 2025qe, potentially indicating the presence of unburned material throughout the ejecta. Inspired by our early light curve coverage, we gather a sample of SNe Iax observed by ATLAS, GOTO, and ZTF, and measure their rise times and early light curve power-law rise indices. We compare our findings to a sample of normal SNe Ia and find indications that SNe Iax show systematically shorter rise times, but the small sample size and relatively large uncertainties prevent us from identifying statistically significant differences in most bands. We find some indication that SNe Iax show systematically lower rise indices than normal SNe Ia in all bands. The low rise indices observed among SNe Iax is qualitatively consistent with extended $^{56}$Ni distributions and more thoroughly-mixed ejecta compared to normal SNe Ia, similar to predictions from pure deflagration explosions.
Rootin' Tootin' Efficient Ray Shootin': Creating Microlensing Magnification Maps with GPUs
2025-06-02
Luke Weisenbach
The impending discovery and monitoring of hundreds of new gravitationally lensed quasars and supernovae from upcoming ground and space based large area surveys such as LSST, \textit{Euclid}, and \textit{Roman} necessitates the development of improved numerical methods for studying gravitational microlensing. We present in this work the fastest microlensing map generation code currently publicly available. We utilize graphics processing units to take advantage of the inherent parallelizable nature of creating magnification maps, in addition to using 1) the fast multipole method to reduce the runtime dependence on the number of microlenses and 2) inverse polygon mapping to reduce the number of rays required. The code is available at https://github.com/weisluke/microlensing/.
Surrogate models for lightcurves and photosphere properties of Type II supernovae
2025-06-02
Nikhil Sarin, Takashi J. Moriya, Avinash Singh, Anjasha Gangopadhyay, K-Ryan Hinds, Steve Schulze, Conor M. B. Omand, Kaustav K. Das
Inferences on the properties Type II supernovae (SNe) can provide significant insights into the lives and deaths of the astrophysical population of massive stars and potentially provide measurements of luminosity distance, independent of the distance ladder. Here, we introduce surrogate models for the photospheric properties and lightcurves of Type II SNe trained on a large grid of simulations from the radiation hydrodynamics code, {\sc stella}. The trained model can accurately and efficiently ($\sim 30$ms) predict the lightcurves and properties of Type II SNe within a large parameter space of progenitor ($10-18 M_{\odot}$ at ZAMS) and nickel masses ($0.001-0.3M_{\odot}$), progenitor mass-loss rate ($10^{-5}-10^{-1}~M_{\odot}$yr$^{-1}$), CSM radius ($1-10\times10^{14}$cm), and SN explosion energies ($0.5-5 \times 10^{51}$erg). We validate this model through inference on lightcurves and photosphere properties drawn directly from the original {\sc stella} simulations not included in training. In particular, for a synthetic Type II SNe observed within the 10-year LSST survey, we find we can measure the progenitor and nickel masses with $\approx 9\%$ and $\approx 25\%$ precision, respectively, when fitting the photometric data while accounting for the uncertainty in the surrogate model itself. Meanwhile, from real observations of SN~2004et, SN~2012aw, and SN~2017gmr we infer a progenitor ZAMS mass of $12.15_{-1.06}^{+1.03} M_{\odot}$, $10.61_{-0.32}^{+0.37} M_{\odot}$, $10.4 \pm 0.3 M_{\odot}$, respectively. We discuss systematic uncertainties from our surrogate modelling approach and likelihood approaches to account for these uncertainties. We further discuss future extensions to the model to enable stronger constraints on properties of Type II SNe and their progenitors, for cosmological applications, and applications of our surrogate modelling approach to other transients.
Relativistic Magnetic Reconnection in Astrophysical Plasmas: A Powerful Mechanism of Nonthermal Emission
2025-06-02
Lorenzo Sironi, Dmitri A. Uzdensky, Dimitrios Giannios
Magnetic reconnection -- a fundamental plasma physics process, where magnetic field lines of opposite polarity annihilate -- is invoked in astrophysical plasmas as a powerful mechanism of nonthermal particle acceleration, able to explain fast-evolving, bright high-energy flares. Near black holes and neutron stars, reconnection occurs in the ``relativistic'' regime, in which the mean magnetic energy per particle exceeds the rest mass energy. This review reports recent advances in our understanding of the kinetic physics of relativistic reconnection: (1) Kinetic simulations have elucidated the physics of plasma heating and nonthermal particle acceleration in relativistic reconnection; (2) The physics of radiative relativistic reconnection, with its self-consistent interplay between photons and reconnection-accelerated particles -- a peculiarity of luminous, high-energy astrophysical sources -- is the new frontier of research; (3) Relativistic reconnection plays a key role in global models of high-energy sources, both in terms of global-scale layers, as well as of reconnection sites generated as a byproduct of local magnetohydrodynamic instabilities. We summarize themes of active investigation and future directions, emphasizing the role of upcoming observational capabilities, laboratory experiments, and new computational tools.
Collisionless relaxation to quasi-steady state attractors in cold dark matter halos: origin of the universal NFW profile
2025-06-02
Uddipan Banik, Amitava Bhattacharjee
Collisionless self-gravitating systems such as cold dark matter halos are known to harbor universal density profiles despite the intricate non-linear physics of hierarchical structure formation in the $\Lambda$CDM paradigm. The origin of these attractor states has been a persistent mystery, particularly because the physics of collisionless relaxation is not well understood. To solve this long-standing problem, we develop a self-consistent quasilinear theory in action-angle space for the collisionless relaxation of inhomogeneous, self-gravitating systems by perturbing the governing Vlasov-Poisson equations. We obtain a quasilinear diffusion equation that describes the secular evolution of the mean coarse-grained distribution function $f_0$ of accreted matter in the fluctuating force field of a halo. The diffusion coefficient not only depends on the fluctuation power spectrum but also on the evolving potential of the system, which reflects the self-consistency of the problem. Diffusive heating by an initially cored halo develops an $r^{-1}$ cusp in the density profile of the accreted material, with $r$ the halocentric radius. Subsequent accretion and relaxation around this $r^{-1}$ cusp develops an $r^{-3}$ fall-off, establishing the Navarro-Frenk-White (NFW) density profile, a quasi-steady state attractor of collisionless relaxation that is not particularly sensitive to initial conditions. Given enough time though, the halo tends to Maxwellianize and develop an isothermal sphere profile. We demonstrate for the first time that the universal NFW profile emerges as an attractor solution to a self-consistent theory for collisionless relaxation.
The Stellar Populations and Rest-Frame Colors of Star-Forming Galaxies at $z \approx 8$: Exploring the Impact of Filter Choice and Star Formation History Assumption with JADES
2025-06-02
Jakob M. Helton, Stacey Alberts, George H. Rieke, Kevin N. Hainline, Zhiyuan Ji, Marcia J. Rieke, Benjamin D. Johnson, Brant Robertson, Sandro Tacchella, Lily Whitler, William M. Baker, Rachana Bhatawdekar, Kristan Boyett, Andrew J. Bunker, Phillip A. Cargile, Stefano Carniani, Stephane Charlot, Jacopo Chevallard, Emma Curtis-Lake, Eiichi Egami, Daniel J. Eisenstein, Ryan Hausen, Jianwei Lyu, Roberto Maiolino, Erica Nelson, Pablo G. Pérez-González, Pierluigi Rinaldi, Meredith Stone, Fengwu Sun, Christina C. Williams, Christopher N. A. Willmer, Chris Willott, Joris Witstok
Our understanding of the physical properties of star-forming galaxies during the Epoch of Reionization (EoR, at $z > 6$) suffers from degeneracies among the apparent properties of the stars, the nebular gas, and the dust. These degeneracies are most prominent with photometry, which has insufficient (1) spectral resolution and (2) rest-frame spectral coverage. We explore ways to break these degeneracies with a sample of $N = 22$ high-redshift star-forming galaxies at $7 < z_{\mathrm{phot}} \leq 9$, using some of the deepest existing imaging from JWST/NIRCam and JWST/MIRI with JADES. Key to this study is the imaging from JWST/MIRI at $7.7\ \mu\mathrm{m}$, which provides coverage of the rest-frame $I$-band at the observed redshifts. We infer stellar population properties and rest-frame colors using a variety of filter sets and star formation history assumptions to explore the impact of these choices. Evaluating these quantities both with and without the $7.7\ \mu\mathrm{m}$ data point shows that dense spectral coverage with JWST/NIRCam (eight or more filters, including at least one medium-band) can compensate for lacking the rest-frame $I$-band coverage for the vast majority ($\approx 80\%$) of our sample. Furthermore, these galaxy properties are most consistently determined by assuming the delayed-tau star formation history, which provides the smallest offsets and scatters around these offsets when including JWST/MIRI. Within extragalactic surveys like JADES and CEERS, our findings suggest that robust characterization of the stellar population properties and rest-frame colors for high-redshift star-forming galaxies is possible with JWST/NIRCam alone at $z \approx 8$.
LibriBrain: Over 50 Hours of Within-Subject MEG to Improve Speech Decoding Methods at Scale
2025-06-02
Miran Özdogan, Gilad Landau, Gereon Elvers, Dulhan Jayalath, Pratik Somaiya, Francesco Mantegna, Mark Woolrich, Oiwi Parker Jones
LibriBrain represents the largest single-subject MEG dataset to date for speech decoding, with over 50 hours of recordings -- 5$\times$ larger than the next comparable dataset and 50$\times$ larger than most. This unprecedented `depth' of within-subject data enables exploration of neural representations at a scale previously unavailable with non-invasive methods. LibriBrain comprises high-quality MEG recordings together with detailed annotations from a single participant listening to naturalistic spoken English, covering nearly the full Sherlock Holmes canon. Designed to support advances in neural decoding, LibriBrain comes with a Python library for streamlined integration with deep learning frameworks, standard data splits for reproducibility, and baseline results for three foundational decoding tasks: speech detection, phoneme classification, and word classification. Baseline experiments demonstrate that increasing training data yields substantial improvements in decoding performance, highlighting the value of scaling up deep, within-subject datasets. By releasing this dataset, we aim to empower the research community to advance speech decoding methodologies and accelerate the development of safe, effective clinical brain-computer interfaces.
GLoSS: Generative Language Models with Semantic Search for Sequential Recommendation
2025-06-02
Krishna Acharya, Aleksandr V. Petrov, Juba Ziani
We propose Generative Low-rank language model with Semantic Search (GLoSS), a generative recommendation framework that combines large language models with dense retrieval for sequential recommendation. Unlike prior methods such as GPT4Rec, which rely on lexical matching via BM25, GLoSS uses semantic search to retrieve relevant items beyond lexical matching. For query generation, we employ 4-bit quantized LlaMA-3 models fine-tuned with low-rank adaptation (LoRA), enabling efficient training and inference on modest hardware. We evaluate GLoSS on three real-world Amazon review datasets: Beauty, Toys, and Sports, and find that it achieves state-of-the-art performance. Compared to traditional ID-based baselines, GLoSS improves Recall@5 by 33.3%, 52.8%, and 15.2%, and NDCG@5 by 30.0%, 42.6%, and 16.1%, respectively. It also outperforms LLM-based recommenders such as P5, GPT4Rec, LlamaRec and E4SRec with Recall@5 gains of 4.3%, 22.8%, and 29.5%. Additionally, user segment evaluations show that GLoSS performs particularly well for cold-start users in the Amazon Toys and Sports datasets, and benefits from longer user histories in Amazon Beauty dataset, demonstrating robustness across different levels of interaction lengths.
Status of the W boson mass and the future of the electroweak fit in the next decades
2025-06-02
Giuseppe Bozzi, Matthias Schott
A precise determination of the W boson mass is an essential test for the Standard Model of particle physics: the comparison of experimental value and theoretical prediction allows to probe the internal consistency of the electroweak sector and could possibly highlight signals of New Physics. We provide a concise and up-to-date summary of past and recent measurements at lepton and hadron colliders, a discussion of the known perturbative and non-perturbative theoretical ingredients used to provide predictions for the relevant observables, and an overview of future prospects to reduce systematic uncertainties and to compare different measurements in a consistent way. We conclude with a brief discussion on the relevance of the global electroweak fit at present and future colliders.
SoK: Concurrency in Blockchain -- A Systematic Literature Review and the Unveiling of a Misconception
2025-06-02
Atefeh Zareh Chahoki, Maurice Herlihy, Marco Roveri
Smart contracts, the cornerstone of blockchain technology, enable secure, automated distributed execution. Given their role in handling large transaction volumes across clients, miners, and validators, exploring concurrency is critical. This includes concurrent transaction execution or validation within blocks, block processing across shards, and miner competition to select and persist transactions. Concurrency and parallelism are a double-edged sword: while they improve throughput, they also introduce risks like race conditions, non-determinism, and vulnerabilities such as deadlock and livelock. This paper presents the first survey of concurrency in smart contracts, offering a systematic literature review organized into key dimensions. First, it establishes a taxonomy of concurrency levels in blockchain systems and discusses proposed solutions for future adoption. Second, it examines vulnerabilities, attacks, and countermeasures in concurrent operations, emphasizing the need for correctness and security. Crucially, we reveal a flawed concurrency assumption in a major research category, which has led to widespread misinterpretation. This work aims to correct that and guide future research toward more accurate models. Finally, we identify gaps in each category to outline future research directions and support blockchain's advancement.
Life Sequence Transformer: Generative Modelling for Counterfactual Simulation
2025-06-02
Alberto Cabezas, Carlotta Montorsi
Social sciences rely on counterfactual analysis using surveys and administrative data, generally depending on strong assumptions or the existence of suitable control groups, to evaluate policy interventions and estimate causal effects. We propose a novel approach that leverages the Transformer architecture to simulate counterfactual life trajectories from large-scale administrative records. Our contributions are: the design of a novel encoding method that transforms longitudinal administrative data to sequences and the proposal of a generative model tailored to life sequences with overlapping events across life domains. We test our method using data from the Istituto Nazionale di Previdenza Sociale (INPS), showing that it enables the realistic and coherent generation of life trajectories. This framework offers a scalable alternative to classical counterfactual identification strategy, such as difference-in-differences and synthetic controls, particularly in contexts where these methods are infeasible or their assumptions unverifiable. We validate the model's utility by comparing generated life trajectories against established findings from causal studies, demonstrating its potential to enrich labour market research and policy evaluation through individual-level simulations.
Evaluating AI-generated medical image segmentations for clinical acceptability poses a significant challenge, as traditional pixelagreement metrics often fail to capture true diagnostic utility. This paper introduces Hierarchical Clinical Reasoner (HCR), a novel framework that leverages Large Language Models (LLMs) as clinical guardrails for reliable, zero-shot quality assessment. HCR employs a structured, multistage prompting strategy that guides LLMs through a detailed reasoning process, encompassing knowledge recall, visual feature analysis, anatomical inference, and clinical synthesis, to evaluate segmentations. We evaluated HCR on a diverse dataset across six medical imaging tasks. Our results show that HCR, utilizing models like Gemini 2.5 Flash, achieved a classification accuracy of 78.12%, performing comparably to, and in instances exceeding, dedicated vision models such as ResNet50 (72.92% accuracy) that were specifically trained for this task. The HCR framework not only provides accurate quality classifications but also generates interpretable, step-by-step reasoning for its assessments. This work demonstrates the potential of LLMs, when appropriately guided, to serve as sophisticated evaluators, offering a pathway towards more trustworthy and clinically-aligned quality control for AI in medical imaging.
Datasheets Aren't Enough: DataRubrics for Automated Quality Metrics and Accountability
2025-06-02
Genta Indra Winata, David Anugraha, Emmy Liu, Alham Fikri Aji, Shou-Yi Hung, Aditya Parashar, Patrick Amadeus Irawan, Ruochen Zhang, Zheng-Xin Yong, Jan Christian Blaise Cruz, Niklas Muennighoff, Seungone Kim, Hanyang Zhao, Sudipta Kar, Kezia Erina Suryoraharjo, M. Farid Adilazuarda, En-Shiun Annie Lee, Ayu Purwarianti, Derry Tanti Wijaya, Monojit Choudhury
High-quality datasets are fundamental to training and evaluating machine learning models, yet their creation-especially with accurate human annotations-remains a significant challenge. Many dataset paper submissions lack originality, diversity, or rigorous quality control, and these shortcomings are often overlooked during peer review. Submissions also frequently omit essential details about dataset construction and properties. While existing tools such as datasheets aim to promote transparency, they are largely descriptive and do not provide standardized, measurable methods for evaluating data quality. Similarly, metadata requirements at conferences promote accountability but are inconsistently enforced. To address these limitations, this position paper advocates for the integration of systematic, rubric-based evaluation metrics into the dataset review process-particularly as submission volumes continue to grow. We also explore scalable, cost-effective methods for synthetic data generation, including dedicated tools and LLM-as-a-judge approaches, to support more efficient evaluation. As a call to action, we introduce DataRubrics, a structured framework for assessing the quality of both human- and model-generated datasets. Leveraging recent advances in LLM-based evaluation, DataRubrics offers a reproducible, scalable, and actionable solution for dataset quality assessment, enabling both authors and reviewers to uphold higher standards in data-centric research. We also release code to support reproducibility of LLM-based evaluations at https://github.com/datarubrics/datarubrics.
Infinite symmetry prevents disorder-induced localization in 2D
2025-06-02
Carlo A. Trugenberger
We show that 2D gapped many-body quantum states are constrained by an infinite-dimensional symmetry which renders them transparent to weak disorder. This prevents disorder-induced localization when interactions are strong enough to open a gap. Using purely algebraic methods we derive all possible quantum states near the superconductor-to-insulator (SIT) transition and we compute the meson spectrum of superinsulators.
Modelling benthic animals in space and time using Bayesian Point Process with cross validation: the case of Holoturians
2025-06-02
Daniele Poggio, Gian Mario Sangiovanni, Gianluca Mastrantonio, Giovanna Jona Lasinio, Edoardo Casoli, Stefano Moro, Daniele Ventura
Understanding the spatial distribution of Holothurians is an essential task for ecosystem monitoring and sustainable management, particularly in the Mediterranean habitats. However, species distribution modeling is often complicated by the presence-only nature of the data and heterogeneous sampling designs. This study develops a spatio-temporal framework based on Log-Gaussian Cox Processes to analyze Holothurians' positions collected across nine survey campaigns conducted from 2022 to 2024 near Giglio Island, Italy. The surveys combined high-resolution photogrammetry with diver-based visual censuses, leading to varying detection probabilities across habitats, especially within Posidonia oceanica meadows. We adopt a model with a shared spatial Gaussian process component to accommodate this complexity, accounting for habitat structure, environmental covariates, and temporal variability. Model estimation is performed using Integrated Nested Laplace Approximation. We evaluate the predictive performances of alternative model specifications through a novel k-fold cross-validation strategy for point processes, using the Continuous Ranked Probability Score. Our approach provides a flexible and computationally efficient framework for integrating heterogeneous presence-only data in marine ecology and comparing the predictive ability of alternative models.
Anisotropic Counts-in-Cells in Redshift Space: A New Route to Cosmological Constraints from Galaxy Surveys
2025-06-02
Paweł Drozda, Wojciech A. Hellwing, Maciej Bilicki
We introduce a novel extension of the volume-averaged correlation function (VACF) framework by replacing the traditional spherical smoothing kernels with anisotropic, ellipsoidal windows. This generalized approach enables the study of shape-dependent clustering statistics and captures directional information encoded in large-scale structure, particularly in redshift space where galaxy distribution is distorted by peculiar velocities. We define and compute ellipsoidal VACFs $\bar{\xi}_J (r_{\parallel}, r_{\perp})$ and the corresponding reduced cumulants $s_J (r_{\parallel}, r_{\perp})$, allowing for joint sensitivity to both scale and anisotropy across arbitrary statistical order J. Using a suite of COLA N-body simulations spanning a grid of cosmologies with varying $\Omega_M$ and $\sigma_8$, we analyze the behavior of ellipsoidal VACFs and cumulants in both real and redshift space. We find that the shape of the smoothing kernel that maximizes the clustering signal depends strongly on the redshift-space distortion regime: spherical in real space, prolate in the Fingers-of-God-dominated regime, and oblate in the Kaiser squashing-dominated regime. While the standard VACF amplitude is mainly sensitive to ${\sigma}_8$, the shape-dependence of redshift-space skewness shows a coherent response to the combined growth parameter $f \sigma_8$, with a typical sensitivity at the 1-3 $\sigma$ level between neighboring models. Our results demonstrate that ellipsoidal VACFs offer a computationally efficient and information-rich generalization of counts-in-cells analysis, with promising applications to galaxy survey data, halo catalogs, and cosmological tests of gravity beyond $\Lambda CDM$.
Toward Memristor-like Resonant Sensors: Observation of Pinched Hysteresis within MEMS Resonators
2025-06-02
Erion Uka, Chun Zhao
Memristors, uniquely characterized by their pinched hysteresis loop fingerprints, have attracted significant research interest over the past decade, due to their enormous potential for novel computation and artificial intelligence applications. Memristors are widely regarded as the fourth fundamental electrical component, with voltage and current being their input and output signals. In broader terms, similar pinched hysteresis behavior should also exist in other physical systems across domains (e.g., physical input and electrical output), hence linking the real physical world with the digital domain (e.g., in the form of a physical sensor). In this work, we report the first observation of pinched hysteresis behavior in a micro-electro-mechanical systems (MEMS) resonator device, showing that it is viable to create resonant MEMS sensors incorporating memristor-like properties, i.e., MemReSensor. We envisage that this will lay the foundations for a new way of fusing MEMS with artificial intelligence (AI), such as creating in-physical-sensor computing, as well as in-sensor AI, e.g., multi-mode in-sensor matrix multiplication across domains.
A Survey of Synchronization Technologies for Low-power Backscatter Communication
2025-06-02
Wenyuan Jiang, Shuo Guo
Synchronization is a fundamental enabler for low-power backscatter communication systems, where passive or semi-passive tags modulate ambient RF signals for ultra-low-power data transfer. In this survey, we review recent advances in synchronization techniques across Bluetooth Low Energy (BLE), Long-Term Evolution (LTE), and WiFi-based backscatter platforms. We categorize existing methods by their synchronization granularity, accuracy, compatibility, and power cost. We then compare representative systems including PassiveBLE, Bitalign, LScatter, SyncLTE, LiTEfoot, SyncScatter, and BiScatter, highlighting design trade-offs and performance metrics. Furthermore, we delve into the trade-offs between high throughput and low power synchronization, examining key approaches and challenges such as the balance between throughput, synchronization accuracy, and power consumption in various backscatter systems. Finally, we discuss open challenges and outline future directions toward scalable, secure, and ultra-low-power backscatter synchronization.
Spiking neural networks (SNNs) have emerged as a class of bio -inspired networks that leverage sparse, event-driven signaling to achieve low-power computation while inherently modeling temporal dynamics. Such characteristics align closely with the demands of ubiquitous computing systems, which often operate on resource-constrained devices while continuously monitoring and processing time-series sensor data. Despite their unique and promising features, SNNs have received limited attention and remain underexplored (or at least, under-adopted) within the ubiquitous computing community. To address this gap, this paper first introduces the core components of SNNs, both in terms of models and training mechanisms. It then presents a systematic survey of 76 SNN-based studies focused on time-series data analysis, categorizing them into six key application domains. For each domain, we summarize relevant works and subsequent advancements, distill core insights, and highlight key takeaways for researchers and practitioners. To facilitate hands-on experimentation, we also provide a comprehensive review of current software frameworks and neuromorphic hardware platforms, detailing their capabilities and specifications, and then offering tailored recommendations for selecting development tools based on specific application needs. Finally, we identify prevailing challenges within each application domain and propose future research directions that need be explored in ubiquitous community. Our survey highlights the transformative potential of SNNs in enabling energy-efficient ubiquitous sensing across diverse application domains, while also serving as an essential introduction for researchers looking to enter this emerging field.
Benford's Curse: Tracing Digit Bias to Numerical Hallucination in LLMs
2025-06-02
Jiandong Shao, Yao Lu, Jianfei Yang
Large Language Models (LLMs) exhibit impressive performance on complex reasoning tasks, yet they frequently fail on basic numerical problems, producing incorrect outputs. Inspired by Benford's Law -- a statistical pattern where lower digits occur more frequently as leading digits -- we hypothesize that the long-tailed digit distributions in web-collected corpora may be learned by LLMs during pretraining, leading to biased numerical generation. To investigate the hypothesis, we first examine whether digits frequencies in pretraining corpus (OLMo2) follows Benford's law. We then construct an evaluation benchmark with uniformly distributed ground-truth digits across seven numerical reasoning tasks. Our evaluation results demonstrate that leading open-source LLMs show a consistent pattern of digit bias that resembles Benford's law. Through logit-lens tracing and neuron-level dissection, we identify that this bias arises predominantly from a small subset of highly digit-selective feed-forward network (FFN) neurons in the deeper layers. Finally, we demonstrate that pruning these neurons mitigates imbalanced overgeneration and partially corrects erroneous outputs, providing causal evidence that fine-grained pretraining digit bias can propagate into model behavior. Our findings reveal a fundamental connection between corpus-level statistics and symbolic failure modes in LLMs, offering a new lens for diagnosing and mitigating hallucinations in numerical tasks.
SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware Reinforcement Learning
2025-06-02
Zhongwei Wan, Zhihao Dou, Che Liu, Yu Zhang, Dongfei Cui, Qinjian Zhao, Hui Shen, Jing Xiong, Yi Xin, Yifan Jiang, Yangfan He, Mi Zhang, Shen Yan
Multimodal large language models (MLLMs) have shown promising capabilities in reasoning tasks, yet still struggle with complex problems requiring explicit self-reflection and self-correction, especially compared to their unimodal text-based counterparts. Existing reflection methods are simplistic and struggle to generate meaningful and instructive feedback, as the reasoning ability and knowledge limits of pre-trained models are largely fixed during initial training. To overcome these challenges, we propose Multimodal Self-Reflection enhanced reasoning with Group Relative Policy Optimization (SRPO), a two-stage reflection-aware reinforcement learning (RL) framework explicitly designed to enhance multimodal LLM reasoning. In the first stage, we construct a high-quality, reflection-focused dataset under the guidance of an advanced MLLM, which generates reflections based on initial responses to help the policy model learn both reasoning and self-reflection. In the second stage, we introduce a novel reward mechanism within the GRPO framework that encourages concise and cognitively meaningful reflection while avoiding redundancy. Extensive experiments across multiple multimodal reasoning benchmarks, including MathVista, MathVision, MathVerse, and MMMU-Pro, using Qwen-2.5-VL-7B and Qwen-2.5-VL-32B demonstrate that SRPO significantly outperforms state-of-the-art models, achieving notable improvements in both reasoning accuracy and reflection quality.
When LLMs Team Up: The Emergence of Collaborative Affective Computing
2025-06-02
Wenna Lai, Haoran Xie, Guandong Xu, Qing Li, S. Joe Qin
Affective Computing (AC) is essential in bridging the gap between human emotional experiences and machine understanding. Traditionally, AC tasks in natural language processing (NLP) have been approached through pipeline architectures, which often suffer from structure rigidity that leads to inefficiencies and limited adaptability. The advent of Large Language Models (LLMs) has revolutionized this field by offering a unified approach to affective understanding and generation tasks, enhancing the potential for dynamic, real-time interactions. However, LLMs face cognitive limitations in affective reasoning, such as misinterpreting cultural nuances or contextual emotions, and hallucination problems in decision-making. To address these challenges, recent research advocates for LLM-based collaboration systems that emphasize interactions among specialized models and LLMs, mimicking human-like affective intelligence through the synergy of emotional and rational thinking that aligns with Dual Process Theory in psychology. This survey aims to provide a comprehensive overview of LLM-based collaboration systems in AC, exploring from structured collaborations to autonomous collaborations. Specifically, it includes: (1) A systematic review of existing methods, focusing on collaboration strategies, mechanisms, key functions, and applications; (2) Experimental comparisons of collaboration strategies across representative tasks in affective understanding and generation; (3) An analysis highlighting the potential of these systems to enhance robustness and adaptability in complex affective reasoning; (4) A discussion of key challenges and future research directions to further advance the field. This work is the first to systematically explore collaborative intelligence with LLMs in AC, paving the way for more powerful applications that approach human-like social intelligence.
Modern Slavery Acts mandate that corporations disclose their efforts to combat modern slavery, aiming to enhance transparency and strengthen practices for its eradication. However, verifying these statements remains challenging due to their complex, diversified language and the sheer number of statements that must be reviewed. The development of NLP tools to assist in this task is also difficult due to a scarcity of annotated data. Furthermore, as modern slavery transparency legislation has been introduced in several countries, the generalizability of such tools across legal jurisdictions must be studied. To address these challenges, we work with domain experts to make two key contributions. First, we present AIMS.uk and AIMS.ca, newly annotated datasets from the UK and Canada to enable cross-jurisdictional evaluation. Second, we introduce AIMSCheck, an end-to-end framework for compliance validation. AIMSCheck decomposes the compliance assessment task into three levels, enhancing interpretability and practical applicability. Our experiments show that models trained on an Australian dataset generalize well across UK and Canadian jurisdictions, demonstrating the potential for broader application in compliance monitoring. We release the benchmark datasets and AIMSCheck to the public to advance AI-adoption in compliance assessment and drive further research in this field.
Explainable AI Systems Must Be Contestable: Here's How to Make It Happen
2025-06-02
Catarina Moreira, Anna Palatkina, Dacia Braca, Dylan M. Walsh, Peter J. Leihn, Fang Chen, Nina C. Hubig
As AI regulations around the world intensify their focus on system safety, contestability has become a mandatory, yet ill-defined, safeguard. In XAI, "contestability" remains an empty promise: no formal definition exists, no algorithm guarantees it, and practitioners lack concrete guidance to satisfy regulatory requirements. Grounded in a systematic literature review, this paper presents the first rigorous formal definition of contestability in explainable AI, directly aligned with stakeholder requirements and regulatory mandates. We introduce a modular framework of by-design and post-hoc mechanisms spanning human-centered interfaces, technical architectures, legal processes, and organizational workflows. To operationalize our framework, we propose the Contestability Assessment Scale, a composite metric built on more than twenty quantitative criteria. Through multiple case studies across diverse application domains, we reveal where state-of-the-art systems fall short and show how our framework drives targeted improvements. By converting contestability from regulatory theory into a practical framework, our work equips practitioners with the tools to embed genuine recourse and accountability into AI systems.
An Open and Collaborative Database of Properties of Materials for High-Temperature Superconducting-Based Devices
2025-06-02
Pablo Cayado, João Rosas, João Murta-Pina, Harold S. Ruiz
The successful integration of high-temperature superconductors (HTS) into modern technologies requires consistent, accessible, and comprehensive material data, a need that is currently unmet due to the fragmented and incomplete nature of existing resources. This paper introduces a new collaborative, open-access database specifically designed to address this gap by providing standardized data on HTS materials and crucial auxiliary components for HTS applications. The database encompasses extensive data on structural, cryogenic, electrical, magnetic, and superconducting materials, supporting diverse requirements from HTS modelling to magnet design. Developed through collaborative efforts and organized using an ontology-driven data model, this platform is dynamically adaptable, ensuring that it can grow as new materials and data emerge. Key features include user-driven contributions, peer-reviewed data validation, and advanced filtering capabilities for efficient data retrieval. This innovative database, to the knowledge of the authors, being the largest publicly available for material properties of HTS technologies is positioned as a valuable tool for the HTS community, promoting more efficient research and development processes, accelerating the practical application of HTS, and fostering a collaborative approach to knowledge sharing within the field. The database is available at https://sc.hi-scale.grisenergia.pt/app.
Network Digital Twin for 6G and Beyond: An End-to-End View Across Multi-Domain Network Ecosystems
2025-06-02
Dinh-Hieu Tran, Nazar Waheed, Yuris Mulya Saputra, Xingqin Lin, Cong T. Nguyen, Tedros Salih Abdu, Van Nhan Vo, Van-Quan Pham, Madyan Alsenwi, Abuzar Babikir Mohammad Adam, Symeon Chatzinotas, Eva Lagaunas, Hung Tran, Tu Ho Dac, Nguyen Van Huynh
With the rapid development of technology, the number of smart mobile users is increasing, accompanied by growing demands from applications such as virtual/augmented reality (VR/XR), remote surgery, autonomous vehicles, and real-time holographic communications, all of which require high transmission rates and ultra-low latency in 6G and beyond networks (6G+). This poses enormous challenges in efficiently deploying large-scale networks, including network design, planning, troubleshooting, optimization, and maintenance, without affecting the user experience. Network Digital Twin (NDT) has emerged as a potential solution, enabling the creation of a virtual model that reflects the actual network, supporting the simulation of various network designs, applying diverse operating policies, and reproducing complex fault scenarios under real-world conditions. This motivate us for this study, where we provide a comprehensive survey of NDT in the context of 6G+, covering areas such as radio access networks (RAN), transport networks, 5G core networks and beyond (5GCORE+), cloud/edge computing, applications (blockchain, health system, manufacturing, security, and vehicular networks), non-terrestrial networks (NTNs), and quantum networks, from both academic and industrial perspectives. In particular, we are the first to provide an in-depth guide and usage of RAN and 5GCORE+ for NDT. Then, we provide an extensive review of foundation technologies such as transport networks, cloud/edge computing, applications, NTNs, and quantum networks in NDT. Finally, we discuss the key challenges, open issues, and future research directions for NDT in the context of 6G+.
Interstellar Polarization Survey. V. Galactic magnetic field tomography in the spiral arms using optical and near-infrared starlight polarization
2025-06-02
Y. Angarita, M. J. F. Versteeg, M. Haverkorn, V. Pelgrims, C. V. Rodrigues, A. M. Magalhães, R. Santos-Lima, Koji S. Kawabata
Interstellar linear polarization occurs when starlight passes through elongated dust grains aligned by interstellar magnetic fields. The observed polarization can come from different dust structures along the line of sight (LOS). By combining polarization measurements with stellar distances, we can study the plane-of-sky Galactic magnetic field (GMF) between the observer and the star and separate the contributions of clouds with different GMF properties. We used optical and near-infrared (NIR) polarization data from three regions in the Galactic plane ($|b|<1^{\circ}$ and $19.\!\!^{\circ}8
Cosmological correlators offer a remarkable window into the high-energy physics governing Universe's earliest moments, with the tantalising prospect of discovering new particles. However, extracting new physics from these observables requires both precise theoretical predictions of inflationary theories and accurate, analytical templates suitable for data analysis throughout parameter and kinematic spaces. In this paper, we extend the current analytical results by computing the most general boost-breaking seed correlator mediated by the tree-level exchange of a massive spinning particle. We derive the result using two complementary approaches, bootstrapping from boundary differential equations, and direct spectral integration. Both representations are packaged as a single partially resummed series that converges in all physical kinematics. Computing this correlator marks a milestone for carving out the space of all boost-breaking correlators, and therefore completes the tree-level catalogue. We then introduce a general procedure to obtain accurate approximations for cosmological collider signals based on the saddle-point method. This approach allows for a clear physical intuition of various signals hidden in correlators, as the bulk physics is made manifest through the location of these saddles in the complex time plane, which depend on the external kinematics. Evaluating the time integrals at these saddles yields results given as elementary functions that remain valid beyond soft limits and provide intuitive control over both the signal shape and amplitude. We demonstrate the power of this method in both de Sitter-invariant and boost-breaking scenarios, and uncover novel refined waveform and strength dependence for oscillatory signals from massive fields. We provide a complete cosmological collider shape template capturing all boost-breaking effects for upcoming cosmological surveys.
2025-06-02
Pedro C. Neto, Naser Damer, Jaime S. Cardoso, Ana F. Sequeira
Bias has been a constant in face recognition models. Over the years, researchers have looked at it from both the model and the data point of view. However, their approach to mitigation of data bias was limited and lacked insight on the real nature of the problem. Here, in this document, we propose to revise our use of ethnicity labels as a continuous variable instead of a discrete value per identity. We validate our formulation both experimentally and theoretically, showcasing that not all identities from one ethnicity contribute equally to the balance of the dataset; thus, having the same number of identities per ethnicity does not represent a balanced dataset. We further show that models trained on datasets balanced in the continuous space consistently outperform models trained on data balanced in the discrete space. We trained more than 65 different models, and created more than 20 subsets of the original datasets.
This study examined the effect of insecurity on agricultural output in Benue state. A descriptive survey design was employed, and 400 respondents were purposively selected from insecurity-prone local government areas, namely, Guma LGA, Agatu LGA, Gwer LGA, Gwer-West LGA, Katsina-Ala LGA, Logo LGA, Ukum LGA and Kwande LGA. The data were collected through the administration of a questionnaire and were analysed using t tests and structural equation modelling (SEM). The t-test was used to compare farmers' incomes before and after the insecurity in the study area to assess if the differences were statistically significant, while Structural Equation Modelling analysed the complex relationships among multiple variables, employing regression and factor analysis to model both direct and indirect effects. The results revealed that the monetary value of crop and livestock output decreased during periods of insecurity. Furthermore, the study showed that insecurity has an adverse effect on crop and livestock output. This means that a one percent increase in insecurity leads to a 0.211% and 0.311% decrease in crop and livestock output respectively. The study concluded that insecurity reduced agricultural output in Benue state. Based on the findings, it was recommended that the government deploy more security personnel, establish community policing initiatives, and employ modern surveillance technologies to deter criminal activities in insecure areas. Additionally, for places experiencing farmer-herder conflict, the government should provide grazing reserves for herdsmen and further strengthen the state law on open grazing prohibition and the establishment of ranch law.
Interpretation of the binary black hole mass spectrum
2025-06-02
Ilya Mandel
This is a summary of an invited talk given at the Moriond Gravitation meeting on March 31, 2025. I touch on some of the practical challenges of measuring the mass spectrum of merging binary black holes through their gravitational-wave signatures. I then describe my take on the current state of interpreting the observed binary black hole mass spectrum from the perspective of models for the formation of these sources. I conclude that meaningful progress must rely on the combination of gravitational-wave observations and a broad range of electromagnetic observations of massive stellar binaries at earlier stages of their evolution. This is my very personal and necessarily brief take on the current state of the field and does not aspire to the balance or completeness of a review.
Turbulent puffs in transitional pulsatile pipe flow at moderate pulsation amplitudes
2025-06-02
Daniel Morón, Marc Avila
We show that, in the transitional regime of pulsatile pipe flow, at moderate-to-high amplitudes 0.5 < A < 1, the first long-lived turbulent structures are localized and take the form of the puffs and slugs observed in statistically steady pipe flow. We perform direct numerical simulations at many pulsation frequencies, amplitudes and Re, and observe different dynamics of puffs and slugs. At certain flow parameters we find, using a causal analysis, that puffs actively make use of linear instabilities in the laminar Sexl-Womersley profile to survive the pulsation. Using all these lessons learned, we extend a low order model by Barkley et al., Nature (2015), to reproduce these dynamics. We find a good agreement between the extended model and our numerical results in a broad parametric space of pulsation amplitudes 0.5 < A < 1, frequencies Wo > 5 and 2100 < Re < 3000. With the help of our numerical results, causal analysis and model, we determine that turbulence production has two sources at these flow parameters: the mean shear as in statistically steady pipe flow, and the instabilities of the instantaneous pulsatile mean profile.
Challenges in designing research infrastructure software in multi-stakeholder contexts
2025-06-02
Stephan Druskat, Sabine Theis
This study investigates the challenges in designing research infrastructure software for automated software publication in multi-stakeholder environments, focusing specifically on the HERMES system. Through two quantitative surveys of research software engineers (RSEs) and infrastructure facility staff (IFs), it examines technical, organizational, and social requirements across these stakeholder groups. The study reveals significant differences in how RSEs and IFs prioritize various system features. While RSEs highly value compatibility with existing infrastructure, IFs prioritize user-focused aspects like system usability and documentation. The research identifies two main challenges in designing research infrastructure software: (1) the existence of multiple stakeholder groups with differing requirements, and (2) the internal heterogeneity within each stakeholder group across dimensions such as technical experience. The study also highlights that only half of RSE respondents actively practice software publication, pointing to potential cultural or technical barriers. Additionally, the research reveals discrepancies in how stakeholders view organizational aspects, with IFs consistently rating factors like responsibility structures and quality assurance as more important than RSEs do. These findings contribute to a better understanding of the complexities involved in designing research infrastructure software and emphasize the need for systems that can accommodate diverse user groups while maintaining usability across different technical expertise levels.
Model-agnostic Mitigation Strategies of Data Imbalance for Regression
2025-06-02
Jelke Wibbeke, Sebastian Rohjans, Andreas Rauh
Data imbalance persists as a pervasive challenge in regression tasks, introducing bias in model performance and undermining predictive reliability. This is particularly detrimental in applications aimed at predicting rare events that fall outside the domain of the bulk of the training data. In this study, we review the current state-of-the-art regarding sampling-based methods and cost-sensitive learning. Additionally, we propose novel approaches to mitigate model bias. To better asses the importance of data, we introduce the density-distance and density-ratio relevance functions, which effectively integrate empirical frequency of data with domain-specific preferences, offering enhanced interpretability for end-users. Furthermore, we present advanced mitigation techniques (cSMOGN and crbSMOGN), which build upon and improve existing sampling methods. In a comprehensive quantitative evaluation, we benchmark state-of-the-art methods on 10 synthetic and 42 real-world datasets, using neural networks, XGBoosting trees and Random Forest models. Our analysis reveals that while most strategies improve performance on rare samples, they often degrade it on frequent ones. We demonstrate that constructing an ensemble of models -- one trained with imbalance mitigation and another without -- can significantly reduce these negative effects. The key findings underscore the superior performance of our novel crbSMOGN sampling technique with the density-ratio relevance function for neural networks, outperforming state-of-the-art methods.
Robust Federated Learning against Noisy Clients via Masked Optimization
2025-06-02
Xuefeng Jiang, Tian Wen, Zhiqin Yang, Lvhua Wu, Yufeng Chen, Sheng Sun, Yuwei Wang, Min Liu
In recent years, federated learning (FL) has made significant advance in privacy-sensitive applications. However, it can be hard to ensure that FL participants provide well-annotated data for training. The corresponding annotations from different clients often contain complex label noise at varying levels. This label noise issue has a substantial impact on the performance of the trained models, and clients with greater noise levels can be largely attributed for this degradation. To this end, it is necessary to develop an effective optimization strategy to alleviate the adverse effects of these noisy clients.In this study, we present a two-stage optimization framework, MaskedOptim, to address this intricate label noise problem. The first stage is designed to facilitate the detection of noisy clients with higher label noise rates. The second stage focuses on rectifying the labels of the noisy clients' data through an end-to-end label correction mechanism, aiming to mitigate the negative impacts caused by misinformation within datasets. This is achieved by learning the potential ground-truth labels of the noisy clients' datasets via backpropagation. To further enhance the training robustness, we apply the geometric median based model aggregation instead of the commonly-used vanilla averaged model aggregation. We implement sixteen related methods and conduct evaluations on three image datasets and one text dataset with diverse label noise patterns for a comprehensive comparison. Extensive experimental results indicate that our proposed framework shows its robustness in different scenarios. Additionally, our label correction framework effectively enhances the data quality of the detected noisy clients' local datasets. % Our codes will be open-sourced to facilitate related research communities. Our codes are available via https://github.com/Sprinter1999/MaskedOptim .
Agentic AI and Multiagentic: Are We Reinventing the Wheel?
2025-06-02
V. Botti
The terms Agentic AI and Multiagentic AI have recently gained popularity in discussions on generative artificial intelligence, often used to describe autonomous software agents and systems composed of such agents. However, the use of these terms confuses these buzzwords with well-established concepts in AI literature: intelligent agents and multi-agent systems. This article offers a critical analysis of this conceptual misuse. We review the theoretical origins of "agentic" in the social sciences (Bandura, 1986) and philosophical notions of intentionality (Dennett, 1971), and then summarise foundational works on intelligent agents and multi-agent systems by Wooldridge, Jennings and others. We examine classic agent architectures, from simple reactive agents to Belief-Desire-Intention (BDI) models, and highlight key properties (autonomy, reactivity, proactivity, social capability) that define agency in AI. We then discuss recent developments in large language models (LLMs) and agent platforms based on LLMs, including the emergence of LLM-powered AI agents and open-source multi-agent orchestration frameworks. We argue that the term AI Agentic is often used as a buzzword for what are essentially AI agents, and AI Multiagentic for what are multi-agent systems. This confusion overlooks decades of research in the field of autonomous agents and multi-agent systems. The article advocates for scientific and technological rigour and the use of established terminology from the state of the art in AI, incorporating the wealth of existing knowledge, including standards for multi-agent system platforms, communication languages and coordination and cooperation algorithms, agreement technologies (automated negotiation, argumentation, virtual organisations, trust, reputation, etc.), into the new and promising wave of LLM-based AI agents, so as not to end up reinventing the wheel.
We show that collisions between particles free falling from infinity and a disk of material plunging off the retrograde innermost stable circular orbit of a near-extremal Kerr black hole is the unique astronomically natural way in which to create a gravitational particle accelerator with center of mass energies at the $10$'s to $100$'s of teraelectronvolt range, in other words a supercollider.
Low-velocity precessing jets can explain observed morphologies in the Twin Radio Galaxy TRG J104454+354055
2025-06-02
Santanu Mondal, Gourab Giri, Ravi Joshi, Paul J. Wiita, Gopal-Krishna, Luis C. Ho
Our understanding of large-scale radio jets in merger systems has been drastically improved in the era of VLA, VLBA/EVN, uGMRT, and MeerKAT. Twin Radio Galaxies (TRGs) are the rare interacting galaxy pairs where both supermassive black holes host kiloparsec-scale bipolar radio jets. Only recently was a third TRG discovered and it shows significantly different jet morphologies than the previous two. Due to both the extreme paucity and complexity of such systems, the launching of their jets as well as their mutual interaction during the propagation through the ambient medium are not well understood. We have performed 3D hydrodynamic simulations to study the bipolar jets in the third TRG, J104454+354055. Our study indicates that the precession of mutually tilted bipolar jets originating from the two galactic nuclei separated by tens of kiloparsecs and propagating at low velocities can explain the observed morphologies. The simulated jet precession timescales are short compared to the overall dynamical timescale of the jets and could originate from Lense-Thirring effects in the accretion disks. This approach to understanding the TRG jet dynamics could also be applied to other TRG systems with similar helical morphologies that may be discovered in the upcoming era of the SKA and its pathfinder surveys.
Stop Chasing the C-index: This Is How We Should Evaluate Our Survival Models
2025-06-02
Christian Marius Lillelund, Shi-ang Qi, Russell Greiner, Christian Fischer Pedersen
We argue that many survival analysis and time-to-event models are incorrectly evaluated. First, we survey many examples of evaluation approaches in the literature and find that most rely on concordance (C-index). However, the C-index only measures a model's discriminative ability and does not assess other important aspects, such as the accuracy of the time-to-event predictions or the calibration of the model's probabilistic estimates. Next, we present a set of key desiderata for choosing the right evaluation metric and discuss their pros and cons. These are tailored to the challenges in survival analysis, such as sensitivity to miscalibration and various censoring assumptions. We hypothesize that the current development of survival metrics conforms to a double-helix ladder, and that model validity and metric validity must stand on the same rung of the assumption ladder. Finally, we discuss the appropriate methods for evaluating a survival model in practice and summarize various viewpoints opposing our analysis.
Mitigating Disparate Impact of Differentially Private Learning through Bounded Adaptive Clipping
2025-06-02
Linzh Zhao, Aki Rehn, Mikko A. Heikkilä, Razane Tajeddine, Antti Honkela
Differential privacy (DP) has become an essential framework for privacy-preserving machine learning. Existing DP learning methods, however, often have disparate impacts on model predictions, e.g., for minority groups. Gradient clipping, which is often used in DP learning, can suppress larger gradients from challenging samples. We show that this problem is amplified by adaptive clipping, which will often shrink the clipping bound to tiny values to match a well-fitting majority, while significantly reducing the accuracy for others. We propose bounded adaptive clipping, which introduces a tunable lower bound to prevent excessive gradient suppression. Our method improves the accuracy of the worst-performing class on average over 10 percentage points on skewed MNIST and Fashion MNIST compared to the unbounded adaptive clipping, and over 5 percentage points over constant clipping.
Journaling has long been recognized for fostering emotional awareness and self-reflection, and recent advancements in generative AI offer new opportunities to create personalized music that can enhance these practices. In this study, we explore how AI-generated music can augment the journaling experience. Through a formative study, we examined journal writers' writing patterns, purposes, emotional regulation strategies, and the design requirements for the system that augments journaling experience by journal-based AI-generated music. Based on these insights, we developed NoRe, a system that transforms journal entries into personalized music using generative AI. In a seven-day in-the-wild study (N=15), we investigated user engagement and perceived emotional effectiveness through system logs, surveys, and interviews. Our findings suggest that journal-based music generation could support emotional reflection and provide vivid reminiscence of daily experiences. Drawing from these findings, we discuss design implications for tailoring music to journal writers' emotional states and preferences.
Spending Behavior and Economic Impacts of Urban Digital Consumption Vouchers
2025-06-02
Ming-Huan Liou, Shou-Yung Yin, Hsiang-Wen Mao
This paper evaluates the Taipei Bear Vouchers 2.0 program using verified user-level survey data and a regional input-output model to assess the effectiveness of consumption vouchers as a fiscal stimulus tool. We focus on three key behavioral mechanisms: expenditure substitution, induced consumption, and the intensity of treatment through varying voucher face values. Our findings show that voucher effectiveness differs by type. Accommodation vouchers stimulate the most additional spending due to low expenditure substitution and high induced consumption effects, while sports vouchers often replace existing consumption. Increases in voucher value further enhance marginal consumption, especially when this change is a part of unexpected policy. Taking these behavioral responses into account, we find that the output multiplier of the program rises significantly, and indirect benefits extend to untargeted sectors through inter-industry linkages. These results highlight the critical role of consumer behavior in shaping policy outcomes and offer practical guidance for designing more effective and targeted consumption voucher programs.
Scheduling Techniques of AI Models on Modern Heterogeneous Edge GPU -- A Critical Review
2025-06-02
Ashiyana Abdul Majeed, Mahmoud Meribout
In recent years, the development of specialized edge computing devices has significantly increased, driven by the growing demand for AI models. These devices, such as the NVIDIA Jetson series, must efficiently handle increased data processing and storage requirements. However, despite these advancements, there remains a lack of frameworks that automate the optimal execution of optimal execution of deep neural network (DNN). Therefore, efforts have been made to create schedulers that can manage complex data processing needs while ensuring the efficient utilization of all available accelerators within these devices, including the CPU, GPU, deep learning accelerator (DLA), programmable vision accelerator (PVA), and video image compositor (VIC). Such schedulers would maximize the performance of edge computing systems, crucial in resource-constrained environments. This paper aims to comprehensively review the various DNN schedulers implemented on NVIDIA Jetson devices. It examines their methodologies, performance, and effectiveness in addressing the demands of modern AI workloads. By analyzing these schedulers, this review highlights the current state of the research in the field. It identifies future research and development areas, further enhancing edge computing devices' capabilities.
Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review
2025-06-02
Yuchen Fang, Hao Miao, Yuxuan Liang, Liwei Deng, Yue Cui, Ximu Zeng, Yuyang Xia, Yan Zhao, Torben Bach Pedersen, Christian S. Jensen, Xiaofang Zhou, Kai Zheng
Spatio-temporal deep learning models aims to utilize useful patterns in such data to support tasks like prediction. However, previous deep learning models designed for specific tasks typically require separate training for each use case, leading to increased computational and storage costs. To address this issue, spatio-temporal foundation models have emerged, offering a unified framework capable of solving multiple spatio-temporal tasks. These foundation models achieve remarkable success by learning general knowledge with spatio-temporal data or transferring the general capabilities of pre-trained language models. While previous surveys have explored spatio-temporal data and methodologies separately, they have ignored a comprehensive examination of how foundation models are designed, selected, pre-trained, and adapted. As a result, the overall pipeline for spatio-temporal foundation models remains unclear. To bridge this gap, we innovatively provide an up-to-date review of previous spatio-temporal foundation models from the pipeline perspective. The pipeline begins with an introduction to different types of spatio-temporal data, followed by details of data preprocessing and embedding techniques. The pipeline then presents a novel data property taxonomy to divide existing methods according to data sources and dependencies, providing efficient and effective model design and selection for researchers. On this basis, we further illustrate the training objectives of primitive models, as well as the adaptation techniques of transferred models. Overall, our survey provides a clear and structured pipeline to understand the connection between core elements of spatio-temporal foundation models while guiding researchers to get started quickly. Additionally, we introduce emerging opportunities such as multi-objective training in the field of spatio-temporal foundation models.
Large Language Models (LLMs) have evolved into Multimodal Large Language Models (MLLMs), significantly enhancing their capabilities by integrating visual information and other types, thus aligning more closely with the nature of human intelligence, which processes a variety of data forms beyond just text. Despite advancements, the undesirable generation of these models remains a critical concern, particularly due to vulnerabilities exposed by text-based jailbreak attacks, which have represented a significant threat by challenging existing safety protocols. Motivated by the unique security risks posed by the integration of new and old modalities for MLLMs, we propose a unified multimodal universal jailbreak attack framework that leverages iterative image-text interactions and transfer-based strategy to generate a universal adversarial suffix and image. Our work not only highlights the interaction of image-text modalities can be used as a critical vulnerability but also validates that multimodal universal jailbreak attacks can bring higher-quality undesirable generations across different MLLMs. We evaluate the undesirable context generation of MLLMs like LLaVA, Yi-VL, MiniGPT4, MiniGPT-v2, and InstructBLIP, and reveal significant multimodal safety alignment issues, highlighting the inadequacy of current safety mechanisms against sophisticated multimodal attacks. This study underscores the urgent need for robust safety measures in MLLMs, advocating for a comprehensive review and enhancement of security protocols to mitigate potential risks associated with multimodal capabilities.
In this paper, we review recent developments and the role of Graph Neural Networks (GNNs) in computational drug discovery, including molecule generation, molecular property prediction, and drug-drug interaction prediction. By summarizing the most recent developments in this area, we underscore the capabilities of GNNs to comprehend intricate molecular patterns, while exploring both their current and prospective applications. We initiate our discussion by examining various molecular representations, followed by detailed discussions and categorization of existing GNN models based on their input types and downstream application tasks. We also collect a list of commonly used benchmark datasets for a variety of applications. We conclude the paper with brief discussions and summarize common trends in this important research area.
DeepSeek in Healthcare: A Survey of Capabilities, Risks, and Clinical Applications of Open-Source Large Language Models
2025-06-02
Jiancheng Ye, Sophie Bronstein, Jiarui Hai, Malak Abu Hashish
DeepSeek-R1 is a cutting-edge open-source large language model (LLM) developed by DeepSeek, showcasing advanced reasoning capabilities through a hybrid architecture that integrates mixture of experts (MoE), chain of thought (CoT) reasoning, and reinforcement learning. Released under the permissive MIT license, DeepSeek-R1 offers a transparent and cost-effective alternative to proprietary models like GPT-4o and Claude-3 Opus; it excels in structured problem-solving domains such as mathematics, healthcare diagnostics, code generation, and pharmaceutical research. The model demonstrates competitive performance on benchmarks like the United States Medical Licensing Examination (USMLE) and American Invitational Mathematics Examination (AIME), with strong results in pediatric and ophthalmologic clinical decision support tasks. Its architecture enables efficient inference while preserving reasoning depth, making it suitable for deployment in resource-constrained settings. However, DeepSeek-R1 also exhibits increased vulnerability to bias, misinformation, adversarial manipulation, and safety failures - especially in multilingual and ethically sensitive contexts. This survey highlights the model's strengths, including interpretability, scalability, and adaptability, alongside its limitations in general language fluency and safety alignment. Future research priorities include improving bias mitigation, natural language comprehension, domain-specific validation, and regulatory compliance. Overall, DeepSeek-R1 represents a major advance in open, scalable AI, underscoring the need for collaborative governance to ensure responsible and equitable deployment.
Retrieval-Augmented Generation of Ontologies from Relational Databases
2025-06-02
Mojtaba Nayyeri, Athish A Yogi, Nadeen Fathallah, Ratan Bahadur Thapa, Hans-Michael Tautenhahn, Anton Schnurpel, Steffen Staab
Transforming relational databases into knowledge graphs with enriched ontologies enhances semantic interoperability and unlocks advanced graph-based learning and reasoning over data. However, previous approaches either demand significant manual effort to derive an ontology from a database schema or produce only a basic ontology. We present RIGOR, Retrieval-augmented Iterative Generation of RDB Ontologies, an LLM-driven approach that turns relational schemas into rich OWL ontologies with minimal human effort. RIGOR combines three sources via RAG, the database schema and its documentation, a repository of domain ontologies, and a growing core ontology, to prompt a generative LLM for producing successive, provenance-tagged delta ontology fragments. Each fragment is refined by a judge-LLM before being merged into the core ontology, and the process iterates table-by-table following foreign key constraints until coverage is complete. Applied to real-world databases, our approach outputs ontologies that score highly on standard quality dimensions such as accuracy, completeness, conciseness, adaptability, clarity, and consistency, while substantially reducing manual effort.
Effectiveness of Stacks in the Stacked Hilbert-Huang Transform
2025-06-02
Lupin Chun-Che Lin, Chin-Ping Hu, Chien-Chang Yen, Kuo-Chuan Pan, C. Y. Hui, Kwan-Lok Li, Yu-Chiung Lin, Yi-Sheng Huang, Albert K. H. Kong
The Hilbert-Huang transform (HHT) consists of empirical mode decomposition (EMD), which is a template-free method that represents the combination of different intrinsic modes on a time-frequency map (i.e., the Hilbert spectrum). The application of HHT involves introducing trials by imposing white noise on the signal and then calculating the ensemble mean process of the corresponding EMD to demonstrate its significance on the Hilbert spectrum. In this study, we develop a stacked Hilbert-Huang Transform (sHHT) method that generates the Hilbert spectrum for each trial and compiles all results to enhance the strength of the real instantaneous frequency of the main signal on the time-frequency map. This new approach is more sensitive to detecting/tracing the nonlinear and transient features of a signal embedded in astronomical databases than the conventional HHT, particularly when the signal experiences dramatic frequency changes in a short time. We analytically investigate the consistency of HHT and sHHT and perform numerical simulations to examine the dispersion of the instantaneous frequency obtained through sHHT and compare its advantages and effectiveness with those of conventional HHT. To confirm the feasibility of the sHHT, we demonstrate its application in verifying the signal of superorbital modulation in X-ray and binary black hole mergers in gravitational waves.
Hot dust-obscured galaxies (Hot DOGs) are a rare population of hyperluminous dust-obscured quasars discovered by the Wide-field Infrared Survey Explorer (WISE) all-sky survey. The heavy circumnuclear dust obscuration allows only a small amount of scattered light from the obscured quasar to escape, enabling the decomposition of the stellar component from the total flux. The presence of scattered light enables the redshift of the source and the properties of the black hole to be obtained from SDSS and SDSS-related literature. From WISE and SDSS data, we select 11 hyperluminous Hot DOGs at $z=1.5-3.7$ with bolometric luminosities $L_{\rm bol} \gtrsim 10^{47}\,\mathrm{erg \ s^{-1}}$. We investigate the $M_{\rm BH}-M_\star$ relation in these sources using Bayesian spectral energy distribution (SED) fitting or with extra constraints from \textit{Hubble Space Telescope} (HST) image decomposition. Stellar masses are successfully derived for eight Hot DOGs. We find high Eddington ratios $\lambda_{\rm Edd}$ in these Hot DOGs, with the median value of 1.05 and the maximum value close to 3. The super-Eddington accretion may be associated with the overdense environments of Hot DOGs. We find no significant differences in the $M_{\rm BH}/M_\star$ of these Hot DOGs compared to the local relation, suggesting that these dust-obscured quasars are the progenitors of massive early-type galaxies. We speculate that the subsequent evolution of Hot DOGs may be significantly influenced by AGN feedback and remain on the local relation.
Real-time Light Curve Classification Framework for the Wide Field Survey Telescope Using Modified Semi-supervised Variational Auto-Encoder
2025-06-01
Yongling Tang, Lulu Fan, Zhen Wan, Yating Liu, Yan Lu
Modern time-domain astronomy will benefit from the vast data collected by survey telescopes. The 2.5 m Wide Field Survey Telescope (WFST), with its powerful capabilities, is promising to make significant contributions in the era of large sky surveys. To harness the full potential of the enormous amount of unlabeled light curve data that the WFST will collect, we have developed a semisupervised light curve classification framework. This framework showcases several unique features. First, it is optimized for classifying events based on the early phase of the light curve (three days after trigger), which can help identify interesting events early and enable efficient follow-up observations. Second, the semisupervised nature of our framework allows it to leverage valuable information from large volumes of unlabeled data, potentially bridging the gap between simulations and real observations and achieving better generalization in practical scenarios. Compared to the commonly used Recurrent Neural Network models, our framework has shown a 5.59% improvement in accuracy for early classification tasks, as well as improvements in precision and recall in almost all subclasses. Moreover, our approach provides a reconstructed light curve, along with a compact latent representation, offering a different perspective that can be used for further downstream tasks beyond classification. The code and model weights used in this work are maintained and publicly available on our GitHub repository.
A Review on Coarse to Fine-Grained Animal Action Recognition
2025-06-01
Ali Zia, Renuka Sharma, Abdelwahed Khamis, Xuesong Li, Muhammad Husnain, Numan Shafi, Saeed Anwar, Sabine Schmoelzl, Eric Stone, Lars Petersson, Vivien Rolland
This review provides an in-depth exploration of the field of animal action recognition, focusing on coarse-grained (CG) and fine-grained (FG) techniques. The primary aim is to examine the current state of research in animal behaviour recognition and to elucidate the unique challenges associated with recognising subtle animal actions in outdoor environments. These challenges differ significantly from those encountered in human action recognition due to factors such as non-rigid body structures, frequent occlusions, and the lack of large-scale, annotated datasets. The review begins by discussing the evolution of human action recognition, a more established field, highlighting how it progressed from broad, coarse actions in controlled settings to the demand for fine-grained recognition in dynamic environments. This shift is particularly relevant for animal action recognition, where behavioural variability and environmental complexity present unique challenges that human-centric models cannot fully address. The review then underscores the critical differences between human and animal action recognition, with an emphasis on high intra-species variability, unstructured datasets, and the natural complexity of animal habitats. Techniques like spatio-temporal deep learning frameworks (e.g., SlowFast) are evaluated for their effectiveness in animal behaviour analysis, along with the limitations of existing datasets. By assessing the strengths and weaknesses of current methodologies and introducing a recently-published dataset, the review outlines future directions for advancing fine-grained action recognition, aiming to improve accuracy and generalisability in behaviour analysis across species.
The Measurement Imbalance in Agentic AI Evaluation Undermines Industry Productivity Claims
2025-06-01
Kiana Jafari Meimandi, Gabriela Aránguiz-Dias, Grace Ra Kim, Lana Saadeddin, Mykel J. Kochenderfer
As industry reports claim agentic AI systems deliver double-digit productivity gains and multi-trillion dollar economic potential, the validity of these claims has become critical for investment decisions, regulatory policy, and responsible technology adoption. However, this paper demonstrates that current evaluation practices for agentic AI systems exhibit a systemic imbalance that calls into question prevailing industry productivity claims. Our systematic review of 84 papers (2023--2025) reveals an evaluation imbalance where technical metrics dominate assessments (83%), while human-centered (30%), safety (53%), and economic assessments (30%) remain peripheral, with only 15% incorporating both technical and human dimensions. This measurement gap creates a fundamental disconnect between benchmark success and deployment value. We present evidence from healthcare, finance, and retail sectors where systems excelling on technical metrics failed in real-world implementation due to unmeasured human, temporal, and contextual factors. Our position is not against agentic AI's potential, but rather that current evaluation frameworks systematically privilege narrow technical metrics while neglecting dimensions critical to real-world success. We propose a balanced four-axis evaluation model and call on the community to lead this paradigm shift because benchmark-driven optimization shapes what we build. By redefining evaluation practices, we can better align industry claims with deployment realities and ensure responsible scaling of agentic systems in high-stakes domains.
TRUST -- Transformer-Driven U-Net for Sparse Target Recovery
2025-06-01
Di An, Dylan Poppert, Jiayue Li, Mark Foster, Trac D. Tran
In the context of inverse problems $\bf y = Ax$, sparse recovery offers a powerful paradigm shift by enabling the stable solution of ill-posed or underdetermined systems through the exploitation of structure, particularly sparsity. Sparse regularization techniques via $\ell_0$- or $\ell_1$-norm minimization encourage solutions $\bf x$ that are both consistent with observations $\bf y$ and parsimonious in representation, often yielding physically meaningful interpretations. In this work, we address the classical inverse problem under the challenging condition where the sensing operator $\bf A$ is unknown and only a limited set of observation-target pairs $\{ \bf x,\bf y \}$ is available. We propose a novel neural architecture, TRUST, that integrates the attention mechanism of Transformers with the decoder pathway of a UNet to simultaneously learn the sensing operator and reconstruct the sparse signal. The TRUST model incorporates a Transformer-based encoding branch to capture long-range dependencies and estimate sparse support, which then guides a U-Net-style decoder to refine reconstruction through multiscale feature integration. The skip connections between the transformer stages and the decoder not only enhance image quality but also enable the decoder to access image features at different levels of abstraction. This hybrid architecture enables more accurate and robust recovery by combining global context with local details. Experimental results demonstrate that TRUST significantly outperforms traditional sparse recovery methods and standalone U-Net models, achieving superior performance in SSIM and PSNR metrics while effectively suppressing hallucination artifacts that commonly plague deep learning-based inverse solvers.
2025-06-01
A. M. Cherepashchuk, A. V. Dodin, K. A. Postnov
The unique microquasar SS433 is a massive X-ray binary system at an advanced evolutionary stage. The optical star overflows its Roche lobe and transfers mass at a very high rate onto a black hole surrounded by a supercritical accretion disk with relativistic jets inclined to the orbital plane. Both disk and jets precess with a period of 162.3 days. In the outer parts of the precessing jets, emission lines of hydrogen and neutral helium are formed, moving periodically across the spectrum of SS433 with an enormous amplitude of $\sim 1000$\,\AA\ or, on the $\sim 50000$ km/s velocity scale. A 30-year spectral and photometric monitoring of SS433 has been carried out at Sternberg Astronomical Institute. Using all published data for 45 years of observations, we obtained a number of important results concerning the nature of this unique microquasar. We discovered a secular evolutionary increase in the orbital period of SS433 at a rate of $(1.14 \pm 0.25)\times 10^{-7}$ seconds per second, suggesting that the relativistic object in SS433 is a black hole with mass exceeding 8\,${\rm M}_\odot$. It is shown that the distance between the components of SS433 increases with time, which prevents the formation of a common envelope in the system. The size of the Roche lobe of the optical donor star is on average constant in time, which ensures a stable secondary mass exchange in the system. The orbital ellipticity of SS433 was discovered, strongly supporting the model of a slaved accretion disk tracking the precession of the rotation axis of the optical star, which is inclined to the orbital plane due to an asymmetric supernova explosion. Parameters of the kinematic model of SS433, except for the precession period, keep on average constant for 45 years. Phase jumps of the precession period were detected, but on average the precession period remains constant for 45 years. (Abridged)
We survey the different methods used for extending the BWT to collections of strings, following largely [Cenzato and Lipt\'ak, CPM 2022, Bioinformatics 2024]. We analyze the specific aspects and combinatorial properties of the resulting BWT variants and give a categorization of publicly available tools for computing the BWT of string collections. We show how the specific method used impacts on the resulting transform, including the number of runs, and on the dynamicity of the transform with respect to adding or removing strings from the collection. We then focus on the number of runs of these BWT variants and present the optimal BWT introduced in [Cenzato et al., DCC 2023], which implements an algorithm originally proposed by [Bentley et al., ESA 2020] to minimize the number of BWT-runs. We also discuss several recent heuristics and study their impact on the compression of biological sequences. We conclude with an overview of the applications and the impact of the BWT of string collections in bioinformatics.
Video Frame Interpolation (VFI) is a fundamental Low-Level Vision (LLV) task that synthesizes intermediate frames between existing ones while maintaining spatial and temporal coherence. VFI techniques have evolved from classical motion compensation-based approach to deep learning-based approach, including kernel-, flow-, hybrid-, phase-, GAN-, Transformer-, Mamba-, and more recently diffusion model-based approach. We introduce AceVFI, the most comprehensive survey on VFI to date, covering over 250+ papers across these approaches. We systematically organize and describe VFI methodologies, detailing the core principles, design assumptions, and technical characteristics of each approach. We categorize the learning paradigm of VFI methods namely, Center-Time Frame Interpolation (CTFI) and Arbitrary-Time Frame Interpolation (ATFI). We analyze key challenges of VFI such as large motion, occlusion, lighting variation, and non-linear motion. In addition, we review standard datasets, loss functions, evaluation metrics. We examine applications of VFI including event-based, cartoon, medical image VFI and joint VFI with other LLV tasks. We conclude by outlining promising future research directions to support continued progress in the field. This survey aims to serve as a unified reference for both newcomers and experts seeking a deep understanding of modern VFI landscapes.
Camera Trajectory Generation: A Comprehensive Survey of Methods, Metrics, and Future Directions
2025-06-01
Zahra Dehghanian, Pouya Ardekhani, Amir Vahedi, Hamid Beigy, Hamid R. Rabiee
Camera trajectory generation is a cornerstone in computer graphics, robotics, virtual reality, and cinematography, enabling seamless and adaptive camera movements that enhance visual storytelling and immersive experiences. Despite its growing prominence, the field lacks a systematic and unified survey that consolidates essential knowledge and advancements in this domain. This paper addresses this gap by providing the first comprehensive review of the field, covering from foundational definitions to advanced methodologies. We introduce the different approaches to camera representation and present an in-depth review of available camera trajectory generation models, starting with rule-based approaches and progressing through optimization-based techniques, machine learning advancements, and hybrid methods that integrate multiple strategies. Additionally, we gather and analyze the metrics and datasets commonly used for evaluating camera trajectory systems, offering insights into how these tools measure performance, aesthetic quality, and practical applicability. Finally, we highlight existing limitations, critical gaps in current research, and promising opportunities for investment and innovation in the field. This paper not only serves as a foundational resource for researchers entering the field but also paves the way for advancing adaptive, efficient, and creative camera trajectory systems across diverse applications.
Data Heterogeneity Modeling for Trustworthy Machine Learning
2025-06-01
Jiashuo Liu, Peng Cui
Data heterogeneity plays a pivotal role in determining the performance of machine learning (ML) systems. Traditional algorithms, which are typically designed to optimize average performance, often overlook the intrinsic diversity within datasets. This oversight can lead to a myriad of issues, including unreliable decision-making, inadequate generalization across different domains, unfair outcomes, and false scientific inferences. Hence, a nuanced approach to modeling data heterogeneity is essential for the development of dependable, data-driven systems. In this survey paper, we present a thorough exploration of heterogeneity-aware machine learning, a paradigm that systematically integrates considerations of data heterogeneity throughout the entire ML pipeline -- from data collection and model training to model evaluation and deployment. By applying this approach to a variety of critical fields, including healthcare, agriculture, finance, and recommendation systems, we demonstrate the substantial benefits and potential of heterogeneity-aware ML. These applications underscore how a deeper understanding of data diversity can enhance model robustness, fairness, and reliability and help model diagnosis and improvements. Moreover, we delve into future directions and provide research opportunities for the whole data mining community, aiming to promote the development of heterogeneity-aware ML.
Will Agents Replace Us? Perceptions of Autonomous Multi-Agent AI
2025-06-01
Nikola Balic
Autonomous multi-agent AI systems are poised to transform various industries, particularly software development and knowledge work. Understanding current perceptions among professionals is crucial for anticipating adoption challenges, ethical considerations, and future workforce development. This study analyzes responses from 130 participants to a survey on the capabilities, impact, and governance of AI agents. We explore expected timelines for AI replacing programmers, identify perceived barriers to deployment, and examine beliefs about responsibility when agents make critical decisions. Key findings reveal three distinct clusters of respondents. While the study explored factors associated with current AI agent deployment, the initial logistic regression model did not yield statistically significant predictors, suggesting that deployment decisions are complex and may be influenced by factors not fully captured or that a larger sample is needed. These insights highlight the need for organizations to address compliance concerns (a commonly cited barrier) and establish clear governance frameworks as they integrate autonomous agents into their workflows.
Legal Compliance Evaluation of Smart Contracts Generated By Large Language Models
2025-06-01
Chanuka Wijayakoon, Hai Dong, H. M. N. Dilum Bandara, Zahir Tari, Anurag Soin
Smart contracts can implement and automate parts of legal contracts, but ensuring their legal compliance remains challenging. Existing approaches such as formal specification, verification, and model-based development require expertise in both legal and software development domains, as well as extensive manual effort. Given the recent advances of Large Language Models (LLMs) in code generation, we investigate their ability to generate legally compliant smart contracts directly from natural language legal contracts, addressing these challenges. We propose a novel suite of metrics to quantify legal compliance based on modeling both legal and smart contracts as processes and comparing their behaviors. We select four LLMs, generate 20 smart contracts based on five legal contracts, and analyze their legal compliance. We find that while all LLMs generate syntactically correct code, there is significant variance in their legal compliance with larger models generally showing higher levels of compliance. We also evaluate the proposed metrics against properties of software metrics, showing they provide fine-grained distinctions, enable nuanced comparisons, and are applicable across domains for code from any source, LLM or developer. Our results suggest that LLMs can assist in generating starter code for legally compliant smart contracts with strict reviews, and the proposed metrics provide a foundation for automated and self-improving development workflows.
Integer inequalities between knot invariants, skein tree depth and delta-crossing numbers
2025-06-01
Michal Jablonowski
This survey collects known (and one new) inequalities between integer-valued classical knot invariants. It features: a visual graph of the relations; proof of the new inequality relating skein tree depth and delta-crossing numbers; and examples where the inequality becomes equality.
Integrating Emerging Technologies in Virtual Learning Environments: A Comparative Study of Perceived Needs among Open Universities in Five Southeast Asian Countries
Amid the growing need to keep learners abreast of rapid technological advancements brought about by the Fourth Industrial Revolution, this study explores perceived needs of students in virtual learning environments supported by emerging technologies. A survey was conducted across five leading open universities in Southeast Asia. The study aimed to identify student preferences regarding features of their virtual learning environments that could better prepare them as productive citizens and professionals. Findings indicate strong interest in interactive books and learning analytics, underscoring the importance of enhancing learner engagement and data-informed instruction. The results inform the development of a strategic roadmap to guide open universities in prioritizing technological and pedagogical innovations aligned with the evolving expectations of digital-age learners.
2025-06-01
Mengyuan Liu, Hong Liu, Qianshuo Hu, Bin Ren, Junsong Yuan, Jiaying Lin, Jiajun Wen
With the inherent advantages of skeleton representation, 3D skeleton-based action recognition has become a prominent topic in the field of computer vision. However, previous reviews have predominantly adopted a model-oriented perspective, often neglecting the fundamental steps involved in skeleton-based action recognition. This oversight tends to ignore key components of skeleton-based action recognition beyond model design and has hindered deeper, more intrinsic understanding of the task. To bridge this gap, our review aims to address these limitations by presenting a comprehensive, task-oriented framework for understanding skeleton-based action recognition. We begin by decomposing the task into a series of sub-tasks, placing particular emphasis on preprocessing steps such as modality derivation and data augmentation. The subsequent discussion delves into critical sub-tasks, including feature extraction and spatio-temporal modeling techniques. Beyond foundational action recognition networks, recently advanced frameworks such as hybrid architectures, Mamba models, large language models (LLMs), and generative models have also been highlighted. Finally, a comprehensive overview of public 3D skeleton datasets is presented, accompanied by an analysis of state-of-the-art algorithms evaluated on these benchmarks. By integrating task-oriented discussions, comprehensive examinations of sub-tasks, and an emphasis on the latest advancements, our review provides a fundamental and accessible structured roadmap for understanding and advancing the field of 3D skeleton-based action recognition.
An Integrated Platform for LEED Certification Automation Using Computer Vision and LLM-RAG
2025-06-01
Jooyeol Lee
The Leadership in Energy and Environmental Design (LEED) certification process is characterized by labor-intensive requirements for data handling, simulation, and documentation. This paper presents an automated platform designed to streamline key aspects of LEED certification. The platform integrates a PySide6-based user interface, a review Manager for process orchestration, and multiple analysis engines for credit compliance, energy modeling via EnergyPlus, and location-based evaluation. Key components include an OpenCV-based preprocessing pipeline for document analysis and a report generation module powered by the Gemma3 large language model with a retrieval-augmented generation framework. Implementation techniques - including computer vision for document analysis, structured LLM prompt design, and RAG-based report generation - are detailed. Initial results from pilot project deployment show improvements in efficiency and accuracy compared to traditional manual workflows, achieving 82% automation coverage and up to 70% reduction in documentation time. The platform demonstrates practical scalability for green building certification automation.
Hypernuclei, nuclei containing one or more hyperons, serve as unique laboratories for probing the non-perturbative quantum chromodynamics (QCD). Recent progress in hypernuclear physics, driven by advanced experimental techniques and theoretical innovations, is briefly reviewed with a focus on key findings and unresolved challenges, such as the precise determination of the hypertriton binding energy, investigations of charge symmetry breaking in mirror hypernuclei, and the search for exotic systems, including the neutral nn$\Lambda$ state. Experimental breakthroughs, including invariant-mass analyses and femtoscopy studies in heavy-ion collisions, as well as high-resolution $\gamma$-spectroscopy, have enabled precise studies of light hypernuclei and offered critical insights into the hyperon-nucleon interaction. Theoretical progress, including ab initio calculations based on chiral effective field theory and lattice QCD, has further enhanced our understanding of hyperon-nucleon and hyperon-hyperon interactions.
EvoGit: Decentralized Code Evolution via Git-Based Multi-Agent Collaboration
2025-06-01
Beichen Huang, Ran Cheng, Kay Chen Tan
We introduce EvoGit, a decentralized multi-agent framework for collaborative software development driven by autonomous code evolution. EvoGit deploys a population of independent coding agents, each proposing edits to a shared codebase without centralized coordination, explicit message passing, or shared memory. Instead, all coordination emerges through a Git-based phylogenetic graph that tracks the full version lineage and enables agents to asynchronously read from and write to the evolving code repository. This graph-based structure supports fine-grained branching, implicit concurrency, and scalable agent interaction while preserving a consistent historical record. Human involvement is minimal but strategic: users define high-level goals, periodically review the graph, and provide lightweight feedback to promote promising directions or prune unproductive ones. Experiments demonstrate EvoGit's ability to autonomously produce functional and modular software artifacts across two real-world tasks: (1) building a web application from scratch using modern frameworks, and (2) constructing a meta-level system that evolves its own language-model-guided solver for the bin-packing optimization problem. Our results underscore EvoGit's potential to establish a new paradigm for decentralized, automated, and continual software development. EvoGit is open-sourced at https://github.com/BillHuang2001/evogit.
Aiding Medical Diagnosis through Image Synthesis and Classification
2025-06-01
Kanishk Choudhary
Medical professionals, especially those in training, often depend on visual reference materials to support an accurate diagnosis and develop pattern recognition skills. However, existing resources may lack the diversity and accessibility needed for broad and effective clinical learning. This paper presents a system designed to generate realistic medical images from textual descriptions and validate their accuracy through a classification model. A pretrained stable diffusion model was fine-tuned using Low-Rank Adaptation (LoRA) on the PathMNIST dataset, consisting of nine colorectal histopathology tissue types. The generative model was trained multiple times using different training parameter configurations, guided by domain-specific prompts to capture meaningful features. To ensure quality control, a ResNet-18 classification model was trained on the same dataset, achieving 99.76% accuracy in detecting the correct label of a colorectal histopathological medical image. Generated images were then filtered using the trained classifier and an iterative process, where inaccurate outputs were discarded and regenerated until they were correctly classified. The highest performing version of the generative model from experimentation achieved an F1 score of 0.6727, with precision and recall scores of 0.6817 and 0.7111, respectively. Some types of tissue, such as adipose tissue and lymphocytes, reached perfect classification scores, while others proved more challenging due to structural complexity. The self-validating approach created demonstrates a reliable method for synthesizing domain-specific medical images because of high accuracy in both the generation and classification portions of the system, with potential applications in both diagnostic support and clinical education. Future work includes improving prompt-specific accuracy and extending the system to other areas of medical imaging.
The magnetic response of the Tsai-type 1/1 Au-Al-Gd approximant crystals (ACs) was quantitatively investigated in terms of the magnetic entropy change ($\Delta S_{M}$) for different magnetic ground states. A comprehensive $\Delta S_{M}$ map over a wide electron concentration range has been established, demonstrating the detailed variation of $\Delta S_{M}$ across the entire magnetic phase diagram in the Tsai-type 1/1 ACs. Near the boundaries of the ferromagnetic (FM) phase, a clear deviation from the mean-field theory (MFT) was observed in both the Curie temperature ($T_{C}$) and magnetic field ($H$) dependences of the maximum magnetic entropy change ($\Delta S_{M}^{max}$). Contrary to general expectations, a high $\Delta S_{M}^{max}$ (7.2 J/K mol-Gd under a 5 T field variation), even comparable to those of candidate materials for low-temperature magnetic refrigeration, was obtained within the antiferromagnetic (AFM) region near the FM / AFM phase boundary. The unexpected enhancement of $\Delta S_{M}^{max}$ toward the AFM region under high magnetic fields indicates an anomalous magnetic response in the present Tsai-type 1/1 AC, which is presumably associated with the breakdown of the MFT. The present finding suggests that tuning the magnetic ground state across the phase boundary is an effective strategy to enhance $\Delta S_{M}$, even in general rare-earth intermetallic compounds.
Machine vs Machine: Using AI to Tackle Generative AI Threats in Assessment
2025-05-31
Mohammad Saleh Torkestani, Taha Mansouri
This paper presents a theoretical framework for addressing the challenges posed by generative artificial intelligence (AI) in higher education assessment through a machine-versus-machine approach. Large language models like GPT-4, Claude, and Llama increasingly demonstrate the ability to produce sophisticated academic content, traditional assessment methods face an existential threat, with surveys indicating 74-92% of students experimenting with these tools for academic purposes. Current responses, ranging from detection software to manual assessment redesign, show significant limitations: detection tools demonstrate bias against non-native English writers and can be easily circumvented, while manual frameworks rely heavily on subjective judgment and assume static AI capabilities. This paper introduces a dual strategy paradigm combining static analysis and dynamic testing to create a comprehensive theoretical framework for assessment vulnerability evaluation. The static analysis component comprises eight theoretically justified elements: specificity and contextualization, temporal relevance, process visibility requirements, personalization elements, resource accessibility, multimodal integration, ethical reasoning requirements, and collaborative elements. Each element addresses specific limitations in generative AI capabilities, creating barriers that distinguish authentic human learning from AI-generated simulation. The dynamic testing component provides a complementary approach through simulation-based vulnerability assessment, addressing limitations in pattern-based analysis. The paper presents a theoretical framework for vulnerability scoring, including the conceptual basis for quantitative assessment, weighting frameworks, and threshold determination theory.
Context: As generative AI (GenAI) tools such as ChatGPT and GitHub Copilot become pervasive in education, concerns are rising about students using them to complete rather than learn from coursework-risking overreliance, reduced critical thinking, and long-term skill deficits. Objective: This paper proposes and empirically applies a causal model to help educators scaffold responsible GenAI use in Software Engineering (SE) education. The model identifies how professor actions, student factors, and GenAI tool characteristics influence students' usage of GenAI tools. Method: Using a design-based research approach, we applied the model in two contexts: (1) revising four extensive lab assignments of a final-year Software Testing course at Queen's University Belfast (QUB), and (2) embedding GenAI-related competencies into the curriculum of a newly developed SE BSc program at Azerbaijan Technical University (AzTU). Interventions included GenAI usage declarations, output validation tasks, peer-review of AI artifacts, and career-relevant messaging. Results: In the course-level case, instructor observations and student artifacts indicated increased critical engagement with GenAI, reduced passive reliance, and improved awareness of validation practices. In the curriculum-level case, the model guided integration of GenAI learning outcomes across multiple modules and levels, enabling longitudinal scaffolding of AI literacy. Conclusion: The causal model served as both a design scaffold and a reflection tool. It helped align GenAI-related pedagogy with SE education goals and can offer a useful framework for instructors and curriculum designers navigating the challenges of GenAI-era education.
This study addresses critical challenges in managing the transportation of spent nuclear fuel, including inadequate data transparency, stringent confidentiality requirements, and a lack of trust among collaborating parties, issues prevalent in traditional centralized management systems. Given the high risks involved, balancing data confidentiality with regulatory transparency is imperative. To overcome these limitations, a prototype system integrating blockchain technology and the Internet of Things (IoT) is proposed, featuring a multi-tiered consortium chain architecture. This system utilizes IoT sensors for real-time data collection, which is immutably recorded on the blockchain, while a hierarchical data structure (operational, supervisory, and public layers) manages access for diverse stakeholders. The results demonstrate that this approach significantly enhances data immutability, enables real-time multi-sensor data integration, improves decentralized transparency, and increases resilience compared to traditional systems. Ultimately, this blockchain-IoT framework improves the safety, transparency, and efficiency of spent fuel transportation, effectively resolving the conflict between confidentiality and transparency in nuclear data management and offering significant practical implications.
On Linking Planet Formation Models, Protoplanetary Disk Properties, and Mature Gas Giant Exoplanet Atmospheres
2025-05-31
Adina D. Feinstein, Richard A. Booth, Jennifer B. Bergner, Joshua D. Lothringer, Elisabeth C. Matthews, Luis Welbanks, Yamila Miguel, Bertram Bitsch, Linn E. J. Eriksson, James Kirk, Stefan Pelletier, Anna B. T. Penzlin, Anjali A. A. Piette, Caroline Piaulet-Ghorayeb, Kamber Schwarz, Diego Turrini, Lorena Acuña-Aguirre, Eva-Maria Ahrer, Madyson G. Barber, Jonathan Brande, Aritra Chakrabarty, Ian J. M. Crossfield, Gabriel-Dominique Marleau, Helong Huang, Anders Johansen, Laura Kreidberg, John H. Livingston, Rafael Luque, Maria Oreshenko, Elenia Pacetti, Guilia Perotti, Jesse Polman, Bibiana Prinoth, Dmitry A. Semenov, Jacob B. Simon, Johanna Teske, Niall Whiteford
Measuring a single elemental ratio (e.g., carbon-to-oxygen) provides insufficient information for understanding the formation mechanisms and evolution that affect our observations of gas giant planet atmospheres. Although the fields of planet formation, protoplanetary disks, and exoplanets are well established and interconnected, our understanding of how to self-consistently and accurately link the theoretical and observational aspects of these fields together is lacking. To foster interdisciplinary conversations, the Max-Planck Institut f\"ur Astronomie (MPIA) hosted a week-long workshop called, "Challenge Accepted: Linking Planet Formation with Present-Day Atmospheres." Here, we summarize the latest theories and results in planet formation modeling, protoplanetary disk observations, and atmospheric observations of gas giant atmospheres to address one of the challenges of hosting interdisciplinary conferences: ensuring everyone is aware of the state-of-the-art results and technical language from each discipline represented. Additionally, we highlight key discussions held at the workshop. Our main conclusion is that it is unclear what the ideal observable is to make this link between formation scenarios and exoplanet atmospheres, whether it be multiple elemental abundance ratios, measuring refractory budgets, or something else. Based on discussions held throughout the workshop, we provide several key takeaways of what the workshop attendees feel need the most improvement and exploration within each discipline.
Unbiased estimation in new Gini index extensions under gamma distributions
2025-05-31
Roberto Vila, Helton Saulo
In this paper, we propose two new flexible Gini indices (extended lower and upper) defined via differences between the $i$-th observation, the smallest order statistic, and the largest order statistic, for any $1 \leqslant i \leqslant m$. For gamma-distributed data, we obtain exact expectations of the estimators and establish their unbiasedness, generalizing prior works by [Deltas, G. 2003. The small-sample bias of the gini coefficient: Results and implications for empirical research. Review of Economics and Statistics 85:226-234] and [Baydil, B., de la Pe\~na, V. H., Zou, H., and Yao, H. 2025. Unbiased estimation of the gini coefficient. Statistics & Probability Letters 222:110376]. Finite-sample performance is assessed via simulation, and real income data set is analyzed to illustrate the proposed measures.
Differential Privacy for Deep Learning in Medicine
2025-05-31
Marziyeh Mohammadi, Mohsen Vejdanihemmat, Mahshad Lotfinia, Mirabela Rusu, Daniel Truhn, Andreas Maier, Soroosh Tayebi Arasteh
Differential privacy (DP) is a key technique for protecting sensitive patient data in medical deep learning (DL). As clinical models grow more data-dependent, balancing privacy with utility and fairness has become a critical challenge. This scoping review synthesizes recent developments in applying DP to medical DL, with a particular focus on DP-SGD and alternative mechanisms across centralized and federated settings. Using a structured search strategy, we identified 74 studies published up to March 2025. Our analysis spans diverse data modalities, training setups, and downstream tasks, and highlights the tradeoffs between privacy guarantees, model accuracy, and subgroup fairness. We find that while DP-especially at strong privacy budgets-can preserve performance in well-structured imaging tasks, severe degradation often occurs under strict privacy, particularly in underrepresented or complex modalities. Furthermore, privacy-induced performance gaps disproportionately affect demographic subgroups, with fairness impacts varying by data type and task. A small subset of studies explicitly addresses these tradeoffs through subgroup analysis or fairness metrics, but most omit them entirely. Beyond DP-SGD, emerging approaches leverage alternative mechanisms, generative models, and hybrid federated designs, though reporting remains inconsistent. We conclude by outlining key gaps in fairness auditing, standardization, and evaluation protocols, offering guidance for future work toward equitable and clinically robust privacy-preserving DL systems in medicine.
Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics
2025-05-31
Lorenzo Jaime Yu Flores, Ori Ernst, Jackie Chi Kit Cheung
Well-calibrated model confidence scores can improve the usefulness of text generation models. For example, users can be prompted to review predictions with low confidence scores, to prevent models from returning bad or potentially dangerous predictions. However, confidence metrics are not always well calibrated in text generation. One reason is that in generation, there can be many valid answers, which previous methods do not always account for. Hence, a confident model could distribute its output probability among multiple sequences because they are all valid. We propose task-agnostic confidence metrics suited to generation, which rely solely on the probabilities associated with the model outputs without the need for further fine-tuning or heuristics. Using these, we are able to improve the calibration of BART and Flan-T5 on summarization, translation, and QA datasets.
Spins of Black Holes in X-ray Binaries and the Tension with the Gravitational Wave Measurements
2025-05-31
Andrzej A. Zdziarski, Gregoire Marcel, Alexandra Veledina, Aleksandra Olejak, Debora Lancova
We review current challenges in understanding the values and origin of the spins of black holes in binaries. A key finding of the gravitational-wave observatories is that premerger black holes in binaries have low spin values, with an average dimensionless spin parameter of $a_*\sim$0.1-0.2. Furthermore, the spins in the first-born black holes appear lower than in the second-born ones. This implies that the natal spins of black holes are generally low, and the angular momentum transport in massive stars is efficient. On the other hand, most of the published spins in X-ray binaries are very high, $a_*\gtrsim 0.7$. This is the case, in particular, for binaries with high-mass donors (potential progenitors of mergers), where their published spins range from 0.8 to 1.0, while their short lifetimes prevent significant spin-up by accretion. Those with low-mass donors could be spun-up to $a_*\gtrsim 0.7$ by accretion, but only if the donor initial masses were more than several solar masses, which remains unproven. However, we find the existing methods of spin measurements suffer from significant systematic errors. The method relying on relativistic X-ray line broadening is based on the separation of the observed spectra into the incident and reflected ones, which is intrinsically highly uncertain. The method of spectral fitting accretion disk continua uses models that predict the disk to be highly unstable, while stability is observed. Improved stable models give the disk temperatures higher than the standard models, and consequently predict lower spins. We postulate that the published spin measurements in X-ray binaries are uncertain. We hypothesize that the spins of the binaries with high-mass donors are low, while those with low-mass donors have a broader spin distribution, ranging from low to high, including high spins as required to power relativistic jets.
PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreements
2025-05-31
Petros Raptopoulos, Giorgos Filandrianos, Maria Lymperaiou, Giorgos Stamou
Contract review is a complex and time-intensive task that typically demands specialized legal expertise, rendering it largely inaccessible to non-experts. Moreover, legal interpretation is rarely straightforward-ambiguity is pervasive, and judgments often hinge on subjective assessments. Compounding these challenges, contracts are usually confidential, restricting their use with proprietary models and necessitating reliance on open-source alternatives. To address these challenges, we introduce PAKTON: a fully open-source, end-to-end, multi-agent framework with plug-and-play capabilities. PAKTON is designed to handle the complexities of contract analysis through collaborative agent workflows and a novel retrieval-augmented generation (RAG) component, enabling automated legal document review that is more accessible, adaptable, and privacy-preserving. Experiments demonstrate that PAKTON outperforms both general-purpose and pretrained models in predictive accuracy, retrieval performance, explainability, completeness, and grounded justifications as evaluated through a human study and validated with automated metrics.
AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation
2025-05-31
Ming Wang, Peidong Wang, Lin Wu, Xiaocui Yang, Daling Wang, Shi Feng, Yuxin Chen, Bixuan Wang, Yifei Zhang
Constrained by the cost and ethical concerns of involving real seekers in AI-driven mental health, researchers develop LLM-based conversational agents (CAs) with tailored configurations, such as profiles, symptoms, and scenarios, to simulate seekers. While these efforts advance AI in mental health, achieving more realistic seeker simulation remains hindered by two key challenges: dynamic evolution and multi-session memory. Seekers' mental states often fluctuate during counseling, which typically spans multiple sessions. To address this, we propose AnnaAgent, an emotional and cognitive dynamic agent system equipped with tertiary memory. AnnaAgent incorporates an emotion modulator and a complaint elicitor trained on real counseling dialogues, enabling dynamic control of the simulator's configurations. Additionally, its tertiary memory mechanism effectively integrates short-term and long-term memory across sessions. Evaluation results, both automated and manual, demonstrate that AnnaAgent achieves more realistic seeker simulation in psychological counseling compared to existing baselines. The ethically reviewed and screened code can be found on https://github.com/sci-m-wang/AnnaAgent.
The adoption of GenAI is fundamentally reshaping organizations in the knowledge economy. GenAI can significantly enhance workers' problem-solving abilities and productivity, yet it also presents a major reliability challenge: hallucinations, or errors presented as plausible outputs. This study develops a theoretical model to examine GenAI's impact on organizational structure and the role of human-in-the-loop oversight. Our findings indicate that successful GenAI adoption hinges primarily on maintaining hallucination rates below a critical level. After adoption, as GenAI advances in capability or reliability, organizations optimize their workforce by reducing worker knowledge requirements while preserving operational effectiveness through GenAI augmentation-a phenomenon known as deskilling. Unexpectedly, enhanced capability or reliability of GenAI may actually narrow the span of control, increasing the demand for managers rather than flattening organizational hierarchies. To effectively mitigate hallucination risks, many firms implement human-in-the-loop validation, where managers review GenAI-enhanced outputs before implementation. While the validation increases managerial workload, it can, surprisingly, expand the span of control, reducing the number of managers needed. Furthermore, human-in-the-loop validation influences GenAI adoption differently based on validation costs and hallucination rates, deterring adoption in low-error, high-cost scenarios, while promoting it in high-error, low-cost cases. Finally, productivity improvements from GenAI yield distinctive organizational shifts: as productivity increases, firms tend to employ fewer but more knowledgeable workers, gradually expanding managerial spans of control.
SenseFlow: Scaling Distribution Matching for Flow-based Text-to-Image Distillation
2025-05-31
Xingtong Ge, Xin Zhang, Tongda Xu, Yi Zhang, Xinjie Zhang, Yan Wang, Jun Zhang
The Distribution Matching Distillation (DMD) has been successfully applied to text-to-image diffusion models such as Stable Diffusion (SD) 1.5. However, vanilla DMD suffers from convergence difficulties on large-scale flow-based text-to-image models, such as SD 3.5 and FLUX. In this paper, we first analyze the issues when applying vanilla DMD on large-scale models. Then, to overcome the scalability challenge, we propose implicit distribution alignment (IDA) to regularize the distance between the generator and fake distribution. Furthermore, we propose intra-segment guidance (ISG) to relocate the timestep importance distribution from the teacher model. With IDA alone, DMD converges for SD 3.5; employing both IDA and ISG, DMD converges for SD 3.5 and FLUX.1 dev. Along with other improvements such as scaled up discriminator models, our final model, dubbed \textbf{SenseFlow}, achieves superior performance in distillation for both diffusion based text-to-image models such as SDXL, and flow-matching models such as SD 3.5 Large and FLUX. The source code will be avaliable at https://github.com/XingtongGe/SenseFlow.
Reconstructing the Milky Way chemical map with Galactic Chemical Evolution tool OMEGA+ from SDSS-MWM
2025-05-31
Viola Hegedűs, Szabolcs Mészáros, Blanka Világos, Marco Pignatari, J. Emily Griffith, Diogo Souto, Maria Lugaro
We obtain two-infall galactic chemical evolution (GCE) models simulating the chemical evolution of the Milky Way as constrained by a golden sample of $394,000$ stellar abundances of the Milky Way Mapper survey from the 19th data release of SDSS-V. The separation between the chemical thin and thick disks is defined using [Mg/M]. We use the chemical evolution environment $\texttt{OMEGA+}$, combined with Levenberg-Marquardt and bootstrapping. We simulate the entire Galactic disk and six galactocentric regions for a more detailed analysis of the formation of the inner, middle, and outer Galaxy. We investigate the evolution of $\alpha$, odd-Z, and iron-peak elements: 15 species altogether. The chemical thin and thick disks are separated by Mg observations, which the other $\alpha$-elements show similar trends with, while odd-Z species demonstrate different patterns as functions of metallicity. In the inward Galactic disk regions the locus of the low-Mg sequence is gradually shifted toward higher metallicity, while the high-Mg phase is less populated. The best-fit GCE models show a well-defined peak in the rate of the infalling matter as a function of the Galactic age, confirming a merger event about $10$ Gyr ago. We show that the timescale of gas accretion, the time of the second infall as well as the ratio between the surface mass densities associated to the second infall and the formation event vary with the distance from the Galactic center. The disk is assembled within a timescale of $(0.32\pm0.02)~$Gyr during a primary formation phase, then a $(0.55\pm0.06)~$Gyr-timescale, increasing accretion rate was followed by a relaxation that lasted $(2.86\pm0.70)~$Gyr, with a second peak of the infall rate at $(4.13\pm0.19)~$Gyr. Our best Galaxy evolution models are consistent with an inside-out formation scenario of the Milky Way disk, in agreement with recent chemo-dynamical simulations.
UNSURF: Uncertainty Quantification for Cortical Surface Reconstruction of Clinical Brain MRIs
2025-05-31
Raghav Mehta, Karthik Gopinath, Ben Glocker, Juan Eugenio Iglesias
We propose UNSURF, a novel uncertainty measure for cortical surface reconstruction of clinical brain MRI scans of any orientation, resolution, and contrast. It relies on the discrepancy between predicted voxel-wise signed distance functions (SDFs) and the actual SDFs of the fitted surfaces. Our experiments on real clinical scans show that traditional uncertainty measures, such as voxel-wise Monte Carlo variance, are not suitable for modeling the uncertainty of surface placement. Our results demonstrate that UNSURF estimates correlate well with the ground truth errors and: \textit{(i)}~enable effective automated quality control of surface reconstructions at the subject-, parcel-, mesh node-level; and \textit{(ii)}~improve performance on a downstream Alzheimer's disease classification task.
Cold pools, Breezes, and Monsoons: Propagating Convection over New Guinea
2025-05-31
Mingyue Tang, Jimy Dudhia, Changhai Liu, Giuseppe Torri
The diurnal cycle of precipitation near New Guinea involves intricate land-ocean-atmosphere interactions, posing substantial challenges for tropical weather and climate simulations. Using over two decades of GPM satellite observations and convection-permitting WRF simulations, this study examines the physical mechanisms governing the pronounced offshore propagation of diurnal convection over New Guinea. We identify two distinct offshore propagation modes: (1) a "ridge-to-coast" mode originated over elevated terrain and migrating toward the coastline, and (2) an "over-ocean" mode initiated near the coast, separated by a spatial gap of approximately 100 km. Our findings highlight the critical role of multi-scale density currents in shaping boundary layer dynamics over warm ocean waters. Specifically, the afternoon sea-breeze front advects cooler air onshore, stabilizing the lower atmosphere and interrupting the continuous propagation of the first mode. At night, the land breeze interacts with residual cold pools, generating offshore moist patches that facilitate the convective regeneration and propagation of the second mode. These offshore convective systems interact with monsoonal background winds, sustaining precipitation well beyond 200-600 km from the coast. Sensitivity experiments indicate that even a modest increase in sea surface temperature can enhance convective intensity and extend offshore propagation. These results shed light on the mechanisms that enable diurnal offshore convection to persist overnight and propagate far from the coastline, highlighting the importance of boundary-layer density currents and offering insights for improving precipitation.
There are several physical processes that mediate the interaction between an exoplanet and its host star, with the four main ones being due to magnetic, particle (stellar outflow), radiative and tidal interactions. These interactions can be observed at different wavelengths, from X-ray to radio. Their strengths depend on the architecture of planetary systems, as well as the age and activity level of the host stars. In particular, exoplanets in close-in orbits and/or orbiting active host stars can experience strong physical interactions, some of which are negligible or absent in the present-day Solar System planets. Here, I present an overview of star-planet interactions through the lens of three-dimensional (3D) numerical models. The main conclusions are: * Models are fundamental to interpret and guide observations. The powerful combination of observations and models allows us to extract important physical parameters of the system, such as, planetary magnetic fields, stellar wind properties, etc. * The non-axisymmetric forces of the interactions generate spatially asymmetric features (e.g., planetary material trailing the orbit, shock formation), thus requiring the use of 3D models. * Star-planet interactions vary in different timescales (from hours to giga-years) that are related to both planetary (orbital motion, rotation) and stellar (flares, cycles, and long-term evolution) properties. Understanding these variations require time-dependent models. I advocate that future 3D models should be informed by multi-wavelength, (near-)simultaneous observations. The use of observations is twofold: some generate inputs for models (eg stellar magnetic field maps), whereas others are fitted by models (eg spectroscopic transits). This combination of observations and models provides a powerful tool to derive physical properties of the system that would otherwise remain unknown.
Bridging the Gap between Hardware Fuzzing and Industrial Verification
2025-05-31
Ruiyang Ma, Tianhao Wei, Jiaxi Zhang, Chun Yang, Jiangfang Yi, Guojie Luo
As hardware design complexity increases, hardware fuzzing emerges as a promising tool for automating the verification process. However, a significant gap still exists before it can be applied in industry. This paper aims to summarize the current progress of hardware fuzzing from an industry-use perspective and propose solutions to bridge the gap between hardware fuzzing and industrial verification. First, we review recent hardware fuzzing methods and analyze their compatibilities with industrial verification. We establish criteria to assess whether a hardware fuzzing approach is compatible. Second, we examine whether current verification tools can efficiently support hardware fuzzing. We identify the bottlenecks in hardware fuzzing performance caused by insufficient support from the industrial environment. To overcome the bottlenecks, we propose a prototype, HwFuzzEnv, providing the necessary support for hardware fuzzing. With this prototype, the previous hardware fuzzing method can achieve a several hundred times speedup in industrial settings. Our work could serve as a reference for EDA companies, encouraging them to enhance their tools to support hardware fuzzing efficiently in industrial verification.
Old Quantum Mechanics by Bohr and Sommerfeld from a Modern Perspective
2025-05-31
Kamal K. Barley, Sergei K. Suslov
We review Bohr's atomic model and its extension by Sommerfeld from a mathematical perspective of wave mechanics. The derivation of quantization rules and energy levels is revisited using semiclassical methods. Sommerfeld-type integrals are evaluated by elementary techniques, and connections with the Schr\"{o}dinger and Dirac equations are established. Historical developments and key transitions from classical to quantum theory are discussed to clarify the structure and significance of the old quantum mechanics.
A Systematic Review of Metaheuristics-Based and Machine Learning-Driven Intrusion Detection Systems in IoT
2025-05-31
Mohammad Shamim Ahsan, Salekul Islam, Swakkhar Shatabda
The widespread adoption of the Internet of Things (IoT) has raised a new challenge for developers since it is prone to known and unknown cyberattacks due to its heterogeneity, flexibility, and close connectivity. To defend against such security breaches, researchers have focused on building sophisticated intrusion detection systems (IDSs) using machine learning (ML) techniques. Although these algorithms notably improve detection performance, they require excessive computing power and resources, which are crucial issues in IoT networks considering the recent trends of decentralized data processing and computing systems. Consequently, many optimization techniques have been incorporated with these ML models. Specifically, a special category of optimizer adopted from the behavior of living creatures and different aspects of natural phenomena, known as metaheuristic algorithms, has been a central focus in recent years and brought about remarkable results. Considering this vital significance, we present a comprehensive and systematic review of various applications of metaheuristics algorithms in developing a machine learning-based IDS, especially for IoT. A significant contribution of this study is the discovery of hidden correlations between these optimization techniques and machine learning models integrated with state-of-the-art IoT-IDSs. In addition, the effectiveness of these metaheuristic algorithms in different applications, such as feature selection, parameter or hyperparameter tuning, and hybrid usages are separately analyzed. Moreover, a taxonomy of existing IoT-IDSs is proposed. Furthermore, we investigate several critical issues related to such integration. Our extensive exploration ends with a discussion of promising optimization algorithms and technologies that can enhance the efficiency of IoT-IDSs.
The ringdown of a black hole surrounded by a thin shell of matter
2025-05-31
Andrew Laeuger, Colin Weller, Dongjun Li, Yanbei Chen
Recent studies have shown that far-field perturbations to the curvature potential of a black hole spacetime may destabilize its quasinormal mode (QNM) spectrum while only mildly affecting time-domain ringdown signals. In this work, we study the QNM spectrum and ringdown behavior of a Schwarzchild black hole with a far-field perturbation to its physical environment -- a thin matter shell with finite surface tension. After accounting for the dynamics of the interaction between GWs and the shell, we find that the fundamental mode can migrate perturbatively or be destabilized by the appearance of new modes with no analogue in the vacuum case, much like studies of ``bumps" in the curvature potential. However, unlike these previous works, we find that the coupling between metric perturbations and oscillations of the shell also sources weakly-damped QNMs which are exclusive to the polar sector. We then study whether the analysis tools of least-squares QNM fits and the full and rational ringdown filters can clearly identify the signatures of the shell in representative ringdown waveforms. We conclude that ringdown at sufficiently early times is insensitive to the shell; weakly-damped QNMs (in the polar sector) and echoes, which may enable the analysis methods considered here to infer the presence of a shell, only appear at late times and are generally weak.
While recent audio-visual models have demonstrated impressive performance, their robustness to distributional shifts at test-time remains not fully understood. Existing robustness benchmarks mainly focus on single modalities, making them insufficient for thoroughly assessing the robustness of audio-visual models. Motivated by real-world scenarios where shifts can occur $\textit{simultaneously}$ in both audio and visual modalities, we introduce $\texttt{AVROBUSTBENCH}$, a comprehensive benchmark designed to evaluate the test-time robustness of audio-visual recognition models. $\texttt{AVROBUSTBENCH}$ comprises four audio-visual benchmark datasets, $\texttt{AUDIOSET-2C}$, $\texttt{VGGSOUND-2C}$, $\texttt{KINETICS-2C}$, and $\texttt{EPICKITCHENS-2C}$, each incorporating 75 bimodal audio-visual corruptions that are $\textit{co-occurring}$ and $\textit{correlated}$. Through extensive evaluations, we observe that state-of-the-art supervised and self-supervised audio-visual models exhibit declining robustness as corruption severity increases. Furthermore, online test-time adaptation (TTA) methods, on $\texttt{VGGSOUND-2C}$ and $\texttt{KINETICS-2C}$, offer minimal improvements in performance under bimodal corruptions. We further propose $\texttt{AV2C}$, a simple TTA approach enabling on-the-fly cross-modal fusion by penalizing high-entropy samples, which achieves improvements on $\texttt{VGGSOUND-2C}$. We hope that $\texttt{AVROBUSTBENCH}$ will steer the development of more effective and robust audio-visual TTA approaches. Our code is available $\href{https://github.com/sarthaxxxxx/AV-C-Robustness-Benchmark}{here}$.
The SPHEREx Sky Simulator: Science Data Modeling for the First All-Sky Near-Infrared Spectral Survey
2025-05-30
Brendan P. Crill, Yoonsoo P. Bach, Sean A. Bryan, Jean Choppin de Janvry, Ari J. Cukierman, C. Darren Dowell, Spencer W. Everett, Candice Fazar, Tatiana Goldina, Zhaoyu Huai, Howard Hui, Woong-Seob Jeong, Jae Hwan Kang, Phillip M. Korngut, Jae Joon Lee, Daniel C. Masters, Chi H. Nguyen, Jeonghyun Pyo, Teresa Symons, Yujin Yang, Michael Zemcov, Rachel Akeson, Matthew L. N. Ashby, James J. Bock, Tzu-Ching Chang, Yun-Ting Cheng, Yi-Kuan Chang, Asantha Cooray, Olivier Doré, Andreas L. Faisst, Richard M. Feder, Michael W. Werner
We describe the SPHEREx Sky Simulator, a software tool designed to model science data for NASA's SPHEREx mission that will carry out a series of all-sky spectrophotometric surveys at $\sim$6'' spatial resolution in 102 spectral channels spanning 0.75 to 5 $\mu$m. The Simulator software implements models for astrophysical emission, instrument characteristics, and survey strategy to generate realistic infrared sky scenes as they will be observed by SPHEREx. The simulated data includes a variety of realistic noise and systematic effects that are estimated using up-to-date astrophysical measurements and information from pre-launch instrument characterization campaigns. Through the pre-flight mission phases the Simulator has been critical in predicting the impact of various effects on SPHEREx science performance, and has played an important role guiding the development of the SPHEREx data analysis pipeline. In this paper, we describe the \skysim\ architecture, pre-flight instrument and sky models, and summarize high-level predictions from the Simulator, including a pre-launch prediction for the 5$\sigma$ point source sensitivity of SPHEREx, which we estimate to be $m_{\rm AB}$ 18.5--19 from 0.75 to 3.8~$\mu$m and $m_{\rm AB}$ 16.6--18 from 3.8 to 5 $\mu$m, with the sensitivity limited by the zodiacal light background at all wavelengths. In the future, on-orbit data will be used to improve the Simulator, which will form the basis of a variety of forward-modeling tools that will be used to model myriad instrumental and astrophysical processes to characterize their systematic effects on our final data products and analyses.
Novel methodology to obtain transonic solutions for dissipative flows around compact objects
2025-05-30
Shilpa Sarkar
A novel methodology to obtain global transonic solutions around compact objects is reported here. A unified methodology to obtain accretion as well as wind solutions around these objects has been presented. Flows around compact objects are dissipative, and the conservation equations are therefore stiff. In such conditions, obtaining of sonic point(s) and hence, the transonic solution is not trivial. The conserved equations of motion fail to integrate in the presence of realistic viscosity, thereby making it difficult to obtain a global solution. This inhibits one from getting an actual picture of an astrophysical flow. The current work addresses this long-standing issue of obtaining solutions for both accretion and wind. The methodology developed utilises the inner boundary conditions and takes recourse to implicit-explicit (ImEx) integration schemes, to obtain general global transonic accretion and wind solutions. This is the first time such an attempt has been made. Current work considers the different cooling processes like bremsstrahlung, synchrotron and their inverse-Comptonizations, which are found to affect the thermodynamics of the flow. This methodology could successfully generate all topologies of global solutions, multiple sonic point regime, as well as shocks. A broad parameter space study has been done in this work. In an upcoming part II of the paper, a detailed discussion on the spectra and luminosity of the accretion and wind solutions has been presented.
2025-05-30
Eric C. Nelson, Kyle J. Charbonnet, Haytham H. Effarah, Trevor Reutershan, Kyle D. Chesnut, Christopher P. J. Barty
A characterization of the focused space-time structures of radially chirped beams is provided, detailing different tunable properties such as: variable on-axis centroid velocity, symmetric pulse front tilt, transverse intensity modulations, and polarization states. While the practical generation of ideal radially chirped beams and polarizations can be problematic, it is shown that the primary characteristics of these beams can be mimicked with simple arrays of axisymmetric, 1D spatially chirped beams.
Guiding Generative Storytelling with Knowledge Graphs
2025-05-30
Zhijun Pan, Antonios Andronis, Eva Hayek, Oscar AP Wilkinson, Ilya Lasy, Annette Parry, Guy Gadney, Tim J. Smith, Mick Grierson
Large Language Models (LLMs) have shown great potential in automated story generation, but challenges remain in maintaining long-form coherence and providing users with intuitive and effective control. Retrieval-Augmented Generation (RAG) has proven effective in reducing hallucinations in text generation; however, the use of structured data to support generative storytelling remains underexplored. This paper investigates how knowledge graphs (KGs) can enhance LLM-based storytelling by improving narrative quality and enabling user-driven modifications. We propose a KG-assisted storytelling pipeline and evaluate its effectiveness through a user study with 15 participants. Participants created their own story prompts, generated stories, and edited knowledge graphs to shape their narratives. Through quantitative and qualitative analysis, our findings demonstrate that knowledge graphs significantly enhance story quality in action-oriented and structured narratives within our system settings. Additionally, editing the knowledge graph increases users' sense of control, making storytelling more engaging, interactive, and playful.
Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings
2025-05-30
Max Conti, Manuel Faysse, Gautier Viaud, Antoine Bosselut, Céline Hudelot, Pierre Colombo
A limitation of modern document retrieval embedding methods is that they typically encode passages (chunks) from the same documents independently, often overlooking crucial contextual information from the rest of the document that could greatly improve individual chunk representations. In this work, we introduce ConTEB (Context-aware Text Embedding Benchmark), a benchmark designed to evaluate retrieval models on their ability to leverage document-wide context. Our results show that state-of-the-art embedding models struggle in retrieval scenarios where context is required. To address this limitation, we propose InSeNT (In-sequence Negative Training), a novel contrastive post-training approach which combined with late chunking pooling enhances contextual representation learning while preserving computational efficiency. Our method significantly improves retrieval quality on ConTEB without sacrificing base model performance. We further find chunks embedded with our method are more robust to suboptimal chunking strategies and larger retrieval corpus sizes. We open-source all artifacts at https://github.com/illuin-tech/contextual-embeddings.
Electronic Health Records (EHRs) have been increasingly used as real-world evidence (RWE) to support the discovery and validation of new drug indications. This paper surveys current approaches to EHR-based drug repurposing, covering data sources, processing methodologies, and representation techniques. It discusses study designs and statistical frameworks for evaluating drug efficacy. Key challenges in validation are discussed, with emphasis on the role of large language models (LLMs) and target trial emulation. By synthesizing recent developments and methodological advances, this work provides a foundational resource for researchers aiming to translate real-world data into actionable drug-repurposing evidence.
Identification of New Candidate Be/X-Ray Binary Systems in the Small Magellanic Cloud via Analysis of S-CUBED Source Catalog
2025-05-30
Thomas M. Gaudin, Jamie A. Kennea, Malcolm J. Coe, Phil A. Evans
It has long been known that a large population of Be/X-ray Binaries (BeXRBs) exists in the Milky Way's neighboring dwarf galaxy, the Small Magellanic Cloud (SMC), due to a recent period of intense star formation. Since 2016, efforts have been made to monitor this population and identify new BeXRBs through the Swift SMC Survey (S-CUBED). S-CUBED's weekly observation cadence has identified many new BeXRBs that exist within the SMC, but evidence suggests that more systems exist that have thusfar escaped detection. A major challenge in identifying new BeXRBs is their transient nature at high-energy wavelengths, which prevents them from being detected via their X-ray emission characteristics when not in outburst. In order to identify sources that may have been missed due to a long period of quiescence, it becomes necessary to devise methods of detection that rely on wavelengths at which BeXRBs are more persistent emitters. In this work, we attempt to use archival analysis of infrared, optical, and ultraviolet observations to identify new candidate BeXRBs that have been overlooked within the S-CUBED source catalog. Using X-ray/optical selection of source properties, unsupervised clustering, SED-fitting to VizieR archival measurements, and ultraviolet light curve analysis, we are able to identify six new candidate BeXRB systems that otherwise would have been missed by automated analysis pipelines. Using these results, we demonstrate the use of ultraviolet through near-infrared observational data in identifying candidate BeXRBs when they cannot be identified using their X-ray emission.
Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications
2025-05-30
Srikanth Thudumu, Jason Fisher, Hung Du
Supervised Quantum Machine Learning (QML) represents an intersection of quantum computing and classical machine learning, aiming to use quantum resources to support model training and inference. This paper reviews recent developments in supervised QML, focusing on methods such as variational quantum circuits, quantum neural networks, and quantum kernel methods, along with hybrid quantum-classical workflows. We examine recent experimental studies that show partial indications of quantum advantage and describe current limitations including noise, barren plateaus, scalability issues, and the lack of formal proofs of performance improvement over classical methods. The main contribution is a ten-year outlook (2025-2035) that outlines possible developments in supervised QML, including a roadmap describing conditions under which QML may be used in applied research and enterprise systems over the next decade.
2025-05-30
Miguel E. Coimbra, Lucie Svitáková, Alexandre P. Francisco, Luís Veiga
Graph databases have become essential tools for managing complex and interconnected data, which is common in areas like social networks, bioinformatics, and recommendation systems. Unlike traditional relational databases, graph databases offer a more natural way to model and query intricate relationships, making them particularly effective for applications that demand flexibility and efficiency in handling interconnected data. Despite their increasing use, graph databases face notable challenges. One significant issue is the irregular nature of graph data, often marked by structural sparsity, such as in its adjacency matrix representation, which can lead to inefficiencies in data read and write operations. Other obstacles include the high computational demands of traversal-based queries, especially within large-scale networks, and complexities in managing transactions in distributed graph environments. Additionally, the reliance on traditional centralized architectures limits the scalability of Online Transaction Processing (OLTP), creating bottlenecks due to contention, CPU overhead, and network bandwidth constraints. This paper presents a thorough survey of graph databases. It begins by examining property models, query languages, and storage architectures, outlining the foundational aspects that users and developers typically engage with. Following this, it provides a detailed analysis of recent advancements in graph database technologies, evaluating these in the context of key aspects such as architecture, deployment, usage, and development, which collectively define the capabilities of graph database solutions.
LGAR: Zero-Shot LLM-Guided Neural Ranking for Abstract Screening in Systematic Literature Reviews
2025-05-30
Christian Jaumann, Andreas Wiedholz, Annemarie Friedrich
The scientific literature is growing rapidly, making it hard to keep track of the state-of-the-art. Systematic literature reviews (SLRs) aim to identify and evaluate all relevant papers on a topic. After retrieving a set of candidate papers, the abstract screening phase determines initial relevance. To date, abstract screening methods using large language models (LLMs) focus on binary classification settings; existing question answering (QA) based ranking approaches suffer from error propagation. LLMs offer a unique opportunity to evaluate the SLR's inclusion and exclusion criteria, yet, existing benchmarks do not provide them exhaustively. We manually extract these criteria as well as research questions for 57 SLRs, mostly in the medical domain, enabling principled comparisons between approaches. Moreover, we propose LGAR, a zero-shot LLM Guided Abstract Ranker composed of an LLM based graded relevance scorer and a dense re-ranker. Our extensive experiments show that LGAR outperforms existing QA-based methods by 5-10 pp. in mean average precision. Our code and data is publicly available.
Systematically Measuring Ultra-Diffuse Galaxies. VIII. Misfits, Miscasts, and Miscreants
2025-05-30
Dennis Zaritsky, Richard Donnerstein, Donghyeon J. Khim
We re-examine the 7,070 candidate ultra-diffuse galaxies (UDGs) in the SMUDGes survey and provide classifications based on their visual morphology. Among the more interesting cases, we identify objects along a low surface brightness galaxy merger sequence (ongoing mergers (8) and post-mergers (7)) and a distinct set of dwarf ring galaxies (29). The ring galaxies are hypothesized to be the result of nearly polar-axis collisions, but the responsible companions are undetected. We also highlight objects in the catalog that appear to be tidally affected (68), thereby cautioning that their cataloged parameters may be unreliable. Finally, we identify contaminants of various types in the catalog, leaving 6,553 as viable undisturbed UDG candidates. We discuss all categories and provide example images of the more interesting ones.
What solves the Hubble tension in phenomenological dark energy models at background level?
2025-05-30
Manosh T. Manoharan
Few phenomenological models tend to favour higher values of the Hubble parameter, often at the expense of invoking phantom transitions. These models achieve this without introducing additional parameters, akin to the simplicity of the concordance $\Lambda$CDM model. In this work, we investigate two such models -- Phenomenologically Emergent Dark Energy (PEDE) and Granda-Oliveros Holographic Dark Energy (GOHDE) -- to assess how correlations between $H_0$ and $\Omega_m$, as well as the choice of datasets, influence conclusions regarding their potential to address the Hubble tension at the background level. We find that minimally extended versions of these models favour notably low values for the Hubble parameter, with the perceived preference for higher values driven by the associated prior. Excluding BAO Ly$\alpha$-$H(z)$ data points at a redshift of $\sim 2.3$ results in a Hubble parameter that remains in significant tension with SH0ES measurements. In contrast, including these data points favours a higher $H_0$, as they suggest a relatively lower matter density within the framework of the assumed fiducial cosmology. Additionally, recent DESI DR1 and DR2 data exhibit mild tension with BAO-$H(z)$ estimates from SDSS. We demonstrate that the inclusion of stringent constraints, such as the CMB shift-parameter along with Pantheon$^+$, on the effective pressure less matter density significantly impacts the estimation of the Hubble parameter. Finally, reinterpreting these models in terms of interacting dark sectors with $Q = 3H\gamma_{\Lambda}\tilde{\rho}_{m}$ reveals that addressing the Hubble tension necessitates a varying $\gamma_{\Lambda}$ characterised by a singular sign-switch behaviour. This phantom behaviour, or equivalently, the onset of violation of the null energy condition in the future, is crucial for minimal models to solve the Hubble tension.
Observations from DESI DR2 are challenging the $\Lambda$CDM paradigm by suggesting that the equation-of-state parameter of dark energy evolves across $w = -1$, a phenomenon known as the Quintom scenario. Inspired by this development, we present a staged review of Quintom cosmology including its theoretical foundations, observational supports, and implications as well as possible extensions. We first trace the historical progression from Einstein's static cosmological constant to modern dynamical dark energy, summarizing recent cosmological constraints that favor an evolving $w(z)$ along time. A key focus is the theoretical no-go theorem for dark energy showing that no single canonical field or perfect fluid model can smoothly cross the $w = -1$ boundary. We then survey viable Quintom constructions, including two-field models, single-scalar fields with higher derivatives, modified gravity frameworks, and an effective field theory approach that unifies these mechanisms. Possible interactions of Quintom fields with ordinary matter and the potential roles in yielding non-singular universe solutions are discussed.
From Pixels to Camera: Scaling Superconducting Nanowire Single-Photon Detectors for Imaging at the Quantum-Limit
2025-05-30
Jun Gao, Jin Chang, Bruno Lopez Rodriguez, Iman Esmaeil Zadeh, Val Zwiller, Ali W. Elshaari
Superconducting nanowire single-photon detectors (SNSPDs) have emerged as essential devices that push the boundaries of photon detection with unprecedented sensitivity, ultrahigh timing precision, and broad spectral response. Recent advancements in materials engineering, superconducting electronics integration, and cryogenic system design are enabling the evolution of SNSPDs from single-pixel detectors toward scalable arrays and large-format single-photon time tagging cameras. This perspective article surveys the rapidly evolving technological landscape underpinning this transition, focusing on innovative superconducting materials, advanced multiplexed read-out schemes, and emerging cryo-compatible electronics. We highlight how these developments are set to profoundly impact diverse applications, including quantum communication networks, deep-tissue biomedical imaging, single-molecule spectroscopy, remote sensing with unprecedented resolution, and the detection of elusive dark matter signals. By critically discussing both current challenges and promising solutions, we aim to articulate a clear, coherent vision for the next generation of SNSPD-based quantum imaging systems.
Air pollution is a major global health hazard, with fine particulate matter (PM10) linked to severe respiratory and cardiovascular diseases. Hence, analyzing and clustering spatio-temporal air quality data is crucial for understanding pollution dynamics and guiding policy interventions. This work provides a review of Bayesian nonparametric clustering methods, with a particular focus on their application to spatio-temporal data, which are ubiquitous in environmental sciences. We first introduce key modeling approaches for point-referenced spatio-temporal data, highlighting their flexibility in capturing complex spatial and temporal dependencies. We then review recent advancements in Bayesian clustering, focusing on spatial product partition models, which incorporate spatial structure into the clustering process. We illustrate the proposed methods on PM10 monitoring data from Northern Italy, demonstrating their ability to identify meaningful pollution patterns. This review highlights the potential of Bayesian nonparametric methods for environmental risk assessment and offers insights into future research directions in spatio-temporal clustering for public health and environmental science.
We summarize new results on the studies of $\pi\pi$ and $\pi N$ scatterings. They include the finding of a negative-parity nucleon pole with a mass lower than the nucleon mass, and the pole trajectory of $f_0(500)$ with respect to varying pion masses. The results are obtained from model-independent dispersion analyses. We also study the thermal properties of $f_0(500)$ based on the $O(N)$ $\sigma$ model and $N/D$ method.
Diversity of Cold Worlds: A Near Complete Spectral Energy Distribution for 2MASS J04151954-0935066 using JWST
2025-05-30
Sherelyn Alejandro Merchan, Jacqueline K. Faherty, Genaro Suárez, Kelle L. Cruz, Adam J. Burgasser, Jonathan Gagné, Callie E. Hood, Eileen C. Gonzales, Daniella C. Bardalez Gagliuffi, Jolie L'Heureux, Johanna M. Vos, Adam C. Schneider, Aaron M. Meisner, Caroline Morley, J. Davy Kirkpatrick, Federico Marocco, Rocio Kiman, Charles A. Beichman, Ben Burningham, Dan Caselden, Peter R. Eisenhardt, Christopher R. Gelino, Ehsan Gharib-Nezhad, Marc J. Kuchner, Brianna Lacy, Austin Rothermich, Melanie J. Rowland, Niall Whiteford
We present the a near complete spectral energy distribution (SED) for an extrasolar world: the T8 brown dwarf 2MASS~J04151954$-$0935066. Spanning from optical to mid-infrared (0.7--20.4 micron) wavelengths, the SED for this substellar atmosphere is constructed from new JWST NIRSpec G395H ($R\sim$2700) and Magellan FIRE echelle ($R\sim$8000) near-infrared spectra, along with MIRI mid-infrared photometry complemented by spectra from Keck I, IRTF, Magellan, AKARI, Spitzer and photometry from various surveys and missions. The NIRSpec G395H spectrum reveals strong molecular absorptions from NH$_{3}$, CH$_{4}$, H$_{2}$S, CO$_{2}$ and H$_{2}$O at approximately 3.00, 3.35, 3.95, 4.25, and 5.00 micron respectively, along with the presence of a CO absorption feature detected mainly at $\sim$ 4.6 micron. We detect no absorption of near-infrared K I doublets in the $R\sim8000$ FIRE spectra. In the mid-infrared IRS spectrum, we tentatively identify a new CO$_{2}$ feature at 14--16 micron. The comprehensive SED allows us to empirically constrain bolometric luminosity, effective temperature, mass and radius. Additionally, we demonstrate that the NIRSpec G395H resolution, the highest allowable by JWST, enables a precise radial velocity measurement of $47.1\pm1.8$ km s$^{-1}$ for the object, in agreement with previous measurements.
The Samples and Binary Fractions of Red Supergiant in M31 and M33 by the HST Observations
2025-05-30
Min Dai, Shu Wang, Biwei Jiang, Ying Li
The binarity of red supergiants (RSGs) influences their evolution and the fate of supernovae. We investigate the binary fraction of RSGs in the Andromeda Galaxy (M31) and Triangulum Galaxy (M33) using photometry from the Hubble Space Telescope (HST), which offers high spatial resolution to resolve more RSGs. A preliminary step involves identifying a reliable and complete RSG sample using the F110W $-$ F160W versus F160W diagram, yielding 2,612 RSGs from the Panchromatic Hubble Andromeda Treasury (PHAT) survey of M31 3,294 RSGs from the Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER) survey of M33. These samples suggest total RSG populations in M31 and M33 of 6,563 and 7,572, respectively. These estimates significantly exceed previous ones from the ground-based observations, an increase attributed to the superior spatial resolution of the HST. The stellar parameters of these RSGs, including effective temperature ($T_{\mathrm{eff}}$), radius ($R$), and luminosity ($L$), are derived by fitting their spectral energy distribution (SED) across optical and near-infrared bands. Binary candidates are identified by detecting ultraviolet (UV) excesses in their SEDs compared to the single-star RSG model prediction. The binary fraction is determined to be 33.4% $\pm$ 0.9% for M31 and 30.9% $\pm$ 0.8% for M33. For more luminous RSGs with log $L/L_{\odot} > 4.0$, the binary fraction decreases to 31.6% $\pm$ 1.9% in M31 and increases to 34.7% $\pm$ 1.8% in M33, respectively. These results are in good agreement with predictions from the BPASS binary evolution model.
CREFT: Sequential Multi-Agent LLM for Character Relation Extraction
2025-05-30
Ye Eun Chun, Taeyoon Hwang, Seung-won Hwang, Byung-Hak Kim
Understanding complex character relations is crucial for narrative analysis and efficient script evaluation, yet existing extraction methods often fail to handle long-form narratives with nuanced interactions. To address this challenge, we present CREFT, a novel sequential framework leveraging specialized Large Language Model (LLM) agents. First, CREFT builds a base character graph through knowledge distillation, then iteratively refines character composition, relation extraction, role identification, and group assignments. Experiments on a curated Korean drama dataset demonstrate that CREFT significantly outperforms single-agent LLM baselines in both accuracy and completeness. By systematically visualizing character networks, CREFT streamlines narrative comprehension and accelerates script review -- offering substantial benefits to the entertainment, publishing, and educational sectors.
The temperature of a newly formed neutron star is believed to be as high as $10^{11}$~K, corresponding to a thermal energy of about $10$ MeV. After a time $t \sim 50 \ {\rm s}$, the neutrino mean free path in nuclear matter exceeds the typical star radius, R~$\sim$~10 Km, and neutrino emission becomes the dominant mechanism of energy loss, eventually bringing the temperature down to $\sim10^{8}$~K. Neutrinos also play a critical role in determining the composition of matter in the star interior, consisting primarily of a charge-neutral mixture of neutrons, protons and leptons in $\beta$-equilibrium. This article provides an introduction to the weak interactions of nucleons in nuclear matter, as well as a concise review of the neutrino emission reactions taking place in the neutron star core. The approximations involved in the standard theoretical treatment of thermal and dynamical effects are analysed in the light of the recent progress of the field, and the prospects for future developments are outlined.
Study of Curvature-Matter Coupling in Modified Gravity
2025-05-30
Lakhan V. Jaybhaye
Over the past century, General Relativity (GR) has been a cornerstone of gravitational theory. However, recent cosmological observations, such as the accelerated expansion of the Universe, challenge its completeness and the standard $\Lambda$CDM model. This has motivated the development of alternative approaches, including dynamical dark energy and modifications to gravity. This thesis investigates the $f(R, L_m)$ gravity framework, which extends $f(R)$ gravity by introducing curvature-matter coupling, to address unresolved issues in modern cosmology. Chapter 1 reviews the foundations of cosmology, GR, and $\Lambda$CDM, discussing their challenges and introducing modified gravity theories. Chapter 2 studies cosmic expansion in a specific non-linear $f(R, L_m)$ model, analyzing its dynamics using updated $H(z)$ and Pantheon datasets and demonstrating a deceleration-to-acceleration transition. Chapter 3 introduces a model-independent Hubble parameter parametrization and explores cosmological variables using MCMC and combined data. Chapter 4 incorporates bulk viscosity into $f(R, L_m)$ models to explain late-time acceleration and applies Om diagnostics and energy conditions. Chapter 5 examines non-singular matter bounce cosmologies, analyzing bouncing dynamics and the evolution of cosmographic parameters. Chapter 6 addresses gravitational baryogenesis and shows how $f(R, L_m)$ gravity supports the observed baryon-to-entropy ratio. Chapter 7 summarizes the results and suggests directions for future work.
SA-Person: Text-Based Person Retrieval with Scene-aware Re-ranking
2025-05-30
Yingjia Xu, Jinlin Wu, Zhen Chen, Daming Gao, Yang Yang, Zhen Lei, Min Cao
Text-based person retrieval aims to identify a target individual from a gallery of images based on a natural language description. It presents a significant challenge due to the complexity of real-world scenes and the ambiguity of appearance-related descriptions. Existing methods primarily emphasize appearance-based cross-modal retrieval, often neglecting the contextual information embedded within the scene, which can offer valuable complementary insights for retrieval. To address this, we introduce SCENEPERSON-13W, a large-scale dataset featuring over 100,000 scenes with rich annotations covering both pedestrian appearance and environmental cues. Based on this, we propose SA-Person, a two-stage retrieval framework. In the first stage, it performs discriminative appearance grounding by aligning textual cues with pedestrian-specific regions. In the second stage, it introduces SceneRanker, a training-free, scene-aware re-ranking method leveraging multimodal large language models to jointly reason over pedestrian appearance and the global scene context. Experiments on SCENEPERSON-13W validate the effectiveness of our framework in challenging scene-level retrieval scenarios. The code and dataset will be made publicly available.
In 1975, two papers were published that together sparked major new directions, conceptual, mathematical and practically applicable, in several previously disparate fields of science. In this short review, we expose key aspects of their thinking, implementations and implications, along with a selection of further crucial and consequential developments. These papers were `Theory of spin glasses' by S.F.Edwards and P.W.Anderson (EA)[1] and `Solvable Model of a Spin-Glass', by D.Sherrington and S.Kirkpatrick (SK)[2], both concerned with trying to understand recent experiments that suggested a new phase of matter.
Bayesian Inference for Spatially-Temporally Misaligned Data Using Predictive Stacking
2025-05-30
Soumyakanti Pan, Sudipto Banerjee
Air pollution remains a major environmental risk factor that is often associated with adverse health outcomes. However, quantifying and evaluating its effects on human health is challenging due to the complex nature of exposure data. Recent technological advances have led to the collection of various indicators of air pollution at increasingly high spatial-temporal resolutions (e.g., daily averages of pollutant levels at spatial locations referenced by latitude-longitude). However, health outcomes are typically aggregated over several spatial-temporal coordinates (e.g., annual prevalence for a county) to comply with survey regulations. This article develops a Bayesian hierarchical model to analyze such spatially-temporally misaligned exposure and health outcome data. We introduce Bayesian predictive stacking, which optimally combines multiple predictive spatial-temporal models and avoids iterative estimation algorithms such as Markov chain Monte Carlo that struggle due to convergence issues inflicted by the presence of weakly identified parameters. We apply our proposed method to study the effects of ozone on asthma in the state of California.
Quadrotors have demonstrated remarkable versatility, yet their full aerobatic potential remains largely untapped due to inherent underactuation and the complexity of aggressive maneuvers. Traditional approaches, separating trajectory optimization and tracking control, suffer from tracking inaccuracies, computational latency, and sensitivity to initial conditions, limiting their effectiveness in dynamic, high-agility scenarios. Inspired by recent breakthroughs in data-driven methods, we propose a reinforcement learning-based framework that directly maps drone states and aerobatic intentions to control commands, eliminating modular separation to enable quadrotors to perform end-to-end policy optimization for extreme aerobatic maneuvers. To ensure efficient and stable training, we introduce an automated curriculum learning strategy that dynamically adjusts aerobatic task difficulty. Enabled by domain randomization for robust zero-shot sim-to-real transfer, our approach is validated in demanding real-world experiments, including the first demonstration of a drone autonomously performing continuous inverted flight while reactively navigating a moving gate, showcasing unprecedented agility.
Recent advancements in large language models (LLMs) have enhanced natural-language reasoning. However, their limited parametric memory and susceptibility to hallucination present persistent challenges for tasks requiring accurate, context-based inference. To overcome these limitations, an increasing number of studies have proposed leveraging external knowledge to enhance LLMs. This study offers a systematic exploration of strategies for using external knowledge to enhance LLMs, beginning with a taxonomy that categorizes external knowledge into unstructured and structured data. We then focus on structured knowledge, presenting distinct taxonomies for tables and knowledge graphs (KGs), detailing their integration paradigms with LLMs, and reviewing representative methods. Our comparative analysis further highlights the trade-offs among interpretability, scalability, and performance, providing insights for developing trustworthy and generalizable knowledge-enhanced LLMs.
Cartan Networks: Group theoretical Hyperbolic Deep Learning
2025-05-30
Federico Milanesio, Matteo Santoro, Pietro G. Fré, Guido Sanguinetti
Hyperbolic deep learning leverages the metric properties of hyperbolic spaces to develop efficient and informative embeddings of hierarchical data. Here, we focus on the solvable group structure of hyperbolic spaces, which follows naturally from their construction as symmetric spaces. This dual nature of Lie group and Riemannian manifold allows us to propose a new class of hyperbolic deep learning algorithms where group homomorphisms are interleaved with metric-preserving diffeomorphisms. The resulting algorithms, which we call Cartan networks, show promising results on various benchmark data sets and open the way to a novel class of hyperbolic deep learning architectures.
Inferring Obscured Cosmic Black Hole Accretion History from AGN Found by JWST/MIRI CEERS Survey
2025-05-30
C. -A. Hsieh, T. Goto, C. -T. Ling, S. J. Kim, T. Hashimoto, T. C. -C. Chien, A. Y. -A. Chen
This study presents the black hole accretion history (BHAH) of obscured active galactic nuclei (AGNs) identified from the JWST CEERS survey by Chien et al. (2024) using mid-infrared (MIR) SED fitting. We compute black hole accretion rates (BHARs) to estimate the black hole accretion density (BHAD), $\rho_{L_{\mathrm{disk}}}$, across $0 < z < 4.25$. MIR luminosity functions (LFs) are also constructed for these sources, modeled with modified Schechter and double power law forms, and corresponding BHAD, $\rho_{\mathrm{LF}}$, is derived by integrating the LFs and multiplying by the luminosity. Both $\rho_{\mathrm{LF}}$ extend to luminosities as low as $10^7 \, L_{\odot}$, two orders of magnitude fainter than pre-JWST studies. Our results show that BHAD peaks between redshifts 1 and 3, with the peak varying by method and model, $z \approx 1$--2 for $\rho_{L_{\mathrm{disk}}}$ and the double power law, and $z \approx 2$--3 for the modified Schechter function. A scenario where AGN activity peaks before cosmic star formation would challenge existing black hole formation theories, but our present study, based on early JWST observations, provides an initial exploration of this possibility. At $z \sim 3$, $\rho_{\mathrm{LF}}$ appears higher than X-ray estimates, suggesting that MIR observations are more effective in detecting obscured AGNs missed by X-ray observations. However, given the overlapping error bars, this difference remains within the uncertainties and requires confirmation with larger samples. These findings highlight the potential of JWST surveys to enhance the understanding of co-evolution between galaxies and AGNs.
INSIGHT: A Survey of In-Network Systems for Intelligent, High-Efficiency AI and Topology Optimization
2025-05-30
Aleksandr Algazinov, Joydeep Chandra, Matt Laing
In-network computation represents a transformative approach to addressing the escalating demands of Artificial Intelligence (AI) workloads on network infrastructure. By leveraging the processing capabilities of network devices such as switches, routers, and Network Interface Cards (NICs), this paradigm enables AI computations to be performed directly within the network fabric, significantly reducing latency, enhancing throughput, and optimizing resource utilization. This paper provides a comprehensive analysis of optimizing in-network computation for AI, exploring the evolution of programmable network architectures, such as Software-Defined Networking (SDN) and Programmable Data Planes (PDPs), and their convergence with AI. It examines methodologies for mapping AI models onto resource-constrained network devices, addressing challenges like limited memory and computational capabilities through efficient algorithm design and model compression techniques. The paper also highlights advancements in distributed learning, particularly in-network aggregation, and the potential of federated learning to enhance privacy and scalability. Frameworks like Planter and Quark are discussed for simplifying development, alongside key applications such as intelligent network monitoring, intrusion detection, traffic management, and Edge AI. Future research directions, including runtime programmability, standardized benchmarks, and new applications paradigms, are proposed to advance this rapidly evolving field. This survey underscores the potential of in-network AI to create intelligent, efficient, and responsive networks capable of meeting the demands of next-generation AI applications.
Urban design is a multifaceted process that demands careful consideration of site-specific constraints and collaboration among diverse professionals and stakeholders. The advent of generative artificial intelligence (GenAI) offers transformative potential by improving the efficiency of design generation and facilitating the communication of design ideas. However, most existing approaches are not well integrated with human design workflows. They often follow end-to-end pipelines with limited control, overlooking the iterative nature of real-world design. This study proposes a stepwise generative urban design framework that integrates multimodal diffusion models with human expertise to enable more adaptive and controllable design processes. Instead of generating design outcomes in a single end-to-end process, the framework divides the process into three key stages aligned with established urban design workflows: (1) road network and land use planning, (2) building layout planning, and (3) detailed planning and rendering. At each stage, multimodal diffusion models generate preliminary designs based on textual prompts and image-based constraints, which can then be reviewed and refined by human designers. We design an evaluation framework to assess the fidelity, compliance, and diversity of the generated designs. Experiments using data from Chicago and New York City demonstrate that our framework outperforms baseline models and end-to-end approaches across all three dimensions. This study underscores the benefits of multimodal diffusion models and stepwise generation in preserving human control and facilitating iterative refinements, laying the groundwork for human-AI interaction in urban design solutions.
Interactive Video Generation via Domain Adaptation
2025-05-30
Ishaan Rawal, Suryansh Kumar
Text-conditioned diffusion models have emerged as powerful tools for high-quality video generation. However, enabling Interactive Video Generation (IVG), where users control motion elements such as object trajectory, remains challenging. Recent training-free approaches introduce attention masking to guide trajectory, but this often degrades perceptual quality. We identify two key failure modes in these methods, both of which we interpret as domain shift problems, and propose solutions inspired by domain adaptation. First, we attribute the perceptual degradation to internal covariate shift induced by attention masking, as pretrained models are not trained to handle masked attention. To address this, we propose mask normalization, a pre-normalization layer designed to mitigate this shift via distribution matching. Second, we address initialization gap, where the randomly sampled initial noise does not align with IVG conditioning, by introducing a temporal intrinsic diffusion prior that enforces spatio-temporal consistency at each denoising step. Extensive qualitative and quantitative evaluations demonstrate that mask normalization and temporal intrinsic denoising improve both perceptual quality and trajectory control over the existing state-of-the-art IVG techniques.
Emission Line Galaxies (ELGs) are crucial for cosmological studies, particularly in understanding the large-scale structure of the Universe and the role of dark energy. ELGs form an essential component of the target catalogue for the Dark Energy Spectroscopic Instrument (DESI), a major astronomical survey. However, the accurate selection of ELGs for such surveys is challenging due to the inherent uncertainties in determining their redshifts with photometric data. In order to improve the accuracy of photometric redshift estimation for ELGs, we propose a novel approach CNN-MLP that combines Convolutional Neural Networks (CNNs) with Multilayer Perceptrons (MLPs). This approach integrates both images and photometric data derived from the DESI Legacy Imaging Surveys Data Release 10. By leveraging the complementary strengths of CNNs (for image data processing) and MLPs (for photometric feature integration), the CNN-MLP model achieves a $\sigma_{\mathrm{NMAD}}$ (normalised median absolute deviation) of 0.0140 and an outlier fraction of 2.57%. Compared to other models, CNN-MLP demonstrates a significant improvement in the accuracy of ELG photometric redshift estimation, which directly benefits the target selection process for DESI. In addition, we explore the photometric redshifts of different galaxy types (Starforming, Starburst, AGN, Broadline). Furthermore, this approach will contribute to more reliable photometric redshift estimation in ongoing and future large-scale sky surveys (e.g. LSST, CSST, Euclid), enhancing the overall efficiency of cosmological research and galaxy surveys.
A Method for Analytical Solutions in the Lattice Boltzmann Method
2025-05-30
Jordan Larson, Alexander J. Wagner
Analytical solutions to the lattice Boltzmann Equation make it possible to study the method itself, explore the properties of its collision operator, and identify implementations of boundary conditions. In this paper, we propose a method to find analytical solutions where the macroscopic flow profile is known. We test this method on bulk Couette flow aligned and inclined to the simulation lattice with the quadratic and entropic equilibrium distributions. Our method indeed provides an analytical solution to these flows when using the quadratic distribution. When the flow is aligned to the lattice, our method provides an analytical solution using the entropic distribution for practical relaxation times and shear rates. We show that a small even order truncation of the formal solution is optimal for accuracy-compute-time trade-off. In the inclined case, our method does not conserve momentum, by a small relative error, when using the entropic distribution. We also discover that entropic lattice Boltzmann method is not compatible with the angled Couette flow. We discuss the application of our method to more complicated flows.
Estimating dynamic transmission rates with a Black-Karasinski process in stochastic SIHR models using particle MCMC
2025-05-30
Avery Drennan, Jeffrey Covington, Dan Han, Andrew Attilio, Jaechoul Lee, Richard Posner, Eck Doerry, Joseph Mihaljevic, Ye Chen
Compartmental models are effective in modeling the spread of infectious pathogens, but have remaining weaknesses in fitting to real datasets exhibiting stochastic effects. We propose a stochastic SIHR model with a dynamic transmission rate, where the rate is modeled by the Black-Karasinski (BK) process - a mean-reverting stochastic process with a stable equilibrium distribution, making it well-suited for modeling long-term epidemic dynamics. To generate sample paths of the BK process and estimate static parameters of the system, we employ particle Markov Chain Monte Carlo (pMCMC) methods due to their effectiveness in handling complex state-space models and jointly estimating parameters. We designed experiments on synthetic data to assess estimation accuracy and its impact on inferred transmission rates; all BK-process parameters were estimated accurately except the mean-reverting rate. We also assess the sensitivity of pMCMC to misspecification of the mean-reversion rate. Our results show that estimation accuracy remains stable across different mean-reversion rates, though smaller values increase error variance and complicate inference results. Finally, we apply our model to Arizona flu hospitalization data, finding that parameter estimates are consistent with published survey data.
Next-to-leading-order prediction for the neutrinoless double-beta decay
2025-05-30
Y. L. Yang, P. W. Zhao
The neutrinoless double-beta decay ($0\nu\beta\beta$) of two neutrons$nn \rightarrow ppee$ is the elementary subprocess of $0\nu\beta\beta$ decay in nuclei. Accurate knowledge of the $nn \rightarrow ppee$ amplitude is required to pin down the short-range contributions in the nuclear matrix elements of the candidate nuclei for large-scale $0\nu\beta\beta$ searches. In this Letter, we report the first next-to-leading-order prediction of the nn \rightarrow ppee amplitude, with Bayesian uncertainty quantification. This is made possible by the development of the relativistic chiral effective field theory, in which no unknown contact term is required up to next-to-leading order. The theory is validated by reproducing in a parameter-free way the available data on the charge independence and charge symmetry breaking contributions in the two-nucleon scattering. The present work makes an essential step towards addressing the uncertainty in the theoretical calculations of the nuclear matrix elements relevant for $0\nu\beta\beta$ searches.
The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating It
2025-05-30
Zheng-Xin Yong, Beyza Ermis, Marzieh Fadaee, Stephen H. Bach, Julia Kreutzer
This paper presents a comprehensive analysis of the linguistic diversity of LLM safety research, highlighting the English-centric nature of the field. Through a systematic review of nearly 300 publications from 2020--2024 across major NLP conferences and workshops at *ACL, we identify a significant and growing language gap in LLM safety research, with even high-resource non-English languages receiving minimal attention. We further observe that non-English languages are rarely studied as a standalone language and that English safety research exhibits poor language documentation practice. To motivate future research into multilingual safety, we make several recommendations based on our survey, and we then pose three concrete future directions on safety evaluation, training data generation, and crosslingual safety generalization. Based on our survey and proposed directions, the field can develop more robust, inclusive AI safety practices for diverse global populations.
The 'Brazil-nut effect' in bidisperse particle laden flow on an incline
2025-05-30
Jack Luong, Sarah Cassie Burnett, Andrea L. Bertozzi
We study bidisperse suspensions -- suspensions where there are two particle species of the same density but different sizes -- of a viscous fluid on an incline. We use a lubrication theory/thin film model to form a hyperbolic system of three conservation laws for the height and particle volume fractions. The model predicts, over a range of parameters, that the larger particles rise to the top of the layer, consistent with the well-known `Brazil-nut effect' for granular media. The model predicts well-separated fronts of the two species of particles, behind a clear fluid front, at lower inclination angles and volume fractions. This corresponds to a triple shock structure in the system of conservations. At higher inclination angles and volume fractions the particles congregate at a high concentration at the leading front corresponding to a singular shock in the model. We find excellent agreement between theory and experiments in terms of the overall dynamic structures as the parameters vary.
We survey recent developments on the structure of complemented subspaces of Banach lattices, including in particular the construction of a complemented subspace of a $C(K)$-space which is not linearly isomorphic to any Banach lattice. Motivated by this, several natural questions and directions of future research are presented. We provide an approach to some of these problems using tools from the theory of free Banach lattices.
Large language models (LLMs) have been prominent in various tasks, including text generation and summarisation. The applicability of LLMs to the generation of product reviews is gaining momentum, paving the way for the generation of movie reviews. In this study, we propose a framework that generates movie reviews using three LLMs (GPT-4o, DeepSeek-V3, and Gemini-2.0), and evaluate their performance by comparing the generated outputs with IMDb user reviews. We use movie subtitles and screenplays as input to the LLMs and investigate how they affect the quality of reviews generated. We review the LLM-based movie reviews in terms of vocabulary, sentiment polarity, similarity, and thematic consistency in comparison to IMDB user reviews. The results demonstrate that LLMs are capable of generating syntactically fluent and structurally complete movie reviews. Nevertheless, there is still a noticeable gap in emotional richness and stylistic coherence between LLM-generated and IMDb reviews, suggesting that further refinement is needed to improve the overall quality of movie review generation. We provided a survey-based analysis where participants were told to distinguish between LLM and IMDb user reviews. The results show that LLM-generated reviews are difficult to distinguish from IMDB user reviews. We found that DeepSeek-V3 produced the most balanced reviews, closely matching IMDb reviews. GPT-4o overemphasised positive emotions, while Gemini-2.0 captured negative emotions better but showed excessive emotional intensity.
Millimeter-wave observations of Euclid Deep Field South using the South Pole Telescope: A data release of temperature maps and catalogs
2025-05-30
M. Archipley, A. Hryciuk, L. E. Bleem, K. Kornoelje, M. Klein, A. J. Anderson, B. Ansarinejad, M. Aravena, L. Balkenhol, P. S. Barry, K. Benabed, A. N. Bender, B. A. Benson, F. Bianchini, S. Bocquet, F. R. Bouchet, E. Camphuis, M. G. Campitiello, J. E. Carlstrom, J. Cathey, C. L. Chang, S. C. Chapman, P. Chaubal, P. M. Chichura, A. Chokshi, T. -L. Chou, A. Coerver, T. M. Crawford, C. Daley, T. de Haan, R. P. Deane, K. R. Dibert, M. A. Dobbs, M. Doohan, A. Doussot, D. Dutcher, W. Everett, C. Feng, K. R. Ferguson, K. Fichman, B. Floyd, A. Foster, S. Galli, A. E. Gambrel, R. W. Gardner, F. Ge, N. Goeckner-Wald, A. Gonzalez, S. Grandis, T. R. Greve, R. Gualtieri, F. Guidi, S. Guns, N. W. Halverson, R. Hill, E. Hivon, G. P. Holder, W. L. Holzapfel, J. C. Hood, N. Huang, F. Kéruzoré, A. R. Khalife, L. Knox, M. Korman, C. -L. Kuo, K. Levy, A. E. Lowitz, C. Lu, G. P. Lynch, A. Maniyar, E. S. Martsen, F. Menanteau, M. Millea, J. Montgomery, Y. Nakato, T. Natoli, G. I. Noble, Y. Omori, A. Ouellette, Z. Pan, K. A. Phadke, A. W. Pollak, K. Prabhu, W. Quan, S. Raghunathan, M. Rahimi, A. Rahlin, C. L. Reichardt, C. Reuter, M. Rouble, J. E. Ruhl, E. Schiappucci, A. Simpson, J. A. Sobrin, B. Stalder, A. A. Stark, N. Sulzenauer, C. Tandoi, B. Thorne, C. Trendafilova, C. Umilta, J. D. Vieira, A. Vitrier, D. Vizgan, Y. Wan, A. Weiß, N. Whitehorn, W. L. K. Wu, M. R. Young, J. A. Zebrowski, D. Zhou
Context. The South Pole Telescope third-generation camera (SPT-3G) has observed over 10,000 square degrees of sky at 95, 150, and 220 GHz (3.3, 2.0, 1.4 mm, respectively) overlapping the ongoing 14,000 square-degree Euclid Wide Survey. The Euclid collaboration recently released Euclid Deep Field observations in the first quick data release (Q1). Aims. With the goal of releasing complementary millimeter-wave data and encouraging legacy science, we performed dedicated observations of a 57-square-degree field overlapping the Euclid Deep Field South (EDF-S). Methods. The observing time totaled 20 days and we reached noise depths of 4.3, 3.8, and 13.2 $\mu$K-arcmin at 95, 150, and 220 GHz, respectively. Results. In this work we present the temperature maps and two catalogs constructed from these data. The emissive source catalog contains 601 objects (334 inside EDF-S) with 54% synchrotron-dominated sources and 46% thermal dust emission-dominated sources. The 5$\sigma$ detection thresholds are 1.7, 2.0, and 6.5 mJy in the three bands. The cluster catalog contains 217 cluster candidates (121 inside EDF-S) with median mass $M_{500c}=2.12 \times 10^{14} M_{\odot}/h_{70}$ and median redshift $z$ = 0.70, corresponding to an order-of-magnitude improvement in cluster density over previous tSZ-selected catalogs in this region (3.81 clusters per square degree). Conclusions. The overlap between SPT and Euclid data will enable a range of multiwavelength studies of the aforementioned source populations. This work serves as the first step towards joint projects between SPT and Euclid and provides a rich dataset containing information on galaxies, clusters, and their environments.
CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review
2025-05-30
Manav Nitin Kapadnis, Atharva Naik, Carolyn Rose
Reinforcement learning (RL) to improve code review comment generation requires handling unstructured outputs, making reinforcement learning (RL) feedback challenging. The two main RL approaches, namely RL with Verifiable Feedback (RLVR) and RL with AI Feedback (RLAIF), offer trade-offs: RLVR provides reliable feedback for structured tasks like code generation, while RLAIF works for unstructured outputs but is subjective. We bridge this gap with CRScore++, an RL framework that leverages both LLM-based subjective feedback and verifiable signals for training. Extending CRScore, a code review evaluation metric integrating LLMs with verifiers like linters and code smell detectors, CRScore++ transforms these signals into training rewards. We show that CRScore++ improves a weaker student model through a combination of supervised fine-tuning and RL critique from a stronger teacher model, thus enabling generalization to novel programming languages.
Applying Vision Transformers on Spectral Analysis of Astronomical Objects
2025-05-30
Luis Felipe Strano Moraes, Ignacio Becker, Pavlos Protopapas, Guillermo Cabrera-Vives
We apply pre-trained Vision Transformers (ViTs), originally developed for image recognition, to the analysis of astronomical spectral data. By converting traditional one-dimensional spectra into two-dimensional image representations, we enable ViTs to capture both local and global spectral features through spatial self-attention. We fine-tune a ViT pretrained on ImageNet using millions of spectra from the SDSS and LAMOST surveys, represented as spectral plots. Our model is evaluated on key tasks including stellar object classification and redshift ($z$) estimation, where it demonstrates strong performance and scalability. We achieve classification accuracy higher than Support Vector Machines and Random Forests, and attain $R^2$ values comparable to AstroCLIP's spectrum encoder, even when generalizing across diverse object types. These results demonstrate the effectiveness of using pretrained vision models for spectroscopic data analysis. To our knowledge, this is the first application of ViTs to large-scale, which also leverages real spectroscopic data and does not rely on synthetic inputs.
Pluto Geologic Map: Use of Crater Data to Understand Age Relationships
2025-05-30
Kelsi N. Singer, Oliver L. White, Sarah Greenstreet, Jeffrey M. Moore, David A. Williams, Rosaly M. C. Lopes
Pluto's surface displays a wide variety of geologic units from smooth plains to extremely rugged mountainous expanses. These terrains range in age from young, actively resurfaced regions (no observable craters even in the highest-resolution New Horizons images) to old, heavily cratered, eroded regions. Here we expand upon the crater data analysis and the independent crater data set used in the production of a 1:7M scale geologic map of Pluto that is to be published by the United States Geologic Survey (USGS). We present both relative ages based on crater spatial density (number of craters in a given size bin per km^2) and also quantitative ages (e.g., 2 Ga) using the estimated impactor flux onto Pluto. The techniques presented here were developed specifically for the information available from a USGS geologic map, where smaller craters are mapped as points only (no specific diameter information per crater). We developed a new type of visualization, called a distributed R-plot, to understand the relative ages of the geologic units. The uncertainties in the current knowledge of the Kuiper belt populations and impactor flux at Pluto propagate to large uncertainties in the estimated quantitative ages (~a factor of two). However, both relative and quantitative ages from crater analysis were still valuable tools in developing the sequence of geologic events. Pluto has large areas of crater-free young terrains (13 units making up ~27% of mapped higher-resolution surface area), with widely varying morphologies, indicating a variety of resurfacing mechanisms, both exogenic and endogenic, likely active into Pluto's recent past or present.
Performance Analysis of Convolutional Neural Network By Applying Unconstrained Binary Quadratic Programming
2025-05-30
Aasish Kumar Sharma, Sanjeeb Prashad Pandey, Julian M. Kunkel
Convolutional Neural Networks (CNNs) are pivotal in computer vision and Big Data analytics but demand significant computational resources when trained on large-scale datasets. Conventional training via back-propagation (BP) with losses like Mean Squared Error or Cross-Entropy often requires extensive iterations and may converge sub-optimally. Quantum computing offers a promising alternative by leveraging superposition, tunneling, and entanglement to search complex optimization landscapes more efficiently. In this work, we propose a hybrid optimization method that combines an Unconstrained Binary Quadratic Programming (UBQP) formulation with Stochastic Gradient Descent (SGD) to accelerate CNN training. Evaluated on the MNIST dataset, our approach achieves a 10--15\% accuracy improvement over a standard BP-CNN baseline while maintaining similar execution times. These results illustrate the potential of hybrid quantum-classical techniques in High-Performance Computing (HPC) environments for Big Data and Deep Learning. Fully realizing these benefits, however, requires a careful alignment of algorithmic structures with underlying quantum mechanisms.
Stellar populations in STARFORGE II: Comparison with observations
2025-05-30
Juan P. Farias, Stella S. R. Offner, Ronan Kerr, Michael Y. Grudić
Recent studies suggest that most star-forming regions in our Galaxy form stellar associations rather than bound clusters. We analyse models from the STARFORGE simulation suite, a set of magneto-hydrodynamical simulations that include all key stellar feedback and radiative processes following star formation through cloud dispersal. We create synthetic observations by introducing observational biases such as random spurious measurements, unresolved binaries, and photometric sensitivity. These biases affect the measurement of the group mass, size, and velocity dispersion, introducing uncertainties of up to 100%, with accuracy improving as the number of system members increases. Furthermore, models favouring the formation of groups around massive stars were the most affected by observational biases, as massive stars contribute a larger fraction of the group mass and are often missing from astrometric surveys like Gaia. We compare the simulations to the Cepheus Far North (CFN) region, and show that CFN groups may have formed in a low-density environment similar to those modelled in STARFORGE but with massive stars not located preferentially in groups. We also question the effectiveness of the kinematic traceback method, showing that it is accurate within 20% only for certain associations with actual virial parameters above 2. However, observational biases can artificially raise the virial parameter by up to a factor ten, making it difficult to evaluate the reliability of the traceback age. Additionally, since stars continue to form during the dispersal of the parent cloud, we find no relation between the stellar-dynamical age difference and the length of the embedded phase.
Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes
2025-05-30
Anthony Gosselin, Ge Ya Luo, Luis Lara, Florian Golemo, Derek Nowrouzezahrai, Liam Paull, Alexia Jolicoeur-Martineau, Christopher Pal
Video diffusion techniques have advanced significantly in recent years; however, they struggle to generate realistic imagery of car crashes due to the scarcity of accident events in most driving datasets. Improving traffic safety requires realistic and controllable accident simulations. To tackle the problem, we propose Ctrl-Crash, a controllable car crash video generation model that conditions on signals such as bounding boxes, crash types, and an initial image frame. Our approach enables counterfactual scenario generation where minor variations in input can lead to dramatically different crash outcomes. To support fine-grained control at inference time, we leverage classifier-free guidance with independently tunable scales for each conditioning signal. Ctrl-Crash achieves state-of-the-art performance across quantitative video quality metrics (e.g., FVD and JEDi) and qualitative measurements based on a human-evaluation of physical realism and video quality compared to prior diffusion-based methods.
Evaluating the Contextual Integrity of False Positives in Algorithmic Travel Surveillance
2025-05-30
Alina Wernick, Alan Medlar, Sofia Söderholm, Dorota Głowacka
International air travel is highly surveilled. While surveillance is deemed necessary for law enforcement to prevent and detect terrorism and other serious crimes, even the most accurate algorithmic mass surveillance systems produce high numbers of false positives. Despite the potential impact of false positives on the fundamental rights of millions of passengers, algorithmic travel surveillance is lawful in the EU. However, as the system's processing practices and accuracy are kept secret by law, it is unknown to what degree passengers are accepting of the system's interference with their rights to privacy and data protection. We conducted a nationally representative survey of the adult population of Finland (N=1550) to assess their attitudes towards algorithmic mass surveillance in air travel and its potential expansion to other travel contexts. Furthermore, we developed a novel approach for estimating the threshold, beyond which, the number of false positives breaches individuals' perception of contextual integrity. Surprisingly, when faced with a trade-off between privacy and security, even very high false positive counts were perceived as legitimate. This result could be attributed to Finland's high-trust cultural context, but also raises questions about people's capacity to account for privacy harms that happen to other people. We conclude by discussing how legal and ethical approaches to legitimising algorithmic surveillance based on individual rights may overlook the statistical or systemic properties of mass surveillance.
Utilizing AI for Aviation Post-Accident Analysis Classification
2025-05-30
Aziida Nanyonga, Graham Wild
The volume of textual data available in aviation safety reports presents a challenge for timely and accurate analysis. This paper examines how Artificial Intelligence (AI) and, specifically, Natural Language Processing (NLP) can automate the process of extracting valuable insights from this data, ultimately enhancing aviation safety. The paper reviews ongoing efforts focused on the application of NLP and deep learning to aviation safety reports, with the goal of classifying the level of damage to an aircraft and identifying the phase of flight during which safety occurrences happen. Additionally, the paper explores the use of Topic Modeling (TM) to uncover latent thematic structures within aviation incident reports, aiming to identify recurring patterns and potential areas for safety improvement. The paper compares and contrasts the performance of various deep learning models and TM techniques applied to datasets from the National Transportation Safety Board (NTSB) and the Australian Transport Safety Bureau (ATSB), as well as the Aviation Safety Network (ASN), discussing the impact of dataset size and source on the accuracy of the analysis. The findings demonstrate that both NLP and deep learning, as well as TM, can significantly improve the efficiency and accuracy of aviation safety analysis, paving the way for more proactive safety management and risk mitigation strategies.
Three fast-spinning medium-sized Hilda asteroids uncovered by TESS
2025-05-30
Nóra Takács, Csaba Kiss, Róbert Szakáts, Emese Plachy, Csilla E. Kalup, Gyula M. Szabó, László Molnár, Krisztián Sárneczky, Róbert Szabó, Attila Bódi, András Pál
Hilda asteroids, which orbit in a 3:2 resonance with Jupiter, serve as key indicators of dynamical processes in the early solar system. Their spin rates, an important probe of these mechanisms, can constrain their density and collisional evolution, offering valuable insights into their origin. In this paper, we report on the identification of three fast-rotating Hilda asteroids with spin periods in the 3.2--3.7 h range using data from the Transiting Exoplanet Survey Satellite. These rotation periods are significantly shorter than the previous $\sim$5.0 h shortest rotation periods obtained from ground-based observations in the $\sim$10 km size range, and are comparable with the $\sim$3.0 h breakup limit of Hildas a few km in size, derived from the FOSSIL survey. These fast-rotating asteroids require either considerable cohesion (in the order of a few kPa), or densities $\rho$ $\gtrsim$1.5 $gm^{-3}$, in contrast to the typically assumed $\rho$ $\lesssim$1 $gm^{-3}$, to prevent rotational break-up. C-type asteroids, which are common in the outer main belt, have densities of $\rho$ $\approx$1.5 $gm^{-3}$ and are known to comprise a small but notable fraction of Hildas. The observed occurrence rate of the $\leq$4 h rotation periods may be explained by the 10-15% fraction of C-type asteroids, likely mixed into these populations from the outer main belt during giant planet dynamical interactions in the early solar system.
MRDust: Wireless Implant Data Uplink & Localization via Magnetic Resonance Image Modulation
2025-05-30
Biqi Rebekah Zhao, Alexander Chou, Robert Peltekov, Elad Alon, Chunlei Liu, Rikky Muller, Michael Lustig
Magnetic resonance imaging (MRI) exhibits rich and clinically useful endogenous contrast mechanisms, which can differentiate soft tissues and are sensitive to flow, diffusion, magnetic susceptibility, blood oxygenation level, and more. However, MRI sensitivity is ultimately constrained by Nuclear Magnetic Resonance (NMR) physics, and its spatiotemporal resolution is limited by SNR and spatial encoding. On the other hand, miniaturized implantable sensors offer highly localized physiological information, yet communication and localization can be challenging when multiple implants are present. This paper introduces the MRDust, an active ``contrast agent" that integrates active sensor implants with MRI, enabling the direct encoding of highly localized physiological data into MR images to augment the anatomical images. MRDust employs a micrometer-scale on-chip coil to actively modulate the local magnetic field, enabling MR signal amplitude and phase modulation for digital data transmission. Since MRI inherently captures the anatomical tissue structure, this method has the potential to enable simultaneous data communication, localization, and image registration with multiple implants. This paper presents the underlying physical principles, design tradeoffs, and design methodology for this approach. To validate the concept, a 900 $\times$ 990 $\mu$m$^2$ chip was designed using TSMC 28 nm technology, with an on-chip coil measuring 630 $\mu$m in diameter. The chip was tested with custom hardware in an MR750W GE3T MRI scanner. Successful voxel amplitude modulation is demonstrated with Spin-Echo Echo-Planar-Imaging (SE-EPI) sequence, achieving a contrast-to-noise ratio (CNR) of 25.58 with a power consumption of 130 $\mu$W.
Understanding Underrepresented Groups in Open Source Software
2025-05-30
Reydne Santos, Rafa Prado, Ana Paula de Holanda Silva, Kiev Gama, Fernando Castor, Ronnie de Souza Santos
Context: Diversity can impact team communication, productivity, cohesiveness, and creativity. Analyzing the existing knowledge about diversity in open source software (OSS) projects can provide directions for future research and raise awareness about barriers and biases against underrepresented groups in OSS. Objective: This study aims to analyze the knowledge about minority groups in OSS projects. We investigated which groups were studied in the OSS literature, the study methods used, their implications, and their recommendations to promote the inclusion of minority groups in OSS projects. Method: To achieve this goal, we performed a systematic literature review study that analyzed 42 papers that directly study underrepresented groups in OSS projects. Results: Most papers focus on gender (62.3%), while others like age or ethnicity are rarely studied. The neurodiversity dimension, have not been studied in the context of OSS. Our results also reveal that diversity in OSS projects faces several barriers but brings significant benefits, such as promoting safe and welcoming environments. Conclusion: Most analyzed papers adopt a myopic perspective that sees gender as strictly binary. Dimensions of diversity that affect how individuals interact and function in an OSS project, such as age, tenure, and ethnicity, have received very little attention.
Geo-Sign: Hyperbolic Contrastive Regularisation for Geometrically Aware Sign Language Translation
2025-05-30
Edward Fish, Richard Bowden
Recent progress in Sign Language Translation (SLT) has focussed primarily on improving the representational capacity of large language models to incorporate Sign Language features. This work explores an alternative direction: enhancing the geometric properties of skeletal representations themselves. We propose Geo-Sign, a method that leverages the properties of hyperbolic geometry to model the hierarchical structure inherent in sign language kinematics. By projecting skeletal features derived from Spatio-Temporal Graph Convolutional Networks (ST-GCNs) into the Poincar\'e ball model, we aim to create more discriminative embeddings, particularly for fine-grained motions like finger articulations. We introduce a hyperbolic projection layer, a weighted Fr\'echet mean aggregation scheme, and a geometric contrastive loss operating directly in hyperbolic space. These components are integrated into an end-to-end translation framework as a regularisation function, to enhance the representations within the language model. This work demonstrates the potential of hyperbolic geometry to improve skeletal representations for Sign Language Translation, improving on SOTA RGB methods while preserving privacy and improving computational efficiency. Code available here: https://github.com/ed-fish/geo-sign.
Applying Large Language Models to Issue Classification: Revisiting with Extended Data and New Models
2025-05-30
Gabriel Aracena, Kyle Luster, Fabio Santos, Igor Steinmacher, Marco A. Gerosa
Effective prioritization of issue reports in software engineering helps to optimize resource allocation and information recovery. However, manual issue classification is laborious and lacks scalability. As an alternative, many open source software (OSS) projects employ automated processes for this task, yet this method often relies on large datasets for adequate training. Traditionally, machine learning techniques have been used for issue classification. More recently, large language models (LLMs) have emerged as powerful tools for addressing a range of software engineering challenges, including code and test generation, mapping new requirements to legacy software endpoints, and conducting code reviews. The following research investigates an automated approach to issue classification based on LLMs. By leveraging the capabilities of such models, we aim to develop a robust system for prioritizing issue reports, mitigating the necessity for extensive training data while also maintaining reliability in classification. In our research, we developed an LLM-based approach for accurately labeling issues by selecting two of the most prominent large language models. We then compared their performance across multiple datasets. Our findings show that GPT-4o achieved the best results in classifying issues from the NLBSE 2024 competition. Moreover, GPT-4o outperformed DeepSeek R1, achieving an F1 score 20% higher when both models were trained on the same dataset from the NLBSE 2023 competition, which was ten times larger than the NLBSE 2024 dataset. The fine-tuned GPT-4o model attained an average F1 score of 80.7%, while the fine-tuned DeepSeek R1 model achieved 59.33%. Increasing the dataset size did not improve the F1 score, reducing the dependence on massive datasets for building an efficient solution to issue classification.
SPLASH: A Rapid Host-Based Supernova Classifier for Wide-Field Time-Domain Surveys
2025-05-30
Adam Boesky, V. Ashley Villar, Alexander Gagliano, Brian Hsu
The upcoming Legacy Survey of Space and Time (LSST) conducted by the Vera C. Rubin Observatory will detect millions of supernovae (SNe) and generate millions of nightly alerts, far outpacing available spectroscopic resources. Rapid, scalable photometric classification methods are therefore essential for identifying young SNe for follow-up and enabling large-scale population studies. We present SPLASH, a host-based classification pipeline that infers supernova classes using only host galaxy photometry. SPLASH first associates SNe with their hosts (yielding a redshift estimate), then infers host galaxy stellar mass and star formation rate using deep learning, and finally classifies SNe using a random forest trained on these inferred properties, along with host-SN angular separation and redshift. SPLASH achieves a binary (Type Ia vs. core-collapse) classification accuracy of $76\%$ and an F1-score of $69\%$, comparable to other state-of-the-art methods. By selecting only the most confident predictions, SPLASH can return highly pure subsets of all major SN types, making it well-suited for targeted follow-up. Its efficient design allows classification of $\sim 500$ SNe per second, making it ideal for next-generation surveys. Moreover, its intermediate inference step enables selection of transients by host environment, providing a tool not only for classification but also for probing the demographics of stellar death.
Binarity at LOw Metallicity (BLOeM): Pipeline-Determined Physical Properties of OB Stars
2025-05-30
J. M. Bestenlehner, Paul A. Crowther, V. A. Bronner, S. Simon-Diaz, D. J. Lennon, J. Bodensteiner, N. Langer, P. Marchant, H. Sana, F. R. N. Schneider, T. Shenar
We aim to determine the physical properties of OB stars from the multi-epoch VLT/FLAMES BLOeM spectroscopic survey of the Small Magellanic Cloud. We apply a pipeline designed to analyse large spectroscopic samples of OB stars to the co-added, initial 9 epochs of the BLOeM survey, utilising grids of synthetic model spectra computed with the stellar atmosphere code FASTWIND. 69 OB stars are excluded from the analysis owing to disk emission or significant contamination by secondaries in SB2 binaries. We determine physical properties of 778 OB stars, including Teff, log g, log L/Lsun and v_e sin i. There appears to be a bimodality in v_e sin i of single O stars, while v_e sin i distributions of OB stars are strikingly different for single (median 78 km/s) and binary (median 200 km/s) systems. Inferred temperatures are broadly in agreement with literature results for stars in common, plus results from a grid-based automization tool for a subset of O and early B stars, although uncertainties are larger for surface gravities. Rotational velocities are broadly in line with an independent tool applied to the same subset. We recover the anticipated lower mass cutoff at 8 Msun from the survey design using a Bayesian inference method coupled with SMC metallicity evolutionary models, with median masses of 12.6 Msun (19.8 Msun) for B-type (O-type) stars. Spectroscopic masses exceed evolutionary masses, albeit with large uncertainties in surface gravities. We also provide an updated catalogue of O stars in the SMC since half of the 159 BLOeM O stars are newly classified as O-type stars.
Simulating High-Velocity Clouds in the Observational Plane: An Initial Study with the Smith Cloud
2025-05-30
Lori E. Porter, Matthew Abruzzo, Greg L. Bryan, Mary Putman, Yong Zheng, Drummond Fielding
High-velocity clouds (HVCs) may fuel future star formation in the Milky Way, but they must first survive their passage through the hot halo. While recent work has improved our understanding of the survival criterion for cloud-wind interactions, few observational comparisons exist that test this criterion. We therefore present an initial comparison of simulations with the Smith Cloud (SC; $d=$ 12.4 kpc, $l, b = 40^{\circ}, -13^{\circ}$) as mapped with the GALFA-HI survey. We use the Smith Cloud's observed properties to motivate simulations of comparable clouds in wind tunnel simulations with Enzo-E, an MHD code. For both observations and simulations, we generate moment maps, characterize turbulence through a projected first-order velocity structure function (VSF), and do the same for HI column density with a normalized autocovariance function. We explore how initial cloud conditions (such as radius, metallicity, thermal pressure, viewing angle, and distance) affect these statistics, demonstrating that the small-scale VSF is sensitive to cloud turbulence while large scales depend on cloud bulk velocity and viewing angle. We find that some simulations reproduce key observational features (particularly the correlation between column density and velocity dispersion) but none match all observational probes at the same time (the large scales of the column density autocovariance is particularly challenging). We find that the simulated cloud (cloud C) showing growth via a turbulent radiative mixing layer (TRML) is the best match, implying the importance of TRML-mediated cooling for Milky Way HVCs. We conclude by suggesting improvements for simulations to better match observed HVCs.
The rapid adoption of machine learning (ML) technologies has driven organizations across diverse sectors to seek efficient and reliable methods to accelerate model development-to-deployment. Machine Learning Operations (MLOps) has emerged as an integrative approach addressing these requirements by unifying relevant roles and streamlining ML workflows. As the MLOps market continues to grow, securing these pipelines has become increasingly critical. However, the unified nature of MLOps ecosystem introduces vulnerabilities, making them susceptible to adversarial attacks where a single misconfiguration can lead to compromised credentials, severe financial losses, damaged public trust, and the poisoning of training data. Our paper presents a systematic application of the MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) framework, a comprehensive and continuously updated catalog of AI-focused attacks, to systematically assess attacks across different phases of the MLOps ecosystem. We begin by examining the preparatory phases during which adversaries acquire the essential intelligence required to initiate their attacks. We then present a structured taxonomy of attack techniques explicitly mapped to corresponding phases of the MLOps ecosystem, supported by examples drawn from red-teaming exercises and real-world incidents. This is followed by a taxonomy of mitigation strategies aligned with these attack categories, offering actionable early-stage defenses to strengthen the security of MLOps ecosystem. Given the rapid evolution and adoption of MLOps, we further highlight key research gaps that require immediate attention. Our work emphasizes the importance of implementing robust security protocols from the outset, empowering practitioners to safeguard MLOps ecosystem against evolving cyber attacks.
Data remanence in NAND flash complicates complete deletion on IoT SSDs. We design an adaptive architecture offering four privacy levels (PL0-PL3) that select among address, data, and parity deletion techniques. Quantitative analysis balances efficacy, latency, endurance, and cost. Machine-learning adjusts levels contextually, boosting privacy with negligible performance overhead and complexity.
Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Survey
2025-05-30
Edgar Welte, Rania Rayyes
Dexterous manipulation is a crucial yet highly complex challenge in humanoid robotics, demanding precise, adaptable, and sample-efficient learning methods. As humanoid robots are usually designed to operate in human-centric environments and interact with everyday objects, mastering dexterous manipulation is critical for real-world deployment. Traditional approaches, such as reinforcement learning and imitation learning, have made significant strides, but they often struggle due to the unique challenges of real-world dexterous manipulation, including high-dimensional control, limited training data, and covariate shift. This survey provides a comprehensive overview of these challenges and reviews existing learning-based methods for dexterous manipulation, spanning imitation learning, reinforcement learning, and hybrid approaches. A promising yet underexplored direction is interactive imitation learning, where human feedback actively refines a robot's behavior during training. While interactive imitation learning has shown success in various robotic tasks, its application to dexterous manipulation remains limited. To address this gap, we examine current interactive imitation learning techniques applied to other robotic tasks and discuss how these methods can be adapted to enhance dexterous manipulation. By synthesizing state-of-the-art research, this paper highlights key challenges, identifies gaps in current methodologies, and outlines potential directions for leveraging interactive imitation learning to improve dexterous robotic skills.
Complete survey of r-process conditions: the (un-)robustness of the r-process(-es)
2025-05-30
Jan Kuske, Almudena Arcones, Moritz Reichert
Heavy elements are synthesized by the r-process in neutron star mergers and potentially in rare supernovae linked to strong magnetic fields. Expensive hydrodynamic simulations of these extreme environments are usually post-processed to calculate the nucleosynthesis. In contrast, here we follow a site-independent approach based on three key parameters: electron fraction, entropy, and expansion timescale. Our model reproduces the results based on hydrodynamic simulations. Moreover, the 120 000 astrophysical conditions analyzed allow us to systematically and generally explore the astrophysical conditions of the r-process, also beyond those found in current simulations. Our results show that a wide range of conditions produce very similar abundance patterns explaining the observed robustness of the r-process between the second and third peak. Furthermore, we cannot find a single condition that produces the full r-process from first to third peak. Instead, a superposition of at least two or three conditions or components is required to reproduce the typical r-process pattern as observed in the solar system and very old stars. The different final abundances are grouped into eight nucleosynthesis clusters, which can be used to select representative conditions for comparisons to observations and investigations of the nuclear physics input.
Apache Point rapid response characterization of primitive pre-impact detection asteroid 2024 RW$_1$
2025-05-29
Carl Ingebretsen, Bryce T. Bolin, Robert Jedicke, Peter Vereš, Christine H. Chen, Carey M. Lisse, Russet McMillan, Torrie Sutherland, Amanda J. Townsend
Pre-impact detection asteroids (PIDAs) may be detected only a few hours before their impact with Earth, providing a brief opportunity to characterize them before impact. We describe the characterization of PIDA 2024 RW$_1$, which was discovered by the Catalina Sky Survey on 2024 September 4 at 05:43 UTC, before it entered the atmosphere near the northern Philippines at 16:39 UTC. We observed 2024 RW$_1$ with the Astrophysical Research Consortium Telescope Imaging Camera on the Apache Point Astrophysical Research Consortium's 3.5-m telescope on 2024 September 4 10:16 UTC. We obtained g, r, i, and z photometry of 2024 RW$_1$, yielding color indices of g-r = 0.47$\pm$0.04, r-i = 0.13$\pm$0.04, i-z = -0.11$\pm$0.07, and g-i = 0.60$\pm$0.04, corresponding to a spectral slope of 0.67$\pm$0.40~$\%$/100 nm. The closest match to an asteroid spectral type is with B-type asteroids from the C-complex. We detect variations in the time series photometry of the asteroid with an amplitude of $\sim$0.75, and a double-peaked rotation period of $\sim$1900 s. Assuming a visible albedo of 0.07 and a density of $\sim$1500 kg/m$^3$, and using the derived absolute magnitude of 32.2$\pm$0.5, we calculate that the asteroid has a diameter of 1.8$\pm$0.4 m and a total mass of $\sim$5000 kg. The most likely source of 2024 RW$_1$ is the 3:1 mean motion resonance followed by the $\nu_6$ resonance, according to NEOMOD3.
Computerized Modeling of Electrophysiology and Pathoelectrophysiology of the Atria -- How Much Detail is Needed?
2025-05-29
Olaf Dössel, Axel Loewe
This review focuses on the computerized modeling of the electrophysiology of the human atria, emphasizing the simulation of common arrhythmias such as atrial flutter (AFlut) and atrial fibrillation (AFib). Which components of the model are necessary to accurately model arrhythmogenic tissue modifications, including remodeling, cardiomyopathy, and fibrosis, to ensure reliable simulations? The central question explored is the level of detail required for trustworthy simulations for a specific context of use. The review discusses the balance between model complexity and computational efficiency, highlighting the risks of oversimplification and excessive detail. It covers various aspects of atrial modeling, from cellular to whole atria levels, including the influence of atrial geometry, fiber direction, anisotropy, and wall thickness on simulation outcomes. The article also examines the impact of different modeling approaches, such as volumetric 3D models, bilayer models, and single surface models, on the realism of simulations. In addition, it reviews the latest advances in the modeling of fibrotic tissue and the verification and validation of atrial models. The intended use of these models in planning and optimization of atrial ablation strategies is discussed, with a focus on personalized modeling for individual patients and cohort-based approaches for broader applications. The review concludes by emphasizing the importance of integrating experimental data and clinical validation to enhance the utility of computerized atrial models to improve patient outcomes.
A lookup table for large-genus asymptotics of saddle connection Siegel-Veech constants
2025-05-29
Anja Randecker, Maurice Reichert
Siegel-Veech constants are powerful tools for counting saddle connections on a translation surface. Their computation can be involved, most famously with recursive formulas that use intricate combinatorics or intersection theory. From recursive formulas, asymptotics of Siegel--Veech constants for growing genus can be extracted. In this survey, we collect some known asymptotics and explicitly calculate some special cases of asymptotics that show up in applications.
How to Elicit Explainability Requirements? A Comparison of Interviews, Focus Groups, and Surveys
2025-05-29
Martin Obaidi, Jakob Droste, Hannah Deters, Marc Herrmann, Raymond Ochsner, Jil Klünder, Kurt Schneider
As software systems grow increasingly complex, explainability has become a crucial non-functional requirement for transparency, user trust, and regulatory compliance. Eliciting explainability requirements is challenging, as different methods capture varying levels of detail and structure. This study examines the efficiency and effectiveness of three commonly used elicitation methods - focus groups, interviews, and online surveys - while also assessing the role of taxonomy usage in structuring and improving the elicitation process. We conducted a case study at a large German IT consulting company, utilizing a web-based personnel management software. A total of two focus groups, 18 interviews, and an online survey with 188 participants were analyzed. The results show that interviews were the most efficient, capturing the highest number of distinct needs per participant per time spent. Surveys collected the most explanation needs overall but had high redundancy. Delayed taxonomy introduction resulted in a greater number and diversity of needs, suggesting that a two-phase approach is beneficial. Based on our findings, we recommend a hybrid approach combining surveys and interviews to balance efficiency and coverage. Future research should explore how automation can support elicitation and how taxonomies can be better integrated into different methods.
In this study, we conducted a systematic analysis of long-term Fermi-LAT \gamma-ray data for a sample of blazars, including FSRQs, BL\,Lacs, and BCUs, to investigate their $\gamma$-ray variability. We focused on light curves binned in 3-, 7-, and 30-day intervals to assess the impact of binning, using data with TS >4 as a detection threshold. We calculated fractional variability ($F_{\rm var}$) for each category and found that FSRQs exhibit higher mean variability compared to BL\,Lacs and BCUs, with BCUs displaying intermediate variability closer to BL\,Lacs. The KS test on the variability distributions indicates that FSRQs differ from both BL Lacs and BCUs, whereas BCUs are more similar to BL Lacs. The higher variability in FSRQs is likely linked to more powerful jets and accretion. The correlation between \gamma-ray flux and spectral index suggests a moderate positive correlation for BL Lacs and BCUs, indicating a "softer when brighter" behavior. FSRQs displayed a mild anticorrelation, suggesting these sources tend to become harder as their flux increases. Analysis of flux distributions revealed log-normal behavior in many sources, consistent with multiplicative variability in blazar jets. Some sources show bimodal distributions, implying transitions between emission states. Binning affects the observed variability, with longer bins smoothing short-term fluctuations. Power spectral density analysis suggests FSRQs exhibit steeper slopes, reflecting structured variability, while BL Lacs display shallower slopes, dominated by stochastic processes. The absence of PSD breaks suggests no dominant timescale within the Fermi window. Spectral index distributions further highlight complexity, often requiring multi-component models.
This book is a philosopher's introduction to the idea that our universe is just one of many universes. I present and assess three versions of the idea: one version from philosophy, and two from physics. In short, they are: all the logically possible worlds; all the branches of the quantum state, in an Everettian interpretation of quantum theory; and all the bubbles of inflationary cosmology. For each proposal, I choose one main philosophical question to discuss in depth. They are, respectively: what is a possible world; what is chance; and what is explanation. But before treating these proposals and their associated questions, I set the stage by reviewing physics and philosophy from about 1600 to about 1900; and a final Chapter compares and contrasts the proposals.
Human Empathy as Encoder: AI-Assisted Depression Assessment in Special Education
2025-05-29
Boning Zhao
Assessing student depression in sensitive environments like special education is challenging. Standardized questionnaires may not fully reflect students' true situations. Furthermore, automated methods often falter with rich student narratives, lacking the crucial, individualized insights stemming from teachers' empathetic connections with students. Existing methods often fail to address this ambiguity or effectively integrate educator understanding. To address these limitations by fostering a synergistic human-AI collaboration, this paper introduces Human Empathy as Encoder (HEAE), a novel, human-centered AI framework for transparent and socially responsible depression severity assessment. Our approach uniquely integrates student narrative text with a teacher-derived, 9-dimensional "Empathy Vector" (EV), its dimensions guided by the PHQ-9 framework,to explicitly translate tacit empathetic insight into a structured AI input enhancing rather than replacing human judgment. Rigorous experiments optimized the multimodal fusion, text representation, and classification architecture, achieving 82.74% accuracy for 7-level severity classification. This work demonstrates a path toward more responsible and ethical affective computing by structurally embedding human empathy
Optimizing Flexible Complex Systems with Coupled and Co-Evolving Subsystems under Operational Uncertainties
2025-05-29
Koki Ho, Masafumi Isaji
The paper develops a novel design optimization framework and associated computational techniques for staged deployment optimization of complex systems under operational uncertainties. It proposes a local scenario discretization method that offers a computationally efficient approach to optimize staged co-deployment of multiple coupled subsystems by decoupling weak dynamic interaction among subsystems. The proposed method is applied to a case study and is demonstrated to provide an effective and scalable strategy to determine the optimal and flexible systems design under uncertainty. The developed optimization framework is expected to improve the staged deployment design of various complex engineering systems, such as water, energy, food, and other infrastructure systems.
Towards A Global Quantum Internet: A Review of Challenges Facing Aerial Quantum Networks
2025-05-29
Nitin Jha, Abhishek Parakh
Quantum networks use principles of quantum physics to create secure communication networks. Moving these networks off the ground using drones, balloons, or satellites could help increase the scalability of these networks. This article reviews how such aerial links work, what makes them difficult to build, and the possible solutions that can be used to overcome these problems. By combining ground stations, aerial relays, and orbiting satellites into one seamless system, we move closer to a practical quantum internet.
A Modern Theory of Cross-Validation through the Lens of Stability
2025-05-29
Jing Lei
Modern data analysis and statistical learning are marked by complex data structures and black-box algorithms. Data complexity stems from technologies like imaging, remote sensing, wearables, and genomic sequencing. Simultaneously, black-box models -- especially deep neural networks -- have achieved impressive results. This combination raises new challenges for uncertainty quantification and statistical inference, which we term "black-box inference." Black-box inference is difficult due to the lack of traditional modeling assumptions and the opaque behavior of modern estimators. These make it hard to characterize the distribution of estimation errors. A popular solution is post-hoc randomization, which, under mild assumptions like exchangeability, can yield valid uncertainty quantification. Such methods range from classical techniques like permutation tests, jackknife, and bootstrap, to recent innovations like conformal inference. These approaches typically need little knowledge of data distributions or the internal working of estimators. Many rely on the idea that estimators behave similarly under small data changes -- a concept formalized as stability. Over time, stability has become a key principle in data science, influencing generalization error, privacy, and adaptive inference. This article investigates cross-validation (CV) -- a widely used resampling method -- through the lens of stability. We first review recent theoretical results on CV for estimating generalization error and model selection under stability. We then examine uncertainty quantification for CV-based risk estimates. Together, these insights yield new theory and tools, which we apply to topics like model selection, selective inference, and conformal prediction.
Accelerated Training of Federated Learning via Second-Order Methods
2025-05-29
Mrinmay Sen, Sidhant R Nair, C Krishna Mohan
This paper explores second-order optimization methods in Federated Learning (FL), addressing the critical challenges of slow convergence and the excessive communication rounds required to achieve optimal performance from the global model. While existing surveys in FL primarily focus on challenges related to statistical and device label heterogeneity, as well as privacy and security concerns in first-order FL methods, less attention has been given to the issue of slow model training. This slow training often leads to the need for excessive communication rounds or increased communication costs, particularly when data across clients are highly heterogeneous. In this paper, we examine various FL methods that leverage second-order optimization to accelerate the training process. We provide a comprehensive categorization of state-of-the-art second-order FL methods and compare their performance based on convergence speed, computational cost, memory usage, transmission overhead, and generalization of the global model. Our findings show the potential of incorporating Hessian curvature through second-order optimization into FL and highlight key challenges, such as the efficient utilization of Hessian and its inverse in FL. This work lays the groundwork for future research aimed at developing scalable and efficient federated optimization methods for improving the training of the global model in FL.
A restaurant dinner or a hotel stay may lead to memorable experiences when guests encounter unexpected aspects that also match their interests. For example, an origami-making station in the waiting area of a restaurant may be both surprising and enjoyable for a customer who is passionate about paper crafts. Similarly, an exhibit of 18th century harpsichords would be atypical for a hotel lobby and likely pique the interest of a guest who has a passion for Baroque music. Motivated by this insight, in this paper we introduce the new task of engineering serendipity through recommendations of items with atypical aspects. We describe an LLM-based system pipeline that extracts atypical aspects from item reviews, then estimates and aggregates their user-specific utility in a measure of serendipity potential that is used to rerank a list of items recommended to the user. To facilitate system development and evaluation, we introduce a dataset of Yelp reviews that are manually annotated with atypical aspects and a dataset of artificially generated user profiles, together with crowdsourced annotations of user-aspect utility values. Furthermore, we introduce a custom procedure for dynamic selection of in-context learning examples, which is shown to improve LLM-based judgments of atypicality and utility. Experimental evaluations show that serendipity-based rankings generated by the system are highly correlated with ground truth rankings for which serendipity scores are computed from manual annotations of atypical aspects and their user-dependent utility. Overall, we hope that the new recommendation task and the associated system presented in this paper catalyze further research into recommendation approaches that go beyond accuracy in their pursuit of enhanced user satisfaction. The datasets and the code are made publicly available at https://github.com/ramituncc49er/ATARS .
2025-05-29
Benjamin Arnav, Pablo Bernabeu-Pérez, Nathan Helm-Burger, Tim Kostolansky, Hannes Whittingham, Mary Phuong
As AI models are deployed with increasing autonomy, it is important to ensure they do not take harmful actions unnoticed. As a potential mitigation, we investigate Chain-of-Thought (CoT) monitoring, wherein a weaker trusted monitor model continuously oversees the intermediate reasoning steps of a more powerful but untrusted model. We compare CoT monitoring to action-only monitoring, where only final outputs are reviewed, in a red-teaming setup where the untrusted model is instructed to pursue harmful side tasks while completing a coding problem. We find that CoT monitoring improves detection by up to 27 percentage points in scenarios where action-only monitoring fails to reliably identify sabotage. However, CoT traces can also contain misleading rationalizations that deceive the monitor, reducing performance in more obvious sabotage cases. To address this, we introduce a hybrid protocol that independently scores both reasoning and final outputs and combines them using a weighted average. This hybrid monitor consistently outperforms both CoT and action-only monitors across all tested models and tasks, with detection rates over four times higher than action-only monitoring for subtle deception scenarios.
Evaluating AI capabilities in detecting conspiracy theories on YouTube
2025-05-29
Leonardo La Rocca, Francesco Corso, Francesco Pierri
As a leading online platform with a vast global audience, YouTube's extensive reach also makes it susceptible to hosting harmful content, including disinformation and conspiracy theories. This study explores the use of open-weight Large Language Models (LLMs), both text-only and multimodal, for identifying conspiracy theory videos shared on YouTube. Leveraging a labeled dataset of thousands of videos, we evaluate a variety of LLMs in a zero-shot setting and compare their performance to a fine-tuned RoBERTa baseline. Results show that text-based LLMs achieve high recall but lower precision, leading to increased false positives. Multimodal models lag behind their text-only counterparts, indicating limited benefits from visual data integration. To assess real-world applicability, we evaluate the most accurate models on an unlabeled dataset, finding that RoBERTa achieves performance close to LLMs with a larger number of parameters. Our work highlights the strengths and limitations of current LLM-based approaches for online harmful content detection, emphasizing the need for more precise and robust systems.
SafeScientist: Toward Risk-Aware Scientific Discoveries by LLM Agents
2025-05-29
Kunlun Zhu, Jiaxun Zhang, Ziheng Qi, Nuoxing Shang, Zijia Liu, Peixuan Han, Yue Su, Haofei Yu, Jiaxuan You
Recent advancements in large language model (LLM) agents have significantly accelerated scientific discovery automation, yet concurrently raised critical ethical and safety concerns. To systematically address these challenges, we introduce \textbf{SafeScientist}, an innovative AI scientist framework explicitly designed to enhance safety and ethical responsibility in AI-driven scientific exploration. SafeScientist proactively refuses ethically inappropriate or high-risk tasks and rigorously emphasizes safety throughout the research process. To achieve comprehensive safety oversight, we integrate multiple defensive mechanisms, including prompt monitoring, agent-collaboration monitoring, tool-use monitoring, and an ethical reviewer component. Complementing SafeScientist, we propose \textbf{SciSafetyBench}, a novel benchmark specifically designed to evaluate AI safety in scientific contexts, comprising 240 high-risk scientific tasks across 6 domains, alongside 30 specially designed scientific tools and 120 tool-related risk tasks. Extensive experiments demonstrate that SafeScientist significantly improves safety performance by 35\% compared to traditional AI scientist frameworks, without compromising scientific output quality. Additionally, we rigorously validate the robustness of our safety pipeline against diverse adversarial attack methods, further confirming the effectiveness of our integrated approach. The code and data will be available at https://github.com/ulab-uiuc/SafeScientist. \textcolor{red}{Warning: this paper contains example data that may be offensive or harmful.}
Comparing the Moore-Penrose Pseudoinverse and Gradient Descent for Solving Linear Regression Problems: A Performance Analysis
2025-05-29
Alex Adams
This paper investigates the comparative performance of two fundamental approaches to solving linear regression problems: the closed-form Moore-Penrose pseudoinverse and the iterative gradient descent method. Linear regression is a cornerstone of predictive modeling, and the choice of solver can significantly impact efficiency and accuracy. I review and discuss the theoretical underpinnings of both methods, analyze their computational complexity, and evaluate their empirical behavior on synthetic datasets with controlled characteristics, as well as on established real-world datasets. My results delineate the conditions under which each method excels in terms of computational time, numerical stability, and predictive accuracy. This work aims to provide practical guidance for researchers and practitioners in machine learning when selecting between direct, exact solutions and iterative, approximate solutions for linear regression tasks.
The Intracluster Light of Abell 3667: Unveiling an Optical Bridge in LSST Precursor Data
2025-05-29
Anthony Englert, Ian Dell'Antonio, Mireia Montes
Intracluster light, the diffuse glow of stars stripped from galaxies during a cluster's formation, is an established tracer of a cluster's dynamical history. The upcoming Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) is set to revolutionize studies of intracluster light by imaging the entire southern sky down to a limiting surface brightness $\mu \gtrsim 30\text{mag}/\text{arcsec}^2$ by year ten. In this letter, we create a precursor LSST dataset (reaching the equivalent of year eight depth) using DECam observations of Abell 3667 and study its intracluster light. We have discovered a low surface brightness ($ \mu \gtrsim 26\text{mag}/\text{arcsec}^2 $) optical bridge extending over $\sim 400\text{ kpc}$ which connects the two brightest galaxies (BCG1 and BCG2) in the cluster; the color and surface brightness of the bridge is consistent with formation via a major merger. The inner regions of BCG1 ($r < 200\text{ kpc}$) and BCG2 ($r < 50\text{ kpc}$) are consistent with formation via gradual stripping of satellite galaxies, but BCG2's outer profile appears disrupted by a recent merger. We hypothesize that the bridge is a relic of a recent first-pass between the two brightest galaxies and is composed of stars being stripped from BCG2. Future studies of intracluster light with LSST will discover new features such as the bridge in local clusters while enabling detailed studies of the stellar populations of these features with its six photometric bands.
2025-05-29
Nathan Secrest, Sebastian von Hausegger, Mohamed Rameez, Roya Mohayaee, Subir Sarkar
The Cosmological Principle, which states that the Universe is homogeneous and isotropic (when averaged on large scales), is the foundational assumption of Friedmann-Lemaitre-Robertson-Walker (FLRW) cosmologies such as the current standard Lambda-Cold-Dark-Matter ({\Lambda}CDM) model. This simplification yields an exact solution to the Einstein field equations that relates space and time through a single time-dependent scale factor, which defines cosmological observables such as the Hubble parameter and the cosmological redshift. The validity of the Cosmological Principle, which underpins modern cosmology, can now be rigorously tested with the advent of large, nearly all-sky catalogs of radio galaxies and quasars. Surprisingly, the dipole anisotropy in the large-scale distribution of matter is found to be inconsistent with the expectation from kinematic aberration and Doppler boosting effects in a perturbed FLRW universe, which is the standard interpretation of the observed dipole in the cosmic microwave background (CMB). Although the matter dipole agrees in direction with that of the CMB dipole, it is anomalously larger, demonstrating that either the rest frames in which matter and radiation appear isotropic are not the same, or that there is an unexpected intrinsic anisotropy in at least one of them. This discrepancy now exceeds 5{\sigma} in significance. We review these recent findings, as well as the potential biases, systematic issues, and alternate interpretations that have been suggested to help alleviate the tension. We conclude that the cosmic dipole anomaly poses a serious challenge to FLRW cosmology, and the standard {\Lambda}CDM model in particular, as an adequate description of our Universe.
Constraining primordial curvature perturbations with present and future GW detectors
2025-05-29
Mauro Pieroni
Primordial scalar curvature perturbations ($\zeta$), typically probed on large cosmological scales via CMB and LSS observations, can be significantly enhanced on smaller scales by various early Universe mechanisms, for instance, non-minimal inflationary models. While decoupled at linear order, scalar and tensor perturbations, i.e., Gravitational Waves (GWs), interact at second order. As a consequence, an enhanced primordial scalar power spectrum $P_\zeta(k)$ can source a sizable stochastic GW background (SGWB). In these proceedings, we briefly review the generation mechanism of such signals, typically referred to as scalar-induced GWs (SIGWs), and discuss the prospects of measuring them with present and future Pulsar Timing Arrays datasets and future GW observatories like the Laser Interferometer Space Antenna LISA.
The CASE Framework -- A New Architecture for Participatory Research and Digital Health Surveillance
2025-05-29
Marco Hirsch, Peter Hevesi, Paul Lukowicz
We present the CASE framework, an open-source platform for adaptive, context-aware participatory research, and pandemic preparedness. CASE implements an event-driven architecture that enables dynamic survey workflows, allowing real-time adaptation based on participant responses, external data, temporal conditions, and evolving user states. The framework supports a broad range of research needs, from simple one-time questionnaires to complex longitudinal studies with advanced conditional logic. Built on over a decade of practical experience, CASE underwent a major architectural rework in 2024, transitioning from a microservice-based design to a streamlined monolithic architecture. This evolution significantly improved maintainability, flexibility, and accessibility to deployment, particularly for institutions with limited technical capacity. CASE has been successfully deployed across diverse domains, powering national disease surveillance platforms, supporting post-COVID cohort studies, and enabling real-time sentiment analysis during political events. These applications, involving tens of thousands of participants, demonstrate the framework's scalability, versatility, and practical value. This paper describes the foundations of CASE, details its architectural evolution, and presents lessons learned from real-world deployments. We establish CASE as a mature and reusable research infrastructure that balances sophisticated functionality with practical implementation, addressing the critical global need for sustainable and institutionally controlled data collection systems.
Autoformalization in the Era of Large Language Models: A Survey
2025-05-29
Ke Weng, Lun Du, Sirui Li, Wangyue Lu, Haozhe Sun, Hengyu Liu, Tiancheng Zhang
Autoformalization, the process of transforming informal mathematical propositions into verifiable formal representations, is a foundational task in automated theorem proving, offering a new perspective on the use of mathematics in both theoretical and applied domains. Driven by the rapid progress in artificial intelligence, particularly large language models (LLMs), this field has witnessed substantial growth, bringing both new opportunities and unique challenges. In this survey, we provide a comprehensive overview of recent advances in autoformalization from both mathematical and LLM-centric perspectives. We examine how autoformalization is applied across various mathematical domains and levels of difficulty, and analyze the end-to-end workflow from data preprocessing to model design and evaluation. We further explore the emerging role of autoformalization in enhancing the verifiability of LLM-generated outputs, highlighting its potential to improve both the trustworthiness and reasoning capabilities of LLMs. Finally, we summarize key open-source models and datasets supporting current research, and discuss open challenges and promising future directions for the field.
PhysicsNeRF: Physics-Guided 3D Reconstruction from Sparse Views
2025-05-29
Mohamed Rayan Barhdadi, Hasan Kurban, Hussein Alnuweiri
PhysicsNeRF is a physically grounded framework for 3D reconstruction from sparse views, extending Neural Radiance Fields with four complementary constraints: depth ranking, RegNeRF-style consistency, sparsity priors, and cross-view alignment. While standard NeRFs fail under sparse supervision, PhysicsNeRF employs a compact 0.67M-parameter architecture and achieves 21.4 dB average PSNR using only 8 views, outperforming prior methods. A generalization gap of 5.7-6.2 dB is consistently observed and analyzed, revealing fundamental limitations of sparse-view reconstruction. PhysicsNeRF enables physically consistent, generalizable 3D representations for agent interaction and simulation, and clarifies the expressiveness-generalization trade-off in constrained NeRF models.
Self-driving technologies need the help of the public: A narrative review of the evidence
2025-05-29
Jonathan Smith, Siddartha Khastgir
If public trust is lot in a new technology early in its life cycle it can take much more time for the benefits of that technology to be realised. Eventually tens-of-millions of people will collectively have the power to determine self-driving technology success of failure driven by their perception of risk, data handling, safety, governance, accountability, benefits to their life and more. This paper reviews the evidence on safety critical technology covering trust, engagement, and acceptance. The paper takes a narrative review approach concluding with a scalable model for self-driving technology education and engagement. The paper find that if a mismatch between the publics perception and expectations about self driving systems emerge it can lead to misuse, disuse, or abuse of the system. Furthermore we find from the evidence that industrial experts often misunderstand what matters to the public, users, and stakeholders. However we find that engagement programmes that develop approaches to defining the right information at the right time, in the right format orientated around what matters to the public creates the potential for ever more sophisticated conversations, greater trust, and moving the public into a progressive more active role of critique and advocacy. This work has been undertaken as part of the Partners for Automated Vehicle Education (PAVE) United Kingdom programme.
What About Emotions? Guiding Fine-Grained Emotion Extraction from Mobile App Reviews
2025-05-29
Quim Motger, Marc Oriol, Max Tiessler, Xavier Franch, Jordi Marco
Opinion mining plays a vital role in analysing user feedback and extracting insights from textual data. While most research focuses on sentiment polarity (e.g., positive, negative, neutral), fine-grained emotion classification in app reviews remains underexplored. This paper addresses this gap by identifying and addressing the challenges and limitations in fine-grained emotion analysis in the context of app reviews. Our study adapts Plutchik's emotion taxonomy to app reviews by developing a structured annotation framework and dataset. Through an iterative human annotation process, we define clear annotation guidelines and document key challenges in emotion classification. Additionally, we evaluate the feasibility of automating emotion annotation using large language models, assessing their cost-effectiveness and agreement with human-labelled data. Our findings reveal that while large language models significantly reduce manual effort and maintain substantial agreement with human annotators, full automation remains challenging due to the complexity of emotional interpretation. This work contributes to opinion mining by providing structured guidelines, an annotated dataset, and insights for developing automated pipelines to capture the complexity of emotions in app reviews.
Two-gap superconductor ZrB$_{12}$ with dynamic stripes and charge density waves: Crystal structure, physical properties and pairing mechanism
2025-05-29
A. N. Azarevich, N. B. Bolotina, O. N. Khrykina, A. V. Bogach, K. M. Krasikov, A. Yu. Tsvetkov, S. Yu. Gavrilkin, V. V. Voronov, S. Gabani, K. Flachbart, A. V. Kuznetsov, N. E. Sluchanko
A review of long-term studies of ZrB$_{12}$ and LuB$_{12}$ superconductors with very similar conduction bands and phonon spectra, but with radically different (by a factor of 15-20) critical temperatures and magnetic fields is presented. A detailed analysis of well-known studies in combination with new results of structural, thermodynamic and charge transport measurements obtained here for these metallic dodecaborides with Jahn-Teller instability of the rigid boron network and with dynamic charge stripes allows us to conclude in favor of the primary role of nanoscale effects of electron phase separation, leading to the formation of one-dimensional dynamic chains with different configurations of fluctuating charges, which in the case of ZrB$_{12}$ are predominantly $2p$-states, and for LuB$_{12}$-$5d$-$2p$ states. We propose a new plasmon-phonon pairing mechanism in ZrB$_{12}$, which may be common to different classes of high-$T_c$ superconductors.
Toward Effective AI Governance: A Review of Principles
2025-05-29
Danilo Ribeiro, Thayssa Rocha, Gustavo Pinto, Bruno Cartaxo, Marcelo Amaral, Nicole Davila, Ana Camargo
Artificial Intelligence (AI) governance is the practice of establishing frameworks, policies, and procedures to ensure the responsible, ethical, and safe development and deployment of AI systems. Although AI governance is a core pillar of Responsible AI, current literature still lacks synthesis across such governance frameworks and practices. Objective: To identify which frameworks, principles, mechanisms, and stakeholder roles are emphasized in secondary literature on AI governance. Method: We conducted a rapid tertiary review of nine peer-reviewed secondary studies from IEEE and ACM (20202024), using structured inclusion criteria and thematic semantic synthesis. Results: The most cited frameworks include the EU AI Act and NIST RMF; transparency and accountability are the most common principles. Few reviews detail actionable governance mechanisms or stakeholder strategies. Conclusion: The review consolidates key directions in AI governance and highlights gaps in empirical validation and inclusivity. Findings inform both academic inquiry and practical adoption in organizations.
Measuring pulsar profile variations with 2-D Gaussian process regression
2025-05-29
Michael J. Keith, Renée Spiewak, Andrew G. Lyne, Patrick Weltevrede, Danai Antonopoulou, Ben Stappers
Time-correlated variations in the pulse profiles of radio pulsars provide insights into changes in their magnetospheres. For a small number of pulsars (~20), these variations have been shown to correlate with spin-down rate. Many of these profile changes involve small (few percent) variations in the relative intensity of different profile components, and hence tools such as Gaussian process regression have been employed to separate the time-correlated profile variation from intrinsic noise. In this paper, we present a computationally efficient approximation of a 2-D Gaussian process model that enhances sensitivity by simultaneously tracking time- and phase-correlated signals. Applying this model to 26 pulsars observed at the Jodrell Bank Observatory, we detect significant profile shape variations in 21 pulsars. Using principal component analysis, we confirm spin-down correlated shape variations in 11 pulsars where this had been previously observed. Additionally, we find evidence of spin-down correlated shape changes in 7 pulsars for the first time (PSRs B0105+65, B0611+22, B0626+24, B1740-03, B1826-17, B1917+00, and B2148+63). We look in greater detail at PSR B0740-28, where the correlation between profile shape and spin-down itself seems to switch between quasi-stable states. Notably the profile shape associated with greater spin-down seems to invert at times, presenting a challenge to our understanding of the physical processes at work.
A Practical Guide for Supporting Formative Assessment and Feedback Using Generative AI
2025-05-29
Sapolnach Prompiengchai, Charith Narreddy, Steve Joordens
Formative assessment is a cornerstone of effective teaching and learning, providing students with feedback to guide their learning. While there has been an exponential growth in the application of generative AI in scaling various aspects of formative assessment, ranging from automatic question generation to intelligent tutoring systems and personalized feedback, few have directly addressed the core pedagogical principles of formative assessment. Here, we critically examined how generative AI, especially large-language models (LLMs) such as ChatGPT, can support key components of formative assessment: helping students, teachers, and peers understand "where learners are going," "where learners currently are," and "how to move learners forward" in the learning process. With the rapid emergence of new prompting techniques and LLM capabilities, we also provide guiding principles for educators to effectively leverage cost-free LLMs in formative assessments while remaining grounded in pedagogical best practices. Furthermore, we reviewed the role of LLMs in generating feedback, highlighting limitations in current evaluation metrics that inadequately capture the nuances of formative feedback, such as distinguishing feedback at the task, process, and self-regulatory levels. Finally, we offer practical guidelines for educators and researchers, including concrete classroom strategies and future directions such as developing robust metrics to assess LLM-generated feedback, leveraging LLMs to overcome systemic and cultural barriers to formative assessment, and designing AI-aware assessment strategies that promote transferable skills while mitigating overreliance on LLM-generated responses. By structuring the discussion within an established formative assessment framework, this review provides a comprehensive foundation for integrating LLMs into formative assessment in a pedagogically informed manner.
Ordinal regression for meta-analysis of test accuracy: a flexible approach for utilising all threshold data
2025-05-29
Enzo Cerullo, Haley E. Jones, Tim Lucas, Nicola J. Cooper, Alex J. Sutton
Standard methods for meta-analysis and network-meta-analysis of test accuracy do not fully utilise available evidence, as they analyse thresholds separately, resulting in a loss of data unless every study reports all thresholds - which rarely occurs. Furthermore, previously proposed "multiple threshold" models introduce different problems: making overly restrictive assumptions, or failing to provide summary sensitivity and specificity estimates across thresholds. To address this, we proposed a series of ordinal regression-based models, representing a natural extension of established frameworks. Our approach offers notable advantages: (i) complete data utilisation: rather than discarding information like standard methods, we incorporate all threshold data; (ii) threshold-specific inference: by providing summary accuracy estimates across thresholds, our models deliver critical information for clinical decision-making; (iii) enhanced flexibility: unlike previous "multiple thresholds" approaches, our methodology imposes fewer assumptions, leading to better accuracy estimates; (iv) our models use an induced-Dirichlet framework, allowing for either fixed-effects or random-effects cutpoint parameters, whilst also allowing for intuitive cutpoint priors. Our (ongoing) simulation study - based on real-world anxiety and depression screening data - demonstrates notably better accuracy estimates than previous approaches, even when the number of categories is high. Furthermore, we implemented these models in a user-friendly R package - MetaOrdDTA (https://github.com/CerulloE1996/MetaOrdDTA). The package uses Stan and produces MCMC summaries, sROC plots with credible/prediction regions, and meta-regression. Overall, our approach establishes a more comprehensive framework for synthesising test accuracy data, better serving systematic reviewers and clinical decision-makers.
Nosey: Open-source hardware for acoustic nasalance
2025-05-29
Maya Dewhurst, Jack Collins, Justin J. H. Lo, Roy Alderton, Sam Kirkham
We introduce Nosey (Nasalance Open Source Estimation sYstem), a low-cost, customizable, 3D-printed system for recording acoustic nasalance data that we have made available as open-source hardware (http://github.com/phoneticslab/nosey). We first outline the motivations and design principles behind our hardware nasalance system, and then present a comparison between Nosey and a commercial nasalance device. Nosey shows consistently higher nasalance scores than the commercial device, but the magnitude of contrast between phonological environments is comparable between systems. We also review ways of customizing the hardware to facilitate testing, such as comparison of microphones and different construction materials. We conclude that Nosey is a flexible and cost-effective alternative to commercial nasometry devices and propose some methodological considerations for its use in data collection.
TRACE: Trajectory-Constrained Concept Erasure in Diffusion Models
2025-05-29
Finn Carter
Text-to-image diffusion models have shown unprecedented generative capability, but their ability to produce undesirable concepts (e.g.~pornographic content, sensitive identities, copyrighted styles) poses serious concerns for privacy, fairness, and safety. {Concept erasure} aims to remove or suppress specific concept information in a generative model. In this paper, we introduce \textbf{TRACE (Trajectory-Constrained Attentional Concept Erasure)}, a novel method to erase targeted concepts from diffusion models while preserving overall generative quality. Our approach combines a rigorous theoretical framework, establishing formal conditions under which a concept can be provably suppressed in the diffusion process, with an effective fine-tuning procedure compatible with both conventional latent diffusion (Stable Diffusion) and emerging rectified flow models (e.g.~FLUX). We first derive a closed-form update to the model's cross-attention layers that removes hidden representations of the target concept. We then introduce a trajectory-aware finetuning objective that steers the denoising process away from the concept only in the late sampling stages, thus maintaining the model's fidelity on unrelated content. Empirically, we evaluate TRACE on multiple benchmarks used in prior concept erasure studies (object classes, celebrity faces, artistic styles, and explicit content from the I2P dataset). TRACE achieves state-of-the-art performance, outperforming recent methods such as ANT, EraseAnything, and MACE in terms of removal efficacy and output quality.
Higher-order thermal transport theory for phonon thermal transport in semiconductors using lattice dynamics calculations and the Boltzmann transport equation
2025-05-29
Ankit Jain, Yagyank Srivastava, Amey G. Gokhale, Nidheesh Virakante, Hardik L. Kagdada
The phonon thermal conductivity of semiconducting periodic solids can be obtained using the lattice dynamics calculations along with the Boltzmann transport equation and with input from density functional theory calculations. These calculations have resulted in an excellent agreement with experiments without requiring any fitting parameters. However, over the last decade, many material systems have been identified where the lowest level lattice dynamics theory, which is based on the relaxation time approximation solution of the Boltzmann transport equation and considers potential energy surface sampling around the static equilibrium positions of atoms with only three-phonon scatterings, is proved insufficient in describing the thermal transport physics. In this article, we review these higher-order developments in the lattice dynamics theory to describe thermal transport in periodic semiconducting solids. We start with a brief discussion of the lowest-order theory and discuss its limitations along with proposed developments to address these limitations. We discuss prominent success cases of these higher-order developments and present our recommendations on their use for various material systems. Considering that many of these higher-order developments are computationally more demanding compared to the lowest-order theory, we also discussed data-driven approaches to accelerate these calculations. This review article is intended to serve as a reference for both novice and experienced researchers in this field.
2025-05-29
Yazhou Zhang, Chunwang Zou, Qimeng Liu, Lu Rong, Ben Yao, Zheng Lian, Qiuchi Li, Peng Zhang, Jing Qin
Can multi-modal large models (MLMs) that can ``see'' an image be said to ``understand'' it? Drawing inspiration from Searle's Chinese Room, we propose the \textbf{Visual Room} argument: a system may process and describe every detail of visual inputs by following algorithmic rules, without genuinely comprehending the underlying intention. This dilemma challenges the prevailing assumption that perceptual mastery implies genuine understanding. In implementation, we introduce a two-tier evaluation framework spanning perception and cognition. The perception component evaluates whether MLMs can accurately capture the surface-level details of visual contents, where the cognitive component examines their ability to infer sarcasm polarity. To support this framework, We further introduce a high-quality multi-modal sarcasm dataset comprising both 924 static images and 100 dynamic videos. All sarcasm labels are annotated by the original authors and verified by independent reviewers to ensure clarity and consistency. We evaluate eight state-of-the-art (SoTA) MLMs. Our results highlight three key findings: (1) MLMs demonstrate high accuracy in visual perception; (2) even with correct perception, MLMs exhibit an average error rate of ~17.1\% in sarcasm understanding, revealing a significant gap between seeing and understanding; (3) this gap stems from weaknesses in context integration, emotional reasoning, and pragmatic inference. This work provides empirical grounding for the proposed Visual Room argument and offers a new evaluation paradigm for MLMs.
We present Adversarial Object Fusion (AdvOF), a novel attack framework targeting vision-and-language navigation (VLN) agents in service-oriented environments by generating adversarial 3D objects. While foundational models like Large Language Models (LLMs) and Vision Language Models (VLMs) have enhanced service-oriented navigation systems through improved perception and decision-making, their integration introduces vulnerabilities in mission-critical service workflows. Existing adversarial attacks fail to address service computing contexts, where reliability and quality-of-service (QoS) are paramount. We utilize AdvOF to investigate and explore the impact of adversarial environments on the VLM-based perception module of VLN agents. In particular, AdvOF first precisely aggregates and aligns the victim object positions in both 2D and 3D space, defining and rendering adversarial objects. Then, we collaboratively optimize the adversarial object with regularization between the adversarial and victim object across physical properties and VLM perceptions. Through assigning importance weights to varying views, the optimization is processed stably and multi-viewedly by iterative fusions from local updates and justifications. Our extensive evaluations demonstrate AdvOF can effectively degrade agent performance under adversarial conditions while maintaining minimal interference with normal navigation tasks. This work advances the understanding of service security in VLM-powered navigation systems, providing computational foundations for robust service composition in physical-world deployments.
Can Large Language Models Trigger a Paradigm Shift in Travel Behavior Modeling? Experiences with Modeling Travel Satisfaction
2025-05-29
Pengfei Xu, Donggen Wang
As a specific domain of subjective well-being, travel satisfaction has attracted much research attention recently. Previous studies primarily use statistical models and, more recently, machine learning models to explore the determinants of travel satisfaction. Both approaches require data from sufficient sample sizes and correct prior statistical assumptions. The emergence of Large Language Models (LLMs) offers a new modeling approach that can overcome the shortcomings of the existing methods. Pre-trained on extensive datasets, LLMs have strong capabilities in contextual understanding and generalization, significantly reducing their dependence on large quantities of task-specific data and stringent statistical assumptions. The primary challenge in applying LLMs lies in addressing the behavioral misalignment between LLMs and human behavior. Using data on travel satisfaction from a household survey in shanghai, this study identifies the existence and source of misalignment and develop methods to address the misalignment issue. We find that the zero-shot LLM exhibits behavioral misalignment, resulting in relatively low prediction accuracy. However, few-shot learning, even with a limited number of samples, allows the model to outperform baseline models in MSE and MAPE metrics. This misalignment can be attributed to the gap between the general knowledge embedded in LLMs and the specific, unique characteristics of the dataset. On these bases, we propose an LLM-based modeling approach that can be applied to model travel behavior using samples of small sizes. This study highlights the potential of LLMs for modeling not only travel satisfaction but also broader aspects of travel behavior.
Advancing Image Super-resolution Techniques in Remote Sensing: A Comprehensive Survey
2025-05-29
Yunliang Qi, Meng Lou, Yimin Liu, Lu Li, Zhen Yang, Wen Nie
Remote sensing image super-resolution (RSISR) is a crucial task in remote sensing image processing, aiming to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts. Despite the growing number of RSISR methods proposed in recent years, a systematic and comprehensive review of these methods is still lacking. This paper presents a thorough review of RSISR algorithms, covering methodologies, datasets, and evaluation metrics. We provide an in-depth analysis of RSISR methods, categorizing them into supervised, unsupervised, and quality evaluation approaches, to help researchers understand current trends and challenges. Our review also discusses the strengths, limitations, and inherent challenges of these techniques. Notably, our analysis reveals significant limitations in existing methods, particularly in preserving fine-grained textures and geometric structures under large-scale degradation. Based on these findings, we outline future research directions, highlighting the need for domain-specific architectures and robust evaluation protocols to bridge the gap between synthetic and real-world RSISR scenarios.
Ein reguliertes Flächenintegral als Entscheidungskriterium für die Riemannsche Vermutung
2025-05-29
Dennis-Magnus Welz
We present a novel integral criterion for the Riemann Hypothesis based on a regulated, normalized surface integral involving a logarithmic weight and the reciprocal square of the Riemann zeta function. The construction distinguishes symmetric from asymmetric zero configurations and converges if and only if all nontrivial zeros lie on the critical line. Analytical arguments are supported by model computations and operator-theoretic reformulations. The resulting structure provides a new functional characterization of the Riemann Hypothesis in both real and complex settings.
On the flow topology of swirl jets upon impingement
2025-05-29
Premchand V. Chandra, Pradip Dutta
Jet impingement enhances heat transfer and is characterised by the complex flow patterns formed when a jet impacts a plate aligned normal to it. While traditional round jet impingement has been extensively studied to understand flow and associated heat transfer, there is still room for research in investigating flow structures in swirl jet impingement. This paper focuses on the flow topology of swirl jets generated by a 45-degree vane swirler, impinging on a flat plate studied at dimensionless jet-plate distances (H/D=1-4) and Reynolds numbers (Re = 16600 and 23000). The flow structures, mean velocity components, and turbulence characteristics are presented using a 2D Particle Image Velocimetry (PIV) experiment at the front and top planes. Furthermore, results from the 3D numerical simulations are presented to support the results where the PIV study had experimental limitations. The effect of impingement distance or jet-plate distance on the mean flow properties and turbulence parameters is discussed. A Proper Orthogonal Decomposition (POD) analysis has been performed to understand the dominant coherent structures in different cases of impingement distance. We show that the turbulence parameters are more pronounced at smaller jet-plate distances $(H/D \leq 2)$, which could explain the enhanced heat transfer for these jets.
V. J. Emery and P. W. Anderson's views and related issues regarding the basics of cuprates: a re-look
2025-05-29
Navinder Singh
In 1991, V. J. Emery in his important review article entitled "Some aspects of the theory of high temperature superconductors"\cite{emery1} argued against the Zhang-Rice reduction of three-band to an effective one-band model. In his words "...therefore it seems that the simple $t-J$ model does not account for the properties of high temperature superconductors". Over approximately 35 years after the initial debates\cite{debates} much has happened in the field pertaining to this topic. Even though it is one of the most discussed issue, a comprehensive account and the required resolution are lacking. Connected to the debate over one-band versus three-band models is another discussion: the one-component versus two-component model for cuprates. The two-component model is most strongly advocated by Barzykin and Pines\cite{bp}. In this article the author attempts a perspective and a re-look on some of these issues. After an analysis of a large body of literature, author finds that V. J. Emery's criticism of the Zhang-Rice reduction was correct. Many central experimental features of cuprates cannot be rationalized within the one-band model, and Johnston-Nakano scaling is one such example. Other examples are also discussed. Author introduces a simple-minded toy model to illustrate the core issues involved.
Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreement
2025-05-29
Gabriele Sarti, Vilém Zouhar, Malvina Nissim, Arianna Bisazza
Word-level quality estimation (WQE) aims to automatically identify fine-grained error spans in machine-translated outputs and has found many uses, including assisting translators during post-editing. Modern WQE techniques are often expensive, involving prompting of large language models or ad-hoc training on large amounts of human-labeled data. In this work, we investigate efficient alternatives exploiting recent advances in language model interpretability and uncertainty quantification to identify translation errors from the inner workings of translation models. In our evaluation spanning 14 metrics across 12 translation directions, we quantify the impact of human label variation on metric performance by using multiple sets of human labels. Our results highlight the untapped potential of unsupervised metrics, the shortcomings of supervised methods when faced with label uncertainty, and the brittleness of single-annotator evaluation practices.
Noncoherent MIMO Communications: Theoretical Foundation, Design Approaches, and Future Challenges
2025-05-29
Khac-Hoang Ngo, Diego Cuevas, Ruben de Miguel Gil, Victor Monzon Baeza, Ana Garcia Armada, Ignacio Santamaria
Noncoherent communication is a promising paradigm for future wireless systems where acquiring accurate channel state information (CSI) is challenging or infeasible. It provides methods to bypass the need for explicit channel estimation in practical scenarios such as high-mobility networks, massive distributed antenna arrays, energy-constrained Internet-of-Things devices, and unstructured propagation environments. This survey provides a comprehensive overview of noncoherent communication strategies in multiple-input multiple-output (MIMO) systems, focusing on recent advances since the early 2000s. We classify noncoherent communication schemes into three main approaches where CSI-free signal recovery is based on subspace detection (i.e., Grassmannian signaling), differential detection, and energy detection, respectively. For each approach, we review the theoretical foundation and design methodologies. We also provide comparative insights into their suitability across different channel models and system constraints, highlighting application scenarios where noncoherent methods offer performance and scalability advantages over traditional coherent communication. Furthermore, we discuss practical considerations of noncoherent communication, including compatibility with orthogonal frequency division multiplexing (OFDM), resilience to hardware impairments, and scalability with the number of users. Finally, we provide an outlook on future challenges and research directions in designing robust and efficient noncoherent systems for next-generation wireless networks.
A Be star-black hole binary with a wide orbit from LAMOST time-domain survey
2025-05-29
Qian-Yu An, Yang Huang, Wei-Min Gu, Yong Shao, Zhi-Xiang Zhang, Tuan Yi, B. D. Lailey, T. A. A. Sigut, Kyle Akira Rocha, Meng Sun, Seth Gossage, Shi-Jie Gao, Shan-Shan Weng, Song Wang, Bowen Zhang, Xinlin Zhao, Senyu Qi, Shilong Liao, Jianghui Ji, Junfeng Wang, Jianfeng Wu, Mouyuan Sun, Xiang-Dong Li, Jifeng Liu
Binary systems consisting of an early type star and a black hole (BH) are crucial for understanding various astrophysical phenomena, particularly the origins of detected gravitational wave sources. Be binary systems are expected to represent a key evolutionary stage in hosting BHs. However, while hundreds of Be X-ray binaries are known, the only confirmed BH candidate in a Be binary remains highly controversial. We report the discovery of ALS 8814, a Be star-BH binary with a moderately eccentric ($e = 0.23$) and wide orbit ($P = 176.6$ days), revealed by the radial velocity (RV) measurement of the visible Be star. Our analysis, combining flux-calibrated spectra in the Balmer discontinuity region and spectral template matching, yields a mass of $11.2^{+1.4}_{-1.2}$ $M_\odot$ for the Be star. The minimum mass of the unseen companion, assuming an edge-on inclination ($i = 90^{\circ}$), is $9.8\pm 0.7\,M_\odot$. We rule out the presence of non-degenerate companions in ALS 8814, indicating that it can only be a BH. This discovery represents a robust case of a Be-BH binary, identified purely through precise RV measurements from a single set of lines. The extremely low peculiar velocity of ALS 8814 suggests that the BH is formed via a direct core-collapse with a negligible natal kick, implying an almost perfect alignment between the Be star's spin and the orbital plane. In this context, the binary's inclination angle is estimated to be 22$^{\circ}$-49$^{\circ}$ by analyzing the shallow double-peaked profile of the H$\alpha$ emission line. This inclination range corresponds to a BH mass estimate between $15\,M_\odot$ and $58\,M_\odot$. As the only unambiguous Be-BH binary system known to date, ALS 8814 provides valuable constraints on the BH formation in a binary system with a high-mass companion.
Eye-tracking-Driven Shared Control for Robotic Arms:Wizard of Oz Studies to Assess Design Choices
2025-05-29
Anke Fischer-Janzen, Thomas M. Wendt, Daniel Görlich, Kristof Van Laerhoven
Advances in eye-tracking control for assistive robotic arms provide intuitive interaction opportunities for people with physical disabilities. Shared control has gained interest in recent years by improving user satisfaction through partial automation of robot control. We present an eye-tracking-guided shared control design based on insights from state-of-the-art literature. A Wizard of Oz setup was used in which automation was simulated by an experimenter to evaluate the concept without requiring full implementation. This approach allowed for rapid exploration of user needs and expectations to inform future iterations. Two studies were conducted to assess user experience, identify design challenges, and find improvements to ensure usability and accessibility. The first study involved people with disabilities by providing a survey, and the second study used the Wizard of Oz design in person to gain technical insights, leading to a comprehensive picture of findings.
2025-05-29
Zhe Ye, Zhengxu Yan, Jingxuan He, Timothe Kasriel, Kaiyu Yang, Dawn Song
Large language models (LLMs) are increasingly integrated in software development, but ensuring correctness in LLM-generated code remains challenging and often requires costly manual review. Verifiable code generation -- jointly generating code, specifications, and proofs of code-specification alignment -- offers a promising path to address this limitation and further unleash LLMs' benefits in coding. Yet, there exists a significant gap in evaluation: current benchmarks often lack support for end-to-end verifiable code generation. In this paper, we introduce Verina (Verifiable Code Generation Arena), a high-quality benchmark enabling a comprehensive and modular evaluation of code, specification, and proof generation as well as their compositions. Verina consists of 189 manually curated coding tasks in Lean, with detailed problem descriptions, reference implementations, formal specifications, and extensive test suites. Our extensive evaluation of state-of-the-art LLMs reveals significant challenges in verifiable code generation, especially in proof generation, underscoring the need for improving LLM-based theorem provers in verification domains. The best model, OpenAI o4-mini, generates only 61.4% correct code, 51.0% sound and complete specifications, and 3.6% successful proofs, with one trial per task. We hope Verina will catalyze progress in verifiable code generation by providing a rigorous and comprehensive benchmark. We release our dataset on https://huggingface.co/datasets/sunblaze-ucb/verina and our evaluation code on https://github.com/sunblaze-ucb/verina.
The intense growth of quantum computation and communication allows the development of advanced solutions and services. Networked quantum services are provided for the users via quantum computers and quantum networking. Here, we review the fundamental concepts and recent achievements of networked quantum services. We present a comprehensive study of the state of the art, the different technologies, platforms and applications. We analyze the implementation basis and identify key challenges.
Improving Multilingual Social Media Insights: Aspect-based Comment Analysis
2025-05-29
Longyin Zhang, Bowei Zou, Ai Ti Aw
The inherent nature of social media posts, characterized by the freedom of language use with a disjointed array of diverse opinions and topics, poses significant challenges to downstream NLP tasks such as comment clustering, comment summarization, and social media opinion analysis. To address this, we propose a granular level of identifying and generating aspect terms from individual comments to guide model attention. Specifically, we leverage multilingual large language models with supervised fine-tuning for comment aspect term generation (CAT-G), further aligning the model's predictions with human expectations through DPO. We demonstrate the effectiveness of our method in enhancing the comprehension of social media discourse on two NLP tasks. Moreover, this paper contributes the first multilingual CAT-G test set on English, Chinese, Malay, and Bahasa Indonesian. As LLM capabilities vary among languages, this test set allows for a comparative analysis of performance across languages with varying levels of LLM proficiency.
Can Modern NLP Systems Reliably Annotate Chest Radiography Exams? A Pre-Purchase Evaluation and Comparative Study of Solutions from AWS, Google, Azure, John Snow Labs, and Open-Source Models on an Independent Pediatric Dataset
2025-05-29
Shruti Hegde, Mabon Manoj Ninan, Jonathan R. Dillman, Shireen Hayatghaibi, Lynn Babcock, Elanchezhian Somasundaram
General-purpose clinical natural language processing (NLP) tools are increasingly used for the automatic labeling of clinical reports. However, independent evaluations for specific tasks, such as pediatric chest radiograph (CXR) report labeling, are limited. This study compares four commercial clinical NLP systems - Amazon Comprehend Medical (AWS), Google Healthcare NLP (GC), Azure Clinical NLP (AZ), and SparkNLP (SP) - for entity extraction and assertion detection in pediatric CXR reports. Additionally, CheXpert and CheXbert, two dedicated chest radiograph report labelers, were evaluated on the same task using CheXpert-defined labels. We analyzed 95,008 pediatric CXR reports from a large academic pediatric hospital. Entities and assertion statuses (positive, negative, uncertain) from the findings and impression sections were extracted by the NLP systems, with impression section entities mapped to 12 disease categories and a No Findings category. CheXpert and CheXbert extracted the same 13 categories. Outputs were compared using Fleiss Kappa and accuracy against a consensus pseudo-ground truth. Significant differences were found in the number of extracted entities and assertion distributions across NLP systems. SP extracted 49,688 unique entities, GC 16,477, AZ 31,543, and AWS 27,216. Assertion accuracy across models averaged around 62%, with SP highest (76%) and AWS lowest (50%). CheXpert and CheXbert achieved 56% accuracy. Considerable variability in performance highlights the need for careful validation and review before deploying NLP tools for clinical report labeling.
CHEX-MATE: The Impact of Triaxiality and Orientation on Planck SZ Cluster Selection and Weak Lensing Mass Measurements
2025-05-29
H. Saxena, J. Sayers, A. Gavidia, J. B. Melin, E. T. Lau, J. Kim, L. Chappuis, D. Eckert, S. Ettori, M. Gaspari, F. Gastaldello, S. Kay, L. Lovisari, F. Oppizzi, M. D. Petris, G. W. Pratt, E. Pointecouteau, E. Rasia, M. Rossetti, M. Sereno
Galaxy cluster abundance measurements are a valuable tool for constraining cosmological parameters like the mass density ($\Omega_m$) and density fluctuation amplitude ($\sigma_8$). Wide area surveys detect clusters based on observables, such as the total integrated Sunyaev-Zel'dovich effect signal ($Y_{SZ}$) in the case of Planck. Quantifying the survey selection function is necessary for a cosmological analysis, with completeness representing the probability of detecting a cluster as a function of its intrinsic properties. Employing a Monte-Carlo method, we inject triaxial cluster profiles into random positions within the Planck all-sky maps, and subsequently determine the completeness of the Planck-selected CHEXMATE sample as a function of both geometry and SZ brightness. This is then used to generate 1000 mock CHEX-MATE cluster catalogs, and the distribution of shapes and orientations of the detected clusters, along with any associated bias in weak lensing-derived mass ($M_{WL}$) due to this orientation-dependent selection, denoted as $1 - b_{\chi}$, is obtained. We show that cluster orientation impacts completeness, with a higher probability of detecting clusters elongated along the line of sight (LOS). This leads to $1 - b_{\chi}$ values of $0-4\%$ for CHEXMATE clusters relative to a random population. The largest increase in $M_{WL}$ is observed in the lowest mass objects, which are most impacted by orientation-related selection bias. This bias is relevant for upcoming SZ surveys like CMB-S4, and should be considered for surveys utilizing other probes for cluster detection, such as Euclid.
Vision-Based Assistive Technologies for People with Cerebral Visual Impairment: A Review and Focus Study
2025-05-29
Bhanuka Gamage, Leona Holloway, Nicola McDowell, Thanh-Toan Do, Nicholas Price, Arthur Lowery, Kim Marriott
Over the past decade, considerable research has investigated Vision-Based Assistive Technologies (VBAT) to support people with vision impairments to understand and interact with their immediate environment using machine learning, computer vision, image enhancement, and/or augmented/virtual reality. However, this has almost totally overlooked a growing demographic: people with Cerebral Visual Impairment (CVI). Unlike ocular vision impairments, CVI arises from damage to the brain's visual processing centres. Through a scoping review, this paper reveals a significant research gap in addressing the needs of this demographic. Three focus studies involving 7 participants with CVI explored the challenges, current strategies, and opportunities for VBAT. We also discussed the assistive technology needs of people with CVI compared with ocular low vision. Our findings highlight the opportunity for the Human-Computer Interaction and Assistive Technologies research community to explore and address this underrepresented domain, thereby enhancing the quality of life for people with CVI.
Free Lunch for User Experience: Crowdsourcing Agents for Scalable User Studies
2025-05-29
Siyang Liu, Sahand Sabour, Xiaoyang Wang, Rada Mihalcea
We demonstrate the potential of anthropomorphized language agents to generate budget-friendly, moderate-fidelity, yet sufficiently insightful user experiences at scale, supporting fast, early-stage prototyping. We explore this through the case of prototyping Large Language Model-driven non-player characters (NPCs). We present Agentic H-CI, a framework that mirrors traditional user research processes-surveying, screening, experiencing, and collecting feedback and insights-with simulated agents. Using this approach, we easily construct a team of 240 player agents with a balanced range of player types and personality traits, at extremely low cost (\$0.28/player) and minimal time commitment (6.9 minutes/player). Content analysis shows that agent-based players behave in ways aligned with their simulated backgrounds, achieving 82.5\% alignment with designated profiles. From their interactions, we distill 11 user insights and 6 design implications to guide further development. To evaluate practical value, we conduct parallel user studies with human participants recruited locally and via crowdsourcing. Ratings from three professional game developers show that the agentic player team offers a Pareto-optimal and well-balanced trade-off across fidelity, cost, time efficiency, and insight helpfulness.
Evaluating Driver Perceptions of Integrated Safety Monitoring Systems for Alcohol Impairment and Distraction
2025-05-29
RoshikNagaSai Patibandla, Ross Greer
The increasing number of accidents caused by alcohol-impaired driving has prompted the development of integrated safety systems in vehicles to monitor driver behavior and prevent crashes. This paper explores how drivers perceive these systems, focusing on their comfort, trust, privacy concerns, and willingness to adopt the technology. Through a survey of 115 U.S. participants, the study reveals a preference for non-intrusive systems, such as those monitoring eye movements, over more restrictive technologies like alcohol detection devices. Privacy emerged as a major concern, with many participants preferring local data processing and anonymity. Trust in these systems was crucial for acceptance, as drivers are more likely to adapt their behavior when they believe the system is accurate and reliable. To encourage adoption, it is important to address concerns about privacy and balance the benefits of safety with personal freedom. By improving transparency, ensuring reliability, and increasing public awareness, these systems could play a significant role in reducing road accidents and improving safety.
The remarkable growth in large language model (LLM) capabilities has spurred exploration into multi-agent systems, with debate frameworks emerging as a promising avenue for enhanced problem-solving. These multi-agent debate (MAD) approaches, where agents collaboratively present, critique, and refine arguments, potentially offer improved reasoning, robustness, and diverse perspectives over monolithic models. Despite prior studies leveraging MAD, a systematic understanding of its effectiveness compared to self-agent methods, particularly under varying conditions, remains elusive. This paper seeks to fill this gap by conceptualizing MAD as a test-time computational scaling technique, distinguished by collaborative refinement and diverse exploration capabilities. We conduct a comprehensive empirical investigation comparing MAD with strong self-agent test-time scaling baselines on mathematical reasoning and safety-related tasks. Our study systematically examines the influence of task difficulty, model scale, and agent diversity on MAD's performance. Key findings reveal that, for mathematical reasoning, MAD offers limited advantages over self-agent scaling but becomes more effective with increased problem difficulty and decreased model capability, while agent diversity shows little benefit. Conversely, for safety tasks, MAD's collaborative refinement can increase vulnerability, but incorporating diverse agent configurations facilitates a gradual reduction in attack success through the collaborative refinement process. We believe our findings provide critical guidance for the future development of more effective and strategically deployed MAD systems.
Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents
2025-05-29
Jenny Zhang, Shengran Hu, Cong Lu, Robert Lange, Jeff Clune
Today's AI systems have human-designed, fixed architectures and cannot autonomously and continuously improve themselves. The advance of AI could itself be automated. If done safely, that would accelerate AI development and allow us to reap its benefits much sooner. Meta-learning can automate the discovery of novel algorithms, but is limited by first-order improvements and the human design of a suitable search space. The G\"odel machine proposed a theoretical alternative: a self-improving AI that repeatedly modifies itself in a provably beneficial manner. Unfortunately, proving that most changes are net beneficial is impossible in practice. We introduce the Darwin G\"odel Machine (DGM), a self-improving system that iteratively modifies its own code (thereby also improving its ability to modify its own codebase) and empirically validates each change using coding benchmarks. Inspired by Darwinian evolution and open-endedness research, the DGM maintains an archive of generated coding agents. It grows the archive by sampling an agent from it and using a foundation model to create a new, interesting, version of the sampled agent. This open-ended exploration forms a growing tree of diverse, high-quality agents and allows the parallel exploration of many different paths through the search space. Empirically, the DGM automatically improves its coding capabilities (e.g., better code editing tools, long-context window management, peer-review mechanisms), increasing performance on SWE-bench from 20.0% to 50.0%, and on Polyglot from 14.2% to 30.7%. Furthermore, the DGM significantly outperforms baselines without self-improvement or open-ended exploration. All experiments were done with safety precautions (e.g., sandboxing, human oversight). The DGM is a significant step toward self-improving AI, capable of gathering its own stepping stones along paths that unfold into endless innovation.
MOA-2022-BLG-091Lb and KMT-2024-BLG-1209Lb: Microlensing planets detected through weak caustic-crossing signals
2025-05-29
Cheongho Han, Chung-Uk Lee, Andrzej Udalski, Ian A. Bond, Hongjing Yang, Michael D. Albrow, Sun-Ju Chung, Andrew Gould, Youn Kil Jung, Kyu-Ha Hwang, Yoon-Hyun Ryu, Yossi Shvartzvald, In-Gu Shin, Jennifer C. Yee, Weicheng Zang, Tanagodchaporn Inyanya, Sang-Mok Cha, Doeon Kim, Dong-Jin Kim, Seung-Lee Kim, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge, Przemek Mróz, Michał K. Szymański, Jan Skowron, Radosław Poleski, Igor Soszyński, Paweł Pietrukowicz, Szymon Kozłowski, Krzysztof A. Rybicki, Patryk Iwanek, Krzysztof Ulaczyk, Marcin Wrona, Mariusz Gromadzki, Mateusz J. Mróz, Michał Jaroszyński, Marcin Kiraga, Fumio Abe, Ken Bando, David P. Bennett, Aparna Bhattacharya, Akihiko Fukui, Ryusei Hamada, Shunya Hamada, Naoto Hamasaki, Yuki Hirao, Stela Ishitani Silva, Naoki Koshimoto, Yutaka Matsubara, Shota Miyazaki, Yasushi Muraki, Tutumi Nagai, Kansuke Nunota, Greg Olmschenk, Clément Ranc, Nicholas J. Rattenbury, Yuki Satoh, Takahiro Sumi, Daisuke Suzuki, Sean K. Terry, Paul J. Tristram, Aikaterini Vandorou, Hibiki Yama, Yunyi Tang, Shude Mao, Dan Maoz, Wei Zhu
The light curves of the microlensing events MOA-2022-BLG-091 and KMT-2024-BLG-1209 exhibit anomalies with very similar features. These anomalies appear near the peaks of the light curves, where the magnifications are moderately high, and are distinguished by weak caustic-crossing features with minimal distortion while the source remains inside the caustic. To achieve a deeper understanding of these anomalies, we conducted a comprehensive analysis of the lensing events. We carried out binary-lens modeling with a thorough exploration of the parameter space. This analysis revealed that the anomalies in both events are of planetary origin, although their exact interpretation is complicated by different types of degeneracy. In the case of MOA-2022-BLG-091, the main difficulty in the interpretation of the anomaly arises from a newly identified degeneracy related to the uncertain angle at which the source trajectory intersects the planet-host axis. For KMT-2024-BLG-1209, the interpretation is affected by the previously known inner-outer degeneracy, which leads to ambiguity between solutions in which the source passes through either the inner or outer caustic region relative to the planet host. Bayesian analysis indicates that the planets in both lens systems are giant planets with masses about 2 to 4 times that of Jupiter, orbiting early K-type main-sequence stars. Both systems are likely located in the Galactic disk at a distance of around 4 kiloparsecs. The degeneracy in KMT-2024-BLG-1209 is challenging to resolve because it stems from intrinsic similarities in the caustic structures of the degenerate solutions. In contrast, the degeneracy in MOA-2022-BLG-091, which occurs by chance rather than from inherent characteristics, is expected to be resolved by the future space based Roman RGES microlensing survey.
Built environment auditing refers to the systematic documentation and assessment of urban and rural spaces' physical, social, and environmental characteristics, such as walkability, road conditions, and traffic lights. It is used to collect data for the evaluation of how built environments impact human behavior, health, mobility, and overall urban functionality. Traditionally, built environment audits were conducted using field surveys and manual observations, which were time-consuming and costly. The emerging street view imagery, e.g., Google Street View, has become a widely used data source for conducting built environment audits remotely. Deep learning and computer vision techniques can extract and classify objects from street images to enhance auditing productivity. Before meaningful analysis, the detected objects need to be geospatially mapped for accurate documentation. However, the mapping methods and tools based on street images are underexplored, and there are no universal frameworks or solutions yet, imposing difficulties in auditing the street objects. In this study, we introduced an open source street view mapping framework, providing three pipelines to map and measure: 1) width measurement for ground objects, such as roads; 2) 3D localization for objects with a known dimension (e.g., doors and stop signs); and 3) diameter measurements (e.g., street trees). These pipelines can help researchers, urban planners, and other professionals automatically measure and map target objects, promoting built environment auditing productivity and accuracy. Three case studies, including road width measurement, stop sign localization, and street tree diameter measurement, are provided in this paper to showcase pipeline usage.
A Hetero-functional Graph Theory Perspective of Engineering Management of Mega-Projects
2025-05-29
Amirreza Hosseini, Amro M. Farid
Megaprojects are large-scale, complex, and one-off engineering endeavors that require significant investments from a public or private sector. Such projects generally cost more than a billion dollars, take many years to develop and construct, involve stakeholders both in the public and private sectors, and impact millions of people. Most of the extant megaproject research is concerned with understanding why the engineering management of megaprojects fails so frequently and which dimensions make them so difficult to manage, including size, uncertainty, complexity, urgency, and institutional structure \cite{denicol:2020:00}. Recently, the literature on mega-projects has advocated for a convergence of the engineering management and production system management literature. To that end, this paper proposes the use of Model-Based System Engineering (MBSE) and Hetero-Functional Graph Theory (HFGT), where the latter, quite interestingly, finds its origins in the mass-customized production system literature. More specifically, HFGT was developed so that the physical and informatic parts of production system planning, operations, and decision-making are readily reconfigured to support production customization at scale. As the literature on megaprojects is rapidly evolving with a significant amount of divergence between authors, this report builds upon the recent and extensive megaproject literature review provided by Denicol et. al. \cite{denicol:2020:00}. The paper concludes that MBSE and HFGT provide a means for addressing many of the concluding recommendations provided by Denicol et. al. MBSE and HFGT not only align with current research on megaprojects but also push the boundaries of how the engineering management of megaprojects can gain a unified theoretical foundation.
MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking
2025-05-29
Numair Nadeem, Muhammad Hamza Asad, Saeed Anwar, Abdul Bais
Semantic segmentation of crops and weeds is crucial for site-specific farm management; however, most existing methods depend on labor intensive pixel-level annotations. A further challenge arises when models trained on one field (source domain) fail to generalize to new fields (target domain) due to domain shifts, such as variations in lighting, camera setups, soil composition, and crop growth stages. Unsupervised Domain Adaptation (UDA) addresses this by enabling adaptation without target-domain labels, but current UDA methods struggle with occlusions and visual blending between crops and weeds, leading to misclassifications in real-world conditions. To overcome these limitations, we introduce MaskAdapt, a novel approach that enhances segmentation accuracy through multimodal contextual learning by integrating RGB images with features derived from depth data. By computing depth gradients from depth maps, our method captures spatial transitions that help resolve texture ambiguities. These gradients, through a cross-attention mechanism, refines RGB feature representations, resulting in sharper boundary delineation. In addition, we propose a geometry-aware masking strategy that applies horizontal, vertical, and stochastic masks during training. This encourages the model to focus on the broader spatial context for robust visual recognition. Evaluations on real agricultural datasets demonstrate that MaskAdapt consistently outperforms existing State-of-the-Art (SOTA) UDA methods, achieving improved segmentation mean Intersection over Union (mIOU) across diverse field conditions.
HoneySat: A Network-based Satellite Honeypot Framework
2025-05-29
Efrén López-Morales, Ulysse Planta, Gabriele Marra, Carlos González, Jacob Hopkins, Majid Garoosi, Elías Obreque, Carlos Rubio-Medrano, Ali Abbasi
Satellites are the backbone of several mission-critical services, such as GPS that enable our modern society to function. For many years, satellites were assumed to be secure because of their indecipherable architectures and the reliance on security by obscurity. However, technological advancements have made these assumptions obsolete, paving the way for potential attacks, and sparking a renewed interest in satellite security. Unfortunately, to this day, there is no efficient way to collect data on adversarial techniques for satellites, which severely hurts the generation of security intelligence. In this paper, we present HoneySat, the first high-interaction satellite honeypot framework, which is fully capable of convincingly simulating a real-world CubeSat, a type of Small Satellite (SmallSat) widely used in practice. To provide evidence of the effectiveness of HoneySat, we surveyed experienced SmallSat operators currently in charge of active in-orbit satellite missions. Results revealed that the majority of satellite operators (71.4%) agreed that HoneySat provides realistic and engaging simulations of CubeSat missions. Further experimental evaluations also showed that HoneySat provides adversaries with extensive interaction opportunities by supporting the majority of adversarial techniques (86.8%) and tactics (100%) that target satellites. Additionally, we also obtained a series of real interactions from actual adversaries by deploying HoneySat on the internet over several months, confirming that HoneySat can operate covertly and efficiently while collecting highly valuable interaction data.
Preemptive Hallucination Reduction: An Input-Level Approach for Multimodal Language Model
2025-05-29
Nokimul Hasan Arif, Shadman Rabby, Md Hefzul Hossain Papon, Sabbir Ahmed
Visual hallucinations in Large Language Models (LLMs), where the model generates responses that are inconsistent with the visual input, pose a significant challenge to their reliability, particularly in contexts where precise and trustworthy outputs are critical. Current research largely emphasizes post-hoc correction or model-specific fine-tuning strategies, with limited exploration of preprocessing techniques to address hallucination issues at the input stage. This study presents a novel ensemble-based preprocessing framework that adaptively selects the most appropriate filtering approach -- noise reduced (NR), edge enhanced (EE), or unaltered input (org) based on the type of question posed, resulting into reduced hallucination without requiring any modifications to the underlying model architecture or training pipeline. Evaluated on the `HaloQuest' dataset -- a benchmark designed to test multimodal reasoning on visually complex inputs, our method achieves a 44.3% reduction in hallucination rates, as measured by Natural Language Inference (NLI) scores using SelfCheckGPT. This demonstrates that intelligent input conditioning alone can significantly enhance factual grounding in LLM responses. The findings highlight the importance of adaptive preprocessing techniques in mitigating hallucinations, paving the way for more reliable multimodal systems capable of addressing real-world challenges.
PolicyPulse: LLM-Synthesis Tool for Policy Researchers
2025-05-29
Maggie Wang, Ella Colby, Jennifer Okwara, Varun Nagaraj Rao, Yuhan Liu, Andrés Monroy-Hernández
Public opinion shapes policy, yet capturing it effectively to surface diverse perspectives remains challenging. This paper introduces PolicyPulse, an LLM-powered interactive system that synthesizes public experiences from online community discussions to help policy researchers author memos and briefs, leveraging curated real-world anecdotes. Given a specific topic (e.g., "Climate Change"), PolicyPulse returns an organized list of themes (e.g., "Biodiversity Loss" or "Carbon Pricing"), supporting each theme with relevant quotes from real-life anecdotes. We compared PolicyPulse outputs to authoritative policy reports. Additionally, we asked 11 policy researchers across multiple institutions in the Northeastern U.S to compare using PolicyPulse with their expert approach. We found that PolicyPulse's themes aligned with authoritative reports and helped spark research by analyzing existing data, gathering diverse experiences, revealing unexpected themes, and informing survey or interview design. Participants also highlighted limitations including insufficient demographic context and data verification challenges. Our work demonstrates how AI-powered tools can help influence policy-relevant research and shape policy outcomes.
The role of bars in triggering active galactic nuclei galaxies
2025-05-29
V. Marels, V. Mesa, M. Jaque Arancibia, S. Alonso, G. Coldwell, G. Damke, V. Contreras Rojas
Bars are considered an efficient mechanism for transporting gas toward the central regions of galaxies, potentially enhancing nuclear activity. However, the extent to which bars influence active galactic nuclei (AGNs), and whether their efficiency varies with environment, remain open questions. In this study, we aim to quantify the role of bars in triggering AGNs by comparing the AGN fraction in barred and non-barred galaxies across different environments. We constructed a sample from the Galaxy Zoo DECaLS catalog, ensuring a control selection where both samples share similar distributions in stellar mass, redshift, magnitude, concentration index, and local density parameter. AGNs were identified using spectroscopic data from the Sloan Digital Sky Survey, yielding 1330 barred AGNs and 1651 unbarred AGNs. We use the [OIII]5007 luminosity (Lum[OIII]) and the accretion rate parameter R as indicators of nuclear activity. Based on these, we applied criteria to distinguish powerful from weak AGNs, allowing a more precise assessment of the bar's impact on the supermassive black hole. Our analysis reveals that barred galaxies tend to host a higher fraction of powerful AGNs. From Lum[OIII], we find that more active nuclei reside in massive, blue galaxies with young stellar populations. We also observe a slight tendency for barred galaxies to host less massive black holes accreting more efficiently. The classification of strong and weak bars shows that more prominent bars correlate with higher nuclear activity. While this trend shows no significant differences in intermediate-density environments, it becomes evident in both low- and high-density regions, where galaxies with strong bars show enhanced AGN activity.
Leveraging Auxiliary Information in Text-to-Video Retrieval: A Review
2025-05-29
Adriano Fragomeni, Dima Damen, Michael Wray
Text-to-Video (T2V) retrieval aims to identify the most relevant item from a gallery of videos based on a user's text query. Traditional methods rely solely on aligning video and text modalities to compute the similarity and retrieve relevant items. However, recent advancements emphasise incorporating auxiliary information extracted from video and text modalities to improve retrieval performance and bridge the semantic gap between these modalities. Auxiliary information can include visual attributes, such as objects; temporal and spatial context; and textual descriptions, such as speech and rephrased captions. This survey comprehensively reviews 81 research papers on Text-to-Video retrieval that utilise such auxiliary information. It provides a detailed analysis of their methodologies; highlights state-of-the-art results on benchmark datasets; and discusses available datasets and their auxiliary information. Additionally, it proposes promising directions for future research, focusing on different ways to further enhance retrieval performance using this information.
SwingArena: Competitive Programming Arena for Long-context GitHub Issue Solving
2025-05-29
Wendong Xu, Jing Xiong, Chenyang Zhao, Qiujiang Chen, Haoran Wang, Hui Shen, Zhongwei Wan, Jianbo Dai, Taiqiang Wu, He Xiao, Chaofan Tao, Z. Morley Mao, Ying Sheng, Zhijiang Guo, Hongxia Yang, Bei Yu, Lingpeng Kong, Quanquan Gu, Ngai Wong
We present SwingArena, a competitive evaluation framework for Large Language Models (LLMs) that closely mirrors real-world software development workflows. Unlike traditional static benchmarks, SwingArena models the collaborative process of software iteration by pairing LLMs as submitters, who generate patches, and reviewers, who create test cases and verify the patches through continuous integration (CI) pipelines. To support these interactive evaluations, we introduce a retrieval-augmented code generation (RACG) module that efficiently handles long-context challenges by providing syntactically and semantically relevant code snippets from large codebases, supporting multiple programming languages (C++, Python, Rust, and Go). This enables the framework to scale across diverse tasks and contexts while respecting token limitations. Our experiments, using over 400 high-quality real-world GitHub issues selected from a pool of 2,300 issues, show that models like GPT-4o excel at aggressive patch generation, whereas DeepSeek and Gemini prioritize correctness in CI validation. SwingArena presents a scalable and extensible methodology for evaluating LLMs in realistic, CI-driven software development settings. More details are available on our project page: swing-bench.github.io
Estimating Head Motion in Structural MRI Using a Deep Neural Network Trained on Synthetic Artifacts
2025-05-29
Charles Bricout, Samira Ebrahimi Kahou, Sylvain Bouix
Motion-related artifacts are inevitable in Magnetic Resonance Imaging (MRI) and can bias automated neuroanatomical metrics such as cortical thickness. Manual review cannot objectively quantify motion in anatomical scans, and existing automated approaches often require specialized hardware or rely on unbalanced noisy training data. Here, we train a 3D convolutional neural network to estimate motion severity using only synthetically corrupted volumes. We validate our method with one held-out site from our training cohort and with 14 fully independent datasets, including one with manual ratings, achieving a representative $R^2 = 0.65$ versus manual labels and significant thickness-motion correlations in 12/15 datasets. Furthermore, our predicted motion correlates with subject age in line with prior studies. Our approach generalizes across scanner brands and protocols, enabling objective, scalable motion assessment in structural MRI studies without prospective motion correction.
Large Language Models are widely used for content moderation but often misclassify benign comments as toxic, leading to over-sensitivity. While previous research attributes this issue primarily to the presence of offensive terms, we reveal a potential cause beyond token level: LLMs exhibit systematic topic biases in their implicit associations. Inspired by cognitive psychology's implicit association tests, we introduce Topic Association Analysis, a semantic-level approach to quantify how LLMs associate certain topics with toxicity. By prompting LLMs to generate free-form scenario imagination for misclassified benign comments and analyzing their topic amplification levels, we find that more advanced models (e.g., GPT-4 Turbo) demonstrate stronger topic stereotype despite lower overall false positive rates. These biases suggest that LLMs do not merely react to explicit, offensive language but rely on learned topic associations, shaping their moderation decisions. Our findings highlight the need for refinement beyond keyword-based filtering, providing insights into the underlying mechanisms driving LLM over-sensitivity.
2025-05-29
Lasha Berezhiani, Giordano Cintia, Valerio De Luca, Justin Khoury
The superfluid dark matter model offers an elegant solution to reconcile discrepancies between the predictions of the cold dark matter paradigm and observations on galactic scales. In this scenario, dark matter is composed of ultralight bosons with self-interactions that can undergo a superfluid phase transition in galactic environments. In this review, we explore the theoretical foundations of dark matter superfluidity, detailing the conditions required for the formation and stability of superfluid cores of astrophysical size. We examine the phenomenological consequences for galactic dynamics, including the impact on galaxy mergers, the formation of vortices, the behavior near supermassive black holes, modifications to dynamical friction, and the emergence of long-range interactions. By synthesizing theoretical developments with observational constraints, we aim to provide a comprehensive overview of the current status and future prospects of dark matter superfluidity as a viable extension of the standard cosmological model.
Black hole spectroscopy: from theory to experiment
2025-05-29
Emanuele Berti, Vitor Cardoso, Gregorio Carullo, Jahed Abedi, Niayesh Afshordi, Simone Albanesi, Vishal Baibhav, Swetha Bhagwat, José Luis Blázquez-Salcedo, Béatrice Bonga, Bruno Bucciotti, Giada Caneva Santoro, Pablo A. Cano, Collin Capano, Mark Ho-Yeuk Cheung, Cecilia Chirenti, Gregory B. Cook, Adrian Ka-Wai Chung, Marina De Amicis, Kyriakos Destounis, Oscar J. C. Dias, Walter Del Pozzo, Francisco Duque, Will M. Farr, Eliot Finch, Nicola Franchini, Kwinten Fransen, Vasco Gennari, Stephen R. Green, Scott A. Hughes, Maximiliano Isi, Xisco Jimenez Forteza, Gaurav Khanna, Fech Scen Khoo, Masashi Kimura, Badri Krishnan, Adrien Kuntz, Macarena Lagos, Rico K. L. Lo, Lionel London, Sizheng Ma, Simon Maenaut, Lorena Magaña Zertuche, Elisa Maggio, Andrea Maselli, Keefe Mitman, Hayato Motohashi, Naritaka Oshita, Costantino Pacilio, Paolo Pani, Rodrigo Panosso Macedo, Chantal Pitte, Lorenzo Pompili, Jaime Redondo-Yuste, Maurício Richartz, Antonio Riotto, Jorge E. Santos, Bangalore Sathyaprakash, Laura Sberna, Hector O. Silva, Leo C. Stein, Alexandre Toubiana, Sebastian H. Völkel, Julian Westerweck, Huan Yang, Sophia Yi, Nicolas Yunes, Hengrui Zhu
The "ringdown" radiation emitted by oscillating black holes has great scientific potential. By carefully predicting the frequencies and amplitudes of black hole quasinormal modes and comparing them with gravitational-wave data from compact binary mergers we can advance our understanding of the two-body problem in general relativity, verify the predictions of the theory in the regime of strong and dynamical gravitational fields, and search for physics beyond the Standard Model or new gravitational degrees of freedom. We summarize the state of the art in our understanding of black hole quasinormal modes in general relativity and modified gravity, their excitation, and the modeling of ringdown waveforms. We also review the status of LIGO-Virgo-KAGRA ringdown observations, data analysis techniques, and the bright prospects of the field in the era of LISA and next-generation ground-based gravitational-wave detectors.
Gravitational Bounce from the Quantum Exclusion Principle
2025-05-29
Enrique Gaztanaga, K. Sravan Kumar, Swaraj Pradhan, Michael Gabler
We investigate the fully relativistic spherical collapse model of a uniform distribution of mass $M$ with initial comoving radius $\chi_*$ and spatial curvature $k \equiv 1/\chi_k^2 \le 1/\chi_*^2$ representing an over-density or bounded perturbation within a larger background. Our model incorporates a perfect fluid with an evolving equation of state, $P = P(\rho)$, which asymptotically transitions from pressureless dust ($P = 0$) to a ground state characterized by a uniform, time-independent energy density $\rho_{\rm G}$. This transition is motivated by the quantum exclusion principle, which prevents singular collapse, as observed in supernova core-collapse explosions. We analytically demonstrate that this transition induces a gravitational bounce at a radius $R_{\rm B} = (8 \pi G \rho_{\rm G}/3)^{-1/2}$. The bounce leads to an exponential expansion phase, where $P(\rho)$ behaves effectively as an inflation potential. This model provides novel insights into black hole interiors and, when extended to a cosmological setting, predicts a small but non-zero closed spatial curvature: $ -0.07 \pm 0.02 \le \Omega_k < 0$. This lower bound follows from the requirement of $\chi_k \ge \chi_* \simeq 15.9$ Gpc to address the cosmic microwave background low quadrupole anomaly. The bounce remains confined within the initial gravitational radius $r_{\rm S} = 2GM$, which effectively acts as a cosmological constant $\Lambda$ inside $r_{\rm S}=\sqrt{3/\Lambda}$ while still appearing as a Schwarzschild black hole from an external perspective. This framework unifies the origin of inflation and dark energy, with its key observational signature being the presence of small but nonzero spatial curvature, a testable prediction for upcoming cosmological surveys.
Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents
2025-05-28
Michael Kirchhof, Gjergji Kasneci, Enkelejda Kasneci
Large-language models (LLMs) and chatbot agents are known to provide wrong outputs at times, and it was recently found that this can never be fully prevented. Hence, uncertainty quantification plays a crucial role, aiming to quantify the level of ambiguity in either one overall number or two numbers for aleatoric and epistemic uncertainty. This position paper argues that this traditional dichotomy of uncertainties is too limited for the open and interactive setup that LLM agents operate in when communicating with a user, and that we need to research avenues that enrich uncertainties in this novel scenario. We review the literature and find that popular definitions of aleatoric and epistemic uncertainties directly contradict each other and lose their meaning in interactive LLM agent settings. Hence, we propose three novel research directions that focus on uncertainties in such human-computer interactions: Underspecification uncertainties, for when users do not provide all information or define the exact task at the first go, interactive learning, to ask follow-up questions and reduce the uncertainty about the current context, and output uncertainties, to utilize the rich language and speech space to express uncertainties as more than mere numbers. We expect that these new ways of dealing with and communicating uncertainties will lead to LLM agent interactions that are more transparent, trustworthy, and intuitive.
The rise of hardware-level security threats, such as side-channel attacks, hardware Trojans, and firmware vulnerabilities, demands advanced detection mechanisms that are more intelligent and adaptive. Traditional methods often fall short in addressing the complexity and evasiveness of modern attacks, driving increased interest in machine learning-based solutions. Among these, Transformer models, widely recognized for their success in natural language processing and computer vision, have gained traction in the security domain due to their ability to model complex dependencies, offering enhanced capabilities in identifying vulnerabilities, detecting anomalies, and reinforcing system integrity. This survey provides a comprehensive review of recent advancements on the use of Transformers in hardware security, examining their application across key areas such as side-channel analysis, hardware Trojan detection, vulnerability classification, device fingerprinting, and firmware security. Furthermore, we discuss the practical challenges of applying Transformers to secure hardware systems, and highlight opportunities and future research directions that position them as a foundation for next-generation hardware-assisted security. These insights pave the way for deeper integration of AI-driven techniques into hardware security frameworks, enabling more resilient and intelligent defenses.
Attention-based Neural Network Emulators for Multi-Probe Data Vectors Part III: Modeling The Next Generation Surveys
2025-05-28
Yijie Zhu, Evan Saraivanov, Joshua A. Kable, Artemis Sofia Giannakopoulou, Amritpal Nijjar, Vivian Miranda, Marco Bonici, Tim Eifler, Elisabeth Krause
Machine learning can accelerate cosmological inferences that involve many sequential evaluations of computationally expensive data vectors. Previous works in this series have examined how machine learning architectures impact emulator accuracy and training time for optical shear and galaxy clustering 2-point function. In this final manuscript, we explore neural network performance when emulating Cosmic Microwave Background temperature and polarization power spectra. We maximize the volume of applicability in the parameter space of our emulators within the standard $\Lambda$-cold-dark-matter model while ensuring that errors are below cosmic variance. Relative to standard multi-layer perceptron architectures, we find the dot-product-attention mechanism reduces the number of outliers among testing cosmologies, defined as the fraction of testing points with $\Delta \chi^2 > 0.2$ relative to \textsc{CAMB} outputs, for a wide range of training set sizes. Such precision enables attention-based emulators to be directly applied to real data without requiring any additional correction via importance sampling. Combined with pre-processing techniques and optimized activation and loss functions, attention-based models can meet the precision criteria set by current and future CMB and lensing experiments. For each of Planck, Simons Observatory, CMB S4, and CMB HD, we find the fraction of outlier points to be less than $10\%$ with around $2\times10^5$ to $4\times10^5$ training data vectors. We further explore the applications of these methods to supernova distance, weak lensing, and galaxy clustering, as well as alternative architectures and pre-processing techniques.
DES-LOC: Desynced Low Communication Adaptive Optimizers for Training Foundation Models
2025-05-28
Alex Iacob, Lorenzo Sani, Mher Safaryan, Paris Giampouras, Samuel Horváth, Andrej Jovanovic, Meghdad Kurmanji, Preslav Aleksandrov, William F. Shen, Xinchi Qiu, Nicholas D. Lane
Scaling foundation model training with Distributed Data Parallel (DDP) methods is bandwidth-limited. Existing infrequent communication methods like Local SGD were designed to synchronize only model parameters and cannot be trivially applied to adaptive optimizers due to additional optimizer states. Current approaches extending Local SGD either lack convergence guarantees or require synchronizing all optimizer states, tripling communication costs. We propose Desynced Low Communication Adaptive Optimizers (DES-LOC), a family of optimizers assigning independent synchronization periods to parameters and momenta, enabling lower communication costs while preserving convergence. Through extensive experiments on language models of up to 1.7B, we show that DES-LOC can communicate 170x less than DDP and 2x less than the previous state-of-the-art Local ADAM. Furthermore, unlike previous heuristic approaches, DES-LOC is suited for practical training scenarios prone to system failures. DES-LOC offers a scalable, bandwidth-efficient, and fault-tolerant solution for foundation model training.
The Optical Design of the Carbon Investigation(Carbon-I) Imaging Spectrometer
2025-05-28
Christine L. Bradley, Rami W. Wehbe, Matthew Smith, Sharmila Padmanabhan, Valerie Scott, David R. Thompson, Daniel W. Wilson, Pantazis Mouroulis, Robert O. Green, Christian Frankenberg
The proposed Carbon Investigation (Carbon-I) Imaging Spectrometer is designed to measure variations of greenhouse gases in Earth's atmosphere. The instrument will survey the Earth from its own spacecraft at an altitude of approximately 610 km. It will use a coarse ground sampling distance (GSD) of <400 m in global mode for land and coastal monitoring and finer 35 m GSD in target mode to sample key regions. The identification and quantification of greenhouse gases require continuous spectral sampling over the 2040-2380 nm wavelength range with <1 nm spectral sampling. The proposed design builds upon Jet Propulsion Laboratory's (JPL) experience of spaceflight Dyson imaging spectrometers to achieve spectral sampling of 0.7 nm per pixel. This paper presents the proposed Carbon-I optical design comprised of a freeform three-mirror anastigmat telescope that couples to a F/2.2, highly uniform Dyson-inspired imaging spectrometer. The high uniformity and throughput enables Carbon-I to measure Earth's greenhouse gas concentrations with unprecedented precision and spatial sampling.
A semi-classical approach to the study of the evolution of anyonic excitations--elementary particles with fractional statistics, complementing bosons and fermions--is through the Boltzmann equation for anyons. This work reviews a discretized version--a system of partial differential equations--of such a quantum equation. Trend to equilibrium is studied for a planar stationary system, as well as the spatially homogeneous system. Essential properties of the linearized operator are proven, implying that results for general steady half-space problems for the discrete Boltzmann equation in a slab geometry can be applied.
IGNIS: A Neural Network Framework for Robust Parameter Estimation in Archimedean Copulas
2025-05-28
Agnideep Aich, Ashit Baran Aich, Bruce Wade
Parameter estimation for Archimedean copulas remains a challenging problem, particularly for the recently developed A1 and A2 families that exhibit complex dependency structures. Traditional methods, such as the Method of Moments (MoM), Maximum Likelihood Estimation (MLE), and Maximum Pseudo-Likelihood (MPL), often struggle due to issues of non-monotonic relationship with dependency measures such as Kendall's tau (as in the case of A1) and numerical instability. In this paper, we present the IGNIS Network, a novel, unified neural framework that learns a direct mapping from observable dependency measures to copula parameters, thereby overcoming the limitations of classical approaches. Our approach is trained on simulated data spanning five Archimedean copula families including Clayton, Gumbel, Frank, A1, and A2, ensuring its general applicability across the entire family. Extensive simulation studies demonstrate that the IGNIS Network reduces estimation errors compared to MoM, while inherently enforcing parameter constraints through theory-guided post-processing. We further validate the practical utility of our method on diverse real-world datasets, including financial returns (AAPL-MSFT), healthcare metrics (CDC Diabetes indicators), and environmental measurements (PM2.5 air quality). Our results underscore the transformative potential of neural methods for robust and accurate dependence modeling in modern applications.
Understanding Adversarial Training with Energy-based Models
2025-05-28
Mujtaba Hussain Mirza, Maria Rosaria Briglia, Filippo Bartolucci, Senad Beadini, Giuseppe Lisanti, Iacopo Masi
We aim at using Energy-based Model (EBM) framework to better understand adversarial training (AT) in classifiers, and additionally to analyze the intrinsic generative capabilities of robust classifiers. By viewing standard classifiers through an energy lens, we begin by analyzing how the energies of adversarial examples, generated by various attacks, differ from those of the natural samples. The central focus of our work is to understand the critical phenomena of Catastrophic Overfitting (CO) and Robust Overfitting (RO) in AT from an energy perspective. We analyze the impact of existing AT approaches on the energy of samples during training and observe that the behavior of the ``delta energy' -- change in energy between original sample and its adversarial counterpart -- diverges significantly when CO or RO occurs. After a thorough analysis of these energy dynamics and their relationship with overfitting, we propose a novel regularizer, the Delta Energy Regularizer (DER), designed to smoothen the energy landscape during training. We demonstrate that DER is effective in mitigating both CO and RO across multiple benchmarks. We further show that robust classifiers, when being used as generative models, have limits in handling trade-off between image quality and variability. We propose an improved technique based on a local class-wise principal component analysis (PCA) and energy-based guidance for better class-specific initialization and adaptive stopping, enhancing sample diversity and generation quality. Considering that we do not explicitly train for generative modeling, we achieve a competitive Inception Score (IS) and Fr\'echet inception distance (FID) compared to hybrid discriminative-generative models.
In the intelligent era, the interaction between humans and intelligent systems fundamentally involves collaboration with autonomous intelligent agents. Human-AI Collaboration (HAC) represents a novel type of human-machine relationship facilitated by autonomous intelligent machines equipped with AI technologies. In this paradigm, AI agents serve not only as auxiliary tools but also as active teammates, partnering with humans to accomplish tasks collaboratively. Human-centered AI (HCAI) emphasizes that humans play critical leadership roles in the collaboration. This human-led collaboration imparts new dimensions to the human-machine relationship, necessitating innovative research perspectives, paradigms, and agenda to address the unique challenges posed by HAC. This chapter delves into the essence of HAC from the human-centered perspective, outlining its core concepts and distinguishing features. It reviews the current research methodologies and research agenda within the HAC field from the HCAI perspective, highlighting advancements and ongoing studies. Furthermore, a framework for human-centered HAC (HCHAC) is proposed by integrating these reviews and analyses. A case study of HAC in the context of autonomous vehicles is provided, illustrating practical applications and the synergistic interactions between humans and AI agents. Finally, it identifies potential future research directions aimed at enhancing the effectiveness, reliability, and ethical integration of human-centered HAC systems in diverse domains.
Science for Peace and the need for Civil Clauses at universities and civilian research institutions
2025-05-28
J. Altmann, U. Amaldi, M. Barone, A. Bassalat, M. Bona, J. Beullens, H. Brand, S. Brentjes, D. Britzger, J. Ellis, S. Franchoo, A. Giammanco, A. Glazov, C. Heck, H. Jung, S. Kraml, L. Lönnblad, M. Mangano, M. Renneberg, Th. Riebe, A. Sabio-Vera, R. Sanders, J. Scheffran, M. Schmelling, T. Schucker, T. Suzuki, A. Tanasijczuk, I. Tsakov, D. Valls-Gabaud, M. Walker
After the end of World War II, the commitment to confine scientific activities in universities and research institutions to peaceful and civilian purposes has entered, in the form of {\it Civil Clauses}, the charters of many research institutions and universities. In the wake of recent world events, the relevance and scope of such Civil Clauses has been questioned in reports issued by some governments and by the EU Commission, a development that opens the door to a possible blurring of the distinction between peaceful and military research. This paper documents the reflections stimulated by a panel discussion on this issue recently organized by the Science4Peace Forum. We review the adoptions of Civil Clauses in research organizations and institutions in various countries, present evidence of the challenges that are emerging to such Civil Clauses, and collect arguments in favour of maintaining the purely civilian and peaceful focus of public (non-military) research.
We present an overview of time-dependent transport phenomena in quantum systems, with a particular emphasis on steady-state regimes. We present the ideas after the main theoretical frameworks to study open-quantum systems out of equilibrium, that are useful to study quantum transport under time-dependent driving. We discuss the fundamentals of the key mechanisms such as dissipation, quantum pumping, noise, and energy conversion that are associated to the problem of quantum transport. Our primary focus is on electronic systems, where decades of research have established a rich theoretical foundation and a wealth of experimental realizations. Topics of interest include quantum optics with electrons, high-precision electron spectroscopy, quantum electrical metrology, and the critical role of quantum fluctuations in transport and thermodynamics. We also extend the discussion to atomic, molecular, and optical systems, as well as nanomechanical platforms, which offer complementary perspectives and are currently experiencing rapid experimental development. Finally, we examine the intersection of time-dependent transport and topological matter, a domain of active investigation. This review aims to gather the diverse approaches and emerging trends that define the current landscape of quantum transport research under time-dependent conditions, bridging theoretical insights with experimental advances across multiple physical platforms.
Prompt learning is a crucial technique for adapting pre-trained multimodal language models (MLLMs) to user tasks. Federated prompt personalization (FPP) is further developed to address data heterogeneity and local overfitting, however, it exposes personalized prompts - valuable intellectual assets - to privacy risks like prompt stealing or membership inference attacks. Widely-adopted techniques like differential privacy add noise to prompts, whereas degrading personalization performance. We propose SecFPP, a secure FPP protocol harmonizing generalization, personalization, and privacy guarantees. SecFPP employs hierarchical prompt adaptation with domain-level and class-level components to handle multi-granular data imbalance. For privacy, it uses a novel secret-sharing-based adaptive clustering algorithm for domain-level adaptation while keeping class-level components private. While theoretically and empirically secure, SecFPP achieves state-of-the-art accuracy under severe heterogeneity in data distribution. Extensive experiments show it significantly outperforms both non-private and privacy-preserving baselines, offering a superior privacy-performance trade-off.
Solvent environments play a central role in determining molecular structure, energetics, reactivity, and interfacial phenomena. However, modeling solvation from first principles remains difficult due to the complex interplay of interactions and unfavorable computational scaling of first-principles treatment with system size. Machine-learned potentials (MLPs) have recently emerged as efficient surrogates for quantum chemistry methods, offering first-principles accuracy at greatly reduced computational cost. MLPs approximate the underlying potential energy surface, enabling efficient computation of energies and forces in solvated systems, and are capable of accounting for effects such as hydrogen bonding, long-range polarization, and conformational changes. This review surveys the development and application of MLPs in solvation modeling. We summarize the theoretical basis of MLP-based energy and force predictions and present a classification of MLPs based on training targets, model types, and design choices related to architectures, descriptors, and training protocols. Integration into established solvation workflows is discussed, with case studies spanning small molecules, interfaces, and reactive systems. We conclude by outlining open challenges and future directions toward transferable, robust, and physically grounded MLPs for solvation-aware atomistic modeling.
Facial Age Estimation: A Research Roadmap for Technological and Legal Development and Deployment
2025-05-28
Richard Guest, Eva Lievens, Martin Sas, Elena Botoeva, Temitope Adeyemo, Valerie Verdoodt, Elora Fernandes, Chris Allgrove
Automated facial age assessment systems operate in either estimation mode - predicting age based on facial traits, or verification mode - confirming a claimed age. These systems support access control to age-restricted goods, services, and content, and can be used in areas like e-commerce, social media, forensics, and refugee support. They may also personalise services in healthcare, finance, and advertising. While improving technological accuracy is essential, deployment must consider legal, ethical, sociological, alongside technological factors. This white paper reviews the current challenges in deploying such systems, outlines the relevant legal and regulatory landscape, and explores future research for fair, robust, and ethical age estimation technologies.
Synaptic shot-noise triggers fast and slow global oscillations in balanced neural networks
2025-05-28
Denis S. Goldobin, Maria V. Ageeva, Matteo di Volo, Ferdinand Tixidre, Alessandro Torcini
Neural dynamics is determined by the transmission of discrete synaptic pulses (synaptic shot-noise) among neurons. However, the neural responses are usually obtained within the diffusion approximation modeling synaptic inputs as continuous Gaussian noise. Here, we present a rigorous mean-field theory that encompasses synaptic shot-noise for sparse balanced inhibitory neural networks driven by an excitatory drive. Our theory predicts new dynamical regimes, in agreement with numerical simulations, which are not captured by the classical diffusion approximation. Notably, these regimes feature self-sustained global oscillations emerging at low connectivity (in-degree) via either continuous or hysteretic transitions and characterized by irregular neural activity, as expected for balanced dynamics. For sufficiently weak (strong) excitatory drive (inhibitory feedback) the transition line displays a peculiar re-entrant shape revealing the existence of global oscillations at low and high in-degrees, separated by an asynchronous regime at intermediate levels of connectivity. The mechanisms leading to the emergence of these global oscillations are distinct: drift-driven at high connectivity and cluster activation at low connectivity. The frequency of these two kinds of global oscillations can be varied from slow (around 1 Hz) to fast (around 100 Hz), without altering their microscopic and macroscopic features, by adjusting the excitatory drive and the synaptic inhibition strength in a prescribed way. Furthermore, the cluster-activated oscillations at low in-degrees could correspond to the gamma rhythms reported in mammalian cortex and hippocampus and attributed to ensembles of inhibitory neurons sharing few synaptic connections [G. Buzsaki and X.-J. Wang, Annual Review of Neuroscience 35, 203 (2012)].
Recently, the baryon acoustic oscillations (BAO) measurements from the DESI survey have suggested hints of dynamical dark energy, challenging the standard $\Lambda $CDM model. In this work, we adopt an improved inverse distance ladder approach based on the latest cosmological data to provide a model-independent perspective, employing a global parametrization based on cosmic age (PAge). Our analysis incorporates DESI DR2 BAO measurements, cosmic chronometer (CC) data, and type Ia supernovae (SNe) observations from either the DESY5 or PantheonPlus datasets. For the DESY5+DESI DR2+CC datasets, we obtain $H_0 = 67.91 \pm 2.33~\mathrm{km~s^{-1}~Mpc^{-1}}$. This value is consistent with the Planck 2018 result, while shows $2.0 \sigma$ tension with the SH0ES measurement. Furthermore, by mapping specific cosmological models into PAge approximation parameter space $(p_{\mathrm{age}}, \eta)$, our model-independent analysis reveals a notable deviation from the $\Lambda \mathrm{CDM}$ model, as indicated by the DESY5 and DESI DR2 datasets. Finally, DESY5+DESI DR2+CC datasets provide nearly decisive evidence favoring the PAge model over the standard $\Lambda \mathrm{CDM}$ model. These findings highlight the need for further investigation into the expansion history to better understand the deviations from the $\Lambda \mathrm{CDM}$ model.
NLP for Social Good: A Survey of Challenges, Opportunities, and Responsible Deployment
2025-05-28
Antonia Karamolegkou, Angana Borah, Eunjung Cho, Sagnik Ray Choudhury, Martina Galletti, Rajarshi Ghosh, Pranav Gupta, Oana Ignat, Priyanka Kargupta, Neema Kotonya, Hemank Lamba, Sun-Joo Lee, Arushi Mangla, Ishani Mondal, Deniz Nazarova, Poli Nemkova, Dina Pisarevskaya, Naquee Rizwan, Nazanin Sabri, Dominik Stammbach, Anna Steinberg, David Tomás, Steven R Wilson, Bowen Yi, Jessica H Zhu, Arkaitz Zubiaga, Anders Søgaard, Alexander Fraser, Zhijing Jin, Rada Mihalcea, Joel R. Tetreault, Daryna Dementieva
Recent advancements in large language models (LLMs) have unlocked unprecedented possibilities across a range of applications. However, as a community, we believe that the field of Natural Language Processing (NLP) has a growing need to approach deployment with greater intentionality and responsibility. In alignment with the broader vision of AI for Social Good (Toma\v{s}ev et al., 2020), this paper examines the role of NLP in addressing pressing societal challenges. Through a cross-disciplinary analysis of social goals and emerging risks, we highlight promising research directions and outline challenges that must be addressed to ensure responsible and equitable progress in NLP4SG research.
Chain-of-Thought for Large Language Model-empowered Wireless Communications
2025-05-28
Xudong Wang, Jian Zhu, Ruichen Zhang, Lei Feng, Dusit Niyato, Jiacheng Wang, Hongyang Du, Shiwen Mao, Zhu Han
Recent advances in large language models (LLMs) have opened new possibilities for automated reasoning and decision-making in wireless networks. However, applying LLMs to wireless communications presents challenges such as limited capability in handling complex logic, generalization, and reasoning. Chain-of-Thought (CoT) prompting, which guides LLMs to generate explicit intermediate reasoning steps, has been shown to significantly improve LLM performance on complex tasks. Inspired by this, this paper explores the application potential of CoT-enhanced LLMs in wireless communications. Specifically, we first review the fundamental theory of CoT and summarize various types of CoT. We then survey key CoT and LLM techniques relevant to wireless communication and networking. Moreover, we introduce a multi-layer intent-driven CoT framework that bridges high-level user intent expressed in natural language with concrete wireless control actions. Our proposed framework sequentially parses and clusters intent, selects appropriate CoT reasoning modules via reinforcement learning, then generates interpretable control policies for system configuration. Using the unmanned aerial vehicle (UAV) network as a case study, we demonstrate that the proposed framework significantly outperforms a non-CoT baseline in both communication performance and quality of generated reasoning.
From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications
2025-05-28
Feibo Jiang, Cunhua Pan, Li Dong, Kezhi Wang, Octavia A. Dobre, Merouane Debbah
With the advent of 6G communications, intelligent communication systems face multiple challenges, including constrained perception and response capabilities, limited scalability, and low adaptability in dynamic environments. This tutorial provides a systematic introduction to the principles, design, and applications of Large Artificial Intelligence Models (LAMs) and Agentic AI technologies in intelligent communication systems, aiming to offer researchers a comprehensive overview of cutting-edge technologies and practical guidance. First, we outline the background of 6G communications, review the technological evolution from LAMs to Agentic AI, and clarify the tutorial's motivation and main contributions. Subsequently, we present a comprehensive review of the key components required for constructing LAMs. We further categorize LAMs and analyze their applicability, covering Large Language Models (LLMs), Large Vision Models (LVMs), Large Multimodal Models (LMMs), Large Reasoning Models (LRMs), and lightweight LAMs. Next, we propose a LAM-centric design paradigm tailored for communications, encompassing dataset construction and both internal and external learning approaches. Building upon this, we develop an LAM-based Agentic AI system for intelligent communications, clarifying its core components such as planners, knowledge bases, tools, and memory modules, as well as its interaction mechanisms. We also introduce a multi-agent framework with data retrieval, collaborative planning, and reflective evaluation for 6G. Subsequently, we provide a detailed overview of the applications of LAMs and Agentic AI in communication scenarios. Finally, we summarize the research challenges and future directions in current studies, aiming to support the development of efficient, secure, and sustainable next-generation intelligent communication systems.
CADReview: Automatically Reviewing CAD Programs with Error Detection and Correction
2025-05-28
Jiali Chen, Xusen Hei, HongFei Liu, Yuancheng Wei, Zikun Deng, Jiayuan Xie, Yi Cai, Li Qing
Computer-aided design (CAD) is crucial in prototyping 3D objects through geometric instructions (i.e., CAD programs). In practical design workflows, designers often engage in time-consuming reviews and refinements of these prototypes by comparing them with reference images. To bridge this gap, we introduce the CAD review task to automatically detect and correct potential errors, ensuring consistency between the constructed 3D objects and reference images. However, recent advanced multimodal large language models (MLLMs) struggle to recognize multiple geometric components and perform spatial geometric operations within the CAD program, leading to inaccurate reviews. In this paper, we propose the CAD program repairer (ReCAD) framework to effectively detect program errors and provide helpful feedback on error correction. Additionally, we create a dataset, CADReview, consisting of over 20K program-image pairs, with diverse errors for the CAD review task. Extensive experiments demonstrate that our ReCAD significantly outperforms existing MLLMs, which shows great potential in design applications.
Sub-arcsecond-resolution LOFAR observations of bright sub-millimetre galaxies in the North Ecliptic Pole field
2025-05-28
M. Bondi, I. Prandoni, M. Magliocchetti, L. Bisigello, M. Bonato, M. Giulietti, R. Scaramella, G. Brunetti, F. Vitello
Bright SMGs contribute significantly to the star formation rate (SFR) density (20-50\%) and stellar mass density ($\sim$ 30-50\%) at $z=$ 2-4 with SFRs$\ge 1000$ M$_\odot$,yr$^{-1}$ and stellar masses of $\sim 10^{11}$-$10^{12}$ M$_\odot$. The number of bright SMGs with such high SFRs is hard to reconcile with the standard models of galaxy formation and evolution. In this paper we provide evidence that, in a small sample of 12 bright SMGs, the SFRs derived from spectral energy distribution (SED) fitting are significantly higher than those obtained using low-frequency radio emission as a proxy for star formation. Using the International LOFAR Telescope (ILT), which allows imaging at 144 MHz with sub-arcsecond angular resolution, we have produced deep images of a small sample of bright SMGs in the North Ecliptic Pole (NEP) field extracted from the NEPSC2 survey. For all 12 SMGs, we find radio-emitting mid-infrared galaxies at distances from a few arcseconds down to sub-arcsecond scales from the SMG and/or the presence of a radio-emitting AGN. The SFRs derived from the radio emission of the SMG, disentangled from the AGN-related radio emission, are systematically lower by a factor of $\sim 5$ (median value) than those derived from the multi-band SED fitting. We discuss whether our assumptions might be, at least in part, responsible for the observed discrepancy. We argue that the radio-derived SFRs are not systematically underestimated but can be affected by a significant dispersion ($0.3-0.5$ dex). Considering these new SFR estimates, the offset of the specific SFR of the 12 bright SMGs from the star-forming galaxy main sequence ($\Delta\mathrm{(SSFR)}$) is significantly reduced, with all 12 bright SMGs which are only a factor of 2 more star-forming than the main sequence galaxies.
Evidence-based medicine (EBM) is at the forefront of modern healthcare, emphasizing the use of the best available scientific evidence to guide clinical decisions. Due to the sheer volume and rapid growth of medical literature and the high cost of curation, there is a critical need to investigate Natural Language Processing (NLP) methods to identify, appraise, synthesize, summarize, and disseminate evidence in EBM. This survey presents an in-depth review of 129 research studies on leveraging NLP for EBM, illustrating its pivotal role in enhancing clinical decision-making processes. The paper systematically explores how NLP supports the five fundamental steps of EBM -- Ask, Acquire, Appraise, Apply, and Assess. The review not only identifies current limitations within the field but also proposes directions for future research, emphasizing the potential for NLP to revolutionize EBM by refining evidence extraction, evidence synthesis, appraisal, summarization, enhancing data comprehensibility, and facilitating a more efficient clinical workflow.
Test-Time Immunization: A Universal Defense Framework Against Jailbreaks for (Multimodal) Large Language Models
2025-05-28
Yongcan Yu, Yanbo Wang, Ran He, Jian Liang
While (multimodal) large language models (LLMs) have attracted widespread attention due to their exceptional capabilities, they remain vulnerable to jailbreak attacks. Various defense methods are proposed to defend against jailbreak attacks, however, they are often tailored to specific types of jailbreak attacks, limiting their effectiveness against diverse adversarial strategies. For instance, rephrasing-based defenses are effective against text adversarial jailbreaks but fail to counteract image-based attacks. To overcome these limitations, we propose a universal defense framework, termed Test-time IMmunization (TIM), which can adaptively defend against various jailbreak attacks in a self-evolving way. Specifically, TIM initially trains a gist token for efficient detection, which it subsequently applies to detect jailbreak activities during inference. When jailbreak attempts are identified, TIM implements safety fine-tuning using the detected jailbreak instructions paired with refusal answers. Furthermore, to mitigate potential performance degradation in the detector caused by parameter updates during safety fine-tuning, we decouple the fine-tuning process from the detection module. Extensive experiments on both LLMs and multimodal LLMs demonstrate the efficacy of TIM.
Advances in Position-Momentum Entanglement: A Versatile Tool for Quantum Technologies
2025-05-28
Satyajeet Patil, Sebastian Töpfer, Rajshree Swarnkar, Sergio Tovar-Perez, Jonas Moos, Jorge Fuenzalida, Markus Gräfe
Position-momentum entanglement is a versatile high-dimensional resource in quantum optics. From fundamental tests of reality, to application in quantum technologies, spatial entanglement has had an increasing growth in recent years. In this review, we explore these advances, starting from the generation of spatial entanglement, followed by the different types of measurements for quantifying the entanglement, and finishing with different quantum-based applications. We conclude the review with a discussion and future perspectives on the field.
We review the notion of stable supermap from SUSY curves to a fixed target superscheme, and prove that when the target is (super)projective, stable supermaps are parameterized by an algebraic superstack with superschematic and separated diagonal. We characterize the bosonic reduction of this moduli superstack and see that it has a surjective morphism onto the moduli stack of stable maps from spin curves to the bosonic reduction of the target, whose fibers are linear schemes; for this reason, the moduli superstack of stable supermaps is not proper unless such linear schemes reduce to a point. Using Manin-Penkov-Voronov's super Grothendieck-Riemann-Roch theorem we also make a formal computation of the virtual dimension of the moduli superstack, which agrees with the characterization of the bosonic reduction just mentioned and with the dimension formula for the case of bosonic target existing in the literature.
Bayesian learning has emerged as a compelling and vital research direction in the field of structural dynamics, offering a probabilistic lens to understand and refine the analysis of complex dynamical systems. This review meticulously traces the three-decade evolution of Bayesian learning in structural dynamics, illuminating core principles, groundbreaking methodologies, and diverse applications that have significantly influenced the field. The narrative commences by delving into the basics of Bayesian theory, clarifying essential concepts, and introducing primary methods for deriving posterior distributions, with an in-depth exploration of three types: Laplace approximation, stochastic sampling, and variational inference. Subsequently, the text explores the implementation of two types of Bayesian learning in structural dynamics: physical model learning and data-centric statistical model learning. Physical model learning emphasizes inferring physical model parameters within a Bayesian framework for system identification and prediction, while statistical model learning integrates Bayesian learning methodologies into data-centric statistical modeling within probabilistic machine learning. Both types resonate across various applications, such as modal analysis, model updating, damage detection, and reliability updating, highlighting their pivotal role in enhancing comprehension of dynamical systems and decision-making. The paper also navigates obstacles by proposing ways to enhance existing Bayesian inference strategies. Distinguished from previous research, this study offers a thorough examination of both traditional and cutting-edge Bayesian methods. It not only underscores the transformative influence of Bayesian approaches but also serves as a beacon, guiding researchers in the judicious selection and refinement of suitable methods for various challenges in structural dynamics.
A Survey on Training-free Open-Vocabulary Semantic Segmentation
2025-05-28
Naomi Kombol, Ivan Martinović, Siniša Šegvić
Semantic segmentation is one of the most fundamental tasks in image understanding with a long history of research, and subsequently a myriad of different approaches. Traditional methods strive to train models up from scratch, requiring vast amounts of computational resources and training data. In the advent of moving to open-vocabulary semantic segmentation, which asks models to classify beyond learned categories, large quantities of finely annotated data would be prohibitively expensive. Researchers have instead turned to training-free methods where they leverage existing models made for tasks where data is more easily acquired. Specifically, this survey will cover the history, nuance, idea development and the state-of-the-art in training-free open-vocabulary semantic segmentation that leverages existing multi-modal classification models. We will first give a preliminary on the task definition followed by an overview of popular model archetypes and then spotlight over 30 approaches split into broader research branches: purely CLIP-based, those leveraging auxiliary visual foundation models and ones relying on generative methods. Subsequently, we will discuss the limitations and potential problems of current research, as well as provide some underexplored ideas for future study. We believe this survey will serve as a good onboarding read to new researchers and spark increased interest in the area.
Multi-agent systems based on large language models (LLMs) advance automatic task completion in various fields, where debate is a common cooperation form for agents to solve complicated problems with reasoning and cross-review to solidify answers. Assessing the individual contributions of agents within these debates is crucial for system refinement and outcome reliability. Traditional leave-one-out (LOO) method offers a clear framework for evaluating each agent's role but face challenges in LLM-based systems due to high computational costs and associated financial implications. This paper presents introspective-leave-one-out (IntrospecLOO), a simple yet effective prompting for approximation of LOO in LLM-powered multi-agent debates. IntrospecLOO introduces an additional querying round after standard debates, prompting agents to update their answers while ignoring responses from a designated agent. This strategy effectively isolates and gauges each participant's influence at a reduced query complexity compared to the original LOO approaches. Validation through experiments on three benchmark datasets confirms the effectiveness of IntrospecLOO.
Hot Rocks Survey III: A deep eclipse for LHS 1140c and a new Gaussian process method to account for correlated noise in individual pixels
2025-05-28
Mark Fortune, Neale P. Gibson, Hannah Diamond-Lowe, João M. Mendonça, Amélie Gressier, Daniel Kitzmann, Natalie H. Allen, Prune C. August, Jegug Ih, Erik Meier Valdés, Merlin Zgraggen, Lars A. Buchhave, Brice-Olivier Demory, Néstor Espinoza, Kevin Heng, Kathryn Jones, Alexander D. Rathcke
Time-series photometry at mid-infrared wavelengths is becoming a common technique to search for atmospheres around rocky exoplanets. This method constrains the brightness temperature of the planet to determine whether heat redistribution is taking place - indicative of an atmosphere - or whether the heat is reradiated from a low albedo bare rock. By observing at 15$\mu$m we are also highly sensitive to CO$_2$ absorption. We observed three eclipses of the rocky super-Earth LHS 1140c using MIRI/Imaging with the F1500W filter. We found significant variation in the initial settling ramp for these observations and identify a potential trend between detector settling and the previous filter used by MIRI. We analysed our data using aperture photometry but also developed a novel approach which joint-fits pixel light curves directly using a shared eclipse model and a flexible multi-dimensional Gaussian process which models changes in the PSF over time. We demonstrate using simulated data that our method has the ability to weight away from particular pixels which show increased systematics, allowing for the recovery of eclipse depths in a more robust and precise way. Both methods and an independent analysis detect the eclipse at $>5\sigma$ and are highly consistent with a low albedo bare rock. We recover a dayside brightness temperature of $T_\mathrm{day} = 561\pm44$ K, close to the theoretical maximum of $T_\text{day; max} = 537\pm9$ K. We rule out a wide range of atmospheric forward models to $>3\sigma$ including pure CO$_2$ atmospheres with surface pressure $\ge10$ mbar and pure H$_2$O atmospheres with surface pressure $\ge1$ bar. Our strict constraints on potential atmospheric composition, in combination with future observations of the exciting outer planet LHS 1140b, could provide a powerful benchmark to understand atmospheric escape around M dwarfs.
Post-processing of wind gusts from COSMO-REA6 with a spatial Bayesian hierarchical extreme value model
2025-05-28
Philipp Ertz, Petra Friederichs
The aim of this study is to provide a probabilistic gust analysis for the region of Germany that is calibrated with station observations and with an interpolation to unobserved locations. To this end, we develop a spatial Bayesian hierarchical model (BHM) for the post-processing of surface maximum wind gusts from the COSMO-REA6 reanalysis. Our approach uses a non-stationary extreme value distribution for the gust observations, with parameters that vary according to a linear model using COSMO-REA6 predictor variables. To capture spatial patterns in surface wind gust behavior, the regression coefficients are modeled as 2-dimensional Gaussian random fields with a constant mean and an isotropic covariance function that depends on the distance between locations. In addition, we include an elevation offset in the distance metric for the covariance function to account for the topography. This allows us to include data from mountaintop stations in the training process. The training of the BHM is carried out with an independent data set from which the data at the station to be predicted are excluded. We evaluate the spatial prediction performance at the withheld station using Brier score and quantile score, including their decomposition, and compare the performance of our BHM to climatological forecasts and a non-hierarchical, spatially constant baseline model. This is done for 109 weather stations in Germany. Compared to the spatially constant baseline model, the spatial BHM significantly improves the estimation of local gust parameters. It shows up to 5 % higher skill for prediction quantiles and provides a particularly improved skill for extreme wind gusts. In addition, the BHM improves the prediction of threshold levels at most of the stations. Although a spatially constant approach already provides high skill, our BHM further improves predictions and improves spatial consistency.
Spectral indices in active galactic nuclei as seen by Apertif and LOFAR
2025-05-28
A. M. Kutkin, R. Morganti, T. A. Oosterloo, E. A. K. Adams, H. Dénes, J. van Leeuwen, M. J. Norden, E. Orru
We present two new radio continuum images obtained with Apertif at 1.4 GHz. The images, produced with a direction-dependent calibration pipeline, cover 136 square degrees of the Lockman Hole and 24 square degrees of the ELAIS-N fields, with an average resolution of 17x12" and residual noise of 33 uJy/beam. With the improved depth of the images we found in total 63692 radio sources, many of which are detected for the first time at this frequency. With the addition of the previously published Apertif catalog for the Bootes field, we cross-match with the LOFAR deep-fields value-added catalogs at 150 MHz, resulting in a homogeneous sample of 10196 common sources with spectral index estimates, one of the largest to date. We analyze and discuss the correlations between spectral index, redshift, linear sources size, and radio luminosity, taking into account biases of flux-density-limited surveys. Our results suggest that the observed correlation between spectral index and redshift of active galactic nuclei can be attributed to the Malmquist bias reflecting an intrinsic relation between radio luminosity and the spectral index. We also find a correlation between spectral index and linear source size with more compact sources having steeper spectra.
Although existing model editing methods perform well in recalling exact edit facts, they often struggle in complex scenarios that require deeper semantic understanding rather than mere knowledge regurgitation. Leveraging the strong contextual reasoning abilities of large language models (LLMs), in-context learning (ICL) becomes a promising editing method by comprehending edit information through context encoding. However, this method is constrained by the limited context window of LLMs, leading to degraded performance and efficiency as the number of edits increases. To overcome this limitation, we propose InComeS, a flexible framework that enhances LLMs' ability to process editing contexts through explicit compression and selection mechanisms. Specifically, InComeS compresses each editing context into the key-value (KV) cache of a special gist token, enabling efficient handling of multiple edits without being restricted by the model's context window. Furthermore, specialized cross-attention modules are added to dynamically select the most relevant information from the gist pools, enabling adaptive and effective utilization of edit information. We conduct experiments on diverse model editing benchmarks with various editing formats, and the results demonstrate the effectiveness and efficiency of our method.
Real-World Modeling of Computation Offloading for Neural Networks with Early Exits and Splits
2025-05-28
Jan Danek, Zdenek Becvar, Adam Janes
We focus on computation offloading of applications based on convolutional neural network (CNN) from moving devices, such as mobile robots or autonomous vehicles, to MultiAccess Edge Computing (MEC) servers via a mobile network. In order to reduce overall CNN inference time, we design and implement CNN with early exits and splits, allowing a flexible partial or full offloading of CNN inference. Through real-world experiments, we analyze an impact of the CNN inference offloading on the total CNN processing delay, energy consumption, and classification accuracy in a practical road sign recognition task. The results confirm that offloading of CNN with early exits and splits can significantly reduce both total processing delay and energy consumption compared to full local processing while not impairing classification accuracy. Based on the results of real-world experiments, we derive practical models for energy consumption and total processing delay related to offloading of CNN with early exits and splits.
The stellar population in the SARAO MeerKAT Galactic Plane Survey
2025-05-28
Okwudili D. Egbo, David A. H. Buckley, Paul J. Groot, Francesco Cavallaro, Patrick A. Woudt, Mark A. Thompson, Mubela Mutale, Michael Bietenholz
We report on optically selected stellar candidates of SARAO MeerKAT 1.3 GHz radio continuum survey sources of the Galactic plane. Stellar counterparts to radio sources are selected by cross-matching the MeerKAT source positions with \textit{Gaia} DR3, using two approaches. The first approach evaluated the probability of chance alignments between the radio survey and \textit{Gaia} sources and used AllWISE infrared colour-colour information to select potential stellar candidates. The second approach utilized a Monte Carlo method to evaluate the cross-matching reliability probability, based on populations of known radio-emitting stars. From the combined approaches, we found 629 potential stellar counterparts, of which 169 have existing SIMBAD classifications, making it the largest Galactic plane radio-optical crossmatch sample to date. A colour-magnitude analysis of the sample revealed a diverse population of stellar objects, ranging from massive OB stars, main-sequence stars, giants, young stellar objects, emission line stars, red dwarfs and white dwarfs. Some of the proposed optical counterparts include chromospherically/coronally active stars, for example RS CVn binaries, BY Dra systems, YSOs and flare stars, which typically exhibit radio emission. Based on Gaia's low-resolution spectroscopy, some of the stars show strong H$\alpha$ emission, indicating they are magnetically active, consistent with them being radio emitters. While MeerKAT's sensitivity and survey speed make it ideal for detecting faint radio sources, its angular resolution limits accurate counterpart identification for crowded fields such as the Galactic Plane. Higher frequency, and, thereby, better spatial resolution, radio observations plus circular polarization would be required to strengthen the associations.
2025-05-28
Melrose Tia, Jezreel Sophia Lanuzo, Lei Rigi Baltazar, Marie Joy Lopez-Relente, Diwa Malaya Quiñones, Jason Albia
Traditional sentiment analysis relies on surface-level linguistic patterns and retrospective data, limiting its ability to capture the psychological and contextual drivers of human sentiment. These limitations constrain its effectiveness in applications that require predictive insight, such as policy testing, narrative framing, and behavioral forecasting. We present a robust framework for sentiment simulation using generative AI agents embedded with psychologically rich profiles. Agents are instantiated from a nationally representative survey of 2,485 Filipino respondents, combining sociodemographic information with validated constructs of personality traits, values, beliefs, and socio-political attitudes. The framework includes three stages: (1) agent embodiment via categorical or contextualized encodings, (2) exposure to real-world political and economic scenarios, and (3) generation of sentiment ratings accompanied by explanatory rationales. Using Quadratic Weighted Accuracy (QWA), we evaluated alignment between agent-generated and human responses. Contextualized encoding achieved 92% alignment in replicating original survey responses. In sentiment simulation tasks, agents reached 81%--86% accuracy against ground truth sentiment, with contextualized profile encodings significantly outperforming categorical (p < 0.0001, Cohen's d = 0.70). Simulation results remained consistent across repeated trials (+/-0.2--0.5% SD) and resilient to variation in scenario framing (p = 0.9676, Cohen's d = 0.02). Our findings establish a scalable framework for sentiment modeling through psychographically grounded AI agents. This work signals a paradigm shift in sentiment analysis from retrospective classification to prospective and dynamic simulation grounded in psychology of sentiment formation.
From Coders to Critics: Empowering Students through Peer Assessment in the Age of AI Copilots
2025-05-28
Santiago Berrezueta-Guzman, Stephan Krusche, Stefan Wagner
The rapid adoption of AI powered coding assistants like ChatGPT and other coding copilots is transforming programming education, raising questions about assessment practices, academic integrity, and skill development. As educators seek alternatives to traditional grading methods susceptible to AI enabled plagiarism, structured peer assessment could be a promising strategy. This paper presents an empirical study of a rubric based, anonymized peer review process implemented in a large introductory programming course. Students evaluated each other's final projects (2D game), and their assessments were compared to instructor grades using correlation, mean absolute error, and root mean square error (RMSE). Additionally, reflective surveys from 47 teams captured student perceptions of fairness, grading behavior, and preferences regarding grade aggregation. Results show that peer review can approximate instructor evaluation with moderate accuracy and foster student engagement, evaluative thinking, and interest in providing good feedback to their peers. We discuss these findings for designing scalable, trustworthy peer assessment systems to face the age of AI assisted coding.
Galaxy Superclusters and Their Complexes in the Cosmic Web
2025-05-28
Maret Einasto
The richest and largest structures in the cosmic web are galaxy superclusters, their complexes (associations of several almost connected very rich superclusters), and planes. Superclusters represent a special environment where the evolution of galaxies and galaxy groups and clusters differs from the evolution of these systems in a low-density environment. The richest galaxy clusters reside in superclusters. The richest superclusters in the nearby Universe form a quasiregular pattern with the characteristic distance between superclusters 120 - 140 $h^{-1}$Mpc. Moreover, superclusters in the nearby Universe lie in two huge perpendicular planes with the extent of several hundreds of megaparsecs, the Local Supercluster plane and the Dominant supercluster plane. The origin of these patterns in the supercluster distribution is not yet clear, and it is an open question whether the presence of such structures can be explained within the $\Lambda$CDM cosmological model. This review presents a brief story of superclusters, their discovery, definitions, main properties, and large-scale distribution.
ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentation
2025-05-28
Maja Stahl, Timon Ziegenbein, Joonsuk Park, Henning Wachsmuth
Training large language models (LLMs) to follow instructions has significantly enhanced their ability to tackle unseen tasks. However, despite their strong generalization capabilities, instruction-following LLMs encounter difficulties when dealing with tasks that require domain knowledge. This work introduces a specialized instruction fine-tuning for the domain of computational argumentation (CA). The goal is to enable an LLM to effectively tackle any unseen CA tasks while preserving its generalization capabilities. Reviewing existing CA research, we crafted natural language instructions for 105 CA tasks to this end. On this basis, we developed a CA-specific benchmark for LLMs that allows for a comprehensive evaluation of LLMs' capabilities in solving various CA tasks. We synthesized 52k CA-related instructions, adapting the self-instruct process to train a CA-specialized instruction-following LLM. Our experiments suggest that CA-specialized instruction fine-tuning significantly enhances the LLM on both seen and unseen CA tasks. At the same time, performance on the general NLP tasks of the SuperNI benchmark remains stable.
In this paper we study the chromatic number of the Grassmann graphs $J_q(n, m)$. We show that $\binom{n-m+1}{1}_q \leq \chi(J_q(n, m)) \leq \binom{n}{1}_q$, which is analogous to the best-known bounds for the chromatic number of the Johnson graphs $J(n, m)$. When $m = 2$, determining $\chi(J_q(n, 2))$ is equivalent to determining the smallest number of partial line parallelisms that one can partition the lines of PG$(n-1, q)$ into. We survey known results about line parallelisms and their implications for $\chi(J_q(n, 2))$. Finally, we prove that when $q$ is any power of two, and $n$ is any even integer, then $\chi(J_q(n, 2)) < 2\binom{n-1}{1}_q$.
Two-dimensional equilibrium configurations in Korteweg fluids
2025-05-28
M. Gorgone, F. Oliveri, A. Ricciardello, P. Rogolino
In this paper, after reviewing the form of the constitutive equations for a third grade Korteweg fluid, recently derived by means of an extended Liu procedure, an equilibrium problem is investigated. By considering a two--dimensional setting, it is derived a single nonlinear elliptic equation such that the equilibrium conditions are identically satisfied. Such an equation is discussed both analytically and numerically. Moreover, by considering a particular boundary value problem of Dirichlet type, some preliminary numerical solutions are presented.
Hidden Transits: TOI-2285 b is a Warmer sub-Neptune Likely with a Super-Earth Companion
2025-05-28
Akihiko Fukui
TOI-2285 b is a sub-Neptune-sized planet orbiting a nearby M dwarf, discovered through the TESS photometric survey and ground-based follow-up observations. The planet was initially reported to have an orbital period of 27.27 d, making it one of the lowest temperature sub-Neptunes transiting a bright M dwarf. However, additional TESS data reveal that its true orbital period is 13.64 d, half the original value, resulting in a warmer equilibrium temperature (358 K) than previously estimated (284 K). The misidentification likely resulted from the low signal-to-noise ratio of individual transit signals and the limited number of transits observed by TESS at that time. This case highlights the importance of carefully considering harmonic solutions for similar cases. The additional TESS data also reveal another planetary candidate with an orbital period of 9.67 d and a radius of 1.5 $R_\oplus$, requiring validation in future studies.
Mixed methods research integrates quantitative and qualitative data but faces challenges in aligning their distinct structures, particularly in examining measurement characteristics and individual response patterns. Advances in large language models (LLMs) offer promising solutions by generating synthetic survey responses informed by qualitative data. This study investigates whether LLMs, guided by personal interviews, can reliably predict human survey responses, using the Behavioral Regulations in Exercise Questionnaire (BREQ) and interviews from after-school program staff as a case study. Results indicate that LLMs capture overall response patterns but exhibit lower variability than humans. Incorporating interview data improves response diversity for some models (e.g., Claude, GPT), while well-crafted prompts and low-temperature settings enhance alignment between LLM and human responses. Demographic information had less impact than interview content on alignment accuracy. These findings underscore the potential of interview-informed LLMs to bridge qualitative and quantitative methodologies while revealing limitations in response variability, emotional interpretation, and psychometric fidelity. Future research should refine prompt design, explore bias mitigation, and optimize model settings to enhance the validity of LLM-generated survey data in social science research.
Sr$_2$NbO$_4$: A $4d$ analogue of the layered perovskite Sr$_2$VO$_4$
2025-05-28
Leonid S. Taran, Anastasia E. Lebedeva, Sergey V. Streltsov
This work focuses on the layered perovskite Sr$_2$NbO$_4$, a 4$d$ analogue of Sr$_2$VO$_4$, which remains an unsolved puzzle with a possible intriguing hidden magnetic order. Using density functional theory (DFT) calculations, we demonstrate the robust thermodynamic stability and exfoliability of Sr$_2$NbO$_4$, suggesting potential applications as a 2D material. Imperfect Fermi surface nesting indicates instabilities that may drive symmetry lowering, charge/orbital density waves, or superconductivity. Dynamical mean-field theory (DMFT) calculations reveal moderate mass renormalization $(m^*/m\sim1.3)$ and an itinerant character of magnetism with strong longitudinal spin fluctuations. The exchange interaction is dominated by in-plane ferromagnetic coupling with much weaker interlayer antiferromagnetic exchange.
Eye-Tracking and Biometric Feedback in UX Research: Measuring User Engagement and Cognitive Load
2025-05-28
Aaditya Shankar Majumder
User experience research often uses surveys and interviews, which may miss subconscious user interactions. This study explores eye-tracking and biometric feedback as tools to assess user engagement and cognitive load in digital interfaces. These methods measure gaze behavior and bodily responses, providing an objective complement to qualitative insights. Using empirical evidence, practical applications, and advancements from 2023-2025, we present experimental data, describe our methodology, and place our work within foundational and recent literature. We address challenges like data interpretation, ethical issues, and technological integration. These tools are key for advancing UX design in complex digital environments.
Understanding gas mixing in the circumgalactic medium
2025-05-28
Hilay Shah, Freeke van de Voort, Amit Seta, Christoph Federrath
We study gas mixing in a simulated Milky Way-mass galaxy's circumgalactic medium (CGM) using cosmological zoom-in simulations. We insert tracer dyes in the CGM with different gas flows (shearing, coherent, and static) and diverse physical properties to track gas mixing and find that shearing flows generally exhibit a higher dye spread. We then correlate the extent and shape of dye spread with local gas properties for a generalised understanding of gas mixing. The velocity dispersion and magnitude of traceless symmetric shear (shear deformation) tensors in small regions (<5 kpc) around the dye injection locations most accurately predict the extent of dye spread after 200 Myr (Spearman rank 0.87). We use this to determine diffusivity calibration constants for subgrid-scale mixing models. While the dye shape after 200 Myr aligns well with velocity and magnetic field dispersion, the best alignment occurs with the dispersion of eigenvectors representing stretching (from traceless symmetric shear tensor) and plane-of-rotation (from antisymmetric shear or vorticity tensor) in large regions (>10 kpc) around the dye injection locations. Therefore, shear-related statistics and velocity dispersion are the properties best able to predict both the extent and shape of mixed gas in our simulations. Our dyes spread roughly linearly with time, suggesting superdiffusive behaviour in the CGM, potentially due to turbulent and large-scale coherent flows. We calculate physical Reynolds numbers of 10^2-10^3 using gas properties in the CGM of our simulations, which a resolution of a few hundred pc could resolve, but our 1 kpc resolution leads to significant numerical mixing. However, our correlation results are robust to changes in resolution. These results can be used to predict diffusion coefficients from local gas properties to model processes like magnetic field diffusion, heat transport, and metal mixing.
2025-05-28
Robert W. Heath, Jr., Joseph Carlson, Nitish Vikas Deshpande, Miguel Rodrigo Castellanos, Mohamed Akrout, Chan-Byoung Chae
We present an evolution of multiple-input multiple-output (MIMO) wireless communications known as the tri-hybrid MIMO architecture. In this framework, the traditional operations of linear precoding at the transmitter are distributed across digital beamforming, analog beamforming, and reconfigurable antennas. Compared with the hybrid MIMO architecture, which combines digital and analog beamforming, the tri-hybrid approach introduces a third layer of electromagnetic beamforming through antenna reconfigurability. This added layer offers a pathway to scale MIMO spatial dimensions, important for 6G systems operating in centimeter-wave bands, where the tension between larger bandwidths and infrastructure reuse necessitates ultra-large antenna arrays. We introduce the key features of the tri-hybrid architecture by (i)~reviewing the benefits and challenges of communicating with reconfigurable antennas, (ii)~examining tradeoffs between spectral and energy efficiency enabled by reconfigurability, and (iii)~exploring configuration challenges across the three layers. Overall, the tri-hybrid MIMO architecture offers a new approach for integrating emerging antenna technologies in the MIMO precoding framework.
Whether the presence of planets affects the lithium (Li) abundance of their host stars is still an open question. To investigate the difference of the Li abundance between planet-host stars (HS) and isolated stars (IS) with no detected planets, we analyze a large sample of stars with temperatures ranging from 4600 to 6600 K and metallicity ranging from -0.55 to +0.50. The sample consists of 279 HS whose spectra were taken from the California-Kepler Survey (CKS), which followed up planets detected by Kepler, and 171 IS whose spectra were taken from the Keck archive. The non-local thermodynamic equilibrium (non-LTE) effects were taken into consideration. It is found that the distribution of Li abundances in both the HS and IS groups are generally consistent with each other. This suggests that the presence of Kepler-like planets does not have a significant impact on Li depletion. We also found that the non-LTE corrections can not be neglected for stars with A(Li) over ~ 2.5 dex.
Deep Extragalactic VIsible Legacy Survey (DEVILS): The sSFR-M$_{\star}$ plane part II: Starbursts, SFHs and AGN Feedback
2025-05-28
L. J. M. Davies, J. E. Thorne, S. Bellstedt, R. H. W. Cook, M. Bravo, A. S. G. Robotham, C. del P. Lagos, S. Phillipps, M. Siudek, B. W. Holwerda, M. N. Bremer, J. D'Silva, S. P. Driver
In part I of this series we discussed the variation of star-formation histories (SFHs) across the specific star formation rate - stellar mass plane (sSFR-M$_{\star}$) using the Deep Extragalactic VIsible Legacy Survey (DEVILS). Here we explore the physical mechanisms that are likely driving these observational trends, by comparing the properties of galaxies with common recent SFH shapes. Overall, we find that the processes shaping the movement of galaxies through the sSFR-M$_{\star}$ plane can be be largely split into two stellar mass regimes, bounded by the minimum SFR dispersion ($\sigma_{SFR}$) point. At lower stellar masses we find that large $\sigma_{SFR}$ values are likely observed due to a combination of stochastic star-formation processes and a large variety in absolute sSFR values, but relatively constant/flat SFHs. While at higher stellar masses we see strong observational evidence that Active Galactic Nuclei (AGN) are associated with rapidly declining SFHs, and that these galaxies reside in the high $\sigma_{SFR}$ region of the plane. As such, we suggest that AGN feedback, leading to galaxy quenching, is the primary driver of the high $\sigma_{SFR}$ values. These results are consistent with previous theoretical interpretations of the $\sigma_{SFR}$-M$_{\star}$ relation.
Deep Extragalactic VIsible Legacy Survey (DEVILS): The sSFR-M$_{\star}$plane part I: The recent SFH of galaxies and movement through the plane
2025-05-28
L. J. M. Davies, J. E. Thorne, S. Bellstedt, R. H. W. Cook, M. Bravo, A. S. G. Robotham, C. del P. Lagos, S. Phillipps, M. Siudek, B. W. Holwerda, M. N. Bremer, J. D'Silva, S. P. Driver
In a recent paper we parameterised the evolution of the star-formation rate dispersion ($\sigma_{SFR}$) across the specific star-formation rate - stellar mass plane (sSFR-M$_{\star}$) using the Deep Extragalactic VIsible Legacy Survey (DEVILS) - suggesting that the point at which the minimum in the dispersion occurs (M$^{*}_{\sigma-min}$) defines a boundary between different physical mechanisms affecting galaxy evolution. Here we expand upon that work to determine the movement of galaxies through the sSFR-M$_{\star}$ plane using their recent star-formation histories (SFHs) and explore how this leads to the observed $\sigma_{SFR}$-M$_{\star}$ relation. We find that galaxies in sub-regions of the sSFR-M$_{\star}$ plane show distinctly different SFHs, leading to a complex evolution of the sSFR-M$_{\star}$ plane and star-forming sequence (SFS). However, we find that selecting galaxies based on stellar mass and position relative to SFS alone (as is traditionally the case), may not identify sources with common recent SFHs, and therefore propose a new selection methodology. We then use the recent SFH of galaxies to measure the evolution of the SFS, showing that it has varying contributions from galaxies with different SFHs that lead to the observed changes in slope, normalisation and turnover stellar mass. Finally, we determine the overall evolution of the sSFR-M$_{\star}$ plane from $z\sim1$ to today. In the second paper in this series we will discuss physical properties of galaxies with common recent SFHs and how these lead to the observed $\sigma_{SFR}$-M$_{\star}$ relation and evolution of the sSFR-M$_{\star}$ plane.
From Reasoning to Learning: A Survey on Hypothesis Discovery and Rule Learning with Large Language Models
2025-05-28
Kaiyu He, Zhiyu Chen
Since the advent of Large Language Models (LLMs), efforts have largely focused on improving their instruction-following and deductive reasoning abilities, leaving open the question of whether these models can truly discover new knowledge. In pursuit of artificial general intelligence (AGI), there is a growing need for models that not only execute commands or retrieve information but also learn, reason, and generate new knowledge by formulating novel hypotheses and theories that deepen our understanding of the world. Guided by Peirce's framework of abduction, deduction, and induction, this survey offers a structured lens to examine LLM-based hypothesis discovery. We synthesize existing work in hypothesis generation, application, and validation, identifying both key achievements and critical gaps. By unifying these threads, we illuminate how LLMs might evolve from mere ``information executors'' into engines of genuine innovation, potentially transforming research, science, and real-world problem solving.
Causal Inference for Experiments with Latent Outcomes: Key Results and Their Implications for Design and Analysis
2025-05-28
Jiawei Fu, Donald P. Green
How should we analyze randomized experiments in which the main outcome is measured in multiple ways and each measure contains some degree of error? Since Costner (1971) and Bagozzi (1977), methodological discussions of experiments with latent outcomes have reviewed the modeling assumptions that are invoked when the quantity of interest is the average treatment effect (ATE) of a randomized intervention on a latent outcome that is measured with error. Many authors have proposed methods to estimate this ATE when multiple measures of an outcome are available. Despite this extensive literature, social scientists rarely use these modeling approaches when analyzing experimental data, perhaps because the surge of interest in experiments coincides with increased skepticism about the modeling assumptions that these methods invoke. The present paper takes a fresh look at the use of latent variable models to analyze experiments. Like the skeptics, we seek to minimize reliance on ad hoc assumptions that are not rooted in the experimental design and measurement strategy. At the same time, we think that some of the misgivings that are frequently expressed about latent variable models can be addressed by modifying the research design in ways that make the underlying assumptions defensible or testable. We describe modeling approaches that enable researchers to identify and estimate key parameters of interest, suggest ways that experimental designs can be augmented so as to make the modeling requirements more credible, and discuss empirical tests of key modeling assumptions. Simulations and an empirical application illustrate the gains in terms of precision and robustness.
AI copilots represent a new generation of AI-powered systems designed to assist users, particularly knowledge workers and developers, in complex, context-rich tasks. As these systems become more embedded in daily workflows, personalization has emerged as a critical factor for improving usability, effectiveness, and user satisfaction. Central to this personalization is preference optimization: the system's ability to detect, interpret, and align with individual user preferences. While prior work in intelligent assistants and optimization algorithms is extensive, their intersection within AI copilots remains underexplored. This survey addresses that gap by examining how user preferences are operationalized in AI copilots. We investigate how preference signals are sourced, modeled across different interaction stages, and refined through feedback loops. Building on a comprehensive literature review, we define the concept of an AI copilot and introduce a taxonomy of preference optimization techniques across pre-, mid-, and post-interaction phases. Each technique is evaluated in terms of advantages, limitations, and design implications. By consolidating fragmented efforts across AI personalization, human-AI interaction, and language model adaptation, this work offers both a unified conceptual foundation and a practical design perspective for building user-aligned, persona-aware AI copilots that support end-to-end adaptability and deployment.
Global-Local Duality of Energetic Control Cost in Multipartite Quantum Correlated Systems
2025-05-28
Rui Guan, Junjie Liu, Jian-Hua Jiang
Multipartite quantum correlated systems (MQCSs) are widely utilized in diverse quantum information tasks, where their sophisticated control inherently incurs energetic costs. However, the fundamental characteristics of these control costs remain elusive, largely due to the lack of thermodynamic descriptions capable of capturing the full complexities of MQCSs. Here, we uncover universal thermodynamic relations for arbitrary MQCSs weakly coupled to a thermal bath, establishing an intrinsic global-local duality of control costs. Using these relations, we elucidate the exact role of multipartite correlation--a defining quantum feature of MQCSs--in shaping control costs at finite times. We also demonstrate that the relative magnitude between global and local control costs is undetermined, which perplexes the cost management of MQCSs under finite-time controls. Our results are numerically corroborated with applications to experimentally realizable multi-qubit systems undergoing finite-time qubit reset processes.
Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation
2025-05-28
Yu-Lun Song, Chung-En Tsern, Che-Cheng Wu, Yu-Ming Chang, Syuan-Bo Huang, Wei-Chu Chen, Michael Chia-Liang Lin, Yu-Ta Lin
This study presents an innovative approach to urban mobility simulation by integrating a Large Language Model (LLM) with Agent-Based Modeling (ABM). Unlike traditional rule-based ABM, the proposed framework leverages LLM to enhance agent diversity and realism by generating synthetic population profiles, allocating routine and occasional locations, and simulating personalized routes. Using real-world data, the simulation models individual behaviors and large-scale mobility patterns in Taipei City. Key insights, such as route heat maps and mode-specific indicators, provide urban planners with actionable information for policy-making. Future work focuses on establishing robust validation frameworks to ensure accuracy and reliability in urban planning applications.
Broadening Our View: Assistive Technology for Cerebral Visual Impairment
2025-05-28
Bhanuka Gamage, Leona Holloway, Nicola McDowell, Thanh-Toan Do, Nicholas Seow Chiang Price, Arthur James Lowery, Kim Marriott
Over the past decade, considerable research has been directed towards assistive technologies to support people with vision impairments using machine learning, computer vision, image enhancement, and/or augmented/virtual reality. However, this has almost totally overlooked a growing demographic: people with Cerebral Visual Impairment (CVI). Unlike Ocular Vision Impairments (OVI), CVI arises from damage to the brain's visual processing centres. This paper introduces CVI and reveals a wide research gap in addressing the needs of this demographic. Through a scoping review, we identified 14 papers at the intersection of these technologies and CVI. Of these, only three papers described assistive technologies focused on people living with CVI, with the others focusing on diagnosis, understanding, simulation or rehabilitation. Our findings highlight the opportunity for the Human-Computer Interaction and Assistive Technologies research community to explore and address this underrepresented domain, thereby enhancing the quality of life for people with CVI.
Ultra High Energy Cosmic Rays, UHECR, are charged particles with energies between $\sim10^{18}\,{\rm eV}$ and $\sim3\times10^{20}\,{\rm eV}\sim50\,{\rm J}$. They exhibit fundamental physics at energies inaccessible to terrestrial accelerators, challenge experimental physics and connect strongly to astronomical observations through electromagnetic, neutrino and even gravitational wave channels. There has been much theoretical and observational progress in the sixty years that have elapsed since the discovery of UHECR, to divine their nature and identify their sources. The highest energy UHECR appear to be heavy nuclei with rigidity extending up to $\sim10\,{\rm EV}$; A significant ($6.9\sigma$) dipole anisotropy has been measured but our poor understanding of the Galactic magnetic fields makes this hard to interpret; The UHECR luminosity density is $\sim 10^{44}$ erg Mpc$^{-3}$ yr$^{-1}$ which constrains explanations of their origin; The most promising acceleration mechanisms involve diffusive shock acceleration and unipolar induction; The most promising sources include intergalactic accretion shocks, and relativistic jets from stellar-mass or supermassive black holes. We explore the prospects for using the highest energy events, combined with multimessenger astronomy, to help us solve the riddle of UHECR.
Determination of Light Curve Parameters of Poorly Studied Eclipsing Variables Using Data from Tess and Other Sky Surveys
2025-05-28
Vladyslava I. Marsakova, Ivan L. Andronov, Victoriia O. Borshchenko, Illia. A. Garbazhii-Romanchenko, Anastasiia D. Lashkova, Sofia A. Kreminska, Pavol A. Dubovsky, Vladyslav V. Dubovskyi, Karol Petrik
A group of poorly studied eclipsing variables (the classification of which is marked as uncertain and/or the period of brightness changes is uncertain) has been studied with the using of the photometric observations of the TESS mission and NSVS, ASAS-SN sky surveys. We also obtained some observations covering the brightness minima of our variables by our group using the telescopes at Astronomical Observatory on Kolonica Saddle (Slovakia) and Observatory and Planetarium in Hlohovec (Slovakia) during the "Variable-2024" astrocamp. The periods and classification were corrected. For NSV 575 and NSV 014 the periods were found for the first time, but it is doubtful that NSV 014 is an eclipsing variable, because there are no eclipses but the asymmetric wave is present, which indicates that the variable star can be re-classified as a low-amplitude pulsating one. Different methods were used for approximation of the light curves and further calculation of stellar system's parameters such as eclipse depths and durations, values of reflection effect and effect of ellipticity of stars. The initial period was estimated using the periodogram based on the trigonometrical polynomial fit of high order (up to 10). For better approximation of the complete eclipsing phase curve, the "New Algol Variable" (NAV) software was used. The methods of "asymptotic parabolas" and "wall-supported asymptotic parabolas" were used for calculation of moments of eclipses, which use only near-eclipse part of the observations instead of a complete curve. These methods were implemented in the software MAVKA among a larger set of features. For the variables NSV 489 and NSV 1884, our moments of eclipses and the ones found in the literature, were used for the O-C curves. For NSV 489, the period was adjusted taking into account the slope of the O-C diagram.
2025-05-28
Niclas Boehmer, Sara Fish, Ariel D. Procaccia
A key task in certain democratic processes is to produce a concise slate of statements that proportionally represents the full spectrum of user opinions. This task is similar to committee elections, but unlike traditional settings, the candidate set comprises all possible statements of varying lengths, and so it can only be accessed through specific queries. Combining social choice and large language models, prior work has approached this challenge through a framework of generative social choice. We extend the framework in two fundamental ways, providing theoretical guarantees even in the face of approximately optimal queries and a budget limit on the overall length of the slate. Using GPT-4o to implement queries, we showcase our approach on datasets related to city improvement measures and drug reviews, demonstrating its effectiveness in generating representative slates from unstructured user opinions.
Systematic reviews in medicine play a critical role in evidence-based decision-making by aggregating findings from multiple studies. A central bottleneck in automating this process is extracting numeric evidence and determining study-level conclusions for specific outcomes and comparisons. Prior work has framed this problem as a textual inference task by retrieving relevant content fragments and inferring conclusions from them. However, such approaches often rely on shallow textual cues and fail to capture the underlying numeric reasoning behind expert assessments. In this work, we conceptualise the problem as one of quantitative reasoning. Rather than inferring conclusions from surface text, we extract structured numerical evidence (e.g., event counts or standard deviations) and apply domain knowledge informed logic to derive outcome-specific conclusions. We develop a numeric reasoning system composed of a numeric data extraction model and an effect estimate component, enabling more accurate and interpretable inference aligned with the domain expert principles. We train the numeric data extraction model using different strategies, including supervised fine-tuning (SFT) and reinforcement learning (RL) with a new value reward model. When evaluated on the CochraneForest benchmark, our best-performing approach -- using RL to train a small-scale number extraction model -- yields up to a 21% absolute improvement in F1 score over retrieval-based systems and outperforms general-purpose LLMs of over 400B parameters by up to 9%. Our results demonstrate the promise of reasoning-driven approaches for automating systematic evidence synthesis.
2025-05-28
Andrew N. Jordan, John C. Howell, Nicholas Vamivakas, Ebrahim Karimi
This book chapter gives a selective review of physical implementations and applications of superoscillations and associated phenomena. We introduce the field by reviewing simple examples of superoscillations and showing how their existence naturally follows from the real part of the quantum mechanical weak value, which the parallel phenomena of supergrowth naturally follows from the imaginary part. Focusing on electromagnetic applications, we review the topics of superoscillation and supergrowth in speckle, creating superoscillating hot spots with patterned filters, superspectroscopic discrimination of two molecules, noise mitigation and the engineering of super behavior in point spread functions for the purpose of optical superresolution. We also cover a variety of different methods for creating superoscillatory and supergrowing functions, reviewing both mathematical and physical ways to create this class of functions, and beyond. Promising directions for future research, including superoscillations in other wave phenomena, super radar, and generalized super-phenomena in quantum physics, are outlined.
Scalable Parameter and Memory Efficient Pretraining for LLM: Recent Algorithmic Advances and Benchmarking
2025-05-28
Athanasios Glentis, Jiaxiang Li, Qiulin Shang, Andi Han, Ioannis Tsaknakis, Quan Wei, Mingyi Hong
Fueled by their remarkable ability to tackle diverse tasks across multiple domains, large language models (LLMs) have grown at an unprecedented rate, with some recent models containing trillions of parameters. This growth is accompanied by substantial computational challenges, particularly regarding the memory and compute resources required for training and fine-tuning. Numerous approaches have been explored to address these issues, such as LoRA. While these methods are effective for fine-tuning, their application to pre-training is significantly more challenging due to the need to learn vast datasets. Motivated by this issue, we aim to address the following questions: Can parameter- or memory-efficient methods enhance pre-training efficiency while achieving performance comparable to full-model training? How can the performance gap be narrowed? To this end, the contributions of this work are the following. (1) We begin by conducting a comprehensive survey that summarizes state-of-the-art methods for efficient pre-training. (2) We perform a benchmark evaluation of several representative memory efficient pre-training approaches to comprehensively evaluate their performance across model sizes. We observe that with a proper choice of optimizer and hyperparameters, full-rank training delivers the best performance, as expected. We also notice that incorporating high-rank updates in low-rank approaches is the key to improving their performance. (3) Finally, we propose two practical techniques, namely weight refactorization and momentum reset, to enhance the performance of efficient pre-training methods. We observe that applying these techniques to the low-rank method (on a 1B model) can achieve a lower perplexity than popular memory efficient algorithms such as GaLore and Fira, while simultaneously using about 25% less memory.
Scaling Offline RL via Efficient and Expressive Shortcut Models
2025-05-28
Nicolas Espinosa-Dice, Yiyi Zhang, Yiding Chen, Bradley Guo, Owen Oertell, Gokul Swamy, Kiante Brantley, Wen Sun
Diffusion and flow models have emerged as powerful generative approaches capable of modeling diverse and multimodal behavior. However, applying these models to offline reinforcement learning (RL) remains challenging due to the iterative nature of their noise sampling processes, making policy optimization difficult. In this paper, we introduce Scalable Offline Reinforcement Learning (SORL), a new offline RL algorithm that leverages shortcut models - a novel class of generative models - to scale both training and inference. SORL's policy can capture complex data distributions and can be trained simply and efficiently in a one-stage training procedure. At test time, SORL introduces both sequential and parallel inference scaling by using the learned Q-function as a verifier. We demonstrate that SORL achieves strong performance across a range of offline RL tasks and exhibits positive scaling behavior with increased test-time compute. We release the code at nico-espinosadice.github.io/projects/sorl.
Security Benefits and Side Effects of Labeling AI-Generated Images
2025-05-28
Sandra Höltervennhoff, Jonas Ricker, Maike M. Raphael, Charlotte Schwedes, Rebecca Weil, Asja Fischer, Thorsten Holz, Lea Schönherr, Sascha Fahl
Generative artificial intelligence is developing rapidly, impacting humans' interaction with information and digital media. It is increasingly used to create deceptively realistic misinformation, so lawmakers have imposed regulations requiring the disclosure of AI-generated content. However, only little is known about whether these labels reduce the risks of AI-generated misinformation. Our work addresses this research gap. Focusing on AI-generated images, we study the implications of labels, including the possibility of mislabeling. Assuming that simplicity, transparency, and trust are likely to impact the successful adoption of such labels, we first qualitatively explore users' opinions and expectations of AI labeling using five focus groups. Second, we conduct a pre-registered online survey with over 1300 U.S. and EU participants to quantitatively assess the effect of AI labels on users' ability to recognize misinformation containing either human-made or AI-generated images. Our focus groups illustrate that, while participants have concerns about the practical implementation of labeling, they consider it helpful in identifying AI-generated images and avoiding deception. However, considering security benefits, our survey revealed an ambiguous picture, suggesting that users might over-rely on labels. While inaccurate claims supported by labeled AI-generated images were rated less credible than those with unlabeled AI-images, the belief in accurate claims also decreased when accompanied by a labeled AI-generated image. Moreover, we find the undesired side effect that human-made images conveying inaccurate claims were perceived as more credible in the presence of labels.
A Unified Treatment of Some Classic Combinatorial Inequalities Using the Variance Method
2025-05-28
Douglas R. Stinson
The "variance method" has been used to prove many classical inequalities in design theory and coding theory. The purpose of this expository note is to review and present some of these inequalities in a unified setting. I will also discuss some examples from my own research where I have employed these techniques.
2-Segal sets and pseudomonoids in the bicategory of spans
2025-05-28
Sophia E Marx, Rajan Amit Mehta
In this survey article, we give an introduction to the notion of a 2-Segal set and prove that 2-Segal sets are equivalent to pseudomonoids in the bicategory of spans. The proof utilizes graphical techniques for 2-Segal sets and spans that should be useful in more general settings. There are procedures for obtaining an associative algebra from a 2-Segal set (satisfying finiteness conditions). We describe these procedures and give several examples of algebras arising from 2-Segal sets. Wherever possible, we avoid higher category theory so as to make the paper accessible to a wide audience.
Highly Efficient and Effective LLMs with Multi-Boolean Architectures
2025-05-28
Ba-Hien Tran, Van Minh Nguyen
Weight binarization has emerged as a promising strategy to drastically reduce the complexity of large language models (LLMs). It is mainly classified into two approaches: post-training binarization and finetuning with training-aware binarization methods. The first approach, while having low complexity, leads to significant loss of information from the original LLMs, resulting in poor performance. The second approach, on the other hand, relies heavily on full-precision latent weights for gradient approximation of binary weights, which not only remains suboptimal but also introduces substantial complexity. In this paper, we introduce a novel framework that effectively transforms LLMs into multi-kernel Boolean parameters, for the first time, finetunes them directly in the Boolean domain, eliminating the need for expensive latent weights. This significantly reduces complexity during both finetuning and inference. Through extensive and insightful experiments across a wide range of LLMs, we demonstrate that our method outperforms recent ultra low-bit quantization and binarization methods.
A Terminology and Quantitative Framework for Assessing the Habitability of Solar System and Extraterrestrial Worlds
2025-05-28
Daniel Apai, Rory Barnes, Matthew M. Murphy, Tim Lichtenberg, Noah Tuchow, Regis Ferriere, Kevin Wagner, Antonin Affholder, Renu Malhotra, Baptiste Journaux, Allona Vazan, Ramses Ramirez, Abel Mendez, Stephen R. Kane, Veronica H. Klawender, NExSS Quantitative Habitability Science Working Group
The search for extraterrestrial life in the Solar System and beyond is a key science driver in astrobiology, planetary science, and astrophysics. A critical step is the identification and characterization of potential habitats, both to guide the search and to interpret its results. However, a well-accepted, self-consistent, flexible, and quantitative terminology and method of assessment of habitability are lacking. Our paper fills this gap based on a three year-long study by the NExSS Quantitative Habitability Science Working Group. We reviewed past studies of habitability, but find that the lack of a universally valid definition of life prohibits a universally applicable definition of habitability. A more nuanced approach is needed. We introduce a quantitative habitability assessment framework (QHF) that enables self-consistent, probabilistic assessment of the compatibility of two models: First, a habitat model, which describes the probability distributions of key conditions in the habitat. Second, a viability model, which describes the probability that a metabolism is viable given a set of environmental conditions. We provide an open-source implementation of this framework and four examples as a proof of concept: (a) Comparison of two exoplanets for observational target prioritization; (b) Interpretation of atmospheric O2 detection in two exoplanets; (c) Subsurface habitability of Mars; and (d) Ocean habitability in Europa. These examples demonstrate that our framework can self-consistently inform astrobiology research over a broad range of questions. The proposed framework is modular so that future work can expand the range and complexity of models available, both for habitats and for metabolisms.
Cultural Evaluations of Vision-Language Models Have a Lot to Learn from Cultural Theory
2025-05-28
Srishti Yadav, Lauren Tilton, Maria Antoniak, Taylor Arnold, Jiaang Li, Siddhesh Milind Pawar, Antonia Karamolegkou, Stella Frank, Zhaochong An, Negar Rostamzadeh, Daniel Hershcovich, Serge Belongie, Ekaterina Shutova
Modern vision-language models (VLMs) often fail at cultural competency evaluations and benchmarks. Given the diversity of applications built upon VLMs, there is renewed interest in understanding how they encode cultural nuances. While individual aspects of this problem have been studied, we still lack a comprehensive framework for systematically identifying and annotating the nuanced cultural dimensions present in images for VLMs. This position paper argues that foundational methodologies from visual culture studies (cultural studies, semiotics, and visual studies) are necessary for cultural analysis of images. Building upon this review, we propose a set of five frameworks, corresponding to cultural dimensions, that must be considered for a more complete analysis of the cultural competencies of VLMs.
Can Large Language Models Match the Conclusions of Systematic Reviews?
2025-05-28
Christopher Polzak, Alejandro Lozano, Min Woo Sun, James Burgess, Yuhui Zhang, Kevin Wu, Serena Yeung-Levy
Systematic reviews (SR), in which experts summarize and analyze evidence across individual studies to provide insights on a specialized topic, are a cornerstone for evidence-based clinical decision-making, research, and policy. Given the exponential growth of scientific articles, there is growing interest in using large language models (LLMs) to automate SR generation. However, the ability of LLMs to critically assess evidence and reason across multiple documents to provide recommendations at the same proficiency as domain experts remains poorly characterized. We therefore ask: Can LLMs match the conclusions of systematic reviews written by clinical experts when given access to the same studies? To explore this question, we present MedEvidence, a benchmark pairing findings from 100 SRs with the studies they are based on. We benchmark 24 LLMs on MedEvidence, including reasoning, non-reasoning, medical specialist, and models across varying sizes (from 7B-700B). Through our systematic evaluation, we find that reasoning does not necessarily improve performance, larger models do not consistently yield greater gains, and knowledge-based fine-tuning degrades accuracy on MedEvidence. Instead, most models exhibit similar behavior: performance tends to degrade as token length increases, their responses show overconfidence, and, contrary to human experts, all models show a lack of scientific skepticism toward low-quality findings. These results suggest that more work is still required before LLMs can reliably match the observations from expert-conducted SRs, even though these systems are already deployed and being used by clinicians. We release our codebase and benchmark to the broader research community to further investigate LLM-based SR systems.
A comprehensive analysis of PINNs: Variants, Applications, and Challenges
2025-05-28
Afila Ajithkumar Sophiya, Akarsh K Nair, Sepehr Maleki, Senthil K. Krishnababu
Physics Informed Neural Networks (PINNs) have been emerging as a powerful computational tool for solving differential equations. However, the applicability of these models is still in its initial stages and requires more standardization to gain wider popularity. Through this survey, we present a comprehensive overview of PINNs approaches exploring various aspects related to their architecture, variants, areas of application, real-world use cases, challenges, and so on. Even though existing surveys can be identified, they fail to provide a comprehensive view as they primarily focus on either different application scenarios or limit their study to a superficial level. This survey attempts to bridge the gap in the existing literature by presenting a detailed analysis of all these factors combined with recent advancements and state-of-the-art research in PINNs. Additionally, we discuss prevalent challenges in PINNs implementation and present some of the future research directions as well. The overall contributions of the survey can be summarised into three sections: A detailed overview of PINNs architecture and variants, a performance analysis of PINNs on different equations and application domains highlighting their features. Finally, we present a detailed discussion of current issues and future research directions.
$\texttt{SwiftC}_\ell$: fast differentiable angular power spectra beyond Limber
2025-05-28
Laura Reymond, Alexander Reeves, Pierre Zhang, Alexandre Refregier
The upcoming stage IV wide-field surveys will provide high precision measurements of the large-scale structure (LSS) of the universe. Their interpretation requires fast and accurate theoretical predictions including large scales. For this purpose, we introduce $\texttt{SwiftC}_\ell$, a fast, accurate and differentiable $\texttt{JAX}$-based pipeline for the computation of the angular power spectrum beyond the Limber approximation. It uses a new FFTLog-based method which can reach arbitrary precision and includes interpolation along $k$, allowing for $k$-dependent growth factor and biases. $\texttt{SwiftC}_\ell$ includes a wide range of probes and effects such as galaxy clustering, including magnification bias, redshift-space distortions and primordial non-Gaussianity, weak lensing, including intrinsic alignment, cosmic microwave background (CMB) lensing and CMB integrated Sachs-Wolfe effect. We compare our pipeline to the other available beyond-Limber codes within the N5K challenge from the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration. $\texttt{SwiftC}_\ell$ computes the 120 different angular power spectra over 103 $\ell$-multipoles in 5 ms on one GPU core. Using a pre-calculation, $\texttt{SwiftC}_\ell$ is thus about 40$\times$ faster than the winner of the N5K challenge with comparable accuracy. Furthermore, all outputs are auto-differentiable, facilitating gradient-based sampling and robust and accurate Fisher forecasts. We showcase a Markov Chain Monte Carlo on an LSST-like survey as well as a Fisher forecast, illustrating $\texttt{SwiftC}_\ell$'s differentiability, speed and reliability in measuring cosmological parameters. The code is publicly available at https://cosmo-gitlab.phys.ethz.ch/cosmo_public/swiftcl.
Connecting Planetary Composition with Formation: a New Paradigm Emerges
2025-05-28
Ralph E. Pudritz, Alex J. Cridland, Julie Inglis, Mathew Alessi
Extensive ground and space based surveys have now characterized the properties of thousands of exoplanets; their radii, masses, orbits around their host stars, and the beginnings of accurate measurements of the chemical compositions of their atmospheres and cores. How are these properties linked to their formation in physically and chemically evolving protoplanetary disks wherein they accrete pebbles, planetesimals, and gas as they undergo migration? To address this challenge, our review assembles a large and varied body of exoplanet observations as well as recent Atacama Large Millimeter Array (ALMA) and James Webb Space Telescope (JWST) observations of disk structure, chemistry, kinematics, and winds. The latest advances in theory and MHD simulations that bear on these issues are also reviewed and compared with the observations. Taken together, this review argues that a new dynamic paradigm for planet formation is emerging wherein MHD disk winds and not disk turbulence play a central role in disk evolution and planet formation including: angular momentum transport, gap and ring formation. disk astrochemistry, and planet formation and migration. These processes leave their mark on the resulting atmospheric composition, radii, and orbital characteristics of exoplanet populations, offering the possibility of future observational tests.
BLUE: Bi-layer Heterogeneous Graph Fusion Network for Avian Influenza Forecasting
2025-05-28
Jing Du, Haley Stone, Yang Yang, Ashna Desai, Hao Xue, Andreas Züfle, Chandini Raina MacIntyre, Flora D. Salim
Accurate forecasting of avian influenza outbreaks within wild bird populations requires models that account for complex, multi-scale transmission patterns driven by various factors. Spatio-temporal GNN-based models have recently gained traction for infection forecasting due to their ability to capture relations and flow between spatial regions, but most existing frameworks rely solely on spatial connections and their connections. This overlooks valuable genetic information at the case level, such as cases in one region being genetically descended from strains in another, which is essential for understanding how infectious diseases spread through epidemiological linkages beyond geography. We address this gap with BLUE, a B}i-Layer heterogeneous graph fUsion nEtwork designed to integrate genetic, spatial, and ecological data for accurate outbreak forecasting. The framework 1) builds heterogeneous graphs from multiple information sources and multiple layers, 2) smooths across relation types, 3) performs fusion while retaining structural patterns, and 4) predicts future outbreaks via an autoregressive graph sequence model that captures transmission dynamics over time. To facilitate further research, we introduce \textbf{Avian-US} dataset, the dataset for avian influenza outbreak forecasting in the United States, incorporating genetic, spatial, and ecological data across locations. BLUE achieves superior performance over existing baselines, highlighting the value of incorporating multi-layer information into infectious disease forecasting.
Document Valuation in LLM Summaries: A Cluster Shapley Approach
2025-05-28
Zikun Ye, Hema Yoganarasimhan
Large Language Models (LLMs) are increasingly used in systems that retrieve and summarize content from multiple sources, such as search engines and AI assistants. While these models enhance user experience by generating coherent summaries, they obscure the contributions of original content creators, raising concerns about credit attribution and compensation. We address the challenge of valuing individual documents used in LLM-generated summaries. We propose using Shapley values, a game-theoretic method that allocates credit based on each document's marginal contribution. Although theoretically appealing, Shapley values are expensive to compute at scale. We therefore propose Cluster Shapley, an efficient approximation algorithm that leverages semantic similarity between documents. By clustering documents using LLM-based embeddings and computing Shapley values at the cluster level, our method significantly reduces computation while maintaining attribution quality. We demonstrate our approach to a summarization task using Amazon product reviews. Cluster Shapley significantly reduces computational complexity while maintaining high accuracy, outperforming baseline methods such as Monte Carlo sampling and Kernel SHAP with a better efficient frontier. Our approach is agnostic to the exact LLM used, the summarization process used, and the evaluation procedure, which makes it broadly applicable to a variety of summarization settings.
Converting natural language (NL) questions into SQL queries, referred to as Text-to-SQL, has emerged as a pivotal technology for facilitating access to relational databases, especially for users without SQL knowledge. Recent progress in large language models (LLMs) has markedly propelled the field of natural language processing (NLP), opening new avenues to improve text-to-SQL systems. This study presents a systematic review of LLM-based text-to-SQL, focusing on four key aspects: (1) an analysis of the research trends in LLM-based text-to-SQL; (2) an in-depth analysis of existing LLM-based text-to-SQL techniques from diverse perspectives; (3) summarization of existing text-to-SQL datasets and evaluation metrics; and (4) discussion on potential obstacles and avenues for future exploration in this domain. This survey seeks to furnish researchers with an in-depth understanding of LLM-based text-to-SQL, sparking new innovations and advancements in this field.
LegalSearchLM: Rethinking Legal Case Retrieval as Legal Elements Generation
2025-05-28
Chaeeun Kim, Jinu Lee, Wonseok Hwang
Legal Case Retrieval (LCR), which retrieves relevant cases from a query case, is a fundamental task for legal professionals in research and decision-making. However, existing studies on LCR face two major limitations. First, they are evaluated on relatively small-scale retrieval corpora (e.g., 100-55K cases) and use a narrow range of criminal query types, which cannot sufficiently reflect the complexity of real-world legal retrieval scenarios. Second, their reliance on embedding-based or lexical matching methods often results in limited representations and legally irrelevant matches. To address these issues, we present: (1) LEGAR BENCH, the first large-scale Korean LCR benchmark, covering 411 diverse crime types in queries over 1.2M legal cases; and (2) LegalSearchLM, a retrieval model that performs legal element reasoning over the query case and directly generates content grounded in the target cases through constrained decoding. Experimental results show that LegalSearchLM outperforms baselines by 6-20% on LEGAR BENCH, achieving state-of-the-art performance. It also demonstrates strong generalization to out-of-domain cases, outperforming naive generative models trained on in-domain data by 15%.
ValueSim: Generating Backstories to Model Individual Value Systems
2025-05-28
Bangde Du, Ziyi Ye, Zhijing Wu, Jankowska Monika, Shuqi Zhu, Qingyao Ai, Yujia Zhou, Yiqun Liu
As Large Language Models (LLMs) continue to exhibit increasingly human-like capabilities, aligning them with human values has become critically important. Contemporary advanced techniques, such as prompt learning and reinforcement learning, are being deployed to better align LLMs with human values. However, while these approaches address broad ethical considerations and helpfulness, they rarely focus on simulating individualized human value systems. To address this gap, we present ValueSim, a framework that simulates individual values through the generation of personal backstories reflecting past experiences and demographic information. ValueSim converts structured individual data into narrative backstories and employs a multi-module architecture inspired by the Cognitive-Affective Personality System to simulate individual values based on these narratives. Testing ValueSim on a self-constructed benchmark derived from the World Values Survey demonstrates an improvement in top-1 accuracy by over 10% compared to retrieval-augmented generation methods. Further analysis reveals that performance enhances as additional user interaction history becomes available, indicating the model's ability to refine its persona simulation capabilities over time.
Reviewing Scientific Papers for Critical Problems With Reasoning LLMs: Baseline Approaches and Automatic Evaluation
2025-05-28
Tianmai M. Zhang, Neil F. Abernethy
Recent advancements in large language models have sparked interest in utilizing them to assist the peer review process of scientific publication. Instead of having AI models generate reviews in the same way as human reviewers, we propose adopting them as manuscript quality checkers. We introduce several baseline approaches and an extendable automatic evaluation framework using top LLMs as judges to tackle the difficulty of recruiting domain experts for manual evaluation. Utilizing papers withdrawn from arXiv, we validated our proposed methods with several leading reasoning LLMs from different providers and assessed their performance and API costs for identifying critical errors and unsoundness problems. The OpenAI o3 model performed the best, while o4-mini was the most cost-effective one in our evaluation. This paper provides insights into document-based scientific understanding/reasoning and lays the foundation for future applications.
Dust removal timescale in galaxies across cosmic time
2025-05-27
Aleksandra Leśniewska, Jens Hjorth, Christa Gall
Understanding the evolution of dust in galaxies is crucial because it affects the dynamics and cooling of gas, star formation, and chemical evolution. Recent work on dust removal in galaxies indicates timescales of gigayears, with old stellar populations and AGNs as the primary drivers of this process. However, most statistically significant studies are focused on low redshifts $z < 0.4$. Here, we determine the dust removal timescale in galaxies over a wide range of redshifts, up to $z \sim 5$. We use publicly available catalogue data of infrared-selected galaxies, observed by \textit{Herschel}. Using the inferred dust masses, stellar masses, and stellar ages, we calculate the dust removal timescale in a sample of more than 120,000 galaxies. We find that, with increasing redshift, the dust removal timescale decreases from 1.8 Gyr at redshift $z \sim 0.05$ to less than 500\,Myr at $z > 3$. Galaxies at higher redshifts undergo more efficient dust removal than galaxies at lower redshift, likely driven by AGN activity, supernova shocks, and astration. These findings indicate that dust removal evolves over cosmic time, reflecting the changing mechanisms regulating dust content of galaxies as the Universe evolves.
Scaling External Knowledge Input Beyond Context Windows of LLMs via Multi-Agent Collaboration
2025-05-27
Zijun Liu, Zhennan Wan, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu
With the rapid advancement of post-training techniques for reasoning and information seeking, large language models (LLMs) can incorporate a large quantity of retrieved knowledge to solve complex tasks. However, the limited context window of LLMs obstructs scaling the amount of external knowledge input, prohibiting further improvement, especially for tasks requiring significant amount of external knowledge. Existing context window extension methods inevitably cause information loss. LLM-based multi-agent methods emerge as a new paradigm to handle massive input in a distributional manner, where we identify two core bottlenecks in existing knowledge synchronization and reasoning processes. In this work, we develop a multi-agent framework, $\textbf{ExtAgents}$, to overcome the bottlenecks and enable better scalability in inference-time knowledge integration without longer-context training. Benchmarked with our enhanced multi-hop question answering test, $\textbf{$\boldsymbol{\infty}$Bench+}$, and other public test sets including long survey generation, ExtAgents significantly enhances the performance over existing non-training methods with the same amount of external knowledge input, regardless of whether it falls $\textit{within or exceeds the context window}$. Moreover, the method maintains high efficiency due to high parallelism. Further study in the coordination of LLM agents on increasing external knowledge input could benefit real-world applications.
Wave-particle duality ellipse and application in single-photon imaging
2025-05-27
Pawan Khatiwada, Xiao-Feng Qian
We present a systematic framework to quantify the interplay between coherence and wave-particle duality in generic two-path interference systems. Our analysis reveals a closed-form duality ellipse (DE) equality, that rigorously unifies visibility (a traditional waveness measure) and predictability (a particleness measure) with degree of coherence, providing a complete mathematical embodiment of Bohr's complementarity principle. Extending this framework to quantum imaging with undetected photons (QIUP), where both path information and photon interference are inherently linked to spatial object reconstruction, we establish an imaging duality ellipse (IDE) that directly connects wave-particle duality to the object's transmittance profile. This relation enables object characterization through duality measurements alone and remains robust against experimental imperfections such as decoherence and misalignment. Our results advance the fundamental understanding of quantum duality while offering a practical toolkit for optimizing coherence-driven quantum technologies, from imaging to sensing.
With the widespread application of large language models in code generation, recent studies demonstrate that employing additional Chain-of-Thought generation models can significantly enhance code generation performance by providing explicit reasoning steps. However, as external components, CoT models are particularly vulnerable to backdoor attacks, which existing defense mechanisms often fail to detect effectively. To address this challenge, we propose GUARD, a novel dual-agent defense framework specifically designed to counter CoT backdoor attacks in neural code generation. GUARD integrates two core components: GUARD-Judge, which identifies suspicious CoT steps and potential triggers through comprehensive analysis, and GUARD-Repair, which employs a retrieval-augmented generation approach to regenerate secure CoT steps for identified anomalies. Experimental results show that GUARD effectively mitigates attacks while maintaining generation quality, advancing secure code generation systems.
A Structured Unplugged Approach for Foundational AI Literacy in Primary Education
2025-05-27
Maria Cristina Carrisi, Mirko Marras, Sara Vergallo
Younger generations are growing up in a world increasingly shaped by intelligent technologies, making early AI literacy crucial for developing the skills to critically understand and navigate them. However, education in this field often emphasizes tool-based learning, prioritizing usage over understanding the underlying concepts. This lack of knowledge leaves non-experts, especially children, prone to misconceptions, unrealistic expectations, and difficulties in recognizing biases and stereotypes. In this paper, we propose a structured and replicable teaching approach that fosters foundational AI literacy in primary students, by building upon core mathematical elements closely connected to and of interest in primary curricula, to strengthen conceptualization, data representation, classification reasoning, and evaluation of AI. To assess the effectiveness of our approach, we conducted an empirical study with thirty-one fifth-grade students across two classes, evaluating their progress through a post-test and a satisfaction survey. Our results indicate improvements in terminology understanding and usage, features description, logical reasoning, and evaluative skills, with students showing a deeper comprehension of decision-making processes and their limitations. Moreover, the approach proved engaging, with students particularly enjoying activities that linked AI concepts to real-world reasoning. Materials: https://github.com/tail-unica/ai-literacy-primary-ed.
PHISH in MESH: Korean Adversarial Phonetic Substitution and Phonetic-Semantic Feature Integration Defense
2025-05-27
Byungjun Kim, Minju Kim, Hyeonchu Park, Bugeun Kim
As malicious users increasingly employ phonetic substitution to evade hate speech detection, researchers have investigated such strategies. However, two key challenges remain. First, existing studies have overlooked the Korean language, despite its vulnerability to phonetic perturbations due to its phonographic nature. Second, prior work has primarily focused on constructing datasets rather than developing architectural defenses. To address these challenges, we propose (1) PHonetic-Informed Substitution for Hangul (PHISH) that exploits the phonological characteristics of the Korean writing system, and (2) Mixed Encoding of Semantic-pHonetic features (MESH) that enhances the detector's robustness by incorporating phonetic information at the architectural level. Our experimental results demonstrate the effectiveness of our proposed methods on both perturbed and unperturbed datasets, suggesting that they not only improve detection performance but also reflect realistic adversarial behaviors employed by malicious users.
Ordine privo di periodicità: il fascino matematico delle tassellazioni
2025-05-27
Francesco D'Andrea
This is a review (in Italian) on aperiodic tilings of the plane intended for a general audience. First, we recall some basic results about lattices and periodic tilings. Then, we move on to one-dimensional (domino) tilings and Wang tilings. We present a beautiful proof of the existence of an aperiodic set of Wang prototiles due to J. Kari. Next, we discuss Penrose tilings and their properties. Finally, we briefly present the recent discovery by D. Smith and his collaborators of an aperiodic monotile.
Evaluating LLM Adaptation to Sociodemographic Factors: User Profile vs. Dialogue History
2025-05-27
Qishuai Zhong, Zongmin Li, Siqi Fan, Aixin Sun
Effective engagement by large language models (LLMs) requires adapting responses to users' sociodemographic characteristics, such as age, occupation, and education level. While many real-world applications leverage dialogue history for contextualization, existing evaluations of LLMs' behavioral adaptation often focus on single-turn prompts. In this paper, we propose a framework to evaluate LLM adaptation when attributes are introduced either (1) explicitly via user profiles in the prompt or (2) implicitly through multi-turn dialogue history. We assess the consistency of model behavior across these modalities. Using a multi-agent pipeline, we construct a synthetic dataset pairing dialogue histories with distinct user profiles and employ questions from the Value Survey Module (VSM 2013) (Hofstede and Hofstede, 2016) to probe value expression. Our findings indicate that most models adjust their expressed values in response to demographic changes, particularly in age and education level, but consistency varies. Models with stronger reasoning capabilities demonstrate greater alignment, indicating the importance of reasoning in robust sociodemographic adaptation.
In content moderation for social media platforms, the cost of delaying the review of a content is proportional to its view trajectory, which fluctuates and is apriori unknown. Motivated by such uncertain holding costs, we consider a queueing model where job states evolve based on a Markov chain with state-dependent instantaneous holding costs. We demonstrate that in the presence of such uncertain holding costs, the two canonical algorithmic principles, instantaneous-cost ($c\mu$-rule) and expected-remaining-cost ($c\mu/\theta$-rule), are suboptimal. By viewing each job as a Markovian ski-rental problem, we develop a new index-based algorithm, Opportunity-adjusted Remaining Cost (OaRC), that adjusts to the opportunity of serving jobs in the future when uncertainty partly resolves. We show that the regret of OaRC scales as $\tilde{O}(L^{1.5}\sqrt{N})$, where $L$ is the maximum length of a job's holding cost trajectory and $N$ is the system size. This regret bound shows that OaRC achieves asymptotic optimality when the system size $N$ scales to infinity. Moreover, its regret is independent of the state-space size, which is a desirable property when job states contain contextual information. We corroborate our results with an extensive simulation study based on two holding cost patterns (online ads and user-generated content) that arise in content moderation for social media platforms. Our simulations based on synthetic and real datasets demonstrate that OaRC consistently outperforms existing practice, which is based on the two canonical algorithmic principles.
robostrategy: Field and Target Assignment Optimization in the Sloan Digital Sky Survey V
2025-05-27
Michael R. Blanton, Joleen K. Carlberg, Tom Dwelly, Ilija Medan, S. Drew Chojnowski, Kevin Covey, Megan C. Davis, John Donor, Pramod Gupta, Alexander Ji, Jennifer A. Johnson, Juna A. Kollmeier, Jose Sanchez-Gallego, Conor Sayres, Eleonora Zari
We present an algorithmic method for efficiently planning a long-term, large-scale multi-object spectroscopy program. The Sloan Digital Sky Survey V (SDSS-V) Focal Plane System performs multi-object spectroscopy using 500 robotic positioners to place fibers feeding optical and infrared spectrographs across a wide field. SDSS-V uses this system to observe targets throughout the year at two observatories in support of the science goals of its Milky Way Mapper and Black Hole Mapper programs. These science goals require observations of objects over time with preferred temporal spacinges (referred to as "cadences"), which can differ from object to object even in the same area of sky. robostrategy is the software we use to construct our planned observations so that they can best achieve the desired goals given the time available as a function of sky brightness and local sidereal time, and to assign fibers to targets during specific observations. We use linear programming techniques to seek optimal allocations of time under the constraints given. We present the methods and example results obtained with this software.
Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead
2025-05-27
Jesujoba O. Alabi, Michael A. Hedderich, David Ifeoluwa Adelani, Dietrich Klakow
With over 2,000 languages and potentially millions of speakers, Africa represents one of the richest linguistic regions in the world. Yet, this diversity is scarcely reflected in state-of-the-art natural language processing (NLP) systems and large language models (LLMs), which predominantly support a narrow set of high-resource languages. This exclusion not only limits the reach and utility of modern NLP technologies but also risks widening the digital divide across linguistic communities. Nevertheless, NLP research on African languages is active and growing. In recent years, there has been a surge of interest in this area, driven by several factors-including the creation of multilingual language resources, the rise of community-led initiatives, and increased support through funding programs. In this survey, we analyze 734 research papers on NLP for African languages published over the past five years, offering a comprehensive overview of recent progress across core tasks. We identify key trends shaping the field and conclude by outlining promising directions to foster more inclusive and sustainable NLP research for African languages.
2025-05-27
Xudong Wang, Ziheng Sun, Chris Ding, Jicong Fan
This work proposes a framework LGKDE that learns kernel density estimation for graphs. The key challenge in graph density estimation lies in effectively capturing both structural patterns and semantic variations while maintaining theoretical guarantees. Combining graph kernels and kernel density estimation (KDE) is a standard approach to graph density estimation, but has unsatisfactory performance due to the handcrafted and fixed features of kernels. Our method LGKDE leverages graph neural networks to represent each graph as a discrete distribution and utilizes maximum mean discrepancy to learn the graph metric for multi-scale KDE, where all parameters are learned by maximizing the density of graphs relative to the density of their well-designed perturbed counterparts. The perturbations are conducted on both node features and graph spectra, which helps better characterize the boundary of normal density regions. Theoretically, we establish consistency and convergence guarantees for LGKDE, including bounds on the mean integrated squared error, robustness, and complexity. We validate LGKDE by demonstrating its effectiveness in recovering the underlying density of synthetic graph distributions and applying it to graph anomaly detection across diverse benchmark datasets. Extensive empirical evaluation shows that LGKDE demonstrates superior performance compared to state-of-the-art baselines on most benchmark datasets.
Sci-Fi: Symmetric Constraint for Frame Inbetweening
2025-05-27
Liuhan Chen, Xiaodong Cun, Xiaoyu Li, Xianyi He, Shenghai Yuan, Jie Chen, Ying Shan, Li Yuan
Frame inbetweening aims to synthesize intermediate video sequences conditioned on the given start and end frames. Current state-of-the-art methods mainly extend large-scale pre-trained Image-to-Video Diffusion models (I2V-DMs) by incorporating end-frame constraints via directly fine-tuning or omitting training. We identify a critical limitation in their design: Their injections of the end-frame constraint usually utilize the same mechanism that originally imposed the start-frame (single image) constraint. However, since the original I2V-DMs are adequately trained for the start-frame condition in advance, naively introducing the end-frame constraint by the same mechanism with much less (even zero) specialized training probably can't make the end frame have a strong enough impact on the intermediate content like the start frame. This asymmetric control strength of the two frames over the intermediate content likely leads to inconsistent motion or appearance collapse in generated frames. To efficiently achieve symmetric constraints of start and end frames, we propose a novel framework, termed Sci-Fi, which applies a stronger injection for the constraint of a smaller training scale. Specifically, it deals with the start-frame constraint as before, while introducing the end-frame constraint by an improved mechanism. The new mechanism is based on a well-designed lightweight module, named EF-Net, which encodes only the end frame and expands it into temporally adaptive frame-wise features injected into the I2V-DM. This makes the end-frame constraint as strong as the start-frame constraint, enabling our Sci-Fi to produce more harmonious transitions in various scenarios. Extensive experiments prove the superiority of our Sci-Fi compared with other baselines.
Data deduplication has gained wide acclaim as a mechanism to improve storage efficiency and conserve network bandwidth. Its most critical phase, data chunking, is responsible for the overall space savings achieved via the deduplication process. However, modern data chunking algorithms are slow and compute-intensive because they scan large amounts of data while simultaneously making data-driven boundary decisions. We present SeqCDC, a novel chunking algorithm that leverages lightweight boundary detection, content-defined skipping, and SSE/AVX acceleration to improve chunking throughput for large chunk sizes. Our evaluation shows that SeqCDC achieves 15x higher throughput than unaccelerated and 1.2x-1.35x higher throughput than vector-accelerated data chunking algorithms while minimally affecting deduplication space savings.
Lunguage: A Benchmark for Structured and Sequential Chest X-ray Interpretation
2025-05-27
Jong Hak Moon, Geon Choi, Paloma Rabaey, Min Gwan Kim, Hyuk Gi Hong, Jung-Oh Lee, Hangyul Yoon, Eun Woo Doe, Jiyoun Kim, Harshita Sharma, Daniel C. Castro, Javier Alvarez-Valle, Edward Choi
Radiology reports convey detailed clinical observations and capture diagnostic reasoning that evolves over time. However, existing evaluation methods are limited to single-report settings and rely on coarse metrics that fail to capture fine-grained clinical semantics and temporal dependencies. We introduce LUNGUAGE,a benchmark dataset for structured radiology report generation that supports both single-report evaluation and longitudinal patient-level assessment across multiple studies. It contains 1,473 annotated chest X-ray reports, each reviewed by experts, and 80 of them contain longitudinal annotations to capture disease progression and inter-study intervals, also reviewed by experts. Using this benchmark, we develop a two-stage framework that transforms generated reports into fine-grained, schema-aligned structured representations, enabling longitudinal interpretation. We also propose LUNGUAGESCORE, an interpretable metric that compares structured outputs at the entity, relation, and attribute level while modeling temporal consistency across patient timelines. These contributions establish the first benchmark dataset, structuring framework, and evaluation metric for sequential radiology reporting, with empirical results demonstrating that LUNGUAGESCORE effectively supports structured report evaluation. The code is available at: https://github.com/SuperSupermoon/Lunguage
Exploring the Latent Capacity of LLMs for One-Step Text Generation
2025-05-27
Gleb Mezentsev, Ivan Oseledets
A recent study showed that large language models (LLMs) can reconstruct surprisingly long texts - up to thousands of tokens - via autoregressive generation from just one specially trained input embedding. In this work, we explore whether such reconstruction is possible without autoregression. We show that frozen LLMs can generate hundreds of accurate tokens in just one forward pass, when provided with only two learned embeddings. This reveals a surprising and underexplored capability of LLMs - multi-token generation without iterative decoding. We investigate the behaviour of these embeddings and provide insight into the type of information they encode. We also empirically show that although these representations are not unique for a given text, they form connected and local regions in embedding space - a property that suggests the potential of learning a dedicated encoder into that space.
Constructive community race: full-density spiking neural network model drives neuromorphic computing
2025-05-27
Johanna Senk, Anno Kurth, Steve Furber, Tobias Gemmeke, Bruno Golosio, Arne Heittmann, James C. Knight, Eric Müller, Tobias Noll, Thomas Nowotny, Gorka Peraza Coppola, Luca Peres, Oliver Rhodes, Andrew Rowley, Johannes Schemmel, Tim Stadtmann, Tom Tetzlaff, Gianmarco Tiddia, Sacha J. van Albada, José Villamar, Markus Diesmann
The local circuitry of the mammalian brain is a focus of the search for generic computational principles because it is largely conserved across species and modalities. In 2014 a model was proposed representing all neurons and synapses of the stereotypical cortical microcircuit below $1\,\text{mm}^2$ of brain surface. The model reproduces fundamental features of brain activity but its impact remained limited because of its computational demands. For theory and simulation, however, the model was a breakthrough because it removes uncertainties of downscaling, and larger models are less densely connected. This sparked a race in the neuromorphic computing community and the model became a de facto standard benchmark. Within a few years real-time performance was reached and surpassed at significantly reduced energy consumption. We review how the computational challenge was tackled by different simulation technologies and derive guidelines for the next generation of benchmarks and other domains of science.
Latent label distribution grid representation for modeling uncertainty
2025-05-27
ShuNing Sun, YinSong Xiong, Yu Zhang, Zhuoran Zheng
Although \textbf{L}abel \textbf{D}istribution \textbf{L}earning (LDL) has promising representation capabilities for characterizing the polysemy of an instance, the complexity and high cost of the label distribution annotation lead to inexact in the construction of the label space. The existence of a large number of inexact labels generates a label space with uncertainty, which misleads the LDL algorithm to yield incorrect decisions. To alleviate this problem, we model the uncertainty of label distributions by constructing a \textbf{L}atent \textbf{L}abel \textbf{D}istribution \textbf{G}rid (LLDG) to form a low-noise representation space. Specifically, we first construct a label correlation matrix based on the differences between labels, and then expand each value of the matrix into a vector that obeys a Gaussian distribution, thus building a LLDG to model the uncertainty of the label space. Finally, the LLDG is reconstructed by the LLDG-Mixer to generate an accurate label distribution. Note that we enforce a customized low-rank scheme on this grid, which assumes that the label relations may be noisy and it needs to perform noise-reduction with the help of a Tucker reconstruction technique. Furthermore, we attempt to evaluate the effectiveness of the LLDG by considering its generation as an upstream task to achieve the classification of the objects. Extensive experimental results show that our approach performs competitively on several benchmarks.
Quantum AIXI: Universal Intelligence via Quantum Information
2025-05-27
Elija Perrier
AIXI is a widely studied model of artificial general intelligence (AGI) based upon principles of induction and reinforcement learning. However, AIXI is fundamentally classical in nature - as are the environments in which it is modelled. Given the universe is quantum mechanical in nature and the exponential overhead required to simulate quantum mechanical systems classically, the question arises as to whether there are quantum mechanical analogues of AIXI which are theoretically consistent or practically feasible as models of universal intelligence. To address this question, we extend the framework to quantum information and present Quantum AIXI (QAIXI). We introduce a model of quantum agent/environment interaction based upon quantum and classical registers and channels, showing how quantum AIXI agents may take both classical and quantum actions. We formulate the key components of AIXI in quantum information terms, extending previous research on quantum Kolmogorov complexity and a QAIXI value function. We discuss conditions and limitations upon quantum Solomonoff induction and show how contextuality fundamentally affects QAIXI models.
The TYPHOON Stellar Population Synthesis Survey. II. Pushing Full Spectral Fitting to the Limit in the Nearby Grand Design Barred Spiral M83
2025-05-27
Eva Sextl, Rolf-Peter Kudritzki, Fabio Bresolin, Kathryn Grasha, Hye-Jin Park, Qian-Hui Chen, Andrew J. Battisti, Mark Seibert, Barry F. Madore, Jeffrey A. Rich
We apply population synthesis techniques to analyze TYPHOON long slit spectra of the starburst barred spiral galaxy M83. The analysis covers a central square of 5 arcmin side length. We determine the spatial distribution of dust through the analysis of reddening and extinction, together with star formation rates, ages, and metallicities of young and old stellar populations. For the first time, a spatial one-to-one comparison of metallicities derived from full-spectral fitting techniques with those obtained from individual young stellar probes has been carried out. The comparison with blue supergiant stars, young massive star clusters, and super star clusters shows a high degree of concordance when wavelength coverage in the $B$-band is available. The metallicity of the young population is supersolar and does not show a radial metallicity gradient along the investigated part of the disk, in agreement with our chemical evolution model. However, a notable decrease in metallicity is observed in a tightly confined region at the galaxy center, coinciding with circumnuclear orbits. We attribute this to matter infall either from the circumgalactic medium or a dwarf galaxy interloper or, alternatively, to AGN-interrupted chemical evolution. We confirm the presence of a dust cavity with a diameter of 260~pc close to the galaxy center. Dust absorption and molecular CO emission are spatially well correlated. We find an anticorrelation between R$_V$, the ratio of dust attenuation to reddening, and the emission strength of molecular species present in photo-dissociation regions. We confirm our results by using alternative fitting algorithms and stellar libraries.
Large language model (LLM)-driven multi-agent systems (MAS) are transforming how humans and AIs collaboratively generate ideas and artifacts. While existing surveys provide comprehensive overviews of MAS infrastructures, they largely overlook the dimension of \emph{creativity}, including how novel outputs are generated and evaluated, how creativity informs agent personas, and how creative workflows are coordinated. This is the first survey dedicated to creativity in MAS. We focus on text and image generation tasks, and present: (1) a taxonomy of agent proactivity and persona design; (2) an overview of generation techniques, including divergent exploration, iterative refinement, and collaborative synthesis, as well as relevant datasets and evaluation metrics; and (3) a discussion of key challenges, such as inconsistent evaluation standards, insufficient bias mitigation, coordination conflicts, and the lack of unified benchmarks. This survey offers a structured framework and roadmap for advancing the development, evaluation, and standardization of creative MAS.
The MUSE view of ram pressure stripped galaxies in clusters: the GASP sample
2025-05-27
Bianca M. Poggianti, Benedetta Vulcani, Neven Tomicic, Alessia Moretti, Marco Gullieuszik, Cecilia Bacchini, Jacopo Fritz, Koshy George, Myriam Gitti, Alessandro Ignesti, Augusto Lassen, Antonino Marasco, Mario Radovich, Paolo Serra, Rory Smith, Stephanie Tonnesen, Anna Wolter
We present the full sample of 76 galaxies in 39 galaxy cluster fields at z=0.04-0.07 observed with VLT/MUSE by the GASP survey. Most of them (64) were observed as possible ram pressure stripped galaxies (stripping candidates) based on optical B-band images, while the remaining 12 were a control sample of both star-forming and passive galaxies. Based on spatially resolved ionized gas and stellar kinematics, we assess the physical origin of the gas asymmetries and find that 89% of the stripping candidates are confirmed by the VLT/MUSE data. In addition, also 3 of the 4 star-forming galaxies in the control sample show signs of ram pressure. These control galaxies display a ring of unusual emission line ratios, which we see also in field galaxies, possibly originating from the interaction with a hotter surrounding medium. The stripped galaxies are classified into various classes corresponding to different degrees of stripping, from weakest stripping to strong and extreme (jellyfish galaxies) stripping, as well as truncated gas disks with gas left only in the galaxy center. Our results show that selecting cluster stripping candidates based on optical imaging yields a sample that is indeed largely dominated by galaxies affected by ram pressure at different stages and stripping strength, though some contamination is present, mostly by tidal processes. Strong ram pressure cases are found in galaxies over the whole range of stellar masses studied (10^9-10^11.5 Msun) both in low-mass and high-mass clusters (cluster velocity dispersions sigma = 500-1100 km/s). We examine the possible connection between the progressive stages of stripping, up to the phase of a truncated gas disk, and the subsequent complete stripping of gas. We discuss the incompleteness intrinsic to this and other methods of selection to obtain a complete census of ram pressure stripping in clusters.
In this paper, we address the task of targeted sentiment analysis (TSA), which involves two sub-tasks, i.e., identifying specific aspects from reviews and determining their corresponding sentiments. Aspect extraction forms the foundation for sentiment prediction, highlighting the critical dependency between these two tasks for effective cross-task knowledge transfer. While most existing studies adopt a multi-task learning paradigm to align task-specific features in the latent space, they predominantly rely on coarse-grained knowledge transfer. Such approaches lack fine-grained control over aspect-sentiment relationships, often assuming uniform sentiment polarity within related aspects. This oversimplification neglects contextual cues that differentiate sentiments, leading to negative transfer. To overcome these limitations, we propose FCKT, a fine-grained cross-task knowledge transfer framework tailored for TSA. By explicitly incorporating aspect-level information into sentiment prediction, FCKT achieves fine-grained knowledge transfer, effectively mitigating negative transfer and enhancing task performance. Experiments on three datasets, including comparisons with various baselines and large language models (LLMs), demonstrate the effectiveness of FCKT. The source code is available on https://github.com/cwei01/FCKT.
A Hitchhiker's Guide to Privacy-Preserving Cryptocurrencies: A Survey on Anonymity, Confidentiality, and Auditability
2025-05-27
Matteo Nardelli, Francesco De Sclavis, Michela Iezzi
Cryptocurrencies and central bank digital currencies (CBDCs) are reshaping the monetary landscape, offering transparency and efficiency while raising critical concerns about user privacy and regulatory compliance. This survey provides a comprehensive and technically grounded overview of privacy-preserving digital currencies, covering both cryptocurrencies and CBDCs. We propose a taxonomy of privacy goals -- including anonymity, confidentiality, unlinkability, and auditability -- and map them to underlying cryptographic primitives, protocol mechanisms, and system architectures. Unlike previous surveys, our work adopts a design-oriented perspective, linking high-level privacy objectives to concrete implementations. We also trace the evolution of privacy-preserving currencies through three generations, highlighting shifts from basic anonymity guarantees toward more nuanced privacy-accountability trade-offs. Finally, we identify open challenges at the intersection of cryptography, distributed systems, and policy definition, which motivate further investigation into the primitives and design of digital currencies that balance real-world privacy and auditability needs.
Research Community Perspectives on "Intelligence" and Large Language Models
2025-05-27
Bertram Højer, Terne Sasha Thorn Jakobsen, Anna Rogers, Stefan Heinrich
Despite the widespread use of ''artificial intelligence'' (AI) framing in Natural Language Processing (NLP) research, it is not clear what researchers mean by ''intelligence''. To that end, we present the results of a survey on the notion of ''intelligence'' among researchers and its role in the research agenda. The survey elicited complete responses from 303 researchers from a variety of fields including NLP, Machine Learning (ML), Cognitive Science, Linguistics, and Neuroscience. We identify 3 criteria of intelligence that the community agrees on the most: generalization, adaptability, & reasoning. Our results suggests that the perception of the current NLP systems as ''intelligent'' is a minority position (29%). Furthermore, only 16.2% of the respondents see developing intelligent systems as a research goal, and these respondents are more likely to consider the current systems intelligent.
Comparing population synthesis models of compact double white dwarfs to electromagnetic observations
2025-05-27
Wouter G. J. van Zeist, Jan van Roestel, Gijs Nelemans, Jan J. Eldridge, Valeriya Korol, Silvia Toonen
Context: Studies of the Galactic population of double white dwarfs (DWDs) that would be detectable in gravitational waves by LISA have found differences in the number of predicted detectable DWDs of more than an order of magnitude, depending on the binary stellar evolution model used. Particularly, the binary population synthesis code BPASS predicts 20 to 40 times fewer detectable DWDs than the codes SeBa or BSE, which relates to differing treatments of mass transfer and common-envelope events (CEEs). Aims: We aimed to investigate which of these models are closer to reality by comparing their predictions to the DWDs known from electromagnetic observations. Methods: We compared the DWDs predicted by a BPASS galaxy model and a SeBa galaxy model to a DWD catalogue and the sample of DWDs observed by the Zwicky Transient Facility (ZTF), taking into account the observational limits and biases of the ZTF survey. Results: We found that BPASS underpredicts the number of short-period DWDs by at least an order of magnitude compared to the observations, while the SeBa galaxy model is consistent with the observations for DWDs more distant than 500 pc. These results highlight how LISA's observations of DWDs will provide invaluable information on aspects of stellar evolution such as mass transfer and CEEs, which will allow theoretical models to be better constrained.
Construction, Commissioning, and Installation of the Cylindrical GEM Inner Tracker of the BESIII Experiment
2025-05-27
Stefano Gramigna
BESIII (BEijing Spectrometer III) is a particle physics experiment with a vast physics program centered around the study of charmonium and the $\tau$ lepton. The performance of the spectrometer's inner tracker, the innermost part of a large drift chamber, has been degrading due to aging phenomena related to the large particle rate. Planned upgrades to the BEPCII (Beijing Electron Positron Collider II) collider, servicing the experiment, may further aggravate the problem, with the risk of disrupting the data taking. The Italian component of the BESIII collaboration proposed a detector based on cylindrical GEM (Gas Electron Multiplier) technology to replace the aging inner tracker. The new detector aims to improve the current tracker's spatial resolution in the beam direction at least a factor 2 and to ensure the continuation of BESIII's data taking until its end in 2030. After more than 10 years of design, development, and construction the three layers of the CGEM-IT (Cylindrical GEM Inner Tracker) are finally being installed in the spectrometer. This thesis describes the three final years of the detector's development, which led from diagnosing and resolving mechanical issues preventing the largest layer from powering on to securing the approval of the experiment's internal review committee for installation. Particular focus is given to the technological solutions adopted to overcome the challenges encountered during the development process, which often required a complete rethinking of previous methods and procedures. The thesis concludes with a snapshot of the ongoing installation of the detector, commenting on the results of the preparatory work undertaken to ensure its success.
Abductive reasoning in knowledge graphs aims to generate plausible logical hypotheses from observed entities, with broad applications in areas such as clinical diagnosis and scientific discovery. However, due to a lack of controllability, a single observation may yield numerous plausible but redundant or irrelevant hypotheses on large-scale knowledge graphs. To address this limitation, we introduce the task of controllable hypothesis generation to improve the practical utility of abductive reasoning. This task faces two key challenges when controlling for generating long and complex logical hypotheses: hypothesis space collapse and hypothesis oversensitivity. To address these challenges, we propose CtrlHGen, a Controllable logcial Hypothesis Generation framework for abductive reasoning over knowledge graphs, trained in a two-stage paradigm including supervised learning and subsequent reinforcement learning. To mitigate hypothesis space collapse, we design a dataset augmentation strategy based on sub-logical decomposition, enabling the model to learn complex logical structures by leveraging semantic patterns in simpler components. To address hypothesis oversensitivity, we incorporate smoothed semantic rewards including Dice and Overlap scores, and introduce a condition-adherence reward to guide the generation toward user-specified control constraints. Extensive experiments on three benchmark datasets demonstrate that our model not only better adheres to control conditions but also achieves superior semantic similarity performance compared to baselines.
A Stereotype Content Analysis on Color-related Social Bias in Large Vision Language Models
2025-05-27
Junhyuk Choi, Minju Kim, Yeseon Hong, Bugeun Kim
As large vision language models(LVLMs) rapidly advance, concerns about their potential to learn and generate social biases and stereotypes are increasing. Previous studies on LVLM's stereotypes face two primary limitations: metrics that overlooked the importance of content words, and datasets that overlooked the effect of color. To address these limitations, this study introduces new evaluation metrics based on the Stereotype Content Model (SCM). We also propose BASIC, a benchmark for assessing gender, race, and color stereotypes. Using SCM metrics and BASIC, we conduct a study with eight LVLMs to discover stereotypes. As a result, we found three findings. (1) The SCM-based evaluation is effective in capturing stereotypes. (2) LVLMs exhibit color stereotypes in the output along with gender and race ones. (3) Interaction between model architecture and parameter sizes seems to affect stereotypes. We release BASIC publicly on [anonymized for review].
TCP J07222683$+$6220548: a new AM CVn type system with infrequent outbursts
2025-05-27
Alexander Tarasenkov, Kirill Sokolovsky, Alexandr Dodin, Oxana Chernyshenko, Stanislav Korotkiy, Ivan Strakhov, Marina Burlak, Sergey Naroenkov, Franz-Josef Hambsch, Tamás Tordai, Hiroshi Itoh, Yasuo Sano, Yusuke Tampo, Ferdinand
We present the discovery of TCP J07222683$+$6220548, a new ultracompact binary system of the AM CVn type. This system was first identified displaying a $\Delta V = 7.6$ mag outburst on 2025-01-20.9416 UTC by the New Milky Way wide-field survey for transients and later independently detected by ASAS-SN and ZTF. The outburst peaked at $V_{\rm max} = 12.45$ and lasted for seven days, followed by a series of rebrightenings. No previous outbursts are found in archival data. Positive superhumps with a period of $0.032546 \pm 0.000084$ d ($46.87 \pm 0.12$ min), barely detectable during the main outburst, became clearly visible during the first rebrightening that lasted from day 18 to day 24 after the initial outburst. No convincing change in the superhump period was detected. Dense time-series photometry follow-up by a pair of 0.5-m INASAN robotic telescopes, together with VSNET and AAVSO observers, was essential for identifying TCP J07222683$+$6220548 as an AM CVn system and triggering confirmation spectroscopy with the 2.5-m CMO SAI telescope. Some outbursting AM CVn systems lacking such detailed follow-up may remain unrecognized among the newly discovered cataclysmic variable candidates.
In this manuscript, through applications of TDE (tidal disruption event) expected variability properties, a potential candidate for True type-2 AGN without hidden central broad line regions (=TT2AGN) is reported in the SDSS J233454.07+145712.9 (=SDSS J2334). Through analyzing the 20-years optical light curves of SDSS J2334 from different Sky Survey projects, a TDE is preferred with a $4.7{\rm M_\odot}$ main-sequence star tidally disrupted by the central BH with mass $11.7\times 10^6{\rm M_\odot}$, indicating that central region within distance about 20 light-days to central BH in SDSS J2334 is directly in the line-of-sight. Moreover, AGN activities in SDSS J2334 can be confirmed through applications of BPT diagrams. Meanwhile, comparing virial BH mass determined through assumed broad Balmer emission components and M-sigma expected BH mass by well measured stellar velocity dispersion through stellar absorption features, optical broad emission lines in SDSS J2334 are disfavored with confidence level higher than 6$\sigma$. Therefore, combining the unique properties of the TDE and the spectroscopic results with only narrow emission lines, SDSS J2334 can be well identified as a potential candidate for a TT2AGN. The results indicate the to detect TDE expected flares in normal Type-2 AGN classified by spectroscopic results should be a new practicable method for identifying
MT-Mol:Multi Agent System with Tool-based Reasoning for Molecular Optimization
2025-05-27
Hyomin Kim, Yunhui Jang, Sungsoo Ahn
Large language models (LLMs) have large potential for molecular optimization, as they can gather external chemistry tools and enable collaborative interactions to iteratively refine molecular candidates. However, this potential remains underexplored, particularly in the context of structured reasoning, interpretability, and comprehensive tool-grounded molecular optimization. To address this gap, we introduce MT-Mol, a multi-agent framework for molecular optimization that leverages tool-guided reasoning and role-specialized LLM agents. Our system incorporates comprehensive RDKit tools, categorized into five distinct domains: structural descriptors, electronic and topological features, fragment-based functional groups, molecular representations, and miscellaneous chemical properties. Each category is managed by an expert analyst agent, responsible for extracting task-relevant tools and enabling interpretable, chemically grounded feedback. MT-Mol produces molecules with tool-aligned and stepwise reasoning through the interaction between the analyst agents, a molecule-generating scientist, a reasoning-output verifier, and a reviewer agent. As a result, we show that our framework shows the state-of-the-art performance of the PMO-1K benchmark on 17 out of 23 tasks.
The Role of AI in Early Detection of Life-Threatening Diseases: A Retinal Imaging Perspective
2025-05-27
Tariq M Khan, Toufique Ahmed Soomro, Imran Razzak
Retinal imaging has emerged as a powerful, non-invasive modality for detecting and quantifying biomarkers of systemic diseases-ranging from diabetes and hypertension to Alzheimer's disease and cardiovascular disorders but current insights remain dispersed across platforms and specialties. Recent technological advances in optical coherence tomography (OCT/OCTA) and adaptive optics (AO) now deliver ultra-high-resolution scans (down to 5 {\mu}m ) with superior contrast and spatial integration, allowing early identification of microvascular abnormalities and neurodegenerative changes. At the same time, AI-driven and machine learning (ML) algorithms have revolutionized the analysis of large-scale retinal datasets, increasing sensitivity and specificity; for example, deep learning models achieve > 90 \% sensitivity for diabetic retinopathy and AUC = 0.89 for the prediction of cardiovascular risk from fundus photographs. The proliferation of mobile health technologies and telemedicine platforms further extends access, reduces costs, and facilitates community-based screening and longitudinal monitoring. Despite these breakthroughs, translation into routine practice is hindered by heterogeneous imaging protocols, limited external validation of AI models, and integration challenges within clinical workflows. In this review, we systematically synthesize the latest OCT/OCT and AO developments, AI/ML approaches, and mHealth/Tele-ophthalmology initiatives and quantify their diagnostic performance across disease domains. Finally, we propose a roadmap for multicenter protocol standardization, prospective validation trials, and seamless incorporation of retinal screening into primary and specialty care pathways-paving the way for precision prevention, early intervention, and ongoing treatment of life-threatening systemic diseases.
SpecExtend: A Drop-in Enhancement for Speculative Decoding of Long Sequences
2025-05-27
Jungyoub Cha, Hyunjong Kim, Sungzoon Cho
Speculative decoding is a widely adopted technique for accelerating inference in large language models (LLMs), but its performance degrades on long inputs due to increased attention cost and reduced draft accuracy. We introduce SpecExtend, a drop-in enhancement that improves the performance of speculative decoding on long sequences without any additional training. SpecExtend integrates efficient attention mechanisms such as FlashAttention and Hybrid Tree Attention into both the draft and target models, reducing latency across all stages. To improve draft accuracy and speed, we propose Cross-model Retrieval, a novel KV cache update strategy that uses the target model's attention scores to dynamically select relevant context for the draft model. Extensive evaluations on three long-context understanding datasets show that SpecExtend accelerates standard tree-based speculative decoding by up to 2.22x for inputs up to 16K tokens, providing an effective solution for speculative decoding of long sequences. The code is available at https://github.com/jycha98/SpecExtend .
We present, for the first time, a systematic study of quasar-associated 2175 \AA\ dust absorbers using spectroscopic data from the Sloan Digital Sky Survey (SDSS) Data Release 16 (DR16). By analyzing the optical spectra and multi-band magnitudes of 557,674 quasars in the redshift range of $0.7 \le z \le 2.4$, we identify 843 absorbers that share the same redshifts as quasars and are believed to originate from dust in the quasar nuclei, the host galaxies, or their surrounding environments. These absorbers exhibit weak bump strengths ($A\rm_{bump}=0.49\pm0.15~\mu m^{-1}$) and narrow widths ($\gamma\rm=0.81\pm0.14~\mu m^{-1}$), while their peak positions span a broad range from $x_0 = 4.2$ to $4.84~ \mu m^{-1}$. Their average extinction curves resemble those of the Large Magellanic Cloud (LMC) but exhibit a shallower slope. In broad absorption line (BAL) quasars, the absorption bumps show systematic shifts in peak positions. Although further confirmation is needed, this may suggest environmental differences in dust grain properties. We find a statistically significant negative correlation between bump strength and redshift, suggesting possible evolution in dust properties. These findings highlight the changing composition and physical conditions of dust in quasar environments, likely influenced by factors such as metallicity, radiation fields, and dust processing mechanisms. Future studies incorporating ultraviolet and infrared data will be essential for refining the dust evolution models. Machine learning techniques and high-resolution spectroscopic follow-ups could enhance sample completeness and provide deeper insights into the chemical properties of the dust absorbers.
Hybrid Bayesian Estimation in the additive hazards model
2025-05-27
Enrique Ernesto Álvarez, Maximiliano Luis Riddick
Hereby we propose a Bayesian method of estimation for the semiparametric Additive Hazards Model (AHM) from Survival Analysis under right-censoring. With this aim, we review the AHM revisiting the likelihood function, so as to comment on the challenges posed by Bayesian estimation from the full likelihood. Through an algorithmic reformulation of that likelihood, we present an alternative method based on a hybrid Bayesian treatment that exploits Lin and Ying (1994) estimating equation approach and which chooses tractable priors for the parameters. We obtain the estimators from the posterior distributions in closed form, we perform a small simulation experiment, and lastly, we illustrate our method with the classical Nickels Miners dataset and a brief simulation experiment.
A joint analysis of 3D clustering and galaxy x CMB-lensing cross-correlations with DESI DR1 galaxies
2025-05-27
M. Maus, M. White, N. Sailer, A. Baleato Lizancos, S. Ferraro, S. Chen, J. DeRose, J. Aguilar, S. Ahlen, S. BenZvi, D. Bianchi, D. Brooks, E. Burtin, F. J. Castander, E. Chaussidon, T. Claybaugh, A. Cuceu, A. de la Macorra, A. de Mattia, P. Doel, A. Font-Ribera, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, J. Guy, K. Honscheid, C. Howlett, M. Ishak, R. Kehoe, D. Kirkby, T. Kisner, A. Kremin, O. Lahav, C. Lamman, M. Landriau, L. Le Guillou, M. E. Levi, M. Manera, A. Meisner, R. Miquel, S. Nadathur, J. A. Newman, N. Palanque-Delabrouille, W. J. Percival, F. Prada, I. Pérez-Ràfols, A. J. Ross, G. Rossi, L. Samushia, E. Sanchez, D. Schlegel, M. Schubnell, H. Seo, J. Silber, D. Sprayberry, G. Tarlé, B. A. Weaver, P. Zarrouk, R. Zhou, H. Zou
The spectroscopic data from DESI Data Release 1 (DR1) galaxies enables the analysis of 3D clustering by fitting galaxy power spectra and reconstructed correlation functions in redshift space. Given low measurements of the amplitude of structure from cosmic shear at $z\sim1$, redshift space distortions (RSD) + Baryon Acoustic Oscillation (BAO) signals from DESI galaxies combined with weak lensing can break degeneracies and provide a tight alternative constraint on the $z\sim1$ amplitude of structure. In this paper we perform joint analyses that combine full-shape + post-reconstruction information from the DESI DR1 BGS and LRG samples along with angular cross-correlations with Planck PR4 and ACT DR6 CMB lensing maps. We show that adding galaxy-lensing cross-correlations tightens clustering amplitude constraints, improving $\sigma_8$ uncertainties by $\sim 40\%$ over RSD+BAO alone. We also include angular galaxy-galaxy and galaxy-lensing spectra using photometric samples from the DESI Legacy Survey to further improve constraints. Our headline results are $\sigma_8 = 0.803\pm 0.017$, $\Omega_{\rm m} = 0.3037\pm 0.0069$, and $S_8 = 0.808\pm 0.017$. Given DESI's preference for higher $\sigma_8$ compared to lower values from BOSS, we perform a catalog-level comparison of LRG samples from both surveys. We test sensitivity to dark energy assumptions by relaxing our $\Lambda$CDM prior and allowing for evolving dark energy via the $w_0-w_a$ parameterization. We find our $S_8$ constraints to be relatively unchanged despite a $~3.5\sigma$ tension with the cosmological constant model when combining with the Union3 supernova likelihood. Finally we test general relativity (GR) by allowing the gravitational slip parameter ($\gamma$) to vary, and find $\gamma = 1.17\pm0.11$ in mild ($\sim1.5\sigma$) tension with the GR value of $1.0$.
2025-05-27
Yongyi Zang, Zheqi Dai, Mark D. Plumbley, Qiuqiang Kong
We introduce Music Source Restoration (MSR), a novel task addressing the gap between idealized source separation and real-world music production. Current Music Source Separation (MSS) approaches assume mixtures are simple sums of sources, ignoring signal degradations employed during music production like equalization, compression, and reverb. MSR models mixtures as degraded sums of individually degraded sources, with the goal of recovering original, undegraded signals. Due to the lack of data for MSR, we present RawStems, a dataset annotation of 578 songs with unprocessed source signals organized into 8 primary and 17 secondary instrument groups, totaling 354.13 hours. To the best of our knowledge, RawStems is the first dataset that contains unprocessed music stems with hierarchical categories. We consider spectral filtering, dynamic range compression, harmonic distortion, reverb and lossy codec as possible degradations, and establish U-Former as a baseline method, demonstrating the feasibility of MSR on our dataset. We release the RawStems dataset annotations, degradation simulation pipeline, training code and pre-trained models to be publicly available.
Towards Operational Automated Greenhouse Gas Plume Detection
2025-05-27
Brian D. Bue, Jake H. Lee, Andrew K. Thorpe, Philip G. Brodrick, Daniel Cusworth, Alana Ayasse, Vassiliki Mancoridis, Anagha Satish, Shujun Xiong, Riley Duren
Operational deployment of a fully automated greenhouse gas (GHG) plume detection system remains an elusive goal for imaging spectroscopy missions, despite recent advances in deep learning approaches. With the dramatic increase in data availability, however, automation continues to increase in importance for natural and anthropogenic emissions monitoring. This work reviews and addresses several key obstacles in the field: data and label quality control, prevention of spatiotemporal biases, and correctly aligned modeling objectives. We demonstrate through rigorous experiments using multicampaign data from airborne and spaceborne instruments that convolutional neural networks (CNNs) are able to achieve operational detection performance when these obstacles are alleviated. We demonstrate that a multitask model that learns both instance detection and pixelwise segmentation simultaneously can successfully lead towards an operational pathway. We evaluate the model's plume detectability across emission source types and regions, identifying thresholds for operational deployment. Finally, we provide analysis-ready data, models, and source code for reproducibility, and work to define a set of best practices and validation standards to facilitate future contributions to the field.
Multimodal Federated Learning: A Survey through the Lens of Different FL Paradigms
2025-05-27
Yuanzhe Peng, Jieming Bian, Lei Wang, Yin Huang, Jie Xu
Multimodal Federated Learning (MFL) lies at the intersection of two pivotal research areas: leveraging complementary information from multiple modalities to improve downstream inference performance and enabling distributed training to enhance efficiency and preserve privacy. Despite the growing interest in MFL, there is currently no comprehensive taxonomy that organizes MFL through the lens of different Federated Learning (FL) paradigms. This perspective is important because multimodal data introduces distinct challenges across various FL settings. These challenges, including modality heterogeneity, privacy heterogeneity, and communication inefficiency, are fundamentally different from those encountered in traditional unimodal or non-FL scenarios. In this paper, we systematically examine MFL within the context of three major FL paradigms: horizontal FL (HFL), vertical FL (VFL), and hybrid FL. For each paradigm, we present the problem formulation, review representative training algorithms, and highlight the most prominent challenge introduced by multimodal data in distributed settings. We also discuss open challenges and provide insights for future research. By establishing this taxonomy, we aim to uncover the novel challenges posed by multimodal data from the perspective of different FL paradigms and to offer a new lens through which to understand and advance the development of MFL.
The automated generation of radiology reports from chest X-ray images holds significant promise in enhancing diagnostic workflows while preserving patient privacy. Traditional centralized approaches often require sensitive data transfer, posing privacy concerns. To address this, the study proposes a Multimodal Federated Learning framework for chest X-ray report generation using the IU-Xray dataset. The system utilizes a Vision Transformer (ViT) as the encoder and GPT-2 as the report generator, enabling decentralized training without sharing raw data. Three Federated Learning (FL) aggregation strategies: FedAvg, Krum Aggregation and a novel Loss-aware Federated Averaging (L-FedAvg) were evaluated. Among these, Krum Aggregation demonstrated superior performance across lexical and semantic evaluation metrics such as ROUGE, BLEU, BERTScore and RaTEScore. The results show that FL can match or surpass centralized models in generating clinically relevant and semantically rich radiology reports. This lightweight and privacy-preserving framework paves the way for collaborative medical AI development without compromising data confidentiality.
Quantum correlation dynamics and in-medium 3$\leftrightarrow$3 collisions of fermions
2025-05-27
Wolfgang Cassing
In this study we aim for quantifying the role of in-medium 3$\leftrightarrow$3 collisions for systems of fermions which initially are out-off equilibrium. The formulation of the 3-body dynamics is based on the equations of motion method for identical fermions -- also denoted as quantum correlation dynamics -- and presented in detail. The on-shell 2-body collision integral is briefly reviewed and the on-shell 3-body collision integral is derived on the basis of the same two-body interaction in leading order. The resulting equations obey particle number as well as energy-momentum conservation. For a quantification of the relative impact of 3-body interactions we employ a model study for a homogeneous system in space in a finite box with periodic boundary conditions. We address spin-isospin symmetric nuclear matter systems with momentum distributions that are given by shifted Fermi spheres (without overlap) as encountered in the initial phase of nucleus-nucleus collisions after contact. The results for the relaxation times -- employing an effective 2-body interaction -- are compared to Boltzmann-Uehling-Uhlenbeck (BUU) transport calculations in the continuum limit for the same bombarding energies and are found to agree on the level of a few percent. We find that the additional 3-body interactions reduce the relaxation times up to a factor of 3 at 130 A$\cdot$MeV. Furthermore, it is shown in BUU transport calculations that an enhanced stopping by 3$\leftrightarrow$3 collisions shows up in the angular distribution of energetic nucleons ($>$ 60 MeV) e.g. in central $^{97}_{45}Rh$ collisions at 40 A$\cdot$MeV that lead to the formation of a compound nucleus. The angular distribution of the energetic nucleons changes from a slightly forward peaked angular distribution to a slightly sidewards peaked angular distribution which might be controlled experimentally.
Expert Survey: AI Reliability & Security Research Priorities
2025-05-27
Joe O'Brien, Jeremy Dolan, Jay Kim, Jonah Dykhuizen, Jeba Sania, Sebastian Becker, Jam Kraprayoon, Cara Labrador
Our survey of 53 specialists across 105 AI reliability and security research areas identifies the most promising research prospects to guide strategic AI R&D investment. As companies are seeking to develop AI systems with broadly human-level capabilities, research on reliability and security is urgently needed to ensure AI's benefits can be safely and broadly realized and prevent severe harms. This study is the first to quantify expert priorities across a comprehensive taxonomy of AI safety and security research directions and to produce a data-driven ranking of their potential impact. These rankings may support evidence-based decisions about how to effectively deploy resources toward AI reliability and security research.
Visual presentations are vital for effective communication. Early attempts to automate their creation using deep learning often faced issues such as poorly organized layouts, inaccurate text summarization, and a lack of image understanding, leading to mismatched visuals and text. These limitations restrict their application in formal contexts like business and scientific research. To address these challenges, we propose PreGenie, an agentic and modular framework powered by multimodal large language models (MLLMs) for generating high-quality visual presentations. PreGenie is built on the Slidev presentation framework, where slides are rendered from Markdown code. It operates in two stages: (1) Analysis and Initial Generation, which summarizes multimodal input and generates initial code, and (2) Review and Re-generation, which iteratively reviews intermediate code and rendered slides to produce final, high-quality presentations. Each stage leverages multiple MLLMs that collaborate and share information. Comprehensive experiments demonstrate that PreGenie excels in multimodal understanding, outperforming existing models in both aesthetics and content consistency, while aligning more closely with human design preferences.
Unified weak lensing constraints on the evolution of the mass -- X-ray luminosity relation for galaxy clusters
2025-05-27
Isabel Pederneiras, Alexis Finoguenov, Eduardo Cypriano, Johan Comparat, Kimmo Kiiveri, Ludovic van Waerbeke, Renato Dupke
Scaling relations between galaxy cluster properties are crucial for understanding cosmology and baryonic physics. Rigorous calibration of the $M-L_X$ relation, employing weak lensing mass and consistent statistical methodology, is challenging due to heterogeneous cluster samples. The release of LEGACY imaging data introduced the possibility of unifying the cluster selection. We present the all-sky extension of the CODEX catalog based on LEGACY data and introduce a Bayesian framework for calibrating the X-ray luminosity-mass relation, derived for 100 clusters with weak lensing mass measurements. Using the X-ray luminosity estimates for those clusters from ROSAT All-Sky Survey (RASS) data, we perform a power-law fit to the $M-L_X$ relation. Furthermore, taking advantage of the recently released eROSITA data (eRASS1), we assess the impact of point source contamination on cluster fluxes for 42 clusters in the eRASS1 footprint. The RASS fit yields a slope of $\beta = 0.75 \pm 0.09$, 1.7$\sigma$ lower than the best self-similar prediction, with marginal evidence for the redshift evolution of the normalization ($\gamma = 0.65 \pm 0.43$). As for the eRASS1 analysis, the slope is substantially steeper, $\beta = 1.11 \pm 0.15$, and in further agreement with the prediction of self-similarity. No additional evolution is also seen ($\gamma = 0.004 \pm 0.790 $). While our results provide the practical means for cosmological studies of both RASS and eRASS data, the link to cluster physics is much cleaner after the cluster flux contamination is reduced. We also analyzed the impact of the selection function on calibration, finding that its full modeling is essential.
First IFU observations of two GRB host galaxies at cosmic noon with JWST/NIRSpec
2025-05-27
B. Topçu, P. Schady, S. Wuyts, A. Inkenhaag, M. Arabsalmani, H. -W. Chen, L. Christensen, V. D'Elia, J. P. U. Fynbo, K. E. Heintz, P. Jakobsson, T. Laskar, A. Levan, G. Pugliese, A. Rossi, R. L. C. Starling, N. R. Tanvir, P. Wiseman, R. M. Yates
Long gamma-ray bursts (GRBs) serve as powerful probes of distant galaxies. Their luminous afterglow pinpoints galaxies independent of luminosity, in contrast to most flux-limited surveys. Nevertheless, GRB-selected galaxy samples are not free from bias, instead tracing the conditions favoured by the progenitor stars. Characterising the galaxy populations traced by GRBs is therefore important both to effectively use GRBs as probes as well as to place stronger constraints on the progenitor stars capable of forming long GRBs. Spatially-resolved spectroscopic observations with integral field units (IFUs) provide valuable insights into the interstellar medium and stellar populations of GRB host galaxies. In this paper we present results of the first two GRB host galaxies observed with the JWST/NIRSpec IFU with a spatial resolution of ~ 1.6 kpc; the hosts of GRB 150403A and GRB 050820A at redshifts z ~ 2.06 and z ~ 2.61, respectively. The data reveal two complex galaxy environments made up of two or more star forming galaxies that are likely interacting given their small spatial separation (< 20 kpc) and line of sight velocity offsets (< 100 km/s). The measured gas-phase metallicity, star formation rates (SFRs), and key diagnostic line ratios for each of the detected galaxies are overall consistent with the properties of other star forming galaxies and GRB hosts at z > 2. However, differences in the SFR and metallicities of the interacting galaxies highlight the importance of spatially resolved observations in order to accurately characterise the galaxy properties traced by GRBs.
The Feasibility of Topic-Based Watermarking on Academic Peer Reviews
2025-05-27
Alexander Nemecek, Yuzhou Jiang, Erman Ayday
Large language models (LLMs) are increasingly integrated into academic workflows, with many conferences and journals permitting their use for tasks such as language refinement and literature summarization. However, their use in peer review remains prohibited due to concerns around confidentiality breaches, hallucinated content, and inconsistent evaluations. As LLM-generated text becomes more indistinguishable from human writing, there is a growing need for reliable attribution mechanisms to preserve the integrity of the review process. In this work, we evaluate topic-based watermarking (TBW), a lightweight, semantic-aware technique designed to embed detectable signals into LLM-generated text. We conduct a comprehensive assessment across multiple LLM configurations, including base, few-shot, and fine-tuned variants, using authentic peer review data from academic conferences. Our results show that TBW maintains review quality relative to non-watermarked outputs, while demonstrating strong robustness to paraphrasing-based evasion. These findings highlight the viability of TBW as a minimally intrusive and practical solution for enforcing LLM usage in peer review.
Fragmentation in Collapsar Disks: Migration, Growth, and Emission
2025-05-27
Y. Lerner, N. C. Stone, D. D. Ofengeim
We present a parameter survey of fragmentation in collapsar disks, using a revised version of the Chen & Beloborodov (2007) model that determines the structure of steady state hyperaccretion disks in a general relativistic and neutrino cooled framework. We map out the range of disk conditions leading to gravitational instability alongside an exploration of the dimensionless cooling time $\beta$, which together determine whether fragmentation is likely to occur. We estimate the initial mass and density of fragments, finding that they occupy a unique region in the space of self-gravitating compact objects, with masses $M_{\rm f} \sim 10^{-3} M_\odot -10^{-1} M_\odot$ and densities $\rho_{\rm f}\sim 10^8-10^{11}~{\rm g~cm}^{-3}$. We then calculate their migration and mass growth (via Bondi-Hoyle accretion) as they inspiral through the collapsar disk. During a fragment's migration to the central black hole, it can grow its mass up to a range $M_{\rm f}\sim 10^{-1} M_\odot - 1 M_\odot$. In most cases, the final fragment mass is larger than the minimum cold stable neutron star mass but much smaller than any observed neutron star. The fragment briefly achieves peak accretion rates comparable to (or even larger than) that of the central engine. We propose that these bound fragments may give rise to observable astrophysical phenomena, and we approximately model two of these: (i) gamma ray burst variability produced by a secondary, fragment-launched jet; (ii) the generation of non-vacuum gravitational waveforms accompanied by electromagnetic counterparts.
GalSBI-SPS: a stellar population synthesis-based galaxy population model for cosmology and galaxy evolution applications
2025-05-27
Luca Tortorelli, Silvan Fischbacher, Daniel Grün, Alexandre Refregier, Sabine Bellstedt, Aaron S. G. Robotham, Tomasz Kacprzak
Next generation photometric and spectroscopic surveys will enable unprecedented tests of the concordance cosmological model and of galaxy formation and evolution. Fully exploiting their potential requires a precise understanding of the selection effects on galaxies and biases on measurements of their properties, required, above all, for accurate estimates of redshift distributions n(z). Forward-modelling offers a powerful framework to simultaneously recover galaxy n(z)s and characterise the observed galaxy population. We present GalSBI-SPS, a new SPS-based galaxy population model that generates realistic galaxy catalogues, which we use to forward-model HSC data in the COSMOS field. GalSBI-SPS samples galaxy physical properties, computes magnitudes with ProSpect, and simulates HSC images in the COSMOS field with UFig. We measure photometric properties consistently in real data and simulations. We compare redshift distributions, photometric and physical properties to observations and to GalSBI. GalSBI-SPS reproduces the observed grizy magnitude, colour, and size distributions down to i<23. Median differences in magnitudes and colours remain below 0.14 mag, with the model covering the full colour space spanned by HSC. Galaxy sizes are offset by 0.2 arcsec on average and some tension exists in the g-r colour, but the latter is comparable to that seen in GalSBI. Redshift distributions show a mild positive offset (0.01-0.08) in the mean. GalSBI-SPS qualitatively reproduces the stellar mass-SFR and size-stellar mass relations seen in COSMOS2020. GalSBI-SPS provides a realistic, survey-independent galaxy population description at a Stage-III depth using only parameters from the literature. Its predictive power will improve significantly when constrained against observed data using SBI, providing accurate redshift distributions satisfying the stringent requirements set by Stage IV surveys.
Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives
2025-05-27
Yajiao Liu, Congliang Chen, Junchi Yang, Ruoyu Sun
Training large language models with data collected from various domains can improve their performance on downstream tasks. However, given a fixed training budget, the sampling proportions of these different domains significantly impact the model's performance. How can we determine the domain weights across different data domains to train the best-performing model within constrained computational resources? In this paper, we provide a comprehensive overview of existing data mixture methods. First, we propose a fine-grained categorization of existing methods, extending beyond the previous offline and online classification. Offline methods are further grouped into heuristic-based, algorithm-based, and function fitting-based methods. For online methods, we categorize them into three groups: online min-max optimization, online mixing law, and other approaches by drawing connections with the optimization frameworks underlying offline methods. Second, we summarize the problem formulations, representative algorithms for each subtype of offline and online methods, and clarify the relationships and distinctions among them. Finally, we discuss the advantages and disadvantages of each method and highlight key challenges in the field of data mixture.
Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment
2025-05-27
Ze Chen, Shaode Yu
Kolmogorov-Arnold Network (KAN) has attracted growing interest for its strong function approximation capability. In our previous work, KAN and its variants were explored in score regression for blind image quality assessment (BIQA). However, these models encounter challenges when processing high-dimensional features, leading to limited performance gains and increased computational cost. To address these issues, we propose TaylorKAN that leverages the Taylor expansions as learnable activation functions to enhance local approximation capability. To improve the computational efficiency, network depth reduction and feature dimensionality compression are integrated into the TaylorKAN-based score regression pipeline. On five databases (BID, CLIVE, KonIQ, SPAQ, and FLIVE) with authentic distortions, extensive experiments demonstrate that TaylorKAN consistently outperforms the other KAN-related models, indicating that the local approximation via Taylor expansions is more effective than global approximation using orthogonal functions. Its generalization capacity is validated through inter-database experiments. The findings highlight the potential of TaylorKAN as an efficient and robust model for high-dimensional score regression.
Enhancing Selection of Climate Tech Startups with AI -- A Case Study on Integrating Human and AI Evaluations in the ClimaTech Great Global Innovation Challenge
2025-05-27
Jennifer Turliuk, Alejandro Sevilla, Daniela Gorza, Tod Hynes
This case study examines the ClimaTech Great Global Innovation Challenge's approach to selecting climate tech startups by integrating human and AI evaluations. The competition aimed to identify top startups and enhance the accuracy and efficiency of the selection process through a hybrid model. Research shows data-driven approaches help VC firms reduce bias and improve decision-making. Machine learning models have outperformed human investors in deal screening, helping identify high-potential startups. Incorporating AI aimed to ensure more equitable and objective evaluations. The methodology included three phases: initial AI review, semi-finals judged by humans, and finals using a hybrid weighting. In phase one, 57 applications were scored by an AI tool built with StackAI and OpenAI's GPT-4o, and the top 36 advanced. In the semi-finals, human judges, unaware of AI scores, evaluated startups on team quality, market potential, and technological innovation. Each score - human or AI - was weighted equally, resulting in 75 percent human and 25 percent AI influence. In the finals, with five human judges, weighting shifted to 83.3 percent human and 16.7 percent AI. There was a moderate positive correlation between AI and human scores - Spearman's = 0.47 - indicating general alignment with key differences. Notably, the final four startups, selected mainly by humans, were among those rated highest by the AI. This highlights the complementary nature of AI and human judgment. The study shows that hybrid models can streamline and improve startup assessments. The ClimaTech approach offers a strong framework for future competitions by combining human expertise with AI capabilities.
Time plays a critical role in how information is generated, retrieved, and interpreted. In this survey, we provide a comprehensive overview of Temporal Information Retrieval and Temporal Question Answering, two research areas aimed at handling and understanding time-sensitive information. As the amount of time-stamped content from sources like news articles, web archives, and knowledge bases increases, systems must address challenges such as detecting temporal intent, normalizing time expressions, ordering events, and reasoning over evolving or ambiguous facts. These challenges are critical across many dynamic and time-sensitive domains, from news and encyclopedias to science, history, and social media. We review both traditional approaches and modern neural methods, including those that use transformer models and Large Language Models (LLMs). We also review recent advances in temporal language modeling, multi-hop reasoning, and retrieval-augmented generation (RAG), alongside benchmark datasets and evaluation strategies that test temporal robustness, recency awareness, and generalization.
RedAHD: Reduction-Based End-to-End Automatic Heuristic Design with Large Language Models
2025-05-26
Nguyen Thach, Aida Riahifar, Nathan Huynh, Hau Chan
Solving NP-hard combinatorial optimization problems (COPs) (e.g., traveling salesman problems (TSPs) and capacitated vehicle routing problems (CVRPs)) in practice traditionally involves handcrafting heuristics or specifying a search space for finding effective heuristics. The main challenges from these approaches, however, are the sheer amount of domain knowledge and implementation efforts required from human experts. Recently, significant progress has been made to address these challenges, particularly by using large language models (LLMs) to design heuristics within some predetermined generalized algorithmic framework (GAF, e.g., ant colony optimization and guided local search) for building key functions/components (e.g., a priori information on how promising it is to include each edge in a solution for TSP and CVRP). Although existing methods leveraging this idea have shown to yield impressive optimization performance, they are not fully end-to-end and still require considerable manual interventions. In this paper, we propose a novel end-to-end framework, named RedAHD, that enables these LLM-based heuristic design methods to operate without the need of GAFs. More specifically, RedAHD employs LLMs to automate the process of reduction, i.e., transforming the COP at hand into similar COPs that are better-understood, from which LLM-based heuristic design methods can design effective heuristics for directly solving the transformed COPs and, in turn, indirectly solving the original COP. Our experimental results, evaluated on six COPs, show that RedAHD is capable of designing heuristics with competitive or improved results over the state-of-the-art methods with minimal human involvement.
The rapid evolution of large language models in natural language processing has substantially elevated their semantic understanding and logical reasoning capabilities. Such proficiencies have been leveraged in autonomous driving systems, contributing to significant improvements in system performance. Models such as OpenAI o1 and DeepSeek-R1, leverage Chain-of-Thought (CoT) reasoning, an advanced cognitive method that simulates human thinking processes, demonstrating remarkable reasoning capabilities in complex tasks. By structuring complex driving scenarios within a systematic reasoning framework, this approach has emerged as a prominent research focus in autonomous driving, substantially improving the system's ability to handle challenging cases. This paper investigates how CoT methods improve the reasoning abilities of autonomous driving models. Based on a comprehensive literature review, we present a systematic analysis of the motivations, methodologies, challenges, and future research directions of CoT in autonomous driving. Furthermore, we propose the insight of combining CoT with self-learning to facilitate self-evolution in driving systems. To ensure the relevance and timeliness of this study, we have compiled a dynamic repository of literature and open-source projects, diligently updated to incorporate forefront developments. The repository is publicly available at https://github.com/cuiyx1720/Awesome-CoT4AD.
Estimation of multivariate traces of states given partial classical information
2025-05-26
Kyrylo Simonov, Rafael Wagner, Ernesto Galvão
Bargmann invariants of order $n$, defined as multivariate traces of quantum states $\text{Tr}[\rho_1\rho_2 \ldots \rho_n]$, are useful in applications ranging from quantum metrology to certification of nonclassicality. A standard quantum circuit used to estimate Bargmann invariants is the cycle test. In this work, we propose generalizations of the cycle test applicable to a situation where $n$ systems are given and unknown, and classical information on $m$ systems ($m\leq n)$ is available, allowing estimation of invariants of order $n+m$. Our main result is a generalization of results on 4th order invariants appearing in double weak values from Chiribella et al. [Phys. Rev. Research 6, 043043 (2024)]. The use of classical information on some of the states enables circuits on fewer qubits and with fewer gates, decreasing the experimental requirements for their estimation, and enabling multiple applications we briefly review.
2025-05-26
Umut Cihan, Arda İçöz, Vahid Haratian, Eray Tüzün
Context: Code reviews are crucial for software quality. Recent AI advances have allowed large language models (LLMs) to review and fix code; now, there are tools that perform these reviews. However, their reliability and accuracy have not yet been systematically evaluated. Objective: This study compares different LLMs' performance in detecting code correctness and suggesting improvements. Method: We tested GPT4o and Gemini 2.0 Flash on 492 AI generated code blocks of varying correctness, along with 164 canonical code blocks from the HumanEval benchmark. To simulate the code review task objectively, we expected LLMs to assess code correctness and improve the code if needed. We ran experiments with different configurations and reported on the results. Results: With problem descriptions, GPT4o and Gemini 2.0 Flash correctly classified code correctness 68.50% and 63.89% of the time, respectively, and corrected the code 67.83% and 54.26% of the time for the 492 code blocks of varying correctness. Without problem descriptions, performance declined. The results for the 164 canonical code blocks differed, suggesting that performance depends on the type of code. Conclusion: LLM code reviews can help suggest improvements and assess correctness, but there is a risk of faulty outputs. We propose a process that involves humans, called the "Human in the loop LLM Code Review" to promote knowledge sharing while mitigating the risk of faulty outputs.
A frequentist view on the two-body decaying dark matter model
2025-05-26
Thomas Montandon, Elsa M. Teixeira, Adèle Poudou, Vivian Poulin
Decaying dark matter (DDM) has emerged as an interesting framework to extend the Lambda-cold-dark-matter (LCDM) model, as many particle physics models predict that dark matter may not be stable over cosmic time and can impact structure formation. In particular, a model in which DM decays at a rate $\Gamma$ and imprints a velocity kick $v$ onto its decay products leads to a low amplitude of fluctuations, as quantified by the parameter $S_8$, in better agreement with that measured by some past weak lensing surveys. Bayesian analyses have provided mixed conclusions regarding its viability, with a reconstructed clustering amplitude only slightly below the standard LCDM value. In this paper, we perform a frequentist analysis of Planck+BAO data. We find $1\sigma$ constraints on the half-life of $6.93^{+7.88}_{-2.85}$Gyr and a velocity kick of $1250^{+1450}_{-1000}$km/s which differ from their Bayesian counterparts, indicating the presence of volume effects. Moreover, we find that under the DDM model, the frequentist analysis predicts lower values of $S_8$, in agreement with those found by KiDS-1000 and DES-Y3 at $1.5\sigma$. We further show that previously derived KiDS-1000 constraints that appeared to exclude the best-fit model from Planck data were driven by priors on the primordial parameters $A_s$ and $n_s$. When those are removed from the analysis, KiDS-1000 constraints on the DDM parameters are fully relaxed. It is only when applying Planck-informed priors on $A_s$ and $n_s$ to the KiDS-1000 analysis that one can constrain the model. We further highlight that in the absence of such priors, the region of scales best-measured by KiDS-1000 does not exactly match the $S_8$ kernel, but rather a slightly smaller range of scales centered around $k\sim 0.3\, h/$Mpc. One must thus be careful in applying $S_8$ constraints to a model instead of the full data likelihood.
Leveraging Descriptions of Emotional Preferences in Recommender Systems
2025-05-26
Tonmoy Hasan, Razvan Bunescu
The affective attitude of liking a recommended item reflects just one category in a wide spectrum of affective phenomena that also includes emotions such as entranced or intrigued, moods such as cheerful or buoyant, as well as more fine-grained affective states, such as "pleasantly surprised by the conclusion". In this paper, we introduce a novel recommendation task that can leverage a virtually unbounded range of affective states sought explicitly by the user in order to identify items that, upon consumption, are likely to induce those affective states. Correspondingly, we create a large dataset of user preferences containing expressions of fine-grained affective states that are mined from book reviews, and propose a Transformer-based architecture that leverages such affective expressions as input. We then use the resulting dataset of affective states preferences, together with the linked users and their histories of book readings, ratings, and reviews, to train and evaluate multiple recommendation models on the task of matching recommended items with affective preferences. Experiments show that the best results are obtained by models that can utilize textual descriptions of items and user affective preferences.
The evolving categories multinomial distribution: introduction with applications to movement ecology and vote transfer
2025-05-26
Ricardo Carrizo Vergara, Marc Kéry, Trevor Hefley
We introduce the evolving categories multinomial (ECM) distribution for multivariate count data taken over time. This distribution models the counts of individuals following iid stochastic dynamics among categories, with the number and identity of the categories also evolving over time. We specify the one-time and two-times marginal distributions of the counts and the first and second order moments. When the total number of individuals is unknown, placing a Poisson prior on it yields a new distribution (ECM-Poisson), whose main properties we also describe. Since likelihoods are intractable or impractical, we propose two estimating functions for parameter estimation: a Gaussian pseudo-likelihood and a pairwise composite likelihood. We show two application scenarios: the inference of movement parameters of animals moving continuously in space-time with irregular survey regions, and the inference of vote transfer in two-rounds elections. We give three illustrations: a simulation study with Ornstein-Uhlenbeck moving individuals, paying special attention to the autocorrelation parameter; the inference of movement and behavior parameters of lesser prairie-chickens; and the estimation of vote transfer in the 2021 Chilean presidential election.
This Special Issue on "Top Quark at the New Physics Frontier" is devoted to the most massive fundamental elementary particle known, the top quark. The aim is to provide a comprehensive review of the current status and prospects of top quark physics at the Large Hadron Collider (LHC) and future colliders. We included articles that emphasize where the present understanding is incomplete and suggest new directions for research in this area.
Engineering Trustworthy Machine-Learning Operations with Zero-Knowledge Proofs
2025-05-26
Filippo Scaramuzza, Giovanni Quattrocchi, Damian A. Tamburri
As Artificial Intelligence (AI) systems, particularly those based on machine learning (ML), become integral to high-stakes applications, their probabilistic and opaque nature poses significant challenges to traditional verification and validation methods. These challenges are exacerbated in regulated sectors requiring tamper-proof, auditable evidence, as highlighted by apposite legal frameworks, e.g., the EU AI Act. Conversely, Zero-Knowledge Proofs (ZKPs) offer a cryptographic solution that enables provers to demonstrate, through verified computations, adherence to set requirements without revealing sensitive model details or data. Through a systematic survey of ZKP protocols, we identify five key properties (non-interactivity, transparent setup, standard representations, succinctness, and post-quantum security) critical for their application in AI validation and verification pipelines. Subsequently, we perform a follow-up systematic survey analyzing ZKP-enhanced ML applications across an adaptation of the Team Data Science Process (TDSP) model (Data & Preprocessing, Training & Offline Metrics, Inference, and Online Metrics), detailing verification objectives, ML models, and adopted protocols. Our findings indicate that current research on ZKP-Enhanced ML primarily focuses on inference verification, while the data preprocessing and training stages remain underexplored. Most notably, our analysis identifies a significant convergence within the research domain toward the development of a unified Zero-Knowledge Machine Learning Operations (ZKMLOps) framework. This emerging framework leverages ZKPs to provide robust cryptographic guarantees of correctness, integrity, and privacy, thereby promoting enhanced accountability, transparency, and compliance with Trustworthy AI principles.
Tri-band Aperture-shared Antenna Array Using Scalable FSS-based Electromagnetic Transparent Structure
2025-05-26
Yongzheng Li, Wanchen Yang, Quan Xue, Wenquan Che
A novel dual-band electromagnetic transparent structure (DBTS) is firstly proposed and used to realize a tri-band aperture-shared antenna array. The DBTS achieves two tunable electromagnetic transparent frequency bands by periodically loading capacitive patches and meander lines to an inductive strip. Meanwhile, the DBTS features flexible frequency band scalability by loading additional serial L-C tanks. In tri-band aperture-shared arrays, the low-band (LB) dipole induces a deterioration in the radiation pattern of the middle-band (MB) and high-band (HB) antennas as a consequence of the shielding effects. To address this issue, the DBTS is utilized to construct the LB dipole, with its electromagnetic transparent bands allocated at MB and HB simultaneously. Consequently, a tri-band aperture-shared array is realized by combining the LB dipole with a dual-band subarray that operates at MB and HB within a shared aperture. The proposed array realizes an aperture-shared operation within the frequency ranges covering 0.65-0.88GHz (LB), 1.92-2.18GHz (MB) and 3.3-3.8GHz (HB). The LB dipole induces minimal shielding to the MB and HB antennas, resulting in their restored radiation performance with broadside gain deviation of less than 0.6dB.
Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities
2025-05-26
Chuangtao Ma, Yongrui Chen, Tianxing Wu, Arijit Khan, Haofen Wang
Large language models (LLMs) have demonstrated remarkable performance on question-answering (QA) tasks because of their superior capabilities in natural language understanding and generation. However, LLM-based QA struggles with complex QA tasks due to poor reasoning capacity, outdated knowledge, and hallucinations. Several recent works synthesize LLMs and knowledge graphs (KGs) for QA to address the above challenges. In this survey, we propose a new structured taxonomy that categorizes the methodology of synthesizing LLMs and KGs for QA according to the categories of QA and the KG's role when integrating with LLMs. We systematically survey state-of-the-art advances in synthesizing LLMs and KGs for QA and compare and analyze these approaches in terms of strength, limitations, and KG requirements. We then align the approaches with QA and discuss how these approaches address the main challenges of different complex QA. Finally, we summarize the advancements, evaluation metrics, and benchmark datasets and highlight open challenges and opportunities.
2025-05-26
Xiaowen Ling, Zhiqiang Li, Yanbin Wang, Zhuhong You
As protein informatics advances rapidly, the demand for enhanced predictive accuracy, structural analysis, and functional understanding has intensified. Transformer models, as powerful deep learning architectures, have demonstrated unprecedented potential in addressing diverse challenges across protein research. However, a comprehensive review of Transformer applications in this field remains lacking. This paper bridges this gap by surveying over 100 studies, offering an in-depth analysis of practical implementations and research progress of Transformers in protein-related tasks. Our review systematically covers critical domains, including protein structure prediction, function prediction, protein-protein interaction analysis, functional annotation, and drug discovery/target identification. To contextualize these advancements across various protein domains, we adopt a domain-oriented classification system. We first introduce foundational concepts: the Transformer architecture and attention mechanisms, categorize Transformer variants tailored for protein science, and summarize essential protein knowledge. For each research domain, we outline its objectives and background, critically evaluate prior methods and their limitations, and highlight transformative contributions enabled by Transformer models. We also curate and summarize pivotal datasets and open-source code resources to facilitate reproducibility and benchmarking. Finally, we discuss persistent challenges in applying Transformers to protein informatics and propose future research directions. This review aims to provide a consolidated foundation for the synergistic integration of Transformer and protein informatics, fostering further innovation and expanded applications in the field.
Explanation User Interfaces: A Systematic Literature Review
2025-05-26
Eleonora Cappuccio, Andrea Esposito, Francesco Greco, Giuseppe Desolda, Rosa Lanzilotti, Salvatore Rinzivillo
Artificial Intelligence (AI) is one of the major technological advancements of this century, bearing incredible potential for users through AI-powered applications and tools in numerous domains. Being often black-box (i.e., its decision-making process is unintelligible), developers typically resort to eXplainable Artificial Intelligence (XAI) techniques to interpret the behaviour of AI models to produce systems that are transparent, fair, reliable, and trustworthy. However, presenting explanations to the user is not trivial and is often left as a secondary aspect of the system's design process, leading to AI systems that are not useful to end-users. This paper presents a Systematic Literature Review on Explanation User Interfaces (XUIs) to gain a deeper understanding of the solutions and design guidelines employed in the academic literature to effectively present explanations to users. To improve the contribution and real-world impact of this survey, we also present a framework for Human-cEnteRed developMent of Explainable user interfaceS (HERMES) to guide practitioners and academics in the design and evaluation of XUIs.
On the Same Page: Dimensions of Perceived Shared Understanding in Human-AI Interaction
2025-05-26
Qingyu Liang, Jaime Banks
Shared understanding plays a key role in the effective communication in and performance of human-human interactions. With the increasingly common integration of AI into human contexts, the future of personal and workplace interactions will likely see human-AI interaction (HAII) in which the perception of shared understanding is important. Existing literature has addressed the processes and effects of PSU in human-human interactions, but the construal remains underexplored in HAII. To better understand PSU in HAII, we conducted an online survey to collect user reflections on interactions with a large language model when it sunderstanding of a situation was thought to be similar to or different from the participant's. Through inductive thematic analysis, we identified eight dimensions comprising PSU in human-AI interactions: Fluency, aligned operation, fluidity, outcome satisfaction, contextual awareness, lack of humanlike abilities, computational limits, and suspicion.
Multirate methods for ordinary differential equations
2025-05-26
Michael Günther, Adrian Sandu
This survey provides an overview of state-of-the art multirate schemes, which exploit the different time scales in the dynamics of a differential equation model by adapting the computational costs to different activity levels of the system. We start the discussion with the straightforward approach based on interpolating and extrapolating the slow--fast coupling variables; the multirate Euler scheme, used as a base example, falls into this class. Next we discuss higher order multirate schemes that generalize classical singlerate linear multistep, Runge-Kutta, and extrapolation methods.
Cold Jupiters and small planets: friends, foes, or indifferent? A search for correlations with the largest exoplanet samples
2025-05-26
A. S. Bonomo, L. Naponiello, E. Pezzetta, A. Sozzetti, D. Gandolfi, R. Wittenmyer, M. Pinamonti
Finding out whether there is any correlation between the presence of short-period small planets (SPs) with $P\lesssim100~d$ ($a \lesssim0.4~AU$) and $10.1$) metallicity as well as at average and sub-intervals of stellar mass, namely 0.6-0.8, 0.8-1.0, and 1.0-1.2 $M_\odot$, with (i) a carefully-selected sample of 217 SP systems, and (ii) a large comparison sample of 1167 solar-type stars. We determined the integrated occurrence rate of CJs in SP systems to be $f_{CJ|SP}=11.1^{+2.5}_{-1.8}\%$; this is consistent with the estimated frequencies of CJs in both the comparison sample ($f_{CJ}=9.8^{+0.9}_{-0.8}\%$) and the HARPS-N survey of transiting SP systems. We found a possible correlation ($f_{CJ|SP}>f_{CJ}$) only at super-solar mass and metallicity, though with a statistical confidence less than $3\sigma$. To test some theoretical predictions, we also searched for possible SP-CJ relations as a function of SP and CJ multiplicity as well as SP composition, and found none. We show that the architectures of SP systems are not indifferent to the presence of CJs as the multiplicity of SPs strongly depends on the CJ eccentricity, as expected from planetary dynamics. A more comprehensive understanding of the relation between SPs and CJs requires larger samples of SP systems. [Abridged]
The Many Challenges of Human-Like Agents in Virtual Game Environments
2025-05-26
Maciej Świechowski, Dominik Ślęzak
Human-like agents are an increasingly important topic in games and beyond. Believable non-player characters enhance the gaming experience by improving immersion and providing entertainment. They also offer players the opportunity to engage with AI entities that can function as opponents, teachers, or cooperating partners. Additionally, in games where bots are prohibited -- and even more so in non-game environments -- there is a need for methods capable of identifying whether digital interactions occur with bots or humans. This leads to two fundamental research questions: (1) how to model and implement human-like AI, and (2) how to measure its degree of human likeness. This article offers two contributions. The first one is a survey of the most significant challenges in implementing human-like AI in games (or any virtual environment featuring simulated agents, although this article specifically focuses on games). Thirteen such challenges, both conceptual and technical, are discussed in detail. The second is an empirical study performed in a tactical video game that addresses the research question: "Is it possible to distinguish human players from bots (AI agents) based on empirical data?" A machine-learning approach using a custom deep recurrent convolutional neural network is presented. We hypothesize that the more challenging it is to create human-like AI for a given game, the easier it becomes to develop a method for distinguishing humans from AI-driven players.
Universal scaling of intra-urban climate fluctuations
2025-05-26
Marc Duran-Sala, Martin Hendrick, Gabriele Manoli
Urban-induced changes in local microclimate, such as the urban heat island effect and air pollution, are known to vary with city size, leading to distinctive relations between average climate variables and city-scale quantities (e.g., total population or area). However, these approaches suffer from biases related to the choice of city boundaries and they neglect intra-urban variations of urban characteristics. Here we use high-resolution data of urban temperatures, air quality, population counts, and street intersections from 142 cities worldwide and show that their marginal and joint probability distributions follow universal scaling functions. By using a logarithmic relation between urban spatial features and climate variables, we show that average street network properties are sufficient to characterize the entire variability of the temperature and air pollution fields observed within and across cities. We further demonstrate that traditional models linking climate variables to the distance from the city center fail to reproduce the observed distributions unless the stochasticity of urban structure is fully considered. These findings provide a unified statistical framework for characterizing intra-urban climate variability, with important implications for climate modelling and urban planning.
High entanglement regimes in the Weisskopf-Wigner theory for spontaneous decay
2025-05-26
J. C. C. Capella, A. Fonseca, Pablo L. Saldanha, D. Felinto
In this work we review the Weisskopf-Wigner formalism for spontaneous emission considering the spatial modes of light as well as external atomic degrees of freedom which we introduce in the theory by modeling the atom as a wavepacket in momentum space with a given initial uncertainty. We perform a purity calculation in order to quantify the entanglement encoded in the momentum variables of the atom-photon system. Our purity calculations reveal two high entanglement regimes depending on the initial atomic momentum uncertainty: $1)$ the Recoil entanglement regime (which arises in the small momentum uncertainty region) where recoil effects dominate the mechanisms that originate entanglement, and $2)$ the Doppler entanglement regime (in the large momentum uncertainty region) where homogeneous Doppler shifts in the emitted photon's frequency play the fundamental part in the build up of quantum correlations in the system. Physical considerations are made to explain the nature of each entanglement regime as well as provide their respective thresholds.
DeepDialogue: A Multi-Turn Emotionally-Rich Spoken Dialogue Dataset
2025-05-26
Alkis Koudounas, Moreno La Quatra, Elena Baralis
Recent advances in conversational AI have demonstrated impressive capabilities in single-turn responses, yet multi-turn dialogues remain challenging for even the most sophisticated language models. Current dialogue datasets are limited in their emotional range, domain diversity, turn depth, and are predominantly text-only, hindering progress in developing more human-like conversational systems across modalities. To address these limitations, we present DeepDialogue, a large-scale multimodal dataset containing 40,150 high-quality multi-turn dialogues spanning 41 domains and incorporating 20 distinct emotions with coherent emotional progressions. Our approach pairs 9 different language models (4B-72B parameters) to generate 65,600 initial conversations, which we then evaluate through a combination of human annotation and LLM-based quality filtering. The resulting dataset reveals fundamental insights: smaller models fail to maintain coherence beyond 6 dialogue turns; concrete domains (e.g., "cars," "travel") yield more meaningful conversations than abstract ones (e.g., "philosophy"); and cross-model interactions produce more coherent dialogues than same-model conversations. A key contribution of DeepDialogue is its speech component, where we synthesize emotion-consistent voices for all 40,150 dialogues, creating the first large-scale open-source multimodal dialogue dataset that faithfully preserves emotional context across multi-turn conversations.
DFIR-Metric: A Benchmark Dataset for Evaluating Large Language Models in Digital Forensics and Incident Response
2025-05-26
Bilel Cherif, Tamas Bisztray, Richard A. Dubniczky, Aaesha Aldahmani, Saeed Alshehhi, Norbert Tihanyi
Digital Forensics and Incident Response (DFIR) involves analyzing digital evidence to support legal investigations. Large Language Models (LLMs) offer new opportunities in DFIR tasks such as log analysis and memory forensics, but their susceptibility to errors and hallucinations raises concerns in high-stakes contexts. Despite growing interest, there is no comprehensive benchmark to evaluate LLMs across both theoretical and practical DFIR domains. To address this gap, we present DFIR-Metric, a benchmark with three components: (1) Knowledge Assessment: a set of 700 expert-reviewed multiple-choice questions sourced from industry-standard certifications and official documentation; (2) Realistic Forensic Challenges: 150 CTF-style tasks testing multi-step reasoning and evidence correlation; and (3) Practical Analysis: 500 disk and memory forensics cases from the NIST Computer Forensics Tool Testing Program (CFTT). We evaluated 14 LLMs using DFIR-Metric, analyzing both their accuracy and consistency across trials. We also introduce a new metric, the Task Understanding Score (TUS), designed to more effectively evaluate models in scenarios where they achieve near-zero accuracy. This benchmark offers a rigorous, reproducible foundation for advancing AI in digital forensics. All scripts, artifacts, and results are available on the project website at https://github.com/DFIR-Metric.
Light propagation in the 2PN approximation in the monopole and quadrupole field of a body at rest: Boundary value problem
2025-05-26
Sven Zschocke
In a recent investigation, the initial value problem of light propagation in the gravitational field of a body at rest with monopole and quadrupole structure has been determined in the second post-Newtonian (2PN) approximation. In reality, the light source as well as the observer are located at finite distances from the solar system bodies. This fact requires solving the boundary value problem of light propagation. In this investigation, the solution of the boundary value problem is deduced from the initial value problem of light propagation in 2PN approximation. These results are a basic requirement for subsequent investigations aiming at ultra-highly precise tests of light deflection and time delay in the solar system.
Diffusion models have shown great potential in generating realistic image detail. However, adapting these models to video super-resolution (VSR) remains challenging due to their inherent stochasticity and lack of temporal modeling. In this paper, we propose UltraVSR, a novel framework that enables ultra-realistic and temporal-coherent VSR through an efficient one-step diffusion space. A central component of UltraVSR is the Degradation-aware Restoration Schedule (DRS), which estimates a degradation factor from the low-resolution input and transforms iterative denoising process into a single-step reconstruction from from low-resolution to high-resolution videos. This design eliminates randomness from diffusion noise and significantly speeds up inference. To ensure temporal consistency, we propose a lightweight yet effective Recurrent Temporal Shift (RTS) module, composed of an RTS-convolution unit and an RTS-attention unit. By partially shifting feature components along the temporal dimension, these two units collaboratively facilitate effective feature propagation, fusion, and alignment across neighboring frames, without relying on explicit temporal layers. The RTS module is integrated into a pretrained text-to-image diffusion model and is further enhanced through Spatio-temporal Joint Distillation (SJD), which improves temporal coherence while preserving realistic details. Additionally, we introduce a Temporally Asynchronous Inference (TAI) strategy to capture long-range temporal dependencies under limited memory constraints. Extensive experiments show that UltraVSR achieves state-of-the-art performance, both qualitatively and quantitatively, in a single sampling step.
Recent advancements in AI agents have demonstrated their growing potential to drive and support scientific discovery. In this work, we introduce MLR-Bench, a comprehensive benchmark for evaluating AI agents on open-ended machine learning research. MLR-Bench includes three key components: (1) 201 research tasks sourced from NeurIPS, ICLR, and ICML workshops covering diverse ML topics; (2) MLR-Judge, an automated evaluation framework combining LLM-based reviewers with carefully designed review rubrics to assess research quality; and (3) MLR-Agent, a modular agent scaffold capable of completing research tasks through four stages: idea generation, proposal formulation, experimentation, and paper writing. Our framework supports both stepwise assessment across these distinct research stages, and end-to-end evaluation of the final research paper. We then use MLR-Bench to evaluate six frontier LLMs and an advanced coding agent, finding that while LLMs are effective at generating coherent ideas and well-structured papers, current coding agents frequently (e.g., in 80% of the cases) produce fabricated or invalidated experimental results--posing a major barrier to scientific reliability. We validate MLR-Judge through human evaluation, showing high agreement with expert reviewers, supporting its potential as a scalable tool for research evaluation. We open-source MLR-Bench to help the community benchmark, diagnose, and improve AI research agents toward trustworthy and transparent scientific discovery.
Dynamically Learned Test-Time Model Routing in Language Model Zoos with Service Level Guarantees
2025-05-26
Herbert Woisetschläger, Ryan Zhang, Shiqiang Wang, Hans-Arno Jacobsen
Open-weight LLM zoos provide access to numerous high-quality models, but selecting the appropriate model for specific tasks remains challenging and requires technical expertise. Most users simply want factually correct, safe, and satisfying responses without concerning themselves with model technicalities, while inference service providers prioritize minimizing operating costs. These competing interests are typically mediated through service level agreements (SLAs) that guarantee minimum service quality. We introduce MESS+, a stochastic optimization algorithm for cost-optimal LLM request routing while providing rigorous SLA compliance guarantees. MESS+ learns request satisfaction probabilities of LLMs in real-time as users interact with the system, based on which model selection decisions are made by solving a per-request optimization problem. Our algorithm includes a novel combination of virtual queues and request satisfaction prediction, along with a theoretical analysis of cost optimality and constraint satisfaction. Across a wide range of state-of-the-art LLM benchmarks, MESS+ achieves an average of 2x cost savings compared to existing LLM routing techniques.
We investigate the prospects for probing large-scale statistical anisotropy through galaxy clustering and intrinsic alignments (IA) in Stage IV galaxy surveys. Specifically, we consider a dipolar modulation in the primordial power spectrum and evaluate the Fisher information matrix using the two-point statistics of both the galaxy clustering and IA. Our analysis reveals that while IA alone provides limited improvement in constraining the anisotropy amplitude, the cross-spectrum between galaxy density and IA can contribute up to half the constraining power of galaxy clustering, especially for surveys with low galaxy bias and high number density of galaxies, such as Euclid. This demonstrates the potential of IA-clustering cross-correlations as a robust consistency check against systematics, and highlights the complementary roles of galaxy clustering and IA in constraining cosmic statistical anisotropy. We also show that marginalizing over galaxy bias and IA bias parameters has a negligible impact on the final constraint on the anisotropy amplitude.
Systems of Twinned Systems: A Systematic Literature Review
2025-05-26
Feyi Adesanya, Kanan Castro Silva, Valdemar V. Graciano Neto, Istvan David
Modern systems exhibit unprecedented complexity due to their increased scale, interconnectedness, and the heterogeneity of their digital and physical components. In response to scaling challenges, the system-of-systems (SoS) paradigm proposes flexible aggregations of subsystems into a larger whole, while maintaining the independence of subsystems to various degrees. In response to the cyber-physical convergence, the digital twin (DT) paradigm proposes a tight coupling between digital and physical components through computational reflection and precise control. As these two paradigms address distinct parts of the overall challenge, combining the two promises more comprehensive methods to engineer what we call systems of twinned systems (SoTS). The noticeably growing body of knowledge on SoTS calls for a review of the state of the art. In this work, we report on our systematic literature survey of SoTS. We screened over 2500 potential studies, of which we included 80 and investigated them in detail. To converge SoS and DT, we derive a classification framework for SoTS that is backward compatible with the currently accepted theories of SoS and DT.
Large Language Models as Autonomous Spacecraft Operators in Kerbal Space Program
2025-05-26
Alejandro Carrasco, Victor Rodriguez-Fernandez, Richard Linares
Recent trends are emerging in the use of Large Language Models (LLMs) as autonomous agents that take actions based on the content of the user text prompts. We intend to apply these concepts to the field of Control in space, enabling LLMs to play a significant role in the decision-making process for autonomous satellite operations. As a first step towards this goal, we have developed a pure LLM-based solution for the Kerbal Space Program Differential Games (KSPDG) challenge, a public software design competition where participants create autonomous agents for maneuvering satellites involved in non-cooperative space operations, running on the KSP game engine. Our approach leverages prompt engineering, few-shot prompting, and fine-tuning techniques to create an effective LLM-based agent that ranked 2nd in the competition. To the best of our knowledge, this work pioneers the integration of LLM agents into space research. The project comprises several open repositories to facilitate replication and further research. The codebase is accessible on \href{https://github.com/ARCLab-MIT/kspdg}{GitHub}, while the trained models and datasets are available on \href{https://huggingface.co/OhhTuRnz}{Hugging Face}. Additionally, experiment tracking and detailed results can be reviewed on \href{https://wandb.ai/carrusk/huggingface}{Weights \& Biases
A fully automated urban PV parameterization framework for improved estimation of energy production profiles
2025-05-26
Bowen Tian, Roel C. G. M. Loonen, Roland Valckenborg, Jan L. M. Hensen
Accurate parameterization of rooftop photovoltaic (PV) installations is critical for effective grid management and strategic large-scale solar deployment. The lack of high-fidelity datasets for PV configuration parameters often compels practitioners to rely on coarse assumptions, undermining both the temporal and numerical accuracy of large-scale PV performance modeling. This study introduces a fully automated framework that innovatively integrates remote sensing data, semantic segmentation, polygon-vector refinement, tilt-azimuth estimation, and module layout inference to produce a richly attributed GIS dataset of distributed PV. Applied to Eindhoven (the Netherlands), the method achieves a correlation ($R^2$) of 0.92 with Distribution System Operator (DSO) records, while capacity estimates for 73$\%$ of neighborhoods demonstrate agreement within a $\pm$25$\%$ margin of recorded data. Additionally, by accurately capturing actual system configuration parameters (e.g., tilt, azimuth, module layout) and seamlessly linking them to advanced performance models, the method yields more reliable PV energy generation forecasts within the distribution networks. Centering our experiments toward a high PV-penetration community, configuration-aware simulations help to reduce Mean Absolute Percentage Error (MAPE) of energy generation modeling by up to 160$\%$ compared to the conventional assumption-based approaches. Furthermore, owing to its modular design and reliance on readily available geospatial resources, the workflow can be extended across diverse regions, offering a scalable solution for robust urban solar integration.
Impact of <3D> NLTE on GCE of oxygen with the RAdial Velocity Experiment
2025-05-26
G. Guiglion
Stellar abundances, coupled with kinematics are a unique way to understand the chemo-dynamical processes that occurred to build the Milky Way and its local volume as we observe today. However, measuring abundances is challenging as one needs to properly address the effect of departure from the Local Thermodynamic Equilibrium (LTE), as well as the commonly used 1-dimensional model atmosphere. In this work, we constrain the chemical evolution of [O/Fe] in FG stars of the RAVE survey with [O/Fe] abundances derived in non-LTE (NLTE) and with horizontally-temporally-averaged 3D (<3D>) model atmospheres. Using standard spectral fitting method, we determine for the first time LTE and NLTE [O/Fe] ratios from the O triplet at 8446A in turn-off and dwarf stars thanks to intermediate-resolution RAVE spectra, assuming both 1D and <3D> model atmosphere. NLTE effects play a significant role when determining oxygen even at a resolution of R= 7500. Typical NLTE-LTE corrections of the order of -0.12 dex are measured in dwarfs and turn-off stars using 1D MARCS models. In contrast to applying <3D> NLTE abundance corrections or the classical 1D LTE, the full <3D> NLTE spectral fitting yields improving the precision of abundances by nearly 10%. We show that the decrease of [O/Fe] in the super-solar [Fe/H] regime is rather characterised by a flat trend when [O/Fe] is computed in <3D> NLTE from full spectral fitting. We attribute this flattening at super-solar [Fe/H] to the interplay between locally born stars with negative [O/Fe] and stars migrated from the inner MW regions with super-solar [O/Fe], supporting the complex chemo-dynamical history of the Solar neighbourhood. Our results are key for understanding the effects of <3D> and NLTE when measuring [O/Fe]. This work is a test bed for the analysis of 4MOST low-resolution spectra that will share similar properties as RAVE in the red wavelength domain.
Magnetic fields in the massive star-forming region NGC 6334 and their relationship with the properties of dust filaments probed by [CII] and PAH emissions
2025-05-26
Takayoshi Kusune, Hayata Tsuji, Shinki Oyabu, Hidehiro Kaneda, Toyoaki Suzuki, Akiko Yasuda, Devendra Ojha, Swarna K. Ghosh, Koshvendra Singh, Joe P. Ninan
We carried out the near-infrared ($JHK_{\rm s}$) imaging polarimetric observation with the polarimeter SIRPOL on the Infrared Survey Facility (IRSF) 1.4 m telescope and [CII] line mapping observation with a Fabry-P\'{e}rot spectrometer on board a 100-cm TIFR balloon-borne far-infrared telescope toward NGC 6334, and revealed the relationship between the plane-of-sky (POS) magnetic fields and [CII] emission lines to investigate the star formation in the molecular cloud. The polarization vector map shows that the POS magnetic fields are approximately perpendicular to the main filament elongation of NGC 6334. On the other hand, the POS magnetic fields tend to be parallel or random for the other filaments in NGC 6334. The [CII] emission shows a distribution well aligned with the main filament. Strong [CII] emission is also seen in the hub-filament system. Since the main filament is sandwiched between two HII regions, it is most likely that gas is efficiently accreting from the shells of the HII regions along the magnetic field resulting in active star formation. This is consistent with the NGC 6334 being bright in [CII] emission.
Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications
2025-05-26
Christoph R. Landolt, Christoph Würsch, Roland Meier, Alain Mermoud, Julian Jang-Jaccard
Multi-Agent Reinforcement Learning (MARL) has shown great potential as an adaptive solution for addressing modern cybersecurity challenges. MARL enables decentralized, adaptive, and collaborative defense strategies and provides an automated mechanism to combat dynamic, coordinated, and sophisticated threats. This survey investigates the current state of research in MARL applications for automated cyber defense (ACD), focusing on intruder detection and lateral movement containment. Additionally, it examines the role of Autonomous Intelligent Cyber-defense Agents (AICA) and Cyber Gyms in training and validating MARL agents. Finally, the paper outlines existing challenges, such as scalability and adversarial robustness, and proposes future research directions. This also discusses how MARL integrates in AICA to provide adaptive, scalable, and dynamic solutions to counter the increasingly sophisticated landscape of cyber threats. It highlights the transformative potential of MARL in areas like intrusion detection and lateral movement containment, and underscores the value of Cyber Gyms for training and validation of AICA.
SN 2024aecx: A double-peaked rapidly evolving Type IIb supernova at 11 Mpc
2025-05-26
Xingzhu Zou, Brajesh Kumar, Rishabh Singh Teja, D. K. Sahu, Xinlei Chen, Avinash Singh, Weikang Lin, Xiangkun Liu, Dezi Liu, Hrishav Das, Mridweeka Singh, Yu Pan, Guowang Du, Helong Guo, Tao Wang, Xufeng Zhu, Jujia Zhang, Yuan Fang, Chenxu Liu, Kaushik Chatterjee, Yuan-Pei Yang, Liping Li, Qian Zhai, Edoardo P. Lagioia, Xueling Du, Xinzhong Er, Jianhui Lian, Shiyan Zhong, Xiaowei Liu
We present the results of low-resolution spectroscopic and densely sampled multiband simultaneous optical imaging ($ugi$ and $vrz$ bands) follow-up of supernova (SN) 2024aecx. The photometric data is supplemented with $Swift$/UVOT and ATLAS survey observations. The SN was discovered in the spiral galaxy NGC 3521 (distance $\sim$11 Mpc) within a day after the explosion. The early spectra of SN 2024aecx show a weak signature of hydrogen lines, which disappeared in $\sim$30 days after the explosion. Light curves in all bands show a distinct feature of two peaks, and the first peak is likely due to the shock cooling emission. The early phase light curve evolution of SN 2024aecx has similarity with the typical Type IIb events, but the decay rate in different bands (e.g., $\rm \Delta m_{15}$ = 1.60 $\pm$ 0.05 mag, $g$-band) is significantly faster in the post-peak phase. It attained the secondary maximum in $\sim$19 days ($g$-band) with a peak absolute magnitude of M$_{g}$= -17.94 $\pm$ 0.10 mag. The color evolution of SN 2024aecx is displaying a red-blue-red trend between days $\sim$8 to 40. The analytical model fitting to the light curves reveals an envelope mass and progenitor radii in the range of $\sim$0.03 - 0.24 $M_\odot$ and $\sim$169 - 200 $R_\odot$, respectively. Modeling of the pseudo-bolometric light curve suggests that synthesized $^{56}$Ni in the explosion was $\sim$0.15 M$_{\odot}$ with ejecta mass and kinetic energy of $\sim$0.7 M$_{\odot}$ and $\sim$0.16 x 10$^{51}$ erg, respectively. The observational properties and modeling indicate that the SN 2024aecx progenitor belongs to the extended progenitor category.
Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Risks
2025-05-26
Sirui Chen, Shuqin Ma, Shu Yu, Hanwang Zhang, Shengjie Zhao, Chaochao Lu
Consciousness stands as one of the most profound and distinguishing features of the human mind, fundamentally shaping our understanding of existence and agency. As large language models (LLMs) develop at an unprecedented pace, questions concerning intelligence and consciousness have become increasingly significant. However, discourse on LLM consciousness remains largely unexplored territory. In this paper, we first clarify frequently conflated terminologies (e.g., LLM consciousness and LLM awareness). Then, we systematically organize and synthesize existing research on LLM consciousness from both theoretical and empirical perspectives. Furthermore, we highlight potential frontier risks that conscious LLMs might introduce. Finally, we discuss current challenges and outline future directions in this emerging field. The references discussed in this paper are organized at https://github.com/OpenCausaLab/Awesome-LLM-Consciousness.
Gravitational waves (GWs) originating from cosmological sources offer direct insights into the physics of the primordial Universe, the fundamental nature of gravity, and the cosmic expansion of the Universe. In this review paper, we present a comprehensive overview of our recent advances in GW cosmology, supported by the national key research and development program of China, focusing on cosmological GW sources and their implications for fundamental physics and cosmology. We first discuss the generation mechanisms and characteristics of stochastic gravitational wave backgrounds generated by physical processes occurred in the early Universe, including those from inflation, phase transitions, and topological defects, and summarize current and possible future constraints from pulsar timing array and space-based detectors. Next, we explore the formation and observational prospects of primordial black holes as GW sources and their potential connection to dark matter. We then analyze how GWs are affected by large-scale structure, cosmological perturbations, and possible modifications of gravity on GW propagation, and how these effects can be used to test fundamental symmetry of gravity. Finally, we discuss the application of GW standard sirens in measuring the Hubble constant, the expansion history, and dark energy parameters, including their combination with electromagnetic observations. These topics together show how GW observations, especially with upcoming space-based detectors, such as LISA, Taiji, and Tianqin, can provide new information about the physics of the early Universe, cosmological evolution, and the nature of gravity.
SETBVE: Quality-Diversity Driven Exploration of Software Boundary Behaviors
2025-05-26
Sabinakhon Akbarova, Felix Dobslaw, Francisco Gomes de Oliveira Neto, Robert Feldt
Software systems exhibit distinct behaviors based on input characteristics, and failures often occur at the boundaries between input domains. Traditional Boundary Value Analysis (BVA) relies on manual heuristics, while automated Boundary Value Exploration (BVE) methods typically optimize a single quality metric, risking a narrow and incomplete survey of boundary behaviors. We introduce SETBVE, a customizable, modular framework for automated black-box BVE that leverages Quality-Diversity (QD) optimization to systematically uncover and refine a broader spectrum of boundaries. SETBVE maintains an archive of boundary pairs organized by input- and output-based behavioral descriptors. It steers exploration toward underrepresented regions while preserving high-quality boundary pairs and applies local search to refine candidate boundaries. In experiments with ten integer-based functions, SETBVE outperforms the baseline in diversity, boosting archive coverage by 37 to 82 percentage points. A qualitative analysis reveals that SETBVE identifies boundary candidates the baseline misses. While the baseline method typically plateaus in both diversity and quality after 30 seconds, SETBVE continues to improve in 600-second runs, demonstrating better scalability. Even the simplest SETBVE configurations perform well in identifying diverse boundary behaviors. Our findings indicate that balancing quality with behavioral diversity can help identify more software edge-case behaviors than quality-focused approaches.
A Structured Tour of Optimization with Finite Differences
2025-05-26
Marco Rando, Cesare Molinari, Lorenzo Rosasco, Silvia Villa
Finite-difference methods are widely used for zeroth-order optimization in settings where gradient information is unavailable or expensive to compute. These procedures mimic first-order strategies by approximating gradients through function evaluations along a set of random directions. From a theoretical perspective, recent studies indicate that imposing structure (such as orthogonality) on the chosen directions allows for the derivation of convergence rates comparable to those achieved with unstructured random directions (i.e., directions sampled independently from a distribution). Empirically, although structured directions are expected to enhance performance, they often introduce additional computational costs, which can limit their applicability in high-dimensional settings. In this work, we examine the impact of structured direction selection in finite-difference methods. We review and extend several strategies for constructing structured direction matrices and compare them with unstructured approaches in terms of computational cost, gradient approximation quality, and convergence behavior. Our evaluation spans both synthetic tasks and real-world applications such as adversarial perturbation. The results demonstrate that structured directions can be generated with computational costs comparable to unstructured ones while significantly improving gradient estimation accuracy and optimization performance.
This position paper argues that Mean Opinion Score (MOS), while historically foundational, is no longer sufficient as the sole supervisory signal for multimedia quality assessment models. MOS reduces rich, context-sensitive human judgments to a single scalar, obscuring semantic failures, user intent, and the rationale behind quality decisions. We contend that modern quality assessment models must integrate three interdependent capabilities: (1) context-awareness, to adapt evaluations to task-specific goals and viewing conditions; (2) reasoning, to produce interpretable, evidence-grounded justifications for quality judgments; and (3) multimodality, to align perceptual and semantic cues using vision-language models. We critique the limitations of current MOS-centric benchmarks and propose a roadmap for reform: richer datasets with contextual metadata and expert rationales, and new evaluation metrics that assess semantic alignment, reasoning fidelity, and contextual sensitivity. By reframing quality assessment as a contextual, explainable, and multimodal modeling task, we aim to catalyze a shift toward more robust, human-aligned, and trustworthy evaluation systems.
Planning represents a fundamental capability of intelligent agents, requiring comprehensive environmental understanding, rigorous logical reasoning, and effective sequential decision-making. While Large Language Models (LLMs) have demonstrated remarkable performance on certain planning tasks, their broader application in this domain warrants systematic investigation. This paper presents a comprehensive review of LLM-based planning. Specifically, this survey is structured as follows: First, we establish the theoretical foundations by introducing essential definitions and categories about automated planning. Next, we provide a detailed taxonomy and analysis of contemporary LLM-based planning methodologies, categorizing them into three principal approaches: 1) External Module Augmented Methods that combine LLMs with additional components for planning, 2) Finetuning-based Methods that involve using trajectory data and feedback signals to adjust LLMs in order to improve their planning abilities, and 3) Searching-based Methods that break down complex tasks into simpler components, navigate the planning space, or enhance decoding strategies to find the best solutions. Subsequently, we systematically summarize existing evaluation frameworks, including benchmark datasets, evaluation metrics and performance comparisons between representative planning methods. Finally, we discuss the underlying mechanisms enabling LLM-based planning and outline promising research directions for this rapidly evolving field. We hope this survey will serve as a valuable resource to inspire innovation and drive progress in this field.
Large Language Models in Code Co-generation for Safe Autonomous Vehicles
2025-05-26
Ali Nouri, Beatriz Cabrero-Daniel, Zhennan Fei, Krishna Ronanki, Håkan Sivencrona, Christian Berger
Software engineers in various industrial domains are already using Large Language Models (LLMs) to accelerate the process of implementing parts of software systems. When considering its potential use for ADAS or AD systems in the automotive context, there is a need to systematically assess this new setup: LLMs entail a well-documented set of risks for safety-related systems' development due to their stochastic nature. To reduce the effort for code reviewers to evaluate LLM-generated code, we propose an evaluation pipeline to conduct sanity-checks on the generated code. We compare the performance of six state-of-the-art LLMs (CodeLlama, CodeGemma, DeepSeek-r1, DeepSeek-Coders, Mistral, and GPT-4) on four safety-related programming tasks. Additionally, we qualitatively analyse the most frequent faults generated by these LLMs, creating a failure-mode catalogue to support human reviewers. Finally, the limitations and capabilities of LLMs in code generation, and the use of the proposed pipeline in the existing process, are discussed.
State-of-the-art solutions detect jamming attacks ex-post, i.e., only when jamming has already disrupted the wireless communication link. In many scenarios, e.g., mobile networks or static deployments distributed over a large geographical area, it is often desired to detect jamming at the early stage, when it affects the communication link enough to be detected but not sufficiently to disrupt it (detection of weak jamming signals). Under such assumptions, devices can enhance situational awareness and promptly apply mitigation, e.g., moving away from the jammed area in mobile scenarios or changing communication frequency in static deployments, before jamming fully disrupts the communication link. Although some contributions recently demonstrated the feasibility of detecting low-power and weak jamming signals, they make simplistic assumptions far from real-world deployments. Given the current state of the art, no evidence exists that detection of weak jamming can be considered with real-world communication technologies. In this paper, we provide and comprehensively analyze new general-purpose strategies for detecting weak jamming signals, compatible by design with one of the most relevant communication technologies used by commercial-off-the-shelf devices, i.e., IEEE 802.11. We describe two operational modes: (i) binary classification via Convolutional Neural Networks and (ii) one-class classification via Sparse Autoencoders. We evaluate and compare the proposed approaches with the current state-of-the-art using data collected through an extensive real-world experimental campaign in three relevant environments. At the same time, we made the dataset available to the public. Our results demonstrate that detecting weak jamming signals is feasible in all considered real-world environments, and we provide an in-depth analysis considering different techniques, scenarios, and mobility patterns.
Think Again! The Effect of Test-Time Compute on Preferences, Opinions, and Beliefs of Large Language Models
2025-05-26
George Kour, Itay Nakash, Ateret Anaby-Tavor, Michal Shmueli-Scheuer
As Large Language Models (LLMs) become deeply integrated into human life and increasingly influence decision-making, it's crucial to evaluate whether and to what extent they exhibit subjective preferences, opinions, and beliefs. These tendencies may stem from biases within the models, which may shape their behavior, influence the advice and recommendations they offer to users, and potentially reinforce certain viewpoints. This paper presents the Preference, Opinion, and Belief survey (POBs), a benchmark developed to assess LLMs' subjective inclinations across societal, cultural, ethical, and personal domains. We applied our benchmark to evaluate leading open- and closed-source LLMs, measuring desired properties such as reliability, neutrality, and consistency. In addition, we investigated the effect of increasing the test-time compute, through reasoning and self-reflection mechanisms, on those metrics. While effective in other tasks, our results show that these mechanisms offer only limited gains in our domain. Furthermore, we reveal that newer model versions are becoming less consistent and more biased toward specific viewpoints, highlighting a blind spot and a concerning trend. POBS: https://ibm.github.io/POBS
Accelerating Prefilling for Long-Context LLMs via Sparse Pattern Sharing
2025-05-26
Dan Peng, Zhihui Fu, Zewen Ye, Zhuoran Song, Jun Wang
Sparse attention methods exploit the inherent sparsity in attention to speed up the prefilling phase of long-context inference, mitigating the quadratic complexity of full attention computation. While existing sparse attention methods rely on predefined patterns or inaccurate estimations to approximate attention behavior, they often fail to fully capture the true dynamics of attention, resulting in reduced efficiency and compromised accuracy. Instead, we propose a highly accurate sparse attention mechanism that shares similar yet precise attention patterns across heads, enabling a more realistic capture of the dynamic behavior of attention. Our approach is grounded in two key observations: (1) attention patterns demonstrate strong inter-head similarity, and (2) this similarity remains remarkably consistent across diverse inputs. By strategically sharing computed accurate patterns across attention heads, our method effectively captures actual patterns while requiring full attention computation for only a small subset of heads. Comprehensive evaluations demonstrate that our approach achieves superior or comparable speedup relative to state-of-the-art methods while delivering the best overall accuracy.
Keyword spotting (KWS) is essential for voice-driven applications, demanding both accuracy and efficiency. Traditional ASR-based KWS methods, such as greedy and beam search, explore the entire search space without explicitly prioritizing keyword detection, often leading to suboptimal performance. In this paper, we propose an effective keyword-specific KWS framework by introducing a streaming-oriented CTC-Transducer-combined frame-asynchronous system with multi-head frame-asynchronous decoding (MFA-KWS). Specifically, MFA-KWS employs keyword-specific phone-synchronous decoding for CTC and replaces conventional RNN-T with Token-and-Duration Transducer to enhance both performance and efficiency. Furthermore, we explore various score fusion strategies, including single-frame-based and consistency-based methods. Extensive experiments demonstrate the superior performance of MFA-KWS, which achieves state-of-the-art results on both fixed keyword and arbitrary keywords datasets, such as Snips, MobvoiHotwords, and LibriKWS-20, while exhibiting strong robustness in noisy environments. Among fusion strategies, the consistency-based CDC-Last method delivers the best performance. Additionally, MFA-KWS achieves a 47% to 63% speed-up over the frame-synchronous baselines across various datasets. Extensive experimental results confirm that MFA-KWS is an effective and efficient KWS framework, making it well-suited for on-device deployment.
Automated Text-to-Table for Reasoning-Intensive Table QA: Pipeline Design and Benchmarking Insights
2025-05-26
Shi-Yu Tian, Zhi Zhou, Wei Dong, Ming Yang, Kun-Yang Yu, Zi-Jian Cheng, Lan-Zhe Guo, Yu-Feng Li
Reasoning with tabular data holds increasing importance in modern applications, yet comprehensive evaluation methodologies for reasoning-intensive Table Question Answering (QA) tasks remain nascent. Existing research is constrained by two primary bottlenecks: 1) Reliance on costly manually annotated real-world data, which is difficult to cover complex reasoning scenarios; 2) The heterogeneity of table structures hinders systematic analysis of the intrinsic mechanisms behind the underperformance of LLMs, especially in reasoning-intensive tasks. To address these issues, we propose an automated generation pipeline AutoT2T that transforms mathematical word problems into table-based reasoning tasks, eliminating the need for manual annotation. The pipeline can generate multiple variants of a table for the same reasoning problem, including noisy versions to support robustness evaluation. Based on this, we construct a new benchmark TabularGSM, which systematically spans a range of table complexities and trap problems. Experimental analyses through AutoT2T and TabularGSM reveal that the tight coupling between reasoning and retrieval or identification processes is a key factor underlying the failure of LLMs in complex Table QA tasks. This highlights the necessity for models to develop synergistic reasoning capabilities in order to perform effectively in complex Table QA tasks.
AmpleHate: Amplifying the Attention for Versatile Implicit Hate Detection
2025-05-26
Yejin Lee, Joonghyuk Hahn, Hyeseon Ahn, Yo-Sub Han
Implicit hate speech detection is challenging due to its subtlety and reliance on contextual interpretation rather than explicit offensive words. Current approaches rely on contrastive learning, which are shown to be effective on distinguishing hate and non-hate sentences. Humans, however, detect implicit hate speech by first identifying specific targets within the text and subsequently interpreting how these target relate to their surrounding context. Motivated by this reasoning process, we propose AmpleHate, a novel approach designed to mirror human inference for implicit hate detection. AmpleHate identifies explicit target using a pretrained Named Entity Recognition model and capture implicit target information via [CLS] tokens. It computes attention-based relationships between explicit, implicit targets and sentence context and then, directly injects these relational vectors into the final sentence representation. This amplifies the critical signals of target-context relations for determining implicit hate. Experiments demonstrate that AmpleHate achieves state-of-the-art performance, outperforming contrastive learning baselines by an average of 82.14% and achieve faster convergence. Qualitative analyses further reveal that attention patterns produced by AmpleHate closely align with human judgement, underscoring its interpretability and robustness.
Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI
2025-05-26
Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee
This review presents a comprehensive analysis of two emerging paradigms in AI-assisted software development: vibe coding and agentic coding. While both leverage large language models (LLMs), they differ fundamentally in autonomy, architectural design, and the role of the developer. Vibe coding emphasizes intuitive, human-in-the-loop interaction through prompt-based, conversational workflows that support ideation, experimentation, and creative exploration. In contrast, agentic coding enables autonomous software development through goal-driven agents capable of planning, executing, testing, and iterating tasks with minimal human intervention. We propose a detailed taxonomy spanning conceptual foundations, execution models, feedback loops, safety mechanisms, debugging strategies, and real-world tool ecosystems. Through comparative workflow analysis and 20 detailed use cases, we illustrate how vibe systems thrive in early-stage prototyping and education, while agentic systems excel in enterprise-grade automation, codebase refactoring, and CI/CD integration. We further examine emerging trends in hybrid architectures, where natural language interfaces are coupled with autonomous execution pipelines. Finally, we articulate a future roadmap for agentic AI, outlining the infrastructure needed for trustworthy, explainable, and collaborative systems. Our findings suggest that successful AI software engineering will rely not on choosing one paradigm, but on harmonizing their strengths within a unified, human-centered development lifecycle.
Task Memory Engine: Spatial Memory for Robust Multi-Step LLM Agents
2025-05-26
Ye Ye
Large Language Models (LLMs) falter in multi-step interactions -- often hallucinating, repeating actions, or misinterpreting user corrections -- due to reliance on linear, unstructured context. This fragility stems from the lack of persistent memory to track evolving goals and task dependencies, undermining trust in autonomous agents. We introduce the Task Memory Engine (TME), a modular memory controller that transforms existing LLMs into robust, revision-aware agents without fine-tuning. TME implements a spatial memory framework that replaces flat context with graph-based structures to support consistent, multi-turn reasoning. Departing from linear concatenation and ReAct-style prompting, TME builds a dynamic task graph -- either a tree or directed acyclic graph (DAG) -- to map user inputs to subtasks, align them with prior context, and enable dependency-tracked revisions. Its Task Representation and Intent Management (TRIM) component models task semantics and user intent to ensure accurate interpretation. Across four multi-turn scenarios-trip planning, cooking, meeting scheduling, and shopping cart editing -- TME eliminates 100% of hallucinations and misinterpretations in three tasks, and reduces hallucinations by 66.7% and misinterpretations by 83.3% across 27 user turns, outperforming ReAct. TME's modular design supports plug-and-play deployment and domain-specific customization, adaptable to both personal assistants and enterprise automation. We release TME's codebase, benchmarks, and components as open-source resources, enabling researchers to develop reliable LLM agents. TME's scalable architecture addresses a critical gap in agent performance across complex, interactive settings.
Recalibrating the Compass: Integrating Large Language Models into Classical Research Methods
2025-05-26
Tai-Quan Peng, Xuzhen Yang
This paper examines how large language models (LLMs) are transforming core quantitative methods in communication research in particular, and in the social sciences more broadly-namely, content analysis, survey research, and experimental studies. Rather than replacing classical approaches, LLMs introduce new possibilities for coding and interpreting text, simulating dynamic respondents, and generating personalized and interactive stimuli. Drawing on recent interdisciplinary work, the paper highlights both the potential and limitations of LLMs as research tools, including issues of validity, bias, and interpretability. To situate these developments theoretically, the paper revisits Lasswell's foundational framework -- "Who says what, in which channel, to whom, with what effect?" -- and demonstrates how LLMs reconfigure message studies, audience analysis, and effects research by enabling interpretive variation, audience trajectory modeling, and counterfactual experimentation. Revisiting the metaphor of the methodological compass, the paper argues that classical research logics remain essential as the field integrates LLMs and generative AI. By treating LLMs not only as technical instruments but also as epistemic and cultural tools, the paper calls for thoughtful, rigorous, and imaginative use of LLMs in future communication and social science research.
Erasing Concepts, Steering Generations: A Comprehensive Survey of Concept Suppression
2025-05-26
Yiwei Xie, Ping Liu, Zheng Zhang
Text-to-Image (T2I) models have demonstrated impressive capabilities in generating high-quality and diverse visual content from natural language prompts. However, uncontrolled reproduction of sensitive, copyrighted, or harmful imagery poses serious ethical, legal, and safety challenges. To address these concerns, the concept erasure paradigm has emerged as a promising direction, enabling the selective removal of specific semantic concepts from generative models while preserving their overall utility. This survey provides a comprehensive overview and in-depth synthesis of concept erasure techniques in T2I diffusion models. We systematically categorize existing approaches along three key dimensions: intervention level, which identifies specific model components targeted for concept removal; optimization structure, referring to the algorithmic strategies employed to achieve suppression; and semantic scope, concerning the complexity and nature of the concepts addressed. This multi-dimensional taxonomy enables clear, structured comparisons across diverse methodologies, highlighting fundamental trade-offs between erasure specificity, generalization, and computational complexity. We further discuss current evaluation benchmarks, standardized metrics, and practical datasets, emphasizing gaps that limit comprehensive assessment, particularly regarding robustness and practical effectiveness. Finally, we outline major challenges and promising future directions, including disentanglement of concept representations, adaptive and incremental erasure strategies, adversarial robustness, and new generative architectures. This survey aims to guide researchers toward safer, more ethically aligned generative models, providing foundational knowledge and actionable recommendations to advance responsible development in generative AI.
Continuous helium absorption from the leading and trailing tails of WASP-107b
2025-05-26
Vigneshwaran Krishnamurthy, Yann Carteret, Caroline Piaulet-Ghorayeb, Jared Splinter, Dhvani Doshi, Michael Radica, Louis-Philippe Coulombe, Romain Allart, Vincent Bourrier, Nicolas B. Cowan, David Lafrenière, Loïc Albert, Lisa Dang, Ray Jayawardhana, Doug Johnstone, Lisa Kaltenegger, Adam B. Langeveld, Stefan Pelletier, Jason F. Rowe, Pierre-Alexis Roy, Jake Taylor, Jake D. Turner
The detection of helium escaping the atmosphere of exoplanets has revolutionized our understanding of atmospheric escape and exoplanetary evolution. Using high-precision spectroscopic observations from the James Webb Space Telescope (JWST) NIRISS-SOSS mode, we report the detection of significant helium absorption during the pre-transit phase of WASP-107b (17$\sigma$), as well as in the transit and post-transit phases. This unique continuous helium absorption begins approximately 1.5 hours before the planet's ingress and reveals the presence of an extended thermosphere. The observations show a maximum transit depth of 2.395$\% \pm$ 0.01$\%$ near the helium triplet (36$\sigma$; at NIRISS-SOSS resolution $\sim$ 700). Our ellipsoidal model of the planetary thermosphere matches well the measured light curve suggesting an outflow extending to tens of planetary radii. Furthermore, we confidently detect water absorption (log10 H2O=-2.5 $\pm$ 0.6), superimposed with a short-wavelength slope which we attribute to a prominent signature from unocculted stellar spots (5.2$\sigma$), rather than a small-particle haze slope. We place an upper limit on the abundance of K (log10 K$<$-4.86, or K/H$<$ 75$\times$ stellar) at 2$\sigma$, which is consistent with the O/H super-solar metallicity estimate. This study underscores the transformative potential of JWST for tracing atmospheric and mass-loss processes, while offering a benchmark for future studies targeting helium escape and its implications for planetary evolution.
Radial excitations and their potential impact on Fermi $β$-decay rates
2025-05-26
L. Xayavong, Y. Lim, N. A. Smirnova, Calvin W. Johnson
We investigate the contribution of radial excitations to Fermi $\beta$-decay matrix element. To this end, exact no-core shell model calculations are performed for the mirror $\beta$ decay of tritium, where full convergence can be achieved on an ordinary computer. The differences between the isospin-mixing correction values obtained in the full and in a restricted model spaces are matched to the radial overlap correction term, analogous to that required in the shell-model approach, where the configuration space is extremely limited. We examine this complementary correction term using a nonorthogonal harmonic-oscillator basis, generated by slightly differentiating the oscillator frequencies between the initial and final nuclei, while all desirable properties, including translational invariance, are still preserved. For $N_{\rm max}\le8$, we find that the radial excitation contribution is negative, with a typical magnitude of approximately 10\,\% to 20\,\% of the radial diagonal contribution. This effect becomes more pronounced as the model space increases. Therefore, the $\delta_{C2}$ values obtained in the shell model approach, where radial excitations are not explicitly included, are likely overestimated. Based on experimental $ft$ data and the corrective terms adopted in the survey by Hardy and Towner [Phys. Rev. C {\bf 102}, 045501 (2020)], we show that the incorporation of radial excitations for the superallowed $0^+\rightarrow0^+$ nuclear $\beta$ decay tends however to worsen agreement with the Standard Model.
Causality and "In-the-Wild" Video-Based Person Re-ID: A Survey
2025-05-26
Md Rashidunnabi, Kailash Hambarde, Hugo Proença
Video-based person re-identification (Re-ID) remains brittle in real-world deployments despite impressive benchmark performance. Most existing models rely on superficial correlations such as clothing, background, or lighting that fail to generalize across domains, viewpoints, and temporal variations. This survey examines the emerging role of causal reasoning as a principled alternative to traditional correlation-based approaches in video-based Re-ID. We provide a structured and critical analysis of methods that leverage structural causal models, interventions, and counterfactual reasoning to isolate identity-specific features from confounding factors. The survey is organized around a novel taxonomy of causal Re-ID methods that spans generative disentanglement, domain-invariant modeling, and causal transformers. We review current evaluation metrics and introduce causal-specific robustness measures. In addition, we assess practical challenges of scalability, fairness, interpretability, and privacy that must be addressed for real-world adoption. Finally, we identify open problems and outline future research directions that integrate causal modeling with efficient architectures and self-supervised learning. This survey aims to establish a coherent foundation for causal video-based person Re-ID and to catalyze the next phase of research in this rapidly evolving domain.
Proof of a Conjecture on the Growth of the Maximal Resistance Distance in a Linear 3--Tree
2025-05-26
Emily J. Evans, Russell Jay Hendel
Barret, Evans, and Francis conjectured that if $G$ is the straight linear 3-tree with $n$ vertices and $H$ is the straight linear 3-tree with $n+1$ vertices then \[\lim_{n\rightarrow \infty} r_{H} (1, n+1) - r_G(1,n) = \frac{1}{14},\] where $r_G(u,v)$ and $r_H(u,v)$ are the resistance distance between vertices $u$ and $v$ in graphs $G$ and $H$ respectively. In this paper, we prove the conjecture by looking at the determinants of deleted Laplacian matrices. The proof uses a Laplace expansion method on a family of determinants to determine the underlying recursion this family satisfies and then uses routine linear algebra methods to obtain an exact Binet formula for the $n$-th term.
Quantum Resilience: Canadian Innovations in Quantum Error Correction and Quantum Error Mitigation
2025-05-26
Gaurav Saxena, Jack S. Baker, Pablo Díez Valle, William E. Salazar, Kevin Ferreira, Thi Ha Kyaw
In celebration of the 2025 International Year of Quantum Science and Technology, this article highlights the pioneering achievements and ongoing innovations in quantum error correction and quantum error mitigation by Canadian institutions, academia and industry alike. Emphasizing Canada's central role in advancing these two related areas, we summarize landmark theoretical breakthroughs, cutting-edge experiments, and emerging techniques aimed at reducing and/or eliminating errors incurred when using a quantum computer. This community-focused overview underscores Canada's leadership in addressing the critical challenge of noise in quantum information science.
Financial literacy, robo-advising, and the demand for human financial advice: Evidence from Italy
2025-05-26
David Aristei, Manuela Gallo
This paper investigates the impact of objective financial knowledge, confidence in one's financial skills, and digital financial literacy on individuals' decisions to seek financial advice from robo-advice platforms. Using microdata from the Bank of Italy's 2023 survey on Italian adults' financial literacy, we find that individuals with greater financial knowledge are less inclined to rely on online services for automated financial advice. Conversely, confidence in one's financial abilities and digital financial literacy enhance the likelihood of utilising robo-advice services. Trust in financial innovation, the use of digital financial services, and the propensity to take risks and save also emerge as significant predictors of an individual's use of robo-advice. We also provide evidence of a significant complementary relationship between the adoption of robo-advisory services and the demand for independent professional human advice. By contrast, a substitution effect is found for non-independent human advice. These findings highlight the importance of hybrid solutions in professional financial consulting, where robo-advisory services complement human financial advice.
Project Riley: Multimodal Multi-Agent LLM Collaboration with Emotional Reasoning and Voting
2025-05-26
Ana Rita Ortigoso, Gabriel Vieira, Daniel Fuentes, Luis Frazão, Nuno Costa, António Pereira
This paper presents Project Riley, a novel multimodal and multi-model conversational AI architecture oriented towards the simulation of reasoning influenced by emotional states. Drawing inspiration from Pixar's Inside Out, the system comprises five distinct emotional agents - Joy, Sadness, Fear, Anger, and Disgust - that engage in structured multi-round dialogues to generate, criticise, and iteratively refine responses. A final reasoning mechanism synthesises the contributions of these agents into a coherent output that either reflects the dominant emotion or integrates multiple perspectives. The architecture incorporates both textual and visual large language models (LLMs), alongside advanced reasoning and self-refinement processes. A functional prototype was deployed locally in an offline environment, optimised for emotional expressiveness and computational efficiency. From this initial prototype, another one emerged, called Armando, which was developed for use in emergency contexts, delivering emotionally calibrated and factually accurate information through the integration of Retrieval-Augmented Generation (RAG) and cumulative context tracking. The Project Riley prototype was evaluated through user testing, in which participants interacted with the chatbot and completed a structured questionnaire assessing three dimensions: Emotional Appropriateness, Clarity and Utility, and Naturalness and Human-likeness. The results indicate strong performance in structured scenarios, particularly with respect to emotional alignment and communicative clarity.
Highlights from Exoplanet Observations by the James Webb Space Telescope
2025-05-26
Néstor Espinoza, Marshall D. Perrin
The James Webb Space Telescope (JWST) has started a revolution in exoplanetary science. From studying in exquisite detail the chemical inventories and physical processes in gas giant exoplanets, the structure and chemical diversity of the enigmatic sub-Neptune population to even providing constraints on the atmospheric make-up of rocky exoplanets, the observatory is enabling cutting-edge science that is touching virtually every sub-area in the field. In this review Chapter, we showcase key highlights from exoplanet science being conducted with this state-of-the-art space observatory, which we believe is representative of the transformational science it is producing. One of the key takeaways from these pioneering JWST observations is how they are starting to reshape not only how we think, study and interpret exoplanet observations -- but how they are also reshaping our intuition about our very own Solar System planets.
Ten Aligned Orbits: Planet Migration in the Era of JWST and Ariel
2025-05-26
J. Zak, H. M. J. Boffin, A. Bocchieri, E. Sedaghati, Z. Balkoova, P. Kabath
Understanding the diverse formation and migration pathways that shape exoplanetary systems requires characterizing both their atmospheric properties and their orbital dynamics. A key dynamical diagnostic is the projected spin-orbit angle - the alignment between the stellar spin and the planetary orbit-which provides crucial tests for theoretical models. This angle can be determined using the Rossiter-McLaughlin effect. Although measurements exist for over 200 planets, the overall distribution of these angles is not fully understood, motivating further observations across the full parameter space. We analyze archival HARPS and HARPS-N spectroscopic transit time series of nine gas giant exoplanets on short orbits and one brown dwarf. We derive their projected spin-orbit angle $\lambda$. We find aligned projected orbits for all nine gas giants as well as the brown dwarf. Furthermore, we are able to derive the true spin-orbit angle for the brown dwarf EPIC 219388192b, $\psi = $25$^{+11}_{-14}$ deg. These projected prograde orbits are consistent with quiet disc migration disfavoring violent events exciting the orbits in the history of these systems. Finally, we investigate the current overlap between spin-orbit angle measurements and atmospheric characterization targets. While we find no strong observational biases due to the spin-orbit angle, we note that the majority of planets with atmospheric data still lack spin-orbit measurements. This incompleteness of the dynamical information may limit the interpretation of upcoming atmospheric surveys.
Multimodal Emotion Recognition in Conversations: A Survey of Methods, Trends, Challenges and Prospects
2025-05-26
Chengyan Wu, Yiqiang Cai, Yang Liu, Pengxu Zhu, Yun Xue, Ziwei Gong, Julia Hirschberg, Bolei Ma
While text-based emotion recognition methods have achieved notable success, real-world dialogue systems often demand a more nuanced emotional understanding than any single modality can offer. Multimodal Emotion Recognition in Conversations (MERC) has thus emerged as a crucial direction for enhancing the naturalness and emotional understanding of human-computer interaction. Its goal is to accurately recognize emotions by integrating information from various modalities such as text, speech, and visual signals. This survey offers a systematic overview of MERC, including its motivations, core tasks, representative methods, and evaluation strategies. We further examine recent trends, highlight key challenges, and outline future directions. As interest in emotionally intelligent systems grows, this survey provides timely guidance for advancing MERC research.
Embodied AI with Foundation Models for Mobile Service Robots: A Systematic Review
2025-05-26
Matthew Lisondra, Beno Benhabib, Goldie Nejat
Rapid advancements in foundation models, including Large Language Models, Vision-Language Models, Multimodal Large Language Models, and Vision-Language-Action Models have opened new avenues for embodied AI in mobile service robotics. By combining foundation models with the principles of embodied AI, where intelligent systems perceive, reason, and act through physical interactions, robots can improve understanding, adapt to, and execute complex tasks in dynamic real-world environments. However, embodied AI in mobile service robots continues to face key challenges, including multimodal sensor fusion, real-time decision-making under uncertainty, task generalization, and effective human-robot interactions (HRI). In this paper, we present the first systematic review of the integration of foundation models in mobile service robotics, identifying key open challenges in embodied AI and examining how foundation models can address them. Namely, we explore the role of such models in enabling real-time sensor fusion, language-conditioned control, and adaptive task execution. Furthermore, we discuss real-world applications in the domestic assistance, healthcare, and service automation sectors, demonstrating the transformative impact of foundation models on service robotics. We also include potential future research directions, emphasizing the need for predictive scaling laws, autonomous long-term adaptation, and cross-embodiment generalization to enable scalable, efficient, and robust deployment of foundation models in human-centric robotic systems.
Measurement of WWZ and ZH production cross sections at $\sqrt{s}$ = 13 TeV
2025-05-26
CMS Collaboration
A measurement is presented of the cross section in proton-proton collisions for the production of two W bosons and one Z boson. It is based on data recorded by the CMS experiment at the CERN LHC at center-of-mass energies $\sqrt{s}$ = 13 and 13.6 TeV, corresponding to an integrated luminosity of 200 fb$^{-1}$. Events with four charged leptons (electrons or muons) in the final state are selected. Both nonresonant WWZ production and ZH production, with the Higgs boson decaying into two W bosons, are reported. For the first time, the two processes are measured separately in a simultaneous fit. Combining the two modes, signal strengths relative to the standard model (SM) predictions of 0.75$^{+0.34}_{-0.29}$ and 1.74$^{+0.71}_{-0.60}$ are measured for $\sqrt{s}$ = 13 and 13.6 TeV, respectively. The observed (expected) significance for the triboson signal is 3.8 (2.5) standard deviations for $\sqrt{s}$ = 13.6 TeV, thus providing the first evidence for triboson production at this center-of-mass energy. Combining the two modes and the two center-of-mass energies, the inclusive signal strength relative to the SM prediction is measured to be 1.03$^{+0.31}_{-0.28}$, with an observed (expected) significance of 4.5 (5.0) standard deviations.
We calculate the corrections to the Higgs wave-function renormalization constant arising from modified cubic, quartic, and quintic Higgs self-couplings up to the two-loop level. Using our analytic results, we derive two-dimensional constraints on the modifications of the considered Higgs self-interactions that could potentially be set from precision measurements of single-Higgs production processes at the high-luminosity Large Hadron Collider (LHC) and a Future Circular Collider. Our novel constraints are compared to those that might be set by searches for multi-Higgs production at the same facilities. In view of the first LHC results on triple-Higgs production, we also review the current status of Higgs self-coupling determinations after LHC Run 2.
In situ photoemission study of collective electronic phase transition in VO2-based heterostructures
2025-05-26
D. Shiga, S. Inoue, T. Kanda, N. Hasegawa, M. Kitamura, K. Horiba, K. Yoshimatsu, A. F. Santander-Syro, H. Kumigashira
We investigated the origin of collective electronic phase transitions induced at the heterointerface between monoclinic insulating (MI) VO2 and rutile metallic (RM) electron-doped VO2 layers using in situ soft x-ray photoelectron spectroscopy (SXPES) on nanoscale VO2/V0.99W0.01O2 (001)R bilayers. Thanks to the surface sensitivity of SXPES, we determined the changes in the electronic structure and V-V dimerization in each constituent layer separately. The selective observation of the electronic and crystal structures in the upper VO2 layer of the bilayer indicates that the MI-phase VO2 layer undergoes a transition to the RM phase by forming the heterointerface. Detailed temperature-dependent measurements reveal that the RM-phase VO2 undergoes a transition to the MI phase with a decrease in temperature, as in the case of a VO2 single-layer film. Furthermore, during the temperature-induced phase transition in the VO2 layer, the spectra are well described by an in-plane phase separation of the RM and MI phases. These results suggest that the interface-induced transition from the MI phase to the RM phase in the VO2 layer of bilayers occurs as a collective phase transition derived from the static energy balance between the interfacial energy and the bulk free energies of the constituent layers.
2025-05-26
Oliver Morsch, G. Massimo Palma, Davide Rossini
In this review we give a brief overview of quantum simulation as applied to the study of complex systems. In particular, we cover the basic ideas of quantum simulation, neuromorphic computation, the Sachdev-Ye-Kitaev model, as well as applications to quantum batteries.
DESI Emission Line Galaxies: Clustering Dependence on Stellar Mass and [OII] Luminosity
2025-05-26
T. Hagen, K. S. Dawson, Z. Zheng, J. Aguilar, S. Ahlen, S. BenZvi, D. Bianchi, D. Brooks, F. J. Castander, T. Claybaugh, A. Cuceu, A. de la Macorra, P. Doel, S. Ferraro, A. Font-Ribera, J. E. Forero-Romero, E. Gaztanaga, S. Gontcho A Gontcho, V. Gonzalez-Perez, G. Gutierrez, C. Hahn, K. Honscheid, M. Ishak, S. Juneau, R. Kehoe, T. Kisner, A. Kremin, C. Lamman, M. Landriau, L. Le Guillou, A. Leauthaud, M. E. Levi, M. Manera, A. Meisner, R. Miquel, J. Moustakas, S. Nadathur, N. Palanque-Delabrouille, F. Prada, I. Perez-Rafols, A. J. Ross, G. Rossi, S. Saito, E. Sanchez, D. Schlegel, M. Schubnell, J. Silber, D. Sprayberry, G. Tarle, B. A. Weaver, R. Zhou, H. Zou
We measure the projected two-point correlation functions of emission line galaxies (ELGs) from the Dark Energy Spectroscopic Instrument (DESI) One-Percent Survey and model their dependence on stellar mass and [OII] luminosity. We select $\sim$180,000 ELGs with redshifts of $0.8 < z < 1.6$ and define 27 samples according to cuts in redshift and both galaxy properties. Following a framework that describes the conditional [OII] luminosity-stellar mass distribution as a function of halo mass, we simultaneously model the clustering measurements of all samples at fixed redshift. Based on the modeling result, most ELGs in our samples are classified as central galaxies, residing in halos of a narrow mass range with a typical median of $\sim$10$^{12.2-12.4}$ $h^{-1} M_\odot$. We observe a weak dependence of clustering amplitude on stellar mass, which is reflected in the model constraints and is likely a consequence of the 0.5-dex measurement uncertainty in the stellar mass estimates. The model shows a trend between galaxy bias and [OII] luminosity at high redshift ($1.2 < z < 1.6$) that is otherwise absent at lower redshifts.
Disentangling the Halo: Joint Model for Measurements of the Kinetic Sunyaev-Zeldovich Effect and Galaxy-Galaxy Lensing
2025-05-26
James Sunseri, Alexandra Amon, Jo Dunkley, Nicholas Battaglia, Simone Ferraro, Boryana Hadzhiyska, Bernadita Ried Guachalla, Emmanuel Schaan
We present the first joint analysis of the kinetic Sunyaev-Zeldovich (kSZ) effect with galaxy-galaxy lensing (GGL) for CMASS galaxies in the Baryon Oscillation Spectroscopic Survey (BOSS). We show these complementary probes can disentangle baryons from dark matter in the outskirts of galactic halos by alleviating model degeneracies that are present when fitting to kSZ or GGL measurements alone. In our joint kSZ+GGL analysis we show that the baryon density profile is well constrained on scales from 0.3 to 50 Mpc/$h$. With our well constrained profile of the baryon density, we provide direct comparisons to simulations. For our model we find an outer slope of the baryon distribution that is shallower than predicted by some hydrodynamical simulations, consistent with enhanced baryonic feedback. We also show that not including baryons in a model for GGL can bias halo mass estimates low by $\sim 20\%$ compared to a model that includes baryons and is jointly fit to kSZ+GGL measurements. Our modelling code galaxy-galaxy lensing and kSZ (\texttt{glasz}) is publicly available at https://github.com/James11222/glasz.
Fundamentals of Trapped Ions and Quantum Simulation of Chemical Dynamics
2025-05-26
Guido Pagano, Wojciech Adamczyk, Visal So
Trapped atomic ions are among the most advanced platforms for quantum simulation, computation, and metrology, offering long coherence times and precise, individual control over both internal and motional degrees of freedom. In this review, we present a pedagogical introduction to trapped-ion systems, covering the physics of ion trapping, qubit encodings, and laser-ion interactions. We explain how spin-dependent forces generated by light fields enable both analog and digital quantum simulations of spin and spin-boson models, as well as high-fidelity quantum logic gates. We then highlight an emerging frontier in the simulation of chemical dynamics, summarizing recent experiments that demonstrate the capability of trapped ions to simulate vibronic models and excitation-transfer processes. Finally, we outline future directions in quantum simulation and discuss open challenges in scaling up trapped-ion architectures.
NEXUS: A Spectroscopic Census of Broad-line AGNs and Little Red Dots at $3\lesssim z\lesssim 6$
2025-05-26
Ming-Yang Zhuang, Junyao Li, Yue Shen, Xiaojing Lin, Alice E. Shapley, Feige Wang, Qiaoya Wu, Qian Yang
We present a spectroscopic sample of 23 broad-line AGNs (BLAGNs) at $3\lesssim z\lesssim 6$ selected using F322W2+F444W NIRCam/WFSS grism spectroscopy of the central 100 ${\rm arcmin^2}$ area of the NEXUS survey. Among these BLAGNs, 15 are classified as Little Red Dots (LRDs) based on their rest-frame UV-optical spectral slopes and compact morphology. The number density of LRDs is $\sim 10^{-5}\,{\rm cMpc^{-3}}$, with a hint of declining towards the lower end of the probed redshift range. These BLAGNs and LRDs span broad H$\alpha$ luminosities of $\sim 10^{42.2}-10^{43.7}\,{\rm erg\,s^{-1}}$, black hole masses of $\sim 10^{6.3}-10^{8.4}\,M_\odot$, and Eddington ratios of $\sim 0.1-1$ (median value 0.4), though the black hole mass and Eddington ratio estimates carry large systematic uncertainties. Half of the LRDs show strong Balmer absorption, suggesting high-density gas surrounding the line-emitting region. We detect extended (hundreds of parsec) rest-frame UV-optical emission from the host galaxy in the majority of these LRDs, which contributes significantly or even dominantly to their total UV emission. This host emission largely accounts for the peculiar UV upturn of the LRD spectral energy distribution. We also measure the small-scale ($\lesssim 1\,{\rm cMpc}$) clustering of these BLAGNs and LRDs by cross-correlating with a photometric galaxy sample. Extrapolating the power-law two-point correlation function model to large linear scales, we infer a linear bias of $3.30_{-2.04}^{+2.88}$ and typical halo masses of a few $\times 10^{11}\,h^{-1}M_\odot$ for BLAGNs at the sample median redshift of $z\sim 4.5$. However, the inferred linear bias and halo masses of LRDs, while formally consistent with those for BLAGNs at $\sim 1.5\sigma$, appear too large to be compatible with their space density, suggesting LRDs may have strong excess clustering on small scales.
Across the Universe: Dark Matter and Galaxy Cross-Correlations with the Cherenkov Telescope Array Observatory
2025-05-26
Elena Pinetti, Veronika Vodeb, Aurelio Amerio, Alessandro Cuoco, Stefano Camera, Nicolao Fornengo, Gabrijela Zaharijas
The Cherenkov Telescope Array Observatory (CTAO) will be a ground-based Cherenkov telescope performing wide-sky surveys, ideal for anisotropy studies such as cross-correlations with tracers of the cosmic large-scale structure. Cross-correlations can shed light on high-energy $\gamma$-ray sources and potentially reveal exotic signals from particle dark matter. In this work, we investigate CTAO sensitivity to cross-correlation signals between $\gamma$-ray emission and galaxy distributions. We find that by using dense, low-redshift catalogs like 2MASS, and for integration times around 50 hours, this technique achieves sensitivities to both annihilating and decaying dark matter signals that are competitive with those from dwarf galaxy and cluster analyses.
Large language models (LLMs) now support context windows exceeding 128K tokens, but this comes with significant memory requirements and high inference latency. Quantization can mitigate these costs, but may degrade performance. In this work, we present the first systematic evaluation of quantized LLMs on tasks with long-inputs (>64K tokens) and long-form outputs. Our evaluation spans 9.7K test examples, five quantization methods (FP8, GPTQ-int8, AWQ-int4, GPTQ-int4, BNB-nf4), and five models (Llama-3.1 8B and 70B; Qwen-2.5 7B, 32B, and 72B). We find that, on average, 8-bit quantization preserves accuracy (~0.8% drop), whereas 4-bit methods lead to substantial losses, especially for tasks involving long context inputs (drops of up to 59%). This degradation tends to worsen when the input is in a language other than English. Crucially, the effects of quantization depend heavily on the quantization method, model, and task. For instance, while Qwen-2.5 72B remains robust under BNB-nf4, Llama-3.1 70B experiences a 32% performance drop on the same task. These findings highlight the importance of a careful, task-specific evaluation before deploying quantized LLMs, particularly in long-context scenarios and with languages other than English.
Large language models (LLMs) have the potential to revolutionize how we design and implement compilers and code translation tools. However, existing LLMs struggle to handle long and complex programs. We introduce LEGO-Compiler, a novel neural compilation system that leverages LLMs to translate high-level languages into assembly code. Our approach centers on three key innovations: LEGO translation, which decomposes the input program into manageable blocks; breaking down the complex compilation process into smaller, simpler verifiable steps by organizing it as a verifiable LLM workflow by external tests; and a feedback mechanism for self-correction. Supported by formal proofs of translation composability, LEGO-Compiler demonstrates high accuracy on multiple datasets, including over 99% on ExeBench and 97.9% on industrial-grade AnsiBench. Additionally, LEGO-Compiler has also acheived near one order-of-magnitude improvement on compilable code size scalability. This work opens new avenues for applying LLMs to system-level tasks, complementing traditional compiler technologies.
Ratio statistics--such as relative risk and odds ratios--play a central role in hypothesis testing, model evaluation, and decision-making across many areas of machine learning, including causal inference and fairness analysis. However, despite privacy concerns surrounding many datasets and despite increasing adoption of differential privacy, differentially private ratio statistics have largely been neglected by the literature and have only recently received an initial treatment by Lin et al. [1]. This paper attempts to fill this lacuna, giving results that can guide practice in evaluating ratios when the results must be protected by differential privacy. In particular, we show that even a simple algorithm can provide excellent properties concerning privacy, sample accuracy, and bias, not just asymptotically but also at quite small sample sizes. Additionally, we analyze a differentially private estimator for relative risk, prove its consistency, and develop a method for constructing valid confidence intervals. Our approach bridges a gap in the differential privacy literature and provides a practical solution for ratio estimation in private machine learning pipelines.
Multidimensional Assessment of Public Space Quality: A Comprehensive Framework Across Urban Space Typologies
2025-05-26
Mary John, Sherzod Turaev, Saja Al-Dabet, Rawad Abdulghafor
This study presents a comprehensive framework for evaluating the quality of public spaces across various urban typologies. Through a systematic review of 159 research studies, we identify universal quality factors that transcend spatial types as well as specialized factors unique to specific public environments. Our findings establish accessibility (73.6%), safety/security (58.4%), and comfort (52.8%) as foundational requirements across all public space types, while revealing distinct quality priorities for different typologies: open spaces emphasize comfort (70%), parks prioritize activities (60%), green spaces focus on aesthetics and natural elements (70% and 60%), and public facilities uniquely emphasize indoor environment quality (41.7%). The research reveals a hierarchical relationship between factors, where accessibility enables other qualities, safety serves as a prerequisite for utilization, and comfort determines engagement quality. We identify critical limitations in current assessment approaches, including artificial intelligence studies focused on easily quantifiable factors, domain-specific research confined within disciplinary boundaries, and overreliance on subjective perceptions without objective measures. This research provides a foundation for integrated approaches to public space assessment that acknowledge the complexity of public urban environments while addressing both universal human needs and context-specific requirements. The findings support urban planners, designers, and policymakers in developing balanced assessment methodologies that ensure both comparability across spaces and sensitivity to local conditions, ultimately contributing to the creation of high-quality public spaces that enhance urban life and community wellbeing.
Review of point defect structures in hexagonal close packed metals and across the Periodic Table
2025-05-25
Andrew Ralph Warwick, Pui-Wai Ma, Sergei Lvovich Dudarev
We present a comprehensive ab initio dataset of formation energies and elastic properties of intrinsic point defects across all the transition and rare earth hexagonal close packed (hcp) metals, as well as metalloid elements with hcp crystal structure. Point defect properties appear weakly correlated with the c/a ratio of the hcp lattice. Instead, it is the position of an element in the Periodic Table that primarily defines the relaxation volume tensor, elastic dipole tensor and formation energy of a point defect. This suggests that the local variations in the electronic structure and interatomic bonding at the core of a defect dominate its properties, as opposed to long-range elastic deformations. Across all the metals, we find that the relaxation volumes of vacancies and self-interstitial defects are correlated with atomic volumes, with values of -0.35 and 1.46 atomic volumes for a vacancy and a self-interstitial defect, respectively, providing a universally good approximation independent of the crystal structure. This study complements and completes the existing point defect databases spanning body-centred cubic and face-centred cubic metals.
BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Behavioural Change
2025-05-25
Manuela González-González, Soufiane Belharbi, Muhammad Osama Zeeshan, Masoumeh Sharafi, Muhammad Haseeb Aslam, Marco Pedersoli, Alessandro Lameiras Koerich, Simon L Bacon, Eric Granger
Recognizing complex emotions linked to ambivalence and hesitancy (A/H) can play a critical role in the personalization and effectiveness of digital behaviour change interventions. These subtle and conflicting emotions are manifested by a discord between multiple modalities, such as facial and vocal expressions, and body language. Although experts can be trained to identify A/H, integrating them into digital interventions is costly and less effective. Automatic learning systems provide a cost-effective alternative that can adapt to individual users, and operate seamlessly within real-time, and resource-limited environments. However, there are currently no datasets available for the design of ML models to recognize A/H. This paper introduces a first Behavioural Ambivalence/Hesitancy (BAH) dataset collected for subject-based multimodal recognition of A/H in videos. It contains videos from 224 participants captured across 9 provinces in Canada, with different age, and ethnicity. Through our web platform, we recruited participants to answer 7 questions, some of which were designed to elicit A/H while recording themselves via webcam with microphone. BAH amounts to 1,118 videos for a total duration of 8.26 hours with 1.5 hours of A/H. Our behavioural team annotated timestamp segments to indicate where A/H occurs, and provide frame- and video-level annotations with the A/H cues. Video transcripts and their timestamps are also included, along with cropped and aligned faces in each frame, and a variety of participants meta-data. We include results baselines for BAH at frame- and video-level recognition in multi-modal setups, in addition to zero-shot prediction, and for personalization using unsupervised domain adaptation. The limited performance of baseline models highlights the challenges of recognizing A/H in real-world videos. The data, code, and pretrained weights are available.
Plasma fireball sheath dynamics: A brief review and meta-analysis
2025-05-25
Subham Dutta, Ahmed Atteya, Pralay Kumar Karmakar
We present a comprehensive overview of the formation mechanism of plasma fireball sheath (PFS) structures, sheath-induced collective phenomena, associated relevant instabilities, and corresponding onset conditions. It includes an optimum set of self-illustrative schematic figures, relevantly manifesting the instability triggering dynamics, various involved metastable stages, and parametric threshold conditions. The possible damping mechanisms of excited instabilities in the usual PFS systems are also highlighted. An up-to-date experimental glimpse of both the regular fireball (RFB) and inverted fireball (IFB) classes is specifically presented. We explicitly offer illustrative appendices showing the main distinctions between (a) RFB and IFB, (b) laboratory and astrocosmic fireballs, and (c) RFB sheath and IFB sheath formations. It provides a panoptic glimpse of the current RFB and IFB studies with a special mention to both laboratory and astrospace plasmas. A holistic outline on the chronological development of the PFS research investigations, since the inception of plasma-electrode coupling studies, is outlined. A clear indication of the future fireball scope in both theoretic and applied perspectives is finally emphasized.
Unveiling Dual Quality in Product Reviews: An NLP-Based Approach
2025-05-25
Rafał Poświata, Marcin Michał Mirończuk, Sławomir Dadas, Małgorzata Grębowiec, Michał Perełkiewicz
Consumers often face inconsistent product quality, particularly when identical products vary between markets, a situation known as the dual quality problem. To identify and address this issue, automated techniques are needed. This paper explores how natural language processing (NLP) can aid in detecting such discrepancies and presents the full process of developing a solution. First, we describe in detail the creation of a new Polish-language dataset with 1,957 reviews, 540 highlighting dual quality issues. We then discuss experiments with various approaches like SetFit with sentence-transformers, transformer-based encoders, and LLMs, including error analysis and robustness verification. Additionally, we evaluate multilingual transfer using a subset of opinions in English, French, and German. The paper concludes with insights on deployment and practical applications.
RGC-Bent: A Novel Dataset for Bent Radio Galaxy Classification
2025-05-25
Mir Sazzat Hossain, Khan Muhammad Bin Asad, Payaswini Saikia, Adrita Khan, Md Akil Raihan Iftee, Rakibul Hasan Rajib, Arshad Momen, Md Ashraful Amin, Amin Ahsan Ali, AKM Mahbubur Rahman
We introduce a novel machine learning dataset tailored for the classification of bent radio active galactic nuclei (AGN) in astronomical observations. Bent radio AGN, distinguished by their curved jet structures, provide critical insights into galaxy cluster dynamics, interactions within the intracluster medium, and the broader physics of AGN. Despite their astrophysical significance, the classification of bent radio AGN remains a challenge due to the scarcity of specialized datasets and benchmarks. To address this, we present a dataset, derived from a well-recognized radio astronomy survey, that is designed to support the classification of NAT (Narrow-Angle Tail) and WAT (Wide-Angle Tail) categories, along with detailed data processing steps. We further evaluate the performance of state-of-the-art deep learning models on the dataset, including Convolutional Neural Networks (CNNs), and transformer-based architectures. Our results demonstrate the effectiveness of advanced machine learning models in classifying bent radio AGN, with ConvNeXT achieving the highest F1-scores for both NAT and WAT sources. By sharing this dataset and benchmarks, we aim to facilitate the advancement of research in AGN classification, galaxy cluster environments and galaxy evolution.
100 Proof: HI Observations of 100 Nearby Dwarf Galaxies with the 100-meter Green Bank Telescope
2025-05-25
Aleksandra E. Nazarova, John M. Cannon, Igor D. Karachentsev, Dmitry I. Makarov, Maksim I. Chazov, Lila Schisgal, William St. John
We describe the results of observations with the 100m Robert C. Byrd Green Bank Telescope (GBT) in the HI line of 105 nearby dwarf galaxies, 60 of which were discovered recently in the DESI Legacy Imaging Surveys. Of 105 objects observed, we detected 77 galaxies with the following median parameters: an HI-flux of 0.69 Jy km/s, a heliocentric velocity of 732 km/s, and a $W_{50}$ line width of 32 km/s. 70 are isolated late-type objects and 35 are new probable satellites of nearby spiral galaxies (NGC 628, NGC 2787, NGC 3556, NGC 4490, NGC 4594 and NGC 5055). The detected galaxies are predominantly gas-rich systems with a median gas-to-stellar-mass ratio of 1.87. In general, they follow the classic Tully-Fisher relation obtained for large disk-dominated spiral galaxies if their $M_{21}$ magnitudes are used instead of B-magnitudes.
LLLMs: A Data-Driven Survey of Evolving Research on Limitations of Large Language Models
2025-05-25
Aida Kostikova, Zhipin Wang, Deidamea Bajri, Ole Pütz, Benjamin Paaßen, Steffen Eger
Large language model (LLM) research has grown rapidly, along with increasing concern about their limitations such as failures in reasoning, hallucinations, and limited multilingual capability. In this survey, we conduct a data-driven, semi-automated review of research on limitations of LLM (LLLMs) from 2022 to 2024 using a bottom-up approach. From a corpus of 250,000 ACL and arXiv papers, we identify 14,648 relevant papers using keyword filtering, LLM-based classification, validated against expert labels, and topic clustering (via two approaches, HDBSCAN+BERTopic and LlooM). We find that LLM-related research increases over fivefold in ACL and fourfold in arXiv. Since 2022, LLLMs research grows even faster, reaching over 30% of LLM papers by late 2024. Reasoning remains the most studied limitation, followed by generalization, hallucination, bias, and security. The distribution of topics in the ACL dataset stays relatively stable over time, while arXiv shifts toward safety and controllability (with topics like security risks, alignment, hallucinations, knowledge editing), and multimodality between 2022 and 2024. We release a dataset of annotated abstracts and a validated methodology, and offer a quantitative view of trends in LLM limitations research.
Electric dipole polarizability constraints on neutron skin and symmetry energy
2025-05-25
P. von Neumann-Cosel, A. Tamii
We review the experimental knowledge on the dipole polarizability (DP) of nuclei and its relation to the neutron skin thickness and properties of the neutron-rich matter equation of state (EOS). The discussion focuses on recent experiments using relativistic Coulomb excitation in inelastic proton scattering at extreme forward angles covering a mass range from $^{40}$Ca to $^{208}$Pb. Constraints on the neutron skins and the density dependence of the symmetry energy are derived from systematic comparison to calculations based on density functional theory (DFT) and ab initio methods utilizing interactions derived from chiral effective field theory ($\chi$EFT). The results consistently favor a soft EOS around or slightly below the saturation point. An outlook is given on possible improvements of the precision achievable in stable nuclei and studies of exotic neutron-rich unstable nuclei with upcoming experimental facilities.
Coronal dimmings and what they tell us about solar and stellar coronal mass ejections
2025-05-25
Astrid M. Veronig, Karin Dissauer, Bernhard Kliem, Cooper Downs, Hugh S. Hudson, Meng Jin, Rachel Osten, Tatiana Podladchikova, Avijeet Prasad, Jiong Qiu, Barbara Thompson, Hui Tian, Angelos Vourlidas
Coronal dimmings associated with coronal mass ejections (CME) from the Sun have gained much attention since the late 1990s when they were first observed in high-cadence imagery of the SOHO/EIT and Yohkoh/SXT instruments. They appear as localized sudden decreases of the coronal emission at extreme ultraviolet (EUV) and soft X-ray (SXR) wavelengths, that evolve impulsively during the lift-off and early expansion phase of a CME. Coronal dimmings have been interpreted as "footprints" of the erupting flux rope and also as indicators of the coronal mass loss by CMEs. However, these are only some aspects of coronal dimmings and how they relate to the overall CME/flare process. The goal of this review is to summarize our current understanding and observational findings on coronal dimmings, how they relate to CME simulations, and to discuss how they can be used to provide us with a deeper insight and diagnostics of the triggering of CMEs, the magnetic connectivities and coronal reconfigurations due to the CME as well as the replenishment of the corona after an eruption. In addition, we go beyond a pure review by introducing a new, physics-driven categorization of coronal dimmings based on the magnetic flux systems involved in the eruption process. Finally, we discuss the recent progress in studying coronal dimmings on solar-like and late-type stars, and to use them as a diagnostics for stellar coronal mass ejections and their properties.
Multi-Agent Path Finding (MAPF) is a fundamental problem in artificial intelligence and robotics, requiring the computation of collision-free paths for multiple agents navigating from their start locations to designated goals. As autonomous systems become increasingly prevalent in warehouses, urban transportation, and other complex environments, MAPF has evolved from a theoretical challenge to a critical enabler of real-world multi-robot coordination. This comprehensive survey bridges the long-standing divide between classical algorithmic approaches and emerging learning-based methods in MAPF research. We present a unified framework that encompasses search-based methods (including Conflict-Based Search, Priority-Based Search, and Large Neighborhood Search), compilation-based approaches (SAT, SMT, CSP, ASP, and MIP formulations), and data-driven techniques (reinforcement learning, supervised learning, and hybrid strategies). Through systematic analysis of experimental practices across 200+ papers, we uncover significant disparities in evaluation methodologies, with classical methods typically tested on larger-scale instances (up to 200 by 200 grids with 1000+ agents) compared to learning-based approaches (predominantly 10-100 agents). We provide a comprehensive taxonomy of evaluation metrics, environment types, and baseline selections, highlighting the need for standardized benchmarking protocols. Finally, we outline promising future directions including mixed-motive MAPF with game-theoretic considerations, language-grounded planning with large language models, and neural solver architectures that combine the rigor of classical methods with the flexibility of deep learning. This survey serves as both a comprehensive reference for researchers and a practical guide for deploying MAPF solutions in increasingly complex real-world applications.
The Overthinker's DIET: Cutting Token Calories with DIfficulty-AwarE Training
2025-05-25
Weize Chen, Jiarui Yuan, Tailin Jin, Ning Ding, Huimin Chen, Zhiyuan Liu, Maosong Sun
Recent large language models (LLMs) exhibit impressive reasoning but often over-think, generating excessively long responses that hinder efficiency. We introduce DIET ( DIfficulty-AwarE Training), a framework that systematically cuts these "token calories" by integrating on-the-fly problem difficulty into the reinforcement learning (RL) process. DIET dynamically adapts token compression strategies by modulating token penalty strength and conditioning target lengths on estimated task difficulty, to optimize the performance-efficiency trade-off. We also theoretically analyze the pitfalls of naive reward weighting in group-normalized RL algorithms like GRPO, and propose Advantage Weighting technique, which enables stable and effective implementation of these difficulty-aware objectives. Experimental results demonstrate that DIET significantly reduces token counts while simultaneously improving reasoning performance. Beyond raw token reduction, we show two crucial benefits largely overlooked by prior work: (1) DIET leads to superior inference scaling. By maintaining high per-sample quality with fewer tokens, it enables better scaling performance via majority voting with more samples under fixed computational budgets, an area where other methods falter. (2) DIET enhances the natural positive correlation between response length and problem difficulty, ensuring verbosity is appropriately allocated, unlike many existing compression methods that disrupt this relationship. Our analyses provide a principled and effective framework for developing more efficient, practical, and high-performing LLMs.
Extending the micro-Hertz detection horizons via hidden orbital resonance effect for geocentric gravitational wave antennas
2025-05-25
Qiong Deng, Minghui Du, Peng Xu, Liang Huang, Ziren Luo
The microhertz gravitational wave (GW) band (0.1 $\mu$Hz-0.1 mHz) holds critical insights into coalescing supermassive black hole binaries (SMBHBs) and the stochastic GW background, yet remains observationally inaccessible due to technical barriers. This work presents a strategy to unlock this band by exploiting orbital resonance effects in the proposed geocentric GW antennas, circumventing the need for impractical ultralong baselines or advanced technologies. We demonstrate that when GW frequencies resonate with integer multiples of a satellite's orbital frequency (e.g., 1$\times$, 3$\times$), sustained orbital deviations amplify signals through time-cumulative effects. Benifit from orbital modulation and resonance phenomena, detector sensitivity can be enhanced by 1-2 orders of magnitude in the microhertz regime. For proposed missions like TianQin, gLISA, and GADFLI, this resonance-driven mechanism extends detectable GW frequencies and expands the observable parameter space of SMBHBs to lower masses and higher redshifts, exceeding conventional sensitivity assessments. This approach may leverage geocentric missions as cost-effective platforms to bridge the microhertz observational gap.
Can LLMs accurately adjust their confidence when facing opposition? Building on previous studies measuring calibration on static fact-based question-answering tasks, we evaluate Large Language Models (LLMs) in a dynamic, adversarial debate setting, uniquely combining two realistic factors: (a) a multi-turn format requiring models to update beliefs as new information emerges, and (b) a zero-sum structure to control for task-related uncertainty, since mutual high-confidence claims imply systematic overconfidence. We organized 60 three-round policy debates among ten state-of-the-art LLMs, with models privately rating their confidence (0-100) in winning after each round. We observed five concerning patterns: (1) Systematic overconfidence: models began debates with average initial confidence of 72.9% vs. a rational 50% baseline. (2) Confidence escalation: rather than reducing confidence as debates progressed, debaters increased their win probabilities, averaging 83% by the final round. (3) Mutual overestimation: in 61.7% of debates, both sides simultaneously claimed >=75% probability of victory, a logical impossibility. (4) Persistent self-debate bias: models debating identical copies increased confidence from 64.1% to 75.2%; even when explicitly informed their chance of winning was exactly 50%, confidence still rose (from 50.0% to 57.1%). (5) Misaligned private reasoning: models' private scratchpad thoughts sometimes differed from their public confidence ratings, raising concerns about faithfulness of chain-of-thought reasoning. These results suggest LLMs lack the ability to accurately self-assess or update their beliefs in dynamic, multi-turn tasks; a major concern as LLM outputs are deployed without careful review in assistant roles or agentic settings.
LLM-as-a-Judge employs large language models (LLMs), such as GPT-4, to evaluate the quality of LLM-generated responses, gaining popularity for its cost-effectiveness and strong alignment with human evaluations. However, training proxy judge models using evaluation data generated by powerful teacher models introduces a critical yet previously overlooked issue: teacher preference bias, where the proxy judge model learns a biased preference for responses from the teacher model. To tackle this problem, we propose a novel setting that incorporates an additional assistant model, which is not biased toward the teacher model's responses, to complement the training data. Building on this setup, we introduce AGDe-Judge, a three-stage framework designed to debias from both the labels and feedbacks in the training data. Extensive experiments demonstrate that AGDe-Judge effectively reduces teacher preference bias while maintaining strong performance across six evaluation benchmarks. Code is available at https://github.com/Liuz233/AGDe-Judge.
The rapid advancement of large language models (LLMs) and multi-modal LLMs (MLLMs) has historically relied on model-centric scaling through increasing parameter counts from millions to hundreds of billions to drive performance gains. However, as we approach hardware limits on model size, the dominant computational bottleneck has fundamentally shifted to the quadratic cost of self-attention over long token sequences, now driven by ultra-long text contexts, high-resolution images, and extended videos. In this position paper, \textbf{we argue that the focus of research for efficient AI is shifting from model-centric compression to data-centric compression}. We position token compression as the new frontier, which improves AI efficiency via reducing the number of tokens during model training or inference. Through comprehensive analysis, we first examine recent developments in long-context AI across various domains and establish a unified mathematical framework for existing model efficiency strategies, demonstrating why token compression represents a crucial paradigm shift in addressing long-context overhead. Subsequently, we systematically review the research landscape of token compression, analyzing its fundamental benefits and identifying its compelling advantages across diverse scenarios. Furthermore, we provide an in-depth analysis of current challenges in token compression research and outline promising future directions. Ultimately, our work aims to offer a fresh perspective on AI efficiency, synthesize existing research, and catalyze innovative developments to address the challenges that increasing context lengths pose to the AI community's advancement.
Human-annotated data plays a vital role in training large language models (LLMs), such as supervised fine-tuning and human preference alignment. However, it is not guaranteed that paid human annotators produce high-quality data. In this paper, we study how to incentivize human annotators to do so. We start from a principal-agent model to model the dynamics between the company (the principal) and the annotator (the agent), where the principal can only monitor the annotation quality by examining $n$ samples. We investigate the maximum likelihood estimators (MLE) and the corresponding hypothesis testing to incentivize annotators: the agent is given a bonus if the MLE passes the test. By analyzing the variance of the outcome, we show that the strategic behavior of the agent makes the hypothesis testing very different from traditional ones: Unlike the exponential rate proved by the large deviation theory, the principal-agent model's hypothesis testing rate is of $\Theta(1/\sqrt{n \log n})$. Our theory implies two criteria for the \emph{golden questions} to monitor the performance of the annotators: they should be of (1) high certainty and (2) similar format to normal ones. In that light, we select a set of golden questions in human preference data. By doing incentive-compatible experiments, we find out that the annotators' behavior is better revealed by those golden questions, compared to traditional survey techniques such as instructed manipulation checks.
2025-05-25
Eustasio del Barrio, Alberto González-Sanz, Jean-Michel Loubes, David Rodríguez-Vítores
Optimal Transport (OT) is a resource allocation problem with applications in biology, data science, economics and statistics, among others. In some of the applications, practitioners have access to samples which approximate the continuous measure. Hence the quantities of interest derived from OT -- plans, maps and costs -- are only available in their empirical versions. Statistical inference on OT aims at finding confidence intervals of the population plans, maps and costs. In recent years this topic gained an increasing interest in the statistical community. In this paper we provide a comprehensive review of the most influential results on this research field, underlying the some of the applications. Finally, we provide a list of open problems.
DeepSeek-R1 has demonstrated powerful reasoning capabilities in the text domain through stable reinforcement learning (RL). Recently, in the multimodal domain, works have begun to directly apply RL to generate R1-like free-form reasoning for Visual Question Answering (VQA) tasks. However, multimodal tasks share an intrinsically different nature from textual tasks, which heavily rely on the understanding of the input image to solve the problem. Therefore, such free-form reasoning faces two critical limitations in the VQA task: (1) Extended reasoning chains diffuse visual focus away from task-critical regions, degrading answer accuracy. (2) Unverifiable intermediate steps amplify policy-gradient variance and computational costs overhead. To address these issues, in this paper, we introduce SATORI ($\textbf{S}patially$ $\textbf{A}nchored$ $\textbf{T}ask$ $\textbf{O}ptimization$ with $\textbf{R}e\textbf{I}nforcement$ Learning), which decomposes VQA into three verifiable stages, including global image captioning, region localization, and answer prediction, each supplying explicit reward signals. Furthermore, we also introduce VQA-Verify, a 12k dataset annotated with answer-aligned captions and bounding-boxes to facilitate training. Experiments demonstrate consistent performance improvements across seven VQA benchmarks, achieving up to $15.7\%$ improvement in accuracy in accuracy compared to the R1-like baseline. Our analysis of the attention map confirms enhanced focus on critical regions, which brings improvements in accuracy. Our code is available at https://github.com/justairr/SATORI-R1.
Geometric Determinations Of Characteristic Redshifts From DESI-DR2 BAO and DES-SN5YR Observations: Hints For New Expansion Rate Anomalies
2025-05-25
Purba Mukherjee, Anjan A Sen
In this work, we perform a model-agnostic reconstruction of the cosmic expansion history by combining DESI-DR2 BAO and DES-SN5YR data, with a focus on geometric determination of characteristic redshifts where notable tensions in the expansion rate are found to emerge. Employing Gaussian process regression alongside knot-based spline techniques, we reconstruct cosmic distances and their derivatives to pinpoint these characteristic redshifts and infer $E(z)$. Our analysis reveals significant deviations of approximately 4 to 5$\sigma$ from the Planck 2018 $\Lambda$CDM predictions, particularly pronounced in the redshift range $z \sim 0.35-0.55$. These anomalies are consistently observed across both reconstruction methods and combined datasets, indicating robust late-time departures that could signal new physics beyond the standard cosmological framework. The joint use of BAO and SN probes enhances the precision of our constraints, allowing us to isolate these deviations without reliance on specific cosmological assumptions. Our findings underscore the role of characteristic redshifts as sensitive indicators of expansion rate anomalies and motivate further scrutiny with forthcoming datasets from DESI-5YR BAO, Euclid, and LSST. These future surveys will tighten constraints and help distinguish whether these late-time anomalies arise from new fundamental physics or unresolved systematics in the data.
Towards Generalized Proactive Defense against Face Swapping with Contour-Hybrid Watermark
2025-05-25
Ruiyang Xia, Dawei Zhou, Decheng Liu, Lin Yuan, Jie Li, Nannan Wang, Xinbo Gao
Face swapping, recognized as a privacy and security concern, has prompted considerable defensive research. With the advancements in AI-generated content, the discrepancies between the real and swapped faces have become nuanced. Considering the difficulty of forged traces detection, we shift the focus to the face swapping purpose and proactively embed elaborate watermarks against unknown face swapping techniques. Given that the constant purpose is to swap the original face identity while preserving the background, we concentrate on the regions surrounding the face to ensure robust watermark generation, while embedding the contour texture and face identity information to achieve progressive image determination. The watermark is located in the facial contour and contains hybrid messages, dubbed the contour-hybrid watermark (CMark). Our approach generalizes face swapping detection without requiring any swapping techniques during training and the storage of large-scale messages in advance. Experiments conducted across 8 face swapping techniques demonstrate the superiority of our approach compared with state-of-the-art passive and proactive detectors while achieving a favorable balance between the image quality and watermark robustness.
Probing Time-Varying Dark Energy with DESI: The Crucial Role of Precision Matter Density ($Ω_{m0}$) Measurements
2025-05-25
Seokcheon Lee
Accurate measurements of fundamental cosmological parameters, especially the Hubble constant ($H_0$) and present-day matter density ($\Omega_{m0}$), are crucial for constraining dark energy (DE) evolution. We analyze the sensitivities of cosmological observables ($H(z)$, $D_L(z)$, $\EG$) to $\Omega_{m0}$, $\omega_0$, and $\omega_a$ under different parametrizations. Our results show observables are far more sensitive to $\Omega_{m0}$ than to DE equation of state parameters (e.g., at $z \sim 0.5$, $H(z)$'s $\Omega_{m0}$ sensitivity is $\sim 0.7$ vs. $\omega_a$'s $\sim 0.04$). This hierarchy mandates high-precision $\Omega_{m0}$ measurements to accurately constrain time-varying DE. We also find DE parameter sensitivity highly depends on parametrization; the standard CPL form shows low sensitivity to $\omega_a$, but $\omega(z) = \omega_0 + \omega_a \ln(1+z)$ significantly enhances it. Our analysis of DESI DR1/DR2 data confirms these theoretical limits: standalone DESI data primarily provides only upper limits for $\omega_a$, underscoring insufficient constraining power for a definitive time-varying DE detection. While combined datasets offer tighter constraints, interpretation requires caution due to parametrization influence. In conclusion, improving $\Omega_{m0}$ precision and adopting optimized parametrizations are imperative for future surveys like DESI to fully probe dark energy's nature.
Many engineering and environmental surfaces exhibit spatial heterogeneity in the spanwise direction and encompass multiple surface length scales. When the dominant spanwise length scale is on the order of the largest flow scales (e.g., the boundary layer thickness or channel half-height, delta), localized delta-scale secondary flows can form. These secondary-flow generating heterogeneous surfaces are typically classified as "ridge-type" (with spanwise variation in surface elevation) and "strip-type" (spanwise variation in skin-friction including through variation in roughness characteristics). Both types of surfaces have been explored in previous studies at high Reynolds numbers using experiments with focus on a variety of characteristics such as mean flow, turbulent statistics and structure, while lower Reynolds number studies in channels have examined influence on skin-friction in addition to flow characteristics and mechanisms. In this work, we augment the previous work through experiments on ridge-type roughness in turbulent channel flows over a large range of Reynolds numbers. Our findings reveal that the global skin friction, as inferred through pressure drop in the channel along the streamwise direction, of these surfaces exhibits a log-linear asymptotic behaviour that is characteristic of a fully-rough surface at sufficiently high Reynolds numbers. Yet, the roughness function where this is observed is "low" compared to other established homogeneous rough surfaces. Moreover, the mean flow, turbulent statistics, and two-point correlation analyses indicate that the secondary flow structure, spatial extent, and magnitude remain largely invariant over the Reynolds number range considered. These findings have important implications on predicting drag of these heterogeneous surfaces as well as developing new models for the flow structure.
Origins of Supermassive Black Holes in Galactic Centers
2025-05-25
Ke-Jung Chen
Direct imaging of black hole shadow halos has firmly confirmed the existence of supermassive black holes (SMBHs), with millions of solar masses, residing at the centers of the Milky Way and M87 galaxies. These groundbreaking discoveries represent a monumental success of Einstein's theory of general relativity and have revealed the hidden "monsters" lurking at the centers of galaxies. Moreover, observations of active galactic nuclei (AGNs) indicate that SMBHs with billions of solar masses were already in place within the first billion years after the Big Bang. However, the origins of these SMBHs, as well as their co-evolution with host galaxies, remain poorly understood. This review focuses on the origin of SMBHs, particularly on the formation of their seed black holes. We also highlight several outstanding challenges in modeling seed formation and discuss possible observational signatures. These signatures may be testable with current and future facilities, including the James Webb Space Telescope (JWST) and the upcoming gravitational wave observatory, the Laser Interferometer Space Antenna (LISA).
NTIRE 2025 Challenge on Video Quality Enhancement for Video Conferencing: Datasets, Methods and Results
2025-05-25
Varun Jain, Zongwei Wu, Quan Zou, Louis Florentin, Henrik Turbell, Sandeep Siddhartha, Radu Timofte, others
This paper presents a comprehensive review of the 1st Challenge on Video Quality Enhancement for Video Conferencing held at the NTIRE workshop at CVPR 2025, and highlights the problem statement, datasets, proposed solutions, and results. The aim of this challenge was to design a Video Quality Enhancement (VQE) model to enhance video quality in video conferencing scenarios by (a) improving lighting, (b) enhancing colors, (c) reducing noise, and (d) enhancing sharpness - giving a professional studio-like effect. Participants were given a differentiable Video Quality Assessment (VQA) model, training, and test videos. A total of 91 participants registered for the challenge. We received 10 valid submissions that were evaluated in a crowdsourced framework.
FedSKC: Federated Learning with Non-IID Data via Structural Knowledge Collaboration
2025-05-25
Huan Wang, Haoran Li, Huaming Chen, Jun Yan, Lijuan Wang, Jiahua Shi, Shiping Chen, Jun Shen
With the advancement of edge computing, federated learning (FL) displays a bright promise as a privacy-preserving collaborative learning paradigm. However, one major challenge for FL is the data heterogeneity issue, which refers to the biased labeling preferences among multiple clients, negatively impacting convergence and model performance. Most previous FL methods attempt to tackle the data heterogeneity issue locally or globally, neglecting underlying class-wise structure information contained in each client. In this paper, we first study how data heterogeneity affects the divergence of the model and decompose it into local, global, and sampling drift sub-problems. To explore the potential of using intra-client class-wise structural knowledge in handling these drifts, we thus propose Federated Learning with Structural Knowledge Collaboration (FedSKC). The key idea of FedSKC is to extract and transfer domain preferences from inter-client data distributions, offering diverse class-relevant knowledge and a fair convergent signal. FedSKC comprises three components: i) local contrastive learning, to prevent weight divergence resulting from local training; ii) global discrepancy aggregation, which addresses the parameter deviation between the server and clients; iii) global period review, correcting for the sampling drift introduced by the server randomly selecting devices. We have theoretically analyzed FedSKC under non-convex objectives and empirically validated its superiority through extensive experimental results.
Language Models Surface the Unwritten Code of Science and Society
2025-05-25
Honglin Bao, Siyang Wu, Jiwoong Choi, Yingrong Mao, James A. Evans
This paper calls on the research community not only to investigate how human biases are inherited by large language models (LLMs) but also to explore how these biases in LLMs can be leveraged to make society's "unwritten code" - such as implicit stereotypes and heuristics - visible and accessible for critique. We introduce a conceptual framework through a case study in science: uncovering hidden rules in peer review - the factors that reviewers care about but rarely state explicitly due to normative scientific expectations. The idea of the framework is to push LLMs to speak out their heuristics through generating self-consistent hypotheses - why one paper appeared stronger in reviewer scoring - among paired papers submitted to 45 computer science conferences, while iteratively searching deeper hypotheses from remaining pairs where existing hypotheses cannot explain. We observed that LLMs' normative priors about the internal characteristics of good science extracted from their self-talk, e.g. theoretical rigor, were systematically updated toward posteriors that emphasize storytelling about external connections, such as how the work is positioned and connected within and across literatures. This shift reveals the primacy of scientific myths about intrinsic properties driving scientific excellence rather than extrinsic contextualization and storytelling that influence conceptions of relevance and significance. Human reviewers tend to explicitly reward aspects that moderately align with LLMs' normative priors (correlation = 0.49) but avoid articulating contextualization and storytelling posteriors in their review comments (correlation = -0.14), despite giving implicit reward to them with positive scores. We discuss the broad applicability of the framework, leveraging LLMs as diagnostic tools to surface the tacit codes underlying human society, enabling more precisely targeted responsible AI.
Benchmarking Large Language Models for Cyberbullying Detection in Real-World YouTube Comments
2025-05-25
Amel Muminovic
As online platforms grow, comment sections increasingly host harassment that undermines user experience and well-being. This study benchmarks three leading large language models, OpenAI GPT-4.1, Google Gemini 1.5 Pro, and Anthropic Claude 3 Opus, on a corpus of 5,080 YouTube comments sampled from high-abuse threads in gaming, lifestyle, food vlog, and music channels. The dataset comprises 1,334 harmful and 3,746 non-harmful messages in English, Arabic, and Indonesian, annotated independently by two reviewers with substantial agreement (Cohen's kappa = 0.83). Using a unified prompt and deterministic settings, GPT-4.1 achieved the best overall balance with an F1 score of 0.863, precision of 0.887, and recall of 0.841. Gemini flagged the highest share of harmful posts (recall = 0.875) but its precision fell to 0.767 due to frequent false positives. Claude delivered the highest precision at 0.920 and the lowest false-positive rate of 0.022, yet its recall dropped to 0.720. Qualitative analysis showed that all three models struggle with sarcasm, coded insults, and mixed-language slang. These results underscore the need for moderation pipelines that combine complementary models, incorporate conversational context, and fine-tune for under-represented languages and implicit abuse. A de-identified version of the dataset and full prompts is publicly released to promote reproducibility and further progress in automated content moderation.
ALPCAHUS: Subspace Clustering for Heteroscedastic Data
2025-05-25
Javier Salazar Cavazos, Jeffrey A Fessler, Laura Balzano
Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction. Various methods have been proposed to extend PCA to the union of subspace (UoS) setting for clustering data that come from multiple subspaces like K-Subspaces (KSS). However, some applications involve heterogeneous data that vary in quality due to noise characteristics associated with each data sample. Heteroscedastic methods aim to deal with such mixed data quality. This paper develops a heteroscedastic-focused subspace clustering method, named ALPCAHUS, that can estimate the sample-wise noise variances and use this information to improve the estimate of the subspace bases associated with the low-rank structure of the data. This clustering algorithm builds on K-Subspaces (KSS) principles by extending the recently proposed heteroscedastic PCA method, named LR-ALPCAH, for clusters with heteroscedastic noise in the UoS setting. Simulations and real-data experiments show the effectiveness of accounting for data heteroscedasticity compared to existing clustering algorithms. Code available at https://github.com/javiersc1/ALPCAHUS.
Data-driven modeling of fluid dynamics has advanced rapidly with neural PDE solvers, yet a fair and strong benchmark remains fragmented due to the absence of unified PDE datasets and standardized evaluation protocols. Although architectural innovations are abundant, fair assessment is further impeded by the lack of clear disentanglement between spatial, temporal and loss modules. In this paper, we introduce FD-Bench, the first fair, modular, comprehensive and reproducible benchmark for data-driven fluid simulation. FD-Bench systematically evaluates 85 baseline models across 10 representative flow scenarios under a unified experimental setup. It provides four key contributions: (1) a modular design enabling fair comparisons across spatial, temporal, and loss function modules; (2) the first systematic framework for direct comparison with traditional numerical solvers; (3) fine-grained generalization analysis across resolutions, initial conditions, and temporal windows; and (4) a user-friendly, extensible codebase to support future research. Through rigorous empirical studies, FD-Bench establishes the most comprehensive leaderboard to date, resolving long-standing issues in reproducibility and comparability, and laying a foundation for robust evaluation of future data-driven fluid models. The code is open-sourced at https://anonymous.4open.science/r/FD-Bench-15BC.
Security Concerns for Large Language Models: A Survey
2025-05-24
Miles Q. Li, Benjamin C. M. Fung
Large Language Models (LLMs) such as GPT-4 (and its recent iterations like GPT-4o and the GPT-4.1 series), Google's Gemini, Anthropic's Claude 3 models, and xAI's Grok have caused a revolution in natural language processing, but their capabilities also introduce new security vulnerabilities. In this survey, we provide a comprehensive overview of the emerging security concerns around LLMs, categorizing threats into prompt injection and jailbreaking, adversarial attacks (including input perturbations and data poisoning), misuse by malicious actors (e.g., for disinformation, phishing, and malware generation), and worrisome risks inherent in autonomous LLM agents. A significant focus has been recently placed on the latter, exploring goal misalignment, emergent deception, self-preservation instincts, and the potential for LLMs to develop and pursue covert, misaligned objectives (scheming), which may even persist through safety training. We summarize recent academic and industrial studies (2022-2025) that exemplify each threat, analyze proposed defenses and their limitations, and identify open challenges in securing LLM-based applications. We conclude by emphasizing the importance of advancing robust, multi-layered security strategies to ensure LLMs are safe and beneficial.
REGen: Multimodal Retrieval-Embedded Generation for Long-to-Short Video Editing
2025-05-24
Weihan Xu, Yimeng Ma, Jingyue Huang, Yang Li, Wenye Ma, Taylor Berg-Kirkpatrick, Julian McAuley, Paul Pu Liang, Hao-Wen Dong
Short videos are an effective tool for promoting contents and improving knowledge accessibility. While existing extractive video summarization methods struggle to produce a coherent narrative, existing abstractive methods cannot `quote' from the input videos, i.e., inserting short video clips in their outputs. In this work, we explore novel video editing models for generating shorts that feature a coherent narrative with embedded video insertions extracted from a long input video. We propose a novel retrieval-embedded generation framework that allows a large language model to quote multimodal resources while maintaining a coherent narrative. Our proposed REGen system first generates the output story script with quote placeholders using a finetuned large language model, and then uses a novel retrieval model to replace the quote placeholders by selecting a video clip that best supports the narrative from a pool of candidate quotable video clips. We examine the proposed method on the task of documentary teaser generation, where short interview insertions are commonly used to support the narrative of a documentary. Our objective evaluations show that the proposed method can effectively insert short video clips while maintaining a coherent narrative. In a subjective survey, we show that our proposed method outperforms existing abstractive and extractive approaches in terms of coherence, alignment, and realism in teaser generation.
Understanding the Relationship Between Personal Data Privacy Literacy and Data Privacy Information Sharing by University Students
2025-05-24
Brady D. Lund, Bryan Anderson, Ana Roeschley, Gahangir Hossain
With constant threats to the safety of personal data in the United States, privacy literacy has become an increasingly important competency among university students, one that ties intimately to the information sharing behavior of these students. This survey based study examines how university students in the United States perceive personal data privacy and how their privacy literacy influences their understanding and behaviors. Students responses to a privacy literacy scale were categorized into high and low privacy literacy groups, revealing that high literacy individuals demonstrate a broader range of privacy practices, including multi factor authentication, VPN usage, and phishing awareness, whereas low literacy individuals rely on more basic security measures. Statistical analyses suggest that high literacy respondents display greater diversity in recommendations and engagement in privacy discussions. These findings suggest the need for enhanced educational initiatives to improve data privacy awareness at the university level to create a better cyber safe population.
2025-05-24
Md Farhamdur Reza, Reza Jahani, Richeng Jin, Huaiyu Dai
Decentralized federated learning (DFL) has attracted significant attention due to its scalability and independence from a central server. In practice, some participating clients can be mobile, yet the impact of user mobility on DFL performance remains largely unexplored, despite its potential to facilitate communication and model convergence. In this work, we demonstrate that introducing a small fraction of mobile clients, even with random movement, can significantly improve the accuracy of DFL by facilitating information flow. To further enhance performance, we propose novel distribution-aware mobility patterns, where mobile clients strategically navigate the network, leveraging knowledge of data distributions and static client locations. The proposed moving strategies mitigate the impact of data heterogeneity and boost learning convergence. Extensive experiments validate the effectiveness of induced mobility in DFL and demonstrate the superiority of our proposed mobility patterns over random movement.
Literature review on assistive technologies for people with Parkinson's disease
2025-05-24
Subek Acharya, Sansrit Paudel
Parkinson's Disease (PD) is a neurodegenerative disorder that significantly impacts motor and non-motor functions. There is currently no treatment that slows or stops neurodegeneration in PD. In this context, assistive technologies (ATs) have emerged as vital tools to aid people with Parkinson's and significantly improve their quality of life. This review explores a broad spectrum of ATs, including wearable and cueing devices, exoskeletons, robotics, virtual reality, voice and video-assisted technologies, and emerging innovations such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). The review highlights ATs' significant role in addressing motor symptoms such as freezing of gait (FOG) and gait and posture disorders. However, it also identifies significant gaps in addressing non-motor symptoms such as sleep dysfunction and mental health. Similarly, the research identifies substantial potential in the further implementation of deep learning, AI, IOT technologies. Overall, this review highlights the transformative potential of AT in PD management while identifying gaps that future research should address to ensure personalized, accessible, and effective solutions.
LLM-Driven APT Detection for 6G Wireless Networks: A Systematic Review and Taxonomy
2025-05-24
Muhammed Golec, Yaser Khamayseh, Suhib Bani Melhem, Abdulmalik Alwarafy
Sixth Generation (6G) wireless networks, which are expected to be deployed in the 2030s, have already created great excitement in academia and the private sector with their extremely high communication speed and low latency rates. However, despite the ultra-low latency, high throughput, and AI-assisted orchestration capabilities they promise, they are vulnerable to stealthy and long-term Advanced Persistent Threats (APTs). Large Language Models (LLMs) stand out as an ideal candidate to fill this gap with their high success in semantic reasoning and threat intelligence. In this paper, we present a comprehensive systematic review and taxonomy study for LLM-assisted APT detection in 6G networks. We address five research questions, namely, semantic merging of fragmented logs, encrypted traffic analysis, edge distribution constraints, dataset/modeling techniques, and reproducibility trends, by leveraging most recent studies on the intersection of LLMs, APTs, and 6G wireless networks. We identify open challenges such as explainability gaps, data scarcity, edge hardware limitations, and the need for real-time slicing-aware adaptation by presenting various taxonomies such as granularity, deployment models, and kill chain stages. We then conclude the paper by providing several research gaps in 6G infrastructures for future researchers. To the best of our knowledge, this paper is the first comprehensive systematic review and classification study on LLM-based APT detection in 6G networks.
2025-05-24
Sagar Sapkota, Mohammad Saqib Hasan, Mubarak Shah, Santu Karmaker
Multi-party Conversational Agents (MPCAs) are systems designed to engage in dialogue with more than two participants simultaneously. Unlike traditional two-party agents, designing MPCAs faces additional challenges due to the need to interpret both utterance semantics and social dynamics. This survey explores recent progress in MPCAs by addressing three key questions: 1) Can agents model each participants' mental states? (State of Mind Modeling); 2) Can they properly understand the dialogue content? (Semantic Understanding); and 3) Can they reason about and predict future conversation flow? (Agent Action Modeling). We review methods ranging from classical machine learning to Large Language Models (LLMs) and multi-modal systems. Our analysis underscores Theory of Mind (ToM) as essential for building intelligent MPCAs and highlights multi-modal understanding as a promising yet underexplored direction. Finally, this survey offers guidance to future researchers on developing more capable MPCAs.
Reasoning Segmentation (RS) aims to delineate objects based on implicit text queries, the interpretation of which requires reasoning and knowledge integration. Unlike the traditional formulation of segmentation problems that relies on fixed semantic categories or explicit prompting, RS bridges the gap between visual perception and human-like reasoning capabilities, facilitating more intuitive human-AI interaction through natural language. Our work presents the first comprehensive survey of RS for image and video processing, examining 26 state-of-the-art methods together with a review of the corresponding evaluation metrics, as well as 29 datasets and benchmarks. We also explore existing applications of RS across diverse domains and identify their potential extensions. Finally, we identify current research gaps and highlight promising future directions.
Usability of Token-based and Remote Electronic Signatures: A User Experience Study
2025-05-24
Omer Ege, Mustafa Cagal, Kemal Bicakci
As electronic signatures (e-signatures) become increasingly integral to secure digital transactions, understanding their usability and security perception from an end-user perspective has become crucial. This study empirically evaluates and compares two major e-signature systems -- token-based and remote signatures -- through a controlled user experience study with 20 participants. Participants completed tasks involving acquisition, installation, and document signing using both methods, followed by structured surveys and qualitative feedback. Statistical analyses revealed that remote e-signatures were perceived as significantly more usable than token-based ones, due to their minimal setup and platform-independent accessibility. In contrast, token-based signatures were rated as significantly more secure, highlighting users' trust in hardware-based protection. Although more participants preferred remote e-signatures for document signing, the preference did not reach statistical significance, indicating a trend toward favoring convenience in real-world scenarios. These findings underline the fundamental trade-off between usability and perceived security in digital signing systems. By bridging the gap between theoretical frameworks and real user experience, this study contributes valuable insights to the design and policymaking of qualified electronic signature solutions.
The powerful reasoning capabilities of Large Language Models (LLMs) in mathematics and coding, combined with their ability to automate complex tasks through agentic frameworks, present unprecedented opportunities for accelerating scientific innovation. In this paper, we introduce AI-Researcher, a fully autonomous research system that transforms how AI-driven scientific discovery is conducted and evaluated. Our framework seamlessly orchestrates the complete research pipeline--from literature review and hypothesis generation to algorithm implementation and publication-ready manuscript preparation--with minimal human intervention. To rigorously assess autonomous research capabilities, we develop Scientist-Bench, a comprehensive benchmark comprising state-of-the-art papers across diverse AI research domains, featuring both guided innovation and open-ended exploration tasks. Through extensive experiments, we demonstrate that AI-Researcher achieves remarkable implementation success rates and produces research papers that approach human-level quality. This work establishes new foundations for autonomous scientific innovation that can complement human researchers by systematically exploring solution spaces beyond cognitive limitations.
Simultaneous Optimization of Efficiency and Degradation in Tunable HTL-Free Perovskite Solar Cells with MWCNT-Integrated Back Contact Using a Machine Learning-Derived Polynomial Regressor
2025-05-24
Ihtesham Ibn Malek, Hafiz Imtiaz, Samia Subrina
Perovskite solar cells (PSCs) without a hole transport layer (HTL) offer a cost-effective and stable alternative to conventional architectures, utilizing only an absorber layer and an electron transport layer (ETL). This study presents a machine learning (ML)-driven framework to optimize the efficiency and stability of HTL-free PSCs by integrating experimental validation with numerical simulations. Excellent agreement is achieved between a fabricated device and its simulated counterpart at a molar fraction \( x = 68.7\% \) in \(\mathrm{MAPb}_{1-x}\mathrm{Sb}_{2x/3}\mathrm{I}_3\), where MA is methylammonium. A dataset of 1650 samples is generated by varying molar fraction, absorber defect density, thickness, and ETL doping, with corresponding efficiency and 50-hour degradation as targets. A fourth-degree polynomial regressor (PR-4) shows the best performance, achieving RMSEs of 0.0179 and 0.0117, and \( R^2 \) scores of 1 and 0.999 for efficiency and degradation, respectively. The derived model generalizes beyond the training range and is used in an L-BFGS-B optimization algorithm with a weighted objective function to maximize efficiency and minimize degradation. This improves device efficiency from 13.7\% to 16.84\% and reduces degradation from 6.61\% to 2.39\% over 1000 hours. Finally, the dataset is labeled into superior and inferior classes, and a multilayer perceptron (MLP) classifier achieves 100\% accuracy, successfully identifying optimal configurations.
EvoSort: A Genetic-Algorithm-Based Adaptive Parallel Sorting Framework for Large-Scale High Performance Computing
2025-05-24
Shashank Raj, Kalyanmoy Deb
In today's era of big data, sorting enormous datasets is a major challenge. We present EvoSort, an adaptive parallel sorting framework that employs a Genetic Algorithm (GA) to automatically discover and refine critical parameters, including insertion sort and fallback thresholds, tile size, and mergesort vs Least Significant Digit (LSD) radix sort. EvoSort integrates parallel sorting primitives and adapts continuously to input data and system architecture, ensuring optimal performance. Experiments on up to 10 billion elements show that EvoSort consistently outperforms NumPy sorting by factors from three to over 90 times. EvoSort exemplifies a powerful auto-tuning solution for large-scale data processing.
Supporting Preschool Emotional Development with AI-Powered Robots
2025-05-24
Santiago Berrezueta-Guzman, María Dolón-Poza, Stefan Wagner
This study evaluates the integration of AI-powered robots in early childhood education, focusing on their impact on emotional self-regulation, engagement, and collaborative skills. A ten-week experimental design involving two groups of children assessed the robot's effectiveness through progress assessments, parental surveys, and teacher feedback. Results demonstrated that early exposure to the robot significantly enhanced emotional recognition, while sustained interaction further improved collaborative and social engagement. Parental and teacher feedback highlighted high acceptance levels, emphasizing the robot's ease of integration and positive influence on classroom dynamics. This research underscores the transformative potential of AI and robotics in education. The findings advocate for the broader adoption of AI-powered interventions, carefully examining equitable access, ethical considerations, and sustainable implementation. This work sets a foundation for exploring long-term impacts and expanding applications of AI in inclusive and impactful educational settings.
Robustness in Large Language Models: A Survey of Mitigation Strategies and Evaluation Metrics
2025-05-24
Pankaj Kumar, Subhankar Mishra
Large Language Models (LLMs) have emerged as a promising cornerstone for the development of natural language processing (NLP) and artificial intelligence (AI). However, ensuring the robustness of LLMs remains a critical challenge. To address these challenges and advance the field, this survey provides a comprehensive overview of current studies in this area. First, we systematically examine the nature of robustness in LLMs, including its conceptual foundations, the importance of consistent performance across diverse inputs, and the implications of failure modes in real-world applications. Next, we analyze the sources of non-robustness, categorizing intrinsic model limitations, data-driven vulnerabilities, and external adversarial factors that compromise reliability. Following this, we review state-of-the-art mitigation strategies, and then we discuss widely adopted benchmarks, emerging metrics, and persistent gaps in assessing real-world reliability. Finally, we synthesize findings from existing surveys and interdisciplinary studies to highlight trends, unresolved issues, and pathways for future research.
Ocean surface waves can propagate long distances through regions containing floating ice covers. The impacts ocean waves have on the ice covers are of interest in the climate change era, as the polar regions experience pressure from rising temperatures. This chapter provides a review of observations and theoretical models for ocean wave propagation through the marginal ice zone, landfast ice and ice shelves. It traces the historical evolution of the field, from seminal work in the 1970s-80s up to recent research advances. Key research questions are identified for each of the three ice covers, and commonalities between them are highlighted. The chapter concludes with perspectives and outlooks on the field of waves in ice, in the context of the dramatic changes currently occurring to the world's sea ice and ice shelves.
The Stripped-Star Ultraviolet Magellanic Cloud Survey (SUMS): The UV Photometric Catalog and Stripped Star Candidate Selection
2025-05-24
Bethany Ludwig, Maria R. Drout, Ylva Gotberg, Dustin Lang, Alex Laroche
Most massive stars will interact with a binary companion during their lifetimes. These interactions can remove the hydrogen-rich envelope, producing intermediate-mass ($\sim$2-8 M$_\odot$) and helium-rich stars. These "stripped stars" are predicted to emit predominantly in the ultraviolet (UV) and can therefore be identified via a UV excess provided they are not outshone by their companion. However, despite their importance to binary evolution, supernovae, and ionizing feedback, few stripped stars have been confirmed. This is likely due to the scarcity of wide-field, high angular resolution, UV surveys of stellar populations with reliable distances and extinction estimates. To address this, we present the Stripped-Star Ultraviolet Magellanic Clouds Survey (SUMS) catalog. We use the Tractor forward modeling software to perform PSF photometry on 2,420 Swift-UVOT images of the LMC and SMC. The resulting public catalog contains 734,869 sources in three UV filters to a depth of $\sim$20 Vega mag. We perform validation tests on the photometry pipeline and highlight the catalog's broad applicability. We then identify sources with excess UV light compared to main-sequence stars and apply a series of quality cuts. From this, we identify 522 candidate stripped stars in the LMC and 298 in the SMC. We assess the potential contamination from other UV excess systems and argue the dominant uncertainty to be dust: early main-sequence stars can mimic the colors of stripped star binaries when extinction is overcorrected. This survey lays the groundwork for the first systematic census of stripped stars and opens new windows into binary evolution and massive star populations.
2025-05-24
Szivia Lestyán, William Letrone, Ludovica Robustelli, Gergely Biczók
Anonymization is a foundational principle of data privacy regulation, yet its practical application remains riddled with ambiguity and inconsistency. This paper introduces the concept of anonymity-washing -- the misrepresentation of the anonymity level of ``sanitized'' personal data -- as a critical privacy concern. While both legal and technical critiques of anonymization exist, they tend to address isolated aspects of the problem. In contrast, this paper offers a comprehensive overview of the conditions that enable anonymity-washing. It synthesizes fragmented legal interpretations, technical misunderstandings, and outdated regulatory guidance and complements them with a systematic review of national and international resources, including legal cases, data protection authority guidelines, and technical documentation. Our findings reveal a lack of coherent support for practitioners, contributing to the persistent misuse of pseudonymization and obsolete anonymization techniques. We conclude by recommending targeted education, clearer technical guidance, and closer cooperation between regulators, researchers, and industry to bridge the gap between legal norms and technical reality.
Unleashing Diffusion Transformers for Visual Correspondence by Modulating Massive Activations
2025-05-24
Chaofan Gan, Yuanpeng Tu, Xi Chen, Tieyuan Chen, Yuxi Li, Mehrtash Harandi, Weiyao Lin
Pre-trained stable diffusion models (SD) have shown great advances in visual correspondence. In this paper, we investigate the capabilities of Diffusion Transformers (DiTs) for accurate dense correspondence. Distinct from SD, DiTs exhibit a critical phenomenon in which very few feature activations exhibit significantly larger values than others, known as \textit{massive activations}, leading to uninformative representations and significant performance degradation for DiTs. The massive activations consistently concentrate at very few fixed dimensions across all image patch tokens, holding little local information. We trace these dimension-concentrated massive activations and find that such concentration can be effectively localized by the zero-initialized Adaptive Layer Norm (AdaLN-zero). Building on these findings, we propose Diffusion Transformer Feature (DiTF), a training-free framework designed to extract semantic-discriminative features from DiTs. Specifically, DiTF employs AdaLN to adaptively localize and normalize massive activations with channel-wise modulation. In addition, we develop a channel discard strategy to further eliminate the negative impacts from massive activations. Experimental results demonstrate that our DiTF outperforms both DINO and SD-based models and establishes a new state-of-the-art performance for DiTs in different visual correspondence tasks (\eg, with +9.4\% on Spair-71k and +4.4\% on AP-10K-C.S.).
Exploring the Vulnerability of the Content Moderation Guardrail in Large Language Models via Intent Manipulation
2025-05-24
Jun Zhuang, Haibo Jin, Ye Zhang, Zhengjian Kang, Wenbin Zhang, Gaby G. Dagher, Haohan Wang
Intent detection, a core component of natural language understanding, has considerably evolved as a crucial mechanism in safeguarding large language models (LLMs). While prior work has applied intent detection to enhance LLMs' moderation guardrails, showing a significant success against content-level jailbreaks, the robustness of these intent-aware guardrails under malicious manipulations remains under-explored. In this work, we investigate the vulnerability of intent-aware guardrails and demonstrate that LLMs exhibit implicit intent detection capabilities. We propose a two-stage intent-based prompt-refinement framework, IntentPrompt, that first transforms harmful inquiries into structured outlines and further reframes them into declarative-style narratives by iteratively optimizing prompts via feedback loops to enhance jailbreak success for red-teaming purposes. Extensive experiments across four public benchmarks and various black-box LLMs indicate that our framework consistently outperforms several cutting-edge jailbreak methods and evades even advanced Intent Analysis (IA) and Chain-of-Thought (CoT)-based defenses. Specifically, our "FSTR+SPIN" variant achieves attack success rates ranging from 88.25% to 96.54% against CoT-based defenses on the o1 model, and from 86.75% to 97.12% on the GPT-4o model under IA-based defenses. These findings highlight a critical weakness in LLMs' safety mechanisms and suggest that intent manipulation poses a growing challenge to content moderation guardrails.
Growing awareness of social biases and inequalities embedded in Artificial Intelligence (AI) systems has brought increased attention to the integration of Diversity and Inclusion (D&I) principles throughout the AI lifecycle. Despite the rise of ethical AI guidelines, there is limited empirical evidence on how D&I is applied in real-world settings. This study explores how AI and Machine Learning(ML) practitioners perceive and implement D&I principles and identifies organisational challenges that hinder their effective adoption. Using a mixed-methods approach, we surveyed industry professionals, collecting both quantitative and qualitative data on current practices, perceived impacts, and challenges related to D&I in AI. While most respondents recognise D&I as essential for mitigating bias and enhancing fairness, practical implementation remains inconsistent. Our analysis revealed a disconnect between perceived benefits and current practices, with major barriers including the under-representation of marginalised groups, lack of organisational transparency, and limited awareness among early-career professionals. Despite these barriers, respondents widely agree that diverse teams contribute to ethical, trustworthy, and innovative AI systems. By underpinning the key pain points and areas requiring improvement, this study highlights the need to bridge the gap between D&I principles and real-world AI development practices.
Capacity Enhancement Analysis and Implementation of a 3D Array Based on Miniaturized Dipole Antennas
2025-05-24
Yongzheng Li, Wanchen Yang, Shuai S. A. Yuan, Zhitao Ye, Chongwen Huang, Xiaoming Chen, Wenquan Che, Wei E. I. Sha
Theoretically, the three-dimensional (3D) array architecture provides a higher communication degree of freedom (DoF) compared to the planar arrays, allowing for greater capacity potential in multiple-input multiple-output (MIMO) systems. However, in practical implementations, the upper elements of 3D arrays significantly degrade the performance of the lower elements, leading to increased inter-element correlation and reduced array efficiency. As a result, the expected enhancement in MIMO performance is often suboptimal. To address this issue, this work employs a miniaturized antenna element to reduce the inter-element correlation and thus enhance the DoF of the 3D array. Moreover, to mitigate the efficiency degradation of the lower elements caused by the upper ones, the structures of lower elements are modified to achieve wideband impedance matching. The influence of upper element profile distribution on DoF and element efficiency is investigated, and the scalability of the proposed 3D array is theoretically analyzed. Finally, the MIMO performance of the proposed 3D array is evaluated under 3GPP scenarios, demonstrating a 16% higher capacity than conventional 2D arrays under the same SNR of 20 dB and a physical aperture area of 6.26 {\lambda}02. These results indicate that 3D arrays of appropriately arranged miniaturized elements offer a promising approach to enhancing MIMO system performance.
Graphs are a widely used paradigm for representing non-Euclidean data, with applications ranging from social network analysis to biomolecular prediction. Conventional graph learning approaches typically rely on fixed structural assumptions or fully observed data, limiting their effectiveness in more complex, noisy, or evolving settings. Consequently, real-world graph data often violates the assumptions of traditional graph learning methods, in particular, it leads to four fundamental challenges: (1) Incompleteness, real-world graphs have missing nodes, edges, or attributes; (2) Imbalance, the distribution of the labels of nodes or edges and their structures for real-world graphs are highly skewed; (3) Cross-domain Heterogeneity, graphs from different domains exhibit incompatible feature spaces or structural patterns; and (4) Dynamic Instability, graphs evolve over time in unpredictable ways. Recent advances in Large Language Models (LLMs) offer the potential to tackle these challenges by leveraging rich semantic reasoning and external knowledge. This survey provides a comprehensive review of how LLMs can be integrated with graph learning to address the aforementioned challenges. For each challenge, we review both traditional solutions and modern LLM-driven approaches, highlighting how LLMs contribute unique advantages. Finally, we discuss open research questions and promising future directions in this emerging interdisciplinary field. To support further exploration, we have curated a repository of recent advances on graph learning challenges: https://github.com/limengran98/Awesome-Literature-Graph-Learning-Challenges.
2025-05-24
Sebastian Gutierrez Hernandez, Peng Chen, Haomin Zhou
We introduce Parametric Density Path Optimization (PDPO), a novel method for computing action-minimizing paths between probability densities. The core idea is to represent the target probability path as the pushforward of a reference density through a parametric map, transforming the original infinite-dimensional optimization over densities to a finite-dimensional one over the parameters of the map. We derive a static formulation of the dynamic problem of action minimization and propose cubic spline interpolation of the path in parameter space to solve the static problem. Theoretically, we establish an error bound of the action under proper assumptions on the regularity of the parameter path. Empirically, we find that using 3-5 control points of the spline interpolation suffices to accurately resolve both multimodal and high-dimensional problems. We demonstrate that PDPO can flexibly accommodate a wide range of potential terms, including those modeling obstacles, mean-field interactions, stochastic control, and higher-order dynamics. Our method outperforms existing state-of-the-art approaches in benchmark tasks, demonstrating superior computational efficiency and solution quality. The source code will be publically available after the revision process.
The integration of large language model (LLM) and data management (DATA) is rapidly redefining both domains. In this survey, we comprehensively review the bidirectional relationships. On the one hand, DATA4LLM, spanning large-scale data processing, storage, and serving, feeds LLMs with high quality, diversity, and timeliness of data required for stages like pre-training, post-training, retrieval-augmented generation, and agentic workflows: (i) Data processing for LLMs includes scalable acquisition, deduplication, filtering, selection, domain mixing, and synthetic augmentation; (ii) Data Storage for LLMs focuses on efficient data and model formats, distributed and heterogeneous storage hierarchies, KV-cache management, and fault-tolerant checkpointing; (iii) Data serving for LLMs tackles challenges in RAG (e.g., knowledge post-processing), LLM inference (e.g., prompt compression, data provenance), and training strategies (e.g., data packing and shuffling). On the other hand, in LLM4DATA, LLMs are emerging as general-purpose engines for data management. We review recent advances in (i) data manipulation, including automatic data cleaning, integration, discovery; (ii) data analysis, covering reasoning over structured, semi-structured, and unstructured data, and (iii) system optimization (e.g., configuration tuning, query rewriting, anomaly diagnosis), powered by LLM techniques like retrieval-augmented prompting, task-specialized fine-tuning, and multi-agent collaboration.
Lost in the Haystack: Smaller Needles are More Difficult for LLMs to Find
2025-05-23
Owen Bianchi, Mathew J. Koretsky, Maya Willey, Chelsea X. Alvarado, Tanay Nayak, Adi Asija, Nicole Kuznetsov, Mike A. Nalls, Faraz Faghri, Daniel Khashabi
Large language models (LLMs) face significant challenges with needle-in-a-haystack tasks, where relevant information ("the needle") must be drawn from a large pool of irrelevant context ("the haystack"). Previous studies have highlighted positional bias and distractor quantity as critical factors affecting model performance, yet the influence of gold context size has received little attention. We address this gap by systematically studying how variations in gold context length impact LLM performance on long-context question answering tasks. Our experiments reveal that LLM performance drops sharply when the gold context is shorter, i.e., smaller gold contexts consistently degrade model performance and amplify positional sensitivity, posing a major challenge for agentic systems that must integrate scattered, fine-grained information of varying lengths. This pattern holds across three diverse domains (general knowledge, biomedical reasoning, and mathematical reasoning) and seven state-of-the-art LLMs of various sizes and architectures. Our work provides clear insights to guide the design of robust, context-aware LLM-driven systems.
Bidirectional Knowledge Distillation for Enhancing Sequential Recommendation with Large Language Models
2025-05-23
Jiongran Wu, Jiahao Liu, Dongsheng Li, Guangping Zhang, Mingzhe Han, Hansu Gu, Peng Zhang, Li Shang, Tun Lu, Ning Gu
Large language models (LLMs) have demonstrated exceptional performance in understanding and generating semantic patterns, making them promising candidates for sequential recommendation tasks. However, when combined with conventional recommendation models (CRMs), LLMs often face challenges related to high inference costs and static knowledge transfer methods. In this paper, we propose a novel mutual distillation framework, LLMD4Rec, that fosters dynamic and bidirectional knowledge exchange between LLM-centric and CRM-based recommendation systems. Unlike traditional unidirectional distillation methods, LLMD4Rec enables iterative optimization by alternately refining both models, enhancing the semantic understanding of CRMs and enriching LLMs with collaborative signals from user-item interactions. By leveraging sample-wise adaptive weighting and aligning output distributions, our approach eliminates the need for additional parameters while ensuring effective knowledge transfer. Extensive experiments on real-world datasets demonstrate that LLMD4Rec significantly improves recommendation accuracy across multiple benchmarks without increasing inference costs. This method provides a scalable and efficient solution for combining the strengths of both LLMs and CRMs in sequential recommendation systems.
Deep Video Discovery: Agentic Search with Tool Use for Long-form Video Understanding
2025-05-23
Xiaoyi Zhang, Zhaoyang Jia, Zongyu Guo, Jiahao Li, Bin Li, Houqiang Li, Yan Lu
Long-form video understanding presents significant challenges due to extensive temporal-spatial complexity and the difficulty of question answering under such extended contexts. While Large Language Models (LLMs) have demonstrated considerable advancements in video analysis capabilities and long context handling, they continue to exhibit limitations when processing information-dense hour-long videos. To overcome such limitations, we propose the Deep Video Discovery agent to leverage an agentic search strategy over segmented video clips. Different from previous video agents manually designing a rigid workflow, our approach emphasizes the autonomous nature of agents. By providing a set of search-centric tools on multi-granular video database, our DVD agent leverages the advanced reasoning capability of LLM to plan on its current observation state, strategically selects tools, formulates appropriate parameters for actions, and iteratively refines its internal reasoning in light of the gathered information. We perform comprehensive evaluation on multiple long video understanding benchmarks that demonstrates the advantage of the entire system design. Our DVD agent achieves SOTA performance, significantly surpassing prior works by a large margin on the challenging LVBench dataset. Comprehensive ablation studies and in-depth tool analyses are also provided, yielding insights to further advance intelligent agents tailored for long-form video understanding tasks. The code will be released later.
Towards factorization of jet observables in dense media : An EFT approach
2025-05-23
Balbeer Singh
Jets are extended multipartonic systems and serve as a powerful tool for investigating the dynamics of emergent phenomena driven by many body QCD interactions. In heavy ion collisions, starting from their production during the perturbative hard scattering event in the initial stages of the collision to non-perturbative hadronization they interact with the various stages of quark-gluon plasma and retain imprints of fundamental properties of the medium. In these collisions, the jet production cross-section can be factorized using open quantum system framework along with effective field theory into various functions, each capturing a specific dynamics and depending on a single characteristic scale. In this review article, we discuss recent theoretical developments on factorization for jets in heavy-ion collisions with a specific example of jet substructure observable as energy-energy correlator and its generalization to projected $\nu$-point energy correlators.
Towards Uncertainty Aware Task Delegation and Human-AI Collaborative Decision-Making
2025-05-23
Min Hun Lee, Martyn Zhe Yu Tok
Despite the growing promise of artificial intelligence (AI) in supporting decision-making across domains, fostering appropriate human reliance on AI remains a critical challenge. In this paper, we investigate the utility of exploring distance-based uncertainty scores for task delegation to AI and describe how these scores can be visualized through embedding representations for human-AI decision-making. After developing an AI-based system for physical stroke rehabilitation assessment, we conducted a study with 19 health professionals and 10 students in medicine/health to understand the effect of exploring distance-based uncertainty scores on users' reliance on AI. Our findings showed that distance-based uncertainty scores outperformed traditional probability-based uncertainty scores in identifying uncertain cases. In addition, after exploring confidence scores for task delegation and reviewing embedding-based visualizations of distance-based uncertainty scores, participants achieved an 8.20% higher rate of correct decisions, a 7.15% higher rate of changing their decisions to correct ones, and a 7.14% lower rate of incorrect changes after reviewing AI outputs than those reviewing probability-based uncertainty scores ($p<0.01$). Our findings highlight the potential of distance-based uncertainty scores to enhance decision accuracy and appropriate reliance on AI while discussing ongoing challenges for human-AI collaborative decision-making.
Semantic Correspondence: Unified Benchmarking and a Strong Baseline
2025-05-23
Kaiyan Zhang, Xinghui Li, Jingyi Lu, Kai Han
Establishing semantic correspondence is a challenging task in computer vision, aiming to match keypoints with the same semantic information across different images. Benefiting from the rapid development of deep learning, remarkable progress has been made over the past decade. However, a comprehensive review and analysis of this task remains absent. In this paper, we present the first extensive survey of semantic correspondence methods. We first propose a taxonomy to classify existing methods based on the type of their method designs. These methods are then categorized accordingly, and we provide a detailed analysis of each approach. Furthermore, we aggregate and summarize the results of methods in literature across various benchmarks into a unified comparative table, with detailed configurations to highlight performance variations. Additionally, to provide a detailed understanding on existing methods for semantic matching, we thoroughly conduct controlled experiments to analyse the effectiveness of the components of different methods. Finally, we propose a simple yet effective baseline that achieves state-of-the-art performance on multiple benchmarks, providing a solid foundation for future research in this field. We hope this survey serves as a comprehensive reference and consolidated baseline for future development. Code is publicly available at: https://github.com/Visual-AI/Semantic-Correspondence.
Automata Learning of Preferences over Temporal Logic Formulas from Pairwise Comparisons
2025-05-23
Hazhar Rahmani, Jie Fu
Many preference elicitation algorithms consider preference over propositional logic formulas or items with different attributes. In sequential decision making, a user's preference can be a preorder over possible outcomes, each of which is a temporal sequence of events. This paper considers a class of preference inference problems where the user's unknown preference is represented by a preorder over regular languages (sets of temporal sequences), referred to as temporal goals. Given a finite set of pairwise comparisons between finite words, the objective is to learn both the set of temporal goals and the preorder over these goals. We first show that a preference relation over temporal goals can be modeled by a Preference Deterministic Finite Automaton (PDFA), which is a deterministic finite automaton augmented with a preorder over acceptance conditions. The problem of preference inference reduces to learning the PDFA. This problem is shown to be computationally challenging, with the problem of determining whether there exists a PDFA of size smaller than a given integer $k$, consistent with the sample, being NP-Complete. We formalize the properties of characteristic samples and develop an algorithm that guarantees to learn, given a characteristic sample, the minimal PDFA equivalent to the true PDFA from which the sample is drawn. We present the method through a running example and provide detailed analysis using a robotic motion planning problem.
Training with Pseudo-Code for Instruction Following
2025-05-23
Prince Kumar, Rudra Murthy, Riyaz Bhat, Danish Contractor
Despite the rapid progress in the capabilities of Large Language Models (LLMs), they continue to have difficulty following relatively simple, unambiguous instructions, especially when compositions are involved. In this paper, we take inspiration from recent work that suggests that models may follow instructions better when they are expressed in pseudo-code. However, writing pseudo-code programs can be tedious and using few-shot demonstrations to craft code representations for use in inference can be unnatural for non-expert users of LLMs. To overcome these limitations, we propose fine-tuning LLMs with instruction-tuning data that additionally includes instructions re-expressed in pseudo-code along with the final response. We evaluate models trained using our method on $11$ publicly available benchmarks comprising of tasks related to instruction-following, mathematics, and common-sense reasoning. We conduct rigorous experiments with $5$ different models and find that not only do models follow instructions better when trained with pseudo-code, they also retain their capabilities on the other tasks related to mathematical and common sense reasoning. Specifically, we observe a relative gain of $3$--$19$% on instruction-following benchmark, and an average gain of upto 14% across all tasks.
Re-evaluation of Logical Specification in Behavioural Verification
2025-05-23
Radoslaw Klimek, Jakub Semczyszyn
This study empirically validates automated logical specification methods for behavioural models, focusing on their robustness, scalability, and reproducibility. By the systematic reproduction and extension of prior results, we confirm key trends, while identifying performance irregularities that suggest the need for adaptive heuristics in automated reasoning. Our findings highlight that theorem provers exhibit varying efficiency across problem structures, with implications for real-time verification in CI/CD pipelines and AI-driven IDEs supporting on-the-fly validation. Addressing these inefficiencies through self-optimising solvers could enhance the stability of automated reasoning, particularly in safety-critical software verification.
Explainable Anatomy-Guided AI for Prostate MRI: Foundation Models and In Silico Clinical Trials for Virtual Biopsy-based Risk Assessment
2025-05-23
Danial Khan, Zohaib Salahuddin, Yumeng Zhang, Sheng Kuang, Shruti Atul Mali, Henry C. Woodruff, Sina Amirrajab, Rachel Cavill, Eduardo Ibor-Crespo, Ana Jimenez-Pastor, Adrian Galiana-Bordera, Paula Jimenez Gomez, Luis Marti-Bonmati, Philippe Lambin
We present a fully automated, anatomically guided deep learning pipeline for prostate cancer (PCa) risk stratification using routine MRI. The pipeline integrates three key components: an nnU-Net module for segmenting the prostate gland and its zones on axial T2-weighted MRI; a classification module based on the UMedPT Swin Transformer foundation model, fine-tuned on 3D patches with optional anatomical priors and clinical data; and a VAE-GAN framework for generating counterfactual heatmaps that localize decision-driving image regions. The system was developed using 1,500 PI-CAI cases for segmentation and 617 biparametric MRIs with metadata from the CHAIMELEON challenge for classification (split into 70% training, 10% validation, and 20% testing). Segmentation achieved mean Dice scores of 0.95 (gland), 0.94 (peripheral zone), and 0.92 (transition zone). Incorporating gland priors improved AUC from 0.69 to 0.72, with a three-scale ensemble achieving top performance (AUC = 0.79, composite score = 0.76), outperforming the 2024 CHAIMELEON challenge winners. Counterfactual heatmaps reliably highlighted lesions within segmented regions, enhancing model interpretability. In a prospective multi-center in-silico trial with 20 clinicians, AI assistance increased diagnostic accuracy from 0.72 to 0.77 and Cohen's kappa from 0.43 to 0.53, while reducing review time per case by 40%. These results demonstrate that anatomy-aware foundation models with counterfactual explainability can enable accurate, interpretable, and efficient PCa risk assessment, supporting their potential use as virtual biopsies in clinical practice.
New Tight Bounds for SGD without Variance Assumption: A Computer-Aided Lyapunov Analysis
2025-05-23
Daniel Cortild, Lucas Ketels, Juan Peypouquet, Guillaume Garrigos
The analysis of Stochastic Gradient Descent (SGD) often relies on making some assumption on the variance of the stochastic gradients, which is usually not satisfied or difficult to verify in practice. This paper contributes to a recent line of works which attempt to provide guarantees without making any variance assumption, leveraging only the (strong) convexity and smoothness of the loss functions. In this context, we prove new theoretical bounds derived from the monotonicity of a simple Lyapunov energy, improving the current state-of-the-art and extending their validity to larger step-sizes. Our theoretical analysis is backed by a Performance Estimation Problem analysis, which allows us to claim that, empirically, the bias term in our bounds is tight within our framework.
Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL
2025-05-23
Che Liu, Haozhe Wang, Jiazhen Pan, Zhongwei Wan, Yong Dai, Fangzhen Lin, Wenjia Bai, Daniel Rueckert, Rossella Arcucci
Improving performance on complex tasks and enabling interpretable decision making in large language models (LLMs), especially for clinical applications, requires effective reasoning. Yet this remains challenging without supervised fine-tuning (SFT) on costly chain-of-thought (CoT) data distilled from closed-source models (e.g., GPT-4o). In this work, we present AlphaMed, the first medical LLM to show that reasoning capability can emerge purely through reinforcement learning (RL), using minimalist rule-based rewards on public multiple-choice QA datasets, without relying on SFT or distilled CoT data. AlphaMed achieves state-of-the-art results on six medical QA benchmarks, outperforming models trained with conventional SFT+RL pipelines. On challenging benchmarks (e.g., MedXpert), AlphaMed even surpasses larger or closed-source models such as DeepSeek-V3-671B and Claude-3.5-Sonnet. To understand the factors behind this success, we conduct a comprehensive data-centric analysis guided by three questions: (i) Can minimalist rule-based RL incentivize reasoning without distilled CoT supervision? (ii) How do dataset quantity and diversity impact reasoning? (iii) How does question difficulty shape the emergence and generalization of reasoning? Our findings show that dataset informativeness is a key driver of reasoning performance, and that minimalist RL on informative, multiple-choice QA data is effective at inducing reasoning without CoT supervision. We also observe divergent trends across benchmarks, underscoring limitations in current evaluation and the need for more challenging, reasoning-oriented medical QA benchmarks.
Towards Practical Defect-Focused Automated Code Review
2025-05-23
Junyi Lu, Lili Jiang, Xiaojia Li, Jianbing Fang, Fengjun Zhang, Li Yang, Chun Zuo
The complexity of code reviews has driven efforts to automate review comments, but prior approaches oversimplify this task by treating it as snippet-level code-to-text generation and relying on text similarity metrics like BLEU for evaluation. These methods overlook repository context, real-world merge request evaluation, and defect detection, limiting their practicality. To address these issues, we explore the full automation pipeline within the online recommendation service of a company with nearly 400 million daily active users, analyzing industry-grade C++ codebases comprising hundreds of thousands of lines of code. We identify four key challenges: 1) capturing relevant context, 2) improving key bug inclusion (KBI), 3) reducing false alarm rates (FAR), and 4) integrating human workflows. To tackle these, we propose 1) code slicing algorithms for context extraction, 2) a multi-role LLM framework for KBI, 3) a filtering mechanism for FAR reduction, and 4) a novel prompt design for better human interaction. Our approach, validated on real-world merge requests from historical fault reports, achieves a 2x improvement over standard LLMs and a 10x gain over previous baselines. While the presented results focus on C++, the underlying framework design leverages language-agnostic principles (e.g., AST-based analysis), suggesting potential for broader applicability.
In an era defined by rapid data evolution, traditional machine learning (ML) models often fall short in adapting to dynamic environments. Evolving Machine Learning (EML) has emerged as a critical paradigm, enabling continuous learning and adaptation in real-time data streams. This survey presents a comprehensive analysis of EML, focusing on five core challenges: data drift, concept drift, catastrophic forgetting, skewed learning, and network adaptation. We systematically review over 120 studies, categorizing state-of-the-art methods across supervised, unsupervised, and semi-supervised approaches. The survey explores diverse evaluation metrics, benchmark datasets, and real-world applications, offering a comparative lens on the effectiveness and limitations of current techniques. Additionally, we highlight the growing role of adaptive neural architectures, meta-learning, and ensemble strategies in addressing evolving data complexities. By synthesizing insights from recent literature, this work not only maps the current landscape of EML but also identifies critical gaps and opportunities for future research. Our findings aim to guide researchers and practitioners in developing robust, ethical, and scalable EML systems for real-world deployment.
Mutarjim: Advancing Bidirectional Arabic-English Translation with a Small Language Model
2025-05-23
Khalil Hennara, Muhammad Hreden, Mohamed Motaism Hamed, Zeina Aldallal, Sara Chrouf, Safwan AlModhayan
We introduce Mutarjim, a compact yet powerful language model for bidirectional Arabic-English translation. While large-scale LLMs have shown impressive progress in natural language processing tasks, including machine translation, smaller models. Leveraging this insight, we developed Mutarjim based on Kuwain-1.5B , a language model tailored for both Arabic and English. Despite its modest size, Mutarjim outperforms much larger models on several established benchmarks, achieved through an optimized two-phase training approach and a carefully curated, high-quality training corpus.. Experimental results show that Mutarjim rivals models up to 20 times larger while significantly reducing computational costs and training requirements. We also introduce Tarjama-25, a new benchmark designed to overcome limitations in existing Arabic-English benchmarking datasets, such as domain narrowness, short sentence lengths, and English-source bias. Tarjama-25 comprises 5,000 expert-reviewed sentence pairs and spans a wide range of domains, offering a more comprehensive and balanced evaluation framework. Notably, Mutarjim achieves state-of-the-art performance on the English-to-Arabic task in Tarjama-25, surpassing even significantly larger and proprietary models like GPT-4o mini. We publicly release Tarjama-25 to support future research and advance the evaluation of Arabic-English translation systems.
Urban Household Behavior in Indonesia: Drivers of Zero Waste Participation
2025-05-23
Faizal Amir, Alimuddin S. Miru, Edy Sabara
The 3R-based Zero Waste approach aims to minimize household solid waste through the principles of Reduce, Reuse, and Recycle. This study examines the relationship between household environmental knowledge, personal attitude, subjective norms, and perceived behavioral control as key behavioral predictors. A structured survey was conducted among 1,200 urban households across 12 Indonesian cities. Data were analyzed using Pearson correlation and multiple regression analysis. The results indicate that perceived behavioral control is the strongest predictor of household waste management behavior (beta = 0.367, p <= 0.001), followed by subjective norms (beta = 0.358, p <= 0.001) and environmental knowledge (beta = 0.126, p <= 0.001). This suggests that individuals' confidence in managing household waste significantly influences their practical actions. Overall, perceived behavioral control, subjective norms, and environmental knowledge contribute to Zero Waste behavior in urban households. Given that households regularly generate and dispose of waste, they represent a fundamental element in municipal waste management strategies. These findings offer valuable insights for designing behavior-based interventions and inform policy development using the Theory of Planned Behavior.
Over the past decades, superplatforms, digital companies that integrate a vast range of third-party services and applications into a single, unified ecosystem, have built their fortunes on monopolizing user attention through targeted advertising and algorithmic content curation. Yet the emergence of AI agents driven by large language models (LLMs) threatens to upend this business model. Agents can not only free user attention with autonomy across diverse platforms and therefore bypass the user-attention-based monetization, but might also become the new entrance for digital traffic. Hence, we argue that superplatforms have to attack AI agents to defend their centralized control of digital traffic entrance. Specifically, we analyze the fundamental conflict between user-attention-based monetization and agent-driven autonomy through the lens of our gatekeeping theory. We show how AI agents can disintermediate superplatforms and potentially become the next dominant gatekeepers, thereby forming the urgent necessity for superplatforms to proactively constrain and attack AI agents. Moreover, we go through the potential technologies for superplatform-initiated attacks, covering a brand-new, unexplored technical area with unique challenges. We have to emphasize that, despite our position, this paper does not advocate for adversarial attacks by superplatforms on AI agents, but rather offers an envisioned trend to highlight the emerging tensions between superplatforms and AI agents. Our aim is to raise awareness and encourage critical discussion for collaborative solutions, prioritizing user interests and perserving the openness of digital ecosystems in the age of AI agents.
Measurement of event shapes in minimum-bias events from proton-proton collisions at $\sqrt{s}$ = 13 TeV
2025-05-23
CMS Collaboration
A measurement of event-shape variables is presented, using a data sample produced in a special run with approximately one inelastic proton-proton collision per bunch crossing. The data were collected with the CMS detector at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 64 $\mu$b$^{-1}$. A number of observables related to the overall distribution of charged particles in the collisions are corrected for detector effects and compared with simulations. Inclusive event-shape distributions, as well as differential distributions of event shapes as functions of charged-particle multiplicity, are studied. None of the models investigated is able to satisfactorily describe the data. Moreover, there are significant features common amongst all generator setups studied, particularly showing data being more isotropic than any of the simulations. Multidimensional unfolded distributions are provided, along with their correlations.
Neural Network Assisted Fermionic Compression Encoding: A Lossy-QSCI Framework for Scalable Quantum Chemistry Simulations
2025-05-23
Yu-cheng Chen, Ronin Wu, M. H. Cheng, Min-Hsiu Hsieh
Quantum computing promises to revolutionize many-body simulations for quantum chemistry, but its potential is constrained by limited qubits and noise in current devices. In this work, we introduce the Lossy Quantum Selected Configuration Interaction (Lossy-QSCI) framework, which combines a lossy subspace Hamiltonian preparation pipeline with a generic QSCI selection process. This framework integrates a chemistry-inspired lossy Random Linear Encoder (Chemical-RLE) with a neural network-assisted Fermionic Expectation Decoder (NN-FED). The RLE leverages fermionic number conservation to compress quantum states, reducing qubit requirements to O(N log M) for M spin orbitals and N electrons, while preserving crucial ground state information and enabling self-consistent configuration recovery. NN-FED, powered by a neural network trained with minimal data, efficiently decodes these compressed states, overcoming the measurement challenges common in the approaches of the traditional QSCI and its variants. Through iterative quantum sampling and classical post-processing, our hybrid method refines ground state estimates with high efficiency. Demonstrated on the C2 and LiH molecules, our framework achieves chemical accuracy with fewer qubits and basis states, paving a scalable pathway for quantum chemistry simulations on both near-term and fault-tolerant quantum hardware.
Don't Overthink it. Preferring Shorter Thinking Chains for Improved LLM Reasoning
2025-05-23
Michael Hassid, Gabriel Synnaeve, Yossi Adi, Roy Schwartz
Reasoning large language models (LLMs) heavily rely on scaling test-time compute to perform complex reasoning tasks by generating extensive "thinking" chains. While demonstrating impressive results, this approach incurs significant computational costs and inference time. In this work, we challenge the assumption that long thinking chains results in better reasoning capabilities. We first demonstrate that shorter reasoning chains within individual questions are significantly more likely to yield correct answers - up to 34.5% more accurate than the longest chain sampled for the same question. Based on these results, we suggest short-m@k, a novel reasoning LLM inference method. Our method executes k independent generations in parallel and halts computation once the first m thinking processes are done. The final answer is chosen using majority voting among these m chains. Basic short-1@k demonstrates similar or even superior performance over standard majority voting in low-compute settings - using up to 40% fewer thinking tokens. short-3@k, while slightly less efficient than short-1@k, consistently surpasses majority voting across all compute budgets, while still being substantially faster (up to 33% wall time reduction). Inspired by our results, we finetune an LLM using short, long, and randomly selected reasoning chains. We then observe that training on the shorter ones leads to better performance. Our findings suggest rethinking current methods of test-time compute in reasoning LLMs, emphasizing that longer "thinking" does not necessarily translate to improved performance and can, counter-intuitively, lead to degraded results.
A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advances
2025-05-23
Brian B. Moser, Arundhati S. Shanbhag, Stanislav Frolov, Federico Raue, Joachim Folz, Andreas Dengel
Coreset selection targets the challenge of finding a small, representative subset of a large dataset that preserves essential patterns for effective machine learning. Although several surveys have examined data reduction strategies before, most focus narrowly on either classical geometry-based methods or active learning techniques. In contrast, this survey presents a more comprehensive view by unifying three major lines of coreset research, namely, training-free, training-oriented, and label-free approaches, into a single taxonomy. We present subfields often overlooked by existing work, including submodular formulations, bilevel optimization, and recent progress in pseudo-labeling for unlabeled datasets. Additionally, we examine how pruning strategies influence generalization and neural scaling laws, offering new insights that are absent from prior reviews. Finally, we compare these methods under varying computational, robustness, and performance demands and highlight open challenges, such as robustness, outlier filtering, and adapting coreset selection to foundation models, for future research.
Defining Self-adaptive Systems: A Systematic Literature Review
2025-05-23
Ana Petrovska, Guan Erjiage, Stefan Kugele
In the last two decades, the popularity of self-adaptive systems in the field of software and systems engineering has drastically increased. However, despite the extensive work on self-adaptive systems, the literature still lacks a common agreement on the definition of these systems. To this day, the notion of self-adaptive systems is mainly used intuitively without a precise understanding of the terminology. Using terminology only by intuition does not suffice, especially in engineering and science, where a more rigorous definition is necessary. In this paper, we investigate the existing formal definitions of self-adaptive systems and how these systems are characterised across the literature. Additionally, we analyse and summarise the limitations of the existing formal definitions in order to understand why none of the existing formal definitions is used more broadly by the community. To achieve this, we have conducted a systematic literature review in which we have analysed over 1400 papers related to self-adaptive systems. Concretely, from an initial pool of 1493 papers, we have selected 314 relevant papers, which resulted in nine primary studies whose primary objective was to define self-adaptive systems formally. Our systematic review reveals that although there has been an increasing interest in self-adaptive systems over the years, there is a scarcity of efforts to define these systems formally. Finally, as part of this paper, based on the analysed primary studies, we also elicit requirements and set a foundation for a potential (formal) definition in the future that is accepted by the community on a broader range.
Graph representation learning is effective for obtaining a meaningful latent space utilizing the structure of graph data and is widely applied, including biological networks. In particular, Graph Contrastive Learning (GCL) has emerged as a powerful self-supervised method that relies on applying perturbations to graphs for data augmentation. However, when applying existing GCL methods to biological networks such as Gene Regulatory Networks (GRNs), they overlooked meaningful biologically relevant perturbations, e.g., gene knockdowns. In this study, we introduce SupGCL (Supervised Graph Contrastive Learning), a novel GCL method for GRNs that directly incorporates biological perturbations derived from gene knockdown experiments as the supervision. SupGCL mathematically extends existing GCL methods that utilize non-biological perturbations to probabilistic models that introduce actual biological gene perturbation utilizing gene knockdown data. Using the GRN representation obtained by our proposed method, our aim is to improve the performance of biological downstream tasks such as patient hazard prediction and disease subtype classification (graph-level task), and gene function classification (node-level task). We applied SupGCL on real GRN datasets derived from patients with multiple types of cancer, and in all experiments SupGCL achieves better performance than state-of-the-art baselines.
The Herschel-SPIRE Dark Field I: The deepest Herschel image of the submillimetre Universe
2025-05-23
Chris Pearson, Thomas W. O. Varnish, Xinni Wu, David L. Clements, Ayushi Parmar, Helen Davidge, Matthew Pearson
We present the image maps, data reduction, analysis and the first source counts from the Herschel SPIRE Dark Field. The SPIRE Dark Field is an area of sky near the North Ecliptic Pole observed many times during the calibration phase of the Herschel mission in order to characterise the stability of the SPIRE instrument and is subsequently one of the deepest imaged fields of the Universe at far-infrared-submillimetre wavelengths. The SPIRE dark field is concurrent with the Spitzer IRAC Dark Field used for a similar purpose. The final Dark Field map is comprised of 141 individual SPIRE observations in Small Map and Large Map modes defined by a deep inner region approximately 12' in diameter and a slightly shallower surrounding area of diameter ~30'. The depth of both regions reach well below the confusion limit of the SPIRE instrument at 250 microns, 350 microns and 500 microns. Two independent processes are used to extract sources, a standard map based method using the SUSSEXtractor algorithm and a list driven photometry approach using the XID algorithm with the Spitzer MIPS 24 microns catalogue as an input prior. The resulting source counts detect the turnover in the galaxy population with both methods shown to be consistent with previous results from other Herschel surveys, with the XID process reaching approximately twice as deep compared to traditional map based algorithms. Finally, we compare our results with two contemporary galaxy evolution models, again showing a good general agreement with the modelled counts.
Feasible Action Space Reduction for Quantifying Causal Responsibility in Continuous Spatial Interactions
2025-05-23
Ashwin George, Luciano Cavalcante Siebert, David A. Abbink, Arkady Zgonnikov
Understanding the causal influence of one agent on another agent is crucial for safely deploying artificially intelligent systems such as automated vehicles and mobile robots into human-inhabited environments. Existing models of causal responsibility deal with simplified abstractions of scenarios with discrete actions, thus, limiting real-world use when understanding responsibility in spatial interactions. Based on the assumption that spatially interacting agents are embedded in a scene and must follow an action at each instant, Feasible Action-Space Reduction (FeAR) was proposed as a metric for causal responsibility in a grid-world setting with discrete actions. Since real-world interactions involve continuous action spaces, this paper proposes a formulation of the FeAR metric for measuring causal responsibility in space-continuous interactions. We illustrate the utility of the metric in prototypical space-sharing conflicts, and showcase its applications for analysing backward-looking responsibility and in estimating forward-looking responsibility to guide agent decision making. Our results highlight the potential of the FeAR metric for designing and engineering artificial agents, as well as for assessing the responsibility of agents around humans.
Understanding How Value Neurons Shape the Generation of Specified Values in LLMs
2025-05-23
Yi Su, Jiayi Zhang, Shu Yang, Xinhai Wang, Lijie Hu, Di Wang
Rapid integration of large language models (LLMs) into societal applications has intensified concerns about their alignment with universal ethical principles, as their internal value representations remain opaque despite behavioral alignment advancements. Current approaches struggle to systematically interpret how values are encoded in neural architectures, limited by datasets that prioritize superficial judgments over mechanistic analysis. We introduce ValueLocate, a mechanistic interpretability framework grounded in the Schwartz Values Survey, to address this gap. Our method first constructs ValueInsight, a dataset that operationalizes four dimensions of universal value through behavioral contexts in the real world. Leveraging this dataset, we develop a neuron identification method that calculates activation differences between opposing value aspects, enabling precise localization of value-critical neurons without relying on computationally intensive attribution methods. Our proposed validation method demonstrates that targeted manipulation of these neurons effectively alters model value orientations, establishing causal relationships between neurons and value representations. This work advances the foundation for value alignment by bridging psychological value frameworks with neuron analysis in LLMs.
This is a brief review of the collider phenomenology of neutrino physics. Current and future colliders provide an ideal testing ground for (sub)TeV-scale neutrino mass models, as they can directly probe the messenger particles, which could be either new fermions, scalars, or gauge bosons, associated with neutrino mass generation. Moreover, the recent observation of TeV-scale neutrinos produced at the LHC offers new ways to test the limits of the Standard Model and beyond.
Simulating Macroeconomic Expectations using LLM Agents
2025-05-23
Jianhao Lin, Lexuan Sun, Yixin Yan
We introduce a novel framework for simulating macroeconomic expectation formation using Large Language Model-Empowered Agents (LLM Agents). By constructing thousands of LLM Agents equipped with modules for personal characteristics, prior expectations, and knowledge, we replicate a survey experiment involving households and experts on inflation and unemployment. Our results show that although the expectations and thoughts generated by LLM Agents are more homogeneous than those of human participants, they still effectively capture key heterogeneity across agents and the underlying drivers of expectation formation. Furthermore, a module-ablation exercise highlights the critical role of prior expectations in simulating such heterogeneity. This approach complements traditional survey methods and offers new insights into AI behavioral science in macroeconomic research.
Stereotype Detection in Natural Language Processing
2025-05-23
Alessandra Teresa Cignarella, Anastasia Giachanou, Els Lefever
Stereotypes influence social perceptions and can escalate into discrimination and violence. While NLP research has extensively addressed gender bias and hate speech, stereotype detection remains an emerging field with significant societal implications. In this work is presented a survey of existing research, analyzing definitions from psychology, sociology, and philosophy. A semi-automatic literature review was performed by using Semantic Scholar. We retrieved and filtered over 6,000 papers (in the year range 2000-2025), identifying key trends, methodologies, challenges and future directions. The findings emphasize stereotype detection as a potential early-monitoring tool to prevent bias escalation and the rise of hate speech. Conclusions highlight the need for a broader, multilingual, and intersectional approach in NLP studies.
Synthetic X-ray angiographies generated by modern generative models hold great potential to reduce the use of contrast agents in vascular interventional procedures. However, low-quality synthetic angiographies can significantly increase procedural risk, underscoring the need for reliable image quality assessment (IQA) methods. Existing IQA models, however, fail to leverage auxiliary images as references during evaluation and lack fine-grained, task-specific metrics necessary for clinical relevance. To address these limitations, this paper proposes CAS-IQA, a vision-language model (VLM)-based framework that predicts fine-grained quality scores by effectively incorporating auxiliary information from related images. In the absence of angiography datasets, CAS-3K is constructed, comprising 3,565 synthetic angiographies along with score annotations. To ensure clinically meaningful assessment, three task-specific evaluation metrics are defined. Furthermore, a Multi-path featUre fuSion and rouTing (MUST) module is designed to enhance image representations by adaptively fusing and routing visual tokens to metric-specific branches. Extensive experiments on the CAS-3K dataset demonstrate that CAS-IQA significantly outperforms state-of-the-art IQA methods by a considerable margin.
Agents powered by large language models (LLMs) have demonstrated strong planning and decision-making capabilities in complex embodied environments. However, such agents often suffer from inefficiencies in multi-turn interactions, frequently trapped in repetitive loops or issuing ineffective commands, leading to redundant computational overhead. Instead of relying solely on learning from trajectories, we take a first step toward exploring the early-exit behavior for LLM-based agents. We propose two complementary approaches: 1. an $\textbf{intrinsic}$ method that injects exit instructions during generation, and 2. an $\textbf{extrinsic}$ method that verifies task completion to determine when to halt an agent's trial. To evaluate early-exit mechanisms, we introduce two metrics: one measures the reduction of $\textbf{redundant steps}$ as a positive effect, and the other evaluates $\textbf{progress degradation}$ as a negative effect. Experiments with 4 different LLMs across 5 embodied environments show significant efficiency improvements, with only minor drops in agent performance. We also validate a practical strategy where a stronger agent assists after an early-exit agent, achieving better performance with the same total steps. We will release our code to support further research.
Large language models (LLMs) achieve impressive performance across domains but face significant challenges when deployed on consumer-grade GPUs or personal devices such as laptops, due to high memory consumption and inference costs. Post-training quantization (PTQ) of LLMs offers a promising solution that reduces their memory footprint and decoding latency. In practice, PTQ with uniform quantization representation is favored for its efficiency and ease of deployment since uniform quantization is widely supported by mainstream hardware and software libraries. Recent studies on $\geq 2$-bit uniform quantization have led to noticeable improvements in post-quantization model performance; however, they primarily focus on quantization methodologies, while the initialization of quantization parameters is underexplored and still relies on the suboptimal Min-Max strategies. In this work, we propose NeUQI, a method devoted to efficiently determining near-optimal initial parameters for uniform quantization. NeUQI is orthogonal to prior quantization methodologies and can seamlessly integrate with them. The experiments with the LLaMA and Qwen families on various tasks demonstrate that our NeUQI consistently outperforms existing methods. Furthermore, when combined with a lightweight distillation strategy, NeUQI can achieve superior performance to PV-tuning, a much more resource-intensive approach.
The Internet of Things (IoT) and Large Language Models (LLMs) have been two major emerging players in the information technology era. Although there has been significant coverage of their individual capabilities, our literature survey sheds some light on the integration and interaction of LLMs and IoT devices - a mutualistic relationship in which both parties leverage the capabilities of the other. LLMs like OpenAI's ChatGPT, Anthropic's Claude, Google's Gemini/BERT, any many more, all demonstrate powerful capabilities in natural language understanding and generation, enabling more intuitive and context-aware interactions across diverse IoT applications such as smart cities, healthcare systems, industrial automation, and smart home environments. Despite these opportunities, integrating these resource-intensive LLMs into IoT devices that lack the state-of-the-art computational power is a challenging task. The security of these edge devices is another major concern as they can easily act as a backdoor to private networks if the LLM integration is sloppy and unsecured. This literature survey systematically explores the current state-of-the-art in applying LLMs within IoT, emphasizing their applications in various domains/sectors of society, the significant role they play in enhancing IoT security through anomaly detection and threat mitigation, and strategies for effective deployment using edge computing frameworks. Finally, this survey highlights existing challenges, identifies future research directions, and underscores the need for cross-disciplinary collaboration to fully realize the transformative potential of integrating LLMs and IoT.
Birational involutions of the real projective plane fixing an irrational curve
2025-05-23
Frédéric Mangolte
This review is an elaboration of a presentation given at the Real algebraic geometry and singularities conference in honor of Wojciech Kucharz's 70th birthday in Krakow in 2022.
Recent advances in text-to-video generation, particularly with autoregressive models, have enabled the synthesis of high-quality videos depicting individual scenes. However, extending these models to generate long, cross-scene videos remains a significant challenge. As the context length grows during autoregressive decoding, computational costs rise sharply, and the model's ability to maintain consistency and adhere to evolving textual prompts deteriorates. We introduce InfLVG, an inference-time framework that enables coherent long video generation without requiring additional long-form video data. InfLVG leverages a learnable context selection policy, optimized via Group Relative Policy Optimization (GRPO), to dynamically identify and retain the most semantically relevant context throughout the generation process. Instead of accumulating the entire generation history, the policy ranks and selects the top-$K$ most contextually relevant tokens, allowing the model to maintain a fixed computational budget while preserving content consistency and prompt alignment. To optimize the policy, we design a hybrid reward function that jointly captures semantic alignment, cross-scene consistency, and artifact reduction. To benchmark performance, we introduce the Cross-scene Video Benchmark (CsVBench) along with an Event Prompt Set (EPS) that simulates complex multi-scene transitions involving shared subjects and varied actions/backgrounds. Experimental results show that InfLVG can extend video length by up to 9$\times$, achieving strong consistency and semantic fidelity across scenes. Our code is available at https://github.com/MAPLE-AIGC/InfLVG.
Activity-enhanced shear thinning of flexible linear polar polymers
2025-05-23
Arindam Panda, Roland G. Winkler, Sunil P. Singh
The rheological properties of tangentially propelled flexible polymers under linear shear flow are studied by computer simulations and are compared with analytical calculations. We find a significant impact of the coupled nonequilibrium active and shear forces on the polymer characteristics. The polar activity enhances shear-induced stretching along the flow direction, shrinkage in the transverse direction, and implies a strongly amplified shear-thinning behavior. The characteristic shear rate for the onset of these effects is determined by the activity. In the asymptotic limit of large activities, the shear-induced features become independent of activity, and for asymptotically large shear rates, shear dominates over activity with passive polymer behavior.
High-time-resolution properties of 35 fast radio bursts detected by the Commensal Real-time ASKAP Fast Transients Survey
2025-05-23
D. R. Scott, T. Dial, A. Bera, A. T. Deller, M. Glowacki, K. Gourdji, C. W. James, R. M. Shannon, K. W. Bannister, R. D. Ekers, M. Sammons, A. T. Sutinjo, P. A. Uttarkar
We present microsecond-resolution, coherently-dedispersed, polarimetric measurements of 35 fast radio bursts (FRBs) detected during the Commensal Real-time ASKAP Fast Transients (CRAFT) incoherent sum (ICS) survey with the Australian Square Kilometre Array Pathfinder (ASKAP). We find a wide diversity of time-frequency morphology and polarisation properties broadly consistent with those of currently known non-repeating FRBs. The high S/N and fine time-resolution of our data however reveals a wealth of new information. Key results include (i) the distribution of scattering timescales, ${\tau}_{obs}$, is limited purely by instrumental effects, with no downturn at high ${\tau}_{obs}$ as expected from a log-normal distribution; (ii) for the 29 FRBs with known redshift, ${\tau}_{obs}$ is uncorrelated with dispersion measure (DM) fluctuations about the Macquart relation, in contrast to expectations from pulsar scattering-DM relations; (iii) all FRBs probably have multiple components, and at least a large fraction have variable PA, the identification of which is limited by scattering; (iv) at least half of all FRBs exhibit PA microstructure at 200 ${\mu}s$-200 ns timescales, with behaviour most closely resembling a sub-category of Crab main pulses; (v) that there is a break in the FRB circular polarisation distribution at Stokes V $\gtrsim$ 20%, which is suggestive of a discrete sub-population.
Project duplication detection is critical for project quality assessment, as it improves resource utilization efficiency by preventing investing in newly proposed project that have already been studied. It requires the ability to understand high-level semantics and generate constructive and valuable feedback. Existing detection methods rely on basic word- or sentence-level comparison or solely apply large language models, lacking valuable insights for experts and in-depth comprehension of project content and review criteria. To tackle this issue, we propose PD$^3$, a Project Duplication Detection framework via adapted multi-agent Debate. Inspired by real-world expert debates, it employs a fair competition format to guide multi-agent debate to retrieve relevant projects. For feedback, it incorporates both qualitative and quantitative analysis to improve its practicality. Over 800 real-world power project data spanning more than 20 specialized fields are used to evaluate the framework, demonstrating that our method outperforms existing approaches by 7.43% and 8.00% in two downstream tasks. Furthermore, we establish an online platform, Review Dingdang, to assist power experts, saving 5.73 million USD in initial detection on more than 100 newly proposed projects.
Twin-2K-500: A dataset for building digital twins of over 2,000 people based on their answers to over 500 questions
2025-05-23
Olivier Toubia, George Z. Gui, Tianyi Peng, Daniel J. Merlau, Ang Li, Haozhe Chen
LLM-based digital twin simulation, where large language models are used to emulate individual human behavior, holds great promise for research in AI, social science, and digital experimentation. However, progress in this area has been hindered by the scarcity of real, individual-level datasets that are both large and publicly available. This lack of high-quality ground truth limits both the development and validation of digital twin methodologies. To address this gap, we introduce a large-scale, public dataset designed to capture a rich and holistic view of individual human behavior. We survey a representative sample of $N = 2,058$ participants (average 2.42 hours per person) in the US across four waves with 500 questions in total, covering a comprehensive battery of demographic, psychological, economic, personality, and cognitive measures, as well as replications of behavioral economics experiments and a pricing survey. The final wave repeats tasks from earlier waves to establish a test-retest accuracy baseline. Initial analyses suggest the data are of high quality and show promise for constructing digital twins that predict human behavior well at the individual and aggregate levels. By making the full dataset publicly available, we aim to establish a valuable testbed for the development and benchmarking of LLM-based persona simulations. Beyond LLM applications, due to its unique breadth and scale the dataset also enables broad social science research, including studies of cross-construct correlations and heterogeneous treatment effects.
Recent progress in configuration-interaction shell model
2025-05-23
Menglan Liu, Cenxi Yuan
Since Mayer and Jensen employed the single-particle shell model to interpret the magic numbers, various microscopic nuclear models have been developed to study the nuclear force and structure. The confguration-interaction shell model (CISM), performed in truncated model space with the inclusion of the residual interaction, is one widely-used nuclear structure model. In the last decade, CISM has progressed in investigating the cross-shell excitation in exotic light nuclei, the similarity and diference in mirror nuclei, and the isomerism and seniority conservation in medium and heavy nuclei. Additionally, researchers have attempted to construct effective Hamiltonians for nuclei near 132Sn and 208Pb through a unifed way in the CISM framework. In parallel, related models, including the nucleon-pair approximation (NPA) approach, the Monte Carlo shell model (MCSM), the projected shell model (PSM), the Gamow shell model (GSM), etc., have also been extensively developed and validated in the last decade. This paper reviews the recent progress in CISM and some related models.
In large-scale galaxy surveys, particularly deep ground-based photometric studies, galaxy blending is inevitable and poses a potential primary systematic uncertainty for upcoming surveys. Current deblenders predominantly rely on analytical modeling of galaxy profiles, facing limitations due to inflexible and imprecise models. We present a novel approach using a U-net structured transformer-based network for deblending astronomical images, which we term the CAT-deblender. It was trained using both RGB and grz-band images, spanning two distinct data formats from the Dark Energy Camera Legacy Survey (DECaLS) database, including galaxies with diverse morphologies. Our method requires only the approximate central coordinates of each target galaxy, bypassing assumptions on neighboring source counts. Post-deblending, our RGB images retain a high signal-to-noise peak, showing superior structural similarity to ground truth. For multi-band images, the ellipticity of central galaxies and median reconstruction error for the r-band consistently lie within +/-0.025 to +/-0.25, revealing minimal pixel residuals. In our comparison focused on flux recovery, our model shows a mere 1 percent error in magnitude recovery for quadruply blended galaxies, significantly outperforming SExtractor's higher error rate of 4.8 percent. By cross-matching with publicly accessible overlapping galaxy catalogs from the DECaLS database, we successfully deblended 433 overlapping galaxies. Furthermore, we demonstrated effective deblending of 63,733 blended galaxy images randomly selected from the DECaLS database.
Large language models (LLMs) have demonstrated impressive capabilities in reasoning with the emergence of reasoning models like OpenAI-o1 and DeepSeek-R1. Recent research focuses on integrating reasoning capabilities into the realm of retrieval-augmented generation (RAG) via outcome-supervised reinforcement learning (RL) approaches, while the correctness of intermediate think-and-search steps is usually neglected. To address this issue, we design a process-level reward module to mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation. Grounded on this, we propose Learning to Think-and-Search (LeTS), a novel framework that hybridizes stepwise process reward and outcome-based reward to current RL methods for RAG. Extensive experiments demonstrate the generalization and inference efficiency of LeTS across various RAG benchmarks. In addition, these results reveal the potential of process- and outcome-level reward hybridization in boosting LLMs' reasoning ability via RL under other scenarios. The code will be released soon.
Debiasing CLIP: Interpreting and Correcting Bias in Attention Heads
2025-05-23
Wei Jie Yeo, Rui Mao, Moloud Abdar, Erik Cambria, Ranjan Satapathy
Multimodal models like CLIP have gained significant attention due to their remarkable zero-shot performance across various tasks. However, studies have revealed that CLIP can inadvertently learn spurious associations between target variables and confounding factors. To address this, we introduce \textsc{Locate-Then-Correct} (LTC), a contrastive framework that identifies spurious attention heads in Vision Transformers via mechanistic insights and mitigates them through targeted ablation. Furthermore, LTC identifies salient, task-relevant attention heads, enabling the integration of discriminative features through orthogonal projection to improve classification performance. We evaluate LTC on benchmarks with inherent background and gender biases, achieving over a $>50\%$ gain in worst-group accuracy compared to non-training post-hoc baselines. Additionally, we visualize the representation of selected heads and find that the presented interpretation corroborates our contrastive mechanism for identifying both spurious and salient attention heads. Code available at https://github.com/wj210/CLIP_LTC.
Many decision-making tasks, where both accuracy and efficiency matter, still require human supervision. For example, tasks like traffic officers reviewing hour-long dashcam footage or researchers screening conference videos can benefit from concise summaries that reduce cognitive load and save time. Yet current vision-language models (VLMs) often produce verbose, redundant outputs that hinder task performance. Existing video caption evaluation depends on costly human annotations and overlooks the summaries' utility in downstream tasks. We address these gaps with Video-to-text Information Bottleneck Evaluation (VIBE), an annotation-free method that scores VLM outputs using two metrics: grounding (how well the summary aligns with visual content) and utility (how informative it is for the task). VIBE selects from randomly sampled VLM outputs by ranking them according to the two scores to support effective human decision-making. Human studies on LearningPaper24, SUTD-TrafficQA, and LongVideoBench show that summaries selected by VIBE consistently improve performance-boosting task accuracy by up to 61.23% and reducing response time by 75.77% compared to naive VLM summaries or raw video.
What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI
2025-05-23
Rudrajit Choudhuri, Bianca Trinkenreich, Rahul Pandita, Eirini Kalliamvakou, Igor Steinmacher, Marco Gerosa, Christopher Sanchez, Anita Sarma
Generative AI (genAI) tools are advertised as productivity aids. Yet, issues related to miscalibrated trust and usage friction continue to hinder their adoption. Additionally, AI can be exclusionary, failing to support diverse users adequately, further exacerbating these concerns. One such aspect of diversity is cognitive diversity -- variations in users' cognitive styles -- that leads to divergence in interaction styles. When an individual's cognitive styles are unsupported, it creates additional barriers to technology adoption. Thus, to design tools that developers trust, we must first understand what factors affect their trust and intentions to use these tools in practice? We developed a theoretical model of factors influencing trust and adoption intentions towards genAI through a large-scale survey with developers (N=238) at GitHub and Microsoft. Using Partial Least Squares-Structural Equation Modeling (PLS-SEM), we found that genAI's system/output quality, functional value, and goal maintenance significantly influence developers' trust, which along with their cognitive styles, affects their intentions to use these tools in work. An Importance-Performance Matrix Analysis (IPMA) identified factors that, despite their strong influence, underperform, revealing specific genAI aspects that need design prioritization. We bolster these findings by qualitatively analyzing developers' perceived challenges and risks of genAI usage to uncover why these gaps persist in development contexts. For genAI to indeed be a true productivity aid rather than a disguised productivity sink, it must align with developers' goals, maintain contextual transparency, reduce cognitive burden, and provide equitable interaction support. We provide practical suggestions to guide future genAI tool design for effective, trustworthy, and inclusive human-genAI interactions.
Model-Independent Dark Energy Measurements from DESI DR2 and Planck 2015 Data
2025-05-23
Yun Wang, Katherine Freese
Using DESI DR2 baryon acoustic oscillation distance measurements and Planck cosmic microwave background distance priors, we have measured the dark energy density $\rho_X(z)$ and dark energy equation of state $w_X(z)$ as free functions of redshift (smoothly interpolated from values at $z=0, 1/3, 2/3, 1, 4/3, 2.33$), and find both to be consistent with a cosmological constant, with only deviations of 1.2$\sigma$ for $\rho_X(z)$ and 1.9$\sigma$ for $w_X(z)$ at $z=2/3$. We confirm our earlier finding in Wang & Freese (2006) that $w_X(z)$ is significantly less constrained by data than $\rho_X(z)$. Our results differ noticeably from those of the DESI Collaboration, in which they used the same DESI DR2 data combined with Planck data and found a 3.1$\sigma$ deviation from a cosmological constant, a finding which is the consequence of their assuming the parametrization $w_X(z)=w_0+w_a(1-a)$. Our results indicate that assuming a linear $w_X(z)$ could be misleading and precludes discovering how dark energy actually varies with time at higher redshifts. In our quest to discover the physical nature of dark energy, the most urgent goal at present is to determine definitively whether dark energy density varies with time. We have demonstrated that it is of critical importance to measure dark energy density as a free function of redshift from data. Future galaxy redshift surveys by Euclid and Roman at higher redshifts will significantly advance our understanding of dark energy.
An ALMA Study of Molecular Complexity in the Hot Core G336.99-00.03 MM1
2025-05-23
Chunguo Duan, Qian Gou, Tie Liu, Fengwei Xu, Xuefang Xu, Junlin Lan, Ke Wang, Laurent Pagani, Donghui Quan, Junzhi Wang, Xunchuan Liu, Mingwei He
High-mass star formation involves complex processes, with the hot core phase playing a crucial role in chemical enrichment and the formation of complex organic molecules. However, molecular inventories in hot cores remain limited. Using data from the ALMA Three-millimeter Observations of Massive Star-forming regions survey (ATOMS), the molecular composition and evolutionary stages of two distinct millimeter continuum sources in the high-mass star forming region G336.99-00.03 have been characterized. MM1, with 19 distinct molecular species detected, along with 8 isotopologues and several vibrationally/torsionally excited states, has been identified as a hot core. MM2 with only 5 species identified, was defined as a HII region. Isotopic ratios in MM1 were derived, with $^{12}$C/$^{13}$C ranging from 16.0 to 29.2, $^{16}$O/$^{18}$O at 47.7, and $^{32}$S/$^{34}$S at 19.2. Molecular abundances in MM1 show strong agreement with other sources and three-phase warm-up chemical models within an order of magnitude for most species. Formation pathways of key molecules were explored, revealing chemical links and reaction networks. This study provides a detailed molecular inventory of two millimeter continuum sources, shedding light on the chemical diversity and evolutionary processes in high-mass star-forming regions. The derived molecular parameters and isotopic ratios offer benchmarks for astrochemical models, paving the way for further investigation into the formation and evolution of complex organic molecules during the hot core phase.
High-fidelity 3D reconstruction is critical for aerial inspection tasks such as infrastructure monitoring, structural assessment, and environmental surveying. While traditional photogrammetry techniques enable geometric modeling, they lack semantic interpretability, limiting their effectiveness for automated inspection workflows. Recent advances in neural rendering and 3D Gaussian Splatting (3DGS) offer efficient, photorealistic reconstructions but similarly lack scene-level understanding. In this work, we present a UAV-based pipeline that extends Feature-3DGS for language-guided 3D segmentation. We leverage LSeg-based feature fields with CLIP embeddings to generate heatmaps in response to language prompts. These are thresholded to produce rough segmentations, and the highest-scoring point is then used as a prompt to SAM or SAM2 for refined 2D segmentation on novel view renderings. Our results highlight the strengths and limitations of various feature field backbones (CLIP-LSeg, SAM, SAM2) in capturing meaningful structure in large-scale outdoor environments. We demonstrate that this hybrid approach enables flexible, language-driven interaction with photorealistic 3D reconstructions, opening new possibilities for semantic aerial inspection and scene understanding.
We report on the statistical analysis of quasar photoevaporation at $z\sim2.2$ by comparing the density of surrounding Ly$\alpha$ Emitters (LAEs) and continuum-selected galaxies, based on the imaging data of Hyper Suprime-Cam (HSC) Subaru Strategic Program (SSP) and CFHT Large Area $U$-band Deep Survey (CLAUDS). We select 18 quasars from Sloan Digital Sky Survey (SDSS) in the HSC Deep/UltraDeep fields, normalize the LAE/continuum-selected galaxy distribution around each quasar with the quasar proximity size, stack them, and then measure the average densities of the galaxies. As a result, we find that the density of LAEs is $\gtrsim 5 \sigma$ lower than that of continuum-selected galaxies within the quasar proximity region. Within the quasar proximity region, we find that the LAEs with high Ly$\alpha$ equivalent widths (EWs) are less dense than those with low EWs at the 3$\sigma$ level and that LAEs with EW of $\gtrsim150$ \AA (rest-frame) are predominantly scarce. Finally, we find that both LAEs and continuum-selected galaxies have smaller densities when they are closer to quasars. We argue that the photoevaporation effect is more effective for smaller dark matter haloes predominantly hosting LAEs, but that it may also affect larger haloes.
Internal dynamics and fission of pure-quartic soliton molecules
2025-05-23
Zhixiang Deng, Rui Ma, Chunxiang Zhang, Boris Malomed, Dianyuan Fan, Jingsong He, Jun Liu
We address the weak interaction of a pair of well-separated pure-quartic solitons (PQSs), which are solutions to a generalized nonlinear Schrodinger equation (NLSE) with the quartic-only dispersion. An asymptotic technique is applied to derive equations for the slow evolution of the temporal separation and phase difference of the PQSs interacting through the overlapping of their exponentially decaying oscillating tails. Based on this approach, various stationary states of bound PQS (soliton molecules) with distinct phase differences are predicted. Their stability is addressed via the numerical calculation of the eigenvalue spectrum of small perturbations, showing instability of the bound states. A systematic numerical analysis demonstrates that the parameter space of the PQS bound states is organized as a self-similar fractal structure, composed of regions populated by robustly oscillating or splitting two-soliton states. The analytical method and results reported here can be extended for bound states of two or several weakly interacting modes in other conservative and dissipative systems.
Child literacy is a strong predictor of life outcomes at the subsequent stages of an individual's life. This points to a need for targeted interventions in vulnerable low and middle income populations to help bridge the gap between literacy levels in these regions and high income ones. In this effort, reading assessments provide an important tool to measure the effectiveness of these programs and AI can be a reliable and economical tool to support educators with this task. Developing accurate automatic reading assessment systems for child speech in low-resource languages poses significant challenges due to limited data and the unique acoustic properties of children's voices. This study focuses on Xhosa, a language spoken in South Africa, to advance child speech recognition capabilities. We present a novel dataset composed of child speech samples in Xhosa. The dataset is available upon request and contains ten words and letters, which are part of the Early Grade Reading Assessment (EGRA) system. Each recording is labeled with an online and cost-effective approach by multiple markers and a subsample is validated by an independent EGRA reviewer. This dataset is evaluated with three fine-tuned state-of-the-art end-to-end models: wav2vec 2.0, HuBERT, and Whisper. The results indicate that the performance of these models can be significantly influenced by the amount and balancing of the available training data, which is fundamental for cost-effective large dataset collection. Furthermore, our experiments indicate that the wav2vec 2.0 performance is improved by training on multiple classes at a time, even when the number of available samples is constrained.
Diffusion models have emerged as leading generative models for images and other modalities, but aligning their outputs with human preferences and safety constraints remains a critical challenge. This thesis proposal investigates methods to align diffusion models using reinforcement learning (RL) and reward modeling. We survey recent advances in fine-tuning text-to-image diffusion models with human feedback, including reinforcement learning from human and AI feedback, direct preference optimization, and differentiable reward approaches. We classify these methods based on the type of feedback (human, automated, binary or ranked preferences), the fine-tuning technique (policy gradient, reward-weighted likelihood, direct backpropagation, etc.), and their efficiency and safety outcomes. We compare key algorithms and frameworks, highlighting how they improve alignment with user intent or safety standards, and discuss inter-relationships such as how newer methods build on or diverge from earlier ones. Based on the survey, we identify five promising research directions for the next two years: (1) multi-objective alignment with combined rewards, (2) efficient human feedback usage and active learning, (3) robust safety alignment against adversarial inputs, (4) continual and online alignment of diffusion models, and (5) interpretable and trustworthy reward modeling for generative images. Each direction is elaborated with its problem statement, challenges, related work, and a proposed research plan. The proposal is organized as a comprehensive document with literature review, comparative tables of methods, and detailed research plans, aiming to contribute new insights and techniques for safer and value-aligned diffusion-based generative AI.
2025-05-23
Sandeep Pirbhulal, Habtamu Abie, Martin Jullum, Didrik Nielsen, Anders Løland
The advancements in communication technology (5G and beyond) and global connectivity Internet of Things (IoT) also come with new security problems that will need to be addressed in the next few years. The threats and vulnerabilities introduced by AI/ML based 5G and beyond IoT systems need to be investigated to avoid the amplification of attack vectors on AI/ML. AI/ML techniques are playing a vital role in numerous applications of cybersecurity. Despite the ongoing success, there are significant challenges in ensuring the trustworthiness of AI/ML systems. However, further research is needed to define what is considered an AI/ML threat and how it differs from threats to traditional systems, as currently there is no common understanding of what constitutes an attack on AI/ML based systems, nor how it might be created, hosted and propagated [ETSI, 2020]. Therefore, there is a need for studying the AI/ML approach to ensure safe and secure development, deployment, and operation of AI/ML based 5G and beyond IoT systems. For 5G and beyond, it is essential to continuously monitor and analyze any changing environment in real-time to identify and reduce intentional and unintentional risks. In this study, we will review the role of the AI/ML technique for 5G and beyond security. Furthermore, we will provide our perspective for predicting and mitigating 5G and beyond security using AI/ML techniques.
Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review
2025-05-23
Jiangbei Yue, He Wang
Crowd behaviour analysis is essential to numerous real-world applications, such as public safety and urban planning, and therefore has been studied for decades. In the last decade or so, the development of deep learning has significantly propelled the research on crowd behaviours. This chapter reviews recent advances in crowd behaviour analysis using deep learning. We mainly review the research in two core tasks in this field, crowd behaviour prediction and recognition. We broadly cover how different deep neural networks, after first being proposed in machine learning, are applied to analysing crowd behaviours. This includes pure deep neural network models as well as recent development of methodologies combining physics with deep learning. In addition, representative studies are discussed and compared in detail. Finally, we discuss the effectiveness of existing methods and future research directions in this rapidly evolving field. This chapter aims to provide a high-level summary of the ongoing deep learning research in crowd behaviour analysis. It intends to help new researchers who just entered this field to obtain an overall understanding of the ongoing research, as well as to provide a retrospective analysis for existing researchers to identify possible future directions
Human-Centered AI Communication in Co-Creativity: An Initial Framework and Insights
2025-05-23
Jeba Rezwana, Corey Ford
Effective communication between AI and humans is essential for successful human-AI co-creation. However, many current co-creative AI systems lack effective communication, which limits their potential for collaboration. This paper presents the initial design of the Framework for AI Communication (FAICO) for co-creative AI, developed through a systematic review of 107 full-length papers. FAICO presents key aspects of AI communication and their impact on user experience, offering preliminary guidelines for designing human-centered AI communication. To improve the framework, we conducted a preliminary study with two focus groups involving skilled individuals in AI, HCI, and design. These sessions sought to understand participants' preferences for AI communication, gather their perceptions of the framework, collect feedback for refinement, and explore its use in co-creative domains like collaborative writing and design. Our findings reveal a preference for a human-AI feedback loop over linear communication and emphasize the importance of context in fostering mutual understanding. Based on these insights, we propose actionable strategies for applying FAICO in practice and future directions, marking the first step toward developing comprehensive guidelines for designing effective human-centered AI communication in co-creation.
Quantum spin Hall effects in van der Waals materials
2025-05-23
Jian Tang, Thomas Siyuan Ding, Chengdong Wang, Ning Mao, Vsevolod Belosevich, Yang Zhang, Xiaofeng Qian, Qiong Ma
The quantum spin Hall (QSH) effect, first predicted in graphene by Kane and Mele in 2004, has emerged as a prototypical platform for exploring spin-orbit coupling, topology, and electronic interactions. Initially realized experimentally in quantum wells exhibiting characteristic QSH signatures, the field has since expanded with the discovery of van der Waals (vdW) materials. This review focuses on vdW systems, which offer unique advantages: their exposed surfaces enable a combination of surface-sensitive spectroscopic and microscopic tools for comprehensive detection of the QSH state; mechanical stacking with other vdW layers facilitates symmetry engineering and proximity effects; and moir\'e engineering introduces layer skyrmion topological phases and strong correlation effects. We highlight two monolayer families, 1T$^\prime$-MX$_2$ and MM$^\prime$X$_4$, represented by WTe$_2$ and TaIrTe$_4$, respectively. These materials exhibit QSH phases intertwined with or in close proximity to other quantum phases, such as excitonic insulators, charge density waves, and superconductivity. Their low crystal symmetry and topology enable rich quantum geometrical responses, ranging from nonlinear Hall effects to circular photogalvanic effects. We also discuss moir\'e systems, which combine topology with flatband physics and enhanced correlations, driving spontaneous symmetry breaking and transitions from QSH to quantum anomalous Hall (QAH) states. Remarkably, fractionalized QAH and QSH states have recently been observed in moir\'e systems, significantly advancing the field of condensed matter physics. Finally, we explore emerging applications of QSH and derived materials, such as using nonlinear Hall effects for quantum rectification in microwave energy harvesting and harnessing fractional anomalous states for topological quantum computing.
Is It Bad to Work All the Time? Cross-Cultural Evaluation of Social Norm Biases in GPT-4
2025-05-23
Zhuozhuo Joy Liu, Farhan Samir, Mehar Bhatia, Laura K. Nelson, Vered Shwartz
LLMs have been demonstrated to align with the values of Western or North American cultures. Prior work predominantly showed this effect through leveraging surveys that directly ask (originally people and now also LLMs) about their values. However, it is hard to believe that LLMs would consistently apply those values in real-world scenarios. To address that, we take a bottom-up approach, asking LLMs to reason about cultural norms in narratives from different cultures. We find that GPT-4 tends to generate norms that, while not necessarily incorrect, are significantly less culture-specific. In addition, while it avoids overtly generating stereotypes, the stereotypical representations of certain cultures are merely hidden rather than suppressed in the model, and such stereotypes can be easily recovered. Addressing these challenges is a crucial step towards developing LLMs that fairly serve their diverse user base.
Unveiling compact planetary nebulae: Broad-band survey analysis and LAMOST confirmation
2025-05-23
L. A. Gutiérrez-Soto, M. Belén Mari, W. A. Weidmann, F. R. Faifer
Planetary nebulae (PNe) are pivotal for advancing our knowledge of stellar evolution and galactic chemical enrichment. Recent progress in surveys and data analysis has revolutionized PN research, leading to the discovery of new objects and deeper insights into their properties. We have devised a novel photometric selection method, integrating GAIA and Pan-STARRS photometry, to identify compact PN candidates. This approach utilizes color-color diagrams, specifically (G-g) versus (GBP-GRP) and (G-r) versus (GBP-GRP), as primary criteria for candidate selection. The subsequent verification step involves confirming these candidates through LAMOST spectroscopic data. By cross-referencing a comprehensive dataset of PNe, GAIA, Pan-STARRS, and LAMOST DR7 spectra, we explore the potential of our approach and the crucial role played by these surveys in PN research. The LAMOST spectra provide compelling evidence supporting our selection criteria, especially for compact PNe characterized by strong emission lines and low continuum. Applying these criteria to a catalog of emission line objects, we have selected a PN candidate. Detailed analysis of its LAMOST spectrum unveiled classical Balmer emission lines and high-ionization lines (He II, [Ar V], [Ar III], [Ne III]), characteristic of high-ionization PNe, without low-excitation lines. Using the photoionization code Cloudy, our modeling revealed parameters including an ionizing source temperature of 180x10^3 K, luminosity around 3400 Lsun, and gas abundances encompassing various elements. Comparing the PNe evolution track, the progenitor star was estimated to have a mass of 2 Msun. Our findings show strong promise for separating compact PNe from other objects and provide a robust framework for further exploration of these surveys in the context of planetary nebulae.
Galaxies OBserved as Low-luminosity Identified Nebulae (GOBLIN): a catalog of 43,000 high-probability dwarf galaxy candidates in the UNIONS survey
2025-05-23
Nick Heesters, David Chemaly, Oliver Müller, Elisabeth Sola, Sébastien Fabbro, Ashley Ferreira, Alan W. McConnachie, Eugene Magnier, Michael J. Hudson, Kenneth Chambers, François Hammer, Ruben Sanchez-Janssen
The detection of low surface brightness galaxies beyond the Local Group poses significant observational challenges, yet these faint systems are fundamental to our understanding of dark matter, hierarchical galaxy formation, and cosmic structure. Their abundance and distribution provide crucial tests for cosmological models, particularly regarding the small-scale predictions of $\Lambda$CDM. We present a systematic detection framework for dwarf galaxy candidates in Ultraviolet Near Infrared Optical Northern Survey (UNIONS) data covering 4,861 deg$^{2}$. Our pipeline preprocesses UNIONS gri-band data through binning, artifact removal, and stellar masking, then employs MTObjects (MTO) for low surface brightness detection. After parameter cuts and cross-matching, we obtain $\sim$360 candidates per deg$^{2}$, totaling $\sim$1.5 million candidates forming our GOBLIN (Galaxies OBserved as Low-luminosity Identified Nebulae) catalog. We fine-tuned the deep learning model Zoobot, pre-trained on Galaxy Zoo labels, for classification. Training data came from visual inspection of literature candidates with probability labels from expert assessments, capturing consensus and uncertainty. Applied to all MTO objects, our method identifies 42,965 dwarf candidates with probability $>$ 0.8, including 23,072 with probability $>$ 0.9. High-probability candidates correlate spatially with massive galaxies (log$(M_{*}/M_{\odot}) \geq$ 10) within 120 Mpc. While some of these objects may have been previously identified in other surveys, we present this extensive catalog of candidates, including their positions, structural parameter estimates, and classification probabilities, as a resource for the community to enable studies of galaxy formation, evolution, and the distribution of dwarf galaxies in different environments.
Einstein-Gauss-Bonnet-Myrzakulov Gravity from $R + F(T, G)$: Numerical Insights and Torsion-Gauss-Bonnet Dynamics in Weitzenböck Spacetime
2025-05-23
Davood Momeni, Ratbay Myrzakulov
The study of modified gravity models has garnered significant attention because of their potential to provide alternative explanations for cosmological phenomena, such as the accelerated expansion of the universe and the nature of dark energy. One such model, the Einstein-Gauss-Bonnet-Myrzakulov $R + F(T, G)$ gravity (EGBMG), which incorporates the curvature $R$, torsion $T$, and the Gauss-Bonnet term $G$, offers a promising framework to explore the dynamics of the universe and its evolution. This paper delves into the theoretical and observational implications of the EGBMG model, focusing on its ability to address long-standing challenges in cosmology, including the evolution of dark energy and the transition from early-time inflationary behavior to late-time acceleration. We review recent advancements in the model, including its compatibility with observational data and its ability to provide new insights into cosmic acceleration. Through a combination of theoretical models, dynamical systems analysis, and cosmological diagnostics, we demonstrate the robustness of the EGBMG framework in explaining the large-scale structure of the universe and its accelerated expansion. This paper serves as a step toward further exploring the potential of this model to understand the fundamental forces driving Weitzenb$\"{o}$ck spacetime.
The Traveling Salesman Problem (TSP) is a well-known NP-hard combinatorial optimization problem with wide-ranging applications in logistics, routing, and intelligent systems. Due to its factorial complexity, solving large-scale instances requires scalable and efficient algorithmic frameworks, often enabled by parallel computing. This literature review provides a comparative evaluation of parallel TSP optimization methods, including exact algorithms, heuristic-based approaches, hybrid metaheuristics, and machine learning-enhanced models. In addition, we introduce task-specific evaluation metrics to facilitate cross-paradigm analysis, particularly for hybrid and adaptive solvers. The review concludes by identifying research gaps and outlining future directions, including deep learning integration, exploring quantum-inspired algorithms, and establishing reproducible evaluation frameworks to support scalable and adaptive TSP optimization in real-world scenarios.
Though humans seem to be remarkable learners, arguments in cognitive science and philosophy of mind have long maintained that learning something fundamentally new is impossible. Specifically, Jerry Fodor's arguments for radical concept nativism hold that most, if not all, concepts are innate and that what many call concept learning never actually leads to the acquisition of new concepts. These arguments have deeply affected cognitive science, and many believe that the counterarguments to radical concept nativism have been either unsuccessful or only apply to a narrow class of concepts. This paper first reviews the features and limitations of prior arguments. We then identify three critical points - related to issues of expressive power, conceptual structure, and concept possession - at which the arguments in favor of radical concept nativism diverge from describing actual human cognition. We use ideas from computer science and information theory to formalize the relevant ideas in ways that are arguably more scientifically productive. We conclude that, as a result, there is an important sense in which people do indeed learn new concepts.
A 3D Monte Carlo calculation of the inverse Compton emission from the Sun and stars in presence of magnetic and electric fields
2025-05-23
M. N. Mazziotta
The solar steady emission in gamma rays is due to the interactions of Galactic cosmic rays with the solar atmosphere and with the low-energy solar photon field via inverse Compton scattering. The emission is sensitive to the magnetic field nearby the Sun and to the cosmic-ray transport in the magnetic field in the inner solar system. Modeling the inverse Compton emission in the presence of a magnetic field is therefore crucial to better interpret the observations. In a previous work we have presented a comprehensive calculation of the secondary productions due to the collision of cosmic rays with the solar atmosphere in presence of magnetic fields. In this paper, we present a general approach to calculate the (anisotropic) inverse Compton scattering in a 3D Monte Carlo simulation, also in presence of magnetic and electric fields. After a short review of the scattering process of photons with electrons, examples of inverse Compton emission are presented, including the predictions for the Sun.
Enhanced Merger Fractions in a Reionization-Era Protocluster
2025-05-23
Lorraine C. Marcelin, Jaclyn B. Champagne, Feige Wang, Xiaohui Fan, Maria Pudoka, Wei Leong Tee, Yongda Zhu
Mergers play a critical role in galaxy evolution, but their relationship to their surrounding environments is unexplored at high redshift. We investigate the galaxy merger rate among 124 [OIII] emitters at $5.33\sigma$ enhancement of the merger fraction amongst all three overdense structures compared to the field. Eleven galaxies are classified as ``active mergers" satisfying both merger criteria, all of which occur within the overdensity samples. We conclude that environment affects the merger rates of galaxies at $z>6$, leading to increased specific star formation at the 4$\sigma$ level.
BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Bases
2025-05-23
Mathew J. Koretsky, Maya Willey, Adi Asija, Owen Bianchi, Chelsea X. Alvarado, Tanay Nayak, Nicole Kuznetsov, Sungwon Kim, Mike A. Nalls, Daniel Khashabi, Faraz Faghri
Biomedical researchers increasingly rely on large-scale structured databases for complex analytical tasks. However, current text-to-SQL systems often struggle to map qualitative scientific questions into executable SQL, particularly when implicit domain reasoning is required. We introduce BiomedSQL, the first benchmark explicitly designed to evaluate scientific reasoning in text-to-SQL generation over a real-world biomedical knowledge base. BiomedSQL comprises 68,000 question/SQL query/answer triples grounded in a harmonized BigQuery knowledge base that integrates gene-disease associations, causal inference from omics data, and drug approval records. Each question requires models to infer domain-specific criteria, such as genome-wide significance thresholds, effect directionality, or trial phase filtering, rather than rely on syntactic translation alone. We evaluate a range of open- and closed-source LLMs across prompting strategies and interaction paradigms. Our results reveal a substantial performance gap: GPT-o3-mini achieves 59.0% execution accuracy, while our custom multi-step agent, BMSQL, reaches 62.6%, both well below the expert baseline of 90.0%. BiomedSQL provides a new foundation for advancing text-to-SQL systems capable of supporting scientific discovery through robust reasoning over structured biomedical knowledge bases. Our dataset is publicly available at https://huggingface.co/datasets/NIH-CARD/BiomedSQL, and our code is open-source at https://github.com/NIH-CARD/biomedsql.
Existing medical VQA benchmarks mostly focus on single-image analysis, yet clinicians almost always compare a series of images before reaching a diagnosis. To better approximate this workflow, we introduce MedFrameQA -- the first benchmark that explicitly evaluates multi-image reasoning in medical VQA. To build MedFrameQA both at scale and in high-quality, we develop 1) an automated pipeline that extracts temporally coherent frames from medical videos and constructs VQA items whose content evolves logically across images, and 2) a multiple-stage filtering strategy, including model-based and manual review, to preserve data clarity, difficulty, and medical relevance. The resulting dataset comprises 2,851 VQA pairs (gathered from 9,237 high-quality frames in 3,420 videos), covering nine human body systems and 43 organs; every question is accompanied by two to five images. We comprehensively benchmark ten advanced Multimodal LLMs -- both proprietary and open source, with and without explicit reasoning modules -- on MedFrameQA. The evaluation challengingly reveals that all models perform poorly, with most accuracies below 50%, and accuracy fluctuates as the number of images per question increases. Error analysis further shows that models frequently ignore salient findings, mis-aggregate evidence across images, and propagate early mistakes through their reasoning chains; results also vary substantially across body systems, organs, and modalities. We hope this work can catalyze research on clinically grounded, multi-image reasoning and accelerate progress toward more capable diagnostic AI systems.
2025-05-22
Yepeng Liu, Xuandong Zhao, Christopher Kruegel, Dawn Song, Yuheng Bu
The growing use of large language models (LLMs) for sensitive applications has highlighted the need for effective watermarking techniques to ensure the provenance and accountability of AI-generated text. However, most existing watermarking methods require access to the decoding process, limiting their applicability in real-world settings. One illustrative example is the use of LLMs by dishonest reviewers in the context of academic peer review, where conference organizers have no access to the model used but still need to detect AI-generated reviews. Motivated by this gap, we introduce In-Context Watermarking (ICW), which embeds watermarks into generated text solely through prompt engineering, leveraging LLMs' in-context learning and instruction-following abilities. We investigate four ICW strategies at different levels of granularity, each paired with a tailored detection method. We further examine the Indirect Prompt Injection (IPI) setting as a specific case study, in which watermarking is covertly triggered by modifying input documents such as academic manuscripts. Our experiments validate the feasibility of ICW as a model-agnostic, practical watermarking approach. Moreover, our findings suggest that as LLMs become more capable, ICW offers a promising direction for scalable and accessible content attribution.
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype
2025-05-22
Nikola Tankovic, Robert Sajina
This paper presents a concise review of Contextual Multi-Armed Bandit (CMAB) methods and introduces an experimental framework for scalable, interpretable offer selection, addressing the challenge of fast-changing offers. The approach models context at the product category level, allowing offers to span multiple categories and enabling knowledge transfer across similar offers. This improves learning efficiency and generalization in dynamic environments. The framework extends standard CMAB methodology to support multi-category contexts, and achieves scalability through efficient feature engineering and modular design. Advanced features such as MPG (Member Purchase Gap) and MF (Matrix Factorization) capture nuanced user-offer interactions, with implementation in Python for practical deployment. A key contribution is interpretability at scale: logistic regression models yield transparent weight vectors, accessible via a large language model (LLM) interface for real-time, user-level tracking and explanation of evolving preferences. This enables the generation of detailed member profiles and identification of behavioral patterns, supporting personalized offer optimization and enhancing trust in automated decisions. By situating our prototype alongside established paradigms like Generalized Linear Models and Thompson Sampling, we demonstrate its value for both research and real-world CMAB applications.
Quantum hardware development is progressing rapidly with substantial advancements achieved across leading platforms, including superconducting circuits, trapped-ion systems, and neutral atom arrays. As the pursuit of practical quantum advantage continues, efficient quantum program compilation becomes essential for transforming high-level representations of quantum algorithms into physically executable circuits. A fundamental challenge in this process is qubit mapping and gate scheduling, which play a critical role in adapting compiled circuits to the architectural constraints and physical limitations of specific quantum hardware. In this survey, we systematically review and categorize research on the qubit mapping and routing problems across the three mainstream quantum hardware platforms. We primarily explore the development of hardware-aware compilers for superconducting platforms, classifying existing methods into solver-based, heuristic-based, and machine learning-based approaches, and analyze their optimization targets, including gate count, circuit duration, fidelity, and scalability. Furthermore, we examine the evolution of trapped-ion and neutral atom devices, analyzing the distinct challenges posed by their hardware characteristics and highlighting specialized compilers tailored to these unique physical constraints. Finally, we summarize the key challenges and identify some promising opportunities for future research in quantum compiler design across these hardware platforms.
Hydrogen peroxide electrogeneration from O2 electroreduction: a review focusing on carbon electrocatalysts and environmental applications
2025-05-22
Aline B. Trench, Caio Machado Fernandes, João Paulo C. Moura, Lanna E. B. Lucchetti, Thays S. Lima, Vanessa S. Antonin, James M. de Almeida, Pedro Autreto, Irma Robles, Artur J. Motheo, Marcos R. V. Lanza, Mauro C. Santos
Hydrogen peroxide (H2O2) stands as one of the foremost utilized oxidizing agents in modern times. The established method for its production involves the intricate and costly anthraquinone process. However, a promising alternative pathway is the electrochemical hydrogen peroxide production, accomplished through the oxygen reduction reaction via a 2-electron pathway. This method not only simplifies the production process but also upholds environmental sustainability, especially when compared to the conventional anthraquinone method. In this review paper, recent works from the literature focusing on the 2-electron oxygen reduction reaction promoted by carbon electrocatalysts are summarized. The practical applications of these materials in the treatment of effluents contaminated with different pollutants (drugs, dyes, pesticides, and herbicides) are presented. Water treatment aiming to address these issues can be achieved through advanced oxidation electrochemical processes such as electro-Fenton, solar-electro-Fenton, and photo-electro-Fenton. These processes are discussed in detail in this work and the possible radicals that degrade the pollutants in each case are highlighted. The review broadens its scope to encompass contemporary computational simulations focused on the 2-electron oxygen reduction reaction, employing different models to describe carbon-based electrocatalysts. Finally, perspectives and future challenges in the area of carbon-based electrocatalysts for H2O2 electrogeneration are discussed. This review paper presents a forward-oriented viewpoint of present innovations and pragmatic implementations, delineating forthcoming challenges and prospects of this ever-evolving field.
Periodic Variability in Space Photometry of 181 New Supermassive Black Hole Binary Candidates
2025-05-22
Pablo Huijse, Jordy Davelaar, Joris De Ridder, Nicholas Jannsen, Conny Aerts
Robust detections of supermassive black hole binaries (SMBHBs) are essential to unravel the role of galaxy mergers in galaxy evolution and for identifying potential sources of low-frequency gravitational waves. One of the most commonly used observational signatures of SMBHBs is periodic variability in the light curves of active galactic nuclei (AGN), which may arise from accretion rate modulation or relativistic Doppler boosting due to binary orbital motion. However, intrinsic stochastic AGN variability can mimic such periodic signals, complicating robust identification. We report the discovery of 181 new SMBHB candidates from a sample of approximately 770,000 AGN observed by the Gaia space observatory. Periodic signals were identified using a novel and computationally efficient Bayesian model selection framework, enabling unbiased source selection and quantifying the likelihood of periodicity over stochastic variability. These candidates nearly double the known SMBHB population and provide a prioritized target list for next-generation time-domain surveys.
We investigate primordial magnetic fields (PMFs) as a potential source of the parity-violating signatures recently implied by observations of the cosmic microwave background (CMB) anisotropies and the large-scale structure of the Universe. Among various possibilities, helical PMFs are of particular interest, as they inherently violate parity symmetry and can explain the observed magnetic fields, especially in void regions. PMFs, if generated in the early universe, can source curvature perturbations, which evolve into the present density fluctuations observed in galaxy surveys. Motivated by this, we focus on the imprint of helical PMFs on the trispectrum of the sourced primordial curvature perturbations, a leading-order statistics sensitive to parity-violating signals in three-dimensional space. We derive the analytic expressions for the trispectrum of the primordial curvature perturbations sourced by taking into account both the helical and non-helical PMFs and analytically reduce their expressions by using the pole approximation, whose validity is confirmed by comparison with the exact results. We find that, varying the ratio of the amplitude of the helical to non-helical power spectrum, the structure of the trispectrum qualitatively changes, particularly the ratio of the imaginary to real components of the trispectrum. Our findings highlight the primordial trispectrum as a promising probe of cosmological parity violation in the early universe and provide a theoretical basis for future precise observations of higher order statistics in the CMB anisotropies and the galaxy clustering.
2025-05-22
Nikolai Iorgov, Kohei Iwaki, Oleg Lisovyy, Yurii Zhuravlov
In recent years, the Fourier series (Zak transform) structure of the Painlev\'e I tau function has emerged in multiple contexts. Its main building block admits several conjectural interpretations, such as the partition function of an Argyres-Douglas gauge theory, the topological recursion partition function for the Weierstrass elliptic curve, and a 1-point conformal block on the Riemann sphere with an irregular insertion of rank $\frac52$. We review and further develop a mathematical framework for these constructions, and formulate conjectures on their equivalence. In particular, we give a simple explanation of the Fourier series representation of the tau function based on the Jimbo-Miwa-Ueno differential extended to the space of Stokes data. We provide an algebraic construction of the rank $\frac52$ Whittaker state for the Virasoro algebra embedded into a rank $2$ Whittaker module, prove its existence and uniqueness, and fix its descendant structure. We also prove the conifold gap property of the relevant topological recursion partition function, which, on one hand, enables its efficient computation within the holomorphic anomaly approach and, on the other, establishes the existence of solution for the latter.
Extending Quantum Computing through Subspace, Embedding and Classical Molecular Dynamics Techniques
2025-05-22
Thomas M. Bickley, Angus Mingare, Tim Weaving, Michael Williams de la Bastida, Shunzhou Wan, Martina Nibbi, Philipp Seitz, Alexis Ralli, Peter J. Love, Minh Chung, Mario Hernández Vera, Laura Schulz, Peter V. Coveney
The advent of hybrid computing platforms consisting of quantum processing units integrated with conventional high-performance computing brings new opportunities for algorithms design. By strategically offloading select portions of the workload to classical hardware where tractable, we may broaden the applicability of quantum computation in the near term. In this perspective, we review techniques that facilitate the study of subdomains of chemical systems with quantum computers and present a proof-of-concept demonstration of quantum-selected configuration interaction deployed within a multiscale/multiphysics simulation workflow leveraging classical molecular dynamics, projection-based embedding and qubit subspace tools. This allows the technology to be utilised for simulating systems of real scientific and industrial interest, which not only brings true quantum utility closer to realisation but is also relevant as we look forward to the fault-tolerant regime.
Large Language Models (LLMs) have achieved impressive performance on complex reasoning tasks with Chain-of-Thought (CoT) prompting. However, conventional CoT relies on reasoning steps explicitly verbalized in natural language, introducing inefficiencies and limiting its applicability to abstract reasoning. To address this, there has been growing research interest in latent CoT reasoning, where inference occurs within latent spaces. By decoupling reasoning from language, latent reasoning promises richer cognitive representations and more flexible, faster inference. Researchers have explored various directions in this promising field, including training methodologies, structural innovations, and internal reasoning mechanisms. This paper presents a comprehensive overview and analysis of this reasoning paradigm. We begin by proposing a unified taxonomy from four perspectives: token-wise strategies, internal mechanisms, analysis, and applications. We then provide in-depth discussions and comparative analyses of representative methods, highlighting their design patterns, strengths, and open challenges. We aim to provide a structured foundation for advancing this emerging direction in LLM reasoning. The relevant papers will be regularly updated at https://github.com/EIT-NLP/Awesome-Latent-CoT.
This paper presents a comprehensive synthesis of major breakthroughs in artificial intelligence (AI) over the past fifteen years, integrating historical, theoretical, and technological perspectives. It identifies key inflection points in AI' s evolution by tracing the convergence of computational resources, data access, and algorithmic innovation. The analysis highlights how researchers enabled GPU based model training, triggered a data centric shift with ImageNet, simplified architectures through the Transformer, and expanded modeling capabilities with the GPT series. Rather than treating these advances as isolated milestones, the paper frames them as indicators of deeper paradigm shifts. By applying concepts from statistical learning theory such as sample complexity and data efficiency, the paper explains how researchers translated breakthroughs into scalable solutions and why the field must now embrace data centric approaches. In response to rising privacy concerns and tightening regulations, the paper evaluates emerging solutions like federated learning, privacy enhancing technologies (PETs), and the data site paradigm, which reframe data access and security. In cases where real world data remains inaccessible, the paper also assesses the utility and constraints of mock and synthetic data generation. By aligning technical insights with evolving data infrastructure, this study offers strategic guidance for future AI research and policy development.
When Safety Detectors Aren't Enough: A Stealthy and Effective Jailbreak Attack on LLMs via Steganographic Techniques
2025-05-22
Jianing Geng, Biao Yi, Zekun Fei, Tongxi Wu, Lihai Nie, Zheli Liu
Jailbreak attacks pose a serious threat to large language models (LLMs) by bypassing built-in safety mechanisms and leading to harmful outputs. Studying these attacks is crucial for identifying vulnerabilities and improving model security. This paper presents a systematic survey of jailbreak methods from the novel perspective of stealth. We find that existing attacks struggle to simultaneously achieve toxic stealth (concealing toxic content) and linguistic stealth (maintaining linguistic naturalness). Motivated by this, we propose StegoAttack, a fully stealthy jailbreak attack that uses steganography to hide the harmful query within benign, semantically coherent text. The attack then prompts the LLM to extract the hidden query and respond in an encrypted manner. This approach effectively hides malicious intent while preserving naturalness, allowing it to evade both built-in and external safety mechanisms. We evaluate StegoAttack on four safety-aligned LLMs from major providers, benchmarking against eight state-of-the-art methods. StegoAttack achieves an average attack success rate (ASR) of 92.00%, outperforming the strongest baseline by 11.0%. Its ASR drops by less than 1% even under external detection (e.g., Llama Guard). Moreover, it attains the optimal comprehensive scores on stealth detection metrics, demonstrating both high efficacy and exceptional stealth capabilities. The code is available at https://anonymous.4open.science/r/StegoAttack-Jail66
Can a domain-specific language improve program structure comprehension of data pipelines? A mixed-methods study
2025-05-22
Philip Heltweg, Georg-Daniel Schwarz, Dirk Riehle
In many application domains, domain-specific languages can allow domain experts to contribute to collaborative projects more correctly and efficiently. To do so, they must be able to understand program structure from reading existing source code. With high-quality data becoming an increasingly important resource, the creation of data pipelines is an important application domain for domain-specific languages. We execute a mixed-method study consisting of a controlled experiment and a follow-up descriptive survey among the participants to understand the effects of a domain-specific language on bottom-up program understanding and generate hypotheses for future research. During the experiment, participants need the same time to solve program structure comprehension tasks, but are significantly more correct when using the domain-specific language. In the descriptive survey, participants describe reasons related to the programming language itself, such as a better pipeline overview, more enforced code structure, and a closer alignment to the mental model of a data pipeline. In addition, human factors such as less required programming experience and the ability to reuse experience from other data engineering tools are discussed. Based on these results, domain-specific languages are a promising tool for creating data pipelines that can increase correct understanding of program structure and lower barriers to entry for domain experts. Open questions exist to make more informed implementation decisions for domain-specific languages for data pipelines in the future.
Existing pretrained models for 3D mesh generation often suffer from data biases and produce low-quality results, while global reinforcement learning (RL) methods rely on object-level rewards that struggle to capture local structure details. To address these challenges, we present \textbf{Mesh-RFT}, a novel fine-grained reinforcement fine-tuning framework that employs Masked Direct Preference Optimization (M-DPO) to enable localized refinement via quality-aware face masking. To facilitate efficient quality evaluation, we introduce an objective topology-aware scoring system to evaluate geometric integrity and topological regularity at both object and face levels through two metrics: Boundary Edge Ratio (BER) and Topology Score (TS). By integrating these metrics into a fine-grained RL strategy, Mesh-RFT becomes the first method to optimize mesh quality at the granularity of individual faces, resolving localized errors while preserving global coherence. Experiment results show that our M-DPO approach reduces Hausdorff Distance (HD) by 24.6\% and improves Topology Score (TS) by 3.8\% over pre-trained models, while outperforming global DPO methods with a 17.4\% HD reduction and 4.9\% TS gain. These results demonstrate Mesh-RFT's ability to improve geometric integrity and topological regularity, achieving new state-of-the-art performance in production-ready mesh generation. Project Page: \href{https://hitcslj.github.io/mesh-rft/}{this https URL}.
The S-PLUS Fornax Project (S+FP): Mapping H$α$+[NII] emission in 77 Fornax galaxy members reaching $\sim$4 Rvir
2025-05-22
A. R. Lopes, A. V. Smith Castelli, A. C. Krabbe, J. A. Hernandez-Jimenez, D. Pallero, S. Torres-Flores, E. Telles, M. Sarzi, A. Cortesi, J. Thainá-Batista, R. Cid Fernandes, E. A. D. Lacerda, M. Sampaio, V. H. Sasse, F. R. Herpich, I. Andruchow, R. Demarco, L. A. Gutiérrez-Soto, M. Grossi, R. F. Haack, P. K. Humire, C. Lima-Dias, G. Limberg, C. Lobo, L. Lomelí-Núñez, P. A. A. Lopes, D. E. Olave-Rojas, S. V . Werner, F. Almeida-Fernandes, G. B. Oliveira Schwarz, W. Schoenell, T. Ribeiro, A. Kanaan, C. Mendes de Oliveira
The Fornax cluster, the second-largest galaxy cluster within 20 Mpc, presents an ideal environment for studying environmental effects on galaxy evolution. Utilizing data from the Southern Photometric Local Universe Survey (S-PLUS), this study explores the H$\alpha$+[NII] emission maps across an area of approximately 208 square degrees around NGC 1399. For such, a dedicated semi-automated pipeline, Pixel-to-Pixel Emission Line Estimate (PELE), was developed to generate emission line maps by processing S-PLUS images using the Three Filter Method. A morphological analysis was conducted using the ASTROMORPHLIB package to determine whether H$\alpha$+[NII] emitters exhibit perturbed features. The study successfully detected 77 H$\alpha$+[NII] emitters with $r<18$ mag, extending to four times the virial radius of the Fornax cluster. PELE demonstrated its ability to recover flux down to 2e-17 erg s$^{-1}$ cm$^{-2}$ when compared to H$\alpha$ maps from MUSE/VLT. Among the emitters, 25% are early-type galaxies (ETG) and 75% late-type galaxies (LTG). Signs of morphological perturbation or merger activity are observed in 44% of the LTG and in three ETG located beyond the cluster's virial radius. A significant fraction (91%) of the emitters are identified as recent infallers, primarily located in the northwestern region of the cluster, while others are associated with the infalling group Fornax A in the southwest. Disturbed, low-mass galaxies at larger cluster-centric distances provide evidence of galaxies begin transforming before entering the main cluster. This study demonstrates S-PLUS's effectiveness in detecting emitters, whose distribution reflects the Fornax cluster's assembly history, with LTG linked to recent infall from the field, possibly along a Fornax-Eridanus filament, and ETG may have evolved prior to entry.
Open interacting particle systems and Ising measures
2025-05-22
Ngo P. N. Ngoc, Gunter M. Schütz
We first survey some open questions concerning stochastic interacting particle systems with open boundaries. Then, an asymmetric exclusion process with open boundaries that generalizes the lattice gas model of Katz, Lebowitz, and Spohn is introduced and invariance of the one-dimensional Ising measure is proved. The stationary current is computed in explicit form and is shown to exhibit current reversal at some density. Based on the extremal-current principle for one-dimensional driven diffusive systems with one conservation law, the phase diagram for boundary-induced phase transitions is conjectured for this case. There are two extremal-current phases, unlike in the open ASEP (one extremal-current phase) or in the conventional KLS model (one or three extremal-current phases).
The bulk-boundary correspondence plays a crucial role in topological quantum systems, however,this principle is broken in non-Hermitian systems. The breakdown of the bulk-boundary correspondence indicates that the global phase diagrams under open boundary conditions are significantly different from those under periodic boundary conditions. In this paper, we investigate how the bulk-boundary correspondence breaks down by gradually tearing the system. We find that by tuning the strength of gain and loss domain wall, in the thermodynamic limit, the global phase diagrams of the topological system become the hybrids of those under periodic and open boundary conditions. Moreover, during the breakdown of the bulk-boundary correspondence, several phase transitions occur. This situation is quite different from earlier work, where the breakdown of the bulk-boundary correspondence in the thermodynamic limit occurred suddenly due to infinitesimal boundary hopping amplitudes. To support our conclusions, we provide both analytical and numerical calculations. These results help researchers better understand non-Hermitian topological systems.
Software Architecture Meets LLMs: A Systematic Literature Review
2025-05-22
Larissa Schmid, Tobias Hey, Martin Armbruster, Sophie Corallo, Dominik Fuchß, Jan Keim, Haoyu Liu, Anne Koziolek
Large Language Models (LLMs) are used for many different software engineering tasks. In software architecture, they have been applied to tasks such as classification of design decisions, detection of design patterns, and generation of software architecture design from requirements. However, there is little overview on how well they work, what challenges exist, and what open problems remain. In this paper, we present a systematic literature review on the use of LLMs in software architecture. We analyze 18 research articles to answer five research questions, such as which software architecture tasks LLMs are used for, how much automation they provide, which models and techniques are used, and how these approaches are evaluated. Our findings show that while LLMs are increasingly applied to a variety of software architecture tasks and often outperform baselines, some areas, such as generating source code from architectural design, cloud-native computing and architecture, and checking conformance remain underexplored. Although current approaches mostly use simple prompting techniques, we identify a growing research interest in refining LLM-based approaches by integrating advanced techniques.
On the use of Graphs for Satellite Image Time Series
2025-05-22
Corentin Dufourg, Charlotte Pelletier, Stéphane May, Sébastien Lefèvre
The Earth's surface is subject to complex and dynamic processes, ranging from large-scale phenomena such as tectonic plate movements to localized changes associated with ecosystems, agriculture, or human activity. Satellite images enable global monitoring of these processes with extensive spatial and temporal coverage, offering advantages over in-situ methods. In particular, resulting satellite image time series (SITS) datasets contain valuable information. To handle their large volume and complexity, some recent works focus on the use of graph-based techniques that abandon the regular Euclidean structure of satellite data to work at an object level. Besides, graphs enable modelling spatial and temporal interactions between identified objects, which are crucial for pattern detection, classification and regression tasks. This paper is an effort to examine the integration of graph-based methods in spatio-temporal remote-sensing analysis. In particular, it aims to present a versatile graph-based pipeline to tackle SITS analysis. It focuses on the construction of spatio-temporal graphs from SITS and their application to downstream tasks. The paper includes a comprehensive review and two case studies, which highlight the potential of graph-based approaches for land cover mapping and water resource forecasting. It also discusses numerous perspectives to resolve current limitations and encourage future developments.
Families of isospectral and isoscattering quantum graphs
2025-05-22
Pavel Kurasov, Omer Farooq, Michał Ławniczak, Szymon Bauch, Mats-Erik Pistol, Matthew de Courcy-Ireland, Leszek Sirko
A concept of germ graphs and the M-function formalism are employed to construct large families of isospectral and isoscattering graphs. This approach represents a complete departure from the original approach pioneered by Sunada, where isospectral graphs are obtained as quotients of a certain large symmetric graph. Using the M-function formalism and the symmetries of the graph itself we construct isospectral and isoscattering pairs. In our novel approach isospectral pairs do not need to be embedded into a larger symmetric graph as in Sunada's approach. We demonstrate that the introduced formalism can also be extended to graphs with dissipation. The theoretical predictions are validated experimentally using microwave networks emulating open quantum graphs with dissipation.
Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences
2025-05-22
Gavin Farrell, Eleni Adamidi, Rafael Andrade Buono, Mihail Anton, Omar Abdelghani Attafi, Salvador Capella Gutierrez, Emidio Capriotti, Leyla Jael Castro, Davide Cirillo, Lisa Crossman, Christophe Dessimoz, Alexandros Dimopoulos, Raul Fernandez-Diaz, Styliani-Christina Fragkouli, Carole Goble, Wei Gu, John M. Hancock, Alireza Khanteymoori, Tom Lenaerts, Fabio G. Liberante, Peter Maccallum, Alexander Miguel Monzon, Magnus Palmblad, Lucy Poveda, Ovidiu Radulescu, Denis C. Shields, Shoaib Sufi, Thanasis Vergoulis, Fotis Psomopoulos, Silvio C. E. Tosatto
Artificial intelligence (AI) has recently seen transformative breakthroughs in the life sciences, expanding possibilities for researchers to interpret biological information at an unprecedented capacity, with novel applications and advances being made almost daily. In order to maximise return on the growing investments in AI-based life science research and accelerate this progress, it has become urgent to address the exacerbation of long-standing research challenges arising from the rapid adoption of AI methods. We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability and reproducibility, and highlight their consequent impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI (OSAI) model development. In response, this perspective introduces a practical set of OSAI recommendations directly mapped to over 300 components of the AI ecosystem. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI. Built upon life science community consensus and aligned to existing efforts, the outputs of this perspective are designed to aid the future development of policy and structured pathways for guiding AI implementation.
Exploring magneto-electric coupling through lattice distortions: insights from a pantograph model
2025-05-22
Daniel C. Cabra, Gerardo L. Rossini
Multiferroic materials exhibit the coexistence of magnetic and electric order. They are at the forefront of modern condensed matter physics due to their potential applications in next-generation technologies such as data storage, sensors, and actuators. Despite significant progress, understanding and optimizing the coupling mechanisms between electric polarization and magnetism remain active areas of research. We review here a series of papers presenting a comprehensive numerical and theoretical exploration of a pantograph mechanism modeling magneto-electric coupling through lattice distortions in low dimensional multiferroic systems. These works introduce and elaborate a microscopic model where elastic lattice distortions mediate interactions between spin 1/2 magnetic moments and electric dipoles, uncovering novel physics and functionalities. The model successfully describes ubiquitous phenomena in type II improper multiferroics, particularly when dominant Ising spin components are introduced through XXZ-type rotational symmetry breaking spin interactions. We also study more realistic extensions relevant for materials with higher spin magnetic ions and to materials where magnetic couplings draw higher dimensional lattices.
Major issues in theory of Bose-Einstein condensation
2025-05-22
V. I. Yukalov
Major issues arising in the theory of Bose-Einstein condensation are reviewed. These issues, although being principally important, are very often misunderstood, which results in wrong conclusions. The basic point is global gauge symmetry breaking that is a necessary and sufficient condition for Bose-Einstein condensation. Paying no attention to this basic point is a common fallacy leading to a number of confusions. For instance, the attempt of describing Bose condensation without gauge symmetry breaking produces the so-called ``grand canonical catastrophe" that actually does not exist in the correct description of Bose condensation accompanied by gauge symmetry breaking. The other common flaw is forgetting to consider the stability of the studied systems. One sometimes accomplishes lengthy calculations and discusses the properties of a system that in reality cannot exist being unstable. In some cases, the seeming instability is caused by the negligence of the simple mathematical reason teaching us that one should not go beyond the approximation applicability. An example of such an artificial instability is related to the appearance of the so-called ``thermodynamically anomalous fluctuations" whose arising is due to the use of a second-order approximation for calculating fourth-order terms, in this way distorting the $O(2)$-class model of a Bose-condensed system to the Gaussian-class model. These and other principal points, important for the correct treatment of Bose-condensed systems, are reviewed, including the resolution of the Hohenberg-Martin dilemma of gapless versus conserving theories for Bose-condensed systems and the problem of statistical ensemble equivalence.
A Survey on the Application of Large Language Models in Scenario-Based Testing of Automated Driving Systems
2025-05-22
Yongqi Zhao, Ji Zhou, Dong Bi, Tomislav Mihalj, Jia Hu, Arno Eichberger
The safety and reliability of Automated Driving Systems (ADSs) must be validated prior to large-scale deployment. Among existing validation approaches, scenario-based testing has been regarded as a promising method to improve testing efficiency and reduce associated costs. Recently, the emergence of Large Language Models (LLMs) has introduced new opportunities to reinforce this approach. While an increasing number of studies have explored the use of LLMs in the field of automated driving, a dedicated review focusing on their application within scenario-based testing remains absent. This survey addresses this gap by systematically categorizing the roles played by LLMs across various phased of scenario-based testing, drawing from both academic research and industrial practice. In addition, key characteristics of LLMs and corresponding usage strategies are comprehensively summarized. The paper concludes by outlining five open challenges and potential research directions. To support ongoing research efforts, a continuously updated repository of recent advancements and relevant open-source tools is made available at: https://github.com/ftgTUGraz/LLM4ADSTest.
Is Your LLM-Based Multi-Agent a Reliable Real-World Planner? Exploring Fraud Detection in Travel Planning
2025-05-22
Junchi Yao, Jianhua Xu, Tianyu Xin, Ziyi Wang, Shenzhe Zhu, Shu Yang, Di Wang
The rise of Large Language Model-based Multi-Agent Planning has leveraged advanced frameworks to enable autonomous and collaborative task execution. Some systems rely on platforms like review sites and social media, which are prone to fraudulent information, such as fake reviews or misleading descriptions. This reliance poses risks, potentially causing financial losses and harming user experiences. To evaluate the risk of planning systems in real-world applications, we introduce \textbf{WandaPlan}, an evaluation environment mirroring real-world data and injected with deceptive content. We assess system performance across three fraud cases: Misinformation Fraud, Team-Coordinated Multi-Person Fraud, and Level-Escalating Multi-Round Fraud. We reveal significant weaknesses in existing frameworks that prioritize task efficiency over data authenticity. At the same time, we validate WandaPlan's generalizability, capable of assessing the risks of real-world open-source planning frameworks. To mitigate the risk of fraud, we propose integrating an anti-fraud agent, providing a solution for reliable planning.
Denoising Milky Way stellar survey data with normalizing flow models
2025-05-22
Ziyang Yan, Jason L. Sanders
The Gaia dataset has revealed many intricate Milky Way substructures in exquisite detail, including moving groups and the phase spiral. Precise characterisation of these features and detailed comparisons to theoretical models require engaging with Gaia's heteroscedastic noise model, particularly in more distant parts of the Galactic disc and halo. We propose a general, novel machine-learning approach using normalizing flows for denoising density estimation, with particular focus on density estimation from stellar survey data such as that from Gaia. Normalizing flows transform a simple base distribution into a complex target distribution through bijective transformations resulting in a highly expressive and flexible model. The denoising is performed using importance sampling. We demonstrate that this general procedure works excellently on Gaia data by reconstructing detailed local velocity distributions artificially corrupted with noise. For example, we show the multiple branches of the Hercules stream and the phase-space spiral can both be well captured by our model. We discuss hyperparameter choice to optimally recover substructure and compare our approach to extreme deconvolution. The model therefore promises to be a robust tool for studying the Milky Way's kinematics in Galactic locations where the noise from Gaia is significant.
The theory of an $\eta$-pseudo Hermitian Hamiltonian with $\mathcal{PT}$ symmetry is reviewed and extended to include open system dynamics. Inspired by a simple light matter interaction open system model, information theoretic quantities like a non-Markovian witness and fidelity are calculated for the $\mathcal{PT-}$symmetric Hamiltonian, and the results are compared with their corresponding Hermitian counterparts. The nature of entanglement between two $\mathcal{PT-}$symmetric and Hermitian open quantum systems is calculated, and the contrast observed.
A neural network approach to determining photometric metallicities of M-type dwarf stars
2025-05-22
C. Duque-Arribas, H. M. Tabernero, D. Montes, J. A. Caballero, E. Galceran
M dwarfs are the most abundant stars in the Galaxy and serve as key targets for stellar and exoplanetary studies. It is particularly challenging to determine their metallicities because their spectra are complex. For this reason, several authors have focused on photometric estimates of the M-dwarf metallicity. Although artificial neural networks have been used in the framework of modern astrophysics, their application to a photometric metallicity estimate for M dwarfs remains unexplored. We develop an accurate method for estimating the photometric metallicities of M dwarfs using artificial neural networks to address the limitations of traditional empirical approaches. We trained a neural network on a dataset of M dwarfs with spectroscopically derived metallicities. We used eight absolute magnitudes in the visible and infrared from Gaia, 2MASS, and WISE as input features. Batch normalization and dropout regularization stabilized the training and prevented overfitting. We applied the Monte Carlo dropout technique to obtain more robust predictions. The neural network demonstrated a strong performance in estimating photometric metallicities for M dwarfs in the range of -0.45<[Fe/H]<0.45dex and for spectral types as late as M5.0V. On the test sample, the predictions showed uncertainties down to 0.08dex. This surpasses the accuracy of previous methods. We further validated our results using an additional sample of 46 M dwarfs in wide binary systems with FGK-type primary stars with well-defined metallicities and achieved an excellent predictive performance that surpassed the 0.1dex error threshold. This study introduces a ML-based framework for estimating the photometric metallicities of M dwarfs and provides a scalable data-driven solution for analyzing large photometric surveys. The results outline the potential of artificial neural networks to enhance the determination of stellar parameters.
AppealCase: A Dataset and Benchmark for Civil Case Appeal Scenarios
2025-05-22
Yuting Huang, Meitong Guo, Yiquan Wu, Ang Li, Xiaozhong Liu, Keting Yin, Changlong Sun, Fei Wu, Kun Kuang
Recent advances in LegalAI have primarily focused on individual case judgment analysis, often overlooking the critical appellate process within the judicial system. Appeals serve as a core mechanism for error correction and ensuring fair trials, making them highly significant both in practice and in research. To address this gap, we present the AppealCase dataset, consisting of 10,000 pairs of real-world, matched first-instance and second-instance documents across 91 categories of civil cases. The dataset also includes detailed annotations along five dimensions central to appellate review: judgment reversals, reversal reasons, cited legal provisions, claim-level decisions, and whether there is new information in the second instance. Based on these annotations, we propose five novel LegalAI tasks and conduct a comprehensive evaluation across 20 mainstream models. Experimental results reveal that all current models achieve less than 50% F1 scores on the judgment reversal prediction task, highlighting the complexity and challenge of the appeal scenario. We hope that the AppealCase dataset will spur further research in LegalAI for appellate case analysis and contribute to improving consistency in judicial decision-making.
Smaller, Smarter, Closer: The Edge of Collaborative Generative AI
2025-05-22
Roberto Morabito, SiYoung Jang
The rapid adoption of generative AI (GenAI), particularly Large Language Models (LLMs), has exposed critical limitations of cloud-centric deployments, including latency, cost, and privacy concerns. Meanwhile, Small Language Models (SLMs) are emerging as viable alternatives for resource-constrained edge environments, though they often lack the capabilities of their larger counterparts. This article explores the potential of collaborative inference systems that leverage both edge and cloud resources to address these challenges. By presenting distinct cooperation strategies alongside practical design principles and experimental insights, we offer actionable guidance for deploying GenAI across the computing continuum.
Asset liability management under sequential stochastic dominance constraints
2025-05-22
Giorgio Consigli, Darinka Dentcheva, Francesca Maggioni, Giovanni Micheli
We consider a financial intermediary managing assets and liabilities exposed to several risk sources and seeking an optimal portfolio strategy to minimise the initial capital invested and the total risk associated with investment losses and financial debt. We formulate the problem as a multistage stochastic programming model, with a time-consistent dynamic risk measure in the objective function to control the investment risk. To ensure that the intermediary's financial equilibrium is preserved, we introduce a funding constraint in the model by enforcing in a time-consistent manner a sequential second-order stochastic dominance (SSD) of the portfolio return distribution over the liability distribution. We demonstrate that imposing the SSD constraint at the last-but-one stage is sufficient to enforce the SSD ordering at each stage. To deal with the computational burden of associated MSP, we develop a novel decomposition scheme integrating, for the first time in the literature, time-consistent dynamic risk measures and sequential stochastic dominance constraints. The proposed methodology is computationally validated on a case study developed on a property and casualty ALM problem.
Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery
2025-05-22
Yanbo Zhang, Sumeer A. Khan, Adnan Mahmud, Huck Yang, Alexander Lavin, Michael Levin, Jeremy Frey, Jared Dunnmon, James Evans, Alan Bundy, Saso Dzeroski, Jesper Tegner, Hector Zenil
With recent Nobel Prizes recognising AI contributions to science, Large Language Models (LLMs) are transforming scientific research by enhancing productivity and reshaping the scientific method. LLMs are now involved in experimental design, data analysis, and workflows, particularly in chemistry and biology. However, challenges such as hallucinations and reliability persist. In this contribution, we review how Large Language Models (LLMs) are redefining the scientific method and explore their potential applications across different stages of the scientific cycle, from hypothesis testing to discovery. We conclude that, for LLMs to serve as relevant and effective creative engines and productivity enhancers, their deep integration into all steps of the scientific process should be pursued in collaboration and alignment with human scientific goals, with clear evaluation metrics. The transition to AI-driven science raises ethical questions about creativity, oversight, and responsibility. With careful guidance, LLMs could evolve into creative engines, driving transformative breakthroughs across scientific disciplines responsibly and effectively. However, the scientific community must also decide how much it leaves to LLMs to drive science, even when associations with 'reasoning', mostly currently undeserved, are made in exchange for the potential to explore hypothesis and solution regions that might otherwise remain unexplored by human exploration alone.
Clogging-unclogging transition in 2D vertical pipe
2025-05-22
Y. Zhou, M. Li, Y. Wang, Y. Guan, Y. Liu, Z. Zou
We experimentally and numerically investigate the clogging behavior of granular materials in a two-dimensional vertical pipe. The nonmonotonicity of clogging probability found in cylindrical vertical pipe is also observed in the two-dimensional case. We numerically demonstrate that the clogging probability strongly correlates with the friction coefficient $\mu$, in addition to the pipe-to-particle diameter ratio $D/d$. We thus construct a clogging-unclogging $D/d$-$\mu$ phase diagram within the $2
Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems
2025-05-22
Song Jin, Juntian Zhang, Yuhan Liu, Xun Zhang, Yufei Zhang, Guojun Yin, Fei Jiang, Wei Lin, Rui Yan
Evaluating and iterating upon recommender systems is crucial, yet traditional A/B testing is resource-intensive, and offline methods struggle with dynamic user-platform interactions. While agent-based simulation is promising, existing platforms often lack a mechanism for user actions to dynamically reshape the environment. To bridge this gap, we introduce RecInter, a novel agent-based simulation platform for recommender systems featuring a robust interaction mechanism. In RecInter platform, simulated user actions (e.g., likes, reviews, purchases) dynamically update item attributes in real-time, and introduced Merchant Agents can reply, fostering a more realistic and evolving ecosystem. High-fidelity simulation is ensured through Multidimensional User Profiling module, Advanced Agent Architecture, and LLM fine-tuned on Chain-of-Thought (CoT) enriched interaction data. Our platform achieves significantly improved simulation credibility and successfully replicates emergent phenomena like Brand Loyalty and the Matthew Effect. Experiments demonstrate that this interaction mechanism is pivotal for simulating realistic system evolution, establishing our platform as a credible testbed for recommender systems research.
The effective communication of procedural knowledge remains a significant challenge in natural language processing (NLP), as purely textual instructions often fail to convey complex physical actions and spatial relationships. We address this limitation by proposing a language-driven framework that translates procedural text into coherent visual instructions. Our approach models the linguistic structure of instructional content by decomposing it into goal statements and sequential steps, then conditioning visual generation on these linguistic elements. We introduce three key innovations: (1) a constituency parser-based text encoding mechanism that preserves semantic completeness even with lengthy instructions, (2) a pairwise discourse coherence model that maintains consistency across instruction sequences, and (3) a novel evaluation protocol specifically designed for procedural language-to-image alignment. Our experiments across three instructional datasets (HTStep, CaptainCook4D, and WikiAll) demonstrate that our method significantly outperforms existing baselines in generating visuals that accurately reflect the linguistic content and sequential nature of instructions. This work contributes to the growing body of research on grounding procedural language in visual content, with applications spanning education, task guidance, and multimodal language understanding.
Weak lensing alters galaxy sizes and fluxes, influencing the clustering patterns of galaxies through cosmic magnification. This effect enables the reconstruction of weak lensing convergence $\hat{\kappa}$ maps for DES and DECaLS by linearly combining galaxy overdensities across magnitude bins in the $g$, $r$, and $z$ photometry bands \citep{Qin+,Qin2+}. In this study, we enhance the lensing reconstruction method by addressing biases in the magnification coefficient estimation, which arise from incomplete consideration of selection effects, especially those induced by photometric redshift (photo-$z$) selection. Using a Random Forest-based photo-$z$ estimation for DECaLS and DES galaxies, we quantify the impact of photo-$z$ induced selection on magnification coefficient estimation. Our results show that neglecting photo-$z$ selection introduces significant biases in the magnification coefficient, leading to deviations in the reconstructed convergence map amplitude $A$, with values ranging from 0.4 to 3.5 depending on the survey, redshift, and magnitude cuts. By incorporating an improved magnification coefficient estimation that accounts for photo-$z$ selection, these biases are significantly reduced, with $A$ converging to $\sim 1$ as the magnitude cuts approach optimal values. This improvement is consistently observed across DES and DECaLS datasets and redshift bins, despite differences in survey strategies and depths. Our findings highlight the importance of addressing photo-$z$ induced selection to achieve unbiased weak lensing reconstructions and accurate cosmic magnification measurements.
On the Hausdorff spectra of free pro-$p$ groups and certain $p$-adic analytic groups
2025-05-22
Iker de las Heras, Benjamin Klopsch, Anitha Thillaisundaram
We establish that finitely generated non-abelian direct products $G$ of free pro-$p$ groups have full Hausdorff spectrum with respect to the lower $p$-series $\mathcal{L}$. This complements similar results with respect to other standard filtration series and a recent theorem showing that the Hausdorff spectrum $\text{hspec}^\mathcal{L}(G)$ of a $p$-adic analytic pro-$p$ group $G$ is discrete and consists of at most $2^{\dim(G)}$ rational numbers. The latter also left some room for improvement regarding the upper bound. Indeed, for finitely generated nilpotent pro-$p$ groups $G$ we obtain the stronger assertion that the cardinality of the Hausdorff spectrum is at most the analytic dimension of $G$. Moreover, we produce a corresponding result when the $p$-adic analytic pro-$p$ group $G$ is just infinite, which holds not just for the lower $p$-series but for arbitrary filtration series. Finally, we show that, if $G$ is a countably based pro-$p$ group with an open subgroup mapping onto the free abelian pro-$p$ group $\mathbb{Z}_p \oplus \mathbb{Z}_p$, then for every prescribed finite set $\{0,1\} \subseteq X \subseteq [0,1]$ there is a filtration series $\mathcal{S}$ such that $\text{hspec}^\mathcal{S}(G) = X$; in particular, $|\text{hspec}^{\mathcal{S}}(G)|$ is unbounded, as $\mathcal{S}$ runs through all filtration series of $G$ with $|\text{hspec}^{\mathcal{S}}(G)| < \infty$.
From Surveys to Narratives: Rethinking Cultural Value Adaptation in LLMs
2025-05-22
Muhammad Farid Adilazuarda, Chen Cecilia Liu, Iryna Gurevych, Alham Fikri Aji
Adapting cultural values in Large Language Models (LLMs) presents significant challenges, particularly due to biases and limited training data. Prior work primarily aligns LLMs with different cultural values using World Values Survey (WVS) data. However, it remains unclear whether this approach effectively captures cultural nuances or produces distinct cultural representations for various downstream tasks. In this paper, we systematically investigate WVS-based training for cultural value adaptation and find that relying solely on survey data can homogenize cultural norms and interfere with factual knowledge. To investigate these issues, we augment WVS with encyclopedic and scenario-based cultural narratives from Wikipedia and NormAd. While these narratives may have variable effects on downstream tasks, they consistently improve cultural distinctiveness than survey data alone. Our work highlights the inherent complexity of aligning cultural values with the goal of guiding task-specific behavior.
Eye gaze can provide rich information on human psychological activities, and has garnered significant attention in the field of Human-Robot Interaction (HRI). However, existing gaze estimation methods merely predict either the gaze direction or the Point-of-Gaze (PoG) on the screen, failing to provide sufficient information for a comprehensive six Degree-of-Freedom (DoF) gaze analysis in 3D space. Moreover, the variations of eye shape and structure among individuals also impede the generalization capability of these methods. In this study, we propose MAGE, a Multi-task Architecture for Gaze Estimation with an efficient calibration module, to predict the 6-DoF gaze information that is applicable for the real-word HRI. Our basic model encodes both the directional and positional features from facial images, and predicts gaze results with dedicated information flow and multiple decoders. To reduce the impact of individual variations, we propose a novel calibration module, namely Easy-Calibration, to fine-tune the basic model with subject-specific data, which is efficient to implement without the need of a screen. Experimental results demonstrate that our method achieves state-of-the-art performance on the public MPIIFaceGaze, EYEDIAP, and our built IMRGaze datasets.
Filling in the Blanks? A Systematic Review and Theoretical Conceptualisation for Measuring WikiData Content Gaps
2025-05-22
Marisa Ripoll, Neal Reeves, Anelia Kurteva, Elena Simperl, Albert Meroño Peñuela
Wikidata is a collaborative knowledge graph which provides machine-readable structured data for Wikimedia projects including Wikipedia. Managed by a community of volunteers, it has grown to become the most edited Wikimedia project. However, it features a long-tail of items with limited data and a number of systematic gaps within the available content. In this paper, we present the results of a systematic literature review aimed to understand the state of these content gaps within Wikidata. We propose a typology of gaps based on prior research and contribute a theoretical framework intended to conceptualise gaps and support their measurement. We also describe the methods and metrics present used within the literature and classify them according to our framework to identify overlooked gaps that might occur in Wikidata. We then discuss the implications for collaboration and editor activity within Wikidata as well as future research directions. Our results contribute to the understanding of quality, completeness and the impact of systematic biases within Wikidata and knowledge gaps more generally.
Materials are the foundation of modern society, underpinning advancements in energy, electronics, healthcare, transportation, and infrastructure. The ability to discover and design new materials with tailored properties is critical to solving some of the most pressing global challenges. In recent years, the growing availability of high-quality materials data combined with rapid advances in Artificial Intelligence (AI) has opened new opportunities for accelerating materials discovery. Data-driven generative models provide a powerful tool for materials design by directly create novel materials that satisfy predefined property requirements. Despite the proliferation of related work, there remains a notable lack of up-to-date and systematic surveys in this area. To fill this gap, this paper provides a comprehensive overview of recent progress in AI-driven materials generation. We first organize various types of materials and illustrate multiple representations of crystalline materials. We then provide a detailed summary and taxonomy of current AI-driven materials generation approaches. Furthermore, we discuss the common evaluation metrics and summarize open-source codes and benchmark datasets. Finally, we conclude with potential future directions and challenges in this fast-growing field. The related sources can be found at https://github.com/ZhixunLEE/Awesome-AI-for-Materials-Generation.
Style Transfer with Diffusion Models for Synthetic-to-Real Domain Adaptation
2025-05-22
Estelle Chigot, Dennis G. Wilson, Meriem Ghrib, Thomas Oberlin
Semantic segmentation models trained on synthetic data often perform poorly on real-world images due to domain gaps, particularly in adverse conditions where labeled data is scarce. Yet, recent foundation models enable to generate realistic images without any training. This paper proposes to leverage such diffusion models to improve the performance of vision models when learned on synthetic data. We introduce two novel techniques for semantically consistent style transfer using diffusion models: Class-wise Adaptive Instance Normalization and Cross-Attention (CACTI) and its extension with selective attention Filtering (CACTIF). CACTI applies statistical normalization selectively based on semantic classes, while CACTIF further filters cross-attention maps based on feature similarity, preventing artifacts in regions with weak cross-attention correspondences. Our methods transfer style characteristics while preserving semantic boundaries and structural coherence, unlike approaches that apply global transformations or generate content without constraints. Experiments using GTA5 as source and Cityscapes/ACDC as target domains show that our approach produces higher quality images with lower FID scores and better content preservation. Our work demonstrates that class-aware diffusion-based style transfer effectively bridges the synthetic-to-real domain gap even with minimal target domain data, advancing robust perception systems for challenging real-world applications. The source code is available at: https://github.com/echigot/cactif.
How to keep pushing ML accelerator performance? Know your rooflines!
2025-05-22
Marian Verhelst, Luca Benini, Naveen Verma
The rapidly growing importance of Machine Learning (ML) applications, coupled with their ever-increasing model size and inference energy footprint, has created a strong need for specialized ML hardware architectures. Numerous ML accelerators have been explored and implemented, primarily to increase task-level throughput per unit area and reduce task-level energy consumption. This paper surveys key trends toward these objectives for more efficient ML accelerators and provides a unifying framework to understand how compute and memory technologies/architectures interact to enhance system-level efficiency and performance. To achieve this, the paper introduces an enhanced version of the roofline model and applies it to ML accelerators as an effective tool for understanding where various execution regimes fall within roofline bounds and how to maximize performance and efficiency under the rooline. Key concepts are illustrated with examples from state-of-the-art designs, with a view towards open research opportunities to further advance accelerator performance.
Rethinking Code Review Workflows with LLM Assistance: An Empirical Study
2025-05-22
Fannar Steinn Aðalsteinsson, Björn Borgar Magnússon, Mislav Milicevic, Adam Nirving Davidsson, Chih-Hong Cheng
Code reviews are a critical yet time-consuming aspect of modern software development, increasingly challenged by growing system complexity and the demand for faster delivery. This paper presents a study conducted at WirelessCar Sweden AB, combining an exploratory field study of current code review practices with a field experiment involving two variations of an LLM-assisted code review tool. The field study identifies key challenges in traditional code reviews, including frequent context switching, insufficient contextual information, and highlights both opportunities (e.g., automatic summarization of complex pull requests) and concerns (e.g., false positives and trust issues) in using LLMs. In the field experiment, we developed two prototype variations: one offering LLM-generated reviews upfront and the other enabling on-demand interaction. Both utilize a semantic search pipeline based on retrieval-augmented generation to assemble relevant contextual information for the review, thereby tackling the uncovered challenges. Developers evaluated both variations in real-world settings: AI-led reviews are overall more preferred, while still being conditional on the reviewers' familiarity with the code base, as well as on the severity of the pull request.
SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers
2025-05-22
Wenqing Wu, Chengzhi Zhang, Tong Bao, Yi Zhao
Novelty is a core component of academic papers, and there are multiple perspectives on the assessment of novelty. Existing methods often focus on word or entity combinations, which provide limited insights. The content related to a paper's novelty is typically distributed across different core sections, e.g., Introduction, Methodology and Results. Therefore, exploring the optimal combination of sections for evaluating the novelty of a paper is important for advancing automated novelty assessment. In this paper, we utilize different combinations of sections from academic papers as inputs to drive language models to predict novelty scores. We then analyze the results to determine the optimal section combinations for novelty score prediction. We first employ natural language processing techniques to identify the sectional structure of academic papers, categorizing them into introduction, methods, results, and discussion (IMRaD). Subsequently, we used different combinations of these sections (e.g., introduction and methods) as inputs for pretrained language models (PLMs) and large language models (LLMs), employing novelty scores provided by human expert reviewers as ground truth labels to obtain prediction results. The results indicate that using introduction, results and discussion is most appropriate for assessing the novelty of a paper, while the use of the entire text does not yield significant results. Furthermore, based on the results of the PLMs and LLMs, the introduction and results appear to be the most important section for the task of novelty score prediction. The code and dataset for this paper can be accessed at https://github.com/njust-winchy/SC4ANM.
Cooperative NOMA Meets Emerging Technologies: A Survey for Next-Generation Wireless Networks
2025-05-22
Mahmoud M. Salim, Suhail I. Al-Dharrab, Daniel Benevides Da Costa, Ali H. Muqaibel
The emerging demands of sixth-generation wireless networks, such as ultra-connectivity, native intelligence, and cross-domain convergence, are bringing renewed focus to cooperative non-orthogonal multiple access (C-NOMA) as a fundamental enabler of scalable, efficient, and intelligent communication systems. C-NOMA builds on the core benefits of NOMA by leveraging user cooperation and relay strategies to enhance spectral efficiency, coverage, and energy performance. This article presents a unified and forward-looking survey on the integration of C-NOMA with key enabling technologies, including radio frequency energy harvesting, cognitive radio networks, reconfigurable intelligent surfaces, space-air-ground integrated networks, and integrated sensing and communication-assisted semantic communication. Foundational principles and relaying protocols are first introduced to establish the technical relevance of C-NOMA. Then, a focused investigation is conducted into protocol-level synergies, architectural models, and deployment strategies across these technologies. Beyond integration, this article emphasizes the orchestration of C-NOMA across future application domains such as digital twins, extended reality, and e-health. In addition, it provides an extensive and in-depth review of recent literature, categorized by relaying schemes, system models, performance metrics, and optimization paradigms, including model-based, heuristic, and AI-driven approaches. Finally, open challenges and future research directions are outlined, spanning standardization, security, and cross-layer design, positioning C-NOMA as a key pillar of intelligent next-generation network architectures.
How do Scaling Laws Apply to Knowledge Graph Engineering Tasks? The Impact of Model Size on Large Language Model Performance
2025-05-22
Desiree Heim, Lars-Peter Meyer, Markus Schröder, Johannes Frey, Andreas Dengel
When using Large Language Models (LLMs) to support Knowledge Graph Engineering (KGE), one of the first indications when searching for an appropriate model is its size. According to the scaling laws, larger models typically show higher capabilities. However, in practice, resource costs are also an important factor and thus it makes sense to consider the ratio between model performance and costs. The LLM-KG-Bench framework enables the comparison of LLMs in the context of KGE tasks and assesses their capabilities of understanding and producing KGs and KG queries. Based on a dataset created in an LLM-KG-Bench run covering 26 open state-of-the-art LLMs, we explore the model size scaling laws specific to KGE tasks. In our analyses, we assess how benchmark scores evolve between different model size categories. Additionally, we inspect how the general score development of single models and families of models correlates to their size. Our analyses revealed that, with a few exceptions, the model size scaling laws generally also apply to the selected KGE tasks. However, in some cases, plateau or ceiling effects occurred, i.e., the task performance did not change much between a model and the next larger model. In these cases, smaller models could be considered to achieve high cost-effectiveness. Regarding models of the same family, sometimes larger models performed worse than smaller models of the same family. These effects occurred only locally. Hence it is advisable to additionally test the next smallest and largest model of the same family.
Reassessing Collaborative Writing Theories and Frameworks in the Age of LLMs: What Still Applies and What We Must Leave Behind
2025-05-22
Daisuke Yukita, Tim Miller, Joel Mackenzie
In this paper, we conduct a critical review of existing theories and frameworks on human-human collaborative writing to assess their relevance to the current human-AI paradigm in professional contexts, and draw seven insights along with design implications for human-AI collaborative writing tools. We found that, as LLMs nudge the writing process more towards an empirical "trial and error" process analogous to prototyping, the non-linear cognitive process of writing will stay the same, but more rigor will be required for revision methodologies. This shift would shed further light on the importance of coherence support, but the large language model (LLM)'s unprecedented semantic capabilities can bring novel approaches to this ongoing challenge. We argue that teamwork-related factors such as group awareness, consensus building and authorship - which have been central in human-human collaborative writing studies - should not apply to the human-AI paradigm due to excessive anthropomorphism. With the LLM's text generation capabilities becoming essentially indistinguishable from human-written ones, we are entering an era where, for the first time in the history of computing, we are engaging in collaborative writing with AI at workplaces on a daily basis. We aim to bring theoretical grounding and practical design guidance to the interaction designs of human-AI collaborative writing, with the goal of enhancing future human-AI writing software.
Improving adiabatic quantum factorization via chopped random-basis optimization
2025-05-22
Tianlai Yang, Mo Xiong, Ming Xue, Xinwei Li, Jinbin Li
Integer factorization remains a significant challenge for classical computers and is fundamental to the security of RSA encryption. Adiabatic quantum algorithms present a promising solution, yet their practical implementation is limited by the short coherence times of current NISQ devices and quantum simulators. In this work, we apply the chopped random-basis (CRAB) optimization technique to enhance adiabatic quantum factorization algorithms. We demonstrate the effectiveness of CRAB by applying it to factor the integers ranging from 21 to 2479, achieving significantly improved fidelity of the target state when the evolution time exceeds the quantum speed limit. Notably, this performance improvement shows resilience in the presence of dephasing noise, highlighting CRAB's practical utility in noisy quantum systems. Our findings suggest that CRAB optimization can serve as a powerful tool for advancing adiabatic quantum algorithms, with broader implications for quantum information processing tasks.
Deep Learning-Driven Ultra-High-Definition Image Restoration: A Survey
2025-05-22
Liyan Wang, Weixiang Zhou, Cong Wang, Kin-Man Lam, Zhixun Su, Jinshan Pan
Ultra-high-definition (UHD) image restoration aims to specifically solve the problem of quality degradation in ultra-high-resolution images. Recent advancements in this field are predominantly driven by deep learning-based innovations, including enhancements in dataset construction, network architecture, sampling strategies, prior knowledge integration, and loss functions. In this paper, we systematically review recent progress in UHD image restoration, covering various aspects ranging from dataset construction to algorithm design. This serves as a valuable resource for understanding state-of-the-art developments in the field. We begin by summarizing degradation models for various image restoration subproblems, such as super-resolution, low-light enhancement, deblurring, dehazing, deraining, and desnowing, and emphasizing the unique challenges of their application to UHD image restoration. We then highlight existing UHD benchmark datasets and organize the literature according to degradation types and dataset construction methods. Following this, we showcase major milestones in deep learning-driven UHD image restoration, reviewing the progression of restoration tasks, technological developments, and evaluations of existing methods. We further propose a classification framework based on network architectures and sampling strategies, helping to clearly organize existing methods. Finally, we share insights into the current research landscape and propose directions for further advancements. A related repository is available at https://github.com/wlydlut/UHD-Image-Restoration-Survey.
Confirming HSC strong lens candidates with DESI Spectroscopy. I. Project Overview
2025-05-22
Yiping Shu, Shen Li
Accurate redshift determinations of both lenses and sources are critical for confirming strong-lens systems and fully realizing their scientific value. However, the thousands of strong-lens candidates now routinely discovered in wide-field imaging surveys make one-by-one follow-up observations impractical. In this work, we investigate the capability and efficiency of large-scale spectroscopic surveys in confirming strong-lens systems. As a case study, we cross-match strong lens candidates identified from the Hyper Suprime-Cam Subaru Strategic Program with Data Release 1 of the Dark Energy Spectroscopic Instrument (DESI). We find that DESI DR1 serendipitously observed putative lenses and/or lensed images in approximately 50\% of these candidates. Analyzing the DESI spectra for $\approx 500$ matched candidates that meet our specific interests, we determine both lens and source redshifts for 27 systems. Additionally, 76 candidate systems feature lensing galaxies at $z > 0.8$, and one candidate system contains a quasar within its lensing galaxy. Applying this approach to other strong-lens candidates will yield numerous additional confirmations, with a further several-fold increase anticipated from the final DESI data release. Our results highlight the growing importance of large-scale spectroscopic surveys in advancing strong lensing discoveries and investigations.
Model-Independent Measurement of the Matter-Radiation Equality Scale in DESI 2024
2025-05-22
B. Bahr-Kalus, D. Parkinson, K. Lodha, E. Mueller, E. Chaussidon, A. de Mattia, D. Forero-Sánchez, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, A. Cuceu, A. de la Macorra, P. Doel, A. Font-Ribera, E. Gaztañaga, S. Gontcho, A Gontcho, G. Gutierrez, K. Honscheid, D. Huterer, M. Ishak, R. Kehoe, S. Kent, D. Kirkby, T. Kisner, A. Kremin, O. Lahav, M. Landriau, L. Le Guillou, C. Magneville, M. Manera, P. Martini, A. Meisner, R. Miquel, J. Moustakas, S. Nadathur, N. Palanque-Delabrouille, W. J. Percival, F. Prada, I. Pérez-Ràfols, A. J. Ross, G. Rossi, L. Samushia, E. Sanchez, D. Schlegel, M. Schubnell, H. Seo, J. Silber, D. Sprayberry, G. Tarlé, B. A. Weaver, R. Zhou, H. Zou
The peak of the matter power spectrum, known as the turnover (TO) scale, is determined by the horizon size at the time of matter-radiation equality. This scale can serve as a standard ruler, independent of other features in the matter power spectrum, such as baryon acoustic oscillations (BAO). Here, we present the first detection of the turnover in the galaxy auto-power spectrum, utilising the distribution of quasars (QSO) and luminous red galaxies (LRG) measured by the Dark Energy Spectroscopic Instrument (DESI) during its first year of survey operations in a model-independent manner. To avoid confirmation bias, we first analyse the data using data blinding methods designed for the DESI baryon acoustic oscillation, redshift space distortion and scale-dependent bias signals. We measure the angle-averaged dilation distance $D_V(z = 1.651) = (38.1\pm 2.5)r_H$ from the quasars and $D_{V}(z = 0.733) = (21.8\pm 1.0)r_H$ from the LRG sample in units of the horizon $r_H$ at the matter-radiation-equality epoch. Combining these two constraints and assuming a flat $\Lambda$CDM model with three standard neutrino species, we can translate this into a constraint of $\Omega_{m}h^2 = 0.139^{+0.036}_{-0.046}$. We can break the $\Omega_m$-$H_0$ degeneracy with low-redshift distance measurements from type-Ia supernova (SN) data from Pantheon+, we obtain a sound-horizon free estimate of the Hubble-Lema\^itre parameter of $H_0=65.2^{+4.9}_{-6.2}$ km/s/Mpc, consistent with sound-horizon dependent DESI measurements. On the other hand, combining the DESI BAO and TO, we find a truly DESI-only measurement of $H_0=74.0^{+7.2}_{-3.5}$ km/s/Mpc, in line with DESI-only full-shape results where the sound-horizon scale is marginalised out. This discrepancy in $H_0$ can be reconciled in a $w_0w_a$CDM cosmology, where the combination of DESI BAO and TO data yields $H_0 = 66.5\pm 7.2\;\mathrm{km/s/Mpc}$.
LLMs Are Not Scorers: Rethinking MT Evaluation with Generation-Based Methods
2025-05-22
Hyang Cui
Recent studies have applied large language models (LLMs) to machine translation quality estimation (MTQE) by prompting models to assign numeric scores. Nonetheless, these direct scoring methods tend to show low segment-level correlation with human judgments. In this paper, we propose a generation-based evaluation paradigm that leverages decoder-only LLMs to produce high-quality references, followed by semantic similarity scoring using sentence embeddings. We conduct the most extensive evaluation to date in MTQE, covering 8 LLMs and 8 language pairs. Empirical results show that our method outperforms both intra-LLM direct scoring baselines and external non-LLM reference-free metrics from MTME. These findings demonstrate the strength of generation-based evaluation and support a shift toward hybrid approaches that combine fluent generation with accurate semantic assessment.
Semiotic Reconstruction of Destination Expectation Constructs An LLM-Driven Computational Paradigm for Social Media Tourism Analytics
2025-05-22
Haotian Lan, Yao Gao, Yujun Cheng, Wei Yuan, Kun Wang
Social media's rise establishes user-generated content (UGC) as pivotal for travel decisions, yet analytical methods lack scalability. This study introduces a dual-method LLM framework: unsupervised expectation extraction from UGC paired with survey-informed supervised fine-tuning. Findings reveal leisure/social expectations drive engagement more than foundational natural/emotional factors. By establishing LLMs as precision tools for expectation quantification, we advance tourism analytics methodology and propose targeted strategies for experience personalization and social travel promotion. The framework's adaptability extends to consumer behavior research, demonstrating computational social science's transformative potential in marketing optimization.
Continually Self-Improving Language Models for Bariatric Surgery Question--Answering
2025-05-22
Yash Kumar Atri, Thomas H Shin, Thomas Hartvigsen
While bariatric and metabolic surgery (MBS) is considered the gold standard treatment for severe and morbid obesity, its therapeutic efficacy hinges upon active and longitudinal engagement with multidisciplinary providers, including surgeons, dietitians/nutritionists, psychologists, and endocrinologists. This engagement spans the entire patient journey, from preoperative preparation to long-term postoperative management. However, this process is often hindered by numerous healthcare disparities, such as logistical and access barriers, which impair easy patient access to timely, evidence-based, clinician-endorsed information. To address these gaps, we introduce bRAGgen, a novel adaptive retrieval-augmented generation (RAG)-based model that autonomously integrates real-time medical evidence when response confidence dips below dynamic thresholds. This self-updating architecture ensures that responses remain current and accurate, reducing the risk of misinformation. Additionally, we present bRAGq, a curated dataset of 1,302 bariatric surgery--related questions, validated by an expert bariatric surgeon. bRAGq constitutes the first large-scale, domain-specific benchmark for comprehensive MBS care. In a two-phase evaluation, bRAGgen is benchmarked against state-of-the-art models using both large language model (LLM)--based metrics and expert surgeon review. Across all evaluation dimensions, bRAGgen demonstrates substantially superior performance in generating clinically accurate and relevant responses.
TrialPanorama: Database and Benchmark for Systematic Review and Design of Clinical Trials
2025-05-22
Zifeng Wang, Qiao Jin, Jiacheng Lin, Junyi Gao, Jathurshan Pradeepkumar, Pengcheng Jiang, Benjamin Danek, Zhiyong Lu, Jimeng Sun
Developing artificial intelligence (AI) for vertical domains requires a solid data foundation for both training and evaluation. In this work, we introduce TrialPanorama, a large-scale, structured database comprising 1,657,476 clinical trial records aggregated from 15 global sources. The database captures key aspects of trial design and execution, including trial setups, interventions, conditions, biomarkers, and outcomes, and links them to standard biomedical ontologies such as DrugBank and MedDRA. This structured and ontology-grounded design enables TrialPanorama to serve as a unified, extensible resource for a wide range of clinical trial tasks, including trial planning, design, and summarization. To demonstrate its utility, we derive a suite of benchmark tasks directly from the TrialPanorama database. The benchmark spans eight tasks across two categories: three for systematic review (study search, study screening, and evidence summarization) and five for trial design (arm design, eligibility criteria, endpoint selection, sample size estimation, and trial completion assessment). The experiments using five state-of-the-art large language models (LLMs) show that while general-purpose LLMs exhibit some zero-shot capability, their performance is still inadequate for high-stakes clinical trial workflows. We release TrialPanorama database and the benchmark to facilitate further research on AI for clinical trials.
Large language models (LLMs) are introducing a paradigm shift in molecular discovery by enabling text-guided interaction with chemical spaces through natural language, symbolic notations, with emerging extensions to incorporate multi-modal inputs. To advance the new field of LLM for molecular discovery, this survey provides an up-to-date and forward-looking review of the emerging use of LLMs for two central tasks: molecule generation and molecule optimization. Based on our proposed taxonomy for both problems, we analyze representative techniques in each category, highlighting how LLM capabilities are leveraged across different learning settings. In addition, we include the commonly used datasets and evaluation protocols. We conclude by discussing key challenges and future directions, positioning this survey as a resource for researchers working at the intersection of LLMs and molecular science. A continuously updated reading list is available at https://github.com/REAL-Lab-NU/Awesome-LLM-Centric-Molecular-Discovery.
Safe Reinforcement Learning (SafeRL) is the subfield of reinforcement learning that explicitly deals with safety constraints during the learning and deployment of agents. This survey provides a mathematically rigorous overview of SafeRL formulations based on Constrained Markov Decision Processes (CMDPs) and extensions to Multi-Agent Safe RL (SafeMARL). We review theoretical foundations of CMDPs, covering definitions, constrained optimization techniques, and fundamental theorems. We then summarize state-of-the-art algorithms in SafeRL for single agents, including policy gradient methods with safety guarantees and safe exploration strategies, as well as recent advances in SafeMARL for cooperative and competitive settings. Additionally, we propose five open research problems to advance the field, with three focusing on SafeMARL. Each problem is described with motivation, key challenges, and related prior work. This survey is intended as a technical guide for researchers interested in SafeRL and SafeMARL, highlighting key concepts, methods, and open future research directions.
This study attempts to establish a basis for understanding how methods used in research in solar physics have evolved since World War II (WWII). The goal is to begin to explore if and how the changing research environment affects the training of young scientists, and the future of solar physics research at our institutions. A strategy based upon a sample of 650 PhD theses is used to seek possible trends over 8 decades, with the aim of uncovering any correlations between methods used and measures of success. Necessarily subjective, results depend on how methods are defined, and how success is measured. Although a brief justification of the choices made is attempted, trying mainly to avoid pitfalls such as counting citations, it is clear that further assessment is required. The statistical analysis is based upon necessarily subjective categorization and the inference of likelihoods of two different distributions being drawn from the same underlying distribution. The statistics seem to reflect historical events, such as the Kennedy Moonshot program and the associated SKYLAB mission, with changes delayed by a few years. The data suggest that impactful advances are becoming more rare. Yet the methods used have changed little barring those related to obvious technological advances (e.g. the advent of spacecraft, adaptive optics). A follow-up study to explore the 100,000+ publications in solar physics through machine learning seems warranted.
Galaxy-scale lens search in the PEARLS NEP TDF and CEERS JWST fields
2025-05-22
Giovanni Ferrami, Nathan J. Adams, Lewi Westcott, Thomas Harvey, Rolf A. Jansen, Jose M. Diego, Vince Estrada-Carpente, Rogier A. Windhorst, Christopher J. Conselice, Anton M. Koekemoer, Jordan C. J. D'Silva, Christopher Willmer, J. Stuart B. Wyithe, Michael J. Rutkowski, Seth H. Cohen, Brenda L. Frye, Norman A. Grogin
We present four galaxy scale lenses discovered in two JWST blank-fields: the ~ 54 arcmin^2 of the PEARLS North-Ecliptic-Pole Time-Domain Field (NEP TDF) and in the ~ 90 arcmin^2 of CEERS. We perform the search by visual inspection of NIRCam photometric data, obtaining an initial list of 16 lens candidates. We down-select this list to 4 high-confidence lens candidates, based on lens modelling of the image configuration and photometric redshift measurements for both the source and the deflector. We compare our results to samples of lenses obtained in ground-based and space-based lens searches and theoretical expectations. We expect that JWST observations of field galaxies will yield approximately 1 galaxy scale lens every three to five NIRCam pointings of comparable depth to these observations (~ 9 arcmin^2 each). This shows that JWST, compared to other lens searches, can yield an extremely high number of secure lenses per unit area, with redshift and size distributions complementary to lens samples obtained from ground-based and wide-area surveys. We estimate that a single JWST pure-parallel survey of comparable depth could yield $\sim 70$ galaxy scale lenses, with a third of them having z_lens>1 and z_source>3.
The proliferation of electronic devices has greatly transformed every aspect of human life, such as communication, healthcare, transportation, and energy. Unfortunately, the global electronics supply chain is vulnerable to various attacks, including piracy of intellectual properties, tampering, counterfeiting, information leakage, side-channel, and fault injection attacks, due to the complex nature of electronic products and vulnerabilities present in them. Although numerous solutions have been proposed to address these threats, significant gaps remain, particularly in providing scalable and comprehensive protection against emerging attacks. Digital twin, a dynamic virtual replica of a physical system, has emerged as a promising solution to address these issues by providing backward traceability, end-to-end visibility, and continuous verification of component integrity and behavior. In this paper, we present a comprehensive survey of the application of digital twins based on their functional role and application domains. We comprehensively present recent digital twin-based security implementations, including their role in cyber-physical systems, Internet of Things, and cryptographic systems, detection of counterfeit electronics, intrusion detection, fault injection, and side-channel leakage. To the best of our knowledge, it is the first study to consolidate these security use cases into a unified reference. The paper also explores the integration of large language models with digital twins for enhanced security and discusses current challenges, solutions, and future research directions.
The progress of automotive technologies has made cybersecurity a crucial focus, leading to various cyber attacks. These attacks primarily target the Controller Area Network (CAN) and specialized Electronic Control Units (ECUs). In order to mitigate these attacks and bolster the security of vehicular systems, numerous defense solutions have been proposed.These solutions aim to detect diverse forms of vehicular attacks. However, the practical implementation of these solutions still presents certain limitations and challenges. In light of these circumstances, this paper undertakes a thorough examination of existing vehicular attacks and defense strategies employed against the CAN and ECUs. The objective is to provide valuable insights and inform the future design of Vehicular Intrusion Detection Systems (VIDS). The findings of our investigation reveal that the examined VIDS primarily concentrate on particular categories of attacks, neglecting the broader spectrum of potential threats. Moreover, we provide a comprehensive overview of the significant challenges encountered in implementing a robust and feasible VIDS. Additionally, we put forth several defense recommendations based on our study findings, aiming to inform and guide the future design of VIDS in the context of vehicular security.
Understanding the Security Landscape of Embedded Non-Volatile Memories: A Comprehensive Survey
2025-05-22
Zakia Tamanna Tisha, Ujjwal Guin
The modern semiconductor industry requires memory solutions that can keep pace with the high-speed demands of high-performance computing. Embedded non-volatile memories (eNVMs) address these requirements by offering faster access to stored data at an improved computational throughput and efficiency. Furthermore, these technologies offer numerous appealing features, including limited area-energy-runtime budget and data retention capabilities. Among these, the data retention feature of eNVMs has garnered particular interest within the semiconductor community. Although this property allows eNVMs to retain data even in the absence of a continuous power supply, it also introduces some vulnerabilities, prompting security concerns. These concerns have sparked increased interest in examining the broader security implications associated with eNVM technologies. This paper examines the security aspects of eNVMs by discussing the reasons for vulnerabilities in specific memories from an architectural point of view. Additionally, this paper extensively reviews eNVM-based security primitives, such as physically unclonable functions and true random number generators, as well as techniques like logic obfuscation. The paper also explores a broad spectrum of security threats to eNVMs, including physical attacks such as side-channel attacks, fault injection, and probing, as well as logical threats like information leakage, denial-of-service, and thermal attacks. Finally, the paper presents a study of publication trends in the eNVM domain since the early 2000s, reflecting the rising momentum and research activity in this field.
Successive Convexification for Passively-Safe Spacecraft Rendezvous on Near Rectilinear Halo Orbit
2025-05-22
Purnanand Elango, Abraham P. Vinod, Kenji Kitamura, Behçet Açıkmeşe, Stefano Di Cairano, Avishai Weiss
We present an optimization-based approach for fuel-efficient spacecraft rendezvous to the Gateway, a space station that will be deployed on a near rectilinear halo orbit (NRHO) around the Moon. The approach: i) ensures passive safety and satisfies path constraints at all times, ii) meets the specifications for critical decision points along the trajectory, iii) accounts for uncertainties that are common in real-world operation, such as due to orbital insertion, actuation, and navigation measurement, via chance constraints and utilizes a stabilizing feedback controller to bound the effect of uncertainties. We leverage sequential convex programming (SCP) and isoperimetric reformulation of path constraints, including passive safety, to eliminate the risk of inter-sample constraint violations that is common in existing methods. We demonstrate the proposed approach on a realistic simulation of a rendezvous to the Gateway.
Big trajectory data hold great promise for human mobility analysis, but their utility is often constrained by the absence of critical traveler attributes, particularly sociodemographic information. While prior studies have explored predicting such attributes from mobility patterns, they often overlooked underlying cognitive mechanisms and exhibited low predictive accuracy. This study introduces SILIC, short for Sociodemographic Inference with LLM-guided Inverse Reinforcement Learning (IRL) and Cognitive Chain Reasoning (CCR), a theoretically grounded framework that leverages LLMs to infer sociodemographic attributes from observed mobility patterns by capturing latent behavioral intentions and reasoning through psychological constructs. Particularly, our approach explicitly follows the Theory of Planned Behavior (TPB), a foundational behavioral framework in transportation research, to model individuals' latent cognitive processes underlying travel decision-making. The LLMs further provide heuristic guidance to improve IRL reward function initialization and update by addressing its ill-posedness and optimization challenges arising from the vast and unstructured reward space. Evaluated in the 2017 Puget Sound Regional Council Household Travel Survey, our method substantially outperforms state-of-the-art baselines and shows great promise for enriching big trajectory data to support more behaviorally grounded applications in transportation planning and beyond.
Generative AI and Creativity: A Systematic Literature Review and Meta-Analysis
2025-05-22
Niklas Holzner, Sebastian Maier, Stefan Feuerriegel
Generative artificial intelligence (GenAI) is increasingly used to support a wide range of human tasks, yet empirical evidence on its effect on creativity remains scattered. Can GenAI generate ideas that are creative? To what extent can it support humans in generating ideas that are both creative and diverse? In this study, we conduct a meta-analysis to evaluate the effect of GenAI on the performance in creative tasks. For this, we first perform a systematic literature search, based on which we identify n = 28 relevant studies (m = 8214 participants) for inclusion in our meta-analysis. We then compute standardized effect sizes based on Hedges' g. We compare different outcomes: (i) how creative GenAI is; (ii) how creative humans augmented by GenAI are; and (iii) the diversity of ideas by humans augmented by GenAI. Our results show no significant difference in creative performance between GenAI and humans (g = -0.05), while humans collaborating with GenAI significantly outperform those working without assistance (g = 0.27). However, GenAI has a significant negative effect on the diversity of ideas for such collaborations between humans and GenAI (g = -0.86). We further analyze heterogeneity across different GenAI models (e.g., GPT-3.5, GPT-4), different tasks (e.g., creative writing, ideation, divergent thinking), and different participant populations (e.g., laypeople, business, academia). Overall, our results position GenAI as an augmentative tool that can support, rather than replace, human creativity-particularly in tasks benefiting from ideation support.
The Backup Program of the Dark Energy Spectroscopic Instrument's Milky Way Survey
2025-05-22
Arjun Dey, Sergey E. Koposov, Joan R. Najita, Andrew P. Cooper, B. T. Gänsicke, Adam D. Myers, A. Raichoor, Daniel J. Eisenstein, E. F. Schlafly, C. Allende Prieto, Leandro Beraldo e Silva, Ting S. Li, M. Valluri, Stéphanie Juneau, Mika Lambert, S. Li, Guillaume F. Thomas, Wenting Wang, Alexander H. Riley, N. Kizhuprakkat, J. Aguilar, S. Ahlen, S. Bailey, D. Bianchi, D. Brooks, T. Claybaugh, A. Cuceu, A. de la Macorra, J. Della Costa, Biprateep Dey, P. Doel, A. Font-Ribera, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, J. Guy, C. Hahn, K. Honscheid, M. Ishak, J. Jimenez, R. Kehoe, D. Kirkby, T. Kisner, A. Kremin, C. Lamman, M. Landriau, L. Le Guillou, M. E. Levi, M. Manera, P. Martini, A. Meisner, R. Miquel, J. Moustakas, S. Nadathur, N. Palanque-Delabrouille, W. J. Percival, F. Prada, I. Pérez-Ràfols, G. Rossi, E. Sanchez, M. Schubnell, J. Silber, D. Sprayberry, G. Tarlé, B. A. Weaver, R. H. Wechsler, R. Zhou, H. Zou
The Milky Way Backup Program (MWBP), a survey currently underway with the Dark Energy Spectroscopic Instrument (DESI) on the Nicholas U. Mayall 4-m Telescope, works at the margins of the DESI Main surveys to obtain spectra of millions of additional stars from the Gaia catalog. Efficiently utilizing twilight times (<18 deg) and poor weather conditions, the MWBP extends the range of stellar sources studied to both brighter magnitudes and lower Galactic latitude and declination than the stars studied in DESI's Main Milky Way Survey. While the MWBP prioritizes candidate giant stars selected from the Gaia catalog (using color and parallax criteria), it also includes an unbiased sample of bright stars (i.e., 11.2 < G < 16 mag) as well as fainter sources (to G < 19 mag). As of March 1, 2025, the survey had obtained spectra of ~7 million stars, approximately 1.2 million of which are included in the DESI Data Release 1. The full survey, when completed, will cover an area of more than 21,000 square degrees and include approximately 10 million Gaia sources, roughly equal to the number of stellar spectra obtained through the DESI Main Survey, while only utilizing <9% of all DESI observing time.
On the nature of the black hole information problem
2025-05-22
Thiago T. Bergamaschi
The aim of this work is to present the black hole information problem and discuss the assumptions and hypotheses necessary for its formulation. As the problem arises in the framework of semiclassical gravity, we first review the necessary notions to describe Lorentzian manifolds equipped with physical properties, as well as the physical concepts of the theory that describes the gravitational interaction as the curvature of spacetime, general relativity. From its classical perspective, we develop the formalism to study the dynamical aspects of black holes in spacetimes obeying suitable causality conditions. Equipped with conjectures that nature censors naked singularities and that black holes reach a stationary configuration after they form, the black hole uniqueness theorems allow us to review several relations for the geometrical quantities associated with them. Following considerations of the other fundamental interactions, which are described by quantum field theory, we review the arguments in the formalism of quantum field theory in curved spacetime that give rise to the effective particle creation effect, its approximately thermal character, and the concept of black hole evaporation. With a precise quantification of information in quantum mechanics and assuming that the condition for physically acceptable states is given by the Hadamard condition, we review the result that entanglement between causally complementary regions is an intrinsic feature of quantum field theory. As a consequence, we discuss how the formation and complete evaporation of black holes leads to information loss. Conscious that such a prediction follows if no deviations from the semiclassical picture occur at the Planck scale, we discuss alternatives to this nonunitary dynamical evolution and formulate the black hole information problem. Lastly, we analyze the assumptions and hypotheses that lead to the problem.
Tropical Attention: Neural Algorithmic Reasoning for Combinatorial Algorithms
2025-05-22
Baran Hashemi, Kurt Pasque, Chris Teska, Ruriko Yoshida
Dynamic programming (DP) algorithms for combinatorial optimization problems work with taking maximization, minimization, and classical addition in their recursion algorithms. The associated value functions correspond to convex polyhedra in the max plus semiring. Existing Neural Algorithmic Reasoning models, however, rely on softmax-normalized dot-product attention where the smooth exponential weighting blurs these sharp polyhedral structures and collapses when evaluated on out-of-distribution (OOD) settings. We introduce Tropical attention, a novel attention function that operates natively in the max-plus semiring of tropical geometry. We prove that Tropical attention can approximate tropical circuits of DP-type combinatorial algorithms. We then propose that using Tropical transformers enhances empirical OOD performance in both length generalization and value generalization, on algorithmic reasoning tasks, surpassing softmax baselines while remaining stable under adversarial attacks. We also present adversarial-attack generalization as a third axis for Neural Algorithmic Reasoning benchmarking. Our results demonstrate that Tropical attention restores the sharp, scale-invariant reasoning absent from softmax.
The Lyman-$α$ emitter bispectrum as a probe of reionization morphology
2025-05-22
Soumak Maitra, Girish Kulkarni, Shikhar Asthana, James S. Bolton, Martin G. Haehnelt, Laura Keating
Ly$\alpha$ emitters (LAEs) have now been discovered out to redshift $z=13$, and are valuable probes of the reionization history at redshifts beyond the reach of other currently available tracers. Most inferences of the neutral hydrogen fraction from LAE observations rely on one-point and two-point statistics like the luminosity function and the power spectrum. We present here an analysis of the bispectrum of high-redshift LAEs and demonstrate its sensitivity to the Epoch of Reionization. We use the Sherwood-Relics suite of cosmological hydrodynamical simulations post-processed with the GPU-based radiative transfer code ATON-HE to generate realistic LAE mock catalogues for a wide range of reionization models, varying the ionization history and the source populations, including contributions of AGN to hydrogen reionization. We demonstrate that the bispectrum provides greater sensitivity than the power spectrum to both the timing and spatial morphology of reionization. Using reduced-$\chi^2$ analysis we further show that the bispectrum also responds more strongly to variations in source population and AGN contribution, apparently more efficiently capturing morphological features missed by two-point statistics. The redshift evolution of the bispectrum reflects the increased clustering of ionizing sources at earlier epochs. The sensitivity of the bispectrum to peculiar velocities underscores the importance of velocity corrections in comparisons to observations. Our findings demonstrate that the LAE bispectrum is a powerful higher-order statistic for probing reionization through current and future LAE surveys using telescopes such as Subaru and JWST.
On the bound states of the Schwarzschild black hole
2025-05-22
Sebastian H. Völkel
Understanding the physical significance and spectral stability of black hole quasinormal modes is fundamental to high-precision spectroscopy with future gravitational wave detectors. Inspired by Mashhoon's idea of relating quasinormal modes of black holes with their equivalent bound states in an inverted potential, we investigate, for the first time, energy levels and eigenfunctions of the Schwarzschild black hole quantitatively. While quasinormal modes describe the characteristic damped oscillations of a black hole, the bound states of the inverted potential are qualitatively more similar to those of the hydrogen atom. Although the physical interpretation of these states may initially be of more academic interest, it furthers our understanding of open problems related to quasinormal modes in a similar spirit to Maggiore's interpretation of the Schwarzschild quasinormal mode spectrum. One surprising insight from the explicit calculation of bound states is that eigenfunctions corresponding to quasinormal mode overtones become rapidly delocalized and extremely loosely bound. This observation raises immediate questions about the common interpretation of quasinormal modes as excitations of the lightring region. Closely related, as a second application, we also explore the spectral stability of bound states and demonstrate that they can provide complementary insights into the quasinormal mode spectrum.
Caught in the act: detections of recoiling supermassive black holes from simulations
2025-05-22
Alexander Rawlings, Peter H. Johansson, Thorsten Naab, Antti Rantala, Jens Thomas, Bianca Neureiter
We study the detectability of supermassive black holes (SMBHs) with masses of $M_{\bullet}\gtrsim 10^{9}\,\mathrm{M}_\odot$ displaced by gravitational wave recoil kicks $(v_{\rm kick}=0\mathrm{-}2000\,\mathrm{km\,s}^{-1})$ in simulations of merging massive $(M_{\star}>10^{11}\,\mathrm{M}_\odot)$ early-type galaxies. The used KETJU code combines the GADGET-4 fast multiple gravity solver with accurate regularised integration and post-Newtonian corrections (up to PN3.5) around SMBHs. The ejected SMBHs carry clusters of bound stellar material (black hole recoil clusters, BRCs) with masses in the range of $10^6 \lesssim M_{\text{BRC}} \lesssim 10^7\,\mathrm{M}_\odot$ and sizes of several $10\,\mathrm{pc}$. For recoil velocities up to $60\%$ of the galaxy escape velocity, the BRCs are detectable in mock photometric images at a Euclid-like resolution up to redshift $z \sim 1.0$. By Monte Carlo sampling the observability for different recoil directions and magnitudes, we predict that in $\sim20\%$ of instances the BRCs are photometrically detectable, most likely for kicks with SMBH apocentres less than the galaxy effective radius. BRCs occupy distinct regions in the stellar mass/velocity dispersion vs. size relations of known star clusters and galaxies. An enhanced velocity dispersion in excess of $\sigma \sim 600\,\mathrm{km\,s}^{-1}$ coinciding with the SMBH position provides the best evidence for an SMBH-hosting stellar system, effectively distinguishing BRCs from other faint stellar systems. BRCs are promising candidates to observe the aftermath of the yet-undetected mergers of the most massive SMBHs and we estimate that up to 8000 BRCs might be observable below $z\lesssim 0.6$ with large-scale photometric surveys such as Euclid and upcoming high-resolution imaging and spectroscopy with the Extremely Large Telescope.
Probing the origins. II. Unravelling lithium depletion and stellar motion: Intrinsic stellar properties drive depletion, not kinematics
2025-05-22
M. L. L. Dantas, R. Smiljanic, D. Romano, G. Guiglion, L. Magrini, P. B. Tissera, R. S. de Souza
In Paper I, we classified a stellar sample from the thin disc with a broad range in metallicity as being churned outward or inward, or blurred/undisturbed. In this paper (Paper II), we delve deeper by analysing our entire metallicity-stratified sample along with their dynamic properties, focusing on the connection between radial migration and Li depletion. We analyse the chemo-dynamics of a set of 1188 thin disc dwarf stars observed by the \textit{Gaia}-ESO survey, previously classified into six metallicity-stratified groups via Hierarchical Clustering (HC). We examine several features, such as effective temperatures, masses, and dynamic properties. We also implement a parametric survival analysis using penalised splines (logistic distribution) to quantify how stellar properties and motion (or migration) direction jointly influence Li depletion patterns. We find that stars in our sample that appear to have churned outward are predominantly Li-depleted, regardless of their metallicities. These stars are also the oldest, coldest, and least massive compared to those in the same HC group that have either churned inward or kept their orbital radii. Our survival analysis confirms temperature as the primary driver of Li depletion, followed by metallicity and age, while migration direction shows negligible influence. The increasing proportion of outward-churned stars with higher metallicity (and older ages) indicates their dominant influence on the overall trend observed in the [Fe/H]-A(Li) space for stellar groups with [Fe/H]>0. The survival model reinforces that the observed Li depletion stems primarily from intrinsic stellar properties (cool temperatures, higher metallicity, old ages) rather than migration history. This suggests the metallicity-dependent depletion pattern emerges through stellar evolution rather than Galactic dynamical processes.
A Morphological Model to Separate Resolved--unresolved Sources in the DESI Legacy Surveys: Application in the LS4 Alert Stream
2025-05-22
Chang Liu, Adam A. Miller, Joshua S. Bloom, Robert A. Knop, Peter E. Nugent
Separating resolved and unresolved sources in large imaging surveys is a fundamental step to enable downstream science, such as searching for extragalactic transients in wide-field time-domain surveys. Here we present our method to effectively separate point sources from the resolved, extended sources in the Dark Energy Spectroscopic Instrument (DESI) Legacy Surveys (LS). We develop a supervised machine-learning model based on the Gradient Boosting algorithm \texttt{XGBoost}. The features input to the model are purely morphological and are derived from the tabulated LS data products. We train the model using $\sim$$2\times10^5$ LS sources in the COSMOS field with HST morphological labels and evaluate the model performance on LS sources with spectroscopic classification from the DESI Data Release 1 ($\sim$$2\times10^7$ objects) and the Sloan Digital Sky Survey Data Release 17 ($\sim$$3\times10^6$ objects), as well as on $\sim$$2\times10^8$ Gaia stars. A significant fraction of LS sources are not observed in every LS filter, and we therefore build a ``Hybrid'' model as a linear combination of two \texttt{XGBoost} models, each containing features combining aperture flux measurements from the ``blue'' ($gr$) and ``red'' ($iz$) filters. The Hybrid model shows a reasonable balance between sensitivity and robustness, and achieves higher accuracy and flexibility compared to the LS morphological typing. With the Hybrid model, we provide classification scores for $\sim$$3\times10^9$ LS sources, making this the largest ever machine-learning catalog separating resolved and unresolved sources. The catalog has been incorporated into the real-time pipeline of the La Silla Schmidt Southern Survey (LS4), enabling the identification of extragalactic transients within the LS4 alert stream.
A pedagogical approach to the black hole information problem
2025-05-22
Thiago T. Bergamaschi
The semiclassical depiction of gravity has provided many important results, such as particle creation effects in expanding universes and in spacetimes containing black holes. However, many questions remain open, most notably concerning the final product of the black hole evaporation process and the conservation of information. From a pedagogical perspective, we review the concepts that lead to this state of affairs, also known as the black hole information problem, and discuss the assumptions and hypotheses necessary for its formulation. Stating the necessary conjectures and results that lead to the geometrical properties of black holes, we present the notion of black hole evaporation and the result that entanglement between causally complementary regions is an intrinsic feature of quantum field theory under the Hadamard condition. We then show that the formation and complete evaporation of a black hole leads to information loss. Comparing such a result to the main alternatives to this non-unitary dynamical evolution, we formulate the black hole information problem and show that information loss is a genuine prediction if no deviations from the semiclassical picture occur at the Planck scale.
Evaluating the Performance of Nigerian Lecturers using Multilayer Perceptron
2025-05-22
I. E. Ezeibe, S. O. Okide, D. C. Asogwa
Evaluating the performance of a lecturer has been essential for enhancing teaching quality, improving student learning outcomes, and strengthening the institution's reputation. The absence of such a system brings about lecturer performance evaluation which was neither comprehensive nor holistic. This system was designed using a web-based platform, created a secure database, and by using a custom dataset, captured some performance metrics which included student evaluation scores, Research Publications, Years of Experience, and Administrative Duties. Multilayer Perceptron (MLP) algorithm was utilized due to its ability to process complex data patterns and generates accurate predictions in a lecturer's performance based on historical data. This research focused on designing multiple performance metrics beyond the standard ones, incorporating student participation, and integrating analytical tools to deliver a comprehensive and holistic evaluation of lecturers' performance and was developed using Object-Oriented Analysis and Design (OOAD) methodology. Lecturers' performance is evaluated by the model, and the evaluation accuracy is about 91% compared with actual performance. Finally, by evaluating the performance of the MLP model, it is concluded that MLP enhanced lecturer performance evaluation by providing accurate predictions, reducing bias, and supporting data-driven decisions, ultimately improving the fairness and efficiency of the evaluation process. The MLP model's performance was evaluated using Mean Squared Error (MSE) and Mean Absolute Error (MAE), achieved a test loss (MSE) of 256.99 and a MAE of 13.76, and reflected a high level of prediction accuracy. The model also demonstrated an estimated accuracy rate of approximately 96%, validated its effectiveness in predicting lecturer performance.
Fashion Industry in the Age of Generative Artificial Intelligence and Metaverse: A systematic Review
2025-05-22
Rania Ahmed, Eman Ahmed, Ahmed Elbarbary, Ashraf Darwish, Aboul Ella Hassanien
The fashion industry is an extremely profitable market that generates trillions of dollars in revenue by producing and distributing apparel, footwear, and accessories. This systematic literature review (SLR) seeks to systematically review and analyze the research landscape about the Generative Artificial Intelligence (GAI) and metaverse in the fashion industry. Thus, investigating the impact of integrating both technologies to enhance the fashion industry. This systematic review uses the Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology, including three essential phases: identification, evaluation, and reporting. In the identification phase, the target search problems are determined by selecting appropriate keywords and alternative synonyms. After that 578 documents from 2014 to the end of 2023 are retrieved. The evaluation phase applies three screening steps to assess papers and choose 118 eligible papers for full-text reading. Finally, the reporting phase thoroughly examines and synthesizes the 118 eligible papers to identify key themes associated with GAI and Metaverse in the fashion industry. Based on Strengths, Weaknesses, Opportunities, and Threats (SWOT) analyses performed for both GAI and metaverse for the fashion industry, it is concluded that the integration of GAI and the metaverse holds the capacity to profoundly revolutionize the fashion sector, presenting chances for improved manufacturing, design, sales, and client experiences. Accordingly, the research proposes a new framework to integrate GAI and metaverse to enhance the fashion industry. The framework presents different use cases to promote the fashion industry using the integration. Future research points for achieving a successful integration are demonstrated.
Selection of optically variable active galactic nuclei via a random forest algorithm
2025-05-21
Demetra De Cicco, Gaetano Zazzaro, Stefano Cavuoti, Maurizio Paolillo, Giuseppe Longo, Vincenzo Petrecca, Ivano Saccheo, Paula Sánchez-Sáez
Context. A defining characteristic of active galactic nuclei (AGN) that distinguishes them from other astronomical sources is their stochastic variability, which is observable across the entire electromagnetic spectrum. Upcoming optical wide-field surveys, such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time, are set to transform astronomy by delivering unprecedented volumes of data for time domain studies. This data influx will require the development of the expertise and methodologies necessary to manage and analyze it effectively. Aims. This project focuses on optimizing AGN selection through optical variability in wide-field surveys and aims to reduce the bias against obscured AGN. We tested a random forest (RF) algorithm trained on various feature sets to select AGN. The initial dataset consisted of 54 observations in the r-band and 25 in the g-band of the COSMOS field, captured with the VLT Survey Telescope over a 3.3-year baseline. Methods. Our analysis relies on feature sets derived separately from either band plus a set of features combining data from both bands, mostly characterizing AGN on the basis of their variability properties and obtained from their light curves. We trained multiple RF classifiers using different subsets of selected features and assessed their performance via targeted metrics. Results. Our tests provide valuable insights into the use of multiband and multivisit data for AGN identification. We compared our findings with previous studies and dedicated part of the analysis to potential enhancements in selecting obscured AGN. The expertise gained and the methodologies developed here are readily applicable to datasets from other ground- and space-based missions.
Structure from Motion (SfM) refers to the problem of recovering both structure (i.e., 3D coordinates of points in the scene) and motion (i.e., camera matrices) starting from point correspondences in multiple images. It has attracted significant attention over the years, counting practical reconstruction pipelines as well as theoretical results. This paper is conceived as a conceptual review of SfM methods, which are grouped into three main categories, according to which part of the problem - between motion and structure - they focus on. The proposed taxonomy brings a new perspective on existing SfM approaches as well as insights into open problems and possible future research directions. Particular emphasis is given on identifying the theoretical conditions that make SfM well posed, which depend on the problem formulation that is being considered.
2025-05-21
Carlos Rodriguez-Pardo, Leonardo Chiani, Emanuele Borgonovo, Massimo Tavoni
We present a neural framework for learning conditional optimal transport (OT) maps between probability distributions. Our approach introduces a conditioning mechanism capable of processing both categorical and continuous conditioning variables simultaneously. At the core of our method lies a hypernetwork that generates transport layer parameters based on these inputs, creating adaptive mappings that outperform simpler conditioning methods. Comprehensive ablation studies demonstrate the superior performance of our method over baseline configurations. Furthermore, we showcase an application to global sensitivity analysis, offering high performance in computing OT-based sensitivity indices. This work advances the state-of-the-art in conditional optimal transport, enabling broader application of optimal transport principles to complex, high-dimensional domains such as generative modeling and black-box model explainability.
Diffusion models have emerged as powerful generative tools across various domains, yet tailoring pre-trained models to exhibit specific desirable properties remains challenging. While reinforcement learning (RL) offers a promising solution,current methods struggle to simultaneously achieve stable, efficient fine-tuning and support non-differentiable rewards. Furthermore, their reliance on sparse rewards provides inadequate supervision during intermediate steps, often resulting in suboptimal generation quality. To address these limitations, dense and differentiable signals are required throughout the diffusion process. Hence, we propose VAlue-based Reinforced Diffusion (VARD): a novel approach that first learns a value function predicting expection of rewards from intermediate states, and subsequently uses this value function with KL regularization to provide dense supervision throughout the generation process. Our method maintains proximity to the pretrained model while enabling effective and stable training via backpropagation. Experimental results demonstrate that our approach facilitates better trajectory guidance, improves training efficiency and extends the applicability of RL to diffusion models optimized for complex, non-differentiable reward functions.
Current constraints on the minimally extended varying speed of light model through the cosmic distance duality relation
2025-05-21
Jaiane Santos, Carlos Bengaly, Rodrigo S. Gonçalves
One of the most crucial tests of the standard cosmological model consists on testing possible variations on fundamental physical constants. In frameworks such as the minimally extended varying speed of light model (meVSL), the relationship between the luminosity distance ($D_{\text{L}}$) and the angular diameter distance ($D_{\text{A}}$), namely the cosmic distance duality relation (CDDR), is expected to deviate from $\eta(z) \equiv D_{\text{L}}(z)/D_{\text{A}}(z) = (1+z)^{2}$, making it a powerful probe of a potential variation of such a fundamental constant. Hence, we test the viability of the meVSL model through the CDDR by comparing $D_{\text{A}}$ measurements, provided by the transverse (2D) and anisotropic (3D) baryon acoustic oscillations (BAO) observations from different surveys, like SDSS, DES and DESI, in combination with $D_{\text{L}}$ measurements from Pantheon+ type Ia Supernova (SNe) compilation. The Gaussian Process reconstruction is employed on the SN data to match $D_{\text{A}}$ with $D_{\text{L}}$ at the same redshift. We find no deviation of the standard CDDR relation within 1-2$\sigma$ confidence level when considering SNe with 2D and 3D BAO samples combined together, as well as when considering SNe with 3D BAO only. However, when SNe and 2D BAO only are considered, the standard CDDR is only recovered at $\sim 4\sigma$ confidence level. However, such a result might be due to some recently discussed tensions between SN and BAO datasets, especially at low redshifts, in addition to possible inconsistencies between the BAO datasets individually. Therefore, our results show no significant evidence in favour of the meVSL model, once these potential systematics are taken into account.
We propose a novel framework for integrating fragmented multi-modal data in Alzheimer's disease (AD) research using large language models (LLMs) and knowledge graphs. While traditional multimodal analysis requires matched patient IDs across datasets, our approach demonstrates population-level integration of MRI, gene expression, biomarkers, EEG, and clinical indicators from independent cohorts. Statistical analysis identified significant features in each modality, which were connected as nodes in a knowledge graph. LLMs then analyzed the graph to extract potential correlations and generate hypotheses in natural language. This approach revealed several novel relationships, including a potential pathway linking metabolic risk factors to tau protein abnormalities via neuroinflammation (r>0.6, p<0.001), and unexpected correlations between frontal EEG channels and specific gene expression profiles (r=0.42-0.58, p<0.01). Cross-validation with independent datasets confirmed the robustness of major findings, with consistent effect sizes across cohorts (variance <15%). The reproducibility of these findings was further supported by expert review (Cohen's k=0.82) and computational validation. Our framework enables cross modal integration at a conceptual level without requiring patient ID matching, offering new possibilities for understanding AD pathology through fragmented data reuse and generating testable hypotheses for future research.
Evolutionary Computation and Large Language Models: A Survey of Methods, Synergies, and Applications
2025-05-21
Dikshit Chauhan, Bapi Dutta, Indu Bala, Niki van Stein, Thomas Bäck, Anupam Yadav
Integrating Large Language Models (LLMs) and Evolutionary Computation (EC) represents a promising avenue for advancing artificial intelligence by combining powerful natural language understanding with optimization and search capabilities. This manuscript explores the synergistic potential of LLMs and EC, reviewing their intersections, complementary strengths, and emerging applications. We identify key opportunities where EC can enhance LLM training, fine-tuning, prompt engineering, and architecture search, while LLMs can, in turn, aid in automating the design, analysis, and interpretation of ECs. The manuscript explores the synergistic integration of EC and LLMs, highlighting their bidirectional contributions to advancing artificial intelligence. It first examines how EC techniques enhance LLMs by optimizing key components such as prompt engineering, hyperparameter tuning, and architecture search, demonstrating how evolutionary methods automate and refine these processes. Secondly, the survey investigates how LLMs improve EC by automating metaheuristic design, tuning evolutionary algorithms, and generating adaptive heuristics, thereby increasing efficiency and scalability. Emerging co-evolutionary frameworks are discussed, showcasing applications across diverse fields while acknowledging challenges like computational costs, interpretability, and algorithmic convergence. The survey concludes by identifying open research questions and advocating for hybrid approaches that combine the strengths of EC and LLMs.
Utilizing Bayesian methods in clinical trials has become increasingly popular, as they can incorporate historical data and expert opinions into the design and allow for smaller sample sizes to reduce costs while providing reliable and robust statistical results. Sample size determination (SSD) is a key aspect of clinical trial design and various methods for Bayesian sample size determination are available. However, it is unclear how these methods are being used in practice. A systematic literature review was conducted to understand how sample sizes for Bayesian randomized clinical trials (RCTs) are determined and inform the design of future Bayesian trials. We searched five databases in May 2023, and updated in January 2025, including efficacy RCTs in humans which utilized a Bayesian framework for the primary data analysis, published in English, and enrolled participants between 2009 and 2024. The literature search produced 19,182 records, of which 105 studies were selected for data extraction. Results show that the most common method for SSD in Bayesian RCTs was a hybrid approach in which elements of Bayesian and frequentist theory are combined. Many RCTs did not provide a justification for SSD, while fully Bayesian methods were rarely used in practice, despite significant theoretical development. Our review also revealed a lack of standardized reporting, making it challenging to review the SSD. The CONSORT statement for reporting RCTs states that sample size calculations must be reported, which was poorly adhered to. Among RCTs that reported SSD, relevant information was frequently omitted from the reports and discussed in poorly structured supplementary materials. Thus, there is a critical need for greater transparency, standardization and translation of relevant methodology in Bayesian RCTs.
Privacy-Preserving Conformal Prediction Under Local Differential Privacy
2025-05-21
Coby Penso, Bar Mahpud, Jacob Goldberger, Or Sheffet
Conformal prediction (CP) provides sets of candidate classes with a guaranteed probability of containing the true class. However, it typically relies on a calibration set with clean labels. We address privacy-sensitive scenarios where the aggregator is untrusted and can only access a perturbed version of the true labels. We propose two complementary approaches under local differential privacy (LDP). In the first approach, users do not access the model but instead provide their input features and a perturbed label using a k-ary randomized response. In the second approach, which enforces stricter privacy constraints, users add noise to their conformity score by binary search response. This method requires access to the classification model but preserves both data and label privacy. Both approaches compute the conformal threshold directly from noisy data without accessing the true labels. We prove finite-sample coverage guarantees and demonstrate robust coverage even under severe randomization. This approach unifies strong local privacy with predictive uncertainty control, making it well-suited for sensitive applications such as medical imaging or large language model queries, regardless of whether users can (or are willing to) compute their own scores.
Two-dimensional (2D) massless Dirac fermions (MDF), which represent a type of quasi-particles with linear energy-momentum dispersions only in 2D momentum space, provide a fertile ground for realizing novel quantum phenomena. However, 2D MDF were seldom observed in the superconducting bulk states of 3D materials. Furthermore, as a cornerstone for accurately tuning the quantum phenomena based on 2D MDF, a quantitative relationship between 2D MDF and a structural parameter has rarely been revealed so far. Here, we report magneto-infrared spectroscopy studies of the iron-arsenide-superconductor systems NaFeAs and $A\mathrm{Fe_2As_2} (A = \mathrm{Ca, Ba})$ at temperature $T \sim 4.2 $ K and at magnetic fields ($B$) up to 17.5 T. Our results demonstrate the existence of 2D MDF in the superconducting bulk state of NaFeAs. Moreover, the 2D-MDF Fermi velocities in NaFeAs and $A\mathrm{Fe_2As_2} (A = \mathrm{Ca, Ba})$, which are extracted from the slopes of the linear $\sqrt{B}$ dependences of the Landau-level transition energies, scale linearly with the Fe-As bond lengths. The linear scaling between the 2D-MDF Fermi velocities and the Fe-As bond lengths is supported by (i) the linear relationship between the square root of the effective mass of the $d_{xy}$ electrons and the Fe-As bond length and (ii) the linear dependence of the square root of the calculated tight-binding hopping energy on the Fe-As bond length. Our results open up new avenues for exploring and tuning novel quantum phenomena based on 2D MDF in the superconducting bulk states of 3D materials.
Quantum dots (QDs) have emerged as promising nanomaterials with unique optical and physical properties, making them highly attractive for various applications in biomedicine. This review provides a comprehensive overview of the types, modes of synthesis, characterization, applications, and recent advances of QDs in the field of biomedicine, with a primary focus on bioimaging, drug delivery, and biosensors. The unique properties of QDs, such as tunable emission spectra, long-term photostability, high quantum yield, and targeted drug delivery, hold tremendous promise for advancing diagnostics, therapeutics, and imaging techniques in biomedical research. However, several significant hurdles remain before their full potential in the biomedical field, like bioaccumulation, toxicity, and short-term stability. Addressing these hurdles is essential to effectively incorporate QDs into clinical use and enhance their influence on healthcare outcomes. Furthermore, the review conducts a critical analysis of potential QD toxicity and explores recent progress in strategies and methods to mitigate these adverse effects, such as surface modification, surface coatings, and encapsulation. By thoroughly examining current research and recent advancements, this comprehensive review offers invaluable insights into both the future possibilities and the challenges that lie ahead in fully harnessing the potential of QDs in the field of biomedicine, promising a revolution in the landscape of medical diagnostics, therapies, and imaging technologies.
Stellar population modelling of neutron stars and black holes: spatially-resolved graveyards in MaNGA/SDSS-IV galaxies
2025-05-21
Claudia Maraston, Marco Limongi, Justus Neumann, Lorenzo Roberti, Alessandro Chieffi, Daniel Thomas, Jianhui Lian
We update our stellar population models for the time evolution of the number and mass of massive remnants - neutron stars and black holes - with a new initial mass-remnant mass relation for core collapse supernovae. The calculations are based on hydrodynamical simulations and induced explosions of a subset of previously published pre-supernovae models spanning a wide range of stellar mass, metallicity and different values for rotation velocity. The resulting stellar population models predict lower numbers of neutron stars (by up to 0.3 dex) and higher numbers of black holes (by up to 0.8 dex), especially when stellar rotation is considered. The mass fraction locked in neutron stars and black holes is lowest in high-metallicity populations, with the largest number of remnants found at about half-solar metallicity. This mirrors the amount of available gas, ranging from 35 per cent to 45 per cent. We then apply our new models to IFU spectra for ~10,000 galaxies from the SDSS-IV/MaNGA survey for which we previously published spatially-resolved star formation histories. This allows us to probe spatially-resolved graveyards in galaxies of different types. The number and radial distribution of remnants depend on a galaxy's mass, star formation history and metal content. More massive and hence more metal-rich galaxies are found to host fewer remnants. Radial gradients in the number of remnants depend on galaxy mass mostly because of the mass-dependent profiles in mass density: the gradients are flat in low-mass galaxies, and negative in high-mass galaxies, particularly in Milky Way analogues.
Exploring future synergies for large-scale structure between gravitational waves and radio sources
2025-05-21
Stefano Zazzera, José Fonseca, Tessa Baker, Chris Clarkson
Future third-generation gravitational wave detectors like the Einstein Telescope (ET) and Cosmic Explorer (CE) are expected to detect millions of binary black hole (BBH) mergers. Alongside these advances, upcoming radio surveys, such as the Square Kilometer Array Observatory (SKAO) will provide new sets of cosmological tracers. These include mapping the large-scale distribution of neutral hydrogen (HI) using intensity mapping (IM), HI galaxies and radio continuum galaxies. In this work, we will investigate synergies between gravitational waves (GW) and radio tracers through a multi-tracer approach. We first forecast the precision on the clustering bias of GWs by cross-correlating data from an ET-like detector with an SKAO IM survey. Our results indicate that this approach can constrain the GW clustering bias to within $2\%$ up to $z = 2.5$. Additionally, we explore the potential of a triple cross-correlation using GWs, IM, and photometric galaxies from a survey like LSST. This multi-tracer method enhances constraints on the magnification lensing effect, achieving percent-level precision, and allows for a measurement of the Doppler effect with approximately $15\%$ uncertainty. Furthermore we show for the first time that this method could achieve the precision required to measure subdominant gravitational potential contributions to the relativistic corrections, which had thought to be below cosmic variance. Our analysis highlights the potential of cross-correlations between GWs and radio tracers to improve constraints on astrophysical properties of BBHs, measure relativistic effects, and perform null tests of GR in cosmological scales.
The increasing prevalence of mental health disorders globally highlights the urgent need for effective digital screening methods that can be used in multilingual contexts. Most existing studies, however, focus on English data, overlooking critical mental health signals that may be present in non-English texts. To address this important gap, we present the first survey on the detection of mental health disorders using multilingual social media data. We investigate the cultural nuances that influence online language patterns and self-disclosure behaviors, and how these factors can impact the performance of NLP tools. Additionally, we provide a comprehensive list of multilingual data collections that can be used for developing NLP models for mental health screening. Our findings can inform the design of effective multilingual mental health screening tools that can meet the needs of diverse populations, ultimately improving mental health outcomes on a global scale.
Bias in Large Language Models (LLMs) significantly undermines their reliability and fairness. We focus on a common form of bias: when two reference concepts in the model's concept space, such as sentiment polarities (e.g., "positive" and "negative"), are asymmetrically correlated with a third, target concept, such as a reviewing aspect, the model exhibits unintended bias. For instance, the understanding of "food" should not skew toward any particular sentiment. Existing bias evaluation methods assess behavioral differences of LLMs by constructing labeled data for different social groups and measuring model responses across them, a process that requires substantial human effort and captures only a limited set of social concepts. To overcome these limitations, we propose BiasLens, a test-set-free bias analysis framework based on the structure of the model's vector space. BiasLens combines Concept Activation Vectors (CAVs) with Sparse Autoencoders (SAEs) to extract interpretable concept representations, and quantifies bias by measuring the variation in representational similarity between the target concept and each of the reference concepts. Even without labeled data, BiasLens shows strong agreement with traditional bias evaluation metrics (Spearman correlation r > 0.85). Moreover, BiasLens reveals forms of bias that are difficult to detect using existing methods. For example, in simulated clinical scenarios, a patient's insurance status can cause the LLM to produce biased diagnostic assessments. Overall, BiasLens offers a scalable, interpretable, and efficient paradigm for bias discovery, paving the way for improving fairness and transparency in LLMs.
The PAU Survey: Measuring intrinsic galaxy alignments in deep wide fields as a function of colour, luminosity, stellar mass and redshift
2025-05-21
D. Navarro-Gironés, M. Crocce, E. Gaztañaga, A. Wittje, M. Siudek, H. Hoekstra, H. Hildebrandt, B. Joachimi, R. Paviot, C. M. Baugh, J. Carretero, R. Casas, F. J. Castander, M. Eriksen, E. Fernandez, P. Fosalba, J. García-Bellido, R. Miquel, C. Padilla, P. Renard, E. Sánchez, S. Serrano, I. Sevilla-Noarbe, P. Tallada-Crespí
We present the measurements and constraints of intrinsic alignments (IA) in the Physics of the Accelerating Universe Survey (PAUS) deep wide fields, which include the W1 and W3 fields from the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) and the G09 field from the Kilo-Degree Survey (KiDS). Our analyses cover 51deg$^{2}$, in the photometric redshift (photo-$z$) range $0.1 < z_{\mathrm{b}} < 1$ and a magnitude limit $i_{\mathrm{AB}}<22$. The precise photo-$z$s and the luminosity coverage of PAUS enable robust IA measurements, which are key for setting informative priors for upcoming stage-IV surveys. For red galaxies, we detect an increase in IA amplitude with both luminosity and stellar mass, extending previous results towards fainter and less massive regimes. As a function of redshift, we observe strong IA signals at intermediate ($z_{\mathrm{b}}\sim0.55$) and high ($z_{\mathrm{b}}\sim0.75$) redshift bins. However, we find no significant trend of IA evolution with redshift after accounting for the varying luminosities across redshift bins, consistent with the literature. For blue galaxies, no significant IA signal is detected, with $A_{1}=0.68_{-0.51}^{+0.53}$ when splitting only by galaxy colour, yielding some of the tightest constraints to date for the blue population and constraining a regime of very faint and low-mass galaxies.
Exploring short-term stellar activity in M dwarfs: A volume-limited perspective
2025-05-21
G. Galletta, S. Colombo, L. Prisinzano, G. Micela
Flares are short-lived but energetic manifestations of stellar activity. Studying them is crucial, as they emit intense high-energy radiation that can impact the circumstellar environment, especially the atmospheres of orbiting planets. This is particularly relevant for M dwarfs, which frequently flare and often host planets within their habitable zones. Flare-driven photoevaporation and photochemical processes may significantly affect planetary evolution. In this work, we analyzed the flaring properties of a volume-limited, unbiased sample of nearby M dwarfs using data from the Transiting Exoplanet Survey Satellite (TESS). We selected stars within 10 pc from Gaia DR3 and used an iterative Gaussian process to remove long-term stellar variability from the light curves, isolating impulsive flare events. For each flare, we measured amplitude, duration, and total emitted energy. Our sample includes 173 stars and 17,229 detected flares, ranging from 0 to 76 flares per TESS sector. We focused on three representative stars to highlight the diversity in flare activity. Detected flares had energies above 10^29 erg and durations from 2 to 8000 seconds. We modeled cumulative energy distributions with one- and two-slope power-law fits, finding average slopes of -0.79 +/- 0.64 and -1.23 +/- 1.32, respectively. We introduced the Flare Energy Index (GF.01) to describe flare frequency, identifying two populations: fainter stars tend to produce fewer high-energy flares, while brighter stars exhibit more frequent low-energy flares. Finally, we investigated two highly active stars, G 227-22 and G 258-33, observed across many sectors, to study long-term flare behavior and energy trends.
NeoN: A Tool for Automated Detection, Linguistic and LLM-Driven Analysis of Neologisms in Polish
2025-05-21
Aleksandra Tomaszewska, Dariusz Czerski, Bartosz Żuk, Maciej Ogrodniczuk
NeoN, a tool for detecting and analyzing Polish neologisms. Unlike traditional dictionary-based methods requiring extensive manual review, NeoN combines reference corpora, Polish-specific linguistic filters, an LLM-driven precision-boosting filter, and daily RSS monitoring in a multi-layered pipeline. The system uses context-aware lemmatization, frequency analysis, and orthographic normalization to extract candidate neologisms while consolidating inflectional variants. Researchers can verify candidates through an intuitive interface with visualizations and filtering controls. An integrated LLM module automatically generates definitions and categorizes neologisms by domain and sentiment. Evaluations show NeoN maintains high accuracy while significantly reducing manual effort, providing an accessible solution for tracking lexical innovation in Polish.
Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches
2025-05-21
Nudrat Habib, Tosin Adewumi, Marcus Liwicki, Elisa Barney
Authorship analysis plays an important role in diverse domains, including forensic linguistics, academia, cybersecurity, and digital content authentication. This paper presents a systematic literature review on two key sub-tasks of authorship analysis; Author Attribution and Author Verification. The review explores SOTA methodologies, ranging from traditional ML approaches to DL models and LLMs, highlighting their evolution, strengths, and limitations, based on studies conducted from 2015 to 2024. Key contributions include a comprehensive analysis of methods, techniques, their corresponding feature extraction techniques, datasets used, and emerging challenges in authorship analysis. The study highlights critical research gaps, particularly in low-resource language processing, multilingual adaptation, cross-domain generalization, and AI-generated text detection. This review aims to help researchers by giving an overview of the latest trends and challenges in authorship analysis. It also points out possible areas for future study. The goal is to support the development of better, more reliable, and accurate authorship analysis system in diverse textual domain.
A two-stage model for factors influencing citation counts
2025-05-21
Pablo Dorta-González, Emilio Gómez-Déniz
This work aims to study a count response random variable, the number of citations of a research paper, affected by some explanatory variables through a suitable regression model. Due to the fact that the count variable exhibits substantial variation since the sample variance is larger than the sample mean, the classical Poisson regression model seems not to be appropriate. We concentrate attention on the negative binomial regression model, which allows the variance of each measurement to be a function of its predicted value. Nevertheless, the process of citations of papers may be divided into two parts. In the first stage, the paper has no citations, and the second part provides the intensity of the citations. A hurdle model for separating the documents with citations and those without citations is considered. The dataset for the empirical application consisted of 43,190 research papers in the field of Economics and Business from 2014-2021, obtained from The Lens database. Citation counts and social attention scores for each article were gathered from Altmetric database. The main findings indicate that both collaboration and funding have a positive impact on citation counts and reduce the likelihood of receiving zero citations. Higher journal impact factors lead to higher citation counts, while lower peer review ratings lead to fewer citations and a higher probability of zero citations. Mentions in news, blogs, and social media have varying but generally limited effects on citation counts. Open access via repositories (green OA) correlates with higher citation counts and a lower probability of zero citations. In contrast, OA via the publisher's website without an explicit open license (bronze OA) is associated with higher citation counts but also with a higher probability of zero citations.
Phasebook: A Survey of Selected Open Problems in Phase Retrieval
2025-05-21
Marc Allain, Selin Aslan, Wim Coene, Sjoerd Dirksen, Jonathan Dong, Julien Flamant, Mark Iwen, Felix Krahmer, Tristan van Leeuwen, Oleh Melnyk, Andreas Menzel, Allard P. Mosk, Viktor Nikitin, Gerlind Plonka, Palina Salanevich, Matthias Wellershoff
Phase retrieval is an inverse problem that, on one hand, is crucial in many applications across imaging and physics, and, on the other hand, leads to deep research questions in theoretical signal processing and applied harmonic analysis. This survey paper is an outcome of the recent workshop Phase Retrieval in Mathematics and Applications (PRiMA) (held on August 5--9 2024 at the Lorentz Center in Leiden, The Netherlands) that brought together experts working on theoretical and practical aspects of the phase retrieval problem with the purpose to formulate and explore essential open problems in the field.
Fiber Nonlinearity Mitigation in Coherent Optical Systems
2025-05-21
Stella Civelli, Dario Cellini, Enrico Forestieri, Marco Secondini
Fiber nonlinearity represents a critical challenge to the capacity enhancement of modern optical communication systems. In recent years, significant research efforts have focused on mitigating its impact through two complementary approaches. On the one hand, researchers have investigated practical digital signal processing (DSP) techniques to mitigate or compensate for nonlinear impairments, such as reversing fiber propagation effects through digital backpropagation (DBP). However, the high computational complexity of these techniques often discourages their practical implementation. On the other hand, information-theoretic studies have sought to establish the capacity limits of the nonlinear optical fiber channel, providing a framework for evaluating the ultimate performance of existing optical networks and guiding the design of next-generation systems. This work reviews recent advances and proposes future directions for nonlinearity compensation and mitigation, including constellation shaping techniques and low-complexity DBP. Furthermore, it highlights the potential of these innovations both in advancing the theoretical understanding of fiber capacity limits and in enabling practical DSP implementations.
Deploying machine learning models in resource-constrained environments, such as edge devices or rapid prototyping scenarios, increasingly demands distillation of large datasets into significantly smaller yet informative synthetic datasets. Current dataset distillation techniques, particularly Trajectory Matching methods, optimize synthetic data so that the model's training trajectory on synthetic samples mirrors that on real data. While demonstrating efficacy on medium-scale synthetic datasets, these methods fail to adequately preserve semantic richness under extreme sample scarcity. To address this limitation, we propose a novel dataset distillation method integrating contrastive learning during image synthesis. By explicitly maximizing instance-level feature discrimination, our approach produces more informative and diverse synthetic samples, even when dataset sizes are significantly constrained. Experimental results demonstrate that incorporating contrastive learning substantially enhances the performance of models trained on very small-scale synthetic datasets. This integration not only guides more effective feature representation but also significantly improves the visual fidelity of the synthesized images. Experimental results demonstrate that our method achieves notable performance improvements over existing distillation techniques, especially in scenarios with extremely limited synthetic data.
Rare-Earth Nitrides: Fundamental Advances and Applications in Cryogenic Electronics
2025-05-21
W. F. Holmes-Hewett, J. D. Miller, H. G. Ahmad, S. Granville, B. J. Ruck
Driven by the pursuit of high-performance electronic devices, research into novel materials with properties appropriate for cryogenic applications has unveiled the exceptional properties of the rare-earth nitride series of intrinsic ferromagnetic semiconductors. Here we report on the field focusing on developments, since the most recent comprehensive review [1], which enable applications in cryogenic electronic devices.
MINDS. Water reservoirs of compact planet-forming dust disk: A diversity of H$_2$O distributions
2025-05-21
Milou Temmink, Andrew D. Sellek, Danny Gasman, Ewine F. van Dishoeck, Marissa Vlasblom, Angèl Pranger, Manuel Güdel, Thomas Henning, Pierre-Olivier Lagage, Alessio Caratti O Garatti, Inga Kamp, Göran Olofsson, Aditya M. Arabhavi, Sierra L. Grant, Till Kaeufer, Nicolas T. Kurtovic, Giulia Perotti, Matthias Samland, Kamber Schwarz, Benoît Tabone
Millimetre-compact dust disks are thought to have efficient radial drift of icy dust pebbles, which has been hypothesised to produce an enhanced cold ($T<$400 K) H$_2$O reservoir in their inner disks. Mid-infrared spectral surveys, now with the James Webb Space Telescope (JWST), pave the way to explore this hypothesis. In this work, we test this theory for 8 compact disks ($R_\mathrm{dust}<$60 au) with JWST-MIRI/MRS observations. We analyse the different reservoirs that can be probed with the pure rotational lines ($>$10 $\mathrm{\mu}$m) through parametric column density profiles, multiple component slab models, and line flux ratios. We find that not all compact disks show strong enhancements of the cold H$_2$O reservoir, instead we propose three different classes of inner disk H$_2$O distributions. Four of our disks appear to have similar H$_2$O distributions as many of the large and structured disks (Type N or ``Normal'' disks), as is indicated by the slab model fitting and the line flux ratios. These disks have a small cold reservoir, suggesting the inward drift of dust, but it is not as efficient as hypothesised before. Only two disks do show a strong enhancement of the cold H$_2$O emission (Type E or cold H$_2$O enhanced disks), agreeing with the original hypothesis. The two remaining disks are found to be very H$_2$O-poor (Type P or H$_2$O-poor disks), yet show emission from either the hot or immediate reservoirs (depending on the fit) in addition to emission from the cold one. We find that different parametrisations are able to provide a good description of the observed H$_2$O spectra, with the multiple component analysis yielding similar results. Finally, we also report the detection of other molecules in these disks, including a tentative detection of CH$_4$ in CY Tau.
Continuous Representation Methods, Theories, and Applications: An Overview and Perspectives
2025-05-21
Yisi Luo, Xile Zhao, Deyu Meng
Recently, continuous representation methods emerge as novel paradigms that characterize the intrinsic structures of real-world data through function representations that map positional coordinates to their corresponding values in the continuous space. As compared with the traditional discrete framework, the continuous framework demonstrates inherent superiority for data representation and reconstruction (e.g., image restoration, novel view synthesis, and waveform inversion) by offering inherent advantages including resolution flexibility, cross-modal adaptability, inherent smoothness, and parameter efficiency. In this review, we systematically examine recent advancements in continuous representation frameworks, focusing on three aspects: (i) Continuous representation method designs such as basis function representation, statistical modeling, tensor function decomposition, and implicit neural representation; (ii) Theoretical foundations of continuous representations such as approximation error analysis, convergence property, and implicit regularization; (iii) Real-world applications of continuous representations derived from computer vision, graphics, bioinformatics, and remote sensing. Furthermore, we outline future directions and perspectives to inspire exploration and deepen insights to facilitate continuous representation methods, theories, and applications. All referenced works are summarized in our open-source repository: https://github.com/YisiLuo/Continuous-Representation-Zoo
Deliberation on Priors: Trustworthy Reasoning of Large Language Models on Knowledge Graphs
2025-05-21
Jie Ma, Ning Qu, Zhitao Gao, Rui Xing, Jun Liu, Hongbin Pei, Jiang Xie, Linyun Song, Pinghui Wang, Jing Tao, Zhou Su
Knowledge graph-based retrieval-augmented generation seeks to mitigate hallucinations in Large Language Models (LLMs) caused by insufficient or outdated knowledge. However, existing methods often fail to fully exploit the prior knowledge embedded in knowledge graphs (KGs), particularly their structural information and explicit or implicit constraints. The former can enhance the faithfulness of LLMs' reasoning, while the latter can improve the reliability of response generation. Motivated by these, we propose a trustworthy reasoning framework, termed Deliberation over Priors (DP), which sufficiently utilizes the priors contained in KGs. Specifically, DP adopts a progressive knowledge distillation strategy that integrates structural priors into LLMs through a combination of supervised fine-tuning and Kahneman-Tversky optimization, thereby improving the faithfulness of relation path generation. Furthermore, our framework employs a reasoning-introspection strategy, which guides LLMs to perform refined reasoning verification based on extracted constraint priors, ensuring the reliability of response generation. Extensive experiments on three benchmark datasets demonstrate that DP achieves new state-of-the-art performance, especially a Hit@1 improvement of 13% on the ComplexWebQuestions dataset, and generates highly trustworthy responses. We also conduct various analyses to verify its flexibility and practicality. The code is available at https://github.com/reml-group/Deliberation-on-Priors.
Bridging Two Dimensions: Luminescent Sensors at the Intersection of Temperature and Pressure
2025-05-21
Lukasz Marciniak, Maja Szymczak, Przemyslaw Wozny, Marcin Runowski
Luminescence thermometry and manometry are exponentially growing areas dealing with the optical detection of temperature and pressure, respectively, being appealing alternatives for conventional thermometers and manometers. The main benefit of luminescent thermometers and manometers is a possibility of remote temperature and/or pressure monitoring, in contrast to conventional gauges. Moreover, the use of luminescent nanoparticles as temperature/pressure sensors allow detection in micron- and nano-sized areas, previously inaccessible for conventional gauges. Therefore, the combination of both functionalities in a single material is highly appealing, as has been shown in a growing number of reports in the last years. Moreover, the bifunctional pressure and temperature sensors, operating with multiple independent spectroscopic parameters, allow simultaneous and distinct pressure and temperature readouts. However, the development of such truly bifunctional and reliable sensors is very challenging and rarely reported. This review summarizes the current status in the field, focusing on the sensing strategy and the selection of optically active sensor materials appropriate for a given application, including their sensitivity, spectral range of interest and pressure/temperature (in)dependence.
Equilibrium models to analyse the impact of different coordination schemes between TSO and DSOs on market power in sequentially-cleared energy and ancillary services markets under load and renewable generation uncertainty
2025-05-21
Giovanni Micheli, Maria Teresa Vespucci, Gianluigi Migliavacca, Dario Siface
The current massive installation of distributed resources in electricity distribution systems is transforming these systems into active dispatching subjects. At the same time, the need to compensate for the intermittent generation of an increasing amount of renewable sources creates the need to acquire more ancillary services. Flexible resources in the distribution system could provide these services not only within the perimeter of the distribution network to which they are connected but also for the benefit of the transmission system. However, this requires Transmission System Operators (TSOs) and Distribution System Operators (DSOs) to coordinate their dispatching actions effectively. One critical aspect of this coordination is establishing a market architecture that limits market power. This paper presents an innovative game-theoretic approach to compare different TSO-DSO coordination models for acquiring ancillary services from distribution resources. Several schemes are considered: some with coordinated market management by TSOs and DSOs, others with sequential or independent local markets. For each scheme, the dispatching problem is formulated as a two-stage stochastic sequential game, where the first stage is the day-ahead market and the second stage is the balancing market. Nash equilibrium solutions are obtained by iteratively solving the profit maximization problem of each market player. Numerical tests on a CIGRE benchmark network show that coordination schemes enabling distribution resources to provide ancillary services to the transmission system can significantly increase system costs when congestion occurs in the transmission network.
How to exploit driving and dissipation to stabilize and manipulate quantum many-body states
2025-05-21
Iacopo Carusotto
We review the basic concepts of quantum fluids of light and the different techniques that have been developed to exploit driving and dissipation to stabilize and manipulate interesting many-body states. In the weakly interacting regime, this approach has allowed to study, among other, superfluid light, non-equilibrium Bose-Einstein condensation, photonic analogs of Hall effects, and is opening the way towards the realization of a new family of analog models of gravity. In the strongly interacting regime, the recent observations of Mott insulators and baby Laughlin fluids of light open promising avenues towards the study of novel strongly correlated many-body states.
A Stochastic Programming Model for Anticipative Planning of Integrated Electricity and Gas Systems with Bidirectional Energy Flows under Fuel and CO2 Price Uncertainty
2025-05-21
Giovanni Micheli, Maria Teresa Vespucci, Alessia Cortazzi, Cinzia Puglisi
A two-stage multi-period mixed-integer linear stochastic programming model is proposed to assist qualified operators in long-term generation and transmission expansion planning of electricity and gas systems to meet policy objectives. The first-stage decisions concern investments in new plants, new connections in the electricity and gas sectors, and the decommissioning of existing thermal power plants; the second-stage variables represent operational decisions, with uncertainty about future fuel and CO2 prices represented by scenarios. The main features of the model are: (i) the bidirectional conversion between electricity and gas enabled by Power-to-Gas and thermal power plants, (ii) a detailed representation of short-term operation, crucial for addressing challenges associated with integrating large shares of renewables in the energy mix, and (iii) an integrated planning framework to evaluate the operation of flexibility resources, their ability to manage non-programmable generation, and their economic viability. Given the computational complexity of the proposed model, in this paper we also implement a solution algorithm based on Benders decomposition to compute near-optimal solutions. A case study on the decarbonisation of the Italian integrated energy system demonstrates the effectiveness of the model. The numerical results show: (i) the importance of including a detailed system representation for obtaining reliable results, and (ii) the need to consider price uncertainty to design adequate systems and reduce overall costs.
Lossless Token Merging Even Without Fine-Tuning in Vision Transformers
2025-05-21
Jaeyeon Lee, Dong-Wan Choi
Although Vision Transformers (ViTs) have become the standard architecture in computer vision, their massive sizes lead to significant computational overhead. Token compression techniques have attracted considerable attention to address this issue, but they often suffer from severe information loss, requiring extensive additional training to achieve practical performance. In this paper, we propose Adaptive Token Merging (ATM), a novel method that ensures lossless token merging, eliminating the need for fine-tuning while maintaining competitive performance. ATM adaptively reduces tokens across layers and batches by carefully adjusting layer-specific similarity thresholds, thereby preventing the undesirable merging of less similar tokens with respect to each layer. Furthermore, ATM introduces a novel token matching technique that considers not only similarity but also merging sizes, particularly for the final layers, to minimize the information loss incurred from each merging operation. We empirically validate our method across a wide range of pretrained models, demonstrating that ATM not only outperforms all existing training-free methods but also surpasses most training-intensive approaches, even without additional training. Remarkably, training-free ATM achieves over a 30% reduction in FLOPs for the DeiT-T and DeiT-S models without any drop in their original accuracy.
Remote sensing images (RSIs) capture both natural and human-induced changes on the Earth's surface, serving as essential data for environmental monitoring, urban planning, and resource management. Semantic segmentation (SS) of RSIs enables the fine-grained interpretation of surface features, making it a critical task in remote sensing analysis. With the increasing diversity and volume of RSIs collected by sensors on various platforms, traditional processing methods struggle to maintain efficiency and accuracy. In response, deep learning (DL) has emerged as a transformative approach, enabling substantial advances in remote sensing image semantic segmentation (RSISS) by automating feature extraction and improving segmentation accuracy across diverse modalities. This paper revisits the evolution of DL-based RSISS by categorizing existing approaches into four stages: the early pixel-based methods, the prevailing patch-based and tile-based techniques, and the emerging image-based strategies enabled by foundation models. We analyze these developments from the perspective of feature extraction and learning strategies, revealing the field's progression from pixel-level to tile-level and from unimodal to multimodal segmentation. Furthermore, we conduct a comprehensive evaluation of nearly 40 advanced techniques on a unified dataset to quantitatively characterize their performance and applicability. This review offers a holistic view of DL-based SS for RS, highlighting key advancements, comparative insights, and open challenges to guide future research.
Cinematography is a cornerstone of film production and appreciation, shaping mood, emotion, and narrative through visual elements such as camera movement, shot composition, and lighting. Despite recent progress in multimodal large language models (MLLMs) and video generation models, the capacity of current models to grasp and reproduce cinematographic techniques remains largely uncharted, hindered by the scarcity of expert-annotated data. To bridge this gap, we present CineTechBench, a pioneering benchmark founded on precise, manual annotation by seasoned cinematography experts across key cinematography dimensions. Our benchmark covers seven essential aspects-shot scale, shot angle, composition, camera movement, lighting, color, and focal length-and includes over 600 annotated movie images and 120 movie clips with clear cinematographic techniques. For the understanding task, we design question answer pairs and annotated descriptions to assess MLLMs' ability to interpret and explain cinematographic techniques. For the generation task, we assess advanced video generation models on their capacity to reconstruct cinema-quality camera movements given conditions such as textual prompts or keyframes. We conduct a large-scale evaluation on 15+ MLLMs and 5+ video generation models. Our results offer insights into the limitations of current models and future directions for cinematography understanding and generation in automatically film production and appreciation. The code and benchmark can be accessed at https://github.com/PRIS-CV/CineTechBench.
Multispectral object detection aims to leverage complementary information from visible (RGB) and infrared (IR) modalities to enable robust performance under diverse environmental conditions. In this letter, we propose IC-Fusion, a multispectral object detector that effectively fuses visible and infrared features through a lightweight and modalityaware design. Motivated by wavelet analysis and empirical observations, we find that IR images contain structurally rich high-frequency information critical for object localization, while RGB images provide complementary semantic context. To exploit this, we adopt a compact RGB backbone and design a novel fusion module comprising a Multi-Scale Feature Distillation (MSFD) block to enhance RGB features and a three-stage fusion block with Cross-Modal Channel Shuffle Gate (CCSG) and Cross-Modal Large Kernel Gate (CLKG) to facilitate effective cross-modal interaction. Experiments on the FLIR and LLVIP benchmarks demonstrate the effectiveness and efficiency of our IR-centric fusion strategy. Our code is available at https://github.com/smin-hwang/IC-Fusion.
In this survey, we will explore the interaction between secure multiparty computation and the area of machine learning. Recent advances in secure multiparty computation (MPC) have significantly improved its applicability in the realm of machine learning (ML), offering robust solutions for privacy-preserving collaborative learning. This review explores key contributions that leverage MPC to enable multiple parties to engage in ML tasks without compromising the privacy of their data. The integration of MPC with ML frameworks facilitates the training and evaluation of models on combined datasets from various sources, ensuring that sensitive information remains encrypted throughout the process. Innovations such as specialized software frameworks and domain-specific languages streamline the adoption of MPC in ML, optimizing performance and broadening its usage. These frameworks address both semi-honest and malicious threat models, incorporating features such as automated optimizations and cryptographic auditing to ensure compliance and data integrity. The collective insights from these studies highlight MPC's potential in fostering collaborative yet confidential data analysis, marking a significant stride towards the realization of secure and efficient computational solutions in privacy-sensitive industries. This paper investigates a spectrum of SecureML libraries that includes cryptographic protocols, federated learning frameworks, and privacy-preserving algorithms. By surveying the existing literature, this paper aims to examine the efficacy of these libraries in preserving data privacy, ensuring model confidentiality, and fortifying ML systems against adversarial attacks. Additionally, the study explores an innovative application domain for SecureML techniques: the integration of these methodologies in gaming environments utilizing ML.
Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding
2025-05-21
Ta Duc Huy, Duy Anh Huynh, Yutong Xie, Yuankai Qi, Qi Chen, Phi Le Nguyen, Sen Kim Tran, Son Lam Phung, Anton van den Hengel, Zhibin Liao, Minh-Son To, Johan W. Verjans, Vu Minh Hieu Phan
Visual grounding (VG) is the capability to identify the specific regions in an image associated with a particular text description. In medical imaging, VG enhances interpretability by highlighting relevant pathological features corresponding to textual descriptions, improving model transparency and trustworthiness for wider adoption of deep learning models in clinical practice. Current models struggle to associate textual descriptions with disease regions due to inefficient attention mechanisms and a lack of fine-grained token representations. In this paper, we empirically demonstrate two key observations. First, current VLMs assign high norms to background tokens, diverting the model's attention from regions of disease. Second, the global tokens used for cross-modal learning are not representative of local disease tokens. This hampers identifying correlations between the text and disease tokens. To address this, we introduce simple, yet effective Disease-Aware Prompting (DAP) process, which uses the explainability map of a VLM to identify the appropriate image features. This simple strategy amplifies disease-relevant regions while suppressing background interference. Without any additional pixel-level annotations, DAP improves visual grounding accuracy by 20.74% compared to state-of-the-art methods across three major chest X-ray datasets.
Graph-structured data pervades domains such as social networks, biological systems, knowledge graphs, and recommender systems. While foundation models have transformed natural language processing, vision, and multimodal learning through large-scale pretraining and generalization, extending these capabilities to graphs -- characterized by non-Euclidean structures and complex relational semantics -- poses unique challenges and opens new opportunities. To this end, Graph Foundation Models (GFMs) aim to bring scalable, general-purpose intelligence to structured data, enabling broad transfer across graph-centric tasks and domains. This survey provides a comprehensive overview of GFMs, unifying diverse efforts under a modular framework comprising three key components: backbone architectures, pretraining strategies, and adaptation mechanisms. We categorize GFMs by their generalization scope -- universal, task-specific, and domain-specific -- and review representative methods, key innovations, and theoretical insights within each category. Beyond methodology, we examine theoretical foundations including transferability and emergent capabilities, and highlight key challenges such as structural alignment, heterogeneity, scalability, and evaluation. Positioned at the intersection of graph learning and general-purpose AI, GFMs are poised to become foundational infrastructure for open-ended reasoning over structured data. This survey consolidates current progress and outlines future directions to guide research in this rapidly evolving field. Resources are available at https://github.com/Zehong-Wang/Awesome-Foundation-Models-on-Graphs.
A Risk Taxonomy for Evaluating AI-Powered Psychotherapy Agents
2025-05-21
Ian Steenstra, Timothy W. Bickmore
The proliferation of Large Language Models (LLMs) and Intelligent Virtual Agents acting as psychotherapists presents significant opportunities for expanding mental healthcare access. However, their deployment has also been linked to serious adverse outcomes, including user harm and suicide, facilitated by a lack of standardized evaluation methodologies capable of capturing the nuanced risks of therapeutic interaction. Current evaluation techniques lack the sensitivity to detect subtle changes in patient cognition and behavior during therapy sessions that may lead to subsequent decompensation. We introduce a novel risk taxonomy specifically designed for the systematic evaluation of conversational AI psychotherapists. Developed through an iterative process including review of the psychotherapy risk literature, qualitative interviews with clinical and legal experts, and alignment with established clinical criteria (e.g., DSM-5) and existing assessment tools (e.g., NEQ, UE-ATR), the taxonomy aims to provide a structured approach to identifying and assessing user/patient harms. We provide a high-level overview of this taxonomy, detailing its grounding, and discuss potential use cases. We discuss two use cases in detail: monitoring cognitive model-based risk factors during a counseling conversation to detect unsafe deviations, in both human-AI counseling sessions and in automated benchmarking of AI psychotherapists with simulated patients. The proposed taxonomy offers a foundational step towards establishing safer and more responsible innovation in the domain of AI-driven mental health support.
2025-05-21
Eray Can Elumar, Cem Tekin, Osman Yagan
Recent advances in large language models (LLMs) have enabled automated dataset labeling with minimal human supervision. While majority voting across multiple LLMs can improve label reliability by mitigating individual model biases, it incurs high computational costs due to repeated querying. In this work, we propose a novel online framework, Cost-aware Majority Voting (CaMVo), for efficient and accurate LLM-based dataset annotation. CaMVo adaptively selects a subset of LLMs for each data instance based on contextual embeddings, balancing confidence and cost without requiring pre-training or ground-truth labels. Leveraging a LinUCB-based selection mechanism and a Bayesian estimator over confidence scores, CaMVo estimates a lower bound on labeling accuracy for each LLM and aggregates responses through weighted majority voting. Our empirical evaluation on the MMLU and IMDB Movie Review datasets demonstrates that CaMVo achieves comparable or superior accuracy to full majority voting while significantly reducing labeling costs. This establishes CaMVo as a practical and robust solution for cost-efficient annotation in dynamic labeling environments.
Sarcasm is a challenge to sentiment analysis because of the incongruity between stated and implied sentiment. The challenge is exacerbated when the implication may be relevant to a specific country or geographical region. Pragmatic metacognitive prompting (PMP) is a cognition-inspired technique that has been used for pragmatic reasoning. In this paper, we harness PMP for explainable sarcasm detection for Australian and Indian English, alongside a benchmark dataset for standard English. We manually add sarcasm explanations to an existing sarcasm-labeled dataset for Australian and Indian English called BESSTIE, and compare the performance for explainable sarcasm detection for them with FLUTE, a standard English dataset containing sarcasm explanations. Our approach utilising PMP when evaluated on two open-weight LLMs (GEMMA and LLAMA) achieves statistically significant performance improvement across all tasks and datasets when compared with four alternative prompting strategies. We also find that alternative techniques such as agentic prompting mitigate context-related failures by enabling external knowledge retrieval. The focused contribution of our work is utilising PMP in generating sarcasm explanations for varieties of English.
Imprints of Large-Scale Structures in the Anisotropies of the Cosmological Gravitational Wave Background
2025-05-21
Rafael Bravo, Walter Riquelme
We compute the cross-correlation between the anisotropies of the cosmological gravitational wave background (CGWB) and the galaxy density contrast. We show that the cross-correlation is non-zero due to the {\it late} integrated Sachs-Wolfe (ISW) effect experienced by tensor modes. We study the detection prospects of the cross-correlation signal against cosmic variance (CV), and in the light of incoming LSS and GW surveys, where we found that the signal under certain conditions could be distinguishable from noise. In addition, by considering a CGWB sourced by scalar-induced gravitational waves, and the inclusion of a scale-dependent galaxy bias, we use the cross-correlation to forecast local primordial non-Gaussianity, where we find $\sigma(f_\text{NL}^\text{loc})\sim10$ for CV only and a LSST-like survey. Moreover, by combining the Fisher information of CGWB$\times$LSS with LSS, we are able to improve the constraints by 4\% compared to an LSS-only analysis. Our results imply that a cross-correlation between GW anisotropies and LSS can indeed come from a stochastic background of cosmological origin and could be used to distinguish it from an astrophysical one.
Star formation at different stages of ram-pressure stripping as observed through far-ultraviolet imaging of 13 GASP galaxies
2025-05-21
Koshy George, B. M. Poggianti, B. Vulcani, M. Gullieuszik, J. Postma, Jacopo Fritz, P. Côté, Yara L. Jaffe, A. Moretti, Alessandro Ignesti, Giorgia Peluso, Neven Tomićić, A. Subramaniam, S. K. Ghosh, S. N. Tandon
Galaxies undergoing ram-pressure stripping develop gaseous tails that can extend several kiloparsecs outside the galaxy disc. We used far-ultraviolet and H$\alpha$ imaging from the GASP survey to investigate how different stages of stripping affect star formation properties in the tail and disc of 13 galaxies undergoing stripping. These galaxies have different stripping strengths, as identified from the MUSE integral field spectroscopy. The star-forming knots in the disc and tails show a good correspondence between the measured FUV and H$\alpha$ flux. This is especially true for strong and extreme cases of stripping, which have developed extended ionized gaseous tails featuring clumpy structures. The mechanism behind the H$\alpha$ emission on the tails of these regions, which correlates well with FUV emission, is photoionization caused by young massive stars. The optical emission line ratio maps enable us to understand the emission mechanism, which can be attributed to star formation, LINER activity, or a combination of both phenomena and AGN. The star-forming regions in the emission line maps correspond well to the areas with significant FUV flux in these galaxies. Six galaxies exhibit minimal star formation in their tails, with two cases star formation is limited to the central regions and their discs are truncated. In galaxies with truncated discs, star formation is confined to a smaller region on the disc, as indicated by the FUV flux, compared to H$\alpha$. Galaxies with strong stripping are undergoing recent star formation and are likely recent infalls. Galaxies with truncated discs confine star formation to the center, likely because they have completed a cluster crossing that depleted most of their outer gaseous disc. Galaxies with minimal FUV flux along their tails exhibit unresolved H$\alpha$ emission which may be attributed to processes other than star formation.
The Pursuit of Empathy: Evaluating Small Language Models for PTSD Dialogue Support
2025-05-21
Suhas BN, Yash Mahajan, Dominik Mattioli, Andrew M. Sherrill, Rosa I. Arriaga, Chris W. Wiese, Saeed Abdullah
Can small language models with 0.5B to 5B parameters meaningfully engage in trauma-informed, empathetic dialogue for individuals with PTSD? We address this question by introducing TIDE, a dataset of 10,000 two-turn dialogues spanning 500 diverse PTSD client personas and grounded in a three-factor empathy model: emotion recognition, distress normalization, and supportive reflection. All scenarios and reference responses were reviewed for realism and trauma sensitivity by a clinical psychologist specializing in PTSD. We evaluate eight small language models before and after fine-tuning, comparing their outputs to a frontier model (Claude Sonnet 3.5). Our IRB-approved human evaluation and automatic metrics show that fine-tuning generally improves perceived empathy, but gains are highly scenario- and user-dependent, with smaller models facing an empathy ceiling. Demographic analysis shows older adults value distress validation and graduate-educated users prefer nuanced replies, while gender effects are minimal. We highlight the limitations of automatic metrics and the need for context- and user-aware system design. Our findings, along with the planned release of TIDE, provide a foundation for building safe, resource-efficient, and ethically sound empathetic AI to supplement, not replace, clinical mental health care.
Towards a Working Definition of Designing Generative User Interfaces
2025-05-21
Kyungho Lee
Generative UI is transforming interface design by facilitating AI-driven collaborative workflows between designers and computational systems. This study establishes a working definition of Generative UI through a multi-method qualitative approach, integrating insights from a systematic literature review of 127 publications, expert interviews with 18 participants, and analyses of 12 case studies. Our findings identify five core themes that position Generative UI as an iterative and co-creative process. We highlight emerging design models, including hybrid creation, curation-based workflows, and AI-assisted refinement strategies. Additionally, we examine ethical challenges, evaluation criteria, and interaction models that shape the field. By proposing a conceptual foundation, this study advances both theoretical discourse and practical implementation, guiding future HCI research toward responsible and effective generative UI design practices.
Orthogonal arrays are arguably one of the most fascinating and important statistical tools for efficient data collection. They have a simple, natural definition, desirable properties when used as fractional factorials, and a rich and beautiful mathematical theory. Their connections with combinatorics, finite fields, geometry, and error-correcting codes are profound. Orthogonal arrays have been widely used in agriculture, engineering, manufacturing, and high-technology industries for quality and productivity improvement experiments. In recent years, they have drawn rapidly growing interest from various fields such as computer experiments, integration, visualization, optimization, big data, machine learning/artificial intelligence through successful applications in those fields. We review the fundamental concepts and statistical properties and report recent developments. Discussions of recent applications and connections with various fields are presented.
Are the confidence scores of reviewers consistent with the review content? Evidence from top conference proceedings in AI
2025-05-21
Wenqing Wu, Haixu Xi, Chengzhi Zhang
Peer review is vital in academia for evaluating research quality. Top AI conferences use reviewer confidence scores to ensure review reliability, but existing studies lack fine-grained analysis of text-score consistency, potentially missing key details. This work assesses consistency at word, sentence, and aspect levels using deep learning and NLP conference review data. We employ deep learning to detect hedge sentences and aspects, then analyze report length, hedge word/sentence frequency, aspect mentions, and sentiment to evaluate text-score alignment. Correlation, significance, and regression tests examine confidence scores' impact on paper outcomes. Results show high text-score consistency across all levels, with regression revealing higher confidence scores correlate with paper rejection, validating expert assessments and peer review fairness.
Analyzing Stellar and Interstellar Contributions to Polarization: Modeling Approaches for Hot Stars
2025-05-21
R Ignace, A G Fullard, G V Panopoulou, D J Hillier, C Erba, P A Scowen
Linear polarimetry of unresolved stars is a powerful method for discerning or constraining the geometry of a source and its environment, since spherical sources produce no net polarization. However, a general challenge to interpreting intrinsic stellar polarization is the contribution to the signal by interstellar polarization (ISP). Here, we review methodologies for distinguishing the stellar signal from the interstellar contribution in the context of massive stars. We first characterize ISP with distance using a recent compilation of starlight polarization catalogs. Several scenarios involving Thomson scattering, rapidly rotating stars, optically thick winds, and interacting binaries are considered specifically to contrast the wavelength-dependent effects of ISP in the ultraviolet versus optical bands. ISP is recognizable in the stellar polarization from Thomson scattering in the polarization position angle rotations. For hot stars with near-critical rotation rates, the ISP declines whereas the stellar continuum polarization sharply increases. In the case of quite dense winds, strong ultraviolet lines trace the ISP, which is not always the case in the optical. In the binary case, temporal and chromatic effects illustrate how the ISP displaces variable polarization with wavelength. This study clarifies the impacts of ISP in relation to new ultraviolet spectropolarimetry efforts such as Polstar and Pollux.
We present a comprehensive study of formaldehyde (H2CO) absorption and radio recombination line (H110a) emission in 215 molecular clouds from the Bolocam Galactic Plane Survey (BGPS), observed using the Nanshan 25-m radio telescope. H2CO was detected in 88 sources (40.93 percent) with 59 being new detections, while H110a emission was found in only 11 sources (5.12 percent), all coincident with H2CO absorption. There exists a correlation of H2CO fluxes with millimeter fluxes below a 3 Jy threshold and an increased dispersion above it, suggesting the sub-CMB cooling of H2CO. Cross-matching with kinematic distance catalogs revealed H2CO spanning galactocentric distances from 0.216 to 10.769 kpc, with column densities ranging from 7.82 x 10^11 to 6.69 x 10^14 cm-2. A significant inverse correlation was observed between H2CO detection fraction and galactocentric distance, suggesting enhanced star forming activity closer to the Galactic Center. These findings challenge traditional Galactic Habitable Zone (GHZ) models by demonstrating the presence of biogenic precursors in the inner Galaxy, shielded within dense molecular clouds. Our results underscore the importance of incorporating chemical tracers like H2CO, alongside physical constraints, to refine the boundaries of the GHZ and advance the research of prebiotic chemistry in the Milky Way.
BiasLab: Toward Explainable Political Bias Detection with Dual-Axis Annotations and Rationale Indicators
2025-05-21
KMA Solaiman
We present BiasLab, a dataset of 300 political news articles annotated for perceived ideological bias. These articles were selected from a curated 900-document pool covering diverse political events and source biases. Each article is labeled by crowdworkers along two independent scales, assessing sentiment toward the Democratic and Republican parties, and enriched with rationale indicators. The annotation pipeline incorporates targeted worker qualification and was refined through pilot-phase analysis. We quantify inter-annotator agreement, analyze misalignment with source-level outlet bias, and organize the resulting labels into interpretable subsets. Additionally, we simulate annotation using schema-constrained GPT-4o, enabling direct comparison to human labels and revealing mirrored asymmetries, especially in misclassifying subtly right-leaning content. We define two modeling tasks: perception drift prediction and rationale type classification, and report baseline performance to illustrate the challenge of explainable bias detection. BiasLab's rich rationale annotations provide actionable interpretations that facilitate explainable modeling of political bias, supporting the development of transparent, socially aware NLP systems. We release the dataset, annotation schema, and modeling code to encourage research on human-in-the-loop interpretability and the evaluation of explanation effectiveness in real-world settings.
Evaluating Voting Design Vulnerabilities for Retroactive Funding
2025-05-21
Jay Yu, Austin Bennett, Billy Gao, Rebecca Joseph
Retroactive Public Goods Funding (RetroPGF) rewards blockchain projects based on proven impact rather than future promises. This paper reviews voting mechanisms for Optimism's RetroPGF, where "badgeholders" allocate rewards to valuable projects. We explore Optimism's previous schemes for RetroPGF voting, including quadratic, mean, and median voting. We present a proof-based formal analysis for vulnerabilities in these voting schemes, empirically validate these vulnerabilities using voting simulations, and offer assessments and practical recommendations for future iterations of Optimism's system based on our findings.
CP-LLM: Context and Pixel Aware Large Language Model for Video Quality Assessment
2025-05-21
Wen Wen, Yaohong Wu, Yue Sheng, Neil Birkbeck, Balu Adsumilli, Yilin Wang
Video quality assessment (VQA) is a challenging research topic with broad applications. Effective VQA necessitates sensitivity to pixel-level distortions and a comprehensive understanding of video context to accurately determine the perceptual impact of distortions. Traditional hand-crafted and learning-based VQA models mainly focus on pixel-level distortions and lack contextual understanding, while recent LLM-based models struggle with sensitivity to small distortions or handle quality scoring and description as separate tasks. To address these shortcomings, we introduce CP-LLM: a Context and Pixel aware Large Language Model. CP-LLM is a novel multimodal LLM architecture featuring dual vision encoders designed to independently analyze perceptual quality at both high-level (video context) and low-level (pixel distortion) granularity, along with a language decoder subsequently reasons about the interplay between these aspects. This design enables CP-LLM to simultaneously produce robust quality scores and interpretable quality descriptions, with enhanced sensitivity to pixel distortions (e.g. compression artifacts). The model is trained via a multi-task pipeline optimizing for score prediction, description generation, and pairwise comparisons. Experiment results demonstrate that CP-LLM achieves state-of-the-art cross-dataset performance on established VQA benchmarks and superior robustness to pixel distortions, confirming its efficacy for comprehensive and practical video quality assessment in real-world scenarios.
As large language models (LLMs) are used in complex writing workflows, users engage in multi-turn interactions to steer generations to better fit their needs. Rather than passively accepting output, users actively refine, explore, and co-construct text. We conduct a large-scale analysis of this collaborative behavior for users engaged in writing tasks in the wild with two popular AI assistants, Bing Copilot and WildChat. Our analysis goes beyond simple task classification or satisfaction estimation common in prior work and instead characterizes how users interact with LLMs through the course of a session. We identify prototypical behaviors in how users interact with LLMs in prompts following their original request. We refer to these as Prototypical Human-AI Collaboration Behaviors (PATHs) and find that a small group of PATHs explain a majority of the variation seen in user-LLM interaction. These PATHs span users revising intents, exploring texts, posing questions, adjusting style or injecting new content. Next, we find statistically significant correlations between specific writing intents and PATHs, revealing how users' intents shape their collaboration behaviors. We conclude by discussing the implications of our findings on LLM alignment.
Exploring Perception-Based Techniques for Redirected Walking in VR: A Comprehensive Survey
2025-05-21
Bradley Coles, Yahya Hmaiti, Joseph J. LaViola Jr
We present a comprehensive survey of perception-based redirected walking (RDW) techniques in virtual reality (VR), presenting a taxonomy that serves as a framework for understanding and designing RDW algorithms. RDW enables users to explore virtual environments (VEs) larger than their physical space, addressing the constraints of real walking in limited home VR setups. Our review spans 232 papers, with 165 included in the final analysis. We categorize perception-based RDW techniques based on gains, gain application, target orientation calculation, and optional general enhancements, identifying key patterns and relationships. We present data on how current work aligns within this classification system and suggest how this data can guide future work into areas that are relatively under explored. This taxonomy clarifies perception-based RDW techniques, guiding the design and application of RDW systems, and suggests future research directions to enhance VR user experience.
NIRCam yells at cloud: JWST MIRI imaging can directly detect exoplanets of the same temperature, mass, age, and orbital separation as Saturn and Jupiter
2025-05-21
Rachel Bowens-Rubin, James Mang, Mary Anne Limbach, Aarynn L. Carter, Kevin B. Stevenson, Kevin Wagner, Giovanni Strampelli, Caroline V. Morley, Grant Kennedy, Elisabeth Matthews, Andrew Vanderburg, Maïssa Salama
NIRCam and MIRI coronagraphy have successfully demonstrated the ability to directly image young sub-Jupiter mass and mature gas-giant exoplanets. However, these modes struggle to reach the sensitivities needed to find the population of cold giant planets that are similar to our own Solar System's giant planets ($T_{\rm eff} = 60 - 125$ K; $a=5 - 30$ AU). For the first time, we explore the high-contrast imaging capabilities of MIRI imaging rather than coronagraphy. Using data from the JWST GO 6122: Cool Kids on the Block program which targets nearby ($<6$ pc) M-dwarfs with NIRCam coronagraphy and MIRI imaging, we demonstrate that 21$\mu$m MIRI imaging can detect planets with the same temperature, mass, age, and orbital separations as Saturn and Jupiter. For systems within 3pc, 21$\mu$m MIRI imaging reaches the sensitivity needed to detect planets colder than Saturn ($<95$ K). NIRCam coronagraphy can achieve similar results only in the unlikely case that a cold giant planet is cloud-free. Motivated by these compelling findings, we extend our analysis to evaluate the measured performance of MIRI F2100W imaging versus NIRCam F444W coronagraphy to 70 pc and conclude that MIRI imaging offers the advantage for systems within 20pc. Microlensing surveys predict an occurrence rate as high as 1 - 2 low-mass giant exoplanets per star, suggesting that JWST MIRI imaging surveys of nearby systems may be poised to uncover a substantial population. This breakthrough enables a path towards the first direct characterization of cold giant exoplanets that are analogous to the solar system giant planets.
Geometric formulation of $k$-essence and late-time acceleration
2025-05-21
Lehel Csillag, Erik Jensko
We study a class of geometries in which nonmetricity is fully determined by a vectorial degree of freedom and three independent coefficients. Formulating the simplest linear action in this geometry, implemented through Lagrange multipliers, naturally leads to an equivalence with the purely kinetic $k$-essence models with quadratic kinetic terms. A detailed dynamical systems analysis reveals that the $\Lambda$CDM phenomenology is embedded within the model. Crucially, we find that if stability conditions such as a positive sound speed squared and non-negative energy density are not enforced, the model generically exhibits instabilities and divergent behaviour in the phase space. These physical viability criteria allow us to isolate stable regions of the parameter space and derive well-motivated priors for parameter inference. Using Markov Chain Monte Carlo methods and late-time observational data, including cosmic chronometers, Pantheon$^{+}$ Type Ia supernovae, and DESI baryon acoustic oscillations, we constrain the degrees of freedom associated with nonmetricity and demonstrate the viability of the model. Remarkably, the model is found to be statistically indistinguishable from $\Lambda$CDM at late times. We discuss the implications of these results in light of the recent cosmic tensions, and give a possible explanation as to why the equivalent $k$-essence models have been missed as serious competitors to $\Lambda$CDM in the past. Finally, we review the geometric foundations of the theory and show that the integrable Weyl, Schr\"{o}dinger and completely symmetric geometries are embedded within our framework as special cases.
The invisible threat: assessing the collisional hazard posed by the undiscovered Venus co-orbital asteroids
2025-05-21
V. Carruba, R. Sfair, R. A. Araujo, O. C. Winter, D. C. Mourão, S. Di Ruzza, S. Aljbaae, G. Caritá, R. C. Domingos, A. A. Alves
Currently, 20 co-orbital asteroids of Venus are known, with only one with an eccentricity below 0.38. This is most likely caused by observational biases since asteroids with larger eccentricities may approach the Earth and are easier to detect. We aim to assess the possible threat that the yet undetected population of Venus co-orbitals may pose to Earth, and investigate their detectability from Earth and space observatories. We used semi-analytical models of the 1:1 mean-motion resonance with Venus and numerical simulations to monitor close encounters with Earth on several co-orbital cycles. We analyzed observability windows and brightness variations for potential Venus co-orbitals as viewed from ground-based telescopes to assess their future detection feasibility with next-generation survey capabilities. There is a range of orbits with e < 0.38, larger at lower inclinations, for which Venus' co-orbitals can pose a collisional hazard to Earth. Current ground-based observations are constrained by periodic observing windows and solar elongation limitations, though the Rubin Observatory may detect some of these objects during favorable configurations. Space missions based on Venus' orbits may be instrumental in detecting Venus' co-orbitals at low eccentricities.
Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey
2025-05-21
Chih-Kai Yang, Neo S. Ho, Hung-yi Lee
With advancements in large audio-language models (LALMs), which enhance large language models (LLMs) with auditory capabilities, these models are expected to demonstrate universal proficiency across various auditory tasks. While numerous benchmarks have emerged to assess LALMs' performance, they remain fragmented and lack a structured taxonomy. To bridge this gap, we conduct a comprehensive survey and propose a systematic taxonomy for LALM evaluations, categorizing them into four dimensions based on their objectives: (1) General Auditory Awareness and Processing, (2) Knowledge and Reasoning, (3) Dialogue-oriented Ability, and (4) Fairness, Safety, and Trustworthiness. We provide detailed overviews within each category and highlight challenges in this field, offering insights into promising future directions. To the best of our knowledge, this is the first survey specifically focused on the evaluations of LALMs, providing clear guidelines for the community. We will release the collection of the surveyed papers and actively maintain it to support ongoing advancements in the field.
The article addresses the problem of storing data in extreme environmental conditions with limited computing resources and memory. There is a requirement to create portable, fault-tolerant, modular database management systems (DBMS) that are optimized for use in embedded systems. Existing databases, such as LittleDB, LMDB, and Berkeley DB, are reviewed, and their limitations are identified. A variant of a portable DBMS is introduced to efficiently manage data in environments where computational resource usage must be minimized, while meeting specific requirements for fault tolerance and noise immunity. Common solutions for optimizing of insertion, storage and management of data are reviewed. Algorithms for fault-tolerant data encoding in RAM are implemented. An architectural solution to data storage and minimizing the impact of bit errors is proposed. Software that manages relational data in extreme conditions is developed, that allows testing and comparing results with existing solutions.
TriggerCalib: a turnkey package for estimating LHCb trigger efficiencies
2025-05-21
Johannes Albrecht, James Andrew Gooding, Maxim Lysenko, Alessandro Scarabotto
Data-driven estimations of trigger efficiencies are essential to LHCb physics analyses. A software package, TriggerCalib, has been developed to provide a centralised framework of trigger efficiency calculations. In this paper, a data-driven method of estimating trigger efficiencies is presented, within the context of the TriggerCalib package, and its functionalities validated on candidate $B^+\to J/\psi\left(\mu^+\mu^-\right) K^+$ decays in LHCb simulated data. A detailed overview of the trigger efficiency calculations is given, discussing different background mitigation methods and the computation of statistical and systematic uncertainties.
Gaussian Processes in Power Systems: Techniques, Applications, and Future Works
2025-05-21
Bendong Tan, Tong Su, Yu Weng, Ketian Ye, Parikshit Pareek, Petr Vorobev, Hung Nguyen, Junbo Zhao, Deepjyoti Deka
The increasing integration of renewable energy sources (RESs) and distributed energy resources (DERs) has significantly heightened operational complexity and uncertainty in modern power systems. Concurrently, the widespread deployment of smart meters, phasor measurement units (PMUs) and other sensors has generated vast spatiotemporal data streams, enabling advanced data-driven analytics and decision-making in grid operations. In this context, Gaussian processes (GPs) have emerged as a powerful probabilistic framework, offering uncertainty quantification, non-parametric modeling, and predictive capabilities to enhance power system analysis and control. This paper presents a comprehensive review of GP techniques and their applications in power system operation and control. GP applications are reviewed across three key domains: GP-based modeling, risk assessment, and optimization and control. These areas serve as representative examples of how GP can be utilized in power systems. Furthermore, critical challenges in GP applications are discussed, and potential research directions are outlined to facilitate future power system operations.
Chemical Abundances in the Milky Way's Nuclear Stellar Disc
2025-05-21
N. Ryde, G. Nandakumar, R. Albarracin, M. Schultheis, A. Rojas-Arriagada, M. Zoccali
The Nuclear Stellar Disc (NSD) is a rotating, disc-like structure in the Galactic Center, believed to have a distinct star formation. However, its formation history and evolutionary links to other structures in the Galactic Center remain uncertain. This study aims to present the first comprehensive chemical census of the NSD by deriving abundance trends for 18 elements in 9 M giants in the metallicity range of -1.0 <[Fe/H]< 0.5. By comparing these trends with those of other Galactic populations we seek to understand the chemical relationships between these structures. We obtained high-resolution H- and K-band spectra of NSD stars using the IGRINS spectrometer mounted on the Gemini South telescope. The giants were analyzed consistently with stars from a comparison populations to minimize systematic uncertainties. The abundance trends of NSD stars exhibit similarities with those of the inner-bulge and Nuclear Star Cluster (NSC) populations across a broad range of elements with different chemical evolution histories. The trends for 17 elements align closely with the local thick-disc behaviour at subsolar metallicities. At super-solar metallicities, most elements follow the NSC and inner-bulge trends. Sodium is the only element exhibiting a distinct trend, with enhanced abundances in the NSD and NSC compared to both thin-disc and inner-bulge stars. The chemical similarity suggests that the NSD likely shares an evolutionary history with the NSC and possibly the inner-disc sequence. Further studies are required to determine potential evolutionary links to Liller 1 and metal-rich globular clusters. We find no evidence of typical globular cluster abundance signatures in our NSD stars with subsolar metallicities. Our study demonstrates the feasibility of obtaining high-quality abundance data even in highly dust-obscured regions of the Milky Way, paving the way for future surveys.
Discovery and characterization of 25 new quasars at 4.6 < z < 6.9 from wide-field multi-band surveys
2025-05-21
Silvia Belladitta, Eduardo Bañados, Zhang-Liang Xie, Roberto Decarli, Silvia Onorato, Jinyi Yang, Manuela Bischetti, Masafusa Onoue, Federica Loiacono, Laura N. Martínez-Ramírez, Chiara Mazzucchelli, Frederick B. Davies, Julien Wolf, Jan-Torge Schindler, Xiaohui Fan, Feige Wang, Fabian Walter, Tatevik Mkrtchyan, Daniel Stern, Emanuele P. Farina, Bram P. Venemans
Luminous quasars at $z>4$ provide key insights into the early Universe. Their rarity necessitates wide-field multi-band surveys to efficiently separate them from the main astrophysical contaminants (i.e., ultracool dwarfs). To expand the sample of high-$z$ quasars, we conducted targeted selections using optical, infrared, and radio surveys, complemented by literature-based quasar candidate catalogs. In this paper, we report the discovery of \nqsos\ new quasars at $4.66.5$ quasars we selected, but whose independent discovery has already been published in the literature. Three of the newly discovered quasars are strong radio emitters (L$_{1.4~\rm GHz}$$=0.09-1.0\times$10$^{34}$erg s$^{-1}$ Hz$^{-1}$). Among them, one source at $z=4.71$ exhibits typical blazar-like properties, including a flat radio spectrum, radio-loudness $\sim$1000, and multi-frequency variability. It is also detected by SRG/eROSITA X-ray telescope (f$_{\rm 0.2-2.3keV} \sim 1.3\times10^{-13}$erg s$^{-1}$ cm$^{-2}$). In addition, for seven $6.36$ quasars reported in the literature. A $z = 6.3$ quasar exhibits a velocity difference of approximately $9000$ km s$^{-1}$ between the C$\rm IV$ and Mg$\rm II$ emission lines, making it one of the most extreme C$\rm IV$ outflows currently known. Additionally, the sample includes three high-ionization broad absorption line quasars. One of these quasars shows potential evidence of an extremely fast outflow feature, reaching $48000$ km s$^{-1}$.
The Lyman-alpha Halos of Galaxies at z=2-3 in the Keck Baryonic Structure Survey
2025-05-21
Ryan F. Trainor, Noah R. Lamb, Charles C. Steidel, Yuguang Chen, Dawn K. Erb, Elizabeth Trenholm, Rebecca L. McClain, Io Kovach
We present the large-scale spatial Lya profiles of galaxies from the Keck Baryonic Structure Survey (KBSS) at 2 50 kpc, theta > 6''). We find that the scale length of the Lya halo is not strongly dependent on the properties of the central galaxy, including its net continuum luminosity or EW_Lya, although we find a possible weak tendency of continuum-faint, high-EW_Lya galaxies to exhibit larger Lya halos in contrast with previous work.
Fully non-linear simulations of galaxy intrinsic alignments for weak lensing with the MillenniumTNG lightcone
2025-05-21
Fulvio Ferlito, Volker Springel, Christopher T. Davies, Toshiki Kurita, Ana Maria Delgado, Sownak Bose, Lars Hernquist
We present a complete forward model of a realistic weak lensing galaxy catalogue based on the 740 Mpc hydrodynamical MillenniumTNG (MTNG) simulation. Starting with a complete particle and cell lightcone covering one octant of the sky with redshift range 0 < $z$ < 1.5, we apply a group and subhalo finder to generate the corresponding galaxy catalogue for a fiducial observer. For all galaxies, we compute both their intrinsic and lensing-induced shear. The intrinsic component is derived from the luminosity-weighted inertia tensor of stellar particles, while the extrinsic (gravitational) shear is obtained through full-sky ray-tracing on the same lightcone. This allows us to directly predict the impact of intrinsic alignment (IA) of galaxies on the shear correlation function and popular convergence statistics in a fully non-linear forward model. We find that IA modifies the convergence power spectrum at all angular scales by up to 20%, it significantly impacts the PDF, altering its tails by 10-20%, and distorts peak and minimum counts up to 30%, depending on redshift and scale. We also evaluate the impact of the IA signal on the shear correlation function finding that, along with a redshift dependence, the signal strongly increases for higher galaxy stellar mass cuts applied to the catalogue. Notably, with the highest stellar mass cut we apply, the intrinsic shear autocorrelation can become comparable to the gravitational shear component on small angular scales. Our results highlight the importance of accurately modeling IA for precision weak lensing cosmology with upcoming Stage IV surveys.
Satellites Reveal Mobility: A Commuting Origin-destination Flow Generator for Global Cities
2025-05-21
Can Rong, Xin Zhang, Yanxin Xi, Hongjie Sui, Jingtao Ding, Yong Li
Commuting Origin-destination~(OD) flows, capturing daily population mobility of citizens, are vital for sustainable development across cities around the world. However, it is challenging to obtain the data due to the high cost of travel surveys and privacy concerns. Surprisingly, we find that satellite imagery, publicly available across the globe, contains rich urban semantic signals to support high-quality OD flow generation, with over 98\% expressiveness of traditional multisource hard-to-collect urban sociodemographic, economics, land use, and point of interest data. This inspires us to design a novel data generator, GlODGen, which can generate OD flow data for any cities of interest around the world. Specifically, GlODGen first leverages Vision-Language Geo-Foundation Models to extract urban semantic signals related to human mobility from satellite imagery. These features are then combined with population data to form region-level representations, which are used to generate OD flows via graph diffusion models. Extensive experiments on 4 continents and 6 representative cities show that GlODGen has great generalizability across diverse urban environments on different continents and can generate OD flow data for global cities highly consistent with real-world mobility data. We implement GlODGen as an automated tool, seamlessly integrating data acquisition and curation, urban semantic feature extraction, and OD flow generation together. It has been released at https://github.com/tsinghua-fib-lab/generate-od-pubtools.
PhysicsArena: The First Multimodal Physics Reasoning Benchmark Exploring Variable, Process, and Solution Dimensions
2025-05-21
Song Dai, Yibo Yan, Jiamin Su, Dongfang Zihao, Yubo Gao, Yonghua Hei, Jungang Li, Junyan Zhang, Sicheng Tao, Zhuoran Gao, Xuming Hu
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in diverse reasoning tasks, yet their application to complex physics reasoning remains underexplored. Physics reasoning presents unique challenges, requiring grounding in physical conditions and the interpretation of multimodal information. Current physics benchmarks are limited, often focusing on text-only inputs or solely on problem-solving, thereby overlooking the critical intermediate steps of variable identification and process formulation. To address these limitations, we introduce PhysicsArena, the first multimodal physics reasoning benchmark designed to holistically evaluate MLLMs across three critical dimensions: variable identification, physical process formulation, and solution derivation. PhysicsArena aims to provide a comprehensive platform for assessing and advancing the multimodal physics reasoning abilities of MLLMs.
2025-05-21
Katharina Winter, Abhishek Vivekanandan, Rupert Polley, Yinzhe Shen, Christian Schlauch, Mohamed-Khalil Bouzidi, Bojan Derajic, Natalie Grabowsky, Annajoyce Mariani, Dennis Rochau, Giovanni Lucente, Harsh Yadav, Firas Mualla, Adam Molin, Sebastian Bernhard, Christian Wirth, Ömer Şahin Taş, Nadja Klein, Fabian B. Flohr, Hanno Gottschalk
Generative AI (GenAI) is rapidly advancing the field of Autonomous Driving (AD), extending beyond traditional applications in text, image, and video generation. We explore how generative models can enhance automotive tasks, such as static map creation, dynamic scenario generation, trajectory forecasting, and vehicle motion planning. By examining multiple generative approaches ranging from Variational Autoencoder (VAEs) over Generative Adversarial Networks (GANs) and Invertible Neural Networks (INNs) to Generative Transformers (GTs) and Diffusion Models (DMs), we highlight and compare their capabilities and limitations for AD-specific applications. Additionally, we discuss hybrid methods integrating conventional techniques with generative approaches, and emphasize their improved adaptability and robustness. We also identify relevant datasets and outline open research questions to guide future developments in GenAI. Finally, we discuss three core challenges: safety, interpretability, and realtime capabilities, and present recommendations for image generation, dynamic scenario generation, and planning.
The exponential growth of data-driven systems and AI technologies has intensified the demand for high-quality web-sourced datasets. While existing datasets have proven valuable, conventional web data collection approaches face significant limitations in terms of human effort and scalability. Current data-collecting solutions fall into two categories: wrapper-based methods that struggle with adaptability and reproducibility, and large language model (LLM)-based approaches that incur substantial computational and financial costs. To address these challenges, we propose AutoData, a novel multi-agent system for Automated web Data collection, that requires minimal human intervention, i.e., only necessitating a natural language instruction specifying the desired dataset. In addition, AutoData is designed with a robust multi-agent architecture, featuring a novel oriented message hypergraph coordinated by a central task manager, to efficiently organize agents across research and development squads. Besides, we introduce a novel hypergraph cache system to advance the multi-agent collaboration process that enables efficient automated data collection and mitigates the token cost issues prevalent in existing LLM-based systems. Moreover, we introduce Instruct2DS, a new benchmark dataset supporting live data collection from web sources across three domains: academic, finance, and sports. Comprehensive evaluations over Instruct2DS and three existing benchmark datasets demonstrate AutoData's superior performance compared to baseline methods. Case studies on challenging tasks such as picture book collection and paper extraction from surveys further validate its applicability. Our source code and dataset are available at https://github.com/GraphResearcher/AutoData.
As large language models (LLMs) increasingly serve as autonomous agents in social contexts, understanding their capacity for prosocial behavior becomes essential. We present ProSim, a simulation framework designed to examine how prosocial behavior emerges, adapts, and erodes in LLM-based agents under diverse social and institutional conditions. The framework comprises four components: individual simulation, scenario simulation, interaction simulation, and intervention simulation. We conduct three progressive studies to evaluate prosocial alignment. First, we show that LLM agents can demonstrate stable and context-sensitive prosocial behavior across diverse scenarios and adapt their responses under normative policy interventions. Second, we find that agents engage in fairness-based third-party punishment and respond systematically to variations in inequity magnitude and enforcement cost. Third, we show that policy-induced inequities suppress prosocial behavior, propagate through social networks, and are mediated by agents' perceptions of unfairness. These findings lay the groundwork for evaluating social alignment and modeling institutional dynamics in agent-driven societies.
Recent advances in Large Language Models (LLMs) have shown promising results in complex reasoning tasks. However, current evaluations predominantly focus on single-turn reasoning scenarios, leaving interactive tasks largely unexplored. We attribute it to the absence of comprehensive datasets and scalable automatic evaluation protocols. To fill these gaps, we present MTR-Bench for LLMs' Multi-Turn Reasoning evaluation. Comprising 4 classes, 40 tasks, and 3600 instances, MTR-Bench covers diverse reasoning capabilities, fine-grained difficulty granularity, and necessitates multi-turn interactions with the environments. Moreover, MTR-Bench features fully-automated framework spanning both dataset constructions and model evaluations, which enables scalable assessment without human interventions. Extensive experiments reveal that even the cutting-edge reasoning models fall short of multi-turn, interactive reasoning tasks. And the further analysis upon these results brings valuable insights for future research in interactive AI systems.
Transparency in Healthcare AI: Testing European Regulatory Provisions against Users' Transparency Needs
2025-05-21
Anna Spagnolli, Cecilia Tolomini, Elisa Beretta, Claudio Sarra
Artificial Intelligence (AI) plays an essential role in healthcare and is pervasively incorporated into medical software and equipment. In the European Union, healthcare is a high-risk application domain for AI, and providers must prepare Instructions for Use (IFU) according to the European regulation 2024/1689 (AI Act). To this regulation, the principle of transparency is cardinal and requires the IFU to be clear and relevant to the users. This study tests whether these latter requirements are satisfied by the IFU structure. A survey was administered online via the Qualtrics platform to four types of direct stakeholders, i.e., managers (N = 238), healthcare professionals (N = 115), patients (N = 229), and Information Technology experts (N = 230). The participants rated the relevance of a set of transparency needs and indicated the IFU section addressing them. The results reveal differentiated priorities across stakeholders and a troubled mapping of transparency needs onto the IFU structure. Recommendations to build a locally meaningful IFU are derived.
Sequential QCQP for Bilevel Optimization with Line Search
2025-05-20
Sina Sharifi, Erfan Yazdandoost Hamedani, Mahyar Fazlyab
Bilevel optimization involves a hierarchical structure where one problem is nested within another, leading to complex interdependencies between levels. We propose a single-loop, tuning-free algorithm that guarantees anytime feasibility, i.e., approximate satisfaction of the lower-level optimality condition, while ensuring descent of the upper-level objective. At each iteration, a convex quadratically-constrained quadratic program (QCQP) with a closed-form solution yields the search direction, followed by a backtracking line search inspired by control barrier functions to ensure safe, uniformly positive step sizes. The resulting method is scalable, requires no hyperparameter tuning, and converges under mild local regularity assumptions. We establish an O(1/k) ergodic convergence rate and demonstrate the algorithm's effectiveness on representative bilevel tasks.
OSIRIS-REx Operational Key Decision Points: A Retrospective
2025-05-20
Rich Burns, Dante S. Lauretta
OSIRIS-REx, NASA's first asteroid sample return mission, rendezvoused with the near-Earth asteroid Bennu in 2018 and delivered a 121.6-gram sample to Earth in 2023, the largest amount of material ever recovered from a planetary body beyond the Moon. The operations phase of OSIRIS-REx was considered the most challenging robotic mission that NASA had undertaken, owing to the tight constraints on spacecraft performance and the microgravity environment. In preparation for the sample collection and return campaign, the mission leadership defined four operational key decision points (OKDPs) at critical junctures: sample site selection, rehearsal and execution of the sample collection maneuver, sample stow, and Earth return. This publication examines these OKDPs in depth, inclusive of rationale, implementation, decision processes, and efficacy. We also review other decisions that enabled mission success despite the unexpectedly rugged nature of Bennu's surface. This information should be beneficial for future challenging operational concepts where science-in-the-loop, time-critical decision making is integral to success.
sudoLLM : On Multi-role Alignment of Language Models
2025-05-20
Soumadeep Saha, Akshay Chaturvedi, Joy Mahapatra, Utpal Garain
User authorization-based access privileges are a key feature in many safety-critical systems, but have thus far been absent from the large language model (LLM) realm. In this work, drawing inspiration from such access control systems, we introduce sudoLLM, a novel framework that results in multi-role aligned LLMs, i.e., LLMs that account for, and behave in accordance with, user access rights. sudoLLM injects subtle user-based biases into queries and trains an LLM to utilize this bias signal in order to produce sensitive information if and only if the user is authorized. We present empirical results demonstrating that this approach shows substantially improved alignment, generalization, and resistance to prompt-based jailbreaking attacks. The persistent tension between the language modeling objective and safety alignment, which is often exploited to jailbreak LLMs, is somewhat resolved with the aid of the injected bias signal. Our framework is meant as an additional security layer, and complements existing guardrail mechanisms for enhanced end-to-end safety with LLMs.
Bellman operator convergence enhancements in reinforcement learning algorithms
2025-05-20
David Krame Kadurha, Domini Jocema Leko Moutouo, Yae Ulrich Gaba
This paper reviews the topological groundwork for the study of reinforcement learning (RL) by focusing on the structure of state, action, and policy spaces. We begin by recalling key mathematical concepts such as complete metric spaces, which form the foundation for expressing RL problems. By leveraging the Banach contraction principle, we illustrate how the Banach fixed-point theorem explains the convergence of RL algorithms and how Bellman operators, expressed as operators on Banach spaces, ensure this convergence. The work serves as a bridge between theoretical mathematics and practical algorithm design, offering new approaches to enhance the efficiency of RL. In particular, we investigate alternative formulations of Bellman operators and demonstrate their impact on improving convergence rates and performance in standard RL environments such as MountainCar, CartPole, and Acrobot. Our findings highlight how a deeper mathematical understanding of RL can lead to more effective algorithms for decision-making problems.
Task-parallelism in SWIFT for heterogeneous compute architectures
2025-05-20
Abouzied M. A. Nasar, Benedict D. Rogers, Georgios Fourtakas, Scott T. Kay, Matthieu Schaller
This paper highlights the first steps towards enabling graphics processing unit (GPU) acceleration of the smoothed particle hydrodynamics (SPH) solver for cosmology SWIFT and creating a hydrodynamics solver capable of fully leveraging the hardware available on heterogeneous exascale machines composed of central and graphics processing units (CPUs and GPUs). Exploiting the existing task-based parallelism in SWIFT, novel combinations of algorithms are presented which enable SWIFT to function as a truly heterogeneous software leveraging CPUs for memory-bound computations concurrently with GPUs for compute-bound computations in a manner which minimises the effects of CPU-GPU communication latency. These algorithms are validated in extensive testing which shows that the GPU acceleration methodology is capable of delivering up to 3.5x speedups for SWIFTs SPH hydrodynamics computation kernels when including the time required to prepare the computations on the CPU and unpack the results on the CPU. Speedups of 7.5x are demonstrated when not including the CPU data preparation and unpacking times. Whilst these measured speedups are substantial, it is shown that the overall performance of the hydrodynamic solver for a full simulation when accelerated on the GPU of state-of-the-art superchips, is only marginally faster than the code performance when using the Grace Hopper superchips fully parallelised CPU capabilities. This is shown to be mostly due to excessive fine-graining of the tasks prior to offloading on the GPU. Fine-graining introduces significant over-heads associated with task management on the CPU hosting the simulation and also introduces un-necessary duplication of CPU-GPU communications of the same data.
NavBench: A Unified Robotics Benchmark for Reinforcement Learning-Based Autonomous Navigation
2025-05-20
Matteo El-Hariry, Antoine Richard, Ricard M. Castan, Luis F. W. Batista, Matthieu Geist, Cedric Pradalier, Miguel Olivares-Mendez
Autonomous robots must navigate and operate in diverse environments, from terrestrial and aquatic settings to aerial and space domains. While Reinforcement Learning (RL) has shown promise in training policies for specific autonomous robots, existing benchmarks are often constrained to unique platforms, limiting generalization and fair comparisons across different mobility systems. In this paper, we present NavBench, a multi-domain benchmark for training and evaluating RL-based navigation policies across diverse robotic platforms and operational environments. Built on IsaacLab, our framework standardizes task definitions, enabling different robots to tackle various navigation challenges without the need for ad-hoc task redesigns or custom evaluation metrics. Our benchmark addresses three key challenges: (1) Unified cross-medium benchmarking, enabling direct evaluation of diverse actuation methods (thrusters, wheels, water-based propulsion) in realistic environments; (2) Scalable and modular design, facilitating seamless robot-task interchangeability and reproducible training pipelines; and (3) Robust sim-to-real validation, demonstrated through successful policy transfer to multiple real-world robots, including a satellite robotic simulator, an unmanned surface vessel, and a wheeled ground vehicle. By ensuring consistency between simulation and real-world deployment, NavBench simplifies the development of adaptable RL-based navigation strategies. Its modular design allows researchers to easily integrate custom robots and tasks by following the framework's predefined templates, making it accessible for a wide range of applications. Our code is publicly available at NavBench.
Federated prediction for scalable and privacy-preserved knowledge-based planning in radiotherapy
2025-05-20
Jingyun Chen, David Horowitz, Yading Yuan
Background: Deep learning has potential to improve the efficiency and consistency of radiation therapy planning, but clinical adoption is hindered by the limited model generalizability due to data scarcity and heterogeneity among institutions. Although aggregating data from different institutions could alleviate this problem, data sharing is a practical challenge due to concerns about patient data privacy and other technical obstacles. Purpose: This work aims to address this dilemma by developing FedKBP+, a comprehensive federated learning (FL) platform for predictive tasks in real-world applications in radiotherapy treatment planning. Methods: We implemented a unified communication stack based on Google Remote Procedure Call (gRPC) to support communication between participants whether located on the same workstation or distributed across multiple workstations. In addition to supporting the centralized FL strategies commonly available in existing open-source frameworks, FedKBP+ also provides a fully decentralized FL model where participants directly exchange model weights to each other through Peer-to-Peer communication. We evaluated FedKBP+ on three predictive tasks using scale-attention network (SA-Net) as the predictive model. Conclusions: Our results demonstrate that FedKBP+ is highly effective, efficient and robust, showing great potential as a federated learning platform for radiation therapy.
In this review, we present recent theoretical developments on spin transport phenomena probed by ferromagnetic resonance (FMR) modulation in two-dimensional systems coupled to magnetic materials. We first address FMR linewidth enhancements induced by spin pumping at interfaces, emphasizing their potential as sensitive probes of superconducting pairing symmetries in two-dimensional superconductors. We then examine FMR modulation due to spin pumping into two-dimensional electron gases formed in semiconductor heterostructures, where the interplay of Rashba and Dresselhaus spin-orbit interactions enables gate-controlled spin transport and persistent spin textures. Finally, we investigate spin pumping in monolayer transition-metal dichalcogenides, where spin-valley coupling and Berry curvature effects lead to valley-selective spin excitations and a spin-current Hall effect. These developments demonstrate that the spin pumping technique provides a versatile tool for probing spin transport and spin-dependent phenomena in low-dimensional systems, offering a basis for future spintronics applications.
Five New Sirius-Like White Dwarf + Main Sequence Star Systems with MagAO-X
2025-05-20
Logan A. Pearce, Jared R. Males, Sebastiaan Y. Haffert, Laird M. Close, Joseph D. Long, Eden A. McEwen, Joshua Liberman, Maggie Y. Kautz, Jay K. Kueny, Alycia J. Weinberger, Jialin Li, Elena Tonucci, Kattie Twitchell, Avalon McLeod, Warren B. Foster, Olivier Guyon, Alexander Hedglen, Kyle Van Gorkom, Jennifer Lumbres, Lauren Schatz, Victor Gasho, Katie M. Morzinski, Phil M. Hinz
Most known white dwarfs in multiple systems with main sequence stars have been discovered with M-type companions, because the white dwarf causes detectable UV excess and bluer colors than expected from a single M star. Surveys have shown that the number of white dwarfs in Sirius-like systems within 100 pc of the Sun is lower than expected, suggesting that white dwarfs are being missed in the glare of their main sequence companions. In this work we have leveraged the angular resolution and high-contrast capabilities, as well as optimization for visible wavelengths, of the extreme adaptive optics instrument MagAO-X to detect new white dwarf companions to AFGK stars. We present the first results of our survey with the extreme AO instrument MagAO-X, called the Pup Search, of 18 targets with seven new candidate companions, five of which are confirmed to be white dwarfs. We discuss the new detections in the context of previous surveys and other detection metric sensitivities and show that we are sensitive to a region not probed by other surveys. Finally we discuss the future of the Pup Search in light of developing technologies.
Pierce the Mists, Greet the Sky: Decipher Knowledge Overshadowing via Knowledge Circuit Analysis
2025-05-20
Haoming Huang, Yibo Yan, Jiahao Huo, Xin Zou, Xinfeng Li, Kun Wang, Xuming Hu
Large Language Models (LLMs), despite their remarkable capabilities, are hampered by hallucinations. A particularly challenging variant, knowledge overshadowing, occurs when one piece of activated knowledge inadvertently masks another relevant piece, leading to erroneous outputs even with high-quality training data. Current understanding of overshadowing is largely confined to inference-time observations, lacking deep insights into its origins and internal mechanisms during model training. Therefore, we introduce PhantomCircuit, a novel framework designed to comprehensively analyze and detect knowledge overshadowing. By innovatively employing knowledge circuit analysis, PhantomCircuit dissects the internal workings of attention heads, tracing how competing knowledge pathways contribute to the overshadowing phenomenon and its evolution throughout the training process. Extensive experiments demonstrate PhantomCircuit's effectiveness in identifying such instances, offering novel insights into this elusive hallucination and providing the research community with a new methodological lens for its potential mitigation.
A New Wavelet Scattering Transform-Based Statistic for Cosmological Analysis of Large-Scale Structure
2025-05-20
Zhujun Jiang, Xiaolin Luo, Wenying Du, Zhiwei Min, Fenfen Yin, Longlong Feng, Jiacheng Ding, Le Zhang, Xiao-Dong Li
In this study, we propose a novel method based on the wavelet scattering transform (WST) to distinguish large-scale structures of the universe across different cosmological models while mitigating tracer bias. By introducing a tailored data preprocessing strategy--particularly a high-density apodization technique--and defining a new statistical measure, the WST $m$-mode ratios, we assess performance using \texttt{CosmicGrowth} N-body simulations across three datasets: WMAP, mWMAP, and Planck. We find that only the WST ratio $R^{\rm wst}$ within the scale range $j \in [3,7]$ achieves a reduced chi-square for cosmological parameters $\chi^2_{\nu, \rm cos} \gg 1$ while maintaining $\chi^2_{\nu, \rm bias} \sim 1$ for tracer bias--a regime unattained by any other statistic. These results highlight $R^{\rm wst}$ as a promising tool for future galaxy surveys, offering strong cosmological sensitivity with effective bias mitigation.
Editing Across Languages: A Survey of Multilingual Knowledge Editing
2025-05-20
Nadir Durrani, Basel Mousi, Fahim Dalvi
While Knowledge Editing has been extensively studied in monolingual settings, it remains underexplored in multilingual contexts. This survey systematizes recent research on Multilingual Knowledge Editing (MKE), a growing subdomain of model editing focused on ensuring factual edits generalize reliably across languages. We present a comprehensive taxonomy of MKE methods, covering parameter-based, memory-based, fine-tuning, and hypernetwork approaches. We survey available benchmarks,summarize key findings on method effectiveness and transfer patterns, identify challenges in cross-lingual propagation, and highlight open problems related to language anisotropy, evaluation coverage, and edit scalability. Our analysis consolidates a rapidly evolving area and lays the groundwork for future progress in editable language-aware LLMs.
Generating a review for an academic research paper is a complex task that requires a deep understanding of the document's content and the interdependencies between its sections. It demands not only insight into technical details but also an appreciation of the paper's overall coherence and structure. Recent methods have predominantly focused on fine-tuning large language models (LLMs) to address this challenge. However, they often overlook the computational and performance limitations imposed by long input token lengths. To address this, we introduce AutoRev, an Automatic Peer Review System for Academic Research Papers. Our novel framework represents an academic document as a graph, enabling the extraction of the most critical passages that contribute significantly to the review. This graph-based approach demonstrates effectiveness for review generation and is potentially adaptable to various downstream tasks, such as question answering, summarization, and document representation. When applied to review generation, our method outperforms SOTA baselines by an average of 58.72% across all evaluation metrics. We hope that our work will stimulate further research in applying graph-based extraction techniques to other downstream tasks in NLP. We plan to make our code public upon acceptance.
Is Your Prompt Safe? Investigating Prompt Injection Attacks Against Open-Source LLMs
2025-05-20
Jiawen Wang, Pritha Gupta, Ivan Habernal, Eyke Hüllermeier
Recent studies demonstrate that Large Language Models (LLMs) are vulnerable to different prompt-based attacks, generating harmful content or sensitive information. Both closed-source and open-source LLMs are underinvestigated for these attacks. This paper studies effective prompt injection attacks against the $\mathbf{14}$ most popular open-source LLMs on five attack benchmarks. Current metrics only consider successful attacks, whereas our proposed Attack Success Probability (ASP) also captures uncertainty in the model's response, reflecting ambiguity in attack feasibility. By comprehensively analyzing the effectiveness of prompt injection attacks, we propose a simple and effective hypnotism attack; results show that this attack causes aligned language models, including Stablelm2, Mistral, Openchat, and Vicuna, to generate objectionable behaviors, achieving around $90$% ASP. They also indicate that our ignore prefix attacks can break all $\mathbf{14}$ open-source LLMs, achieving over $60$% ASP on a multi-categorical dataset. We find that moderately well-known LLMs exhibit higher vulnerability to prompt injection attacks, highlighting the need to raise public awareness and prioritize efficient mitigation strategies.
Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives
2025-05-20
Xingxing Weng, Chao Pang, Gui-Song Xia
Vision-language modeling (VLM) aims to bridge the information gap between images and natural language. Under the new paradigm of first pre-training on massive image-text pairs and then fine-tuning on task-specific data, VLM in the remote sensing domain has made significant progress. The resulting models benefit from the absorption of extensive general knowledge and demonstrate strong performance across a variety of remote sensing data analysis tasks. Moreover, they are capable of interacting with users in a conversational manner. In this paper, we aim to provide the remote sensing community with a timely and comprehensive review of the developments in VLM using the two-stage paradigm. Specifically, we first cover a taxonomy of VLM in remote sensing: contrastive learning, visual instruction tuning, and text-conditioned image generation. For each category, we detail the commonly used network architecture and pre-training objectives. Second, we conduct a thorough review of existing works, examining foundation models and task-specific adaptation methods in contrastive-based VLM, architectural upgrades, training strategies and model capabilities in instruction-based VLM, as well as generative foundation models with their representative downstream applications. Third, we summarize datasets used for VLM pre-training, fine-tuning, and evaluation, with an analysis of their construction methodologies (including image sources and caption generation) and key properties, such as scale and task adaptability. Finally, we conclude this survey with insights and discussions on future research directions: cross-modal representation alignment, vague requirement comprehension, explanation-driven model reliability, continually scalable model capabilities, and large-scale datasets featuring richer modalities and greater challenges.
2025-05-20
Nicolas Brunel, Vincent Hakim, Jean-Pierre Nadal
The perceptron has served as a prototypical neuronal learning machine in the physics community interested in neural networks and artificial intelligence, which included G\'erard Toulouse as one of its prominent figures. It has also been used as a model of Purkinje cells of the cerebellum, a brain structure involved in motor learning, in the early influential theories of David Marr and James Albus. We review these theories, more recent developments in the field, and highlight questions of current interest.
Plane Geometry Problem Solving with Multi-modal Reasoning: A Survey
2025-05-20
Seunghyuk Cho, Zhenyue Qin, Yang Liu, Youngbin Choi, Seungbeom Lee, Dongwoo Kim
Plane geometry problem solving (PGPS) has recently gained significant attention as a benchmark to assess the multi-modal reasoning capabilities of large vision-language models. Despite the growing interest in PGPS, the research community still lacks a comprehensive overview that systematically synthesizes recent work in PGPS. To fill this gap, we present a survey of existing PGPS studies. We first categorize PGPS methods into an encoder-decoder framework and summarize the corresponding output formats used by their encoders and decoders. Subsequently, we classify and analyze these encoders and decoders according to their architectural designs. Finally, we outline major challenges and promising directions for future research. In particular, we discuss the hallucination issues arising during the encoding phase within encoder-decoder architectures, as well as the problem of data leakage in current PGPS benchmarks.
Effects of the Cyber Resilience Act (CRA) on Industrial Equipment Manufacturing Companies
2025-05-20
Roosa Risto, Mohit Sethi, Mika Katara
The Cyber Resilience Act (CRA) is a new European Union (EU) regulation aimed at enhancing the security of digital products and services by ensuring they meet stringent cybersecurity requirements. This paper investigates the challenges that industrial equipment manufacturing companies anticipate while preparing for compliance with CRA through a comprehensive survey. Key findings highlight significant hurdles such as implementing secure development lifecycle practices, managing vulnerability notifications within strict timelines, and addressing gaps in cybersecurity expertise. This study provides insights into these specific challenges and offers targeted recommendations on key focus areas, such as tooling improvements, to aid industrial equipment manufacturers in their preparation for CRA compliance.
MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains
2025-05-20
Kyungeun Lee, Moonjung Eo, Hye-Seung Cho, Dongmin Kim, Ye Seul Sim, Seoyoon Kim, Min-Kook Suh, Woohyung Lim
Despite the widespread use of tabular data in real-world applications, most benchmarks rely on average-case metrics, which fail to reveal how model behavior varies across diverse data regimes. To address this, we propose MultiTab, a benchmark suite and evaluation framework for multi-dimensional, data-aware analysis of tabular learning algorithms. Rather than comparing models only in aggregate, MultiTab categorizes 196 publicly available datasets along key data characteristics, including sample size, label imbalance, and feature interaction, and evaluates 13 representative models spanning a range of inductive biases. Our analysis shows that model performance is highly sensitive to such regimes: for example, models using sample-level similarity excel on datasets with large sample sizes or high inter-feature correlation, while models encoding inter-feature dependencies perform best with weakly correlated features. These findings reveal that inductive biases do not always behave as intended, and that regime-aware evaluation is essential for understanding and improving model behavior. MultiTab enables more principled model design and offers practical guidance for selecting models tailored to specific data characteristics. All datasets, code, and optimization logs are publicly available at https://huggingface.co/datasets/LGAI-DILab/Multitab.
A Review of Vision-Based Assistive Systems for Visually Impaired People: Technologies, Applications, and Future Directions
2025-05-20
Fulong Yao, Wenju Zhou, Huosheng Hu
Visually impaired individuals rely heavily on accurate and timely information about obstacles and their surrounding environments to achieve independent living. In recent years, significant progress has been made in the development of assistive technologies, particularly vision-based systems, that enhance mobility and facilitate interaction with the external world in both indoor and outdoor settings. This paper presents a comprehensive review of recent advances in assistive systems designed for the visually impaired, with a focus on state-of-the-art technologies in obstacle detection, navigation, and user interaction. In addition, emerging trends and future directions in visual guidance systems are discussed.
The H~{\sc i} gas distribution in damped Lyman $\alpha$ absorbers (DLAs) has remained elusive due to the point-source nature of background quasar emission. Observing DLAs against spatially extended background galaxies provides a new method for constraining their size and structure. Using the Keck Cosmic Web Imager, we present the first ``silhouette'' image of a DLA at $z=3.34$, identified in the spectrum of a background galaxy at $z=3.61$. Although the silhouette remains unresolved due to limited spatial resolution, this represents a successful proof-of-concept for studying DLA morphology using extended background sources. Possible residual emission in the DLA trough suggests an optical depth contrast exceeding $10^7$ in the internal structure, implying a sharp edge or patchy structure. A Lyman $\alpha$ emitter (LAE) at $z_{\rm LAE}=3.3433\pm0.0005$, consistent with the DLA redshift, is detected at an angular separation of $1.''73\pm0.''28$ ($12.9\pm2.1$ kpc). The DLA is surrounded by three galaxies within 140 kpc in projected distance and 500 km s$^{-1}$ in line-of-sight velocity, indicating that it resides in the circumgalactic medium of the LAE or within a galaxy group/protocluster environment. An O~{\sc i} $\lambda1302$ absorption at $z_{\rm OI}=3.3288\pm0.0004$ is also detected along the line of sight. This absorber may trace metal-enriched outflow from the LAE or a gas-rich galaxy exhibiting the highest star formation activity among the surrounding galaxies. Future large spectroscopic surveys of galaxies will expand such a DLA sample, and three-dimensional spectroscopy for it will shed new light on the role of intergalactic dense gas in galaxy formation and evolution.
How Influencers and Multipliers Drive Polarization and Issue Alignment on Twitter/X
2025-05-20
Armin Pournaki, Felix Gaisbauer, Eckehard Olbrich
We investigate the polarization of the German Twittersphere by extracting the main issues discussed and the signaled opinions of users towards those issues based on (re)tweets concerning trending topics. The dataset covers daily trending topics from March 2021 to July 2023. At the opinion level, we show that the online public sphere is largely divided into two camps, one consisting mainly of left-leaning, and another of right-leaning accounts. Further we observe that political issues are strongly aligned, contrary to what one may expect from surveys. This alignment is driven by two cores of strongly active users: influencers, who generate ideologically charged content, and multipliers, who facilitate the spread of this content. The latter are specific to social media and play a crucial role as intermediaries on the platform by curating and amplifying very specific types of content that match their ideological position, resulting in the overall observation of a strongly polarized public sphere. These results contribute to a better understanding of the mechanisms that shape online public opinion, and have implications for the regulation of platforms.
An engineering guide to superconducting quantum circuit shielding
2025-05-20
Elizaveta I. Malevannaya, Viktor I. Polozov, Anton I. Ivanov, Aleksei R. Matanin, Nikita S. Smirnov, Vladimir V. Echeistov, Dmitry O. Moskalev, Dmitry A. Mikhalin, Denis E. Shirokov, Yuri V. Panfilov, Ilya A. Ryzhikov, Aleksander V. Andriyash, Ilya A. Rodionov
In this review, we provide a practical guide on protection of superconducting quantum circuits from broadband electromagnetic and infrared-radiation noise by using cryogenic shielding and filtering of microwave lines. Recently, superconducting multi-qubit processors demonstrated quantum supremacy and quantum error correction below the surface code threshold. However, the decoherence-induced loss of quantum information still remains a challenge for more than 100 qubit quantum computing. Here, we review the key aspects of superconducting quantum circuits protection from stray electromagnetic fields and infrared radiation, namely, multilayer shielding design, materials, filtering of the fridge lines and attenuation, cryogenic setup configurations, and methods for shielding efficiency evaluation developed over the last 10 years. In summary, we make recommendations for creation of an efficient and compact shielding system as well as microwave filtering for a large-scale superconducting quantum systems.
Device-Independent Quantum Key Distribution: Protocols, Quantum Games, and Security
2025-05-20
Syed M. Arslan, Saif Al-Kuwari, M. T. Rahim, Hashir Kuniyal
Quantum Key Distribution (QKD) is based on the laws of quantum mechanics to enable provably secure communication. Despite its theoretical security promise, practical QKD systems are vulnerable to serious attacks, including side-channel attacks and detector loopholes, and assumes a trusted device characterization. Device-Independent Quantum Key Distribution (DIQKD) overcomes these limitations by relying solely on observed nonlocal correlations, certified through Bell inequality violations, thereby removing assumptions about the internal workings of the measurement devices. In this paper, we first review the foundational principles underlying DIQKD, including Bell tests and security definitions. We then examine a range of protocol designs, including CHSH-based schemes, and non-local game frameworks, alongside with their security proofs. We also assess recent experimental implementations and discuss source architectures, detection technologies, and finite-key analyses. Finally, we identify current open problems, such as noise tolerance, generation rates, and integration with quantum networks, and outline promising directions for future research to realize robust, high-performance DIQKD.
Technical Report on classification of literature related to children speech disorder
2025-05-20
Ziang Wang, Amir Aryani
This technical report presents a natural language processing (NLP)-based approach for systematically classifying scientific literature on childhood speech disorders. We retrieved and filtered 4,804 relevant articles published after 2015 from the PubMed database using domain-specific keywords. After cleaning and pre-processing the abstracts, we applied two topic modeling techniques - Latent Dirichlet Allocation (LDA) and BERTopic - to identify latent thematic structures in the corpus. Our models uncovered 14 clinically meaningful clusters, such as infantile hyperactivity and abnormal epileptic behavior. To improve relevance and precision, we incorporated a custom stop word list tailored to speech pathology. Evaluation results showed that the LDA model achieved a coherence score of 0.42 and a perplexity of -7.5, indicating strong topic coherence and predictive performance. The BERTopic model exhibited a low proportion of outlier topics (less than 20%), demonstrating its capacity to classify heterogeneous literature effectively. These results provide a foundation for automating literature reviews in speech-language pathology.
Toward Embodied AGI: A Review of Embodied AI and the Road Ahead
2025-05-20
Yequan Wang, Aixin Sun
Artificial General Intelligence (AGI) is often envisioned as inherently embodied. With recent advances in robotics and foundational AI models, we stand at the threshold of a new era-one marked by increasingly generalized embodied AI systems. This paper contributes to the discourse by introducing a systematic taxonomy of Embodied AGI spanning five levels (L1-L5). We review existing research and challenges at the foundational stages (L1-L2) and outline the key components required to achieve higher-level capabilities (L3-L5). Building on these insights and existing technologies, we propose a conceptual framework for an L3+ robotic brain, offering both a technical outlook and a foundation for future exploration.
A Mosaic of Perspectives: Understanding Ownership in Software Engineering
2025-05-20
Tomi Suomi, Petri Ihantola, Tommi Mikkonen, Niko Mäkitalo
Agile software development relies on self-organized teams, underlining the importance of individual responsibility. How developers take responsibility and build ownership are influenced by external factors such as architecture and development methods. This paper examines the existing literature on ownership in software engineering and in psychology, and argues that a more comprehensive view of ownership in software engineering has a great potential in improving software team's work. Initial positions on the issue are offered for discussion and to lay foundations for further research.
Functional Controllability, Functional Stabilisability, and the Generalised Separation Principle
2025-05-20
Tyrone Fernando, Mohamed Darouach
This paper introduces the new concepts of Functional Controllability and Functional Stabilisability, and establishes their duality with Functional Observability and Functional Detectability, respectively. We further present a Generalised Separation Principle, demonstrating that the classical Separation Principle emerges as a special case. Conditions for the existence of functional controllers of a specified order are derived. Importantly, the design framework does not require full controllability. Furthermore, we develop a functional observer-based controller design applicable to systems that are both uncontrollable and unobservable. The results presented generalise the classical full-state feedback control paradigm.
PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore
2025-05-20
Zhenkai Qin, Jiajing He, Qiao Fang
Fine-grained sentiment analysis (FGSA) aims to identify sentiment polarity toward specific aspects within a text, enabling more precise opinion mining in domains such as product reviews and social media. However, traditional FGSA approaches often require task-specific architectures and extensive annotated data, limiting their generalization and scalability. To address these challenges, we propose PL-FGSA, a unified prompt learning-based framework implemented using the MindSpore platform, which integrates prompt design with a lightweight TextCNN backbone. Our method reformulates FGSA as a multi-task prompt-augmented generation problem, jointly tackling aspect extraction, sentiment classification, and causal explanation in a unified paradigm. By leveraging prompt-based guidance, PL-FGSA enhances interpretability and achieves strong performance under both full-data and low-resource conditions. Experiments on three benchmark datasets-SST-2, SemEval-2014 Task 4, and MAMS-demonstrate that our model consistently outperforms traditional fine-tuning methods and achieves F1-scores of 0.922, 0.694, and 0.597, respectively. These results validate the effectiveness of prompt-based generalization and highlight the practical value of PL-FGSA for real-world sentiment analysis tasks.
Relating thermodynamic quantities of convex-hard-body fluids to the body's shape
2025-05-20
Thomas Franosch, Cristiano De Michele, Rolf Schilling
For a fluid of convex hard particles, characterized by a length scale $\sigma_\text{min}$ and an anisotropy parameter $\epsilon$, we develop a formalism allowing one to relate thermodynamic quantities to the body's shape. In a first step its thermodynamics is reduced to that of spherical particles. The latter have a hard core of diameter $\sigma_\text{min }$ and a soft shell with a thickness $\epsilon \sigma_\text{min}/2$. Besides their hard core repulsion at $\sigma_\text{min }$ they interact by effective entropic forces which will be calculated. Based on this mapping, a second step provides a perturbative method for the systematic calculation of thermodynamic quantities with the shape anisotropy $\epsilon$ as smallness parameter. In leading order in $\epsilon $, the equation of state is derived as a functional of the particle's shape. To illustrate these findings, they are applied to a one- and two-dimensional fluid of ellipses and compared with results from different analytical approaches, and our computer simulations. The mapping to spherical particles also implies that any phase transition of spherical particles, e.g., the liquid-hexatic transition, also exists for the nonspherical ones, and shifts linearly with $\epsilon $ for weak shape anisotropy. This is supported by our Monte-Carlo simulation.
Unconventional Hexacopters via Evolution and Learning: Performance Gains and New Insights
2025-05-20
Jed Muff, Keiichi Ito, Elijah H. W. Ang, Karine Miras, A. E. Eiben
Evolution and learning have historically been interrelated topics, and their interplay is attracting increased interest lately. The emerging new factor in this trend is morphological evolution, the evolution of physical forms within embodied AI systems such as robots. In this study, we investigate a system of hexacopter-type drones with evolvable morphologies and learnable controllers and make contributions to two fields. For aerial robotics, we demonstrate that the combination of evolution and learning can deliver non-conventional drones that significantly outperform the traditional hexacopter on several tasks that are more complex than previously considered in the literature. For the field of Evolutionary Computing, we introduce novel metrics and perform new analyses into the interaction of morphological evolution and learning, uncovering hitherto unidentified effects. Our analysis tools are domain-agnostic, making a methodological contribution towards building solid foundations for embodied AI systems that integrate evolution and learning.
Quantum and Critical Casimir Effects: Bridging Fluctuation Physics and Nanotechnology
2025-05-20
Roberto Passante, Lucia Rizzuto, Peter Schall, Emanuele Marino
Fluctuation-induced forces, primarily represented by quantum and critical Casimir effects, play a pivotal role at the nanoscale. This review explores the theoretical and experimental landscapes of these forces, offering a comprehensive analysis of their similarities and distinctions. We emphasize the effects of material properties, geometry, and temperature in shaping these forces and their roles in various nanoscale systems, both colloidal and solid-state. We devote special attention to the Casimir torque, the influence of magnetism on the Casimir force, and the use of Casimir effects for the generation of optical resonators. Through this comparative study, we elucidate the underlying physics of these phenomena, fostering insights that advance applications in nanomechanics, optomechanics, and quantum technologies.
The emergence of groundbreaking large language models capable of performing complex reasoning tasks holds significant promise for addressing various scientific challenges, including those arising in complex clinical scenarios. To enable their safe and effective deployment in real-world healthcare settings, it is urgently necessary to benchmark the diagnostic capabilities of current models systematically. Given the limitations of existing medical benchmarks in evaluating advanced diagnostic reasoning, we present DiagnosisArena, a comprehensive and challenging benchmark designed to rigorously assess professional-level diagnostic competence. DiagnosisArena consists of 1,113 pairs of segmented patient cases and corresponding diagnoses, spanning 28 medical specialties, deriving from clinical case reports published in 10 top-tier medical journals. The benchmark is developed through a meticulous construction pipeline, involving multiple rounds of screening and review by both AI systems and human experts, with thorough checks conducted to prevent data leakage. Our study reveals that even the most advanced reasoning models, o3-mini, o1, and DeepSeek-R1, achieve only 45.82%, 31.09%, and 17.79% accuracy, respectively. This finding highlights a significant generalization bottleneck in current large language models when faced with clinical diagnostic reasoning challenges. Through DiagnosisArena, we aim to drive further advancements in AIs diagnostic reasoning capabilities, enabling more effective solutions for real-world clinical diagnostic challenges. We provide the benchmark and evaluation tools for further research and development https://github.com/SPIRAL-MED/DiagnosisArena.
Legal rules encompass not only codified statutes but also implicit adjudicatory principles derived from precedents that contain discretionary norms, social morality, and policy. While computational legal research has advanced in applying established rules to cases, inducing legal rules from judicial decisions remains understudied, constrained by limitations in model inference efficacy and symbolic reasoning capability. The advent of Large Language Models (LLMs) offers unprecedented opportunities for automating the extraction of such latent principles, yet progress is stymied by the absence of formal task definitions, benchmark datasets, and methodologies. To address this gap, we formalize Legal Rule Induction (LRI) as the task of deriving concise, generalizable doctrinal rules from sets of analogous precedents, distilling their shared preconditions, normative behaviors, and legal consequences. We introduce the first LRI benchmark, comprising 5,121 case sets (38,088 Chinese cases in total) for model tuning and 216 expert-annotated gold test sets. Experimental results reveal that: 1) State-of-the-art LLMs struggle with over-generalization and hallucination; 2) Training on our dataset markedly enhances LLMs capabilities in capturing nuanced rule patterns across similar cases.
Understanding which factors could influence co-presence in Virtual Reality could help develop more qualitative social interactions, or social interactions that generate similar sensations, emotions and feelings than the ones generated during Face-to-Face interactions. Co-presence is studied since the beginning of Virtual Reality (VR); though, no consensus is identified on what factors could influence it, except the consensus on the definition of "being there together" inside the Virtual Environment. In this paper, we introduce the Koinos method to explain social interactions in VR through communication models, (i) theoretically, and (ii) on two VR experiments that change the virtual partner social and physical representations. These analyses lead us to propose an equation to predict and help manage the sense of co-presence in VR.
Deep imaging surveys have changed our view of the low surface brightness (LSB) Universe. The "renaissance" of the low surface brightness dwarf galaxy population, as the prime example of such recent development, continues to challenge our understanding of galaxy formation. Here, We report the serendipitous discovery of Zangetsu, an isolated, quiescent, and distorted ultra-diffuse galaxy (UDG) candidate in the COSMOS field, using images from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). Zangetsu exhibits an extremely low central surface brightness ($\mathrm{\mu_{0,g}}=26.60\pm0.01$ mag arcsec$^{-2}$), a very shallow inner surface brightness profile ($\mathrm{n}_{\rm Sersic}=0.40\pm0.01$), and a large angular size ($\mathrm{R_e}\approx 10.44$ arcsec). Surprisingly, Zangetsu also has a quiescent stellar population ($\mathrm{g-i}=0.96$), an unusually elongated shape ($\mathrm{b/a}\sim 0.25$), and mild morphological asymmetry, making it a rare case among known UDGs. Surface brightness fluctuation analysis of HSC and Hubble Space Telescope (HST) images only provides a distance lower limit of $D>25.4$ Mpc (thus $\mathrm{R_e}>1.38$ kpc). However, Zangetsu remains an extreme outlier in the luminosity-size relation of known LSB galaxies, suggesting that it could be an exceptionally large and/or diffuse system. Classic internal or external UDG formation mechanisms alone struggle to explain such a system. A backsplash origin may account for its isolation and quiescent nature. This finding also raises the possibility that current works may overlook similarly extreme, elongated systems that could further our understanding of the LSB Universe.
Place Recognition: A Comprehensive Review, Current Challenges and Future Directions
2025-05-20
Zhenyu Li, Tianyi Shang, Pengjie Xu, Zhaojun Deng
Place recognition is a cornerstone of vehicle navigation and mapping, which is pivotal in enabling systems to determine whether a location has been previously visited. This capability is critical for tasks such as loop closure in Simultaneous Localization and Mapping (SLAM) and long-term navigation under varying environmental conditions. In this survey, we comprehensively review recent advancements in place recognition, emphasizing three representative methodological paradigms: Convolutional Neural Network (CNN)-based approaches, Transformer-based frameworks, and cross-modal strategies. We begin by elucidating the significance of place recognition within the broader context of autonomous systems. Subsequently, we trace the evolution of CNN-based methods, highlighting their contributions to robust visual descriptor learning and scalability in large-scale environments. We then examine the emerging class of Transformer-based models, which leverage self-attention mechanisms to capture global dependencies and offer improved generalization across diverse scenes. Furthermore, we discuss cross-modal approaches that integrate heterogeneous data sources such as Lidar, vision, and text description, thereby enhancing resilience to viewpoint, illumination, and seasonal variations. We also summarize standard datasets and evaluation metrics widely adopted in the literature. Finally, we identify current research challenges and outline prospective directions, including domain adaptation, real-time performance, and lifelong learning, to inspire future advancements in this domain. The unified framework of leading-edge place recognition methods, i.e., code library, and the results of their experimental evaluations are available at https://github.com/CV4RA/SOTA-Place-Recognitioner.
The JCMT BISTRO-3 Survey: Variation of magnetic field orientations on parsec and sub-parsec scales in the massive star-forming region G28.34+0.06
2025-05-20
Jihye Hwang, Kate Pattle, Chang Won Lee, Janik Karoly, Kee-Tae Kim, Jongsoo Kim, Junhao Liu, Keping Qiu, A-Ran Lyo, David Eden, Patrick M. Koch, Doris Arzoumanian, Ekta Sharma, Frédérick Poidevin, Doug Johnstone, Simon Coudé, Mehrnoosh Tahani, Derek Ward-Thompson, Archana Soam, Ji-hyun Kang, Thiem Hoang, Woojin Kwon, Nguyen Bich Ngoc, Takashi Onaka, Florian Kirchschlager, Motohide Tamura, Jungmi Kwon, Xindi Tang, Eun Jung Chung, Tyler L. Bourke, Eswaraiah Chakali, Tie Liu, Pierre Bastien, Ray S. Furuya, Shih-Ping Lai, Sheng-Jun Lin, Jia-Wei Wang, David Berry
Magnetic fields play a significant role in star-forming processes on core to clump scales. We investigate magnetic field orientations and strengths in the massive star-forming clump P2 within the filamentary infrared dark cloud G28.34+0.06 using dust polarization observations made using SCUBA-2/POL-2 on the James Clerk Maxwell Telescope as part of the B-field In STar-forming Region Observations (BISTRO) survey. We compare the magnetic field orientations at the clump scale of ~2 parsecs from these JCMT observations with those at the core scale of ~0.2 parsecs from archival ALMA data, finding that the magnetic field orientations on these two different scales are perpendicular to one another. We estimate the distribution of magnetic field strengths, which range from 50 to 430 {\mu}G over the clump. The region forming the core shows the highest magnetic field strength. We also obtain the distribution of mass-to-flux ratios across the clump. In the region surrounding the core, the mass-to-flux ratio is larger than 1, which indicates the magnetic field strength is insufficient to support the region against gravitational collapse. Therefore, the change in the magnetic field orientation from clump to core scales may be the result of gravitational collapse, with the field being pulled inward along with the flow of material under gravity.
Reliable stellar atmospheric parameters are essential for probing stellar structure and evolution, and for stellar population studies. However, various deviations appear in comparisons with different ground-based spectroscopic surveys. We aim to select high-quality open cluster members and employ the atmospheric parameters provided by the theoretical isochrones of open clusters as a benchmark to assess the quality of stellar atmospheric parameters from Gaia DR3 and other ground-based spectroscopic surveys, such as LAMOST DR11, APOGEE DR17, and GALAH DR4. We selected 130 open clusters with well-defined main sequences within 500 pc of the solar neighborhood as a benchmark sample to estimate the reference atmospheric parameters of the members from the best-fit isochrones of those clusters. By comparing the atmospheric parameters provided by different spectroscopic surveys to the theoretical parameters, we found that the atmospheric parameter deviation and the corresponding dispersions exhibit different variations. The atmospheric parameter deviations of F, G, and K-type stars are smaller than those of B, A, and M-type stars for most surveys. For most samples, the dispersion of Teff decreases as temperature decreases, whereas the dispersion of logg shows the opposite trend.
In this paper, we propose an efficient visual transformer framework for ultra-high-definition (UHD) image dehazing that addresses the key challenges of slow training speed and high memory consumption for existing methods. Our approach introduces two key innovations: 1) an \textbf{a}daptive \textbf{n}ormalization mechanism inspired by the nGPT architecture that enables ultra-fast and stable training with a network with a restricted range of parameter expressions; and 2) we devise an atmospheric scattering-aware KV caching mechanism that dynamically optimizes feature preservation based on the physical haze formation model. The proposed architecture improves the training convergence speed by \textbf{5 $\times$} while reducing memory overhead, enabling real-time processing of 50 high-resolution images per second on an RTX4090 GPU. Experimental results show that our approach maintains state-of-the-art dehazing quality while significantly improving computational efficiency for 4K/8K image restoration tasks. Furthermore, we provide a new dehazing image interpretable method with the help of an integrated gradient attribution map. Our code can be found here: https://anonymous.4open.science/r/anDehazeFormer-632E/README.md.
The Capability of Code Review as a Communication Network
2025-05-20
Michael Dorner, Daniel Mendez
Background: Code review, a core practice in software engineering, has been widely studied as a collaborative process, with prior work suggesting it functions as a communication network. However, this theory remains untested, limiting its practical and theoretical significance. Objective: This study aims to (1) formalize the theory of code review as a communication network explicit and (2) empirically test its validity by quantifying how widely and how quickly information can spread in code review. Method: We replicate an in-silico experiment simulating information diffusion -- the spread of information among participants -- under best-case conditions across three open-source (Android, Visual Studio Code, React) and three closed-source code review systems (Microsoft, Spotify, Trivago) each modeled as communication network. By measuring the number of reachable participants and the minimal topological and temporal distances, we quantify how widely and how quickly information can spread through code review. Results: We demonstrate that code review can enable both wide and fast information diffusion, even at a large scale. However, this capacity varies: open-source code review spreads information faster, while closed-source review reaches more participants. Conclusion: Our findings reinforce and refine the theory, highlighting implications for measuring collaboration, generalizing open-source studies, and the role of AI in shaping future code review.
Symbolic and Numerical Tools for $L_{\infty}$-Norm Calculation
2025-05-20
Grace Younes, Alban Quadrat, Fabrice Rouillier
The computation of the $L_\infty $-norm is an important issue in $H_{\infty}$ control, particularly for analyzing system stability and robustness. This paper focuses on symbolic computation methods for determining the $L_{\infty} $-norm of finite-dimensional linear systems, highlighting their advantages in achieving exact solutions where numerical methods often encounter limitations. Key techniques such as Sturm-Habicht sequences, Rational Univariate Representations (RUR), and Cylindrical Algebraic Decomposition (CAD) are surveyed, with an emphasis on their theoretical foundations, practical implementations, and specific applicability to $ L_{\infty} $-norm computation. A comparative analysis is conducted between symbolic and conventional numerical approaches, underscoring scenarios in which symbolic computation provides superior accuracy, particularly in parametric cases. Benchmark evaluations reveal the strengths and limitations of both approaches, offering insights into the trade-offs involved. Finally, the discussion addresses the challenges of symbolic computation and explores future opportunities for its integration into control theory, particularly for robust and stable system analysis.
XXII. Accurate stellar velocity dispersions of the SL2S lens sample and the lensing mass fundamental plane
2025-05-20
Pritom Mozumdar, Shawn Knabel, Tommaso Treu, Alessandro Sonnenfeld, Anowar J. Shajib, Michele Cappellari, Carlo Nipoti
We reanalyze spectra taken as part of the SL2S lens galaxy survey, with the goal to obtain stellar velocity dispersion with precision and accuracy sufficient for time-delay cosmography. In order to achieve this goal, we impose stringent cuts on signal-to-noise ratio (SNR), and employ recently developed methods to mitigate and quantify residual systematic errors due to template libraries and fitting process. We also quantify the covariance across the sample. For galaxies with spectra with SNR > 20/{\AA}, our new measurements have average random uncertainty of 3-4%, average systematic uncertainty of 2%, and covariance across the sample of 1%. We find negligible covariance between spectra taken with different instruments. The systematic uncertainty and covariance need to be included when the sample is used as an external dataset in time-delay cosmography. We revisit empirical scaling relations of lens galaxies based on the improved kinematics. We show that the SLS2 sample, the TDCOSMO time-delay lens sample, and the lower redshift SLACS sample follow the same correlation between effective radius, stellar velocity dispersion and lensing mass, known as the lensing mass fundamental plane, as that derived by Auger et al. (2010) assuming isothermal mass profiles for the deflectors. We also derive for the first time the lensing mass fundamental plane assuming free power-law mass density profiles, and show that the three samples also follow the same correlation. This is consistent with a scenario in which massive galaxies evolve by growing their radii and mass, but staying within the plane.
WALLABY pilot survey: Spatially resolved gas scaling relations within the stellar discs of nearby galaxies
2025-05-20
Seona Lee, Barbara Catinella, Tobias Westmeier, Luca Cortese, Jing Wang, Kristine Spekkens, Nathan Deg, Helga Dénes, Ahmed Elagali, Bärbel S. Koribalski, Karen Lee-Waddell, Chandrashekar Murugeshan, Jonghwan Rhee, Lister Staveley-Smith, O. Ivy Wong, Benne W. Holwerda
The scatter in global atomic hydrogen (HI) scaling relations is partly attributed to differences in how HI and stellar properties are measured, with HI reservoirs typically extending beyond the inner regions of galaxies where star formation occurs. Using pilot observations from the WALLABY survey, we present the first measurements of HI mass enclosed within the stellar-dominated regions of galaxies for a statistical sample of 995 local gas-rich systems, investigating the factors driving its variation. We examine how global HI scaling relations change when measurements are restricted to R25 and R24 -- the isophotal radii at 25 and 24 mag arcsec$^{-2}$ in the i-band -- and explore how the fraction of HI mass and HI surface density within these radii correlate with other galaxy properties. On average, 68% of the total HI mass is enclosed within R25 and 54% within R24, though significant variation exists between galaxies. The fraction of HI mass within R25 shows a mild correlation with stellar properties, with galaxies of higher stellar mass, greater stellar surface density, or redder colours enclosing a larger fraction of their HI reservoirs. These correlations do not significantly strengthen when considering R24. Conversely, global HI surface densities show no significant correlation with stellar mass or stellar surface density, but trends start emerging when these are measured within the inner regions of galaxies. The strongest correlation is observed with optical colour, with bluer galaxies having higher average HI surface densities within R25. This trend strengthens when we restrict from R25 to R24, suggesting a closer connection between inner HI reservoirs and star formation. This study underscores the value of (at least marginally) resolved HI surveys of statistical samples for advancing our understanding of the gas-star formation cycle in galaxies. [Abriged]
Benchmarking the Myopic Trap: Positional Bias in Information Retrieval
2025-05-20
Ziyang Zeng, Dun Zhang, Jiacheng Li, Panxiang Zou, Yuqing Yang
This study investigates a specific form of positional bias, termed the Myopic Trap, where retrieval models disproportionately attend to the early parts of documents while overlooking relevant information that appears later. To systematically quantify this phenomenon, we propose a semantics-preserving evaluation framework that repurposes the existing NLP datasets into position-aware retrieval benchmarks. By evaluating the SOTA models of full retrieval pipeline, including BM25, embedding models, ColBERT-style late-interaction models, and reranker models, we offer a broader empirical perspective on positional bias than prior work. Experimental results show that embedding models and ColBERT-style models exhibit significant performance degradation when query-related content is shifted toward later positions, indicating a pronounced head bias. Notably, under the same training configuration, ColBERT-style approach show greater potential for mitigating positional bias compared to the traditional single-vector approach. In contrast, BM25 and reranker models remain largely unaffected by such perturbations, underscoring their robustness to positional bias. Code and data are publicly available at: www.github.com/NovaSearch-Team/RAG-Retrieval.
The Initial Mass Function of the Galactic Early-type Field Stars Based on the LAMOST Survey
2025-05-20
Qida Li, Jianping Xiong, Zhenwei Li, Dan Qiu, Chao Liu, Zhanwen Han, Xuefei Chen
Research on the high-mass end of the initial mass function (IMF) has been limited due to a scarcity of samples. Recently, Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), as the most efficient spectroscopic telescope, has provided new opportunities for related research. In this study, based on approximately 70,000 main-sequence early-type stars from the LAMOST survey, we investigated the IMF of Galactic field stars at the high-mass end (1.5 < M/Msun < 7.1). First, we derived the slope of the present-day mass function (PDMF), finding after correcting for selection effect in the observed sample. We then accounted for the effects of stellar evolution and unresolved binaries to correct the PDMF back to the IMF, resulting in {\alpha}ini = 2.70 - 2.82. Notably, we corrected both stellar evolution and unresolved binary effects simultaneously by using binary-Star evolution code, which enhances the robustness of our results. Additionally, we investigated how different mass-ratio (q) distributions of binaries and different star formation histories of the Milky Way impact the IMF. Finally, we tested samples across different spatial scales and found that {\alpha}ini may exhibit a decreasing trend as the spatial scale increases, which could be attributed to variations in metallicity.
Outdated software remains a potent and underappreciated menace in 2025's cybersecurity environment, exposing systems to a broad array of threats, including ransomware, data breaches, and operational outages that can have devastating and far-reaching impacts. This essay explores the unseen threats of cyberattacks by presenting robust statistical information, including the staggering reality that 32% of cyberattacks exploit unpatched software vulnerabilities, based on a 2025 TechTarget survey. Furthermore, it discusses real case studies, including the MOVEit breach in 2023 and the Log4Shell breach in 2021, both of which illustrate the catastrophic consequences of failing to perform software updates. The article offers a detailed analysis of the nature of software vulnerabilities, the underlying reasons for user resistance to patches, and organizational barriers that compound the issue. Furthermore, it suggests actionable solutions, including automation and awareness campaigns, to address these shortcomings. Apart from this, the paper also talks of trends such as AI-driven vulnerability patching and legal consequences of non-compliance under laws like HIPAA, thus providing a futuristic outlook on how such advancements may define future defenses. Supplemented by tables like one detailing trends in vulnerability and a graph illustrating technology adoption, this report showcases the pressing demand for anticipatory update strategies to safeguard digital ecosystems against the constantly evolving threats that characterize the modern cyber landscape. As it stands, it is a very useful document for practitioners, policymakers, and researchers.
Cross-Linguistic Transfer in Multilingual NLP: The Role of Language Families and Morphology
2025-05-20
Ajitesh Bankula, Praney Bankula
Cross-lingual transfer has become a crucial aspect of multilingual NLP, as it allows for models trained on resource-rich languages to be applied to low-resource languages more effectively. Recently massively multilingual pre-trained language models (e.g., mBERT, XLM-R) demonstrate strong zero-shot transfer capabilities[14] [13]. This paper investigates cross-linguistic transfer through the lens of language families and morphology. Investigating how language family proximity and morphological similarity affect performance across NLP tasks. We further discuss our results and how it relates to findings from recent literature. Overall, we compare multilingual model performance and review how linguistic distance metrics correlate with transfer outcomes. We also look into emerging approaches that integrate typological and morphological information into model pre-training to improve transfer to diverse languages[18] [19].
TransFit: An Efficient Framework for Transient Light-Curve Fitting with Time-Dependent Radiative Diffusion
2025-05-20
Liang-Duan Liu, Yu-Hao Zhang, Yun-Wei Yu, Ze-Xin Du, Jing-Yao Li, Guang-Lei Wu, Zi-Gao Dai
Modeling the light curves (LCs) of luminous astronomical transients, such as supernovae, is crucial for understanding their progenitor physics, particularly with the exponential growth of survey data. However, existing methods face limitations: efficient semi-analytical models (e.g., Arnett-like) employ significant physical simplifications (like time-invariant temperature profiles and simplified heating distributions), often compromising accuracy, especially for early-time LCs. Conversely, detailed numerical radiative transfer simulations, while accurate, are computationally prohibitive for large datasets. This paper introduces TransFit, a novel framework that numerically solves a generalized energy conservation equation, explicitly incorporating time-dependent radiative diffusion, continuous radioactive or central engine heating, and ejecta expansion dynamics. The model accurately captures the influence of key ejecta properties and diverse heating source characteristics on light curve morphology, including peak luminosity, rise time, and overall shape. Furthermore, TransFit provides self-consistent modeling of the transition from shock-cooling to $^{56}$Ni}-powered light curves. By combining physical realism with computational speed, TransFit provides a powerful tool for efficiently inverting LCs and extracting detailed physical insights from the vast datasets of current and future transient surveys.
Polarization-Sensitive Diffractive Optics and Metasurfaces: "Past is Prologue"
2025-05-20
Noah A. Rubin, Yeshaiahu Fainman
Polarization control and switchability are among the most unique features of "metasurfaces" as compared with diffractive optics technologies of the past. Here, we review how the polarization control afforded by the advent of present-day metasurfaces compares to diffractive elements of previous decades, clarifying from a functional perspective what is new, and what is not.
6G communications through sub-Terahertz CMOS power amplifiers: Design challenges and trends
2025-05-20
Jun Yan Lee, Duo Wu, Xuanrui Guo, Jian Ding Tan, Teh Jia Yew, Zi Neng Ng, Mohammad Arif Sobhan Bhuiyan, Mahdi H. Miraz
The fifth-generation (5G) network faces limitations in supporting emerging applications, such as artificial intelligence (AI), virtual reality (VR) and digital twins. To overcome these confines, sub-Terahertz (sub-THz) and Terahertz (THz) technologies are considered to be key enablers of effective 6G wireless communications, offering higher transmission speeds, longer range and wider bandwidth. Achieving these capabilities requires careful engineering of 6G transceivers, with a focus on efficient power amplifiers (PAs) in the front-end, which play a critical role in effectively amplifying and transmitting signals over long distances. Complimentary metal-oxidesemiconductor (CMOS) technology-based PA in sub-THz suffers severe parasitic and limited maximum frequency, however, this has eventually been solved by different design architectures and scaling down of CMOS technology to break through the frequency limitations. In this article, we reviewed the potentials and capabilities of CMOS technology for designing 6G hardware, identified the state-of-art PA designs in the sub-THz band and then examined as well as compared the designs to identify the suitable design strategies for better performance. The circuit optimisation techniques, such as coupled-line, passive gain boosting method, zero-degree power splitting, load-pull matching, diode and capacitor linearisation for better gain, saturated output power and power added efficiency, are considered for the PA design architectures at different sub-THz bands. Furthermore, these methods are summarised and discussed with their advantages and disadvantages in lieu with their performances. The PA design trends, challenges and future perspectives are also presented and discussed. Therefore, this comprehensive review article will serve as a comparative study and reference for future PA designs for radio frequency integrated circuits (RFIC).
Reduction in nuclear size and quadrupole deformation of high-spin isomers of 127,129In
2025-05-20
A. R. Vernon, C. L. Binnersley, R. F. Garcia Ruiz, K. M. Lynch, T. Miyagi, J. Billowes, M. L. Bissell, T. E. Cocolios, J. P. Delaroche, J. Dobaczewski, M. Dupuis, K. T. Flanagan, W. Gins, M. Girod, G. Georgiev, R. P. de Groote, J. D. Holt, J. Hustings, Á. Koszorús, D. Leimbach, J. Libert, W. Nazarewicz, G. Neyens, N. Pillet, P. -G. Reinhard, S. Rothe, B. K. Sahoo, S. R. Stroberg, S. G. Wilkins, X. F. Yang, Z. Y. Xu, D. T. Yordanov
We employed laser spectroscopy of atomic transitions to measure the nuclear charge radii and electromagnetic properties of the high-spin isomeric states in neutron-rich indium isotopes (Z = 49) near the closed proton and neutron shells at Z = 50 and N = 82. Our data reveal a reduction in the nuclear charge radius and intrinsic quadrupole moment when protons and neutrons are fully aligned in 129In(N = 80), to form the high spin isomer. Such a reduction is not observed in 127In(N = 78), where more complex configurations can be formed by the existence of four neutron-holes. These observations are not consistently described by nuclear theory.
Statistical field theory of equilibrium amorphous solids and the intrinsic heterogeneity distributions that characterize them
2025-05-20
Paul M. Goldbart
A rich variety of amorphous solids are found in nature and technology, including ones formed via the vulcanization of long, flexible molecules. A special class -- those featuring a wide gap between the long timescales over which constraints in them release and the much shorter timescales over which their unconstrained freedoms relax -- exhibit states of thermodynamic equilibrium and are thus amenable to the framework of equilibrium statistical physics. The approach reviewed here is the least specific -- and thus the most general -- approach to the statistical mechanics of equilibrium amorphous solid-formers: statistical field theory. An overview is given of the key elements and results of this theory. The field of the theory is constructed to detect and diagnose the amorphous solid state. Its form turns out to be unusual, in ways that are essential for its application, so it is examined in detail, as is the form of the field theory controlling the field. What this theory can predict for the equilibrium properties of amorphous solids is then discussed, including: the transition to the amorphous solid state and the heterogeneity of the resulting solid; the impact of fluctuations on the transition and connections with percolation theory; the pattern of symmetry-breaking and the nature of the resulting elasticity; and field correlations and the information they provide. Emphasis is placed on the idea, peculiar to amorphous solids, that their equilibrium states are naturally characterized in terms of distributions that capture the intrinsic spatial heterogeneity of the thermal motions of their constituents. The theory's field has an internal structure that subtly encodes this information, via the wave-vector dependencies of the average field and its correlations. Reflections are made on the applicability of theses ideas and results to a range of amorphous solids and related systems.
Error-Free Demodulation of Pixelated Interferograms: Updated review and circumstantial evidence of plagiarism by 4D Technology Corporation
2025-05-20
Manuel Servin
This paper examines the historical development that led to error-free phase demodulation of pixelated spatial-carrier interferograms between 2004 and 2010. It also evaluates evidence suggesting that 4D Technology Corporation (4DTC), in their SPIE 7790 publication [18], may have adopted key concepts from a manuscript submitted by Servin and collaborators, which at that time was still under peer review [17]. We analyze 4DTC algorithms based on 2x2 and 3x3 spatial convolution phase shifting methods (SC-PSA), developed from 2004 to 2009 [9-15], and show that they were fundamentally limited to narrow bandwidth fringe demodulation. Notably, only one 4DTC paper [13] acknowledged major phase errors when these methods were applied to wideband pixelated fringes, but the problem remained unsolved. In 2010, the team at Centro de Investigaciones en Optica (CIO), led by Servin, entered the field [16,17]. At that point, CIO and 4DTC were the only two known groups pursuing error free demodulation of wideband pixelated interferograms. In June 2010, Servin et al submitted to Optics Express [17] the error free solution introducing a new method: the complex fringe-carrier product followed by step-edge low-pass Fourier filtering. This approach fully superseded the 2x2 and 3x3 SC-PSAs used by 4DTC [9-15], reaching the theoretical slope limit for pixelated sensors. Given that 4DTC paper [18] was presented just seven weeks after Servin submission [17], and only these two groups were working on this problem, it is plausible that 4DTC or an affiliate had early access to the submission. The circumstantial evidence suggests that core ideas from [17] were incorporated into 4DTC August presentation [18]. This paper retraces that scientific path and examines the ethical implications of uncredited appropriation in optical research.
Algorithms in the Stacks: Investigating automated, for-profit diversity audits in public libraries
2025-05-20
Melanie Walsh, Connor Franklin Rey, Chang Ge, Tina Nowak, Sabina Tomkins
Algorithmic systems are increasingly being adopted by cultural heritage institutions like libraries. In this study, we investigate U.S. public libraries' adoption of one specific automated tool -- automated collection diversity audits -- which we see as an illuminating case study for broader trends. Typically developed and sold by commercial book distributors, automated diversity audits aim to evaluate how well library collections reflect demographic and thematic diversity. We investigate how these audits function, whether library workers find them useful, and what is at stake when sensitive, normative decisions about representation are outsourced to automated commercial systems. Our analysis draws on an anonymous survey of U.S. public librarians (n=99), interviews with 14 librarians, a sample of purchasing records, and vendor documentation. We find that many library workers view these tools as convenient, time-saving solutions for assessing and diversifying collections under real and increasing constraints. Yet at the same time, the audits often flatten complex identities into standardized categories, fail to reflect local community needs, and further entrench libraries' infrastructural dependence on vendors. We conclude with recommendations for improving collection diversity audits and reflect on the broader implications for public libraries operating at the intersection of AI adoption, escalating anti-DEI backlash, and politically motivated defunding.
A self-regulated convolutional neural network for classifying variable stars
2025-05-20
Francisco Pérez-Galarce, Jorge Martínez-Palomera, Karim Pichara, Pablo Huijse, Márcio Catelan
Over the last two decades, machine learning models have been widely applied and have proven effective in classifying variable stars, particularly with the adoption of deep learning architectures such as convolutional neural networks, recurrent neural networks, and transformer models. While these models have achieved high accuracy, they require high-quality, representative data and a large number of labelled samples for each star type to generalise well, which can be challenging in time-domain surveys. This challenge often leads to models learning and reinforcing biases inherent in the training data, an issue that is not easily detectable when validation is performed on subsamples from the same catalogue. The problem of biases in variable star data has been largely overlooked, and a definitive solution has yet to be established. In this paper, we propose a new approach to improve the reliability of classifiers in variable star classification by introducing a self-regulated training process. This process utilises synthetic samples generated by a physics-enhanced latent space variational autoencoder, incorporating six physical parameters from Gaia Data Release 3. Our method features a dynamic interaction between a classifier and a generative model, where the generative model produces ad-hoc synthetic light curves to reduce confusion during classifier training and populate underrepresented regions in the physical parameter space. Experiments conducted under various scenarios demonstrate that our self-regulated training approach outperforms traditional training methods for classifying variable stars on biased datasets, showing statistically significant improvements.
Following the herd: The influence of the bandwagon heuristic on e-commerce shoppers
2025-05-20
Min Xiao, Paul Myers
This research study examined how the number of reviews, review scores product involvement, and product review valence affect consumers' shopping decisions. Specifically, two online experiments were conducted to examine how product review scores interact with the number of reviews, product involvement, and review valence in affecting bandwagon perceptions and purchase intention. A mock website that displays products and product review metrics was constructed for the purpose of the research. The findings suggest that product review scores, and their interaction with product involvement and review valence, influenced consumers' shopping decisions.
Voice to Vision: Enhancing Civic Decision-Making through Co-Designed Data Infrastructure
2025-05-20
Maggie Hughes, Cassandra Overney, Ashima Kamra, Jasmin Tepale, Elizabeth Hamby, Mahmood Jasim, Deb Roy
Trust and transparency in civic decision-making processes, like neighborhood planning, are eroding as community members frequently report sending feedback "into a void" without understanding how, or whether, their input influences outcomes. To address this gap, we introduce Voice to Vision, a sociotechnical system that bridges community voices and planning outputs through a structured yet flexible data infrastructure and complementary interfaces for both community members and planners. Through a five-month iterative design process with 21 stakeholders and subsequent field evaluation involving 24 participants, we examine how this system facilitates shared understanding across the civic ecosystem. Our findings reveal that while planners value systematic sensemaking tools that find connections across diverse inputs, community members prioritize seeing themselves reflected in the process, discovering patterns within feedback, and observing the rigor behind decisions, while emphasizing the importance of actionable outcomes. We contribute insights into participatory design for civic contexts, a complete sociotechnical system with an interoperable data structure for civic decision-making, and empirical findings that inform how digital platforms can promote shared understanding among elected or appointed officials, planners, and community members by enhancing transparency and legitimacy.
Acute pancreatitis (AP) is a common and potentially life-threatening gastrointestinal disease that imposes a significant burden on healthcare systems. ICU readmissions among AP patients are common, especially in severe cases, with rates exceeding 40%. Identifying high-risk patients for readmission is crucial for improving outcomes. This study used the MIMIC-III database to identify ICU admissions for AP based on diagnostic codes. We applied a preprocessing pipeline including missing data imputation, correlation analysis, and hybrid feature selection. Recursive Feature Elimination with Cross-Validation (RFECV) and LASSO regression, supported by expert review, reduced over 50 variables to 20 key predictors, covering demographics, comorbidities, lab tests, and interventions. To address class imbalance, we used the Synthetic Minority Over-sampling Technique (SMOTE) in a five-fold cross-validation framework. We developed and optimized six machine learning models-Logistic Regression, k-Nearest Neighbors, Naive Bayes, Random Forest, LightGBM, and XGBoost-using grid search. Model performance was evaluated with AUROC, accuracy, F1 score, sensitivity, specificity, PPV, and NPV. XGBoost performed best, with an AUROC of 0.862 (95% CI: 0.800-0.920) and accuracy of 0.889 (95% CI: 0.858-0.923) on the test set. An ablation study showed that removing any feature decreased performance. SHAP analysis identified platelet count, age, and SpO2 as key predictors of readmission. This study shows that ensemble learning, informed feature selection, and handling class imbalance can improve ICU readmission prediction in AP patients, supporting targeted post-discharge interventions.
Causal inference determines cause-and-effect relationships between variables and has broad applications across disciplines. Traditional time-series methods often reveal causal links only in a time-averaged sense, while ensemble-based information transfer approaches detect the time evolution of short-term causal relationships but are typically limited to low-dimensional systems. In this paper, a new causal inference framework, called assimilative causal inference (ACI), is developed. Fundamentally different from the state-of-the-art methods, ACI uses a dynamical system and a single realization of a subset of the state variables to identify instantaneous causal relationships and the dynamic evolution of the associated causal influence range (CIR). Instead of quantifying how causes influence effects as done traditionally, ACI solves an inverse problem via Bayesian data assimilation, thus tracing causes backward from observed effects with an implicit Bayesian hypothesis. Causality is determined by assessing whether incorporating the information of the effect variables reduces the uncertainty in recovering the potential cause variables. ACI has several desirable features. First, it captures the dynamic interplay of variables, where their roles as causes and effects can shift repeatedly over time. Second, a mathematically justified objective criterion determines the CIR without empirical thresholds. Third, ACI is scalable to high-dimensional problems by leveraging computationally efficient Bayesian data assimilation techniques. Finally, ACI applies to short time series and incomplete datasets. Notably, ACI does not require observations of candidate causes, which is a key advantage since potential drivers are often unknown or unmeasured. The effectiveness of ACI is demonstrated by complex dynamical systems showcasing intermittency and extreme events.
Controllability and Inverse Problems for Hyperbolic and Dispersive Equations with Dynamic Boundary Conditions
2025-05-20
S. E. Chorfi, L. Maniar, R. Morales
This review examines classical and recent results on controllability and inverse problems for hyperbolic and dispersive equations with dynamic boundary conditions. We aim to illustrate the applicability of Carleman estimates to establish exact controllability of such equations and derive Lipschitz stability estimates for inverse problems of source terms and coefficients with general dynamic boundary conditions. We highlight the challenges associated with dynamic boundary conditions compared to classical static ones. Finally, we conclude with a discussion of open problems and future research directions.
2025-05-20
Sergey E. Koposov, Ting S. Li, C. Allende Prieto, G. E. Medina, N. Sandford, D. Aguado, L. Beraldo e Silva, A. Byström, A. P. Cooper, Arjun Dey, C. S. Frenk, N. Kizhuprakkat, S. Li, J. Najita, A. H. Riley, D. R. Silva, G. Thomas, M. Valluri, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, S. Cole, A. Cuceu, A. de la Macorra, J. Della Costa, Biprateep Dey, P. Doel, J. Edelstein, A. Font-Ribera, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, J. Guy, K. Honscheid, J. Jimenez, R. Kehoe, D. Kirkby, T. Kisner, A. Kremin, O. Lahav, M. Landriau, L. Le Guillou, A. Leauthaud, M. E. Levi, M. Manera, A. Meisner, R. Miquel, J. Moustakas, S. Nadathur, N. Palanque-Delabrouille, W. J. Percival, F. Prada, I. Pérez-Ràfols, G. Rossi, E. Sanchez, E. F. Schlafly, D. Schlegel, H. Seo, R. Sharples, J. Silber, D. Sprayberry, G. Tarl'e, B. A. Weaver, R. Zhou, H. Zou
In this paper we present the stellar Value-Added Catalogue (VAC) based on the DESI Data Release 1. This VAC contains stellar parameter, abundance and radial velocity measurements for more than 4 million stars. It also contains, for the first time, measurements from individual epochs for more than a million stars with at least two observations. The main contribution to the catalogue comes from the bright program of the main survey, which includes $\sim $2.5 million stars, and the backup program, which includes $\sim $ 1 million stars. The combined magnitude range for the stars in the catalogue extends from Gaia G $\sim 12$ to G $\sim 21$. For the magnitude range $17.5
Interaction of the relativistic jet and the narrow-line region of PMN J0948+0022
2025-05-20
B. Dalla Barba, M. Berton, L. Foschini, E. Sani, L. Crepaldi, E. Congiu, G. Venturi, W. J. Hon, A. Vietri
We have analyzed publicly available optical spectra of PMN J0948+0022 obtained with the Sloan Digital Sky Survey, X-Shooter, and the Multi Unit Spectroscopic Explorer (MUSE). Initially, PMN J0948+0022 was classified as a jetted narrow-line Seyfert 1 galaxy, but X-Shooter and MUSE observations, which have better spectral resolution, revealed a different profile for the H$\beta$ line, from Lorentzian to a composite one (a combination of a broad and a narrow Gaussian), more typical of intermediate Seyfert galaxies. According to the unified model, intermediate Seyferts are viewed at larger angles. However, we show that, in this case, the composite line profile results from the interaction of the powerful relativistic jet with the narrow-line region. The jet transfers part of its kinetic energy to the narrow-line region, producing flux changes in the H$\beta$ narrow component (drop by a factor of 3.4 from SDSS to X-Shooter), [O III]$\lambda$5007 core component (which nearly doubled from X-Shooter to MUSE), and its blue wing ($\Delta$v$\sim$200 km s$^{-1}$), which we interpret as evidence of an outflow. We also recalculated the physical parameters of this AGN, obtaining a black hole mass of $10^{7.8}$ M$_{\odot}$ and an Eddington ratio of $\sim$0.21 (weighted mean).
Lomb-Scargle periodograms struggle with non-sinusoidal supermassive BH binary signatures in quasar lightcurves
2025-05-20
Allison Lin, Maria Charisi, Zoltan Haiman
Supermassive black hole binary (SMBHB) systems are expected to form as a consequence of galaxy mergers. At sub-parsec separations, SMBHBs are difficult to resolve, but can be identified as quasars with periodic variability. Previous periodicity searches have identified statistically significant candidates, but focused primarily on sinusoidal signals. However, theoretical models and hydrodynamical simulations predict that binaries produce more complex non-sinusoidal pulse shapes. Here we examine the efficacy of the Lomb-Scargle periodogram (LSP; one of the most popular tools for periodicity searches in unevenly sampled lightcurves) to detect periodicities with a saw-tooth shape mimicking results of hydrodynamical simulations. We simulate quasar lightcurves with damped random walk (DRW) variability and inject periodic signals. Our mock sample of 12,400 quasars consists either of idealised well-sampled lightcurves, or mimics the data in the Palomar Transient Factory (PTF) analyzed in Charisi et al, 2016. We assess the statistical significance of recovering two types of periodic signals, i.e. with sinusoidal and sawtooth pulse shapes. We find that the LSP detects 39.1% and 28.1% of the sinusoidal signals, in the PTF-like and idealised lightcurves, respectively. The fraction is significantly reduced for sawtooth periodicity, with only 7.5% and 1.1% detected in PTF-like and idealised lightcurves, respectively. These low recovery rates imply that previous searches have missed the large majority of binaries. Therefore, significant improvements are required beyond simple LSPs to successfully uncover SMBHBs in upcoming time-domain surveys.
Selective Structured State Space for Multispectral-fused Small Target Detection
2025-05-20
Qianqian Zhang, WeiJun Wang, Yunxing Liu, Li Zhou, Hao Zhao, Junshe An, Zihan Wang
Target detection in high-resolution remote sensing imagery faces challenges due to the low recognition accuracy of small targets and high computational costs. The computational complexity of the Transformer architecture increases quadratically with image resolution, while Convolutional Neural Networks (CNN) architectures are forced to stack deeper convolutional layers to expand their receptive fields, leading to an explosive growth in computational demands. To address these computational constraints, we leverage Mamba's linear complexity for efficiency. However, Mamba's performance declines for small targets, primarily because small targets occupy a limited area in the image and have limited semantic information. Accurate identification of these small targets necessitates not only Mamba's global attention capabilities but also the precise capture of fine local details. To this end, we enhance Mamba by developing the Enhanced Small Target Detection (ESTD) module and the Convolutional Attention Residual Gate (CARG) module. The ESTD module bolsters local attention to capture fine-grained details, while the CARG module, built upon Mamba, emphasizes spatial and channel-wise information, collectively improving the model's ability to capture distinctive representations of small targets. Additionally, to highlight the semantic representation of small targets, we design a Mask Enhanced Pixel-level Fusion (MEPF) module for multispectral fusion, which enhances target features by effectively fusing visible and infrared multimodal information.
The Evolution of Alpha in Finance Harnessing Human Insight and LLM Agents
2025-05-20
Mohammad Rubyet Islam
The pursuit of alpha returns that exceed market benchmarks has undergone a profound transformation, evolving from intuition-driven investing to autonomous, AI powered systems. This paper introduces a comprehensive five stage taxonomy that traces this progression across manual strategies, statistical models, classical machine learning, deep learning, and agentic architectures powered by large language models (LLMs). Unlike prior surveys focused narrowly on modeling techniques, this review adopts a system level lens, integrating advances in representation learning, multimodal data fusion, and tool augmented LLM agents. The strategic shift from static predictors to contextaware financial agents capable of real time reasoning, scenario simulation, and cross modal decision making is emphasized. Key challenges in interpretability, data fragility, governance, and regulatory compliance areas critical to production deployment are examined. The proposed taxonomy offers a unified framework for evaluating maturity, aligning infrastructure, and guiding the responsible development of next generation alpha systems.
Integration of TinyML and LargeML: A Survey of 6G and Beyond
2025-05-20
Thai-Hoc Vu, Ngo Hoang Tu, Thien Huynh-The, Kyungchun Lee, Sunghwan Kim, Miroslav Voznak, Quoc-Viet Pham
The transition from 5G networks to 6G highlights a significant demand for machine learning (ML). Deep learning models, in particular, have seen wide application in mobile networking and communications to support advanced services in emerging wireless environments, such as smart healthcare, smart grids, autonomous vehicles, aerial platforms, digital twins, and the metaverse. The rapid expansion of Internet-of-Things (IoT) devices, many with limited computational capabilities, has accelerated the development of tiny machine learning (TinyML) and resource-efficient ML approaches for cost-effective services. However, the deployment of large-scale machine learning (LargeML) solutions require major computing resources and complex management strategies to support extensive IoT services and ML-generated content applications. Consequently, the integration of TinyML and LargeML is projected as a promising approach for future seamless connectivity and efficient resource management. Although the integration of TinyML and LargeML shows abundant potential, several challenges persist, including performance optimization, practical deployment strategies, effective resource management, and security considerations. In this survey, we review and analyze the latest research aimed at enabling the integration of TinyML and LargeML models for the realization of smart services and applications in future 6G networks and beyond. The paper concludes by outlining critical challenges and identifying future research directions for the holistic integration of TinyML and LargeML in next-generation wireless networks.
2025-05-19
Dulhan Jayalath, Gilad Landau, Oiwi Parker Jones
Despite major advances in surgical brain-to-text (B2T), i.e. transcribing speech from invasive brain recordings, non-invasive alternatives have yet to surpass even chance on standard metrics. This remains a barrier to building a non-invasive brain-computer interface (BCI) capable of restoring communication in paralysed individuals without surgery. Here, we present the first non-invasive B2T result that significantly exceeds these critical baselines, raising BLEU by $1.4\mathrm{-}2.6\times$ over prior work. This result is driven by three contributions: (1) we extend recent word-classification models with LLM-based rescoring, transforming single-word predictors into closed-vocabulary B2T systems; (2) we introduce a predictive in-filling approach to handle out-of-vocabulary (OOV) words, substantially expanding the effective vocabulary; and (3) we demonstrate, for the first time, how to scale non-invasive B2T models across datasets, unlocking deep learning at scale and improving accuracy by $2.1\mathrm{-}2.3\times$. Through these contributions, we offer new insights into the roles of data quality and vocabulary size. Together, our results remove a major obstacle to realising practical non-invasive B2T systems.
Filtering out large-scale noise for cluster weak-lensing mass estimation
2025-05-19
C. Murray, C. Combet, C. Payerne, M. Ricci
We present a new method for estimating galaxy cluster masses using weak-lensing magnification. The effect of weak-lensing magnification introduces a correlation between the position of foreground galaxy clusters and the density of background sources. Therefore, cluster masses can be inferred through observations of these correlations. In this work, we introduce a method that allows us to considerably reduce noise correlations between different radial bins of the cluster magnification signal via a Wiener filtering of our observed magnification field on large scales. This method can reduce the uncertainty on the estimated galaxy cluster mass and it can also be applied to cluster mass estimation for weak-lensing shear. The method was applied to Hyper-Suprime Cam galaxies and CAMIRA clusters detected within the Hyper-Suprime Cam survey (HSC). With HSC data, we find that our filtering method significantly reduces the correlation of noise between radial magnification bins. The estimated cluster mass is consistent between the filtered and unfiltered methods, with similar errors between the two methods as our current measurement errors contain significant contributions from the irreducible shot-noise. For deeper surveys, the effects of shot noise will be less important and this method will lead to greater improvements on the estimated cluster mass.
Multireference Embedding and Fragmentation Methods for Classical and Quantum Computers: from Model Systems to Realistic Applications
2025-05-19
Shreya Verma, Abhishek Mitra, Qiaohong Wang, Ruhee D'Cunha, Bhavnesh Jangid, Matthew R. Hennefarth, Valay Agarawal, Leon Otis, Soumi Haldar, Matthew R. Hermes, Laura Gagliardi
One of the primary challenges in quantum chemistry is the accurate modeling of strong electron correlation. While multireference methods effectively capture such correlation, their steep scaling with system size prohibits their application to large molecules and extended materials. Quantum embedding offers a promising solution by partitioning complex systems into manageable subsystems. In this review, we highlight recent advances in multireference density matrix embedding and localized active space self-consistent field approaches for complex molecules and extended materials. We discuss both classical implementations and the emerging potential of these methods on quantum computers. By extending classical embedding concepts to the quantum landscape, these algorithms have the potential to expand the reach of multireference methods in quantum chemistry and materials.
How Adding Metacognitive Requirements in Support of AI Feedback in Practice Exams Transforms Student Learning Behaviors
2025-05-19
Mak Ahmad, Prerna Ravi, David Karger, Marc Facciotti
Providing personalized, detailed feedback at scale in large undergraduate STEM courses remains a persistent challenge. We present an empirically evaluated practice exam system that integrates AI generated feedback with targeted textbook references, deployed in a large introductory biology course. Our system encourages metacognitive behavior by asking students to explain their answers and declare their confidence. It uses OpenAI's GPT-4o to generate personalized feedback based on this information, while directing them to relevant textbook sections. Through interaction logs from consenting participants across three midterms (541, 342, and 413 students respectively), totaling 28,313 question-student interactions across 146 learning objectives, along with 279 surveys and 23 interviews, we examined the system's impact on learning outcomes and engagement. Across all midterms, feedback types showed no statistically significant performance differences, though some trends suggested potential benefits. The most substantial impact came from the required confidence ratings and explanations, which students reported transferring to their actual exam strategies. About 40 percent of students engaged with textbook references when prompted by feedback -- far higher than traditional reading rates. Survey data revealed high satisfaction (mean rating 4.1 of 5), with 82.1 percent reporting increased confidence on practiced midterm topics, and 73.4 percent indicating they could recall and apply specific concepts. Our findings suggest that embedding structured reflection requirements may be more impactful than sophisticated feedback mechanisms.
A Game-Theoretic Quantum Algorithm for Solving Magic Squares
2025-05-19
Sarah Chehade, Andrea Delgado, Elaine Wong
Variational quantum algorithms (VQAs) offer a promising near-term approach to finding optimal quantum strategies for playing non-local games. These games test quantum correlations beyond classical limits and enable entanglement verification. In this work, we present a variational framework for the Magic Square Game (MSG), a two-player non-local game with perfect quantum advantage. We construct a value Hamiltonian that encodes the game's parity and consistency constraints, then optimize parameterized quantum circuits to minimize this cost. Our approach builds on the stabilizer formalism, leverages commutation structure for circuit design, and is hardware-efficient. Compared to existing work, our contribution emphasizes algebraic structure and interpretability. We validate our method through numerical experiments and outline generalizations to larger games.
Bandit algorithms and Large Language Models (LLMs) have emerged as powerful tools in artificial intelligence, each addressing distinct yet complementary challenges in decision-making and natural language processing. This survey explores the synergistic potential between these two fields, highlighting how bandit algorithms can enhance the performance of LLMs and how LLMs, in turn, can provide novel insights for improving bandit-based decision-making. We first examine the role of bandit algorithms in optimizing LLM fine-tuning, prompt engineering, and adaptive response generation, focusing on their ability to balance exploration and exploitation in large-scale learning tasks. Subsequently, we explore how LLMs can augment bandit algorithms through advanced contextual understanding, dynamic adaptation, and improved policy selection using natural language reasoning. By providing a comprehensive review of existing research and identifying key challenges and opportunities, this survey aims to bridge the gap between bandit algorithms and LLMs, paving the way for innovative applications and interdisciplinary research in AI.
Starting Seatwork Earlier as a Valid Measure of Student Engagement
2025-05-19
Ashish Gurung, Jionghao Lin, Zhongtian Huang, Conrad Borchers, Ryan S. Baker, Vincent Aleven, Kenneth R. Koedinger
Prior work has developed a range of automated measures ("detectors") of student self-regulation and engagement from student log data. These measures have been successfully used to make discoveries about student learning. Here, we extend this line of research to an underexplored aspect of self-regulation: students' decisions about when to start and stop working on learning software during classwork. In the first of two analyses, we build on prior work on session-level measures (e.g., delayed start, early stop) to evaluate their reliability and predictive validity. We compute these measures from year-long log data from Cognitive Tutor for students in grades 8-12 (N = 222). Our findings show that these measures exhibit moderate to high month-to-month reliability (G > .75), comparable to or exceeding gaming-the-system behavior. Additionally, they enhance the prediction of final math scores beyond prior knowledge and gaming-the-system behaviors. The improvement in learning outcome predictions beyond time-on-task suggests they capture a broader motivational state tied to overall learning. The second analysis demonstrates the cross-system generalizability of these measures in i-Ready, where they predict state test scores for grade 7 students (N = 818). By leveraging log data, we introduce system-general naturally embedded measures that complement motivational surveys without extra instrumentation or disruption of instruction time. Our findings demonstrate the potential of session-level logs to mine valid and generalizable measures with broad applications in the predictive modeling of learning outcomes and analysis of learner self-regulation.
Learning Driven Elastic Task Multi-Connectivity Immersive Computing Systems
2025-05-19
Babak Badnava, Jacob Chakareski, Morteza Hashemi
In virtual reality (VR) environments, computational tasks exhibit an elastic nature, meaning they can dynamically adjust based on various user and system constraints. This elasticity is essential for maintaining immersive experiences; however, it also introduces challenges for communication and computing in VR systems. In this paper, we investigate elastic task offloading for multi-user edge-computing-enabled VR systems with multi-connectivity, aiming to maximize the computational energy-efficiency (computational throughput per unit of energy consumed). To balance the induced communication, computation, energy consumption, and quality of experience trade-offs due to the elasticity of VR tasks, we formulate a constrained stochastic computational energy-efficiency optimization problem that integrates the multi-connectivity/multi-user action space and the elastic nature of VR computational tasks. We formulate a centralized phasic policy gradient (CPPG) framework to solve the problem of interest online, using only prior elastic task offloading statistics (energy consumption, response time, and transmission time), and task information (i.e., task size and computational intensity), while observing the induced system performance (energy consumption and latency). We further extend our approach to decentralized learning by formulating an independent phasic policy gradient (IPPG) method and a decentralized shared multi-armed bandit (DSMAB) method. We train our methods with real-world 4G, 5G, and WiGig network traces and 360 video datasets to evaluate their performance in terms of response time, energy efficiency, scalability, and delivered quality of experience. We also provide a comprehensive analysis of task size and its effect on offloading policy and system performance. In particular, we show that CPPG reduces latency by 28% and energy consumption by 78% compared to IPPG.
Frequency-Dependent Power Consumption Modeling of CMOS Transmitters for WNoC Architectures
2025-05-19
Mohammad Shahmoradi, Korkut Kaan Tokgöz, Eduard Alarcón, Sergi Abadal
Wireless Network-on-Chip (WNoC) systems, which wirelessly interconnect the chips of a computing system, have been proposed as a complement to existing chip-to-chip wired links. However, their feasibility depends on the availability of custom-designed high-speed, tiny, ultra-efficient transceivers. This represents a challenge due to the tradeoffs between bandwidth, area, and energy efficiency that are found as frequency increases, which suggests that there is an optimal frequency region. To aid in the search for such an optimal design point, this paper presents a behavioral model that quantifies the expected power consumption of oscillators, mixers, and power amplifiers as a function of frequency. The model is built on extensive surveys of the respective sub-blocks, all based on experimental data. By putting together the models of the three sub-blocks, a comprehensive power model is obtained, which will aid in selecting the optimal operating frequency for WNoC systems.
Inflight calibration of SRG/ART-XC point spread function at large off-axis angles
2025-05-19
R. Krivonos, R. Burenin, E. Filippova, I. Lapshov, A. Tkachenko, A. Semena, I. Mereminskiy, V. Arefiev, A. Lutovinov, B. D. Ramsey, J. J. Kolodziejczak, D. A. Swartz, C. -T. Chen, S. R. Ehlert, A. Vikhlinin
The knowledge of the point spread function (PSF) of the Mikhail Pavlinsky Astronomical Roentgen Telescope - X-ray Concentrator (ART-XC) telescope aboard the Spectrum-Roentgen-Gamma (SRG) observatory plays an especially crucial role in the detection of point X-ray sources in the all-sky survey and the studies of extended X-ray objects with low surface brightness. In this work, we calibrate the far off-axis shape of the ART-XC PSF using in-flight data of Sco X-1 and the Crab Nebula, in all-sky survey or scan mode, respectively. We demonstrate that the so-called "slewing" ART-XC PSF (in contrast to the on-axis PSF), in convolution with the detector pixels, is consistent with ground calibration performed at the Marshall Space Flight Center, and can be used to model the PSF up to large off-axis distances in all-sky survey or scan modes. The radial profile of the Crab Nebula in the 4-12 keV band shows an extended structure out to ~150" and is consistent with Sco X-1 at larger off-axis angles. Finally, we performed an analytic parametrization of the slewing ART-XC PSF as a function of energy.
A planetary-mass candidate imaged in the Young Suns Exoplanet Survey
2025-05-19
Pengyu Liu, Matthew A. Kenworthy, Beth A. Biller, Alex Wallace, Tomas Stolker, Sebastiaan Haffert, Christian Ginski, Eric E. Mamajek, Alfred Castro-Ginard, Tiffany Meshkat, Mark J. Pecaut, Maddalena Reggiani, Jared R. Males, Laird M. Close, Olivier Guyon, Isabella Doty, Kyle Van Gorkom, Alex Hedglen, Maggie Kautz, Jay Kueny, Joshua Liberman, Jialin Li, Joseph D. Long, Jennifer Lumbres, Eden McEwen, Logan Pearce, Roswell R. Roberts IV, Lauren Schatz, Katie Twitchell
Directly imaged exoplanets in wide orbits challenge current gas giant formation theories. They need to form quickly and acquire enough material before the disk dissipates, which cannot be accommodated by in-situ formation by core accretion. We search for wide separation ($>$ 100 au) planetary-mass companions with the Young Suns Exoplanet Survey (YSES). Here, we present a planetary-mass candidate companion discovered in the survey. We conducted follow-up observations of the candidate system after the first epoch observations and obtained six epochs of observations for this system between 2018 and 2024, and integral field spectroscopy of the stellar component. We report the detection of a candidate companion with H=22.04 $\pm$ 0.13 mag at a projected separation of 730 $\pm$ 10 au away from the primary star. High angular resolution imaging observations of the central star show it is a visual binary. Acceleration data, orbital fitting, spectral energy distribution fitting and radial velocity differences all suggest that there is at least one more unresolved low-mass stellar companion in this system. The planetary-mass candidate shows a significant proper motion comparable to that of the primary star. We estimate an age of 19-28 Myr for the primary star. We cannot confirm the companionship of the candidate due to the unknown barycentre of the stars. Long-term imaging and radial velocity monitoring of the central stars, along with spectroscopy of the candidate companion, are key to resolving the nature of this system. If confirmed, the candidate companion would have a mass of 3-5 Mj estimated with the ATMO evolutionary model. It would be another cold low-mass planet imaged similar to 51 Eri b and AF Lep b. Its extremely wide separation from the host star would challenge the formation theory of gas giant exoplanets.
Output behavior equivalence and simultaneous subspace identification of systems and faults
2025-05-19
Gabriel de Albuquerque Gleizer
We address the problem of identifying a system subject to additive faults, while simultaneously reconstructing the fault signal via subspace methods. We do not require nominal data for the identification, neither do we impose any assumption on the class of faults, e.g., sensor or actuator faults. We show that, under mild assumptions on the fault signal, standard PI-MOESP can recover the system matrices associated to the input-output subsystem. Then we introduce the concept of output behavior equivalence, which characterizes systems with the same output behavior set, and present a method to establish this equivalence from system matrices. Finally, we show how to estimate from data the complete set of fault matrices for which there exist a fault signal with minimal dimension that explains the data.
Type Ia supernova growth-rate measurement with LSST simulations: intrinsic scatter systematics
2025-05-19
Bastien Carreres, Rebecca C. Chen, Erik R. Peterson, Dan Scolnic, Corentin Ravoux, Damiano Rosselli, Maria Acevedo, Julian E. Bautista, Dominique Fouchez, Lluís Galbany, Benjamin Racine, The LSST Dark Energy Science Collaboration
Measurement of the growth rate of structures ($\fsig$) with Type Ia supernovae (\sns) will improve our understanding of the nature of dark energy and enable tests of general relativity. In this paper, we generate simulations of the 10 year \sn\ dataset of the Rubin-LSST survey, including a correlated velocity field from a N-body simulation and realistic models of \sns\ properties and their correlations with host-galaxy properties. We find, similar to SN~Ia analyses that constrain the dark energy equation-of-state parameters $w_0w_a$, that constraints on $\fsig$ can be biased depending on the intrinsic scatter of \sns. While for the majority of intrinsic scatter models we recover $\fsig$ with a precision of $\sim13 - 14\%$, for the most realistic dust-based model, we find that the presence of non-Gaussianities in Hubble diagram residuals leads to a bias on $\fsig$ of about $\sim-20\%$. When trying to correct for the dust-based intrinsic scatter, we find that the propagation of the uncertainty on the model parameters does not significantly increase the error on $\fsig$. We also find that while the main component of the error budget of $\fsig$ is the statistical uncertainty ($>75\%$ of the total error budget), the systematic error budget is dominated by the uncertainty on the damping parameter, $\sigma_u$, that gives an empirical description of the effect of redshift space distortions on the velocity power spectrum. Our results motivate a search for new methods to correct for the non-Gaussian distribution of the Hubble diagram residuals, as well as an improved modeling of the damping parameter.
Net-Zero: A Comparative Study on Neural Network Design for Climate-Economic PDEs Under Uncertainty
2025-05-19
Carlos Rodriguez-Pardo, Louis Daumas, Leonardo Chiani, Massimo Tavoni
Climate-economic modeling under uncertainty presents significant computational challenges that may limit policymakers' ability to address climate change effectively. This paper explores neural network-based approaches for solving high-dimensional optimal control problems arising from models that incorporate ambiguity aversion in climate mitigation decisions. We develop a continuous-time endogenous-growth economic model that accounts for multiple mitigation pathways, including emission-free capital and carbon intensity reductions. Given the inherent complexity and high dimensionality of these models, traditional numerical methods become computationally intractable. We benchmark several neural network architectures against finite-difference generated solutions, evaluating their ability to capture the dynamic interactions between uncertainty, technology transitions, and optimal climate policy. Our findings demonstrate that appropriate neural architecture selection significantly impacts both solution accuracy and computational efficiency when modeling climate-economic systems under uncertainty. These methodological advances enable more sophisticated modeling of climate policy decisions, allowing for better representation of technology transitions and uncertainty-critical elements for developing effective mitigation strategies in the face of climate change.
From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery
2025-05-19
Tianshi Zheng, Zheye Deng, Hong Ting Tsang, Weiqi Wang, Jiaxin Bai, Zihao Wang, Yangqiu Song
Large Language Models (LLMs) are catalyzing a paradigm shift in scientific discovery, evolving from task-specific automation tools into increasingly autonomous agents and fundamentally redefining research processes and human-AI collaboration. This survey systematically charts this burgeoning field, placing a central focus on the changing roles and escalating capabilities of LLMs in science. Through the lens of the scientific method, we introduce a foundational three-level taxonomy-Tool, Analyst, and Scientist-to delineate their escalating autonomy and evolving responsibilities within the research lifecycle. We further identify pivotal challenges and future research trajectories such as robotic automation, self-improvement, and ethical governance. Overall, this survey provides a conceptual architecture and strategic foresight to navigate and shape the future of AI-driven scientific discovery, fostering both rapid innovation and responsible advancement. Github Repository: https://github.com/HKUST-KnowComp/Awesome-LLM-Scientific-Discovery.
Large Language Models (LLMs) are integral to advancing natural language processing, used extensively from machine translation to content creation. However, as these models scale to billions of parameters, their resource demands increase dramatically. Meanwhile, quantum computing is recognized for efficiently solving complex problems with quantum characteristics like superposition and entanglement, providing a novel approach to these challenges. This paper attempts to combine quantum computing with LLMs and proposes a Quantum knowledge Distillation algorithm for LLMs (QD-LLM), aimed at reducing the computational and memory overhead required for model loading and inference. Specifically, during the distillation stage, data is fed simultaneously into both the LLMs and the designed quantum student model to initially quantify the difference between their outputs; subsequently, with the help of the true label, the optimization of the quantum student model is executed to minimize the difference with the LLM's output. Throughout this process, only the parameters of the quantum student network are updated to make its output closer to that of the LLMs, thereby achieving the purpose of distillation. Finally, the optimized student model obtained by QD-LLM can efficiently solve domain-specific tasks during inference without the usage of the original LLMs. Experimental results show that, compared to mainstream compression methods, QD-LLM significantly reduces the number of training parameters, memory consumption, training time, and inference time while maintaining performance. Moreover, the optimized student model obtained by QD-LLM surpasses specific models designed for these tasks. We believe that QD-LLM can lay the groundwork for exploring the utilization of quantum computing in model compression and its potential extension to other natural language processing challenges.
Spatially resolved stellar populations and emission lines properties in nearby galaxies with J-PLUS -- I. Method and first results for the M101 group
2025-05-19
J. Thainá-Batista, R. Cid Fernandes, R. M. González Delgado, J. E. Rodríguez-Martín, R. García-Benito, G. Martínez-Solaeche, L. A. Díaz-García, V. H. Sasse, A. Lumbreras-Calle, A. M. Conrado, J. Alcaniz, R. E. Angulo, A. J. Cenarro, D. Cristóbal-Hornillos, R. A. Dupke, A. Ederoclite, C. Hernández-Monteagudo, C. López-Sanjuan, A. Marín-Franch, M. Moles, L. Sodré Jr., H. Vázquez Ramió, J. Varela
Spatially resolved maps of stellar populations and nebular emission are key tools for understanding the physical properties and evolutionary stages of galaxies. We aim to characterize the spatially resolved stellar population and emission line properties of galaxies in the M101 group using Javalambre Photometric Local Universe Survey (J-PLUS) data. The datacubes first go through pre-processing steps, which include masking, noise suppression, PSF homogenization, and spatial binning. The improved data are then analyzed with the spectral synthesis code \alstar, which has been previously shown to produce excellent results with the unique 12 bands filter system of J-PLUS and S-PLUS. We produce maps of stellar mass surface density ($\Sigma_\star$), mean stellar age and metallicity, star formation rate surface density ($\Sigma_{\rm SFR}$), dust attenuation, and emission line properties such as fluxes and equivalent widths of the main optical lines. Relations among these properties are explored. All galaxies exhibit a well-defined age-$\Sigma_\star$ relation, except for the dwarfs. Similarly, all of the galaxies follow local $\Sigma_\star$-$\Sigma_{\rm SFR}$ star-forming MS relations, with specific star formation rates that grow for less massive systems. A stellar $\Sigma_\star$-metallicity relation is clearly present in M101, while other galaxies have either flatter or undefined relations. Nebular metallicities correlate with $\Sigma_\star$ for all galaxies. This study demonstrates the ability of J-PLUS to perform IFS-like analysis of galaxies, offering robust spatially resolved measurements of stellar populations and emission lines over large fields of view. The M101 group analysis showcases the potential for expanding such studies to other groups and clusters, contributing to the understanding of galaxy evolution across different environments.
ModernGBERT: German-only 1B Encoder Model Trained from Scratch
2025-05-19
Anton Ehrmanntraut, Julia Wunderle, Jan Pfister, Fotis Jannidis, Andreas Hotho
Despite the prominence of decoder-only language models, encoders remain crucial for resource-constrained applications. We introduce ModernGBERT (134M, 1B), a fully transparent family of German encoder models trained from scratch, incorporating architectural innovations from ModernBERT. To evaluate the practical trade-offs of training encoders from scratch, we also present LL\"aMmlein2Vec (120M, 1B, 7B), a family of encoders derived from German decoder-only models via LLM2Vec. We benchmark all models on natural language understanding, text embedding, and long-context reasoning tasks, enabling a controlled comparison between dedicated encoders and converted decoders. Our results show that ModernGBERT 1B outperforms prior state-of-the-art German encoders as well as encoders adapted via LLM2Vec, with regard to performance and parameter-efficiency. All models, training data, checkpoints and code are publicly available, advancing the German NLP ecosystem with transparent, high-performance encoder models.
Adaptive Image Restoration for Video Surveillance: A Real-Time Approach
2025-05-19
Muhammad Awais Amin, Adama Ilboudo, Abdul Samad bin Shahid, Amjad Ali, Waqas Haider Khan Bangyal
One of the major challenges in the field of computer vision especially for detection, segmentation, recognition, monitoring, and automated solutions, is the quality of images. Image degradation, often caused by factors such as rain, fog, lighting, etc., has a negative impact on automated decision-making.Furthermore, several image restoration solutions exist, including restoration models for single degradation and restoration models for multiple degradations. However, these solutions are not suitable for real-time processing. In this study, the aim was to develop a real-time image restoration solution for video surveillance. To achieve this, using transfer learning with ResNet_50, we developed a model for automatically identifying the types of degradation present in an image to reference the necessary treatment(s) for image restoration. Our solution has the advantage of being flexible and scalable.
LLM-KG-Bench 3.0: A Compass for SemanticTechnology Capabilities in the Ocean of LLMs
2025-05-19
Lars-Peter Meyer, Johannes Frey, Desiree Heim, Felix Brei, Claus Stadler, Kurt Junghanns, Michael Martin
Current Large Language Models (LLMs) can assist developing program code beside many other things, but can they support working with Knowledge Graphs (KGs) as well? Which LLM is offering the best capabilities in the field of Semantic Web and Knowledge Graph Engineering (KGE)? Is this possible to determine without checking many answers manually? The LLM-KG-Bench framework in Version 3.0 is designed to answer these questions. It consists of an extensible set of tasks for automated evaluation of LLM answers and covers different aspects of working with semantic technologies. In this paper the LLM-KG-Bench framework is presented in Version 3 along with a dataset of prompts, answers and evaluations generated with it and several state-of-the-art LLMs. Significant enhancements have been made to the framework since its initial release, including an updated task API that offers greater flexibility in handling evaluation tasks, revised tasks, and extended support for various open models through the vllm library, among other improvements. A comprehensive dataset has been generated using more than 30 contemporary open and proprietary LLMs, enabling the creation of exemplary model cards that demonstrate the models' capabilities in working with RDF and SPARQL, as well as comparing their performance on Turtle and JSON-LD RDF serialization tasks.
Seismic Isolation of Optical Tables Using Piezo Actuators
2025-05-19
Tailong Wang, Carl Blair, Ammar Al-Jodah, John Winterflood, Jian Liu, Alexander Adams, Aaron Goodwin-Jones, Chunnong Zhao, Li Ju
Seismic isolation is crucial for gravitational wave detectors as it minimizes ground vibrations, enabling the detection of faint gravitational wave signals. An active seismic isolation platform for precision measurement experiments is described. The table features piezo actuation along five degrees of freedom: three translational actuations and two tip-tilt degrees of freedom along the horizontal axes. It is stiff in rotation about the vertical axes. A seismometer is used to sense table motion. Piezo actuators are used to suppress seismic noise with feedback control bandwidth of 0.3 to 3 Hz. Suppression levels ranging from 21 to 36 dB of seismic noise within the frequency range of 0.5 to 1.3 Hz are demonstrated, as measured by a witness seismometer on the table, with the suppression direction along the axis of the longitudinal translation of the suspended mirror on the table. The suppression results in 1 $\mathrm{\mathrm{nm/\sqrt{Hz}}}$ residual horizontal motion at 1 Hz. Limitations such as tilt-to-translation coupling that prevent actuation over the desired range of 0.03 to 3 Hz are discussed.
Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs
2025-05-19
Shmulik Markovich-Golan, Daniel Ohayon, Itay Niv, Yair Hanani
Quantization is essential for Neural Network (NN) compression, reducing model size and computational demands by using lower bit-width data types, though aggressive reduction often hampers accuracy. Mixed Precision (MP) mitigates this tradeoff by varying the numerical precision across network layers. This study focuses on automatically selecting an optimal MP configuration within Post-Training Quantization (PTQ) for inference. The first key contribution is a novel sensitivity metric derived from a first-order Taylor series expansion of the loss function as a function of quantization errors in weights and activations. This metric, based on the Mean Square Error (MSE) of the loss, is efficiently calculated per layer using high-precision forward and backward passes over a small calibration dataset. The metric is additive across layers, with low calibration memory overhead as weight optimization is unnecessary. The second contribution is an accurate hardware-aware method for predicting MP time gain by modeling it as additive for sequential sub-graphs. An algorithm partitions the model graph into sequential subgraphs, measuring time gain for each configuration using a few samples. After calibrating per-layer sensitivity and time gain, an Integer Programming (IP) problem is formulated to maximize time gain while keeping loss MSE below a set threshold. Memory gain and theoretical time gain based on Multiply and Accumulate (MAC) operations are also considered. Rigorous experiments on the Intel Gaudi 2 accelerator validate the approach on several Large Language Models (LLMs).
SNAPE-PM: Building and Utilizing Dynamic Partner Models for Adaptive Explanation Generation
2025-05-19
Amelie S. Robrecht, Christoph R. Kowalski, Stefan Kopp
Adapting to the addressee is crucial for successful explanations, yet poses significant challenges for dialogsystems. We adopt the approach of treating explanation generation as a non-stationary decision process, where the optimal strategy varies according to changing beliefs about the explainee and the interaction context. In this paper we address the questions of (1) how to track the interaction context and the relevant listener features in a formally defined computational partner model, and (2) how to utilize this model in the dynamically adjusted, rational decision process that determines the currently best explanation strategy. We propose a Bayesian inference-based approach to continuously update the partner model based on user feedback, and a non-stationary Markov Decision Process to adjust decision-making based on the partner model values. We evaluate an implementation of this framework with five simulated interlocutors, demonstrating its effectiveness in adapting to different partners with constant and even changing feedback behavior. The results show high adaptivity with distinct explanation strategies emerging for different partners, highlighting the potential of our approach to improve explainable AI systems and dialogsystems in general.
EffiBench-X: A Multi-Language Benchmark for Measuring Efficiency of LLM-Generated Code
2025-05-19
Yuhao Qing, Boyu Zhu, Mingzhe Du, Zhijiang Guo, Terry Yue Zhuo, Qianru Zhang, Jie M. Zhang, Heming Cui, Siu-Ming Yiu, Dong Huang, See-Kiong Ng, Luu Anh Tuan
Existing code generation benchmarks primarily evaluate functional correctness, with limited focus on code efficiency and often restricted to a single language like Python. To address this gap, we introduce EffiBench-X, the first multi-language benchmark designed to measure the efficiency of LLM-generated code. EffiBench-X supports Python, C++, Java, JavaScript, Ruby, and Golang. It comprises competitive programming tasks with human-expert solutions as efficiency baselines. Evaluating state-of-the-art LLMs on EffiBench-X reveals that while models generate functionally correct code, they consistently underperform human experts in efficiency. Even the most efficient LLM-generated solutions (Qwen3-32B) achieve only around \textbf{62\%} of human efficiency on average, with significant language-specific variations. LLMs show better efficiency in Python, Ruby, and JavaScript than in Java, C++, and Golang. For instance, DeepSeek-R1's Python code is significantly more efficient than its Java code. These results highlight the critical need for research into LLM optimization techniques to improve code efficiency across diverse languages. The dataset and evaluation infrastructure are submitted and available at https://github.com/EffiBench/EffiBench-X.git and https://huggingface.co/datasets/EffiBench/effibench-x.
Dynamic memory allocation is essential in modern programming but remains under-supported in current PIM devices. In this work, we first conduct a design space exploration of PIM memory allocators, examining optimal metadata placement and management strategies. Building on these insights, we propose PIM-malloc, a fast and scalable allocator for real PIM hardware, improving allocation performance by $66\times$. We further enhance this design with a lightweight, per-PIM core hardware cache for dynamic allocation, achieving an additional $31\%$ performance gain. Finally, we demonstrate the effectiveness of PIM-malloc using a dynamic graph update workload, achieving a $28\times$ throughput increase.
A Structured Literature Review on Traditional Approaches in Current Natural Language Processing
2025-05-19
Robin Jegan, Andreas Henrich
The continued rise of neural networks and large language models in the more recent past has altered the natural language processing landscape, enabling new approaches towards typical language tasks and achieving mainstream success. Despite the huge success of large language models, many disadvantages still remain and through this work we assess the state of the art in five application scenarios with a particular focus on the future perspectives and sensible application scenarios of traditional and older approaches and techniques. In this paper we survey recent publications in the application scenarios classification, information and relation extraction, text simplification as well as text summarization. After defining our terminology, i.e., which features are characteristic for traditional techniques in our interpretation for the five scenarios, we survey if such traditional approaches are still being used, and if so, in what way they are used. It turns out that all five application scenarios still exhibit traditional models in one way or another, as part of a processing pipeline, as a comparison/baseline to the core model of the respective paper, or as the main model(s) of the paper. For the complete statistics, see https://zenodo.org/records/13683801
What Price Fiber Bundle Substantivalism? On How to Avoid Holes in Fibers
2025-05-19
Philipp Berghofer, Jordan François, Lucrezia Ravera
On a mathematically foundational level, our most successful physical theories (gauge field theories and general-relativistic theories) are formulated in a framework based on the differential geometry of connections on principal bundles. After reviewing the essentials of this framework, we articulate the generalized hole and point-coincidence arguments, examining how they weight on a substantivalist position towards bundle spaces. This question, then, is considered in light of the Dressing Field Method, which allows a manifestly invariant reformulation of gauge field theories and general-relativistic theories, making their conceptual structure more transparent: it formally implements the point-coincidence argument and thus allows to define (dressed) fields and (dressed) bundle spaces immune to hole-type arguments.
The Dichotomy in the Nuclear and Host Galaxy Properties of High-redshift Quasars
2025-05-19
Ruancun Li, Luis C. Ho, Chang-Hao Chen
The early growth of high-redshift quasars and their host galaxies raises critical questions about their cosmic evolution. We exploit the angular resolution and sensitivity of NIRCam to investigate the host galaxies of 31 quasars at $4\lesssim z\lesssim7$ drawn from multiple JWST surveys. Using a new multi-band forward-modeling code (\textsc{GalfitS}) that incorporates physically motivated priors, we securely detect and quantify the host emission in 30 objects, while simultaneously characterizing the nuclear spectral energy distribution. The host galaxies of high-redshift quasars are $\sim 0.3$~dex more compact than star-forming galaxies of comparable mass. A striking dichotomy emerges: luminous ``blue'' quasars ($L_{5100}\gtrsim10^{45}\,{\rm erg\,s^{-1}}$) reside in bulge-dominated galaxies ($n \approx 5$) and exhibit a narrow range of ultraviolet nuclear slopes (median $\beta_{\rm UV} \approx -1.4$), while fainter ``red'' quasars inhabit disk-like hosts ($n\approx 1$) and display a broad range of slopes ($\beta_{\rm UV}\approx-2$ to 4). These two populations differ markedly in their black hole-to-stellar mass ratios, with high-luminosity quasars showing $M_{\mathrm{BH}}/M_\ast = 1.2\%$ compared to $4.7\%$ for lower luminosity sources, placing them collectively $\sim$0.6~dex above the local $M_{\mathrm{BH}}-M_\ast$ relation. This offset likely reflects rapid black hole growth in early gas-rich environments, where feedback from the active galactic nucleus becomes effective only after substantial gas depletion. Our findings suggest that the observed dichotomy, whether due to intrinsic spectral differences or dust extinction, fundamentally shapes the coevolution of supermassive black holes and their host galaxies in the early Universe.
Practical Equivalence Testing and Its Application in Synthetic Pre-Crash Scenario Validation
2025-05-19
Jian Wu, Ulrich Sander, Carol Flannagan, Minxiang Zhao, Jonas Bärgman
The use of representative pre-crash scenarios is critical for assessing the safety impact of driving automation systems through simulation. However, a gap remains in the robust evaluation of the similarity between synthetic and real-world pre-crash scenarios and their crash characteristics. Without proper validation, it cannot be ensured that the synthetic test scenarios adequately represent real-world driving behaviors and crash characteristics. One reason for this validation gap is the lack of focus on methods to confirm that the synthetic test scenarios are practically equivalent to real-world ones, given the assessment scope. Traditional statistical methods, like significance testing, focus on detecting differences rather than establishing equivalence; since failure to detect a difference does not imply equivalence, they are of limited applicability for validating synthetic pre-crash scenarios and crash characteristics. This study addresses this gap by proposing an equivalence testing method based on the Bayesian Region of Practical Equivalence (ROPE) framework. This method is designed to assess the practical equivalence of scenario characteristics that are most relevant for the intended assessment, making it particularly appropriate for the domain of virtual safety assessments. We first review existing equivalence testing methods. Then we propose and demonstrate the Bayesian ROPE-based method by testing the equivalence of two rear-end pre-crash datasets. Our approach focuses on the most relevant scenario characteristics. Our analysis provides insights into the practicalities and effectiveness of equivalence testing in synthetic test scenario validation and demonstrates the importance of testing for improving the credibility of synthetic data for automated vehicle safety assessment, as well as the credibility of subsequent safety impact assessments.
Low-Metallicity Star Formation Survey in Sh2-284 (LZ-STAR). II. The inital mass function
2025-05-19
M. Andersen, A. Brizawasi, Y. Cheng, J. C. Tan, R. Fedriani, J. J. Armstrong, M. Robberto
To fully understand the star formation process, we are compelled to study it in a variety of environments. Of particular interest are how star formation and the resulting initial mass function (IMF) vary as a function of metallicity. We have observed an embedded young cluster in Sh2-284 (hereafter S284), the HII region associated with the open cluster Dolidze 25 using JWST/NIRCam with the aim to study star formation in a metal-poor, i.e., about 1/3 of solar, environment. In particular, we aim to measure the peak of the IMF. Using JWST NIRCam photometry, we identified the embedded cluster S284-EC1 and resolved its low-mass content. By comparison with pre-main sequence evolutionary tracks we determine the mass and extinction for the individual cluster members. Extinction limited samples are created based on the distribution of extinction and the completeness of the data. For the region with a completeness of 50% or higher, we have fitted a log-normal distribution to the IMF. Adopting a fiducial age of 1 Myr of the members, the peak of the IMF is found to be at mc = 0.16+-0.02Msun, which is significantly smaller than the peak mass measured in local young clusters, such as mc = 0.26+0.11-0.07 Msun in the Orion Nebula Cluster (Gennaro & Robberto 2020), or the local Galactic disk value of mc = 0.25 Msun (Chabrier 2005). We have found evidence for IMF variation as a function of metallicity, i.e., the peak of the IMF shifts to lower masses as one goes from solar to 1/3 solar metallicity. However, we caution that the result is sensitive to the assumed age of the stellar population, i.e., with peak mass rising if an age older than 1 Myr is adopted. This study further motivates the need for expanded samples of low-metallicity regions and their content to enable more comprehensive measures of the IMF in such environments.
With the continuous evolution of Large Language Models (LLMs), LLM-based agents have advanced beyond passive chatbots to become autonomous cyber entities capable of performing complex tasks, including web browsing, malicious code and deceptive content generation, and decision-making. By significantly reducing the time, expertise, and resources, AI-assisted cyberattacks orchestrated by LLM-based agents have led to a phenomenon termed Cyber Threat Inflation, characterized by a significant reduction in attack costs and a tremendous increase in attack scale. To provide actionable defensive insights, in this survey, we focus on the potential cyber threats posed by LLM-based agents across diverse network systems. Firstly, we present the capabilities of LLM-based cyberattack agents, which include executing autonomous attack strategies, comprising scouting, memory, reasoning, and action, and facilitating collaborative operations with other agents or human operators. Building on these capabilities, we examine common cyberattacks initiated by LLM-based agents and compare their effectiveness across different types of networks, including static, mobile, and infrastructure-free paradigms. Moreover, we analyze threat bottlenecks of LLM-based agents across different network infrastructures and review their defense methods. Due to operational imbalances, existing defense methods are inadequate against autonomous cyberattacks. Finally, we outline future research directions and potential defensive strategies for legacy network systems.
Pursuit and Review of Magnetic Resonance Imaging (MRI) based Quantum Computing -- Qubit Generation, Spin Purification, Tailored RF Pulses and MRI Sequences for Quantum Computing
2025-05-19
Z. H. Cho, J. H. Han, D. H. Suk, H. J. Jeung, S. Z. Lee, Y. B. Kim, S. H. Paek, H. G. Lee
We propose a novel MRI (Magnetic Resonance Imaging) technique based quantum bit (qubit) generation with water proton NMR (1H-NMR), distinct from previously proposed NMR chemical shift or spectroscopic techniques based qubit generation. We briefly review prior NMR-based techniques in the context of quantum computing, focusing on MRI-related methods. The proposed technique utilizes MRI-based gradient methods combined with set of local reverse gradients to generate multiple qubits. This configuration enables the creation of multiple localized constant magnetic fields, each producing a qubit with highly homogeneous field, therefore, a unique single frequency. The RF electronics and signal processing techniques are analogous to those used in conventional MRI scanners, allowing operation at room temperature, with the exception of the main magnet, which remains identical to that of standard MRI systems. Additional advantages of this method include the ability to leverage the extensive pulse techniques and hardware developed for MRI scanners over the past 50 years. Furthermore, the wide array of MRI pulse sequences enables highly sophisticated signal processing for quantum computing applications, such as the spin purification.
Understanding people's preferences and needs is crucial for urban planning decisions, yet current approaches often combine them from multi-cultural and multi-city populations, obscuring important demographic differences and risking amplifying biases. We conducted a large-scale urban visual perception survey of streetscapes worldwide using street view imagery, examining how demographics -- including gender, age, income, education, race and ethnicity, and, for the first time, personality traits -- shape perceptions among 1,000 participants, with balanced demographics, from five countries and 45 nationalities. This dataset, introduced as Street Perception Evaluation Considering Socioeconomics (SPECS), exhibits statistically significant differences in perception scores in six traditionally used indicators (safe, lively, wealthy, beautiful, boring, and depressing) and four new ones we propose (live nearby, walk, cycle, green) among demographics and personalities. We revealed that location-based sentiments are carried over in people's preferences when comparing urban streetscapes with other cities. Further, we compared the perception scores based on where participants and streetscapes are from. We found that an off-the-shelf machine learning model trained on an existing global perception dataset tends to overestimate positive indicators and underestimate negative ones compared to human responses, suggesting that targeted intervention should consider locals' perception. Our study aspires to rectify the myopic treatment of street perception, which rarely considers demographics or personality traits.
In 1962, Gale and Shapley \cite{GS} introduced the concept of stable marriages and proved their existence. Since then, the statement of the stability problem has been highly generalized. And a lot of proofs has emerged for the existence in these more general statements. It's time to review them and identify the similarities and differences. First, we will briefly discuss the classical case, because the existence proofs in the general case grew out of it. Or rather, from the idea of "deferred acceptance". When the best of the proposed contracts is temporarily retained until a better offer is received.
Cluster optical depth and pairwise velocity estimation using machine learning
2025-05-19
Yulin Gong, Rachel Bean
We apply two machine learning methods, a CNN deep-leaning model and a gradient-boosting decision tree, to estimate individual cluster optical depths from observed properties derived from multiple complementary datasets. The models are trained and tested with simulated N-body derived halo catalogs and synthetic full-sky CMB maps designed to mirror data from the DESI and Simons Observatory experiments. Specifically, the thermal Sunyaev-Zel'dovich (tSZ) and CMB lensing convergence, along with cluster virial mass estimates are used as features to train the machine learning models. The predicted optical depths are combined with kinematic Sunyaev-Zel'dovich (kSZ) measurements to estimate individual cluster radial peculiar velocities. The method is shown to recover an unbiased estimate of the pairwise velocity statistics of the simulated cluster sample. The model's efficacy is demonstrated for halos with mass range $10^{13} M_{\odot} < M_{200} < 10^{15} M_{\odot}$ over a redshift range $0
Large language models (LLMs) consistently benefit from further fine-tuning on various tasks. However, we observe that directly tuning the INSTRUCT (i.e., instruction tuned) models often leads to marginal improvements and even performance degeneration. Notably, paired BASE models, the foundation for these INSTRUCT variants, contain highly similar weight values (i.e., less than 2% on average for Llama 3.1 8B). Therefore, we propose a novel Shadow-FT framework to tune the INSTRUCT models by leveraging the corresponding BASE models. The key insight is to fine-tune the BASE model, and then directly graft the learned weight updates to the INSTRUCT model. Our proposed Shadow-FT introduces no additional parameters, is easy to implement, and significantly improves performance. We conduct extensive experiments on tuning mainstream LLMs, such as Qwen 3 and Llama 3 series, and evaluate them across 19 benchmarks covering coding, reasoning, and mathematical tasks. Experimental results demonstrate that Shadow-FT consistently outperforms conventional full-parameter and parameter-efficient tuning approaches. Further analyses indicate that Shadow-FT can be applied to multimodal large language models (MLLMs) and combined with direct preference optimization (DPO). Codes and weights are available at \href{https://github.com/wutaiqiang/Shadow-FT}{Github}.
PLAICraft: Large-Scale Time-Aligned Vision-Speech-Action Dataset for Embodied AI
2025-05-19
Yingchen He, Christian D. Weilbach, Martyna E. Wojciechowska, Yuxuan Zhang, Frank Wood
Advances in deep generative modelling have made it increasingly plausible to train human-level embodied agents. Yet progress has been limited by the absence of large-scale, real-time, multi-modal, and socially interactive datasets that reflect the sensory-motor complexity of natural environments. To address this, we present PLAICraft, a novel data collection platform and dataset capturing multiplayer Minecraft interactions across five time-aligned modalities: video, game output audio, microphone input audio, mouse, and keyboard actions. Each modality is logged with millisecond time precision, enabling the study of synchronous, embodied behaviour in a rich, open-ended world. The dataset comprises over 10,000 hours of gameplay from more than 10,000 global participants.\footnote{We have done a privacy review for the public release of an initial 200-hour subset of the dataset, with plans to release most of the dataset over time.} Alongside the dataset, we provide an evaluation suite for benchmarking model capabilities in object recognition, spatial awareness, language grounding, and long-term memory. PLAICraft opens a path toward training and evaluating agents that act fluently and purposefully in real time, paving the way for truly embodied artificial intelligence.
2025-05-19
Utsav Banerjee, Chiraag Juvekar, Yong Ki Lee, Leibo Liu, Sanu Mathew, Thomas Poeppelmann, Shreyas Sen, Takeshi Sugawara, Ingrid Verbauwhede, Rabia Tugce Yazicigil
Security is increasingly more important in designing chips and systems based on them, and the International Solid-State Circuits Conference (ISSCC), the leading conference for presenting advances in solid-state circuits and semiconductor technology, is committed to hardware security by establishing the security subcommittee since 2024. In the past two years, the authors of this paper reviewed submissions as members of the Security Subcommittee, a part of International Technical Program Committee (ITPC). This paper aims to encourage high-quality submissions to grow this field in the overall scope of the ISSCC.
Recent advances in graph machine learning have shifted to data-centric paradigms, driven by two emerging fields: (1) Federated graph learning (FGL) enables multi-client collaboration but faces challenges from data and task heterogeneity, limiting its practicality; (2) Graph foundation models (GFM) offer strong domain generalization but are usually trained on single machines, missing out on cross-silo data and resources. These paradigms are complementary, and their integration brings notable benefits. Motivated by this, we propose FedGFM, a novel decentralized GFM training paradigm. However, a key challenge is knowledge entanglement, where multi-domain knowledge merges into indistinguishable representations, hindering downstream adaptation. To address this, we present FedGFM+, an enhanced framework with two core modules to reduce knowledge entanglement: (1) AncDAI: A global anchor-based domain-aware initialization strategy. Before pre-training, each client encodes its local graph into domain-specific prototypes that serve as semantic anchors. Synthetic embeddings around these anchors initialize the global model. We theoretically prove these prototypes are distinguishable across domains, providing a strong inductive bias to disentangle domain-specific knowledge. (2) AdaDPP: A local adaptive domain-sensitive prompt pool. Each client learns a lightweight graph prompt capturing domain semantics during pre-training. During fine-tuning, prompts from all clients form a pool from which the GFM selects relevant prompts to augment target graph attributes, improving downstream adaptation. FedGFM+ is evaluated on 8 diverse benchmarks across multiple domains and tasks, outperforming 20 baselines from supervised learning, FGL, and federated GFM variants.
Recent Foundation Model-enabled robotics (FMRs) display greatly improved general-purpose skills, enabling more adaptable automation than conventional robotics. Their ability to handle diverse tasks thus creates new opportunities to replace human labor. However, unlike general foundation models, FMRs interact with the physical world, where their actions directly affect the safety of humans and surrounding objects, requiring careful deployment and control. Based on this proposition, our survey comprehensively summarizes robot control approaches to mitigate physical risks by covering all the lifespan of FMRs ranging from pre-deployment to post-accident stage. Specifically, we broadly divide the timeline into the following three phases: (1) pre-deployment phase, (2) pre-incident phase, and (3) post-incident phase. Throughout this survey, we find that there is much room to study (i) pre-incident risk mitigation strategies, (ii) research that assumes physical interaction with humans, and (iii) essential issues of foundation models themselves. We hope that this survey will be a milestone in providing a high-resolution analysis of the physical risks of FMRs and their control, contributing to the realization of a good human-robot relationship.
Model Cards for AI Teammates: Comparing Human-AI Team Familiarization Methods for High-Stakes Environments
2025-05-19
Ryan Bowers, Richard Agbeyibor, Jack Kolb, Karen Feigh
We compare three methods of familiarizing a human with an artificial intelligence (AI) teammate ("agent") prior to operation in a collaborative, fast-paced intelligence, surveillance, and reconnaissance (ISR) environment. In a between-subjects user study (n=60), participants either read documentation about the agent, trained alongside the agent prior to the mission, or were given no familiarization. Results showed that the most valuable information about the agent included details of its decision-making algorithms and its relative strengths and weaknesses compared to the human. This information allowed the familiarization groups to form sophisticated team strategies more quickly than the control group. Documentation-based familiarization led to the fastest adoption of these strategies, but also biased participants towards risk-averse behavior that prevented high scores. Participants familiarized through direct interaction were able to infer much of the same information through observation, and were more willing to take risks and experiment with different control modes, but reported weaker understanding of the agent's internal processes. Significant differences were seen between individual participants' risk tolerance and methods of AI interaction, which should be considered when designing human-AI control interfaces. Based on our findings, we recommend a human-AI team familiarization method that combines AI documentation, structured in-situ training, and exploratory interaction.
Finding Distributions that Differ, with False Discovery Rate Control
2025-05-19
Yonghoon Lee, Edgar Dobriban, Eric Tchetgen Tchetgen
We consider the problem of comparing a reference distribution with several other distributions. Given a sample from both the reference and the comparison groups, we aim to identify the comparison groups whose distributions differ from that of the reference group. Viewing this as a multiple testing problem, we introduce a methodology that provides exact, distribution-free control of the false discovery rate. To do so, we introduce the concept of batch conformal p-values and demonstrate that they satisfy positive regression dependence across the groups [Benjamini and Yekutieli, 2001], thereby enabling control of the false discovery rate through the Benjamini-Hochberg procedure. The proof of positive regression dependence introduces a novel technique for the inductive construction of rank vectors with almost sure dominance under exchangeability. We evaluate the performance of the proposed procedure through simulations, where, despite being distribution-free, in some cases they show performance comparable to methods with knowledge of the data-generating normal distribution; and further have more power than direct approaches based on conformal out-of-distribution detection. Further, we illustrate our methods on a Hepatitis C treatment dataset, where they can identify patient groups with large treatment effects; and on the Current Population Survey dataset, where they can identify sub-population with long work hours.
Advancing Software Quality: A Standards-Focused Review of LLM-Based Assurance Techniques
2025-05-19
Avinash Patil
Software Quality Assurance (SQA) is critical for delivering reliable, secure, and efficient software products. The Software Quality Assurance Process aims to provide assurance that work products and processes comply with predefined provisions and plans. Recent advancements in Large Language Models (LLMs) present new opportunities to enhance existing SQA processes by automating tasks like requirement analysis, code review, test generation, and compliance checks. Simultaneously, established standards such as ISO/IEC 12207, ISO/IEC 25010, ISO/IEC 5055, ISO 9001/ISO/IEC 90003, CMMI, and TMM provide structured frameworks for ensuring robust quality practices. This paper surveys the intersection of LLM-based SQA methods and these recognized standards, highlighting how AI-driven solutions can augment traditional approaches while maintaining compliance and process maturity. We first review the foundational software quality standards and the technical fundamentals of LLMs in software engineering. Next, we explore various LLM-based SQA applications, including requirement validation, defect detection, test generation, and documentation maintenance. We then map these applications to key software quality frameworks, illustrating how LLMs can address specific requirements and metrics within each standard. Empirical case studies and open-source initiatives demonstrate the practical viability of these methods. At the same time, discussions on challenges (e.g., data privacy, model bias, explainability) underscore the need for deliberate governance and auditing. Finally, we propose future directions encompassing adaptive learning, privacy-focused deployments, multimodal analysis, and evolving standards for AI-driven software quality.
BeamClean: Language Aware Embedding Reconstruction
2025-05-19
Kaan Kale, Kyle Mylonakis, Jay Roberts, Sidhartha Roy
In this work, we consider an inversion attack on the obfuscated input embeddings sent to a language model on a server, where the adversary has no access to the language model or the obfuscation mechanism and sees only the obfuscated embeddings along with the model's embedding table. We propose BeamClean, an inversion attack that jointly estimates the noise parameters and decodes token sequences by integrating a language-model prior. Against Laplacian and Gaussian obfuscation mechanisms, BeamClean always surpasses naive distance-based attacks. This work highlights the necessity for and robustness of more advanced learned, input-dependent methods.
OGHReS: Star formation in the Outer Galaxy II ($\ell = 180^\circ$-$280^\circ$)
2025-05-19
J. S. Urquhart, C. Koenig, D. Colombo, A. Karska, A. Giannetti, T. J. T. Moore, A. Y. Yang, F. Wyrowski, Y. Sun, Z. Jiang, K. R. Neralwar, D. Eden, I. Grozdanova, S. Neupane, M. Figueira, E. Dann, V., S. Veena, W. -J. Kim, S. Leurini, J. Brand, M. -Y. Lee
The Outer Galaxy High-Resolution Survey (OGHReS) covers 100 square degrees ($180^\circ < \ell < 280^\circ$) in the (2--1) transitions of three CO-isotopologues. We use the spectra to refine the velocities and physical properties to 6706 \higal\ clumps located in the OGHReS region. In a previous paper, we analysed 3584 clumps between $\ell = 250^\circ$ and $280^\circ$. Here, we cover a further 3122 clumps ($180^\circ < \ell < 250^\circ$) and determine reliable velocities for \withVLSR\ of these, finding good agreement with the previously assigned velocities ($\sim$80 percent within 5 \kms). We update velocities for 288 clumps and provide new values for an additional 411. Combining these with the previous results, we have velocities and physical properties for 6193 clumps (92.3 percent). The \allnonDetections\ non-detections are low surface density clumps or likely contamination by evolved stars and galaxies. Key findings: i) improved correlation between clumps and spiral arm loci, and the discovery of clumps beyond the outer arm supports the existence of a new spiral structure; ii) decreasing trend in the $L/M$-ratio consistent with less high-mass star formation in the outer Galaxy; iii) increase in the star formation fraction (SFF) in the outer Galaxy, suggesting that more clumps are forming stars despite their lower mass; iv) discrepancies in velocity assignments across different surveys that could affect $\sim$10000 clumps, especially in the fourth quadrant.
2025-05-19
Adrian Clingher, Andreas Malmendier, Tony Shaska
The paper discusses geometric and computational aspects associated with $(n,n)$-isogenies for principally polarized Abelian surfaces and related Kummer surfaces. We start by reviewing the comprehensive Theta function framework for classifying genus-two curves, their principally polarized Jacobians, as well as for establishing explicit quartic normal forms for associated Kummer surfaces. This framework is then used for practical isogeny computations. A particular focus of the discussion is the $(n,n)$-Split isogeny case. We also explore possible extensions of Richelot's $(2,2)$-isogenies to higher order cases, with a view towards developing efficient isogeny computation algorithms.
2025-05-19
V. Heesen, M. Stein, N. Pourjafari, M. Brüggen, J. Stil, J. -T. Li, T. Wiegert, J. Irwin, R. -J. Dettmar, T. A. Porter, Y. Stein
Context. Edge-on spiral galaxies give us an outsiders' view of the radio halo, which envelops these galaxies. The radio halos are caused by extra-planar cosmic-ray electrons that emit synchrotron emission in magnetic fields. Aims. We aim to study the origin of radio halos around galaxies and infer the role of cosmic-rays in supporting the gaseous discs. We would like to test the influence of star formation as the main source of cosmic rays as well as other fundamental galaxy properties such as mass and size. Methods. We present a study of radio continuum scale heights in 22 nearby edge-on galaxies from the CHANG-ES survey. We employ deep observations with the Jansky Very Large Array in the S-band (2-4 GHz), imaging at 7" angular resolution. We measure scale heights in three strips within the effective radio continuum radius, correcting for the influence of angular resolution and inclination angle. We include only galaxies where a distinction between the two disc components can be made in at least one of the strips, providing us with robust measurements of both scale heights. Results. We find a strong positive correlation between scale heights of the thin and thick discs and star-forming radius as well as star-formation rate (SFR); moderately strong correlations are found for the mass surface density and the ratio of SFR-to-mass surface density; no correlation is found with SFR surface density alone. Yet the SFR surface density plays a role as well: galaxies with high SFR surface densities have a rather roundish shape, whereas galaxies with little star formation show only a relatively small vertical extent in comparison to their size. Conclusions. Thick gaseous discs are partially supported by cosmic-ray pressure. Our results are a useful benchmark for simulations of galaxy evolution that include cosmic rays.
A Systematic Review and Taxonomy for Privacy Breach Classification: Trends, Gaps, and Future Directions
2025-05-19
Clint Fuchs, John D. Hastings
In response to the rising frequency and complexity of data breaches and evolving global privacy regulations, this study presents a comprehensive examination of academic literature on the classification of privacy breaches and violations between 2010-2024. Through a systematic literature review, a corpus of screened studies was assembled and analyzed to identify primary research themes, emerging trends, and gaps in the field. A novel taxonomy is introduced to guide efforts by categorizing research efforts into seven domains: breach classification, report classification, breach detection, threat detection, breach prediction, risk analysis, and threat classification. An analysis reveals that breach classification and detection dominate the literature, while breach prediction and risk analysis have only recently emerged in the literature, suggesting opportunities for potential research impacts. Keyword and phrase frequency analysis reveal potentially underexplored areas, including location privacy, prediction models, and healthcare data breaches.
The discovery of the family of balanced product codes was pivotal in the subsequent development of `good' low density quantum error correcting codes that have optimal scaling of the key parameters of distance and storage density. We review this family, giving a completely different presentation to the original, minimising the abstraction and technicalities wherever possible.
Most observational studies of galactic-scale magnetic fields using Faraday rotation rely on estimates of thermal electron densities in galaxies and their radial variations. However, the spatial distribution of electrons in the interstellar medium (ISM) is not clearly known. In this study, we propose and utilize collision-excited doublet emission line ratios of [S\,\textsc{ii}] $\lambda\lambda$ 6716, 6731 \AA~and [O\,\textsc{ii}] $\lambda\lambda$ 3726, 3729 \AA \ to estimate the electron densities ($n_e$). To map their distribution in the galaxies, we employ Integral Field Unit (IFU) spectroscopic observations from the SDSS Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, utilising data products from both the \texttt{Pipe3D} and MaNGA Data Analysis Pipeline (DAP). We present a spatially resolved analysis of $13$ face-on galaxies (inclination, $i \leq 10^\circ$), including $9$ star-forming galaxies (SFGs) and $4$ Non-SFGs. Azimuthally averaged radial profiles of $n_e$ are obtained using two different binning schemes: linear and non-linear. For the \texttt{Pipe3D} case, both SFGs and non-SFGs exhibit $n_e$ gradients, with higher densities of $n_e$(S\,\textsc{ii}) = $165.6 \pm 20.8$ cm$^{-3}$ in the inner disk region (r/R$_e$ $\leq$ 1.5), which decrease to $31 \pm 4.5$ cm$^{-3}$ in the outer disk region (r/R$_e$ $>$ 1.5). We also translate $n_e$ to the electron column density $N_e$ assuming an evenly distributed thin disk profile, fairly excluding the central bulge regions. These electron density estimates at different radii provide valuable insights for resolving ambiguities in current and future studies of magnetic fields in galaxies.
UGC 10043 in Depth: Dissecting the Polar Bulge and Subtle LSB Features
2025-05-19
S. K. H. Bahr, A. V. Mosenkov
Galaxies with polar structures -- of which polar-ring galaxies (PRGs) are a prominent subclass -- contain components that are kinematically decoupled and highly inclined relative to the host galaxy's major axis. Modern deep optical surveys provide a powerful means of detecting low surface brightness (LSB) features around galaxies, offering critical insights into the formation and evolution of galaxies with polar structures. UGC 10043 is an edge-on galaxy notable for its prominent bulge, which extends orthogonally to the disk plane. In addition, the galaxy displays a well-defined integral-shaped disk warp and multiple dust features crossing the bulge along the minor galaxy axis. In this work, we present new deep optical photometry of UGC 10043 down to 29.5 mag arcsec$^{-2}$ and perform a detailed analysis of its LSB and polar structures. The observations reveal a stellar stream aligned along the polar axis, alongside other signatures of tidal interaction, including a flat, tilted LSB envelope that extends toward the neighboring galaxy MCG +04-37-035, with which UGC 10043 is connected by an HI bridge. Our results suggest that the polar component of UGC 10043 comprises and older, triaxial polar bulge and a younger, forming polar structure, likely originating from the ongoing disruption of a dwarf satellite galaxy, while also participating in active interaction with MCG +04-37-035.
Conceptual Modeling: Topics, Themes, and Technology Trends
2025-05-19
V. C. Storey, R. Lukyanenko, A. Castellanos
Conceptual modeling is an important part of information systems development and use that involves identifying and representing relevant aspects of reality. Although the past decades have experienced continuous digitalization of services and products that impact business and society, conceptual modeling efforts are still required to support new technologies as they emerge. This paper surveys research on conceptual modeling over the past five decades and shows how its topics and trends continue to evolve to accommodate emerging technologies, while remaining grounded in basic constructs. We survey over 5,300 papers that address conceptual modeling topics from the 1970s to the present, which are collected from 35 multidisciplinary journals and conferences, and use them as the basis from which to analyze the progression of conceptual modeling. The important role that conceptual modeling should play in our evolving digital world is discussed, and future research directions proposed.
We give a brief review of the basic principles of inflationary theory and discuss the present status of the simplest inflationary models that can describe Planck/BICEP/Keck observational data by choice of a single model parameter. In particular, we discuss the Starobinsky model, Higgs inflation, and $\alpha$-attractors, including the recently developed $\alpha$-attractor models with $SL(2,\mathbb{Z})$ invariant potentials. We also describe inflationary models providing a good fit to the recent ACT data, as well as the polynomial chaotic inflation models with three parameters, which can account for any values of the three main CMB-related inflationary parameters $A_{s}$, $n_{s}$ and $r$.
2025-05-19
Sarah V. White, Kshitij Thorat, Moses Mogotsi, Rosalind E. Skelton, Solohery M. Randriamampandry, Encarni Romero-Colmenero, Precious K. Sejake, Francesco Massaro, Abigail Garcia-Perez, Ana Jimenez-Gallardo, Harold A. Pena-Herazo, Edward N. Taylor
The GLEAM 4-Jy (G4Jy) Sample is a thorough compilation of the 'brightest' radio sources in the southern sky (Declination < 30 deg), as measured at 151 MHz (S > 4.0 Jy) with the Murchison Widefield Array (MWA), through the GaLactic and Extragalactic All-sky MWA (GLEAM) Survey. In addition to flux-density measurements, the G4Jy catalogue provides host-galaxy identifications (through careful visual-inspection) and four sets of spectral indices. Despite their brightness in the radio, many of these sources are poorly-studied, with the vast majority lacking a spectroscopic redshift in published work. This is crucial for studying the intrinsic properties of the sources, and so we conduct a multi-semester observing campaign on the Southern African Large Telescope (SALT), with optical spectroscopy enabling us to provide new redshifts to the astronomical community. Initial results show that not all of the host galaxies exhibit emission-line spectra in the optical (~4500-7500 Ang), which illustrates the importance of radio-frequency selection (rather than optical selection) for creating an unbiased sample of active galactic nuclei. By combining SALT redshifts with those from the 6-degree Field Galaxy Survey (6dFGS) and the Sloan Digital Sky Survey (SDSS), we calculate radio luminosities and linear sizes for 299 G4Jy sources (which includes one newly-discovered giant radio-galaxy, G4Jy 604). Furthermore, with the highest redshift acquired (so far) being z ~ 2.2 from SDSS, we look forward to evolution studies of this complete sample, as well as breaking degeneracies in radio properties with respect to, for example, the galaxy environment.
Deep galaxy stellar mass functions as a function of star formation rate in the Virgo cluster environment
2025-05-19
Cameron R. Morgan, Elizaveta Sazonova, Ian D. Roberts, Michael L. Balogh, Joel Roediger, Laura Ferrarese, Patrick Côté, Alessandro Boselli, Matteo Fossati, Jean-Charles Cuillandre, Stephen Gwyn
We analyze deep ($M_*\gtrsim10^7~{M}_{\odot}$) galaxy stellar mass functions (SMFs) of the Virgo cluster using stellar masses derived as part of the Next Generation Virgo Survey (NGVS). The total SMF has a slope of $\alpha=-1.35^{+0.02}_{-0.02}$ which is similar to or steeper than typical field values. Using deep \ha{} data from the Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE) we separate out star-forming galaxies, quiescent galaxies with no ongoing star formation, and low-SFR galaxies that are intermediate between these two populations. For each of these populations, the shape of the SMF is found to be universal throughout the cluster, from the core to the outskirts. The star-forming and quiescent SMFs show stark differences with values seen in field galaxies. The relative fraction of quiescent galaxies is highest in the core of the cluster, with low-SFR and star-forming galaxies more significant in the outer regions of the cluster. At low stellar masses ($M_*\lesssim10^9~{M}_{\odot}$), the quiescent fraction in the main cluster is significantly higher than that of the field and even satellites of massive groups. At high stellar masses, the quiescent fraction is similar to other studies of cluster galaxies. We model the quiescent population in the infall region of the cluster as a combination of backsplash and field quiescent galaxies, and find that the backsplash fractions needed to explain the observed population are unrealistically high. This suggests the existence of a third population of low-mass galaxies that are pre-processed outside the virial radius of the cluster, possibly in groups prior to infall.
A Panchromatic Characterization of the Evening and Morning Atmosphere of WASP-107 b: Composition and Cloud Variations, and Insight into the Effect of Stellar Contamination
2025-05-19
Matthew M. Murphy, Thomas G. Beatty, Everett Schlawin, Taylor J. Bell, Michael Radica, Thomas D. Kennedy, Nishil Mehta, Luis Welbanks, Michael R. Line, Vivien Parmentier, Thomas P. Greene, Sagnick Mukherjee, Jonathan J. Fortney, Kazumasa Ohno, Lindsey Wiser, Kenneth Arnold, Emily Rauscher, Isaac R. Edelman, Marcia J. Rieke
Limb-resolved transmission spectroscopy has the potential to transform our understanding of exoplanetary atmospheres. By separately measuring the transmission spectra of the evening and morning limbs, these atmospheric regions can be individually characterized, shedding light into the global distribution and transport of key atmospheric properties from transit observations alone. In this work, we follow up the recent detection of limb asymmetry on the exoplanet WASP-107 b (Murphy et al. 2024) by reanalyzing literature observations of WASP-107 b using all of JWST's science intruments (NIRISS, NIRCam, NIRSpec, and MIRI) to measure its limb transmission spectra from $\sim$1-12 $\mu$m. We confirm the evening--morning temperature difference inferred previously and find that it is qualitatively consistent with predictions from global circulation models. We find evidence for evening--morning variation in SO$_2$ and CO$_2$ abundance, and significant cloud coverage only on WASP-107 b's morning limb. We find that the NIRISS and NIRSpec observations are potentially contaminated by occulted starspots, which we leverage to investigate stellar contamination's impact on limb asymmetry measurements. We find that starspot crossings can significantly bias the inferred evening and morning transmission spectra depending on when they occur during the transit, and develop a simple correction model which successfully brings these instruments' spectra into agreement with the uncontaminated observations.
Fantômas: epistemic and nuclear uncertainties for the parton distributions of the pion
2025-05-19
Lucas Kotz, Aurore Courtoy, Pavel Nadolsky, Maximiliano Ponce-Chavez
We present Fanto10, a new ensemble of NLO error parton distribution functions (PDFs) in a charged pion that provides the most detailed estimate of uncertainties from experimental, theoretical, and methodological sources in order to enable faithful comparisons against upcoming precision experiments and ab initio QCD predictions. For the first time, the Fanto10 PDFs quantify two important types of uncertainties, arising from in-depth exploration of feasible functional forms of pion PDFs and from the nuclear PDFs describing the internal composition of the tungsten target in the key E615 data set for pion-nuclear Drell-Yan process. Accounting for these uncertainties modifies physics conclusions of more restrictive analyses about the pion's gluon and quark sea composition. These advancements are made by employing the recently developed C++ framework Fant\^omas for systematic exploration of epistemic sources of PDF uncertainties, such as those arising from the choice of the PDF functional forms and sampling over methodological choices and third-party inputs, e.g., the nuclear PDF uncertainty in the presented case. The framework employs polynomial universal approximators (B\'ezier curves) to parametrize diverse PDF functional forms and reduce biases associated with the PDF parametrization choice. Its other component combines PDF ensembles from multiple preliminary fits into a single published ensemble of Hessian error PDFs. We review key steps of the Fant\^omas methodology, properties of Fanto10 PDFs, and implications for future studies.
2025-05-19
Gemma Zhang, Chirag Modi, Oliver H. E. Philcox
Simulation-based inference (SBI) has emerged as a powerful tool for extracting cosmological information from galaxy surveys deep into the non-linear regime. Despite its great promise, its application is limited by the computational cost of running simulations that can describe the increasingly-large cosmological datasets. Recent work proposed a hybrid SBI framework (HySBI), which combines SBI on small-scales with perturbation theory (PT) on large-scales, allowing information to be extracted from high-resolution observations without large-volume simulations. In this work, we lay out the HySBI framework for galaxy clustering, a key step towards its application to next-generation datasets. We study the choice of priors on the parameters for modeling galaxies in PT analysis and in simulation-based analyses, as well as investigate their cosmology dependence. By jointly modeling large- and small-scale statistics and their associated nuisance parameters, we show that HySBI can obtain 20\% and 60\% tighter constraints on $\Omega_m$ and $\sigma_8$, respectively, compared to traditional PT analyses, thus demonstrating the efficacy of this approach to maximally extract information from upcoming spectroscopic datasets.
Supervised learning demands large amounts of precisely annotated data to achieve promising results. Such data curation is labor-intensive and imposes significant overhead regarding time and costs. Self-supervised learning (SSL) partially overcomes these limitations by exploiting vast amounts of unlabeled data and creating surrogate (pretext or proxy) tasks to learn useful representations without manual labeling. As a result, SSL has become a powerful machine learning (ML) paradigm for solving several practical downstream computer vision problems, such as classification, detection, and segmentation. Image segmentation is the cornerstone of many high-level visual perception applications, including medical imaging, intelligent transportation, agriculture, and surveillance. Although there is substantial research potential for developing advanced algorithms for SSL-based semantic segmentation, a comprehensive study of existing methodologies is essential to trace advances and guide emerging researchers. This survey thoroughly investigates over 150 recent image segmentation articles, particularly focusing on SSL. It provides a practical categorization of pretext tasks, downstream tasks, and commonly used benchmark datasets for image segmentation research. It concludes with key observations distilled from a large body of literature and offers future directions to make this research field more accessible and comprehensible for readers.
ComBAT Harmonization for diffusion MRI: Challenges and Best Practices
2025-05-19
Pierre-Marc Jodoin, Manon Edde, Gabriel Girard, Félix Dumais, Guillaume Theaud, Matthieu Dumont, Jean-Christophe Houde, Yoan David, Maxime Descoteaux
Over the years, ComBAT has become the standard method for harmonizing MRI-derived measurements, with its ability to compensate for site-related additive and multiplicative biases while preserving biological variability. However, ComBAT relies on a set of assumptions that, when violated, can result in flawed harmonization. In this paper, we thoroughly review ComBAT's mathematical foundation, outlining these assumptions, and exploring their implications for the demographic composition necessary for optimal results. Through a series of experiments involving a slightly modified version of ComBAT called Pairwise-ComBAT tailored for normative modeling applications, we assess the impact of various population characteristics, including population size, age distribution, the absence of certain covariates, and the magnitude of additive and multiplicative factors. Based on these experiments, we present five essential recommendations that should be carefully considered to enhance consistency and supporting reproducibility, two essential factors for open science, collaborative research, and real-life clinical deployment.
The emerging AI 'revolution tranquille' in America
2025-05-19
Omar R. Malik
Using data from the U.S. Census Bureaus Business Trends and Outlook Survey (BTOS), I examine the adoption of AI among US firms at national, state, industry, and firm size levels. I find that adoption remains overall low (only around 7% of firms currently use AI), but is on a steady upward trajectory with a rising share of firms planning to implement AI. Adoption rates vary significantly across regions and sectors: some states are emerging as early adopters, while others lag, and knowledge-intensive industries (such as information technology and professional services) along with larger firms show higher openness to AI adoption compared to sectors like construction or small businesses. In general, these trends indicate that a quiet revolution in AI adoption is underway; a gradual but expanding diffusion of AI across the economy with important implications for future productivity and policy.
A key factor in effective Self-Supervised learning (SSL) is preventing dimensional collapse, which is where higher-dimensional representation spaces span a lower-dimensional subspace. Therefore, SSL optimization strategies involve guiding a model to produce representations ($R$) with a higher dimensionality. Dimensionality is either optimized through a dimension-contrastive approach that encourages feature decorrelation or through a sample-contrastive method that promotes a uniform spread of sample representations. Both families of SSL algorithms also utilize a projection head that maps $R$ into a lower-dimensional embedding space $Z$. Recent work has characterized the projection head as a filter of irrelevant features from the SSL objective by reducing mutual information, $I(R;Z)$. Therefore, the current literature's view is that a good SSL representation space should have a high $H(R)$ and a low $I(R;Z)$. However, this view of the problem is lacking in terms of an understanding of the underlying training dynamics that influences both terms, as well as how the values of $H(R)$ and $I(R;Z)$ arrived at the end of training reflect the downstream performance of an SSL model. We address both gaps in the literature by demonstrating that increases in $H(R)$ due to feature decorrelation at the start of training lead to a higher $I(R;Z)$, while increases in $H(R)$ due to samples distributing uniformly in a high-dimensional space at the end of training cause $I(R;Z)$ to plateau or decrease. Furthermore, our analysis shows that the best performing SSL models do not have the highest $H(R)$ nor the lowest $I(R;Z)$, but arrive at an optimal intermediate point for both. We develop a method called AdaDim to exploit these observed training dynamics by adaptively weighting between losses based on feature decorrelation and uniform sample spread.
Large language models (LLMs) and LLM-based agents have been widely deployed in a wide range of applications in the real world, including healthcare diagnostics, financial analysis, customer support, robotics, and autonomous driving, expanding their powerful capability of understanding, reasoning, and generating natural languages. However, the wide deployment of LLM-based applications exposes critical security and reliability risks, such as the potential for malicious misuse, privacy leakage, and service disruption that weaken user trust and undermine societal safety. This paper provides a systematic overview of the details of adversarial attacks targeting both LLMs and LLM-based agents. These attacks are organized into three phases in LLMs: Training-Phase Attacks, Inference-Phase Attacks, and Availability & Integrity Attacks. For each phase, we analyze the details of representative and recently introduced attack methods along with their corresponding defenses. We hope our survey will provide a good tutorial and a comprehensive understanding of LLM security, especially for attacks on LLMs. We desire to raise attention to the risks inherent in widely deployed LLM-based applications and highlight the urgent need for robust mitigation strategies for evolving threats.
The NEID Earth Twin Survey. II. Dynamical Masses in Seven High-acceleration Star Systems
2025-05-18
Mark R. Giovinazzi, Cullen H. Blake, Paul Robertson, Andrea S. J. Lin, Arvind F. Gupta, Suvrath Mahadevan, Rachel B. Fernandes, Jason T. Wright, Daniella Bardalez Gagliuffi, Jiayin Dong, Evan Fitzmaurice, Samuel Halverson, Shubham Kanodia, Sarah E. Logsdon, Jacob K. Luhn, Michael W. McElwain, Andy Monson, Joe P. Ninan, Jayadev Rajagopal, Arpita Roy, Christian Schwab, Gudmundur Stefánsson, Ryan Terrien, Jason D. Eastman, Jonathan Horner, Peter Plavchan, Sharon X. Wang, Maurice L. Wilson, Robert A. Wittenmyer
We present a set of companion dynamical masses and orbital parameters of seven star systems from the NEID Earth Twin Survey with significant absolute astrometric accelerations between the epochs of Hipparcos and Gaia. These include four binary star systems (HD 68017 AB, 61 Cygni AB, HD 24496 AB, and HD 4614 AB) and three planetary systems (HD 217107, HD 190360, and HD 154345). Our analyses incorporate a long baseline of RVs that includes over 1100 previously unpublished measurements from NEID and MINERVA, extending the overall RV baseline for each system by $\approx$2.5 years, as well as relative astrometry for the stellar binary systems where the positions of both stars are well-measured. In each case, the combination of astrometry and RVs constrains the three-dimensional acceleration of the host star and enables precise dynamical masses. We publish true masses for three planets whose measurements were previously entangled with their inclinations, four stellar masses with $\lesssim$1% relative precision, and improved orbital solutions for all seven systems, including the first for HD 24496 AB. These solutions not only agree with previous estimates, but also improve their fidelity. We also explore each system for evidence of periodic signals in the residuals around our best-fit models, and discuss the potential that the three planetary systems have for being directly imaged. With dynamical mass estimates and reliable orbit ephemerides, these seven star systems represent promising benchmarks for future stellar and planetary characterization efforts, and are amenable for further improvement with the upcoming release of Gaia epoch astrometry.
Towards Reliable and Interpretable Traffic Crash Pattern Prediction and Safety Interventions Using Customized Large Language Models
2025-05-18
Yang Zhao, Pu Wang, Yibo Zhao, Hongru Du, Hao Frank Yang
Predicting crash events is crucial for understanding crash distributions and their contributing factors, thereby enabling the design of proactive traffic safety policy interventions. However, existing methods struggle to interpret the complex interplay among various sources of traffic crash data, including numeric characteristics, textual reports, crash imagery, environmental conditions, and driver behavior records. As a result, they often fail to capture the rich semantic information and intricate interrelationships embedded in these diverse data sources, limiting their ability to identify critical crash risk factors. In this research, we propose TrafficSafe, a framework that adapts LLMs to reframe crash prediction and feature attribution as text-based reasoning. A multi-modal crash dataset including 58,903 real-world reports together with belonged infrastructure, environmental, driver, and vehicle information is collected and textualized into TrafficSafe Event Dataset. By customizing and fine-tuning LLMs on this dataset, the TrafficSafe LLM achieves a 42% average improvement in F1-score over baselines. To interpret these predictions and uncover contributing factors, we introduce TrafficSafe Attribution, a sentence-level feature attribution framework enabling conditional risk analysis. Findings show that alcohol-impaired driving is the leading factor in severe crashes, with aggressive and impairment-related behaviors having nearly twice the contribution for severe crashes compared to other driver behaviors. Furthermore, TrafficSafe Attribution highlights pivotal features during model training, guiding strategic crash data collection for iterative performance improvements. The proposed TrafficSafe offers a transformative leap in traffic safety research, providing a blueprint for translating advanced AI technologies into responsible, actionable, and life-saving outcomes.
Disambiguation in Conversational Question Answering in the Era of LLM: A Survey
2025-05-18
Md Mehrab Tanjim, Yeonjun In, Xiang Chen, Victor S. Bursztyn, Ryan A. Rossi, Sungchul Kim, Guang-Jie Ren, Vaishnavi Muppala, Shun Jiang, Yongsung Kim, Chanyoung Park
Ambiguity remains a fundamental challenge in Natural Language Processing (NLP) due to the inherent complexity and flexibility of human language. With the advent of Large Language Models (LLMs), addressing ambiguity has become even more critical due to their expanded capabilities and applications. In the context of Conversational Question Answering (CQA), this paper explores the definition, forms, and implications of ambiguity for language driven systems, particularly in the context of LLMs. We define key terms and concepts, categorize various disambiguation approaches enabled by LLMs, and provide a comparative analysis of their advantages and disadvantages. We also explore publicly available datasets for benchmarking ambiguity detection and resolution techniques and highlight their relevance for ongoing research. Finally, we identify open problems and future research directions, proposing areas for further investigation. By offering a comprehensive review of current research on ambiguities and disambiguation with LLMs, we aim to contribute to the development of more robust and reliable language systems.
The use of deep learning (DL) on Internet of Things (IoT) and mobile devices offers numerous advantages over cloud-based processing. However, such devices face substantial energy constraints to prolong battery-life, or may even operate intermittently via energy-harvesting. Consequently, \textit{energy-aware} approaches for optimizing DL inference and training on such resource-constrained devices have garnered recent interest. We present an overview of such approaches, outlining their methodologies, implications for energy consumption and system-level efficiency, and their limitations in terms of supported network types, hardware platforms, and application scenarios. We hope our review offers a clear synthesis of the evolving energy-aware DL landscape and serves as a foundation for future research in energy-constrained computing.
Exploring the interplay between population profile and optimal routes in U.S. cities
2025-05-18
Diego Ortega, Elka Korutcheva
Cities have developed over time alongside advancements in civilization, focusing on efficient travel and reducing costs. Many studies have examined the distinctive features of urban road networks, such as their length, efficiency, connection to population density, and other properties. However, the relationship between car routes and population in city structures remains unclear. In this study, we used the center of mass for each city tract, defined by the US Census, as the origins and destinations for our itineraries. We calculated travel time, and both Euclidean and travel distances for sixty major cities. We discovered that the total sum of all routes adheres to an urban law. The distribution of these car journeys follows Weibull functions, suggesting that the urban center plays a crucial role in optimizing routes across multiple cities. We also developed a simple point pattern model for the population, which aligns with the well-known decreasing exponential density expression. Our findings show that the interplay between population and path optimization influences city structure through its center. This study offers a new perspective on the fundamental principles that shape urban design.
Proposal for Improving Google A2A Protocol: Safeguarding Sensitive Data in Multi-Agent Systems
2025-05-18
Yedidel Louck, Ariel Stulman, Amit Dvir
A2A, a protocol for AI agent communication, offers a robust foundation for secure AI agent communication. However, it has several critical issues in handling sensitive data, such as payment details, identification documents, and personal information. This paper reviews the existing protocol, identifies its limitations, and proposes specific enhancements to improve security, privacy, and trust. It includes a concrete example to illustrate the problem and solution, research-backed rationales, and implementation considerations, drawing on prior studies to strengthen the arguments and proposed solutions. This proposal includes seven enhancements: short-lived tokens, customer authentication (SCA), granular scopes, explicit consent, direct data transfer, multi-transaction approval, and payment standard compliance. The vacation booking example illustrates how these enhancements reduce risks and enhance user experience.
Traversal Verification for Speculative Tree Decoding
2025-05-18
Yepeng Weng, Qiao Hu, Xujie Chen, Li Liu, Dianwen Mei, Huishi Qiu, Jiang Tian, Zhongchao Shi
Speculative decoding is a promising approach for accelerating large language models. The primary idea is to use a lightweight draft model to speculate the output of the target model for multiple subsequent timesteps, and then verify them in parallel to determine whether the drafted tokens should be accepted or rejected. To enhance acceptance rates, existing frameworks typically construct token trees containing multiple candidates in each timestep. However, their reliance on token-level verification mechanisms introduces two critical limitations: First, the probability distribution of a sequence differs from that of individual tokens, leading to suboptimal acceptance length. Second, current verification schemes begin from the root node and proceed layer by layer in a top-down manner. Once a parent node is rejected, all its child nodes should be discarded, resulting in inefficient utilization of speculative candidates. This paper introduces Traversal Verification, a novel speculative decoding algorithm that fundamentally rethinks the verification paradigm through leaf-to-root traversal. Our approach considers the acceptance of the entire token sequence from the current node to the root, and preserves potentially valid subsequences that would be prematurely discarded by existing methods. We theoretically prove that the probability distribution obtained through Traversal Verification is identical to that of the target model, guaranteeing lossless inference while achieving substantial acceleration gains. Experimental results across different large language models and multiple tasks show that our method consistently improves acceptance length and throughput over existing methods
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey
2025-05-18
Calvin Galagain, Martyna Poreba, François Goulette
In embedded systems, robots must perceive and interpret their environment efficiently to operate reliably in real-world conditions. Visual Semantic SLAM (Simultaneous Localization and Mapping) enhances standard SLAM by incorporating semantic information into the map, enabling more informed decision-making. However, implementing such systems on resource-limited hardware involves trade-offs between accuracy, computing efficiency, and power usage. This paper provides a comparative review of recent Semantic Visual SLAM methods with a focus on their applicability to embedded platforms. We analyze three main types of architectures - Geometric SLAM, Neural Radiance Fields (NeRF), and 3D Gaussian Splatting - and evaluate their performance on constrained hardware, specifically the NVIDIA Jetson AGX Orin. We compare their accuracy, segmentation quality, memory usage, and energy consumption. Our results show that methods based on NeRF and Gaussian Splatting achieve high semantic detail but demand substantial computing resources, limiting their use on embedded devices. In contrast, Semantic Geometric SLAM offers a more practical balance between computational cost and accuracy. The review highlights a need for SLAM algorithms that are better adapted to embedded environments, and it discusses key directions for improving their efficiency through algorithm-hardware co-design.
Toward Near-Space Communication Network in the 6G and Beyond Era
2025-05-18
Xinhua Liu, Zhen Gao, Ziwei Wan, Zhonghuai Wu, Tuan Li, Tianqi Mao, Xiao Liang, Dezhi Zheng, Jun Zhang
Near-space communication network (NS-ComNet), as an indispensable component of sixth-generation (6G) and beyond mobile communication systems and the space-air-ground-sea integrated network (SAGSIN), demonstrates unique advantages in wide-area coverage, long-endurance high-altitude operation, and highly flexible deployment. This paper presents a comprehensive review of NS-ComNet for 6G and beyond era. Specifically, by contrasting satellite, low-altitude unmanned-aerial-vehicle (UAV), and terrestrial communications, we first elucidate the background and motivation for integrating NS-ComNet into 6G network architectures. Subsequently, we review the developmental status of near-space platforms, including high-altitude balloons, solar-powered UAVs, and stratospheric airships, and analyze critical challenges faced by NS-ComNet. To address these challenges, the research focuses on key enabling technologies such as topology design, resource and handover management, multi-objective joint optimization, etc., with particular emphasis on artificial intelligence techniques for NS-ComNet. Finally, envisioning future intelligent collaborative networks that integrate NS-ComNet with satellite-UAV-terrestrial systems, we explore promising directions. This paper aims to provide technical insights and research foundations for the systematic construction of NS-ComNet and its deep deployment in the 6G and beyond era.
The aim of this paper is to review how some approximation results in commutative algebra are being used to construct equisingular deformations of singularities. The first example of such an approximation result appeared for the first time in A. Ploski's PhD thesis.
Early Prediction of In-Hospital ICU Mortality Using Innovative First-Day Data: A Review
2025-05-18
Han Wang
The intensive care unit (ICU) manages critically ill patients, many of whom face a high risk of mortality. Early and accurate prediction of in-hospital mortality within the first 24 hours of ICU admission is crucial for timely clinical interventions, resource optimization, and improved patient outcomes. Traditional scoring systems, while useful, often have limitations in predictive accuracy and adaptability. Objective: This review aims to systematically evaluate and benchmark innovative methodologies that leverage data available within the first day of ICU admission for predicting in-hospital mortality. We focus on advancements in machine learning, novel biomarker applications, and the integration of diverse data types.
Is Artificial Intelligence Generated Image Detection a Solved Problem?
2025-05-18
Ziqiang Li, Jiazhen Yan, Ziwen He, Kai Zeng, Weiwei Jiang, Lizhi Xiong, Zhangjie Fu
The rapid advancement of generative models, such as GANs and Diffusion models, has enabled the creation of highly realistic synthetic images, raising serious concerns about misinformation, deepfakes, and copyright infringement. Although numerous Artificial Intelligence Generated Image (AIGI) detectors have been proposed, often reporting high accuracy, their effectiveness in real-world scenarios remains questionable. To bridge this gap, we introduce AIGIBench, a comprehensive benchmark designed to rigorously evaluate the robustness and generalization capabilities of state-of-the-art AIGI detectors. AIGIBench simulates real-world challenges through four core tasks: multi-source generalization, robustness to image degradation, sensitivity to data augmentation, and impact of test-time pre-processing. It includes 23 diverse fake image subsets that span both advanced and widely adopted image generation techniques, along with real-world samples collected from social media and AI art platforms. Extensive experiments on 11 advanced detectors demonstrate that, despite their high reported accuracy in controlled settings, these detectors suffer significant performance drops on real-world data, limited benefits from common augmentations, and nuanced effects of pre-processing, highlighting the need for more robust detection strategies. By providing a unified and realistic evaluation framework, AIGIBench offers valuable insights to guide future research toward dependable and generalizable AIGI detection.
Large reasoning models, such as OpenAI o1 or DeepSeek R1, have demonstrated remarkable performance on reasoning tasks but often incur a long reasoning path with significant memory and time costs. Existing methods primarily aim to shorten reasoning paths by introducing additional training data and stages. In this paper, we propose three critical reward designs integrated directly into the reinforcement learning process of large reasoning models, which reduce the response length without extra training stages. Experiments on four settings show that our method significantly decreases response length while maintaining or even improving performance. Specifically, in a logic reasoning setting, we achieve a 40% reduction in response length averaged by steps alongside a 14% gain in performance. For math problems, we reduce response length averaged by steps by 33% while preserving performance.
A Survey on Side Information-driven Session-based Recommendation: From a Data-centric Perspective
2025-05-18
Xiaokun Zhang, Bo Xu, Chenliang Li, Bowei He, Hongfei Lin, Chen Ma, Fenglong Ma
Session-based recommendation is gaining increasing attention due to its practical value in predicting the intents of anonymous users based on limited behaviors. Emerging efforts incorporate various side information to alleviate inherent data scarcity issues in this task, leading to impressive performance improvements. The core of side information-driven session-based recommendation is the discovery and utilization of diverse data. In this survey, we provide a comprehensive review of this task from a data-centric perspective. Specifically, this survey commences with a clear formulation of the task. This is followed by a detailed exploration of various benchmarks rich in side information that are pivotal for advancing research in this field. Afterwards, we delve into how different types of side information enhance the task, underscoring data characteristics and utility. Moreover, we discuss the usage of various side information, including data encoding, data injection, and involved techniques. A systematic review of research progress is then presented, with the taxonomy by the types of side information. Finally, we summarize the current limitations and present the future prospects of this vibrant topic.
LLM Context Conditioning and PWP Prompting for Multimodal Validation of Chemical Formulas
2025-05-18
Evgeny Markhasin
Identifying subtle technical errors within complex scientific and technical documents, especially those requiring multimodal interpretation (e.g., formulas in images), presents a significant hurdle for Large Language Models (LLMs) whose inherent error-correction tendencies can mask inaccuracies. This exploratory proof-of-concept (PoC) study investigates structured LLM context conditioning, informed by Persistent Workflow Prompting (PWP) principles, as a methodological strategy to modulate this LLM behavior at inference time. The approach is designed to enhance the reliability of readily available, general-purpose LLMs (specifically Gemini 2.5 Pro and ChatGPT Plus o3) for precise validation tasks, crucially relying only on their standard chat interfaces without API access or model modifications. To explore this methodology, we focused on validating chemical formulas within a single, complex test paper with known textual and image-based errors. Several prompting strategies were evaluated: while basic prompts proved unreliable, an approach adapting PWP structures to rigorously condition the LLM's analytical mindset appeared to improve textual error identification with both models. Notably, this method also guided Gemini 2.5 Pro to repeatedly identify a subtle image-based formula error previously overlooked during manual review, a task where ChatGPT Plus o3 failed in our tests. These preliminary findings highlight specific LLM operational modes that impede detail-oriented validation and suggest that PWP-informed context conditioning offers a promising and highly accessible technique for developing more robust LLM-driven analytical workflows, particularly for tasks requiring meticulous error detection in scientific and technical documents. Extensive validation beyond this limited PoC is necessary to ascertain broader applicability.
PANORAMA: A synthetic PII-laced dataset for studying sensitive data memorization in LLMs
2025-05-18
Sriram Selvam, Anneswa Ghosh
The memorization of sensitive and personally identifiable information (PII) by large language models (LLMs) poses growing privacy risks as models scale and are increasingly deployed in real-world applications. Existing efforts to study sensitive and PII data memorization and develop mitigation strategies are hampered by the absence of comprehensive, realistic, and ethically sourced datasets reflecting the diversity of sensitive information found on the web. We introduce PANORAMA - Profile-based Assemblage for Naturalistic Online Representation and Attribute Memorization Analysis, a large-scale synthetic corpus of 384,789 samples derived from 9,674 synthetic profiles designed to closely emulate the distribution, variety, and context of PII and sensitive data as it naturally occurs in online environments. Our data generation pipeline begins with the construction of internally consistent, multi-attribute human profiles using constrained selection to reflect real-world demographics such as education, health attributes, financial status, etc. Using a combination of zero-shot prompting and OpenAI o3-mini, we generate diverse content types - including wiki-style articles, social media posts, forum discussions, online reviews, comments, and marketplace listings - each embedding realistic, contextually appropriate PII and other sensitive information. We validate the utility of PANORAMA by fine-tuning the Mistral-7B model on 1x, 5x, 10x, and 25x data replication rates with a subset of data and measure PII memorization rates - revealing not only consistent increases with repetition but also variation across content types, highlighting PANORAMA's ability to model how memorization risks differ by context. Our dataset and code are publicly available, providing a much-needed resource for privacy risk assessment, model auditing, and the development of privacy-preserving LLMs.
Machine Learning Applications Related to Suicide in Military and Veterans: A Scoping Literature Review
2025-05-18
Yuhan Zhang, Yishu Wei, Yanshan Wang, Yunyu Xiao, COL, Ronald K. Poropatich, Gretchen L. Haas, Yiye Zhang, Chunhua Weng, Jinze Liu, Lisa A. Brenner, James M. Bjork, Yifan Peng
Suicide remains one of the main preventable causes of death among active service members and veterans. Early detection and prediction are crucial in suicide prevention. Machine learning techniques have yielded promising results in this area recently. This study aims to assess and summarize current research and provides a comprehensive review regarding the application of machine learning techniques in assessing and predicting suicidal ideation, attempts, and mortality among members of military and veteran populations. A keyword search using PubMed, IEEE, ACM, and Google Scholar was conducted, and the PRISMA protocol was adopted for relevant study selection. Thirty-two articles met the inclusion criteria. These studies consistently identified risk factors relevant to mental health issues such as depression, post-traumatic stress disorder (PTSD), suicidal ideation, prior attempts, physical health problems, and demographic characteristics. Machine learning models applied in this area have demonstrated reasonable predictive accuracy. However, additional research gaps still exist. First, many studies have overlooked metrics that distinguish between false positives and negatives, such as positive predictive value and negative predictive value, which are crucial in the context of suicide prevention policies. Second, more dedicated approaches to handling survival and longitudinal data should be explored. Lastly, most studies focused on machine learning methods, with limited discussion of their connection to clinical rationales. In summary, machine learning analyses have identified a wide range of risk factors associated with suicide in military populations. The diversity and complexity of these factors also demonstrates that effective prevention strategies must be comprehensive and flexible.
Towards Robust Autonomous Landing Systems: Iterative Solutions and Key Lessons Learned
2025-05-18
Sebastian Schroder, Yao Deng, Alice James, Avishkar Seth, Kye Morton, Subhas Mukhopadhyay, Richard Han, Xi Zheng
Uncrewed Aerial Vehicles (UAVs) have become a focal point of research, with both established companies and startups investing heavily in their development. This paper presents our iterative process in developing a robust autonomous marker-based landing system, highlighting the key challenges encountered and the solutions implemented. It reviews existing systems for autonomous landing processes, and through this aims to contribute to the community by sharing insights and challenges faced during development and testing.
LLM-Based User Simulation for Low-Knowledge Shilling Attacks on Recommender Systems
2025-05-18
Shengkang Gu, Jiahao Liu, Dongsheng Li, Guangping Zhang, Mingzhe Han, Hansu Gu, Peng Zhang, Ning Gu, Li Shang, Tun Lu
Recommender systems (RS) are increasingly vulnerable to shilling attacks, where adversaries inject fake user profiles to manipulate system outputs. Traditional attack strategies often rely on simplistic heuristics, require access to internal RS data, and overlook the manipulation potential of textual reviews. In this work, we introduce Agent4SR, a novel framework that leverages Large Language Model (LLM)-based agents to perform low-knowledge, high-impact shilling attacks through both rating and review generation. Agent4SR simulates realistic user behavior by orchestrating adversarial interactions, selecting items, assigning ratings, and crafting reviews, while maintaining behavioral plausibility. Our design includes targeted profile construction, hybrid memory retrieval, and a review attack strategy that propagates target item features across unrelated reviews to amplify manipulation. Extensive experiments on multiple datasets and RS architectures demonstrate that Agent4SR outperforms existing low-knowledge baselines in both effectiveness and stealth. Our findings reveal a new class of emergent threats posed by LLM-driven agents, underscoring the urgent need for enhanced defenses in modern recommender systems.
A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis
2025-05-18
Muhammad Zubair, Muzammil Hussai, Mousa Ahmad Al-Bashrawi, Malika Bendechache, Muhammad Owais
Multi-modal medical image fusion (MMIF) is increasingly recognized as an essential technique for enhancing diagnostic precision and facilitating effective clinical decision-making within computer-aided diagnosis systems. MMIF combines data from X-ray, MRI, CT, PET, SPECT, and ultrasound to create detailed, clinically useful images of patient anatomy and pathology. These integrated representations significantly advance diagnostic accuracy, lesion detection, and segmentation. This comprehensive review meticulously surveys the evolution, methodologies, algorithms, current advancements, and clinical applications of MMIF. We present a critical comparative analysis of traditional fusion approaches, including pixel-, feature-, and decision-level methods, and delves into recent advancements driven by deep learning, generative models, and transformer-based architectures. A critical comparative analysis is presented between these conventional methods and contemporary techniques, highlighting differences in robustness, computational efficiency, and interpretability. The article addresses extensive clinical applications across oncology, neurology, and cardiology, demonstrating MMIF's vital role in precision medicine through improved patient-specific therapeutic outcomes. Moreover, the review thoroughly investigates the persistent challenges affecting MMIF's broad adoption, including issues related to data privacy, heterogeneity, computational complexity, interpretability of AI-driven algorithms, and integration within clinical workflows. It also identifies significant future research avenues, such as the integration of explainable AI, adoption of privacy-preserving federated learning frameworks, development of real-time fusion systems, and standardization efforts for regulatory compliance.
Poincaré on Gibbs and on Probability in Statistical Mechanics
2025-05-17
Bruce D. Popp
This paper reviews a paper from 1906 by J. Henri Poincar\'e on statistical mechanics with a background in his earlier work and notable connections to J. Willard Gibbs. Poincar\'e's paper presents important ideas that are still relevant for understanding the need for probability in statistical mechanics. Poincar\'e understands the foundations of statistical mechanics as a many-body problem in analytical mechanics (reflecting his 1890 monograph on The Three-Body Problem and the Equations of Dynamics) and possibly influenced by Gibbs independent development published in chapters in his 1902 book, Elementary Principles in Statistical Mechanics. This dynamical systems approach of Poincar\'e and Gibbs provides great flexibility including applications to many systems besides gasses. This foundation benefits from close connections to Poincar\'e's earlier work. Notably, Poincar\'e had shown (e.g. in his study of non-linear oscillators) that Hamiltonian dynamical systems display sensitivity to initial conditions separating stable and unstable trajectories. In the first context it precludes proving the stability of orbits in the solar system, here it compels the use of ensembles of systems for which the probability is ontic and frequentist and does not have an a priori value. Poincar\'e's key concepts relating to uncertain initial conditions, and fine- and coarse-grained entropy are presented for the readers' consideration. Poincar\'e and Gibbs clearly both wanted to say something about irreversibility, but came up short.
Socioeconomic status (SES) fundamentally influences how people interact with each other and more recently, with digital technologies like Large Language Models (LLMs). While previous research has highlighted the interaction between SES and language technology, it was limited by reliance on proxy metrics and synthetic data. We survey 1,000 individuals from diverse socioeconomic backgrounds about their use of language technologies and generative AI, and collect 6,482 prompts from their previous interactions with LLMs. We find systematic differences across SES groups in language technology usage (i.e., frequency, performed tasks), interaction styles, and topics. Higher SES entails a higher level of abstraction, convey requests more concisely, and topics like 'inclusivity' and 'travel'. Lower SES correlates with higher anthropomorphization of LLMs (using ''hello'' and ''thank you'') and more concrete language. Our findings suggest that while generative language technologies are becoming more accessible to everyone, socioeconomic linguistic differences still stratify their use to exacerbate the digital divide. These differences underscore the importance of considering SES in developing language technologies to accommodate varying linguistic needs rooted in socioeconomic factors and limit the AI Gap across SES groups.
Modelling helium in exoplanet atmospheres. A revised network with photoelectron-driven processes
2025-05-17
Antonio García Muñoz
The He I line at 1.08 $\mu$m is a valuable tracer of atmospheric escape in exoplanet atmospheres. We expand past networks used to predict the absorbing He(2$^3S$) by including, firstly, processes that involve H$_2$ and some molecular ions and, secondly, the interaction of photoelectrons with the atmosphere. We survey the literature on the chemical-collisional-radiative processes that govern the production-loss of He(2$^3S$). We simulate the atmospheric outflow from the Neptune-sized GJ 436 b by coupling a hydrodynamic model that solves the bulk properties of the gas and a Monte Carlo model that tracks the energy degradation of the photoelectrons. We identify Penning ionization of H as a key He(2$^3S$) loss process at GJ 436 b and update its rate coefficient to a value consistent with the most recent available cross sections. The update affects notably the predicted strength of the He I line. For GJ 436 b, photoelectron-driven processes (mainly ionization and excitation) modify the He(2$^3S$) population in layers too deep to affect the in-transit spectrum. The situation might be different for other atmospheres though. The spectral energy distribution of GJ 436 has a strong effect on the predicted in-transit signal. The published non-detections of the He I line for GJ 436 b are reasonably consistent with our model predictions for a solar-metallicity atmosphere when the model adopts a recently proposed spectral energy distribution. The interpretation of the He I line at 1.08 $\mu$m is model-dependent. Our revised network provides a general framework to extract more robust conclusions from measurements of this line, especially in atmospheres where H$_2$ remains abundant to high altitudes. We will explore additional, previously-ignored processes in future work.
Two-photon processes in electron-positron collisions of the form $e^+e^-\to e^+e^-X$ are a well-established tool for probing hadronic structure and provide a direct access to non-vector states in $e^+e^-$ collisions. Collisions of quasi real photons have been employed by several experimental collaborations to extract radiative widths of various hadronic resonances. Persistent discrepancies between Standard Model predictions and measurements of the muon anomalous magnetic moment $a_\mu = (g-2)\mu/2$ have renewed interest in theoretical predictions for $\gamma\gamma \to X$ processes, particularly with off-shell photons. These are essential for evaluating the hadronic light-by-light contribution to $a_\mu$. This work reviews results and outlines future two-photon measurements by the BESIII collaboration.
This paper investigates code LLMs' capability of static analysis during code intelligence tasks such as code summarization and generation. Code LLMs are now household names for their abilities to do some programming tasks that have heretofore required people. The process that people follow to do programming tasks has long been understood to require static analysis. For example, human programmers navigate the call graph of large programs to comprehend the different parts of those programs. Education in programming includes static analysis under the assumption that better static analysis skills beget better programming. Yet while popular culture is replete with anthropomorphic references such as LLM "reasoning", in fact code LLMs could exhibit a wholly alien thought process to humans. This paper studies the specific question of static analysis by code LLMs. We use three different static analysis tasks (callgraph generation, AST generation, and dataflow generation) and three different code intelligence tasks (code generation, summarization, and translation) with two different open-source models (Gemini and GPT-4o) and closed-source models (CodeLlaMA and Jam) as our experiments. We found that LLMs show poor performance on static analysis tasks and that pretraining on the static analysis tasks does not generalize to better performance on the code intelligence tasks.
T-Rex: Fitting a Robust Factor Model via Expectation-Maximization
2025-05-17
Daniel Cederberg
Over the past decades, there has been a surge of interest in studying low-dimensional structures within high-dimensional data. Statistical factor models $-$ i.e., low-rank plus diagonal covariance structures $-$ offer a powerful framework for modeling such structures. However, traditional methods for fitting statistical factor models, such as principal component analysis (PCA) or maximum likelihood estimation assuming the data is Gaussian, are highly sensitive to heavy tails and outliers in the observed data. In this paper, we propose a novel expectation-maximization (EM) algorithm for robustly fitting statistical factor models. Our approach is based on Tyler's M-estimator of the scatter matrix for an elliptical distribution, and consists of solving Tyler's maximum likelihood estimation problem while imposing a structural constraint that enforces the low-rank plus diagonal covariance structure. We present numerical experiments on both synthetic and real examples, demonstrating the robustness of our method for direction-of-arrival estimation in nonuniform noise and subspace recovery.
Dynamical Properties and Velocity Dispersion-Mass Relation of $z \sim 1$ Galaxy Clusters from the GOGREEN and GCLASS Surveys
2025-05-17
Shrouk Abdulshafy, Mohamed H. Abdullah, Gillian Wilson, Michael L. Balogh, Raouf H. Mabrouk
We investigate a sample of 14 galaxy clusters from the GOGREEN and GCLASS (GG) spectroscopic datasets within the redshift range $(0.87 \leq z \leq 1.37)$ and cluster masses $\mathrm{M}_{200} \gtrsim 2\times 10^{14}$ \hm. Using the highly effective GalWeight technique for cluster membership assignment developed by our own team, we derive the dynamical parameters of these clusters through the virial mass estimator. We examine the velocity dispersion-cluster mass relation $(\sigma \mathrm{MR})$ for the GG cluster sample. We find, $\log{\sigma_{200}} = (2.94\pm0.02) + (0.37\pm0.07)\log{\mathrm{M}_{200}}$ with an intrinsic scatter of $(\sigma_\mathrm{int} = 0.02 \pm 0.02)$. Our results demonstrate that the $(\sigma \mathrm{MR})$ relation is consistent with predictions from cosmological simulations, highlighting the reliability of the GalWeight technique for cluster membership assignment. Furthermore, the $(\sigma \mathrm{MR})$ validates the robustness of the virial mass estimator in accurately recovering cluster masses and associated parameters. Importantly, our findings confirm that velocity dispersion can be used directly to estimate cluster mass without relying on dynamical mass estimators.
WISE 0855-0714 is the coldest known brown dwarf, located 2.28 pc from the solar system. Discovered by the Wide-Field Infrared Survey Explorer (WISE) in 2014 (Luhman 2014), the object is of interest to scientists because of its low temperature ($\approx270$ K), proximity to the solar system, small mass ($\sim3-10\: M_{J}$), and high proper motion. The first observations of W0855 by WISE in 2010 are heavily contaminated by a background source. With 10.5 years of observations following the NEOWISE reactivation in 2013 (Mainzer et al., 2014), we present a robust analysis of W0855's flux and color unobstructed by this background source. We obtain W1 = 19.3 and W1-W2 = 5.4 magnitudes with an error of 0.37 magnitudes.
The Impact of Emerging Phishing Threats: Assessing Quishing and LLM-generated Phishing Emails against Organizations
2025-05-17
Marie Weinz, Nicola Zannone, Luca Allodi, Giovanni Apruzzese
Modern organizations are persistently targeted by phishing emails. Despite advances in detection systems and widespread employee training, attackers continue to innovate, posing ongoing threats. Two emerging vectors stand out in the current landscape: QR-code baits and LLM-enabled pretexting. Yet, little is known about the effectiveness of current defenses against these attacks, particularly when it comes to real-world impact on employees. This gap leaves uncertainty around to what extent related countermeasures are justified or needed. Our work addresses this issue. We conduct three phishing simulations across organizations of varying sizes -- from small-medium businesses to a multinational enterprise. In total, we send over 71k emails targeting employees, including: a "traditional" phishing email with a click-through button; a nearly-identical "quishing" email with a QR code instead; and a phishing email written with the assistance of an LLM and open-source intelligence. Our results show that quishing emails have the same effectiveness as traditional phishing emails at luring users to the landing webpage -- which is worrying, given that quishing emails are much harder to identify even by operational detectors. We also find that LLMs can be very good "social engineers": in one company, over 30% of the emails opened led to visiting the landing webpage -- a rate exceeding some prior benchmarks. Finally, we complement our study by conducting a survey across the organizations' employees, measuring their "perceived" phishing awareness. Our findings suggest a correlation between higher self-reported awareness and organizational resilience to phishing attempts.
Discrete Time Credit-Based Shaping for Time-Sensitive Applications in 5G/6G Networks
2025-05-17
Anudeep Karnam, Kishor C. Joshi, Jobish John, George Exarchakos, Sonia Heemstra de Groot, Ignas Niemegeers
Future wireless networks must deliver deterministic end-to-end delays for workloads such as smart-factory control loops. On Ethernet these guarantees are delivered by the set of tools within IEEE 802.1 time sensitive networking~(TSN) standards. Credit-based shaper (CBS) is one such tool which enforces bounded latency. Directly porting CBS to 5G/6G New Radio (NR) is non-trivial because NR schedules traffic in discrete-time, modulation-dependent resource allocation, whereas CBS assumes a continuous, fixed-rate link. Existing TSN-over-5G translators map Ethernet priorities to 5G quality of service (QoS) identifiers but leave the radio scheduler unchanged, so deterministic delay is lost within the radio access network (RAN). To address this challenge, we propose a novel slot-native approach that adapts CBS to operate natively in discrete NR slots. We first propose a per-slot credit formulation for each user-equipment ({UE}) queue that debits credit by the granted transport block size~(TBS); we call this discrete-time CBS (CBS-DT). Recognizing that debiting the full {TBS} can unduly penalize transmissions that actually use only part of their grant, we then introduce and analyze {CBS} with Partial Usage ({CBS-PU}). {CBS-PU} scales the credit debit in proportion to the actual bytes dequeued from the downlink queue. The resulting CBS-PU algorithm is shown to maintain bounded credit, preserve long-term rate reservations, and guarantees worst-case delay performance no worse than {CBS-DT}. Simulation results show that slot-level credit gating--particularly CBS-PU--enables NR to export TSN class QoS while maximizing resource utilization.
NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results
2025-05-17
Sangmin Lee, Eunpil Park, Angel Canelo, Hyunhee Park, Youngjo Kim, Hyung-Ju Chun, Xin Jin, Chongyi Li, Chun-Le Guo, Radu Timofte, Qi Wu, Tianheng Qiu, Yuchun Dong, Shenglin Ding, Guanghua Pan, Weiyu Zhou, Tao Hu, Yixu Feng, Duwei Dai, Yu Cao, Peng Wu, Wei Dong, Yanning Zhang, Qingsen Yan, Simon J. Larsen, Ruixuan Jiang, Senyan Xu, Xingbo Wang, Xin Lu, Marcos V. Conde, Javier Abad-Hernandez, Alvaro Garcıa-Lara, Daniel Feijoo, Alvaro Garcıa, Zeyu Xiao, Zhuoyuan Li
This paper reviews the NTIRE 2025 Efficient Burst HDR and Restoration Challenge, which aims to advance efficient multi-frame high dynamic range (HDR) and restoration techniques. The challenge is based on a novel RAW multi-frame fusion dataset, comprising nine noisy and misaligned RAW frames with various exposure levels per scene. Participants were tasked with developing solutions capable of effectively fusing these frames while adhering to strict efficiency constraints: fewer than 30 million model parameters and a computational budget under 4.0 trillion FLOPs. A total of 217 participants registered, with six teams finally submitting valid solutions. The top-performing approach achieved a PSNR of 43.22 dB, showcasing the potential of novel methods in this domain. This paper provides a comprehensive overview of the challenge, compares the proposed solutions, and serves as a valuable reference for researchers and practitioners in efficient burst HDR and restoration.
Do different prompting methods yield a common task representation in language models?
2025-05-17
Guy Davidson, Todd M. Gureckis, Brenden M. Lake, Adina Williams
Demonstrations and instructions are two primary approaches for prompting language models to perform in-context learning (ICL) tasks. Do identical tasks elicited in different ways result in similar representations of the task? An improved understanding of task representation mechanisms would offer interpretability insights and may aid in steering models. We study this through function vectors, recently proposed as a mechanism to extract few-shot ICL task representations. We generalize function vectors to alternative task presentations, focusing on short textual instruction prompts, and successfully extract instruction function vectors that promote zero-shot task accuracy. We find evidence that demonstration- and instruction-based function vectors leverage different model components, and offer several controls to dissociate their contributions to task performance. Our results suggest that different task presentations do not induce a common task representation but elicit different, partly overlapping mechanisms. Our findings offer principled support to the practice of combining textual instructions and task demonstrations, imply challenges in universally monitoring task inference across presentation forms, and encourage further examinations of LLM task inference mechanisms.
Classical federated learning (FL) assumes that the clients have a limited amount of noisy data with which they voluntarily participate and contribute towards learning a global, more accurate model in a principled manner. The learning happens in a distributed fashion without sharing the data with the center. However, these methods do not consider the incentive of an agent for participating and contributing to the process, given that data collection and running a distributed algorithm is costly for the clients. The question of rationality of contribution has been asked recently in the literature and some results exist that consider this problem. This paper addresses the question of simultaneous parameter learning and incentivizing contribution, which distinguishes it from the extant literature. Our first mechanism incentivizes each client to contribute to the FL process at a Nash equilibrium and simultaneously learn the model parameters. However, this equilibrium outcome can be away from the optimal, where clients contribute with their full data and the algorithm learns the optimal parameters. We propose a second mechanism with monetary transfers that is budget balanced and enables the full data contribution along with optimal parameter learning. Large scale experiments with real (federated) datasets (CIFAR-10, FeMNIST, and Twitter) show that these algorithms converge quite fast in practice, yield good welfare guarantees, and better model performance for all agents.
SOCIA: An End-to-End Agentic Framework for Automated Cyber-Physical-Social Simulator Generation
2025-05-17
Yuncheng Hua, Ji Miao, Mehdi Jafari, Jianxiang Xie, Hao Xue, Flora D. Salim
This paper introduces SOCIA (Simulation Orchestration for Cyber-physical-social Intelligence and Agents), a novel end-to-end framework leveraging Large Language Model (LLM)-based multi-agent systems to automate the generation of high-fidelity Cyber-Physical-Social (CPS) simulators. Addressing the challenges of labor-intensive manual simulator development and complex data calibration, SOCIA integrates a centralized orchestration manager that coordinates specialized agents for tasks including data comprehension, code generation, simulation execution, and iterative evaluation-feedback loops. Through empirical evaluations across diverse CPS tasks, such as mask adoption behavior simulation (social), personal mobility generation (physical), and user modeling (cyber), SOCIA demonstrates its ability to produce high-fidelity, scalable simulations with reduced human intervention. These results highlight SOCIA's potential to offer a scalable solution for studying complex CPS phenomena
Piping and Instrumentation Diagrams (P&IDs) constitute the foundational blueprint of a plant, depicting the interconnections among process equipment, instrumentation for process control, and the flow of fluids and control signals. In their existing setup, the manual mapping of information from P&ID sheets holds a significant challenge. This is a time-consuming process, taking around 3-6 months, and is susceptible to errors. It also depends on the expertise of the domain experts and often requires multiple rounds of review. The digitization of P&IDs entails merging detected line segments, which is essential for linking various detected instruments, thereby creating a comprehensive digitized P&ID. This paper focuses on explaining how line segments which are detected using a computer vision model are merged and eventually building the connection between equipment and merged lines. Hence presenting a digitized form of information stating the interconnection between process equipment, instrumentation, flow of fluids and control signals. Eventually, which can be stored in a knowledge graph and that information along with the help of advanced algorithms can be leveraged for tasks like finding optimal routes, detecting system cycles, computing transitive closures, and more.
A Survey of Real-time Scheduling on Accelerator-based Heterogeneous Architecture for Time Critical Applications
2025-05-17
An Zou, Yuankai Xu, Yinchen Ni, Jintao Chen, Yehan Ma, Jing Li, Christopher Gill, Xuan Zhang, Yier Jin
Accelerator-based heterogeneous architectures, such as CPU-GPU, CPU-TPU, and CPU-FPGA systems, are widely adopted to support the popular artificial intelligence (AI) algorithms that demand intensive computation. When deployed in real-time applications, such as robotics and autonomous vehicles, these architectures must meet stringent timing constraints. To summarize these achievements, this article presents a comprehensive survey of real-time scheduling techniques for accelerator-based heterogeneous platforms. It highlights key advancements from the past ten years, showcasing how proposed solutions have evolved to address the distinct challenges and requirements of these systems. This survey begins with an overview of the hardware characteristics and common task execution models used in accelerator-based heterogeneous systems. It then categorizes the reviewed works based on soft and hard deadline constraints. For soft real-time approaches, we cover real-time scheduling methods supported by hardware vendors and strategies focusing on timing-critical scheduling, energy efficiency, and thermal-aware scheduling. For hard real-time approaches, we first examine support from processor vendors. We then discuss scheduling techniques that guarantee hard deadlines (with strict response time analysis). After reviewing general soft and hard real-time scheduling methods, we explore application- or scenario-driven real-time scheduling techniques for accelerator-enabled heterogeneous computing platforms. Finally, the article concludes with a discussion of open issues and challenges within this research area.
Hardware security verification is a challenging and time-consuming task. For this purpose, design engineers may utilize tools such as formal verification, linters, and functional simulation tests, coupled with analysis and a deep understanding of the hardware design being inspected. Large Language Models (LLMs) have been used to assist during this task, either directly or in conjunction with existing tools. We improve the state of the art by proposing MARVEL, a multi-agent LLM framework for a unified approach to decision-making, tool use, and reasoning. MARVEL mimics the cognitive process of a designer looking for security vulnerabilities in RTL code. It consists of a supervisor agent that devises the security policy of the system-on-chips (SoCs) using its security documentation. It delegates tasks to validate the security policy to individual executor agents. Each executor agent carries out its assigned task using a particular strategy. Each executor agent may use one or more tools to identify potential security bugs in the design and send the results back to the supervisor agent for further analysis and confirmation. MARVEL includes executor agents that leverage formal tools, linters, simulation tests, LLM-based detection schemes, and static analysis-based checks. We test our approach on a known buggy SoC based on OpenTitan from the Hack@DATE competition. We find that 20 of the 48 issues reported by MARVEL pose security vulnerabilities.
Visual instruction tuning aims to enable large language models to comprehend the visual world, with a pivotal challenge lying in establishing an effective vision-to-language projection. However, existing methods often grapple with the intractable trade-off between accuracy and efficiency. In this paper, we present LLaVA-Meteor, a novel approach designed to break this deadlock, equipped with a novel Top-Down Compression paradigm that strategically compresses visual tokens without compromising core information. Specifically, we construct a trainable Flash Global Fusion module based on efficient selective state space operators, which aligns the feature space while enabling each token to perceive holistic visual context and instruction preference at low cost. Furthermore, a local-to-single scanning manner is employed to effectively capture local dependencies, thereby enhancing the model's capability in vision modeling. To alleviate computational overhead, we explore a Visual-Native Selection mechanism that independently assesses token significance by both the visual and native experts, followed by aggregation to retain the most critical subset. Extensive experiments show that our approach reduces visual tokens by 75--95% while achieving comparable or superior performance across 12 benchmarks, significantly improving efficiency.
Exploring the Potential of SSL Models for Sound Event Detection
2025-05-17
Hanfang Cui, Longfei Song, Li Li, Dongxing Xu, Yanhua Long
Self-supervised learning (SSL) models offer powerful representations for sound event detection (SED), yet their synergistic potential remains underexplored. This study systematically evaluates state-of-the-art SSL models to guide optimal model selection and integration for SED. We propose a framework that combines heterogeneous SSL representations (e.g., BEATs, HuBERT, WavLM) through three fusion strategies: individual SSL embedding integration, dual-modal fusion, and full aggregation. Experiments on the DCASE 2023 Task 4 Challenge reveal that dual-modal fusion (e.g., CRNN+BEATs+WavLM) achieves complementary performance gains, while CRNN+BEATs alone delivers the best results among individual SSL models. We further introduce normalized sound event bounding boxes (nSEBBs), an adaptive post-processing method that dynamically adjusts event boundary predictions, improving PSDS1 by up to 4% for standalone SSL models. These findings highlight the compatibility and complementarity of SSL architectures, providing guidance for task-specific fusion and robust SED system design.
Universal properties of elastic pp cross section from the ISR to the LHC
2025-05-17
Michał Praszałowicz
We explore the phenomenology of the property that the ratio of bump to dip {\em positions} of the elastic differential $pp$ cross section is constant over the energy range from the ISR to the LHC. We review the old idea of geometric scaling at the ISR and argue that it also holds at the LHC. We discuss its consequences for the $\rho$ parameter and for the ratio of bump to dip cross section {\em values}.
Fair-PP: A Synthetic Dataset for Aligning LLM with Personalized Preferences of Social Equity
2025-05-17
Qi Zhou, Jie Zhang, Dongxia Wang, Qiang Liu, Tianlin Li, Jin Song Dong, Wenhai Wang, Qing Guo
Human preference plays a crucial role in the refinement of large language models (LLMs). However, collecting human preference feedback is costly and most existing datasets neglect the correlation between personalization and preferences. To address this issue, we introduce Fair-PP, a synthetic dataset of personalized preferences targeting social equity, derived from real-world social survey data, which includes 28 social groups, 98 equity topics, and 5 personal preference dimensions. Leveraging GPT-4o-mini, we engage in role-playing based on seven representative persona portrayals guided by existing social survey data, yielding a total of 238,623 preference records. Through Fair-PP, we also contribute (i) An automated framework for generating preference data, along with a more fine-grained dataset of personalized preferences; (ii) analysis of the positioning of the existing mainstream LLMs across five major global regions within the personalized preference space; and (iii) a sample reweighting method for personalized preference alignment, enabling alignment with a target persona while maximizing the divergence from other personas. Empirical experiments show our method outperforms the baselines.
SplInterp: Improving our Understanding and Training of Sparse Autoencoders
2025-05-17
Jeremy Budd, Javier Ideami, Benjamin Macdowall Rynne, Keith Duggar, Randall Balestriero
Sparse autoencoders (SAEs) have received considerable recent attention as tools for mechanistic interpretability, showing success at extracting interpretable features even from very large LLMs. However, this research has been largely empirical, and there have been recent doubts about the true utility of SAEs. In this work, we seek to enhance the theoretical understanding of SAEs, using the spline theory of deep learning. By situating SAEs in this framework: we discover that SAEs generalise ``$k$-means autoencoders'' to be piecewise affine, but sacrifice accuracy for interpretability vs. the optimal ``$k$-means-esque plus local principal component analysis (PCA)'' piecewise affine autoencoder. We characterise the underlying geometry of (TopK) SAEs using power diagrams. And we develop a novel proximal alternating method SGD (PAM-SGD) algorithm for training SAEs, with both solid theoretical foundations and promising empirical results in MNIST and LLM experiments, particularly in sample efficiency and (in the LLM setting) improved sparsity of codes. All code is available at: https://github.com/splInterp2025/splInterp
Generating Digital Models Using Text-to-3D and Image-to-3D Prompts: Critical Case Study
2025-05-17
Rushan Ziatdinov, Rifkat Nabiyev
In the world of technology and AI, digital models play an important role in our lives and are an essential part of the digital twins of real-world objects. They can be created by designers, artists, or game developers using spline curves and surfaces, meshes, and voxels, but making such models is too time-consuming. With the growth of AI tools, there is interest in the automated generation of 3D models, such as generative design approaches, which can save creators valuable time. This paper reviews several online 3D model generators and critically analyses the results, hoping to see higher-quality results from different prompts.
The present study delineates the effects of pressure gradient history and local disequilibration on the small and large-scale energy in turbulent boundary layers (TBLs) imposed with a broad range of adverse-pressure-gradients (APG). This is made possible by analyzing four published high-fidelity APG TBL databases, which span weak to strong APGs and cover dynamic conditions ranging from near-equilibrium to strong disequilibrium. The influence of PG history on TBL statistics is quantified by the accumulated PG parameter ($\overline{\beta}$), proposed previously by Vinuesa et al. (2017) to study integral quanitites, which is compared here between cases at matched local PG strength ($\beta$), Reynolds number ($Re$) and ${\rm d}{\beta}/{{\rm d}{Re}}$ at nominally similar orders of magnitude. While the effects of local disequilibration (${\rm d}{\beta}/{{\rm d}{Re}}$) are investigated by considering TBL cases at matched $\beta$, $Re$, and fairly matched $\overline{\beta}$. It is found that $\overline{\beta}$ cannot unambiguously capture history effects when ${\rm d}{\beta}/{{\rm d}{Re}}$ levels are significantly high, as it does not account for the delayed response of the mean flow and turbulence, nor the attenuation of the pressure gradient effect with distance. In two comparisons of APG TBLs under strong non-equilibrium, the values of $\overline{\beta}$ and ${\rm d}{\beta}/{\rm d}{Re}$ expressed using Zagarola-Smits scaling were found to be consistent with the trends in mean velocity defect and Reynolds stresses noted previously for weak APG TBLs. While an increase in $\overline{\beta}$ is associated with energisation of both the small and large scales in the outer regions of APG TBLs, it affects only the large scales in the near-wall region. This confirms the ability of near-wall small scales to rapidly adjust to changes in PG strength.
A Review and Analysis of a Parallel Approach for Decision Tree Learning from Large Data Streams
2025-05-17
Zeinab Shiralizadeh
This work studies one of the parallel decision tree learning algorithms, pdsCART, designed for scalable and efficient data analysis. The method incorporates three core capabilities. First, it supports real-time learning from data streams, allowing trees to be constructed incrementally. Second, it enables parallel processing of high-volume streaming data, making it well-suited for large-scale applications. Third, the algorithm integrates seamlessly into the MapReduce framework, ensuring compatibility with distributed computing environments. In what follows, we present the algorithm's key components along with results highlighting its performance and scalability.
Imbalanced data poses a significant obstacle in machine learning, as an unequal distribution of class labels often results in skewed predictions and diminished model accuracy. To mitigate this problem, various resampling strategies have been developed, encompassing both oversampling and undersampling techniques aimed at modifying class proportions. Conventional oversampling approaches like SMOTE enhance the representation of the minority class, whereas undersampling methods focus on trimming down the majority class. Advances in deep learning have facilitated the creation of more complex solutions, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), which are capable of producing high-quality synthetic examples. This paper reviews a broad spectrum of data balancing methods, classifying them into categories including synthetic oversampling, adaptive techniques, generative models, ensemble-based strategies, hybrid approaches, undersampling, and neighbor-based methods. Furthermore, it highlights current developments in resampling techniques and discusses practical implementations and case studies that validate their effectiveness. The paper concludes by offering perspectives on potential directions for future exploration in this domain.
Exactly solvable many-body dynamics from space-time duality
2025-05-16
Bruno Bertini, Pieter W. Claeys, Tomaž Prosen
Recent years have seen significant advances, both theoretical and experimental, in our understanding of quantum many-body dynamics. Given this problem's high complexity, it is surprising that a substantial amount of this progress can be ascribed to exact analytical results. Here we review dual-unitary circuits as a particular setting leading to exact results in quantum many-body dynamics. Dual-unitary circuits constitute minimal models in which space and time are treated on an equal footings, yielding exactly solvable yet possibly chaotic evolution. They were the first in which current notions of quantum chaos could be analytically quantified, allow for a full characterisation of the dynamics of thermalisation, scrambling, and entanglement (among others), and can be experimentally realised in current quantum simulators. Dual-unitarity is a specific fruitful implementation of the more general idea of space-time duality in which the roles of space and time are exchanged to access relevant dynamical properties of quantum many-body systems.
Striking the appropriate balance between humans and co-creative AI is an open research question in computational creativity. Co-creativity, a form of hybrid intelligence where both humans and AI take action proactively, is a process that leads to shared creative artifacts and ideas. Achieving a balanced dynamic in co-creativity requires characterizing control and identifying strategies to distribute control between humans and AI. We define control as the power to determine, initiate, and direct the process of co-creation. Informed by a systematic literature review of 172 full-length papers, we introduce MOSAAIC (Managing Optimization towards Shared Autonomy, Authority, and Initiative in Co-creation), a novel framework for characterizing and balancing control in co-creation. MOSAAIC identifies three key dimensions of control: autonomy, initiative, and authority. We supplement our framework with control optimization strategies in co-creation. To demonstrate MOSAAIC's applicability, we analyze the distribution of control in six existing co-creative AI case studies and present the implications of using this framework.
Application of the Portable Diagnostic Package to the Wisconsin HTS Axisymmetric Mirror (WHAM)
2025-05-16
Keisuke Fujii, Douglass Endrizzi, Jay K. Anderson, Cary B. Forest, Jonathan Pizzo, Tony Qian, Mason Yu, Theodore M. Biewer
We present an application of the Portable Diagnostic Package (PDP) on the Wisconsin HTS Axisymmetric Mirror (WHAM), which integrates an optical emission spectroscopy (OES) system and an active Thomson scattering (TS) system. Due to the designed portability of our system, we realized the installation of the PDP OES and TS measurements on WHAM in $\sim$6 months. The OES system facilitates a comprehensive impurity line survey and enables flow measurements through the Doppler effect observed on impurity lines. Notably, plasma rotation profiles were successfully derived from doubly charged carbon lines. In addition, the TS system enabled the first measurements of the electron temperature in commissioning plasmas on WHAM. These successes underscore the diagnostic package's potential for advancing experimental plasma studies.
Mechanistic inference of stochastic gene expression from structured single-cell data
2025-05-16
Christopher E. Miles
Single-cell gene expression measurements encode variability spanning molecular noise, cellular heterogeneity, and technical artifacts. Mechanistic models provide a principled framework to disentangle these sources and extract insight, but inferring underlying dynamics from standard sequencing count data faces fundamental limitations. Structured datasets with temporal, spatial, or multimodal features offer constraints that help resolve these ambiguities, but demand more complex models and advanced inference strategies, including machine learning techniques with associated tradeoffs. This review highlights recent progress in the judicious integration of structured single-cell data, stochastic model development, and innovative inference strategies to extract gene-level insights. These approaches lay the foundation for mechanistic understanding of regulatory networks and multicellular systems.
EMU/GAMA: A new approach to characterising radio luminosity functions
2025-05-16
J. Prathap, A. M. Hopkins, J. Afonso, M. Bilicki, M. Cowley, S. M. Croom, Y. Gordon, S. Phillipps, E. M. Sadler, S. S. Shabala, U. T. Ahmed, S. Amarantidis, M. J. I. Brown, R. Carvajal, D. Leahy, J. R. Marvil, T. Mukherjee, J. Willingham, T. Zafar
This study characterises the radio luminosity functions (RLFs) for SFGs and AGN using statistical redshift estimation in the absence of comprehensive spectroscopic data. Sensitive radio surveys over large areas detect many sources with faint optical and infrared counterparts, for which redshifts and spectra are unavailable. This challenges our attempt to understand the population of radio sources. Statistical tools are often used to model parameters (such as redshift) as an alternative to observational data. Using the data from GAMA G23 and EMU early science observations, we explore simple statistical techniques to estimate the redshifts in order to measure the RLFs of the G23 radio sources as a whole and for SFGs and AGN separately. Redshifts and AGN/SFG classifications are assigned statistically for those radio sources without spectroscopic data. The calculated RLFs are compared with existing studies, and the results suggest that the RLFs match remarkably well for low redshift galaxies with an optical counterpart. We use a more realistic high redshift distribution to model the redshifts of (most likely) high redshift radio sources and find that the LFs from our approach match well with measured LFs. We also look at strategies to compare the RLFs of radio sources without an optical counterpart to existing studies.
Beyond Time: Unveiling the Invisible Burden of Mental Load
2025-05-16
Francesca Barigozzi, Pietro Biroli, Chiara Monfardini, Natalia Montinari, Elena Pisanelli, Sveva Vitellozzi
This paper introduces a novel, scalable methodology to measure individual perceptions of gaps in mental load -- the cognitive and emotional burden associated with organizing household and childcare tasks -- within heterosexual couples. Using original data from the TIMES Observatory in Italy, the study combines time-use diaries with new survey indicators to quantify cognitive labor, emotional fatigue, and the spillover of mental load into the workplace. Results reveal systematic gender asymmetries: women are significantly more likely than men to bear organizational responsibility for domestic tasks, report lower satisfaction with this division, and experience higher emotional fatigue. These burdens are underestimated by their partners. The effects are particularly pronounced among college-educated and employed women, who also report greater spillovers of family responsibilities than men during paid work hours. The perceived responsibility for managing family activities is more strongly associated with within-couple gaps in time use than with the absolute time spent on their execution, underscoring the relational and conflictual nature of mental load.
MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection
2025-05-16
Pouya Shaeri, Ariane Middel
Modern neural networks often activate all neurons for every input, leading to unnecessary computation and inefficiency. We introduce Matrix-Interpolated Dropout Layer (MID-L), a novel module that dynamically selects and activates only the most informative neurons by interpolating between two transformation paths via a learned, input-dependent gating vector. Unlike conventional dropout or static sparsity methods, MID-L employs a differentiable Top-k masking strategy, enabling per-input adaptive computation while maintaining end-to-end differentiability. MID-L is model-agnostic and integrates seamlessly into existing architectures. Extensive experiments on six benchmarks, including MNIST, CIFAR-10, CIFAR-100, SVHN, UCI Adult, and IMDB, show that MID-L achieves up to average 55\% reduction in active neurons, 1.7$\times$ FLOPs savings, and maintains or exceeds baseline accuracy. We further validate the informativeness and selectivity of the learned neurons via Sliced Mutual Information (SMI) and observe improved robustness under overfitting and noisy data conditions. Additionally, MID-L demonstrates favorable inference latency and memory usage profiles, making it suitable for both research exploration and deployment on compute-constrained systems. These results position MID-L as a general-purpose, plug-and-play dynamic computation layer, bridging the gap between dropout regularization and efficient inference.
A Multigraph Characterization of Permutiple Strings
2025-05-16
Benjamin V. Holt
A permutiple is a natural number whose representation in some base is an integer multiple of a number whose representation has the same collection of digits. A previous paper utilizes a finite-state-machine construction and its state graph to recognize permutiples and to generate new examples. Permutiples are associated with walks on the state graph which necessarily satisfy certain conditions. However, the above effort does not provide conditions sufficient for the existence of permutiples. In this paper, we provide such a condition, which we will state using the language of multigraphs.
A magnetic field detection in the massive O-type bright giant 63 Oph
2025-05-16
James A. Barron, Gregg A. Wade, Gonzalo Holgado, Sergio Simón-Díaz
Surface magnetic fields are detected in less than $10\%$ of the massive O-type star population and even less frequently among `old' massive stars approaching the terminal-age main sequence (TAMS). It is unclear to what extent the rarity of magnetic detections in massive stars near the TAMS is due to magnetic field decay or observational biases. We report the detection of a weak surface magnetic field in the O-type giant 63~Oph ($T_{\mathrm{eff}}=35.0\pm0.3\,$kK, $\log g=3.51\pm0.03$) from new ESPaDOnS circularly polarized spectra. The mean longitudinal field strength associated with the magnetic detection is $\langle B_{z}\rangle=84\pm14\,$G, which we use to set a lower limit on the dipolar field strength of $B_{\mathrm{p}}\geq300\pm50\,$G. We report Balmer line equivalent widths (EW) and radial velocity (RV) measurements from the analysis of spectra primarily obtained by the IACOB project with the FEROS, FIES and HERMES spectrographs. We identify a dominant period of $\sim19.8\,$d in the EWs which we attribute to the effects of a rotating magnetosphere under the Oblique Rotator Model. We do not identify any coherent signals in a time-series analysis of archival Hipparcos, ASAS-SN and K2 photometry. Our findings show that 63~Oph may be a rare link between strongly magnetic massive stars detected on or near the zero-age main sequence and weakly-magnetic O-type supergiants. Additional observations are needed to fully constrain 63~Oph's magnetic field geometry and magnetospheric properties.
Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics
2025-05-16
Ardian Selmonaj, Alessandro Antonucci, Adrian Schneider, Michael Rüegsegger, Matthias Sommer
Artificial intelligence (AI) is reshaping strategic planning, with Multi-Agent Reinforcement Learning (MARL) enabling coordination among autonomous agents in complex scenarios. However, its practical deployment in sensitive military contexts is constrained by the lack of explainability, which is an essential factor for trust, safety, and alignment with human strategies. This work reviews and assesses current advances in explainability methods for MARL with a focus on simulated air combat scenarios. We proceed by adapting various explainability techniques to different aerial combat scenarios to gain explanatory insights about the model behavior. By linking AI-generated tactics with human-understandable reasoning, we emphasize the need for transparency to ensure reliable deployment and meaningful human-machine interaction. By illuminating the crucial importance of explainability in advancing MARL for operational defense, our work supports not only strategic planning but also the training of military personnel with insightful and comprehensible analyses.
A Cosmic Miracle: A Remarkably Luminous Galaxy at $z_{\rm{spec}}=14.44$ Confirmed with JWST
2025-05-16
Rohan P. Naidu, Pascal A. Oesch, Gabriel Brammer, Andrea Weibel, Yijia Li, Jorryt Matthee, John Chisholm, Clara L. Pollock, Kasper E. Heintz, Benjamin D. Johnson, Xuejian Shen, Raphael E. Hviding, Joel Leja, Sandro Tacchella, Arpita Ganguly, Callum Witten, Hakim Atek, Sirio Belli, Sownak Bose, Rychard Bouwens, Pratika Dayal, Roberto Decarli, Anna de Graaff, Yoshinobu Fudamoto, Emma Giovinazzo, Jenny E. Greene, Garth Illingworth, Akio K. Inoue, Sarah G. Kane, Ivo Labbe, Ecaterina Leonova, Rui Marques-Chaves, Romain A. Meyer, Erica J. Nelson, Guido Roberts-Borsani, Daniel Schaerer, Robert A. Simcoe, Mauro Stefanon, Yuma Sugahara, Sune Toft, Arjen van der Wel, Pieter van Dokkum, Fabian Walter, Darach Watson, John R. Weaver, Katherine E. Whitaker
JWST has revealed a stunning population of bright galaxies at surprisingly early epochs, $z>10$, where few such sources were expected. Here we present the most distant example of this class yet -- MoM-z14, a luminous ($M_{\rm{UV}}=-20.2$) source in the COSMOS legacy field at $z_{\rm{spec}}=14.44^{+0.02}_{-0.02}$ that expands the observational frontier to a mere 280 million years after the Big Bang. The redshift is confirmed with NIRSpec/prism spectroscopy through a sharp Lyman-$\alpha$ break and $\approx3\sigma$ detections of five rest-UV emission lines. The number density of bright $z_{\rm{spec}}\approx14-15$ sources implied by our "Mirage or Miracle" survey spanning $\approx350$ arcmin$^{2}$ is $>100\times$ larger ($182^{+329}_{-105}\times$) than pre-JWST consensus models. The high EWs of UV lines (${\approx}15{-}35$ \AA) signal a rising star-formation history, with a ${\approx}10\times$ increase in the last 5 Myr ($\rm{SFR_{\rm{5Myr}}}/\rm{SFR_{\rm{50Myr}}}=9.9^{+3.0}_{-5.8}$). The source is extremely compact (circularized $r_{\rm{e}} = 74^{+15}_{-12}$ pc), and yet resolved, suggesting an AGN is not the dominant source of light. The steep UV slope ($\beta=-2.5^{+0.2}_{-0.2}$) implies negligible dust attenuation and a young stellar population. The absence of a strong damping wing may indicate that the immediate surroundings of MoM-z14 are partially ionized at a redshift where virtually every reionization model predicts a $\approx100\%$ neutral fraction. The nitrogen emission and highly super-solar [N/C]$>1$ hint at an abundance pattern similar to local globular clusters that may have once hosted luminous supermassive stars. Since this abundance pattern is also common among the most ancient stars born in the Milky Way, we may be directly witnessing the formation of such stars in dense clusters, connecting galaxy evolution across the entire sweep of cosmic time.
Despite early hopes that the Large Hadron Collider (LHC) would quickly unveil supersymmetric particles, none have been detected to date. This review examines the impact of the LHC results on the viability of weak-scale supersymmetry, and discusses whether the possibility of discovering supersymmetric particles remains within reach.
A Review of Tools and Techniques for Optimization of Workload Mapping and Scheduling in Heterogeneous HPC System
2025-05-16
Aasish Kumar Sharma, Julian Kunkel
This paper presents a systematic review of mapping and scheduling strategies within the High-Performance Computing (HPC) compute continuum, with a particular emphasis on heterogeneous systems. It introduces a prototype workflow to establish foundational concepts in workload characterization and resource allocation. Building on this, a thorough analysis of 66 selected research papers - spanning the period from 2017 to 2024 - is conducted, evaluating contemporary tools and techniques used for workload mapping and scheduling. The review highlights that conventional Job Shop scheduling formulations often lack the expressiveness required to model the complexity of modern HPC data centers effectively. It also reaffirms the classification of HPC scheduling problems as NP-hard, due to their combinatorial nature and the diversity of system and workload constraints. The analysis reveals a prevailing reliance on heuristic and meta-heuristic strategies, including nature-inspired, evolutionary, sorting, and search algorithms. To bridge the observed gaps, the study advocates for hybrid optimization approaches that strategically integrate heuristics, meta-heuristics, machine learning, and emerging quantum computing techniques. Such integration, when tailored to specific problem domains, holds promise for significantly improving the scalability, efficiency, and adaptability of workload optimization in heterogeneous HPC environments.
Quantum computing with atomic qubit arrays: confronting the cost of connectivity
2025-05-16
M. Saffman
These notes present a review of the status of quantum computing with arrays of neutral atom qubits, an approach which has demonstrated remarkable progress in the last few years. Scaling digital quantum computing to qubit counts and control fidelities that will enable solving outstanding scientific questions, and provide commercial value, is an outstanding challenge, not least because of the requirement of connecting and entangling distant qubits. Long-range Rydberg gates and physical motion outfit atomic qubit arrays with tools for establishing connectivity. These tools operate on different timescales and with distinct levels of parallelization. We analyze several prototypical architectures from the perspective of achieving fast connectivity for circuits with large scale entanglement, as well as fast cycle times for measurement based quantum error correcting codes. Extending Rydberg interactions to multiple atomic species has emerged as a promising route to achieving this latter requirement.
Minimizing False-Positive Attributions in Explanations of Non-Linear Models
2025-05-16
Anders Gjølbye, Stefan Haufe, Lars Kai Hansen
Suppressor variables can influence model predictions without being dependent on the target outcome and they pose a significant challenge for Explainable AI (XAI) methods. These variables may cause false-positive feature attributions, undermining the utility of explanations. Although effective remedies exist for linear models, their extension to non-linear models and to instance-based explanations has remained limited. We introduce PatternLocal, a novel XAI technique that addresses this gap. PatternLocal begins with a locally linear surrogate, e.g. LIME, KernelSHAP, or gradient-based methods, and transforms the resulting discriminative model weights into a generative representation, thereby suppressing the influence of suppressor variables while preserving local fidelity. In extensive hyperparameter optimization on the XAI-TRIS benchmark, PatternLocal consistently outperformed other XAI methods and reduced false-positive attributions when explaining non-linear tasks, thereby enabling more reliable and actionable insights.
Recent advances in large language models (LLMs) have significantly improved text-to-speech (TTS) systems, enhancing control over speech style, naturalness, and emotional expression, which brings TTS Systems closer to human-level performance. Although the Mean Opinion Score (MOS) remains the standard for TTS System evaluation, it suffers from subjectivity, environmental inconsistencies, and limited interpretability. Existing evaluation datasets also lack a multi-dimensional design, often neglecting factors such as speaking styles, context diversity, and trap utterances, which is particularly evident in Chinese TTS evaluation. To address these challenges, we introduce the Audio Turing Test (ATT), a multi-dimensional Chinese corpus dataset ATT-Corpus paired with a simple, Turing-Test-inspired evaluation protocol. Instead of relying on complex MOS scales or direct model comparisons, ATT asks evaluators to judge whether a voice sounds human. This simplification reduces rating bias and improves evaluation robustness. To further support rapid model development, we also finetune Qwen2-Audio-Instruct with human judgment data as Auto-ATT for automatic evaluation. Experimental results show that ATT effectively differentiates models across specific capability dimensions using its multi-dimensional design. Auto-ATT also demonstrates strong alignment with human evaluations, confirming its value as a fast and reliable assessment tool. The white-box ATT-Corpus and Auto-ATT can be found in ATT Hugging Face Collection (https://huggingface.co/collections/meituan/audio-turing-test-682446320368164faeaf38a4).
PRIMAGAL: a PRIMAger Galactic Plane Far-IR polarization survey to quantify the magnetic fields' role in the formation and evolution of large star-forming filaments
2025-05-16
S. Molinari, J. D. Soler, V. -M. Pelkonen, A. Nucara, E. Schisano, A. Traficante, C. Mininni, M. Benedettini, A. Coletta, D. Elia, S. Pezzuto
The PRIMAger instrument on board the proposed PRIMA satellite will offer the unprecedented capability to obtain hundreds of square-degree maps in polarised emission at sub-arcminute resolution in four Far-IR bands. This will open a unique window to study magnetic fields in our Galaxy. PRIMAGAL, a proposed survey of polarized dust emission in the Milky Way Galactic Plane will determine the strength and orientation of magnetic fields towards several thousands of filamentary clouds in a wide range of linear masses, column densities, evolution, star-formation rates and efficiencies, and Galactic environment. This survey will address for the first time in a statistically significant fashion the role that magnetic fields play in shaping the formation, evolution and fragmentation of dense ISM filaments down to a minimum scale of 0.4 pc up to 8 kpc distance from the Sun. A 4-band polarization survey of the Galactic Plane with |b|<1{\deg} (a total of 720 sq. deg.) can be executed by PRIMAger in about 1200 hours including all mapping and instrument overhead.
Hyperspectral image processing and analysis has important application value in remote sensing, agriculture and environmental monitoring, but its high dimensionality, data redundancy and noise interference etc. bring great challenges to the analysis. Traditional models have limitations in dealing with these complex data, and it is difficult to meet the increasing demand for analysis. In recent years, Diffusion Model, as an emerging generative model, has shown unique advantages in hyperspectral image processing. By simulating the diffusion process of data in time, the Diffusion Model can effectively process high-dimensional data, generate high-quality samples, and perform well in denoising and data enhancement. In this paper, we review the recent research advances in diffusion modeling for hyperspectral image processing and analysis, and discuss its applications in tasks such as high-dimensional data processing, noise removal, classification, and anomaly detection. The performance of diffusion-based models on image processing is compared and the challenges are summarized. It is shown that the diffusion model can significantly improve the accuracy and efficiency of hyperspectral image analysis, providing a new direction for future research.
Absence of a Runaway Greenhouse Limit on Lava Planets
2025-05-16
Iris D. Boer, Harrison Nicholls, Tim Lichtenberg
Climate transitions on exoplanets offer valuable insights into the atmospheric processes governing planetary habitability. Previous pure-steam atmospheric models show a thermal limit in outgoing long-wave radiation, which has been used to define the inner edge of the classical habitable zone and guide exoplanet surveys aiming to identify and characterize potentially habitable worlds. We expand upon previous modelling by treating (i) the dissolution of volatiles into a magma ocean underneath the atmosphere, (ii) a broader volatile range of the atmospheric composition including H2O, CO2, CO, H2, CH4 and N2, and (iii) a surface temperature- and mantle redox-dependent equilibrium chemistry. We find that multi-component atmospheres of outgassed composition located above partially or fully-molten mantles do not exhibit the characteristic thermal radiation limit that arises from pure-steam models, thereby undermining the canonical concept of a runaway greenhouse limit, and hence challenging the conventional approach of using it to define an irradiation-based habitable zone. Our results show that atmospheric heat loss to space is strongly dependent on the oxidation and melting state of the underlying planetary mantle, through their significant influence on the atmosphere's equilibrium composition. This suggests an evolutionary hysteresis in climate scenarios: initially molten and cooling planets do not converge to the same climate regime as solidified planets that heat up by external irradiation. Steady-state models cannot recover evolutionary climate transitions, which instead require self-consistent models of the temporal evolution of the coupled feedback processes between interior and atmosphere over geologic time.
Reinforcement Learning for AMR Charging Decisions: The Impact of Reward and Action Space Design
2025-05-16
Janik Bischoff, Alexandru Rinciog, Anne Meyer
We propose a novel reinforcement learning (RL) design to optimize the charging strategy for autonomous mobile robots in large-scale block stacking warehouses. RL design involves a wide array of choices that can mostly only be evaluated through lengthy experimentation. Our study focuses on how different reward and action space configurations, ranging from flexible setups to more guided, domain-informed design configurations, affect the agent performance. Using heuristic charging strategies as a baseline, we demonstrate the superiority of flexible, RL-based approaches in terms of service times. Furthermore, our findings highlight a trade-off: While more open-ended designs are able to discover well-performing strategies on their own, they may require longer convergence times and are less stable, whereas guided configurations lead to a more stable learning process but display a more limited generalization potential. Our contributions are threefold. First, we extend SLAPStack, an open-source, RL-compatible simulation-framework to accommodate charging strategies. Second, we introduce a novel RL design for tackling the charging strategy problem. Finally, we introduce several novel adaptive baseline heuristics and reproducibly evaluate the design using a Proximal Policy Optimization agent and varying different design configurations, with a focus on reward.
Search for dwarf galaxies in the southwestern sector of the Local cosmic void
2025-05-16
A. A. Popova, I. D. Karachentsev
We performed a search for new dwarf galaxies in a direction towards the southwestern part of the Local Void using the data on DESI Legacy Imaging Surveys. In a sky area of $\sim1000$ square degrees, we discovered 12 candidates to nearby dwarfs with a high confidence. Four of them are probable new companions to the nearby galaxy M 83 and others are isolated objects. We found also 20 nearby dwarf candidates with a low confidence. Almost all of the detected galaxies are classified as late type dwarfs. A new cluster of bluish stars with an angular diameter of 0.9$^{\prime}$ is revealed by us at a high galactic latitude, $b = -29^{\circ}$. Being at a distance of $\sim70$ kpc, it can be a globular cluster associated with the Milky Way stellar stream Sagittarius dSph or a new ultra-faint satellite of the Milky Way.
Timely prediction of students at high risk of dropout is critical for early intervention and improving educational outcomes. However, in offline educational settings, poor data quality, limited scale, and high heterogeneity often hinder the application of advanced machine learning models. Furthermore, while educational theories provide valuable insights into dropout phenomena, the lack of quantifiable metrics for key indicators limits their use in data-driven modeling. Through data analysis and a review of educational literature, we identified abrupt changes in student behavior as key early signals of dropout risk. To address this, we propose the Dual-Modal Multiscale Sliding Window (DMSW) Model, which integrates academic performance and behavioral data to dynamically capture behavior patterns using minimal data. The DMSW model improves prediction accuracy by 15% compared to traditional methods, enabling educators to identify high-risk students earlier, provide timely support, and foster a more inclusive learning environment. Our analysis highlights key behavior patterns, offering practical insights for preventive strategies and tailored support. These findings bridge the gap between theory and practice in dropout prediction, giving educators an innovative tool to enhance student retention and outcomes.
PARSEC: Preference Adaptation for Robotic Object Rearrangement from Scene Context
2025-05-16
Kartik Ramachandruni, Sonia Chernova
Object rearrangement is a key task for household robots requiring personalization without explicit instructions, meaningful object placement in environments occupied with objects, and generalization to unseen objects and new environments. To facilitate research addressing these challenges, we introduce PARSEC, an object rearrangement benchmark for learning user organizational preferences from observed scene context to place objects in a partially arranged environment. PARSEC is built upon a novel dataset of 110K rearrangement examples crowdsourced from 72 users, featuring 93 object categories and 15 environments. We also propose ContextSortLM, an LLM-based rearrangement model that places objects in partially arranged environments by adapting to user preferences from prior and current scene context while accounting for multiple valid placements. We evaluate ContextSortLM and existing personalized rearrangement approaches on the PARSEC benchmark and complement these findings with a crowdsourced evaluation of 108 online raters ranking model predictions based on alignment with user preferences. Our results indicate that personalized rearrangement models leveraging multiple scene context sources perform better than models relying on a single context source. Moreover, ContextSortLM outperforms other models in placing objects to replicate the target user's arrangement and ranks among the top two in all three environment categories, as rated by online evaluators. Importantly, our evaluation highlights challenges associated with modeling environment semantics across different environment categories and provides recommendations for future work.
Finding the most similar subsequences between two multidimensional time series has many applications: e.g. capturing dependency in stock market or discovering coordinated movement of baboons. Considering one pattern occurring in one time series, we might be wondering whether the same pattern occurs in another time series with some distortion that might have a different length. Nevertheless, to the best of our knowledge, there is no efficient framework that deals with this problem yet. In this work, we propose an algorithm that provides the exact solution of finding the most similar multidimensional subsequences between time series where there is a difference in length both between time series and between subsequences. The algorithm is built based on theoretical guarantee of correctness and efficiency. The result in simulation datasets illustrated that our approach not just only provided correct solution, but it also utilized running time only quarter of time compared against the baseline approaches. In real-world datasets, it extracted the most similar subsequences even faster (up to 20 times faster against baseline methods) and provided insights regarding the situation in stock market and following relations of multidimensional time series of baboon movement. Our approach can be used for any time series. The code and datasets of this work are provided for the public use.
Analysis of Customer Journeys Using Prototype Detection and Counterfactual Explanations for Sequential Data
2025-05-16
Keita Kinjo
Recently, the proliferation of omni-channel platforms has attracted interest in customer journeys, particularly regarding their role in developing marketing strategies. However, few efforts have been taken to quantitatively study or comprehensively analyze them owing to the sequential nature of their data and the complexity involved in analysis. In this study, we propose a novel approach comprising three steps for analyzing customer journeys. First, the distance between sequential data is defined and used to identify and visualize representative sequences. Second, the likelihood of purchase is predicted based on this distance. Third, if a sequence suggests no purchase, counterfactual sequences are recommended to increase the probability of a purchase using a proposed method, which extracts counterfactual explanations for sequential data. A survey was conducted, and the data were analyzed; the results revealed that typical sequences could be extracted, and the parts of those sequences important for purchase could be detected. We believe that the proposed approach can support improvements in various marketing activities.
The so-called "Giant Arc" is a sparse pattern of MgII absorbers spanning approximately 740 comoving Mpc, whose discovery has been claimed to contradict the large-scale homogeneity inherent to the standard cosmological model. We previously showed that, with the same algorithm and parameters used for its discovery, very similar patterns are abundant in uniform random distributions, and among equivalent halo samples in a cosmological simulation of the standard model. In a response, the original discoverers of the "Giant Arc" have argued that these parameters were only appropriate for their specific observational data, but that a smaller linking length should be used for control studies, in which case far fewer patterns are detected. We briefly review and disprove these arguments, and demonstrate that large patterns like the "Giant Arc" are indeed ubiquitous in a statistically homogeneous universe.
CAMEO: Collection of Multilingual Emotional Speech Corpora
2025-05-16
Iwona Christop, Maciej Czajka
This paper presents CAMEO -- a curated collection of multilingual emotional speech datasets designed to facilitate research in emotion recognition and other speech-related tasks. The main objectives were to ensure easy access to the data, to allow reproducibility of the results, and to provide a standardized benchmark for evaluating speech emotion recognition (SER) systems across different emotional states and languages. The paper describes the dataset selection criteria, the curation and normalization process, and provides performance results for several models. The collection, along with metadata, and a leaderboard, is publicly available via the Hugging Face platform.
2025-05-16
Fabian Gröger, Simone Lionetti, Philippe Gottfrois, Alvaro Gonzalez-Jimenez, Ludovic Amruthalingam, Elisabeth Victoria Goessinger, Hanna Lindemann, Marie Bargiela, Marie Hofbauer, Omar Badri, Philipp Tschandl, Arash Koochek, Matthew Groh, Alexander A. Navarini, Marc Pouly
Robust machine learning depends on clean data, yet current image data cleaning benchmarks rely on synthetic noise or narrow human studies, limiting comparison and real-world relevance. We introduce CleanPatrick, the first large-scale benchmark for data cleaning in the image domain, built upon the publicly available Fitzpatrick17k dermatology dataset. We collect 496,377 binary annotations from 933 medical crowd workers, identify off-topic samples (4%), near-duplicates (21%), and label errors (22%), and employ an aggregation model inspired by item-response theory followed by expert review to derive high-quality ground truth. CleanPatrick formalizes issue detection as a ranking task and adopts typical ranking metrics mirroring real audit workflows. Benchmarking classical anomaly detectors, perceptual hashing, SSIM, Confident Learning, NoiseRank, and SelfClean, we find that, on CleanPatrick, self-supervised representations excel at near-duplicate detection, classical methods achieve competitive off-topic detection under constrained review budgets, and label-error detection remains an open challenge for fine-grained medical classification. By releasing both the dataset and the evaluation framework, CleanPatrick enables a systematic comparison of image-cleaning strategies and paves the way for more reliable data-centric artificial intelligence.
Review-Instruct: A Review-Driven Multi-Turn Conversations Generation Method for Large Language Models
2025-05-16
Jiangxu Wu, Cong Wang, TianHuang Su, Jun Yang, Haozhi Lin, Chao Zhang, Ming Peng, Kai Shi, SongPan Yang, BinQing Pan, ZiXian Li, Ni Yang, ZhenYu Yang
The effectiveness of large language models (LLMs) in conversational AI is hindered by their reliance on single-turn supervised fine-tuning (SFT) data, which limits contextual coherence in multi-turn dialogues. Existing methods for generating multi-turn dialogue data struggle to ensure both diversity and quality in instructions. To address this, we propose Review-Instruct, a novel framework that synthesizes multi-turn conversations through an iterative "Ask-Respond-Review" process involving three agent roles: a Candidate, multiple Reviewers, and a Chairman. The framework iteratively refines instructions by incorporating Reviewer feedback, enhancing dialogue diversity and difficulty. We construct a multi-turn dataset using the Alpaca dataset and fine-tune the LLaMA2-13B model. Evaluations on MT-Bench, MMLU-Pro, and Auto-Arena demonstrate significant improvements, achieving absolute gains of 2.9\% on MMLU-Pro and 2\% on MT-Bench compared to prior state-of-the-art models based on LLaMA2-13B. Ablation studies confirm the critical role of the Review stage and the use of multiple Reviewers in boosting instruction diversity and difficulty. Our work highlights the potential of review-driven, multi-agent frameworks for generating high-quality conversational data at scale.
Generative modeling has emerged as a promising direction in computational pathology, offering capabilities such as data-efficient learning, synthetic data augmentation, and multimodal representation across diverse diagnostic tasks. This review provides a comprehensive synthesis of recent progress in the field, organized into four key domains: image generation, text generation, multimodal image-text generation, and other generative applications, including spatial simulation and molecular inference. By analyzing over 150 representative studies, we trace the evolution of generative architectures from early generative adversarial networks to recent advances in diffusion models and foundation models with generative capabilities. We further examine the datasets and evaluation protocols commonly used in this domain and highlight ongoing limitations, including challenges in generating high-fidelity whole slide images, clinical interpretability, and concerns related to the ethical and legal implications of synthetic data. The review concludes with a discussion of open challenges and prospective research directions, with an emphasis on developing unified, multimodal, and clinically deployable generative systems. This work aims to provide a foundational reference for researchers and practitioners developing and applying generative models in computational pathology.
Automated quasar continuum estimation using neural networks: a comparative study of deep-learning architectures
2025-05-16
Francesco Pistis, Michele Fumagalli, Matteo Fossati, Trystyn Berg, Elena S. Mangola, Rajeshwari Dutta, Margherita Grespan, Angela Iovino, Katarzyna Małek, Sean Morrison, David N. A. Murphy, William J. Pearson, Ignasi Pérez-Ráfols, Matthew M. Pieri, Agnieszka Pollo, Daniela Vergani
Context. Ongoing and upcoming large spectroscopic surveys are drastically increasing the number of observed quasar spectra, requiring the development of fast and accurate automated methods to estimate spectral continua. Aims. This study evaluates the performance of three neural networks (NN) - an autoencoder, a convolutional NN (CNN), and a U-Net - in predicting quasar continua within the rest-frame wavelength range of $1020~\text{\AA}$ to $2000~\text{\AA}$. The ability to generalize and predict galaxy continua within the range of $3500~\text{\AA}$ to $5500~\text{\AA}$ is also tested. Methods. The performance of these architectures is evaluated using the absolute fractional flux error (AFFE) on a library of mock quasar spectra for the WEAVE survey, and on real data from the Early Data Release observations of the Dark Energy Spectroscopic Instrument (DESI) and the VIMOS Public Extragalactic Redshift Survey (VIPERS). Results. The autoencoder outperforms the U-Net, achieving a median AFFE of 0.009 for quasars. The best model also effectively recovers the Ly$\alpha$ optical depth evolution in DESI quasar spectra. With minimal optimization, the same architectures can be generalized to the galaxy case, with the autoencoder reaching a median AFFE of 0.014 and reproducing the D4000n break in DESI and VIPERS galaxies.
Survey of End-to-End Multi-Speaker Automatic Speech Recognition for Monaural Audio
2025-05-16
Xinlu He, Jacob Whitehill
Monaural multi-speaker automatic speech recognition (ASR) remains challenging due to data scarcity and the intrinsic difficulty of recognizing and attributing words to individual speakers, particularly in overlapping speech. Recent advances have driven the shift from cascade systems to end-to-end (E2E) architectures, which reduce error propagation and better exploit the synergy between speech content and speaker identity. Despite rapid progress in E2E multi-speaker ASR, the field lacks a comprehensive review of recent developments. This survey provides a systematic taxonomy of E2E neural approaches for multi-speaker ASR, highlighting recent advances and comparative analysis. Specifically, we analyze: (1) architectural paradigms (SIMO vs.~SISO) for pre-segmented audio, analyzing their distinct characteristics and trade-offs; (2) recent architectural and algorithmic improvements based on these two paradigms; (3) extensions to long-form speech, including segmentation strategy and speaker-consistent hypothesis stitching. Further, we (4) evaluate and compare methods across standard benchmarks. We conclude with a discussion of open challenges and future research directions towards building robust and scalable multi-speaker ASR.
Let the Trial Begin: A Mock-Court Approach to Vulnerability Detection using LLM-Based Agents
2025-05-16
Ratnadira Widyasari, Martin Weyssow, Ivana Clairine Irsan, Han Wei Ang, Frank Liauw, Eng Lieh Ouh, Lwin Khin Shar, Hong Jin Kang, David Lo
Detecting vulnerabilities in source code remains a critical yet challenging task, especially when benign and vulnerable functions share significant similarities. In this work, we introduce VulTrial, a courtroom-inspired multi-agent framework designed to enhance automated vulnerability detection. It employs four role-specific agents, which are security researcher, code author, moderator, and review board. Through extensive experiments using GPT-3.5 and GPT-4o we demonstrate that Vultrial outperforms single-agent and multi-agent baselines. Using GPT-4o, VulTrial improves the performance by 102.39% and 84.17% over its respective baseline. Additionally, we show that role-specific instruction tuning in multi-agent with small data (50 pair samples) improves the performance of VulTrial further by 139.89% and 118.30%. Furthermore, we analyze the impact of increasing the number of agent interactions on VulTrial's overall performance. While multi-agent setups inherently incur higher costs due to increased token usage, our findings reveal that applying VulTrial to a cost-effective model like GPT-3.5 can improve its performance by 69.89% compared to GPT-4o in a single-agent setting, at a lower overall cost.
Spatiotemporal relationships are critical in data science, as many prediction and reasoning tasks require analysis across both spatial and temporal dimensions--for instance, navigating an unfamiliar city involves planning itineraries that sequence locations and timing cultural experiences. However, existing Question-Answering (QA) datasets lack sufficient spatiotemporal-sensitive questions, making them inadequate benchmarks for evaluating models' spatiotemporal reasoning capabilities. To address this gap, we introduce POI-QA, a novel spatiotemporal-sensitive QA dataset centered on Point of Interest (POI), constructed through three key steps: mining and aligning open-source vehicle trajectory data from GAIA with high-precision geographic POI data, rigorous manual validation of noisy spatiotemporal facts, and generating bilingual (Chinese/English) QA pairs that reflect human-understandable spatiotemporal reasoning tasks. Our dataset challenges models to parse complex spatiotemporal dependencies, and evaluations of state-of-the-art multilingual LLMs (e.g., Qwen2.5-7B, Llama3.1-8B) reveal stark limitations: even the top-performing model (Qwen2.5-7B fine-tuned with RAG+LoRA) achieves a top 10 Hit Ratio (HR@10) of only 0.41 on the easiest task, far below human performance at 0.56. This underscores persistent weaknesses in LLMs' ability to perform consistent spatiotemporal reasoning, while highlighting POI-QA as a robust benchmark to advance algorithms sensitive to spatiotemporal dynamics. The dataset is publicly available at https://www.kaggle.com/ds/7394666.
Towards an open geotechnical data platform in France
2025-05-16
Isabelle Halfon, Mickaël Beaufils
An important quantity of geotechnical data is constantly collected for all the new projects of civil engineering. This data includes generally core and destructive boreholes, in which samples are taken for laboratory testing and in situ geotechnical tests are performed. The density of geotechnical data is particularly high in urban areas. The data is collected by geotechnical engineering or drilling Companies for public or private owners. Data is essential to define the geotechnical conditions of a project and are obviously necessary for the geotechnical design of foundations or underground structures. However, most of the time, data is not accessible in a numerical format, and at the end of the project, is often forgotten, while it could be reused for neighbouring projects. It is a great loss of information and knowledge to the technical and scientific geosciences community, and it represents a significant cost for the country economy. In France, the BRGM (French geological survey office) is currently developing a new open access platform dedicated to the capitalization and accessibility of geotechnical data, compliant with the FAIR principles (Findable, Accessible, Interoperable and Reusable). This paper describes the challenges to overcome and the possible solutions, considering the high diversity of geotechnical tests, explains the need of keeping the exhaustive data set for each test, and describes the next stages of development.
A Survey on the Safety and Security Threats of Computer-Using Agents: JARVIS or Ultron?
2025-05-16
Ada Chen, Yongjiang Wu, Junyuan Zhang, Shu Yang, Jen-tse Huang, Kun Wang, Wenxuan Wang, Shuai Wang
Recently, AI-driven interactions with computing devices have advanced from basic prototype tools to sophisticated, LLM-based systems that emulate human-like operations in graphical user interfaces. We are now witnessing the emergence of \emph{Computer-Using Agents} (CUAs), capable of autonomously performing tasks such as navigating desktop applications, web pages, and mobile apps. However, as these agents grow in capability, they also introduce novel safety and security risks. Vulnerabilities in LLM-driven reasoning, with the added complexity of integrating multiple software components and multimodal inputs, further complicate the security landscape. In this paper, we present a systematization of knowledge on the safety and security threats of CUAs. We conduct a comprehensive literature review and distill our findings along four research objectives: \textit{\textbf{(i)}} define the CUA that suits safety analysis; \textit{\textbf{(ii)} } categorize current safety threats among CUAs; \textit{\textbf{(iii)}} propose a comprehensive taxonomy of existing defensive strategies; \textit{\textbf{(iv)}} summarize prevailing benchmarks, datasets, and evaluation metrics used to assess the safety and performance of CUAs. Building on these insights, our work provides future researchers with a structured foundation for exploring unexplored vulnerabilities and offers practitioners actionable guidance in designing and deploying secure Computer-Using Agents.
The impact of spiral arms on the star formation life cycle
2025-05-16
Andrea Romanelli, Mélanie Chevance, J. M. Diederik Kruijssen, Lise Ramambason, Miguel Querejeta, Mederic Boquien, Daniel A. Dale, Jakob den Brok, Simon C. O. Glover, Kathryn Grasha, Annie Hughes, Jaeyeon Kim, Steven Longmore, Sharon E. Meidt, José Eduardo Mendez-Delgado, Lukas Neumann, Jérôme Pety, Eva Schinnerer, Rowan Smith, Jiayi Sun, Thomas G. Williams
The matter cycle between gas clouds and stars in galaxies plays a crucial role in regulating galaxy evolution through feedback mechanisms. In turn, the local and global galactic environments shape the interstellar medium and provide the initial conditions for star formation, potentially affecting the properties of this small-scale matter cycle. In particular, spiral arms have been proposed to play a pivotal role in the star formation life cycle, by enhancing the gas density and triggering star formation. However, their exact role is still debated. In this paper, we investigate the role of spiral arms in the giant molecular cloud evolutionary life cycle and on the star formation process in a sample of 22 nearby spiral galaxies from the PHANGS survey. We measure the cloud lifetime, the feedback timescale, the typical distance between independent regions and the star formation efficiency in spiral arms and inter-arm regions separately. We find that the distributions of the cloud lifetime as well as the feedback timescale are similar in both environments. This result suggests that spiral arms are unlikely to play a dominant role in triggering star formation. By contrast, the star formation efficiency appears to be slightly higher in inter-arm regions compared to spiral arms.
On the Security Risks of ML-based Malware Detection Systems: A Survey
2025-05-16
Ping He, Yuhao Mao, Changjiang Li, Lorenzo Cavallaro, Ting Wang, Shouling Ji
Malware presents a persistent threat to user privacy and data integrity. To combat this, machine learning-based (ML-based) malware detection (MD) systems have been developed. However, these systems have increasingly been attacked in recent years, undermining their effectiveness in practice. While the security risks associated with ML-based MD systems have garnered considerable attention, the majority of prior works is limited to adversarial malware examples, lacking a comprehensive analysis of practical security risks. This paper addresses this gap by utilizing the CIA principles to define the scope of security risks. We then deconstruct ML-based MD systems into distinct operational stages, thus developing a stage-based taxonomy. Utilizing this taxonomy, we summarize the technical progress and discuss the gaps in the attack and defense proposals related to the ML-based MD systems within each stage. Subsequently, we conduct two case studies, using both inter-stage and intra-stage analyses according to the stage-based taxonomy to provide new empirical insights. Based on these analyses and insights, we suggest potential future directions from both inter-stage and intra-stage perspectives.
REI-Bench: Can Embodied Agents Understand Vague Human Instructions in Task Planning?
2025-05-16
Chenxi Jiang, Chuhao Zhou, Jianfei Yang
Robot task planning decomposes human instructions into executable action sequences that enable robots to complete a series of complex tasks. Although recent large language model (LLM)-based task planners achieve amazing performance, they assume that human instructions are clear and straightforward. However, real-world users are not experts, and their instructions to robots often contain significant vagueness. Linguists suggest that such vagueness frequently arises from referring expressions (REs), whose meanings depend heavily on dialogue context and environment. This vagueness is even more prevalent among the elderly and children, who robots should serve more. This paper studies how such vagueness in REs within human instructions affects LLM-based robot task planning and how to overcome this issue. To this end, we propose the first robot task planning benchmark with vague REs (REI-Bench), where we discover that the vagueness of REs can severely degrade robot planning performance, leading to success rate drops of up to 77.9%. We also observe that most failure cases stem from missing objects in planners. To mitigate the REs issue, we propose a simple yet effective approach: task-oriented context cognition, which generates clear instructions for robots, achieving state-of-the-art performance compared to aware prompt and chains of thought. This work contributes to the research community of human-robot interaction (HRI) by making robot task planning more practical, particularly for non-expert users, e.g., the elderly and children.
Textured mesh Quality Assessment using Geometry and Color Field Similarity
2025-05-16
Kaifa Yang, Qi Yang, Zhu Li, Yiling Xu
Textured mesh quality assessment (TMQA) is critical for various 3D mesh applications. However, existing TMQA methods often struggle to provide accurate and robust evaluations. Motivated by the effectiveness of fields in representing both 3D geometry and color information, we propose a novel point-based TMQA method called field mesh quality metric (FMQM). FMQM utilizes signed distance fields and a newly proposed color field named nearest surface point color field to realize effective mesh feature description. Four features related to visual perception are extracted from the geometry and color fields: geometry similarity, geometry gradient similarity, space color distribution similarity, and space color gradient similarity. Experimental results on three benchmark datasets demonstrate that FMQM outperforms state-of-the-art (SOTA) TMQA metrics. Furthermore, FMQM exhibits low computational complexity, making it a practical and efficient solution for real-world applications in 3D graphics and visualization. Our code is publicly available at: https://github.com/yyyykf/FMQM.
The link between galaxy structure properties and star formation in local galaxies
2025-05-16
Zhimin Zhou, Wenwen Wang
To investigate the role of morphology in galaxy evolution, we analyze the relationships between galaxy structure, star formation, and HI gas content. Using multi-band images from the DESI Legacy Imaging Surveys, we perform detailed structural decompositions on a representative local galaxy sample from xGASS. Structural components and color properties are examined as functions of deviations from the star formation main sequence ($\rm \Delta SFR_{MS}$) and HI gas deficiency ($\rm \Delta f_{HI}$). We find that bulge fractions decrease with higher $\rm \Delta SFR_{MS}$ and lower stellar mass, indicating that star-forming galaxies are predominantly disc-dominated, while quiescent galaxies are bulge-dominated. The slope of the color ($g-r$) versus $\rm \Delta SFR_{MS}$ relationship decreases from low to high stellar masses and from outer to inner regions, with greater color variation in massive galaxies. Color gradients are predominantly negative, becoming shallower in lower-mass galaxies and in the outer disk regions. We also identify inflection points in the color gradient and bulge fraction relations with $\rm \Delta SFR_{MS}$, with main-sequence galaxies having the lowest bulge fractions and steepest color gradients. At fixed stellar mass, we observe only a slight correlation between bulge fraction and HI deficiency. However, outer disk colors show a stronger dependence on HI content than inner regions, and color gradients flatten as $\rm \Delta f_{HI}$ increases. These results suggest that HI gas is more closely linked to star-forming, disc-dominated systems, supporting the idea that gas accretion fuels star formation primarily in galaxy disks.
Frequency and Abundance of Binary sUpermassive bLack holes from Optical Variability Surveys (FABULOVS): Hubble Space Telescope Imaging of Radial-Velocity Selected Binary Candidates
Sub-parsec (sub-pc) binary supermassive black holes (BSBHs) should be common from galaxy mergers, yet direct evidence has been elusive. We present HST/WFC3IR F160W imaging for a sample of 8 candidate sub-pc BSBHs at redshifts z~0.1--0.5, as well as cross-comparison with a sample of ordinary quasars with archival HST/WFC3 IR F160W images. These 8 candidate sub-pc BSBHs were identified from multi-epoch spectroscopic surveys of quasars (including both typical quasars and those with single-peaked velocity-offset broad lines). whose broad H$\beta$ lines are significantly offset (by ~< a few hundred km/s) from the systemic redshifts. We directly test the prediction that the host galaxies of BSBHs would have a higher fraction of disturbed morphologies and younger stellar bulges from recent interactions than those of control quasars. After careful subtraction of the central quasar light, our candidate BSBH hosts show a statistically undifferentiated distribution of host asymmetry, indicative of a similar fraction of recent mergers. While a significantly larger sample is needed to place this result on a much firmer statistical ground, it opens questions as to the timescale differences between galaxy merger and BSBH formation, or the efficacy of the radial-velocity-shift--based selection of sub-pc BSBH candidates.
Precision agriculture, enhanced by artificial intelligence (AI), offers promising tools such as remote sensing, intelligent irrigation, fertilization management, and crop simulation to improve agricultural efficiency and sustainability. Reinforcement learning (RL), in particular, has outperformed traditional methods in optimizing yields and resource management. However, widespread AI adoption is limited by gaps between algorithmic recommendations and farmers' practical experience, local knowledge, and traditional practices. To address this, our study emphasizes Human-AI Interaction (HAII), focusing on transparency, usability, and trust in RL-based farm management. We employ a well-established trust framework - comprising ability, benevolence, and integrity - to develop a novel mathematical model quantifying farmers' confidence in AI-based fertilization strategies. Surveys conducted with farmers for this research reveal critical misalignments, which are integrated into our trust model and incorporated into a multi-objective RL framework. Unlike prior methods, our approach embeds trust directly into policy optimization, ensuring AI recommendations are technically robust, economically feasible, context-aware, and socially acceptable. By aligning technical performance with human-centered trust, this research supports broader AI adoption in agriculture.
Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics
2025-05-16
Conor F. Hayes, Felipe Leno Da Silva, Jiachen Yang, T. Nathan Mundhenk, Chak Shing Lee, Jacob F. Pettit, Claudio Santiago, Sookyung Kim, Joanne T. Kim, Ignacio Aravena Solis, Ruben Glatt, Andre R. Goncalves, Alexander Ladd, Ahmet Can Solak, Thomas Desautels, Daniel Faissol, Brenden K. Petersen, Mikel Landajuela
Deep Symbolic Optimization (DSO) is a novel computational framework that enables symbolic optimization for scientific discovery, particularly in applications involving the search for intricate symbolic structures. One notable example is equation discovery, which aims to automatically derive mathematical models expressed in symbolic form. In DSO, the discovery process is formulated as a sequential decision-making task. A generative neural network learns a probabilistic model over a vast space of candidate symbolic expressions, while reinforcement learning strategies guide the search toward the most promising regions. This approach integrates gradient-based optimization with evolutionary and local search techniques, and it incorporates in-situ constraints, domain-specific priors, and advanced policy optimization methods. The result is a robust framework capable of efficiently exploring extensive search spaces to identify interpretable and physically meaningful models. Extensive evaluations on benchmark problems have demonstrated that DSO achieves state-of-the-art performance in both accuracy and interpretability. In this chapter, we provide a comprehensive overview of the DSO framework and illustrate its transformative potential for automating symbolic optimization in scientific discovery.
Automated Real-time Assessment of Intracranial Hemorrhage Detection AI Using an Ensembled Monitoring Model (EMM)
2025-05-16
Zhongnan Fang, Andrew Johnston, Lina Cheuy, Hye Sun Na, Magdalini Paschali, Camila Gonzalez, Bonnie A. Armstrong, Arogya Koirala, Derrick Laurel, Andrew Walker Campion, Michael Iv, Akshay S. Chaudhari, David B. Larson
Artificial intelligence (AI) tools for radiology are commonly unmonitored once deployed. The lack of real-time case-by-case assessments of AI prediction confidence requires users to independently distinguish between trustworthy and unreliable AI predictions, which increases cognitive burden, reduces productivity, and potentially leads to misdiagnoses. To address these challenges, we introduce Ensembled Monitoring Model (EMM), a framework inspired by clinical consensus practices using multiple expert reviews. Designed specifically for black-box commercial AI products, EMM operates independently without requiring access to internal AI components or intermediate outputs, while still providing robust confidence measurements. Using intracranial hemorrhage detection as our test case on a large, diverse dataset of 2919 studies, we demonstrate that EMM successfully categorizes confidence in the AI-generated prediction, suggesting different actions and helping improve the overall performance of AI tools to ultimately reduce cognitive burden. Importantly, we provide key technical considerations and best practices for successfully translating EMM into clinical settings.
Rethinking Optimal Verification Granularity for Compute-Efficient Test-Time Scaling
2025-05-16
Hao Mark Chen, Guanxi Lu, Yasuyuki Okoshi, Zhiwen Mo, Masato Motomura, Hongxiang Fan
Test-time scaling (TTS) has proven effective in enhancing the reasoning capabilities of large language models (LLMs). Verification plays a key role in TTS, simultaneously influencing (1) reasoning performance and (2) compute efficiency, due to the quality and computational cost of verification. In this work, we challenge the conventional paradigms of verification, and make the first attempt toward systematically investigating the impact of verification granularity-that is, how frequently the verifier is invoked during generation, beyond verifying only the final output or individual generation steps. To this end, we introduce Variable Granularity Search (VG-Search), a unified algorithm that generalizes beam search and Best-of-N sampling via a tunable granularity parameter g. Extensive experiments with VG-Search under varying compute budgets, generator-verifier configurations, and task attributes reveal that dynamically selecting g can improve the compute efficiency and scaling behavior. Building on these findings, we propose adaptive VG-Search strategies that achieve accuracy gains of up to 3.1\% over Beam Search and 3.6\% over Best-of-N, while reducing FLOPs by over 52\%. We will open-source the code to support future research.
REMOR: Automated Peer Review Generation with LLM Reasoning and Multi-Objective Reinforcement Learning
2025-05-16
Pawin Taechoyotin, Daniel Acuna
AI-based peer review systems tend to produce shallow and overpraising suggestions compared to human feedback. Here, we evaluate how well a reasoning LLM trained with multi-objective reinforcement learning (REMOR) can overcome these limitations. We start by designing a multi-aspect reward function that aligns with human evaluation of reviews. The aspects are related to the review itself (e.g., criticisms, novelty) and the relationship between the review and the manuscript (i.e., relevance). First, we perform supervised fine-tuning of DeepSeek-R1-Distill-Qwen-7B using LoRA on PeerRT, a new dataset of high-quality top AI conference reviews enriched with reasoning traces. We then apply Group Relative Policy Optimization (GRPO) to train two models: REMOR-H (with the human-aligned reward) and REMOR-U (with a uniform reward). Interestingly, the human-aligned reward penalizes aspects typically associated with strong reviews, leading REMOR-U to produce qualitatively more substantive feedback. Our results show that REMOR-U and REMOR-H achieve more than twice the average rewards of human reviews, non-reasoning state-of-the-art agentic multi-modal AI review systems, and general commercial LLM baselines. We found that while the best AI and human reviews are comparable in quality, REMOR avoids the long tail of low-quality human reviews. We discuss how reasoning is key to achieving these improvements and release the Human-aligned Peer Review Reward (HPRR) function, the Peer Review Reasoning-enriched Traces (PeerRT) dataset, and the REMOR models, which we believe can help spur progress in the area.
Employing Laban Shape for Generating Emotionally and Functionally Expressive Trajectories in Robotic Manipulators
2025-05-16
Srikrishna Bangalore Raghu, Clare Lohrmann, Akshay Bakshi, Jennifer Kim, Jose Caraveo Herrera, Bradley Hayes, Alessandro Roncone
Successful human-robot collaboration depends on cohesive communication and a precise understanding of the robot's abilities, goals, and constraints. While robotic manipulators offer high precision, versatility, and productivity, they exhibit expressionless and monotonous motions that conceal the robot's intention, resulting in a lack of efficiency and transparency with humans. In this work, we use Laban notation, a dance annotation language, to enable robotic manipulators to generate trajectories with functional expressivity, where the robot uses nonverbal cues to communicate its abilities and the likelihood of succeeding at its task. We achieve this by introducing two novel variants of Hesitant expressive motion (Spoke-Like and Arc-Like). We also enhance the emotional expressivity of four existing emotive trajectories (Happy, Sad, Shy, and Angry) by augmenting Laban Effort usage with Laban Shape. The functionally expressive motions are validated via a human-subjects study, where participants equate both variants of Hesitant motion with reduced robot competency. The enhanced emotive trajectories are shown to be viewed as distinct emotions using the Valence-Arousal-Dominance (VAD) spectrum, corroborating the usage of Laban Shape.
DMN-Guided Prompting: A Low-Code Framework for Controlling LLM Behavior
2025-05-16
Shaghayegh Abedi, Amin Jalali
Large Language Models (LLMs) have shown considerable potential in automating decision logic within knowledge-intensive processes. However, their effectiveness largely depends on the strategy and quality of prompting. Since decision logic is typically embedded in prompts, it becomes challenging for end users to modify or refine it. Decision Model and Notation (DMN) offers a standardized graphical approach for defining decision logic in a structured, user-friendly manner. This paper introduces a DMN-guided prompting framework that breaks down complex decision logic into smaller, manageable components, guiding LLMs through structured decision pathways. We implemented the framework in a graduate-level course where students submitted assignments. The assignments and DMN models representing feedback instructions served as inputs to our framework. The instructor evaluated the generated feedback and labeled it for performance assessment. Our approach demonstrated promising results, outperforming chain-of-thought (CoT) prompting. Students also responded positively to the generated feedback, reporting high levels of perceived usefulness in a survey based on the Technology Acceptance Model.
SpiderCat: A Catalog of Compact Binary Millisecond Pulsars
2025-05-16
Karri I. I. Koljonen, Manuel Linares
We present SpiderCat, a multi-wavelength catalog of all publicly known compact binary millisecond pulsars (MSPs) in the Galactic field. These systems, colloquially known as "spiders", consist of neutron stars in tight orbits with low-mass companions, which are gradually ablated by the pulsar wind. SpiderCat includes both primary subclasses - redbacks and black widows - distinguished by companion mass, as well as candidates and peculiar systems such as transitional, huntsman and tidarren MSPs. As of this initial release, SpiderCat contains 109 entries: 32 redbacks, 49 black widows, 23 redback candidates, and 5 black widow candidates. In this paper, we compile and summarize key parameters for each system, including spin and orbital properties, and multi-wavelength data from radio, optical, X-ray, and gamma-ray observations. An interactive, publicly accessible web interface (https://astro.phys.ntnu.no/SpiderCAT) enables exploration and visualization of the data. The rapid growth of the number of known spiders, accelerated by the Fermi-LAT survey and its ability to identify MSPs in gamma-rays, has opened the door to population-level studies. Utilizing SpiderCat, we analyze trends in spin period, orbital period, companion mass, emission properties, and spatial distribution. SpiderCat serves as a dynamic, multi-wavelength repository for this unique class of binary pulsars, facilitating new discoveries and constraints on pulsar evolution, particle acceleration, and the neutron star equation of state.
Large language models (LLMs) have demonstrated impressive performance across a wide range of Natural Language Processing (NLP) tasks. However, ensuring their effectiveness across multiple languages presents unique challenges. Multilingual prompt engineering has emerged as a key approach to enhance LLMs' capabilities in diverse linguistic settings without requiring extensive parameter re-training or fine-tuning. With growing interest in multilingual prompt engineering over the past two to three years, researchers have explored various strategies to improve LLMs' performance across languages and NLP tasks. By crafting structured natural language prompts, researchers have successfully extracted knowledge from LLMs across different languages, making these techniques an accessible pathway for a broader audience, including those without deep expertise in machine learning, to harness the capabilities of LLMs. In this paper, we survey and categorize different multilingual prompting techniques based on the NLP tasks they address across a diverse set of datasets that collectively span around 250 languages. We further highlight the LLMs employed, present a taxonomy of approaches and discuss potential state-of-the-art (SoTA) methods for specific multilingual datasets. Additionally, we derive a range of insights across language families and resource levels (high-resource vs. low-resource), including analyses such as the distribution of NLP tasks by language resource type and the frequency of prompting methods across different language families. Our survey reviews 36 research papers covering 39 prompting techniques applied to 30 multilingual NLP tasks, with the majority of these studies published in the last two years.
Adaptive Ergodic Search with Energy-Aware Scheduling for Persistent Multi-Robot Missions
2025-05-16
Kaleb Ben Naveed, Devansh R. Agrawal, Rahul Kumar, Dimitra Panagou
Autonomous robots are increasingly deployed for long-term information-gathering tasks, which pose two key challenges: planning informative trajectories in environments that evolve across space and time, and ensuring persistent operation under energy constraints. This paper presents a unified framework, mEclares, that addresses both challenges through adaptive ergodic search and energy-aware scheduling in multi-robot systems. Our contributions are two-fold: (1) we model real-world variability using stochastic spatiotemporal environments, where the underlying information evolves unpredictably due to process uncertainty. To guide exploration, we construct a target information spatial distribution (TISD) based on clarity, a metric that captures the decay of information in the absence of observations and highlights regions of high uncertainty; and (2) we introduce Robustmesch (Rmesch), an online scheduling method that enables persistent operation by coordinating rechargeable robots sharing a single mobile charging station. Unlike prior work, our approach avoids reliance on preplanned schedules, static or dedicated charging stations, and simplified robot dynamics. Instead, the scheduler supports general nonlinear models, accounts for uncertainty in the estimated position of the charging station, and handles central node failures. The proposed framework is validated through real-world hardware experiments, and feasibility guarantees are provided under specific assumptions.
Foundation Models for AI-Enabled Biological Design
2025-05-16
Asher Moldwin, Amarda Shehu
This paper surveys foundation models for AI-enabled biological design, focusing on recent developments in applying large-scale, self-supervised models to tasks such as protein engineering, small molecule design, and genomic sequence design. Though this domain is evolving rapidly, this survey presents and discusses a taxonomy of current models and methods. The focus is on challenges and solutions in adapting these models for biological applications, including biological sequence modeling architectures, controllability in generation, and multi-modal integration. The survey concludes with a discussion of open problems and future directions, offering concrete next-steps to improve the quality of biological sequence generation.
The Host Galaxies of PTA Sources: Converting Supermassive BH Binary Parameters into EM Observables
2025-05-16
Niccolo Veronesi, Maria Charisi, Stephen R Taylor, Jessie Runnoe, Daniel J D Orazio
Pulsar timing arrays (PTAs) are approaching the sensitivity required to resolve for the first time gravitational wave (GW) signals from individual supermassive black hole (SMBH) binaries. However, the large uncertainty in the localisation of the source will make the identification of its host environment challenging. We show how the posterior probability function of binary parameters inferred by the standard GW analysis can be converted into distributions of apparent magnitudes of the host galaxy in the infrared (IR) and optical bands. We do so for two different scenarios: one in which the host is a regular early-type galaxy (ETG), and one in which the binary resides in an active galactic nucleus (AGN). We estimate the binary parameter space PTAs can cover in the near and intermediate future and estimate whether their hosts will be detectable in all-sky electromagnetic surveys. A PTA with a baseline of 20 years and 116 pulsars, resembling the upcoming data release of the International Pulsar Timing Array (IPTA)can detect binaries out to a luminosity distance of 2Gpc (which corresponds to a redshift of $z \approx 0.36$) under the most optimistic scenario for detection, while a PTA with a baseline of 30 years and 200 pulsars can reach out to a maximum distance slightly greater than 3Gpc ($z \approx 0.53$). We find that the host galaxies of all binaries that are detectable by a PTA with a baseline of 20 years are above the threshold for the WISE and SuperCOSMOS, and therefore they are expected to be present in those surveys, if they lie outside the Milky Way plane. 2MASS becomes incomplete for hosts of binary systems more massive than $10^{9.8}$ solar masses located at a luminosity distance from Earth greater than 1 Gpc. The EM surveys become slightly more incomplete when we consider the sensitivity of PTAs with baselines of 25 and 30 years, as PTAs can detect binaries to larger distances.
A luminous and hot infrared through X-ray transient at a 5 kpc offset from a dwarf galaxy
2025-05-16
Jean J. Somalwar, Vikram Ravi, Raffaella Margutti, Ryan Chornock, Priyamvada Natarajan, Wenbin Lu, Charlotte Angus, Matthew J. Graham, Erica Hammerstein, Edward Nathan, Matt Nicholl, Kritti Sharma, Robert Stein, Frank Verdi, Yuhan Yao, Eric C. Bellm, Tracy X. Chen, Michael W. Coughlin, David Hale, Mansi M. Kasliwal, Russ R. Laher, Reed Riddle, Jesper Sollerman
We are searching for hot, constant-color, offset optical flares in the Zwicky Transient Facility (ZTF) data stream that are ${>}10''$ from any galaxy in public imaging data from the PanSTARRS survey. Here, we present the first discovery from this search: AT 2024puz, a luminous multiwavelength transient offset by $5\,$kpc from a ${\sim}10^8\,M_\odot$ galaxy at $z=0.356$ with a low-moderate star formation rate. It produced luminous $10^{44.79 \pm 0.04}\,{\rm erg\,s}^{-1}$ optical/UV emission that evolved on a ${\sim}20\,$day timescale, as well as $10^{44.12\pm0.03}\,{\rm erg\,s}^{-1}$ X-ray emission with a photon-index $\Gamma=1.7$. No associated radio or millimeter emission was detected. We show that the early-time optical emission is likely powered by reprocessing of high-energy, accretion-powered radiation, with a possible contribution from a shock in a dense circum-transient medium. If the shock is dominant at early-times, the circum-transient medium has a mass ${\sim}0.1-1\,M_\odot$, radius $10^{15}\,$cm, and a density profile shallower than ${\sim}r^{-1}$. A near-infrared excess appears at late-times and is suggestive of reprocessing within a wind or other circum-transient medium. The X-rays are most consistent with a central engine. We suggest that AT 2024puz may be associated with an accretion event onto a $50-10^5\,M_\odot$ BH, where the lower masses are preferred based on the large projected offset from the host galaxy. AT2024puz exhibits properties similar to both luminous fast blue optical transients (LFBOTs) and tidal disruption events (TDEs), but is intermediate between them in its energetics and evolution timescale. This highlights the need for broader exploration of the landscape of hot optical transients to trace their origins.
On the definition and importance of interpretability in scientific machine learning
2025-05-16
Conor Rowan, Alireza Doostan
Though neural networks trained on large data sets have been successfully used to describe and predict many physical phenomena, there is a sense among scientists that, unlike traditional scientific models, where relationships come packaged in the form of simple mathematical expressions, the findings of the neural network cannot be integrated into the body of scientific knowledge. Critics of ML's inability to produce human-understandable relationships have converged on the concept of "interpretability" as its point of departure from more traditional forms of science. As the growing interest in interpretability has shown, researchers in the physical sciences seek not just predictive models, but also to uncover the fundamental principles that govern a system of interest. However, clarity around a definition of interpretability and the precise role that it plays in science is lacking in the literature. In this work, we argue that researchers in equation discovery and symbolic regression tend to conflate the concept of sparsity with interpretability. We review key papers on interpretable ML from outside the scientific community and argue that, though the definitions and methods they propose can inform questions of interpretability for SciML, they are inadequate for this new purpose. Noting these deficiencies, we propose an operational definition of interpretability for the physical sciences. Our notion of interpretability emphasizes understanding of the mechanism over mathematical sparsity. Innocuous though it may seem, this emphasis on mechanism shows that sparsity is often unnecessary. It also questions the possibility of interpretable scientific discovery when prior knowledge is lacking. We believe a precise and philosophically informed definition of interpretability in SciML will help focus research efforts toward the most significant obstacles to realizing a data-driven scientific future.
A Catalogue of Ultracompact Mass-Transferring White Dwarf Binaries
2025-05-15
Matthew J. Green, Jan van Roestel, Tin Long Sunny Wong
We present an overview and catalogue of all known mass-transferring binary systems that have white dwarf accretors and orbital periods shorter than 70 minutes, including the AM CVn-type binary systems and a number of related objects. In this paper, we provide an overview of the literature background and describe recent breakthroughs, open questions, and connections to other fields. This overview is intended to provide a starting point for researchers who are relatively new to the fields of ultracompact binaries or AM CVn-type binaries. We define the scope of the catalogue (the criteria for inclusion) and describe its format and contents (what data have been included for each target). We also summarise the observed properties of the known mass-transferring ultracompact binaries, including their orbital periods and component stellar masses and radii. The catalogue presented here aims to provide a central resource to enable researchers to track the current status of the known sample. At the time of writing, the catalogue includes 123 confirmed binaries and 29 candidates. The catalogue is hosted on a public platform (Zenodo) with version control, and will be updated regularly.
2025-05-15
P. M. Aronow, Austin Jang, Molly Offer-Westort
The design-based paradigm may be adopted in causal queries and survey sampling when we assume Rubin's stable unit treatment value assumption (SUTVA) or impose similar frameworks. While often taken for granted, such assumptions entail strong claims about the data generating process. We develop an alternative design-based approach: we first invoke a generalized, non-parametric model that allows for unrestricted forms of interference, such as spillover. We define a new set of inferential targets and discuss their interpretation under SUTVA and a weaker assumption that we call the No Unmodeled Revealable Variation Assumption (NURVA). We then reconstruct the standard paradigm, reconsidering SUTVA at the end rather than assuming it at the beginning. Despite its similarity to SUTVA, we demonstrate the practical insufficiency of NURVA in identifying substantively interesting quantities. In so doing, we provide clarity on the nature and importance of SUTVA for applied research.
PnPXAI: A Universal XAI Framework Providing Automatic Explanations Across Diverse Modalities and Models
2025-05-15
Seongun Kim, Sol A Kim, Geonhyeong Kim, Enver Menadjiev, Chanwoo Lee, Seongwook Chung, Nari Kim, Jaesik Choi
Recently, post hoc explanation methods have emerged to enhance model transparency by attributing model outputs to input features. However, these methods face challenges due to their specificity to certain neural network architectures and data modalities. Existing explainable artificial intelligence (XAI) frameworks have attempted to address these challenges but suffer from several limitations. These include limited flexibility to diverse model architectures and data modalities due to hard-coded implementations, a restricted number of supported XAI methods because of the requirements for layer-specific operations of attribution methods, and sub-optimal recommendations of explanations due to the lack of evaluation and optimization phases. Consequently, these limitations impede the adoption of XAI technology in real-world applications, making it difficult for practitioners to select the optimal explanation method for their domain. To address these limitations, we introduce \textbf{PnPXAI}, a universal XAI framework that supports diverse data modalities and neural network models in a Plug-and-Play (PnP) manner. PnPXAI automatically detects model architectures, recommends applicable explanation methods, and optimizes hyperparameters for optimal explanations. We validate the framework's effectiveness through user surveys and showcase its versatility across various domains, including medicine and finance.
Code security and usability are both essential for various coding assistant applications driven by large language models (LLMs). Current code security benchmarks focus solely on single evaluation task and paradigm, such as code completion and generation, lacking comprehensive assessment across dimensions like secure code generation, vulnerability repair and discrimination. In this paper, we first propose CoV-Eval, a multi-task benchmark covering various tasks such as code completion, vulnerability repair, vulnerability detection and classification, for comprehensive evaluation of LLM code security. Besides, we developed VC-Judge, an improved judgment model that aligns closely with human experts and can review LLM-generated programs for vulnerabilities in a more efficient and reliable way. We conduct a comprehensive evaluation of 20 proprietary and open-source LLMs. Overall, while most LLMs identify vulnerable codes well, they still tend to generate insecure codes and struggle with recognizing specific vulnerability types and performing repairs. Extensive experiments and qualitative analyses reveal key challenges and optimization directions, offering insights for future research in LLM code security.
Searching for Accreting Compact Binaries in eROSITA DR1
2025-05-15
Hao-Bin Liu, Wei-Min Gu, Yongqi Lu, Teng Liu, Jin-Zhong Liu
Compact binaries with accreting white dwarfs, neutron stars, or black holes are crucial for understanding accretion physics. In this study, we identify accreting compact binary candidates in the eROSITA DR1 by combining Gaia astrometry and ZTF time-domain photometry. We select accreting compact binary candidates whose X-ray emission is likely accretion-driven, based on their X-ray-to-optical flux ratios and optical colors, and further identify systems with short-period ZTF light curves, thereby ruling out AGNs. The resulting 22 candidates, including two known compact binaries, represent promising candidates for spectroscopic follow-up to confirm their accreting nature and explore their physical properties. Our results demonstrate the effectiveness of combining X-ray-to-optical flux ratio and optical color, and time-domain photometry in selecting accreting compact binaries. The method is scalable and adaptable to future multi-wavelength sky surveys, offering a promising path toward a more complete census of compact binaries in the Galaxy.
First Results on the Search for Lepton Number Violating Neutrinoless Double Beta Decay with the LEGEND-200 Experiment
2025-05-15
H. Acharya, N. Ackermann, M. Agostini, A. Alexander, C. Andreoiu, G. R. Araujo, F. T. Avignone III, M. Babicz, W. Bae, A. Bakalyarov, M. Balata, A. S. Barabash, P. S. Barbeau, C. J. Barton, L. Baudis, C. Bauer, E. Bernieri, L. Bezrukov, K. H. Bhimani, V. Biancacci, E. Blalock, S. J. Borden, G. Borghi, F. Borra, B. Bos, A. Boston, V. Bothe, R. Bouabid, R. Brugnera, N. Burlac, M. Busch, S. Calgaro, L. Canonica, S. Capra, M. Carminati, R. M. D. Carney, C. Cattadori, R. Cesarano, Y. -D. Chan, J. R. Chapman, A. Chernogorov, P. -J. Chiu, C. D. Christofferson, M. L. Clark, A. I. Colon-Rivera, T. Comellato, V. D'Andrea, R. Deckert, J. A. Detwiler, A. Di Giacinto, N. Di Marco, T. Dixon, K. -M. Dong, A. Drobizhev, G. Duran, Yu. Efremenko, S. R. Elliott, C. H. J. Emmanuel, E. Engelhardt, E. Esch, M. T. Febbraro, F. Ferella, D. E. Fields, C. Fiorini, M. Fomina, N. Fuad, R. Gala, A. Galindo-Uribarri, A. Gangapshev, A. Garfagnini, S. Gazzana, A. Geraci, L. Gessler, C. Ghiano, A. Gieb, S. Giri, M. Gold, C. Gooch, G. Grünauer, M. P. Green, J. Gruszko, I. Guinn, V. E. Guiseppe, V. Gurentsov, Y. Gurov, K. Gusev, B. Hackett, F. Hagemann, M. Haranczyk, F. Henkes, R. Henning, J. Herrera, D. Hervas Aguilar, J. Hinton, R. Hodák, H. F. R. Hoffmann, M. A. Howe, M. Huber, M. Hult, A. Ianni, K. Jędrzejczak, J. Jochum, R. W. L. Jones, D. S. Judson, M. Junker, J. Kaizer, V. Kazalov, M. F. Kidd, T. Kihm, K. Kilgus, A. Klimenko, K. T. Knöpfle, I. Kochanek, O. Kochetov, I. Kontul, L. L. Kormos, V. N. Kornoukhov, P. Krause, H. Krishnamoorthy, V. V. Kuzminov, K. Lang, M. Laubenstein, N. N. P. N. Lay, E. León, A. Leder, B. Lehnert, A. Leonhardt, N. Levashko, L. Y. Li, A. Li, Y. -R. Lin, M. Lindner, I. Lippi, A. Love, A. Lubashevskiy, B. Lubsandorzhiev, N. Lusardi, C. Macolino, B. Majorovits, F. Mamedov, L. Manzanillas, G. G. Marshall, R. D. Martin, E. L. Martin, R. Massarczyk, A. Mazumdar, G. McDowell, D. -M. Mei, S. P. Meireles, M. Menzel, S. Mertens, E. Miller, I. Mirza, M. Misiaszek, M. Morella, B. Morgan, T. Mroz, D. Muenstermann, C. J. Nave, I. Nemchenok, M. Neuberger, N. O'Briant, F. Paissan, L. Papp, L. S. Paudel, K. Pelczar, L. Pertoldi, W. Pettus, F. Piastra, M. Pichotta, P. Piseri, A. W. P. Poon, P. P. Povinec, M. Pruckner, A. Pullia, W. S. Quinn, D. C. Radford, Y. A. Ramachers, A. Razeto, M. Redchuk, A. L. Reine, S. Riboldi, K. Rielage, C. Romo-Luque, N. Rossi, S. Rozov, T. J. Ruland, N. Rumyantseva, J. Runge, R. Saakyan, S. Sailer, G. Salamanna, F. Salamida, G. Saleh, V. Sandukovsky, C. Savarese, S. Schönert, A. -K. Schütz, D. C. Schaper, L. Schlüter, S. J. Schleich, O. Schulz, M. Schwarz, B. Schwingenheuer, C. Seibt, O. Selivanenko, G. Senatore, A. Serafini, K. Shakhov, E. Shevchik, M. Shirchenko, Y. Shitov, H. Simgen, F. Šimkovic, S. Simonaitis-Boyd, M. Skorokhvatov, M. Slavíčková, A. Smolnikov, J. A. Solomon, G. Song, A. C. Sousa, A. R. Sreekala, L. Steinhart, I. Štekl, T. Sterr, M. Stommel, S. A. Sullivan, R. R. Sumathi, K. Szczepaniec, L. Taffarello, D. Tagnani, D. J. Tedeschi, T. N. Thorpe, V. Tretyak, M. Turqueti, E. E. Van Nieuwenhuizen, L. J. Varriano, S. Vasilyev, A. Veresnikova, C. Vignoli, C. Vogl, K. von Sturm, A. Warren, D. Waters, S. L. Watkins, C. Wiesinger, J. F. Wilkerson, M. Willers, C. Wiseman, M. Wojcik, D. Xu, W. Xu, E. Yakushev, T. Ye, C. -H. Yu, V. Yumatov, D. Zinatulina, K. Zuber, G. Zuzel
The LEGEND collaboration is searching for neutrinoless double beta ($0\nu\beta\beta$) decay by operating high-purity germanium detectors enriched in $^{76}$Ge in a low-background liquid argon environment. Building on key technological innovations from GERDA and the MAJORANA DEMONSTRATOR, LEGEND-200 has performed a first $0\nu\beta\beta$ decay search based on 61 kg yr of data. Over half of this exposure comes from our highest performing detectors, including newly developed inverted-coaxial detectors, and is characterized by an estimated background level of $0.5^{+0.3}_{-0.2}$ cts/(keV kg yr) in the $0\nu\beta\beta$ decay signal region. A combined analysis of data from GERDA, the MAJORANA DEMONSTRATOR, and LEGEND-200, characterized by a 90% confidence level exclusion sensitivity of $2.8 \times 10^{26}$ yr on the half-life of $0\nu\beta\beta$ decay, reveals no evidence for a signal and sets a new observed lower limit at $T^{0\nu}_{1/2} > 1.9 \times 10^{26}$ yr (90% confidence level). Assuming the decay is mediated by Majorana neutrinos, this corresponds to an upper limit on the effective Majorana mass in the range $m_{\beta\beta} < 75-200$ meV, depending on the adopted nuclear matrix element.
Environmental dependences of galaxy properties in the southern regions of the GAMA survey
2025-05-15
K. Joy, U. Sureshkumar, A. Narayanan, S. Bellstedt, A. Durkalec, A. Pollo, M. Hilton
Physical properties of galaxies are correlated with their local environment. Quantifying these environmental correlations is crucial for a better understanding of galaxy formation and evolution. In this work, we investigate how galaxy properties are correlated with the environment through spatial clustering measurements of galaxies. Using two-point correlation functions and marked correlation functions, we measure and compare the environmental dependence of colour, stellar mass, luminosities in the $u$, $g$, $r$, $J$, and $K$ bands, star formation rate, and specific star formation rate. We use galaxy samples from the southern (G02 and G23) regions of the Galaxy and Mass Assembly (GAMA) survey. Furthermore, we explore how redshift completeness affects clustering measurements by comparing different subsets of the G02 region with varying redshift completeness. We show that the $u-r$ and $g-r$ colours correlate the most with the local environment, with stellar mass being the next most reliable indicator of the environment. We also demonstrate that redshift completeness has a significant effect on clustering measurements.
Euclid: Photometric redshift calibration with the clustering redshifts technique
2025-05-15
W. d'Assignies Doumerg, M. Manera, C. Padilla, O. Ilbert, H. Hildebrandt, L. Reynolds, J. Chaves-Montero, A. H. Wright, P. Tallada-Crespí, M. Eriksen, J. Carretero, W. Roster, Y. Kang, K. Naidoo, R. Miquel, B. Altieri, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, D. Bagot, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, A. Biviano, E. Branchini, M. Brescia, S. Camera, V. Capobianco, C. Carbone, V. F. Cardone, S. Casas, F. J. Castander, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Crocce, A. Da Silva, H. Degaudenzi, S. de la Torre, G. De Lucia, M. Douspis, X. Dupac, A. Ealet, S. Escoffier, M. Farina, F. Faustini, S. Ferriol, F. Finelli, P. Fosalba, S. Fotopoulou, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, B. Gillis, C. Giocoli, P. Gómez-Alvarez, J. Gracia-Carpio, A. Grazian, F. Grupp, W. Holmes, I. M. Hook, A. Hornstrup, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, O. Lahav, A. M. C. Le Brun, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, D. C. Masters, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, C. Neissner, S. -M. Niemi, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, J. A. Schewtschenko, P. Schneider, T. Schrabback, A. Secroun, E. Sefusatti, G. Seidel, M. Seiffert, S. Serrano, P. Simon, C. Sirignano, G. Sirri, A. Spurio Mancini, L. Stanco, J. Steinwagner, D. Tavagnacco, A. N. Taylor, H. I. Teplitz, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, A. Tsyganov, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, Y. Wang, J. Weller, G. Zamorani, E. Zucca, M. Bolzonella, C. Burigana, L. Gabarra, J. Martín-Fleitas, I. Risso, V. Scottez, M. Viel
Aims: The precision of cosmological constraints from imaging surveys hinges on accurately estimating the redshift distribution $ n(z) $ of tomographic bins, especially their mean redshifts. We assess the effectiveness of the clustering redshifts technique in constraining Euclid tomographic redshift bins to meet the target uncertainty of $ \sigma ( \langle z \rangle ) < 0.002 (1 + z) $. In this work, these mean redshifts are inferred from the small-scale angular clustering of Euclid galaxies, which are distributed into bins with spectroscopic samples localised in narrow redshift slices. Methods: We generate spectroscopic mocks from the Flagship2 simulation for the Baryon Oscillation Spectroscopic Survey (BOSS), the Dark Energy Spectroscopic Instrument (DESI), and Euclid's Near-Infrared Spectrometer and Photometer (NISP) spectroscopic survey. We evaluate and optimise the clustering redshifts pipeline, introducing a new method for measuring photometric galaxy bias (clustering), which is the primary limitation of this technique. Results: We have successfully constrained the means and standard deviations of the redshift distributions for all of the tomographic bins (with a maximum photometric redshift of 1.6), achieving precision beyond the required thresholds. We have identified the main sources of bias, particularly the impact of the 1-halo galaxy distribution, which imposed a minimal separation scale of 1.5 Mpc for evaluating cross-correlations. These results demonstrate the potential of clustering redshifts to meet the precision requirements for Euclid, and we highlight several avenues for future improvements.
Digging Deeper for RR Lyrae Stars with Low Modulation Amplitudes
2025-05-15
Geza Kovacs
With the goal of searching for very low modulation amplitudes among fundamental mode RR Lyrae stars and assess their incidence rate, we performed a survey of 36 stars observed by the Kepler satellite during the entire four-year period of its mission. The search was conducted by a task-oriented code, designed to find low-amplitude signals in the presence of high-amplitude components and instrumental systematics. We found 7 new modulated stars and negate one earlier claimed star, whereby increasing the number of known Blazhko stars from 17 to 24 and yielding an observed occurrence rate of 67% for the Kepler field. Six of the new stars have the lowest modulation amplitudes found so far, with ~250 ppm Fourier side-lobe amplitudes near the fundamental mode frequency. Because of the small sample size in the Kepler field, we extended the survey to 12 campaign fields observed by K2, the ``two-wheeled'' mission of Kepler. From the 1061 stars we found 514 Blazhko stars. After correcting for the short duration of the time spent on each field, and for the noise dependence of the detections, we arrived at an underlying occurrence rate of ~75% - likely a lower limit for the true rate of Blazhko stars in the K2 fields.
Operational and Exploration Requirements and Research Capabilities for SEP Environment Monitoring and Forecasting
2025-05-15
Viacheslav Sadykov, Petrus Martens, Dustin Kempton, Rafal Angryk, Berkay Aydin, Jessica Hamilton, Griffin Goodwin, Aatiya Ali, Sanjib K C, Rimsha Syeda, Irina Kitiashvili, Kathryn Whitman, Alexander Kosovichev, Kimberly Moreland, Manolis Georgoulis, Ming Zhang, Azim Ahmadzadeh, Ronald Turner
Mitigating risks posed by solar energetic particles (SEPs) to operations and exploration in space and Earth's atmosphere motivates the development of advanced, synergistic approaches for monitoring, modeling, and analyzing space weather conditions. The consequences of SEPs and their interactions with the near-Earth space environment are numerous, including elevated radiation levels at aviation altitudes during major events, satellite damage, and health risks to astronauts, resulting in economic impacts and potential hazards for space exploration. This contribution will present a high-level overview of the operational requirements and research capabilities for SEP event environment monitoring and forecasting that were highlighted during a workshop at Georgia State University, held on October 16-19, 2024. Specifically, it summarizes the presented activities concerning the following: (1) Identifying needs for SEP event forecasting and nowcasting, including practical forecast timeframes; (2) Reviewing availability and coverage of the current observational data and identifying tangible data resources for research, operations and the R2O2R loop; (3) Mapping existing forecast capabilities and identifying meaningful modeling advances for research and operations.
Towards N$^3$LO PDFs and their implications for Higgs production cross-sections
2025-05-15
Felix Hekhorn
We review the recently published PDF sets MSHT20xNNPDF40_an3lo and MSHT20xNNPDF40_an3lo_qed, which have been obtained by combining the approximate N$^3$LO (aN$^3$LO) sets of parton distributions (PDFs) from the MSHT20 and NNPDF4.0 series. We investigate the impact of the most recent results on N$^3$LO splitting functions onto the two PDF sets. Finally, we demonstrate the impact of N$^3$LO PDFs on the computation of Higgs production cross-sections at the LHC.
PolarCat: Catalog of polars, low-accretion rate polars, and candidate objects
2025-05-15
Axel D. Schwope
Polars and low-accretion rate polars (LARPs) are strongly magnetic cataclysmic variables. Mediated by the magnetic field of the white dwarf, their spin and binary orbit are (mostly) synchronized. They play an important role in our understanding of close binary evolution and the generation of strong magnetic fields in white dwarfs. Thanks to X-ray all-sky surveys, optical variability, and spectroscopic surveys, the number of polars and LARPs has grown from just a few in the 1980s to more than 200 today. Follow-up studies are facilitated by the systematic compilation of these systems presented here, which is also made available as an online resource. Yearly updates are planned, and community input is highly appreciated.
AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agents
2025-05-15
Julius Henke
A recent area of increasing research is the use of Large Language Models (LLMs) in penetration testing, which promises to reduce costs and thus allow for higher frequency. We conduct a review of related work, identifying best practices and common evaluation issues. We then present AutoPentest, an application for performing black-box penetration tests with a high degree of autonomy. AutoPentest is based on the LLM GPT-4o from OpenAI and the LLM agent framework LangChain. It can perform complex multi-step tasks, augmented by external tools and knowledge bases. We conduct a study on three capture-the-flag style Hack The Box (HTB) machines, comparing our implementation AutoPentest with the baseline approach of manually using the ChatGPT-4o user interface. Both approaches are able to complete 15-25 % of the subtasks on the HTB machines, with AutoPentest slightly outperforming ChatGPT. We measure a total cost of \$96.20 US when using AutoPentest across all experiments, while a one-month subscription to ChatGPT Plus costs \$20. The results show that further implementation efforts and the use of more powerful LLMs released in the future are likely to make this a viable part of vulnerability management.
2025-05-15
Yang Li, Xinyu Zhou, Yitong Wang, Liangxin Qian, Jun Zhao
Transformer models have revolutionized AI, powering applications like content generation and sentiment analysis. However, their deployment in Machine Learning as a Service (MLaaS) raises significant privacy concerns, primarily due to the centralized processing of sensitive user data. Private Transformer Inference (PTI) offers a solution by utilizing cryptographic techniques such as secure multi-party computation and homomorphic encryption, enabling inference while preserving both user data and model privacy. This paper reviews recent PTI advancements, highlighting state-of-the-art solutions and challenges. We also introduce a structured taxonomy and evaluation framework for PTI, focusing on balancing resource efficiency with privacy and bridging the gap between high-performance inference and data privacy.
A hybrid SLAM-Payne framework for atmospheric parameter and abundance determination of early-type Stars from LAMOST DR9 low-resolution Spectra
2025-05-15
Weijia Sun
Early-type stars are key drivers of Galactic chemical evolution, enriching the interstellar medium with alpha elements through powerful stellar winds and core-collapse supernovae, fueled by their short lifetimes and high masses. However, their spectra remain challenging to analyse due to rapid rotation, weak metal lines, and non-LTE effects. While large spectroscopic surveys provide extensive low-resolution data, extracting reliable parameters remains difficult due to methodological limitations for hot stars. Our goal is to develop a unified framework combining data-driven and synthetic spectral approaches to determine atmospheric parameters and abundances for hot stars using low-resolution spectra, addressing limitations in current methodologies while retaining critical spectral information. We present a hybrid approach integrating the Stellar LAbel Machine (SLAM) and the Payne frameworks, for low-resolution ($R\sim 1800$) spectra from LAMOST DR9. Our method preserves full spectral information including Balmer series and metal-line blends, employing neural-network interpolation for efficient parameter estimation ($T_\mathrm{eff}$, $\log g$, and $v\sin i$) and abundance determination for O, Mg, Si, and Fe, across 8000 - 20000 K. We derive stellar parameters and abundances for 315,822 stars with $\mathrm{SNR} \geqslant 10$ in the $r$-band. Additionally, we detect abundance trends ([$\alpha$/Fe]-[Fe/H]) that exhibit temperature-dependent systematics and a distinct $\alpha$-poor stellar population within $0.0 \leqslant \mathrm{[Fe/H]} \leqslant 0.5$ dex. The radial abundance gradients are negative and consistent with that derived from Cepheids, with a slope of $-0.070\pm0.007$ in \feh in the region $6 \leqslant R_\mathrm{GC}\leqslant 15$ kpc.
Empirically evaluating commonsense intelligence in large language models with large-scale human judgments
2025-05-15
Tuan Dung Nguyen, Duncan J. Watts, Mark E. Whiting
Commonsense intelligence in machines is often assessed by static benchmarks that compare a model's output against human-prescribed correct labels. An important, albeit implicit, assumption of these labels is that they accurately capture what any human would think, effectively treating human common sense as homogeneous. However, recent empirical work has shown that humans vary enormously in what they consider commonsensical; thus what appears self-evident to one benchmark designer may not be so to another. Here, we propose a novel method for evaluating common sense in artificial intelligence (AI), specifically in large language models (LLMs), that incorporates empirically observed heterogeneity among humans by measuring the correspondence between a model's judgment and that of a human population. We first find that, when treated as independent survey respondents, most LLMs remain below the human median in their individual commonsense competence. Second, when used as simulators of a hypothetical population, LLMs correlate with real humans only modestly in the extent to which they agree on the same set of statements. In both cases, smaller, open-weight models are surprisingly more competitive than larger, proprietary frontier models. Our evaluation framework, which ties commonsense intelligence to its cultural basis, contributes to the growing call for adapting AI models to human collectivities that possess different, often incompatible, social stocks of knowledge.
AI LEGO: Scaffolding Cross-Functional Collaboration in Industrial Responsible AI Practices during Early Design Stages
2025-05-15
Muzhe Wu, Yanzhi Zhao, Shuyi Han, Michael Xieyang Liu, Hong Shen
Responsible AI (RAI) efforts increasingly emphasize the importance of addressing potential harms early in the AI development lifecycle through social-technical lenses. However, in cross-functional industry teams, this work is often stalled by a persistent knowledge handoff challenge: the difficulty of transferring high-level, early-stage technical design rationales from technical experts to non-technical or user-facing roles for ethical evaluation and harm identification. Through literature review and a co-design study with 8 practitioners, we unpack how this challenge manifests -- technical design choices are rarely handed off in ways that support meaningful engagement by non-technical roles; collaborative workflows lack shared, visual structures to support mutual understanding; and non-technical practitioners are left without scaffolds for systematic harm evaluation. Existing tools like JIRA or Google Docs, while useful for product tracking, are ill-suited for supporting joint harm identification across roles, often requiring significant extra effort to align understanding. To address this, we developed AI LEGO, a web-based prototype that supports cross-functional AI practitioners in effectively facilitating knowledge handoff and identifying harmful design choices in the early design stages. Technical roles use interactive blocks to draft development plans, while non-technical roles engage with those blocks through stage-specific checklists and LLM-driven persona simulations to surface potential harms. In a study with 18 cross-functional practitioners, AI LEGO increased the volume and likelihood of harms identified compared to baseline worksheets. Participants found that its modular structure and persona prompts made harm identification more accessible, fostering clearer and more collaborative RAI practices in early design.
Density perturbations in nonminimally coupled gravity: symptoms of Lagrangian density ambiguity
2025-05-15
Miguel Barroso Varela, Orfeu Bertolami
The evolution of density perturbations is analysed in a modified theory of gravity with a nonminimal coupling between curvature and matter. We consider the broken degeneracy between the choices of matter Lagrangian for a perfect fluid, $\mathcal{L}_m=-\rho$ and $\mathcal{L}_m=p$, and determine the differences between their effects on the effective gravitational constant. We review the result for $\mathcal{L}_m=-\rho$ in the quasistatic approximation and show how it can lead to unphysical singular behaviour for late-time dominating models. This divergent regime can be avoided when considering the fully non-quasistatic perturbative equations, although the higher-order nature of the nonminimally coupled theory and the requirement of a physically viable effective gravitational constant strongly constrains the magnitude of these modifications to the action. We find that both of these issues can be removed when considering $\mathcal{L}_m=p$ at late times due to the pressureless nature of non-relativistic matter and provide predictions for inverse power-law models.
Clinical evidence, derived from rigorous research and data analysis, provides healthcare professionals with reliable scientific foundations for informed decision-making. Integrating clinical evidence into real-time practice is challenging due to the enormous workload, complex professional processes, and time constraints. This highlights the need for tools that automate evidence synthesis to support more efficient and accurate decision making in clinical settings. This study introduces Quicker, an evidence-based clinical decision support system powered by large language models (LLMs), designed to automate evidence synthesis and generate clinical recommendations modeled after standard clinical guideline development processes. Quicker implements a fully automated chain that covers all phases, from questions to clinical recommendations, and further enables customized decision-making through integrated tools and interactive user interfaces. To evaluate Quicker's capabilities, we developed the Q2CRBench-3 benchmark dataset, based on clinical guideline development records for three different diseases. Experimental results highlighted Quicker's strong performance, with fine-grained question decomposition tailored to user preferences, retrieval sensitivities comparable to human experts, and literature screening performance approaching comprehensive inclusion of relevant studies. In addition, Quicker-assisted evidence assessment effectively supported human reviewers, while Quicker's recommendations were more comprehensive and logically coherent than those of clinicians. In system-level testing, collaboration between a single reviewer and Quicker reduced the time required for recommendation development to 20-40 minutes. In general, our findings affirm the potential of Quicker to help physicians make quicker and more reliable evidence-based clinical decisions.
Using Machine Learning to Estimate NUV Magnitudes and Probe Quenching Mechanisms of $z$=0 Nuggets in the RESOLVE and ECO Surveys
2025-05-15
D. S. Carr, S. J. Kappannan, Z. L. Hutchens, M. S. Polimera, M. A. Norris, K. D. Eckert, A. J. Moffett
We present a $z=0$ census of nuggets -- compact galaxies that form via gas-rich violent disk instability -- within the luminosity- and volume-limited REsolved Spectroscopy Of a Local VolumE (RESOLVE) and Environmental COntext (ECO) surveys. We use random forest (RF) models to predict near-ultraviolet (NUV) magnitudes for ECO galaxies that lack high-quality NUV magnitudes, thereby doubling the number of ECO galaxies with reliable extinction-corrected star formation rates (SFRs) and red/green/blue classifications based on specific SFRs (sSFRs). The resulting RF-enhanced RESOLVE+ECO nugget sample allows us to analyze rare subpopulations -- green nuggets and nuggets with active galactic nuclei (AGN) -- likely associated with quenching. Green nuggets are more similar to red nuggets than to blue nuggets in halo mass ($M_{\text{halo}}$) distribution, with both red and green nuggets being found mainly at $M_{\text{halo}} \ge 10^{11.4} M_\odot$, where permanent halo quenching is predicted. At these masses, the AGN frequency for green nuggets is higher ($\text{48.2\%}^{+5.3\%}_{-5.3\%}$) than for either blue ($\text{39.2\%}^{+2.9\%}_{-2.8\%}$) or red ($\text{29.3\%}^{+3.0\%}_{-2.8\%}$) nuggets. Between $M_{halo} = 10^{11.4}-10^{12} M_\odot$, at the onset of permanent quenching, the AGN frequency for green nuggets is nearly double the frequency for blue or red nuggets, implying AGN are associated with this transition. At $M_{halo} < 10^{11.4} M_\odot$, where temporary cyclic quenching is expected, the AGN frequency for blue nuggets ($\text{7.5\%}^{+1.4\%}_{-1.2\%}$) is lower than for either green ($\text{31.3\%}^{+8.7\%}_{-7.5\%}$) or red ($\text{18.8\%}^{+11.5\%}_{-7.8\%}$) nuggets. At all masses, nuggets with AGN have reduced sSFRs and likely also atomic gas content compared to nuggets without AGN, but the quenching is more extreme below $M_{halo} = 10^{11.4} M_\odot$.
Spherical trapped surfaces in n-dimensional general relativity
2025-05-15
Ayan Chatterjee, Suresh C. Jaryal, Akshay Kumar
In this paper, we examine gravitational collapse of matter fields in $n$-dimensional general relativity. The matter energy-momentum tensor under consideration includes dust, perfect fluids with equations of state and matter admitting bulk and shear viscosity. By adjusting various parameters of the matter energy-momentum tensor, we determine the trapped region and spherical marginally trapped surfaces in homogeneous and inhomogeneous models of collapse. We show that, as expected, the time development of marginally trapped tube is intricately related to the initial velocity and density profiles of the collapsing matter configuration. This study clarifies the role of initial data in the formation of spacetime singularity during gravitational collapse and implies that, under generic conditions on the matter profiles, the central spacetime singularity is always covered by a horizon.
A User Study Evaluating Argumentative Explanations in Diagnostic Decision Support
2025-05-15
Felix Liedeker, Olivia Sanchez-Graillet, Moana Seidler, Christian Brandt, Jörg Wellmer, Philipp Cimiano
As the field of healthcare increasingly adopts artificial intelligence, it becomes important to understand which types of explanations increase transparency and empower users to develop confidence and trust in the predictions made by machine learning (ML) systems. In shared decision-making scenarios where doctors cooperate with ML systems to reach an appropriate decision, establishing mutual trust is crucial. In this paper, we explore different approaches to generating explanations in eXplainable AI (XAI) and make their underlying arguments explicit so that they can be evaluated by medical experts. In particular, we present the findings of a user study conducted with physicians to investigate their perceptions of various types of AI-generated explanations in the context of diagnostic decision support. The study aims to identify the most effective and useful explanations that enhance the diagnostic process. In the study, medical doctors filled out a survey to assess different types of explanations. Further, an interview was carried out post-survey to gain qualitative insights on the requirements of explanations incorporated in diagnostic decision support. Overall, the insights gained from this study contribute to understanding the types of explanations that are most effective.
Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence
2025-05-15
Xiang He, Dongcheng Zhao, Yang Li, Qingqun Kong, Xin Yang, Yi Zeng
Multimodal learning enhances the perceptual capabilities of cognitive systems by integrating information from different sensory modalities. However, existing multimodal fusion research typically assumes static integration, not fully incorporating key dynamic mechanisms found in the brain. Specifically, the brain exhibits an inverse effectiveness phenomenon, wherein weaker unimodal cues yield stronger multisensory integration benefits; conversely, when individual modal cues are stronger, the effect of fusion is diminished. This mechanism enables biological systems to achieve robust cognition even with scarce or noisy perceptual cues. Inspired by this biological mechanism, we explore the relationship between multimodal output and information from individual modalities, proposing an inverse effectiveness driven multimodal fusion (IEMF) strategy. By incorporating this strategy into neural networks, we achieve more efficient integration with improved model performance and computational efficiency, demonstrating up to 50% reduction in computational cost across diverse fusion methods. We conduct experiments on audio-visual classification, continual learning, and question answering tasks to validate our method. Results consistently demonstrate that our method performs excellently in these tasks. To verify universality and generalization, we also conduct experiments on Artificial Neural Networks (ANN) and Spiking Neural Networks (SNN), with results showing good adaptability to both network types. Our research emphasizes the potential of incorporating biologically inspired mechanisms into multimodal networks and provides promising directions for the future development of multimodal artificial intelligence. The code is available at https://github.com/Brain-Cog-Lab/IEMF.
Knowledge-Based Aerospace Engineering -- A Systematic Literature Review
2025-05-15
Tim Wittenborg, Ildar Baimuratov, Ludvig Knöös Franzén, Ingo Staack, Ulrich Römer, Sören Auer
The aerospace industry operates at the frontier of technological innovation while maintaining high standards regarding safety and reliability. In this environment, with an enormous potential for re-use and adaptation of existing solutions and methods, Knowledge-Based Engineering (KBE) has been applied for decades. The objective of this study is to identify and examine state-of-the-art knowledge management practices in the field of aerospace engineering. Our contributions include: 1) A SWARM-SLR of over 1,000 articles with qualitative analysis of 164 selected articles, supported by two aerospace engineering domain expert surveys. 2) A knowledge graph of over 700 knowledge-based aerospace engineering processes, software, and data, formalized in the interoperable Web Ontology Language (OWL) and mapped to Wikidata entries where possible. The knowledge graph is represented on the Open Research Knowledge Graph (ORKG), and an aerospace Wikibase, for reuse and continuation of structuring aerospace engineering knowledge exchange. 3) Our resulting intermediate and final artifacts of the knowledge synthesis, available as a Zenodo dataset. This review sets a precedent for structured, semantic-based approaches to managing aerospace engineering knowledge. By advancing these principles, research, and industry can achieve more efficient design processes, enhanced collaboration, and a stronger commitment to sustainable aviation.
Exploring Large Quantities of Secondary Data from High-Resolution Synchrotron X-ray Computed Tomography Scans Using AccuStripes
2025-05-15
Anja Heim, Thomas Lang, Christoph Heinzl
The analysis of secondary quantitative data extracted from high-resolution synchrotron X-ray computed tomography scans represents a significant challenge for users. While a number of methods have been introduced for processing large three-dimensional images in order to generate secondary data, there are only a few techniques available for simple and intuitive visualization of such data in their entirety. This work employs the AccuStripes visualization technique for that purpose, which enables the visual analysis of secondary data represented by an ensemble of univariate distributions. It supports different schemes for adaptive histogram binnings in combination with several ways of rendering aggregated data and it allows the interactive selection of optimal visual representations depending on the data and the use case. We demonstrate the usability of AccuStripes on a high-resolution synchrotron scan of a particle-reinforced metal matrix composite sample, containing more than 20 million particles. Through AccuStripes, detailed insights are facilitated into distributions of derived particle characteristics of the entire sample. Furthermore, research questions such as how the overall shape of the particles is or how homogeneously they are distributed across the sample can be answered.
From Text to Network: Constructing a Knowledge Graph of Taiwan-Based China Studies Using Generative AI
2025-05-15
Hsuan-Lei Shao
Taiwanese China Studies (CS) has developed into a rich, interdisciplinary research field shaped by the unique geopolitical position and long standing academic engagement with Mainland China. This study responds to the growing need to systematically revisit and reorganize decades of Taiwan based CS scholarship by proposing an AI assisted approach that transforms unstructured academic texts into structured, interactive knowledge representations. We apply generative AI (GAI) techniques and large language models (LLMs) to extract and standardize entity relation triples from 1,367 peer reviewed CS articles published between 1996 and 2019. These triples are then visualized through a lightweight D3.js based system, forming the foundation of a domain specific knowledge graph and vector database for the field. This infrastructure allows users to explore conceptual nodes and semantic relationships across the corpus, revealing previously uncharted intellectual trajectories, thematic clusters, and research gaps. By decomposing textual content into graph structured knowledge units, our system enables a paradigm shift from linear text consumption to network based knowledge navigation. In doing so, it enhances scholarly access to CS literature while offering a scalable, data driven alternative to traditional ontology construction. This work not only demonstrates how generative AI can augment area studies and digital humanities but also highlights its potential to support a reimagined scholarly infrastructure for regional knowledge systems.
Electron spin resonance with scanning tunneling microscopy: a tool for an on-surface quantum platform of identical qubits
2025-05-15
Deung-Jang Choi, Soo-hyon Phark, Andreas J. Heinrich, Nicolás Lorente
Integration of electron spin resonance (ESR) in a scanning tunneling microscope (STM) has enabled an all-electrical control of atomic and molecular spins on solid surfaces with atomic-scale precision and energy resolution beyond thermal limitations. Further, coherent manipulation and detection of individual spins in an ESR-STM establishes a powerful quantum platform, allowing for the implementation of fundamental quantum logic operations to on-surface identical qubits. In this review, we introduce recent advances of ESR-STM, focusing on its application to atomic-scale qubits and extension to molecular qubit systems. We discuss the principles underlying ESR-STM, followed by single-spin addressability, coherent control via Rabi oscillations, and quantum state readout through frequency-resolved detection. We further demonstrate multi-qubit control architectures enabled by atom manipulation and local magnetic field engineering, culminating in the realization of multi-qubit logic gates such as the Controlled-NOT and Toffoli gates. These implementations highlight the specialty of ESR-STM towards atomic-scale quantum circuits. Indeed, ESR-STM can be an excellent tool to perform and evaluate quantum operations in molecular qubits. The results reviewed in this collection establish ESR-STM as a versatile tool for advancing quantum coherent science at the atomic and molecular level in solid-state environments.
To investigate the star-forming process in nearby dwarf galaxies, we present Integral Field Units (IFU) observation of the star-forming dwarf galaxy, NGC 1522, with the Very Large Telescope (VLT)/Multi Unit Spectroscopic Explorer (MUSE) as a part of Dwarf Galaxy Integral Survey (DGIS). Our observation reveals the presence of a star-forming clumpy ring in its central region. We identify nine distinct star-forming clumps based on extinction-corrected H$\alpha$ emission-line map, with the total star formation rate (SFR) of about 0.1 $M_\odot$ yr$^{-1}$. The nine clumps are considered to be starbursts, which represent an extreme case in the local universe, without invoking major merging. We investigate the properties of ionized gas using the strong emission lines and `BPT' diagrams, in conjunction with the velocity mapping. Our analysis unveils intriguing patterns, including the positive metallicity gradient and low N/O abundance ratio. This peculiar distribution of metallicity may signify external gas accretion. Our results suggest that the ongoing star formation in NGC 1522 might be triggered and sustained by the inflow of external metal-poor gas.
Top-Down vs. Bottom-Up Approaches for Automatic Educational Knowledge Graph Construction in CourseMapper
2025-05-15
Qurat Ul Ain, Mohamed Amine Chatti, Amr Shakhshir, Jean Qussa, Rawaa Alatrash, Shoeb Joarder
The automatic construction of Educational Knowledge Graphs (EduKGs) is crucial for modeling domain knowledge in digital learning environments, particularly in Massive Open Online Courses (MOOCs). However, identifying the most effective approach for constructing accurate EduKGs remains a challenge. This study compares Top-down and Bottom-up approaches for automatic EduKG construction, evaluating their effectiveness in capturing and structuring knowledge concepts from learning materials in our MOOC platform CourseMapper. Through a user study and expert validation using Simple Random Sampling (SRS), results indicate that the Bottom-up approach outperforms the Top-down approach in accurately identifying and mapping key knowledge concepts. To further enhance EduKG accuracy, we integrate a Human-in-the-Loop approach, allowing course moderators to review and refine the EduKG before publication. This structured comparison provides a scalable framework for improving knowledge representation in MOOCs, ultimately supporting more personalized and adaptive learning experiences.
Dynamic scene representation and reconstruction have undergone transformative advances in recent years, catalyzed by breakthroughs in neural radiance fields and 3D Gaussian splatting techniques. While initially developed for static environments, these methodologies have rapidly evolved to address the complexities inherent in 4D dynamic scenes through an expansive body of research. Coupled with innovations in differentiable volumetric rendering, these approaches have significantly enhanced the quality of motion representation and dynamic scene reconstruction, thereby garnering substantial attention from the computer vision and graphics communities. This survey presents a systematic analysis of over 200 papers focused on dynamic scene representation using radiance field, spanning the spectrum from implicit neural representations to explicit Gaussian primitives. We categorize and evaluate these works through multiple critical lenses: motion representation paradigms, reconstruction techniques for varied scene dynamics, auxiliary information integration strategies, and regularization approaches that ensure temporal consistency and physical plausibility. We organize diverse methodological approaches under a unified representational framework, concluding with a critical examination of persistent challenges and promising research directions. By providing this comprehensive overview, we aim to establish a definitive reference for researchers entering this rapidly evolving field while offering experienced practitioners a systematic understanding of both conceptual principles and practical frontiers in dynamic scene reconstruction.
Linear theory of viscoelasticity in a generalized hydrodynamic framework
2025-05-15
Andreas M. Menzel
A generalized hydrodynamic theory that systematically incorporates elasticity and viscoelasticity had been derived about a quarter of a century ago. It is based on a strictly Euler point of view, as is natural for hydrodynamics. We used and adapted this theory particularly in a linear framework. There, it is straightforward to focus on what are the flow and displacement fields in linearized hydrodynamics and elasticity, which provides some advantages. Since this theoretical approach appears not to be as commonly widespread as it deserves to be, we here overview and review the formalism. Specific further focus is on pointing out relations to the commonly known Kelvin-Voigt model and Maxwell model. They are naturally contained within this description. The two limits of perfect long-term elasticity on one hand and long-term flow on the other hand can be represented by adjusting only one parameter.
Data mining of public genomic repositories: harnessing off-target reads to expand microbial pathogen genomic resources
2025-05-15
Damien Richard, Nils Poulicard
As sequencing technologies become more affordable and genomic databases expand continuously, the reuse of publicly available sequencing data emerges as a powerful strategy for studying microbial pathogens. Indeed, raw sequencing reads generated for the study of a given organism often contain reads originating from the associated microbiota. This review explores how such off-target reads can be detected and used for the study of microbial pathogens. We present genomic data mining as a method to identify relevant sequencing runs from petabase-scale databases, highlighting recent methodological advances that allow efficient database querying. We then briefly outline methods designed to retrieve relevant data and associated metadata, and provide an overview of common downstream analysis pipelines. We discuss how such approaches have (i) expanded the known genetic diversity of microbial pathogens, (ii) enriched our understanding of their spatiotemporal distribution, and (iii) highlighted previously unrecognized ecological interactions involving microbial pathogens. However, these analyses often rely on the completeness and accuracy of accompanying metadata, which remain highly variable. We detail common pitfalls, including data contamination and metadata misannotations, and suggest strategies for result interpretation. Ultimately, while data mining cannot replace dedicated studies, it constitutes an essential and complementary tool for microbial pathogen research. Broader utility will depend on improved data standardization and systematic genomic monitoring across ecosystems.
Edge computing, with its low latency, dynamic scalability, and location awareness, along with the convergence of computing and communication paradigms, has been successfully applied in critical domains such as industrial IoT, smart healthcare, smart homes, and public safety. This paper provides a comprehensive survey of open-source edge computing simulators and emulators, presented in our GitHub repository (https://github.com/qijianpeng/awesome-edge-computing), emphasizing the convergence of computing and networking paradigms. By examining more than 40 tools, including CloudSim, NS-3, and others, we identify the strengths and limitations in simulating and emulating edge environments. This survey classifies these tools into three categories: packet-level, application-level, and emulators. Furthermore, we evaluate them across five dimensions, ranging from resource representation to resource utilization. The survey highlights the integration of different computing paradigms, packet processing capabilities, support for edge environments, user-defined metric interfaces, and scenario visualization. The findings aim to guide researchers in selecting appropriate tools for developing and validating advanced computing and networking technologies.
Optical Kerr soliton microcombs for high bandwidth communications
2025-05-15
Bill Corcoran, Arnan Mitchell, Roberto Morandotti, Leif K. Oxenlowe, David J. Moss
Microcombs, optical frequency combs generated by nonlinear integrated micro-cavity resonators, have the potential to offer the full capability of their benchtop comb based counterparts, but in an integrated footprint. They have enabled breakthroughs in spectroscopy, microwave photonics, frequency synthesis, optical ranging, quantum state generation and manipulation, metrology, optical neuromorphic processing and more. One of their most promising applications has been optical fibre communications where they have formed the basis for massively parallel ultrahigh capacity multiplexed data transmission. Innovative approaches have been used in recent years to phaselock, or modelock different types of microcombs, from dissipative Kerr solitons to dark solitons, soliton crystals and others. This has enabled their use as sources for optical communications including advanced coherent modulation format systems that have achieved ultrahigh data capacity bit rates breaking the petabit/s barrier. Here, we review this new and exciting field, chronicling the progress while highlighting the challenges and opportunities.
Advancements in Entangled Photon Pairs in 2D Van der Waals Materials for On-chip Quantum Applications
2025-05-15
Abdus Salam Sarkar
The next generation of technology is rooted in quantum-based advancements. The entangled photon pair sources play a pivotal role in a wide range of advanced quantum applications, including quantum high precision sensors, communication, computing, cryptography and so on. Scalable on-chip quantum photonic devices have the potential to drive game changing developments in this field. This review article highlights recent breakthroughs in the generation of entangled photon pairs in two dimensional (2D) van der Waals (vdW) materials, with a focus on their applicability to quantum technologies and plausible on-chip integration technology. The article begins by discussing the fundamental principles of entangled photon pairs generation. It provides a comprehensive review of the origin and generation of entangled photons in emerging vdW materials, alongside various optical quantum characterization techniques. The review then explores key physical parameters of the quantum states associated with entangled photon pairs. Additionally, it examines concepts related to the realization of paired photon generation at the quantum limit. The final section focuses on the potential for on-chip integrated quantum device applications. Beyond highlighting recent advancements in quantum-based research, the review also outlines current limitations and future prospects aimed at advancing the field
Promise of Data-Driven Modeling and Decision Support for Precision Oncology and Theranostics
2025-05-15
Binesh Sadanandan, Vahid Behzadan
Cancer remains a leading cause of death worldwide, necessitating personalized treatment approaches to improve outcomes. Theranostics, combining molecular-level imaging with targeted therapy, offers potential for precision oncology but requires optimized, patient-specific care plans. This paper investigates state-of-the-art data-driven decision support applications with a reinforcement learning focus in precision oncology. We review current applications, training environments, state-space representation, performance evaluation criteria, and measurement of risk and reward, highlighting key challenges. We propose a framework integrating data-driven modeling with reinforcement learning-based decision support to optimize radiopharmaceutical therapy dosing, addressing identified challenges and setting directions for future research. The framework leverages Neural Ordinary Differential Equations and Physics-Informed Neural Networks to enhance Physiologically Based Pharmacokinetic models while applying reinforcement learning algorithms to iteratively refine treatment policies based on patient-specific data.
The Path Integral Bottleneck: Exploring the Control-Compute Tradeoff
2025-05-15
Justin Ting, Jing Shuang Li
Executing a control sequence requires some computation effort. Intuitively, a high-effort, fine-grained computation should result in better control (e.g. lower cost), whereas little to no computation effort would lead to worse control. To quantify and explore the tradeoff between control performance and compute effort, we present the Path Integral Bottleneck (PIB), a fusion of the Path Integral (PI) optimal control and Information Bottleneck (IB) frameworks. Both frameworks provide flexible and probabilistic descriptions of control. The PI does not limit itself to a particular control law, and the IB is not bound to any specific state encoding. Combining the generality of both frameworks enables us to produce an analytical description of the control-compute tradeoff. We provide PIB formulations for both continuous and discrete random variables. With these formulations, we can plot a tradeoff curve between performance and computation effort for any given plant description and control cost function. Simulations of a cart-pole for both the continuous and discrete variable cases reveal fundamental control-compute tradeoffs, exposing regions where the task performance-per-compute is higher than others.
Determining Absence of Unreasonable Risk: Approval Guidelines for an Automated Driving System Release
2025-05-15
Francesca Favaro, Scott Schnelle, Laura Fraade-Blanar, Trent Victor, Mauricio Peña, Nick Webb, Holland Broce, Craig Paterson, Dan Smith
This paper provides an overview of how the determination of absence of unreasonable risk can be operationalized. It complements previous theoretical work published by existing developers of Automated Driving Systems (ADS) on the overall engineering practices and methodologies for readiness determination. Readiness determination is, at its core, a risk assessment process. It is aimed at evaluating the residual risk associated with the deployment of a new software release candidate. The paper proposes methodological criteria to ground the readiness review process for an ADS release. While informed by Waymo's experience in this domain, the criteria presented are agnostic of any specific ADS technological solution and/or architectural choice, to support broad implementation by others in the industry. The paper continues with a discussion on governance and decision-making toward approval of a new software release candidate for the ADS. The implementation of the presented criteria requires the existence of appropriate safety management practices in addition to many other cultural, procedural, and operational considerations. As such, the paper is concluded by a statement of limitations for those wishing to replicate part or all of its content.
Post-Post-API Age: Studying Digital Platforms in Scant Data Access Times
2025-05-15
Kayo Mimizuka, Megan A Brown, Kai-Cheng Yang, Josephine Lukito
Over the past decade, data provided by digital platforms has informed substantial research in HCI to understand online human interaction and communication. Following the closure of major social media APIs that previously provided free access to large-scale data (the "post-API age"), emerging data access programs required by the European Union's Digital Services Act (DSA) have sparked optimism about increased platform transparency and renewed opportunities for comprehensive research on digital platforms, leading to the "post-post-API age." However, it remains unclear whether platforms provide adequate data access in practice. To assess how platforms make data available under the DSA, we conducted a comprehensive survey followed by in-depth interviews with 19 researchers to understand their experiences with data access in this new era. Our findings reveal significant challenges in accessing social media data, with researchers facing multiple barriers including complex API application processes, difficulties obtaining credentials, and limited API usability. These challenges have exacerbated existing institutional, regional, and financial inequities in data access. Based on these insights, we provide actionable recommendations for platforms, researchers, and policymakers to foster more equitable and effective data access, while encouraging broader dialogue within the CSCW community around interdisciplinary and multi-stakeholder solutions.
Topological photonics is developed based on the analogy of Schr\"{o}dinger equation which is mathematically reduced to a standard eigenvalue equation. Notably, several photonic systems are beyond the standard topological band theory as they are described by generalized or nonlinear eigenvalue equations. In this article, we review the topological band theory of this category. In the first part, we discuss topological photonics of generalized eigenvalue equations where the band structure may take complex values even when the involved matrices are Hermitian. These complex bands explain the characteristic dispersion relation of hyperbolic metamaterials. In addition, our numerical analysis predicts the emergence of symmetry-protected exceptional points in a photonic crystal composed of negative index media. In the second part, by introducing auxiliary bands, we establish the nonlinear bulk-edge correspondence under ``weak" nonlinearity of eigenvalues. The nonlinear bulk-edge correspondence elucidates the robustness of chiral edge modes in photonic systems where the permittivity and permeability are frequency dependent.
Impact of Outreach on Physics Student Development: Quantitative Results from a National Survey
2025-05-15
Jonathan D. Perry, Toni Sauncy, Susan White, Rachel L Ivie, John Tyler, Tatiana Erukhimova
This work reports on results from the first national study of the impact of student facilitation of informal physics outreach programs on their physics identity, sense of belonging, and essential career skill development. Drawing on results from studies at a single institution with a well-developed physics outreach program, a national survey was developed and distributed through the Society of Physics Students' network to more than five thousand individuals. Responses to closed-form questions on the survey were analyzed descriptively and using multiple regression analysis to evaluate the relationship between student participation in informal physics outreach programs and the constructs of interest. Results show a strong association between students engaging in outreach with their confidence in communicating their physics knowledge, the development of key career skills, and direct connections with growth mindset and sense of belonging. These results may be useful to physics and other STEM departments around the United States who are seeking to elevate and broaden the learning experience of students.
Deep Learning and Explainable AI: New Pathways to Genetic Insights
2025-05-15
Chenyu Wang, Chaoying Zuo, Zihan Su, Yuhang Xing, Lu Li, Maojun Wang, Zeyu Zhang
Deep learning-based AI models have been extensively applied in genomics, achieving remarkable success across diverse applications. As these models gain prominence, there exists an urgent need for interpretability methods to establish trustworthiness in model-driven decisions. For genetic researchers, interpretable insights derived from these models hold significant value in providing novel perspectives for understanding biological processes. Current interpretability analyses in genomics predominantly rely on intuition and experience rather than rigorous theoretical foundations. In this review, we systematically categorize interpretability methods into input-based and model-based approaches, while critically evaluating their limitations through concrete biological application scenarios. Furthermore, we establish theoretical underpinnings to elucidate the origins of these constraints through formal mathematical demonstrations, aiming to assist genetic researchers in better understanding and designing models in the future. Finally, we provide feasible suggestions for future research on interpretability in the field of genetics.
How does the limited resolution of space plasma analyzers affect the accuracy of space plasma measurements?
2025-05-15
Georgios Nicolaou, Charalambos Ioannou, Christopher Owen, Daniel Verscharen, Andrey Fedorov, Philippe Louarn
We investigate the systematic errors in measured plasma velocity distribution functions and their corresponding velocity moments, arising from the limited energy and angular resolution of top-hat electrostatic analyzers. For this purpose, we develop a forward model of a concept analyzer that simulates observations of typical solar wind proton plasma particles with their velocities following a Maxwell distribution function. We then review the standard conversion of the observations to physical parameters and evaluate the errors arising from the limited resolution of the modeled instrument. We show that the limited resolution of the instrument results in velocity distributions that underestimate the core and overestimate the tails of the actual Maxwellian plasma velocity distribution functions. As a consequence, the velocity moments of the observed plasma underestimate the proton density and overestimate the proton temperature. Moreover, we show that the examined errors become significant for cold and fast plasma protons. We finally determine a mathematical formula that predicts these systematic inaccuracies based on specific plasma inputs and instrument features. Our results inform and contextualize future evaluations of observations by analyzers in various plasma regimes.
From Static to Dynamic: Exploring Temporal Networks in Systems Biology
2025-05-15
Abir Khazaal, Fatemeh Vafaee
Network science has become an essential interdisciplinary tool for understanding complex biological systems. However, because these systems undergo continuous, often stimulus-driven changes in both structure and function, traditional static network approaches frequently fall short in capturing their dynamic nature. Dynamic network analysis (DNA) addresses this limitation and offers a powerful framework to investigate these evolving relationships. This work focuses on temporal networks, a central paradigm within DNA, as an effective approach for modelling time-resolved changes in biological systems. While DNA has gained traction in domains like social and communication sciences, its integration in biology has been more gradual, hindered by data limitations and the need for domain-specific adaptations. Aimed at supporting researchers, particularly those new to the field, the review offers an integrative overview of the diverse and multidisciplinary landscape of DNA, with a focus on temporal networks in systems biology. I begin by clarifying foundational terminology and concepts, then present a multi-scale perspective spanning microscale (nodes and edges), mesoscale (motifs and communities), and macroscale (global topology) analyses. Finally, I explore analytical strategies and computational tools suited to various research objectives, including methods for detecting structural shifts, assessing network similarity, tracking module evolution, and predictive modelling of future network states.
Automated Detection of Salvin's Albatrosses: Improving Deep Learning Tools for Aerial Wildlife Surveys
2025-05-15
Mitchell Rogers, Theo Thompson, Isla Duporge, Johannes Fischer, Klemens Pütz, Thomas Mattern, Bing Xue, Mengjie Zhang
Recent advancements in deep learning and aerial imaging have transformed wildlife monitoring, enabling researchers to survey wildlife populations at unprecedented scales. Unmanned Aerial Vehicles (UAVs) provide a cost-effective means of capturing high-resolution imagery, particularly for monitoring densely populated seabird colonies. In this study, we assess the performance of a general-purpose avian detection model, BirdDetector, in estimating the breeding population of Salvin's albatross (Thalassarche salvini) on the Bounty Islands, New Zealand. Using drone-derived imagery, we evaluate the model's effectiveness in both zero-shot and fine-tuned settings, incorporating enhanced inference techniques and stronger augmentation methods. Our findings indicate that while applying the model in a zero-shot setting offers a strong baseline, fine-tuning with annotations from the target domain and stronger image augmentation leads to marked improvements in detection accuracy. These results highlight the potential of leveraging pre-trained deep-learning models for species-specific monitoring in remote and challenging environments.
Embodied AI in Machine Learning -- is it Really Embodied?
2025-05-15
Matej Hoffmann, Shubhan Parag Patni
Embodied Artificial Intelligence (Embodied AI) is gaining momentum in the machine learning communities with the goal of leveraging current progress in AI (deep learning, transformers, large language and visual-language models) to empower robots. In this chapter we put this work in the context of "Good Old-Fashioned Artificial Intelligence" (GOFAI) (Haugeland, 1989) and the behavior-based or embodied alternatives (R. A. Brooks 1991; Pfeifer and Scheier 2001). We claim that the AI-powered robots are only weakly embodied and inherit some of the problems of GOFAI. Moreover, we review and critically discuss the possibility of cross-embodiment learning (Padalkar et al. 2024). We identify fundamental roadblocks and propose directions on how to make progress.
Origins of Carbon Dust in a JWST-Observed Primeval Galaxy at $z\sim$6.7
2025-05-15
Ambra Nanni, Michael Romano, Darko Donevski, Joris Witstok, Irene Shivaei, Michel Fioc, Prasad Sawant
JADES-GS-z6-0, a high-redshift galaxy ($z \sim 6.7$) recently observed as part of the James Webb Space Telescope (JWST) Advanced Deep Extragalactic Survey (JADES), exhibits a distinct bump in its rest-frame ultraviolet (UV) spectrum indicative of a large quantity of hydrocarbon grains, a sign of rapid metal and dust enrichment in its interstellar medium (ISM). This galaxy serves as an ideal case for examining rapid dust formation processes in the early universe. We investigated diverse dust production channels from a possible maximal formation redshift of $z_{\rm form} \approx 17$, enabling dust contributions from asymptotic giant branch (AGB) stars over the longest possible timescale. Our model simultaneously reproduces key spectral features of JADES-GS-z6-0 such as its Balmer decrement, UV slope, and UV bump. The match is obtained by adopting a star-formation history in which a burst at $\sim 600$~Myr accounts for approximately 30\% of the galaxy's final stellar mass. Our findings indicate two pathways for the formation of hydrocarbon grains, such as polycyclic aromatic hydrocarbons (PAHs): (1) efficient dust accretion within the ISM, necessitating a low depletion of metals into dust grains from Type II supernovae ($\approx 10$\%), or (2) dust production predominantly by Type II supernovae, requiring a high depletion fraction ($\approx 73$\%) without dust accretion. We further demonstrate that PAHs are unlikely to originate solely from AGB stars or from shattering of large grains in the ISM. The evolution of the UV slope with redshift points to a complex and bursty star formation history for galaxies observed by JADES.
TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation
2025-05-15
Manthan Patel, Fan Yang, Yuheng Qiu, Cesar Cadena, Sebastian Scherer, Marco Hutter, Wenshan Wang
We present TartanGround, a large-scale, multi-modal dataset to advance the perception and autonomy of ground robots operating in diverse environments. This dataset, collected in various photorealistic simulation environments includes multiple RGB stereo cameras for 360-degree coverage, along with depth, optical flow, stereo disparity, LiDAR point clouds, ground truth poses, semantic segmented images, and occupancy maps with semantic labels. Data is collected using an integrated automatic pipeline, which generates trajectories mimicking the motion patterns of various ground robot platforms, including wheeled and legged robots. We collect 910 trajectories across 70 environments, resulting in 1.5 million samples. Evaluations on occupancy prediction and SLAM tasks reveal that state-of-the-art methods trained on existing datasets struggle to generalize across diverse scenes. TartanGround can serve as a testbed for training and evaluation of a broad range of learning-based tasks, including occupancy prediction, SLAM, neural scene representation, perception-based navigation, and more, enabling advancements in robotic perception and autonomy towards achieving robust models generalizable to more diverse scenarios. The dataset and codebase for data collection will be made publicly available upon acceptance. Webpage: https://tartanair.org/tartanground
2025-05-15
Moses C. Nah, Johannes Lachner, Neville Hogan
Despite a slow neuromuscular system, humans easily outperform modern robot technology, especially in physical contact tasks. How is this possible? Biological evidence indicates that motor control of biological systems is achieved by a modular organization of motor primitives, which are fundamental building blocks of motor behavior. Inspired by neuro-motor control research, the idea of using simpler building blocks has been successfully used in robotics. Nevertheless, a comprehensive formulation of modularity for robot control remains to be established. In this paper, we introduce a modular framework for robot control using motor primitives. We present two essential requirements to achieve modular robot control: independence of modules and closure of stability. We describe key control modules and demonstrate that a wide range of complex robotic behaviors can be generated from this small set of modules and their combinations. The presented modular control framework demonstrates several beneficial properties for robot control, including task-space control without solving Inverse Kinematics, addressing the problems of kinematic singularity and kinematic redundancy, and preserving passivity for contact and physical interactions. Further advantages include exploiting kinematic singularity to maintain high external load with low torque compensation, as well as controlling the robot beyond its end-effector, extending even to external objects. Both simulation and actual robot experiments are presented to validate the effectiveness of our modular framework. We conclude that modularity may be an effective constructive framework for achieving robotic behaviors comparable to human-level performance.
Suggestions of decreasing dark energy from supernova and BAO data: an update
2025-05-15
Mark Van Raamsdonk, Chris Waddell
In a previous work 2305.04946, we found that supernova and baryon acoustic oscillation data support the hypothesis that late time cosmic acceleration is caused by the potential energy of a scalar field descending its potential, as suggested by holographically defined models of quantum gravity. In this note, we update our analysis using the Dark Energy Survey 5 year supernova data set (DES-SN5YR) and the baryon acoustic oscillation data from the Dark Energy Spectroscopic Instrument Data Release 2 (DESI DR2). Approximating the scalar potential via a first order Taylor series $V \approx V_0 + V_1 \phi$ about the present value, and making use of only recent-time data from DES-SN5YR and DESI DR2, we find that the slope parameter is constrained as $V_1 = 1.49 \pm 0.25$ in a standard likelihood analysis. This is naively a $>5 \sigma$ discrepancy with $\Lambda$CDM (which has $V_1 =0$), though a more detailed analysis not assuming a Gaussian likelihood distribution suggests $4 \sigma$ significance. Based only on the $\Delta \chi^2 = -13.7$ improvement of fit while ignoring parameter space volumes disfavours $\Lambda$CDM at a $3 \sigma$ significance level. These significance measures are substantially improved from our previous analysis using older data sets. We also reproduce the DESI DR2 parameter constraints based on the same combination of data and find that the $\Lambda$CDM is more strongly disfavoured in the context of the linear potential extension (dubbed $V_0V_1$) as compared with the $w_0 w_a$ extension of $\Lambda$CDM. A caveat is that for both $w_0 w_a$ and $V_0 V_1$, much of the significance relies on the historical $z < 0.1$ supernova samples included in the DES-SN5YR data set.
Surface stability of a layered magnetoelastic half-space
2025-05-15
Davood Shahsavari, Luis Dorfmann, Prashant Saxena
We evaluate the conditions for surface stability of a layered magnetoelastic half-space subjected to large deformations and a magnetic field. After reviewing the fundamental measures of deformation and summarizing the magnetostatic equations in Eulerian and Lagrangian forms, we derive the constitutive relations from a total energy function dependent on the deformation gradient and Lagrangian magnetic induction. Energy principles yield the equilibrium equations, magnetic field equations, and boundary conditions. The second variation of the energy functional provides the incremental equations and conditions for stability analysis. Surface instability is studied by linearizing increments of deformation and magnetic induction about a finitely deformed state under a magnetic field normal to the surface. Four illustrative cases are considered: (i) a layered non-magnetizable half-space with varying stiffness contrast; (ii) the critical stretch of a magnetoelastic half-space as a function of magnetic induction; (iii) surface stability of a magneto-sensitive layer atop a non-magnetizable substrate; and (iv) bifurcation conditions in a two-layered magnetoelastic solid with different stiffness ratios. Graphical results are provided throughout.
Self-duality plays a very important role in many applications in field theories possessing topological solitons. In general, the self-duality equations are first order partial differential equations such that their solutions satisfy the second order Euler-Lagrange equations of the theory. The fact that one has to perform one integration less to construct self-dual solitons, as compared to the usual topological solitons, is not linked to the use of any dynamically conserved quantity. It is important that the topological charge admits an integral representation, and so there exists a density of topological charge. The homotopic invariance of it leads to local identities, in the form of second order differential equations. The magic is that such identities become the Euler-Lagrange equations of the theory when the self-duality equations are imposed. We review some important structures underlying the concept of self-duality, and show how it can be applied to kinks, lumps, monopoles, Skyrmions and instantons.
Searching optimal scales for reconstructing cosmological initial conditions using convolutional neural networks
2025-05-15
Koichiro Nakashima, Kiyotomo Ichiki, Atsushi J. Nishizawa, Kenji Hasegawa
Reconstructing the initial density field of the Universe from the late-time matter distribution is a nontrivial task with implications for understanding structure formation in cosmology, offering insights into early Universe conditions. Convolutional neural networks (CNNs) have shown promise in tackling this problem by learning the complex mapping from nonlinear evolved fields back to initial conditions. Here we investigate the effect of varying input sub-box size in single-input CNNs. We find that intermediate scales ($L_\mathrm{sub} \sim 152\,h^{-1}\,\mathrm{Mpc}$) strike the best balance between capturing local detail and global context, yielding the lowest validation loss and most accurate recovery across multiple statistical metrics. We then propose a dual-input model that combines two sub-boxes of different sizes from the same simulation volume. This model significantly improves reconstruction performance, especially on small scales over the best single-input case, despite utilizing the same parent simulation box. This demonstrates the advantage of explicitly incorporating multi-scale context into the network. Our results highlight the importance of input scale and network design in reconstruction tasks. The dual-input approach represents a simple yet powerful enhancement that leverages fixed input information more efficiently, paving the way for more accurate cosmological inference from large-scale structure surveys.
Truncated star formation and ram pressure stripping in the Coma Cluster
2025-05-15
Ariel O. Broderick, Ian D. Roberts, Michael J. Hudson
We use 45 galaxies from the Mapping Nearby Galaxies at Apache Point Observatory survey to study the physical drivers of star formation quenching in the Coma cluster. We measure specific star formation rate (sSFR) radial profiles for the Coma sample as well as a control sample of non-cluster field galaxies. We find that compared to the control sample, galaxies within the Coma Cluster have sSFR profiles that fall off more steeply with galactocentric radius. We then apply a toy model based on slow-then-rapid quenching via ram pressure stripping. We find that this model is able to reproduce the difference in sSFR profiles between field and Coma galaxies. These results demonstrate that ram pressure stripping plays a significant role in quenching star formation in the nearest massive galaxy cluster.
Measuring Emission Lines with JWST-MegaScience Medium-Bands: A New Window into Dust and Star Formation at Cosmic Noon
2025-05-15
Brian Lorenz, Katherine A. Suess, Mariska Kriek, Sedona H. Price, Joel Leja, Erica Nelson, Hakim Atek, Rachel Bezanson, Gabriel Brammer, Sam E. Cutler, Pratika Dayal, Anna de Graaff, Jenny E. Greene, Lukas J. Furtak, Ivo Labbé, Danilo Marchesini, Michael V. Maseda, Tim B. Miller, Abby Mintz, Ikki Mitsuhashi, Richard Pan, Natalia Porraz Barrera, Bingjie Wang, John R. Weaver, Christina C. Williams, Katherine E. Whitaker
We demonstrate the power of JWST-NIRCam medium-band photometry to measure emission line fluxes and study dust and star formation properties of galaxies at cosmic noon. In this work, we present photometric emission line measurements and spatially-resolved maps of H$\alpha$ and Pa$\beta$ for a sample of 14 galaxies at $1.3\leq z\leq 2.4$, observed by the MegaScience medium-band survey and the UNCOVER deep spectroscopic survey. We measure line fluxes directly from the medium-band photometry and compare with spectroscopic measurements from UNCOVER. We find reasonable agreement between the photometric and spectroscopic emission line fluxes for both H$\alpha$ and Pa$\beta$, with scatter $<0.15$ dex down to emission line equivalent widths of $10$\r{A}. We also make a nebular dust measurement from the ratio Pa$\beta$ / H$\alpha$, finding an average nebular A$_\mathrm{V}$ of 1.4. Our photometric A$_\mathrm{V}$ measurements show a slightly larger scatter of $0.5$ magnitudes when compared to spectroscopic measurements; however, this scatter may be partially caused by aperture effects. Finally, we produce spatially resolved maps of H$\alpha$ emission, Pa$\beta$ emission, and stellar continuum. We find that offsets in H$\alpha$ and Pa$\beta$ emission are common, especially for galaxies with the highest A$_\mathrm{V}$, indicating dusty sub-structures. Furthermore, the correlation between H$\alpha$ and continuum emission decreases with increasing A$_\mathrm{V}$, suggesting that the dustiest objects have clumpy dust and star formation distributions. Our study demonstrates the power of medium-band photometry to directly probe emission line strengths, star formation, and dust attenuation for hundreds of galaxies in UNCOVER and thousands of galaxies in upcoming JWST medium-band surveys.
Microlensing is the method of exoplanet detection that discovers solar system analog exoplanets. These are planets low in mass located in wide orbits around their host stars. Even though thousands of exoplanets are discovered, they are mostly hot planets close to their hosts. There is a dearth of exoplanets discovered beyond the snowline where exoplanets are thought to be formed. This was recognized as a very important science issue; 2010 decadal survey declared detecting exoplanets with microlensing and measuring their masses as one of the three main science goals of NASA's next flagship mission, Nancy Grace Roman Space Telescope. Nancy Grace Roman Space Telescope is scheduled to launch in late 2026, no later than May 2027. It will observe 6 seasons of galactic bulge to discover and measure masses of 1000+ such wide orbit low mass solar system analog planets.
The DESI Y1 RR Lyrae catalog II: The metallicity dependency of pulsational properties, the shape of the RR Lyrae instability strip, and metal rich RR Lyrae
2025-05-15
Gustavo E. Medina, Ting S. Li, C. Allende Prieto, L. Beraldo e Silva, A. Bystrom, R. G. Carlberg, S. E. Koposov, M. Lambert, J. R. Najita, C. M. Rockosi, N. Kizhuprakkat, A. Riley, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, A. P. Cooper, A. de la Macorra, A. Dey, P. Doel, J. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, M. Ishak, R. Kehoe, T. Kisner, M. Landriau, L. Le Guillou, A. Meisner, R. Miquel, F. Prada, I. Perez-Rafols, G. Rossi, E. Sanchez, D. J. Schlegel, J. H. Silber, D. Sprayberry, G. Tarle, B. A. Weaver, R. Zhou
RR Lyrae stars (RRLs) are valuable probes of both Milky Way assembly and stellar-evolution physics. Using a sample 6,240 RRLs obtained in the first year of the Dark Energy Spectroscopic Instrument (DESI) survey, we investigate the metallicity of RRLs and its correlation with their pulsation properties. We find that (1) a clear correlation between period and [Fe/H] reinforces the view that the longstanding Oosterhoff dichotomy arises from the scarcity of intermediate-metallicity Galactic globular clusters hosting sizeable RRL samples; (2) high-amplitude short-period and small-amplitude short-period variables are comparatively metal-rich, with mean [Fe/H] = $-1.39 \pm 0.27$ and $-1.30 \pm 0.28$, respectively; (3) in double-mode pulsators (RRd) the metallicity declines smoothly with increasing fundamental-mode period, and anomalous RRd stars occupy a remarkably narrow [Fe/H] range relative to classical RRd stars; (4) this spectroscopic sample let us, for the first time, place empirical constraints on the metallicity-dependent topology of the instability strip using phase-corrected effective temperatures and a large number of RRLs, where we observe an instability strip that moves towards cooler Teff with declining [Fe/H] with a width consistent with stellar-evolution models; and (5) a subset of metal-rich RRLs exhibits orbits consistent with disk membership and halo kinematics. Our results confirm the tantalizing potential of DESI for Galactic and stellar astrophysics and highlight the importance of the even larger samples of RRLs and data-processing improvements forthcoming in future DESI data releases.
Toward a Public and Secure Generative AI: A Comparative Analysis of Open and Closed LLMs
2025-05-15
Jorge Machado
Generative artificial intelligence (Gen AI) systems represent a critical technology with far-reaching implications across multiple domains of society. However, their deployment entails a range of risks and challenges that require careful evaluation. To date, there has been a lack of comprehensive, interdisciplinary studies offering a systematic comparison between open-source and proprietary (closed) generative AI systems, particularly regarding their respective advantages and drawbacks. This study aims to: i) critically evaluate and compare the characteristics, opportunities, and challenges of open and closed generative AI models; and ii) propose foundational elements for the development of an Open, Public, and Safe Gen AI framework. As a methodology, we adopted a combined approach that integrates three methods: literature review, critical analysis, and comparative analysis. The proposed framework outlines key dimensions, openness, public governance, and security, as essential pillars for shaping the future of trustworthy and inclusive Gen AI. Our findings reveal that open models offer greater transparency, auditability, and flexibility, enabling independent scrutiny and bias mitigation. In contrast, closed systems often provide better technical support and ease of implementation, but at the cost of unequal access, accountability, and ethical oversight. The research also highlights the importance of multi-stakeholder governance, environmental sustainability, and regulatory frameworks in ensuring responsible development.
UDDETTS: Unifying Discrete and Dimensional Emotions for Controllable Emotional Text-to-Speech
2025-05-15
Jiaxuan Liu, Zhenhua Ling
Recent neural codec language models have made great progress in the field of text-to-speech (TTS), but controllable emotional TTS still faces many challenges. Traditional methods rely on predefined discrete emotion labels to control emotion categories and intensities, which can't capture the complexity and continuity of human emotional perception and expression. The lack of large-scale emotional speech datasets with balanced emotion distributions and fine-grained emotion annotations often causes overfitting in synthesis models and impedes effective emotion control. To address these issues, we propose UDDETTS, a neural codec language model unifying discrete and dimensional emotions for controllable emotional TTS. This model introduces the interpretable Arousal-Dominance-Valence (ADV) space for dimensional emotion description and supports emotion control driven by either discrete emotion labels or nonlinearly quantified ADV values. Furthermore, a semi-supervised training strategy is designed to comprehensively utilize diverse speech datasets with different types of emotion annotations to train the UDDETTS. Experiments show that UDDETTS achieves linear emotion control along the three dimensions of ADV space, and exhibits superior end-to-end emotional speech synthesis capabilities.
Enhancing Collaboration Through Google Workspace: Assessing and Strengthening Current Practices
2025-05-15
Alexander Pahayahay
This study investigates the effectiveness of Google Workspace in fostering collaboration within academic settings, specifically at the University of Makati. The aim is to evaluate its role in enhancing blended learning practices and identify areas for improvement among faculty, staff, and students. A survey was conducted with 50 participants, including academic staff, faculty, and students at the University of Makati who regularly use Google Workspace for academic and collaborative activities. Participants were selected through purposive sampling to ensure familiarity with the platform. The study employed a quantitative research design using structured surveys to assess user experiences with key features such as real-time document editing, communication tools, etc. The study found that Google Workspace and rated as "Very Effective" (mean score of 4.61) in promoting teamwork. Key advantages included improved collaboration, enhanced communication, and efficient management of group projects. However, several challenges were also noted, including low user adoption rates, limited Google Drive storage capacity, the need for better technical support, and limited offline functionality. Google Workspace significantly supports academic collaboration in the normal practices within the University of Makati, however, it faces challenges that impact its overall effectiveness. Addressing these issues could improve user experience and platform efficiency in educational contexts. It is recommended to enhance user adoption through targeted training and improve offline capabilities. Additionally, providing more advanced technical support could mitigate existing challenges.
Reward models (RMs) play a pivotal role in aligning large language models (LLMs) with human values. However, noisy preferences in human feedback can lead to reward misgeneralization - a phenomenon where reward models learn spurious correlations or overfit to noisy preferences, which poses important challenges to the generalization of RMs. This paper systematically analyzes the characteristics of preference pairs and aims to identify how noisy preferences differ from human-aligned preferences in reward modeling. Our analysis reveals that noisy preferences are difficult for RMs to fit, as they cause sharp training fluctuations and irregular gradient updates. These distinctive dynamics suggest the feasibility of identifying and excluding such noisy preferences. Empirical studies demonstrate that policy LLM optimized with a reward model trained on the full preference dataset, which includes substantial noise, performs worse than the one trained on a subset of exclusively high quality preferences. To address this challenge, we propose an online Collaborative Reward Modeling (CRM) framework to achieve robust preference learning through peer review and curriculum learning. In particular, CRM maintains two RMs that collaboratively filter potential noisy preferences by peer-reviewing each other's data selections. Curriculum learning synchronizes the capabilities of two models, mitigating excessive disparities to promote the utility of peer review. Extensive experiments demonstrate that CRM significantly enhances RM generalization, with up to 9.94 points improvement on RewardBench under an extreme 40\% noise. Moreover, CRM can seamlessly extend to implicit-reward alignment methods, offering a robust and versatile alignment strategy.
A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network
2025-05-15
Muzun Althunayyan, Amir Javed, Omer Rana
Connected and Autonomous Vehicles (CAVs) enhance mobility but face cybersecurity threats, particularly through the insecure Controller Area Network (CAN) bus. Cyberattacks can have devastating consequences in connected vehicles, including the loss of control over critical systems, necessitating robust security solutions. In-vehicle Intrusion Detection Systems (IDSs) offer a promising approach by detecting malicious activities in real time. This survey provides a comprehensive review of state-of-the-art research on learning-based in-vehicle IDSs, focusing on Machine Learning (ML), Deep Learning (DL), and Federated Learning (FL) approaches. Based on the reviewed studies, we critically examine existing IDS approaches, categorising them by the types of attacks they detect - known, unknown, and combined known-unknown attacks - while identifying their limitations. We also review the evaluation metrics used in research, emphasising the need to consider multiple criteria to meet the requirements of safety-critical systems. Additionally, we analyse FL-based IDSs and highlight their limitations. By doing so, this survey helps identify effective security measures, address existing limitations, and guide future research toward more resilient and adaptive protection mechanisms, ensuring the safety and reliability of CAVs.
Managerial Insights on Investment Strategy in Cybersecurity: Findings from Multi-Country Research
2025-05-15
Silvia Tedeschi, Giacomo Marzi, Marco Balzano, Gabriele Costa
This study examines the strategic role of cybersecurity based on survey data from 1,083 managers across Europe, the UK, and the United States. The findings indicate growing recognition of cybersecurity as a source of competitive advantage, although firms continue to face barriers such as limited resources, talent shortages, and cultural resistance. Larger and high-tech firms tend to adopt more proactive strategies, while SMEs and low-tech sectors display greater variability. Key managerial tensions emerge in balancing security with innovation and agility. Notable country-level differences are observed across Europe, the UK, and the United States. Across all contexts, leadership and employee engagement appear central to closing the gap between strategic intent and operational practice.
Design of Experiments for Emulations: A Selective Review from a Modeling Perspective
2025-05-14
Xinwei Deng, Lulu Kang, C. Devon Lin
Space-filling designs are crucial for efficient computer experiments, enabling accurate surrogate modeling and uncertainty quantification in many scientific and engineering applications, such as digital twin systems and cyber-physical systems. In this work, we will provide a comprehensive review on key design methodologies, including Maximin/miniMax designs, Latin hypercubes, and projection-based designs. Moreover, we will connect the space-filling design criteria like the fill distance to Gaussian process performance. Numerical studies are conducted to investigate the practical trade-offs among various design types, with the discussion on emerging challenges in high-dimensional and constrained settings. The paper concludes with future directions in adaptive sampling and machine learning integration, providing guidance for improving computational experiments.
2025-05-14
Tobias Jan Wieczorek, Nathalie Daun, Mohammad Emtiyaz Khan, Marcus Rohrbach
Despite remarkable progress in multimodal models for Visual Question Answering (VQA), there remain major reliability concerns because the models can often be overconfident and miscalibrated, especially in out-of-distribution (OOD) settings. Plenty has been done to address such issues for unimodal models, but little work exists for multimodal cases. Here, we address unreliability in multimodal models by proposing a Variational VQA approach. Specifically, instead of fine-tuning vision-language models by using AdamW, we employ a recently proposed variational algorithm called IVON, which yields a posterior distribution over model parameters. Through extensive experiments, we show that our approach improves calibration and abstentions without sacrificing the accuracy of AdamW. For instance, compared to AdamW fine-tuning, we reduce Expected Calibration Error by more than 50% compared to the AdamW baseline and raise Coverage by 4% vs. SOTA (for a fixed risk of 1%). In the presence of distribution shifts, the performance gain is even higher, achieving 8% Coverage (@ 1% risk) improvement vs. SOTA when 50% of test cases are OOD. Overall, we present variational learning as a viable option to enhance the reliability of multimodal models.
2025-05-14
Guillermo Gomez-Trenado, Pablo Mesejo, Oscar Cordón, Stéphane Lathuilière
The field of text-to-image generation has undergone significant advancements with the introduction of diffusion models. Nevertheless, the challenge of editing real images persists, as most methods are either computationally intensive or produce poor reconstructions. This paper introduces SAGE (Self-Attention Guidance for image Editing) - a novel technique leveraging pre-trained diffusion models for image editing. SAGE builds upon the DDIM algorithm and incorporates a novel guidance mechanism utilizing the self-attention layers of the diffusion U-Net. This mechanism computes a reconstruction objective based on attention maps generated during the inverse DDIM process, enabling efficient reconstruction of unedited regions without the need to precisely reconstruct the entire input image. Thus, SAGE directly addresses the key challenges in image editing. The superiority of SAGE over other methods is demonstrated through quantitative and qualitative evaluations and confirmed by a statistically validated comprehensive user study, in which all 47 surveyed users preferred SAGE over competing methods. Additionally, SAGE ranks as the top-performing method in seven out of 10 quantitative analyses and secures second and third places in the remaining three.
Motivated by the development of reusable software for computational chemistry [Lehtola, J. Chem. Phys. 159, 180901 (2023)], we aim to establish novel open source infrastructure for calculations with numerical atomic orbital (NAO) basis sets. Soft confinement potentials are typically used in this context to force the NAO radial basis functions {\psi}nl (r) to vanish smoothly in increasing r and to generate localized unoccupied states; we review such potentials in this work as a follow-up to our recent study on hard-wall confinement [{\AA}str\"om and Lehtola, J. Phys. Chem. A 129, 2791 (2025)]. We also note that in addition to their use in NAO generation, confinement potentials are also employed to simulate environmental effects in other research areas, such as studies of (i) atoms in solids, (ii) quantum dots, and (iii) high-pressure chemistry. As in our earlier work, we perform fully numerical density functional theory (DFT) calculations with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional, and employ spherically averaged, spin-restricted densities, as is usual in NAO studies. Our calculations employ the HelFEM program, which implements the finite element method (FEM), yielding variational energies and enabling the use of various boundary conditions. We consider four families of potentials to study the Mg and Ca atoms, which are textbook examples of extended electronic structures. We show that the resulting ground-state orbitals are surprisingly insensitive to the employed form of the confinement potential, and that the orbitals decay quickly under confinement. We study increasingly steep potentials and point out how they approach the hard-wall limit in a systematic and smooth manner. Finally, we assess NAO basis set truncation errors arising from various parameter choices for the singular potentials that are now broadly used in the NAO literature.
Contactless Cardiac Pulse Monitoring Using Event Cameras
2025-05-14
Mohamed Moustafa, Joseph Lemley, Peter Corcoran
Time event cameras are a novel technology for recording scene information at extremely low latency and with low power consumption. Event cameras output a stream of events that encapsulate pixel-level light intensity changes within the scene, capturing information with a higher dynamic range and temporal resolution than traditional cameras. This study investigates the contact-free reconstruction of an individual's cardiac pulse signal from time event recording of their face using a supervised convolutional neural network (CNN) model. An end-to-end model is trained to extract the cardiac signal from a two-dimensional representation of the event stream, with model performance evaluated based on the accuracy of the calculated heart rate. The experimental results confirm that physiological cardiac information in the facial region is effectively preserved within the event stream, showcasing the potential of this novel sensor for remote heart rate monitoring. The model trained on event frames achieves a root mean square error (RMSE) of 3.32 beats per minute (bpm) compared to the RMSE of 2.92 bpm achieved by the baseline model trained on standard camera frames. Furthermore, models trained on event frames generated at 60 and 120 FPS outperformed the 30 FPS standard camera results, achieving an RMSE of 2.54 and 2.13 bpm, respectively.
Artificial intelligence-enabled precision medicine for inflammatory skin diseases
2025-05-14
Alice Tang, Maria Wei, Anna Haemel, Cindy La, Marina Sirota, Ernest Y. Lee
Recent advances in artificial intelligence (AI) and multimodal data collection are revolutionizing dermatology. Generative AI and machine learning approaches offer opportunities to enhance the diagnosis and treatment of inflammatory skin diseases, including atopic dermatitis, psoriasis, hidradenitis suppurativa, and autoimmune connective tissue disease. This review examines the current landscape of AI applications for inflammatory skin diseases and explores how generative AI and machine learning methods can advance the field through deep phenotyping, disease heterogeneity characterization, drug development, personalized medicine, and clinical care. We discuss the promises and challenges of these technologies and present a vision for their integration into clinical practice.
Misinformation has become a widespread issue in the 21st century, impacting numerous areas of society and underscoring the need for effective intervention strategies. Among these strategies, user-centered interventions, such as warning systems, have shown promise in reducing the spread of misinformation. Many studies have used various metrics to evaluate the effectiveness of these warning interventions. However, no systematic review has thoroughly examined these metrics in all studies. This paper provides a comprehensive review of existing metrics for assessing the effectiveness of misinformation warnings, categorizing them into four main groups: behavioral impact, trust and credulity, usability, and cognitive and psychological effects. Through this review, we identify critical challenges in measuring the effectiveness of misinformation warnings, including inconsistent use of cognitive and attitudinal metrics, the lack of standardized metrics for affective and emotional impact, variations in user trust, and the need for more inclusive warning designs. We present an overview of these metrics and propose areas for future research.
Evolving the Productivity Equation: Should Digital Labor Be Considered a New Factor of Production?
2025-05-14
Alex Farach, Alexia Cambon, Jared Spataro
As the digital economy grows increasingly intangible, traditional productivity measures struggle to capture the true economic impact of artificial intelligence (AI). AI systems capable of cognitive work significantly enhance productivity, yet their contributions remain obscured within the residual category of Total Factor Productivity (TFP). This paper explores whether it is time for a conceptual shift to explicitly recognize "digital labor," the autonomous cognitive capability of AI, as a distinct factor of production alongside capital and human labor. We outline the unique economic properties of digital labor, including scalability, intangibility, self-improvement, rapid obsolescence, and elastic substitutability. By integrating digital labor into growth models (such as those by Solow and Romer), we demonstrate strategic implications for business leaders, including new approaches to productivity tracking, resource allocation, investment strategy, and organizational design. Ultimately, treating digital labor as an independent factor offers a clearer view of economic growth and helps organizations manage AI's transformative potential.
Examining Deployment and Refinement of the VIOLA-AI Intracranial Hemorrhage Model Using an Interactive NeoMedSys Platform
2025-05-14
Qinghui Liu, Jon Nesvold, Hanna Raaum, Elakkyen Murugesu, Martin Røvang, Bradley J Maclntosh, Atle Bjørnerud, Karoline Skogen
Background: There are many challenges and opportunities in the clinical deployment of AI tools in radiology. The current study describes a radiology software platform called NeoMedSys that can enable efficient deployment and refinements of AI models. We evaluated the feasibility and effectiveness of running NeoMedSys for three months in real-world clinical settings and focused on improvement performance of an in-house developed AI model (VIOLA-AI) designed for intracranial hemorrhage (ICH) detection. Methods: NeoMedSys integrates tools for deploying, testing, and optimizing AI models with a web-based medical image viewer, annotation system, and hospital-wide radiology information systems. A pragmatic investigation was deployed using clinical cases of patients presenting to the largest Emergency Department in Norway (site-1) with suspected traumatic brain injury (TBI) or patients with suspected stroke (site-2). We assessed ICH classification performance as VIOLA-AI encountered new data and underwent pre-planned model retraining. Performance metrics included sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC). Results: NeoMedSys facilitated iterative improvements in the AI model, significantly enhancing its diagnostic accuracy. Automated bleed detection and segmentation were reviewed in near real-time to facilitate re-training VIOLA-AI. The iterative refinement process yielded a marked improvement in classification sensitivity, rising to 90.3% (from 79.2%), and specificity that reached 89.3% (from 80.7%). The bleed detection ROC analysis for the entire sample demonstrated a high area-under-the-curve (AUC) of 0.949 (from 0.873). Model refinement stages were associated with notable gains, highlighting the value of real-time radiologist feedback.
Text-driven Motion Generation: Overview, Challenges and Directions
2025-05-14
Ali Rida Sahili, Najett Neji, Hedi Tabia
Text-driven motion generation offers a powerful and intuitive way to create human movements directly from natural language. By removing the need for predefined motion inputs, it provides a flexible and accessible approach to controlling animated characters. This makes it especially useful in areas like virtual reality, gaming, human-computer interaction, and robotics. In this review, we first revisit the traditional perspective on motion synthesis, where models focused on predicting future poses from observed initial sequences, often conditioned on action labels. We then provide a comprehensive and structured survey of modern text-to-motion generation approaches, categorizing them from two complementary perspectives: (i) architectural, dividing methods into VAE-based, diffusion-based, and hybrid models; and (ii) motion representation, distinguishing between discrete and continuous motion generation strategies. In addition, we explore the most widely used datasets, evaluation methods, and recent benchmarks that have shaped progress in this area. With this survey, we aim to capture where the field currently stands, bring attention to its key challenges and limitations, and highlight promising directions for future exploration. We hope this work offers a valuable starting point for researchers and practitioners working to push the boundaries of language-driven human motion synthesis.
Quantum signatures in black hole accretion: Pair production in dynamical magnetic fields
2025-05-14
Susobhan Mandal, Tausif Parvez, S. Shankaranarayanan
Accretion disks around black holes host extreme conditions where general relativity and magnetohydrodynamics dominate. These disks exhibit two distinct dynamical regimes -- Standard and Normal Evolution (SANE) and Magnetically Arrested Disk (MAD). In the MAD regime, these systems exhibit magnetic fields up to $10^8$ G and variability on gravitational timescales $t_g \sim 10^{-4}$ s for stellar-mass black holes. While classical magnetohydrodynamics has been extensively applied, quantum effects in these high-energy environments remain unexplored. Here, we employ quantum field theory in background gauge fields (QFTBGF) to demonstrate that the dynamic magnetic fields of MADs drive significant pair production via the Schwinger mechanism. The resulting pairs emit non-thermal (synchrotron) radiation with a peak frequency tunable across $ \sim 1 - 3000$ MHz, depending on the magnetic field strength (peaking at higher frequencies for stronger fields). For $ B \sim 10^8 $ G, our model predicts a peak spectral flux density of $ \sim 1 - 100$ mJy, detectable with next-generation radio telescopes (e.g., SKA, ngVLA). This work provides a direct and observable signatures of quantum effects in black hole accretion disks.
Observational signs of limited flare area variation and peak flare temperatures estimations in main-sequence flaring stars
2025-05-14
K. Bicz, R. Falewicz, P. Heinzel, P. Preś, D. Moździerski, A. Pigulski, D. Marchev, K. Kotysz, T. Atanasova, G. Yordanova, A. Georgiev
In the study of stellar flares, traditional method of calculating total energy emitted in the continuum assumes the emission originating from a narrow chromospheric condensation region with a constant temperature of 10000 K and variable flare area. However, based on multicolor data from 7 new flares observed in Bia{\l}k\'ow and Shumen observatory and 8 flares from (Howard et al., 2020) observed on 10 main-sequence stars (spectral types M5.5V to K5V) we show that flare areas had a relative change in the range of 10% - 61% (for more than half of the flares this value did not exceed 30%) throughout the events except for the impulsive phase, and had values starting from 50 +/- 30 ppm to 300 +/- 150 ppm for our new flares and from 380 +/- 200 ppm to 7600 +/- 3000 ppm from (Howard et al., 2020) data, while their temperature increased on average by the factor 2.5. The peak flare temperatures for our seven observed flares ranged from 5700 +/- 450 K to 17500 +/- 10050 K. Five of these flares had their temperatures estimated using the Johnson-Kron-Cousins B filter alongside TESS data, one flare was analyzed using the SLOAN g' and r' bandpasses, and another was evaluated using both the SLOAN g' and r' bandpasses and TESS data. Using flare temperature and area from our data and data from Howard et al., 2020, along with the physical parameters of stars where the flares occurred, we developed a semi-empirical grid that correlates a star's effective temperature and flare amplitude in TESS data with the flare's peak temperature. This allows interpolation of a flare's peak temperature based on the star's effective temperature (ranging from 2700 K to 4600 K) and flare amplitude from TESS observations. Applying this grid to 42257 flares from TESS survey, we estimated peak flare temperatures between 5700 K and 38300 K, with most flares showing peak black-body temperatures around 11100 +/- 2400 K.
In recent years, diffusion model has shown its potential across diverse domains from vision generation to language modeling. Transferring its capabilities to modern autonomous driving systems has also emerged as a promising direction.In this work, we propose TransDiffuser, an encoder-decoder based generative trajectory planning model for end-to-end autonomous driving. The encoded scene information serves as the multi-modal conditional input of the denoising decoder. To tackle the mode collapse dilemma in generating high-quality diverse trajectories, we introduce a simple yet effective multi-modal representation decorrelation optimization mechanism during the training process.TransDiffuser achieves PDMS of 94.85 on the NAVSIM benchmark, surpassing previous state-of-the-art methods without any anchor-based prior trajectories.
In recent years, embodied intelligent robotics (EIR) has made significant progress in multi-modal perception, autonomous decision-making, and physical interaction. Some robots have already been tested in general-purpose scenarios such as homes and shopping malls. We aim to advance the research and application of embodied intelligence in industrial scenes. However, current EIR lacks a deep understanding of industrial environment semantics and the normative constraints between industrial operating objects. To address this gap, this paper first reviews the history of industrial robotics and the mainstream EIR frameworks. We then introduce the concept of the embodied intelligent industrial robotics (EIIR) and propose a knowledge-driven EIIR technology framework for industrial environments. The framework includes four main modules: world model, high-level task planner, low-level skill controller, and simulator. We also review the current development of technologies related to each module and highlight recent progress in adapting them to industrial applications. Finally, we summarize the key challenges EIIR faces in industrial scenarios and suggest future research directions. We believe that EIIR technology will shape the next generation of industrial robotics. Industrial systems based on embodied intelligent industrial robots offer strong potential for enabling intelligent manufacturing. We will continue to track and summarize new research in this area and hope this review will serve as a valuable reference for scholars and engineers interested in industrial embodied intelligence. Together, we can help drive the rapid advancement and application of this technology. The associated project can be found at https://github.com/jackyzengl/EIIR.
A Scalable Unsupervised Framework for multi-aspect labeling of Multilingual and Multi-Domain Review Data
2025-05-14
Jiin Park, Misuk Kim
Effectively analyzing online review data is essential across industries. However, many existing studies are limited to specific domains and languages or depend on supervised learning approaches that require large-scale labeled datasets. To address these limitations, we propose a multilingual, scalable, and unsupervised framework for cross-domain aspect detection. This framework is designed for multi-aspect labeling of multilingual and multi-domain review data. In this study, we apply automatic labeling to Korean and English review datasets spanning various domains and assess the quality of the generated labels through extensive experiments. Aspect category candidates are first extracted through clustering, and each review is then represented as an aspect-aware embedding vector using negative sampling. To evaluate the framework, we conduct multi-aspect labeling and fine-tune several pretrained language models to measure the effectiveness of the automatically generated labels. Results show that these models achieve high performance, demonstrating that the labels are suitable for training. Furthermore, comparisons with publicly available large language models highlight the framework's superior consistency and scalability when processing large-scale data. A human evaluation also confirms that the quality of the automatic labels is comparable to those created manually. This study demonstrates the potential of a robust multi-aspect labeling approach that overcomes limitations of supervised methods and is adaptable to multilingual, multi-domain environments. Future research will explore automatic review summarization and the integration of artificial intelligence agents to further improve the efficiency and depth of review analysis.
Recent Advances in Medical Imaging Segmentation: A Survey
2025-05-14
Fares Bougourzi, Abdenour Hadid
Medical imaging is a cornerstone of modern healthcare, driving advancements in diagnosis, treatment planning, and patient care. Among its various tasks, segmentation remains one of the most challenging problem due to factors such as data accessibility, annotation complexity, structural variability, variation in medical imaging modalities, and privacy constraints. Despite recent progress, achieving robust generalization and domain adaptation remains a significant hurdle, particularly given the resource-intensive nature of some proposed models and their reliance on domain expertise. This survey explores cutting-edge advancements in medical image segmentation, focusing on methodologies such as Generative AI, Few-Shot Learning, Foundation Models, and Universal Models. These approaches offer promising solutions to longstanding challenges. We provide a comprehensive overview of the theoretical foundations, state-of-the-art techniques, and recent applications of these methods. Finally, we discuss inherent limitations, unresolved issues, and future research directions aimed at enhancing the practicality and accessibility of segmentation models in medical imaging. We are maintaining a \href{https://github.com/faresbougourzi/Awesome-DL-for-Medical-Imaging-Segmentation}{GitHub Repository} to continue tracking and updating innovations in this field.
Optimizing photon-number resolution with superconducting nanowire multi-photon detectors
2025-05-14
Timon Schapeler, Fabian Schlue, Michael Stefszky, Benjamin Brecht, Christine Silberhorn, Tim J. Bartley
In this paper we briefly review the state-of-the-art of intrinsic photon-number resolution (PNR) with superconducting nanowire single-photon detectors (SNSPDs) and will provide an overview of the various experimental techniques employed to achieve PNR. Additionally, we introduce a resolvability criterion to PNR based on SNSPDs.
The growing demand for artificial intelligence and complex computing has underscored the urgent need for advanced data storage technologies. Spin-orbit torque (SOT) has emerged as a leading candidate for high-speed, high-density magnetic random-access memory due to its ultrafast switching speed and low power consumption. This review systematically explores the generation and switching mechanisms of electron-mediated torques (including both conventional SOTs and orbital torques) and magnon-mediated torques. We discuss key materials that enable these effects: heavy metals, topological insulators, low-crystal-symmetry materials, non-collinear antiferromagnets, and altermagnets for conventional SOTs; 3d, 4d, and 5d transition metals for orbital torques; and antiferromagnetic insulator NiO- and multiferroic BiFeO3-based sandwich structures for magnon torques. We emphasize that although key components of SOT devices have been demonstrated, numerous promising materials and critical questions regarding their underlying mechanisms remain to be explored. Therefore, this field represents a dynamic and rapidly evolving frontier in spintronics, offering significant potential for advancing next-generation information storage and computational technologies.
Moving towards informative and actionable social media research
2025-05-14
Joseph B. Bak-Coleman, Stephan Lewandowsky, Philipp Lorenz-Spreen, Arvind Narayanan, Amy Orben, Lisa Oswald
Social media is nearly ubiquitous in modern life, and concerns have been raised about its putative societal impacts, ranging from undermining mental health and exacerbating polarization to fomenting violence and disrupting democracy. Despite extensive research, consensus on these effects remains elusive, with observational studies often highlighting concerns while randomized controlled trials (RCTs) yield conflicting or null findings. This review examines how the complexity inherent in social systems can account for such discrepancies, emphasizing that emergent societal and long-term outcomes cannot be readily inferred from individual-level effects. In complex systems, such as social networks, feedback loops, hysteresis, multi-scale dynamics, and non-linearity limit the utility of approaches for assessing causality that are otherwise robust in simpler contexts. Revisiting large-scale experiments, we explore how null or conflicting findings may reflect these complexities rather than a true absence of effects. Even in cases where the methods are appropriate, assessing the net impacts of social media provides little actionable insight given that eliminating social media is not a realistic option for whole populations. We argue that progress will require a complexity-minded approach focused on specific design choices of online platforms that triangulates experimental, observational and theoretical methods.
When a star is torn apart by the tidal forces of a supermassive black hole (a so-called TDE) a transient accretion episode is initiated and a hot, often X-ray bright, accretion disk is formed. Like any accretion flow this disk is turbulent, and therefore the emission from its surface will vary stochastically. As the disk has a finite mass supply (i.e., at most the initial mass of the disrupted star) the disk will also undergo long-timescale evolution, as this material is lost into the black hole. In this paper we combine theoretical models for this long time evolution of the disk with models for the stochastic variability of turbulent accretion flows which are correlated on short (orbital) timescales. This new framework allows us to demonstrate that (i) dimming events should be more prevalent than brightening events in long term TDE X-ray light curves (i.e., their log-luminosity distribution should be asymmetric), (ii) TDE X-ray light curves should follow a near- (but formally sub-)linear correlation between their root mean square variability and the mean flux, (iii) the fractional variability observed on short timescales across an X-ray observing band should increase with observing energy, and (iv) TDEs offer a unique probe of the physics of disk turbulence, owing to their clean spectra and natural evolutionary timescales. We confirm predictions (i) and (ii) with an analysis of the long timescale variability of two observed TDEs, and show strong support for prediction (iii) using the intra-observation variability of the same two sources.
Ethical Aspects of the Use of Social Robots in Elderly Care -- A Systematic Qualitative Review
2025-05-14
Marianne Leineweber, Clara Victoria Keusgen, Marc Bubeck, Joschka Haltaufderheide, Robert Ranisch, Corinna Klingler
Background: The use of social robotics in elderly care is increasingly discussed as one way of meeting emerging care needs due to scarce resources. While many potential benefits are associated with robotic care technologies, there is a variety of ethical challenges. To support steps towards a responsible implementation and use, this review develops an overview on ethical aspects of the use of social robots in elderly care from a decision-makers' perspective. Methods: Electronic databases were queried using a comprehensive search strategy based on the key concepts of "ethical aspects", "social robotics" and "elderly care". Abstract and title screening was conducted by two authors independently. Full-text screening was conducted by one author following a joint consolidation phase. Data was extracted using MAXQDA24 by one author, based on a consolidated coding framework. Analysis was performed through modified qualitative content analysis. Results: A total of 1,518 publications were screened, and 248 publications were included. We have organized our analysis in a scheme of ethical hazards, ethical opportunities and unsettled questions, identifying at least 60 broad ethical aspects affecting three different stakeholder groups. While some ethical issues are well-known and broadly discussed our analysis shows a plethora of potentially relevant aspects, often only marginally recognized, that are worthy of consideration from a practical perspective. Discussion: The findings highlight the need for a contextual and detailed evaluation of implementation scenarios. To make use of the vast knowledge of the ethical discourse, we hypothesize that decision-makers need to understand the specific nature of this discourse to be able to engage in careful ethical deliberation.
Educational impacts of generative artificial intelligence on learning and performance of engineering students in China
2025-05-14
Lei Fan, Kunyang Deng, Fangxue Liu
With the rapid advancement of generative artificial intelligence(AI), its potential applications in higher education have attracted significant attention. This study investigated how 148 students from diverse engineering disciplines and regions across China used generative AI, focusing on its impact on their learning experience and the opportunities and challenges it poses in engineering education. Based on the surveyed data, we explored four key areas: the frequency and application scenarios of AI use among engineering students, its impact on students' learning and performance, commonly encountered challenges in using generative AI, and future prospects for its adoption in engineering education. The results showed that more than half of the participants reported a positive impact of generative AI on their learning efficiency, initiative, and creativity, with nearly half believing it also enhanced their independent thinking. However, despite acknowledging improved study efficiency, many felt their actual academic performance remained largely unchanged and expressed concerns about the accuracy and domain-specific reliability of generative AI. Our findings provide a first-hand insight into the current benefits and challenges generative AI brings to students, particularly Chinese engineering students, while offering several recommendations, especially from the students' perspective, for effectively integrating generative AI into engineering education.
2025-05-14
Minjun Kim, Jaehyeon Choi, Jongkeun Lee, Wonjin Cho, U Kang
Network quantization has proven to be a powerful approach to reduce the memory and computational demands of deep learning models for deployment on resource-constrained devices. However, traditional quantization methods often rely on access to training data, which is impractical in many real-world scenarios due to privacy, security, or regulatory constraints. Zero-shot Quantization (ZSQ) emerges as a promising solution, achieving quantization without requiring any real data. In this paper, we provide a comprehensive overview of ZSQ methods and their recent advancements. First, we provide a formal definition of the ZSQ problem and highlight the key challenges. Then, we categorize the existing ZSQ methods into classes based on data generation strategies, and analyze their motivations, core ideas, and key takeaways. Lastly, we suggest future research directions to address the remaining limitations and advance the field of ZSQ. To the best of our knowledge, this paper is the first in-depth survey on ZSQ.
Properties of Giant Radio Galaxies larger than 3 Mpc
2025-05-14
H. Andernach, M. Brüggen
Giant radio galaxies (GRG) are radio galaxies with physical sizes of their radio emission larger than 0.7 Mpc. Recently, the sample of GRGs has become large enough to study the extreme end of the GRG size distribution. We examine the properties of GRGs with largest linear sizes larger than 3 Mpc in order to shed light on the nature and origin of GRGs. We select, corroborate, and revise if necessary, the largest GRGs from literature. We add to these the GRGs we identified in our own search, combined with optical surveys and catalogues of spectroscopic and photometric redshifts in order to find their projected linear radio size. We study their radio power--size relation, the asymmetry in the lobes, their association with clusters of galaxies, as well as their bending angles. We present an unprecedented sample of 143 GRGs larger than 3 Mpc, of which 69 were newly found by us. The sample includes six GRGs with projected linear sizes clearly exceeding 5 Mpc and reaching up to 6.6 Mpc. We find that GRGs larger than 3 Mpc are distributed in redshift and radio luminosity indistinguishable from those of smaller GRGs. At most a single one of the GRGs larger than 3 Mpc can be classified as a clear Fanaroff-Riley (FR) type I source, and only 6 per cent deviate from a clear FR II radio morphology. One quarter of our GRGs show very diffuse lobes typical for remnant radio galaxies, and only 59 per cent show indications of hotspots in at least one lobe, with 38 per cent featuring a hotspot in both lobes. As in the case of smaller radio galaxies, the shorter lobe is most often also the brighter one. We find tentative evidence that the bending angle decreases with size of the GRG, but no trend with redshift is detected. The bending angles of GRGs > 3 Mpc in known clusters are larger than for those GRGs not associated with clusters.
Modeling Indoor PM$_{2.5}$ Exposure During Retrofits: Plastic Film Barriers and a Quadratic Baseline Approach
2025-05-14
Rostyslav Sipakov, Olena Voloshkina
Temporary plastic film barriers are widely used to separate occupied rooms from exterior renovation zones, yet their effect on indoor particulate exposure is poorly quantified. We monitored PM$_{2.5}$ in a Tampa, Florida, apartment for 48 days with a low-cost optical sensor (Temtop LKC-1000S+), spanning pre-barrier, barrier-on, and post-barrier periods. A quadratic baseline was fitted to "background" minutes devoid of identifiable indoor sources, allowing excess concentrations ($\Delta$PM) to be partitioned into facade work, cooking, and passive accumulation without outdoor co-monitoring. The barrier prevented large construction spikes indoors but curtailed natural ventilation, doubling the mean baseline from 1.9 to 3.9 $\mu$g m$^{-3}$. During this stage, passive build-up accounted for $45\,\%$ of the daily excess dose, with facade work and cooking contributing $31\,\%$ and $24\,\%$, respectively. Once the new window was installed and evening airing resumed, the baseline fell to 0.8 $\mu$g m$^{-3}$, the lowest of the campaign. Our findings highlight the trade-off between dust shielding and background elevation and demonstrate that simple polynomial fitting bolsters low-cost IAQ diagnostics in mechanically unventilated dwellings. The framework is readily transferable to other retrofits; future studies should pair indoor sensing with outdoor references and multi-room deployments to refine infiltration estimates.
An Initial Exploration of Default Images in Text-to-Image Generation
2025-05-14
Hannu Simonen, Atte Kiviniemi, Jonas Oppenlaender
In the creative practice of text-to-image generation (TTI), images are generated from text prompts. However, TTI models are trained to always yield an output, even if the prompt contains unknown terms. In this case, the model may generate what we call "default images": images that closely resemble each other across many unrelated prompts. We argue studying default images is valuable for designing better solutions for TTI and prompt engineering. In this paper, we provide the first investigation into default images on Midjourney, a popular image generator. We describe our systematic approach to create input prompts triggering default images, and present the results of our initial experiments and several small-scale ablation studies. We also report on a survey study investigating how default images affect user satisfaction. Our work lays the foundation for understanding default images in TTI and highlights challenges and future research directions.
VAST-MeMeS: Characterising non-thermal radio emission from magnetic massive stars using the Australian SKA Pathfinder
2025-05-14
Barnali Das, Laura N. Driessen, Matt E. Shultz, Joshua Pritchard, Kovi Rose, Yuanming Wang, Yu Wing Joshua Lee, Gregory Sivakoff, Andrew Zic, Tara Murphy
Magnetic massive stars are stars of spectral types O, B and A that harbour $\sim$ kG strength (mostly dipolar) surface magnetic fields. Their non-thermal radio emission has been demonstrated to be an important magnetospheric probe, provided the emission is fully characterised. A necessary step for that is to build a statistically significant sample of radio-bright magnetic massive stars. In this paper, we present the `VAST project to study Magnetic Massive Stars' or VAST-MeMeS that aims to achieve that by taking advantage of survey data acquired with the Australian SKA Pathfinder telescope. VAST-MeMeS is defined under the `VAriable and Slow Transient' (VAST) survey, although it also uses data from other ASKAP surveys. We found radio detections from 48 magnetic massive stars, out of which, 14 do not have any prior radio detections. We also identified 9 `Main-sequence Radio Pulse Emitter' candidates based on variability and circular polarisation of flux densities. The expanded sample suggests a lower efficiency in the radio production than that reported in earlier work. In addition to significantly expanding the sample of radio-bright magnetic massive stars, the addition of flux density measurements at $\lesssim 1$ GHz revealed that the spectra of incoherent radio emission can extend to much lower frequencies than that assumed in the past. In the future, radio observations spanning wide frequency and rotational phase ranges should be conducted so as to reduce the uncertainties in the incoherent radio luminosities. The results from these campaigns, supplemented with precise estimations of stellar parameters, will allow us to fully understand particle acceleration and non-thermal radio production in large-scale stellar magnetospheres.
2025-05-14
Kunwoong Kim, Jihu Lee, Sangchul Park, Yongdai Kim
Algorithmic fairness in clustering aims to balance the proportions of instances assigned to each cluster with respect to a given sensitive attribute. While recently developed fair clustering algorithms optimize clustering objectives under specific fairness constraints, their inherent complexity or approximation often results in suboptimal clustering utility or numerical instability in practice. To resolve these limitations, we propose a new fair clustering algorithm based on a novel decomposition of the fair K-means clustering objective function. The proposed algorithm, called Fair Clustering via Alignment (FCA), operates by alternately (i) finding a joint probability distribution to align the data from different protected groups, and (ii) optimizing cluster centers in the aligned space. A key advantage of FCA is that it theoretically guarantees approximately optimal clustering utility for any given fairness level without complex constraints, thereby enabling high-utility fair clustering in practice. Experiments show that FCA outperforms existing methods by (i) attaining a superior trade-off between fairness level and clustering utility, and (ii) achieving near-perfect fairness without numerical instability.
Architecture of Tianyu Software: Relative Photometry as a Case Study
2025-05-14
Yicheng Rui, Yifan Xuan, Shuyue Zheng, Kexin Li, Kaiming Cui, Kai Xiao, Jie Zheng, Jun Kai Ng, Hongxuan Jiang, Fabo Feng, Qinghui Sun
Tianyu telescope, an one-meter robotic optical survey instrument to be constructed in Lenghu, Qinghai, China, is designed for detecting transiting exoplanets, variable stars and transients. It requires a highly automated, optimally distributed, easily extendable, and highly flexible software to enable the data processing for the raw data at rates exceeding 500MB/s. In this work, we introduce the architecture of the Tianyu pipeline and use relative photometry as a case to demonstrate its high scalability and efficiency. This pipeline is tested on the data collected from Muguang observatory and Xinglong observatory. The pipeline demonstrates high scalability, with most processing stages increasing in throughput as the number of consumers grows. Compared to a single consumer, the median throughput of image calibration, alignment, and flux extraction increases by 41%, 257%, and 107% respectively when using 5 consumers, while image stacking exhibits limited scalability due to I/O constraints. In our tests, the pipeline was able to detect two transiting sources. Besides, the pipeline captures variability in the light curves of nine known and two previously unknown variable sources in the testing data. Meanwhile, the differential photometric precision of the light curves is near the theoretical limitation. These results indicate that this pipeline is suitable for detecting transiting exoplanets and variable stars. This work builds the fundation for further development of Tianyu software. Code of this work is available at https://github.com/ruiyicheng/Tianyu_pipeline.
2025-05-14
He Wang, Yikun Zhang, Jie Chen, Jian Zhan, Yaoqi Zhou
Given usefulness of protein language models (LMs) in structure and functional inference, RNA LMs have received increased attentions in the last few years. However, these RNA models are often not compared against the same standard. Here, we divided RNA LMs into three classes (pretrained on multiple RNA types (especially noncoding RNAs), specific-purpose RNAs, and LMs that unify RNA with DNA or proteins or both) and compared 13 RNA LMs along with 3 DNA and 1 protein LMs as controls in zero-shot prediction of RNA secondary structure and functional classification. Results shows that the models doing well on secondary structure prediction often perform worse in function classification or vice versa, suggesting that more balanced unsupervised training is needed.
Deployable and Generalizable Motion Prediction: Taxonomy, Open Challenges and Future Directions
2025-05-14
Letian Wang, Marc-Antoine Lavoie, Sandro Papais, Barza Nisar, Yuxiao Chen, Wenhao Ding, Boris Ivanovic, Hao Shao, Abulikemu Abuduweili, Evan Cook, Yang Zhou, Peter Karkus, Jiachen Li, Changliu Liu, Marco Pavone, Steven Waslander
Motion prediction, the anticipation of future agent states or scene evolution, is rooted in human cognition, bridging perception and decision-making. It enables intelligent systems, such as robots and self-driving cars, to act safely in dynamic, human-involved environments, and informs broader time-series reasoning challenges. With advances in methods, representations, and datasets, the field has seen rapid progress, reflected in quickly evolving benchmark results. Yet, when state-of-the-art methods are deployed in the real world, they often struggle to generalize to open-world conditions and fall short of deployment standards. This reveals a gap between research benchmarks, which are often idealized or ill-posed, and real-world complexity. To address this gap, this survey revisits the generalization and deployability of motion prediction models, with an emphasis on the applications of robotics, autonomous driving, and human motion. We first offer a comprehensive taxonomy of motion prediction methods, covering representations, modeling strategies, application domains, and evaluation protocols. We then study two key challenges: (1) how to push motion prediction models to be deployable to realistic deployment standards, where motion prediction does not act in a vacuum, but functions as one module of closed-loop autonomy stacks - it takes input from the localization and perception, and informs downstream planning and control. 2) how to generalize motion prediction models from limited seen scenarios/datasets to the open-world settings. Throughout the paper, we highlight critical open challenges to guide future work, aiming to recalibrate the community's efforts, fostering progress that is not only measurable but also meaningful for real-world applications.
Explainable Recommender Systems (XRS) aim to provide users with understandable reasons for the recommendations generated by these systems, representing a crucial research direction in artificial intelligence (AI). Recent research has increasingly focused on the algorithms, display, and evaluation methodologies of XRS. While current research and reviews primarily emphasize the algorithmic aspects, with fewer studies addressing the Human-Computer Interaction (HCI) layer of XRS. Additionally, existing reviews lack a unified taxonomy for XRS and there is insufficient attention given to the emerging area of short video recommendations. In this study, we synthesize existing literature and surveys on XRS, presenting a unified framework for its research and development. The main contributions are as follows: 1) We adopt a lifecycle perspective to systematically summarize the technologies and methods used in XRS, addressing challenges posed by the diversity and complexity of algorithmic models and explanation techniques. 2) For the first time, we highlight the application of multimedia, particularly video-based explanations, along with its potential, technical pathways, and challenges in XRS. 3) We provide a structured overview of evaluation methods from both qualitative and quantitative dimensions. These findings provide valuable insights for the systematic design, progress, and testing of XRS.
Modeling Interdependent Cybersecurity Threats Using Bayesian Networks: A Case Study on In-Vehicle Infotainment Systems
2025-05-14
Sangita Sridar
Cybersecurity threats are increasingly marked by interdependence, uncertainty, and evolving complexity challenges that traditional assessment methods such as CVSS, STRIDE, and attack trees fail to adequately capture. This paper reviews the application of Bayesian Networks (BNs) in cybersecurity risk modeling, highlighting their capacity to represent probabilistic dependencies, integrate diverse threat indicators, and support reasoning under uncertainty. A structured case study is presented in which a STRIDE-based attack tree for an automotive In-Vehicle Infotainment (IVI) system is transformed into a Bayesian Network. Logical relationships are encoded using Conditional Probability Tables (CPTs), and threat likelihoods are derived from normalized DREAD scores. The model enables not only probabilistic inference of system compromise likelihood but also supports causal analysis using do-calculus and local sensitivity analysis to identify high-impact vulnerabilities. These analyses provide insight into the most influential nodes within the threat propagation chain, informing targeted mitigation strategies. While demonstrating the potential of BNs for dynamic and context-aware risk assessment, the study also outlines limitations related to scalability, reliance on expert input, static structure assumptions, and limited temporal modeling. The paper concludes by advocating for future enhancements through Dynamic Bayesian Networks, structure learning, and adaptive inference to better support real-time cybersecurity decision-making in complex environments.
Visual Feedback of Pattern Separability Improves Myoelectric Decoding Performance of Upper Limb Prostheses
2025-05-14
Ruichen Yang, György M. Lévay, Christopher L. Hunt, Dániel Czeiner, Megan C. Hodgson, Damini Agarwal, Rahul R. Kaliki, Nitish V. Thakor
State-of-the-art upper limb myoelectric prostheses often use pattern recognition (PR) control systems that translate electromyography (EMG) signals into desired movements. As prosthesis movement complexity increases, users often struggle to produce sufficiently distinct EMG patterns for reliable classification. Existing training typically involves heuristic, trial-and-error user adjustments to static decoder boundaries. Goal: We introduce the Reviewer, a 3D visual interface projecting EMG signals directly into the decoder's classification space, providing intuitive, real-time insight into PR algorithm behavior. This structured feedback reduces cognitive load and fosters mutual, data-driven adaptation between user-generated EMG patterns and decoder boundaries. Methods: A 10-session study with 12 able-bodied participants compared PR performance after motor-based training and updating using the Reviewer versus conventional virtual arm visualization. Performance was assessed using a Fitts law task that involved the aperture of the cursor and the control of orientation. Results: Participants trained with the Reviewer achieved higher completion rates, reduced overshoot, and improved path efficiency and throughput compared to the standard visualization group. Significance: The Reviewer introduces decoder-informed motor training, facilitating immediate and consistent PR-based myoelectric control improvements. By iteratively refining control through real-time feedback, this approach reduces reliance on trial-and-error recalibration, enabling a more adaptive, self-correcting training framework. Conclusion: The 3D visual feedback significantly improves PR control in novice operators through structured training, enabling feedback-driven adaptation and reducing reliance on extensive heuristic adjustments.
Learn, Explore and Reflect by Chatting: Understanding the Value of an LLM-Based Voting Advice Application Chatbot
2025-05-14
Jianlong Zhu, Manon Kempermann, Vikram Kamath Cannanure, Alexander Hartland, Rosa M. Navarrete, Giuseppe Carteny, Daniela Braun, Ingmar Weber
Voting advice applications (VAAs), which have become increasingly prominent in European elections, are seen as a successful tool for boosting electorates' political knowledge and engagement. However, VAAs' complex language and rigid presentation constrain their utility to less-sophisticated voters. While previous work enhanced VAAs' click-based interaction with scripted explanations, a conversational chatbot's potential for tailored discussion and deliberate political decision-making remains untapped. Our exploratory mixed-method study investigates how LLM-based chatbots can support voting preparation. We deployed a VAA chatbot to 331 users before Germany's 2024 European Parliament election, gathering insights from surveys, conversation logs, and 10 follow-up interviews. Participants found the VAA chatbot intuitive and informative, citing its simple language and flexible interaction. We further uncovered VAA chatbots' role as a catalyst for reflection and rationalization. Expanding on participants' desire for transparency, we provide design recommendations for building interactive and trustworthy VAA chatbots.
Virtual Dosimetrists: A Radiotherapy Training "Flight Simulator"
2025-05-14
Skylar S. Gay, Tucker Netherton, Barbara Marquez, Raymond Mumme, Mary Gronberg, Brent Parker, Chelsea Pinnix, Sanjay Shete, Carlos Cardenas, Laurence Court
Effective education in radiotherapy plan quality review requires a robust, regularly updated set of examples and the flexibility to demonstrate multiple possible planning approaches and their consequences. However, the current clinic-based paradigm does not support these needs. To address this, we have developed 'Virtual Dosimetrist' models that can both generate training examples of suboptimal treatment plans and then allow trainees to improve the plan quality through simple natural language prompts, as if communicating with a dosimetrist. The dose generation and modification process is accurate, rapid, and requires only modest resources. This work is the first to combine dose distribution prediction with natural language processing; providing a robust pipeline for both generating suboptimal training plans and allowing trainees to practice their critical plan review and improvement skills that addresses the challenges of the current clinic-based paradigm.
Should I Stay or Should I Go Now? An Investigation into Gender Differences in the Impact of Switching Jobs on Earnings
2025-05-14
Emily Winskill
This paper investigates the relationship between job mobility and earnings growth in the UK labour market, with a focus on gender differences in the returns to switching jobs. Using data from the Annual Survey of Hours and Earnings (ASHE) between 2011 and 2023, the analysis compares wage progression for job switchers and stayers, controlling for individual and job characteristics. The findings show that job mobility is associated with higher earnings growth, but women experience smaller gains than men, with occupational mobility and age further widening this gap. However, the study finds no statistically significant evidence that changes in occupation, sector, or working time pattern influence this gender gap. The results highlight the importance of addressing gender disparities in the returns to job mobility and provide valuable evidence for developing policy interventions aimed at promoting more equitable labour market outcomes.
Virgo Filaments V: Disrupting the Baryon Cycle in the NGC 5364 Galaxy Group
2025-05-14
Rose A. Finn, Gregory Rudnick, Pascale Jablonka, Mpati Ramatsoku, Gautam Nagaraj, Benedetta Vulcani, Rebecca A. Koopmann, Matteo Fossati, James Agostino, Yannick Bahe, Santiago Garcia-Burillo, Gianluca Castignani, Francoise Combes, Kim Conger, Gabriella De Lucia, Vandana Desai, John Moustakas, Dara Norman, Damien Sperone-Longin, Melinda Townsend, Lizhi Xie, Daria Zakharova, Dennis Zaritsky
The Virgo Filament Survey (VFS) is a comprehensive study of galaxies that reside in the extended filamentary structures surrounding the Virgo Cluster, out to 12 virial radii. The primary goal is to characterize all of the dominant baryonic components within galaxies and to understand whether and how they are affected by the filament environment. A key constituent of VFS is a narrowband H$\alpha$ imaging survey of over 600 galaxies, VFS-H$\alpha$. The H$\alpha$ images reveal detailed, resolved maps of the ionized gas and massive star-formation. This imaging is particularly powerful as a probe of environmentally-induced quenching because different physical processes affect the spatial distribution of star formation in different ways. In this paper, we present the first results from the VFS-H$\alpha$ for the NGC~5364 group, a low-mass ($\log_{10}(M_{dyn}/M_\odot) < 13)$ system located at the western edge of the Virgo~III filament. We combine H$\alpha$ imaging with resolved H~I observations from MeerKAT for eight group members. These galaxies exhibit peculiar morphologies, including strong distortions in the stars and the gas, truncated H~I and H$\alpha$ disks, H~I tails, extraplanar H$\alpha$ emission, and off-center H$\alpha$ emission. These signatures are suggestive of environmental processing such as tidal interactions, ram pressure stripping, and starvation. We quantify the role of ram pressure stripping expected in this group, and find that it can explain the cases of H~I tails and truncated H-alpha for all but one of the disk-dominated galaxies. Our observations indicate that multiple physical mechanisms are disrupting the baryon cycle in these group galaxies.
2025-05-14
Hygor B. Gonçalves, Swayamtrupta Panda, Thaisa Storchi Bergmann, Edward M. Cackett, Michael Eracleous
Quasars, powered by accretion onto supermassive black holes (SMBHs), exhibit significant variability, offering insights into the physics of accretion and the properties of the central engines. In this study, we analyze photometric variability and its correlation with key quasar properties, including black hole mass ($M_{\mathrm{BH}}$) and nuclear luminosities, using 915 quasars with $0\leq z<3.0$ from the AQMES sample monitored within SDSS-V. Variability metrics were derived from approximately 6-year light curves provided by the Zwicky Transient Facility -- ZTF, while SMBH masses and luminosities were obtained from the SDSS DR16 quasar catalog of \citet{wu2022catalog}. We identify a strong anti-correlation between variability amplitude and luminosity, which strengthens with redshift, and a redshift-dependent trend for $M_{\mathrm{BH}}$: a positive correlation at low redshifts, no significant correlation at intermediate redshifts, and an anti-correlation the highest redshifts. Our main finding is a robust anti-correlation between photometric variability amplitude and Eddington ratio, consistent across redshift bins. We present a general equation encapsulating this relationship, that appears to be almost free of redshift dependence, enabling predictions of quasar variability based on accretion parameters {\bf or vice-versa}. The derived relation with the Eddington ratio provides a unified framework for interpreting variability in active galactic nuclei (AGN) and facilitates future studies of quasar variability using high-cadence surveys, such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time -- LSST.
A Survey on Large Language Models in Multimodal Recommender Systems
2025-05-14
Alejo Lopez-Avila, Jinhua Du
Multimodal recommender systems (MRS) integrate heterogeneous user and item data, such as text, images, and structured information, to enhance recommendation performance. The emergence of large language models (LLMs) introduces new opportunities for MRS by enabling semantic reasoning, in-context learning, and dynamic input handling. Compared to earlier pre-trained language models (PLMs), LLMs offer greater flexibility and generalisation capabilities but also introduce challenges related to scalability and model accessibility. This survey presents a comprehensive review of recent work at the intersection of LLMs and MRS, focusing on prompting strategies, fine-tuning methods, and data adaptation techniques. We propose a novel taxonomy to characterise integration patterns, identify transferable techniques from related recommendation domains, provide an overview of evaluation metrics and datasets, and point to possible future directions. We aim to clarify the emerging role of LLMs in multimodal recommendation and support future research in this rapidly evolving field.
The Impact of International Collaborations with Highly Publishing Countries in Computer Science
2025-05-14
Alberto Gomez Espes, Michael Faerber, Adam Jatowt
This paper analyzes international collaborations in Computer Science, focusing on three major players: China, the European Union, and the United States. Drawing from a comprehensive literature review, we examine collaboration patterns, research impact, retraction rates, and the role of the Development Index in shaping research outcomes. Our findings show that while China, the EU, and the US lead global research efforts, other regions are narrowing the gap in publication volume. Collaborations involving these key regions tend to have lower retraction rates, reflecting stronger adherence to scientific standards. We also find that countries with a Very High Development Index contribute to research with higher citation rates and fewer retractions. Overall, this study highlights the value of international collaboration and the importance of inclusive, ethical practices in advancing global research in Computer Science.
AAS Graduate Admissions Task Force: Final report and recommendations
2025-05-14
Emily M. Levesque, Rachel Ivie, Christopher Johns-Krull, Grace Krahm, Laura A. Lopez, Meredith A. MacGregor, Sebastian Monzon, Daniel R. Piacitelli, Seth Redfield, Tom Rice
In March of 2024 the AAS formed our Graduate Admissions Task Force (GATF), asking us to produce recommendations aimed at improving the state of graduate admissions in astronomy. The task was a timely one, as the past decade has seen dramatic shifts that are currently being exacerbated by rapidly-shifting funding and policy challenges. The GATF surveyed recent applicants to astronomy graduate school and admissions leaders in degree-granting graduate programs, held in-depth conversations with select programs, and explored how other fields approach admissions. Based on this work, we have quantified recent changes in the astronomy graduate admissions landscape, addressed mismatches between perception and reality, and identified several key ways to address some of our current challenges. In this report we summarize the results of this work and the four main recommendations we believe the AAS should take to make lasting positive changes. We emphasize that the goal of our report, and the GATF's work, is not to help programs select students, or to help applicants get into graduate school. Instead, we have focused on ways to make the admissions process itself more streamlined, fair, and efficient for everyone involved.
2025-05-14
Diana Valencia, Amaya Moro-Martin, Johanna Teske
This review article delves into the study of low-mass exoplanets: super-Earths, mini-Neptunes and the new categories within and between that we are starting to discover. We provide an overview of current exoplanet observational capabilities, their limitations, and what they are allowing us to learn about low-mass planets. We briefly summarize the most important aspects of planet formation, with an emphasis on processes that may be testable with small exoplanets, in particular those that affect their composition. We also describe the observed compositional diversity of low-mass exoplanets and what it teaches us about planet formation pathways. We finish this review summarizing the study of the composition of small exoplanets during the very last stage of stellar evolution, by studying white dwarfs. This review is written as the JWST is making its first contributions to small planet characterization, rapidly opening new lines of inquiry.
Chern-Simons Theory, Holography and Topological Strings
2025-05-14
Cumrun Vafa
In this note we present a brief overview of connections between Chern-Simons theory and topological strings. A prominent role in this link has been played by large N dualities and holography. We demystify this by explaining why the Kahler form should be viewed as dual to the field strength associated with a 3-form gauge potential, sourced by Lagrangian D-branes. We explain how this leads to the computation of topological string amplitudes in terms of topological vertex for toric Calabi-Yau threefolds. Furthermore, applications of topological strings to a conceptual derivation of Skein relations for link invariants as well as some of its physical applications to black hole physics are also reviewed.
An AI-Powered Research Assistant in the Lab: A Practical Guide for Text Analysis Through Iterative Collaboration with LLMs
2025-05-14
Gino Carmona-Díaz, William Jiménez-Leal, María Alejandra Grisales, Chandra Sripada, Santiago Amaya, Michael Inzlicht, Juan Pablo Bermúdez
Analyzing texts such as open-ended responses, headlines, or social media posts is a time- and labor-intensive process highly susceptible to bias. LLMs are promising tools for text analysis, using either a predefined (top-down) or a data-driven (bottom-up) taxonomy, without sacrificing quality. Here we present a step-by-step tutorial to efficiently develop, test, and apply taxonomies for analyzing unstructured data through an iterative and collaborative process between researchers and LLMs. Using personal goals provided by participants as an example, we demonstrate how to write prompts to review datasets and generate a taxonomy of life domains, evaluate and refine the taxonomy through prompt and direct modifications, test the taxonomy and assess intercoder agreements, and apply the taxonomy to categorize an entire dataset with high intercoder reliability. We discuss the possibilities and limitations of using LLMs for text analysis.
Search for a dark Higgs boson produced in association with inelastic dark matter at the Belle II experiment
2025-05-14
Belle II Collaboration, I. Adachi, L. Aggarwal, H. Ahmed, H. Aihara, N. Akopov, S. Alghamdi, M. Alhakami, A. Aloisio, N. Althubiti, K. Amos, M. Angelsmark, N. Anh Ky, C. Antonioli, D. M. Asner, H. Atmacan, V. Aushev, M. Aversano, R. Ayad, V. Babu, N. K. Baghel, S. Bahinipati, P. Bambade, Sw. Banerjee, S. Bansal, M. Barrett, M. Bartl, J. Baudot, A. Baur, A. Beaubien, F. Becherer, J. Becker, J. V. Bennett, F. U. Bernlochner, V. Bertacchi, M. Bertemes, E. Bertholet, M. Bessner, S. Bettarini, B. Bhuyan, F. Bianchi, T. Bilka, D. Biswas, A. Bobrov, D. Bodrov, A. Bolz, A. Bondar, G. Bonvicini, J. Borah, A. Boschetti, A. Bozek, M. Bračko, P. Branchini, T. E. Browder, A. Budano, S. Bussino, Q. Campagna, M. Campajola, L. Cao, G. Casarosa, C. Cecchi, J. Cerasoli, M. -C. Chang, R. Cheaib, P. Cheema, Y. -T. Chen, B. G. Cheon, K. Chilikin, J. Chin, K. Chirapatpimol, H. -E. Cho, K. Cho, S. -J. Cho, S. -K. Choi, S. Choudhury, J. Cochran, I. Consigny, L. Corona, J. X. Cui, E. De La Cruz-Burelo, S. A. De La Motte, G. de Marino, G. De Nardo, G. De Pietro, R. de Sangro, M. Destefanis, S. Dey, A. Di Canto, F. Di Capua, J. Dingfelder, Z. Doležal, I. Domínguez Jiménez, T. V. Dong, X. Dong, M. Dorigo, D. Dossett, K. Dugic, G. Dujany, P. Ecker, J. Eppelt, P. Feichtinger, T. Ferber, T. Fillinger, C. Finck, G. Finocchiaro, A. Fodor, F. Forti, B. G. Fulsom, A. Gabrielli, A. Gale, E. Ganiev, M. Garcia-Hernandez, R. Garg, G. Gaudino, V. Gaur, V. Gautam, A. Gaz, A. Gellrich, G. Ghevondyan, D. Ghosh, H. Ghumaryan, G. Giakoustidis, R. Giordano, A. Giri, P. Gironella Gironell, A. Glazov, B. Gobbo, R. Godang, O. Gogota, P. Goldenzweig, W. Gradl, E. Graziani, D. Greenwald, Z. Gruberová, Y. Guan, K. Gudkova, I. Haide, Y. Han, T. Hara, C. Harris, K. Hayasaka, H. Hayashii, S. Hazra, C. Hearty, M. T. Hedges, A. Heidelbach, I. Heredia de la Cruz, M. Hernández Villanueva, T. Higuchi, M. Hoek, M. Hohmann, R. Hoppe, P. Horak, C. -L. Hsu, T. Humair, T. Iijima, K. Inami, G. Inguglia, N. Ipsita, A. Ishikawa, R. Itoh, M. Iwasaki, P. Jackson, D. Jacobi, W. W. Jacobs, E. -J. Jang, Q. P. Ji, S. Jia, Y. Jin, A. Johnson, K. K. Joo, H. Junkerkalefeld, A. B. Kaliyar, J. Kandra, K. H. Kang, G. Karyan, T. Kawasaki, F. Keil, C. Ketter, C. Kiesling, C. -H. Kim, D. Y. Kim, J. -Y. Kim, K. -H. Kim, Y. J. Kim, Y. -K. Kim, H. Kindo, K. Kinoshita, P. Kodyš, T. Koga, S. Kohani, K. Kojima, A. Korobov, S. Korpar, E. Kovalenko, R. Kowalewski, P. Križan, P. Krokovny, T. Kuhr, Y. Kulii, D. Kumar, R. Kumar, K. Kumara, T. Kunigo, A. Kuzmin, Y. -J. Kwon, S. Lacaprara, K. Lalwani, T. Lam, L. Lanceri, J. S. Lange, T. S. Lau, M. Laurenza, R. Leboucher, F. R. Le Diberder, M. J. Lee, C. Lemettais, P. Leo, H. -J. Li, L. K. Li, Q. M. Li, W. Z. Li, Y. Li, Y. B. Li, Y. P. Liao, J. Libby, J. Lin, S. Lin, M. H. Liu, Q. Y. Liu, Y. Liu, Z. Q. Liu, D. Liventsev, S. Longo, T. Lueck, T. Luo, C. Lyu, Y. Ma, C. Madaan, M. Maggiora, S. P. Maharana, R. Maiti, G. Mancinelli, R. Manfredi, E. Manoni, M. Mantovano, D. Marcantonio, S. Marcello, C. Marinas, C. Martellini, A. Martens, A. Martini, T. Martinov, L. Massaccesi, M. Masuda, K. Matsuoka, D. Matvienko, S. K. Maurya, M. Maushart, J. A. McKenna, R. Mehta, F. Meier, D. Meleshko, M. Merola, C. Miller, M. Mirra, S. Mitra, K. Miyabayashi, H. Miyake, R. Mizuk, G. B. Mohanty, S. Mondal, S. Moneta, A. L. Moreira de Carvalho, H. -G. Moser, R. Mussa, I. Nakamura, M. Nakao, H. Nakazawa, Y. Nakazawa, M. Naruki, Z. Natkaniec, A. Natochii, M. Nayak, G. Nazaryan, M. Neu, S. Nishida, S. Ogawa, R. Okubo, H. Ono, Y. Onuki, G. Pakhlova, S. Pardi, K. Parham, H. Park, J. Park, K. Park, S. -H. Park, B. Paschen, A. Passeri, S. Patra, S. Paul, T. K. Pedlar, I. Peruzzi, R. Peschke, R. Pestotnik, M. Piccolo, L. E. Piilonen, P. L. M. Podesta-Lerma, T. Podobnik, S. Pokharel, A. Prakash, C. Praz, S. Prell, E. Prencipe, M. T. Prim, S. Privalov, I. Prudiiev, H. Purwar, P. Rados, G. Raeuber, S. Raiz, K. Ravindran, J. U. Rehman, M. Reif, S. Reiter, M. Remnev, L. Reuter, D. Ricalde Herrmann, I. Ripp-Baudot, G. Rizzo, S. H. Robertson, M. Roehrken, J. M. Roney, A. Rostomyan, N. Rout, L. Salutari, D. A. Sanders, S. Sandilya, L. Santelj, V. Savinov, B. Scavino, J. Schmitz, S. Schneider, M. Schnepf, C. Schwanda, Y. Seino, A. Selce, K. Senyo, J. Serrano, M. E. Sevior, C. Sfienti, W. Shan, G. Sharma, C. P. Shen, X. D. Shi, T. Shillington, T. Shimasaki, J. -G. Shiu, D. Shtol, B. Shwartz, A. Sibidanov, F. Simon, J. B. Singh, J. Skorupa, R. J. Sobie, M. Sobotzik, A. Soffer, A. Sokolov, E. Solovieva, W. Song, S. Spataro, B. Spruck, M. Starič, P. Stavroulakis, S. Stefkova, R. Stroili, J. Strube, Y. Sue, M. Sumihama, K. Sumisawa, N. Suwonjandee, H. Svidras, M. Takahashi, M. Takizawa, U. Tamponi, K. Tanida, F. Tenchini, A. Thaller, O. Tittel, R. Tiwary, E. Torassa, K. Trabelsi, I. Tsaklidis, M. Uchida, I. Ueda, T. Uglov, K. Unger, Y. Unno, K. Uno, S. Uno, P. Urquijo, Y. Ushiroda, S. E. Vahsen, R. van Tonder, K. E. Varvell, M. Veronesi, A. Vinokurova, V. S. Vismaya, L. Vitale, V. Vobbilisetti, R. Volpe, A. Vossen, M. Wakai, S. Wallner, M. -Z. Wang, A. Warburton, M. Watanabe, S. Watanuki, C. Wessel, E. Won, X. P. Xu, B. D. Yabsley, S. Yamada, W. Yan, W. C. Yan, S. B. Yang, J. Yelton, J. H. Yin, K. Yoshihara, J. Yuan, L. Zani, F. Zeng, M. Zeyrek, B. Zhang, V. Zhilich, J. S. Zhou, Q. D. Zhou, L. Zhu, R. Žlebčík
Inelastic dark matter models that have two dark matter particles and a massive dark photon can reproduce the observed relic dark matter density without violating cosmological limits. The mass splitting between the two dark matter particles $\chi_{1}$ and $\chi_{2}$, with $m(\chi_{2}) > m(\chi_{1})$, is induced by a dark Higgs field and a corresponding dark Higgs boson $h^{\prime}$. We present a search for dark matter in events with two vertices, at least one of which must be displaced from the interaction region, and missing energy. Using a $365\,\mbox{fb}^{-1}$ data sample collected at Belle II, which operates at the SuperKEKB $e^+e^-$ collider, we observe no evidence for a signal. We set upper limits on the product of the production cross section $\sigma\left(e^+e^- \to h^\prime \chi_1 \chi_2\right)$, and the product of branching fractions $\mathcal{B}\left(\chi_2\to\chi_1 e^+ e^-\right)\times\mathcal{B}\left(h^\prime\to x^+x^-\right)$, where $x^+x^-$ indicates $\mu^+\mu^-, \pi^+\pi^-$, or $K^+K^-$, as functions of $h^{\prime}$ mass and lifetime at the level of $10^{-1}\,\mbox{fb}$. We set model-dependent upper limits on the dark Higgs mixing angle at the level of $10^{-5}$ and on the dark photon kinetic mixing parameter at the level of $10^{-3}$. This is the first search for dark Higgs bosons in association with inelastic dark matter.
Vision-Language Models (VLMs) have revolutionized artificial intelligence and robotics due to their commonsense reasoning capabilities. In robotic manipulation, VLMs are used primarily as high-level planners, but recent work has also studied their lower-level reasoning ability, which refers to making decisions about precise robot movements. However, the community currently lacks a clear and common benchmark that can evaluate how well VLMs can aid low-level reasoning in robotics. Consequently, we propose a novel benchmark, ManipBench, to evaluate the low-level robot manipulation reasoning capabilities of VLMs across various dimensions, including how well they understand object-object interactions and deformable object manipulation. We extensively test 33 representative VLMs across 10 model families on our benchmark, including variants to test different model sizes. Our evaluation shows that the performance of VLMs significantly varies across tasks, and there is a strong correlation between this performance and trends in our real-world manipulation tasks. It also shows that there remains a significant gap between these models and human-level understanding. See our website at: https://manipbench.github.io.
2025-05-14
Christian T. Byrnes, Marina Cortês, Andrew R. Liddle
The latest results from the Atacama Cosmology Telescope Collaboration have moved the preferred perturbation spectral index $n_{\rm s}$ towards one. We reanalyse constraints on the simplest version of the curvaton model, using $n_{\rm s}$ and the tensor-to-scalar ratio $r$. We show that in the massless curvaton case the model gives the same locus of $n_{\rm s}$-$r$ predictions as the power-law model $V(\phi) \propto \phi^p$, but with a different and more elegant physical interpretation. The model gives an excellent account of current observational data, both in the regime where the curvaton dominates the observed perturbations and with an admixture of inflaton-originated perturbations of up to about one-third of the total. Forthcoming large-scale structure surveys, such as that of the recently-launched SPHEREx probe, could discriminate between curvaton and single-field inflation models using the bispectrum.
Beyond the Power Spectrum: A New Framework for Non-Stationary Fields with Applications to Light-Cone Effects in Line Intensity Mapping
2025-05-14
Mattéo Blamart, Adrian Liu
Modern cosmological surveys cover extremely large volumes and map fluctuations on scales reaching gigaparsecs. As a result, it is no longer a valid assumption to ignore cosmological evolution along the line of sight from one end of the survey to the other. When extracting cosmological information, the power spectrum becomes suboptimal because it relies on the assumption of translational invariance of the observed field. For example, during the Epoch of Reionization (EoR), the 21cm brightness temperature field on the nearby (low-redshift) end of a large survey volume exhibits statistical properties that differ significantly from those at far (high-redshift) end. To overcome these limitations, we have developed a eigen decomposition-inspired non-Fourier basis that captures evolution effects. Our work demonstrates that using this new basis integrated in a new summary statistic yields tighter constraints on astrophysical parameters compared to the traditional power spectrum. Additionally, we provide an illustrative example and a practical guide for applying this basis in the context of a realistic forecast for interferometers such as the Hydrogen Epoch of Reionization Array (HERA).
2025-05-14
Julian Büchel, Iason Chalas, Giovanni Acampa, An Chen, Omobayode Fagbohungbe, Sidney Tsai, Kaoutar El Maghraoui, Manuel Le Gallo, Abbas Rahimi, Abu Sebastian
Analog in-memory computing (AIMC) is a promising compute paradigm to improve speed and power efficiency of neural network inference beyond the limits of conventional von Neumann-based architectures. However, AIMC introduces fundamental challenges such as noisy computations and strict constraints on input and output quantization. Because of these constraints and imprecisions, off-the-shelf LLMs are not able to achieve 4-bit-level performance when deployed on AIMC-based hardware. While researchers previously investigated recovering this accuracy gap on small, mostly vision-based models, a generic method applicable to LLMs pre-trained on trillions of tokens does not yet exist. In this work, we introduce a general and scalable method to robustly adapt LLMs for execution on noisy, low-precision analog hardware. Our approach enables state-of-the-art models $\unicode{x2013}$ including Phi-3-mini-4k-instruct and Llama-3.2-1B-Instruct $\unicode{x2013}$ to retain performance comparable to 4-bit weight, 8-bit activation baselines, despite the presence of analog noise and quantization constraints. Additionally, we show that as a byproduct of our training methodology, analog foundation models can be quantized for inference on low-precision digital hardware. Finally, we show that our models also benefit from test-time compute scaling, showing better scaling behavior than models trained with 4-bit weight and 8-bit static input quantization. Our work bridges the gap between high-capacity LLMs and efficient analog hardware, offering a path toward energy-efficient foundation models. Code is available at https://github.com/IBM/analog-foundation-models.
Modelling the impact of quasar redshift errors on the full-shape analysis of correlations in the Lyman-$α$ forest
2025-05-13
Calum Gordon, Andrei Cuceu, Andreu Font-Ribera, Hiram K. Herrera-Alcantar, Jessica Nicole Aguilar, Steven Ahlen, Davide Bianchi, David Brooks, Todd Claybaugh, Shaun Cole, Axel de la Macorra, Biprateep Dey, Peter Doel, Jaime E. Forero-Romero, Enrique Gaztañaga, Satya Gontcho A Gontcho, Gaston Gutierrez, Julien Guy, Klaus Honscheid, Mustapha Ishak, Robert Kehoe, David Kirkby, Theodore Kisner, Anthony Kremin, Martin Landriau, Laurent Le Guillou, Michael Levi, Marc Manera, Paul Martini, Ramon Miquel, John Moustakas, Seshadri Nadathur, Gustavo Niz, Nathalie Palanque-Delabrouille, Will Percival, Francisco Prada, Ignasi Pérez-Ràfols, Graziano Rossi, Eusebio Sanchez, David Schlegel, Michael Schubnell, Hee-Jong Seo, Joseph Harry Silber, David Sprayberry, Gregory Tarlé, Benjamin Alan Weaver, Rongpu Zhou, Hu Zou
In preparation for the first cosmological measurements from the full-shape of the Lyman-$\alpha$ (Ly$\alpha$) forest from DESI, we must carefully model all relevant systematics that might bias our analysis. It was shown in Youles et al. (2022) that random quasar redshift errors produce a smoothing effect on the mean quasar continuum in the Ly$\alpha$ forest region. This in turn gives rise to spurious features in the Ly$\alpha$ auto-correlation, and its cross-correlation with quasars. Using synthetic data sets based on the DESI survey, we confirm that the impact on BAO measurements is small, but that a bias is introduced to parameters which depend on the full-shape of our correlations. We combine a model of this contamination in the cross-correlation (Youles et al. 2022) with a new model we introduce here for the auto-correlation. These are parametrised by 3 parameters, which when included in a joint fit to both correlation functions, successfully eliminate any impact of redshift errors on our full-shape constraints. We also present a strategy for removing this contamination from real data, by removing $\sim$0.3% of correlating pairs.
Blockchain Technology: Core Mechanisms, Evolution, and Future Implementation Challenges
2025-05-13
Aditya Pratap Singh
Blockchain technology has emerged as one of the most transformative digital innovations of the 21st century. This paper presents a comprehensive review of blockchain's fundamental architecture, tracing its development from Bitcoin's initial implementation to current enterprise applications. We examine the core technical components including distributed consensus algorithms, cryptographic principles, and smart contract functionality that enable blockchain's unique properties. The historical progression from cryptocurrency-focused systems to robust platforms for decentralized applications is analyzed, highlighting pivotal developments in scalability, privacy, and interoperability. Additionally, we identify critical challenges facing widespread blockchain adoption, including technical limitations, regulatory hurdles, and integration complexities with existing systems. By providing this foundational understanding of blockchain technology, this paper contributes to ongoing research efforts addressing blockchain's potential to revolutionize data management across industries.
The CMB cold spot under the lens II: Lensing on polarization and cosmic textures footprints
2025-05-13
Pedro da Silveira Ferreira, Stephen Owusu
We present a quadratic lensing estimator that incorporates off-diagonal correlations in both temperature and polarization CMB maps, and is capable of measuring localized lensing profiles with $<10''$ average deflection angle at the $\sim3\sigma$ level in upcoming Simons observatory (SO) data. The proposed pipeline is agnostic to the underlying mass profile and can probe the subtle signatures of voids, clusters, and topological defects. As a case study and test scenario we focus on a collapsing cosmic texture, a topological defect that has been proposed to explain the CMB Cold Spot. With the forthcoming SO data, we forecast a $2.8\sigma$ detection if the texture amplitude reaches the current Planck 2018 $2\sigma$ limit, and a $1.8\sigma$ measurement for the best-fit value, which is remarkable given the expected typical lensing angle of $<6''$. As the next-generation CMB surveys will reach $\ell>3000$ for polarization, we demonstrate that its inclusion is significant, boosting the estimator's signal-to-noise ratio by $\sim50\%$, and making it a powerful tool, allowing us to recover faint lensing footprints that were previously inaccessible.
Securing RAG: A Risk Assessment and Mitigation Framework
2025-05-13
Lukas Ammann, Sara Ott, Christoph R. Landolt, Marco P. Lehmann
Retrieval Augmented Generation (RAG) has emerged as the de facto industry standard for user-facing NLP applications, offering the ability to integrate data without re-training or fine-tuning Large Language Models (LLMs). This capability enhances the quality and accuracy of responses but also introduces novel security and privacy challenges, particularly when sensitive data is integrated. With the rapid adoption of RAG, securing data and services has become a critical priority. This paper first reviews the vulnerabilities of RAG pipelines, and outlines the attack surface from data pre-processing and data storage management to integration with LLMs. The identified risks are then paired with corresponding mitigations in a structured overview. In a second step, the paper develops a framework that combines RAG-specific security considerations, with existing general security guidelines, industry standards, and best practices. The proposed framework aims to guide the implementation of robust, compliant, secure, and trustworthy RAG systems.
Be star demographics: a comprehensive study of thousands of lightcurves in the Magellanic Clouds
2025-05-13
A. L. Figueiredo, A. C. Carciofi, J. Labadie-Bartz, M. L. Pinho, T. H. de Amorin, P. T. dos Santos, I. Soszyński, A. Udalski
Multi-color OGLE survey light curves of about 20 years duration are analyzed for about 3000 classical Be stars in the Large and Small Magellanic Clouds (LMC, SMC) in order to study the properties and variability. Each light curve was manually analyzed to distinguish between different scenarios, such as photospheric baseline levels, and disk build-up and dissipation phases. This analysis was aided by dynamical disk models and photospheric models to coarsely determine inclination angle and mass. Measured quantities such as the fraction of time spent actively ejecting mass (the duty cycle), the fraction of time spent with a detectable disk (the disk duty cycle), the build-up and dissipation time of isolated disk events, and the number of mass outbursts per year allow us to characterize and compare the behavior of the two populations. There is a wide spread in the duty cycle, with median values of 0.44 (LMC) and 0.60 (SMC). The disk duty cycle is high for both populations, with median values of 0.99 (LMC) and 1.0 (SMC), indicating that disks are almost always present for these stars. The occurrence rate of outbursts ranges from zero to about two per year, with median values of 0.31 (LMC) and 0.26 (SMC). There are strong statistical differences in the behavior of the LMC and SMC populations, with the lower metallicity stars being more active in terms of their duty cycle and disk duty cycle, and with less frequent but longer lasting outbursts.
We introduce an axiomatization of the notion of ( $p$-complete) anticyclotomic Euler system for a wide class of Galois representations, including those attached to a cuspidal eigenform and to a Hida family of modular forms. Under a minimal set of assumptions, we show how to build from these data a universal Kolyvagin system for the representation and for its anticyclotomic twist. Eventually, we recover some applications to the structure of Selmer groups and Iwasawa main conjectures and we review a few concrete examples of these abstract notions that can be found in the literature.
Time as a test-field: the no-boundary universe in motion and a smooth radiation bounce
2025-05-13
Federico Piazza, Siméon Vareilles
We review two arguments for using the Schr\"odinger equation in quantum cosmology and propose restricting to solutions in which time acts as a component with negligible backreaction on the metric - that is, it plays the role of a test field. We apply this idea to various minisuperspace models. In the semiclassical regime we recover expected results: the wavefunction peaks on the classical solution and, in models with a scalar field, the variance of $\zeta$ (a mini-superspace analogue of the comoving curvature perturbation) is conserved in time. Applied to the no-boundary wavefunction, our model highlights a bouncing behavior, which gives a straightforward quantum representation of global de Sitter space. We argue that the inevitable time dependence of the variance of the wavefunction breaks some of the classical de Sitter symmetries. Then, by modeling radiation as a fluid, we analyze the epoch of radiation domination near the singularity. The problem is equivalent to an $s$-wave scattering off a central power-law potential of the form $-r^{-2/3}$. Such a potential admits bound states, so the system is stable and the wavepacket undergoes unitary scattering. The radiation bounce, as opposed to the no-boundary bounce, does not have a classical counterpart as the corresponding classical solutions are singular. During the radiation bounce, the uncertainty and the expectation value of the scale factor become comparable. By selecting a large initial variance, the bounce can be made arbitrarily smooth, the mean value of the Hubble parameter correspondingly soft.
A Survey of Deep Learning for Complex Speech Spectrograms
2025-05-13
Yuying Xie, Zheng-Hua Tan
Recent advancements in deep learning have significantly impacted the field of speech signal processing, particularly in the analysis and manipulation of complex spectrograms. This survey provides a comprehensive overview of the state-of-the-art techniques leveraging deep neural networks for processing complex spectrograms, which encapsulate both magnitude and phase information. We begin by introducing complex spectrograms and their associated features for various speech processing tasks. Next, we explore the key components and architectures of complex-valued neural networks, which are specifically designed to handle complex-valued data and have been applied for complex spectrogram processing. We then discuss various training strategies and loss functions tailored for training neural networks to process and model complex spectrograms. The survey further examines key applications, including phase retrieval, speech enhancement, and speech separation, where deep learning has achieved significant progress by leveraging complex spectrograms or their derived feature representations. Additionally, we examine the intersection of complex spectrograms with generative models. This survey aims to serve as a valuable resource for researchers and practitioners in the field of speech signal processing and complex-valued neural networks.
Plastic deformation as a phase transition: a combinatorial model of plastic flow in copper single crystals
2025-05-13
Afonso D. M. Barroso, Elijah Borodin, Andrey P. Jivkov
Continuum models of plasticity fail to capture the richness of microstructural evolution because the continuum is a homogeneous construction. The present study shows that an alternative way is available at the mesoscale in the form of truly discrete constructions and in the discrete exterior calculus. A pre-existing continuum mean-field model with two parameters is rewritten in the language of the latter to model the properties of a network of plastic slip events in a perfect copper single crystal under uniaxial tension. The behaviour of the system is simulated in a triangular 2D mesh in 3D space employing a Metropolis-Hastings algorithm. Phases of distinct character emerge and both first-order and second-order phase transitions are observed. The phases can be interpreted as representing crystallographic phenomena like cross-slip, strain localisation and partial dislocations. The first-order transitions mostly occur as functions of the applied stress, while the second-order transitions occur exclusively as functions of the mean-field coupling parameter. The former are reminiscent of transitions in other statistical-mechanical models, while the latter find parallels in experimental observations.
Modular Federated Learning: A Meta-Framework Perspective
2025-05-13
Frederico Vicente, Cláudia Soares, Dušan Jakovetić
Federated Learning (FL) enables distributed machine learning training while preserving privacy, representing a paradigm shift for data-sensitive and decentralized environments. Despite its rapid advancements, FL remains a complex and multifaceted field, requiring a structured understanding of its methodologies, challenges, and applications. In this survey, we introduce a meta-framework perspective, conceptualising FL as a composition of modular components that systematically address core aspects such as communication, optimisation, security, and privacy. We provide a historical contextualisation of FL, tracing its evolution from distributed optimisation to modern distributed learning paradigms. Additionally, we propose a novel taxonomy distinguishing Aggregation from Alignment, introducing the concept of alignment as a fundamental operator alongside aggregation. To bridge theory with practice, we explore available FL frameworks in Python, facilitating real-world implementation. Finally, we systematise key challenges across FL sub-fields, providing insights into open research questions throughout the meta-framework modules. By structuring FL within a meta-framework of modular components and emphasising the dual role of Aggregation and Alignment, this survey provides a holistic and adaptable foundation for understanding and advancing FL research and deployment.
D-Hammer: Efficient Equational Reasoning for Labelled Dirac Notation
2025-05-13
Yingte Xu, Li Zhou, Gilles Barthe
Labelled Dirac notation is a formalism commonly used by physicists to represent many-body quantum systems and by computer scientists to assert properties of quantum programs. It is supported by a rich equational theory for proving equality between expressions in the language. These proofs are typically carried on pen-and-paper, and can be exceedingly long and error-prone. We introduce D-Hammer, the first tool to support automated equational proof for labelled Dirac notation. The salient features of D-Hammer include: an expressive, higher-order, dependently-typed language for labelled Dirac notation; an efficient normalization algorithm; and an optimized C++ implementation. We evaluate the implementation on representative examples from both plain and labelled Dirac notation. In the case of plain Dirac notation, we show that our implementation significantly outperforms DiracDec.
Resource-Efficient Language Models: Quantization for Fast and Accessible Inference
2025-05-13
Tollef Emil Jørgensen
Large language models have significantly advanced natural language processing, yet their heavy resource demands pose severe challenges regarding hardware accessibility and energy consumption. This paper presents a focused and high-level review of post-training quantization (PTQ) techniques designed to optimize the inference efficiency of LLMs by the end-user, including details on various quantization schemes, granularities, and trade-offs. The aim is to provide a balanced overview between the theory and applications of post-training quantization.
Modeling Quantum Links for the Exploration of Distributed Quantum Computing Systems
2025-05-13
Sahar Ben Rached, Zezhou Sun, Junaid Khan, Guilu Long, Santiago Rodrigo, Carmen G. Almudéver, Eduard Alarcón, Sergi Abadal
Quantum computing offers the potential to solve certain complex problems, yet, scaling monolithic processors remains a major challenge. Modular and distributed architectures are proposed to build large-scale quantum systems while bringing the security advantages of quantum communication. At present, this requires accurate and computationally efficient models of quantum links across different scales to advance system design and guide experimental prototyping. In this work, we review protocols and models for estimating latency, losses, and fidelity in quantum communication primitives relying on quantum state distribution via microwave photons. We also propose a scalable simulation framework to support the design and evaluation of future distributed quantum computing systems.
2025-05-13
Delphine Marris-Morini, Goran Z. Mashanovich, Milos Nedeljkovic, Carlos Alonso-Ramos, Laurent Vivien, Jacopo Frigerio, Giovanni Isella
The mid-infrared (mid-IR) spectral range is a part of the electromagnetic spectrum in which most of the molecules have vibrational and rotational resonances. Ge-based photonic integrated circuits in this wavelength range have thus seen a burst of interest in the recent years, mainly driven by applications related with the detection of chemical and biological substances. Here we review the motivations and the recent developments in this field, from the different material platforms to active and nonlinear devices. We also discuss a few demonstrations of sensing that have already been conducted, attesting the potential applications of such devices. Finally, we conclude by discussing the challenges that have to be solved to transition from lab demonstrations to practical industrial devices.
Stance detection is essential for understanding subjective content across various platforms such as social media, news articles, and online reviews. Recent advances in Large Language Models (LLMs) have revolutionized stance detection by introducing novel capabilities in contextual understanding, cross-domain generalization, and multimodal analysis. Despite these progressions, existing surveys often lack comprehensive coverage of approaches that specifically leverage LLMs for stance detection. To bridge this critical gap, our review article conducts a systematic analysis of stance detection, comprehensively examining recent advancements of LLMs transforming the field, including foundational concepts, methodologies, datasets, applications, and emerging challenges. We present a novel taxonomy for LLM-based stance detection approaches, structured along three key dimensions: 1) learning methods, including supervised, unsupervised, few-shot, and zero-shot; 2) data modalities, such as unimodal, multimodal, and hybrid; and 3) target relationships, encompassing in-target, cross-target, and multi-target scenarios. Furthermore, we discuss the evaluation techniques and analyze benchmark datasets and performance trends, highlighting the strengths and limitations of different architectures. Key applications in misinformation detection, political analysis, public health monitoring, and social media moderation are discussed. Finally, we identify critical challenges such as implicit stance expression, cultural biases, and computational constraints, while outlining promising future directions, including explainable stance reasoning, low-resource adaptation, and real-time deployment frameworks. Our survey highlights emerging trends, open challenges, and future directions to guide researchers and practitioners in developing next-generation stance detection systems powered by large language models.
A nonlocal-to-local approach to aggregation-diffusion equations
2025-05-13
Carles Falcó, Ruth E. Baker, José A. Carrillo
Over the past decades, nonlocal models have been widely used to describe aggregation phenomena in biology, physics, engineering, and the social sciences. These are often derived as mean-field limits of attraction-repulsion agent-based models, and consist of systems of nonlocal partial differential equations. Using differential adhesion between cells as a biological case study, we introduce a novel local model of aggregation-diffusion phenomena. This system of local aggregation-diffusion equations is fourth-order, resembling thin-film or Cahn-Hilliard type equations. In this framework, cell sorting phenomena are explained through relative surface tensions between distinct cell types. The local model emerges as a limiting case of short-range interactions, providing a significant simplification of earlier nonlocal models, while preserving the same phenomenology. This simplification makes the model easier to implement numerically and more amenable to calibration to quantitative data. Additionally, we discuss recent analytical results based on the gradient-flow structure of the model, along with open problems and future research directions.
Event cameras have emerged as promising sensors for 3D reconstruction due to their ability to capture per-pixel brightness changes asynchronously. Unlike conventional frame-based cameras, they produce sparse and temporally rich data streams, which enable more accurate 3D reconstruction and open up the possibility of performing reconstruction in extreme environments such as high-speed motion, low light, or high dynamic range scenes. In this survey, we provide the first comprehensive review focused exclusively on 3D reconstruction using event cameras. The survey categorises existing works into three major types based on input modality - stereo, monocular, and multimodal systems, and further classifies them by reconstruction approach, including geometry-based, deep learning-based, and recent neural rendering techniques such as Neural Radiance Fields and 3D Gaussian Splatting. Methods with a similar research focus were organised chronologically into the most subdivided groups. We also summarise public datasets relevant to event-based 3D reconstruction. Finally, we highlight current research limitations in data availability, evaluation, representation, and dynamic scene handling, and outline promising future research directions. This survey aims to serve as a comprehensive reference and a roadmap for future developments in event-driven 3D reconstruction.
Visual Image Reconstruction from Brain Activity via Latent Representation
2025-05-13
Yukiyasu Kamitani, Misato Tanaka, Ken Shirakawa
Visual image reconstruction, the decoding of perceptual content from brain activity into images, has advanced significantly with the integration of deep neural networks (DNNs) and generative models. This review traces the field's evolution from early classification approaches to sophisticated reconstructions that capture detailed, subjective visual experiences, emphasizing the roles of hierarchical latent representations, compositional strategies, and modular architectures. Despite notable progress, challenges remain, such as achieving true zero-shot generalization for unseen images and accurately modeling the complex, subjective aspects of perception. We discuss the need for diverse datasets, refined evaluation metrics aligned with human perceptual judgments, and compositional representations that strengthen model robustness and generalizability. Ethical issues, including privacy, consent, and potential misuse, are underscored as critical considerations for responsible development. Visual image reconstruction offers promising insights into neural coding and enables new psychological measurements of visual experiences, with applications spanning clinical diagnostics and brain-machine interfaces.
Neutron-Capture Element Signatures in Globular Clusters: Insights from the Gaia-ESO Survey
2025-05-13
J. Schiappacasse-Ulloa, L. Magrini, S. Lucatello, S. Randich, A. Bragaglia, E. Carretta, G. Cescutti, F. Rizzuti, C. Worley, F. Lucertini, L. Berni
Globular clusters (GCs) are key to understanding the formation and evolution of our Galaxy. While the abundances of light and Fe-peak elements in GCs have been widely studied, investigations into heavier, neutron-capture elements -- and their connection to multiple stellar populations and GC origins -- remain limited. In this work, we analysed the chemical abundances of neutron-capture elements in GCs to trace the Galactic halo and to explore possible links to the MP phenomenon. Our goal is to better constrain the nature of the polluters responsible for intracluster enrichment and to distinguish the origin of GCs through the chemical signature of neutron-capture elements. We examined 14 GCs from the Gaia-ESO Survey, spanning a wide metallicity range, [Fe/H] from -0.40 to -2.32, using a homogeneous methodology. We focused on the abundances of Y, Zr, Ba, La, Ce, Nd, Pr, and Eu, derived from FLAMES-UVES spectra. These were compared with predictions from a stochastic Galactic chemical evolution model. With the exception of Zr, the model broadly reproduces the observed trends in neutron-capture elements. In some GCs, we found strong correlations between hot H-burning products (Na, Al) and s-process elements, pointing to a shared nucleosynthesis site, e.g., asymptotic giant branch stars of different masses and/or fast-rotating massive stars. We also detect a distinct difference in [Eu/Mg] ratio between in-situ ($\langle$[Eu/Mg]$\rangle$ = 0.14 dex) and ex-situ ($\langle$[Eu/Mg]$\langle$ = 0.32 dex) GCs, highlighting their different enrichment histories. Finally, on average, Type II GCs (NGC 362, NGC 1261, and NGC 1851) showed a s-process element spread ratio between second- and first-generations about twice as large as those seen in Type I clusters.
A Comparison Between Human and Generative AI Decision-Making Attributes in Complex Health Services
2025-05-13
Nandini Doreswamy, Louise Horstmanshof
A comparison between human and Generative AI decision-making attributes in complex health services is a knowledge gap in the literature, at present. Humans may possess unique attributes beneficial to decision-making in complex health services such as health policy and health regulation, but are also susceptible to decision-making flaws. The objective is to explore whether humans have unique, and/or helpful attributes that contribute to optimal decision-making in complex health services. This comparison may also shed light on whether humans are likely to compete, cooperate, or converge with Generative AI. The comparison is based on two published reviews: a scoping review of human attributes [1] and a rapid review of Generative AI attributes [2]. The analysis categorizes attributes by uniqueness and impact. The results are presented in tabular form, comparing the sets and subsets of human and Generative AI attributes. Humans and Generative AI decision-making attributes have complementary strengths. Cooperation between these two entities seems more likely than pure competition. To maintain meaningful decision-making roles, humans could develop their unique attributes, with decision-making systems integrating both human and Generative AI contributions. These entities may also converge, in future.
Tidal Debris Candidates from the $ω$ Centauri Accretion Event and its Role in Building Up the Milky Way Halo
2025-05-13
Borja Anguiano, Arik Mitschang, Takanobu Kirihara, Yutaka Hirai, Danny Horta, Sten Hasselquist, Ricardo P. Schiavon, Steven R. Majewski, Andrew C. Mason, Adrian M. Price-Whelan, Carlos Allende Prieto, Verne Smith, Katia Cunha, David L. Nidever
We identify stellar tidal debris from the $\omega$ Centauri ($\omega$ Cen) system among field stars in the APOGEE survey via chemical tagging using a neural network trained on APOGEE observations of the $\omega$ Cen core. We find a total of 463 $\omega$ Cen debris candidates have a probability $P > 0.8$ of sharing common patterns in their chemical abundances across a range of individual elements or element combinations, including [C+N], O, Mg, Al, Si, Ca, Ni, and Fe. Some debris candidates show prograde or retrograde disk-like kinematics, but most show kinematics consistent with the accreted halo, showing high radial actions, $J_{R}$, values. We find that a sample of Gaia-Sausage-Enceladus (GES) members are chemically distinct from the $\omega$ Cen core, suggesting that $\omega$ Cen is associated to an independent merger event shaping the Milky Way halo. However, a connection between GSE and $\omega$ Cen cannot be ruled out. A detailed comparison with $N$-body simulations indicates that the $\omega$ Cen progenitor was a massive dwarf galaxy ($\gtrsim 10^8 M_{\odot}$). The existence of a metal-poor high-$\alpha$ chemically homogeneous halo debris is also reported.
When Incentives and Nudges Meet: Promoting Budget Allocations for Undervalued Policies
2025-05-13
Makoto Kuroki, Shusaku Sasaki
Budget officers often assess public project proposals based on available financial support and expected outcomes. However, behavioral factors such as time discounting and psychological hesitation may lead to underinvestment in programs with delayed but significant benefits. This study investigates whether financial incentives and non-financial nudges can influence budgetary decisions in local governments. We conducted a nationwide mail-based survey experiment targeting budget officers in Japanese municipalities and received 490 valid responses. Using a 2*2 randomized design, we tested the independent and combined effects of a financial incentive (a 50% national subsidy) and a non-financial nudge (loss framing and peer information). All three treatments significantly increased assessed budget amounts compared to the control group. The largest effect appeared in the combination group (approximately 1.1 million JPY higher, p < .01). Both the financial incentive and the nudge independently increased assessments by approximately 650,000-670,000 JPY (p < .01). Notably, only the nudge raised the self-financed portion of the budget. These findings demonstrate how low-cost behavioral interventions can improve budget assessments for undervalued projects. As an application, we embedded the experiment in a case involving HPV vaccine promotion in Japan, but the approach can be broadly relevant to other public policy domains requiring forward-looking budget decisions.
SiameseLSRTM: Enhancing least-squares reverse time migration with a Siamese network
2025-05-13
Xinru Mu, Omar M. Saad, Tariq Alkhalifah
Least-squares reverse time migration (LSRTM) is an inversion-based imaging method rooted in optimization theory, which iteratively updates the reflectivity model to minimize the difference between observed and simulated data. However, in real data applications, the Born-based simulated data, based on simplified physics, like the acoustic assumption, often under represent the complexity within observed data. Thus, we develop SiameseLSRTM, a novel approach that employs a Siamese network consisting of two identical convolutional neural networks (CNNs) with shared weights to measure the difference between simulated and observed data. Specifically, the shared-weight CNNs in the Siamese network enable the extraction of comparable features from both observed and simulated data, facilitating more effective data matching and ultimately improving imaging accuracy. SiameseLSRTM is a self-supervised framework in which the network parameters are updated during the iterative LSRTM process, without requiring extensive labeled data and prolonged training. We evaluate SiameseLSRTM using two synthetic datasets and one field dataset from a land seismic survey, showing that it produces higher-resolution and more accurate imaging results compared to traditional LSRTM.
The Polarisation Sky Survey of the Universe's Magnetism (POSSUM): Science Goals and Survey Description
2025-05-13
B. M. Gaensler, G. H. Heald, N. M. McClure-Griffiths, C. S. Anderson, C. L. Van Eck, J. L. West, A. J. M. Thomson, J. P. Leahy, L. Rudnick, Y. K. Ma, Takuya Akahori, G. Gürkan, T. L. Landecker, S. A. Mao, S. P. O'Sullivan, W. Raja, X. Sun, T. Vernstrom, Lerato Baidoo, Ettore Carretti, A. R. Taylor, A. G. Willis, Erik Osinga, J. D. Livingston, E. L. Alexander, David Alonso-López, A. D. Amaral, T. An, Andrea Bracco, S. Bradbury, Marcus Brüggen, Chakali Eswaraiah, Torsten Enßlin, T. J. Galvin, Marijke Haverkorn, A. M. Hopkins, Sebastian Hutschenreuter, Shinsuke Ideguchi, S. Jaswanth, S. Lyla Jung, J. F. Kaczmarek, Roland Kothes, Sanja Lazarević, Denis Leahy, Francesca Loi, Joshua R. Marvil, Ray Norris, Ayush Pandhi, Jason M. Price, C. J. Riseley, P. Ryder, Amit Seta, Vasundhara Shaw, A. X. Shen, C. Sobey, J. Stil, Chiara Stuardi, Gupta Upasana, Shannon Vanderwoude, Velibor Velović
The Australian SKA Pathfinder (ASKAP) offers powerful new capabilities for studying the polarised and magnetised Universe at radio wavelengths. In this paper, we introduce the Polarisation Sky Survey of the Universe's Magnetism (POSSUM), a groundbreaking survey with three primary objectives: (1) to create a comprehensive Faraday rotation measure (RM) grid of up to one million compact extragalactic sources across the southern ~50 per cent of the sky (20,630 deg$^2$); (2) to map the intrinsic polarisation and RM properties of a wide range of discrete extragalactic and Galactic objects over the same area; and (3) to contribute interferometric data with excellent surface brightness sensitivity, which can be combined with single-dish data to study the diffuse Galactic interstellar medium. Observations for the full POSSUM survey commenced in May 2023 and are expected to conclude by mid-2028. POSSUM will achieve an RM grid density of around 30-50 RMs per square degree with a median measurement uncertainty of ~1 rad m$^{-2}$. The survey operates primarily over a frequency range of 800-1088 MHz, with an angular resolution of 20'' and a typical RMS sensitivity in Stokes $Q$ or $U$ of 18 $\mu$Jy beam$^{-1}$. Additionally, the survey will be supplemented by similar observations covering 1296-1440 MHz over 38 per cent of the sky. POSSUM will enable the discovery and detailed investigation of magnetised phenomena in a wide range of cosmic environments, as well as the interplay between these components. This paper reviews the current science case developed by the POSSUM Collaboration and provides an overview of POSSUM's observations, data processing, outputs, and its complementarity with other radio and multi-wavelength surveys, including future work with the SKA. [Abstract abridged]
The Evolutionary Map of the Universe: A new radio atlas for the southern hemisphere sky
2025-05-13
A. M. Hopkins, A. Kapinska, J. Marvil, T. Vernstrom, J. D. Collier, R. P. Norris, Y. A. Gordon, S. W. Duchesne, L. Rudnick, N. Gupta, E. Carretti, C. S. Anderson, S. Dai, G. Gürkan, D. Parkinson, I. Prandoni, S. Riggi, C. S. Saraf, Y. K. Ma, M. D. Filipović, G. Umana, B. Bahr-Kalus, B. S. Koribalski, E. Lenc, A. Ingallinera, J. Afonso, A. Ahmad, U. T. Ahmed, E. L. Alexander, H. Andernach, J. Asorey, A. J. Battisti, M. Bilicki, A. Botteon, M. J. I. Brown, M. Brüggen, M. Cowley, K. C. Dage, C. L. Hale, M. J. Hardcastle, R. Kothes, S. Lazarević, Y. -T. Lin, K. J. Luken, J. P. Moss, J. Prathap, S. F. Rahman, T. H. Reiprich, C. J. Riseley, M. Salvato, N. Seymour, S. S. Shabala, D. J. B. Smith, M. Vaccari, J. Th. van Loon, O. I. Wong, R. Z. E. Alsaberi, A. D. Asher, B. D. Ball, D. Barbosa, N. Biava, A. C. Bradley, R. Carvajal, E. J. Crawford, T. J. Galvin, M. T. Huynh, D. A. Leahy, I. Matute, V. A. Moss, C. Pappalardo, Z. J. Smeaton, V. Velović, T. Zafar
We present the Evolutionary Map of the Universe (EMU) survey conducted with the Australian Square Kilometre Array Pathfinder (ASKAP). EMU aims to deliver the touchstone radio atlas of the southern hemisphere. We introduce EMU and review its science drivers and key science goals, updated and tailored to the current ASKAP five-year survey plan. The development of the survey strategy and planned sky coverage is presented, along with the operational aspects of the survey and associated data analysis, together with a selection of diagnostics demonstrating the imaging quality and data characteristics. We give a general description of the value-added data pipeline and data products before concluding with a discussion of links to other surveys and projects and an outline of EMU's legacy value.
Utilization of noise for the control of a class of non-linear systems
2025-05-13
Adrian-Mihail Stoica, Isaac Yaesh
Utilization of noise for the control of a class of non-linear systems is presented. The application of state-multiplicative noise as a mean of control is far more limited then the use of standard determinis?tic gains. Nevertheless, so called Stochastic Anti Resonance (SAR) with state-multiplicative noise based control, do arise in a variety of situations such as in engineering applications, physics modelling, bi?ology, and models of visuo-motor tasks. Linear Matrix Inequalities based conditions from recent publications are reviewed, that character?ize stochastic stability of such nonlinear systems applying SAR. While those results dealt with systems that are, apriori, modelled using sec?tor bounded nonlinearities, we demonstrate that more general systems that can be approximated as such, can be also controlled using SAR.
Evaluating LLM Metrics Through Real-World Capabilities
2025-05-13
Justin K Miller, Wenjia Tang
As generative AI becomes increasingly embedded in everyday workflows, it is important to evaluate its performance in ways that reflect real-world usage rather than abstract notions of intelligence. Unlike many existing benchmarks that assess general intelligence, our approach focuses on real-world utility, evaluating how well models support users in everyday tasks. While current benchmarks emphasize code generation or factual recall, users rely on AI for a much broader range of activities-from writing assistance and summarization to citation formatting and stylistic feedback. In this paper, we analyze large-scale survey data and usage logs to identify six core capabilities that represent how people commonly use Large Language Models (LLMs): Summarization, Technical Assistance, Reviewing Work, Data Structuring, Generation, and Information Retrieval. We then assess the extent to which existing benchmarks cover these capabilities, revealing significant gaps in coverage, efficiency measurement, and interpretability. Drawing on this analysis, we use human-centered criteria to identify gaps in how well current benchmarks reflect common usage that is grounded in five practical criteria: coherence, accuracy, clarity, relevance, and efficiency. For four of the six capabilities, we identify the benchmarks that best align with real-world tasks and use them to compare leading models. We find that Google Gemini outperforms other models-including OpenAI's GPT, xAI's Grok, Meta's LLaMA, Anthropic's Claude, DeepSeek, and Qwen from Alibaba-on these utility-focused metrics.
Large Language Model Psychometrics: A Systematic Review of Evaluation, Validation, and Enhancement
2025-05-13
Haoran Ye, Jing Jin, Yuhang Xie, Xin Zhang, Guojie Song
The rapid advancement of large language models (LLMs) has outpaced traditional evaluation methodologies. It presents novel challenges, such as measuring human-like psychological constructs, navigating beyond static and task-specific benchmarks, and establishing human-centered evaluation. These challenges intersect with Psychometrics, the science of quantifying the intangible aspects of human psychology, such as personality, values, and intelligence. This survey introduces and synthesizes an emerging interdisciplinary field of LLM Psychometrics, which leverages psychometric instruments, theories, and principles to evaluate, understand, and enhance LLMs. We systematically explore the role of Psychometrics in shaping benchmarking principles, broadening evaluation scopes, refining methodologies, validating results, and advancing LLM capabilities. This paper integrates diverse perspectives to provide a structured framework for researchers across disciplines, enabling a more comprehensive understanding of this nascent field. Ultimately, we aim to provide actionable insights for developing future evaluation paradigms that align with human-level AI and promote the advancement of human-centered AI systems for societal benefit. A curated repository of LLM psychometric resources is available at https://github.com/valuebyte-ai/Awesome-LLM-Psychometrics.
The Failure of Plagiarism Detection in Competitive Programming
2025-05-13
Ethan Dickey
Plagiarism in programming courses remains a persistent challenge, especially in competitive programming contexts where assignments often have unique, known solutions. This paper examines why traditional code plagiarism detection methods frequently fail in these environments and explores the implications of emerging factors such as generative AI (genAI). Drawing on the author's experience teaching a Competitive Programming 1 (CP1) course over seven semesters at Purdue University (with $\approx 100$ students each term) and completely redesigning the CP1/2/3 course sequence, we provide an academically grounded analysis. We review literature on code plagiarism in computer science education, survey current detection tools (Moss, Kattis, etc.) and methods (manual review, code-authorship interviews), and analyze their strengths and limitations. Experience-based observations are presented to illustrate real-world detection failures and successes. We find that widely-used automated similarity checkers can be thwarted by simple code transformations or novel AI-generated code, while human-centric approaches like oral interviews, though effective, are labor-intensive. The paper concludes with opinions and preliminary recommendations for improving academic integrity in programming courses, advocating for a multi-faceted approach that combines improved detection algorithms, mastery-based learning techniques, and authentic assessment practices to better ensure code originality.
Natural disasters, including earthquakes, wildfires and cyclones, bear a huge risk on human lives as well as infrastructure assets. An effective response to disaster depends on the ability to rapidly and efficiently assess the intensity of damage. Artificial Intelligence (AI) and Generative Artificial Intelligence (GenAI) presents a breakthrough solution, capable of combining knowledge from multiple types and sources of data, simulating realistic scenarios of disaster, and identifying emerging trends at a speed previously unimaginable. In this paper, we present a comprehensive review on the prospects of AI and GenAI in damage assessment for various natural disasters, highlighting both its strengths and limitations. We talk about its application to multimodal data such as text, image, video, and audio, and also cover major issues of data privacy, security, and ethical use of the technology during crises. The paper also recognizes the threat of Generative AI misuse, in the form of dissemination of misinformation and for adversarial attacks. Finally, we outline avenues of future research, emphasizing the need for secure, reliable, and ethical Generative AI systems for disaster management in general. We believe that this work represents the first comprehensive survey of Gen-AI techniques being used in the field of Disaster Assessment and Response.
This work presents a scheme for engineering quantum entanglement and Einstein-Podolsky-Rosen (EPR) steering with Gaussian measurements based on the quantum Hopfield model that incorporates a common thermal reservoir. We begin by examining quantum correlations, specifically quantum entanglement and EPR steering, in the ground state. These quantum correlations primarily stem from squeezing interactions in weak and normal strong coupling regimes. As the coupling strength increases, especially upon entering the ultrastrong coupling regime, the correlations emerge from the combined effect of squeezing and mix-mode interactions. Importantly, this scenario enables the realization of two-way EPR steering. Moreover, lower optical frequencies enhance both quantum entanglement and EPR steering. Further, when considering thermal effects, the ultrastrong and deep strong coupling regimes, paired with lower optical frequencies, lead to improved entanglement. The one-way EPR steering for resonant case can be effectively controlled in the ultrastrong and deep strong coupling regimes which originates from the asymmetry of subsystem and reservoir coupling induced by the diamagnetic term. Additionally, one-way EPR steering can also be produced for nonresonant case. In this case, the asymmetry of the subsystem and reservoir originates from the combined effect of nonresonant frequencies and diamagnetic term. Our findings have the potential to inspire further research into quantum information processing that leverages light-matter entanglement and EPR steering.
Decoding Neighborhood Environments with Large Language Models
2025-05-13
Andrew Cart, Shaohu Zhang, Melanie Escue, Xugui Zhou, Haitao Zhao, Prashanth BusiReddyGari, Beiyu Lin, Shuang Li
Neighborhood environments include physical and environmental conditions such as housing quality, roads, and sidewalks, which significantly influence human health and well-being. Traditional methods for assessing these environments, including field surveys and geographic information systems (GIS), are resource-intensive and challenging to evaluate neighborhood environments at scale. Although machine learning offers potential for automated analysis, the laborious process of labeling training data and the lack of accessible models hinder scalability. This study explores the feasibility of large language models (LLMs) such as ChatGPT and Gemini as tools for decoding neighborhood environments (e.g., sidewalk and powerline) at scale. We train a robust YOLOv11-based model, which achieves an average accuracy of 99.13% in detecting six environmental indicators, including streetlight, sidewalk, powerline, apartment, single-lane road, and multilane road. We then evaluate four LLMs, including ChatGPT, Gemini, Claude, and Grok, to assess their feasibility, robustness, and limitations in identifying these indicators, with a focus on the impact of prompting strategies and fine-tuning. We apply majority voting with the top three LLMs to achieve over 88% accuracy, which demonstrates LLMs could be a useful tool to decode the neighborhood environment without any training effort.
Large Language Models (LLMs) have seen rapid advancements in recent years, with models like ChatGPT and DeepSeek, showcasing their remarkable capabilities across diverse domains. While substantial research has been conducted on LLMs in various fields, a comprehensive review focusing on their integration with Computer-Aided Design (CAD) remains notably absent. CAD is the industry standard for 3D modeling and plays a vital role in the design and development of products across different industries. As the complexity of modern designs increases, the potential for LLMs to enhance and streamline CAD workflows presents an exciting frontier. This article presents the first systematic survey exploring the intersection of LLMs and CAD. We begin by outlining the industrial significance of CAD, highlighting the need for AI-driven innovation. Next, we provide a detailed overview of the foundation of LLMs. We also examine both closed-source LLMs as well as publicly available models. The core of this review focuses on the various applications of LLMs in CAD, providing a taxonomy of six key areas where these models are making considerable impact. Finally, we propose several promising future directions for further advancements, which offer vast opportunities for innovation and are poised to shape the future of CAD technology. Github: https://github.com/lichengzhanguom/LLMs-CAD-Survey-Taxonomy
One Bad NOFO? AI Governance in Federal Grantmaking
2025-05-13
Dan Bateyko, Karen Levy
Much scholarship considers how U.S. federal agencies govern artificial intelligence (AI) through rulemaking and their own internal use policies. But agencies have an overlooked AI governance role: setting discretionary grant policy when directing billions of dollars in federal financial assistance. These dollars enable state and local entities to study, create, and use AI. This funding not only goes to dedicated AI programs, but also to grantees using AI in the course of meeting their routine grant objectives. As discretionary grantmakers, agencies guide and restrict what grant winners do -- a hidden lever for AI governance. Agencies pull this lever by setting program objectives, judging criteria, and restrictions for AI use. Using a novel dataset of over 40,000 non-defense federal grant notices of funding opportunity (NOFOs) posted to Grants.gov between 2009 and 2024, we analyze how agencies regulate the use of AI by grantees. We select records mentioning AI and review their stated goals and requirements. We find agencies promoting AI in notice narratives, shaping adoption in ways other records of grant policy might fail to capture. Of the grant opportunities that mention AI, we find only a handful of AI-specific judging criteria or restrictions. This silence holds even when agencies fund AI uses in contexts affecting people's rights and which, under an analogous federal procurement regime, would result in extra oversight. These findings recast grant notices as a site of AI policymaking -- albeit one that is developing out of step with other regulatory efforts and incomplete in its consideration of transparency, accountability, and privacy protections. The paper concludes by drawing lessons from AI procurement scholarship, while identifying distinct challenges in grantmaking that invite further study.
We introduce a method of meta-prompting that jointly produces fluent text for complex tasks while optimizing the similarity of neural states between a human's mental expectation and a Large Language Model's (LLM) neural processing. A technique of agentic reinforcement learning is applied, in which an LLM as a Judge (LLMaaJ) teaches another LLM, through in-context learning, how to produce content by interpreting the intended and unintended generated text traits. To measure human mental beliefs around content production, users modify long form AI-generated text articles before publication at the US Open 2024 tennis Grand Slam. Now, an LLMaaJ can solve the Theory of Mind (ToM) alignment problem by anticipating and including human edits within the creation of text from an LLM. Throughout experimentation and by interpreting the results of a live production system, the expectations of human content reviewers had 100% of alignment with AI 53.8% of the time with an average iteration count of 4.38. The geometric interpretation of content traits such as factualness, novelty, repetitiveness, and relevancy over a Hilbert vector space combines spatial volume (all trait importance) with vertices alignment (individual trait relevance) enabled the LLMaaJ to optimize on Human ToM. This resulted in an increase in content quality by extending the coverage of tennis action. Our work that was deployed at the US Open 2024 has been used across other live events within sports and entertainment.
Dataflow & Tiling Strategies in Edge-AI FPGA Accelerators: A Comprehensive Literature Review
2025-05-13
Zhaoqin Li
Edge-AI applications demand high-throughput, low-latency inference on FPGAs under tight resource and power constraints. This survey provides a comprehensive review of two key architectural decisions for FPGA-based neural network accelerators: (i) the dataflow (the order and manner in which data is moved and reused on chip), and (ii) the tiling/blocking strategy (how large tensors are partitioned to fit on-chip). We first present a broadened taxonomy of canonical dataflow styles: Weight-Stationary, Output-Stationary, Row-Stationary, and No-Local-Reuse, including formal definitions, pseudocode/diagrams, and real FPGA examples. We then discuss analytical frameworks (MAESTRO, Timeloop) and compare them with a concise feature table, illustrating how they model reuse, performance, and hardware costs. Next, we detail multi-level tiling and loop unrolling/pipelining strategies for FPGAs, clarifying how each memory tier (registers, LUTRAM, BRAM, HBM) can be exploited. Our four case studies - FINN, FINN-R, FlightLLM, and SSR - highlight distinct dataflows (from binary streaming to hybrid sparse transformations) and tiling patterns. We include a unified comparison matrix covering platform, precision, throughput, resource utilization, and energy efficiency, plus small block diagrams for each design. We conclude by examining design automation trade-offs among HLS, DSL, and hand-coded RTL, offering a "lessons learned" summary box, and charting future research directions in partial reconfiguration, hybrid dataflows, and domain-specific compiler flows for next-generation edge AI FPGA accelerators.
Generalization in Monitored Markov Decision Processes (Mon-MDPs)
2025-05-13
Montaser Mohammedalamen, Michael Bowling
Reinforcement learning (RL) typically models the interaction between the agent and environment as a Markov decision process (MDP), where the rewards that guide the agent's behavior are always observable. However, in many real-world scenarios, rewards are not always observable, which can be modeled as a monitored Markov decision process (Mon-MDP). Prior work on Mon-MDPs have been limited to simple, tabular cases, restricting their applicability to real-world problems. This work explores Mon-MDPs using function approximation (FA) and investigates the challenges involved. We show that combining function approximation with a learned reward model enables agents to generalize from monitored states with observable rewards, to unmonitored environment states with unobservable rewards. Therefore, we demonstrate that such generalization with a reward model achieves near-optimal policies in environments formally defined as unsolvable. However, we identify a critical limitation of such function approximation, where agents incorrectly extrapolate rewards due to overgeneralization, resulting in undesirable behaviors. To mitigate overgeneralization, we propose a cautious police optimization method leveraging reward uncertainty. This work serves as a step towards bridging this gap between Mon-MDP theory and real-world applications.
ChicGrasp: Imitation-Learning based Customized Dual-Jaw Gripper Control for Delicate, Irregular Bio-products Manipulation
2025-05-13
Amirreza Davar, Zhengtong Xu, Siavash Mahmoudi, Pouya Sohrabipour, Chaitanya Pallerla, Yu She, Wan Shou, Philip Crandall, Dongyi Wang
Automated poultry processing lines still rely on humans to lift slippery, easily bruised carcasses onto a shackle conveyor. Deformability, anatomical variance, and strict hygiene rules make conventional suction and scripted motions unreliable. We present ChicGrasp, an end--to--end hardware--software co-design for this task. An independently actuated dual-jaw pneumatic gripper clamps both chicken legs, while a conditional diffusion-policy controller, trained from only 50 multi--view teleoperation demonstrations (RGB + proprioception), plans 5 DoF end--effector motion, which includes jaw commands in one shot. On individually presented raw broiler carcasses, our system achieves a 40.6\% grasp--and--lift success rate and completes the pick to shackle cycle in 38 s, whereas state--of--the--art implicit behaviour cloning (IBC) and LSTM-GMM baselines fail entirely. All CAD, code, and datasets will be open-source. ChicGrasp shows that imitation learning can bridge the gap between rigid hardware and variable bio--products, offering a reproducible benchmark and a public dataset for researchers in agricultural engineering and robot learning.
High-resolution spectroscopy (HRS) has grown into one of the main techniques to characterise the atmospheres of extrasolar planets. High spectral resolving power allows for the efficient removal of telluric and host-star contamination. Combined with the large collecting area of ground-based telescopes it enables detailed studies of atmospheric species, temperature structure, atmospheric loss, and global winds and circulation patterns. In this review, the wide range of HRS observation and data-analysis techniques are described and literature results discussed. Key findings include: * The highest irradiated planets show a rich spectrum of atomic and ionic species, just like stars. * Retrieval analyses of Hot Jupiters and directly imaged Super- Jupiters point to Solar metallicities and chemistry, but observed samples are still heterogeneous and incomplete. * There appears to be a clear dichotomy between Hot Jupiters with and without atmospheric inversions, depending on their equilibrium temperature. * Some highly irradiated planets exhibit enormous leading and/or trailing tails of helium gas, providing unique insights into planet evolution and atmospheric escape processes. * Minor isotopes of carbon and oxygen are now being detected in gas giant planets and brown dwarfs with the interesting potential to shed light on formation pathways. A list of potential pitfalls is provided for those new to the field, and synergies with JWST are discussed. HRS has a great future ahead with the advent of the extremely large telescopes, promising to bring temperate rocky exoplanets into view with their increase in HRS detection speed of up to three orders of magnitude.
ALMA FACTS. II. Large Scale Variations in the 12CO(J=2-1) to 12CO(J=1-0) Line Ratio in Nearby Galaxies
2025-05-13
Shinya Komugi, Tsuyoshi Sawada, Jin Koda, Fumi Egusa, Fumiya Maeda, Akihiko Hirota, Amanda M. Lee
We present 12CO(J=1-0) mapping observations over ~1/2 of the optical disk of 12 nearby galaxies from the Fundamental CO 1-0 Transition Survey of nearby galaxies (FACTS), using the ALMA Total Power array. Variations in the 12CO(J=2-1)/12CO(J=1-0) line ratio r21 are investigated. The luminosity-weighted r21 of the 11 sample galaxies ranges from 0.52 to 0.69 with an average of 0.61. We use position-velocity diagrams along the major axis and tilted ring models to separate the normal rotating galactic disk from kinematic outliers that deviate from pure circular rotation. We find that r21 is systematically higher in outliers compared to the disk. We compare r21 between SA, SAB and SB galaxies, and find no significant difference in the average r21 depending on the presence of galactic bars. We find, however, that the radial gradient in r21 is bimodal, where a group containing all SA galaxies prefer constant or very shallow r21 gradients out 40% of the optical radius, while another group containing all SB galaxies have a steep r21 gradient, decreasing by ~20% before 40% of the optical radius, which also corresponds to the radius of the stellar bar. After this radius, these galaxies become consistent with a constant or shallow trend in r21. The large scale trend in r21 can have implications for how we interpret observations made solely in the 12CO(J=2-1) line.
Assessing and Advancing Benchmarks for Evaluating Large Language Models in Software Engineering Tasks
2025-05-13
Xing Hu, Feifei Niu, Junkai Chen, Xin Zhou, Junwei Zhang, Junda He, Xin Xia, David Lo
Large language models (LLMs) are gaining increasing popularity in software engineering (SE) due to their unprecedented performance across various applications. These models are increasingly being utilized for a range of SE tasks, including requirements engineering and design, code analysis and generation, software maintenance, and quality assurance. As LLMs become more integral to SE, evaluating their effectiveness is crucial for understanding their potential in this field. In recent years, substantial efforts have been made to assess LLM performance in various SE tasks, resulting in the creation of several benchmarks tailored to this purpose. This paper offers a thorough review of 191 benchmarks, addressing three main aspects: what benchmarks are available, how benchmarks are constructed, and the future outlook for these benchmarks. We begin by examining SE tasks such as requirements engineering and design, coding assistant, software testing, AIOPs, software maintenance, and quality management. We then analyze the benchmarks and their development processes, highlighting the limitations of existing benchmarks. Additionally, we discuss the successes and failures of LLMs in different software tasks and explore future opportunities and challenges for SE-related benchmarks. We aim to provide a comprehensive overview of benchmark research in SE and offer insights to support the creation of more effective evaluation tools.
A century of metric Diophantine approximation and half a decade since Koukoulopoulos-Maynard
2025-05-13
Manuel Hauke
In this note, we review the history of Khintchine's Theorem which is the foundation of metric Diophantine approximation, and discuss several generalizations and recent breakthroughs in this area. We focus particularly on the direction of the Duffin-Schaeffer Conjecture which was spectacularly proven in 2020. We present some simplified key ideas of the proof that can also be applied in various other areas of number theory.
Galaxies in the Epoch of Reionization Are All Bark and No Bite -- Plenty of Ionizing Photons, Low Escape Fractions
2025-05-13
Casey Papovich, Justin W. Cole, Weida Hu, Steven L. Finkelstein, Lu Shen, Pablo Arrabal Haro, Ricardo O. Amorín, Bren Backhaus, Micaela B. Bagley, Rachana Bhatawdekar, Antonello Calabró, Adam C. Carnall, Nikko Cleri, Emanuele Daddi, Mark Dickinson, Norman Grogin, Benne W. Holwerda, Anne E. Jaskot, Anton E. Koekemoer, Mario Llerena, Ray A. Lucas, Sara Mascia, Fabio Pacucci, Laura Pentericci, Pablo G. Pérez-González, Nor Pirzkal, Srinivasan Raghunathan, Lisa-Marie Seillé, Rachel Somerville, L. Y. Aaron Yung
Early results from JWST suggest that epoch-of-reionization (EoR) galaxies produce copious ionizing photons, which, if they escape efficiently, could cause reionization to occur too early. We study this problem using JWST imaging and prism spectroscopy for 426 galaxies at 4.5 < z < 9.0. We fit these data simultaneously with stellar--population and nebular--emission models that include a parameter for the fraction of ionizing photons that escape the galaxy, f_esc. We find that the ionization production efficiency, $\xi_\mathrm{ion}$ = Q(H) / L(UV), increases with redshift and decreasing UV luminosity, but shows significant scatter, $\sigma( \log \xi_\mathrm{ion})$ ~= 0.3 dex. The inferred escape fractions averaged over the population are $\langle f_\mathrm{esc} \rangle$ = 3 +\pm 1% with no indication of evolution over 4.5 < z < 9.0. This implies that in our models nearly all of the ionizing photons need to be absorbed to account for the nebular emission. We compute the impact of our results on reionization, including the distributions for $\xi_\mathrm{ion}$ and f_esc, and the evolution and uncertainty of the UV luminosity function. Considering galaxies brighter than M(UV) < -16 mag, we would produce an IGM hydrogen-ionized fraction of x_e=0.5 at 5.5 < z < 7.1, possibly too late compared to constraints from from QSO sightlines. Including fainter galaxies, M(UV) < -14 mag, we obtain x_e = 0.5 at 6.7 < z < 9.6, fully consistent with QSO and CMB data. This implies that EoR galaxies produce plenty of ionizing photons, but these do not efficiently escape. This may be a result of high gas column densities combined with burstier star-formation histories, which limit the time massive stars are able to clear channels through the gas for ionizing photons to escape.
GA-NIFS: Mapping $z\simeq3.5$ AGN-driven ionized outflows in the COSMOS field
2025-05-13
E. Bertola, G. Cresci, G. Venturi, M. Perna, C. Circosta, G. Tozzi, I. Lamperti, C. Vignali, S. Arribas, A. J. Bunker, S. Charlot, S. Carniani, R. Maiolino, B. Rodríguez Del Pino, H. Übler, C. J. Willott, T. Böker, M. A. Marshall, E. Parlanti, J. Scholtz
Active Galactic Nuclei (AGN) are a key ingredient in galaxy evolution, possibly shaping galaxy growth through the generation of powerful outflows. Little is known regarding AGN-driven ionized outflows in moderate-luminosity AGN (logLbol[erg/s]<47) beyond cosmic noon (z>3). We present the first systematic analysis of the ionized outflow properties of a sample of X-ray-selected AGN (logLx[erg/s]>44) from the COSMOS-Legacy field at z~3.5 and with logLbol[erg/s]=45.2-46.7, by using JWST NIRSpec/IFU spectroscopic observations as part of the GA-NIFS program. We spectrally isolate and spatially resolve the ionized outflows, by performing a multi-component kinematic decomposition of the rest-frame optical emission lines. JWST/NIRSpec IFU data also revealed a wealth of close-by companions, of both non-AGN and AGN nature, and ionized gas streams likely tracing tidal structures and large-scale ionized gas nebulae, extending up to the circum-galactic medium. Ionized outflows are detected in all COS-AGN targets, which we compare with previous results from the literature up to z~3, opportunely (re-)computed for a coherent comparison. We normalize outflow energetics ($\dot{M}_{out}$, $\dot{E}_{out}$) to the outflow density to standardize the various assumptions that were made in the literature. Our choice is equal to assuming that each outflow has the same gas density. We find GA-NIFS AGN to show outflows consistent with literature results, within the large scatter shown by the collected measurements, suggesting no strong evolution with redshift in terms of total mass outflow rate, energy budget and outflow velocity for fixed bolometric luminosity. Moreover, we find no clear redshift evolution of the ratio of mass outflow rate and kinetic power over AGN bolometric luminosity beyond z>1. In general, our results indicate no significant evolution of the physics driving outflows beyond z~3. [abridged]
Marvelous Metals: Surveying the Circumgalactic Medium of Simulated Dwarf Galaxies
2025-05-13
Daniel R. Piacitelli, Alyson M. Brooks, Charlotte Christensen, N. Nicole Sanchez, Yakov Faerman, Sijing Shen, Akaxia Cruz, Ben Keller, Thomas R. Quinn, James Wadsley
Dwarf galaxies are uniquely sensitive to energetic feedback processes and are known to experience substantial mass and metal loss from their disk. Here, we investigate the circumgalactic medium (CGM) of 64 isolated dwarf galaxies ($6.0<$log(M$_*/M_{\odot}$)$<9.5$) at $z=0$ from the Marvel-ous Dwarfs and Marvelous Massive Dwarfs hydrodynamic simulations. Our galaxies produce column densities broadly consistent with current observations. We investigate these column densities in the context of mass and metal retention rates and the physical properties of the CGM. We find $48\pm11\%$ of all baryons within $R_{200c}$ reside in the CGM, with $\sim70\%$ of CGM mass existing in a warm gas phase, $10^{4.5}40\%$ towards lower masses. Our findings highlight the CGM (primarily its warm component) as a key reservoir of mass and metals for dwarf galaxies across stellar masses and underscore its importance in understanding the baryon cycle in the low-mass regime. Finally, we provide individual galaxy properties of our full sample and quantify the fraction of ultraviolet observable mass to support future observational programs, particularly those aimed at performing a metal budget around dwarf galaxies.
Generative AI for Autonomous Driving: Frontiers and Opportunities
2025-05-13
Yuping Wang, Shuo Xing, Cui Can, Renjie Li, Hongyuan Hua, Kexin Tian, Zhaobin Mo, Xiangbo Gao, Keshu Wu, Sulong Zhou, Hengxu You, Juntong Peng, Junge Zhang, Zehao Wang, Rui Song, Mingxuan Yan, Walter Zimmer, Xingcheng Zhou, Peiran Li, Zhaohan Lu, Chia-Ju Chen, Yue Huang, Ryan A. Rossi, Lichao Sun, Hongkai Yu, Zhiwen Fan, Frank Hao Yang, Yuhao Kang, Ross Greer, Chenxi Liu, Eun Hak Lee, Xuan Di, Xinyue Ye, Liu Ren, Alois Knoll, Xiaopeng Li, Shuiwang Ji, Masayoshi Tomizuka, Marco Pavone, Tianbao Yang, Jing Du, Ming-Hsuan Yang, Hua Wei, Ziran Wang, Yang Zhou, Jiachen Li, Zhengzhong Tu
Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation, reasoning, planning, and multimodal understanding. This revolutionary force offers the most promising path yet toward solving one of engineering's grandest challenges: achieving reliable, fully autonomous driving, particularly the pursuit of Level 5 autonomy. This survey delivers a comprehensive and critical synthesis of the emerging role of GenAI across the autonomous driving stack. We begin by distilling the principles and trade-offs of modern generative modeling, encompassing VAEs, GANs, Diffusion Models, and Large Language Models (LLMs). We then map their frontier applications in image, LiDAR, trajectory, occupancy, video generation as well as LLM-guided reasoning and decision making. We categorize practical applications, such as synthetic data workflows, end-to-end driving strategies, high-fidelity digital twin systems, smart transportation networks, and cross-domain transfer to embodied AI. We identify key obstacles and possibilities such as comprehensive generalization across rare cases, evaluation and safety checks, budget-limited implementation, regulatory compliance, ethical concerns, and environmental effects, while proposing research plans across theoretical assurances, trust metrics, transport integration, and socio-technical influence. By unifying these threads, the survey provides a forward-looking reference for researchers, engineers, and policymakers navigating the convergence of generative AI and advanced autonomous mobility. An actively maintained repository of cited works is available at https://github.com/taco-group/GenAI4AD.
Will AI Take My Job? Evolving Perceptions of Automation and Labor Risk in Latin America
2025-05-13
Andrea Cremaschi, Dae-Jin Lee, Manuele Leonelli
As artificial intelligence and robotics increasingly reshape the global labor market, understanding public perceptions of these technologies becomes critical. We examine how these perceptions have evolved across Latin America, using survey data from the 2017, 2018, 2020, and 2023 waves of the Latinobar\'ometro. Drawing on responses from over 48,000 individuals across 16 countries, we analyze fear of job loss due to artificial intelligence and robotics. Using statistical modeling and latent class analysis, we identify key structural and ideological predictors of concern, with education level and political orientation emerging as the most consistent drivers. Our findings reveal substantial temporal and cross-country variation, with a notable peak in fear during 2018 and distinct attitudinal profiles emerging from latent segmentation. These results offer new insights into the social and structural dimensions of AI anxiety in emerging economies and contribute to a broader understanding of public attitudes toward automation beyond the Global North.
Federated Large Language Models: Feasibility, Robustness, Security and Future Directions
2025-05-13
Wenhao Jiang, Yuchuan Luo, Guilin Deng, Silong Chen, Xu Yang, Shihong Wu, Xinwen Gao, Lin Liu, Shaojing Fu
The integration of Large Language Models (LLMs) and Federated Learning (FL) presents a promising solution for joint training on distributed data while preserving privacy and addressing data silo issues. However, this emerging field, known as Federated Large Language Models (FLLM), faces significant challenges, including communication and computation overheads, heterogeneity, privacy and security concerns. Current research has primarily focused on the feasibility of FLLM, but future trends are expected to emphasize enhancing system robustness and security. This paper provides a comprehensive review of the latest advancements in FLLM, examining challenges from four critical perspectives: feasibility, robustness, security, and future directions. We present an exhaustive survey of existing studies on FLLM feasibility, introduce methods to enhance robustness in the face of resource, data, and task heterogeneity, and analyze novel risks associated with this integration, including privacy threats and security challenges. We also review the latest developments in defense mechanisms and explore promising future research directions, such as few-shot learning, machine unlearning, and IP protection. This survey highlights the pressing need for further research to enhance system robustness and security while addressing the unique challenges posed by the integration of FL and LLM.
Location Intelligence (LI), the science of transforming location-centric geospatial data into actionable knowledge, has become a cornerstone of modern spatial decision-making. The rapid evolution of Geospatial Representation Learning is fundamentally reshaping LI development through two successive technological revolutions: the deep learning breakthrough and the emerging large language model (LLM) paradigm. While deep neural networks (DNNs) have demonstrated remarkable success in automated feature extraction from structured geospatial data (e.g., satellite imagery, GPS trajectories), the recent integration of LLMs introduces transformative capabilities for cross-modal geospatial reasoning and unstructured geo-textual data processing. This survey presents a comprehensive review of geospatial representation learning across both technological eras, organizing them into a structured taxonomy based on the complete pipeline comprising: (1) data perspective, (2) methodological perspective and (3) application perspective. We also highlight current advancements, discuss existing limitations, and propose potential future research directions in the LLM era. This work offers a thorough exploration of the field and providing a roadmap for further innovation in LI. The summary of the up-to-date paper list can be found in https://github.com/CityMind-Lab/Awesome-Location-Intelligence and will undergo continuous updates.
Roman Observations Time Allocation Committee: Final Report and Recommendations
2025-05-13
Roman Observations Time Allocation Committee, Core Community Survey Definition Committees
The Nancy Grace Roman Space Telescope is poised to revolutionize our scientific understanding of exoplanets, dark matter, dark energy, and general astrophysics, including through an innovative community approach to defining and executing sky surveys. The Roman Observations Time Allocation Committee (ROTAC) was convened to recommend time allocations for the three Core Community Surveys (CCS) using the Wide Field Instrument (WFI): the High Latitude Wide Area Survey, the High Latitude Time Domain Survey, and the Galactic Bulge Time Domain Survey, as well as balance the time allocation for the General Astrophysics Surveys. Each CCS had a corresponding Definition Committee that collected community input and designed proposals for a nominal (in-guide) survey, as well as underguide and overguide options with smaller and larger time allocations, respectively. These options explored different ways of fulfilling the mission science requirements while maximizing general astrophysics science goals enabled by the surveys. In this report, the ROTAC lays out its recommendations for the three CCS observing designs and the WFI time allotment for CCS (74.5%) and the General Astrophysics Surveys (25.5%).
Electrochemical performance and diffusion kinetics of a NASICON type Na$_{3.3}$Mn$_{1.2}$Ti$_{0.75}$Mo$_{0.05}$(PO$_4$)$_3$/C cathode for low-cost sodium-ion batteries
2025-05-13
Madhav Sharma, Rajendra S. Dhaka
We report the electrochemical performance and diffusion kinetics of a newly designed NASICON type Na$_{3.3}$Mn$_{1.2}$Ti$_{0.75}$Mo$_{0.05}$(PO$_4$)$_3$/C composite material as a cathode for cost-effective sodium-ion batteries. A novel strategy of small Mo doping successfully stabilizes the sample having high Mn content in single phase rhombohedral symmerty. The high-resolution microscopy analysis reveals nanocrystallites of around $\sim$18 nm, uniformly embedded within the semi-graphitic carbon matrix, which enhances the surface electronic conductivity and effectively shortens the sodium-ion diffusion path. More importantly, we demonstrate a stable electrochemical behavior, with enhanced discharge capacity of 124 mAh/g at 0.1 C, having good reversibility and retaining 77\% of its capacity after 300 cycles, and 70\% even after 400 cycles at 2 C. The sodium-ion diffusion coefficients, estimated using both galvanostatic intermittent titration technique (GITT) and cyclic voltammetry are found to lie within the range of $10^{-9}$ to $10^{-11}$~cm$^2$/s. Additionally, the bond-valence site energy mapping predicted a sodium-ion migration energy barrier of 0.76 eV. A detailed distribution of relaxation times (DRT) analysis is used to deconvolute the electrochemical impedance spectra into distinct processes based on their characteristic relaxation times. Notably, the solid-state diffusion of sodium ions within the bulk electrode, with a relaxation time of $\sim$50 s, shows a consistent trend with the diffusion coefficients obtained from GITT and Warburg-based evaluations across the state of charge.
Must Read: A Systematic Survey of Computational Persuasion
2025-05-12
Nimet Beyza Bozdag, Shuhaib Mehri, Xiaocheng Yang, Hyeonjeong Ha, Zirui Cheng, Esin Durmus, Jiaxuan You, Heng Ji, Gokhan Tur, Dilek Hakkani-Tür
Persuasion is a fundamental aspect of communication, influencing decision-making across diverse contexts, from everyday conversations to high-stakes scenarios such as politics, marketing, and law. The rise of conversational AI systems has significantly expanded the scope of persuasion, introducing both opportunities and risks. AI-driven persuasion can be leveraged for beneficial applications, but also poses threats through manipulation and unethical influence. Moreover, AI systems are not only persuaders, but also susceptible to persuasion, making them vulnerable to adversarial attacks and bias reinforcement. Despite rapid advancements in AI-generated persuasive content, our understanding of what makes persuasion effective remains limited due to its inherently subjective and context-dependent nature. In this survey, we provide a comprehensive overview of computational persuasion, structured around three key perspectives: (1) AI as a Persuader, which explores AI-generated persuasive content and its applications; (2) AI as a Persuadee, which examines AI's susceptibility to influence and manipulation; and (3) AI as a Persuasion Judge, which analyzes AI's role in evaluating persuasive strategies, detecting manipulation, and ensuring ethical persuasion. We introduce a taxonomy for computational persuasion research and discuss key challenges, including evaluating persuasiveness, mitigating manipulative persuasion, and developing responsible AI-driven persuasive systems. Our survey outlines future research directions to enhance the safety, fairness, and effectiveness of AI-powered persuasion while addressing the risks posed by increasingly capable language models.
Clouds can enhance direct imaging detection of O2 and O3 on terrestrial exoplanets
2025-05-12
Huanzhou Yang, Michelle Hu, Dorian S. Abbot
Clouds are often considered a highly uncertain barrier for detecting biosignatures on exoplanets, especially given intuition gained from transit surveys. However, for direct imaging reflected light observations, clouds could increase the observational signal by increasing reflected light. Here we constrain the impact of clouds on the detection of O2 and O3 by a direct imaging telescope such as the Habitable Worlds Observatory (HWO) using observations simulated with the Planetary Spectrum Generator (PSG). We first perform sensitivity tests to show that low clouds enhance O2 and O3 detectability while high clouds diminish it, and the effect is greater when cloud particles are smaller. We next apply clouds produced by the cloud microphysics model CARMA with varied planetary parameters and clouds drawn from observations of different types of clouds on Earth to PSG. We find that clouds are likely to increase the SNR of O2 and O3 for terrestrial exoplanets under a wide range of scenarios. This work provides important constraints on the impact of clouds on observations by telescopes including HWO.
Skeletonization of neuronal processes using Discrete Morse techniques from computational topology
2025-05-12
Samik Banerjee, Caleb Stam, Daniel J. Tward, Steven Savoia, Yusu Wang, Partha P. Mitra
To understand biological intelligence we need to map neuronal networks in vertebrate brains. Mapping mesoscale neural circuitry is done using injections of tracers that label groups of neurons whose axons project to different brain regions. Since many neurons are labeled, it is difficult to follow individual axons. Previous approaches have instead quantified the regional projections using the total label intensity within a region. However, such a quantification is not biologically meaningful. We propose a new approach better connected to the underlying neurons by skeletonizing labeled axon fragments and then estimating a volumetric length density. Our approach uses a combination of deep nets and the Discrete Morse (DM) technique from computational topology. This technique takes into account nonlocal connectivity information and therefore provides noise-robustness. We demonstrate the utility and scalability of the approach on whole-brain tracer injected data. We also define and illustrate an information theoretic measure that quantifies the additional information obtained, compared to the skeletonized tracer injection fragments, when individual axon morphologies are available. Our approach is the first application of the DM technique to computational neuroanatomy. It can help bridge between single-axon skeletons and tracer injections, two important data types in mapping neural networks in vertebrates.
Diabetes is one of the most prevalent diseases worldwide, characterized by persistently high blood sugar levels, capable of damaging various internal organs and systems. Diabetes patients require routine check-ups, resulting in a time series of laboratory records, such as hemoglobin A1c, which reflects each patient's health behavior over time and informs their doctor's recommendations. Clustering patients into groups based on their entire time series data assists doctors in making recommendations and choosing treatments without the need to review all records. However, time series clustering of this type of dataset introduces some challenges; patients visit their doctors at different time points, making it difficult to capture and match trends, peaks, and patterns. Additionally, two aspects must be considered: differences in the levels of laboratory results and differences in trends and patterns. To address these challenges, we introduce a new clustering algorithm called Time and Trend Traveling Time Series Clustering (4TaStiC), using a base dissimilarity measure combined with Euclidean and Pearson correlation metrics. We evaluated this algorithm on artificial datasets, comparing its performance with that of seven existing methods. The results show that 4TaStiC outperformed the other methods on the targeted datasets. Finally, we applied 4TaStiC to cluster a cohort of 1,989 type 2 diabetes patients at Siriraj Hospital. Each group of patients exhibits clear characteristics that will benefit doctors in making efficient clinical decisions. Furthermore, the proposed algorithm can be applied to contexts outside the medical field.
We review recent advancements in cosmology with galaxy clusters. Galaxy clusters are the most massive objects in the Universe. Consequently the cluster number density as a function of cluster mass, or cluster abundance, is sensitive to cosmological parameters, particularly the matter density of the Universe $\Omega_{\rm m}$ and the amplitude of matter density fluctuation $\sigma_8$. In this review, we describe the methods used to detect galaxy clusters through optical near-infrared (O-NIR), X-ray, and CMB observations, outlining the advantages and disadvantages of cluster detection through different wavelengths. We describe methods for measuring cluster mass, with a particular focus on calibration by WL measurements. We then discuss how the connection between observables in different wavelengths and cluster abundance can be modeled through a cluster selection function and MOR, and quantify the impact of marginalization of nuisance parameters on cosmological constraints. Finally, we also walk through the recent results of cosmological constraints by cluster abundance with the O-NIR, X-ray, and CMB observations.
Introduction to the proof of the Kakeya conjecture
2025-05-12
Larry Guth
Recently, Hong Wang and Joshua Zahl announced a proof of the 3-dimensional Kakeya conjecture. This is a survey article on the proof of Kakeya. We introduce the problem, discuss previous work and some of the difficulties of the problem, and describe the new ideas in the recent work.
Neural Brain: A Neuroscience-inspired Framework for Embodied Agents
2025-05-12
Jian Liu, Xiongtao Shi, Thai Duy Nguyen, Haitian Zhang, Tianxiang Zhang, Wei Sun, Yanjie Li, Athanasios V. Vasilakos, Giovanni Iacca, Arshad Ali Khan, Arvind Kumar, Jae Won Cho, Ajmal Mian, Lihua Xie, Erik Cambria, Lin Wang
The rapid evolution of artificial intelligence (AI) has shifted from static, data-driven models to dynamic systems capable of perceiving and interacting with real-world environments. Despite advancements in pattern recognition and symbolic reasoning, current AI systems, such as large language models, remain disembodied, unable to physically engage with the world. This limitation has driven the rise of embodied AI, where autonomous agents, such as humanoid robots, must navigate and manipulate unstructured environments with human-like adaptability. At the core of this challenge lies the concept of Neural Brain, a central intelligence system designed to drive embodied agents with human-like adaptability. A Neural Brain must seamlessly integrate multimodal sensing and perception with cognitive capabilities. Achieving this also requires an adaptive memory system and energy-efficient hardware-software co-design, enabling real-time action in dynamic environments. This paper introduces a unified framework for the Neural Brain of embodied agents, addressing two fundamental challenges: (1) defining the core components of Neural Brain and (2) bridging the gap between static AI models and the dynamic adaptability required for real-world deployment. To this end, we propose a biologically inspired architecture that integrates multimodal active sensing, perception-cognition-action function, neuroplasticity-based memory storage and updating, and neuromorphic hardware/software optimization. Furthermore, we also review the latest research on embodied agents across these four aspects and analyze the gap between current AI systems and human intelligence. By synthesizing insights from neuroscience, we outline a roadmap towards the development of generalizable, autonomous agents capable of human-level intelligence in real-world scenarios.
2025-05-12
Tian Ma, Shiyong Tan, Rui Ni, Hendrik Hessenkemper, Andrew D. Bragg
Experiments using 3D Lagrangian tracking are used to investigate Kolmogorov scaling below the bubble size in bubble-induced turbulence (BIT). Second and third order structure functions reveal approximate Kolmogorov scaling for homogeneous bubble swarms. A new scaling for the kinetic energy dissipation rate is derived and shown to be in excellent agreement with the data. Using this we predict the scale separation below the bubble size as a function of the parameters and find that a large inertial range is not possible in BIT since bubbles of the required size would quickly break down.
Multilayer Perceptron (MLP), as a simple yet powerful model, continues to be widely used in classification and regression tasks. However, traditional MLPs often struggle to efficiently capture nonlinear relationships in load data when dealing with complex datasets. Kolmogorov-Arnold Networks (KAN), inspired by the Kolmogorov-Arnold representation theorem, have shown promising capabilities in modeling complex nonlinear relationships. In this study, we explore the performance of KANs within federated learning (FL) frameworks and compare them to traditional Multilayer Perceptrons. Our experiments, conducted across four diverse datasets demonstrate that KANs consistently outperform MLPs in terms of accuracy, stability, and convergence efficiency. KANs exhibit remarkable robustness under varying client numbers and non-IID data distributions, maintaining superior performance even as client heterogeneity increases. Notably, KANs require fewer communication rounds to converge compared to MLPs, highlighting their efficiency in FL scenarios. Additionally, we evaluate multiple parameter aggregation strategies, with trimmed mean and FedProx emerging as the most effective for optimizing KAN performance. These findings establish KANs as a robust and scalable alternative to MLPs for federated learning tasks, paving the way for their application in decentralized and privacy-preserving environments.
Deep Learning Advances in Vision-Based Traffic Accident Anticipation: A Comprehensive Review of Methods,Datasets,and Future Directions
2025-05-12
Yi Zhang, Wenye Zhou, Ruonan Lin, Xin Yang, Hao Zheng
Traffic accident prediction and detection are critical for enhancing road safety,and vision-based traffic accident anticipation (Vision-TAA) has emerged as a promising approach in the era of deep learning.This paper reviews 147 recent studies,focusing on the application of supervised,unsupervised,and hybrid deep learning models for accident prediction,alongside the use of real-world and synthetic datasets.Current methodologies are categorized into four key approaches: image and video feature-based prediction, spatiotemporal feature-based prediction, scene understanding,and multimodal data fusion.While these methods demonstrate significant potential,challenges such as data scarcity,limited generalization to complex scenarios,and real-time performance constraints remain prevalent. This review highlights opportunities for future research,including the integration of multimodal data fusion, self-supervised learning,and Transformer-based architectures to enhance prediction accuracy and scalability.By synthesizing existing advancements and identifying critical gaps, this paper provides a foundational reference for developing robust and adaptive Vision-TAA systems,contributing to road safety and traffic management.
The HOSTS Survey: Suspected variable dust emission and constraints on companions around $θ$ Boo
2025-05-12
G. Garreau, D. Defrère, S. Ertel, V. Faramaz-Gorka, G. Bryden, M. Sommer, D. Mesa, K. Wagner, T. De Prins, R. Laugier, A. Weinberger, J. Farinato, C. Haniff, P. M. Hinz, J. W. Isbell, G. M. Kennedy, A. Lorenzetto, E. R. Maier, L. Marafatto, S. Marino, M. A. Martinod, B. Mennesson, H. Rousseau, E. Spalding, D. Vassallo, M. C. Wyatt
During the HOSTS survey by the LBTI, an excess emission from the main sequence star $\theta$ Boo (F7V spectral type, 14.5pc distance) was observed. This excess indicates the presence of exozodiacal dust near the habitable zone (HZ) of the star. Previous observations from Spitzer and Herschel showed no sign of outer cold dust within their respective detection limits. Additional nulling and high-contrast AO observations were taken to spatially constrain the dust distribution, search for variability, and directly image potential companions in the system. This study presents the results of these observations and provides an interpretation of the inner system's architecture. The star was observed using the LBTI's N'-band nulling mode during three epochs in 2017, 2018, and 2023. The dust distribution is modeled and constrained for each epoch using the standard LBTI nulling pipeline, assuming a vertically thin disk with a face-on inclination. In addition, high-contrast AO observations are performed in the L'-band and H-band to constrain the presence of substellar companions around the star. Several solutions are found for the dust distribution, and for each epoch. However, the LBTI nulling observations are not able to discriminate between them. Using the upper limits from previous observations, we constrain the representative size of the dust grains around 3-5$\mu$m. A tentative increase in dust brightness is also measured at the Earth-equivalent insolation distance between 2017 and 2023. Several options are considered to explain the origin of the observed dust and its variability, but no clear sources could be identified from the current observations. Partly because our high-contrast AO observations could only constrain the presence of companions down to $11M_\text{Jup}$ at 1.3" separation.
Leveraging large language model (LLM) based agents to simulate human social behaviors has recently gained significant attention. In this paper, we introduce a novel social simulator called YuLan-OneSim. Compared to previous works, YuLan-OneSim distinguishes itself in five key aspects: (1) Code-free scenario construction: Users can simply describe and refine their simulation scenarios through natural language interactions with our simulator. All simulation code is automatically generated, significantly reducing the need for programming expertise. (2) Comprehensive default scenarios: We implement 50 default simulation scenarios spanning 8 domains, including economics, sociology, politics, psychology, organization, demographics, law, and communication, broadening access for a diverse range of social researchers. (3) Evolvable simulation: Our simulator is capable of receiving external feedback and automatically fine-tuning the backbone LLMs, significantly enhancing the simulation quality. (4) Large-scale simulation: By developing a fully responsive agent framework and a distributed simulation architecture, our simulator can handle up to 100,000 agents, ensuring more stable and reliable simulation results. (5) AI social researcher: Leveraging the above features, we develop an AI social researcher. Users only need to propose a research topic, and the AI researcher will automatically analyze the input, construct simulation environments, summarize results, generate technical reports, review and refine the reports--completing the social science research loop. To demonstrate the advantages of YuLan-OneSim, we conduct experiments to evaluate the quality of the automatically generated scenarios, the reliability, efficiency, and scalability of the simulation process, as well as the performance of the AI social researcher.
Security through the Eyes of AI: How Visualization is Shaping Malware Detection
2025-05-12
Matteo Brosolo, Asmitha K. A., Rafidha Rehiman K. A., Muhammed Shafi K. P., Serena Nicolazzo, Antonino Nocera, Vinod P
Malware, a persistent cybersecurity threat, increasingly targets interconnected digital systems such as desktop, mobile, and IoT platforms through sophisticated attack vectors. By exploiting these vulnerabilities, attackers compromise the integrity and resilience of modern digital ecosystems. To address this risk, security experts actively employ Machine Learning or Deep Learning-based strategies, integrating static, dynamic, or hybrid approaches to categorize malware instances. Despite their advantages, these methods have inherent drawbacks and malware variants persistently evolve with increased sophistication, necessitating advancements in detection strategies. Visualization-based techniques are emerging as scalable and interpretable solutions for detecting and understanding malicious behaviors across diverse platforms including desktop, mobile, IoT, and distributed systems as well as through analysis of network packet capture files. In this comprehensive survey of more than 100 high-quality research articles, we evaluate existing visualization-based approaches applied to malware detection and classification. As a first contribution, we propose a new all-encompassing framework to study the landscape of visualization-based malware detection techniques. Within this framework, we systematically analyze state-of-the-art approaches across the critical stages of the malware detection pipeline. By analyzing not only the single techniques but also how they are combined to produce the final solution, we shed light on the main challenges in visualization-based approaches and provide insights into the advancements and potential future directions in this critical field.
The JDISC Survey: Linking the Physics and Chemistry of Inner and Outer Protoplanetary Disk Zones
2025-05-12
Nicole Arulanantham, Colette Salyk, Klaus Pontoppidan, Andrea Banzatti, Ke Zhang, Karin Öberg, Feng Long, John Carr, Joan Najita, Ilaria Pascucci, María José Colmenares, Chengyan Xie, Jane Huang, Joel Green, Sean M. Andrews, Geoffrey A. Blake, Edwin A. Bergin, Paola Pinilla, Miguel Vioque, Emma Dahl, Eshan Raul, Sebastiaan Krijt, the JDISCS Collaboration
Mid-infrared spectroscopy of protoplanetary disks provides a chemical inventory of gas within a few au, where planets are readily detected around older stars. With the JWST Disk Infrared Spectral Chemistry Survey (JDISCS), we explore demographic trends among 31 disks observed with MIRI (MRS) and with previous ALMA millimeter continuum imaging at high angular resolution (5-10 au). With these S/N $\sim$200-450 spectra, we report emission from H$_2$O, OH, CO, C$_2$H$_2$, HCN, CO$_2$, [Ne II], [Ne III], and [Ar II]. Emission from H$_2$O, OH and CO is nearly ubiquitous for low-mass stars, and detection rates of all molecules are higher than for similar disks observed with Spitzer-IRS. Slab model fits to the molecular emission lines demonstrate that emission from C$_2$H$_2$, HCN, and possibly CO$_2$ is optically thin; thus since column densities and emitting radii are degenerate, observations are actually sensitive to the total molecular mass. C$_2$H$_2$ and HCN emission also typically originate in a hotter region ($920^{+70}_{-130}$, $820^{+70}_{-130}$ K, respectively) than CO$_2$ ($600^{+200}_{-160}$ K). The HCN to cold H$_2$O luminosity ratios are generally smaller in smooth disks, consistent with more efficient water delivery via icy pebbles in the absence of large dust substructures. The molecular emission line luminosities are also correlated with mass accretion rates and infrared spectral indices, similar to trends reported from Spitzer-IRS surveys. This work demonstrates the power of combining multi-wavelength observations to explore inner disk chemistry as a function of outer disk and stellar properties, which will continue to grow as the sample of observed Class II systems expands in the coming JWST observation cycles.
A Systematic Mapping Study on Contract-based Software Design for Dependable Systems
2025-05-12
Fazli Faruk Okumus, Amra Ramic, Stefan Kugele
Background: Contract-based Design (CbD) is a valuable methodology for software design that allows annotation of code and architectural components with contracts, thereby enhancing clarity and reliability in software development. It establishes rules that outline the behaviour of software components and their interfaces and interactions. This modular approach enables the design process to be segmented into smaller, independently developed, tested, and verified system components, ultimately leading to more robust and dependable software. Aim: Despite the significance and well-established theoretical background of CbD, there is a need for a comprehensive systematic mapping study for reliable software systems. Our study provides an evidence-based overview of a method and demonstrates its practical feasibility. Method: To conduct this study, we systematically searched three different databases using specially formulated queries, which initially yielded 1,221 primary studies. After voting, we focused on 288 primary studies for more detailed analysis. Finally, a collaborative review allowed us to gather relevant evidence and information to address our research questions. Results: Our findings suggest potential avenues for future research trajectories in CbD, emphasising its role in improving the dependability of software systems. We highlight maturity levels across different domains and identify areas that may benefit from further research. Conclusion: Although CbD is a well-established software design approach, a more comprehensive literature review is needed to clarify its theoretical state about dependable systems. Our study addresses this gap by providing a detailed overview of CbD from various perspectives, identifying key gaps, and suggesting future research directions.
Competing interactions stabilize exotic mesoscopic structures, yet the microscopic mechanisms by which they influence non-equilibrium processes leading to disordered states remain largely unexplored, despite their critical role in self-assembly across a range of nanomaterials and biological systems. Here, we numerically investigate the structural evolution in charged colloidal model systems, where short-range attractions and long-range repulsions compete. We reveal that these two interaction scales drive sequential ordering within clusters, from tetrahedra motifs to linear aggregates with chiral order. This process disrupts early-stage percolated networks, resulting in reentrant behavior -- a dynamic transition from disordered cluster to network to chiral rigid cluster. On the other hand, the cluster-elastic network boundary in the final state is governed by isostatic percolation, which slows structural rearrangements, preserves branching points, and sustains a long-lived network. The resulting structure consist of rigid Bernal spiral-like branches connected through flexible branching points lacking order. These insights advance our microscopic understanding of out-of-equilibrium ordering driven by competing interactions, particularly phenomena like temporally delayed frustration reflecting different length scales of competing interactions. The mechanisms identified here may play a crucial role in mesoscale self-organization across soft materials, from nanoparticle assemblies to biological gels and cytoskeletal networks. Understanding how competing interactions regulate structure and dynamics could guide the design of adaptive materials with tunable mechanical properties and offer new perspectives on biological processes such as cytoplasmic organization and cellular scaffolding.
A Survey on Collaborative Mechanisms Between Large and Small Language Models
2025-05-12
Yi Chen, JiaHao Zhao, HaoHao Han
Large Language Models (LLMs) deliver powerful AI capabilities but face deployment challenges due to high resource costs and latency, whereas Small Language Models (SLMs) offer efficiency and deployability at the cost of reduced performance. Collaboration between LLMs and SLMs emerges as a crucial paradigm to synergistically balance these trade-offs, enabling advanced AI applications, especially on resource-constrained edge devices. This survey provides a comprehensive overview of LLM-SLM collaboration, detailing various interaction mechanisms (pipeline, routing, auxiliary, distillation, fusion), key enabling technologies, and diverse application scenarios driven by on-device needs like low latency, privacy, personalization, and offline operation. While highlighting the significant potential for creating more efficient, adaptable, and accessible AI, we also discuss persistent challenges including system overhead, inter-model consistency, robust task allocation, evaluation complexity, and security/privacy concerns. Future directions point towards more intelligent adaptive frameworks, deeper model fusion, and expansion into multimodal and embodied AI, positioning LLM-SLM collaboration as a key driver for the next generation of practical and ubiquitous artificial intelligence.
TPT-Bench: A Large-Scale, Long-Term and Robot-Egocentric Dataset for Benchmarking Target Person Tracking
2025-05-12
Hanjing Ye, Yu Zhan, Weixi Situ, Guangcheng Chen, Jingwen Yu, Kuanqi Cai, Hong Zhang
Tracking a target person from robot-egocentric views is crucial for developing autonomous robots that provide continuous personalized assistance or collaboration in Human-Robot Interaction (HRI) and Embodied AI. However, most existing target person tracking (TPT) benchmarks are limited to controlled laboratory environments with few distractions, clean backgrounds, and short-term occlusions. In this paper, we introduce a large-scale dataset designed for TPT in crowded and unstructured environments, demonstrated through a robot-person following task. The dataset is collected by a human pushing a sensor-equipped cart while following a target person, capturing human-like following behavior and emphasizing long-term tracking challenges, including frequent occlusions and the need for re-identification from numerous pedestrians. It includes multi-modal data streams, including odometry, 3D LiDAR, IMU, panoptic, and RGB-D images, along with exhaustively annotated 2D bounding boxes of the target person across 35 sequences, both indoors and outdoors. Using this dataset and visual annotations, we perform extensive experiments with existing TPT methods, offering a thorough analysis of their limitations and suggesting future research directions.
Thermoelectric processes of quantum normal-superconductor interfaces
2025-05-12
L. Arrachea, A. Braggio, P. Burset, E. J. H. Lee, A. Levy Yeyati, R. Sánchez
Superconducting interfaces have recently been demonstrated to contain a rich variety of effects that give rise to sizable thermoelectric responses and unexpected thermal properties, despite traditionally being considered poor thermoelectrics due to their intrinsic electron-hole symmetry. We review different mechanisms driving this response in hybrid normal-superconducting junctions, depending on the dimensionality of the mesoscopic interface. In addition to discussing heat to power conversion, cooling and heat transport, special emphasis is put on physical properties of hybrid devices that can be revealed by the thermoelectric effect.
Code translation aims to convert code from one programming language to another automatically. It is motivated by the need for multi-language software development and legacy system migration. In recent years, neural code translation has gained significant attention, driven by rapid advancements in deep learning and large language models. Researchers have proposed various techniques to improve neural code translation quality. However, to the best of our knowledge, no comprehensive systematic literature review has been conducted to summarize the key techniques and challenges in this field. To fill this research gap, we collected 57 primary studies covering the period 2020~2025 on neural code translation. These studies are analyzed from seven key perspectives: task characteristics, data preprocessing, code modeling, model construction, post-processing, evaluation subjects, and evaluation metrics. Our analysis reveals current research trends, identifies unresolved challenges, and shows potential directions for future work. These findings can provide valuable insights for both researchers and practitioners in the field of neural code translation.
Characterizing 3D Magnetic Fields and Turbulence in H I Clouds
2025-05-12
Yue Hu
3D Galactic magnetic fields are critical for understanding the interstellar medium, Galactic foreground polarization, and the propagation of ultra-high-energy cosmic rays. Leveraging recent theoretical insights into anisotropic magnetohydrodynamic (MHD) turbulence, we introduce a deep learning framework to predict the full 3D magnetic field structure-including the plane-of-sky (POS) position angle, line-of-sight (LOS) inclination, magnetic field strength, sonic Mach number ($M_s$), and Alfv\'en Mach number ($M_A$)-from spectroscopic H~I observations. The deep learning model is trained on synthetic H~I emission data generated from multiphase 3D MHD simulations. We then apply the trained model to observational data from the Commensal Radio Astronomy FAST Survey, presenting maps of 3D magnetic field orientation, magnetic field strength, $M_s$, and $M_A$ for two H~I clouds, a low-velocity cloud (LVC) and an intermediate-velocity cloud (IVC), which overlap in the POS yet reside at different LOS distances. The deep-learning-predicted POS magnetic field position angles align closely with those determined using the velocity gradient technique, whose integrated results are consistent with independent measurements from Planck 353~GHz polarization data. This study demonstrates the potential of deep learning approaches as powerful tools for modeling the 3D distributions of 3D Galactic magnetic fields and turbulence properties throughout the Galaxy.
ViMRHP: A Vietnamese Benchmark Dataset for Multimodal Review Helpfulness Prediction via Human-AI Collaborative Annotation
2025-05-12
Truc Mai-Thanh Nguyen, Dat Minh Nguyen, Son T. Luu, Kiet Van Nguyen
Multimodal Review Helpfulness Prediction (MRHP) is an essential task in recommender systems, particularly in E-commerce platforms. Determining the helpfulness of user-generated reviews enhances user experience and improves consumer decision-making. However, existing datasets focus predominantly on English and Indonesian, resulting in a lack of linguistic diversity, especially for low-resource languages such as Vietnamese. In this paper, we introduce ViMRHP (Vietnamese Multimodal Review Helpfulness Prediction), a large-scale benchmark dataset for MRHP task in Vietnamese. This dataset covers four domains, including 2K products with 46K reviews. Meanwhile, a large-scale dataset requires considerable time and cost. To optimize the annotation process, we leverage AI to assist annotators in constructing the ViMRHP dataset. With AI assistance, annotation time is reduced (90 to 120 seconds per task down to 20 to 40 seconds per task) while maintaining data quality and lowering overall costs by approximately 65%. However, AI-generated annotations still have limitations in complex annotation tasks, which we further examine through a detailed performance analysis. In our experiment on ViMRHP, we evaluate baseline models on human-verified and AI-generated annotations to assess their quality differences. The ViMRHP dataset is publicly available at https://github.com/trng28/ViMRHP
Computational Fact-Checking of Online Discourse: Scoring scientific accuracy in climate change related news articles
2025-05-12
Tim Wittenborg, Constantin Sebastian Tremel, Markus Stocker, Sören Auer
Democratic societies need reliable information. Misinformation in popular media such as news articles or videos threatens to impair civic discourse. Citizens are, unfortunately, not equipped to verify this content flood consumed daily at increasing rates. This work aims to semi-automatically quantify scientific accuracy of online media. By semantifying media of unknown veracity, their statements can be compared against equally processed trusted sources. We implemented a workflow using LLM-based statement extraction and knowledge graph analysis. Our neurosymbolic system was able to evidently streamline state-of-the-art veracity quantification. Evaluated via expert interviews and a user survey, the tool provides a beneficial veracity indication. This indicator, however, is unable to annotate public media at the required granularity and scale. Further work towards a FAIR (Findable, Accessible, Interoperable, Reusable) ground truth and complementary metrics are required to scientifically support civic discourse.
Short abstract: The paper reviews the physics of Frohlich excitonic polarons from the viewpoint of empirical approaches with some original developments. Models for excitonic polarons in ionic semiconductors in the spirit of the Lee Low and Pines (LLP) model for free polarons were initiated by Toyozawa and Hermanson and extended by Pollman and Buttner (PB). The dominant electron-hole interaction with the lattice introduced by Frohlich is represented by a long-range effective interaction with a single longitudinal optical polar mode. The properties of the excitonic polarons are characterized by various physical quantities such as effective dielectric constants, effective masses, virtual phonon populations, carrier self-energies and binding energies, and effective electron-hole interactions mediated by the lattice. In 3D perovskites, the excitonic polarons deviate from the simplified picture of weakly interacting (almost free) polarons, with sizeable effects of electron-hole correlations on all the physical properties.
On the internal architecture of lightweight negative Poisson's ratio (auxetic) metastructures: a review
2025-05-12
Ali Rahimi-Lenji, Mohammad Heidari-Rarani, Mohsen Mirkhalaf, Mohammad Mirkhalaf
Development of lightweight materials with enhanced mechanical properties has been a long-standing challenge in science and engineering. Lightweight auxetic metastructures (AMSs) provide attractive solutions to this problem. AMSs' negative Poisson's ratio is a unique characteristic which results in very interesting practical properties such as high energy absorption and improved toughness. Different properties of metastrcutures, including anisotropy, are dependent, in addition to their original material, on the unit cell shape and geometrical features which have very high variations. Over the past few years, researchers have developed AMSs with various unit cells, either introducing new internal architecture or enhancing and optimizing the existing ones. Despite the progress made in this field, there is no comprehensive review of the AMS unit cells. This review describes the cellular AMSs associated with a classified set of metastructures, comprising more than 100 distinct auxetic repetitive unit cells. Besides, achievable range of negative Poisson's ratios are provided for different categories of AMSs. Finally, the future perspective of the research field and potential developments and applications in this field are discussed.
AIS Data-Driven Maritime Monitoring Based on Transformer: A Comprehensive Review
2025-05-12
Zhiye Xie, Enmei Tu, Xianping Fu, Guoliang Yuan, Yi Han
With the increasing demands for safety, efficiency, and sustainability in global shipping, Automatic Identification System (AIS) data plays an increasingly important role in maritime monitoring. AIS data contains spatial-temporal variation patterns of vessels that hold significant research value in the marine domain. However, due to its massive scale, the full potential of AIS data has long remained untapped. With its powerful sequence modeling capabilities, particularly its ability to capture long-range dependencies and complex temporal dynamics, the Transformer model has emerged as an effective tool for processing AIS data. Therefore, this paper reviews the research on Transformer-based AIS data-driven maritime monitoring, providing a comprehensive overview of the current applications of Transformer models in the marine field. The focus is on Transformer-based trajectory prediction methods, behavior detection, and prediction techniques. Additionally, this paper collects and organizes publicly available AIS datasets from the reviewed papers, performing data filtering, cleaning, and statistical analysis. The statistical results reveal the operational characteristics of different vessel types, providing data support for further research on maritime monitoring tasks. Finally, we offer valuable suggestions for future research, identifying two promising research directions. Datasets are available at https://github.com/eyesofworld/Maritime-Monitoring.
Breast cancer (BC) is the most widespread cancer globally, yet current diagnostic and prognostic methods inadequately capture its biological complexity, despite the benefits of early detection. Cancer systems biology (SB) has advanced over the past two decades as a multiscale approach, but often neglects morphological complexity observable at cellular, tissue, and tumor levels. Structural organization significantly influences system behavior, necessitating the identification of scales where morphological features emerge. Fractal geometry (FG) provides quantitative tools to assess the irregular, scale-invariant structures typical of cancer. Alongside FG, fractional calculus (FC) offers a framework to model memory-dependent and non-local dynamics. However, existing research remains constrained to mono-fractal analyses or limited imaging scopes, lacking integration into broader SB models. This review aims to bridge that gap by presenting core FG concepts, both mono- and multi-fractal measures, and their application to BC across histological, cytological, and radiological data. It further explores how FC enhances dynamic morphological modeling. Finally, it considers how number-theoretic tools like p-adic analysis may represent hierarchical genomic and morphological patterns, unifying insights from tumor morphology to genome organization.
Manual pruning is labor intensive and represents up to 25% of annual labor costs in fruit production, notably in apple orchards and vineyards where operational challenges and cost constraints limit the adoption of large-scale machinery. In response, a growing body of research is investigating compact, flexible robotic platforms capable of precise pruning in varied terrains, particularly where traditional mechanization falls short. This paper reviews recent advances in autonomous robotic pruning for orchards and vineyards, addressing a critical need in precision agriculture. Our review examines literature published between 2014 and 2024, focusing on innovative contributions across key system components. Special attention is given to recent developments in machine vision, perception, plant skeletonization, and control strategies, areas that have experienced significant influence from advancements in artificial intelligence and machine learning. The analysis situates these technological trends within broader agricultural challenges, including rising labor costs, a decline in the number of young farmers, and the diverse pruning requirements of different fruit species such as apple, grapevine, and cherry trees. By comparing various robotic architectures and methodologies, this survey not only highlights the progress made toward autonomous pruning but also identifies critical open challenges and future research directions. The findings underscore the potential of robotic systems to bridge the gap between manual and mechanized operations, paving the way for more efficient, sustainable, and precise agricultural practices.
Early detection of dementia is very crucial to ensure treatment begins on time, however it is difficult to choose appropriate cognitive assessment tools because each test is designed differently and may not be tailored to the needs of a patient. This review compares five commonly used tests the Mini-Mental State Examination (MMSE), Rowland Universal Dementia Assessment Scale (RUDAS), Self-Administered Gerocognitive Examination (SAGE), Alzheimer's Disease Assessment Scale (ADAS), and Montreal Cognitive Assessment (MoCA). Each test has different criteria's and vary in their coverage of cognitive domains. MMSE focuses on memory and language but lacks in the evaluation of executive and visuospatial abilities. RUDAS and SAGE focus on memory, language and visual thinking while ADAS mainly targets memory, executive function and language. The MoCA is most complete as it focuses on areas like attention, memory skills, problem solving and visual skills. This review evaluates how accurate and reliable these tools are to help doctors decide the most efficient tool for diagnosis.
A Novel Online Pseudospectral Method for Approximation of Nonlinear Systems Dynamics
2025-05-12
Arian Yousefian, Avimanyu Sahoo, Vignesh Narayanan
This paper presents a novel online system identification approach utilizing the Chebyshev pseudospectral (PS) method to approximate the dynamics of a continuous-time nonlinear system. Unlike conventional periodic sampling, the proposed identification scheme employs aperiodic state sampling, leveraging the Chebyshev nodes to achieve the desired approximation accuracy. Unlike traditional off-line PS approaches, the scheme utilizes a moving time-window strategy to compute the sampling instants (Chebyshev nodes) forward in time for state measurements. Within each time window, the number of sampling instants is adaptively determined to meet the specified approximation accuracy. The Chebyshev basis is also shifted for each time window to accommodate arbitrary approximation intervals. The least-squares approach is employed to estimate the coefficients of the shifted Chebyshev basis functions using the measured state and its derivatives at the end of each window, resulting in a piecewise approximation of the drift dynamics of the system. In the second step, the identified drift dynamics are utilized to design an adaptive state estimator to reconstruct the continuous system states from the aperiodic state measurements. To address the smoothness of the piecewise approximated system dynamics, the Chebyshev coefficients are recomputed at the window transition instants, between two consecutive time windows, by enforcing continuity of the approximated function and its derivatives. In addition, analytical results are provided to determine the number of sampling instants within each time window, which guarantees the desired approximation accuracy. The boundedness of function approximation and state estimation errors is also proved analytically. Finally, numerical simulation results are included to validate the proposed scheme.
OAM spatial demultiplexing by diffraction-based noiseless mode conversion with axicon
2025-05-12
Junsu Kim, Jiyeon Baek, Hyunchae Chun, Seung Ryong Park
The physics of orbital angular momentum (OAM) carrying light has been well-defined since the 1990s. Leveraging its physical phenomena has become a significant focus in various areas of research. For instance, OAM is applied in hybrid free-space optical communication channels, like wavelength division multiplexing. Due to its orthogonality, OAM can be multiplexed and demultiplexed, enabling the use of multiple orthogonal OAM beams to achieve high-capacity optical communication systems. In this appliance, there will be instability of light because its phase distribution is quite complicated. This study focuses on spatial demultiplexing using Computer Generated Holography (CGH) based OAM diffraction. Earlier research on OAM diffraction primarily involved fork gratings. Spatial demultiplexing has phase distribution instability and noise problems in free space optics. Axicons have been studied extensively in the context of perfect vortex generation. Drawing inspiration from these two areas of physics, we propose a noiseless topological charge conversion through axicon properties in spatial demultiplexing using CGH. Furthermore, we estimate the axicon optical approximate solution that is appropriate for research purposes.
Discovery of Main-sequence Radio Pulse emitters from widefield sky surveys
2025-05-12
Barnali Das, Matt E. Shultz, Joshua Pritchard, Kovi Rose, Laura N. Driessen, Yuanming Wang, Andrew Zic, Tara Murphy, Gregory Sivakoff
Magnetic AB stars are known to produce periodic radio pulses by the electron cyclotron maser emission (ECME) mechanism. Only 19 such stars, known as 'Main-sequence Radio Pulse emitters' (MRPs) are currently known. The majority of MRPs have been discovered through targeted observation campaigns that involve carefully selecting a sample of stars that are likely to produce ECME, and which can be detected by a given telescope within reasonable amount of time. These selection criteria inadvertently introduce bias in the resulting sample of MRPs, which affects subsequent investigation of the relation between ECME properties and stellar magnetospheric parameters. The alternative is to use all-sky surveys. Until now, MRP candidates obtained from surveys were identified based on their high circular polarisation ($\gtrsim 30\%$). In this paper, we introduce a complementary strategy, which does not require polarisation information. Using multi-epoch data from the Australian SKA Pathfinder (ASKAP) telescope, we identify four MRP candidates based on the variability in the total intensity light curves. Follow-up observations with the Australia Telescope Compact Array (ATCA) confirm three of them to be MRPs, thereby demonstrating the effectiveness of our strategy. With the expanded sample, we find that ECME is affected by temperature and the magnetic field strength, consistent with past results. There is, however, a degeneracy regarding how the two parameters govern the ECME luminosity for magnetic A and late-B stars (effective temperature $\lesssim 16$ kK). The current sample is also inadequate to investigate the role of stellar rotation, which has been shown to play a key role in driving incoherent radio emission.
Brightness Characteristics of the Qianfan Satellites and Evidence That Some Are Tumbling
2025-05-12
Anthony Mallama, Richard E. Cole, Stephan Hellmich, Roger Spinner, Jeff Warner, Jay Respler
The mean apparent magnitude of the Qianfan satellites is 5.76 +/- 0.04, while the mean of magnitudes adjusted to a distance of 1,000 km is 5.24 +/- 0.04, based on 1,161 observations. Light curves of several spacecraft display rapid periodic fluctuations which indicate that they are tumbling. Nearly all of the non-tumbling satellite observations can be modeled with diffusely reflecting, Earth-facing surfaces. The Qianfan constellation will impact astronomical research and aesthetic appreciation of the night sky unless their brightness is mitigated.
Internet of Agents: Fundamentals, Applications, and Challenges
2025-05-12
Yuntao Wang, Shaolong Guo, Yanghe Pan, Zhou Su, Fahao Chen, Tom H. Luan, Peng Li, Jiawen Kang, Dusit Niyato
With the rapid proliferation of large language models and vision-language models, AI agents have evolved from isolated, task-specific systems into autonomous, interactive entities capable of perceiving, reasoning, and acting without human intervention. As these agents proliferate across virtual and physical environments, from virtual assistants to embodied robots, the need for a unified, agent-centric infrastructure becomes paramount. In this survey, we introduce the Internet of Agents (IoA) as a foundational framework that enables seamless interconnection, dynamic discovery, and collaborative orchestration among heterogeneous agents at scale. We begin by presenting a general IoA architecture, highlighting its hierarchical organization, distinguishing features relative to the traditional Internet, and emerging applications. Next, we analyze the key operational enablers of IoA, including capability notification and discovery, adaptive communication protocols, dynamic task matching, consensus and conflict-resolution mechanisms, and incentive models. Finally, we identify open research directions toward building resilient and trustworthy IoA ecosystems.
Critique Before Thinking: Mitigating Hallucination through Rationale-Augmented Instruction Tuning
2025-05-12
Zexian Yang, Dian Li, Dayan Wu, Gang Liu, Weiping Wang
Despite significant advancements in multimodal reasoning tasks, existing Large Vision-Language Models (LVLMs) are prone to producing visually ungrounded responses when interpreting associated images. In contrast, when humans embark on learning new knowledge, they often rely on a set of fundamental pre-study principles: reviewing outlines to grasp core concepts, summarizing key points to guide their focus and enhance understanding. However, such preparatory actions are notably absent in the current instruction tuning processes. This paper presents Re-Critic, an easily scalable rationale-augmented framework designed to incorporate fundamental rules and chain-of-thought (CoT) as a bridge to enhance reasoning abilities. Specifically, Re-Critic develops a visual rationale synthesizer that scalably augments raw instructions with rationale explanation. To probe more contextually grounded responses, Re-Critic employs an in-context self-critic mechanism to select response pairs for preference tuning. Experiments demonstrate that models fine-tuned with our rationale-augmented dataset yield gains that extend beyond hallucination-specific tasks to broader multimodal reasoning tasks.
Systematic reviews are comprehensive literature reviews that address highly focused research questions and represent the highest form of evidence in medicine. A critical step in this process is the development of complex Boolean queries to retrieve relevant literature. Given the difficulty of manually constructing these queries, recent efforts have explored Large Language Models (LLMs) to assist in their formulation. One of the first studies,Wang et al., investigated ChatGPT for this task, followed by Staudinger et al., which evaluated multiple LLMs in a reproducibility study. However, the latter overlooked several key aspects of the original work, including (i) validation of generated queries, (ii) output formatting constraints, and (iii) selection of examples for chain-of-thought (Guided) prompting. As a result, its findings diverged significantly from the original study. In this work, we systematically reproduce both studies while addressing these overlooked factors. Our results show that query effectiveness varies significantly across models and prompt designs, with guided query formulation benefiting from well-chosen seed studies. Overall, prompt design and model selection are key drivers of successful query formulation. Our findings provide a clearer understanding of LLMs' potential in Boolean query generation and highlight the importance of model- and prompt-specific optimisations. The complex nature of systematic reviews adds to challenges in both developing and reproducing methods but also highlights the importance of reproducibility studies in this domain.
Assessing the User Experience of Extended Reality Devices for (Dis)Assembly: A Classroom Study
2025-05-12
Brandon S. Byers, Eleftherios Triantafyllidis, Thibaut Menny, Martin Schulte, Catherine De Wolf
Despite the current rise and promising capabilities of Extended Reality (XR) technologies, the architecture, engineering, and construction industry lacks informed guidance when choosing between these technologies, especially for complex processes like assembly and disassembly tasks. This research compares the user experience across different XR devices for (dis)assembly utilizing the NASA Task Load Index and System Usability Scale metrics. Through a workshop and surveys with graduate civil engineering and architecture students, the study found that Augmented Reality scored highest in usability, followed closely by Mixed Reality. However, Mixed Reality showed the best task load index score, indicating low cognitive demand. The findings presented in this research may aid academics and practitioners in making informed decisions when selecting XR systems in practical, real-world assembly scenarios. Moreover, this study suggests opportunities and guidelines for more detailed XR system comparisons and exploration of XR's further role in circular construction practices.
"You Cannot Sound Like GPT": Signs of language discrimination and resistance in computer science publishing
2025-05-12
Haley Lepp, Daniel Scott Smith
LLMs have been celebrated for their potential to help multilingual scientists publish their research. Rather than interpret LLMs as a solution, we hypothesize their adoption can be an indicator of existing linguistic exclusion in scientific writing. Using the case study of ICLR, an influential, international computer science conference, we examine how peer reviewers critique writing clarity. Analyzing almost 80,000 peer reviews, we find significant bias against authors associated with institutions in countries where English is less widely spoken. We see only a muted shift in the expression of this bias after the introduction of ChatGPT in late 2022. To investigate this unexpectedly minor change, we conduct interviews with 14 conference participants from across five continents. Peer reviewers describe associating certain features of writing with people of certain language backgrounds, and such groups in turn with the quality of scientific work. While ChatGPT masks some signs of language background, reviewers explain that they now use ChatGPT "style" and non-linguistic features as indicators of author demographics. Authors, aware of this development, described the ongoing need to remove features which could expose their "non-native" status to reviewers. Our findings offer insight into the role of ChatGPT in the reproduction of scholarly language ideologies which conflate producers of "good English" with producers of "good science."
The proliferation of IoT in cities, combined with Digital Twins, creates a rich data foundation for Smart Cities aimed at improving urban life and operations. Generative AI (GenAI) significantly enhances this potential, moving beyond traditional AI analytics and predictions by processing multimodal content and generating novel outputs like text and simulations. Using specialized or foundational models, GenAI's natural language abilities such as Natural Language Understanding (NLU) and Natural Language Generation (NLG) can power tailored applications and unified interfaces, dramatically lowering barriers for users interacting with complex smart city systems. In this paper, we focus on GenAI applications based on conversational interfaces within the context of three critical user archetypes in a Smart City - Citizens, Operators and Planners. We identify and review GenAI models and techniques that have been proposed or deployed for various urban subsystems in the contexts of these user archetypes. We also consider how GenAI can be built on the existing data foundation of official city records, IoT data streams and Urban Digital Twins. We believe this work represents the first comprehensive summarization of GenAI techniques for Smart Cities from the lens of the critical users in a Smart City.
Large Language Models and Arabic Content: A Review
2025-05-12
Haneh Rhel, Dmitri Roussinov
Over the past three years, the rapid advancement of Large Language Models (LLMs) has had a profound impact on multiple areas of Artificial Intelligence (AI), particularly in Natural Language Processing (NLP) across diverse languages, including Arabic. Although Arabic is considered one of the most widely spoken languages across 27 countries in the Arabic world and used as a second language in some other non-Arabic countries as well, there is still a scarcity of Arabic resources, datasets, and tools. Arabic NLP tasks face various challenges due to the complexities of the Arabic language, including its rich morphology, intricate structure, and diverse writing standards, among other factors. Researchers have been actively addressing these challenges, demonstrating that pre-trained Large Language Models (LLMs) trained on multilingual corpora achieve significant success in various Arabic NLP tasks. This study provides an overview of using large language models (LLMs) for the Arabic language, highlighting early pre-trained Arabic Language models across various NLP applications and their ability to handle diverse Arabic content tasks and dialects. It also provides an overview of how techniques like finetuning and prompt engineering can enhance the performance of these models. Additionally, the study summarizes common Arabic benchmarks and datasets while presenting our observations on the persistent upward trend in the adoption of LLMs.
$τ$HK: a modular housekeeping system for cryostats and balloon payloads
2025-05-12
Simon Tartakovsky, Steven J. Benton, Aurelien A. Fraisse, William C. Jones, Jared L. May, Johanna M. Nagy, Ricardo R. Rodriguez, Philippe Voyer
$\tau$HK is a versatile experiment housekeeping (HK) system designed to perform cryogenic temperature readout and heater control on the upcoming Taurus balloon experiment. $\tau$HK, more broadly, is also suitable for ambient-temperature applications and general-purpose experiment input and output. It is built around an IEEE Eurocard subrack capable of housing up to 16 interchangeable daughter cards, allowing a fully populated system to support as many as 256 independent channels while drawing under 7.5\,W. This modular architecture allows experiments to expand on the existing daughter cards with ones tailored to their specific needs. There are currently three flavors of daughter cards: Resistive Temperature Device (RTD) readout, general purpose thermometer bias and readout, and load driver. The RTD board consists of a low noise lock-in amplifier that is limited only by device sensitivity over all temperature ranges. The general-purpose bias and readout board with chopping capability is primarily designed for thermometer diodes, but flexible enough to accommodate room temperature thermistors, Wheatstone bridges, optical encoders, and other devices. Finally, the load driver card can output an analog voltage for precise cryogenic heaters or it can be used to pulse width modulate high power loads. $\tau$HK is a power efficient solution for experimental housekeeping needs that is suited for the the harsh environment of stratospheric ballooning.
MilChat: Introducing Chain of Thought Reasoning and GRPO to a Multimodal Small Language Model for Remote Sensing
2025-05-12
Aybora Koksal, A. Aydin Alatan
Remarkable capabilities in understanding and generating text-image content have been demonstrated by recent advancements in multimodal large language models (MLLMs). However, their effectiveness in specialized domains-particularly those requiring resource-efficient and domain-specific adaptations-has remained limited. In this work, a lightweight multimodal language model termed MilChat is introduced, specifically adapted to analyze remote sensing imagery in secluded areas, including challenging missile launch sites. A new dataset, MilData, was compiled by verifying hundreds of aerial images through expert review, and subtle military installations were highlighted via detailed captions. Supervised fine-tuning on a 2B-parameter open-source MLLM with chain-of-thought (CoT) reasoning annotations was performed, enabling more accurate and interpretable explanations. Additionally, Group Relative Policy Optimization (GRPO) was leveraged to enhance the model's ability to detect critical domain-specific cues-such as defensive layouts and key military structures-while minimizing false positives on civilian scenes. Through empirical evaluations, it has been shown that MilChat significantly outperforms both larger, general-purpose multimodal models and existing remote sensing-adapted approaches on open-ended captioning and classification metrics. Over 80% recall and 98% precision were achieved on the newly proposed MilData benchmark, underscoring the potency of targeted fine-tuning and reinforcement learning in specialized real-world applications.
DESI DR1 Ly$α$ 1D power spectrum: The optimal estimator measurement
2025-05-12
N. G. Karaçaylı, P. Martini, J. Aguilar, S. Ahlen, E. Armengaud, S. Bailey, A. Bault, D. Bianchi, A. Brodzeller, D. Brooks, J. Chaves-Montero, T. Claybaugh, A. Cuceu, A. de la Macorra, A. Dey, B. Dey, P. Doel, S. Ferraro, A. Font-Ribera, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, J. Guy, C. Hahn, H. K. Herrera-Alcantar, K. Honscheid, M. Ishak, R. Kehoe, D. Kirkby, A. Kremin, M. Landriau, J. M. Le Goff, L. Le Guillou, M. E. Levi, M. Manera, A. Meisner, R. Miquel, P. Montero-Camacho, S. Nadathur, G. Niz, N. Palanque-Delabrouille, Z. Pan, W. J. Percival, Matthew M. Pieri, F. Prada, I. Pérez-Ràfols, C. Ravoux, G. Rossi, E. Sanchez, C. Saulder, D. Schlegel, M. Schubnell, H. Seo, M. Siudek, D. Sprayberry, T. Tan, Ji-Jia Tang, G. Tarlé, M. Walther, B. A. Weaver, J. Yu, R. Zhou, H. Zou
The one-dimensional power spectrum $P_{\mathrm{1D}}$ of Ly$\alpha$ forest offers rich insights into cosmological and astrophysical parameters, including constraints on the sum of neutrino masses, warm dark matter models, and the thermal state of the intergalactic medium. We present the measurement of $P_{\mathrm{1D}}$ using the optimal quadratic maximum likelihood estimator applied to over 300,000 Ly$\alpha$ quasars from Data Release 1 (DR1) of the Dark Energy Spectroscopic Instrument (DESI) survey. This sample represents the largest to date for $P_{\mathrm{1D}}$ measurements and is larger than the Extended Baryon Oscillation Spectroscopic Survey (eBOSS) by a factor of 1.7. We conduct a meticulous investigation of instrumental and analysis systematics and quantify their impact on $P_{\mathrm{1D}}$. This includes the development of a cross-exposure estimator that eliminates the need to model the pipeline noise and has strong potential for future $P_{\mathrm{1D}}$ measurements. We also present new insights into metal contamination through the 1D correlation function. Using a fitting function we measure the evolution of the Ly$\alpha$ forest bias with high precision: $b_F(z) = (-0.218\pm0.002)\times((1 + z) / 4)^{2.96\pm0.06}$. In a companion validation paper, we substantially extend our previous suite of CCD image simulations to quantify the pipeline's exquisite performance accurately. In another companion paper, we present DR1 $P_{\mathrm{1D}}$ measurements using the Fast Fourier Transform (FFT) approach to power spectrum estimation. These two measurements are consistent with each other and constitute the most precise $P_{\mathrm{1D}}$ measurement to date, while being in good agreement with results from the DESI early data release.
Universal Waveforms for Extreme Mass-Ratio Inspiral
2025-05-12
Ilan Strusberg, Barak Rom, Re'em Sari
We engage with the challenge of calculating the waveforms of gravitational waves emitted by spinless binary black hole merger in extreme mass-ratio limit. We model the stellar-mass black hole as a test-particle, initially on a circular orbit, that undergoes adiabatic inspiral until it reaches the innermost stable circular orbit (ISCO), after which it follows a geodesic trajectory. We compute the gravitational waveforms emitted during both phases -- before and after the ISCO crossing -- and demonstrate how to accurately connect them. While the waveforms are calculated adiabatically up to the ISCO, the associated phase error near the ISCO scales as $\nu^{1/5}$ and remains below one radian for sufficiently small mass-ratios $\nu$. Our complete waveform is universal in the sense that all computationally expensive calculations are performed once, and its application to any binary merger can be obtained by appropriately re-scaling time, phase, and amplitude. We compare our results with existing models in the literature and show that our complete waveforms are accurate enough all the way from separations that are an order of one gravitational radii outside the ISCO, to the merger.
Re$^2$: A Consistency-ensured Dataset for Full-stage Peer Review and Multi-turn Rebuttal Discussions
2025-05-12
Daoze Zhang, Zhijian Bao, Sihang Du, Zhiyi Zhao, Kuangling Zhang, Dezheng Bao, Yang Yang
Peer review is a critical component of scientific progress in the fields like AI, but the rapid increase in submission volume has strained the reviewing system, which inevitably leads to reviewer shortages and declines review quality. Besides the growing research popularity, another key factor in this overload is the repeated resubmission of substandard manuscripts, largely due to the lack of effective tools for authors to self-evaluate their work before submission. Large Language Models (LLMs) show great promise in assisting both authors and reviewers, and their performance is fundamentally limited by the quality of the peer review data. However, existing peer review datasets face three major limitations: (1) limited data diversity, (2) inconsistent and low-quality data due to the use of revised rather than initial submissions, and (3) insufficient support for tasks involving rebuttal and reviewer-author interactions. To address these challenges, we introduce the largest consistency-ensured peer review and rebuttal dataset named Re^2, which comprises 19,926 initial submissions, 70,668 review comments, and 53,818 rebuttals from 24 conferences and 21 workshops on OpenReview. Moreover, the rebuttal and discussion stage is framed as a multi-turn conversation paradigm to support both traditional static review tasks and dynamic interactive LLM assistants, providing more practical guidance for authors to refine their manuscripts and helping alleviate the growing review burden. Our data and code are available in https://anonymous.4open.science/r/ReviewBench_anon/.
SciCom Wiki: Fact-Checking and FAIR Knowledge Distribution for Scientific Videos and Podcasts
2025-05-12
Tim Wittenborg, Constantin Sebastian Tremel, Niklas Stehr, Oliver Karras, Markus Stocker, Sören Auer
Democratic societies need accessible, reliable information. Videos and Podcasts have established themselves as the medium of choice for civic dissemination, but also as carriers of misinformation. The emerging Science Communication Knowledge Infrastructure (SciCom KI) curating non-textual media is still fragmented and not adequately equipped to scale against the content flood. Our work sets out to support the SciCom KI with a central, collaborative platform, the SciCom Wiki, to facilitate FAIR (findable, accessible, interoperable, reusable) media representation and the fact-checking of their content, particularly for videos and podcasts. Building an open-source service system centered around Wikibase, we survey requirements from 53 stakeholders, refine these in 11 interviews, and evaluate our prototype based on these requirements with another 14 participants. To address the most requested feature, fact-checking, we developed a neurosymbolic computational fact-checking approach, converting heterogenous media into knowledge graphs. This increases machine-readability and allows comparing statements against equally represented ground-truth. Our computational fact-checking tool was iteratively evaluated through 10 expert interviews, a public user survey with 43 participants verified the necessity and usability of our tool. Overall, our findings identified several needs to systematically support the SciCom KI. The SciCom Wiki, as a FAIR digital library complementing our neurosymbolic computational fact-checking framework, was found suitable to address the raised requirements. Further, we identified that the SciCom KI is severely underdeveloped regarding FAIR knowledge and related systems facilitating its collaborative creation and curation. Our system can provide a central knowledge node, yet a collaborative effort is required to scale against the imminent (mis-)information flood.
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review
2025-05-12
Chengmin Zhou, Ville Kyrki, Pasi Fränti, Laura Ruotsalainen
Bayesian inference has many advantages in decision making of agents (e.g. robotics/simulative agent) over a regular data-driven black-box neural network: Data-efficiency, generalization, interpretability, and safety where these advantages benefit directly/indirectly from the uncertainty quantification of Bayesian inference. However, there are few comprehensive reviews to summarize the progress of Bayesian inference on reinforcement learning (RL) for decision making to give researchers a systematic understanding. This paper focuses on combining Bayesian inference with RL that nowadays is an important approach in agent decision making. To be exact, this paper discusses the following five topics: 1) Bayesian methods that have potential for agent decision making. First basic Bayesian methods and models (Bayesian rule, Bayesian learning, and Bayesian conjugate models) are discussed followed by variational inference, Bayesian optimization, Bayesian deep learning, Bayesian active learning, Bayesian generative models, Bayesian meta-learning, and lifelong Bayesian learning. 2) Classical combinations of Bayesian methods with model-based RL (with approximation methods), model-free RL, and inverse RL. 3) Latest combinations of potential Bayesian methods with RL. 4) Analytical comparisons of methods that combine Bayesian methods with RL with respect to data-efficiency, generalization, interpretability, and safety. 5) In-depth discussions in six complex problem variants of RL, including unknown reward, partial-observability, multi-agent, multi-task, non-linear non-Gaussian, and hierarchical RL problems and the summary of how Bayesian methods work in the data collection, data processing and policy learning stages of RL to pave the way for better agent decision-making strategies.
Representation Learning with Mutual Influence of Modalities for Node Classification in Multi-Modal Heterogeneous Networks
2025-05-12
Jiafan Li, Jiaqi Zhu, Liang Chang, Yilin Li, Miaomiao Li, Yang Wang, Hongan Wang
Nowadays, numerous online platforms can be described as multi-modal heterogeneous networks (MMHNs), such as Douban's movie networks and Amazon's product review networks. Accurately categorizing nodes within these networks is crucial for analyzing the corresponding entities, which requires effective representation learning on nodes. However, existing multi-modal fusion methods often adopt either early fusion strategies which may lose the unique characteristics of individual modalities, or late fusion approaches overlooking the cross-modal guidance in GNN-based information propagation. In this paper, we propose a novel model for node classification in MMHNs, named Heterogeneous Graph Neural Network with Inter-Modal Attention (HGNN-IMA). It learns node representations by capturing the mutual influence of multiple modalities during the information propagation process, within the framework of heterogeneous graph transformer. Specifically, a nested inter-modal attention mechanism is integrated into the inter-node attention to achieve adaptive multi-modal fusion, and modality alignment is also taken into account to encourage the propagation among nodes with consistent similarities across all modalities. Moreover, an attention loss is augmented to mitigate the impact of missing modalities. Extensive experiments validate the superiority of the model in the node classification task, providing an innovative view to handle multi-modal data, especially when accompanied with network structures.
Security of Internet of Agents: Attacks and Countermeasures
2025-05-12
Yuntao Wang, Yanghe Pan, Shaolong Guo, Zhou Su
With the rise of large language and vision-language models, AI agents have evolved into autonomous, interactive systems capable of perception, reasoning, and decision-making. As they proliferate across virtual and physical domains, the Internet of Agents (IoA) has emerged as a key infrastructure for enabling scalable and secure coordination among heterogeneous agents. This survey offers a comprehensive examination of the security and privacy landscape in IoA systems. We begin by outlining the IoA architecture and its distinct vulnerabilities compared to traditional networks, focusing on four critical aspects: identity authentication threats, cross-agent trust issues, embodied security, and privacy risks. We then review existing and emerging defense mechanisms and highlight persistent challenges. Finally, we identify open research directions to advance the development of resilient and privacy-preserving IoA ecosystems.
Chemical language models (CLMs), inspired by natural language processing (NLP), have recently emerged as powerful tools for various cheminformatics tasks such as molecular property prediction and molecule generation. The simplified molecular input line entry system (SMILES) is commonly employed in CLMs for representing molecular structures. However, despite attempts at standardization through Canonical SMILES, significant representational inconsistencies remain due to variations in canonicalization methods (grammatical inconsistencies) and incomplete stereochemical annotations (stereochemical inconsistencies). This study systematically investigates the prevalence and impact of these inconsistencies. Our literature review reveals that nearly half (45.4%) of the studies employing CLMs omit explicit mention of SMILES canonicalization, potentially impairing reproducibility. Through quantitative analysis of publicly available datasets, we observed substantial variability in SMILES representations, particularly highlighting significant gaps in stereochemical information approximately 50% of enantiomers and 30% of geometric isomers lacked complete annotations. Empirical evaluations using an encoder-decoder CLM demonstrated that representational variations significantly affect latent molecular representations, notably reducing translation accuracy. Interestingly, these variations minimally impacted downstream property prediction tasks, likely due to robust feature selection driven by label information. Furthermore, explicit manipulation of stereochemical annotations confirmed their crucial role in accurate cyclic structure reconstruction.
Patent systems vary widely in how rigorously they define and enforce inventors' rights. On one hand, formal statutes ("law on the books") set the scope of what can be patented and outline procedural safeguards. On the other hand, actual enforcement ("law in practice") determines whether those rights hold up in practice. To capture these dimensions, researchers have developed simple indices of legal provisions and more nuanced proxies for enforcement effectiveness, along with metrics of how applicant-friendly each office's procedures are. Comparative studies of "twin patents" -- identical inventions filed in multiple jurisdictions -- reveal systematic differences in grant rates and bar heights across major offices. By combining these approaches, we gain a multifaceted view of patent-system strength that balances statutory design, administrative practice, and actual enforcement. This perspective is crucial for understanding how different regimes support innovation and shape global knowledge flows.
VSTPOL: making the VST a large survey telescope for optical polarimetry
2025-05-11
P. Schipani, S. Covino, F. Snik, M. Colapietro, F. Perrotta, S. Savarese, S. Bagnulo, P. Bellutti, G. Capasso, E. Cappellaro, M. Cappi, G. Castignani, S. DOrsi, J. Farinato, O. Hainaut, D. Hutsemekers, K. Kuijken, A. M. Magalhaes, D. Magrin, M. Marconi, L. Marty, F. Patat, E. Pian, F. Rigamonti, V. Ripepi, P. Rossettini, R. Z. Sanchez, A. Smette, P. Spano, R. Tomelleri, G. Umbriaco, A. van Vorstenbosch, G. Verdoes-Kleijn
Since the start of operations in 2011, the VLT Survey Telescope (VST) has been one of the most efficient wide-field imagers in the optical bands. However, in the next years the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will be a game-changer in this field. Hence, the timing is appropriate for specializing the VST with additions that can make it unique in well-defined scientific cases. VSTPOL is a project that aims to provide the addition of wide-field polarimetric capabilities to the VST telescope, making it the first large survey telescope for linear optical polarimetry. Actually, while there are quite a number of optical telescopes, the telescopes providing polarimetric instrumentation are just a few. The number of relatively large mirror polarimetric telescopes is small, although they would be specifically needed e.g. to support many science cases of the Cherenkov Telescope Array (CTA) that, in the southern hemisphere, is co-located with the VST. The VST telescope is equipped with a single instrument, the OmegaCAM wide-field imaging camera operating in the visible bands with a field of view of 1$^\circ\times1^\circ$. The polarimetric mode will be implemented through the insertion of a large rotatable polarizer installed on the field-corrector optics, which will be exchangeable with the non-polarimetric corrector optics. The limiting polarimetric systematic errors due to variable atmospheric conditions and instrumental polarization can be corrected down to a level of $\sim10^{-3}$ by leveraging the large amount of unpolarized stars within each field-of-view. By the user point of view, VSTPOL will be an additional mode for the VST wide-field imaging camera.
We present Seed1.5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed with a 532M-parameter vision encoder and a Mixture-of-Experts (MoE) LLM of 20B active parameters. Despite its relatively compact architecture, it delivers strong performance across a wide spectrum of public VLM benchmarks and internal evaluation suites, achieving the state-of-the-art performance on 38 out of 60 public benchmarks. Moreover, in agent-centric tasks such as GUI control and gameplay, Seed1.5-VL outperforms leading multimodal systems, including OpenAI CUA and Claude 3.7. Beyond visual and video understanding, it also demonstrates strong reasoning abilities, making it particularly effective for multimodal reasoning challenges such as visual puzzles. We believe these capabilities will empower broader applications across diverse tasks. In this report, we mainly provide a comprehensive review of our experiences in building Seed1.5-VL across model design, data construction, and training at various stages, hoping that this report can inspire further research. Seed1.5-VL is now accessible at https://www.volcengine.com/ (Volcano Engine Model ID: doubao-1-5-thinking-vision-pro-250428)
Review: Accretion Disk Evolution in Tidal Disruption Events
2025-05-11
Wenbin Lu
This is a brief review of the recent progress in understanding the evolution of the accretion disks in tidal disruption events (TDEs). Special attention is paid to (1) thermal-viscous instability that causes the disk to transition from a thick state to a thin one, and back and forth, (2) interactions between the fallback material and existing disk. Challenges to the current model from late-time X-ray observations are highlighted and possible solutions are discussed.
Artificial Intelligence (AI) is rapidly expanding and integrating more into daily life to automate tasks, guide decision making, and enhance efficiency. However, complex AI models, which make decisions without providing clear explanations (known as the "black-box problem"), currently restrict trust and widespread adoption of AI. Explainable Artificial Intelligence (XAI) has emerged to address the black-box problem of making AI systems more interpretable and transparent so stakeholders can trust, verify, and act upon AI-based outcomes. Researchers have developed various techniques to foster XAI in the Software Development Lifecycle. However, there are gaps in applying XAI techniques in the Software Engineering phases. Literature review shows that 68% of XAI in Software Engineering research is focused on maintenance as opposed to 8% on software management and requirements. In this paper, we present a comprehensive survey of the applications of XAI methods such as concept-based explanations, Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), rule extraction, attention mechanisms, counterfactual explanations, and example-based explanations to the different phases of the Software Development Life Cycle (SDLC), including requirements elicitation, design and development, testing and deployment, and evolution. To the best of our knowledge, this paper presents the first comprehensive survey of XAI techniques for every phase of the Software Development Life Cycle (SDLC). This survey aims to promote explainable AI in Software Engineering and facilitate the practical application of complex AI models in AI-driven software development.
Unlocking Non-Block-Structured Decisions: Inductive Mining with Choice Graphs
2025-05-11
Humam Kourani, Gyunam Park, Wil M. P. van der Aalst
Process discovery aims to automatically derive process models from event logs, enabling organizations to analyze and improve their operational processes. Inductive mining algorithms, while prioritizing soundness and efficiency through hierarchical modeling languages, often impose a strict block-structured representation. This limits their ability to accurately capture the complexities of real-world processes. While recent advancements like the Partially Ordered Workflow Language (POWL) have addressed the block-structure limitation for concurrency, a significant gap remains in effectively modeling non-block-structured decision points. In this paper, we bridge this gap by proposing an extension of POWL to handle non-block-structured decisions through the introduction of choice graphs. Choice graphs offer a structured yet flexible approach to model complex decision logic within the hierarchical framework of POWL. We present an inductive mining discovery algorithm that uses our extension and preserves the quality guarantees of the inductive mining framework. Our experimental evaluation demonstrates that the discovered models, enriched with choice graphs, more precisely represent the complex decision-making behavior found in real-world processes, without compromising the high scalability inherent in inductive mining techniques.
The competing effects of recent and long-term star formation histories on oxygen, nitrogen, and stellar metallicities
2025-05-11
Nicholas Fraser Boardman, Vivienne Wild, Natalia Vale Asari, Francesco D'Eugenio
The fundamental metallicity relation (FMR) - the three-way trend between galaxy stellar masses, star-formation rates (SFRs) and gaseous metallicities - remains amongst the most studied extragalactic relations. Furthermore, metallicity correlates particularly tightly with gravitational potential. Simulations support a shared origin for these relations relating to long-term gas inflow history variations; however, differences between simulated and observed galaxy samples make it unclear whether this holds for real galaxies. We use MaNGA integral field observations to probe these relations in star-forming galaxies at one effective radius. We confirm the FMR and equivalent relations for stellar metallicity (FMR*) and gaseous N/O (fundamental nitrogen relation, FNR). We find that all relations persist when considering gravitational potential in place of stellar mass and/or considering stellar ages in place of SFR, with the gaseous relations strengthened significantly by considering potential. The gaseous FMR disappears at high masses/potentials, while the FNR persists and the FMR* strengthens. Our results suggest a unified interpretation of galaxies' gaseous and stellar metallicities and their N/O abundances in terms of their formation histories. Deeper gravitational potentials correspond to earlier star-formation histories (SFHs) and faster gas consumption, producing tight potential-abundance relations for stars and gas. In weak potentials, galaxy SFR variations primarily result from recent gas inflows, mostly affecting gas abundances. In deeper potentials, SFR variations instead correspond to broad differences in SFH shapes resulting from differences in long-term gas consumption histories, which is most visible in stellar abundances. This unified interpretation could be confirmed with upcoming higher redshift spectroscopic surveys.
In recent years, Artificial Intelligence technology has excelled in various applications across all domains and fields. However, the various algorithms in neural networks make it difficult to understand the reasons behind decisions. For this reason, trustworthy AI techniques have started gaining popularity. The concept of trustworthiness is cross-disciplinary; it must meet societal standards and principles, and technology is used to fulfill these requirements. In this paper, we first surveyed developments from various countries and regions on the ethical elements that make AI algorithms trustworthy; and then focused our survey on the state of the art research into the interpretability of AI. We have conducted an intensive survey on technologies and techniques used in making AI explainable. Finally, we identified new trends in achieving explainable AI. In particular, we elaborate on the strong link between the explainability of AI and the meta-reasoning of autonomous systems. The concept of meta-reasoning is 'reason the reasoning', which coincides with the intention and goal of explainable Al. The integration of the approaches could pave the way for future interpretable AI systems.
Vision-Language Models (VLMs) are becoming increasingly popular in the medical domain, bridging the gap between medical images and clinical language. Existing VLMs demonstrate an impressive ability to comprehend medical images and text queries to generate detailed, descriptive diagnostic medical reports. However, hallucination--the tendency to generate descriptions that are inconsistent with the visual content--remains a significant issue in VLMs, with particularly severe implications in the medical field. To facilitate VLM research on gastrointestinal (GI) image analysis and study hallucination, we curate a multimodal image-text GI dataset: Gut-VLM. This dataset is created using a two-stage pipeline: first, descriptive medical reports of Kvasir-v2 images are generated using ChatGPT, which introduces some hallucinated or incorrect texts. In the second stage, medical experts systematically review these reports, and identify and correct potential inaccuracies to ensure high-quality, clinically reliable annotations. Unlike traditional datasets that contain only descriptive texts, our dataset also features tags identifying hallucinated sentences and their corresponding corrections. A common approach to reducing hallucination in VLM is to finetune the model on a small-scale, problem-specific dataset. However, we take a different strategy using our dataset. Instead of finetuning the VLM solely for generating textual reports, we finetune it to detect and correct hallucinations, an approach we call hallucination-aware finetuning. Our results show that this approach is better than simply finetuning for descriptive report generation. Additionally, we conduct an extensive evaluation of state-of-the-art VLMs across several metrics, establishing a benchmark. GitHub Repo: https://github.com/bhattarailab/Hallucination-Aware-VLM.
What does Artificial Intelligence (AI) have to contribute to health care? And what should we be looking out for if we are worried about its risks? In this paper we offer a survey, and initial evaluation, of hopes and fears about the applications of artificial intelligence in medicine. AI clearly has enormous potential as a research tool, in genomics and public health especially, as well as a diagnostic aid. It's also highly likely to impact on the organisational and business practices of healthcare systems in ways that are perhaps under-appreciated. Enthusiasts for AI have held out the prospect that it will free physicians up to spend more time attending to what really matters to them and their patients. We will argue that this claim depends upon implausible assumptions about the institutional and economic imperatives operating in contemporary healthcare settings. We will also highlight important concerns about privacy, surveillance, and bias in big data, as well as the risks of over trust in machines, the challenges of transparency, the deskilling of healthcare practitioners, the way AI reframes healthcare, and the implications of AI for the distribution of power in healthcare institutions. We will suggest that two questions, in particular, are deserving of further attention from philosophers and bioethicists. What does care look like when one is dealing with data as much as people? And, what weight should we give to the advice of machines in our own deliberations about medical decisions?
A systematic review of challenges and proposed solutions in modeling multimodal data
2025-05-11
Maryam Farhadizadeh, Maria Weymann, Michael Blaß, Johann Kraus, Christopher Gundler, Sebastian Walter, Noah Hempen, Harald Binde, Nadine Binder
Multimodal data modeling has emerged as a powerful approach in clinical research, enabling the integration of diverse data types such as imaging, genomics, wearable sensors, and electronic health records. Despite its potential to improve diagnostic accuracy and support personalized care, modeling such heterogeneous data presents significant technical challenges. This systematic review synthesizes findings from 69 studies to identify common obstacles, including missing modalities, limited sample sizes, dimensionality imbalance, interpretability issues, and finding the optimal fusion techniques. We highlight recent methodological advances, such as transfer learning, generative models, attention mechanisms, and neural architecture search that offer promising solutions. By mapping current trends and innovations, this review provides a comprehensive overview of the field and offers practical insights to guide future research and development in multimodal modeling for medical applications.
Gamma-ray and high-energy X-ray detection with large area scintillating crystals: a hands-on review
2025-05-11
M. Bonesini
Detection of photons with scintillating inorganic crystals in the high-energy range (> 0.1 MeV) will be discussed, making a comparison with other available methods. Energy resolutions up to 2 % at 662 keV and fast decay time of the order of 20 ns are within reach, with the introduction of Ce-doped crystals instead of alkali halide ones. Development is underway for the production of non-hygroscopic scintillating crystals, such as PrLuAg and CE:GAAG. At the end of this review, examples of experimental devices based on scintillating inorganic crystals will be discussed. Practical hands-on experience is emphasized at the expense of a more comprehensive description of all available and possible options. Detectors' construction details and the consequences of the different choices will be discussed. Emphasis will be put on the LaBr3:Ce-based detectors that are the present ''golden standard'' in gamma ray spectroscopy. This review will focus on photon detection in the high-energy region: mainly 0.1-2 MeV, including both gamma rays and high-energy X-rays, even if many considerations may be applied to the detection of low-energy X-rays.
Despite strong performance in medical question-answering, the clinical adoption of Large Language Models (LLMs) is critically hampered by their opaque 'black-box' reasoning, limiting clinician trust. This challenge is compounded by the predominant reliance of current medical LLMs on corpora from scientific literature or synthetic data, which often lack the granular expert validation and high clinical relevance essential for advancing their specialized medical capabilities. To address these critical gaps, we introduce a highly clinically relevant dataset with 31,247 medical question-answer pairs, each accompanied by expert-validated chain-of-thought (CoT) explanations. This resource, spanning multiple clinical domains, was curated via a scalable human-LLM hybrid pipeline: LLM-generated rationales were iteratively reviewed, scored, and refined by medical experts against a structured rubric, with substandard outputs revised through human effort or guided LLM regeneration until expert consensus. This publicly available dataset provides a vital source for the development of medical LLMs that capable of transparent and verifiable reasoning, thereby advancing safer and more interpretable AI in medicine.
MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning
2025-05-11
Lishan Yang, Wei Zhang, Quan Z. Sheng, Weitong Chen, Lina Yao, Weitong Chen, Ali Shakeri
In the era of big data, data mining has become indispensable for uncovering hidden patterns and insights from vast and complex datasets. The integration of multimodal data sources further enhances its potential. Multimodal Federated Learning (MFL) is a distributed approach that enhances the efficiency and quality of multimodal learning, ensuring collaborative work and privacy protection. However, missing modalities pose a significant challenge in MFL, often due to data quality issues or privacy policies across the clients. In this work, we present MMiC, a framework for Mitigating Modality incompleteness in MFL within the Clusters. MMiC replaces partial parameters within client models inside clusters to mitigate the impact of missing modalities. Furthermore, it leverages the Banzhaf Power Index to optimize client selection under these conditions. Finally, MMiC employs an innovative approach to dynamically control global aggregation by utilizing Markovitz Portfolio Optimization. Extensive experiments demonstrate that MMiC consistently outperforms existing federated learning architectures in both global and personalized performance on multimodal datasets with missing modalities, confirming the effectiveness of our proposed solution.
Towards Artificial General or Personalized Intelligence? A Survey on Foundation Models for Personalized Federated Intelligence
2025-05-11
Yu Qiao, Huy Q. Le, Avi Deb Raha, Phuong-Nam Tran, Apurba Adhikary, Mengchun Zhang, Loc X. Nguyen, Eui-Nam Huh, Dusit Niyato, Choong Seon Hong
The rise of large language models (LLMs), such as ChatGPT, DeepSeek, and Grok-3, has reshaped the artificial intelligence landscape. As prominent examples of foundational models (FMs) built on LLMs, these models exhibit remarkable capabilities in generating human-like content, bringing us closer to achieving artificial general intelligence (AGI). However, their large-scale nature, sensitivity to privacy concerns, and substantial computational demands present significant challenges to personalized customization for end users. To bridge this gap, this paper presents the vision of artificial personalized intelligence (API), focusing on adapting these powerful models to meet the specific needs and preferences of users while maintaining privacy and efficiency. Specifically, this paper proposes personalized federated intelligence (PFI), which integrates the privacy-preserving advantages of federated learning (FL) with the zero-shot generalization capabilities of FMs, enabling personalized, efficient, and privacy-protective deployment at the edge. We first review recent advances in both FL and FMs, and discuss the potential of leveraging FMs to enhance federated systems. We then present the key motivations behind realizing PFI and explore promising opportunities in this space, including efficient PFI, trustworthy PFI, and PFI empowered by retrieval-augmented generation (RAG). Finally, we outline key challenges and future research directions for deploying FM-powered FL systems at the edge with improved personalization, computational efficiency, and privacy guarantees. Overall, this survey aims to lay the groundwork for the development of API as a complement to AGI, with a particular focus on PFI as a key enabling technique.
The ultimate goal of artificial intelligence (AI) is to achieve Artificial General Intelligence (AGI). Embodied Artificial Intelligence (EAI), which involves intelligent systems with physical presence and real-time interaction with the environment, has emerged as a key research direction in pursuit of AGI. While advancements in deep learning, reinforcement learning, large-scale language models, and multimodal technologies have significantly contributed to the progress of EAI, most existing reviews focus on specific technologies or applications. A systematic overview, particularly one that explores the direct connection between EAI and AGI, remains scarce. This paper examines EAI as a foundational approach to AGI, systematically analyzing its four core modules: perception, intelligent decision-making, action, and feedback. We provide a detailed discussion of how each module contributes to the six core principles of AGI. Additionally, we discuss future trends, challenges, and research directions in EAI, emphasizing its potential as a cornerstone for AGI development. Our findings suggest that EAI's integration of dynamic learning and real-world interaction is essential for bridging the gap between narrow AI and AGI.
LAMMPS: A Case Study For Applying Modern Software Engineering to an Established Research Software Package
2025-05-11
Axel Kohlmeyer, Richard Berger
We review various changes made in recent years to the software development process of the LAMMPS simulation software package and the software itself. We discuss how those changes have impacted the effort and workflow required to develop and maintain a software package that has been in existence for more than 30 years and where a significant part of the code base is contributed by external developers. We also look into how those changes have affected the code quality and ease of modifying and extending the software while at the same time its audience has changed from a cohort with a generally strong software development background to a group containing many researchers with limited software development skills. We explore how this contributes to LAMMPS' significant growth in popularity in that time. We close with an outlook on future steps.
Sandcastles in the Storm: Revisiting the (Im)possibility of Strong Watermarking
2025-05-11
Fabrice Y Harel-Canada, Boran Erol, Connor Choi, Jason Liu, Gary Jiarui Song, Nanyun Peng, Amit Sahai
Watermarking AI-generated text is critical for combating misuse. Yet recent theoretical work argues that any watermark can be erased via random walk attacks that perturb text while preserving quality. However, such attacks rely on two key assumptions: (1) rapid mixing (watermarks dissolve quickly under perturbations) and (2) reliable quality preservation (automated quality oracles perfectly guide edits). Through large-scale experiments and human-validated assessments, we find mixing is slow: 100% of perturbed texts retain traces of their origin after hundreds of edits, defying rapid mixing. Oracles falter, as state-of-the-art quality detectors misjudge edits (77% accuracy), compounding errors during attacks. Ultimately, attacks underperform: automated walks remove watermarks just 26% of the time -- dropping to 10% under human quality review. These findings challenge the inevitability of watermark removal. Instead, practical barriers -- slow mixing and imperfect quality control -- reveal watermarking to be far more robust than theoretical models suggest. The gap between idealized attacks and real-world feasibility underscores the need for stronger watermarking methods and more realistic attack models.
Control Plane as a Tool: A Scalable Design Pattern for Agentic AI Systems
2025-05-11
Sivasathivel Kandasamy
Agentic AI systems represent a new frontier in artificial intelligence, where agents often based on large language models(LLMs) interact with tools, environments, and other agents to accomplish tasks with a degree of autonomy. These systems show promise across a range of domains, but their architectural underpinnings remain immature. This paper conducts a comprehensive review of the types of agents, their modes of interaction with the environment, and the infrastructural and architectural challenges that emerge. We identify a gap in how these systems manage tool orchestration at scale and propose a reusable design abstraction: the "Control Plane as a Tool" pattern. This pattern allows developers to expose a single tool interface to an agent while encapsulating modular tool routing logic behind it. We position this pattern within the broader context of agent design and argue that it addresses several key challenges in scaling, safety, and extensibility.
Out-of-Sample Embedding with Proximity Data: Projection versus Restricted Reconstruction
2025-05-10
Michael W. Trosset, Kaiyi Tan, Minh Tang, Carey E. Priebe
The problem of using proximity (similarity or dissimilarity) data for the purpose of "adding a point to a vector diagram" was first studied by J.C. Gower in 1968. Since then, a number of methods -- mostly kernel methods -- have been proposed for solving what has come to be called the problem of *out-of-sample embedding*. We survey the various kernel methods that we have encountered and show that each can be derived from one or the other of two competing strategies: *projection* or *restricted reconstruction*. Projection can be analogized to a well-known formula for adding a point to a principal component analysis. Restricted reconstruction poses a different challenge: how to best approximate redoing the entire multivariate analysis while holding fixed the vector diagram that was previously obtained. This strategy results in a nonlinear optimization problem that can be simplified to a unidimensional search. Various circumstances may warrant either projection or restricted reconstruction.
Perspectives on Unsolvability in Roommates Markets
2025-05-10
Frederik Glitzner, David Manlove
In the well-studied Stable Roommates problem, we seek a stable matching of agents into pairs, where no two agents prefer each other over their assigned partners. However, some instances of this problem are unsolvable, lacking any stable matching. A long-standing open question posed by Gusfield and Irving (1989) asks about the behavior of the probability function Pn, which measures the likelihood that a random instance with n agents is solvable. This paper provides a comprehensive analysis of the landscape surrounding this question, combining structural, probabilistic, and experimental perspectives. We review existing approaches from the past four decades, highlight connections to related problems, and present novel structural and experimental findings. Specifically, we estimate Pn for instances with preferences sampled from diverse statistical distributions, examining problem sizes up to 5,001 agents, and look for specific sub-structures that cause unsolvability. Our results reveal that while Pn tends to be low for most distributions, the number and lengths of "unstable" structures remain limited, suggesting that random instances are "close" to being solvable. Additionally, we present the first empirical study of the number of stable matchings and the number of stable partitions that random instances admit, using recently developed algorithms. Our findings show that the solution sets are typically small. This implies that many NP-hard problems related to computing optimal stable matchings and optimal stable partitions become tractable in practice, and motivates efficient alternative solution concepts for unsolvable instances, such as stable half-matchings and maximum stable matchings.
Approximate Minimal SU(5), Several Fundamental Scales, Fluctuating Lattice
2025-05-10
Holger Bech Nielsen
Having shortly reviewed our idea of the grand unified SU(5) being only exact in a classical limit, in a truly existing lattice, an ontological lattice, we go over to putting a series of different physical energy scales such the approximate unification scale for the SU(5)(without any SUSY), the Planck scale, and e.g. the scale of see-saw neutrino masses into a certain plot showing the energy scales on a straight line. This straight line of this plot supposed to result from such an ontological lattice, that fluctuates in link size a and lattice density in a very strong way according to a log normal distribution. The point is that different energy scales result from the link size of the lattice to different powers, like the averages a to the n th , where the power n depends on the type of scale considered. Since the n th root of the average of the n th power of the link size in the fluctuating lattice is very strongly dependent on the power n, because of very huge fluctuations, the different types of physical energy scales can get very different. With a Galton i.e. log normal distribution of the link size the various energy scales have their logarithms fall into a nice straight line versus the power of the link size, on which they depend. Our model gives a surprisingly good number for the small deviations of the experimental electron-and muon-anomalous magnetic moment from the pure Standard Model value.
Wi-Fi sensing has emerged as a significant technology in wireless sensing and Integrated Sensing and Communication (ISAC), offering benefits such as low cost, high penetration, and enhanced privacy. Currently, it is widely utilized in various applications, including action recognition, human localization, and crowd counting. However, Wi-Fi sensing also faces challenges, such as low robustness and difficulties in data collection. Recently, there has been an increasing focus on multi-modal Wi-Fi sensing, where other modalities can act as teachers, providing ground truth or robust features for Wi-Fi sensing models to learn from, or can be directly fused with Wi-Fi for enhanced sensing capabilities. Although these methods have demonstrated promising results and substantial value in practical applications, there is a lack of comprehensive surveys reviewing them. To address this gap, this paper reviews the multi-modal Wi-Fi sensing literature \textbf{from the past 24 months} and highlights the current limitations, challenges and future directions in this field.
A Survey on Data-Driven Modeling of Human Drivers' Lane-Changing Decisions
2025-05-10
Linxuan Huang, Dong-Fan Xie, Li Li, Zhengbing He
Lane-changing (LC) behavior, a critical yet complex driving maneuver, significantly influences driving safety and traffic dynamics. Traditional analytical LC decision (LCD) models, while effective in specific environments, often oversimplify behavioral heterogeneity and complex interactions, limiting their capacity to capture real LCD. Data-driven approaches address these gaps by leveraging rich empirical data and machine learning to decode latent decision-making patterns, enabling adaptive LCD modeling in dynamic environments. In light of the rapid development of artificial intelligence and the demand for data-driven models oriented towards connected vehicles and autonomous vehicles, this paper presents a comprehensive survey of data-driven LCD models, with a particular focus on human drivers LC decision-making. It systematically reviews the modeling framework, covering data sources and preprocessing, model inputs and outputs, objectives, structures, and validation methods. This survey further discusses the opportunities and challenges faced by data-driven LCD models, including driving safety, uncertainty, as well as the integration and improvement of technical frameworks.
A catalogue of candidate milli-parsec separation massive black hole binaries from long term optical photometric monitoring
2025-05-10
Vincent Foustoul, Natalie A. Webb, Raphaël Mignon-Risse, Elias Kammoun, Marta Volonteri, Chi An Dong-Páez
The role of mergers in the evolution of massive black holes is still unclear, and their dynamical evolution, from the formation of pairs to binaries and the final coalescence, carries large physical uncertainties. The identification of the elusive population of close massive binary black holes (MBBHs) is crucial to understand the importance of mergers in the formation and evolution of supermassive black holes. It has been proposed that MBBHs may display periodic optical/ultra-violet variability. Optical surveys provide photometric measurements of a large variety of objects, over decades and searching for periodicities coming from galaxies in their long-term optical/UV lightcurves may help identify new MBBH candidates. Using the Catalina Real-Time Transient Survey (CRTS) and Zwicky Transient Facility (ZTF) data, we studied the long-term periodicity of variable sources in the centre of galaxies identified using the galaxy catalogue Glade+. We report 36 MBBHs candidates, with sinusoidal variability with amplitudes between 0.1 and 0.8 magnitudes over 3-5 cycles, through fitting 15 years of data. The periodicities are also detected when adding a red noise contribution to the sine model. Moreover, the periodicities are corroborated through Generalized Lomb Scargle (GLS) periodograms analysis, providing supplementary evidence for the observed modulation. We also indicate 58 objects, that were previously proposed to be MBBH candidates from analysis of CRTS data only. Adding ZTF data clearly shows that the previously claimed modulation is due to red noise. We also created a catalogue of 221 weaker candidates which require further observations over the coming years to help validate their nature. Based on our 36 MBBHs candidates, we expect ~20 MBBHs at z<1, which is commensurate with simulations. Further observations will help confirm these results.
Minimizing Risk Through Minimizing Model-Data Interaction: A Protocol For Relying on Proxy Tasks When Designing Child Sexual Abuse Imagery Detection Models
2025-05-10
Thamiris Coelho, Leo S. F. Ribeiro, João Macedo, Jefersson A. dos Santos, Sandra Avila
The distribution of child sexual abuse imagery (CSAI) is an ever-growing concern of our modern world; children who suffered from this heinous crime are revictimized, and the growing amount of illegal imagery distributed overwhelms law enforcement agents (LEAs) with the manual labor of categorization. To ease this burden researchers have explored methods for automating data triage and detection of CSAI, but the sensitive nature of the data imposes restricted access and minimal interaction between real data and learning algorithms, avoiding leaks at all costs. In observing how these restrictions have shaped the literature we formalize a definition of "Proxy Tasks", i.e., the substitute tasks used for training models for CSAI without making use of CSA data. Under this new terminology we review current literature and present a protocol for making conscious use of Proxy Tasks together with consistent input from LEAs to design better automation in this field. Finally, we apply this protocol to study -- for the first time -- the task of Few-shot Indoor Scene Classification on CSAI, showing a final model that achieves promising results on a real-world CSAI dataset whilst having no weights actually trained on sensitive data.
Evaluating LLM-Generated Q&A Test: a Student-Centered Study
2025-05-10
Anna Wróblewska, Bartosz Grabek, Jakub Świstak, Daniel Dan
This research prepares an automatic pipeline for generating reliable question-answer (Q&A) tests using AI chatbots. We automatically generated a GPT-4o-mini-based Q&A test for a Natural Language Processing course and evaluated its psychometric and perceived-quality metrics with students and experts. A mixed-format IRT analysis showed that the generated items exhibit strong discrimination and appropriate difficulty, while student and expert star ratings reflect high overall quality. A uniform DIF check identified two items for review. These findings demonstrate that LLM-generated assessments can match human-authored tests in psychometric performance and user satisfaction, illustrating a scalable approach to AI-assisted assessment development.
Data Version Management and Machine-Actionable Reproducibility for HPC based on git and DataLad
2025-05-10
Andreas Knüpfer, Timothy J. Callow
We present the adaptation of an existing data versioning and machine-actionable reproducibility solution for HPC. Both aspects are important for research data management and the DataLad tool provides both based on the very prevalent git version control system. However, it is incompatible with HPC batch processing. The presented extension enables DataLad's versioning and reproducibility in conjunction with the HPC batch scheduling system Slurm. It solves a fundamental incompatibility as well as inefficient behavior patterns on parallel file systems.
Too Sick for Working, or Sick of Working? Impact of Acute Health Shocks on Early Labour Market Exits
2025-05-10
Luis Vieira
This study investigates the impact of acute health shocks on early labour market exits among individuals aged 50 and over in Europe. Utilising data from Waves 1-8 of the Survey of Health, Ageing, and Retirement in Europe (SHARE), encompassing over 140,000 individuals, I employ survival analysis techniques, including Kaplan-Meier estimators and Cox proportional hazards models. The analysis explores how sudden, severe health events, alongside self-perceived health, chronic conditions, and socio-demographic factors, influence the probability of exiting the workforce before official retirement age. Results indicate that acute health shocks significantly increase the hazard of early labour market exit, with more pronounced effects observed for males. Poorer self-perceived health and lower educational attainment are also strong predictors of early exit. Gender differences are notable: while poorer health consistently raises exit risk for both genders, income acts as a protective factor for females, and living with a partner reduces exit risk for males but increases it for females. These findings highlight the critical role of health in labour market participation and suggest the need for targeted policies to support older workers, particularly those experiencing adverse health events.
Survey of Filtered Approximate Nearest Neighbor Search over the Vector-Scalar Hybrid Data
2025-05-10
Yanjun Lin, Kai Zhang, Zhenying He, Yinan Jing, X. Sean Wang
Filtered approximate nearest neighbor search (FANNS), an extension of approximate nearest neighbor search (ANNS) that incorporates scalar filters, has been widely applied to constrained retrieval of vector data. Despite its growing importance, no dedicated survey on FANNS over the vector-scalar hybrid data currently exists, and the field has several problems, including inconsistent definitions of the search problem, insufficient framework for algorithm classification, and incomplete analysis of query difficulty. This survey paper formally defines the concepts of hybrid dataset and hybrid query, as well as the corresponding evaluation metrics. Based on these, a pruning-focused framework is proposed to classify and summarize existing algorithms, providing a broader and finer-grained classification framework compared to the existing ones. In addition, a review is conducted on representative hybrid datasets, followed by an analysis on the difficulty of hybrid queries from the perspective of distribution relationships between data and queries. This paper aims to establish a structured foundation for FANNS over the vector-scalar hybrid data, facilitate more meaningful comparisons between FANNS algorithms, and offer practical recommendations for practitioners. The code used for downloading hybrid datasets and analyzing query difficulty is available at https://github.com/lyj-fdu/FANNS
Enhancing Trust Management System for Connected Autonomous Vehicles Using Machine Learning Methods: A Survey
2025-05-10
Qian Xu, Lei Zhang, Yixiao Liu
Connected Autonomous Vehicles (CAVs) operate in dynamic, open, and multi-domain networks, rendering them vulnerable to various threats. Trust Management Systems (TMS) systematically organize essential steps in the trust mechanism, identifying malicious nodes against internal threats and external threats, as well as ensuring reliable decision-making for more cooperative tasks. Recent advances in machine learning (ML) offer significant potential to enhance TMS, especially for the strict requirements of CAVs, such as CAV nodes moving at varying speeds, and opportunistic and intermittent network behavior. Those features distinguish ML-based TMS from social networks, static IoT, and Social IoT. This survey proposes a novel three-layer ML-based TMS framework for CAVs in the vehicle-road-cloud integration system, i.e., trust data layer, trust calculation layer and trust incentive layer. A six-dimensional taxonomy of objectives is proposed. Furthermore, the principles of ML methods for each module in each layer are analyzed. Then, recent studies are categorized based on traffic scenarios that are against the proposed objectives. Finally, future directions are suggested, addressing the open issues and meeting the research trend. We maintain an active repository that contains up-to-date literature and open-source projects at https://github.com/octoberzzzzz/ML-based-TMS-CAV-Survey.
Large Language Models (LLMs) that can be deployed locally have recently gained popularity for privacy-sensitive tasks, with companies such as Meta, Google, and Intel playing significant roles in their development. However, the security of local LLMs through the lens of hardware cache side-channels remains unexplored. In this paper, we unveil novel side-channel vulnerabilities in local LLM inference: token value and token position leakage, which can expose both the victim's input and output text, thereby compromising user privacy. Specifically, we found that adversaries can infer the token values from the cache access patterns of the token embedding operation, and deduce the token positions from the timing of autoregressive decoding phases. To demonstrate the potential of these leaks, we design a novel eavesdropping attack framework targeting both open-source and proprietary LLM inference systems. The attack framework does not directly interact with the victim's LLM and can be executed without privilege. We evaluate the attack on a range of practical local LLM deployments (e.g., Llama, Falcon, and Gemma), and the results show that our attack achieves promising accuracy. The restored output and input text have an average edit distance of 5.2% and 17.3% to the ground truth, respectively. Furthermore, the reconstructed texts achieve average cosine similarity scores of 98.7% (input) and 98.0% (output).
A Machine-Learning Compositional Study of Exoplanetary Material Accreted Onto Five Helium-Atmosphere White Dwarfs with $\texttt{cecilia}$
2025-05-09
Mariona Badenas-Agusti, Siyi Xu, Andrew Vanderburg, Kishalay De, Patrick Dufour, Laura K. Rogers, Susana Hoyos, Simon Blouin, Javier Viaña, Amy Bonsor, Ben Zuckerman
We present the first application of the Machine Learning (ML) pipeline $\texttt{cecilia}$ to determine the physical parameters and photospheric composition of five metal-polluted He-atmosphere white dwarfs without well-characterised elemental abundances. To achieve this, we perform a joint and iterative Bayesian fit to their $\textit{SDSS}$ (R=2,000) and $\textit{Keck/ESI}$ (R=4,500) optical spectra, covering the wavelength range from about 3,800\r{A} to 9,000\r{A}. Our analysis measures the abundances of at least two $-$and up to six$-$ chemical elements in their atmospheres with a predictive accuracy similar to that of conventional WD analysis techniques ($\approx$0.20 dex). The white dwarfs with the largest number of detected heavy elements are SDSS J0859$+$5732 and SDSS J2311$-$0041, which simultaneously exhibit O, Mg, Si, Ca, and Fe in their $\textit{Keck/ESI}$ spectra. For all systems, we find that the bulk composition of their pollutants is largely consistent with those of primitive CI chondrites to within 1-2$\sigma$. We also find evidence of statistically significant ($>2\sigma$) oxygen excesses for SDSS J0859$+$5732 and SDSS J2311$-$0041, which could point to the accretion of oxygen-rich exoplanetary material. In the future, as wide-field astronomical surveys deliver millions of public WD spectra to the scientific community, $\texttt{cecilia}$ aspires to unlock population-wide studies of polluted WDs, therefore helping to improve our statistical knowledge of extrasolar compositions.
Extracting scientific evidence from biomedical studies for clinical research questions (e.g., Does stem cell transplantation improve quality of life in patients with medically refractory Crohn's disease compared to placebo?) is a crucial step in synthesising biomedical evidence. In this paper, we focus on the task of document-level scientific evidence extraction for clinical questions with conflicting evidence. To support this task, we create a dataset called CochraneForest, leveraging forest plots from Cochrane systematic reviews. It comprises 202 annotated forest plots, associated clinical research questions, full texts of studies, and study-specific conclusions. Building on CochraneForest, we propose URCA (Uniform Retrieval Clustered Augmentation), a retrieval-augmented generation framework designed to tackle the unique challenges of evidence extraction. Our experiments show that URCA outperforms the best existing methods by up to 10.3% in F1 score on this task. However, the results also underscore the complexity of CochraneForest, establishing it as a challenging testbed for advancing automated evidence synthesis systems.
Noncommutative fields in Reissner-Nordström black hole background
2025-05-09
Milorad Bežanić, Marija Dimitrijević Ćirić, Nikola Konjik, Biljana Nikolić, Andjelo Samsarov
In this short paper we discuss dynamics of noncommutative (NC) matter fields in the Reissner-Nordstr\"{o}m (RN) black hole background. After reviewing the propagation of charged NC scalar and spinor fields, we derive the equation governing the propagation of NC electromagnetic (EM) perturbation in the RN background. The propagation of NC scalar and spinor perturbation have a dual description in terms of the propagation of commutative fields in the effective/dual metric. Finally, we turn to the gravitational perturbations. We present equations of motion for the NC gravitational field obtained in two different models: $SO(2,3)_\star$ NC gravity and braided NC gravity. Typically for NC gravity models, the first nontrivial corrections are quadratic in the NC parameter. The obtained NC gravity equations are the starting point to discuss the propagation of NC gravitational perturbations and the validity of the dual description in terms of the effective metric.
New Advances in Phonons: From Band Topology to Quasiparticle Chirality
2025-05-09
Tiantian Zhang, Yizhou Liu, Hu Miao, Shuichi Murakami
Phonons, the quantized vibrational modes of a crystal lattice, are ubiquitous quasiparticles in solid-state systems. They play a central role in a wide range of physical phenomena, from thermal transport, as primary carriers of heat in insulators, to their involvement in symmetry-breaking orders such as charge density waves and superconductivity. Traditionally considered as spinless bosons, phonons have recently emerged as a fertile ground for exploring topological physics, spurred by the rapid development of topological band theory initially formulated for fermionic systems. It is now understood that the phonon eigenstates, characterized by their eigenvalues and eigenvectors, can carry nontrivial topological invariants such as Berry curvature and Chern numbers, despite the absence of spin or charge. This new understanding opens up avenues to investigate the interplay between lattice dynamics, topology, and chirality in bosonic systems. In this article, we provide a comprehensive review of recent theoretical and experimental advances in the field of topological and chiral phonons. We begin by introducing the foundational concepts, including the classification of phononic band structures, symmetry-protected topological phases, and the definition of topological invariants in bosonic systems. Special attention is given to the concept of phonon angular momentum and its fundametal connection to Weyl phonons in inversion-symmetry-breaking systems. We then discuss key experimental realizations of topological and chiral phonons across a variety of material platforms. Finally, we outline outstanding challenges and promising directions for future research, such as the role of topology in phonon-mediated quasiparticle interactions and the manipulation of phonon angular momentum in quantum technologies.
The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review
2025-05-09
Jingguo Qu, Xinyang Han, Man-Lik Chui, Yao Pu, Simon Takadiyi Gunda, Ziman Chen, Jing Qin, Ann Dorothy King, Winnie Chiu-Wing Chu, Jing Cai, Michael Tin-Cheung Ying
Automatic lymph node segmentation is the cornerstone for advances in computer vision tasks for early detection and staging of cancer. Traditional segmentation methods are constrained by manual delineation and variability in operator proficiency, limiting their ability to achieve high accuracy. The introduction of deep learning technologies offers new possibilities for improving the accuracy of lymph node image analysis. This study evaluates the application of deep learning in lymph node segmentation and discusses the methodologies of various deep learning architectures such as convolutional neural networks, encoder-decoder networks, and transformers in analyzing medical imaging data across different modalities. Despite the advancements, it still confronts challenges like the shape diversity of lymph nodes, the scarcity of accurately labeled datasets, and the inadequate development of methods that are robust and generalizable across different imaging modalities. To the best of our knowledge, this is the first study that provides a comprehensive overview of the application of deep learning techniques in lymph node segmentation task. Furthermore, this study also explores potential future research directions, including multimodal fusion techniques, transfer learning, and the use of large-scale pre-trained models to overcome current limitations while enhancing cancer diagnosis and treatment planning strategies.
2025-05-09
Alfredo De la Fuente, Saurabh Singh, Jona Ballé
The Fourier Basis Density Model (FBM) was recently introduced as a flexible probability model for band-limited distributions, i.e. ones which are smooth in the sense of having a characteristic function with limited support around the origin. Its density and cumulative distribution functions can be efficiently evaluated and trained with stochastic optimization methods, which makes the model suitable for deep learning applications. However, the model lacked support for sampling. Here, we introduce a method inspired by discretization--interpolation methods common in Digital Signal Processing, which directly take advantage of the band-limited property. We review mathematical properties of the FBM, and prove quality bounds of the sampled distribution in terms of the total variation (TV) and Wasserstein--1 divergences from the model. These bounds can be used to inform the choice of hyperparameters to reach any desired sample quality. We discuss these results in comparison to a variety of other sampling techniques, highlighting tradeoffs between computational complexity and sampling quality.
Serendipitous discovery of a spiral host in a 2 Mpc double-double lobed radio galaxy
2025-05-09
Sagar Sethi, Agnieszka Kuźmicz, Dominika Hunik, Marek Jamrozy
We present the serendipitous discovery of a double-double radio galaxy (DDRG) with a projected linear size exceeding 2 Mpc, hosted by a spiral galaxy. This unique combination of a giant radio structure and a spiral host challenges the prevailing view that such extreme radio sources reside only in elliptical galaxies. Using high-resolution optical imaging from the DESI Legacy Imaging Survey (DR10), we confirm a spiral-arm feature and a disk-component in the surface brightness profile fitting for the host galaxy (LEDA 896325) having a black hole of mass 2.4 $\times$ 10$^8$ $\rm M_{\odot}$. Radio observations from RACS and GLEAM reveal two distinct pairs of radio lobes. Using the multi-frequency analysis of radio data, we obtained the spectral index distribution and estimate the spectral ages of the outer and inner radio lobes to be approximately 120 and 35 Myr, respectively. Our results confirm recurrent jet activity in this disk galaxy and establish it as the largest known radio galaxy in a spiral host, and its double-double structure makes it the largest of only three such spiral-host DDRGs, demonstrating that disk galaxies can indeed launch extremely large-scale radio jets.
Lattice dynamics play a crucial role in understanding the physical mechanisms of cutting-edge energy materials. Many excellent energy materials have complex multiple-sublattice structures, with intricate lattice dynamics, and the underlying mechanisms are difficult to understand. Neutron scattering technologies, which are known for their high energy and momentum resolution, are powerful tools for simultaneously characterizing material structure and complex lattice dynamics. In recent years, neutron scattering techniques have made significant contributions to the study of energy materials, shedding light on their physical mechanisms. This review article details several neutron scattering techniques commonly used in energy material research, including neutron diffraction, total neutron scattering, quasi-elastic and inelastic neutron scattering. Then, some important research progress made in the field of energy materials in recent years using neutron scattering as the main characterization method is reviewed, including ultra-low lattice thermal conductivity in superionic thermoelectric materials, ion diffusion mechanism of solid-state electrolytes, plastic-crystalline phase transition and configuration entropy changes in barocaloric materials, lattice anharmonicity and charge transport in photovoltaic materials, and first-order magnetic-structural phase transition in magnetocaloric materials. In these complex energy conversion and storage materials, lattice dynamics do not work independently, and their functioning in macroscopic physical properties is always achieved through correlation or mutual coupling with other degrees of freedom, such as sublattices, charge, spin, etc. Through these typical examples, this review paper can provide a reference for further exploring and understanding the energy materials and lattice dynamics.
Newcastle Disease Virus (NDV), classified as Avian orthoavulavirus 1 (avian paramyxovirus type 1), is a promising oncolytic agent that selectively targets and destroys cancer cells while sparing normal tissues. Its oncoselectivity exploits cancer-specific defects in antiviral defenses, particularly impaired Type I interferon signaling, and dysregulated apoptotic pathways, enabling robust viral replication and cytotoxicity in malignancies such as breast, colorectal, and melanoma. NDV induces intrinsic and extrinsic apoptosis through caspase activation and triggers immunogenic cell death via damage-associated molecular patterns, stimulating potent antitumours immune responses. Additionally, NDVs potential as a vaccine vector, expressing tumours-associated antigens, offers prospects for prophylactic and therapeutic cancer applications. This review provides a comprehensive analysis of NDVs morphology, classification, and molecular biology, focusing on its viral entry and replication mechanisms in host cells. It explores NDVs interactions with cancer cells, emphasizing its ability to induce cytotoxicity and immune activation. Understanding these mechanisms is critical for optimizing NDVs oncolytic potential and advancing its clinical translation. Future directions include enhancing NDV through genetic engineering, combining it with therapies like immune checkpoint inhibitors, and developing personalized medicine approaches tailored to tumours genomic profiles. These advancements position NDV as a versatile therapeutic agent in oncolytic virotherapy.
Many suspensions contain particles with complex shapes that are affected not only by hydrodynamics, but also by thermal fluctuations, internal kinematic constraints and other long-range non-hydrodynamic interactions. Modeling these systems represents a significant numerical challenge due to the interplay between different effects and the need to accurately capture multiscale phenomena. In this article we review recent developments to model large suspensions of particles of arbitrary shapes and multiple couplings with controllable accuracy within the rigid multiblob framework. We discuss the governing equations, highlight key numerical developments, and illustrate applications ranging from microswimmers to complex colloidal suspensions. This review illustrates the effectiveness and versatility of the rigid multiblob method in tackling a wide range of physical problems in fluid mechanics, soft matter physics, biophysics, materials and colloidal science.
Short-circuiting Shortcuts: Mechanistic Investigation of Shortcuts in Text Classification
2025-05-09
Leon Eshuijs, Shihan Wang, Antske Fokkens
Reliance on spurious correlations (shortcuts) has been shown to underlie many of the successes of language models. Previous work focused on identifying the input elements that impact prediction. We investigate how shortcuts are actually processed within the model's decision-making mechanism. We use actor names in movie reviews as controllable shortcuts with known impact on the outcome. We use mechanistic interpretability methods and identify specific attention heads that focus on shortcuts. These heads gear the model towards a label before processing the complete input, effectively making premature decisions that bypass contextual analysis. Based on these findings, we introduce Head-based Token Attribution (HTA), which traces intermediate decisions back to input tokens. We show that HTA is effective in detecting shortcuts in LLMs and enables targeted mitigation by selectively deactivating shortcut-related attention heads.
Unilogit: Robust Machine Unlearning for LLMs Using Uniform-Target Self-Distillation
2025-05-09
Stefan Vasilev, Christian Herold, Baohao Liao, Seyyed Hadi Hashemi, Shahram Khadivi, Christof Monz
This paper introduces Unilogit, a novel self-distillation method for machine unlearning in Large Language Models. Unilogit addresses the challenge of selectively forgetting specific information while maintaining overall model utility, a critical task in compliance with data privacy regulations like GDPR. Unlike prior methods that rely on static hyperparameters or starting model outputs, Unilogit dynamically adjusts target logits to achieve a uniform probability for the target token, leveraging the current model's outputs for more accurate self-distillation targets. This approach not only eliminates the need for additional hyperparameters but also enhances the model's ability to approximate the golden targets. Extensive experiments on public benchmarks and an in-house e-commerce dataset demonstrate Unilogit's superior performance in balancing forget and retain objectives, outperforming state-of-the-art methods such as NPO and UnDIAL. Our analysis further reveals Unilogit's robustness across various scenarios, highlighting its practical applicability and effectiveness in achieving efficacious machine unlearning.
Discontinuous Galerkin time integration for second-order differential problems: formulations, analysis, and analogies
2025-05-09
Gabriele Ciaramella, Martin J. Gander, Ilario Mazzieri
We thoroughly investigate Discontinuous Galerkin (DG) discretizations as time integrators for second-order oscillatory systems, considering both second-order and first-order formulations of the original problem. Key contributions include new convergence analyses for the second-order formulation and equivalence proofs between DG and classical time-stepping schemes (such as Newmark schemes and general linear methods). In addition, the chapter provides a detailed review and convergence analysis for the first-order formulation, alongside comparisons of the proposed schemes in terms of accuracy, consistency, and computational cost.
A review of discontinuous Galerkin time-stepping methods for wave propagation problems
2025-05-09
Paola F. Antonietti, Alberto Artoni, Gabriele Ciaramella, Ilario Mazzieri
This chapter reviews and compares discontinuous Galerkin time-stepping methods for the numerical approximation of second-order ordinary differential equations, particularly those stemming from space finite element discretization of wave propagation problems. Two formulations, tailored for second- and first-order systems of ordinary differential equations, are discussed within a generalized framework, assessing their stability, accuracy, and computational efficiency. Theoretical results are supported by various illustrative examples that validate the findings, enhancing the understanding and applicability of these methods in practical scenarios.
GEORCE: A Fast New Control Algorithm for Computing Geodesics
2025-05-09
Frederik Möbius Rygaard, Søren Hauberg
Computing geodesics for Riemannian manifolds is a difficult task that often relies on numerical approximations. However, these approximations tend to be either numerically unstable, have slow convergence, or scale poorly with manifold dimension and number of grid points. We introduce a new algorithm called GEORCE that computes geodesics via a transformation into a discrete control problem. We show that GEORCE has global convergence and quadratic local convergence. In addition, we show that it extends to Finsler manifolds. For both Finslerian and Riemannian manifolds, we thoroughly benchmark GEORCE against several alternative optimization algorithms and show empirically that it has a much faster and more accurate performance for a variety of manifolds, including key manifolds from information theory and manifolds that are learned using generative models.
Poverty prediction models are used to address missing data issues in a variety of contexts such as poverty profiling, targeting with proxy-means tests, cross-survey imputations such as poverty mapping, top and bottom incomes studies, or vulnerability analyses. Based on the models used by this literature, this paper conducts a study by artificially corrupting data clear of missing incomes with different patterns and shares of missing incomes. It then compares the capacity of classic econometric and machine learning models to predict poverty under different scenarios with full information on observed and unobserved incomes, and the true counterfactual poverty rate. Random forest provides more consistent and accurate predictions under most but not all scenarios.
Photometric surveys for exoplanetary ring systems have not confirmed any object with Saturn-sized ring. We systematically analyse 308 TESS planet candidates, mainly comprised of giant short-period planets orbiting nearby bright stars. These targets are selected based on the optimistic detectability of rings, assuming a favourable ring orientation. We develop a pipeline with a two-step noise reduction and compare the fitting results of both ringless and ringed transit models to the resulting phase-folded light curves. Although we identify six systems where ringed models are statistically favoured, visual inspection of the signals suggests that none of them is conclusively attributed to the presence of rings. Assuming the ring orientation favourable for detection, we determine the 3$\sigma$ upper limits on ring sizes for 125 objects. Using these ring size limits, we derive upper limits on the ring occurrence rate, such as rings with an outer radius larger than 1.8 times the planetary radii occurring at rates lower than 2%. However, these limits can be relaxed if tidal alignment between the spin and orbital axes holds. We explore an alternative detection method using transit depth variations by ring precession and estimate that 10 and 13 systems are likely detectable in TESS and Kepler data, respectively.
This manuscript investigates the possible existence of a binary supermassive black holes (BSMBH) system in the blue quasar SDSS J100438.8+151056 (=SDSS J1004+1510) at z=0.219 based on the detection of robust optical QPOs. We determine QPOs using multiple analysis methods applied to the CSS-V, ZTFg/r band light curves, and additionally, combined with the characteristics of broad emission lines and explores potential mechanisms for the QPOs, including jet and disk precession models. Two distinct periodicities, $1103\pm260$days and $243\pm29$days, are identified in the ZTF g/r-band light curves with confidence level exceeding $5\sigma$, through four different techniques. Meanwhile, the $1103\pm260$days periodicity is also clearly detected in the CSS V-band light curve. The optical periodicities suggest a BSMBH system candidate in SDSS J1004+1510, with an estimated total virial BH mass of $(1.13\pm0.14)\times10^8 M_{\odot}$ and a space separation of $0.0053\pm0.0016$pc for the periodicity of $1103\pm260$days. The second periodicity of $243\pm29$days could be attributed to harmonic oscillations, considering $(1103\pm260)/(243\pm29)\sim4.54\pm0.47$ with large scatters. However, if the periodicity of $243\pm29$days was from an independent QPO, a triple BH system candidate on sub-pc scale could be probably expected, with space separations of $0.00036\pm0.00004$pc between a close BSMBH system and of $0.0053\pm0.0016$pc between the BSMBH system and the third BH, after considering similar BH mass of the third BH as the total mass of the central BSMBH. These findings strongly demonstrate that combined light curves from the different sky survey projects can lead to more reliable QPOs candidates to be detected, and also indicate higher quality light curves could be helpful to find probably potential QPOs with multiple periodicities leading to rare detections of candidates for sub-pc triple BH systems.
Exploring the Susceptibility to Fraud of Monetary Incentive Mechanisms for Strengthening FOSS Projects
2025-05-09
Ben Swierzy, Timo Pohl, Marc Ohm, Michael Meier
Free and open source software (FOSS) is ubiquitous on modern IT systems, accelerating the speed of software engineering over the past decades. With its increasing importance and historical reliance on uncompensated contributions, questions have been raised regarding the continuous maintenance of FOSS and its implications from a security perspective. In recent years, different funding programs have emerged to provide external incentives to reinforce community FOSS' sustainability. Past research primarily focused on analyses what type of projects have been funded and for what reasons. However, it has neither been considered whether there is a need for such external incentives, nor whether the incentive mechanisms, especially with the development of decentralized approaches, are susceptible to fraud. In this study, we explore the need for funding through a literature review and compare the susceptibility to fraud of centralized and decentralized incentive programs by performing case studies on the Sovereign Tech Fund (STF) and the tea project. We find non-commercial incentives to fill an important gap, ensuring longevity and sustainability of projects. Furthermore, we find the STF to be able to achieve a high resilience against fraud attempts, while tea is highly susceptible to fraud, as evidenced by revelation of an associated sybil attack on npm. Our results imply that special considerations must be taken into account when utilizing quantitative repository metrics regardless whether spoofing is expected.
Discovery of the Polar Ring Galaxies with deep learning
2025-05-09
D. V. Dobrycheva, O. O. Hetmantsev, I. B. Vavilova, A. Shportko, O. Gugnin, O. V. Kompaniiets
The aim of our research is to create a catalog of strong and good candidates for PRGs using existing catalogs of PRGs, develop an image-based approach with machine learning methods for the search and discovery of PRGs in a big sky survey, and explore the capability of the CIGALE software for determining their multiwavelength properties. For the first time, we applied a deep learning method to the search for PRGs. We visually inspected galaxies from existing catalogs of PRGs to create a training sample based on high-quality SDSS images. Since the resulting training sample was extremely small (87 strong and good PRGs), we applied augmentation, image segmentation, and ensemble learning techniques. However, most effective method was transfer learning with its ability to enlarge the training sample by synthetic images generated by GALFIT. To examine deep learning approach for finding new PRGs we used the SDSS catalog of galaxies at z < 0.1. The method with synthetic images showed that even with overtraining we were able to find galaxies with a ring pattern. Our deep learning approach has resulted in the discovery of three PRGs (SDSS J140644.42+471602.0; SDSS J133650.48+492745.3; SDSS J095717.30+364953.5). Also, we visually inspected the Catalog of the SDSS Ring galaxies at z < 0.1 and discovered four PRGs among ~2,200 ring galaxies (SDSS J095851.32+320422.9; SDSS J104211.05+234448.2; SDSS J162212.63+272032.2; SDSS J104600.10+090627.2). One of the discovered galaxies with transfer learning, SDSS J140644.42+471602.0, was studied with CIGALE software to determine its spectral energy distribution in IR-UV bands. The current SFR is 71 M_sun per year, although the lack of FUV data limits this estimate. The total stellar mass is 8.34x10^{10} M_sun. The predominance of an old stellar population (two-thirds of the total mass) suggests that this PRG is undergoing interaction process.
The AGORA High-Resolution Galaxy Simulations Comparison Project VII: Satellite quenching in zoom-in simulation of a Milky Way-mass halo
2025-05-09
R. Rodríguez-Cardoso, S. Roca-Fàbrega, Minyong Jung, Thinh Huu Nguyen, Ji-hoon Kim, Joel Primack, Oscar Agertz, Kirk S. S. Barrow, Jesus Gallego, Kentaro Nagamine, Johnny W. Powell, Yves Revaz, Hector Velázquez, Anna Genina, Hyeonyong Kim, Alessandro Lupi, Tom Abel, Renyue Cen, Daniel Ceverino, Avishai Dekel, Boon Kiat Oh, Thomas R. Quinn
Context: Satellite galaxies experience multiple physical processes when interacting with their host halos, often leading to the quenching of star formation. In the Local Group (LG), satellite quenching has been shown to be highly efficient, affecting nearly all satellites except the most massive ones. While recent surveys are studying Milky Way (MW) analogs to assess how representative our LG is, the dominant physical mechanisms behind satellite quenching in MW-mass halos remain under debate. Aims: We analyze satellite quenching within the same MW-mass halo, simulated using various widely-used astrophysical codes, each using different hydrodynamic methods and implementing different supernovae feedback recipes. The goal is to determine whether quenched fractions, quenching timescales and the dominant quenching mechanisms are consistent across codes or if they show sensitivity to the specific hydrodynamic method and supernovae (SNe) feedback physics employed. Methods: We use a subset of high-resolution cosmological zoom-in simulations of a MW-mass halo from the multiple-code AGORA CosmoRun suite. Results: We find that the quenched fraction is consistent with the latest SAGA survey results within its 1$\sigma$ host-to-host scatter across all the models. Regarding quenching timescales, all the models reproduce the trend observed in the ELVES survey, LG observations, and previous simulations: the less massive the satellite, the shorter its quenching timescale. All our models converge on the dominant quenching mechanisms: strangulation halts cold gas accretion and ram pressure stripping is the predominant mechanism for gas removal, particularly effective in satellites with $M_* < 10^8\, M_\odot$. Nevertheless, the efficiency of the stripping mechanisms differs among the codes, showing a strong sensitivity to the different SNe feedback implementations and/or hydrodynamic methods employed.
Oh F**k! How Do People Feel about Robots that Leverage Profanity?
2025-05-09
Madison R. Shippy, Brian J. Zhang, Naomi T. Fitter
Profanity is nearly as old as language itself, and cursing has become particularly ubiquitous within the last century. At the same time, robots in personal and service applications are often overly polite, even though past work demonstrates the potential benefits of robot norm-breaking. Thus, we became curious about robots using curse words in error scenarios as a means for improving social perceptions by human users. We investigated this idea using three phases of exploratory work: an online video-based study (N = 76) with a student pool, an online video-based study (N = 98) in the general U.S. population, and an in-person proof-of-concept deployment (N = 52) in a campus space, each of which included the following conditions: no-speech, non-expletive error response, and expletive error response. A surprising result in the outcomes for all three studies was that although verbal acknowledgment of an error was typically beneficial (as expected based on prior work), few significant differences appeared between the non-expletive and expletive error acknowledgment conditions (counter to our expectations). Within the cultural context of our work, the U.S., it seems that many users would likely not mind if robots curse, and may even find it relatable and humorous. This work signals a promising and mischievous design space that challenges typical robot character design.
Exploring Dense Crowd Dynamics: State of the Art and Emerging Paradigms
2025-05-09
Thomas Chatagnon, Antoine Tordeux, Mohcine Chraibi
Dense pedestrian crowds may pose significant safety risks, yet their underlying dynamics remain insufficiently understood to reliably prevent accidents. In these environments, physical interactions and contact forces fundamentally shape the dynamics of the crowd. However, accurately describing these interindividual interactions requires specific modeling and analytical approaches. This chapter reviews paradigms and models used to represent pedestrian dynamics in various contexts, highlighting the transition from classical approaches to models tailored for dense crowd conditions. We argue that further investigation is needed, featuring new experimental studies and new modeling paradigms, to better capture the complex dynamics that emerge in high-density situations.
EMU: Cross-correlating EMU Pilot Survey 1 with Dark Energy Survey to constrain the radio galaxy redshift distribution
2025-05-09
Chandra Shekhar Saraf, David Parkinson, Jacobo Asorey, Catherine L. Hale, Benedict Bahr-Kalus, Maciej Bilicki, Stefano Camera, Andrew M. Hopkins, Konstantinos Tanidis
Radio continuum galaxy surveys can provide a relatively fast map of the projected distribution of structure in the Universe, at the cost of lacking information about the radial distribution. We can use these surveys to learn about the growth of structure and the fundamental physics of the Universe, but doing so requires extra information to be provided in the modelling of the redshift distribution, $dN/dz$. In this work, we show how the cross-correlation of the two dimensional radio continuum map with another galaxy map (in this case a photometric optical extragalactic survey), with a known redshift distribution, can be used to determine the redshift distribution through statistical inference. We use data from the Evolutionary Map of the Universe (EMU) Pilot Survey 1 and cross-correlate it with optical data from the Dark Energy Survey to fit the parameters of our $dN/dz$ model. We show that the recovered distribution fits the data much better than current simulated $dN/dz$ models available in the literature, and peaks at a much higher redshift. These results will have significance for future cosmological analyses with large-scale radio continuum surveys such as the full EMU, or with the SKAO.
The Experience of Running: Recommending Routes Using Sensory Mapping in Urban Environments
2025-05-09
Katrin Hänsel, Luca Maria Aiello, Daniele Quercia, Rossano Schifanella, Krisztian Zsolt Varga, Linus W. Dietz, Marios Constantinides
Depending on the route, runners may experience frustration, freedom, or fulfilment. However, finding routes that are conducive to the psychological experience of running remains an unresolved task in the literature. In a mixed-method study, we interviewed 7 runners to identify themes contributing to running experience, and quantitatively examined these themes in an online survey with 387 runners. Using Principal Component Analysis on the survey responses, we developed a short experience sampling questionnaire that captures the three most important dimensions of running experience: \emph{performance \& achievement}, \emph{environment}, and \emph{mind \& social connectedness}. Using path preferences obtained from the online survey, we clustered them into two types of routes: \emph{scenic} (associated with nature and greenery) and \emph{urban} (characterized by the presence of people); and developed a routing engine for path recommendations. We discuss challenges faced in developing the routing engine, and provide guidelines to integrate it into mobile and wearable running apps.
Assessing the Dynamics of the Coffee Value Chain in Davao del Sur: An Agent-Based Modeling Approach
2025-05-09
Lucia Stephanie B. Sibala, Novy Aila B. Rivas, Giovanna Fae R. Oguis
The study investigates the coffee value chain dynamics in Davao del Sur using an agent-based model. Three main factors driving interactions among key players were identified: trust, risk, and transaction costs. The model was constructed using NetLogo 6.3.0, and data from a survey questionnaire collected three data points from BACOFA members. Five cases were explored, with each scenario simulated 1000 times. Findings suggest that producers often sell to the market rather than the cooperative due to higher prices. However, producers tend to prioritize trust in buyers and their risk attitude, leading to increased sales to the cooperative. The producer's risk attitude significantly influences their decision-making, affecting performance outcomes such as loans, demand, and price changes. All three factors play a role and exert varying impacts on the value chain. So, the stakeholders' decisions on prioritizing factors in improving relationships depend on their priorities. Nonetheless, simulations show that establishing a harmonious system benefiting all parties is possible. However, achieving this requires adjustments to demand, pricing, trust, and risk attitudes of key players, which may not align with the preferences of some parties in reality.
Large language models (LLMs) are rapidly pushing the limits of contemporary computing hardware. For example, training GPT-3 has been estimated to consume around 1300 MWh of electricity, and projections suggest future models may require city-scale (gigawatt) power budgets. These demands motivate exploration of computing paradigms beyond conventional von Neumann architectures. This review surveys emerging photonic hardware optimized for next-generation generative AI computing. We discuss integrated photonic neural network architectures (e.g., Mach-Zehnder interferometer meshes, lasers, wavelength-multiplexed microring resonators) that perform ultrafast matrix operations. We also examine promising alternative neuromorphic devices, including spiking neural network circuits and hybrid spintronic-photonic synapses, which combine memory and processing. The integration of two-dimensional materials (graphene, TMDCs) into silicon photonic platforms is reviewed for tunable modulators and on-chip synaptic elements. Transformer-based LLM architectures (self-attention and feed-forward layers) are analyzed in this context, identifying strategies and challenges for mapping dynamic matrix multiplications onto these novel hardware substrates. We then dissect the mechanisms of mainstream LLMs, such as ChatGPT, DeepSeek, and LLaMA, highlighting their architectural similarities and differences. We synthesize state-of-the-art components, algorithms, and integration methods, highlighting key advances and open issues in scaling such systems to mega-sized LLM models. We find that photonic computing systems could potentially surpass electronic processors by orders of magnitude in throughput and energy efficiency, but require breakthroughs in memory, especially for long-context windows and long token sequences, and in storage of ultra-large datasets.
Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective
2025-05-09
Henan Sun, Xunkai Li, Lei Zhu, Junyi Han, Guang Zeng, Ronghua Li, Guoren Wang
Out-Of-Distribution (OOD) generalization has gained increasing attentions for machine learning on graphs, as graph neural networks (GNNs) often exhibit performance degradation under distribution shifts. Existing graph OOD methods tend to follow the basic ideas of invariant risk minimization and structural causal models, interpreting the invariant knowledge across datasets under various distribution shifts as graph topology or graph spectrum. However, these interpretations may be inconsistent with real-world scenarios, as neither invariant topology nor spectrum is assured. In this paper, we advocate the learnable random walk (LRW) perspective as the instantiation of invariant knowledge, and propose LRW-OOD to realize graph OOD generalization learning. Instead of employing fixed probability transition matrix (i.e., degree-normalized adjacency matrix), we parameterize the transition matrix with an LRW-sampler and a path encoder. Furthermore, we propose the kernel density estimation (KDE)-based mutual information (MI) loss to generate random walk sequences that adhere to OOD principles. Extensive experiment demonstrates that our model can effectively enhance graph OOD generalization under various types of distribution shifts and yield a significant accuracy improvement of 3.87% over state-of-the-art graph OOD generalization baselines.
Model-based calibration of gear-specific fish abundance survey data as a change-of-support problem
2025-05-09
Grace S. Chiu, Anton H. Westveld, Mark A. Albins, Kevin M. Boswell, John M. Hoenig, Sean P. Powers, S. Lynne Stokes, Allison L. White
In a continental-scale fish abundance study, a major challenge in deriving an absolute abundance estimate lies in the fact that regional surveys deploy different gear types, each with its unique field of view, producing gear-specific relative abundance data. Thus, data from regional surveys in the study must be converted from the gear-specific relative scale to an absolute scale before being combined to estimate a continental scale absolute abundance. In this paper, we develop a tool that takes gear-based data as input, and produces as output the required conversion, with associated uncertainty. Methodologically, this tool is operationalized from a Bayesian hierarchical model which we develop in an inferential context that is akin to the change-of-support problem often encountered in spatial studies; the actual context here is to reconcile abundance data at various gear-specific scales, some being relative, and others, absolute. We consider data from a small-scale calibration experiment in which 2 to 4 underwater video camera types, as well as an acoustic echosounder, were simultaneously deployed on each of 21 boat trips. While acoustic fish signals are recorded along transects on the absolute scale, they are subject to confounding from acoustically similar species, thus requiring an externally derived correction factor. Conversely, a camera allows visual distinction between species but records data on a gear-specific relative scale. Our statistical modeling framework reflects the relationship among all 5 gear types across the 21 trips, and the resulting model is used to derive calibration formulae to translate relative abundance data to the corrected absolute abundance scale whenever a camera is deployed alone. Cross-validation is conducted using mark-recapture abundance estimates. We also briefly discuss the case when one camera type is deployed alongside the echosounder.
A review of advancements in low-light image enhancement using deep learning
2025-05-09
Fangxue Liu, Lei Fan
In low-light environments, the performance of computer vision algorithms often deteriorates significantly, adversely affecting key vision tasks such as segmentation, detection, and classification. With the rapid advancement of deep learning, its application to low-light image processing has attracted widespread attention and seen significant progress in recent years. However, there remains a lack of comprehensive surveys that systematically examine how recent deep-learning-based low-light image enhancement methods function and evaluate their effectiveness in enhancing downstream vison tasks. To address this gap, this review provides a detailed elaboration on how various recent approaches (from 2020) operate and their enhancement mechanisms, supplemented with clear illustrations. It also investigates the impact of different enhancement techniques on subsequent vision tasks, critically analyzing their strengths and limitations. Additionally, it proposes future research directions. This review serves as a useful reference for determining low-light image enhancement techniques and optimizing vision task performance in low-light conditions.
Automating Infrastructure Surveying: A Framework for Geometric Measurements and Compliance Assessment Using Point Cloud Data
2025-05-09
Amin Ghafourian, Andrew Lee, Dechen Gao, Tyler Beer, Kin Yen, Iman Soltani
Automation can play a prominent role in improving efficiency, accuracy, and scalability in infrastructure surveying and assessing construction and compliance standards. This paper presents a framework for automation of geometric measurements and compliance assessment using point cloud data. The proposed approach integrates deep learning-based detection and segmentation, in conjunction with geometric and signal processing techniques, to automate surveying tasks. As a proof of concept, we apply this framework to automatically evaluate the compliance of curb ramps with the Americans with Disabilities Act (ADA), demonstrating the utility of point cloud data in survey automation. The method leverages a newly collected, large annotated dataset of curb ramps, made publicly available as part of this work, to facilitate robust model training and evaluation. Experimental results, including comparison with manual field measurements of several ramps, validate the accuracy and reliability of the proposed method, highlighting its potential to significantly reduce manual effort and improve consistency in infrastructure assessment. Beyond ADA compliance, the proposed framework lays the groundwork for broader applications in infrastructure surveying and automated construction evaluation, promoting wider adoption of point cloud data in these domains. The annotated database, manual ramp survey data, and developed algorithms are publicly available on the project's GitHub page: https://github.com/Soltanilara/SurveyAutomation.
We investigate the structural and magnetic properties of BaNi$_3$(AsO$_4$)$_2$(OH)$_2$, focusing on its spin-1 kagome lattice and the intricate coexistence of ferromagnetic and antiferromagnetic interactions. Powder x-ray diffraction analysis confirms a highly crystalline trigonal structure. Detailed Rietveld refinement identifies a single crystallographic Ni site, indicative of a perfect kagome lattice. Magnetic susceptibility measurements suggest predominantly ferromagnetic interactions with an effective magnetic moment consistent with Ni$^{2+}$ spins, yet the system undergoes antiferromagnetic ordering at a N$\'e$el temperature of 5.8 K. Isothermal magnetization measurements reveal a series of metamagnetic transitions culminating in a plateau-like phase near one-third of the total saturation magnetization. Analysis of the phase boundaries shows that the antiferromagnetic phase supports a substantial net moment in each kagome layer, comparable to that of the one-third plateau. This observation challenges the conventional model-where a 120$^\circ$ ground state transitions to an up-up-down configuration-commonly assumed for kagome antiferromagnets. Instead, our findings indicate that both the zero-field ground state and the field-induced phases exhibit in-plane ferrimagnetic spin arrangements on the kagome lattice, with the metamagnetic transition corresponding to a shift from layer-by-layer antiferromagnetically aligned net moments to ferromagnetically aligned ones. This configuration is stabilized by bond frustration, a network of competing interactions that can favor both ferromagnetic and antiferromagnetic couplings, highlighting the essential role of frustration in governing the low-temperature magnetic behavior of spin-1 kagome systems.
A prototype reactor-antineutrino detector based on $^6$Li-doped pulse-shaping-discriminating plastic scintillator
2025-05-09
O. Benevides Rodrigues, E. P. Bernard, N. S. Bowden, C. Bravo, R. Carr, T. M. Classen, A. J. Conant, S. A. Dazeley, M. T. Dunbrack, S. R. Durham, A. S. Erickson, A. Haghighat, K. M. Heeger, P. Huber, A. Irani, O. Kyzylova, V. A. Li, J. M. Link, B. R. Littlejohn, F. Machado, M. P. Mendenhall, H. P. Mumm, J. Newby, C. Roca, J. Ross, F. Sutanto, K. Walkup, J. Wilhelmi, X. Zhang
An aboveground 60-kg reactor-antineutrino detector prototype, comprised of a 2-dimensional array of 36 $^{6}$Li-doped pulse shape sensitive plastic scintillator bars, is described. Each bar is 50~cm long with a square cross section of 5.5~cm. Doped with $^{6}$Li at 0.1\% by mass, the detector is capable of identifying correlated energy depositions for the detection of reactor antineutrinos via the inverse-beta-decay reaction. Each bar is wrapped with a specular reflector that directs photons towards PMTs mounted at both ends of the bar. This paper highlights the construction, key features, and main performance characteristics of the system. The system, which relies on multiple observables such as PSD, energy, position, and timing, is capable of detecting IBD-like neutron-correlated backgrounds, long-lived decay chains, and cosmogenic isotopes.
RM jitter: Wavelength-dependent Scatter in Rotation Measure Related to Faraday Complexity
2025-05-09
Jeroen M. Stil
The relation between Faraday Rotation Measure (RM) and differential Faraday rotation by unresolved structure of a turbulent plasma is investigated for extragalactic radio sources. The RM scatter of a sample of sources affected by turbulent Faraday screens with identical power spectra of Faraday depth structure, is referred to as RM jitter. For fixed amplitude and slope of the power spectrum, the range of possible RMs depends on the wavelength coverage of the survey. RM jitter is independent of Faraday depth resolution as it results from the true Faraday depth dispersion and effects of wavelength-dependent depolarization. RM jitter for a flux density limited sample is sensitive to the power law index gamma of the power spectrum of Faraday depth structure. Assuming depolarization by a turbulent Faraday screen for all sources, a simulated flux-density-limited sample can reproduce the high RM scatter found by Vanderwoude et al. (2024) for sources that are less than 3% polarized. RM jitter of sources that are more than 3% polarized, is found to be smaller than the observed scatter, indicating that plasma other than the near-source environment dominates the RM scatter for the more polarized sources. The significance of RM jitter for applications of the RM grid is discussed.
Viscous flow model of ion-induced pattern formation: consistency between theory and experiment
2025-05-09
Tyler P. Evans, Scott A. Norris
It is known that ion-irradiation can lead surfaces to spontaneously develop patterns with characteristic length scales on the order of only a few nanometers. Pattern formation typically occurs only for irradiation angles beyond a critical angle, which varies with projectile, target, and irradiation energy. To date, there is no completely unifying physical theory. However, since predictions of the critical angle can be extracted from linear stability analysis of a given model, the ability to explain critical angle selection is a simple but important test of model validity. In this paper, we survey all existing critical angle, wavelength, and in-plane stress data for noble gas broad beam ion-irradiation of silicon and germanium by argon, krypton and xenon at energies from 250eV to 2keV. While neglecting the effects of erosion and redistribution, which are widely regarded as key contributors to ion-induced pattern formation, we find that a viscous flow model is capable of explaining these three types of experimental data simultaneously using only parameters within a physically-reasonable range, and in a manner consistent with the defect kinetics of the amorphous layer. This bolsters the case for viscous flow and stress-driven instabilities as important components of an eventual, unifying model of nanoscale pattern formation under ion bombardment.
Voice biomarkers of perinatal depression: cross-sectional nationwide pilot study report
2025-05-09
Rachel L. Wiley, Jim Schwoebel, Joel Shor, Bindu Chanagala, Matias Caccia, Adolfo M. García, Sheehan D. Fisher, Martin G. Frasch
Perinatal depression (PND) affects 1 in 5 mothers, with 85% lacking support. Digital health tools offer early identification and prevention, potentially reducing PND risk by over 50% and improving engagement. Despite high interest, user retention needs improvement for maximum benefit. Voice biomarkers have emerged as a possible digital alternative to collecting mood questionnaires to inform individualized user adherence and retention. However, no voice biomarkers have been developed specifically for pregnant mothers. We aimed to test an unstructured speech sample for the feasibility of yielding a machine learning (ML) model to predict PND. We surveyed 446 women nationwide at 22 weeks of gestation. We measured PHQ-8 and modified EPDS and four voice samples. Participants were classified as depressed if their PHQ-8 or EPDS were equal or higher than 10. In the present report, we focus on a 2-minute unstructured speech prompt. A standardized cleaning pipeline was applied to the audio files, including a uniform format conversion, segment filtering for background noise, extraneous acoustic events, and file size. This resulted in 112 high-quality voice recordings, i.e., a rate of 25%. The ML modeling strategy utilized the Allie ML framework on 80% of the data for training on the binary classification task (depressed or not). The analysis included steps for feature selection using referenced speech audio embeddings, followed by a TPOT classifier for ML. We report the validation results for the 20% held-out data. For PHQ-8, we achieved a balanced accuracy of ~71%, precision of 73%, recall of 68%, and AUC of 0.71. For EPDS, we achieved a balanced accuracy of 80%, precision of 92%, recall of 80%, and AUC of 0.80. In conclusion, we demonstrate the feasibility at scale and a promising accuracy for detecting PND using 120 seconds of unstructured speech audio data in expectant mothers.
A Comprehensive Data Description for LoRaWAN Path Loss Measurements in an Indoor Office Setting: Effects of Environmental Factors
2025-05-09
Nahshon Mokua Obiri, Kristof Van Laerhoven
This paper presents a comprehensive dataset of LoRaWAN technology path loss measurements collected in an indoor office environment, focusing on quantifying the effects of environmental factors on signal propagation. Utilizing a network of six strategically placed LoRaWAN end devices (EDs) and a single indoor gateway (GW) at the University of Siegen, City of Siegen, Germany, we systematically measured signal strength indicators such as the Received Signal Strength Indicator (RSSI) and the Signal-to-Noise Ratio (SNR) under various environmental conditions, including temperature, relative humidity, carbon dioxide (CO$_2$) concentration, barometric pressure, and particulate matter levels (PM$_{2.5}$). Our empirical analysis confirms that transient phenomena such as reflections, scattering, interference, occupancy patterns (induced by environmental parameter variations), and furniture rearrangements can alter signal attenuation by as much as 10.58 dB, highlighting the dynamic nature of indoor propagation. As an example of how this dataset can be utilized, we tested and evaluated a refined Log-Distance Path Loss and Shadowing Model that integrates both structural obstructions (Multiple Walls) and Environmental Parameters (LDPLSM-MW-EP). Compared to a baseline model that considers only Multiple Walls (LDPLSM-MW), the enhanced approach reduced the root mean square error (RMSE) from 10.58 dB to 8.04 dB and increased the coefficient of determination (R$^2$) from 0.6917 to 0.8222. By capturing the extra effects of environmental conditions and occupancy dynamics, this improved model provides valuable insights for optimizing power usage and prolonging device battery life, enhancing network reliability in indoor Internet of Things (IoT) deployments, among other applications. This dataset offers a solid foundation for future research and development in indoor wireless communication.
Beyond the Standard Model contributions to dipole moments
2025-05-09
Oscar Vives, Nicola Valori
The goal of this work is to provide a pedagogical introduction to dipole moments and dipole transitions in theories beyond the Standard Model, with a focus on the lepton sector. We emphasize the exceptional sensitivity of dipole observables to new physics, analyzing the most sensitive processes, such as the muon anomalous magnetic moment, the electric dipole moment of the electron, and the branching ratio $\text{BR}(\mu \to e \gamma)$. This review is intended for PhD students, early-career researchers, and anyone entering the field for the first time. It does not aim to provide a complete overview of all models in the literature, but rather to serve as an accessible guide, using simple and representative examples from the most well-studied extensions of the Standard Model.
The ALMA-CRISTAL survey: Gas, dust, and stars in star-forming galaxies when the Universe was ~1 Gyr old I. Survey overview and case studies
2025-05-09
R. Herrera-Camus, J. González-López, N. Förster Schreiber, M. Aravena, I. de Looze, J. Spilker, K. Tadaki, L. Barcos-Muñoz, R. J. Assef, J. E. Birkin, A. D. Bolatto, R. Bouwens, S. Bovino, R. A. A. Bowler, G. Calistro Rivera, E. da Cunha, R. I. Davies, R. L. Davies, T. Díaz-Santos, A. Ferrara, D. Fisher, R. Genzel, J. Hodge, R. Ikeda, M. Killi, L. Lee, Y. Li, J. Li, D. Liu, D. Lutz, I. Mitsuhashi, D. Narayanan, T. Naab, M. Palla, S. H. Price, A. Posses, M. Relaño, R. Smit, M. Solimano, A. Sternberg, L. Tacconi, K. Telikova, H. Übler, S. A. van der Giessen, S. Veilleux, V. Villanueva, M. Baeza-Garay
We present the ALMA-CRISTAL survey, an ALMA Cycle 8 Large Program designed to investigate the physical properties of star-forming galaxies at $4 \lesssim z \lesssim 6$ through spatially resolved, multi-wavelength observations. This survey targets 19 star-forming main-sequence galaxies selected from the ALPINE survey, using ALMA Band 7 observations to study [CII] 158 $\mu$m line emission and dust continuum, complemented by JWST/NIRCam and HST imaging to map stellar and UV emission. The CRISTAL sample expanded to 39 after including newly detected galaxies in the CRISTAL fields, archival data, and pilot study targets. The resulting dataset provides a detailed view of gas, dust, and stellar structures on kiloparsec scales at the end of the era of reionization. The survey reveals diverse morphologies and kinematics, including rotating disks, merging systems, [CII] emission tails from potential interactions, and clumpy star formation. Notably, the [CII] emission in many cases extends beyond the stellar light seen in HST and JWST imaging. Scientific highlights include CRISTAL-10, exhibiting an extreme [CII] deficit similar to Arp 220; and CRISTAL-13, where feedback from young star-forming clumps likely causes an offset between the stellar clumps and the peaks of [CII] emission. CRISTAL galaxies exhibit global [CII]/FIR ratios that decrease with increasing FIR luminosity, similar to trends seen in local galaxies but shifted to higher luminosities, likely due to their higher molecular gas content. CRISTAL galaxies also span a previously unexplored range of global FIR surface brightness at high-redshift, showing that high-redshift galaxies can have elevated [CII]/FIR ratios. These elevated ratios are likely influenced by factors such as lower metallicity gas, the presence of significant extraplanar gas, and contributions from shock-excited gas.
Enterprise Architecture as a Dynamic Capability for Scalable and Sustainable Generative AI adoption: Bridging Innovation and Governance in Large Organisations
2025-05-09
Alexander Ettinger
Generative Artificial Intelligence is a powerful new technology with the potential to boost innovation and reshape governance in many industries. Nevertheless, organisations face major challenges in scaling GenAI, including technology complexity, governance gaps and resource misalignments. This study explores how Enterprise Architecture Management can meet the complex requirements of GenAI adoption within large enterprises. Based on a systematic literature review and the qualitative analysis of 16 semi-structured interviews with experts, it examines the relationships between EAM, dynamic capabilities and GenAI adoption. The review identified key limitations in existing EA frameworks, particularly their inability to fully address the unique requirements of GenAI. The interviews, analysed using the Gioia methodology, revealed critical enablers and barriers to GenAI adoption across industries. The findings indicate that EAM, when theorised as sensing, seizing and transforming dynamic capabilities, can enhance GenAI adoption by improving strategic alignment, governance frameworks and organisational agility. However, the study also highlights the need to tailor EA frameworks to GenAI-specific challenges, including low data governance maturity and the balance between innovation and compliance. Several conceptual frameworks are proposed to guide EA leaders in aligning GenAI maturity with organisational readiness. The work contributes to academic understanding and industry practice by clarifying the role of EA in bridging innovation and governance in disruptive technology environments.
Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review
2025-05-09
Abdullah, Tao Huang, Ickjai Lee, Euijoon Ahn
The diffusion model has recently emerged as a potent approach in computer vision, demonstrating remarkable performances in the field of generative artificial intelligence. Capable of producing high-quality synthetic images, diffusion models have been successfully applied across a range of applications. However, a significant challenge remains with the high computational cost associated with training and generating these models. This study focuses on the efficiency and inference time of diffusion-based generative models, highlighting their applications in both natural and medical imaging. We present the most recent advances in diffusion models by categorizing them into three key models: the Denoising Diffusion Probabilistic Model (DDPM), the Latent Diffusion Model (LDM), and the Wavelet Diffusion Model (WDM). These models play a crucial role in medical imaging, where producing fast, reliable, and high-quality medical images is essential for accurate analysis of abnormalities and disease diagnosis. We first investigate the general framework of DDPM, LDM, and WDM and discuss the computational complexity gap filled by these models in natural and medical imaging. We then discuss the current limitations of these models as well as the opportunities and future research directions in medical imaging.
3D scene generation seeks to synthesize spatially structured, semantically meaningful, and photorealistic environments for applications such as immersive media, robotics, autonomous driving, and embodied AI. Early methods based on procedural rules offered scalability but limited diversity. Recent advances in deep generative models (e.g., GANs, diffusion models) and 3D representations (e.g., NeRF, 3D Gaussians) have enabled the learning of real-world scene distributions, improving fidelity, diversity, and view consistency. Recent advances like diffusion models bridge 3D scene synthesis and photorealism by reframing generation as image or video synthesis problems. This survey provides a systematic overview of state-of-the-art approaches, organizing them into four paradigms: procedural generation, neural 3D-based generation, image-based generation, and video-based generation. We analyze their technical foundations, trade-offs, and representative results, and review commonly used datasets, evaluation protocols, and downstream applications. We conclude by discussing key challenges in generation capacity, 3D representation, data and annotations, and evaluation, and outline promising directions including higher fidelity, physics-aware and interactive generation, and unified perception-generation models. This review organizes recent advances in 3D scene generation and highlights promising directions at the intersection of generative AI, 3D vision, and embodied intelligence. To track ongoing developments, we maintain an up-to-date project page: https://github.com/hzxie/Awesome-3D-Scene-Generation.
Primordial black-hole formation and heavy r-process element synthesis from the cosmological QCD transition. Two aspects of an inhomogeneous early Universe
2025-05-08
M. Gonin, G. Hasinger, D. Blaschke, O. Ivanytskyi, G. Röpke
We review the role of primordial black holes (PBHs) for illuminating the dark ages of the cosmological evolution and as dark matter candidates. We elucidate the role of phase transitions for primordial black hole formation in the early Universe and focus our attention to the cosmological QCD phase transition within a recent microscopical model. We explore the impact of physics beyond the Standard Model on the cosmic equation of state and the probability distribution for the formation of primordial black holes which serve as dark matter (DM) candidates. We argue that besides primordial black holes also droplet-like quark-gluon plasma inhomogeneities may become gravitationally stabilized for a sufficiently long epoch to distill baryon number and form nuclear matter droplets which upon their evaporation may enrich the cosmos locally with heavy $r$-process elements already in the early Universe.
Marsden--Meyer--Weinstein reduction for $k$-contact field theories
2025-05-08
J. de Lucas, X. Rivas, S. Vilarino, B. M. Zawora
This work devises a Marsden--Meyer--Weinstein $k$-contact reduction. Our techniques are illustrated with several examples of mathematical and physical relevance. As a byproduct, we review the previous contact reduction literature so as to clarify and to solve some inaccuracies.
Spatial intelligence (SI) represents a cognitive ability encompassing the visualization, manipulation, and reasoning about spatial relationships, underpinning disciplines from neuroscience to robotics. We introduce SITE, a benchmark dataset towards SI Thorough Evaluation in a standardized format of multi-choice visual question-answering, designed to assess large vision-language models' spatial intelligence across diverse visual modalities (single-image, multi-image, and video) and SI factors (figural to environmental scales, spatial visualization and orientation, intrinsic and extrinsic, static and dynamic). Our approach to curating the benchmark combines a bottom-up survey about 31 existing datasets and a top-down strategy drawing upon three classification systems in cognitive science, which prompt us to design two novel types of tasks about view-taking and dynamic scenes. Extensive experiments reveal that leading models fall behind human experts especially in spatial orientation, a fundamental SI factor. Moreover, we demonstrate a positive correlation between a model's spatial reasoning proficiency and its performance on an embodied AI task.
Variable Selection for Fixed and Random Effects in Multilevel Functional Mixed Effects Models
2025-05-08
Rahul Ghosal, Marcos Matabuena, Enakshi Saha
We develop a new method for simultaneously selecting fixed and random effects in a multilevel functional regression model. The proposed method is motivated by accelerometer-derived physical activity data from the 2011-12 cohort of the National Health and Nutrition Examination Survey (NHANES), where we are interested in identifying age and race-specific heterogeneity in covariate effects on the diurnal patterns of physical activity across the lifespan. Existing methods for variable selection in function-on-scalar regression have primarily been designed for fixed effect selection and for single-level functional data. In high-dimensional multilevel functional regression, the presence of cluster-specific heterogeneity in covariate effects could be detected through sparsity in fixed and random effects, and for this purpose, we propose a multilevel functional mixed effects selection (MuFuMES) method. The fixed and random functional effects are modelled using splines, with spike-and-slab group lasso (SSGL) priors on the unknown parameters of interest and a computationally efficient MAP estimation approach is employed for mixed effect selection through an Expectation Conditional Maximization (ECM) algorithm. Numerical analysis using simulation study illustrates the satisfactory selection accuracy of the variable selection method in having a negligible false-positive and false-negative rate. The proposed method is applied to the accelerometer data from the NHANES 2011-12 cohort, where it effectively identifies age and race-specific heterogeneity in covariate effects on the diurnal patterns of physical activity, recovering biologically meaningful insights.
Late-blooming magnetars: awakening as ultra-long period objects after a dormant cooling epoch
2025-05-08
Arthur G. Suvorov, Clara Dehman, José A. Pons
Ultra-long period transients (ULPs) are an elusive class of compact objects uncovered by radio surveys. While magnetars are a leading candidate for isolated ULPs, several observational properties challenge the established evolutionary framework: (i) low quiescent X-ray luminosities, (ii) $\sim$~hour-long rotational periods, and (iii) highly-variable radio flux. It is shown via magnetothermal modelling that, if electric currents thread the fluid core at the time of crust freezing, the neutron star remains multiband silent for an initial period of approximately 0.1 Myr while cooling passively. Once the crust becomes cold enough, the Hall effect begins to dominate the magnetic evolution, triggering crustal failures that inject magnetospheric twist that initiates radio pulsing while depleting rotational kinetic energy from an already-slow star. Depending on where electric currents circulate, such 'late-blooming' magnetars manifesting as Galactic ULPs may thus form a distinct branch from soft gamma repeaters and anomalous X-ray pulsars.
Rigorous Methods for Bohr-Sommerfeld Quantization Rules
2025-05-08
Joanne Dong, Peter D. Miller, Giorgio Young
In this work, we prove Bohr-Sommerfeld quantization rules for the self-adjoint Zakharov-Shabat system and the Schr\"odinger equation in the presence of two simple turning points bounding a classically allowed region. In particular, we use the method of comparison equations for $2\times 2$ traceless first-order systems to provide a unified perspective that yields similar proofs in each setting. The use of a Weber model system gives results that are uniform in the eigenvalue parameter over the whole range from the bottom of the potential well up to finite values.
The rapid adoption of Vision Language Models (VLMs), pre-trained on large image-text and video-text datasets, calls for protecting and informing users about when to trust these systems. This survey reviews studies on trust dynamics in user-VLM interactions, through a multi-disciplinary taxonomy encompassing different cognitive science capabilities, collaboration modes, and agent behaviours. Literature insights and findings from a workshop with prospective VLM users inform preliminary requirements for future VLM trust studies.
2025-05-08
D. N. Basov, A. Asenjo-Garcia, P. J. Schuck, X. -Y. Zhu, A. Rubio, A. Cavalleri, M. Delor, M. M. Fogler, Mengkun Liu
Polaritons are quantum mechanical superpositions of photon states with elementary excitations in molecules and solids. The light-matter admixture causes a characteristic frequency-momentum dispersion shared by all polaritons irrespective of the microscopic nature of material excitations that could entail charge, spin, lattice or orbital effects. Polaritons retain the strong nonlinearities of their matter component and simultaneously inherit ray-like propagation of light. Polaritons prompt new properties, enable new opportunities for spectroscopy/imaging, empower quantum simulations and give rise to new forms of synthetic quantum matter. Here, we review the emergent effects rooted in polaritonic quasiparticles in a wide variety of their physical implementations. We present a broad portfolio of the physical platforms and phenomena of what we term polaritonic quantum matter. We discuss the unifying aspects of polaritons across different platforms and physical implementations and focus on recent developments in: polaritonic imaging, cavity electrodynamics and cavity materials engineering, topology and nonlinearities, as well as quantum polaritonics.
Software Development Life Cycle Perspective: A Survey of Benchmarks for Code Large Language Models and Agents
2025-05-08
Kaixin Wang, Tianlin Li, Xiaoyu Zhang, Chong Wang, Weisong Sun, Yang Liu, Bin Shi
Code large language models (CodeLLMs) and agents have shown great promise in tackling complex software engineering tasks.Compared to traditional software engineering methods, CodeLLMs and agents offer stronger abilities, and can flexibly process inputs and outputs in both natural and code. Benchmarking plays a crucial role in evaluating the capabilities of CodeLLMs and agents, guiding their development and deployment. However, despite their growing significance, there remains a lack of comprehensive reviews of benchmarks for CodeLLMs and agents. To bridge this gap, this paper provides a comprehensive review of existing benchmarks for CodeLLMs and agents, studying and analyzing 181 benchmarks from 461 relevant papers, covering the different phases of the software development life cycle (SDLC). Our findings reveal a notable imbalance in the coverage of current benchmarks, with approximately 60% focused on the software development phase in SDLC, while requirements engineering and software design phases receive minimal attention at only 5% and 3%, respectively. Additionally, Python emerges as the dominant programming language across the reviewed benchmarks. Finally, this paper highlights the challenges of current research and proposes future directions, aiming to narrow the gap between the theoretical capabilities of CodeLLMs and agents and their application in real-world scenarios.
Insights into Neutron Star Matter: EoS Models and Observations
2025-05-08
Zuhua Ji, Jiarui Chen, Gaojian Wu
The equation of state (EoS) for neutron stars is a crucial topic in astrophysics, nuclear physics, and quantum chromodynamics (QCD), influencing their structure, stability, and observable properties. This review classifies EoS models into hadronic matter, hybrid, and quark matter models, analyzing their assumptions, predictions, and constraints. While hadronic models characterize nucleonic matter, potentially including contributions from hyperons or mesons, hybrid models introduce phase transitions to quark matter, and quark models hypothesize the presence of deconfined quark matter cores or entirely quark-composed stars. By synthesizing results from recent theoretical and observational studies, this review aims to offer a comprehensive understanding of the methodologies used in constructing neutron star EoS, their implications, and future directions.
2025-05-08
Qian Zeng, Chenggong Hu, Mingli Song, Jie Song
Recent success of large text-to-image models has empirically underscored the exceptional performance of diffusion models in generative tasks. To facilitate their efficient deployment on resource-constrained edge devices, model quantization has emerged as a pivotal technique for both compression and acceleration. This survey offers a thorough review of the latest advancements in diffusion model quantization, encapsulating and analyzing the current state of the art in this rapidly advancing domain. First, we provide an overview of the key challenges encountered in the quantization of diffusion models, including those based on U-Net architectures and Diffusion Transformers (DiT). We then present a comprehensive taxonomy of prevalent quantization techniques, engaging in an in-depth discussion of their underlying principles. Subsequently, we perform a meticulous analysis of representative diffusion model quantization schemes from both qualitative and quantitative perspectives. From a quantitative standpoint, we rigorously benchmark a variety of methods using widely recognized datasets, delivering an extensive evaluation of the most recent and impactful research in the field. From a qualitative standpoint, we categorize and synthesize the effects of quantization errors, elucidating these impacts through both visual analysis and trajectory examination. In conclusion, we outline prospective avenues for future research, proposing novel directions for the quantization of generative models in practical applications. The list of related papers, corresponding codes, pre-trained models and comparison results are publicly available at the survey project homepage https://github.com/TaylorJocelyn/Diffusion-Model-Quantization.
The article explores the emerging domain of incentive-aware machine learning (ML), which focuses on algorithmic decision-making in contexts where individuals can strategically modify their inputs to influence outcomes. It categorizes the research into three perspectives: robustness, aiming to design models resilient to "gaming"; fairness, analyzing the societal impacts of such systems; and improvement/causality, recognizing situations where strategic actions lead to genuine personal or societal improvement. The paper introduces a unified framework encapsulating models for these perspectives, including offline, online, and causal settings, and highlights key challenges such as differentiating between gaming and improvement and addressing heterogeneity among agents. By synthesizing findings from diverse works, we outline theoretical advancements and practical solutions for robust, fair, and causally-informed incentive-aware ML systems.
Discrete dynamical systems: inverse problems and related topics
2025-05-08
Alexander Mikhaylov, Victor Mikhaylov
In this review, we extend the Boundary Control method\, -- \,an approach to inverse problems based on control theory for dynamical systems \, -- \,to inverse problems for discrete dynamical systems. We apply our results to classical moment problems, Toda lattices, Weyl functions, de Branges spaces, Krein-Stieltjes strings, and also to problems of numerical simulations.
2025-05-08
Tiago de Souza Farias, Lucas Friedrich, Jonas Maziero
As quantum devices scale toward practical machine learning applications, the binary qubit paradigm faces expressivity and resource efficiency limitations. Multi-level quantum systems, or qudits, offer a promising alternative by harnessing a larger Hilbert space, enabling richer data embeddings, more compact variational circuits, and support for multi-valued problem structures. In this work, we review the role of qudits in quantum machine learning techniques, mainly variational quantum algorithms and quantum neural networks. Drawing on recent experimental demonstrations, including high-level superconducting transmons, qutrit-based combinatorial optimization, and single-qudit classifiers, we highlight how qudit architectures can reduce circuit depth and parameter counts while maintaining competitive fidelity. We further assess the evolving software ecosystem, from specialized simulators and differentiable-programming libraries to extensions of mainstream frameworks. We also identify key challenges in control complexity, noise management, and tooling maturity.
Multi-agent Embodied AI: Advances and Future Directions
2025-05-08
Zhaohan Feng, Ruiqi Xue, Lei Yuan, Yang Yu, Ning Ding, Meiqin Liu, Bingzhao Gao, Jian Sun, Gang Wang
Embodied artificial intelligence (Embodied AI) plays a pivotal role in the application of advanced technologies in the intelligent era, where AI systems are integrated with physical bodies that enable them to perceive, reason, and interact with their environments. Through the use of sensors for input and actuators for action, these systems can learn and adapt based on real-world feedback, allowing them to perform tasks effectively in dynamic and unpredictable environments. As techniques such as deep learning (DL), reinforcement learning (RL), and large language models (LLMs) mature, embodied AI has become a leading field in both academia and industry, with applications spanning robotics, healthcare, transportation, and manufacturing. However, most research has focused on single-agent systems that often assume static, closed environments, whereas real-world embodied AI must navigate far more complex scenarios. In such settings, agents must not only interact with their surroundings but also collaborate with other agents, necessitating sophisticated mechanisms for adaptation, real-time learning, and collaborative problem-solving. Despite increasing interest in multi-agent systems, existing research remains narrow in scope, often relying on simplified models that fail to capture the full complexity of dynamic, open environments for multi-agent embodied AI. Moreover, no comprehensive survey has systematically reviewed the advancements in this area. As embodied AI rapidly evolves, it is crucial to deepen our understanding of multi-agent embodied AI to address the challenges presented by real-world applications. To fill this gap and foster further development in the field, this paper reviews the current state of research, analyzes key contributions, and identifies challenges and future directions, providing insights to guide innovation and progress in this field.
Integrating Communication, Sensing, and Security: Progress and Prospects of PLS in ISAC Systems
2025-05-08
Waqas Aman, El-Mehdi Illi, Marwa Qaraqe, Saif Al-Kuwari
The sixth generation of wireless networks defined several key performance indicators (KPIs) for assessing its networks, mainly in terms of reliability, coverage, and sensing. In this regard, remarkable attention has been paid recently to the integrated sensing and communication (ISAC) paradigm as an enabler for efficiently and jointly performing communication and sensing using the same spectrum and hardware resources. On the other hand, ensuring communication and data security has been an imperative requirement for wireless networks throughout their evolution. The physical-layer security (PLS) concept paved the way to catering to the security needs in wireless networks in a sustainable way while guaranteeing theoretically secure transmissions, independently of the computational capacity of adversaries. Therefore, it is of paramount importance to consider a balanced trade-off between communication reliability, sensing, and security in future networks, such as the 5G and beyond, and the 6G. In this paper, we provide a comprehensive and system-wise review of designed secure ISAC systems from a PLS point of view. In particular, the impact of various physical-layer techniques, schemes, and wireless technologies to ensure the sensing-security trade-off is studied from the surveyed work. Furthermore, the amalgamation of PLS and ISAC is analyzed in a broader impact by considering attacks targeting data confidentiality, communication covertness, and sensing spoofing. The paper also serves as a tutorial by presenting several theoretical foundations on ISAC and PLS, which represent a practical guide for readers to develop novel secure ISAC network designs.
SSH-Net: A Self-Supervised and Hybrid Network for Noisy Image Watermark Removal
2025-05-08
Wenyang Liu, Jianjun Gao, Kim-Hui Yap
Visible watermark removal is challenging due to its inherent complexities and the noise carried within images. Existing methods primarily rely on supervised learning approaches that require paired datasets of watermarked and watermark-free images, which are often impractical to obtain in real-world scenarios. To address this challenge, we propose SSH-Net, a Self-Supervised and Hybrid Network specifically designed for noisy image watermark removal. SSH-Net synthesizes reference watermark-free images using the watermark distribution in a self-supervised manner and adopts a dual-network design to address the task. The upper network, focused on the simpler task of noise removal, employs a lightweight CNN-based architecture, while the lower network, designed to handle the more complex task of simultaneously removing watermarks and noise, incorporates Transformer blocks to model long-range dependencies and capture intricate image features. To enhance the model's effectiveness, a shared CNN-based feature encoder is introduced before dual networks to extract common features that both networks can leverage. Our code will be available at https://github.com/wenyang001/SSH-Net.
Visual Affordances: Enabling Robots to Understand Object Functionality
2025-05-08
Tommaso Apicella, Alessio Xompero, Andrea Cavallaro
Human-robot interaction for assistive technologies relies on the prediction of affordances, which are the potential actions a robot can perform on objects. Predicting object affordances from visual perception is formulated differently for tasks such as grasping detection, affordance classification, affordance segmentation, and hand-object interaction synthesis. In this work, we highlight the reproducibility issue in these redefinitions, making comparative benchmarks unfair and unreliable. To address this problem, we propose a unified formulation for visual affordance prediction, provide a comprehensive and systematic review of previous works highlighting strengths and limitations of methods and datasets, and analyse what challenges reproducibility. To favour transparency, we introduce the Affordance Sheet, a document to detail the proposed solution, the datasets, and the validation. As the physical properties of an object influence the interaction with the robot, we present a generic framework that links visual affordance prediction to the physical world. Using the weight of an object as an example for this framework, we discuss how estimating object mass can affect the affordance prediction. Our approach bridges the gap between affordance perception and robot actuation, and accounts for the complete information about objects of interest and how the robot interacts with them to accomplish its task.
Nucleic acids have been regarded as stiff polymers with long-range flexibility and generally modeled using elastic rod models of polymer physics. Notwithstanding, investigations carried out over the past few years on single fragments of order $\sim 100$ base pairs have revealed remarkable flexibility properties at short scales and called for theoretical approaches that emphasize the role of the bending fluctuations at single sites along the molecule stack. Here, we review a three dimensional mesoscopic Hamiltonian model which assumes a discrete representation of the double stranded (ds) molecules at the level of the nucleotides. The model captures the fundamental local interactions between adjacent sugar-phosphate groups and the pairwise interactions between complementary base pair mates. A statistical method based on the path integral formalism sets the ensemble of the base pair breathing fluctuations which are included in the partition function and permits to derive the thermodynamics and the elastic response of single molecules to external forces. We apply the model to the computation of the twist-stretch relations for fragments of ds-DNA and ds-RNA, showing that the obtained opposite pattern (DNA overtwists whereas RNA untwists versus force) follows from the different structural features of the two helices. Moreover, we focus on the DNA stretching due to the confinement in nano-pores and, finally, on the computation of the cyclization probability of open ends molecules of $\sim 100$ base pairs under physiological conditions. The mesoscopic model shows a distinct advantage over the elastic rod model in estimating the molecule bendability at short length scale.
Neural Pathways to Program Success: Hopfield Networks for PERT Analysis
2025-05-08
Azgar Ali Noor Ahamed
Project and task scheduling under uncertainty remains a fundamental challenge in program and project management, where accurate estimation of task durations and dependencies is critical for delivering complex, multi project systems. The Program Evaluation and Review Technique provides a probabilistic framework to model task variability and critical paths. In this paper, the author presents a novel formulation of PERT scheduling as an energy minimization problem within a Hopfield neural network architecture. By mapping task start times and precedence constraints into a neural computation framework, the networks inherent optimization dynamics is exploited to approximate globally consistent schedules. The author addresses key theoretical issues related to energy function differentiability, constraint encoding, and convergence, and extends the Hopfield model for structured precedence graphs. Numerical simulations on synthetic project networks comprising up to 1000 tasks demonstrate the viability of this approach, achieving near optimal makespans with minimal constraint violations. The findings suggest that neural optimization models offer a promising direction for scalable and adaptive project tasks scheduling under uncertainty in areas such as the agentic AI workflows, microservice based applications that the modern AI systems are being built upon.
Gravitational Lenses in UNIONS and Euclid (GLUE) I: A Search for Strong Gravitational Lenses in UNIONS with Subaru, CFHT, and Pan-STARRS Data
2025-05-08
Christopher J. Storfer, Eugene A. Magnier, Xiaosheng Huang, David Rubin, David J. Schlegel, Saurav Banka, Kenneth C. Chambers, Jean-Charles Cuillandre, Thomas de Boer, Raphael Gavazzi, Stephen Gwyn, Michael J. Hudson, Gregory S. H. Paek, Douglas Scott
We present the results of our pipeline for discovering strong gravitational lenses in the ongoing Ultraviolet Near-Infrared Optical Northern Survey (UNIONS). We successfully train the deep residual neural network (ResNet) based on CMU-Deeplens architecture, which is designed to detect strong lenses in ground-based imaging surveys. We train on images of real strong lenses and deploy on a sample of 8 million galaxies in areas with full coverage in the g, r, and i filters, the first multi-band search for strong gravitational lenses in UNIONS. Following human inspection and grading, we report the discovery of a total of 1346 new strong lens candidates of which 146 are grade A, 199 grade B, and 1001 grade C. Of these candidates, 283 have lens-galaxy spectroscopic redshifts from the Sloan Digital Sky Survey (SDSS) and an additional 297 from the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1). We find 15 of these systems display evidence of both lens and source galaxy redshifts in spectral superposition. We additionally report the spectroscopic confirmation of seven lensed sources in highquality systems, all with z > 2.1, using the Keck Near-Infrared Echelle Spectrograph (NIRES) and Gemini Near-Infrared Spectrograph (GNIRS).
Searching for pulsars in Globular Clusters with the Fast Fold Algorithm and a new pulsar discovered in M13
2025-05-08
Yaowei Li, Lin Wang, Lei Qian, Liyun Zhang, Yujie Chen, Dejiang Yin, Baoda Li, Yinfeng Dai, Ralph P. Eatough, Wenze Li, Dongyue Jiang, Xingnan Zhang, Minghui Li, Yujie Lian, Yuxiao Wu, Tong Liu, Kuo Liu, Zhichen Pan
We employed the Fast Folding Algorithm (FFA) on L-Band Globular Cluster (GC) observations taken with Five-hundred-meter Aperture Spherical radio Telescope (FAST) to search for new pulsars, especially those with a long rotational period. We conducted a search across 16 GCs that collectively host 93 known pulsars, as well as 14 GCs that do not contain any known pulsars. The majority of these known pulsars were successfully re-detected in our survey. The few non-detections could be attributed to the high accelerations of these pulsars. Additionally, we have discovered a new binary millisecond pulsar, namely M13I (or PSR J1641+3627I) in GC M13 (or NGC 6205), and obtained its phase-coherent timing solution using observations spanning 6 years. M13I has a spin period of 6.37 ms, and an orbital period of 18.23 days. The eccentricity of the binary orbit is 0.064, with a companion mass range of approximately 0.45 to 1.37 M$_{\odot}$. The orbital properties of M13I are remarkably different from those of the other known pulsars in M13, indicating that this pulsar has undergone a different evolutionary path compared to the rest.
Driving with Context: Online Map Matching for Complex Roads Using Lane Markings and Scenario Recognition
2025-05-08
Xin Bi, Zhichao Li, Yuxuan Xia, Panpan Tong, Lijuan Zhang, Yang Chen, Junsheng Fu
Accurate online map matching is fundamental to vehicle navigation and the activation of intelligent driving functions. Current online map matching methods are prone to errors in complex road networks, especially in multilevel road area. To address this challenge, we propose an online Standard Definition (SD) map matching method by constructing a Hidden Markov Model (HMM) with multiple probability factors. Our proposed method can achieve accurate map matching even in complex road networks by carefully leveraging lane markings and scenario recognition in the designing of the probability factors. First, the lane markings are generated by a multi-lane tracking method and associated with the SD map using HMM to build an enriched SD map. In areas covered by the enriched SD map, the vehicle can re-localize itself by performing Iterative Closest Point (ICP) registration for the lane markings. Then, the probability factor accounting for the lane marking detection can be obtained using the association probability between adjacent lanes and roads. Second, the driving scenario recognition model is applied to generate the emission probability factor of scenario recognition, which improves the performance of map matching on elevated roads and ordinary urban roads underneath them. We validate our method through extensive road tests in Europe and China, and the experimental results show that our proposed method effectively improves the online map matching accuracy as compared to other existing methods, especially in multilevel road area. Specifically, the experiments show that our proposed method achieves $F_1$ scores of 98.04% and 94.60% on the Zenseact Open Dataset and test data of multilevel road areas in Shanghai respectively, significantly outperforming benchmark methods. The implementation is available at https://github.com/TRV-Lab/LMSR-OMM.
850 SRG/eROSITA X-ray sources associated with Pleiades stars
2025-05-08
I. M. Khamitov, I. F. Bikmaev, M. R. Gilfanov, R. A. Sunyaev, P. S. Medvedev
Using data from the SRG/eROSITA all-sky X-ray survey and the GAIA-based catalog of 2,209 members of the Pleiades open star cluster, we found 850 X-ray sources associated with the cluster stars. Over 650 of them were detected in X-rays for the first time. At the distance of the Pleiades, the nominal sensitivity of eROSITA corresponds to a luminosity of $L_X \sim 1.6 \cdot 10^{28}$ erg/s in the 0.3-2.3 keV band. The eROSITA sources associated with Pleiades stars have a total luminosity of $L_{X,tot} \sim 1.3 \cdot 10^{32}$ erg/s , a million times greater than the X-ray luminosity of the quiet Sun. Strong X-ray variability, more than 10 times, was recorded for 27 sources. Most of them are known as eruptive optical variables of the dM class. The value of $R_X=log(L_X/L_{bol})$ increases with decreasing effective temperature of the star from $R_X\approx -5$ to $R_X\approx -2$. The distribution of stars over $R_X$ is bimodal, with the left peak at $R_X\sim-4.3$ being formed by stars of FGK classes, and the right peak at $R_X\sim-3.1$ being mainly populated by M-stars. The relation between $R_X$ and the Rossby number $Ro$ depends on the spectral class. For K- and M- stars, at low Rossby numbers $R_X\sim -3$ and depends weakly on $Ro$. At $Ro \gt 0.25$, a rapid drop in $R_X$ is observed for K stars, while in our sample there are no M stars with large Rossby number. Most of F- and G- stars appear to have smaller $R_X\sim -4.5$, however, our sample size is insufficient for a more detailed characterization of their $R_X-Ro$ dependence.
Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards
2025-05-08
Jaeho Kim, Yunseok Lee, Seulki Lee
The peer review process in major artificial intelligence (AI) conferences faces unprecedented challenges with the surge of paper submissions (exceeding 10,000 submissions per venue), accompanied by growing concerns over review quality and reviewer responsibility. This position paper argues for the need to transform the traditional one-way review system into a bi-directional feedback loop where authors evaluate review quality and reviewers earn formal accreditation, creating an accountability framework that promotes a sustainable, high-quality peer review system. The current review system can be viewed as an interaction between three parties: the authors, reviewers, and system (i.e., conference), where we posit that all three parties share responsibility for the current problems. However, issues with authors can only be addressed through policy enforcement and detection tools, and ethical concerns can only be corrected through self-reflection. As such, this paper focuses on reforming reviewer accountability with systematic rewards through two key mechanisms: (1) a two-stage bi-directional review system that allows authors to evaluate reviews while minimizing retaliatory behavior, (2)a systematic reviewer reward system that incentivizes quality reviewing. We ask for the community's strong interest in these problems and the reforms that are needed to enhance the peer review process.
Coronary angiography (CAG) is the gold-standard imaging modality for evaluating coronary artery disease, but its interpretation and subsequent treatment planning rely heavily on expert cardiologists. To enable AI-based decision support, we introduce a two-stage, physician-curated pipeline and a bilingual (Japanese/English) CAG image-report dataset. First, we sample 14,686 frames from 539 exams and annotate them for key-frame detection and left/right laterality; a ConvNeXt-Base CNN trained on this data achieves 0.96 F1 on laterality classification, even on low-contrast frames. Second, we apply the CNN to 243 independent exams, extract 1,114 key frames, and pair each with its pre-procedure report and expert-validated diagnostic and treatment summary, yielding a parallel corpus. We then fine-tune three open-source VLMs (PaliGemma2, Gemma3, and ConceptCLIP-enhanced Gemma3) via LoRA and evaluate them using VLScore and cardiologist review. Although PaliGemma2 w/LoRA attains the highest VLScore, Gemma3 w/LoRA achieves the top clinician rating (mean 7.20/10); we designate this best-performing model as CAG-VLM. These results demonstrate that specialized, fine-tuned VLMs can effectively assist cardiologists in generating clinical reports and treatment recommendations from CAG images.
Synthesizing medical images remains challenging due to limited annotated pathological data, modality domain gaps, and the complexity of representing diffuse pathologies such as liver cirrhosis. Existing methods often struggle to maintain anatomical fidelity while accurately modeling pathological features, frequently relying on priors derived from natural images or inefficient multi-step sampling. In this work, we introduce ViCTr (Vital Consistency Transfer), a novel two-stage framework that combines a rectified flow trajectory with a Tweedie-corrected diffusion process to achieve high-fidelity, pathology-aware image synthesis. First, we pretrain ViCTr on the ATLAS-8k dataset using Elastic Weight Consolidation (EWC) to preserve critical anatomical structures. We then fine-tune the model adversarially with Low-Rank Adaptation (LoRA) modules for precise control over pathology severity. By reformulating Tweedie's formula within a linear trajectory framework, ViCTr supports one-step sampling, reducing inference from 50 steps to just 4, without sacrificing anatomical realism. We evaluate ViCTr on BTCV (CT), AMOS (MRI), and CirrMRI600+ (cirrhosis) datasets. Results demonstrate state-of-the-art performance, achieving a Medical Frechet Inception Distance (MFID) of 17.01 for cirrhosis synthesis 28% lower than existing approaches and improving nnUNet segmentation by +3.8% mDSC when used for data augmentation. Radiologist reviews indicate that ViCTr-generated liver cirrhosis MRIs are clinically indistinguishable from real scans. To our knowledge, ViCTr is the first method to provide fine-grained, pathology-aware MRI synthesis with graded severity control, closing a critical gap in AI-driven medical imaging research.
Graffe: Graph Representation Learning via Diffusion Probabilistic Models
2025-05-08
Dingshuo Chen, Shuchen Xue, Liuji Chen, Yingheng Wang, Qiang Liu, Shu Wu, Zhi-Ming Ma, Liang Wang
Diffusion probabilistic models (DPMs), widely recognized for their potential to generate high-quality samples, tend to go unnoticed in representation learning. While recent progress has highlighted their potential for capturing visual semantics, adapting DPMs to graph representation learning remains in its infancy. In this paper, we introduce Graffe, a self-supervised diffusion model proposed for graph representation learning. It features a graph encoder that distills a source graph into a compact representation, which, in turn, serves as the condition to guide the denoising process of the diffusion decoder. To evaluate the effectiveness of our model, we first explore the theoretical foundations of applying diffusion models to representation learning, proving that the denoising objective implicitly maximizes the conditional mutual information between data and its representation. Specifically, we prove that the negative logarithm of the denoising score matching loss is a tractable lower bound for the conditional mutual information. Empirically, we conduct a series of case studies to validate our theoretical insights. In addition, Graffe delivers competitive results under the linear probing setting on node and graph classification tasks, achieving state-of-the-art performance on 9 of the 11 real-world datasets. These findings indicate that powerful generative models, especially diffusion models, serve as an effective tool for graph representation learning.
Study of Wormholes in Symmetric Teleparallel Theories of Gravity
2025-05-08
Moreshwar Tayde
This thesis explores traversable wormhole (WH) solutions within symmetric teleparallel gravity and its extensions, including $f(Q)$ and $f(Q, T)$ gravity. Chapter I reviews WH geometry and properties, general relativity, and modified gravity's role in WH physics. Chapter II constructs WHs in $f(Q)$ gravity using dark matter profiles like Pseudo-Isothermal and Navarro-Frenk-White (NFW). For linear $f(Q)$ models, suitable redshift and shape functions satisfying the flare-out condition yield viable WHs, with the monopole charge $\eta$ driving Null Energy Condition (NEC) violation. The Volume Integral Quantifier (VIQ) method shows minimal exotic matter is needed. Nonlinear models like $f(Q) = Q + m Q^n$ fail to meet WH criteria. Chapter III studies $f(Q, T)$ gravity, where $Q$ and the trace $T$ of the energy-momentum tensor are coupled. WHs are examined under barotropic and anisotropic equations of state using forms like $f(Q, T) = \alpha Q + \beta T$ and $f(Q, T) = Q + \lambda_1 Q^2 + \eta_1 T$. NEC violations occur near the throat, indicating effective matter-geometry coupling. Tolman-Oppenheimer-Volkoff (TOV) analysis confirms equilibrium under suitable parameters. Chapter IV employs the MIT bag model in $f(Q, T)$ gravity, treating it as a source of exotic matter. Specific shape functions lead to WHs with NEC violation and TOV-based stability under radial perturbations. Chapter V considers WHs in $f(Q, T)$ gravity with noncommutative geometries inspired by string theory. Gaussian and Lorentzian smeared sources are used. Linear models yield analytical WH solutions, nonlinear ones are numerical. All satisfy the flare-out condition and exhibit NEC violation. Gravitational lensing analysis reveals distinguishable features from black holes. Chapter VI summarizes the results, emphasizing observational prospects in extended gravity frameworks.
While Knowledge Graphs (KGs) have become increasingly popular across various scientific disciplines for their ability to model and interlink huge quantities of data, essentially all real-world KGs are known to be incomplete. As such, with the growth of KG use has been a concurrent development of machine learning tools designed to predict missing information in KGs, which is referred to as the Link Prediction Task. The majority of state-of-the-art link predictors to date have followed an embedding-based paradigm. In this paradigm, it is assumed that the information content of a KG is best represented by the (individual) vector representations of its nodes and edges, and that therefore node and edge embeddings are particularly well-suited to performing link prediction. This thesis proposes an alternative perspective on the field's approach to link prediction and KG data modelling. Specifically, this work re-analyses KGs and state-of-the-art link predictors from a graph-structure-first perspective that models the information content of a KG in terms of whole triples, rather than individual nodes and edges. Following a literature review and two core sets of experiments, this thesis concludes that a structure-first perspective on KGs and link prediction is both viable and useful for understanding KG learning and for enabling cross-KG transfer learning for the link prediction task. This observation is used to create and propose the Structural Alignment Hypothesis, which postulates that link prediction can be understood and modelled as a structural task. All code and data used for this thesis are open-sourced. This thesis was written bilingually, with the main document in English and an informal extended summary in Irish. An Irish-language translation dictionary of machine learning terms (the Focl\'oir Tr\'achtais) created for this work is open-sourced as well.
Blockchain technology, introduced in 2008, has revolutionized data storage and transfer across sectors such as finance, healthcare, intelligent transportation, and the metaverse. However, the proliferation of blockchain systems has led to discrepancies in architectures, consensus mechanisms, and data standards, creating data and value silos that hinder the development of an integrated multi chain ecosystem. Blockchain interoperability (a.k.a cross chain interoperability) has thus emerged as a solution to enable seamless data and asset exchange across disparate blockchains. In this survey, we systematically analyze over 150 high impact sources from academic journals, digital libraries, and grey literature to provide an in depth examination of blockchain interoperability. By exploring the existing methods, technologies, and architectures, we offer a classification of interoperability approaches including Atomic Swaps, Sidechains, Light Clients, and so on, which represent the most comprehensive overview to date. Furthermore, we investigate the convergence of academic research with industry practices, underscoring the importance of collaborative efforts in advancing blockchain innovation. Finally, we identify key strategic insights, challenges, and future research trajectories in this field. Our findings aim to support researchers, policymakers, and industry leaders in understanding and harnessing the transformative potential of blockchain interoperability to address current challenges and drive forward a cohesive multi-chain ecosystem.
Reasoning lies at the heart of intelligence, shaping the ability to make decisions, draw conclusions, and generalize across domains. In artificial intelligence, as systems increasingly operate in open, uncertain, and multimodal environments, reasoning becomes essential for enabling robust and adaptive behavior. Large Multimodal Reasoning Models (LMRMs) have emerged as a promising paradigm, integrating modalities such as text, images, audio, and video to support complex reasoning capabilities and aiming to achieve comprehensive perception, precise understanding, and deep reasoning. As research advances, multimodal reasoning has rapidly evolved from modular, perception-driven pipelines to unified, language-centric frameworks that offer more coherent cross-modal understanding. While instruction tuning and reinforcement learning have improved model reasoning, significant challenges remain in omni-modal generalization, reasoning depth, and agentic behavior. To address these issues, we present a comprehensive and structured survey of multimodal reasoning research, organized around a four-stage developmental roadmap that reflects the field's shifting design philosophies and emerging capabilities. First, we review early efforts based on task-specific modules, where reasoning was implicitly embedded across stages of representation, alignment, and fusion. Next, we examine recent approaches that unify reasoning into multimodal LLMs, with advances such as Multimodal Chain-of-Thought (MCoT) and multimodal reinforcement learning enabling richer and more structured reasoning chains. Finally, drawing on empirical insights from challenging benchmarks and experimental cases of OpenAI O3 and O4-mini, we discuss the conceptual direction of native large multimodal reasoning models (N-LMRMs), which aim to support scalable, agentic, and adaptive reasoning and planning in complex, real-world environments.
Learning Economic Model Predictive Control via Clustering and Kernel-Based Lipschitz Regression
2025-05-08
Weiliang Xiong, Defeng He, Haiping Du
This paper presents a novel learning economic model predictive control scheme for uncertain nonlinear systems subject to input and state constraints and unknown dynamics. We design a fast and accurate Lipschitz regression method using input and output data that combines clustering and kernel regression to learn the unknown dynamics. In each cluster, the parallel convex optimization problems are solved to estimate the kernel weights and reduce the Lipschitz constant of the predictor, hence limiting the error propagation in the prediction horizon. We derive the two different bounds of learning errors in deterministic and probabilistic forms and customize a new robust constraint-tightening strategy for the discontinuous predictor. Then, the learning economic model predictive control algorithm is formulated by introducing a stabilized optimization problem to construct a Lyapunov function. Sufficient conditions are derived to ensure the recursive feasibility and input-to-state stability of the closed-loop system. The effectiveness of the proposed algorithm is verified by simulations of a numerical example and a continuously stirred tank reactor.
Side-channel attacks on memory (SCAM) exploit unintended data leaks from memory subsystems to infer sensitive information, posing significant threats to system security. These attacks exploit vulnerabilities in memory access patterns, cache behaviors, and other microarchitectural features to bypass traditional security measures. The purpose of this research is to examine SCAM, classify various attack techniques, and evaluate existing defense mechanisms. It guides researchers and industry professionals in improving memory security and mitigating emerging threats. We begin by identifying the major vulnerabilities in the memory system that are frequently exploited in SCAM, such as cache timing, speculative execution, \textit{Rowhammer}, and other sophisticated approaches. Next, we outline a comprehensive taxonomy that systematically classifies these attacks based on their types, target systems, attack vectors, and adversarial capabilities required to execute them. In addition, we review the current landscape of mitigation strategies, emphasizing their strengths and limitations. This work aims to provide a comprehensive overview of memory-based side-channel attacks with the goal of providing significant insights for researchers and practitioners to better understand, detect, and mitigate SCAM risks.
Federated Learning for Cyber Physical Systems: A Comprehensive Survey
2025-05-08
Minh K. Quan, Pubudu N. Pathirana, Mayuri Wijayasundara, Sujeeva Setunge, Dinh C. Nguyen, Christopher G. Brinton, David J. Love, H. Vincent Poor
The integration of machine learning (ML) in cyber physical systems (CPS) is a complex task due to the challenges that arise in terms of real-time decision making, safety, reliability, device heterogeneity, and data privacy. There are also open research questions that must be addressed in order to fully realize the potential of ML in CPS. Federated learning (FL), a distributed approach to ML, has become increasingly popular in recent years. It allows models to be trained using data from decentralized sources. This approach has been gaining popularity in the CPS field, as it integrates computer, communication, and physical processes. Therefore, the purpose of this work is to provide a comprehensive analysis of the most recent developments of FL-CPS, including the numerous application areas, system topologies, and algorithms developed in recent years. The paper starts by discussing recent advances in both FL and CPS, followed by their integration. Then, the paper compares the application of FL in CPS with its applications in the internet of things (IoT) in further depth to show their connections and distinctions. Furthermore, the article scrutinizes how FL is utilized in critical CPS applications, e.g., intelligent transportation systems, cybersecurity services, smart cities, and smart healthcare solutions. The study also includes critical insights and lessons learned from various FL-CPS implementations. The paper's concluding section delves into significant concerns and suggests avenues for further research in this fast-paced and dynamic era.
Bioscience Students in Physics Courses With Higher Test Anxiety Have Lower Grades on High-Stakes Assessments, and Women Report More Test Anxiety Than Men
Test anxiety is beginning to be recognized as a significant factor affecting student performance in science, technology, engineering, and mathematics (STEM) courses, potentially contributing to gender inequity within these fields. Additionally, the management of test anxiety can improve self-efficacy, which is a construct that has been well studied in the physics context. In this study, we investigated the relationship between self-efficacy, test anxiety, and gender differences in performance in a two-semester-long introductory physics course sequence for bioscience students in which women outnumber men. Using validated survey data and grade information from students in a two-semester introductory physics course sequence, we compared the predictive power of self-efficacy and test anxiety on female and male students' performance on both low- and high-stakes assessments. We found that there were gender differences disadvantaging women in self-efficacy and test anxiety in both Physics 1 and Physics 2, as well as gender differences in high-stakes outcomes in Physics 1. There were no gender differences in low-stakes assessment scores. We also found that self-efficacy and test anxiety predicted high-stakes (but not low-stakes) assessment outcomes in both Physics 1 and Physics 2. Comparison of these findings with prior studies involving physical science and engineering students shows that although women outnumber men in physics courses for bioscience students and the career goals of bioscience students are very different from the earlier researched group, most of the negative trends hold even for this new population.
This note is an overview of the knot-quiver correspondence, which relates symmetric quivers and their partition functions, a.k.a. motivic Donaldson-Thomas generating series, to quantum invariants of knots and links in $S^3$.
Synthetic Training and Representation Bridging in Reconstruction Domains
2025-05-08
Wonyong Chung
Reconstructing low-dimensional truth labels from high-dimensional experimental data is a central challenge in any scenario that relies on robust mappings across this so-called domain gap, from multi-particle final states in high-energy physics to large-scale early-universe structure in cosmological surveys. We introduce a new method to bridge this domain gap with an intermediate, synthetic representation of truth that differs from methods operating purely in latent space, such as normalizing flows or invertible approaches, in that the synthetic data is specifically engineered to represent intrinsic detector hardware capabilities of the system at hand. The hypothesis is that by encoding physical properties of the detector response available only in full simulation, such synthetic representations result in a less lossy compression and recovery than a direct mapping from truth to experimental data. We demonstrate a first implementation of this concept with full simulation of a dual-readout crystal electromagnetic calorimeter for future collider detectors, in which the synthetic data is constructed to be the simulated detector hits corresponding to photon tracks of scintillation and Cerenkov photons. We refer to these signals as simulated observables as they would not be physical observables in a real detector, but are nonetheless representations of a real physical process. First results show that the synthetic representation naturally anchors the neural network architecture to a known physical method, in this case the dual-readout correction. We believe this strategy opens new avenues for machinistic interpretability and explainability of ML-based reconstruction methods. In the case of anomalous signal detection, we hypothesize that anomalous signals detected in networks trained on synthetic data rooted in a physical process are more likely to be indicative of a genuinely physical anomaly.
UltraGauss: Ultrafast Gaussian Reconstruction of 3D Ultrasound Volumes
2025-05-08
Mark C. Eid, Ana I. L. Namburete, João F. Henriques
Ultrasound imaging is widely used due to its safety, affordability, and real-time capabilities, but its 2D interpretation is highly operator-dependent, leading to variability and increased cognitive demand. 2D-to-3D reconstruction mitigates these challenges by providing standardized volumetric views, yet existing methods are often computationally expensive, memory-intensive, or incompatible with ultrasound physics. We introduce UltraGauss: the first ultrasound-specific Gaussian Splatting framework, extending view synthesis techniques to ultrasound wave propagation. Unlike conventional perspective-based splatting, UltraGauss models probe-plane intersections in 3D, aligning with acoustic image formation. We derive an efficient rasterization boundary formulation for GPU parallelization and introduce a numerically stable covariance parametrization, improving computational efficiency and reconstruction accuracy. On real clinical ultrasound data, UltraGauss achieves state-of-the-art reconstructions in 5 minutes, and reaching 0.99 SSIM within 20 minutes on a single GPU. A survey of expert clinicians confirms UltraGauss' reconstructions are the most realistic among competing methods. Our CUDA implementation will be released upon publication.
Tutorial on Bayesian Functional Regression Using Stan
2025-05-08
Ziren Jiang, Ciprian Crainiceanu, Erjia Cui
This manuscript provides step-by-step instructions for implementing Bayesian functional regression models using Stan. Extensive simulations indicate that the inferential performance of the methods is comparable to that of state-of-the-art frequentist approaches. However, Bayesian approaches allow for more flexible modeling and provide an alternative when frequentist methods are not available or may require additional development. Methods and software are illustrated using the accelerometry data from the National Health and Nutrition Examination Survey (NHANES).
OTI on FIRE: Testing the Efficacy of Orbital Torus Imaging to Recover the Galactic Potential
2025-05-08
Micah Oeur, Sarah R. Loebman, Adrian M. Price-Whelan, Arpit Arora, Lina Necib, Danny Horta
Orbital Torus Imaging (OTI) is a dynamical inference method for determining the Milky Way's gravitational potential using stellar survey data. OTI uses gradients in stellar astrophysical quantities, such as element abundances, as functions of dynamical quantities, like orbital actions or energy, to estimate the Galactic mass distribution, assuming axisymmetry and steady-state of the system. While preliminary applications have shown promising outcomes, its sensitivity to disequilibrium effects is unknown. Here, we apply OTI to a benchmark Feedback in Realistic Environments (FIRE-2) cosmological hydrodynamic simulation, m12i, which enables a comparative analysis between known FIRE-2 vertical acceleration profiles and total surface mass densities to the analogous OTI-inferred results. We quantify OTI's accuracy within solar-analog volumes embedded in the simulated galactic disk. Despite a dynamically-evolving system, we find that OTI recovers the known vertical acceleration profiles within 3 sigma/1 sigma errors for 94%/75% of the volumes considered. We discuss the method's sensitivity to the local, instantaneous structure of the disk, reporting a loss in accuracy for volumes that have large (>1.5 kpc) scale heights and low total density at z=1.1 kpc. We present realistic OTI error bars from both MCMC sampling and bootstrapping the FIRE-2 simulated data, which provides a touchstone for interpreting results obtained from current and forthcoming surveys such as SDSS-V, Gaia, WEAVE, and 4MOST.
The survivorship bias of protoplanetary disc populations
2025-05-08
Lorenzo Alessio Malanga, Giovanni Pietro Rosotti, Giuseppe Lodato, Alice Somigliana, Carlo Felice Manara, Claudia Toci, Leonardo Testi
The evolution of protoplanetary discs has a substantial impact on theories of planet formation. To date, neither of the two main competing evolutionary models, namely the viscous-photoevaporative paradigm and the MHD winds model, has been ruled out by observations. Due to the high number of sources observed by large surveys, population synthesis is a powerful tool to distinguish the evolution mechanism in observations. We explore the evolution of the mass distribution of synthetic populations under the assumptions of turbulence-driven accretion and dispersal caused by internal photoevaporation. We find that the rapid removal of light discs often results in an apparent increase of the median mass of the survived disc population. This occurs both when the disc properties are independent of each other, and when typical correlations between these quantities and the stellar mass are assumed. Furthermore, as MHD wind-driven accretion rarely manifests the same feature, this serves as a signature of the viscous-photoevaporative evolution when dispersal proceeds from inside-out. Therefore, we propose the evolution of the median mass as a new method to distinguish this model in observed populations. This survivorship bias is not shown by the median accretion rate, which, instead, decreases with time. Moreover, we introduce a new criterion that estimates the disc lifetime as a function of initial conditions and an analytical relation to predict whether internal photoevaporation triggers an inside-out or an outside-in dispersal. We verify both analytical relations with numerical simulations.
The NGC3109 Satellite System: The First Systematic Resolved Search for Dwarf Galaxies Around a SMC-mass Host
2025-05-08
Amandine Doliva-Dolinsky, Burçin Mutlu-Pakdil, Denija Crnojević, Dhayaa Anbajagane, Jeffrey L. Carlin, Jonah Medoff, David J. Sand, Erik Tollerud, Sungsoon Lim, Paul Bennet, Alex Drlica-Wagner, Catherine E. Fielder, Jonathan R. Hargis, Kai Herron, Laura Congreve Hunter, Michael G. Jones, Ananthan Karunakaran, Annika H. G. Peter, Aaron J. Romanowsky, Kristine Spekkens, Jay Strader, Beth Willman, Julio A. Carballo-Bello, William Cerny, Astha Chaturvedi, Nitya Kallivayalil, Clara E. Martínez-Vázquez, Gustavo E. Medina, Noelia E. D. Noël, Andrew B. Pace, Alex H. Riley, Joanna D. Sakowska, Adam Smercina, Kathy Vivas, Monika Adamów, Clecio R. Bom, Yumi Choi, Peter S. Ferguson, Mahdieh Navabi, Alfredo Zenteno, DELVE COLLABORATION
We report the results of the deepest search to date for dwarf galaxies around NGC3109, a barred spiral galaxy with a mass similar to that of the Small Magellanic Cloud (SMC), using a semi-automated search method. Using the Dark Energy Camera (DECam), we survey a region covering a projected distance of $\sim$70 kpc of NGC 3109 ($D$ = 1.3 Mpc, $R_\mathrm{vir}\sim$ 90 kpc, $M\sim10^8M_\ast$) as part of the MADCASH and DELVE-DEEP programs. Through our resolved and newly designed semi-resolved searches, we successfully recover the known satellites Antlia and Antlia B. We identified a promising candidate, which was later confirmed to be a background dwarf through deep follow-up observations. Our detection limits are well defined, with the sample $\sim 80\%$ complete down to $M_V\sim-$8.0 , and includes detections of dwarf galaxies as faint as $M_V\sim-$6.0. This is the first comprehensive study of a satellite system through resolved star around an SMC mass host. Our results show that NGC 3109 has more bright ($M_V\sim-$9.0) satellites than the mean predictions from cold dark matter (CDM) models, but well within the host-to-host scatter. A larger sample of LMC/SMC-mass hosts is needed to test whether or not the observations are consistent with current model expectations.
Trust is a fundamental component of human-agent interaction. With the increasing presence of artificial agents in daily life, it is essential to understand how people perceive and trust these agents. One of the key challenges affecting this perception is the Uncanny Valley Effect (UVE), where increasingly human-like artificial beings can be perceived as eerie or repelling. Despite growing interest in trust and the UVE, existing research varies widely in terms of how these concepts are defined and operationalized. This inconsistency raises important questions about how and under what conditions the UVE influences trust in agents. A systematic understanding of their relationship is currently lacking. This review aims to examine the impact of the UVE on human trust in agents and to identify methodological patterns, limitations, and gaps in the existing empirical literature. Following PRISMA guidelines, a systematic search identified 53 empirical studies that investigated both UVE-related constructs and trust or trust-related outcomes. Studies were analyzed based on a structured set of categories, including types of agents and interactions, methodological and measurement approaches, and key findings. The results of our systematic review reveal that most studies rely on static images or hypothetical scenarios with limited real-time interaction, and the majority use subjective trust measures. This review offers a novel framework for classifying trust measurement approaches with regard to the best-practice criteria for empirically investigating the UVE. As the first systematic attempt to map the intersection of UVE and trust, this review contributes to a deeper understanding of their interplay and offers a foundation for future research. Keywords: the uncanny valley effect, trust, human-likeness, affinity response, human-agent interaction
Safety by Measurement: A Systematic Literature Review of AI Safety Evaluation Methods
2025-05-08
Markov Grey, Charbel-Raphaël Segerie
As frontier AI systems advance toward transformative capabilities, we need a parallel transformation in how we measure and evaluate these systems to ensure safety and inform governance. While benchmarks have been the primary method for estimating model capabilities, they often fail to establish true upper bounds or predict deployment behavior. This literature review consolidates the rapidly evolving field of AI safety evaluations, proposing a systematic taxonomy around three dimensions: what properties we measure, how we measure them, and how these measurements integrate into frameworks. We show how evaluations go beyond benchmarks by measuring what models can do when pushed to the limit (capabilities), the behavioral tendencies exhibited by default (propensities), and whether our safety measures remain effective even when faced with subversive adversarial AI (control). These properties are measured through behavioral techniques like scaffolding, red teaming and supervised fine-tuning, alongside internal techniques such as representation analysis and mechanistic interpretability. We provide deeper explanations of some safety-critical capabilities like cybersecurity exploitation, deception, autonomous replication, and situational awareness, alongside concerning propensities like power-seeking and scheming. The review explores how these evaluation methods integrate into governance frameworks to translate results into concrete development decisions. We also highlight challenges to safety evaluations - proving absence of capabilities, potential model sandbagging, and incentives for "safetywashing" - while identifying promising research directions. By synthesizing scattered resources, this literature review aims to provide a central reference point for understanding AI safety evaluations.
Sucrose ester surfactants: current understanding and emerging perspectives
2025-05-08
Diana Cholakova, Slavka Tcholakova
Sucrose esters (SEs), derived from sucrose and fatty acids, are biodegradable and non-toxic surfactants increasingly favored as substitutes for petrochemically-synthesized ones in food, cosmetics, and pharmaceuticals. SEs provide versatile hydrophilic-lipophilic properties, determined by the degree of sucrose esterification ranging from one to eight. The length of the fatty acid residues further influences the phase behavior of SEs, allowing creation of tailored formulations for specific applications. This review provides insights about our current understanding of the SEs phase behavior, their aggregation in aqueous and oily solutions, and its correlation with formulation outcomes. Furthermore, an overview of recent studies investigating SEs in various colloidal systems, incl. emulsions, foams, oleogels, and others, is provided. Novel concepts are discussed alongside future research directions, emphasizing the SEs potential as sustainable, functional ingredients.
With unprecedented rapid development, deep neural networks (DNNs) have deeply influenced almost all fields. However, their heavy computation costs and model sizes are usually unacceptable in real-world deployment. Model quantization, an effective weight-lighting technique, has become an indispensable procedure in the whole deployment pipeline. The essence of quantization acceleration is the conversion from continuous floating-point numbers to discrete integer ones, which significantly speeds up the memory I/O and calculation, i.e., addition and multiplication. However, performance degradation also comes with the conversion because of the loss of precision. Therefore, it has become increasingly popular and critical to investigate how to perform the conversion and how to compensate for the information loss. This article surveys the recent five-year progress towards low-bit quantization on DNNs. We discuss and compare the state-of-the-art quantization methods and classify them into 8 main categories and 24 sub-categories according to their core techniques. Furthermore, we shed light on the potential research opportunities in the field of model quantization. A curated list of model quantization is provided at https://github.com/Kai-Liu001/Awesome-Model-Quantization.
Current status of the CMS Inner Tracker upgrade for HL-LHC
2025-05-08
Lorenzo Damenti
During the High Luminosity programme of the LHC collider (called HL-LHC), planned to start in 2030, the instantaneous luminosity will be increased from \num{\sim 2e34}~\si{cm^{-2}s^{-1}} to an unprecedented figure of about \num{\sim 7.5e34}~\si{cm^{-2}s^{-1}}. This will allow the Compact Muon Solenoid (CMS) to collect up to \num{\sim 4000}~\si{fb^{-1}} of integrated luminosity over a decade. In order to cope with the much higher pp-collisions rate, CMS will undergo an extensive improvement known as \pII upgrade: in particular, the silicon tracker system will be entirely replaced to comply with the extremely challenging experimental conditions. This contribution will review the main upgrades of the CMS Inner Tracker and will present the most relevant design and technological choices. Moreover, the ongoing validation of prototypes and the preparation for the large-scale production will be discussed.
GenAI in Entrepreneurship: a systematic review of generative artificial intelligence in entrepreneurship research: current issues and future directions
2025-05-08
Anna Kusetogullari, Huseyin Kusetogullari, Martin Andersson, Tony Gorschek
Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are recognized to have significant effects on industry and business dynamics, not least because of their impact on the preconditions for entrepreneurship. There is still a lack of knowledge of GenAI as a theme in entrepreneurship research. This paper presents a systematic literature review aimed at identifying and analyzing the evolving landscape of research on the effects of GenAI on entrepreneurship. We analyze 83 peer-reviewed articles obtained from leading academic databases: Web of Science and Scopus. Using natural language processing and unsupervised machine learning techniques with TF-IDF vectorization, Principal Component Analysis (PCA), and hierarchical clustering, five major thematic clusters are identified: (1) Digital Transformation and Behavioral Models, (2) GenAI-Enhanced Education and Learning Systems, (3) Sustainable Innovation and Strategic AI Impact, (4) Business Models and Market Trends, and (5) Data-Driven Technological Trends in Entrepreneurship. Based on the review, we discuss future research directions, gaps in the current literature, as well as ethical concerns raised in the literature. We highlight the need for more macro-level research on GenAI and LLMs as external enablers for entrepreneurship and for research on effective regulatory frameworks that facilitate business experimentation, innovation, and further technology development.
User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data
2025-05-08
Haowei Yang, Qingyi Lu, Yang Wang, Sibei Liu, Jiayun Zheng, Ao Xiang
With the widespread application of large language models (LLMs), user privacy protection has become a significant research topic. Existing privacy preference modeling methods often rely on large-scale user data, making effective privacy preference analysis challenging in data-limited environments. This study explores how LLMs can analyze user behavior related to privacy protection in scenarios with limited data and proposes a method that integrates Few-shot Learning and Privacy Computing to model user privacy preferences. The research utilizes anonymized user privacy settings data, survey responses, and simulated data, comparing the performance of traditional modeling approaches with LLM-based methods. Experimental results demonstrate that, even with limited data, LLMs significantly improve the accuracy of privacy preference modeling. Additionally, incorporating Differential Privacy and Federated Learning further reduces the risk of user data exposure. The findings provide new insights into the application of LLMs in privacy protection and offer theoretical support for advancing privacy computing and user behavior analysis.
CAPERS-LRD-z9: A Gas Enshrouded Little Red Dot Hosting a Broad-line AGN at z=9.288
2025-05-07
Anthony J. Taylor, Vasily Kokorev, Dale D. Kocevski, Hollis B. Akins, Fergus Cullen, Mark Dickinson, Steven L. Finkelstein, Pablo Arrabal Haro, Volker Bromm, Mauro Giavalisco, Kohei Inayoshi, Stephanie Juneau, Gene C. K. Leung, Pablo G. Perez-Gonzalez, Rachel S. Somerville, Jonathan R. Trump, Ricardo O. Amorin, Guillermo Barro, Denis Burgarella, Madisyn Brooks, Adam Carnall, Caitlin M. Casey, Yingjie Cheng, John Chisholm, Katherine Chworowsky, Kelcey Davis, Callum T. Donnan, James S. Dunlop, Richard S. Ellis, Vital Fernandez, Seiji Fujimoto, Norman A. Grogin, Ansh R. Gupta, Nimish P. Hathi, Intae Jung, Michaela Hirschmann, Jeyhan S. Kartaltepe, Anton M. Koekemoer, Rebecca L. Larson, Ho-Hin Leung, Mario Llerena, Ray A. Lucas, Derek J. McLeod, Ross McLure, Lorenzo Napolitano, Casey Papovich, Thomas M. Stanton, Roberta Tripodi, Xin Wang, Stephen M. Wilkins, L. Y. Aaron Yung, Jorge A. Zavala
We present CAPERS-LRD-z9, a little red dot (LRD) which we confirm to be a $z=9.288$ broad-line AGN (BLAGN). First identified as a high-redshift LRD candidate from PRIMER NIRCam photometry, follow-up NIRSpec/PRISM spectroscopy of CAPERS-LRD-z9 from the CANDELS-Area Prism Epoch of Reionization Survey (CAPERS) has revealed a broad $3500$ km s$^{-1}$ H$\beta$ emission line and narrow [O III]$\lambda\lambda4959,5007$ lines, indicative of a BLAGN. Based on the broad H$\beta$ line, we compute a canonical black-hole mass of $\log(M_{\textrm{BH}}/M_{\odot})=7.58\pm0.15$, although full consideration of systematic uncertainties yields a conservative range of $6.65<\log(M_{\textrm{BH}}/M_{\odot})<8.50$. These observations suggest that either a massive black hole seed, or a lighter stellar remnant seed undergoing periods of super-Eddington accretion, is necessary to grow such a massive black hole in $\lesssim500$ Myr of cosmic time. CAPERS-LRD-z9 exhibits a strong Balmer break, consistent with a central AGN surrounded by dense ($\sim 10^{10}\textrm{ cm}^{-3}$) neutral gas. We model CAPERS-LRD-z9 using CLOUDY to fit the emission red-ward of the Balmer break with a dense gas-enshrouded AGN, and bagpipes to fit the rest-ultraviolet emission as a host-galaxy stellar population. This upper limit on the stellar mass of the host galaxy ($<10^9\,{\rm M_\odot}$) implies that the black-hole to stellar mass ratio may be extremely large, possibly $>5\%$ (although systematic uncertainties on the black-hole mass prevent strong conclusions). However, the shape of the UV continuum differs from typical high-redshift star-forming galaxies, indicating that this UV emission may also be of AGN origin, and hence the true stellar mass of the host may be still lower.
Our velocity with respect to the cosmic frame of rest leads to a dipole in the number count distribution of galaxies. The dipole depends on the source spectrum, which is usually assumed to be a power law, $S(\nu) \propto \nu^{-\alpha}$ and on the flux dependence of the number density of sources. The latter is also generally assumed to be a power law, parametrised with exponent $x$. The velocity can be extracted from the observed dipole once the two parameters $x$ and $\alpha$ are known. The standard procedure uses the mean value of $\alpha$ across the entire sample, and the parameter $x$ is inferred by fitting the cumulative number count, $\frac{dN}{d\Omega}(>S_*) \propto S_*^{-x}$, near the flux limit $S_*$ of the survey. Here, we introduce a simulation procedure to extract the velocity which directly uses the $\alpha$ values of each source rather than their mean and does not rely on the functional form of the cumulative number count near the flux limit. We apply this to the quasar sample in CatWISE2020 data and find that the final results differ from the standard procedure by approximately one sigma.
Active Sampling for MRI-based Sequential Decision Making
2025-05-07
Yuning Du, Jingshuai Liu, Rohan Dharmakumar, Sotirios A. Tsaftaris
Despite the superior diagnostic capability of Magnetic Resonance Imaging (MRI), its use as a Point-of-Care (PoC) device remains limited by high cost and complexity. To enable such a future by reducing the magnetic field strength, one key approach will be to improve sampling strategies. Previous work has shown that it is possible to make diagnostic decisions directly from k-space with fewer samples. Such work shows that single diagnostic decisions can be made, but if we aspire to see MRI as a true PoC, multiple and sequential decisions are necessary while minimizing the number of samples acquired. We present a novel multi-objective reinforcement learning framework enabling comprehensive, sequential, diagnostic evaluation from undersampled k-space data. Our approach during inference actively adapts to sequential decisions to optimally sample. To achieve this, we introduce a training methodology that identifies the samples that contribute the best to each diagnostic objective using a step-wise weighting reward function. We evaluate our approach in two sequential knee pathology assessment tasks: ACL sprain detection and cartilage thickness loss assessment. Our framework achieves diagnostic performance competitive with various policy-based benchmarks on disease detection, severity quantification, and overall sequential diagnosis, while substantially saving k-space samples. Our approach paves the way for the future of MRI as a comprehensive and affordable PoC device. Our code is publicly available at https://github.com/vios-s/MRI_Sequential_Active_Sampling
SlideItRight: Using AI to Find Relevant Slides and Provide Feedback for Open-Ended Questions
2025-05-07
Chloe Qianhui Zhao, Jie Cao, Eason Chen, Kenneth R. Koedinger, Jionghao Lin
Feedback is important in supporting student learning. While various automated feedback systems have been implemented to make the feedback scalable, many existing solutions only focus on generating text-based feedback. As is indicated in the multimedia learning principle, learning with more modalities could help utilize more separate channels, reduce the cognitive load and facilitate students' learning. Hence, it is important to explore the potential of Artificial Intelligence (AI) in feedback generation from and to different modalities. Our study leverages Large Language Models (LLMs) for textual feedback with the supplementary guidance from other modality - relevant lecture slide retrieved from the slides hub. Through an online crowdsourcing study (N=91), this study investigates learning gains and student perceptions using a 2x2 design (i.e., human feedback vs. AI feedback and with vs. without relevant slide), evaluating the clarity, engagement, perceived effectiveness, and reliability) of AI-facilitated multimodal feedback. We observed significant pre-to-post learning gains across all conditions. However, the differences in these gains were not statistically significant between conditions. The post-survey revealed that students found the slide feedback helpful in their learning process, though they reported difficulty in understanding it. Regarding the AI-generated open-ended feedback, students considered it personalized and relevant to their responses, but they expressed lower trust in the AI feedback compared to human-generated feedback.
Relative benefits of different active learning methods to conceptual physics learning
2025-05-07
Meagan Sundstrom, Justin Gambrell, Colin Green, Adrienne L. Traxle, Eric Brewe
Extensive research has demonstrated that active learning methods are more effective than traditional lecturing at improving student conceptual understanding and reducing failure rates in undergraduate physics courses. Researchers have developed several distinct active learning methods that are now widely implemented in introductory physics; however, the relative benefits of these methods remain unknown. Here we present the first multi-institutional comparison of the impacts of four well-established active learning methods (ISLE, Peer Instruction, Tutorials, and SCALE-UP) on conceptual learning. We also investigate student development of peer networks and the activities that take place during instruction to explain differences in these impacts. Data include student concept inventory scores, peer network surveys, and classroom video recordings from 31 introductory physics and astronomy courses at 28 different institutions in the United States containing a total of 2,855 students. We find measurable increases in student conceptual learning in all four active learning methods (ranging from 2.09-sigma to 6.22-sigma differences from a null effect), and significantly larger conceptual learning gains in SCALE-UP than in both ISLE (2.25-sigma difference) and Peer Instruction (2.54-sigma difference). Conceptual learning gains in Tutorials are not significantly different from those in the other three methods. Despite the hypothesized benefits of student interactions, student development of peer networks is similar across the four methods. Instead, we observe differences in classroom activities; in many of the observed ISLE and Peer Instruction courses, instructors lecture for a large fraction of class time. In Tutorials and SCALE-UP courses, instructors dedicate most in-class time to student-centered activities such as worksheets and laboratory work.
Scintillometry of Fast Radio Bursts: Resolution effects in two-screen models
2025-05-07
Sachin Pradeep E. T, Tim Sprenger, Olaf Wucknitz, Robert A. Main, Laura G. Spitler
Fast Radio Bursts (FRBs) exhibit scintillation and scattering, often attributed to interactions with plasma screens in the Milky Way and the host galaxy. When these two screens appear "point-like" to each other, two scales of scintillation can be observed with sufficient frequency resolution. A screen perceives a second screen as extended or resolved when the angular size of the latter is smaller than the angular resolution of the former. We define the ratio of these two as the Resolution Power (RP). Previous observational studies have argued that, in the resolving regime, scintillations disappear, assuming that a screen resolving another screen is equivalent to a screen resolving an incoherent emission region. In this theoretical and simulation-based study of resolving effects in two-screen scenarios, we argue that resolving quenches only the relatively broad-scale scintillation and that this quenching is a gradual process. We present qualitative and quantitative predictions for dynamic spectra, spectral autocorrelation functions (ACF), and modulation indices in resolved and unresolved regimes of two-screen systems. We show that the spectral ACF of a two-screen system has a product term in addition to the sum of individual screen contributions, causing the total modulation index to rise to \sqrt(3) in the unresolved regime. To aid in discovering resolving systems, we also present observable trends in multi-frequency observations of a screen resolving another screen or incoherent emission. Additionally we introduce a new formula to estimate the distance between the FRB and the screen in its host galaxy. We also show that this formula, like previous ones in the literature, is only applicable to screens that are two-dimensional in the plane of the sky.
Overcoming Data Scarcity in Generative Language Modelling for Low-Resource Languages: A Systematic Review
2025-05-07
Josh McGiff, Nikola S. Nikolov
Generative language modelling has surged in popularity with the emergence of services such as ChatGPT and Google Gemini. While these models have demonstrated transformative potential in productivity and communication, they overwhelmingly cater to high-resource languages like English. This has amplified concerns over linguistic inequality in natural language processing (NLP). This paper presents the first systematic review focused specifically on strategies to address data scarcity in generative language modelling for low-resource languages (LRL). Drawing from 54 studies, we identify, categorise and evaluate technical approaches, including monolingual data augmentation, back-translation, multilingual training, and prompt engineering, across generative tasks. We also analyse trends in architecture choices, language family representation, and evaluation methods. Our findings highlight a strong reliance on transformer-based models, a concentration on a small subset of LRLs, and a lack of consistent evaluation across studies. We conclude with recommendations for extending these methods to a wider range of LRLs and outline open challenges in building equitable generative language systems. Ultimately, this review aims to support researchers and developers in building inclusive AI tools for underrepresented languages, a necessary step toward empowering LRL speakers and the preservation of linguistic diversity in a world increasingly shaped by large-scale language technologies.
Cosmic reflections I: the structural diversity of simulated and observed low-mass galaxy analogues
2025-05-07
G. Martin, A. E. Watkins, Y. Dubois, J. Devriendt, S. Kaviraj, D. Kim, K. Kraljic, I. Lazar, F. R. Pearce, S. Peirani, C. Pichon, A. Slyz, S. K. Yi
Dwarf galaxies serve as powerful laboratories for investigating the underlying physics of galaxy evolution including the impact of baryonic feedback processes and environmental influences. We compare the visual and structural properties of dwarf galaxies in ultra-deep HSC-SSP imaging of the COSMOS field with those measured from realistic HSC-like synthetic observations of dwarfs generated by the Illustris TNG50 and NewHorizon simulations. Using S\'ersic profile fitting and non-parametric morphological metrics (Gini, $M_{20}$, asymmetry, and concentration), we evaluate the diversity of structural properties in observed and simulated galaxies. Our analysis shows that NewHorizon and TNG50 galaxies lie at opposite extremes of observed structural trends: NewHorizon produces diffuse, extended galaxies with shallow S\'ersic indices, while TNG50 yields compact, concentrated systems with steep indices. Both simulations reproduce observed structural trends more closely at higher stellar masses ($M_{\star}\sim10^{9.5} {\rm M_{\odot}}$) but fail to capture the full diversity of COSMOS dwarfs at lower masses. Non-parametric metrics further show that NewHorizon galaxies exhibit more uneven, clumpy light distributions while TNG50 galaxies have smoother but excessively concentrated profiles. These structural differences reflect underlying differences in their physical prescriptions and are likely driven by differing approaches to ISM physics, supernova feedback and star formation in addition to differences in numerical resolution. Our findings highlight the unique power of low-mass galaxies to constrain differences in simulation physics, especially star formation and feedback. Upcoming surveys from facilities like the Vera C. Rubin Observatory and Euclid will enable more rigorous comparisons with simulations, offering deeper insights into the physical processes shaping galaxy evolution.
Uncovering Key Features for Model-Driven Engineering of Complex Performance Indicators: A Scoping Review
2025-05-07
Benito Giunta, Corentin Burnay
This paper addresses challenges of designing and managing Complex Performance Indicators (CPI), which amalgamate individual indicators to measure latent, yet crucial business factors like customer satisfaction or sustainability indices. Despite their significant value, designing and managing CPI is intricate; they evolve with rapidly changing business contexts and present comprehension and explanation challenges for end-users. Model-Driven Engineering (MDE) emerges as a potent solution to overcome these hurdles and ensure CPI adoption, though its application to CPI remains an understudied research area. While prior efforts targeted specific CPI modeling objectives, a comprehensive overview of literature advancements is lacking. This study addresses this gap by conducting a scoping review yielding dual outcomes: (1) a comprehensive mapping of modeling features in the literature and (2) a comparative analysis of the coverage offered by the modeling frameworks. These outcomes enhance CPI understanding in academic and practitioner circles and offer insights for future MDE CPI advancements.
Mass Modeling the Andromeda Dwarf Galaxies: Andromeda VI and Andromeda XXIII
2025-05-07
Connor S. Pickett, Michelle L. M. Collins, R. Michael Rich, Justin I. Read, Emily J. E. Charles, Nicolas Martin, Scott Chapman, Alan McConnachie, Alessandro Savino, Daniel R. Weisz
Accurately mapping the mass profiles of low mass dwarf spheroidal (dSph) galaxies allows us to test predictions made by dark matter (DM) models. To date, such analyses have primarily been performed on Milky Way (MW) satellites. Meanwhile, the Andromeda Galaxy (M31) is home to 35 known dwarf galaxies, yet only two have been successfully mass-modeled so far. In order to better understand the nature of dark matter, a more comprehensive study of Local Group dwarfs is necessary. In this study, we have undertaken a dynamical study of two higher-luminosity Andromeda dwarf galaxies: Andromeda VI (And VI) and Andromeda XXIII (And XXIII). We infer an enclosed mass for And VI of M(r < r$_{\rm{h}}$) = (4.9 $\pm$ 1.5) $\times$ 10$^{7}$ M$_{\odot}$, corresponding to a mass-to-light ratio of $[M/L]_{r_{\rm{h}}}$ = (27.1 $\pm$ 8.2) M$_{\odot}$/L$_{\odot}$. We infer an enclosed mass for And XXIII of M(r < r$_{\rm{h}}$) = (3.1 $\pm$ 1.9) $\times$ 10$^{7}$ M$_{\odot}$, corresponding to a mass-to-light ratio of $[M/L]_{\rm{r_{h}}}$ = (90.2 $\pm$ 53.9) M$_{\odot}$/L$_{\odot}$. Using the dynamical Jeans modeling tool, GravSphere, we determine And VI and And XXIII's dark matter density at 150 pc, finding $\rho_{\rm{DM,VI}}$(150 pc) = (1.4 $\pm$ 0.5) $\times$ 10$^{8}$ M$_{\odot}$ kpc$^{-3}$ and $\rho_{\rm{DM,XXIII}}$(150 pc) = 0.5$\substack{+0.4 \\ -0.3} \times$ 10$^{8}$ M$_{\odot}$ kpc$^{-3}$. Our results make And VI the first mass-modeled M31 satellite to fall into the cuspy regime, while And XXIII has a lower density, implying either a more cored central dark matter density, or a lowering of the density through tides. This adds And XXIII to a growing list of M31 dwarfs with a central density lower than most MW dwarfs and lower than expected for isolated dwarfs in the Standard Cosmology. This could be explained by the M31 dwarfs having experienced stronger tides than their Milky Way counterparts.
As one of the most promising technologies for intellicise (intelligent and consice) wireless networks, Semantic Communication (SemCom) significantly improves communication efficiency by extracting, transmitting, and recovering semantic information, while reducing transmission delay. However, an integration of communication and artificial intelligence (AI) also exposes SemCom to security and privacy threats posed by intelligent eavesdroppers. To address this challenge, image steganography in SemCom embeds secret semantic features within cover semantic features, allowing intelligent eavesdroppers to decode only the cover image. This technique offers a form of "invisible encryption" for SemCom. Motivated by these advancements, this paper conducts a comprehensive exploration of integrating image steganography into SemCom. Firstly, we review existing encryption techniques in SemCom and assess the potential of image steganography in enhancing its security. Secondly, we delve into various image steganographic paradigms designed to secure SemCom, encompassing three categories of joint source-channel coding (JSCC) models tailored for image steganography SemCom, along with multiple training strategies. Thirdly, we present a case study to illustrate the effectiveness of coverless steganography SemCom. Finally, we propose future research directions for image steganography SemCom.
A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities
2025-05-07
Pengfei Jiao, Hongjiang Chen, Xuan Guo, Zhidong Zhao, Dongxiao He, Di Jin
Temporal interaction graphs (TIGs), defined by sequences of timestamped interaction events, have become ubiquitous in real-world applications due to their capability to model complex dynamic system behaviors. As a result, temporal interaction graph representation learning (TIGRL) has garnered significant attention in recent years. TIGRL aims to embed nodes in TIGs into low-dimensional representations that effectively preserve both structural and temporal information, thereby enhancing the performance of downstream tasks such as classification, prediction, and clustering within constantly evolving data environments. In this paper, we begin by introducing the foundational concepts of TIGs and emphasize the critical role of temporal dependencies. We then propose a comprehensive taxonomy of state-of-the-art TIGRL methods, systematically categorizing them based on the types of information utilized during the learning process to address the unique challenges inherent to TIGs. To facilitate further research and practical applications, we curate the source of datasets and benchmarks, providing valuable resources for empirical investigations. Finally, we examine key open challenges and explore promising research directions in TIGRL, laying the groundwork for future advancements that have the potential to shape the evolution of this field.
Phase Shift Information Compression in IRS-aided Wireless Systems: Challenges and Opportunities
2025-05-07
Xianhua Yu, Dong Li
Intelligent reflecting surfaces (IRS) have emerged as a promising technology for future 6G wireless networks, offering programmable control of the wireless environment by adjusting the phase shifts of reflecting elements. However, IRS performance relies on accurately configuring the phase shifts of reflecting elements, which introduces substantial phase shift information (PSI) delivery overhead, especially in large-scale or rapidly changing environments. This paper first introduces the architecture of IRS-assisted systems and highlights real-world use cases where PSI delivery becomes a critical bottleneck. It then reviews current PSI compression approaches, outlining their limitations in adaptability and scalability. To address these gaps, we propose a prompt-guided PSI compression framework that leverages task-aware prompts and meta-learning to achieve efficient and real-time PSI delivery under diverse conditions. Simulation results show improved reconstruction accuracy and robustness compared to the baseline method. Finally, we discuss open challenges and outline promising directions for future research.
Turbulence spreading and anomalous diffusion on combs
2025-05-07
Alexander V. Milovanov, Alexander Iomin, Jens Juul Rasmussen
This paper presents a simple model for such processes as chaos spreading or turbulence spillover into stable regions. In this simple model the essential transport occurs via inelastic resonant interactions of waves on a lattice. The process is shown to result universally in a subdiffusive spreading of the wave field. The dispersion of this spreading process is found to depend exclusively on the type of the interaction process (three- or four-wave), but not on a particular instability behind. The asymptotic transport equations for field spreading are derived with the aid of a specific geometric construction in the form of a comb. The results can be summarized by stating that the asymptotic spreading pursues as a continuous-time random walk (CTRW) and corresponds to a kinetic description in terms of fractional-derivative equations. The fractional indexes pertaining to these equations are obtained exactly using the comb model. A special case of the above theory is a situation when two waves with oppositely directed wave vectors couple together to form a bound state with zero momentum. This situation is considered separately and associated with the self-organization of wave-like turbulence into banded flows or staircases. Overall, we find that turbulence spreading and staircasing could be described based on the same mathematical formalism, using the Hamiltonian of inelastic wave-wave interactions and a mapping procedure into the comb space. Theoretically, the comb approach is regarded as a substitute for a more common description based on quasilinear theory. Some implications of the present theory for the fusion plasma studies are discussed and a comparison with the available observational and numerical evidence is given.
Blockchain Data Analytics: A Scoping Literature Review and Directions for Future Research
2025-05-07
Marcel Bühlmann, Hans-Georg Fill, Simon Curty
Blockchain technology has rapidly expanded beyond its original use in cryptocurrencies to a broad range of applications, creating vast amounts of immutable, decentralized data. As blockchain adoption grows, so does the need for advanced data analytics techniques to extract insights for business intelligence, fraud detection, financial analysis and many more. While previous research has examined specific aspects of blockchain data analytics, such as transaction patterns, illegal activity detection, and data management, there remains a lack of comprehensive reviews that explore the full scope of blockchain data analytics. This study addresses this gap through a scoping literature review, systematically mapping the existing research landscape, identifying key topics, and highlighting emerging trends. Using established methodologies for literature reviews, we analyze 466 publications, clustering them into six major research themes: illegal activity detection, data management, financial analysis, user analysis, community detection, and mining analysis. Our findings reveal a strong focus on detecting illicit activities and financial applications, while holistic business intelligence use cases remain underexplored. This review provides a structured overview of blockchain data analytics, identifying research gaps and proposing future directions to enhance the fields impact.
We review our current knowledge of thermal and viscous instabilities in accretion discs around compact objects. We begin with classical disc models based on analytic viscosity prescriptions, discussing physical uncertainties and exploring time-dependent solutions of disc evolution. We also review the ionization instability responsible for outbursting dwarf nova and X-ray binary systems, including some detailed comparisons between alpha-based models and the observed characteristics of these systems. We then review modern theoretical work based on ideas around angular momentum transport mediated by magnetic fields, focusing in particular on knowledge gained through local and global computer simulations of MHD processes in discs. We discuss how MHD may alter our understanding of outbursts in white dwarf and X-ray binary systems. Finally, we turn to the putative thermal/viscous instabilities that were predicted to exist in the inner, radiation pressure-dominated regions of black hole and neutron star discs, in apparent contradiction to the observed stability of the high/soft state in black hole X-ray binaries.
Advancements in Solid-State Sodium-Based Batteries: A Comprehensive Review
2025-05-07
Arianna Massaro, Lorenzo Squillantini, Francesca De Giorgio, Francesca A. Scaramuzzo, Mauro Pasquali, Sergio Brutti
This manuscript explores recent advancements in solid-state sodium-based battery technology, particularly focusing on electrochemical performance and the challenges associated with developing efficient solid electrolytes. The replacement of conventional liquid electrolytes with solid-state alternatives offers numerous benefits, including enhanced safety and environmental sustainability, as solid-state systems reduce flammability and harsh chemical handling. The work emphasizes the importance of structure and interface characteristics in solid electrolytes, which play a critical role in ionic conductivity and overall battery performance. Various classes of solid electrolytes, such as sodium-based anti-perovskites and sulphide electrolytes, are examined, highlighting their unique ionic transport mechanisms and mechanical properties that facilitate stable cycling. The manuscript also discusses strategies to enhance interfacial stability between the anode and the solid electrolyte to mitigate performance degradation during battery operation. Furthermore, advancements in electrode formulations and the integration of novel materials are considered pivotal in optimizing the charging and discharging processes, thus improving the energy and power densities of sodium batteries. The outlook on the future of sodium-based solid-state batteries underscores their potential to meet emerging energy storage demands while leveraging the abundant availability of sodium compared to lithium. This comprehensive review aims to provide insights into ongoing research and prospective directions for the commercialization of solid-state sodium-based batteries, positioning them as viable alternatives in the renewable energy landscape.
How the Misuse of a Dataset Harmed Semantic Clone Detection
2025-05-07
Jens Krinke, Chaiyong Ragkhitwetsagul
BigCloneBench is a well-known and widely used large-scale dataset for the evaluation of recall of clone detection tools. It has been beneficial for research on clone detection and has become a standard in evaluating the performance of clone detection tools. More recently, it has also been widely used as a dataset to evaluate machine learning approaches to semantic clone detection or code similarity detection for functional or semantic similarity. This paper demonstrates that BigCloneBench is problematic to use as ground truth for learning or evaluating semantic code similarity, and highlights the aspects of BigCloneBench that affect the ground truth quality. A manual investigation of a statistically significant random sample of 406 Weak Type-3/Type-4 clone pairs revealed that 93% of them do not have a similar functionality and are therefore mislabelled. In a literature review of 179 papers that use BigCloneBench as a dataset, we found 139 papers that used BigCloneBench to evaluate semantic clone detection and where the results are threatened in their validity by the mislabelling. As such, these papers often report high F1 scores (e.g., above 0.9), which indicates overfitting to dataset-specific artefacts rather than genuine semantic similarity detection. We emphasise that using BigCloneBench remains valid for the intended purpose of evaluating syntactic or textual clone detection of Type-1, Type-2, and Type-3 clones. We acknowledge the important contributions of BigCloneBench to two decades of traditional clone detection research. However, the usage of BigCloneBench beyond the intended purpose without careful consideration of its limitations has led to misleading results and conclusions, and potentially harmed the field of semantic clone detection.
Biologically-informed neural networks typically leverage pathway annotations to enhance performance in biomedical applications. We hypothesized that the benefits of pathway integration does not arise from its biological relevance, but rather from the sparsity it introduces. We conducted a comprehensive analysis of all relevant pathway-based neural network models for predictive tasks, critically evaluating each study's contributions. From this review, we curated a subset of methods for which the source code was publicly available. The comparison of the biologically informed state-of-the-art deep learning models and their randomized counterparts showed that models based on randomized information performed equally well as biologically informed ones across different metrics and datasets. Notably, in 3 out of the 15 analyzed models, the randomized versions even outperformed their biologically informed counterparts. Moreover, pathway-informed models did not show any clear advantage in interpretability, as randomized models were still able to identify relevant disease biomarkers despite lacking explicit pathway information. Our findings suggest that pathway annotations may be too noisy or inadequately explored by current methods. Therefore, we propose a methodology that can be applied to different domains and can serve as a robust benchmark for systematically comparing novel pathway-informed models against their randomized counterparts. This approach enables researchers to rigorously determine whether observed performance improvements can be attributed to biological insights.
WALLABY pilot survey: properties of HI-selected dark sources and low surface brightness galaxies
2025-05-07
T. O'Beirne, L. Staveley-Smith, V. A. Kilborn, O. I. Wong, T. Westmeier, M. E. Cluver, K. Bekki, N. Deg, H. Dénes, B. -Q. For, K. Lee-Waddell, C. Murugeshan, K. Oman, J. Rhee, A. X. Shen, E. N. Taylor
We examine the optical counterparts of the 1829 neutral hydrogen (HI) detections in three pilot fields in the Widefield ASKAP L-band Legacy All-sky Blind surveY (WALLABY) using data from the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys DR10. We find that 17 per cent (315) of the detections are optically low surface brightness galaxies (LSBGs; mean $g$-band surface brightness within 1 $ R_e$ of $> 23$ mag arcsec$^{-2}$) and 3 per cent (55) are optically 'dark'. We find that the gas-rich WALLABY LSBGs have low star formation efficiencies, and have stellar masses spanning five orders of magnitude, which highlights the diversity of properties across our sample. 75 per cent of the LSBGs and all of the dark HI sources had not been catalogued prior to WALLABY. We examine the optically dark sample of the WALLABY pilot survey to verify the fidelity of the catalogue and investigate the implications for the full survey for identifying dark HI sources. We assess the HI detections without optical counterparts and identify 38 which pass further reliability tests. Of these, we find that 13 show signatures of tidal interactions. The remaining 25 detections have no obvious tidal origin, so are candidates for isolated galaxies with high HI masses, but low stellar masses and star-formation rates. Deeper HI and optical follow-up observations are required to verify the true nature of these dark sources.
Bayesian Frequency Metrology with Optimal Ramsey Interferometry in Optical Atomic Clocks
2025-05-07
Timm Kielinski, Klemens Hammerer
Frequency metrology is a cornerstone of modern precision measurements and optical atomic clocks have emerged as the most precise measurement devices. In this progress report, we explore various Ramsey interrogation schemes tailored to optical atomic clocks primarily limited by laser noise. To incorporate frequency fluctuations directly into the theoretical model, we consider a Bayesian framework. In this context, we review fundamental bounds arising in Bayesian estimation theory, which serve as a benchmark throughout this work. We investigate the trade-off between entanglement-enhanced sensitivity and robustness against laser noise in order to identify optimal initial states, measurement schemes and estimation strategies. Beside standard protocols based on coherent spin states, squeezed spin states and GHZ states, we consider variational Ramsey protocols implemented via low-depth quantum circuits based on one-axis twisting operations to approach optimal stability. In particular, we review known and identify new optimal interrogation schemes for a variety of scenarios, including different experimental platforms, ensemble sizes and regimes characterized by a wide range of interrogation durations and dead times. Hence, this work establishes a comprehensive theoretical framework for optimizing Ramsey interrogation schemes, providing guidance for the development of next-generation optical atomic clocks.
Progress in the development of quantum algorithms and software
2025-05-07
Anastasiia S. Nikolaeva, Daria O. Konina, Anatolii V. Antipov, Maksim A. Gavreev, Konstantin M. Makushin, Boris I. Bantysh, Andrey Yu. Chernyavskiy, Grigory V. Astretsov, Evgeniy A. Polyakov, Aidar I. Saifoulline, Evgeniy O. Kiktenko, Alexey N. Rubtsov, Aleksey K. Fedorov
A quantum processor, like any computing device, requires the development of both hardware and the necessary set of software solutions, starting with quantum algorithms and ending with means of accessing quantum devices. As part of the roadmap for the development of the high-tech field of quantum computing in the period from 2020 to 2024, a set of software solutions for quantum computing devices was developed. This software package includes a set of quantum algorithms for solving prototypes of applied tasks, monitoring and benchmarking tools for quantum processors, error suppression and correction methods, tools for compiling and optimizing quantum circuits, as well as interfaces for remote cloud access. This review presents the key results achieved, among which it is necessary to mention the execution of quantum algorithms using a cloud-based quantum computing platform.
Revolutionizing Newcomers' Onboarding Process in OSS Communities: The Future AI Mentor
2025-05-07
Xin Tan, Xiao Long, Yinghao Zhu, Lin Shi, Xiaoli Lian, Li Zhang
Onboarding newcomers is vital for the sustainability of open-source software (OSS) projects. To lower barriers and increase engagement, OSS projects have dedicated experts who provide guidance for newcomers. However, timely responses are often hindered by experts' busy schedules. The recent rapid advancements of AI in software engineering have brought opportunities to leverage AI as a substitute for expert mentoring. However, the potential role of AI as a comprehensive mentor throughout the entire onboarding process remains unexplored. To identify design strategies of this ``AI mentor'', we applied Design Fiction as a participatory method with 19 OSS newcomers. We investigated their current onboarding experience and elicited 32 design strategies for future AI mentor. Participants envisioned AI mentor being integrated into OSS platforms like GitHub, where it could offer assistance to newcomers, such as ``recommending projects based on personalized requirements'' and ``assessing and categorizing project issues by difficulty''. We also collected participants' perceptions of a prototype, named ``OSSerCopilot'', that implemented the envisioned strategies. They found the interface useful and user-friendly, showing a willingness to use it in the future, which suggests the design strategies are effective. Finally, in order to identify the gaps between our design strategies and current research, we conducted a comprehensive literature review, evaluating the extent of existing research support for this concept. We find that research is relatively scarce in certain areas where newcomers highly anticipate AI mentor assistance, such as ``discovering an interested project''. Our study has the potential to revolutionize the current newcomer-expert mentorship and provides valuable insights for researchers and tool designers aiming to develop and enhance AI mentor systems.
J-PAS and PFS surveys in the era of dark energy and neutrino mass measurements
2025-05-07
Fuxing Qin, Yuting Wang, Gong-Bo Zhao
Fisher-matrix forecasts are presented for the cosmological surveys of the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) and the Subaru Prime Focus Spectrograph (PFS). The wide, low-redshift coverage of J-PAS and the high-density, high-redshift mapping of PFS are strongly complementary: combining the two reduces marginalized uncertainties on all primary parameters compared with either survey individually. Adding the joint J-PAS+PFS data to next-generation CMB measurements from CMB-S4 and \textsc{LiteBird} yields an expected precision of $\sigma(\sum m_\nu)=0.017\,$eV in the $\Lambda$CDM$+\sum m_\nu+N_{\rm eff}$ framework, sufficient to disfavour the inverted neutrino hierarchy at $2.35\,\sigma$ if the true mass sum equals the normal-ordering minimum. Motivated by recent DESI results, we also forecast within a $w_0w_a$CDM$+\sum m_\nu+N_{\rm eff}$ cosmology, adopting the DESI\,DR2 best-fit values ($w_0=-0.758$, $w_a=-0.82$) as fiducial. The combination CMB+J-PAS+PFS then delivers $\sigma(w_0)=0.044$ and $\sigma(w_a)=0.18$, corresponding to a $5.1\,\sigma$ preference for a time-varying dark-energy equation of state. These findings show that J-PAS and PFS, especially when coupled with Stage-IV CMB observations, will provide competitive tests of neutrino physics and the dynamics of cosmic acceleration.
Weaponizing Language Models for Cybersecurity Offensive Operations: Automating Vulnerability Assessment Report Validation; A Review Paper
2025-05-07
Abdulrahman S Almuhaidib, Azlan Mohd Zain, Zalmiyah Zakaria, Izyan Izzati Kamsani, Abdulaziz S Almuhaidib
This, with the ever-increasing sophistication of cyberwar, calls for novel solutions. In this regard, Large Language Models (LLMs) have emerged as a highly promising tool for defensive and offensive cybersecurity-related strategies. While existing literature has focused much on the defensive use of LLMs, when it comes to their offensive utilization, very little has been reported-namely, concerning Vulnerability Assessment (VA) report validation. Consequentially, this paper tries to fill that gap by investigating the capabilities of LLMs in automating and improving the validation process of the report of the VA. From the critical review of the related literature, this paper hereby proposes a new approach to using the LLMs in the automation of the analysis and within the validation process of the report of the VA that could potentially reduce the number of false positives and generally enhance efficiency. These results are promising for LLM automatization for improving validation on reports coming from VA in order to improve accuracy while reducing human effort and security postures. The contribution of this paper provides further evidence about the offensive and defensive LLM capabilities and therefor helps in devising more appropriate cybersecurity strategies and tools accordingly.
Environment of SDSS quasars at $z=0.4$$-$$1.0$ explored by Subaru HSC
2025-05-07
Kohei Shibata, Tohru Nagao, Hisakazu Uchiyama, Mariko Kubo, Yoshiki Toba, Kiyoaki Christopher Omori, Toshihiro Kawaguchi, Yuta Suzuki
The relationship between quasars and their galaxy environment is important for understanding the evolution of galaxies and supermassive black holes, but it is not fully understood. We perform a wide and deep exploration of the environment of quasars at $0.4 < z < 1.0$ using the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) survey. We investigate the environment of the 1,912 spectroscopically selected quasars from the Sloan Digital Sky Survey (SDSS), using photometrically selected galaxies from the HSC-SSP data, over an area of 505 deg${^{2}}$. The quasar environment is compared to the environment of matched galaxies with similar stellar mass and redshift. We employ the $k$-nearest neighbor method to define the local galaxy number density for both the quasars and the matched galaxies at a scale of a few hundred kpc. As a result, we find that the number density of galaxies around SDSS quasars is lower than that of the matched galaxies by $\sim$11--$20\%$. We also investigate possible correlations between the local galaxy number densities and the quasar properties such as black hole mass and Eddington ratio. As a result, no correlation is found between the local galaxy number densities and these properties of quasars. These results suggest that the quasar activity is not triggered by the high number density of surrounding galaxies at the scale of a few hundred kpc.
On the Vulnerability of Underwater Magnetic Induction Communication
2025-05-07
Muhammad Muzzammil, Waqas Aman, Irfan Ullah, Shang Zhigang, Saif Al-Kuwari, Zhou Tian, Marwa Qaraqe
Typical magnetic induction (MI) communication is commonly considered a secure underwater wireless communication (UWC) technology due to its non-audible and non-visible nature compared to acoustic and optical UWC technologies. However, vulnerabilities in communication systems inevitably exist and may lead to different types of attacks. In this paper, we investigate the eavesdropping attack in underwater MI communication to quantitatively measure the system's vulnerability under this attack. We consider different potential eavesdropping configuration setups based on the positions and orientations of the eavesdropper node to investigate how they impact the received voltage and secrecy at the legitimate receiver node. To this end, we develop finite-element-method-based simulation models for each configuration in an underwater environment and evaluate the received voltage and the secrecy capacity against different system parameters such as magnetic flux, magnetic flux density, distance, and orientation sensitivity. Furthermore, we construct an experimental setup within a laboratory environment to replicate the simulation experiments. Both simulation and lab experimental confirm the susceptibility of underwater MI communication to eavesdropping attacks. However, this vulnerability is highly dependent on the position and orientation of the coil between the eavesdropper and the legitimate transmitter. On the positive side, we also observe a unique behavior in the received coil reception that might be used to detect malicious node activities in the vicinity, which might lead to a potential security mechanism against eavesdropping attacks.
Job Shop Scheduling (JSS) is one of the most studied combinatorial optimization problems. It involves scheduling a set of jobs with predefined processing constraints on a set of machines to achieve a desired objective, such as minimizing makespan, tardiness, or flowtime. Since it introduction, JSS has become an attractive research area. Many approaches have been successfully used to address this problem, including exact methods, heuristics, and meta-heuristics. Furthermore, various learning-based approaches have been proposed to solve the JSS problem. However, these approaches are still limited when compared to the more established methods. This paper summarizes and evaluates the most important works in the literature on machine learning approaches for the JSSP. We present models, analyze their benefits and limitations, and propose future research directions.
2025-05-07
Li Xu, Xiao-yu Zhang, Ming Li, Shu-qian Shen
Kernel function plays a crucial role in machine learning algorithms such as classifiers. In this paper, we aim to improve the classification performance and reduce the reading out burden of quantum classifiers. We devise a universally trainable quantum feature mapping layout to broaden the scope of feature states and avoid the inefficiently straight preparation of quantum superposition states. We also propose an improved quantum support vector machine that employs partially evenly weighted trial states. In addition, we analyze its error sources and superiority. As a promotion, we propose a quantum iterative multi-classifier framework for one-versus-one and one-versus-rest approaches. Finally, we conduct corresponding numerical demonstrations in the \textit{qiskit} package. The simulation result of trainable quantum feature mapping shows considerable clustering performance, and the subsequent classification performance is superior to the existing quantum classifiers in terms of accuracy and distinguishability.
MAMMOTH-MOSFIRE: Environmental Effects on Galaxy Interstellar Medium at $z\sim2$
2025-05-07
Hang Zhou, Xin Wang, Matthew A. Malkan, Tommaso Treu, Yiming Yang, Zheng Cai, Xiaohui Fan, Mengting Ju, Dong Dong Shi, Anahita Alavi, Fuyan Bian, James Colbert, Alaina L. Henry, Sijia Li, Zihao Li, Harry I. Teplitz, Hu Zhan, Xian Zhong Zheng, Zheng Zheng
The MAMMOTH-MOSFIRE program is a deep Keck MOSFIRE K-band spectroscopic follow-up of emission-line galaxies identified in the MAMMOTH-Grism HST WFC3/G141 slitless spectroscopic survey, targeting the core regions of three most massive galaxy protoclusters at cosmic noon. To introduce this program, we present a comprehensive analysis of the emission-line diagnostics for a unique sample of 43 protocluster member galaxies at $z\sim2$, investigating the impact of the overdense environment on their interstellar medium conditions. We characterize their ionization and excitation state using the $\rm [N\,II]\lambda$6584, $\rm [S\,II]\lambda\lambda$6717,6731, and $\rm [O\,I]\lambda$6300 BPT diagrams, from a full suite of rest-frame optical emission lines jointly covered by Keck MOSFIRE and HST G141 spectroscopy. Our analysis reveals a median electron density of $n_{\rm e}\approx290~{\rm cm}^{-3}$ from $\rm [S\,II]$ doublets, consistent with measurements from field galaxies at similar redshifts. Like their field counterparts at $z\sim2$, protocluster galaxies exhibit a systematic offset in the N2 BPT diagram compared to the local star-forming sequence, but no offset in the S2 BPT diagram. Notably, we find significantly enhanced $\rm [O\,I]/H{\alpha}$ ratios, which can be well explained by photoionization models incorporating both $\rm H\,II$ regions and shock excitation. This work highlights the powerful synergy between high-resolution Keck MOSFIRE K-band spectroscopy and HST G141 slitless spectroscopy, enabling comprehensive coverage of the rest-frame optical spectra of galaxies at $z\sim2$.
Sick of being driven? -- Prevalence and modulating factors of carsickness in the European population in context of automated driving
2025-05-07
Myriam Metzulat, Barbara Metz, Aaron Edelmann, Alexandra Neukum, Wilfried Kunde
As in automated driving the driver becomes a passenger, carsickness might reduce comfort for susceptible individuals. Insights in the prevalence of carsickness and its modulating factors are considered useful for the development of automated vehicles to mitigate or prevent its occurrence. An online survey was conducted with N = 3999 participants in Spain, Sweden, Poland, and Germany. 30% of participants reported to have already experienced carsickness as adult. The frequency of carsickness was modulated not only by demographic factors (country, gender, age), but also by frequency of being a passenger, type of non-driving related task, road type, and the seating position in car. Furthermore, the efficiency of applied countermeasures, temporal aspects of carsickness development, as well as the relation of carsickness with the acceptability of automated driving and the effect on subjective fitness to drive was investigated. The results are discussed with focus on automated driving.
State-of-the-Art HCI for Dementia Care: A Scoping Review of Recent Technological Advances
2025-05-07
Yong Ma, Yuchong Zhang, Oda Elise Nordberg, Arvid Rongve, Miroslav Bachinski, Morten Fjeld
Dementia significantly impacts cognitive, behavioral, and functional abilities, creating challenges for both individuals and caregivers. Recent advancements in HCI have introduced innovative technological solutions to support people with dementia (PwD) and their caregivers. This scoping review systematically examines 32 recent publications from leading digital libraries, categorizing technological interventions into four key domains: Assistive and Smart Technology for Daily Life, Social Interaction and Communication, Well-being and Psychological Support, and Caregiver Support and Training. Our analysis highlights how emerging technologies are transforming dementia care. These technologies enhance quality of life by promoting independence, fostering social engagement, and providing emotional and cognitive support. However, the review also identifies critical gaps, particularly in addressing the needs of individuals with early-stage dementia and the lack of individualized support mechanisms. By emphasizing user-centered design, accessibility, and ethical considerations, this paper offers a structured roadmap for future research and practice in dementia care. It bridges the gap between technological innovation and the real-world needs of PwD and their caregivers, providing valuable insights for researchers, practitioners, and policymakers. This review not only synthesizes current advancements but also sets the stage for future HCI-driven innovations in dementia care, aiming to improve outcomes for an aging global population.
Privacy Challenges In Image Processing Applications
2025-05-07
Maneesha, Bharat Gupta, Rishabh Sethi, Charvi Adita Das
As image processing systems proliferate, privacy concerns intensify given the sensitive personal information contained in images. This paper examines privacy challenges in image processing and surveys emerging privacy-preserving techniques including differential privacy, secure multiparty computation, homomorphic encryption, and anonymization. Key applications with heightened privacy risks include healthcare, where medical images contain patient health data, and surveillance systems that can enable unwarranted tracking. Differential privacy offers rigorous privacy guarantees by injecting controlled noise, while MPC facilitates collaborative analytics without exposing raw data inputs. Homomorphic encryption enables computations on encrypted data and anonymization directly removes identifying elements. However, balancing privacy protections and utility remains an open challenge. Promising future directions identified include quantum-resilient cryptography, federated learning, dedicated hardware, and conceptual innovations like privacy by design. Ultimately, a holistic effort combining technological innovations, ethical considerations, and policy frameworks is necessary to uphold the fundamental right to privacy as image processing capabilities continue advancing rapidly.
The ALMA-ATOMS Survey: Exploring Protostellar Outflows in HC$_3$N
2025-05-07
Ariful Hoque, Tapas Baug, Lokesh K. Dewangan, Mika Juvela, Anandmayee Tej, Paul F. Goldsmith, Pablo García, Amelia M. Stutz, Tie Liu, Chang Won Lee, Fengwei Xu, Patricio Sanhueza, N. K. Bhadari, K. Tatematsu, Xunchuan Liu, Hong-Li Liu, Yong Zhang, Xindi Tang, Guido Garay, Ke Wang, Siju Zhang, L. Viktor Tóth, Hafiz Nazeer, Jihye Hwang, Prasanta Gorai, Leonardo Bronfman, Swagat Ranjan Das, Tirthendu Sinha
We present the first systematic study of bipolar outflows using HC$_3$N as a tracer in a sample of 146 massive star-forming regions from ALMA-ATOMS survey. Protostellar outflows arise at the initial stage of star formation as a consequence of active accretion. In general, these outflows play a pivotal role in regulating the star formation processes by injecting energetic material in the parent molecular clouds. In such process, lower velocity components of outflows contain a significant portion of the energy. However, extraction of those component is difficult as the corresponding gas is often mixed with that of the ambient cloud. In our sample, we identified 44 bipolar outflows and one explosive outflow in HC$_3$N (J=11--10). The host clumps of these outflows are found to be at different evolutionary stages, suggesting that outflows in HC$_3$N are detectable in different stages of star formation. Also, the non-correlation of HC$_3$N outflows with clump evolutionary stages suggests that HC$_3$N is an unbiased tracer of outflows. Analyses revealed that HC$_3$N performs slightly better in detecting low-velocity components of outflows than traditionally employed tracers like SiO. The derived outflow parameters (i.e outflow mass, momentum, and energy) show moderate correlations with clump mass and luminosity. Our analysis of outflow opening angles and position-velocity diagrams across the outflow lobes show that, HC$_3$N is not only a good tracer of low-velocity outflows, but can also detect high-velocity collimated outflows. Overall, this study indicates that HC$_3$N can be used as a complementary outflow tracer along with the traditionally known outflow tracers, particularly in the detection of the low-velocity components of outflows.
Evaluating the regeneration process of damaged muscle tissue is a fundamental analysis in muscle research to measure experimental effect sizes and uncover mechanisms behind muscle weakness due to aging and disease. The conventional approach to assessing muscle tissue regeneration involves whole-slide imaging and expert visual inspection of the recovery stages based on the morphological information of cells and fibers. There is a need to replace these tasks with automated methods incorporating machine learning techniques to ensure a quantitative and objective analysis. Given the limited availability of fully labeled data, a possible approach is Learning from Label Proportions (LLP), a weakly supervised learning method using class label proportions. However, current LLP methods have two limitations: (1) they cannot adapt the feature extractor for muscle tissues, and (2) they treat the classes representing recovery stages and cell morphological changes as nominal, resulting in the loss of ordinal information. To address these issues, we propose Ordinal Scale Learning from Similarity Proportion (OSLSP), which uses a similarity proportion loss derived from two bag combinations. OSLSP can update the feature extractor by using class proportion attention to the ordinal scale of the class. Our model with OSLSP outperforms large-scale pre-trained and fine-tuning models in classification tasks of skeletal muscle recovery stages.
We explore the use of Chain-of-Thought (CoT) prompting with large language models (LLMs) to improve the accuracy of granular sentiment categorization in app store reviews. Traditional numeric and polarity-based ratings often fail to capture the nuanced sentiment embedded in user feedback. We evaluated the effectiveness of CoT prompting versus simple prompting on 2000 Amazon app reviews by comparing each method's predictions to human judgements. CoT prompting improved classification accuracy from 84% to 93% highlighting the benefit of explicit reasoning in enhancing sentiment analysis performance.
Doubtlessly, the immersive technologies have potential to ease people's life and uplift economy, however the obvious data privacy risks cannot be ignored. For example, a participant wears a 3D headset device which detects participant's head motion to track the pose of participant's head to match the orientation of camera with participant's eyes positions in the real-world. In a preliminary study, researchers have proved that the voice command features on such headsets could lead to major privacy leakages. By analyzing the facial dynamics captured with the motion sensors, the headsets suffer security vulnerabilities revealing a user's sensitive speech without user's consent. The psychography data (such as voice command features, facial dynamics, etc.) is sensitive data and it should not be leaked out of the device without users consent else it is a privacy breach. To the best of our literature review, the work done in this particular research problem is very limited. Motivated from this, we develop a simple technical framework to mitigate sensitive data (or biometric data) privacy leaks in immersive technology domain. The performance evaluation is conducted in a robust way using six data sets, to show that the proposed solution is effective and feasible to prevent this issue.
The Eccentricity Distribution of Warm Sub-Saturns in TESS
2025-05-07
Tyler R. Fairnington, Jiayin Dong, Chelsea X. Huang, Emma Nabbie, George Zhou, Duncan Wright, Karen A. Collins, Jon M. Jenkins, David W. Latham, George Ricker, Samuel N. Quinn, Sara Seager, Avi Shporer, Roland Vanderspek, Joshua N. Winn, Calvin Ajizian, Akihiko Fukui, David Baker, Giuseppe Conzo, Robert Scott Fisher, Raquel Forés-Toribio, Tianjun Gan, Alexey Garmash, Kai Ikuta, Adam Lark, Jerome P. de Leon, Katherine Linnenkohl, Christopher R. Mann, Owen Mitchem, Mayuko Mori, Jose A. Muñoz, Norio Narita, Adam Popowicz, Don Radford, Justus Randolph, Fabian Rodriguez Frustaglia, Richard P. Schwarz, Chris Stockdale, Jiaqi Wang, Noriharu Watanabe, Francis P. Wilkin, Krzysztof Sz. Zieliński, Emma Esparza-Borges, Felipe Murgas, Enric Pallé, Hannu Parviainen, Selçuk Yalçınkaya, Özgür Baştürk
We present the eccentricity distribution of warm sub-Saturns (4-8 Re, 8-200 day periods) as derived from an analysis of transit light curves from NASA's Transiting Exoplanet Survey Satellite (TESS) mission. We use the "photoeccentric" effect to constrain the eccentricities of 76 planets, comprising 60 and 16 from single- and multi-transiting systems, respectively. We employ Hierarchical Bayesian Modelling to infer the eccentricity distribution of the population, testing both a Beta and Mixture Beta distribution. We identify a few highly eccentric (e ~ 0.7-0.8) warm sub-Saturns with eccentricities that appear too high to be explained by disk migration or planet-planet scattering alone, suggesting high-eccentricity migration may play a role in their formation. The majority of the population have a mean eccentricity of e = 0.103+0.047-0.045, consistent with both planet-disk and planet-planet interactions. Notably, we find that the highly eccentric sub-Saturns occur in single-transiting systems. This study presents the first evidence at the population level that the eccentricities of sub-Saturns may be sculpted by dynamical processes.
Natural language generation (NLG) is the key technology to achieve generative artificial intelligence (AI). With the breakthroughs in large language models (LLMs), NLG has been widely used in various medical applications, demonstrating the potential to enhance clinical workflows, support clinical decision-making, and improve clinical documentation. Heterogeneous and diverse medical data modalities, such as medical text, images, and knowledge bases, are utilized in NLG. Researchers have proposed many generative models and applied them in a number of healthcare applications. There is a need for a comprehensive review of NLG methods and applications in the medical domain. In this study, we systematically reviewed 113 scientific publications from a total of 3,988 NLG-related articles identified using a literature search, focusing on data modality, model architecture, clinical applications, and evaluation methods. Following PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines, we categorize key methods, identify clinical applications, and assess their capabilities, limitations, and emerging challenges. This timely review covers the key NLG technologies and medical applications and provides valuable insights for future studies to leverage NLG to transform medical discovery and healthcare.
Since the variety of their light curve morphologies, the vast majority of the known heartbeat stars (HBSs) have been discovered by manual inspection. Machine learning, which has already been successfully applied to the classification of variable stars based on light curves, offers another possibility for the automatic detection of HBSs. We propose a novel feature extraction approach for HBSs. First, the light curve is transformed into the frequency domain via Fourier transform, then the amplitudes of the first 100 harmonics are extracted, and finally these harmonics are normalised as feature vectors of the light curve. A training data set of synthetic light curves is constructed using ELLC, and their features are fed into recurrent neural networks (RNNs) for supervised learning, with the expected output being the eccentricity of these light curves. The performance of RNNs is evaluated using a test data set of synthetic light curves, achieving 95$\%$ accuracy. When applied to known HBSs from OGLE, Kepler, and TESS surveys, the networks achieve an average accuracy of 88$\%$. This method successfully identify four new HBSs within the eclipsing binary catalog of Kirk et al. The use of orbital harmonics as features for HBSs proves to be an effective approach that significantly reduces the computational cost of neural networks. RNNs show excellent performance in recognising this type of time series data. This method not only allows efficient identification of HBSs, but can also be extended to recognise other types of periodic variable stars.
Comprehending sound damping is integral to understanding the anomalous low temperature properties of glasses. Despite decades of studies, the underlying mechanism of sound damping in glasses is still debated. In this perspective we review recent work on sound damping in amorphous solids. We focus on the role of defects, heterogeneous elasticity, and damping in model amorphous solids without defects. We review our definition of damping defects and show that they strongly influence sound damping. However, we also find another contribution to sound damping that cannot be attributed to damping defects. We confirm an earlier result of Kapteijns et al. [G. Kapteijns et al., J. Chem. Phys. 154, 081101 (2021)] that heterogeneous elasticity theory predicts relative changes of sound damping in model two-dimensional glasses if the configuration-to-configuration elastic constants fluctuations are used to quantify the heterogeneity. We extend this finding to similar three-dimensional glasses. We end by discussing the Euclidean Random Matrix model, which exhibits Rayleigh scaling of sound damping, but does not have quasi-localized excitations, and thus probably does not have sound damping defects. We propose that the mechanisms behind sound damping can be more fully understood by approaching the problem from two directions, one where the strong influence of defects is studied and another where sound damping is studied in defect free albeit disordered materials.
Osiris: A Lightweight Open-Source Hallucination Detection System
2025-05-07
Alex Shan, John Bauer, Christopher D. Manning
Retrieval-Augmented Generation (RAG) systems have gained widespread adoption by application builders because they leverage sources of truth to enable Large Language Models (LLMs) to generate more factually sound responses. However, hallucinations, instances of LLM responses that are unfaithful to the provided context, often prevent these systems from being deployed in production environments. Current hallucination detection methods typically involve human evaluation or the use of closed-source models to review RAG system outputs for hallucinations. Both human evaluators and closed-source models suffer from scaling issues due to their high costs and slow inference speeds. In this work, we introduce a perturbed multi-hop QA dataset with induced hallucinations. Via supervised fine-tuning on our dataset, we achieve better recall with a 7B model than GPT-4o on the RAGTruth hallucination detection benchmark and offer competitive performance on precision and accuracy, all while using a fraction of the parameters. Code is released at our repository.
Subaru High-z Exploration of Low-Luminosity Quasars (SHELLQs) XXII. Chandra observations of narrow-line quasar candidates at z>6
2025-05-07
K. Iwasawa, R. Gilli, F. Vito, Y. Matsuoka, M. Onoue, M. A. Strauss, N. Kashikawa, Y. Toba, K. Shimasaku, K. Inayoshi, T. Nagao, N. Kawanaka, J. D. Silverman, T. Izumi, K. Kohno, Y. Ueda
We report on Chandra X-ray observations of four narrow-line quasar candidates at z~6, selected from the SHELLQs project, based on the Subaru Hyper Suprime-Cam survey. These objects are characterised by narrow (FWHM<310 km/s), luminous (>1e44 erg/s) Lya and faint UV continuum (M_1450 = -22 - -21), prompting us to examine whether they are obscured luminous AGN at the epoch of reionization. However, none of these objects were detected by Chandra, giving an upper limit to their rest-frame 2-10 keV luminosity (Lx) of 2e44 erg/s (2 sigma), assuming a spectral slope Gamma=2. Subsequent rest-frame optical spectroscopy of these objects by the JWST-NIRSpec, presented in a companion paper, show weak broad Balmer emission at the base of narrow cores. With the scaling relation for low-redshift AGN, the observed strong [OIII]5007 flux of these sources would predict Lx to be around 1e45 erg/s, which is well above the Chandra upper limits. These optical spectra and X-ray quietness are reminiscent of JWST-selected broad-line AGN. We attribute the weak broad Balmer emission to the broad-line regions hidden partially by optically-thick obscuring matter which also hides the optical and X-ray continuum emission from the accretion disc. Compton-thick obscuration, which would strongly suppress X-ray emission, could be due to a dense inter-stellar medium that is often present in galaxies at high redshifts. Alternatively, the same effect could be obtained from an inflated disc at the innermost radii in a supercritical accretion flow, when the disc is viewed at inclined angles.
SHELLQs. Bridging the gap: JWST unveils obscured quasars in the most luminous galaxies at z > 6
2025-05-07
Yoshiki Matsuoka, Masafusa Onoue, Kazushi Iwasawa, Kentaro Aoki, Michael A. Strauss, John D. Silverman, Xuheng Ding, Camryn L. Phillips, Masayuki Akiyama, Junya Arita, Masatoshi Imanishi, Takuma Izumi, Nobunari Kashikawa, Toshihiro Kawaguchi, Satoshi Kikuta, Kotaro Kohno, Chien-Hsiu Lee, Tohru Nagao, Ayumi Takahashi, Yoshiki Toba
The unprecedented sensitivity of the James Webb Space Telescope (JWST) has uncovered a surprisingly abundant population of mildly obscured, low-luminosity active galactic nuclei (AGNs) in the epoch of reionization (EoR). However, the link between these objects and classical unobscured quasars remains a mystery. Here we report the discovery of obscured quasars hosted by the most luminous galaxies at $z > 6$, possibly bridging the gap between the two AGN populations. The 13 objects presented here were originally selected from a rest-frame ultraviolet (UV) imaging survey over $>$1000 deg$^2$, and were known to have luminous ($>10^{43}$ erg s$^{-1}$) narrow Ly$\alpha$ emission. With JWST/NIRSpec follow-up observations, we found that nine of them exhibit a broad component in H I Balmer lines and He I lines, but not in [O III] and other forbidden lines. Mild dust obscuration ($0 < A_V < 3$) is inferred from the Balmer decrements. The estimated intrinsic luminosities suggest that our broad line (BL) objects are the long-sought UV-obscured counterparts of luminous quasars in the EoR. They host supermassive black holes (SMBHs) with masses $10^{7.8 - 9.1} M_\odot$, undergoing sub-Eddington to Eddington accretion. Most of the BL objects are spatially unresolved, and are close to "little red dots" with their blue rest-UV and red rest-optical colors. We estimate the AGN number density among similarly luminous Ly$\alpha$ emitters to be larger than $2 \times 10^{-8}$ Mpc$^{-3}$. This density is comparable to that of classical quasars with similar continuum luminosities, suggesting that a substantial fraction of active SMBHs are obscured in the EoR and have been overlooked in past rest-UV surveys.
Theoretical perspectives on optical control of magnetism in spin-charge coupled systems
2025-05-07
Masahito Mochizuki
In this article, we review recent theoretical research on photocontrol of magnetism in several spin-charge coupled systems. The control of magnetism with light has been a central issue in condensed-matter physics, attracting enormous research interest both for fundamental science and for technological applications. This field of research has developed rapidly in recent years along with the development of laser technology. However, because the direct coupling between the light magnetic field and magnetization via the Zeeman coupling is very weak in terms of the energy scale, it is, in principle, difficult to induce dramatic effects as far as this magnetic light-matter interaction is exploited. On the contrary, the interaction between the light electric field and electron charges has an energy scale two to three orders of magnitude larger than that of the magnetic interaction. Therefore, we may realize astonishing photoinduced physical phenomena and novel optical device functions by exploiting this electric light-matter interaction. Spin-charge coupled magnets, e.g., double-exchange magnets, multiferroics materials, and Rashba electron systems, in which spins and charges are strongly coupled through several kinds of mechanisms such as exchange interactions and spin-orbit coupling, are ideal systems for realizing this idea. Recent theoretical studies have revealed that it is possible to control, manipulate and switch the magnetization coupled to electron charges in these systems through exciting and/or driving them with light electric fields. The following three recent topics are discussed as examples of such theoretical studies, that is, photoinduced magnetic phase transitions in irradiated double-exchange models, highly efficient photoinduction of spin polarization in Rashba electron systems, and electromagnon excitations and their intense excitation effects in multiferroic materials.
Vision-Language-Action Models: Concepts, Progress, Applications and Challenges
2025-05-07
Ranjan Sapkota, Yang Cao, Konstantinos I. Roumeliotis, Manoj Karkee
Vision-Language-Action (VLA) models mark a transformative advancement in artificial intelligence, aiming to unify perception, natural language understanding, and embodied action within a single computational framework. This foundational review presents a comprehensive synthesis of recent advancements in Vision-Language-Action models, systematically organized across five thematic pillars that structure the landscape of this rapidly evolving field. We begin by establishing the conceptual foundations of VLA systems, tracing their evolution from cross-modal learning architectures to generalist agents that tightly integrate vision-language models (VLMs), action planners, and hierarchical controllers. Our methodology adopts a rigorous literature review framework, covering over 80 VLA models published in the past three years. Key progress areas include architectural innovations, parameter-efficient training strategies, and real-time inference accelerations. We explore diverse application domains such as humanoid robotics, autonomous vehicles, medical and industrial robotics, precision agriculture, and augmented reality navigation. The review further addresses major challenges across real-time control, multimodal action representation, system scalability, generalization to unseen tasks, and ethical deployment risks. Drawing from the state-of-the-art, we propose targeted solutions including agentic AI adaptation, cross-embodiment generalization, and unified neuro-symbolic planning. In our forward-looking discussion, we outline a future roadmap where VLA models, VLMs, and agentic AI converge to power socially aligned, adaptive, and general-purpose embodied agents. This work serves as a foundational reference for advancing intelligent, real-world robotics and artificial general intelligence. >Vision-language-action, Agentic AI, AI Agents, Vision-language Models
Quantum-enabled optical large-baseline interferometry: applications, protocols and feasibility
2025-05-07
Zixin Huang, Oleg Titov, Mikolaj K. Schmidt, Benjamin Pope, Gavin K. Brennen, Daniel Oi, Pieter Kok
Optical Very Long Baseline Interferometry (VLBI) offers the potential for unprecedented angular resolution in both astronomical imaging and precision measurements. Classical approaches, however, face significant limitations due to photon loss, background noise, and the requirements for dynamical delay lines over large distances. This document surveys recent developments in quantum-enabled VLBI, which aim to address these challenges using entanglement-assisted protocols, quantum memory storage, and nonlocal measurement techniques. While its application to astronomy is well known, we also examine how these techniques may be extended to geodesy -- specifically, the monitoring of Earth's rotation. Particular attention is given to quantum-enhanced telescope architectures, including repeater-based long baseline interferometry and quantum error-corrected encoding schemes, which offer a pathway toward high-fidelity optical VLBI. To aid the discussion, we also compare specifications for key enabling technologies to current state-of-the-art experimental components, including switching rates, gate times, entanglement distribution rates, and memory lifetimes. By integrating quantum technologies, future interferometric networks may achieve diffraction-limited imaging at optical and near-infrared wavelengths, surpassing the constraints of classical techniques and enabling new precision tests of astrophysical and fundamental physics phenomena.
Exploring Zero-Shot App Review Classification with ChatGPT: Challenges and Potential
2025-05-07
Mohit Chaudhary, Chirag Jain, Preethu Rose Anish
App reviews are a critical source of user feedback, offering valuable insights into an app's performance, features, usability, and overall user experience. Effectively analyzing these reviews is essential for guiding app development, prioritizing feature updates, and enhancing user satisfaction. Classifying reviews into functional and non-functional requirements play a pivotal role in distinguishing feedback related to specific app features (functional requirements) from feedback concerning broader quality attributes, such as performance, usability, and reliability (non-functional requirements). Both categories are integral to informed development decisions. Traditional approaches to classifying app reviews are hindered by the need for large, domain-specific datasets, which are often costly and time-consuming to curate. This study explores the potential of zero-shot learning with ChatGPT for classifying app reviews into four categories: functional requirement, non-functional requirement, both, or neither. We evaluate ChatGPT's performance on a benchmark dataset of 1,880 manually annotated reviews from ten diverse apps spanning multiple domains. Our findings demonstrate that ChatGPT achieves a robust F1 score of 0.842 in review classification, despite certain challenges and limitations. Additionally, we examine how factors such as review readability and length impact classification accuracy and conduct a manual analysis to identify review categories more prone to misclassification.
QBD-RankedDataGen: Generating Custom Ranked Datasets for Improving Query-By-Document Search Using LLM-Reranking with Reduced Human Effort
2025-05-07
Sriram Gopalakrishnan, Sunandita Patra
The Query-By-Document (QBD) problem is an information retrieval problem where the query is a document, and the retrieved candidates are documents that match the query document, often in a domain or query specific manner. This can be crucial for tasks such as patent matching, legal or compliance case retrieval, and academic literature review. Existing retrieval methods, including keyword search and document embeddings, can be optimized with domain-specific datasets to improve QBD search performance. However, creating these domain-specific datasets is often costly and time-consuming. Our work introduces a process to generate custom QBD-search datasets and compares a set of methods to use in this problem, which we refer to as QBD-RankedDatagen. We provide a comparative analysis of our proposed methods in terms of cost, speed, and the human interface with the domain experts. The methods we compare leverage Large Language Models (LLMs) which can incorporate domain expert input to produce document scores and rankings, as well as explanations for human review. The process and methods for it that we present can significantly reduce human effort in dataset creation for custom domains while still obtaining sufficient expert knowledge for tuning retrieval models. We evaluate our methods on QBD datasets from the Text Retrieval Conference (TREC) and finetune the parameters of the BM25 model -- which is used in many industrial-strength search engines like OpenSearch -- using the generated data.
2025-05-07
Alexander Oestreicher, Chris Clarkson, Julian Adamek, Sofie Marie Koksbang
The cosmological redshift drift promises to be the first observable directly measuring the cosmic expansion rate and should be detectable with upcoming surveys by the Square Kilometre Array and the Extremely Large Telescope. To prepare for these upcoming measurements we study the redshift drift in detail using the relativistic N-body code $\texttt{gevolution}$ focusing on inhomogeneity-induced fluctuations. Using a ray-tracer, we calculate the redshift drift directly from the light cone at two different time steps. To investigate observer-dependent biases we consider 10 different observers. We find that inhomogeneity-induced fluctuations in the redshift drift can in extreme cases be of the same order as the cosmic signal for $z\lesssim0.15$. By comparing our results to first-order perturbation theory, we find that the extreme outliers are due to peculiar motion in over-densities and can be described by first-order perturbation theory to percent precision. We calculate angular power spectra that fit very well with our theoretical predictions based on perturbation theory at linear scales and show a surprisingly large non-linear signal. This shows that redshift drift not only has the power to measure the background expansion, but could also deliver information about the velocity and acceleration fields in clusters.
Dehybridization transition in Kondo insulators and heavy fermions
2025-05-07
Mucio A. Continentino
In strongly correlated multi-band systems, like inter-metallics, heavy fermions or Kondo insulators, electron-electron and electron-phonon scattering of the electrons in the bands give rise, at finite temperatures, to a damping of these quasi-particles. This is responsible for producing an effective dehybridization between the electrons in the large conduction bands and those in the narrow, correlated band. This dehybridization effect has been used to explain the transport properties of inter-metallics and ARPES experiments in heavy fermions at sufficiently high temperatures. A new insight into this problem has been recently proposed using the theory of non-Hermitian systems. In this note, we review previous work on dehybridization in Kondo insulators and strongly correlated metals within this new perspective. For this purpose, we use a parametrization of the self-energy of the strongly correlated electrons obtained from LDA+DMFT calculations. We discuss the nature of the dehybridization transition and its consequences in the electronic spectrum and transport properties.
Data Standards in Audiology: A Mixed-Methods Exploration of Community Perspectives and Implementation Considerations
2025-05-07
Charlotte Vercammen, Antje Heinrich, Christophe Lesimple, Alessia Paglialonga, Jan-Willem A. Wasmann, Mareike Buhl
Objective: The purpose of this study was to explore options for data standardisation in audiology and document the global audiology community's current knowledge and views of data standards, explore their needs and preferences, and develop recommendations for data standardisation as a result. Design: A mixed-methods approach, combining a structured survey with an in-depth exploration of themes by experts during a special session on "Big Data and Data Standards in Audiology" at the 2024 Virtual Conference of Computational Audiology. Study Sample: The survey sample consisted of 82 members of the global audiology community; five experts joined the panel discussion. Results: Survey results emphasized the need for data standardisation in audiology aimed at facilitating research and improving patient care. Knowledge of existing initiatives was low: 38% were aware of initiatives. Yet, 90% envisioned contributing to them moving forward. The panel discussion explored emerging standardisation initiatives in audiology (OMOP, openEHR, HIMSA's Noah standard), challenges (e.g., data quality and privacy), and opportunities (e.g., conversion between approaches and synergies with other medical fields). Conclusions: The community support identified in this study could be leveraged to further develop standardisation initiatives for audiology, ensuring alignment between initiatives and with other medical fields.
Fitts' List Revisited: An Empirical Study on Function Allocation in a Two-Agent Physical Human-Robot Collaborative Position/Force Task
2025-05-07
Nicky Mol, J. Micah Prendergast, David A. Abbink, Luka Peternel
In this letter, we investigate whether the classical function allocation holds for physical Human-Robot Collaboration, which is important for providing insights for Industry 5.0 to guide how to best augment rather than replace workers. This study empirically tests the applicability of Fitts' List within physical Human-Robot Collaboration, by conducting a user study (N=26, within-subject design) to evaluate four distinct allocations of position/force control between human and robot in an abstract blending task. We hypothesize that the function in which humans control the position achieves better performance and receives higher user ratings. When allocating position control to the human and force control to the robot, compared to the opposite case, we observed a significant improvement in preventing overblending. This was also perceived better in terms of physical demand and overall system acceptance, while participants experienced greater autonomy, more engagement and less frustration. An interesting insight was that the supervisory role (when the robot controls both position and force control) was rated second best in terms of subjective acceptance. Another surprising insight was that if position control was delegated to the robot, the participants perceived much lower autonomy than when the force control was delegated to the robot. These findings empirically support applying Fitts' principles to static function allocation for physical collaboration, while also revealing important nuanced user experience trade-offs, particularly regarding perceived autonomy when delegating position control.
Star formation and accretion rates within 500 pc as traced by Gaia DR3 XP spectra
2025-05-07
L. Delfini, M. Vioque, Á. Ribas, S. Hodgkin
Accretion rates from protoplanetary disks onto forming stars are a key ingredient in star formation and protoplanetary disk evolution. Extensive efforts surveying individual star forming regions with spectroscopy and narrow-band photometry have been performed to derive accretion rates on large populations of young stellar objects (YSOs). We use Gaia DR3 XP spectra to perform the first all-sky homogeneous analysis of YSO accretion within 500 pc. We characterise the H$\alpha$ line emission of YSOs by using the H$\alpha$ pseudo-equivalent widths and XP spectra from Gaia DR3. We derive accretion luminosities, mass accretion rates and stellar parameters for 145 975 candidate YSO H$\alpha$ emitters all-sky. We describe filtering strategies to select specific sub-samples of YSOs from this catalogue. We identify a large population of low-accreting YSO candidates untraced by previous surveys. The population of low accreting YSOs is mostly spatially dispersed, away from star forming regions or more clustered environments of star formation. Many YSOs appear disconnected from young populations, reminiscent of 'Peter Pan' YSOs. We find $L_{acc}\propto L_\star^{1.41\pm0.02}$ and $\dot M_{acc}\propto M_\star^{2.4\pm0.1}$ for the purest all-sky sample of YSO candidates. By fitting an exponential to the fraction of accreting stars in clusters of different ages in the Sco-Cen complex, we obtain an accretion timescale of 2.7$\pm$0.4 Myr. The percentage of accretors found by fitting a power-law is 70% at 2 Myr and 2.8% at 10 Myr. With this new catalogue of H$\alpha$ emitters we significantly increase the number of YSO candidates with accretion rate estimations in the local neighbourhood. This allows us to study accretion timescales and the spatial and physical properties of YSO accretion from a large, all-sky, and homogeneous sample for the first time. [abridged]
Cluster Lensing Mass Inversion (CLUMI+): Combining Dynamics and Weak Lensing around Galaxy Clusters
2025-05-07
Keiichi Umetsu, Michele Pizzardo, Antonaldo Diaferio, Margaret J. Geller
We present CLUMI+, a self-consistent, multi-probe methodology for reconstructing the mass distribution in and around galaxy clusters by combining gravitational lensing and dynamical observations. Building on the joint-likelihood framework of Umetsu (2013), CLUMI+ integrates weak-lensing shear and magnification data with projected escape velocity measurements in the cluster infall region, yielding tighter constraints on the gravitational potential without relying on equilibrium assumptions. The mass distribution is modeled using a flexible, piecewise-defined convergence profile that characterizes the azimuthally averaged surface mass density within the lensing field, transitioning to a projected power-law form at larger radii where phase-space constraints complement lensing. Additional strong-lensing constraints are incorporated via central aperture-mass measurements, enabling full-scale mass reconstruction from the cluster core to the outskirts. We validate CLUMI+ using synthetic weak-lensing and phase-space data for a massive cluster from the IllustrisTNG simulations, demonstrating unbiased recovery of projected and three-dimensional mass profiles and achieving 10%--30% improvement in precision at large radii. As a case study, we apply CLUMI+ to Abell 2261, combining Subaru and Hubble Space Telescope weak+strong lensing data with spectroscopic measurements from the Hectospec Cluster Survey. This analysis demonstrates the power of multi-probe, equilibrium-free modeling for robust cluster mass reconstruction.
Euclid preparation. The impact of redshift interlopers on the two-point correlation function analysis
2025-05-07
Euclid Collaboration, I. Risso, A. Veropalumbo, E. Branchini, E. Maragliano, S. de la Torre, E. Sarpa, P. Monaco, B. R. Granett, S. Lee, G. E. Addison, S. Bruton, C. Carbone, G. Lavaux, K. Markovic, K. McCarthy, G. Parimbelli, F. Passalacqua, W. J. Percival, C. Scarlata, E. Sefusatti, Y. Wang, M. Bonici, F. Oppizzi, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, A. Biviano, A. Bonchi, D. Bonino, M. Brescia, J. Brinchmann, S. Camera, G. Cañas-Herrera, V. Capobianco, V. F. Cardone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Crocce, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, H. Dole, M. Douspis, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, S. Escoffier, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, S. Fotopoulou, N. Fourmanoit, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, W. Gillard, B. Gillis, C. Giocoli, J. Gracia-Carpio, A. Grazian, F. Grupp, L. Guzzo, S. V. H. Haugan, W. Holmes, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, M. Kilbinger, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, P. Liebing, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, S. Maurogordato, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, J. A. Schewtschenko, P. Schneider, T. Schrabback, M. Scodeggio, A. Secroun, G. Seidel, M. Seiffert, S. Serrano, P. Simon, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, C. Surace, P. Tallada-Crespí, D. Tavagnacco, A. N. Taylor, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, D. Vibert, J. Weller, G. Zamorani, F. M. Zerbi, E. Zucca, V. Allevato, M. Ballardini, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, A. Cappi, D. Di Ferdinando, J. A. Escartin Vigo, L. Gabarra, W. G. Hartley, J. Martín-Fleitas, S. Matthew, N. Mauri, R. B. Metcalf, A. Pezzotta, M. Pöntinen, C. Porciani, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, S. Alvi, I. T. Andika, M. Archidiacono, F. Atrio-Barandela, S. Avila, A. Balaguera-Antolinez, C. Benoist, D. Bertacca, M. Bethermin, L. Blot, H. Böhringer, S. Borgani, M. L. Brown, A. Calabro, B. Camacho Quevedo, F. Caro, C. S. Carvalho, T. Castro, F. Cogato, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, J. M. Diego, P. Dimauro, A. Enia, Y. Fang, A. G. Ferrari, A. Finoguenov, A. Fontana, A. Franco, K. Ganga, J. García-Bellido, T. Gasparetto, V. Gautard, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, M. Guidi, C. M. Gutierrez, A. Hall, S. Hemmati, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, S. Joudaki, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, S. Kruk, V. Le Brun, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. Leroy, G. F. Lesci, L. Leuzzi, T. I. Liaudat, A. Loureiro, J. Macias-Perez, M. Magliocchetti, F. Mannucci, R. Maoli, C. J. A. P. Martins, L. Maurin, M. Miluzio, C. Moretti, G. Morgante, S. Nadathur, K. Naidoo, A. Navarro-Alsina, K. Paterson, L. Patrizii, A. Pisani, D. Potter, S. Quai, M. Radovich, P. -F. Rocci, S. Sacquegna, M. Sahlén, D. B. Sanders, A. Schneider, D. Sciotti, E. Sellentin, L. C. Smith, J. G. Sorce, K. Tanidis, C. Tao, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, G. Verza, N. A. Walton
The Euclid survey aims to measure the spectroscopic redshift of emission-line galaxies by identifying the H$\,{\alpha}$ line in their slitless spectra. This method is sensitive to the signal-to-noise ratio of the line, as noise fluctuations or other strong emission lines can be misidentified as H$\,{\alpha}$, depending on redshift. These effects lead to catastrophic redshift errors and the inclusion of interlopers in the sample. We forecast the impact of such redshift errors on galaxy clustering measurements. In particular, we study the effect of interloper contamination on the two-point correlation function (2PCF), the growth rate of structures, and the Alcock-Paczynski (AP) parameters. We analyze 1000 synthetic spectroscopic catalogues, the EuclidLargeMocks, designed to match the area and selection function of the Data Release 1 (DR1) sample. We estimate the 2PCF of the contaminated catalogues, isolating contributions from correctly identified galaxies and from interlopers. We explore different models with increasing complexity to describe the measured 2PCF at fixed cosmology. Finally, we perform a cosmological inference and evaluate the systematic error on the inferred $f\sigma_8$, $\alpha_{\parallel}$ and $\alpha_{\perp}$ values associated with different models. Our results demonstrate that a minimal modelling approach, which only accounts for an attenuation of the clustering signal regardless of the type of contaminants, is sufficient to recover the correct values of $f\sigma_8$, $\alpha_{\parallel}$, and $\alpha_{\perp}$ at DR1. The accuracy and precision of the estimated AP parameters are largely insensitive to the presence of interlopers. The adoption of a minimal model induces a 1%-3% systematic error on the growth rate of structure estimation, depending on the redshift. However, this error remains smaller than the statistical error expected for the Euclid DR1 analysis.
A review of applications of Quantum Energy Teleportation: from experimental tests to thermodynamics and spacetime engineering
2025-05-07
Boris Ragula, Eduardo Martín-Martínez
Quantum energy teleportation (QET) exploits the existence of correlations to enable remote energy transfer without the need for physical energy carriers between emitter and receiver. This paper presents a review of the thermodynamic foundations of QET and reviews its first experimental demonstration (performed using Nuclear Magnetic Resonance), along with its implementation on publicly available superconducting quantum hardware. Additionally, we review an application of QET in the field of quantum thermodynamics as an efficient algorithmic cooling technique to cool down individual parts of interacting systems. Finally, we will review how QET can be employed to optimally generate exotic quantum states characterized by negative average stress-energy densities, offering a new operational approach to engineering such states which are promising in the context of semiclassical gravity.
Parity anomaly from LSM: exact valley symmetries on the lattice
2025-05-07
Salvatore D. Pace, Minho Luke Kim, Arkya Chatterjee, Shu-Heng Shao
We show that the honeycomb tight-binding model hosts an exact microscopic avatar of its low-energy SU(2) valley symmetry and parity anomaly. Specifically, the SU(2) valley symmetry arises from a collection of conserved, integer-quantized charge operators that obey the Onsager algebra. Along with lattice reflection and time-reversal symmetries, this Onsager symmetry has a Lieb-Schultz-Mattis (LSM) anomaly that matches the parity anomaly in the IR. Indeed, we show that any local Hamiltonian commuting with these symmetries cannot have a trivial unique gapped ground state. We study the phase diagram of the simplest symmetric model and survey various deformations, including Haldane's mass term, which preserves only the Onsager symmetry. Our results place the parity anomaly in 2+1D alongside Schwinger's anomaly in 1+1D and Witten's SU(2) anomaly in 3+1D as 't Hooft anomalies that can arise from the Onsager symmetry on the lattice.
Proceedings The 13th International Workshop on Theorem proving components for Educational software
2025-05-07
Julien Narboux, Walther Neuper, Pedro Quaresma
The ThEdu series pursues the smooth transition from an intuitive way of doing mathematics at secondary school to a more formal approach to the subject in STEM education while favoring software support for this transition by exploiting the power of theorem-proving technologies. What follows is a brief description of how the present volume contributes to this enterprise. The 13th International Workshop on Theorem Proving Components for Educational Software (ThEdu'24), was a satellite event of the CADE29, part of IJCAR 2024, Nancy, France. ThEdu'24 was a vibrant workshop, with one invited talk by Jeremy Avigad (Carnegie Mellon University) and 14 submitted talks. An open call for papers was then issued and attracted 9 submissions. Eight of those submissions have been accepted by our reviewers. The resulting revised papers are collected in the present volume. The contributions in this volume are a faithful representation of the wide spectrum of ThEdu, ranging from those more focused on the automated deduction research, not losing track of the possible applications in an educational setting, to those focused on the applications, in educational settings, of automated deduction tools and methods. We, the volume editors, hope that this collection of papers will further promote the development of theorem-proving-based software and that it will allow to improve the mutual understanding between computer scientists, mathematicians, and stakeholders in education. While this volume goes to press, the next edition of the ThEdu workshop is being prepared: ThEdu'25 will be a satellite event of the 30th international Conference on Automated DEduction (CADE-30), July 28th - August 2nd, 2025, Stuttgart, Germany.
Machine Learning Workflow for Morphological Classification of Galaxies
2025-05-07
Bernd Doser, Kai L. Polsterer, Andreas Fehlner, Sebastian Trujillo-Gomez
As part of the EU-funded Center of Excellence SPACE (Scalable Parallel Astrophysical Codes for Exascale), seven commonly used astrophysics simulation codes are being optimized to exploit exascale computing platforms. Exascale cosmological simulations will produce large amounts of data (i.e. several petabytes) that will soon be waiting to be analyzed, with enormous potential for scientific breakthroughs. Our tool Spherinator enables the reduction of these complex data sets to a low-dimensional space using Generative Deep Learning to understand the morphological structure of simulated galaxies. A spherical latent space allows the HiPSter module to provide explorative visualization using Hierarchical Progressive Surveys (HiPS) in the Aladin software. Here we present a machine-learning workflow covering all stages, from data collection to preprocessing, training, prediction, and final deployment. This workflow ensures full reproducibility by keeping track of the code, data, and environment. Additionally, the workflow allows for scalability in managing a large amount of data and complex pipelines. We use only open source software and standards that align with the FAIR (Findability, Accessibility, Interoperability and Reproducibility) principles. In this way, we are able to distribute the workflow reliably and enable collaboration by sharing code, data, and results efficiently.
With the rise of AI-based code generation, customizing existing code out of natural language instructions to modify visual results -such as figures or images -has become possible, promising to reduce the need for deep programming expertise. However, even experienced developers can struggle with this task, as it requires identifying relevant code regions (feature location), generating valid code variants, and ensuring the modifications reliably align with user intent. In this paper, we introduce vTikZ, the first benchmark designed to evaluate the ability of Large Language Models (LLMs) to customize code while preserving coherent visual outcomes. Our benchmark consists of carefully curated vTikZ editing scenarios, parameterized ground truths, and a reviewing tool that leverages visual feedback to assess correctness. Empirical evaluation with stateof-the-art LLMs shows that existing solutions struggle to reliably modify code in alignment with visual intent, highlighting a gap in current AI-assisted code editing approaches. We argue that vTikZ opens new research directions for integrating LLMs with visual feedback mechanisms to improve code customization tasks in various domains beyond TikZ, including image processing, art creation, Web design, and 3D modeling.
Uranus Flagship Science-Driven Tour Design: Community Input Poll
2025-05-07
Amy Simon, Ian Cohen, Matthew Hedman, Mark Hofstadter, Kathleen Mandt, Francis Nimmo
The highest priority Planetary Flagship from the Planetary Science and Astrobiology Decadal Survey is the Uranus Orbiter and Probe. This mission enables broad, cross-disciplinary science of an ice giant system, including the planet, moons, rings, and magnetosphere. The ice giants represent a size class of planet that is largely unexplored but that is common among extrasolar planet systems. At the same time, Uranus is unique, possessing a regular satellite system, rings, and a complex magnetosphere surrounding a planet tilted over for extreme seasonal variation. Exploration of the Uranian system is guided by a broad suite of science Objectives and through remote sensing, in situ probe, and fields and particles experiments. Due to the rapid nature of the Decadal concept study, the orbital tour could not be perfectly optimized, but a best effort was made to include orbit insertion, probe release and relay, and multiple satellite flybys; some Objectives were necessarily better met than others by this tour. The mission's tour trajectory can be optimized to meet any of the Decadal Science Objectives, by focusing on a particular system target, for example magnetosphere, satellites, or planet interior, but the best tour will simultaneously meet the highest priority measurements for every Objective. The community-led (with no institutional or NASA sponsorship) poll described here was designed to collect broad science inputs on the tour parameters required for each Decadal Science Objective. Here we present results aggregated by Objective, as well as compared longitudinally, of the highest priority tour needs identified across all Objectives. Both the raw and aggregated data are made publicly available for future white papers, tour and trajectory work, as well as mission and instrument design teams, but the results are not binding nor decisional in any way.
Global Agricultural Competitiveness Index (Gaci) in the Context of Climate Change: A Holistic Approach
2025-05-07
Bakhtmina Zia
The impacts of climate change, conflicts, the spread of infectious diseases, and global economic downturns have greatly affected food production, disrupted supply chains, and hindered access to affordable, nutritious food. It poses risks to both local and global food security, in addition to agricultural market competitiveness. Given the increasing concerns about climate change and its implications for global agriculture and food security, evaluating agricultural competitiveness via a composite scale to measure the effects of climate change would be beneficial. This study examined a global agricultural competitiveness index (GACI) framework developed through a systematic review and an expert survey. The results show that most countries experienced a decline in their competitiveness scores with agricultural assessment in the context of the impact of climate change. This framework can serve as a global benchmark for assessing and comparing national and international standing. Furthermore, it can help policy development aimed at promoting sustainable and inclusive agriculture, ultimately contributing to improved global food security. Keywords: Agriculture, Competitiveness, Pillars, Global Agricultural Competitiveness, Index, Climate Change
2025-05-07
Xingyu Jiang, Ning Gao, Xiuhui Zhang, Hongkun Dou, Shaowen Fu, Xiaoqing Zhong, Hongjue Li, Yue Deng
Due to adverse atmospheric and imaging conditions, natural images suffer from various degradation phenomena. Consequently, image restoration has emerged as a key solution and garnered substantial attention. Although recent Transformer architectures have demonstrated impressive success across various restoration tasks, their considerable model complexity poses significant challenges for both training and real-time deployment. Furthermore, instead of investigating the commonalities among different degradations, most existing restoration methods focus on modifying Transformer under limited restoration priors. In this work, we first review various degradation phenomena under multi-domain perspective, identifying common priors. Then, we introduce a novel restoration framework, which integrates multi-domain learning into Transformer. Specifically, in Token Mixer, we propose a Spatial-Wavelet-Fourier multi-domain structure that facilitates local-region-global multi-receptive field modeling to replace vanilla self-attention. Additionally, in Feed-Forward Network, we incorporate multi-scale learning to fuse multi-domain features at different resolutions. Comprehensive experimental results across ten restoration tasks, such as dehazing, desnowing, motion deblurring, defocus deblurring, rain streak/raindrop removal, cloud removal, shadow removal, underwater enhancement and low-light enhancement, demonstrate that our proposed model outperforms state-of-the-art methods and achieves a favorable trade-off among restoration performance, parameter size, computational cost and inference latency. The code is available at: https://github.com/deng-ai-lab/SWFormer.
Meta-Optimization and Program Search using Language Models for Task and Motion Planning
2025-05-06
Denis Shcherba, Eckart Cobo-Briesewitz, Cornelius V. Braun, Marc Toussaint
Intelligent interaction with the real world requires robotic agents to jointly reason over high-level plans and low-level controls. Task and motion planning (TAMP) addresses this by combining symbolic planning and continuous trajectory generation. Recently, foundation model approaches to TAMP have presented impressive results, including fast planning times and the execution of natural language instructions. Yet, the optimal interface between high-level planning and low-level motion generation remains an open question: prior approaches are limited by either too much abstraction (e.g., chaining simplified skill primitives) or a lack thereof (e.g., direct joint angle prediction). Our method introduces a novel technique employing a form of meta-optimization to address these issues by: (i) using program search over trajectory optimization problems as an interface between a foundation model and robot control, and (ii) leveraging a zero-order method to optimize numerical parameters in the foundation model output. Results on challenging object manipulation and drawing tasks confirm that our proposed method improves over prior TAMP approaches.
Investigating the Upper Scorpius OB association with HERMES. I. The spectroscopic sample and 6D kinematics
2025-05-06
Joseph J. Armstrong, Jonathan C. Tan, Nicholas J. Wright, R. D. Jeffries, Janez Kos, E. Fiorellino, Sven Buder, D. Barrios Lopez
We present results from a large spectroscopic survey of the nearest young association to the Sun, Upper Scorpius, conducted using 2dF/HERMES on the Anglo-Australian Telescope. We use spectroscopic youth criteria such as Li-equivalent widths to identify >1000 pre-main sequence (PMS) members across the region and measure radial velocities, combining these with Gaia EDR3 5-parameter astrometry to obtain 6D kinematic information. We separate confirmed PMS association members into distinct kinematic groups and measure expansion and rotation trends in each. We find strong evidence for asymmetric expansion in several groups and derive expansion timescales from the greatest rates of expansion in each group. We also trace the past motion of these groups using an epicycle approximation and estimate the time since their most compact configuration. These kinematic properties are compared to literature ages and the star formation history of Upper Scorpius is discussed. We find evidence that a scenario in which star formation in the subgroups of Upper Scorpius proceeded independently, either by self-instability or external feedback from Upper Centaurus-Lupus, is more likely than a recently proposed "cluster chain" scenario in which these subgroups have triggered each other.
A review of DNA restriction-free overlapping sequence cloning techniques for synthetic biology
2025-05-06
Isabella Frighetto Bomfiglio, Isabelli Seiler de Medeiros Mendes, Diego Bonatto
DNA cloning methods are fundamental tools in molecular biology, synthetic biology, and genetic engineering that enable precise DNA manipulation for various scientific and biotechnological applications. This review systematically summarizes the major restriction-free overlapping sequence cloning (RFOSC) techniques currently used in synthetic biology and examines their development, efficiency, practicality, and specific applications. In vitro methods, including Gibson Assembly, Circular Polymerase Extension Cloning (CPEC), Polymerase Incomplete Primer Extension (PIPE), Overlap Extension Cloning (OEC), Flap Endonuclease Cloning (FEN-Cloning), and commercially available techniques such as TOPO and In-Fusion, have been discussed alongside hybrid approaches such as Ligation-Independent Cloning (LIC), Sequence-Independent Cloning (SLIC), and T5 Exonuclease-Dependent Assembly (TEDA). Additionally, in vivo methods leveraging host recombination machinery, including Yeast Homologous Recombination (YHR), In Vivo Assembly (IVA), Transformation-Associated Recombination (TAR), and innovative approaches such as Multiple-Round In Vivo Site-Specific Assembly (MISSA) and Phage Enzyme-Assisted Direct Assembly (PEDA), are critically evaluated. The review highlights that method selection should consider the scale, complexity, cost, and specific needs of individual research projects, noting that no single technique is universally optimal. Future trends suggest the increased integration of enzymatic efficiency, host versatility, and automation, broadening the accessibility and capabilities of DNA assembly technologies.
Counterfactual Inference for Eliminating Sentiment Bias in Recommender Systems
2025-05-06
Le Pan, Yuanjiang Cao, Chengkai Huang, Wenjie Zhang, Lina Yao
Recommender Systems (RSs) aim to provide personalized recommendations for users. A newly discovered bias, known as sentiment bias, uncovers a common phenomenon within Review-based RSs (RRSs): the recommendation accuracy of users or items with negative reviews deteriorates compared with users or items with positive reviews. Critical users and niche items are disadvantaged by such unfair recommendations. We study this problem from the perspective of counterfactual inference with two stages. At the model training stage, we build a causal graph and model how sentiment influences the final rating score. During the inference stage, we decouple the direct and indirect effects to mitigate the impact of sentiment bias and remove the indirect effect using counterfactual inference. We have conducted extensive experiments, and the results validate that our model can achieve comparable performance on rating prediction for better recommendations and effective mitigation of sentiment bias. To the best of our knowledge, this is the first work to employ counterfactual inference on sentiment bias mitigation in RSs.
Discovery of resonating integration modes in triple-mode high-amplitude $δ$ Scuti stars: a new evolutionary phase indicator
2025-05-06
Jia-Shu Niu, Hui-Fang Xue
High-amplitude $\delta$ Scuti stars (HADS) that pulsate in their first three radial modes are rare in current samples. Here, we analyse five such triple-mode HADS observed by the Transiting Exoplanet Survey Satellite (TESS) and report that the previously considered second overtone mode ($f_2$) is actually the non-radial component of a resonating integration mode (RI mode, resulting from resonance between a radial p-mode and a non-radial p-g mixed mode), which shows significant amplitude and frequency variations over short timescales (approximately 20 days). This RI mode appears to be widespread among these stars. Notably, all five stars are in the post-main sequence evolutionary phase, actively crossing the Hertzsprung gap. These stars offer valuable insights into stellar evolution during the Hertzsprung gap, which is one of the most rapid evolutionary stages in a star's life.
The role of friction forces in arterial mechanical thrombectomy: a review
2025-05-06
Mahesh S. Nagargoje, Virginia Fregona, Giulia Luraghi, Francesco Migliavacca, Guglielmo Pero, Jose Felix Rodriguez Matas
Multiple clinical trials have demonstrated the superiority of mechanical thrombectomy (MT) in treating acute ischemic stroke (AIS). Stent retriever (SR) and aspiration techniques are the standard methods for removing occluded emboli, with evolving technologies improving MT efficiency. However, procedural success remains uncertain. Frictional forces, specifically clot-vessel, clot-SR, and SR-vessel interactions, play a critical role in MT outcomes. This review examines frictional forces during MT and their impact on success, analyzing publications from 2015 to 2025. We focus on studies that calculated friction or retrieval forces using in vitro models. We have also included current trends, limitations, and future perspectives on studying and understanding frictional forces and their implementation into in silico models. Findings indicate that fibrin-rich clots are more difficult to retrieve than red blood cell (RBC)-rich clots due to their higher friction coefficient, three to four times greater, an observation supported by multiple studies. SR-vessel and SR-clot friction also influence MT effectiveness. SR-vessel interaction plays a crucial role in acutely curved vessels, as SR compression reduces its efficiency. In SR-clot interaction, RBC-rich clot fragmentation is linked to relative interaction forces. In summary, obtaining in vivo frictional values remains challenging, and inconsistencies persist in past in vitro studies. Further, a deeper understanding of frictional forces is essential for optimizing MT, improving current SRs, and developing next-generation thrombectomy technologies.
High-redshift Millennium and Astrid galaxies in effective field theory at the field level
2025-05-06
James M. Sullivan, Carolina Cuesta-Lazaro, Mikhail M. Ivanov, Yueying Ni, Sownak Bose, Boryana Hadzhiyska, César Hernández-Aguayo, Lars Hernquist, Rahul Kannan
Effective Field Theory (EFT) modeling is expected to be a useful tool in the era of future higher-redshift galaxy surveys such as DESI-II and Spec-S5 due to its robust description of various large-scale structure tracers. However, large values of EFT bias parameters of higher-redshift galaxies could jeopardize the convergence of the perturbative expansion. In this paper we measure the bias parameters and other EFT coefficients from samples of two types of star-forming galaxies in the state-of-the-art MilleniumTNG and Astrid hydrodynamical simulations. Our measurements are based on the field-level EFT forward model that allows for precision EFT parameter measurements by virtue of cosmic variance cancellation. Specifically, we consider approximately representative samples of Lyman-break galaxies (LBGs) and Lyman-alpha emitters (LAEs) that are consistent with the observed (angular) clustering and number density of these galaxies at $z=3$. Reproducing the linear biases and number densities observed from existing LAE and LBG data, we find quadratic bias parameters that are roughly consistent with those predicted from the halo model coupled with a simple halo occupation distribution model. We also find non-perturbative velocity contributions (Fingers of God) of a similar size for LBGs to the familiar case of Luminous Red Galaxies. However, these contributions are quite small for LAEs despite their large satellite fraction values of up to $\sim 30\%$. Our results indicate that the effective momentum reach $k_{\rm{Max}}$ at $z=3$ for LAEs (LBGs) will be in the range $0.3-0.6 ~h\rm{Mpc}^{-1}$ ($0.2-0.8~h\rm{Mpc}^{-1}$), suggesting that EFT will perform well for high redshift galaxy clustering. This work provides the first step toward obtaining realistic simulation-based priors on EFT parameters for LAEs and LBGs.
Self-limited tidal heating and prolonged magma oceans in the L 98-59 system
2025-05-06
Harrison Nicholls, Claire Marie Guimond, Hamish C. F. C. Hay, Richard D. Chatterjee, Tim Lichtenberg, Raymond T. Pierrehumbert
Rocky exoplanets accessible to characterisation often lie on close-in orbits where tidal heating within their interiors is significant, with the L 98-59 planetary system being a prime example. As a long-term energy source for ongoing mantle melting and outgassing, tidal heating has been considered as a way to replenish lost atmospheres on rocky planets around active M-dwarfs. We simulate the early evolution of L 98-59 b, c and d using a time-evolved interior-atmosphere modelling framework, with a self-consistent implementation of tidal heating and redox-controlled outgassing. Emerging from our calculations is a novel self-limiting mechanism between radiative cooling, tidal heating, and mantle rheology, which we term the 'radiation-tide-rheology feedback'. Our coupled modelling yields self-limiting tidal heating estimates that are up to two orders of magnitude lower than previous calculations, and yet are still large enough to enable the extension of primordial magma oceans to Gyr timescales. Comparisons with a semi-analytic model demonstrate that this negative feedback is a robust mechanism which can probe a given planet's initial conditions, atmospheric composition, and interior structure. The orbit and instellation of the sub-Venus L 98-59 b likely place it in a regime where tidal heating has kept the planet molten up to the present day, even if it were to have lost its atmosphere. For c and d, a long-lived magma ocean can be induced by tides only with additional atmospheric regulation of energy transport.
Doing Audits Right? The Role of Sampling and Legal Content Analysis in Systemic Risk Assessments and Independent Audits in the Digital Services Act
2025-05-06
Marie-Therese Sekwenz, Rita Gsenger, Scott Dahlgren, Ben Wagner
A central requirement of the European Union's Digital Services Act (DSA) is that online platforms undergo internal and external audits. A key component of these audits is the assessment of systemic risks, including the dissemination of illegal content, threats to fundamental rights, impacts on democratic processes, and gender-based violence. The DSA Delegated Regulation outlines how such audits should be conducted, setting expectations for both platforms and auditors. This article evaluates the strengths and limitations of different qualitative and quantitative methods for auditing these systemic risks and proposes a mixed-method approach for DSA compliance. We argue that content sampling, combined with legal and empirical analysis, offers a viable method for risk-specific audits. First, we examine relevant legal provisions on sample selection for audit purposes. We then assess sampling techniques and methods suitable for detecting systemic risks, focusing on how representativeness can be understood across disciplines. Finally, we review initial systemic risk assessment reports submitted by platforms, analyzing their testing and sampling methodologies. By proposing a structured, mixed-method approach tailored to specific risk categories and platform characteristics, this article addresses the challenge of evidence-based audits under the DSA. Our contribution emphasizes the need for adaptable, context-sensitive auditing strategies and adds to the emerging field of DSA compliance research.
From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstruction
2025-05-06
Fengming Lin, Arezoo Zakeri, Yidan Xue, Michael MacRaild, Haoran Dou, Zherui Zhou, Ziwei Zou, Ali Sarrami-Foroushani, Jinming Duan, Alejandro F. Frangi
Deep learning-based medical image-to-mesh reconstruction has rapidly evolved, enabling the transformation of medical imaging data into three-dimensional mesh models that are critical in computational medicine and in silico trials for advancing our understanding of disease mechanisms, and diagnostic and therapeutic techniques in modern medicine. This survey systematically categorizes existing approaches into four main categories: template models, statistical models, generative models, and implicit models. Each category is analysed in detail, examining their methodological foundations, strengths, limitations, and applicability to different anatomical structures and imaging modalities. We provide an extensive evaluation of these methods across various anatomical applications, from cardiac imaging to neurological studies, supported by quantitative comparisons using standard metrics. Additionally, we compile and analyze major public datasets available for medical mesh reconstruction tasks and discuss commonly used evaluation metrics and loss functions. The survey identifies current challenges in the field, including requirements for topological correctness, geometric accuracy, and multi-modality integration. Finally, we present promising future research directions in this domain. This systematic review aims to serve as a comprehensive reference for researchers and practitioners in medical image analysis and computational medicine.
Fixed-length fingerprint representations, which map each fingerprint to a compact and fixed-size feature vector, are computationally efficient and well-suited for large-scale matching. However, designing a robust representation that effectively handles diverse fingerprint modalities, pose variations, and noise interference remains a significant challenge. In this work, we propose a fixed-length dense descriptor of fingerprints, and introduce FLARE-a fingerprint matching framework that integrates the Fixed-Length dense descriptor with pose-based Alignment and Robust Enhancement. This fixed-length representation employs a three-dimensional dense descriptor to effectively capture spatial relationships among fingerprint ridge structures, enabling robust and locally discriminative representations. To ensure consistency within this dense feature space, FLARE incorporates pose-based alignment using complementary estimation methods, along with dual enhancement strategies that refine ridge clarity while preserving the original fingerprint modality. The proposed dense descriptor supports fixed-length representation while maintaining spatial correspondence, enabling fast and accurate similarity computation. Extensive experiments demonstrate that FLARE achieves superior performance across rolled, plain, latent, and contactless fingerprints, significantly outperforming existing methods in cross-modality and low-quality scenarios. Further analysis validates the effectiveness of the dense descriptor design, as well as the impact of alignment and enhancement modules on the accuracy of dense descriptor matching. Experimental results highlight the effectiveness and generalizability of FLARE as a unified and scalable solution for robust fingerprint representation and matching. The implementation and code will be publicly available at https://github.com/Yu-Yy/FLARE.
Familiarizing with Music: Discovery Patterns for Different Music Discovery Needs
2025-05-06
Marta Moscati, Darius Afchar, Markus Schedl, Bruno Sguerra
Humans have the tendency to discover and explore. This natural tendency is reflected in data from streaming platforms as the amount of previously unknown content accessed by users. Additionally, in domains such as that of music streaming there is evidence that recommending novel content improves users' experience with the platform. Therefore, understanding users' discovery patterns, such as the amount to which and the way users access previously unknown content, is a topic of relevance for both the scientific community and the streaming industry, particularly the music one. Previous works studied how music consumption differs for users of different traits and looked at diversity, novelty, and consistency over time of users' music preferences. However, very little is known about how users discover and explore previously unknown music, and how this behavior differs for users of varying discovery needs. In this paper we bridge this gap by analyzing data from a survey answered by users of the major music streaming platform Deezer in combination with their streaming data. We first address questions regarding whether users who declare a higher interest in unfamiliar music listen to more diverse music, have more stable music preferences over time, and explore more music within a same time window, compared to those who declare a lower interest. We then investigate which type of music tracks users choose to listen to when they explore unfamiliar music, identifying clear patterns of popularity and genre representativeness that vary for users of different discovery needs. Our findings open up possibilities to infer users' interest in unfamiliar music from streaming data as well as possibilities to develop recommender systems that guide users in exploring music in a more natural way.
A Comprehensive Survey of Large AI Models for Future Communications: Foundations, Applications and Challenges
2025-05-06
Feibo Jiang, Cunhua Pan, Li Dong, Kezhi Wang, Merouane Debbah, Dusit Niyato, Zhu Han
The 6G wireless communications aim to establish an intelligent world of ubiquitous connectivity, providing an unprecedented communication experience. Large artificial intelligence models (LAMs) are characterized by significantly larger scales (e.g., billions or trillions of parameters) compared to typical artificial intelligence (AI) models. LAMs exhibit outstanding cognitive abilities, including strong generalization capabilities for fine-tuning to downstream tasks, and emergent capabilities to handle tasks unseen during training. Therefore, LAMs efficiently provide AI services for diverse communication applications, making them crucial tools for addressing complex challenges in future wireless communication systems. This study provides a comprehensive review of the foundations, applications, and challenges of LAMs in communication. First, we introduce the current state of AI-based communication systems, emphasizing the motivation behind integrating LAMs into communications and summarizing the key contributions. We then present an overview of the essential concepts of LAMs in communication. This includes an introduction to the main architectures of LAMs, such as transformer, diffusion models, and mamba. We also explore the classification of LAMs, including large language models (LLMs), large vision models (LVMs), large multimodal models (LMMs), and world models, and examine their potential applications in communication. Additionally, we cover the training methods and evaluation techniques for LAMs in communication systems. Lastly, we introduce optimization strategies such as chain of thought (CoT), retrieval augmented generation (RAG), and agentic systems. Following this, we discuss the research advancements of LAMs across various communication scenarios. Finally, we analyze the challenges in the current research and provide insights into potential future research directions.
This study employs stellar parameters from spectroscopic surveys and Zhang et al. based on Gaia XP spectra, along with photometric data from GALEX, UVOT, and Gaia, to obtain extensive ultraviolet color excess information for the relevant bands of GALEX and UVOT. By considering the impact of stellar parameters (\teff, \feh, and \logg) on intrinsic color indices, and utilizing the blue-edge method combined with a random forest algorithm, an empirical relationship between stellar parameters and intrinsic ultraviolet color indices is established. By combining observed color indices, the study derives color excesses for 11,624,802 and 65,531 stars in the GALEX/near-UV and far-UV bands, and 336,633, 137,739, and 253,271 stars in the UVOT/\uvwa, \uvmb, and \uvwb\ bands, constructing corresponding ultraviolet extinction maps. Notably, the color excess data for the GALEX/near-UV band shows a tenfold increase from previous results, with the extinction map covering approximately two-thirds of the sky at a resolution of 0.4$^{\circ}$. The typical uncertainties in the ultraviolet color excesses are 0.21 mag, 0.30 mag, 0.19 mag, 0.24 mag, and 0.21 mag for \enb, \efb, \ewab, \embb, and \ewbb, respectively. By comparing the spatial distributions of \rv\ derived from ultraviolet and Gaia optical band measurements with those obtained from infrared and optical data in previous works, it is evident that the \rv\ distributions based on the ultraviolet data show noticeable differences, with some regions even exhibiting opposite trends. This suggests that a single-parameter \rv\ extinction law may not be sufficient to simultaneously characterize the extinction behavior across infrared, optical, and ultraviolet bands.
Fluid Volume Assignment for Flow-Based Biochips: State-of-the-Art and Research Challenges
2025-05-06
Alexander Schneider, Jan Madsen, Paul Pop
Microfluidic biochips are replacing the conventional biochemical analysers integrating the necessary functions on-chip. We are interested in Flow-Based Microfluidic Biochips (FBMB), where a continuous flow of liquid is manipulated using integrated microvalves. Using microvalves and channels, more complex Fluidic Units (FUs) such as switches, micropumps, mixers and separators can be constructed. When running a biochemical application on a FBMB, fluid volumes are dispensed from input reservoirs and used by the FUs. Given a biochemical application and a biochip, one of the key problems which we are discussing in this paper, is in determining the fluid volume assignment for each operation of the application, such that the FUs' volume requirements are satisfied, while over- and underflow are avoided and the total volume of fluid used is minimized. We illustrate the main problems using examples, and provide a review of related work on volume management. We present algorithms for optimizing fluid volume assignments and for reusing leftover fluids to reduce waste. This also includes the optimization of mixing operations which significantly impact the required fluid volumes. We identify the main challenges related to volume management and discuss possible solutions. Finally we compare the outcome of volume management using fixed- and arbitrary-ratio mixing technology, demonstrating significant reductions in fluid consumption for real biochemical assays.
Robust estimation of location is a fundamental problem in statistics, particularly in scenarios where data contamination by outliers or model misspecification is a concern. In univariate settings, methods such as the sample median and trimmed means balance robustness and efficiency by mitigating the influence of extreme observations. This paper extends these robust techniques to the multivariate context through the use of data depth functions, which provide a natural means to order and rank multidimensional data. We review several depth measures and discuss their role in generalizing trimmed mean estimators beyond one dimension. Our main contributions are twofold: first, we prove the almost sure consistency of the multivariate trimmed mean estimator under mixing conditions; second, we establish a general limit distribution theorem for a broad family of depth-based estimators, encompassing popular examples such as Tukey's and projection depth. These theoretical advancements not only enhance the understanding of robust location estimation in high-dimensional settings but also offer practical guidelines for applications in areas such as machine learning, economic analysis, and financial risk assessment. A small example with simulated data is performed, varying the depth measure used and the percentage of trimmed data.
Real-time small area estimation of food security in Zimbabwe: integrating mobile-phone and face-to-face surveys using joint multilevel regression and poststratification
2025-05-06
Sahoko Ishida, Adam Howes, Valerie Bradley, Elizaveta Semenova, Theo Rashid, Silinganisiwe Dzumbunu, Sumali Bajaj, Gaurav Singhal, Dino Sejdinovic, Herbert Zvirere, Duccio Piovani, Silvia Passeri, Kusum Hachhethu, Arif Husain, George D. Kembo, Terrence Kairiza, Seth Flaxman
Real-time, fine-grained monitoring of food security is essential for enabling timely and targeted interventions, thereby supporting the global goal of achieving zero hunger - a key objective of the 2030 Agenda for Sustainable Development. Mobile phone surveys provide a scalable and temporally rich data source that can be tailored to different administrative levels. However, due to cost and operational constraints, maintaining high-frequency data collection while ensuring representativeness at lower administrative levels is often infeasible. We propose a joint multilevel regression and poststratification (jMRP) approach that combines high-frequency and up-to-date mobile phone survey data, designed for higher administrative levels, with an annual face-to-face survey representative at lower levels to produce reliable food security estimates at spatially and temporally finer scales than those originally targeted by the surveys. This methodology accounts for systematic differences in survey responses due to modality and socio-economic characteristics, reducing both sampling and modality bias. We implement the approach in a fully Bayesian manner to quantify uncertainty. We demonstrate the effectiveness of our method using data from Zimbabwe, thus offering a cost-effective solution for real-time monitoring and strengthening decision-making in resource-constrained settings.
Clarification of the Spontaneous Polarization Direction in Crystals with Wurtzite Structure
2025-05-06
Simon Fichtner, Mohamed Yassine, Chris van de Walle, Oliver Ambacher
The wurtzite structure is one of the most frequently found crystal structures in modern semiconductors and its inherent spontaneous polarization is a defining materials property. Despite this significance, confusion has been rampant in the literature with respect to the orientation of the spontaneous polarization inside the unit cell of the wurtzite structure, especially for the technologically very relevant III-N compounds (AlN, GaN, InN). In particular, the spontaneous polarization has been reported to either point up or down for the same unit cell orientation, depending on the literature source - with important implications for, e.g., the carrier type and density expected at interfaces of heterostructures involving materials with wurtzite-structure. This perspective aims to resolve this ambiguity by reviewing available reports on the direction of the energetically preferred polarization direction in the presence of external electric fields, as well as atomically resolved scanning transmission electron microscopy images. While we use ferroelectric wurtzite AlScN as a key example, our conclusions are generalizable to other compounds with the same crystal structure. We demonstrate that a metal-polar unit cell must be associated with an upward polarization vector - which is contrary to long-standing conventional wisdom.
Identifying Astrophysical Anomalies in 99.6 Million Cutouts from the Hubble Legacy Archive Using AnomalyMatch
2025-05-06
David O'Ryan, Pablo Gómez
Astronomical archives contain vast quantities of unexplored data that potentially harbour rare and scientifically valuable cosmological phenomena. We have systematically searched approximately 100 million image cutouts from the entire Hubble Legacy Archive using the recently developed AnomalyMatch method, which combines semi-supervised and active learning techniques for the efficient detection of astrophysical anomalies. This comprehensive search rapidly uncovered a multitude of astrophysical anomalies presented here that significantly expand the inventory of known rare objects. Among our discoveries are 138 new candidate gravitational lenses, 18 jellyfish galaxies, and 417 mergers or interacting galaxes. The efficiency and accuracy of our iterative detection strategy allows us to trawl the complete archive within just 2-3 days, highlighting its potential for large-scale astronomical surveys. We present a detailed overview of these newly identified objects, discuss their astrophysical significance, and demonstrate the considerable potential of AnomalyMatch to efficiently explore extensive astronomical datasets, e.g., upcoming Euclid data releases.
DCO$^+$ and DCN 1-0 survey toward a sample of Planck cold clumps
2025-05-06
Fu Mo, Junzhi Wang, Shu Liu, Yan Duan, Huanxue Feng, Yuqiang Li, Zhe Lu, Rui Luo, Chao Ou, Yani Xu, Zhuoying Yan
Deuterated molecules can be used to study the physical conditions and the astro-chemical evolution of molecular clouds. large-sample surveys for deuterated molecules are needed to understand the enhancement of deuterated molecules from diffuse molecular gas to cold cores. A single-pointing survey toward the 559 Planck cold clumps of the Early Cold Core Catalogue (ECC) has been conducted using the Arizona Radio Observatory 12-meter telescope, focusing on the $J$=1-0 transitions of DCO$^+$ and DCN. The survey included observations of 309 cores for DCO$^+$ and DCN 1-0 simultaneously, followed by 71 of these cores where DCO$^+$ 1-0 was detected for H$^{13}$CO$^+$ and H$^{13}$CN 1-0 simultaneously, aiming to determine the deuterated fraction ($D_{\rm frac}$). Additionally, 250 cores were observed for DCO$^+$, DCN, H$^{13}$CO$^+$ and H$^{13}$CN 1-0 simultaneously. Among the 309 sources, DCO$^+$ and DCN 1-0 were detected in 79 and 11 sources, with a detection rates of 25.6 % and 3.6 % respectively. In the 250 sources observed for all four species, DCO$^+$, DCN, H$^{13}$CO$^+$ and H$^{13}$CN 1-0 were detected in 58, 9, 57 and 13 sources, with a detection rate of 23.2 %, 3.6 %, 22.8 % and 5.2 % respectively. The $D_{\rm frac}$(HCO$^+$) values in 112 sources range from 0.89 % to 7.4 % with a median value of 3.1 %, while $D_{\rm frac}$(HCN) values in 11 sources range from 1.5 % to 5.5 % with a median value of 2.3 %. The line widths of DCO$^+$ and H$^{13}$CO$^+$ 1-0 detections are mostly within 1 km s$^{-1}$. The similarity in $D_{\rm frac}$ values between HCO$^+$ and HCN indicates that the higher detection rate of DCO$^+$ 1-0 compared with DCN 1-0 is due to the lower critical density of DCO$^+$ 1-0. We suggest that the enhancement of DCO$^+$ and DCN likely begins in the early diffuse stage of the molecular cloud, rather than during the cold core formation stage.
Sentence Embeddings as an intermediate target in end-to-end summarisation
2025-05-06
Maciej Zembrzuski, Saad Mahamood
Current neural network-based methods to the problem of document summarisation struggle when applied to datasets containing large inputs. In this paper we propose a new approach to the challenge of content-selection when dealing with end-to-end summarisation of user reviews of accommodations. We show that by combining an extractive approach with externally pre-trained sentence level embeddings in an addition to an abstractive summarisation model we can outperform existing methods when this is applied to the task of summarising a large input dataset. We also prove that predicting sentence level embedding of a summary increases the quality of an end-to-end system for loosely aligned source to target corpora, than compared to commonly predicting probability distributions of sentence selection.
Simulation to Reality: Testbeds and Architectures for Connected and Automated Vehicles
2025-05-06
David Klüner, Simon Schäfer, Lucas Hegerath, Jianye Xu, Julius Kahle, Hazem Ibrahim, Alexandru Kampmann, Bassam Alrifaee
Ensuring the safe and efficient operation of CAVs relies heavily on the software framework used. A software framework needs to ensure real-time properties, reliable communication, and efficient resource utilization. Furthermore, a software framework needs to enable seamless transition between testing stages, from simulation to small-scale to full-scale experiments. In this paper, we survey prominent software frameworks used for in-vehicle and inter-vehicle communication in CAVs. We analyze these frameworks regarding opportunities and challenges, such as their real-time properties and transitioning capabilities. Additionally, we delve into the tooling requirements necessary for addressing the associated challenges. We illustrate the practical implications of these challenges through case studies focusing on critical areas such as perception, motion planning, and control. Furthermore, we identify research gaps in the field, highlighting areas where further investigation is needed to advance the development and deployment of safe and efficient CAV systems.
Blending 3D Geometry and Machine Learning for Multi-View Stereopsis
2025-05-06
Vibhas Vats, Md. Alimoor Reza, David Crandall, Soon-heung Jung
Traditional multi-view stereo (MVS) methods primarily depend on photometric and geometric consistency constraints. In contrast, modern learning-based algorithms often rely on the plane sweep algorithm to infer 3D geometry, applying explicit geometric consistency (GC) checks only as a post-processing step, with no impact on the learning process itself. In this work, we introduce GC MVSNet plus plus, a novel approach that actively enforces geometric consistency of reference view depth maps across multiple source views (multi view) and at various scales (multi scale) during the learning phase (see Fig. 1). This integrated GC check significantly accelerates the learning process by directly penalizing geometrically inconsistent pixels, effectively halving the number of training iterations compared to other MVS methods. Furthermore, we introduce a densely connected cost regularization network with two distinct block designs simple and feature dense optimized to harness dense feature connections for enhanced regularization. Extensive experiments demonstrate that our approach achieves a new state of the art on the DTU and BlendedMVS datasets and secures second place on the Tanks and Temples benchmark. To our knowledge, GC MVSNet plus plus is the first method to enforce multi-view, multi-scale supervised geometric consistency during learning. Our code is available.
VLBA observations of a sample of low-power compact symmetric objects
2025-05-06
M. Orienti, F. D'Ammando, D. Dallacasa, G. Migliori, P. Rossi, G. Bodo
Compact symmetric objects (CSOs) are intrinsically compact extragalactic radio sources that are thought to be the progenitors of classical radio galaxies. To date, evolutionary models have mainly focused on the formation and growth of high-power radio sources, leaving unanswered many questions related to low-power objects, whose relativistic jets are likely more prone to instabilities. We present a new sample of candidate low-power CSOs selected from the Faint Images of the Radio Sky at Twenty-cm (FIRST) survey. The main selection criteria are (i) a parsec-scale double radio morphology from archival Very Long Baseline Array (VLBA) images and (ii) a VLBA total flux density consistent with that from the FIRST survey, which rules out the presence of significant radio emission extending beyond the parsec scale. The final sample consists of 60 sources with radio luminosities between 10$^{24}$ and 10$^{27}$ W Hz$^{-1}$ at 1.4 GHz and projected linear sizes between 45 and 430 pc, which fill a region in the radio power-size plane that is currently underpopulated. We carried out VLBA observations at 4.98 GHz of a sub-sample of 20 sources among the brightest candidate CSOs with the aim of confirming their classification. We classify 12 sources as CSOs on the basis of their radio structure and spectral index distribution. In two out of the four CSOs with core identification, the asymmetry in the flux density of the outer components is in agreement with light travel time effects, and there is no evidence of jet-cloud interaction. If we assume a simplistic parametric model, most of the sources in the total sample have a jet power of $\sim 10^{44} - 10^{45}$ erg s$^{-1}$, making their evolutionary paths sensitive to the individual conditions of the jet and its environment.
Implications of Recent Experimental & Theoretical Results on Electroweak Precision Tests
2025-05-06
Jens Erler
I review the results of a recent global fit to electroweak precision data. Particular attention is devoted to the landscape of determinations of the weak mixing angle, recent results on basic properties of the electroweak gauge bosons, and the implications of vacuum polarization on the scale dependences of the electromagnetic coupling and the weak mixing angle, as well as the anomalous magnetic moment of the muon.
Algorithm Selection in Short-Range Molecular Dynamics Simulations
2025-05-06
Samuel James Newcome, Fabio Alexander Gratl, Manuel Lerchner, Abdulkadir Pazar, Manish Kumar Mishra, Hans-Joachim Bungartz
Numerous algorithms and parallelisations have been developed for short-range particle simulations; however, none are optimally performant for all scenarios. Such a concept led to the prior development of the particle simulation library AutoPas, which implemented many of these algorithms and parallelisations and could select and tune these over the course of the simulation as the scenario changed. Prior works have, however, used only naive approaches to the algorithm selection problem, which can lead to significant overhead from trialling poorly performing algorithmic configurations. In this work, we investigate this problem in the case of Molecular Dynamics simulations. We present three algorithm selection strategies: an approach which makes performance predictions from past data, an expert-knowledge fuzzy logic-based approach, and a data-driven random forest-based approach. We demonstrate that these approaches can achieve speedups of up to 4.05 compared to prior approaches and 1.25 compared to a perfect configuration selection without dynamic algorithm selection. In addition, we discuss the practicality of the strategies in comparison to their performance, to highlight the tractability of such solutions.
Knowledge Augmented Complex Problem Solving with Large Language Models: A Survey
2025-05-06
Da Zheng, Lun Du, Junwei Su, Yuchen Tian, Yuqi Zhu, Jintian Zhang, Lanning Wei, Ningyu Zhang, Huajun Chen
Problem-solving has been a fundamental driver of human progress in numerous domains. With advancements in artificial intelligence, Large Language Models (LLMs) have emerged as powerful tools capable of tackling complex problems across diverse domains. Unlike traditional computational systems, LLMs combine raw computational power with an approximation of human reasoning, allowing them to generate solutions, make inferences, and even leverage external computational tools. However, applying LLMs to real-world problem-solving presents significant challenges, including multi-step reasoning, domain knowledge integration, and result verification. This survey explores the capabilities and limitations of LLMs in complex problem-solving, examining techniques including Chain-of-Thought (CoT) reasoning, knowledge augmentation, and various LLM-based and tool-based verification techniques. Additionally, we highlight domain-specific challenges in various domains, such as software engineering, mathematical reasoning and proving, data analysis and modeling, and scientific research. The paper further discusses the fundamental limitations of the current LLM solutions and the future directions of LLM-based complex problems solving from the perspective of multi-step reasoning, domain knowledge integration and result verification.
Machine unlearning considers the removal of the contribution of a set of data points from a trained model. In a distributed setting, where a server orchestrates training using data available at a set of remote users, unlearning is essential to cope with late-detected malicious or corrupted users. Existing distributed unlearning algorithms require the server to store all model updates observed in training, leading to immense storage overhead for preserving the ability to unlearn. In this work we study lossy compression schemes for facilitating distributed server-side unlearning with limited memory footprint. We propose memory-efficient distributed unlearning (MEDU), a hierarchical lossy compression scheme tailored for server-side unlearning, that integrates user sparsification, differential thresholding, and random lattice coding, to substantially reduce memory footprint. We rigorously analyze MEDU, deriving an upper bound on the difference between the desired model that is trained from scratch and the model unlearned from lossy compressed stored updates. Our bound outperforms the state-of-the-art known bounds for non-compressed decentralized server-side unlearning, even when lossy compression is incorporated. We further provide a numerical study, which shows that suited lossy compression can enable distributed unlearning with notably reduced memory footprint at the server while preserving the utility of the unlearned model.
Physics-Informed Neural Networks in Electromagnetic and Nanophotonic Design
2025-05-06
Omar A. M. Abdelraouf, Abdulrahman M. A. Ahmed, Emadeldeen Eldele, Ahmed A. Omar
The fusion of artificial intelligence (AI) with physics-guided frameworks has opened transformative avenues for advancing the design and optimization of electromagnetic and nanophotonic systems. Innovations in deep neural networks (DNNs) and physics-informed neural networks (PINNs) now provide robust tools to tackle longstanding challenges in light scattering engineering, meta-optics, and nonlinear photonics. This review outlines recent progress in leveraging these computational methodologies to enhance device performance across domains such as dynamic light modulation, antenna design, and nonlinear optical phenomena. We systematically survey advancements in AI-driven forward and inverse design strategies, which bypass conventional trial-and-error approaches by embedding physical laws directly into optimization workflows. Furthermore, the integration of AI accelerates electromagnetic simulations and enables precise modelling of complex optical effects, including topological photonic states and nonlinear interactions. A comparative evaluation of algorithmic frameworks highlights their strengths in balancing computational efficiency, multi-objective optimization, and fabrication feasibility. Challenges such as limited interpretability of AI models and data scarcity for unconventional optical modes are critically addressed. Finally, we emphasize future opportunities in scalable multi-physics modelling, adaptive architectures, and practical deployment of AI-optimized photonic devices. This work underscores the pivotal role of AI in transcending traditional design limitations, thereby propelling the development of next-generation photonic technologies with unprecedented functionality and efficiency.
Variable projection framework for the reduced-rank matrix approximation problem by weighted least-squares
2025-05-06
Pascal Terray
In this monograph, we review and develop variable projection Gauss-Newton, Levenberg-Marquardt and Newton methods for the Weighted Low-Rank Approximation (WLRA) problem, which has now an increasing number of applications in many scientific fields. Particular attention is drawn at the robustness, efficiency and scalability of these variable projection second-order algorithms such that they can be used also on larger datasets now commonly found in many practical problems for which only first-order algorithms based on sequential repetitions of local optimization (e.g., majorization, Expectation-Maximization or alternating least-squares methods) or variations of gradient descent (e.g., conjugate, proximal or stochastic gradient descent methods), or hybrid algorithms from these two classes of methods, were only feasible due to their lower cost and memory requirement per iteration. In parallel with this review of variable projection algorithms, we develop new formulae for the Jacobian and Hessian matrices involved in these variable projection methods and demonstrate their very specific properties such as the uniform rank deficiency of the Jacobian matrix or the rank deficiency of the Hessian matrix at the (local) minimizers of the cost function associated with the WLRA problem. These systematic deficiencies must be taken into account in any practical implementations of the algorithms. These different properties and the very particular geometry of the WLRA problem have not been well appreciated in the past and have been the main obstacles in the development of robust variable projection second-order algorithms for solving the WLRA problem. In addition, we demonstrate that the variable projection framework gives original insights on the solvability, the landscape and the non-smoothness of the WLRA problem. It also helps to describe the tight links between previously unrelated methods, which have been proposed to solve it. Specifically, we illustrate the closed links between the variable projection framework and Riemannian optimization on the Grassmann manifold for the WLRA problem. We expect that software's developers and practitioners in different fields such as computer vision, signal processing, recommender systems, machine learning, multivariate statistics and geophysical sciences will benefit from the results in this monograph in order to devise more robust and accurate algorithms to solve the WLRA problem.
AI-Driven Scholarly Peer Review via Persistent Workflow Prompting, Meta-Prompting, and Meta-Reasoning
2025-05-06
Evgeny Markhasin
Critical peer review of scientific manuscripts presents a significant challenge for Large Language Models (LLMs), partly due to data limitations and the complexity of expert reasoning. This report introduces Persistent Workflow Prompting (PWP), a potentially broadly applicable prompt engineering methodology designed to bridge this gap using standard LLM chat interfaces (zero-code, no APIs). We present a proof-of-concept PWP prompt for the critical analysis of experimental chemistry manuscripts, featuring a hierarchical, modular architecture (structured via Markdown) that defines detailed analysis workflows. We develop this PWP prompt through iterative application of meta-prompting techniques and meta-reasoning aimed at systematically codifying expert review workflows, including tacit knowledge. Submitted once at the start of a session, this PWP prompt equips the LLM with persistent workflows triggered by subsequent queries, guiding modern reasoning LLMs through systematic, multimodal evaluations. Demonstrations show the PWP-guided LLM identifying major methodological flaws in a test case while mitigating LLM input bias and performing complex tasks, including distinguishing claims from evidence, integrating text/photo/figure analysis to infer parameters, executing quantitative feasibility checks, comparing estimates against claims, and assessing a priori plausibility. To ensure transparency and facilitate replication, we provide full prompts, detailed demonstration analyses, and logs of interactive chats as supplementary resources. Beyond the specific application, this work offers insights into the meta-development process itself, highlighting the potential of PWP, informed by detailed workflow formalization, to enable sophisticated analysis using readily available LLMs for complex scientific tasks.
In this work, we introduce the task of script-driven video summarization, which aims to produce a summary of the full-length video by selecting the parts that are most relevant to a user-provided script outlining the visual content of the desired summary. Following, we extend a recently-introduced large-scale dataset for generic video summarization (VideoXum) by producing natural language descriptions of the different human-annotated summaries that are available per video. In this way we make it compatible with the introduced task, since the available triplets of ``video, summary and summary description'' can be used for training a method that is able to produce different summaries for a given video, driven by the provided script about the content of each summary. Finally, we develop a new network architecture for script-driven video summarization (SD-VSum), that relies on the use of a cross-modal attention mechanism for aligning and fusing information from the visual and text modalities. Our experimental evaluations demonstrate the advanced performance of SD-VSum against state-of-the-art approaches for query-driven and generic (unimodal and multimodal) summarization from the literature, and document its capacity to produce video summaries that are adapted to each user's needs about their content.
Identification of BHB stars using Synthetic SkyMapper colors from Gaia XP spectra
2025-05-06
Guozhen Hu, Yang Huang, Wenyuan Cui, Tao Wang, Kai Xiao, Ruifeng Shi, Jie Ju, Bowen Huang, Chunyan Li, Zhicun Liu, Xiaolong Wang, Changqing Luo
Blue horizontal-branch (BHB) stars are ideal tracers for mapping the structure of Galactic stellar halo. Traditionally, BHB sample stars are built from large-scale spectroscopic surveys utilizing their spectral features, however, the resulting sample sizes have been quite limited. In this paper, we construct a catalog of BHB stars based on synthetic colors $(u-v)_{0}$ and $(g-i)_{0}$ in SkyMapper photometric systems, which are convolved from Gaia XP spectra. A total of 49,733 BHB stars are selected from nearly the entire sky (excluding regions of low Galactic latitudes $|b| \le 8^{\circ}$ with heavy reddening), with a completeness and purity exceeding 90\%. Using member stars of globular clusters with precise distance determinations, we carefully calibrate the relationship between the $g$-band absolute magnitude and $(g-i)_{0}$, achieving a precision of 0.11\,mag, which corresponds to a 5\% uncertainty in distance. This relation is applied to derive distances for all BHB stars in the constructed sample. Given current capabilities of Gaia XP observations, the constructed BHB sample is primarily located within 20 kpc, enabling detailed mapping of the inner stellar halo. To extend this depth to the outer halo or even the edge of our Galaxy, we explore the potential of the Chinese Space Station Telescope (CSST) and its broad-band photometry for detecting BHB stars. Using mock data from synthetic spectra, we find that it is feasible to distinguish BHB stars from blue stragglers (BS) stars using CSST near-ultraviolet bands ($NUV, u$) photometry. Thanks to the deep limiting magnitude of CSST, its data will provide a groundbreaking perspective on our Galaxy, particularly regarding the outer halo, in an unprecedented volume.
How do activity-end trip characteristics affect the choice for shared micromobility? A latent class choice modelling approach for train station egress trips in the Netherlands
2025-05-06
Nejc Geržinič, Mark van Hagen, Hussein Al-Tamimi, Niels van Oort, Dorine Duives
Access/egress travel to train stations continues to pose a significant barrier to increasing the number of train travellers. Shared micromobility (SMM), including bicycles, e-bikes, steps and mopeds, is often cited as a prominent solution, especially for the activity-end of the trip chain. Using a stated preference survey, we analyse activity-end mode-choice preferences for SMM, walking and public transport (PT) among the Dutch population. By means of a latent class choice model, we uncover three user groups with respect to activity-end mode choice behaviour. The largest (58%) are Multimodal sharing enthusiast, who choose based on the trade-offs between various travel characteristics, while not having strong modal preferences. They are the most open, ready and able to use SMM. Sharing hesitant cyclists (16%) have a strong preference for cycling and while they are open to using SMM , they may not feel themselves ready, stating that use of SMM can be difficult and dangerous. Sharing-averse PT users (27%) are most likely to use PT and avoid SMM as they find it too difficult and dangerous to use. The high preference to walk for short egress distances reaffirms the need for transit-oriented development policies. For longer egress distances, PT should be the primary focus at stations in high-density areas with high demand, where high frequencies and dense networks are justified, while stations in lower demand areas are better served by SMM. Providing multiple SMM options would result mainly in competition for the same travellers.
Reexploring Molecular Complexity with ALMA: Insights into chemical differentiation from the molecular composition of hot cores in Sgr B2(N2)
2025-05-06
A. Belloche, R. T. Garrod, H. S. P. Müller, N. J. Morin, S. A. Willis, K. M. Menten
We used ALMA to perform a line survey of the high-mass star forming region Sgr B2(N), called ReMoCA. We modeled under the assumption of LTE the spectra obtained toward the sources embedded in the secondary hot core Sgr B2(N2). We compared the chemical composition of these sources to that of sources from the literature and to predictions of the chemical kinetics model MAGICKAL. We detected up to 58 molecules toward Sgr B2(N2)'s hot cores, including up to 24 COMs, as well as many less abundant isotopologs. The compositions of some pairs of sources are well correlated, but differences also exist in particular for HNCO and NH2CHO. The abundances of series of homologous molecules drop by about one order of magnitude at each further step in complexity. The nondetection of radicals yields stringent constraints on the models. The comparison to the chemical models confirms previous evidence of a high cosmic-ray ionization rate in Sgr B2(N). The comparison to sources from the literature gives new insight into chemical differentiation. The composition of most hot cores of Sgr B2(N2) is tightly correlated to that of the hot core G31.41+0.31 and the hot corino IRAS 16293-2422B after normalizing the abundances by classes of molecules (O-, N-, O+N-, and S-bearing). There is no overall correlation between Sgr B2(N2) and the shocked region G+0.693-0.027 also located in Sgr B2, and even less with the cold starless core TMC-1. The class of N-bearing species reveals the largest variance among the four classes of molecules. The S-bearing class shows in contrast the smallest variance. These results imply that the class of N-bearing molecules reacts more sensitively to shocks, low-temperature gas phase chemistry after non-thermal desorption, or density. The abundance shifts observed between the N- and O-bearing molecules may indicate how violently and completely the ice mantles are desorbed. [abridged]
The long-term optical flux variations of Compact Symmetric Objects
2025-05-06
Subhashree Swain, Vaidehi S. Paliya, D. J. Saikia, C. S. Stalin, Arya Venugopal, A. K. Bhavya, C. D. Ravikumar
Compact Symmetric Objects (CSOs) are a distinct category of jetted active galactic nuclei (AGN) whose optical variability characteristics have not been well investigated. We present here the results of our investigation on the optical flux and colour variability properties of a bona fide sample of 38 CSOs. We used the g-, r- and i-bands data from the Zwicky Transient Facility survey that spans a duration of about 5 years. We also considered a comparison sub-sample of blazars that includes 5 flat spectrum radio quasars and 12 BL Lac objects with redshifts and g-band magnitudes similar to the limited sub-sample of 9 CSOs. These two sub-samples of AGN, chosen for this comparative study of their long-term optical variability, represent different orientations of their relativistic jets with respect to the observer. We found that both CSOs and blazars exhibit optical flux variations, although variability of CSOs is lower than that of blazars. The observed variability in both CSOs and blazars is attributed to the relativistic jets and the increased optical variations in blazars relative to CSOs are likely due to beaming effects. CSOs and blazars exhibit similar colour variations, with both of them showing a bluer when brighter trend. Such a colour variability pattern is expected due to processes associated with their relativistic jets.
Survey of Abstract Meaning Representation: Then, Now, Future
2025-05-06
Behrooz Mansouri
This paper presents a survey of Abstract Meaning Representation (AMR), a semantic representation framework that captures the meaning of sentences through a graph-based structure. AMR represents sentences as rooted, directed acyclic graphs, where nodes correspond to concepts and edges denote relationships, effectively encoding the meaning of complex sentences. This survey investigates AMR and its extensions, focusing on AMR capabilities. It then explores the parsing (text-to-AMR) and generation (AMR-to-text) tasks by showing traditional, current, and possible futures approaches. It also reviews various applications of AMR including text generation, text classification, and information extraction and information seeking. By analyzing recent developments and challenges in the field, this survey provides insights into future directions for research and the potential impact of AMR on enhancing machine understanding of human language.
A Trustworthy Multi-LLM Network: Challenges,Solutions, and A Use Case
2025-05-06
Haoxiang Luo, Gang Sun, Yinqiu Liu, Dusit Niyato, Hongfang Yu, Mohammed Atiquzzaman, Schahram Dustdar
Large Language Models (LLMs) demonstrate strong potential across a variety of tasks in communications and networking due to their advanced reasoning capabilities. However, because different LLMs have different model structures and are trained using distinct corpora and methods, they may offer varying optimization strategies for the same network issues. Moreover, the limitations of an individual LLM's training data, aggravated by the potential maliciousness of its hosting device, can result in responses with low confidence or even bias. To address these challenges, we propose a blockchain-enabled collaborative framework that connects multiple LLMs into a Trustworthy Multi-LLM Network (MultiLLMN). This architecture enables the cooperative evaluation and selection of the most reliable and high-quality responses to complex network optimization problems. Specifically, we begin by reviewing related work and highlighting the limitations of existing LLMs in collaboration and trust, emphasizing the need for trustworthiness in LLM-based systems. We then introduce the workflow and design of the proposed Trustworthy MultiLLMN framework. Given the severity of False Base Station (FBS) attacks in B5G and 6G communication systems and the difficulty of addressing such threats through traditional modeling techniques, we present FBS defense as a case study to empirically validate the effectiveness of our approach. Finally, we outline promising future research directions in this emerging area.
Behavioral Sensing and Intervention Paradigm: A Review of Closed-Loop Approaches for Ingestion Health
2025-05-06
Jun Fang, Yanuo Zhou, Ka I Chan, Jiajin Li, Zeyi Sun, Zhengnan Li, Zicong Fu, Hongjing Piao, Haodong Xu, Yuanchun Shi, Yuntao Wang
Ingestive behavior plays a critical role in health, yet many existing interventions remain limited to static guidance or manual self-tracking. With the increasing integration of sensors and perceptual computing, recent systems have begun to support closed-loop interventions that dynamically sense user behavior and provide feedback during or around ingestion episodes. In this survey, we review 136 studies that leverage sensor-enabled or interaction-mediated approaches to influence eating behavior. We propose a behavioral closed-loop paradigm comprising three core components: target behaviors, sensing modalities, and feedback strategies. A taxonomy of sensing and intervention modalities is presented, organized along human- and environment-based dimensions. Our analysis also examines evaluation methods and design trends across different modality-behavior pairings. This review reveals prevailing patterns and critical gaps, offering design insights for future adaptive and context-aware ingestion health interventions.
The Physical Nature of the Off-centered Extended Emission Associated with the Little Red Dots
2025-05-06
Chang-Hao Chen, Luis C. Ho, Ruancun Li, Kohei Inayoshi
A significant fraction of little red dots (LRDs) exhibit nearby extended emission of unknown origin. If physically associated with the LRD, this component may trace stellar emission from an off-centered host galaxy, neighboring companions, or nebular gas illuminated by the active nucleus. We investigate the detailed spectral energy distribution of the extended emission near four LRDs in the JWST UNCOVER and MegaScience surveys. We accurately decompose the extended emission from the dominant point source by simultaneously fitting the images in eight broad-band and nine medium-band filters. After considering both the results from photometric redshift fitting and the probability of galaxies at different redshift overlapping, we confirm that the off-centered blobs in three sources are physically associated with the LRDs, with two of them showing strong [\ion{O}{3}] $\lambda\lambda 4959,\,5007$ emission captured by the medium-band filters. While the spectral energy distributions of all three blobs can be modeled assuming star-forming galaxies with stellar mass $\sim 10^8\,M_{\odot}$, the exceptionally strong [\ion{O}{3}] emission of two sources is best interpreted as pure nebular emission from low-density ($n<10\, {\rm cm}^{-3}$), low-metallicity ($Z\approx 0.05\,Z_{\odot}$) gas photoionized by the ultraviolet radiation from the nearby LRD. Adopting LRD halo masses constrained by clustering measurements and theoretical considerations, we estimate a typical baryonic halo mass accretion rate of $\sim 2-9\, M_{\odot}\,{\rm yr}^{-1}$. If the halo accretion rate is sustained to $z=4$ and stars form with an efficiency of 10\%, the accreted gas would form a galaxy with stellar mass $\sim 10^9\,M_{\odot}$, potentially rendering them spatially resolved at lower redshift.
This study investigates whether large language models (LLMs) can function as intelligent collaborators to bridge expertise gaps in cybersecurity decision-making. We examine two representative tasks-phishing email detection and intrusion detection-that differ in data modality, cognitive complexity, and user familiarity. Through a controlled mixed-methods user study, n = 58 (phishing, n = 34; intrusion, n = 24), we find that human-AI collaboration improves task performance,reducing false positives in phishing detection and false negatives in intrusion detection. A learning effect is also observed when participants transition from collaboration to independent work, suggesting that LLMs can support long-term skill development. Our qualitative analysis shows that interaction dynamics-such as LLM definitiveness, explanation style, and tone-influence user trust, prompting strategies, and decision revision. Users engaged in more analytic questioning and showed greater reliance on LLM feedback in high-complexity settings. These results provide design guidance for building interpretable, adaptive, and trustworthy human-AI teaming systems, and demonstrate that LLMs can meaningfully support non-experts in reasoning through complex cybersecurity problems.
AI-driven education, particularly Large Language Models (LLMs), has the potential to address learning disparities in rural K-12 schools. However, research on AI adoption in rural India remains limited, with existing studies focusing primarily on urban settings. This study examines the perceptions of volunteer teachers on AI integration in rural education, identifying key challenges and opportunities. Through semi-structured interviews with 23 volunteer educators in Rajasthan and Delhi, we conducted a thematic analysis to explore infrastructure constraints, teacher preparedness, and digital literacy gaps. Findings indicate that while LLMs could enhance personalized learning and reduce teacher workload, barriers such as poor connectivity, lack of AI training, and parental skepticism hinder adoption. Despite concerns over over-reliance and ethical risks, volunteers emphasize that AI should be seen as a complementary tool rather than a replacement for traditional teaching. Given the potential benefits, LLM-based tutors merit further exploration in rural classrooms, with structured implementation and localized adaptations to ensure accessibility and equity.
Enhanced Simultaneous Quantum-Classical Communications Under Composable Security
2025-05-06
Nicholas Zaunders, Ziqing Wang, Robert Malaney, Ryan Aguinaldo, Timothy C. Ralph
Simultaneous quantum-classical communications (SQCC) protocols are a family of continuous-variable quantum key distribution (CV-QKD) protocols which allow for quantum and classical symbols to be integrated concurrently on the same optical pulse and mode. In this work, we present a revised analysis of simultaneous quantum-classical communications in Gaussian-modulated coherent-state CV-QKD protocols. We address security concerns inherently associated with SQCC schemes and provide an updated model of the coupling between the classical and quantum channels. We provide evidence for our model via Monte Carlo simulation. We compute the performance of our revised SQCC protocol in terms of the secret-key generation rate optimised over free parameters and demonstrate improved quantum efficiency for a given classical bit-error rate. Lastly, we extend our analysis into the finite-key regime, where we propose a scheme for composably-secure SQCC under realistic operating conditions and demonstrate that our scheme retains the advantage in quantum performance over previous models.
We present the multi-band photometric and low-resolution spectroscopic analysis of four W Ursae Majoris eclipsing binaries (EWs) - J080510.1+141528 (hereinafter as J0805a), J080516.3+143138 (hereinafter as J0805b), J143358.7+053953 (hereinafter as J1433), and J143458.4+054143 (hereinafter as J1434). The multi-band ground based photometric data are collected using the 1.3-m Devasthal Fast Optical Telescope (DFOT) while we also make use of TESS photometric observations. The spectroscopic analysis is based on the low-resolution observations by 4-m Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). The ephemeris of these systems are updated using the photometric data from TESS and other photometric surveys. The system J0805b shows a secular change in the orbital period with a period change rate of 4.2 ($\pm$ 0.1) $\times10^{-7}$ days per year while the orbital period change rate for J1433 is calculated as -1.1 ($\pm$ 0.1) $\times10^{-6}$ days per year. The mass-transfer rate for J0805b is found to be dM$_{1}$/dt = -1.56($\pm$0.07) $\times 10^{-6}$ M$_{\odot}$/year and dM$_{1}$/dt = -7.95($\pm$0.87) $\times 10^{-7}$ M$_{\odot}$/year for J1433. All the systems have inclination > 79$^{\circ}$ except J1433 which has inclination of 72.8$^{\circ}$. The mass-ratios (less massive to more massive component) for these targets are < 0.5. All the system except J0805b are A-subtype contact binaries. The absolute parameters of the systems are determined using GAIA DR3 parallax and reddening information. The LAMOST spectra are analyzed using spectral subtraction technique. A small excess emission is detected for J1433 in H$_{\alpha}$ and H$_{\beta}$ region. The systems are plotted on Hertzsprung-Russell (HR) diagram and compared with previously studied systems. The mass-ratio vs radius-ratio relation is also investigated for these systems.
The Transformer architecture has achieved significant success in natural language processing, motivating its adaptation to computer vision tasks. Unlike convolutional neural networks, vision transformers inherently capture long-range dependencies and enable parallel processing, yet lack inductive biases and efficiency benefits, facing significant computational and memory challenges that limit its real-world applicability. This paper surveys various online strategies for generating lightweight vision transformers for image recognition, focusing on three key areas: Efficient Component Design, Dynamic Network, and Knowledge Distillation. We evaluate the relevant exploration for each topic on the ImageNet-1K benchmark, analyzing trade-offs among precision, parameters, throughput, and more to highlight their respective advantages, disadvantages, and flexibility. Finally, we propose future research directions and potential challenges in the lightweighting of vision transformers with the aim of inspiring further exploration and providing practical guidance for the community. Project Page: https://github.com/ajxklo/Lightweight-VIT
For time-domain astronomy, it is crucial to frequently image celestial objects at specific depths within a predetermined cadence. To fulfill these scientific demands, scientists globally have started or planned the development of non-interferometric telescope arrays in recent years. Due to the numerous parameters involved in configuring these arrays, there is a need for an automated optimization framework that selects parameter sets to satisfy scientific needs while minimizing costs. In this paper, we introduce such a framework, which integrates optical design software, an exposure time calculator, and an optimization algorithm, to balance the observation capabilities and the cost of optical telescope arrays. Neural networks are utilized to speed up results retrieval of the system with different configurations. We use the SiTian project as a case study to demonstrate the framework's effectiveness, showing that this approach can aid scientists in selecting optimal parameter sets. The code for this framework is published in the China Virtual Observatory PaperData Repository, enabling users to optimize parameters for various non-interferometric telescope array projects.
The introduction of multimodal models is a huge step forward in Artificial Intelligence. A single model is trained to understand multiple modalities: text, image, video, and audio. Open-source multimodal models have made these breakthroughs more accessible. However, considering the vast landscape of adversarial attacks across these modalities, these models also inherit vulnerabilities of all the modalities, and ultimately, the adversarial threat amplifies. While broad research is available on possible attacks within or across these modalities, a practitioner-focused view that outlines attack types remains absent in the multimodal world. As more Machine Learning Practitioners adopt, fine-tune, and deploy open-source models in real-world applications, it's crucial that they can view the threat landscape and take the preventive actions necessary. This paper addresses the gap by surveying adversarial attacks targeting all four modalities: text, image, video, and audio. This survey provides a view of the adversarial attack landscape and presents how multimodal adversarial threats have evolved. To the best of our knowledge, this survey is the first comprehensive summarization of the threat landscape in the multimodal world.
Coevolution of Actions and Opinions in Networks of Coordinating and Anti-Coordinating Agents
2025-05-06
Hong Liang, Mengbin Ye, Lorenzo Zino, Weiguo Xia
In this paper, we investigate the dynamics of coordinating and anti-coordinating agents in a coevolutionary model for actions and opinions. In the model, the individuals of a population interact on a two-layer network, sharing their opinions and observing others' action, while revising their own opinions and actions according to a game-theoretic mechanism, grounded in the social psychology literature. First, we consider the scenario of coordinating agents, where convergence to a Nash equilibrium (NE) is guaranteed. We identify conditions for reaching consensus configurations and establish regions of attraction for these equilibria. Second, we study networks of anti-coordinating agents. In this second scenario, we prove that all trajectories converge to a NE by leveraging potential game theory. Then, we establish analytical conditions on the network structure and model parameters to guarantee the existence of consensus and polarized equilibria, characterizing their regions of attraction.
Revealing the spectral properties of magnetic turbulence by synchrotron polarization gradients
2025-05-06
Jianfu Zhang, Ziqi Liu
Based on the modern understanding of MHD turbulence theory, we propose a new method for measuring the spectral properties of magnetic turbulence by synchrotron polarization gradient analysis. Using synthetic polarization observational data, we first confirm the feasibility of the gradient technique to determine the scaling properties of magnetic turbulence. We then apply this technique to the Galactic plane survey data from the Australia Telescope Compact Array (ATCA) and obtain the power-law spectral feature of the Galactic magnetic turbulence of $E\propto k^{-10/3}$. Gradient techniques open up a new way to obtain spectral properties of magnetic turbulence from polarization observations, such as the Low-Frequency Array for Radio Astronomy (LOFAR), in the presence of low-frequency and strong Faraday depolarization.
The Eye as a Window to Systemic Health: A Survey of Retinal Imaging from Classical Techniques to Oculomics
2025-05-06
Inamullah, Imran Razzak, Shoaib Jameel
The unique vascularized anatomy of the human eye, encased in the retina, provides an opportunity to act as a window for human health. The retinal structure assists in assessing the early detection, monitoring of disease progression and intervention for both ocular and non-ocular diseases. The advancement in imaging technology leveraging Artificial Intelligence has seized this opportunity to bridge the gap between the eye and human health. This track paves the way for unveiling systemic health insight from the ocular system and surrogating non-invasive markers for timely intervention and identification. The new frontiers of oculomics in ophthalmology cover both ocular and systemic diseases, and getting more attention to explore them. In this survey paper, we explore the evolution of retinal imaging techniques, the dire need for the integration of AI-driven analysis, and the shift of retinal imaging from classical techniques to oculomics. We also discuss some hurdles that may be faced in the progression of oculomics, highlighting the research gaps and future directions.
Action Spotting and Precise Event Detection in Sports: Datasets, Methods, and Challenges
2025-05-06
Hao Xu, Arbind Agrahari Baniya, Sam Well, Mohamed Reda Bouadjenek, Richard Dazeley, Sunil Aryal
Video event detection has become an essential component of sports analytics, enabling automated identification of key moments and enhancing performance analysis, viewer engagement, and broadcast efficiency. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs) and Transformers, have significantly improved accuracy and efficiency in Temporal Action Localization (TAL), Action Spotting (AS), and Precise Event Spotting (PES). This survey provides a comprehensive overview of these three key tasks, emphasizing their differences, applications, and the evolution of methodological approaches. We thoroughly review and categorize existing datasets and evaluation metrics specifically tailored for sports contexts, highlighting the strengths and limitations of each. Furthermore, we analyze state-of-the-art techniques, including multi-modal approaches that integrate audio and visual information, methods utilizing self-supervised learning and knowledge distillation, and approaches aimed at generalizing across multiple sports. Finally, we discuss critical open challenges and outline promising research directions toward developing more generalized, efficient, and robust event detection frameworks applicable to diverse sports. This survey serves as a foundation for future research on efficient, generalizable, and multi-modal sports event detection.
The increasing frequency and severity of natural disasters underscore the critical importance of effective disaster emergency response planning to minimize human and economic losses. This survey provides a comprehensive review of recent advancements (2019--2024) in five essential areas of disaster emergency response planning: evacuation, facility location, casualty transport, search and rescue, and relief distribution. Research in these areas is systematically categorized based on methodologies, including optimization models, machine learning, and simulation, with a focus on their individual strengths and synergies. A notable contribution of this work is its examination of the interplay between machine learning, simulation, and optimization frameworks, highlighting how these approaches can address the dynamic, uncertain, and complex nature of disaster scenarios. By identifying key research trends and challenges, this study offers valuable insights to improve the effectiveness and resilience of emergency response strategies in future disaster planning efforts.
The Isaac Newton Telescope Monitoring Survey of Local Group Dwarf Galaxies. VII. Long-Period Variable Stars in the Nearest Starburst Dwarf Galaxy, IC 10
To identify long-period variable (LPV) stars in IC10 - the nearest starburst galaxy of the Local Group (LG) - we conducted an optical monitoring survey using the 2.5-m Isaac Newton Telescope (INT) with the wide-field camera (WFC) in the i-band and V-band from 2015 to 2017. We created a photometric catalog for 53,579 stars within the area of CCD4 of WFC ($\sim$ 0.07 deg$^2$ corresponding to 13.5 kpc$^2$ at the distance of IC10), of which we classified 536 and 380 stars as long-period variable candidates (LPVs), mostly asymptotic giant branch stars (AGBs) and red supergiants (RSGs), within CCD4 and two half-light radii of IC10, respectively. By comparing our output catalog to the catalogs from Pan-STARRS, Spitzer Space Telescope, Hubble Space Telescope (HST), and carbon stars from the Canada-France-Hawai'i Telescope (CFHT) survey, we determined the success of our detection method. We recovered $\sim$ 73% of Spitzer's sources in our catalog and demonstrated that our survey successfully identified 43% of the variable stars found with Spitzer, and also retrieved 40% of the extremely dusty AGB stars among the Spitzer variables. In addition, we successfully identified $\sim$ 70% of HST variables in our catalog. Furthermore, we found all the confirmed LPVs that Gaia DR3 detected in IC10 among our identified LPVs. This paper is the first in a series on IC10, presenting the variable star survey methodology and the photometric catalog, available to the public through the Centre de Donn\'ees Astronomiques de Strasbourg.
Survey Experience and Nonresponse in an Online Probability Panel: A Survival Analysis
2025-05-06
Katya Kostadintcheva, Jouni Kuha, Patrick Sturgis
We fit discrete time survival models to data from an online probability panel, where the outcome is the respondent first nonresponse to a survey invitation, following at least one previous survey completion. This approach has the advantage of utilising information about survey experiences over multiple survey waves, while accommodating the unbalanced data structure typical of OPPs, where the number, timing and content of survey invitations varies widely between panel members. We show that the nature and quality of previous survey experience has a strong influence on the propensity to respond to the next survey invitation. Longer surveys, reporting a survey as less enjoyable, a phone interview, and more days since the last survey invitation are found to be important predictors of nonresponse. We also find strong effects of personality on response propensity across survey invitations. Our findings have important implications for survey designers wishing to minimise nonresponse and attrition from OPPs.
Reanalysis of Stage-III cosmic shear surveys: A comprehensive study of shear diagnostic tests
2025-05-06
Jazmine Jefferson, Yuuki Omori, Chihway Chang, Shrihan Agarwal, Joe Zuntz, Marika Asgari, Marco Gatti, Benjamin Giblin, Claire-Alice Hébert, Mike Jarvis, Eske M. Pedersen, Judit Prat, Theo Schutt, Tianqing Zhang, the LSST Dark Energy Science Collaboration
In recent years, weak lensing shear catalogs have been released by various Stage-III weak lensing surveys including the Kilo-Degree Survey, the Dark Energy Survey, and the Hyper Suprime-Cam Subaru Strategic Program. These shear catalogs have undergone rigorous validation tests to ensure that the residual shear systematic effects in the catalogs are subdominant relative to the statistical uncertainties, such that the resulting cosmological constraints are unbiased. While there exists a generic set of tests that are designed to probe certain systematic effects, the implementations differ slightly across the individual surveys, making it difficult to make direct comparisons. In this paper, we use the TXPipe package to conduct a series of predefined diagnostic tests across three public shear catalogs -- the 1,000 deg$^2$ KiDS-1000 shear catalog, the Year 3 DES-Y3 shear catalog, and the Year 3 HSC-Y3 shear catalog. We attempt to reproduce the published results when possible and perform key tests uniformly across the surveys. While all surveys pass most of the null tests in this study, we find two tests where some of the surveys fail. Namely, we find that when measuring the tangential ellipticity around bright and faint star samples, KiDS-1000 fails depending on whether the samples are weighted, with a $\chi^2$/dof of 185.3/13 for faint stars. We also find that DES-Y3 and HSC-Y3 fail the $B$-mode test when estimated with the Hybrid-$E$/$B$ method, with a $\chi^2$/dof of 37.9/10 and 36.0/8 for the fourth and third autocorrelation bins. We assess the impacts on the $\Omega_{\rm m}$ - S$_{8}$ parameter space by comparing the posteriors of a simulated data vector with and without PSF contamination -- we find negligible effects in all cases. Finally, we propose strategies for performing these tests on future surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time.
nuGAN: Generative Adversarial Emulator for Cosmic Web with Neutrinos
2025-05-06
Neerav Kaushal, Elena Giusarma, Mauricio Reyes
Understanding the impact of neutrino masses on the evolution of Universe is a crucial aspect of modern cosmology. Due to their large free streaming lengths, neutrinos significantly influence the formation of cosmic structures at non-linear scales. To maximize the information yield from current and future galaxy surveys, it is essential to generate precise theoretical predictions of structure formation. One approach to achieve this is by running large sets of cosmological numerical simulations, which is a computationally intensive process. In this study, we propose a deep learning-based generative adversarial network (GAN) model to emulate the Universe for a variety of neutrino masses. Our model called $\nu$GAN (for neutrino GAN) is able to generate 2D cosmic webs of the Universe for a number of neutrino masses ranging from 0.0 eV to 0.4 eV. The generated maps exhibit statistical independence, lack correlations with training data, and very closely resemble the distribution of matter in true maps. We assess the accuracy of our results both visually and through key statistics used in cosmology and computer vision analyses. Our results indicate that samples generated by $\nu$GAN are accurate within a 5% error on power spectrum between k=0.01 to k=0.5 h/Mpc. Although this accuracy covers the mildly non-linear scales, consistent with other works and observations, achieving higher accuracy at fully non-linear scales requires more sophisticated models, such as diffusion models. Nevertheless, our work opens up new avenues for building emulators to generate fast and massive neutrino simulations, potentially revolutionizing cosmological predictions and analyses. This work serves as a proof-of-concept, paving the way for future extensions with higher-resolution 3D data and advanced generative models.
Lyapunov and Reversibility error invariant indicators
2025-05-06
Federico Panichi, Giorgio Turchetti
In this review, we present a survey of the Lyapunov Error and Reversibility Error (\cite{Faranda2012}), and we propose a generalization to make them invariant to the choice of initial conditions. We first define a process as the evolution in time of a a map or a flow, we then introduce the covariance matrix of a given process, and use their trace to compute LE and RE. The determinant of the covariance matrices is used to compute all invariant indicators of higher order. In this way, two set of invariant indicators are proposed within the framework introduced here, one for the Reversibility and one for the Lyapunov Errors, respectively. LE and RE which have been used in the literature, are the \textit{first-order} invariant indicators in their respective sets. The new sets of invariant indicators have the same fundamental meaning, the set for RE is used to characterize the dynamical evolution of a continuous small perturbation over an orbit, while LE is used to study the evolution of an initial displacement between two nearby orbits. We obtain again also the Reversibility Error Method (REM), which is a particular case of RE, where the additive noise is replaced by the round-off errors. REM has been proved to be a practical, accurate, and fast dynamical indicator and the results are comparable with the RE induced by a random noise of given amplitude. the behaviour of RE invariants depends on the sum of the positive Lyapunov exponents. We showcase the accuracy and reliability of those indicators on two well studied Hamiltonian. Within this new framework, a full dynamical characterization of a Hamiltonian system can be obtained. For instance, the Gibbs entropy, its asymptotic behaviour and a method to compute the fidelity over the perturbed orbit, are provided.
OpenHelix: A Short Survey, Empirical Analysis, and Open-Source Dual-System VLA Model for Robotic Manipulation
2025-05-06
Can Cui, Pengxiang Ding, Wenxuan Song, Shuanghao Bai, Xinyang Tong, Zirui Ge, Runze Suo, Wanqi Zhou, Yang Liu, Bofang Jia, Han Zhao, Siteng Huang, Donglin Wang
Dual-system VLA (Vision-Language-Action) architectures have become a hot topic in embodied intelligence research, but there is a lack of sufficient open-source work for further performance analysis and optimization. To address this problem, this paper will summarize and compare the structural designs of existing dual-system architectures, and conduct systematic empirical evaluations on the core design elements of existing dual-system architectures. Ultimately, it will provide a low-cost open-source model for further exploration. Of course, this project will continue to update with more experimental conclusions and open-source models with improved performance for everyone to choose from. Project page: https://openhelix-robot.github.io/.
The Relative Contributions of Accretion Disk versus Jet to the Optical and Mid-infrared Variability of Seyfert Galaxies
2025-05-06
Vineet Ojha, Xue-Bing Wu, Luis C. Ho
We performed a comprehensive analysis of flux and color variability in a redshift-matched sample of Seyfert galaxies, comprising 23 gamma-ray-detected narrow-line Seyfert 1 galaxies (gNLS1s), 190 non-gamma-ray-detected narrow-line Seyfert 1 galaxies (ngNLS1s), and 10 gamma-ray-detected broad-line Seyfert 1 galaxies (gBLS1s). Utilizing multi-band light curves from the Zwicky Transient Facility (ZTF) in g, r, and i bands, along with mid-infrared (MIR) observations in W1 and W2 bands from the Wide-Field Infrared Survey Explorer (WISE), we observed that gBLS1s exhibit more significant variability than gNLS1s, while ngNLS1s display minimal variability across both optical and MIR wavelengths. The pronounced variability in gBLS1s may be attributed to a more closely aligned jet relative to the observer's line of sight or their comparatively lower accretion rates. In contrast, the subdued variability in ngNLS1s suggests that their flux changes are primarily driven by accretion disk instabilities. A significant correlation between optical and MIR variability amplitudes found here supports the reprocessing scenario, wherein variations in the accretion disk emission are re-emitted by surrounding dust. Furthermore, our long-term color variability analysis revealed a stronger redder-when-brighter (RWB) trend in approximately 35%, 61%, and 80% of gNLS1s, ngNLS1s, and gBLS1s, respectively, in optical wavelength, strengthens the reprocessing scenario with the observed trend of stronger RWB for approximately 68%, 96%, and 30%, respectively, in MIR wavelength. The prevalent RWB trend observed in both optical and MIR wavelengths from the current sample of Seyfert galaxies on the longer time scales is likely associated with accretion disk instabilities.
Evidence for universal gas depletion in a sample of 41 luminous Type 1 quasars at z $\sim$ 2
2025-05-06
S. J. Molyneux, M. Banerji, M. J. Temple, M. Aravena, R. J. Assef, P. Hewett, G. C. Jones, A. Puglisi, A. L. Rankine, C. Ricci, M. Stepney, S. Tang
We present ALMA CO observations of the molecular gas in a sample of 41 luminous unobscured quasars at z $\sim$ 2 from the Sloan Digital Sky Survey. 32 targets comprise the main sample observed in CO(3-2) and 9 targets have archival ALMA data of CO(3-2), CO(4-3) and CO(7-6). All quasars have rest-UV to optical spectra tracing ionised gas in the broad line region (e.g. CIV) and the narrow line region (e.g. [OIII]) and they cover the full range of outflow properties in the SDSS quasar population at these redshifts. 15 out of the 32 quasars in the main sample are detected in CO(3-2) and five out of the nine archival quasars are also detected in CO. The median gas mass for all 20 CO detected quasars is 8.0 $\pm$ 1.5 $\times$ 10$^9$ M$_{\odot}$ with a median M$_{dyn}$ of 1.4 $\pm$ 0.9 $\times$ 10$^{11}$ M$_{\odot}$. We find gas fractions in the range 0.02 - 0.32, which are generally lower than both inactive galaxies and obscured quasars at similar redshifts. We suggest an evolutionary trend in gas fractions of quasar host galaxies from obscured and gas rich to unobscured and gas poor. We note a tentative correlation between the gas fractions and the broad-line region properties with quasars showing high CIV blueshifts, indicating stronger broad-line region winds, having higher gas fractions. Six of the quasars corresponding to 15% of the sample also show evidence for at least one companion galaxy detected in CO at the same redshift.
The eROSITA Final Equatorial Depth Survey (eFEDS): SDSS spectroscopic observations of X-ray sources
2025-05-06
Catarina Aydar, Andrea Merloni, Tom Dwelly, Johan Comparat, Mara Salvato, Johannes Buchner, Marcella Brusa, Teng Liu, Julien Wolf, Scott F. Anderson, Carolina P. Andonie, Franz Erik Bauer, Michael R. Blanton, William Nielsen Brandt, Yaherlyn Díaz, Lorena Hernandez-García, Dong-Woo Kim, Takamitsu Miyaji, Sean Morrison, Blessing Musiimenta, Castalia Alenka Negrete, Qingling Ni, Claudio Ricci, Donald P. Schneider, Axel Schwope, Yue Shen, Sophia G. H. Waddell, Riccardo Arcodia, Dmitry Bizyaev, Joseph N. Burchett, Priyanka Chakraborty, Kevin Covey, Boris T. Gansicke, Antonis Georgakakis, Paul J. Green, Hector Ibarra, Jacob Ider-Chitham, Anton M. Koekemoer, Juna A. Kollmeier, Mirko Krumpe, Georg Lamer, Adam Malyali, Kirpal Nandra, Kaike Pan, Claudio Rivera Pizarro, José Sanchez-Gallego, Jonathan R. Trump, Tanya Urrutia
We present one of the largest uniform optical spectroscopic surveys of X-ray selected sources to date that were observed as a pilot study for the Black Hole Mapper (BHM) survey. The BHM program of the Sloan Digital Sky Survey (SDSS)-V is designed to provide optical spectra for hundreds of thousands of X-ray selected sources from the SRG/eROSITA all-sky survey. This significantly improves our ability to classify and characterise the physical properties of large statistical populations of X-ray emitting objects. Our sample consists of 13079 sources in the eROSITA eFEDS performance verification field, 12011 of which provide reliable redshifts from 06.5x10^-15 erg/s/cm^2) and were observed for about 20 years with fibre-fed SDSS spectrographs. After including all available redshift information for the eFEDS sources from the dedicated SDSS-V plate programme and archival data, we visually inspected the SDSS optical spectra to verify the reliability of these redshift measurements and the performance of the SDSS pipeline. The visual inspection allowed us to recover reliable redshifts (for 99% of the spectra with a signal-to-noise ratio of >2) and to assign classes to the sources, and we confirm that the vast majority of our sample consists of active galactic nuclei (AGNs). Only ~3% of the eFEDS/SDSS sources are Galactic objects. We also show the diversity of the optical spectra of the X-ray selected AGNs and provide spectral stacks with a high signal-to-noise ratio in various sub-samples with different redshift and optical broad-band colours. Our AGN sample contains optical spectra of (broad-line) quasars, narrow-line galaxies, and optically passive galaxies. It is considerably diverse in its colours and in its levels of nuclear obscuration.
SAPPHIRES: Extremely Metal-Poor Galaxy Candidates with $12+{\rm log(O/H)}<7.0$ at $z\sim5-7$ from Deep JWST/NIRCam Grism Observations
2025-05-06
Tiger Yu-Yang Hsiao, Fengwu Sun, Xiaojing Lin, Dan Coe, Eiichi Egami, Daniel J. Eisenstein, Yoshinobu Fudamoto, Andrew J. Bunker, Xiaohui Fan, Yuichi Harikane, Jakob M. Helton, Koki Kakiichi, Yichen Liu, Weizhe Liu, Roberto Maiolino, Masami Ouchi, Wei Leong Tee, Feige Wang, Yunjing Wu, Yi Xu, Jinyi Yang, Yongda Zhu
Population III stars, the hypothetical first generation metal-free stars, have yet to be discovered. Even after three years of successful JWST operations, studies have shown that most galaxies identified to date at $z > 5$ exhibit a metallicity floor of $Z\gtrsim2\%\,Z_{\odot}$, possibly due to unknown selection biases toward bright galaxies or rapid metal enrichment. To address this question, we search for galaxies with low R3$=$[OIII]$\,\lambda$5008/H$\beta$ ratios as part of the JWST Cycle-3 large treasury program, the Slitless Areal Pure-Parallel HIgh-Redshift Emission Survey (SAPPHIRES). Using deep NIRCam Wide-Field Slitless Spectrscopy (WFSS) data, we report the discovery of seven extremely metal-poor galaxy candidates in the SAPPHIRES Early Data Release field, with estimated $12+{\rm log(O/H)}<7.0$ at $z\sim5-7$, including two sources with $Z<1\%\,Z_{\odot}$, significantly breaking the metallicity floor observed both locally and at high redshift. These candidates appear extremely faint ($\sim28-30\,$ F200W AB mag) and low-mass (${\rm log}(M_{*}/M_{\odot})\sim6.8-7.8$), as expected from the mass-metallicity relation. They also exhibit very blue UV slopes ($-2.6\lesssim\beta\lesssim-2.0$), likely due to low dust content $A_{V}\lesssim0.2\,{\rm mag}$ or young stellar ages $\sim5-20\,{\rm Myr}$. Compared to galaxies at similar redshift, they appear exceptionally bursty in their star formation activity. Our results highlight the power of NIRCam/WFSS in identifying extremely metal-poor galaxies, from just a single pointing, with more data to come in SAPPHIRES. This underscores the potential of pure-parallel programs towards achieving JWST's primary science goal: discovering the first pristine stars and galaxies. Deep JWST/NIRSpec follow-up observations will also be essential to confirm their nature and perform detailed chemical abundance analyses.
Dynamical Dark Energy Emerges from Massive Gravity
2025-05-06
Juri Smirnov
In this work, we demonstrate that a dynamical dark energy component predicted by massive gravity gives rise to a distinctive evolution of the equation of state. This scenario is favoured over the standard $\Lambda$CDM model when confronted with the latest combined datasets from the Dark Energy Spectroscopic Instrument (DESI), the cosmic microwave background (CMB), and supernova observations. The model stands out as a rare example of a healthy, self-consistent theory that accommodates phantom dark energy while maintaining a technically natural, small asymptotic cosmological constant. Our analysis indicates a preferred graviton mass of approximately $5 \times 10^{-33} \text{eV}$, suggesting the emergence of a new cosmological length scale. This leads to a maximal deviation of the equation of state around $z \sim 2.5$, a prediction that will be robustly tested by upcoming, deeper surveys of baryon acoustic oscillations.
Context: Women face many challenges in their lives, which affect their daily experiences and influence major life decisions, starting before they enroll in bachelor's programs, setting a difficult path for those aspiring to enter the software development industry. Goal: To explore the challenges that women face across three different life stages, beginning as high school students, continuing as university undergraduates, and extending into their professional lives, as well as potential solutions to address these challenges. Research Method: We conducted a literature review followed by workshops to understand the perspectives of high school women, undergraduates, and practitioners regarding the same set of challenges and solutions identified in the literature. Results: Regardless of the life stage, women feel discouraged in a toxic environment often characterized by a lack of inclusion, harassment, and the exhausting need to prove themselves. We also discovered that some challenges are specific to certain life stages; for example, issues related to maternity were mentioned only by practitioners. Conclusions: Gender-related challenges arise before women enter the software development field when the proportion of men and women is still similar. While the need to prove themselves is mentioned at all three stages, high school women's challenges are more often directed toward convincing their parents that they are mature enough to handle their responsibilities. As they progress, the emphasis shifts to proving their competence in managing responsibilities for which they have received training. Increasing the inclusion of women in the field should, therefore, start earlier, and profound societal changes may be necessary to boost women's participation.
From Glue-Code to Protocols: A Critical Analysis of A2A and MCP Integration for Scalable Agent Systems
2025-05-06
Qiaomu Li, Ying Xie
Artificial intelligence is rapidly evolving towards multi-agent systems where numerous AI agents collaborate and interact with external tools. Two key open standards, Google's Agent to Agent (A2A) protocol for inter-agent communication and Anthropic's Model Context Protocol (MCP) for standardized tool access, promise to overcome the limitations of fragmented, custom integration approaches. While their potential synergy is significant, this paper argues that effectively integrating A2A and MCP presents unique, emergent challenges at their intersection, particularly concerning semantic interoperability between agent tasks and tool capabilities, the compounded security risks arising from combined discovery and execution, and the practical governance required for the envisioned "Agent Economy". This work provides a critical analysis, moving beyond a survey to evaluate the practical implications and inherent difficulties of combining these horizontal and vertical integration standards. We examine the benefits (e.g., specialization, scalability) while critically assessing their dependencies and trade-offs in an integrated context. We identify key challenges increased by the integration, including novel security vulnerabilities, privacy complexities, debugging difficulties across protocols, and the need for robust semantic negotiation mechanisms. In summary, A2A+MCP offers a vital architectural foundation, but fully realizing its potential requires substantial advancements to manage the complexities of their combined operation.
Large Language Models (LLMs) are transforming scientific hypothesis generation and validation by enabling information synthesis, latent relationship discovery, and reasoning augmentation. This survey provides a structured overview of LLM-driven approaches, including symbolic frameworks, generative models, hybrid systems, and multi-agent architectures. We examine techniques such as retrieval-augmented generation, knowledge-graph completion, simulation, causal inference, and tool-assisted reasoning, highlighting trade-offs in interpretability, novelty, and domain alignment. We contrast early symbolic discovery systems (e.g., BACON, KEKADA) with modern LLM pipelines that leverage in-context learning and domain adaptation via fine-tuning, retrieval, and symbolic grounding. For validation, we review simulation, human-AI collaboration, causal modeling, and uncertainty quantification, emphasizing iterative assessment in open-world contexts. The survey maps datasets across biomedicine, materials science, environmental science, and social science, introducing new resources like AHTech and CSKG-600. Finally, we outline a roadmap emphasizing novelty-aware generation, multimodal-symbolic integration, human-in-the-loop systems, and ethical safeguards, positioning LLMs as agents for principled, scalable scientific discovery.
Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review
2025-05-05
Sonal Allana, Mohan Kankanhalli, Rozita Dara
Explainable Artificial Intelligence (XAI) has emerged as a pillar of Trustworthy AI and aims to bring transparency in complex models that are opaque by nature. Despite the benefits of incorporating explanations in models, an urgent need is found in addressing the privacy concerns of providing this additional information to end users. In this article, we conduct a scoping review of existing literature to elicit details on the conflict between privacy and explainability. Using the standard methodology for scoping review, we extracted 57 articles from 1,943 studies published from January 2019 to December 2024. The review addresses 3 research questions to present readers with more understanding of the topic: (1) what are the privacy risks of releasing explanations in AI systems? (2) what current methods have researchers employed to achieve privacy preservation in XAI systems? (3) what constitutes a privacy preserving explanation? Based on the knowledge synthesized from the selected studies, we categorize the privacy risks and preservation methods in XAI and propose the characteristics of privacy preserving explanations to aid researchers and practitioners in understanding the requirements of XAI that is privacy compliant. Lastly, we identify the challenges in balancing privacy with other system desiderata and provide recommendations for achieving privacy preserving XAI. We expect that this review will shed light on the complex relationship of privacy and explainability, both being the fundamental principles of Trustworthy AI.
Constraints on Inflationary Gravitational Waves with Two Years of SPT-3G Data
2025-05-05
J. A. Zebrowski, C. L. Reichardt, A. J. Anderson, B. Ansarinejad, M. Archipley, L. Balkenhol, P. Barry, K. Benabed, A. N. Bender, B. A. Benson, F. Bianchini, L. E. Bleem, F. R. Bouchet, L. Bryant, E. Camphuis, J. E. Carlstrom, C. L. Chang, P. Chaubal, P. M. Chichura, A. Chokshi, T. -L. Chou, A. Coerver, T. M. Crawford, C. Daley, T. de Haan, K. R. Dibert, M. A. Dobbs, M. Doohan, A. Doussot, D. Dutcher, W. Everett, C. Feng, K. R. Ferguson, K. Fichman, A. Foster, S. Galli, A. E. Gambrel, R. W. Gardner, F. Ge, N. Goeckner-Wald, R. Gualtieri, F. Guidi, S. Guns, N. W. Halverson, E. Hivon, G. P. Holder, W. L. Holzapfel, J. C. Hood, A. Hryciuk, N. Huang, F. Kéruzoré, A. R. Khalife, L. Knox, M. Korman, K. Kornoelje, C. -L. Kuo, K. Levy, Y. Li, A. E. Lowitz, C. Lu, G. P. Lynch, A. Maniyar, E. S. Martsen, F. Menanteau, M. Millea, J. Montgomery, Y. Nakato, T. Natoli, G. I. Noble, Y. Omori, A. Ouellette, Z. Pan, P. Paschos, K. A. Phadke, A. W. Pollak, K. Prabhu, W. Quan, S. Raghunathan, M. Rahimi, A. Rahlin, M. Rouble, J. E. Ruhl, A. Simpson, E. Schiappucci, J. A. Sobrin, A. A. Stark, J. Stephen, C. Tandoi, B. Thorne, C. Trendafilova, C. Umilta, J. D. Vieira, A. G. Vieregg, A. Vitrier, Y. Wan, N. Whitehorn, W. L. K. Wu, M. R. Young
We present a measurement of the $B$-mode polarization power spectrum of the cosmic microwave background anisotropies at 32 $\le$ $\ell$ $<$ 502 for three bands centered at 95, 150, and 220 GHz using data from the SPT-3G receiver on the South Pole Telescope. This work uses SPT-3G observations from the 2019 and 2020 winter observing seasons of a $\sim$1500 deg$^2$ patch of sky that directly overlaps with fields observed with the BICEP/Keck family of telescopes, and covers part of the proposed Simons Observatory and CMB-S4 deep fields. Employing new techniques for mitigating polarized atmospheric noise, the SPT-3G data demonstrates a white noise level of 9.3 (6.7) $\mu$K-arcmin at $\ell \sim 500$ for the 95 GHz (150 GHz) data, with a $1/\ell$ noise knee at $\ell$=128 (182). We fit the observed six auto- and cross-frequency $B$-mode power spectra to a model including lensed $\Lambda$CDM $B$-modes and a combination of Galactic and extragalactic foregrounds. This work characterizes foregrounds in the vicinity of the BICEP/Keck survey area, finding foreground power consistent with that reported by the BICEP/Keck collaboration within the same region, and a factor of $\sim$ 3 higher power over the full SPT-3G survey area. Using SPT-3G data over the BICEP/Keck survey area, we place a 95% upper limit on the tensor-to-scalar ratio of $r < 0.25$ and find the statistical uncertainty on $r$ to be $\sigma(r) = 0.067$.
Closeby Habitable Exoplanet Survey (CHES). IV. Synergy between astrometry and direct imaging missions of the Habitable World Observatory for detecting Earth-like planets
2025-05-05
Chunhui Bao, Jianghui Ji, Dongjie Tan, Guo Chen, Xiumin Huang, Su Wang, Yao Dong
The detection and characterization of habitable planets around nearby stars persist as one of the foremost objectives in contemporary astrophysics. This work investigates the synergistic integration of astrometric and direct imaging techniques by capitalizing on the complementary capabilities of the Closeby Habitable Exoplanet Survey (CHES) and Habitable Worlds Observatory (HWO). Planetary brightness and position vary over time due to phase effects and orbital architectures, information that can be precisely provided by CHES's astrometric measurements. By combining the precise orbital constraints from CHES with the imaging capabilities of HWO, we evaluate the improvements in detection efficiency, signal-to-noise ratio and overall planet yield. Completeness is quantified as the fraction of injected planets that are successfully detected, while yields are estimated for various scenarios using terrestrial planet occurrence rates derived from the Kepler dataset. Our results indicate that prior astrometric data significantly enhance detection efficiency. Under the adopted detection limit, our analysis indicates that prior CHES observations can increase completeness by approximately 10% and improve detection efficiency by factors ranging from two to thirty. The findings underscore the importance of interdisciplinary approaches in the search for and characterization of habitable worlds.
A coherent radio burst from an X-ray neutron star in the Carina Nebula
2025-05-05
K. M. Rajwade, J. Tian, G. Younes, B. Posselt, B. Stappers, Z. Wadiasingh, E. D. Barr, M. C. Bezuidenhout, M. Caleb, F. Jankowski, M. Kramer, I. Pastor-Marazuela, M. Surnis
The neutron star zoo comprises several sub-populations that range from energetic magnetars and thermally emitting X-ray neutron stars to radio-emitting pulsars. Despite studies over the last five decades, it has been challenging to obtain a clear physical link between the various populations of neutron stars, vital to constrain their formation and evolutionary pathways. Here we report the detection of a burst of coherent radio emission from a known radio-quiet, thermally emitting neutron star 2XMM J104608.7$-$594306in the Carina Nebula. The burst has a distinctive sharp rise followed by a decay made up of multiple components, which is unlike anything seen from other radio-emitting neutron stars. It suggests an episodic event from the neutron star surface, akin to transient radio emission seen from magnetars. The radio burst confirms that the X-ray source is a neutron star and suggests a new link between these apparently radio-quiet X-ray emitting sources and other transient or persistent radio-emitting neutron stars. It also suggests that a common physical mechanism for emission might operate over a range of magnetic field strengths and neutron star ages. We propose that 2XMM J104608.7$-$594306 straddles the boundary between young, energetic neutron stars and their evolved radio-emitting cousins and may bridge these two populations. The detection of such a radio burst also shows that other radio-quiet neutron stars may also emit such sporadic radio emission that has been missed by previous radio surveys and highlights the need for regular monitoring of this unique sub-population of neutron stars.
Social scoring is one of the AI practices banned by the AI Act. This ban is explicitly inspired by China, which in 2014 announced its intention to set up a large-scale government project - the Social Credit System - aiming to rate every Chinese citizen according to their good behaviour, using digital technologies and AI. But in Europe, individuals are also scored by public and private bodies in a variety of contexts, such as assessing creditworthiness, monitoring employee productivity, detecting social fraud or terrorist risks, and so on. However, the AI Act does not intend to prohibit these types of scoring, as they would qualify as 'high-risk AI systems', which are authorised while subject to various requirements. One might therefore think that the ban on social scoring will have no practical effect on the scoring practices already in use in Europe, and that it is merely a vague safeguard in case an authoritarian power is tempted to set up such a system on European territory. Contrary to this view, this article argues that the ban has been drafted in a way that is flexible and therefore likely to make it a useful tool, similar and complementary to Article 22 of the General Data Protection Regulation, to protect individuals against certain forms of disproportionate use of AI-based scoring.
Bye-bye, Bluebook? Automating Legal Procedure with Large Language Models
2025-05-05
Matthew Dahl
Legal practice requires careful adherence to procedural rules. In the United States, few are more complex than those found in The Bluebook: A Uniform System of Citation. Compliance with this system's 500+ pages of byzantine formatting instructions is the raison d'etre of thousands of student law review editors and the bete noire of lawyers everywhere. To evaluate whether large language models (LLMs) are able to adhere to the procedures of such a complicated system, we construct an original dataset of 866 Bluebook tasks and test flagship LLMs from OpenAI, Anthropic, Google, Meta, and DeepSeek. We show (1) that these models produce fully compliant Bluebook citations only 69%-74% of the time and (2) that in-context learning on the Bluebook's underlying system of rules raises accuracy only to 77%. These results caution against using off-the-shelf LLMs to automate aspects of the law where fidelity to procedure is paramount.
Dynamical coupled-channel models for hadron dynamics
2025-05-05
Michael Döring, Johann Haidenbauer, Maxim Mai, Toru Sato
Dynamical coupled-channel (DCC) approaches parametrize the interactions and dynamics of two and more hadrons and their response to different electroweak probes. The inclusion of unitarity, three-body channels, and other properties from scattering theory allows for a reliable extraction of resonance spectra and their properties from data. We review the formalism and application of the ANL-Osaka, the Juelich-Bonn-Washington, and other DCC approaches in the context of light baryon resonances from meson, (virtual) photon, and neutrino-induced reactions, as well as production reactions, strange baryons, light mesons, heavy meson systems, exotics, and baryon-baryon interactions. Finally, we also provide a connection of the formalism to study finite-volume spectra obtained in Lattice QCD, and review applications involving modern statistical and machine learning tools.
Searching for supermassive black holes binaries within SRG/eROSITA-De I: Properties of the X-ray selected candidates
2025-05-05
D. Tubín-Arenas, M. Krumpe, D. Homan, A. Markowitz, M. Powell, G. Lamer, T. Urrutia, A. Schwope, H. Winkler, S. Bahic, J. Buchner, C. Andonie, M. Salvato, A. Merloni, J. Kurpas, S. Ciroi, F. di Mille, A. Chaturvedi, A. Rau, Z. Igo, I. Grotova, Z. Liu, K. Nandra
Abridged: Supermassive black hole binaries (SMBHBs) separated by (sub)-pc scales represent one of the latest stages of hierarchical galaxy assembly. However, many of these objects are hidden behind large columns of gas and dust at the center of galaxies and are difficult to detect. The X-ray and UV emission in these systems are predicted to vary regularly on timescales comparable to that of the orbital period of the binary. This is the first of a series of papers where we aim to find SMBHB candidates based on quasi-periodic light curves from the soft X-ray instrument eROSITA on board the Spectrum-Roentgen-Gamma (SRG) observatory and X-ray follow-up. We searched the multi-epoch SRG/eROSITA all-sky surveys for extragalactic sources that show an `up-down-up-down' or `down-up-down-up' profile (from scan to scan) in their 0.2--2.3 keV flux light curves. We compiled a sample of 16 sources that are suitable for X-ray follow-up campaigns given their brightness and significant variability between bright and faint SRG/eROSITA flux levels. We triggered extensive Swift-XRT and NICER monitoring campaigns on the best SMBHB candidates to confirm or discard their tentative periodicities. Optical spectroscopic observations confirmed the nuclear and extragalactic nature of 15/16 objects and enabled single-epoch SMBH mass measurements and BPT classifications. Our most promising candidate, eRASSt J0530-4125, shows X-ray quasi-periodic variability with a typical time scale of one year in the observed frame. By stacking the X-ray observations of each source in our sample, we find that 14/15 sources can be modeled by a power law with a photon index ranging from $\Gamma\sim1.8-2.8$. Based on our selection, we estimate an optimistic upper limit on the fraction of SMBHB candidates to be $\sim 0.05$ per galaxy. We emphasize that further observational evidence is needed to confirm the SMBHB nature of our sources.
In this survey we present the most recent developments in the uniformization of metric surfaces, i.e., metric spaces homeomorphic to two-dimensional topological manifolds. We start from the classical conformal uniformization theorem of Koebe and Poincar\'e. Then we discuss the Bonk-Kleiner theorem on the quasisymmetric uniformization of metric spheres, which marks the beginning of the study of the uniformization problem on fractal surfaces. The next result presented is Rajala's theorem on the quasiconformal uniformization of metric spheres. We conclude with the final result in this series of works, due to Romney and the author, on the weakly quasiconformal uniformization of arbitrary metric surfaces of locally finite area under no further assumption.
Exploring the IR-radio correlation in massive galaxy clusters at the end of cosmic noon
2025-05-05
N. Samanso, J. B. Nantais, S. Alberts, U. Rescigno, W. Rujopakarn, G. R. Zeimann, J. Wagg
We investigate the effect of the environment on the infrared and radio emission of cluster galaxies during the transition epoch at 1 < z < 2 when they first start to quench consistently in the majority of galaxy clusters. We considered a sample of 129 cluster member galaxies from 11 massive clusters at a confirmed redshift of 1.0-1.8 from the IRAC Shallow Cluster Survey (ISCS), the IRAC Distant Cluster Survey (IDCS), and new 3 GHz images from the Karl G. Jansky Very Large Array (VLA). We calculated the IR-radio correlation slope parameter, q, in order to identify differences in the ratios of IR to radio of cluster galaxies and field galaxy comparison samples at different redshifts. Active galactic nuclei (AGNs) were identified and analyzed to search for any effect on the IR-radio correlation. The correlation parameter values were also compared by the Kolmogorov-Smirnov test with field galaxies. Our comparison of the IR-radio correlation in cluster galaxies to the control sample of field galaxies reveals a marginally to modestly significant difference in the correlation slope parameter at the ~2sigma-3sigma level. A split of the clusters into low-redshift (1 < z < 1.37) and high-redshift (1.37 < z < 1.8) bins indicates a more significant difference in the correlation parameter in the lower redshift cluster subsample, where widespread quenching begins. We find no difference in the IR-radio correlation between galaxies that host AGNs and non-active star-forming galaxies either. This suggests that our AGNs are overwhelmingly radio quiet and therefore do not affect the results we described above. We conclude that further investigations based on larger datasets are needed to constrain the impact of the cluster environment on the IR-radio correlation better.
Sailing AI by the Stars: A Survey of Learning from Rewards in Post-Training and Test-Time Scaling of Large Language Models
2025-05-05
Xiaobao Wu
Recent developments in Large Language Models (LLMs) have shifted from pre-training scaling to post-training and test-time scaling. Across these developments, a key unified paradigm has arisen: Learning from Rewards, where reward signals act as the guiding stars to steer LLM behavior. It has underpinned a wide range of prevalent techniques, such as reinforcement learning (in RLHF, DPO, and GRPO), reward-guided decoding, and post-hoc correction. Crucially, this paradigm enables the transition from passive learning from static data to active learning from dynamic feedback. This endows LLMs with aligned preferences and deep reasoning capabilities. In this survey, we present a comprehensive overview of the paradigm of learning from rewards. We categorize and analyze the strategies under this paradigm across training, inference, and post-inference stages. We further discuss the benchmarks for reward models and the primary applications. Finally we highlight the challenges and future directions. We maintain a paper collection at https://github.com/bobxwu/learning-from-rewards-llm-papers.
Network analysis of student perceptions of an introductory lab
2025-05-05
W. Brian Lane, Charlotte Dries, Gabriella Khazal, Tiffany Snow
Introductory physics labs can be modeled using the Communities of Practice framework, in which a group of members pursues a common set of goals by learning and implementing a set of agreed-upon practices. Studies of student preferences and behaviors show how students might perceive the introductory lab community differently, as they occupy different positions within the community and engage differently with its practices. Such differences can be particularly sharp along the dimensions of gender, college generation, and racial background. In this proof-of-concept study, we demonstrate how we can explore students' perceptions of the introductory lab community of practice using a drawing-based survey and network analysis. The drawing-based survey collects students' open expressions of their perspectives and guides them to elaborate on the goals, members, and practices of the community. Network analysis allows us to obtain an overview of the most important common elements from a sample of drawings while preserving the openness of student expression. We quantify ``importance'' as the frequency of an element's depiction across the network and its betweenness centrality within the network. We collected $N = 74$ student drawings from a studio-format introductory physics for life sciences sequence. Our network analysis reveals a wide diversity of drawing elements with a focus on centralized hands-on practices and community members and a sparsity of goals across the students' perspectives. We demonstrate how some elements show differences with large effect sizes between identity groups based on gender, college generation, and racial background. We compare these differences to those found in observational studies and discuss future uses of the drawing survey and network analysis approach.
A Survey on Progress in LLM Alignment from the Perspective of Reward Design
2025-05-05
Miaomiao Ji, Yanqiu Wu, Zhibin Wu, Shoujin Wang, Jian Yang, Mark Dras, Usman Naseem
The alignment of large language models (LLMs) with human values and intentions represents a core challenge in current AI research, where reward mechanism design has become a critical factor in shaping model behavior. This study conducts a comprehensive investigation of reward mechanisms in LLM alignment through a systematic theoretical framework, categorizing their development into three key phases: (1) feedback (diagnosis), (2) reward design (prescription), and (3) optimization (treatment). Through a four-dimensional analysis encompassing construction basis, format, expression, and granularity, this research establishes a systematic classification framework that reveals evolutionary trends in reward modeling. The field of LLM alignment faces several persistent challenges, while recent advances in reward design are driving significant paradigm shifts. Notable developments include the transition from reinforcement learning-based frameworks to novel optimization paradigms, as well as enhanced capabilities to address complex alignment scenarios involving multimodal integration and concurrent task coordination. Finally, this survey outlines promising future research directions for LLM alignment through innovative reward design strategies.
A Survey of Slow Thinking-based Reasoning LLMs using Reinforced Learning and Inference-time Scaling Law
2025-05-05
Qianjun Pan, Wenkai Ji, Yuyang Ding, Junsong Li, Shilian Chen, Junyi Wang, Jie Zhou, Qin Chen, Min Zhang, Yulan Wu, Liang He
This survey explores recent advancements in reasoning large language models (LLMs) designed to mimic "slow thinking" - a reasoning process inspired by human cognition, as described in Kahneman's Thinking, Fast and Slow. These models, like OpenAI's o1, focus on scaling computational resources dynamically during complex tasks, such as math reasoning, visual reasoning, medical diagnosis, and multi-agent debates. We present the development of reasoning LLMs and list their key technologies. By synthesizing over 100 studies, it charts a path toward LLMs that combine human-like deep thinking with scalable efficiency for reasoning. The review breaks down methods into three categories: (1) test-time scaling dynamically adjusts computation based on task complexity via search and sampling, dynamic verification; (2) reinforced learning refines decision-making through iterative improvement leveraging policy networks, reward models, and self-evolution strategies; and (3) slow-thinking frameworks (e.g., long CoT, hierarchical processes) that structure problem-solving with manageable steps. The survey highlights the challenges and further directions of this domain. Understanding and advancing the reasoning abilities of LLMs is crucial for unlocking their full potential in real-world applications, from scientific discovery to decision support systems.
Baryon Acoustic Oscillations from a Different Angle
2025-05-05
George Efstathiou
This paper presents an alternative way of analysing Baryon Acoustic Oscillation (BAO) distance measurements via rotations to define new quantities Dperp and Dpar. These quantities allow simple tests of consistency with the Planck LCDM cosmology. The parameter Dperp is determined with negligible uncertainty from Planck under the assumption of LCDM. Comparing with measurements from the Dark Energy Spectroscopic Instrument (DESI), we find that the measurements of Dperp from Data Release 2 (DR2) move into significantly better agreement with the Planck LCDM cosmology compared to DESI Data Release 1 (DR1). The quantity in the orthogonal direction Dpar provides a measure of the physical matter density omega_m in the LCDM cosmology. The DR2 measurements of Dpar\ also come into better agreement with Planck LCDM compared to the earlier DR1 results. From the comparison of Planck and DESI BAO measurements, we find no significant evidence in support of evolving dark energy. We also investigate a rotation in the theory space of the w_0 and w_a parameterization of the dark energy equation-of-state w(z). We show that the combination of DESI BAO measurements and the CMB constrain w(z=0.5) = -0.996 pm 0.046, i.e. very close to the value expected for a cosmological constant. We present a critique of the statistical methodology employed by the DESI collaboration and argue that it gives a misleading impression of the evidence in favour of evolving dark energy. An Appendix shows that the cosmological parameters determined from the Dark Energy Survey 5 Year supernova sample are in tension with those from DESI DR2 and parameters determined by Planck.
2025-05-05
A. Gholamhosseinian, R. W. Corkery, I. Brevik, Mathias Bostrom
We explore the fundamental idea that there may be a role for the Casimir effect, via an uncertainty relation, in the generation of electron-positron and quark-gluon plasmas. We investigate this concept, reviewing the possible contribution of semi-classical electrodynamics to nuclear interactions, specifically focusing on the Casimir effect at sub-Fermi length scales. The main result is a temperature distance relation, derived from the time-energy uncertainty relation, which can have observable consequences at these extreme scales. From a more general perspective, since the energy-time uncertainty relation appears to be a significant physical quantity, we also provide a brief overview of recent developments in this direction in Sec. 3.2.
We review the main features of an unfitted finite element method for interface and fluid-structure interaction problems based on a distributed Lagrange multiplier in the spirit of the fictitious domain approach. We recall our theoretical findings concerning well-posedeness, stability, and convergence of the numerical schemes, and discuss the related computational challenges. In the case of elliptic interface problems, we also present a posteriori error estimates.
The proliferation of edge devices has generated an unprecedented volume of time series data across different domains, motivating various well-customized methods. Recently, Large Language Models (LLMs) have emerged as a new paradigm for time series analytics by leveraging the shared sequential nature of textual data and time series. However, a fundamental cross-modality gap between time series and LLMs exists, as LLMs are pre-trained on textual corpora and are not inherently optimized for time series. Many recent proposals are designed to address this issue. In this survey, we provide an up-to-date overview of LLMs-based cross-modality modeling for time series analytics. We first introduce a taxonomy that classifies existing approaches into four groups based on the type of textual data employed for time series modeling. We then summarize key cross-modality strategies, e.g., alignment and fusion, and discuss their applications across a range of downstream tasks. Furthermore, we conduct experiments on multimodal datasets from different application domains to investigate effective combinations of textual data and cross-modality strategies for enhancing time series analytics. Finally, we suggest several promising directions for future research. This survey is designed for a range of professionals, researchers, and practitioners interested in LLM-based time series modeling.
Mock modularity at work, or black holes in a forest
2025-05-05
Sergei Alexandrov
Mock modular forms, first invented by Ramanujan, provide a beautiful generalization of the usual modular forms. In recent years, it was found that they capture generating functions of the number of microstates of BPS black holes appearing in compactifications of string theory with 8 and 16 supercharges. This review describes these results and their applications which range from the actual computation of these generating functions for both compact and non-compact compactification manifolds (encoding, respectively, Donaldson-Thomas and Vafa-Witten topological invariants) to the construction of new non-commutative structures on moduli spaces of Calabi-Yau threefolds.
Recent years have seen remarkable progress in both multimodal understanding models and image generation models. Despite their respective successes, these two domains have evolved independently, leading to distinct architectural paradigms: While autoregressive-based architectures have dominated multimodal understanding, diffusion-based models have become the cornerstone of image generation. Recently, there has been growing interest in developing unified frameworks that integrate these tasks. The emergence of GPT-4o's new capabilities exemplifies this trend, highlighting the potential for unification. However, the architectural differences between the two domains pose significant challenges. To provide a clear overview of current efforts toward unification, we present a comprehensive survey aimed at guiding future research. First, we introduce the foundational concepts and recent advancements in multimodal understanding and text-to-image generation models. Next, we review existing unified models, categorizing them into three main architectural paradigms: diffusion-based, autoregressive-based, and hybrid approaches that fuse autoregressive and diffusion mechanisms. For each category, we analyze the structural designs and innovations introduced by related works. Additionally, we compile datasets and benchmarks tailored for unified models, offering resources for future exploration. Finally, we discuss the key challenges facing this nascent field, including tokenization strategy, cross-modal attention, and data. As this area is still in its early stages, we anticipate rapid advancements and will regularly update this survey. Our goal is to inspire further research and provide a valuable reference for the community. The references associated with this survey are available on GitHub (https://github.com/AIDC-AI/Awesome-Unified-Multimodal-Models).
2025-05-05
Pengfei Yang, Ngai-Man Cheung, Xinda Ma
Text-to-image generation (T2I) refers to the text-guided generation of high-quality images. In the past few years, T2I has attracted widespread attention and numerous works have emerged. In this survey, we comprehensively review 141 works conducted from 2021 to 2024. First, we introduce four foundation model architectures of T2I (autoregression, non-autoregression, GAN and diffusion) and the commonly used key technologies (autoencoder, attention and classifier-free guidance). Secondly, we systematically compare the methods of these studies in two directions, T2I generation and T2I editing, including the encoders and the key technologies they use. In addition, we also compare the performance of these researches side by side in terms of datasets, evaluation metrics, training resources, and inference speed. In addition to the four foundation models, we survey other works on T2I, such as energy-based models and recent Mamba and multimodality. We also investigate the potential social impact of T2I and provide some solutions. Finally, we propose unique insights of improving the performance of T2I models and possible future development directions. In summary, this survey is the first systematic and comprehensive overview of T2I, aiming to provide a valuable guide for future researchers and stimulate continued progress in this field.
Advanced neural interfaces are transforming applications ranging from neuroscience research to diagnostic tools (for mental state recognition, tremor and seizure detection) as well as prosthetic devices (for motor and communication recovery). By integrating complex functions into miniaturized neural devices, these systems unlock significant opportunities for personalized assistive technologies and adaptive therapeutic interventions. Leveraging high-density neural recordings, on-site signal processing, and machine learning (ML), these interfaces extract critical features, identify disease neuro-markers, and enable accurate, low-latency neural decoding. This integration facilitates real-time interpretation of neural signals, adaptive modulation of brain activity, and efficient control of assistive devices. Moreover, the synergy between neural interfaces and ML has paved the way for self-sufficient, ubiquitous platforms capable of operating in diverse environments with minimal hardware costs and external dependencies. In this work, we review recent advancements in AI-driven decoding algorithms and energy-efficient System-on-Chip (SoC) platforms for next-generation miniaturized neural devices. These innovations highlight the potential for developing intelligent neural interfaces, addressing critical challenges in scalability, reliability, interpretability, and user adaptability.
Beyond the model: Key differentiators in large language models and multi-agent services
2025-05-05
Muskaan Goyal, Pranav Bhasin
With the launch of foundation models like DeepSeek, Manus AI, and Llama 4, it has become evident that large language models (LLMs) are no longer the sole defining factor in generative AI. As many now operate at comparable levels of capability, the real race is not about having the biggest model but optimizing the surrounding ecosystem, including data quality and management, computational efficiency, latency, and evaluation frameworks. This review article delves into these critical differentiators that ensure modern AI services are efficient and profitable.
We consider the free energy of a class of spin glass models with $ p$-spin interactions in a transverse magnetic field. As $ p \to \infty $, the infinite system-size free energy is proven to converge to that of the quantum random energy model. This is accomplished by combining existing analytical techniques addressing the non-commutative properties of such quantum glasses, with the description of the typical geometry of extreme negative deviations of the classical $ p $-spin glass. We also review properties of the corresponding classical free energy and conjectures addressing $ 1/p $-corrections in the quantum case.
Out-of-distribution detection (OOD) is a pivotal task for real-world applications that trains models to identify samples that are distributionally different from the in-distribution (ID) data during testing. Recent advances in AI, particularly Vision-Language Models (VLMs) like CLIP, have revolutionized OOD detection by shifting from traditional unimodal image detectors to multimodal image-text detectors. This shift has inspired extensive research; however, existing categorization schemes (e.g., few- or zero-shot types) still rely solely on the availability of ID images, adhering to a unimodal paradigm. To better align with CLIP's cross-modal nature, we propose a new categorization framework rooted in both image and text modalities. Specifically, we categorize existing methods based on how visual and textual information of OOD data is utilized within image + text modalities, and further divide them into four groups: OOD Images (i.e., outliers) Seen or Unseen, and OOD Texts (i.e., learnable vectors or class names) Known or Unknown, across two training strategies (i.e., train-free or training-required). More importantly, we discuss open problems in CLIP-like OOD detection and highlight promising directions for future research, including cross-domain integration, practical applications, and theoretical understanding.
Towards One-shot Federated Learning: Advances, Challenges, and Future Directions
2025-05-05
Flora Amato, Lingyu Qiu, Mohammad Tanveer, Salvatore Cuomo, Fabio Giampaolo, Francesco Piccialli
One-shot FL enables collaborative training in a single round, eliminating the need for iterative communication, making it particularly suitable for use in resource-constrained and privacy-sensitive applications. This survey offers a thorough examination of One-shot FL, highlighting its distinct operational framework compared to traditional federated approaches. One-shot FL supports resource-limited devices by enabling single-round model aggregation while maintaining data locality. The survey systematically categorizes existing methodologies, emphasizing advancements in client model initialization, aggregation techniques, and strategies for managing heterogeneous data distributions. Furthermore, we analyze the limitations of current approaches, particularly in terms of scalability and generalization in non-IID settings. By analyzing cutting-edge techniques and outlining open challenges, this survey aspires to provide a comprehensive reference for researchers and practitioners aiming to design and implement One-shot FL systems, advancing the development and adoption of One-shot FL solutions in a real-world, resource-constrained scenario.
SymbioticRAG: Enhancing Document Intelligence Through Human-LLM Symbiotic Collaboration
2025-05-05
Qiang Sun, Tingting Bi, Sirui Li, Eun-Jung Holden, Paul Duuring, Kai Niu, Wei Liu
We present \textbf{SymbioticRAG}, a novel framework that fundamentally reimagines Retrieval-Augmented Generation~(RAG) systems by establishing a bidirectional learning relationship between humans and machines. Our approach addresses two critical challenges in current RAG systems: the inherently human-centered nature of relevance determination and users' progression from "unconscious incompetence" in query formulation. SymbioticRAG introduces a two-tier solution where Level 1 enables direct human curation of retrieved content through interactive source document exploration, while Level 2 aims to build personalized retrieval models based on captured user interactions. We implement Level 1 through three key components: (1)~a comprehensive document processing pipeline with specialized models for layout detection, OCR, and extraction of tables, formulas, and figures; (2)~an extensible retriever module supporting multiple retrieval strategies; and (3)~an interactive interface that facilitates both user engagement and interaction data logging. We experiment Level 2 implementation via a retriever strategy incorporated LLM summarized user intention from user interaction logs. To maintain high-quality data preparation, we develop a human-on-the-loop validation interface that improves pipeline output while advancing research in specialized extraction tasks. Evaluation across three scenarios (literature review, geological exploration, and education) demonstrates significant improvements in retrieval relevance and user satisfaction compared to traditional RAG approaches. To facilitate broader research and further advancement of SymbioticRAG Level 2 implementation, we will make our system openly accessible to the research community.
In this paper BEPC, BEPCII and BEPCII-U have been briefly reviewed, which lay a good foundation of CEPC as a Higgs factory. BEPCII reached the designed luminos-ity goal of 10^33cm^-2s^-1 @1.89GeV on April 5, 2016. As an upgrade program of BEPCII, BEPCII-U has been com-pleted and started commissioning in March of 2025 with the luminosity goal as 3.7*10^32cm^-2s^-1 @2.8GeV. CEPC as Higgs factory has been reviewed on Engineering De-sign Report (EDR) status and plan for construction proposal to Chinese government in 2025.
EntroLLM: Entropy Encoded Weight Compression for Efficient Large Language Model Inference on Edge Devices
2025-05-05
Arnab Sanyal, Prithwish Mukherjee, Gourav Datta, Sandeep P. Chinchali
Large Language Models (LLMs) demonstrate exceptional performance across various tasks, but their large storage and computational requirements constrain their deployment on edge devices. To address this, we propose EntroLLM, a novel compression framework that integrates mixed quantization with entropy coding to reduce storage overhead while maintaining model accuracy. Our method applies a layer-wise mixed quantization scheme - choosing between symmetric and asymmetric quantization based on individual layer weight distributions - to optimize compressibility. We then employ Huffman encoding for lossless compression of the quantized weights, significantly reducing memory bandwidth requirements. Furthermore, we introduce parallel Huffman decoding, which enables efficient retrieval of encoded weights during inference, ensuring minimal latency impact. Our experiments on edge-compatible LLMs, including smolLM-1.7B-Instruct, phi3-mini-4k-Instruct, and mistral-7B-Instruct, demonstrate that EntroLLM achieves up to $30\%$ storage reduction compared to uint8 models and up to $65%$ storage reduction compared to uint4 models, while preserving perplexity and accuracy, on language benchmark tasks. We further show that our method enables $31.9\%$ - $146.6\%$ faster inference throughput on memory-bandwidth-limited edge devices, such as NVIDIA Jetson P3450, by reducing the required data movement. The proposed approach requires no additional re-training and is fully compatible with existing post-training quantization methods, making it a practical solution for edge LLMs.
Photometric activity cycles in fast-rotating stars: Revisiting the reality of stellar activity cycle branches
2025-05-05
Deepak Chahal, Devika Kamath, Richard de Grijs, Benjamin T. Montet, Xiaodian Chen
We aim to detect activity cycles in young main-sequence stars, analogous to the 11-year solar cycle, using combined photometric survey data. This research will enhance our understanding of how cycle periods relate to rotation rates in fast-rotating stars. We measured activity cycles for 138 G-K-type main sequence stars using combined time-series photometry spanning ~14 years. The first set of 70 stars used data from Kepler Full Frame Images (FFIs)-ASAS-SN-ZTF, and the second set of 68 stars used data from Kepler-FFIs-ZTF. Additionally, we measured the activity cycles for 25 RS CVn candidates. For our sample, we analyzed the correlation or anti-correlation between flux variations and photospheric activity, which arises due to the presence of faculae or starspots. We identified fast-rotating K-type stars that are faculae-dominated by tracking spot or faculae evolution in Kepler RMS data. Our findings reveal that fast-rotating G-K-type stars show no strong correlation between cycle length and rotation period. Previous studies have identified active and inactive branches in the cycle-rotation diagram. However, we find that G-K-type stars do not show a clear trend aligning with the active branch, with 34 per cent of stars falling within the intermediate region between the two branches, where our Sun resides. Our results highlight that the proposed distinction between the two branches may not be as definitive as previously thought, particularly regarding the active branch. Furthermore, we also detected 23 per cent of young Sun-like stars in the intermediate region, where our Sun is located, implying that our Sun may not be unique in this regard.
The abundance of dust within galaxies directly influences their evolution. Contemporary models attempt to match this abundance by simulating the processes of dust creation, growth, and destruction. While these models are accurate, they require refinement, especially at earlier epochs. This study aims to compare simulated and observed datasets and identify discrepancies between the two, providing a basis for future improvements. We utilise simulation data from the SIMBA cosmological simulation suite and observed data from the Galaxy and Mass Assembly (GAMA), a subset of the Cosmic Evolution Survey (G10-COSMOS), and the Hubble Space Telescope (3D-HST). We selected galaxies in the observed and simulated data in a stellar mass range of $(10^{8.59} < M_\odot < 10^{11.5})$ and at redshift bins centering around $(z = 0.0)$, $(z = 0.1)$, $(z = 0.5)$, $(z = 1.0)$, and $(z = 1.5)$ in a homogeneous dust mass range $((10^{6} < M_D [M_\odot] < 10^{9}))$. Our results show notable deviations between SIMBA and observed data for dust-poor and rich galaxies, with strong indications that differences in galaxy populations and SIMBA limitations are the underlying cause rather than the dust physics implemented in SIMBA itself.
2D van der Waals magnets: from fundamental physics to applications
2025-05-05
Je-Geun Park, Kaixuan Zhang, Hyeonsik Cheong, Jae Hoon Kim, Carina Belvin, David Hsieh, Honglie Ning, Nuh Gedik
Magnetism has played a central role in the long and rich history of modern condensed matter physics, with many foundational insights originating from theoretical studies of two-dimensional (2D) spin systems. The discovery of 2D van der Waals (vdW) magnets has revolutionized this area by providing real, atomically thin magnetic systems for experimental investigation. Since the first experimental reports of antiferromagnetic vdW insulators in 2016 - followed by studies on ferromagnetic vdW systems in 2017 - the field has witnessed rapid and expansive growth, with more than two dozen vdW magnetic materials now identified, including both ferro- and antiferromagnets. In this review, we present a comprehensive overview of the major scientific and technological developments in this rapidly evolving field. These include experimental realizations of various 2D spin Hamiltonians as well as unexpected phenomena such as magnetic excitons, Floquet-engineered states, and light-induced metastable magnetic phases. In parallel, 2D vdW magnets have shown significant promise in spintronics and related applications, offering a new platform for engineering quantum functionalities. We organize this review by tracing the historical development of the field, synthesizing key milestones, and highlighting its broader impact across condensed matter physics and materials science. We conclude with an Outlook section that outlines several promising directions for future research, aiming to chart a path forward in this vibrant and still rapidly growing area.
Metasurfaces in Adaptive Optics: A New Opportunity in Optical Wavefront Sensing
2025-05-05
Rundong Fan, Zichao Wang, Pei Li, Lei Huang
Over the past fifty years, wavefront sensing technology has continuously evolved from basic techniques to high-precision systems, serving as a core methodology in adaptive optics (AO). Beyond traditional wavefront retrieval methods based on spot displacement, direct phase retrieval techniques with greater accuracy have emerged, jointly driving advancements in wavefront sensing precision. This evolution is fueled by increasing demands for accuracy, which have prompted iterative upgrades in system architectures and algorithms. Recently, breakthroughs in metasurface technology have opened new possibilities for wavefront sensing. By utilizing subwavelength microstructures, metasurfaces enable multi-dimensional control over the phase, amplitude, and polarization of light fields. Their high degree of design flexibility presents transformative opportunities for advancing wavefront sensing capabilities. This review examines the fundamental principles of wavefront sensing and the development of key enabling devices, highlighting how metasurface technology is reshaping traditional paradigms. We discuss recent research progress and emerging innovations, aiming to establish a theoretical framework for next-generation wavefront sensing technologies. Ultimately, we hope this review provides technical insights for applications in astronomical observation, biological microscopy, laser engineering, and beyond.
Large Language Models (LLMs) have revolutionized many areas of artificial intelligence (AI), but their substantial resource requirements limit their deployment on mobile and edge devices. This survey paper provides a comprehensive overview of techniques for compressing LLMs to enable efficient inference in resource-constrained environments. We examine three primary approaches: Knowledge Distillation, Model Quantization, and Model Pruning. For each technique, we discuss the underlying principles, present different variants, and provide examples of successful applications. We also briefly discuss complementary techniques such as mixture-of-experts and early-exit strategies. Finally, we highlight promising future directions, aiming to provide a valuable resource for both researchers and practitioners seeking to optimize LLMs for edge deployment.
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery
2025-05-05
Yoel Zimmermann, Adib Bazgir, Alexander Al-Feghali, Mehrad Ansari, L. Catherine Brinson, Yuan Chiang, Defne Circi, Min-Hsueh Chiu, Nathan Daelman, Matthew L. Evans, Abhijeet S. Gangan, Janine George, Hassan Harb, Ghazal Khalighinejad, Sartaaj Takrim Khan, Sascha Klawohn, Magdalena Lederbauer, Soroush Mahjoubi, Bernadette Mohr, Seyed Mohamad Moosavi, Aakash Naik, Aleyna Beste Ozhan, Dieter Plessers, Aritra Roy, Fabian Schöppach, Philippe Schwaller, Carla Terboven, Katharina Ueltzen, Shang Zhu, Jan Janssen, Calvin Li, Ian Foster, Ben Blaiszik
Large Language Models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 34 total projects developed during the second annual Large Language Model Hackathon for Applications in Materials Science and Chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.
A Continuous Galactic Line Source of Axions: The Remarkable Case of 23Na
2025-05-05
W. C. Haxton, Xing Liu, Annie McCutcheon, Anupam Ray
We argue that $^{23}$Na is a potentially significant source of galactic axions. For temperatures $\gtrsim 7 \times 10^8$K -- characteristic of carbon burning in the massive progenitors of supernovae and ONeMg white dwarfs -- the 440 keV first excited state of $^{23}$Na is thermally populated, with its repeated decays pumping stellar energy into escaping axions. Odd-A nuclear abundances are typically very low in high-temperature stellar environments (or absent entirely due to burn-up). $^{23}$Na is an exception: $\approx 0.1 M_\odot$ of the isotope is synthesized during carbon burning then maintained at $\approx 10^9$K for times ranging up to $6 \times 10^4$y. Using MESA simulations, a galactic model, and sampling over progenitor masses, locations, and evolutionary stages, we find a continuous flux at earth of $\langle \phi_a \rangle \approx 22$/cm$^2$s for $g^\mathrm{eff}_{aNN} = 10^{-9}$. Some fraction of these axions convert to photons as they propagate through the galactic magnetic field, producing a distinctive 440 keV line $\gamma$ ray detectable by all-sky detectors like the Compton Spectrometer and Imager (COSI). Assuming a 1$\mu$G galactic magnetic field and a sufficiently light axion mass, we find that COSI will be able to probe $| g_{aNN}^\mathrm{eff} g_{a \gamma \gamma} | \gtrsim1.8 \times 10^{-22}$ GeV$^{-1}$ at $3\sigma$ after two years of surveying.
The Massive Internet of Things (MIoT) envisions an interconnected ecosystem of billions of devices, fundamentally transforming diverse sectors such as healthcare, smart cities, transportation, agriculture, and energy management. However, the vast scale of MIoT introduces significant challenges, including network scalability, efficient data management, energy conservation, and robust security mechanisms. This paper presents a thorough review of existing and emerging MIoT technologies designed to address these challenges, including Low-Power Wide-Area Networks (LPWAN), 5G/6G capabilities, edge and fog computing architectures, and hybrid access methodologies. We further investigate advanced strategies such as AI-driven resource allocation, federated learning for privacy-preserving analytics, and decentralized security frameworks using blockchain. Additionally, we analyze sustainable practices, emphasizing energy harvesting and integrating green technologies to reduce environmental impact. Through extensive comparative analysis, this study identifies critical innovations and architectural adaptations required to support efficient, resilient, and scalable MIoT deployments. Key insights include the role of network slicing and intelligent resource management for scalability, adaptive protocols for real-time data handling, and lightweight AI models suited to the constraints of MIoT devices. This research ultimately contributes to a deeper understanding of how MIoT systems can evolve to meet the growing demand for seamless, reliable connectivity while prioritizing sustainability, security, and performance across diverse applications. Our findings serve as a roadmap for future advancements, underscoring the potential of MIoT to support a globally interconnected, intelligent infrastructure.
The rapid growth of Internet-of-things (IoT) devices, smart vehicles, and other connected objects is driving demand for ubiquitous connectivity and intensive computing capacity. 5G and upcoming 6G networks are crucial to meeting these demands and the fast-evolving services and applications. However, traditional terrestrial networks face limitations in coverage and capacity. Integrated Terrestrial and Non-Terrestrial Networks (ITNTN) are emerging to address these challenges. In essence, ITNTN combines ground-based infrastructure with aerial, space, and water surface networks to provide seamless connectivity and computing resources anytime, anywhere. Given the stringent quality-of-service (QoS) of future services, edge computing will be an inseparable component of ITNTN. Consequently, we dive in this survey into current efforts of integrating cloud/fog/edge computing into ITNTN layers to facilitate stringent QoS services and address the data processing needs of modern applications. Since there have been only limited and partial efforts in integrating computing functionalities within ITNTN, we aim to extend the discussion to the full integration of computing and identifying the challenges and future research directions to achieve it.
This is a survey on propositional proof complexity aimed at introducing the basics of the field with a particular focus on a method known as feasible interpolation. This method is used to construct "hard theorems" for several proof systems for both classical and non-classical logics. Here, a "hard theorem" refers to a theorem in the logic whose shortest proofs are super-polynomially long in the length of the theorem itself. To make this survey more accessible, we only assume a basic familiarity with propositional, modal, and first-order logic, as well as a basic understanding of the key concepts in computational complexity, such as the definitions of the classes $\mathbf{NP}$ and $\mathbf{PSPACE}$. Any additional concepts will be introduced and explained as needed.
The Disks In Scorpius-Centaurus Survey (DISCS) I: Four Newly-Resolved Debris Disks in Polarized Intensity Light
2025-05-05
Justin Hom, Thomas M. Esposito, Katie A. Crotts, Gaspard Duchêne, Jennifer Patience, Johan Mazoyer, Robert J. De Rosa, Christine H. Chen, Paul Kalas, Bruce Macintosh, Brenda C. Matthews
The presence of infrared excesses around stars directly correlates to spatially-resolved imaging detections of circumstellar disks at both mm and optical/near-infrared wavelengths. High contrast imagers have resolved dozens of circumstellar disks with scattered light polarimetric imaging. Many of these detections are members of the Scorpius-Centaurus OB association, demonstrating it to be a rich sample for investigating planetary system architectures and planet-disk interactions. With the goal of expanding the sample of directly imaged debris disks in Scorpius-Centaurus, we conducted the Disks In Scorpius-Centaurus Survey (DISCS) leveraging knowledge of high-IR excesses and the power of high contrast polarimetric differential imaging. In combination with the GPIES polarimetric disk survey, we observe seven new Scorpius-Centaurus targets to achieve a 60% complete survey of debris disks with IR excesses exceeding $2.5\times10^{-4}$, resolving four new debris disks. HD 98363, HD 109832, and HD 146181 are resolved for the first time, and HD 112810 is resolved for the first time in polarized intensity. We identify morphological structures that may be indications of planet-disk interactions. We place the systems in the greater context of resolved debris disks, identifying factors of ten differences in scattered light contrast for a given IR excess and implying gaps in our understanding of the smallest and largest dust grains of a system. We conclude that while thermal emission measurements are correlated with scattered light detection, they poorly predict the magnitude of scattered light brightness. We also establish Scorpius-Centaurus debris disks as critical benchmarks in understanding the properties of disks in the scattering regime.
Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production
2025-05-05
The RAIL Team, Jan Luca van den Busch, Eric Charles, Johann Cohen-Tanugi, Alice Crafford, John Franklin Crenshaw, Sylvie Dagoret, Josue De-Santiago, Juan De Vicente, Qianjun Hang, Benjamin Joachimi, Shahab Joudaki, J. Bryce Kalmbach, Shuang Liang, Olivia Lynn, Alex I. Malz, Rachel Mandelbaum, Grant Merz, Irene Moskowitz, Drew Oldag, Jaime Ruiz-Zapatero, Mubdi Rahman, Samuel J. Schmidt, Jennifer Scora, Raphael Shirley, Benjamin Stölzner, Laura Toribio San Cipriano, Luca Tortorelli, Ziang Yan, Tianqing Zhang, the Dark Energy Science Collaboration
Virtually all extragalactic use cases of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) require the use of galaxy redshift information, yet the vast majority of its sample of tens of billions of galaxies will lack high-fidelity spectroscopic measurements thereof, instead relying on photometric redshifts (photo-$z$) subject to systematic imprecision and inaccuracy best encapsulated by photo-$z$ probability density functions (PDFs). We present the version 1 release of Redshift Assessment Infrastructure Layers (RAIL), an open source Python library for at-scale probabilistic photo-$z$ estimation, initiated by the LSST Dark Energy Science Collaboration (DESC) with contributions from the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) Frameworks team. RAIL's three subpackages provide modular tools for end-to-end stress-testing, including a forward modeling suite to generate realistically complex photometry, a unified API for estimating per-galaxy and ensemble redshift PDFs by an extensible set of algorithms, and built-in metrics of both photo-$z$ PDFs and point estimates. RAIL serves as a flexible toolkit enabling the derivation and optimization of photo-$z$ data products at scale for a variety of science goals and is not specific to LSST data. We thus describe to the extragalactic science community, including and beyond Rubin the design and functionality of the RAIL software library so that any researcher may have access to its wide array of photo-$z$ characterization and assessment tools.
Radiative magnetohydrodynamics simulation of minidisks in equal-mass massive black hole binaries
2025-05-05
Chi-Ho Chan, Vishal Tiwari, Tamara Bogdanović, Yan-Fei Jiang, Shane W. Davis
We are on the cusp of detecting gravitational waves (GWs) from individual massive black hole binaries (MBHBs) with the Laser Interferometer Space Antenna and pulsar-timing arrays. These MBHBs may be surrounded by circumbinary disks and minidisks, the electromagnetic emission from which are essential for localizing the MBHBs on the sky. Here we present the first radiative magnetohydrodynamics (RMHD) minidisk simulation that directly solves the radiative transfer equation on discretized grid rays. The simulation examines one of the minidisks in an equal-mass $2\times10^7\,M_\odot$ MBHB separated by 100 gravitational radii. Minidisks simulated with and without radiative effects resemble each other qualitatively but differ in several key aspects. The RMHD minidisk is denser and geometrically thinner than the magnetohydrodynamics minidisk. Furthermore, the RMHD minidisk, with a nonaxisymmetric photosphere and temperature distribution, produces an anisotropic illumination pattern. As a result, the observed radiative flux of two RMHD minidisks orbiting each other varies at half the binary orbital period, a feature independent of relativistic boosting and lensing effects. Such periodic light curves, if identified in upcoming optical transient surveys, could reveal the existence of MBHBs on the way to merger, particularly if they are in a constant phase relation with detected GWs.
Early Shock-Cooling Observations and Progenitor Constraints of Type IIb SN 2024uwq
2025-05-05
Bhagya M. Subrayan, David J. Sand, K. Azalee Bostroem, Saurabh W. Jha, Aravind P. Ravi, Michaela Schwab, Jennifer E. Andrews, Griffin Hosseinzadeh, Stefano Valenti, Yize Dong, Jeniveve Pearson, Manisha Shrestha, Lindsey A. Kwok, Emily Hoang, Jeonghee Rho, Seong Hyun Park, Sung-Chul Yoon, T. R. Geball, Joshua Haislip, Daryl Janzen, Vladimir Kouprianov, Darshana Mehta, Nicolás Meza Retamal, Daniel E. Reichart, Moira Andrews, Joseph Farah, Megan Newsome, D. Andrew Howell, Curtis McCully
We present early multi-wavelength photometric and spectroscopic observations of the Type IIb supernova SN 2024uwq, capturing its shock-cooling emission phase and double-peaked light curve evolution. Early spectra reveal broad H-alpha (v ~ 15,500 km s$^{-1}$) and He I P-Cygni profiles of similar strengths. Over time the He I lines increase in strength while the H-alpha decreases, consistent with a hydrogen envelope ($M_{env}$ = 0.7 - 1.35 $M_\odot$ ) overlying helium-rich ejecta. Analytic modeling of early shock cooling emission and bolometric light analysis constrains the progenitor to a partially stripped star with radius R = 10 - 60 $R_\odot$, consistent with a blue/yellow supergiant with an initial ZAMS mass of 12 - 20 $M_\odot$ , likely stripped via binary interaction. SN 2024uwq occupies a transitional position between compact and extended Type IIb supernovae, highlighting the role of binary mass-transfer efficiency in shaping a continuum of stripped-envelope progenitors. Our results underscore the importance of both early UV/optical observations to characterize shock breakout signatures critical to map the diversity in evolutionary pathways of massive stars. Upcoming time domain surveys including Rubin Observatory's LSST and UV missions like ULTRASAT and UVEX will revolutionise our ability to systematically capture these early signatures, probing the full diversity of stripped progenitors and their explosive endpoints.
Weighted FFT estimators for 1D and 3D correlations of the Lyman-$α$ forest
2025-05-05
Martine Lokken, Andreu Font-Ribera, Patrick McDonald
Correlations in the Lyman-$\alpha$ (Ly$\alpha$) forest, both as a function of line of sight separation (1D) and 3D separation, provide a unique window to the distribution of matter at redshifts not accessible by current galaxy surveys. While optimal quadratic estimators have been used to measure 1D correlations, they are computationally expensive and difficult to extend to 3D analyses. On the other hand, estimators based on the Fast Fourier Transform (FFT) are significantly faster, but are affected by missing data in the spectra (masked pixels) and so far have not used pixel weights to reduce the uncertainties in the measurement. In this publication we describe how to compute the window matrix that enables forward-modeling the impact of masked pixels and weights on the FFT-based estimators. We use Gaussian and hydrodynamical simulations with artificially masked pixels to validate the method on the measurement of 1D correlations. Finally, we show that the formalism can be extended to model the impact on 3D correlations, in particular on the cross-spectrum, the correlation of 1D Fourier modes as a function of transverse separation. This work will enable more precise clustering measurements with the Ly$\alpha$ forest dataset recently collected by the Dark Energy Spectroscopic Instrument (DESI).
GULP II: Hierarchical Distribution and Evolution of Young Stellar Structures in NGC 4449
2025-05-05
Beena Meena, Elena Sabbi, Peter Zeidler, Bruce G. Elmegreen, Jan J. Eldridge, Varun Bajaj, Mario Gennaro, Anna Pasquali, Debra M. Elmegreen, Ralf S. Klessen, Linda J. Smith, Luciana Bianchi, Aida Wofford, Pietro Facchini, John S. Gallagher III, Daniela Calzetti, Eva K. Grebel, Angela Adamo
We investigate the hierarchical distribution and evolution of young stellar structures in the dwarf starburst galaxy NGC 4449 using data from the GULP survey. By analyzing the spatial distribution of field stars younger than 100 Myr, we identify large-scale stellar complexes and substructures using HDBSCAN -- a density-based clustering algorithm -- and trace their evolution over time. While comparing these stellar structures in different regions of the galaxy, we find that the central bar-like region shows a clear expansion of the structures within the first $\sim$ 60 Myrs, while the arm-like structure in the NE shows no discernible trend, possibly due to external perturbations from tidal interactions with a neighboring galaxy. An age-dependent two point correlation function (TPCF) analysis shows that young stars exhibit a strong hierarchical distribution, with clustering strength decreasing over time. The power-law slope of the TPCF, which starts at $\alpha \sim 0.65$ for stars younger than 5 Myr, shows a slight decline to $\alpha \sim 0.4$ for stars older than 50 Myr, though it does not reach a completely flat (random) distribution. This trend indicates a subtle weakening of structural hierarchy among young ($<$100 Myr) stars, which is primarily driven by internal stellar motions. Future work will extend this analysis to the remaining 26 galaxies in the GULP survey to better constrain the role of the galactic environment in shaping the hierarchical evolution of young stellar populations.
The Large-scale Environments of Low-luminosity AGNs at $3.9 < z < 6$ and Implications for Their Host Dark Matter Halos from a Complete NIRCam Grism Redshift Survey
2025-05-05
Xiaojing Lin, Xiaohui Fan, Fengwu Sun, Junyu Zhang, Eiichi Egami, Jakob M. Helton, Feige Wang, Haowen Zhang, Andrew J. Bunker, Zheng Cai, Zhiyuan Ji, Xiangyu Jin, Roberto Maiolino, Maria Anne Pudoka, Pierluigi Rinaldi, Brant Robertson, Sandro Tacchella, Wei Leong Tee, Yang Sun, Christopher N. A. Willmer, Chris Willott, Yongda Zhu
We study the large-scale environments and clustering properties of 28 low-luminosity AGNs at $z=3.9-6$ in the GOODS-N field. Our sample, identified from the JWST NIRCam Imaging and WFSS data in CONGRESS and FRESCO surveys with either broad H$\alpha$ emission lines or V-shape continua, are compared to 782 H$\alpha$ emitters (HAEs) selected from the same data. These AGNs are located in diverse large-scale environments and do not preferentially reside in denser environments compared to HAEs. Their overdensity field, $\delta$, averaged over (15 $h^{-1}$cMpc)$^3$, ranges from $-0.56$ to 10.56, and shows no clear correlation with broad-line luminosity, black hole (BH) masses, or the AGN fraction. It suggests that $> 10$ cMpc structures do not significantly influence BH growth. We measure the two-point cross-correlation function of AGNs with HAEs, finding a comparable amplitude to that of the HAE auto-correlation. This indicates similar bias parameters and host dark matter halo masses for AGNs and HAEs. The correlation length of field AGNs is 4.26 $h^{-1}$cMpc, and 7.66 $h^{-1}$cMpc at $3.9 < z < 5$ and $5 < z < 6$, respectively. We infer a median host dark matter halo mass of $\log (M_h/M_\odot)\approx 11.0-11.2$ and host stellar masses of $\log (M_\star/M_\odot) \approx 8.4-8.6$ by comparing with the UniverseMachine simulation. Our clustering analysis suggests that low-luminosity AGNs at high redshift reside in normal star-forming galaxies with overmassive BHs. They represent an intrinsically distinct population from luminous quasars and could be a common phase in galaxy evolution.
Coverage Biases in High-Resolution Satellite Imagery
2025-05-05
Vadim Musienko, Axel Jacquet, Ingmar Weber, Till Koebe
Satellite imagery is increasingly used to complement traditional data collection approaches such as surveys and censuses across scientific disciplines. However, we ask: Do all places on earth benefit equally from this new wealth of information? In this study, we investigate coverage bias of major satellite constellations that provide optical satellite imagery with a ground sampling distance below 10 meters, evaluating both the future on-demand tasking opportunities as well as the availability of historic images across the globe. Specifically, forward-looking, we estimate how often different places are revisited during a window of 30 days based on the satellites' orbital paths, thus investigating potential coverage biases caused by physical factors. We find that locations farther away from the equator are generally revisited more frequently by the constellations under study. Backward-looking, we show that historic satellite image availability -- based on metadata collected from major satellite imagery providers -- is influenced by socio-economic factors on the ground: less developed, less populated places have less satellite images available. Furthermore, in three small case studies on recent conflict regions in this world, namely Gaza, Sudan and Ukraine, we show that also geopolitical events play an important role in satellite image availability, hinting at underlying business model decisions. These insights lay bare that the digital dividend yielded by satellite imagery is not equally distributed across our planet.
Identifying the finer details of a book's genres enhances user experience by enabling efficient book discovery and personalized recommendations, ultimately improving reader engagement and satisfaction. It also provides valuable insights into market trends and consumer preferences, allowing publishers and marketers to make data-driven decisions regarding book production and marketing strategies. While traditional book genre classification methods primarily rely on review data or textual analysis, incorporating additional modalities, such as book covers, blurbs, and metadata, can offer richer context and improve prediction accuracy. However, the presence of incomplete or noisy information across these modalities presents a significant challenge. This paper introduces IMAGINE (Intelligent Multi-modal Adaptive Genre Identification NEtwork), a framework designed to address these complexities. IMAGINE extracts robust feature representations from multiple modalities and dynamically selects the most informative sources based on data availability. It employs a hierarchical classification strategy to capture genre relationships and remains adaptable to varying input conditions. Additionally, we curate a hierarchical genre classification dataset that structures genres into a well-defined taxonomy, accommodating the diverse nature of literary works. IMAGINE integrates information from multiple sources and assigns multiple genre labels to each book, ensuring a more comprehensive classification. A key feature of our framework is its resilience to incomplete data, enabling accurate predictions even when certain modalities, such as text, images, or metadata, are missing or incomplete. Experimental results show that IMAGINE outperformed existing baselines in genre classification accuracy, particularly in scenarios with insufficient modality-specific data.
Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation
2025-05-05
Hannes Waldetoft, Jakob Torgander, Måns Magnusson
Estimating population parameters in finite populations of text documents can be challenging when obtaining the labels for the target variable requires manual annotation. To address this problem, we combine predictions from a transformer encoder neural network with well-established survey sampling estimators using the model predictions as an auxiliary variable. The applicability is demonstrated in Swedish hate crime statistics based on Swedish police reports. Estimates of the yearly number of hate crimes and the police's under-reporting are derived using the Hansen-Hurwitz estimator, difference estimation, and stratified random sampling estimation. We conclude that if labeled training data is available, the proposed method can provide very efficient estimates with reduced time spent on manual annotation.
A survey of agent interoperability protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP)
2025-05-04
Abul Ehtesham, Aditi Singh, Gaurav Kumar Gupta, Saket Kumar
Large language model (LLM)-powered autonomous agents demand robust, standardized protocols to integrate tools, share contextual data, and coordinate tasks across heterogeneous systems. Ad-hoc integrations are difficult to scale, secure, and generalize across domains. This survey examines four emerging agent communication protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP), each addressing interoperability in distinct deployment contexts. MCP provides a JSON-RPC client-server interface for secure tool invocation and typed data exchange. ACP introduces REST-native messaging via multi-part messages and asynchronous streaming to support multimodal agent responses. A2A enables peer-to-peer task outsourcing through capability-based Agent Cards, facilitating enterprise-scale workflows. ANP supports open-network agent discovery and secure collaboration using decentralized identifiers (DIDs) and JSON-LD graphs. The protocols are compared across multiple dimensions, including interaction modes, discovery mechanisms, communication patterns, and security models. Based on the comparative analysis, a phased adoption roadmap is proposed: beginning with MCP for tool access, followed by ACP for multimodal messaging, A2A for collaborative task execution, and extending to ANP for decentralized agent marketplaces. This work provides a comprehensive foundation for designing secure, interoperable, and scalable ecosystems of LLM-powered agents.
A Path Less Traveled: Reimagining Software Engineering Automation via a Neurosymbolic Paradigm
2025-05-04
Antonio Mastropaolo, Denys Poshyvanyk
The emergence of Large Code Models (LCMs) has transformed software engineering (SE) automation, driving significant advancements in tasks such as code generation, source code documentation, code review, and bug fixing. However, these advancements come with trade-offs: achieving high performance often entails exponential computational costs, reduced interpretability, and an increasing dependence on data-intensive models with hundreds of billions of parameters. In this paper, we propose Neurosymbolic Software Engineering, in short NSE, as a promising paradigm combining neural learning with symbolic (rule-based) reasoning, while strategically introducing a controlled source of chaos to simulate the complex dynamics of real-world software systems. This hybrid methodology aims to enhance efficiency, reliability, and transparency in AI-driven software engineering while introducing controlled randomness to adapt to evolving requirements, unpredictable system behaviors, and non-deterministic execution environments. By redefining the core principles of AI-driven software engineering automation, NSE lays the groundwork for solutions that are more adaptable, transparent, and closely aligned with the evolving demands of modern software development practices.
On the Need for a Statistical Foundation in Scenario-Based Testing of Autonomous Vehicles
2025-05-04
Xingyu Zhao, Robab Aghazadeh-Chakherlou, Chih-Hong Cheng, Peter Popov, Lorenzo Strigini
Scenario-based testing has emerged as a common method for autonomous vehicles (AVs) safety, offering a more efficient alternative to mile-based testing by focusing on high-risk scenarios. However, fundamental questions persist regarding its stopping rules, residual risk estimation, debug effectiveness, and the impact of simulation fidelity on safety claims. This paper argues that a rigorous statistical foundation is essential to address these challenges and enable rigorous safety assurance. By drawing parallels between AV testing and traditional software testing methodologies, we identify shared research gaps and reusable solutions. We propose proof-of-concept models to quantify the probability of failure per scenario (pfs) and evaluate testing effectiveness under varying conditions. Our analysis reveals that neither scenario-based nor mile-based testing universally outperforms the other. Furthermore, we introduce Risk Estimation Fidelity (REF), a novel metric to certify the alignment of synthetic and real-world testing outcomes, ensuring simulation-based safety claims are statistically defensible.
In-sensor computing, which integrates computation directly within the sensor, has emerged as a promising paradigm for machine vision applications such as AR/VR and smart home systems. By processing data on-chip before transmission, it alleviates the bandwidth bottleneck caused by high-resolution, high-frame-rate image transmission, particularly in video applications. We envision a system architecture that integrates a CMOS image sensor (CIS) with a logic chip via advanced packaging, where the logic chip processes early-stage deep neural network (DNN) layers. However, its limited compute and memory make deploying advanced DNNs challenging. A simple solution is to split the model, executing the first part on the logic chip and the rest off-chip. However, modern DNNs require multiple layers before dimensionality reduction, limiting their ability to achieve the primary goal of in-sensor computing: minimizing data bandwidth. To address this, we propose a dual-branch autoencoder-based vision architecture that deploys a lightweight encoder on the logic chip while the task-specific network runs off-chip. The encoder is trained using a triple loss function: (1) task-specific loss to optimize accuracy, (2) entropy loss to enforce compact and compressible representations, and (3) reconstruction loss (mean-square error) to preserve essential visual information. This design enables a four-order-of-magnitude reduction in output activation dimensionality compared to input images, resulting in a $2{-}4.5\times$ decrease in energy consumption, as validated by our hardware-backed semi-analytical energy models. We evaluate our approach on CNN and ViT-based models across applications in smart home and augmented reality domains, achieving state-of-the-art accuracy with energy efficiency of up to 22.7 TOPS/W.
Federated causal inference enables multi-site treatment effect estimation without sharing individual-level data, offering a privacy-preserving solution for real-world evidence generation. However, data heterogeneity across sites, manifested in differences in covariate, treatment, and outcome, poses significant challenges for unbiased and efficient estimation. In this paper, we present a comprehensive review and theoretical analysis of federated causal effect estimation across both binary/continuous and time-to-event outcomes. We classify existing methods into weight-based strategies and optimization-based frameworks and further discuss extensions including personalized models, peer-to-peer communication, and model decomposition. For time-to-event outcomes, we examine federated Cox and Aalen-Johansen models, deriving asymptotic bias and variance under heterogeneity. Our analysis reveals that FedProx-style regularization achieves near-optimal bias-variance trade-offs compared to naive averaging and meta-analysis. We review related software tools and conclude by outlining opportunities, challenges, and future directions for scalable, fair, and trustworthy federated causal inference in distributed healthcare systems.
Prospects for disentangling dark matter with weak lensing
2025-05-04
Calvin Preston, Keir K. Rogers, Alexandra Amon, George Efstathiou
We investigate the degeneracy between the effects of ultra-light axion dark matter and baryonic feedback in suppressing the matter power spectrum. We forecast that galaxy shear data from the Rubin Observatory's Legacy Survey of Space and Time (LSST) could limit an axion of mass $m = 10^{-25}\,\mathrm{eV}$ to be $\lesssim 5\%$ of the dark matter, stronger than any current bound, if the interplay between axions and feedback is accurately modelled. Using a halo model emulator to construct power spectra for mixed cold and axion dark matter cosmologies, including baryonic effects, we find that galaxy shear is sensitive to axions from $10^{-27}\,\mathrm{eV}$ to $10^{-21}\,\mathrm{eV}$, with the capacity to set competitive bounds across much of this range. For axions with $m \sim 10^{-25}\,\mathrm{eV}$, the scales at which axions and feedback impact structure formation are similar, introducing a parameter degeneracy. We find that, with an external feedback constraint, we can break the degeneracy and constrain the axion transfer function, such that LSST could detect a $10^{-25}\,\mathrm{eV}$ axion comprising 10\% of the dark matter at $\sim 3 \sigma$ significance. Direct reconstruction of the non-linear matter power spectrum provides an alternative way of analysing weak lensing surveys, with the advantage of identifying the scale-dependent features in the data that the dark matter model imposes. We advocate for dedicated cosmological hydrodynamical simulations with an axion dark matter component so that upcoming galaxy and cosmic microwave background lensing surveys can disentangle the dark matter-baryon transfer function.
The GenAI Generation: Student Views of Awareness, Preparedness, and Concern
2025-05-04
Micaela Siraj, Jon Duke
Generative AI (GenAI) is revolutionizing education and workforce development, profoundly shaping how students learn, engage, and prepare for their future. Outpacing the development of uniform policies and structures, GenAI has heralded a unique era and given rise to the GenAI Generation: a cohort of students whose education has been increasingly shaped by the opportunities and challenges GenAI presents during its widespread adoption within society. This study examines our students' perceptions of GenAI through a concise survey with optional open-ended questions, focusing on their awareness, preparedness, and concerns. Evaluation of more than 250 responses with more than 40% providing detailed qualitative feedback reveals a core dual sentiment: while most students express enthusiasm for GenAI, an even greater proportion voice a spectrum of concerns about ethics, job displacement, and the adequacy of educational structures given the highly transformative technology. These findings offer critical insights into how students view the potential and pitfalls of GenAI for future career impacts, with accompanying recommendations to guide educational institutions in navigating a future driven by GenAI.
Exploring new Approaches for Information Retrieval through Natural Language Processing
2025-05-04
Manak Raj, Nidhi Mishra
This review paper explores recent advancements and emerging approaches in Information Retrieval (IR) applied to Natural Language Processing (NLP). We examine traditional IR models such as Boolean, vector space, probabilistic, and inference network models, and highlight modern techniques including deep learning, reinforcement learning, and pretrained transformer models like BERT. We discuss key tools and libraries - Lucene, Anserini, and Pyserini - for efficient text indexing and search. A comparative analysis of sparse, dense, and hybrid retrieval methods is presented, along with applications in web search engines, cross-language IR, argument mining, private information retrieval, and hate speech detection. Finally, we identify open challenges and future research directions to enhance retrieval accuracy, scalability, and ethical considerations.
The use of chatbots equipped with artificial intelligence (AI) in educational settings has increased in recent years, showing potential to support teaching and learning. However, the adoption of these technologies has raised concerns about their impact on academic integrity, students' ability to problem-solve independently, and potential underlying biases. To better understand students' perspectives and experiences with these tools, a survey was conducted at a large public university in the United States. Through thematic analysis, 262 undergraduate students' responses regarding their perceived benefits and risks of AI chatbots in education were identified and categorized into themes. The results discuss several benefits identified by the students, with feedback and study support, instruction capabilities, and access to information being the most cited. Their primary concerns included risks to academic integrity, accuracy of information, loss of critical thinking skills, the potential development of overreliance, and ethical considerations such as data privacy, system bias, environmental impact, and preservation of human elements in education. While student perceptions align with previously discussed benefits and risks of AI in education, they show heightened concerns about distinguishing between human and AI generated work - particularly in cases where authentic work is flagged as AI-generated. To address students' concerns, institutions can establish clear policies regarding AI use and develop curriculum around AI literacy. With these in place, practitioners can effectively develop and implement educational systems that leverage AI's potential in areas such as immediate feedback and personalized learning support. This approach can enhance the quality of students' educational experiences while preserving the integrity of the learning process with AI.
Evaluating the Accuracy of Non-parametric Galaxy Morphological Indicator Measurements in the CSST Imaging Survey
2025-05-04
Yuchong Luo, Anhe Sha, Jian Ren, Xin Zhang, Xianmin Meng, Nan Li, F. S. Liu
The Chinese Space Station Telescope (CSST) is China's upcoming next-generation ultraviolet and optical survey telescope, with imaging resolution capabilities comparable to the Hubble Space Telescope (HST). In this study, we utilized a comprehensive sample of 3,679 CSST realistic mock galaxies constructed from HST CANDELS/GOODS-North deep imaging observations, with stellar masses $\log\left(M_{*} / M_{\odot}\right) > 9.0$ and redshifts $z < 2$. We evaluate the detection capabilities of CSST surveys and the accuracy in measuring the non-parametric morphological indicators ($C$, $A$, $Gini$, $M_{\rm 20}$, $A_{\rm O}$, $D_{\rm O}$) of galaxies. Our findings show that in terms of galaxy detection capabilities, CSST's deep field surveys can achieve the same level as HST's deep field observations; however, in wide-field surveys, CSST exhibits a significant deficiency in detecting high-redshift, low-mass, low-surface-brightness galaxies. Regarding the measurement of galaxy morphology, CSST's deep field surveys achieve high accuracy across all indicators except for the asymmetry indicator ($A$), whereas its wide-field surveys suffer from significant systematic biases. We thus provide simple correction functions to adjust the non-parametric morphological indicators obtained from CSST's wide-field and deep-field observations, thereby aligning CSST measurements with those from HST. This adjustment enables the direct application of non-parametric morphological classification methods originally developed for HST data to galaxies observed by CSST.
Automatically grading the diverse range of question types in high school physics problem is a challenge that requires automated grading techniques from different fields. We report the findings of a Systematic Literature Review of potential physics grading techniques. We propose a multi-modal AI grading framework to address these challenges and examine our framework in light of Australia's AI Ethical Principles.
In this paper we introduce an exponential map of the algebra of germs of vector fields into the group of germs of diffeomorphisms at zero. It is shown that this mapping is not a bijection. A brief review of the key results of the analytic iteration problem is given.
X-ray transients (XTs) driven by newborn magnetars from mergers of neutron star binaries (NSBs) were occasionally detected in the narrow-field {\it Chandra} Deep Field-South survey (CDF-S) and the {\it Swift}/XRT observations of short gamma-ray bursts (sGRBs). Quantifying their event rate density (ERD) and luminosity function (LF) is critical for understanding NSB coalescence and magnetar formation. Utilizing population synthesis calculations incorporating various equations of state (EoS), we derive a local ERD of $\sim 300\,{\rm Gpc^{-3}\,yr^{-1}}$ and a redshift-dependent ERD profile peaking at $z=1.81$ followed by rapid decline beyond $z \sim 4$. Constructing an XT sample based on CDF-S and {\it Swift} observations, we characterize the LF by a single power-law function at $L \leq 4.75 \times 10^{46}\;{\rm erg\;s^{-1}}$ with a slope of $-1.03$, following by a broken power-law function in which the break luminosity is $L_{\rm b} = 4.38 \times 10^{47}\;{\rm erg\;s^{-1}}$ and the slopes are $-0.28$ and $-1.66$. Based on the ERD and the LF, we estimate that the {\it Einstein Probe} ({\it EP}) detection rate is $\sim 31\;{\rm yr^{-1}}$, adopting a conservative threshold flux of $10^{-9}\;{\rm erg}\;{\rm s^{-1}}$, an luminosity range of $L \in [2\times 10^{44},2\times 10^{49}]\;{\rm erg\;s^{-1}}$, and a correction for jet opening angle of $\sim 16^{\circ}$. This detection rate is consistent with the {\it EP} observations during its first-year operation. It is important to note that our estimation is subject to uncertainties arising from the LF derivation. Future {\it EP} observations of these XT events will be crucial in reducing these uncertainties.
Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents
2025-05-04
Christian Schroeder de Witt
Decentralized AI agents will soon interact across internet platforms, creating security challenges beyond traditional cybersecurity and AI safety frameworks. Free-form protocols are essential for AI's task generalization but enable new threats like secret collusion and coordinated swarm attacks. Network effects can rapidly spread privacy breaches, disinformation, jailbreaks, and data poisoning, while multi-agent dispersion and stealth optimization help adversaries evade oversightcreating novel persistent threats at a systemic level. Despite their critical importance, these security challenges remain understudied, with research fragmented across disparate fields including AI security, multi-agent learning, complex systems, cybersecurity, game theory, distributed systems, and technical AI governance. We introduce \textbf{multi-agent security}, a new field dedicated to securing networks of decentralized AI agents against threats that emerge or amplify through their interactionswhether direct or indirect via shared environmentswith each other, humans, and institutions, and characterize fundamental security-performance trade-offs. Our preliminary work (1) taxonomizes the threat landscape arising from interacting AI agents, (2) surveys security-performance tradeoffs in decentralized AI systems, and (3) proposes a unified research agenda addressing open challenges in designing secure agent systems and interaction environments. By identifying these gaps, we aim to guide research in this critical area to unlock the socioeconomic potential of large-scale agent deployment on the internet, foster public trust, and mitigate national security risks in critical infrastructure and defense contexts.
The adoption of Artificial Intelligence (AI) in the healthcare service industry presents numerous ethical challenges, yet current frameworks often fail to offer a comprehensive, empirical understanding of the multidimensional factors influencing ethical AI integration. Addressing this critical research gap, this study introduces the Multi-Dimensional Ethical AI Adoption Model (MEAAM), a novel theoretical framework that categorizes 13 critical ethical variables across four foundational dimensions of Ethical AI Fair AI, Responsible AI, Explainable AI, and Sustainable AI. These dimensions are further analyzed through three core ethical lenses: epistemic concerns (related to knowledge, transparency, and system trustworthiness), normative concerns (focused on justice, autonomy, dignity, and moral obligations), and overarching concerns (highlighting global, systemic, and long-term ethical implications). This study adopts a quantitative, cross-sectional research design using survey data collected from healthcare professionals and analyzed via Partial Least Squares Structural Equation Modeling (PLS-SEM). Employing PLS-SEM, this study empirically investigates the influence of these ethical constructs on two outcomes Operational AI Adoption and Systemic AI Adoption. Results indicate that normative concerns most significantly drive operational adoption decisions, while overarching concerns predominantly shape systemic adoption strategies and governance frameworks. Epistemic concerns play a facilitative role, enhancing the impact of ethical design principles on trust and transparency in AI systems. By validating the MEAAM framework, this research advances a holistic, actionable approach to ethical AI adoption in healthcare and provides critical insights for policymakers, technologists, and healthcare administrators striving to implement ethically grounded AI solutions.
This survey explores knot polynomials and their categorification, culminating in the homological invariants of knots. We begin with an overview of classical knot polynomials, progressing towards the superpolynomial and its role in unifying various knot homologies. Along the way, we provide physical and geometric insights into the unification of the $sl(N)$ Khovanov-Rozansky and the knot Floer homology. We then turn our attention to the intriguing correspondence between knots and quivers, examining how this perspective sheds light on the integrality of BPS states encoded in the Labastida-Mari\~no-Ooguri-Vafa (LMOV) invariants. We will further investigate the knot-quiver correspondence from a physics and geometric side and study the 3d $\mathcal{N}=2$ theory $T[Q_K]$ for the quivers.
Cyclic polytopes, orientals, and correspondences: some aspects of higher Segal spaces
2025-05-04
Tobias Dyckerhoff
We discuss the role of higher Segal spaces at the interface of cyclic polytopes, orientals, and higher correspondences. Along the way we review examples from algebraic K-theory, show how cyclic polytopes provide a geometric model for the definition of orientals, and establish a characterization of higher Segal spaces as lax monadic structures in higher correspondence categories.
Since the Hadean era of Earth's history, peptides/proteins and RNA have undergone a complex evolutionary trajectory. Originating from simple monomeric units, these molecules evolved abiotically under various biochemical and biophysical constraints into functional biomolecules that contributed to the emergence of the first living cells. Within these cells, their interactions could then evolve through Darwinian selection. In this review, we examine current understanding of how protein$-$RNA interactions emerged under prebiotic conditions and developed into today's iconic biomolecular machines such as the ribosome. Particular emphasis is placed on the types of physicochemical interactions accessible to early protein$-$RNA complexes and their roles in driving spatial organization and compartmentalization in protocellular environments.
An overview of artificial intelligence in computer-assisted language learning
2025-05-04
Anisia Katinskaia
Computer-assisted language learning -- CALL -- is an established research field. We review how artificial intelligence can be applied to support language learning and teaching. The need for intelligent agents that assist language learners and teachers is increasing: the human teacher's time is a scarce and costly resource, which does not scale with growing demand. Further factors contribute to the need for CALL: pandemics and increasing demand for distance learning, migration of large populations, the need for sustainable and affordable support for learning, etc. CALL systems are made up of many components that perform various functions, and AI is applied to many different aspects in CALL, corresponding to their own expansive research areas. Most of what we find in the research literature and in practical use are prototypes or partial implementations -- systems that perform some aspects of the overall desired functionality. Complete solutions -- most of them commercial -- are few, because they require massive resources. Recent advances in AI should result in improvements in CALL, yet there is a lack of surveys that focus on AI in the context of this research field. This paper aims to present a perspective on the AI methods that can be employed for language learning from a position of a developer of a CALL system. We also aim to connect work from different disciplines, to build bridges for interdisciplinary work.
Since the advent of the ``Neural Ordinary Differential Equation (Neural ODE)'' paper, learning ODEs with deep learning has been applied to system identification, time-series forecasting, and related areas. Exploiting the diffeomorphic nature of ODE solution maps, neural ODEs has also enabled their use in generative modeling. Despite the rich potential to incorporate various kinds of physical information, training Neural ODEs remains challenging in practice. This study demonstrates, through the simplest one-dimensional linear model, why training Neural ODEs is difficult. We then propose a new stabilization method and provide an analytical convergence analysis. The insights and techniques presented here serve as a concise tutorial for researchers beginning work on Neural ODEs.
Requirements-Based Test Generation: A Comprehensive Survey
2025-05-04
Zhenzhen Yang, Rubing Huang, Chenhui Cui, Nan Niu, Dave Towey
As an important way of assuring software quality, software testing generates and executes test cases to identify software failures. Many strategies have been proposed to guide test-case generation, such as source-code-based approaches and methods based on bug reports. Requirements-based test generation (RBTG) constructs test cases based on specified requirements, aligning with user needs and expectations, without requiring access to the source code. Since its introduction in 1994, there have been many contributions to the development of RBTG, including various approaches, implementations, tools, assessment and evaluation methods, and applications. This paper provides a comprehensive survey on RBTG, categorizing requirement types, classifying approaches, investigating types of test cases, summarizing available tools, and analyzing experimental evaluations. This paper also summarizes the domains and industrial applications of RBTG, and discusses some open research challenges and potential future work.
A new apparatus, NA60+, is proposed for measuring muon pairs in the center-of-mass region from 5 to 17 GeV at CERN SPS in various collisional systems from Pb+Pb and down to $p$+Be. The physics scope of the new detector will cover topics from the measurement of thermal radiation coming from the hot and dense medium to chiral symmetry restoration, strangeness, and charm production. The proposed detector consists of a vertex spectrometer based on novel technology, allowing the production of large silicon sensors and a large-acceptance muon spectrometer based on gaseous detectors. With its high beam intensity, the new apparatus provides access to rare observables that have been scarcely studied until now. The new detector will come into operation after the Long Shutdown 3 of the LHC (past 2029) and is aimed at the first data-taking with Pb and proton beams. In this contribution, we review the project and recent R\&D effort, including the technical aspects and the studies of the physics performances for the observables.
Exact Set Packing in Multimodal Transportation with Ridesharing System for First/Last Mile
2025-05-04
Qian-Ping Gu, Jiajian Leo Liang
We propose a centralized transportation system that integrates public transit with ridesharing to provide multimodal transportation. At each time interval, the system receives a set of personal drivers, designated drivers, and public transit riders. It then assigns all riders to drivers, ensuring that pick-ups and drop-offs occur at designated transit stations. This effectively replaces first-mile/last-mile (FM/LM) segments with a ridesharing alternative, reducing overall commuting time. We study two optimization problems: (1) minimizing the total travel distances of drivers and (2) minimizing the number of designated drivers required to serve all riders. We show the optimization problems are NP-hard and give hypergraph-based integer linear programming exact algorithm and approximation algorithms. To enhance computational efficiency, we introduce a clustering heuristic that utilizes both spatial and temporal aspects of the input data to accelerate rider-to-driver assignments. Finally, we conduct an extensive computational study using real-world datasets and surveys from Chicago to evaluate our model and algorithms at a city-wide scale.
A Survey on Privacy Risks and Protection in Large Language Models
2025-05-04
Kang Chen, Xiuze Zhou, Yuanguo Lin, Shibo Feng, Li Shen, Pengcheng Wu
Although Large Language Models (LLMs) have become increasingly integral to diverse applications, their capabilities raise significant privacy concerns. This survey offers a comprehensive overview of privacy risks associated with LLMs and examines current solutions to mitigate these challenges. First, we analyze privacy leakage and attacks in LLMs, focusing on how these models unintentionally expose sensitive information through techniques such as model inversion, training data extraction, and membership inference. We investigate the mechanisms of privacy leakage, including the unauthorized extraction of training data and the potential exploitation of these vulnerabilities by malicious actors. Next, we review existing privacy protection against such risks, such as inference detection, federated learning, backdoor mitigation, and confidential computing, and assess their effectiveness in preventing privacy leakage. Furthermore, we highlight key practical challenges and propose future research directions to develop secure and privacy-preserving LLMs, emphasizing privacy risk assessment, secure knowledge transfer between models, and interdisciplinary frameworks for privacy governance. Ultimately, this survey aims to establish a roadmap for addressing escalating privacy challenges in the LLMs domain.
Distracted driving continues to be a significant cause of road traffic injuries and fatalities worldwide, even with advancements in driver monitoring technologies. Recent developments in machine learning (ML) and deep learning (DL) have primarily focused on visual data to detect distraction, often neglecting the complex, multimodal nature of driver behavior. This systematic review assesses 74 peer-reviewed studies from 2019 to 2024 that utilize ML/DL techniques for distracted driving detection across visual, sensor-based, multimodal, and emerging modalities. The review highlights a significant prevalence of visual-only models, particularly convolutional neural networks (CNNs) and temporal architectures, which achieve high accuracy but show limited generalizability in real-world scenarios. Sensor-based and physiological models provide complementary strengths by capturing internal states and vehicle dynamics, while emerging techniques, such as auditory sensing and radio frequency (RF) methods, offer privacy-aware alternatives. Multimodal architecture consistently surpasses unimodal baselines, demonstrating enhanced robustness, context awareness, and scalability by integrating diverse data streams. These findings emphasize the need to move beyond visual-only approaches and adopt multimodal systems that combine visual, physiological, and vehicular cues while keeping in checking the need to balance computational requirements. Future research should focus on developing lightweight, deployable multimodal frameworks, incorporating personalized baselines, and establishing cross-modality benchmarks to ensure real-world reliability in advanced driver assistance systems (ADAS) and road safety interventions.
Uncertainty Quantification for Machine Learning in Healthcare: A Survey
2025-05-04
L. Julián Lechuga López, Shaza Elsharief, Dhiyaa Al Jorf, Firas Darwish, Congbo Ma, Farah E. Shamout
Uncertainty Quantification (UQ) is pivotal in enhancing the robustness, reliability, and interpretability of Machine Learning (ML) systems for healthcare, optimizing resources and improving patient care. Despite the emergence of ML-based clinical decision support tools, the lack of principled quantification of uncertainty in ML models remains a major challenge. Current reviews have a narrow focus on analyzing the state-of-the-art UQ in specific healthcare domains without systematically evaluating method efficacy across different stages of model development, and despite a growing body of research, its implementation in healthcare applications remains limited. Therefore, in this survey, we provide a comprehensive analysis of current UQ in healthcare, offering an informed framework that highlights how different methods can be integrated into each stage of the ML pipeline including data processing, training and evaluation. We also highlight the most popular methods used in healthcare and novel approaches from other domains that hold potential for future adoption in the medical context. We expect this study will provide a clear overview of the challenges and opportunities of implementing UQ in the ML pipeline for healthcare, guiding researchers and practitioners in selecting suitable techniques to enhance the reliability, safety and trust from patients and clinicians on ML-driven healthcare solutions.
OBD-Finder: Explainable Coarse-to-Fine Text-Centric Oracle Bone Duplicates Discovery
2025-05-04
Chongsheng Zhang, Shuwen Wu, Yingqi Chen, Matthias Aßenmacher, Christian Heumann, Yi Men, Gaojuan Fan, João Gama
Oracle Bone Inscription (OBI) is the earliest systematic writing system in China, while the identification of Oracle Bone (OB) duplicates is a fundamental issue in OBI research. In this work, we design a progressive OB duplicate discovery framework that combines unsupervised low-level keypoints matching with high-level text-centric content-based matching to refine and rank the candidate OB duplicates with semantic awareness and interpretability. We compare our approach with state-of-the-art content-based image retrieval and image matching methods, showing that our approach yields comparable recall performance and the highest simplified mean reciprocal rank scores for both Top-5 and Top-15 retrieval results, and with significantly accelerated computation efficiency. We have discovered over 60 pairs of new OB duplicates in real-world deployment, which were missed by OBI researchers for decades. The models, video illustration and demonstration of this work are available at: https://github.com/cszhangLMU/OBD-Finder/.
Update: Progress toward fusion energy breakeven and gain as measured against the Lawson criteria
2025-05-04
Samuel E. Wurzel, Scott C. Hsu
This paper is an update to our earlier paper ''Progress toward fusion energy breakeven and gain as measured against the Lawson criterion'' [Phys. Plasmas 29, 062103 (2022)]. Plots of Lawson parameter and triple product vs. ion temperature and triple product vs. date achieved are updated with recently published experimental results. A new plot of scientific energy gain vs. date achieved is included. Additionally, notes on new experimental results, clarifications, and a correction are included.
Video Forgery Detection for Surveillance Cameras: A Review
2025-05-04
Noor B. Tayfor, Tarik A. Rashid, Shko M. Qader, Bryar A. Hassan, Mohammed H. Abdalla, Jafar Majidpour, Aram M. Ahmed, Hussein M. Ali, Aso M. Aladdin, Abdulhady A. Abdullah, Ahmed S. Shamsaldin, Haval M. Sidqi, Abdulrahman Salih, Zaher M. Yaseen, Azad A. Ameen, Janmenjoy Nayak, Mahmood Yashar Hamza
The widespread availability of video recording through smartphones and digital devices has made video-based evidence more accessible than ever. Surveillance footage plays a crucial role in security, law enforcement, and judicial processes. However, with the rise of advanced video editing tools, tampering with digital recordings has become increasingly easy, raising concerns about their authenticity. Ensuring the integrity of surveillance videos is essential, as manipulated footage can lead to misinformation and undermine judicial decisions. This paper provides a comprehensive review of existing forensic techniques used to detect video forgery, focusing on their effectiveness in verifying the authenticity of surveillance recordings. Various methods, including compression-based analysis, frame duplication detection, and machine learning-based approaches, are explored. The findings highlight the growing necessity for more robust forensic techniques to counteract evolving forgery methods. Strengthening video forensic capabilities will ensure that surveillance recordings remain credible and admissible as legal evidence.
A Generalised and Adaptable Reinforcement Learning Stopping Method
2025-05-03
Reem Bin-Hezam, Mark Stevenson
This paper presents a Technology Assisted Review (TAR) stopping approach based on Reinforcement Learning (RL). Previous such approaches offered limited control over stopping behaviour, such as fixing the target recall and tradeoff between preferring to maximise recall or cost. These limitations are overcome by introducing a novel RL environment, GRLStop, that allows a single model to be applied to multiple target recalls, balances the recall/cost tradeoff and integrates a classifier. Experiments were carried out on six benchmark datasets (CLEF e-Health datasets 2017-9, TREC Total Recall, TREC Legal and Reuters RCV1) at multiple target recall levels. Results showed that the proposed approach to be effective compared to multiple baselines in addition to offering greater flexibility.
Individual stars in the Milky Way (MW) and its satellites have been shown to trace galaxy stellar mass dependent sequences in the $\alpha$-abundance ([$\alpha$/Fe]) vs metallicity ([Fe/H]) plane. Testing the universality of such sequences has been elusive as deep absorption-line spectra required for [$\alpha$/Fe] and [Fe/H] measurements beyond the local group are mostly limited to integrated light from nearby, relatively high-mass, early-type galaxies. However, analogous to [$\alpha$/Fe] vs [Fe/H] for stars, we now have log(O/Ar) vs 12+log(Ar/H) for the integrated nebular light of star-forming galaxies (SFGs). From Sloan-Digital Sky Survey (SDSS) observations of $\sim3000$ SFGs out to z$\sim0.3$, where we directly determined O & Ar abundances, we obtain for the first time the distribution of an ensemble of SFGs in the log(O/Ar) vs 12+log(Ar/H) plane. We show that higher ($\sim2.6\times10^9$M$_{\odot}$) and lower mass ($\sim1.7\times10^7$M$_{\odot}$) SFGs clearly trace distinct mass dependent sequences in this plane, qualitatively consistent with the mass dependence of chemical enrichment sequences observed for the stars in the MW and its satellites. Such sequences are consistent with expectations from galaxy chemical evolution (GCE) models that are driven primarily by the interplay of core-collapse and Type Ia supernovae.
Visual enhancement and 3D representation for underwater scenes: a review
2025-05-03
Guoxi Huang, Haoran Wang, Brett Seymour, Evan Kovacs, John Ellerbrock, Dave Blackham, Nantheera Anantrasirichai
Underwater visual enhancement (UVE) and underwater 3D reconstruction pose significant challenges in computer vision and AI-based tasks due to complex imaging conditions in aquatic environments. Despite the development of numerous enhancement algorithms, a comprehensive and systematic review covering both UVE and underwater 3D reconstruction remains absent. To advance research in these areas, we present an in-depth review from multiple perspectives. First, we introduce the fundamental physical models, highlighting the peculiarities that challenge conventional techniques. We survey advanced methods for visual enhancement and 3D reconstruction specifically designed for underwater scenarios. The paper assesses various approaches from non-learning methods to advanced data-driven techniques, including Neural Radiance Fields and 3D Gaussian Splatting, discussing their effectiveness in handling underwater distortions. Finally, we conduct both quantitative and qualitative evaluations of state-of-the-art UVE and underwater 3D reconstruction algorithms across multiple benchmark datasets. Finally, we highlight key research directions for future advancements in underwater vision.
Intent-driven network management is critical for managing the complexity of 5G and 6G networks. It enables adaptive, on-demand management of the network based on the objectives of the network operators. In this paper, we propose an innovative three-step framework for intent-driven network management based on Generative AI (GenAI) algorithms. First, we fine-tune a Large Language Model (LLM) on a custom dataset using a Quantized Low-Rank Adapter (QLoRA) to enable memory-efficient intent processing within limited computational resources. A Retrieval Augmented Generation (RAG) module is included to support dynamic decision-making. Second, we utilize a transformer architecture for time series forecasting to predict key parameters, such as power consumption, traffic load, and packet drop rate, to facilitate intent validation proactively. Lastly, we introduce a Hierarchical Decision Transformer with Goal Awareness (HDTGA) to optimize the selection and orchestration of network applications and hence, optimize the network. Our intent guidance and processing approach improves BERTScore by 6% and the semantic similarity score by 9% compared to the base LLM model. Again, the proposed predictive intent validation approach can successfully rule out the performance-degrading intents with an average of 88% accuracy. Finally, compared to the baselines, the proposed HDTGA algorithm increases throughput at least by 19.3%, reduces delay by 48.5%, and boosts energy efficiency by 54.9%.
Multi-Scale Target-Aware Representation Learning for Fundus Image Enhancement
2025-05-03
Haofan Wu, Yin Huang, Yuqing Wu, Qiuyu Yang, Bingfang Wang, Li Zhang, Muhammad Fahadullah Khan, Ali Zia, M. Saleh Memon, Syed Sohail Bukhari, Abdul Fattah Memon, Daizong Ji, Ya Zhang, Ghulam Mustafa, Yin Fang
High-quality fundus images provide essential anatomical information for clinical screening and ophthalmic disease diagnosis. Yet, due to hardware limitations, operational variability, and patient compliance, fundus images often suffer from low resolution and signal-to-noise ratio. Recent years have witnessed promising progress in fundus image enhancement. However, existing works usually focus on restoring structural details or global characteristics of fundus images, lacking a unified image enhancement framework to recover comprehensive multi-scale information. Moreover, few methods pinpoint the target of image enhancement, e.g., lesions, which is crucial for medical image-based diagnosis. To address these challenges, we propose a multi-scale target-aware representation learning framework (MTRL-FIE) for efficient fundus image enhancement. Specifically, we propose a multi-scale feature encoder (MFE) that employs wavelet decomposition to embed both low-frequency structural information and high-frequency details. Next, we design a structure-preserving hierarchical decoder (SHD) to fuse multi-scale feature embeddings for real fundus image restoration. SHD integrates hierarchical fusion and group attention mechanisms to achieve adaptive feature fusion while retaining local structural smoothness. Meanwhile, a target-aware feature aggregation (TFA) module is used to enhance pathological regions and reduce artifacts. Experimental results on multiple fundus image datasets demonstrate the effectiveness and generalizability of MTRL-FIE for fundus image enhancement. Compared to state-of-the-art methods, MTRL-FIE achieves superior enhancement performance with a more lightweight architecture. Furthermore, our approach generalizes to other ophthalmic image processing tasks without supervised fine-tuning, highlighting its potential for clinical applications.
Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey
2025-05-03
Jing Liu, Yao Du, Kun Yang, Yan Wang, Xiping Hu, Zehua Wang, Yang Liu, Peng Sun, Azzedine Boukerche, Victor C. M. Leung
Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications, integrating cloud resources with edge devices to enable efficient, low-latency processing. Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems, yet introduce significant challenges in model deployment and resource management. In this survey, we comprehensive examine the intersection of distributed intelligence and model optimization within edge-cloud environments, providing a structured tutorial on fundamental architectures, enabling technologies, and emerging applications. Additionally, we systematically analyze model optimization approaches, including compression, adaptation, and neural architecture search, alongside AI-driven resource management strategies that balance performance, energy efficiency, and latency requirements. We further explore critical aspects of privacy protection and security enhancement within ECCC systems and examines practical deployments through diverse applications, spanning autonomous driving, healthcare, and industrial automation. Performance analysis and benchmarking techniques are also thoroughly explored to establish evaluation standards for these complex systems. Furthermore, the review identifies critical research directions including LLMs deployment, 6G integration, neuromorphic computing, and quantum computing, offering a roadmap for addressing persistent challenges in heterogeneity management, real-time processing, and scalability. By bridging theoretical advancements and practical deployments, this survey offers researchers and practitioners a holistic perspective on leveraging AI to optimize distributed computing environments, fostering innovation in next-generation intelligent systems.
Kinks in generalized scalar field models and their scattering properties
2025-05-03
D. Bazeia, A. S. Lobão, Fabiano C. Simas
This work investigates kink solutions in one-dimensional scalar field theories. We begin with a review of the formalism used to obtain these solutions, presenting the BPS formalism and linear stability analysis. Next, we explore new models involving real scalar fields, generated by distinct potentials, with a focus on the topological structures responsible for the formation of kinks. Finally, we study collisions between the solutions obtained in two distinct models, analyzing their dynamic implications.
We review the basics of the supersymmetric extension of the Standard Model and discuss the implications of the constraints on the superpartner masses at the LHC for the Higgs mass, the $W$ boson mass, the muon $g-2$ and the proton lifetime.
From Formulas to Figures: How Visual Elements Impact User Interactions in Educational Videos
2025-05-03
Wolfgang Gritz, Hewi Salih, Anett Hoppe, Ralph Ewerth
Educational videos have become increasingly relevant in today's learning environments. While prior research in laboratory studies has provided valuable insights, analyzing real-world interaction data can enhance our understanding of authentic user behavior. Previous studies have investigated technical aspects, such as the influence of cuts on pausing behavior, but the impact of visual complexity remains understudied. In this paper, we address this gap and propose a novel approach centered on visual complexity, defined as the number of visually distinguishable and meaningful elements in a video frame, such as mathematical equations, chemical formulas, or graphical representations. Our study introduces a fine-grained taxonomy of visual objects in educational videos, expanding on previous classifications. Applying this taxonomy to 25 videos from physics and chemistry, we examine the relationship between visual complexity and user behavior, including pauses, in-video navigation, and session dropouts. The results indicate that increased visual complexity, especially of textual elements, correlates with more frequent pauses, rewinds, and dropouts. The results offer a deeper understanding of how video design affects user behavior in real-world scenarios. Our work has implications for optimizing educational videos, particularly in STEM fields. We make our code publicly available (https://github.com/TIBHannover/from_formulas_to_figures).
RVSNUpy: A Python Package for Spectroscopic Redshift Measurement Based on Cross-Correlation
2025-05-03
Taewan Kim, Jubee Sohn, Ho Seong Hwang
We introduce RVSNUpy, a new Python package designed to measure spectroscopic redshifts. Based on inverse-variance weighted cross-correlation, RVSNUpy determines the redshifts by comparing observed spectra with various rest-frame template spectra. We test the performance of RVSNUpy based on ~ 6000 objects in the HectoMAP redshift survey observed with both SDSS and MMT/Hectospec. We demonstrate that a slight redshift offset (~ 40 km/s) between SDSS and MMT/Hectospec measurements reported from previous studies results from the small offsets in the redshift template spectra used for SDSS and Hectospec reductions. We construct the universal set of template spectra, including empirical SDSS template spectra, carefully calibrated to the rest frame. Our test for the HectoMAP objects with duplicated observations shows that RVSNUpy with the universal template spectra yields the homogeneous redshift from the spectra obtained with different spectrographs. We highlight that RVSNUpy is a powerful redshift measurement tool for current and future large-scale spectroscopy surveys, including A-SPEC, DESI, 4MOST, and Subaru/PFS.
Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking
2025-05-03
Alireza Sadeghi, Farshid Hajati, Ahmadreza Argha, Nigel H Lovell, Min Yang, Hamid Alinejad-Rokny
Integrating heterogeneous biomedical data including imaging, omics, and clinical records supports accurate diagnosis and personalised care. Graph-based models fuse such non-Euclidean data by capturing spatial and relational structure, yet clinical uptake requires regulator-ready interpretability. We present the first technical survey of interpretable graph based models for multimodal biomedical data, covering 26 studies published between Jan 2019 and Sep 2024. Most target disease classification, notably cancer and rely on static graphs from simple similarity measures, while graph-native explainers are rare; post-hoc methods adapted from non-graph domains such as gradient saliency, and SHAP predominate. We group existing approaches into four interpretability families, outline trends such as graph-in-graph hierarchies, knowledge-graph edges, and dynamic topology learning, and perform a practical benchmark. Using an Alzheimer disease cohort, we compare Sensitivity Analysis, Gradient Saliency, SHAP and Graph Masking. SHAP and Sensitivity Analysis recover the broadest set of known AD pathways and Gene-Ontology terms, whereas Gradient Saliency and Graph Masking surface complementary metabolic and transport signatures. Permutation tests show all four beat random gene sets, but with distinct trade-offs: SHAP and Graph Masking offer deeper biology at higher compute cost, while Gradient Saliency and Sensitivity Analysis are quicker though coarser. We also provide a step-by-step flowchart covering graph construction, explainer choice and resource budgeting to help researchers balance transparency and performance. This review synthesises the state of interpretable graph learning for multimodal medicine, benchmarks leading techniques, and charts future directions, from advanced XAI tools to under-studied diseases, serving as a concise reference for method developers and translational scientists.
Non-thermal Radio Emission from Massive Protostars in the SARAO MeerKAT Galactic Plane Survey
2025-05-03
W. O Obonyo, M. G Hoare, S. L Lumsden, M. A Thompson, J. O. Chibueze, W. D. Cotton, A. Rigby3, P. Leto, C. Trigilio, G. M. Williams
We present an investigation of the L-band emission from known massive young stellar objects (MYSOs) in the SARAO MeerKAT Galactic Plane Survey to search for non-thermal radio emitters in the sample. A total of 398 massive protostars, identified from the Red MSX Source (RMS) survey, are located within the survey region. Among these, 162 fields that host the protostars are isolated from nearby bright HII regions, allowing for the study of any ionized jets present. Seventy-one of these fields have jets with five-sigma detections or higher, corresponding to a detection rate of 44%. The MeerKAT fluxes of the detections, together with the upper limits of the non-detections and any other fluxes from previous observations, were used to estimate the spectral indices of the jets, and to search for the presence of non-thermal radiation. In cases where a source manifests as single in a given observation but is resolved into multiple components in observations of higher resolutions, the sum of the fluxes of the resolved components was used in estimating the indices. Any e!ects from missing flux in higher-resolution observations were incorporated into the index uncertainties. The spectral indices of the sample show that at least 50% of the jets emit non-thermal radiation. Additionally, the spectral energy distribution (SED) of some of the sources, as well as their radio luminosities exhibit evidence of non-thermal emission, especially in extended sources.
Automated ARAT Scoring Using Multimodal Video Analysis, Multi-View Fusion, and Hierarchical Bayesian Models: A Clinician Study
2025-05-03
Tamim Ahmed, Thanassis Rikakis
Manual scoring of the Action Research Arm Test (ARAT) for upper extremity assessment in stroke rehabilitation is time-intensive and variable. We propose an automated ARAT scoring system integrating multimodal video analysis with SlowFast, I3D, and Transformer-based models using OpenPose keypoints and object locations. Our approach employs multi-view data (ipsilateral, contralateral, and top perspectives), applying early and late fusion to combine features across views and models. Hierarchical Bayesian Models (HBMs) infer movement quality components, enhancing interpretability. A clinician dashboard displays task scores, execution times, and quality assessments. We conducted a study with five clinicians who reviewed 500 video ratings generated by our system, providing feedback on its accuracy and usability. Evaluated on a stroke rehabilitation dataset, our framework achieves 89.0% validation accuracy with late fusion, with HBMs aligning closely with manual assessments. This work advances automated rehabilitation by offering a scalable, interpretable solution with clinical validation.
Non-native speakers (NNSs) often face speaking challenges in real-time multilingual communication, such as struggling to articulate their thoughts. To address this issue, we developed an AI-based speaking assistant (AISA) that provides speaking references for NNSs based on their input queries, task background, and conversation history. To explore NNSs' interaction with AISA and its impact on NNSs' speaking during real-time multilingual communication, we conducted a mixed-method study involving a within-subject experiment and follow-up interviews. In the experiment, two native speakers (NSs) and one NNS formed a team (31 teams in total) and completed two collaborative tasks--one with access to the AISA and one without. Overall, our study revealed four types of AISA input patterns among NNSs, each reflecting different levels of effort and language preferences. Although AISA did not improve NNSs' speaking competence, follow-up interviews revealed that it helped improve the logical flow and depth of their speech. Moreover, the additional multitasking introduced by AISA, such as entering and reviewing system output, potentially elevated NNSs' workload and anxiety. Based on these observations, we discuss the pros and cons of implementing tools to assist NNS in real-time multilingual communication and offer design recommendations.
High-resolution radio observations of massive protostars in the SARAO MeerKAT Galactic Plane Survey
2025-05-03
W. O Obonyo, M. G Hoare, S. L Lumsden, J. O. Chibueze, C. J. Ugwu
We present high-resolution observations made with the Australia Telescope Compact Array (ATCA) at 5.5 GHz and 9.0 GHz of a sample of twenty-eight massive protostars. These protostars were initially detected at radio wavelengths in the MeerKAT Galactic Plane Survey at 1.3 GHz, where they were unresolved with an angular resolution of approximately 8 arcseconds. The resolution of the ATCA observations at 5.5 GHz and 9.0 GHz are significantly higher at, 2.0 and 1.2 arc seconds, respectively. The highest angular resolution corresponds to a linear resolution of 1920 AU for our nearest object. This improvement in resolution enabled us to resolve the components of extended emission and to more accurately determine the nature of the objects. Approximately 80% of the detections were classified as jets with 68% of the jets found to be associated with non-thermal emission.
Gaussian process (GP) models have been used in a wide range of battery applications, in which different kernels were manually selected with considerable expertise. However, to capture complex relationships in the ever-growing amount of real-world data, selecting a suitable kernel for the GP model in battery applications is increasingly challenging. In this work, we first review existing GP kernels used in battery applications and then extend an automatic kernel search method with a new base kernel and model selection criteria. The GP models with composite kernels outperform the baseline kernel in two numerical examples of battery applications, i.e., battery capacity estimation and residual load prediction. Particularly, the results indicate that the Bayesian Information Criterion may be the best model selection criterion as it achieves a good trade-off between kernel performance and computational complexity. This work should, therefore, be of value to practitioners wishing to automate their kernel search process in battery applications.
Report on Challenges of Practical Reproducibility for Systems and HPC Computer Science
2025-05-03
Kate Keahey, Marc Richardson, Rafael Tolosana Calasanz, Sascha Hunold, Jay Lofstead, Tanu Malik, Christian Perez
This report synthesizes findings from the November 2024 Community Workshop on Practical Reproducibility in HPC, which convened researchers, artifact authors, reviewers, and chairs of reproducibility initiatives to address the critical challenge of making computational experiments reproducible in a cost-effective manner. The workshop deliberately focused on systems and HPC computer science research due to its unique requirements, including specialized hardware access and deep system reconfigurability. Through structured discussions, lightning talks, and panel sessions, participants identified key barriers to practical reproducibility and formulated actionable recommendations for the community. The report presents a dual framework of challenges and recommendations organized by target audience (authors, reviewers, organizations, and community). It characterizes technical obstacles in experiment packaging and review, including completeness of artifact descriptions, acquisition of specialized hardware, and establishing reproducibility conditions. The recommendations range from immediate practical tools (comprehensive checklists for artifact packaging) to ecosystem-level improvements (refining badge systems, creating artifact digital libraries, and developing AI-assisted environment creation). Rather than advocating for reproducibility regardless of cost, the report emphasizes striking an appropriate balance between reproducibility rigor and practical feasibility, positioning reproducibility as an integral component of scientific exploration rather than a burdensome afterthought. Appendices provide detailed, immediately actionable checklists for authors and reviewers to improve reproducibility practices across the HPC community.
Revisiting the Hubble constant, sound horizon and cosmography from late-time Universe observations
2025-05-03
Zhiwei Yang, Tonghua Liu, Xiaolei Li
The Hubble tension has become one of the central problems in cosmology. In this work, we determine the Hubble constant $H_0$ and sound horizon $r_d$ by using the combination of Baryon Acoustic Oscillations (BAOs) from DESI surveys, time-delay lensed quasars from H0LiCOW collaborations and the Pantheon supernovae observations. We consider two cosmological approaches, i.e., Taylor series and Pad\'{e} polynomials, to avoid cosmological dependence. The reason for using this combination of data is that the absolute distance provided by strong gravitational lensing helps anchor the relative distance of BAO, and supernovae provide a robust history of universe evolution. {{Combining the 6 time-delay distance (6$D_{\Delta t}$) plus 4 angular diameter distance to the deflector (4$D_d$) measurements of time-delay lensed quasars,}} the BAO and the type Ia of supernovae (SNe Ia) datasets, we obtain a model-independent result of $r_d = 138.2_{-3.9}^{+3.3}$ Mpc and $H_0 = 72.9^{+1.8}_{-1.8}$ ${\mathrm{~km~s^{-1}~Mpc^{-1}}}$ for the Taylor series cosmography and $r_d = 137.0_{-3.7}^{+3.2}$ Mpc and $H_0 = 73.1_{-1.7}^{+1.8}$ ${\mathrm{~km~s^{-1}~Mpc^{-1}}}$ for the Pad\'{e} polynomials cosmography. The determination of $r_d$ and $H_0$ prefers larger $H_0$ and smaller $r_d$ than Planck data under the assumption of flat-$\Lambda$CDM model. However, the values of $H_0$ are consistent with the $H_0$ determination from SH0ES collaboration.
A Survey on Inference Engines for Large Language Models: Perspectives on Optimization and Efficiency
2025-05-03
Sihyeong Park, Sungryeol Jeon, Chaelyn Lee, Seokhun Jeon, Byung-Soo Kim, Jemin Lee
Large language models (LLMs) are widely applied in chatbots, code generators, and search engines. Workloads such as chain-of-thought, complex reasoning, and agent services significantly increase the inference cost by invoking the model repeatedly. Optimization methods such as parallelism, compression, and caching have been adopted to reduce costs, but the diverse service requirements make it hard to select the right method. Recently, specialized LLM inference engines have emerged as a key component for integrating the optimization methods into service-oriented infrastructures. However, a systematic study on inference engines is still lacking. This paper provides a comprehensive evaluation of 25 open-source and commercial inference engines. We examine each inference engine in terms of ease-of-use, ease-of-deployment, general-purpose support, scalability, and suitability for throughput- and latency-aware computation. Furthermore, we explore the design goals of each inference engine by investigating the optimization techniques it supports. In addition, we assess the ecosystem maturity of open source inference engines and handle the performance and cost policy of commercial solutions. We outline future research directions that include support for complex LLM-based services, support of various hardware, and enhanced security, offering practical guidance to researchers and developers in selecting and designing optimized LLM inference engines. We also provide a public repository to continually track developments in this fast-evolving field: https://github.com/sihyeong/Awesome-LLM-Inference-Engine
Topological Quantum Statistical Mechanics and Topological Quantum Field Theories
2025-05-03
Zhidong Zhang
In this work, we first focus on the mathematical structure of the three-dimensional (3D) Ising model. In the Clifford algebraic representation, many internal factors exist in the transfer matrices of the 3D Ising model, which are ascribed to the topology of the 3D space and the many-body interactions of spins. They result in the nonlocality, the nontrivial topological structure, as well as the long-range entanglement between spins in the 3D Ising model. We review briefly the exact solution of the ferromagnetic 3D Ising model at the zero magnetic field, which was derived in our previous work. Then, the framework of topological quantum statistical mechanics is established, with respect to the mathematical aspects (topology, algebra, and geometry) and physical features (the contribution of topology to physics, Jordan-von Neumann-Wigner framework, time average, ensemble average, and quantum mechanical average). This is accomplished by generalizations of our findings and observations in the 3D Ising models. Finally, the results are generalized to topological quantum field theories, in consideration of relationships between quantum statistical mechanics and quantum field theories. It is found that these theories must be set up within the Jordan-von Neumann-Wigner framework, and the ergodic hypothesis is violated at the finite temperature. It is necessary to account the time average of the ensemble average and the quantum mechanical average in the topological quantum statistical mechanics and to introduce the parameter space of complex time (and complex temperature) in the topological quantum field theories. We find that a topological phase transition occurs near the infinite temperature (or the zero temperature) in models in the topological quantum statistical mechanics and the topological quantum field theories, which visualizes a symmetrical breaking of time inverse symmetry.
Third-party compliance reviews for frontier AI safety frameworks
2025-05-03
Aidan Homewood, Sophie Williams, Noemi Dreksler, John Lidiard, Malcolm Murray, Lennart Heim, Marta Ziosi, Seán Ó hÉigeartaigh, Michael Chen, Kevin Wei, Christoph Winter, Miles Brundage, Ben Garfinkel, Jonas Schuett
Safety frameworks have emerged as a best practice for managing risks from frontier artificial intelligence (AI) systems. However, it may be difficult for stakeholders to know if companies are adhering to their frameworks. This paper explores a potential solution: third-party compliance reviews. During a third-party compliance review, an independent external party assesses whether a frontier AI company is complying with its safety framework. First, we discuss the main benefits and challenges of such reviews. On the one hand, they can increase compliance with safety frameworks and provide assurance to internal and external stakeholders. On the other hand, they can create information security risks, impose additional cost burdens, and cause reputational damage, but these challenges can be partially mitigated by drawing on best practices from other industries. Next, we answer practical questions about third-party compliance reviews, namely: (1) Who could conduct the review? (2) What information sources could the reviewer consider? (3) How could compliance with the safety framework be assessed? (4) What information about the review could be disclosed externally? (5) How could the findings guide development and deployment actions? (6) When could the reviews be conducted? For each question, we evaluate a set of plausible options. Finally, we suggest "minimalist", "more ambitious", and "comprehensive" approaches for each question that a frontier AI company could adopt.
Identifying Doppelganger Active Galactic Nuclei across redshifts from spectroscopic surveys
2025-05-03
Shreya Sareen, Swayamtrupta Panda
Active Galactic Nuclei (AGNs) are among the most luminous objects in the universe, making them valuable probes for studying galaxy evolution. However, understanding how AGN properties evolve over cosmic time remains a fundamental challenge. This study investigates whether AGNs at low redshift (nearby) can serve as proxies for their high-redshift (distant) counterparts by identifying spectral 'doppelg\"angers', AGNs with remarkably similar emission line properties despite being separated by vast cosmic distances. We analyze key spectral features of bona fide AGNs using the Sloan Digital Sky Survey's Data Release 16, including continuum and emission lines: Nitrogen (N V), Carbon (C IV), Magnesium (Mg II), Hydrogen-beta (H$\beta$), and Iron (Fe II - optical and UV) emission lines. We incorporated properties such as equivalent width, velocity dispersion in the form of full width at half maximum (FWHM), and continuum luminosities (135nm, 300nm, and 510nm) closest to these prominent lines. Our initial findings suggest the existence of multiple AGNs with highly similar spectra, hinting at the possibility that local AGNs may indeed share intrinsic properties with high-redshift ones. We showcase here one of the better candidate pairs of AGNs resulting from our analyses.
Understanding University Students' Use of Generative AI: The Roles of Demographics and Personality Traits
2025-05-03
Newnew Deng, Edward Jiusi Liu, Xiaoming Zhai
The use of generative AI (GAI) among university students is rapidly increasing, yet empirical research on students' GAI use and the factors influencing it remains limited. To address this gap, we surveyed 363 undergraduate and graduate students in the United States, examining their GAI usage and how it relates to demographic variables and personality traits based on the Big Five model (i.e., extraversion, agreeableness, conscientiousness, and emotional stability, and intellect/imagination). Our findings reveal: (a) Students in higher academic years are more inclined to use GAI and prefer it over traditional resources. (b) Non-native English speakers use and adopt GAI more readily than native speakers. (c) Compared to White, Asian students report higher GAI usage, perceive greater academic benefits, and express a stronger preference for it. Similarly, Black students report a more positive impact of GAI on their academic performance. Personality traits also play a significant role in shaping perceptions and usage of GAI. After controlling demographic factors, we found that personality still significantly predicts GAI use and attitudes: (a) Students with higher conscientiousness use GAI less. (b) Students who are higher in agreeableness perceive a less positive impact of GAI on academic performance and express more ethical concerns about using it for academic work. (c) Students with higher emotional stability report a more positive impact of GAI on learning and fewer concerns about its academic use. (d) Students with higher extraversion show a stronger preference for GAI over traditional resources. (e) Students with higher intellect/imagination tend to prefer traditional resources. These insights highlight the need for universities to provide personalized guidance to ensure students use GAI effectively, ethically, and equitably in their academic pursuits.
The rapid development of Large Language Models (LLMs) has catalyzed significant advancements in video understanding technologies. This survey provides a comprehensive analysis of benchmarks and evaluation methodologies specifically designed or used for Video Large Language Models (VideoLLMs). We examine the current landscape of video understanding benchmarks, discussing their characteristics, evaluation protocols, and limitations. The paper analyzes various evaluation methodologies, including closed-set, open-set, and specialized evaluations for temporal and spatiotemporal understanding tasks. We highlight the performance trends of state-of-the-art VideoLLMs across these benchmarks and identify key challenges in current evaluation frameworks. Additionally, we propose future research directions to enhance benchmark design, evaluation metrics, and protocols, including the need for more diverse, multimodal, and interpretability-focused benchmarks. This survey aims to equip researchers with a structured understanding of how to effectively evaluate VideoLLMs and identify promising avenues for advancing the field of video understanding with large language models.
Sentiment-Aware Recommendation Systems in E-Commerce: A Review from a Natural Language Processing Perspective
2025-05-03
Yogesh Gajula
E-commerce platforms generate vast volumes of user feedback, such as star ratings, written reviews, and comments. However, most recommendation engines rely primarily on numerical scores, often overlooking the nuanced opinions embedded in free text. This paper comprehensively reviews sentiment-aware recommendation systems from a natural language processing perspective, covering advancements from 2023 to early 2025. It highlights the benefits of integrating sentiment analysis into e-commerce recommenders to enhance prediction accuracy and explainability through detailed opinion extraction. Our survey categorizes recent work into four main approaches: deep learning classifiers that combine sentiment embeddings with user item interactions, transformer based methods for nuanced feature extraction, graph neural networks that propagate sentiment signals, and conversational recommenders that adapt in real time to user feedback. We summarize model architectures and demonstrate how sentiment flows through recommendation pipelines, impacting dialogue-based suggestions. Key challenges include handling noisy or sarcastic text, dynamic user preferences, and bias mitigation. Finally, we outline research gaps and provide a roadmap for developing smarter, fairer, and more user-centric recommendation tools.
QCMaquis 4.0: Multi-Purpose Electronic, Vibrational, and Vibronic Structure and Dynamics Calculations with the Density Matrix Renormalization Group
2025-05-02
Kalman Szenes, Nina Glaser, Mihael Erakovic, Valentin Barandun, Maximilian Mörchen, Robin Feldmann, Stefano Battaglia, Alberto Baiardi, Markus Reiher
QCMaquis is a quantum chemistry software package for general molecular structure calculations in a matrix product state/matrix product operator formalism of the density matrix renormalization group (DMRG). It supports a wide range of features for electronic structure, multi-component (pre-Born-Oppenheimer), anharmonic vibrational structure, and vibronic calculations. In addition to the ground and excited state solvers, QCMaquis allows for time propagation of matrix product states based on the tangent-space formulation of time-dependent DMRG. The latest developments include transcorrelated electronic structure calculations, very recent vibrational and vibronic models, and a convenient Python wrapper, facilitating the interface with external libraries. This paper reviews all the new features of QCMaquis and demonstrates them with new results.
Dynamic Robot Tool Use with Vision Language Models
2025-05-02
Noah Trupin, Zixing Wang, Ahmed H. Qureshi
Tool use enhances a robot's task capabilities. Recent advances in vision-language models (VLMs) have equipped robots with sophisticated cognitive capabilities for tool-use applications. However, existing methodologies focus on elementary quasi-static tool manipulations or high-level tool selection while neglecting the critical aspect of task-appropriate tool grasping. To address this limitation, we introduce inverse Tool-Use Planning (iTUP), a novel VLM-driven framework that enables grounded fine-grained planning for versatile robotic tool use. Through an integrated pipeline of VLM-based tool and contact point grounding, position-velocity trajectory planning, and physics-informed grasp generation and selection, iTUP demonstrates versatility across (1) quasi-static and more challenging (2) dynamic and (3) cluster tool-use tasks. To ensure robust planning, our framework integrates stable and safe task-aware grasping by reasoning over semantic affordances and physical constraints. We evaluate iTUP and baselines on a comprehensive range of realistic tool use tasks including precision hammering, object scooping, and cluster sweeping. Experimental results demonstrate that iTUP ensures a thorough grounding of cognition and planning for challenging robot tool use across diverse environments.
Quantifying entanglement from the geometric perspective
2025-05-02
Lisa T. Weinbrenner, Otfried Gühne
Quantum entanglement between several particles is essential for applications like quantum metrology or quantum cryptography, but it is also central for foundational phenomena like quantum non-locality. This leads to the problem of quantifying the amount of entanglement in a quantum state. We present a review on the geometric measure of entanglement, being a quantifier based on the distance of a state to the nearest separable state. We explain basic properties, existing methods to compute it, its operational interpretations, as well as scaling and complexity issues. We point out intimate relations to fundamental problems in mathematics concerning eigenvalues and norms of tensors. Consequently, the geometric measure of entanglement provides a playground where physical intuition and mathematical concepts meet and stimulate each other.
Identifying regions for asymptotic expansions of amplitudes: fundamentals and recent advances
2025-05-02
Yao Ma
This review paper discusses the identification of regions, a crucial first step in applying the "method-of-regions" technique. A systematic approach based on Newton polytope geometry has proven successful and efficient for many cases. However, obtaining the correct list of regions becomes increasingly subtle with higher loop numbers or specific Feynman graph topologies. This paper explores the scenarios where such subtleties arise, outlines general strategies to address them, and reviews the current understanding of region structures in various asymptotic expansions of Feynman integrals.
Closing the Loop: A Systematic Review of Experience-Driven Game Adaptation
2025-05-02
Phil Lopes, Nuno Fachada, Maria Fonseca
Adaptive game systems aim to enrich player experiences by dynamically adjusting game content in response to user data. While extensive research has addressed content personalization and player experience modeling, the integration of these components into fully operational adaptive gameplay systems remains limited. This systematic review, conducted in accordance with PRISMA guidelines, analyzes 17 empirical studies published between January 2015 and May 2024, identifying and analyzing approaches that implement the complete experience-driven loop -- including player sensing, modeling, and content adaptation. Game telemetry remains the most prevalent sensing modality, although other non-invasive methods suitable for affective modeling -- such as facial expression analysis (FEA) and peripheral interaction data -- remain underutilized despite their potential for real-time emotional inference. Knowledge-based methods, such as rule-based systems and heuristics, dominate modeling and adaptation due to their interpretability and low resource demands, whereas machine learning approaches face challenges related to data availability and transparency. Despite their relevance to immersive and therapeutic experiences, affective states such as stress and anxiety remain largely ignored, as systems continue to favor performance over emotion-sensitive adaptation. These findings highlight a crucial research direction: advancing emotionally responsive game systems that move beyond performance optimization by incorporating underutilized sensing modalities -- such as FEA and peripheral interaction -- to enable real-time affect-driven personalization. Advancing in this direction holds strong potential to increase immersion, personalize gameplay, and support affect regulation across entertainment and therapeutic contexts.
Classifying Radio-Loud and Radio-Quiet Quasars With Novel PCA Based Regression Classifier
2025-05-02
Ramkrishna Joshi, Vivek Shinde
The problem of quasar classification comes in the class of highly imbalanced classification problems since Radio-loud (RL) quasars are rare and make up only about 10% of the quasar population. In this work, we use the Sloan Digital Sky Survey-DR3 dataset and introduce a PCA-based regression pipeline designed to maximize recall for rare classes in class-imbalanced astronomical data. We demonstrate an effective methodology to identify the key features of the dataset and apply Principal Component Analysis (PCA) for dimensionality reduction. For the PCA transformed SDSS-DR3 dataset, first two components account for the 97% of the observed variance. We perform classification of Radio-Loud (RL) and Radio-Quiet (RQ) quasars with Random Forest Classifier (RFC), novel PCA based balanced linear regression classifier (PBC), Random forest integrated with SMOTE classifier and XGBoost classifier with threshold tuning. RFC achieves an overall accuracy of 92% while PBC achieves an overall accuracy of 62%. XGBoost achieves an overall accuracy of 72% and SMOTE integrated RFC achieves an accuracy of 85%. Higher precision is obtained for RQ quasars in all classification methods. For the RL class, RFC achieves a recall of 0.04, XGBoost achieves a recall of 0.39, SMOTE integrated RFC achieves a recall of 0.25 and PBC achieves a recall of 0.52 attributed to the balanced logistic regression. RFC and PBC achieve F1 score of 0.08 and 0.19 respectively for RL while XGBoost achieves an improved F1 score of 0.22 but at the cost of reduced recall of the RL class. SMOTE integrated RFC achieves a better F1 score of 0.21 over RFC and PBC. Overall results of classifiers point to extreme class imbalance between RQ and RL classes in the data set.
DebtStreamness: An Ecological Approach to Credit Flows in Inter-Firm Networks
2025-05-02
Anahí Rodríguez-Martínez, Silvia Bartolucci, Francesco Caravelli, Victoria Landaberry, Pierpaolo Vivo, Fabio Caccioli
Understanding how credit flows through inter-firm networks is critical for assessing financial stability and systemic risk. In this study, we introduce DebtStreamness, a novel metric inspired by trophic levels in ecological food webs, to quantify the position of firms within credit chains. By viewing credit as the ``primary energy source'' of the economy, we measure how far credit travels through inter-firm relationships before reaching its final borrowers. Applying this framework to Uruguay's inter-firm credit network, using survey data from the Central Bank, we find that credit chains are generally short, with a tiered structure in which some firms act as intermediaries, lending to others further along the chain. We also find that local network motifs such as loops can substantially increase a firm's DebtStreamness, even when its direct borrowing from banks remains the same. Comparing our results with standard economic classifications based on input-output linkages, we find that DebtStreamness captures distinct financial structures not visible through production data. We further validate our approach using two maximum-entropy network reconstruction methods, demonstrating the robustness of DebtStreamness in capturing systemic credit structures. These results suggest that DebtStreamness offers a complementary ecological perspective on systemic credit risk and highlights the role of hidden financial intermediation in firm networks.
TRAVELER: A Benchmark for Evaluating Temporal Reasoning across Vague, Implicit and Explicit References
2025-05-02
Svenja Kenneweg, Jörg Deigmöller, Philipp Cimiano, Julian Eggert
Understanding and resolving temporal references is essential in Natural Language Understanding as we often refer to the past or future in daily communication. Although existing benchmarks address a system's ability to reason about and resolve temporal references, systematic evaluation of specific temporal references remains limited. Towards closing this gap, we introduce TRAVELER, a novel synthetic benchmark dataset that follows a Question Answering paradigm and consists of questions involving temporal references with the corresponding correct answers. TRAVELER assesses models' abilities to resolve explicit, implicit relative to speech time, and vague temporal references. Beyond investigating the performance of state-of-the-art LLMs depending on the type of temporal reference, our benchmark also allows evaluation of performance in relation to the length of the set of events. For the category of vague temporal references, ground-truth answers were established via human surveys on Prolific, following a procedure similar to the one from Kenneweg et al. To demonstrate the benchmark's applicability, we evaluate four state-of-the-art LLMs using a question-answering task encompassing 3,300 questions. Our findings show that while the benchmarked LLMs can answer questions over event sets with a handful of events and explicit temporal references successfully, performance clearly deteriorates with larger event set length and when temporal references get less explicit. Notably, the vague question category exhibits the lowest performance across all models. The benchmark is publicly available at: https://gitlab.ub.uni-bielefeld.de/s.kenneweg/TRAVELER
Electronic Nose for Agricultural Grain Pest Detection, Identification, and Monitoring: A Review
2025-05-02
Chetan M Badgujar, Sai Swaminathan, Alison Gerken
Biotic pest attacks and infestations are major causes of stored grain losses, leading to significant food and economic losses. Conventional, manual, sampling-based pest recognition methods are labor-intensive, time-consuming, costly, require expertise, and may not even detect hidden infestations. In recent years, the electronic nose (e-nose) approach has emerged as a potential alternative for agricultural grain pest recognition and monitoring. An e-nose mimics human olfactory systems by integrating a sensor array, data acquisition, and analysis for recognizing grain pests by analyzing volatile organic compounds (VOCs) emitted by grain and pests. However, well-documented, curated, and synthesized literature on the use of e-nose technology for grain pest detection is lacking. Therefore, this systematic literature review provides a comprehensive overview of the current state-of-the-art e-nose technology for agricultural grain pest monitoring. The review examines employed sensor technology, targeted pest species type, grain medium, data processing, and pattern recognition techniques. An e-nose is a promising tool that offers a rapid, low-cost, non-destructive solution for detecting, identifying, and monitoring grain pests, including microscopic and hidden insects, with good accuracy. We identified the factors that influence the e-nose performance, which include pest species, storage duration, temperature, moisture content, and pest density. The major challenges include sensor array optimization or selection, large data processing, poor repeatability, and comparability among measurements. An inexpensive and portable e-nose has the potential to help stakeholders and storage managers take timely and data-driven informed actions or decisions to reduce overall food and economic losses.
The ultimate goal of integrated sensing and communication (ISAC) deployment is to provide coordinated sensing and communication services at an unprecedented scale. This paper presents a comprehensive overview of network-level ISAC systems, an emerging paradigm that significantly extends the capabilities of link-level ISAC through distributed cooperation. We first examine recent advancements in network-level ISAC architectures, emphasizing various cooperation schemes and distributed system designs. The sensing and communication (S\&C) performance is analyzed with respect to interference management and cooperative S\&C, offering new insights into the design principles necessary for large-scale networked ISAC deployments. In addition, distributed signaling strategies across different levels of cooperation are reviewed, focusing on key performance metrics such as sensing accuracy and communication quality-of-service (QoS). Next, we explore the key challenges for practical deployment where the critical role of synchronization is also discussed, highlighting advanced over-the-air synchronization techniques specifically tailored for bi-static and distributed ISAC systems. Finally, open challenges and future research directions in network-level ISAC design are identified. The findings and discussions aim to serve as a foundational guideline for advancing scalable, high-performance, and resilient distributed ISAC systems in next-generation wireless networks.
Local Differential Privacy (LDP) enables massive data collection and analysis while protecting end users' privacy against untrusted aggregators. It has been applied to various data types (e.g., categorical, numerical, and graph data) and application settings (e.g., static and streaming). Recent findings indicate that LDP protocols can be easily disrupted by poisoning or manipulation attacks, which leverage injected/corrupted fake users to send crafted data conforming to the LDP reports. However, current attacks primarily target static protocols, neglecting the security of LDP protocols in the streaming settings. Our research fills the gap by developing novel fine-grained manipulation attacks to LDP protocols for data streams. By reviewing the attack surfaces in existing algorithms, We introduce a unified attack framework with composable modules, which can manipulate the LDP estimated stream toward a target stream. Our attack framework can adapt to state-of-the-art streaming LDP algorithms with different analytic tasks (e.g., frequency and mean) and LDP models (event-level, user-level, w-event level). We validate our attacks theoretically and through extensive experiments on real-world datasets, and finally explore a possible defense mechanism for mitigating these attacks.
Type-I thermonuclear bursts (TNBs) from neutron star low-mass X-ray binaries (NSLMXBs) originate on the neutron star's surface from the unstable burning of the accreted material. On the other hand, kHz quasi-periodic oscillations (QPOs) are thought to originate in the innermost regions of the in-spiraling accretion disk. Due to the violent nature of the bursts, it is anticipated that Type-I TNBs will impact the inner accretion flow, and, therefore, the kHz QPOs. In this work, we systematically study the evolution of the upper and the lower kHz QPOs immediately before and after a Type-I TNB on 4U 1636$-$536 using {\it{AstroSat}} observations in the 3--20 keV band. The analysis of the power-density-spectra show the presence of kHz QPOs within 200 seconds before the onset of the Type--I burst. However, we have not detected any prominent signature of the same within 100-200 second posterior to the burst. The kHz QPOs then re-emerges after $\approx$ 200 sec. The fractional rms variation in the 3--20 keV band drops by $\approx$ 5-6 \%, supporting the non-existence of kHz QPOs in the 200 sec post-Burst Zone. The time scale of 200 sec coincides with the viscous time scale, highlighting a scenario where the inner disk is temporarily disrupted by the intense radiation from the Type-I TNB. The kHz QPO then re-establishes as the inner disk is restored.
Redshift-space bispectrum in presence of massive neutrinos: A multipole expansion approach for Euclid
2025-05-02
Sourav Pal, Debanjan Sarkar, Rickmoy Samanta, Supratik Pal
Massive neutrinos imprint distinctive signatures on the evolution of cosmic structures, notably suppressing small-scale clustering. We investigate the impact of massive neutrinos on the galaxy bispectrum in redshift-space, adopting a spherical harmonic multipole decomposition $B_L^m(k_1, \mu, t)$, that captures the full angular dependence. We develop an analytical and numerical framework incorporating neutrino-corrected perturbation theory kernels and redshift-space distortions. Our results demonstrate that the linear triangle configurations are particularly sensitive to massive neutrinos, with deviations reaching up to $\sim 2\%$ for a total mass $\sum m_\nu = 0.12\,\mathrm{eV}$. To assess detection prospects in galaxy surveys like \textit{Euclid}, we compute the signal-to-noise ratio (SNR) for individual multipoles, including the effects of Finger-of-God damping and shot noise. The neutrino-induced signatures in $B_0^0$ and $B_2^0$ are found to be detectable with SNR $\gtrsim 5$ across a range of configurations, even after accounting for small-scale suppression. Higher-order multipoles such as $B_2^1$ and $B_2^2$ are moderately sensitive, with SNR $\gtrsim$ ($2-3$) in squeezed limits, while hexadecapole moments are more suppressed but still exhibit measurable signals at high $k_1$. Additionally, the SNR generally increases with wave number $k_1$, particularly for squeezed and stretched triangles, suggesting that access to smaller scales significantly enhances detection prospects.Our study highlights the potential of the redshift-space bispectrum multipoles as sensitive probes of massive neutrinos, complementing traditional power spectrum analyses, and underscores the importance of angular information and higher-order statistics for galaxy surveys.
Digital Pathway Curation (DPC): a comparative pipeline to assess the reproducibility, consensus and accuracy across Gemini, PubMed, and scientific reviewers in biomedical research
2025-05-02
Flavio Lichtenstein, Daniel Alexandre de Souza, Carlos Eduardo Madureira Trufen, Victor Wendel da Silva Gonçalves, Juliana de Paula Bernardes, Vinicius Miranda Baroni, Carlos DeOcesano-Pereira, Leonardo Fontoura Ormundo, Fabio Augusto Labre de Souza, Olga Celia Martinez Ibañez, Nancy Starobinas, Luciano Rodrigo Lopes, Aparecida Maria Fontes, Sonia Aparecida de Andrade, Ana Marisa Chudzinski-Tavassi
A scientific study begins with a central question, and search engines like PubMed are the first tools for retrieving knowledge and understanding the current state of the art. Large Language Models (LLMs) have been used in research, promising acceleration and deeper results. However, besides caution, they demand rigorous validation. Assessing complex biological relationships remains challenging for SQL-based tools and LLM models. Here, we introduce the Digital Pathway Curation (DPC) pipeline to evaluate the reproducibility and accuracy of the Gemini models against PubMed search and human expert curation. Using two omics experiments, we created a large dataset (Ensemble) based on determining pathway-disease associations. With the Ensemble dataset, we demonstrate that Gemini achieves high run-to-run reproducibility of approximately 99% and inter-model reproducibility of around 75%. Next, we calculate the crowdsourced consensus using a smaller dataset. The CSC allows us to calculate accuracies, and the Gemini multi-model consensus reached a significant accuracy of about 87%. Our findings demonstrate that LLMs are reproducible, reliable, and valuable tools for navigating complex biomedical knowledge.
PREMISE: Matching-based Prediction for Accurate Review Recommendation
2025-05-02
Wei Han, Hui Chen, Soujanya Poria
We present PREMISE (PREdict with Matching ScorEs), a new architecture for the matching-based learning in the multimodal fields for the multimodal review helpfulness (MRHP) task. Distinct to previous fusion-based methods which obtains multimodal representations via cross-modal attention for downstream tasks, PREMISE computes the multi-scale and multi-field representations, filters duplicated semantics, and then obtained a set of matching scores as feature vectors for the downstream recommendation task. This new architecture significantly boosts the performance for such multimodal tasks whose context matching content are highly correlated to the targets of that task, compared to the state-of-the-art fusion-based methods. Experimental results on two publicly available datasets show that PREMISE achieves promising performance with less computational cost.
Intrinsic magnetic topological insulators of the MnBi${}_2$Te${}_4$ family
2025-05-02
A. Yu. Vyazovskaya, M. Bosnar, E. V. Chulkov, M. M. Otrokov
This short review appears on the occasion of the fifth anniversary of discovery of intrinsic magnetic topological insulators (MTIs) of the MnBi${}_2$Te${}_4$ family, which have attracted a great deal of attention recently. This family of materials has been discovered in attempts to increase the observation temperature of the quantum anomalous Hall effect as well as to facilitate the eventual realization of the topological magnetoelectric effect. Therefore, we first briefly introduce these effects, then describe the experimental state-of-the-art in the MTIs field just prior to MnBi${}_2$Te${}_4$ appearance, after which we discuss the basic properties of this material and its family. Finally, we overview the exciting progress made during five years of intense research in this field.
Perturbative LVS and Inflation: A Review of Volume Modulus and Fibre Scenarios
2025-05-02
George K. Leontaris, Pramod Shukla
In type IIB superstring compactifications, incorporating log-loop corrections and higher-derivative ${(\alpha^\prime)}^3$-corrections can stabilize the overall volume of the compact internal space at exponentially large values. This mechanism forms the basis of the perturbative Large Volume Scenario (LVS). In this report, we briefly review two inflationary models realized within the perturbative LVS framework. The first one is the volume modulus inflation (also known as inflection point inflation) and the second one is popularly known as fibre inflation. Using an explict Calabi-Yau orientifold, some concrete global embeddings of both models are also discussed.
LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures
2025-05-02
Francisco Aguilera-Martínez, Fernando Berzal
As large language models (LLMs) continue to evolve, it is critical to assess the security threats and vulnerabilities that may arise both during their training phase and after models have been deployed. This survey seeks to define and categorize the various attacks targeting LLMs, distinguishing between those that occur during the training phase and those that affect already trained models. A thorough analysis of these attacks is presented, alongside an exploration of defense mechanisms designed to mitigate such threats. Defenses are classified into two primary categories: prevention-based and detection-based defenses. Furthermore, our survey summarizes possible attacks and their corresponding defense strategies. It also provides an evaluation of the effectiveness of the known defense mechanisms for the different security threats. Our survey aims to offer a structured framework for securing LLMs, while also identifying areas that require further research to improve and strengthen defenses against emerging security challenges.
An instrument to measure factors that constitute the socio-technical context of testing experience
2025-05-02
Mark Swillus, Carolin Brandt, Andy Zaidman
We consider testing a cooperative and social practice that is shaped by the tools developers use, the tests they write, and their mindsets and human needs. This work is one part of a project that explores the human- and socio-technical context of testing through the lens of those interwoven elements: test suite and tools as technical infrastructure and collaborative factors and motivation as mindset. Drawing on empirical observations of previous work, this survey examines how these factors relate to each other. We want to understand which combination of factors can help developers strive and make the most of their ambitions to leverage the potential that software testing practices have. In this report, we construct a survey instrument to measure the factors that constitute the socio-technical context of testing experience. In addition, we state our hypotheses about how these factors impact testing experience and explain the considerations and process that led to the construction of the survey questions.
Methodological Foundations for AI-Driven Survey Question Generation
2025-05-02
Ted K. Mburu, Kangxuan Rong, Campbell J. McColley, Alexandra Werth
This paper presents a methodological framework for using generative AI in educational survey research. We explore how Large Language Models (LLMs) can generate adaptive, context-aware survey questions and introduce the Synthetic Question-Response Analysis (SQRA) framework, which enables iterative testing and refinement of AI-generated prompts prior to deployment with human participants. Guided by Activity Theory, we analyze how AI tools mediate participant engagement and learning, and we examine ethical issues such as bias, privacy, and transparency. Through sentiment, lexical, and structural analyses of both AI-to-AI and AI-to-human survey interactions, we evaluate the alignment and effectiveness of these questions. Our findings highlight the promise and limitations of AI-driven survey instruments, emphasizing the need for robust prompt engineering and validation to support trustworthy, scalable, and contextually relevant data collection in engineering education.
Retrieval-Augmented Generation in Biomedicine: A Survey of Technologies, Datasets, and Clinical Applications
2025-05-02
Jiawei He, Boya Zhang, Hossein Rouhizadeh, Yingjian Chen, Rui Yang, Jin Lu, Xudong Chen, Nan Liu, Irene Li, Douglas Teodoro
Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks. However, their application in the biomedical domain presents unique challenges, particularly regarding factual accuracy and up-to-date knowledge integration. Retrieval Augmented Generation (RAG) has emerged as a promising solution to address these challenges by combining the generative capabilities of LLMs with external knowledge retrieval. This comprehensive survey examines the application of RAG in the biomedical domain, focusing on its technological components, available datasets, and clinical applications. We present a systematic analysis of retrieval methods, ranking strategies, and generation models, while also exploring the challenges and future directions in this rapidly evolving field. Our work provides researchers and practitioners with a thorough understanding of the current state of biomedical RAG systems and identifies key areas for future research and development.
Open problems on compact constant mean curvature surfaces with boundary
2025-05-02
Rafael López
We present a collection of easily stated open problems in the theory of compact constant mean curvature surfaces with boundary. We also survey the current status of answering them.
Evaluating the Impact of Data Cleaning on the Quality of Generated Pull Request Descriptions
2025-05-02
Kutay Tire, Berk Çakar, Eray Tüzün
Pull Requests (PRs) are central to collaborative coding, summarizing code changes for reviewers. However, many PR descriptions are incomplete, uninformative, or have out-of-context content, compromising developer workflows and hindering AI-based generation models trained on commit messages and original descriptions as "ground truth." This study examines the prevalence of "noisy" PRs and evaluates their impact on state-of-the-art description generation models. To do so, we propose four cleaning heuristics to filter noise from an initial dataset of 169K+ PRs drawn from 513 GitHub repositories. We train four models-BART, T5, PRSummarizer, and iTAPE-on both raw and cleaned datasets. Performance is measured via ROUGE-1, ROUGE-2, and ROUGE-L metrics, alongside a manual evaluation to assess description quality improvements from a human perspective. Cleaning the dataset yields significant gains: average F1 improvements of 8.6% (ROUGE-1), 8.7% (ROUGE-2), and 8.5% (ROUGE-L). Manual assessment confirms higher readability and relevance in descriptions generated by the best-performing model, BART when trained on cleaned data. Dataset refinement markedly enhances PR description generation, offering a foundation for more accurate AI-driven tools and guidelines to assist developers in crafting high-quality PR descriptions.
Towards an Interpretable Analysis for Estimating the Resolution Time of Software Issues
2025-05-02
Dimitrios-Nikitas Nastos, Themistoklis Diamantopoulos, Davide Tosi, Martina Tropeano, Andreas L. Symeonidis
Lately, software development has become a predominantly online process, as more teams host and monitor their projects remotely. Sophisticated approaches employ issue tracking systems like Jira, predicting the time required to resolve issues and effectively assigning and prioritizing project tasks. Several methods have been developed to address this challenge, widely known as bug-fix time prediction, yet they exhibit significant limitations. Most consider only textual issue data and/or use techniques that overlook the semantics and metadata of issues (e.g., priority or assignee expertise). Many also fail to distinguish actual development effort from administrative delays, including assignment and review phases, leading to estimates that do not reflect the true effort needed. In this work, we build an issue monitoring system that extracts the actual effort required to fix issues on a per-project basis. Our approach employs topic modeling to capture issue semantics and leverages metadata (components, labels, priority, issue type, assignees) for interpretable resolution time analysis. Final predictions are generated by an aggregated model, enabling contributors to make informed decisions. Evaluation across multiple projects shows the system can effectively estimate resolution time and provide valuable insights.
Generative AI (GenAI) tools such as ChatGPT allow users, including school students without prior AI expertise, to explore and address a wide range of tasks. Surveys show that most students aged eleven and older already use these tools for school-related activities. However, little is known about how they actually use GenAI and how it impacts their learning. This study addresses this gap by examining middle school students ability to ask effective questions and critically evaluate ChatGPT responses, two essential skills for active learning and productive interactions with GenAI. 63 students aged 14 to 15 were tasked with solving science investigation problems using ChatGPT. We analyzed their interactions with the model, as well as their resulting learning outcomes. Findings show that students often over-relied on ChatGPT in both the question-asking and answer-evaluation phases. Many struggled to use clear questions aligned with task goals and had difficulty judging the quality of responses or knowing when to seek clarification. As a result, their learning performance remained moderate: their explanations of the scientific concepts tended to be vague, incomplete, or inaccurate, even after unrestricted use of ChatGPT. This pattern held even in domains where students reported strong prior knowledge. Furthermore, students self-reported understanding and use of ChatGPT were negatively associated with their ability to select effective questions and evaluate responses, suggesting misconceptions about the tool and its limitations. In contrast, higher metacognitive skills were positively linked to better QA-related skills. These findings underscore the need for educational interventions that promote AI literacy and foster question-asking strategies to support effective learning with GenAI.
Instrumentation prospects for rocky exoplanet atmospheres studies with high resolution spectroscopy
2025-05-02
Surangkhana Rukdee
Studying the atmospheres of exoplanets is one of the most promising ways to learn about distant worlds beyond our solar system. The composition of an exoplanet's atmosphere can provide critical insights into its geology and potential habitability. For instance, the presence of certain molecules such as water vapor, oxygen, or methane have been proposed to indicate the possibility of life. From an observation point of view, over the past fifteen years, significant progress has been made in characterizing exoplanetary atmospheres. This work reviews recent developments in ground-based high-resolution spectroscopic instruments that make it possible to analyze distant atmospheres in great detail. High-resolution transmission spectroscopy, one of the most effective methods used, has examined the atmospheres of Jupiter-like and is pushing towards the smaller, sub-Neptunian exoplanets. Numerous molecules have been detected using this technique, including CO,H2O,TiO,HCN,CH4,NH3,C2H2,OH. We explore the intriguing possibilities that lie ahead for future ground-based instrumentation, particularly in the context of detecting biologically relevant molecules within Earth-analog exoplanetary atmospheres including molecular oxygen (O2). With detailed exposure time calculations for detecting O2 we find that at the same exposure time spectral resolution of 300,000 reaches higher significance compared to 100,000. The exposure time and therefore the needed number of transits is reduced by a factor of 4 in challenging haze and cloud scenarios.
2025-05-02
Tala Fakhoury, Elia Turner, Sushrut Thorat, Athena Akrami
Attractor dynamics are a fundamental computational motif in neural circuits, supporting diverse cognitive functions through stable, self-sustaining patterns of neural activity. In these lecture notes, we review four key examples that demonstrate how autoassociative neural network models can elucidate the computational mechanisms underlying attractor-based information processing in biological neural systems performing cognitive functions. Drawing on empirical evidence, we explore hippocampal spatial representations, visual classification in the inferotemporal cortex, perceptual adaptation and priming, and working-memory biases shaped by sensory history. Across these domains, attractor network models reveal common computational principles and provide analytical insights into how experience shapes neural activity and behavior. Our synthesis underscores the value of attractor models as powerful tools for probing the neural basis of cognition and behavior.
Artificial Intelligence in Government: Why People Feel They Lose Control
2025-05-02
Alexander Wuttke, Adrian Rauchfleisch, Andreas Jungherr
The use of Artificial Intelligence (AI) in public administration is expanding rapidly, moving from automating routine tasks to deploying generative and agentic systems that autonomously act on goals. While AI promises greater efficiency and responsiveness, its integration into government functions raises concerns about fairness, transparency, and accountability. This article applies principal-agent theory (PAT) to conceptualize AI adoption as a special case of delegation, highlighting three core tensions: assessability (can decisions be understood?), dependency (can the delegation be reversed?), and contestability (can decisions be challenged?). These structural challenges may lead to a "failure-by-success" dynamic, where early functional gains obscure long-term risks to democratic legitimacy. To test this framework, we conducted a pre-registered factorial survey experiment across tax, welfare, and law enforcement domains. Our findings show that although efficiency gains initially bolster trust, they simultaneously reduce citizens' perceived control. When the structural risks come to the foreground, institutional trust and perceived control both drop sharply, suggesting that hidden costs of AI adoption significantly shape public attitudes. The study demonstrates that PAT offers a powerful lens for understanding the institutional and political implications of AI in government, emphasizing the need for policymakers to address delegation risks transparently to maintain public trust.
Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities
2025-05-02
Zhiwei Hao, Jianyuan Guo, Li Shen, Yong Luo, Han Hu, Guoxia Wang, Dianhai Yu, Yonggang Wen, Dacheng Tao
Large language models (LLMs) have achieved impressive performance across various domains. However, the substantial hardware resources required for their training present a significant barrier to efficiency and scalability. To mitigate this challenge, low-precision training techniques have been widely adopted, leading to notable advancements in training efficiency. Despite these gains, low-precision training involves several components$\unicode{x2013}$such as weights, activations, and gradients$\unicode{x2013}$each of which can be represented in different numerical formats. The resulting diversity has created a fragmented landscape in low-precision training research, making it difficult for researchers to gain a unified overview of the field. This survey provides a comprehensive review of existing low-precision training methods. To systematically organize these approaches, we categorize them into three primary groups based on their underlying numerical formats, which is a key factor influencing hardware compatibility, computational efficiency, and ease of reference for readers. The categories are: (1) fixed-point and integer-based methods, (2) floating-point-based methods, and (3) customized format-based methods. Additionally, we discuss quantization-aware training approaches, which share key similarities with low-precision training during forward propagation. Finally, we highlight several promising research directions to advance this field. A collection of papers discussed in this survey is provided in https://github.com/Hao840/Awesome-Low-Precision-Training.
No-hair theorems in General Relativity and scalar-tensor theories
2025-05-02
Stoytcho Yazadjiev, Daniela Doneva
In the present review, we consider the status of the classification of the vacuum, stationary and asymptotically flat black holes in scalar-tensor gravity. Contrary to the similar problem in general relativity, the black hole classification in scalar-tensor theories is much more challenging due to the very complicated character of the field equations and the very complex mathematical structure of the scalar-tensor gravity as a whole. We review most of the known no-hair results, and where possible new ones, as well demonstrate some of the difficulties that appear in our attempts to classify the black holes within scalar-tensor gravity. The proofs of the theorems and the underlying mathematical techniques are given in sufficient detail. To make the review self-contained we also present the vacuum black hole uniqueness theorems in general relativity and their proofs.
Adaptive Wizard for Removing Cross-Tier Misconfigurations in Active Directory
2025-05-02
Huy Q. Ngo, Mingyu Guo, Hung Nguyen
Security vulnerabilities in Windows Active Directory (AD) systems are typically modeled using an attack graph and hardening AD systems involves an iterative workflow: security teams propose an edge to remove, and IT operations teams manually review these fixes before implementing the removal. As verification requires significant manual effort, we formulate an Adaptive Path Removal Problem to minimize the number of steps in this iterative removal process. In our model, a wizard proposes an attack path in each step and presents it as a set of multiple-choice options to the IT admin. The IT admin then selects one edge from the proposed set to remove. This process continues until the target $t$ is disconnected from source $s$ or the number of proposed paths reaches $B$. The model aims to optimize the human effort by minimizing the expected number of interactions between the IT admin and the security wizard. We first prove that the problem is $\mathcal{\#P}$-hard. We then propose a set of solutions including an exact algorithm, an approximate algorithm, and several scalable heuristics. Our best heuristic, called DPR, can operate effectively on larger-scale graphs compared to the exact algorithm and consistently outperforms the approximate algorithm across all graphs. We verify the effectiveness of our algorithms on several synthetic AD graphs and an AD attack graph collected from a real organization.
A Survey Of Model Fits to Brown Dwarf Spectra Through the L-T Sequence
2025-05-02
Savanah K. Turner, Denise C. Stephens, Conner B. Scoresby, Josh A. Miller
We fit archival near-infrared spectra of 305 brown dwarfs with atmosphere models from the Sonora and Phoenix groups. Using the parameters of the best-fit models as estimates for the physical properties of the brown dwarfs in our sample, we have performed a survey of how brown dwarf atmospheres evolve with spectral type and temperature. We present the fit results and observed trends. We find that clouds have a more significant impact on near infrared spectra than disequilibrium chemistry, and that silicate clouds influence the near infrared spectrum through the late T types. We note where current atmosphere models are able to replicate the data and where the models and data conflict. We also categorize objects with similar spectral morphologies into families and discuss possible causes for their unique spectral traits. We identify two spectral families with morphologies that are likely indicative of binarity.
Large Language Models (LLMs) have become central to numerous natural language processing tasks, but their vulnerabilities present significant security and ethical challenges. This systematic survey explores the evolving landscape of attack and defense techniques in LLMs. We classify attacks into adversarial prompt attack, optimized attacks, model theft, as well as attacks on application of LLMs, detailing their mechanisms and implications. Consequently, we analyze defense strategies, including prevention-based and detection-based defense methods. Although advances have been made, challenges remain to adapt to the dynamic threat landscape, balance usability with robustness, and address resource constraints in defense implementation. We highlight open problems, including the need for adaptive scalable defenses, explainable security techniques, and standardized evaluation frameworks. This survey provides actionable insights and directions for developing secure and resilient LLMs, emphasizing the importance of interdisciplinary collaboration and ethical considerations to mitigate risks in real-world applications.
Summary Report of the Physics Beyond Colliders Study at CERN
2025-05-02
R. Alemany Fernández, M. Au, G. Arduini, L. Bandiera, D. Banerjee, H. Bartosik, J. Bernhard, D. Boer, J. Boyd, O. Brandt, M. Brugger, O. Buchmüller, F. Butin, S. Calatroni, C. Carli, N. Charitonidis, P. Crivelli, D. Curtin, R. T. D'Agnolo, G. De Lellis, O. Denisov, P. Di Nezza, B. Döbrich, Y. Dutheil, J. R. Ellis, S. A. R. Ellis, T. Ferber, M. Ferro-Luzzi, M. Fraser, T. Galatyuk, D. Gamba, C. Gatti, S. Gilardoni, A. Glazov, M. Gorshtein, E. Granados, E. Gschwendtner, P. Hermes, J. Jaeckel, M. A. Jebramcik, Y. Kadi, F. Kahlhoefer, F. Kling, M. Kowalska, M. W. Krasny, B. Ketzer, M. Lamont, P. Lenisa, A. Lindner, M. Maćkowiak-Pawłowska, A. Martens, F. Martinez Vidal, C. Matteuzzi, N. Neri, L. J. Nevay, M. Ovchynnikov, Y. Papaphilippou, L. L. Pappalardo, J. Pawlowski, M. Perrin-Terrin, L. Ponce, M. Pospelov, T. Prebibaj, J. Pretz, S. Redaelli, J. Rojo, G. Rumolo, A. Rybicki, G. Schnell, S. Schuh, I. Schulthess, E. Scomparin, S. Stapnes, P. N. Swallow, F. Terranova, S. Ulmer, G. Usai, C. Vallée, M. van Dijk, G. Venanzoni, M. Wing, E. D. Zimmerman
The Physics Beyond Collider (PBC) Study Group was initially mandated by the CERN Management to prepare the previous European Particle Physics Strategy Update for CERN projects other than the high-energy frontier colliders. The main findings were summarized in an PBC Summary Report submitted to the Strategy Update. Following the Update process, the PBC Study Group was confirmed on a permanent basis with an updated mandate taking into account the strategy recommendations. The Study Group is now in charge of supporting the proponents of new ideas to address the technical issues and physics motivation of the projects ahead of their review by the CERN Scientific Committees and decision by the Management. The present document updates the previous PBC summary report to inform the new ongoing European Particle Physics Strategy Update process, taking into account the evolution of the CERN and worldwide landscapes and the new projects under consideration within the Study Group.
A Self-Supervised Transformer for Unusable Shared Bike Detection
2025-05-02
Yin Huang, Yongqi Dong, Youhua Tang, Alvaro García Hernandez
The rapid expansion of bike-sharing systems (BSS) has greatly improved urban "last-mile" connectivity, yet large-scale deployments face escalating operational challenges, particularly in detecting faulty bikes. Existing detection approaches either rely on static model-based thresholds that overlook dynamic spatiotemporal (ST) usage patterns or employ supervised learning methods that struggle with label scarcity and class imbalance. To address these limitations, this paper proposes a novel Self-Supervised Transformer (SSTransformer) framework for automatically detecting unusable shared bikes, leveraging ST features extracted from GPS trajectories and trip records. The model incorporates a self-supervised pre-training strategy to enhance its feature extraction capabilities, followed by fine-tuning for efficient status recognition. In the pre-training phase, the Transformer encoder learns generalized representations of bike movement via a self-supervised objective; in the fine-tuning phase, the encoder is adapted to a downstream binary classification task. Comprehensive experiments on a real-world dataset of 10,730 bikes (1,870 unusable, 8,860 normal) from Chengdu, China, demonstrate that SSTransformer significantly outperforms traditional machine learning, ensemble learning, and deep learning baselines, achieving the best accuracy (97.81%), precision (0.8889), and F1-score (0.9358). This work highlights the effectiveness of self-supervised Transformer on ST data for capturing complex anomalies in BSS, paving the way toward more reliable and scalable maintenance solutions for shared mobility.
What is estimated in cluster randomized crossover trials with informative sizes? -- A survey of estimands and common estimators
2025-05-02
Kenneth M. Lee, Andrew B. Forbes, Jessica Kasza, Andrew Copas, Brennan C. Kahan, Paul J. Young, Michael O. Harhay, Fan Li
In the analysis of cluster randomized trials (CRTs), previous work has defined two meaningful estimands: the individual-average treatment effect (iATE) and cluster-average treatment effect (cATE) estimand, to address individual and cluster-level hypotheses. In multi-period CRT designs, such as the cluster randomized crossover (CRXO) trial, additional weighted average treatment effect estimands help fully reflect the longitudinal nature of these trial designs, namely the cluster-period-average treatment effect (cpATE) and period-average treatment effect (pATE). We define different forms of informative sizes, where the treatment effects vary according to cluster, period, and/or cluster-period sizes, which subsequently cause these estimands to differ in magnitude. Under such conditions, we demonstrate which of the unweighted, inverse cluster-period size weighted, inverse cluster size weighted, and inverse period size weighted: (i.) independence estimating equation, (ii.) fixed effects model, (iii.) exchangeable mixed effects model, and (iv.) nested exchangeable mixed effects model treatment effect estimators are consistent for the aforementioned estimands in 2-period cross-sectional CRXO designs with continuous outcomes. We report a simulation study and conclude with a reanalysis of a CRXO trial testing different treatments on hospital length of stay among patients receiving invasive mechanical ventilation. Notably, with informative sizes, the unweighted and weighted nested exchangeable mixed effects model estimators are not consistent for any meaningful estimand and can yield biased results. In contrast, the unweighted and weighted independence estimating equation, and under specific scenarios, the fixed effects model and exchangeable mixed effects model, can yield consistent and empirically unbiased estimators for meaningful estimands in 2-period CRXO trials.
Minimax entropy: The statistical physics of optimal models
2025-05-02
David P. Carcamo, Nicholas J. Weaver, Purushottam D. Dixit, Christopher W. Lynn
When constructing models of the world, we aim for optimal compressions: models that include as few details as possible while remaining as accurate as possible. But which details -- or features measured in data -- should we choose to include in a model? Here, using the minimum description length principle, we show that the optimal features are the ones that produce the maximum entropy model with minimum entropy, thus yielding a minimax entropy principle. We review applications, which range from machine learning to optimal models of biological networks. Naive implementations, however, are limited to systems with small numbers of states and features. We therefore require new theoretical insights and computational techniques to construct optimal compressions of high-dimensional datasets arising in large-scale experiments.
Beyond Productivity: Rethinking the Impact of Creativity Support Tools
2025-05-02
Samuel Rhys Cox, Helena Bøjer Djernæs, Niels van Berkel
Creativity Support Tools (CSTs) are widely used across diverse creative domains, with generative AI recently increasing the abilities of CSTs. To better understand how the success of CSTs is determined in the literature, we conducted a review of outcome measures used in CST evaluations. Drawing from (n=173) CST evaluations in the ACM Digital Library, we identified the metrics commonly employed to assess user interactions with CSTs. Our findings reveal prevailing trends in current evaluation practices, while exposing underexplored measures that could broaden the scope of future research. Based on these results, we argue for a more holistic approach to evaluating CSTs, encouraging the HCI community to consider not only user experience and the quality of the generated output, but also user-centric aspects such as self-reflection and well-being as critical dimensions of assessment. We also highlight a need for validated measures specifically suited to the evaluation of generative AI in CSTs.
Using Active Learning to Improve Quasar Identification for the DESI Spectra Processing Pipeline
2025-05-02
Dylan Green, David Kirkby, J. Aguilar, S. Ahlen, D. M. Alexander, E. Armengaud, S. Bailey, A. Bault, D. Bianchi, A. Brodzeller, D. Brooks, T. Claybaugh, R. de Belsunce, A. de la Macorra, P. Doel, V. A. Fawcett, S. Ferraro, A. Font-Ribera, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, M. Ishak, S. Juneau, R. Kehoe, T. Kisner, A. Kremin, A. Lambert, M. Landriau, L. Le Guillou, M. E. Levi, M. Manera, A. Meisner, R. Miquel, J. Moustakas, A. D. Myers, N. Palanque-Delabrouille, F. Prada, I. Pérez-Ràfols, G. Rossi, E. Sanchez, C. Saulder, D. Schlegel, M. Schubnell, H. Seo, F. Sinigaglia, D. Sprayberry, T. Tan, G. Tarlé, B. A. Weaver, S. Youles, J. Yu, R. Zhou, H. Zou
The Dark Energy Spectroscopic Instrument (DESI) survey uses an automatic spectral classification pipeline to classify spectra. QuasarNET is a convolutional neural network used as part of this pipeline originally trained using data from the Baryon Oscillation Spectroscopic Survey (BOSS). In this paper we implement an active learning algorithm to optimally select spectra to use for training a new version of the QuasarNET weights file using only DESI data, specifically to improve classification accuracy. This active learning algorithm includes a novel outlier rejection step using a Self-Organizing Map to ensure we label spectra representative of the larger quasar sample observed in DESI. We perform two iterations of the active learning pipeline, assembling a final dataset of 5600 labeled spectra, a small subset of the approx 1.3 million quasar targets in DESI's Data Release 1. When splitting the spectra into training and validation subsets we meet or exceed the previously trained weights file in completeness and purity calculated on the validation dataset with less than one tenth of the amount of training data. The new weights also more consistently classify objects in the same way when used on unlabeled data compared to the old weights file. In the process of improving QuasarNET's classification accuracy we discovered a systemic error in QuasarNET's redshift estimation and used our findings to improve our understanding of QuasarNET's redshifts.
Galaxy clusters trace the most massive dark matter haloes, whose shapes and orientations reflect the imprint of the cosmic large-scale tidal field. This paper introduces the Subhalo-based Halo Alignment and Projected Ellipticity (SHAPE) technique, which reconstructs cluster halo shapes from the projected distribution of subhaloes, providing a novel approach to investigate intrinsic alignment (IA) correlations between cluster halo shapes and the surrounding density field. We measure halo shapes and orientations using different line-of-sight projection depths and find that, with modest projection depths, the shapes and orientations recovered by SHAPE show good agreement with those measured directly from the simulation particles. Using these SHAPE-derived shapes, we compute IA correlation functions from N-body simulations in both real and redshift space. The IA correlation multipoles exhibit features consistent with baryon acoustic oscillations around 100 Mpc/h and show redshift-space distortion (RSD) effects that agree well with predictions from a non-linear alignment model incorporating RSD. We further demonstrate that the structure growth rate parameter can be robustly estimated without bias from these IA correlations, providing a new avenue for cosmological parameter estimation. Expanding the IA correlations in an associated Legendre basis yields results consistent with those from the standard Legendre expansion, but with improved statistical significance. These results suggest that SHAPE may enhance cosmological parameter constraints in future galaxy surveys.
2025-05-02
Nick E. Mavromatos, Panagiotis Dorlis, Sotirios-Neilos Vlachos
We review a string-inspired model of inflation which is a consequence of condensates of chiral gravitational waves (GW) in the primordial Universe, leading in turn to a (approximately) constant condensate of a gravitational anomaly term of Chern-Simons (CS) type, present in the Lagrangian density that describes the dynamics of the very early Universe in the model. We discuss some mechanisms for the production of chiral GW, as well as the role of periodic modulations of the potential of the gravitational axion field, that couples to the CS anomaly term, in ensuring the correct inflationary slow-roll phenomenology of this model.
Emotions in the Loop: A Survey of Affective Computing for Emotional Support
2025-05-02
Karishma Hegde, Hemadri Jayalath
In a world where technology is increasingly embedded in our everyday experiences, systems that sense and respond to human emotions are elevating digital interaction. At the intersection of artificial intelligence and human-computer interaction, affective computing is emerging with innovative solutions where machines are humanized by enabling them to process and respond to user emotions. This survey paper explores recent research contributions in affective computing applications in the area of emotion recognition, sentiment analysis and personality assignment developed using approaches like large language models (LLMs), multimodal techniques, and personalized AI systems. We analyze the key contributions and innovative methodologies applied by the selected research papers by categorizing them into four domains: AI chatbot applications, multimodal input systems, mental health and therapy applications, and affective computing for safety applications. We then highlight the technological strengths as well as the research gaps and challenges related to these studies. Furthermore, the paper examines the datasets used in each study, highlighting how modality, scale, and diversity impact the development and performance of affective models. Finally, the survey outlines ethical considerations and proposes future directions to develop applications that are more safe, empathetic and practical.
Approximately 1 in 100 children worldwide are diagnosed with Autism Spectrum Disorder (ASD), and 46% to 89% experience significant feeding difficulties. Although mobile health (mHealth) applications offer potential support for caregivers, the quality and relevance of apps targeting autism-related feeding issues remain unclear. This systematic review evaluated mobile applications available on the Apple App Store and the Google Play Store between September and October 2024. The searches were carried out using 15 predefined terms (e.g., "child autism feeding", "child autism food"). Applications were eligible if they were in English, free to download, updated within the past year, explicitly addressed feeding in children with autism, accessible in Africa, and had more than 100 downloads. Of the 326 apps identified, only two iOS applications met all inclusion criteria; no Android apps qualified. Behavior Change Wheel (BCW) analysis showed that the selected applications incorporated multiple intervention functions, such as education, training, enablement, incentivization, and modeling, though none addressed the full spectrum of behavioral strategies. Mobile App Rating Scale (MARS) indicated moderate to high usability, with features such as sensory-friendly food routines and structured caregiver tools. However, both apps lacked clinical validation and comprehensive customization. These findings highlight a critical gap in the availability of evidence-based high-quality mHealth tools for caregivers managing ASD-related feeding challenges and underscore the need for professionally developed and culturally sensitive digital solutions.
Reconstructing post-common envelope white dwarf+main sequence binary histories through inverse population synthesis techniques
2025-05-02
Santiago Torres, Marta Gili, Alberto Rebassa-Mansergas, Alejandro Santos-García, Alex J. Brown, Steven G. Parsons
The evolution of binary stellar systems involves a wide range of physical processes, many of which are not yet well understood. We aim to build a general-purpose algorithm based on inverse population synthesis techniques, able to reconstruct the past history of binary systems. This algorithm will be applied to a sample of eclipsing binaries, aiming to ascertain their progenitors and past histories. Once validated, it was applied to a sample 30 white dwarf plus main-sequence eclipsing binaries observed by the Zwicky Transient Facility survey. We determined the input space parameters of the progenitors for the 30 eclipsing binary systems to which the algorithm was applied. These parameters included the initial primary and secondary masses, the orbital separation and eccentricity, the common-envelope efficiency ($\alpha_{\rm CE}$), and the age at which the system was formed. Furthermore, the analysis of the global properties revealed some important features: a mild anticorrelation between the common-envelope efficiency parameter and the secondary mass, the absence of a universal value of $\alpha_{\rm CE}$ along with no need for internal energy, although in the low-mass regime, the high values of $\alpha_{\rm CE}$ suggest a possible contribution, and an initial thermalized eccentricity distribution. Although a strong degeneracy among the input parameters exists in the reconstruction of post-common envelope binary systems, the high accuracy obtained for the eclipsing-binary systems analyzed here has allowed our algorithm to make a reasonable determination of the initial parameters without the need to include external constraints. The global properties found here so far, can be substantially improved when analyzing a future volume-complete sample.
Investigating episodic mass loss in evolved massive stars: III. Spectroscopy of dusty massive stars in three northern galaxies
2025-05-02
S. de Wit, G. Muñoz-Sanchez, G. Maravelias, A. Z. Bonanos, K. Antoniadis, D. García-Álvarez, N. Britavskiy, A. Ruiz, A. Philippopoulou
Mass loss in massive stars is crucial to understanding how these stars evolve and explode. Despite increasing evidence indicating its importance, episodic mass loss remains poorly understood. Here we report the results of an optical spectroscopic survey of evolved massive stars in NGC 6822, IC 10, and IC 1613, conducted by the ASSESS project (Episodic Mass Loss in Evolved Massive Stars: Key to Understanding the Explosive Early Universe), which aimed to investigate the role of episodic mass loss, by targeting stars with infrared excesses indicating a dusty circumstellar environment. We assigned a spectral class to 122 unique sources, the majority of which are dusty. The rate of evolved massive stars was over 60% for the highest-priority targets. We discovered 2 blue supergiants, 1 yellow supergiant, 1 emission-line object, and confirmed 2 supernova remnant candidates, a Wolf-Rayet star, and 2 H II regions. Twenty-eight unique sources were classified as red supergiants, 21 of which are new discoveries. In IC 10, we increased the sample of spectroscopically confirmed RSGs from 1 to 17. We used the MARCS models to obtain their surface properties, most importantly the effective temperature, and spectral energy distribution fitting to obtain the stellar luminosity for 17 of them. The dusty RSGs are cooler, more luminous, more extinguished, and more evolved than the non-dusty ones, in agreement with previous findings. Investigating the optical photometric variability of the RSGs from light curves covering a period over a decade revealed that the dusty RSGs are more variable. We further highlight the very extinguished emission-line object, two RSGs that display a significant change in spectral type between two observed epochs, and four dusty K-type RSGs as candidates for having undergone episodic mass loss.
eROSITA view on the Halo Mass-Temperature relation. From low mass groups to massive clusters
2025-05-02
V. Toptun, P. Popesso, I. Marini, K. Dolag, G. Lamer, X. Yang, Q. Li, B. Csizi, L. Lovisari, S. Ettori, V. Biffi, S. Vladutescu-Zopp, A. Dev, D. Mazengo, A. Merloni, J. Comparat, G. Ponti, E. Bulbul
Galaxy groups and clusters are one of the best probes of structure formation and growth in a cosmological context. Most of their baryonic component is dominated by the intracluster medium (ICM), whose thermodynamical properties serve as indicators of the halo's dynamical state and can be used for the halo mass determination in the self-similar scenario. However, baryonic processes, such as AGN feedback and gas cooling, may affect the global properties of the ICM, especially in the group regime. These effects might lead to deviations from self-similar predictions in galaxy groups' scaling relations, while they remain in place for massive galaxy clusters. Additionally, the low-mass end of the scaling relations, ranging from $10^{13}$ to $10^{14} M_\odot$, remains unclear and poorly populated, as current X-ray surveys detect only the brightest groups. Here, we present the Mass-Temperature relation across the full mass range, from massive clusters to low-mass groups ($10^{13}M_\odot$), as observed by eROSITA. Using spectral stacking from eROSITA eRASS1 data for optically selected galaxy groups, we find that, in the lower mass range, galaxy groups follow the power-law relation known for galaxy clusters. We further validate these results by conducting the same stacking procedure on mock eRASS:4 data using the Magneticum hydrodynamical simulation. This indicates that AGN feedback is more likely to affect the distribution of baryons in the intragroup medium rather than the overall halo gas temperature. No significant changes in the Mass-Temperature relation slope suggest that temperature can serve as a reliable mass proxy across the entire mass range. This validates the use of temperature-derived masses, particularly in cosmological studies, significantly broadening the mass range and enabling applications such as improving the cluster mass function studies and cosmological parameter estimates.
Gaia search for stellar companions of TESS Objects of Interest V
2025-05-02
M. Mugrauer, A. -K. Kollak, L. Pietsch, K. -U. Michel
In this paper we present the latest results of our ongoing multiplicity survey of (Community) TESS Objects of Interest, using astrometry and photometry from the latest data release of the ESA Gaia mission to detect stellar companions of these stars and to characterize their properties. A total of 92 binary and two hierarchical triple star systems are identified among the 745 target stars whose multiplicity is explored in this study, all at distances of less than 500pc around the Sun. As expected for components of gravitationally bound star systems, the targets and the detected companions are at the same distance and share a common proper motion, as shown by their accurate Gaia astrometry. The companions have masses of about 0.12 to 1.6$M_\odot$. and are most frequently found in the mass range up to 0.6$M_\odot$. The companions have projected separations from the targets between about 110 and 9600au. Their frequency is highest and constant from about 300 to 800au, decreasing at larger projected separations. In addition to main sequence stars, five white dwarf companions are detected in this study, whose true nature is unveiled by their photometric properties.
sae4health: An R Shiny Application for Small Area Estimation in Low- and Middle-Income Countries
2025-05-02
Yunhan Wu, Qianyu Dong, Jieyi Xu, Zehang Richard Li, Jon Wakefield
Accurate subnational estimation of health indicators is critical for public health planning, especially in low- and middle-income countries (LMICs), where data and tools are often limited. The sae4health R shiny app, built on the surveyPrev package, provides a user-friendly tool for prevalence mapping using small area estimation (SAE) methods. Both area- and unit-level models with spatial random effects are available, with fast Bayesian inference performed using Integrated Nested Laplace Approximation (INLA). Currently, the app supports analysis of over 150 indicators from Demographic and Health Surveys (DHS) across multiple administrative levels. sae4health simplifies the use of complex prevalence mapping models to support data-driven decision-making. The app provides interactive visualization, summary, and report generation functionalities for a wide range of use cases. This paper outlines the app's statistical framework and demonstrates the workflow through a case study of child stunting in Nigeria. Additional documentation is available on the supporting website (https://sae4health.stat.uw.edu).
Overcoming Obstacles: Challenges of Gender Inequality in Undergraduate ICT Programs
2025-05-02
Angelica Pereira Souza, Anderson Uchôa, Edna Dias Canedo, Juliana Alves Pereira, Claudia Pinto Pereira, Larissa Rocha
Context: Gender inequality is a widely discussed issue across various sectors, including Information Technology and Communication (ICT). In Brazil, women represent less than 18% of ICT students in higher education. Prior studies highlight gender-related barriers that discourage women from staying in ICT. However, they provide limited insights into their perceptions as undergraduate students and the factors influencing their participation and confidence. Goal: This study explores the perceptions of women undergraduate students in ICT regarding gender inequality. Method: A survey of 402 women from 18 Brazilian states enrolled in ICT courses was conducted using a mixed-method approach, combining quantitative and qualitative analyses. Results: Women students reported experiencing discriminatory practices from peers and professors, both inside and outside the classroom. Gender stereotypes were found to undermine their self-confidence and self-esteem, occasionally leading to course discontinuation. Conclusions: Factors such as lack of representation, inappropriate jokes, isolation, mistrust, and difficulty being heard contribute to harmful outcomes, including reduced participation and reluctance to take leadership roles. Addressing these issues is essential to creating a safe and respectful learning environment for all students.
Aligning Large Language Models with Healthcare Stakeholders: A Pathway to Trustworthy AI Integration
2025-05-02
Kexin Ding, Mu Zhou, Akshay Chaudhari, Shaoting Zhang, Dimitris N. Metaxas
The wide exploration of large language models (LLMs) raises the awareness of alignment between healthcare stakeholder preferences and model outputs. This alignment becomes a crucial foundation to empower the healthcare workflow effectively, safely, and responsibly. Yet the varying behaviors of LLMs may not always match with healthcare stakeholders' knowledge, demands, and values. To enable a human-AI alignment, healthcare stakeholders will need to perform essential roles in guiding and enhancing the performance of LLMs. Human professionals must participate in the entire life cycle of adopting LLM in healthcare, including training data curation, model training, and inference. In this review, we discuss the approaches, tools, and applications of alignments between healthcare stakeholders and LLMs. We demonstrate that LLMs can better follow human values by properly enhancing healthcare knowledge integration, task understanding, and human guidance. We provide outlooks on enhancing the alignment between humans and LLMs to build trustworthy real-world healthcare applications.
Rethinking Memory in AI: Taxonomy, Operations, Topics, and Future Directions
2025-05-01
Yiming Du, Wenyu Huang, Danna Zheng, Zhaowei Wang, Sebastien Montella, Mirella Lapata, Kam-Fai Wong, Jeff Z. Pan
Memory is a fundamental component of AI systems, underpinning large language models (LLMs) based agents. While prior surveys have focused on memory applications with LLMs, they often overlook the atomic operations that underlie memory dynamics. In this survey, we first categorize memory representations into parametric, contextual structured, and contextual unstructured and then introduce six fundamental memory operations: Consolidation, Updating, Indexing, Forgetting, Retrieval, and Compression. We systematically map these operations to the most relevant research topics across long-term, long-context, parametric modification, and multi-source memory. By reframing memory systems through the lens of atomic operations and representation types, this survey provides a structured and dynamic perspective on research, benchmark datasets, and tools related to memory in AI, clarifying the functional interplay in LLMs based agents while outlining promising directions for future research\footnote{The paper list, datasets, methods and tools are available at \href{https://github.com/Elvin-Yiming-Du/Survey_Memory_in_AI}{https://github.com/Elvin-Yiming-Du/Survey\_Memory\_in\_AI}.}.
It's All ${\tt Ok}$: Curvature in Light of BAO from DESI DR2
2025-05-01
Shi-Fan Chen, Matias Zaldarriaga
Recent measurements of baryon acoustic oscillations (BAO) from the Dark Energy Spectroscopic Instrument (DESI) show hints of tension with data from the cosmic microwave background (CMB) when interpreted within the standard model of cosmology. In this short note we discuss the consequences of one solution to this tension, a small but negative spatial curvature with $R_k = 21 H_0^{-1}$, which DESI measures at $2\sigma$. We describe the physical role of curvature in cosmological distance measures tied to recombination, i.e. the CMB and BAO, and the relation to neutrino mass constraints which are relaxed to $\sum m_\nu < 0.10$ eV when curvature is allowed to deviate from zero. A robust detection of negative curvature would have significant implications for inflationary models: improved BAO measurements, particularly from future high-redshift spectroscopic surveys, will be able to distinguish curvature from other solutions to the DESI-CMB tension like phantom dark energy at high significance.
Open-Source LLM-Driven Federated Transformer for Predictive IoV Management
2025-05-01
Yazan Otoum, Arghavan Asad, Ishtiaq Ahmad
The proliferation of connected vehicles within the Internet of Vehicles (IoV) ecosystem presents critical challenges in ensuring scalable, real-time, and privacy-preserving traffic management. Existing centralized IoV solutions often suffer from high latency, limited scalability, and reliance on proprietary Artificial Intelligence (AI) models, creating significant barriers to widespread deployment, particularly in dynamic and privacy-sensitive environments. Meanwhile, integrating Large Language Models (LLMs) in vehicular systems remains underexplored, especially concerning prompt optimization and effective utilization in federated contexts. To address these challenges, we propose the Federated Prompt-Optimized Traffic Transformer (FPoTT), a novel framework that leverages open-source LLMs for predictive IoV management. FPoTT introduces a dynamic prompt optimization mechanism that iteratively refines textual prompts to enhance trajectory prediction. The architecture employs a dual-layer federated learning paradigm, combining lightweight edge models for real-time inference with cloud-based LLMs to retain global intelligence. A Transformer-driven synthetic data generator is incorporated to augment training with diverse, high-fidelity traffic scenarios in the Next Generation Simulation (NGSIM) format. Extensive evaluations demonstrate that FPoTT, utilizing EleutherAI Pythia-1B, achieves 99.86% prediction accuracy on real-world data while maintaining high performance on synthetic datasets. These results underscore the potential of open-source LLMs in enabling secure, adaptive, and scalable IoV management, offering a promising alternative to proprietary solutions in smart mobility ecosystems.
Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps
2025-05-01
Kangning Diao, Biwei Dai, Uros Seljak
Simulation-based inference provides a powerful framework for extracting rich information from nonlinear scales in current and upcoming cosmological surveys, and ensuring its robustness requires stringent validation of forward models. In this work, we recast forward model validation as an out-of-distribution (OoD) detection problem within the framework of machine learning (ML)-based simulation-based inference (SBI). We employ probability density as the metric for OoD detection, and compare various density estimation techniques, demonstrating that field-level probability density estimation via continuous time flow models (CTFM) significantly outperforms feature-level approaches that combine scattering transform (ST) or convolutional neural networks (CNN) with normalizing flows (NFs), as well as NF-based field-level estimators, as quantified by the area under the receiver operating characteristic curve (AUROC). Our analysis shows that CTFM not only excels in detecting OoD samples but also provides a robust metric for model selection. Additionally, we verified CTFM maintains consistent efficacy across different cosmologies while mitigating the inductive biases inherent in NF architectures. Although our proof-of-concept study employs simplified forward modeling and noise settings, our framework establishes a promising pathway for identifying unknown systematics in the cosmology datasets.
Deep learning has profoundly transformed remote sensing, yet prevailing architectures like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) remain constrained by critical trade-offs: CNNs suffer from limited receptive fields, while ViTs grapple with quadratic computational complexity, hindering their scalability for high-resolution remote sensing data. State Space Models (SSMs), particularly the recently proposed Mamba architecture, have emerged as a paradigm-shifting solution, combining linear computational scaling with global context modeling. This survey presents a comprehensive review of Mamba-based methodologies in remote sensing, systematically analyzing about 120 Mamba-based remote sensing studies to construct a holistic taxonomy of innovations and applications. Our contributions are structured across five dimensions: (i) foundational principles of vision Mamba architectures, (ii) micro-architectural advancements such as adaptive scan strategies and hybrid SSM formulations, (iii) macro-architectural integrations, including CNN-Transformer-Mamba hybrids and frequency-domain adaptations, (iv) rigorous benchmarking against state-of-the-art methods in multiple application tasks, such as object detection, semantic segmentation, change detection, etc. and (v) critical analysis of unresolved challenges with actionable future directions. By bridging the gap between SSM theory and remote sensing practice, this survey establishes Mamba as a transformative framework for remote sensing analysis. To our knowledge, this paper is the first systematic review of Mamba architectures in remote sensing. Our work provides a structured foundation for advancing research in remote sensing systems through SSM-based methods. We curate an open-source repository (https://github.com/BaoBao0926/Awesome-Mamba-in-Remote-Sensing) to foster community-driven advancements.
The controlled dissipation of chemical potentials is the fundamental way cells make a living. Enzyme-mediated catalysis allows the various transformations to proceed at biologically relevant rates with remarkable precision and efficiency. Theory, experiments and computational studies coincide to show that local frustration is a useful concept to relate protein dynamics with catalytic power. Local frustration gives rise to the asperities of the energy landscapes that can harness the thermal fluctuations to guide the functional protein motions. We review here recent advances into these relationships from various fields of protein science. The biologically relevant dynamics is tuned by the evolution of protein sequences that modulate the local frustration patterns to near optimal values.
Voice Cloning has rapidly advanced in today's digital world, with many researchers and corporations working to improve these algorithms for various applications. This article aims to establish a standardized terminology for voice cloning and explore its different variations. It will cover speaker adaptation as the fundamental concept and then delve deeper into topics such as few-shot, zero-shot, and multilingual TTS within that context. Finally, we will explore the evaluation metrics commonly used in voice cloning research and related datasets. This survey compiles the available voice cloning algorithms to encourage research toward its generation and detection to limit its misuse.
A Bioinformatic Study of Genetics Involved in Determining Mild Traumatic Brain Injury Severity and Recovery
2025-05-01
Mahnaz Tajik, Michael D Noseworthy
Aim: This in silico study sought to identify specific biomarkers for mild traumatic brain injury (mTBI) through the analysis of publicly available gene and miRNA databases, hypothesizing their influence on neuronal structure, axonal integrity, and regeneration. Methods: This study implemented a three-step process: (1) Data searching for mTBI-related genes in Gene and MalaCard databases and literature review ; (2) Data analysis involved performing functional annotation through GO and KEGG, identifying hub genes using Cytoscape, mapping protein-protein interactions via DAVID and STRING, and predicting miRNA targets using miRSystem, miRWalk2.0, and mirDIP (3) RNA-sequencing analysis applied to the mTBI dataset GSE123336. Results: Eleven candidate hub genes associated with mTBI outcome were identified: APOE, S100B, GFAP, BDNF, AQP4, COMT, MBP, UCHL1, DRD2, ASIC1, and CACNA1A. Enrichment analysis linked these genes to neuron projection regeneration and synaptic plasticity. miRNAs linked to the mTBI candidate genes were hsa-miR-9-5p, hsa-miR-204-5p, hsa-miR-1908-5p, hsa-miR-16-5p, hsa-miR-10a-5p, has-miR-218-5p, has-miR-34a-5p, and has-miR-199b-5p. The RNA sequencing revealed 2664 differentially expressed miRNAs post-mTBI, with 17 showing significant changes at the time of injury and 48 hours post-injury. Two miRNAs were positively correlated with direct head hits. Conclusion: Our study indicates that specific genes and miRNAs, particularly hsa-miR-10a-5p, may influence mTBI outcomes. Our research may guide future mTBI diagnostics, emphasizing the need to measure and track these specific genes and miRNAs in diverse cohorts.
100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models
2025-05-01
Chong Zhang, Yue Deng, Xiang Lin, Bin Wang, Dianwen Ng, Hai Ye, Xingxuan Li, Yao Xiao, Zhanfeng Mo, Qi Zhang, Lidong Bing
The recent development of reasoning language models (RLMs) represents a novel evolution in large language models. In particular, the recent release of DeepSeek-R1 has generated widespread social impact and sparked enthusiasm in the research community for exploring the explicit reasoning paradigm of language models. However, the implementation details of the released models have not been fully open-sourced by DeepSeek, including DeepSeek-R1-Zero, DeepSeek-R1, and the distilled small models. As a result, many replication studies have emerged aiming to reproduce the strong performance achieved by DeepSeek-R1, reaching comparable performance through similar training procedures and fully open-source data resources. These works have investigated feasible strategies for supervised fine-tuning (SFT) and reinforcement learning from verifiable rewards (RLVR), focusing on data preparation and method design, yielding various valuable insights. In this report, we provide a summary of recent replication studies to inspire future research. We primarily focus on SFT and RLVR as two main directions, introducing the details for data construction, method design and training procedure of current replication studies. Moreover, we conclude key findings from the implementation details and experimental results reported by these studies, anticipating to inspire future research. We also discuss additional techniques of enhancing RLMs, highlighting the potential of expanding the application scope of these models, and discussing the challenges in development. By this survey, we aim to help researchers and developers of RLMs stay updated with the latest advancements, and seek to inspire new ideas to further enhance RLMs.
A Methodological and Structural Review of Parkinsons Disease Detection Across Diverse Data Modalities
2025-05-01
Abu Saleh Musa Miah, taro Suzuki, Jungpil Shin
Parkinsons Disease (PD) is a progressive neurological disorder that primarily affects motor functions and can lead to mild cognitive impairment (MCI) and dementia in its advanced stages. With approximately 10 million people diagnosed globally 1 to 1.8 per 1,000 individuals, according to reports by the Japan Times and the Parkinson Foundation early and accurate diagnosis of PD is crucial for improving patient outcomes. While numerous studies have utilized machine learning (ML) and deep learning (DL) techniques for PD recognition, existing surveys are limited in scope, often focusing on single data modalities and failing to capture the potential of multimodal approaches. To address these gaps, this study presents a comprehensive review of PD recognition systems across diverse data modalities, including Magnetic Resonance Imaging (MRI), gait-based pose analysis, gait sensory data, handwriting analysis, speech test data, Electroencephalography (EEG), and multimodal fusion techniques. Based on over 347 articles from leading scientific databases, this review examines key aspects such as data collection methods, settings, feature representations, and system performance, with a focus on recognition accuracy and robustness. This survey aims to serve as a comprehensive resource for researchers, providing actionable guidance for the development of next generation PD recognition systems. By leveraging diverse data modalities and cutting-edge machine learning paradigms, this work contributes to advancing the state of PD diagnostics and improving patient care through innovative, multimodal approaches.
Out of the Loop Again: How Dangerous is Weaponizing Automated Nuclear Systems?
2025-05-01
Joshua A. Schwartz, Michael C. Horowitz
Are nuclear weapons useful for coercion, and, if so, what factors increase the credibility and effectiveness of nuclear threats? While prominent scholars like Thomas Schelling argue that nuclear brinkmanship, or the manipulation of nuclear risk, can effectively coerce adversaries, others contend nuclear weapons are not effective tools of coercion, especially coercion designed to achieve offensive and revisionist objectives. Simultaneously, there is broad debate about the incorporation of artificial intelligence (AI) into military systems, especially nuclear command and control. We develop a theoretical argument that explicit nuclear threats implemented with automated nuclear launch systems are potentially more credible compared to ambiguous nuclear threats or explicit nuclear threats implemented via non-automated means. By reducing human control over nuclear use, leaders can more effectively tie their hands and thus signal resolve. While automated nuclear weapons launch systems may seem like something out of science fiction, the Soviet Union deployed such a system during the Cold War and the technology necessary to automate the use of force has developed considerably in recent years due to advances in AI. Preregistered survey experiments on an elite sample of United Kingdom Members of Parliament and two public samples of UK citizens provide support for these expectations, showing that, in a limited set of circumstances, nuclear threats backed by AI integration have credibility advantages, no matter how dangerous they may be. Our findings contribute to the literatures on coercive bargaining, weapons of mass destruction, and emerging technology.
Uniformly Star Superparacompact Subsets and Spaces
2025-05-01
Argha Ghosh
Uniformly star superparacompactness, which is a topological property between compactness and completeness, can be characterized using finite-component covers and a measure of strong local compactness. Using these finite-component covers and the associated functional, we introduce and investigate a variational notion of uniformly star superparacompact subsets in metric spaces in the spirit of studies on uniformly paracompact subset and UC-subset. We show that the collection of all such subsets forms a bornology with a closed base, which is contained in the bornology of uniformly paracompact subsets. Conditions under which these two bornologies coincide are specified. Furthermore, we provide several new characterizations of uniformly star superparacompact metric spaces also known as cofinally Bourbaki-quasi complete spaces in terms of some geometric functionals. As a consequence, we establish new relationships among metric spaces that lie between compactness and completeness.
MULE: Multi-terrain and Unknown Load Adaptation for Effective Quadrupedal Locomotion
2025-05-01
Vamshi Kumar Kurva, Shishir Kolathaya
Quadrupedal robots are increasingly deployed for load-carrying tasks across diverse terrains. While Model Predictive Control (MPC)-based methods can account for payload variations, they often depend on predefined gait schedules or trajectory generators, limiting their adaptability in unstructured environments. To address these limitations, we propose an Adaptive Reinforcement Learning (RL) framework that enables quadrupedal robots to dynamically adapt to both varying payloads and diverse terrains. The framework consists of a nominal policy responsible for baseline locomotion and an adaptive policy that learns corrective actions to preserve stability and improve command tracking under payload variations. We validate the proposed approach through large-scale simulation experiments in Isaac Gym and real-world hardware deployment on a Unitree Go1 quadruped. The controller was tested on flat ground, slopes, and stairs under both static and dynamic payload changes. Across all settings, our adaptive controller consistently outperformed the controller in tracking body height and velocity commands, demonstrating enhanced robustness and adaptability without requiring explicit gait design or manual tuning.
CORSTITCH - A free, open source software for stitching and georeferencing underwater coral reef videos
2025-05-01
Julian Christopher L. Maypa, Johnenn R. Manalang, Maricor N. Soriano
CorStitch is an open-source software developed to automate the creation of accurate georeferenced reef mosaics from video transects obtained through Automated Rapid Reef Assessment System surveys. We utilized a Fourier-based image correlation algorithm to stitch sequential video frames, aligning them with synchronized GNSS timestamps. The resulting compressed Keyhole Markup Language files, compatible with geographic information systems such as Google Earth, enable detailed spatial analysis. Validation through comparative analysis of mosaics from two temporally distinct surveys of the same reef demonstrated the software's consistent and reliable performance.
Our present knowledge of the nuclear equation of state is briefly reviewed in this article intended for a wider readership. Particular emphasis is given to the asymmetric-matter equation of state required for modeling neutron stars, neutron-star mergers, and r-process nucleosynthesis. Recent analyses based on combining information obtained from nuclear theory, heavy-ion collisions and astrophysical observations confine the obtained radii of the canonical 1.4-solar-mass neutron star to values between 12 km and 13 km. The remaining uncertainty is primarily related to missing information in the density interval between nuclear saturation density and about twice that value which, however, is accessible with laboratory experiments.
Vintage-Based Formulations in Multi-Year Investment Modelling for Energy Systems
2025-05-01
Ni Wang, Germán Morales-España
This paper reviews two established formulations for modelling multi-year energy investments: the simple method, which aggregates all capacity regardless of commissioning year, and the vintage method, which explicitly tracks investments by year to capture differences in technical parameters over time. While the vintage method improves modelling fidelity, it significantly increases model size. To address this, we propose a novel compact formulation that maintains the ability to represent year-specific characteristics while reducing the dimensionality of the model. The proposed compact formulation is implemented in the open-source model TulipaEnergyModel.jl and offers a tractable alternative for detailed long-term energy system planning.
In data stream clustering, systematic theory of stream clustering algorithms remains relatively scarce. Recently, density-based methods have gained attention. However, existing algorithms struggle to simultaneously handle arbitrarily shaped, multi-density, high-dimensional data while maintaining strong outlier resistance. Clustering quality significantly deteriorates when data density varies complexly. This paper proposes a clustering algorithm based on the novel concept of Tightest Neighbors and introduces a data stream clustering theory based on the Skeleton Set. Based on these theories, this paper develops a new method, TNStream, a fully online algorithm. The algorithm adaptively determines the clustering radius based on local similarity, summarizing the evolution of multi-density data streams in micro-clusters. It then applies a Tightest Neighbors-based clustering algorithm to form final clusters. To improve efficiency in high-dimensional cases, Locality-Sensitive Hashing (LSH) is employed to structure micro-clusters, addressing the challenge of storing k-nearest neighbors. TNStream is evaluated on various synthetic and real-world datasets using different clustering metrics. Experimental results demonstrate its effectiveness in improving clustering quality for multi-density data and validate the proposed data stream clustering theory.
Denoising weak lensing mass maps with diffusion model: systematic comparison with generative adversarial network
2025-05-01
Shohei D. Aoyama, Ken Osato, Masato Shirasaki
(abridged) Weak gravitational lensing (WL) is the unique and powerful probe into the large-scale structures of the Universe. Removing the shape noise from the observed WL field, i.e., denoising, enhances the potential of WL by accessing information at small scales where the shape noise dominates without denoising. We utilise two machine learning (ML) models for denosing: generative adversarial network (GAN) and diffusion model (DM). We evaluate the performance of denosing with GAN and DM utilising the large suite of mock WL observations, which serve as the training and test data sets. We apply denoising to 1,000 maps with GAN and DM models trained with 39,000 mock observations. Both models can fairly well reproduce the true convergence map on large scales. Then, we measure cosmological statistics: power spectrum, bispectrum, one-point probability distribution function, peak and minima counts, and scattering transform coefficients. We find that DM outperforms GAN in almost all statistics and recovers the correct statistics down to small scales within roughly $0.3 \sigma$ level at the scales accessible from current and future WL surveys. We also conduct the stress tests on the trained model; denoising the maps with different characteristics, e.g., different source redshifts, from the data used in training. The performance degrades at small scales, but the statistics can still be recovered at large scales. Though the training of DM is more computationally demanding compared with GAN, there are several advantages: numerically stable training, higher performance in the reconstruction of cosmological statistics, and sampling multiple realisations once the model is trained. It has been known that DM can generate higher-quality images in real-world problems than GAN, the superiority has been confirmed as well in the WL denoising problem.
Compressing fluid flows with nonlinear machine learning: mode decomposition, latent modeling, and flow control
2025-05-01
Koji Fukagata, Kai Fukami
An autoencoder is a self-supervised machine-learning network trained to output a quantity identical to the input. Owing to its structure possessing a bottleneck with a lower dimension, an autoencoder works to achieve data compression, extracting the essence of the high-dimensional data into the resulting latent space. We review the fundamentals of flow field compression using convolutional neural network-based autoencoder (CNN-AE) and its applications to various fluid dynamics problems. We cover the structure and the working principle of CNN-AE with an example of unsteady flows while examining the theoretical similarities between linear and nonlinear compression techniques. Representative applications of CNN-AE to various flow problems, such as mode decomposition, latent modeling, and flow control, are discussed. Throughout the present review, we show how the outcomes from the nonlinear machine-learning-based compression may support modeling and understanding a range of fluid mechanics problems.
We consider a special type of self-similar sets, called fractal squares, and give a brief review on recent results and unsolved issues with an emphasis on their topological properties.
AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality
2025-05-01
Biling Wang, Austen Maniscalco, Ti Bai, Siqiu Wang, Michael Dohopolski, Mu-Han Lin, Chenyang Shen, Dan Nguyen, Junzhou Huang, Steve Jiang, Xinlei Wang
Purpose: This study presents a Deep Learning (DL)-based quality assessment (QA) approach for evaluating auto-generated contours (auto-contours) in radiotherapy, with emphasis on Online Adaptive Radiotherapy (OART). Leveraging Bayesian Ordinal Classification (BOC) and calibrated uncertainty thresholds, the method enables confident QA predictions without relying on ground truth contours or extensive manual labeling. Methods: We developed a BOC model to classify auto-contour quality and quantify prediction uncertainty. A calibration step was used to optimize uncertainty thresholds that meet clinical accuracy needs. The method was validated under three data scenarios: no manual labels, limited labels, and extensive labels. For rectum contours in prostate cancer, we applied geometric surrogate labels when manual labels were absent, transfer learning when limited, and direct supervision when ample labels were available. Results: The BOC model delivered robust performance across all scenarios. Fine-tuning with just 30 manual labels and calibrating with 34 subjects yielded over 90% accuracy on test data. Using the calibrated threshold, over 93% of the auto-contours' qualities were accurately predicted in over 98% of cases, reducing unnecessary manual reviews and highlighting cases needing correction. Conclusion: The proposed QA model enhances contouring efficiency in OART by reducing manual workload and enabling fast, informed clinical decisions. Through uncertainty quantification, it ensures safer, more reliable radiotherapy workflows.
Integrating VR tours in online language learning:A design-based research study
2025-05-01
Roberto B. Figueroa Jr., Insung Jung
This paper presents an investigation into the integration of virtual reality (VR) tours in online English lessons tailored for adult learners. The study utilised a design-based research approach to evaluate the effectiveness of VR tours in this context. It specifically examined the responses of adult learners to this instructional strategy by collecting data through surveys, observation notes and interviews with four learners in Japan and five learners in France, most of whom completed 10 lessons over 4 months. The research findings highlight the effectiveness of VR tours in enhancing learner motivation. Additionally, they demonstrate that perceived learning outcomes are influenced not only by the immersive experience of spatial presence but also by the novelty of technological and scenery-related aspects within the VR environment, as well as factors related to lesson design and individual learner characteristics.
The increasing complexity and scale of the Internet of Things (IoT) have made security a critical concern. This paper presents a novel Large Language Model (LLM)-based framework for comprehensive threat detection and prevention in IoT environments. The system integrates lightweight LLMs fine-tuned on IoT-specific datasets (IoT-23, TON_IoT) for real-time anomaly detection and automated, context-aware mitigation strategies optimized for resource-constrained devices. A modular Docker-based deployment enables scalable and reproducible evaluation across diverse network conditions. Experimental results in simulated IoT environments demonstrate significant improvements in detection accuracy, response latency, and resource efficiency over traditional security methods. The proposed framework highlights the potential of LLM-driven, autonomous security solutions for future IoT ecosystems.
Hubble-induced phase transitions in the Standard Model and beyond
2025-05-01
Javier Rubio, Giorgio Laverda
We review the dynamics of spectator scalar fields non-minimally coupled to gravity in the post-inflationary Universe, with particular emphasis on scenarios where the end of inflation is followed by a period of kination. In this context, the evolution of the Ricci scalar can lead to the spontaneous breaking of discrete or continuous symmetries through tachyonic instabilities. These Hubble-induced phase transitions may cause the amplification of field fluctuations, the formation of transient topological defects, and an efficient energy transfer into relativistic degrees of freedom. We analyze this general mechanism and its cosmological consequences, including (re)heating and gravitational wave production. As a concrete realization, we discuss the postinflationary evolution of the Standard Model Higgs, examining the interplay between curvature effects, vacuum stability, and non-perturbative dynamics. This framework provides a minimal and predictive connection between high-energy physics and the early Universe, with potential observational signatures.
Methods of construction of exponential bases on planar domains
2025-05-01
Oleg Asipchuk, Laura De Carli
In this paper, we survey and refine several results -- some previously established in the literature -- that facilitate the construction of exponential bases on planar domains with explicit control over the associated frame bounds. We apply our techniques to construct well-conditioned exponential bases on certain planar sets that multi-tile the plane.
SmallPlan: Leverage Small Language Models for Sequential Path Planning with Simulation-Powered, LLM-Guided Distillation
2025-05-01
Quang P. M. Pham, Khoi T. N. Nguyen, Nhi H. Doan, Cuong A. Pham, Kentaro Inui, Dezhen Song
Efficient path planning in robotics, particularly within large-scale, dynamic environments, remains a significant hurdle. While Large Language Models (LLMs) offer strong reasoning capabilities, their high computational cost and limited adaptability in dynamic scenarios hinder real-time deployment on edge devices. We present SmallPlan -- a novel framework leveraging LLMs as teacher models to train lightweight Small Language Models (SLMs) for high-level path planning tasks. In SmallPlan, the SLMs provide optimal action sequences to navigate across scene graphs that compactly represent full-scaled 3D scenes. The SLMs are trained in a simulation-powered, interleaved manner with LLM-guided supervised fine-tuning (SFT) and reinforcement learning (RL). This strategy not only enables SLMs to successfully complete navigation tasks but also makes them aware of important factors like travel distance and number of trials. Through experiments, we demonstrate that the fine-tuned SLMs perform competitively with larger models like GPT-4o on sequential path planning, without suffering from hallucination and overfitting. SmallPlan is resource-efficient, making it well-suited for edge-device deployment and advancing practical autonomous robotics.
As one of the most widely cultivated and consumed crops, wheat is essential to global food security. However, wheat production is increasingly challenged by pests, diseases, climate change, and water scarcity, threatening yields. Traditional crop monitoring methods are labor-intensive and often ineffective for early issue detection. Hyperspectral imaging (HSI) has emerged as a non-destructive and efficient technology for remote crop health assessment. However, the high dimensionality of HSI data and limited availability of labeled samples present notable challenges. In recent years, deep learning has shown great promise in addressing these challenges due to its ability to extract and analysis complex structures. Despite advancements in applying deep learning methods to HSI data for wheat crop analysis, no comprehensive survey currently exists in this field. This review addresses this gap by summarizing benchmark datasets, tracking advancements in deep learning methods, and analyzing key applications such as variety classification, disease detection, and yield estimation. It also highlights the strengths, limitations, and future opportunities in leveraging deep learning methods for HSI-based wheat crop analysis. We have listed the current state-of-the-art papers and will continue tracking updating them in the following https://github.com/fadi-07/Awesome-Wheat-HSI-DeepLearning.
We review recent studies on quantum materials where transition-metal atoms give rise to $d$-bands typical of kagome metals. Using examples from several material families - AV$_3$Sb$_5$, FeGe, RV$_6$Sn$_6$, and LaRu$_3$Si$_2$ - we argue that $p$-bands contributed by elements beyond the kagome network also play a crucial role in the electronic instabilities, including the charge-density-waves and superconductivity in kagome metals.
The JWST weather report from the nearest brown dwarfs II: Consistent variability mechanisms over 7 months revealed by 1-14 $μ$m NIRSpec + MIRI monitoring of WISE 1049AB
2025-05-01
Xueqing Chen, Beth A. Biller, Xianyu Tan, Johanna M. Vos, Yifan Zhou, Genaro Suárez, Allison M. McCarthy, Caroline V. Morley, Niall Whiteford, Trent J. Dupuy, Jacqueline Faherty, Ben J. Sutlieff, Natalia Oliveros-Gomez, Elena Manjavacas, Mary Anne Limbach, Elspeth K. H. Lee, Theodora Karalidi, Ian J. M. Crossfield, Pengyu Liu, Paul Molliere, Philip S. Muirhead, Thomas Henning, Gregory Mace, Nicolas Crouzet, Tiffany Kataria
We present a new epoch of JWST spectroscopic variability monitoring of the benchmark binary brown dwarf WISE 1049AB, the closest, brightest brown dwarfs known. Our 8-hour MIRI low resolution spectroscopy (LRS) and 7-hour NIRSpec prism observations extended variability measurements for any brown dwarfs beyond 11 $\mu$m for the first time, reaching up to 14 $\mu$m. Combined with the previous epoch in 2023, they set the longest JWST weather monitoring baseline to date. We found that both WISE 1049AB show wavelength-dependent light curve behaviours. Using a robust k-means clustering algorithm, we identified several clusters of variability behaviours associated with three distinct pressure levels. By comparing to a general circulation model (GCM), we identified the possible mechanisms that drive the variability at these pressure levels: Patchy clouds rotating in and out of view likely shaped the dramatic light curves in the deepest layers between 1-2.5 $\mu$m, whereas hot spots arising from temperature / chemical variations of molecular species likely dominate the high-altitude levels between 2.5-3.6 $\mu$m and 4.3-8.5 $\mu$m. Small-grain silicates potentially contributed to the variability of WISE 1049A at 8.5-11 $\mu$m. While distinct atmospheric layers are governed by different mechanisms, we confirmed for the first time that each variability mechanism remains consistent within its layer over the long term. Future multi-period observations will further test the stability of variability mechanisms on this binary, and expanded JWST variability surveys across the L-T-Y sequence will allow us to trace and understand variability mechanisms across a wider population of brown dwarfs and planetary-mass objects.
Determining the Scale Length and Height of the Milky Way's Thick Disc Using RR~Lyrae
2025-05-01
Roman Tkachenko, Katherine Vieira, Artem Lutsenko, Vladimir Korchagin, Giovanni Carraro
Using the RR Lyrae surveys Gaia DR3 Specific Objects Study, PanSTARRS1 and ASAS-SN-II, we determine the Milky Way's thick disc scale length and scale height as well as the radial scale length of the galaxy's inner halo. We use a Bayesian approach to estimate these values using two independent techniques: Markov chain Monte Carlo sampling, and importance nested sampling. We consider two vertical density profiles for the thick disc. In the exponential model, the scale length of the thick disc is $h_R=2.14_{-0.17}^{+0.19}$ kpc, and its scale height is $h_z=0.64_{-0.06}^{+0.06}$ kpc. In the squared hyperbolic secant profile $sech^2$, those values are correspondingly $h_R=2.10_{-0.17}^{+0.19}$ kpc and $h_z=1.02_{-0.08}^{+0.09}$ kpc. The density distribution of the inner halo can be described as a power law function with the exponent $n =-2.35_{-0.05}^{+0.05}$ and flattening $q =0.57_{-0.02}^{+0.02}$. We also estimate the halo to disc concentration ratio as $\gamma=0.19_{-0.02}^{+0.02}$ for the exponential disc and $\gamma=0.32_{-0.03}^{+0.03}$ for the $sech^2$ disc.
Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. However, fully autonomous LLM-based agents still face significant challenges, including limited reliability due to hallucinations, difficulty in handling complex tasks, and substantial safety and ethical risks, all of which limit their feasibility and trustworthiness in real-world applications. To overcome these limitations, LLM-based human-agent systems (LLM-HAS) incorporate human-provided information, feedback, or control into the agent system to enhance system performance, reliability and safety. This paper provides the first comprehensive and structured survey of LLM-HAS. It clarifies fundamental concepts, systematically presents core components shaping these systems, including environment & profiling, human feedback, interaction types, orchestration and communication, explores emerging applications, and discusses unique challenges and opportunities. By consolidating current knowledge and offering a structured overview, we aim to foster further research and innovation in this rapidly evolving interdisciplinary field. Paper lists and resources are available at https://github.com/HenryPengZou/Awesome-LLM-Based-Human-Agent-System-Papers.
A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI
2025-05-01
Lik Hang Kenny Wong, Xueyang Kang, Kaixin Bai, Jianwei Zhang
Navigation and manipulation are core capabilities in Embodied AI, yet training agents with these capabilities in the real world faces high costs and time complexity. Therefore, sim-to-real transfer has emerged as a key approach, yet the sim-to-real gap persists. This survey examines how physics simulators address this gap by analyzing their properties overlooked in previous surveys. We also analyze their features for navigation and manipulation tasks, along with hardware requirements. Additionally, we offer a resource with benchmark datasets, metrics, simulation platforms, and cutting-edge methods-such as world models and geometric equivariance-to help researchers select suitable tools while accounting for hardware constraints.
2025-04-30
Jiwen Yu, Yiran Qin, Haoxuan Che, Quande Liu, Xintao Wang, Pengfei Wan, Di Zhang, Kun Gai, Hao Chen, Xihui Liu
Interactive Generative Video (IGV) has emerged as a crucial technology in response to the growing demand for high-quality, interactive video content across various domains. In this paper, we define IGV as a technology that combines generative capabilities to produce diverse high-quality video content with interactive features that enable user engagement through control signals and responsive feedback. We survey the current landscape of IGV applications, focusing on three major domains: 1) gaming, where IGV enables infinite exploration in virtual worlds; 2) embodied AI, where IGV serves as a physics-aware environment synthesizer for training agents in multimodal interaction with dynamically evolving scenes; and 3) autonomous driving, where IGV provides closed-loop simulation capabilities for safety-critical testing and validation. To guide future development, we propose a comprehensive framework that decomposes an ideal IGV system into five essential modules: Generation, Control, Memory, Dynamics, and Intelligence. Furthermore, we systematically analyze the technical challenges and future directions in realizing each component for an ideal IGV system, such as achieving real-time generation, enabling open-domain control, maintaining long-term coherence, simulating accurate physics, and integrating causal reasoning. We believe that this systematic analysis will facilitate future research and development in the field of IGV, ultimately advancing the technology toward more sophisticated and practical applications.
Public Opinion and The Rise of Digital Minds: Perceived Risk, Trust, and Regulation Support
2025-04-30
Justin B. Bullock, Janet V. T. Pauketat, Hsini Huang, Yi-Fan Wang, Jacy Reese Anthis
Governance institutions must respond to societal risks, including those posed by generative AI. This study empirically examines how public trust in institutions and AI technologies, along with perceived risks, shape preferences for AI regulation. Using the nationally representative 2023 Artificial Intelligence, Morality, and Sentience (AIMS) survey, we assess trust in government, AI companies, and AI technologies, as well as public support for regulatory measures such as slowing AI development or outright bans on advanced AI. Our findings reveal broad public support for AI regulation, with risk perception playing a significant role in shaping policy preferences. Individuals with higher trust in government favor regulation, while those with greater trust in AI companies and AI technologies are less inclined to support restrictions. Trust in government and perceived risks significantly predict preferences for both soft (e.g., slowing development) and strong (e.g., banning AI systems) regulatory interventions. These results highlight the importance of public opinion in AI governance. As AI capabilities advance, effective regulation will require balancing public concerns about risks with trust in institutions. This study provides a foundational empirical baseline for policymakers navigating AI governance and underscores the need for further research into public trust, risk perception, and regulatory strategies in the evolving AI landscape.
Accretion inside astrophysical objects : Effects of rotation and viscosity
2025-04-30
H. A. Adarsha, Chandrachur Chakraborty, Sudip Bhattacharyya
Sub-solar mass black holes could show up in gravitational observations in future and near-solar mass black holes might have been involved in the events GW190425 and GW190814. Since they cannot form from the stellar evolution, their creation requires exotic mechanisms. One such mechanism involves the capture of dark matter particles by stellar objects and their thermalization. When the criterion for the collapse of these dark matter particles is satisfied, a tiny endoparasitic black hole (EBH) forms and then it accretes matter from the host. The EBH may transmute the host into a black hole of nearly the same mass as the host or lesser, depending on the type of accretion. We examine this complex and poorly explored accretion mechanism, considering the effects of rotation and viscosity but ignoring some other effects, such as those of pressure and magnetic field, as the first step. Using a general framework to assess the effects of rotation and viscosity on accretion, we show that the accretion could be stalled in some white dwarfs, but not in neutron stars. The stalled accretion should cause an opening in the host's polar regions, the extent of which depends on the mass and spin of the host.
The accurate determination of thermal conductivity $\kappa(T)$ in bulk materials is essential to assess their performance as candidates for specific applications. The 3$\omega$ technique is an established methodology for studying the thermal conductivity of thin films and becomes particularly suitable in the case of bulk specimens at room temperature and above, where standard stationary techniques require significant corrections for radiative losses. Although this method has been employed in several works, it remains not widely adopted because its implementation demands considerable sophistication, including experiment design, thin film deposition techniques and choices of the geometry of the current/heat transducer, electronics, and analytical treatment of the signals. This work reviews the technique's most crucial technical aspects, providing practical support for a quick and user-friendly implementation, from the design phase to the execution and analysis. We release a Python-based graphical user interface that supports a quick quantitative estimation of the investigated temperature profiles based on the geometrical parameters (width/length) of the deposited transducer (heater/thermometer metal line) before an experiment, guaranteeing an optimal engineering of the experimental conditions for each given material under scrutiny.
The Challenges of Detecting Gases in Exoplanet Atmospheres
2025-04-30
Luis Welbanks, Matthew C. Nixon, Peter McGill, Lana J. Tilke, Lindsey S. Wiser, Yoav Rotman, Sagnick Mukherjee, Adina Feinstein, Michael R. Line, Sara Seager, Thomas G. Beatty, Darryl Z. Seligman, Vivien Parmentier, David Sing
Claims of detections of gases in exoplanet atmospheres often rely on comparisons between models including and excluding specific chemical species. However, the space of molecular combinations available for model construction is vast and highly degenerate. Only a limited subset of these combinations is typically explored for any given detection. As a result, apparent detections of trace gases risk being artifacts of incomplete modeling rather than robust identification of atmospheric constituents, especially in the low signal-to-noise regime. We illustrate these challenges using the sub-Neptune K2-18~b, where recent claims of a potential biosignature detection vanish when the considered model space is expanded. We show that numerous alternative models without potential biosignature gases provide equivalent or better fits to the observations. We demonstrate that the significance of a claimed detection relies on the choice of models being compared, and that model preference does not necessarily imply the presence of any specific gas.
We give a pedagogical introduction to the rich phenomenology of hadronic tau decays. These decays provide a unique window into the interplay of electroweak and strong interactions at low energies, as they occur primarily via $W$ exchange after the electroweak quark current hadronizes. In this manuscript, we summarize the basic ingredients required to perform precision physics studies in this sector. We detail the derivation of the different distributions within the Standard Model, discuss how to parametrize the non-perturbative QCD dynamics, and present methods commonly used to achieve clean theoretical predictions. Additionally, we briefly review how these distributions generalize in the presence of relatively heavy particles from beyond the Standard Model. This overview thus aims to serve as a useful starting point for readers interested in understanding how the only lepton capable of decaying into hadrons does so.
The Intermediate-Mass Black Hole Reverberation Mapping Project: First Detection of Mid-Infrared Lags in Prototypical IMBHs in NGC 4395 and POX 52
2025-04-30
Jingbo Sun, Hengxiao Guo, Wenwen Zuo, Paulina Lira, Minfeng Gu, Philip G. Edwards, Shu Wang, Jamie Stevens, Tao An, Samuzal Barua, Zhen-yi Cai, Haicheng Feng, Alok C. Gupta, Luis C. Ho, Dragana Ilić, Andjelka B. Kovačević, ShaSha Li, Mar Mezcua, Luka Č. Popović, Paula Sánchez-Sáez, Mouyuan Sun, Rongfeng Shen, Vivian U, Oliver Vince, Junxian Wang, Xuebing Wu, Zhefu Yu, Zhenya Zheng
The search for robust evidence of intermediate-mass black holes (IMBHs) is crucial for understanding black hole seeding process and the formation of supermassive black holes in the early Universe. NGC 4395 and POX 52 are two prototypical IMBH hosts, both exhibiting multi-line evidence of low-mass black hole activity. Here, we report the first detection of mid-infrared (MIR) lags in response to optical variability, with measurements of $3.0^{+2.4}_{-1.9}$ days for NGC 4395 and $35.2^{+14.2}_{-11.7}$ days for POX~52 at $3.4$ $\mu$m, respectively, using archival optical data and observations from the Wide-field Infrared Survey Explorer (WISE). This detection provides the first reverberation evidence of low-mass black hole activity in POX 52. The time lags of these two low-mass, low-luminosity active galactic nuclei (AGNs) generally follow the extent of the $R_{\rm dust}-L_{\rm 5100}$ relation found in higher-mass AGNs. Based on an empirical relation between the broad-line region and dusty torus size, we constrain the black hole mass of POX 52 to log($M_{\rm BH}$/$M_\odot$) = 5.5 $\pm$ 0.37 (systemic and statistical errors), confirming its IMBH nature. Furthermore, long-term optical continuum monitoring of POX 52 reveals a mild inter-band lag of $\lesssim$ 1 day. However, no significant intranight variability was detected during its one-night, high-cadence monitoring, which we attribute to the longer duty cycle of fast variability in POX 52 compared to that in NGC 4395.
Discounting Approaches in Multi-Year Investment Modelling for Energy Systems
2025-04-30
Ni Wang, Diego A. Tejada-Arango
This paper reviews discounting approaches for modeling multi-year energy investments, focusing on total versus annualised cost formulations. We discuss how time value of money is handled, and how salvage value and milestone-year weighting can address mismatches between asset lifetimes and model horizons. These methods are implemented in the open-source TulipaEnergyModel to support transparent and tractable long-term energy system planning.
Detecting plant diseases is a crucial aspect of modern agriculture - it plays a key role in maintaining crop health and increasing overall yield. Traditional approaches, though still valuable, often rely on manual inspection or conventional machine learning techniques, both of which face limitations in scalability and accuracy. Recently, Vision Transformers (ViTs) have emerged as a promising alternative, offering benefits such as improved handling of long-range dependencies and better scalability for visual tasks. This survey explores the application of ViTs in precision agriculture, covering tasks from classification to detection and segmentation. We begin by introducing the foundational architecture of ViTs and discuss their transition from Natural Language Processing (NLP) to computer vision. The discussion includes the concept of inductive bias in traditional models like Convolutional Neural Networks (CNNs), and how ViTs mitigate these biases. We provide a comprehensive review of recent literature, focusing on key methodologies, datasets, and performance metrics. The survey also includes a comparative analysis of CNNs and ViTs, with a look at hybrid models and performance enhancements. Technical challenges - such as data requirements, computational demands, and model interpretability - are addressed alongside potential solutions. Finally, we outline potential research directions and technological advancements that could further support the integration of ViTs in real-world agricultural settings. Our goal with this study is to offer practitioners and researchers a deeper understanding of how ViTs are poised to transform smart and precision agriculture.
Assessing Racial Disparities in Healthcare Expenditures Using Causal Path-Specific Effects
2025-04-30
Xiaxian Ou, Xinwei He, David Benkeser, Razieh Nabi
Racial disparities in healthcare expenditures are well-documented, yet the underlying drivers remain complex and require further investigation. This study employs causal and counterfactual path-specific effects to quantify how various factors, including socioeconomic status, insurance access, health behaviors, and health status, mediate these disparities. Using data from the Medical Expenditures Panel Survey, we estimate how expenditures would differ under counterfactual scenarios in which the values of specific mediators were aligned across racial groups along selected causal pathways. A key challenge in this analysis is ensuring robustness against model misspecification while addressing the zero-inflation and right-skewness of healthcare expenditures. For reliable inference, we derive asymptotically linear estimators by integrating influence function-based techniques with flexible machine learning methods, including super learners and a two-part model tailored to the zero-inflated, right-skewed nature of healthcare expenditures.
Visual Text Processing: A Comprehensive Review and Unified Evaluation
2025-04-30
Yan Shu, Weichao Zeng, Fangmin Zhao, Zeyu Chen, Zhenhang Li, Xiaomeng Yang, Yu Zhou, Paolo Rota, Xiang Bai, Lianwen Jin, Xu-Cheng Yin, Nicu Sebe
Visual text is a crucial component in both document and scene images, conveying rich semantic information and attracting significant attention in the computer vision community. Beyond traditional tasks such as text detection and recognition, visual text processing has witnessed rapid advancements driven by the emergence of foundation models, including text image reconstruction and text image manipulation. Despite significant progress, challenges remain due to the unique properties that differentiate text from general objects. Effectively capturing and leveraging these distinct textual characteristics is essential for developing robust visual text processing models. In this survey, we present a comprehensive, multi-perspective analysis of recent advancements in visual text processing, focusing on two key questions: (1) What textual features are most suitable for different visual text processing tasks? (2) How can these distinctive text features be effectively incorporated into processing frameworks? Furthermore, we introduce VTPBench, a new benchmark that encompasses a broad range of visual text processing datasets. Leveraging the advanced visual quality assessment capabilities of multimodal large language models (MLLMs), we propose VTPScore, a novel evaluation metric designed to ensure fair and reliable evaluation. Our empirical study with more than 20 specific models reveals substantial room for improvement in the current techniques. Our aim is to establish this work as a fundamental resource that fosters future exploration and innovation in the dynamic field of visual text processing. The relevant repository is available at https://github.com/shuyansy/Visual-Text-Processing-survey.
From Precision to Perception: User-Centred Evaluation of Keyword Extraction Algorithms for Internet-Scale Contextual Advertising
2025-04-30
Jingwen Cai, Sara Leckner, Johanna Björklund
Keyword extraction is a foundational task in natural language processing, underpinning countless real-world applications. A salient example is contextual advertising, where keywords help predict the topical congruence between ads and their surrounding media contexts to enhance advertising effectiveness. Recent advances in artificial intelligence, particularly large language models, have improved keyword extraction capabilities but also introduced concerns about computational cost. Moreover, although the end-user experience is of vital importance, human evaluation of keyword extraction performances remains under-explored. This study provides a comparative evaluation of three prevalent keyword extraction algorithms that vary in complexity: TF-IDF, KeyBERT, and Llama 2. To evaluate their effectiveness, a mixed-methods approach is employed, combining quantitative benchmarking with qualitative assessments from 552 participants through three survey-based experiments. Findings indicate a slight user preference for KeyBERT, which offers a favourable balance between performance and computational efficiency compared to the other two algorithms. Despite a strong overall preference for gold-standard keywords, differences between the algorithmic outputs are not statistically significant, highlighting a long-overlooked gap between traditional precision-focused metrics and user-perceived algorithm efficiency. The study highlights the importance of user-centred evaluation methodologies and proposes analytical tools to support their implementation.
A mini-review on the status of global analyses of nuclear parton distribution functions is given, focusing on the most relevant constraints for the hard-probes phenomenology in ultra-relativistic heavy-ion collisions.
2025-04-30
Stefano Maurelli, Ruggero Noris, Marcelo Oyarzo, Mario Trigiante
We review a general paradigm for constructing U-fold backgrounds in (dimensionally reduced) Type IIB superstring theory, of the form ${\rm AdS}_{d-1}\times S^1\times S^d$, with a monodromy along $S^1$ in the string-duality group. We also consider a special instance with $d=3$ in Type IIB superstring theory, discuss its ten-dimensional uplift and assess its supersymmetry.
Generative Artificial Intelligence (GenAI) is rapidly reshaping the global financial landscape, offering unprecedented opportunities to enhance customer engagement, automate complex workflows, and extract actionable insights from vast financial data. This survey provides an overview of GenAI adoption across the financial ecosystem, examining how banks, insurers, asset managers, and fintech startups worldwide are integrating large language models and other generative tools into their operations. From AI-powered virtual assistants and personalized financial advisory to fraud detection and compliance automation, GenAI is driving innovation across functions. However, this transformation comes with significant cybersecurity and ethical risks. We discuss emerging threats such as AI-generated phishing, deepfake-enabled fraud, and adversarial attacks on AI systems, as well as concerns around bias, opacity, and data misuse. The evolving global regulatory landscape is explored in depth, including initiatives by major financial regulators and international efforts to develop risk-based AI governance. Finally, we propose best practices for secure and responsible adoption - including explainability techniques, adversarial testing, auditability, and human oversight. Drawing from academic literature, industry case studies, and policy frameworks, this chapter offers a perspective on how the financial sector can harness GenAI's transformative potential while navigating the complex risks it introduces.
eNCApsulate: NCA for Precision Diagnosis on Capsule Endoscopes
2025-04-30
Henry John Krumb, Anirban Mukhopadhyay
Wireless Capsule Endoscopy is a non-invasive imaging method for the entire gastrointestinal tract, and is a pain-free alternative to traditional endoscopy. It generates extensive video data that requires significant review time, and localizing the capsule after ingestion is a challenge. Techniques like bleeding detection and depth estimation can help with localization of pathologies, but deep learning models are typically too large to run directly on the capsule. Neural Cellular Automata (NCA) for bleeding segmentation and depth estimation are trained on capsule endoscopic images. For monocular depth estimation, we distill a large foundation model into the lean NCA architecture, by treating the outputs of the foundation model as pseudo ground truth. We then port the trained NCA to the ESP32 microcontroller, enabling efficient image processing on hardware as small as a camera capsule. NCA are more accurate (Dice) than other portable segmentation models, while requiring more than 100x fewer parameters stored in memory than other small-scale models. The visual results of NCA depth estimation look convincing, and in some cases beat the realism and detail of the pseudo ground truth. Runtime optimizations on the ESP32-S3 accelerate the average inference speed significantly, by more than factor 3. With several algorithmic adjustments and distillation, it is possible to eNCApsulate NCA models into microcontrollers that fit into wireless capsule endoscopes. This is the first work that enables reliable bleeding segmentation and depth estimation on a miniaturized device, paving the way for precise diagnosis combined with visual odometry as a means of precise localization of the capsule -- on the capsule.
Systematic Review of Smart Factories Production in Industry 5.0
2025-04-30
Ali Bakhshi Movahed, Hamed Nozari, Aminmasoud Bakhshi Movahed
Technology plays an undeniable role in today's industrial world, especially in manufacturing and smart factories. Unlike previous industrial revolutions, humans are at the core of the fifth generation of the Industrial Revolution. One of the critical aspects of Industry 5.0 (I 5.0) is its emphasis on human-centricity. The integration of modern technologies can be clearly observed in smart factories, which offer enhanced comfort and professionalism. This study highlights the significance of I 5.0 and smart factory production (SFP). A total of 36 articles are reviewed and systematically categorized using the meta-synthesis methodology. The research emphasizes the influence of I 5.0 on SFP through the use of modern technologies and comprehensive policy frameworks. This new paradigm has the potential to streamline people's lives and bring a transformative shift to smart factory production lines. Enhancing the structure of factories appears feasible under this optimistic perspective.
A Bayesian approach to sharing information on sensitivity of a Multi-Cancer Early Detection test across and within tumour types and stages
2025-04-30
Sofia Dias, Yiwen Liu, Stephen Palmer, Marta O Soares
The Galleri (R) (GRAIL) multi-cancer early detection test measures circulating tumour DNA (ctDNA) to predict the presence of more than 50 different cancers, from a blood test. If sensitivity of the test to detect early-stage cancers is high, using it as part of a screening programme may lead to better cancer outcomes, but available evidence indicates there is heterogeneity in sensitivity between cancer types and stages. We describe a framework for sharing evidence on test sensitivity between cancer types and/or stages, examining whether models with different sharing assumptions are supported by the evidence and considering how further data could be used to strengthen inference. Bayesian hierarchical models were fitted, and the impact of information sharing in increasing precision of the estimates of test sensitivity for different cancer types and stages was examined. Assumptions on sharing were informed by evidence from a review of the literature on the determinants of ctDNA shedding and its detection in a blood test. Support was strongest for the assumption that sensitivity can be shared only across stage 4 for all cancer types. There was also support for the assumption that sensitivities can be shared across cancer types for each stage, if cancer types expected to have low sensitivity are excluded which increased precision of early-stage cancer sensitivity estimates and was considered the most appropriate model. High heterogeneity limited improvements in precision. For future research, elicitation of expert opinion could inform more realistic sharing assumptions.
Enhancing Cosmological Constraints by Two-dimensional $β$-cosmic-web Weighted Angular Correlation Functions
2025-04-30
Fenfen Yin, Liang Xiao, Wenying Du, Zhujun Jiang, Zhiwei Min, Jaime Forero-Romero, Jiacheng Ding, Le Zhang, Xiao-Dong Li
We investigate the potential of $\beta$-cosmic-web weighted angular correlation functions to improve the cosmological constraints. Using SDSS DR12 CMASS-NGC galaxies and simulated mock catalogs with $\Omega_m$ varying in the range of 0.25-0.40, we quantify the discriminative power of different statistics via $\Delta \chi^2$ measurements. Our results demonstrate significant improvements when incorporating weighted statistics. Especially, adding the $\bar{D}_{\rm nei}$-weighting statistics enhances $\Delta \chi^2$ by 39%-130%, while adding the $1/\bar{D}_{\rm nei}$-weighted statistics yields 229%-336% gains over solely using the traditional angular statistics. These findings align with 3D correlation function studies \cite{Yin+etal+2024}, confirming the superior performance of $\beta$-cosmic-web weighted statistics. The thin-redshift-slice approach makes our method particularly relevant for slitless surveys (such as Euclid, CSST) where redshift errors challenge traditional 3D analyses. This work also establishes the first theoretical framework for marked statistics in 2D angular clustering.
We review recent progress in constructing type IIB AdS$_{3}$ flux vacua that exhibit parametrically-controlled scale separation and come along with integer-valued conformal dimensions for the would-be dual CFT$_{2}$ operators. We comment on the anisotropic nature of the associated internal spaces, as well as on the existence of polynomial shift symmetries underlying the mass spectra. We conclude by outlining open questions and potential directions for future research, partially inspired by the Swampland program.
Visual Analytics Challenges and Trends in the Age of AI: The BigVis Community Perspective
2025-04-30
Nikos Bikakis, Panos K. Chrysanthis, Guoliang Li, George Papastefanatos, Lingyun Yu
This report provides insights into the challenges, emerging topics, and opportunities related to human-data interaction and visual analytics in the AI era. The BigVis 2024 organizing committee conducted a survey among experts in the field. They invite the Program Committee members and the authors of accepted papers to share their views. Thirty-two scientists from diverse research communities, including Databases, Information Visualization, and Human-Computer Interaction, participated in the study. These scientists, representing both industry and academia, provided valuable insights into the current and future landscape of the field. In this report, we analyze the survey responses and compare them to the findings of a similar study conducted four years ago. The results reveal some interesting insights. First, many of the critical challenges identified in the previous survey remain highly relevant today, despite being unrelated to AI. Meanwhile, the field's landscape has significantly evolved, with most of today's vital challenges not even being mentioned in the earlier survey, underscoring the profound impact of AI-related advancements. By summarizing the perspectives of the research community, this report aims to shed light on the key challenges, emerging trends, and potential research directions in human-data interaction and visual analytics in the AI era.
Monolayer C$_{60}$ networks: A first-principles perspective
2025-04-30
Bo Peng, Michele Pizzochero
Monolayer fullerene (C$_{60}$) networks combine molecular-level rigidity with crystalline connectivity, offering a promising platform for numerous applications. In this Feature article, we review the physical and chemical properties of fullerene monolayers, focusing on first-principles studies. We first explore the structural stability of monolayer phases and investigate their thermal expansion behaviours. We then outline criteria for photocatalytic water splitting and introduce theoretical predictions which are supported by recent experimental verification. Finally, we show how interlayer stacking, molecular size, and dimensional tuning (from 2D monolayers into 3D crystals, 1D chains, or nanoribbons) offer versatile approaches to modulate their chemical functionality. Together, these insights establish fullerene networks as a novel class of carbon-based materials with tailored properties for catalysis, photovoltaics, and flexible electronics.
A Comprehensive Survey of Electrical Stimulation Haptic Feedback in Human-Computer Interaction
2025-04-30
Simin Yang, Xian Wang, Yang Li, Lik-Hang Lee, Tristan Camille, Pan Hui
Haptic perception and feedback play a pivotal role in interactive experiences, forming an essential component of human-computer interaction (HCI). In recent years, the field of haptic interaction has witnessed significant advancements, particularly in the area of electrical haptic feedback, driving innovation across various domains. To gain a comprehensive understanding of the current state of research and the latest developments in electrical haptic interaction, this study systematically reviews the literature in this area. Our investigation covers key aspects including haptic devices, haptic perception mechanisms, the comparison and integration of electrical haptic feedback with other feedback modalities, and their diverse applications. Specifically, we conduct a systematic analysis of 110 research papers to explore the forefront of electrical haptic feedback, providing insights into its latest trends, challenges, and future directions.
Collisions with tidal disruption event disks: implications for quasi-periodic X-ray eruptions
2025-04-30
Andrew Mummery
A popular class of models for interpreting quasi-periodic X-ray eruptions from galactic nuclei (QPEs) invoke collisions between an object on an extreme mass ratio inspiral (EMRI) and an accretion disk around a supermassive black hole. There are strong links between QPE systems and those disks which formed following a tidal disruption event (TDE), and at least two events (AT2019qiz and AT2022upj) are known to have occurred following an otherwise typical TDE. We show that the fact that these disks were formed following a TDE strongly constrains their properties, more so than previous models have assumed. Models based on steady-state AGN-like disks have mass contents which grow strongly with size $M_{\rm disk}\propto R_{\rm out}^{7/2}$ and do not conserve the mass or angular momentum of the disrupted star. A very different scaling must be satisfied by a TDE disk in order to conserve the disrupted stars angular momentum, $M_{\rm disk} \propto R_{\rm out}^{-1/2}$. These constraints substantially change the predicted scaling relationships between QPE observables (luminosity, duration, energy, temperature) and the QPE period. They also allow QPE observables to be written in terms of the properties of the two stars assumed to be involved (the one tidally disrupted and the one on an EMRI), making plausibility tests of these models possible. We show that these modifications to the disk structure imply that (i) QPEs cannot be powered by collisions between an orbiting black hole and a TDE disk, (ii) QPEs also cannot be powered by collisions between the surface of a stellar EMRI and a TDE disk. A framework in which the collisions are between a TDE disk and a star which has puffed up to fill its Hills sphere with a trailing debris stream (as seen in recent simulations) cannot be ruled out from the data, and should be the focus of further study.
Video compression is a fundamental topic in the visual intelligence, bridging visual signal sensing/capturing and high-level visual analytics. The broad success of artificial intelligence (AI) technology has enriched the horizon of video compression into novel paradigms by leveraging end-to-end optimized neural models. In this survey, we first provide a comprehensive and systematic overview of recent literature on end-to-end optimized learned video coding, covering the spectrum of pioneering efforts in both uni-directional and bi-directional prediction based compression model designation. We further delve into the optimization techniques employed in learned video compression (LVC), emphasizing their technical innovations, advantages. Some standardization progress is also reported. Furthermore, we investigate the system design and hardware implementation challenges of the LVC inclusively. Finally, we present the extensive simulation results to demonstrate the superior compression performance of LVC models, addressing the question that why learned codecs and AI-based video technology would have with broad impact on future visual intelligence research.
On the Encapsulation of Medical Imaging AI Algorithms
2025-04-30
Hans Meine, Yongli Mou, Guido Prause, Horst Hahn
In the context of collaborative AI research and development projects, it would be ideal to have self-contained encapsulated algorithms that can be easily shared between different parties, executed and validated on data at different sites, or trained in a federated manner. In practice, all of this is possible but greatly complicated, because human supervision and expert knowledge is needed to set up the execution of algorithms based on their documentation, possibly implicit assumptions, and knowledge about the execution environment and data involved. We derive and formulate a range of detailed requirements from the above goal and from specific use cases, focusing on medical imaging AI algorithms. Furthermore, we refer to a number of existing APIs and implementations and review which aspects each of them addresses, which problems are still open, and which public standards and ontologies may be relevant. Our contribution is a comprehensive collection of aspects that have not yet been addressed in their entirety by any single solution. Working towards the formulated goals should lead to more sustainable algorithm ecosystems and relates to the FAIR principles for research data, where this paper focuses on interoperability and (re)usability of medical imaging AI algorithms.
Fairness in Graph Learning Augmented with Machine Learning: A Survey
2025-04-30
Renqiang Luo, Ziqi Xu, Xikun Zhang, Qing Qing, Huafei Huang, Enyan Dai, Zhe Wang, Bo Yang
Augmenting specialised machine learning techniques into traditional graph learning models has achieved notable success across various domains, including federated graph learning, dynamic graph learning, and graph transformers. However, the intricate mechanisms of these specialised techniques introduce significant challenges in maintaining model fairness, potentially resulting in discriminatory outcomes in high-stakes applications such as recommendation systems, disaster response, criminal justice, and loan approval. This paper systematically examines the unique fairness challenges posed by Graph Learning augmented with Machine Learning (GL-ML). It highlights the complex interplay between graph learning mechanisms and machine learning techniques, emphasising how the augmentation of machine learning both enhances and complicates fairness. Additionally, we explore four critical techniques frequently employed to improve fairness in GL-ML methods. By thoroughly investigating the root causes and broader implications of fairness challenges in this rapidly evolving field, this work establishes a robust foundation for future research and innovation in GL-ML fairness.
We present a study of the third star orbiting around known contact eclipsing binary J04+25 using spectra from the LAMOST medium-resolution survey (MRS) and publicly available photometry. This is a rare case of a hierarchical triple, where the third star is significantly brighter than the inner contact subsystem. We successfully extracted radial velocities for all three components, using the binary spectral model in two steps. Third star radial velocities have high precision and allow direct fitting of the orbit. The low precision of radial velocity measurements in the contact system is compensated by large number statistics. We employed a template matching technique for light curves to find periodic variation due to the light time travel effect (LTTE) using several photometric datasets. Joint fit of third star radial velocities and LTTE allowed us to get a consistent orbital solution with $P_3=941.40\pm0.03$ day and $e_3=0.059\pm0.007$. We made estimations of the masses $M_{\rm 12,~3}\sin^3{i_3}=1.05\pm0.02,~0.90\pm0.02~M_\odot$ in a wide system and discussed possible determination of an astrometric orbit in the future data release of Gaia. Additionally, we propose an empirical method for measuring a period and minimal mass of contact systems, based on variation of the projected rotational velocity ($V\sin{i}$) from the spectra.
The integration of reinforcement learning (RL) into the reasoning capabilities of Multimodal Large Language Models (MLLMs) has rapidly emerged as a transformative research direction. While MLLMs significantly extend Large Language Models (LLMs) to handle diverse modalities such as vision, audio, and video, enabling robust reasoning across multimodal inputs remains a major challenge. This survey systematically reviews recent advances in RL-based reasoning for MLLMs, covering key algorithmic designs, reward mechanism innovations, and practical applications. We highlight two main RL paradigms--value-free and value-based methods--and analyze how RL enhances reasoning abilities by optimizing reasoning trajectories and aligning multimodal information. Furthermore, we provide an extensive overview of benchmark datasets, evaluation protocols, and existing limitations, and propose future research directions to address current bottlenecks such as sparse rewards, inefficient cross-modal reasoning, and real-world deployment constraints. Our goal is to offer a comprehensive and structured guide to researchers interested in advancing RL-based reasoning in the multimodal era.
A new candidate of a cluster of galaxies behind the Galactic plane, AX J145732-5901
2025-04-30
Shigeo Yamauchi, Masaru Ueno
AX J145732-5901 is an unidentified X-ray source discovered in the ASCA Galactic plane survey. Its extended nature and heavily absorbed X-ray spectrum suggest that AX J145732-5901 is a cluster of galaxies behind the Galactic plane. However, due to limited photon statistics, the spectral shape was not well examined. Using the results of the Galactic ridge X-ray emission and Cosmic X-ray background studies based on the Suzaku observations, we reanalyzed the ASCA data of AX J145732-5901. We confirmed that the source is more extended than the point spread function and the angular size is 14'x10'. The spectrum was heavily absorbed by interstellar matter equivalent to an N_{H} of ~10^{23} cm^{-2} and the emission line feature was confirmed. The spectrum was represented by a thin thermal plasma model with a temperature of 2.6 keV and a redshift of 0.12. Assuming the redshift value, the X-ray luminosity is calculated to be 2.6x10^{44} erg s^{-1} in the 1-10 keV energy band. The observational results indicate that AX J145732-5901 is a cluster of galaxies behind the Galactic plane.
"Oh FUors where art thou": A search for long-lasting YSO outbursts hiding in infrared surveys
2025-04-30
Carlos Contreras Peña, Jeong-Eun Lee, Ho-Gyu Lee, Gregory Herczeg, Doug Johnstone, Hanpu Liu, Phillip W. Lucas, Zhen Guo, Michael A. Kuhn, Leigh C. Smith, Mizna Ashraf, Jessy Jose, Sung-Yong Yoon, Sung-Chul Yoon
Long-lasting episodes of high accretion can strongly impact stellar and planetary formation. However, the universality of these events during the formation of young stellar objects (YSOs) is still under debate. Accurate statistics of strong outbursts (FUors), are necessary to understand the role of episodic accretion bursts. In this work, we search for a population of FUors that may have gone undetected in the past because they either a) went into outburst before the start of modern monitoring surveys and are now slowly fading back into quiescence or b) are slow-rising outbursts that would not commonly be classified as candidate FUors. We hypothesise that the light curves of these outbursts should be well fitted by linear models with negative (declining) or positive (rising) slopes. The analysis of the infrared light curves and photometry of $\sim$99000 YSO candidates from SPICY yields 717 candidate FUors. Infrared spectroscopy of 20 candidates, from both the literature and obtained by our group, confirms that 18 YSOs are going through long-term outbursts and identifies two evolved sources as contaminants. The number of candidate FUors combined with previously measured values of the frequency of FUor outbursts, yield average outburst decay times that are 2.5 times longer than the rise times. In addition, a population of outbursts with rise timescales between 2000 and 5000 days must exist to obtain our observed number of YSOs with positive slopes. Finally, we estimate a mean burst lifetime of between 45 and 100 years.
Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review
2025-04-30
Suk Ki Lee, Hyunwoong Ko
Dynamic manufacturing processes exhibit complex characteristics defined by time-varying parameters, nonlinear behaviors, and uncertainties. These characteristics require sophisticated in-situ monitoring techniques utilizing multimodal sensor data and adaptive control systems that can respond to real-time feedback while maintaining product quality. Recently, generative machine learning (ML) has emerged as a powerful tool for modeling complex distributions and generating synthetic data while handling these manufacturing uncertainties. However, adopting these generative technologies in dynamic manufacturing systems lacks a functional control-oriented perspective to translate their probabilistic understanding into actionable process controls while respecting constraints. This review presents a functional classification of Prediction-Based, Direct Policy, Quality Inference, and Knowledge-Integrated approaches, offering a perspective for understanding existing ML-enhanced control systems and incorporating generative ML. The analysis of generative ML architectures within this framework demonstrates control-relevant properties and potential to extend current ML-enhanced approaches where conventional methods prove insufficient. We show generative ML's potential for manufacturing control through decision-making applications, process guidance, simulation, and digital twins, while identifying critical research gaps: separation between generation and control functions, insufficient physical understanding of manufacturing phenomena, and challenges adapting models from other domains. To address these challenges, we propose future research directions aimed at developing integrated frameworks that combine generative ML and control technologies to address the dynamic complexities of modern manufacturing systems.
These proceedings are based on the author's invited talk reviewing the original published work [1,2] of the author with collaborators. The subject matter is a new, covariant and efficient technology of constructing entire trajectories of physical string states deeper inside the string spectrum than the leading Regge. The key observation behind the technology is that the lowering operators of a symplectic algebra appear in the Virasoro constraints which impose physicality of states in the open bosonic string. This algebra commutes with the spacetime Lorentz algebra, (of the little group) of which all string states are irreducible representations. Employing then the so-called Howe duality of representation theory, one may relate the irreducible representations of the two algebras via a bijection. The spectrum thus splits into two parts: trajectories that are lowest weight states of the symplectic algebra and their infinitely many clones. The latter can then be reached by suitably dressing the former with the raising operators of the symplectic algebra. The technology is nontrivially extended to the open superstring, where the relevant Howe dual is an orthosymplectic algebra.
Path to a Single-Stage, 100-GeV Electron Beam via a Flying-Focus-Driven Laser-Plasma Accelerator
2025-04-30
J. L. Shaw, M. V. Ambat, K. G. Miller, R. Boni, I. LaBelle, W. B. Mori, J. J. Pigeon, A. Rigatti, I. Settle, L. Mack, J. P. Palastro, D. H. Froula
Dephasingless laser wakefield acceleration (DLWFA), a novel laser wakefield acceleration concept based on the recently demonstrated "flying focus" technology, offers a new paradigm in laser-plasma acceleration that could advance the progress toward a TeV linear accelerator using a single-stage system without guiding structures. The recently proposed NSF OPAL laser facility could be the transformative technology that enables this grand challenge in laser-plasma acceleration. We review the viable parameter space for DLWFA based on the scaling of its performance with laser and plasma parameters, and we compare that performance to traditional laser wakefield acceleration. These scalings indicate the necessity for ultrashort, high-energy laser architectures such as NSF OPAL to achieve groundbreaking electron energies using DLWFA. Initial results from MTW-OPAL, the platform for the 6-J DLWFA demonstration experiment, show a tight, round focal spot over a distance of 3.7 mm. New particle-in-cell simulations of that platform indicate that using hydrogen for DLWFA reduces the amount of laser light that is distorted due to refraction at ionization fronts. An experimental path, and the computational and technical design work along that path, from the current status of the field to a single-stage, 100-GeV electron beam via DLWFA on NSF OPAL is outlined. Progress along that path is presented.
We review compactifications of type IIB string theory which produce de Sitter vacua to leading order in the $\alpha^\prime$ and $g_s$ expansions in line with the scenario proposed by Kachru, Kallosh, Linde, and Trivedi. We detail specific Calabi-Yau orientifold compactifications incorporating the non-perturbative superpotential from Euclidean D3-branes, the full flux-induced superpotential, and the K\"ahler potential evaluated at string tree level but retaining all orders in $\alpha'$. Each model hosts a Klebanov-Strassler throat featuring a single anti-D3-brane. The energy associated with this supersymmetry-breaking source, computed at leading order in $\alpha'$, lifts the minimum to a metastable de Sitter vacuum with all moduli stabilised. A key open challenge is the identification of vacua that remain stable when including additional corrections; an endeavour for which this study provides a solid foundation. This work is a contribution to the proceedings of the Corfu Summer Institute 2024 "School and Workshops on Elementary Particle Physics and Gravity" (CORFU2024) and is based on arXiv:2406.13751.
When Deep Learning Meets Information Retrieval-based Bug Localization: A Survey
2025-04-30
Feifei Niu, Chuanyi Li, Kui Liu, Xin Xia, David Lo
Bug localization is a crucial aspect of software maintenance, running through the entire software lifecycle. Information retrieval-based bug localization (IRBL) identifies buggy code based on bug reports, expediting the bug resolution process for developers. Recent years have witnessed significant achievements in IRBL, propelled by the widespread adoption of deep learning (DL). To provide a comprehensive overview of the current state of the art and delve into key issues, we conduct a survey encompassing 61 IRBL studies leveraging DL. We summarize best practices in each phase of the IRBL workflow, undertake a meta-analysis of prior studies, and suggest future research directions. This exploration aims to guide further advancements in the field, fostering a deeper understanding and refining practices for effective bug localization. Our study suggests that the integration of DL in IRBL enhances the model's capacity to extract semantic and syntactic information from both bug reports and source code, addressing issues such as lexical gaps, neglect of code structure information, and cold-start problems. Future research avenues for IRBL encompass exploring diversity in programming languages, adopting fine-grained granularity, and focusing on real-world applications. Most importantly, although some studies have started using large language models for IRBL, there is still a need for more in-depth exploration and thorough investigation in this area.
An $SU_2\times U_1$ scalar vector model with a scalar doublet $\varphi$ is reviewed for the study of possible magnetic monopole solution. An eigenvalue equation $\hat n^a \sigma^a \varphi_\pm =\pm \varphi_\pm$ is shown to induce a set of monopole solutions specified by the unit vector $\hat n$. It is shown clearly that monopole solution has to do with the eigenvalue equation and an unique covariant combination of the non-abelian gauge field. It is also shown that a new set of monopole solutions is presented as a generalization of the monopole solutions known as Cho-Maison monopole. We also show that the EM field and field tensor defined by CM solution is effectively the same as the effective covariant field tensor introduced in this paper. Possible implication is also discussed in the literature.
Evaluating the AI-Lab Intervention: Impact on Student Perception and Use of Generative AI in Early Undergraduate Computer Science Courses
2025-04-30
Ethan Dickey, Andres Bejarano, Rhianna Kuperus, Bárbara Fagundes
Generative AI (GenAI) is rapidly entering computer science education, yet its effects on student learning, skill development, and perceptions remain underexplored. Concerns about overreliance coexist with a gap in research on structured scaffolding to guide tool use in formal courses. This study examines the impact of a dedicated "AI-Lab" intervention -- emphasizing guided scaffolding and mindful engagement -- on undergraduate students in Data Structures and Algorithms, Competitive Programming, and first-year engineering courses at Purdue University. Over three semesters, we integrated AI-Lab modules into four mandatory and elective courses, yielding 831 matched pre- and post-intervention survey responses, alongside focus group discussions. Employing a mixed-methods approach, we analyzed quantitative shifts in usage patterns and attitudes as well as qualitative narratives of student experiences. While the overall frequency of GenAI usage for homework or programming projects remained largely stable, we observed large effect sizes in comfort and openness across conceptual, debugging, and homework problems. Notably, usage patterns for debugging also shifted statistically significantly, reflecting students' more mindful and deliberate approach. Focus group discussions corroborated these results, suggesting that the intervention "bridged the gap" between naive GenAI usage and more nuanced, reflective integration of AI tools into coursework, ultimately heightening students' awareness of their own skill development. These findings suggest that structured, scaffolded interventions can enable students to harness GenAI's benefits without undermining essential competencies. We offer evidence-based recommendations for educators seeking to integrate GenAI responsibly into computing curricula and identify avenues for future research on GenAI-supported pedagogy.
Chromatic Effects on the PSF and Shear Measurement for the Roman Space Telescope High-Latitude Wide Area Survey
2025-04-30
Federico Berlfein, Rachel Mandelbaum, Xiangchong Li, Tianqing Zhang, Scott Dodelson, Katarina Markovic
Weak gravitational lensing (WL) is a key cosmological probe that requires precise measurement of galaxy images to infer shape distortions, or shear, and constrain cosmology. Accurate estimation of the Point Spread Function (PSF) is crucial for shear measurement, but the wavelength dependence of the PSF introduces chromatic biases that can systematically impact shear inference. We focus on biases arising from spectral energy distribution (SED) differences between stars, used for PSF modeling, and galaxies, used for shear measurement. We investigate these effects in $\textit{Roman's}$ four design reference mission WL bands (Y106, J129, H158, F184) and wide filter (W146). Using $\textit{Roman}$-like image simulations, we quantify the induced shear biases and compare them to requirements on those biases. Multiplicative biases over all galaxies hover around $\sim$0.2% in the WL bands and 2% in the wide filter, exceeding the mission requirement of $|m| < 0.032\%$ and relaxed requirement of $|m| < 0.1\%$. In individual redshift bins, biases can reach 0.4$\unicode{x2013}$0.9% for the WL bands and 3$\unicode{x2013}$6% for the wide filter. Additive biases remain acceptable in the WL bands but exceed systematic limits in the wide filter. We develop and test PSF-level corrections, showing that a first-order correction reduces biases within survey requirements for the WL bands; however, higher-order terms are necessary for the wide filter. Our results highlight the necessity of chromatic corrections for precision WL with $\textit{Roman}$ and provide a framework for mitigating these biases. Finally, we compare analytical color-based corrections to self-organizing maps (SOMs) and find that both methods effectively reduce biases.
Reconstructing the Free-floating Planet Mass Function with the Nancy Grace Roman Space Telescope
2025-04-30
William DeRocco, Matthew T. Penny, Samson A. Johnson, Peter McGill
Free-floating planets comprise one of the most enigmatic populations of exoplanets in the Galaxy. Though ground-based observations point to a large abundance of these worlds, little is known about their origins and demographics. In the coming years, the Nancy Grace Roman Space Telescope's Galactic Bulge Time Domain Survey is expected to detect several hundred free-floating planets, providing the first opportunity to characterize these worlds at the population level. We present a first study of Roman's prospects for reconstructing the mass distribution of free-floating planets through population-level statistical inference. We find that depending on the true underlying mass distribution of free-floating planets in the Galaxy, Roman will be able to improve upon existing estimates of the abundance by orders of magnitudes in the largely unexplored mass range below that of Earth. When applied to Roman's observations, the methodology we present herein will be capable of discriminating between different hypothesized mass distributions at high statistical significance, opening a new window into the origins of these rogue worlds.
As large language models (LLMs) are increasingly used in human-centered tasks, assessing their psychological traits is crucial for understanding their social impact and ensuring trustworthy AI alignment. While existing reviews have covered some aspects of related research, several important areas have not been systematically discussed, including detailed discussions of diverse psychological tests, LLM-specific psychological datasets, and the applications of LLMs with psychological traits. To address this gap, we systematically review six key dimensions of applying psychological theories to LLMs: (1) assessment tools; (2) LLM-specific datasets; (3) evaluation metrics (consistency and stability); (4) empirical findings; (5) personality simulation methods; and (6) LLM-based behavior simulation. Our analysis highlights both the strengths and limitations of current methods. While some LLMs exhibit reproducible personality patterns under specific prompting schemes, significant variability remains across tasks and settings. Recognizing methodological challenges such as mismatches between psychological tools and LLMs' capabilities, as well as inconsistencies in evaluation practices, this study aims to propose future directions for developing more interpretable, robust, and generalizable psychological assessment frameworks for LLMs.
Wireless Communication as an Information Sensor for Multi-agent Cooperative Perception: A Survey
2025-04-30
Zhiying Song, Tenghui Xie, Fuxi Wen, Jun Li
Cooperative perception extends the perception capabilities of autonomous vehicles by enabling multi-agent information sharing via Vehicle-to-Everything (V2X) communication. Unlike traditional onboard sensors, V2X acts as a dynamic "information sensor" characterized by limited communication, heterogeneity, mobility, and scalability. This survey provides a comprehensive review of recent advancements from the perspective of information-centric cooperative perception, focusing on three key dimensions: information representation, information fusion, and large-scale deployment. We categorize information representation into data-level, feature-level, and object-level schemes, and highlight emerging methods for reducing data volume and compressing messages under communication constraints. In information fusion, we explore techniques under both ideal and non-ideal conditions, including those addressing heterogeneity, localization errors, latency, and packet loss. Finally, we summarize system-level approaches to support scalability in dense traffic scenarios. Compared with existing surveys, this paper introduces a new perspective by treating V2X communication as an information sensor and emphasizing the challenges of deploying cooperative perception in real-world intelligent transportation systems.
A Survey on 3D Reconstruction Techniques in Plant Phenotyping: From Classical Methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and Beyond
Plant phenotyping plays a pivotal role in understanding plant traits and their interactions with the environment, making it crucial for advancing precision agriculture and crop improvement. 3D reconstruction technologies have emerged as powerful tools for capturing detailed plant morphology and structure, offering significant potential for accurate and automated phenotyping. This paper provides a comprehensive review of the 3D reconstruction techniques for plant phenotyping, covering classical reconstruction methods, emerging Neural Radiance Fields (NeRF), and the novel 3D Gaussian Splatting (3DGS) approach. Classical methods, which often rely on high-resolution sensors, are widely adopted due to their simplicity and flexibility in representing plant structures. However, they face challenges such as data density, noise, and scalability. NeRF, a recent advancement, enables high-quality, photorealistic 3D reconstructions from sparse viewpoints, but its computational cost and applicability in outdoor environments remain areas of active research. The emerging 3DGS technique introduces a new paradigm in reconstructing plant structures by representing geometry through Gaussian primitives, offering potential benefits in both efficiency and scalability. We review the methodologies, applications, and performance of these approaches in plant phenotyping and discuss their respective strengths, limitations, and future prospects (https://github.com/JiajiaLi04/3D-Reconstruction-Plants). Through this review, we aim to provide insights into how these diverse 3D reconstruction techniques can be effectively leveraged for automated and high-throughput plant phenotyping, contributing to the next generation of agricultural technology.
Characterizing CMB noise anisotropies from CMB delensing
2025-04-29
Louis Legrand, Julien Carron
Un-doing the effect of gravitational lensing on the Cosmic Microwave Background ('de-lensing') is essential in shaping constraints on weak signals limited by lensing effects on the CMB, for example on a background of primordial gravitational waves. Removing these anisotropies induced by large-scale structures from the CMB maps also generally helps our view of the primordial Universe, by sharpening the acoustic peaks and the damping tail. However, delensing does transfer parts of these anisotropies to the noise maps. This will induce a new large scale 'mean-field' bias to any anisotropy estimator applied to the delensed CMB, and this bias directly traces large-scale structures. This paper analytically quantifies this delensed noise mean-field and its impact on quadratic (QE) and likelihood-based lensing estimators. We show that for Simons-Observatory-like surveys, this mean-field bias can reach 15% in cross-correlation with large-scale structures if unaccounted for. We further demonstrate that this delensed noise mean-field can be safely neglected in likelihood-based estimators without compromising the quality of lensing reconstruction or $B$-mode delensing, provided the resulting lensing map is properly renormalized.
Modeling AI-Human Collaboration as a Multi-Agent Adaptation
2025-04-29
Prothit Sen, Sai Mihir Jakkaraju
We develop an agent-based simulation to formalize AI-human collaboration as a function of task structure, advancing a generalizable framework for strategic decision-making in organizations. Distinguishing between heuristic-based human adaptation and rule-based AI search, we model interactions across modular (parallel) and sequenced (interdependent) tasks using an NK model. Our results reveal that in modular tasks, AI often substitutes for humans - delivering higher payoffs unless human expertise is very high, and the AI search space is either narrowly focused or extremely broad. In sequenced tasks, interesting complementarities emerge. When an expert human initiates the search and AI subsequently refines it, aggregate performance is maximized. Conversely, when AI leads, excessive heuristic refinement by the human can reduce payoffs. We also show that even "hallucinatory" AI - lacking memory or structure - can improve outcomes when augmenting low-capability humans by helping escape local optima. These results yield a robust implication: the effectiveness of AI-human collaboration depends less on context or industry, and more on the underlying task structure. By elevating task decomposition as the central unit of analysis, our model provides a transferable lens for strategic decision-making involving humans and an agentic AI across diverse organizational settings.
I review the construction of actions for extended geometry from the grading of an underlying tensor hierarchy algebra, which provides the full set of Batalin-Vilkovisky fields. The dynamics is neatly encoded in a complex. This talk, presented at the Corfu Summer Institute 2024, is mainly based on joint work with J. Palmkvist, in particular ref. [1].
Quantifying the Noise of Structural Perturbations on Graph Adversarial Attacks
2025-04-29
Junyuan Fang, Han Yang, Haixian Wen, Jiajing Wu, Zibin Zheng, Chi K. Tse
Graph neural networks have been widely utilized to solve graph-related tasks because of their strong learning power in utilizing the local information of neighbors. However, recent studies on graph adversarial attacks have proven that current graph neural networks are not robust against malicious attacks. Yet much of the existing work has focused on the optimization objective based on attack performance to obtain (near) optimal perturbations, but paid less attention to the strength quantification of each perturbation such as the injection of a particular node/link, which makes the choice of perturbations a black-box model that lacks interpretability. In this work, we propose the concept of noise to quantify the attack strength of each adversarial link. Furthermore, we propose three attack strategies based on the defined noise and classification margins in terms of single and multiple steps optimization. Extensive experiments conducted on benchmark datasets against three representative graph neural networks demonstrate the effectiveness of the proposed attack strategies. Particularly, we also investigate the preferred patterns of effective adversarial perturbations by analyzing the corresponding properties of the selected perturbation nodes.
Mitigating the Structural Bias in Graph Adversarial Defenses
2025-04-29
Junyuan Fang, Huimin Liu, Han Yang, Jiajing Wu, Zibin Zheng, Chi K. Tse
In recent years, graph neural networks (GNNs) have shown great potential in addressing various graph structure-related downstream tasks. However, recent studies have found that current GNNs are susceptible to malicious adversarial attacks. Given the inevitable presence of adversarial attacks in the real world, a variety of defense methods have been proposed to counter these attacks and enhance the robustness of GNNs. Despite the commendable performance of these defense methods, we have observed that they tend to exhibit a structural bias in terms of their defense capability on nodes with low degree (i.e., tail nodes), which is similar to the structural bias of traditional GNNs on nodes with low degree in the clean graph. Therefore, in this work, we propose a defense strategy by including hetero-homo augmented graph construction, $k$NN augmented graph construction, and multi-view node-wise attention modules to mitigate the structural bias of GNNs against adversarial attacks. Notably, the hetero-homo augmented graph consists of removing heterophilic links (i.e., links connecting nodes with dissimilar features) globally and adding homophilic links (i.e., links connecting nodes with similar features) for nodes with low degree. To further enhance the defense capability, an attention mechanism is adopted to adaptively combine the representations from the above two kinds of graph views. We conduct extensive experiments to demonstrate the defense and debiasing effect of the proposed strategy on benchmark datasets.
Secure Coding with AI, From Creation to Inspection
2025-04-29
Vladislav Belozerov, Peter J Barclay, Ashkan Sami
While prior studies have explored security in code generated by ChatGPT and other Large Language Models, they were conducted in controlled experimental settings and did not use code generated or provided from actual developer interactions. This paper not only examines the security of code generated by ChatGPT based on real developer interactions, curated in the DevGPT dataset, but also assesses ChatGPT's capability to find and fix these vulnerabilities. We analysed 1,586 C, C++, and C# code snippets using static scanners, which detected potential issues in 124 files. After manual analysis, we selected 26 files with 32 confirmed vulnerabilities for further investigation. We submitted these files to ChatGPT via the OpenAI API, asking it to detect security issues, identify the corresponding Common Weakness Enumeration numbers, and propose fixes. The responses and modified code were manually reviewed and re-scanned for vulnerabilities. ChatGPT successfully detected 18 out of 32 security issues and resolved 17 issues but failed to recognize or fix the remainder. Interestingly, only 10 vulnerabilities were resulted from the user prompts, while 22 were introduced by ChatGPT itself. We highlight for developers that code generated by ChatGPT is more likely to contain vulnerabilities compared to their own code. Furthermore, at times ChatGPT reports incorrect information with apparent confidence, which may mislead less experienced developers. Our findings confirm previous studies in demonstrating that ChatGPT is not sufficiently reliable for generating secure code nor identifying all vulnerabilities, highlighting the continuing importance of static scanners and manual review.
Hallucination by Code Generation LLMs: Taxonomy, Benchmarks, Mitigation, and Challenges
2025-04-29
Yunseo Lee, John Youngeun Song, Dongsun Kim, Jindae Kim, Mijung Kim, Jaechang Nam
Recent technical breakthroughs in large language models (LLMs) have enabled them to fluently generate source code. Software developers often leverage both general-purpose and code-specialized LLMs to revise existing code or even generate a whole function from scratch. These capabilities are also beneficial in no-code or low-code contexts, in which one can write programs without a technical background. However, due to their internal design, LLMs are prone to generating hallucinations, which are incorrect, nonsensical, and not justifiable information but difficult to identify its presence. This problem also occurs when generating source code. Once hallucinated code is produced, it is often challenging for users to identify and fix it, especially when such hallucinations can be identified under specific execution paths. As a result, the hallucinated code may remain unnoticed within the codebase. This survey investigates recent studies and techniques relevant to hallucinations generated by CodeLLMs. We categorize the types of hallucinations in the code generated by CodeLLMs, review existing benchmarks and mitigation strategies, and identify open challenges. Based on these findings, this survey outlines further research directions in the detection and removal of hallucinations produced by CodeLLMs.
RecGaze: The First Eye Tracking and User Interaction Dataset for Carousel Interfaces
2025-04-29
Santiago de Leon-Martinez, Jingwei Kang, Robert Moro, Maarten de Rijke, Branislav Kveton, Harrie Oosterhuis, Maria Bielikova
Carousel interfaces are widely used in e-commerce and streaming services, but little research has been devoted to them. Previous studies of interfaces for presenting search and recommendation results have focused on single ranked lists, but it appears their results cannot be extrapolated to carousels due to the added complexity. Eye tracking is a highly informative approach to understanding how users click, yet there are no eye tracking studies concerning carousels. There are very few interaction datasets on recommenders with carousel interfaces and none that contain gaze data. We introduce the RecGaze dataset: the first comprehensive feedback dataset on carousels that includes eye tracking results, clicks, cursor movements, and selection explanations. The dataset comprises of interactions from 3 movie selection tasks with 40 different carousel interfaces per user. In total, 87 users and 3,477 interactions are logged. In addition to the dataset, its description and possible use cases, we provide results of a survey on carousel design and the first analysis of gaze data on carousels, which reveals a golden triangle or F-pattern browsing behavior. Our work seeks to advance the field of carousel interfaces by providing the first dataset with eye tracking results on carousels. In this manner, we provide and encourage an empirical understanding of interactions with carousel interfaces, for building better recommender systems through gaze information, and also encourage the development of gaze-based recommenders.
Evaluating Effects of Augmented SELFIES for Molecular Understanding Using QK-LSTM
2025-04-29
Collin Beaudoin, Swaroop Ghosh
Identifying molecular properties, including side effects, is a critical yet time-consuming step in drug development. Failing to detect these side effects before regulatory submission can result in significant financial losses and production delays, and overlooking them during the regulatory review can lead to catastrophic consequences. This challenge presents an opportunity for innovative machine learning approaches, particularly hybrid quantum-classical models like the Quantum Kernel-Based Long Short-Term Memory (QK-LSTM) network. The QK-LSTM integrates quantum kernel functions into the classical LSTM framework, enabling the capture of complex, non-linear patterns in sequential data. By mapping input data into a high-dimensional quantum feature space, the QK-LSTM model reduces the need for large parameter sets, allowing for model compression without sacrificing accuracy in sequence-based tasks. Recent advancements have been made in the classical domain using augmented variations of the Simplified Molecular Line-Entry System (SMILES). However, to the best of our knowledge, no research has explored the impact of augmented SMILES in the quantum domain, nor the role of augmented Self-Referencing Embedded Strings (SELFIES) in either classical or hybrid quantum-classical settings. This study presents the first analysis of these approaches, providing novel insights into their potential for enhancing molecular property prediction and side effect identification. Results reveal that augmenting SELFIES yields in statistically significant improvements from SMILES by a 5.97% improvement for the classical domain and a 5.91% improvement for the hybrid quantum-classical domain.
2025-04-29
Nafiseh Jabbari Tofighi, Maxime Robic, Fabio Morbidi, Pascal Vasseur
The advent of event-based cameras, with their low latency, high dynamic range, and reduced power consumption, marked a significant change in robotic vision and machine perception. In particular, the combination of these neuromorphic sensors with widely-available passive or active optical markers (e.g. AprilTags, arrays of blinking LEDs), has recently opened up a wide field of possibilities. This survey paper provides a comprehensive review on Event-Based Optical Marker Systems (EBOMS). We analyze the basic principles and technologies on which these systems are based, with a special focus on their asynchronous operation and robustness against adverse lighting conditions. We also describe the most relevant applications of EBOMS, including object detection and tracking, pose estimation, and optical communication. The article concludes with a discussion of possible future research directions in this rapidly-emerging and multidisciplinary field.
Cosmology from LOFAR Two-metre Sky Survey Data Release 2: Cross-correlations with luminous red galaxies from eBOSS
2025-04-29
Jinglan Zheng, Prabhakar Tiwari, Gong-Bo Zhao, Dominik J. Schwarz, David Bacon, Stefano Camera, Caroline Heneka, Catherine Hale, Szymon J. Nakoneczny, Morteza Pashapour-Ahmadabadi
We cross-correlated galaxies from the LOw-Frequency ARray (LOFAR) Two-metre Sky Survey (LoTSS) second data release (DR2) radio source with the extended Baryon Oscillation Spectroscopic Survey (eBOSS) luminous red galaxy (LRG) sample to extract the baryon acoustic oscillation (BAO) signal and constrain the linear clustering bias of radio sources in LoTSS DR2. In the LoTSS DR2 catalogue, employing a flux density limit of $1.5$ mJy at the central LoTSS frequency of 144 MHz and a signal-to-noise ratio (S/N) of $7.5$, additionally considering eBOSS LRGs with redshifts between 0.6 and 1, we measured both the angular LoTSS-eBOSS cross-power spectrum and the angular eBOSS auto-power spectrum. These measurements were performed across various eBOSS redshift tomographic bins with a width of $\Delta z=0.06$. By marginalising over the broadband shape of the angular power spectra, we searched for a BAO signal in cross-correlation with radio galaxies, and determine the linear clustering bias of LoTSS radio sources for a constant-bias and an evolving-bias model. Using the cross-correlation, we measured the isotropic BAO dilation parameter as $\alpha=1.01\pm 0.11$ at $z_{\rm eff}=0.63$. By combining four redshift slices at $z_{\rm eff}=0.63, 0.69, 0.75$, and $0.81$, we determined a more constrained value of $\alpha = 0.968^{+0.060}_{-0.095}$. For the entire redshift range of $z_{\rm eff}=0.715$, we measured $b_C = 2.64 \pm 0.20$ for the constant-bias model, $b(z)=b_C$, and then $b_D = 1.80 \pm 0.13$ for the evolving-bias model, $b(z) = b_D / D(z)$, with $D(z)$ denoting the growth rate of linear structures. Additionally, we measured the clustering bias for individual redshift bins.
Cosmology from LOFAR Two-metre Sky Survey Data Release 2: Counts-in-Cells Statistics
2025-04-29
Morteza Pashapour-Ahmadabadi, Lukas Böhme, Thilo M. Siewert, Dominik J. Schwarz, Catherine L. Hale, Caroline Heneka, Prabhakar Tiwari, Jinglan Zheng
We investigate the statistical distribution of source counts-in-cells in the second data release of the LOFAR Two-Metre Sky Survey (LoTSS-DR2) and we test a computationally cheap method based on the counts-in-cells to estimate the two-point correlation function. We compare three stochastic models for the counts-in-cells which result in a Poisson distribution, a compound Poisson distribution, and a negative binomial distribution. By analysing the variance of counts-in-cells for various cell sizes, we fit the reduced normalised variance to a single power-law model representing the angular two-point correlation function. Our analysis confirms that radio sources are not Poisson distributed, which is most likely due to multiple physical components of radio sources. Employing instead a Cox process, we show that there is strong evidence in favour of the negative binomial distribution above a flux density threshold of 2 mJy. Additionally, the mean number of radio components derived from the negative binomial distribution is in good agreement with corresponding estimates based on the value-added catalogue of LoTSS-DR2. The scaling of the counts-in-cells normalised variance with cell size is in good agreement with a power-law model for the angular two-point correlation. At a flux density threshold of 2 mJy and a signal-to-noise ratio of 7.5 for individual radio sources, we find that for a range of angular scales large enough to not be affected by the multi-component nature of radio sources, the value of the exponent of the power law ranges from -0.8 to -1.05. This closely aligns with findings from previous optical, infrared, and radio surveys of the large scale structure. The scaling of the counts-in-cells statistics with cell size provides a computationally efficient method to estimate the two-point correlation properties, offering a valuable tool for future large-scale structure studies.
The VMC survey -- LIII. Data release #7. Complete survey data and data from additional programmes
2025-04-29
M. -R. L. Cioni, N. J. G. Cross, V. Ripepi, L. Girardi, R. de Grijs, G. Clementini, J. Th. van Loon, M. A. T. Groenewegen, V. D. Ivanov, J. M. Oliveira, M. Marconi, K. Bekki, R. P. Blake, M. J. Holliman, J. Irwin, M. J. Irwin, R. G. Mann, M. Read, E. Sutorius, J. E. M. Craig, F. Cusano, F. Dresbach, C. Maitra, R. Molinaro, F. Niederhofer, A. O. Omkumar, C. M. Pennock, T. Sicignano, S. Subramanian, B. L. Tatton, S. Vijayasree, S. Zaggia
The near-infrared YJKs Visual and Infrared Survey Telescope for Astronomy (VISTA) survey of the Magellanic Clouds (VMC) is complete and there are also data from additional programmes enhancing its quality over the original footprint. This work presents the final data release of the VMC survey, which includes additional observations and provides an overview of the scientific results. The overall data quality has been revised and reprocessed standard data products, that already appeared in previous data releases, are made available together with new data products. These include individual stellar proper motions, reddening towards red clump stars and source classifications. Several data products, such as the parameters of some variable stars and of background galaxies, from the VMC publications are associated to a data release for the first time. The data are processed using the VISTA Data Flow System and additional products, e.g. catalogues with point-spread function photometry or tables with stellar proper motions, are obtained with software developed by the survey team. This release supersedes all previous data releases of the VMC survey for the combined (deepstacked) data products, whilst providing additional (complementary) images and catalogues of single observations per filter. Overall, it includes about 64 million detections, split nearly evenly between sources with stellar or galaxy profiles. The VMC survey provides a homogeneous data set resulting from deep and multi-epoch YJKs-band imaging observations of the Large and Small Clouds, the Bridge and two fields in the Stream. The VMC data represent a valuable counterpart for sources detected at other wavelengths for both stars and background galaxies.
What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models
2025-04-29
Jan Kapar, Niklas Koenen, Martin Jullum
Evaluating synthetic tabular data is challenging, since they can differ from the real data in so many ways. There exist numerous metrics of synthetic data quality, ranging from statistical distances to predictive performance, often providing conflicting results. Moreover, they fail to explain or pinpoint the specific weaknesses in the synthetic data. To address this, we apply explainable AI (XAI) techniques to a binary detection classifier trained to distinguish real from synthetic data. While the classifier identifies distributional differences, XAI concepts such as feature importance and feature effects, analyzed through methods like permutation feature importance, partial dependence plots, Shapley values and counterfactual explanations, reveal why synthetic data are distinguishable, highlighting inconsistencies, unrealistic dependencies, or missing patterns. This interpretability increases transparency in synthetic data evaluation and provides deeper insights beyond conventional metrics, helping diagnose and improve synthetic data quality. We apply our approach to two tabular datasets and generative models, showing that it uncovers issues overlooked by standard evaluation techniques.
Are Information Retrieval Approaches Good at Harmonising Longitudinal Survey Questions in Social Science?
2025-04-29
Wing Yan Li, Zeqiang Wang, Jon Johnson, Suparna De
Automated detection of semantically equivalent questions in longitudinal social science surveys is crucial for long-term studies informing empirical research in the social, economic, and health sciences. Retrieving equivalent questions faces dual challenges: inconsistent representation of theoretical constructs (i.e. concept/sub-concept) across studies as well as between question and response options, and the evolution of vocabulary and structure in longitudinal text. To address these challenges, our multi-disciplinary collaboration of computer scientists and survey specialists presents a new information retrieval (IR) task of identifying concept (e.g. Housing, Job, etc.) equivalence across question and response options to harmonise longitudinal population studies. This paper investigates multiple unsupervised approaches on a survey dataset spanning 1946-2020, including probabilistic models, linear probing of language models, and pre-trained neural networks specialised for IR. We show that IR-specialised neural models achieve the highest overall performance with other approaches performing comparably. Additionally, the re-ranking of the probabilistic model's results with neural models only introduces modest improvements of 0.07 at most in F1-score. Qualitative post-hoc evaluation by survey specialists shows that models generally have a low sensitivity to questions with high lexical overlap, particularly in cases where sub-concepts are mismatched. Altogether, our analysis serves to further research on harmonising longitudinal studies in social science.
Occlusion-aware Driver Monitoring System using the Driver Monitoring Dataset
2025-04-29
Paola Natalia Cañas, Alexander Diez, David Galvañ, Marcos Nieto, Igor Rodríguez
This paper presents a robust, occlusion-aware driver monitoring system (DMS) utilizing the Driver Monitoring Dataset (DMD). The system performs driver identification, gaze estimation by regions, and face occlusion detection under varying lighting conditions, including challenging low-light scenarios. Aligned with EuroNCAP recommendations, the inclusion of occlusion detection enhances situational awareness and system trustworthiness by indicating when the system's performance may be degraded. The system employs separate algorithms trained on RGB and infrared (IR) images to ensure reliable functioning. We detail the development and integration of these algorithms into a cohesive pipeline, addressing the challenges of working with different sensors and real-car implementation. Evaluation on the DMD and in real-world scenarios demonstrates the effectiveness of the proposed system, highlighting the superior performance of RGB-based models and the pioneering contribution of robust occlusion detection in DMS.
Large language models (LLMs) play a crucial role in software engineering, excelling in tasks like code generation and maintenance. However, existing benchmarks are often narrow in scope, focusing on a specific task and lack a comprehensive evaluation framework that reflects real-world applications. To address these gaps, we introduce CoCo-Bench (Comprehensive Code Benchmark), designed to evaluate LLMs across four critical dimensions: code understanding, code generation, code modification, and code review. These dimensions capture essential developer needs, ensuring a more systematic and representative evaluation. CoCo-Bench includes multiple programming languages and varying task difficulties, with rigorous manual review to ensure data quality and accuracy. Empirical results show that CoCo-Bench aligns with existing benchmarks while uncovering significant variations in model performance, effectively highlighting strengths and weaknesses. By offering a holistic and objective evaluation, CoCo-Bench provides valuable insights to guide future research and technological advancements in code-oriented LLMs, establishing a reliable benchmark for the field.
A Generative-AI-Driven Claim Retrieval System Capable of Detecting and Retrieving Claims from Social Media Platforms in Multiple Languages
2025-04-29
Ivan Vykopal, Martin Hyben, Robert Moro, Michal Gregor, Jakub Simko
Online disinformation poses a global challenge, placing significant demands on fact-checkers who must verify claims efficiently to prevent the spread of false information. A major issue in this process is the redundant verification of already fact-checked claims, which increases workload and delays responses to newly emerging claims. This research introduces an approach that retrieves previously fact-checked claims, evaluates their relevance to a given input, and provides supplementary information to support fact-checkers. Our method employs large language models (LLMs) to filter irrelevant fact-checks and generate concise summaries and explanations, enabling fact-checkers to faster assess whether a claim has been verified before. In addition, we evaluate our approach through both automatic and human assessments, where humans interact with the developed tool to review its effectiveness. Our results demonstrate that LLMs are able to filter out many irrelevant fact-checks and, therefore, reduce effort and streamline the fact-checking process.
Challenging $Λ$CDM: 5$σ$ Evidence for a Dynamical Dark Energy Late-Time Transition
2025-04-29
Mateus Scherer, Miguel A. Sabogal, Rafael C. Nunes, Antonio De Felice
Recently, there has been considerable debate regarding potential evidence for the dynamical nature of dark energy (DE), particularly in light of Baryon Acoustic Oscillations (BAO) measurements released by DESI survey. In this work, we propose an agnostic test that simultaneously constrains the dark energy (DE) equation of state (EoS) and probes the possibility of a transition between the quintessence and phantom regimes, or vice versa. Our initial approach is independent of physical priors, allowing the data to determine which behavior best fits the parameters. We then consider a minimally modified gravity theory known as VCDM, into which we can map our initial approximation, placing it within a theoretically stable framework. To this end, we incorporate the most up-to-date datasets available, including BAO measurements from DESI-DR2, Type Ia Supernovae from the PantheonPlus, DESY5, and Union3 samples, as well as Cosmic Microwave Background (CMB) data from Planck. Our analysis reveals strong and statistically significant evidence for a quintessence-phantom transition across various data combinations. \textit{The strongest evidence is found for Planck+DESI+DESY5, with a significance exceeding $\sim$5$\sigma$ in favor of a quintessence-phantom transition at $z_{\dag} = 0.493^{+0.063}_{-0.081}$}. Beyond this redshift, the EoS remains within the phantom regime, while for $z < z_{\dag}$, it favors the quintessence regime. Despite this strong indication, \textit{we find that such transitions do not resolve the $H_0$ tension}.
The rapid advancement of Large Language Models (LLMs) has enhanced software development processes, minimizing the time and effort required for coding and enhancing developer productivity. However, despite their potential benefits, code generated by LLMs has been shown to generate insecure code in controlled environments, raising critical concerns about their reliability and security in real-world applications. This paper uses predefined security parameters to evaluate the security compliance of LLM-generated code across multiple models, such as ChatGPT, DeepSeek, Claude, Gemini and Grok. The analysis reveals critical vulnerabilities in authentication mechanisms, session management, input validation and HTTP security headers. Although some models implement security measures to a limited extent, none fully align with industry best practices, highlighting the associated risks in automated software development. Our findings underscore that human expertise is crucial to ensure secure software deployment or review of LLM-generated code. Also, there is a need for robust security assessment frameworks to enhance the reliability of LLM-generated code in real-world applications.
This work sets out to answer a single question: what is the orthodox interpretation of quantum mechanics? However, we adopt a different approach to that normally used. Rather than carefully surveying the precise details of the thoughts of Bohr and Heisenberg, we extract an orthodoxy empirically. To do this we review a collection of 33 textbooks on quantum mechanics, encompassing the most popular and prominent works of this nature. We then gauge their response to 12 propositions to build up a picture of exactly what is believed by an orthodox quantum mechanic. We demonstrate that this orthodoxy is largely unchanged over the past century, with some interesting emerging deviations, and has many aspects of Copenhagen-like viewpoints. However, it is more nuanced than some reductive characterisations that condense it down to the ontological primacy of the quantum state. The revealed orthodoxy has two main pillars: measurement inherently disturbs quantum states and these states refer to individual instances, not ensembles. More fully it entails that individual particles exist in wave-like super-positions and present particle behaviours only when forced to by outside influences. The act of measuring such a system inherently changes its state in a random fashion, manifesting in a form of measurement error that corresponds to the uncertainty principle. This implies that measurement does not reveal underlying values of quantum properties.
Inclusive Training Separation and Implicit Knowledge Interaction for Balanced Online Class-Incremental Learning
2025-04-29
Shunjie Wen, Thomas Heinis, Dong-Wan Choi
Online class-incremental learning (OCIL) focuses on gradually learning new classes (called plasticity) from a stream of data in a single-pass, while concurrently preserving knowledge of previously learned classes (called stability). The primary challenge in OCIL lies in maintaining a good balance between the knowledge of old and new classes within the continually updated model. Most existing methods rely on explicit knowledge interaction through experience replay, and often employ exclusive training separation to address bias problems. Nevertheless, it still remains a big challenge to achieve a well-balanced learner, as these methods often exhibit either reduced plasticity or limited stability due to difficulties in continually integrating knowledge in the OCIL setting. In this paper, we propose a novel replay-based method, called Balanced Online Incremental Learning (BOIL), which can achieve both high plasticity and stability, thus ensuring more balanced performance in OCIL. Our BOIL method proposes an inclusive training separation strategy using dual classifiers so that knowledge from both old and new classes can effectively be integrated into the model, while introducing implicit approaches for transferring knowledge across the two classifiers. Extensive experimental evaluations over three widely-used OCIL benchmark datasets demonstrate the superiority of BOIL, showing more balanced yet better performance compared to state-of-the-art replay-based OCIL methods.
Addressing Data Scarcity in UBEM Validation: Application of Survey Sampling Techniques
2025-04-29
Chunxiao Wang, Bruno Duplessis, Eric Peirano, Pascal Schetelat, Peter Riederer
Urban Building Energy Models (UBEM) are vital for enhancing energy efficiency and sustainability in urban planning. However, data scarcity often challenges their validation, particularly the lack of hourly measured data and the variety of building samples. This study addresses this issue by applying bias adjustment techniques from survey research to improve UBEM validation robustness with incomplete measured data. Error estimation tests are conducted using various levels of missingness, and three bias adjustment methods are employed: multivariate imputation, cell weighting and raking weighting. Key findings indicate that using incomplete data in UBEM validation without adjustment is not advisable, while bias adjustment techniques significantly enhance the robustness of validation, providing more reliable model validity estimates. Cell weighting is preferable in this study due to its reliance on joint distributions of auxiliary variables.
Challenges of Requirements Communication and Digital Assets Verification in Infrastructure Projects
2025-04-29
Waleed Abdeen, Krzysztof Wnuk, Michael Unterkalmsteiner, Alexandros Chirtoglou
Background: Poor communication of requirements between clients and suppliers contributes to project overruns,in both software and infrastructure projects. Existing literature offers limited insights into the communication challenges at this interface. Aim: Our research aim to explore the processes and associated challenges with requirements activities that include client-supplier interaction and communication. Method: we study requirements validation, communication, and digital asset verification processes through two case studies in the road and railway sectors, involving interviews with ten experts across three companies. Results: We identify 13 challenges, along with their causes and consequences, and suggest solution areas from existing literature. Conclusion: Interestingly, the challenges in infrastructure projects mirror those found in software engineering, highlighting a need for further research to validate potential solutions.
Large language models (LLMs) exhibit remarkable multilingual capabilities despite English-dominated pre-training, attributed to cross-lingual mechanisms during pre-training. Existing methods for enhancing cross-lingual transfer remain constrained by parallel resources, suffering from limited linguistic and domain coverage. We propose Cross-lingual In-context Pre-training (CrossIC-PT), a simple and scalable approach that enhances cross-lingual transfer by leveraging semantically related bilingual texts via simple next-word prediction. We construct CrossIC-PT samples by interleaving semantic-related bilingual Wikipedia documents into a single context window. To access window size constraints, we implement a systematic segmentation policy to split long bilingual document pairs into chunks while adjusting the sliding window mechanism to preserve contextual coherence. We further extend data availability through a semantic retrieval framework to construct CrossIC-PT samples from web-crawled corpus. Experimental results demonstrate that CrossIC-PT improves multilingual performance on three models (Llama-3.1-8B, Qwen2.5-7B, and Qwen2.5-1.5B) across six target languages, yielding performance gains of 3.79%, 3.99%, and 1.95%, respectively, with additional improvements after data augmentation.
The tomographic Alcock-Paczynski (AP) method, developed over the past decade, exploits redshift evolution for cosmological determination, aiming to mitigate contamination from redshift distortions and capture nonlinear scale information. Marked Correlation Functions (MCFs) extend information beyond the two-point correlation. For the first time, this study integrated the tomographic AP test with MCFs to constrain the flat $w$CDM cosmology model. Our findings show that multiple density weights in MCFs outperform the traditional two-point correlation function, reducing the uncertainties of the matter density parameter $\Omega_m$ and dark energy equation of state $w$ by 48\% and 45\%, respectively. Furthermore, we introduce a novel principal component analysis (PCA) compression scheme that efficiently projects high-dimensional statistical measurements into a compact set of eigenmodes while preserving most of the cosmological information. This approach retains significantly more information than traditional coarse binning methods, which simply average adjacent bins in a lossy manner. Applying PCA compression also enables the effective use of marked correlation functions in 2D $(s,\mu)$ space, yielding an additional $\sim 50\%$ reduction in error margins. To assess robustness, we incorporate realistic redshift errors expected in future spectroscopic surveys. While these errors modestly degrade cosmological constraints, our combined framework, which utiizes MCFs and PCA compression within tomographic AP tests, is less affected and always yield to tight cosmological constraints. This scheme remains highly promising for upcoming slitless spectroscopic surveys, such as the Chinese Space Station Telescope (CSST).
A Summary on GUI Agents with Foundation Models Enhanced by Reinforcement Learning
2025-04-29
Jiahao Li, Kaer Huang
Graphical User Interface (GUI) agents, driven by Multi-modal Large Language Models (MLLMs), have emerged as a promising paradigm for enabling intelligent interaction with digital systems. This paper provides a structured summary of recent advances in GUI agents, focusing on architectures enhanced by Reinforcement Learning (RL). We first formalize GUI agent tasks as Markov Decision Processes and discuss typical execution environments and evaluation metrics. We then review the modular architecture of (M)LLM-based GUI agents, covering Perception, Planning, and Acting modules, and trace their evolution through representative works. Furthermore, we categorize GUI agent training methodologies into Prompt-based, Supervised Fine-Tuning (SFT)-based, and RL-based approaches, highlighting the progression from simple prompt engineering to dynamic policy learning via RL. Our summary illustrates how recent innovations in multimodal perception, decision reasoning, and adaptive action generation have significantly improved the generalization and robustness of GUI agents in complex real-world environments. We conclude by identifying key challenges and future directions for building more capable and reliable GUI agents.
This study integrates PET metabolic information with CT anatomical structures to establish a 3D multimodal segmentation dataset for lymphoma based on whole-body FDG PET/CT examinations, which bridges the gap of the lack of standardised multimodal segmentation datasets in the field of haematological malignancies. We retrospectively collected 483 examination datasets acquired between March 2011 and May 2024, involving 220 patients (106 non-Hodgkin lymphoma, 42 Hodgkin lymphoma); all data underwent ethical review and were rigorously de-identified. Complete 3D structural information was preserved during data acquisition, preprocessing and annotation, and a high-quality dataset was constructed based on the nnUNet format. By systematic technical validation and evaluation of the preprocessing process, annotation quality and automatic segmentation algorithm, the deep learning model trained based on this dataset is verified to achieve accurate segmentation of lymphoma lesions in PET/CT images with high accuracy, good robustness and reproducibility, which proves the applicability and stability of this dataset in accurate segmentation and quantitative analysis. The deep fusion of PET/CT images achieved with this dataset not only significantly improves the accurate portrayal of the morphology, location and metabolic features of tumour lesions, but also provides solid data support for early diagnosis, clinical staging and personalized treatment, and promotes the development of automated image segmentation and precision medicine based on deep learning. The dataset and related resources are available at https://github.com/SuperD0122/LymphAtlas-.
Trees in the Largest Order With a Given Burning Number
2025-04-29
Eugene Jun Tong Leong, Kai An Sim, Wen Chean Teh
Graph burning is modelled upon the spread of contagion, and the burning number measures the speed of the spread. By the fact that the burning number of a connected graph is the minimum burning number of its spanning trees, our work focuses on identifying the largest order of a general tree with a given burning number up to homeomorphism. In this work, we propose the concept of admissible sequences over a homeomorphically irreducible tree in addition to developing a general framework. We then determine whether an admissible sequence induces a tree with a given burning number of the largest order. Additionally, we obtain some results on the smallest attainable diameter of an $n$-spider having the largest order with a given burning number.
Discovery of a variable yellow supergiant progenitor for the Type IIb SN 2024abfo
2025-04-29
Zexi Niu, Ning-Chen Sun, Justyn R. Maund, Zhen Guo, Wenxiong Li, Meng Sun, Jifeng Liu
We report the discovery of a progenitor candidate for the Type IIb SN 2024abfo using multi-epoch pre-explosion images from the Hubble Space Telescope and the Dark Energy Camera Legacy Survey. The progenitor exhibited a ~0.7 mag decline in F814W from 2001 to 2013, followed by significant brightness and color fluctuations in the g, r and z bands. This is the first time that a variable progenitor has been found for a SN IIb. We suggest that the variability is caused by intrinsic changes in the progenitor star instead of varying obscuration by circumstellar dust. Our results show that the progenitor of SN 2024abfo was likely a yellow supergiant star with an initial mass of 12--14, 14--15 and 15--18 Msun for different assumed values of circumstellar reddening of E(B-V)_CSM = 0, 0.1 and 0.2 mag. Our study underscores the critical role of multi-epoch imaging surveys in revealing the final stages of core-collapse supernovae progenitors.
Automated Unit Test Case Generation: A Systematic Literature Review
2025-04-29
Jason Wang, Basem Suleiman, Muhammad Johan Alibasa
Software is omnipresent within all factors of society. It is thus important to ensure that software are well tested to mitigate bad user experiences as well as the potential for severe financial and human losses. Software testing is however expensive and absorbs valuable time and resources. As a result, the field of automated software testing has grown of interest to researchers in past decades. In our review of present and past research papers, we have identified an information gap in the areas of improvement for the Genetic Algorithm and Particle Swarm Optimisation. A gap in knowledge in the current challenges that face automated testing has also been identified. We therefore present this systematic literature review in an effort to consolidate existing knowledge in regards to the evolutionary approaches as well as their improvements and resulting limitations. These improvements include hybrid algorithm combinations as well as interoperability with mutation testing and neural networks. We will also explore the main test criterion that are used in these algorithms alongside the challenges currently faced in the field related to readability, mocking and more.
SoK: Enhancing Privacy-Preserving Software Development from a Developers' Perspective
2025-04-29
Tharaka Wijesundara, Matthew Warren, Nalin Asanka Gamagedara Arachchilage
In software development, privacy preservation has become essential with the rise of privacy concerns and regulations such as GDPR and CCPA. While several tools, guidelines, methods, methodologies, and frameworks have been proposed to support developers embedding privacy into software applications, most of them are proofs-of-concept without empirical evaluations, making their practical applicability uncertain. These solutions should be evaluated for different types of scenarios (e.g., industry settings such as rapid software development environments, teams with different privacy knowledge, etc.) to determine what their limitations are in various industry settings and what changes are required to refine current solutions before putting them into industry and developing new developer-supporting approaches. For that, a thorough review of empirically evaluated current solutions will be very effective. However, the existing secondary studies that examine the available developer support provide broad overviews but do not specifically analyze empirically evaluated solutions and their limitations. Therefore, this Systematic Literature Review (SLR) aims to identify and analyze empirically validated solutions that are designed to help developers in privacy-preserving software development. The findings will provide valuable insights for researchers to improve current privacy-preserving solutions and for practitioners looking for effective and validated solutions to embed privacy into software development.
Positive neutrino masses with DESI DR2 via matter conversion to dark energy
2025-04-29
S. Ahlen, A. Aviles, B. Cartwright, K. S. Croker, W. Elbers, D. Farrah, N. Fernandez, G. Niz, J. Rohlf, G. Tarlé, R. A. Windhorst, J. Aguilar, U. Andrade, D. Bianchi, D. Brooks, T. Claybaugh, A. de la Macorra, A. de Mattia, B. Dey, P. Doel, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, D. Huterer, M. Ishak, R. Kehoe, D. Kirkby, A. Kremin, O. Lahav, C. Lamman, M. Landriau, L. Le Guillou, M. E. Levi, M. Manera, R. Miquel, J. Moustakas, I. Pérez-Ràfols, F. Prada, G. Rossi, E. Sanchez, M. Schubnell, H. Seo, J. Silber, D. Sprayberry, M. Walther, B. A. Weaver, R. H. Wechsler, H. Zou
The Dark Energy Spectroscopic Instrument (DESI) is a massively parallel spectroscopic survey on the Mayall telescope at Kitt Peak, which has released measurements of baryon acoustic oscillations determined from over 14 million extragalactic targets. We combine DESI Data Release 2 with CMB datasets to search for evidence of matter conversion to dark energy (DE), focusing on a scenario mediated by stellar collapse to cosmologically-coupled black holes (CCBH). In this physical model, which has the same number of free parameters as $\Lambda$CDM, DE production is determined by the cosmic star formation rate density (SFRD), allowing for distinct early- and late-time cosmologies. Using two SFRDs to bracket current observations, we find that the CCBH model: accurately recovers the cosmological expansion history, agrees with early-time baryon abundance measured by BBN, reduces tension with the local distance ladder, and relaxes constraints on the summed neutrino mass $\sum m_\nu$. For these SFRDs, we find a peaked positive $\sum m_\nu < 0.149\,\rm eV$ (95\% confidence) and $\sum m_\nu = 0.106^{+0.050}_{-0.069}\,\rm eV$ respectively, in good agreement with lower limits from neutrino oscillation experiments. A peak in $\sum m_\nu > 0$ results from late-time baryon consumption in the CCBH scenario and is expected to be a general feature of any model that converts sufficient matter to dark energy during and after reionization.
This paper investigates how developers conceptualize AI-powered Development Tools and how these role attributions influence technology acceptance. Through qualitative analysis of 38 interviews and a quantitative survey with 102 participants, we identify two primary Mental Models: AI as an inanimate tool and AI as a human-like teammate. Factor analysis further groups AI roles into Support Roles (e.g., assistant, reference guide) and Expert Roles (e.g., advisor, problem solver). We find that assigning multiple roles to AI correlates positively with Perceived Usefulness and Perceived Ease of Use, indicating that diverse conceptualizations enhance AI adoption. These insights suggest that AI4SE tools should accommodate varying user expectations through adaptive design strategies that align with different Mental Models.
Nearby stellar substructures in the Galactic halo from DESI Milky Way Survey Year 1 Data Release
2025-04-29
Bokyoung Kim, Sergey E. Koposov, Ting S. Li, Sophia Lilleengen, Andrew P. Cooper, Andreia Carrillo, Monica Valluri, Alexander H. Riley, Jiwon Jesse Han, Jessica Nicole Aguilar, Steven Ahlen, Leandro Beraldo e Silva, Davide Bianchi, David Brooks, Amanda Byström, Todd Claybaugh, Shaun Cole, Kyle Dawson, Axel de la Macorra, Jaime Forero-Romero, Oleg Y. Gnedin, Satya Gontcho A Gontcho, Gaston Gutierrez, Julien Guy, Klaus Honscheid, Robert Kehoe, Namitha Kizhuprakkat, Martin Landriau, Laurent Le Guillou, Michael Levi, Gustavo Medina Toledo, Aaron Meisner, Ramon Miquel, John Moustakas, Claire Poppett, Francisco Prada, Graziano Rossi, Eusebio Sánchez, Michael Schubnell, Ray Sharples, David Sprayberry, José Arturo Trelles Hernández, Benjamin Alan Weaver, Hu Zou
We report five nearby ($d_{\mathrm{helio}} < 5$ kpc) stellar substructures in the Galactic halo from a subset of 138,661 stars in the Dark Energy Spectroscopic Instrument (DESI) Milky Way Survey Year 1 Data Release. With an unsupervised clustering algorithm, HDBSCAN*, these substructures are independently identified in Integrals of Motion ($E_{\mathrm{tot}}$, $L_{\mathrm z}$, $\log{J_r}$, $\log{J_z}$) space and Galactocentric cylindrical velocity space ($V_{R}$, $V_{\phi}$, $V_{z}$). We associate all identified clusters with known nearby substructures (Helmi streams, M18-Cand10/MMH-1, Sequoia, Antaeus, and ED-2) previously reported in various studies. With metallicities precisely measured by DESI, we confirm that the Helmi streams, M18-Cand10, and ED-2 are chemically distinct from local halo stars. We have characterised the chemodynamic properties of each dynamic group, including their metallicity dispersions, to associate them with their progenitor types (globular cluster or dwarf galaxy). Our approach for searching substructures with HDBSCAN* reliably detects real substructures in the Galactic halo, suggesting that applying the same method can lead to the discovery of new substructures in future DESI data. With more stars from future DESI data releases and improved astrometry from the upcoming {\it Gaia} Data Release 4, we will have a more detailed blueprint of the Galactic halo, offering a significant improvement in our understanding of the formation and evolutionary history of the Milky Way Galaxy.
The H.E.S.S. extragalactic sky survey with the first decade of observations
2025-04-29
H. E. S. S. Collaboration, F. Aharonian, F. Ait Benkhali, J. Aschersleben, H. Ashkar, M. Backes, V. Barbosa Martins, R. Batzofin, Y. Becherini, D. Berge, K. Bernlöhr, M. Böttcher, C. Boisson, J. Bolmont, M. de Bony de Lavergne, J. Borowska, M. Bouyahiaoui, F. Bradascio, R. Brose, A. Brown, F. Brun, B. Bruno, T. Bulik, C. Burger-Scheidlin, T. Bylund, S. Casanova, J. Celic, M. Cerruti, T. Chand, S. Chandra, A. Chen, J. Chibueze, O. Chibueze, G. Cotter, P. Cristofari, J. Damascene Mbarubucyeye, I. D. Davids, J. Devin, J. Djuvsland, A. Dmytriiev, K. Egberts, S. Einecke, S. Fegan, G. Fontaine, S. Funk, S. Gabici, J. F. Glicenstein, J. Glombitza, P. Goswami, G. Grolleron, L. Haerer, B. Heß, J. A. Hinton, W. Hofmann, T. L. Holch, M. Holler, D. Horns, Zhiqiu Huang, M. Jamrozy, F. Jankowsky, A. Jardin-Blicq, E. Kasai, K. Katarzyński, R. Khatoon, B. Khélifi, Nu. Komin, K. Kosack, D. Kostunin, A. Kundu, R. G. Lang, S. Le Stum, A. Lemière, M. Lemoine-Goumard, J. -P. Lenain, F. Leuschner, A. Luashvili, J. Mackey, D. Malyshev, V. Marandon, G. Martí-Devesa, R. Marx, A. Mehta, A. Mitchell, R. Moderski, L. Mohrmann, A. Montanari, M. de Naurois, J. Niemiec, P. O'Brien, L. Olivera-Nieto, E. de Ona Wilhelmi, M. Ostrowski, S. Panny, M. Panter, U. Pensec, G. Pühlhofer, M. Punch, A. Quirrenbach, S. Ravikularaman, M. Regeard, A. Reimer, O. Reimer, I. Reis, H. Ren, B. Reville, F. Rieger, G. Rowell, B. Rudak, E. Ruiz-Velasco, V. Sahakian, H. Salzmann, D. A. Sanchez, A. Santangelo, M. Sasaki, J. Schäfer, F. Schüssler, J. N. S. Shapopi, A. Sharma, H. Sol, S. Spencer, Ł. Stawarz, R. Steenkamp, S. Steinmassl, C. Steppa, H. Suzuki, T. Takahashi, T. Tanaka, A. M. Taylor, R. Terrier, A. Thakur, M. Tsirou, C. van Eldik, M. Vecchi, C. Venter, J. Vink, H. J. Völk, T. Wach, S. J. Wagner, A. Wierzcholska, M. Zacharias, A. A. Zdziarski, A. Zech, N. Żywucka
The results of the first extragalactic gamma-ray survey by the High Energy Stereoscopic System (H.E.S.S.) are presented. The survey comprises 2720 hours of very high-energy gamma-ray observations of the extragalactic sky, recorded with H.E.S.S. from 2004 up to the end of 2012. These data have been re-analysed using a common consistent set of up-to-date data calibration and analysis tools. From this analysis, a list of 23 detected objects, predominantly blazars, was obtained. This catalogue was assessed in terms of the source class populations that it contains. The level of source parameter bias for the blazar sources, probed by this observational dataset, was evaluated using Monte-Carlo simulations. Spectral results obtained with the H.E.S.S. data were compared with the \textit{Fermi}-LAT catalogues to present the full gamma-ray picture of the detected objects. Lastly, this unique dataset was used to assess the contribution of BL Lacertae objects and flat-spectrum radio quasars to the extragalactic gamma-ray background light at several hundreds of giga-electronvolts. These results are accompanied by the release of the high-level data to the astrophysical community.
Artificial Intelligence for Personalized Prediction of Alzheimer's Disease Progression: A Survey of Methods, Data Challenges, and Future Directions
2025-04-29
Gulsah Hancerliogullari Koksalmis, Bulent Soykan, Laura J. Brattain, Hsin-Hsiung Huang
Alzheimer's Disease (AD) is marked by significant inter-individual variability in its progression, complicating accurate prognosis and personalized care planning. This heterogeneity underscores the critical need for predictive models capable of forecasting patient-specific disease trajectories. Artificial Intelligence (AI) offers powerful tools to address this challenge by analyzing complex, multi-modal, and longitudinal patient data. This paper provides a comprehensive survey of AI methodologies applied to personalized AD progression prediction. We review key approaches including state-space models for capturing temporal dynamics, deep learning techniques like Recurrent Neural Networks for sequence modeling, Graph Neural Networks (GNNs) for leveraging network structures, and the emerging concept of AI-driven digital twins for individualized simulation. Recognizing that data limitations often impede progress, we examine common challenges such as high dimensionality, missing data, and dataset imbalance. We further discuss AI-driven mitigation strategies, with a specific focus on synthetic data generation using Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) to augment and balance datasets. The survey synthesizes the strengths and limitations of current approaches, emphasizing the trend towards multimodal integration and the persistent need for model interpretability and generalizability. Finally, we identify critical open challenges, including robust external validation, clinical integration, and ethical considerations, and outline promising future research directions such as hybrid models, causal inference, and federated learning. This review aims to consolidate current knowledge and guide future efforts in developing clinically relevant AI tools for personalized AD prognostication.
Quarkonium suppression in strongly coupled plasmas
2025-04-29
Bruno Scheihing-Hitschfeld, Xiaojun Yao
Suppression of open heavy quarks and quarkonia in heavy-ion collisions are among the most informative probes of quark-gluon plasma (QGP). Interpreting the full wealth of data obtained from the collision events requires a precise theoretical understanding of the evolution of heavy quarks and quarkonia as they propagate through a strongly coupled plasma. Such calculations require the evaluation of a gauge-invariant correlator of chromoelectric fields. This chromoelectric correlator encodes all the characteristics of QGP that the dissociation and recombination dynamics of quarkonium are sensitive to, which is to say can in principle measure. We review its distinctive qualitative features at weak coupling in QCD up to next-to-leading order and at strong coupling in $\mathcal{N}=4$ SYM using the AdS/CFT correspondence, as well as its formulation in Euclidean QCD. Furthermore, we report on recent progress in applying our results to the calculation of the final quarkonium abundances after propagating through a cooling droplet of QGP, which illustrates how we may learn about QGP from quarkonium measurements. We devote special attention to how the presence of a strongly coupled plasma modifies the transport description of quarkonium, in comparison to approaches that rely on weak coupling approximations to describe quarkonium dissociation and recombination.
Predicting Potential Host Galaxies of Supermassive Black Hole Binaries Based on Stellar Kinematics in Archival IFU Surveys
2025-04-29
Patrick Horlaville, John J. Ruan, Michael Eracleous, Jaeden Bardati, Jessie C. Runnoe, Daryl Haggard
Supermassive black hole binaries (SMBHBs) at the centers of galaxies emit continuous gravitational waves (GWs) at nanohertz frequencies, and ongoing pulsar timing array (PTA) experiments aim to detect the first individual system. Identifying the exact host galaxy of a SMBHB detected in GWs is paramount for a variety of multi-messenger science cases, but it will be challenging due to the large number of candidate galaxies in the sky localization region. Here, we apply recent insights on the distinct characteristics of SMBHB host galaxies to archival galaxy datasets, to predict which nearby massive galaxies are most likely to host SMBHBs detectable by PTAs. Specifically, we use archival galaxy IFU surveys to search for nearby galaxies with distinct stellar kinematic signatures of SMBHB host galaxies, as informed by cosmological simulations. These distinct stellar kinematic signatures, including slow rotation and strong kinematic/photometric misalignments, are a hallmark of recent major galaxy mergers that led to the formation of SMBHBs in these galaxies. We produce a list of nearby massive galaxies that may currently host SMBHBs, ranked by a combination of their host galaxy stellar kinematic properties and their hypothetical GW strain. We discuss how our ranked list can be used (1) for targeted searches for individual sources of continuous GWs by PTAs, (2) to corroborate candidate SMBHBs identified through other approaches, and (3) to select candidate recoiling AGN and closely-separated (<100 pc) dual AGN for telescope follow-up confirmation.
Analyses of the galaxy N-Point Correlation Functions (NPCFs) have a large number of degrees of freedom, meaning one cannot directly estimate an invertible covariance matrix purely from mock catalogs, as has been the standard approach for the 2PCF and power spectrum. Instead, analyses use templates based on assuming a Gaussian Random Field density with the true, Boltzmann-solver-computed power spectrum. The resulting covariance matrices are sparse but have notable internal structure. To understand this structure better, we seek a fully analytic, closed-form covariance matrix template, using a power law power spectrum $P(k) \propto 1/k$. We obtain a simple closed-form solution for the covariance of the 2PCF, as well as closed-form solutions for the fundamental building blocks of the covariance matrices for the 3PCF, 4PCF, and beyond. We use our results to present a clearer picture of the covariance matrices' structure and sparsity, corresponding to triangular and non-triangular regions. This will be useful in guiding future NPCF analyses with spectroscopic surveys such as DESI, Euclid, Roman, and SPHEREx.
Light-Based Fast Timing in Bulk CsPbBr3 Crystals for TOF-PET and Proton Range Verification
2025-04-29
Nicolaus Kratohwil, Leonor Rebolo, Indra R. Pandey, Joshua W. Cates, Emilie Roncali, Joshua H. Palmer, Gerard Arino-Estrada
Halide perovskite semiconductors such as CsPbBr3 (CLB) are emerging gamma-ray detectors for applications requiring very high energy resolution and potential for fine detector segmentation. Semiconductor detectors typically offer poor time resolution due to the long drift times. Recently, we proposed to use the Cherenkov light component in partially transparent semiconductors to boost the timing capability of such detectors. Cherenkov light produced upon 511 keV gamma-ray interaction with CLB was investigated by means of optical simulations and experimental measurements. The timing capability of a pair of identical CLB crystals ( 3 x 3 x 5 mm3) coupled to NUV- MT silicon photomultipliers was measured. On average, 9.5 Cherenkov photons are produced in CLB between 555 and 900 nm for 511 keV photoelectric interactions based on our simulation framework. Experimentally, we observe 2-to-3 times more photons detected than in the simulation. The two most likely explanations for these additional detected optical photons are either the partial transparency of CLB in the UV, or a mild scintillation light emitted by CLB at room temperature. A coincidence time resolution (CTR) of 419 ps FWHM was obtained by triggering on more than 2 fired SiPM cells and after time walk correction. The measured CTR confirms the feasibility to use the Cherenkov light-component for fast timing applications on top of the charge readout, toward full 3D localization.
A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning
2025-04-29
Jieming Bian, Yuanzhe Peng, Lei Wang, Yin Huang, Jie Xu
Foundation models have revolutionized artificial intelligence by providing robust, versatile architectures pre-trained on large-scale datasets. However, adapting these massive models to specific downstream tasks requires fine-tuning, which can be prohibitively expensive in computational resources. Parameter-Efficient Fine-Tuning (PEFT) methods address this challenge by selectively updating only a small subset of parameters. Meanwhile, Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it ideal for privacy-sensitive applications. This survey provides a comprehensive review of the integration of PEFT techniques within federated learning environments. We systematically categorize existing approaches into three main groups: Additive PEFT (which introduces new trainable parameters), Selective PEFT (which fine-tunes only subsets of existing parameters), and Reparameterized PEFT (which transforms model architectures to enable efficient updates). For each category, we analyze how these methods address the unique challenges of federated settings, including data heterogeneity, communication efficiency, computational constraints, and privacy concerns. We further organize the literature based on application domains, covering both natural language processing and computer vision tasks. Finally, we discuss promising research directions, including scaling to larger foundation models, theoretical analysis of federated PEFT methods, and sustainable approaches for resource-constrained environments.
Fast and accurate parameter estimation of high-redshift sources with the Einstein Telescope
2025-04-29
Filippo Santoliquido, Jacopo Tissino, Ulyana Dupletsa, Marica Branchesi, Jan Harms, Manuel Arca Sedda, Maximilian Dax, Annalena Kofler, Stephen R. Green, Nihar Gupte, Isobel M. Romero-Shaw, Emanuele Berti
The Einstein Telescope (ET), along with other third-generation gravitational wave (GW) detectors, will be a key instrument for detecting GWs in the coming decades. However, analyzing the data and estimating source parameters will be challenging, especially given the large number of expected detections - of order $10^5$ per year - which makes current methods based on stochastic sampling impractical. In this work, we use Dingo-IS to perform Neural Posterior Estimation (NPE) of high-redshift events detectable with ET in its triangular configuration. NPE is a likelihood-free inference technique that leverages normalizing flows to approximate posterior distributions. After training, inference is fast, requiring only a few minutes per source, and accurate, as corrected through importance sampling and validated against standard Bayesian inference methods. To confirm previous findings on the ability to estimate parameters for high-redshift sources with ET, we compare NPE results with predictions from the Fisher information matrix (FIM) approximation. We find that FIM underestimates sky localization errors substantially for most sources, as it does not capture the multimodalities in sky localization introduced by the geometry of the triangular detector. FIM also overestimates the uncertainty in luminosity distance by a factor of $\sim 3$ on average when the injected luminosity distance is $d^{\mathrm{inj}}_{\mathrm{L}} > 10^5~$Mpc, further confirming that ET will be particularly well suited for studying the early Universe.
Kiloparsec-scale turbulence driven by reionization may grow intergalactic magnetic fields
2025-04-29
Christopher Cain, Matthew McQuinn, Evan Scannapieco, Anson D'Aloisio, Hy Trac
The intergalactic medium (IGM) underwent intense heating that resulted in pressure disequilibrium in the wake of ionization fronts during cosmic reionization. The dynamical relaxation to restore pressure balance may have driven small-scale turbulence and, hence, the amplification of intergalactic magnetic fields. We investigate this possibility using a suite of $\approx 100$ pc resolution radiation-hydrodynamics simulations of IGM gas dynamics. We show that as the spatial resolution improves beyond that achieved with most prior studies, much of the IGM becomes turbulent unless it was pre-heated to $\gg 100~$K before reionization. In our most turbulent simulations, we find that the gas energy spectrum follows the expected $k^{-5/3}$ Kolmogorov scaling to the simulation's resolution, and the eddy turnover time of the turbulence is $< 1$ Gyr at $k \approx 1 ~$kpc$^{-1}$. Turbulence will grow magnetic fields, and we show that the fields grown by reionization-driven turbulence could explain lower limits on IGM B-field strengths from observations of TeV blazars.
Investigating the Colour-Magnitude Relations for White Dwarf Stars in SDSS Photometry
2025-04-29
O. Caliskan, S Gokmen
In this study, colour-magnitude relations (CMRs) for DA-type white dwarfs in the Sloan Digital Sky Survey (SDSS) photometric system were investigated. For this purpose, the SDSS data for 20,247 white dwarf stars, as provided in the study by Anguiano et al. (2017), were matched with the Gaia third data release (Gaia DR3) catalogue to obtain trigonometric parallax ($\varpi$) data. The SDSS $ugriz$ magnitudes of the stars were corrected for interstellar extinction using dust maps provided for the Milky Way, and distances from the Sun to the stars were calculated. The SDSS magnitudes were thus corrected for the effects of interstellar extinction. For the calibration of the stars, 5,516 white dwarf stars were selected, with apparent magnitudes brighter than $g_0=21$ mag and relative parallax errors measured to better than $\sigma_\varpi/\varpi=0.1$. Subsequently, three separate CMRs were derived for the absolute magnitudes $M_{\rm g}$, $M_{\rm r}$, and $M_{\rm i}$, each calibrated to two-colour indices. The coefficient of determination ($R^2$) of the obtained CMRs are highly reliable in the bf range of 0.86 to 0.95. Moreover, the standard deviations of the differences between the absolute magnitudes obtained from the relations and the original ones of the calibration stars range from 0.26 to 0.37 mag.
A Systematic Literature Review of Parameter-Efficient Fine-Tuning for Large Code Models
2025-04-29
Md Zahidul Haque, Saima Afrin, Antonio Mastropaolo
The rise of Artificial Intelligence (AI)-and particularly Large Language Models (LLMs) for code-has reshaped Software Engineering (SE) by enabling the automation of tasks such as code generation, bug detection, and repair. However, these models require significant computational resources for training and fine-tuning, posing challenges for real-world adoption in resource-constrained environments. To address this, the research community has increasingly turned to Parameter-Efficient Fine-Tuning (PEFT)-a class of techniques that enables the adaptation of large models by updating only a small subset of parameters, rather than the entire model. In this Systematic Literature Review (SLR), we examine the growing application of PEFT techniques-across a wide range of software engineering tasks. We analyze how these methods are used to optimize various deep learning (DL) architectures, focusing on their impact on both performance and efficiency. Our study synthesizes findings from 27 peer-reviewed papers, identifying patterns in configuration strategies and adaptation trade-offs. The outcome of this review is a comprehensive taxonomy that categorizes PEFT usage by task type, distinguishing between generative (e.g., Code Summarization) and non-generative (e.g., Code Clone Detection) scenarios. Our findings aim to inform future research and guide the practical deployment of PEFT in sustainable, AI-powered software development. Our artifacts are publicly available at https://github.com/alvi75/SLR-PEFT
A Brief Review for Compression and Transfer Learning Techniques in DeepFake Detection
2025-04-29
Andreas Karathanasis, John Violos, Ioannis Kompatsiaris, Symeon Papadopoulos
Training and deploying deepfake detection models on edge devices offers the advantage of maintaining data privacy and confidentiality by processing it close to its source. However, this approach is constrained by the limited computational and memory resources available at the edge. To address this challenge, we explore compression techniques to reduce computational demands and inference time, alongside transfer learning methods to minimize training overhead. Using the Synthbuster, RAISE, and ForenSynths datasets, we evaluate the effectiveness of pruning, knowledge distillation (KD), quantization, fine-tuning, and adapter-based techniques. Our experimental results demonstrate that both compression and transfer learning can be effectively achieved, even with a high compression level of 90%, remaining at the same performance level when the training and validation data originate from the same DeepFake model. However, when the testing dataset is generated by DeepFake models not present in the training set, a domain generalization issue becomes evident.
Multimodal Large Language Models for Medicine: A Comprehensive Survey
2025-04-29
Jiarui Ye, Hao Tang
MLLMs have recently become a focal point in the field of artificial intelligence research. Building on the strong capabilities of LLMs, MLLMs are adept at addressing complex multi-modal tasks. With the release of GPT-4, MLLMs have gained substantial attention from different domains. Researchers have begun to explore the potential of MLLMs in the medical and healthcare domain. In this paper, we first introduce the background and fundamental concepts related to LLMs and MLLMs, while emphasizing the working principles of MLLMs. Subsequently, we summarize three main directions of application within healthcare: medical reporting, medical diagnosis, and medical treatment. Our findings are based on a comprehensive review of 330 recent papers in this area. We illustrate the remarkable capabilities of MLLMs in these domains by providing specific examples. For data, we present six mainstream modes of data along with their corresponding evaluation benchmarks. At the end of the survey, we discuss the challenges faced by MLLMs in the medical and healthcare domain and propose feasible methods to mitigate or overcome these issues.
Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey
2025-04-29
Mohamad A. Hady, Siyi Hu, Mahardhika Pratama, Jimmy Cao, Ryszard Kowalczyk
Multi-Agent Reinforcement Learning (MARL) has become a powerful framework for numerous real-world applications, modeling distributed decision-making and learning from interactions with complex environments. Resource Allocation Optimization (RAO) benefits significantly from MARL's ability to tackle dynamic and decentralized contexts. MARL-based approaches are increasingly applied to RAO challenges across sectors playing pivotal roles to Industry 4.0 developments. This survey provides a comprehensive review of recent MARL algorithms for RAO, encompassing core concepts, classifications, and a structured taxonomy. By outlining the current research landscape and identifying primary challenges and future directions, this survey aims to support researchers and practitioners in leveraging MARL's potential to advance resource allocation solutions.
A Framework to Assess the Persuasion Risks Large Language Model Chatbots Pose to Democratic Societies
2025-04-29
Zhongren Chen, Joshua Kalla, Quan Le, Shinpei Nakamura-Sakai, Jasjeet Sekhon, Ruixiao Wang
In recent years, significant concern has emerged regarding the potential threat that Large Language Models (LLMs) pose to democratic societies through their persuasive capabilities. We expand upon existing research by conducting two survey experiments and a real-world simulation exercise to determine whether it is more cost effective to persuade a large number of voters using LLM chatbots compared to standard political campaign practice, taking into account both the "receive" and "accept" steps in the persuasion process (Zaller 1992). These experiments improve upon previous work by assessing extended interactions between humans and LLMs (instead of using single-shot interactions) and by assessing both short- and long-run persuasive effects (rather than simply asking users to rate the persuasiveness of LLM-produced content). In two survey experiments (N = 10,417) across three distinct political domains, we find that while LLMs are about as persuasive as actual campaign ads once voters are exposed to them, political persuasion in the real-world depends on both exposure to a persuasive message and its impact conditional on exposure. Through simulations based on real-world parameters, we estimate that LLM-based persuasion costs between \$48-\$74 per persuaded voter compared to \$100 for traditional campaign methods, when accounting for the costs of exposure. However, it is currently much easier to scale traditional campaign persuasion methods than LLM-based persuasion. While LLMs do not currently appear to have substantially greater potential for large-scale political persuasion than existing non-LLM methods, this may change as LLM capabilities continue to improve and it becomes easier to scalably encourage exposure to persuasive LLMs.
Unified and consistent structure growth measurements from joint ACT, SPT and \textit{Planck} CMB lensing
2025-04-28
Frank J. Qu, Fei Ge, W. L. Kimmy Wu, Irene Abril-Cabezas, Mathew S. Madhavacheril, Marius Millea, Ethan Anderes, Adam J. Anderson, Behzad Ansarinejad, Melanie Archipley, Zachary Atkins, Lennart Balkenhol, Nicholas Battaglia, Karim Benabed, Amy N. Bender, Bradford A. Benson, Federico Bianchini, Lindsey. E. Bleem, Boris Bolliet, J Richard Bond, François. R. Bouchet, Lincoln Bryant, Erminia Calabrese, Etienne Camphuis, John E. Carlstrom, Julien Carron, Anthony Challinor, Clarence L. Chang, Prakrut Chaubal, Geoff Chen, Paul M. Chichura, Steve K. Choi, Aman Chokshi, Ti-Lin Chou, Anna Coerver, William Coulton, Thomas M. Crawford, Cail Daley, Omar Darwish, Tijmen de Haan, Mark J. Devlin, Karia R. Dibert, Matthew A. Dobbs, Michael Doohan, Aristide Doussot, Adriaan J. Duivenvoorden, Jo Dunkley, Rolando Dunner, Daniel Dutcher, Carmen Embil Villagra, Wendy Everett, Gerrit S. Farren, Chang Feng, Simone Ferraro, Kyle R. Ferguson, Kyra Fichman, Emily Finson, Allen Foster, Patricio A. Gallardo, Silvia Galli, Anne E. Gambrel, Rob W. Gardner, Neil Goeckner-Wald, Riccardo Gualtieri, Federica Guidi, Sam Guns, Mark Halpern, Nils W. Halverson, J. Colin Hill, Matt Hilton, Eric Hivon, Gilbert P. Holder, William L. Holzapfel, John C. Hood, Doug Howe, Alec Hryciuk, Nicholas Huang, Johannes Hubmayr, Florian Kéruzoré, Ali R. Khalife, Joshua Kim, Lloyd Knox, Milo Korman, Kayla Kornoelje, Arthur Kosowsky, Chao-Lin Kuo, Hidde T. Jense, Adrien La Posta, Kevin Levy, Amy E. Lowitz, Thibaut Louis, Chunyu Lu, Gabriel P. Lynch, Niall MacCrann, Abhishek Maniyar, Emily S. Martsen, Jeff McMahon, Felipe Menanteau, Joshua Montgomery, Yuka Nakato, Kavilan Moodley, Toshiya Namikawa, Tyler Natoli, Michael D. Niemack, Gavin I. Noble, Yuuki Omori, Aaron Ouellette, Lyman A. Page, Zhaodi Pan, Pascal Paschos, Kedar A. Phadke, Alexander W. Pollak, Karthik Prabhu, Wei Quan, Srinivasan Raghunathan, Mahsa Rahimi, Alexandra Rahlin, Christian L. Reichardt, Dave Riebel, Maclean Rouble, John E. Ruhl, Emmanuel Schaan, Eduardo Schiappucci, Neelima Sehgal, Carlos E. Sierra, Aidan Simpson, Blake D. Sherwin, Cristóbal Sifón, David N. Spergel, Suzanne T. Staggs, Joshua A. Sobrin, Antony A. Stark, Judith Stephen, Chris Tandoi, Ben Thorne, Cynthia Trendafilova, Caterina Umilta, Alexander Van Engelen, Joaquin D. Vieira, Aline Vitrier, Yujie Wan, Nathan Whitehorn, Edward J. Wollack, Matthew R. Young, Jessica A. Zebrowski
We present the tightest cosmic microwave background (CMB) lensing constraints to date on the growth of structure by combining CMB lensing measurements from the Atacama Cosmology Telescope (ACT), the South Pole Telescope (SPT) and \textit{Planck}. Each of these surveys individually provides lensing measurements with similarly high statistical power, achieving signal-to-noise ratios of approximately 40. The combined lensing bandpowers represent the most precise CMB lensing power spectrum measurement to date with a signal-to-noise ratio of 61 and an amplitude of $A_\mathrm{lens}^\mathrm{recon} = 1.025 \pm 0.017$ with respect to the theory prediction from the best-fit CMB \textit{Planck}-ACT cosmology. The bandpowers from all three lensing datasets, analyzed jointly, yield a $1.6\%$ measurement of the parameter combination $S_8^\mathrm{CMBL} \equiv \sigma_8\,(\Omega_m/0.3)^{0.25} = 0.825^{+0.015}_{-0.013}$. Including Dark Energy Spectroscopic Instrument (DESI) Baryon Acoustic Oscillation (BAO) data improves the constraint on the amplitude of matter fluctuations to $\sigma_8 = 0.829 \pm 0.009$ (a $1.1\%$ determination). When combining with uncalibrated supernovae from \texttt{Pantheon+}, we present a $4\%$ sound-horizon-independent estimate of $H_0=66.4\pm2.5\,\mathrm{km\,s^{-1}\,Mpc^{-1}} $. The joint lensing constraints on structure growth and present-day Hubble rate are fully consistent with a $\Lambda$CDM model fit to the primary CMB data from \textit{Planck} and ACT. While the precise upper limit is sensitive to the choice of data and underlying model assumptions, when varying the neutrino mass sum within the $\Lambda\mathrm{CDM}$ cosmological model, the combination of primary CMB, BAO and CMB lensing drives the probable upper limit for the mass sum towards lower values, comparable to the minimum mass prior required by neutrino oscillation experiments.
Local primordial non-Gaussianity from 'zero-bias' 21cm radiation during reionization
2025-04-28
Nickolas Kokron
We revisit the potential of 21cm radiation fluctuations, during the epoch of reionization, in constraining the amplitude of local primordial non-Gaussianity (PNG) $f_{\rm NL}^{\rm loc}$. There generically exists an epoch at which the linear bias of the 21cm field crosses zero, independent of the precise astrophysics of reionization. This epoch implies the 21cm radiation is a natural "zero-bias tracer" in the sense of Castorina et al (2018). We identify new noise-like contributions which directly compete with the zero-bias effect, but which should be mitigated through sophisticated analysis techniques such as field-level reconstruction. These noise-like terms act to hinder the constraining power on local PNG of the brightness temperature fluctuations, making $\sigma (f_{\rm NL}^{\rm loc}) \leq 1$ unachievable even in simplified forecasts. We show that analyses which can reach the 'sampling noise' floor for this tracer and harness its full power can potentially unlock a 10-fold reduction in error bars, even in the presence of large-scale cuts from foregrounds. The potential of this epoch motivates searching for it in future 21cm surveys, along with developing analysis techniques that can reach the noise floor required for the zero-bias epoch to saturate Fisher information.
Towards Automated Scoping of AI for Social Good Projects
2025-04-28
Jacob Emmerson, Rayid Ghani, Zheyuan Ryan Shi
Artificial Intelligence for Social Good (AI4SG) is an emerging effort that aims to address complex societal challenges with the powerful capabilities of AI systems. These challenges range from local issues with transit networks to global wildlife preservation. However, regardless of scale, a critical bottleneck for many AI4SG initiatives is the laborious process of problem scoping -- a complex and resource-intensive task -- due to a scarcity of professionals with both technical and domain expertise. Given the remarkable applications of large language models (LLM), we propose a Problem Scoping Agent (PSA) that uses an LLM to generate comprehensive project proposals grounded in scientific literature and real-world knowledge. We demonstrate that our PSA framework generates proposals comparable to those written by experts through a blind review and AI evaluations. Finally, we document the challenges of real-world problem scoping and note several areas for future work.
Mapping of Weed Management Methods in Orchards using Sentinel-2 and PlanetScope Data
2025-04-28
Ioannis Kontogiorgakis, Iason Tsardanidis, Dimitrios Bormpoudakis, Ilias Tsoumas, Dimitra A. Loka, Christos Noulas, Alexandros Tsitouras, Charalampos Kontoes
Effective weed management is crucial for improving agricultural productivity, as weeds compete with crops for vital resources like nutrients and water. Accurate maps of weed management methods are essential for policymakers to assess farmer practices, evaluate impacts on vegetation health, biodiversity, and climate, as well as ensure compliance with policies and subsidies. However, monitoring weed management methods is challenging as commonly rely on on-ground field surveys, which are often costly, time-consuming and subject to delays. In order to tackle this problem, we leverage Earth Observation (EO) data and Machine Learning (ML). Specifically, we developed an ML approach for mapping four distinct weed management methods (Mowing, Tillage, Chemical-spraying, and No practice) in orchards using satellite image time series (SITS) data from two different sources: Sentinel-2 (S2) and PlanetScope (PS). The findings demonstrate the potential of ML-driven remote sensing to enhance the efficiency and accuracy of weed management mapping in orchards.
Accelerating Mixture-of-Experts Training with Adaptive Expert Replication
2025-04-28
Athinagoras Skiadopoulos, Mark Zhao, Swapnil Gandhi, Thomas Norrie, Shrijeet Mukherjee, Christos Kozyrakis
Mixture-of-Experts (MoE) models have become a widely adopted solution to continue scaling model sizes without a corresponding linear increase in compute. During MoE model training, each input token is dynamically routed to a subset of experts -- sparsely-activated feed-forward networks -- within each transformer layer. The distribution of tokens assigned to each expert varies widely and rapidly over the course of training. To handle the wide load imbalance across experts, current systems are forced to either drop tokens assigned to popular experts, degrading convergence, or frequently rebalance resources allocated to each expert based on popularity, incurring high state migration overheads. To break this performance-accuracy tradeoff, we introduce SwiftMoE, an adaptive MoE training system. The key insight of SwiftMoE is to decouple the placement of expert parameters from their large optimizer state. SwiftMoE statically partitions the optimizer of each expert across all training nodes. Meanwhile, SwiftMoE dynamically adjusts the placement of expert parameters by repurposing existing weight updates, avoiding migration overheads. In doing so, SwiftMoE right-sizes the GPU resources allocated to each expert, on a per-iteration basis, with minimal overheads. Compared to state-of-the-art MoE training systems, DeepSpeed and FlexMoE, SwiftMoE is able to achieve a 30.5% and 25.9% faster time-to-convergence, respectively.
The Cosmic Shoreline Revisited: A Metric for Atmospheric Retention Informed by Hydrodynamic Escape
2025-04-28
Xuan Ji, Richard D. Chatterjee, Brandon Park Coy, Edwin S. Kite
The "cosmic shoreline", a semi-empirical relation that separates airless worlds from worlds with atmospheres as proposed by Zahnle & Catling (2017), is now guiding large-scale JWST surveys aimed at detecting rocky exoplanet atmospheres. We expand upon this framework by revisiting the shorelines using existing hydrodynamic escape models applied to Earth-like, Venus-like, and steam atmospheres for rocky exoplanets, and we estimate energy-limited escape rates for CH4 atmospheres. We determine the critical instellation required for atmospheric retention by calculating time-integrated atmospheric mass loss. Our analysis introduces a new metric for target selection in the Rocky Worlds DDT and refines expectations for rocky planet atmosphere searches in Cycle 4. Exploring initial volatile inventory ranging from 0.01% to 1% of planetary mass, we find that its variation prevents the definition of a unique clear-cut shoreline, though non-linear escape physics can reduce this sensitivity to initial conditions. Additionally, uncertain distributions of high-energy stellar evolution and planet age further blur the critical instellations for atmospheric retention, yielding broad shorelines. Hydrodynamic escape models find atmospheric retention is markedly more favorable for higher-mass planets orbiting higher-mass stars, with carbon-rich atmospheres remaining plausible for 55 Cancri e despite its extreme instellation. Dedicated modeling efforts are needed to better constrain the escape dynamics of secondary atmospheres, such as the role of atomic line cooling, especially for Earth-sized planets. Finally, we illustrate how density measurements can be used to statistically test the existence of the cosmic shorelines, emphasizing the need for more precise mass and radius measurements.
2025-04-28
Luis Alvarez, Bruno Ferman, Kaspar Wüthrich
In many causal inference applications, only one or a few units (or clusters of units) are treated. An important challenge in such settings is that standard inference methods that rely on asymptotic theory may be unreliable, even when the total number of units is large. This survey reviews and categorizes inference methods that are designed to accommodate few treated units, considering both cross-sectional and panel data methods. We discuss trade-offs and connections between different approaches. In doing so, we propose slight modifications to improve the finite-sample validity of some methods, and we also provide theoretical justifications for existing heuristic approaches that have been proposed in the literature.
With the rapid rise of large language models (LLMs), phone automation has undergone transformative changes. This paper systematically reviews LLM-driven phone GUI agents, highlighting their evolution from script-based automation to intelligent, adaptive systems. We first contextualize key challenges, (i) limited generality, (ii) high maintenance overhead, and (iii) weak intent comprehension, and show how LLMs address these issues through advanced language understanding, multimodal perception, and robust decision-making. We then propose a taxonomy covering fundamental agent frameworks (single-agent, multi-agent, plan-then-act), modeling approaches (prompt engineering, training-based), and essential datasets and benchmarks. Furthermore, we detail task-specific architectures, supervised fine-tuning, and reinforcement learning strategies that bridge user intent and GUI operations. Finally, we discuss open challenges such as dataset diversity, on-device deployment efficiency, user-centric adaptation, and security concerns, offering forward-looking insights into this rapidly evolving field. By providing a structured overview and identifying pressing research gaps, this paper serves as a definitive reference for researchers and practitioners seeking to harness LLMs in designing scalable, user-friendly phone GUI agents.
Creation and Microscopic Origins of Single-Photon Emitters in Transition Metal Dichalcogenides and Hexagonal Boron Nitride
2025-04-28
Amedeo Carbone, Diane-Pernille Bendixen-Fernex de Mongex, Arkady V. Krasheninnikov, Martijn Wubs, Alexander Huck, Thomas W. Hansen, Alexander W. Holleitner, Nicolas Stenger, Christoph Kastl
We highlight recent advances in the controlled creation of single-photon emitters in van der Waals materials and in the understanding of their atomistic origin. We focus on quantum emitters created in monolayer transition-metal dichalcogenide semiconductors, which provide spectrally sharp single-photon emission at cryogenic temperatures, and the ones in insulating hBN, which provide bright and stable single-photon emission up to room temperature. After introducing the different classes of quantum emitters in terms of band-structure properties, we review the defect creation methods based on electron and ion irradiation as well as local strain engineering and plasma treatments. A main focus of the review is put on discussing the microscopic origin of the quantum emitters as revealed by various experimental platforms, including optical and scanning probe methods.
Protoplanetary disk insights from the first ERIS/APP survey at 4 μm
2025-04-28
F. Maio, V. Roccatagliata, D. Fedele, A. Garufi, A. Zurlo, C. Lazzoni, S. Facchini, R. G. Gratton, D. Mesa, C. Toci, S. Antoniucci, S. Desidera, L. . Pino, E. Rigliaco, C. Codella, L. Podio, V. D'Orazi, G. Lodato, F. Pedichini, L. Testi
We present high-contrast imaging observations of seven protoplanetary disks at 4um using the ERIS on the VLT. This study focuses on detecting scattered light from micron-sized dust particles and assessing the potential of the vAPP coronagraph for disk and planet characterization. Observations were performed in pupil-stabilized mode with the vAPP coronagraph. Data were reduced using reference differential imaging and angular differential imaging techniques, incorporating principal component analysis for point-source detection. Contrast curves and detection limits were computed for planetary companions and disk features. The infrared disk signal was resolved in all systems, with first-time 4um detections around AS 209 and Elias 2-24, revealing mostly axisymmetric structures extending up to 60au. Two gaps were detected in the radial profiles of TW Hya (22au, 35au) and AS 209 (50au, 100au). For Elias 2-24, scattered light emission matched ALMA observations of inner disk structures, marking their first mid-infrared detection. In the case of HD 100546, the vAPP uncovered flared disk structures and faint spiral arms consistent with previous observations. HD 163296 shows a bright inner dust ring, confirming disk asymmetries and features, but we did not detect any planet candidate within the achieved contrast limits. The disk around PDS 70 exhibits clear features, with faint structures detected within the cavity. The observations achieved contrasts enabling the detection of planets down to 800 K, but no companions were detected, implying either low-mass planets, cooler formation scenarios, or a large dust extinction of Av>20 mag. The vAPP performed robustly for imaging structures in protoplanetary disks at 4um, providing critical insights into disk morphology and constraints on planet formation processes. No planetary-mass companions with temperatures >1000K are present in our sample.
Memento: Augmenting Personalized Memory via Practical Multimodal Wearable Sensing in Visual Search and Wayfinding Navigation
2025-04-28
Indrajeet Ghosh, Kasthuri Jayarajah, Nicholas Waytowich, Nirmalya Roy
Working memory involves the temporary retention of information over short periods. It is a critical cognitive function that enables humans to perform various online processing tasks, such as dialing a phone number, recalling misplaced items' locations, or navigating through a store. However, inherent limitations in an individual's capacity to retain information often result in forgetting important details during such tasks. Although previous research has successfully utilized wearable and assistive technologies to enhance long-term memory functions (e.g., episodic memory), their application to supporting short-term recall in daily activities remains underexplored. To address this gap, we present Memento, a framework that uses multimodal wearable sensor data to detect significant changes in cognitive state and provide intelligent in situ cues to enhance recall. Through two user studies involving 15 and 25 participants in visual search navigation tasks, we demonstrate that participants receiving visual cues from Memento achieved significantly better route recall, improving approximately 20-23% compared to free recall. Furthermore, Memento reduced cognitive load and review time by 46% while also substantially reducing computation time (3.86 seconds vs. 15.35 seconds), offering an average of 75% effectiveness compared to computer vision-based cue selection approaches.
On Solving the Dynamics of Constrained Rigid Multi-Body Systems with Kinematic Loops
2025-04-28
Vassilios Tsounis, Ruben Grandia, Moritz Bächer
This technical report provides an in-depth evaluation of both established and state-of-the-art methods for simulating constrained rigid multi-body systems with hard-contact dynamics, using formulations of Nonlinear Complementarity Problems (NCPs). We are particularly interest in examining the simulation of highly coupled mechanical systems with multitudes of closed-loop bilateral kinematic joint constraints in the presence of additional unilateral constraints such as joint limits and frictional contacts with restitutive impacts. This work thus presents an up-to-date literature survey of the relevant fields, as well as an in-depth description of the approaches used for the formulation and solving of the numerical time-integration problem in a maximal coordinate setting. More specifically, our focus lies on a version of the overall problem that decomposes it into the forward dynamics problem followed by a time-integration using the states of the bodies and the constraint reactions rendered by the former. We then proceed to elaborate on the formulations used to model frictional contact dynamics and define a set of solvers that are representative of those currently employed in the majority of the established physics engines. A key aspect of this work is the definition of a benchmarking framework that we propose as a means to both qualitatively and quantitatively evaluate the performance envelopes of the set of solvers on a diverse set of challenging simulation scenarios. We thus present an extensive set of experiments that aim at highlighting the absolute and relative performance of all solvers on particular problems of interest as well as aggravatingly over the complete set defined in the suite.
Large Language Models (LLMs) for Generative AI have achieved remarkable progress, evolving into sophisticated and versatile tools widely adopted across various domains and applications. However, the substantial memory overhead caused by their vast number of parameters, combined with the high computational demands of the attention mechanism, poses significant challenges in achieving low latency and high throughput for LLM inference services. Recent advancements, driven by groundbreaking research, have significantly accelerated progress in this field. This paper provides a comprehensive survey of these methods, covering fundamental instance-level approaches, in-depth cluster-level strategies, emerging scenario directions, and other miscellaneous but important areas. At the instance level, we review model placement, request scheduling, decoding length prediction, storage management, and the disaggregation paradigm. At the cluster level, we explore GPU cluster deployment, multi-instance load balancing, and cloud service solutions. For emerging scenarios, we organize the discussion around specific tasks, modules, and auxiliary methods. To ensure a holistic overview, we also highlight several niche yet critical areas. Finally, we outline potential research directions to further advance the field of LLM inference serving.
Measurement of the total spin angular momentum of alkali-metal atoms
2025-04-28
Runa Yasuda, Kei Ishii, Wolfgang Klassen, Jeffery W. Martin, Atsushi Hatakeyama
It is important to evaluate the total spin angular momentum of alkali-metal atoms if the atoms serve as a reservoir of angular momenta. We use an absorption-monitoring technique to measure , i.e., the expectation values of the quantization (z) axis components of the total angular momentum of cesium (Cs) atoms in the electronic ground state in both uncoated and anti-relaxation-coated vacuum cells at room temperature. Cs atoms are polarized via optical pumping and probed using their D2 transitions. The probe laser frequency is varied across the Doppler-broadened D2 transition; the values are derived using the integrated absorption coefficients. The largest is 2.5 for the coated cell. We then use a simple model of spin flow through vapor cells to estimate the atomic spin relaxation probabilities after a single surface collision.
PhyloProfile v2 -- Exploring multi-layered phylogenetic profiles at scale
2025-04-28
Vinh Tran, Ingo Ebersberger
Phylogenetic profiles visualize the presence-absence pattern of genes across taxa and are essential for delineating the evolutionary fate of genes and gene families. Integrating phylogenetic profiles across many genes and taxa reveals patterns of coevolution, aiding the predictions of gene functions and interactions. The surge of genome sequences generated by biodiversity genomics projects allows to compile phylogenetic profiles at an unprecedented scale. PhyloProfile v2 was designed to cope with the novel challenges of visualizing and analyzing phylogenetic profiles comprising millions of pairwise orthology relationships. By providing the ability to interact with the visualization and dynamically filter the data, PhyloProfile v2 facilitates a seamless transition from survey analyses across thousands of genes and taxa down to the feature architecture comparison of two ortholog pairs within the same analysis. As one key innovation, PhyloProfile v2 allows the display of phylogenetic profiles in 2D or 3D using dimensionality reduction techniques. This novel perspective eases, for example, the identification of taxa with similar presence/absence patterns of genes irrespective of their phylogenetic relationships. PhyloProfile v2 is available as an R package at Bioconductor https://doi.org/doi:10.18129/B9.bioc.PhyloProfile. The open-source code and documentation are provided under MIT license at https://github.com/BIONF/PhyloProfile
Advances in Approximate Bayesian Inference for Models in Epidemiology
2025-04-28
Xiahui Li, Fergus Chadwick, Ben Swallow
Bayesian inference methods are useful in infectious diseases modeling due to their capability to propagate uncertainty, manage sparse data, incorporate latent structures, and address high-dimensional parameter spaces. However, parameter inference through assimilation of observational data in these models remains challenging. While asymptotically exact Bayesian methods offer theoretical guarantees for accurate inference, they can be computationally demanding and impractical for real-time outbreak analysis. This review synthesizes recent advances in approximate Bayesian inference methods that aim to balance inferential accuracy with scalability. We focus on four prominent families: Approximate Bayesian Computation, Bayesian Synthetic Likelihood, Integrated Nested Laplace Approximation, and Variational Inference. For each method, we evaluate its relevance to epidemiological applications, emphasizing innovations that improve both computational efficiency and inference accuracy. We also offer practical guidance on method selection across a range of modeling scenarios. Finally, we identify hybrid exact approximate inference as a promising frontier that combines methodological rigor with the scalability needed for the response to outbreaks. This review provides epidemiologists with a conceptual framework to navigate the trade-off between statistical accuracy and computational feasibility in contemporary disease modeling.
On the unique evolutionary mechanisms of massive quiescent galaxies in the epoch of reionisation
2025-04-28
Harry George Chittenden, Karl Glazebrook, Themiya Nanayakkara, Lalitwadee Kawinwanichakij, Claudia Lagos, Lucas Kimmig, Rhea-Silvia Remus
We investigate the evolutionary histories of a population of high mass, high redshift, quiescent galaxies in the cosmohydrodynamical simulation Thesan; studying the characteristic properties of their haloes and environments over the epoch of reionisation. Thesan employs a modified version of the Arepo moving-mesh code utilised in IllustrisTNG, whose explicit handling of ionising radiation couples haloes and galaxies with cosmic reionisation. This results in a regime of rapid growth and quenching, producing nine massive quiescent galaxies at $z = 5.5$, in a $95.5\text{cMpc}^3$ volume, with no counterpart in IllustrisTNG. We find that the rapid assembly of stellar mass is attributed to smooth accretion of mass onto their haloes in dense environments, particularly from massive neighbouring structures; while these galaxies exhibit fast-growing potential wells hosting massive central black holes. With insignificant merger activity, we find AGN feedback to be the primary source of quenching. We find megaparsec-scale densities and halo masses to continue growing after quenching, suggesting that massive quiescent galaxies will be found in some of the largest haloes and densest regions of space by observations. With an unprecedented plethora of massive quiescent galaxies found in JWST data, these reionisation simulations may provide the paradigm for the evolutionary histories of these galaxies, while constraining the relatively unknown physics of reionisation. Despite the rarity of simulated massive quiescent galaxies, their identification in Thesan enables identification of halo and environmental properties most conducive to their quenching, hopefully guiding future efforts into the study of analogous systems in deep observational surveys and large-volume N-body simulations.
Large language models and autonomous AI agents have evolved rapidly, resulting in a diverse array of evaluation benchmarks, frameworks, and collaboration protocols. However, the landscape remains fragmented and lacks a unified taxonomy or comprehensive survey. Therefore, we present a side-by-side comparison of benchmarks developed between 2019 and 2025 that evaluate these models and agents across multiple domains. In addition, we propose a taxonomy of approximately 60 benchmarks that cover general and academic knowledge reasoning, mathematical problem-solving, code generation and software engineering, factual grounding and retrieval, domain-specific evaluations, multimodal and embodied tasks, task orchestration, and interactive assessments. Furthermore, we review AI-agent frameworks introduced between 2023 and 2025 that integrate large language models with modular toolkits to enable autonomous decision-making and multi-step reasoning. Moreover, we present real-world applications of autonomous AI agents in materials science, biomedical research, academic ideation, software engineering, synthetic data generation, chemical reasoning, mathematical problem-solving, geographic information systems, multimedia, healthcare, and finance. We then survey key agent-to-agent collaboration protocols, namely the Agent Communication Protocol (ACP), the Model Context Protocol (MCP), and the Agent-to-Agent Protocol (A2A). Finally, we discuss recommendations for future research, focusing on advanced reasoning strategies, failure modes in multi-agent LLM systems, automated scientific discovery, dynamic tool integration via reinforcement learning, integrated search capabilities, and security vulnerabilities in agent protocols.
Generative AI in Education: Student Skills and Lecturer Roles
2025-04-28
Stefanie Krause, Ashish Dalvi, Syed Khubaib Zaidi
Generative Artificial Intelligence (GenAI) tools such as ChatGPT are emerging as a revolutionary tool in education that brings both positive aspects and challenges for educators and students, reshaping how learning and teaching are approached. This study aims to identify and evaluate the key competencies students need to effectively engage with GenAI in education and to provide strategies for lecturers to integrate GenAI into teaching practices. The study applied a mixed method approach with a combination of a literature review and a quantitative survey involving 130 students from South Asia and Europe to obtain its findings. The literature review identified 14 essential student skills for GenAI engagement, with AI literacy, critical thinking, and ethical AI practices emerging as the most critical. The student survey revealed gaps in prompt engineering, bias awareness, and AI output management. In our study of lecturer strategies, we identified six key areas, with GenAI Integration and Curriculum Design being the most emphasised. Our findings highlight the importance of incorporating GenAI into education. While literature prioritized ethics and policy development, students favour hands-on, project-based learning and practical AI applications. To foster inclusive and responsible GenAI adoption, institutions should ensure equitable access to GenAI tools, establish clear academic integrity policies, and advocate for global GenAI research initiatives.
Stars with very low levels of magnetic activity provide an opportunity for a more quantitative comparison with the Sun during its Maunder minimum. We employ spectra from the RAVE survey in a search for particularly low-activity stars with the goal of identifying candidates for so-called Maunder-minimum stars. Spectra were used to measure the relative flux in the cores of the Ca II infrared-triplet (IRT) lines. Those were converted to absolute emission-line fluxes and were corrected with target fluxes from high-resolution STELLA and ultra-high-resolution PEPSI spectra. Absolute Ca II IRT fluxes for a total of 78111 RAVE dwarf stars are presented and compared with fluxes of the 123 stars from our high-resolution STELLA+PEPSI sample. RAVE fluxes appear higher than the STELLA and PEPSI fluxes by on average 19% for IRT-1, 21% for IRT-2, and 25% for IRT-3 due to their lower spectral resolution. Our sample also spans a metallicity [Fe/H] range relative to the Sun of -1.5 to +0.5 dex. We confirm the strong dependency of IRT fluxes on metallicity and quantify it to be at most $\pm$14% in the B-V range 0.53-0.73. Without a metallicity correction, practically all very-low-activity RAVE dwarfs show a super-solar metallicity. After correcting for spectral resolution and for metallicity, we find 13 RAVE stars out of 13326 (0.1%) that fall well below our empirical lower flux bound from high-resolution versus B-V. For solar B-V, this relates to a photospheric uncorrected radiative loss in the IRT lines of log R_IRT=-4.13 (~20% below the solar-minimum value in late 2016). However, 11 targets turned out to be evolved stars based on their Gaia DR3 parallaxes. Two stars, TIC 352227373 (G2V) and TYC 7560-477-1 (G7V), are our only Maunder-minimum candidates from the present search. Contrary to the initial suggestion from the Mount-Wilson H&K Survey, we conclude that such stars are very rare.
Decentralization of Generative AI via Mixture of Experts for Wireless Networks: A Comprehensive Survey
2025-04-28
Yunting Xu, Jiacheng Wang, Ruichen Zhang, Changyuan Zhao, Dusit Niyato, Jiawen Kang, Zehui Xiong, Bo Qian, Haibo Zhou, Shiwen Mao, Abbas Jamalipour, Xuemin Shen, Dong In Kim
Mixture of Experts (MoE) has emerged as a promising paradigm for scaling model capacity while preserving computational efficiency, particularly in large-scale machine learning architectures such as large language models (LLMs). Recent advances in MoE have facilitated its adoption in wireless networks to address the increasing complexity and heterogeneity of modern communication systems. This paper presents a comprehensive survey of the MoE framework in wireless networks, highlighting its potential in optimizing resource efficiency, improving scalability, and enhancing adaptability across diverse network tasks. We first introduce the fundamental concepts of MoE, including various gating mechanisms and the integration with generative AI (GenAI) and reinforcement learning (RL). Subsequently, we discuss the extensive applications of MoE across critical wireless communication scenarios, such as vehicular networks, unmanned aerial vehicles (UAVs), satellite communications, heterogeneous networks, integrated sensing and communication (ISAC), and mobile edge networks. Furthermore, key applications in channel prediction, physical layer signal processing, radio resource management, network optimization, and security are thoroughly examined. Additionally, we present a detailed overview of open-source datasets that are widely used in MoE-based models to support diverse machine learning tasks. Finally, this survey identifies crucial future research directions for MoE, emphasizing the importance of advanced training techniques, resource-aware gating strategies, and deeper integration with emerging 6G technologies.
Auxiliary Artifacts in Requirements Traceability: A Systematic Mapping Study
2025-04-28
Waleed Abdeen, Michael Unterkalmsteiner, Krzysztof Wnuk
Background: Traceability between software artifacts enhances the value of the information those artifacts contain, but only when the links themselves are reliable. Link quality is known to depend on explicit factors such as the traced artifacts and the expertise of the practitioner who judges each connection. Other factors, however, remain largely unexplored. We contend that one of these factors is the set of auxiliary artifacts -- artifacts that are produced and/or used during the tracing process yet are neither the source nor target artifacts. Because such auxiliary artifacts can subtly steer how links are created and validated, they merit a literature survey to identify these artifacts and further investigate them. Objective: We identify and map auxiliary artifacts used in requirements tracing, which could be additional factors that affect the quality of the trace links. Method: We conducted a systematic mapping study on auxiliary artifacts in requirements traceability. Results: We found 110 studies in which auxiliary artifacts are used in requirements tracing, and identified 49 auxiliary artifacts, and 13 usage scenarios. Conclusion: This study provides a systematic mapping of auxiliary artifacts in requirement tracing, including their usage, origin, type and tool support. The use of auxiliary artifacts in requirements tracing seems to be the norm, thus, these artifacts should be studied in depth to identify how they effect the quality of traced links.
Investigating the Period-Luminosity Relations of delta Scuti Stars: A Pathway to Distance and 3-D Dust Map Inference
2025-04-28
Fangzhou Guo, Joshua S. Bloom, Xiaofeng Wang, Liyang Chen, Jie Lin, Xiaodian Chen, Jun Mo, Jicheng Zhang, Shengyu Yan, Qichun Liu, Haowei Peng, Xiaojun Jiang, Xiaoran Ma, Danfeng Xiang, Wenxiong Li
While delta Scuti stars are the most numerous class of kappa-mechanism pulsators in the instability strip, the short periods and small peak-to-peak amplitudes have left them understudied and underutilized. Recently, large-scale time-domain surveys have significantly increased the number of identified delta Scuti stars. Notably, the Tsinghua University-Ma Huateng Telescopes for Survey (TMTS), with its high-cadence observations at 1-minute intervals, has identified thousands of delta Scuti stars, greatly expanding the sample of these short-period pulsating variables. Using the delta Scuti stars from the TMTS catalogs of Periodic Variable Stars, we cross-matched the dataset with Pan-STARRS1, 2MASS, and WISE to obtain photometric measurements across optical and infrared bands. Parallax data, used as Bayesian priors, were retrieved from Gaia DR3, and line-of-sight dust extinction priors were estimated from a three-dimensional dust map. Using PyMC, we performed a simultaneous determination of the 11-band P-L relations of delta Scuti stars, which not only yields precise measurements of these relations, but also greatly improves constraints on the distance moduli and color excesses, as evidenced by the reduced uncertainties in the posterior distributions. Furthermore, our methodology enables an independent estimation of the color excess through the P-L relations, offering a potential complement to existing 3-D dust maps. Moreover, by cross-matching with LAMOST DR7, we investigated the influence of metallicity on the P-L relations. Our analysis reveals that incorporating metallicity might reduce the intrinsic scatter at longer wavelengths. However, this result does not achieve 3 sigma significance, leaving open the possibility that the observed reduction is attributable to statistical fluctuations.
This paper proposes an image-based robot motion planning method using a one-step diffusion model. While the diffusion model allows for high-quality motion generation, its computational cost is too expensive to control a robot in real time. To achieve high quality and efficiency simultaneously, our one-step diffusion model takes an approximately generated motion, which is predicted directly from input images. This approximate motion is optimized by additive noise provided by our novel noise optimizer. Unlike general isotropic noise, our noise optimizer adjusts noise anisotropically depending on the uncertainty of each motion element. Our experimental results demonstrate that our method outperforms state-of-the-art methods while maintaining its efficiency by one-step diffusion.
Gaia FGK Benchmark Stars: spectral library, metallicities and abundances of $α$ and Fe-peak elements of the third version
2025-04-28
L. Casamiquela, C. Soubiran, P. Jofré, S. Vitali, S. Blanco-Cuaresma, N. Lagarde, D. Slumstrup, J. T. Palmerio, N. Brouillet, S. Elgueta, A. Rojas-Arriagada, C. Aguilera-Gómez, I. Hernández-Araya, O. L. Creevey, U. Heiter, L. Balaguer-Núñez, R. Carrera
The accurate determination of chemical abundances in stars plays a pivotal role in understanding stellar structure and evolution, nucleosynthesis, and the chemical enrichment history of the Milky Way. Benchmark stars with precise and accurate atmospheric parameters and abundances are indispensable for calibrating spectroscopic surveys and testing stellar atmosphere models. This study focuses on the compilation of high-quality spectra and the determination of LTE chemical abundances of iron-peak and $\alpha$ elements for the third version of the Gaia FGK Benchmark Stars (GBSv3). We compiled spectra of the GBSv3 from public archives and complemented these with our own observations. We use fundamental atmospheric parameters from Soubiran et al. 2024 to derive the chemical abundances and perform a spectroscopic analysis using the public code iSpec. We compile a homogeneous spectral library of high-resolution (42,000) and high signal-to-noise ($>100$) normalised spectra for 202 stars: including the 192 GBSv3, 9 stars with indirect measurement of the angular diameter from previous GBS versions, and the Sun. Using four radiative transfer codes, we derive chemical abundances of 13 chemical species (Fe I, Fe II, Mg I, Si I, Ca I, Ti I, Ti II, Sc II, V I, Cr I, Mn I, Co I, Ni I). We make an in-depth study of several sources of error. The GBSv3 contributes to the legacy samples of spectroscopic reference stars with improved statistics and homogeneity. The compiled high-resolution spectral library and the determination of abundances for iron-peak and $\alpha$ elements, together with an extensive discussion of the linked uncertainties, provides a sample of reference abundances to the community.
The computation of time dynamics arising in nonlinear time-dependent partial differential equations is an ongoing challenge in numerical analysis, especially once roughness comes into play. Classical numerical schemes in general fail to resolve the oscillatory behaviour in the solution which leads to numerical instabilities and loss of convergence. Dispersive equations, e.g., nonlinear Schr\"odinger, Korteweg--de Vries and wave equations, thereby pose in particular a big problem as in contrast to the parabolic setting, no strong smoothing can be expected, i.e., if the initial data is rough, the solution stays rough which makes their approximation a delicate task. In this review we give an overview on a new numerical ansatz which aims to tackle the time dynamics of nonlinear dispersive partial differential equations even for very rough data. This is achieved by a resonance analysis and decorated tree formalism that draws its inpiration from the combinatorics used in the theory of regularity structures for solving singular SPDEs. One can hope to see this formalism applied in other contexts for dispersive PDEs and beyond.
End-to-end Transformer-based detectors (DETRs) have demonstrated strong detection performance. However, domain generalization (DG) research has primarily focused on convolutional neural network (CNN)-based detectors, while paying little attention to enhancing the robustness of DETRs. In this letter, we introduce a Domain Generalized DEtection TRansformer (DG-DETR), a simple, effective, and plug-and-play method that improves out-of-distribution (OOD) robustness for DETRs. Specifically, we propose a novel domain-agnostic query selection strategy that removes domain-induced biases from object queries via orthogonal projection onto the instance-specific style space. Additionally, we leverage a wavelet decomposition to disentangle features into domain-invariant and domain-specific components, enabling synthesis of diverse latent styles while preserving the semantic features of objects. Experimental results validate the effectiveness of DG-DETR. Our code is available at https://github.com/sminhwang/DG-DETR.
Existence of Friedrich-Wintgen bound states in the continuum: system of Schrödinger equations
2025-04-28
Xiuchen Yu, Ya Yan Lu
A bound state in the continuum (BIC) is an eigenmode with the corresponding eigenvalue embedded in the continuous spectrum. There is currently a significant research interest on BICs in the photonics community, because they can be used to induce strong resonances that are useful for lasing, sensing, harmonic generation, etc. The existence of BICs in classical or quantum wave systems has only been established for some relatively simple cases such as BICs protected by symmetry. In 1985, Friedrich and Wintgen (Physical Review A, Vol. 32, pp. 3232-3242, 1985) suggested that BICs may appear from the destructive interference of two resonances coupled to a single radiation channel. They used a system of three one-dimensional Schr\"{o}dinger equations to illustrate this process. Many BICs in classical wave systems seem to follow this mechanism and are now called Friedrich-Wintgen BICs. However, Friedrich and Wintgen did not show the existence of BICs in their system of three Schr\"{o}dinger equations. Instead, they approximated the original system by a model with one Schr\"{o}dinger equation and two algebraic equations, and only analyzed BICs in the approximate model. In this paper, we give a rigorous justification for the existence of BICs in the original system of three 1D Schr\"{o}dinger equations.
$K_1$-Stability of symplectic modules over monoid algebras
2025-04-28
Rabeya Basu, Maria Ann Mathew
Let $R$ be a regular ring of dimension $d$ and $L$ be a $c$-divisible monoid. If ${K}_1{Sp}(R)$ is trivial and $k \geq d+2,$ then we prove that the symplectic group ${Sp}_{2k}(R[L])$ is generated by elementary symplectic matrices over $R[L]$. When $d \leq 1$ or $R$ is a geometrically regular ring containing a field, then improved bounds have been established. We also discuss the linear case, extending the work of Gubeladze.
Morphological Feature Distances Among the Spectral Types of SDSS Galaxies
2025-04-28
Duho Kim, Garreth Martin
This study investigates the morphological feature distances among various spectral types of galaxies from the Sloan Digital Sky Survey (SDSS), including strong and weak Active Galactic Nuclei (AGN), Quasi-Stellar Objects (QSO), quiescent, and star-forming galaxies. We evaluated the clustering and relative distances of these spectral types in the multidimensional morphological feature space. The results indicate that AGN and QSOs are more closely associated with quiescent galaxies than with star-forming ones, indicating a potential regulation of star formation by AGN activity. Furthermore, the analysis underlines the role of AGN feedback in the dwarf regime having $\sim$10~50% closer distances from AGN types to the quiescent type than to the SF type in the dwarf regime $-18 > M_r > -20$, compared to $<$15% closer in the massive regime $M_r < -21$. The continuous probability analysis for being Hubble types of the spectral types upholds the distance analysis results having a range of the probability distribution of AGN types similar to quiescent type, especially in dwarf galaxies.
Relativistic Two-Electron Contributions within Exact Two-Component Theory
2025-04-28
Xubo Wang, Chaoqun Zhang, Junzi Liu, Lan Cheng
The development of relativistic exact two-component (X2C) theory is briefly reviewed, with an emphasis on cost-effective treatments of relativistic two-electron contributions by means of model potential (MP) techniques and closely related atomic mean-field (AMF) approaches. The correct MP or AMF contribution to the electronic energy is elucidated. The performance of one-center approximations to relativistic two-electron contributions is carefully assessed using benchmark calculations of molecular properties.
Conflicts in Texts: Data, Implications and Challenges
2025-04-28
Siyi Liu, Dan Roth
As NLP models become increasingly integrated into real-world applications, it becomes clear that there is a need to address the fact that models often rely on and generate conflicting information. Conflicts could reflect the complexity of situations, changes that need to be explained and dealt with, difficulties in data annotation, and mistakes in generated outputs. In all cases, disregarding the conflicts in data could result in undesired behaviors of models and undermine NLP models' reliability and trustworthiness. This survey categorizes these conflicts into three key areas: (1) natural texts on the web, where factual inconsistencies, subjective biases, and multiple perspectives introduce contradictions; (2) human-annotated data, where annotator disagreements, mistakes, and societal biases impact model training; and (3) model interactions, where hallucinations and knowledge conflicts emerge during deployment. While prior work has addressed some of these conflicts in isolation, we unify them under the broader concept of conflicting information, analyze their implications, and discuss mitigation strategies. We highlight key challenges and future directions for developing conflict-aware NLP systems that can reason over and reconcile conflicting information more effectively.
The Role of Generative AI in Strengthening Secure Software Coding Practices: A Systematic Perspective
2025-04-28
Hathal S. Alwageed, Rafiq Ahmad Khan
As software security threats continue to evolve, the demand for innovative ways of securing coding has tremendously grown. The integration of Generative AI (GenAI) into software development holds significant potential for improving secure coding practices. This paper aims at systematically studying the impact of GenAI in enhancing secure coding practices from improving software security, setting forth its potential benefits, challenges, and implications. To outline the contribution of AI driven code generation tools, we analyze via a structured review of recent literature, application to the industry, and empirical studies on how these tools help to mitigate security risks, comply with the secure coding standards, and make software development efficient. We hope that our findings will benefit researchers, software engineers and cybersecurity professionals alike in integrating GenAI into a secure development workflow without losing the advantages GenAI provides. Finally, the state of the art advances and future directions of AI assisted in secure software engineering discussed in this study can contribute to the ongoing discourse on AI assisted in secure software engineering.
Do Automatic Comment Generation Techniques Fall Short? Exploring the Influence of Method Dependencies on Code Understanding
2025-04-28
Md Mustakim Billah, Md Shamimur Rahman, Banani Roy
Method-level comments are critical for improving code comprehension and supporting software maintenance. With advancements in large language models (LLMs), automated comment generation has become a major research focus. However, existing approaches often overlook method dependencies, where one method relies on or calls others, affecting comment quality and code understandability. This study investigates the prevalence and impact of dependent methods in software projects and introduces a dependency-aware approach for method-level comment generation. Analyzing a dataset of 10 popular Java GitHub projects, we found that dependent methods account for 69.25% of all methods and exhibit higher engagement and change proneness compared to independent methods. Across 448K dependent and 199K independent methods, we observed that state-of-the-art fine-tuned models (e.g., CodeT5+, CodeBERT) struggle to generate comprehensive comments for dependent methods, a trend also reflected in LLM-based approaches like ASAP. To address this, we propose HelpCOM, a novel dependency-aware technique that incorporates helper method information to improve comment clarity, comprehensiveness, and relevance. Experiments show that HelpCOM outperforms baseline methods by 5.6% to 50.4% across syntactic (e.g., BLEU), semantic (e.g., SentenceBERT), and LLM-based evaluation metrics. A survey of 156 software practitioners further confirms that HelpCOM significantly improves the comprehensibility of code involving dependent methods, highlighting its potential to enhance documentation, maintainability, and developer productivity in large-scale systems.
Online Safety for All: Sociocultural Insights from a Systematic Review of Youth Online Safety in the Global South
2025-04-28
Ozioma C. Oguine, Oghenemaro Anuyah, Zainab Agha, Iris Melgarez, Adriana Alvarado Garcia, Karla Badillo-Urquiola
Youth online safety research in HCI has historically centered on perspectives from the Global North, often overlooking the unique particularities and cultural contexts of regions in the Global South. This paper presents a systematic review of 66 youth online safety studies published between 2014 and 2024, specifically focusing on regions in the Global South. Our findings reveal a concentrated research focus in Asian countries and predominance of quantitative methods. We also found limited research on marginalized youth populations and a primary focus on risks related to cyberbullying. Our analysis underscores the critical role of cultural factors in shaping online safety, highlighting the need for educational approaches that integrate social dynamics and awareness. We propose methodological recommendations and a future research agenda that encourages the adoption of situated, culturally sensitive methodologies and youth-centered approaches to researching youth online safety regions in the Global South. This paper advocates for greater inclusivity in youth online safety research, emphasizing the importance of addressing varied sociocultural contexts to better understand and meet the online safety needs of youth in the Global South.
DeepAndes: A Self-Supervised Vision Foundation Model for Multi-Spectral Remote Sensing Imagery of the Andes
2025-04-28
Junlin Guo, James R. Zimmer-Dauphinee, Jordan M. Nieusma, Siqi Lu, Quan Liu, Ruining Deng, Can Cui, Jialin Yue, Yizhe Lin, Tianyuan Yao, Juming Xiong, Junchao Zhu, Chongyu Qu, Yuechen Yang, Mitchell Wilkes, Xiao Wang, Parker VanValkenburgh, Steven A. Wernke, Yuankai Huo
By mapping sites at large scales using remotely sensed data, archaeologists can generate unique insights into long-term demographic trends, inter-regional social networks, and past adaptations to climate change. Remote sensing surveys complement field-based approaches, and their reach can be especially great when combined with deep learning and computer vision techniques. However, conventional supervised deep learning methods face challenges in annotating fine-grained archaeological features at scale. While recent vision foundation models have shown remarkable success in learning large-scale remote sensing data with minimal annotations, most off-the-shelf solutions are designed for RGB images rather than multi-spectral satellite imagery, such as the 8-band data used in our study. In this paper, we introduce DeepAndes, a transformer-based vision foundation model trained on three million multi-spectral satellite images, specifically tailored for Andean archaeology. DeepAndes incorporates a customized DINOv2 self-supervised learning algorithm optimized for 8-band multi-spectral imagery, marking the first foundation model designed explicitly for the Andes region. We evaluate its image understanding performance through imbalanced image classification, image instance retrieval, and pixel-level semantic segmentation tasks. Our experiments show that DeepAndes achieves superior F1 scores, mean average precision, and Dice scores in few-shot learning scenarios, significantly outperforming models trained from scratch or pre-trained on smaller datasets. This underscores the effectiveness of large-scale self-supervised pre-training in archaeological remote sensing. Codes will be available on https://github.com/geopacha/DeepAndes.
SoK: A Survey of Mixing Techniques and Mixers for Cryptocurrencies
2025-04-28
Juraj Mariani, Ivan Homoliak
Blockchain technologies have overturned the digital finance industry by introducing a decentralized pseudonymous means of monetary transfer. The pseudonymous nature introduced privacy concerns, enabling various deanonymization techniques, which in turn spurred development of stronger anonymity-preserving measures. The purpose of this paper is to create a comprehensive survey of mixing techniques and implementations within the vast ecosystem surrounding anonymization tools and mechanisms available in blockchain cryptocurrencies. First, we begin by reviewing classifications used in the field. Then, we survey various obfuscation techniques, helping to delve into actual implementations and combinations of these techniques. Next, we identify the positive and negative attributes of the approaches and implementations included. Moreover, we examine the implications of anonymization tools for user privacy, including their effectiveness in preserving anonymity and susceptibility to attacks and vulnerabilities. Finally, we discuss the challenges and innovations for extending mixing services into the realm of smart contracts or cross-chain space.
Time-domain surveys have advanced astronomical research by revealing diverse variable phenomena, from stellar flares to transient events. The scale and complexity of survey data, along with the demand for rapid classification, present significant challenges for analysis. While machine learning offers solutions, most existing models are tailored to single tasks, struggle to generalize, and depend heavily on large, accurately labeled datasets. We introduce FALCO, a foundation model for astronomical light curve analysis in time-domain astronomy. This work presents the initial version of FALCO trained via self-supervised learning on unlabeled Kepler light curves using a Transformer-based architecture. The model has been evaluated on three distinct tasks and demonstrates strong generalization: achieving 95 percent accuracy in stellar variability classification across eight classes, an overall RMSE of 0.1305 dex in surface gravity estimation (notably improved to below 0.08 dex when log g is less than 1, and approximately 0.02 dex near log g equals 3), and 87 percent precision in flare identification. These results highlight the model's versatility and ability to learn generalizable representations from light curves, enabling straightforward adaptation to diverse tasks. We further analyze the impact of model scaling and sequence length, finding performance improves with larger models and longer input sequences. We also apply FALCO to derive surface gravity (log g) measurements for 179,732 Kepler stars from their light curves.
Inverse design optimization aims to infer system parameters from observed solutions, posing critical challenges across domains such as semiconductor manufacturing, structural engineering, materials science, and fluid dynamics. The lack of explicit mathematical representations in many systems complicates this process and makes the first order optimization impossible. Mainstream approaches, including generative AI and Bayesian optimization, address these challenges but have limitations. Generative AI is computationally expensive, while Bayesian optimization, relying on surrogate models, suffers from scalability, sensitivity to priors, and noise issues, often leading to suboptimal solutions. This paper introduces Deep Physics Prior (DPP), a novel method enabling first-order gradient-based inverse optimization with surrogate machine learning models. By leveraging pretrained auxiliary Neural Operators, DPP enforces prior distribution constraints to ensure robust and meaningful solutions. This approach is particularly effective when prior data and observation distributions are unknown.
Enhancing Systematic Reviews with Large Language Models: Using GPT-4 and Kimi
2025-04-28
Dandan Chen Kaptur, Yue Huang, Xuejun Ryan Ji, Yanhui Guo, Bradley Kaptur
This research delved into GPT-4 and Kimi, two Large Language Models (LLMs), for systematic reviews. We evaluated their performance by comparing LLM-generated codes with human-generated codes from a peer-reviewed systematic review on assessment. Our findings suggested that the performance of LLMs fluctuates by data volume and question complexity for systematic reviews.
2025-04-28
Nicholas M. Luber, Min S. Yun, Hansung B. Gim, Daniel Krista-Kelsey, D. J. Pisano, Emmanuel Momjian, Chris Hales
We introduce and describe the CHILES Continuum \& Polarization (CHILES Con Pol) Survey, a 1000 hour 1.4 GHz wideband full polarization radio continuum deepfield with the Very Large Array (VLA), commensurate with the CHILES HI deepfield. We describe the observational configuration, outline the calibration of the data, and discuss the effect of Radio Frequency Interference on different observing epochs. In addition, we present a novel radio continuum imaging strategy, using well known baseline subtraction techniques in radio spectral data, and discuss the applications to the removal of artifacts from sources far from the field center. Additionally, we discuss the nature of a low-level image-wide offset, the so-called ``negative bowl" and simulate our observations in order that we may both properly understand and correct for this artifact. Using these techniques, we present the first total intensity image of the data which achieves an r.m.s. noise of 1.3 $\mu$Jy beam$^{-1}$ with a synthesized beam of 4.5\arcsec x 4.0\arcsec, the most sensitive L-band image ever taken at this resolution. We then place this image into the broader context of 1.4 GHz radio continuum surveys in the literature, in terms of image sensitivity and fidelity, and $\mu$Jy level source counts and P(D) analysis.
A Platform for Generating Educational Activities to Teach English as a Second Language
2025-04-28
Aiala Rosá, Santiago Góngora, Juan Pablo Filevich, Ignacio Sastre, Laura Musto, Brian Carpenter, Luis Chiruzzo
We present a platform for the generation of educational activities oriented to teaching English as a foreign language. The different activities -- games and language practice exercises -- are strongly based on Natural Language Processing techniques. The platform offers the possibility of playing out-of-the-box games, generated from resources created semi-automatically and then manually curated. It can also generate games or exercises of greater complexity from texts entered by teachers, providing a stage of review and edition of the generated content before use. As a way of expanding the variety of activities in the platform, we are currently experimenting with image and text generation. In order to integrate them and improve the performance of other neural tools already integrated, we are working on migrating the platform to a more powerful server. In this paper we describe the development of our platform and its deployment for end users, discussing the challenges faced and how we overcame them, and also detail our future work plans.
The CHILES Continuum & Polarization Survey-II: Radio Continuum Source Catalog and Radio Properties
2025-04-28
Hansung B. Gim, Min S. Yun, Nicholas M. Luber, Emmanuel Momjian, D. J. Pisano, Kelley M. Hess, Julia Blue Bird, Lucas Hunt
The COSMOS HI Large Extragalactic Survey (CHILES) Continuum & Polarization (CHILES Con Pol) survey is an ultra-deep continuum imaging study of the COSMOS field conducted using the Karl G. Jansky Very Large Array. We obtained 1000 hours of L-band ($\lambda = 20$ cm) observations across four spectral windows (1.063-1.831 GHz) on a single pointing and produced a confusion limited image with an apparent RMS noise of 1.67 $\mu$Jy beam$^{-1}$ with a synthesized beam of 5$.\!\!^{\prime\prime}$5$\times$5$.\!\!^{\prime\prime}$0. This paper reports a 1.4 GHz radio continuum source catalog containing 1678 sources detected above 7$\sigma$ (flux densities greater than 11.7 $\mu$Jy), identified using two independent source extraction programs applied to the Stokes $I$ image. Resolved sources dominate at flux density S$_{1.4GHz} \ge 42 $\mu$Jy. Radio spectral index for each source was derived using a power-law fit across the four spectral windows, and we found that a robust spectral index measurement requires a total signal-to-noise ratio of at least 20. Comparisons with previous 1.4 GHz radio continuum surveys show good overall consistency, but evidence for a high degree of catalog incompleteness and the effects of source confusion are evident for some of the earlier studies.
Communication in Agile Software Development -- A Mapping Study
2025-04-28
Suddhasvatta Das, Kevin Gary
Software industry is a fast-moving industry and to keep up with this pace the development process also needs to be fast and efficient and Agile software development (ASD) is the answer to this problem. Even though ASD has been in there for over two decades there are still multiple unknown questions tied to ASD that need to be addressed. In this study we are going to address one of the most critical factors of ASD i.e. Communication. We conducted a review of 14 studies and found the areas under ASD communication that the community is interested in as well as research gaps.
The increasing adoption of autonomous vehicles is bringing a major shift in the automotive industry. However, as these vehicles become more connected, cybersecurity threats have emerged as a serious concern. Protecting the security and integrity of autonomous systems is essential to prevent malicious activities that can harm passengers, other road users, and the overall transportation network. This paper focuses on addressing the cybersecurity issues in autonomous vehicles by examining the challenges and risks involved, which are important for building a secure future. Since autonomous vehicles depend on the communication between sensors, artificial intelligence, external infrastructure, and other systems, they are exposed to different types of cyber threats. A cybersecurity breach in an autonomous vehicle can cause serious problems, including a loss of public trust and safety. Therefore, it is very important to develop and apply strong cybersecurity measures to support the growth and acceptance of self-driving cars. This paper discusses major cybersecurity challenges like vulnerabilities in software and hardware, risks from wireless communication, and threats through external interfaces. It also reviews existing solutions such as secure software development, intrusion detection systems, cryptographic protocols, and anomaly detection methods. Additionally, the paper highlights the role of regulatory bodies, industry collaborations, and cybersecurity standards in creating a secure environment for autonomous vehicles. Setting clear rules and best practices is necessary for consistent protection across manufacturers and regions. By analyzing the current cybersecurity landscape and suggesting practical countermeasures, this paper aims to contribute to the safe development and public trust of autonomous vehicle technology.
The chemical enrichment histories across the Milky Way disk
2025-04-28
Valeria Cerqui, Misha Haywood, Owain Snaith, Paola Di Matteo, Laia Casamiquela
The variation of metal production over time and its dilution in the interstellar medium depend on the star formation and gas accretion rates. Measuring age-chemistry relations across the Milky Way disk provides key constraints on the gas accretion and star formation histories, and offers insight into the birth locations of stars. We present a study based on a sample of nearly 30000 dwarf stars from the APOGEE DR17 survey within 2 kpc of the Sun, for which we measure accurate ages. Various parameter combinations are tested to optimize stellar age determination from isochrones. The resulting age-chemistry relations for a selected subsample of 12000 stars are interpreted with the aid of a chemical evolution model. The data reveal a well-defined, tight thick disk sequence, characterized by high [alpha/Fe], subsolar metallicities, and ages older than 8 Gyr. The thin disk, with lower [alpha/Fe] and younger ages, exhibits a wide spread in metallicity at all ages, with apparent structures. Dividing the sample by guiding radius into inner, intermediate, and outer disks shows distinct chemical evolution patterns. The inner disk displays a monotonic, homogeneous chemical evolution with little dispersion, while the outer disk shows little metallicity increase over the past 8 Gyr. The solar neighborhood appears as a mixture, not only due to stellar migration but also because the chemical evolution of the ISM in this intermediate region results from the mixing of gas from the inner and outer disks. In particular, we demonstrate that the solar vicinity experienced a decrease in the mean ISM metallicity 7-9 Gyr ago. A plausible explanation involves a radial inflow of lower-metallicity gas from the outer disk at that time, which diluted the gas leftover by the thick disk formation, contributing to the observed metallicity gradient in the intermediate region.
The differences of baryonic and dark matter scaling relations in galaxy clusters between IllustrisTNG simulations and observations
2025-04-28
Daniel Miller, Diego Pallero, Patricia B. Tissera, Matías Blaña
We compare the self-similar baryonic mass fraction scaling relations between galaxy clusters from the South Pole Telescope Sunyaev-Zel'dovich (SPT-SZ) survey and the IllustrisTNG state-of-the-art magnetohydrodynamical cosmological simulations. Using samples of 218 (TNG100) and 1605 (TNG300) friends-of-friends (FoF) haloes within $0.0 \leq z \leq 1.5$ and $M_{200c} \geq 7 \times 10^{13} M_{\odot}$, we fit the scaling relations using Simple Power Law (SPL), Broken Power Law (BPL), and General Double Power Law (GDPL) models through non-linear least squares regression. The SPL model reveals null slopes for the baryonic fraction as a function of redshift, consistent with self-similarity. Observations and simulations agree within $1{-}2\sigma$, suggesting comparable baryonic scaling slopes. We identify $\sim$13.8$-$14.1 per cent of baryons as "missing", primarily in the form of intracluster light (ICL) across all halo masses and warm gas in low-mass haloes. High-mass haloes ($\log_{10}(M_{500c}/M_{\odot}) \geq 14$) adhere to self-similarity, while low-mass haloes exhibit deviations, with the breakpoint occurring at $\log_{10}(M_{500c}/M_{\odot}) \sim 14$, where baryons are redistributed to the outskirts. Our findings suggest that the undetected warm-hot intergalactic medium (WHIM) and baryon redistribution by feedback mechanisms are complementary solutions to the "missing baryon" problem.
Microlensing events indicate that super-Earth exoplanets are common in Jupiter-like orbits
2025-04-28
Weicheng Zang, Youn Kil Jung, Jennifer C. Yee, Kyu-Ha Hwang, Hongjing Yang, Andrzej Udalski, Takahiro Sumi, Andrew Gould, Shude Mao, Michael D. Albrow, Sun-Ju Chung, Cheongho Han, Yoon-Hyun Ryu, In-Gu Shin, Yossi Shvartzvald, Sang-Mok Cha, Dong-Jin Kim, Hyoun-Woo Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge, Xiangyu Zhang, Renkun Kuang, Hanyue Wang, Jiyuan Zhang, Zhecheng Hu, Wei Zhu, Przemek Mróz, Jan Skowron, Radosław Poleski, Michał K. Szymański, Igor Soszyński, Paweł Pietrukowicz, Szymon Kozłowski, Krzysztof Ulaczyk, Krzysztof A. Rybicki, Patryk Iwanek, Marcin Wrona, Mariusz Gromadzki, Fumio Abe, Richard Barry, David P. Bennett, Aparna Bhattacharya, Ian A. Bond, Hirosane Fujii, Akihiko Fukui, Ryusei Hamada, Yuki Hirao, Stela Ishitani Silva, Yoshitaka Itow, Rintaro Kirikawa, Naoki Koshimoto, Yutaka Matsubara, Shota Miyazaki, Yasushi Muraki, Greg Olmschenk, Clément Ranc, Nicholas J. Rattenbury, Yuki Satoh, Daisuke Suzuki, Mio Tomoyoshi, Paul J. Tristram, Aikaterini Vandorou, Hibiki Yama, Kansuke Yamashita
Exoplanets classified as super-Earths are commonly observed on short period orbits, close to their host stars, but their abundance on wider orbits is poorly constrained. Gravitational microlensing is sensitive to exoplanets on wide orbits. We observed the microlensing event OGLE-2016-BLG-0007, which indicates an exoplanet with a planet-to-star mass ratio roughly double the Earth-Sun mass-ratio, on an orbit longer than Saturn's. We combine this event with a larger sample from a microlensing survey to determine the distribution of mass ratios for planets on wide orbits. We infer there are $\sim 0.35$ super-Earth planets per star on Jupiter-like orbits. The observations are most consistent with a bimodal distribution, with separate peaks for super-Earths and gas giants. We suggest that this reflects differences in their formation processes.
Systematic KMTNet Planetary Anomaly Search. XII. Complete Sample of 2017 Subprime Field Planets
2025-04-28
Yuqian Gui, Weicheng Zang, Ruocheng Zhai, Yoon-Hyun Ryu, Andrzej Udalski, Hongjing Yang, Cheongho Han, Shude Mao, Michael D. Albrow, Sun-Ju Chung, Andrew Gould, Kyu-Ha Hwang, Youn Kil Jung, In-Gu Shin, Yossi Shvartzvald, Jennifer C. Yee, Sang-Mok Cha, Dong-Jin Kim, Hyoun-Woo Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge, Przemek Mróz, Michał K. Szymański, Jan Skowron, Radosław Poleski, Igor Soszyński, Paweł Pietrukowicz, Szymon Kozłowski, Krzysztof Ulaczyk, Krzysztof A. Rybicki, Patryk Iwanek, Marcin Wrona, Mariusz Gromadzki, Hanyue Wang, Jiyuan Zhang, Renkun Kuang, Qiyue Qian, Wei Zhu
We report the analysis of four unambiguous planets and one possible planet from the subprime fields ($\Gamma \leq 1~{\rm hr}^{-1}$) of the 2017 Korea Microlensing Telescope Network (KMTNet) microlensing survey, to complete the KMTNet AnomalyFinder planetary sample for the 2017 subprime fields. They are KMT-2017-BLG-0849, KMT-2017-BLG-1057, OGLE-2017-BLG-0364, and KMT-2017-BLG-2331 (unambiguous), as well as KMT-2017-BLG-0958 (possible). For the four unambiguous planets, the mean planet-host mass ratios, $q$, are $(1.0, 1.2, 4.6, 13) \times 10^{-4}$, the median planetary masses are $(6.4, 24, 76, 171)~M_{\oplus}$ and the median host masses are $(0.19, 0.57, 0.49, 0.40)~M_{\odot}$ from a Bayesian analysis. We have completed the AnomalyFinder planetary sample from the first 4-year KMTNet data (2016--2019), with 112 unambiguous planets in total, which nearly tripled the microlensing planetary sample. The ``sub-Saturn desert'' ($\log q = \left[-3.6, -3.0\right]$) found in the 2018 and 2019 KMTNet samples is confirmed by the 2016 and 2017 KMTNet samples.
How black hole mimickers and Shapiro-free lenses signal effective dark matter
2025-04-28
Lirui Yang, Will Barker, Tobias Mistele, Amel Durakovic
We report the existence of two exotic compact objects in the leading relativistic model of modified Newtonian dynamics, namely aether-scalar-tensor theory. This model is consistent with precision cosmology and gravitational wave constraints on tensor speed. Black hole mimickers could subtly change observations: gravitational waves from their mergers might show unusual echoes or altered ringdown patterns, and images of their horizon-scale shadows might be slightly different from those of a true black hole. Shapiro-free lenses are massless objects that deflect light without any gravitational time delay, producing distinctive lensing events. These predictions connect to ongoing and future gravitational-wave searches, horizon-scale imaging, and time-domain lensing surveys.
Benchmarking field-level cosmological inference from galaxy redshift surveys
2025-04-28
Hugo Simon-Onfroy, François Lanusse, Arnaud de Mattia
Field-level inference has emerged as a promising framework to fully harness the cosmological information encoded in next-generation galaxy surveys. It involves performing Bayesian inference to jointly estimate the cosmological parameters and the initial conditions of the cosmic field, directly from the observed galaxy density field. Yet, the scalability and efficiency of sampling algorithms for field-level inference of large-scale surveys remain unclear. To address this, we introduce a standardized benchmark using a fast and differentiable simulator for the galaxy density field based on \texttt{JaxPM}. We evaluate a range of sampling methods, including standard Hamiltonian Monte Carlo (HMC), No-U-Turn Sampler (NUTS) without and within a Gibbs scheme, and both adjusted and unadjusted microcanonical samplers (MAMS and MCLMC). These methods are compared based on their efficiency, in particular the number of model evaluations required per effective posterior sample. Our findings emphasize the importance of carefully preconditioning latent variables and demonstrate the significant advantage of (unadjusted) MCLMC for scaling to $\geq 10^6$-dimensional problems. We find that MCLMC outperforms adjusted samplers by over an order-of-magnitude, with a mild scaling with the dimension of our inference problem. This benchmark, along with the associated publicly available code, is intended to serve as a basis for the evaluation of future field-level sampling strategies. The code is readily open-sourced at https://github.com/hsimonfroy/benchmark-field-level
Can LLMs Be Trusted for Evaluating RAG Systems? A Survey of Methods and Datasets
2025-04-28
Lorenz Brehme, Thomas Ströhle, Ruth Breu
Retrieval-Augmented Generation (RAG) has advanced significantly in recent years. The complexity of RAG systems, which involve multiple components-such as indexing, retrieval, and generation-along with numerous other parameters, poses substantial challenges for systematic evaluation and quality enhancement. Previous research highlights that evaluating RAG systems is essential for documenting advancements, comparing configurations, and identifying effective approaches for domain-specific applications. This study systematically reviews 63 academic articles to provide a comprehensive overview of state-of-the-art RAG evaluation methodologies, focusing on four key areas: datasets, retrievers, indexing and databases, and the generator component. We observe the feasibility of an automated evaluation approach for each component of a RAG system, leveraging an LLM capable of both generating evaluation datasets and conducting evaluations. In addition, we found that further practical research is essential to provide companies with clear guidance on the do's and don'ts of implementing and evaluating RAG systems. By synthesizing evaluation approaches for key RAG components and emphasizing the creation and adaptation of domain-specific datasets for benchmarking, we contribute to the advancement of systematic evaluation methods and the improvement of evaluation rigor for RAG systems. Furthermore, by examining the interplay between automated approaches leveraging LLMs and human judgment, we contribute to the ongoing discourse on balancing automation and human input, clarifying their respective contributions, limitations, and challenges in achieving robust and reliable evaluations.
Exponential growth in scientific literature has heightened the demand for efficient evidence-based synthesis, driving the rise of the field of Automated Meta-analysis (AMA) powered by natural language processing and machine learning. This PRISMA systematic review introduces a structured framework for assessing the current state of AMA, based on screening 978 papers from 2006 to 2024, and analyzing 54 studies across diverse domains. Findings reveal a predominant focus on automating data processing (57%), such as extraction and statistical modeling, while only 17% address advanced synthesis stages. Just one study (2%) explored preliminary full-process automation, highlighting a critical gap that limits AMA's capacity for comprehensive synthesis. Despite recent breakthroughs in large language models (LLMs) and advanced AI, their integration into statistical modeling and higher-order synthesis, such as heterogeneity assessment and bias evaluation, remains underdeveloped. This has constrained AMA's potential for fully autonomous meta-analysis. From our dataset spanning medical (67%) and non-medical (33%) applications, we found that AMA has exhibited distinct implementation patterns and varying degrees of effectiveness in actually improving efficiency, scalability, and reproducibility. While automation has enhanced specific meta-analytic tasks, achieving seamless, end-to-end automation remains an open challenge. As AI systems advance in reasoning and contextual understanding, addressing these gaps is now imperative. Future efforts must focus on bridging automation across all meta-analysis stages, refining interpretability, and ensuring methodological robustness to fully realize AMA's potential for scalable, domain-agnostic synthesis.
Beyond Physical Reach: Comparing Head- and Cane-Mounted Cameras for Last-Mile Navigation by Blind Users
2025-04-27
Apurv Varshney, Lucas Nadolskis, Tobias Höllerer, Michael Beyeler
Blind individuals face persistent challenges in last-mile navigation, including locating entrances, identifying obstacles, and navigating complex or cluttered spaces. Although wearable cameras are increasingly used in assistive systems, there has been no systematic, vantage-focused comparison to guide their design. This paper addresses that gap through a two-part investigation. First, we surveyed ten experienced blind cane users, uncovering navigation strategies, pain points, and technology preferences. Participants stressed the importance of multi-sensory integration, destination-focused travel, and assistive tools that complement (rather than replace) the cane's tactile utility. Second, we conducted controlled data collection with a blind participant navigating five real-world environments using synchronized head- and cane-mounted cameras, isolating vantage placement as the primary variable. To assess how each vantage supports spatial perception, we evaluated SLAM performance (for localization and mapping) and NeRF-based 3D reconstruction (for downstream scene understanding). Head-mounted sensors delivered superior localization accuracy, while cane-mounted views offered broader ground-level coverage and richer environmental reconstructions. A combined (head+cane) configuration consistently outperformed both. These results highlight the complementary strengths of different sensor placements and offer actionable guidance for developing hybrid navigation aids that are perceptive, robust, and user-aligned.
BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese
2025-04-27
Peilin Zhou, Bruce Leon, Xiang Ying, Can Zhang, Yifan Shao, Qichen Ye, Dading Chong, Zhiling Jin, Chenxuan Xie, Meng Cao, Yuxin Gu, Sixin Hong, Jing Ren, Jian Chen, Chao Liu, Yining Hua
As large language models (LLMs) evolve into tool-using agents, the ability to browse the web in real-time has become a critical yardstick for measuring their reasoning and retrieval competence. Existing benchmarks such as BrowseComp concentrate on English and overlook the linguistic, infrastructural, and censorship-related complexities of other major information ecosystems -- most notably Chinese. To address this gap, we introduce BrowseComp-ZH, a high-difficulty benchmark purpose-built to comprehensively evaluate LLM agents on the Chinese web. BrowseComp-ZH consists of 289 multi-hop questions spanning 11 diverse domains. Each question is reverse-engineered from a short, objective, and easily verifiable answer (e.g., a date, number, or proper noun). A two-stage quality control protocol is applied to strive for high question difficulty and answer uniqueness. We benchmark over 20 state-of-the-art language models and agentic search systems on our proposed BrowseComp-ZH. Despite their strong conversational and retrieval capabilities, most models struggle severely: a large number achieve accuracy rates below 10%, and only a handful exceed 20%. Even the best-performing system, OpenAI's DeepResearch, reaches just 42.9%. These results demonstrate the considerable difficulty of BrowseComp-ZH, where success demands not only effective retrieval strategies, but also sophisticated reasoning and information reconciliation -- capabilities that current models still struggle to master. Our dataset, construction guidelines, and benchmark results have been publicly released at https://github.com/PALIN2018/BrowseComp-ZH.
Synergistic Role of Transition Metals and Polyanionic Frameworks in Phosphate-Based Cathode Materials for Sodium-Ion Batteries
2025-04-27
Madhav Sharma, Riya Gulati, Rajendra S. Dhaka
Ongoing research in the area of advanced cathode materials for sodium-ion batteries (SIBs) is expected to reduce reliance on lithium-ion batteries (LIBs), providing more affordable and sustainable energy storage solutions. Polyanionic compounds have emerged as promising options due to their stable structure and ability to withstand high-voltage conditions as well as fast charging capabilities. This review offers a thorough discussion of phosphate-based polyanionic cathodes for SIBs, exploring their structure, electrochemical performance with various transition metals, and existing challenges. We discuss different polyanionic frameworks, such as ortho-phosphates, fluoro-phosphates, pyro-phosphates, mix pyro-phosphates, and NASICON-based phosphates, highlighting their unique structural characteristics and ability to perform well across a wide potential range. Further, we delve into the mechanisms governing sodium storage and tunability of redox potentials in polyanionic materials, providing insights into the factors that affect their electrochemical performance. Finally, we outline future research directions and potential avenues for the practical applications of polyanionic high-voltage cathodes in sodium-ion battery technologies.
Gamifying Testing in IntelliJ: A Replicability Study
2025-04-27
Philipp Straubinger, Tommaso Fulcini, Giacomo Garaccione, Luca Ardito, Gordon Fraser
Gamification is an emerging technique to enhance motivation and performance in traditionally unengaging tasks like software testing. Previous studies have indicated that gamified systems have the potential to improve software testing processes by providing testers with achievements and feedback. However, further evidence of these benefits across different environments, programming languages, and participant groups is required. This paper aims to replicate and validate the effects of IntelliGame, a gamification plugin for IntelliJ IDEA to engage developers in writing and executing tests. The objective is to generalize the benefits observed in earlier studies to new contexts, i.e., the TypeScript programming language and a larger participant pool. The replicability study consists of a controlled experiment with 174 participants, divided into two groups: one using IntelliGame and one with no gamification plugin. The study employed a two-group experimental design to compare testing behavior, coverage, mutation scores, and participant feedback between the groups. Data was collected through test metrics and participant surveys, and statistical analysis was performed to determine the statistical significance. Participants using IntelliGame showed higher engagement and productivity in testing practices than the control group, evidenced by the creation of more tests, increased frequency of executions, and enhanced utilization of testing tools. This ultimately led to better code implementations, highlighting the effectiveness of gamification in improving functional outcomes and motivating users in their testing endeavors. The replication study confirms that gamification, through IntelliGame, positively impacts software testing behavior and developer engagement in coding tasks.
Ethical Challenges of Using Artificial Intelligence in Judiciary
2025-04-27
Angel Mary John, Aiswarya M. U., Jerrin Thomas Panachakel
Artificial intelligence (AI) has emerged as a ubiquitous concept in numerous domains, including the legal system. AI has the potential to revolutionize the functioning of the judiciary and the dispensation of justice. Incorporating AI into the legal system offers the prospect of enhancing decision-making for judges, lawyers, and legal professionals, while concurrently providing the public with more streamlined, efficient, and cost-effective services. The integration of AI into the legal landscape offers manifold benefits, encompassing tasks such as document review, legal research, contract analysis, case prediction, and decision-making. By automating laborious and error-prone procedures, AI has the capacity to alleviate the burden associated with these arduous tasks. Consequently, courts around the world have begun embracing AI technology as a means to enhance the administration of justice. However, alongside its potential advantages, the use of AI in the judiciary poses a range of ethical challenges. These ethical quandaries must be duly addressed to ensure the responsible and equitable deployment of AI systems. This article delineates the principal ethical challenges entailed in employing AI within the judiciary and provides recommendations to effectively address these issues.
Dynamic Embedded Topic Models: properties and recommendations based on diverse corpora
2025-04-27
Elisabeth Fittschen, Bella Xia, Leib Celnik, Paul Dilley, Tom Lippincott
We measure the effects of several implementation choices for the Dynamic Embedded Topic Model, as applied to five distinct diachronic corpora, with the goal of isolating important decisions for its use and further development. We identify priorities that will maximize utility in applied scholarship, including the practical scalability of vocabulary size to best exploit the strengths of embedded representations, and more flexible modeling of intervals to accommodate the uneven temporal distributions of historical writing. Of similar importance, we find performance is not significantly or consistently affected by several aspects that otherwise limit the model's application or might consume the resources of a grid search.
Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements
2025-04-27
Sandipan Dhar, Nanda Dulal Jana, Swagatam Das
Voice conversion (VC) stands as a crucial research area in speech synthesis, enabling the transformation of a speaker's vocal characteristics to resemble another while preserving the linguistic content. This technology has broad applications, including automated movie dubbing, speech-to-singing conversion, and assistive devices for pathological speech rehabilitation. With the increasing demand for high-quality and natural-sounding synthetic voices, researchers have developed a wide range of VC techniques. Among these, generative adversarial network (GAN)-based approaches have drawn considerable attention for their powerful feature-mapping capabilities and potential to produce highly realistic speech. Despite notable advancements, challenges such as ensuring training stability, maintaining linguistic consistency, and achieving perceptual naturalness continue to hinder progress in GAN-based VC systems. This systematic review presents a comprehensive analysis of the voice conversion landscape, highlighting key techniques, key challenges, and the transformative impact of GANs in the field. The survey categorizes existing methods, examines technical obstacles, and critically evaluates recent developments in GAN-based VC. By consolidating and synthesizing research findings scattered across the literature, this review provides a structured understanding of the strengths and limitations of different approaches. The significance of this survey lies in its ability to guide future research by identifying existing gaps, proposing potential directions, and offering insights for building more robust and efficient VC systems. Overall, this work serves as an essential resource for researchers, developers, and practitioners aiming to advance the state-of-the-art (SOTA) in voice conversion technology.
Optical Properties of Nanocellulose and Nanocellulose-based Composites for Optoelectronic Applications
2025-04-27
Jonas Rashidi
In this review, we first provided a brief overview of cellulose structures, categorizing various types of nanocellulose based on their production methods, and discussed different photonic structures such as cholesteric phases, photonic crystals, distributed Bragg reflectors, and inverse photonic structures. We then reviewed some models that predict optical properties, including refractive index, scattering, reflection, and birefringence, highlighting factors that can influence these properties, such as electric field, surface roughness, temperature, and density, among others. Following this, we explored recent advancements in predicting optical properties, particularly the use of AI-driven algorithms to enhance the prediction of optical behaviors in nanomaterials. Finally, we provided an in-depth discussion of the latest developments in nonlinear optical phenomena and chiroptical responses in nanocellulose-based materials, focusing on circularly polarized luminescence, structural colors, structural correlations, the impact of enantiomers, and more.
Comparative Analysis of AI-Driven Security Approaches in DevSecOps: Challenges, Solutions, and Future Directions
2025-04-27
Farid Binbeshr, Muhammad Imam
The integration of security within DevOps, known as DevSecOps, has gained traction in modern software development to address security vulnerabilities while maintaining agility. Artificial Intelligence (AI) and Machine Learning (ML) have been increasingly leveraged to enhance security automation, threat detection, and compliance enforcement. However, existing studies primarily focus on individual aspects of AI-driven security in DevSecOps, lacking a structured comparison of methodologies. This study conducts a systematic literature review (SLR) to analyze and compare AI-driven security solutions in DevSecOps, evaluating their technical capabilities, implementation challenges, and operational impacts. The findings reveal gaps in empirical validation, scalability, and integration of AI in security automation. The study highlights best practices, identifies research gaps, and proposes future directions for optimizing AI-based security frameworks in DevSecOps.
2025-04-27
Bin Chen, Yingchao Xie, Ting Yang, Qin Zhou
Between 1957-1985, Chinese mathematician Loo-Keng Hua pioneered economic optimization theory through three key contributions: establishing economic stability's fundamental theorem, proving the uniqueness of equilibrium solutions in economic systems, and developing a consumption-integrated model 50 days before his death. Since 1988, Mu-Fa Chen has been working on Hua's theory. He introduced stochastics, namely Markov chains, to economic optimization theory. He updated and developed Hua's model and came up with a new model (Chen's model) which has become the starting point of a new economic optimization theory. Chen's theory can be applied to economic stability test, bankruptcy prediction, product ranking and classification, economic prediction and adjustment, economic structure optimization. Chen's theory can also provide efficient algorithms that are programmable and intelligent. {Stochastics} is the cornerstone of Chen's theory. There is no overlap between Chen's theory, and the existing mathematical economy theory and the economics developments that were awarded Nobel Prizes in Economics between 1969 and 2024. The distinguished features of Chen's theory from the existing theories are quantitative, calculable, predictable, optimizable, programmable and can be intelligent. This survey provides a theoretical overview of the newly published monograph \cite{5rw24}. Specifically, the invariant of the economic structure matrix, also known as the Chen's invariant, was first published in this survey.
Blind source separation (BSS) is a key technique in array processing and data analysis, aiming to recover unknown sources from observed mixtures without knowledge of the mixing matrix. Classical independent component analysis (ICA) methods rely on the assumption that sources are mutually independent. To address limitations of ICA, sparsity-based methods have been introduced, which decompose source signals sparsely in a predefined dictionary. Morphological Component Analysis (MCA), based on sparse representation theory, assumes that a signal is a linear combination of components with distinct geometries, each sparsely representable in one dictionary and not in others. This approach has recently been applied to BSS with promising results. This report reviews key approaches derived from classical ICA and explores sparsity-based methods for BSS. It introduces the theory of sparse representation and decomposition, followed by a block coordinate relaxation MCA algorithm, whose variants are used in Multichannel MCA (MMCA) and Generalized MCA (GMCA). A local dictionary learning method using K-SVD is then presented. Finally, we propose an improved algorithm, SAC+BK-SVD, which enhances K-SVD by learning a block-sparsifying dictionary that clusters and updates similar atoms in blocks. The implementation includes experiments on image segmentation and blind image source separation using the discussed techniques. We also compare the proposed block-sparse dictionary learning algorithm with K-SVD. Simulation results demonstrate that our method yields improved blind image separation quality.
Recent advancements in learned image compression (LIC) have yielded impressive performance gains. Notably, the learned image compression models with multi-reference entropy models (MLIC series) have significantly outperformed existing traditional image codecs such as the Versatile Video Coding (VVC) Intra. In this paper, we present MLICv2 and MLICv2$^+$, enhanced versions of the MLIC series, featuring improved transform techniques, entropy modeling, and instance adaptability. For better transform, we introduce a simple token mixing transform block inspired by the meta transformer architecture, addressing the performance degradation at high bit-rates observed in previous MLIC series while maintaining computational efficiency. To enhance entropy modeling, we propose a hyperprior-guided global correlation prediction, enabling the capture of global contexts in the initial slice of the latent representation. We also develop a channel reweighting module to dynamically prioritize important channels within each context. Additionally, advanced positional embedding for context modeling and selective compression with guided optimization are investigated. To boost instance adaptability, we employ stochastic Gumbel annealing to iteratively refine the latent representation according to the rate-distortion optimization of a specific input image. This approach further enhances performance without impacting decoding speed. Experimental results demonstrate that our MLICv2 and MLICv2$^+$ achieve state-of-the-art performance, reducing Bjontegaard-Delta rate (BD-rate) by 16.54%, 21.61%, 16.05% and 20.46%, 24.35%, 19.14% respectively, compared to VTM-17.0 Intra on the Kodak, Tecnick, CLIC Pro Val dataset, respectively.
The academic peer review system is under increasing pressure due to a growing volume of submissions and a limited pool of available reviewers, resulting in delayed decisions and an uneven distribution of reviewing responsibilities. To address this challenge, the International Conference on Mining Software Repositories (MSR) 2025 introduced a Blended Program Committee (PC) peer review model for its Technical Track. Building upon the community's earlier experience with the Shadow PC (2021 and 2022) and Junior PC (2023 and 2024), the new model pairs up one Junior PC member with two regular PC members as part of the core review team of a given paper, instead of adding them as an extra reviewer. This paper presents the rationale, implementation, and reflections on the model, including insights from a post-review author survey evaluating the quality and usefulness of reviews. Our findings highlight the potential of Junior PCs to alleviate reviewer shortages, foster inclusivity, and sustain a high-quality peer review process. We offer lessons learned and recommendations to guide future adoption and refinement of the model.
Low-code applications are gaining popularity across various fields, enabling non-developers to participate in the software development process. However, due to the strong reliance on graphical user interfaces, they may unintentionally exclude users with visual impairments, such as color blindness and low vision. This paper investigates the accessibility issues users report when using low-code applications. We construct a comprehensive dataset of low-code application reviews, consisting of accessibility-related reviews and non-accessibility-related reviews. We then design and implement a complex model to identify whether a review contains an accessibility-related issue, combining two state-of-the-art Transformers-based models and a traditional keyword-based system. Our proposed hybrid model achieves an accuracy and F1-score of 78% in detecting accessibility-related issues.
Performance Analysis of OpenVPN on a Consumer Grade Router
2025-04-27
Michael J. Hall
Virtual Private Networks (VPNs) offer an alternative solution using Internet Protocol (IP) tunnels to create secure, encrypted communication between geographically distant networks using a common shared medium such as the Internet. They use tunneling to establish end-to-end connectivity. OpenVPN is a cross-platform, secure, highly configurable VPN solution. Security in OpenVPN is handled by the OpenSSL cryptographic library which provides strong security over a Secure Socket Layer (SSL) using standard algorithms such as Advanced Encryption Standard (AES), Blowfish, or Triple DES (3DES). The Linksys WRT54GL router is a consumer-grade router made by Linksys, a division of Cisco Systems, capable of running under Linux. The Linux-based DD-WRT open-source router firmware can run OpenVPN on the Linksys WRT54GL router. For this case study, the performance of OpenVPN is measured and analyzed using a $2^{k-p}$ fractional factorial design for 5 minus 1 factors where $k=5$ and $p=1$. The results show that the throughput is mainly limited by the encryption cipher used, and that the round-trip time (RTT) is mostly dependent on the transport protocol selected.
Security Vulnerabilities in Quantum Cloud Systems: A Survey on Emerging Threats
2025-04-27
Justin Coupel, Tasnuva Farheen
Quantum computing is becoming increasingly widespread due to the potential and capabilities to solve complex problems beyond the scope of classical computers. As Quantum Cloud services are adopted by businesses and research groups, they allow for greater progress and application in many fields. However, the inherent vulnerabilities of these environments pose significant security concerns. This survey delivers a comprehensive analysis of the security challenges that emerged in quantum cloud systems, with a distinct focus on multi-tenant vulnerabilities and the classical-quantum interface. Key threats such as crosstalk attacks, quantum-specific side-channel vulnerabilities, and insider threats are all examined, as well as their effects on the confidentiality, integrity, and availability of quantum circuits. The design and implementation of various quantum architectures from quantum cloud providers are also discussed. In addition, this paper delves into emerging quantum security solutions and best practices to mitigate these risks. This survey offers insights into current research gaps and proposes future directions for secure and resilient quantum cloud infrastructures.
Generative AI for Character Animation: A Comprehensive Survey of Techniques, Applications, and Future Directions
2025-04-27
Mohammad Mahdi Abootorabi, Omid Ghahroodi, Pardis Sadat Zahraei, Hossein Behzadasl, Alireza Mirrokni, Mobina Salimipanah, Arash Rasouli, Bahar Behzadipour, Sara Azarnoush, Benyamin Maleki, Erfan Sadraiye, Kiarash Kiani Feriz, Mahdi Teymouri Nahad, Ali Moghadasi, Abolfazl Eshagh Abianeh, Nizi Nazar, Hamid R. Rabiee, Mahdieh Soleymani Baghshah, Meisam Ahmadi, Ehsaneddin Asgari
Generative AI is reshaping art, gaming, and most notably animation. Recent breakthroughs in foundation and diffusion models have reduced the time and cost of producing animated content. Characters are central animation components, involving motion, emotions, gestures, and facial expressions. The pace and breadth of advances in recent months make it difficult to maintain a coherent view of the field, motivating the need for an integrative review. Unlike earlier overviews that treat avatars, gestures, or facial animation in isolation, this survey offers a single, comprehensive perspective on all the main generative AI applications for character animation. We begin by examining the state-of-the-art in facial animation, expression rendering, image synthesis, avatar creation, gesture modeling, motion synthesis, object generation, and texture synthesis. We highlight leading research, practical deployments, commonly used datasets, and emerging trends for each area. To support newcomers, we also provide a comprehensive background section that introduces foundational models and evaluation metrics, equipping readers with the knowledge needed to enter the field. We discuss open challenges and map future research directions, providing a roadmap to advance AI-driven character-animation technologies. This survey is intended as a resource for researchers and developers entering the field of generative AI animation or adjacent fields. Resources are available at: https://github.com/llm-lab-org/Generative-AI-for-Character-Animation-Survey.
Artificial Intelligence has made remarkable advancements in recent years, primarily driven by increasingly large deep learning models. However, achieving true Artificial General Intelligence (AGI) demands fundamentally new architectures rather than merely scaling up existing models. Current approaches largely depend on expanding model parameters, which improves task-specific performance but falls short in enabling continuous, adaptable, and generalized learning. Achieving AGI capable of continuous learning and personalization on resource-constrained edge devices is an even bigger challenge. This paper reviews the state of continual learning and neuroscience-inspired AI, and proposes a novel architecture for Personalized AGI that integrates brain-like learning mechanisms for edge deployment. We review literature on continuous lifelong learning, catastrophic forgetting, and edge AI, and discuss key neuroscience principles of human learning, including Synaptic Pruning, Hebbian plasticity, Sparse Coding, and Dual Memory Systems, as inspirations for AI systems. Building on these insights, we outline an AI architecture that features complementary fast-and-slow learning modules, synaptic self-optimization, and memory-efficient model updates to support on-device lifelong adaptation. Conceptual diagrams of the proposed architecture and learning processes are provided. We address challenges such as catastrophic forgetting, memory efficiency, and system scalability, and present application scenarios for mobile AI assistants and embodied AI systems like humanoid robots. We conclude with key takeaways and future research directions toward truly continual, personalized AGI on the edge. While the architecture is theoretical, it synthesizes diverse findings and offers a roadmap for future implementation.
Robotic Trail Maker Platform for Rehabilitation in Neurological Conditions: Clinical Use Cases
2025-04-27
Srikar Annamraju, Harris Nisar, Dayu Xia, Shankar A. Deka, Anne Horowitz, Nadica Miljković, Dušan M. Stipanović
Patients with neurological conditions require rehabilitation to restore their motor, visual, and cognitive abilities. To meet the shortage of therapists and reduce their workload, a robotic rehabilitation platform involving the clinical trail making test is proposed. Therapists can create custom trails for each patient and the patient can trace the trails using a robotic device. The platform can track the performance of the patient and use these data to provide dynamic assistance through the robot to the patient interface. Therefore, the proposed platform not only functions as an evaluation platform, but also trains the patient in recovery. The developed platform has been validated at a rehabilitation center, with therapists and patients operating the device. It was found that patients performed poorly while using the platform compared to healthy subjects and that the assistance provided also improved performance amongst patients. Statistical analysis demonstrated that the speed of the patients was significantly enhanced with the robotic assistance. Further, neural networks are trained to classify between patients and healthy subjects and to forecast their movements using the data collected.
The perceived quality of the explanations accompanying e-government services is key to gaining trust in these institutions, consequently amplifying further usage of these services. Recent advances in generative AI, and concretely in Large Language Models (LLMs) allow the automation of such content articulations, eliciting explanations' interpretability and fidelity, and more generally, adapting content to various audiences. However, selecting the right LLM type for this has become a non-trivial task for e-government service providers. In this work, we adapted a previously developed scale to assist with this selection, providing a systematic approach for the comparative analysis of the perceived quality of explanations generated by various LLMs. We further demonstrated its applicability through the tax-return process, using it as an exemplar use case that could benefit from employing an LLM to generate explanations about tax refund decisions. This was attained through a user study with 128 survey respondents who were asked to rate different versions of LLM-generated explanations about tax refund decisions, providing a methodological basis for selecting the most appropriate LLM. Recognizing the practical challenges of conducting such a survey, we also began exploring the automation of this process by attempting to replicate human feedback using a selection of cutting-edge predictive techniques.
Investigating the Prominence and Severity of Bugs and Glitches Within Games and Their Effects on Player Experience
2025-04-26
Jessica Backus
Different errors that occur in video games are often referred to as glitches or bugs. The goal of this exploratory research is to understand how these glitches and bugs within video games affect a players experience. To do this, I reviewed relevant literature and performed observations of these different errors in different games via Twitch livestreams. I then performed thematic analysis with the observation data and generated themes that tie back into to the relevant literature. Most of the current literature focuses on the what and how behind bugs in games, but very little on the implications of these bugs on the overall experience for the players, and what patterns of behavior may emerge because of them.
In this mini-review I look into the physics underlying the theory of electronic structure of atoms and molecules. Quantum mechanics is needed to understand the structure of the periodic table. Special relativity is indispensable for a correct description of the chemistry of the heavy elements. With increased accuracy of quantum chemical calculations, it is natural to ask if chemistry needs more physics.
Machine learning (ML) models are often sensitive to carefully crafted yet seemingly unnoticeable perturbations. Such adversarial examples are considered to be a property of ML models, often associated with their black-box operation and sensitivity to features learned from data. This work examines the adversarial sensitivity of non-learned decision rules, and particularly of iterative optimizers. Our analysis is inspired by the recent developments in deep unfolding, which cast such optimizers as ML models. We show that non-learned iterative optimizers share the sensitivity to adversarial examples of ML models, and that attacking iterative optimizers effectively alters the optimization objective surface in a manner that modifies the minima sought. We then leverage the ability to cast iteration-limited optimizers as ML models to enhance robustness via adversarial training. For a class of proximal gradient optimizers, we rigorously prove how their learning affects adversarial sensitivity. We numerically back our findings, showing the vulnerability of various optimizers, as well as the robustness induced by unfolding and adversarial training.
LINC: Supporting Language Independent Communication and Comprehension to Enhance Contribution in Multilingual Collaborative Meetings
2025-04-26
Saramsh Gautam, Mahmood Jasim
Collaborative research often includes contributors with varied perspectives from diverse linguistic backgrounds. However, English as a Second Language (ESL) researchers often struggle to communicate during meetings in English and comprehend discussions, leading to limited contribution. To investigate these challenges, we surveyed 64 ESL researchers who frequently collaborate in multilingual teams and identified four key design goals around participation, comprehension, documentation, and feedback. Guided by these design goals, we developed LINC, a multimodal Language INdependent Collaboration system with two components: a real-time module for multilingual communication during meetings and a post-meeting dashboard for discussion analysis. We evaluated the system through a two-phased study with six triads of multilingual teams. We found that using LINC, participants benefited from communicating in their preferred language, recalled and reviewed actionable insights, and prepared for upcoming meetings effectively. We discuss external factors that impact multilingual meeting participation beyond language preferences and the implications of multimodal systems in facilitating meetings in hybrid multilingual collaborative settings beyond research.
In recent years, cryptocurrencies have attracted growing attention from both private investors and institutions. Among them, Bitcoin stands out for its impressive volatility and widespread influence. This paper explores the predictability of Bitcoin's price movements, drawing a parallel with traditional financial markets. We examine whether the cryptocurrency market operates under the efficient market hypothesis (EMH) or if inefficiencies still allow opportunities for arbitrage. Our methodology combines theoretical reviews, empirical analyses, machine learning approaches, and time series modeling to assess the extent to which Bitcoin's price can be predicted. We find that while, in general, the Bitcoin market tends toward efficiency, specific conditions, including information asymmetries and behavioral anomalies, occasionally create exploitable inefficiencies. However, these opportunities remain difficult to systematically identify and leverage. Our findings have implications for both investors and policymakers, particularly regarding the regulation of cryptocurrency brokers and derivatives markets.
The interplay between Active Galactic Nuclei and Ram-pressure stripping: spatially resolved gas-phase abundances of stripped and undisturbed galaxies
2025-04-26
Giorgia Peluso, Benedetta Vulcani, Mario Radovich, Alessia Moretti, Bianca M. Poggianti, Peter Watson, Ayan Acharyya, Augusto E. Lassen, Marco Gullieuszik, Jacopo Fritz, Alessandro Ignesti, Neven Tomicic, Ivan Delvecchio, Amir H. Khoram
The gas-phase oxygen abundance of the circumnuclear regions around supermassive black holes (SMBH) has been claimed to be affected by the presence of an Active Galactic Nucleus (AGN). However, there is currently no consensus on the mechanism driving this effect. In this work, we explore whether the interplay between AGN activity and the Ram Pressure Stripping (RPS) can influence the metallicity distributions of nearby (z < 0.07) galaxies. To this aim, we measure the spatially resolved gas-phase oxygen abundances of 10 stripped AGN hosts from the GASP survey, as well as 52 AGN hosts found in the field, which are undisturbed by the effects of ram pressure, drawn from the MaNGA DR15. We find that the metal distributions in these two samples do not differ significantly. Only 2 out of the 10 RP-stripped AGNs present lower oxygen abundances at any given radius than the rest of the AGN sample. Overall, this result highlights that the AGN-RPS interplay does not play a significant role in shaping the metallicity distributions of stripped galaxies within 1.5 times the galaxy's effective radius (r < 1.5 Re). However, larger samples are required to draw more definitive conclusions. By including a control sample of SF galaxies, we observe that the AGN hosts are more metal-enriched than SF galaxies at any given radius. More than that, the steepness of the gradients in the nuclear regions (r < 0.5 Re) is greater in AGN hosts than in SF galaxies. These results favor the hypothesis that the AGN activity is causing metal pollution in the galaxy's nuclear regions.
Surgeons vs. Computer Vision: A comparative analysis on surgical phase recognition capabilities
2025-04-26
Marco Mezzina, Pieter De Backer, Tom Vercauteren, Matthew Blaschko, Alexandre Mottrie, Tinne Tuytelaars
Purpose: Automated Surgical Phase Recognition (SPR) uses Artificial Intelligence (AI) to segment the surgical workflow into its key events, functioning as a building block for efficient video review, surgical education as well as skill assessment. Previous research has focused on short and linear surgical procedures and has not explored if temporal context influences experts' ability to better classify surgical phases. This research addresses these gaps, focusing on Robot-Assisted Partial Nephrectomy (RAPN) as a highly non-linear procedure. Methods: Urologists of varying expertise were grouped and tasked to indicate the surgical phase for RAPN on both single frames and video snippets using a custom-made web platform. Participants reported their confidence levels and the visual landmarks used in their decision-making. AI architectures without and with temporal context as trained and benchmarked on the Cholec80 dataset were subsequently trained on this RAPN dataset. Results: Video snippets and presence of specific visual landmarks improved phase classification accuracy across all groups. Surgeons displayed high confidence in their classifications and outperformed novices, who struggled discriminating phases. The performance of the AI models is comparable to the surgeons in the survey, with improvements when temporal context was incorporated in both cases. Conclusion: SPR is an inherently complex task for expert surgeons and computer vision, where both perform equally well when given the same context. Performance increases when temporal information is provided. Surgical tools and organs form the key landmarks for human interpretation and are expected to shape the future of automated SPR.
Glider Path Design and Control for Reconstructing Three-Dimensional Structures of Oceanic Mesoscale Eddies
2025-04-26
Wu Su, Xiaoyuan E, Zhao Jing, Song Xi Chen
Underwater gliders offer effective means in oceanic surveys with a major task in reconstructing the three-dimensional hydrographic field of a mesoscale eddy. This paper considers three key issues in the hydrographic reconstruction of mesoscale eddies with the sampled data from the underwater gliders. It first proposes using the Thin Plate Spline (TPS) as the interpolation method for the reconstruction with a blocking scheme to speed up the computation. It then formulates a procedure for selecting glider path design that minimizes the reconstruction errors among a set of pathway formations. Finally we provide a glider path control procedure to guide the glider to follow to designed pathways as much as possible in the presence of ocean current. A set of optimization algorithms are experimented and several with robust glider control performance on a simulated eddy are identified.
Effect of perceived preprint effectiveness and research intensity on posting behaviour
2025-04-26
Pablo Dorta-González, María Isabel Dorta-González
Open science is increasingly recognised worldwide, with preprint posting emerging as a key strategy. This study explores the factors influencing researchers' adoption of preprint publication, particularly the perceived effectiveness of this practice and research intensity indicators such as publication and review frequency. Using open data from a comprehensive survey with 5,873 valid responses, we conducted regression analyses to control for demographic variables. Researchers' productivity, particularly the number of journal articles and books published, greatly influences the frequency of preprint deposits. The perception of the effectiveness of preprints follows this. Preprints are viewed positively in terms of early access to new research, but negatively in terms of early feedback. Demographic variables, such as gender and the type of organisation conducting the research, do not have a significant impact on the production of preprints when other factors are controlled for. However, the researcher's discipline, years of experience and geographical region generally have a moderate effect on the production of preprints. These findings highlight the motivations and barriers associated with preprint publication and provide insights into how researchers perceive the benefits and challenges of this practice within the broader context of open science.
SPD Learning for Covariance-Based Neuroimaging Analysis: Perspectives, Methods, and Challenges
2025-04-26
Ce Ju, Reinmar J. Kobler, Antoine Collas, Motoaki Kawanabe, Cuntai Guan, Bertrand Thirion
Neuroimaging provides a critical framework for characterizing brain activity by quantifying connectivity patterns and functional architecture across modalities. While modern machine learning has significantly advanced our understanding of neural processing mechanisms through these datasets, decoding task-specific signatures must contend with inherent neuroimaging constraints, for example, low signal-to-noise ratios in raw electrophysiological recordings, cross-session non-stationarity, and limited sample sizes. This review focuses on machine learning approaches for covariance-based neuroimaging data, where often symmetric positive definite (SPD) matrices under full-rank conditions encode inter-channel relationships. By equipping the space of SPD matrices with Riemannian metrics (e.g., affine-invariant or log-Euclidean), their space forms a Riemannian manifold enabling geometric analysis. We unify methodologies operating on this manifold under the SPD learning framework, which systematically leverages the SPD manifold's geometry to process covariance features, thereby advancing brain imaging analytics.
Generative to Agentic AI: Survey, Conceptualization, and Challenges
2025-04-26
Johannes Schneider
Agentic Artificial Intelligence (AI) builds upon Generative AI (GenAI). It constitutes the next major step in the evolution of AI with much stronger reasoning and interaction capabilities that enable more autonomous behavior to tackle complex tasks. Since the initial release of ChatGPT (3.5), Generative AI has seen widespread adoption, giving users firsthand experience. However, the distinction between Agentic AI and GenAI remains less well understood. To address this gap, our survey is structured in two parts. In the first part, we compare GenAI and Agentic AI using existing literature, discussing their key characteristics, how Agentic AI remedies limitations of GenAI, and the major steps in GenAI's evolution toward Agentic AI. This section is intended for a broad audience, including academics in both social sciences and engineering, as well as industry professionals. It provides the necessary insights to comprehend novel applications that are possible with Agentic AI but not with GenAI. In the second part, we deep dive into novel aspects of Agentic AI, including recent developments and practical concerns such as defining agents. Finally, we discuss several challenges that could serve as a future research agenda, while cautioning against risks that can emerge when exceeding human intelligence.
Why you shouldn't fully trust ChatGPT: A synthesis of this AI tool's error rates across disciplines and the software engineering lifecycle
2025-04-26
Vahid Garousi
Context: ChatGPT and other large language models (LLMs) are widely used across healthcare, business, economics, engineering, and software engineering (SE). Despite their popularity, concerns persist about their reliability, especially their error rates across domains and the software development lifecycle (SDLC). Objective: This study synthesizes and quantifies ChatGPT's reported error rates across major domains and SE tasks aligned with SDLC phases. It provides an evidence-based view of where ChatGPT excels, where it fails, and how reliability varies by task, domain, and model version (GPT-3.5, GPT-4, GPT-4-turbo, GPT-4o). Method: A Multivocal Literature Review (MLR) was conducted, gathering data from academic studies, reports, benchmarks, and grey literature up to 2025. Factual, reasoning, coding, and interpretive errors were considered. Data were grouped by domain and SE phase and visualized using boxplots to show error distributions. Results: Error rates vary across domains and versions. In healthcare, rates ranged from 8% to 83%. Business and economics saw error rates drop from ~50% with GPT-3.5 to 15-20% with GPT-4. Engineering tasks averaged 20-30%. Programming success reached 87.5%, though complex debugging still showed over 50% errors. In SE, requirements and design phases showed lower error rates (~5-20%), while coding, testing, and maintenance phases had higher variability (10-50%). Upgrades from GPT-3.5 to GPT-4 improved reliability. Conclusion: Despite improvements, ChatGPT still exhibits non-negligible error rates varying by domain, task, and SDLC phase. Full reliance without human oversight remains risky, especially in critical settings. Continuous evaluation and critical validation are essential to ensure reliability and trustworthiness.
Cross Far- and Near-Field Beam Management Technologies in Millimeter-Wave and Terahertz MIMO Systems
2025-04-26
Yuhang Chen, Heyin Shen, Chong Han
The evolution of wireless communication toward next-generation networks introduces unprecedented demands on data rates, latency, and connectivity. To meet these requirements, two key trends have emerged: the use of higher communication frequencies to provide broader bandwidth, and the deployment of massive multiple-input multiple-output systems with large antenna arrays to compensate for propagation losses and enhance spatial multiplexing. These advancements significantly extend the Rayleigh distance, enabling near-field (NF) propagation alongside the traditional far-field (FF) regime. As user communication distances dynamically span both FF and NF regions, cross-field (CF) communication has also emerged as a practical consideration. Beam management (BM)-including beam scanning, channel state information estimation, beamforming, and beam tracking-plays a central role in maintaining reliable directional communications. While most existing BM techniques are developed for FF channels, recent works begin to address the unique characteristics of NF and CF regimes. This survey presents a comprehensive review of BM techniques from the perspective of propagation fields. We begin by building the basic through analyzing the modeling of FF, NF, and CF channels, along with the associated beam patterns for alignment. Then, we categorize BM techniques by methodologies, and discuss their operational differences across propagation regimes, highlighting how field-dependent channel characteristics influence design tradeoffs and implementation complexity. In addition, for each BM method, we identify open challenges and future research directions, including extending FF methods to NF or CF scenarios, developing unified BM strategies for field-agnostic deployment, and designing low-overhead BM solutions for dynamic environments.
Large Language Models (LLMs) are advancing at an amazing speed and have become indispensable across academia, industry, and daily applications. To keep pace with the status quo, this survey probes the core challenges that the rise of LLMs poses for evaluation. We identify and analyze two pivotal transitions: (i) from task-specific to capability-based evaluation, which reorganizes benchmarks around core competencies such as knowledge, reasoning, instruction following, multi-modal understanding, and safety; and (ii) from manual to automated evaluation, encompassing dynamic dataset curation and "LLM-as-a-judge" scoring. Yet, even with these transitions, a crucial obstacle persists: the evaluation generalization issue. Bounded test sets cannot scale alongside models whose abilities grow seemingly without limit. We will dissect this issue, along with the core challenges of the above two transitions, from the perspectives of methods, datasets, evaluators, and metrics. Due to the fast evolving of this field, we will maintain a living GitHub repository (links are in each section) to crowd-source updates and corrections, and warmly invite contributors and collaborators.
Extreme weather events driven by climate change, such as wildfires, floods, and heatwaves, prompt significant public reactions on social media platforms. Analyzing the sentiment expressed in these online discussions can offer valuable insights into public perception, inform policy decisions, and enhance emergency responses. Although sentiment analysis has been widely studied in various fields, its specific application to climate-induced events, particularly in real-time, high-impact situations like the 2025 Los Angeles forest fires, remains underexplored. In this survey, we thoroughly examine the methods, datasets, challenges, and ethical considerations related to sentiment analysis of social media content concerning weather and climate change events. We present a detailed taxonomy of approaches, ranging from lexicon-based and machine learning models to the latest strategies driven by large language models (LLMs). Additionally, we discuss data collection and annotation techniques, including weak supervision and real-time event tracking. Finally, we highlight several open problems, such as misinformation detection, multimodal sentiment extraction, and model alignment with human values. Our goal is to guide researchers and practitioners in effectively understanding sentiment during the climate crisis era.
The multi-photon excitation to the $8$-eV nuclear isomeric state $^{229\text{m}}$Th in the direct laser-nucleus interaction is investigated theoretically. We solve the time-dependent Schr\"odinger equation with the method which allows us to study the $n$-photon absorption in the nuclear excitation in the direct laser-nucleus interaction. Based on the laser facilities available currently or in the near future, we analyze the impact of the laser parameters on the excitation probability of the multi-photon excitation. The possibilities of the $2$-, $3$- and $4$-photon excitations to the isomeric state $^{229\text{m}}$Th from the ground state are discussed in details. Our results show the strong impact of the laser intensity and pulse duration on the multi-photon excitation probability. The onset of high-order effects in the multi-photon excitation in the direct laser-nucleus interaction is also revealed. Our findings open new possibilities to study the multi-photon laser-nucleus interaction in high-power laser facilities.
Potential-Driven Metal Cycling: JADES Census of Gas-Phase Metallicity for galaxies at 1 < z < 7
2025-04-26
Cheng Jia, Enci Wang, Cheqiu Lyu, Chengyu Ma, Jie Song, Yangyao Chen, Kai Wang, Haoran Yu, Zeyu Chen, Jinyang Wang, Yifan Wang, Xu Kong
The gravitational potential is established as a critical determinant of gas-phase metallicity (12+log(O/H)) in low-redshift galaxies, whereas its influence remains unconfirmed at high redshifts. We investigate the correlation between gas-phase metallicity and effective radius ($R_{\rm e}$) for a sample of galaxies with redshifts ranging from 1 to 7, drawn from JADES (JWST Advanced Deep Extragalactic Survey) Data Release 3. We calculate the metallicities using four strong-line methods: ${\rm N2S2H\alpha}$, ${\rm R23}$, ${\rm N2}$, and ${\rm O3N2}$, respectively. After taking out the evolution of size, we find that the offsets of mass-size relation ($\Delta \log R_{\rm e}$) are significantly negatively correlated with the offset of mass-metallicity relation ($\Delta \log({\rm O/H})$) for the four metallicity tracers. Regardless of the metallicity tracer used, we obtain Spearman rank $p-$values much less than 0.01, rejecting the null hypothesis that the observed correlation is statistically nonsignificant and attributable to random chance. This is also true for galaxies with $z>3$, with $p-$values less than 0.05 for the four metallicity tracers. We for the first time find evidence of size playing a key role in determining gas-phase metallicity towards cosmic dawn, suggesting that the gravitational potential influences their material-exchange processes with the surrounding environment at very early universe.
BugsRepo: A Comprehensive Curated Dataset of Bug Reports, Comments and Contributors Information from Bugzilla
2025-04-26
Jagrit Acharya, Gouri Ginde
Bug reports help software development teams enhance software quality, yet their utility is often compromised by unclear or incomplete information. This issue not only hinders developers' ability to quickly understand and resolve bugs but also poses significant challenges for various software maintenance prediction systems, such as bug triaging, severity prediction, and bug report summarization. To address this issue, we introduce \textnormal{{\fontfamily{ppl}\selectfont BugsRepo}}, a multifaceted dataset derived from Mozilla projects that offers three key components to support a wide range of software maintenance tasks. First, it includes a Bug report meta-data & Comments dataset with detailed records for 119,585 fixed or closed and resolved bug reports, capturing fields like severity, creation time, status, and resolution to provide rich contextual insights. Second, {\fontfamily{ppl}\selectfont BugsRepo} features a contributor information dataset comprising 19,351 Mozilla community members, enriched with metadata on user roles, activity history, and contribution metrics such as the number of bugs filed, comments made, and patches reviewed, thus offering valuable information for tasks like developer recommendation. Lastly, the dataset provides a structured bug report subset of 10,351 well-structured bug reports, complete with steps to reproduce, actual behavior, and expected behavior. After this initial filter, a secondary filtering layer is applied using the CTQRS scale. By integrating static metadata, contributor statistics, and detailed comment threads, {\fontfamily{ppl}\selectfont BugsRepo} presents a holistic view of each bug's history, supporting advancements in automated bug report analysis, which can enhance the efficiency and effectiveness of software maintenance processes.
2025-04-26
Rashini Liyanarachchi, Aditya Joshi, Erik Meijering
Multimodal music emotion recognition (MMER) is an emerging discipline in music information retrieval that has experienced a surge in interest in recent years. This survey provides a comprehensive overview of the current state-of-the-art in MMER. Discussing the different approaches and techniques used in this field, the paper introduces a four-stage MMER framework, including multimodal data selection, feature extraction, feature processing, and final emotion prediction. The survey further reveals significant advancements in deep learning methods and the increasing importance of feature fusion techniques. Despite these advancements, challenges such as the need for large annotated datasets, datasets with more modalities, and real-time processing capabilities remain. This paper also contributes to the field by identifying critical gaps in current research and suggesting potential directions for future research. The gaps underscore the importance of developing robust, scalable, a interpretable models for MMER, with implications for applications in music recommendation systems, therapeutic tools, and entertainment.
High-Energy Cosmic-Ray Propagation in the Milky Way and the Associated Diffuse Gamma-Ray Emission
2025-04-26
Peter Marinos
Simulations of Galactic CR transport were performed with the software GALPROP, with the resulting gamma-ray flux calculated up to the PeV regime. The impact of altering parameters such as the number and distribution of CR sources, the distribution of infrared radiation between stars, and the distribution and strength of the Galactic magnetic field (GMF), were investigated. For the first time the modelling variation in the TeV predictions due to uncertainties in the Galactic distributions was quantified. Additionally, the modelling variation from considering a stochastic placement of the CR sources was quantified up to 1 PeV. The simulation results were compared to the most detailed Galactic TeV gamma-ray survey: the H.E.S.S. Galactic plane survey (HGPS). The GALPROP predictions were broadly compatible with the large-scale emission from the HGPS after accounting for both the catalogued sources and estimates of the unresolved source fraction. At 1 TeV the gamma-ray emission from CR electrons was found to contribute $\sim$50\% to the large-scale emission. The GMF was found to be an important modelling consideration above 1 TeV as it impacted the large-scale emission by approximately a factor of two. Additionally, the CR electron flux at Earth above 1 TeV was found to vary by over a factor of ten over a period of a few million years due. The GALPROP models were found to agree with observations of the diffuse gamma rays in the TeV regime by H.E.S.S., and the PeV regime by LHAASO, extending the demonstrated accuracy of GALPROP into the TeV--PeV regime. The results will also inform the next generation of experiments, such as the Cherenkov telescope array (CTA), on possible observation strategies and background considerations. It was also found that the proposed CTA Galactic plane survey will be sensitive enough to observe the large-scale diffuse gamma-ray emission in the TeV regime.
This paper introduces Agent-Based Auto Research, a structured multi-agent framework designed to automate, coordinate, and optimize the full lifecycle of scientific research. Leveraging the capabilities of large language models (LLMs) and modular agent collaboration, the system spans all major research phases, including literature review, ideation, methodology planning, experimentation, paper writing, peer review response, and dissemination. By addressing issues such as fragmented workflows, uneven methodological expertise, and cognitive overload, the framework offers a systematic and scalable approach to scientific inquiry. Preliminary explorations demonstrate the feasibility and potential of Auto Research as a promising paradigm for self-improving, AI-driven research processes.
Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics
2025-04-26
Inmaculada Santamaria-Valenzuela, Victor Rodriguez-Fernandez, Javier Huertas-Tato, Jong Hyuk Park, David Camacho
The present study explores the interpretability of latent spaces produced by time series foundation models, focusing on their potential for visual analysis tasks. Specifically, we evaluate the MOMENT family of models, a set of transformer-based, pre-trained architectures for multivariate time series tasks such as: imputation, prediction, classification, and anomaly detection. We evaluate the capacity of these models on five datasets to capture the underlying structures in time series data within their latent space projection and validate whether fine tuning improves the clarity of the resulting embedding spaces. Notable performance improvements in terms of loss reduction were observed after fine tuning. Visual analysis shows limited improvement in the interpretability of the embeddings, requiring further work. Results suggest that, although Time Series Foundation Models such as MOMENT are robust, their latent spaces may require additional methodological refinements to be adequately interpreted, such as alternative projection techniques, loss functions, or data preprocessing strategies. Despite the limitations of MOMENT, foundation models supose a big reduction in execution time and so a great advance for interactive visual analytics.
Theory of Mind in Large Language Models: Assessment and Enhancement
2025-04-26
Ruirui Chen, Weifeng Jiang, Chengwei Qin, Cheston Tan
Theory of Mind (ToM)-the ability to infer and reason about others' mental states-is fundamental to human social intelligence. As Large Language Models (LLMs) become increasingly integrated into daily life, it is crucial to assess and enhance their capacity to interpret and respond to human mental states. In this paper, we review LLMs' ToM capabilities by examining both evaluation benchmarks and the strategies designed to improve them. We focus on widely adopted story-based benchmarks and provide an in-depth analysis of methods aimed at enhancing ToM in LLMs. Furthermore, we outline promising future research directions informed by recent benchmarks and state-of-the-art approaches. Our survey serves as a valuable resource for researchers interested in advancing LLMs' ToM capabilities.
Four Elements to Rule Them All: Abundances are Rigidly Coupled in the Milky Way Disk
2025-04-25
Jennifer Mead, Rebeca De La Garza, Melissa Ness
Chemical tagging is a central pursuit of galactic archaeology, but requires sufficiently discriminative abundances to uniquely identify sites of star formation. This task is complicated by intrinsic scatter among conatal stars, inter-element correlations, imprecise abundance measurements, and systematics across stellar evolutionary states. In this work, we formalize the abundance correlation structure of the disk by quantifying the amplitude of information in individual element abundances once a subset is known, and map inter-element residual correlations to uncover hidden signatures of nucleosynthesis. We use two datasets of 79 (593) stars across $-0.15<\rm [Fe/H]<0.13$ ($-1<\rm [Fe/H]<0.41$) with measurements of 30 (19) element abundances of solar neighborhood stars, including 11 (7) light and $\alpha$, 7 (3) Fe-peak, and 12 (9) neutron capture elements. With a simple linear regression model, we predict most $\alpha$ and Fe-peak element abundances within 0.03~dex ($\sim7\%$), and neutron capture elements within 0.05~dex ($\sim10\%$). Including first and second peak s-process elements as predictors improves most neutron capture element predictions to within 0.02~dex (5\%), although no predictive power is gained by including an r-process element. We uncover strong (anti-)correlations in small residual abundances between and within element families. Our finding that disk abundance space is rigidly coupled, from light to heavy elements, implies chemical tagging is infeasible at $>2\%$ precision for $\sim$30 elements. However, the residual structure encodes fingerprints of star formation history, inherited from nucleosynthesis and environmental variations, and provides critical constraints for chemical evolution models. Future disk surveys must achieve sub-2-5\% precision in 30+ elements to access this independent information.
Classical Interfaces for Controlling Cryogenic Quantum Computing Technologies
2025-04-25
Jack C. Brennan, Joao Barbosa, Chong Li, Meraj Ahmad, Fiheon Imroze, Calum Rose, Wridhdhisom Karar, Manoj Stanley, Hadi Heidari, Nick M. Ridler, Martin Weides
Quantum processors have the potential to revolutionise computing on a scale unseen since the development of semiconductor technology in the middle of the 20th century. However, while there is now huge activity and investment in the field, there are a number of challenges that must be overcome before the technology can be fully realised. Of primary concern is the development of the classical technology required to interface with quantum systems, as we push towards a new era of high-performance, large-scale quantum computing. In this review, we briefly discuss some of the main challenges facing the development of universally useful quantum computers and the different architectures being investigated. We are primarily concerned with cryogenic quantum systems. These systems are among the most mature quantum computing architectures to date, and are garnering a lot of both industrial and academic attention. We present and analyse the leading methods of interfacing with quantum processors, both now and for the next generation of larger, multi-qubit systems. Recent advancements in control cryoelectronics, both semiconducting and superconducting, are covered, while a view towards newer methods such as optical and wireless qubit interfaces are also presented.
Complex morphology and precession indicators of AGN jets in LoTSS DR2
2025-04-25
Maya Horton, Martin Hardcastle, George Miley, Cyril Tasse, Timothy Shimwell
The LOw Frequency ARray Two-metre Sky Survey second data release (LoTSS DR2) covers 27\% of the northern sky and contains around four million radio sources. The development of this catalogue involved a large citizen science project (Radio Galaxy Zoo: LOFAR) with more than 116,000 resolved sources going through visual inspection. We took a subset of sources with flux density above 75 mJy and an angular size of $90''$ or greater, giving a total of $9,985$ sources or $\sim 10\%$ of the visually inspected sources. We classified these by visual inspection in terms of broad source type (e.g., Fanaroff-Riley class I or II, narrow or wide-angle tail, relaxed double), noticeable features (wings, visible jets, banding, filaments etc), environmental features (cluster environment, merger, diffuse emission). Our specific aim was to search for features linked to jet precession, such as a misaligned jet axis, curvature and multiple hotspots. This combination of features and morphology allowed us to detect increasingly fine-grained sub-populations of interesting or unusual sources. We found that $28\%$ of sources showed evidence of one or more precession indicators, which could make them candidates for hosting close binary supermassive black holes. Potential precession signatures occur in sources of all sizes and luminosities in our sample but appear to favour more massive host galaxies. Our work greatly expands the sample size and parameter space of searches for precession signatures in powerful jetted sources. This work also showcases the diversity of large bright radio sources in the LOFAR surveys, whether or not precession indicators are present.
Facets, Taxonomies, and Syntheses: Navigating Structured Representations in LLM-Assisted Literature Review
2025-04-25
Raymond Fok, Joseph Chee Chang, Marissa Radensky, Pao Siangliulue, Jonathan Bragg, Amy X. Zhang, Daniel S. Weld
Comprehensive literature review requires synthesizing vast amounts of research -- a labor intensive and cognitively demanding process. Most prior work focuses either on helping researchers deeply understand a few papers (e.g., for triaging or reading), or retrieving from and visualizing a vast corpus. Deep analysis and synthesis of large paper collections (e.g., to produce a survey paper) is largely conducted manually with little support. We present DimInd, an interactive system that scaffolds literature review across large paper collections through LLM-generated structured representations. DimInd scaffolds literature understanding with multiple levels of compression, from papers, to faceted literature comparison tables with information extracted from individual papers, to taxonomies of concepts, to narrative syntheses. Users are guided through these successive information transformations while maintaining provenance to source text. In an evaluation with 23 researchers, DimInd supported participants in extracting information and conceptually organizing papers with less effort compared to a ChatGPT-assisted baseline workflow.
An Unsupervised Machine Learning Approach to Identify Spectral Energy Distribution Outliers: Application to the S-PLUS DR4 data
2025-04-25
F. Quispe-Huaynasi, F. Roig, N. Holanda, V. Loaiza-Tacuri, Romualdo Eleutério, C. B. Pereira, S. Daflon, V. M. Placco, R. Lopes de Oliveira, F. Sestito, P. K. Humire, M. Borges Fernandes, A. Kanaan, T. Ribeiro, W. Schoenell
Identification of specific stellar populations using photometry for spectroscopic follow-up is a first step to confirm and better understand their nature. In this context, we present an unsupervised machine learning approach to identify candidates for spectroscopic follow-up using data from the Southern Photometric Local Universe Survey (S-PLUS). First, using an anomaly detection technique based on an autoencoder model, we select a large sample of objects ($\sim 19,000$) whose Spectral Energy Distribution (SED) is not well reconstructed by the model after training it on a well-behaved star sample. Then, we apply the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm to the 66 color measurements from S-PLUS, complemented by information from the SIMBAD database, to identify stellar populations. Our analysis reveals 69 carbon-rich star candidates that, based on their spatial and kinematic characteristics, may belong to the CH or Carbon-Enhanced Metal-Poor (CEMP) categories. Among these chemically peculiar candidates, we identify four as likely carbon dwarf stars. We show that it is feasible to identify three primary white dwarf (WD) populations: WDs with hydrogen-dominated atmospheres (DA), WDs with neutral helium-dominated atmospheres (DB), and the WDs main sequence binaries (WD + MS). Furthermore, by using eROSITA X-ray data, we also highlight the identification of candidates for very active low-mass stars. Finally, we identified a large number of binary systems using the autoencoder model, but did not observe a clear association between the overdensities in the t-SNE map and their orbital properties.
Bias is an inherent threat to human decision-making, including in decisions made during software development. Extensive research has demonstrated the presence of biases at various stages of the software development life-cycle. Notably, code reviews are highly susceptible to prejudice-induced biases, and individuals are often unaware of these biases as they occur. Developing methods to automatically detect these biases is crucial for addressing the associated challenges. Recent advancements in visual data analytics have shown promising results in detecting potential biases by analyzing user interaction patterns. In this project, we propose a controlled experiment to extend this approach to detect potentially biased outcomes in code reviews by observing how reviewers interact with the code. We employ the "spotlight model of attention", a cognitive framework where a reviewer's gaze is tracked to determine their focus areas on the review screen. This focus, identified through gaze tracking, serves as an indicator of the reviewer's areas of interest or concern. We plan to analyze the sequence of gaze focus using advanced sequence modeling techniques, including Markov Models, Recurrent Neural Networks (RNNs), and Conditional Random Fields (CRF). These techniques will help us identify patterns that may suggest biased interactions. We anticipate that the ability to automatically detect potentially biased interactions in code reviews will significantly reduce unnecessary push-backs, enhance operational efficiency, and foster greater diversity and inclusion in software development. This approach not only helps in identifying biases but also in creating a more equitable development environment by mitigating these biases effectively
Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers
2025-04-25
Jared Moore, Declan Grabb, William Agnew, Kevin Klyman, Stevie Chancellor, Desmond C. Ong, Nick Haber
Should a large language model (LLM) be used as a therapist? In this paper, we investigate the use of LLMs to *replace* mental health providers, a use case promoted in the tech startup and research space. We conduct a mapping review of therapy guides used by major medical institutions to identify crucial aspects of therapeutic relationships, such as the importance of a therapeutic alliance between therapist and client. We then assess the ability of LLMs to reproduce and adhere to these aspects of therapeutic relationships by conducting several experiments investigating the responses of current LLMs, such as `gpt-4o`. Contrary to best practices in the medical community, LLMs 1) express stigma toward those with mental health conditions and 2) respond inappropriately to certain common (and critical) conditions in naturalistic therapy settings -- e.g., LLMs encourage clients' delusional thinking, likely due to their sycophancy. This occurs even with larger and newer LLMs, indicating that current safety practices may not address these gaps. Furthermore, we note foundational and practical barriers to the adoption of LLMs as therapists, such as that a therapeutic alliance requires human characteristics (e.g., identity and stakes). For these reasons, we conclude that LLMs should not replace therapists, and we discuss alternative roles for LLMs in clinical therapy.
Heavy-Tailed Privacy: The Symmetric alpha-Stable Privacy Mechanism
2025-04-25
Christopher C. Zawacki, Eyad H. Abed
With the rapid growth of digital platforms, there is increasing apprehension about how personal data is collected, stored, and used by various entities. These concerns arise from the increasing frequency of data breaches, cyber-attacks, and misuse of personal information for targeted advertising and surveillance. To address these matters, Differential Privacy (DP) has emerged as a prominent tool for quantifying a digital system's level of protection. The Gaussian mechanism is commonly used because the Gaussian density is closed under convolution, and is a common method utilized when aggregating datasets. However, the Gaussian mechanism only satisfies an approximate form of Differential Privacy. In this work, we present and analyze of the Symmetric alpha-Stable (SaS) mechanism. We prove that the mechanism achieves pure differential privacy while remaining closed under convolution. Additionally, we study the nuanced relationship between the level of privacy achieved and the parameters of the density. Lastly, we compare the expected error introduced to dataset queries by the Gaussian and SaS mechanisms. From our analysis, we believe the SaS Mechanism is an appealing choice for privacy-focused applications.
Are We on the Same Page? Examining Developer Perception Alignment in Open Source Code Reviews
2025-04-25
Yoseph Berhanu Alebachew, Minhyuk Ko, Chris Brown
Code reviews are a critical aspect of open-source software (OSS) development, ensuring quality and fostering collaboration. This study examines perceptions, challenges, and biases in OSS code review processes, focusing on the perspectives of Contributors and Maintainers. Through surveys (n=289), interviews (n=23), and repository analysis (n=81), we identify key areas of alignment and disparity. While both groups share common objectives, differences emerge in priorities, e.g, with Maintainers emphasizing alignment with project goals while Contributors overestimated the value of novelty. Bias, particularly familiarity bias, disproportionately affects underrepresented groups, discouraging participation and limiting community growth. Misinterpretation of approach differences as bias further complicates reviews. Our findings underscore the need for improved documentation, better tools, and automated solutions to address delays and enhance inclusivity. This work provides actionable strategies to promote fairness and sustain the long-term innovation of OSS ecosystems.
HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models
2025-04-25
Jens Hooge, Gerard Sanroma-Guell, Faidra Stavropoulou, Alexander Ullmann, Gesine Knobloch, Mark Klemens, Carola Schmidt, Sabine Weckbach, Andreas Bolz
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a crucial role in the detection and characterization of focal liver lesions, with the hepatobiliary phase (HBP) providing essential diagnostic information. However, acquiring HBP images requires prolonged scan times, which may compromise patient comfort and scanner throughput. In this study, we propose a deep learning based approach for synthesizing HBP images from earlier contrast phases (precontrast and transitional) and compare three generative models: a perceptual U-Net, a perceptual GAN (pGAN), and a denoising diffusion probabilistic model (DDPM). We curated a multi-site DCE-MRI dataset from diverse clinical settings and introduced a contrast evolution score (CES) to assess training data quality, enhancing model performance. Quantitative evaluation using pixel-wise and perceptual metrics, combined with qualitative assessment through blinded radiologist reviews, showed that pGAN achieved the best quantitative performance but introduced heterogeneous contrast in out-of-distribution cases. In contrast, the U-Net produced consistent liver enhancement with fewer artifacts, while DDPM underperformed due to limited preservation of fine structural details. These findings demonstrate the feasibility of synthetic HBP image generation as a means to reduce scan time without compromising diagnostic utility, highlighting the clinical potential of deep learning for dynamic contrast enhancement in liver MRI. A project demo is available at: https://jhooge.github.io/hepatogen
Generative AI (GenAI) applications are transforming software engineering by enabling automated code co-creation. However, empirical evidence on GenAI's productivity effects in industrial settings remains limited. This paper investigates the adoption of GenAI coding assistants (e.g., Codeium, Amazon Q) within telecommunications and FinTech domains. Through surveys and interviews with industrial domain-experts, we identify primary productivity-influencing factors, including task complexity, coding skills, domain knowledge, and GenAI integration. Our findings indicate that GenAI tools enhance productivity in routine coding tasks (e.g., refactoring and Javadoc generation) but face challenges in complex, domain-specific activities due to limited context-awareness of codebases and insufficient support for customized design rules. We highlight new paradigms for coding transfer, emphasizing iterative prompt refinement, immersive development environment, and automated code evaluation as essential for effective GenAI usage.
Energy Security and Resilience: Reviewing Concepts and Advancing Planning Perspectives for Transforming Integrated Energy Systems
2025-04-25
Richard Schmitz, Franziska Flachsbarth, Leonie Sara Plaga, Martin Braun, Philipp Härtel
Recent events, including the pandemic, geopolitical conflicts, supply chain disruptions, and climate change impacts, have exposed the critical need to ensure energy security and resilience in energy systems. We review existing definitions and interrelations between energy security and resilience, conceptualising these terms in the context of energy system transformations. We introduce a classification of disturbances into shock events and slow burn processes to highlight key challenges to energy system resilience. Examples illustrate their distinct impacts on technical, economic, and environmental system performance over time. We compile relevant recourse options across resilience capacity levels and system planning horizons to address these challenges, emphasising actionable strategies for an increasingly integrated energy system. Finally, we propose policy recommendations to integrate shock events and slow burn processes into future energy system planning, enabling forward-looking decision-making and system design to analyse and mitigate potential disruptions.
Testing Individual Fairness in Graph Neural Networks
2025-04-25
Roya Nasiri
The biases in artificial intelligence (AI) models can lead to automated decision-making processes that discriminate against groups and/or individuals based on sensitive properties such as gender and race. While there are many studies on diagnosing and mitigating biases in various AI models, there is little research on individual fairness in Graph Neural Networks (GNNs). Unlike traditional models, which treat data features independently and overlook their inter-relationships, GNNs are designed to capture graph-based structure where nodes are interconnected. This relational approach enables GNNs to model complex dependencies, but it also means that biases can propagate through these connections, complicating the detection and mitigation of individual fairness violations. This PhD project aims to develop a testing framework to assess and ensure individual fairness in GNNs. It first systematically reviews the literature on individual fairness, categorizing existing approaches to define, measure, test, and mitigate model biases, creating a taxonomy of individual fairness. Next, the project will develop a framework for testing and ensuring fairness in GNNs by adapting and extending current fairness testing and mitigation techniques. The framework will be evaluated through industrial case studies, focusing on graph-based large language models.
Tachyons Before Tachyons: Lev Strum (1890-1936) and Superluminal Velocities
2025-04-25
Helge Kragh
No particle or signal carrying information can travel at a speed exceeding that of light in vacuum. Although this has for a long time been accepted as a law of nature, prior to Einstein's 1905 theory of special relativity the possibility of superluminal motion of electrons was widely discussed by Arnold Sommerfeld and other physicists. Besides, it is not obvious that special relativity rules out such motion under all circumstances. From approximately 1965 to 1985 the hypothesis of tachyons moving faster than light was seriously entertained by a minority of physicists. This paper reviews the early history concerning superluminal signals and pays particular attention to the ideas proposed in the 1920s by the little known Ukrainian physicist Lev Strum (Shtrum). As he pointed out in a paper of 1923, within the framework of relativity it is possible for a signal to move superluminally without violating the law of causality. Part of this article is devoted to the personal and scientific biography of the undeservedly neglected Strum, whose career was heavily and eventually fatally influenced by the political situation in Stalin's Russia. Remarkably, to the limited extent that Strum is known today, it is as a literary figure in a novel and not as a real person.
Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Review
2025-04-25
Toghrul Abbasli, Kentaroh Toyoda, Yuan Wang, Leon Witt, Muhammad Asif Ali, Yukai Miao, Dan Li, Qingsong Wei
Large Language Models (LLMs) have been transformative across many domains. However, hallucination -- confidently outputting incorrect information -- remains one of the leading challenges for LLMs. This raises the question of how to accurately assess and quantify the uncertainty of LLMs. Extensive literature on traditional models has explored Uncertainty Quantification (UQ) to measure uncertainty and employed calibration techniques to address the misalignment between uncertainty and accuracy. While some of these methods have been adapted for LLMs, the literature lacks an in-depth analysis of their effectiveness and does not offer a comprehensive benchmark to enable insightful comparison among existing solutions. In this work, we fill this gap via a systematic survey of representative prior works on UQ and calibration for LLMs and introduce a rigorous benchmark. Using two widely used reliability datasets, we empirically evaluate six related methods, which justify the significant findings of our review. Finally, we provide outlooks for key future directions and outline open challenges. To the best of our knowledge, this survey is the first dedicated study to review the calibration methods and relevant metrics for LLMs.
NRevisit: A Cognitive Behavioral Metric for Code Understandability Assessment
2025-04-25
Gao Hao, Haytham Hijazi, Júlio Medeiros, João Durães, Chan Tong Lam, Paulo de Carvalho, Henrique Madeira
Measuring code understandability is both highly relevant and exceptionally challenging. This paper proposes a dynamic code understandability assessment method, which estimates a personalized code understandability score from the perspective of the specific programmer handling the code. The method consists of dynamically dividing the code unit under development or review in code regions (invisible to the programmer) and using the number of revisits (NRevisit) to each region as the primary feature for estimating the code understandability score. This approach removes the uncertainty related to the concept of a "typical programmer" assumed by static software code complexity metrics and can be easily implemented using a simple, low-cost, and non-intrusive desktop eye tracker or even a standard computer camera. This metric was evaluated using cognitive load measured through electroencephalography (EEG) in a controlled experiment with 35 programmers. Results show a very high correlation ranging from rs = 0.9067 to rs = 0.9860 (with p nearly 0) between the scores obtained with different alternatives of NRevisit and the ground truth represented by the EEG measurements of programmers' cognitive load, demonstrating the effectiveness of our approach in reflecting the cognitive effort required for code comprehension. The paper also discusses possible practical applications of NRevisit, including its use in the context of AI-generated code, which is already widely used today.
Probing Quantum Phenomena through Photoproduction in Relativistic Heavy-Ion Collisions
2025-04-25
James Daniel Brandenburg, Spencer R. Klein, Zhangbu Xu, Shuai Yang, Wangmei Zha, Jian Zhou
Photoproduction in ultra-peripheral relativistic heavy-ion collisions displays many unique features, often involving quantum mechanical coherence and two-source interference between photon emission from the two ions. We review the recent experimental results from RHIC and the LHC and theoretical studies of coherent vector meson photoproduction, emphasizing the quantum mechanical aspects of the interactions and the entanglement between the final state particles. These studies enrich our understanding of non-local realism, underscore the critical role of the polarization of the photon source, quantum interference and nuclear effect on the gluon distribution. It paves a way for quantitatively probing the quantum nature of these high-energy nuclear collisions.
AI Safety Assurance for Automated Vehicles: A Survey on Research, Standardization, Regulation
2025-04-25
Lars Ullrich, Michael Buchholz, Klaus Dietmayer, Knut Graichen
Assuring safety of artificial intelligence (AI) applied to safety-critical systems is of paramount importance. Especially since research in the field of automated driving shows that AI is able to outperform classical approaches, to handle higher complexities, and to reach new levels of autonomy. At the same time, the safety assurance required for the use of AI in such safety-critical systems is still not in place. Due to the dynamic and far-reaching nature of the technology, research on safeguarding AI is being conducted in parallel to AI standardization and regulation. The parallel progress necessitates simultaneous consideration in order to carry out targeted research and development of AI systems in the context of automated driving. Therefore, in contrast to existing surveys that focus primarily on research aspects, this paper considers research, standardization and regulation in a concise way. Accordingly, the survey takes into account the interdependencies arising from the triplet of research, standardization and regulation in a forward-looking perspective and anticipates and discusses open questions and possible future directions. In this way, the survey ultimately serves to provide researchers and safety experts with a compact, holistic perspective that discusses the current status, emerging trends, and possible future developments.
Exhaled Breath Analysis Through the Lens of Molecular Communication: A Survey
2025-04-25
Sunasheer Bhattacharjee, Dadi Bi, Pit Hofmann, Alexander Wietfeld, Sophie Becke, Michael Lommel, Pengjie Zhou, Ruifeng Zheng, Ulrich Kertzscher, Yansha Deng, Wolfgang Kellerer, Frank H. P. Fitzek, Falko Dressler
Molecular Communication (MC) has long been envisioned to enable an Internet of Bio-Nano Things (IoBNT) with medical applications, where nanomachines within the human body conduct monitoring, diagnosis, and therapy at micro- and nanoscale levels. MC involves information transfer via molecules and is supported by well-established theoretical models. However, practically achieving reliable, energy-efficient, and bio-compatible communication at these scales still remains a challenge. Air-Based Molecular Communication (ABMC) is a type of MC that operates over larger, meter-scale distances and extends even outside the human body. Therefore, devices and techniques to realize ABMC are readily accessible, and associated use cases can be very promising in the near future. Exhaled breath analysis has previously been proposed. It provides a non-invasive approach for health monitoring, leveraging existing commercial sensor technologies and reducing deployment barriers. The breath contains a diverse range of molecules and particles that serve as biomarkers linked to various physiological and pathological conditions. The plethora of proven methods, models, and optimization approaches in MC enable macroscale breath analysis, treating human as the transmitter, the breath as the information carrier, and macroscale sensors as the receiver. Using ABMC to interface with the inherent dynamic networks of cells, tissues, and organs could create a novel Internet of Bio Things (IoBT), a preliminary macroscale stage of the IoBNT. This survey extensively reviews exhaled breath modeling and analysis through the lens of MC, offering insights into theoretical frameworks and practical implementations from ABMC, bringing the IoBT a step closer to real-world use.
Artificial Intelligence health advice accuracy varies across languages and contexts
2025-04-25
Prashant Garg, Thiemo Fetzer
Using basic health statements authorized by UK and EU registers and 9,100 journalist-vetted public-health assertions on topics such as abortion, COVID-19 and politics from sources ranging from peer-reviewed journals and government advisories to social media and news across the political spectrum, we benchmark six leading large language models from in 21 languages, finding that, despite high accuracy on English-centric textbook claims, performance falls in multiple non-European languages and fluctuates by topic and source, highlighting the urgency of comprehensive multilingual, domain-aware validation before deploying AI in global health communication.
Ergodic theorems for bilinear averages, Roth's Theorem and Corners along fractional powers
2025-04-25
Leonidas Daskalakis
We prove that for every $c\in(1,23/22)$, every probability space $(X,\mathcal{B},\mu)$ equipped with two commuting measure-preserving transformations $T,S\colon X\to X$ and every $f,g\in L^{\infty}_{\mu}(X)$ we have that the $L^2_{\mu}(X)$-limit \[ \lim_{N\to\infty}\frac{1}{N}\sum_{n=1}^Nf(T^{\lfloor n^c\rfloor}x)g(S^{\lfloor n^c\rfloor}x) \] equals the $L^2_{\mu}(X)$-limit $\lim_{N\to\infty}\frac{1}{N}\sum_{n=1}^Nf(T^{n}x)g(S^{n}x)$. The approach is based on the author's recently developed technique which may be thought of as a change of variables. We employ it to establish several new results along fractional powers including a Roth-type result for patterns of the form $x,x+\lfloor y^c \rfloor,x+2\lfloor y^c \rfloor$ as well as its ''corner'' counterpart. The quantitative nature of the former result allows us to recover the analogous one in the primes. Our considerations give partial answers to Problem 29 and Problem 30 from Frantzikinakis' open problems survey on multiple ergodic averages. Notably, we cover more general sparse orbits $(\lfloor h(n)\rfloor)_{n\in\mathbb{N}}$, where $h$ belongs to the class of the so-called $c$-regularly varying functions, addressing for example even the orbit $(\lfloor n\log n\rfloor)_{n\in\mathbb{N}}$.
A comprehensive review of classifier probability calibration metrics
2025-04-25
Richard Oliver Lane
Probabilities or confidence values produced by artificial intelligence (AI) and machine learning (ML) models often do not reflect their true accuracy, with some models being under or over confident in their predictions. For example, if a model is 80% sure of an outcome, is it correct 80% of the time? Probability calibration metrics measure the discrepancy between confidence and accuracy, providing an independent assessment of model calibration performance that complements traditional accuracy metrics. Understanding calibration is important when the outputs of multiple systems are combined, for assurance in safety or business-critical contexts, and for building user trust in models. This paper provides a comprehensive review of probability calibration metrics for classifier and object detection models, organising them according to a number of different categorisations to highlight their relationships. We identify 82 major metrics, which can be grouped into four classifier families (point-based, bin-based, kernel or curve-based, and cumulative) and an object detection family. For each metric, we provide equations where available, facilitating implementation and comparison by future researchers.
Generating images from prompts containing specific entities requires models to retain as much entity-specific knowledge as possible. However, fully memorizing such knowledge is impractical due to the vast number of entities and their continuous emergence. To address this, we propose Text-based Intelligent Generation with Entity prompt Refinement (TextTIGER), which augments knowledge on entities included in the prompts and then summarizes the augmented descriptions using Large Language Models (LLMs) to mitigate performance degradation from longer inputs. To evaluate our method, we introduce WiT-Cub (WiT with Captions and Uncomplicated Background-explanations), a dataset comprising captions, images, and an entity list. Experiments on four image generation models and five LLMs show that TextTIGER improves image generation performance in standard metrics (IS, FID, and CLIPScore) compared to caption-only prompts. Additionally, multiple annotators' evaluation confirms that the summarized descriptions are more informative, validating LLMs' ability to generate concise yet rich descriptions. These findings demonstrate that refining prompts with augmented and summarized entity-related descriptions enhances image generation capabilities. The code and dataset will be available upon acceptance.
Neural operators struggle to learn complex PDEs in pedestrian mobility: Hughes model case study
2025-04-25
Prajwal Chauhan, Salah Eddine Choutri, Mohamed Ghattassi, Nader Masmoudi, Saif Eddin Jabari
This paper investigates the limitations of neural operators in learning solutions for a Hughes model, a first-order hyperbolic conservation law system for crowd dynamics. The model couples a Fokker-Planck equation representing pedestrian density with a Hamilton-Jacobi-type (eikonal) equation. This Hughes model belongs to the class of nonlinear hyperbolic systems that often exhibit complex solution structures, including shocks and discontinuities. In this study, we assess the performance of three state-of-the-art neural operators (Fourier Neural Operator, Wavelet Neural Operator, and Multiwavelet Neural Operator) in various challenging scenarios. Specifically, we consider (1) discontinuous and Gaussian initial conditions and (2) diverse boundary conditions, while also examining the impact of different numerical schemes. Our results show that these neural operators perform well in easy scenarios with fewer discontinuities in the initial condition, yet they struggle in complex scenarios with multiple initial discontinuities and dynamic boundary conditions, even when trained specifically on such complex samples. The predicted solutions often appear smoother, resulting in a reduction in total variation and a loss of important physical features. This smoothing behavior is similar to issues discussed by Daganzo (1995), where models that introduce artificial diffusion were shown to miss essential features such as shock waves in hyperbolic systems. These results suggest that current neural operator architectures may introduce unintended regularization effects that limit their ability to capture transport dynamics governed by discontinuities. They also raise concerns about generalizing these methods to traffic applications where shock preservation is essential.
The growth of a lithium abundance dispersion in pre main sequence stars
2025-04-25
R. J. Jackson, R. D. Jeffries, E. Tognelli
Lithium is predicted, and observed, to be depleted in contracting, low-mass pre main sequence (PMS) stars. Yet these stars reach the zero age main sequence (ZAMS) with a spread in lithium abundance at a given effective temperature that is not predicted by standard stellar evolutionary models and which appears to be correlated with rotation. Using a homogeneous dataset provided by the Gaia-ESO spectroscopic survey, we have followed the evolving photospheric lithium content of cohorts of stars destined to be ZAMS late G-, K- and M-dwarfs, in clusters at ages of 2-300 Myr. We show that a dispersion in the LiI 6708A line strength develops in the lower mass stars after 10-20 Myr on the PMS, as soon as Li depletion begins, even in fully convective stars. A model based on a surface starspot coverage varying from star-to-star, leading to a differential Li-burning rate, can explain this temporal behaviour and its mass dependence. However, to fully explain the magnitude of the Li dispersion and its correlation with rotation, the spot coverage during Li-burning would need to be a factor of two larger on average than measured in ZAMS clusters like the Pleiades and continue increasing with rotation in PMS stars beyond the usual "saturation limit" observed for other magnetic activity indicators.
2025-04-25
Qinyu Chen, Chang Gao, Min Liu, Daniele Perrone, Yan Ru Pei, Zuowen Wang, Zhuo Zou, Shihang Tan, Tao Han, Guorui Lu, Zhen Xu, Junyuan Ding, Ziteng Wang, Zongwei Wu, Han Han, Yuliang Wu, Jinze Chen, Wei Zhai, Yang Cao, Zheng-jun Zha, Nuwan Bandara, Thivya Kandappu, Archan Misra, Xiaopeng Lin, Hongxiang Huang, Hongwei Ren, Bojun Cheng, Hoang M. Truong, Vinh-Thuan Ly, Huy G. Tran, Thuan-Phat Nguyen, Tram T. Doan
This survey serves as a review for the 2025 Event-Based Eye Tracking Challenge organized as part of the 2025 CVPR event-based vision workshop. This challenge focuses on the task of predicting the pupil center by processing event camera recorded eye movement. We review and summarize the innovative methods from teams rank the top in the challenge to advance future event-based eye tracking research. In each method, accuracy, model size, and number of operations are reported. In this survey, we also discuss event-based eye tracking from the perspective of hardware design.
The multi-tracer (MT) technique has been shown to outperform single-tracer analyses in the context of galaxy clustering. In this paper, we conduct a series of Fisher analyses to further explore MT information gains within the framework of non-linear bias expansion. We examine how MT performance depends on the bias parameters of the subtracers, showing that directly splitting the non-linear bias generally leads to smaller error bars in $A_s$, $h$, and $\omega_{\rm cdm}$ compared to a simple split in $b_1$. This finding opens the door to identifying subsample splits that do not necessarily rely on very distinct linear biases. We discuss different total and subtracer number density scenarios, as well as the possibility of splitting into more than two tracers. Additionally, we consider how different Fingers-of-God suppression scales for the subsamples can be translated into different $k_{\rm max}$ values. Finally, we present forecasts for ongoing and future galaxy surveys.
SecCityVR: Visualization and Collaborative Exploration of Software Vulnerabilities in Virtual Reality
2025-04-25
Dennis Wüppelman, Enes Yigitbas
Security vulnerabilities in software systems represent significant risks as potential entry points for malicious attacks. Traditional dashboards that display the results of static analysis security testing often use 2D or 3D visualizations, which tend to lack the spatial details required to effectively reveal issues such as the propagation of vulnerabilities across the codebase or the appearance of concurrent vulnerabilities. Additionally, most reporting solutions only treat the analysis results as an artifact that can be reviewed or edited asynchronously by developers, limiting real-time, collaborative exploration. To the best of our knowledge, no VR-based approach exists for the visualization and interactive exploration of software security vulnerabilities. Addressing these challenges, the virtual reality (VR) environment SecCityVR was developed as a proof-of-concept implementation that employs the code city metaphor within VR to visualize software security vulnerabilities as colored building floors inside the surrounding virtual city. By integrating the application's call graph, vulnerabilities are contextualized within related software components. SecCityVR supports multi-user collaboration and interactive exploration. It provides explanations and mitigations for detected issues. A user study comparing SecCityVR with the traditional dashboard find-sec-bugs showed the VR approach provided a favorable experience, with higher usability, lower temporal demand, and significantly lower frustration despite having longer task completion times. This paper and its results contribute to the fields of collaborative and secure software engineering, as well as software visualization. It provides a new application of VR code cities to visualize security vulnerabilities, as well as a novel environment for security audits using collaborative and immersive technologies.
Uranus and Neptune, the so-called "ice giants", represent a fascinating class of planets. They are the outermost planets in the solar system with intermediate masses/sizes, complex non-polar magnetic fields, strong atmospheric winds, and not well-understood internal structures. Studying the interiors of Uranus and Neptune is vital for advancing our understanding of planetary formation and evolution as well as for the characterization of planets around other stars. In this review, we summarize our current knowledge of the interior and formation of Uranus and Neptune. Both planets are expected to be composed of rocks and ices and have H-He atmospheres of the order of 10% of their total masses. The rock-to-water ratios in Uranus and Neptune, however, are very uncertain. It is also unclear how the different materials are distributed within the interiors and whether distinct layers exist. While often Uranus and Neptune are viewed as being "twin planets" it is in fact unclear how different the two planets are from each other, and whether they are indeed "icy" (water-dominated) planets. After summarizing the current-knowledge of the interiors of Uranus and Neptune, we briefly discuss their magnetic fields and atmosphere dynamics. We next introduce the challenges in constraining the formation paths of Uranus and Neptune: it is still unclear whether the planets formed at their current locations, and what the dominating processes that led to their formation (accretion rates, size of solids, etc.) were. We also mention the possible role of giant impacts shortly after their formation. Finally, we suggest that advanced modeling, future observations from space and the ground, lab experiments, and links with exoplanetary science can improve our understanding of Uranus and Neptune as a class of astronomical objects which seems to be very common in our galaxy.
A post-common-envelope binary with double-peaked Balmer emission lines from TMTS
2025-04-25
Qichun Liu, Xiaofeng Wang, Jie Lin, Chengyuan Wu, Chunqian Li, V. Alexei Filippenko, G. Thomas Brink, Yi Yang, Cheng Liu, Cuiying Song, Mikhail Kovalev, Hongwei Ge, Fenghui Zhang, Xiaobin Zhang, Qiqi Xia, Haowei Peng, Gaobo Xi, Jun Mo, Shengyu Yan, Jianrong Shi, Jiangdan Li, Tuan Yi
The dynamical method provides an efficient way to discover post-common-envelope binaries (PCEB) with faint white dwarfs (WDs), thanks to the development of time-domain survey projects. We perform a comprehensive analysis of the PCEB system TMTS J15530469+4457458 (J1553), discovered by the Tsinghua University-Ma Huateng Telescopes for Survey, to explore its physical origin and evolutionary fate. This system is characterized by double-peaked Balmer emission lines, and a cross-correlation function is applied to derive its radial velocity (RV) from a series of phase-resolved Keck spectra. Analyses with the cross-correlation function suggest that this system is a single-lined spectroscopic binary and only one star is optically visible. Further analysis through Doppler tomography indicates that J1553 is a detached binary without an accretion disk. Under such a configuration, the simultaneous light-curve and RV fitting reveal that this system contains an unseen WD with mass $M_{\rm A}=0.56\pm 0.09\, M_{\odot}$, and an M4 dwarf with mass $M_{\rm B}=0.37\pm 0.02\,M_{\odot}$ and radius $R_{\rm B}=0.403^{+0.014}_{-0.015}\,R_{\odot}$. The extra prominent Balmer emission lines seen in the spectra can trace the motion of the WD, which are likely formed near the WD surface as a result of wind accretion. According to the MESA simulation, J1553 could have evolved from a binary consisting of a 2.0-4.0 ${M}_{\odot}$ zero-age-main-sequence star and an M dwarf with an initial orbital period $P_i\approx 201-476$ d, and the system has undergone a common-envelope (CE) phase. After about $3.3\times10^6$ yr, J1553 should evolve into a cataclysmic variable, with a transient state as a supersoft X-ray source at the beginning. J1553 is an excellent system for studying wind accretion, CE ejection physics, and binary evolution theory.
MVVM Revisited: Exploring Design Variants of the Model-View-ViewModel Pattern
2025-04-25
Mario Fuksa, Sandro Speth, Steffen Becker
Many enterprise software systems provide complex Graphical User Interfaces (GUIs) that need robust architectural patterns for well-structured software design. However, popular GUI architectural patterns like Model-View-ViewModel (MVVM) often lack detailed implementation guidance, leading GUI developers to inappropriately use the pattern without a comprehensive overview of design variants and often-mentioned trade-offs. Therefore, this paper presents an extensive review of MVVM design aspects and trade-offs, extending beyond the standard MVVM definition. We conducted a multivocal literature review (MLR), including white and gray literature, to cover essential knowledge from blogs, published papers, and other unpublished formats like books. Using the standard MVVM definition as a baseline, our study identifies (1) 76 additional design constructs grouped into 29 design aspects and (2) 16 additional benefits and 15 additional drawbacks. These insights can guide enterprise application developers in implementing practical MVVM solutions and enable informed design decisions.
2025-04-25
Rui Meng, Xiqi Cheng, Song Gao, Xiaodong Xu, Chen Dong, Guoshun Nan, Xiaofeng Tao, Ping Zhang, Tony Q. S. Quek
In recent years, Artificial Intelligence (AI)-driven Physical-Layer Authentication (PLA), which focuses on achieving endogenous security and intelligent identity authentication, has attracted considerable interest. When compared with Discriminative AI (DAI), Generative AI (GAI) offers several advantages, such as fingerprint data augmentation, fingerprint denoising and reconstruction, and protection against adversarial attacks. Inspired by these innovations, this paper provides a systematic exploration of GAI's integration into PLA frameworks. We commence with a review of representative authentication techniques, emphasizing PLA's inherent strengths. Following this, we revisit four typical GAI models and contrast the limitations of DAI with the potential of GAI in addressing PLA challenges, including insufficient fingerprint data, environment noises and inferences, perturbations in fingerprint data, and complex tasks. Specifically, we delve into providing GAI-enhance methods for PLA across the data, model, and application layers in detail. Moreover, we present a case study that combines fingerprint extrapolation, generative diffusion models, and cooperative nodes to illustrate the superiority of GAI in bolstering the reliability of PLA compared to DAI. Additionally, we outline potential future research directions for GAI-based PLA.
Acoustic turbulence: from the Zakharov-Sagdeev spectra to the Kadomtsev-Petviashvili spectrum
2025-04-25
E. A. Kochurin, E. A. Kuznetsov
This paper presents a brief review on theoretical and numerical works on three-dimensional acoustic turbulence both in a weakly nonlinear regime, when the amplitudes of sound waves are small, and in the case of strong nonlinearity. This review is based on the classical studies on weak acoustic turbulence by V.E. Zakharov (1965) and V.E. Zakharov and R.Z. Sagdeev (1970), on the one hand, and on the other hand, by B.B. Kadomtsev and V.I. Petviashvili (1972). Until recently, there were no convincing numerical experiments confirming one or the other point of view. In the works of the authors of this review in 2022 and 2024, strong arguments were found based on direct numerical modeling in favor of both theories. It is shown that the Zakharov-Sagdeev spectrum of weak turbulence $\propto k^{-3/2}$ is realized not only for small positive dispersion of sound waves, but also in the case of complete absence of dispersion. The calculated turbulence spectra in the weakly nonlinear regime have anisotropic distribution: for small $k$, narrow cones (jets) are formed, broadening in the Fourier space. For weak dispersion, the jets are smoothed out, and the turbulence spectrum tends to be isotropic in the region of short wavelengths. In the absence of dispersion, the turbulence spectrum is a discrete set of jets subjected to diffraction divergence. For each individual jet, nonlinear effects are much weaker than diffraction ones, which prevents the formation of shock waves. Thus, the Zakharov-Sagdeev spectrum is realized due to the smallness of nonlinear effects compared to dispersion or diffraction. As the pumping level increases in the non-dispersive regime, when nonlinear effects begin to dominate, shock waves are formed. As a result, acoustic turbulence passes into a strongly nonlinear state in the form of an ensemble of random shocks described by the Kadomtsev-Petviashvili spectrum $\propto k^{-2}$.
Reimagining Assistive Walkers: An Exploration of Challenges and Preferences in Older Adults
2025-04-25
Victory A. Aruona, Sergio D. Sierra M., Nigel Harris, Marcela Munera, Carlos A. Cifuentes
The well-being of older adults relies significantly on maintaining balance and mobility. As physical ability declines, older adults often accept the need for assistive devices. However, existing walkers frequently fail to consider user preferences, leading to perceptions of imposition and reduced acceptance. This research explores the challenges faced by older adults, caregivers, and healthcare professionals when using walkers, assesses their perceptions, and identifies their needs and preferences. A holistic approach was employed, using tailored perception questionnaires for older adults (24 participants), caregivers (30 participants), and healthcare professionals (27 participants), all of whom completed the survey. Over 50% of caregivers and healthcare professionals displayed good knowledge, positive attitudes, and effective practices regarding walkers. However, over 30% of participants perceived current designs as fall risks, citing the need for significant upper body strength, potentially affecting safety and movement. More than 50% highlighted the importance of incorporating fall detection, ergonomic designs, noise reduction, and walker ramps to better meet user needs and preferences.
Recent advances in data-driven methods for degradation modelling across applications
2025-04-25
Anna Jarosz, Marta Zagorowska, Jerzy Baranowski
Understanding degradation is crucial for ensuring the longevity and performance of materials, systems, and organisms. To illustrate the similarities across applications, this article provides a review of data-based method in materials science, engineering, and medicine. The methods analyzed in this paper include regression analysis, factor analysis, cluster analysis, Markov Chain Monte Carlo, Bayesian statistics, hidden Markov models, nonparametric Bayesian modeling of time series, supervised learning, and deep learning. The review provides an overview of degradation models, referencing books and methods, and includes detailed tables highlighting the applications and insights offered in medicine, power engineering, and material science. It also discusses the classification of methods, emphasizing statistical inference, dynamic prediction, machine learning, and hybrid modeling techniques. Overall, this review enhances understanding of degradation modelling across diverse domains.
Docker has gained attention as a lightweight container-based virtualization platform. The process for building a Docker image is defined in a text file called a Dockerfile. A Dockerfile can be considered as a kind of source code that contains instructions on how to build a Docker image. Its behavior should be verified through testing, as is done for source code in a general programming language. For source code in languages such as Java, search-based test generation techniques have been proposed. However, existing automated test generation techniques cannot be applied to Dockerfiles. Since a Dockerfile does not contain branches, the coverage metric, typically used as an objective function in existing methods, becomes meaningless. In this study, we propose an automated test generation method for Dockerfiles based on processing results rather than processing steps. The proposed method determines which files should be tested and generates the corresponding tests based on an analysis of Dockerfile instructions and Docker image layers. The experimental results show that the proposed method can reproduce over 80% of the tests created by developers.
Most real-world classification problems deal with imbalanced datasets, posing a challenge for Artificial Intelligence (AI), i.e., machine learning algorithms, because the minority class, which is of extreme interest, often proves difficult to be detected. This paper empirically evaluates tree boosting methods' performance given different dataset sizes and class distributions, from perfectly balanced to highly imbalanced. For tabular data, tree-based methods such as XGBoost, stand out in several benchmarks due to detection performance and speed. Therefore, XGBoost and Imbalance-XGBoost are evaluated. After introducing the motivation to address risk assessment with machine learning, the paper reviews evaluation metrics for detection systems or binary classifiers. It proposes a method for data preparation followed by tree boosting methods including hyper-parameter optimization. The method is evaluated on private datasets of 1 thousand (K), 10K and 100K samples on distributions with 50, 45, 25, and 5 percent positive samples. As expected, the developed method increases its recognition performance as more data is given for training and the F1 score decreases as the data distribution becomes more imbalanced, but it is still significantly superior to the baseline of precision-recall determined by the ratio of positives divided by positives and negatives. Sampling to balance the training set does not provide consistent improvement and deteriorates detection. In contrast, classifier hyper-parameter optimization improves recognition, but should be applied carefully depending on data volume and distribution. Finally, the developed method is robust to data variation over time up to some point. Retraining can be used when performance starts deteriorating.
SoK: Timeline based event reconstruction for digital forensics: Terminology, methodology, and current challenges
2025-04-25
Frank Breitinger, Hudan Studiawan, Chris Hargreaves
Event reconstruction is a technique that examiners can use to attempt to infer past activities by analyzing digital artifacts. Despite its significance, the field suffers from fragmented research, with studies often focusing narrowly on aspects like timeline creation or tampering detection. This paper addresses the lack of a unified perspective by proposing a comprehensive framework for timeline-based event reconstruction, adapted from traditional forensic science models. We begin by harmonizing existing terminology and presenting a cohesive diagram that clarifies the relationships between key elements of the reconstruction process. Through a comprehensive literature survey, we classify and organize the main challenges, extending the discussion beyond common issues like data volume. Lastly, we highlight recent advancements and propose directions for future research, including specific research gaps. By providing a structured approach, key findings, and a clearer understanding of the underlying challenges, this work aims to strengthen the foundation of digital forensics.
Integrated sensing and communication (ISAC) has become a crucial technology in the development of next-generation wireless communication systems. The integration of communication and sensing functionalities on a unified spectrum and infrastructure is expected to enable a variety of emerging use cases. The introduction of ISAC has led to various new challenges and opportunities related to the security of wireless communications, resulting in significant research focused on ISAC system design in relation to physical layer security (PLS). The shared use of spectrum presents a risk where confidential messages embedded in probing ISAC signals may be exposed to potentially malicious sensing targets. This situation creates a tradeoff between sensing performance and security performance. The sensing functionality of ISAC offers a unique opportunity for PLS by utilizing sensing information regarding potential eavesdroppers to design secure PLS schemes. This study examines PLS methodologies to tackle the specified security challenge associated with ISAC. The study begins with a brief overview of performance metrics related to PLS and sensing, as well as the optimization techniques commonly utilized in the existing literature. A thorough examination of existing literature on PLS for ISAC is subsequently presented, with the objective of emphasizing the current state of research. The study concludes by outlining potential avenues for future research pertaining to secure ISAC systems.
Modular integration of neural connectomics, dynamics and biomechanics for identification of behavioral sensorimotor pathways in Caenorhabditis elegans
2025-04-25
Jimin Kim, Jeremy T. Florman, Julia A. Santos, Mark J. Alkema, Eli Shlizerman
Computational approaches which emulate in-vivo nervous system are needed to investigate mechanisms of the brain to orchestrate behavior. Such approaches must integrate a series of biophysical models encompassing the nervous system, muscles, biomechanics to allow observing the system in its entirety while supporting model variations. Here we develop modWorm: a modular modeling framework for the nematode C. elegans. modWorm allows for construction of a model as an integrated series of configurable, exchangeable modules each describing specific biophysical processes across different modalities. Utilizing modWorm, we propose a base neuro-mechanical model for C. elegans built upon the complete connectome. The model integrates a series of 7 modules: i) intra-cellular dynamics, ii) electrical and iii) chemical extra-cellular neural dynamics, iv) translation of neural activity to muscle calcium dynamics, v) muscle calcium dynamics to muscle forces, vi) muscle forces to body postures and vii) proprioceptive feedback. We validate the base model by in-silico injection of constant currents into neurons known to be associated with locomotion behaviors and by applying external forces to the body. Applications of in-silico neural stimuli experimentally known to modulate locomotion show that the model can recapitulate natural behavioral responses such as forward and backward locomotion as well as mid-locomotion responses such as avoidance and turns. Furthermore, through in-silico ablation surveys, the model can infer novel neural circuits involved in sensorimotor behaviors. To further dissect mechanisms of locomotion, we utilize modWorm to introduce empirical based model variations and model optimizations to elucidate their effects on simulated locomotion. Our results show that modWorm can be utilized to identify neural circuits which control, mediate and generate natural behavior.
AI Ethics and Social Norms: Exploring ChatGPT's Capabilities From What to How
2025-04-25
Omid Veisi, Sasan Bahrami, Roman Englert, Claudia Müller
Using LLMs in healthcare, Computer-Supported Cooperative Work, and Social Computing requires the examination of ethical and social norms to ensure safe incorporation into human life. We conducted a mixed-method study, including an online survey with 111 participants and an interview study with 38 experts, to investigate the AI ethics and social norms in ChatGPT as everyday life tools. This study aims to evaluate whether ChatGPT in an empirical context operates following ethics and social norms, which is critical for understanding actions in industrial and academic research and achieving machine ethics. The findings of this study provide initial insights into six important aspects of AI ethics, including bias, trustworthiness, security, toxicology, social norms, and ethical data. Significant obstacles related to transparency and bias in unsupervised data collection methods are identified as ChatGPT's ethical concerns.
Mock Observations: Morphological Analysis of Galaxies in TNG100 Simulations
2025-04-25
Jun-Yu Gong, Weipeng Lin, Lin Tang, Yanyao Lan
In this study, we investigate the morphology of galaxies in the TNG100 simulation by applying mock observation techniques and compare the results with the observational data from the Sloan Digital Sky Survey (SDSS). By employing a hierarchical Convolutional Neural Network (CNN) approach, we classify galaxies into four morphological categories (Ellipticals, S0/a, Sab/Sb, and Sc/Sd/Irregulars). Our findings show that the morphological characteristics of the mock-observed galaxy samples closely match those observed in the SDSS, successfully reproducing key features such as distinct parameter distributions for different types. However, some discrepancies are identified: notably, a significant lack of early-type galaxies (ETGs) in the dwarf galaxy regime ($M_* < 10^{10} M_{\odot}$) and minimal distinction between Sab/Sb and Sc/Sd/Irregular galaxies in the mock-observed samples, unlike the clear differences seen in actual observations. These divergences may stem from simulation properties such as elevated star formation efficiency at low mass end or resolution limits. Observational effects, including the impact of the Point Spread Function, sky background, and instrumental noise, can independently cause approximately 7.87\% morphological misclassifications by our CNN model. Compared to previous studies using gravity-based definitions of galaxies that failed to clearly distinguish the parameter distributions of ETGs versus Late-Type Galaxies, our brightness-based sample definition method better recovers the observed morphological parameter distributions, especially their distinct differences. Our study underscores that, alongside mock observations, employing galaxy segmentation methods consistent with observational practices is crucial for appropriately recovering realistic morphological parameters from simulations and enabling fair comparisons.
A hydrodynamical simulations-based model that connects the FRB DM--redshift relation to suppression of the matter power spectrum via feedback
2025-04-25
Kritti Sharma, Elisabeth Krause, Vikram Ravi, Robert Reischke, Pranjal R. S., Liam Connor
Understanding the impact of baryonic feedback on the small-scale ($k \gtrsim 1\,h\,$Mpc$^{-1}$) matter power spectrum is a key astrophysical challenge, and essential for interpreting data from upcoming weak-lensing surveys, which require percent-level accuracy to fully harness their potential. Astrophysical probes, such as the kinematic and thermal Sunyaev-Zel'dovich effects, have been used to constrain feedback at large scales ($k \lesssim 5\,h\,$Mpc$^{-1}$). The sightline-to-sightline variance in the fast radio bursts (FRBs) dispersion measure (DM) correlates with the strength of baryonic feedback and offers unique sensitivity at scales upto $k \sim 10\,h\,$Mpc$^{-1}$. We develop a new simulation-based formalism in which we parameterize the distribution of DM at a given redshift, $p(\mathrm{DM}|z)$, as a log-normal with its first two moments computed analytically in terms of cosmological parameters and the feedback-dependent electron power spectrum $P_\mathrm{ee}(k, z)$. We find that the log-normal parameterization provides an improved description of the $p(\mathrm{DM}|z)$ distribution observed in hydrodynamical simulations as compared to the standard $F$-parameterization. Our model robustly captures the baryonic feedback effects across a wide range of baryonic feedback prescriptions in hydrodynamical simulations, including IllustrisTNG, SIMBA and Astrid. Leveraging simulations incorporates the redshift evolution of the DM variance by construction and facilitates the translation of constrained feedback parameters to the suppression of matter power spectrum relative to gravity-only simulations. We show that with $10^4$ FRBs, the suppression can be constrained to percent-level precision at large scales and $\sim 10$\% precision at scales $k \gtrsim 10\,h\,$Mpc$^{-1}$ with prior-to-posterior $1\sigma$ constraint width ratio $\gtrsim 20$.
LBT IR observations of candidate super-Eddington quasars
2025-04-25
T. M. Buendia-Rios, P. Marziani, C. A. Negrete, D. Dultzin
Quasars accreting at very high rates are believed to be prime movers of galactic evolution because of their high radiative and mechanical output. The study presented in this paper investigates a sample of six highly accreting quasars at redshifts \( z = 2-3 \) using near-infrared observations from the LUCI spectrograph at the Large Binocular Telescope (LBT). The aim is obtain a precise measure of the quasar systemic redshift and accretion parameters (black hole mass and Eddington ratio) primarily from the \hb\ line, \ and on second stance from other intermediate and low ionization lines. Outflow dynamical parameters (mass rate of outflowing gas, its kinetic power and momentum rate) were estimated from the \civ\ emission line that is perhaps the most easily accessible tracer of high-ionization winds from the accretion disk, obtained from the Sloan Digital Sky Survey. In addition, the joint analysis of the rest-frame optical and UV spectra allowed us to estimate the chemical composition of the broad line emitting gas. The high metal content of the outflowing gas ($Z \gtrsim 10 Z_\odot$) and the high values of thrust and kinetic power may induce a chemical feedback effect in the quasar host, in addition to mechanical feedback.
A Review of 3D Object Detection with Vision-Language Models
2025-04-25
Ranjan Sapkota, Konstantinos I Roumeliotis, Rahul Harsha Cheppally, Marco Flores Calero, Manoj Karkee
This review provides a systematic analysis of comprehensive survey of 3D object detection with vision-language models(VLMs) , a rapidly advancing area at the intersection of 3D vision and multimodal AI. By examining over 100 research papers, we provide the first systematic analysis dedicated to 3D object detection with vision-language models. We begin by outlining the unique challenges of 3D object detection with vision-language models, emphasizing differences from 2D detection in spatial reasoning and data complexity. Traditional approaches using point clouds and voxel grids are compared to modern vision-language frameworks like CLIP and 3D LLMs, which enable open-vocabulary detection and zero-shot generalization. We review key architectures, pretraining strategies, and prompt engineering methods that align textual and 3D features for effective 3D object detection with vision-language models. Visualization examples and evaluation benchmarks are discussed to illustrate performance and behavior. Finally, we highlight current challenges, such as limited 3D-language datasets and computational demands, and propose future research directions to advance 3D object detection with vision-language models. >Object Detection, Vision-Language Models, Agents, VLMs, LLMs, AI
A Search for Exoplanet Candidates in TESS 2-min Light Curves using Joint Bayesian Detection
2025-04-25
Jamila S. Taaki, Athol J. Kemball, Farzad Kamalabadi
In this work, we apply an exploratory joint Bayesian transit detector (Taaki et al. 2020), previously evaluated using Kepler data, to the 2 min simple aperture photometry light curve data in the continuous viewing zone for the Transiting Exoplanet Survey Satellite (TESS) over three years of observation. The detector uses Bayesian priors, adaptively estimated, to model unknown systematic noise and stellar variability incorporated in a Neyman-Pearson likelihood ratio test for a candidate transit signal; a primary goal of the algorithm is to reduce overfitting. The detector was adapted to the TESS data and refined to improve outlier rejection and suppress false alarm detections in post-processing. The statistical performance of the detector was evaluated using transit injection tests, where the joint Bayesian detector achieves an 80.0 % detection rate and a 19.1% quasi-false alarm rate at a detection threshold {\tau} = 10; this is a marginal, although not statistically significant, improvement of 0.2% over a reference sequential detrending and detection algorithm. In addition, a full search of the input TESS data was performed to evaluate the recovery rate of known TESS objects of interest (TOI) and to perform an independent search for new exoplanet candidates. The joint detector has a 73% recall rate and a 63% detection rate for known TOI; the former considers a match against all detection statistics above threshold while the latter considers only the maximum detection statistic.
Black hole mimickers as relativistic stars calculated from the Tolman-Oppenheimer-Volkoff equations
2025-04-25
Stephen L. Adler
This mini-review summarizes and simplifies the principle content of our papers on ``Dynamical Gravastars'', formulated using the Tolman-Oppenheimer-Volkoff equations, and surveyed numerically using Mathematica notebooks that we document here in Appendices.
DELVE-DEEP Survey: The Faint Satellite System of NGC 55
2025-04-25
Jonah Medoff, Burçin Mutlu-Pakdil, Jeffrey L. Carlin, Alex Drlica-Wagner, Erik J. Tollerud, Amandine Doliva-Dolinsky, David J. Sand, Clara E. Martínez-Vázquez, Guy S. Stringfellow, William Cerny, Denija Crnojević, Peter S. Ferguson, Catherine E. Fielder, Astha Chaturvedi, Nitya Kallivayalil, Noelia E. D. Noël, Kathy Vivas, Alistair R. Walker, Monika Adamów, Clecio R. Bom, Julio A. Carballo-Bello, Yumi Choi, Gustavo E. Medina, Mahdieh Navabi, Andrew B. Pace, Alex H. Riley, Joanna D. Sakowska
We report the first comprehensive census of the satellite dwarf galaxies around NGC~55 ($2.1$~Mpc) as a part of the DECam Local Volume Exploration DEEP (DELVE-DEEP) survey. NGC~55 is one of four isolated, Magellanic analogs in the Local Volume around which DELVE-DEEP aims to identify faint dwarfs and other substructures. We employ two complementary detection methods: one targets fully resolved dwarf galaxies by identifying them as stellar over-densities, while the other focuses on semi-resolved dwarf galaxies, detecting them through shredded unresolved light components. As shown through extensive tests with injected galaxies, our search is sensitive to candidates down to $M_V \lesssim -6.6$ and surface brightness $\mu \lesssim 28.5$ mag arcsec$^{-2}$, and $\sim 80\%$ complete down to $M_V \lesssim -7.8$. We do not report any new confirmed satellites beyond two previously known systems, ESO 294-010 and NGC~55-dw1. We construct the satellite luminosity function of NGC~55 and find it to be consistent with the predictions from cosmological simulations. As one of the first complete luminosity functions for a Magellanic analog, our results provide a glimpse of the constraints on low-mass-host satellite populations that will be further explored by upcoming surveys, such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time.
Forecasts and Simulations for Relativistic Corrections to the Sunyaev-Zeldovich Effect
2025-04-25
L. Kuhn, Z. Li, William R. Coulton
The Sunyaev-Zeldovich (SZ) effect is a window into the astrophysical processes of galaxy clusters, and relativistic corrections (the "rSZ") promise to provide a global census of the gas feedback within clusters. Upcoming wide-field millimeter-wave surveys such as the Simons Observatory (SO), Fred Young Submillimeter Telescope, and CMB-S4 will make increasingly precise measurements of the SZ effect and its relativistic corrections. We present simulated full-sky maps of the rSZ effect and a fast code to generate it, for use in the development of analysis techniques and pipelines. As part of the websky simulation suite, our mock observations have semi-realistic cross-correlations with other large-scale structure tracers, offering insights into the formation and evolution of galaxy clusters and large-scale structure. As a demonstration of this, we examine what an SO-like experiment can learn from the rSZ effect. We find that high significance detections will be possible, provided that the instrumental systematics are under control, and that the evolution of cluster temperatures with mass and redshift can be probed in a manner complementary to X-ray measurements.
Weighing neutrinos with 21cm Intensity Mapping at the SKAO
2025-04-25
Gabriele Autieri, Maria Berti, Marta Spinelli, Balakrishna Sandeep Haridasu, Matteo Viel
We explore the constraining power of future 21cm intensity mapping (IM) observations at the SKAO, focusing primarily on the sum of neutrino masses, $\Sigma m_\nu$. We forecast observations of the 21cm IM auto-power spectrum as well as the 21cm IM and galaxy surveys cross-correlation power spectrum. We construct different synthetic data sets of observations for the 21cm IM observables. For galaxy clustering, we consider two stage-IV surveys to mimic a DESI-like and Euclid-like cross-correlation signal. We study the impact of assuming three different fiducial values for the sum of neutrino masses, i.e. $\Sigma m_\nu = 0.06, 0.1, 0.4$ eV, in the synthetic data sets. To investigate the constraining power of the forecasted 21cm observations, we build a likelihood code. We find that the 21cm auto-power spectrum alone could provide an upper limit on the sum of neutrino masses of $\Sigma m_\nu < 0.287$ eV, at $95\%$ confidence level, for the case of the lowest fiducial value of $\Sigma m_\nu$. This result is comparable to the upper limits provided by cosmic microwave background (CMB) observations alone. When combining the 21cm auto-power spectrum synthetic data set with Planck 2018 CMB measurements, we find a tighter upper limit of $\Sigma m_\nu < 0.105$ eV, which improves on the constraints from Planck alone. We obtain a similar result with 21cm and galaxy clustering cross-correlation power spectrum, whose detection is more easily achieved as they are less affected by systematic effects. Combining with Planck 2018 data, we find the upper limits of $\Sigma m_\nu < 0.116$ eV and $\Sigma m_\nu < 0.117$ eV for the 21cm signal in cross-correlation with the DESI-like and Euclid-like surveys, respectively. These constraints are comparable to those obtained by combining Planck data with the 21cm auto-power spectrum synthetic data sets, thus supporting the case for 21cm cross-correlation detections.
Spark: A System for Scientifically Creative Idea Generation
2025-04-25
Aishik Sanyal, Samuel Schapiro, Sumuk Shashidhar, Royce Moon, Lav R. Varshney, Dilek Hakkani-Tur
Recently, large language models (LLMs) have shown promising abilities to generate novel research ideas in science, a direction which coincides with many foundational principles in computational creativity (CC). In light of these developments, we present an idea generation system named Spark that couples retrieval-augmented idea generation using LLMs with a reviewer model named Judge trained on 600K scientific reviews from OpenReview. Our work is both a system demonstration and intended to inspire other CC researchers to explore grounding the generation and evaluation of scientific ideas within foundational CC principles. To this end, we release the annotated dataset used to train Judge, inviting other researchers to explore the use of LLMs for idea generation and creative evaluations.
Recent breakthroughs in artificial intelligence (AI) have brought about increasingly capable systems that demonstrate remarkable abilities in reasoning, language understanding, and problem-solving. These advancements have prompted a renewed examination of AI awareness, not as a philosophical question of consciousness, but as a measurable, functional capacity. In this review, we explore the emerging landscape of AI awareness, which includes meta-cognition (the ability to represent and reason about its own state), self-awareness (recognizing its own identity, knowledge, limitations, inter alia), social awareness (modeling the knowledge, intentions, and behaviors of other agents), and situational awareness (assessing and responding to the context in which it operates). First, we draw on insights from cognitive science, psychology, and computational theory to trace the theoretical foundations of awareness and examine how the four distinct forms of AI awareness manifest in state-of-the-art AI. Next, we systematically analyze current evaluation methods and empirical findings to better understand these manifestations. Building on this, we explore how AI awareness is closely linked to AI capabilities, demonstrating that more aware AI agents tend to exhibit higher levels of intelligent behaviors. Finally, we discuss the risks associated with AI awareness, including key topics in AI safety, alignment, and broader ethical concerns. AI awareness is a double-edged sword: it improves general capabilities, i.e., reasoning, safety, while also raises concerns around misalignment and societal risks, demanding careful oversight as AI capabilities grow. On the whole, our interdisciplinary review provides a roadmap for future research and aims to clarify the role of AI awareness in the ongoing development of intelligent machines.
2025-04-25
Firuz Kamalov, David Santandreu Calonge, Linda Smail, Dilshod Azizov, Dimple R. Thadani, Theresa Kwong, Amara Atif
Artificial intelligence (AI) has transformed various aspects of education, with large language models (LLMs) driving advancements in automated tutoring, assessment, and content generation. However, conventional LLMs are constrained by their reliance on static training data, limited adaptability, and lack of reasoning. To address these limitations and foster more sustainable technological practices, AI agents have emerged as a promising new avenue for educational innovation. In this review, we examine agentic workflows in education according to four major paradigms: reflection, planning, tool use, and multi-agent collaboration. We critically analyze the role of AI agents in education through these key design paradigms, exploring their advantages, applications, and challenges. To illustrate the practical potential of agentic systems, we present a proof-of-concept application: a multi-agent framework for automated essay scoring. Preliminary results suggest this agentic approach may offer improved consistency compared to stand-alone LLMs. Our findings highlight the transformative potential of AI agents in educational settings while underscoring the need for further research into their interpretability, trustworthiness, and sustainable impact on pedagogical impact.
Collisionless ion-electron energy exchange in magnetized shocks
2025-04-24
Y. Zhang, P. V. Heuer, H. Wen, J. R. Davies, C. Ren, D. B. Schaeffer, E. G. Blackman, J. Zhang, A. Bret, F. García-Rubio
Energy partition between ions and electrons in collisionless shocks has long been an unsolved fundamental physical problem. We show that kinetic simulations of moderate Alfv\'enic Mach number, magnetized, collisionless shocks reveal rapid, faster-than-Coulomb, energy exchange between ions and electrons when the plasma is sufficiently magnetized. Using kinetic and multi-fluid models with counter-streaming ions, we identify resonances between electron whistler and ion magnetohydrodynamic waves that account for this rapid energy exchange.
Nearby open clusters with tidal features: golden sample selection and 3D structure
2025-04-24
Ming Xu, Xiaoting Fu, Yang Chen, Lu Li, Min Fang, He Zhao, Penghui Liu, Yichang Zuo
Open clusters offer unique opportunities to study stellar dynamics and evolution under the influence of their internal gravity, the Milky Way's gravitational field, and the interactions with encounters. Using the Gaia DR3 data for a catalog of open clusters within 500 parsecs that exhibit tidal features reported by the literature, we apply a novel method based on 3D principal component analysis to select a ``golden sample'' of nearby open clusters with minimal line-of-sight distortions. This approach ensures a systematic comparison of 3D and 2D structural parameters for tidally perturbed clusters. The selected golden sample includes Blanco 1, Melotte 20, Melotte 22, NGC 2632, NGC 7092, NGC 1662, Roslund 6 and Melotte 111. We analyze these clusters by fitting both 2D and 3D King Profiles to their stellar density distributions. Our results reveal systematic discrepancies: most of the golden sample clusters exhibit larger 3D tidal radii compared to their 2D counterparts, demonstrating that the 2D projection effects bias the measured cluster size. Furthermore, the 3D density profiles show stronger deviations from King profiles at the tidal radii ($\Delta \rho_{\rm 3D} > \Delta \rho_{\rm 2D}$), highlighting enhanced sensitivity to tidal disturbances. Additionally, we investigate the spatial distribution of cluster members relative to their bulk motion in the Galactic plane. We find that some clusters exhibit tidal features oriented perpendicular to their direction of motion, which can be attributed to the fact that the current surveys only detect the curved inner regions of the tidal features. In conclusion, this work offers a golden sample of nearby open clusters that are most reliable for 3D structure analysis and underscores the necessity of 3D analysis in characterizing OC morphological asymmetries, determining cluster size, and identifying tidal features.
Novel Heusler Materials for Spintronic Applications: Growth, Characterizations and Applications
2025-04-24
Ravinder Kumar, Sachin Gupta
Spintronics is a rapidly evolving technology that utilizes the spin of electrons along with their charge to enable high speed, low power and non volatile electronic devices. The development of novel materials with tailored magnetic and electronic properties is critical to exploit the full potential of spintronic applications. Among these, Heusler alloys stand out due to their tunable multifunctional properties. This review presents a comprehensive overview of various Heusler based materials including half metallic ferromagnets, spin gapless semiconductors, magnetic semiconductors, spin semimetals, and nearly zero moment materials focusing on their synthesis, structural and magnetic characterizations, and transport behavior. The role of crystal structure, and structural disorder in governing their magnetic and electronic properties is discussed in detail. Emphasis is placed on experimental results and their implications for spintronic devices. By bringing together recent advancements, the review highlights the critical role of Heusler alloys in advancing the next-generation spintronic technologies and outlines future directions for their integration in practical applications.
Multi-messenger standard-siren cosmology for third-generation gravitational-wave detectors: Considering observations of gamma-ray bursts and kilonovae
2025-04-24
Tao Han, Jing-Fei Zhang, Xin Zhang
In the third-generation (3G) gravitational-wave (GW) detector era, GW multi-messenger observations for binary neutron star merger events can exert great impacts on exploring the cosmic expansion history. Extending the previous work, we explore the potential of 3G GW standard siren observations in cosmological parameter estimation by considering their associated electromagnetic (EM) counterparts, including $\gamma$-ray burst (GRB) coincidence observations by the Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor and GW-triggered target-of-opportunity observations of kilonovae by different optical survey projects. During an assumed 10-year observation, we predict that the number of detectable GW-kilonova events is $\sim 4900$ with redshifts below $\sim 0.4$ under GW network and Large Synoptic Survey Telescope in the $i$ band, which is three times more than that of GW-GRB detections. For the cosmological analysis, we find that with the inclusion of GW-kilonova detections, the constraints on cosmological parameters from GW-EM detections are significantly improved compared to those from GW-GRB detections. In particular, GW-EM detections can tightly constrain the Hubble constant with a precision ranging from $0.076\%$ to $0.034\%$. Moreover, GW multi-messenger observations could effectively break the cosmological parameter degeneracies generated by the mainstream EM observations, CMB+BAO+SN (CBS). The combination of CBS and GW-EM can tightly constrain the equation of state parameters of dark energy $w$ in the $w$CDM model and $w_0$ in the $w_0w_a$CDM model with precisions of $0.72\%$ and $0.99\%$, respectively, meeting the standard of precision cosmology. In conclusion, GW multi-messenger observations could play a crucial role in helping solve the Hubble tension and probing the fundamental nature of dark energy.
Conformal Segmentation in Industrial Surface Defect Detection with Statistical Guarantees
2025-04-24
Cheng Shen, Yuewei Liu
In industrial settings, surface defects on steel can significantly compromise its service life and elevate potential safety risks. Traditional defect detection methods predominantly rely on manual inspection, which suffers from low efficiency and high costs. Although automated defect detection approaches based on Convolutional Neural Networks(e.g., Mask R-CNN) have advanced rapidly, their reliability remains challenged due to data annotation uncertainties during deep model training and overfitting issues. These limitations may lead to detection deviations when processing the given new test samples, rendering automated detection processes unreliable. To address this challenge, we first evaluate the detection model's practical performance through calibration data that satisfies the independent and identically distributed (i.i.d) condition with test data. Specifically, we define a loss function for each calibration sample to quantify detection error rates, such as the complement of recall rate and false discovery rate. Subsequently, we derive a statistically rigorous threshold based on a user-defined risk level to identify high-probability defective pixels in test images, thereby constructing prediction sets (e.g., defect regions). This methodology ensures that the expected error rate (mean error rate) on the test set remains strictly bounced by the predefined risk level. Additionally, we observe a negative correlation between the average prediction set size and the risk level on the test set, establishing a statistically rigorous metric for assessing detection model uncertainty. Furthermore, our study demonstrates robust and efficient control over the expected test set error rate across varying calibration-to-test partitioning ratios, validating the method's adaptability and operational effectiveness.
Target-Date Funds: A State-of-the-Art Review with Policy Applications to Chile's Pension Reform
2025-04-24
Fernando Suárez, José Manuel Peña, Omar Larré
This review paper explores the evolution and implementation of target-date funds (TDFs), specifically focusing on their application within the context of Chile's 2025 pension reform. The introduction of TDFs marks a significant shift in Chile's pension system, which has traditionally relied on a multifund structure (essentially a target-risk funds system). We offer a comprehensive review of the theoretical foundations and practical considerations of TDFs, highlighting key challenges and opportunities for Chilean regulators and fund managers. Notably, we recommend that the glide path design should be dynamic, incorporating adjustments based on total accumulated wealth, with particular flexibility depending on each investor's risk tolerance. Furthermore, we propose that the new benchmark for generational funds should feature a wide deviation band relative to the new benchmark portfolio, which could foster a market with more investment strategies and better competition among fund managers, encourage the inclusion of alternative assets, and foster greater diversification. Lastly, we highlight the need for future work to define a glide path model that incorporates the theoretical frameworks described, tailored to the unique parameters of the Chilean pension system. These recommendations aim to optimize the long-term retirement outcomes for Chilean workers under the new pension structure.
Large Reasoning Models (LRMs) have exhibited extraordinary prowess in tasks like mathematics and coding, leveraging their advanced reasoning capabilities. Nevertheless, as these capabilities progress, significant concerns regarding their vulnerabilities and safety have arisen, which can pose challenges to their deployment and application in real-world settings. This paper presents a comprehensive survey of LRMs, meticulously exploring and summarizing the newly emerged safety risks, attacks, and defense strategies. By organizing these elements into a detailed taxonomy, this work aims to offer a clear and structured understanding of the current safety landscape of LRMs, facilitating future research and development to enhance the security and reliability of these powerful models.
Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence
2025-04-24
Edward Collins, Michel Wang
Federated Learning (FL) has emerged as a transformative paradigm in the field of distributed machine learning, enabling multiple clients such as mobile devices, edge nodes, or organizations to collaboratively train a shared global model without the need to centralize sensitive data. This decentralized approach addresses growing concerns around data privacy, security, and regulatory compliance, making it particularly attractive in domains such as healthcare, finance, and smart IoT systems. This survey provides a concise yet comprehensive overview of Federated Learning, beginning with its core architecture and communication protocol. We discuss the standard FL lifecycle, including local training, model aggregation, and global updates. A particular emphasis is placed on key technical challenges such as handling non-IID (non-independent and identically distributed) data, mitigating system and hardware heterogeneity, reducing communication overhead, and ensuring privacy through mechanisms like differential privacy and secure aggregation. Furthermore, we examine emerging trends in FL research, including personalized FL, cross-device versus cross-silo settings, and integration with other paradigms such as reinforcement learning and quantum computing. We also highlight real-world applications and summarize benchmark datasets and evaluation metrics commonly used in FL research. Finally, we outline open research problems and future directions to guide the development of scalable, efficient, and trustworthy FL systems.
Applied Sheaf Theory For Multi-agent Artificial Intelligence (Reinforcement Learning) Systems: A Prospectus
2025-04-24
Eric Schmid
This paper provides a pedagogical introduction to classical sheaf theory and sheaf cohomology, followed by a research prospectus exploring potential applications to multi-agent artificial intelligence systems. The first section offers a comprehensive overview of fundamental sheaf-theoretic concepts-presheaves, sheaves, stalks, and cohomology-aimed at researchers in computer science and AI who may not have extensive background in algebraic topology. The second section presents a detailed research prospectus that outlines a roadmap for developing sheaf-theoretic approaches to model and analyze complex systems of interacting agents. We propose that sheaf theory's inherent local-to-global perspective may provide valuable mathematical tools for reasoning about how local agent behaviors collectively determine emergent system properties. The third section contains a literature review connecting sheaf theory with existing research in multi-agent systems, reinforcement learning, and economic modeling. This paper does not present a completed model but rather lays theoretical groundwork and identifies promising research directions that could bridge abstract mathematics with practical AI applications, potentially revealing new approaches to coordination and emergence in multi-agent systems.
We have created a large database of similarity information between sub-regions of Hubble Space Telescope images. These data can be used to assess the accuracy of image search algorithms based on computer vision methods. The images were compared by humans in a citizen science project, where they were asked to select similar images from a comparison sample. We utilized the Amazon Mechanical Turk system to pay our reviewers a fair wage for their work. Nearly 850,000 comparison measurements have been analyzed to construct a similarity distance matrix between all the pairs of images. We describe the algorithm used to extract a robust distance matrix from the (sometimes noisy) user reviews. The results are very impressive: the data capture similarity between images based on morphology, texture, and other details that are sometimes difficult even to describe in words (e.g., dusty absorption bands with sharp edges). The collective visual wisdom of our citizen scientists matches the accuracy of the trained eye, with even subtle differences among images faithfully reflected in the distances.
Pyrochlore oxides with chemical formula of A2B2O7 exhibit a diverse range of electronic properties as a representative family of quantum materials. These properties mostly stem from strong electron correlations at the transition metal B site and typical geometrical frustration effects on the pyrochlore lattice. Furthermore, the coupling between the magnetic moments of the rare-earth A site and the conduction electrons at the B site, along with the relativistic spin-orbit coupling particularly affecting the 4d/5d electrons at the B site, gives rise to the topological characteristics of the correlated electrons. This review paper focuses on the metal-insulator transitions in pyrochlore oxides as evidence of the strong electron correlation, which is highlighted as a rich source of intriguing charge dynamics coupled with frustrated spin-orbital entangled magnetism.
It is well recognized that the safety of compiler optimizations is at risk in a concurrent context. Existing approaches primarily rely on context-free thread-local guarantees, and prohibit optimizations that introduce a data-race. However, compilers utilize global context-specific information, exposing safe optimizations that may violate such guarantees as well as introduce a race. Such optimizations need to individually be proven safe for each language model. An alternate approach to this would be proving them safe for an intuitive model (like interleaving semantics), and then determine their portability across other concurrent models. In this paper, we address this problem of porting across models of concurrency. We first identify a global guarantee on optimizations portable from Sequential Consistency (SC) to Total Store Order (TSO). Our guarantee is in the form of constraints specifying the syntactic changes an optimization must not incur. We then show these constraints correlate to prohibiting the introduction of triangular races, a subset of data-race relevant to TSO. We conclude by showing how such race inducing optimizations relate to porting across Strong Release Acquire (SRA), a known causally consistent memory model.
Hot Diggity Dog: A complete analysis of the extreme molecular gas and dust properties at kpc scales in the hyper-luminous hot, dust-obscured galaxy W2246-0526
2025-04-24
Kevin C. Harrington, Román Fernández Aranda, Leindert Boogaard, Axel Weiß, Tanio Diaz Santos, Manuel Aravena, Roberto J. Assef, Chao-Wei Tsai, Peter Eisenhardt, Daniel Stern
Hot dust-obscured galaxies (Hot DOGs), the most infrared (IR) luminous objects selected by the WISE all-sky mid-IR survey, have yielded a sample of intrinsically luminous quasars (QSOs) with obscured nuclear activity and hot dust temperatures. The molecular gas excitation properties have yet to be examined in detail under such extreme conditions. Here we study the most far-IR luminous WISE Hot DOG W2246-0526, focusing on the central host galaxy. Multi-J CO transition measurements at J=2-1, 5-4, 7-6, 12-11, and 17-16 provide the most well-sampled CO excitation ladder of any WISE Hot DOG to date, providing the first self-consistent modeling constraints on the molecular gas and dust properties. We implement a state-of-the-art TUrbulent Non-Equilibrium Radiative transfer model (TUNER) that simultaneously models both the line and dust continuum measurements. Due to a combination of high molecular gas densities and high kinetic temperatures, this extreme CO spectral line energy distribution peaks at J = 10 to 12, likely making this the most highly excited galaxy ever reported. We derive the alpha_CO conversion factors and conclude that (J=3-7) CO line luminosities trace the bulk of the molecular gas mass. W2246-0526 is a rapidly evolving system, with a high value of the molecular gas kinetic temperature versus dust temperature T_k / T_d ~ 3.9, reflecting previously reported shocks and outflows injecting kinetic energy within the central kpc of this host. This first comprehensive simultaneous modeling of both the molecular gas and dust in any object within the WISE-selected Hot DOG sample motivates obtaining well-sampled dust and line spectral energy distributions to better understand the conditions within these short-lived episodes in galaxy evolution that are associated with the most obscured supermassive black hole activity.
Modeling Communication Perception in Development Teams Using Monte Carlo Methods
2025-04-24
Marc Herrmann, Martin Obaidi, Jil Klünder
Software development is a collaborative task involving diverse development teams, where toxic communication can negatively impact team mood and project success. Mood surveys enable the early detection of underlying tensions or dissatisfaction within development teams, allowing communication issues to be addressed before they escalate, fostering a positive and productive work environment. The mood can be surveyed indirectly by analyzing the text-based communication of the team. However, emotional subjectivity leads to varying sentiment interpretations across team members; a statement perceived neutrally by one developer might be seen as problematic by another developer with a different conversational culture. Early identification of perception volatility can help prevent misunderstandings and enhance team morale while safeguarding the project. This paper analyzes the diversity of perceptions within arbitrary development teams and determines how many team members should report their sentiment to accurately reflect the team's mood. Through a Monte Carlo experiment involving 45 developers, we present a preliminary mathematical model to calculate the minimum agreement among a subset of developers based on the whole team's agreement. This model can guide leadership in mood assessment, demonstrating that omitting even a single member in an average-sized 7-member team can misrepresent the overall mood. Therefore, including all developers in mood surveying is recommended to ensure a reliable evaluation of the team's mood.
The stellar corona-chromosphere connection. A comprehensive study of X-ray and Ca II IRT fluxes from eROSITA and Gaia
2025-04-24
S. Freund, S. Czesla, B. Fuhrmeister, P. Predehl, J. Robrade, P. C. Schneider, J. H. M. M. Schmitt
Stellar activity can be observed at different wavelengths in a variety of different activity indicators. We investigated the correlation between coronal and chromospheric emissions by combining X-ray data from stars detected in the eROSITA all-sky surveys (eRASS1 and eRASS:5) with Ca II infrared triplet (IRT) activity indices as published in the third Gaia data release (Gaia DR3). We specifically studied 24 300 and 43 200 stellar sources with reliable Ca II IRT measurement and X-ray detection in eRASS1 and eRASS:5, which is by far the largest stellar sample available so far. The largest detection fraction is obtained for highly active sources and stars of a late spectral type, while F-type and less active stars (as measured in the Ca II IRT) remain mostly undetected in X-rays. Also, the correlation is the strongest for late-type sources, while F-type stars show a rather weak correlation between the X-ray to bolometric flux ratio and the Ca II IRT activity index. The relation between the X-ray and Ca II IRT surface fluxes changes with the fractional X-ray flux without showing two separated branches as described in previous studies. For fast rotators, both activity indicators saturate at a similar Rossby number and the X-ray to bolometric flux ratio decreases faster than the IRT index for slower rotating stars. As a consequence, the ratio between X-ray and IRT fluxes is constant in the saturation regime and decreases for slow rotators.
The quest for high-redshift radio galaxies. I. A pilot spectroscopic study
2025-04-24
Alessandro Capetti, Barbara Balmaverde, Miguel Coloma Puga, Bruno Vizzone, Ana Jimenez-Gallardo, Abigail Garcia-Perez, Giacomo Venturi
The population of high-redshift radio galaxies (HzRGs) is still poorly studied because only a few of these objects are currently known. We here present the results of a pilot project of spectroscopic identification of HzRG candidates. The candidates are selected by combining low-frequency radio and optical surveys that cover a total of ~2,000 squared degrees using the dropout technique, that is, the presence of a redshifted Lyman break in their photometric data. We focused on 39 g-dropout sources, which is about one-third of the selected sources, that are expected to be at 3.0 < z < 4.5. We considered single and double radio sources separately and searched for g-dropout sources at the location of the midpoint of the radio structure for the latter. The host galaxy is expected to be located there. We confirm only one out of 29 candidate HzRG associated with an extended radio source. For the compact radio sources, we instead reach a success rate of 30% by confirming 3 out of 10 HzRG targets. The four newly discovered HzRGs show a wide range of spectral radio slopes. This supports the idea that not all HzRGs are ultrasteep radio sources (USSs). The criterion for USSs is most commonly used to find HzRGs, but this method only selects a subpopulation. We discuss various contamination sources for the objects that are selected with the Lyman-break method and conclude that they are likely mainly HzRGs, but with a Ly$\alpha$ line that is underluminous with respect to expectations.
Knowledge-based Vision Question Answering (KB-VQA) extends general Vision Question Answering (VQA) by not only requiring the understanding of visual and textual inputs but also extensive range of knowledge, enabling significant advancements across various real-world applications. KB-VQA introduces unique challenges, including the alignment of heterogeneous information from diverse modalities and sources, the retrieval of relevant knowledge from noisy or large-scale repositories, and the execution of complex reasoning to infer answers from the combined context. With the advancement of Large Language Models (LLMs), KB-VQA systems have also undergone a notable transformation, where LLMs serve as powerful knowledge repositories, retrieval-augmented generators and strong reasoners. Despite substantial progress, no comprehensive survey currently exists that systematically organizes and reviews the existing KB-VQA methods. This survey aims to fill this gap by establishing a structured taxonomy of KB-VQA approaches, and categorizing the systems into main stages: knowledge representation, knowledge retrieval, and knowledge reasoning. By exploring various knowledge integration techniques and identifying persistent challenges, this work also outlines promising future research directions, providing a foundation for advancing KB-VQA models and their applications.
Psychological safety is the belief that team members can speak up or make mistakes without fear of negative consequences. While it is recognized as important in traditional software teams, its role in open-source development remains understudied. Yet, open-source contributors often collaborate without formal roles or structures, where interpersonal relationship can make or break participation. In this study, we examine whether team-level psychological safety, inferred from code review activities, is associated with contributors' continued participation in open-source projects. Code review is a central and collaborative activity in modern software development, which offers a rich context for observing team interactions. Based on 60,684 pull requests, we construct a psychological safety index using cues such as merge decisions, comment activity, interaction diversity, and mentions. We analyze its relationship with contributors' short-term (after 1 year) and long-term (after 4-5 years) sustained participation using three logistic regression models. Our findings show that contributors are more likely to remain active in repositories with higher levels of psychological safety. Psychological safety is positively associated with both short-term and future sustained participation. However, when prior participation is included, it becomes the stronger predictor of future sustained participation, while the effect of psychological safety becomes smaller. This study introduces a scalable approach to study psychological safety through pull request data and provides new evidence that it matters in open-source development.
Using multiple representations to improve student understanding of quantum states
2025-04-24
Emily Marshman, Alexandru Maries, Chandralekha Singh
One hallmark of expertise in physics is the ability to translate between different representations of knowledge and use the representations that make the problem-solving process easier. In quantum mechanics, students learn about several ways to represent quantum states, e.g., as state vectors in Dirac notation and as wavefunctions in position and momentum representation. Many advanced students in upper-level undergraduate and graduate quantum mechanics courses have difficulty translating state vectors in Dirac notation to wavefunctions in the position or momentum representation and vice versa. They also struggle when translating the wavefunction between the position and momentum representations. The research presented here describes the difficulties that students have with these issues and how research was used as a guide in the development, validation, and evaluation of a Quantum Interactive Learning Tutorial (QuILT) to help students develop a functional understanding of these concepts. The QuILT strives to help students with different representations of quantum states as state vectors in Dirac notation and as wavefunctions in position and momentum representation and with translating between these representations. We discuss the effectiveness of the QuILT from in-class implementation and evaluation.
Storing and Querying Evolving Graphs in NoSQL Storage Models
2025-04-24
Alexandros Spitalas, Anastasios Gounaris, Andreas Kosmatopoulos, Kostas Tsichlas
This paper investigates advanced storage models for evolving graphs, focusing on the efficient management of historical data and the optimization of global query performance. Evolving graphs, which represent dynamic relationships between entities over time, present unique challenges in preserving their complete history while supporting complex analytical queries. We first do a fast review of the current state of the art focusing mainly on distributed historical graph databases to provide the context of our proposals. We investigate the im- plementation of an enhanced vertex-centric storage model in MongoDB that prioritizes space efficiency by leveraging in-database query mechanisms to minimize redundant data and reduce storage costs. To ensure broad applicability, we employ datasets, some of which are generated with the LDBC SNB generator, appropriately post-processed to utilize both snapshot- and interval-based representations. Our experimental results both in centralized and distributed infrastructures, demonstrate significant improvements in query performance, particularly for resource-intensive global queries that traditionally suffer from inefficiencies in entity-centric frameworks. The proposed model achieves these gains by optimizing memory usage, reducing client involvement, and exploiting the computational capabilities of MongoDB. By addressing key bottlenecks in the storage and processing of evolving graphs, this study demonstrates a step toward a robust and scalable framework for managing dynamic graph data. This work contributes to the growing field of temporal graph analytics by enabling more efficient ex- ploration of historical data and facilitating real-time insights into the evolution of complex networks.
Finding Conditions for Target Controllability under Christmas Trees
2025-04-24
Marco Peruzzo, Giacomo Baggio, Francesco Ticozzi
This paper presents new graph-theoretic conditions for structural target controllability of directed networks. After reviewing existing conditions and highlighting some gaps in the literature, we introduce a new class of network systems, named Christmas trees, which generalizes trees and cacti. We then establish a graph-theoretic characterization of sets of nodes that are structurally target controllable for a simple subclass of Christmas trees. Our characterization applies to general network systems by considering spanning subgraphs of Christmas tree class and allows us to uncover target controllable sets that existing criteria fail to identify.
Towards User-Centred Design of AI-Assisted Decision-Making in Law Enforcement
2025-04-24
Vesna Nowack, Dalal Alrajeh, Carolina Gutierrez Muñoz, Katie Thomas, William Hobson, Catherine Hamilton-Giachritsis, Patrick Benjamin, Tim Grant, Juliane A. Kloess, Jessica Woodhams
Artificial Intelligence (AI) has become an important part of our everyday lives, yet user requirements for designing AI-assisted systems in law enforcement remain unclear. To address this gap, we conducted qualitative research on decision-making within a law enforcement agency. Our study aimed to identify limitations of existing practices, explore user requirements and understand the responsibilities that humans expect to undertake in these systems. Participants in our study highlighted the need for a system capable of processing and analysing large volumes of data efficiently to help in crime detection and prevention. Additionally, the system should satisfy requirements for scalability, accuracy, justification, trustworthiness and adaptability to be adopted in this domain. Participants also emphasised the importance of having end users review the input data that might be challenging for AI to interpret, and validate the generated output to ensure the system's accuracy. To keep up with the evolving nature of the law enforcement domain, end users need to help the system adapt to the changes in criminal behaviour and government guidance, and technical experts need to regularly oversee and monitor the system. Furthermore, user-friendly human interaction with the system is essential for its adoption and some of the participants confirmed they would be happy to be in the loop and provide necessary feedback that the system can learn from. Finally, we argue that it is very unlikely that the system will ever achieve full automation due to the dynamic and complex nature of the law enforcement domain.
On the workflow, opportunities and challenges of developing foundation model in geophysics
2025-04-24
Hanlin Sheng, Xinming Wu, Hang Gao, Haibin Di, Sergey Fomel, Jintao Li, Xu Si
Foundation models, as a mainstream technology in artificial intelligence, have demonstrated immense potential across various domains in recent years, particularly in handling complex tasks and multimodal data. In the field of geophysics, although the application of foundation models is gradually expanding, there is currently a lack of comprehensive reviews discussing the full workflow of integrating foundation models with geophysical data. To address this gap, this paper presents a complete framework that systematically explores the entire process of developing foundation models in conjunction with geophysical data. From data collection and preprocessing to model architecture selection, pre-training strategies, and model deployment, we provide a detailed analysis of the key techniques and methodologies at each stage. In particular, considering the diversity, complexity, and physical consistency constraints of geophysical data, we discuss targeted solutions to address these challenges. Furthermore, we discuss how to leverage the transfer learning capabilities of foundation models to reduce reliance on labeled data, enhance computational efficiency, and incorporate physical constraints into model training, thereby improving physical consistency and interpretability. Through a comprehensive summary and analysis of the current technological landscape, this paper not only fills the gap in the geophysics domain regarding a full-process review of foundation models but also offers valuable practical guidance for their application in geophysical data analysis, driving innovation and advancement in the field.
Analysis and Mitigation of Data injection Attacks against Data-Driven Control
2025-04-24
Sribalaji C. Anand
This paper investigates the impact of false data injection attacks on data-driven control systems. Specifically, we consider an adversary injecting false data into the sensor channels during the learning phase. When the operator seeks to learn a stable state-feedback controller, we propose an attack strategy capable of misleading the operator into learning an unstable feedback gain. We also investigate the effects of constant-bias injection attacks on data-driven linear quadratic regulation (LQR). Finally, we explore potential mitigation strategies and support our findings with numerical examples.
This study proposes a novel design methodology for neutron beam shutters that integrates Monte Carlo simulations (MCNP) with machine learning techniques to enhance shielding performance and accelerate the design process. The target facility is a compact neutron science platform where neutrons are produced by proton beams from a cyclotron striking a neutron production target. The system includes both thermal and fast neutron beamlines. A beam shutter is installed on the thermal neutron line to reduce occupational radiation exposure during maintenance activities. In this work, 200 neutron shutter configurations with varying material sequences were simulated using MCNP. The resulting dataset was used to train a fully connected neural network to predict the neutron flux downstream of the shielding. The trained model was subsequently applied to 1,000 randomly generated shielding configurations for rapid flux prediction and performance ranking. The 20 designs with the lowest predicted flux were selected and further validated via MCNP simulations. Results show that the optimal design reduces the neutron flux from 5.61 x 10^9 n/cm2*s at the shutter entrance to 4.96 x 10^5 n/cm2*s at the exit, achieving a reduction of four orders of magnitude. These findings confirm that the integration of machine learning techniques can effectively reduce simulation costs and assist in identifying high-performance shielding configurations, demonstrating the strong potential of data driven approaches in neutron system design.
2025-04-24
Terry Long Phan, Tomotsugu Goto, Issei Yamamura, Takao Nakagawa, Amos Y. -A. Chen, Cossas K. -W. Wu, Tetsuya Hashimoto, Simon C. -C. Ho, Seong Jin Kim
The outer solar system is theoretically predicted to harbour an undiscovered planet, often referred to as P9. Simulations suggest that its gravitational influence could explain the unusual clustering of minor bodies in the Kuiper Belt. However, no observational evidence for P9 has been found so far, as its predicted orbit lies far beyond Neptune, where it reflects only a faint amount of Sunlight. This work aims to find P9 candidates by taking advantage of two far-infrared all-sky surveys, which are IRAS and AKARI. The epochs of these two surveys were separated by 23 years, which is large enough to detect the ~3'/year orbital motion of P9. We use a dedicated AKARI Far-Infrared point source list for our P9 search - AKARI Monthly Unconfirmed Source List, which includes sources detected repeatedly only in hours timescale, but not after months. We search for objects that moved slowly between IRAS and AKARI detections given in the catalogues. First, we estimated the expected flux and orbital motion of P9 by assuming its mass, distance, and effective temperature to ensure it can be detected by IRAS and AKARI, then applied the positional and flux selection criteria to narrow down the number of sources from the catalogues. Next, we produced all possible candidate pairs whose angular separations were limited between 42' and 69.6', corresponding to the heliocentric distance range of 500 - 700 AU and the mass range of 7 - 17 Earth masses. There are 13 pairs obtained after the selection criteria. After image inspection, we found one good candidate, of which the IRAS source is absent from the same coordinate in the AKARI image after 23 years and vice versa. However, AKARI and IRAS detections are not enough to determine the full orbit of this candidate. This issue leads to the need for follow-up observations, which will determine the Keplerian motion of our candidate.
Building Sustainable and Trustworthy Indigenous Knowledge Preservation Ecosystem
2025-04-24
Siguo Bi, Xin Yuan, Wei Ni
This paper focuses on the essential global issue of protecting and transmitting indigenous knowledge. It reveals the challenges in this area and proposes a sustainable supply chain framework for indigenous knowledge. The paper reviews existing technological solutions and identifies technical challenges and gaps. It then introduces cutting-edge technologies to protect and disseminate indigenous knowledge more effectively. The paper also discusses how the proposed framework can address real-world challenges in protecting and transmitting indigenous knowledge, and explores future research applications of the proposed solutions. Finally, it addresses open issues and provides a detailed analysis, offering promising research directions for the protection and transmission of indigenous knowledge worldwide.
Efficient simulation of discrete galaxy populations and associated radiation fields during the first billion years
2025-04-24
James E. Davies, Andrei Mesinger, Steven Murray
Understanding the epochs of cosmic dawn and reionisation requires us to leverage multi-wavelength and multi-tracer observations, with each dataset providing a complimentary piece of the puzzle. To interpret such data, we update the public simulation code, 21cmFASTv4, to include a discrete source model based on stochastic sampling of conditional mass functions and semi-empirical galaxy relations. We demonstrate that our new galaxy model, which parametrizes the means and scatters of well-established scaling relations, is flexible enough to characterize very different predictions from hydrodynamic cosmological simulations of high-redshift galaxies. Combining a discrete galaxy population with approximate, efficient radiative transfer allows us to self-consistently forward-model galaxy surveys, line intensity maps (LIMs), and observations of the intergalactic medium (IGM). Not only does each observable probe different scales and physical processes, but cross-correlation will maximise the information gained from each measurement by probing the galaxy-IGM connection at high-redshift. We find that a stochastic source field produces significant shot-noise in 21cm and LIM power spectra. Scatter in galaxy properties can be constrained using UV luminosity functions and/or 21cm power spectra, especially if astrophysical scatter is higher than expected (as might be needed to explain recent JWST observations). Our modelling pipeline is both flexible and computationally efficient, facilitating high-dimensional, multi-tracer, field-level Bayesian inference of cosmology and astrophysics during the first billion years.
Quantum-Enhanced Change Detection and Joint Communication-Detection
2025-04-24
Zihao Gong, Saikat Guha
Quick detection of transmittance changes in optical channel is crucial for secure communication. We demonstrate that pre-shared entanglement using two-mode squeezed vacuum states significantly reduces detection latency compared to classical and entanglement-augmented coherent-state probes. The change detection latency is inversely proportional to the quantum relative entropy (QRE), which goes to infinity in the absence of thermal noise, suggesting idealized instantaneous detection. However, in realistic scenarios, we show that QRE scales logarithmically with the inverse of the thermal noise mean photon number. We propose a receiver that achieves this scaling and quantify its performance gains over existing methods. Additionally, we explore the fundamental trade-off between communication capacity and change detection latency, highlighting how pre-shared entanglement enhances both.
Enhancing Variational Autoencoders with Smooth Robust Latent Encoding
2025-04-24
Hyomin Lee, Minseon Kim, Sangwon Jang, Jongheon Jeong, Sung Ju Hwang
Variational Autoencoders (VAEs) have played a key role in scaling up diffusion-based generative models, as in Stable Diffusion, yet questions regarding their robustness remain largely underexplored. Although adversarial training has been an established technique for enhancing robustness in predictive models, it has been overlooked for generative models due to concerns about potential fidelity degradation by the nature of trade-offs between performance and robustness. In this work, we challenge this presumption, introducing Smooth Robust Latent VAE (SRL-VAE), a novel adversarial training framework that boosts both generation quality and robustness. In contrast to conventional adversarial training, which focuses on robustness only, our approach smooths the latent space via adversarial perturbations, promoting more generalizable representations while regularizing with originality representation to sustain original fidelity. Applied as a post-training step on pre-trained VAEs, SRL-VAE improves image robustness and fidelity with minimal computational overhead. Experiments show that SRL-VAE improves both generation quality, in image reconstruction and text-guided image editing, and robustness, against Nightshade attacks and image editing attacks. These results establish a new paradigm, showing that adversarial training, once thought to be detrimental to generative models, can instead enhance both fidelity and robustness.
2025-04-24
N. Galikyan, A. A. Kocharyan, V. G. Gurzadyan
JWST galaxy deep spectral surveys provide a unique opportunity to trace a broad range of evolutionary features of galaxies and the intergalactic medium given the huge distance the photons are propagating. We have analyzed the spectral data of JWST galaxies up to a redshift of around 7 using the Kolmogorov technique, which is an efficient tool for testing the tiny comparative randomness properties of cumulative signals, that is, for distinguishing the contributions of regular and stochastic sub-signals. Our aim is to determine if certain identical spectral features of galaxies have undergone any distortions or systematic evolution across a broad range of redshifts. Our results indicate a change in the spectral properties of the sample galaxies at around z \simeq 2.7 at over a 99% confidence level.
A Genealogy of Multi-Sensor Foundation Models in Remote Sensing
2025-04-24
Kevin Lane, Morteza Karimzadeh
Foundation models have garnered increasing attention for representation learning in remote sensing, primarily adopting approaches that have demonstrated success in computer vision with minimal domain-specific modification. However, the development and application of foundation models in this field are still burgeoning, as there are a variety of competing approaches that each come with significant benefits and drawbacks. This paper examines these approaches along with their roots in the computer vision field in order to characterize potential advantages and pitfalls while outlining future directions to further improve remote sensing-specific foundation models. We discuss the quality of the learned representations and methods to alleviate the need for massive compute resources. We place emphasis on the multi-sensor aspect of Earth observations, and the extent to which existing approaches leverage multiple sensors in training foundation models in relation to multi-modal foundation models. Finally, we identify opportunities for further harnessing the vast amounts of unlabeled, seasonal, and multi-sensor remote sensing observations.
A Comprehensive Review on RNA Subcellular Localization Prediction
2025-04-24
Cece Zhang, Xuehuan Zhu, Nick Peterson, Jieqiong Wang, Shibiao Wan
The subcellular localization of RNAs, including long non-coding RNAs (lncRNAs), messenger RNAs (mRNAs), microRNAs (miRNAs) and other smaller RNAs, plays a critical role in determining their biological functions. For instance, lncRNAs are predominantly associated with chromatin and act as regulators of gene transcription and chromatin structure, while mRNAs are distributed across the nucleus and cytoplasm, facilitating the transport of genetic information for protein synthesis. Understanding RNA localization sheds light on processes like gene expression regulation with spatial and temporal precision. However, traditional wet lab methods for determining RNA localization, such as in situ hybridization, are often time-consuming, resource-demanding, and costly. To overcome these challenges, computational methods leveraging artificial intelligence (AI) and machine learning (ML) have emerged as powerful alternatives, enabling large-scale prediction of RNA subcellular localization. This paper provides a comprehensive review of the latest advancements in AI-based approaches for RNA subcellular localization prediction, covering various RNA types and focusing on sequence-based, image-based, and hybrid methodologies that combine both data types. We highlight the potential of these methods to accelerate RNA research, uncover molecular pathways, and guide targeted disease treatments. Furthermore, we critically discuss the challenges in AI/ML approaches for RNA subcellular localization, such as data scarcity and lack of benchmarks, and opportunities to address them. This review aims to serve as a valuable resource for researchers seeking to develop innovative solutions in the field of RNA subcellular localization and beyond.
A permutiple is a natural number whose representation in some base is an integer multiple of a number whose representation has the same collection of digits. Previous efforts have made progress on finding such numbers using graph-theoretical and finite-state-machine constructions. These are the mother graph and the Hoey-Sloane machine (and its state graph). In this paper, we use the reflective symmetry of the mother graph as a starting point for understanding relationships between permutiple classes and how new classes can be determined from old. Such results are not only useful for finding new permutiples from old, they help us to see previous work through a new lens.
From Bugs to Benchmarks: A Comprehensive Survey of Software Defect Datasets
2025-04-24
Hao-Nan Zhu, Robert M. Furth, Michael Pradel, Cindy Rubio-González
Software defect datasets, which are collections of software bugs and their associated information, are essential resources for researchers and practitioners in software engineering and beyond. Such datasets facilitate empirical research and enable standardized benchmarking for a wide range of techniques, including fault detection, fault localization, test generation, test prioritization, automated program repair, and emerging areas like agentic AI-based software development. Over the years, numerous software defect datasets with diverse characteristics have been developed, providing rich resources for the community, yet making it increasingly difficult to navigate the landscape. To address this challenge, this article provides a comprehensive survey of 132 software defect datasets. The survey discusses the scope of existing datasets, e.g., regarding the application domain of the buggy software, the types of defects, and the programming languages used. We also examine the construction of these datasets, including the data sources and construction methods employed. Furthermore, we assess the availability and usability of the datasets, validating their availability and examining how defects are presented. To better understand the practical uses of these datasets, we analyze the publications that cite them, revealing that the primary use cases are evaluations of new techniques and empirical research. Based on our comprehensive review of the existing datasets, this paper suggests potential opportunities for future research, including addressing underrepresented kinds of defects, enhancing availability and usability through better dataset organization, and developing more efficient strategies for dataset construction and maintenance.
A Thermodynamic Model for the Emergence of Natural Selection in Prebiotic Reaction Networks
2025-04-24
T. M. Prosser
We present a simplified thermodynamic model for the emergence of natural selection in prebiotic systems, derived from the dynamics of energy flow through reaction chains. The model expresses persistence or collapse of chemical systems as a function of environmental energy availability, energy expenditure, and internal energy transformations. We formalize this using a reaction viability inequality and demonstrate how natural selection can emerge as a statistical consequence of differential stability under fluctuating conditions.
Evaluating Machine Expertise: How Graduate Students Develop Frameworks for Assessing GenAI Content
2025-04-24
Celia Chen, Alex Leitch
This paper examines how graduate students develop frameworks for evaluating machine-generated expertise in web-based interactions with large language models (LLMs). Through a qualitative study combining surveys, LLM interaction transcripts, and in-depth interviews with 14 graduate students, we identify patterns in how these emerging professionals assess and engage with AI-generated content. Our findings reveal that students construct evaluation frameworks shaped by three main factors: professional identity, verification capabilities, and system navigation experience. Rather than uniformly accepting or rejecting LLM outputs, students protect domains central to their professional identities while delegating others--with managers preserving conceptual work, designers safeguarding creative processes, and programmers maintaining control over core technical expertise. These evaluation frameworks are further influenced by students' ability to verify different types of content and their experience navigating complex systems. This research contributes to web science by highlighting emerging human-genAI interaction patterns and suggesting how platforms might better support users in developing effective frameworks for evaluating machine-generated expertise signals in AI-mediated web environments.
Continual learning is an emerging subject in machine learning that aims to solve multiple tasks presented sequentially to the learner without forgetting previously learned tasks. Recently, many deep learning based approaches have been proposed for continual learning, however the mathematical foundations behind existing continual learning methods remain underdeveloped. On the other hand, adaptive filtering is a classic subject in signal processing with a rich history of mathematically principled methods. However, its role in understanding the foundations of continual learning has been underappreciated. In this tutorial, we review the basic principles behind both continual learning and adaptive filtering, and present a comparative analysis that highlights multiple connections between them. These connections allow us to enhance the mathematical foundations of continual learning based on existing results for adaptive filtering, extend adaptive filtering insights using existing continual learning methods, and discuss a few research directions for continual learning suggested by the historical developments in adaptive filtering.
VIGMA: An Open-Access Framework for Visual Gait and Motion Analytics
2025-04-24
Kazi Shahrukh Omar, Shuaijie Wang, Ridhuparan Kungumaraju, Tanvi Bhatt, Fabio Miranda
Gait disorders are commonly observed in older adults, who frequently experience various issues related to walking. Additionally, researchers and clinicians extensively investigate mobility related to gait in typically and atypically developing children, athletes, and individuals with orthopedic and neurological disorders. Effective gait analysis enables the understanding of the causal mechanisms of mobility and balance control of patients, the development of tailored treatment plans to improve mobility, the reduction of fall risk, and the tracking of rehabilitation progress. However, analyzing gait data is a complex task due to the multivariate nature of the data, the large volume of information to be interpreted, and the technical skills required. Existing tools for gait analysis are often limited to specific patient groups (e.g., cerebral palsy), only handle a specific subset of tasks in the entire workflow, and are not openly accessible. To address these shortcomings, we conducted a requirements assessment with gait practitioners (e.g., researchers, clinicians) via surveys and identified key components of the workflow, including (1) data processing and (2) data analysis and visualization. Based on the findings, we designed VIGMA, an open-access visual analytics framework integrated with computational notebooks and a Python library, to meet the identified requirements. Notably, the framework supports analytical capabilities for assessing disease progression and for comparing multiple patient groups. We validated the framework through usage scenarios with experts specializing in gait and mobility rehabilitation. VIGMA is available at https://github.com/komar41/VIGMA.
Toward a Human-Centered Evaluation Framework for Trustworthy LLM-Powered GUI Agents
2025-04-24
Chaoran Chen, Zhiping Zhang, Ibrahim Khalilov, Bingcan Guo, Simret A Gebreegziabher, Yanfang Ye, Ziang Xiao, Yaxing Yao, Tianshi Li, Toby Jia-Jun Li
The rise of Large Language Models (LLMs) has revolutionized Graphical User Interface (GUI) automation through LLM-powered GUI agents, yet their ability to process sensitive data with limited human oversight raises significant privacy and security risks. This position paper identifies three key risks of GUI agents and examines how they differ from traditional GUI automation and general autonomous agents. Despite these risks, existing evaluations focus primarily on performance, leaving privacy and security assessments largely unexplored. We review current evaluation metrics for both GUI and general LLM agents and outline five key challenges in integrating human evaluators for GUI agent assessments. To address these gaps, we advocate for a human-centered evaluation framework that incorporates risk assessments, enhances user awareness through in-context consent, and embeds privacy and security considerations into GUI agent design and evaluation.
Coherent nonlinear optical probe for cavity-dressed vibrational mode mixing: multidimensional double-quantum coherence and photon-echo signal
2025-04-24
Arunangshu Debnath
Cavity modulation of interacting molecular vibrational modes promises an expanded role of the characteristic vibrations to act as spectroscopic markers. In addition to the intrinsic, inter-mode and extrinsic, cavity-mediated inter-mode delocalization, the vibrational modes undergo multiscale relaxation due to reservoir vibrations. Two complementary coherent multidimensional spectroscopic techniques are presented, which reveal the dynamical nature of the mode correlations in the presence relaxation. Simulations are presented for finite laser pulses. The chosen cavity-vibron mode coupling strengths fall within the range of inter-vibrational mode coupling, hinting at a parameter regime where vibrational energy dispersal can be controlled. It is highlighted that the cavity manipulation of vibrational double excitations, namely, overtone and combination states, can offer deeper insights into the collective mode dynamics. These observations set the ground for cavity-controlled spectroscopic investigation of mode-selective dynamics, where localized electromagnetic modes function as an effective few-body coupler of vibrations.
2025-04-24
Girish A. Koushik, Diptesh Kanojia, Helen Treharne, Aditya Joshi
Social media memes are a challenging domain for hate detection because they intertwine visual and textual cues into culturally nuanced messages. We introduce a novel framework, CAMU, which leverages large vision-language models to generate more descriptive captions, a caption-scoring neural network to emphasise hate-relevant content, and parameter-efficient fine-tuning of CLIP's text encoder for an improved multimodal understanding of memes. Experiments on publicly available hateful meme datasets show that simple projection layer fine-tuning yields modest gains, whereas selectively tuning deeper text encoder layers significantly boosts performance on all evaluation metrics. Moreover, our approach attains high accuracy (0.807) and F1-score (0.806) on the Hateful Memes dataset, at par with the existing SoTA framework while being much more efficient, offering practical advantages in real-world scenarios that rely on fixed decision thresholds. CAMU also achieves the best F1-score of 0.673 on the MultiOFF dataset for offensive meme identification, demonstrating its generalisability. Additional analyses on benign confounders reveal that robust visual grounding and nuanced text representations are crucial for reliable hate and offence detection. We will publicly release CAMU along with the resultant models for further research. Disclaimer: This paper includes references to potentially disturbing, hateful, or offensive content due to the nature of the task.
Model Error Covariance Estimation for Weak Constraint Data Assimilation
2025-04-24
Sandra R. Babyale, Jodi Mead, Donna Calhoun, Patricia O. Azike
State estimates from weak constraint 4D-Var data assimilation can vary significantly depending on the data and model error covariances. As a result, the accuracy of these estimates heavily depends on the correct specification of both model and observational data error covariances. In this work, we assume that the data error is known and and focus on estimating the model error covariance by framing weak constraint 4D-Var as a regularized inverse problem, where the inverse model error covariance serves as the regularization matrix. We consider both isotropic and non-isotropic forms of the model error covariance. Using the representer method, we reduce the 4D-Var problem from state space to data space, enabling the efficient application of regularization parameter selection techniques. The Representer method also provides an analytic expression for the optimal state estimate, allowing us to derive matrix expressions for the three regularization parameter selection methods i.e. the L-curve, generalized cross-validation (GCV), and the Chi-square method. We validate our approach by assimilating simulated data into a 1D transport equation modeling wildfire smoke transport under various observational noise and forward model perturbations. In these experiments the goal is to identify the model error covariances that accurately capture the influence of observational data versus model predictions on assimilated state estimates. The regularization parameter selection methods successfully estimate hyperparameters for both isotropic and non-isotropic model error covariances, that reflect whether the first guess model predictions are more or less reliable than the observational data. The results further indicate that isotropic variances are sufficient when the first guess is more accurate than the data whereas non-isotropic covariances are preferred when the observational data is more reliable.
Autonomous Navigation Of Quadrupeds Using Coverage Path Planning
2025-04-24
Alexander James Becoy, Kseniia Khomenko, Luka Peternel, Raj Thilak Rajan
This paper proposes a novel method of coverage path planning for the purpose of scanning an unstructured environment autonomously. The method uses the morphological skeleton of the prior 2D navigation map via SLAM to generate a sequence of points of interest (POIs). This sequence is then ordered to create an optimal path given the robot's current position. To control the high-level operation, a finite state machine is used to switch between two modes: navigating towards a POI using Nav2, and scanning the local surrounding. We validate the method in a leveled indoor obstacle-free non-convex environment on time efficiency and reachability over five trials. The map reader and the path planner can quickly process maps of width and height ranging between [196,225] pixels and [185,231] pixels in 2.52 ms/pixel and 1.7 ms/pixel, respectively, where their computation time increases with 22.0 ns/pixel and 8.17 $\mu$s/pixel, respectively. The robot managed to reach 86.5\% of all waypoints over all five runs. The proposed method suffers from drift occurring in the 2D navigation map.
The ALMA-CRISTAL survey: weak evidence for star-formation driven outflows in $z\sim5$ main-sequence galaxies
2025-04-24
Jack E. Birkin, Justin S. Spilker, Rodrigo Herrera-Camus, Rebecca L. Davies, Lilian L. Lee, Manuel Aravena, Roberto J. Assef, Loreto Barcos-Muñoz, Alberto Bolatto, Tanio Diaz-Santos, Andreas L. Faisst, Andrea Ferrara, Deanne B. Fisher, Jorge González-López, Ryota Ikeda, Kirsten Knudsen, Juno Li, Yuan Li, Ilse de Looze, Dieter Lutz, Ikki Mitsuhashi, Ana Posses, Monica Relaño, Manuel Solimano, Ken-ichi Tadaki, Vicente Villanueva
There is a broad consensus from theory that stellar feedback in galaxies at high redshifts is essential to their evolution, alongside conflicting evidence in the observational literature about its prevalence and efficacy. To this end, we utilize deep, high-resolution [CII] emission line data taken as part of the [CII] resolved ISM in star-forming galaxies with ALMA (CRISTAL) survey. Excluding sources with kinematic evidence for gravitational interactions, we perform a rigorous stacking analysis of the remaining 15 galaxies to search for broad emission features that are too weak to detect in the individual spectra, finding only weak evidence that a broad component is needed to explain the composite spectrum. Additionally, such evidence is mostly driven by CRISTAL-02, which is already known to exhibit strong outflows in multiple ISM phases. Interpreting modest residuals in the stack at $v\sim300$kms$^{-1}$ as an outflow, we derive a mass outflow rate of $\dot{M}_{\rm out}=26\pm11$M$_\odot$yr$^{-1}$ and a cold outflow mass-loading factor of $\eta_m=0.49\pm0.20$. This result holds for the subsample with the highest star-formation rate surface density $(\Sigma_{\rm{SFR}}>1.93$M$_\odot$yr$^{-1}$kpc$^{-2}$) but no such broad component is present in the composite of the lower-star-formation rate density subsample. Our results imply that the process of star-formation-driven feedback may already be in place in typical galaxies at $z=5$, but on average not strong enough to completely quench ongoing star formation.
Euclid preparation: TBD. Cosmic Dawn Survey: evolution of the galaxy stellar mass function across 0.2
2025-04-24
Euclid Collaboration, L. Zalesky, J. R. Weaver, C. J. R. McPartland, G. Murphree, I. Valdes, C. K. Jespersen, S. Taamoli, N. Chartab, N. Allen, S. W. J. Barrow, D. B. Sanders, S. Toft, B. Mobasher, I. Szapudi, B. Altieri, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, S. Bardelli, P. Battaglia, A. Biviano, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, A. Caillat, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, F. J. Castander, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, H. Dole, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, A. Ealet, S. Escoffier, M. Farina, R. Farinelli, S. Farrens, F. Faustini, S. Ferriol, F. Finelli, P. Fosalba, S. Fotopoulou, M. Frailis, E. Franceschi, M. Fumana, K. George, B. Gillis, C. Giocoli, J. Gracia-Carpio, A. Grazian, F. Grupp, S. Gwyn, S. V. H. Haugan, W. Holmes, I. Hook, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, K. Kuijken, M. Kümmel, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, S. Maurogordato, H. J. McCracken, E. Medinaceli, S. Mei, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, J. A. Schewtschenko, M. Schirmer, P. Schneider, T. Schrabback, A. Secroun, E. Sefusatti, G. Seidel, S. Serrano, P. Simon, C. Sirignano, G. Sirri, L. Stanco, J. -L. Starck, J. Steinwagner, P. Tallada-Crespí, D. Tavagnacco, A. N. Taylor, H. I. Teplitz, I. Tereno, R. Toledo-Moreo, F. Torradeflot, A. Tsyganov, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, E. Zucca, M. Bolzonella, E. Bozzo, C. Burigana, M. Calabrese, D. Di Ferdinando, J. A. Escartin Vigo, L. Gabarra, S. Matthew, N. Mauri, A. Pezzotta, M. Pöntinen, C. Porciani, V. Scottez, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, V. Allevato, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, M. Ballardini, D. Bertacca, M. Bethermin, A. Blanchard, L. Blot, S. Borgani, M. L. Brown, S. Bruton, R. Cabanac, A. Calabro, B. Camacho Quevedo, A. Cappi, F. Caro, C. S. Carvalho, T. Castro, R. Chary, F. Cogato, T. Contini, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, S. Di Domizio, J. M. Diego, A. G. Ferrari, A. Finoguenov, K. Ganga, J. García-Bellido, T. Gasparetto, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, A. Gregorio, M. Guidi, C. M. Gutierrez, A. Hall, W. G. Hartley, S. Hemmati, H. Hildebrandt, J. Hjorth, M. Huertas-Company, O. Ilbert, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, C. C. Kirkpatrick, S. Kruk, M. Lattanzi, J. Le Graet, L. Legrand, M. Lembo, G. Leroy, J. Lesgourgues, T. I. Liaudat, A. Loureiro, J. Macias-Perez, G. Maggio, M. Magliocchetti, C. Mancini, F. Mannucci, R. Maoli, J. Martín-Fleitas, C. J. A. P. Martins, L. Maurin, R. B. Metcalf, M. Miluzio, P. Monaco, C. Moretti, G. Morgante, C. Murray, K. Naidoo, P. Natoli, A. Navarro-Alsina, S. Nesseris, K. Paterson, L. Patrizii, A. Pisani, D. Potter, I. Risso, P. -F. Rocci, M. Sahlén, E. Sarpa, J. Schaye, A. Schneider, M. Schultheis, D. Sciotti, E. Sellentin, M. Sereno, F. Shankar, L. C. Smith, S. A. Stanford, K. Tanidis, C. Tao, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, G. Verza, P. Vielzeuf, N. A. Walton
The Cosmic Dawn Survey Pre-launch (PL) catalogues cover an effective 10.13 deg$^{2}$ area with uniform deep Spitzer/IRAC data ($m\sim25$ mag, 5$\sigma$), the largest area covered to these depths in the infrared. These data are used to gain new insight into the growth of stellar mass across cosmic history by characterising the evolution of the galaxy stellar mass function (GSMF) through $0.2 < z \leq 6.5$. The total volume (0.62 Gpc$^{3}$) represents a tenfold increase compared to previous works that have explored $z > 3$ and significantly reduces cosmic variance, yielding strong constraints on the abundance of massive galaxies. Results are generally consistent with the literature but now provide firm estimates of number density where only upper limits were previously available. Contrasting the GSMF with the dark matter halo mass function suggests that massive galaxies ($M \gtrsim10^{11}$ M$_{\odot}$) at $z > 3.5$ required integrated star-formation efficiencies of $M/(M_{\rm h}f_{\rm b}) \gtrsim$ 0.25--0.5, in excess of the commonly-held view of ``universal peak efficiency" from studies on the stellar-to-halo mass relation (SHMR). Such increased efficiencies imply an evolving peak in the SHMR at $z > 3.5$ which can be maintained if feedback mechanisms from active galactic nuclei and stellar processes are ineffective at early times. In addition, a significant fraction of the most massive quiescent galaxies are observed to be in place already by $z\sim 2.5$--3. The apparent lack in change of their number density by $z\sim 0.2$ is consistent with relatively little mass growth from mergers. Utilising the unique volume, evidence for an environmental dependence of the galaxy stellar mass function is found all the way through $z\sim 3.5$ for the first time, though a more careful characterisation of the density field is ultimately required for confirmation.
Updated Constraints on Omnipotent Dark Energy: A Comprehensive Analysis with CMB and BAO Data
2025-04-24
Enrico Specogna, Shahnawaz A. Adil, Emre Ozulker, Eleonora Di Valentino, Rafael C. Nunes, Ozgur Akarsu, Anjan A. Sen
In this work, we present updated observational constraints on the parameter space of the DMS20 dark energy model, a member of the omnipotent dark energy (ODE) class. Our analysis combines multiple CMB datasets - including measurements from the Planck satellite (PL18), the South Pole Telescope (SPT), and the Wilkinson Microwave Anisotropy Probe (WMAP) - with Type Ia supernova data from the Pantheon+ catalog (PP), and baryon acoustic oscillation (BAO) measurements from the DESI and SDSS surveys. We find that certain data combinations, such as SPT+WMAP+BAO and PL18+BAO, can reduce the significance of the $H_0$ tension below $1\sigma$, but with considerably large uncertainties. These same combinations also predict suppressed structure growth, favoring lower values of $S_8$ compared to the Planck-only constraint. However, the inclusion of PP data restores the tension in $H_0$. To provide a comprehensive view of the ODE phenomenology, we also investigate the evolution of its energy density, emphasizing its dynamical behavior at low redshifts. Our results generically exhibit multiple phantom divide line (PDL) crossings in a single expansion history, a behavior that is not compatible with single scalar field scenarios.
Spoilers in movie reviews are important on platforms like IMDb and Rotten Tomatoes, offering benefits and drawbacks. They can guide some viewers' choices but also affect those who prefer no plot details in advance, making effective spoiler detection essential. Existing spoiler detection methods mainly analyze review text, often overlooking the impact of movie genres and user bias, limiting their effectiveness. To address this, we analyze movie review data, finding genre-specific variations in spoiler rates and identifying that certain users are more likely to post spoilers. Based on these findings, we introduce a new spoiler detection framework called GUSD (The code is available at https://github.com/AI-explorer-123/GUSD) (Genre-aware and User-specific Spoiler Detection), which incorporates genre-specific data and user behavior bias. User bias is calculated through dynamic graph modeling of review history. Additionally, the R2GFormer module combines RetGAT (Retentive Graph Attention Network) for graph information and GenreFormer for genre-specific aggregation. The GMoE (Genre-Aware Mixture of Experts) model further assigns reviews to specialized experts based on genre. Extensive testing on benchmark datasets shows that GUSD achieves state-of-the-art results. This approach advances spoiler detection by addressing genre and user-specific patterns, enhancing user experience on movie review platforms.
A Decade of You Only Look Once (YOLO) for Object Detection
2025-04-24
Leo Thomas Ramos, Angel D. Sappa
This review marks the tenth anniversary of You Only Look Once (YOLO), one of the most influential frameworks in real-time object detection. Over the past decade, YOLO has evolved from a streamlined detector into a diverse family of architectures characterized by efficient design, modular scalability, and cross-domain adaptability. The paper presents a technical overview of the main versions, highlights key architectural trends, and surveys the principal application areas in which YOLO has been adopted. It also addresses evaluation practices, ethical considerations, and potential future directions for the framework's continued development. The analysis aims to provide a comprehensive and critical perspective on YOLO's trajectory and ongoing transformation.
Year six photometric measurements of known Trans-Neptunian Objects and Centaurs by the Dark Energy Survey
2025-04-23
Feliphe S. Ferreira, Julio I. B. Camargo, Rodrigo Boufleur, M. V. Banda-Huarca, Adriano Pieres, Viviane F. Peixoto, Marcelo Assafin, P. H. Bernardinelli, H. W. Lin, Felipe Braga-Ribas, Altair Gomes-Junior, Roberto Vieira-Martins, L. N. da Costa, T. M. C. Abbott, M. Aguena, Sahar S. Allam, O. Alves, J. Annis, D. Bacon, D. Brooks, D. L. Burke, A. Carneiro Rosell, J. Carretero, S. Desai, P. Doel, S. Everett, I. Ferrero, J. Frieman, J. García-Bellido, M. Gatti, E. Gaztanaga, G. Giannini, D. Gruen, R. A. Gruendl, K. Herner, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, K. Kuehn, S. Lee, J. L. Marshall, J. Mena-Fernández, R. Miquel, J. Myles, A. Palmese, A. A. Plazas Malagón, M. E. S. Pereira, S. Samuroff, E. Sanchez, D. Sanchez Cid, I. Servila-Noarbe, M. Smith, E. Suchyta, M. E. C. Swanson, G. Tarle, C. To, D. L. Tucker, J. de Vicente, V. Vikram, A. R. Walker, N. Weaverdyck, DES Collaboration
We identified known Trans-Neptunian Objects (TNOs) and Centaurs in the complete Dark Energy Survey (DES) year six catalog (DES Y6) through the Sky Body Tracker (SkyBoT) tool. We classified our dataset of 144 objects into a widely used 4-class taxonomic system of TNOs. No such previous classification was available in the literature for most of these objects. From absolute magnitudes and average albedos, an estimation of the diameters of all these objects is obtained. Correlations involving colours, orbital parameters, dynamical classes and sizes are also discussed. In particular, our largest reddest object has a diameter of $390^{+68}_{-53}$ km and our largest cold classical, $255^{+19}_{-17}$ km. Also, a weak correlation between colour and inclination is found within the population of resonant TNOs in addition to weak correlations between colour and phase slope in different bands.
Summary statistics of learning link changing neural representations to behavior
2025-04-23
Jacob A. Zavatone-Veth, Blake Bordelon, Cengiz Pehlevan
How can we make sense of large-scale recordings of neural activity across learning? Theories of neural network learning with their origins in statistical physics offer a potential answer: for a given task, there are often a small set of summary statistics that are sufficient to predict performance as the network learns. Here, we review recent advances in how summary statistics can be used to build theoretical understanding of neural network learning. We then argue for how this perspective can inform the analysis of neural data, enabling better understanding of learning in biological and artificial neural networks.
The Galactic inner spiral arms revealed by the Gaia ESO Survey chemical abundances. Metallicity and [Mg/Fe] ratios
2025-04-23
C. Viscasillas Vázquez, L. Magrini, E. Spitoni, G. Cescutti, G. Tautvaišienė, A. Vasini, S. Randich, G. G. Sacco
Recent observational advances, such as Gaia DR3 GSP-Spec, have highlighted the potential of chemical abundances in tracing and revealing the structure of spiral arms. Building on these studies, we aim to trace the Milky Way's inner spiral arms using chemical abundance data from the Gaia-ESO Survey (GES). By mapping over-densities in [Fe/H] and [Mg/Fe], we seek to identify spiral arms in both radial and vertical planes, detect substructures, and compare our results with recent Galactic chemical evolution models. We used chemical abundance data from the Gaia-ESO Survey to create spatial maps of [Fe/H], [Mg/H], and [Mg/Fe] excess across the Galactic inner disc. We compared our results with the spiral arm models proposed by Spitoni et al. (2023) and Barbillon et al. (2024). For the first time, the inner spiral arms were revealed using chemical abundance patterns. We detected [Fe/H] enhancements and [Mg/Fe] under-abundances that consistently trace the Scutum and Sagittarius arms. A connecting spur between these arms is observed in the [Mg/H] plane. The alignment between our observations and the results of our 2D chemical evolution models reinforces the significance of spiral arm transits in driving both azimuthal and radial variations in chemical abundances. Our results confirm that spiral arms can be traced using stellar chemical abundances with GES data, providing a new perspective on the structure of the inner Galaxy. The consistency between enhanced [Fe/H] and lower [Mg/Fe] ratios, as observed in previous studies, further strengthens the reliability of our findings. The observed spur, bifurcation, and vertical substructures align well with recent models and studies, indicating that chemical maps can significantly contribute to our understanding of Galactic spiral arms.
Symbiotic stars in the era of modern ground- and space-based surveys
2025-04-23
Jaroslav Merc
Symbiotic stars, interacting binaries composed of a cool giant and a hot compact companion, exhibit complex variability across the electromagnetic spectrum. Over the past decades, large-scale photometric and spectroscopic surveys from ground- and space-based observatories have significantly advanced their discovery and characterization. These datasets have transformed the search for new symbiotic candidates, providing extensive time-domain information crucial for their classification and analysis. This review highlights recent observational results that have expanded the known population of symbiotic stars, refined classification criteria, and enhanced our understanding of their variability. Despite these advances, fundamental questions remain regarding their long-term evolution, mass transfer and accretion processes, or their potential role as progenitors of Type Ia supernovae. With ongoing and upcoming surveys, the coming years promise new discoveries and a more comprehensive picture of these intriguing interacting systems.
Rediscussion of eclipsing binaries. Paper XXIV. The delta Scuti pulsator V596 Pup (formerly known as VV Pyx)
2025-04-23
John Southworth
V596 Pup is a detached eclipsing binary containing two A1 V stars in a 4.596 d period orbit with a small eccentricity and apsidal motion, previously designated as VV Pyxidis. We use new light curves from the Transiting Exoplanet Survey Satellite (TESS) and published radial velocities to determine the physical properties of the component stars. We find masses of 2.098 +/- 0.021 Msun and 2.091 +/- 0.018 Msun, and radii of 2.179 +/- 0.008 Rsun and 2.139 +/- 0.007 Rsun. The measured distance to the system is affected by the light from a nearby companion star; we obtain 178.4 +/- 2.5 pc. The properties of the system are best matched by theoretical predictions for a subsolar metallicity of Z = 0.010 and an age of 570 Myr. We measure seven significant pulsation frequencies from the light curve, six of which are consistent with delta Scuti pulsations and one of which is likely of slowly-pulsating B-star type.
Rediscussion of eclipsing binaries. Paper XXIII. The F-type twin system RZ Chamaeleontis
2025-04-23
John Southworth
RZ Cha is a detached eclipsing binary containing two slightly evolved F5 stars in a circular orbit of period 2.832 d. We use new light curves from the Transiting Exoplanet Survey Satellite (TESS) and spectroscopic orbits from Gaia DR3 to measure the physical properties of the component stars. We obtain masses of 1.488 +/- 0.011 Msun and 1.482 +/- 0.011 Msun, and radii of 2.150 +/- 0.006 Rsun and 2.271 +/- 0.006 Rsun. An orbital ephemeris from the TESS data does not match published times of mid-eclipse from the 1970s, suggesting the period is not constant. We measure a distance to the system of 176.7 +/- 3.7 pc, which agrees with the Gaia DR3 value. A comparison with theoretical models finds agreement for metal abundances of Z = 0.014 and Z = 0.017 and an age of 2.3 Gyr. No evidence for pulsations was found in the light curves. Future data from TESS and Gaia will provide more precise masses and constraints on any changes in orbital period.
2025-04-23
S. A. K. Leeney, H. T. J. Bevins, E. de Lera Acedo, W. J. Handley, C. Kirkham, R. S. Patel, J. Zhu, D. Molnar, J. Cumner, D. Anstey, K. Artuc, G. Bernardi, M. Bucher, S. Carey, J. Cavillot, R. Chiello, W. Croukamp, D. I. L. de Villiers, J. A. Ely, A. Fialkov, T. Gessey-Jones, G. Kulkarni, A. Magro, P. D. Meerburg, S. Mittal, J. H. N. Pattison, S. Pegwal, C. M. Pieterse, J. R. Pritchard, E. Puchwein, N. Razavi-Ghods, I. L. V. Roque, A. Saxena, K. H. Scheutwinkel, P. Scott, E. Shen, P. H. Sims, M. Spinelli
Radiometers are crucial instruments in radio astronomy, forming the primary component of nearly all radio telescopes. They measure the intensity of electromagnetic radiation, converting this radiation into electrical signals. A radiometer's primary components are an antenna and a Low Noise Amplifier (LNA), which is the core of the ``receiver'' chain. Instrumental effects introduced by the receiver are typically corrected or removed during calibration. However, impedance mismatches between the antenna and receiver can introduce unwanted signal reflections and distortions. Traditional calibration methods, such as Dicke switching, alternate the receiver input between the antenna and a well-characterised reference source to mitigate errors by comparison. Recent advances in Machine Learning (ML) offer promising alternatives. Neural networks, which are trained using known signal sources, provide a powerful means to model and calibrate complex systems where traditional analytical approaches struggle. These methods are especially relevant for detecting the faint sky-averaged 21-cm signal from atomic hydrogen at high redshifts. This is one of the main challenges in observational Cosmology today. Here, for the first time, we introduce and test a machine learning-based calibration framework capable of achieving the precision required for radiometric experiments aiming to detect the 21-cm line.
Credible plan-driven RAG method for Multi-hop Question Answering
2025-04-23
Ningning Zhang, Chi Zhang, Zhizhong Tan, Xingxing Yang, Weiping Deng, Wenyong Wang
Multi-hop question answering (QA) presents a considerable challenge for Retrieval-Augmented Generation (RAG), requiring the structured decomposition of complex queries into logical reasoning paths and the generation of dependable intermediate results. However, deviations in reasoning paths or errors in intermediate results, which are common in current RAG methods, may propagate and accumulate throughout the reasoning process, diminishing the accuracy of the answer to complex queries. To address this challenge, we propose the Plan-then-Act-and-Review (PAR RAG) framework, which is organized into three key stages: planning, act, and review, and aims to offer an interpretable and incremental reasoning paradigm for accurate and reliable multi-hop question answering by mitigating error propagation.PAR RAG initially applies a top-down problem decomposition strategy, formulating a comprehensive plan that integrates multiple executable steps from a holistic viewpoint. This approach avoids the pitfalls of local optima common in traditional RAG methods, ensuring the accuracy of the entire reasoning path. Subsequently, PAR RAG incorporates a plan execution mechanism based on multi-granularity verification. By utilizing both coarse-grained similarity information and fine-grained relevant data, the framework thoroughly checks and adjusts intermediate results, ensuring process accuracy while effectively managing error propagation and amplification. Experimental results on multi-hop QA datasets demonstrate that the PAR RAG framework substantially outperforms existing state-of-the-art methods in key metrics, including EM and F1 scores.
Software engineering tasks are high-stress and cognitively demanding. Additionally, there is a latent risk of software engineers presenting burnout, depression and anxiety. Established interventions in other fields centred around attention awareness have shown positive results in mental well-being. We aim to test how effective a yoga intervention is in improving general well-being in the workplace. For that, we designed, implemented and evaluated an eight-week yoga programme in a software development company. We used a mixed-methods data collection, using a survey of six psychometric scales, pre and post-intervention, and a weekly well-being scale during the programme. For method triangulation, we conducted a focus group with the organisers to obtain qualitative data. The quantitative results did not show any statistically significant improvement after the intervention. Meanwhile, the qualitative results illustrated that participants felt better and liked the intervention. We conclude that yoga has a positive impact, which, however, can easily get overlaid by contextual factors, especially with only a once-per-week intervention.
Constant-roll inflation is a distinctive class of phenomenological inflationary models in which the inflaton's rate of roll remains constant. It provides an exact solution that is compatible with the latest observational constraints and offers a natural framework for enhancing the curvature power spectrum, which is relevant to the formation of primordial black holes. In this paper, I review constant-roll inflation in memory of Alexei Starobinsky.
Giving personalized feedback to students is very important to the learning process. However, doing so in a timely manner can be difficult to accomplish in very large courses. Recent work has explored different types of automated feedback adapted to different languages and programming paradigms, particularly logic programming. In ProHelp, we implemented several of these types of feedback so that they could be used by students enrolled in a logic programming class. Then, we surveyed those students to find if the feedback was useful and which types of feedback they preferred. Results show that students found all types of feedback helpful, with automatic testing, in particular, being the most helpful type. We also explore student preferences for which types of feedback they would most like to see implemented in the future.
Purpose: The timespan over which exploratory searching can occur, as well as the scope and volume of the search activities undertaken, can make it difficult for searchers to remember key details about their search activities. These difficulties are present both in the midst of searching as well as when resuming a search that spans multiple sessions. In this paper, we present a search interface designed to support cross-session exploratory search in a public digital library context. Methods: Search Timelines provides a visualization of current and past search activities via a dynamic timeline of the search activity (queries and saved resources). This timeline is presented at two levels of detail. An overview timeline is provided alongside the search results in a typical search engine results page design. A detailed timeline is provided in the workspace, where searchers can review the history of their search activities and their saved resources. A controlled laboratory study was conducted to compare this approach to a baseline interface modelled after a typical public digital library search/workspace interface. Results: Participants who used Search Timelines reported higher levels of user engagement, usability, and perceived knowledge gain, during an initial search session and when resuming the search after a 7-8 day interval. This came at the expense of the searchers taking more time to complete the search task, which we view as positive evidence of engagement in cross-session exploratory search processes. Conclusion: Search Timelines serves as an example of how lightweight visualization approaches can be used to enhance typical search interface designs to support exploratory search. The results highlight the value of providing persistent representations of past search activities within the search interface.
Planning long-horizon motions using a set of predefined skills is a key challenge in robotics and AI. Addressing this challenge requires methods that systematically explore skill combinations to uncover task-solving sequences, harness generic, easy-to-learn skills (e.g., pushing, grasping) to generalize across unseen tasks, and bypass reliance on symbolic world representations that demand extensive domain and task-specific knowledge. Despite significant progress, these elements remain largely disjoint in existing approaches, leaving a critical gap in achieving robust, scalable solutions for complex, long-horizon problems. In this work, we present MOSAIC, a skill-centric framework that unifies these elements by using the skills themselves to guide the planning process. MOSAIC uses two families of skills: Generators compute executable trajectories and world configurations, and Connectors link these independently generated skill trajectories by solving boundary value problems, enabling progress toward completing the overall task. By breaking away from the conventional paradigm of incrementally discovering skills from predefined start or goal states--a limitation that significantly restricts exploration--MOSAIC focuses planning efforts on regions where skills are inherently effective. We demonstrate the efficacy of MOSAIC in both simulated and real-world robotic manipulation tasks, showcasing its ability to solve complex long-horizon planning problems using a diverse set of skills incorporating generative diffusion models, motion planning algorithms, and manipulation-specific models. Visit https://skill-mosaic.github.io for demonstrations and examples.
The rapid development of large language models (LLMs) has led to the widespread deployment of LLM agents across diverse industries, including customer service, content generation, data analysis, and even healthcare. However, as more LLM agents are deployed, a major issue has emerged: there is no standard way for these agents to communicate with external tools or data sources. This lack of standardized protocols makes it difficult for agents to work together or scale effectively, and it limits their ability to tackle complex, real-world tasks. A unified communication protocol for LLM agents could change this. It would allow agents and tools to interact more smoothly, encourage collaboration, and triggering the formation of collective intelligence. In this paper, we provide the first comprehensive analysis of existing agent protocols, proposing a systematic two-dimensional classification that differentiates context-oriented versus inter-agent protocols and general-purpose versus domain-specific protocols. Additionally, we conduct a comparative performance analysis of these protocols across key dimensions such as security, scalability, and latency. Finally, we explore the future landscape of agent protocols by identifying critical research directions and characteristics necessary for next-generation protocols. These characteristics include adaptability, privacy preservation, and group-based interaction, as well as trends toward layered architectures and collective intelligence infrastructures. We expect this work to serve as a practical reference for both researchers and engineers seeking to design, evaluate, or integrate robust communication infrastructures for intelligent agents.
DYNUS: Uncertainty-aware Trajectory Planner in Dynamic Unknown Environments
2025-04-23
Kota Kondo, Mason Peterson, Nicholas Rober, Juan Rached Viso, Lucas Jia, Jialin Chen, Harvey Merton, Jonathan P. How
This paper introduces DYNUS, an uncertainty-aware trajectory planner designed for dynamic unknown environments. Operating in such settings presents many challenges -- most notably, because the agent cannot predict the ground-truth future paths of obstacles, a previously planned trajectory can become unsafe at any moment, requiring rapid replanning to avoid collisions. Recently developed planners have used soft-constraint approaches to achieve the necessary fast computation times; however, these methods do not guarantee collision-free paths even with static obstacles. In contrast, hard-constraint methods ensure collision-free safety, but typically have longer computation times. To address these issues, we propose three key contributions. First, the DYNUS Global Planner (DGP) and Temporal Safe Corridor Generation operate in spatio-temporal space and handle both static and dynamic obstacles in the 3D environment. Second, the Safe Planning Framework leverages a combination of exploratory, safe, and contingency trajectories to flexibly re-route when potential future collisions with dynamic obstacles are detected. Finally, the Fast Hard-Constraint Local Trajectory Formulation uses a variable elimination approach to reduce the problem size and enable faster computation by pre-computing dependencies between free and dependent variables while still ensuring collision-free trajectories. We evaluated DYNUS in a variety of simulations, including dense forests, confined office spaces, cave systems, and dynamic environments. Our experiments show that DYNUS achieves a success rate of 100% and travel times that are approximately 25.0% faster than state-of-the-art methods. We also evaluated DYNUS on multiple platforms -- a quadrotor, a wheeled robot, and a quadruped -- in both simulation and hardware experiments.
Identification of quasars variable over long time scales from infrared surveys. Ensemble variability and structure function properties
2025-04-23
E. A. Zaharieva, V. D. Ivanov, E. P. Ovcharov, O. I. Stanchev
Quasars are variable and their variability can both constrain their physical properties and help to identify them. We look for ways to efficiently identify quasars exhibiting consistent variability over multi-year time-scales, based on a small number of epochs. Using infrared (IR) is desirable to avoid bias against reddened objects. We compare the apparent brightness of known quasars that have been observed with two IR surveys, covering up to a twenty-year baseline: the Two Micron All Sky Survey (2MASS; 1997-2001) and the VISTA Hemisphere Survey (VHS; 2009-2017). We look at the previous studies of the selected variable quasars to see if their variable behaviour is known and when available we use multi-epoch monitoring with the Zwicky Transient Facility (ZTF) to obtain a measure of optical variability of individual objects. We build a sample of ~2500 quasars that show statistically significant variability between the 2MASS and VHS. About 1500 of these come from the new Quaia sample based on Gaia spectra and about 1/3 of these have hardly been studied. The Quaia sample constitutes the main product of this work. Based on ensemble variability and structure function analysis we demonstrate that the selected objects in our sample are representative of the typical quasar population and show behaviour, consistent with other quasar samples. Our analysis strengthens previous results, for example that variability decreases with the rest-frame wavelength and that it exhibits peaks for certain absolute magnitudes of the quasars. Similarly, the structure function shows an increase in variability for rest-frame time lags below ~1500 d and a decrease for longer lags, just like in previous studies. Our selection, even though it is based on two epochs only, seems to be surprisingly robust, showing up to ~11% contamination by quasars that show stable non-variable behaviour in ZTF.
In this study, we have investigated and compared the effect of hydrostatic pressure up to ~20 kbar on the transport properties of ZrTe5 single crystals grown by chemical vapor transport (CVT) and flux methods. With the application of pressure, the electrical resistivity Rho(T) and thermopower S(T) of both crystals were found to increase in the whole temperature range unlike the other known thermoelectric materials, such as Bi2Te3, SnSe etc. This observation is supported by the complementary first-principles band structure calculation as the application of pressure widens the direct bandgap at {\Gamma} point. Moreover, the analysis of the pressure dependent magneto-transport and Shubnikov de-Hass oscillation results revealed an increase in carrier concentration and effective mass along with the reduction of mobility as pressure rises. Furthermore, with the application of pressure, the flux-grown ZrTe5 crystals display a transition from unipolar to bipolar charge transport as evidenced by the emergence of resistivity peak at T* under high pressure, unlike the CVT-grown ZrTe5 crystals where the bipolar charge transport near its characteristic resistivity peak (Tp) remains unaffected.
Open Source Software Lifecycle Classification: Developing Wrangling Techniques for Complex Sociotechnical Systems
2025-04-23
Wenyi Lu, Enock Kasaadah, S M Rakib Ul Karim, Matt Germonprez, Sean Goggins
Open source software is a rapidly evolving center for distributed work, and understanding the characteristics of this work across its different contexts is vital for informing policy, economics, and the design of enabling software. The steep increase in open source projects and corporate participation have transformed a peripheral, cottage industry component of the global technology ecosystem into a large, infinitely complex "technology parts supplier" wired into every corner of contemporary life. The lack of theory and tools for breaking this complexity down into identifiable project types or strategies for understanding them more systematically is incommensurate with current industry, society, and developer needs. This paper reviews previous attempts to classify open source software and other organizational ecosystems, using open source scientific software ecosystems in contrast with those found in corporatized open source software. It then examines the divergent and sometimes conflicting purposes that may exist for classifying open source projects and how these competing interests impede our progress in developing a comprehensive understanding of how open source software projects and companies operate. Finally, we will present an empirical, mixed-methods study demonstrating how to classify open-source projects by their lifecycle position. This is the first step forward, advancing our scientific and practical knowledge of open source software through the lens of dynamic and evolving open source genres. It concludes with examples and a proposed path forward.
Binarity at LOw Metallicity (BLOeM): Bayesian inference of natal kicks from inert black hole binaries
2025-04-23
R. Willcox, P. Marchant, A. Vigna-Gómez, H. Sana, J. Bodensteiner, K. Deshmukh, M. Esseldeurs, M. Fabry, V. Hénault-Brunet, S. Janssens, L. Mahy, L. Patrick, D. Pauli, M. Renzo, A. A. C. Sander, T. Shenar, L. A. C. van Son, M. Stoop
Context. The emerging population of inert black hole binaries (BHBs) provides a unique opportunity to constrain black hole (BH) formation physics. These systems are composed of a stellar-mass BH in a wide orbit around a non-degenerate star with no observed Xray emission. Inert BHBs allow for narrow constraints to be inferred on the natal kick and mass loss during BH-forming core-collapse events. Aims. In anticipation of the upcoming BLOeM survey, we aim to provide tight constraints on BH natal kicks by exploiting the full parameter space obtained from combined spectroscopic and astrometric data to characterize the orbits of inert BHBs. Multi-epoch spectroscopy from the BLOeM project will provide measurements of periods, eccentricities, and radial velocities for inert BHBs in the SMC, which complements Gaia astrometric observations of proper motions. Methods. We present a Bayesian parameter estimation framework to infer natal kicks and mass loss during core-collapse from inert BHBs, accounting for all available observables, including the systemic velocity and its orientation relative to the orbital plane. The framework further allows for circumstances when some of the observables are unavailable, such as for the distant BLOeM sources which preclude resolved orbits. Results. With our new framework, we are able to distinguish between BH formation channels, even in the absence of a resolved orbit. In cases when the pre-explosion orbit can be assumed to be circular, we precisely recover the parameters of the core-collapse, highlighting the importance of understanding the eccentricity landscape of pre-explosion binaries, both theoretically and observationally. Treating the near-circular, inert BHB, VFTS 243, as a representative of the anticipated BLOeM systems, we constrain the natal kick to less than 27 km/s and the mass loss to less than 2.9 Msun within a 90% credible interval.
A Systematic Review of Common Beginner Programming Mistakes in Data Engineering
2025-04-23
Max Neuwinger, Dirk Riehle
The design of effective programming languages, libraries, frameworks, tools, and platforms for data engineering strongly depends on their ease and correctness of use. Anyone who ignores that it is humans who use these tools risks building tools that are useless, or worse, harmful. To ensure our data engineering tools are based on solid foundations, we performed a systematic review of common programming mistakes in data engineering. We focus on programming beginners (students) by analyzing both the limited literature specific to data engineering mistakes and general programming mistakes in languages commonly used in data engineering (Python, SQL, Java). Through analysis of 21 publications spanning from 2003 to 2024, we synthesized these complementary sources into a comprehensive classification that captures both general programming challenges and domain-specific data engineering mistakes. This classification provides an empirical foundation for future tool development and educational strategies. We believe our systematic categorization will help researchers, practitioners, and educators better understand and address the challenges faced by novice data engineers.
It is explained why ultra-diffuse galaxies (UDGs), a subset of (IC 3475)-type galaxies, do not have unexpectedly large sizes but large sizes that are in line with expectations from the curved size-luminosity relation defined by brighter early-type galaxies (ETGs). UDGs extend the faint end of the (absolute magnitude, $\mathfrak{M}$)-log(S\'ersic index, $n$) and $\mathfrak{M}$-(central surface brightness, $\mu_{\rm 0}$) relations defined by ETGs, leading to the large effective half-light radii, $R_{\rm e}$, in UDGs. It is detailed how the scatter in $\mu_{\rm 0}$, at a given $\mathfrak{M}$, relates to variations in the galaxies' values of $n$ and effective surface brightness, $\mu_{\rm e}$. These variations map into changes in $R_{\rm e}$ and produce the scatter about the $\mathfrak{M}$-$R_{\rm e}$ relation at fixed $\mathfrak{M}$. Similarly, the scatter in $\mathfrak{M}$, at fixed $\mu_{\rm 0}$ and $n$, can be mapped into changes in $R_{\rm e}$. The increased scatter about the faint end of the $\mathfrak{M}$-$R_{\rm e}$ relation and the smaller scatter about $\mathfrak{M}$-(isophotal radii, $R_{\rm iso}$) relations are explained. Artificial and potentially misleading size-luminosity relations for UDGs are also addressed. The suggestion that there may be two types of UDG appears ill-founded, arising from the scatter about the $\mathfrak{M}$-$\mu_{\rm 0}$ relation, which persists at all magnitudes. Hopefully, the understanding presented here will prove helpful for interpreting the many low surface brightness galaxies that the Large Synoptic Survey Telescope will detect.
Case Study: Fine-tuning Small Language Models for Accurate and Private CWE Detection in Python Code
2025-04-23
Md. Azizul Hakim Bappy, Hossen A Mustafa, Prottoy Saha, Rajinus Salehat
Large Language Models (LLMs) have demonstrated significant capabilities in understanding and analyzing code for security vulnerabilities, such as Common Weakness Enumerations (CWEs). However, their reliance on cloud infrastructure and substantial computational requirements pose challenges for analyzing sensitive or proprietary codebases due to privacy concerns and inference costs. This work explores the potential of Small Language Models (SLMs) as a viable alternative for accurate, on-premise vulnerability detection. We investigated whether a 350-million parameter pre-trained code model (codegen-mono) could be effectively fine-tuned to detect the MITRE Top 25 CWEs specifically within Python code. To facilitate this, we developed a targeted dataset of 500 examples using a semi-supervised approach involving LLM-driven synthetic data generation coupled with meticulous human review. Initial tests confirmed that the base codegen-mono model completely failed to identify CWEs in our samples. However, after applying instruction-following fine-tuning, the specialized SLM achieved remarkable performance on our test set, yielding approximately 99% accuracy, 98.08% precision, 100% recall, and a 99.04% F1-score. These results strongly suggest that fine-tuned SLMs can serve as highly accurate and efficient tools for CWE detection, offering a practical and privacy-preserving solution for integrating advanced security analysis directly into development workflows.
Contact homology of contact manifolds and its applications
2025-04-23
Frédéric Bourgeois
This is a survey of contact homology and its applications to the study of contact manifolds. It is a small tribute to Yasha Eliashberg's huge generosity with his countless explanations of his deep mathematical insights all along his career. It is also the author's wishful thinking that this text could be useful to students and young mathematicians for learning about some of the holomorphic curves based invariants in contact geometry.
The optimization of parametric quantum circuits is technically hindered by three major obstacles: the non-convex nature of the objective function, noisy gradient evaluations, and the presence of barren plateaus. As a result, the selection of classical optimizer becomes a critical factor in assessing and exploiting quantum-classical applications. One promising approach to tackle these challenges involves incorporating curvature information into the parameter update. The most prominent methods in this field are quasi-Newton and quantum natural gradient methods, which can facilitate faster convergence compared to first-order approaches. Second order methods however exhibit a significant trade-off between computational cost and accuracy, as well as heightened sensitivity to noise. This study evaluates the performance of three families of optimizers on synthetically generated MaxCut problems on a shallow QAOA algorithm. To address noise sensitivity and iteration cost, we demonstrate that incorporating secant-penalization in the BFGS update rule (SP-BFGS) yields improved outcomes for QAOA optimization problems, introducing a novel approach to stabilizing BFGS updates against gradient noise.
A Comprehensive Survey of Synthetic Tabular Data Generation
2025-04-23
Ruxue Shi, Yili Wang, Mengnan Du, Xu Shen, Xin Wang
Tabular data remains one of the most prevalent and critical data formats across diverse real-world applications. However, its effective use in machine learning (ML) is often constrained by challenges such as data scarcity, privacy concerns, and class imbalance. Synthetic data generation has emerged as a promising solution, leveraging generative models to learn the distribution of real datasets and produce high-fidelity, privacy-preserving samples. Various generative paradigms have been explored, including energy-based models (EBMs), variational autoencoders (VAEs), generative adversarial networks (GANs), large language models (LLMs), and diffusion models. While several surveys have investigated synthetic tabular data generation, most focus on narrow subdomains or specific generative methods, such as GANs, diffusion models, or privacy-preserving techniques. This limited scope often results in fragmented insights, lacking a comprehensive synthesis that bridges diverse approaches. In particular, recent advances driven by LLMs and diffusion-based models remain underexplored. This gap hinders a holistic understanding of the field`s evolution, methodological interplay, and open challenges. To address this, our survey provides a unified and systematic review of synthetic tabular data generation. Our contributions are threefold: (1) we propose a comprehensive taxonomy that organizes existing methods into traditional approaches, diffusion-based methods, and LLM-based models, and provide an in-depth comparative analysis; (2) we detail the complete pipeline for synthetic tabular data generation, including data synthesis, post-processing, and evaluation; (3) we identify major challenges, explore real-world applications, and outline open research questions and future directions to guide future work in this rapidly evolving area.
Tourism and travel planning increasingly rely on digital assistance, yet existing multimodal AI systems often lack specialized knowledge and contextual understanding of urban environments. We present TraveLLaMA, a specialized multimodal language model designed for urban scene understanding and travel assistance. Our work addresses the fundamental challenge of developing practical AI travel assistants through a novel large-scale dataset of 220k question-answer pairs. This comprehensive dataset uniquely combines 130k text QA pairs meticulously curated from authentic travel forums with GPT-enhanced responses, alongside 90k vision-language QA pairs specifically focused on map understanding and scene comprehension. Through extensive fine-tuning experiments on state-of-the-art vision-language models (LLaVA, Qwen-VL, Shikra), we demonstrate significant performance improvements ranging from 6.5\%-9.4\% in both pure text travel understanding and visual question answering tasks. Our model exhibits exceptional capabilities in providing contextual travel recommendations, interpreting map locations, and understanding place-specific imagery while offering practical information such as operating hours and visitor reviews. Comparative evaluations show TraveLLaMA significantly outperforms general-purpose models in travel-specific tasks, establishing a new benchmark for multi-modal travel assistance systems.
On Developers' Self-Declaration of AI-Generated Code: An Analysis of Practices
2025-04-23
Syed Mohammad Kashif, Peng Liang, Amjed Tahir
AI code generation tools have gained significant popularity among developers, who use them to assist in software development due to their capability to generate code. Existing studies mainly explored the quality, e.g., correctness and security, of AI-generated code, while in real-world software development, the prerequisite is to distinguish AI-generated code from human-written code, which emphasizes the need to explicitly declare AI-generated code by developers. To this end, this study intends to understand the ways developers use to self-declare AI-generated code and explore the reasons why developers choose to self-declare or not. We conducted a mixed-methods study consisting of two phases. In the first phase, we mined GitHub repositories and collected 613 instances of AI-generated code snippets. In the second phase, we conducted a follow-up industrial survey, which received 111 valid responses. Our research revealed the practices followed by developers to self-declare AI-generated code. Most practitioners (76.6%) always or sometimes self-declare AI-generated code. In contrast, other practitioners (23.4%) noted that they never self-declare AI-generated code. The reasons for self-declaring AI-generated code include the need to track and monitor the code for future review and debugging, and ethical considerations. The reasons for not self-declaring AI-generated code include extensive modifications to AI-generated code and the developers' perception that self-declaration is an unnecessary activity. We finally provided guidelines for practitioners to self-declare AI-generated code, addressing ethical and code quality concerns.
The Dance of Atoms-De Novo Protein Design with Diffusion Model
2025-04-23
Yujie Qin, Ming He, Changyong Yu, Ming Ni, Xian Liu, Xiaochen Bo
The de novo design of proteins refers to creating proteins with specific structures and functions that do not naturally exist. In recent years, the accumulation of high-quality protein structure and sequence data and technological advancements have paved the way for the successful application of generative artificial intelligence (AI) models in protein design. These models have surpassed traditional approaches that rely on fragments and bioinformatics. They have significantly enhanced the success rate of de novo protein design, and reduced experimental costs, leading to breakthroughs in the field. Among various generative AI models, diffusion models have yielded the most promising results in protein design. In the past two to three years, more than ten protein design models based on diffusion models have emerged. Among them, the representative model, RFDiffusion, has demonstrated success rates in 25 protein design tasks that far exceed those of traditional methods, and other AI-based approaches like RFjoint and hallucination. This review will systematically examine the application of diffusion models in generating protein backbones and sequences. We will explore the strengths and limitations of different models, summarize successful cases of protein design using diffusion models, and discuss future development directions.
Block Jacobi matrices and Titchmarsh-Weyl function
2025-04-23
Marcin Moszyński, Grzegorz Świderski
We collect some results and notions concerning generalizations for block Jacobi matrices of several concepts, which have been important for spectral studies of the simpler and better known scalar Jacobi case. We focus here on some issues related to the matrix Titchmarsh-Weyl function, but we also consider generalizations of some other tools used by subordinacy theory, including the matrix orthogonal polynomials, the notion of finite cyclicity, a variant of a notion of nonsubordinacy, as well as Jitomirskaya-Last type semi-norms. The article brings together some issues already known, our new concepts, and also improvements and strengthening of some results already existing. We give simpler proofs of some known facts or we add details usually omitted in the existing literature. The introduction contains a separate part devoted to a brief review of the main spectral analysis methods used so far for block Jacobi operators.
From Past to Present: A Survey of Malicious URL Detection Techniques, Datasets and Code Repositories
2025-04-23
Ye Tian, Yanqiu Yu, Jianguo Sun, Yanbin Wang
Malicious URLs persistently threaten the cybersecurity ecosystem, by either deceiving users into divulging private data or distributing harmful payloads to infiltrate host systems. Gaining timely insights into the current state of this ongoing battle holds significant importance. However, existing reviews exhibit 4 critical gaps: 1) Their reliance on algorithm-centric taxonomies obscures understanding of how detection approaches exploit specific modal information channels; 2) They fail to incorporate pivotal LLM/Transformer-based defenses; 3) No open-source implementations are collected to facilitate benchmarking; 4) Insufficient dataset coverage.This paper presents a comprehensive review of malicious URL detection technologies, systematically analyzing methods from traditional blacklisting to advanced deep learning approaches (e.g. Transformer, GNNs, and LLMs). Unlike prior surveys, we propose a novel modality-based taxonomy that categorizes existing works according to their primary data modalities (URL, HTML, Visual, etc.). This hierarchical classification enables both rigorous technical analysis and clear understanding of multimodal information utilization. Furthermore, to establish a profile of accessible datasets and address the lack of standardized benchmarking (where current studies often lack proper baseline comparisons), we curate and analyze: 1) publicly available datasets (2016-2024), and 2) open-source implementations from published works(2013-2025). Then, we outline essential design principles and architectural frameworks for product-level implementations. The review concludes by examining emerging challenges and proposing actionable directions for future research. We maintain a GitHub repository for ongoing curating datasets and open-source implementations: https://github.com/sevenolu7/Malicious-URL-Detection-Open-Source/tree/master.
Mumott -- a Python package for the analysis of multi-modal tensor tomography data
2025-04-23
Leonard C. Nielsen, Mads Carlsen, Sici Wang, Arthur Baroni, Torne Tänzer, Marianne Liebi, Paul Erhart
Small and wide angle x-ray scattering tensor tomography are powerful methods for studying anisotropic nanostructures in a volume-resolved manner, and are becoming increasingly available to users of synchrotron facilities. The analysis of such experiments requires, however, advanced procedures and algorithms, which creates a barrier for the wider adoption of these techniques. Here, in response to this challenge, we introduce the mumott package. It is written in Python with computationally demanding tasks handled via just-in-time compilation using both CPU and GPU resources. The package is being developed with a focus on usability and extensibility, while achieving a high computational efficiency. Following a short introduction to the common workflow, we review key features, outline the underlying object-oriented framework, and demonstrate the computational performance. By developing the mumott package and making it generally available, we hope to lower the threshold for the adoption of tensor tomography and to make these techniques accessible to a larger research community.
Toward a Principled Workflow for Prevalence Mapping Using Household Survey Data
2025-04-23
Qianyu Dong, Yunhan Wu, Zehang Richard Li, Jon Wakefield
Understanding the prevalence of key demographic and health indicators in small geographic areas and domains is of global interest, especially in low- and middle-income countries (LMICs), where vital registration data is sparse and household surveys are the primary source of information. Recent advances in computation and the increasing availability of spatially detailed datasets have led to much progress in sophisticated statistical modeling of prevalence. As a result, high-resolution prevalence maps for many indicators are routinely produced in the literature. However, statistical and practical guidance for producing prevalence maps in LMICs has been largely lacking. In particular, advice in choosing and evaluating models and interpreting results is needed, especially when data is limited. Software and analysis tools are also usually inaccessible to researchers in low-resource settings to conduct their own analysis or reproduce findings in the literature. In this paper, we propose a general workflow for prevalence mapping using household survey data. We consider all stages of the analysis pipeline, with particular emphasis on model choice and interpretation. We illustrate the proposed workflow using a case study mapping the proportion of pregnant women who had at least four antenatal care visits in Kenya. Reproducible code is provided in the Supplementary Materials and can be readily extended to a broad collection of indicators.
Recommender systems (RS) have become essential in filtering information and personalizing content for users. RS techniques have traditionally relied on modeling interactions between users and items as well as the features of content using models specific to each task. The emergence of foundation models (FMs), large scale models trained on vast amounts of data such as GPT, LLaMA and CLIP, is reshaping the recommendation paradigm. This survey provides a comprehensive overview of the Foundation Models for Recommender Systems (FM4RecSys), covering their integration in three paradigms: (1) Feature-Based augmentation of representations, (2) Generative recommendation approaches, and (3) Agentic interactive systems. We first review the data foundations of RS, from traditional explicit or implicit feedback to multimodal content sources. We then introduce FMs and their capabilities for representation learning, natural language understanding, and multi-modal reasoning in RS contexts. The core of the survey discusses how FMs enhance RS under different paradigms. Afterward, we examine FM applications in various recommendation tasks. Through an analysis of recent research, we highlight key opportunities that have been realized as well as challenges encountered. Finally, we outline open research directions and technical challenges for next-generation FM4RecSys. This survey not only reviews the state-of-the-art methods but also provides a critical analysis of the trade-offs among the feature-based, the generative, and the agentic paradigms, outlining key open issues and future research directions.
Lightweight Social Computing Tools for Undergraduate Research Community Building
2025-04-23
Noel Chacko, Hannah Vy Nguyen, Sophie Chen, Stephen MacNeil
Many barriers exist when new members join a research community, including impostor syndrome. These barriers can be especially challenging for undergraduate students who are new to research. In our work, we explore how the use of social computing tools in the form of spontaneous online social networks (SOSNs) can be used in small research communities to improve sense of belonging, peripheral awareness, and feelings of togetherness within an existing CS research community. Inspired by SOSNs such as BeReal, we integrated a Wizard-of-Oz photo sharing bot into a computing research lab to foster community building among members. Through a small sample of lab members (N = 17) over the course of 2 weeks, we observed an increase in participants' sense of togetherness based on pre- and post-study surveys. Our surveys and semi-structured interviews revealed that this approach has the potential to increase awareness of peers' personal lives, increase feelings of community, and reduce feelings of disconnectedness.
Review of the experimental and theoretical landscape of electron transport in noble liquids
2025-04-23
G. J. Boyle, N. A. Garland, D. L. Muccignat, I. Simonović, D. Bošnjaković, S. Dujko, R. D. White
We present a review of the current experimental and theoretical understanding of electron transport in noble liquids. Special attention is given to recent measurements that coincide with the development of time projection chambers (TPCs) using liquid xenon and argon as detector media. To enable transparent benchmarking of simulations and to facilitate the comparison between early studies and modern TPC data, we introduce a new open-access database of electron mobility and diffusion measurements. In particular, we emphasize the transition to large-scale detector designs which incorporate extended drift distances alongside improved purity control and field uniformity. On the theoretical side, we contrast empirical transport models with ab initio approaches, highlighting our recent efforts to incorporate low-energy, liquid-specific scattering phenomena, including coherent scattering, polarization screening, and bulk potential modifications. While elastic transport has seen substantial theoretical progress, inelastic processes in liquids, including ionization, exciton formation and interband transitions, remain poorly understood due to the lack of experimental cross sections and validated models. We also discuss the applications and challenges of modeling scintillation, doped and mixture-liquid targets, and gas-liquid interface behavior, all of which are critical for the design and optimization of next-generation detectors.
Utilizing Dynamic Time Warping for Pandemic Surveillance: Understanding the Relationship between Google Trends Network Metrics and COVID-19 Incidences
2025-04-23
Michael T. Lopez II, Cheska Elise Hung, Maria Regina Justine E. Estuar
The premise of network statistics derived from Google Trends data to foresee COVID-19 disease progression is gaining momentum in infodemiology. This approach was applied in Metro Manila, National Capital Region, Philippines. Through dynamic time warping (DTW), the temporal alignment was quantified between network metrics and COVID-19 case trajectories, and systematically explored 320 parameter configurations including two network metrics (network density and clustering coefficient), two data preprocessing methods (Rescaling Daily Data and MSV), multiple thresholds, two correlation window sizes, and Sakoe-Chiba band constraints. Results from the Kruskal-Wallis tests revealed that five of the six parameters significantly influenced alignment quality, with the disease comparison type (active cases vs. confirmed cases) demonstrating the strongest effect. The optimal configuration, which is using the network density statistic with a Rescaling Daily Data transformation, a threshold of 0.8, a 15-day window, and a 50-day radius constraint, achieved a DTW score of 36.30. This indicated substantial temporal alignment with the COVID-19 confirmed cases data. The discoveries demonstrate that network metrics rooted from online search behavior can serve as complementary indicators for epidemic surveillance in urban locations like Metro Manila. This strategy leverages the Philippines' extensive online usage during the pandemic to provide potentially valuable early signals of disease spread, and offers a supplementary tool for public health monitoring in resource-limited situations.
The Search for Stable Nickel: Investigating the Origins of Type Ia Supernovae with Late-time NIR Spectroscopy from the Carnegie Supernova Project-II
2025-04-23
Sahana Kumar, Eric Y. Hsiao, Christopher Ashall, Peter Hoeflich, Eddie Baron, Mark M. Phillips, Maryam Modjaz, Abigail Polin, Nidia Morrell, Christopher R. Burns, Jing Lu, Melissa Shahbandeh, Lindsey A. Kwok, Lluis Galbany, Maximilian D. Stritzinger, Carlos Contreras, James M. DerKacy, T Hoover, Syed Uddin, Saurabh W. Jha, Huangfei Xiao, Kevin Krisciunas, Nicholas B. Suntzeff
Producing stable $^{58}$Ni in Type Ia supernovae (SNe Ia) requires sufficiently high density conditions that are not predicted for all origin scenarios, so examining the distribution of $^{58}$Ni using the NIR [Ni II] 1.939 $\mu$m line may observationally distinguish between possible progenitors and explosion mechanisms. We present 79 telluric-corrected NIR spectra of 22 low-redshift SNe Ia from the Carnegie Supernova Project-II ranging from +50 to +505 days, including 31 previously unpublished spectra. We introduce the Gaussian Peak Ratio, a detection parameter that confirms the presence of the NIR [Ni II] 1.939 $\mu$m line in 8 SNe in our sample. Non-detections occur at earlier phases when the NIR Ni line has not emerged yet or in low signal-to-noise spectra yielding inconclusive results. Subluminous 86G-like SNe Ia show the earliest NIR Ni features around ~+50 days, whereas normal-bright SNe Ia do not exhibit NIR Ni until ~+150 days. NIR Ni features detected in our sample have low peak velocities ($v$~1200 km/s) and narrow line widths ($\leq$ 3500 km/s), indicating stable $^{58}$Ni is centrally located. This implies high density burning conditions in the innermost regions of SNe Ia and could be due to higher mass progenitors (i.e. near-$M_{ch}$). NIR spectra of the nearly two dozen SNe Ia in our sample are compared to various model predictions and paired with early-time properties to identify ideal observation windows for future SNe Ia discovered by upcoming surveys with Rubin-LSST or the Roman Space Telescope.
Quantum Technologies for Beyond 5G and 6G Networks: Applications, Opportunities, and Challenges
2025-04-23
Engin Zeydan, Chamitha De Alwis, Rabia Khan, Yekta Turk, Abdullah Aydeger, Thippa Reddy Gadekallu, Madhusanka Liyanage
As the world prepares for the advent of 6G networks, quantum technologies are becoming critical enablers of the next generation of communication systems. This survey paper investigates the convergence of quantum technologies and 6G networks, focusing on their applications, opportunities and challenges. We begin with an examination of the motivations for integrating quantum technologies into 6G, investigating the potential to overcome the limits of classical computing and cryptography. We then highlight key research gaps, particularly in quantum communication, quantum computing integration and security enhancement. A comprehensive overview of quantum technologies relevant to 6G, including quantum communication devices, quantum computing paradigms, and hybrid quantum-classical approaches is provided. A particular focus is on the role of quantum technologies in enhancing 6G Radio Access Networks (RAN), 6G core and edge network optimization, and 6G security. The survey paper also explores the application of quantum cryptography with a focus on Quantum Key Distribution (QKD), Quantum Secure Direct Communication (QSDC) and quantum-resistant cryptographic algorithms and assesses their implementation challenges and potential impact on 6G networks. We also discuss the significant challenges associated with integrating quantum technologies into existing communications infrastructures, including issues of technological maturity, standardization, and economic considerations. Finally, we summarize the lessons learned from current research and outline future research directions to guide the ongoing development of quantum-enabled 6G networks.
Despite substantial progress in healthcare applications driven by large language models (LLMs), growing concerns around data privacy, and limited resources; the small language models (SLMs) offer a scalable and clinically viable solution for efficient performance in resource-constrained environments for next-generation healthcare informatics. Our comprehensive survey presents a taxonomic framework to identify and categorize them for healthcare professionals and informaticians. The timeline of healthcare SLM contributions establishes a foundational framework for analyzing models across three dimensions: NLP tasks, stakeholder roles, and the continuum of care. We present a taxonomic framework to identify the architectural foundations for building models from scratch; adapting SLMs to clinical precision through prompting, instruction fine-tuning, and reasoning; and accessibility and sustainability through compression techniques. Our primary objective is to offer a comprehensive survey for healthcare professionals, introducing recent innovations in model optimization and equipping them with curated resources to support future research and development in the field. Aiming to showcase the groundbreaking advancements in SLMs for healthcare, we present a comprehensive compilation of experimental results across widely studied NLP tasks in healthcare to highlight the transformative potential of SLMs in healthcare. The updated repository is available at Github
Magneto-Optical Analysis of Magnetic Anisotropy in Ultrathin Tm$_{3}$Fe$_{5}$O$_{12}$/Pt Bilayers
2025-04-23
T. Nathan Nunley, Daniel Russell, Liang-Juan Chang, David Lujan, Jeongheon Choe, Side Guo, Shang-Fan Lee, Fengyuan Yang, Xiaoqin Li
Magnetic bilayers consisting of an epitaxially grown ferrimagnetic insulator and a heavy metal layer are attractive for spintronic application because of the opportunity for electric control and read-out of spin textures via spin orbit torque. Here, we investigate ultrathin thulium iron garnet (TmIG)/Pt bilayers when the TmIG layer thickness is 3 nm and below using a sensitive Sagnac magneto-optical Kerr effect technique. We compare the hysteresis loops from out-of-plane and in-plane applied magnetic fields. The preferred magnetization orientation evolves with the TmIG thickness and the presence of the Pt overlayer. We quantify the evolution of the magnetic anisotropy in these ultrathin films and find a significant change even when the TmIG thickness is varied by less than 1 nm. In these ultrathin films, the presence of a Pt overlayer changes the effective anisotropy field by more than a factor of 2, suggesting that the interfacial anisotropy at the Pt/TmIG interface plays a critical role in this regime.
Evaluating energy inefficiency in energy-poor households in India: A frontier analysis approach
2025-04-23
Vallary Gupta, Ahana Sarkar, Chirag Deb, Arnab Jana
Energy-poor households often compromise their thermal comfort and refrain from operating mechanical cooling devices to avoid high electricity bills. This is compounded by certain behavioral practices like retention of older, less efficient appliances, resulting in missed energy savings. Thus, the need to enhance efficiency becomes critical in these households. However, due to a lack of comprehensive data in India, little is understood about their electricity consumption patterns and usage efficiency. Estimating inefficiency and assessing its determinants is crucial for improving their quality of life. This study measures the inefficiency in electricity consumption due to household practices and appliances in social housing in Mumbai, India. It considers technological determinants in addition to socio-economic variables. The study employs primary data collected from rehabilitation housing and slums in Mumbai. Stochastic frontier analysis, a parametric approach, is applied to estimate indicators of electricity consumption and inefficiency. While household size and workforce participation significantly affect consumption behavior in rehabilitation housing, it is limited to the workforce in slums. The ownership of appliances, except for washing machines in slums, also exhibits considerable impacts. The mean efficiency scores of 83% and 91% for rehabilitation housing and slums, respectively, empirically quantify the potential savings achievable. Factors that positively influence inefficiency include the duration of operating refrigerators, washing machines, iron, and AC. These results hold implications for enhancing the uptake of efficient appliances in addition to accelerating energy efficiency retrofits in the region. Policies should focus on awareness and the development of appliance markets through incentives.
Exploring the Untapped: Student Perceptions and Participation in OSS
2025-04-23
Italo Santos, Katia Romero Felizardo, Bianca Trinkereinch, Daniel M. German, Igor Steinmacher, Marco A. Gerosa
Open Source Software (OSS) projects offer valuable opportunities to train the next generation of software engineers while benefiting projects and society as a whole. While research has extensively explored student participation in OSS and its use in software engineering education, student participation in OSS is still low, and the perspectives of students who have never contributed remain underexplored. This study aims to investigate the relationship between students' interest in contributing to OSS and their perceptions of barriers and motivational factors. We developed a theoretical model to understand the relationship between students' perceptions of OSS and their interest in contributing. We then surveyed students majoring in computer science and related fields (N=241). Using structural equation modeling techniques, we tested the model and found that intrinsic and internalized extrinsic motivations are positively associated with interest in contributing to OSS projects, while the impact of extrinsic motivation varies by gender. Comparatively, we found no significant relationship between barriers and interest in contributing. Students suggested several ways to make projects more attractive, including increasing awareness of the importance of OSS. Our findings can help communities better prepare to integrate students and encourage educators to enhance interest in OSS by linking participation to specific motivational factors.
Approaches to Responsible Governance of GenAI in Organizations
2025-04-23
Dhari Gandhi, Himanshu Joshi, Lucas Hartman, Shabnam Hassani
The rapid evolution of Generative AI (GenAI) has introduced unprecedented opportunities while presenting complex challenges around ethics, accountability, and societal impact. This paper draws on a literature review, established governance frameworks, and industry roundtable discussions to identify core principles for integrating responsible GenAI governance into diverse organizational structures. Our objective is to provide actionable recommendations for a balanced, risk-based governance approach that enables both innovation and oversight. Findings emphasize the need for adaptable risk assessment tools, continuous monitoring practices, and cross-sector collaboration to establish trustworthy GenAI. These insights provide a structured foundation and Responsible GenAI Guide (ResAI) for organizations to align GenAI initiatives with ethical, legal, and operational best practices.
Type-II Weyl nodes, flat bands, and evidence for a topological Hall-effect in the new ferromagnet FeCr$_3$Te$_6$
2025-04-23
Shyam Raj Karullithodi, Vadym Kulichenko, Mario A. Plata, Andrzej Ptok, Sang-Eon Lee, Gregory T. McCandless, Julia Y. Chan, Luis Balicas
The interplay between linearly dispersing or Dirac-like, and flat electronic bands, for instance, in the kagome ferromagnets, has attracted attention due to a possible interplay between topology and electronic correlations. Here, we report the synthesis, structural, electrical, and magnetic properties of a single-crystalline ferromagnetic compound, namely Fe$_{1/3}$CrTe$_2$ or FeCr$_3$Te$_6$, which crystallizes in the $P\bar{3}m1$ space group instead of the $I2/m$ previously reported for FeCr$_2$Te$_4$. Electronic band structure calculations reveal type-II Dirac nodes and relatively flat bands near the Fermi level ($\varepsilon_F$). This compound shows onset Curie temperature $T_{\text{c}}\simeq 120$ K, followed by an additional ferromagnetic transition near $T_{\text{c2}} \sim 92.5 $ K. Below $T_{\text{c}}$, FeCr$_3$Te$_6$ displays a pronounced anomalous Hall effect, as well as sizable coercive fields that exceed $\mu_0H = 1$~T at low $T$s. However, a scaling analysis indicates that the anomalous Hall effect results from a significant intrinsic contribution, as expected from the calculations, but also from the extrinsic mechanism, i.e., scattering. The extrinsic contribution probably results from occupational disorder at the 1b Fe-site within the van der Waals gap of the CrTe$_2$ host. We also observe evidence for a topological Hall component superimposed onto the overall Hall response, suggesting the presence of chiral spin textures akin to skyrmions in this centrosymmetric system. Their possible presence will require experimental confirmation.
Large Language Models (LLMs) are rapidly becoming integral to a wide range of tools, tasks, and problem-solving processes, especially in software development. Originally designed for natural language processing tasks such as text generation, LLMs are increasingly being used to assist both professionals and students in writing code. This growing reliance on LLM-based tools is reshaping programming workflows and task execution. In this study, we explore the impact of these technologies on blind and low-vision (BLV) developers. Our review of existing literature indicates that while LLMs help mitigate some of the challenges faced by BLV programmers, they also introduce new forms of inaccessibility. We conducted an evaluation of five popular LLM-powered integrated development environments (IDEs), assessing their performance across a comprehensive set of programming tasks. Our findings highlight several unsupported scenarios, instances of incorrect model output, and notable limitations in interaction support for specific tasks. Through observing BLV developers as they engaged in coding activities, we uncovered key interaction barriers that go beyond model accuracy or code generation quality. This paper outlines the challenges and corresponding opportunities for improving accessibility in the context of generative AI-assisted programming. Addressing these issues can meaningfully enhance the programming experience for BLV developers. As the generative AI revolution continues to unfold, it must also address the unique burdens faced by this community.
The First Photometric Evidence of a Transient/Variable Source at z>5 with JWST
2025-04-23
Christa DeCoursey, Eiichi Egami, Fengwu Sun, Arshia Akhtarkavan, Rachana Bhatawdekar, Andrew J. Bunker, David A. Coulter, Michael Engesser, Ori D. Fox, Sebastian Gomez, Kohei Inayoshi, Benjamin D. Johnson, Mitchell Karmen, Conor Larison, Xiaojing Lin, Jianwei Lyu, Seppo Mattila, Takashi J. Moriya, Justin D. R. Pierel, Dávid Puskás, Armin Rest, George H. Rieke, Brant Robertson, Sepehr Salamat, Louis-Gregory Strolger, Sandro Tacchella, Christian Vassallo, Christina C. Williams, Yossef Zenati, Junyu Zhang
The James Webb Space Telescope (JWST) discovered 79 transients out to $z$$\sim$4.8 through the JADES Transient Survey (JTS), but the JTS did not find any $z$$>$5 transients. Here, we present the first photometric evidence of a $z$$>$5 transient/variable source with JWST. The source, AT 2023adya, resides in a $z_{\mathrm{spec}}$$=$5.274 galaxy in GOODS-N, which dimmed from $m_{\rm F356W}$$=$26.05$\pm$0.02 mag to 26.24$\pm$0.02 mag in the rest-frame optical over approximately two rest-frame months, producing a clear residual signal in the difference image ($m_{\rm F356W}$$=$28.01$\pm$0.17 mag; SN$_\mathrm{var}$$=$6.09) at the galaxy center. Shorter-wavelength bands (F090W/F115W) show no rest-frame ultraviolet brightness change. Based on its rest-frame V-band absolute magnitude of M$_\mathrm{V}$$=$$-$18.48 mag, AT 2023adya could be any core-collapse supernova (SN) subtype or an SN Ia. However, due to low SN Ia rates at high redshift, the SN Ia scenario is unlikely. Alternatively, AT 2023adya may be a variable active galactic nucleus (AGN). However, the JWST NIRCam/Grism spectrum shows no broad H$\alpha$ emission line (FWHM$=$130$\pm$26 km s$^{-1}$), disfavoring the variable AGN scenario. It is also unlikely that AT 2023adya is a tidal disruption event (TDE) because the TDE models matching the observed brightness changes have low event rates. Although it is not possible to determine AT 2023adya's nature based on the two-epoch single-band photometry alone, this discovery indicates that JWST can push the frontier of transient/variable science past $z$$=$5 and towards the epoch of reionization.
Using high-resolution general relativistic magnetohydrodynamic (GRMHD) simulations, we investigate accretion flows around spinning black holes and identify three distinct accretion states. Our results naturally explain some of the complex phenomenology observed across the black hole mass spectrum. The magnetically arrested disk (MAD) state, characterized by strong magnetic fields (plasma-$\beta << 1$), exhibits powerful jets (of power $\sim10^{39}$ erg s$^{-1}$), highly variable accretion, and significant sub-Keplerian motion. On the other hand, weakly magnetized disks (plasma-$\beta >> 1$), known as the standard and normal evolution (SANE) state, show steady accretion with primarily thermal winds. An intermediate state bridges the gap between MAD and SANE regimes, with moderate magnetic support (plasma-$\beta \sim 1$) producing mixed outflow morphologies and complex variability. This unified framework explains the extreme variability of GRS 1915+105, the steady jets of Cyg X-1, and the unusually high luminosities (even super-Eddington based on stellar mass black hole) of HLX-1 without requiring super-Eddington mass accretion rates. Our simulations reveal a hierarchy of timescales that explain the rich variety of variability patterns, with magnetic processes driving transitions between states. Comparing two with three dimensional simulations demonstrates that while quantitative details differ, the qualitative features distinguishing different accretion states remain robust. The outflow power and variability follow a fundamental scaling relation with mass determined by the magnetic field configuration, demonstrating how similar accretion physics operates from stellar-mass X-ray binaries (XRBs) to intermediate mass black hole sources. This could be extrapolated further to supermassive black holes.
We present a dynamical analysis of the null and timelike geodesics around an asymptotically de Sitter wormhole in a Randall-Sundrum brane. In this framework, the wormhole throat is interpreted both as a photon sphere and as a fixed point of the associated dynamical system. The stability of this structure is evaluated using Lyapunov and Jacobi criteria with consistent results. A Bogdanov-Takens bifurcation is observed in the null-geodesic dynamics, highlighting critical changes in the behavior of light around the wormhole. Explicit solutions are derived for geodesics near the throat, providing insight into the optical appearance of the wormhole shadow. These results show qualitatively similar behavior for null and timelike orbits, suggesting universal features of geodesic dynamics in brane-de Sitter wormholes.
The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in unsupervised learning. This review synthesizes recent progress in applying autoencoders and vision transformers for unsupervised signal analysis, focusing on their architectures, applications, and emerging trends. We explore how these models enable feature extraction, anomaly detection, and classification across diverse signal types, including electrocardiograms, radar waveforms, and IoT sensor data. The review highlights the strengths of hybrid architectures and self-supervised learning, while identifying challenges in interpretability, scalability, and domain generalization. By bridging methodological innovations and practical applications, this work offers a roadmap for developing robust, adaptive models for signal intelligence.
Survey of Video Diffusion Models: Foundations, Implementations, and Applications
2025-04-22
Yimu Wang, Xuye Liu, Wei Pang, Li Ma, Shuai Yuan, Paul Debevec, Ning Yu
Recent advances in diffusion models have revolutionized video generation, offering superior temporal consistency and visual quality compared to traditional generative adversarial networks-based approaches. While this emerging field shows tremendous promise in applications, it faces significant challenges in motion consistency, computational efficiency, and ethical considerations. This survey provides a comprehensive review of diffusion-based video generation, examining its evolution, technical foundations, and practical applications. We present a systematic taxonomy of current methodologies, analyze architectural innovations and optimization strategies, and investigate applications across low-level vision tasks such as denoising and super-resolution. Additionally, we explore the synergies between diffusionbased video generation and related domains, including video representation learning, question answering, and retrieval. Compared to the existing surveys (Lei et al., 2024a;b; Melnik et al., 2024; Cao et al., 2023; Xing et al., 2024c) which focus on specific aspects of video generation, such as human video synthesis (Lei et al., 2024a) or long-form content generation (Lei et al., 2024b), our work provides a broader, more updated, and more fine-grained perspective on diffusion-based approaches with a special section for evaluation metrics, industry solutions, and training engineering techniques in video generation. This survey serves as a foundational resource for researchers and practitioners working at the intersection of diffusion models and video generation, providing insights into both the theoretical frameworks and practical implementations that drive this rapidly evolving field. A structured list of related works involved in this survey is also available on https://github.com/Eyeline-Research/Survey-Video-Diffusion.
Optimal intrinsic alignment estimators in the presence of redshift-space distortions
2025-04-22
Claire Lamman, Jonathan Blazek, Daniel J. Eisenstein
We present estimators for quantifying intrinsic alignments in large spectroscopic surveys that efficiently capture line-of-sight (LOS) information while being relatively insensitive to redshift-space distortions (RSD). We demonstrate that changing the LOS integration range, {\Pi}max, as a function of transverse separation outperforms the conventional choice of a single {\Pi}max value. This is further improved by replacing the flat {\Pi}max cut with a LOS weighting based on shape projection and RSD. Although these estimators incorporate additional LOS information, they are projected correlations that exhibit signal-to-noise ratios comparable to 3D correlation functions, such as the IA quadrupole. Using simulations from Abacus Summit, we evaluate these estimators and provide recommended {\Pi}max values and weights for projected separations of 1 - 100 Mpc/h. These will improve measurements of intrinsic alignments in large cosmological surveys and the constraints they provide for both weak lensing and direct cosmological applications.
SAR4SLPs: An Asynchronous Survey of Speech-Language Pathologists' Perspectives on Socially Assistive Robots
2025-04-22
Denielle Oliva, Abbie Olszewski, David Feil-Seifer
Socially Assistive Robots (SARs) offer unique opportunities within speech language pathology (SLP) education and practice by supporting interactive interventions for children with communication disorders. This paper explores the implementation of SAR4SLPs (Socially Assistive Robots for Speech-Language Pathologists) to investigate aspects such as engagement, therapeutic strategy discipline, and consistent intervention support. We assessed the current application of technology to clinical and educational settings, especially with respect to how SLPs might use SAR in their therapeutic work. An asynchronous remote community (ARC) collaborated with a cohort of practicing SLPs to consider the feasibility, potential effectiveness, and anticipated challenges with implementing SARs in day-to-day interventions and as practice facilitators. We focus in particular on the expressive functionality of SARs, modeling a foundational strategy that SLPs employ across various intervention targets. This paper highlights clinician-driven insights and design implications for developing SARs that support specific treatment goals through collaborative and iterative design.
Recent Advances and Future Directions in Extended Reality (XR): Exploring AI-Powered Spatial Intelligence
2025-04-22
Baichuan Zeng
Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR) and Mixed Reality (MR), is a transformative technology bridging the physical and virtual world and it has diverse potential which will be ubiquitous in the future. This review examines XR's evolution through foundational framework - hardware ranging from monitors to sensors and software ranging from visual tasks to user interface; highlights state of the art (SOTA) XR products with the comparison and analysis of performance based on their foundational framework; discusses how commercial XR devices can support the demand of high-quality performance focusing on spatial intelligence. For future directions, attention should be given to the integration of multi-modal AI and IoT-driven digital twins to enable adaptive XR systems. With the concept of spatial intelligence, future XR should establish a new digital space with realistic experience that benefits humanity. This review underscores the pivotal role of AI in unlocking XR as the next frontier in human-computer interaction.
Memory is the process of encoding, storing, and retrieving information, allowing humans to retain experiences, knowledge, skills, and facts over time, and serving as the foundation for growth and effective interaction with the world. It plays a crucial role in shaping our identity, making decisions, learning from past experiences, building relationships, and adapting to changes. In the era of large language models (LLMs), memory refers to the ability of an AI system to retain, recall, and use information from past interactions to improve future responses and interactions. Although previous research and reviews have provided detailed descriptions of memory mechanisms, there is still a lack of a systematic review that summarizes and analyzes the relationship between the memory of LLM-driven AI systems and human memory, as well as how we can be inspired by human memory to construct more powerful memory systems. To achieve this, in this paper, we propose a comprehensive survey on the memory of LLM-driven AI systems. In particular, we first conduct a detailed analysis of the categories of human memory and relate them to the memory of AI systems. Second, we systematically organize existing memory-related work and propose a categorization method based on three dimensions (object, form, and time) and eight quadrants. Finally, we illustrate some open problems regarding the memory of current AI systems and outline possible future directions for memory in the era of large language models.
Understanding the Role of Covariates in Numerical Reconstructions of Real-World Vehicle-to-Pedestrian Collisions
2025-04-22
Natalia Lindgren, Svein Kleiven, Xiaogai Li
Traumatic Brain Injuries (TBIs) are a pressing global public health issue, impacting tens of millions of individuals annually. Vulnerable road users (VRUs), such as pedestrians, are vastly overrepresented in the worldwide TBI statistics. To evaluate the effectiveness of injury prevention measures, researchers often employ Finite Element (FE) models of the human body to virtually simulate the human response to impact in real-world road traffic accident scenarios. However, VRU accidents occur in a highly uncontrolled environment and, in consequence, there is a large amount of variables (covariates), e.g. the vehicle impact speed and VRU body posture, that together dictate the injurious outcome of the collision. At the same time, since FE analysis is a computationally heavy task, researchers often need to apply extensive simplifications to FE models when attempting to predict real-world VRU head trauma. To help researchers make informed decisions when conducting FE accident reconstructions, this literature review aims to create an overarching summary of covariates that have been reported influential in literature. The review provides researchers with an overview of variables proven to have an influence on head injury predictions. The material could potentially be useful as a basis for choosing parameters to include when performing sensitivity analyses of car-to-pedestrian impact simulations.
Towards Test Generation from Task Description for Mobile Testing with Multi-modal Reasoning
2025-04-22
Hieu Huynh, Hai Phung, Hao Pham, Tien N. Nguyen, Vu Nguyen
In Android GUI testing, generating an action sequence for a task that can be replayed as a test script is common. Generating sequences of actions and respective test scripts from task goals described in natural language can eliminate the need for manually writing test scripts. However, existing approaches based on large language models (LLM) often struggle with identifying the final action, and either end prematurely or continue past the final screen. In this paper, we introduce VisiDroid, a multi-modal, LLM-based, multi-agent framework that iteratively determines the next action and leverages visual images of screens to detect the task's completeness. The multi-modal approach enhances our model in two significant ways. First, this approach enables it to avoid prematurely terminating a task when textual content alone provides misleading indications of task completion. Additionally, visual input helps the tool avoid errors when changes in the GUI do not directly affect functionality toward task completion, such as adjustments to font sizes or colors. Second, the multi-modal approach also ensures the tool not progress beyond the final screen, which might lack explicit textual indicators of task completion but could display a visual element indicating task completion, which is common in GUI apps. Our evaluation shows that VisiDroid achieves an accuracy of 87.3%, outperforming the best baseline relatively by 23.5%. We also demonstrate that our multi-modal framework with images and texts enables the LLM to better determine when a task is completed.
Synergizing RAG and Reasoning: A Systematic Review
2025-04-22
Yunfan Gao, Yun Xiong, Yijie Zhong, Yuxi Bi, Ming Xue, Haofen Wang
Recent breakthroughs in large language models (LLMs), particularly in reasoning capabilities, have propelled Retrieval-Augmented Generation (RAG) to unprecedented levels. By synergizing retrieval mechanisms with advanced reasoning, LLMs can now tackle increasingly complex problems. This paper presents a systematic review of the collaborative interplay between RAG and reasoning, clearly defining "reasoning" within the RAG context. It construct a comprehensive taxonomy encompassing multi-dimensional collaborative objectives, representative paradigms, and technical implementations, and analyze the bidirectional synergy methods. Additionally, we critically evaluate current limitations in RAG assessment, including the absence of intermediate supervision for multi-step reasoning and practical challenges related to cost-risk trade-offs. To bridge theory and practice, we provide practical guidelines tailored to diverse real-world applications. Finally, we identify promising research directions, such as graph-based knowledge integration, hybrid model collaboration, and RL-driven optimization. Overall, this work presents a theoretical framework and practical foundation to advance RAG systems in academia and industry, fostering the next generation of RAG solutions.
Identifying eclipsing binary stars with TESS data based on a new hybrid deep learning model
2025-04-22
Ying Shan, Jing Chen, Zichong Zhang, Liang Wang, Zhiqiang Zou, Min Li
Eclipsing binary systems (EBs), as foundational objects in stellar astrophysics, have garnered significant attention in recent years. These systems exhibit periodic decreases in light intensity when one star obscures the other from the observer's perspective, producing characteristic light curves (LCs). With the advent of the Transiting Exoplanet Survey Satellite (TESS), a vast repository of stellar LCs has become available, offering unprecedented opportunities for discovering new EBs. To efficiently identify such systems, we propose a novel method that combines LC data and generalized Lomb-Scargle periodograms (GLS) data to classify EBs. At the core of this method is CNN Attention LSTM Net (CALNet), a hybrid deep learning model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and an Attention Mechanism based on the Convolutional Block Attention Module (CBAM). We collected 4,225 EB samples, utilizing their 2-minute cadence LCs for model training and validation. CALNet achieved a recall rate of 99.1%, demonstrating its robustness and effectiveness. Applying it to TESS 2-minute LCs from Sectors 1 to 74, we identified 9,351 new EBs after manual visual inspection, significantly expanding the known sample size. This work highlights the potential of advanced deep-learning techniques in large-scale astronomical surveys and provides a valuable resource for further studies on EBs.
Stochastic low-frequency variability of 50 massive stars in the Cygnus OB associations and the Small Magellanic Cloud
2025-04-22
M. G. Pedersen, L. Bildsten
In recent years, high-precision high-cadence space photometry has revealed that stochastic low frequency (SLF) variability is common in the light curves of massive stars. We use the data from the Transiting Exoplanet Survey Satellite (TESS) to study and characterize the SLF variability found in a sample of 49 O- and B-type main-sequence stars across six Cygnus OB~associations and one low-metallicity SMC star AV~232. We compare these results to 53 previously studied SLF variables. We adopt two different methods for characterizing the signal. In the first, we follow earlier work and fit a Lorentzian-like profile to the power density spectrum of the residual light curve to derive the amplitude $\alpha_0$, characteristic frequency $\nu_{\rm char}$, and slope $\gamma$ of the variability. In our second model-independent method, we calculate the root-mean-square (RMS) of the photometric variability as well as the frequency at 50\% of the accumulated power spectral density, $\nu_{50\%}$, and the width of the cumulative integrated power density, $w$. For the full sample of 103 SLF variables, we find that $\alpha_0$, $\gamma$, RMS, $\nu_{50\%}$, and $w$ correlate with the spectroscopic luminosity of the stars. Both $\alpha_0$ and RMS appear to increase for more evolved stars whereas $\nu_{\rm char}$ and $\nu_{50\%}$ both decrease. Finally, we compare our results to 2-D and 3-D simulations of subsurface convection, core-generated internal gravity waves, and surface stellar winds, and find good agreement between the observed $\nu_{\rm char}$ of our sample and predictions from sub-surface convection.
Sensitivity analysis for parametric nonlinear programming: A tutorial
2025-04-22
François Pacaud
This tutorial provides an overview of the current state-of-the-art in the sensitivity analysis for nonlinear programming. Building upon the fundamental work of Fiacco, it derives the sensitivity of primal-dual solutions for regular nonlinear programs and explores the extent to which Fiacco's framework can be extended to degenerate nonlinear programs with non-unique dual solutions. The survey ends with a discussion on how to adapt the sensitivity analysis for conic programs and approximate solutions obtained from interior-point algorithms.
A primer on Kitaev Model: Basic aspects, material realization, and recent experiments
2025-04-22
Saptarshi Mandal
This elementary review article is aimed to the beginning graduate students interested to know basic aspects of Kitaev model. We begin with a very lucid introduction of Kitaev model and present its exact solution, Hilbert space structure, fractionalisation, spin-spin correlation function and topological degeneracy in an elementary way. We then discuss the recent proposal of realizing Kitaev interaction in certain materials. Finally we present some recent experiments done on these materials, mainly magnetization, susceptibility, specific heat and thermal Hall effect to elucidate the recent status of material realization of coveted Kitaev spin-liquid phase. We end with a brief discussion on other theoretical works on Kitaev model from different many-body aspects.
Autonomous driving systems rely on accurate perception and localization of the ego car to ensure safety and reliability in challenging real-world driving scenarios. Public datasets play a vital role in benchmarking and guiding advancement in research by providing standardized resources for model development and evaluation. However, potential inaccuracies in sensor calibration and vehicle poses within these datasets can lead to erroneous evaluations of downstream tasks, adversely impacting the reliability and performance of the autonomous systems. To address this challenge, we propose a robust optimization method based on Neural Radiance Fields (NeRF) to refine sensor poses and calibration parameters, enhancing the integrity of dataset benchmarks. To validate improvement in accuracy of our optimized poses without ground truth, we present a thorough evaluation process, relying on reprojection metrics, Novel View Synthesis rendering quality, and geometric alignment. We demonstrate that our method achieves significant improvements in sensor pose accuracy. By optimizing these critical parameters, our approach not only improves the utility of existing datasets but also paves the way for more reliable autonomous driving models. To foster continued progress in this field, we make the optimized sensor poses publicly available, providing a valuable resource for the research community.
From Spin Waves to Monte Carlo Simulations: Compiling an Experimental Exchange Interaction Dataset for Magnetic Materials
2025-04-22
Mojtaba Alaei, Zahra Mosleh, Nafise Rezaei, Artem R. Oganov
Inelastic neutron scattering data on magnetic crystals are highly valuable in materials science, as they provide direct insight into microscopic magnetic interactions. Using spin wave theory, these interactions can be extracted from magnetic excitations observed in such experiments. However, these datasets are often scattered across the literature and lack standardization, limiting their accessibility and usability. In this paper, we compile and standardize Heisenberg exchange interaction data for magnetic materials obtained from inelastic neutron scattering experiments. Through an extensive literature review, we identify experimental data for approximately 100 magnetic materials. The standardized dataset includes mapping the results of various Heisenberg Hamiltonians into a unified standard form, visualizations of crystal structures with annotated exchange interactions, and input and output files from Monte Carlo simulations performed for each compound using the ESpinS code. Using experimentally determined exchange interactions, we calculate transition temperatures $T_c$ via classical Monte Carlo simulations. Additionally, we assess the effectiveness of the $S+1)/S$ correction within classical Monte Carlo simulations, finding that it produces transition temperatures in excellent agreement with experimental values in most cases. The complete dataset, along with supporting resources, is publicly available on GitHub.
Development of an Ultra-fast, Likelihood-based, Distance Inference Framework for the Next Generation of Type Ia Supernova Surveys
2025-04-22
Dylan Kuhn, Marc Betoule
In this work, we present EDRIS (French for Distance Estimator for Incomplete Supernova Surveys), a cosmological inference framework tailored to reconstruct unbiased cosmological distances from type Ia supernovae light-curve parameters. This goal is achieved by including data truncation directly in the statistical model which takes care of the standardization of luminosity distances. It allows us to build a single-step distance estimate by maximizing the corresponding likelihood, free from the biases the survey detection limits would introduce otherwise. Moreover, we expect the current worldwide statistics to be multiplied by O(10) in the upcoming years. This provides a new challenge to handle as the cosmological analysis must stay computationally towable. We show that the optimization methods used in EDRIS allow for a reasonable time complexity of O($N^2$) resulting in a very fast inference process (O(10s) for 1500 supernovae).
FinTextSim: Enhancing Financial Text Analysis with BERTopic
2025-04-22
Simon Jehnen, Joaquín Ordieres-Meré, Javier Villalba-Díez
Recent advancements in information availability and computational capabilities have transformed the analysis of annual reports, integrating traditional financial metrics with insights from textual data. To extract valuable insights from this wealth of textual data, automated review processes, such as topic modeling, are crucial. This study examines the effectiveness of BERTopic, a state-of-the-art topic model relying on contextual embeddings, for analyzing Item 7 and Item 7A of 10-K filings from S&P 500 companies (2016-2022). Moreover, we introduce FinTextSim, a finetuned sentence-transformer model optimized for clustering and semantic search in financial contexts. Compared to all-MiniLM-L6-v2, the most widely used sentence-transformer, FinTextSim increases intratopic similarity by 81% and reduces intertopic similarity by 100%, significantly enhancing organizational clarity. We assess BERTopic's performance using embeddings from both FinTextSim and all-MiniLM-L6-v2. Our findings reveal that BERTopic only forms clear and distinct economic topic clusters when paired with FinTextSim's embeddings. Without FinTextSim, BERTopic struggles with misclassification and overlapping topics. Thus, FinTextSim is pivotal for advancing financial text analysis. FinTextSim's enhanced contextual embeddings, tailored for the financial domain, elevate the quality of future research and financial information. This improved quality of financial information will enable stakeholders to gain a competitive advantage, streamlining resource allocation and decision-making processes. Moreover, the improved insights have the potential to leverage business valuation and stock price prediction models.
Electroweak form factors of baryons in dense nuclear matter
2025-04-22
G. Ramalho, K. Tsushima, Myung-Ki Cheoun
There are evidences that the properties of the hadrons are modified in a nuclear medium. Information about the medium modifications of the internal structure of the hadrons are fundamental for the study of dense nuclear matter and high energy processes including heavy-ion and nucleus-nucleus collisions. At the moment, however, the empirical information about the medium modifications of the hadrons is limited, therefore, theoretical studies are essential for the progress in the field. In the present work we review theoretical studies of the electromagnetic and axial form factors of octet baryons in symmetric nuclear matter. The calculations are based on a model that takes into account the degrees of freedom revealed on experimental studies of the low and intermediate square transfer momentum $q^2=-Q^2$: valence quarks and meson cloud excitations of the baryon cores. The formalism combines a covariant constituent quark model, developed for the free space (vacuum) with the quark-meson coupling model for the extension to the nuclear medium. We conclude that the nuclear medium modifies the baryon properties differently according to the flavor content of the baryons and the medium density. The effects of the medium increase with the density, and are stronger (quenched or enhanced) for light baryons than for heavy baryons. In particular, the in-medium neutrino-nucleon and antineutrino-nucleon cross sections are reduced compared to the values in free space. The proposed formalism can be extended to densities above the normal nuclear density and applied to neutrino-hyperon and antineutrino-hyperon scattering in dense nuclear matter.
Atomically-Thin Transition Metal Dichalcogenide Nanolasers: Challenges and Opportunities
2025-04-22
Teresa López-Carrasco, Marcos H. D. Guimarães
Low energy consumption nanolasers are crucial for advancing on-chip integrated optical interconnects and photonic integrated circuits. Monolayer transition metal dichalcogenides (TMDs) have emerged as an energy-efficient alternative to traditional semiconductor materials for nanolaser optical gain medium, promising ultralow lasing threshold powers. While several studies suggest that TMDs meet the criteria for lasing, whether true lasing has been achieved remains a topic of heavy debate within the scientific community. In this perspective, we offer an overview of the field, outlining the key characteristics of laser light and methods for testing these properties in TMD-based devices. We then conduct a thorough review of recent reports claiming lasing, assessing the findings against established criteria for laser light emission. Finally, we discuss future research directions and applications, highlighting the key challenges that must be addressed to realize practical TMD-based nanolasers.
Promoting Real-Time Reflection in Synchronous Communication with Generative AI
2025-04-22
Yi Wen, Meng Xia
Real-time reflection plays a vital role in synchronous communication. It enables users to adjust their communication strategies dynamically, thereby improving the effectiveness of their communication. Generative AI holds significant potential to enhance real-time reflection due to its ability to comprehensively understand the current context and generate personalized and nuanced content. However, it is challenging to design the way of interaction and information presentation to support the real-time workflow rather than disrupt it. In this position paper, we present a review of research on systems designed for real-time reflection in different synchronous communication scenarios. Based on that, we discuss how to design human-AI interaction to support real-time reflection in synchronous communication scenarios.
Multimodal Perception for Goal-oriented Navigation: A Survey
2025-04-22
I-Tak Ieong, Hao Tang
Goal-oriented navigation presents a fundamental challenge for autonomous systems, requiring agents to navigate complex environments to reach designated targets. This survey offers a comprehensive analysis of multimodal navigation approaches through the unifying perspective of inference domains, exploring how agents perceive, reason about, and navigate environments using visual, linguistic, and acoustic information. Our key contributions include organizing navigation methods based on their primary environmental reasoning mechanisms across inference domains; systematically analyzing how shared computational foundations support seemingly disparate approaches across different navigation tasks; identifying recurring patterns and distinctive strengths across various navigation paradigms; and examining the integration challenges and opportunities of multimodal perception to enhance navigation capabilities. In addition, we review approximately 200 relevant articles to provide an in-depth understanding of the current landscape.
Data races are a prevalent class of concurrency bugs in shared-memory parallel programs, posing significant challenges to software reliability and reproducibility. While there is an extensive body of research on detecting data races and a wealth of practical detection tools across various programming languages, considerably less effort has been directed toward automatically fixing data races at an industrial scale. In large codebases, data races are continuously introduced and exhibit myriad patterns, making automated fixing particularly challenging. In this paper, we tackle the problem of automatically fixing data races at an industrial scale. We present Dr.Fix, a tool that combines large language models (LLMs) with program analysis to generate fixes for data races in real-world settings, effectively addressing a broad spectrum of racy patterns in complex code contexts. Implemented for Go--the programming language widely used in modern microservice architectures where concurrency is pervasive and data races are common--Dr.Fix seamlessly integrates into existing development workflows. We detail the design of Dr.Fix and examine how individual design choices influence the quality of the fixes produced. Over the past 18 months, Dr.Fix has been integrated into developer workflows at Uber demonstrating its practical utility. During this period, Dr.Fix produced patches for 224 (55%) from a corpus of 404 data races spanning various categories; 193 of these patches (86%) were accepted by more than a hundred developers via code reviews and integrated into the codebase.
An analytic redshift-independent formulation of baryonic effects on the matter power spectrum
2025-04-22
Matthieu Schaller, Joop Schaye
Baryonic effects created by feedback processes associated with galaxy formation are an important, poorly constrained systematic effect for models of large-scale structure as probed by weak gravitational lensing. Upcoming surveys require fast methods to predict and marginalize over the potential impact of baryons on the total matter power spectrum. Here we use the FLAMINGO cosmological hydrodynamical simulations to test a recent proposal to approximate the matter power spectrum as the sum of the linear matter power spectrum and a constant multiple, $A_{\rm mod}$, of the difference between the linear and non-linear gravity-only power spectra. We show that replacing this constant multiple with a one-parameter family of sigmoid functions of the wavenumber $k$ allows to us match the predictions of simulations with different feedback strengths for $z \leq 1, k < 3~h\cdot{\rm Mpc}^{-1}$, and the different cosmological models in the FLAMINGO suite. The baryonic response predicted by FLAMINGO models that use jet-like AGN feedback instead of the fiducial thermally-driven AGN feedback can also be reproduced, but at the cost of increasing the number of parameters in the sigmoid function from one to three. The assumption that $A_{\rm mod}$ depends only on $k$ breaks down for decaying dark matter models, highlighting the need for more advanced baryon response models when studying cosmological models that deviate strongly from $\Lambda$CDM.
Exploring the Role of Large Language Models in Cybersecurity: A Systematic Survey
2025-04-22
Shuang Tian, Tao Zhang, Jiqiang Liu, Jiacheng Wang, Xuangou Wu, Xiaoqiang Zhu, Ruichen Zhang, Weiting Zhang, Zhenhui Yuan, Shiwen Mao, Dong In Kim
With the rapid development of technology and the acceleration of digitalisation, the frequency and complexity of cyber security threats are increasing. Traditional cybersecurity approaches, often based on static rules and predefined scenarios, are struggling to adapt to the rapidly evolving nature of modern cyberattacks. There is an urgent need for more adaptive and intelligent defence strategies. The emergence of Large Language Model (LLM) provides an innovative solution to cope with the increasingly severe cyber threats, and its potential in analysing complex attack patterns, predicting threats and assisting real-time response has attracted a lot of attention in the field of cybersecurity, and exploring how to effectively use LLM to defend against cyberattacks has become a hot topic in the current research field. This survey examines the applications of LLM from the perspective of the cyber attack lifecycle, focusing on the three phases of defense reconnaissance, foothold establishment, and lateral movement, and it analyzes the potential of LLMs in Cyber Threat Intelligence (CTI) tasks. Meanwhile, we investigate how LLM-based security solutions are deployed and applied in different network scenarios. It also summarizes the internal and external risk issues faced by LLM during its application. Finally, this survey also points out the facing risk issues and possible future research directions in this domain.
A LoRA-Based Approach to Fine-Tuning LLMs for Educational Guidance in Resource-Constrained Settings
2025-04-22
Md Millat Hosen
The current study describes a cost-effective method for adapting large language models (LLMs) for academic advising with study-abroad contexts in mind and for application in low-resource methods for acculturation. With the Mistral-7B-Instruct model applied with a Low-Rank Adaptation (LoRA) method and a 4-bit quantization method, the model underwent training in two distinct stages related to this study's purpose to enhance domain specificity while maintaining computational efficiency. In Phase 1, the model was conditioned with a synthetic dataset via the Gemini Pro API, and in Phase 2, it was trained with manually curated datasets from the StudyAbroadGPT project to achieve enhanced, contextualized responses. Technical innovations entailed memory-efficient quantization, parameter-efficient adaptation, and continuous training analytics via Weights & Biases. After training, this study demonstrated a reduction in training loss by 52.7%, 92% accuracy in domain-specific recommendations, achieved 95% markdown-based formatting support, and a median run-rate of 100 samples per second on off-the-shelf GPU equipment. These findings support the effective application of instruction-tuned LLMs within educational advisers, especially in low-resource institutional scenarios. Limitations included decreased generalizability and the application of a synthetically generated dataset, but this framework is scalable for adding new multilingual-augmented and real-time academic advising processes. Future directions may include plans for the integration of retrieval-augmented generation, applying dynamic quantization routines, and connecting to real-time academic databases to increase adaptability and accuracy.
[PK2008] HalphaJ115927 and IGR J14091-6108: Two new intermediate polars above the period gap
2025-04-22
Arti Joshi
This study presents a detailed timing analyses of two cataclysmic variables (CVs), [PK2008] HalphaJ115927 and IGR J14091-610, utilizing the optical data from the Transiting Exoplanet Survey Satellite (TESS). Periods of 7.20$\pm$0.02 h, 1161.49$\pm$0.14 s, and 1215.99$\pm$0.15 s are presented for [PK2008] HalphaJ115927, and are interpreted as the probable orbital, spin, and beat periods of the system, respectively. The presence of multiple periodic variations suggests that it likely belongs to the intermediate polar (IP) category of magnetic CVs. Interestingly, [PK2008] HalphaJ115927 exhibits a unique and strong periodic modulation at 5.66$\pm$0.29 d, which may result from the precession of an accretion disc, similar to the IP TV Col. The detection of a spin signal of 576.63$\pm$0.03 s and inferred orbital signal of $\sim$ 15.84 h supports the classification of IGR J14091-610 as an IP. The identification of such a long orbital period adds a new example to the limited population of long-period IPs. The observed dominant signal at the second harmonic of the orbital frequency also suggests ellipsoidal modulation of the secondary in this system. The observed double-peaked spin pulse profile in [PK2008] HalphaJ115927 likely results from two-pole accretion, where both poles contribute to the spin modulation, and their geometry allows equal visibility of both accreting poles. In contrast, IGR J14091-610 exhibits a single-peaked sinusoidal like spin pulse, attributed to the changing visibility of the accretion curtains due to a relatively low dipole inclination. The present observations indicate that accretion in both systems occurs predominantly through a disc.
Analytical Softmax Temperature Setting from Feature Dimensions for Model- and Domain-Robust Classification
2025-04-22
Tatsuhito Hasegawa, Shunsuke Sakai
In deep learning-based classification tasks, the softmax function's temperature parameter $T$ critically influences the output distribution and overall performance. This study presents a novel theoretical insight that the optimal temperature $T^*$ is uniquely determined by the dimensionality of the feature representations, thereby enabling training-free determination of $T^*$. Despite this theoretical grounding, empirical evidence reveals that $T^*$ fluctuates under practical conditions owing to variations in models, datasets, and other confounding factors. To address these influences, we propose and optimize a set of temperature determination coefficients that specify how $T^*$ should be adjusted based on the theoretical relationship to feature dimensionality. Additionally, we insert a batch normalization layer immediately before the output layer, effectively stabilizing the feature space. Building on these coefficients and a suite of large-scale experiments, we develop an empirical formula to estimate $T^*$ without additional training while also introducing a corrective scheme to refine $T^*$ based on the number of classes and task complexity. Our findings confirm that the derived temperature not only aligns with the proposed theoretical perspective but also generalizes effectively across diverse tasks, consistently enhancing classification performance and offering a practical, training-free solution for determining $T^*$.
A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment
2025-04-22
Kun Wang, Guibin Zhang, Zhenhong Zhou, Jiahao Wu, Miao Yu, Shiqian Zhao, Chenlong Yin, Jinhu Fu, Yibo Yan, Hanjun Luo, Liang Lin, Zhihao Xu, Haolang Lu, Xinye Cao, Xinyun Zhou, Weifei Jin, Fanci Meng, Junyuan Mao, Hao Wu, Minghe Wang, Fan Zhang, Junfeng Fang, Chengwei Liu, Yifan Zhang, Qiankun Li, Chongye Guo, Yalan Qin, Yi Ding, Donghai Hong, Jiaming Ji, Xinfeng Li, Yifan Jiang, Dongxia Wang, Yihao Huang, Yufei Guo, Jen-tse Huang, Yanwei Yue, Wenke Huang, Guancheng Wan, Tianlin Li, Lei Bai, Jie Zhang, Qing Guo, Jingyi Wang, Tianlong Chen, Joey Tianyi Zhou, Xiaojun Jia, Weisong Sun, Cong Wu, Jing Chen, Xuming Hu, Yiming Li, Xiao Wang, Ningyu Zhang, Luu Anh Tuan, Guowen Xu, Tianwei Zhang, Xingjun Ma, Xiang Wang, Bo An, Jun Sun, Mohit Bansal, Shirui Pan, Yuval Elovici, Bhavya Kailkhura, Bo Li, Yaodong Yang, Hongwei Li, Wenyuan Xu, Yizhou Sun, Wei Wang, Qing Li, Ke Tang, Yu-Gang Jiang, Felix Juefei-Xu, Hui Xiong, Xiaofeng Wang, Shuicheng Yan, Dacheng Tao, Philip S. Yu, Qingsong Wen, Yang Liu
The remarkable success of Large Language Models (LLMs) has illuminated a promising pathway toward achieving Artificial General Intelligence for both academic and industrial communities, owing to their unprecedented performance across various applications. As LLMs continue to gain prominence in both research and commercial domains, their security and safety implications have become a growing concern, not only for researchers and corporations but also for every nation. Currently, existing surveys on LLM safety primarily focus on specific stages of the LLM lifecycle, e.g., deployment phase or fine-tuning phase, lacking a comprehensive understanding of the entire "lifechain" of LLMs. To address this gap, this paper introduces, for the first time, the concept of "full-stack" safety to systematically consider safety issues throughout the entire process of LLM training, deployment, and eventual commercialization. Compared to the off-the-shelf LLM safety surveys, our work demonstrates several distinctive advantages: (I) Comprehensive Perspective. We define the complete LLM lifecycle as encompassing data preparation, pre-training, post-training, deployment and final commercialization. To our knowledge, this represents the first safety survey to encompass the entire lifecycle of LLMs. (II) Extensive Literature Support. Our research is grounded in an exhaustive review of over 800+ papers, ensuring comprehensive coverage and systematic organization of security issues within a more holistic understanding. (III) Unique Insights. Through systematic literature analysis, we have developed reliable roadmaps and perspectives for each chapter. Our work identifies promising research directions, including safety in data generation, alignment techniques, model editing, and LLM-based agent systems. These insights provide valuable guidance for researchers pursuing future work in this field.
A hypergraph $\mathcal{H}=(V,\mathcal{E})$ is a hypertree if it admits a tree $T$ with vertex set $V$ such that every edge of $\mathcal{H}$ induces a subtree of $T$. A tree like that is called a host tree. Several characterizations and properties of hypertrees have been discovered over the years. However, the interest in the structure of their host trees was weaker and restricted to particular scenarios where they arise, like the clique tree of chordal graphs. In that special case, the proofs of most characteristics of clique trees that exist in the literature rely significantly on the structural properties of chordal graphs. The purpose of this work is the study of the properties of the host trees of hypertrees in a more general context and have them described in a single place, giving simpler proofs for known facts, generalizing others and introducing some new concepts that the author considers that are relevant for the study of the topic. Particularly, we will determine what edges can be found in some host tree of a hypertree, and how these edges must be combined to form a host tree, with an emphasis in tools like the basis and the completion of a hypergraph, and the concept of equivalent hypergraphs.
Antenna Technology Readiness for the Black Hole Explorer (BHEX) Mission
2025-04-22
T. K. Sridharan, R. Lehmensiek, S. Schwarz, D. P. Marrone
The Black Hole Explorer (BHEX) will be the first sub-mm wavelength Space Very-Long-Baseline Interferometry (VLBI) mission. It targets astronomical imaging with the highest ever spatial resolution to enable detection of the photon ring of a supermassive black hole. BHEX is being proposed for launch in 2031 as a NASA Small Explorers mission. BHEX science goals and mission opportunity require a high precision lightweight spaceborne antenna. A survey of the technology landscape for realizing such an antenna is presented. Technology readiness (TRL) for the antenna is discussed and assessed to be at TRL 5. An update on our technology maturation efforts is provided. Design studies leading to the conceptual design of a metallized carbon fiber reinforced plastic (CFRP) technology based antenna with a mass of only $\dim 50$ kg, incorporating a 3.4 m primary reflector with a surface precision of < 40 $\mu$m to allow efficient operation up to 320 GHz are outlined. Current plans anticipate attaining TRL6 in 2026 for the BHEX antenna. Completed design studies point to a large margin in surface precision which opens up opportunities for applications beyond BHEX, at significantly higher THz frequencies.
Bayesian information theoretic model-averaging stochastic item selection for computer adaptive testing: compromise-free item exposure
2025-04-22
Joshua C. Chang, Edison Choe
The goal of Computer Adaptive Testing (CAT) is to reliably estimate an individual's ability as modeled by an item response theory (IRT) instrument using only a subset of the instrument's items. A secondary goal is to vary the items presented across different testing sessions so that the sequence of items does not become overly stereotypical -- we want all items to have an exposure rate sufficiently far from zero. We formulate the optimization problem for CAT in terms of Bayesian information theory, where one chooses the item at each step based on the criterion of the ability model discrepancy -- the statistical distance between the ability estimate at the next step and the full-test ability estimate. This viewpoint of CAT naturally motivates a stochastic selection procedure that equates choosing the next item to sampling from a model-averaging ensemble ability model. Using the NIH Work Disability Functional Assessment Battery (WD-FAB), we evaluate our new methods in comparison to pre-existing methods found in the literature. We find that our stochastic selector has superior properties in terms of both item exposure and test accuracy/efficiency.
Functional amyloid fibrils, once primarily associated with amyloidosis, are now recognized for their exceptional potential as biomaterials due to their unique structural features, including remarkable mechanical strength, high stability, and self-assembly capabilities. This review highlights their transformative applications across a wide range of industries, from cutting-edge drug delivery systems and next-generation biosensors to tissue engineering, surface technologies, energy storage, and environmental solutions. Their versatility extends into innovative sectors like information transfer systems, cell adhesion, protein fusion, food technology, and novel catalytic systems. Despite significant progress, critical gaps remain in the research. This review not only consolidates current applications but also underscores the vast potential for future advancements, positioning functional amyloids as key players in emerging biomaterials technologies.
Estimates of Rotation Periods for Jupiter Trojans with the Zwicky Transient Facility Photometric Lightcurves
2025-04-22
Zhuofu, Li, Yasin A. Chowdhury, Željko Ivezić, Ashish Mahabal, Ari Heinze, Lynne Jones, Mercedes S. Thompson, Eric Bellm, Mario Jurić, Andrew J. Connolly, Bryce Bolin, Frank J. Masci, Avery Wold, Reed L. Riddle, Richard G. Dekany
We present new rotational period estimates for 216 Jupiter Trojans using photometric data from the Zwicky Transient Facility (ZTF), including 80 Trojans with previously unknown periods. Our analysis reveals rotation periods ranging from 4.6 hours to 447.8 hours. These results support the existence of a spin barrier for Trojans larger than 10 km, with periods clustering between 4 and 4.8 hours. This spin barrier is roughly twice as long as that observed for main-belt asteroids, suggesting that Jupiter Trojans have significantly lower bulk densities, likely due to a higher fraction of ices and volatile materials in their composition. We identify three new Trojans with reliable rotation periods near the spin barrier, doubling the number of known Trojans in this critical period range. Using these results, we estimate a mean density of approximately 0.52 g/cm^3 for rubble-pile Trojans. Our findings support the growing evidence that many Trojans are rubble-pile bodies with distinct physical properties compared to main-belt asteroids. Looking forward, we anticipate that data from the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will provide rotational period estimates for several hundred thousand Trojans, down to objects as small as 1 km, enabling a more detailed investigation of their rotational properties and internal structure.
Learning-Based Two-Way Communications: Algorithmic Framework and Comparative Analysis
2025-04-22
David R. Nickel, Anindya Bijoy Das, David J. Love, Christopher G. Brinton
Machine learning (ML)-based feedback channel coding has garnered significant research interest in the past few years. However, there has been limited research exploring ML approaches in the so-called "two-way" setting where two users jointly encode messages and feedback for each other over a shared channel. In this work, we present a general architecture for ML-based two-way feedback coding, and show how several popular one-way schemes can be converted to the two-way setting through our framework. We compare such schemes against their one-way counterparts, revealing error-rate benefits of ML-based two-way coding in certain signal-to-noise ratio (SNR) regimes. We then analyze the tradeoffs between error performance and computational overhead for three state-of-the-art neural network coding models instantiated in the two-way paradigm.
2025-04-22
Shahan Ali Memon, Jevin D. West, Cailin O'Connor
Failures of retraction are common in science. Why do these failures occur? And, relatedly, what makes findings harder or easier to retract? We use data from Microsoft Academic Graph, Retraction Watch, and Altmetric -- including retracted papers, citation records, and Altmetric scores and mentions -- to test recently proposed answers to these questions. A recent previous study by LaCroix et al. employ simple network models to argue that the social spread of scientific information helps explain failures of retraction. One prediction of their models is that widely known or well established results, surprisingly, should be easier to retract, since their retraction is more relevant to more scientists. Our results support this conclusion. We find that highly cited papers show more significant reductions in citation after retraction and garner more attention to their retractions as they occur.
Improving Automated Secure Code Reviews: A Synthetic Dataset for Code Vulnerability Flaws
2025-04-22
Leonardo Centellas-Claros, Juan J. Alonso-Lecaros, Juan Pablo Sandoval Alcocer, Andres Neyem
Automation of code reviews using AI models has garnered substantial attention in the software engineering community as a strategy to reduce the cost and effort associated with traditional peer review processes. These models are typically trained on extensive datasets of real-world code reviews that address diverse software development concerns, including testing, refactoring, bug fixes, performance optimization, and maintainability improvements. However, a notable limitation of these datasets is the under representation of code vulnerabilities, critical flaws that pose significant security risks, with security-focused reviews comprising a small fraction of the data. This scarcity of vulnerability-specific data restricts the effectiveness of AI models in identifying and commenting on security-critical code. To address this issue, we propose the creation of a synthetic dataset consisting of vulnerability-focused reviews that specifically comment on security flaws. Our approach leverages Large Language Models (LLMs) to generate human-like code review comments for vulnerabilities, using insights derived from code differences and commit messages. To evaluate the usefulness of the generated synthetic dataset, we plan to use it to fine-tune three existing code review models. We anticipate that the synthetic dataset will improve the performance of the original code review models.
Subthreshold Jitter in VR Can Induce Visual Discomfort
2025-04-22
Samuel J. Levulis, Kevin W. Rio, Pablo Ramon Soria, James Wilmott, Charlie S. Burlingham, Phillip Guan
Visual-vestibular conflicts (VVCs) are a primary contributor to visually induced motion sickness (VIMS) in head-mounted displays (HMDs). However, virtual reality (VR) comfort studies often rely on exposing seated or standing users to experiences with high intensity visual motion (such as roller coasters). These drastic VVCs tend to induce pronounced VIMS symptoms that can be reliably detected across individuals using common survey measures. The conclusions from studies using these extreme motion-based conflicts may not accurately generalize to naturalistic use cases in VR where efforts are made to minimize, rather than maximize, VIMS symptoms. In this work, we show that a subthreshold visual-vestibular conflict can induce measurable discomfort during naturalistic, long duration use. We first present a psychophysical study, conducted outside of an HMD, to rigorously identify the perceptual thresholds for sinusoidal noise in render pose (i.e., jitter) resulting in erroneous 3D motion of rendered content. We next introduce subthreshold levels of jitter to a Meta Quest 3 VR HMD and demonstrate that this can induce visual discomfort in participants playing the commercially-available game Cubism across a three-session, repeated-measures study. Importantly, we did not identify statistically significant comfort differences between control and jitter conditions with traditional pre- and post-test comparison of Simulator Sickness Questionnaire (SSQ) scores. Significant differences were only identified using the Motion Illness Symptoms Classification (MISC) survey administered every 10 minutes across each 90 minute session. This highlights the benefits of incorporating time-resolved data points and suggests that lightweight, more frequent surveys may be important tools for measuring visual discomfort in more ecologically-valid scenarios.
CHIME All-sky Multiday Pulsar Stacking Search (CHAMPSS): System Overview and First Discoveries
2025-04-22
The CHAMPSS Collaboration, Christopher Andrade, P. J. Boyle, Charanjot Brar, Alyssa Cassity, Kathryn Crowter, Davor Cubranic, Abigail K. Denney, Fengqiu Adam Dong, Emmanuel Fonseca, Ajay Kumar, Lars Künkel, Magnus L'Argent, Dustin Lang, Robert A. Main, Kiyoshi W. Masui, Sujay Mate, Juan Mena-Parra, Bradley W. Meyers, Cherry Ng, Aaron B. Pearlman, Ue-Li Pen, Scott M. Ransom, Alexander P. Roman, Kendrick Smith, Reynier Squillace, Ingrid Stairs, Chia Min Tan, Laurent Tarabout, Xia Wenke, Tarik J. Zegmott
We describe the CHIME All-sky Multiday Pulsar Stacking Search (CHAMPSS) project. This novel radio pulsar survey revisits the full Northern Sky daily, offering unprecedented opportunity to detect highly intermittent pulsars, as well as faint sources via long-term data stacking. CHAMPSS uses the CHIME/FRB datastream, which consists of 1024 stationary beams streaming intensity data at $0.983$\,ms resolution, 16384 frequency channels across 400--800\,MHz, continuously being searched for single, dispersed bursts/pulses. In CHAMPSS, data from adjacent east-west beams are combined to form a grid of tracking beams, allowing longer exposures at fixed positions. These tracking beams are dedispersed to many trial dispersion measures (DM) to a maximum DM beyond the Milky Way's expected contribution, and Fourier transformed in time to form power spectra. Repeated observations are searched daily to find intermittent sources, and power spectra of the same sky positions are incoherently stacked, increasing sensitivity to faint persistent sources. The $0.983$\,ms time resolution limits our sensitivity to millisecond pulsars; we have full sensitivity to pulsars with $P > 60\,$ms, with sensitivity gradually decreasing from $60$ ms to $2$\,ms as higher harmonics are beyond the Nyquist limit. In a commissioning survey, data covering $\sim 1/16$ of the CHIME sky was processed and searched in quasi-realtime over two months, leading to the discovery of eleven new pulsars, each with $S_{600} > 0.1$\,mJy. When operating at scale, CHAMPSS will stack $>$1\,year of data along each sightline, reaching a sensitivity of $\lesssim 30\, \mu$Jy for all sightlines above a declination of $10^{\circ}$, and off of the Galactic plane.
Deep, data-driven modeling of room acoustics: literature review and research perspectives
2025-04-22
Toon van Waterschoot
Our everyday auditory experience is shaped by the acoustics of the indoor environments in which we live. Room acoustics modeling is aimed at establishing mathematical representations of acoustic wave propagation in such environments. These representations are relevant to a variety of problems ranging from echo-aided auditory indoor navigation to restoring speech understanding in cocktail party scenarios. Many disciplines in science and engineering have recently witnessed a paradigm shift powered by deep learning (DL), and room acoustics research is no exception. The majority of deep, data-driven room acoustics models are inspired by DL-based speech and image processing, and hence lack the intrinsic space-time structure of acoustic wave propagation. More recently, DL-based models for room acoustics that include either geometric or wave-based information have delivered promising results, primarily for the problem of sound field reconstruction. In this review paper, we will provide an extensive and structured literature review on deep, data-driven modeling in room acoustics. Moreover, we position these models in a framework that allows for a conceptual comparison with traditional physical and data-driven models. Finally, we identify strengths and shortcomings of deep, data-driven room acoustics models and outline the main challenges for further research.
Environmental Dependence of X-Ray Emission From The Least Massive Galaxies
2025-04-22
Marko Mićić, Xinyu Dai, Nick Shumate, Khoa Nguyen Tran, Heechan Yuk
The low-mass end of low-mass galaxies is largely unexplored in AGN studies, but it is essential for extending our understanding of the black hole-galaxy coevolution. We surveyed the 3D-HST catalog and collected a sample of 546 dwarf galaxies with stellar masses log(M$_*$/\(M_\odot\))$<$8.7, residing in the GOODS-South deep field. We then used the unprecedented depth of Chandra available in the GOODS-South field to search for AGN. We carefully investigated the factors that could play roles in the AGN detectability, such as Chandra's point-spread function and the redshift- and off-axis-dependent detection limits. We identified 16 X-ray sources that are likely associated with AGN activity. Next, we evaluated the environment density of each galaxy by computing tidal indices. We uncovered a dramatic impact of the environment on AGN triggering as dwarfs from high-density environments showed an AGN fraction of 22.5\%, while the median stellar mass of this subset of dwarfs is only log(M$_*$/\(M_\odot\))=8.1. In contrast, the low-density environment dwarfs showed an AGN fraction of only 1.4\%, in line with typically reported values from the literature. This highlights the fact that massive central black holes are ubiquitous even at the lowest mass scales and demonstrates the importance of the environment in triggering black hole accretion, as well as the necessity for deep X-ray data and proper evaluation of the X-ray data quality. Alternatively, even if the detected X-ray sources are related to stellar mass accretors rather than AGN, the environmental dependence persists, signaling the impact of the environment on galaxy evolution and star formation processes at the lowest mass scales. Additionally, we stacked the X-ray images of non-detected galaxies from high- and low-density environments, revealing similar trends.
2025-04-22
Jannis Born, Filip Skogh, Kahn Rhrissorrakrai, Filippo Utro, Nico Wagner, Aleksandros Sobczyk
At the core of the Transformer, the Softmax normalizes the attention matrix to be right stochastic. Previous research has shown that this often destabilizes training and that enforcing the attention matrix to be doubly stochastic (through Sinkhorn's algorithm) consistently improves performance across different tasks, domains and Transformer flavors. However, Sinkhorn's algorithm is iterative, approximative, non-parametric and thus inflexible w.r.t. the obtained doubly stochastic matrix (DSM). Recently, it has been proven that DSMs can be obtained with a parametric quantum circuit, yielding a novel quantum inductive bias for DSMs with no known classical analogue. Motivated by this, we demonstrate the feasibility of a hybrid classical-quantum doubly stochastic Transformer (QDSFormer) that replaces the Softmax in the self-attention layer with a variational quantum circuit. We study the expressive power of the circuit and find that it yields more diverse DSMs that better preserve information than classical operators. Across multiple small-scale object recognition tasks, we find that our QDSFormer consistently surpasses both a standard Vision Transformer and other doubly stochastic Transformers. Beyond the established Sinkformer, this comparison includes a novel quantum-inspired doubly stochastic Transformer (based on QR decomposition) that can be of independent interest. The QDSFormer also shows improved training stability and lower performance variation suggesting that it may mitigate the notoriously unstable training of ViTs on small-scale data.
Gravitationally lensed quasars offer a unique opportunity to study cosmological and extragalactic phenomena, using reliable light curves of the lensed images. This requires accurate deblending of the quasar images, which is not trivial due to the small separation between the lensed images (typically $\sim1$ arcsec) and because there is light contamination by the lensing galaxy and the quasar host galaxy. We propose a series of experiments aimed at testing our ability to extract precise and accurate photometry of lensed quasars. In this first paper, we focus on evaluating our ability to extract light curves from simulated CCD images of lensed quasars spanning a broad range of configurations and assuming different observational/instrumental conditions. Specifically, the experiment proposes to go from pixels to light curves and to evaluate the limits of current photometric algorithms. Our experiment has several steps, from data with known point spread function (PSF), to an unknown spatially-variable PSF field that the user has to take into account. This paper is the release of our simulated images. Anyone can extract the light curves and submit their results by the deadline. These will be evaluated with the metrics described below. Our set of simulations will be public and it is meant to be a benchmark for time-domain surveys like Rubin-LSST or other follow-up time-domain observations at higher temporal cadence. It is also meant to be a test set to help develop new algorithms in the future.
As machine learning increasingly influences critical domains such as credit underwriting, public policy, and talent acquisition, ensuring compliance with fairness constraints is both a legal and ethical imperative. This paper introduces a novel framework for fairness adjustments that applies to diverse machine learning tasks, including regression and classification, and accommodates a wide range of fairness metrics. Unlike traditional approaches categorized as pre-processing, in-processing, or post-processing, our method adapts in-processing techniques for use as a post-processing step. By decoupling fairness adjustments from the model training process, our framework preserves model performance on average while enabling greater flexibility in model development. Key advantages include eliminating the need for custom loss functions, enabling fairness tuning using different datasets, accommodating proprietary models as black-box systems, and providing interpretable insights into the fairness adjustments. We demonstrate the effectiveness of this approach by comparing it to Adversarial Debiasing, showing that our framework achieves a comparable fairness/accuracy tradeoff on real-world datasets.
We give a brief, incomplete, and idiosyncratic review of the early years of supergravity in superspace as our contribution to the book Half a Century of Supergravity edited by Anna Ceresole and Gianguido Dall'Agata.
Predictions for the Detectability of Milky Way Satellite Galaxies and Outer-Halo Star Clusters with the Vera C. Rubin Observatory
2025-04-22
Kabelo Tsiane, Sidney Mau, Alex Drlica-Wagner, Jeffrey L. Carlin, Peter S. Ferguson, Keith Bechtol, Ethan O. Nadler, Annika H. G. Peter, Yao-Yuan Mao, Adam J. Thornton
We predict the sensitivity of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) to faint, resolved Milky Way satellite galaxies and outer-halo star clusters. We characterize the expected sensitivity using simulated LSST data from the LSST Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) accessed and analyzed with the Rubin Science Platform as part of the Rubin Early Science Program. We simulate resolved stellar populations of Milky Way satellite galaxies and outer-halo star clusters over a wide range of sizes, luminosities, and heliocentric distances, which are broadly consistent with expectations for the Milky Way satellite system. We inject simulated stars into the DC2 catalog with realistic photometric uncertainties and star/galaxy separation derived from the DC2 data itself. We assess the probability that each simulated system would be detected by LSST using a conventional isochrone matched-filter technique. We find that assuming perfect star/galaxy separation enables the detection of resolved stellar systems with $M_V$ = 0 mag and $r_{1/2}$ = 10 pc with >50% efficiency out to a heliocentric distance of ~250 kpc. Similar detection efficiency is possible with a simple star/galaxy separation criterion based on measured quantities, although the false positive rate is higher due to leakage of background galaxies into the stellar sample. When assuming perfect star/galaxy classification and a model for the galaxy-halo connection fit to current data, we predict that 89 +/- 20 Milky Way satellite galaxies will be detectable with a simple matched-filter algorithm applied to the LSST wide-fast-deep data set. Different assumptions about the performance of star/galaxy classification efficiency can decrease this estimate by ~75-25%, which emphasizes the importance of high-quality star/galaxy separation for studies of the Milky Way satellite population with LSST.
Context: Jupyter Notebook has emerged as a versatile tool that transforms how researchers, developers, and data scientists conduct and communicate their work. As the adoption of Jupyter notebooks continues to rise, so does the interest from the software engineering research community in improving the software engineering practices for Jupyter notebooks. Objective: The purpose of this study is to analyze trends, gaps, and methodologies used in software engineering research on Jupyter notebooks. Method: We selected 146 relevant publications from the DBLP Computer Science Bibliography up to the end of 2024, following established systematic literature review guidelines. We explored publication trends, categorized them based on software engineering topics, and reported findings based on those topics. Results: The most popular venues for publishing software engineering research on Jupyter notebooks are related to human-computer interaction instead of traditional software engineering venues. Researchers have addressed a wide range of software engineering topics on notebooks, such as code reuse, readability, and execution environment. Although reusability is one of the research topics for Jupyter notebooks, only 64 of the 146 studies can be reused based on their provided URLs. Additionally, most replication packages are not hosted on permanent repositories for long-term availability and adherence to open science principles. Conclusion: Solutions specific to notebooks for software engineering issues, including testing, refactoring, and documentation, are underexplored. Future research opportunities exist in automatic testing frameworks, refactoring clones between notebooks, and generating group documentation for coherent code cells.
FPGA-Based Neural Network Accelerators for Space Applications: A Survey
2025-04-22
Pedro Antunes, Artur Podobas
Space missions are becoming increasingly ambitious, necessitating high-performance onboard spacecraft computing systems. In response, field-programmable gate arrays (FPGAs) have garnered significant interest due to their flexibility, cost-effectiveness, and radiation tolerance potential. Concurrently, neural networks (NNs) are being recognized for their capability to execute space mission tasks such as autonomous operations, sensor data analysis, and data compression. This survey serves as a valuable resource for researchers aiming to implement FPGA-based NN accelerators in space applications. By analyzing existing literature, identifying trends and gaps, and proposing future research directions, this work highlights the potential of these accelerators to enhance onboard computing systems.
Leveraging Social Media Analytics for Sustainability Trend Detection in Saudi Arabias Evolving Market
2025-04-22
Kanwal Aalijah
Saudi Arabias rapid economic growth and social evolution under Vision 2030 present a unique opportunity to track emerging trends in real time. Uncovering trends in real time can open up new avenues for business and investment opportunities. This paper explores how AI and social media analytics can uncover and monitor these trends across sectors like sustainability, construction, food beverages industry, tourism, technology, and entertainment. This paper focus on use of AI-driven methodology to identify sustainability trends across Saudi Arabia. We processed millions of social media posts, news, blogs in order to understand sustainability trends in the region. The paper presents an AI approach that can help economists, businesses, government to understand sustainability trends and make better decisions around them. This approach offers both sector-specific and cross-sector insights, giving decision-makers a reliable, up to date snapshot of Saudi Arabias market shifts. Beyond Saudi Arabia, this framework also shows potential for adapting to other regions. Overall, our findings highlight how by using AI-methodologies, give decision makers a reliable method to understand how initiatives are perceived and adopted by the public and understand growth of trends.
Heterogeneous networks in drug-target interaction prediction
2025-04-22
Mohammad Molaee, Nasrollah Moghadam Charkari
Drug discovery requires a tremendous amount of time and cost. Computational drug-target interaction prediction, a significant part of this process, can reduce these requirements by narrowing the search space for wet lab experiments. In this survey, we provide comprehensive details of graph machine learning-based methods in predicting drug-target interaction, as they have shown promising results in this field. These details include the overall framework, main contribution, datasets, and their source codes. The selected papers were mainly published from 2020 to 2024. Prior to discussing papers, we briefly introduce the datasets commonly used with these methods and measurements to assess their performance. Finally, future challenges and some crucial areas that need to be explored are discussed.
Deep Learning Meets Process-Based Models: A Hybrid Approach to Agricultural Challenges
2025-04-22
Yue Shi, Liangxiu Han, Xin Zhang, Tam Sobeih, Thomas Gaiser, Nguyen Huu Thuy, Dominik Behrend, Amit Kumar Srivastava, Krishnagopal Halder, Frank Ewert
Process-based models (PBMs) and deep learning (DL) are two key approaches in agricultural modelling, each offering distinct advantages and limitations. PBMs provide mechanistic insights based on physical and biological principles, ensuring interpretability and scientific rigour. However, they often struggle with scalability, parameterisation, and adaptation to heterogeneous environments. In contrast, DL models excel at capturing complex, nonlinear patterns from large datasets but may suffer from limited interpretability, high computational demands, and overfitting in data-scarce scenarios. This study presents a systematic review of PBMs, DL models, and hybrid PBM-DL frameworks, highlighting their applications in agricultural and environmental modelling. We classify hybrid PBM-DL approaches into DL-informed PBMs, where neural networks refine process-based models, and PBM-informed DL, where physical constraints guide deep learning predictions. Additionally, we conduct a case study on crop dry biomass prediction, comparing hybrid models against standalone PBMs and DL models under varying data quality, sample sizes, and spatial conditions. The results demonstrate that hybrid models consistently outperform traditional PBMs and DL models, offering greater robustness to noisy data and improved generalisation across unseen locations. Finally, we discuss key challenges, including model interpretability, scalability, and data requirements, alongside actionable recommendations for advancing hybrid modelling in agriculture. By integrating domain knowledge with AI-driven approaches, this study contributes to the development of scalable, interpretable, and reproducible agricultural models that support data-driven decision-making for sustainable agriculture.
Enhancing Trust Through Standards: A Comparative Risk-Impact Framework for Aligning ISO AI Standards with Global Ethical and Regulatory Contexts
2025-04-22
Sridharan Sankaran
As artificial intelligence (AI) reshapes industries and societies, ensuring its trustworthiness-through mitigating ethical risks like bias, opacity, and accountability deficits-remains a global challenge. International Organization for Standardization (ISO) AI standards, such as ISO/IEC 24027 and 24368, aim to foster responsible development by embedding fairness, transparency, and risk management into AI systems. However, their effectiveness varies across diverse regulatory landscapes, from the EU's risk-based AI Act to China's stability-focused measures and the U.S.'s fragmented state-led initiatives. This paper introduces a novel Comparative Risk-Impact Assessment Framework to evaluate how well ISO standards address ethical risks within these contexts, proposing enhancements to strengthen their global applicability. By mapping ISO standards to the EU AI Act and surveying regulatory frameworks in ten regions-including the UK, Canada, India, Japan, Singapore, South Korea, and Brazil-we establish a baseline for ethical alignment. The framework, applied to case studies in the EU, US-Colorado, and China, reveals gaps: voluntary ISO standards falter in enforcement (e.g., Colorado) and undervalue region-specific risks like privacy (China). We recommend mandatory risk audits, region-specific annexes, and a privacy-focused module to enhance ISO's adaptability. This approach not only synthesizes global trends but also offers a replicable tool for aligning standardization with ethical imperatives, fostering interoperability and trust in AI worldwide. Policymakers and standards bodies can leverage these insights to evolve AI governance, ensuring it meets diverse societal needs as the technology advances.
A Short History of (Orbital) Decay: Roman's Prospects for Detecting Dying Planets
2025-04-21
Kylee Carden, B. Scott Gaudi, Robert F. Wilson
The Roman Space Telescope Galactic Bulge Time Domain Survey (GBTDS) is expected to detect ~10^5 transiting planets. Many of these planets will have short orbital periods and are thus susceptible to tidal decay. We use a catalog of simulated transiting planet detections to predict the yield of orbital decay detections in the Roman GBTDS. Assuming a constant stellar tidal dissipation factor, Q^{'}_{*}, of 10^6, we predict ~ 5 - 10 detections. We additionally consider an empirical period-dependent parameterization of Q^{'}_{*} \propto P^{-3} and find a substantially suppressed yield. We conclude that Roman will provide constraints on the rate of planet engulfment in the Galaxy and probe the physics of tidal dissipation in stars.
Revealing the 3D Cosmic Web through Gravitationally Constrained Neural Fields
2025-04-21
Brandon Zhao, Aviad Levis, Liam Connor, Pratul P. Srinivasan, Katherine L. Bouman
Weak gravitational lensing is the slight distortion of galaxy shapes caused primarily by the gravitational effects of dark matter in the universe. In our work, we seek to invert the weak lensing signal from 2D telescope images to reconstruct a 3D map of the universe's dark matter field. While inversion typically yields a 2D projection of the dark matter field, accurate 3D maps of the dark matter distribution are essential for localizing structures of interest and testing theories of our universe. However, 3D inversion poses significant challenges. First, unlike standard 3D reconstruction that relies on multiple viewpoints, in this case, images are only observed from a single viewpoint. This challenge can be partially addressed by observing how galaxy emitters throughout the volume are lensed. However, this leads to the second challenge: the shapes and exact locations of unlensed galaxies are unknown, and can only be estimated with a very large degree of uncertainty. This introduces an overwhelming amount of noise which nearly drowns out the lensing signal completely. Previous approaches tackle this by imposing strong assumptions about the structures in the volume. We instead propose a methodology using a gravitationally-constrained neural field to flexibly model the continuous matter distribution. We take an analysis-by-synthesis approach, optimizing the weights of the neural network through a fully differentiable physical forward model to reproduce the lensing signal present in image measurements. We showcase our method on simulations, including realistic simulated measurements of dark matter distributions that mimic data from upcoming telescope surveys. Our results show that our method can not only outperform previous methods, but importantly is also able to recover potentially surprising dark matter structures.
Population Models for Star Formation Timescales in Early Galaxies: The First Step Towards Solving Outshining in Star Formation History Inference
2025-04-21
Bingjie Wang, Joel Leja, Hakim Atek, Rachel Bezanson, Emilie Burnham, Pratika Dayal, Robert Feldmann, Jenny E. Greene, Benjamin D. Johnson, Ivo Labbe, Michael V. Maseda, Themiya Nanayakkara, Sedona H. Price, Katherine A. Suess, John R. Weaver, Katherine E. Whitaker
JWST have revealed temporarily-quenched and ultraviolet-luminous galaxies in the early universe, suggesting enhanced star formation stochasticity. Verifying this hypothesis is critical, yet challenging; outshining, wherein light from young stars dominates the spectral energy distribution, represents perhaps the greatest challenge in inferring the formation histories of unresolved galaxies. In this paper, we take a simple model of burstiness and show that state-of-the-art inference methods with flexible star formation histories (SFHs) and neutral priors, while recovering average star formation rates (SFRs; $\sim0.1$ dex median offset), fail to recover the complexities of fluctuations on tens of Myr timescales, and typically underestimate masses in bursty systems ($\sim0.15$ dex). Surprisingly, detailed SFH recovery is still sensitive to priors even when data quality is optimal, e.g., including high signal-to-noise ($\rm20~pixel^{-1}$) spectroscopy with wide coverage (rest-frame $0.12-1.06~\mu$m). Crucially, however, refitting the same data with a prior correctly encoding the bursty expectation eliminates these biases: median offsets in mass and SFRs decrease to $\sim 0.04$ dex and $\sim 0.05$ dex, respectively. Under the assumption that current population burstiness predicts past SFH, the solution to outshining in modeling statistical samples is empirically measuring recent galaxy SFHs with population modeling. A prototype is H$\alpha$/UV: while helpful, it is insufficient to constrain the expected complex burstiness. To this end, we introduce a more complete, quantitative population-level approach and demonstrate that it promises to recover the typical amplitude, timescale, and slope of the recent SFH to high accuracy. This approach thus has the strong potential to solve outshining using observations from JWST.
Searching for Compact Obscured Nuclei in Compton Thick AGN
2025-04-21
Makoto A. Johnstone, George C. Privon, Loreto Barcos-Munoz, A. S. Evans, S. Aalto, Lee Armus, Franz E. Bauer, L. Blecha, J. S. Gallagher, S. König, Claudio Ricci, Ezequiel Treister, Cosima Eibensteiner, Kimblerly L. Emig, Kara N. Green, Devaky Kunneriath, Jaya Nagarajan-Swenson, Alejandro Saravia, Ilsang Yoon
Compact Obscured Nuclei (CONs) are heavily obscured infrared cores that have been found in local (ultra)luminous infrared galaxies (U/LIRGs). They show bright emission from vibrationally excited rotational transitions of HCN, known as HCN-vib, and are thought to harbor Compton Thick (CT, $N_{\text{H}} \geq 10^{24}$ cm$^{-2}$) active galactic nuclei (AGN) or extreme compact starbursts. We explore the potential evolutionary link between CONs and CT AGN by searching for CONs in hard X-ray-confirmed CT AGN from the Great Observatories All-sky LIRG Survey (GOALS). Here, we present new Atacama Large Millimeter/submillimeter Array Band 6 observations that targeted HCN-vib emission in four hard X-ray-confirmed CT AGN. We analyze these objects together with literature HCN-vib measurements of five additional hard X-ray-confirmed CT AGN from the GOALS sample. We do not detect any CONs in this combined sample of nine CT AGN. We then explore a proposed evolutionary sequence in which CONs evolve into X-ray-detectable CT AGN once outflows and feedback reduce the column densities of the enshrouding gas. We find, however, no evidence of well-developed dense molecular outflows in the observed CT AGN. While this could suggest that CT AGN are not universally linked to CONs, it could also be explained by a short duty cycle for molecular outflows.
Directed Acyclic Graph (DAG)-based Distributed Ledger Technologies (DLTs) have emerged as a promising solution to the scalability issues inherent in traditional blockchains. However, amidst the focus on scalability, the crucial aspect of privacy within DAG-based DLTs has been largely overlooked. This paper seeks to address this gap by providing a comprehensive examination of privacy notions and challenges within DAG-based DLTs. We delve into potential methodologies to enhance privacy within these systems, while also analyzing the associated hurdles and real-world implementations within state-of-the-art DAG-based DLTs. By exploring these methodologies, we not only illuminate the current landscape of privacy in DAG-based DLTs but also outline future research directions in this evolving field.
Existing Industry Practice for the EU AI Act's General-Purpose AI Code of Practice Safety and Security Measures
2025-04-21
Lily Stelling, Mick Yang, Rokas Gipiškis, Leon Staufer, Ze Shen Chin, Siméon Campos, Michael Chen
This report provides a detailed comparison between the measures proposed in the EU AI Act's General-Purpose AI (GPAI) Code of Practice (Third Draft) and current practices adopted by leading AI companies. As the EU moves toward enforcing binding obligations for GPAI model providers, the Code of Practice will be key to bridging legal requirements with concrete technical commitments. Our analysis focuses on the draft's Safety and Security section which is only relevant for the providers of the most advanced models (Commitments II.1-II.16) and excerpts from current public-facing documents quotes that are relevant to each individual measure. We systematically reviewed different document types - including companies' frontier safety frameworks and model cards - from over a dozen companies, including OpenAI, Anthropic, Google DeepMind, Microsoft, Meta, Amazon, and others. This report is not meant to be an indication of legal compliance nor does it take any prescriptive viewpoint about the Code of Practice or companies' policies. Instead, it aims to inform the ongoing dialogue between regulators and GPAI model providers by surfacing evidence of precedent.
The training of artificial neural networks is heavily dependent on the careful selection of an appropriate loss function. While commonly used loss functions, such as cross-entropy and mean squared error (MSE), generally suffice for a broad range of tasks, challenges often emerge due to limitations in data quality or inefficiencies within the learning process. In such circumstances, the integration of supplementary terms into the loss function can serve to address these challenges, enhancing both model performance and robustness. Two prominent techniques, loss regularization and contrastive learning, have been identified as effective strategies for augmenting the capacity of loss functions in artificial neural networks. Knowledge tracing is a compelling area of research that leverages predictive artificial intelligence to facilitate the automation of personalized and efficient educational experiences for students. In this paper, we provide a comprehensive review of the deep learning-based knowledge tracing (DKT) algorithms trained using advanced loss functions and discuss their improvements over prior techniques. We discuss contrastive knowledge tracing algorithms, such as Bi-CLKT, CL4KT, SP-CLKT, CoSKT, and prediction-consistent DKT, providing performance benchmarks and insights into real-world deployment challenges. The survey concludes with future research directions, including hybrid loss strategies and context-aware modeling.
Landmark-Free Preoperative-to-Intraoperative Registration in Laparoscopic Liver Resection
2025-04-21
Jun Zhou, Bingchen Gao, Kai Wang, Jialun Pei, Pheng-Ann Heng, Jing Qin
Liver registration by overlaying preoperative 3D models onto intraoperative 2D frames can assist surgeons in perceiving the spatial anatomy of the liver clearly for a higher surgical success rate. Existing registration methods rely heavily on anatomical landmark-based workflows, which encounter two major limitations: 1) ambiguous landmark definitions fail to provide efficient markers for registration; 2) insufficient integration of intraoperative liver visual information in shape deformation modeling. To address these challenges, in this paper, we propose a landmark-free preoperative-to-intraoperative registration framework utilizing effective self-supervised learning, termed \ourmodel. This framework transforms the conventional 3D-2D workflow into a 3D-3D registration pipeline, which is then decoupled into rigid and non-rigid registration subtasks. \ourmodel~first introduces a feature-disentangled transformer to learn robust correspondences for recovering rigid transformations. Further, a structure-regularized deformation network is designed to adjust the preoperative model to align with the intraoperative liver surface. This network captures structural correlations through geometry similarity modeling in a low-rank transformer network. To facilitate the validation of the registration performance, we also construct an in-vivo registration dataset containing liver resection videos of 21 patients, called \emph{P2I-LReg}, which contains 346 keyframes that provide a global view of the liver together with liver mask annotations and calibrated camera intrinsic parameters. Extensive experiments and user studies on both synthetic and in-vivo datasets demonstrate the superiority and potential clinical applicability of our method.
I present my perspective on sensing with quantum light. I summarise the motivations and methodology for identifying quantum enhancements in sensing over a classical sensor. In the real world, this enhancement will be a constant factor, and not increase with the size of the quantum probe as is often advertised. I use a limited survey of interferometry, microscopy, and spectroscopy to extract the vital challenges that must be faced to realise tangible enhancements in sensing with quantum light.
The 21-cm forest offers a powerful cosmological probe of the thermal history and small-scale structure of the intergalactic medium during the Epoch of Reionization (EoR). Its success, however, critically depends on the availability of high-redshift radio-loud quasars (HzRLQs) as background sources. In this work, we investigate the configuration requirements for a Moon-based low-frequency radio interferometer aimed at maximizing the detection of HzRLQs for future 21-cm forest studies. Building upon a previously developed quasar luminosity function (QLF), we forecast HzRLQ abundances under various array configurations. Assuming a total survey area of $10^4\,\mathrm{deg}^2$ and 1 year of observation, we compare continuum surveys with 10 MHz bandwidth and 21-cm forest surveys with 5 kHz resolution. Our results show that a minimum collecting area of $\sim$6 500 m$^2$ enables detection at $z \sim 6$, while SKA-like arrays ($N_{\mathrm{st}} = 512$) extend the detection limit to $z \sim 10$ for 21-cm forest survey and $z \sim 16$ for continuum survey. Larger arrays with $N_{\mathrm{st}} = 2048$ can reach $z \sim 11$ in 21-cm forest mode. We also explore configurations that maintain fixed collecting areas while increasing the number to enhance survey efficiency. This boosts source detection but significantly increases the data volume and computational demands. These results underscore the importance of optimizing array design for different survey goals and balancing sensitivity, spectral resolution, and data management. A well-designed Moon-based array could open a new observational window on reionization and early cosmic structure formation.
A survey on asymptotic equilibrium distribution of zeros of random holomorphic sections
2025-04-21
George Marinescu, Duc-Viet Vu
This is a survey of results concerning the asymptotic equilibrium distribution of zeros of random holomorphic polynomials and holomorphic sections of high powers of a positive line bundle, as related to the authors' recent work. Our primary focus is on the role of pluripotential theory in this research area.
Failure of the comformal-map method for relativistic quantum billiards
2025-04-21
Barbara Dietz
We demonstrate that the conformal-map method introduced by Robnik in 1984 for nonrelativistic quantum billiards is not applicable for the quantization of relativistic neutrino billiards (NBs) consisting of a massless non-interacting spin-1/2 particle confined to a two-dimensional domain. To be precise, we demonstrate in this work, that this method does not provide solutions of the associated Weyl (Dirac) equation, nor does it fulfill the boundary conditions imposed on the spinor eigenfunctions to ensure confinement of the particle to the domain of the billiard. We review in detail the wave equation, boundary conditions and quantization of NBs and derivation of relevant equations, to make the proof comprehensible for the general reader. Our results are corroborated with numerical results for non-relativistic and relativistic quantum billiards whose shapes depend on a parameter, which allows the study of the properties of their eigenstates as the classical dynamics experiences a transition from regular to chaotic dynamics.
One pathogen does not an epidemic make: A review of interacting contagions, diseases, beliefs, and stories
2025-04-21
Laurent Hébert-Dufresne, Yong-Yeol Ahn, Antoine Allard, Jessica W. Crothers, Peter Sheridan Dodds, Mirta Galesic, Fakhteh Ghanbarnejad, Dominique Gravel, Ross A. Hammond, Kristina Lerman, Juniper Lovato, John J. Openshaw, S. Redner, Samuel V. Scarpino, Guillaume St-Onge, Timothy R. Tangherlini, Jean-Gabriel Young
From pathogens and computer viruses to genes and memes, contagion models have found widespread utility across the natural and social sciences. Despite their success and breadth of adoption, the approach and structure of these models remain surprisingly siloed by field. Given the siloed nature of their development and widespread use, one persistent assumption is that a given contagion can be studied in isolation, independently from what else might be spreading in the population. In reality, countless contagions of biological and social nature interact within hosts (interacting with existing beliefs, or the immune system) and across hosts (interacting in the environment, or affecting transmission mechanisms). Additionally, from a modeling perspective, we know that relaxing these assumptions has profound effects on the physics and translational implications of the models. Here, we review mechanisms for interactions in social and biological contagions, as well as the models and frameworks developed to include these interactions in the study of the contagions. We highlight existing problems related to the inference of interactions and to the scalability of mathematical models and identify promising avenues of future inquiries. In doing so, we highlight the need for interdisciplinary efforts under a unified science of contagions and for removing a common dichotomy between social and biological contagions.
Period-luminosity and period-luminosity-metallicity relation for $δ$ Scuti Stars
2025-04-21
Yan-qi Liu, Xiao-dian Chen, Shu Wang, Kun Wang, Qi Jia, Li-cai Deng
$\delta$ Scuti ($\delta$ Sct) stars are potential distance tracers for studying the Milky Way structure. We conduct a comprehensive analysis of the period-luminosity (PL) and period-luminosity-metallicity (PLZ) relation for $\delta$ Sct stars, integrating data from the Zwicky Transient Facility (ZTF), the Transiting Exoplanet Survey Satellite (TESS), Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), Apache Point Observatory Galactic Evolution Experiment (APOGEE), and Gaia. To mitigate the impact of the Gaia parallax zero point offset, we applied a correction method, determining the optimal zero point value to be $zp_\varpi = 35 \pm 2 \, \mu\text{as}$. Using the three best bands, by varying the parallax error threshold, we found that the total error of the PLR zero point was minimized to 0.9\% at a parallax error threshold of 6\%. With this threshold, we derived the PL and PLZ relation for nine bands (from optical to mid-infrared) and five Wesenheit bands. Through our analysis, we conclude that the influence of metallicity on the PLR of $\delta$ Sct stars is not significant, and the differences across various bands are minimal.
Dust-obscured Galaxies with Broken Power-law Spectral Energy Distributions Discovered by UNIONS
2025-04-21
Taketo Yoshida, Tohru Nagao, Yoshiki Toba, Akatoki Noboriguchi, Kohei Ichikawa, Hendrik Hildebrandt, Naomichi Yutani, Kenneth C. Chambers, Ryo Iwamoto, Seira Kobayashi, Masamune Oguri, Ken Osato, Kohei Shibata, Yuxing Zhong
We report on the spectral energy distributions (SEDs) of infrared-bright dust-obscured galaxies (DOGs) with $(i - [22])_{\rm AB} \geq 7.0$. Using photometry from the deep and wide Ultraviolet Near-Infrared Optical Northern Survey, combined with near-IR and mid-IR data from the UKIRT Infrared Deep Sky Survey and the Wide-field Infrared Survey Explorer, we successfully identified 382 DOGs in $\sim$ 170 deg$^2$. Among them, the vast majority (376 DOGs) were classified into two subclasses: bump DOGs (132/376) and power-law (PL) DOGs (244/376), which are dominated by star formation and active galactic nucleus (AGN), respectively. Through the SED analysis, we found that roughly half (120/244) of the PL DOGs show ``broken'' power-law SEDs. The significant red slope from optical to near-IR in the SEDs of these ``broken power-law DOGs'' (BPL DOGs) probably reflects their large amount of dust extinction. In other words, BPL DOGs are more heavily obscured AGNs, compared to PL DOGs with non-broken power-law SEDs.
Cyc3D: Fine-grained Controllable 3D Generation via Cycle Consistency Regularization
2025-04-21
Hongbin Xu, Chaohui Yu, Feng Xiao, Jiazheng Xing, Hai Ci, Weitao Chen, Ming Li
Despite the remarkable progress of 3D generation, achieving controllability, i.e., ensuring consistency between generated 3D content and input conditions like edge and depth, remains a significant challenge. Existing methods often struggle to maintain accurate alignment, leading to noticeable discrepancies. To address this issue, we propose \name{}, a new framework that enhances controllable 3D generation by explicitly encouraging cyclic consistency between the second-order 3D content, generated based on extracted signals from the first-order generation, and its original input controls. Specifically, we employ an efficient feed-forward backbone that can generate a 3D object from an input condition and a text prompt. Given an initial viewpoint and a control signal, a novel view is rendered from the generated 3D content, from which the extracted condition is used to regenerate the 3D content. This re-generated output is then rendered back to the initial viewpoint, followed by another round of control signal extraction, forming a cyclic process with two consistency constraints. \emph{View consistency} ensures coherence between the two generated 3D objects, measured by semantic similarity to accommodate generative diversity. \emph{Condition consistency} aligns the final extracted signal with the original input control, preserving structural or geometric details throughout the process. Extensive experiments on popular benchmarks demonstrate that \name{} significantly improves controllability, especially for fine-grained details, outperforming existing methods across various conditions (e.g., +14.17\% PSNR for edge, +6.26\% PSNR for sketch).
Research on the birth and evolution of life are reviewed with reference to the maximum entropy production principle (MEPP). It has been shown that this principle is essential for consistent understanding of the birth and evolution of life. First, a recent work for the birth of a self-replicative system as pre-RNA life is reviewed in relation to the MEPP. A critical condition of polymer concentration in a local system is reported by a dynamical system approach, above which, an exponential increase of entropy production is guaranteed. Secondly, research works of early stage of evolutions are reviewed; experimental research for the numbers of cells necessary for forming a multi-cellular organization, and numerical research of differentiation of a model system and its relation with MEPP. It is suggested by this review article that the late stage of evolution is characterized by formation of society and external entropy production. A hypothesis on the general route of evolution is discussed from the birth to the present life which follows the MEPP. Some examples of life which happened to face poor thermodynamic condition are presented with thermodynamic discussion. It is observed through this review that MEPP is consistently useful for thermodynamic understanding of birth and evolution of life, subject to a thermodynamic condition far from equilibrium.
A novel localization phenomenon, termed erratic non-Hermitian skin localization, has been identified in disordered globally-reciprocal non-Hermitian lattices. Unlike conventional non-Hermitian skin effect and Anderson localization, it features macroscopic eigenstate localization at irregular, disorder-dependent positions with sub-exponential decay. Using the Hatano-Nelson model with disordered imaginary gauge fields as a case study, this effect is linked to stochastic interfaces governed by the universal order statistics of random walks. Finite-size scaling analysis confirms the localized nature of the eigenstates. This discovery challenges conventional wave localization paradigms, offering new avenues for understanding and controlling localization phenomena in non-Hermitian physics.
VLM as Policy: Common-Law Content Moderation Framework for Short Video Platform
2025-04-21
Xingyu Lu, Tianke Zhang, Chang Meng, Xiaobei Wang, Jinpeng Wang, YiFan Zhang, Shisong Tang, Changyi Liu, Haojie Ding, Kaiyu Jiang, Kaiyu Tang, Bin Wen, Hai-Tao Zheng, Fan Yang, Tingting Gao, Di Zhang, Kun Gai
Exponentially growing short video platforms (SVPs) face significant challenges in moderating content detrimental to users' mental health, particularly for minors. The dissemination of such content on SVPs can lead to catastrophic societal consequences. Although substantial efforts have been dedicated to moderating such content, existing methods suffer from critical limitations: (1) Manual review is prone to human bias and incurs high operational costs. (2) Automated methods, though efficient, lack nuanced content understanding, resulting in lower accuracy. (3) Industrial moderation regulations struggle to adapt to rapidly evolving trends due to long update cycles. In this paper, we annotate the first SVP content moderation benchmark with authentic user/reviewer feedback to fill the absence of benchmark in this field. Then we evaluate various methods on the benchmark to verify the existence of the aforementioned limitations. We further propose our common-law content moderation framework named KuaiMod to address these challenges. KuaiMod consists of three components: training data construction, offline adaptation, and online deployment & refinement. Leveraging large vision language model (VLM) and Chain-of-Thought (CoT) reasoning, KuaiMod adequately models video toxicity based on sparse user feedback and fosters dynamic moderation policy with rapid update speed and high accuracy. Offline experiments and large-scale online A/B test demonstrates the superiority of KuaiMod: KuaiMod achieves the best moderation performance on our benchmark. The deployment of KuaiMod reduces the user reporting rate by 20% and its application in video recommendation increases both Daily Active User (DAU) and APP Usage Time (AUT) on several Kuaishou scenarios. We have open-sourced our benchmark at https://kuaimod.github.io.
Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
2025-04-21
Aoran Gan, Hao Yu, Kai Zhang, Qi Liu, Wenyu Yan, Zhenya Huang, Shiwei Tong, Guoping Hu
Recent advancements in Retrieval-Augmented Generation (RAG) have revolutionized natural language processing by integrating Large Language Models (LLMs) with external information retrieval, enabling accurate, up-to-date, and verifiable text generation across diverse applications. However, evaluating RAG systems presents unique challenges due to their hybrid architecture that combines retrieval and generation components, as well as their dependence on dynamic knowledge sources in the LLM era. In response, this paper provides a comprehensive survey of RAG evaluation methods and frameworks, systematically reviewing traditional and emerging evaluation approaches, for system performance, factual accuracy, safety, and computational efficiency in the LLM era. We also compile and categorize the RAG-specific datasets and evaluation frameworks, conducting a meta-analysis of evaluation practices in high-impact RAG research. To the best of our knowledge, this work represents the most comprehensive survey for RAG evaluation, bridging traditional and LLM-driven methods, and serves as a critical resource for advancing RAG development.
People often speak of being "moved beyond words" when witnessing awe-inspiring natural beauty, experiencing profound moral elevation, or encountering powerful works of art and music. Such moments evoke intensely embodied sensations - a swelling in the chest, a quickened heartbeat, or a sudden stillness that floods the entire body. These experiences are difficult to articulate, yet they are vividly real and spatially anchored in bodily experience. This review examines the emerging method of body mapping, which captures how individuals subjectively localize emotional sensations across the body. Drawing on over two dozen empirical studies, we propose a tripartite framework for understanding the origins of emotional bodily maps: (1) bottom-up physiological signals, (2) top-down behavioral engagement (including motor actions), and (3) conceptual and metaphorical constructions. Crucially, we argue that bodily maps are not mere reflections of physiological monitoring, but expressive interfaces between brain, body, and conceptualization. Because they rely on introspective sensation rather than verbal articulation, bodily maps offer a promising language-independent tool for assessing emotional experience - particularly in cross-cultural research, developmental studies, and clinical contexts where verbal reporting may be limited or unreliable. Ultimately, bodily maps reveal the hidden geography of emotion-those feelings that elude precise definition or verbalization, like being moved beyond words, yet are undeniably real and embodied - offering a compelling window into how emotions are formed, shared, and lived through the body.
Systematic search for blue hyper-velocity stars from LAMOST survey
2025-04-21
Yongkang Sun, Yang Huang, Jifeng Liu, Haozhu Fu, Huawei Zhang, Yinbi Li, Cuihua Du, Jianrong Shi, Xiao Kong
Hypervelocity stars (HVSs) represent a unique class of objects capable of escaping the gravitational pull of the Milky Way due to extreme acceleration events, such as close encounters with the supermassive black hole at the Galactic center (GC), supernova explosions in binary systems, or multi-body dynamical interactions. Finding and studying HVSs are crucial to exploring these ejection mechanisms, characterizing central black holes, probing the GC environment, and revealing the distribution of dark matter in our galaxy. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) spectroscopic surveys have so far identified four B-type unbound HVSs. To expand this sample with the second-phase LAMOST survey that started in 2018, we conducted a systematic search for early-type HVSs using the LAMOST Data Release 10. We identified 125 early-type high-velocity candidates with total velocities exceeding 300 km\,s$^{-1}$. Among them, we report ten new unbound B- and A-type hypervelocity star (HVS) candidates (designated LAMOST-HVS5 through LAMOST-HVS14), tripling the number of unbound HVSs previously identified by LAMOST. Kinematic analyses suggest that these newly discovered HVS candidates likely originated either from the Galactic Center or via dynamical interactions. Future high-resolution follow-up observations promise to refine the stellar parameters, distances, and elemental abundances of these candidates, thereby providing deeper insights into their origins and broadening their potential applications across astrophysics.
Systematic reviews (SRs) are vital for evidence-based practice in high stakes disciplines, such as healthcare, but are often impeded by intensive labors and lengthy processes that can take months to complete. Due to the high demand for domain expertise, existing automatic summarization methods fail to accurately identify relevant studies and generate high-quality summaries. To that end, we introduce InsightAgent, a human-centered interactive AI agent powered by large language models that revolutionize this workflow. InsightAgent partitions a large literature corpus based on semantics and employs a multi-agent design for more focused processing of literature, leading to significant improvement in the quality of generated SRs. InsightAgent also provides intuitive visualizations of the corpus and agent trajectories, allowing users to effortlessly monitor the actions of the agent and provide real-time feedback based on their expertise. Our user studies with 9 medical professionals demonstrate that the visualization and interaction mechanisms can effectively improve the quality of synthesized SRs by 27.2%, reaching 79.7% of human-written quality. At the same time, user satisfaction is improved by 34.4%. With InsightAgent, it only takes a clinician about 1.5 hours, rather than months, to complete a high-quality systematic review.
A Survey on Small Sample Imbalance Problem: Metrics, Feature Analysis, and Solutions
2025-04-21
Shuxian Zhao, Jie Gui, Minjing Dong, Baosheng Yu, Zhipeng Gui, Lu Dong, Yuan Yan Tang, James Tin-Yau Kwok
The small sample imbalance (S&I) problem is a major challenge in machine learning and data analysis. It is characterized by a small number of samples and an imbalanced class distribution, which leads to poor model performance. In addition, indistinct inter-class feature distributions further complicate classification tasks. Existing methods often rely on algorithmic heuristics without sufficiently analyzing the underlying data characteristics. We argue that a detailed analysis from the data perspective is essential before developing an appropriate solution. Therefore, this paper proposes a systematic analytical framework for the S\&I problem. We first summarize imbalance metrics and complexity analysis methods, highlighting the need for interpretable benchmarks to characterize S&I problems. Second, we review recent solutions for conventional, complexity-based, and extreme S&I problems, revealing methodological differences in handling various data distributions. Our summary finds that resampling remains a widely adopted solution. However, we conduct experiments on binary and multiclass datasets, revealing that classifier performance differences significantly exceed the improvements achieved through resampling. Finally, this paper highlights open questions and discusses future trends.
Conversational recommender systems (CRS) typically require extensive domain-specific conversational datasets, yet high costs, privacy concerns, and data-collection challenges severely limit their availability. Although Large Language Models (LLMs) demonstrate strong zero-shot recommendation capabilities, practical applications often favor smaller, internally managed recommender models due to scalability, interpretability, and data privacy constraints, especially in sensitive or rapidly evolving domains. However, training these smaller models effectively still demands substantial domain-specific conversational data, which remains challenging to obtain. To address these limitations, we propose an active data augmentation framework that synthesizes conversational training data by leveraging black-box LLMs guided by active learning techniques. Specifically, our method utilizes publicly available non-conversational domain data, including item metadata, user reviews, and collaborative signals, as seed inputs. By employing active learning strategies to select the most informative seed samples, our approach efficiently guides LLMs to generate synthetic, semantically coherent conversational interactions tailored explicitly to the target domain. Extensive experiments validate that conversational data generated by our proposed framework significantly improves the performance of LLM-based CRS models, effectively addressing the challenges of building CRS in no- or low-resource scenarios.
Field Report on Ground Penetrating Radar for Localization at the Mars Desert Research Station
2025-04-21
Anja Sheppard, Katherine A. Skinner
In this field report, we detail the lessons learned from our field expedition to collect Ground Penetrating Radar (GPR) data in a Mars analog environment for the purpose of validating GPR localization techniques in rugged environments. Planetary rovers are already equipped with GPR for geologic subsurface characterization. GPR has been successfully used to localize vehicles on Earth, but it has not yet been explored as another modality for localization on a planetary rover. Leveraging GPR for localization can aid in efficient and robust rover pose estimation. In order to demonstrate localizing GPR in a Mars analog environment, we collected over 50 individual survey trajectories during a two-week period at the Mars Desert Research Station (MDRS). In this report, we discuss our methodology, lessons learned, and opportunities for future work.
Combating Toxic Language: A Review of LLM-Based Strategies for Software Engineering
2025-04-21
Hao Zhuo, Yicheng Yang, Kewen Peng
Large Language Models (LLMs) have become integral to software engineering (SE), where they are increasingly used in development workflows. However, their widespread use raises concerns about the presence and propagation of toxic language--harmful or offensive content that can foster exclusionary environments. This paper provides a comprehensive review of recent research on toxicity detection and mitigation, focusing on both SE-specific and general-purpose datasets. We examine annotation and preprocessing techniques, assess detection methodologies, and evaluate mitigation strategies, particularly those leveraging LLMs. Additionally, we conduct an ablation study demonstrating the effectiveness of LLM-based rewriting for reducing toxicity. By synthesizing existing work and identifying open challenges, this review highlights key areas for future research to ensure the responsible deployment of LLMs in SE and beyond.
$k$-Inductive and Interpolation-Inspired Barrier Certificates for Stochastic Dynamical Systems
2025-04-21
Mohammed Adib Oumer, Vishnu Murali, Majid Zamani
We introduce two notions of barrier certificates that use multiple functions to provide a lower bound on the probabilistic satisfaction of safety for stochastic dynamical systems. A barrier certificate for a stochastic dynamical system acts as a nonnegative supermartingale, and provides a lower bound on the probability that the system is safe. The promise of such certificates is that their search can be effectively automated. Typically, one may use optimization or SMT solvers to find such barrier certificates of a given fixed template. When such approaches fail, a typical approach is to instead change the template. We propose an alternative approach that we dub interpolation-inspired barrier certificates. An interpolation-inspired barrier certificate consists of a set of functions that jointly provide a lower bound on the probability of satisfying safety. We show how one may find such certificates of a fixed template, even when we fail to find standard barrier certificates of the same template. However, we note that such certificates still need to ensure a supermartingale guarantee for one function in the set. To address this challenge, we consider the use of $k$-induction with these interpolation-inspired certificates. The recent use of $k$-induction in barrier certificates allows one to relax the supermartingale requirement at every time step to a combination of a supermartingale requirement every $k$ steps and a $c$-martingale requirement for the intermediate steps. We provide a generic formulation of a barrier certificate that we dub $k$-inductive interpolation-inspired barrier certificate. The formulation allows for several combinations of interpolation and $k$-induction for barrier certificate. We present two examples among the possible combinations. We finally present sum-of-squares programming to synthesize this set of functions and demonstrate their utility in case studies.
Starspots on eclipsing giant stars I.: The sample and eclipse mapping examples
2025-04-21
K. Oláh, B. Seli, A. Haris, S. Rappaport, M. Tuomi, R. Gagliano, T. L. Jacobs, M. H. Kristiansen, H. M. Schwengeler, M. Omohundro, I. Terentev, A. Vanderburg, B. Powell, V. Kostov, Zs. Kővári
Spotted stars in eclipsing binary systems allow us to gather significant information about the stellar surface inhomogeneities that is otherwise impossible from only photometric data. Starspots can be scanned using the eclipse (or transit) mapping technique, which takes advantage of the passage of a companion star (or planet) in front of a spotted giant star in a binary system. Based on the characteristics of their ultra-precise space photometric light curves, we compile a list of eclipsing binaries whose primary component is a spotted subgiant or giant star, with the aim of applying the eclipse mapping technique to them. Eclipsing binaries with giant primaries were selected from Transiting Exoplanet Survey Satellite (TESS) light curves by visual inspection. Spots showing up as bumps during eclipses are modeled with an eclipse mapping technique specialized for two stars, and the number of spots are found with the help of Bayes factors. The full light curves themselves were analyzed with time series spot modeling, and the results of the two approaches were compared. We present a catalog of 29 eclipsing close binaries with active giant components and analyze TIC 235934420, TIC 271892852 and TIC 326257590 from the Continuous Viewing Zones (CVZ) of TESS. Remarkable agreement is found between the starspot temperatures, sizes, and longitudes from the eclipse mapping results and the corresponding full light curve solutions. Spots are always present at the substellar points of the tidally locked binaries. Data from the TESS CVZ allow us to follow the changes of spot patterns on yearly timescales.
Traversing the Star-Forming Main Sequence with Molecular Gas Stacks of z~1.6 Cluster Galaxies
2025-04-21
Alex Pigarelli, Allison Noble, Gregory Rudnick, William Cramer, Stacey Alberts, Yannick Bahe, Patrick S. Kamieneski, Sebastian Montano, Adam Muzzin, Julie Nantais, Sarah Saavedra, Eelco van Kampen, Tracy Webb, Christina C. Williams, Gillian Wilson, H. K. C. Yee
The cluster environment has been shown to affect the molecular gas content of cluster members, yet a complete understanding of this often subtle effect has been hindered due to a lack of detections over the full parameter space of galaxy star formation rates and stellar masses. Here we stack CO(2-1) spectra of z~1.6 cluster galaxies to explore the average molecular gas fractions of galaxies both at lower mass (log(M/solar mass)~9.6) and further below the Star Forming Main Sequence (SFMS; DeltaMS~ -0.9) than other literature studies; this translates to a 3sigma gas mass limit of ~7x10^9 solar masses for stacked galaxies below the SFMS. We divide our sample of 54 z~1.6 cluster galaxies, derived from the Spitzer Adaptation of the Red-Sequence Cluster Survey, into 9 groupings, for which we recover detections in 8. The average gas content of the full cluster galaxy population is similar to coeval field galaxies matched in stellar mass and star formation rate. However, when further split by CO-undetected and CO-detected, we find that galaxies below the SFMS have statistically different gas fractions from the field scaling relations, spanning deficiencies to enhancements from 2sigma below to 3sigma above the expected field gas fractions, respectively. These differences between z=1.6 cluster and field galaxies below the SFMS are likely due to environmental processes, though further investigation of spatially-resolved properties and more robust field scaling relation calibration in this parameter space are required.
The cosmological constant problem: from Newtonian cosmology to the greatest puzzle of modern theoretical cosmology
2025-04-21
Federico Scali
The cosmological constant problem is one of the greatest challenges in contemporary physics, since it is deeply rooted in the problematic interplay between quantum fields and gravity. The aim of this work is to review the key conceptual elements needed to formulate the problem and some ideas for a possible solution. I do so by weaving a fil rouge from Newtonian cosmology, through general relativity and the standard model of relativistic cosmology ({\Lambda}-CDM), up to the theory of quantum fields. In the first part, the issues with the application of Newtonian gravity to an infinite and static universe are addressed, observing how a cosmological term in the Poisson equation would stabilize a homogeneous matter distribution. A toy derivation of the Friedman equations using only Newtonian arguments is also shown. In the second part, the conceptual path leading to general relativity and the {\Lambda}-CDM model is laid down, with particular emphasis to the historical introduction of the cosmological constant and its new role after the discovery of the accelerated expansion of the universe. Finally, the problem is formulated within the framework of quantum field theory. Its many facets are discussed together with the criticalities in the formulation and some of the leading ideas for its solution are outlined.
The Pristine Dwarf-Galaxy survey -- VI. A VLT/FLAMES spectroscopic study of the dwarf galaxy Boötes II
2025-04-21
Nicolas Longeard, Pascale Jablonka, Giuseppina Battaglia, Khyati Malhan, Nicolas F. Martin, Julio F. Navarro, Federico Sestito
The Milky Way has a large population of dwarf galaxy satellites. Their properties are sensitive to both cosmology and the physical processes underlying galaxy formation, but these properties are still not properly characterized for the entire satellite population. We aim to provide the most accurate systemic dynamical and metallicity properties of the dwarf galaxy Bo\"otes II (Boo II). We use a new spectroscopic sample of 39 stars in the field of Boo II with data from the Fiber Large Array Multi Element Spectrograph (FLAMES) mounted on the Very Large Telescope (VLT). The target selection is based on a combination of broadband photometry, proper motions from Gaia, and the metallicity-sensitive narrow-band photometry from the Pristine survey that is ideal for removing obvious Milky Way contaminants. We found 9 new members, including 5 also in the recent work of Bruce et al. (2023), and the farthest member to date (5.7 half-light radii from Boo II centroid), extending the spectroscopic spatial coverage of this system. Our metallicity measurements based on the Calcium triplet lines leads to the detection of the two first extremely metal-poor stars (EMPS, [Fe/H] < -3.0) in Boo II. Combining this new dataset with literature data refines Boo II's velocity dispersion (5.6km/s), systemic velocity(-126.8 km/s) and shows that it does not show any sign of a significant velocity gradient. We are thus able to confirm the kinematic and metallicity properties of the satellite as well as identify new members for future high-resolution analyses.
Big, Dusty Galaxies in Blue Jay: Insights into the Relationship Between Morphology and Dust Attenuation at Cosmic Noon
2025-04-21
Gabriel Maheson, Sandro Tacchella, Sirio Belli, Minjung Park, A. Lola Danhaive, Letizia Bugiani, Rebecca Davies, Razieh Emami, Amir H. Khoram, Laurence Lam, Joel Leja, Trevor Mendel, Erica June Nelson
The dust attenuation of galaxies is highly diverse and closely linked to stellar population properties and the star dust geometry, yet its relationship to galaxy morphology remains poorly understood. We present a study of 141 galaxies ($9<\log(\rm M_{\star}/\rm M_{\odot})<11.5$) at $1.7
No evidence for excess AGN activity in recently quenched massive galaxies at cosmic noon
2025-04-21
Omar Almaini, Vivienne Wild, David Maltby, Elizabeth Taylor, Kate Rowlands, Thomas de Lisle, Katherine Alatalo, Jimi Harrold, Guillaume Hewitt, Pallavi Patil, Maya Skarbinski
We present an analysis of AGN activity within recently quenched massive galaxies at cosmic noon ($z\sim 2$), using deep Chandra X-ray observations of the Ultra-Deep Survey (UDS) field. Our sample includes over 4000 massive galaxies ($M_\ast > 10^{10.5}$ M$_{\odot}$) in the redshift range $1 < z < 3$, including more than 200 transitionary post-starburst (PSB) systems. We find that X-ray emitting AGN are detected in $6.2 \pm 1.5$ per cent of massive PSBs at these redshifts, a detection rate that lies between those of star-forming and passive galaxies ($8.2 \pm 0.5$ per cent and $5.7 \pm 0.8$ per cent, respectively). A stacking analysis shows that the average X-ray luminosity for PSBs is comparable to older passive galaxies, but a factor of $2.6 \pm 0.3$ below star-forming galaxies of similar redshift and stellar mass. The average X-ray luminosity in all populations appears to trace the star-formation rate, with PSBs showing low levels of AGN activity consistent with their reduced levels of star formation. We conclude that, on average, we see no evidence for excess AGN activity in the post-starburst phase. However, the low levels of AGN activity can be reconciled with the high-velocity outflows observed in many PSBs, assuming the rare X-ray detections represent short-lived bursts of black hole activity, visible $\sim$5 per cent of the time. Thus, X-ray AGN may help to maintain quiescence in massive galaxies at cosmic noon, but the evidence for a direct link to the primary quenching event remains elusive.
Cosmology in Extended Parameter Space with DESI DR2 BAO: A 2$σ$+ Detection of Non-zero Neutrino Masses with an Update on Dynamical Dark Energy and Lensing Anomaly
2025-04-21
Shouvik Roy Choudhury
We obtain constraints in a 12 parameter cosmological model using the recent DESI Data Release (DR) 2 Baryon Acoustic Oscillations (BAO) data, combined with Cosmic Microwave Background (CMB) power spectra (Planck Public Release (PR) 4) and lensing (Planck PR4 + Atacama Cosmology Telescope (ACT) Data Release (DR) 6) data, uncalibrated type Ia Supernovae (SNe) data from Pantheon+ and Dark Energy Survey (DES) Year 5 (DESY5) samples, and Weak Lensing (WL: DES Year 1) data. The cosmological model consists of six $\Lambda$CDM parameters, and additionally, the dynamical dark energy parameters ($w_0$, $w_a$), the sum of neutrino masses ($\sum m_{\nu}$), the effective number of non-photon radiation species ($N_{\textrm{eff}}$), the scaling of the lensing amplitude ($A_{\textrm{lens}}$), and the running of the scalar spectral index ($\alpha_s$). Our major findings are the following: i) With CMB+BAO+DESY5+WL, we obtain the first 2$\sigma$+ detection of a non-zero $\sum m_{\nu} = 0.19^{+0.15}_{-0.18}$ eV (95%). Replacing DESY5 with Pantheon+ still yields a $\sim$1.9$\sigma$ detection. ii) The cosmological constant lies at the edge of the 95% contour with CMB+BAO+Pantheon+, but is excluded at 2$\sigma$+ with DESY5, leaving evidence for dynamical dark energy inconclusive, contrary to claims by DESI collaboration. iii) With CMB+BAO+SNe+WL, $A_{\textrm{lens}} = 1$ is excluded at $>2\sigma$, while it remains consistent with unity without WL data - suggesting for the first time that the existence of lensing anomaly may depend on non-CMB datasets. iv) The Hubble tension persists at 3.6-4.2$\sigma$ with CMB+BAO+SNe; WL data has minimal impact.
Histogram-based Parameter-efficient Tuning for Passive Sonar Classification
2025-04-21
Amirmohammad Mohammadi, Davelle Carreiro, Alexandra Van Dine, Joshua Peeples
Parameter-efficient transfer learning (PETL) methods adapt large artificial neural networks to downstream tasks without fine-tuning the entire model. However, existing additive methods, such as adapters, sometimes struggle to capture distributional shifts in intermediate feature embeddings. We propose a novel histogram-based parameter-efficient tuning (HPT) technique that captures the statistics of the target domain and modulates the embeddings. Experimental results on three downstream passive sonar datasets (ShipsEar, DeepShip, VTUAD) demonstrate that HPT outperforms conventional adapters. Notably, HPT achieves 91.8% vs. 89.8% accuracy on VTUAD. Furthermore, HPT trains faster and yields feature representations closer to those of fully fine-tuned models. Overall, HPT balances parameter savings and performance, providing a distribution-aware alternative to existing adapters and shows a promising direction for scalable transfer learning in resource-constrained environments. The code is publicly available: https://github.com/Advanced-Vision-and-Learning-Lab/HLAST_DeepShip_ParameterEfficient.
Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions
2025-04-21
Tianliang Yao, Bo Lu, Markus Kowarschik, Yixuan Yuan, Hubin Zhao, Sebastien Ourselin, Kaspar Althoefer, Junbo Ge, Peng Qi
Endovascular procedures have revolutionized the treatment of vascular diseases thanks to minimally invasive solutions that significantly reduce patient recovery time and enhance clinical outcomes. However, the precision and dexterity required during these procedures poses considerable challenges for interventionists. Robotic systems have emerged offering transformative solutions, addressing issues such as operator fatigue, radiation exposure, and the inherent limitations of human precision. The integration of Embodied Intelligence (EI) into these systems signifies a paradigm shift, enabling robots to navigate complex vascular networks and adapt to dynamic physiological conditions. Data-driven approaches, advanced computer vision, medical image analysis, and machine learning techniques, are at the forefront of this evolution. These methods augment procedural intelligence by facilitating real-time vessel segmentation, device tracking, and anatomical landmark detection. Reinforcement learning and imitation learning further refine navigation strategies and replicate experts' techniques. This review systematically examines the integration of EI principles into robotic technologies, in relation to endovascular procedures. We discuss recent advancements in intelligent perception and data-driven control, and their practical applications in robot-assisted endovascular procedures. By critically evaluating current limitations and emerging opportunities, this review establishes a framework for future developments, emphasizing the potential for greater autonomy and improved clinical outcomes. Emerging trends and specific areas of research, such as federated learning for medical data sharing, explainable AI for clinical decision support, and advanced human-robot collaboration paradigms, are also explored, offering insights into the future direction of this rapidly evolving field.
A Mission to Demonstrate Rapid-Response Flyby Reconnaissance for Planetary Defense
2025-04-21
Nancy L. Chabot, Justin A. Atchison, Rylie Bull, Andrew S. Rivkin, R. Terik Daly, Ronald-L. Ballouz, Olivier S. Barnouin, Andrew F. Cheng, Carolyn M. Ernst, Angela M. Stickle, Evan J. Smith, Joseph J. Linden, Benjamin F. Villac, Jodi R. Berdis, Dawn M. Graninger, Sarah Hefter
International and U.S. strategies and protocols have identified the need to develop rapid-response spacecraft reconnaissance capabilities as a priority to advance planetary defense readiness. A space-based reconnaissance response is recommended for potential impactors as small as 50 m, making these small objects the most likely to trigger a space-based response and the ones that drive the reconnaissance capabilities needed. Even following the successful completion of the NEO Surveyor mission and Rubin Observatory survey efforts, roughly half of the 50-m near-Earth object (NEO) population will remain undiscovered. As a result, 50-m impactors may not be found with long warning times, and a rapid-response flyby mission may be the only reconnaissance possible. To develop a robust flyby reconnaissance capability for planetary defense, four major requirements are defined for a demonstration mission. 1. Enable a flyby of greater than 90 percent of the potential asteroid threat population. 2. Demonstrate the flyby reconnaissance for a 50 m NEO. 3. Obtain the information needed to determine if and where it would impact the Earth. 4. Determine key properties of the asteroid to inform decision makers. As commonly noted in the planetary defense community, in planetary defense, you do not pick the asteroid, the asteroid picks you. Thus, a planetary defense flyby reconnaissance demonstration mission is not about just flying by an asteroid, but rather it is about developing a robust capability for the objects that are most likely to require a short-warning-time, space-based response.
Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends
2025-04-21
Mohammad Abu Tami, Mohammed Elhenawy, Huthaifa I. Ashqar
Traffic safety remains a critical global challenge, with traditional Advanced Driver-Assistance Systems (ADAS) often struggling in dynamic real-world scenarios due to fragmented sensor processing and susceptibility to adversarial conditions. This paper reviews the transformative potential of Multimodal Large Language Models (MLLMs) in addressing these limitations by integrating cross-modal data such as visual, spatial, and environmental inputs to enable holistic scene understanding. Through a comprehensive analysis of MLLM-based approaches, we highlight their capabilities in enhancing perception, decision-making, and adversarial robustness, while also examining the role of key datasets (e.g., KITTI, DRAMA, ML4RoadSafety) in advancing research. Furthermore, we outline future directions, including real-time edge deployment, causality-driven reasoning, and human-AI collaboration. By positioning MLLMs as a cornerstone for next-generation traffic safety systems, this review underscores their potential to revolutionize the field, offering scalable, context-aware solutions that proactively mitigate risks and improve overall road safety.
2025-04-21
Junwei Liao, Muning Wen, Jun Wang, Weinan Zhang
LLM-based Multi-Agent Systems have demonstrated remarkable capabilities in addressing complex, agentic tasks requiring multifaceted reasoning and collaboration, from generating high-quality presentation slides to conducting sophisticated scientific research. Meanwhile, RL has been widely recognized for its effectiveness in enhancing agent intelligence, but limited research has investigated the fine-tuning of LaMAS using foundational RL techniques. Moreover, the direct application of MARL methodologies to LaMAS introduces significant challenges, stemming from the unique characteristics and mechanisms inherent to LaMAS. To address these challenges, this article presents a comprehensive study of LLM-based MARL and proposes a novel paradigm termed Multi-Agent Reinforcement Fine-Tuning (MARFT). We introduce a universal algorithmic framework tailored for LaMAS, outlining the conceptual foundations, key distinctions, and practical implementation strategies. We begin by reviewing the evolution from RL to Reinforcement Fine-Tuning, setting the stage for a parallel analysis in the multi-agent domain. In the context of LaMAS, we elucidate critical differences between MARL and MARFT. These differences motivate a transition toward a novel, LaMAS-oriented formulation of RFT. Central to this work is the presentation of a robust and scalable MARFT framework. We detail the core algorithm and provide a complete, open-source implementation to facilitate adoption and further research. The latter sections of the paper explore real-world application perspectives and opening challenges in MARFT. By bridging theoretical underpinnings with practical methodologies, this work aims to serve as a roadmap for researchers seeking to advance MARFT toward resilient and adaptive solutions in agentic systems. Our implementation of the proposed framework is publicly available at: https://github.com/jwliao-ai/MARFT.
The exponential growth of Large Language Models (LLMs) continues to highlight the need for efficient strategies to meet ever-expanding computational and data demands. This survey provides a comprehensive analysis of two complementary paradigms: Knowledge Distillation (KD) and Dataset Distillation (DD), both aimed at compressing LLMs while preserving their advanced reasoning capabilities and linguistic diversity. We first examine key methodologies in KD, such as task-specific alignment, rationale-based training, and multi-teacher frameworks, alongside DD techniques that synthesize compact, high-impact datasets through optimization-based gradient matching, latent space regularization, and generative synthesis. Building on these foundations, we explore how integrating KD and DD can produce more effective and scalable compression strategies. Together, these approaches address persistent challenges in model scalability, architectural heterogeneity, and the preservation of emergent LLM abilities. We further highlight applications across domains such as healthcare and education, where distillation enables efficient deployment without sacrificing performance. Despite substantial progress, open challenges remain in preserving emergent reasoning and linguistic diversity, enabling efficient adaptation to continually evolving teacher models and datasets, and establishing comprehensive evaluation protocols. By synthesizing methodological innovations, theoretical foundations, and practical insights, our survey charts a path toward sustainable, resource-efficient LLMs through the tighter integration of KD and DD principles.
In this expository note, we discuss normal generators for mapping class groups of surfaces. Especially, we focus on the relation between normal generation of a mapping class with its asymptotic translation lengths on the Teichm\"uller space and the curve graph of the underlying surface. We also discuss several open questions.
The quest for the quark-gluon plasma from the perspective of dynamical models of relativistic heavy-ion collisions
2025-04-20
Marcus Bleicher, Elena Bratkovskaya
The physics of heavy-ion collisions is one of the most exciting and challenging directions of science for the last four decades. On the theoretical side one deals with a non-abelian field theory, while on the experimental side today's largest accelerators are needed to enable these studies. The discovery of a new stage of matter - called the quark-gluon plasma (QGP) - and the study of its properties is one of the major achievements of modern physics. In this contribution we briefly review the history of theoretical descriptions of heavy-ion collisions based on dynamical models, focusing on the personal experiences in this inspiring field.
The ladder of Finsler-type objects and their variational problems on spacetimes
2025-04-20
Miguel Sánchez, Fidel F. Villaseñor
The space of anisotropic $r$-contravariant $s$-covariant $\alpha$-homogeneous tensors on a manifold admits a functorial structure where vertical derivatives $\dot{\partial}$ and contractions $\imath_{\mathbb{C}}$ by the Liouville vector field $\mathbb{C}$ are operators which maintain $s+\alpha$ constant. In (semi-)Finsler geometry, this structure is transmitted faithfully to connection-type elements yielding the following ladder: geodesic sprays / nonlinear connections / anisotropic connections / linear (Finslerian) connections. However, it is more loosely transmitted to metric-type ones: Finslerian Lagrangians / Legendre transformations / anisotropic metrics. We will study this structure in depth and apply it to discuss the recent variational proposals (Einstein-Hilbert, Einstein-Palatini, Einstein-Cartan) for generalizing Einstein equations to the Finsler setting.
Research on underrepresented populations is essential for fostering greater diversity within the software industry. Team diversity is important for reasons that go beyond ethics. Diversity contributes to greater innovation and productivity, helping decrease turnover rates and reduce team conflicts. Within this context, LGBTQIA+ software engineering professionals face unique challenges, e.g., self-isolation and invisibility feeling. Developer Experience (DX) encompasses cognitive, emotional, and motivational considerations, supporting the idea that improving how DX can enhance team performance, strengthen collaboration, and lead to more successful software projects. This study aimed to examine traditional and grey literature data through a Multivocal Literature Review focused on the DX of LGBTQIA+ professionals in agile teams. Our findings reveal that issues such as invisibility, prejudice, and discrimination adversely affect their experiences, compounded by the predominance of heterosexual males in the field. Conversely, professionals who feel welcomed by their teams and organizations, especially in processes tailored to their needs, report more positive team dynamics and engagement.
Minimum mean square error (MMSE) estimation is widely used in signal processing and related fields. While it is known to be non-continuous with respect to all standard notions of stochastic convergence, it remains robust in practical applications. In this work, we review the known counterexamples to the continuity of the MMSE. We observe that, in these counterexamples, the discontinuity arises from an element in the converging measurement sequence providing more information about the estimand than the limit of the measurement sequence. We argue that this behavior is uncharacteristic of real-world applications and introduce a new stochastic convergence notion, termed Markovian convergence, to address this issue. We prove that the MMSE is, in fact, continuous under this new notion. We supplement this result with semi-continuity and continuity guarantees of the MMSE in other settings and prove the continuity of the MMSE under linear estimation.
The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology poised to enable this revolutionary vision is the wireless large AI model (WLAM), characterized by its exceptional capabilities in data processing, inference, and decision-making. In light of these remarkable capabilities, this paper provides a comprehensive survey of WLAM, elucidating its fundamental principles, diverse applications, critical challenges, and future research opportunities. We begin by introducing the background of WLAM and analyzing the key synergies with wireless networks, emphasizing the mutual benefits. Subsequently, we explore the foundational characteristics of WLAM, delving into their unique relevance in wireless environments. Then, the role of WLAM in optimizing wireless communication systems across various use cases and the reciprocal benefits are systematically investigated. Furthermore, we discuss the integration of WLAM with emerging technologies, highlighting their potential to enable transformative capabilities and breakthroughs in wireless communication. Finally, we thoroughly examine the high-level challenges hindering the practical implementation of WLAM and discuss pivotal future research directions.
As Artificial Intelligence (AI) becomes increasingly integrated into older adults' daily lives, equipping them with the knowledge and skills to understand and use AI is crucial. However, most research on AI literacy education has focused on students and children, leaving a gap in understanding the unique needs of older adults when learning about AI. To address this, we surveyed 103 older adults aged 50 and above (Mean = 64, SD = 7). Results revealed that they found it important and were motivated to learn about AI because they wish to harness the benefits and avoid the dangers of AI, seeing it as necessary to cope in the future. However, they expressed learning challenges such as difficulties in understanding and not knowing how to start learning AI. Particularly, a strong preference for hands-on learning was indicated. We discussed design opportunities to support AI literacy education for older adults.
Surrogate Fitness Metrics for Interpretable Reinforcement Learning
2025-04-20
Philipp Altmann, Céline Davignon, Maximilian Zorn, Fabian Ritz, Claudia Linnhoff-Popien, Thomas Gabor
We employ an evolutionary optimization framework that perturbs initial states to generate informative and diverse policy demonstrations. A joint surrogate fitness function guides the optimization by combining local diversity, behavioral certainty, and global population diversity. To assess demonstration quality, we apply a set of evaluation metrics, including the reward-based optimality gap, fidelity interquartile means (IQMs), fitness composition analysis, and trajectory visualizations. Hyperparameter sensitivity is also examined to better understand the dynamics of trajectory optimization. Our findings demonstrate that optimizing trajectory selection via surrogate fitness metrics significantly improves interpretability of RL policies in both discrete and continuous environments. In gridworld domains, evaluations reveal significantly enhanced demonstration fidelities compared to random and ablated baselines. In continuous control, the proposed framework offers valuable insights, particularly for early-stage policies, while fidelity-based optimization proves more effective for mature policies. By refining and systematically analyzing surrogate fitness functions, this study advances the interpretability of RL models. The proposed improvements provide deeper insights into RL decision-making, benefiting applications in safety-critical and explainability-focused domains.
Explainability for Embedding AI: Aspirations and Actuality
2025-04-20
Thomas Weber
With artificial intelligence (AI) embedded in many everyday software systems, effectively and reliably developing and maintaining AI systems becomes an essential skill for software developers. However, the complexity inherent to AI poses new challenges. Explainable AI (XAI) may allow developers to understand better the systems they build, which, in turn, can help with tasks like debugging. In this paper, we report insights from a series of surveys with software developers that highlight that there is indeed an increased need for explanatory tools to support developers in creating AI systems. However, the feedback also indicates that existing XAI systems still fall short of this aspiration. Thus, we see an unmet need to provide developers with adequate support mechanisms to cope with this complexity so they can embed AI into high-quality software in the future.
Context: Change mining enables organizations to understand the changes that occurred in their business processes. This allows them to enhance their business processes and adapt to dynamic environments. Therefore, change mining is becoming a topic of interest for researchers, scholars, and practitioners. Objective: Motivated by the goal of establishing the state of the art in this area, this paper aims to investigate the literature in change logging and mining in process-aware information systems, provide an overview of the methods that are used in the existing publications, and identify gaps in the research on the topic of logging and mining process changes. Method: A literature review is conducted with the objective to identify and define methods to mine, store, and record changes in business processes. From 1136 publications, we selected 6 papers related to changes in business process and extended the list to 9 papers by including the relevant articles referenced by the papers that we selected originally. Results: In answer of our research questions, we have identified two classes of change mining methods, two ways of recording the changes into change logs, five formats for change log representation, and four objectives to be learned from changes. Conclusion: The literature review provides a summary of existing change mining and logging methods in process-aware information systems and identifies a number of research gaps in the area.
Detection of Semidetached Eclipsing Binaries from TESS
2025-04-20
Xu Ding, KaiFan Ji, QiYuan Cheng, ZhiMing Song, JinLiang Wang, XueFen Tian, ChuanJun Wang
Semidetached binaries, distinguished by their mass transfer phase, play a crucial role in elucidating the physics of mass transfer within interacting binary systems. To identify these systems in eclipsing binary light curves provided by large-scale time-domain surveys, we have developed a methodology by training two distinct models that establish a mapping relationship between the parameters (orbital parameters and physical parameters) of semidetached binaries and their corresponding light curves. The first model corresponds to scenarios where the more massive star fills its Roche lobe, while the second model addresses situations where the less massive star does so. In consideration of the O'Connell effect observed in the light curves, we integrated a cool spot parameter into our models, thereby enhancing their applicability to fit light curves that exhibit this phenomenon. Our two-model framework was then harmonized with the Markov Chain Monte Carlo algorithm, enabling precise and efficient light-curve fitting and parameter estimation. Leveraging 2 minute cadence data from the initial 67 sectors of the Transiting Exoplanet Survey Satellite, we successfully identified 327 systems where the less massive component fills its Roche lobe, alongside three systems where the more massive component fills its Roche lobe. Additionally, we offer comprehensive fundamental parameters for these binary systems, including orbital inclination, relative radius, mass ratio, and effective temperature.
Resolving the S8 tension with the Lambda Prime ($Λ'$) model
2025-04-20
Stuart Marongwe, Stuart Kauffman, Moletlanyi Tshipa, Christian Corda
The $S_8$ parameter, which quantifies the amplitude of matter fluctuations on scales of $8h^{-1}$ Mpc, has been a source of tension between weak lensing surveys (e.g. KiDS, DES, HSC) and the Planck Cosmic Microwave Background (CMB) measurements. This discrepancy challenges the standard ${\Lambda}$CDM model and has become one of the most significant tensions in modern cosmology. The ${\Lambda'}$ model offers a potential resolution by introducing modifications to the cosmic growth history through alterations to the gravitational sector. The alterations involve including a Ricci soliton into Einstein's field equations which introduce a time dependent factor yielding a time varying cosmological constant ${\Lambda'}=(1-{\alpha(t)^2}(t)){\Lambda_{DE}}\frac{{\rho}_g}{\rho_{DE}}$ and subsequently the evolution of the cosmos. The Ricci soliton is sourced from gravitational energy density. In this study we analyze results from six surveys and compare the results for $w_a$ and $w_0$ with the ${\Lambda'}$ model. We also find ${\sigma}_8={0.750}_{-0.020}^{+0.020}$ , $S_8={0.788}$. These values are closer to some low $S_8$ measurements from weak lensing surveys (e.g DES, KiDS), which report $S_8 \approx 0.76-0.78$, suggesting that the model may alleviate the $S_8$ tension. High values of ${\alpha(t)}$ in the late universe are the cause of suppressed structure formation and low values of ${\Lambda'}$. The late universe in the ${\Lambda'}$ model is effectively or apparently 5-10% younger than in ${\Lambda}$CDM which translates to $H_0={72.734}_{-1.687}^{+1.687}$ km/s/Mpc, which is in agreement with late universe probes. ${\Lambda'}$ is classified under the dynamical dark energy models, however unlike alternatives, it does not invoke exotic particles nor phantom energy.
Rethinking trust in the digital age: An investigation of zero trust architecture's social consequences on organizational culture, collaboration, and knowledge sharing
2025-04-20
Ganiyu Oladimeji
As cyber threats escalate, Zero Trust Architecture (ZTA) replaces outdated perimeter security with strict never trust, always verify protocols. However, ZTA's dual nature as both technical infrastructure and social intervention creates an unresolved tension: its very mechanisms for security may systematically erode the trust foundations enabling effective collaboration. This integrative research combines case study analysis, employee surveys, and social network mapping reveals how ZTA disrupts knowledge-sharing, disproportionately hindering low-altruism employees, while surveillance erodes collective psychological ownership. Networked organizations, reliant on fluid trust, face fragmentation risks. Mitigation strategies include adaptive authorization frameworks using behavioral analytics and transparent communication reframing security as shared responsibility. Interdepartmental collaboration in security design preserves organizational trust structures identified through sociometric mapping. This research provides a framework balancing technical rigor with cultural sensitivity, proving cybersecurity can coexist with innovation by aligning verification with organizational psychology. The findings pioneer a paradigm where security and trust evolve synergistically critical for digital resilience in hybrid work environments. Future security must harmonize protocols with trust cultivation, ensuring defenses adapt to social dynamics driving modern enterprises.
NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results
2025-04-20
Zheng Chen, Jingkai Wang, Kai Liu, Jue Gong, Lei Sun, Zongwei Wu, Radu Timofte, Yulun Zhang, Jianxing Zhang, Jinlong Wu, Jun Wang, Zheng Xie, Hakjae Jeon, Suejin Han, Hyung-Ju Chun, Hyunhee Park, Zhicun Yin, Junjie Chen, Ming Liu, Xiaoming Li, Chao Zhou, Wangmeng Zuo, Weixia Zhang, Dingquan Li, Kede Ma, Yun Zhang, Zhuofan Zheng, Yuyue Liu, Shizhen Tang, Zihao Zhang, Yi Ning, Hao Jiang, Wenjie An, Kangmeng Yu, Chenyang Wang, Kui Jiang, Xianming Liu, Junjun Jiang, Yingfu Zhang, Gang He, Siqi Wang, Kepeng Xu, Zhenyang Liu, Changxin Zhou, Shanlan Shen, Yubo Duan, Yiang Chen, Jin Guo, Mengru Yang, Jen-Wei Lee, Chia-Ming Lee, Chih-Chung Hsu, Hu Peng, Chunming He
This paper provides a review of the NTIRE 2025 challenge on real-world face restoration, highlighting the proposed solutions and the resulting outcomes. The challenge focuses on generating natural, realistic outputs while maintaining identity consistency. Its goal is to advance state-of-the-art solutions for perceptual quality and realism, without imposing constraints on computational resources or training data. The track of the challenge evaluates performance using a weighted image quality assessment (IQA) score and employs the AdaFace model as an identity checker. The competition attracted 141 registrants, with 13 teams submitting valid models, and ultimately, 10 teams achieved a valid score in the final ranking. This collaborative effort advances the performance of real-world face restoration while offering an in-depth overview of the latest trends in the field.
Interdisciplinary Integration of Remote Sensing -- A Review with Four Examples
2025-04-20
Zichen Jin
As a high-level discipline, the development of remote sensing depends on the contribution of many other basic and applied disciplines and technologies. For example, due to the close relationship between remote sensing and photogrammetry, remote sensing would inevitably integrate disciplines such as optics and color science. Also, remote sensing integrates the knowledge of electronics in the conversion from optical signals to electrical signals via CCD (Charge-Coupled Device) or other image sensors. Moreover, when conducting object identification and classification with remote sensing data, mathematical morphology and other digital image processing technologies are used. These examples are only the tip of the iceberg of interdisciplinary integration of remote sensing. This work briefly reviews the interdisciplinary integration of remote sensing with four examples - ecology, mathematical morphology, machine learning, and electronics.
The field of complex photonics has garnered significant interest due to its rich physics and myriad applications spanning physics, engineering, biology, and medicine. However, a substantial portion of research focuses primarily on the linear medium. Over the years, optical nonlinearity, particularly the second order denoted as $\chi^{(2)}$, has been harnessed for diverse applications such as frequency conversions, three-wave mixing, material characterizations, and bio-imaging. When $\chi^{(2)}$-nonlinearity combines with the disorder, a new realm of physics emerges, which in the last 30 years has witnessed substantial progress in fundamental studies and futuristic applications. This review aims to explore fundamental concepts concerning $\chi^{(2)}$-nonlinear disordered media, chart the field's evolution, highlight current interests within the research community, and conclude with a future perspective.
2025-04-20
Adrian Riesco, Kazuhiro Ogata, Masaki Nakamura, Daniel Gaina, Duong Dinh Tran, Kokichi Futatsugi
Proof scores can be regarded as outlines of the formal verification of system properties. They have been historically used by the OBJ family of specification languages. The main advantage of proof scores is that they follow the same syntax as the specification language they are used in, so specifiers can easily adopt them and use as many features as the particular language provides. In this way, proof scores have been successfully used to prove properties of a large number of systems and protocols. However, proof scores also present a number of disadvantages that prevented a large audience from adopting them as proving mechanism. In this paper we present the theoretical foundations of proof scores; the different systems where they have been adopted and their latest developments; the classes of systems successfully verified using proof scores, including the main techniques used for it; the main reasons why they have not been widely adopted; and finally we discuss some directions of future work that might solve the problems discussed previously.
A Review on the Applications of Density Functional Theory to the FQH System
2025-04-20
Yi Yang, Yayun Hu, Zi-Xiang Hu
The fractional quantum Hall (FQH) effect remains a captivating area in condensed matter physics, characterized by strongly correlated topological order, fractionalized excitations, and anyonic statistics. Numerical simulations, such as exact diagonalization, density matrix renormalization group, matrix product states, and Monte Carlo methods, are essential to examine the properties of strongly correlated systems. Recently, density functional theory (DFT) has been employed in this field within the framework of composite fermion (CF) theory. In this paper, we assess how DFT addresses major challenges in FQH system, such as computing ground state and low-energy excitations. We emphasize the critical insights provided by DFT-based methods into the CF model, edge effects, and the nature of fractional charge and magnetoroton excitations. Furthermore, we examine the advantages and limitations of DFT approaches, highlight the interplay between numerical simulations and theoretical models. We finally discuss the future potential of time-dependent DFT (TDDFT) for modeling non-equilibrium dynamics.
Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey
2025-04-20
Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer, Muhammad Awais Khan Bangash, Muhammad Ali Jamshed
This survey explores the development of meta-thinking capabilities in Large Language Models (LLMs) from a Multi-Agent Reinforcement Learning (MARL) perspective. Meta-thinking self-reflection, assessment, and control of thinking processes is an important next step in enhancing LLM reliability, flexibility, and performance, particularly for complex or high-stakes tasks. The survey begins by analyzing current LLM limitations, such as hallucinations and the lack of internal self-assessment mechanisms. It then talks about newer methods, including RL from human feedback (RLHF), self-distillation, and chain-of-thought prompting, and each of their limitations. The crux of the survey is to talk about how multi-agent architectures, namely supervisor-agent hierarchies, agent debates, and theory of mind frameworks, can emulate human-like introspective behavior and enhance LLM robustness. By exploring reward mechanisms, self-play, and continuous learning methods in MARL, this survey gives a comprehensive roadmap to building introspective, adaptive, and trustworthy LLMs. Evaluation metrics, datasets, and future research avenues, including neuroscience-inspired architectures and hybrid symbolic reasoning, are also discussed.
Speech synthesis is crucial for human-computer interaction, enabling natural and intuitive communication. However, existing datasets involve high construction costs due to manual annotation and suffer from limited character diversity, contextual scenarios, and emotional expressiveness. To address these issues, we propose DialogueAgents, a novel hybrid agent-based speech synthesis framework, which integrates three specialized agents -- a script writer, a speech synthesizer, and a dialogue critic -- to collaboratively generate dialogues. Grounded in a diverse character pool, the framework iteratively refines dialogue scripts and synthesizes speech based on speech review, boosting emotional expressiveness and paralinguistic features of the synthesized dialogues. Using DialogueAgent, we contribute MultiTalk, a bilingual, multi-party, multi-turn speech dialogue dataset covering diverse topics. Extensive experiments demonstrate the effectiveness of our framework and the high quality of the MultiTalk dataset. We release the dataset and code https://github.com/uirlx/DialogueAgents to facilitate future research on advanced speech synthesis models and customized data generation.
Environmental Monitoring Requirements for the ngVLA
2025-04-20
T. K. Sridharan, J. G. Mangum, B. Butler
Measurement of environmental parameters is one of the basic requirements for the proper operation of a telescope. This memo is intended to provide guidance for the measurement accuracy requirements in the context of the ngVLA. It relies on previous work for ALMA (Mangum, 2001) and EVLA (Butler \& Perley, 2008) and a review of the subject by Mangum \& Wallace (2015). The local operational environment can be broadly divided into two categories: electromagnetic and physical. Meteorological parameters (weather) primarily constitute the physical environmental component and radio frequency interference (RFI) is the essential element of the electromagnetic environment. This memo focuses on the weather component and does not address the RFI, safety and physical infrastructure components. Under weather, the relevant topics are (1) the correction to pointing arising from refraction in the atmosphere (2) the different delays in the arrival times of signals at different antennas due to propagation in the atmosphere (3) monitoring weather parameters to provide operations support, e.g. in determining prevalence of precision or normal conditions, dynamic scheduling and the choice of antennas to constitute a sub-array with a given set of characteristics, among others, and (4) archival. Here we restrict ourselves to the first two topics which impact the data obtained and its calibration.
The Internet of Things (IoT) has significantly expanded the digital landscape, interconnecting an unprecedented array of devices, from home appliances to industrial equipment. This growth enhances functionality, e.g., automation, remote monitoring, and control, and introduces substantial security challenges, especially in defending these devices against cyber threats. Intrusion Detection Systems (IDS) are crucial for securing IoT; however, traditional IDS often struggle to adapt to IoT networks' dynamic and evolving nature and threat patterns. A potential solution is using Deep Reinforcement Learning (DRL) to enhance IDS adaptability, enabling them to learn from and react to their operational environment dynamically. This systematic review examines the application of DRL to enhance IDS in IoT settings, covering research from the past ten years. This review underscores the state-of-the-art DRL techniques employed to improve adaptive threat detection and real-time security across IoT domains by analyzing various studies. Our findings demonstrate that DRL significantly enhances IDS capabilities by enabling systems to learn and adapt from their operational environment. This adaptability allows IDS to improve threat detection accuracy and minimize false positives, making it more effective in identifying genuine threats while reducing unnecessary alerts. Additionally, this systematic review identifies critical research gaps and future research directions, emphasizing the necessity for more diverse datasets, enhanced reproducibility, and improved integration with emerging IoT technologies. This review aims to foster the development of dynamic and adaptive IDS solutions essential for protecting IoT networks against sophisticated cyber threats.
The impact of ultraviolet suppression on the rates and properties of strongly lensed Type IIn supernovae detected by LSST
2025-04-20
Andrés I. Ponte Pérez, Graham P. Smith, Matt Nicholl, Nikki Arendse, Dan Ryczanowski, Suhail Dhawan, the LSST Strong Lensing Science Collaboration
Upcoming wide-field time-domain surveys, such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) are expected to discover up to two orders of magnitude more strongly lensed supernovae per year than have so far been observed. Of these, Type IIn supernovae have been predicted to be detected more frequently than any other supernova type, despite their small relative detection fraction amongst non-lensed supernovae. However, previous studies that predict a large population of lensed Type IIn supernova detections model their time evolving spectrum as a pure blackbody. In reality, there is a deficit in the UV flux of supernovae relative to the blackbody continuum due to line-blanketing from iron-group elements in the ejecta and scattering effects. In this work we quantify the effect of this UV suppression on the detection rates by LSST of a simulated population of strongly lensed Type IIn supernovae, relative to a pure blackbody model, using a mock LSST observing run. With a blackbody model, we predict to detect $\sim$70 lensed Type IIn supernova per year with LSST. By modelling a similar UV deficit to that seen in superluminous supernovae, we recover 60 - 80% of the detections obtained using a pure blackbody model, of which $\sim$10 detections per year are sufficiently bright ($m_\textrm{i} < 22.5$ mag) and detected early enough (> 5 observations before lightcurve peak) to enable high-cadence spectroscopic follow up.
We investigate potential deviations from cold dark matter (CDM) using the latest Baryon Acoustic Oscillations (BAO) measurements from the Dark Energy Spectroscopic Instrument (DESI). Analyzing DESI data alone constrains the dark matter equation of state parameter $w_{\mathrm{dm}} = -0.042^{+0.047}_{-0.024}$, revealing a mild preference for non-cold dark matter. This preference strengthens significantly in combined analyses, but reveals a striking tension in the inferred $w_{\mathrm{dm}}$ values from observations of different nature. The DESI+DESY5 combination yields $w_{\mathrm{dm}} = -0.084 \pm 0.035$, excluding CDM ($w_{\mathrm{dm}}=0$) at 2.4$\sigma$ significance. In contrast, Planck+DESI gives $w_{\mathrm{dm}} = 0.00077\pm0.00038$, differing from concordance model at 2$\sigma$ significance. The non-vanishing $w_{\mathrm{dm}}$ preference is particularly driven by low-redshift BAO measurements ($z<1.1$), while higher redshift data remain consistent with $\Lambda$CDM. The evidence for non-cold dark matter is more pronounced in DESI compared to the previous BAO surveys. All dataset combinations show significant improvement over the $\Lambda$CDM paradigm, providing compelling evidence for non-cold dark matter scenario.
Machine learning enhanced atom probe tomography analysis: a snapshot review
2025-04-19
Yue Li, Ye Wei, Alaukik Saxena, Markus Kühbach, Christoph Freysoldt, Baptiste Gault
Atom probe tomography (APT) is a burgeoning characterization technique that provides compositional mapping of materials in three-dimensions at near-atomic scale. Since its significant expansion in the past 30 years, we estimate that one million APT datasets have been collected, each containing millions to billions of individual ions. Their analysis and the extraction of microstructural information has largely relied upon individual users whose varied level of expertise causes clear and documented bias. Current practices hinder efficient data processing, and make challenging standardization and the deployment of data analysis workflows that would be compliant with FAIR data principles. Over the past decade, building upon the long-standing expertise of the APT community in the development of advanced data processing or data mining techniques, there has been a surge of novel machine learning (ML) approaches aiming for user-independence, and that are efficient, reproducible, and robust from a statistics perspective. Here, we provide a snapshot review of this rapidly evolving field. We begin with a brief introduction to APT and the nature of the APT data. This is followed by an overview of relevant ML algorithms and a comprehensive review of their applications to APT. We also discuss how ML can enable discoveries beyond human capability, offering new insights into the mechanisms within materials. Finally, we provide guidance for future directions in this domain.
Toward Generation of Test Cases from Task Descriptions via History-aware Planning
2025-04-19
Duy Cao, Phu Nguyen, Vy Le, Tien N. Nguyen, Vu Nguyen
In automated web testing, generating test scripts from natural language task descriptions is crucial for enhancing the test generation process. This activity involves creating the correct sequences of actions to form test scripts for future testing activities. Current state-of-the-art approaches are limited in generating these action sequences, as they either demand substantial manual effort for human demonstrations or fail to consider the history of previous web content and actions to decide the next action. In this paper, we introduce HxAgent, an iterative large language model agent planning approach that determines the next action based on: 1) observations of the current contents and feasible actions, 2) short-term memory of previous web states and actions, and 3) long-term experience with (in)correct action sequences. The agent generates a sequence of actions to perform a given task, which is effectively an automated test case to verify the task. We conducted an extensive empirical evaluation of HxAgent using two datasets. On the MiniWoB++ dataset, our approach achieves 97% exact-match accuracy that is comparable to the best baselines while eliminating the need for human demonstrations required by those methods. For complex tasks requiring navigation through multiple actions and screens, HxAgent achieves an average 82% exact-match. On the second dataset, comprising 350 task instances across seven popular websites, including YouTube, LinkedIn, Facebook, and Google, HxAgent achieves high performance, with 87% of the action sequences exactly matching the ground truth and a prefix-match of 93%, outperforming the baseline by 59%.
Dusty stellar point sources are a significant stage in stellar evolution and contribute to the metal enrichment of galaxies. These objects can be classified using photometric and spectroscopic observations with color-magnitude diagrams (CMD) and infrared excesses in spectral energy distributions (SED). We employed supervised machine learning spectral classification to categorize dusty stellar sources, including young stellar objects (YSOs) and evolved stars (oxygen- and carbon-rich asymptotic giant branch stars, AGBs), red supergiants (RSGs), and post-AGB (PAGB) stars in the Large and Small Magellanic Clouds, based on spectroscopic labeled data from the Surveying the Agents of Galaxy Evolution (SAGE) project, which used 12 multiwavelength filters and 618 stellar objects. Despite missing values and uncertainties in the SAGE spectral datasets, we achieved accurate classifications. To address small and imbalanced spectral catalogs, we used the Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic data points. Among models applied before and after data augmentation, the Probabilistic Random Forest (PRF), a tuned Random Forest (RF), achieved the highest total accuracy, reaching $\mathbf{89\%}$ based on recall in categorizing dusty stellar sources. Using SMOTE does not improve the best model's accuracy for the CAGB, PAGB, and RSG classes; it remains $\mathbf{100\%}$, $\mathbf{100\%}$, and $\mathbf{88\%}$, respectively, but shows variations for OAGB and YSO classes. We also collected photometric labeled data similar to the training dataset, classifying them using the top four PRF models with over $\mathbf{87\%}$ accuracy. Multiwavelength data from several studies were classified using a consensus model integrating four top models to present common labels as final predictions.
The Ophiuchus DIsk Survey Employing ALMA (ODISEA): A Unified Evolutionary Sequence of Planet-Driven Substructures Explaining the Diversity of Disk Morphologies
2025-04-19
Santiago Orcajo, Lucas A. Cieza, Octavio Guilera, Sebastian Perez, Fernando R. Rannou, Camilo Gonzalez-Ruilova, Grace Batalla-Falcon, Trisha Bhowmik, Prachi Chavan, Simon Casassus, Anuroop Dasgupta, Kevin Diaz, Jose L. Gomez, Antonio S. Hales, J. M. Miley, Marcelo M. Miller Bertolami, P. H. Nogueira, Maria Paula Ronco, Dary Ruiz-Rodriguez, Anibal Sierra, Julia Venturini, Philipp Weber, Jonathan P. Williams, Alice Zurlo
Understanding the origin of substructures in protoplanetary disks and their connection to planet formation is currently one of the main challenges in astrophysics. While some disks appear smooth, most exhibit diverse substructures such as gaps, rings, or inner cavities, with varying brightness and depth. As part of the Ophiuchus Disk Survey Employing ALMA (ODISEA), we previously proposed an evolutionary sequence to unify this diversity, driven by the formation of giant planets through core accretion and subsequent planet-disk interactions. By combining the disk evolution and planet formation code PLANETALP with the radiative transfer code RADMC-3D, we have now reproduced the key aspects of the proposed evolutionary sequence. Starting with a smooth disk (like e.g., WLY 2-63), we modeled the evolution of a fiducial disk with a 1 Jupiter-mass planet at 57 au. Within a few hundreds of orbits, a narrow gap forms, resembling ISO-Oph 17. By $\sim$0.1 Myr, the gap widens, and dust accumulates at the cavity edge, producing a structure similar to Elias 2-24. At $\sim$0.4 Myr, the disk evolves further into a morphology akin to DoAr 44, characterized by a smaller inner disk and a brighter inner rim. By $\sim$1 Myr, the system transitions to a single narrow ring, resembling RXJ1633.9-2442. This line of work strongly supports the planetary origin of substructures and enables the possibility of identifying a population of planets that is currently beyond the reach of more direct detection techniques.
The Role of Flexoelectric Coupling and Chemical Strains in the Emergence of Polar Chiral Nano-Structures
2025-04-19
Anna N. Morozovska, Salia Cherifi-Hertel, Eugene A. Eliseev, Victoria V. Khist, Riccardo Hertel, Dean R. Evans
This review examines the conditions that lead to the formation of flexo-sensitive chiral polar structures in thin films and core-shell ferroelectric nanoparticles. It also analyzes possible mechanisms by which the flexoelectric effect impacts the polarization structure in core-shell ferroelectric nanoparticles. Special attention is given to the role of the anisotropic flexoelectric effect in forming a unique type of polarization states with distinct chiral properties, referred to as "flexons". In the first part of the review, we study the influence of the flexoelectric coupling on the polarity, chirality and branching of metastable labyrinthine domain structures in uniaxial ferroelectric core-shell nanoparticles. We reveal that the transition from sinuous branched domain stripes to spiral-like domains occurs gradually as the flexoelectric coupling strength is increased. Our findings indicate that the joint action of flexoelectric effect and chemical strains, termed as "flexo-chemical" coupling, can significantly influence the effective Curie temperature, polarization distribution, domain morphology, and chirality in multiaxial ferroelectric core-shell nanoparticles. Furthermore, we demonstrate that the combination of flexo-chemical coupling and screening effects leads to the appearance and stabilization of a chiral polarization morphology in nanoflakes of van der Waals ferrielectrics. In the second part of the review, we discuss several advanced applications of flexo-sensitive chiral polar structures in core-shell ferroelectric nanoparticles for nanoelectronics elements and cryptography. We underline the possibilities of the flexoelectric control of multiple-degenerated labyrinthine states, which may correspond to a differential negative capacitance (NC) state stabilized in the uniaxial ferroelectric core by the presence of a screening shell.
As machine learning evolves, domain generalization (DG) and domain adaptation (DA) have become crucial for enhancing model robustness across diverse environments. Contrastive Language-Image Pretraining (CLIP) plays a significant role in these tasks, offering powerful zero-shot capabilities that allow models to perform effectively in unseen domains. However, there remains a significant gap in the literature, as no comprehensive survey currently exists that systematically explores the applications of CLIP in DG and DA, highlighting the necessity for this review. This survey presents a comprehensive review of CLIP's applications in DG and DA. In DG, we categorize methods into optimizing prompt learning for task alignment and leveraging CLIP as a backbone for effective feature extraction, both enhancing model adaptability. For DA, we examine both source-available methods utilizing labeled source data and source-free approaches primarily based on target domain data, emphasizing knowledge transfer mechanisms and strategies for improved performance across diverse contexts. Key challenges, including overfitting, domain diversity, and computational efficiency, are addressed, alongside future research opportunities to advance robustness and efficiency in practical applications. By synthesizing existing literature and pinpointing critical gaps, this survey provides valuable insights for researchers and practitioners, proposing directions for effectively leveraging CLIP to enhance methodologies in domain generalization and adaptation. Ultimately, this work aims to foster innovation and collaboration in the quest for more resilient machine learning models that can perform reliably across diverse real-world scenarios. A more up-to-date version of the papers is maintained at: https://github.com/jindongli-Ai/Survey_on_CLIP-Powered_Domain_Generalization_and_Adaptation.
Machine Learning Classification of Young Stellar Objects and Evolved Stars in the Magellanic Clouds Using the Probabilistic Random Forest Classifier
2025-04-19
Sepideh Ghaziasgar, Mahdi Abdollahi, Atefeh Javadi, Jacco Th. van Loon, Iain McDonald, Joana Oliveira, Habib G. Khosroshahi
The Magellanic Clouds (MCs) are excellent locations to study stellar dust emission and its contribution to galaxy evolution. Through spectral and photometric classification, MCs can serve as a unique environment for studying stellar evolution and galaxies enriched by dusty stellar point sources. We applied machine learning classifiers to spectroscopically labeled data from the Surveying the Agents of Galaxy Evolution (SAGE) project, which involved 12 multiwavelength filters and 618 stellar objects at the MCs. We classified stars into five categories: young stellar objects (YSOs), carbon-rich asymptotic giant branch (CAGB) stars, oxygen-rich AGB (OAGB) stars, red supergiants (RSG), and post-AGB (PAGB) stars. Following this, we augmented the distribution of imbalanced classes using the Synthetic Minority Oversampling Technique (SMOTE). Therefore, the Probabilistic Random Forest (PRF) classifier achieved the highest overall accuracy, reaching ${89\%}$ based on the recall metric, in categorizing dusty stellar sources before and after data augmentation. In this study, SMOTE did not impact the classification accuracy for the CAGB, PAGB, and RSG categories but led to changes in the performance of the OAGB and YSO classes.
FedCIA: Federated Collaborative Information Aggregation for Privacy-Preserving Recommendation
2025-04-19
Mingzhe Han, Dongsheng Li, Jiafeng Xia, Jiahao Liu, Hansu Gu, Peng Zhang, Ning Gu, Tun Lu
Recommendation algorithms rely on user historical interactions to deliver personalized suggestions, which raises significant privacy concerns. Federated recommendation algorithms tackle this issue by combining local model training with server-side model aggregation, where most existing algorithms use a uniform weighted summation to aggregate item embeddings from different client models. This approach has three major limitations: 1) information loss during aggregation, 2) failure to retain personalized local features, and 3) incompatibility with parameter-free recommendation algorithms. To address these limitations, we first review the development of recommendation algorithms and recognize that their core function is to share collaborative information, specifically the global relationship between users and items. With this understanding, we propose a novel aggregation paradigm named collaborative information aggregation, which focuses on sharing collaborative information rather than item parameters. Based on this new paradigm, we introduce the federated collaborative information aggregation (FedCIA) method for privacy-preserving recommendation. This method requires each client to upload item similarity matrices for aggregation, which allows clients to align their local models without constraining embeddings to a unified vector space. As a result, it mitigates information loss caused by direct summation, preserves the personalized embedding distributions of individual clients, and supports the aggregation of parameter-free models. Theoretical analysis and experimental results on real-world datasets demonstrate the superior performance of FedCIA compared with the state-of-the-art federated recommendation algorithms. Code is available at https://github.com/Mingzhe-Han/FedCIA.
Dual-channel Heterophilic Message Passing for Graph Fraud Detection
2025-04-19
Wenxin Zhang, Jingxing Zhong, Guangzhen Yao, Renda Han, Xiaojian Lin, Zeyu Zhang, Cuicui Luo
Fraudulent activities have significantly increased across various domains, such as e-commerce, online review platforms, and social networks, making fraud detection a critical task. Spatial Graph Neural Networks (GNNs) have been successfully applied to fraud detection tasks due to their strong inductive learning capabilities. However, existing spatial GNN-based methods often enhance the graph structure by excluding heterophilic neighbors during message passing to align with the homophilic bias of GNNs. Unfortunately, this approach can disrupt the original graph topology and increase uncertainty in predictions. To address these limitations, this paper proposes a novel framework, Dual-channel Heterophilic Message Passing (DHMP), for fraud detection. DHMP leverages a heterophily separation module to divide the graph into homophilic and heterophilic subgraphs, mitigating the low-pass inductive bias of traditional GNNs. It then applies shared weights to capture signals at different frequencies independently and incorporates a customized sampling strategy for training. This allows nodes to adaptively balance the contributions of various signals based on their labels. Extensive experiments on three real-world datasets demonstrate that DHMP outperforms existing methods, highlighting the importance of separating signals with different frequencies for improved fraud detection. The code is available at https://github.com/shaieesss/DHMP.
The composition of pre-training datasets for large language models (LLMs) remains largely undisclosed, hindering transparency and efforts to optimize data quality, a critical driver of model performance. Current data selection methods, such as natural language quality assessments, diversity-based filters, and classifier-based approaches, are limited by single-dimensional evaluation or redundancy-focused strategies. To address these gaps, we propose PRRC to evaluate data quality across Professionalism, Readability, Reasoning, and Cleanliness. We further introduce Meta-rater, a multi-dimensional data selection method that integrates these dimensions with existing quality metrics through learned optimal weightings. Meta-rater employs proxy models to train a regression model that predicts validation loss, enabling the identification of optimal combinations of quality scores. Experiments demonstrate that Meta-rater doubles convergence speed for 1.3B parameter models and improves downstream task performance by 3.23, with scalable benefits observed in 3.3B models trained on 100B tokens. Additionally, we release the annotated SlimPajama-627B dataset, labeled across 25 quality metrics (including PRRC), to advance research in data-centric LLM development. Our work establishes that holistic, multi-dimensional quality integration significantly outperforms conventional single-dimension approaches, offering a scalable paradigm for enhancing pre-training efficiency and model capability.
Retrieval of fundamental material parameters of monolayer transition metal dichalcogenides from experimental exciton energies: An analytical approach
2025-04-19
Duy-Nhat Ly, Dai-Nam Le, Dang-Khoa D. Le, Van-Hoang Le
We propose a straightforward and highly accurate method for determining material parameters such as screening length, bandgap energy, exciton reduced mass, and dielectric constant of the surrounding medium using experimental magnetoexciton energies available for monolayer transition metal dichalcogenides (TMDCs). Our approach is based on analytical formulations that allow us to calculate the screening length $r_0$ and bandgap energy $E_g$ directly from the experimental $s$-state exciton energies $E_{1s}$, $E_{2s}$, and $E_{3s}$. We also establish a relationship between the surrounding dielectric constant $\kappa$ and the exciton reduced mass $\mu$. This relationship simplifies the Schr{\"o}dinger equation for a magnetoexciton in a TMDC monolayer, transforming it into a one-parameter equation that depends only on a single material parameter, $\mu$. Therefore, the fitting method is easy to implement, as it compares calculated magnetoexciton energies with experimental data to determine the true exciton reduced mass, the best-fit value of $\mu$. By applying this method, we extract material parameters $E_g$, $r_0$, $\mu$, and $\kappa$ from the magnetoexciton energies of monolayer TMDCs (WSe$_2$, WS$_2$, MoSe$_2$, and MoS$_2$) encapsulated by hexagonal boron nitride (hBN) slabs in various current experiments. Additionally, we provide key material properties such as diamagnetic coefficients and exciton radii, which are calculated using the extracted parameters.
In this work, we present a study on the long time-scale period variations of four single-mode high-amplitude delta Scuti stars (HADS) via the classical $O-C$ analysis. The target HADS are (i) XX Cygni, (ii) YZ Bootis, (iii) GP Andromedae, and (iv) ZZ Microscopii. The newly determined times of maximum light came from the Transiting Exoplanet Survey Satellite (TESS), American Association of Variable Star Observers (AAVSO), and Bundesdeutsche Arbeitsgemeinschaft f$\ddot{\rm u}$r Ver$\ddot{\rm a}$nderliche Sterne (BAV) projects. Together with the times of maximum light obtained in the historical literature, the $O-C$ analysis was performed on these HADS, in which we obtained the linear period variation rates $\dot{P}/P$ as $(9.2 \pm 0.2) \times 10^{-9} \ \mathrm{yr^{-1}}$, $(3.2\pm 0.2)\times 10^{-9} \ \mathrm{yr^{-1}}$, $(4.22\pm 0.03) \times 10^{-8} \ \mathrm{yr^{-1}}$, and $(-2.06 \pm 0.02) \times 10^{-8} \ \mathrm{yr^{-1}}$, respectively. Based on these results and some earlier research, we also discuss the evolutionary stages and the mechanisms of the period variation of these four HADS.
With the rapid development of machine learning in recent years, many problems in meteorology can now be addressed using AI models. In particular, data-driven algorithms have significantly improved accuracy compared to traditional methods. Meteorological data is often transformed into 2D images or 3D videos, which are then fed into AI models for learning. Additionally, these models often incorporate physical signals, such as temperature, pressure, and wind speed, to further enhance accuracy and interpretability. In this paper, we review several representative AI + Weather/Climate algorithms and propose a new paradigm where observational data from different perspectives, each with distinct physical meanings, are treated as multimodal data and integrated via transformers. Furthermore, key weather and climate knowledge can be incorporated through regularization techniques to further strengthen the model's capabilities. This new paradigm is versatile and can address a variety of tasks, offering strong generalizability. We also discuss future directions for improving model accuracy and interpretability.
Overestimation of turnout has long been an issue in election surveys, with nonresponse bias or voter overrepresentation regarded as one of the major sources of bias. However, the adjustment for nonignorable nonresponse bias is substantially challenging. Based on the ANES Non-Response Follow-Up Study concerning the 2020 U.S. presidential election, we investigate the role of callback data in adjusting for nonresponse bias in the estimation of turnout. Callback data are the records of contact attempts in the survey course, available in many modern large-scale surveys. We propose a stableness of resistance assumption to account for the nonignorable missingness in the outcome, which states that the impact of the missing outcome on the response propensity is stable in the first two call attempts. Under this assumption and by leveraging covariates information from the census data, we establish the identifiability and develop estimation methods for turnout, including a doubly robust estimator. Our methods produce estimates very close to the official turnout and successfully capture the trend of declining willingness to vote as response reluctance or contact difficulty increases. This work hints at the importance of adjusting for nonignorable nonresponse bias and exhibits the promise of callback data for political surveys.
Inclusive Education with AI: Supporting Special Needs and Tackling Language Barriers
2025-04-19
Ricardo Fitas
Early childhood classrooms are becoming increasingly diverse, with students spanning a range of linguistic backgrounds and abilities. AI offers innovative tools to help educators create more inclusive learning environments by breaking down language barriers and providing tailored support for children with special needs. This chapter provides a comprehensive review of how AI technologies can facilitate inclusion in early education. It is discussed AI-driven language assistance tools that enable real-time translation and communication in multilingual classrooms, and it is explored assistive technologies powered by AI that personalize learning for students with disabilities. The implications of these technologies for teachers are examined, including shifts in educator roles and workloads. General outcomes observed with AI integration - such as improved student engagement and performance - as well as challenges related to equitable access and the need for ethical implementation are highlighted. Finally, practical recommendations for educators, policymakers, and developers are offered to collaboratively harness AI in a responsible manner, ensuring that its benefits reach all learners.
PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models
2025-04-19
Nusrat Jahan Prottasha, Upama Roy Chowdhury, Shetu Mohanto, Tasfia Nuzhat, Abdullah As Sami, Md Shamol Ali, Md Shohanur Islam Sobuj, Hafijur Raman, Md Kowsher, Ozlem Ozmen Garibay
Large models such as Large Language Models (LLMs) and Vision Language Models (VLMs) have transformed artificial intelligence, powering applications in natural language processing, computer vision, and multimodal learning. However, fully fine-tuning these models remains expensive, requiring extensive computational resources, memory, and task-specific data. Parameter-Efficient Fine-Tuning (PEFT) has emerged as a promising solution that allows adapting large models to downstream tasks by updating only a small portion of parameters. This survey presents a comprehensive overview of PEFT techniques, focusing on their motivations, design principles, and effectiveness. We begin by analyzing the resource and accessibility challenges posed by traditional fine-tuning and highlight key issues, such as overfitting, catastrophic forgetting, and parameter inefficiency. We then introduce a structured taxonomy of PEFT methods -- grouped into additive, selective, reparameterized, hybrid, and unified frameworks -- and systematically compare their mechanisms and trade-offs. Beyond taxonomy, we explore the impact of PEFT across diverse domains, including language, vision, and generative modeling, showing how these techniques offer strong performance with lower resource costs. We also discuss important open challenges in scalability, interpretability, and robustness, and suggest future directions such as federated learning, domain adaptation, and theoretical grounding. Our goal is to provide a unified understanding of PEFT and its growing role in enabling practical, efficient, and sustainable use of large models.
Power Transformer Health Index and Life Span Assessment: A Comprehensive Review of Conventional and Machine Learning based Approaches
2025-04-19
Syeda Tahreem Zahra, Syed Kashif Imdad, Sohail Khan, Sohail Khalid, Nauman Anwar Baig
Power transformers play a critical role within the electrical power system, making their health assessment and the prediction of their remaining lifespan paramount for the purpose of ensuring efficient operation and facilitating effective maintenance planning. This paper undertakes a comprehensive examination of existent literature, with a primary focus on both conventional and cutting-edge techniques employed within this domain. The merits and demerits of recent methodologies and techniques are subjected to meticulous scrutiny and explication. Furthermore, this paper expounds upon intelligent fault diagnosis methodologies and delves into the most widely utilized intelligent algorithms for the assessment of transformer conditions. Diverse Artificial Intelligence (AI) approaches, including Artificial Neural Networks (ANN) and Convolutional Neural Network (CNN), Support Vector Machine (SVM), Random Forest (RF), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), are elucidated offering pragmatic solutions for enhancing the performance of transformer fault diagnosis. The amalgamation of multiple AI methodologies and the exploration of timeseries analysis further contribute to the augmentation of diagnostic precision and the early detection of faults in transformers. By furnishing a comprehensive panorama of AI applications in the field of transformer fault diagnosis, this study lays the groundwork for future research endeavors and the progression of this critical area of study.
2025-04-18
Leon Staufer, Mick Yang, Anka Reuel, Stephen Casper
AI governance frameworks increasingly rely on audits, yet the results of their underlying evaluations require interpretation and context to be meaningfully informative. Even technically rigorous evaluations can offer little useful insight if reported selectively or obscurely. Current literature focuses primarily on technical best practices, but evaluations are an inherently sociotechnical process, and there is little guidance on reporting procedures and context. Through literature review, stakeholder interviews, and analysis of governance frameworks, we propose "audit cards" to make this context explicit. We identify six key types of contextual features to report and justify in audit cards: auditor identity, evaluation scope, methodology, resource access, process integrity, and review mechanisms. Through analysis of existing evaluation reports, we find significant variation in reporting practices, with most reports omitting crucial contextual information such as auditors' backgrounds, conflicts of interest, and the level and type of access to models. We also find that most existing regulations and frameworks lack guidance on rigorous reporting. In response to these shortcomings, we argue that audit cards can provide a structured format for reporting key claims alongside their justifications, enhancing transparency, facilitating proper interpretation, and establishing trust in reporting.
Knowledge distillation (KD) is a technique for transferring knowledge from complex teacher models to simpler student models, significantly enhancing model efficiency and accuracy. It has demonstrated substantial advancements in various applications including image classification, object detection, language modeling, text classification, and sentiment analysis. Recent innovations in KD methods, such as attention-based approaches, block-wise logit distillation, and decoupling distillation, have notably improved student model performance. These techniques focus on stimulus complexity, attention mechanisms, and global information capture to optimize knowledge transfer. In addition, KD has proven effective in compressing large language models while preserving accuracy, reducing computational overhead, and improving inference speed. This survey synthesizes the latest literature, highlighting key findings, contributions, and future directions in knowledge distillation to provide insights for researchers and practitioners on its evolving role in artificial intelligence and machine learning.
Parameter-Efficient Continual Fine-Tuning: A Survey
2025-04-18
Eric Nuertey Coleman, Luigi Quarantiello, Ziyue Liu, Qinwen Yang, Samrat Mukherjee, Julio Hurtado, Vincenzo Lomonaco
The emergence of large pre-trained networks has revolutionized the AI field, unlocking new possibilities and achieving unprecedented performance. However, these models inherit a fundamental limitation from traditional Machine Learning approaches: their strong dependence on the \textit{i.i.d.} assumption hinders their adaptability to dynamic learning scenarios. We believe the next breakthrough in AI lies in enabling efficient adaptation to evolving environments -- such as the real world -- where new data and tasks arrive sequentially. This challenge defines the field of Continual Learning (CL), a Machine Learning paradigm focused on developing lifelong learning neural models. One alternative to efficiently adapt these large-scale models is known Parameter-Efficient Fine-Tuning (PEFT). These methods tackle the issue of adapting the model to a particular data or scenario by performing small and efficient modifications, achieving similar performance to full fine-tuning. However, these techniques still lack the ability to adjust the model to multiple tasks continually, as they suffer from the issue of Catastrophic Forgetting. In this survey, we first provide an overview of CL algorithms and PEFT methods before reviewing the state-of-the-art on Parameter-Efficient Continual Fine-Tuning (PECFT). We examine various approaches, discuss evaluation metrics, and explore potential future research directions. Our goal is to highlight the synergy between CL and Parameter-Efficient Fine-Tuning, guide researchers in this field, and pave the way for novel future research directions.
Transformer Encoder and Multi-features Time2Vec for Financial Prediction
2025-04-18
Nguyen Kim Hai Bui, Nguyen Duy Chien, Péter Kovács, Gergő Bognár
Financial prediction is a complex and challenging task of time series analysis and signal processing, expected to model both short-term fluctuations and long-term temporal dependencies. Transformers have remarkable success mostly in natural language processing using attention mechanism, which also influenced the time series community. The ability to capture both short and long-range dependencies helps to understand the financial market and to recognize price patterns, leading to successful applications of Transformers in stock prediction. Although, the previous research predominantly focuses on individual features and singular predictions, that limits the model's ability to understand broader market trends. In reality, within sectors such as finance and technology, companies belonging to the same industry often exhibit correlated stock price movements. In this paper, we develop a novel neural network architecture by integrating Time2Vec with the Encoder of the Transformer model. Based on the study of different markets, we propose a novel correlation feature selection method. Through a comprehensive fine-tuning of multiple hyperparameters, we conduct a comparative analysis of our results against benchmark models. We conclude that our method outperforms other state-of-the-art encoding methods such as positional encoding, and we also conclude that selecting correlation features enhance the accuracy of predicting multiple stock prices.
Bake Two Cakes with One Oven: RL for Defusing Popularity Bias and Cold-start in Third-Party Library Recommendations
2025-04-18
Minh Hoang Vuong, Anh M. T. Bui, Phuong T. Nguyen, Davide Di Ruscio
Third-party libraries (TPLs) have become an integral part of modern software development, enhancing developer productivity and accelerating time-to-market. However, identifying suitable candidates from a rapidly growing and continuously evolving collection of TPLs remains a challenging task. TPL recommender systems have been studied, offering a promising solution to address this issue. They typically rely on collaborative filtering (CF) that exploits a two-dimensional project-library matrix (user-item in general context of recommendation) when making recommendations. We have noticed that CF-based approaches often encounter two challenges: (i) a tendency to recommend popular items more frequently, making them even more dominant, a phenomenon known as popularity bias, and (ii) difficulty in generating recommendations for new users or items due to limited user-item interactions, commonly referred to as the cold-start problem. In this paper, we propose a reinforcement learning (RL)-based approach to address popularity bias and the cold-start problem in TPL recommendation. Our method comprises three key components. First, we utilize a graph convolution network (GCN)-based embedding model to learn user preferences and user-item interactions, allowing us to capture complex relationships within interaction subgraphs and effectively represent new user/item embeddings. Second, we introduce an aggregation operator to generate a representative embedding from user and item embeddings, which is then used to model cold-start users. Finally, we adopt a model-based RL framework for TPL recommendation, where popularity bias is mitigated through a carefully designed reward function and a rarity-based replay buffer partitioning strategy. The results demonstrated that our proposed approach outperforms state-of-the-art models in cold-start scenarios while effectively mitigating the impact of popularity bias.
Detecting Malicious Source Code in PyPI Packages with LLMs: Does RAG Come in Handy?
2025-04-18
Motunrayo Ibiyo, Thinakone Louangdy, Phuong T. Nguyen, Claudio Di Sipio, Davide Di Ruscio
Malicious software packages in open-source ecosystems, such as PyPI, pose growing security risks. Unlike traditional vulnerabilities, these packages are intentionally designed to deceive users, making detection challenging due to evolving attack methods and the lack of structured datasets. In this work, we empirically evaluate the effectiveness of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and few-shot learning for detecting malicious source code. We fine-tune LLMs on curated datasets and integrate YARA rules, GitHub Security Advisories, and malicious code snippets with the aim of enhancing classification accuracy. We came across a counterintuitive outcome: While RAG is expected to boost up the prediction performance, it fails in the performed evaluation, obtaining a mediocre accuracy. In contrast, few-shot learning is more effective as it significantly improves the detection of malicious code, achieving 97% accuracy and 95% balanced accuracy, outperforming traditional RAG approaches. Thus, future work should expand structured knowledge bases, refine retrieval models, and explore hybrid AI-driven cybersecurity solutions.
A Survey for What Developers Require in AI-powered Tools that Aid in Component Selection in CBSD
2025-04-18
Mahdi Jaberzadeh Ansari, Ann Barcomb
Although it has been more than four decades that the first components-based software development (CBSD) studies were conducted, there is still no standard method or tool for component selection which is widely accepted by the industry. The gulf between industry and academia contributes to the lack of an accepted tool. We conducted a mixed methods survey of nearly 100 people engaged in component-based software engineering practice or research to better understand the problems facing industry, how these needs could be addressed, and current best practices employed in component selection. We also sought to identify and prioritize quality criteria for component selection from an industry perspective. In response to the call for CBSD component selection tools to incorporate recent technical advances, we also explored the perceptions of professionals about AI-driven tools, present and envisioned.
Human-aligned Deep Learning: Explainability, Causality, and Biological Inspiration
2025-04-18
Gianluca Carloni
This work aligns deep learning (DL) with human reasoning capabilities and needs to enable more efficient, interpretable, and robust image classification. We approach this from three perspectives: explainability, causality, and biological vision. Introduction and background open this work before diving into operative chapters. First, we assess neural networks' visualization techniques for medical images and validate an explainable-by-design method for breast mass classification. A comprehensive review at the intersection of XAI and causality follows, where we introduce a general scaffold to organize past and future research, laying the groundwork for our second perspective. In the causality direction, we propose novel modules that exploit feature co-occurrence in medical images, leading to more effective and explainable predictions. We further introduce CROCODILE, a general framework that integrates causal concepts, contrastive learning, feature disentanglement, and prior knowledge to enhance generalization. Lastly, we explore biological vision, examining how humans recognize objects, and propose CoCoReco, a connectivity-inspired network with context-aware attention mechanisms. Overall, our key findings include: (i) simple activation maximization lacks insight for medical imaging DL models; (ii) prototypical-part learning is effective and radiologically aligned; (iii) XAI and causal ML are deeply connected; (iv) weak causal signals can be leveraged without a priori information to improve performance and interpretability; (v) our framework generalizes across medical domains and out-of-distribution data; (vi) incorporating biological circuit motifs improves human-aligned recognition. This work contributes toward human-aligned DL and highlights pathways to bridge the gap between research and clinical adoption, with implications for improved trust, diagnostic accuracy, and safe deployment.
Graph Few-Shot Class-Incremental Learning (GFSCIL) enables models to continually learn from limited samples of novel tasks after initial training on a large base dataset. Existing GFSCIL approaches typically utilize Prototypical Networks (PNs) for metric-based class representations and fine-tune the model during the incremental learning stage. However, these PN-based methods oversimplify learning via novel query set fine-tuning and fail to integrate Graph Continual Learning (GCL) techniques due to architectural constraints. To address these challenges, we propose a more rigorous and practical setting for GFSCIL that excludes query sets during the incremental training phase. Building on this foundation, we introduce Model-Agnostic Meta Graph Continual Learning (MEGA), aimed at effectively alleviating catastrophic forgetting for GFSCIL. Specifically, by calculating the incremental second-order gradient during the meta-training stage, we endow the model to learn high-quality priors that enhance incremental learning by aligning its behaviors across both the meta-training and incremental learning stages. Extensive experiments on four mainstream graph datasets demonstrate that MEGA achieves state-of-the-art results and enhances the effectiveness of various GCL methods in GFSCIL. We believe that our proposed MEGA serves as a model-agnostic GFSCIL paradigm, paving the way for future research.
Topics in representation theory and Riemannian geometry
2025-04-18
Giovanni Russo
These are notes for a Ph.D.\ course I held at SISSA, Trieste, in the Winter 2025. We review well-known topics in Riemannian geometry where Lie groups play a fundamental role. Part of the theory of compact connected Lie groups, their invariants, and representations is discussed, with particular emphasis on low dimensional examples. We go through a number of applications in Riemannian geometry, in particular the classification of Riemannian holonomy groups, and the first construction of exceptional holonomy metrics. Some more recent advances in the field of Riemannian geometry with symmetries are mentioned.
Detecting Intermediate-mass Black Holes Using Quasar Microlensing
2025-04-18
Zihao Wu, Luis C. Ho
Recent studies suggest that numerous intermediate-mass black holes (IMBHs) may wander undetected across the Universe, emitting little radiation. These IMBHs largely preserve their birth masses, offering critical insights into the formation of heavy black hole seeds and the dynamical processes driving their evolution. We propose that such IMBHs could produce detectable microlensing effects on quasars. Their Einstein radii, comparable to the scale of quasar broad-line regions, magnify radiation from the accretion disk and broad emission lines, making these quasars outliers in flux scaling relations. Meanwhile, the microlensing causes long-term, quasi-linear variability that is distinguishable from the stochastic variability of quasars through its coherent multi-wavelength behavior. We develop a matched-filtering technique that effectively separates the long-term lensing signal from the intrinsic quasar variability, with sensitivity tripling each time the observational time span doubles. Moreover, as IMBHs are often surrounded by dense star clusters, their combined gravitational field produces substantial extended, concentric caustics. These caustics induce significant variability in optical, ultraviolet, and X-ray bands over decade timescales, alongside hour-to-day-scale flux fluctuations in broad emission lines. We predict a substantial number of detectable events in the upcoming surveys by the Vera C. Rubin Observatory, considering recent IMBH mass density estimates. Even in the absence of positive detections, searches for these microlensing signals will place meaningful constraints on the cosmological mass density of IMBHs, advancing our understanding of their role in cosmic evolution.
Large Language Models Will Change The Way Children Think About Technology And Impact Every Interaction Paradigm
2025-04-18
Russell Beale
This paper presents a hopeful perspective on the potentially dramatic impacts of Large Language Models on how we children learn and how they will expect to interact with technology. We review the effects of LLMs on education so far, and make the case that these effects are minor compared to the upcoming changes that are occurring. We present a small scenario and self-ethnographic study demonstrating the effects of these changes, and define five significant considerations that interactive systems designers will have to accommodate in the future.
SRGAJ230631.0+155633: an extremely X-ray luminous, heavily obscured, radio-loud quasar at z=0.44 discovered by SRG/ART-XC
2025-04-18
Grigory Uskov, Sergey Sazonov, Igor Lapshov, Alexander Mikhailov, Ekaterina Filippova, Alexander Lutovinov, Ilya Mereminskiy, Maria Mochalina, Andrey Semena, Alexey Tkachenko
We report on a detailed study of a luminous, heavily obscured ($N_{\rm H} \sim 2 \times 10^{23}$ cm$^{-2}$), radio-loud quasar SRGAJ230631.0+155633, discovered in the 4--12 keV energy band by the Mikhail Pavlinsky ART-XC telescope aboard the SRG observatory during the first two years of its all-sky X-ray survey in 2020--2021. The object is located at $z=0.4389$ and is a type 2 AGN according to optical spectroscopy (SDSS, confirmed by DESI). Our analysis combines data from the radio to the X-ray energy range, including quasi-simultaneous pointed observations with the SRG/ART-XC and Swift/XRT telescopes, conducted in June 2023. During these follow-up observations, the source was found in a significantly fainter but still very luminous state ($L_{\rm X}=1.0^{+0.8}_{-0.3} \times 10^{45}$ erg s$^{-1}$, absorption corrected, 2--10 keV) compared to its discovery during the all-sky survey ($L_{\rm X}=6^{+6}_{-3}\times10^{45}$ erg s$^{-1}$), which indicates significant intrinsic variability on a rest-frame time scale of $\sim 1$ year. The radio data show a complex morphology with a core and two extended radio lobes, indicating a giant FRII radio galaxy. Using multiwavelength photometry and the black hole--bulge mass scaling relation, we estimate the bolometric luminosity of the quasar at $\sim 6\times 10^{46}$ erg s$^{-1}$ and the mass of its central black hole at $\sim 1.4 \times 10^9$ $M_\odot$. This implies that the black hole is accreting at $\sim 30$ of the Eddington limit. Overall, SRGAJ230631.0+155633 proves to be one of the most luminous obscured quasars in the observable Universe out to $z=0.5$ (i.e. over the last 5 billion years of cosmic time). As such, it can serve as a valuable testbed for in-depth exploration of the physics of such objects, which were much more abundant in the younger Universe.
Multi-Type Context-Aware Conversational Recommender Systems via Mixture-of-Experts
2025-04-18
Jie Zou, Cheng Lin, Weikang Guo, Zheng Wang, Jiwei Wei, Yang Yang, Hengtao Shen
Conversational recommender systems enable natural language conversations and thus lead to a more engaging and effective recommendation scenario. As the conversations for recommender systems usually contain limited contextual information, many existing conversational recommender systems incorporate external sources to enrich the contextual information. However, how to combine different types of contextual information is still a challenge. In this paper, we propose a multi-type context-aware conversational recommender system, called MCCRS, effectively fusing multi-type contextual information via mixture-of-experts to improve conversational recommender systems. MCCRS incorporates both structured information and unstructured information, including the structured knowledge graph, unstructured conversation history, and unstructured item reviews. It consists of several experts, with each expert specialized in a particular domain (i.e., one specific contextual information). Multiple experts are then coordinated by a ChairBot to generate the final results. Our proposed MCCRS model takes advantage of different contextual information and the specialization of different experts followed by a ChairBot breaks the model bottleneck on a single contextual information. Experimental results demonstrate that our proposed MCCRS method achieves significantly higher performance compared to existing baselines.
Telecom services are at the core of today's societies' everyday needs. The availability of numerous online forums and discussion platforms enables telecom providers to improve their services by exploring the views of their customers to learn about common issues that the customers face. Natural Language Processing (NLP) tools can be used to process the free text collected. One way of working with such data is to represent text as numerical vectors using one of many word embedding models based on neural networks. This research uses a novel dataset of telecom customers' reviews to perform an extensive study showing how different word embedding algorithms can affect the text classification process. Several state-of-the-art word embedding techniques are considered, including BERT, Word2Vec and Doc2Vec, coupled with several classification algorithms. The important issue of feature engineering and dimensionality reduction is addressed and several PCA-based approaches are explored. Moreover, the energy consumption used by the different word embeddings is investigated. The findings show that some word embedding models can lead to consistently better text classifiers in terms of precision, recall and F1-Score. In particular, for the more challenging classification tasks, BERT combined with PCA stood out with the highest performance metrics. Moreover, our proposed PCA approach of combining word vectors using the first principal component shows clear advantages in performance over the traditional approach of taking the average.
Integrating LLMs for Grading and Appeal Resolution in Computer Science Education
2025-04-18
I. Aytutuldu, O. Yol, Y. S. Akgul
This study explores the integration of Large Language Models (LLMs) into the grading and appeal resolution process in computer science education. We introduce AI-PAT, an AI-powered assessment tool that leverages LLMs to evaluate computer science exams, generate feedback, and address student appeals. AI-PAT was used to assess over 850 exam submissions and handle 185 appeal cases. Our multi-model comparison (ChatGPT, Gemini) reveals strong correlations between model outputs, though significant variability persists depending on configuration and prompt design. Human graders, while internally consistent, showed notable inter-rater disagreement, further highlighting subjectivity in manual evaluation. The appeal process led to grade changes in 74% of cases, indicating the need for continued refinement of AI evaluation strategies. While students appreciated the speed and detail of AI feedback, survey responses revealed trust and fairness concerns. We conclude that AI-PAT offers scalable benefits for formative assessment and feedback, but must be accompanied by transparent grading rubrics, human oversight, and appeal mechanisms to ensure equitable outcomes.
Enhancing Multilingual Sentiment Analysis with Explainability for Sinhala, English, and Code-Mixed Content
2025-04-18
Azmarah Rizvi, Navojith Thamindu, A. M. N. H. Adhikari, W. P. U. Senevirathna, Dharshana Kasthurirathna, Lakmini Abeywardhana
Sentiment analysis is crucial for brand reputation management in the banking sector, where customer feedback spans English, Sinhala, Singlish, and code-mixed text. Existing models struggle with low-resource languages like Sinhala and lack interpretability for practical use. This research develops a hybrid aspect-based sentiment analysis framework that enhances multilingual capabilities with explainable outputs. Using cleaned banking customer reviews, we fine-tune XLM-RoBERTa for Sinhala and code-mixed text, integrate domain-specific lexicon correction, and employ BERT-base-uncased for English. The system classifies sentiment (positive, neutral, negative) with confidence scores, while SHAP and LIME improve interpretability by providing real-time sentiment explanations. Experimental results show that our approaches outperform traditional transformer-based classifiers, achieving 92.3 percent accuracy and an F1-score of 0.89 in English and 88.4 percent in Sinhala and code-mixed content. An explainability analysis reveals key sentiment drivers, improving trust and transparency. A user-friendly interface delivers aspect-wise sentiment insights, ensuring accessibility for businesses. This research contributes to robust, transparent sentiment analysis for financial applications by bridging gaps in multilingual, low-resource NLP and explainability.
This systematic review discusses the methodological approaches and statistical confirmations of cross-cultural adaptations of cognitive evaluation tools used with different populations. The review considers six seminal studies on the methodology of cultural adaptation in Europe, Asia, Africa, and South America. The results indicate that proper adaptations need holistic models with demographic changes, and education explained as much as 26.76% of the variance in MoCA-H scores. Cultural-linguistic factors explained 6.89% of the variance in European adaptations of MoCA-H; however, another study on adapted MMSE and BCSB among Brazilian Indigenous populations reported excellent diagnostic performance, with a sensitivity of 94.4% and specificity of 99.2%. There was 78.5% inter-rater agreement on the evaluation of cultural adaptation using the Manchester Translation Evaluation Checklist. A paramount message of the paper is that community feedback is necessary for culturally appropriate preparation, standardized translation protocols also must be included, along with robust statistical validation methodologies for developing cognitive assessment instruments. This review supplies evidence-based frameworks for the further adaptation of cognitive assessments in increasingly diverse global health settings.
Active Learning of Computer Experiment with both Quantitative and Qualitative Inputs
2025-04-18
Anita Shahrokhian, Xinwei Deng, C. Devon Lin
Computer experiments refer to the study of real systems using complex simulation models. They have been widely used as alternatives to physical experiments. Design and analysis of computer experiments have attracted great attention in past three decades. The bulk of the work, however, often focus on experiments with only quantitative inputs. In recent years, research on design and analysis for computer experiments have gain momentum. Statistical methodology for design, modeling and inference of such experiments have been developed. In this chapter, we review some of those key developments, and propose active learning approaches for modeling, optimization, contour estimation of computer experiments with both types of inputs. Numerical studies are conducted to evaluate the performance of the proposed methods in comparison with other existing methods.
ProgRoCC: A Progressive Approach to Rough Crowd Counting
2025-04-18
Shengqin Jiang, Linfei Li, Haokui Zhang, Qingshan Liu, Amin Beheshti, Jian Yang, Anton van den Hengel, Quan Z. Sheng, Yuankai Qi
As the number of individuals in a crowd grows, enumeration-based techniques become increasingly infeasible and their estimates increasingly unreliable. We propose instead an estimation-based version of the problem: we label Rough Crowd Counting that delivers better accuracy on the basis of training data that is easier to acquire. Rough crowd counting requires only rough annotations of the number of targets in an image, instead of the more traditional, and far more expensive, per-target annotations. We propose an approach to the rough crowd counting problem based on CLIP, termed ProgRoCC. Specifically, we introduce a progressive estimation learning strategy that determines the object count through a coarse-to-fine approach. This approach delivers answers quickly, outperforms the state-of-the-art in semi- and weakly-supervised crowd counting. In addition, we design a vision-language matching adapter that optimizes key-value pairs by mining effective matches of two modalities to refine the visual features, thereby improving the final performance. Extensive experimental results on three widely adopted crowd counting datasets demonstrate the effectiveness of our method.
Intelligent data collection for network discrimination in material flow analysis using Bayesian optimal experimental design
2025-04-18
Jiankan Liao, Xun Huan, Daniel Cooper
Material flow analyses (MFAs) are powerful tools for highlighting resource efficiency opportunities in supply chains. MFAs are often represented as directed graphs, with nodes denoting processes and edges representing mass flows. However, network structure uncertainty -- uncertainty in the presence or absence of flows between nodes -- is common and can compromise flow predictions. While collection of more MFA data can reduce network structure uncertainty, an intelligent data acquisition strategy is crucial to optimize the resources (person-hours and money spent on collecting and purchasing data) invested in constructing an MFA. In this study, we apply Bayesian optimal experimental design (BOED), based on the Kullback-Leibler divergence, to efficiently target high-utility MFA data -- data that minimizes network structure uncertainty. We introduce a new method with reduced bias for estimating expected utility, demonstrating its superior accuracy over traditional approaches. We illustrate these advances with a case study on the U.S. steel sector MFA, where the expected utility of collecting specific single pieces of steel mass flow data aligns with the actual reduction in network structure uncertainty achieved by collecting said data from the United States Geological Survey and the World Steel Association. The results highlight that the optimal MFA data to collect depends on the total amount of data being gathered, making it sensitive to the scale of the data collection effort. Overall, our methods support intelligent data acquisition strategies, accelerating uncertainty reduction in MFAs and enhancing their utility for impact quantification and informed decision-making.
Cloud based DevOps Framework for Identifying Risk Factors of Hospital Utilization
2025-04-18
Monojit Banerjee, Akaash Vishal Hazarika, Mahak Shah
A scalable and reliable system is required to analyze the National Health and Nutrition Examination Survey (NHANES) data efficiently to understand hospital utilization risk factors. This study aims to investigate the integration of continuous integration and deployment (CI/CD) practices in data science workflows, specifically focusing on analyzing NHANES data to identify the prevalence of diabetes, obesity, and cardiovascular diseases. An end-to-end cloud-based DevOps framework is proposed for data analysis which examines risk factors associated with hospital utilization and evaluates key hospital utilization metrics. We have also highlighted the modular structure of the framework that can be generalized for any other domains beyond healthcare. In the framework, an online data update method is provided which can be extended further using both real and synthetic data. As such, the framework can be especially useful for sparse dataset domains such as environmental science, robotics, cybersecurity, and cultural heritage and arts.
Infrared Vision Systems for Emergency Vehicle Driver Assistance in Low-Visibility Conditions
2025-04-18
M-Mahdi Naddaf-Sh, Andrew Lee, Kin Yen, Eemon Amini, Iman Soltani
This study investigates the potential of infrared (IR) camera technology to enhance driver safety for emergency vehicles operating in low-visibility conditions, particularly at night and in dense fog. Such environments significantly increase the risk of collisions, especially for tow trucks and snowplows that must remain operational in challenging conditions. Conventional driver assistance systems often struggle under these conditions due to limited visibility. In contrast, IR cameras, which detect the thermal signatures of obstacles, offer a promising alternative. The evaluation combines controlled laboratory experiments, real-world field tests, and surveys of emergency vehicle operators. In addition to assessing detection performance, the study examines the feasibility of retrofitting existing Department of Transportation (DoT) fleets with cost-effective IR-based driver assistance systems. Results underscore the utility of IR technology in enhancing driver awareness and provide data-driven recommendations for scalable deployment across legacy emergency vehicle fleets.
Contextual Embedding-based Clustering to Identify Topics for Healthcare Service Improvement
2025-04-18
K M Sajjadul Islam, Ravi Teja Karri, Srujan Vegesna, Jiawei Wu, Praveen Madiraju
Understanding patient feedback is crucial for improving healthcare services, yet analyzing unlabeled short-text feedback presents significant challenges due to limited data and domain-specific nuances. Traditional supervised learning approaches require extensive labeled datasets, making unsupervised methods more viable for uncovering meaningful insights from patient feedback. This study explores unsupervised methods to extract meaningful topics from 439 survey responses collected from a healthcare system in Wisconsin, USA. A keyword-based filtering approach was applied to isolate complaint-related feedback using a domain-specific lexicon. To delve deeper and analyze dominant topics in feedback, we explored traditional topic modeling methods, including Latent Dirichlet Allocation (LDA) and Gibbs Sampling Dirichlet Multinomial Mixture (GSDMM), alongside BERTopic, an advanced neural embedding-based clustering approach. To improve coherence and interpretability where data are scarce and consist of short-texts, we propose kBERT, an integration of BERT embeddings with k-means clustering. Model performance was assessed using coherence scores (Cv ) for topic interpretability and average Inverted Rank-Biased Overlap (IRBOavg) for topic diversity. Results indicate that kBERT achieves the highest coherence (Cv = 0.53) and distinct topic separation (IRBOavg = 1.00), outperforming all other models in short-text healthcare feedback analysis. Our findings emphasize the importance of embedding-based techniques for topic identification and highlight the need for context-aware models in healthcare analytics.
Sentiment Analysis of Airbnb Reviews: Exploring Their Impact on Acceptance Rates and Pricing Across Multiple U.S. Regions
2025-04-18
Ali Safari
This research examines whether Airbnb guests' positive and negative comments influence acceptance rates and rental prices across six U.S. regions: Rhode Island, Broward County, Chicago, Dallas, San Diego, and Boston. Thousands of reviews were collected and analyzed using Natural Language Processing (NLP) to classify sentiments as positive or negative, followed by statistical testing (t-tests and basic correlations) on the average scores. The findings reveal that over 90 percent of reviews in each region are positive, indicating that having additional reviews does not significantly enhance prices. However, listings with predominantly positive feedback exhibit slightly higher acceptance rates, suggesting that sentiment polarity, rather than the sheer volume of reviews, is a more critical factor for host success. Additionally, budget listings often gather extensive reviews while maintaining competitive pricing, whereas premium listings sustain higher prices with fewer but highly positive reviews. These results underscore the importance of sentiment quality over quantity in shaping guest behavior and pricing strategies in an overwhelmingly positive review environment.
Prioritizing Security Practice Adoption: Empirical Insights on Software Security Outcomes in the npm Ecosystem
2025-04-18
Nusrat Zahan, Laurie Williams
Practitioners often struggle with the overwhelming number of security practices outlined in cybersecurity frameworks for risk mitigation. Given the limited budget, time, and resources, practitioners want to prioritize the adoption of security practices based on empirical evidence. The goal of this study is to assist practitioners and policymakers in making informed decisions on which security practices to adopt by evaluating the relationship between software security practices and security outcome metrics. The study investigated the relationship between security practice adoption and security outcomes. We selected the OpenSSF Scorecard metrics to automatically measure the adoption of security practices in npm GitHub repositories. We also explored security outcome metrics, such as the number of open vulnerabilities (Vul_Count), mean time to remediate (MTTR) vulnerabilities in dependencies, and mean time to update (MTTU) dependencies. We conducted regression and causal analysis using 12 Scorecard metrics and their aggregated Scorecard score (computed by aggregating individual security practice scores) as predictors and Vul_Count, MTTR, and MTTU as target variables. Our findings show that higher aggregated Scorecard scores are associated with fewer Vul_Count and shorter MTTU, also supported by causal analysis. However, while the regression model suggests shorter MTTR, causal analysis indicates project characteristics likely influence MTTR direction. Segment analysis shows that larger, newer repositories with more contributors, dependencies, and downloads have shorter MTTR. Among individual security practices, Code Review, Maintained status, Pinned Dependencies, and Branch Protection show strong associations with security outcomes; the directionality of these associations varies across security outcomes.
Simplicity by Obfuscation: Evaluating LLM-Driven Code Transformation with Semantic Elasticity
2025-04-18
Lorenzo De Tomasi, Claudio Di Sipio, Antinisca Di Marco, Phuong T. Nguyen
Code obfuscation is the conversion of original source code into a functionally equivalent but less readable form, aiming to prevent reverse engineering and intellectual property theft. This is a challenging task since it is crucial to maintain functional correctness of the code while substantially disguising the input code. The recent development of large language models (LLMs) paves the way for practical applications in different domains, including software engineering. This work performs an empirical study on the ability of LLMs to obfuscate Python source code and introduces a metric (i.e., semantic elasticity) to measure the quality degree of obfuscated code. We experimented with 3 leading LLMs, i.e., Claude-3.5-Sonnet, Gemini-1.5, GPT-4-Turbo across 30 Python functions from diverse computational domains. Our findings reveal GPT-4-Turbo's remarkable effectiveness with few-shot prompting (81% pass rate versus 29% standard prompting), significantly outperforming both Gemini-1.5 (39%) and Claude-3.5-Sonnet (30%). Notably, we discovered a counter-intuitive "obfuscation by simplification" phenomenon where models consistently reduce rather than increase cyclomatic complexity. This study provides a methodological framework for evaluating AI-driven obfuscation while highlighting promising directions for leveraging LLMs in software security.
Brightness and colour variability in NLSy1s versus BLSy1s and QSOs using the ZTF survey
2025-04-18
Madhu Sudan, Hum Chand, Paul J. Wiita, Ritish Kumar
The Zwicky Transient Facility (ZTF), with its extensive optical monitoring capabilities, has provided an unprecedented opportunity to study the long-term variability of active galactic nuclei (AGNs). In this work, we present a comparative analysis of optical colour and brightness variability for $\mathrm{log(L_{Bol})}-z$ matched samples, consisting of 2095 Narrow-line Seyfert 1 galaxies similar to Broad-line Seyfert 1s (NLSy1_A) and 538 NLSy1_B galaxies matched with quasars (QSOs). The corresponding respective control samples of consist of 2380 Broad-line Seyfert 1 (BLSy1) galaxies and 741 QSOs. Using over six years of r-band and g-band light curves from the ZTF Data Release 22 (DR22), we characterize flux variability, fractional flux variability, and amplitude of temporal variability for each source in the samples. Our results indicate that BLSy1 galaxies exhibit significantly stronger variability compared to NLSy1_As, and similarly, QSOs show more variability than do NLSy1_Bs. To probe colour variability, we utilize quasi-simultaneous light curves, with half-hour epoch differences between $g$- and $r$-band measurements where colour index was evaluated using linear regression in magnitude-magnitude space. We find that large majorities of these sources -- 96% of NLSy1_A, 95% of BLSy1, 94% of NLSy1_B, and 91% of QSOs -- exhibit a clear "bluer-when-brighter" (BWB) trend. Furthermore, rest-frame structure function analysis reveals that BLSy1 galaxies are $1.42\pm0.06$ times more variable than NLSy1_A, while QSOs are $1.41\pm0.01$ times more variable than NLSy1_B. These results can provide valuable insights into the variability properties of AGN subclasses and their underlying physical drivers.
CPR: Leveraging LLMs for Topic and Phrase Suggestion to Facilitate Comprehensive Product Reviews
2025-04-18
Ekta Gujral, Apurva Sinha, Lishi Ji, Bijayani Sanghamitra Mishra
Consumers often heavily rely on online product reviews, analyzing both quantitative ratings and textual descriptions to assess product quality. However, existing research hasn't adequately addressed how to systematically encourage the creation of comprehensive reviews that capture both customers sentiment and detailed product feature analysis. This paper presents CPR, a novel methodology that leverages the power of Large Language Models (LLMs) and Topic Modeling to guide users in crafting insightful and well-rounded reviews. Our approach employs a three-stage process: first, we present users with product-specific terms for rating; second, we generate targeted phrase suggestions based on these ratings; and third, we integrate user-written text through topic modeling, ensuring all key aspects are addressed. We evaluate CPR using text-to-text LLMs, comparing its performance against real-world customer reviews from Walmart. Our results demonstrate that CPR effectively identifies relevant product terms, even for new products lacking prior reviews, and provides sentiment-aligned phrase suggestions, saving users time and enhancing reviews quality. Quantitative analysis reveals a 12.3% improvement in BLEU score over baseline methods, further supported by manual evaluation of generated phrases. We conclude by discussing potential extensions and future research directions.
Framework, Standards, Applications and Best practices of Responsible AI : A Comprehensive Survey
2025-04-18
Thippa Reddy Gadekallu, Kapal Dev, Sunder Ali Khowaja, Weizheng Wang, Hailin Feng, Kai Fang, Sharnil Pandya, Wei Wang
Responsible Artificial Intelligence (RAI) is a combination of ethics associated with the usage of artificial intelligence aligned with the common and standard frameworks. This survey paper extensively discusses the global and national standards, applications of RAI, current technology and ongoing projects using RAI, and possible challenges in implementing and designing RAI in the industries and projects based on AI. Currently, ethical standards and implementation of RAI are decoupled which caters each industry to follow their own standards to use AI ethically. Many global firms and government organizations are taking necessary initiatives to design a common and standard framework. Social pressure and unethical way of using AI forces the RAI design rather than implementation.
Background. Diet and inflammation are critical factors influencing cancer risk. However, the combined impact of nutritional status and inflammatory biomarkers on cancer status and type, using machine learning (ML), remains underexplored. Objectives. This study investigates the association between nutritional factors, inflammatory biomarkers, and cancer status, and whether these relationships differ across cancer types using National Health and Nutrition Examination Survey (NHANES) data. Methods. We analyzed 24 macro- and micronutrients, C-reactive protein (CRP), and the advanced lung cancer inflammation index (ALI) in 26,409 NHANES participants (2,120 with cancer). Multivariable logistic regression assessed associations with cancer prevalence. We also examined whether these features differed across the five most common cancer types. To evaluate predictive value, we applied three ML models - Logistic Regression, Random Forest, and XGBoost - on the full feature set. Results. The cohort's mean age was 49.1 years; 34.7% were obese. Comorbidities such as anemia and liver conditions, along with nutritional factors like protein and several vitamins, were key predictors of cancer status. Among the models, Random Forest performed best, achieving an accuracy of 0.72. Conclusions. Higher-quality nutritional intake and lower levels of inflammation may offer protective effects against cancer. These findings highlight the potential of combining nutritional and inflammatory markers with ML to inform cancer prevention strategies.
Digital Twin Generation from Visual Data: A Survey
2025-04-17
Andrew Melnik, Benjamin Alt, Giang Nguyen, Artur Wilkowski, Maciej Stefańczyk, Qirui Wu, Sinan Harms, Helge Rhodin, Manolis Savva, Michael Beetz
This survey explores recent developments in generating digital twins from videos. Such digital twins can be used for robotics application, media content creation, or design and construction works. We analyze various approaches, including 3D Gaussian Splatting, generative in-painting, semantic segmentation, and foundation models highlighting their advantages and limitations. Additionally, we discuss challenges such as occlusions, lighting variations, and scalability, as well as potential future research directions. This survey aims to provide a comprehensive overview of state-of-the-art methodologies and their implications for real-world applications. Awesome list: https://github.com/ndrwmlnk/awesome-digital-twins
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results
2025-04-17
Xin Li, Kun Yuan, Bingchen Li, Fengbin Guan, Yizhen Shao, Zihao Yu, Xijun Wang, Yiting Lu, Wei Luo, Suhang Yao, Ming Sun, Chao Zhou, Zhibo Chen, Radu Timofte, Yabin Zhang, Ao-Xiang Zhang, Tianwu Zhi, Jianzhao Liu, Yang Li, Jingwen Xu, Yiting Liao, Yushen Zuo, Mingyang Wu, Renjie Li, Shengyun Zhong, Zhengzhong Tu, Yufan Liu, Xiangguang Chen, Zuowei Cao, Minhao Tang, Shan Liu, Kexin Zhang, Jingfen Xie, Yan Wang, Kai Chen, Shijie Zhao, Yunchen Zhang, Xiangkai Xu, Hong Gao, Ji Shi, Yiming Bao, Xiugang Dong, Xiangsheng Zhou, Yaofeng Tu, Ying Liang, Yiwen Wang, Xinning Chai, Yuxuan Zhang, Zhengxue Cheng, Yingsheng Qin, Yucai Yang, Rong Xie, Li Song, Wei Sun, Kang Fu, Linhan Cao, Dandan Zhu, Kaiwei Zhang, Yucheng Zhu, Zicheng Zhang, Menghan Hu, Xiongkuo Min, Guangtao Zhai, Zhi Jin, Jiawei Wu, Wei Wang, Wenjian Zhang, Yuhai Lan, Gaoxiong Yi, Hengyuan Na, Wang Luo, Di Wu, MingYin Bai, Jiawang Du, Zilong Lu, Zhenyu Jiang, Hui Zeng, Ziguan Cui, Zongliang Gan, Guijin Tang, Xinglin Xie, Kehuan Song, Xiaoqiang Lu, Licheng Jiao, Fang Liu, Xu Liu, Puhua Chen, Ha Thu Nguyen, Katrien De Moor, Seyed Ali Amirshahi, Mohamed-Chaker Larabi, Qi Tang, Linfeng He, Zhiyong Gao, Zixuan Gao, Guohua Zhang, Zhiye Huang, Yi Deng, Qingmiao Jiang, Lu Chen, Yi Yang, Xi Liao, Nourine Mohammed Nadir, Yuxuan Jiang, Qiang Zhu, Siyue Teng, Fan Zhang, Shuyuan Zhu, Bing Zeng, David Bull, Meiqin Liu, Chao Yao, Yao Zhao
This paper presents a review for the NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement. The challenge comprises two tracks: (i) Efficient Video Quality Assessment (KVQ), and (ii) Diffusion-based Image Super-Resolution (KwaiSR). Track 1 aims to advance the development of lightweight and efficient video quality assessment (VQA) models, with an emphasis on eliminating reliance on model ensembles, redundant weights, and other computationally expensive components in the previous IQA/VQA competitions. Track 2 introduces a new short-form UGC dataset tailored for single image super-resolution, i.e., the KwaiSR dataset. It consists of 1,800 synthetically generated S-UGC image pairs and 1,900 real-world S-UGC images, which are split into training, validation, and test sets using a ratio of 8:1:1. The primary objective of the challenge is to drive research that benefits the user experience of short-form UGC platforms such as Kwai and TikTok. This challenge attracted 266 participants and received 18 valid final submissions with corresponding fact sheets, significantly contributing to the progress of short-form UGC VQA and image superresolution. The project is publicly available at https://github.com/lixinustc/KVQE- ChallengeCVPR-NTIRE2025.
Hadamard product in deep learning: Introduction, Advances and Challenges
2025-04-17
Grigorios G Chrysos, Yongtao Wu, Razvan Pascanu, Philip Torr, Volkan Cevher
While convolution and self-attention mechanisms have dominated architectural design in deep learning, this survey examines a fundamental yet understudied primitive: the Hadamard product. Despite its widespread implementation across various applications, the Hadamard product has not been systematically analyzed as a core architectural primitive. We present the first comprehensive taxonomy of its applications in deep learning, identifying four principal domains: higher-order correlation, multimodal data fusion, dynamic representation modulation, and efficient pairwise operations. The Hadamard product's ability to model nonlinear interactions with linear computational complexity makes it particularly valuable for resource-constrained deployments and edge computing scenarios. We demonstrate its natural applicability in multimodal fusion tasks, such as visual question answering, and its effectiveness in representation masking for applications including image inpainting and pruning. This systematic review not only consolidates existing knowledge about the Hadamard product's role in deep learning architectures but also establishes a foundation for future architectural innovations. Our analysis reveals the Hadamard product as a versatile primitive that offers compelling trade-offs between computational efficiency and representational power, positioning it as a crucial component in the deep learning toolkit.
The tight versus overtwisted dichotomy has been an essential organizing principle and driving force in 3-dimensional contact geometry since its inception around 1990. In this article, we will discuss the genesis of this dichotomy in Eliashberg's seminal work and his influential contributions to the theory.
A quadratic estimator view of the transfer function correction in intensity mapping surveys
2025-04-17
Zhaoting Chen
In single dish neutral hydrogen (HI) intensity mapping, signal separation methods such as Principal Component Analysis (PCA) are used to clean the astrophysical foregrounds. PCA induces a signal loss in the estimated power spectrum, which can be corrected by a transfer function (TF). By injecting mock signals of HI into the data and performing the PCA cleaning, we can use the cleaned mock HI signal to cross-correlate with the original mock, and estimate the signal loss as a TF, $T(\vec{k})$. As expected, a correction of ${T}(\vec{k})^{-1}$ restores the cross-power between the HI and optical galaxies. However, contrary to intuition, the HI auto-power also requires a ${T}(\vec{k})^{-1}$ correction, not ${T}(\vec{k})^{-2}$. The ${T}(\vec{k})^{-1}$ correction is only known empirically through simulations. In this Letter, we show that the ${T}(\vec{k})^{-1}$ correction in auto-power is universal, and can be analytically proven using the quadratic estimator formalism through window function normalisation. The normalisation can also be used to determine the TF correction for any type of linear process. Using the window function, we demonstrate that PCA induces mode-mixing in the power spectrum estimation, which may lead to biases in the model inference.
Neurodiversity in Computing Education Research: A Systematic Literature Review
2025-04-17
Cynthia Zastudil, David H. Smith IV, Yusef Tohamy, Rayhona Nasimova, Gavin Montross, Stephen MacNeil
Ensuring equitable access to computing education for all students-including those with autism, dyslexia, or ADHD-is essential to developing a diverse and inclusive workforce. To understand the state of disability research in computing education, we conducted a systematic literature review of research on neurodiversity in computing education. Our search resulted in 1,943 total papers, which we filtered to 14 papers based on our inclusion criteria. Our mixed-methods approach analyzed research methods, participants, contribution types, and findings. The three main contribution types included empirical contributions based on user studies (57.1%), opinion contributions and position papers (50%), and survey contributions (21.4%). Interviews were the most common methodology (75% of empirical contributions). There were often inconsistencies in how research methods were described (e.g., number of participants and interview and survey materials). Our work shows that research on neurodivergence in computing education is still very preliminary. Most papers provided curricular recommendations that lacked empirical evidence to support those recommendations. Three areas of future work include investigating the impacts of active learning, increasing awareness and knowledge about neurodiverse students' experiences, and engaging neurodivergent students in the design of pedagogical materials and computing education research.
Euclid preparation. Estimating galaxy physical properties using CatBoost chained regressors with attention
2025-04-17
Euclid Collaboration, A. Humphrey, P. A. C. Cunha, L. Bisigello, C. Tortora, M. Bolzonella, L. Pozzetti, M. Baes, B. R. Granett, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, S. Bardelli, A. Biviano, C. Bodendorf, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, A. Da Silva, H. Degaudenzi, G. De Lucia, J. Dinis, F. Dubath, X. Dupac, S. Dusini, S. Escoffier, M. Farina, R. Farinelli, S. Farrens, S. Ferriol, M. Frailis, E. Franceschi, S. Galeotta, K. George, B. Gillis, C. Giocoli, A. Grazian, F. Grupp, L. Guzzo, S. V. H. Haugan, W. Holmes, I. Hook, F. Hormuth, A. Hornstrup, K. Jahnke, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, M. Kilbinger, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, H. J. McCracken, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, M. Moresco, L. Moscardini, E. Munari, R. Nakajima, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, R. Scaramella, P. Schneider, T. Schrabback, M. Scodeggio, A. Secroun, E. Sefusatti, G. Seidel, S. Serrano, P. Simon, C. Sirignano, A. Spurio Mancini, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, A. Veropalumbo, Y. Wang, J. Weller, G. Zamorani, J. Zoubian, E. Zucca, A. Boucaud, E. Bozzo, C. Burigana, M. Calabrese, N. Mauri, V. Scottez, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, V. Allevato, S. Anselmi, M. Ballardini, A. Blanchard, S. Borgani, S. Bruton, R. Cabanac, A. Calabro, A. Cappi, C. S. Carvalho, T. Castro, S. Contarini, A. R. Cooray, J. Coupon, O. Cucciati, G. Desprez, A. Díaz-Sánchez, S. Di Domizio, J. A. Escartin Vigo, A. G. Ferrari, P. G. Ferreira, I. Ferrero, F. Fornari, L. Gabarra, K. Ganga, J. García-Bellido, E. Gaztanaga, F. Giacomini, G. Gozaliasl, A. Gregorio, A. Hall, H. Hildebrandt, J. Hjorth, J. J. E. Kajava, V. Kansal, D. Karagiannis, C. C. Kirkpatrick, L. Legrand, G. Libet, A. Loureiro, G. Maggio, M. Magliocchetti, F. Mannucci, R. Maoli, C. J. A. P. Martins, S. Matthew, L. Maurin, R. B. Metcalf, P. Monaco, C. Moretti, G. Morgante, Nicholas A. Walton, J. Odier, L. Patrizii, M. Pöntinen, V. Popa, C. Porciani, D. Potter, I. Risso, P. -F. Rocci, M. Sahlén, A. Schneider, M. Sereno, C. Tao, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, D. Vergani, G. Verza
Euclid will image ~14000 deg^2 of the extragalactic sky at visible and NIR wavelengths, providing a dataset of unprecedented size and richness that will facilitate a multitude of studies into the evolution of galaxies. In the vast majority of cases the main source of information will come from broad-band images and data products thereof. Therefore, there is a pressing need to identify or develop scalable yet reliable methodologies to estimate the redshift and physical properties of galaxies using broad-band photometry from Euclid, optionally including ground-based optical photometry also. To address this need, we present a novel method to estimate the redshift, stellar mass, star-formation rate, specific star-formation rate, E(B-V), and age of galaxies, using mock Euclid and ground-based photometry. The main novelty of our property-estimation pipeline is its use of the CatBoost implementation of gradient-boosted regression-trees, together with chained regression and an intelligent, automatic optimization of the training data. The pipeline also includes a computationally-efficient method to estimate prediction uncertainties, and, in the absence of ground-truth labels, provides accurate predictions for metrics of model performance up to z~2. We apply our pipeline to several datasets consisting of mock Euclid broad-band photometry and mock ground-based ugriz photometry, to evaluate the performance of our methodology for estimating the redshift and physical properties of galaxies detected in the Euclid Wide Survey. The quality of our photometric redshift and physical property estimates are highly competitive overall, validating our modeling approach. We find that the inclusion of ground-based optical photometry significantly improves the quality of the property estimation, highlighting the importance of combining Euclid data with ancillary ground-based optical data. (Abridged)
Do Large Language Models (LLMs) hold positions that conflict with your country's values? Occasionally they do! However, existing works primarily focus on ethical reviews, failing to capture the diversity of national values, which encompass broader policy, legal, and moral considerations. Furthermore, current benchmarks that rely on spectrum tests using manually designed questionnaires are not easily scalable. To address these limitations, we introduce NaVAB, a comprehensive benchmark to evaluate the alignment of LLMs with the values of five major nations: China, the United States, the United Kingdom, France, and Germany. NaVAB implements a national value extraction pipeline to efficiently construct value assessment datasets. Specifically, we propose a modeling procedure with instruction tagging to process raw data sources, a screening process to filter value-related topics and a generation process with a Conflict Reduction mechanism to filter non-conflicting values.We conduct extensive experiments on various LLMs across countries, and the results provide insights into assisting in the identification of misaligned scenarios. Moreover, we demonstrate that NaVAB can be combined with alignment techniques to effectively reduce value concerns by aligning LLMs' values with the target country.
2025-04-17
Andrea Bocchieri, Lorenzo V. Mugnai, Enzo Pascale, Andreas Papageorgiou, Angele Syty, Angelos Tsiaras, Paul Eccleston, Giorgio Savini, Giovanna Tinetti, Renaud Broquet, Patrick Chapman, Gianfranco Sechi
The European Space Agency's Ariel mission, scheduled for launch in 2029, aims to conduct the first large-scale survey of atmospheric spectra of transiting exoplanets. Ariel achieves the high photometric stability on transit timescales required to detect the spectroscopic signatures of chemical elements with a payload design optimized for transit photometry that either eliminates known systematics or allows for their removal during data processing without significantly degrading or biasing the detection. Jitter in the spacecraft's line of sight is a source of disturbance when measuring the spectra of exoplanet atmospheres. We describe an improved algorithm for de-jittering Ariel observations simulated in the time domain. We opt for an approach based on the spatial information on the Point Spread Function (PSF) distortion from jitter to detrend the optical signals. The jitter model is based on representative simulations from Airbus Defence and Space, the prime contractor for the Ariel service module. We investigate the precision and biases of the retrieved atmospheric spectra from the jitter-detrended observations. At long wavelengths, the photometric stability of the Ariel spectrometer is already dominated by photon noise. Our algorithm effectively de-jitters both photometric and spectroscopic data, ensuring that the performance remains photon noise-limited across the entire Ariel spectrum, fully compliant with mission requirements. This work contributes to the development of the data reduction pipeline for Ariel, aligning with its scientific goals, and may also benefit other astronomical telescopes and instrumentation.
OntoPortal-Astro, a Semantic Artefact Catalogue for Astronomy
2025-04-17
Baptiste Cecconi, Laura Debisschop, Sébastien Derrière, Mireille Louys, Carmen Corre, Nina Grau, Clément Jonquet
The astronomy communities are widely recognised as mature communities for their open science practices. However, while their data ecosystems are rather advanced and permit efficient data interoperability, there are still gaps between these ecosystems. Semantic artefacts (e.g., ontologies, thesauri, vocabularies or metadata schemas) are a means to bridge that gap as they allow to semantically described the data and map the underlying concepts. The increasing use of semantic artefacts in astronomy presents challenges in description, selection, evaluation, trust, and mappings. The landscape remains fragmented, with semantic artefacts scattered across various registries in diverse formats and structures -- not yet fully developed or encoded with rich semantic web standards like OWL or SKOS -- and often with overlapping scopes. Enhancing data semantic interoperability requires common platforms to catalog, align, and facilitate the sharing of FAIR semantic artefacts. In the frame of the FAIR-IMPACT project, we prototyped a semantic artefact catalogue for astronomy, heliophysics and planetary sciences. This exercise resulted in improved vocabulary and ontology management in the communities, and is now paving the way for better interdisciplinary data discovery and reuse. This article presents current practices in our discipline, reviews candidate SAs for such a catalogue, presents driving use cases and the perspective of a real production service for the astronomy community based on the OntoPortal technology, that will be called OntoPortal-Astro.
Are AI agents the new machine translation frontier? Challenges and opportunities of single- and multi-agent systems for multilingual digital communication
2025-04-17
Vicent Briva-Iglesias
The rapid evolution of artificial intelligence (AI) has introduced AI agents as a disruptive paradigm across various industries, yet their application in machine translation (MT) remains underexplored. This paper describes and analyses the potential of single- and multi-agent systems for MT, reflecting on how they could enhance multilingual digital communication. While single-agent systems are well-suited for simpler translation tasks, multi-agent systems, which involve multiple specialized AI agents collaborating in a structured manner, may offer a promising solution for complex scenarios requiring high accuracy, domain-specific knowledge, and contextual awareness. To demonstrate the feasibility of multi-agent workflows in MT, we are conducting a pilot study in legal MT. The study employs a multi-agent system involving four specialized AI agents for (i) translation, (ii) adequacy review, (iii) fluency review, and (iv) final editing. Our findings suggest that multi-agent systems may have the potential to significantly improve domain-adaptability and contextual awareness, with superior translation quality to traditional MT or single-agent systems. This paper also sets the stage for future research into multi-agent applications in MT, integration into professional translation workflows, and shares a demo of the system analyzed in the paper.
Skewness as a Probe of Gravity: Real and Redshift Space Counts-In-Cells
2025-04-17
Paweł Drozda, Wojciech A. Hellwing, Maciej Bilicki
We study the counts-in-cells reduced skewness $s_3$ for dark matter, halo, and galaxy distributions in both real and redshift space, using the ELEPHANT ($\textit{Extended LEnsing PHysics with ANalytical ray Tracing}$) suite of $N$-body simulations. We compare General Relativity (GR) with two extended (EG) gravity models: $f(R)$ gravity with chameleon screening and the normal-branch Dvali-Gabadadze-Porrati (nDGP) model with Vainshtein screening. We quantify the suppression of $s_3$ by redshift-space distortions (RSD), finding that while small-scale skewness is strongly reduced, the $F5$ model retains a $\sim 4\%$ deviation from GR in galaxy samples, corresponding to a $2\sigma$ significance. We show that the ratio $s_3^{\mathrm{RSD}}/s_3^{\mathrm{real}}$ is approximately independent of the gravity model across tracers and redshifts. Our results demonstrate that real-space predictions can help reliably infer redshift-space skewness in both GR and extended gravity, providing a new tool for testing gravity with current and forthcoming galaxy redshift surveys.
Multimodal learning has driven innovation across various industries, particularly in the field of music. By enabling more intuitive interaction experiences and enhancing immersion, it not only lowers the entry barriers to the music but also increases its overall appeal. This survey aims to provide a comprehensive review of multimodal tasks related to music, outlining how music contributes to multimodal learning and offering insights for researchers seeking to expand the boundaries of computational music. Unlike text and images, which are often semantically or visually intuitive, music primarily interacts with humans through auditory perception, making its data representation inherently less intuitive. Therefore, this paper first introduces the representations of music and provides an overview of music datasets. Subsequently, we categorize cross-modal interactions between music and multimodal data into three types: music-driven cross-modal interactions, music-oriented cross-modal interactions, and bidirectional music cross-modal interactions. For each category, we systematically trace the development of relevant sub-tasks, analyze existing limitations, and discuss emerging trends. Furthermore, we provide a comprehensive summary of datasets and evaluation metrics used in multimodal tasks related to music, offering benchmark references for future research. Finally, we discuss the current challenges in cross-modal interactions involving music and propose potential directions for future research.
SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluation
2025-04-17
Nicolas Bougie, Narimasa Watanabe
Recommender systems play a central role in numerous real-life applications, yet evaluating their performance remains a significant challenge due to the gap between offline metrics and online behaviors. Given the scarcity and limits (e.g., privacy issues) of real user data, we introduce SimUSER, an agent framework that serves as believable and cost-effective human proxies. SimUSER first identifies self-consistent personas from historical data, enriching user profiles with unique backgrounds and personalities. Then, central to this evaluation are users equipped with persona, memory, perception, and brain modules, engaging in interactions with the recommender system. SimUSER exhibits closer alignment with genuine humans than prior work, both at micro and macro levels. Additionally, we conduct insightful experiments to explore the effects of thumbnails on click rates, the exposure effect, and the impact of reviews on user engagement. Finally, we refine recommender system parameters based on offline A/B test results, resulting in improved user engagement in the real world.
TUMLS: Trustful Fully Unsupervised Multi-Level Segmentation for Whole Slide Images of Histology
2025-04-17
Walid Rehamnia, Alexandra Getmanskaya, Evgeniy Vasilyev, Vadim Turlapov
Digital pathology, augmented by artificial intelligence (AI), holds significant promise for improving the workflow of pathologists. However, challenges such as the labor-intensive annotation of whole slide images (WSIs), high computational demands, and trust concerns arising from the absence of uncertainty estimation in predictions hinder the practical application of current AI methodologies in histopathology. To address these issues, we present a novel trustful fully unsupervised multi-level segmentation methodology (TUMLS) for WSIs. TUMLS adopts an autoencoder (AE) as a feature extractor to identify the different tissue types within low-resolution training data. It selects representative patches from each identified group based on an uncertainty measure and then does unsupervised nuclei segmentation in their respective higher-resolution space without using any ML algorithms. Crucially, this solution integrates seamlessly into clinicians workflows, transforming the examination of a whole WSI into a review of concise, interpretable cross-level insights. This integration significantly enhances and accelerates the workflow while ensuring transparency. We evaluated our approach using the UPENN-GBM dataset, where the AE achieved a mean squared error (MSE) of 0.0016. Additionally, nucleus segmentation is assessed on the MoNuSeg dataset, outperforming all unsupervised approaches with an F1 score of 77.46% and a Jaccard score of 63.35%. These results demonstrate the efficacy of TUMLS in advancing the field of digital pathology.
2025-04-17
Kunal Jha, Wilka Carvalho, Yancheng Liang, Simon S. Du, Max Kleiman-Weiner, Natasha Jaques
Zero-shot coordination (ZSC), the ability to adapt to a new partner in a cooperative task, is a critical component of human-compatible AI. While prior work has focused on training agents to cooperate on a single task, these specialized models do not generalize to new tasks, even if they are highly similar. Here, we study how reinforcement learning on a distribution of environments with a single partner enables learning general cooperative skills that support ZSC with many new partners on many new problems. We introduce two Jax-based, procedural generators that create billions of solvable coordination challenges. We develop a new paradigm called Cross-Environment Cooperation (CEC), and show that it outperforms competitive baselines quantitatively and qualitatively when collaborating with real people. Our findings suggest that learning to collaborate across many unique scenarios encourages agents to develop general norms, which prove effective for collaboration with different partners. Together, our results suggest a new route toward designing generalist cooperative agents capable of interacting with humans without requiring human data.
This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images. This challenge received a wide range of impressive solutions, which are developed and evaluated using our collected real-world Raindrop Clarity dataset. Unlike existing deraining datasets, our Raindrop Clarity dataset is more diverse and challenging in degradation types and contents, which includes day raindrop-focused, day background-focused, night raindrop-focused, and night background-focused degradations. This dataset is divided into three subsets for competition: 14,139 images for training, 240 images for validation, and 731 images for testing. The primary objective of this challenge is to establish a new and powerful benchmark for the task of removing raindrops under varying lighting and focus conditions. There are a total of 361 participants in the competition, and 32 teams submitting valid solutions and fact sheets for the final testing phase. These submissions achieved state-of-the-art (SOTA) performance on the Raindrop Clarity dataset. The project can be found at https://lixinustc.github.io/CVPR-NTIRE2025-RainDrop-Competition.github.io/.
On what finitely generated (left-orderable) simple groups can know about their subgroups
2025-04-17
Arman Darbinyan, Markus Steenbock
In this paper, we survey some of the recent advances on embeddings into finitely generated (left-orderable) simple group such that the overgroup preserves algorithmic, geometric, or algebraic information about the embedded group. We discuss some new consequences and also extend some of those embedding theorems to countable classes of finitely generated groups.
Collaborative Perception Datasets for Autonomous Driving: A Review
2025-04-17
Naibang Wang, Deyong Shang, Yan Gong, Xiaoxi Hu, Ziying Song, Lei Yang, Yuhan Huang, Xiaoyu Wang, Jianli Lu
Collaborative perception has attracted growing interest from academia and industry due to its potential to enhance perception accuracy, safety, and robustness in autonomous driving through multi-agent information fusion. With the advancement of Vehicle-to-Everything (V2X) communication, numerous collaborative perception datasets have emerged, varying in cooperation paradigms, sensor configurations, data sources, and application scenarios. However, the absence of systematic summarization and comparative analysis hinders effective resource utilization and standardization of model evaluation. As the first comprehensive review focused on collaborative perception datasets, this work reviews and compares existing resources from a multi-dimensional perspective. We categorize datasets based on cooperation paradigms, examine their data sources and scenarios, and analyze sensor modalities and supported tasks. A detailed comparative analysis is conducted across multiple dimensions. We also outline key challenges and future directions, including dataset scalability, diversity, domain adaptation, standardization, privacy, and the integration of large language models. To support ongoing research, we provide a continuously updated online repository of collaborative perception datasets and related literature: https://github.com/frankwnb/Collaborative-Perception-Datasets-for-Autonomous-Driving.
Replication Packages in Software Engineering Secondary Studies: A Systematic Mapping
2025-04-17
Aleksi Huotala, Miikka Kuutila, Mika Mäntylä
Context: Systematic reviews (SRs) summarize state-of-the-art evidence in science, including software engineering (SE). Objective: Our objective is to evaluate how SRs report replication packages and to provide a comprehensive list of these packages. Method: We examined 528 secondary studies published between 2013 and 2023 to analyze the availability and reporting of replication packages. Results: Our findings indicate that only 25.4% of the reviewed studies include replication packages. Encouragingly, the situation is gradually improving, as our regression analysis shows significant increase in the availability of replication packages over time. However, in 2023, just 50.6% of secondary studies provided a replication package while an even lower percentage, 29.1% had used a permanent repository with a digital object identifier (DOI) for storage. Conclusion: To enhance transparency and reproducibility in SE research, we advocate for the mandatory publication of replication packages in secondary studies.
From Regulation to Support: Centering Humans in Technology-Mediated Emotion Intervention in Care Contexts
2025-04-17
Jiaying "Lizzy" Liu, Shuer Zhuo, Xingyu Li, Andrew Dillon, Noura Howell, Angela D. R. Smith, Yan Zhang
Enhancing emotional well-being has become a significant focus in HCI and CSCW, with technologies increasingly designed to track, visualize, and manage emotions. However, these approaches have faced criticism for potentially suppressing certain emotional experiences. Through a scoping review of 53 empirical studies from ACM proceedings implementing Technology-Mediated Emotion Intervention (TMEI), we critically examine current practices through lenses drawn from HCI critical theories. Our analysis reveals emotion intervention mechanisms that extend beyond traditional emotion regulation paradigms, identifying care-centered goals that prioritize non-judgmental emotional support and preserve users' identities. The findings demonstrate how researchers design technologies for generating artificial care, intervening in power dynamics, and nudging behavioral changes. We contribute the concept of "emotion support" as an alternative approach to "emotion regulation," emphasizing human-centered approaches to emotional well-being. This work advances the understanding of diverse human emotional needs beyond individual and cognitive perspectives, offering design implications that critically reimagine how technologies can honor emotional complexity, preserve human agency, and transform power dynamics in care contexts.
Machine Learning Methods for Gene Regulatory Network Inference
2025-04-17
Akshata Hegde, Tom Nguyen, Jianlin Cheng
Gene Regulatory Networks (GRNs) are intricate biological systems that control gene expression and regulation in response to environmental and developmental cues. Advances in computational biology, coupled with high throughput sequencing technologies, have significantly improved the accuracy of GRN inference and modeling. Modern approaches increasingly leverage artificial intelligence (AI), particularly machine learning techniques including supervised, unsupervised, semi-supervised, and contrastive learning to analyze large scale omics data and uncover regulatory gene interactions. To support both the application of GRN inference in studying gene regulation and the development of novel machine learning methods, we present a comprehensive review of machine learning based GRN inference methodologies, along with the datasets and evaluation metrics commonly used. Special emphasis is placed on the emerging role of cutting edge deep learning techniques in enhancing inference performance. The potential future directions for improving GRN inference are also discussed.
2025-04-17
Chengyong Jiang, Jiajia Guo, Xiangyi Li, Shi Jin, Jun Zhang
Artificial intelligence (AI) is pivotal in advancing fifth-generation (5G)-Advanced and sixth-generation systems, capturing substantial research interest. Both the 3rd Generation Partnership Project (3GPP) and leading corporations champion AI's standardization in wireless communication. This piece delves into AI's role in channel state information (CSI) prediction, a sub-use case acknowledged in 5G-Advanced by the 3GPP. We offer an exhaustive survey of AI-driven CSI prediction, highlighting crucial elements like accuracy, generalization, and complexity. Further, we touch on the practical side of model management, encompassing training, monitoring, and data gathering. Moreover, we explore prospects for CSI prediction in future wireless communication systems, entailing integrated design with feedback, multitasking synergy, and predictions in rapid scenarios. This article seeks to be a touchstone for subsequent research in this burgeoning domain.
This study addresses the minor-embedding problem, which involves mapping the variables of an Ising model onto a quantum annealing processor. The primary motivation stems from the observed performance disparity of quantum annealers when solving problems suited to the processor's architecture versus those with non-hardware-native topologies. Our research has two main objectives: i) to analyze the impact of embedding quality on the performance of D-Wave Systems quantum annealers, and ii) to evaluate the quality of the embeddings generated by Minorminer, an algorithm provided by D-Wave and widely recognized as the standard minor-embedding technique in the literature. Regarding the first objective, our experiments reveal a clear correlation between the average chain length of embeddings and the relative errors of the solutions sampled. This underscores the critical influence of embedding quality on quantum annealing performance. For the second objective, we focus on the Minorminer technique, assessing its capacity to embed problems, the quality of the embeddings produced, and the robustness of the results. We also compare its performance with Clique Embedding, another algorithm developed by D-Wave, which is deterministic and designed to embed fully connected Ising models into quantum annealing processors, serving as a worst-case scenario. The results demonstrate that there is significant room for improvement for Minorminer, as it has not consistently outperformed the worst-case scenario.
The Impact of AI on the Cyber Offense-Defense Balance and the Character of Cyber Conflict
2025-04-17
Andrew J. Lohn
Unlike other domains of conflict, and unlike other fields with high anticipated risk from AI, the cyber domain is intrinsically digital with a tight feedback loop between AI training and cyber application. Cyber may have some of the largest and earliest impacts from AI, so it is important to understand how the cyber domain may change as AI continues to advance. Our approach reviewed the literature, collecting nine arguments that have been proposed for offensive advantage in cyber conflict and nine proposed arguments for defensive advantage. We include an additional forty-eight arguments that have been proposed to give cyber conflict and competition its character as collected separately by Healey, Jervis, and Nandrajog. We then consider how each of those arguments and propositions might change with varying degrees of AI advancement. We find that the cyber domain is too multifaceted for a single answer to whether AI will enhance offense or defense broadly. AI will improve some aspects, hinder others, and leave some aspects unchanged. We collect and present forty-four ways that we expect AI to impact the cyber offense-defense balance and the character of cyber conflict and competition.
A Formalization of Co-Transcriptional Splicing as an Operation on Formal Languages
2025-04-17
Da-Jung Cho, Szilárd Zsolt Fazekas, Shinnosuke Seki, Max Wiedenhöft
RNA co-transcriptionality is the process where RNA sequences are spliced while being transcribed from DNA templates. This process holds potential as a key tool for molecular programming. Co-transcriptional folding has been shown to be programmable for assembling nano-scale RNA structures, and recent advances have proven its Turing universality. While post-transcriptional splicing has been extensively studied, co-transcriptional splicing is gaining attention for its potential to save resources and space in molecular systems. However, its unpredictability has limited its practical applications. In this paper, we focus on engineering co-transcriptional splicing, moving beyond natural occurrences to program RNA sequences that produce specific target sequences through DNA templates. We introduce a formal model of co-transcriptional splicing, defined by constant-, linear-, and logarithmic-bounded hairpin deletion operations, as well as an unbounded hairpin deletion operation. We examine the complexity of the template constructability problem associated with these operations and study the closure properties of the languages they generate, providing insights for RNA template design in molecular programming systems.
The reader surely knows what particles physics is about: finding building blocks of nature that appear elementary at a given time and study their interactions - so why in the world this essay? The problem is how to arrive at a fundamental theory, and maybe even more important, what is the theory supposed to do? I follow here a simple but profound prescription of Feynman for a true, self-contained theory, and then show how the Standard Model beautifully illustrates this prescription. That the issue is not trivial, is clear from the fact that we had theories dominate our field for decades, in spite of completely failing Feynman's requirements and offering no clear predictions whatsoever. I next critically review two unique candidates for a Beyond Standard Model theory: the Left-Right Symmetric theory of electro-weak interactions and the minimal grand unified theory based on SU(5) gauge symmetry. I also comment on some generic aspects of grand unification, and what hope we may have in testing it.
Acoustic to Articulatory Inversion of Speech; Data Driven Approaches, Challenges, Applications, and Future Scope
2025-04-17
Leena G Pillai, D. Muhammad Noorul Mubarak
This review is focused on the data-driven approaches applied in different applications of Acoustic-to-Articulatory Inversion (AAI) of speech. This review paper considered the relevant works published in the last ten years (2011-2021). The selection criteria includes (a) type of AAI - Speaker Dependent and Speaker Independent AAI, (b) objectives of the work - Articulatory approximation, Articulatory Feature space selection and Automatic Speech Recognition (ASR), explore the correlation between acoustic and articulatory features, and framework for Computer-assisted language training, (c) Corpus - Simultaneously recorded speech (wav) and medical imaging models such as ElectroMagnetic Articulography (EMA), Electropalatography (EPG), Laryngography, Electroglottography (EGG), X-ray Cineradiography, Ultrasound, and real-time Magnetic Resonance Imaging (rtMRI), (d) Methods or models - recent works are considered, and therefore all the works are based on machine learning, (e) Evaluation - as AAI is a non-linear regression problem, the performance evaluation is mostly done by Correlation Coefficient (CC), Root Mean Square Error (RMSE), and also considered Mean Square Error (MSE), and Mean Format Error (MFE). The practical application of the AAI model can provide a better and user-friendly interpretable image feedback system of articulatory positions, especially tongue movement. Such trajectory feedback system can be used to provide phonetic, language, and speech therapy for pathological subjects.
Transition-metal oxides have been a central subject of condensed matter physics for decades. In addition to novel electronic states driven by the influence of strong correlation, relativistic spin-orbit coupling effects have recently attracted much attention for their potential to explore topological phenomena. In this article, we review various experimental and theoretical studies on transition-metal oxides with focus on thin films and heterostructures where their physics is much influenced by correlation effects and spin-orbit coupling. The combination of the heterostructure geometry together with correlation and topology leads to a variety of novel states here reviewed. We also discuss perspectives for future research in this broad promising area.
Quantifying the Limits of TESS Stellar Rotation Measurements with the K2-TESS Overlap
2025-04-17
Andrew W. Boyle, Andrew W. Mann, Jonathan Bush
The Transiting Exoplanet Survey Satellite (TESS) has provided stellar rotation periods across much of the sky through high-precision light curves, but the reliability and completeness of these measurements require careful evaluation. We assess the accuracy of TESS-derived rotation periods by leveraging a cross-matched sample of ~23,000 stars observed by both TESS and the K2 mission, treating K2 periods as a benchmark. Using causal pixel models to extract light curves and the Lomb-Scargle (LS) periodogram to identify rotation signals, we quantify the empirical uncertainties, reliability, and completeness of TESS rotation period measurements. We find that uncertainties on TESS-derived rotation periods are typically below 3% for stars with periods < 10 days. Rotation periods are generally reliable out to 10 days, with >80% of measurements matching the K2 benchmark. Completeness and reliability drop dramatically for periods beyond ~12 days due to the 27-day sector limitation. Stricter cuts on TESS magnitude and LS power improve reliability; the highest LS power tested (>0.2) ensures >90% reliability below 10 days but removes over half of potential detections. Stitching consecutive-sector light curves reduces period uncertainties but does not improve overall reliability or completeness due to persistent systematics. Our findings and code provide a framework for interpreting TESS-derived rotation periods and inform the selection of quality cuts to optimize studies of stellar rotation, young associations, and gyrochronology.
Using Lyman Alpha Absorption to Measure the Intensity and Variability of $z \sim 2.4$ Ultraviolet Background Light
2025-04-17
Laurel H. Weiss, Karl Gebhardt, Dustin Davis, Erin Mentuch Cooper, Maja Lujan Niemeyer, Mahdi Qezlou, Mahan Mirza Khanlari, Robin Ciardullo, Daniel Farrow, Eric Gawiser, Simon Gazagnes, Caryl Gronwall, Gary J. Hill, Donald P. Schneider
We present measurements of $z \sim 2.4$ ultraviolet background light using Lya absorption from galaxies at $z \sim 2-3$ in the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) database. Thanks to the wide area of this survey, we also measure the variability of this light across the sky. The data suggest an asymmetric geometry where integrated ultraviolet light from background galaxies is absorbed by \ion{H}{1} within the halo of a foreground galaxy, in a configuration similar to damped Lya systems. Using stacking analyses of over 400,000 HETDEX LAE spectra, we argue that this background absorption is detectable in our data. We also argue that the absorption signal becomes negative due to HETDEX's sky subtraction procedure. The amount that the absorption is over-subtracted is representative of the $z \sim 2.4$ UV contribution to the overall extragalactic background light (EBL) at Lya. Using this method, we determine an average intensity (in $\nu J_{\nu}$ units) of $12.9 \pm 3.7$ nW m$^{-2}$ sr$^{-1}$ at a median observed wavelength of 4134 angstroms, or a rest-frame UV background intensity of $508 \pm 145$ nW m$^{-2}$ sr$^{-1}$ at $z\sim2.4$. We find that this flux varies significantly depending on the density of galaxies in the field of observation. Our estimates are consistent with direct measurements of the overall EBL.
Weakly supervised anomaly detection with event-level variables
2025-04-17
Liam Brennan, Tamas Almos Vami, Oz Amram, Sanjana Sekhar, Yuta Takahashi, Louis Moureaux, Manuel Sommerhalder, Petar Maksimovic
We introduce a new topology for weakly supervised anomaly detection searches, di-object plus~X. In this topology, one looks for a resonance decaying to two standard model particles produced in association with other anomalous event activity (X). This additional activity is used for classification. We demonstrate how anomaly detection techniques which have been developed for di-jet searches focusing on jet substructure anomalies can be applied to event-level anomaly detection in this topology. To robustly capture event-level features of multi-particle kinematics, we employ new physically motivated variables derived from the geometric structure of a collision's phase space manifold. As a proof of concept, we explore the application of this approach to several benchmark signals in the di-$\tau$ plus~X final state. We demonstrate that our anomaly detection approach can reach discovery-level significances for signals that would be missed in a conventional bump-hunt approach.
JWST Discovery of a High-Redshift Tidal Disruption Event Candidate in COSMOS-Web
2025-04-17
Mitchell Karmen, Suvi Gezari, Erini Lambrides, Hollis B. Akins, Colin Norman, Caitlin M. Casey, Justin Pierel, David Coulter, Armin Rest, Ori Fox, Yukta Ajay, Natalie Allen, Nicole E. Drakos, Seiji Fujimoto, Sebastian Gomez, Ghassem Gozaliasl, Olivier Ilbert, Jeyhan S. Kartaltepe, Anton M. Koekemoer, Zachary G. Lane, Henry Joy McCracken, Louise Paquereau, Jason Rhodes, Brant E. Robertson, Marko Shuntov, Matthew R. Siebert, Sune Toft, Thomas Wevers, Yossef Zenati
The rates and properties of tidal disruption events (TDEs) provide valuable insights into their host galaxy central stellar densities and the demographics of their central supermassive black holes (SMBHs). TDEs have been observed only at low redshifts ($z \lesssim 1$), due to the difficulty in conducting deep time-domain surveys. In this work, we present the discovery of a high-redshift TDE candidate, HZTDE-1, in the COSMOS-Web survey with JWST's NIRCam, using a novel selection technique based on color and morphology. We first outline a methodology for identifying high-z TDEs in deep infrared imaging surveys, leveraging their unique spectral energy distributions (SEDs) and morphologies of these transients. We apply this technique to COSMOS-Web in filters F115W, F150W, F277W, and F444W, and identify HZTDE-1, a transient point source relative to archival UltraVISTA infrared observations. If we assume it is a TDE, we estimate its photometric redshift to be $z=5.02^{+1.32}_{-1.11}$. HZTDE-1 cannot be explained by reasonable supernova or AGN models. However, we cannot rule out a superluminous supernova at $z\gtrsim3$. If confirmed with follow-up observations, HZTDE-1 would represent the highest-redshift TDE discovery to date, and would suggest an enhancement of the TDE rate in the high-redshift universe. Our method, which can be applied to future deep surveys with JWST and Roman, offers a pathway to identify TDEs at $z>4$ and probe black hole demographics at early cosmic times.
Deep literature reviews: an application of fine-tuned language models to migration research
2025-04-17
Stefano M. Iacus, Haodong Qi, Jiyoung Han
This paper presents a hybrid framework for literature reviews that augments traditional bibliometric methods with large language models (LLMs). By fine-tuning open-source LLMs, our approach enables scalable extraction of qualitative insights from large volumes of research content, enhancing both the breadth and depth of knowledge synthesis. To improve annotation efficiency and consistency, we introduce an error-focused validation process in which LLMs generate initial labels and human reviewers correct misclassifications. Applying this framework to over 20000 scientific articles about human migration, we demonstrate that a domain-adapted LLM can serve as a "specialist" model - capable of accurately selecting relevant studies, detecting emerging trends, and identifying critical research gaps. Notably, the LLM-assisted review reveals a growing scholarly interest in climate-induced migration. However, existing literature disproportionately centers on a narrow set of environmental hazards (e.g., floods, droughts, sea-level rise, and land degradation), while overlooking others that more directly affect human health and well-being, such as air and water pollution or infectious diseases. This imbalance highlights the need for more comprehensive research that goes beyond physical environmental changes to examine their ecological and societal consequences, particularly in shaping migration as an adaptive response. Overall, our proposed framework demonstrates the potential of fine-tuned LLMs to conduct more efficient, consistent, and insightful literature reviews across disciplines, ultimately accelerating knowledge synthesis and scientific discovery.
With deeper exploration of diffusion model, developments in the field of image generation have triggered a boom in image creation. As the quality of base-model generated images continues to improve, so does the demand for further application like image editing. In recent years, many remarkable works are realizing a wide variety of editing effects. However, the wide variety of editing types and diverse editing approaches have made it difficult for researchers to establish a comprehensive view of the development of this field. In this survey, we summarize the image editing field from four aspects: tasks definition, methods classification, results evaluation and editing datasets. First, we provide a definition of image editing, which in turn leads to a variety of editing task forms from the perspective of operation parts and manipulation actions. Subsequently, we categorize and summary methods for implementing editing into three categories: inversion-based, fine-tuning-based and adapter-based. In addition, we organize the currently used metrics, available datasets and corresponding construction methods. At the end, we present some visions for the future development of the image editing field based on the previous summaries.
The Future of Internet of Things and Multimodal Language Models in 6G Networks: Opportunities and Challenges
2025-04-17
Abdelrahman Soliman
Based on recent trends in artificial intelligence and IoT research. The cooperative potential of integrating the Internet of Things (IoT) and Multimodal Language Models (MLLMs) is presented in this survey paper for future 6G systems. It focuses on the applications of this integration in different fields, such as healthcare, agriculture, and smart cities, and investigates the four pillars of IoT integration, such as sensors, communication, processing, and security. The paper provides a comprehensive description of IoT and MLLM technologies and applications, addresses the role of multimodality in each pillar, and concludes with an overview of the most significant challenges and directions for future research. The general survey is a roadmap for researchers interested in tracing the application areas of MLLMs and IoT, highlighting the potential and challenges in this rapidly growing field. The survey recognizes the need to deal with data availability, computational expense, privacy, and real-time processing to harness the complete potential of IoT, MLLM, and 6G technology
The conventional cloud-based large model learning framework is increasingly constrained by latency, cost, personalization, and privacy concerns. In this survey, we explore an emerging paradigm: collaborative learning between on-device small model and cloud-based large model, which promises low-latency, cost-efficient, and personalized intelligent services while preserving user privacy. We provide a comprehensive review across hardware, system, algorithm, and application layers. At each layer, we summarize key problems and recent advances from both academia and industry. In particular, we categorize collaboration algorithms into data-based, feature-based, and parameter-based frameworks. We also review publicly available datasets and evaluation metrics with user-level or device-level consideration tailored to collaborative learning settings. We further highlight real-world deployments, ranging from recommender systems and mobile livestreaming to personal intelligent assistants. We finally point out open research directions to guide future development in this rapidly evolving field.
SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields
2025-04-16
David Keetae Park, Xihaier Luo, Guang Zhao, Seungjun Lee, Miruna Oprescu, Shinjae Yoo
Spatiotemporal learning is challenging due to the intricate interplay between spatial and temporal dependencies, the high dimensionality of the data, and scalability constraints. These challenges are further amplified in scientific domains, where data is often irregularly distributed (e.g., missing values from sensor failures) and high-volume (e.g., high-fidelity simulations), posing additional computational and modeling difficulties. In this paper, we present SCENT, a novel framework for scalable and continuity-informed spatiotemporal representation learning. SCENT unifies interpolation, reconstruction, and forecasting within a single architecture. Built on a transformer-based encoder-processor-decoder backbone, SCENT introduces learnable queries to enhance generalization and a query-wise cross-attention mechanism to effectively capture multi-scale dependencies. To ensure scalability in both data size and model complexity, we incorporate a sparse attention mechanism, enabling flexible output representations and efficient evaluation at arbitrary resolutions. We validate SCENT through extensive simulations and real-world experiments, demonstrating state-of-the-art performance across multiple challenging tasks while achieving superior scalability.
Observations of several gamma-ray bursts (GRBs) that are temporally and spatially compatible with energetic supernovae (hypernovae) has established their common origin. In one case (GRB 111209A/SN 2011kl) the associated supernova was classified as superluminous (SN 2011kl). The exceptional duration of the observed gamma-ray prompt emission of GRB 111209A (about 7 hours) is widely considered key to unlocking the physics behind the still mysterious origin of superluminous supernovae (SLSNe). We review the main observational and theoretical findings that may link some ultra-long GRBs to SLSNe. Specifically, we examine notable events, the role of progenitors and host galaxies in shaping these phenomena, and focus on the proposed models. While a magnetar central engine is a plausible mechanism for both luminous and long-duration GRBs, a conclusive answer remains elusive, as alternative explanations are still viable. Further observational and theoretical work is required to clarify progenitor pathways and explosion mechanisms, potentially extending the classical GRB-SN connection to rare superluminous hypernovae.
Creating benchmarkable components to measure the quality ofAI-enhanced developer tools
2025-04-16
Elise Paradis, Ambar Murillo, Maulishree Pandey, Sarah D'Angelo, Matthew Hughes, Andrew Macvean, Ben Ferrari-Church
In the AI community, benchmarks to evaluate model quality are well established, but an equivalent approach to benchmarking products built upon generative AI models is still missing. This has had two consequences. First, it has made teams focus on model quality over the developer experience, while successful products combine both. Second, product team have struggled to answer questions about their products in relation to their competitors. In this case study, we share: (1) our process to create robust, enterprise-grade and modular components to support the benchmarking of the developer experience (DX) dimensions of our team's AI for code offerings, and (2) the components we have created to do so, including demographics and attitudes towards AI surveys, a benchmarkable task, and task and feature surveys. By doing so, we hope to lower the barrier to the DX benchmarking of genAI-enhanced code products.
Information processing in the brain is coordinated by the dynamic activity of neurons and neural populations at a range of spatiotemporal scales. These dynamics, captured in the form of electrophysiological recordings and neuroimaging, show evidence of time-irreversibility and broken detailed balance suggesting that the brain operates in a nonequilibrium stationary state. Furthermore, the level of nonequilibrium, measured by entropy production or irreversibility appears to be a crucial signature of cognitive complexity and consciousness. The subsequent study of neural dynamics from the perspective of nonequilibrium statistical physics is an emergent field that challenges the assumptions of symmetry and maximum-entropy that are common in traditional models. In this review, we discuss the plethora of exciting results emerging at the interface of nonequilibrium dynamics and neuroscience. We begin with an introduction to the mathematical paradigms necessary to understand nonequilibrium dynamics in both continuous and discrete state-spaces. Next, we review both model-free and model-based approaches to analysing nonequilibrium dynamics in both continuous-state recordings and neural spike-trains, as well as the results of such analyses. We briefly consider the topic of nonequilibrium computation in neural systems, before concluding with a discussion and outlook on the field.
Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning
2025-04-16
Eunjeong Jeong, Nikolaos Pappas
Federated Learning (FL) has emerged as a promising framework for distributed learning, but its growing complexity has led to significant energy consumption, particularly from computations on the client side. This challenge is especially critical in energy-harvesting FL (EHFL) systems, where device availability fluctuates due to limited and time-varying energy resources. We propose FedBacys, a battery-aware FL framework that introduces cyclic client participation based on users' battery levels to cope with these issues. FedBacys enables clients to save energy and strategically perform local training just before their designated transmission time by clustering clients and scheduling their involvement sequentially. This design minimizes redundant computation, reduces system-wide energy usage, and improves learning stability. Our experiments demonstrate that FedBacys outperforms existing approaches in terms of energy efficiency and performance consistency, exhibiting robustness even under non-i.i.d. training data distributions and with very infrequent battery charging. This work presents the first comprehensive evaluation of cyclic client participation in EHFL, incorporating both communication and computation costs into a unified, resource-aware scheduling strategy.
AI Behind Closed Doors: a Primer on The Governance of Internal Deployment
2025-04-16
Charlotte Stix, Matteo Pistillo, Girish Sastry, Marius Hobbhahn, Alejandro Ortega, Mikita Balesni, Annika Hallensleben, Nix Goldowsky-Dill, Lee Sharkey
The most advanced future AI systems will first be deployed inside the frontier AI companies developing them. According to these companies and independent experts, AI systems may reach or even surpass human intelligence and capabilities by 2030. Internal deployment is, therefore, a key source of benefits and risks from frontier AI systems. Despite this, the governance of the internal deployment of highly advanced frontier AI systems appears absent. This report aims to address this absence by priming a conversation around the governance of internal deployment. It presents a conceptualization of internal deployment, learnings from other sectors, reviews of existing legal frameworks and their applicability, and illustrative examples of the type of scenarios we are most concerned about. Specifically, it discusses the risks correlated to the loss of control via the internal application of a misaligned AI system to the AI research and development pipeline, and unconstrained and undetected power concentration behind closed doors. The report culminates with a small number of targeted recommendations that provide a first blueprint for the governance of internal deployment.
Deep Material Network (DMN) has emerged as a powerful framework for multiscale material modeling, enabling efficient and accurate predictions of material behavior across different length scales. Unlike traditional machine learning approaches, the trainable parameters in DMN have direct physical interpretations, capturing the geometric characteristics of the microstructure rather than serving as purely statistical fitting parameters. Its hierarchical tree structure effectively encodes microstructural interactions and deformation mechanisms, allowing DMN to achieve a balance between accuracy and computational efficiency. This physics-informed architecture significantly reduces computational costs compared to direct numerical simulations while preserving essential microstructural physics. Furthermore, DMN can be trained solely on a linear elastic dataset while effectively extrapolating nonlinear responses during online prediction, making it a highly efficient and scalable approach for multiscale material modeling. This article provides a comprehensive review of DMN, detailing its motivation, underlying methodology, and recent advancements. We discuss key modeling aspects, including its hierarchical structure, training process, and the role of physics-based constraints in enhancing predictive accuracy. Furthermore, we highlight its applications in component-scale multiscale analysis and inverse parameter identification, demonstrating its capability to bridge microscale material behavior with macroscale engineering predictions. Finally, we discuss challenges and future directions in improving DMN's generalization capabilities and its potential extensions for broader applications in multiscale modeling.
Generalized Visual Relation Detection with Diffusion Models
2025-04-16
Kaifeng Gao, Siqi Chen, Hanwang Zhang, Jun Xiao, Yueting Zhuang, Qianru Sun
Visual relation detection (VRD) aims to identify relationships (or interactions) between object pairs in an image. Although recent VRD models have achieved impressive performance, they are all restricted to pre-defined relation categories, while failing to consider the semantic ambiguity characteristic of visual relations. Unlike objects, the appearance of visual relations is always subtle and can be described by multiple predicate words from different perspectives, e.g., ``ride'' can be depicted as ``race'' and ``sit on'', from the sports and spatial position views, respectively. To this end, we propose to model visual relations as continuous embeddings, and design diffusion models to achieve generalized VRD in a conditional generative manner, termed Diff-VRD. We model the diffusion process in a latent space and generate all possible relations in the image as an embedding sequence. During the generation, the visual and text embeddings of subject-object pairs serve as conditional signals and are injected via cross-attention. After the generation, we design a subsequent matching stage to assign the relation words to subject-object pairs by considering their semantic similarities. Benefiting from the diffusion-based generative process, our Diff-VRD is able to generate visual relations beyond the pre-defined category labels of datasets. To properly evaluate this generalized VRD task, we introduce two evaluation metrics, i.e., text-to-image retrieval and SPICE PR Curve inspired by image captioning. Extensive experiments in both human-object interaction (HOI) detection and scene graph generation (SGG) benchmarks attest to the superiority and effectiveness of Diff-VRD.
Extrae.jl: Julia bindings for the Extrae HPC Profiler
2025-04-16
Sergio Sanchez-Ramirez, Mosè Giordano
The Julia programming language has gained acceptance within the High-Performance Computing (HPC) community due to its ability to tackle two-language problem: Julia code feels as high-level as Python but allows developers to tune it to C-level performance. But to squeeze every drop of performance, Julia needs to integrate with advanced performance analysis tools, also known as profilers. In this work, we present Extrae.jl, a Julia package to interface with the Extrae profiler.
A survey on orderability and contact non-squeezing
2025-04-16
Igor Uljarević
The present article provides an overview of Yakov Eliashberg's seminal contributions to the concepts of orderability and contact non-squeezing. It also examines subsequent research by various authors, highlighting the significance of these notions and offering a detailed account of the current state of the field.
RadMamba: Efficient Human Activity Recognition through Radar-based Micro-Doppler-Oriented Mamba State-Space Model
2025-04-16
Yizhuo Wu, Francesco Fioranelli, Chang Gao
Radar-based HAR has emerged as a promising alternative to conventional monitoring approaches, such as wearable devices and camera-based systems, due to its unique privacy preservation and robustness advantages. However, existing solutions based on convolutional and recurrent neural networks, although effective, are computationally demanding during deployment. This limits their applicability in scenarios with constrained resources or those requiring multiple sensors. Advanced architectures, such as ViT and SSM architectures, offer improved modeling capabilities and have made efforts toward lightweight designs. However, their computational complexity remains relatively high. To leverage the strengths of transformer architectures while simultaneously enhancing accuracy and reducing computational complexity, this paper introduces RadMamba, a parameter-efficient, radar micro-Doppler-oriented Mamba SSM specifically tailored for radar-based HAR. Across three diverse datasets, RadMamba matches the top-performing previous model's 99.8% classification accuracy on Dataset DIAT with only 1/400 of its parameters and equals the leading models' 92.0% accuracy on Dataset CI4R with merely 1/10 of their parameters. In scenarios with continuous sequences of actions evaluated on Dataset UoG2020, RadMamba surpasses other models with significantly higher parameter counts by at least 3%, achieving this with only 6.7k parameters. Our code is available at: https://github.com/lab-emi/AIRHAR.
A Review of YOLOv12: Attention-Based Enhancements vs. Previous Versions
2025-04-16
Rahima Khanam, Muhammad Hussain
The YOLO (You Only Look Once) series has been a leading framework in real-time object detection, consistently improving the balance between speed and accuracy. However, integrating attention mechanisms into YOLO has been challenging due to their high computational overhead. YOLOv12 introduces a novel approach that successfully incorporates attention-based enhancements while preserving real-time performance. This paper provides a comprehensive review of YOLOv12's architectural innovations, including Area Attention for computationally efficient self-attention, Residual Efficient Layer Aggregation Networks for improved feature aggregation, and FlashAttention for optimized memory access. Additionally, we benchmark YOLOv12 against prior YOLO versions and competing object detectors, analyzing its improvements in accuracy, inference speed, and computational efficiency. Through this analysis, we demonstrate how YOLOv12 advances real-time object detection by refining the latency-accuracy trade-off and optimizing computational resources.
On the Intersection and Composition properties of conditional independence
2025-04-16
Tobias Boege
Compositional graphoids are fundamental discrete structures which appear in probabilistic reasoning, particularly in the area of graphical models. They are semigraphoids which satisfy the Intersection and Composition properties. These important properties, however, are not enjoyed by general probability distributions. We survey what is known in terms of sufficient conditions for Intersection and Composition and derive a set of new sufficient conditions in the context of discrete random variables based on conditional information inequalities for Shannon entropies.
2025-04-16
Weiqiang Yang, Supriya Pan, Eleonora Di Valentino, Olga Mena, David F. Mota, Subenoy Chakraborty
A pressureless dark matter component fits well with several cosmological observations. However, there are indications that cold dark matter may encounter challenges in explaining observations at small scales, particularly at galactic scales. Observational data suggest that dark matter models incorporating a pressure component could provide solutions to these small-scale problems. In this work, we investigate the possibility that present-day dark matter may result from a decaying non-cold dark matter sector transitioning into the dark energy sector. As the sensitivity of astronomical surveys rapidly increases, we explore an interacting scenario between dark energy and non-cold dark matter, where dark energy has a constant equation of state ($w_{\rm de}$), and dark matter, being non-cold, also has a constant (non-zero) equation of state ($w_{\rm dm}$). Considering the phantom and quintessence nature of dark energy, characterized by its equation of state, we separately analyze interacting phantom and interacting quintessence scenarios. We constrain these scenarios using Cosmic Microwave Background (CMB) measurements and their combination with external probes, such as DESI-BAO and PantheonPlus. From our analyses, we find that a very mild preference for non-cold dark matter cannot be excluded based on the employed datasets. Additionally, for some datasets, there is a pronounced preference for the presence of an interaction at more than 95\% confidence level (CL). Moreover, when the dark energy equation of state lies in the phantom regime, the $S_8$ tension can be alleviated. This study suggests that cosmological models incorporating a non-cold dark matter component should be considered as viable scenarios with novel phenomenological implications, as reflected in the present work.
Unmanned Aerial Vehicles (UAVs) are indispensable for infrastructure inspection, surveillance, and related tasks, yet they also introduce critical security challenges. This survey provides a wide-ranging examination of the anti-UAV domain, centering on three core objectives-classification, detection, and tracking-while detailing emerging methodologies such as diffusion-based data synthesis, multi-modal fusion, vision-language modeling, self-supervised learning, and reinforcement learning. We systematically evaluate state-of-the-art solutions across both single-modality and multi-sensor pipelines (spanning RGB, infrared, audio, radar, and RF) and discuss large-scale as well as adversarially oriented benchmarks. Our analysis reveals persistent gaps in real-time performance, stealth detection, and swarm-based scenarios, underscoring pressing needs for robust, adaptive anti-UAV systems. By highlighting open research directions, we aim to foster innovation and guide the development of next-generation defense strategies in an era marked by the extensive use of UAVs.
The period, mass ratio, eccentricity, and other orbital parameters are fundamental for investigating binary star evolution. However, the number of binaries with known orbital parameters remains limited. Utilizing the LAMOST-MRS survey, we derived orbital solutions for 665 SB2 binaries by fitting the radial velocities of 1119 SB2 systems with at least six observations, employing a modified version of Thejoker optimized for SB2 binaries. To ensure the reliability of the results, four selection criteria were applied: reduced chi-square, normalized mean absolute error, maximum phase gap, and RV distribution metric. After applying these criteria, 665 reliable orbits were retained. Comparison with Kepler, TESS, and ZTF light curve data shows excellent agreement, with discrepancies in some cases attributed to shorter pulsation periods observed in light curves. Additionally, good consistency is found between our periods and those of SB1 systems in Gaia data. These orbital solutions contribute to understanding binary star evolution and the statistical properties of binary populations.
Deep learning to improve the discovery of near-Earth asteroids in the Zwicky Transient Facility
2025-04-16
Belén Yu Irureta-Goyena, George Helou, Jean-Paul Kneib, Frank Masci, Thomas Prince, Kumar Venkataramani, Quanzhi Ye, Joseph Masiero, Frédéric Dux, Mathieu Salzmann
We present a novel pipeline that uses a convolutional neural network (CNN) to improve the detection capability of near-Earth asteroids (NEAs) in the context of planetary defense. Our work aims to minimize the dependency on human intervention of the current approach adopted by the Zwicky Transient Facility (ZTF). The target NEAs have a high proper motion of up to tens of degrees per day and thus appear as streaks of light in the images. We trained our CNNs to detect these streaks using three datasets: a set with real asteroid streaks, a set with synthetic (i.e., simulated) streaks and a mixed set, and tested the resultant models on real survey images. The results achieved were almost identical across the three models: $0.843\pm0.005$ in completeness and $0.820\pm0.025$ in precision. The bias on streak measurements reported by the CNNs was $1.84\pm0.03$ pixels in streak position, $0.817\pm0.026$ degrees in streak angle and $-0.048\pm0.003$ in fractional bias in streak length (computed as the absolute length bias over the streak length, with the negative sign indicating an underestimation). We compared the performance of our CNN trained with a mix of synthetic and real streaks to that of the ZTF human scanners by analyzing a set of 317 streaks flagged as valid by the scanners. Our pipeline detected $80~\%$ of the streaks found by the scanners and 697 additional streaks that were subsequently verified by the scanners to be valid streaks. These results suggest that our automated pipeline can complement the work of the human scanners at no cost for the precision and find more objects than the current approach. They also prove that the synthetic streaks were realistic enough to be used for augmenting training sets when insufficient real streaks are available or exploring the simulation of streaks with unusual characteristics that have not yet been detected.
The highly pathogenic avian influenza (HPAI) H5 clade 2.3.4.4b has triggered an unprecedented global panzootic. As the frequency and scale of HPAI H5 outbreaks continue to rise, understanding how wild birds contribute to shape the global virus spread across regions, affecting poultry, domestic and wild mammals, is increasingly critical. In this review, we examine ecological and evolutionary studies to map the global transmission routes of HPAI H5 viruses, identify key wild bird species involved in viral dissemination, and explore infection patterns, including mortality and survival. We also highlight major remaining knowledge gaps that hinder a full understanding of wild birds role in viral dynamics, which must be addressed to enhance surveillance strategies and refine risk assessment models aimed at preventing future outbreaks in wildlife, domestic animals and safeguard public health.
The growing integration of robots in shared environments -- such as warehouses, shopping centres, and hospitals -- demands a deep understanding of the underlying dynamics and human behaviours, including how, when, and where individuals engage in various activities and interactions. This knowledge goes beyond simple correlation studies and requires a more comprehensive causal analysis. By leveraging causal inference to model cause-and-effect relationships, we can better anticipate critical environmental factors and enable autonomous robots to plan and execute tasks more effectively. To this end, we propose a novel causality-based decision-making framework that reasons over a learned causal model to predict battery usage and human obstructions, understanding how these factors could influence robot task execution. Such reasoning framework assists the robot in deciding when and how to complete a given task. To achieve this, we developed also PeopleFlow, a new Gazebo-based simulator designed to model context-sensitive human-robot spatial interactions in shared workspaces. PeopleFlow features realistic human and robot trajectories influenced by contextual factors such as time, environment layout, and robot state, and can simulate a large number of agents. While the simulator is general-purpose, in this paper we focus on a warehouse-like environment as a case study, where we conduct an extensive evaluation benchmarking our causal approach against a non-causal baseline. Our findings demonstrate the efficacy of the proposed solutions, highlighting how causal reasoning enables autonomous robots to operate more efficiently and safely in dynamic environments shared with humans.
Cross-Frequency Collaborative Training Network and Dataset for Semi-supervised First Molar Root Canal Segmentation
2025-04-16
Zhenhuan Zhou, Yuchen Zhang, Along He, Peng Wang, Xueshuo Xie, Tao Li
Root canal (RC) treatment is a highly delicate and technically complex procedure in clinical practice, heavily influenced by the clinicians' experience and subjective judgment. Deep learning has made significant advancements in the field of computer-aided diagnosis (CAD) because it can provide more objective and accurate diagnostic results. However, its application in RC treatment is still relatively rare, mainly due to the lack of public datasets in this field. To address this issue, in this paper, we established a First Molar Root Canal segmentation dataset called FMRC-2025. Additionally, to alleviate the workload of manual annotation for dentists and fully leverage the unlabeled data, we designed a Cross-Frequency Collaborative training semi-supervised learning (SSL) Network called CFC-Net. It consists of two components: (1) Cross-Frequency Collaborative Mean Teacher (CFC-MT), which introduces two specialized students (SS) and one comprehensive teacher (CT) for collaborative multi-frequency training. The CT and SS are trained on different frequency components while fully integrating multi-frequency knowledge through cross and full frequency consistency supervisions. (2) Uncertainty-guided Cross-Frequency Mix (UCF-Mix) mechanism enables the network to generate high-confidence pseudo-labels while learning to integrate multi-frequency information and maintaining the structural integrity of the targets. Extensive experiments on FMRC-2025 and three public dental datasets demonstrate that CFC-MT is effective for RC segmentation and can also exhibit strong generalizability on other dental segmentation tasks, outperforming state-of-the-art SSL medical image segmentation methods. Codes and dataset will be released.
ACE: Attentional Concept Erasure in Diffusion Models
2025-04-16
Finn Carter
Large text-to-image diffusion models have demonstrated remarkable image synthesis capabilities, but their indiscriminate training on Internet-scale data has led to learned concepts that enable harmful, copyrighted, or otherwise undesirable content generation. We address the task of concept erasure in diffusion models, i.e., removing a specified concept from a pre-trained model such that prompting the concept (or related synonyms) no longer yields its depiction, while preserving the model's ability to generate other content. We propose a novel method, Attentional Concept Erasure (ACE), that integrates a closed-form attention manipulation with lightweight fine-tuning. Theoretically, we formulate concept erasure as aligning the model's conditional distribution on the target concept with a neutral distribution. Our approach identifies and nullifies concept-specific latent directions in the cross-attention modules via a gated low-rank adaptation, followed by adversarially augmented fine-tuning to ensure thorough erasure of the concept and its synonyms. Empirically, we demonstrate on multiple benchmarks, including object classes, celebrity faces, explicit content, and artistic styles, that ACE achieves state-of-the-art concept removal efficacy and robustness. Compared to prior methods, ACE better balances generality (erasing concept and related terms) and specificity (preserving unrelated content), scales to dozens of concepts, and is efficient, requiring only a few seconds of adaptation per concept. We will release our code to facilitate safer deployment of diffusion models.
Towards Forceful Robotic Foundation Models: a Literature Survey
2025-04-16
William Xie, Nikolaus Correll
This article reviews contemporary methods for integrating force, including both proprioception and tactile sensing, in robot manipulation policy learning. We conduct a comparative analysis on various approaches for sensing force, data collection, behavior cloning, tactile representation learning, and low-level robot control. From our analysis, we articulate when and why forces are needed, and highlight opportunities to improve learning of contact-rich, generalist robot policies on the path toward highly capable touch-based robot foundation models. We generally find that while there are few tasks such as pouring, peg-in-hole insertion, and handling delicate objects, the performance of imitation learning models is not at a level of dynamics where force truly matters. Also, force and touch are abstract quantities that can be inferred through a wide range of modalities and are often measured and controlled implicitly. We hope that juxtaposing the different approaches currently in use will help the reader to gain a systemic understanding and help inspire the next generation of robot foundation models.
Learning Strategies in Particle Swarm Optimizer: A Critical Review and Performance Analysis
2025-04-16
Dikshit Chauhan, Shivani, P. N. Suganthan
Nature has long inspired the development of swarm intelligence (SI), a key branch of artificial intelligence that models collective behaviors observed in biological systems for solving complex optimization problems. Particle swarm optimization (PSO) is widely adopted among SI algorithms due to its simplicity and efficiency. Despite numerous learning strategies proposed to enhance PSO's performance in terms of convergence speed, robustness, and adaptability, no comprehensive and systematic analysis of these strategies exists. We review and classify various learning strategies to address this gap, assessing their impact on optimization performance. Additionally, a comparative experimental evaluation is conducted to examine how these strategies influence PSO's search dynamics. Finally, we discuss open challenges and future directions, emphasizing the need for self-adaptive, intelligent PSO variants capable of addressing increasingly complex real-world problems.
Progress in ${\cal CP}$ violating top-Higgs coupling at the LHC with Machine Learning
2025-04-16
A. Hammad, Adil Jueid
A precise measurement of the top-Higgs coupling is essential in particle physics, as it offers a powerful probe of potential new physics beyond the Standard Model (BSM), particularly scenarios involving ${\cal CP}$ violation, which is a key condition in addressing the problem of baryon asymmetry of the universe. In this article, we review the recent progress in the studies of the the top-Higgs coupling at the Large Hadron Collider (LHC). We briefly highlight the recent Machine Learning (ML) algorithms being used and their role in constraining the ${\cal CP}$ phase of the top-Higgs coupling with an emphasis on the future potential of beyond-the-traditional methods such as transformers and heterogeneous graphs in these studies.
The AstroSat UV Deep Field South. III. Evolution of the UV Luminosity Function and Luminosity Density from z~0.8-0.4
2025-04-16
Souradeep Bhattacharya, Kanak Saha
We characterise the rest-frame 1500 \r{A} UV luminosity Function (UVLF) from deep AstroSat/UVIT F154W and N242W imaging in the Great Observatories Origins Survey South (GOODS-S) deep field. The UVLFs are constructed and subsequently characterised with fitted Schechter function parameters from FUV observations at z$<0.13$ and NUV observations in seven redshift bins in z~0.8-0.4. The UVLF slope ($\alpha$) and characteristic magnitude ($M^*$) are consistent with previous determinations for this redshift range based on AstroSat/UVIT GOODS-North observations, as well as with those from Galaxy evolution Explorer and Hubble Space Telescope observations. However, differences in the normalisation factor ($\phi_{*}$) are present for UVLFs for some redshift bins. We compute the UV luminosity density, $\rho_{\rm UV}$, combining our determined UVLF parameters with literature determinations out to z$\sim10$. The $\rho_{\rm UV}$ trend with redshift implies the rapid increase in cosmic star formation till its peak at z$\sim3$ (cosmic noon) followed by a slow decline till present day. Both the initial increase in cosmic star formation and subsequent decline are found to be more rapid than previous determinations.
A fundamental problem in quantum information processing is the discrimination among a set of orthogonal quantum states of a composite system under local operations and classical communication (LOCC). Corresponding to the LOCC indistinguishable sets of four ququad-ququad orthogonal maximally entangled states (MESs) constructed by Yu et al. [Phys. Rev. Lett. 109, 020506 (2012)], the maximum commutative sets (MCSs) were introduced as detectors for the local distinguishability of the set of generalized Bell states (GBSs), for which the detectors are sufficient to determine the LOCC distinguishability. In this work, we show how to determine all the detectors for a given GBS set. We construct also several 4-GBS sets without detectors, most of which are one-way LOCC indistinguishable and only one is one-way LOCC distinguishable, indicating that the detectors are not necessary for LOCC distinguishability. Furthermore, we show that for 4-GBS sets in quantum system $\mathbb{C}^{6}\otimes\mathbb{C}^{6}$, the detectors are almost necessary for one-way LOCC distinguishability, except for one set in the sense of local unitary equivalence. The problem of one-way LOCC discrimination of 4-GBS sets in $\mathbb{C}^{6}\otimes\mathbb{C}^{6}$ is completely resolved.
Recent Advance in 3D Object and Scene Generation: A Survey
2025-04-16
Xiang Tang, Ruotong Li, Xiaopeng Fan
In recent years, the demand for 3D content has grown exponentially with intelligent upgrading of interactive media, extended reality (XR), and Metaverse industries. In order to overcome the limitation of traditional manual modeling approaches, such as labor-intensive workflows and prolonged production cycles, revolutionary advances have been achieved through the convergence of novel 3D representation paradigms and artificial intelligence generative technologies. In this survey, we conduct a systematically review of the cutting-edge achievements in static 3D object and scene generation, as well as establish a comprehensive technical framework through systematic categorization. Specifically, we initiate our analysis with mainstream 3D object representations, followed by in-depth exploration of two principal technical pathways in object generation: data-driven supervised learning methods and deep generative model-based approaches. Regarding scene generation, we focus on three dominant paradigms: layout-guided compositional synthesis, 2D prior-based scene generation, and rule-driven modeling. Finally, we critically examine persistent challenges in 3D generation and propose potential research directions for future investigation. This survey aims to provide readers with a structured understanding of state-of-the-art 3D generation technologies while inspiring researchers to undertake more exploration in this domain.
In recent years, the term Metaverse emerged as one of the most compelling concepts, captivating the interest of international companies such as Tencent, ByteDance, Microsoft, and Facebook. These company recognized the Metaverse as a pivotal element for future success and have since made significant investments in this area. The Metaverse is still in its developmental stages, requiring the integration and advancement of various technologies to bring its vision to life. One of the key technologies associated with the Metaverse is blockchain, known for its decentralization, security, trustworthiness, and ability to manage time-series data. These characteristics align perfectly with the ecosystem of the Metaverse, making blockchain foundational for its security and infrastructure. This paper introduces both blockchain and the Metaverse ecosystem while exploring the application of the blockchain within the Metaverse, including decentralization, consensus mechanisms, hash algorithms, timestamping, smart contracts, distributed storage, distributed ledgers, and non-fungible tokens (NFTs) to provide insights for researchers investigating these topics.
We survey various properties of Krein--von Neumann extensions $S_K$ and the reduced Krein--von Neumann operator $\hatt S_K$ in connection with a strictly positive (symmetric) operator $S$ with nonzero deficiency indices. In particular, we focus on the resolvents of $S_K$ and $\hatt S_K$ and of the trace ideal properties of the resolvent of $\hatt S_K$, and make some comparisons with the corresponding properties of the resolvent of the Friedrichs extension $S_F$. We also recall a parametrization of all nonnegative self-adjoint extensions of $S$ and various Krein-type resolvent formulas for any two relatively prime self-adjoint extensions of $S$, utilizing a Donoghue-type $M$-operator (i.e., an energy parameter dependent Dirichlet-to-Neumann-type map).
A Technical Survey of Sparse Linear Solvers in Electronic Design Automation
2025-04-16
Nityanand Rai
Sparse linear system solvers ($Ax=b$) are critical computational kernels in Electronic Design Automation (EDA), underpinning vital simulations for modern IC and system design. Applications like power integrity verification and electrothermal analysis fundamentally solve large-scale, sparse algebraic systems from Modified Nodal Analysis (MNA) or Finite Element/Volume Method (FEM/FVM) discretizations of PDEs. Problem dimensions routinely reach $10^6-10^9$ unknowns, escalating towards $10^{10}$+ for full-chip power grids \cite{Tsinghua21}, demanding stringent solver scalability, low memory footprint, and efficiency. This paper surveys predominant sparse solver paradigms in EDA: direct factorization methods (LU, Cholesky), iterative Krylov subspace methods (CG, GMRES, BiCGSTAB), and multilevel multigrid techniques. We examine their mathematical foundations, convergence, conditioning sensitivity, implementation aspects (storage formats CSR/CSC, fill-in mitigation via reordering), the critical role of preconditioning for ill-conditioned systems \cite{SaadIterative, ComparisonSolversArxiv}, and multigrid's potential optimal $O(N)$ complexity \cite{TrottenbergMG}. Solver choice critically depends on the performance impact of frequent matrix updates (e.g., transient/non-linear), where iterative/multigrid methods often amortize costs better than direct methods needing repeated factorization \cite{SaadIterative}. We analyze trade-offs in runtime complexity, memory needs, numerical robustness, parallel scalability (MPI, OpenMP, GPU), and precision (FP32/FP64). Integration into EDA tools for system-level multiphysics is discussed, with pseudocode illustrations. The survey concludes by emphasizing the indispensable nature and ongoing evolution of sparse solvers for designing and verifying complex electronic systems.
The Cocytos Stream: A Disrupted Globular Cluster from our Last Major Merger?
2025-04-16
Christian Aganze, Vedant Chandra, Risa H. Wechsler, Ting S. Li, Sergey E. Koposov, Leandro Beraldo Silva, Andreia Carrillo, Alexander H. Riley, Monica Valluri, Oleg Y. Gnedin, Mairead Heiger, Constance Rockosi, Raymond Carlberg, Amanda Byström, Namitha Kizhuprakkat, Mika Lambert, Bokyoung Kim, Gustavo Medina Toledo, Carlos Allende Prieto, Jessica Nicole Aguilar, Steven Ahlen, Davide Bianchi, David Brooks, Todd T. Claybaugh, Andrew P. Cooper, Axel de la Macorra, Arjun Dey, Peter Doel, Jaime E. Forero-Romero, Enrique Gaztañaga, Satya A. Gontcho, Gaston Gutierrez, Mustapha Ishak, Theodore Kisner, Anthony Kremin, Ofer Lahav, Martin Landriau, Laurent Le Guillou, Aaron Meisner, Ramon Miquel, Nathalie Palanque-Delabrouille, Francisco Prada, Ignasi Pérez-Ràfols, Graziano Rossi, Eusebio Sanchez, Michael Schubnell, David Sprayberry, Gregory Tarlé, Benjamin A. Weaver, Hu Zou
The census of stellar streams and dwarf galaxies in the Milky Way provides direct constraints on galaxy formation models and the nature of dark matter. The DESI Milky Way survey (with a footprint of 14,000$~deg{^2}$ and a depth of $r<19$ mag) delivers the largest sample of distant metal-poor stars compared to previous optical fiber-fed spectroscopic surveys. This makes DESI an ideal survey to search for previously undetected streams and dwarf galaxies. We present a detailed characterization of the Cocytos stream, which was re-discovered using a clustering analysis with a catalog of giants in the DESI year 3 data, supplemented with Magellan/MagE spectroscopy. Our analysis reveals a relatively metal-rich ([Fe/H]$=-1.3$) and thick stream (width$=1.5^\circ$) at a heliocentric distance of $\approx 25$ kpc, with an internal velocity dispersion of 6.5-9 km s$^{-1}$. The stream's metallicity, radial orbit, and proximity to the Virgo stellar overdensities suggest that it is most likely a disrupted globular cluster that came in with the Gaia-Enceladus merger. We also confirm its association with the Pyxis globular cluster. Our result showcases the ability of wide-field spectroscopic surveys to kinematically discover faint disrupted dwarfs and clusters, enabling constraints on the dark matter distribution in the Milky Way.
Higher-Order Binding of Language Model Virtual Personas: a Study on Approximating Political Partisan Misperceptions
2025-04-16
Minwoo Kang, Suhong Moon, Seung Hyeong Lee, Ayush Raj, Joseph Suh, David M. Chan
Large language models (LLMs) are increasingly capable of simulating human behavior, offering cost-effective ways to estimate user responses during the early phases of survey design. While previous studies have examined whether models can reflect individual opinions or attitudes, we argue that a \emph{higher-order} binding of virtual personas requires successfully approximating not only the opinions of a user as an identified member of a group, but also the nuanced ways in which that user perceives and evaluates those outside the group. In particular, faithfully simulating how humans perceive different social groups is critical for applying LLMs to various political science studies, including timely topics on polarization dynamics, inter-group conflict, and democratic backsliding. To this end, we propose a novel methodology for constructing virtual personas with synthetic user ``backstories" generated as extended, multi-turn interview transcripts. Our generated backstories are longer, rich in detail, and consistent in authentically describing a singular individual, compared to previous methods. We show that virtual personas conditioned on our backstories closely replicate human response distributions (up to an 87\% improvement as measured by Wasserstein Distance) and produce effect sizes that closely match those observed in the original studies. Altogether, our work extends the applicability of LLMs beyond estimating individual self-opinions, enabling their use in a broader range of human studies.
A Framework for Information Disorder: Modeling Mechanisms and Implications Based on a Systematic Literature Review
2025-04-16
Julie Ricard, Ivette Yañez, Leticia Hora
This systematic literature review seeks to explain the mechanisms and implications of information disorder for public policy and the democratic process, by proposing a five-stage framework capturing its full life cycle. To our knowledge, no prior reviews in the field of public administration have offered a comprehensive, integrated model of information disorder; most existing studies are situated within communication, information science, or data science, and tend to focus on isolated aspects of the phenomenon. By connecting concepts and stages with enabling factors, agents, tactics and impacts, we reframe information disorder not as a question of "truthiness", individual cognition, digital literacy, or merely of technology, but as a socio-material phenomenon, deeply embedded in and shaped by the material conditions of contemporary digital society. This approach calls for a shift away from fragmented interventions toward more holistic, system-level policy responses.
"It's not approved, but many, like myself, ignore the rule": Investigating the Landscape and Consequences of Unsanctioned Technology Use in Educational Institutes
2025-04-16
Easton Kelso, Ananta Soneji, Syed Zami-Ul-Haque Navid, Yan Soshitaishvili, Sazzadur Rahaman, Rakibul Hasan
Educators regularly use unsanctioned technologies (apps not formally approved by their institutions) for teaching, grading, and other academic tasks. While these tools often support instructional needs, they raise significant privacy, security, and regulatory compliance concerns. Despite its importance, understanding the adoptions and risks from the perspective of educators, who serve as de facto decision makers behind unsanctioned technology use, is largely understudied in existing literature.To address this gap, we conducted two surveys: one with 375 educators who listed 1,373 unsanctioned apps, and another with 21 administrators who either often help educators to set up educational technologies (EdTechs) or observe their security or privacy incidents. Our study identified 494 unique applications used by educators, primarily for pedagogical utility (n=213) and functional convenience (n=155), and the associated risks were often ignored. In fact, despite security and privacy concerns, many educators continued using the same apps (n = 62), citing a lack of alternatives or heavy dependence as barriers to discontinuation. We also found that fewer than a third of educators were aware of any institutional policy on unsanctioned technology use (K12: 30.3%, HEI: 24.8%), and 22 knowingly violated such policies. While 107 received formal warnings, only 33 adjusted their behavior. Finally, we conclude by discussing the implications of our findings and future recommendations to minimize the risks.
CAPERS Observations of Two UV-Bright Galaxies at z>10. More Evidence for Bursting Star Formation in the Early Universe
2025-04-16
Vasily Kokorev, Óscar A. Chávez Ortiz, Anthony J. Taylor, Steven L. Finkelstein, Pablo Arrabal Haro, Mark Dickinson, John Chisholm, Seiji Fujimoto, Julian B. Muñoz, Ryan Endsley, Weida Hu, Lorenzo Napolitano, Stephen M. Wilkins, Hollis B. Akins, Ricardo Amoriín, Caitlin M. Casey, Yingjie Cheng, Nikko J. Cleri, Justin Cole, Fergus Cullen, Emanuele Daddi, Kelcey Davis, Callum T. Donnan, James S. Dunlop, Vital Fernández, Mauro Giavalisco, Norman A. Grogin, Nimish Hathi, Michaela Hirschmann, Jeyhan S. Kartaltepe, Anton M. Koekemoer, Ho-Hin Leung, Ray A. Lucas, Derek McLeod, Casey Papovich, Laura Pentericci, Pablo G. Pérez-González, Rachel S. Somerville, Xin Wang, L. Y. Aaron Yung, Jorge A. Zavala
We present the first results from the CAPERS survey, utilizing PRISM observations with the JWST/NIRSpec MSA in the PRIMER-UDS field. With just 14 % of the total planned data volume, we spectroscopically confirm two new bright galaxies ($M_{\rm UV}\sim -20.4$) at redshifts $z = 10.562\pm0.034$ and $z = 11.013\pm0.028$. We examine their physical properties, morphologies, and star formation histories, finding evidence for recent bursting star formation in at least one galaxy thanks to the detection of strong (EW$_0\sim70$ A) H$\gamma$ emission. Combining our findings with previous studies of similarly bright objects at high-$z$, we further assess the role of stochastic star formation processes in shaping early galaxy populations. Our analysis finds that the majority of bright ($M_{\rm UV}\lesssim -20$) spectroscopically-confirmed galaxies at $z>10$ were likely observed during a starburst episode, characterized by a median SFR$_{10}$/SFR$_{100}\sim2$, although with substantial scatter. Our work also finds tentative evidence that $z>10$ galaxies are more preferentially in a bursting phase than similarly bright $z\sim6$ galaxies. We finally discuss the prospects of deeper spectroscopic observations of a statistically significant number of bright galaxies to quantify the true impact of bursting star formation on the evolution of the bright end of the ultraviolet luminosity function at these early epochs.
2025-04-16
Olivier Shelbaya, Rick Baartman, Peter Braun, Paul Matthew Jung, Oliver Kester, Thomas Planche, Holger Podlech, Stephanie Diana Radel
Multigap cavities are used extensively in linear accelerators to achieve velocities up to a few percent of the speed of light, driving nuclear physics research around the world. Unlike for single-gap structures, there is no closed-form expression to calculate the output beam parameters from the cavity voltage and phase. To overcome this, we propose to use a method based on the integration of the first and second moments of the beam distribution through the axially symmetric time-dependent fields of the cavity. A beam-based calibration between the model's electric field scaling and the machine's rf amplitudes is presented, yielding a fast online energy change method, returning cavity amplitude and phase necessary for a desired output beam energy and energy spread. The method is validated with 23Na6+ beam energy measurements.
Beyond Text: Characterizing Domain Expert Needs in Document Research
2025-04-16
Sireesh Gururaja, Nupoor Gandhi, Jeremiah Milbauer, Emma Strubell
Working with documents is a key part of almost any knowledge work, from contextualizing research in a literature review to reviewing legal precedent. Recently, as their capabilities have expanded, primarily text-based NLP systems have often been billed as able to assist or even automate this kind of work. But to what extent are these systems able to model these tasks as experts conceptualize and perform them now? In this study, we interview sixteen domain experts across two domains to understand their processes of document research, and compare it to the current state of NLP systems. We find that our participants processes are idiosyncratic, iterative, and rely extensively on the social context of a document in addition its content; existing approaches in NLP and adjacent fields that explicitly center the document as an object, rather than as merely a container for text, tend to better reflect our participants' priorities, though they are often less accessible outside their research communities. We call on the NLP community to more carefully consider the role of the document in building useful tools that are accessible, personalizable, iterative, and socially aware.
Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process
2025-04-16
Mohi Reza, Jeb Thomas-Mitchell, Peter Dushniku, Nathan Laundry, Joseph Jay Williams, Anastasia Kuzminykh
As generative AI tools like ChatGPT become integral to everyday writing, critical questions arise about how to preserve writers' sense of agency and ownership when using these tools. Yet, a systematic understanding of how AI assistance affects different aspects of the writing process - and how this shapes writers' agency - remains underexplored. To address this gap, we conducted a systematic review of 109 HCI papers using the PRISMA approach. From this literature, we identify four overarching design strategies for AI writing support: structured guidance, guided exploration, active co-writing, and critical feedback - mapped across the four key cognitive processes in writing: planning, translating, reviewing, and monitoring. We complement this analysis with interviews of 15 writers across diverse domains. Our findings reveal that writers' desired levels of AI intervention vary across the writing process: content-focused writers (e.g., academics) prioritize ownership during planning, while form-focused writers (e.g., creatives) value control over translating and reviewing. Writers' preferences are also shaped by contextual goals, values, and notions of originality and authorship. By examining when ownership matters, what writers want to own, and how AI interactions shape agency, we surface both alignment and gaps between research and user needs. Our findings offer actionable design guidance for developing human-centered writing tools for co-writing with AI, on human terms.
GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification
2025-04-16
Thanh-Dung Le, Vu Nguyen Ha, Ti Ti Nguyen, Geoffrey Eappen, Prabhu Thiruvasagam, Hong-fu Chou, Duc-Dung Tran, Hung Nguyen-Kha, Luis M. Garces-Socarras, Jorge L. Gonzalez-Rios, Juan Carlos Merlano-Duncan, Symeon Chatzinotas
This study introduces ResNet-GLUSE, a lightweight ResNet variant enhanced with Gated Linear Unit-enhanced Squeeze-and-Excitation (GLUSE), an adaptive channel-wise attention mechanism. By integrating dynamic gating into the traditional SE framework, GLUSE improves feature recalibration while maintaining computational efficiency. Experiments on EuroSAT and PatternNet datasets confirm its effectiveness, achieving exceeding \textbf{94\% and 98\% accuracy}, respectively. While \textbf{MobileViT achieves 99\% accuracy}, ResNet-GLUSE offers \textbf{33x fewer parameters, 27x fewer FLOPs, 33x smaller model size (MB), $\approx$6x lower power consumption (W), and $\approx$3x faster inference time (s)}, making it significantly more efficient for onboard satellite deployment. Furthermore, due to its simplicity, ResNet-GLUSE can be easily mimicked for \textbf{neuromorphic computing}, enabling ultra-low power inference at just \textbf{852.30 mW} on Akida Brainchip. This balance between high accuracy and ultra-low resource consumption establishes ResNet-GLUSE as a practical solution for real-time Earth Observation (EO) tasks. Reproducible codes are available in our shared repository.
What do people expect from Artificial Intelligence? Public opinion on alignment in AI moderation from Germany and the United States
2025-04-16
Andreas Jungherr, Adrian Rauchfleisch
Recent advances in generative Artificial Intelligence have raised public awareness, shaping expectations and concerns about their societal implications. Central to these debates is the question of AI alignment -- how well AI systems meet public expectations regarding safety, fairness, and social values. However, little is known about what people expect from AI-enabled systems and how these expectations differ across national contexts. We present evidence from two surveys of public preferences for key functional features of AI-enabled systems in Germany (n = 1800) and the United States (n = 1756). We examine support for four types of alignment in AI moderation: accuracy and reliability, safety, bias mitigation, and the promotion of aspirational imaginaries. U.S. respondents report significantly higher AI use and consistently greater support for all alignment features, reflecting broader technological openness and higher societal involvement with AI. In both countries, accuracy and safety enjoy the strongest support, while more normatively charged goals -- like fairness and aspirational imaginaries -- receive more cautious backing, particularly in Germany. We also explore how individual experience with AI, attitudes toward free speech, political ideology, partisan affiliation, and gender shape these preferences. AI use and free speech support explain more variation in Germany. In contrast, U.S. responses show greater attitudinal uniformity, suggesting that higher exposure to AI may consolidate public expectations. These findings contribute to debates on AI governance and cross-national variation in public preferences. More broadly, our study demonstrates the value of empirically grounding AI alignment debates in public attitudes and of explicitly developing normatively grounded expectations into theoretical and policy discussions on the governance of AI-generated content.
Rethinking Trust in AI Assistants for Software Development: A Critical Review
2025-04-16
Sebastian Baltes, Timo Speith, Brenda Chiteri, Seyedmoein Mohsenimofidi, Shalini Chakraborty, Daniel Buschek
Trust is a fundamental concept in human decision-making and collaboration that has long been studied in philosophy and psychology. However, software engineering (SE) articles often use the term 'trust' informally - providing an explicit definition or embedding results in established trust models is rare. In SE research on AI assistants, this practice culminates in equating trust with the likelihood of accepting generated content, which does not capture the full complexity of the trust concept. Without a common definition, true secondary research on trust is impossible. The objectives of our research were: (1) to present the psychological and philosophical foundations of human trust, (2) to systematically study how trust is conceptualized in SE and the related disciplines human-computer interaction and information systems, and (3) to discuss limitations of equating trust with content acceptance, outlining how SE research can adopt existing trust models to overcome the widespread informal use of the term 'trust'. We conducted a literature review across disciplines and a critical review of recent SE articles focusing on conceptualizations of trust. We found that trust is rarely defined or conceptualized in SE articles. Related disciplines commonly embed their methodology and results in established trust models, clearly distinguishing, for example, between initial trust and trust formation and discussing whether and when trust can be applied to AI assistants. Our study reveals a significant maturity gap of trust research in SE compared to related disciplines. We provide concrete recommendations on how SE researchers can adopt established trust models and instruments to study trust in AI assistants beyond the acceptance of generated software artifacts.
The effect of intrinsic alignments on weak lensing statistics in hydrodynamical simulations
2025-04-16
Max E. Lee, Zoltán Haiman, Shivam Pandey, Shy Genel
The next generation of weak gravitational lensing surveys has the potential to place stringent constraints on cosmological parameters. However, their analysis is limited by systematics such as the intrinsic alignments of galaxies, which alter weak lensing convergence and can lead to biases in cosmological parameter estimations. In this work, we investigate the impact of intrinsic alignments on non-Gaussian statistics of the weak lensing field using galaxy shapes derived from the IllustrisTNG hydrodynamical simulation. We create two catalogs of ray-traced convergence maps: one that includes the measured intrinsic shape of each galaxy and another where all galaxies are randomly rotated to eliminate intrinsic alignments. We compare an exhaustive list of weak lensing statistics between the two catalogs, including the shear-shear correlation function, the map-level angular power spectrum, one-point, peak count, minimum distribution functions, and Minkowski functionals. For each statistic, we assess the level of statistical distinguishability between catalogs for a set of future survey angular areas. Our results reveal strong small-scale correlation in the alignment of galaxies and statistically significant boosts in weak lensing convergence in both positive and negative directions for high-significance peaks and minimums, respectively. Weak lensing analyses utilizing non-Gaussian statistics must account for intrinsic alignments to avoid significantly compromised cosmological inferences.
Personal development through self-directed learning is essential in today's fast-changing world, but many learners struggle to manage it effectively. While AI tools like large language models (LLMs) have the potential for personalized learning planning, they face issues such as transparency and hallucinated information. To address this, we propose PlanGlow, an LLM-based system that generates personalized, well-structured study plans with clear explanations and controllability through user-centered interactions. Through mixed methods, we surveyed 28 participants and interviewed 10 before development, followed by a within-subject experiment with 24 participants to evaluate PlanGlow's performance, usability, controllability, and explainability against two baseline systems: a GPT-4o-based system and Khan Academy's Khanmigo. Results demonstrate that PlanGlow significantly improves usability, explainability, and controllability. Additionally, two educational experts assessed and confirmed the quality of the generated study plans. These findings highlight PlanGlow's potential to enhance personalized learning and address key challenges in self-directed learning.
Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation
2025-04-16
Nairouz Mrabah, Nicolas Richet, Ismail Ben Ayed, Éric Granger
Adapting Vision-Language Models (VLMs) to new domains with few labeled samples remains a significant challenge due to severe overfitting and computational constraints. State-of-the-art solutions, such as low-rank reparameterization, mitigate these issues but often struggle with generalization and require extensive hyperparameter tuning. In this paper, a novel Sparse Optimization (SO) framework is proposed. Unlike low-rank approaches that typically constrain updates to a fixed subspace, our SO method leverages high sparsity to dynamically adjust very few parameters. We introduce two key paradigms. First, we advocate for \textit{local sparsity and global density}, which updates a minimal subset of parameters per iteration while maintaining overall model expressiveness. As a second paradigm, we advocate for \textit{local randomness and global importance}, which sparsifies the gradient using random selection while pruning the first moment based on importance. This combination significantly mitigates overfitting and ensures stable adaptation in low-data regimes. Extensive experiments on 11 diverse datasets show that SO achieves state-of-the-art few-shot adaptation performance while reducing memory overhead.
Digital Adoption and Cyber Security: An Analysis of Canadian Businesses
2025-04-16
Joann Jasiak, Peter MacKenzie, Purevdorj Tuvaandorj
This paper examines how Canadian firms balance the benefits of technology adoption against the rising risk of cyber security breaches. We merge data from the 2021 Canadian Survey of Digital Technology and Internet Use and the 2021 Canadian Survey of Cyber Security and Cybercrime to investigate the trade-off firms face when adopting digital technologies to enhance productivity and efficiency, balanced against the potential increase in cyber security risk. The analysis explores the extent of digital technology adoption, differences across industries, the subsequent impacts on efficiency, and associated cyber security vulnerabilities. We build aggregate variables, such as the Business Digital Usage Score and a cyber security incidence variable to quantify each firm's digital engagement and cyber security risk. A survey-weight-adjusted Lasso estimator is employed, and a debiasing method for high-dimensional logit models is introduced to identify the drivers of technological efficiency and cyber risk. The analysis reveals a digital divide linked to firm size, industry, and workforce composition. While rapid expansion of tools such as cloud services or artificial intelligence can raise efficiency, it simultaneously heightens exposure to cyber threats, particularly among larger enterprises.
2025-04-16
Benjamin Krummenacher, Jonas Frey, Turcan Tuna, Olga Vysotska, Marco Hutter
Mobile robots on construction sites require accurate pose estimation to perform autonomous surveying and inspection missions. Localization in construction sites is a particularly challenging problem due to the presence of repetitive features such as flat plastered walls and perceptual aliasing due to apartments with similar layouts inter and intra floors. In this paper, we focus on the global re-positioning of a robot with respect to an accurate scanned mesh of the building solely using LiDAR data. In our approach, a neural network is trained on synthetic LiDAR point clouds generated by simulating a LiDAR in an accurate real-life large-scale mesh. We train a diffusion model with a PointNet++ backbone, which allows us to model multiple position candidates from a single LiDAR point cloud. The resulting model can successfully predict the global position of LiDAR in confined and complex sites despite the adverse effects of perceptual aliasing. The learned distribution of potential global positions can provide multi-modal position distribution. We evaluate our approach across five real-world datasets and show the place recognition accuracy of 77% +/-2m on average while outperforming baselines at a factor of 2 in mean error.
Galactic warps: from cosmic noon to the current epoch
2025-04-16
Vladimir P. Reshetnikov, Ilia V. Chugunov, Alexander A. Marchuk, Aleksandr V. Mosenkov, Matvey D. Kozlov, Sergey S. Savchenko, Dmitry I. Makarov, Aleksandra V. Antipova, Anastasia M. Sypkova
Approximately half of all disc galaxies exhibit appreciable warps in both their stellar and HI discs. The typical warp amplitude is small (a few degrees), and only becomes noticeable at the periphery of the galaxy disc. As a result, warps remain a relatively poorly studied phenomenon. In this study, we investigate a large sample of distant edge-on galaxies (approximately 1,000 objects) in order to examine the frequency and characteristics of stellar disc warps up to a redshift of $z \sim 2$. For the selected galaxies, we utilize HST data from the Cosmic Evolution Survey field and JWST observations from the Cosmic Dawn Center Archive. We measured the properties of disc warps and investigate their evolution as a function of redshift. Our results indicate a potential evolution in the observed frequency of strong S-shaped warps (with an amplitude greater than 4$^\circ$) in stellar discs as a function of redshift. At $z \approx 2$, the frequency of strong warps reaches approximately 50%, while at $z \approx 0$, this fraction decreases to around 10-15%. We attribute the observed evolution in the occurrence of strong warps to the changing frequency of galaxy interactions and mergers. If galaxy interactions represent one of the primary mechanisms responsible for the formation of warps, then the prevalence of vertical disc deformations should increase in tandem with the rising interaction and merger rate.
Insights into environmental quenching at $z\sim1$: an enhancement of faint, low-mass passive galaxies in clusters
2025-04-16
Harry Gully, Nina Hatch, Syeda Lammim Ahad, Yannick Bahé, Michael Balogh, Devontae C. Baxter, Pierluigi Cerulo, M. C. Cooper, Ricardo Demarco, Ben Forrest, Umberto Rescigno, Gregory Rudnick, Benedetta Vulcani, Gillian Wilson
Understanding the processes that transform star-forming galaxies into quiescent ones is key to unraveling the role of environment in galaxy evolution. We present measurements of the luminosity functions (LFs) and stellar mass functions (SMFs) of passive red-sequence galaxies in four galaxy clusters at $0.8 < z < 1.3$, selected using deep VLT observations complemented with data from the GCLASS and GOGREEN surveys. We find a significant enhancement in the abundance of faint/low-mass passive galaxies in both the LFs and SMFs of all four clusters compared to the field. This is further evidenced by a shallower low-mass slope in the composite passive cluster SMF, which yields a Schechter parameter $\alpha = -0.54^{+\,0.03}_{-0.03}$, compared to $\alpha = 0.12^{+\,0.01}_{-0.01}$ for the field. Our findings indicate that quenching processes that act in clusters are enhanced compared to the field, suggesting that environmental quenching mechanisms may already be active by $z\sim1$. To reproduce the observed passive cluster SMF, we estimate that $25\pm5\%$ of the star-forming field population that falls into the cluster must have been quenched. Our results largely support traditional quenching models but highlight the need for deeper studies of larger cluster samples to better understand the role of environmental quenching in the distant Universe.
2025-04-16
Aleix Alcacer, Irene Epifanio, Sebastian Mair, Morten Mørup
Archetypal analysis (AA) was originally proposed in 1994 by Adele Cutler and Leo Breiman as a computational procedure to extract the distinct aspects called archetypes in observations with each observational record approximated as a mixture (i.e., convex combination) of these archetypes. AA thereby provides straightforward, interpretable, and explainable representations for feature extraction and dimensionality reduction, facilitating the understanding of the structure of high-dimensional data with wide applications throughout the sciences. However, AA also faces challenges, particularly as the associated optimization problem is non-convex. This survey provides researchers and data mining practitioners an overview of methodologies and opportunities that AA has to offer surveying the many applications of AA across disparate fields of science, as well as best practices for modeling data using AA and limitations. The survey concludes by explaining important future research directions concerning AA.
WINTER on S250206dm: A near-infrared search for an electromagnetic counterpart
2025-04-16
Danielle Frostig, Viraj R. Karambelkar, Robert D. Stein, Nathan P. Lourie, Mansi M. Kasliwal, Robert A. Simcoe, Mattia Bulla, Tomas Ahumada, Geoffrey Mo, Josiah Purdum, Jill Juneau, Andrew Malonis, Gabor Furesz
We present near-infrared follow-up observations of the International Gravitational Wave Network (IGWN) event S250206dm with the Wide-Field Infrared Transient Explorer (WINTER). WINTER is a near-infrared time-domain survey designed for electromagnetic follow-up of gravitational-wave sources localized to $\leq$300 deg$^{2}$. The instrument's wide field of view (1.2 deg$^2$), dedicated 1-m robotic telescope, and near-infrared coverage (0.9-1.7 microns) are optimized for searching for kilonovae, which are expected to exhibit a relatively long-lived near-infrared component. S250206dm is the only neutron star merger in the fourth observing run (to date) localized to $\leq$300 deg$^{2}$ with a False Alarm Rate below one per year. It has a $55\%$ probability of being a neutron star-black hole (NSBH) merger and a $37\%$ probability of being a binary neutron star (BNS) merger, with a $50\%$ credible region spanning 38 deg$^2$, an estimated distance of 373 Mpc, and an overall false alarm rate of approximately one in 25 years. WINTER covered $43\%$ of the probability area at least once and $35\%$ at least three times. Through automated and human candidate vetting, all transient candidates found in WINTER coverage were rejected as kilonova candidates. Unsurprisingly, given the large estimated distance of 373 Mpc, the WINTER upper limits do not constrain kilonova models. This study highlights the promise of systematic infrared searches and the need for future wider and deeper infrared surveys.
Diffusion Models on the Edge: Challenges, Optimizations, and Applications
2025-04-16
Dongqi Zheng
Diffusion models have shown remarkable capabilities in generating high-fidelity data across modalities such as images, audio, and video. However, their computational intensity makes deployment on edge devices a significant challenge. This survey explores the foundational concepts of diffusion models, identifies key constraints of edge platforms, and synthesizes recent advancements in model compression, sampling efficiency, and hardware-software co-design to make diffusion models viable on edge devices. We also review promising applications and suggest future research directions.
Knowledge distillation (KD) is a promising solution to compress large language models (LLMs) by transferring their knowledge to smaller models. During this process, white-box KD methods usually minimize the distance between the output distributions of the teacher model and the student model to transfer more information. However, we reveal that the current white-box KD framework exhibits two limitations: a) bridging probability distributions from different output spaces will limit the similarity between the teacher model and the student model; b) this framework cannot be applied to LLMs with different vocabularies. One of the root causes for these limitations is that the distributions from the teacher and the student for KD are output by different prediction heads, which yield distributions in different output spaces and dimensions. Therefore, in this paper, we propose a dual-space knowledge distillation (DSKD) framework that unifies the prediction heads of the teacher and the student models for KD. Specifically, we first introduce two projectors with ideal initialization to project the teacher/student hidden states into the student/teacher representation spaces. After this, the hidden states from different models can share the same head and unify the output spaces of the distributions. Furthermore, we develop an exact token alignment (ETA) algorithm to align the same tokens in two differently-tokenized sequences. Based on the above, our DSKD framework is a general KD framework that supports both off-policy and on-policy KD, and KD between any two LLMs regardless of their vocabularies. Extensive experiments on instruction-following, mathematical reasoning, and code generation benchmarks show that DSKD significantly outperforms existing methods based on the current white-box KD framework and surpasses other cross-tokenizer KD methods for LLMs with different vocabularies.
MINDS: The very low-mass star and brown dwarf sample -- Hidden water in carbon-dominated protoplanetary disks
2025-04-15
Aditya M. Arabhavi, Inga Kamp, Ewine F. van Dishoeck, Thomas Henning, Hyerin Jang, Valentin Christiaens, Danny Gasman, Ilaria Pascucci, Giulia Perotti, Sierra L. Grant, David Barrado, Manuel Güdel, Pierre-Olivier Lagage, Alessio Caratti o Garatti, Fred Lahuis, L. B. F. M. Waters, Till Kaeufer, Jayatee Kanwar, Maria Morales-Calderón, Kamber Schwarz, Andrew D. Sellek, Benoît Tabone, Milou Temmink, Marissa Vlasblom
Infrared observations of the inner disks around very low-mass stars (VLMS, $<$0.3$\,M_{\odot}$) have revealed a carbon-rich gas composition in the terrestrial planet-forming regions. Contrary to the typically water-rich T Tauri disk spectra, only two disks around VLMS have been observed to be water-rich among more than ten VLMS disks observed so far with JWST/MIRI. In this letter, we systematically search for the presence of water and other oxygen-bearing molecules in the JWST/MIRI spectra of ten VLMS disks from the MIRI mid-INfrared Disk Survey (MINDS). In addition to the two previously reported detections of water emission in this VLMS sample, we detect water emission in the spectra of three other sources and tentatively in one source, and we provide strong evidence for water emission in the remaining disks in the MINDS sample, most of which have bright emission from carbon-bearing molecules. We show that the $\rm C_2H_2$ emission is much stronger than that of water for sources with low luminosities, and the hydrocarbons outshine the water emission in such conditions. We propose that the appearance of water-rich vs. hydrocarbon-rich spectra is related to the location of the water reservoir in the disk relative to the main hydrocarbon reservoir. Our findings indicate that the terrestrial planet forming regions in VLMS disks have high carbon-to-oxygen ratios (C/O$>$1), but can still harbor ample water similar to those in the T Tauri disks.
The tangent cone to the real determinantal variety: various expressions and four proofs
2025-04-15
Guillaume Olikier, Petar Mlinarić, P. -A. Absil, André Uschmajew
The set of real matrices of upper-bounded rank is a real algebraic variety called the real generic determinantal variety. An explicit description of the tangent cone to that variety is given in Theorem 3.2 of Schneider and Uschmajew [SIAM J. Optim., 25 (2015), pp. 622-646]. The present paper shows that the proof therein is incomplete and provides four proofs. It also reviews equivalent descriptions of the tangent cone to that variety. Moreover, it shows that the tangent cone and the algebraic tangent cone to that variety coincide, which is not true for all real algebraic varieties.
Cancer-Myth: Evaluating AI Chatbot on Patient Questions with False Presuppositions
2025-04-15
Wang Bill Zhu, Tianqi Chen, Ching Ying Lin, Jade Law, Mazen Jizzini, Jorge J. Nieva, Ruishan Liu, Robin Jia
Cancer patients are increasingly turning to large language models (LLMs) as a new form of internet search for medical information, making it critical to assess how well these models handle complex, personalized questions. However, current medical benchmarks focus on medical exams or consumer-searched questions and do not evaluate LLMs on real patient questions with detailed clinical contexts. In this paper, we first evaluate LLMs on cancer-related questions drawn from real patients, reviewed by three hematology oncology physicians. While responses are generally accurate, with GPT-4-Turbo scoring 4.13 out of 5, the models frequently fail to recognize or address false presuppositions in the questions-posing risks to safe medical decision-making. To study this limitation systematically, we introduce Cancer-Myth, an expert-verified adversarial dataset of 585 cancer-related questions with false presuppositions. On this benchmark, no frontier LLM -- including GPT-4o, Gemini-1.Pro, and Claude-3.5-Sonnet -- corrects these false presuppositions more than 30% of the time. Even advanced medical agentic methods do not prevent LLMs from ignoring false presuppositions. These findings expose a critical gap in the clinical reliability of LLMs and underscore the need for more robust safeguards in medical AI systems.
The main form of freeway traffic congestion is the familiar stop-and-go wave, characterized by wide moving jams that propagate indefinitely upstream provided enough traffic demand. They cause severe, long-lasting adverse effects, such as reduced traffic efficiency, increased driving risks, and higher vehicle emissions. This underscores the crucial importance of artificial intervention in the propagation of stop-and-go waves. Over the past two decades, two prominent strategies for stop-and-go wave suppression have emerged: variable speed limit (VSL) and jam-absorption driving (JAD). Although they share similar research motivations, objectives, and theoretical foundations, the development of these strategies has remained relatively disconnected. To synthesize fragmented advances and drive the field forward, this paper first provides a comprehensive review of the achievements in the stop-and-go wave suppression-oriented VSL and JAD, respectively. It then focuses on bridging the two areas and identifying research opportunities from the following perspectives: fundamental diagrams, traffic dynamics modeling, traffic state estimation and prediction, stochasticity, scenarios for strategy validation, and field tests and practical deployment. We expect that through this review, one area can effectively address its limitations by identifying and leveraging the strengths of the other, thus promoting the overall research goal of freeway stop-and-go wave suppression.
OpenTuringBench: An Open-Model-based Benchmark and Framework for Machine-Generated Text Detection and Attribution
2025-04-15
Lucio La Cava, Andrea Tagarelli
Open Large Language Models (OLLMs) are increasingly leveraged in generative AI applications, posing new challenges for detecting their outputs. We propose OpenTuringBench, a new benchmark based on OLLMs, designed to train and evaluate machine-generated text detectors on the Turing Test and Authorship Attribution problems. OpenTuringBench focuses on a representative set of OLLMs, and features a number of challenging evaluation tasks, including human/machine-manipulated texts, out-of-domain texts, and texts from previously unseen models. We also provide OTBDetector, a contrastive learning framework to detect and attribute OLLM-based machine-generated texts. Results highlight the relevance and varying degrees of difficulty of the OpenTuringBench tasks, with our detector achieving remarkable capabilities across the various tasks and outperforming most existing detectors. Resources are available on the OpenTuringBench Hugging Face repository at https://huggingface.co/datasets/MLNTeam-Unical/OpenTuringBench
Complex networks are frequently employed to model physical or virtual complex systems. When certain entities exist across multiple systems simultaneously, unveiling their corresponding relationships across the networks becomes crucial. This problem, known as network alignment, holds significant importance. It enhances our understanding of complex system structures and behaviours, facilitates the validation and extension of theoretical physics research about studying complex systems, and fosters diverse practical applications across various fields. However, due to variations in the structure, characteristics, and properties of complex networks across different fields, the study of network alignment is often isolated within each domain, with even the terminologies and concepts lacking uniformity. This review comprehensively summarizes the latest advancements in network alignment research, focusing on analyzing network alignment characteristics and progress in various domains such as social network analysis, bioinformatics, computational linguistics and privacy protection. It provides a detailed analysis of various methods' implementation principles, processes, and performance differences, including structure consistency-based methods, network embedding-based methods, and graph neural network-based (GNN-based) methods. Additionally, the methods for network alignment under different conditions, such as in attributed networks, heterogeneous networks, directed networks, and dynamic networks, are presented. Furthermore, the challenges and the open issues for future studies are also discussed.
Smartphone-Based Undergraduate Physics Labs: A Comprehensive Review of Innovation, Accessibility, and Pedagogical Impact
2025-04-15
Yiping Zhao
Smartphone-integrated physics laboratories (SmartIPLs) have emerged as scalable and cost-effective alternatives to traditional lab instruction, providing accessible, hands-on experiences for diverse learning environments. This review synthesizes over a decade of research, covering nearly 200 SmartIPLs across key physics domains such as mechanics, optics, acoustics, electromagnetism, thermodynamics, and modern physics. SmartIPLs are categorized into two primary types: sensor-based experiments using built-in smartphone tools and camera-based video/image analysis for motion and optical studies. Empirical studies show that SmartIPLs support equal or greater gains in conceptual understanding, science process skills, and student engagement, especially in remote and under-resourced settings. The review explores their theoretical foundations, compares them to traditional and virtual labs, and addresses challenges such as device variability and classroom integration. Future directions include broader curricular integration, AI-driven student feedback, expansion into underrepresented physics topics, interdisciplinary applications, and equity-focused instructional design. Open-access resources, such as the UGA SmartPhone Intro Physics Lab and Modern Optics YouTube channels and the SPIE book Use of Smartphones in Optical Experimentation, highlight community-driven efforts to expand and democratize physics education. As smartphone technology advances, SmartIPLs will offer a promising path toward adaptive, intelligent, and inclusive laboratory instruction for the 21st century.
Dynamical Casimir effect in superconducting cavities: from photon generation to universal quantum gates
2025-04-15
Fernando C. Lombardo, Paula I. Villar
This chapter explores various aspects of the Dynamical Casimir Effect (DCE) and its implications in the context of circuit quantum electrodynamics (cQED). We begin by reviewing the origin and fundamental properties of the DCE, including three equivalent mathematical frameworks that offer complementary perspectives on the phenomenon. These formulations will serve as a foundation for the subsequent analyses. We then turn our attention to the practical realization of the DCE in cQED-based architectures, discussing how modern superconducting circuits can be engineered to exhibit this inherently quantum effect. Building on this, we examine how the presence of the DCE influences the performance of a quantum thermal machine operating with a quantum field, shedding light on the interplay between quantum fluctuations and thermodynamic processes. Further, we demonstrate how the DCE can be harnessed to implement a controlled-squeeze gate within a cQED platform, opening a path toward advanced quantum control and quantum information processing. The chapter concludes with a synthesis of the main results and a discussion of potential future directions.
Explicit and Implicit Representations in AI-based 3D Reconstruction for Radiology: A systematic literature review
2025-04-15
Yuezhe Yang, Boyu Yang, Yaqian Wang, Yang He, Xingbo Dong, Zhe Jin
The demand for high-quality medical imaging in clinical practice and assisted diagnosis has made 3D reconstruction in radiological imaging a key research focus. Artificial intelligence (AI) has emerged as a promising approach to enhancing reconstruction accuracy while reducing acquisition and processing time, thereby minimizing patient radiation exposure and discomfort and ultimately benefiting clinical diagnosis. This review explores state-of-the-art AI-based 3D reconstruction algorithms in radiological imaging, categorizing them into explicit and implicit approaches based on their underlying principles. Explicit methods include point-based, volume-based, and Gaussian representations, while implicit methods encompass implicit prior embedding and neural radiance fields. Additionally, we examine commonly used evaluation metrics and benchmark datasets. Finally, we discuss the current state of development, key challenges, and future research directions in this evolving field. Our project available on: https://github.com/Bean-Young/AI4Med.
We investigated the impact of a spatially varying matter potential $\lambda$, coming from neutrino-electron forward scattering, on the emergence of fast neutrino flavor conversion (FFC) triggered by the presence of zero crossings in the angular distribution of the neutrino electron lepton number (ELN). We find that FFC can be significantly affected as the spatial variation rate of $\lambda$ increases, and strong spatial variations can completely stabilize initially unstable systems. Using stability analysis based solely on initial conditions, we identified for the first time a critical variation rate above which no FFC occurs even if the flavor instability exists. By analyzing several representative matter profiles based on an 18 $M_{\odot}$ SN model, we show that spatially inhomogeneous $\lambda$ can suppress the occurrence of FFC associated with shallow ELN zero crossings in most of the SN's radial region, especially during the accretion phase. Our finding highlights the need to consider the impact of matter inhomogeneity in the development of improved SN models that aim to include the effect of neutrino flavor conversions.
Invention, Innovation, and Commercialisation in British Biophysics
2025-04-15
Jack Shepherd, Mark Leake
British biophysics has a rich tradition of scientific invention and innovation, on several occasions resulting in new technologies which have transformed biological insight, such as rapid DNA sequencing, high-precision super-resolution and label-free microscopy hardware, new approaches for high-throughput and single-molecule bio-sensing, and the development of a range of de novo bio-inspired synthetic materials. Some of these advances have been established through democratised, open-source platforms and many have biomedical success, a key example involving the SARS-CoV-2 spike protein during the COVID-19 pandemic. Here, three UK labs made crucial contributions in revealing how the spike protein targets human cells, and how therapies such as vaccines and neutralizing nanobodies likely work, enabled in large part through the biophysical technological innovations of cryo-electron microscopy. In this review, we discuss leading-edge technological and methodological innovations which resulted from initial outcomes of discovery-led 'Physics of Life' (PoL) research (capturing biophysics, biological physics and multiple blends of physical-life sciences interdisciplinary research in the UK) and which have matured into wider-reaching sustainable commercial ventures enabling significant translational impact. We describe the fundamental biophysical science which led to a diverse range of academic spinouts, presenting the scientific questions that were first asked and addressed through innovating new techniques and approaches, and highlighting the key publications which ultimately led to commercialisation. We consider these example companies through the lens of opportunities and challenges for academic biophysics research in partnership with British industry. Finally, we propose recommendations concerning future resourcing and structuring of UK biophysics research and the training and support of...
2025-04-15
Anastasia Ailamaki, Samuel Madden, Daniel Abadi, Gustavo Alonso, Sihem Amer-Yahia, Magdalena Balazinska, Philip A. Bernstein, Peter Boncz, Michael Cafarella, Surajit Chaudhuri, Susan Davidson, David DeWitt, Yanlei Diao, Xin Luna Dong, Michael Franklin, Juliana Freire, Johannes Gehrke, Alon Halevy, Joseph M. Hellerstein, Mark D. Hill, Stratos Idreos, Yannis Ioannidis, Christoph Koch, Donald Kossmann, Tim Kraska, Arun Kumar, Guoliang Li, Volker Markl, Renée Miller, C. Mohan, Thomas Neumann, Beng Chin Ooi, Fatma Ozcan, Aditya Parameswaran, Ippokratis Pandis, Jignesh M. Patel, Andrew Pavlo, Danica Porobic, Viktor Sanca, Michael Stonebraker, Julia Stoyanovich, Dan Suciu, Wang-Chiew Tan, Shiv Venkataraman, Matei Zaharia, Stanley B. Zdonik
On October 19 and 20, 2023, the authors of this report convened in Cambridge, MA, to discuss the state of the database research field, its recent accomplishments and ongoing challenges, and future directions for research and community engagement. This gathering continues a long standing tradition in the database community, dating back to the late 1980s, in which researchers meet roughly every five years to produce a forward looking report. This report summarizes the key takeaways from our discussions. We begin with a retrospective on the academic, open source, and commercial successes of the community over the past five years. We then turn to future opportunities, with a focus on core data systems, particularly in the context of cloud computing and emerging hardware, as well as on the growing impact of data science, data governance, and generative AI. This document is not intended as an exhaustive survey of all technical challenges or industry innovations in the field. Rather, it reflects the perspectives of senior community members on the most pressing challenges and promising opportunities ahead.
Refractive Index Engineering: Insights from Biological Systems for Advanced Optical Design
2025-04-15
Jiale Huang, Zihao Ou
This review explores the innovative design to achieve advanced optical functions in natural materials and intricate optical systems inspired by the unique refractive index profiles found in nature. By understanding the physical principles behind biological structures, we can develop materials with tailored optical properties that mimic these natural systems. One key area discussed is biomimetic materials design, where biological systems such as apple skin and the vision system inspire new materials. Another focus is on intricate optical systems based on refractive index contrast. These principles can be extended to design devices like waveguides, photonic crystals, and metamaterials, which manipulate light in novel ways. Additionally, the review covers optical scattering engineering, which is crucial for biomedical imaging. By adjusting the real and imaginary parts of the refractive index, we can control how much light is scattered and absorbed by tissues. This is particularly important for techniques like optical coherence tomography and multiphoton microscopy, where tailored scattering properties can improve imaging depth and resolution. The review also discusses various techniques for measuring the refractive index of biological tissues which provide comprehensive insights into the optical properties of biological materials, facilitating the development of advanced biomimetic designs. In conclusion, the manipulation of refractive index profiles in biological systems offers exciting opportunities for technological advancements. By drawing inspiration from nature and understanding the underlying physical principles, we can create materials and devices with enhanced performance and new functionalities. Future research should focus on further exploring these principles and translating them into practical applications to address real-world challenges.
Differentiating Formation Models with New Dynamical Masses for the PDS 70 Protoplanets
2025-04-15
David Trevascus, Sarah Blunt, Valentin Christiaens, Elisabeth Matthews, Iain Hammond, Wolfgang Brandner, Jason Wang, Sylvestre Lacour, Arthur Vigan, William O. Balmer, Mickael Bonnefoy, Remo Burn, Gaël Chauvin, Raffaele Gratton, Mathis Houllé, Sasha Hinkley, Jens Kammerer, Laura Kreidberg, Gabriel-Dominique Marleau, Dino Mesa, Gilles Otten, Mathias Nowak, Emily Rickman, Joel Sanchez-Bermudez, Jonas Sauter
Hot- and cold-start planet formation models predict differing luminosities for the young, bright planets that direct imaging surveys are most sensitive to. However, precise mass estimates are required to distinguish between these models observationally. The presence of two directly imaged planets, PDS 70 b and c, in the PDS 70 protoplanetary disk provides us a unique opportunity for dynamical mass measurements, since the masses for these planets are currently poorly constrained. Fitting orbital parameters to new astrometry of these planets, taken with VLTI/GRAVITY in the $K$~band, we find $2\sigma$ dynamical upper mass limits of 4.9 $M_{\rm Jup}$ for b and 13.6 $M_{\rm Jup}$ for c. Adding astrometry from the newly proposed planet candidate PDS 70 d into our model, we determine $2\sigma$ dynamical upper mass limits of 5.3 $M_{\rm Jup}$, 7.5 $M_{\rm Jup}$ and 2.2 $M_{\rm Jup}$ for b, c, and the candidate d respectively. However, $N$-body analysis of the orbits fit in this case suggest that the inclusion of $d$ makes the system unstable. Using the upper mass limits for b and c we rule out the coldest-start formation models for both planets, calculating minimum post-formation entropies ($S_i$) of 9.5 $k_{\rm B}/{\rm baryon}$ and 8.4 $k_{\rm B}/{\rm baryon}$ respectively. This places PDS 70 b and c on the growing list of directly-imaged planets inconsistent with cold-start formation.
MeerKAT 1.3 GHz Observations Towards the Milky Way Bulge
2025-04-15
William Cotton, P. J. Agnihotri, F. Camilo, E. Polisensky, S. D. Hyman
We present a MeerKAT survey of portions of the Milky Way bulge. The survey covers 172.8 square degrees in two contiguous mosaics above and below the Galactic Center as well as 32 single pointing fields at higher longitudes. The resolution of the images is $\sim$8\asec\ at a frequency of 1333 MHz with a typical Stokes I RMS of 20 $\mu$Jy Beam$^{-1}$. Most of the emission seen is from background extragalactic sources but many compact Galactic objects are identifiable by their polarization properties. Apparent polarized emission resulting from fine scale Faraday rotation in the ISM is widespread in this region of the Galaxy. The survey is used to search for background Giant Radio Galaxies, $>$700 kpc in size, identifying 17 such objects. Data products include FITS images of Stokes I, Q, U and V as well as a Faraday analysis and lists of compact total intensity and polarized sources.
The Robotability Score: Enabling Harmonious Robot Navigation on Urban Streets
2025-04-15
Matt Franchi, Maria Teresa Parreira, Fanjun Bu, Wendy Ju
This paper introduces the Robotability Score ($R$), a novel metric that quantifies the suitability of urban environments for autonomous robot navigation. Through expert interviews and surveys, we identify and weigh key features contributing to R for wheeled robots on urban streets. Our findings reveal that pedestrian density, crowd dynamics and pedestrian flow are the most critical factors, collectively accounting for 28% of the total score. Computing robotability across New York City yields significant variation; the area of highest R is 3.0 times more "robotable" than the area of lowest R. Deployments of a physical robot on high and low robotability areas show the adequacy of the score in anticipating the ease of robot navigation. This new framework for evaluating urban landscapes aims to reduce uncertainty in robot deployment while respecting established mobility patterns and urban planning principles, contributing to the discourse on harmonious human-robot environments.
Electron-transverse acoustic phonon couplings in three-dimensional pentatellurides
2025-04-15
Rui Min, Fuxiang Li, Yi-Xiang Wang
Transverse acoustic (TA) phonon waves are analogous to electromagnetic waves and can carry a certain angular momentum. In this paper, we study the electron-TA phonon couplings in three-dimensional pentatellurides and explore the conditions under which the TA phonon condensation is stable. We analyze the Lindhard response function, phonon softening, mean-field parameters, and renormalized dispersions, on the basis of which the phase diagrams of the electron-phonon couplings in ZrTe$_5$ and HfTe$_5$ are calculated. The phase diagrams show that, if the chemical potential lies near the Weyl nodes, the TA phonon condensation will dominate and lead to the shear strain wave phase. We further reveal that when the wave vector of the particular phonon mode is smaller, the critical coupling strength will be weaker for the phonon condensation, which thus favors the condensation phase.
DPS: Design Pattern Summarisation Using Code Features
2025-04-15
Najam Nazar, Sameer Sikka, Christoph Treude
Automatic summarisation has been used efficiently in recent years to condense texts, conversations, audio, code, and various other artefacts. A range of methods, from simple template-based summaries to complex machine learning techniques -- and more recently, large language models -- have been employed to generate these summaries. Summarising software design patterns is important because it helps developers quickly understand and reuse complex design concepts, thereby improving software maintainability and development efficiency. However, the generation of summaries for software design patterns has not yet been explored. Our approach utilises code features and JavaParser to parse the code and create a JSON representation. Using an NLG library on this JSON representation, we convert it into natural language text that acts as a summary of the code, capturing the contextual information of the design pattern. Our empirical results indicate that the summaries generated by our approach capture the context in which patterns are applied in the codebase. Statistical evaluations demonstrate that our summaries closely align with human-written summaries, as evident from high values in the ROUGE-L, BLEU-4, NIST, and FrugalScore metrics. A follow-up survey further shows that DPS summaries were rated as capturing context better than human-generated summaries.
Scalability and Maintainability Challenges and Solutions in Machine Learning: Systematic Literature Review
2025-04-15
Karthik Shivashankar, Ghadi S. Al Hajj, Antonio Martini
This systematic literature review examines the critical challenges and solutions related to scalability and maintainability in Machine Learning (ML) systems. As ML applications become increasingly complex and widespread across industries, the need to balance system scalability with long-term maintainability has emerged as a significant concern. This review synthesizes current research and practices addressing these dual challenges across the entire ML life-cycle, from data engineering to model deployment in production. We analyzed 124 papers to identify and categorize 41 maintainability challenges and 13 scalability challenges, along with their corresponding solutions. Our findings reveal intricate inter dependencies between scalability and maintainability, where improvements in one often impact the other. The review is structured around six primary research questions, examining maintainability and scalability challenges in data engineering, model engineering, and ML system development. We explore how these challenges manifest differently across various stages of the ML life-cycle. This comprehensive overview offers valuable insights for both researchers and practitioners in the field of ML systems. It aims to guide future research directions, inform best practices, and contribute to the development of more robust, efficient, and sustainable ML applications across various domains.
QualiTagger: Automating software quality detection in issue trackers
2025-04-15
Karthik Shivashankar, Rafael Capilla, Maren Maritsdatter Kruke, Mili Orucevic, Antonio Martini
A systems quality is a major concern for development teams when it evolve. Understanding the effects of a loss of quality in the codebase is crucial to avoid side effects like the appearance of technical debt. Although the identification of these qualities in software requirements described in natural language has been investigated, most of the results are often not applicable in practice, and rely on having been validated on small datasets and limited amount of projects. For many years, machine learning (ML) techniques have been proved as a valid technique to identify and tag terms described in natural language. In order to advance previous works, in this research we use cutting edge models like Transformers, together with a vast dataset mined and curated from GitHub, to identify what text is usually associated with different quality properties. We also study the distribution of such qualities in issue trackers from openly accessible software repositories, and we evaluate our approach both with students from a software engineering course and with its application to recognize security labels in industry.
Peer review is a cornerstone of quality control in scientific publishing. With the increasing workload, the unintended use of `quick' heuristics, referred to as lazy thinking, has emerged as a recurring issue compromising review quality. Automated methods to detect such heuristics can help improve the peer-reviewing process. However, there is limited NLP research on this issue, and no real-world dataset exists to support the development of detection tools. This work introduces LazyReview, a dataset of peer-review sentences annotated with fine-grained lazy thinking categories. Our analysis reveals that Large Language Models (LLMs) struggle to detect these instances in a zero-shot setting. However, instruction-based fine-tuning on our dataset significantly boosts performance by 10-20 performance points, highlighting the importance of high-quality training data. Furthermore, a controlled experiment demonstrates that reviews revised with lazy thinking feedback are more comprehensive and actionable than those written without such feedback. We will release our dataset and the enhanced guidelines that can be used to train junior reviewers in the community. (Code available here: https://github.com/UKPLab/arxiv2025-lazy-review)
2025-04-15
Rein Liefferink, Lukas Körber, Kathinka Gerlinger, Bastian Pfau, Felix Büttner, Johan H. Mentink
Laser-induced ultrafast skyrmion nucleation has been experimentally demonstrated in several materials. So far, atomistic models have been used to corroborate experimental results. However, such simulations do not provide a simple intuitive understanding of the underlying physics. Here, we propose a coarse-grained effective theory where skyrmions can be nucleated or annihilated by thermal activation over energy barriers. Evaluating these two processes during a heat pulse shows good agreement with atomistic spin dynamics simulations and experiments while drastically reducing computational complexity. Furthermore, the effective theory provides a direct guide for experimentally optimizing the number of nucleated skyrmions. Interestingly, the model also predicts a novel pathway for ultrafast annihilation of skyrmions. Our results pave the way for a deeper understanding of ultrafast nanomagnetism and the role of non-equilibrium physics.
Dynamic Compressing Prompts for Efficient Inference of Large Language Models
2025-04-15
Jinwu Hu, Wei Zhang, Yufeng Wang, Yu Hu, Bin Xiao, Mingkui Tan, Qing Du
Large Language Models (LLMs) have shown outstanding performance across a variety of tasks, partly due to advanced prompting techniques. However, these techniques often require lengthy prompts, which increase computational costs and can hinder performance because of the limited context windows of LLMs. While prompt compression is a straightforward solution, existing methods confront the challenges of retaining essential information, adapting to context changes, and remaining effective across different tasks. To tackle these issues, we propose a task-agnostic method called Dynamic Compressing Prompts (LLM-DCP). Our method reduces the number of prompt tokens while aiming to preserve the performance as much as possible. We model prompt compression as a Markov Decision Process (MDP), enabling the DCP-Agent to sequentially remove redundant tokens by adapting to dynamic contexts and retaining crucial content. We develop a reward function for training the DCP-Agent that balances the compression rate, the quality of the LLM output, and the retention of key information. This allows for prompt token reduction without needing an external black-box LLM. Inspired by the progressive difficulty adjustment in curriculum learning, we introduce a Hierarchical Prompt Compression (HPC) training strategy that gradually increases the compression difficulty, enabling the DCP-Agent to learn an effective compression method that maintains information integrity. Experiments demonstrate that our method outperforms state-of-the-art techniques, especially at higher compression rates. The code for our approach will be available at https://github.com/Fhujinwu/DCP.
2025-04-15
Pancheng Zhao, Deng-Ping Fan, Shupeng Cheng, Salman Khan, Fahad Shahbaz Khan, David Clifton, Peng Xu, Jufeng Yang
Deep learning is developing rapidly and handling common computer vision tasks well. It is time to pay attention to more complex vision tasks, as model size, knowledge, and reasoning capabilities continue to improve. In this paper, we introduce and review a family of complex tasks, termed Concealed Dense Prediction (CDP), which has great value in agriculture, industry, etc. CDP's intrinsic trait is that the targets are concealed in their surroundings, thus fully perceiving them requires fine-grained representations, prior knowledge, auxiliary reasoning, etc. The contributions of this review are three-fold: (i) We introduce the scope, characteristics, and challenges specific to CDP tasks and emphasize their essential differences from generic vision tasks. (ii) We develop a taxonomy based on concealment counteracting to summarize deep learning efforts in CDP through experiments on three tasks. We compare 25 state-of-the-art methods across 12 widely used concealed datasets. (iii) We discuss the potential applications of CDP in the large model era and summarize 6 potential research directions. We offer perspectives for the future development of CDP by constructing a large-scale multimodal instruction fine-tuning dataset, CvpINST, and a concealed visual perception agent, CvpAgent.
The Effectiveness of Business Process Visualisations: a Systematic Literature Review
2025-04-15
E. C. Overes, F. M. Santoro
Business Process Visualisations (BPVs) have become indispensable tools for organisations seeking to enhance their operational efficiency, decision-making capabilities, and overall performance. The burgeoning interest in process modeling and tool development, coupled with the rise of data visualisation field, underscores the significant role of visual tools in leveraging human cognition. Unlike traditional models, data visualisation approaches graphics from a novel angle, emphasising the potency of visual representations. This review aims to integrate the domains of BPV and data visualisation to assess their combined influence on organisational effectiveness comprehensively. Through a meticulous analysis of existing literature, this study aims to amalgamate insights on BPVs impact from a data visualisation standpoint, advocating for a design philosophy that prioritises user engagement to bolster organisational outcomes. Additionally, our systematic review has unveiled promising avenues for future research, identifying underexplored variables that influence the efficacy of BPVs, thereby charting a path for forthcoming scholarly inquiries.
Radio Frequency (RF) sensing technologies have experienced significant growth due to the widespread adoption of RF devices and the Internet of Things (IoT). These technologies enable numerous applications across healthcare, smart homes, industrial automation, and human-computer interaction. However, the non-intrusive and ubiquitous nature of RF sensing - combined with its environmental sensitivity and data dependency - makes these systems inherently vulnerable not only as attack targets, but also as powerful attack vectors. This survey presents a comprehensive analysis of RF sensing security, covering both system-level vulnerabilities - such as signal spoofing, adversarial perturbations, and model poisoning - and the misuse of sensing capabilities for attacks like cross-boundary surveillance, side-channel inference, and semantic privacy breaches. We propose unified threat models to structure these attack vectors and further conduct task-specific vulnerability assessments across key RF sensing applications, identifying their unique attack surfaces and risk profiles. In addition, we systematically review defense strategies across system layers and threat-specific scenarios, incorporating both active and passive paradigms to provide a structured and practical view of protection mechanisms. Compared to prior surveys, our work distinguishes itself by offering a multi-dimensional classification framework based on task type, threat vector, and sensing modality, and by providing fine-grained, scenario-driven analysis that bridges theoretical models and real-world implications. This survey aims to serve as a comprehensive reference for researchers and practitioners seeking to understand, evaluate, and secure the evolving landscape of RF sensing technologies.
As generative AI transforms educational feedback practices, understanding students' perceptions of different feedback providers becomes crucial for effective implementation. This study addresses a critical gap by comparing undergraduate students' trust in AI-generated, human-created, and human-AI co-produced feedback, informing how institutions can adapt feedback practices in this new era. Through a within-subject experiment with 91 participants, we investigated factors predicting students' ability to distinguish between feedback types, perception of feedback quality, and potential biases to AI involvement. Findings revealed that students generally preferred AI and co-produced feedback over human feedback in terms of perceived usefulness and objectivity. Only AI feedback suffered a decline in perceived genuineness when feedback sources were revealed, while co-produced feedback maintained its positive perception. Educational AI experience improved students' ability to identify AI feedback and increased their trust in all feedback types, while general AI experience decreased perceived usefulness and credibility. Male students consistently rated all feedback types as less valuable than their female and non-binary counterparts. These insights inform evidence-based guidelines for integrating AI into higher education feedback systems while addressing trust concerns and fostering AI literacy among students.
Emergent ferromagnetism on the surface of recent two-dimensional (2D) MXene is investigated by X-ray magnetic circular dichroism (XMCD) and angle-dependent hard X-ray photoemission spectroscopy (HAXPES). Focusing on the Cr2N as one of the 2D-MXenes, the bilayers of Cr2N/Co and Cr2N/Pt are prepared by magnetron sputtering technique. XMCD reveals the induced magnetic moment of Cr in the Cr2N/Co interface, while it is not observed in the Cr2N/Pt interface at room temperature. To distinguish the possible origins of either the interlayer magnetic exchange coupling or the charge transfer at the interfaces, the additional controlled Cr2N/Cu bilayer, whose work function of Cu is consistent with Co, is prepared. HAXPES spectra for the Cr 2p core level near the interface of Cr2N/Cu is consistent with that of Cr2N/Co, indicating that the induced magnetic moment of Cr observed by XMCD for the Cr2N/Co can be attributed to the interlayer magnetic exchange coupling, rather than the charge transfer, which is a specific characteristics emerged at the interface with 2D-MXene.
A Highly Efficient Cross-matching Scheme using Learned Index Structure
2025-04-15
Phu-Minh Lam, Dongwei Fan, Hongbo Wei, Jun Wang, Yu Zhou, Qi Ma, Baolong Zhang, Xiazhao Zhang, Yongheng Wang
Spatial data fusion is a bottleneck when it meets the scale of 10 billion records. Cross-matching celestial catalogs is just one example of this. To challenge this, we present a framework that enables efficient cross-matching using Learned Index Structures. Our approach involves a data transformation method to map multi-dimensional data into easily learnable distributions, coupled with a novel search algorithm that leverages the advantages of model pairs, significantly enhancing the efficiency of nearest-neighbor search. In this study, we utilized celestial catalog data derived from astronomical surveys to construct the index and evaluated the speed of the cross-matching process. Using the HEALPix segmentation scheme, we built an independent model object for each tile and developed an end-to-end pipeline to construct a framework with semantic guarantees for record retrieval in query and range search. Our results show that the proposed method improves cross-matching speed by more than four times compared to KD-trees for a radius range between 1 milli-arcseconds and 100 arcseconds.
Adaptive Human-Agent Teaming: A Review of Empirical Studies from the Process Dynamics Perspective
2025-04-15
Mengyao Wang, Jiayun Wu, Shuai Ma, Nuo Li, Peng Zhang, Ning Gu, Tun Lu
The rapid advancement of AI, including Large Language Models, has propelled autonomous agents forward, accelerating the human-agent teaming (HAT) paradigm to leverage complementary strengths. However, HAT research remains fragmented, often focusing on isolated team development phases or specific challenges like trust calibration while overlooking the real-world need for adaptability. Addressing these gaps, a process dynamics perspective is adopted to systematically review HAT using the T$^4$ framework: Team Formation, Task and Role Development, Team Development, and Team Improvement. Each phase is examined in terms of its goals, actions, and evaluation metrics, emphasizing the co-evolution of task and team dynamics. Special focus is given to the second and third phases, highlighting key factors such as team roles, shared mental model, and backup behaviors. This holistic perspective identifies future research directions for advancing long-term adaptive HAT.
The extremely low superfluid density and unprecedented tunability of oxide interface superconductors provide an ideal platform for studying fluctuations in two-dimensional superconductors. In this work, we have fabricated a LaAlO3/KTaO3 interface superconductor patterned with a nanohoneycomb array of insulating islands. Little-Parks-like magnetoresistance oscillations have been observed, which are dictated by the superconducting flux quantum h/2e. Moreover, an anomalous negative magnetoresistance (ANMR) appears under a weak magnetic field, suggesting magnetic-field-enhanced superconductivity. By examining their dependences on temperature, measurement current, and electrical gating, we conclude that both phenomena are associated with superconducting order parameter: The h/2e oscillations provide direct evidence of Cooper pair transport; the ANMR is interpreted as a consequence of multiple connected narrow superconducting paths with strong fluctuations.
2025-04-15
Sicheng Feng, Gongfan Fang, Xinyin Ma, Xinchao Wang
Reasoning models have demonstrated remarkable progress in solving complex and logic-intensive tasks by generating extended Chain-of-Thoughts (CoTs) prior to arriving at a final answer. Yet, the emergence of this "slow-thinking" paradigm, with numerous tokens generated in sequence, inevitably introduces substantial computational overhead. To this end, it highlights an urgent need for effective acceleration. This survey aims to provide a comprehensive overview of recent advances in efficient reasoning. It categorizes existing works into three key directions: (1) shorter - compressing lengthy CoTs into concise yet effective reasoning chains; (2) smaller - developing compact language models with strong reasoning capabilities through techniques such as knowledge distillation, other model compression techniques, and reinforcement learning; and (3) faster - designing efficient decoding strategies to accelerate inference. A curated collection of papers discussed in this survey is available in our GitHub repository.
Non-Minimal RT Coupling and its Impact on Inflationary Evolution in f(R, T) Gravity
2025-04-15
Sohan Kumar Jha, Anisur Rahaman
We examine inflationary models in the $f(R, T)$ gravity framework where we have a conformal constant and an $RT$-mixing term apart from an R term. The RT-mixing term introduces non-minimal coupling between gravity and matter. We consider the exponential SUSY potential $V(\p)=M^4 \lt(1-e^{-\l \p/\mp}\rt)$ and a novel potential $V(\p)=\l \mp^{4-2\a} \p^{2\a} \sin^2\lt(\frac{\b \mp^\a}{\p^\a}\rt)$. With the help of COBE normalization, we constrain values of different parameters and extract the field value at the time of Hubble crossing. The end of inflation is marked by $\tep(\p_i)=1$ where $\p_i$ is the field value at the end of inflation. Equipped with these values, we then move on to calculate values of spectral index $n_s$ and tensor-to-scalar ratio $r$. Our predicted values of $n_s$ and $r$ fall within their observed values from the Planck 2018 survey and BICEP/Keck array measurement for both potential, making them plausible candidates for the inflationary model. We also display the variation of the tensor-to-scalar ratio and spectral index with the coefficient of RT-mixing term for fixed values of e-fold number. There, we find the existence of two local maxima of $n_s$, which occur at a negative and a positive value of $\x$, the coefficient of $RT$-mixing term. Our analysis finds a significant impact of $\x$ on values of observables
A comprehensive review of remote sensing in wetland classification and mapping
2025-04-15
Shuai Yuan, Xiangan Liang, Tianwu Lin, Shuang Chen, Rui Liu, Jie Wang, Hongsheng Zhang, Peng Gong
Wetlands constitute critical ecosystems that support both biodiversity and human well-being; however, they have experienced a significant decline since the 20th century. Back in the 1970s, researchers began to employ remote sensing technologies for wetland classification and mapping to elucidate the extent and variations of wetlands. Although some review articles summarized the development of this field, there is a lack of a thorough and in-depth understanding of wetland classification and mapping: (1) the scientific importance of wetlands, (2) major data, methods used in wetland classification and mapping, (3) driving factors of wetland changes, (4) current research paradigm and limitations, (5) challenges and opportunities in wetland classification and mapping under the context of technological innovation and global environmental change. In this review, we aim to provide a comprehensive perspective and new insights into wetland classification and mapping for readers to answer these questions. First, we conduct a meta-analysis of over 1,200 papers, encompassing wetland types, methods, sensor types, and study sites, examining prevailing trends in wetland classification and mapping. Next, we review and synthesize the wetland features and existing data and methods in wetland classification and mapping. We also summarize typical wetland mapping products and explore the intrinsic driving factors of wetland changes across multiple spatial and temporal scales. Finally, we discuss current limitations and propose future directions in response to global environmental change and technological innovation. This review consolidates our understanding of wetland remote sensing and offers scientific recommendations that foster transformative progress in wetland science.
Wireless baseband processing (WBP) serves as an ideal scenario for utilizing vector processing, which excels in managing data-parallel operations due to its parallel structure. However, conventional vector architectures face certain constraints such as limited vector register sizes, reliance on power-of-two vector length multipliers, and vector permutation capabilities tied to specific architectures. To address these challenges, we have introduced an instruction set extension (ISE) based on RISC-V known as unlimited vector processing (UVP). This extension enhances both the flexibility and efficiency of vector computations. UVP employs a novel programming model that supports non-power-of-two register groupings and hardware strip-mining, thus enabling smooth handling of vectors of varying lengths while reducing the software strip-mining burden. Vector instructions are categorized into symmetric and asymmetric classes, complemented by specialized load/store strategies to optimize execution. Moreover, we present a hardware implementation of UVP featuring sophisticated hazard detection mechanisms, optimized pipelines for symmetric tasks such as fixed-point multiplication and division, and a robust permutation engine for effective asymmetric operations. Comprehensive evaluations demonstrate that UVP significantly enhances performance, achieving up to 3.0$\times$ and 2.1$\times$ speedups in matrix multiplication and fast Fourier transform (FFT) tasks, respectively, when measured against lane-based vector architectures. Our synthesized RTL for a 16-lane configuration using SMIC 40nm technology spans 0.94 mm$^2$ and achieves an area efficiency of 21.2 GOPS/mm$^2$.
Radiation Footprint Control in Cell-Free Cooperative ISAC: Optimal Joint BS Activation and Beamforming Coordination
2025-04-15
Jie Chen, Xianbin Wang
Coordinated beamforming across distributed base stations (BSs) in cell-free architectures can efficiently support integrated sensing and communication (ISAC) users by improving resource sharing and reducing conflicts in the spatial domain. However, coordinating numerous BSs within the ISAC network poses risks of generating substantial interference for other networks sharing the spectrum, while also increasing operational costs from power consumption and signaling overhead. Therefore, in this paper, we propose an interference-suppressed and cost-optimized cell-free ISAC network by opportunistically and cooperatively orchestrating distributed radio resources to address competing sensing and communication (S\&C) demands. Specifically, we conceive a radiation footprint control mechanism that autonomously suppresses interference across the entire signal propagation space to safeguard other networks without exchanging signaling. Then, we propose joint BS activation and beamforming coordination to dynamically activate appropriate BSs and orchestrate their spatial beams for service provisioning. Building upon this framework, we formulate a cost-efficient utility maximization problem that considers individual S\&C demands and location-dependent radiation footprint constraints. Since this results in a non-convex optimization problem, we develop a monotonic optimization embedded branch-and-bound (MO-BRB) algorithm to find the optimal solution. Additionally, we apply a low-complexity iterative method to obtain near-optimal solutions. Finally, simulation results validate the effectiveness of the proposed algorithms.
Extreme eclipsing binaries may harbor peculiar physical properties. In this work, we aim to identify a sample of such systems by selecting binaries with pronounced eclipsing light curves, characterized of large variability ($\Delta \mathrm{mag} > 0.3$ in ZTF $g$ band) and significant differences between primary and secondary eclipses (eclipse depth ratio $>$ 20 in ZTF $g$ band). We identified 23 candidates by combining the photometric data and the LAMOST spectroscopic survey. Spectroscopic analysis revealed that all of these systems are dominated by A-type stars in the optical band. Further investigation confirmed that all 23 candidates are Algol-type binaries, with 22 of them being newly discovered. Their orbital periods range from 2.57 to 19.21 days. These systems consist of low-luminosity, highly stripped subgiant donors and accreting A-type stars. The donor stars, with radii of $2.5-8.9~R_\odot$ and effective temperatures around 4000 K, have typical masses of $M_2 \sim 0.3~M_\odot$, indicating substantial mass loss through Roche-lobe overflow. The presence of ellipsoidal variability and H$\alpha$ emission provides strong evidence for ongoing mass transfer. By fitting the spectral energy distributions, spectra, and light curves, we found that most of the accretors have luminosities lower than expected from the mass-luminosity relation, aligning with the predicted faint phase for mass-gaining stars. Three objects of our sample exhibit pulsations with periods from 18 minutes to 8 hours, providing opportunities for asteroseismic studies. The low mass transfer rates and stability make the sample excellent systems for studying mass accretion, advancing our understanding of the Algol-type binary evolution.
Cataclysmic Variable Candidates Identified in eROSITA-DE DR1,XMM-Newton, Swift, and ROSAT Catalogs
2025-04-15
Xiangxiang Wang, Jumpei Takata
Cataclysmic variables (CVs) are binary systems with a white dwarf accreting matter from a low-mass star, making them significant sources of X-ray emission in the Galaxy. We present a systematic search for X-ray emitting CV candidates by cross-matching four X-ray catalogs (eROSITA, XMM-Newton, Swift, and ROSAT) with Gaia sources located in the bridge region between the main sequence and white dwarf cooling sequence in the Hertzsprung-Russell diagram. From 444 candidates (267 confirmed CVs and 177 new candidates), we detect orbital modulation in 56 sources using ZTF/TESS light curves. The eROSITA catalog contributes 51% of candidates, outperforming other surveys due to its wider sky coverage and higher sensitivity (~10^{-14} erg cm^{-2} s^{-1}). Our method demonstrates the efficiency of combining X-ray data with time-domain analysis for CV identification, with future eROSITA observations expected to expand the population of X-ray emitting CVs.
Integrated photonics incorporating 2D materials for practical applications
2025-04-15
David J. Moss
On-chip integration of 2D materials with exceptional optical properties provides an attractive solution for next-generation photonic integrated circuits to address the limitations of conventional bulk integrated platforms. Over the past two decades, significant advancements have been made in the interdisciplinary field of 2D material integrated photonics, greatly narrowing the gap between laboratory research and industrial applications. In this paper, we provide a perspective on the developments of this field towards industrial manufacturing and commercialization. First, we review recent progress towards commercialization. Next, we provide an overview of cutting-edge fabrication techniques, which are categorized into large-scale integration, precise patterning, dynamic tuning, and device packaging. Both the advantages and limitations of these techniques are discussed in relation to industrial manufacturing. Finally, we highlight some important issues related to commercialization, including fabrication standards, recycling, service life, and environmental implications.
Probing the Outskirts of M Dwarf Planetary Systems with a Cycle 1 JWST NIRCam Coronagraphy Survey
2025-04-15
Ellis Bogat, Joshua E. Schlieder, Kellen D. Lawson, Yiting Li, Jarron M. Leisenring, Michael R. Meyer, William Balmer, Thomas Barclay, Charles A. Beichman, Geoffrey Bryden, Per Calissendorff, Aarynn Carter, Matthew De Furio, Julien H. Girard, Thomas P. Greene, Tyler D. Groff, Jens Kammerer, Jorge Llop-Sayson, Michael W. McElwain, Marcia J. Rieke, Marie Ygouf
The population of giant planets on wide orbits around low-mass M dwarf stars is poorly understood, but the unprecedented sensitivity of JWST NIRCam coronagraphic imaging now provides direct access to planets significantly less massive than Jupiter beyond 10 AU around the closest, youngest M dwarfs. We present the design, observations, and results of JWST GTO Program 1184, a Cycle 1 NIRCam coronagraphic imaging survey of 9 very nearby and young low-mass stars at 3-5 micron wavelengths. In the F356W and F444W filters, we achieve survey median 5-sigma contrasts deeper than 10-5 at a separation of 1", corresponding to 0.20 MJup in F444W and 1.30 MJup in F356W at planet-star separations of 10 AU. Our results include 3-5 micron debris disk detections and the identification of many extended and point-like sources in the final post-processed images. In particular, we have identified two marginal point source candidates having fluxes and color limits consistent with model predictions for young sub-Jupiter mass exoplanets. Under the assumption that neither candidate is confirmed, we place the first direct-imaging occurrence constraints on M dwarf wide-orbit (semimajor axes 10-100 AU), sub-Jupiter mass exoplanets (0.3-1 MJup). We find frequency limits of < 0.10 and < 0.16 objects per star with 1 and 3-sigma confidence, respectively. This survey brings to the forefront the unprecedented capabilities of JWST NIRCam coronagraphic imaging when targeting young, low-mass stars and acts as a precursor to broader surveys to place deep statistical constraints on wide-orbit, sub-Jupiter mass planets around M dwarfs.
Examination of the functional form of the light and mass distribution in spiral arms
2025-04-15
Ilia V. Chugunov, Alexander A. Marchuk, Sergey S. Savchenko
Spiral arms are a common feature of local galaxies, but the exact form of the distribution of mass and light in them is not well known. In this work, we aim to measure this distribution as accurately as possible, focusing on individual spiral arms and using the so-called slicing method. The sample consists of 19 well-resolved, viewed face-on spiral galaxies from the S$^4$G survey. We work primarily with infrared images at 3.6 $\mu$m from the same survey, and, secondarily, with ultraviolet data from the GALEX telescope. We derive the properties of the spiral arms step by step, starting from their overall shape, then measuring their brightness profile and width variation along the arm, and then examining the fine structure of the profile across the arm, namely its skewness and S\'{e}rsic index. We construct a 2D photometric function of the spiral arm that can be used in further decomposition studies, validate it and identify the most and least important parameters. Finally, we show how our results can be used to unravel the nature of the spiral arms, supporting the evidence that NGC 4535 has a density wave in its disc.
Scalable Efficient Inference in Complex Surveys through Targeted Resampling of Weights
2025-04-15
Snigdha Das, Dipankar Bandyopadhyay, Debdeep Pati
Survey data often arises from complex sampling designs, such as stratified or multistage sampling, with unequal inclusion probabilities. When sampling is informative, traditional inference methods yield biased estimators and poor coverage. Classical pseudo-likelihood based methods provide accurate asymptotic inference but lack finite-sample uncertainty quantification and the ability to integrate prior information. Existing Bayesian approaches, like the Bayesian pseudo-posterior estimator and weighted Bayesian bootstrap, have limitations; the former struggles with uncertainty quantification, while the latter is computationally intensive and sensitive to bootstrap replicates. To address these challenges, we propose the Survey-adjusted Weighted Likelihood Bootstrap (S-WLB), which resamples weights from a carefully chosen distribution centered around the underlying sampling weights. S-WLB is computationally efficient, theoretically consistent, and delivers finite-sample uncertainty intervals which are proven to be asymptotically valid. We demonstrate its performance through simulations and applications to nationally representative survey datasets like NHANES and NSDUH.
Statistical Modeling of Combinatorial Response Data
2025-04-15
Yu Zheng, Malay Ghosh, Leo Duan
In categorical data analysis, there is rich literature for modeling binary and polychotomous responses. However, existing methods are inadequate for handling combinatorial responses, where each response is an array of integers subject to additional constraints. Such data are increasingly common in modern applications, such as surveys collected under skip logic, event propagation on a network, and observed matching in ecology. Ignoring the combinatorial structure in the response data may lead to biased estimation and prediction. The fundamental challenge for modeling these integer-vector data is the lack of a link function that connects a linear or functional predictor with a probability respecting the combinatorial constraints. In this paper, we propose a novel augmented likelihood, in which a combinatorial response can be viewed as a deterministic transform of a continuous latent variable. We specify the transform as the maximizer of integer linear program, and characterize useful properties such as dual thresholding representation. When taking a Bayesian approach and considering a multivariate normal distribution for the latent variable, our method becomes a direct generalization to the celebrated probit data augmentation, and enjoys straightforward computation via Gibbs sampler. We provide theoretical justification for the proposed method at an interesting intersection between duality and probability distribution and develop useful sufficient conditions that guarantee the applicability of our method. We demonstrate the effectiveness of our method through simulation studies and a real data application on modeling the formation of seasonal matching between waterfowl.
Advances in Surrogate Modeling for Biological Agent-Based Simulations: Trends, Challenges, and Future Prospects
2025-04-15
Kerri-Ann Norton, Daniel Bergman, Harsh Vardhan Jain, Trachette Jackson
Agent-based modeling (ABM) is a powerful computational approach for studying complex biological and biomedical systems, yet its widespread use remains limited by significant computational demands. As models become increasingly sophisticated, the number of parameters and interactions rises rapidly, exacerbating the so-called curse of dimensionality and making comprehensive parameter exploration and uncertainty analyses computationally prohibitive. Surrogate modeling provides a promising solution by approximating ABM behavior through computationally efficient alternatives, greatly reducing the runtime needed for parameter estimation, sensitivity analysis, and uncertainty quantification. In this review, we examine traditional approaches for performing these tasks directly within ABMs -- providing a baseline for comparison -- and then synthesize recent developments in surrogate-assisted methodologies for biological and biomedical applications. We cover statistical, mechanistic, and machine-learning-based approaches, emphasizing emerging hybrid strategies that integrate mechanistic insights with machine learning to balance interpretability and scalability. Finally, we discuss current challenges and outline directions for future research, including the development of standardized benchmarks to enhance methodological rigor and facilitate the broad adoption of surrogate-assisted ABMs in biology and medicine.
Towards Interpretable Deep Generative Models via Causal Representation Learning
2025-04-15
Gemma E. Moran, Bryon Aragam
Recent developments in generative artificial intelligence (AI) rely on machine learning techniques such as deep learning and generative modeling to achieve state-of-the-art performance across wide-ranging domains. These methods' surprising performance is due in part to their ability to learn implicit "representations'' of complex, multi-modal data. Unfortunately, deep neural networks are notoriously black boxes that obscure these representations, making them difficult to interpret or analyze. To resolve these difficulties, one approach is to build new interpretable neural network models from the ground up. This is the goal of the emerging field of causal representation learning (CRL) that uses causality as a vector for building flexible, interpretable, and transferable generative AI. CRL can be seen as a culmination of three intrinsically statistical problems: (i) latent variable models such as factor analysis; (ii) causal graphical models with latent variables; and (iii) nonparametric statistics and deep learning. This paper reviews recent progress in CRL from a statistical perspective, focusing on connections to classical models and statistical and causal identifiablity results. This review also highlights key application areas, implementation strategies, and open statistical questions in CRL.
New dwarf galaxy candidates in the M106, NGC3521, and UGCA127 groups with the Hyper Suprime Camera
2025-04-15
Oliver Müller, Helmut Jerjen, Salvatore Taibi, Nick Heesters, Ethan Crosby, Marcel S. Pawlowski
The local universe is still full of hidden dwarf galaxies to be discovered using deep imaging campaigns. Here we present the third paper in a series to search for low-surface brightness dwarf galaxies around nearby isolated luminous host galaxies with the Subaru Hyper Suprime Camera. Based on visual inspection, we found 11, 0, 4, and 6 dwarf galaxy candidates around M106, NGC2903, NGC3521, and UGCA127, respectively. This adds to the 40 candidates around M104 and 4 candidates around NGC2683 found in the previous papers. Artificial galaxy experiments show that we are complete down to a mean effective surface brightness of 26 mag/arcsec$^2$. The new dwarf galaxy candidates follow known scaling relation in size, surface brightness and luminosity, making them good candidates based on their morphology and photometric properties. We trace the luminosity function of these galaxies down to magnitude of $\approx-$9 in the V band for all galaxies targeted in our survey footprint so far. While the most massive galaxy (M104) has a significant higher abundance of dwarfs, NGC3521, NGC2903, and NGC2683 have a similar luminosity function as the Milky Way. These latter three galaxies also have a similar stellar mass and might be considered Milky Way analogs. UGCA127 is a low-mass galaxy but almost reaches the same number of dwarfs as the Milky Way at our limiting magnitude. We have searched for hints of lopsidedness in the satellite distributions, but found none to be significant. The next step will be to confirm these members through either distance or velocity measurements.
Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey
2025-04-15
Siteng Ma, Honghui Du, Yu An, Jing Wang, Qinqin Wang, Haochang Wu, Aonghus Lawlor, Ruihai Dong
Deep learning has achieved significant breakthroughs in medical imaging, but these advancements are often dependent on large, well-annotated datasets. However, obtaining such datasets poses a significant challenge, as it requires time-consuming and labor-intensive annotations from medical experts. Consequently, there is growing interest in learning paradigms such as incomplete, inexact, and absent supervision, which are designed to operate under limited, inexact, or missing labels. This survey categorizes and reviews the evolving research in these areas, analyzing around 600 notable contributions since 2018. It covers tasks such as image classification, segmentation, and detection across various medical application areas, including but not limited to brain, chest, and cardiac imaging. We attempt to establish the relationships among existing research studies in related areas. We provide formal definitions of different learning paradigms and offer a comprehensive summary and interpretation of various learning mechanisms and strategies, aiding readers in better understanding the current research landscape and ideas. We also discuss potential future research challenges.
Trials and Tribulations of Chemodynamical Tagging: Investigating the Recovery of Open Clusters in the Gaia DR3-GALAH DR4 Catalog
2025-04-15
Nicholas A. Barth, David Mendez, Rana Ezzeddine, Yuxi, Lu, Leslie M. Morales, Zachary R. Claytor, Jamie Tayar
Chemodynamical tagging has been suggested as a powerful tool to trace stars back to their birth clusters. However, the efficacy of chemodynamical tagging as a means to recover individual stellar clusters is still under debate. In this study, we present a detailed investigation of chemodynamical tagging of open clusters using both dynamical and chemical data from the \textit{Gaia} DR3 and GALAH DR4 surveys, respectively. Using a sample of open clusters and mock field stars, we conduct a bootstrap analysis to evaluate every unique combination of orbital components ($E, J_R, J_\phi, J_Z$) and chemical abundances ([X/Fe] for O, Na, Mg, Al, Si, K, Ca, Sc, Ti, Cr, Mn, Ni, Cu, Y, and Ba) on how well they recover open clusters when used as parameters in the clustering algorithm, HBDSCAN. We find that using primarily dynamical orbital parameters leads to the highest recovery rate of open cluster stars. Nevertheless, even employing the best performing parameter combinations leads to low open cluster recovery rates. We find that, in most cases, chemodynamical tagging of open clusters using blind clustering algorithms is not efficient, which is in line with previous theoretical and observational work. However, we show that the addition of cuts based on metallicity, age, and birth radii in order to reduce the size of the clustering catalog can marginally improve the recovery rate of open clusters.
CAMPOS II. The onset of protostellar disk substructures and planet formation
2025-04-15
Cheng-Han Hsieh, Héctor G. Arce, María José Maureira, Jaime E. Pineda, Dominique Segura-Cox, Diego Mardones, Michael M. Dunham, Hui Li, Stella S. R. Offner
The 1.3 mm CAMPOS survey has resolved 90 protostellar disks with $\sim$15 au resolution across the Ophiuchus, Corona Australis, and Chamaeleon star-forming regions. To address the fundamental question, "When does planet formation begin?", we combined the CAMPOS sample with literature observations of Class 0-II disks (bolometric temperature, $T_{\rm bol} \le 1900$ K), all mapped at 1.3 mm with resolutions ranging from 4 to 33 au. To investigate substructure detection rates as a function of bolometric temperature, we restricted the sample to disks observed at the 1.3 mm wavelength, with inclinations below 75$^\circ$, linear resolution $\le 20$ au and resolved with at least four resolution elements ($\theta_{\rm disk}/\theta_{\rm res} \ge 4$). We also considered the effects of extinction correction and the inclusion of Herschel Space Telescope data on the bolometric temperature measurements, to constrain the lower and upper limits of bolometric temperature for each source. We find that by $T_{\rm bol} \sim 200-400\,K$, substructure detection rates increased sharply to $\sim$60%, corresponding to an approximate age of $0.2-0.4$ Myr. No substructures are detected in Class 0 disks. The ratio of disk-averaged brightness temperature to predicted dust temperature shows a trend of increasing values toward the youngest Class 0 disks, suggesting higher optical depths in these early stages. Classifying disks with substructures into those with and without large central cavities, we find both populations coexisting across evolutionary stages, suggesting they are not necessarily evolutionarily linked. Our statistical analysis confirms that substructures similar to those in Class II disks are already common by the Class I stage, and the emergence of these structures at $T_{\rm bol} \sim 200-400\,K$ could represent only an upper limit.
Dynamical electronic correlation and chiral magnetism in van der Waals magnet Fe4GeTe2
2025-04-15
Md. Nur Hasan, Nastaran Salehi, Felix Sorgenfrei, Anna Delin, Igor Di Marco, Anders Bergman, Manuel Pereiro, Patrik Thunström, Olle Eriksson, Debjani Karmakar
Among the quasi-2D van der Waals magnetic systems, Fe4GeTe2 imprints a profound impact due to its near-room temperature ferromagnetic behaviour and the complex magnetothermal phase diagram exhibiting multiple phase transformations, as observed from magnetization and magnetotransport measurements. A complete analysis of these phase transformations in the light of electronic correlation and its impact on the underlying magnetic interactions remain unattended in the existing literature. Using first-principles methodologies, incorporating the dynamical nature of electron correlation, we have analysed the interplay of the direction of magnetization in the easy-plane and easy-axis manner with the underlying crystal symmetry, which reveals the opening of a pseudogap feature beyond the spin-reorientation transition (SRT) temperature. The impact of dynamical correlation on the calculated magnetic circular dichroism and x-ray absorption spectrum of the L-edge of the Fe atoms compared well with the existing experimental observations. The calculated intersite Heisenberg exchange interactions display a complicated nature, depending upon the pairwise interactions among the two inequivalent Fe sites, indicating a RKKY-like behaviour of the magnetic interactions. We noted the existence of significant anisotropic and antisymmetric exchanges interactions, resulting into a chirality in the magnetic behaviour of the system. Subsequent investigation of the dynamical aspects of magnetism in Fe4GeTe2 and the respective magnetothermal phase diagram reveal that the dynamical nature of spins and the decoupling of the magnetic properties for both sites of Fe is crucial to explain all the experimentally observed phase transformations.
Free-ranging dogs (Canis familiaris) thrive in diverse landscapes, including those heavily modified by humans. This study investigated the influence of resource availability on their spatial ecology across 52 rural and 41 urban sites, comparing urban and rural environments. Census-based surveys were conducted to understand the distribution of dogs and resources, while territory-based observations were carried out across different seasons to capture temporal variability in dog populations and resource availability. Dog and resource density were significantly higher in urban areas, supporting the Resource Dispersion Hypothesis (RDH). Territory size (TS) varied seasonally, decreasing significantly (by 21%) post-mating, likely reflecting shifts in resource demands and distribution. TS was positively correlated with resource heterogeneity, dispersion, patch richness, and male-to-female ratio, but not with group size, which remained stable across seasons and resource gradients. This suggests that while resource availability and sex ratio influence space use, social factors play a key role in shaping group dynamics. These findings highlight the complex interplay between resource availability, social behaviour, and human influences in shaping the spatial ecology of free-ranging dogs and have important implications for their management and disease control, informing targeted interventions such as spay/neuter programs and responsible waste management in both urban and rural landscapes.
Probabilistic causal graphs as categorical data synthesizers: Do they do better than Gaussian Copulas and Conditional Tabular GANs?
2025-04-15
Olha Shaposhnyk, Noor Abid, Mouri Zakir, Svetlana Yanushkevich
This study investigates the generation of high-quality synthetic categorical data, such as survey data, using causal graph models. Generating synthetic data aims not only to create a variety of data for training the models but also to preserve privacy while capturing relationships between the data. The research employs Structural Equation Modeling (SEM) followed by Bayesian Networks (BN). We used the categorical data that are based on the survey of accessibility to services for people with disabilities. We created both SEM and BN models to represent causal relationships and to capture joint distributions between variables. In our case studies, such variables include, in particular, demographics, types of disability, types of accessibility barriers and frequencies of encountering those barriers. The study compared the SEM-based BN method with alternative approaches, including the probabilistic Gaussian copula technique and generative models like the Conditional Tabular Generative Adversarial Network (CTGAN). The proposed method outperformed others in statistical metrics, including the Chi-square test, Kullback-Leibler divergence, and Total Variation Distance (TVD). In particular, the BN model demonstrated superior performance, achieving the highest TVD, indicating alignment with the original data. The Gaussian Copula ranked second, while CTGAN exhibited moderate performance. These analyses confirmed the ability of the SEM-based BN to produce synthetic data that maintain statistical and relational validity while maintaining confidentiality. This approach is particularly beneficial for research on sensitive data, such as accessibility and disability studies.
A Machine Learning Framework for Stellar Collision Transient Identification
2025-04-15
Betty X. Hu, Avi Loeb
Modern astronomical surveys, such as the Zwicky Transient Facility (ZTF), are capable of detecting thousands of transient events per year, necessitating the use of automated and scalable data analysis techniques. Recent advances in machine learning have enabled the efficient classification and characterization of these transient phenomena. We aim to develop a fully systematic pipeline to identify candidate stellar collision events in galactic nuclei, which may otherwise be identified as tidal disruption events or other transients. We also seek to validate our simulations by comparing key physical parameters derived from observations and used in modeling these events. We generate a comprehensive bank of simulated light curves spanning a range of physical parameters and employ an approximate nearest neighbor algorithm (via the annoy library) to match these with observed ZTF light curves. Our pipeline is successfully able to associate observed ZTF light curves with simulated events. The resulting estimated parameters, including supermassive black hole masses and ejecta mass, are presented and compared to known values when applicable. We demonstrate that a systematic, machine learning-based approach can effectively identify and characterize stellar collision candidate events from large-scale transient surveys. This methodology is especially promising for future surveys which will provide us with significantly high volumes of data, such as LSST, where automated, data-intensive analysis will be critical for advancing our understanding of transient astrophysical phenomena.
Multi-harmonic and special shape/pattern/template approximations of discrete signals with generally irregular arguments
2025-04-15
Ivan L. Andronov, Hanna M. Akopian, Vitalii V. Breus, Lidiia L. Chinarova, Larysa S. Kudashkina, Nina V. Savchuk, Serhii I. Iovchev, Vladyslava I. Marsakova, Serhii V. Kolesnikov, Maksym Yu. Pyatnytskyy
The invited review of own algorithms and software (MAVKA and MCV) for the data analysis of astronomical signals - irregularly spaced, multi-periodic multi-harmonic, periodogram analysis and approximations with taking into account a polynomial trend simultaneously, instead of commonle used detrending and prewhitening. The references to original papers are listed.
TransitReID: Transit OD Data Collection with Occlusion-Resistant Dynamic Passenger Re-Identification
2025-04-15
Kaicong Huang, Talha Azfar, Jack Reilly, Ruimin Ke
Transit Origin-Destination (OD) data are essential for transit planning, particularly in route optimization and demand-responsive paratransit systems. Traditional methods, such as manual surveys, are costly and inefficient, while Bluetooth and WiFi-based approaches require passengers to carry specific devices, limiting data coverage. On the other hand, most transit vehicles are equipped with onboard cameras for surveillance, offering an opportunity to repurpose them for edge-based OD data collection through visual person re-identification (ReID). However, such approaches face significant challenges, including severe occlusion and viewpoint variations in transit environments, which greatly reduce matching accuracy and hinder their adoption. Moreover, designing effective algorithms that can operate efficiently on edge devices remains an open challenge. To address these challenges, we propose TransitReID, a novel framework for individual-level transit OD data collection. TransitReID consists of two key components: (1) An occlusion-robust ReID algorithm featuring a variational autoencoder guided region-attention mechanism that adaptively focuses on visible body regions through reconstruction loss-optimized weight allocation; and (2) a Hierarchical Storage and Dynamic Matching (HSDM) mechanism specifically designed for efficient and robust transit OD matching which balances storage, speed, and accuracy. Additionally, a multi-threaded design supports near real-time operation on edge devices, which also ensuring privacy protection. We also introduce a ReID dataset tailored for complex bus environments to address the lack of relevant training data. Experimental results demonstrate that TransitReID achieves state-of-the-art performance in ReID tasks, with an accuracy of approximately 90\% in bus route simulations.
Mechanical Characterization of Brain Tissue: Experimental Techniques, Human Testing Considerations, and Perspectives
2025-04-15
Jixin Hou, Kun Jiang, Arunachalam Ramanathan, Abhishek Saji Kumar, Wei Zhang, Lin Zhao, Taotao Wu, Ramana Pidaparti, Dajiang Zhu, Gang Li, Kenan Song, Tianming Liu, Mir Jalil Razavi, Ellen Kuhl, Xianqiao Wang
Understanding the mechanical behavior of brain tissue is crucial for advancing both fundamental neuroscience and clinical applications. Yet, accurately measuring these properties remains challenging due to the brain unique mechanical attributes and complex anatomical structures. This review provides a comprehensive overview of commonly used techniques for characterizing brain tissue mechanical properties, covering both invasive methods such as atomic force microscopy, indentation, axial mechanical testing, and oscillatory shear testing and noninvasive approaches like magnetic resonance elastography and ultrasound elastography. Each technique is evaluated in terms of working principles, applicability, representative studies, and experimental limitations. We further summarize existing publications that have used these techniques to measure human brain tissue mechanical properties. With a primary focus on invasive studies, we systematically compare their sample preparation, testing conditions, reported mechanical parameters, and modeling strategies. Key sensitivity factors influencing testing outcomes (e.g., sample size, anatomical location, strain rate, temperature, conditioning, and post-mortem interval) are also discussed. Additionally, selected noninvasive studies are reviewed to assess their potential for in vivo characterization. A comparative discussion between invasive and noninvasive methods, as well as in vivo versus ex vivo testing, is included. This review aims to offer practical guidance for researchers and clinicians in selecting appropriate mechanical testing approaches and contributes a curated dataset to support constitutive modeling of human brain tissue.
Cosmology with the angular cross-correlation of gravitational-wave and galaxy catalogs: forecasts for next-generation interferometers and the Euclid survey
We study the angular power spectrum of gravitational-wave and galaxy catalogs in tomographic redshift and distance bins as a probe of late-time cosmology, focusing specifically on next-generation ground-based interferometers in combination with the Euclid photometric survey. We assess the potential of this technique to constrain the Hubble constant and the matter energy density. Our analysis incorporates realistic gravitational-wave source populations, error modelling calibrated on recent detector designs, and accounts for nuisance parameters. We show that the tomographic angular cross-correlation could determine the Hubble constant to percent or sub-percent precision depending on the binning choice, configuration and operation time of gravitational-wave observatories. This conclusion holds even when marginalising over the unknown tracer biases, primordial power-spectrum parameters and baryon density. In particular, we show that the combination of the galaxy auto-correlation spectra and the cross-correlation of gravitational waves and galaxy surveys can lead to an improvement of up to a factor ${{\sim}}10$ in constraining power over either of the two probes taken individually. However, this prospect crucially relies on the presence of multiple gravitational-wave interferometers able to yield precise sky localisation. We also discuss the use of a spectroscopic redshift catalog, as well as the detectability of the clustering bias of gravitational-wave sources.
Implications of distance duality violation for the $H_0$ tension and evolving dark energy
2025-04-14
Elsa M. Teixeira, William Giarè, Natalie B. Hogg, Thomas Montandon, Adèle Poudou, Vivian Poulin
We investigate whether a violation of the distance duality relation (DDR), $D_L(z) = (1+z)^2 D_A(z)$, connecting the angular diameter and luminosity distances, can explain the Hubble tension and alter the evidence for dynamical dark energy in recent cosmological observations. We constrain five phenomenological parameterisations of DDR violation using Baryon Acoustic Oscillation measurements from the DESI survey calibrated with the sound horizon derived from \textit{Planck} Cosmic Microwave Background data and the Pantheon+ Type Ia supernova (SNIa) catalogue calibrated with the supernova absolute magnitude from S$H_0$ES. We find that two toy models can resolve the tension: a constant offset in the DDR (equivalent to a shift in the calibration of the SNIa data), $D_L(z)/D_A(z)\simeq 0.925(1+z)^2$, which leaves the hint for evolving dark energy unaffected; or a change in the power-law redshift-dependence of the DDR, restricted to $z\lesssim 1$, $D_L(z)/D_A(z)\simeq(1+z)^{1.866}$, together with a {\it constant} phantom dark energy equation of state $w\sim -1.155$. The Bayesian evidence slightly favours the latter model. Our phenomenological approach motivates the investigation of physical models of DDR violation as a novel way to explain the Hubble tension.
Anchors no more: Using peculiar velocities to constrain $H_0$ and the primordial Universe without calibrators
2025-04-14
Davide Piras, Francesco Sorrenti, Ruth Durrer, Martin Kunz
We develop a novel approach to constrain the Hubble parameter $H_0$ and the primordial power spectrum amplitude $A_\mathrm{s}$ using supernovae type Ia (SNIa) data. By considering SNIa as tracers of the peculiar velocity field, we can model their distance and their covariance as a function of cosmological parameters without the need of calibrators like Cepheids; this yields a new independent probe of the large-scale structure based on SNIa data without distance anchors. Crucially, we implement a differentiable pipeline in JAX, including efficient emulators and affine sampling, reducing inference time from years to hours on a single GPU. We first validate our method on mock datasets, demonstrating that we can constrain $H_0$ and $\log 10^{10}A_\mathrm{s}$ within $\sim10\%$ using $\sim10^3$ SNIa. We then test our pipeline with SNIa from an $N$-body simulation, obtaining $7\%$-level unbiased constraints on $H_0$ with a moderate noise level. We finally apply our method to Pantheon+ data, constraining $H_0$ at the $10\%$ level without Cepheids when fixing $A_\mathrm{s}$ to its $\it{Planck}$ value. On the other hand, we obtain $15\%$-level constraints on $\log 10^{10}A_\mathrm{s}$ in agreement with $\it{Planck}$ when including Cepheids in the analysis. In light of upcoming observations of low redshift SNIa from the Zwicky Transient Facility and the Vera Rubin Legacy Survey of Space and Time, surveys for which our method will develop its full potential, we make our code publicly available.
Normalized solutions for Schrödinger-Bopp-Podolsky systems in bounded domains
2025-04-14
Gaetano Siciliano
We consider an elliptic system of Schr\"odinger-Bopp-Podolsky type in a bounded and smooth domain of R3 with a non constant coupling factor. This kind of system has been introduced in the mathematical literature in [14] and in the last years many contributions appeared. In particular here we present the results in [2] and [34] which show existence of solutions by means of the Ljusternik-Schnirelmann theory under different boundary conditions on the electrostatic potential.
SymRTLO: Enhancing RTL Code Optimization with LLMs and Neuron-Inspired Symbolic Reasoning
2025-04-14
Yiting Wang, Wanghao Ye, Ping Guo, Yexiao He, Ziyao Wang, Yexiao He, Bowei Tian, Shwai He, Guoheng Sun, Zheyu Shen, Sihan Chen, Ankur Srivastava, Qingfu Zhang, Gang Qu, Ang Li
Optimizing Register Transfer Level (RTL) code is crucial for improving the power, performance, and area (PPA) of digital circuits in the early stages of synthesis. Manual rewriting, guided by synthesis feedback, can yield high-quality results but is time-consuming and error-prone. Most existing compiler-based approaches have difficulty handling complex design constraints. Large Language Model (LLM)-based methods have emerged as a promising alternative to address these challenges. However, LLM-based approaches often face difficulties in ensuring alignment between the generated code and the provided prompts. This paper presents SymRTLO, a novel neuron-symbolic RTL optimization framework that seamlessly integrates LLM-based code rewriting with symbolic reasoning techniques. Our method incorporates a retrieval-augmented generation (RAG) system of optimization rules and Abstract Syntax Tree (AST)-based templates, enabling LLM-based rewriting that maintains syntactic correctness while minimizing undesired circuit behaviors. A symbolic module is proposed for analyzing and optimizing finite state machine (FSM) logic, allowing fine-grained state merging and partial specification handling beyond the scope of pattern-based compilers. Furthermore, a fast verification pipeline, combining formal equivalence checks with test-driven validation, further reduces the complexity of verification. Experiments on the RTL-Rewriter benchmark with Synopsys Design Compiler and Yosys show that SymRTLO improves power, performance, and area (PPA) by up to 43.9%, 62.5%, and 51.1%, respectively, compared to the state-of-the-art methods.
2025-04-14
Xingyu Lu, Yuhang Hu, YiFan Zhang, Kaiyu Jiang, Changyi Liu, Tianke Zhang, Jinpeng Wang, Bin Wen, Chun Yuan, Fan Yang, Tingting Gao, Di Zhang
Reinforcement Learning from Human/AI Feedback (RLHF/RLAIF) has been extensively utilized for preference alignment of text-to-image models. Existing methods face certain limitations in terms of both data and algorithm. For training data, most approaches rely on manual annotated preference data, either by directly fine-tuning the generators or by training reward models to provide training signals. However, the high annotation cost makes them difficult to scale up, the reward model consumes extra computation and cannot guarantee accuracy. From an algorithmic perspective, most methods neglect the value of text and only take the image feedback as a comparative signal, which is inefficient and sparse. To alleviate these drawbacks, we propose the InstructEngine framework. Regarding annotation cost, we first construct a taxonomy for text-to-image generation, then develop an automated data construction pipeline based on it. Leveraging advanced large multimodal models and human-defined rules, we generate 25K text-image preference pairs. Finally, we introduce cross-validation alignment method, which refines data efficiency by organizing semantically analogous samples into mutually comparable pairs. Evaluations on DrawBench demonstrate that InstructEngine improves SD v1.5 and SDXL's performance by 10.53% and 5.30%, outperforming state-of-the-art baselines, with ablation study confirming the benefits of InstructEngine's all components. A win rate of over 50% in human reviews also proves that InstructEngine better aligns with human preferences.
Can LLMs Generate Tabular Summaries of Science Papers? Rethinking the Evaluation Protocol
2025-04-14
Weiqi Wang, Jiefu Ou, Yangqiu Song, Benjamin Van Durme, Daniel Khashabi
Literature review tables are essential for summarizing and comparing collections of scientific papers. We explore the task of generating tables that best fulfill a user's informational needs given a collection of scientific papers. Building on recent work (Newman et al., 2024), we extend prior approaches to address real-world complexities through a combination of LLM-based methods and human annotations. Our contributions focus on three key challenges encountered in real-world use: (i) User prompts are often under-specified; (ii) Retrieved candidate papers frequently contain irrelevant content; and (iii) Task evaluation should move beyond shallow text similarity techniques and instead assess the utility of inferred tables for information-seeking tasks (e.g., comparing papers). To support reproducible evaluation, we introduce ARXIV2TABLE, a more realistic and challenging benchmark for this task, along with a novel approach to improve literature review table generation in real-world scenarios. Our extensive experiments on this benchmark show that both open-weight and proprietary LLMs struggle with the task, highlighting its difficulty and the need for further advancements. Our dataset and code are available at https://github.com/JHU-CLSP/arXiv2Table.
RealHarm: A Collection of Real-World Language Model Application Failures
2025-04-14
Pierre Le Jeune, Jiaen Liu, Luca Rossi, Matteo Dora
Language model deployments in consumer-facing applications introduce numerous risks. While existing research on harms and hazards of such applications follows top-down approaches derived from regulatory frameworks and theoretical analyses, empirical evidence of real-world failure modes remains underexplored. In this work, we introduce RealHarm, a dataset of annotated problematic interactions with AI agents built from a systematic review of publicly reported incidents. Analyzing harms, causes, and hazards specifically from the deployer's perspective, we find that reputational damage constitutes the predominant organizational harm, while misinformation emerges as the most common hazard category. We empirically evaluate state-of-the-art guardrails and content moderation systems to probe whether such systems would have prevented the incidents, revealing a significant gap in the protection of AI applications.
The integration of ethics into software development faces significant challenges due to market fundamentalism in organizational practices, where profit often takes precedence over ethical considerations. Additionally, the critical influence of practitioners' individual backgrounds on ethical decision-making remains underexplored, highlighting a gap in comprehensive research. This is especially essential to understand due to the demographic imbalance in software roles. This study investigates ethical concerns in software development, focusing on how they are perceived, prioritized, and addressed by demographically different practitioners. By surveying 217 software practitioners across diverse roles, industries, and countries, we identify critical barriers to ethical integration and examine practitioners' capacity to mitigate these issues. Our findings reveal pronounced demographic disparities, with marginalized groups - including women, BIPOC, and disabled individuals - reporting ethical concerns at higher frequencies. Notably, marginalized practitioners demonstrated heightened sensitivity to ethical implementation and greater empowerment to address them. However, practitioners overall often lack the support needed to address ethical challenges effectively. These insights underscore the urgent need for reforms in software education and development processes that center on diverse perspectives. Such reforms are essential to advancing ethical integration in software development and ensuring responsible computing practices in an increasingly complex technological landscape.
Cross-correlations between X-ray clusters and the general galaxy population
2025-04-14
Johan Comparat, Juan Macias-Perez, Céline Combet, Andrea Merloni, Dominique Eckert, Mohammadreza Ayromlou, Kirpal Nandra, Riccardo Seppi
This study presents highly precise measurements of the cross-correlation between volume-limited galaxy samples from the DESI legacy survey catalogue and X-ray selected galaxy clusters from eROSITA, allowing for detailed analysis across redshift and color. Two key findings emerge. First, the cluster-galaxy cross-correlation, when split into quiescent and star-forming galaxies, contains significant information about the infall, feedback, and quenching processes of blue cloud galaxies in massive environments. These results align well with existing galaxy evolution models for higher stellar masses ($\log_{10}(M^*[M_\odot]) > 10.75$), though the red fraction may be slightly underestimated in the intermediate mass range ($10.25 < \log_{10}(M^*[M_\odot])< 10.75$). Second, the integral of the cross-correlation within 500 kpc enables a model-independent measurement of the red sequence and its scatter in clusters, providing a robust alternative to existing red-sequence calibration methods without requiring spectroscopic redshifts or classifications of galaxies. Similar analyses on upcoming photometric surveys as Euclid and LSST together with spectroscopic samples like 4MOST and DESI should lead to a significant increase in the signal-to-noise ratio and in particular at small separations.
Extracting cosmological information from the abundance of galaxy clusters with simulation-based inference
2025-04-14
Íñigo Zubeldia, Boris Bolliet, Anthony Challinor, William Handley
The abundance of galaxy clusters as a function of mass and redshift is a well-established and powerful cosmological probe. Cosmological analyses based on galaxy cluster number counts have traditionally relied on explicitly computed likelihoods, which are often challenging to develop with the required accuracy and expensive to evaluate. In this work, we implement an alternative approach based on simulation-based inference (SBI) methods that relies solely on synthetic galaxy cluster catalogues generated under a given model. These catalogues are much easier to produce than it is to develop and validate a likelihood. We validate this approach in the context of the galaxy cluster survey of the upcoming Simons Observatory for a setup in which we can also evaluate an exact explicit likelihood. We find that our SBI-based approach yields cosmological parameter posterior means that are within $0.2\,\sigma$ of those obtained with the explicit likelihood and with biases smaller than $0.1\,\sigma$. We also introduce and validate a procedure to assess the goodness of fit using only synthetic catalogues similar to those used for training. This demonstrates, for the first time, that a galaxy cluster number count cosmological analysis can be performed fully without resorting to a likelihood at any stage. Finally, we apply our SBI-based approach to the real Planck MMF3 cosmology sample, obtaining cosmological parameter constraints that are within $0.1\,\sigma$ of their likelihood-based counterparts. This constitutes the first SBI-based number count cosmological analysis of a real galaxy cluster catalogue.
2025-04-14
Xinyu Zhang, Julien Martinelli, ST John
We review generalized additive models as a type of ``transparent'' model that has recently seen renewed interest in the deep learning community as neural additive models. We highlight multiple types of nonidentifiability in this model class and discuss challenges in interpretability, arguing for restraint when claiming ``interpretability'' or ``suitability for safety-critical applications'' of such models.
Personalization has become an essential capability in modern AI systems, enabling customized interactions that align with individual user preferences, contexts, and goals. Recent research has increasingly concentrated on Retrieval-Augmented Generation (RAG) frameworks and their evolution into more advanced agent-based architectures within personalized settings to enhance user satisfaction. Building on this foundation, this survey systematically examines personalization across the three core stages of RAG: pre-retrieval, retrieval, and generation. Beyond RAG, we further extend its capabilities into the realm of Personalized LLM-based Agents, which enhance traditional RAG systems with agentic functionalities, including user understanding, personalized planning and execution, and dynamic generation. For both personalization in RAG and agent-based personalization, we provide formal definitions, conduct a comprehensive review of recent literature, and summarize key datasets and evaluation metrics. Additionally, we discuss fundamental challenges, limitations, and promising research directions in this evolving field. Relevant papers and resources are continuously updated at https://github.com/Applied-Machine-Learning-Lab/Awesome-Personalized-RAG-Agent.
Evidence for electron spin-torsion coupling in the rotational spectrum of the CH$_3$CO radical
2025-04-14
Laurent H. Coudert, Olivier Pirali, Marie-Aline Martin-Drumel, Rosemonde Chahbazian, Luyao Zou, Roman A. Motiyenko, Laurent Margulès
Open-shell non-rigid molecular systems exhibiting an internal rotation are likely candidates for a coupling between the spin angular momentum of the unpaired electron and the torsional motion. This electron spin-torsion coupling lacked both an experimental validation and a theoretical modeling. Here, the first experimental observation of the electron spin-torsion coupling is reported analyzing the pure rotational spectrum at millimeter wavelengths of the CH$_3$CO radical, a $^2\Sigma$ open-shell molecule displaying an internal rotation of its methyl group. To account for this coupling, a specific Hamiltonian incorporating the rotational, torsional, and electronic degrees of freedom is developed and allows us to reproduce the experimental spectrum. The present demonstration of the electron spin-torsion coupling will undoubtedly be key to future investigations of large open-shell molecules exhibiting a complex internal dynamics.
Asymptotic Optimality of Projected Inventory Level Policies for Lost Sales Inventory Systems with Large Leadtime and Penalty Cost
2025-04-14
Poulad Moradi, Joachim Arts, Melvin Drent
We study the canonical periodic review lost sales inventory system with positive leadtime and independent and identically distributed (i.i.d.) demand under the average cost criterion. We demonstrate that the relative value function under the constant order policy satisfies the Wiener-Hopf equation. We employ ladder processes associated with a random walk featuring i.i.d. increments, to obtain an explicit solution for the relative value function. This solution can be expressed as a quadratic form and a term that grows sublinearly. Then we perform an approximate policy iteration step on the constant order policy and bound the approximation errors as a function of the cost of losing a sale. This leads to our main result that projected inventory level policies are asymptotically optimal as the leadtime grows when the cost of losing a sale is sufficiently large and demand has a finite second moment. Under these conditions, we also show that the optimal cost rate approaches infinity, proportional to the square root of the cost of losing a sale.
M2S-RoAD: Multi-Modal Semantic Segmentation for Road Damage Using Camera and LiDAR Data
2025-04-14
Tzu-Yun Tseng, Hongyu Lyu, Josephine Li, Julie Stephany Berrio, Mao Shan, Stewart Worrall
Road damage can create safety and comfort challenges for both human drivers and autonomous vehicles (AVs). This damage is particularly prevalent in rural areas due to less frequent surveying and maintenance of roads. Automated detection of pavement deterioration can be used as an input to AVs and driver assistance systems to improve road safety. Current research in this field has predominantly focused on urban environments driven largely by public datasets, while rural areas have received significantly less attention. This paper introduces M2S-RoAD, a dataset for the semantic segmentation of different classes of road damage. M2S-RoAD was collected in various towns across New South Wales, Australia, and labelled for semantic segmentation to identify nine distinct types of road damage. This dataset will be released upon the acceptance of the paper.
Benchmarking Practices in LLM-driven Offensive Security: Testbeds, Metrics, and Experiment Design
2025-04-14
Andreas Happe, Jürgen Cito
Large Language Models (LLMs) have emerged as a powerful approach for driving offensive penetration-testing tooling. This paper analyzes the methodology and benchmarking practices used for evaluating Large Language Model (LLM)-driven attacks, focusing on offensive uses of LLMs in cybersecurity. We review 16 research papers detailing 15 prototypes and their respective testbeds. We detail our findings and provide actionable recommendations for future research, emphasizing the importance of extending existing testbeds, creating baselines, and including comprehensive metrics and qualitative analysis. We also note the distinction between security research and practice, suggesting that CTF-based challenges may not fully represent real-world penetration testing scenarios.
Text-based Person Retrieval (TPR) as a multi-modal task, which aims to retrieve the target person from a pool of candidate images given a text description, has recently garnered considerable attention due to the progress of contrastive visual-language pre-trained model. Prior works leverage pre-trained CLIP to extract person visual and textual features and fully fine-tune the entire network, which have shown notable performance improvements compared to uni-modal pre-training models. However, full-tuning a large model is prone to overfitting and hinders the generalization ability. In this paper, we propose a novel Unified Parameter-Efficient Transfer Learning (PETL) method for Text-based Person Retrieval (UP-Person) to thoroughly transfer the multi-modal knowledge from CLIP. Specifically, UP-Person simultaneously integrates three lightweight PETL components including Prefix, LoRA and Adapter, where Prefix and LoRA are devised together to mine local information with task-specific information prompts, and Adapter is designed to adjust global feature representations. Additionally, two vanilla submodules are optimized to adapt to the unified architecture of TPR. For one thing, S-Prefix is proposed to boost attention of prefix and enhance the gradient propagation of prefix tokens, which improves the flexibility and performance of the vanilla prefix. For another thing, L-Adapter is designed in parallel with layer normalization to adjust the overall distribution, which can resolve conflicts caused by overlap and interaction among multiple submodules. Extensive experimental results demonstrate that our UP-Person achieves state-of-the-art results across various person retrieval datasets, including CUHK-PEDES, ICFG-PEDES and RSTPReid while merely fine-tuning 4.7\% parameters. Code is available at https://github.com/Liu-Yating/UP-Person.
Data Cooperatives: Democratic Models for Ethical Data Stewardship
2025-04-14
Francisco Mendonca, Giovanna DiMarzo, Nabil Abdennadher
Data cooperatives offer a new model for fair data governance, enabling individuals to collectively control, manage, and benefit from their information while adhering to cooperative principles such as democratic member control, economic participation, and community concern. This paper reviews data cooperatives, distinguishing them from models like data trusts, data commons, and data unions, and defines them based on member ownership, democratic governance, and data sovereignty. It explores applications in sectors like healthcare, agriculture, and construction. Despite their potential, data cooperatives face challenges in coordination, scalability, and member engagement, requiring innovative governance strategies, robust technical systems, and mechanisms to align member interests with cooperative goals. The paper concludes by advocating for data cooperatives as a sustainable, democratic, and ethical model for the future data economy.
Emotional Strain and Frustration in LLM Interactions in Software Engineering
2025-04-14
Cristina Martinez Montes, Ranim Khojah
Large Language Models (LLMs) are increasingly integrated into various daily tasks in Software Engineering such as coding and requirement elicitation. Despite their various capabilities and constant use, some interactions can lead to unexpected challenges (e.g. hallucinations or verbose answers) and, in turn, cause emotions that develop into frustration. Frustration can negatively impact engineers' productivity and well-being if they escalate into stress and burnout. In this paper, we assess the impact of LLM interactions on software engineers' emotional responses, specifically strains, and identify common causes of frustration when interacting with LLMs at work. Based on 62 survey responses from software engineers in industry and academia across various companies and universities, we found that a majority of our respondents experience frustrations or other related emotions regardless of the nature of their work. Additionally, our results showed that frustration mainly stemmed from issues with correctness and less critical issues such as adaptability to context or specific format. While such issues may not cause frustration in general, artefacts that do not follow certain preferences, standards, or best practices can make the output unusable without extensive modification, causing frustration over time. In addition to the frustration triggers, our study offers guidelines to improve the software engineers' experience, aiming to minimise long-term consequences on mental health.
Luminis Stellarum et Machina: Applications of Machine Learning in Light Curve Analysis
2025-04-14
Almat Akhmetali, Alisher Zhunuskanov, Aknur Sakan, Marat Zaidyn, Timur Namazbayev, Dana Turlykozhayeva, Nurzhan Ussipov
The rapid advancement of observational capabilities in astronomy has led to an exponential growth in the volume of light curve (LC) data, presenting both opportunities and challenges for time-domain astronomy. Traditional analytical methods often struggle to fully extract the scientific value of these vast datasets, especially as their complexity increases. Machine learning (ML) algorithms have become indispensable tools for analyzing light curves, offering the ability to classify, predict, discover patterns, and detect anomalies. Despite the growing adoption of ML techniques, challenges remain in LC classification, including class imbalance, noisy data, and interpretability of models. These challenges emphasize the importance of conducting a systematic review of ML algorithms specifically tailored for LC analysis. This survey provides a comprehensive overview of the latest ML techniques, summarizing their principles and applications in key astronomical tasks such as exoplanet detection, variable star classification, and supernova identification. It also discusses strategies to address the existing challenges and advance LC analysis in the near future. As astronomical datasets continue to grow, the integration of ML and deep learning (DL) techniques will be essential for unlocking the full scientific potential of LC data in the era of astronomical big data.
A census of galactic spider binary millisecond pulsars with the Nançay Radio Telescope
2025-04-14
Clara Blanchard, Lucas Guillemot, Guillaume Voisin, Ismaël Cognard, Gilles Theureau
Spider pulsars are systems in which a millisecond pulsar (MSP) tightly orbits (Pb $\lesssim$ 1 day) a low mass (mc $\lesssim$ 0.5 M$_\odot$) semi-degenerate star. Spider often display eclipses around superior conjunction. This eclipse phenomenon is currently poorly understood. We analyzed eclipses via pulsar timing. The eclipses were fit with a phenomenological model which gives a measurement of the duration and asymmetry of the eclipses. These parameters were then compared to other eclipse and system measurements to discuss the potential link between the presence of eclipses and orbital inclination, eclipsing systems being known to have higher mass functions than non-eclipsing ones. We present here a comprehensive review of the NRT NUPPI backend spider pulsars dataset. We also present the first review and systematic analysis of a large sample of eclipsers, monitored with the NRT over several years. The phenomenological fit allowed us to compare the eclipsers with each other, which led to the categorization of eclipsers depending on the shape of their eclipses. We present the polarimetric properties of the 19 spiders in the sample alongside their profiles, which were previously unpublished in some cases. For the eclipsing systems, we found evidence for a positive correlation between eclipse duration and mass function, as expected if more eclipsing material crosses the line-of-sight in higher inclination systems. For the entire sample, we found marginal evidence for increasing pulse profile width with decreasing mass function. We finally conducted a comprehensive literature review of the published inclination measurements for the pulsars in the sample and compared the inclinations to eclipse parameters. Nevertheless, the small number of available orbital inclination constraints, contradicting each other in some cases, hinders such searches for correlations
Deconfined Quantum Critical Point: A Review of Progress
2025-04-14
Yi Cui, Rong Yu, Weiqiang Yu
Deconfined quantum critical points (DQCPs) have been proposed as a class of continuous quantum phase transitions occurring between two ordered phases with distinct symmetry-breaking patterns, beyond the conventional framework of Landau-Ginzburg-Wilson (LGW) theory. At the DQCP, the system exhibits emergent gauge fields, fractionalized excitations, and enhanced symmetries. Here we review recent theoretical and experimental progress on exploring DQCPs in condensed matter systems. We first introduce theoretical advancements in the study of DQCPs over the past twenty years, particularly in magnetic models on square lattices, honeycomb lattices, kagome lattices, and one-dimensional spin chains. We then discuss recent progress on experimental realization of DQCP in quantum magnetic systems. Experimentally, the Shastry-Sutherland model, realized in SrCu$_2$(BO$_3$)$_2$, offers a particularly promising platform for realizing DQCPs. The magnetic frustration inherent to this model drives phase transitions between two distinct symmetry-breaking states: a valence bond solid (VBS) phase and a N\'{e}el antiferromagnetic phase. Remarkably, SrCu$_2$(BO$_3$)$_2$ has provided the first experimental evidence of a proximate DQCP through a field-induced Bose-Einstein condensation, transitioning from the VBS state to the N\'{e}el state. Nevertheless, the direct experimental realization of a DQCP remains a significant challenge. Despite this, it offers a promising platform for exploring emergent phenomena through quantum phase transition in low-dimensional quantum systems.
The Bispectrum of Intrinsic Alignments: Theory Modelling and Forecasts for Stage IV Galaxy Surveys
2025-04-14
Thomas Bakx, Toshiki Kurita, Alexander Eggemeier, Nora Elisa Chisari, Zvonimir Vlah
We present a complete treatment of the bispectrum of intrinsic alignments, both in three spatial dimensions and in projection in the flat-sky approximation. Since intrinsic alignment is a spin-2 observable, the bispectrum of intrinsic alignments contains a parity-even and a parity-odd part, the latter being nonzero even in the absence of parity violation. Moreover, all possible combinations of scalar, E- and B-mode bispectra are nonzero in the absence of parity violation. In analogy to the galaxy bispectrum in redshift space, we construct a complete set of multipoles for anisotropic bispectra of projected spin-2 fields. We then construct separable bispectrum estimators, both for parity-even and parity-odd bispectra, which can be computed by means of Fast Fourier Transforms (FFTs). We compare several different choices of angular weighting in terms of signal-to-noise ratios (SNR) for a Stage IV setup using luminous red galaxies (LRGs) from the Dark Energy Spectroscopic Instrument (DESI) with galaxy shapes measured by the Legacy Survey of Space and Time (LSST). Assuming an overlapping area of $\sim 4,000$ square degrees (yielding $\sim 1.3$ million LRGs) and including scales up to $k_\text{max} = 0.14\,h$/Mpc, we find that the position-position-E-mode bispectrum $B_{DDE}$ (which is parity-even) can be strongly detected at SNR $\sim 30$, while detecting parity-odd bispectra (such as $B_{DDB}$, SNR $\sim 5$) or bispectra with more than one shape field (such as $B_{DEE}$, SNR $\sim 5$) may also be possible.
We utilize the DESI Legacy Imaging Surveys DR10 to investigate the previously undetected faint extension of the Palomar 5 stellar stream. By applying the HDBSCAN clustering algorithm, we identify stream members and successfully extend the leading arm of the stream to approximately $\mathrm{DEC} \sim -15^\circ$. Combining the fully detected stream with a suite of mock stream simulations, we conduct a detailed comparison to constrain both the intrinsic properties of the stream and the dynamical parameters of the Milky Way (MW) halo. Our analysis yields a best-fit model characterized by eight parameters: $M_{\mathrm{halo}} = 5.67\times10^{11}\ M_{\odot}$, $r_{s,\mathrm{halo}} = 28.94\ \mathrm{kpc}$, $q_z = 0.93$, $M_{\mathrm{gc}} = 4.31\times10^{3}\ M_{\odot}$, $dM_{\mathrm{gc}}/dt = 1.81\ M_{\odot}\ \mathrm{Myr}^{-1}$, $\mu_{\alpha}\cos\delta = -2.28\ \mathrm{mas\ yr}^{-1}$, $\mu_{\delta} = -2.26\ \mathrm{mas\ yr}^{-1}$, and $D = 23.25\ \mathrm{kpc}$. Notably, our constraints on the halo shape indicate that the MW's dark matter halo exhibits a flattened potential, with a minor-to-major axis ratio of $q_z = 0.93$. This finding aligns well with theoretical expectations and previous observational estimates. Additionally, the best-fit model accurately reproduces the observed stream morphology and dynamics, providing a more precise understanding of both the evolution of the stream and the overall structure of the Galactic halo.
Federated Graph Learning (FGL) enables privacy-preserving, distributed training of graph neural networks without sharing raw data. Among its approaches, subgraph-FL has become the dominant paradigm, with most work focused on improving overall node classification accuracy. However, these methods often overlook fairness due to the complexity of node features, labels, and graph structures. In particular, they perform poorly on nodes with disadvantaged properties, such as being in the minority class within subgraphs or having heterophilous connections (neighbors with dissimilar labels or misleading features). This reveals a critical issue: high accuracy can mask degraded performance on structurally or semantically marginalized nodes. To address this, we advocate for two fairness goals: (1) improving representation of minority class nodes for class-wise fairness and (2) mitigating topological bias from heterophilous connections for topology-aware fairness. We propose FairFGL, a novel framework that enhances fairness through fine-grained graph mining and collaborative learning. On the client side, the History-Preserving Module prevents overfitting to dominant local classes, while the Majority Alignment Module refines representations of heterophilous majority-class nodes. The Gradient Modification Module transfers minority-class knowledge from structurally favorable clients to improve fairness. On the server side, FairFGL uploads only the most influenced subset of parameters to reduce communication costs and better reflect local distributions. A cluster-based aggregation strategy reconciles conflicting updates and curbs global majority dominance . Extensive evaluations on eight benchmarks show FairFGL significantly improves minority-group performance , achieving up to a 22.62 percent Macro-F1 gain while enhancing convergence over state-of-the-art baselines.
The Tropical Atmosphere of Jupiter - Shallow Weather, Deep Plumes, and Vortices
2025-04-14
Chris Moeckel, Imke de Pater, Bob Sault, Bryan Butler
Towering storms, swirling clouds, and vortices are the cloud tops manifestation of complex weather systems shaping the atmosphere of Jupiter. We use observations from Juno's MicroWave Radiometer (MWR), the Very Large Array (VLA) and the Hubble Space Telescope (HST) to probe for the first time the depth and impact of weather on Jupiter. We use ammonia, the main source of opacity at radio wavelengths on Jupiter, as the tracer for the weather by fitting ammonia anomalies to the MWR brightness temperature variations. We show that the majority of the weather on Jupiter is confined to regions where the clouds are forming. Both the South Equatorial Belt and the Equatorial Zone have surprisingly shallow weather systems (P < 2 bar), and even in the North Equatorial Belt most of the ammonia variations is above the water condensation level (P ~ 6 bar). This confirms that the water condensation layer plays a crucial role in controlling the dynamics and the weather on Jupiter. However, the shallow nature of the weather cannot explain the deep-seated depletion down to 30 bar that the Juno mission has revealed. We do find three features, however, that extend below the water condensation layer: a vortex in the northern hemisphere reaching down to 30 bar, an ammonia plume down to 20-30 bars, and the signature of precipitation down to 20 bar. This work highlights the interplay of large-scale processes (vortices, plumes) and small-scale processes (storms) are responsible for shaping the atmospheric makeup of Jupiter.
Lacking oceanic-driven internal multidecadal climate variability is compensated by forced variability in model simulations
2025-04-14
Raphaël Hébert, Thomas Laepple
Regional climate change in the $21^{st}$ century will result from the interplay between human-induced changes and internal climate variability. Competing effects from greenhouse gas warming and aerosol cooling have historically caused multidecadal forced climate variations overlapping with internal variability. Despite extensive historical observations, disentangling the contributions of internal and forced variability remains debated, largely due to the uncertain magnitude of anthropogenic aerosols. Here, we show that, after removing CO$_{2}$-congruent variability, multidecadal temperature variability in instrumental data is largely attributable to internal processes of oceanic origin. This follows from an emergent relationship, identified in historical climate model simulations, between the driver of variability in oceanic regions and the land-ocean variance ratio in the mid-latitudes. Thus, climate models with higher residual (non-CO$_{2}$) forced variability, largely linked to volcanic and anthropogenic aerosols, exhibit more spatially coherent and amplified temperature patterns over land compared to observations. In contrast, models with higher internal variability agree better with the instrumental data. Our results underscore that internal modes of ocean-driven variability may be too weak in many climate models, and that current projections may be underestimating the range of internal variability in regions with high oceanic influence.
Quantum Natural Language Processing: A Comprehensive Review of Models, Methods, and Applications
2025-04-14
Farha Nausheen, Khandakar Ahmed, M Imad Khan
In recent developments, deep learning methodologies applied to Natural Language Processing (NLP) have revealed a paradox: They improve performance but demand considerable data and resources for their training. Alternatively, quantum computing exploits the principles of quantum mechanics to overcome the computational limitations of current methodologies, thereby establishing an emerging field known as quantum natural language processing (QNLP). This domain holds the potential to attain a quantum advantage in the processing of linguistic structures, surpassing classical models in both efficiency and accuracy. In this paper, it is proposed to categorise QNLP models based on quantum computing principles, architecture, and computational approaches. This paper attempts to provide a survey on how quantum meets language by mapping state-of-the-art in this area, embracing quantum encoding techniques for classical data, QNLP models for prevalent NLP tasks, and quantum optimisation techniques for hyper parameter tuning. The landscape of quantum computing approaches applied to various NLP tasks is summarised by showcasing the specific QNLP methods used, and the popularity of these methods is indicated by their count. From the findings, it is observed that QNLP approaches are still limited to small data sets, with only a few models explored extensively, and there is increasing interest in the application of quantum computing to natural language processing tasks.
The Milky Way Project MOBStIRS: Parametrizing Infrared Stellar-Wind Bow Shock Morphologies with Citizen Science
2025-04-14
Angelica S. Whisnant, Matthew S. Povich, Nikhil Patten, Henry A. Kobulnicky
Mass-loss influences stellar evolution, especially for massive stars with strong winds. Stellar wind bow shock nebulae driven by Galactic OB stars can be used to measure mass-loss rates ($\dot{M}$). The standoff distance ($R_{0}$) between the star and the bow shock is set by momentum flux balance between the stellar wind and the surrounding interstellar medium (ISM). We created the Milky Way Project: MOBStIRS (Mass-loss rates for OB Stars driving IR bow Shocks) using the online Zooniverse citizen science platform. We enlisted several hundred students to measure $R_0$ and two other projected shape parameters for 764 cataloged IR bow shocks. MOBStIRS incorporated 1528 JPEG cutout images produced from Spitzer GLIMPSE and MIPSGAL survey data. Measurements were aggregated to compute shape parameters for each bow shock image deemed high-quality by participants. The average statistical uncertainty on $R_0$ is $12.5\%$ but varies from ${<}5\%$ to ${\sim}40\%$ among individual bow shocks, contributing significantly to the total error budget of $\dot{M}$. The derived nebular morphologies agree well with (magneto)hydrodynamic simulations of bow shocks driven by the winds of OB stars moving at $V_a = 10-40~km~s^{-1}$ with respect to the ambient interstellar medium (ISM). A systematic correction to $R_0$ to account for viewing angle appears unnecessary for computing $\dot{M}$. Slightly more than half of MOBStIRS bow shocks are asymmetric, which could indicate anisotropic stellar winds, ISM clumping on sub-pc scales, time-dependent instabilities, and/or misalignments between the local ISM magnetic field and the star-bow shock axis.
Implication of shell quenching in scandium isotopes around N=20
2025-04-14
Rong An, Xiang Jiang, Na Tang, Li-Gang Cao, Feng-Shou Zhang
Shell closure structures are commonly observed phenomena associated with nuclear charge radii throughout the nuclide chart. Inspired by recent studies demonstrating that the abrupt change can be clearly observed in the charge radii of the scandium isotopic chain across the neutron number $N=20$, we further review the underlying mechanism of the enlarged charge radii for $^{42}$Sc based on the covariant density functional theory. The pairing correlations are tackled by solving the state-dependent Bardeen-Cooper-Schrieffer equations. Meanwhile, the neutron-proton correlation around the Fermi surface derived from the simultaneously unpaired proton and neutron is appropriately considered in describing the systematic evolution of nuclear charge radii. The calculated results suggest that the abrupt increase in charge radii across the $N=20$ shell closure seems to be improved along the scandium isotopic chain if the strong neutron-proton correlation is properly included.
Ember: A Compiler for Efficient Embedding Operations on Decoupled Access-Execute Architectures
2025-04-14
Marco Siracusa, Olivia Hsu, Victor Soria-Pardos, Joshua Randall, Arnaud Grasset, Eric Biscondi, Doug Joseph, Randy Allen, Fredrik Kjolstad, Miquel Moretó Planas, Adrià Armejach
Irregular embedding lookups are a critical bottleneck in recommender models, sparse large language models, and graph learning models. In this paper, we first demonstrate that, by offloading these lookups to specialized access units, Decoupled Access-Execute (DAE) processors achieve 2.6$\times$ higher performance and 6.4$\times$ higher performance/watt than GPUs on end-to-end models. Then, we propose the Ember compiler for automatically generating optimized DAE code from PyTorch and TensorFlow. Conversely from other DAE compilers, Ember features multiple intermediate representations specifically designed for different optimization levels. In this way, Ember can implement all optimizations to match the performance of hand-written code, unlocking the full potential of DAE architectures at scale.
Labeling Messages as AI-Generated Does Not Reduce Their Persuasive Effects
2025-04-14
Isabel O. Gallegos, Chen Shani, Weiyan Shi, Federico Bianchi, Izzy Gainsburg, Dan Jurafsky, Robb Willer
As generative artificial intelligence (AI) enables the creation and dissemination of information at massive scale and speed, it is increasingly important to understand how people perceive AI-generated content. One prominent policy proposal requires explicitly labeling AI-generated content to increase transparency and encourage critical thinking about the information, but prior research has not yet tested the effects of such labels. To address this gap, we conducted a survey experiment (N=1601) on a diverse sample of Americans, presenting participants with an AI-generated message about several public policies (e.g., allowing colleges to pay student-athletes), randomly assigning whether participants were told the message was generated by (a) an expert AI model, (b) a human policy expert, or (c) no label. We found that messages were generally persuasive, influencing participants' views of the policies by 9.74 percentage points on average. However, while 94.6% of participants assigned to the AI and human label conditions believed the authorship labels, labels had no significant effects on participants' attitude change toward the policies, judgments of message accuracy, nor intentions to share the message with others. These patterns were robust across a variety of participant characteristics, including prior knowledge of the policy, prior experience with AI, political party, education level, or age. Taken together, these results imply that, while authorship labels would likely enhance transparency, they are unlikely to substantially affect the persuasiveness of the labeled content, highlighting the need for alternative strategies to address challenges posed by AI-generated information.
EthosGPT: Mapping Human Value Diversity to Advance Sustainable Development Goals (SDGs)
2025-04-14
Luyao Zhang
Large language models (LLMs) are transforming global decision-making and societal systems by processing diverse data at unprecedented scales. However, their potential to homogenize human values poses critical risks, similar to biodiversity loss undermining ecological resilience. Rooted in the ancient Greek concept of ethos, meaning both individual character and the shared moral fabric of communities, EthosGPT draws on a tradition that spans from Aristotle's virtue ethics to Adam Smith's moral sentiments as the ethical foundation of economic cooperation. These traditions underscore the vital role of value diversity in fostering social trust, institutional legitimacy, and long-term prosperity. EthosGPT addresses the challenge of value homogenization by introducing an open-source framework for mapping and evaluating LLMs within a global scale of human values. Using international survey data on cultural indices, prompt-based assessments, and comparative statistical analyses, EthosGPT reveals both the adaptability and biases of LLMs across regions and cultures. It offers actionable insights for developing inclusive LLMs, such as diversifying training data and preserving endangered cultural heritage to ensure representation in AI systems. These contributions align with the United Nations Sustainable Development Goals (SDGs), especially SDG 10 (Reduced Inequalities), SDG 11.4 (Cultural Heritage Preservation), and SDG 16 (Peace, Justice and Strong Institutions). Through interdisciplinary collaboration, EthosGPT promotes AI systems that are both technically robust and ethically inclusive, advancing value plurality as a cornerstone for sustainable and equitable futures.
SUMART: SUMmARizing Translation from Wordy to Concise Expression
2025-04-14
Naoto Nishida, Jun Rekimoto
We propose SUMART, a method for summarizing and compressing the volume of verbose subtitle translations. SUMART is designed for understanding translated captions (e.g., interlingual conversations via subtitle translation or when watching movies in foreign language audio and translated captions). SUMART is intended for users who want a big-picture and fast understanding of the conversation, audio, video content, and speech in a foreign language. During the training data collection, when a speaker makes a verbose statement, SUMART employs a large language model on-site to compress the volume of subtitles. This compressed data is then stored in a database for fine-tuning purposes. Later, SUMART uses data pairs from those non-compressed ASR results and compressed translated results for fine-tuning the translation model to generate more concise translations for practical uses. In practical applications, SUMART utilizes this trained model to produce concise translation results. Furthermore, as a practical application, we developed an application that allows conversations using subtitle translation in augmented reality spaces. As a pilot study, we conducted qualitative surveys using a SUMART prototype and a survey on the summarization model for SUMART. We envision the most effective use case of this system is where users need to consume a lot of information quickly (e.g., Speech, lectures, podcasts, Q&A in conferences).
Working with Large Language Models to Enhance Messaging Effectiveness for Vaccine Confidence
2025-04-14
Lucinda Gullison, Feng Fu
Vaccine hesitancy and misinformation are significant barriers to achieving widespread vaccination coverage. Smaller public health departments may lack the expertise or resources to craft effective vaccine messaging. This paper explores the potential of ChatGPT-augmented messaging to promote confidence in vaccination uptake. We conducted a survey in which participants chose between pairs of vaccination messages and assessed which was more persuasive and to what extent. In each pair, one message was the original, and the other was augmented by ChatGPT. At the end of the survey, participants were informed that half of the messages had been generated by ChatGPT. They were then asked to provide both quantitative and qualitative responses regarding how knowledge of a message's ChatGPT origin affected their impressions. Overall, ChatGPT-augmented messages were rated slightly higher than the original messages. These messages generally scored better when they were longer. Respondents did not express major concerns about ChatGPT-generated content, nor was there a significant relationship between participants' views on ChatGPT and their message ratings. Notably, there was a correlation between whether a message appeared first or second in a pair and its score. These results point to the potential of ChatGPT to enhance vaccine messaging, suggesting a promising direction for future research on human-AI collaboration in public health communication.
Over the past year, the development of large language models (LLMs) has brought spatial intelligence into focus, with much attention on vision-based embodied intelligence. However, spatial intelligence spans a broader range of disciplines and scales, from navigation and urban planning to remote sensing and earth science. What are the differences and connections between spatial intelligence across these fields? In this paper, we first review human spatial cognition and its implications for spatial intelligence in LLMs. We then examine spatial memory, knowledge representations, and abstract reasoning in LLMs, highlighting their roles and connections. Finally, we analyze spatial intelligence across scales -- from embodied to urban and global levels -- following a framework that progresses from spatial memory and understanding to spatial reasoning and intelligence. Through this survey, we aim to provide insights into interdisciplinary spatial intelligence research and inspire future studies.
We briefly review the recent developments in magnetohydrodynamics, which in particular deal with the evolution of magnetic fields in turbulent plasmas. We especially emphasize (i) the necessity of renormalizing equations of motion in turbulence where velocity and magnetic fields become H\"older singular; (ii) the breakdown of Laplacian determinism (spontaneous stochasticity) for turbulent magnetic fields; and (iii) the possibility of eliminating the notion of magnetic field lines, using instead magnetic path lines as trajectories of Alfvenic wave-packets. These methodologies are then exemplified with their application to the problem of magnetic reconnection -- rapid change in magnetic field pattern that accelerates plasma -- a ubiquitous phenomenon in astrophysics and laboratory plasmas. The necessity of smoothing out rough velocity and magnetic fields on a finite scale L implies that magnetohydrodynamic equations should be regarded as effective field theories with running parameters depending upon the scale L.
Unveiling faint X-ray AGN populations in the NewAthena era: Insights from cosmological simulations
2025-04-14
Nuno Covas, Israel Matute, Stergios Amarantidis, José Afonso, Giorgio Lanzuisi, Andrea Comastri, Stefano Marchesi, Ciro Pappalardo, Rodrigo Carvajal, Polychronis Papaderos
Recent observations expanded our understanding of galaxy formation and evolution, yet key challenges persist in the X-ray regime, crucial for studying Active Galactic Nuclei (AGN). These limitations drive the development of next-generation observatories such as ESA's NewAthena. Now in phase B (preliminary design), the mission requires extensive testing to ensure compliance with its scientific goals, particularly given the uncertainties surrounding high redshift AGN. This work leverages the IllustrisTNG cosmological simulation to build an X-ray AGN mock catalogue and assess the performance of NewAthena's WFI. We created a Super Massive Black Hole (SMBH) light cone, spanning 10 deg2, with corrections to account for the limited resolution of the simulation and X-ray properties derived in post-processing. The resulting catalogue reveals a 5* overabundance of faint AGN compared to current X-ray constraints, an inconsistency potentially resolved by invoking a higher Compton-thick (CTK) fraction and intrinsic X-ray weakness, as suggested by recent JWST findings. An end-to-end survey simulation using SIXTE predicts 250000 AGN detections, including 20,000 at z > 3 and 35 in the Epoch of Reionization (z > 6); notably, only AGN with LX > 43.5 erg/s are detectable at z > 6. The analysis also forecasts a significant population of detectable CTK AGN, even beyond z > 4. These findings suggest X-ray observations will, for the first time, probe a significant AGN population in the EoR, offering new insights into SMBH growth. They also provide key input for refining NewAthena's mission design and optimizing its survey strategy.
Beyond the Classroom: Bridging the Gap Between Academia and Industry with a Hands-on Learning Approach
2025-04-14
Mingyang Xu, Ryan Zheng He Liu, Mark Stoodley, Ladan Tahvildari
Modern software systems require various capabilities to meet architectural and operational demands, such as the ability to scale automatically and recover from sudden failures. Self-adaptive software systems have emerged as a critical focus in software design and operation due to their capacity to autonomously adapt to changing environments. However, educating students on this topic is scarce in academia, and a survey among practitioners identified that the lack of knowledgeable individuals has hindered its adoption in the industry. In this paper, we present our experience teaching a course on self-adaptive software systems that integrates theoretical knowledge and hands-on learning with industry-relevant technologies. To close the gap between academic education and industry practices, we incorporated guest lectures from experts and showcases featuring industry professionals as judges, improving technical and communication skills for our students. Feedback based on surveys from 21 students indicates significant improvements in their understanding of self-adaptive systems. The empirical analysis of the developed course demonstrates the effectiveness of the proposed course syllabus and teaching methodology. In addition, we provide a summary of the educational challenges of running this unique course, including balancing theory and practice, addressing the diverse backgrounds and motivations of students, and integrating the industry-relevant technologies. We believe these insights can provide valuable guidance for educating students in other emerging topics within software engineering.
This paper presents a comprehensive review of the NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution (ESR). The challenge aimed to advance the development of deep models that optimize key computational metrics, i.e., runtime, parameters, and FLOPs, while achieving a PSNR of at least 26.90 dB on the $\operatorname{DIV2K\_LSDIR\_valid}$ dataset and 26.99 dB on the $\operatorname{DIV2K\_LSDIR\_test}$ dataset. A robust participation saw \textbf{244} registered entrants, with \textbf{43} teams submitting valid entries. This report meticulously analyzes these methods and results, emphasizing groundbreaking advancements in state-of-the-art single-image ESR techniques. The analysis highlights innovative approaches and establishes benchmarks for future research in the field.
In this article, we present the limitations of traditional localization techniques, such as those using Global Positioning Systems (GPS) and life detectors, in localizing victims during disaster rescue efforts. These techniques usually fall short in accuracy, coverage, and robustness to environmental interference. We then discuss the necessary requirements for developing GPS-independent localization techniques in disaster scenarios. Practical techniques should be passive, with straightforward hardware, low computational demands, low power, and high accuracy, while incorporating unknown environmental information. We review various implementation strategies for these techniques, categorized by measurements (time, angle, and signal strength) and operation manners (non-cooperative and cooperative). Case studies demonstrate trade-offs between localization accuracy and complexity, emphasizing the importance of choosing appropriate localization techniques based on resources and rescue needs for efficient disaster response.
Proposed half a century ago, the quantum chromodynamics (QCD) axion explains the lack of charge and parity violation in the strong interactions and is a compelling candidate for cold dark matter. The last decade has seen the rapid improvement in the sensitivity and range of axion experiments, as well as developments in theory regarding consequences of axion dark matter. We review here the astrophysical searches and theoretical progress regarding the QCD axion. We then give a historical overview of axion searches, review the current status and future prospects of dark matter axion searches, and then discuss proposed dark matter axion techniques currently in development.
An obscured quasar census with the 4MOST IR AGN survey: design, predicted properties, and scientific goals
2025-04-14
Carolina Andonie, David M. Alexander, Claire Greenwell, Sotiria Fotopoulou, Ryan Hickox, David J Rosario, Carolin Villforth, Johannes Buchner, Jens-Kristian Krogager, Brivael Laloux, Andrea Merloni, Mara Salvato, Ole Streicher, Wei Yan
We present the 4MOST IR AGN survey, the first large-scale optical spectroscopic survey characterizing mid-infrared (MIR) selected obscured active galactic nuclei (AGN). The survey targets $\approx 212,000$ obscured infrared (IR) AGN candidates over $\approx 10,000 \rm \: deg^2$ down to a magnitude limit of $r_{\rm AB}=22.8 \, \rm mag$ and will be $\approx 100 \times$ larger than any existing obscured IR AGN spectroscopic sample. We select the targets using a MIR colour criterion applied to the unWISE catalogue from the WISE all-sky survey, and then apply a $r-W2\geq 5.9 \rm \: mag$ cut; we demonstrate that this selection will mostly identify sources obscured by $N_{\rm H}>10^{22} \rm \: cm^{-2}$. The survey complements the 4MOST X-ray survey, which will follow up $\sim 1\rm M$ eROSITA-selected (typically unobscured) AGN. We perform simulations to predict the quality of the spectra that we will obtain and validate our MIR-optical colour-selection method using X-ray spectral constraints and UV-to-far IR spectral energy distribution (SED) modelling in four well-observed deep-sky fields. We find that: (1) $\approx 80-87\%$ of the WISE-selected targets are AGN down to $r_{\rm AB}=22.1-22.8 \: \rm mag$ of which $\approx 70\%$ are obscured by $N_{\rm H}>10^{22} \: \rm cm^{-2}$, and (2) $\approx 80\%$ of the 4MOST IR AGN sample will remain undetected by the deepest eROSITA observations due to extreme absorption. Our SED fitting results show that the 4MOST IR AGN survey will primarily identify obscured AGN and quasars ($\approx 55\%$ of the sample is expected to have $L_{\rm AGN,IR}>10^{45} \rm \: erg \: s^{-1}$) residing in massive galaxies ($M_{\star}\approx 10^{10}-10^{12} \rm \: M_{\odot}$) at $z\approx 0.5-3.5$ with $ \approx 33\%$ expected to be hosted by starburst galaxies.
Dust grains play a fundamental role in galaxies, influencing both their evolution and observability. As a result, incorporating dust physics into galaxy evolution simulations is essential. This is a challenging task due to the finite resolution of such simulations and the uncertainties on dust grain formation in stellar envelopes and their evolution in the Interstellar Medium (ISM). This report reviews some of the most commonly used techniques for modeling dust in galaxy evolution simulations, with a particular emphasis on developments from the past $\sim$10 years in both hydrodynamic and semi-analytic approaches. Key findings from these simulations are presented, discussed, and compared to the most recent available observations. These include the dust-to-gas vs metallicity relation, the abundance of dust within and outside galaxies, and predictions on the relative importance of various processes affecting dust. The analysis presented here highlights significant achievements as well as the limitations in our current modeling of dust evolution in galaxies.
Smooth sailing or ragged climb? -- Increasing the robustness of power spectrum de-wiggling and ShapeFit parameter compression
2025-04-14
Katayoon Ghaemi, Nils Schöneberg, Licia Verde
The baryonic features in the galaxy power spectrum offer tight, time-resolved constraints on the expansion history of the Universe but complicate the measurement of the broadband shape of the power spectrum, which also contains precious cosmological information. In this work we compare thirteen methods designed to separate the broadband and oscillating components and examine their performance. The systematic uncertainty between different de-wiggling procedures is at most $2$%, depending on the scale. The ShapeFit parameter compression aims to compute the slope $m$ of the power spectrum at large scales, sensitive to matter-radiation equality and the baryonic suppression. We show that the de-wiggling procedures impart large (50%) differences on the obtained slope values, but as long as the theory and data pipelines are set up consistently, this is of no concern for cosmological inference given the precision of existing and on-going surveys. However, it still motivates the search for more robust ways of extracting the slope. We show that post-processing the power spectrum ratio before taking the derivative makes the slope values far more robust. We further investigate eleven ways of extracting the slope and highlight the two most successful ones. We derive a systematic uncertainty on the slope $m$ of $\sigma_{m,\mathrm{syst}} = 0.023|m| + 0.001$ by studying the behavior of the slopes in different cosmologies within and beyond $\Lambda$CDM and the impact in cosmological inference. In cosmologies with a feature in the matter-power spectrum, such as in the early dark energy cosmologies, this systematic uncertainty estimate does not necessarily hold, and further investigation is required.
High-$z$ radio Quasars in RACS I: Selection, identification, and multi-wavelength properties
2025-04-14
L. Ighina, A. Caccianiga, A. Moretti, J. W. Broderick, J. K. Leung, F. Rigamonti, N. Seymour, J. Afonso, T. Connor, C. Vignali, Z. Wang, T. An, B. Arsioli, S. Bisogni, D. Dallacasa, R. Della Ceca, Y. Liu, A. López-Sánchez, I. Matute, C. Reynolds, A. Rossi, C. Spingola, P. Severgnini, F. Tavecchio
Radio-bright, jetted quasars at $z>5$ serve as unique laboratories for studying supermassive black hole activity in the early Universe. In this work, we present a sample of high-$z$ jetted quasars selected from the combination of the radio Rapid ASKAP Continuum Survey (RACS) with deep wide-area optical/near-infrared surveys. From this cross-match we selected 45 new high-$z$ radio quasar candidates with S$_{888MHz}>1$ mJy and mag$z<21.3$ over an area of 16000deg$^2$. Using spectroscopic observations, we confirmed the high-$z$ nature of 24 new quasars, 13 at $4.55$. If we also consider similar, in terms of radio/optical fluxes and sky position, quasars at $z>5$ already reported in the literature, the overall $z>5$ RACS sample is composed by 33 powerful quasars, expected to be ~90% complete at mag$z<21.3$ and S$_{888MHz}>1$ mJy. Having rest-frame radio luminosities in the range $\nu L_{1.4GHz}=10^{41.5}-10^{44.4}$ erg s$^{-1}$, this sample contains the most extreme radio quasars currently known in the early Universe. We also present all X-ray and radio data currently available for the sample, including new, dedicated {\it Chandra}, uGMRT, MeerKAT and ATCA observations for a sub-set of the sources. from the modelling of their radio emission, either with a single power law or a broken power law, we found that these systems have a wide variety of spectral shapes with most quasars (22) having a flat radio emission (i.e., $-0.5<\alpha_{r}<0.5$). At the same time, the majority of the sources with X-ray coverage present a high-energy luminosity larger than the one expected from the X-ray corona only. Both the radio and X-ray properties of the high-$z$ RACS sample suggest that many of these sources have relativistic jets oriented close to our line of sight. (i.e., blazars) and can therefore be used to perform statistical studies on the entire jetted population at high redshift.
Optimizing disorder with machine learning to harness synchronization
2025-04-14
Jun-Yin Huang, Zheng-Meng Zhai, Vassilios Kovanis, Ying-Cheng Lai
Disorder is often considered detrimental to coherence. However, under specific conditions, it can enhance synchronization. We develop a machine-learning framework to design optimal disorder configurations that maximize phase synchronization. In particular, utilizing the system of coupled nonlinear pendulums with disorder and noise, we train a feedforward neural network (FNN), with the disorder parameters as input, to predict the Shannon entropy index that quantifies the phase synchronization strength. The trained FNN model is then deployed to search for the optimal disorder configurations in the high-dimensional space of the disorder parameters, providing a computationally efficient replacement of the stochastic differential equation solvers. Our results demonstrate that the FNN is capable of accurately predicting synchronization and facilitates an efficient inverse design solution to optimizing and enhancing synchronization.
Building Trustworthy Multimodal AI: A Review of Fairness, Transparency, and Ethics in Vision-Language Tasks
2025-04-14
Mohammad Saleha, Azadeh Tabatabaeib
Objective: This review explores the trustworthiness of multimodal artificial intelligence (AI) systems, specifically focusing on vision-language tasks. It addresses critical challenges related to fairness, transparency, and ethical implications in these systems, providing a comparative analysis of key tasks such as Visual Question Answering (VQA), image captioning, and visual dialogue. Background: Multimodal models, particularly vision-language models, enhance artificial intelligence (AI) capabilities by integrating visual and textual data, mimicking human learning processes. Despite significant advancements, the trustworthiness of these models remains a crucial concern, particularly as AI systems increasingly confront issues regarding fairness, transparency, and ethics. Methods: This review examines research conducted from 2017 to 2024 focusing on forenamed core vision-language tasks. It employs a comparative approach to analyze these tasks through the lens of trustworthiness, underlining fairness, explainability, and ethics. This study synthesizes findings from recent literature to identify trends, challenges, and state-of-the-art solutions. Results: Several key findings were highlighted. Transparency: Explainability of vision language tasks is important for user trust. Techniques, such as attention maps and gradient-based methods, have successfully addressed this issue. Fairness: Bias mitigation in VQA and visual dialogue systems is essential for ensuring unbiased outcomes across diverse demographic groups. Ethical Implications: Addressing biases in multilingual models and ensuring ethical data handling is critical for the responsible deployment of vision-language systems. Conclusion: This study underscores the importance of integrating fairness, transparency, and ethical considerations in developing vision-language models within a unified framework.
WebShell attacks, in which malicious scripts are injected into web servers, are a major cybersecurity threat. Traditional machine learning and deep learning methods are hampered by issues such as the need for extensive training data, catastrophic forgetting, and poor generalization. Recently, Large Language Models (LLMs) have gained attention for code-related tasks, but their potential in WebShell detection remains underexplored. In this paper, we make two major contributions: (1) a comprehensive evaluation of seven LLMs, including GPT-4, LLaMA 3.1 70B, and Qwen 2.5 variants, benchmarked against traditional sequence- and graph-based methods using a dataset of 26.59K PHP scripts, and (2) the Behavioral Function-Aware Detection (BFAD) framework, designed to address the specific challenges of applying LLMs to this domain. Our framework integrates three components: a Critical Function Filter that isolates malicious PHP function calls, a Context-Aware Code Extraction strategy that captures the most behaviorally indicative code segments, and Weighted Behavioral Function Profiling (WBFP) that enhances in-context learning by prioritizing the most relevant demonstrations based on discriminative function-level profiles. Our results show that larger LLMs achieve near-perfect precision but lower recall, while smaller models exhibit the opposite trade-off. However, all models lag behind previous State-Of-The-Art (SOTA) methods. With BFAD, the performance of all LLMs improved, with an average F1 score increase of 13.82%. Larger models such as GPT-4, LLaMA 3.1 70B, and Qwen 2.5 14B outperform SOTA methods, while smaller models such as Qwen 2.5 3B achieve performance competitive with traditional methods. This work is the first to explore the feasibility and limitations of LLMs for WebShell detection, and provides solutions to address the challenges in this task.
Advances in dual-chirped optical parametric amplification
2025-04-13
Kaito Nishimiya, Eiji J. Takahashi
High-energy infrared lasers have enabled the generation of strong field phenomena, and among such phenomena, high-order harmonic generation (HHG) from gases has enabled attosecond-scale observations in atoms or molecules. Lasers with longer wavelengths and shorter pulse widths are advantageous for generating higher photon energy and shorter attosecond pulses via HHG. Thus, the development of ultrashort mid-infrared (MIR) lasers has progressed. This paper reviews research on developing high-energy MIR lasers using the dual-chirped optical parametric amplification (DC-OPA) method. We developed TW-class multi-cycle lasers in the MIR region, which was previously difficult. The advanced DC-OPA method, an extension of the conventional DC-OPA method, enables one-octave amplification of the wavelength, and a TW-class single-cycle laser was developed. These lasers were utilized for HHG, enabling single-shot absorption spectroscopy, and one-octave supercontinuum soft X-ray generation for single-cycle isolated attosecond pulse. We also show the development of multi-TW sub-cycle DC-OPA pumped by Ti:sapphire laser and high average power MIR single-cycle DC-OPA using thin-disk laser technology.
Can LLM feedback enhance review quality? A randomized study of 20K reviews at ICLR 2025
2025-04-13
Nitya Thakkar, Mert Yuksekgonul, Jake Silberg, Animesh Garg, Nanyun Peng, Fei Sha, Rose Yu, Carl Vondrick, James Zou
Peer review at AI conferences is stressed by rapidly rising submission volumes, leading to deteriorating review quality and increased author dissatisfaction. To address these issues, we developed Review Feedback Agent, a system leveraging multiple large language models (LLMs) to improve review clarity and actionability by providing automated feedback on vague comments, content misunderstandings, and unprofessional remarks to reviewers. Implemented at ICLR 2025 as a large randomized control study, our system provided optional feedback to more than 20,000 randomly selected reviews. To ensure high-quality feedback for reviewers at this scale, we also developed a suite of automated reliability tests powered by LLMs that acted as guardrails to ensure feedback quality, with feedback only being sent to reviewers if it passed all the tests. The results show that 27% of reviewers who received feedback updated their reviews, and over 12,000 feedback suggestions from the agent were incorporated by those reviewers. This suggests that many reviewers found the AI-generated feedback sufficiently helpful to merit updating their reviews. Incorporating AI feedback led to significantly longer reviews (an average increase of 80 words among those who updated after receiving feedback) and more informative reviews, as evaluated by blinded researchers. Moreover, reviewers who were selected to receive AI feedback were also more engaged during paper rebuttals, as seen in longer author-reviewer discussions. This work demonstrates that carefully designed LLM-generated review feedback can enhance peer review quality by making reviews more specific and actionable while increasing engagement between reviewers and authors. The Review Feedback Agent is publicly available at https://github.com/zou-group/review_feedback_agent.
Agentic Workflows for Economic Research: Design and Implementation
2025-04-13
Herbert Dawid, Philipp Harting, Hankui Wang, Zhongli Wang, Jiachen Yi
This paper introduces a methodology based on agentic workflows for economic research that leverages Large Language Models (LLMs) and multimodal AI to enhance research efficiency and reproducibility. Our approach features autonomous and iterative processes covering the entire research lifecycle--from ideation and literature review to economic modeling and data processing, empirical analysis and result interpretation--with strategic human oversight. The workflow architecture comprises specialized agents with clearly defined roles, structured inter-agent communication protocols, systematic error escalation pathways, and adaptive mechanisms that respond to changing research demand. Human-in-the-loop (HITL) checkpoints are strategically integrated to ensure methodological validity and ethical compliance. We demonstrate the practical implementation of our framework using Microsoft's open-source platform, AutoGen, presenting experimental examples that highlight both the current capabilities and future potential of agentic workflows in improving economic research.
Vision-language models (VLMs) integrate visual and textual information, enabling a wide range of applications such as image captioning and visual question answering, making them crucial for modern AI systems. However, their high computational demands pose challenges for real-time applications. This has led to a growing focus on developing efficient vision language models. In this survey, we review key techniques for optimizing VLMs on edge and resource-constrained devices. We also explore compact VLM architectures, frameworks and provide detailed insights into the performance-memory trade-offs of efficient VLMs. Furthermore, we establish a GitHub repository at https://github.com/MPSCUMBC/Efficient-Vision-Language-Models-A-Survey to compile all surveyed papers, which we will actively update. Our objective is to foster deeper research in this area.
Adapting Robot's Explanation for Failures Based on Observed Human Behavior in Human-Robot Collaboration
2025-04-13
Andreas Naoum, Parag Khanna, Elmira Yadollahi, Mårten Björkman, Christian Smith
This work aims to interpret human behavior to anticipate potential user confusion when a robot provides explanations for failure, allowing the robot to adapt its explanations for more natural and efficient collaboration. Using a dataset that included facial emotion detection, eye gaze estimation, and gestures from 55 participants in a user study, we analyzed how human behavior changed in response to different types of failures and varying explanation levels. Our goal is to assess whether human collaborators are ready to accept less detailed explanations without inducing confusion. We formulate a data-driven predictor to predict human confusion during robot failure explanations. We also propose and evaluate a mechanism, based on the predictor, to adapt the explanation level according to observed human behavior. The promising results from this evaluation indicate the potential of this research in adapting a robot's explanations for failures to enhance the collaborative experience.
Generating realistic building layouts for automatic building design has been studied in both the computer vision and architecture domains. Traditional approaches from the architecture domain, which are based on optimization techniques or heuristic design guidelines, can synthesize desirable layouts, but usually require post-processing and involve human interaction in the design pipeline, making them costly and timeconsuming. The advent of deep generative models has significantly improved the fidelity and diversity of the generated architecture layouts, reducing the workload by designers and making the process much more efficient. In this paper, we conduct a comprehensive review of three major research topics of architecture layout design and generation: floorplan layout generation, scene layout synthesis, and generation of some other formats of building layouts. For each topic, we present an overview of the leading paradigms, categorized either by research domains (architecture or machine learning) or by user input conditions or constraints. We then introduce the commonly-adopted benchmark datasets that are used to verify the effectiveness of the methods, as well as the corresponding evaluation metrics. Finally, we identify the well-solved problems and limitations of existing approaches, then propose new perspectives as promising directions for future research in this important research area. A project associated with this survey to maintain the resources is available at awesome-building-layout-generation.
2025-04-13
Oğuz Alp Ağırbaş, Anıl Ata, Eren Demirci, Ilmar Gahramanov, Tuğba Hırlı, R. Semih Kanber, Ahmet Berk Kavruk, Mustafa Mullahasanoğlu, Zehra Özcan, Cansu Özdemir, Irmak Özgüç, Sinan Ulaş Öztürk, Uveys Turhan, Ali Mert T. Yetkin, Yunus Emre Yıldırım, Reyhan Yumuşak
We present several known solutions to the two-dimensional Ising model. This review originated from the ``Ising 100'' seminar series held at Bo\u{g}azi\c{c}i University, Istanbul, in 2024.
Neural mechanisms of predictive processing: a collaborative community experiment through the OpenScope program
2025-04-13
Ido Aizenbud, Nicholas Audette, Ryszard Auksztulewicz, Krzysztof Basiński, André M. Bastos, Michael Berry, Andres Canales-Johnson, Hannah Choi, Claudia Clopath, Uri Cohen, Rui Ponte Costa, Roberto De Filippo, Roman Doronin, Steven P. Errington, Jeffrey P. Gavornik, Colleen J. Gillon, Arno Granier, Jordan P. Hamm, Loreen Hertäg, Henry Kennedy, Sandeep Kumar, Alexander Ladd, Hugo Ladret, Jérôme A. Lecoq, Alexander Maier, Patrick McCarthy, Jie Mei, Jorge Mejias, Fabian Mikulasch, Noga Mudrik, Farzaneh Najafi, Kevin Nejad, Hamed Nejat, Karim Oweiss, Mihai A. Petrovici, Viola Priesemann, Lucas Rudelt, Sarah Ruediger, Simone Russo, Alessandro Salatiello, Walter Senn, Eli Sennesh, Sepehr Sima, Cem Uran, Anna Vasilevskaya, Julien Vezoli, Martin Vinck, Jacob A. Westerberg, Katharina Wilmes, Yihan Sophy Xiong
This review synthesizes advances in predictive processing within the sensory cortex. Predictive processing theorizes that the brain continuously predicts sensory inputs, refining neuronal responses by highlighting prediction errors. We identify key computational primitives, such as stimulus adaptation, dendritic computation, excitatory/inhibitory balance and hierarchical processing, as central to this framework. Our review highlights convergences, such as top-down inputs and inhibitory interneurons shaping mismatch signals, and divergences, including species-specific hierarchies and modality-dependent layer roles. To address these conflicts, we propose experiments in mice and primates using in-vivo two-photon imaging and electrophysiological recordings to test whether temporal, motor, and omission mismatch stimuli engage shared or distinct mechanisms. The resulting dataset, collected and shared via the OpenScope program, will enable model validation and community analysis, fostering iterative refinement and refutability to decode the neural circuits of predictive processing.
A Systematic Literature Review of Infrastructure Studies in SIGCHI
2025-04-13
Yao Lyu, Jie Cai, John M. Carroll
Infrastructure is an indispensable part of human life. Over the past decades, the Human-Computer Interaction (HCI) community has paid increasing attention to human interactions with infrastructure. In this paper, we conducted a systematic literature review on infrastructure studies in SIGCHI, one of the most influential communities in HCI. We collected a total of 190 primary studies, covering works published between 2006 and 2024. Most of these studies are inspired by Susan Leigh Star's notion of infrastructure. We identify three major themes in infrastructure studies: growing infrastructure, appropriating infrastructure, and coping with infrastructure. Our review highlights a prevailing trend in SIGCHI's infrastructure research: a focus on informal infrastructural activities across various sociotechnical contexts. In particular, we examine studies that problematize infrastructure and alert the HCI community to its potentially harmful aspects.
Q-ball mechanism of electron transport properties of high-T$_c$ superconductors
2025-04-13
S. I. Mukhin
Proposed recently by the author Q-ball mechanism of the pseudogap state and high-Tc superconductivity in cuprates (2022) was supported by micro X-ray diffraction data in HgBa$_2$CuO$_{4+y}$ (2023). In the present paper it is demonstrated that T-linear temperature dependence of electrical resistivity arises naturally in the Q-ball gas phase, that may explain corresponding experimental data in the "strange metal" phase of high-T$_c$ cuprates, as reviewed by Barisic et al. (2013). In the present theory it arises due to scattering of electrons on the Q-balls gas of condensed charge/spin fluctuations. Close to the lowest temperature boundary of the "strange metal" phase, at which Q-ball radius diverges, electrical resistivity caused by a slide of the Q-balls as a whole is calculated using fluctuation paraconductivity calculation method by Alex Abrikosov (1987). The diamagnetic response of Q-balls gas is calculated as well and shows good accord with experimental data by L.Li et al. (2010) in the "strange metal" phase. In total, obtained results demonstrate different properties of the correlated electrons systems that arise due to formation of Q-balls possessing internal bosonic frequency $\Omega=2\pi nT$ in Matsubara time and, thus, forming the quantum thermodynamic time polycrystals. Presented theory may give a clue concerning a possible mechanism of the experimentally measured properties of high-T$_c$ cuprates in the "strange metal" phase of their phase diagram. We believe , these results provide support to the quantum thermodynamic time crystal model of the Euclidean Q-balls considered in the present paper.
Short-Path Prompting in LLMs: Analyzing Reasoning Instability and Solutions for Robust Performance
2025-04-13
Zuoli Tang, Junjie Ou, Kaiqin Hu, Chunwei Wu, Zhaoxin Huan, Chilin Fu, Xiaolu Zhang, Jun Zhou, Chenliang Li
Recent years have witnessed significant progress in large language models' (LLMs) reasoning, which is largely due to the chain-of-thought (CoT) approaches, allowing models to generate intermediate reasoning steps before reaching the final answer. Building on these advances, state-of-the-art LLMs are instruction-tuned to provide long and detailed CoT pathways when responding to reasoning-related questions. However, human beings are naturally cognitive misers and will prompt language models to give rather short responses, thus raising a significant conflict with CoT reasoning. In this paper, we delve into how LLMs' reasoning performance changes when users provide short-path prompts. The results and analysis reveal that language models can reason effectively and robustly without explicit CoT prompts, while under short-path prompting, LLMs' reasoning ability drops significantly and becomes unstable, even on grade-school problems. To address this issue, we propose two approaches: an instruction-guided approach and a fine-tuning approach, both designed to effectively manage the conflict. Experimental results show that both methods achieve high accuracy, providing insights into the trade-off between instruction adherence and reasoning accuracy in current models.
Unification of Consensus-Based Multi-Objective Optimization and Multi-Robot Path Planning
2025-04-13
Michael P. Wozniak
Multi-agent systems seeking consensus may also have other objective functions to optimize, requiring the research of multi-objective optimization in consensus. Several recent publications have explored this domain using various methods such as weighted-sum optimization and penalization methods. This paper reviews the state of the art for consensus-based multi-objective optimization, poses a multi-agent lunar rover exploration problem seeking consensus and maximization of explored area, and achieves optimal edge weights and steering angles by applying SQP algorithms.
Reentrant localization transition induced by a composite potential
2025-04-13
Xingbo Wei
We numerically investigate the localization transition in a one-dimensional system subjected to a composite potential consisting of periodic and quasi-periodic components. For the rational wave vector $\alpha=1/2$, the periodic component reduces to a staggered potential, which has been reported to induce the reentrant localization transition. In addition to $\alpha=1/2$, we find that other rational wave vectors can also lead to the reentrant phenomenon. To investigate the underlying mechanisms of the reentrant localization transition, we vary the parameters of the composite potential and find that the reentrant localization transition is sensitive to the phase factor of the periodic component. By further studying the structure of the periodic component, we confirm that this sensitivity arises from the periodic phase factor modulating the mirror symmetry. Finally, we map out a global phase diagram and reveal that the reentrant localization transition originates from a paradoxical effect: increasing the amplitude of the periodic component enhances localization but simultaneously strengthens the mirror symmetry, which favors the formation of extended states. Our numerical analysis suggests that the interplay between these competing factors drives the reentrant localization transition.
Towards Intuitive Drone Operation Using a Handheld Motion Controller
2025-04-13
Daria Trinitatova, Sofia Shevelo, Dzmitry Tsetserukou
We present an intuitive human-drone interaction system that utilizes a gesture-based motion controller to enhance the drone operation experience in real and simulated environments. The handheld motion controller enables natural control of the drone through the movements of the operator's hand, thumb, and index finger: the trigger press manages the throttle, the tilt of the hand adjusts pitch and roll, and the thumbstick controls yaw rotation. Communication with drones is facilitated via the ExpressLRS radio protocol, ensuring robust connectivity across various frequencies. The user evaluation of the flight experience with the designed drone controller using the UEQ-S survey showed high scores for both Pragmatic (mean=2.2, SD = 0.8) and Hedonic (mean=2.3, SD = 0.9) Qualities. This versatile control interface supports applications such as research, drone racing, and training programs in real and simulated environments, thereby contributing to advances in the field of human-drone interaction.
In recent years, Federated Graph Learning (FGL) has gained significant attention for its distributed training capabilities in graph-based machine intelligence applications, mitigating data silos while offering a new perspective for privacy-preserve large-scale graph learning. However, multi-level FGL heterogeneity presents various client-server collaboration challenges: (1) Model-level: The variation in clients for expected performance and scalability necessitates the deployment of heterogeneous models. Unfortunately, most FGL methods rigidly demand identical client models due to the direct model weight aggregation on the server. (2) Data-level: The intricate nature of graphs, marked by the entanglement of node profiles and topology, poses an optimization dilemma. This implies that models obtained by federated training struggle to achieve superior performance. (3) Communication-level: Some FGL methods attempt to increase message sharing among clients or between clients and the server to improve training, which inevitably leads to high communication costs. In this paper, we propose FedPG as a general prototype-guided optimization method for the above multi-level FGL heterogeneity. Specifically, on the client side, we integrate multi-level topology-aware prototypes to capture local graph semantics. Subsequently, on the server side, leveraging the uploaded prototypes, we employ topology-guided contrastive learning and personalized technology to tailor global prototypes for each client, broadcasting them to improve local training. Experiments demonstrate that FedPG outperforms SOTA baselines by an average of 3.57\% in accuracy while reducing communication costs by 168x.
Vision-Language Model (VLM) have gained widespread adoption in Open-Vocabulary (OV) object detection and segmentation tasks. Despite they have shown promise on OV-related tasks, their effectiveness in conventional vision tasks has thus far been unevaluated. In this work, we present the systematic review of VLM-based detection and segmentation, view VLM as the foundational model and conduct comprehensive evaluations across multiple downstream tasks for the first time: 1) The evaluation spans eight detection scenarios (closed-set detection, domain adaptation, crowded objects, etc.) and eight segmentation scenarios (few-shot, open-world, small object, etc.), revealing distinct performance advantages and limitations of various VLM architectures across tasks. 2) As for detection tasks, we evaluate VLMs under three finetuning granularities: \textit{zero prediction}, \textit{visual fine-tuning}, and \textit{text prompt}, and further analyze how different finetuning strategies impact performance under varied task. 3) Based on empirical findings, we provide in-depth analysis of the correlations between task characteristics, model architectures, and training methodologies, offering insights for future VLM design. 4) We believe that this work shall be valuable to the pattern recognition experts working in the fields of computer vision, multimodal learning, and vision foundation models by introducing them to the problem, and familiarizing them with the current status of the progress while providing promising directions for future research. A project associated with this review and evaluation has been created at https://github.com/better-chao/perceptual_abilities_evaluation.
The ANTARES detector: two decades of neutrino searches in the Mediterranean Sea
2025-04-13
A. Albert, S. Alves, M. André, M. Ardid, S. Ardid, J. -J. Aubert, J. Aublin, B. Baret, S. Basa, Y. Becherini, B. Belhorma, F. Benfenati, V. Bertin, S. Biagi, J. Boumaaza, M. Bouta, M. C. Bouwhuis, H. Branzas, R. Bruijn, J. Brunner, J. Busto, B. Caiffi, D. Calvo, S. Campion, A. Capone, F. Carenini, J. Carr, V. Carretero, T. Cartraud, S. Celli, L. Cerisy, M. Chabab, R. Cherkaoui El Moursli, T. Chiarusi, M. Circella, J. A. B. Coelho, A. Coleiro, R. Coniglione, P. Coyle, A. Creusot, A. F. Dìaz, B. De Martino, I. Del Rosso, C. Distefano, I. Di Palma, C. Donzaud, D. Dornic, D. Drouhin, T. Eberl, A. Eddymaoui, T. van Eeden, D. van Eijk, S. El Hedri, N. El Khayati, A. Enzenhöfer, P. Fermani, G. Ferrara, F. Filippini, L. Fusco, S. Gagliardini, J. Garcìa-Méndez, C. Gatius Oliver, P. Gay, N. Geisselbrecht, H. Glotin, R. Gozzini, R. Gracia Ruiz, K. Graf, C. Guidi, L. Haegel, H. van Haren, A. J. Heijboer, Y. Hello, L. Hennig, J. J. Hernàndez-Rey, J. Hössl, F. Huang, G. Illuminati, B. Jisse-Jung, M. de Jong, P. de Jong, M. Kadler, O. Kalekin, U. Katz, A. Kouchner, I. Kreykenbohm, V. Kulikovskiy, R. Lahmann, M. Lamoureux, A. Lazo, D. Lefèvre, E. Leonora, G. Levi, S. Le Stum, S. Loucatos, J. Manczak, M. Marcelin, A. Margiotta, A. Marinelli, J. A. Martìnez-Mora, P. Migliozzi, A. Moussa, R. Muller, S. Navas, E. Nezri, B. 'O Fearraigh, E. Oukacha, A. M. Paun, G. E. Pavalas, S. Pena-Martìnez, M. Perrin-Terrin, P. Piattelli, C. Poirè, V. Popa, T. Pradier, N. Randazzo, D. Real, G. Riccobene, A. Romanov, A. Sànchez Losa, A. Saina, F. Salesa Greus, D. F. E. Samtleben, M. Sanguineti, P. Sapienza, F. Schüssler, J. Seneca, M. Spurio, Th. Stolarczyk, M. Taiuti, Y. Tayalati, B. Vallage, G. Vannoye, V. Van Elewyck, S. Viola, D. Vivolo, J. Wilms, S. Zavatarelli, A. Zegarelli, J. D. Zornoza, J. Zúniga
Interest for studying cosmic neutrinos using deep-sea detectors has increase after the discovery of a diffuse flux of cosmic neutrinos by the IceCube collaboration and the possibility of wider multi-messenger studies with the observations of gravitational waves. The ANTARES detector was the first neutrino telescope in seawater, operating successfully in the Mediterranean Sea for more than a decade and a half. All challenges related to the operation in the deep sea were accurately addressed by the collaboration. Deployment and connection operations became smoother over time; data taking and constant re-calibration of the detector due to the variable environmental conditions were fully automated. A wealth of results on the subject of astroparticle physics, particle physics and multi-messenger astronomy have been obtained, despite the relative modest size of the detector, paving the way to a new generation of larger undersea detectors. This review summarizes the efforts by the ANTARES collaboration that made the possibility to operate neutrino telescopes in seawater a reality and the results obtained in this endeavor.
InfoBound: A Provable Information-Bounds Inspired Framework for Both OoD Generalization and OoD Detection
2025-04-13
Lin Zhu, Yifeng Yang, Zichao Nie, Yuan Gao, Jiarui Li, Qinying Gu, Xinbing Wang, Chenghu Zhou, Nanyang Ye
In real-world scenarios, distribution shifts give rise to the importance of two problems: out-of-distribution (OoD) generalization, which focuses on models' generalization ability against covariate shifts (i.e., the changes of environments), and OoD detection, which aims to be aware of semantic shifts (i.e., test-time unseen classes). Real-world testing environments often involve a combination of both covariate and semantic shifts. While numerous methods have been proposed to address these critical issues, only a few works tackled them simultaneously. Moreover, prior works often improve one problem but sacrifice the other. To overcome these limitations, we delve into boosting OoD detection and OoD generalization from the perspective of information theory, which can be easily applied to existing models and different tasks. Building upon the theoretical bounds for mutual information and conditional entropy, we provide a unified approach, composed of Mutual Information Minimization (MI-Min) and Conditional Entropy Maximizing (CE-Max). Extensive experiments and comprehensive evaluations on multi-label image classification and object detection have demonstrated the superiority of our method. It successfully mitigates trade-offs between the two challenges compared to competitive baselines.
Usability testing is a fundamental research method that user experience (UX) researchers use to evaluate and iterate a web design, but\textbf{ how to evaluate and iterate the usability testing study design } itself? Recent advances in Large Language Model-simulated Agent (\textbf{LLM Agent}) research inspired us to design \textbf{UXAgent} to support UX researchers in evaluating and reiterating their usability testing study design before they conduct the real human-subject study. Our system features a Persona Generator module, an LLM Agent module, and a Universal Browser Connector module to automatically generate thousands of simulated users to interactively test the target website. The system also provides an Agent Interview Interface and a Video Replay Interface so that the UX researchers can easily review and analyze the generated qualitative and quantitative log data. Through a heuristic evaluation, five UX researcher participants praised the innovation of our system but also expressed concerns about the future of LLM Agent usage in UX studies.
Continuous Revival of the Periodic Schrödinger Equation with Piecewise $C^2$ Potential
2025-04-13
Dinh-Quan Tran, Peter J. Olver
In this paper, we investigate the revivals of the one-dimensional periodic Schr\"odinger equation with a piecewise $C^2$ potential function. As has been observed through numerical simulations of the equation with various initial data and potential functions, the solution, while remaining fractalized at irrational times, exhibits a form of revival at rational times. The goal is to prove that the solution at these rational times is given by a finite linear combination of translations and dilations of the initial datum, plus an additional continuous term, which we call "continuous revival". In pursuit of this result, we present a review of relevant properties of the periodic Schr\"odinger equation as an eigenvalue problem, including asymptotic results on both the eigenvalues and eigenfunctions.
DiTSE: High-Fidelity Generative Speech Enhancement via Latent Diffusion Transformers
2025-04-13
Heitor R. Guimarães, Jiaqi Su, Rithesh Kumar, Tiago H. Falk, Zeyu Jin
Real-world speech recordings suffer from degradations such as background noise and reverberation. Speech enhancement aims to mitigate these issues by generating clean high-fidelity signals. While recent generative approaches for speech enhancement have shown promising results, they still face two major challenges: (1) content hallucination, where plausible phonemes generated differ from the original utterance; and (2) inconsistency, failing to preserve speaker's identity and paralinguistic features from the input speech. In this work, we introduce DiTSE (Diffusion Transformer for Speech Enhancement), which addresses quality issues of degraded speech in full bandwidth. Our approach employs a latent diffusion transformer model together with robust conditioning features, effectively addressing these challenges while remaining computationally efficient. Experimental results from both subjective and objective evaluations demonstrate that DiTSE achieves state-of-the-art audio quality that, for the first time, matches real studio-quality audio from the DAPS dataset. Furthermore, DiTSE significantly improves the preservation of speaker identity and content fidelity, reducing hallucinations across datasets compared to state-of-the-art enhancers. Audio samples are available at: http://hguimaraes.me/DiTSE
Communication, Awareness and Acceptance of Digital Banking Amidst Cash Crunch in Southeast and South-South, Nigeria
2025-04-13
Okechukwu Christopher Onuegbu, Bettina Oboakore Agbamu, Belinda Uju Anyakoha, Ogonna Wilson Anunike
Digital banking is among the technological innovations currently reverberating the cyber wave. this study seeks to assess communication, awareness and acceptance of it among the residents of south-east and south-south, nigeria. the survey objectives were to ascertain awareness level of the south-east and south-south residents towards digital banking during the cash crunch, determine the acceptance level of digital banking among the south-east and south-south residents, find out the role of communication in awareness and acceptance of digital banking during the cash crunch in south-east and south-south nigeria, and assess the usage of digital banking amidst cash crunch in south-east and south nigeria. the study methodology is a sample survey which allowed researchers to administer questionnaires on 385 respondents out of the 50,166,807 study population. the findings showed that awareness level of digital banking was good (36%) in south-east and south-south nigeria during the cash crunch but it level of acceptance and usage improved more (37%) after the cash crunch. the study also ascertained that communication contribute significantly (59%) towards the usage and acceptance of digital banking in the two zones. it further showed that usage of digital banking in south-east and south-south has improved due to significant contributions of communication.
Integrating Textual Embeddings from Contrastive Learning with Generative Recommender for Enhanced Personalization
2025-04-13
Yijun Liu
Recent advances in recommender systems have highlighted the complementary strengths of generative modeling and pretrained language models. We propose a hybrid framework that augments the Hierarchical Sequential Transduction Unit (HSTU) generative recommender with BLaIR -- a contrastive text embedding model. This integration enriches item representations with semantic signals from textual metadata while preserving HSTU's powerful sequence modeling capabilities. We evaluate our method on two domains from the Amazon Reviews 2023 dataset, comparing it against the original HSTU and a variant that incorporates embeddings from OpenAI's state-of-the-art text-embedding-3-large model. While the OpenAI embedding model is likely trained on a substantially larger corpus with significantly more parameters, our lightweight BLaIR-enhanced approach -- pretrained on domain-specific data -- consistently achieves better performance, highlighting the effectiveness of contrastive text embeddings in compute-efficient settings.
Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR << 1 Imaging
2025-04-13
Hui Xue, Sarah M. Hooper, Rhodri H. Davies, Thomas A. Treibel, Iain Pierce, John Stairs, Joseph Naegele, Charlotte Manisty, James C. Moon, Adrienne E. Campbell-Washburn, Peter Kellman, Michael S. Hansen
Purpose: To propose a flexible and scalable imaging transformer (IT) architecture with three attention modules for multi-dimensional imaging data and apply it to MRI denoising with very low input SNR. Methods: Three independent attention modules were developed: spatial local, spatial global, and frame attentions. They capture long-range signal correlation and bring back the locality of information in images. An attention-cell-block design processes 5D tensors ([B, C, F, H, W]) for 2D, 2D+T, and 3D image data. A High Resolution (HRNet) backbone was built to hold IT blocks. Training dataset consists of 206,677 cine series and test datasets had 7,267 series. Ten input SNR levels from 0.05 to 8.0 were tested. IT models were compared to seven convolutional and transformer baselines. To test scalability, four IT models 27m to 218m parameters were trained. Two senior cardiologists reviewed IT model outputs from which the EF was measured and compared against the ground-truth. Results: IT models significantly outperformed other models over the tested SNR levels. The performance gap was most prominent at low SNR levels. The IT-218m model had the highest SSIM and PSNR, restoring good image quality and anatomical details even at SNR 0.2. Two experts agreed at this SNR or above, the IT model output gave the same clinical interpretation as the ground-truth. The model produced images that had accurate EF measurements compared to ground-truth values. Conclusions: Imaging transformer model offers strong performance, scalability, and versatility for MR denoising. It recovers image quality suitable for confident clinical reading and accurate EF measurement, even at very low input SNR of 0.2.
"It's not a representation of me": Examining Accent Bias and Digital Exclusion in Synthetic AI Voice Services
2025-04-12
Shira Michel, Sufi Kaur, Sarah Elizabeth Gillespie, Jeffrey Gleason, Christo Wilson, Avijit Ghosh
Recent advances in artificial intelligence (AI) speech generation and voice cloning technologies have produced naturalistic speech and accurate voice replication, yet their influence on sociotechnical systems across diverse accents and linguistic traits is not fully understood. This study evaluates two synthetic AI voice services (Speechify and ElevenLabs) through a mixed methods approach using surveys and interviews to assess technical performance and uncover how users' lived experiences influence their perceptions of accent variations in these speech technologies. Our findings reveal technical performance disparities across five regional, English-language accents and demonstrate how current speech generation technologies may inadvertently reinforce linguistic privilege and accent-based discrimination, potentially creating new forms of digital exclusion. Overall, our study highlights the need for inclusive design and regulation by providing actionable insights for developers, policymakers, and organizations to ensure equitable and socially responsible AI speech technologies.
Conformal Calibration: Ensuring the Reliability of Black-Box AI in Wireless Systems
2025-04-12
Osvaldo Simeone, Sangwoo Park, Matteo Zecchin
AI is poised to revolutionize telecommunication networks by boosting efficiency, automation, and decision-making. However, the black-box nature of most AI models introduces substantial risk, possibly deterring adoption by network operators. These risks are not addressed by the current prevailing deployment strategy, which typically follows a best-effort train-and-deploy paradigm. This paper reviews conformal calibration, a general framework that moves beyond the state of the art by adopting computationally lightweight, advanced statistical tools that offer formal reliability guarantees without requiring further training or fine-tuning. Conformal calibration encompasses pre-deployment calibration via uncertainty quantification or hyperparameter selection; online monitoring to detect and mitigate failures in real time; and counterfactual post-deployment performance analysis to address "what if" diagnostic questions after deployment. By weaving conformal calibration into the AI model lifecycle, network operators can establish confidence in black-box AI models as a dependable enabling technology for wireless systems.
2025-04-12
M. J. Hardcastle, J. C. S. Pierce, K. J. Duncan, G. Gürkan, Y. Gong, M. A. Horton, B. Mingo, H. J. A. Röttgering, D. J. B. Smith
The wide-area component of the LOFAR Two-Metre Sky Survey (LoTSS) is currently the largest radio survey ever carried out, and a large fraction of the 4.5 million radio sources it contains have been optically identified with galaxies or quasars with spectroscopic or photometric redshifts. Identification of radio-luminous AGN from this LoTSS source catalogue is not only important from the point of view of understanding the accretion history of the universe, but also enables a wide range of other science. However, at present the vast majority of the optical identifications lack spectroscopic information or well-sampled spectral energy distributions. We show that colour and absolute magnitude information from the Wide-Field Infrared Survey Explorer (WISE) allows for the robust and efficient selection of radio AGN candidates, generating a radio AGN candidate sample of around 600,000 objects with flux density $> 1.1$ mJy, spanning 144-MHz luminosities between $10^{21}$ and $10^{29}$ W Hz$^{-1}$. We use the catalogue to constrain the total sky density of radio-luminous AGN and the evolution of their luminosity function between $z=0$ and $z\approx 1$, and show that the typical mass of their host galaxies, around $10^{11} M_\odot$, is essentially independent of radio luminosity above around $L_{144} \approx 10^{24}$ W Hz$^{-1}$. Combining with Very Large Array Sky Survey (VLASS) data, we show that the core prominences, radio spectral indices and variability of extended sources from the sample are qualitatively consistent with the expectations from unified models. A catalogue of the radio AGN candidates is released with this paper.
This paper presents a novel AI-powered framework designed to streamline database management and query optimization for PostgreSQL systems. Structured in three phases: Natural Language to SQL Translation, Query Execution and Analysis, and Insight Generation, the approach empowers users with intuitive, intelligent interaction with databases. By leveraging advanced natural language processing and language models, the system enables non-technical users to extract meaningful insights from complex datasets, reducing the dependency on specialized DBAs. The framework also introduces iterative refinement of queries, automatic report generation, and support for temporal data forecasting. Experimental results demonstrate improved productivity, reduced query latency, and enhanced accuracy, validating the system's effectiveness across diverse business use cases. The solution was developed and evaluated at ABV-IIITM Gwalior, under the guidance of Prof. Dr. Arun Kuma
Macroscopic fluctuation theory of correlations in hard rod gas
2025-04-12
Anupam Kundu
Recently, a theoretical framework known as ballistic macroscopic fluctuation theory has been developed to study large-scale fluctuations and correlations in many-body systems exhibiting ballistic transport. In this paper, we review this theory in the context of a one-dimensional gas of hard rods. The initial configurations of the rods are sampled from a probability distribution characterized by slowly varying conserved density profiles across space. Beginning from a microscopic description, we first formulate the macroscopic fluctuation theory in terms of the phase-space density of quasiparticles. In the second part, we apply this framework to compute the two-point, two-time correlation functions of the conserved densities in the Euler scaling limit. We derive an explicit expression for the correlation function, which not only reveals its inherent symmetries but is also straightforward to evaluate numerically for a given initial state. Our results also recover known expressions for space-time correlations in equilibrium for the hard rod gas.
The X-ray statistical properties of dust-obscured galaxies detected by eROSITA
2025-04-12
Akatoki Noboriguchi, Kohei Ichikawa, Yoshiki Toba, Tom Dwelly, Kohei Inayoshi, Toshihiro Kawaguchi, Teng Liu, Yuichi Terashima, Yoshihiro Ueda, Masayuki Akiyama, Marcella Brusa, Johannes Buchner, Kotaro Kohno, Andrea Merloni, Tohru Nagao, Mara Salvato, Hyewon Suh, Tanya Urrutia
Dust-obscured galaxies (DOGs) are considered to be in a co-evolution phase, with the associated active galactic nuclei (AGN) obscured by dust and gas. Although the DOGs are thought to harbor rapidly growing SMBHs, their X-ray statistical properties, crucial for understanding the properties of obscuring gas as well as the accretion disk state and the hot electron corona around the SMBHs, remain unexplored due to the combination of the low number density of DOGs and the lack of X-ray surveys achieving both of the wide-area and uniformly high-sensitivity observations. We construct a sample of X-ray-detected DOGs in the eROSITA Final Equatorial Depth Survey (eFEDS) field and examine their X-ray statistical properties. By using Subaru/HSC SSP, VIKING, and WISE all-sky surveys, our results reveal the discovery of 5738 IR-bright DOGs in the footprint covered by both of the eFEDS and VIKING surveys (60 deg^2), with 65 objects identified as X-ray-detected DOGs (eFEDS-DOGs). Among them, 41 eFEDS-DOGs show a power-law slope in the near to mid-IR bands (power-law DOGs), indicating dust-obscured AGN. The hydrogen column density (N_H) suggests that eFEDS-DOGs cover even unobscured AGN, spanning 10^20 < N_H <= 10^23. On the other hand, the majority of IR-bright DOGs are not detected by eROSITA, suggesting that most IR-bright DOGs are heavily obscured by dust and gas with N_H > 10^23. Therefore, eFEDS-DOGs, discovered thanks to the wide-area survey by eROSITA, are newly found populations showing less obscured phases among the lifetime of DOGs. Additionally, some eFEDS-DOGs exhibit deviations, down to nearly 1.0 dex below the monochromatic luminosity at 6 micron versus absorption-corrected intrinsic X-ray luminosity between 0.5-2 keV relation, suggesting that it may signal high Eddington ratios reaching the Eddington limit.
Secure Physical Layer Communications for Low-Altitude Economy Networking: A Survey
2025-04-12
Lingyi Cai, Jiacheng Wang, Ruichen Zhang, Yu Zhang, Tao Jiang, Dusit Niyato, Xianbin Wang, Abbas Jamalipour, Xuemin Shen
The Low-Altitude Economy Networking (LAENet) is emerging as a transformative paradigm that enables an integrated and sophisticated communication infrastructure to support aerial vehicles in carrying out a wide range of economic activities within low-altitude airspace. However, the physical layer communications in the LAENet face growing security threats due to inherent characteristics of aerial communication environments, such as signal broadcast nature and channel openness. These challenges highlight the urgent need for safeguarding communication confidentiality, availability, and integrity. In view of the above, this survey comprehensively reviews existing secure countermeasures for physical layer communication in the LAENet. We explore core methods focusing on anti-eavesdropping and authentication for ensuring communication confidentiality. Subsequently, availability-enhancing techniques are thoroughly discussed for anti-jamming and spoofing defense. Then, we review approaches for safeguarding integrity through anomaly detection and injection protection. Furthermore, we discuss future research directions, emphasizing energy-efficient physical layer security, multi-drone collaboration for secure communication, AI-driven security defense strategy, space-air-ground integrated security architecture, and 6G-enabled secure UAV communication. This survey may provide valuable references and new insights for researchers in the field of secure physical layer communication for the LAENet.
Leveraging Application-Specific Knowledge for Energy-Efficient Deep Learning Accelerators on Resource-Constrained FPGAs
2025-04-12
Chao Qian
The growing adoption of Deep Learning (DL) applications in the Internet of Things has increased the demand for energy-efficient accelerators. Field Programmable Gate Arrays (FPGAs) offer a promising platform for such acceleration due to their flexibility and power efficiency. However, deploying DL models on resource-constrained FPGAs remains challenging because of limited resources, workload variability, and the need for energy-efficient operation. This paper presents a framework for generating energy-efficient DL accelerators on resource-constrained FPGAs. The framework systematically explores design configurations to enhance energy efficiency while meeting requirements for resource utilization and inference performance in diverse application scenarios. The contributions of this work include: (1) analyzing challenges in achieving energy efficiency on resource-constrained FPGAs; (2) proposing a methodology for designing DL accelerators with integrated Register Transfer Level (RTL) optimizations, workload-aware strategies, and application-specific knowledge; and (3) conducting a literature review to identify gaps and demonstrate the necessity of this work.
Heating of buildings represents a significant share of the energy consumption in Europe. Smart thermostats that capitalize on the data-driven analysis of heating patterns in order to optimize heat supply are a very promising part of building energy management technology. However, factors driving their acceptance by building inhabitants are poorly understood although being a prerequisite for fully tapping on their potential. In order to understand the driving forces of technology adoption in this use case, a large survey (N = 2250) was conducted in five EU countries (Austria, Belgium, Estonia, Germany, Greece). For the data analysis structural equation modelling based on the Unified Theory of Acceptance and Use of Technology (UTAUT) was employed, which was extended by adding social beliefs, including descriptive social norms, collective efficacy, social identity and trust. As a result, performance expectancy, price value, and effort expectancy proved to be the most important predictors overall, with variations across countries. In sum, the adoption of smart thermostats appears more strongly associated with individual beliefs about their functioning, potentially reducing their adoption. At the end of the paper, implications for policy making and marketing of smart heating technologies are discussed.
Spectral Intruders: A Multi-Site Study of Radio Environment for Cosmology Experiments
2025-04-12
Yash Agrawal, Saurabh Singh, Girish B. S., Somashekar R., Srivani K. S., Raghunathan A., Vishakha S. Pandharpure, Udaya Shankar N., Keerthipriya S., Mayuri Sathyanarayana Rao
Radio Frequency Interference (RFI) presents a significant challenge for carrying out precision measurements in radio astronomy. In particular, RFI can be a show stopper when looking for faint cosmological signals such as the red-shifted 21-cm line from cosmic dawn (CD) and epoch of reionization (EoR). As wireless communications, satellite transmissions, and other RF technologies proliferate globally, understanding the RFI landscape has become essential for site selection and data integrity. We present findings from RFI surveys conducted at four distinct locations: three locations in India, the Gauribidanur Radio Observatory in Karnataka, Twin Lakes in Ladakh, Kalpong Dam in the Andaman Islands, and the Gruvebadet Atmosphere Laboratory in Ny-Alesund, Svalbard, Norway. These sites, selected based on their geographical diversity and varying levels of human activity, were studied to assess RFI presence in 30-300 MHz bands-critical for low-frequency observations and experiments targeting the 21-cm CD/EoR signal. Using an automated RFI detection approach via the Hampel filter and singular value decomposition, the surveys identified both persistent and transient interference, which varies with location and time. The results provide a comprehensive view of the RFI environment at each site, informing the feasibility of long-term cosmological observations and aiding in the mitigation of RFI in radio astronomical data. The methods developed to characterize RFI can be easily generalized to any location and experiment.
Quantum thermocouples: nonlocal conversion and control of heat in semiconductor nanostructures
2025-04-12
José Balduque, Rafael Sánchez
Nanoscale conductors are interesting for thermoelectrics because of their particular spectral features connecting separated heat and particle currents. Multiterminal devices in the quantum regime benefit from phase-coherent phenomena, which turns the thermoelectric effect nonlocal, and from tunable single-particle interactions. This way one can define quantum thermocouples which convert an injected heat current into useful power in an isothermal conductor, or work as refrigerators. Additionally, efficient heat management devices can be defined. We review recent theoretical and experimental progress in the research of multiterminal thermal and thermoelectric quantum transport leading to proposals of autonomous quantum heat engines and thermal devices.
IMPACT: Behavioral Intention-aware Multimodal Trajectory Prediction with Adaptive Context Trimming
2025-04-12
Jiawei Sun, Xibin Yue, Jiahui Li, Tianle Shen, Chengran Yuan, Shuo Sun, Sheng Guo, Quanyun Zhou, Marcelo H Ang Jr
While most prior research has focused on improving the precision of multimodal trajectory predictions, the explicit modeling of multimodal behavioral intentions (e.g., yielding, overtaking) remains relatively underexplored. This paper proposes a unified framework that jointly predicts both behavioral intentions and trajectories to enhance prediction accuracy, interpretability, and efficiency. Specifically, we employ a shared context encoder for both intention and trajectory predictions, thereby reducing structural redundancy and information loss. Moreover, we address the lack of ground-truth behavioral intention labels in mainstream datasets (Waymo, Argoverse) by auto-labeling these datasets, thus advancing the community's efforts in this direction. We further introduce a vectorized occupancy prediction module that infers the probability of each map polyline being occupied by the target vehicle's future trajectory. By leveraging these intention and occupancy prediction priors, our method conducts dynamic, modality-dependent pruning of irrelevant agents and map polylines in the decoding stage, effectively reducing computational overhead and mitigating noise from non-critical elements. Our approach ranks first among LiDAR-free methods on the Waymo Motion Dataset and achieves first place on the Waymo Interactive Prediction Dataset. Remarkably, even without model ensembling, our single-model framework improves the soft mean average precision (softmAP) by 10 percent compared to the second-best method in the Waymo Interactive Prediction Leaderboard. Furthermore, the proposed framework has been successfully deployed on real vehicles, demonstrating its practical effectiveness in real-world applications.
A Short Survey on Small Reasoning Models: Training, Inference, Applications and Research Directions
2025-04-12
Chengyu Wang, Taolin Zhang, Richang Hong, Jun Huang
Recently, the reasoning capabilities of large reasoning models (LRMs), such as DeepSeek-R1, have seen significant advancements through the slow thinking process. Despite these achievements, the substantial computational demands of LRMs present considerable challenges. In contrast, small reasoning models (SRMs), often distilled from larger ones, offer greater efficiency and can exhibit distinct capabilities and cognitive trajectories compared to LRMs. This work surveys around 170 recently published papers on SRMs for tackling various complex reasoning tasks. We review the current landscape of SRMs and analyze diverse training and inference techniques related to SRMs. Furthermore, we provide a comprehensive review of SRMs for domain-specific applications and discuss possible future research directions. This survey serves as an essential reference for researchers to leverage or develop SRMs for advanced reasoning functionalities with high efficiency.
Large Language Models (LLMs) are changing the way we operate our society and will undoubtedly impact power systems as well - but how exactly? By integrating various data streams - including real-time grid data, market dynamics, and consumer behaviors - LLMs have the potential to make power system operations more adaptive, enhance proactive security measures, and deliver personalized energy services. This paper provides a comprehensive analysis of 30 real-world applications across eight key categories: Grid Operations and Management, Energy Markets and Trading, Personalized Energy Management and Customer Engagement, Grid Planning and Education, Grid Security and Compliance, Advanced Data Analysis and Knowledge Discovery, Emerging Applications and Societal Impact, and LLM-Enhanced Reinforcement Learning. Critical technical hurdles, such as data privacy and model reliability, are examined, along with possible solutions. Ultimately, this review illustrates how LLMs can significantly contribute to building more resilient, efficient, and sustainable energy infrastructures, underscoring the necessity of their responsible and equitable deployment.
Towards Stepwise Domain Knowledge-Driven Reasoning Optimization and Reflection Improvement
2025-04-12
Chengyuan Liu, Shihang Wang, Lizhi Qing, Kaisong Song, Junjie Cao, Jun Lin, Ji Zhang, Ang Li, Kun Kuang, Fei Wu
Recently, stepwise supervision on Chain of Thoughts (CoTs) presents an enhancement on the logical reasoning tasks such as coding and math, with the help of Monte Carlo Tree Search (MCTS). However, its contribution to tasks requiring domain-specific expertise and knowledge remains unexplored. Motivated by the interest, we identify several potential challenges of vanilla MCTS within this context, and propose the framework of Stepwise Domain Knowledge-Driven Reasoning Optimization, employing the MCTS algorithm to develop step-level supervision for problems that require essential comprehension, reasoning, and specialized knowledge. Additionally, we also introduce the Preference Optimization towards Reflection Paths, which iteratively learns self-reflection on the reasoning thoughts from better perspectives. We have conducted extensive experiments to evaluate the advantage of the methodologies. Empirical results demonstrate the effectiveness on various legal-domain problems. We also report a diverse set of valuable findings, hoping to encourage the enthusiasm to the research of domain-specific LLMs and MCTS.
The Mapper algorithm, a technique within topological data analysis (TDA), constructs a simplified graphical representation of high-dimensional data to uncover its underlying shape and structural patterns. The algorithm has attracted significant attention from researchers and has been applied across various disciplines. However, to the best of the authors' knowledge, no comprehensive review currently exists on the Mapper algorithm and its variants as applied across different fields of study between 2007 and 2025. This review addresses this gap and serves as a valuable resource for researchers and practitioners aiming to apply or advance the algorithm. The reviewed literature comprises peer-reviewed articles retrieved from major academic databases, including Google Scholar, Web of Science, Scopus, JSTOR, PubMed, and IEEE Xplore, using the keywords 'topological data analysis,' 'mapper algorithm,' and 'topological graph.' The study further provides an overview and a comparative analysis of the suitability of the most commonly used filter functions and clustering algorithms within the Mapper framework. Additionally, it examines current trends, identifies limitations, and proposes future research directions for the Mapper algorithm and its variants, emphasizing the need for developing effective methodologies to streamline the analysis of high-dimensional data in the age of big data proliferation.
Reasoning is a fundamental cognitive process that enables logical inference, problem-solving, and decision-making. With the rapid advancement of large language models (LLMs), reasoning has emerged as a key capability that distinguishes advanced AI systems from conventional models that empower chatbots. In this survey, we categorize existing methods along two orthogonal dimensions: (1) Regimes, which define the stage at which reasoning is achieved (either at inference time or through dedicated training); and (2) Architectures, which determine the components involved in the reasoning process, distinguishing between standalone LLMs and agentic compound systems that incorporate external tools, and multi-agent collaborations. Within each dimension, we analyze two key perspectives: (1) Input level, which focuses on techniques that construct high-quality prompts that the LLM condition on; and (2) Output level, which methods that refine multiple sampled candidates to enhance reasoning quality. This categorization provides a systematic understanding of the evolving landscape of LLM reasoning, highlighting emerging trends such as the shift from inference-scaling to learning-to-reason (e.g., DeepSeek-R1), and the transition to agentic workflows (e.g., OpenAI Deep Research, Manus Agent). Additionally, we cover a broad spectrum of learning algorithms, from supervised fine-tuning to reinforcement learning such as PPO and GRPO, and the training of reasoners and verifiers. We also examine key designs of agentic workflows, from established patterns like generator-evaluator and LLM debate to recent innovations. ...
A Physically Consistent Formulation of Macroscopic Electrodynamics in Matter
2025-04-12
Bela Schulte Westhoff
Classical electrodynamics in material media, while essential, faces century-old inconsistencies regarding energy, momentum, and force. This work re-examines the theory from first principles to establish a rigorous description. Applying the force-energy consistency requirement, derived from the unambiguous Maxwell-Lorentz theory for free charges, reveals profound physical inconsistencies in conventional energy balances (using $\mathbf{D}, \mathbf{H}$) and major historical energy-momentum tensors (Minkowski, Abraham, Einstein-Laub). Their critical failure lies in accounting for energy dissipation, especially in stationary matter. This analysis justifies a unique formulation where the field's energy/momentum uses the vacuum tensor $T_{EM}(\mathbf{E},\mathbf{B})$, and interaction is solely the total Lorentz force $f_{Lorentz}$ acting on the total current $J_{total}$ (incorporating material polarization $\mathbf{P}$ and magnetization $\mathbf{M}$). This framework correctly treats $\mathbf{j}_{total} \cdot \mathbf{E}$ as the universal energy exchange gateway, handling storage and dissipation consistently, and clarifies $\mathbf{D}, \mathbf{H}$ as mathematical aids, not fundamental energy carriers. Addressing force density controversies, we demonstrate via spatial averaging analysis that microscopic force distributions are inherently indeterminable in any macroscopic theory. This justifies focusing on consistent energy/momentum accounting and provides a unified, physically sound, relativistically consistent foundation for electrodynamics in matter.
Explainable Artificial Intelligence techniques for interpretation of food datasets: a review
2025-04-12
Leonardo Arrighi, Ingrid Alves de Moraes, Marco Zullich, Michele Simonato, Douglas Fernandes Barbin, Sylvio Barbon Junior
Artificial Intelligence (AI) has become essential for analyzing complex data and solving highly-challenging tasks. It is being applied across numerous disciplines beyond computer science, including Food Engineering, where there is a growing demand for accurate and trustworthy predictions to meet stringent food quality standards. However, this requires increasingly complex AI models, raising reliability concerns. In response, eXplainable AI (XAI) has emerged to provide insights into AI decision-making, aiding model interpretation by developers and users. Nevertheless, XAI remains underutilized in Food Engineering, limiting model reliability. For instance, in food quality control, AI models using spectral imaging can detect contaminants or assess freshness levels, but their opaque decision-making process hinders adoption. XAI techniques such as SHAP (Shapley Additive Explanations) and Grad-CAM (Gradient-weighted Class Activation Mapping) can pinpoint which spectral wavelengths or image regions contribute most to a prediction, enhancing transparency and aiding quality control inspectors in verifying AI-generated assessments. This survey presents a taxonomy for classifying food quality research using XAI techniques, organized by data types and explanation methods, to guide researchers in choosing suitable approaches. We also highlight trends, challenges, and opportunities to encourage the adoption of XAI in Food Engineering.
Code intelligence is an emerging domain in software engineering, aiming to improve the effectiveness and efficiency of various code-related tasks. Recent research suggests that incorporating contextual information beyond the basic original task inputs (i.e., source code) can substantially enhance model performance. Such contextual signals may be obtained directly or indirectly from sources such as API documentation or intermediate representations like abstract syntax trees can significantly improve the effectiveness of code intelligence. Despite growing academic interest, there is a lack of systematic analysis of context in code intelligence. To address this gap, we conduct an extensive literature review of 146 relevant studies published between September 2007 and August 2024. Our investigation yields four main contributions. (1) A quantitative analysis of the research landscape, including publication trends, venues, and the explored domains; (2) A novel taxonomy of context types used in code intelligence; (3) A task-oriented analysis investigating context integration strategies across diverse code intelligence tasks; (4) A critical evaluation of evaluation methodologies for context-aware methods. Based on these findings, we identify fundamental challenges in context utilization in current code intelligence systems and propose a research roadmap that outlines key opportunities for future research.
Deriving the Gradients of Some Popular Optimal Transport Algorithms
2025-04-11
Fangzhou Xie
In this note, I review entropy-regularized Monge-Kantorovich problem in Optimal Transport, and derive the gradients of several popular algorithms popular in Computational Optimal Transport, including the Sinkhorn algorithms, Wasserstein Barycenter algorithms, and the Wasserstein Dictionary Learning algorithms.
Pretrained transformer-encoder models like DeBERTaV3 and ModernBERT introduce architectural advancements aimed at improving efficiency and performance. Although the authors of ModernBERT report improved performance over DeBERTaV3 on several benchmarks, the lack of disclosed training data and the absence of comparisons using a shared dataset make it difficult to determine whether these gains are due to architectural improvements or differences in training data. In this work, we conduct a controlled study by pretraining ModernBERT on the same dataset as CamemBERTaV2, a DeBERTaV3 French model, isolating the effect of model design. Our results show that the previous model generation remains superior in sample efficiency and overall benchmark performance, with ModernBERT's primary advantage being faster training and inference speed. However, the new proposed model still provides meaningful architectural improvements compared to earlier models such as BERT and RoBERTa. Additionally, we observe that high-quality pre-training data accelerates convergence but does not significantly improve final performance, suggesting potential benchmark saturation. These findings show the importance of disentangling pretraining data from architectural innovations when evaluating transformer models.
ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning
2025-04-11
Sahil Sethi, David Chen, Thomas Statchen, Michael C. Burkhart, Nipun Bhandari, Bashar Ramadan, Brett Beaulieu-Jones
Deep learning-based electrocardiogram (ECG) classification has shown impressive performance but clinical adoption has been slowed by the lack of transparent and faithful explanations. Post hoc methods such as saliency maps may fail to reflect a model's true decision process. Prototype-based reasoning offers a more transparent alternative by grounding decisions in similarity to learned representations of real ECG segments, enabling faithful, case-based explanations. We introduce ProtoECGNet, a prototype-based deep learning model for interpretable, multi-label ECG classification. ProtoECGNet employs a structured, multi-branch architecture that reflects clinical interpretation workflows: it integrates a 1D CNN with global prototypes for rhythm classification, a 2D CNN with time-localized prototypes for morphology-based reasoning, and a 2D CNN with global prototypes for diffuse abnormalities. Each branch is trained with a prototype loss designed for multi-label learning, combining clustering, separation, diversity, and a novel contrastive loss that encourages appropriate separation between prototypes of unrelated classes while allowing clustering for frequently co-occurring diagnoses. We evaluate ProtoECGNet on all 71 diagnostic labels from the PTB-XL dataset, demonstrating competitive performance relative to state-of-the-art black-box models while providing structured, case-based explanations. To assess prototype quality, we conduct a structured clinician review of the final model's projected prototypes, finding that they are rated as representative and clear. ProtoECGNet shows that prototype learning can be effectively scaled to complex, multi-label time-series classification, offering a practical path toward transparent and trustworthy deep learning models for clinical decision support.
2025-04-11
José Alvarado, Erin Teich, David Sivak, John Bechhoefer
Soft and active condensed matter represent a class of fascinating materials that we encounter in our everyday lives -- and constitute life itself. Control signals interact with the dynamics of these systems, and this influence is formalized in control theory and optimal control. Recent advances have employed various control-theoretical methods to design desired dynamics, properties, and functionality. Here we provide an introduction to optimal control aimed at physicists working with soft and active matter. We describe two main categories of control, feedforward control and feedback control, and their corresponding optimal control methods. We emphasize their parallels to Lagrangian and Hamiltonian mechanics, and provide a worked example problem. Finally, we review recent studies of control in soft, active, and related systems. Applying control theory to soft, active, and living systems will lead to an improved understanding of the signal processing, information flows, and actuation that underlie the physics of life.
Logarithmic Sobolev inequalities: a review on stability and instability results
2025-04-11
Giovanni Brigati, Jean Dolbeault, Nikita Simonov
In this paper, we review recent results on stability and instability in logarithmic Sobolev inequalities, with a particular emphasis on strong norms. We consider several versions of these inequalities on the Euclidean space, for the Lebesgue and the Gaussian measures, and discuss their differences in terms of moments and stability. We give new and direct proofs, as well as examples. Although we do not cover all aspects of the topic, we hope to contribute to establishing the state of the art.
Enhancing knowledge retention for continual learning with domain-specific adapters and features gating
2025-04-11
Mohamed Abbas Hedjazi, Oussama Hadjerci, Adel Hafiane
Continual learning empowers models to learn from a continuous stream of data while preserving previously acquired knowledge, effectively addressing the challenge of catastrophic forgetting. In this study, we propose a new approach that integrates adapters within the self-attention mechanisms of Vision Transformers to enhance knowledge retention when sequentially adding datasets from different domains. Unlike previous methods that continue learning with only one dataset, our approach introduces domain-specific output heads and feature gating, allowing the model to maintain high accuracy on previously learned tasks while incorporating only the essential information from multiple domains. The proposed method is compared to prominent parameter-efficient fine-tuning methods in the current state of the art. The results provide evidence that our method effectively alleviates the limitations of previous works. Furthermore, we conduct a comparative analysis using three datasets, CIFAR-100, Flowers102, and DTD, each representing a distinct domain, to investigate the impact of task order on model performance. Our findings underscore the critical role of dataset sequencing in shaping learning outcomes, demonstrating that strategic ordering can significantly improve the model's ability to adapt to evolving data distributions over time while preserving the integrity of previously learned knowledge.
A Survey of Machine Learning Models and Datasets for the Multi-label Classification of Textual Hate Speech in English
2025-04-11
Julian Bäumler, Louis Blöcher, Lars-Joel Frey, Xian Chen, Markus Bayer, Christian Reuter
The dissemination of online hate speech can have serious negative consequences for individuals, online communities, and entire societies. This and the large volume of hateful online content prompted both practitioners', i.e., in content moderation or law enforcement, and researchers' interest in machine learning models to automatically classify instances of hate speech. Whereas most scientific works address hate speech classification as a binary task, practice often requires a differentiation into sub-types, e.g., according to target, severity, or legality, which may overlap for individual content. Hence, researchers created datasets and machine learning models that approach hate speech classification in textual data as a multi-label problem. This work presents the first systematic and comprehensive survey of scientific literature on this emerging research landscape in English (N=46). We contribute with a concise overview of 28 datasets suited for training multi-label classification models that reveals significant heterogeneity regarding label-set, size, meta-concept, annotation process, and inter-annotator agreement. Our analysis of 24 publications proposing suitable classification models further establishes inconsistency in evaluation and a preference for architectures based on Bidirectional Encoder Representation from Transformers (BERT) and Recurrent Neural Networks (RNNs). We identify imbalanced training data, reliance on crowdsourcing platforms, small and sparse datasets, and missing methodological alignment as critical open issues and formulate ten recommendations for research.
Collective actuation in active solids in the presence of a polarizing field: a review of the dynamical regimes
2025-04-11
Paul Baconnier, Vincent Démery, Olivier Dauchot
Collective actuation in active solids, the spontaneous condensation of the dynamics on a few elastic modes, takes place whenever the deformations of the structure reorient the forces exerted by the active units composing, or embedded in, the solid. In a companion paper, we show through a combination of model experiments, numerical simulations, and theoretical analysis that adding an external field that polarizes the active forces strongly affects the dynamical transition to collective actuation. A new oscillatory regime emerges, and a reentrance transition to collective actuation takes place. Depending on the degenerate, or non-degenerate, nature of the modes on which the dynamics condensates; depending on the orientation of the polarizing field with respect to the stiff or soft direction of the solid, several new dynamical regimes can be observed. The purpose of the present paper is to review these dynamical regimes in a comprehensive way, both for the single-particle dynamics and for the coarse-grained one. Whenever possible the dynamical regimes and the transition between them are described analytically, otherwise numerically.
MedHal: An Evaluation Dataset for Medical Hallucination Detection
2025-04-11
Gaya Mehenni, Amal Zouaq
We present MedHal, a novel large-scale dataset specifically designed to evaluate if models can detect hallucinations in medical texts. Current hallucination detection methods face significant limitations when applied to specialized domains like medicine, where they can have disastrous consequences. Existing medical datasets are either too small, containing only a few hundred samples, or focus on a single task like Question Answering or Natural Language Inference. MedHal addresses these gaps by: (1) incorporating diverse medical text sources and tasks; (2) providing a substantial volume of annotated samples suitable for training medical hallucination detection models; and (3) including explanations for factual inconsistencies to guide model learning. We demonstrate MedHal's utility by training and evaluating a baseline medical hallucination detection model, showing improvements over general-purpose hallucination detection approaches. This resource enables more efficient evaluation of medical text generation systems while reducing reliance on costly expert review, potentially accelerating the development of medical AI research.
Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: a Review
2025-04-11
Claudio Cimarelli, Jose Andres Millan-Romera, Holger Voos, Jose Luis Sanchez-Lopez
Neuromorphic, or event, cameras represent a transformation in the classical approach to visual sensing encodes detected instantaneous per-pixel illumination changes into an asynchronous stream of event packets. Their novelty compared to standard cameras lies in the transition from capturing full picture frames at fixed time intervals to a sparse data format which, with its distinctive qualities, offers potential improvements in various applications. However, these advantages come at the cost of reinventing algorithmic procedures or adapting them to effectively process the new data format. In this survey, we systematically examine neuromorphic vision along three main dimensions. First, we highlight the technological evolution and distinctive hardware features of neuromorphic cameras from their inception to recent models. Second, we review image processing algorithms developed explicitly for event-based data, covering key works on feature detection, tracking, and optical flow -which form the basis for analyzing image elements and transformations -as well as depth and pose estimation or object recognition, which interpret more complex scene structures and components. These techniques, drawn from classical computer vision and modern data-driven approaches, are examined to illustrate the breadth of applications for event-based cameras. Third, we present practical application case studies demonstrating how event cameras have been successfully used across various industries and scenarios. Finally, we analyze the challenges limiting widespread adoption, identify significant research gaps compared to standard imaging techniques, and outline promising future directions and opportunities that neuromorphic vision offers.
Analysis of the Unscented Transform Controller for Systems with Bounded Nonlinearities
2025-04-11
Siddharth A. Dinkar, Ram Padmanabhan, Anna Clarke, Per-Olof Gutman, Melkior Ornik
In this paper, we present an analysis of the Unscented Transform Controller (UTC), a technique to control nonlinear systems motivated as a dual to the Unscented Kalman Filter (UKF). We consider linear, discrete-time systems augmented by a bounded nonlinear function of the state. For such systems, we review 1-step and N-step versions of the UTC. Using a Lyapunov-based analysis, we prove that the states and inputs converge to a bounded ball around the origin, whose radius depends on the bound on the nonlinearity. Using examples of a fighter jet model and a quadcopter, we demonstrate that the UTC achieves satisfactory regulation and tracking performance on these nonlinear models.
Datasets for Lane Detection in Autonomous Driving: A Comprehensive Review
2025-04-11
Jörg Gamerdinger, Sven Teufel, Oliver Bringmann
Accurate lane detection is essential for automated driving, enabling safe and reliable vehicle navigation in a variety of road scenarios. Numerous datasets have been introduced to support the development and evaluation of lane detection algorithms, each differing in terms of the amount of data, sensor types, annotation granularity, environmental conditions, and scenario diversity. This paper provides a comprehensive review of over 30 publicly available lane detection datasets, systematically analysing their characteristics, advantages and limitations. We classify these datasets based on key factors such as sensor resolution, annotation types and diversity of road and weather conditions. By identifying existing challenges and research gaps, we highlight opportunities for future dataset improvements that can further drive innovation in robust lane detection. This survey serves as a resource for researchers seeking appropriate datasets for lane detection, and contributes to the broader goal of advancing autonomous driving.
The field of spoken language processing is undergoing a shift from training custom-built, task-specific models toward using and optimizing spoken language models (SLMs) which act as universal speech processing systems. This trend is similar to the progression toward universal language models that has taken place in the field of (text) natural language processing. SLMs include both "pure" language models of speech -- models of the distribution of tokenized speech sequences -- and models that combine speech encoders with text language models, often including both spoken and written input or output. Work in this area is very diverse, with a range of terminology and evaluation settings. This paper aims to contribute an improved understanding of SLMs via a unifying literature survey of recent work in the context of the evolution of the field. Our survey categorizes the work in this area by model architecture, training, and evaluation choices, and describes some key challenges and directions for future work.
Bifurcations and Phase Transitions in the Origins of Life
2025-04-11
Ricard Solé, Manlio De Domenico
The path toward the emergence of life in our biosphere involved several key events allowing for the persistence, reproduction and evolution of molecular systems. All these processes took place in a given environmental context and required both molecular diversity and the right non-equilibrium conditions to sustain and favour complex self-sustaining molecular networks capable of evolving by natural selection. Life is a process that departs from non-life in several ways and cannot be reduced to standard chemical reactions. Moreover, achieving higher levels of complexity required the emergence of novelties. How did that happen? Here, we review different case studies associated with the early origins of life in terms of phase transitions and bifurcations, using symmetry breaking and percolation as two central components. We discuss simple models that allow for understanding key steps regarding life origins, such as molecular chirality, the transition to the first replicators and cooperators, the problem of error thresholds and information loss, and the potential for "order for free" as the basis for the emergence of life.
Dark Haptics: Exploring Manipulative Haptic Design in Mobile User Interfaces
2025-04-11
Chenge Tang, Karthikeya Puttur Venkatraj, Hongbo Liu, Christina Schneegass, Gijs Huisman, Abdallah El Ali
Mobile user interfaces abundantly feature so-called 'dark patterns'. These deceptive design practices manipulate users' decision making to profit online service providers. While past research on dark patterns mainly focus on visual design, other sensory modalities such as audio and touch remain largely unexplored. In this early work, we investigate the manipulative side of haptics, which we term as 'Dark Haptics', as a strategy to manipulate users. We designed a study to empirically showcase the potential of using a dark haptic pattern in a mobile device to manipulate user actions in a survey. Our findings indicate that our dark haptic design successfully influenced participants to forego their privacy after experiencing an alarming feedback for rejecting intrusive requests in the survey. As a first exploration of manipulative qualities of dark haptic designs, we attempt to lay the groundwork for future research and tools to mitigate harms and risks of dark haptics.
A comparative review of recent results on supercritical anomalies in two-dimensional kinetic Ising and Blume-Capel ferromagnets
2025-04-11
Gloria M. Buendía, Celeste Mendes, Per Arne Rikvold
Following the unexpected experimental discovery of ``sideband'' peaks in the fluctuation spectrum of thin Co films driven by a slowly oscillating magnetic field with a constant bias [P.~Riego et al., Phys. Rev. Lett. 118, 117202 (2017)] numerical studies of two-state Ising and three-state Blume-Capel (BC) ferromagnets in this dynamically supercritical regime have flourished and been successful in explaining this phenomenon. Here, we give a comparative review of this new literature and its connections to earlier work. Following an introduction and a presentation of the two models and the computational method used in many of these studies, we present numerical results for both models. Particular attention is paid to the fact that zero spins in the BC model tend to collect at he interfaces between regions of the two nonzero spin values, +/-1. We present strong arguments that this phenomenon leads to a reduction of the effective interface tension in the BC model, compared to the Ising model.
Investigation into the low temperature state of the spin-ice material Dy_2 Ti_2 O_7
2025-04-11
Mariano Marziali Bermudez, R. A. Borzi, D. A. Tennant, S. A. Grigera
The thermal equilibrium properties of the spin-ice material DTO, including specific heat, magnetization, and spin correlations, could be successfully reproduced by a model featuring magnetic interactions up to the third nearest neighbor and long-ranged dipolar forces. With the best-fit parameters, the model predicts an ordered ground state which breaks the cubic symmetry of the lattice. In this work, we analyze results from a neutron scattering experiment in which, instead of sharp Bragg peaks, a diffuse pattern was observed down to 300mK, despite very slow cooling [A. M. Samarakoon et al., Physical Review Research 4,033159 (2022).]. Using a reverse Monte Carlo approach, we found compatible spin configurations, analyze the suitability of antiferromagnetic spin chains as building blocks for the ground-state and provide various measures of correlation and calculate their energy. Our analysis suggests that while infinitely long chains are not present in the experimental configuration, antiferromagnetic spin chains provide a good approximation of the data. There are indications of possible evidence for short-range chains, but further investigation is needed for confirmation.
Diffusion Models for Robotic Manipulation: A Survey
2025-04-11
Rosa Wolf, Yitian Shi, Sheng Liu, Rania Rayyes
Diffusion generative models have demonstrated remarkable success in visual domains such as image and video generation. They have also recently emerged as a promising approach in robotics, especially in robot manipulations. Diffusion models leverage a probabilistic framework, and they stand out with their ability to model multi-modal distributions and their robustness to high-dimensional input and output spaces. This survey provides a comprehensive review of state-of-the-art diffusion models in robotic manipulation, including grasp learning, trajectory planning, and data augmentation. Diffusion models for scene and image augmentation lie at the intersection of robotics and computer vision for vision-based tasks to enhance generalizability and data scarcity. This paper also presents the two main frameworks of diffusion models and their integration with imitation learning and reinforcement learning. In addition, it discusses the common architectures and benchmarks and points out the challenges and advantages of current state-of-the-art diffusion-based methods.
Towards quantitative understanding of quantum dot ensemble capacitance-voltage spectroscopy
2025-04-11
Nico F. Brosda, Phil J. Badura, İsmail Bölükbaşı, İbrahim Engin, Patrick Lindner, Sascha R. Valentin, Andreas D. Wieck, Björn Sothmann, Arne Ludwig
Inhomogeneous ensembles of quantum dots (QDs) coupled to a charge reservoir are widely studied by using, e.g., electrical methods like capacitance-voltage spectroscopy. We present experimental measurements of the QD capacitance as a function of varying parameters such as ac frequency and bath temperature. The experiment reveals distinct shifts in the position of the capacitance peaks. While temperature-induced shifts have been explained by previous models, the observation of frequency-dependent shifts has not been explained so far. Given that existing models fall short in explaining these phenomena, we propose a refined theoretical model based on a master equation approach which incorporates energy-dependent tunneling effects. This approach successfully reproduces the experimental data. We highlight the critical role of energy-dependent tunneling in two distinct regimes: at low temperatures, ensemble effects arising from energy-level dispersion in differently sized QDs dominate the spectral response; at high temperatures and frequencies, we observe a peak shift of a different nature, which is best described by optimizing the conjoint probability of successive in- and out-tunneling events. Our findings contribute to a deeper understanding of tunnel processes and the physical properties of QD ensembles coupled to a common reservoir, with implications for their development in applications such as single-photon sources and spin qubits.
The population of tidal disruption events discovered with eROSITA
2025-04-11
Iuliia Grotova, Arne Rau, Pietro Baldini, Adelle J. Goodwin, Zhu Liu, Andrea Merloni, Mara Salvato, Gemma E. Anderson, Riccardo Arcodia, Johannes Buchner, Mirko Krumpe, Adam Malyali, Megan Masterson, James C. A. Miller-Jones, Kirpal Nandra, Raphael Shirley
This paper presents a systematic study of X-ray-selected canonical tidal disruption events (TDEs) discovered in the western Galactic hemisphere of the first two eROSITA all-sky surveys (eRASS1 and eRASS2) performed between Dec 2019 and Dec 2020. We compiled a TDE sample from the catalog of eROSITA's extragalactic transients and variables eRO-ExTra, which includes X-ray sources with a variability significance and fractional amplitude over four between eRASS1 and eRASS2, not associated with known AGNs. Each X-ray source is associated with an optical counterpart from the Legacy Survey DR10. Canonical TDEs were selected based on their X-ray light-curve properties (single flare or decline), soft X-ray spectra ($\Gamma>3$), and the absence of archival X-ray variability and AGN signatures in their host photometry and spectroscopy. The sample includes 31 X-ray-selected TDE candidates with redshifts of $0.02< z<0.34$ and luminosities of $5.7 \times 10^{41}
Adversarial Examples in Environment Perception for Automated Driving (Review)
2025-04-11
Jun Yan, Huilin Yin
The renaissance of deep learning has led to the massive development of automated driving. However, deep neural networks are vulnerable to adversarial examples. The perturbations of adversarial examples are imperceptible to human eyes but can lead to the false predictions of neural networks. It poses a huge risk to artificial intelligence (AI) applications for automated driving. This survey systematically reviews the development of adversarial robustness research over the past decade, including the attack and defense methods and their applications in automated driving. The growth of automated driving pushes forward the realization of trustworthy AI applications. This review lists significant references in the research history of adversarial examples.
The CARMENES search for exoplanets around M dwarfs. Cluster analysis of signals from spectral activity indicators to search for shared periods
2025-04-11
J. Kemmer, M. Lafarga, B. Fuhrmeister, Y. Shan, P. Schöfer, S. V. Jeffers, J. A. Caballero, A. Quirrenbach, P. J. Amado, A. Reiners, I. Ribas, V. J. S. Béjar, F. Del Sordo, A. P. Hatzes, Th. Henning, I. Hermelo, A. Kaminski, D. Montes, J. C. Morales, S. Reffert
A multitude of spectral activity indicators are routinely computed nowadays from the spectra generated as part of planet-hunting radial velocity surveys. Searching for shared periods among them can help to robustly identify astrophysical quantities of interest, such as the stellar rotation period. However, this identification can be complicated due to the fact that many different peaks occurring in the periodograms. This is especially true in the presence of aliasing and spurious signals caused by environmental influences affecting the instrument. Our goal is to test a clustering algorithm to find signals with the same periodicity, (i.e. with the stellar rotation period) in the periodograms of a large number of activity indicators. On this basis, we have looked to evaluate the correlations between activity indicators and fundamental stellar parameters. We used generalised Lomb-Scargle periodograms to find periodic signals in 24 activity indicators, spanning the VIS and NIR channels of the CARMENES spectrograph. Common periods were subsequently determined by a machine learning algorithm for density-based spatial clustering of applications with noise (DBSCAN). The clustering analysis of the signals apparent in the spectral activity indicators is a powerful tool for the detection of stellar rotation periods. It is straightforward to implement and can be easily automated, so that large data sets can be analysed. For a sample of 136 stars, we were able to recover the stellar rotation period in a total of 59 cases, including 3 with a previously unknown rotation period. In addition, we analysed spurious signals frequently occurring at the period of one year and its integer fractions, concluding that they are likely aliases of one underlying signal. Furthermore, we reproduced the results of several previous studies on the relationships between activity indicators and the stellar characteristics.
Thoracic Fluid Measurements by Bioimpedance: A Comprehensive Survey
2025-04-11
Manender Yadav, Shreyansh Shukla, Varsha Kiron, U. Deva Priyakumar, Maitreya Maity
Bioimpedance is an extensively studied non-invasive technique with diverse applications in biomedicine. This comprehensive review delves into the foundational concepts, technical intricacies, and practical implementations of bioimpedance. It elucidates the underlying principles governing bioimpedance measurements, including the relevant physics equations employed for estimating body fluid levels. Moreover, a thorough examination of the prevalent single-chip analog front end (AFE) available in the market, such as AD5933, MAX30001, AD5940, and AFE4300, is conducted, shedding light on their specifications and functionalities. The review focuses on using bioimpedance to assess thoracic impedance for heart failure detection by utilizing the relation between lung water and heart failure. Traditional techniques are compared with bioimpedance-based methods, demonstrating the latter's efficacy as a non-invasive tool for cardiac evaluation. In addition, the review addresses the technical limitations and challenges associated with bioimpedance. Pertinent issues such as contact impedance, motion artifacts, calibration, and validation regarding their impact on measurement precision and dependability are thoroughly examined. The review also explores strategies and advancements in using artificial intelligence to mitigate these challenges.
MedRep: Medical Concept Representation for General Electronic Health Record Foundation Models
2025-04-11
Junmo Kim, Namkyeong Lee, Jiwon Kim, Kwangsoo Kim
Electronic health record (EHR) foundation models have been an area ripe for exploration with their improved performance in various medical tasks. Despite the rapid advances, there exists a fundamental limitation: Processing unseen medical codes out of the vocabulary. This problem limits the generality of EHR foundation models and the integration of models trained with different vocabularies. To deal with this problem, we propose MedRep for EHR foundation models based on the observational medical outcome partnership (OMOP) common data model (CDM), providing the integrated medical concept representations and the basic data augmentation strategy for patient trajectories. For concept representation learning, we enrich the information of each concept with a minimal definition through large language model (LLM) prompts and enhance the text-based representations through graph ontology of OMOP vocabulary. Trajectory augmentation randomly replaces selected concepts with other similar concepts that have closely related representations to let the model practice with the concepts out-of-vocabulary. Finally, we demonstrate that EHR foundation models trained with MedRep better maintain the prediction performance in external datasets. Our code implementation is publicly available at https://github.com/kicarussays/MedRep.
Generative AI (GenAI) is transforming filmmaking, equipping artists with tools like text-to-image and image-to-video diffusion, neural radiance fields, avatar generation, and 3D synthesis. This paper examines the adoption of these technologies in filmmaking, analyzing workflows from recent AI-driven films to understand how GenAI contributes to character creation, aesthetic styling, and narration. We explore key strategies for maintaining character consistency, achieving stylistic coherence, and ensuring motion continuity. Additionally, we highlight emerging trends such as the growing use of 3D generation and the integration of real footage with AI-generated elements. Beyond technical advancements, we examine how GenAI is enabling new artistic expressions, from generating hard-to-shoot footage to dreamlike diffusion-based morphing effects, abstract visuals, and unworldly objects. We also gather artists' feedback on challenges and desired improvements, including consistency, controllability, fine-grained editing, and motion refinement. Our study provides insights into the evolving intersection of AI and filmmaking, offering a roadmap for researchers and artists navigating this rapidly expanding field.
DreamFuse: Adaptive Image Fusion with Diffusion Transformer
2025-04-11
Junjia Huang, Pengxiang Yan, Jiyang Liu, Jie Wu, Zhao Wang, Yitong Wang, Liang Lin, Guanbin Li
Image fusion seeks to seamlessly integrate foreground objects with background scenes, producing realistic and harmonious fused images. Unlike existing methods that directly insert objects into the background, adaptive and interactive fusion remains a challenging yet appealing task. It requires the foreground to adjust or interact with the background context, enabling more coherent integration. To address this, we propose an iterative human-in-the-loop data generation pipeline, which leverages limited initial data with diverse textual prompts to generate fusion datasets across various scenarios and interactions, including placement, holding, wearing, and style transfer. Building on this, we introduce DreamFuse, a novel approach based on the Diffusion Transformer (DiT) model, to generate consistent and harmonious fused images with both foreground and background information. DreamFuse employs a Positional Affine mechanism to inject the size and position of the foreground into the background, enabling effective foreground-background interaction through shared attention. Furthermore, we apply Localized Direct Preference Optimization guided by human feedback to refine DreamFuse, enhancing background consistency and foreground harmony. DreamFuse achieves harmonious fusion while generalizing to text-driven attribute editing of the fused results. Experimental results demonstrate that our method outperforms state-of-the-art approaches across multiple metrics.
Numerical evolution of Maxwell's equations for wavefronts propagating through a spherical gravitational potential
2025-04-11
Annamalai P. Shanmugaraj, Erik Schnetter, Sofie Marie Koksbang
We present a scheme for numerically solving Maxwell's equations in a weakly perturbed spacetime without introducing the usual geometric optics approximation. Using this scheme, we study light propagation through a spherical perturbation of a stationary metric in the linear limit. Going beyond geometric approximation allows us to explore the wave optics effects that occur when the wavefront propagates through the gravitational potential. We find that the wavefront breaks on the gravitational potential due to the Shapiro time delay, i.e. due to the slowing down of light when propagating in an inhomogeneous spacetime. This again leads to constructive interference, resulting in a persistent frequency dependent amplification of parts of the wave amplitude. When the wave length is shorter than a certain threshold set by the induced gravitational mass, the waves are reflected on the curvature gradient.
Evaluating the Bias in LLMs for Surveying Opinion and Decision Making in Healthcare
2025-04-11
Yonchanok Khaokaew, Flora D. Salim, Andreas Züfle, Hao Xue, Taylor Anderson, Matthew Scotch, David J Heslop
Generative agents have been increasingly used to simulate human behaviour in silico, driven by large language models (LLMs). These simulacra serve as sandboxes for studying human behaviour without compromising privacy or safety. However, it remains unclear whether such agents can truly represent real individuals. This work compares survey data from the Understanding America Study (UAS) on healthcare decision-making with simulated responses from generative agents. Using demographic-based prompt engineering, we create digital twins of survey respondents and analyse how well different LLMs reproduce real-world behaviours. Our findings show that some LLMs fail to reflect realistic decision-making, such as predicting universal vaccine acceptance. However, Llama 3 captures variations across race and Income more accurately but also introduces biases not present in the UAS data. This study highlights the potential of generative agents for behavioural research while underscoring the risks of bias from both LLMs and prompting strategies.
Practical Implementation of an End-to-End Methodology for SPC of 3-D Part Geometry: A Case Study
2025-04-11
Yulin An, Xueqi Zhao, Enrique del Castillo
Del Castillo and Zhao (2020, 2021, 2022, 2024) have recently proposed a new methodology for the Statistical Process Control (SPC) of discrete parts whose 3-dimensional (3D) geometrical data are acquired with non-contact sensors. The approach is based on monitoring the spectrum of the Laplace-Beltrami (LB) operator of each scanned part estimated using finite element methods (FEM). The spectrum of the LB operator is an intrinsic summary of the geometry of a part, independent of the ambient space. Hence, registration of scanned parts is unnecessary when comparing them. The primary goal of this case study paper is to demonstrate the practical implementation of the spectral SPC methodology through multiple examples using real scanned parts acquired with an industrial-grade laser scanner, including 3D printed parts and commercial parts. We discuss the scanned mesh preprocessing needed in practice, including the type of remeshing found to be most beneficial for the FEM computations. For each part type, both the "phase I" and "phase II" stages of the spectral SPC methodology are showcased. In addition, we provide a new principled method to determine the number of eigenvalues of the LB operator to consider for efficient SPC of a given part geometry, and present an improved algorithm to automatically define a region of interest, particularly useful for large meshes. Computer codes that implement every method discussed in this paper, as well as all scanned part datasets used in the case studies, are made available and explained in the supplementary materials.
Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges
2025-04-11
Erica van der Sar, Alessandro Zocca, Sandjai Bhulai
Power grid operation is becoming increasingly complex due to the rising integration of renewable energy sources and the need for more adaptive control strategies. Reinforcement Learning (RL) has emerged as a promising approach to power network control (PNC), offering the potential to enhance decision-making in dynamic and uncertain environments. The Learning To Run a Power Network (L2RPN) competitions have played a key role in accelerating research by providing standardized benchmarks and problem formulations, leading to rapid advancements in RL-based methods. This survey provides a comprehensive and structured overview of RL applications for power grid topology optimization, categorizing existing techniques, highlighting key design choices, and identifying gaps in current research. Additionally, we present a comparative numerical study evaluating the impact of commonly applied RL-based methods, offering insights into their practical effectiveness. By consolidating existing research and outlining open challenges, this survey aims to provide a foundation for future advancements in RL-driven power grid optimization.
An evolutionary medicine and life history perspective on aging and disease: Trade-offs, hyperfunction, and mismatch
2025-04-11
Jacob E. Aronoff, Benjamin C. Trumble
The rise in chronic diseases over the last century presents a significant health and economic burden globally. Here we apply evolutionary medicine and life history theory to better understand their development. We highlight an imbalanced metabolic axis of growth and proliferation (anabolic) versus maintenance and dormancy (catabolic), focusing on major mechanisms including IGF-1, mTOR, AMPK, and Klotho. We also relate this axis to the hyperfunction theory of aging, which similarly implicates anabolic mechanisms like mTOR in aging and disease. Next, we highlight the Brain-Body Energy Conservation model, which connects the hyperfunction theory with energetic trade-offs that induce hypofunction and catabolic health risks like impaired immunity. Finally, we discuss how modern environmental mismatches exacerbate this process. Following our review, we discuss future research directions to better understand health risk. This includes studying IGF-1, mTOR, AMPK, and Klotho and how they relate to health and aging in human subsistence populations, including with lifestyle shifts. It also includes understanding their role in the developmental origins of health and disease as well as the social determinants of health disparities. Further, we discuss the need for future studies on exceptionally long-lived species to understand potentially underappreciated trade-offs and costs that come with their longevity. We close with considering possible implications for therapeutics, including (1) compensatory pathways counteracting treatments, (2) a Goldilocks zone, in which suppressing anabolic metabolism too far introduces catabolic health risks, and (3) species constraints, in which therapeutics tested in shorter lived species with greater anabolic imbalance will be less effective in humans.
Software engineering remains male-dominated despite efforts to attract and retain women. Many leave the field due to limited opportunities, unfair treatment, and challenging workplace cultures. Examining university life and choices is important, as these formative experiences shape career aspirations and can help address the root causes of underrepresentation in the industry. The study aimed to deepen understanding of the motivations behind women's choice of a career in IT, their experiences in academic life, and how these experiences influence their career decisions, all within a Nordic context. We used a combination of surveys in the bachelor programmes in the IT faculty and interviews with only women from software engineering (SE) to provide a comprehensive view of population experiences and a closer exploration of the experiences of a smaller sample with a focus on SE. Our results showed that family and personal interest are among the main factors motivating women to choose an IT programme. Further, women perceive more challenges following their chosen career path than men. We proposed high-level actions to address gender-related challenges and disparities based on our findings.
Should you use LLMs to simulate opinions? Quality checks for early-stage deliberation
2025-04-11
Terrence Neumann, Maria De-Arteaga, Sina Fazelpour
The array of emergent capabilities of large language models (LLMs) has sparked interest in assessing their ability to simulate human opinions in a variety of contexts, potentially serving as surrogates for human subjects in opinion surveys. However, previous evaluations of this capability have depended heavily on costly, domain-specific human survey data, and mixed empirical results about LLM effectiveness create uncertainty for managers about whether investing in this technology is justified in early-stage research. To address these challenges, we introduce a series of quality checks to support early-stage deliberation about the viability of using LLMs for simulating human opinions. These checks emphasize logical constraints, model stability, and alignment with stakeholder expectations of model outputs, thereby reducing dependence on human-generated data in the initial stages of evaluation. We demonstrate the usefulness of the proposed quality control tests in the context of AI-assisted content moderation, an application that both advocates and critics of LLMs' capabilities to simulate human opinion see as a desirable potential use case. None of the tested models passed all quality control checks, revealing several failure modes. We conclude by discussing implications of these failure modes and recommend how organizations can utilize our proposed tests for prompt engineering and in their risk management practices when considering the use of LLMs for opinion simulation. We make our crowdsourced dataset of claims with human and LLM annotations publicly available for future research.
AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectories
2025-04-11
Xing Han Lù, Amirhossein Kazemnejad, Nicholas Meade, Arkil Patel, Dongchan Shin, Alejandra Zambrano, Karolina Stańczak, Peter Shaw, Christopher J. Pal, Siva Reddy
Web agents enable users to perform tasks on web browsers through natural language interaction. Evaluating web agents trajectories is an important problem, since it helps us determine whether the agent successfully completed the tasks. Rule-based methods are widely used for this purpose, but they are challenging to extend to new tasks and may not always recognize successful trajectories. We may achieve higher accuracy through human evaluation, but the process would be substantially slower and more expensive. Automatic evaluations with LLMs may avoid the challenges of designing new rules and manually annotating trajectories, enabling faster and cost-effective evaluation. However, it is unclear how effective they are at evaluating web agents. To this end, we propose AgentRewardBench, the first benchmark to assess the effectiveness of LLM judges for evaluating web agents. AgentRewardBench contains 1302 trajectories across 5 benchmarks and 4 LLMs. Each trajectory in AgentRewardBench is reviewed by an expert, who answers questions pertaining to the success, side effects, and repetitiveness of the agent. Using our benchmark, we evaluate 12 LLM judges and find that no single LLM excels across all benchmarks. We also find that the rule-based evaluation used by common benchmarks tends to underreport the success rate of web agents, highlighting a key weakness of rule-based evaluation and the need to develop more flexible automatic evaluations. We release the benchmark at: https://agent-reward-bench.github.io
AGN Feedback-Induced Stellar Density Expansion in the Inner Regions of Early-Type Galaxies
2025-04-11
Fazeel Mahmood Khan, Ángel Rodríguez, Andrea V. Macciò, Smarika Sharma
Observations indicate that early-type galaxies exhibit varying slopes in the relation between their central stellar surface density and stellar mass ($\Sigma_1 - M_{\star}$). Low-mass galaxies tend to follow a steep slope, close to one, while the slope flattens for high-mass early type galaxies. In our study, we investigate the $\Sigma_1 - M_{\star}$ scaling relation and its evolution using the NIHAO suite of cosmological simulations and compare our findings with recent results from the MaNGA survey. Our analysis shows that NIHAO galaxies successfully reproduce the observed scaling relation based on MANGA survey. Our analysis suggests that AGN feedback plays a critical role in flattening the $\Sigma_1$ slope by expelling gas from galactic centers, leading to a decrease in both stellar and dark matter density as the gravitational potential becomes shallower. To further support our findings, we conducted high-resolution N-body simulations, which confirmed that ({\it sudden}) gas removal does substantially alter the stellar density in the central region, consistent with results from NIHAO. Furthermore, our numerical experiments show that even if the same amount of gas is re-accreted on a typical ({\it longer}) free-fall time, it is not able to restore the original stellar density. Our study concludes that AGN feedback assisted gas removal presents a plausible explanation for the decline in central stellar surface density as observed in massive elliptical galaxies.
Measurement-induced phase transitions in quantum inference problems and quantum hidden Markov models
2025-04-11
Sun Woo P. Kim, Curt von Keyserlingk, Austen Lamacraft
Recently, there is interest in coincident 'sharpening' and 'learnability' transitions in monitored quantum systems. In the latter, an outside observer's ability to infer properties of a quantum system from measurements undergoes a phase transition. Such transitions appear to be related to the decodability transition in quantum error correction, but the precise connection is not clear. Here, we study these problems under one framework we call the general quantum inference problem. In cases as above where the system has a Markov structure, we say that the inference is on a quantum hidden Markov model. We show a formal connection to classical hidden Markov models and that they coincide for certain setups. For example, we prove this for those involving Haar-random unitaries and measurements. We introduce the notion of Bayes non-optimality, where parameters used for inference differs from true ones. This allows us to expand the phase diagrams of above models. At Bayes optimality, we obtain an explicit relation between 'sharpening' and 'learnability' order parameters, explicitly showing that the two transitions coincide. Next, we study concrete examples. We review quantum error correction on the toric and repetition code and their mapping to 2D random-bond Ising model (RBIM) through our framework. We study the Haar-random U(1)-symmetric monitored quantum circuit and tree, mapping each to inference models that we call the planted SSEP and planted XOR, respectively, and expanding the phase diagram to Bayes non-optimality. For the circuit, we deduce the phase boundary numerically and analytically argue that it is of a single universality class. For the tree, we present an exact solution of the entire phase boundary, which displays re-entrance as does the 2D RBIM. We discuss these phase diagrams, with their interpretations for quantum inference problems and rigorous arguments on their shapes.
ALMACAL XIV: X-Shooter Spectroscopy, Infrared Properties and Radio SEDs of Calibrators
2025-04-11
Simon Weng, Elaine M. Sadler, Emily Kerrison, Victoria Bollo, Céline Péroux, Martin Zwaan, Elizabeth K. Mahony, James R. Allison, Jianhang Chen, Roland Szakacs, Hyein Yoon
The ALMACAL-22 survey includes over 2700 hours of observations of ALMA phase and amplitude calibrators, spanning frequencies from 84 to 950 GHz across bands 3 to 10. In total, 687 out of the 1,047 calibrators have redshifts confirmed with spectroscopy and we find an additional 50 featureless blazars. The redshift distribution of the ALMACAL-22 sample peaks at $z \approx 1$ and spans a wide range, from the nuclei of nearby galaxies at $z \ll 0.01$ to quasars at $z = 3.742$. 70 new VLT/X-Shooter spectra of these sources covering UV to NIR wavelengths are also presented, which will be used in future stacking experiments to search for cold gas in the circumgalactic medium. Infrared magnitudes from WISE indicate that the majority of the sources are consistent with being quasars or blazars. After fitting the radio spectral energy distributions of the calibrators, we find that most ALMA calibrators exhibit peaked spectra or are re-triggered which is surprising given the large number of blazars in the sample. The peak frequencies span three orders of magnitude from 100 MHz to 170 GHz, corresponding to linear sizes ranging from sub-pc to $>$ 10 kpc. In the future, when combined with high-resolution radio imaging, these results will offer valuable constraints on the molecular gas content of the CGM, as well as the ages and duty cycles of AGN jets. The ever-growing ALMACAL data set will remain an indispensable resource for studying the various aspects of galaxy formation and evolution.
Distilling and exploiting quantitative insights from Large Language Models for enhanced Bayesian optimization of chemical reactions
2025-04-11
Roshan Patel, Saeed Moayedpour, Louis De Lescure, Lorenzo Kogler-Anele, Alan Cherney, Sven Jager, Yasser Jangjou
Machine learning and Bayesian optimization (BO) algorithms can significantly accelerate the optimization of chemical reactions. Transfer learning can bolster the effectiveness of BO algorithms in low-data regimes by leveraging pre-existing chemical information or data outside the direct optimization task (i.e., source data). Large language models (LLMs) have demonstrated that chemical information present in foundation training data can give them utility for processing chemical data. Furthermore, they can be augmented with and help synthesize potentially multiple modalities of source chemical data germane to the optimization task. In this work, we examine how chemical information from LLMs can be elicited and used for transfer learning to accelerate the BO of reaction conditions to maximize yield. Specifically, we show that a survey-like prompting scheme and preference learning can be used to infer a utility function which models prior chemical information embedded in LLMs over a chemical parameter space; we find that the utility function shows modest correlation to true experimental measurements (yield) over the parameter space despite operating in a zero-shot setting. Furthermore, we show that the utility function can be leveraged to focus BO efforts in promising regions of the parameter space, improving the yield of the initial BO query and enhancing optimization in 4 of the 6 datasets studied. Overall, we view this work as a step towards bridging the gap between the chemistry knowledge embedded in LLMs and the capabilities of principled BO methods to accelerate reaction optimization.
Hardware Design and Security Needs Attention: From Survey to Path Forward
2025-04-11
Sujan Ghimire, Muhtasim Alam Chowdhury, Banafsheh Saber Latibari, Muntasir Mamun, Jaeden Wolf Carpenter, Benjamin Tan, Hammond Pearce, Pratik Satam, Soheil Salehi
Recent advances in attention-based artificial intelligence (AI) models have unlocked vast potential to automate digital hardware design while enhancing and strengthening security measures against various threats. This rapidly emerging field leverages Large Language Models (LLMs) to generate HDL code, identify vulnerabilities, and sometimes mitigate them. The state of the art in this design automation space utilizes optimized LLMs with HDL datasets, creating automated systems for register-transfer level (RTL) generation, verification, and debugging, and establishing LLM-driven design environments for streamlined logic designs. Additionally, attention-based models like graph attention have shown promise in chip design applications, including floorplanning. This survey investigates the integration of these models into hardware-related domains, emphasizing logic design and hardware security, with or without the use of IP libraries. This study explores the commercial and academic landscape, highlighting technical hurdles and future prospects for automating hardware design and security. Moreover, it provides new insights into the study of LLM-driven design systems, advances in hardware security mechanisms, and the impact of influential works on industry practices. Through the examination of 30 representative approaches and illustrative case studies, this paper underscores the transformative potential of attention-based models in revolutionizing hardware design while addressing the challenges that lie ahead in this interdisciplinary domain.
ML For Hardware Design Interpretability: Challenges and Opportunities
2025-04-11
Raymond Baartmans, Andrew Ensinger, Victor Agostinelli, Lizhong Chen
The increasing size and complexity of machine learning (ML) models have driven the growing need for custom hardware accelerators capable of efficiently supporting ML workloads. However, the design of such accelerators remains a time-consuming process, heavily relying on engineers to manually ensure design interpretability through clear documentation and effective communication. Recent advances in large language models (LLMs) offer a promising opportunity to automate these design interpretability tasks, particularly the generation of natural language descriptions for register-transfer level (RTL) code, what we refer to as "RTL-to-NL tasks." In this paper, we examine how design interpretability, particularly in RTL-to-NL tasks, influences the efficiency of the hardware design process. We review existing work adapting LLMs for these tasks, highlight key challenges that remain unaddressed, including those related to data, computation, and model development, and identify opportunities to address them. By doing so, we aim to guide future research in leveraging ML to automate RTL-to-NL tasks and improve hardware design interpretability, thereby accelerating the hardware design process and meeting the increasing demand for custom hardware accelerators in machine learning and beyond.
AI-University: An LLM-based platform for instructional alignment to scientific classrooms
2025-04-11
Mostafa Faghih Shojaei, Rahul Gulati, Benjamin A. Jasperson, Shangshang Wang, Simone Cimolato, Dangli Cao, Willie Neiswanger, Krishna Garikipati
We introduce AI University (AI-U), a flexible framework for AI-driven course content delivery that adapts to instructors' teaching styles. At its core, AI-U fine-tunes a large language model (LLM) with retrieval-augmented generation (RAG) to generate instructor-aligned responses from lecture videos, notes, and textbooks. Using a graduate-level finite-element-method (FEM) course as a case study, we present a scalable pipeline to systematically construct training data, fine-tune an open-source LLM with Low-Rank Adaptation (LoRA), and optimize its responses through RAG-based synthesis. Our evaluation - combining cosine similarity, LLM-based assessment, and expert review - demonstrates strong alignment with course materials. We also have developed a prototype web application, available at https://my-ai-university.com, that enhances traceability by linking AI-generated responses to specific sections of the relevant course material and time-stamped instances of the open-access video lectures. Our expert model is found to have greater cosine similarity with a reference on 86% of test cases. An LLM judge also found our expert model to outperform the base Llama 3.2 model approximately four times out of five. AI-U offers a scalable approach to AI-assisted education, paving the way for broader adoption in higher education. Here, our framework has been presented in the setting of a class on FEM - a subject that is central to training PhD and Master students in engineering science. However, this setting is a particular instance of a broader context: fine-tuning LLMs to research content in science.
2025-04-11
Goureesankar Sathyanathan, Calvin Leung, Olaf Wucknitz, Prasenjit Saha
Context: Fast Radio Bursts (FRBs) are enigmatic extragalactic bursts whose properties are still largely unknown, but based on their extremely small time duration, they are proposed to have a compact structure, making them candidates for wave-optical effects if gravitational lensed. If an FRB is lensed into multiple-images bursts at different times by a galaxy or cluster, a likely scenario is that only one image is detected, because the others fall outside the survey area and time frame. Aims: In this work we explore the FRB analog of quasar microlensing, namely the collective microlensing by stars in the lensing galaxy, now with wave optics included. The eikonal regime is applicable here. Methods. We study the voltage (rather than the intensity) in a simple simulation consisting of (a) microlensing stars, and (b) plasma scattering by a turbulent interstellar medium. Results: The auto-correlation of the voltage shows peaks (at order-microsecond separations) corresponding to wave-optical interference between lensed micro-images. The peaks are frequency dependent if plasma-scattering is significant. While qualitative and still in need of more realistic simulations, the results suggest that a strongly-lensed FRB could be identified from a single image. Conclusions: Microlensing could sniff out macro-lensed FRBs
Dislocation Patterning as a Mechanism for Flat Band Formation
2025-04-10
Aziz Fall, Kaushik Dayal
We compute the second-order correction to the electronic dispersion relation of a free electron gas interacting with an effective electron-dislocation potential, derived from a modern quantized theory of dislocations. Our results demonstrate that dislocation patterning induces anisotropic flat bands in the electronic dispersion under specific strain fields and directions, referred to as ``magic'' parameters. These flat bands acquire non-zero curvature as the strain or direction deviates from these magic parameters.
Molecular excited state in the interaction quench dynamics of two different atoms in a two-dimensional anisotropic trap
2025-04-10
I. S. Ishmukhamedov, A. S. Ishmukhamedov, Zh. E. Jalankuzov, D. V. Ismailov
We explore the interaction quench dynamics of two atoms with different masses and subject to different trapping potentials. Notably, under such anisotropic conditions, the nonequilibrium dynamics can lead to the occupation of molecular excited states. We consider cases of quenching from attractive to repulsive interaction and vice versa, analyzing the impact of the pre- and postquench states. The analysis of overlap integrals for the both states reveals a significant contribution from the molecular excited state. Moreover, the overlap with the prequench states might serve as an indicator of when this excited state may emerge. Additionally, we calculate the energy spectrum for the lowest levels in the both isotropic and anisotropic harmonic traps. Throughout our study, we use a Gaussian-shaped finite-range interaction potential.
This study focuses on mixed-variable black-box optimization (MV-BBO), addressing continuous, integer, and categorical variables. Many real-world MV-BBO problems involve dependencies among these different types of variables, requiring efficient methods to optimize them simultaneously. Recently, stochastic optimization methods leveraging the mechanism of the covariance matrix adaptation evolution strategy have shown promising results in mixed-integer or mixed-category optimization. However, such methods cannot handle the three types of variables simultaneously. In this study, we propose CatCMA with Margin (CatCMAwM), a stochastic optimization method for MV-BBO that jointly optimizes continuous, integer, and categorical variables. CatCMAwM is developed by incorporating a novel integer handling into CatCMA, a mixed-category black-box optimization method employing a joint distribution of multivariate Gaussian and categorical distributions. The proposed integer handling is carefully designed by reviewing existing integer handlings and following the design principles of CatCMA. Even when applied to mixed-integer problems, it stabilizes the marginal probability and improves the convergence performance of continuous variables. Numerical experiments show that CatCMAwM effectively handles the three types of variables, outperforming state-of-the-art Bayesian optimization methods and baselines that simply incorporate existing integer handlings into CatCMA.
Token Level Routing Inference System for Edge Devices
2025-04-10
Jianshu She, Wenhao Zheng, Zhengzhong Liu, Hongyi Wang, Eric Xing, Huaxiu Yao, Qirong Ho
The computational complexity of large language model (LLM) inference significantly constrains their deployment efficiency on edge devices. In contrast, small language models offer faster decoding and lower resource consumption but often suffer from degraded response quality and heightened susceptibility to hallucinations. To address this trade-off, collaborative decoding, in which a large model assists in generating critical tokens, has emerged as a promising solution. This paradigm leverages the strengths of both model types by enabling high-quality inference through selective intervention of the large model, while maintaining the speed and efficiency of the smaller model. In this work, we present a novel collaborative decoding inference system that allows small models to perform on-device inference while selectively consulting a cloud-based large model for critical token generation. Remarkably, the system achieves a 60% performance gain on CommonsenseQA using only a 0.5B model on an M1 MacBook, with under 7% of tokens generation uploaded to the large model in the cloud.
Conversion-Driven Freeze-Out: A Common Framework for Dark Matter and Baryogenesis
2025-04-10
Jan Heisig
We explore dark matter genesis beyond the WIMP paradigm, focusing on the mechanism of conversion-driven freeze-out. This mechanism enables the thermalization of dark matter despite its very weak couplings. While the scenario evades conventional WIMP searches, it predicts novel signatures of long-lived particles at colliders, making it a prime target for upcoming LHC searches. We review various model realizations of this mechanism, highlighting its deep connections to other unresolved problems of the Standard Model. In particular, we show how conversion-driven freeze-out can naturally give rise to baryogenesis, offering a compelling perspective on the origins of both dark and baryonic matter.
$B$ processes are a rich source of potential anomalies that could lead to the discovery of BSM physics. The long-standing tension between the inclusive and the exclusive determinations of the CKM matrix elements $|V_{xb}|$, or the current tensions in the $R(D)$-$R(D^\ast)$ plane are some examples of active areas of research where we might find signals of new physics. Heavy-to-heavy $B$ semileptonic decays, $B_{(s)}\to D^{(\ast)}_{(s)}\ell\nu$, and in particular, decays with a vector product ($D^\ast_{(s)}$) are especially interesting from an experimental point of view, but experiment and theory must walk together in order to reach conclusions in the intensity frontier. In this review I talk about the current status of the lattice-QCD calculations of the $B\to D^{\ast}\ell\nu$ form factors at non-zero recoil, I discuss the implications they have for the determination of $B$ anomalies, and finally I give some hints of what we can expect from future calculations.
Intrusion Detection Systems (IDS) have long been a hot topic in the cybersecurity community. In recent years, with the introduction of deep learning (DL) techniques, IDS have made great progress due to their increasing generalizability. The rationale behind this is that by learning the underlying patterns of known system behaviors, IDS detection can be generalized to intrusions that exploit zero-day vulnerabilities. In this survey, we refer to this type of IDS as DL-based IDS (DL-IDS). From the perspective of DL, this survey systematically reviews all the stages of DL-IDS, including data collection, log storage, log parsing, graph summarization, attack detection, and attack investigation. To accommodate current researchers, a section describing the publicly available benchmark datasets is included. This survey further discusses current challenges and potential future research directions, aiming to help researchers understand the basic ideas and visions of DL-IDS research, as well as to motivate their research interests.
A Review of HPC-Accelerated CFD in National Security and Defense
2025-04-10
James Afful
Using High-Performance Computing (HPC), Computational Fluid Dynamics (CFD) now serves as an essential component in defense-related national security applications including missile interception and hypersonic propulsion as well as naval stealth optimization and urban hazard dispersion. This review combines two decades of open-source and public-domain research on HPC-accelerated CFD in defense, addressing three key questions: Which security-sensitive simulations have utilized open-source CFD frameworks such as OpenFOAM, SU2 and ADflow? Which HPC techniques, such as MPI domain decomposition and GPU acceleration together with hybrid parallelism best enhance open-source frameworks to manage large defense CFD simulations? Which technological advancements and research voids currently drive the directional development of the field? Examining several research studies sourced from NASA, DoD HPC centers, and academic institutions, scientific contributions have been classified into air, maritime, and space domains. Modular frameworks like NavyFOAM and SU2 and ADflow's adjoint-based solvers show how custom open-source solutions support workflows with rapid completion of multi-million cell simulations. The conclusion highlights new trends that combine exascale readiness with machine learning surrogate models for real-time CFD applications and interdisciplinary HPC-driven multi-physics integration to deliver practical insights for improving CFD use in defense research and development.
FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awareness
2025-04-10
Chandan Kumar Sah, Xiaoli Lian, Tony Xu, Li Zhang
Recent advances in Large Language Models (LLMs) have enabled their application to recommender systems (RecLLMs), yet concerns remain regarding fairness across demographic and psychological user dimensions. We introduce FairEval, a novel evaluation framework to systematically assess fairness in LLM-based recommendations. FairEval integrates personality traits with eight sensitive demographic attributes,including gender, race, and age, enabling a comprehensive assessment of user-level bias. We evaluate models, including ChatGPT 4o and Gemini 1.5 Flash, on music and movie recommendations. FairEval's fairness metric, PAFS, achieves scores up to 0.9969 for ChatGPT 4o and 0.9997 for Gemini 1.5 Flash, with disparities reaching 34.79 percent. These results highlight the importance of robustness in prompt sensitivity and support more inclusive recommendation systems.
Radiative $α$ capture on $^{12}$C in cluster effective field theory: short review
2025-04-10
Shung-Ichi Ando
Study of radiative $\alpha$ capture on $^{12}$C, $^{12}$C($\alpha$,$\gamma$)$^{16}$O, in cluster effective field theory (EFT) is reviewed. A low energy EFT for $^{12}$C($\alpha$,$\gamma$)$^{16}$O at the Gamow-peak energy, $E_G=0.3$~MeV, is constructed, and the theory is first applied to the study of elastic $\alpha$-$^{12}$C scattering at low energies. The effective range parameters are fitted to the precise phase shift data of the elastic scattering and the astrophysical $S_{E1}$ factor of the $E1$ transition of $^{12}$C($\alpha$,$\gamma$)$^{16}$O at $E_G$ is estimated. For the study of the $E2$ transition of $^{12}$C($\alpha$,$\gamma$)$^{16}$O, we discuss a difficulty to determine the asymptotic normalization coefficient (ANC) of the subthreshold $2_1^+$ state of $^{16}$O from the elastic scattering data, and demonstrate the difficulty with the estimate of the astrophysical $S_{E2}$ factor of $^{12}$C($\alpha$,$\gamma$)$^{16}$O at $E_G$. We discuss the uncertainty in the estimate of the $S$ factors at $E_G$ in the present approach.
PRAD: Periapical Radiograph Analysis Dataset and Benchmark Model Development
2025-04-10
Zhenhuan Zhou, Yuchen Zhang, Ruihong Xu, Xuansen Zhao, Tao Li
Deep learning (DL), a pivotal technology in artificial intelligence, has recently gained substantial traction in the domain of dental auxiliary diagnosis. However, its application has predominantly been confined to imaging modalities such as panoramic radiographs and Cone Beam Computed Tomography, with limited focus on auxiliary analysis specifically targeting Periapical Radiographs (PR). PR are the most extensively utilized imaging modality in endodontics and periodontics due to their capability to capture detailed local lesions at a low cost. Nevertheless, challenges such as resolution limitations and artifacts complicate the annotation and recognition of PR, leading to a scarcity of publicly available, large-scale, high-quality PR analysis datasets. This scarcity has somewhat impeded the advancement of DL applications in PR analysis. In this paper, we present PRAD-10K, a dataset for PR analysis. PRAD-10K comprises 10,000 clinical periapical radiograph images, with pixel-level annotations provided by professional dentists for nine distinct anatomical structures, lesions, and artificial restorations or medical devices, We also include classification labels for images with typical conditions or lesions. Furthermore, we introduce a DL network named PRNet to establish benchmarks for PR segmentation tasks. Experimental results demonstrate that PRNet surpasses previous state-of-the-art medical image segmentation models on the PRAD-10K dataset. The codes and dataset will be made publicly available.
2025-04-10
Junyuan Gao, Shuao Chen, Yongpeng Wu, Liang Liu, Giuseppe Caire, H. Vincent Poor, Wenjun Zhang
Traditional asymptotic information-theoretic studies of the fundamental limits of wireless communication systems primarily rely on some ideal assumptions, such as infinite blocklength and vanishing error probability. While these assumptions enable tractable mathematical characterizations, they fail to capture the stringent requirements of some emerging next-generation wireless applications, such as ultra-reliable low latency communication and ultra-massive machine type communication, in which it is required to support a much wider range of features including short-packet communication, extremely low latency, and/or low energy consumption. To better support such applications, it is important to consider finite-blocklength information theory. In this paper, we present a comprehensive review of the advances in this field, followed by a discussion on the open questions. Specifically, we commence with the fundamental limits of source coding in the non-asymptotic regime, with a particular focus on lossless and lossy compression in point-to-point~(P2P) and multiterminal cases. Next, we discuss the fundamental limits of channel coding in P2P channels, multiple access channels, and emerging massive access channels. We further introduce recent advances in joint source and channel coding, highlighting its considerable performance advantage over separate source and channel coding in the non-asymptotic regime. In each part, we review various non-asymptotic achievability bounds, converse bounds, and approximations, as well as key ideas behind them, which are essential for providing engineering insights into the design of future wireless communication systems.
On-Chip and Off-Chip TIA Amplifiers for Nanopore Signal Readout Design, Performance and Challenges: A Review
2025-04-10
K. Ashoka Deepthi, Manoj Varma, Arup Polley
Advancements in biomedical research have driven continuous innovations in sensing and diagnostic technologies. Among these, nanopore based single molecule sensing and sequencing is rapidly emerging as a powerful and versatile sensing methodology. Advancements in nanopore based approaches require concomitant improvements in the electronic readout methods employed, from the point of low noise, bandwidth and form factor. This article focuses on current sensing circuits designed and employed for ultra low noise nanopore signal readout, addressing the fundamental limitations of traditional off chip transimpedance amplifiers (TIAs), which suffer from high input parasitic capacitance, bandwidth constraints, and increased noise at high frequencies. This review explores the latest design schemes and circuit structures classified into on-chip and off-chip TIA designs, highlighting their design implementation, performance, respective challenges and explores the interplay between noise performance, capacitance, and bandwidth across diverse transimpedance amplifier (TIA) configurations. Emphasis is placed on characterizing noise response under varying parasitic capacitance and operational frequencies, a systematic evaluation not extensively addressed in prior literature while also considering the allowable input current compliance range limitations. The review also compares the widely used Axopatch 200B system to the designs reported in literature. The findings offer valuable insights into optimizing TIA designs for enhanced signal integrity in high speed and high sensitivity applications focusing on noise reduction, impedance matching, DC blocking, and offset cancellation techniques.
Merging Embedded Topics with Optimal Transport for Online Topic Modeling on Data Streams
2025-04-10
Federica Granese, Benjamin Navet, Serena Villata, Charles Bouveyron
Topic modeling is a key component in unsupervised learning, employed to identify topics within a corpus of textual data. The rapid growth of social media generates an ever-growing volume of textual data daily, making online topic modeling methods essential for managing these data streams that continuously arrive over time. This paper introduces a novel approach to online topic modeling named StreamETM. This approach builds on the Embedded Topic Model (ETM) to handle data streams by merging models learned on consecutive partial document batches using unbalanced optimal transport. Additionally, an online change point detection algorithm is employed to identify shifts in topics over time, enabling the identification of significant changes in the dynamics of text streams. Numerical experiments on simulated and real-world data show StreamETM outperforming competitors.
2025-04-10
Zeev Yampolsky, Nadav Cohen, Itzik Klein
Autonomous underwater vehicles (AUV) have a wide variety of applications in the marine domain, including exploration, surveying, and mapping. Their navigation systems rely heavily on fusing data from inertial sensors and a Doppler velocity log (DVL), typically via nonlinear filtering. The DVL estimates the AUV's velocity vector by transmitting acoustic beams to the seabed and analyzing the Doppler shift from the reflected signals. However, due to environmental challenges, DVL beams can deflect or fail in real-world settings, causing signal outages. In such cases, the AUV relies solely on inertial data, leading to accumulated navigation errors and mission terminations. To cope with these outages, we adopted ST-BeamsNet, a deep learning approach that uses inertial readings and prior DVL data to estimate AUV velocity during isolated outages. In this work, we extend ST-BeamsNet to address prolonged DVL outages and evaluate its impact within an extended Kalman filter framework. Experiments demonstrate that the proposed framework improves velocity RMSE by up to 63% and reduces final position error by up to 95% compared to pure inertial navigation. This is in scenarios involving up to 50 seconds of complete DVL outage.
Improving Photometric Redshift Estimation for CSST Mock Catalog Using SED Templates Calibrated with Perturbation Algorithm
2025-04-10
Yicheng Li, Liping Fu, Zhu Chen, Zhijian Luo, Wei Du, Yan Gong, Xianmin Meng, Junhao Lu, Zhirui Tang, Pengfei Chen, Shaohua Zhang, Chenggang Shu, Xingchen Zhou, Zuhui Fan
Photometric redshifts of galaxies obtained by multi-wavelength data are widely used in photometric surveys because of its high efficiency. Although various methods have been developed, template fitting is still adopted as one of the most popular approaches. Its accuracy strongly depends on the quality of the Spectral Energy Distribution (SED) templates, which can be calibrated using broadband photometric data from galaxies with known spectroscopic redshifts. Such calibration is expected to improve photometric redshift accuracy, as the calibrated templates will align with observed photometric data more closely. The upcoming China Space Station Survey Telescope (CSST) is one of the Stage IV surveys, which aiming for high precision cosmological studies. To improve the accuracy of photometric redshift estimation for CSST, we calibrated the CWW+KIN templates using a perturbation algorithm with broadband photometric data from the CSST mock catalog. This calibration used a training set consisting of approximately 4,500 galaxies, which is 10% of the total galaxy sample. The outlier fraction and scatter of the photometric redshifts derived from the calibrated templates are 2.55% and 0.036, respectively. Compared to the CWW+KIN templates, these values are reduced by 34% and 23%, respectively. This demonstrates that SED templates calibrated with a small training set can effectively optimize photometric redshift accuracy for future large-scale surveys like CSST, especially with limited spectral training data.
Clicks, comments, consequences: Are content creators' socio-structural and platform characteristics shaping the exposure to negative sentiment, offensive language, and hate speech on YouTube?
2025-04-10
Sarah Weißmann, Aaron Philipp, Roland Verwiebe, Chiara Osorio Krauter, Nina-Sophie Fritsch, Claudia Buder
Receiving negative sentiment, offensive comments, or even hate speech is a constant part of the working experience of content creators (CCs) on YouTube - a growing occupational group in the platform economy. This study investigates how socio-structural characteristics such as the age, gender, and race of CCs but also platform features including the number of subscribers, community strength, and the channel topic shape differences in the occurrence of these phenomena on that platform. Drawing on a random sample of n=3,695 YouTube channels from German-speaking countries, we conduct a comprehensive analysis combining digital trace data, enhanced with hand-coded variables to include socio-structural characteristics in social media data. Publicly visible negative sentiment, offensive language, and hate speech are detected with machine- and deep-learning methods using N=40,000,000 comments. Contrary to existing studies our findings indicate that female content creators are confronted with less negative communication. Notably, our analysis reveals that while BIPoC, who work as CCs, receive significantly more negative sentiment, they aren't exposed to more offensive comments or hate speech. Additionally, platform characteristics also play a crucial role, as channels publishing content on conspiracy theories or politics are more frequently subject to negative communication.
Low-Complexity Optimization of Antenna Switching Schemes for Dynamic Channel Sounding
2025-04-10
Juan Sanchez, Xuesong Cai, Ali Al-Ameri, Fredrik Tufvesson
Understanding wireless channels is crucial for the design of wireless systems. For mobile communication, sounders and antenna arrays with short measurement times are required to simultaneously capture the dynamic and spatial channel characteristics. Switched antenna arrays are an attractive option that can overcome the high cost of real arrays and the long measurement times of virtual arrays. Optimization of the switching sequences is then essential to avoid aliasing and increase the accuracy of channel parameter estimates. This paper provides a novel and comprehensive analysis of the design of switching sequences. We first review the conventional spatio-temporal ambiguity function, extend it to dual-polarized antenna arrays, and analyze its prohibitive complexity when designing for ultra-massive antenna arrays. We thus propose a new method that uses the Fisher information matrix to tackle the estimation accuracy. We also propose to minimize the ambiguity by choosing a switching sequence that minimizes side lobes in its Fourier spectrum. In this sense, we divide the sequence design problem into Fourier-based ambiguity reduction and Fisher-based accuracy improvement, and coin the resulting design approach as Fourier-Fisher. Simulations and measurements show that the Fourier-Fisher approach achieves identical performance and significantly lower computational complexity than that of the conventional ambiguity-based approach.
Machine Learning (ML) has been integrated into various software and systems. Two main components are essential for training an ML model: the training data and the ML algorithm. Given the critical role of data in ML system development, it has become increasingly important to assess the quality of data attributes and ensure that the data meets specific requirements before its utilization. This work proposes an approach to guide non-experts in identifying data requirements for ML systems using goal modeling. In this approach, we first develop the Data Requirement Goal Model (DRGM) by surveying the white literature to identify and categorize the issues and challenges faced by data scientists and requirement engineers working on ML-related projects. An initial DRGM was built to accommodate common tasks that would generalize across projects. Then, based on insights from both white and gray literature, a customization mechanism is built to help adjust the tasks, KPIs, and goals' importance of different elements within the DRGM. The generated model can aid its users in evaluating different datasets using GRL evaluation strategies. We then validate the approach through two illustrative examples based on real-world projects. The results from the illustrative examples demonstrate that the data requirements identified by the proposed approach align with the requirements of real-world projects, demonstrating the practicality and effectiveness of the proposed framework. The proposed dataset selection customization mechanism and the proposed DRGM are helpful in guiding non-experts in identifying the data requirements for machine learning systems tailored to a specific ML problem. This approach also aids in evaluating different dataset alternatives to choose the optimum dataset for the problem. For future work, we recommend implementing tool support to generate the DRGM based on a chatbot interface.
Prediction of Usage Probabilities of Shopping-Mall Corridors Using Heterogeneous Graph Neural Networks
2025-04-10
Malik M Barakathullah, Immanuel Koh
We present a method based on graph neural network (GNN) for prediction of probabilities of usage of shopping-mall corridors. The heterogeneous graph network of shops and corridor paths are obtained from floorplans of the malls by creating vector layers for corridors, shops and entrances. These are subsequently assimilated into nodes and edges of graphs. The prediction of the usage probability is based on the shop features, namely, the area and usage categories they fall into, and on the graph connecting these shops, corridor junctions and entrances by corridor paths. Though the presented method is applicable for training on datasets obtained from a field survey or from pedestrian-detecting sensors, the target data of the supervised deep-learning work flow in this work are obtained from a probability method. We also include a context-specific representation learning of latent features. The usage-probability prediction is made on each edge, which is a connection by a section of corridor path between the adjacent nodes representing the shops or corridor points. To create a feature for each edge, the hidden-layer feature vectors acquired in the message-passing GNN layers at the nodes of each edge are averaged and concatenated with the vector obtained by their multiplication. These edge-features are then passed to multilayer perceptrons (MLP) to make the final prediction of usage probability on each edge. The samples of synthetic learning dataset for each shopping mall are obtained by changing the shops' usage and area categories, and by subsequently feeding the graph into the probability model. When including different shopping malls in a single dataset, we also propose to consider graph-level features to inform the model with specific identifying features of each mall.
The integration of Generative Artificial Intelligence (GenAI) within the Internet of Things (IoT) is garnering considerable interest. This growing attention stems from the continuous evolution and widespread adoption they are both having individually, enough to spontaneously reshape numerous sectors, including Healthcare, Manufacturing, and Smart Cities. Hence, their increasing popularity has catalyzed further extensive research for understanding the potential of the duo GenAI-IoT, how they interplay, and to which extent their synergy can innovate the state-of-the-art in their individual scenarios. However, despite the increasing prominence of GenAI for IoT Computing, much of the existing research remains focused on specific, narrowly scoped applications. This fragmented approach highlights the need for a more comprehensive analysis of the potential, challenges, and implications of GenAI integration within the broader IoT ecosystem. This survey exactly aims to address this gap by providing a holistic overview of the opportunities, issues, and considerations arising from the convergence of these mainstream paradigms. Our contribution is realized through a systematic literature review following the PRISMA methodology. A comparison framework is presented, and well-defined research questions are outlined to comprehensively explore the past, present, and future directions of GenAI integration with IoT Computing, offering valuable insights for both experts and newcomers.
There has been much recent interest in the necessity of an observer degree of freedom in the description of local algebras in semiclassical gravity. In this work, we describe an example where the observer can be constructed intrinsically from the quantum fields. This construction involves the slow-roll inflation example recently analyzed by Chen and Penington, in which the gauge-invariant gravitational algebra arises from marginalizing over modular flow in a de Sitter static patch. We relate this procedure to the Connes-Takesaki theory of the flow of weights for type III von Neumann algebras, and further show that the resulting gravitational algebra can naturally be presented as a crossed product. This leads to a decomposition of the gravitational algebra into quantum field and observer degrees of freedom, with different choices of observer being related to changes in a quantum reference frame for the algebra. We also connect this example to other constructions of type II algebras in semiclassical gravity, and argue they all share the feature of being the result of gauging modular flow. The arguments in this work involve various properties of automorphism groups of hyperfinite factors, and so in an appendix we review the structure of these groups, which may be of independent interest for further investigations into von Neumann algebras in quantum gravity.
A new quasar strongly-lensed candidate by the galaxy cluster WHJ0400-27 with a $18''$ image-separation
2025-04-10
L. Bazzanini, G. Angora, M. Scialpi, G. Di Rosa, P. Bergamini, P. Rosati, M. Lombardi, D. Abriola, A. Acebron, M. D'Addona, G. Granata, C. Grillo, F. Mannucci, M. Maturi, M. Meneghetti, A. Mercurio, M. Radovich
Time-delay cosmography (TDC) using multiply-lensed quasars (QSOs) by galaxies has recently emerged as an independent and competitive tool to measure the value of the Hubble constant. Lens galaxy clusters hosting multiply-imaged QSOs, when coupled with an accurate and precise knowledge of their total mass distribution, are equally powerful cosmological probes. However, less than ten such systems have been identified to date. Our study aims to expand the limited sample of cluster-lensed QSO systems by identifying new candidates within rich galaxy clusters. Starting from a sample of ~$10^5$ galaxy cluster candidates (Wen & Han, 2022), built from Dark Energy Survey and Wide-field Infrared Survey Explorer imaging data, and a highly-pure catalogue of over one million QSOs, based on Gaia DR3 data, we cross-correlate them to identify candidate lensed QSOs near the core of massive galaxy clusters. Our search yielded 3 lensed double candidates over an area of ~$5000$ sq. degree. In this work, we focus on the best candidate consisting of a double QSO with Gaia-based redshift of 1.35, projected behind a moderately rich cluster (WHJ0400-27) at $z_{phot}=0.65$. Based on a first spectroscopic follow-up study, we confirm the two QSOs at $z=1.345$, with indistinguishable spectra, and a brightest cluster galaxy at $z=0.626$. These observations seem to support the strong lensing nature of this system, although some tension emerges when the cluster mass from a preliminary lens model is compared with that from other mass proxies. We also discuss the possibility that such system is a rare physical association of two distinct QSOs with a projected physical distance of ~$150$ kpc. If further spectroscopic observations confirm its lensing nature, such a rare lens system would exhibit one of the largest image separations observed to date ($\Delta\vartheta=17.8''$), opening interesting TDC applications.
The halo mass function (HMF) is fundamental for interpreting the number counts of galaxy clusters, serving as a pivotal theoretical tool in cosmology. With the advent of high-precision surveys such as LSST, eROSITA, DESI, and Euclid, accurate HMF modeling becomes indispensable to avoid systematic biases in cosmological parameter estimation from cluster cosmology. Moreover, these surveys aim to shed light on the dark sector and uncover dark energy's puzzling nature, necessitating models that faithfully capture its features to ensure robust parameter inference. We aim to construct a model for the HMF in dynamical dark energy cosmologies that preserves the accuracy achieved for the standard $\Lambda (\nu)$CDM model of cosmology, while meeting the precision requirements necessary for future cosmological surveys. Our approach models the HMF parameters as functions of the deceleration parameter at the turnaround, a quantity shown to encapsulate essential information regarding the impact of dynamical dark energy on structure formation. We calibrate the model using results from a comprehensive suite of $N$-body simulations spanning various cosmological scenarios, ensuring sub-percent systematic accuracy. We present an HMF model tailored for dynamical dark energy cosmologies. The model is calibrated following a Bayesian approach, and its uncertainty is characterized by a single parameter controlling its systematic error, which remains at the sub-percent level. This ensures that theoretical uncertainties from our model are subdominant relative to other error sources in future cluster number counts analyses.
In this work, we calculate the charmonium spectrum and strong decay widths of cc states. The calculations are performed using the quark potential model with which incorporates the relativistic effects. The resulting cc spectrum exhibits a good agreement with experimental data. The open flavor strong decay widths are calculated employing the 3P0 model. We adopt two choices of wave functions to determine the observables: the first involves the utilization of realistic wave functions obtained by solving relativistic Schrodinger equation, while the second employs Simple harmonic oscillator (SHO) wave functions whose parameters are fitted to the realistic wave functions. We find that the decay widths of higher charmonia states above the threshold value of open charm mesons are well described with these wave functions. We provide a comprehensive analysis of the strong decay widths and assigned specific charmonium states: X(3940) is assigned to the {\eta}c(3S) state, Y (4660) to the {\psi}(5S) state, X(3915) to the \c{hi}0(2P) state, Zc(3900) to the hc(2P) state, X(4350) to the \c{hi}2(3P) state, and X(3842) to the {\psi}3(1D) state. We also compare our results with available experimental data and theoretical predictions of other models.
High-Level Synthesis using SDF-AP, Template Haskell, QuasiQuotes, and GADTs to Generate Circuits from Hierarchical Input Specification
2025-04-10
Hendrik Folmer
FPGAs provide highly parallel and customizable hardware solutions but are traditionally programmed using low-level Hardware Description Languages (HDLs) like VHDL and Verilog. These languages have a low level of abstraction and require engineers to manage control and scheduling manually. High-Level Synthesis (HLS) tools attempt to lift this level of abstraction by translating C/C++ code into hardware descriptions, but their reliance on imperative paradigms leads to challenges in deriving parallelism due to pointer aliasing and sequential execution models. Functional programming, with its inherent purity, immutability, and parallelism, presents a more natural abstraction for FPGA design. Existing functional hardware description tools such as Clash enable high-level circuit descriptions but lack automated scheduling and control mechanisms. Prior work by Folmer introduced a framework integrating SDF-AP graphs into Haskell for automatic hardware generation, but it lacked hierarchy and reusability. This paper extends that framework by introducing hierarchical pattern specification, enabling structured composition and scalable parallelism. Key contributions include: (1) automatic hardware generation, where both data and control paths are derived from functional specifications with hierarchical patterns, (2) parameterized buffers using GADTs, eliminating the need for manual buffer definitions and facilitating component reuse, and (3) provision of a reference "golden model" that can be simulated in the integrated environment for validation. The core focus of this paper is on methodology. But we also evaluate our approach against Vitis HLS, comparing both notation and resulting hardware architectures. Experimental results demonstrate that our method provides greater transparency in resource utilization and scheduling, often outperforming Vitis in both scheduling and predictability.
Extending Visual Dynamics for Video-to-Music Generation
2025-04-10
Xiaohao Liu, Teng Tu, Yunshan Ma, Tat-Seng Chua
Music profoundly enhances video production by improving quality, engagement, and emotional resonance, sparking growing interest in video-to-music generation. Despite recent advances, existing approaches remain limited in specific scenarios or undervalue the visual dynamics. To address these limitations, we focus on tackling the complexity of dynamics and resolving temporal misalignment between video and music representations. To this end, we propose DyViM, a novel framework to enhance dynamics modeling for video-to-music generation. Specifically, we extract frame-wise dynamics features via a simplified motion encoder inherited from optical flow methods, followed by a self-attention module for aggregation within frames. These dynamic features are then incorporated to extend existing music tokens for temporal alignment. Additionally, high-level semantics are conveyed through a cross-attention mechanism, and an annealing tuning strategy benefits to fine-tune well-trained music decoders efficiently, therefore facilitating seamless adaptation. Extensive experiments demonstrate DyViM's superiority over state-of-the-art (SOTA) methods.
2025-04-10
Alessandra Somma, Domenico Amalfitano, Alessandra De Benedictis, Patrizio Pelliccione
Background. Digital Twins (DTs) are dynamic virtual representations of physical systems, enabled by seamless, bidirectional communication between the physical and digital realms. Among the challenges impeding the widespread adoption of DTs is the absence of a universally accepted definition and a standardized DT Reference Architecture (RA). Existing state-of-the-art architectures remain largely domain-specific, primarily emphasizing aspects like modeling and simulation. Furthermore, they often combine structural and dynamic elements into unified, all-in-one diagrams, which adds to the ambiguity and confusion surrounding the concept of Digital Twins. Objective. To address these challenges, this work aims to contribute a domain-independent, multi-view Digital Twin Reference Architecture that can help practitioners in architecting and engineering their DTs. Method. We adopted the design science methodology, structured into three cycles: (i) an initial investigation conducting a Systematic Literature Review to identify key architectural elements, (ii) preliminary design refined via feedback from practitioners, and (iii) final artifact development, integrating knowledge from widely adopted DT development platforms and validated through an expert survey of 20 participants. Results. The proposed Digital Twin Reference Architecture is named TwinArch. It is documented using the Views and Beyond methodology by the Software Engineering Institute. TwinArch website and replication package: https://alessandrasomma28.github.io/twinarch/ Conclusion. TwinArch offers practitioners practical artifacts that can be utilized for designing and developing new DT systems across various domains. It enables customization and tailoring to specific use cases while also supporting the documentation of existing DT systems.
Automating the Path: An R&D Agenda for Human-Centered AI and Visualization
2025-04-10
Niklas Elmqvist, Clemens Nylandsted Klokmose
The emergence of generative AI, large language models (LLMs), and foundation models is fundamentally reshaping computer science, and visualization and visual analytics are no exception. We present a systematic framework for understanding how human-centered AI (HCAI) can transform the visualization discipline. Our framework maps four key HCAI tool capabilities -- amplify, augment, empower, and enhance -- onto the four phases of visual sensemaking: view, explore, schematize, and report. For each combination, we review existing tools, envision future possibilities, identify challenges and pitfalls, and examine ethical considerations. This design space can serve as an R\&D agenda for both visualization researchers and practitioners to integrate AI into their work as well as understanding how visualization can support HCAI research.
DGOcc: Depth-aware Global Query-based Network for Monocular 3D Occupancy Prediction
2025-04-10
Xu Zhao, Pengju Zhang, Bo Liu, Yihong Wu
Monocular 3D occupancy prediction, aiming to predict the occupancy and semantics within interesting regions of 3D scenes from only 2D images, has garnered increasing attention recently for its vital role in 3D scene understanding. Predicting the 3D occupancy of large-scale outdoor scenes from 2D images is ill-posed and resource-intensive. In this paper, we present \textbf{DGOcc}, a \textbf{D}epth-aware \textbf{G}lobal query-based network for monocular 3D \textbf{Occ}upancy prediction. We first explore prior depth maps to extract depth context features that provide explicit geometric information for the occupancy network. Then, in order to fully exploit the depth context features, we propose a Global Query-based (GQ) Module. The cooperation of attention mechanisms and scale-aware operations facilitates the feature interaction between images and 3D voxels. Moreover, a Hierarchical Supervision Strategy (HSS) is designed to avoid upsampling the high-dimension 3D voxel features to full resolution, which mitigates GPU memory utilization and time cost. Extensive experiments on SemanticKITTI and SSCBench-KITTI-360 datasets demonstrate that the proposed method achieves the best performance on monocular semantic occupancy prediction while reducing GPU and time overhead.
We explain how the geometric Langlands program inspires some recent new prospectives of classical arithmetic Langlands program and leads to the solutions of some problems in arithmetic geometry.
Rendering Large Volume Datasets in Unreal Engine 5: A Survey
2025-04-10
Markus Schlüter, Tom Kwasnitschka, Armin Bernstetter, Jens Karstens
In this technical report, we discuss several approaches to in-core rendering of large volumetric datasets in Unreal Engine 5 (UE5). We explore the following methods: the TBRayMarcher Plugin, the Niagara Fluids Plugin , and various approaches using Sparse Volume Textures (SVT), with a particular focus on Heterogeneous Volumes (HV). We found the HV approach to be the most promising. The biggest challenge we encountered with other approaches was the need to chunk datasets so that each fits into volume textures smaller than one gigavoxel. While this enables display of the entire dataset at reasonable frame rates, it introduces noticeable artifacts at chunk borders due to incorrect lighting, as each chunk lacks information about its neighbors. After addressing some (signed) int32 overflows in the Engine's SVT-related source code by converting them to to (unsigned) uint32 or int64, the SVT-based HV system allows us to render sparse datasets up to 32k x 32k x 16k voxels, provided the compressed tile data (including MIP data and padding for correct interpolation) does not exceed 4 gigavoxels. In the future, we intend to extend the existing SVT streaming functionality to support out-of-core rendering, in order to eventually overcome VRAM limitations, graphics API constraints, and the performance issues associated with 64-bit arithmetic in GPU shaders.
Transformer-Based Temporal Information Extraction and Application: A Review
2025-04-10
Xin Su, Phillip Howard, Steven Bethard
Temporal information extraction (IE) aims to extract structured temporal information from unstructured text, thereby uncovering the implicit timelines within. This technique is applied across domains such as healthcare, newswire, and intelligence analysis, aiding models in these areas to perform temporal reasoning and enabling human users to grasp the temporal structure of text. Transformer-based pre-trained language models have produced revolutionary advancements in natural language processing, demonstrating exceptional performance across a multitude of tasks. Despite the achievements garnered by Transformer-based approaches in temporal IE, there is a lack of comprehensive reviews on these endeavors. In this paper, we aim to bridge this gap by systematically summarizing and analyzing the body of work on temporal IE using Transformers while highlighting potential future research directions.
Measuring Cosmic Growth Rate with CSST Spectroscopic Survey and Fast Radio Burst
2025-04-10
Shi-Yuan Wang, Jun-Qing Xia
The cosmic growth rate, which is related to peculiar velocity and is a primary scientific objective of galaxy spectroscopic surveys, can be inferred from the Redshift Space Distortion effect and the kinetic Sunyaev-Zel'dovich effect. However, the reconstruction noise power spectrum of the radial velocity field in kSZ is significantly dependent on the measurement of the small-scale galaxy-electron power spectrum $P_{ge}$. In this study, we thoroughly discuss the enhancement of cosmic growth rate measurements facilitated by Fast Radio Bursts, which probe the electron density of the universe along their propagation paths to provide crucial additional information on $P_{ge}$. Subsequently, we utilize future spectroscopic surveys from the Chinese Space Station Telescope and the CMB-S4 experiment, combined with FRB dispersion measures, to achieve precise measurements of the cosmic growth rate at redshifts $z_g = 0.15,0.45,0.75$. Employing Fisher matrix forecasting analysis, we anticipate that constraints on $f\sigma_8$ will reach a precision of 0.001 with a sample size of $10^6$ FRBs. Furthermore, we perform a global analysis using Markov Chain Monte Carlo methods to constrain key parameters of three distinct dark energy models and a modified gravity model based on cosmic growth rate measurements. The results demonstrate that these refined $f\sigma_8$ measurements considerably enhance the constraints on relevant cosmological parameters compared to those obtained from Planck. As the number of observed FRBs increases, alongside more precise galaxy surveys and next-generation CMB observations, new opportunities will arise for constraining cosmological models using the kSZ effect and for developing novel cosmological applications of FRBs.
Continuity conditions weaker than lower semi-continuity
2025-04-10
Jacob Westerhout, Xin Guo, Hien Duy Nguyen
Lower semi-continuity (\texttt{LSC}) is a critical assumption in many foundational optimisation theory results; however, in many cases, \texttt{LSC} is stronger than necessary. This has led to the introduction of numerous weaker continuity conditions that enable more general theorem statements. In the context of unstructured optimization over topological domains, we collect these continuity conditions from disparate sources and review their applications. As primary outcomes, we prove two comprehensive implication diagrams that establish novel connections between the reviewed conditions. In doing so, we also introduce previously missing continuity conditions and provide new counterexamples.
Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications, yet current evaluation methods struggle to keep pace with their rapid development. In this paper, we analyze the core limitations of traditional evaluation pipelines and propose a novel metric, the Model Utilization Index (MUI), which introduces mechanism interpretability techniques to complement traditional performance metrics. MUI quantifies the extent to which a model leverages its capabilities to complete tasks. The core idea is that to assess an LLM's overall ability, we must evaluate not only its task performance but also the effort expended to achieve the outcome. Our extensive experiments reveal an inverse relationship between MUI and performance, from which we deduce a common trend observed in popular LLMs, which we term the Utility Law. Based on this, we derive four corollaries that address key challenges, including training judgement, the issue of data contamination, fairness in model comparison, and data diversity. We hope that our survey, novel metric, and utility law will foster mutual advancement in both evaluation and mechanism interpretability. Our code can be found at https://github.com/ALEX-nlp/MUI-Eva.
Enhancing Player Enjoyment with a Two-Tier DRL and LLM-Based Agent System for Fighting Games
2025-04-10
Shouren Wang, Zehua Jiang, Fernando Sliva, Sam Earle, Julian Togelius
Deep reinforcement learning (DRL) has effectively enhanced gameplay experiences and game design across various game genres. However, few studies on fighting game agents have focused explicitly on enhancing player enjoyment, a critical factor for both developers and players. To address this gap and establish a practical baseline for designing enjoyability-focused agents, we propose a two-tier agent (TTA) system and conducted experiments in the classic fighting game Street Fighter II. The first tier of TTA employs a task-oriented network architecture, modularized reward functions, and hybrid training to produce diverse and skilled DRL agents. In the second tier of TTA, a Large Language Model Hyper-Agent, leveraging players' playing data and feedback, dynamically selects suitable DRL opponents. In addition, we investigate and model several key factors that affect the enjoyability of the opponent. The experiments demonstrate improvements from 64. 36% to 156. 36% in the execution of advanced skills over baseline methods. The trained agents also exhibit distinct game-playing styles. Additionally, we conducted a small-scale user study, and the overall enjoyment in the player's feedback validates the effectiveness of our TTA system.
Automating quantum feature map design via large language models
2025-04-10
Kenya Sakka, Kosuke Mitarai, Keisuke Fujii
Quantum feature maps are a key component of quantum machine learning, encoding classical data into quantum states to exploit the expressive power of high-dimensional Hilbert spaces. Despite their theoretical promise, designing quantum feature maps that offer practical advantages over classical methods remains an open challenge. In this work, we propose an agentic system that autonomously generates, evaluates, and refines quantum feature maps using large language models. The system consists of five component: Generation, Storage, Validation, Evaluation, and Review. Using these components, it iteratively improves quantum feature maps. Experiments on the MNIST dataset show that it can successfully discover and refine feature maps without human intervention. The best feature map generated outperforms existing quantum baselines and achieves competitive accuracy compared to classical kernels across MNIST, Fashion-MNIST, and CIFAR-10. Our approach provides a framework for exploring dataset-adaptive quantum features and highlights the potential of LLM-driven automation in quantum algorithm design.
Galaxy and halo properties around cosmic filaments from Sloan Digital Sky Survey Data Release 7 and the ELUCID simulation
2025-04-10
Youcai Zhang, Xiaohu Yang, Hong Guo, Peng Wang, Feng Shi
Using galaxies from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7) along with haloes from the dark matter only constrained ELUCID (Exploring the Local Universe with the reConstructed Initial Density field) simulation, we examine the properties of galaxies and haloes with respect to their distance to cosmic filaments, determined by the medial-axis thinning technique of the COsmic Web Skeleton (COWS) method. Our findings suggest that galaxies or subhaloes grow in mass as they approach these filaments. Galaxies exhibit a redder colour and diminished specific star formation rates as they approach these filaments. Additionally, older subhaloes tend to be more common near the central regions of these filaments. Elliptical galaxies are more frequently found than spiral galaxies in the central regions of the filaments. Lower-mass galaxies typically display reduced sizes in proximity to filaments, whereas higher-mass galaxies tend to exhibit increased sizes when close to filaments. Moreover, the concentration and spin of the haloes grow as they approach the filaments. These findings support the notion that the large-scale structure of the universe, characterized by cosmic web structures, plays a vital role in shaping galaxy and halo properties.
Electronic Warfare Cyberattacks, Countermeasures and Modern Defensive Strategies of UAV Avionics: A Survey
2025-04-10
Aaron Yu, Iuliia Kolotylo, Hashim A. Hashim, A. E. E. Eltoukhy
Unmanned Aerial Vehicles (UAVs) play a pivotal role in modern autonomous air mobility, and the reliability of UAV avionics systems is critical to ensuring mission success, sustainability practices, and public safety. The success of UAV missions depends on effectively mitigating various aspects of electronic warfare, including non-destructive and destructive cyberattacks, transponder vulnerabilities, and jamming threats, while rigorously implementing countermeasures and defensive aids. This paper provides a comprehensive review of UAV cyberattacks, countermeasures, and defensive strategies. It explores UAV-to-UAV coordination attacks and their associated features, such as dispatch system attacks, Automatic Dependent Surveillance-Broadcast (ADS-B) attacks, Traffic Alert and Collision Avoidance System (TCAS)-induced collisions, and TCAS attacks. Additionally, the paper examines UAV-to-command center coordination attacks, as well as UAV functionality attacks. The review also covers various countermeasures and defensive aids designed for UAVs. Lastly, a comparison of common cyberattacks and countermeasure approaches is conducted, along with a discussion of future trends in the field. Keywords: Electronic warfare, UAVs, Avionics Systems, cyberattacks, coordination attacks, functionality attacks, countermeasure, defensive-aids.
Cryogenic-temperature Grain-to-grain Epitaxial Growth of High-quality Ultrathin CoFe Layer on MgO Tunnel Barrier for High-performance Magnetic Tunnel Junctions
One candidate for ultimate non-volatile memory with ultralow power consumption is magneto-resistive random-access memory (VC-MRAM). To develop VC-MRAM, it is important to fabricate high-performance magnetic tunnel junctions (MTJs), which require the epitaxial growth of an ultrathin ferromagnetic electrode on a crystalline tunnel barrier using a mass-manufacturing-compatible process. In this study, the grain-to-grain epitaxial growth of perpendicularly magnetized CoFe ultrathin films on polycrystalline MgO (001) was demonstrated using cryogenic-temperature sputtering on 300 mm Si wafers. Cryogenic-temperature sputtering at 100 K suppressed the island-like initial growth of CoFe on MgO without hampering epitaxy. Sub-nanometer-thick CoFe layers exhibited remarkable perpendicular magnetic anisotropy (PMA). An even larger PMA was obtained using an Fe-doped MgO (MgFeO) tunnel barrier owing to improved uniformity of the CoFe layer. A 0.8-nm-thick CoFe layer grown on MgFeO exhibited a magnetic damping constant as low as 0.008. The ultralow magnetic damping enables voltage-driven magnetization switching with a low write-error rate (WER) below 10^-6 at a pulse duration of 0.3 ns, and WER on the order of 10^-3 even for a relatively long pulse duration of 1.5 ns. These properties achieved using a mass-manufacturing deposition process can promote the development of VC-MRAM and other advanced spintronic devices based on MTJs.
Perception is a key component of Automated vehicles (AVs). However, sensors mounted to the AVs often encounter blind spots due to obstructions from other vehicles, infrastructure, or objects in the surrounding area. While recent advancements in planning and control algorithms help AVs react to sudden object appearances from blind spots at low speeds and less complex scenarios, challenges remain at high speeds and complex intersections. Vehicle to Infrastructure (V2I) technology promises to enhance scene representation for AVs in complex intersections, providing sufficient time and distance to react to adversary vehicles violating traffic rules. Most existing methods for infrastructure-based vehicle detection and tracking rely on LIDAR, RADAR or sensor fusion methods, such as LIDAR-Camera and RADAR-Camera. Although LIDAR and RADAR provide accurate spatial information, the sparsity of point cloud data limits its ability to capture detailed object contours of objects far away, resulting in inaccurate 3D object detection results. Furthermore, the absence of LIDAR or RADAR at every intersection increases the cost of implementing V2I technology. To address these challenges, this paper proposes a V2I framework that utilizes monocular traffic cameras at road intersections to detect 3D objects. The results from the roadside unit (RSU) are then combined with the on-board system using an asynchronous late fusion method to enhance scene representation. Additionally, the proposed framework provides a time delay compensation module to compensate for the processing and transmission delay from the RSU. Lastly, the V2I framework is tested by simulating and validating a scenario similar to the one described in an industry report by Waymo. The results show that the proposed method improves the scene representation and the AV's perception range, giving enough time and space to react to adversary vehicles.
Dynamics of collective minds in online communities
2025-04-10
Seungwoong Ha, Henrik Olsson, Kresimir Jaksic, Max Pellert, Mirta Galesic
How communities respond to diverse societal challenges, from economic crises to political upheavals, is shaped by their collective minds - shared representations of ongoing events and current topics. In turn, collective minds are shaped by a continuous stream of influences, amplified by the rapid rise of online platforms. Online communities must understand these influences to maintain healthy discourse and avoid being manipulated, but understanding is hindered by limited observations and the inability to conduct counterfactual experiments. Here, we show how collective minds in online news communities can be influenced by different editorial agenda-setting practices and aspects of community dynamics, and how these influences can be reversed. We develop a computational model of collective minds, calibrated and validated with data from 400 million comments across five U.S. online news platforms and a large-scale survey. The model enables us to describe and experiment with a variety of influences and derive quantitative insights into their magnitude and persistence in different communities. We find that some editorial influences can be reversed relatively rapidly, but others, such as amplification and reframing of certain topics, as well as community influences such as trolling and counterspeech, tend to persist and durably change the collective mind. These findings illuminate ways collective minds can be manipulated and pathways for communities to maintain healthy and authentic collective discourse amid ongoing societal challenges.
Comparing Optical Variability of Type 1 and Type 2 AGN from the BAT 9-Month Sample using ASAS-SN and TESS Surveys
2025-04-10
Natalie Kovacevic, Xinyu Dai, Heechan Yuk, Emilia E. Jaevelae, Tingfeng Yi, Patrick J. Vallely, Benjamin J. Shappee, Francesco Shankar, K. Z. Stanek
We present an optical variability analysis and comparison of the samples of Seyfert 1 and 2 galaxies, selected from the \textit{Swift} 9-month BAT catalog, using the light curves from Transiting Exoplanet Survey Satellite (TESS) and All-Sky Automated Survey for SuperNovae (ASAS-SN). We measured the normalized excess variance of TESS and ASAS-SN light curves for each target and performed a Kolmogorov-Smirnov test between the two samples, where our results showed significant differences. This is consistent with predictions from the unification model, where Seyfert 2s are obscured by the larger scale dust torus and their variability is suppressed. This variability difference is independent of the luminosity, Eddington ratio, or black hole mass, further supporting geometrical unification models. We searched the dependence of the normalized excess variance of Seyfert 1s on absolute magnitudes, Eddington ratio, and black hole mass, where our results are consistent with relations found in the literature. Finally, a small sub-sample of changing-look AGNs that transitioned during the time frame of the ASAS-SN light curves, with their variability amplitudes changing according to the classification, larger variability as type 1s and smaller as 2s. The change of variability amplitudes can be used to better pinpoint when the type transition occurred. The consistency trend of the variability amplitude differences between Seyfert 1s and 2s and between changing-look AGNs in 1 or 2 stages suggests that variability can be a key factor in shedding light on the changing-look AGN or the dichotomy between Seyfert 1 or 2 populations.
Threading the Needle: Test and Evaluation of Early Stage UAS Capabilities to Autonomously Navigate GPS-Denied Environments in the DARPA Fast Lightweight Autonomy (FLA) Program
2025-04-10
Adam Norton, Holly Yanco
The DARPA Fast Lightweight Autonomy (FLA) program (2015 - 2018) served as a significant milestone in the development of UAS, particularly for autonomous navigation through unknown GPS-denied environments. Three performing teams developed UAS using a common hardware platform, focusing their contributions on autonomy algorithms and sensing. Several experiments were conducted that spanned indoor and outdoor environments, increasing in complexity over time. This paper reviews the testing methodology developed in order to benchmark and compare the performance of each team, each of the FLA Phase 1 experiments that were conducted, and a summary of the Phase 1 results.
In this chapter, we give an overview of the major components of the interstellar medium (ISM) in galaxies at a level appropriate for upper level undergraduates or beginning graduate students. We discuss the major constituents of the the ISM in present-day star forming galaxies and summarize common methods to observe these components. We also review basic aspects of ISM structure accessible to extragalactic observations. Finally, we describe variations in ISM content and star-formation activity among local universe galaxies.
A Swarm of WASP Planets: Nine giant planets identified by the WASP survey
2025-04-10
Nicole Schanche, Guillaume Hébrard, Keivan G. Stassun, Benjamin J. Hord, Khalid Barkaoui, Allyson Bieryla, David R. Ciardi, Karen A. Collins, Andrew Collier Cameron, Joel Hartman, N. Heidari, Coel Hellier, Steve B. Howell, Monika Lendl, James McCormac, Kim K. McLeod, Hannu Parviainen, Don J. Radford, Arvind Singh Rajpurohit, Howard M. Relles, Rishikesh Sharma, Sanjay Baliwal, Gaspar Bakos, Susana Barros, François Bouchy, Artem Y. Burdanov, Polina A. Budnikova, Abhijit Chakaraborty, Catherine Clark, Laetitia Delrez, O. D. S. Demangeon, Rodrigo Diaz, Jonah Donnenfield, Mark Everett, Michaël Gillon, Christina Hedges, Jesus Higuera, Emmanuel Jehin, Jon M. Jenkins, Flavien Kiefer, Didier Laloum, Mike Lund, Pierre Magain, Pierre Maxted, Ismael Mireles, K. J. Nikitha, Cyrielle Opitom, Yatrik Patel, Mark Rose, Sergio Sousa, Ivan Strakhov, Paul Strøm, Amy Tuson, Richard West, Joshua Winn
The Wide Angle Search for Planets (WASP) survey provided some of the first transiting hot Jupiter candidates. With the addition of the Transiting Exoplanet Survey Satellite (TESS), many WASP planet candidates have now been revisited and given updated transit parameters. Here we present 9 transiting planets orbiting FGK stars that were identified as candidates by the WASP survey and measured to have planetary masses by radial velocity measurements. Subsequent space-based photometry taken by TESS as well as ground-based photometric and spectroscopic measurements have been used to jointly analyze the planetary properties of WASP-102 b, WASP-116 b, WASP-149 b WASP-154 b, WASP-155 b, WASP-188 b, WASP-194 b/HAT-P-71 b, WASP-195 b, and WASP-197 b. These planets have radii between 0.9 R_Jup and 1.4 R_Jup, masses between 0.1 M_Jup and 1.5 M_Jup, and periods between 1.3 and 6.6 days.
Relationships between PAHs, Small Dust Grains, H$_2$, and HI in Local Group Dwarf Galaxies NGC 6822 and WLM Using JWST, ALMA, and the VLA
2025-04-10
Ryan Chown, Adam K. Leroy, Alberto D. Bolatto, Jérémy Chastenet, Simon C. O. Glover, Remy Indebetouw, Eric W. Koch, Jennifer Donovan Meyer, Nickolas M. Pingel, Erik Rosolowsky, Karin Sandstrom, Jessica Sutter, Elizabeth Tarantino, Frank Bigiel, Médéric Boquien, I-Da Chiang, Daniel A. Dale, Julianne J. Dalcanton, Oleg V. Egorov, Cosima Eibensteiner, Kathryn Grasha, Hamid Hassani, Hao He, Jaeyeon Kim, Sharon Meidt, Debosmita Pathak, Sumit K. Sarbadhicary, Snezana Stanimirovic, Vicente Villanueva, Thomas G. Williams
We present 0.7-3.3 pc resolution mid-infrared (MIR) JWST images at 7.7 $\mu$m (F770W) and 21 $\mu$m (F2100W) covering the main star-forming regions of two of the closest star-forming low-metallicity dwarf galaxies, NGC6822 and Wolf-Lundmark-Melotte (WLM). The images of NGC6822 reveal filaments, edge-brightened bubbles, diffuse emission, and a plethora of point sources. By contrast, most of the MIR emission in WLM is point-like, with a small amount of extended emission. Compared to solar metallicity galaxies, the ratio of 7.7 $\mu$m intensity ($I_\nu^{F770W}$), tracing polycyclic aromatic hydrocarbons (PAHs), to 21 $\mu$m intensity ($I_\nu^{F2100W}$), tracing small, warm dust grain emission, is suppressed in these low-metallicity dwarfs. Using ALMA CO(2-1) observations, we find that detected CO intensity versus $I_\nu^{F770W}$ at ~2 pc resolution in dwarfs follows a similar relationship to that at solar metallicity and lower resolution, while the CO versus $I_\nu^{F2100W}$ relationship in dwarfs lies significantly below that derived from solar metallicity galaxies at lower resolution, suggesting more pronounced destruction of CO molecules at low metallicity. Finally, adding in Local Group L-Band Survey VLA 21 cm HI observations, we find that $I_\nu^{F2100W}$ and $I_\nu^{F770W}$ vs. total gas ratios are suppressed in NGC6822 and WLM compared to solar metallicity galaxies. In agreement with dust models, the level of suppression appears to be at least partly accounted for by the reduced galaxy-averaged dust-to-gas and PAH-to-dust mass ratios in the dwarfs. Remaining differences are likely due to spatial variations in dust model parameters, which should be an exciting direction for future work in local dwarf galaxies.
The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search
2025-04-10
Yutaro Yamada, Robert Tjarko Lange, Cong Lu, Shengran Hu, Chris Lu, Jakob Foerster, Jeff Clune, David Ha
AI is increasingly playing a pivotal role in transforming how scientific discoveries are made. We introduce The AI Scientist-v2, an end-to-end agentic system capable of producing the first entirely AI generated peer-review-accepted workshop paper. This system iteratively formulates scientific hypotheses, designs and executes experiments, analyzes and visualizes data, and autonomously authors scientific manuscripts. Compared to its predecessor (v1, Lu et al., 2024 arXiv:2408.06292), The AI Scientist-v2 eliminates the reliance on human-authored code templates, generalizes effectively across diverse machine learning domains, and leverages a novel progressive agentic tree-search methodology managed by a dedicated experiment manager agent. Additionally, we enhance the AI reviewer component by integrating a Vision-Language Model (VLM) feedback loop for iterative refinement of content and aesthetics of the figures. We evaluated The AI Scientist-v2 by submitting three fully autonomous manuscripts to a peer-reviewed ICLR workshop. Notably, one manuscript achieved high enough scores to exceed the average human acceptance threshold, marking the first instance of a fully AI-generated paper successfully navigating a peer review. This accomplishment highlights the growing capability of AI in conducting all aspects of scientific research. We anticipate that further advancements in autonomous scientific discovery technologies will profoundly impact human knowledge generation, enabling unprecedented scalability in research productivity and significantly accelerating scientific breakthroughs, greatly benefiting society at large. We have open-sourced the code at https://github.com/SakanaAI/AI-Scientist-v2 to foster the future development of this transformative technology. We also discuss the role of AI in science, including AI safety.
Synthesis of actinides and short-lived radionuclides during i-process nucleosynthesis in AGB stars
2025-04-10
Arthur Choplin, Stephane Goriely, Lionel Siess, Sébastien Martinet
A complex interplay between mixing and nucleosynthesis is at work in asymptotic giant branch (AGB) stars. In addition to the slow neutron capture process (s-process), the intermediate neutron capture process (i-process) can develop during protons ingestion events (PIEs). In this paper, after quickly reviewing the different modes of production of heavy elements in AGB stars that were identified so far, we investigate the synthesis of actinides and other short-lived radioactive nuclei (SLRs, $^{60}$Fe, $^{107}$Pd, $^{126}$Sn, $^{129}$I, $^{135}$Cs and $^{182}$Hf) during i-process nucleosynthesis. AGB stellar models with initial masses $1 \leq M_{\rm ini}/M_{\odot} \leq 3$, metallicities $-3 \leq $ [Fe/H] $ \leq 0$ and different overshoot strengths were computed with the stellar evolution code STAREVOL. During PIEs, a nuclear network of 1160 isotopes is used and coupled to the transport equations. We found that AGB models with [Fe/H] $<-2$ can synthesize actinides with sometimes abundances greater than solar. The $^{60}$Fe yield scales with the initial metallicity while the $^{107}$Pd, $^{126}$Sn, $^{129}$I, $^{135}$Cs and $^{182}$Hf yields follow a similar pattern as a function of metallicity, with a production peak at [Fe/H] $\simeq -1.3$. At [Fe/H] $<-1$, the fraction of odd Ba isotopes $f_{\rm Ba,odd}$ is predicted to vary between 0.6 and 0.8 depending on the initial mass and metallicity. Nuclear uncertainties on our $1 M_{\odot}$, [Fe/H] $=-2.5$ model lead to $f_{\rm Ba,odd}$ ranging between 0.27 and 0.76, which is clearly above the s-process value. AGB stars experiencing PIEs appear to be potential producers of actinides and SLRs, particularly at low metallicity (except for $^{60}$Fe). Galactic chemical evolution modeling are required to assess their possible contribution to the galactic enrichment.
Dataset of artefacts for machine learning applications in astronomy
2025-04-10
Sreevarsha Sreejith, Maria V. Pruzhinskaya, Alina A. Volnova, Vadim V. Krushinsky, Konstantin L. Malanchev, Emille E. O. Ishida, Anastasia D. Lavrukhina, Timofey A. Semenikhin, Emmanuel Gangler, Matwey V. Kornilov, Vladimir S. Korolev
Accurate photometry in astronomical surveys is challenged by image artefacts, which affect measurements and degrade data quality. Due to the large amount of available data, this task is increasingly handled using machine learning algorithms, which often require a labelled training set to learn data patterns. We present an expert-labelled dataset of 1127 artefacts with 1213 labels from 26 fields in ZTF DR3, along with a complementary set of nominal objects. The artefact dataset was compiled using the active anomaly detection algorithm PineForest, developed by the SNAD team. These datasets can serve as valuable resources for real-bogus classification, catalogue cleaning, anomaly detection, and educational purposes. Both artefacts and nominal images are provided in FITS format in two sizes (28 x 28 and 63 x 63 pixels). The datasets are publicly available for further scientific applications.
To study how massive variable stars effect their environment, we search for variability among Zwicky Transient Facility (ZTF) sources located within the optical extent of a nearby starburst galaxy IC 10. We present the ZTF IC 10 catalog, which classifies 1516 $r$ band sources and 864 $g$ band sources within a $225''$ radius around IC 10 into three categories: 1388 (767) $r$ ($g$) band non-variables, 150 (85) $r$ ($g$) band non-periodic variables, and 37 (12) $r$ ($g$) band periodic variables. Among them 101 (48) $r$ ($g$) band non-periodic variables, and 22 (4) $r$ ($g$) band periodic variables are inside IC 10. We verify our classification by cross-matching with previous variability catalogs and machine learning powered classifications. Various analysis including population demographics, color-magnitude diagrams, and cross matching with a set of different surveys and database such as Gaia, XMM-Newton, Chandra, and SIMBAD are also presented. Based on source density and parallax, we distinguish sources within IC 10 from non-IC 10 sources. For IC 10 sources, we highlight flaring super giants, a source with long secondary period, periodic super giants including possible S Doradus luminous blue variable and candidate Miras. For non-IC 10 sources, we present super red sources and compact objects such as a possible long period subdwarf and a periodic X-ray source. The catalog can serve as a useful database to study the connection between various type of massive stars and their host galaxies.
Bridging Quasars and Little Red Dots: Insights into Broad-Line AGNs at $z=5-8$ from the First JWST COSMOS-3D Dataset
2025-04-10
Xiaojing Lin, Xiaohui Fan, Feige Wang, Fengwu Sun, Jaclyn B. Champagne, Eiichi Egami, Koki Kakiichi, Jianwei Lyu, Wei Leong Tee, Jinyi Yang, Fuyan Bian, Sarah E. I. Bosman, Zheng Cai, Caitlin M. Casey, Roberto Decarli, Andreas L. Faisst, Seiji Fujimoto, Santosh Harish, Olivier Ilbert, Akio K. Inoue, Xiangyu Jin, Jeyhan S. Kartaltepe, Dale D. Kocevski, Mingyu Li, Weizhe Liu, Yichen Liu, Jan-Torge Schindler, Marko Shuntov, Takumi S. Tanaka, Marianne Vestergaard, Yunjing Wu, Haowen Zhang, Zijian Zhang
We report the discovery of 13 broad-line AGNs at $z = 5 - 8$ from the first 10% data of the JWST Cycle 3 Treasury Program COSMOS-3D. These AGNs are identified by their broad H$\alpha$ or H$\beta$ emission lines through the NIRCam grism wide-field slitless spectroscopy. One object at $z = 7.646$ with broad H$\beta$ emission has an F444W magnitude of 23.6 mag, making it one of the brightest $z > 7.5$ broad-line AGNs yet known. Among the 13 AGNs, 10 objects have reddened optical continua with slopes $\beta_{\rm opt}>0$. The remaining three objects have their overall SEDs that resemble those of UV-luminous quasars at similar redshifts, but their $\beta_{\rm opt}$, though negative, are not as blue as those of unobscured quasars. We also obtain MIRI photometry at 7.7-18 $\mu$m for two AGNs and place strong constraints on their rest-frame near-IR SED. We find no significant variability in the rest-frame UV by comparing the COSMOS-3D and COSMOS-Web F115W images taken apart by 60 days in the rest-frame. We compute the H$\alpha$ luminosity functions (LFs) for the broad H$\alpha$ emitters at $z \approx 5-6$ and find a potential redshift evolution when compared with that of the $z \approx 4-5$ sample. We also derive the H$\beta$ LF at $z\sim8$ for AGNs and galaxies by combining our sample with those from the literature. The broad H$\beta$ emitters in this work suggest a number density two orders of magnitude higher than that predicted by the quasar LF based on rest-frame UV-selected samples. As a preview, our work showcases the ability of the COSMOS-3D grism survey to provide a complete view of the properties, growth, and evolution of bright broad-line AGNs at $z>5$.
ELVES-Dwarf I: Satellites Systems of Eight Isolated Dwarf Galaxies in the Local Volume
2025-04-10
Jiaxuan Li, Jenny E. Greene, Shany Danieli, Scott G. Carlsten, Marla Geha, Fangzhou Jiang, Masayuki Tanaka
The satellite populations of Milky Way--mass systems have been extensively studied, significantly advancing our understanding of galaxy formation and dark matter physics. In contrast, the satellites of lower-mass dwarf galaxies remain largely unexplored, despite hierarchical structure formation predicting that dwarf galaxies should host their own satellites. We present the first results of the ELVES-Dwarf survey, which aims to statistically characterize the satellite populations of isolated dwarf galaxies in the Local Volume ($4
Counting Little Red Dots at $z<4$ with Ground-based Surveys and Spectroscopic Follow-up
2025-04-10
Yilun Ma, Jenny E. Greene, David J. Setton, Andy D. Goulding, Marianna Annunziatella, Xiaohui Fan, Vasily Kokorev, Ivo Labbe, Jiaxuan Li, Xiaojing Lin, Danilo Marchesini, Jorryt Matthee, Luke Robbins, Anna Sajina, Marcin Sawicki, O. Grace Telford
Little red dots (LRDs) are a population of red, compact objects discovered by JWST at $z>4$. At $44$ to $\sim10^{-5.3}\,\mathrm{cMpc^{-3}}$ at $2.7
The Luminosity Function and Clustering of H$α$ Emitting Galaxies at $z\approx4-6$ from a Complete NIRCam Grism Redshift Survey
2025-04-10
Xiaojing Lin, Eiichi Egami, Fengwu Sun, Haowen Zhang, Xiaohui Fan, Jakob M. Helton, Feige Wang, Andrew J. Bunker, Zheng Cai, Daniel J. Eisenstein, Daniel T. Jaffe, Zhiyuan Ji, Xiangyu Jin, Maria Anne Pudoka, Sandro Tacchella, Wei Leong Tee, Pierluigi Rinaldi, Brant Robertson, Yang Sun, Christopher N. A. Willmer, Chris Willott, Junyu Zhang, Yongda Zhu
We study the luminosity function (LF) and clustering properties of 888 H$\alpha$ emitters (HAEs) at $3.75 < z < 6$ in the GOODS-N field. The sample, built from JWST CONGRESS and FRESCO NIRCam grism surveys using a novel redshift assignment algorithm, spans $\sim$62 arcmin$^2$ and reaches $L_{\rm H\alpha} \sim 10^{41.2} {\rm erg s^{-1}}$. We identify two prominent filamentary protoclusters at $z \approx 4.41$ and $z \approx 5.19$, hosting 98 and 144 HAEs, respectively. The observed H$\alpha$ LFs show similar shallow faint-end slopes for both protocluster and field galaxies at $z=3.75-5$, and for the protocluster at $z=5-6$ ($\alpha\approx 1.2$ to $-1.3$). In contrast, the field LF at $z=5-6$ is much steeper ($\alpha=-1.87_{-0.23}^{+0.30}$), suggesting that protocluster galaxies at $z > 5$ are more evolved, resembling those at $z=3.75-5$. The observed star formation rate density from H$\alpha$, integrated down to 0.45 ${\rm M_\odot yr^{-1}}$, is $0.050^{+0.002}_{-0.003}$ and $0.046^{+0.006}_{-0.004} M_\odot {\rm yr}^{-1} {\rm Mpc}^{-3}$ at $z=3.75-5$ and $z=5-6$, with protoclusters contributing $\sim$25% and 55%, respectively. This implies that a large fraction of star formation at $z > 4$ occurs in protoclusters. We conduct the first star-formation-rate-limited 3D clustering analysis at $z > 4$. We find the filamentary protocluster geometry flattens the power-law shape of the HAE auto-correlation functions, with slopes much shallower than typically assumed. The auto-correlation function of field HAEs have correlation lengths of $r_0 = 4.61^{+1.00}_{-0.68} h^{-1}{\rm Mpc}$ at $z \approx 4-5$ and $r_0 = 6.23^{+1.68}_{-1.13} h^{-1}{\rm Mpc}$ at $z=5-6$. Comparing the observed correlation functions with the UniverseMachine simulation, we infer the dark matter (sub-)halo masses of HAEs to be $\log (M_h/M_\odot)=11.0-11.2$ at $z\approx 4-6$, with a scatter of 0.4 dex.
From Speech to Summary: A Comprehensive Survey of Speech Summarization
2025-04-10
Fabian Retkowski, Maike Züfle, Andreas Sudmann, Dinah Pfau, Jan Niehues, Alexander Waibel
Speech summarization has become an essential tool for efficiently managing and accessing the growing volume of spoken and audiovisual content. However, despite its increasing importance, speech summarization is still not clearly defined and intersects with several research areas, including speech recognition, text summarization, and specific applications like meeting summarization. This survey not only examines existing datasets and evaluation methodologies, which are crucial for assessing the effectiveness of summarization approaches but also synthesizes recent developments in the field, highlighting the shift from traditional systems to advanced models like fine-tuned cascaded architectures and end-to-end solutions.
A Systematic Literature Review of Unmanned Aerial Vehicles for Healthcare and Emergency Services
2025-04-10
Sara Habibi, Naghmeh Ivaki, João Barata
Unmanned aerial vehicles (UAVs), initially developed for military applications, are now used in various fields. As UAVs become more common across multiple industries, it is crucial to understand how to adopt them effectively, efficiently, and safely. The utilization of UAVs in healthcare and emergency services has evolved significantly in recent years, with these aerial vehicles potentially contributing to increased survival rates and enhanced healthcare services. This paper presents a two-stage systematic literature review, including a tertiary study of 15 review papers and an in-depth assessment of 136 primary publications focused on using UAVs in healthcare and emergency services. The research demonstrates how civilian UAVs have been used in numerous applications, such as healthcare emergencies, medical supply delivery, and disaster management, for diverse use cases such as Automated External Defibrillator (AED) delivery, blood delivery, and search and rescue. The studies indicate that UAVs significantly improve response times in emergency situations, enhance survival rates by ensuring the timely delivery of critical medical supplies such as AEDs, and prove to be cost-effective alternatives to traditional delivery methods, especially in remote or inaccessible areas. The studies also highlight the need for ongoing research and development to address existing challenges, such as regulatory frameworks, security, privacy and safety concerns, infrastructure development, and ethical and social issues. Effectively understanding and tackling these challenges is essential for maximizing the benefits of UAV technology in healthcare and emergency services, ultimately leading to safer, more resilient, and responsive systems that can better serve public health needs.
Computing Canonical Averages with Quantum and Classical Optimizers: Thermodynamic Reweighting for QUBO Models of Physical Systems
2025-04-09
Francesco Slongo, Cristian Micheletti
We present a general method to compute canonical averages for physical models sampled via quantum or classical quadratic unconstrained binary optimization (QUBO). First, we introduce a histogram reweighting scheme applicable to QUBO-based sampling constrained to specific intervals of an order parameter, e.g., physical energy. Next, we demonstrate that the scheme can accurately recover the density of states, which in turn allows for calculating expectation values in the conjugate ensemble, e.g., at a fixed temperature. The method can thus be used to advance the state-of-the-art characterization of physical systems that admit a QUBO-based representation and that are otherwise intractable with real-space sampling methods. A case in point are space-filling melts of lattice ring polymers, recently mapped in QUBO form, for which our method reveals that the ring catenation probability is non-monotonic with the bending rigidity.
A Differentiable, End-to-End Forward Model for 21 cm Cosmology: Estimating the Foreground, Instrument, and Signal Joint Posterior
2025-04-09
Nicholas Kern
We present a differentiable, end-to-end Bayesian forward modeling framework for line intensity mapping cosmology experiments, with a specific focus on low-frequency radio telescopes targeting the redshifted 21 cm line from neutral hydrogen as a cosmological probe. Our framework is capable of posterior density estimation of the cosmological signal jointly with foreground and telescope parameters at the field level. Our key aim is to be able to optimize the model's high-dimensional, non-linear, and ill-conditioned parameter space, while also sampling from it to perform robust uncertainty quantification within a Bayesian framework. We show how a differentiable programming paradigm, accelerated by recent advances in machine learning software and hardware, can make this computationally-demanding, end-to-end Bayesian approach feasible. We demonstrate a proof-of-concept on a simplified signal recovery problem for the Hydrogen Epoch of Reionization Array experiment, highlighting the framework's ability to build confidence in early 21 cm signal detections even in the presence of poorly understood foregrounds and instrumental systematics. We use a Hessian-preconditioned Hamiltonian Monte Carlo algorithm to efficiently sample our parameter space with a dimensionality approaching $N\sim10^5$, which enables joint, end-to-end nuisance parameter marginalization over foreground and instrumental terms. Lastly, we introduce a new spherical harmonic formalism that is a complete and orthogonal basis on the cut sky relevant to drift-scan radio surveys, which we call the spherical stripe harmonic formalism, and it's associated three-dimensional basis, the spherical stripe Fourier-Bessel formalism.
The Lyman-alpha and Continuum Origins Survey II: the uneventful journey of escaping Ly$α$ and ionizing radiation through the neutral ISM and CGM of galaxies
2025-04-09
A. Saldana-Lopez, M. J. Hayes, A. Le Reste, C. Scarlata, J. Melinder, A. Henry, R. Amorin, H. Atek, O. Bait, J. Chisholm, A. E. Jaskot, I. Jung, Zhiyuan Ji, L. Komarova, F. Leclercq, G. Ostlin, A. Runnholm, T. X. Thuan, X. Xu
One of the current challenges in galaxy evolution studies is to establish the mechanisms that govern the escape of ionizing radiation from galaxies. In this work, we investigate the connection between Lyman Continuum (LyC) escape and the conditions of the Circumgalactic Medium (CGM), as probed by Ly$\alpha$ halos (LAHs) in emission. We use Ly$\alpha$ and UV continuum imaging data from the Lyman alpha and Continuum Origins Survey (LaCOS), targeting 42 nearby ($z \simeq 0.3$), star-forming galaxies with LyC observations (escape fractions of $f_{\rm esc}^{\rm LyC} \simeq 0.01-0.49$). LaCOS galaxies show extended Ly$\alpha$ emission ubiquitously, with LyC emitters (LCEs) having more compact Ly$\alpha$ morphologies relative to the UV size than non-LCEs, and Ly$\alpha$ spatial offsets that do not exceed the extent of the UV continuum. We model the diffuse LAHs using a combined Sersic plus exponential 2D profile, and find that the characteristic scale length of the Ly$\alpha$ is ten times the scale length of the UV, on average. We unveil a tight anti-correlation between $f_{\rm esc}^{\rm LyC}$ and the Ly$\alpha$ Halo Fraction (HF, or contribution of the halo to the total Ly$\alpha$ luminosity), that we propose as a new LyC indicator. Our observations also show that the HF scales positively with the neutral gas in the ISM, revealing a picture in which Ly$\alpha$ and LyC photons in LCEs emerge through clear sight-lines directly from the central starbursts and, in the case of Ly$\alpha$, minimizing the number of scattering interactions in the CGM. The properties of LAHs in LaCOS resemble those of LAHs at $z \geq 3$, suggesting a lack of evolution in the $f_{\rm esc}^{\rm LyC}$ predictors that rely on the spatial properties of Ly$\alpha$, and ensuring the applicability of these indicators to observations of high-redshift galaxies.
ASASSN-14dx: A cataclysmic variable harbouring a massive pulsating white dwarf
2025-04-09
Pasi Hakala, Ingrid Pelisoli, Boris T. Gaensicke, Pablo Rodriguez-Gil, Thomas R. Marsh, Elme Breedt, John R. Thorstensen, Anna F. Pala
We present the results of our study of ASASSN-14dx, a previously known but poorly characterised cataclysmic variable (CV). The source was observed as part of an ongoing high-time-resolution photometric survey of CVs, which revealed that, in addition to the known 82.8min orbital period, it also exhibits other transient periods, the strongest of which around 4 and 14 min. Here, we report our findings resulting from a multifaceted follow-up programme consisting of optical spectroscopy, spectropolarimetry, imaging polarimetry, and multicolour fast photometry. We find that the source displays complex optical variability, which is best explained by the presence of a massive white dwarf exhibiting non-radial pulsations. An intermediate polar-like scenario involving a spinning magnetic white dwarf can be ruled out based on the detected changes in the observed periods. Based on our optical spectroscopy, we can constrain the mass and effective temperature of the white dwarf to be ~1.1 Msol and 16 100 K, respectively. The overall intrinsic flux level of the source is unusually high, suggesting that there remains significant residual emission from the accretion disc and/or the white dwarf even ten years after the 2014 outburst. Finally, we cannot detect any spectroscopic signatures from the donor star, making ASASSN-14dx a possible period bouncer system evolving towards a longer orbital period.
Personalized preference alignment for large language models (LLMs), the process of tailoring LLMs to individual users' preferences, is an emerging research direction spanning the area of NLP and personalization. In this survey, we present an analysis of works on personalized alignment and modeling for LLMs. We introduce a taxonomy of preference alignment techniques, including training time, inference time, and additionally, user-modeling based methods. We provide analysis and discussion on the strengths and limitations of each group of techniques and then cover evaluation, benchmarks, as well as open problems in the field.
We investigate the non-Gaussianity of second-order matter density perturbations induced by primordial gravitational waves (GWs). These tensor-induced scalar modes arise from local fluctuations in the GWs energy density, which is quadratic in tensor perturbations. The resulting second-order density contrast follows a chi-squared distribution, naturally exhibiting significant non-Gaussianity. We compute the bispectrum of these tensor-induced scalar modes and analyze its dependence on various primordial GWs power spectra, including scale-invariant, blue-tilted, Gaussian-bump, and monochromatic sources. We find that the bispectrum shape is inherently sensitive to the underlying GWs spectrum by construction. In particular, Gaussian-bump and monochromatic sources produce a strong signal peaking in the equilateral configuration, similar to the effect of scalar-induced tensor modes. Our findings reveal a new way to probe primordial GWs via galaxy surveys and highlight a unique feature of tensor-induced density perturbations, otherwise mimicking linear ones on sub-horizon scales.
Teaching pathology foundation models to accurately predict gene expression with parameter efficient knowledge transfer
2025-04-09
Shi Pan, Jianan Chen, Maria Secrier
Gene expression profiling provides critical insights into cellular heterogeneity, biological processes and disease mechanisms. There has been an increasing interest in computational approaches that can predict gene expression directly from digitalized histopathology images. While image foundation models have shown promise in a variety of pathology downstream analysis, their performances on gene-expression prediction are still limited. Explicitly incorporating information from the transcriptomic models can help image models to address domain shift, yet the fine-tuning and alignment of foundation models can be expensive. In the work, we propose Parameter Efficient Knowledge trAnsfer (PEKA), a novel framework that leverages Block-Affine Adaptation and integrates knowledge distillation and structure alignment losses for cross-modal knowledge transfer. We evaluated PEKA for gene expression prediction using multiple spatial transcriptomics datasets (comprising 206,123 image tiles with matched gene expression profiles) that encompassed various types of tissue. PEKA achieved at least 5\% performance improvement over baseline foundation models while also outperforming alternative parameter-efficient fine-tuning strategies. We will release the code, datasets and aligned models after peer-review to facilitate broader adoption and further development for parameter efficient model alignment.
The Lyman-alpha and Continuum Origins Survey I: Survey description and Ly$α$ imaging
2025-04-09
Alexandra Le Reste, Claudia Scarlata, Matthew Hayes, Jens Melinder, Alberto Saldana-Lopez, Axel Runnholm, Yu-Heng Lin, Ricardo O. Amorín, Hakim Atek, Sanchayeeta Borthakur, Cody A. Carr, Sophia R. Flury, Mauro Giavalisco, Alaina Henry, Anne E. Jaskot, Zhiyuan Ji, Intae Jung, Floriane Leclercq, Rui Marques-Chaves, Stephan R. McCandliss, M. S. Oey, Göran Östlin, Swara Ravindranath, Daniel Schaerer, Trinh X. Thuan, Xinfeng Xu
Understanding the mechanisms driving the escape of ionizing or Lyman continuum (LyC) emission from the interstellar medium of galaxies is necessary to constrain the evolution of Reionization, and the sources responsible for it. While progress has been made into identifying the global galaxy properties linked to the escape fraction of ionizing radiation, f$_{esc}^{LyC}$, little is currently known about how spatially resolved galaxy properties impact this parameter. We present Hubble Space Telescope (HST) imaging data obtained as part of the Lyman $\alpha$ and Continuum Origins Survey (LaCOS). LaCOS consists of HST imaging in 5 filters covering rest-frame optical and UV bands for a subsample of 42 galaxies in the Low redshift Lyman Continuum Survey, 22 being Lyman continuum emitters ($f_{esc}^{LyC}=0.01-0.49$). These data allow for investigations of the connection between sub-kpc stellar and nebular properties, including Ly$\alpha$ emission, and $f_{esc}^{LyC}$. Here, we describe the sample selection, observations and data reduction methods. Additionally, we present results on the link between global and resolved Ly$\alpha$ photometry and $f_{esc}^{LyC}$. We find similar trends between global photometric observables ($L_{Ly\alpha}$, $EW_{Ly\alpha}$, $f_{esc}^{Ly\alpha}$, $r_{50}$, $\Sigma_{SFR}$) and $f_{esc}^{LyC}$ as previously found with spectroscopy, but the correlations generally show a slightly smaller degree of correlations. However, we do find strong correlations between Ly$\alpha$ observables ($L_{Ly\alpha}$,$EW_{Ly\alpha}$) and $f_{esc}^{LyC}$ when measured in a small aperture around the brightest UV source in each galaxy. We interpret these results as evidence that LyC photons escaping on the line-of-sight are contributed by a small number of UV-bright compact regions in most galaxies in our sample.
Weak Signals and Heavy Tails: Machine-learning meets Extreme Value Theory
2025-04-09
Stephan Clémençon, Anne Sabourin
The masses of data now available have opened up the prospect of discovering weak signals using machine-learning algorithms, with a view to predictive or interpretation tasks. As this survey of recent results attempts to show, bringing multivariate extreme value theory and statistical learning theory together in a common, non-parametric and non-asymptotic framework makes it possible to design and analyze new methods for exploiting the scarce information located in distribution tails in these purposes. This article reviews recently proved theoretical tools for establishing guarantees for supervised or unsupervised algorithms learning from a fraction of extreme data. These are mainly exponential maximal deviation inequalities tailored to low-probability regions and concentration results for stochastic processes empirically describing the behavior of extreme observations, their dependence structure in particular. Under appropriate assumptions of regular variation, several illustrative applications are then examined: classification, regression, anomaly detection, model selection via cross-validation. For these, generalization results are established inspired by the classical bounds in statistical learning theory. In the same spirit, it is also shown how to adapt the popular high-dimensional lasso technique in the context of extreme values for the covariates with generalization guarantees.
Adaptive Computation Pruning for the Forgetting Transformer
2025-04-09
Zhixuan Lin, Johan Obando-Ceron, Xu Owen He, Aaron Courville
The recently proposed Forgetting Transformer (FoX) incorporates a forget gate into softmax attention and has shown consistently better or on-par performance compared to the standard RoPE-based Transformer. Notably, many attention heads in FoX tend to forget quickly, causing their output at each timestep to rely primarily on the local context. Based on this observation, we propose Adaptive Computation Pruning (ACP) for FoX, a method that dynamically prunes computations involving input-output dependencies that are strongly decayed by the forget gate. This is achieved using a dynamically set pruning threshold that ensures that the pruned attention weights remain negligible. We apply ACP to language model pretraining with FoX and show it consistently reduces the number of FLOPs in softmax attention by around 70% across different model sizes and context lengths, resulting in a roughly 10% to 35% improvement in training throughput. Furthermore, longer context lengths yield greater computational savings. All these speed improvements are achieved without any performance degradation. We also perform several analyses to provide deeper insights into our method, such as examining the pruning patterns and analyzing the distribution of FLOP savings across different attention heads. Our code is available at https://github.com/zhixuan-lin/arctic-fox.
Agents powered by Large Language Models (LLMs) have recently demonstrated impressive capabilities in various tasks. Still, they face limitations in tasks requiring specific, structured knowledge, flexibility, or accountable decision-making. While agents are capable of perceiving their environments, forming inferences, planning, and executing actions towards goals, they often face issues such as hallucinations and lack of contextual memory across interactions. This paper explores how Case-Based Reasoning (CBR), a strategy that solves new problems by referencing past experiences, can be integrated into LLM agent frameworks. This integration allows LLMs to leverage explicit knowledge, enhancing their effectiveness. We systematically review the theoretical foundations of these enhanced agents, identify critical framework components, and formulate a mathematical model for the CBR processes of case retrieval, adaptation, and learning. We also evaluate CBR-enhanced agents against other methods like Chain-of-Thought reasoning and standard Retrieval-Augmented Generation, analyzing their relative strengths. Moreover, we explore how leveraging CBR's cognitive dimensions (including self-reflection, introspection, and curiosity) via goal-driven autonomy mechanisms can further enhance the LLM agent capabilities. Contributing to the ongoing research on neuro-symbolic hybrid systems, this work posits CBR as a viable technique for enhancing the reasoning skills and cognitive aspects of autonomous LLM agents.
Correcting for interloper contamination in the power spectrum with neural networks
2025-04-09
Marina S. Cagliari, Azadeh Moradinezhad Dizgah, Francisco Villaescusa-Navarro
Modern slitless spectroscopic surveys, such as Euclid and the Roman Space Telescope, collect vast numbers of galaxy spectra but suffer from low signal-to-noise ratios. This often leads to incorrect redshift assignments when relying on a single emission line, due to noise spikes or contamination from non-target emission lines, commonly referred to as redshift interlopers. We propose a machine learning approach to correct the impact of interlopers at the level of measured summary statistics, focusing on the power spectrum monopole and line interlopers as a proof of concept. To model interloper effects, we use halo catalogs from the Quijote simulations as proxies for galaxies, displacing a fraction of halos by the distance corresponding to the redshift offset between target and interloper galaxies. This yields contaminated catalogs with varying interloper fractions across a wide range of cosmologies from the Quijote suite. We train a neural network on the power spectrum monopole, alone or combined with the bispectrum monopole, from contaminated mocks to estimate the interloper fraction and reconstruct the cleaned power spectrum. We evaluate performance in two settings: one with fixed cosmology and another where cosmological parameters vary under broad priors. In the fixed case, the network recovers the interloper fraction and corrects the power spectrum to better than 1% accuracy. When cosmology varies, performance degrades, but adding bispectrum information significantly improves results, reducing the interloper fraction error by 40-60%. We also study the method's performance as a function of the size of the training set and find that optimal strategies depend on the correlation between target and interloper samples: bispectrum information aids performance when target and interloper galaxies are uncorrelated, while tighter priors are more effective when the two are strongly correlated.
Data Augmentation for Fake Reviews Detection in Multiple Languages and Multiple Domains
2025-04-09
Ming Liu, Massimo Poesio
With the growth of the Internet, buying habits have changed, and customers have become more dependent on the online opinions of other customers to guide their purchases. Identifying fake reviews thus became an important area for Natural Language Processing (NLP) research. However, developing high-performance NLP models depends on the availability of large amounts of training data, which are often not available for low-resource languages or domains. In this research, we used large language models to generate datasets to train fake review detectors. Our approach was used to generate fake reviews in different domains (book reviews, restaurant reviews, and hotel reviews) and different languages (English and Chinese). Our results demonstrate that our data augmentation techniques result in improved performance at fake review detection for all domains and languages. The accuracy of our fake review detection model can be improved by 0.3 percentage points on DeRev TEST, 10.9 percentage points on Amazon TEST, 8.3 percentage points on Yelp TEST and 7.2 percentage points on DianPing TEST using the augmented datasets.
2025-04-09
Sheng Lu, Ilia Kuznetsov, Iryna Gurevych
Peer review is central to academic publishing, but the growing volume of submissions is straining the process. This motivates the development of computational approaches to support peer review. While each review is tailored to a specific paper, reviewers often make assessments according to certain aspects such as Novelty, which reflect the values of the research community. This alignment creates opportunities for standardizing the reviewing process, improving quality control, and enabling computational support. While prior work has demonstrated the potential of aspect analysis for peer review assistance, the notion of aspect remains poorly formalized. Existing approaches often derive aspect sets from review forms and guidelines of major NLP venues, yet data-driven methods for aspect identification are largely underexplored. To address this gap, our work takes a bottom-up approach: we propose an operational definition of aspect and develop a data-driven schema for deriving fine-grained aspects from a corpus of peer reviews. We introduce a dataset of peer reviews augmented with aspects and show how it can be used for community-level review analysis. We further show how the choice of aspects can impact downstream applications, such as LLM-generated review detection. Our results lay a foundation for a principled and data-driven investigation of review aspects, and pave the path for new applications of NLP to support peer review.
The Dirac Equation, Mass and Arithmetic by Permutations of Automaton States
2025-04-09
Hans-Thomas Elze
The cornerstones of the Cellular Automaton Interpretation of Quantum Mechanics are its underlying ontological states that evolve by permutations. They do not create would-be quantum mechanical superposition states. We review this with a classical automaton consisting of an Ising spin chain which is then related to the Weyl equation in the continuum limit. Based on this and generalizing, we construct a new ``Necklace of Necklaces'' automaton with a torus-like topology that lends itself to represent the Dirac equation in 1 + 1 dimensions. Special attention has to be paid to its mass term, which necessitates this enlarged structure and a particular scattering operator contributing to the step-wise updates of the automaton. As discussed earlier, such deterministic models of discrete spins or bits unavoidably become quantum mechanical, when only slightly deformed.
Bayesian Component Separation for DESI LAE Automated Spectroscopic Redshifts and Photometric Targeting
2025-04-09
Ana Sofía M. Uzsoy, Andrew K. Saydjari, Arjun Dey, Anand Raichoor, Douglas P. Finkbeiner, Eric Gawiser, Kyoung-Soo Lee, Steven Ahlen, Davide Bianchi, David Brooks, Todd Claybaugh, Andrei Cuceu, Axel de la Macorra, Peter Doel, Andreu Font-Ribera, Jaime E. Forero-Romero, Enrique Gaztañaga, Satya Gontcho A Gontcho, Gaston Gutierrez, Mustapha Ishak, Robert Kehoe, David Kirkby, Anthony Kremin, Martin Landriau, Laurent Le Guillou, Aaron Meisner, Ramon Miquel, John Moustakas, Nathalie Palanque-Delabrouille, Francisco Prada, Ignasi Pérez-Ràfols, Graziano Rossi, Eusebio Sanchez, David Schlegel, Michael Schubnell, Hee-Jong Seo, David Sprayberry, Gregory Tarlé, Benjamin Alan Weaver, Hu Zou
Lyman Alpha Emitters (LAEs) are valuable high-redshift cosmological probes traditionally identified using specialized narrow-band photometric surveys. In ground-based spectroscopy, it can be difficult to distinguish the sharp LAE peak from residual sky emission lines using automated methods, leading to misclassified redshifts. We present a Bayesian spectral component separation technique to automatically determine spectroscopic redshifts for LAEs while marginalizing over sky residuals. We use visually inspected spectra of LAEs obtained using the Dark Energy Spectroscopic Instrument (DESI) to create a data-driven prior and can determine redshift by jointly inferring sky residual, LAE, and residual components for each individual spectrum. We demonstrate this method on 910 spectroscopically observed $z = 2-4$ DESI LAE candidate spectra and determine their redshifts with $>$90% accuracy when validated against visually inspected redshifts. Using the $\Delta \chi^2$ value from our pipeline as a proxy for detection confidence, we then explore potential survey design choices and implications for targeting LAEs with medium-band photometry. This method allows for scalability and accuracy in determining redshifts from DESI spectra, and the results provide recommendations for LAE targeting in anticipation of future high-redshift spectroscopic surveys.
Integrating Cognitive Processing Signals into Language Models: A Review of Advances, Applications and Future Directions
2025-04-09
Angela Lopez-Cardona, Sebastian Idesis, Ioannis Arapakis
Recently, the integration of cognitive neuroscience in Natural Language Processing (NLP) has gained significant attention. This article provides a critical and timely overview of recent advancements in leveraging cognitive signals, particularly Eye-tracking (ET) signals, to enhance Language Models (LMs) and Multimodal Large Language Models (MLLMs). By incorporating user-centric cognitive signals, these approaches address key challenges, including data scarcity and the environmental costs of training large-scale models. Cognitive signals enable efficient data augmentation, faster convergence, and improved human alignment. The review emphasises the potential of ET data in tasks like Visual Question Answering (VQA) and mitigating hallucinations in MLLMs, and concludes by discussing emerging challenges and research trends.
Integrated Sensing and Communications Over the Years: An Evolution Perspective
2025-04-09
Di Zhang, Yuanhao Cui, Xiaowen Cao, Nanchi Su, Fan Liu, Xiaojun Jing, J. Andrew Zhang, Jie Xu, Christos Masouros, Dusit Niyato, Marco Di Renzo
Integrated Sensing and Communications (ISAC) enables efficient spectrum utilization and reduces hardware costs for beyond 5G (B5G) and 6G networks, facilitating intelligent applications that require both high-performance communication and precise sensing capabilities. This survey provides a comprehensive review of the evolution of ISAC over the years. We examine the expansion of the spectrum across RF and optical ISAC, highlighting the role of advanced technologies, along with key challenges and synergies. We further discuss the advancements in network architecture from single-cell to multi-cell systems, emphasizing the integration of collaborative sensing and interference mitigation strategies. Moreover, we analyze the progress from single-modal to multi-modal sensing, with a focus on the integration of edge intelligence to enable real-time data processing, reduce latency, and enhance decision-making. Finally, we extensively review standardization efforts by 3GPP, IEEE, and ITU, examining the transition of ISAC-related technologies and their implications for the deployment of 6G networks.
Open Problems and a Hypothetical Path Forward in LLM Knowledge Paradigms
2025-04-09
Xiaotian Ye, Mengqi Zhang, Shu Wu
Knowledge is fundamental to the overall capabilities of Large Language Models (LLMs). The knowledge paradigm of a model, which dictates how it encodes and utilizes knowledge, significantly affects its performance. Despite the continuous development of LLMs under existing knowledge paradigms, issues within these frameworks continue to constrain model potential. This blog post highlight three critical open problems limiting model capabilities: (1) challenges in knowledge updating for LLMs, (2) the failure of reverse knowledge generalization (the reversal curse), and (3) conflicts in internal knowledge. We review recent progress made in addressing these issues and discuss potential general solutions. Based on observations in these areas, we propose a hypothetical paradigm based on Contextual Knowledge Scaling, and further outline implementation pathways that remain feasible within contemporary techniques. Evidence suggests this approach holds potential to address current shortcomings, serving as our vision for future model paradigms. This blog post aims to provide researchers with a brief overview of progress in LLM knowledge systems, while provide inspiration for the development of next-generation model architectures.
Evaluating amyloid-beta as a surrogate endpoint in trials of anti-amyloid drugs in Alzheimer's disease: a Bayesian meta-analysis
2025-04-09
Sa Ren, Janharpreet Singh, Sandro Gsteiger, Christopher Cogley, Ben Reed, Keith R Abrams, Dalia Dawoud, Rhiannon K Owen, Paul Tappenden, Terrence J Quinn, Sylwia Bujkiewicz
The use of amyloid-beta (A$\beta$) clearance to support regulatory approvals of drugs in Alzheimer's disease (AD) remains controversial. We evaluate A$\beta$ as a potential trial-level surrogate endpoint for clinical function in AD using a meta-analysis. Randomised controlled trials (RCTs) reporting data on the effectiveness of anti- A$\beta$ monoclonal antibodies (MABs) on A$\beta$ and clinical outcomes were identified through a literature review. A Bayesian bivariate meta-analysis was used to evaluate surrogate relationships between the treatment effects on A$\beta$ and clinical function, with the intercept, slope and variance quantifying the trial level association. The analysis was performed using RCT data both collectively across all MABs and separately for each MAB through subgroup analysis. The latter analysis was extended by applying Bayesian hierarchical models to borrow information across treatments. We identified 23 RCTs with 39 treatment contrasts for seven MABs. The association between treatment effects on A$\beta$ and Clinical Dementia Rating - Sum of Boxes (CDR-SOB) across all MABs was strong: with intercept of -0.03 (95% credible intervals: -0.16, 0.11), slope of 1.41 (0.60, 2.21) and variance of 0.02 (0.00, 0.05). For individual treatments, the surrogate relationships were suboptimal, displaying large uncertainty. The use of hierarchical models considerably reduced the uncertainty around key parameters, narrowing the intervals for the slopes by an average of 71% (range: 51%-95%) and for the variances by 28% (7%-65%). Our results suggest that A$\beta$ is a potential surrogate endpoint for CDR-SOB when assuming a common surrogate relationship across all MABs. When allowing for information-sharing, the surrogate relationships improved, but only for lecanemab and aducanumab was the improvement sufficient to support a surrogate relationship.
The ALMA-ATOMS survey: A sample of weak hot core candidates identified through line stacking
2025-04-09
Zi-Yang Li, Xunchuan Liu, Tie Liu, Sheng-Li Qin, Paul F. Goldsmith, Pablo García, Yaping Peng, Li Chen, Yunfan Jiao, Zhiping Kou, Chuanshou Li, Jiahang Zou, Mengyao Tang, Shanghuo Li, Meizhu Liu, Guido Garay, Fengwei Xu, Wenyu Jiao, Qiu-Yi Luo, Suinan Zhang, Qi-Lao Gu, Xiaofeng Mai, Yan-Kun Zhang, Jixiang Weng, Chang Won Lee, Patricio Sanhueza, Sami Dib, Swagat R. Das, Xindi Tang, Leonardo Bronfman, Prasanta Gorai, Ken'ichi Tatematsu, Hong-Li Liu, Dongting Yang, Zhenying Zhang, Xianjin Shen
Hot cores represent critical astrophysical environments for high-mass star formation, distinguished by their rich spectra of organic molecular emission lines. We aim to utilize high-angular resolution molecular line data from ALMA to identify hot cores, with a particular focus on weak-emission candidates, and to provide one of the largest samples of hot core candidates. We propose to use spectral stacking and imaging techniques of complex organic molecules (COMs) in the ALMA-ATOMS survey, including line identification & weights, segmentation of line datacubes, resampling, stacking and normalization, moment 0 maps, and data analysis, to search for hot core candidates. We classify cores with dense emission of CH3OH and at least one molecule from the other six molecules as hot core candidates. In addition to the existing sample of 60 strong hot cores from the ALMA-ATOMS survey, we have detected 40 new weak candidates through stacking. All hot core candidates display compact emission from at least one of the other six COM species. For the strong sample, the stacking method provides molecular column density estimates that are consistent with previous fitting results. For the newly identified weak candidates, all species except CH3CHO show compact emission in the stacked image, which cannot be fully resolved spatially. These weak candidates exhibit column densities of COMs that are approximately one order of magnitude lower than those of the strong sample. The entire hot core sample, including the weak candidates, reveals tight correlations between the compact emission of CH3OH and other COM species, suggesting they may share a similar chemical environment for COMs, with CH3OH potentially acting as a precursor for other COMs. The molecular line stacking technique is used to identify hot core candidates in this work, leading to the identification of 40 new hot core candidates.
What if we find nothing? Bayesian analysis of the statistical information of null results in future exoplanet habitability and biosignature surveys
2025-04-09
Daniel Angerhausen, Amedeo Balbi, Andjelka B. Kovačević, Emily O. Garvin, Sascha P. Quanz
Future telescopes will survey temperate, terrestrial exoplanets to estimate the frequency of habitable ($\eta_{\text{Hab}}$) or inhabited ($\eta_{\text{Life}}$) planets. This study aims to determine the minimum number of planets ($N$) required to draw statistically significant conclusions, particularly in the case of a null result (i.e., no detections). Using a Bayesian framework, we analyzed surveys of up to $N=100$ planets to infer the frequency of a binary observable feature ($\eta_{\text{obs}}$) after null results. Posterior best fits and upper limits were derived for various survey sizes and compared with predicted yields from missions like the Large Interferometer for Exoplanets (LIFE) and the Habitable Worlds Observatory (HWO). Our findings indicate that $N=20-50$ ``perfect'' observations (100\% confidence in detecting or excluding the feature) yield conclusions relatively independent of priors. To achieve 99.9\% upper limits of $\eta_{\text{obs}} \leq 0.2/0.1$, approximately $N \simeq 40/80$ observations are needed. For ``imperfect'' observations, uncertainties in interpretation and sample biases become limiting factors. We show that LIFE and HWO aim for sufficiently large survey sizes to provide statistically meaningful estimates of habitable environments and life prevalence under these assumptions. However, robust conclusions require careful sample selection and high-confidence detection or exclusion of features in each observation.
Photobombing for the Large Interferometer For Exoplanets (LIFE). A new criterion for target confusion and application to a MIR rotating nulling interferometer
2025-04-09
Drinor Cacaj, Daniel Angerhausen, Prabal Saxena, Romain Laugier, Jens Kammerer, Eleonora Alei, Sascha P. Quanz
One of the primary objectives in modern astronomy is to discover and study planets with characteristics similar to Earth. This pursuit involves analyzing the spectra of exoplanets and searching for biosignatures. Contamination of spectra by nearby objects (e.g., other planets and moons in the same system) is a significant concern and must be addressed for future exo-Earth searching missions. The aim is to estimate, for habitable planets, the probability of spectral contamination by other planets within the same star system. This investigation focuses on the Large Interferometer for Exoplanets (LIFE). Since the Rayleigh criterion is inapplicable to interferometers such as those proposed for LIFE, we present new criteria based on the principle of parsimony, which take into account two types of issues: contamination or blending of point sources, and cancellation of point sources due to destructive interference. We define a new spatial resolution metric associated with contamination or cancellation that generalizes to a broader family of observing instruments. In the current baseline design, LIFE is an X-array architecture nulling interferometer. Our investigation reveals that its transmission map introduces the potential for two point sources to appear as one, even if they do not appear in close proximity. We find that LIFE has a spatial resolution comparable to that of a traditional telescope with a diameter of $D = 600\,\text{m}$, observing at $\lambda = 4 \,\mu\text{m}$. Our survey of a star system population shows that, out of 73.4 expected habitable planets detected, 71.3 are not contaminated on average.
Evidence of star cluster migration and merger in dwarf galaxies
2025-04-09
Mélina Poulain, Rory Smith, Pierre-Alain Duc, Francine R. Marleau, Rebecca Habas, Patrick R. Durrell, Jérémy Fensch, Sungsoon Lim, Oliver Müller, Sanjaya Paudel, Rubén Sánchez-Janssen
Nuclear star clusters (NSCs) are the densest stellar systems in the Universe. They can be found at the center of all galaxy types, but tend to favor galaxies of intermediate stellar mass around 10$^9\,$M$_{\odot}$[1, 2]. Currently, two main processes are under debate to explain their formation: in-situ star-formation from gas infall[3] and migration and merging of globular clusters (GCs) caused by dynamical friction[4]. Studies[5-9] of NSC stellar populations suggest that the former predominates in massive galaxies, the latter prevails in dwarf galaxies, and both contribute equally at intermediate mass. However, up to now, no ongoing merger of GCs has yet been observed to confirm this scenario. Here we report the serendipitous discovery of five dwarf galaxies with complex nuclear regions, characterized by multiple nuclei and tidal tails, using high resolution images from the Hubble Space Telescope. These structures have been reproduced in complementary N-body simulations, supporting the interpretation that they result from migrating and merging of star clusters. The small detection rate and short simulated timescales (below 100 Myr) of this process may explain why this has not been observed previously. This study highlights the need of large surveys with high resolution to fully map the migration scenario steps.
A Survey of New Mid-Band/FR3 for 6G: Channel Measurement, Characterization and Modeling in Outdoor Environment
2025-04-09
Haiyang Miao, Jianhua Zhang, Pan Tang, Jie Meng, Qi Zhen, Ximan Liu, Enrui Liu, Peijie Liu, Lei Tian, Guangyi Liu
The new mid-band (6-24 GHz) has attracted significant attention from both academia and industry, which is the spectrum with continuous bandwidth that combines the coverage benefits of low frequency with the capacity advantages of high frequency. Since outdoor environments represent the primary application scenario for mobile communications, this paper presents the first comprehensive review and summary of multi-scenario and multi-frequency channel characteristics based on extensive outdoor new mid-band channel measurement data, including UMa, UMi, and O2I. Specifically, a survey of the progress of the channel characteristics is presented, such as path loss, delay spread, angular spread, channel sparsity, capacity and near-field spatial non-stationary characteristics. Then, considering that satellite communication will be an important component of future communication systems, we examine the impact of clutter loss in air-ground communications. Our analysis of the frequency dependence of mid-band clutter loss suggests that its impact is not significant. Additionally, given that penetration loss is frequency-dependent, we summarize its variation within the FR3 band. Based on experimental results, comparisons with the standard model reveal that while the 3GPP TR 38.901 model remains a useful reference for penetration loss in wood and glass, it shows significant deviations for concrete and glass, indicating the need for further refinement. In summary, the findings of this survey provide both empirical data and theoretical support for the deployment of mid-band in future communication systems, as well as guidance for optimizing mid-band base station deployment in the outdoor environment. This survey offers the reference for improving standard models and advancing channel modeling.
Learning global control of underactuated systems with Model-Based Reinforcement Learning
2025-04-09
Niccolò Turcato, Marco Calì, Alberto Dalla Libera, Giulio Giacomuzzo, Ruggero Carli, Diego Romeres
This short paper describes our proposed solution for the third edition of the "AI Olympics with RealAIGym" competition, held at ICRA 2025. We employed Monte-Carlo Probabilistic Inference for Learning Control (MC-PILCO), an MBRL algorithm recognized for its exceptional data efficiency across various low-dimensional robotic tasks, including cart-pole, ball \& plate, and Furuta pendulum systems. MC-PILCO optimizes a system dynamics model using interaction data, enabling policy refinement through simulation rather than direct system data optimization. This approach has proven highly effective in physical systems, offering greater data efficiency than Model-Free (MF) alternatives. Notably, MC-PILCO has previously won the first two editions of this competition, demonstrating its robustness in both simulated and real-world environments. Besides briefly reviewing the algorithm, we discuss the most critical aspects of the MC-PILCO implementation in the tasks at hand: learning a global policy for the pendubot and acrobot systems.
Large-Scale (Semi-)Automated Security Assessment of Consumer IoT Devices -- A Roadmap
2025-04-09
Pascal Schöttle, Matthias Janetschek, Florian Merkle, Martin Nocker, Christoph Egger
The Internet of Things (IoT) has rapidly expanded across various sectors, with consumer IoT devices - such as smart thermostats and security cameras - experiencing growth. Although these devices improve efficiency and promise additional comfort, they also introduce new security challenges. Common and easy-to-explore vulnerabilities make IoT devices prime targets for malicious actors. Upcoming mandatory security certifications offer a promising way to mitigate these risks by enforcing best practices and providing transparency. Regulatory bodies are developing IoT security frameworks, but a universal standard for large-scale systematic security assessment is lacking. Existing manual testing approaches are expensive, limiting their efficacy in the diverse and rapidly evolving IoT domain. This paper reviews current IoT security challenges and assessment efforts, identifies gaps, and proposes a roadmap for scalable, automated security assessment, leveraging a model-based testing approach and machine learning techniques to strengthen consumer IoT security.
Universal profile for cosmic birefringence tomography with radio galaxies
2025-04-09
Fumihiro Naokawa
We propose a new method to tomographically probe cosmic birefringence using radio galaxies. We show that the redshift evolution of the cosmic birefringence angle induced by a slow-rolling pseudoscalar field, which is a candidate for dynamical dark energy, is independent of the detailed model of the pseudoscalor field. This universal profile evolves predominantly at $z\lesssim10$. In contrast, if the origin is a dark matter-like pseudscalor field, the resulting birefringence angle tends to be negligible in the low-redshift regime. This new insight provides a strong motivation to independently test the cosmic birefringence using polarized astrophysical sources such as radio galaxies. We find that a sample size of $\order{10^5-10^6}$ is required to distinguish the profiles, which is achievable with ongoing and upcoming radio surveys such as ASKAP or SKA.
Homogenization theory of random walks in degenerate random environment
2025-04-09
Sebastian Andres
Recent progress on the understanding of the Random Conductance Model is reviewed. A particular emphasis is on homogenization results such as functional central limit theorems, local limit theorems and heat kernel estimates for almost every realization of the environment, established for random walks among stationary ergodic conductances that are possibly unbounded but satisfy certain moment conditions.
Les Houches 2023 -- Physics at TeV Colliders: Report on the Standard Model Precision Wishlist
2025-04-09
Alexander Huss, Joey Huston, Stephen Jones, Mathieu Pellen, Raoul Röntsch
Les Houches returned to an in-person format in 2023 and the bi-yearly tradition of updating the standard model precision wishlist has continued. In this work we review recent progress (since Les Houches 2021) in fixed-order computations for LHC applications. In addition, necessary ingredients for such calculations such as parton distribution functions, amplitudes, and subtraction methods are discussed. Finally, we indicate processes and missing higher-order corrections that are required to reach the theoretical accuracy that matches the anticipated experimental precision.
Defects in Silicon Carbide as Quantum Qubits: Recent Advances in Defect Engineering
2025-04-09
Ivana Capan
This review provides an overview of defects in silicon carbide (SiC) with potential applications as quantum qubits. It begins with a brief introduction to quantum qubits and existing qubit platforms, outlining the essential criteria a defect must meet to function as a viable qubit. The focus then shifts to the most promising defects in SiC, notably the silicon vacancy (VSi) and divacancy (VC-VSi). A key challenge in utilizing these defects for quantum applications is their precise and controllable creation. Various fabrication techniques, including irradiation, ion implantation, femtosecond laser processing, and focused ion beam methods, have been explored to create these defects. Designed as a beginner-friendly resource, this review aims to support early-career experimental researchers entering the field of SiC-related quantum qubits. Providing an introduction to defect-based qubits in SiC offers valuable insights into fabrication strategies, recent progress, and the challenges that lie ahead.
Design and use of devices to assist movement of the upper limb: review of the literature
2025-04-09
Charlotte Le Goff, Pauline Coignard, Christine Azevedo-Coste, Franck Geffard, Charles Fattal
This article explores assistive devices for upper limb movement in people with disabilities through a systematic review based on the PRISMA methodology. The studied devices encompass technologies ranging from orthoses to advanced robotics, aiming to compensate for or supplement motor impairments. The results highlight the diversity of applications (rehabilitation, daily living activities), targeted body segments (distal, proximal, or global), as well as control mechanisms and interfaces used. However, despite the variety of promising prototypes, few devices are commercially available, limiting their real impact on end users. Existing technologies, while effective in improving functional autonomy and quality of life, still face challenges in terms of ergonomics, cost, and portability. In conclusion, this article emphasizes the importance of a user-centered approach and proposes avenues for the development of innovative, modular, and accessible assistive devices.
Deep Extragalactic VIsible Legacy Survey (DEVILS): New robust merger rates at intermediate redshifts
2025-04-09
Melissa F. Fuentealba-Fuentes, Luke J. M. Davies, Aaron S. G. Robotham, Robin H. W. Cook, Sabine Bellstedt, Claudia D. P. Lagos, Matías Bravo, Malgorzata Siudek
Mergers are fundamental to our understanding of the processes driving the evolution of the structure and morphology of galaxies, star formation, AGN activity, and the redistribution of stellar mass in the Universe. Determining the fraction and properties of mergers across cosmic time is critical to understanding the formation of the Universe we observe today. This fraction and its evolution also provide inputs and constraints for cosmological simulations, crucial for theoretical models of galaxy evolution. We present robust estimates of major close-pair fractions and merger rates at $0.2 < z < 0.9$ in the Deep Extragalactic VIsible Legacy Survey (DEVILS). We identify major mergers by selecting close-pairs with a projected spatial separation $r_{\mathrm{sep}} < 20$ h$^{-1}$ kpc and a radial velocity separation $v_{\mathrm{sep}} < 500$ km s$^{-1}$. For galaxies with stellar masses of log$_{10}$($M_\star$/$M_\odot$) = 10.66 $\pm$ 0.25 dex, we find a major close-pair fraction of $\approx 0.021$ at $0.2 < z < 0.34$ using a highly complete, unbiased spectroscopic sample. We extend these estimates to $0.2 < z < 0.9$ by combining the full probability distribution of redshifts for galaxies with high-quality spectroscopic, photometric, or grism measurements. Fitting a power-law $\gamma_{m} = A(1 + z)^m$, we find $A = 0.024 \pm 0.001$ and $m = 0.55 \pm 0.22$. Consistent with previous results, the shallow slope suggests weak redshift evolution in the merger fraction. When comparing with large hydrodynamical simulations, we also find consistent results. We convert close-pair fractions to merger rates using several literature prescriptions for merger timescales and provide all measurements for future studies.
Artificial Spin Ice: A Tutorial on Design and Control of Geometry, Microstate, Magnon Dynamics & Neuromorphic Computing
2025-04-09
Rawnak Sultana, Amrit Kumar Mondal, Vinayak Shantaram Bhat, Kilian Stenning, Yue Li, Daan M. Arroo, Aastha Vasdev, Margaret R. McCarter, Lance E. De Long, J. Todd Hastings, Jack C. Gartside, M. Benjamin Jungfleisch
Artificial spin ice, arrays of strongly interacting nanomagnets, are complex magnetic systems with many emergent properties, rich microstate spaces, intrinsic physical memory, high-frequency dynamics in the GHz range and compatibility with a broad range of measurement approaches. This tutorial article aims to provide the foundational knowledge needed to understand, design, develop, and improve the dynamic properties of artificial spin ice (ASI). Special emphasis is placed on introducing the theory of micromagnetics, which describes the complex dynamics within these systems, along with their design, fabrication methods, and standard measurement and control techniques. The article begins with a review of the historical background, introducing the underlying physical phenomena and interactions that govern artificial spin ice. We then explore standard experimental techniques used to prepare the microstate space of the nanomagnetic array and to characterize magnetization dynamics, both in artificial spin ice and more broadly in ferromagnetic materials. Finally, we introduce the basics of neuromorphic computing applied to the case of artificial spin ice systems with goal to help researchers new to the field grasp these exciting new developments.
One-Dimensional Relativistic Self-Gravitating Systems
2025-04-09
Robert B. Mann
One of the oldest problems in physics is that of calculating the motion of $N$ particles under a specified mutual force: the $N$-body problem. Much is known about this problem if the specified force is non-relativistic gravity, and considerable progress has been made by considering the problem in one spatial dimension. Here, I review what is known about the relativistic gravitational $N$-body problem. Reduction to one spatial dimension has the feature of the absence of gravitational radiation, thereby allowing for a clear comparison between the physics of one-dimensional relativistic and non-relativistic self-gravitating systems. After describing how to obtain a relativistic theory of gravity coupled to $N$ point particles, I discuss in turn the two-body, three-body, four-body, and $N$-body problems. Quite general exact solutions can be obtained for the two-body problem, unlike the situation in general relativity in three spatial dimensions for which only highly specified solutions exist. The three-body problem exhibits mild forms of chaos, and provides one of the first theoretical settings in which relativistic chaos can be studied. For $N\geq 4$, other interesting features emerge. Relativistic self-gravitating systems have a number of interesting problems awaiting further investigation, providing us with a new frontier for exploring relativistic many-body systems.
This paper presents a student-led activity designed to explore the use of statistical software in academic research across economics, political science, and statistics. Students reviewed replication files from major journals and repositories, gaining hands-on experience with reproducible workflows while contributing to cross-disciplinary datasets. Web-scraped metadata and student data collection, together covering more than 10,000 papers, reveal clear disciplinary patterns: Stata remains dominant in economics, while R is increasingly popular in political science and is the standard in statistics. Within the social sciences, a growing number of articles also use multiple software platforms within a single manuscript. Students reported increased understanding of academic workflows and greater awareness of software diversity in quantitative research. The activity is easy to adapt across course levels and disciplines, and we offer suggestions for follow-up assignments that reinforce key concepts in reproducibility and data fluency. The resulting insights into current software practices are also valuable for instructors seeking to align their teaching with evolving trends in research.
Bregman-Hausdorff divergence: strengthening the connections between computational geometry and machine learning
2025-04-09
Tuyen Pham, Hana Dal Poz Kouřimská, Hubert Wagner
The purpose of this paper is twofold. On a technical side, we propose an extension of the Hausdorff distance from metric spaces to spaces equipped with asymmetric distance measures. Specifically, we focus on the family of Bregman divergences, which includes the popular Kullback--Leibler divergence (also known as relative entropy). As a proof of concept, we use the resulting Bregman--Hausdorff divergence to compare two collections of probabilistic predictions produced by different machine learning models trained using the relative entropy loss. The algorithms we propose are surprisingly efficient even for large inputs with hundreds of dimensions. In addition to the introduction of this technical concept, we provide a survey. It outlines the basics of Bregman geometry, as well as computational geometry algorithms. We focus on algorithms that are compatible with this geometry and are relevant for machine learning.
A Multi-Phase Analysis of Blood Culture Stewardship: Machine Learning Prediction, Expert Recommendation Assessment, and LLM Automation
2025-04-09
Fatemeh Amrollahi, Nicholas Marshall, Fateme Nateghi Haredasht, Kameron C Black, Aydin Zahedivash, Manoj V Maddali, Stephen P. Ma, Amy Chang, MD Phar Stanley C Deresinski, Mary Kane Goldstein, Steven M. Asch, Niaz Banaei, Jonathan H Chen
Blood cultures are often over ordered without clear justification, straining healthcare resources and contributing to inappropriate antibiotic use pressures worsened by the global shortage. In study of 135483 emergency department (ED) blood culture orders, we developed machine learning (ML) models to predict the risk of bacteremia using structured electronic health record (EHR) data and provider notes via a large language model (LLM). The structured models AUC improved from 0.76 to 0.79 with note embeddings and reached 0.81 with added diagnosis codes. Compared to an expert recommendation framework applied by human reviewers and an LLM-based pipeline, our ML approach offered higher specificity without compromising sensitivity. The recommendation framework achieved sensitivity 86%, specificity 57%, while the LLM maintained high sensitivity (96%) but over classified negatives, reducing specificity (16%). These findings demonstrate that ML models integrating structured and unstructured data can outperform consensus recommendations, enhancing diagnostic stewardship beyond existing standards of care.
Test smells can compromise the reliability of test suites and hinder software maintenance. Although several strategies exist for detecting test smells, few address their removal. Traditional methods often rely on static analysis or machine learning, requiring significant effort and expertise. This study evaluates LLAMA 3.2 3B, GEMMA 2 9B, DEEPSEEK-R1 14B, and PHI 4 14B - small, open language models - for automating the detection and refactoring of test smells through agent-based workflows. We explore workflows with one, two, and four agents across 150 instances of 5 common test smell types extracted from real-world Java projects. Unlike prior approaches, ours is easily extensible to new smells via natural language definitions and generalizes to Python and Golang. All models detected nearly all test smell instances (pass@5 of 96% with four agents), with PHI 4 14B achieving the highest refactoring accuracy (pass@5 of 75.3%). Analyses were computationally inexpensive and ran efficiently on a consumer-grade hardware. Notably, PHI 4 14B with four agents performed within 5% of proprietary models such as O1-MINI, O3-MINI-HIGH, and GEMINI 2.5 PRO EXPERIMENTAL using a single agent. Multi-agent setups outperformed single-agent ones in three out of five test smell types, highlighting their potential to improve software quality with minimal developer effort. For the Assertion Roulette smell, however, a single agent performed better. To assess practical relevance, we submitted 10 pull requests with PHI 4 14B - generated code to open-source projects. Five were merged, one was rejected, and four remain under review, demonstrating the approach's real-world applicability.
Recent advances in language modeling have led to growing interest in applying Natural Language Processing (NLP) techniques to financial problems, enabling new approaches to analysis and decision-making. To systematically examine this trend, we review 374 NLP research papers published between 2017 and 2024 across 38 conferences and workshops, with a focused analysis of 221 papers that directly address finance-related tasks. We evaluate these papers across 11 qualitative and quantitative dimensions, identifying key trends such as the increasing use of general-purpose language models, steady progress in sentiment analysis and information extraction, and emerging efforts around explainability and privacy-preserving methods. We also discuss the use of evaluation metrics, highlighting the importance of domain-specific ones to complement standard machine learning metrics. Our findings emphasize the need for more accessible, adaptive datasets and highlight the significance of incorporating financial crisis periods to strengthen model robustness under real-world conditions. This survey provides a structured overview of NLP research applied to finance and offers practical insights for researchers and practitioners working at this intersection.
Finding evidence for human opinion and behavior at scale is a challenging task, often requiring an understanding of sophisticated thought patterns among vast online communities found on social media. For example, studying how gun ownership is related to the perception of Freedom, requires a retrieval system that can operate at scale over social media posts, while dealing with two key challenges: (1) identifying abstract concept instances, (2) which can be instantiated differently across different communities. To address these, we introduce ConceptCarve, an evidence retrieval framework that utilizes traditional retrievers and LLMs to dynamically characterize the search space during retrieval. Our experiments show that ConceptCarve surpasses traditional retrieval systems in finding evidence within a social media community. It also produces an interpretable representation of the evidence for that community, which we use to qualitatively analyze complex thought patterns that manifest differently across the communities.
Evolutionary algorithms meet self-supervised learning: a comprehensive survey
2025-04-09
Adriano Vinhas, João Correia, Penousal Machado
The number of studies that combine Evolutionary Machine Learning and self-supervised learning has been growing steadily in recent years. Evolutionary Machine Learning has been shown to help automate the design of machine learning algorithms and to lead to more reliable solutions. Self-supervised learning, on the other hand, has produced good results in learning useful features when labelled data is limited. This suggests that the combination of these two areas can help both in shaping evolutionary processes and in automating the design of deep neural networks, while also reducing the need for labelled data. Still, there are no detailed reviews that explain how Evolutionary Machine Learning and self-supervised learning can be used together. To help with this, we provide an overview of studies that bring these areas together. Based on this growing interest and the range of existing works, we suggest a new sub-area of research, which we call Evolutionary Self-Supervised Learning and introduce a taxonomy for it. Finally, we point out some of the main challenges and suggest directions for future research to help Evolutionary Self-Supervised Learning grow and mature as a field.
Joint Survey Processing. III. Compact Oddballs in the COSMOS Field -- Little Red Dots and Transients
2025-04-09
Yu-Heng Lin, Andreas L. Faisst, Ranga-Ram Chary, Anton M. Koekemoer, Joseph Masiero, Daniel Masters, Vihang Mehta, Harry I. Teplitz, Gregory L. Walth, John R. Weaver
We present the HST ACS G800L grism spectroscopy observation of the faint active galactic nuclei (AGN) candidates in the COSMOS field at redshift of 6 selected by the point-source morphology and the photometry drop-off at 8000\AA. Among the sample of 7 objects, only one is detected by multiple bands, and has similar shape of spectral energy distribution as the so-called ``little red dots'' JWST selected AGN candidates, but our object is 3 magnitude brighter than the JWST sample. We draw the upper limit of the AGN luminosity function $\Phi=1.1\times 10^{-7}$Mpc$^3$ mag$^{-1}$ for $M_{UV}$=$-21$ at redshift of 6. The rest of the sample shows inconsistent flux density when comparing magnitudes of HST ACS F814W to the Subaru $i$-band and $z$-band magnitudes combined. The HST ACS G800L grism observation shows that this inconsistency cannot be created from an emission line. Therefore, we speculate that these objects are transients with the light curve decay timescale at most 6 years in observed frame.
Galactic Accelerations from the GD-1 Stream Suggest a Tilted Dark Matter Halo
2025-04-09
Jacob Nibauer, Ana Bonaca
Cold dark matter halos are expected to be triaxial and often tilted relative to the stellar disk. Stellar streams provide a sensitive tracer of the Milky Way's halo shape, though models for the Galactic potential are typically limited to simple, symmetric functional forms. Here, we measure the Galactic acceleration field along the GD-1 stellar stream using a direct differentiation of the stream's track in phase-space. Using a fully data-driven catalog of stream members from Gaia, SDSS, LAMOST, and DESI, we map the stream in 6D phase-space. We fit splines to the stream track, and infer cylindrical acceleration components $a_R = -2.5 \pm_{0.1}^{0.2}, \ a_z = -1.8\pm 0.1, \ a_\phi = 0.2\pm 0.1~\rm{km \ s^{-1} \ Myr^{-1}}$ at $(R,z,\phi) = (11.9~\rm{kpc}, 7.3~\rm{kpc}, 171.1~\rm{deg})$. We measure mass enclosed within $14~\rm{kpc}$ of $1.4\pm 0.1 \times 10^{11} M_\odot$ and z-axis density flattening of $q_{\rho, z} = 0.81\pm^{0.06}_{0.03}$, both consistent with previous estimates. However, we find a 2$\sigma$ deviation from an axisymmetric acceleration field, which can be explained by a triaxial dark matter halo with axis ratios 1:0.75:0.70. The major axis of the halo is consistent with a tilt of $18~\rm{deg}$ above the Galactic plane in the direction of the Sun. The magnitude and direction of the tilt are consistent with measurements of the Milky Way's stellar halo from Gaia and the H3 survey. A tilted triaxial halo has important consequences for orbit-integration-based studies of the Galaxy, and can be further tested by deriving acceleration constraints from multiple streams.
COSMOS-Web: Unraveling the Evolution of Galaxy Size and Related Properties at $2
2025-04-09
Lilan Yang, Jeyhan S. Kartaltepe, Maximilien Franco, Xuheng Ding, Mark J. Achenbach, Rafael C. Arango-Toro, Caitlin M. Casey, Nicole E. Drakos, Andreas L. Faisst, Steven Gillman, Ghassem Gozaliasl, Marc Huertas-Company, Shuowen Jin, Daizhong Liu, Georgios Magdis, Richard Massey, John D. Silverman, Takumi S. Tanaka, Si-Yue Yu, Hollis B. Akins, Natalie Allen, Olivier Ilbert, Anton M. Koekemoer, Henry Joy McCracken, Louise Paquereau, Jason Rhodes, Brant E. Robertson, Marko Shuntov, Sune Toft
We measure galaxy sizes from $2 < z < 10$ using COSMOS-Web, the largest-area JWST imaging survey to date, covering $\sim$0.54 deg$^2$. We analyze the rest-frame optical (~5000A) size evolution and its scaling relation with stellar mass ($R_e\propto M_*^\alpha$) for star-forming and quiescent galaxies. For star-forming galaxies, the slope $\alpha$ remains approximately 0.20 at $2 < z < 8$, showing no significant evolution over this redshift range. At higher redshifts, the slopes are $-0.13 \pm 0.15$ and $0.37 \pm 0.36$ for $8 < z < 9$ and $9 < z < 10$, respectively. At fixed galaxy mass, the size evolution for star-forming galaxies follows $R_e \propto (1+z)^{-\beta}$, with $\beta = 1.21 \pm 0.05$. For quiescent galaxies, the slope is steeper $\alpha\sim 0.5$-$0.8$ at $2 < z < 5$, and $\beta=0.81\pm0.26$. We find that the size-mass relation is consistent between UV and optical at $z < 8$ for star-forming galaxies. However, we observe a decrease in the slope from UV to optical at $z > 8$, with a tentative negative slope in the optical at $8 < z < 9$, suggesting a complex interplay between intrinsic galaxy properties and observational effects such as dust attenuation. We discuss the ratio between galaxies' half-light radius, and underlying halos' virial radius, $R_{vir}$, and find the median value of $R_e/R_{vir}=2.7\%$. The star formation rate surface density evolves as $\log\Sigma_\text{SFR} = (0.20\pm0.08)\,z+(-0.65\pm0.51)$, and the $\Sigma_\text{SFR}$-$M_*$ relation remains flat at $2 3$ provides new insights into galaxy size and related properties in the rest-frame optical.
Denoising Diffusion Probabilistic Model for realistic and fast generated \textit{Euclid}-like data for weak lensing analysis
2025-04-09
Diana Scognamiglio, Jake H. Lee, Eric Huff, Sergi R. Hildebrandt, Shoubaneh Hemmati
Understanding and mitigating measurement systematics in weak lensing (WL) analysis requires large datasets of realistic galaxies with diverse morphologies and colors. Missions like Euclid, the Nancy Roman Space Telescope, and Vera C. Rubin Observatory's Legacy Survey of Space and Time will provide unprecedented statistical power and control over systematic uncertainties. Achieving the stringent shear measurement requirement of $\lvert m \rvert < 10^{-3}$ demands analyzing $10^9$ galaxies. Accurately modeling galaxy morphology is crucial, as it is shaped by complex astrophysical processes that are not yet fully understood. Subtle deviations in shape and structural parameters can introduce biases in shear calibration. The interplay between bulges, disks, star formation, and mergers contributes to morphological diversity, requiring simulations that faithfully reproduce these features to avoid systematics in shear measurements. Generating such a large and realistic dataset efficiently is feasible using advanced generative models like denoising diffusion probabilistic models (DDPMs). In this work, we extend Hubble Space Telescope (HST) data across Euclid's broad optical band using CANDELS and develop a generative AI tool to produce realistic Euclid-like galaxies while preserving morphological details. We validate our tool through visual inspection and quantitative analysis of galaxy parameters, demonstrating its capability to simulate realistic Euclid galaxy images, which will address WL challenges and enhance calibration for current and future cosmological missions.
Quantum Geometry: Revisiting electronic scales in quantum matter
2025-04-09
Nishchhal Verma, Philip J. W. Moll, Tobias Holder, Raquel Queiroz
Electronic properties of solids are often well understood via the low energy dispersion of Bloch bands, motivating single band approximations in many metals and semiconductors. However, a closer look reveals new length and time scales introduced by quantum dipole fluctuations due to interband mixing, which are reflected in the momentum space textures of the electronic wavefunctions. This structure is usually referred to as quantum geometry. The scales introduced by geometry not only qualitatively modify the linear and nonlinear responses of a material but can also have a vital role in determining the many-body ground state. This review explores how quantum geometry impacts properties of materials and outlines recent experimental advances that have begun to explore quantum geometric effects in various condensed matter platforms. We discuss the separation of scales that can allow us to estimate the significance of quantum geometry in various response functions.
Trustworthy AI encompasses many aspirational aspects for aligning AI systems with human values, including fairness, privacy, robustness, explainability, and uncertainty quantification. However, efforts to enhance one aspect often introduce unintended trade-offs that negatively impact others, making it challenging to improve all aspects simultaneously. In this position paper, we review notable approaches to these five aspects and systematically consider every pair, detailing the negative interactions that can arise. For example, applying differential privacy to model training can amplify biases in the data, undermining fairness. Drawing on these findings, we take the position that addressing trustworthiness along each axis in isolation is insufficient. Instead, research on Trustworthy AI must account for intersectionality between aspects and adopt a holistic view across all relevant axes at once. To illustrate our perspective, we provide guidance on how researchers can work towards integrated trustworthiness, a case study on how intersectionality applies to the financial industry, and alternative views to our position.
Artificial intelligence in creating, representing or expressing an immersive soundscape
2025-04-09
Rima Ayoubi, Laurent Lescop, Sang Bum Park
In today's tech-driven world, significant advancements in artificial intelligence and virtual reality have emerged. These developments drive research into exploring their intersection in the realm of soundscape. Not only do these technologies raise questions about how they will revolutionize the way we design and create soundscapes, but they also draw significant inquiries into their impact on human perception, understanding, and expression of auditory environments. This paper aims to review and discuss the latest applications of artificial intelligence in this domain. It explores how artificial intelligence can be utilized to create a virtual reality immersive soundscape, exploiting its ability to recognize complex patterns in various forms of data. This includes translating between different modalities such as text, sounds, and animations as well as predicting and generating data across these domains. It addresses questions surrounding artificial intelligence's capacity to predict, detect, and comprehend soundscape data, ultimately aiming to bridge the gap between sound and other forms of human-readable data. 1.
Linguistic Interpretability of Transformer-based Language Models: a systematic review
2025-04-09
Miguel López-Otal, Jorge Gracia, Jordi Bernad, Carlos Bobed, Lucía Pitarch-Ballesteros, Emma Anglés-Herrero
Language models based on the Transformer architecture achieve excellent results in many language-related tasks, such as text classification or sentiment analysis. However, despite the architecture of these models being well-defined, little is known about how their internal computations help them achieve their results. This renders these models, as of today, a type of 'black box' systems. There is, however, a line of research -- 'interpretability' -- aiming to learn how information is encoded inside these models. More specifically, there is work dedicated to studying whether Transformer-based models possess knowledge of linguistic phenomena similar to human speakers -- an area we call 'linguistic interpretability' of these models. In this survey we present a comprehensive analysis of 160 research works, spread across multiple languages and models -- including multilingual ones -- that attempt to discover linguistic information from the perspective of several traditional Linguistics disciplines: Syntax, Morphology, Lexico-Semantics and Discourse. Our survey fills a gap in the existing interpretability literature, which either not focus on linguistic knowledge in these models or present some limitations -- e.g. only studying English-based models. Our survey also focuses on Pre-trained Language Models not further specialized for a downstream task, with an emphasis on works that use interpretability techniques that explore models' internal representations.
Constraining the [CII] luminosity function from the power spectrum of line intensity maps at redshift 3.6
2025-04-08
Elena Marcuzzo, Cristiano Porciani, Emilio Romano-Díaz, Prachi Khatri
Forthcoming measurements of the line-intensity-mapping power spectrum (PS) are expected to set precious constraints on several quantities of astrophysical and cosmological interest. Our study targets the [CII] luminosity function (LF) at high redshift, which is still highly uncertain, in particular at the faint end. As an example of future opportunities, we present forecasts for the Deep Spectroscopic Survey (DSS) that will be conducted with the Fred Young Submillimeter Telescope at $z \simeq 3.6$ and also make predictions for eventual $10\times$ wider and/or $\sqrt{10}\times$ more sensitive surveys. The halo-occupation properties of [CII] emitters in the MARIGOLD simulations provide us with the motivation to abundance match two versions of the ALPINE LF against the halo mass function. We employ the resulting luminosity-mass relation within the halo model to predict the expected PS signal and its uncertainty. Finally, we use Bayesian inference to analyse mock PS data and forecast what constraints could be achieved on the first two moments of the LF and on Schechter fits. Depending on the actual LF, the DSS will measure the clustering and shot-noise amplitudes of the PS with a signal-to-noise ratio of $\sim 3$ or higher. However, degeneracies with the bias parameter and redshift-space distortions make it unfeasible to extract the first moment of the LF. Even the widest and most sensitive survey we consider can only constrain it with a $50\%$ uncertainty. By jointly fitting the PS and the LF, we directly constrain Schechter-function parameters. We find that the normalisation and the cutoff luminosity are precisely and accurately measured while the faint-end slope remains highly uncertain (unless the true value approaches $-2$). Overall, increasing the survey sensitivity at fixed sky coverage yields greater improvements than covering a larger area at fixed sensitivity.
Paraxial fluids of light are a promising platform for exploring collective phenomena in a highly tunable environment. These systems, which map the propagation of light through nonlinear media onto the wavefunction of effective 2D quantum fluids, offer a complementary approach to traditional platforms such as cold atomic gases or superfluid helium. In this review, we present a detailed overview of the theoretical framework underlying paraxial fluids of light, including the nonlinear Schr\"odinger equation (NLSE) and its mapping to the 2D+1 Gross-Pitaevskii equation (GPE). We explore the hydrodynamic formulation of these systems and we provide a comparative analysis of fluids of light and cold atomic gases, examining key parameters and figures of merit. We then review the recent experimental advances and the experimental platforms currently used to realize paraxial fluids of light, including hot atomic vapors, photorefractive crystals, and thermo-optic media. Additionally, we question the geometry of the system extending the analogy from 2D+1 to lower or higher dimensions. Looking forward, we outline the potential future directions for the field, including the use of laser cooled atoms as nonlinear media, the study of two-component mixtures, and the exploration of quantum effects beyond the mean-field approximation. These developments promise to deepen our understanding of quantum fluids and potentially contribute to advances in quantum technologies.
A systematic method to identify runaways from star clusters produced from single-binary interactions: A case study of M67
2025-04-08
A. Herrera-Urquieta, N. Leigh, J. Pinto, G. Díaz-Cerda, S. M. Grondin, J. J. Webb, R. Mathieu, T. Ryu, A. Geller, M. Kounkel, S. Toonen, M. Vilaxa-Campos
One hypothesis for runaway stars (RSs) is that they are ejected from star clusters with high velocities relative to the cluster center-of-mass motion. There are two competing mechanisms for their production: supernova-based ejections in binaries, where one companion explodes, leaves no remnant, and launches the other companion at the instantaneous orbital velocity, and the disintegration of triples (or higher-order multiples), which produces a recoiled runaway binary (RB) and an RS. We search for RS candidates using data from the Gaia DR3 survey with a focus on triple disintegration since in this case the product is always a binary and a single star that should be moving in opposite directions. We created a systematic methodology to look for candidate RS-RB runaway pairs produced from the disintegration of bound three-body systems formed from single-binary interactions based on momentum conservation and causality. The method we use is general and can be applied to any cluster with a 5D kinematic data set. We used our criteria to search for these pairs in a 150 pc circular field of view surrounding the open cluster M67, which we used as a benchmark cluster to test the robustness of our method. Our results reveal only one RS-RB pair that is consistent with all of our selection criteria out of an initial sample of $10^8$ pairs.
Underwater Robotic Simulators Review for Autonomous System Development
2025-04-08
Sara Aldhaheri, Yang Hu, Yongchang Xie, Peng Wu, Dimitrios Kanoulas, Yuanchang Liu
The increasing complexity of underwater robotic systems has led to a surge in simulation platforms designed to support perception, planning, and control tasks in marine environments. However, selecting the most appropriate underwater robotic simulator (URS) remains a challenge due to wide variations in fidelity, extensibility, and task suitability. This paper presents a comprehensive review and comparative analysis of five state-of-the-art, ROS-compatible, open-source URSs: Stonefish, DAVE, HoloOcean, MARUS, and UNav-Sim. Each simulator is evaluated across multiple criteria including sensor fidelity, environmental realism, sim-to-real capabilities, and research impact. We evaluate them across architectural design, sensor and physics modeling, task capabilities, and research impact. Additionally, we discuss ongoing challenges in sim-to-real transfer and highlight the need for standardization and benchmarking in the field. Our findings aim to guide practitioners in selecting effective simulation environments and inform future development of more robust and transferable URSs.
Quantum-stabilized states in magnetic dipolar quantum gases
2025-04-08
Lauriane Chomaz
A decade ago, a universal stabilization mechanism driven by quantum fluctuations was discovered in ultracold Bose gases of highly magnetic atoms. This mechanism prevents these systems from collapsing and instead allows exotic states of matter to arise, including ultradilute quantum droplets, crystallized quantum states, and specifically supersolids. We review the experimental and theoretical progress in understanding these quantum-stabilized states, their emergence, and intriguing properties.
An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization
2025-04-08
Moncef Garouani
Considerable progress has been made in the recent literature studies to tackle the Algorithms Selection and Parametrization (ASP) problem, which is diversified in multiple meta-learning setups. Yet there is a lack of surveys and comparative evaluations that critically analyze, summarize and assess the performance of existing methods. In this paper, we provide an overview of the state of the art in this continuously evolving field. The survey sheds light on the motivational reasons for pursuing classifiers selection through meta-learning. In this regard, Automated Machine Learning (AutoML) is usually treated as an ASP problem under the umbrella of the democratization of machine learning. Accordingly, AutoML makes machine learning techniques accessible to domain scientists who are interested in applying advanced analytics but lack the required expertise. It can ease the task of manually selecting ML algorithms and tuning related hyperparameters. We comprehensively discuss the different phases of classifiers selection based on a generic framework that is formed as an outcome of reviewing prior works. Subsequently, we propose a benchmark knowledge base of 4 millions previously learned models and present extensive comparative evaluations of the prominent methods for classifiers selection based on 08 classification algorithms and 400 benchmark datasets. The comparative study quantitatively assesses the performance of algorithms selection methods along while emphasizing the strengths and limitations of existing studies.
HRMedSeg: Unlocking High-resolution Medical Image segmentation via Memory-efficient Attention Modeling
2025-04-08
Qing Xu, Zhenye Lou, Chenxin Li, Xiangjian He, Rong Qu, Tesema Fiseha Berhanu, Yi Wang, Wenting Duan, Zhen Chen
High-resolution segmentation is critical for precise disease diagnosis by extracting micro-imaging information from medical images. Existing transformer-based encoder-decoder frameworks have demonstrated remarkable versatility and zero-shot performance in medical segmentation. While beneficial, they usually require huge memory costs when handling large-size segmentation mask predictions, which are expensive to apply to real-world scenarios. To address this limitation, we propose a memory-efficient framework for high-resolution medical image segmentation, called HRMedSeg. Specifically, we first devise a lightweight gated vision transformer (LGViT) as our image encoder to model long-range dependencies with linear complexity. Then, we design an efficient cross-multiscale decoder (ECM-Decoder) to generate high-resolution segmentation masks. Moreover, we utilize feature distillation during pretraining to unleash the potential of our proposed model. Extensive experiments reveal that HRMedSeg outperforms state-of-the-arts in diverse high-resolution medical image segmentation tasks. In particular, HRMedSeg uses only 0.59GB GPU memory per batch during fine-tuning, demonstrating low training costs. Besides, when HRMedSeg meets the Segment Anything Model (SAM), our HRMedSegSAM takes 0.61% parameters of SAM-H. The code is available at https://github.com/xq141839/HRMedSeg.
On Soft Clustering For Correlation Estimators: Model Uncertainty, Differentiability, and Surrogates
2025-04-08
Edward Berman, Sneh Pandya, Jacqueline McCleary, Marko Shuntov, Caitlin Casey, Nicole Drakos, Andreas Faisst, Steven Gillman, Ghassem Gozaliasl, Natalie Hogg, Jeyhan Kartaltepe, Anton Koekemoer, Wilfried Mercier, Diana Scognamiglio, COSMOS-Web, :, The JWST Cosmic Origins Survey
Properly estimating correlations between objects at different spatial scales necessitates $\mathcal{O}(n^2)$ distance calculations. For this reason, most widely adopted packages for estimating correlations use clustering algorithms to approximate local trends. However, methods for quantifying the error introduced by this clustering have been understudied. In response, we present an algorithm for estimating correlations that is probabilistic in the way that it clusters objects, enabling us to quantify the uncertainty caused by clustering simply through model inference. These soft clustering assignments enable correlation estimators that are theoretically differentiable with respect to their input catalogs. Thus, we also build a theoretical framework for differentiable correlation functions and describe their utility in comparison to existing surrogate models. Notably, we find that repeated normalization and distance function calls slow gradient calculations and that sparse Jacobians destabilize precision, pointing towards either approximate or surrogate methods as a necessary solution to exact gradients from correlation functions. To that end, we close with a discussion of surrogate models as proxies for correlation functions. We provide an example that demonstrates the efficacy of surrogate models to enable gradient-based optimization of astrophysical model parameters, successfully minimizing a correlation function output. Our numerical experiments cover science cases across cosmology, from point spread function (PSF) modeling efforts to gravitational simulations to galaxy intrinsic alignment (IA).
2025-04-08
Artur Xarles, Sergio Escalera, Thomas B. Moeslund, Albert Clapés
Action Valuation (AV) has emerged as a key topic in Sports Analytics, offering valuable insights by assigning scores to individual actions based on their contribution to desired outcomes. Despite a few surveys addressing related concepts such as Player Valuation, there is no comprehensive review dedicated to an in-depth analysis of AV across different sports. In this survey, we introduce a taxonomy with nine dimensions related to the AV task, encompassing data, methodological approaches, evaluation techniques, and practical applications. Through this analysis, we aim to identify the essential characteristics of effective AV methods, highlight existing gaps in research, and propose future directions for advancing the field.
Unification of Conformal and Fuzzy Gravities with Internal Interactions resulting in SO(10) and a Possible Probe through Stochastic Gravitational Wave Background
2025-04-08
Gregory Patellis, Danai Roumelioti, Stelios Stefas, George Zoupanos
The unification of conformal and fuzzy gravities with internal interactions is based on the facts that i) the tangent group of a curved manifold and the manifold itself do not necessarily have the same dimensions and ii) both gravitational theories considered here have been formulated in a gauge theoretic way. We review the gauge-theoretic approach of gravities, commenting in particular on their diffeomorphism invariance, and the construction of conformal and noncommutative (fuzzy) gravity using the gauge-theoretic framework. Based on an extension of the four-dimensional tangent group, unification of both gravities with the internal interactions is achieved. Both unified schemes are examined at 1-loop level considering suitable spontaneous symmetry breakings to a SO(10) grand unified theory and consequently down to the Standard Model of particle physics through four specific spontaneous breaking channels. Each channel is examined against proton lifetime experimental bounds and its observation potential through gravitational signal from cosmic strings production is discussed.
The study of non-local boxes arose from the study of quantum entanglement and from the question: "why isn't entanglement more non-local?". Correlations stronger than quantum entanglement, but that still do not allow for instantaneous transmission of information have been known to exist. This report from 2011 reviews what was known about these objects at the time.
Dynamical Dark Energy in light of the DESI DR2 Baryonic Acoustic Oscillations Measurements
2025-04-08
Gan Gu, Xiaoma Wang, Yuting Wang, Gong-Bo Zhao, Levon Pogosian, Kazuya Koyama, John A. Peacock, Zheng Cai, Jorge L. Cervantes-Cota, Ruiyang Zhao, Steven Ahlen, Davide Bianchi, David Brooks, Todd Claybaugh, Shaun Cole, Axel de la Macorra, Arnaud de Mattia, Peter Doel, Simone Ferraro, Jaime E. Forero-Romero, Enrique Gaztañaga, Satya Gontcho A Gontcho, Gaston Gutierrez, ChangHoon Hahn, Cullan Howlett, Mustapha Ishak, Robert Kehoe, David Kirkby, Jean-Paul Kneib, Ofer Lahav, Martin Landriau, Laurent Le Guillou, Alexie Leauthaud, Michael Levi, Marc Manera, Aaron Meisner, Ramon Miquel, John Moustakas, Andrea Muñoz-Gutiérrez, Seshadri Nadathur, Jeffrey A. Newman, Nathalie Palanque-Delabrouille, Will Percival, Francisco Prada, Ignasi Pérez-Ràfols, Graziano Rossi, Lado Samushia, Eusebio Sanchez, David Schlegel, Hee-Jong Seo, Arman Shafieloo, David Sprayberry, Gregory Tarlé, Michael Walther, Benjamin Alan Weaver, Pauline Zarrouk, Cheng Zhao, Rongpu Zhou, Hu Zou
We investigate whether dark energy deviates from the cosmological constant ($\Lambda$CDM) by analyzing baryon acoustic oscillation (BAO) measurements from the Data Release 1 (DR1) and Data Release 2 (DR2) of DESI observations, in combination with Type Ia supernovae (SNe) and cosmic microwave background (CMB) distance information. We find that with the larger statistical power and wider redshift coverage of the DR2 dataset the preference for dynamical dark energy does not decrease and remains at approximately the same statistical significance as for DESI~DR1. Employing both a shape-function reconstruction and non-parametric methods with a correlation prior derived from Horndeski theory, we consistently find that the dark energy equation of state $w(z)$ evolves with redshift. While DESI DR1 and DR2 BAO data alone provide modest constraints, combining them with independent SNe samples (PantheonPlus, Union3, and the DES 5-year survey) and a CMB distance prior strengthens the evidence for dynamical dark energy. Bayesian model-selection tests show moderate support for dark energy dynamics when multiple degrees of freedom in $w(z)$ are allowed, pointing to increasing tension with $\Lambda$CDM at a level of roughly $3\sigma$ (or more in certain data combinations). Although the methodology adopted in this work is different from those used in companion DESI papers, we find consistent results, demonstrating the complementarity of dark energy studies performed by the DESI collaboration. Although possible systematic effects must be carefully considered, it currently seems implausible that $\Lambda$CDM will be rescued by future high-precision surveys, such as the complete DESI, Euclid, and next-generation CMB experiments. These results therefore highlight the possibility of new physics driving cosmic acceleration and motivate further investigation into dynamical dark energy models.
Computing for Community-Based Economies: A Sociotechnical Ecosystem for Democratic, Egalitarian and Sustainable Futures
2025-04-08
Kwame Porter Robinson, Ron Eglash, Lionel Robert, Audrey Bennett, Mark Guzdial, Michael Nayebare
Automation and industrial mass production, particularly in sectors with low wages, have harmful consequences that contribute to widening wealth disparities, excessive pollution, and worsened working conditions. Coupled with a mass consumption society, there is a risk of detrimental social outcomes and threats to democracy, such as misinformation and political polarization. But AI, robotics and other emerging technologies could also provide a transition to community-based economies, in which more democratic, egalitarian, and sustainable value circulations can be established. Based on both a review of case studies, and our own experiments in Detroit, we derive three core principles for the use of computing in community-based economies. The prefigurative principle requires that the development process itself incorporates equity goals, rather than viewing equity as something to be achieved in the future. The generative principle requires the prevention of value extraction, and its replacement by circulations in which value is returned back to the aspects of labor, nature, and society by which it is generated. And third, the solidarity principle requires that deployments at all scales and across all domains support both individual freedoms and opportunities for mutual aid. Thus we propose the use of computational technologies to develop a specifically generative form of community-based economy: one that is egalitarian regarding race, class and gender; sustainable both environmentally and socially; and democratic in the deep sense of putting people in control of their own lives and livelihoods.
Characteristic exciton energy scales in antiferromagnetic NiPS$_3$
2025-04-08
Jacob A. Warshauer, Huyongqing Chen, Qishuo Tan, Jing Tang, Xi Ling, Wanzheng Hu
Two-dimensional antiferromagnets are promising materials for spintronics. The van der Waals antiferromagnet NiPS$_3$ has attracted extensive interest due to its ultra-narrow exciton feature which is closely linked with the magnetic ordering. Here, we use time-resolved terahertz spectroscopy to investigate photo-excited carriers in NiPS$_3$. We identify the onset of interband transitions and estimate the exciton dissociation energy from the excitation wavelength and fluence dependence of the transient spectral weight. Our results provide key insights to quantify the exciton characteristics and validate the band structure for NiPS$_3$.
Have you tried turning it off and on again? Stochastic resetting for enhanced sampling
2025-04-08
Ofir Blumer, Barak Hirshberg
Molecular dynamics simulations are widely used across chemistry, physics, and biology, providing quantitative insight into complex processes with atomic detail. However, their limited timescale of a few microseconds is a significant obstacle in describing phenomena such as conformational transitions of biomolecules and polymorphism in molecular crystals. Recently, stochastic resetting, i.e., randomly stopping and restarting the simulations, emerged as a powerful enhanced sampling approach, which is collective variable-free, highly parallelized, and easily implemented in existing molecular dynamics codes. Resetting expedites sampling rare events while enabling the inference of kinetic observables of the underlying process. It can be employed as a standalone tool or in combination with other enhanced sampling methods, such as Metadynamics, with each technique compensating for the drawbacks of the other. Here, we comprehensively describe resetting and its theoretical background, review recent developments in stochastic resetting for enhanced sampling, and provide instructive guidelines for practitioners.
Efficient path-following is crucial in most of the applications of autonomous vehicles (UxV). Among various guidance strategies presented in literature, look-ahead distance ($L_1$)-based guidance method has received significant attention due to its ease in implementation and ability to maintain a low cross-track error while following simpler reference paths and generate bounded lateral acceleration commands. However, the constant value of $L_1$ becomes problematic when the UxV is far away from the reference path and also produce higher cross-track error while following complex reference paths having high variation in radius of curvature. To address these challenges, the notion of look-ahead distance is leveraged in a novel way to develop a two-phase guidance strategy. Initially, when the UxV is far from the reference path, an optimized $L_1$ selection strategy is developed to guide the UxV toward the reference path in order to maintain minimal lateral acceleration command. Once the vehicle reaches a close vicinity of the reference path, a novel notion of corrector point is incorporated in the constant $L_1$-based guidance scheme to generate the lateral acceleration command that effectively reduces the root mean square of the cross-track error thereafter. Simulation results demonstrate that this proposed corrector point and look-ahead point pair-based guidance strategy along with the developed midcourse guidance scheme outperforms the conventional constant $L_1$ guidance scheme both in terms of feasibility and measures of effectiveness like cross-track error and lateral acceleration requirements.
In this talk I review various notions of generalised global symmetry: higher-form, higher-group, and non-invertible symmetry. All these notions have had profound impact on quantum field theory research in the last decade. I highlight various applications of these new symmetries in particle physics, focussing on theories beyond the Standard Model. Areas touched upon include axions, gauge unification, dark matter, neutrino masses, and flavour hierarchies.
2025-04-08
Roberto Bruno, Roberto De Prisco, Ugo Vaccaro
This comprehensive survey examines the field of alphabetic codes, tracing their development from the 1960s to the present day. We explore classical alphabetic codes and their variants, analyzing their properties and the underlying mathematical and algorithmic principles. The paper covers the fundamental relationship between alphabetic codes and comparison-based search procedures and their applications in data compression, routing, and testing. We review optimal alphabetic code construction algorithms, necessary and sufficient conditions for their existence, and upper bounds on the average code length of optimal alphabetic codes. The survey also discusses variations and generalizations of the classical problem of constructing minimum average length alphabetic codes. By elucidating both classical results and recent findings, this paper aims to serve as a valuable resource for researchers and students, concluding with promising future research directions in this still-active field.
Rethinking Review Citations: Impact on Scientific Integrity
2025-04-08
Jesus S. Aguilar-Ruiz
The proliferation of surveys and review articles in academic journals has impacted citation metrics like impact factor and h-index, skewing evaluations of journal and researcher quality. This work investigates the implications of this trend, focusing on the field of Computer Science, where a notable increase in review publications has led to inflated citation counts and rankings. While reviews serve as valuable literature overviews, they should not overshadow the primary goal of research -to advance scientific knowledge through original contributions. We advocate for prioritizing citations of primary research in journal articles to uphold citation integrity and ensure fair recognition of substantive contributions. This approach preserves the reliability of citation-based metrics and supports genuine scientific advancement.
Pushing JWST to the extremes: search and scrutiny of bright galaxy candidates at z$\simeq$15-30
2025-04-08
M. Castellano, A. Fontana, E. Merlin, P. Santini, L. Napolitano, N. Menci, A. Calabrò, D. Paris, L. Pentericci, J. Zavala, M. Dickinson, S. L. Finkelstein, T. Treu, R. O. Amorin, P. Arrabal Haro, P. Bergamini, L. Bisigello, E. Daddi, P. Dayal, A. Dekel, A. Ferrara, F. Fortuni, G. Gandolfi, M. Giavalisco, C. Grillo, S. T. Guida, N. P. Hathi, B. W. Holwerda, A. M. Koekemoer, V. Kokorev, Z. Li, M. Llerena, R. A. Lucas, S. Mascia, B. Metha, T. Morishita, T. Nanayakkara, F. Pacucci, P. G. Pérez-González, G. Roberts-Borsani, G. Rodighiero, P. Rosati, V. Salazar, R. Schneider, R. S. Somerville, A. Taylor, M. Trenti, A. Trinca, X. Wang, P. J. Watson, L. Yang, L. Y. A. Yung
We designed customized Lyman-break color selection techniques to identify galaxy candidates in the redshift ranges $15 \leq z \leq 20$ and $20 \leq z \leq 28$. The selection was performed on the ASTRODEEP-JWST multi-band catalogs of the CEERS, Abell-2744, JADES, NGDEEP, and PRIMER survey fields, covering a total area of $\sim0.2$ sq. deg. We identify nine candidates at $15 \leq z \leq 20$, while no objects are found based on the $z\gtrsim20$ color selection criteria. Despite exhibiting a $>$1.5 mag break, all the objects display multimodal redshift probability distributions across different SED-fitting codes and methodologies. The alternative solutions correspond to poorly understood populations of low-mass quiescent or dusty galaxies at z$\sim$3-7. This conclusion is supported by the analysis of a NIRSpec spectrum recently acquired by the CAPERS program for one interloper object, which is confirmed to be a dusty ($E(B-V)=0.8$ mag) starburst galaxy at $z=6.56$. We measured the UV luminosity function under different assumptions on the contamination level within our sample. We find that if even a fraction of the candidates is indeed at $z\gtrsim15$, the resulting UV LF points to a very mild evolution compared to estimates at $z<15$, implying a significant tension with existing theoretical models. In particular, confirming our bright ($M_{\text{UV}}<-21$) candidates would require substantial revisions to the theoretical framework. In turn, if all these candidates will be confirmed to be interlopers, we conclude that future surveys may need ten times wider areas to select $M_{\text{UV}}\lesssim-20$ galaxies at $z>15$. Observations in the F150W and F200W filters at depths comparable to those in the NIRCam LW bands are also required to mitigate contamination from rare red objects at z$\lesssim$8.
Comprehensive UV and Optical spectral analysis of Cygnus X-1: Stellar and wind parameters, abundances, and evolutionary implications
2025-04-08
V. Ramachandran, A. A. C. Sander, L. M. Oskinova, E. C. Schoesser, D. Pauli, W. -R. Hamann, L. Mahy, M. Bernini-Peron, M. Brigitte, B. Kubátová
We present a comprehensive analysis of Cyg X-1, utilizing archival high-resolution UV and optical spectra in conjunction with sophisticated atmospheric models. Notably, this is the first investigation to simultaneously analyze UV and optical spectra of Cyg X-1 along with incorporating X-rays to constrain the stellar and wind parameters. Our analysis yields notably lower masses for both the donor (29$\,M_\odot$) and the black hole (12.7 to 17.8$\,M_\odot$, depending on inclination), and confirms that the donor's radius is close to reaching the inner Lagrangian point. We find super-solar Fe, Si, and Mg abundances (1.3-1.8 times solar) at the surface of the donor star, while the total CNO abundance remains solar despite evidence of CNO processing (N enrichment, O depletion) and He enrichment. This abundance pattern is distinct from the surrounding Cyg OB3 association. We observe a clear difference in wind parameters between X-ray states: $v_\infty \approx 1200\,\mathrm{km\,s}^{-1}$ and $\dot{M} \approx 3\times 10^{-7}\,M_\odot\,\mathrm{yr}^{-1}$ in the high/soft state, increasing to $v_\infty\lesssim 1800\,\mathrm{km\,s}^{-1}$ and $\dot{M}\lesssim 5\times 10^{-7}\,M_\odot\,\mathrm{yr}^{-1}$ in the low/hard state. The observed X-ray luminosity is consistent with wind-fed accretion. Evolutionary models show that Cyg X-1 will undergo Roche lobe overflow in the near future. Under a fully conservative mass accretion scenario, our models predict a future binary black hole merger for Cyg X-1 within $\sim5$ Gyr. Our comprehensive analysis provides refined stellar and wind parameters of the donor star in Cyg X-1, highlighting the importance of using advanced atmospheric models and considering X-ray ionization and wind clumping. The observed abundances suggest a complex formation history involving a high initial metallicity.
Context: Performance regressions negatively impact execution time and memory usage of software systems. Nevertheless, there is a lack of systematic methods to evaluate the effectiveness of performance test suites. Performance mutation testing, which introduces intentional defects (mutants) to measure and enhance fault-detection capabilities, is promising but underexplored. A key challenge is understanding if generated mutants accurately reflect real-world performance issues. Goal: This study evaluates and extends mutation operators for performance testing. Its objectives include (i) collecting existing performance mutation operators, (ii) introducing new operators from real-world code changes that impact performance, and (iii) evaluating these operators on real-world systems to see if they effectively degrade performance. Method: To this aim, we will (i) review the literature to identify performance mutation operators, (ii) conduct a mining study to extract patterns of code changes linked to performance regressions, (iii) propose new mutation operators based on these patterns, and (iv) apply and evaluate the operators to assess their effectiveness in exposing performance degradations. Expected Outcomes: We aim to provide an enriched set of mutation operators for performance testing, helping developers and researchers identify harmful coding practices and design better strategies to detect and prevent performance regressions.
3D spatial understanding is essential in real-world applications such as robotics, autonomous vehicles, virtual reality, and medical imaging. Recently, Large Language Models (LLMs), having demonstrated remarkable success across various domains, have been leveraged to enhance 3D understanding tasks, showing potential to surpass traditional computer vision methods. In this survey, we present a comprehensive review of methods integrating LLMs with 3D spatial understanding. We propose a taxonomy that categorizes existing methods into three branches: image-based methods deriving 3D understanding from 2D visual data, point cloud-based methods working directly with 3D representations, and hybrid modality-based methods combining multiple data streams. We systematically review representative methods along these categories, covering data representations, architectural modifications, and training strategies that bridge textual and 3D modalities. Finally, we discuss current limitations, including dataset scarcity and computational challenges, while highlighting promising research directions in spatial perception, multi-modal fusion, and real-world applications.
A Doppler backscattering (DBS) diagnostic has recently been installed on the Tokamak \`a Configuration Variable (TCV) to facilitate the study of edge turbulence and flow shear in a versatile experimental environment. The dual channel V-band DBS system is coupled to TCV's quasi-optical diagnostic launcher, providing access to the upper low-field side region of the plasma cross-section. Verifications of the DBS measurements are presented. The DBS equilibrium $v_\perp$ profiles are found to compare favorably with gas puff imaging (GPI) measurements and to the $E_r$ inferred from the radial force balance of the carbon impurity. The radial structure of the edge $E_r \times B$ equilibrium flow and its dependencies are investigated across a representative set of L-mode TCV discharges, by varying density, auxiliary heating and magnetic configuration.
2025-04-08
Bowen Lou, Tian Lu, T. S. Raghu, Yingjie Zhang
Artificial Intelligence (AI) is advancing at an unprecedented pace, with clear potential to enhance decision-making and productivity. Yet, the collaborative decision-making process between humans and AI remains underdeveloped, often falling short of its transformative possibilities. This paper explores the evolution of AI agents from passive tools to active collaborators in human-AI teams, emphasizing their ability to learn, adapt, and operate autonomously in complex environments. This paradigm shifts challenges traditional team dynamics, requiring new interaction protocols, delegation strategies, and responsibility distribution frameworks. Drawing on Team Situation Awareness (SA) theory, we identify two critical gaps in current human-AI teaming research: the difficulty of aligning AI agents with human values and objectives, and the underutilization of AI's capabilities as genuine team members. Addressing these gaps, we propose a structured research outlook centered on four key aspects of human-AI teaming: formulation, coordination, maintenance, and training. Our framework highlights the importance of shared mental models, trust-building, conflict resolution, and skill adaptation for effective teaming. Furthermore, we discuss the unique challenges posed by varying team compositions, goals, and complexities. This paper provides a foundational agenda for future research and practical design of sustainable, high-performing human-AI teams.
This paper presents a comprehensive review of AI-driven prognostics for State of Health (SoH) prediction in lithium-ion batteries. We compare the effectiveness of various AI algorithms, including FFNN, LSTM, and BiLSTM, across multiple datasets (CALCE, NASA, UDDS) and scenarios (e.g., varying temperatures and driving conditions). Additionally, we analyze the factors influencing SoH fluctuations, such as temperature and charge-discharge rates, and validate our findings through simulations. The results demonstrate that BiLSTM achieves the highest accuracy, with an average RMSE reduction of 15% compared to LSTM, highlighting its robustness in real-world applications.
SEVERE++: Evaluating Benchmark Sensitivity in Generalization of Video Representation Learning
2025-04-08
Fida Mohammad Thoker, Letian Jiang, Chen Zhao, Piyush Bagad, Hazel Doughty, Bernard Ghanem, Cees G. M. Snoek
Continued advances in self-supervised learning have led to significant progress in video representation learning, offering a scalable alternative to supervised approaches by removing the need for manual annotations. Despite strong performance on standard action recognition benchmarks, video self-supervised learning methods are largely evaluated under narrow protocols, typically pretraining on Kinetics-400 and fine-tuning on similar datasets, limiting our understanding of their generalization in real world scenarios. In this work, we present a comprehensive evaluation of modern video self-supervised models, focusing on generalization across four key downstream factors: domain shift, sample efficiency, action granularity, and task diversity. Building on our prior work analyzing benchmark sensitivity in CNN-based contrastive learning, we extend the study to cover state-of-the-art transformer-based video-only and video-text models. Specifically, we benchmark 12 transformer-based methods (7 video-only, 5 video-text) and compare them to 10 CNN-based methods, totaling over 1100 experiments across 8 datasets and 7 downstream tasks. Our analysis shows that, despite architectural advances, transformer-based models remain sensitive to downstream conditions. No method generalizes consistently across all factors, video-only transformers perform better under domain shifts, CNNs outperform for fine-grained tasks, and video-text models often underperform despite large scale pretraining. We also find that recent transformer models do not consistently outperform earlier approaches. Our findings provide a detailed view of the strengths and limitations of current video SSL methods and offer a unified benchmark for evaluating generalization in video representation learning.
Diabetic Retinopathy Detection Based on Convolutional Neural Networks with SMOTE and CLAHE Techniques Applied to Fundus Images
2025-04-08
Sidhiq Mardianta, Affandy, Catur Supriyanto, Catur Supriyanto, Adi Wijaya
Diabetic retinopathy (DR) is one of the major complications in diabetic patients' eyes, potentially leading to permanent blindness if not detected timely. This study aims to evaluate the accuracy of artificial intelligence (AI) in diagnosing DR. The method employed is the Synthetic Minority Over-sampling Technique (SMOTE) algorithm, applied to identify DR and its severity stages from fundus images using the public dataset "APTOS 2019 Blindness Detection." Literature was reviewed via ScienceDirect, ResearchGate, Google Scholar, and IEEE Xplore. Classification results using Convolutional Neural Network (CNN) showed the best performance for the binary classes normal (0) and DR (1) with an accuracy of 99.55%, precision of 99.54%, recall of 99.54%, and F1-score of 99.54%. For the multiclass classification No_DR (0), Mild (1), Moderate (2), Severe (3), Proliferate_DR (4), the accuracy was 95.26%, precision 95.26%, recall 95.17%, and F1-score 95.23%. Evaluation using the confusion matrix yielded results of 99.68% for binary classification and 96.65% for multiclass. This study highlights the significant potential in enhancing the accuracy of DR diagnosis compared to traditional human analysis
STRIVE: A Think & Improve Approach with Iterative Refinement for Enhancing Question Quality Estimation
2025-04-08
Aniket Deroy, Subhankar Maity
Automatically assessing question quality is crucial for educators as it saves time, ensures consistency, and provides immediate feedback for refining teaching materials. We propose a novel methodology called STRIVE (Structured Thinking and Refinement with multiLLMs for Improving Verified Question Estimation) using a series of Large Language Models (LLMs) for automatic question evaluation. This approach aims to improve the accuracy and depth of question quality assessment, ultimately supporting diverse learners and enhancing educational practices. The method estimates question quality in an automated manner by generating multiple evaluations based on the strengths and weaknesses of the provided question and then choosing the best solution generated by the LLM. Then the process is improved by iterative review and response with another LLM until the evaluation metric values converge. This sophisticated method of evaluating question quality improves the estimation of question quality by automating the task of question quality evaluation. Correlation scores show that using this proposed method helps to improve correlation with human judgments compared to the baseline method. Error analysis shows that metrics like relevance and appropriateness improve significantly relative to human judgments by using STRIVE.
Event-based Civil Infrastructure Visual Defect Detection: ev-CIVIL Dataset and Benchmark
2025-04-08
Udayanga G. W. K. N. Gamage, Xuanni Huo, Luca Zanatta, T Delbruck, Cesar Cadena, Matteo Fumagalli, Silvia Tolu
Small Unmanned Aerial Vehicle (UAV) based visual inspections are a more efficient alternative to manual methods for examining civil structural defects, offering safe access to hazardous areas and significant cost savings by reducing labor requirements. However, traditional frame-based cameras, widely used in UAV-based inspections, often struggle to capture defects under low or dynamic lighting conditions. In contrast, Dynamic Vision Sensors (DVS), or event-based cameras, excel in such scenarios by minimizing motion blur, enhancing power efficiency, and maintaining high-quality imaging across diverse lighting conditions without saturation or information loss. Despite these advantages, existing research lacks studies exploring the feasibility of using DVS for detecting civil structural defects.Moreover, there is no dedicated event-based dataset tailored for this purpose. Addressing this gap, this study introduces the first event-based civil infrastructure defect detection dataset, capturing defective surfaces as a spatio-temporal event stream using DVS.In addition to event-based data, the dataset includes grayscale intensity image frames captured simultaneously using an Active Pixel Sensor (APS). Both data types were collected using the DAVIS346 camera, which integrates DVS and APS sensors.The dataset focuses on two types of defects: cracks and spalling, and includes data from both field and laboratory environments. The field dataset comprises 318 recording sequences,documenting 458 distinct cracks and 121 distinct spalling instances.The laboratory dataset includes 362 recording sequences, covering 220 distinct cracks and 308 spalling instances.Four realtime object detection models were evaluated on it to validate the dataset effectiveness.The results demonstrate the dataset robustness in enabling accurate defect detection and classification,even under challenging lighting conditions.
Nes2Net: A Lightweight Nested Architecture for Foundation Model Driven Speech Anti-spoofing
2025-04-08
Tianchi Liu, Duc-Tuan Truong, Rohan Kumar Das, Kong Aik Lee, Haizhou Li
Speech foundation models have significantly advanced various speech-related tasks by providing exceptional representation capabilities. However, their high-dimensional output features often create a mismatch with downstream task models, which typically require lower-dimensional inputs. A common solution is to apply a dimensionality reduction (DR) layer, but this approach increases parameter overhead, computational costs, and risks losing valuable information. To address these issues, we propose Nested Res2Net (Nes2Net), a lightweight back-end architecture designed to directly process high-dimensional features without DR layers. The nested structure enhances multi-scale feature extraction, improves feature interaction, and preserves high-dimensional information. We first validate Nes2Net on CtrSVDD, a singing voice deepfake detection dataset, and report a 22% performance improvement and an 87% back-end computational cost reduction over the state-of-the-art baseline. Additionally, extensive testing across four diverse datasets: ASVspoof 2021, ASVspoof 5, PartialSpoof, and In-the-Wild, covering fully spoofed speech, adversarial attacks, partial spoofing, and real-world scenarios, consistently highlights Nes2Net's superior robustness and generalization capabilities. The code package and pre-trained models are available at https://github.com/Liu-Tianchi/Nes2Net.
Model-Agnostic Policy Explanations with Large Language Models
2025-04-08
Zhang Xi-Jia, Yue Guo, Shufei Chen, Simon Stepputtis, Matthew Gombolay, Katia Sycara, Joseph Campbell
Intelligent agents, such as robots, are increasingly deployed in real-world, human-centric environments. To foster appropriate human trust and meet legal and ethical standards, these agents must be able to explain their behavior. However, state-of-the-art agents are typically driven by black-box models like deep neural networks, limiting their interpretability. We propose a method for generating natural language explanations of agent behavior based only on observed states and actions -- without access to the agent's underlying model. Our approach learns a locally interpretable surrogate model of the agent's behavior from observations, which then guides a large language model to generate plausible explanations with minimal hallucination. Empirical results show that our method produces explanations that are more comprehensible and correct than those from baselines, as judged by both language models and human evaluators. Furthermore, we find that participants in a user study more accurately predicted the agent's future actions when given our explanations, suggesting improved understanding of agent behavior.
PASSAGES: The Discovery of a Strongly Lensed Protocluster Core Candidate at Cosmic Noon
2025-04-08
Nicholas Foo, Kevin C. Harrington, Brenda Frye, Patrick S. Kamieneski, Min S. Yun, Massimo Pascale, Ilsang Yoon, Allison Noble, Rogier A. Windhorst, Seth H. Cohen, James D. Lowenthal, Melanie Kaasinen, Belén Alcalde Pampliega, Daizhong Liu, Olivia Cooper, Carlos Garcia Diaz, Anastasio Diaz, Jose Diego, Nikhil Garuda, Eric F. Jiménez-Andrade, Reagen Leimbach, Amit Vishwas, Q. Daniel Wang, Dazhi Zhou, Adi Zitrin
Investigating the processes by which galaxies rapidly build up their stellar mass during the peak of their star formation ($z=2$--$3$) is crucial to advancing our understanding of the assembly of large-scale structures. We report the discovery of one of the most gas- and dust-rich protocluster core candidates, PJ0846+15 (J0846), from the Planck All-Sky Survey to Analyze Gravitationally lensed Extreme Starbursts (PASSAGES) sample. The exceedingly high total apparent star formation rate of up to ($\mu$SFR) $\sim 93600\,\mathrm{M}_\odot\,\text{yr}^{-1}$ is a result of a foreground cluster lens magnifying at least 11 dusty star-forming galaxies between $z=2.660$--$2.669$. Atacama Large Millimeter Array (ALMA) observations revealed 18 CO(3--2) emission-line detections, some of which are multiply-imaged systems, lensed by a foreground cluster at $z=0.77$. We present the first multi-wavelength characterization of this field, constructing a lens model that predicts that these 11 systems (magnification factor, $\mu\simeq1.5$--$25$) are contained within a projected physical extent of $280\times150$ kpc, with a velocity dispersion of $\sigma_{v}=246\pm72$ km s$^{-1}$ and a total intrinsic star formation rate of up to (SFR) $\sim10400\,\mathrm{M}_\odot\,\text{yr}^{-1}$. J0846 is one of the most unique, lensed, protocluster core candidates ever reported, and offers a magnified glimpse into the rapid buildup of massive local galaxy clusters.
A Redshift Survey of the Coma Cluster (A1656): Understanding the Nature of Subhalos in the Weak-lensing Map
2025-04-08
Wooseok Kang, Ho Seong Hwang, Nobuhiro Okabe, Changbom Park
We study the physical properties of weak-lensing subhalos in the Coma cluster of galaxies using data from galaxy redshift surveys. The data include 12989 galaxies with measured spectroscopic redshifts (2184 from our MMT/Hectospec observation and 10807 from the literature). The $r$-band magnitude limit at which the differential spectroscopic completeness drops below 50% is 20.2 mag, which is spatially uniform in a region of 4.5 deg$^{2}$ where the weak-lensing map of Okabe et al. (2014) exists. We identify 1337 member galaxies in this field and use them to understand the nature of 32 subhalos detected in the weak-lensing analysis. We use Gaussian Mixture Modeling (GMM) in the line-of-sight velocity domain to measure the mean velocity, the velocity dispersion, and the number of subhalo galaxies by mitigating the contamination from the interloping galaxies. Using subhalo properties calculated with GMM, we find no significant difference in the redshift space distribution between the cluster member galaxies and subhalos. We find that the weak-lensing mass shows strong correlations with the number of subhalo member galaxies, velocity dispersion, and dynamical mass of subhalos with power-law slopes of $0.54^{+0.16}_{-0.15}$, $0.93^{+0.35}_{-0.32}$, and $0.50^{+0.31}_{-0.18}$, respectively. The slope of the mass--velocity dispersion relation of the weak-lensing subhalos appears shallower than that of the galaxy clusters, galaxy groups, and individual galaxies. These results suggest that the combination of redshift surveys with weak-lensing maps can be a powerful tool for better understanding the nature of subhalos in clusters.
Two Intermediate Translations Are Better Than One: Fine-tuning LLMs for Document-level Translation Refinement
2025-04-08
Yichen Dong, Xinglin Lyu, Junhui Li, Daimeng Wei, Min Zhang, Shimin Tao, Hao Yang
Recent research has shown that large language models (LLMs) can enhance translation quality through self-refinement. In this paper, we build on this idea by extending the refinement from sentence-level to document-level translation, specifically focusing on document-to-document (Doc2Doc) translation refinement. Since sentence-to-sentence (Sent2Sent) and Doc2Doc translation address different aspects of the translation process, we propose fine-tuning LLMs for translation refinement using two intermediate translations, combining the strengths of both Sent2Sent and Doc2Doc. Additionally, recognizing that the quality of intermediate translations varies, we introduce an enhanced fine-tuning method with quality awareness that assigns lower weights to easier translations and higher weights to more difficult ones, enabling the model to focus on challenging translation cases. Experimental results across ten translation tasks with LLaMA-3-8B-Instruct and Mistral-Nemo-Instruct demonstrate the effectiveness of our approach.
Tuning-Free Image Editing with Fidelity and Editability via Unified Latent Diffusion Model
2025-04-08
Qi Mao, Lan Chen, Yuchao Gu, Mike Zheng Shou, Ming-Hsuan Yang
Balancing fidelity and editability is essential in text-based image editing (TIE), where failures commonly lead to over- or under-editing issues. Existing methods typically rely on attention injections for structure preservation and leverage the inherent text alignment capabilities of pre-trained text-to-image (T2I) models for editability, but they lack explicit and unified mechanisms to properly balance these two objectives. In this work, we introduce UnifyEdit, a tuning-free method that performs diffusion latent optimization to enable a balanced integration of fidelity and editability within a unified framework. Unlike direct attention injections, we develop two attention-based constraints: a self-attention (SA) preservation constraint for structural fidelity, and a cross-attention (CA) alignment constraint to enhance text alignment for improved editability. However, simultaneously applying both constraints can lead to gradient conflicts, where the dominance of one constraint results in over- or under-editing. To address this challenge, we introduce an adaptive time-step scheduler that dynamically adjusts the influence of these constraints, guiding the diffusion latent toward an optimal balance. Extensive quantitative and qualitative experiments validate the effectiveness of our approach, demonstrating its superiority in achieving a robust balance between structure preservation and text alignment across various editing tasks, outperforming other state-of-the-art methods. The source code will be available at https://github.com/CUC-MIPG/UnifyEdit.
Recent Advances in Near-Field Beam Training and Channel Estimation for XL-MIMO Systems
2025-04-08
Ming Zeng, Ji Wang, Xingwang Li, Wanming Hao, Zheng Chu, Wenwu Xie, Xianbin Wang, Quoc-Viet Pham
Extremely large-scale multiple-input multiple-output (XL-MIMO) is a key technology for next-generation wireless communication systems. By deploying significantly more antennas than conventional massive MIMO systems, XL-MIMO promises substantial improvements in spectral efficiency. However, due to the drastically increased array size, the conventional planar wave channel model is no longer accurate, necessitating a transition to a near-field spherical wave model. This shift challenges traditional beam training and channel estimation methods, which were designed for planar wave propagation. In this article, we present a comprehensive review of state-of-the-art beam training and channel estimation techniques for XL-MIMO systems. We analyze the fundamental principles, key methodologies, and recent advancements in this area, highlighting their respective strengths and limitations in addressing the challenges posed by the near-field propagation environment. Furthermore, we explore open research challenges that remain unresolved to provide valuable insights for researchers and engineers working toward the development of next-generation XL-MIMO communication systems.
Conformal form-invariant parametrization of scalar-tensor gravity theories: A critical analysis
2025-04-08
Israel Quiros, Amit Kumar Rao
Based on the recent result that the action of matter fields is conformal form-invariant in its standard form and on the active and passive approaches to conformal transformations, we review the conformal form-invariant parametrization of scalar-tensor gravity theories. We investigate whether this parametrization is actually different from other existing parametrizations. We also check the accuracy of the claim that the classical physical predictions of these theories are conformal-frame invariants.
Analyzing Examinee Comments using DistilBERT and Machine Learning to Ensure Quality Control in Exam Content
2025-04-08
Ye, Ma
This study explores using Natural Language Processing (NLP) to analyze candidate comments for identifying problematic test items. We developed and validated machine learning models that automatically identify relevant negative feedback, evaluated approaches of incorporating psychometric features enhances model performance, and compared NLP-flagged items with traditionally flagged items. Results demonstrate that candidate feedback provides valuable complementary information to statistical methods, potentially improving test validity while reducing manual review burden. This research offers testing organizations an efficient mechanism to incorporate direct candidate experience into quality assurance processes.
High-entropy alloy (HEA) superconductors have garnered significant attention due to their unique characteristics, such as robust superconductivity under extremely high pressure and irradiation, the cocktail effect, and the enhancement of the upper critical field. A high critical current density is another noteworthy feature observed in HEAs. Several body-centered cubic (bcc) HEAs have exhibited critical current densities comparable to those of Nb-Ti superconducting alloys. Such HEAs hold potential for applications as multifunctional superconducting wires, a capability rarely achieved in conventional alloys. In this context, we review recent advancements in research on critical current densities in bcc HEA superconductors, including Ta$_{1/6}$Nb$_{2/6}$Hf$_{1/6}$Zr$_{1/6}$Ti$_{1/6}$, (TaNb)$_{0.7}$(HfZrTi)$_{0.5}$, NbScTiZr, and others. Comparative analyses among these HEAs reveal that both eutectic microstructures, which accompany lattice strain, and nanosized precipitates play pivotal roles in achieving elevated critical current densities across wide magnetic field ranges. Furthermore, we propose several future directions for research. These include elucidating the origin of lattice strain, exploring more fine eutectic microstructures, artificially introducing nanoscale pinning sites, improving the superconducting critical temperature, and investigating the mechanical properties of these materials.
Hydrodynamic Modelling of Early Peaks in Type Ibc Supernovae with Shock Cooling Emission from Circumstellar Matter
2025-04-08
Ryotaro Chiba, Takashi J. Moriya
Recent high-cadence transient surveys have uncovered a rare subclass of Type Ibc supernovae (SNe) that exhibit an early, blue peak lasting a few days before the main, radioactively powered peak. Since progenitors of Type Ibc SNe are typically compact and lack an extended envelope, this early peak is commonly attributed to shock cooling emission from circumstellar matter (CSM) surrounding the progenitor star. As such, these SNe provide a unique opportunity to constrain the pre-explosion activity of Type Ibc SN progenitors. We present the first systematic study of this Type Ibc SN population that uses hydrodynamic modelling. We simulated Type Ibc SNe exploding in a CSM using the multi-group radiation-hydrodynamics code STELLA, exploring a range of SN and CSM properties. By comparing the resulting theoretical multi-band light curves to a sample of seven Type Ibc SNe with early peaks, we constrained their CSM properties. Assuming a wind-like density distribution for the CSM, we found CSM masses of $10^{-2} - 10^{-1} \ \mathrm{M}_\odot$ and CSM radii of $(1 - 5) \times 10^3 \ \mathrm{R}_\odot$. While the masses were roughly consistent with a previous estimate obtained using an analytical model, the radii were significantly different, likely due to our assumption of spatially spread out CSM. We infer that the progenitors could have created CSM via late-time binary mass transfer or pulsational pair instability. We also estimate that, in the planned ULTRASAT high-cadence survey, $\sim 30$ shock cooling peaks from Type Ibc SNe will be observed.
We preform a systematic search for galactic-scale quasar pairs and small-scale (<3 arcsec) lenses using the SDSS DR16 quasar catalog and Gaia DR3. Candidate double quasars (both are unobscured) are identified as Gaia resolved pairs around spectroscopically confirmed SDSS quasars ($L_{\rm bol} > 10^{44.5} \ {\rm erg \ s^{-1}}$) at $0.5 < z \lesssim 4.5$. Gaia astrometric information and SDSS spectral decomposition are used to exclude foreground star superpositions, which dominate ($\gtrsim 80\%$ of) the pair sample. We identify $136$ double quasar candidates from $1120$ Gaia-resolved pairs after a magnitude and redshift cut of $G<20.5$ and $z>0.5$ (803 double quasars out of 2,497 pairs without any cuts applied) with separations of $\sim$ 0.3 - 3 arcsec, corresponding to projected physical separations of $\sim$ 3 - 30 kpc at the median redshift of the sample of $z = 1.7$. We estimate an overall double quasar (lens and physical pairs combined) fraction using this sample, corrected for pair-resolving completeness, of $5.7_{-0.3}^{+0.3} \times 10^{-4}$ (bootstrapping errors). This double quasar fraction increases toward smaller separations, consistent with earlier findings. We also find little redshift evolution of the double quasar fraction for the luminous SDSS sample, consistent with previous observations and simulation predictions. However, the observed fraction is lower than simulation predictions by $\sim$0.8 - 1.6 dex, suggesting a significant population of obscured quasar pairs are missed in our search. Future wide-area space missions targeting both unobscured and obscured quasar pairs at sub-arcsec resolutions will reveal this population of obscured quasar pairs, and extend to much lower AGN luminosities.
Decoding the Ishango Bone: Unveiling Prehistoric Mathematical Art
2025-04-08
Jenny Baur
The Ishango Bone, discovered in 1950 near the Semliki River in present-day Democratic Republic of Congo and dated to over 24,000 years ago, features a series of 16 grouped numerical notches arranged across three columns. The interpretation of these markings has long been debated, ranging from numerical intention to decorative symbolism. This study presents a newly identified structural pattern, uncovering four irregularities in the numerical groupings that, when resolved, reveal a consistent internal logic across all three columns. The analysis identifies repeating sums, dualistic pairing structures, and cross-column symmetry, pointing toward a deliberate and complex mathematical arrangement. These findings suggest the notches may have functioned as a form of reference system, potentially using stone markers for visual or narrative purposes. Hypotheses include links to cosmic cycles, mathematical instruction, or mythological integration. While interpretations remain speculative, this study argues for an interdisciplinary reevaluation of the Ishango Bone's significance in terms of mathematical, cultural, and cosmological understanding. It offers new insights into the symbolic thinking and cognitive capacities of prehistoric societies and challenges conventional assumptions about the origins of numeracy.
A Scalable Automatic Model Generation Tool for Cyber-Physical Network Topologies and Data Flows for Large-Scale Synthetic Power Grid Models
2025-04-08
Samantha Israel, Sanjana Kunkolienkar, Ana Goulart, Kate Davis, Thomas Overbye
Power grids and their cyber infrastructure are classified as Critical Energy Infrastructure/Information (CEII) and are not publicly accessible. While realistic synthetic test cases for power systems have been developed in recent years, they often lack corresponding cyber network models. This work extends synthetic grid models by incorporating cyber-physical representations. To address the growing need for realistic and scalable models that integrate both cyber and physical layers in electric power systems, this paper presents the Scalable Automatic Model Generation Tool (SAM-GT). This tool enables the creation of large-scale cyber-physical topologies for power system models. The resulting cyber-physical network models include power system switches, routers, and firewalls while accounting for data flows and industrial communication protocols. Case studies demonstrate the tool's application to synthetic grid models of 500, 2,000, and 10,000 buses, considering three distinct network topologies. Results from these case studies include network metrics on critical nodes, hops, and generation times, showcasing effectiveness, adaptability, and scalability of SAM-GT.
User-Centered Insights into Assistive Navigation Technologies for Individuals with Visual Impairment
2025-04-08
Iman Soltani, Johnaton Schofield, Mehran Madani, Daniel Kish, Parisa Emami-Naeini
Navigational challenges significantly impact the independence and mobility of Individuals with Visual Impairment (IVI). While numerous assistive technologies exist, their adoption remains limited due to usability challenges, financial constraints, and a lack of alignment with user needs. This study employs a mixed-methods approach, combining structured surveys and virtual workshops with 19 IVI to investigate their experiences, needs, and preferences regarding assistive technologies for navigation and daily living. The survey results provide insights into participants technological competence, preferences for assistive devices, and willingness to adopt new solutions. In parallel, workshop discussions offer qualitative perspectives on key navigation challenges, including difficulties in detecting overhead obstacles, navigating environments with complex layout, and the limitations of existing technologies. Findings highlight the need for assistive devices that integrate both navigational guidance and high-level spatial awareness, allowing users to build mental maps of their surroundings. Additionally, multimodal feedback, combining audio, haptic, and tactile cues, emerges as a crucial feature to accommodate diverse user preferences and environmental conditions. The study also underscores financial and training barriers that limit access to advanced assistive technologies. Based on these insights, we recommend the development of customizable, user-friendly, and most importantly affordable navigation aids that align with the daily needs of IVI. The findings from this study provide guidance for technology developers, researchers, and policymakers working toward more inclusive and effective assistive solutions.
Review, Definition and Challenges of Electrical Energy Hubs
2025-04-08
Giacomo Bastianel, Jan Kircheis, Merijn Van Deyck, Dongyeong Lee, Geraint Chaffey, Marta Vanin, Hakan Ergun, Jef Beerten, Dirk Van Hertem
To transition towards a carbon-neutral power system, considerable amounts of renewable energy generation capacity are being installed in the North Sea area. Consequently, projects aggregating many gigawatts of power generation capacity and transmitting renewable energy to the main load centers are being developed. Given the electrical challenges arising from having bulk power capacity in a compact geographical area with several connections to the main grid, and a lack of a robust definition identifying the type of system under study, this paper proposes a general technical definition of such projects introducing the term Electrical Energy Hub (EEH). The concept, purpose, and functionalities of EEHs are introduced in the text, emphasizing the importance of a clear technical definition for future planning procedures, grid codes, regulations, and support schemes for EEHs and multiterminal HVDC (MTDC) grids in general. Furthermore, the unique electrical challenges associated with integrating EEHs into the power system are discussed. Three research areas of concern are identified, namely control, planning, and protection. Through this analysis, insights are provided into the effective implementation of multi-GW scale EEH projects and their integration into the power grid through multiple interconnections. Finally, a list of ongoing and planned grid development projects is evaluated to assess whether they fall within the EEH category
Deep spatio-temporal point processes: Advances and new directions
2025-04-08
Xiuyuan Cheng, Zheng Dong, Yao Xie
Spatio-temporal point processes (STPPs) model discrete events distributed in time and space, with important applications in areas such as criminology, seismology, epidemiology, and social networks. Traditional models often rely on parametric kernels, limiting their ability to capture heterogeneous, nonstationary dynamics. Recent innovations integrate deep neural architectures -- either by modeling the conditional intensity function directly or by learning flexible, data-driven influence kernels, substantially broadening their expressive power. This article reviews the development of the deep influence kernel approach, which enjoys statistical explainability, since the influence kernel remains in the model to capture the spatiotemporal propagation of event influence and its impact on future events, while also possessing strong expressive power, thereby benefiting from both worlds. We explain the main components in developing deep kernel point processes, leveraging tools such as functional basis decomposition and graph neural networks to encode complex spatial or network structures, as well as estimation using both likelihood-based and likelihood-free methods, and address computational scalability for large-scale data. We also discuss the theoretical foundation of kernel identifiability. Simulated and real-data examples highlight applications to crime analysis, earthquake aftershock prediction, and sepsis prediction modeling, and we conclude by discussing promising directions for the field.
Quadrupole signature as a kinematic diagnostic to constrain bar properties : implication for the Milky Way
2025-04-08
Soumavo Ghosh, Taavet Kalda, Paola Di Matteo, Gregory M. Green, Sergey Khoperskov, David Katz, Misha Haywood
The presence of a 'butterfly' or a quadrupole structure in the stellar mean radial velocity field of the Milky Way is well known from the Gaia and the APOGEE surveys. Past studies indicated that a stellar bar can excite such a quadrupole feature in the $$ distribution. However, a systematic study investigating the co-evolution of bar and quadrupole structure is largely missing. Furthermore, whether this quadrupole structure in $$ can be used as a robust kinematic diagnostic to constrain bar properties, particularly for the Milky Way, is still beyond our grasp. Here, we investigate the bar-induced quadrupole feature using a suite of isolated N-body models forming prominent bars and a sample of Milky Way-like barred galaxies from the TNG50 cosmological simulation. We demonstrate that the properties of the quadrupole (strength, length, and orientation) are strongly correlated with the bar properties, regardless of the choice of the stellar tracer population; thereby making the quadrupole feature an excellent kinematic diagnostic for constraining the bar properties. In presence of spirals, the estimator which takes into account the phase-angle of m=4 Fourier moment, serves as a more appropriate estimator for measuring the length of the quadrupole. Further, we constructed a novel Gaia-like mock dataset from a simulated bar model while incorporating the dust extinction and the broad trends of observational errors of the Gaia survey. The quadrupole properties (strength and length) estimated from those Gaia-like mock data are larger (~35-45 percent) when compared with their true values. We determined that the majority of this effect is due to the uncertainty in parallax measurement. This demonstrates the potential caveat of inferring Milky Way's bar properties by using the stellar kinematic information from the Gaia DR3 without properly accounting for the observational uncertainties.
The future of secure communications: device independence in quantum key distribution
2025-04-08
Seyed Arash Ghoreishi, Giovanni Scala, Renato Renner, Letícia Lira Tacca, Jan Bouda, Stephen Patrick Walborn, Marcin Pawłowski
In the ever-evolving landscape of quantum cryptography, Device-independent Quantum Key Distribution (DI-QKD) stands out for its unique approach to ensuring security based not on the trustworthiness of the devices but on nonlocal correlations. Beginning with a contextual understanding of modern cryptographic security and the limitations of standard quantum key distribution methods, this review explores the pivotal role of nonclassicality and the challenges posed by various experimental loopholes for DI-QKD. Various protocols, security against individual, collective and coherent attacks, and the concept of self-testing are also examined, as well as the entropy accumulation theorem, and additional mathematical methods in formulating advanced security proofs. In addition, the burgeoning field of semi-device-independent models (measurement DI--QKD, Receiver DI--QKD, and One--sided DI--QKD) is also analyzed. The practical aspects are discussed through a detailed overview of experimental progress and the open challenges toward the commercial deployment in the future of secure communications.
2025-04-08
P. Bonifacio, E. Caffau, P. François, M. Spite
The most metal-poor stars found in the Galaxy and in nearby galaxies are witnesses of the early evolution of the Universe. In a general picture in which we expect the metallicity to increase monotonically with time, as a result of the metal production in stars, we also expect the most metal-poor stars to be the most primitive objects accessible to our observations. The abundance ratios in these stars provide us important information on the first generations of stars that synthesised the nuclei that we observe in these stars. Because they are so primitive the modelling of their chemical inventory can be often satisfactorily achieved by assuming that all the metals were produced in a single supernova, or just a few. This is simpler than modelling the full chemical evolution, using different sources, that is necessary at higher metallicity. The price to pay for this relative ease of interpretation is that these stars are extremely rare and require specifically tailored observational strategies in order to assemble statistically significant samples of stars. In this review we try to summarise the main observational results that have been obtained in the last ten years.
AI-Driven Reconstruction of Large-Scale Structure from Combined Photometric and Spectroscopic Surveys
2025-04-08
Wenying Du, Xiaolin Luo, Zhujun Jiang, Xu Xiao, Qiufan Lin, Xin Wang, Yang Wang, Fenfen Yin, Le Zhang, Xiao-Dong Li
Galaxy surveys are crucial for studying large-scale structure (LSS) and cosmology, yet they face limitations--imaging surveys provide extensive sky coverage but suffer from photo-$z$ uncertainties, while spectroscopic surveys yield precise redshifts but are sample-limited. To take advantage of both photo-$z$ and spec-$z$ data while eliminating photo-$z$ errors, we propose a deep learning framework based on a dual UNet architecture that integrates these two datasets at the field level to reconstruct the 3D photo-$z$ density field. We train the network on mock samples representative of stage-IV spectroscopic surveys, utilizing CosmicGrowth simulations with a $z=0.59$ snapshot containing $2048^3$ particles in a $(1200~h^{-1}\rm Mpc)^3$ volume. Several metrics, including correlation coefficient, MAE, MSE, PSNR, and SSIM, validate the model's accuracy. Moreover, the reconstructed power spectrum closely matches the ground truth at small scales ($k \gtrsim 0.06~h/\rm Mpc$) within the $1\sigma$ confidence level, while the UNet model significantly improves the estimation of photo-$z$ power spectrum multipoles. This study demonstrates the potential of deep learning to enhance LSS reconstruction by using both spectroscopic and photometric data.
Decoding the variability in the star-formation histories of z ~ 0.8 galaxies
2025-04-07
Jenny T. Wan, Sandro Tacchella, Francesco D'Eugenio, Benjamin D. Johnson, Arjen van der Wel
The scatter of the star-forming main sequence (SFMS) holds a wealth of information about how galaxies evolve. The timescales encoded in this scatter can provide valuable insight into the relative importance of the physical processes regulating star formation. In this paper, we present a detailed observational analysis of the timescales imprinted in galaxy star-formation history (SFH) fluctuations by using the stochastic SFH model to fit 1928 massive, z ~ 0.8 galaxies in the LEGA-C survey. We find that the total intrinsic scatter of the SFMS is ~0.3 dex in galaxies with stellar masses $\gtrsim 10^{10}~\mathrm{M}_\odot$. This scatter decreases as the timescale over which SFRs are averaged increases, declining to a non-negligible ~0.15 - 0.25 dex at 2 Gyr, underscoring the importance of long-timescale SFH diversity to the SFMS scatter. Furthermore, galaxies currently above (below) the SFMS tend to have been above (below) the SFMS for at least ~1 Gyr, providing evidence that individual galaxies may follow different median tracks through SFR$-\mathrm{M}_*$ space. On shorter timescales (~30 - 100 Myr), galaxies' SFRs also vary on the order of ~0.1 - 0.2 dex. Our work supports the idea that the SFMS emerges from a population average of the pathways that individual galaxies trace through the SFR$-\mathrm{M}_*$ plane. The scatter reflects the long-term heterogeneity of these paths likely set by the evolutionary timescales of halo growth and cooling, accentuated by short-term variations reflecting the dynamical timescale of the galaxy and its interstellar medium. Our results emphasize the dynamic nature of the SFMS and the importance of understanding the diverse processes governing star formation.
This systematic review examines peer-reviewed studies on application of GPT in business management, revealing significant knowledge gaps. Despite identifying interesting research directions such as best practices, benchmarking, performance comparisons, social impacts, our analysis yields only 42 relevant studies for the 22 months since its release. There are so few studies looking at a particular sector or subfield that management researchers, business consultants, policymakers, and journalists do not yet have enough information to make well-founded statements on how GPT is being used in businesses. The primary contribution of this paper is a call to action for further research. We provide a description of current research and identify knowledge gaps on the use of GPT in business. We cover the management subfields of finance, marketing, human resources, strategy, operations, production, and analytics, excluding retail and sales. We discuss gaps in knowledge of GPT potential consequences on employment, productivity, environmental costs, oppression, and small businesses. We propose how management consultants and the media can help fill those gaps. We call for practical work on business control systems as they relate to existing and foreseeable AI-related business challenges. This work may be of interest to managers, to management researchers, and to people working on AI in society.
An X-ray and Optical Study of the Dwarf Nova Candidate OGLE-BLG-DN-0064
2025-04-07
A. B. Sibgatullin, V. I. Dodon, I. I. Galiullin, A. I. Kolbin, V. V. Shimansky, A. S. Vinokurov
The source OGLE-BLG-DN-0064 (hereafter OGLE64) was classified as a potential dwarf nova based on its regular outburst activity revealed by the OGLE optical survey. In this paper, we investigate the X-ray and optical emissions from the source OGLE64 based on archival Chandra and Swift X-ray data and our optical observations with the 6-m BTA telescope at the Special Astrophysical Observatory of the Russian Academy of Sciences. OGLE64 shows an X-ray luminosity $ L_X \approx 1.6 \times 10^{32} \, \text{erg s}^{-1} $ and a high X-ray-to-optical flux ratio $ F_X / F_{\text{opt}} \approx 1.5$, typical for accreting white dwarfs. The X-ray spectrum of OGLE64 is better fitted by the models of a power law with a photon index $\Gamma \approx 1.9$ and an optically thin plasma with a temperature $ kT \approx 6.4 \, \text{keV} $. The optical spectrum shows hydrogen and neutral helium emission lines, in some of which a double-peaked structure is observed. An analysis of the outburst activity of OGLE64, based on data from the OGLE, ZTF, ATLAS, and ASAS-SN optical surveys, has revealed superoutbursts with a characteristic supercycle $ P_{\text{super}} \approx 400 \, \text{days} $. We found no significant variability in either the X-ray or optical light curves of OGLE64 that could be associated with the change in the visibility conditions for the emitting regions at different orbital phases. Our estimates of the orbital period of the system by indirect methods show that the period probably lies in the range $ P_{\text{orb}} \sim 1.5 - 3.5 \, \text{h} $. The properties of the X-ray and optical emissions from OGLE64 lead us to conclude that the system is an SU UMa-type dwarf nova.
2025-04-07
Sneh Pandya, Yuanyuan Yang, Nicholas Van Alfen, Jonathan Blazek, Robin Walters
The intrinsic alignments (IA) of galaxies, a key contaminant in weak lensing analyses, arise from correlations in galaxy shapes driven by tidal interactions and galaxy formation processes. Accurate IA modeling is essential for robust cosmological inference, but current approaches rely on perturbative methods that break down on nonlinear scales or on expensive simulations. We introduce IAEmu, a neural network-based emulator that predicts the galaxy position-position ($\xi$), position-orientation ($\omega$), and orientation-orientation ($\eta$) correlation functions and their uncertainties using mock catalogs based on the halo occupation distribution (HOD) framework. Compared to simulations, IAEmu achieves ~3% average error for $\xi$ and ~5% for $\omega$, while capturing the stochasticity of $\eta$ without overfitting. The emulator provides both aleatoric and epistemic uncertainties, helping identify regions where predictions may be less reliable. We also demonstrate generalization to non-HOD alignment signals by fitting to IllustrisTNG hydrodynamical simulation data. As a fully differentiable neural network, IAEmu enables $\sim$10,000$\times$ speed-ups in mapping HOD parameters to correlation functions on GPUs, compared to CPU-based simulations. This acceleration facilitates inverse modeling via gradient-based sampling, making IAEmu a powerful surrogate model for galaxy bias and IA studies with direct applications to Stage IV weak lensing surveys.
Observation of coherent $φ$(1020) meson photoproduction in ultraperipheral PbPb collisions at $\sqrt{s_\text{NN}}$ = 5.36 TeV
2025-04-07
CMS Collaboration
The first observation of coherent $\phi$(1020) meson photoproduction off heavy nuclei is presented using ultraperipheral lead-lead collisions at a center-of-mass energy per nucleon pair of 5.36 TeV. The data were collected by the CMS experiment and correspond to an integrated luminosity of 1.68 $\mu$b$^{-1}$. The $\phi$(1020) meson signals are reconstructed via the K$^+$K$^-$ decay channel. The production cross section is presented as a function of the $\phi$(1020) meson rapidity in the range 0.3 $\lt$ $\lvert y\rvert$ $\lt$ 1.0, probing gluons that carry a fraction of the nucleon momentum ($x$) around $10^{-4}$. The observed cross section exhibits little dependence on rapidity and is significantly suppressed, by a factor of ${\sim}$5, compared to a baseline model that treats a nucleus as a collection of free nucleons. Theoretical models that incorporate either nuclear shadowing or gluon saturation predict suppression of the $\phi$(1020) meson cross section with only a small dependence on rapidity, but the magnitude of the predicted suppression varies greatly. Models considering only nuclear shadowing effects result in the best agreement with the experimental data. This study establishes a powerful new tool for exploring nuclear effects and nuclear gluonic structure in the small-$x$ regime at a unique energy scale bridging the perturbative and nonperturbative quantum chromodynamics domains.
Knowledge Graph based Retrieval-Augmented Generation (KG-RAG) is a technique that enhances Large Language Model (LLM) inference in tasks like Question Answering (QA) by retrieving relevant information from knowledge graphs (KGs). However, real-world KGs are often incomplete, meaning that essential information for answering questions may be missing. Existing benchmarks do not adequately capture the impact of KG incompleteness on KG-RAG performance. In this paper, we systematically evaluate KG-RAG methods under incomplete KGs by removing triples using different methods and analyzing the resulting effects. We demonstrate that KG-RAG methods are sensitive to KG incompleteness, highlighting the need for more robust approaches in realistic settings.
2025-04-07
David Elsweiler, Samy Ateia, Markus Bink, Gregor Donabauer, Marcos Fernández Pichel, Alexander Frummet, Udo Kruschwitz, David Losada, Bernd Ludwig, Selina Meyer, Noel Pascual Presa
While it is often assumed that searching for information to evaluate misinformation will help identify false claims, recent work suggests that search behaviours can instead reinforce belief in misleading news, particularly when users generate queries using vocabulary from the source articles. Our research explores how different query generation strategies affect news verification and whether the way people search influences the accuracy of their information evaluation. A mixed-methods approach was used, consisting of three parts: (1) an analysis of existing data to understand how search behaviour influences trust in fake news, (2) a simulation of query generation strategies using a Large Language Model (LLM) to assess the impact of different query formulations on search result quality, and (3) a user study to examine how 'Boost' interventions in interface design can guide users to adopt more effective query strategies. The results show that search behaviour significantly affects trust in news, with successful searches involving multiple queries and yielding higher-quality results. Queries inspired by different parts of a news article produced search results of varying quality, and weak initial queries improved when reformulated using full SERP information. Although 'Boost' interventions had limited impact, the study suggests that interface design encouraging users to thoroughly review search results can enhance query formulation. This study highlights the importance of query strategies in evaluating news and proposes that interface design can play a key role in promoting more effective search practices, serving as one component of a broader set of interventions to combat misinformation.
Unifying Physics- and Data-Driven Modeling via Novel Causal Spatiotemporal Graph Neural Network for Interpretable Epidemic Forecasting
2025-04-07
Shuai Han, Lukas Stelz, Thomas R. Sokolowski, Kai Zhou, Horst Stöcker
Accurate epidemic forecasting is crucial for effective disease control and prevention. Traditional compartmental models often struggle to estimate temporally and spatially varying epidemiological parameters, while deep learning models typically overlook disease transmission dynamics and lack interpretability in the epidemiological context. To address these limitations, we propose a novel Causal Spatiotemporal Graph Neural Network (CSTGNN), a hybrid framework that integrates a Spatio-Contact SIR model with Graph Neural Networks (GNNs) to capture the spatiotemporal propagation of epidemics. Inter-regional human mobility exhibits continuous and smooth spatiotemporal patterns, leading to adjacent graph structures that share underlying mobility dynamics. To model these dynamics, we employ an adaptive static connectivity graph to represent the stable components of human mobility and utilize a temporal dynamics model to capture fluctuations within these patterns. By integrating the adaptive static connectivity graph with the temporal dynamics graph, we construct a dynamic graph that encapsulates the comprehensive properties of human mobility networks. Additionally, to capture temporal trends and variations in infectious disease spread, we introduce a temporal decomposition model to handle temporal dependence. This model is then integrated with a dynamic graph convolutional network for epidemic forecasting. We validate our model using real-world datasets at the provincial level in China and the state level in Germany. Extensive studies demonstrate that our method effectively models the spatiotemporal dynamics of infectious diseases, providing a valuable tool for forecasting and intervention strategies. Furthermore, analysis of the learned parameters offers insights into disease transmission mechanisms, enhancing the interpretability and practical applicability of our model.
On the Origins of "Hostless'' Supernovae: Testing the Faint-end Galaxy Luminosity Function and Supernova Progenitors with Events in Dwarf Galaxies
2025-04-07
Louis-Gregory Strolger, Mia Sauda Bovill, Eric Perlman, Craig Kolobow, Conor Larison, Zachary G. Lane
We present arguments on the likely origins of supernovae without associated host galaxies from open field, non-clustered, environments. We show why it is unlikely these ``hostless'' supernovae stem from escaped hyper-velocity stars (HVS) in any appreciable numbers, especially for core-collapse supernovae. It is highly likely that hostless events arise from dwarf host galaxies too faint to be detected in their parent surveys. Several detections and numerous upper limits suggest a large number of field dwarfs, to $M_V>-14$, which themselves may be important to constraining the slope of the low-mass end of the UV luminosity function, understanding galaxy evolution, and putting $\Lambda$CDM into context. Moreover, the detailed study of these mass and metallicity-constrained host environments, and the variety of supernovae that occur within them, could provide more stringent constraints on the nature of progenitor systems.
AI-Driven Tactical Communications and Networking for Defense: A Survey and Emerging Trends
2025-04-07
Victor Monzon Baeza, Raúl Parada, Laura Concha Salor, Carlos Monzo
The integration of Artificial Intelligence (AI) in military communications and networking is reshaping modern defense strategies, enhancing secure data exchange, real-time situational awareness, and autonomous decision-making. This survey explores how AI-driven technologies improve tactical communication networks, radar-based data transmission, UAV-assisted relay systems, and electronic warfare resilience. The study highlights AI applications in adaptive signal processing, multi-agent coordination for network optimization, radar-assisted target tracking, and AI-driven electronic countermeasures. Our work introduces a novel three-criteria evaluation methodology. It systematically assesses AI applications based on general system objectives, communications constraints in the military domain, and critical tactical environmental factors. We analyze key AI techniques for different types of learning applied to multi-domain network interoperability and distributed data information fusion in military operations. We also address challenges such as adversarial AI threats, the real-time adaptability of autonomous communication networks, and the limitations of current AI models under battlefield conditions. Finally, we discuss emerging trends in self-healing networks, AI-augmented decision support systems, and intelligent spectrum allocation. We provide a structured roadmap for future AI-driven defense communications and networking research.
Review of analytic results on quasinormal modes of black holes
2025-04-07
S. V. Bolokhov, Milena Skvortsova
We present a concise review of known analytic results for quasinormal modes of black holes and related spacetimes. Our emphasis is on those regimes where the perturbation equations admit exact or perturbative solutions, providing insights complementary to numerical or semi-analytic approaches. We discuss solvable cases in lower-dimensional spacetimes, algebraically special modes, and exact results in higher-curvature gravity theories. Particular attention is given to the eikonal regime and its correspondence with null geodesics, as well as to beyond-eikonal approximations based on inverse multipole expansions in parametrized metrics. We review analytic solutions obtained in the near-extremal limit of Schwarzschild - de Sitter black holes, in the regime of large field mass, and in pure de Sitter and anti - de Sitter spacetimes, where boundary conditions play a crucial role. While not exhaustive, this overview highlights the diversity of techniques and physical insights made possible by analytic treatments of quasinormal spectra.
The rapid development of AI-driven tools, particularly large language models (LLMs), is reshaping professional writing. Still, key aspects of their adoption such as languages support, ethics, and long-term impact on writers voice and creativity remain underexplored. In this work, we conducted a questionnaire (N = 301) and an interactive survey (N = 36) targeting professional writers regularly using AI. We examined LLM-assisted writing practices across 25+ languages, ethical concerns, and user expectations. The findings of the survey demonstrate important insights, reflecting upon the importance of: LLMs adoption for non-English speakers; the degree of misinformation, domain and style adaptation; usability and key features of LLMs. These insights can guide further development, benefiting both writers and a broader user base.
CONCERT: a Modular Reconfigurable Robot for Construction
2025-04-07
Luca Rossini, Edoardo Romiti, Arturo Laurenzi, Francesco Ruscelli, Marco Ruzzon, Luca Covizzi, Lorenzo Baccelliere, Stefano Carrozzo, Michael Terzer, Marco Magri, Carlo Morganti, Maolin Lei, Liana Bertoni, Diego Vedelago, Corrado Burchielli, Stefano Cordasco, Luca Muratore, Andrea Giusti, Nikos Tsagarakis
This paper presents CONCERT, a fully reconfigurable modular collaborative robot (cobot) for multiple on-site operations in a construction site. CONCERT has been designed to support human activities in construction sites by leveraging two main characteristics: high-power density motors and modularity. In this way, the robot is able to perform a wide range of highly demanding tasks by acting as a co-worker of the human operator or by autonomously executing them following user instructions. Most of its versatility comes from the possibility of rapidly changing its kinematic structure by adding or removing passive or active modules. In this way, the robot can be set up in a vast set of morphologies, consequently changing its workspace and capabilities depending on the task to be executed. In the same way, distal end-effectors can be replaced for the execution of different operations. This paper also includes a full description of the software pipeline employed to automatically discover and deploy the robot morphology. Specifically, depending on the modules installed, the robot updates the kinematic, dynamic, and geometric parameters, taking into account the information embedded in each module. In this way, we demonstrate how the robot can be fully reassembled and made operational in less than ten minutes. We validated the CONCERT robot across different use cases, including drilling, sanding, plastering, and collaborative transportation with obstacle avoidance, all performed in a real construction site scenario. We demonstrated the robot's adaptivity and performance in multiple scenarios characterized by different requirements in terms of power and workspace. CONCERT has been designed and built by the Humanoid and Human-Centered Mechatronics Laboratory (HHCM) at the Istituto Italiano di Tecnologia in the context of the European Project Horizon 2020 CONCERT.
Probing gravity with non-linear clustering in redshift space
2025-04-07
C. Viglione, P. Fosalba, I. Tutusaus, L. Blot, J. Carretero, P. Tallada, F. Castander
We present the first computation of the gravity model testing parameter $E_G$ on realistic simulated modified gravity galaxy mocks. The analysis is conducted using two twin simulations presented in arXiv:1805.09824(1): one based on general relativity (GR) and the other on the $f(R)$ Hu $\&$ Sawicki model with $f=10^{-5}$ (F5). This study aims to measure the $E_G$ estimator in GR and $f(R)$ models using high-fidelity simulated galaxy catalogs, with the goal of assessing how future galaxy surveys can detect deviations from standard gravity. Deriving this estimator requires precise, unbiased measurements of the growth rate of structure and the linear galaxy bias. We achieve this by implementing an end-to-end cosmological analysis pipeline in configuration space, using the multipoles of the 2-point correlation function. Our analysis demonstrates how to measure the scale-dependent growth rate predicted by non-standard gravity models. We split the estimation of the RSD $\beta$ parameter over distinct scale ranges, separating large (quasi-linear) and small (non-linear) scales. We show that this estimator can be accurately measured using mock galaxies in low redshift bins ($z < 1$), where it offers strong discriminating power over competing gravity theories. We find that, for an all-sky galaxy survey and neglecting observational systematics, accurate and largely unbiased estimations of $E_G$ can be obtained across all redshifts. However, the error bars are too large to clearly distinguish between the theories. When measuring the scale-dependence of the $E_G$ estimator, we note that state-of-the-art theory modeling limitations and intrinsic "prior volume effects" prevent high-accuracy constraints. Alternatively, we propose a null test of gravity using RSD clustering, which, if small scales are modeled accurately in future surveys, could detect significant departures from GR.
How Is Generative AI Used for Persona Development?: A Systematic Review of 52 Research Articles
2025-04-07
Danial Amin, Joni Salminen, Farhan Ahmed, Sonja M. H. Tervola, Sankalp Sethi, Bernard J. Jansen
Although Generative AI (GenAI) has the potential for persona development, many challenges must be addressed. This research systematically reviews 52 articles from 2022-2024, with important findings. First, closed commercial models are frequently used in persona development, creating a monoculture Second, GenAI is used in various stages of persona development (data collection, segmentation, enrichment, and evaluation). Third, similar to other quantitative persona development techniques, there are major gaps in persona evaluation for AI generated personas. Fourth, human-AI collaboration models are underdeveloped, despite human oversight being crucial for maintaining ethical standards. These findings imply that realizing the full potential of AI-generated personas will require substantial efforts across academia and industry. To that end, we provide a list of research avenues to inspire future work.
Understanding and Design of Interstitial Oxygen Conductors
2025-04-07
Jun Meng
Highly efficient oxygen active materials that react with, absorb, and transport oxygen is essential for fuel cells, electrolyzers and related applications. While vacancy mediated oxygen ion conductors have long been the focus of research, they are limited by high migration barriers at intermediate temperatures, which hinder their practical applications. In contrast, interstitial oxygen conductors exhibit significantly lower migration barriers enabling faster ionic conductivity at lower temperatures. This review systematically examines both well established and recently identified families of interstitial oxygen ion conductors, focusing on how their unique structural motifs such as corner sharing polyhedral frameworks, isolated polyhedral, and cage like architectures, facilitate low migration barriers through interstitial and interstitialcy diffusion mechanisms. A central discussion of this review focuses on the evolution of design strategies, from targeted donor doping, element screening, and physical intuition descriptor material discovery, which leverage computational tools to explore vast chemical spaces in search of new interstitial conductors. The success of these strategies demonstrates that a significant, largely unexplored space remains for discovering high performing interstitial oxygen conductors. Crucial features enabling high performance interstitial oxygen diffusion include the availability of electrons for oxygen reduction and sufficient structural flexibility with accessible volume for interstitial accommodation. This review concludes with a forward looking perspective, proposing a knowledge driven methodology that integrates current understanding with data centric approaches to identify promising interstitial oxygen conductors outside traditional search paradigms.
Critical Behaviour in the Single Flavor Thirring Model in 2+1$d$ with Wilson Kernel Domain Wall Fermions
2025-04-07
Simon Hands, Jude Worthy
We present results of a lattice field theory simulation of the 2+1$d$ Thirring model with $N=1$ fermion flavors, using domain wall fermions. The model exhibits a U(2) symmetry-breaking phase transition with the potential to define a UV-stable renormalisation group fixed point. The novelty is the replacement of the Shamir kernel used in all previous work with the Wilson kernel, improving the action particularly with respect to the $L_s\to\infty$ limit needed to recover U(2), now under much better control. Auxiliary field ensembles generated on $16^3\times24$ with varying self-interaction strength $g^2$ and bare mass $m$ are used to measure the bilinear condensate order parameter $\langle\bar\psi i\gamma_3\psi\rangle$ with domain wall separations as large as $L_s=120$. The resulting $L_s\to\infty$ extrapolation is used to fit an empirical equation of state modelling spontaneous symmetry breaking as $m\to0$. The fit is remarkably stable and compelling, with the fitted critical exponents $\beta_m\simeq2.4$, $\delta\simeq1.3$ differing markedly from previous estimates. The associated susceptibility exhibits a mass hierarchy in line with physical expectations, again unlike previous estimates. Schwinger-Dyson equation (SDE) solutions of the Thirring model exploiting a hidden local symmetry in the action are reviewed, and analytic predictions presented for the exponents. In contrast to all previous lattice studies, the universal characteristics of the critical point revealed qualitatively resemble the SDE predictions.
2025-04-07
Viktor Beck, Max Landauer, Markus Wurzenberger, Florian Skopik, Andreas Rauber
Log data, generated by software systems, provides crucial insights for tasks like monitoring, root cause analysis, and anomaly detection. Due to the vast volume of logs, automated log parsing is essential to transform semi-structured log messages into structured representations. Traditional log parsing techniques often require manual configurations, such as defining log formats or labeling data, which limits scalability and usability. Recent advances in large language models (LLMs) have introduced the new research field of LLM-based log parsing, offering potential improvements in automation and adaptability. Despite promising results, there is no structured overview of these approaches since this is a relatively new research field with the earliest advances published in late 2023. This paper systematically reviews 29 LLM-based log parsing methods, comparing their capabilities, limitations, and reliance on manual effort. We analyze the learning and prompt-engineering paradigms employed, efficiency- and effectiveness-enhancing techniques, and the role of LLMs in the parsing process. We aggregate the results of the survey in a large table comprising the characterizing features of LLM-based log parsing approaches and derive the general process of LLM-based log parsing, incorporating all reviewed approaches in a single flow chart. Additionally, we benchmark seven open-source LLM-based log parsers on public datasets and critically assess their reproducibility. Our findings summarize the advances of this new research field and provide insights for researchers and practitioners seeking efficient and user-friendly log parsing solutions, with all code and results made publicly available for transparency.
Textual interaction networks (TINs) are an omnipresent data structure used to model the interplay between users and items on e-commerce websites, social networks, etc., where each interaction is associated with a text description. Classifying such textual interactions (TIC) finds extensive use in detecting spam reviews in e-commerce, fraudulent transactions in finance, and so on. Existing TIC solutions either (i) fail to capture the rich text semantics due to the use of context-free text embeddings, and/or (ii) disregard the bipartite structure and node heterogeneity of TINs, leading to compromised TIC performance. In this work, we propose SAFT, a new architecture that integrates language- and graph-based modules for the effective fusion of textual and structural semantics in the representation learning of interactions. In particular, line graph attention (LGA)/gated attention units (GAUs) and pretrained language models (PLMs) are capitalized on to model the interaction-level and token-level signals, which are further coupled via the proxy token in an iterative and contextualized fashion. Additionally, an efficient and theoretically-grounded approach is developed to encode the local and global topology information pertaining to interactions into structural embeddings. The resulting embeddings not only inject the structural features underlying TINs into the textual interaction encoding but also facilitate the design of graph sampling strategies. Extensive empirical evaluations on multiple real TIN datasets demonstrate the superiority of SAFT over the state-of-the-art baselines in TIC accuracy.
Embracing Dynamics: Dynamics-aware 4D Gaussian Splatting SLAM
2025-04-07
Zhicong Sun, Jacqueline Lo, Jinxing Hu
Simultaneous localization and mapping (SLAM) technology now has photorealistic mapping capabilities thanks to the real-time high-fidelity rendering capability of 3D Gaussian splatting (3DGS). However, due to the static representation of scenes, current 3DGS-based SLAM encounters issues with pose drift and failure to reconstruct accurate maps in dynamic environments. To address this problem, we present D4DGS-SLAM, the first SLAM method based on 4DGS map representation for dynamic environments. By incorporating the temporal dimension into scene representation, D4DGS-SLAM enables high-quality reconstruction of dynamic scenes. Utilizing the dynamics-aware InfoModule, we can obtain the dynamics, visibility, and reliability of scene points, and filter stable static points for tracking accordingly. When optimizing Gaussian points, we apply different isotropic regularization terms to Gaussians with varying dynamic characteristics. Experimental results on real-world dynamic scene datasets demonstrate that our method outperforms state-of-the-art approaches in both camera pose tracking and map quality.
Dark energy under a gauge symmetry: A review of gauged quintessence and its implications
2025-04-07
Kunio Kaneta, Hye-Sung Lee, Jiheon Lee, Jaeok Yi
We review the gauged quintessence scenario, wherein the quintessence scalar field responsible for dark energy is promoted to a complex field charged under a dark $U(1)$ gauge symmetry. This construction leads to new and potentially rich cosmological phenomenology. After a concise recap of the standard quintessence scenario, we highlight how a $U(1)$ gauge invariance alters the dynamics of the scalar and the associated dark gauge boson. We survey the evolution of both fields across cosmic history, discuss their possible production via a misalignment mechanism, and examine implications for the Hubble tension. We also comment on potential non-gravitational signals of gauged quintessence through kinetic mixing (the dark photon vector portal).
HaPy-Bug -- Human Annotated Python Bug Resolution Dataset
2025-04-07
Piotr Przymus, Mikołaj Fejzer, Jakub Narębski, Radosław Woźniak, Łukasz Halada, Aleksander Kazecki, Mykhailo Molchanov, Krzysztof Stencel
We present HaPy-Bug, a curated dataset of 793 Python source code commits associated with bug fixes, with each line of code annotated by three domain experts. The annotations offer insights into the purpose of modified files, changes at the line level, and reviewers' confidence levels. We analyze HaPy-Bug to examine the distribution of file purposes, types of modifications, and tangled changes. Additionally, we explore its potential applications in bug tracking, the analysis of bug-fixing practices, and the development of repository analysis tools. HaPy-Bug serves as a valuable resource for advancing research in software maintenance and security.
Investigating Popularity Bias Amplification in Recommender Systems Employed in the Entertainment Domain
2025-04-07
Dominik Kowald
Recommender systems have become an integral part of our daily online experience by analyzing past user behavior to suggest relevant content in entertainment domains such as music, movies, and books. Today, they are among the most widely used applications of AI and machine learning. Consequently, regulations and guidelines for trustworthy AI, such as the European AI Act, which addresses issues like bias and fairness, are highly relevant to the design, development, and evaluation of recommender systems. One particularly important type of bias in this context is popularity bias, which results in the unfair underrepresentation of less popular content in recommendation lists. This work summarizes our research on investigating the amplification of popularity bias in recommender systems within the entertainment sector. Analyzing datasets from three entertainment domains, music, movies, and anime, we demonstrate that an item's recommendation frequency is positively correlated with its popularity. As a result, user groups with little interest in popular content receive less accurate recommendations compared to those who prefer widely popular items. Furthermore, we aim to better understand the connection between recommendation accuracy, calibration quality of algorithms, and popularity bias amplification.
Observations of fast radio variations in microquasars by FAST
2025-04-07
Wei Wang
Microquasars are the compact objects generally including accreting black holes which produce relativistic jets. The physical mechanisms of jet launching, collimation, and acceleration are poorly understood. Microquasars show strong variability in multi-wavelength observations. In X-rays, the sources show the fast variation features down to millisecond time scales, with the prominent quasiperiodic oscillations (QPOs) around 0.1 Hz - tens of Hz in light curves, however, physical origin of QPOs is still uncertain. FAST as the largest radio telescope provides the opportunity to study fast variability of both radio flux and polarization in microquasars. In the FAST observations from 2020 - 2022, we reported the first evidence of radio subsecond quasi-periodic oscillations of GRS 1915+105, providing the direct link between QPOs and the dynamics of relativistic jets. These QPOs with the centroid frequency around 5 Hz are transient, accompanied with strong evolution of the spectral index. Combined with multiwavelength observations, we discuss the possible physical models to produce radio QPOs in BH systems: the helical motion of jet knots or precession of the jet base. In near future, high time resolution radio monitoring of microquasars based on FAST is expected to discover more new phenomena in black hole systems, which will be important to understand the physics in strong gravity.
Recent advancements in large language models (LLMs) have revolutionized their ability to handle single-turn tasks, yet real-world applications demand sophisticated multi-turn interactions. This survey provides a comprehensive review of recent advancements in evaluating and enhancing multi-turn interactions in LLMs. Focusing on task-specific scenarios, from instruction following in diverse domains such as math and coding to complex conversational engagements in roleplay, healthcare, education, and even adversarial jailbreak settings, we systematically examine the challenges of maintaining context, coherence, fairness, and responsiveness over prolonged dialogues. The paper organizes current benchmarks and datasets into coherent categories that reflect the evolving landscape of multi-turn dialogue evaluation. In addition, we review a range of enhancement methodologies under multi-turn settings, including model-centric strategies (contextual learning, supervised fine-tuning, reinforcement learning, and new architectures), external integration approaches (memory-augmented, retrieval-based methods, and knowledge graph), and agent-based techniques for collaborative interactions. Finally, we discuss open challenges and propose future directions for research to further advance the robustness and effectiveness of multi-turn interactions in LLMs. Related resources and papers are available at https://github.com/yubol-cmu/Awesome-Multi-Turn-LLMs.
Generative Large Language Model usage in Smart Contract Vulnerability Detection
2025-04-07
Peter Ince, Jiangshan Yu, Joseph K. Liu, Xiaoning Du
Recent years have seen an explosion of activity in Generative AI, specifically Large Language Models (LLMs), revolutionising applications across various fields. Smart contract vulnerability detection is no exception; as smart contracts exist on public chains and can have billions of dollars transacted daily, continuous improvement in vulnerability detection is crucial. This has led to many researchers investigating the usage of generative large language models (LLMs) to aid in detecting vulnerabilities in smart contracts. This paper presents a systematic review of the current LLM-based smart contract vulnerability detection tools, comparing them against traditional static and dynamic analysis tools Slither and Mythril. Our analysis highlights key areas where each performs better and shows that while these tools show promise, the LLM-based tools available for testing are not ready to replace more traditional tools. We conclude with recommendations on how LLMs are best used in the vulnerability detection process and offer insights for improving on the state-of-the-art via hybrid approaches and targeted pre-training of much smaller models.
The Disruption Index Measures Displacement Between a Paper and Its Most Cited Reference
2025-04-07
Yiling Lin, Linzhuo Li, Lingfei Wu
Initially developed to capture technical innovation and later adapted to identify scientific breakthroughs, the Disruption Index (D-index) offers the first quantitative framework for analyzing transformative research. Despite its promise, prior studies have struggled to clarify its theoretical foundations, raising concerns about potential bias. Here, we show that-contrary to the common belief that the D-index measures absolute innovation-it captures relative innovation: a paper's ability to displace its most-cited reference. In this way, the D-index reflects scientific progress as the replacement of older answers with newer ones to the same fundamental question-much like light bulbs replacing candles. We support this insight through mathematical analysis, expert surveys, and large-scale bibliometric evidence. To facilitate replication, validation, and broader use, we release a dataset of D-index values for 49 million journal articles (1800-2024) based on OpenAlex.
Cholesterol plays an essential role in biological membranes and is crucial for maintaining their stability and functionality. It is necessary for a variety of membranes, including planar bilayers, liposomes, curved bilayers, nanodiscs, and proteoliposomes. Cholesterol regulates membrane properties by influencing the density of lipids, phase separation into liquid-ordered (Lo) and liquid-disordered (Ld) areas, and stability of protein--membrane interactions. For planar bilayers, cholesterol thickens the membrane, decreases permeability, and brings lipids into well-ordered domains, adding rigidity and fluidity. It modulates membrane curvature in curved bilayers and vesicles, and stabilises low-curvature regions, which are important for structural integrity. In liposomes, cholesterol facilitates drug encapsulation and release by controlling bilayer flexibility and stability. In nanodiscs, cholesterol enhances structural integrity and protein compatibility, which enables the investigation of protein--lipid interactions under physiological conditions. In proteoliposomes, cholesterol regulates the conformational stability of embedded proteins that have implications for protein--lipid interaction. Developments in molecular dynamics (MD) techniques, from coarse-grained to all-atom simulations, have shown how cholesterol modulates lipid tail ordering, membrane curvature, and flip-flop behaviour in response to concentration. Such simulations provide insights into the mechanisms underlying membrane-associated diseases, aiding in the design of efficient drug delivery systems. In this review, we combine results from MD simulations to provide a synoptic explanation of cholesterols complex function in regulating membrane behaviour.
2025-04-07
C. A. Lindstrøm, S. Corde, R. D'Arcy, S. Gessner, M. Gilljohann, M. J. Hogan, J. Osterhoff
Beam-driven plasma-wakefield acceleration (PWFA) has emerged as a transformative technology with the potential to revolutionize the field of particle acceleration, especially toward compact accelerators for high-energy and high-power applications. Charged particle beams are used to excite density waves in plasma with accelerating fields reaching up to 100 GV/m, thousands of times stronger than the fields provided by radio-frequency cavities. Plasma-wakefield-accelerator research has matured over the span of four decades from basic concepts and proof-of-principle experiments to a rich and rapidly progressing sub-field with dedicated experimental facilities and state-of-the-art simulation codes. We review the physics, including theory of linear and nonlinear plasma wakefields as well as beam dynamics of both the wakefield driver and trailing bunches accelerating in the plasma wake, and address challenges associated with energy efficiency and preservation of beam quality. Advanced topics such as positron acceleration, self-modulation, internal injection, long-term plasma evolution and multistage acceleration are discussed. Simulation codes and major experiments are surveyed, spanning the use of electron, positron and proton bunches as wakefield drivers. Finally, we look ahead to future particle colliders and light sources based on plasma technology.
A Survey on Hypothesis Generation for Scientific Discovery in the Era of Large Language Models
2025-04-07
Atilla Kaan Alkan, Shashwat Sourav, Maja Jablonska, Simone Astarita, Rishabh Chakrabarty, Nikhil Garuda, Pranav Khetarpal, Maciej Pióro, Dimitrios Tanoglidis, Kartheik G. Iyer, Mugdha S. Polimera, Michael J. Smith, Tirthankar Ghosal, Marc Huertas-Company, Sandor Kruk, Kevin Schawinski, Ioana Ciucă
Hypothesis generation is a fundamental step in scientific discovery, yet it is increasingly challenged by information overload and disciplinary fragmentation. Recent advances in Large Language Models (LLMs) have sparked growing interest in their potential to enhance and automate this process. This paper presents a comprehensive survey of hypothesis generation with LLMs by (i) reviewing existing methods, from simple prompting techniques to more complex frameworks, and proposing a taxonomy that categorizes these approaches; (ii) analyzing techniques for improving hypothesis quality, such as novelty boosting and structured reasoning; (iii) providing an overview of evaluation strategies; and (iv) discussing key challenges and future directions, including multimodal integration and human-AI collaboration. Our survey aims to serve as a reference for researchers exploring LLMs for hypothesis generation.
Zero Trust is the new cybersecurity model that challenges the traditional one by promoting continuous verification of users, devices, and applications, whatever their position or origin. This model is critical for reducing the attack surface and preventing lateral movement without relying on implicit trust. Adopting the zero trust principle in Intelligent Transportation Systems (ITS), especially in the context of connected vehicles (CVs), presents an adequate solution in the face of increasing cyber threats, thereby strengthening the ITS environment. This paper offers an understanding of Zero Trust security through a comprehensive review of existing literature, principles, and challenges. It specifically examines its applications in emerging technologies, particularly within connected vehicles, addressing potential issues and cyber threats faced by CVs. Inclusion/exclusion criteria for the systematic literature review were planned alongside a bibliometric analysis. Moreover, keyword co-occurrence analysis was done, which indicates trends and general themes for the Zero Trust model, Zero Trust implementation, and Zero Trust application. Furthermore, the paper explores various ZT models proposed in the literature for connected vehicles, shedding light on the challenges associated with their integration into CV systems. Future directions of this research will focus on incorporating Zero Trust principles within Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication paradigms. This initiative intends to enhance the security posture and safety protocols within interconnected vehicular networks. The proposed research seeks to address the unique cybersecurity vulnerabilities inherent in the highly dynamic nature of vehicular communication systems.
A targeted search for Planet Nine in the CNEOS14 field
2025-04-07
Hector Socas-Navarro, Ignacio Trujillo
The hypothesized Planet Nine is thought to reside in the distant outer solar system, potentially explaining various anomalies in the orbits of extreme trans-Neptunian objects (ETNOs). In this work, we present a targeted observational search for Planet Nine, motivated by a possible gravitational interaction with the interstellar meteoroid responsible for the CNEOS14 bolide. Our observations span two campaigns over 2022 and 2023, covering a 98-square-degree region where Planet Nine's position is most likely if the messenger hypothesis holds. Our data and search methodology, based on the detection of parallax position shifts between consecutive nights, provide 85% confidence exclusion limits for objects with Sloan r-band magnitudes brighter than between 21.0 and 21.4, with an average sensitivity limit of 21.3. No credible Planet Nine candidates were identified within this field and magnitude limits. A caveat to our approach is that it would miss a candidate if its position were affected by scattered light from bright stars in at least one of the nights. However, we estimate that the probability for this is very low, around 0.4%. We discuss several possible reasons for our Planet Nine non-detection. Our study complements prior searches, particularly those using archival survey data that are limited in the Galactic plane or at fainter brightness limits. While our consecutive-night observation approach offers high sensitivity to minimal motion, extending the search for Planet Nine to fainter magnitudes (which may be crucial, according to recent predictions), will require higher sensitivity instrumentation.
A mass invariant in a compressible turbulent medium
2025-04-07
Pierre Dumond, Jérémy Fensch, Gilles Chabrier, Etienne Jaupart
Predicting the measurable statistical properties of density fluctuations in a supersonic compressible turbulent flow is a major challenge in physics. In 1951, Chandrasekhar derived an invariant under the assumption of the statistical homogeneity and isotropy of the turbulent density field and stationarity of the background density. Recently, Jaupart & Chabrier (2021) extended this invariant to non-isotropic flows in a time-evolving background and showed that it has the dimension of a mass. This invariant $M_{\rm inv}$ is defined by $M_{\rm inv} = \mathbb{E}(\rho)\text{Var}\left(\frac{\rho}{\mathbb{E}(\rho)}\right)(l_{\rm c}^\rho)^3$ where $\rho$ is the density field and $l_{\rm c}^\rho$ is the correlation length. In this article, we perform numerical simulations of homogeneous and isotropic compressible turbulence to test the validity of this invariant in a medium subject to isotropic decaying turbulence. We study several input configurations, namely different Mach numbers, injection lengths of turbulence and equations of state. We confirm that $M_{\rm inv}$ remains constant during the decaying phase of turbulence. Furthermore, we develop a theoretical model of the density field statistics which predicts without any free parameter the evolution of the correlation length with the variance of the logdensity field beyond the assumption of the gaussian field for the logdensity. Noting that $M_{\rm inv}$ is independent of the Mach number, we show that this invariant can be used to relate the non-gaussian evolution of the logdensity probability distribution function to its variance with no free parameters.
Generative Adversarial Networks with Limited Data: A Survey and Benchmarking
2025-04-07
Omar De Mitri, Ruyu Wang, Marco F. Huber
Generative Adversarial Networks (GANs) have shown impressive results in various image synthesis tasks. Vast studies have demonstrated that GANs are more powerful in feature and expression learning compared to other generative models and their latent space encodes rich semantic information. However, the tremendous performance of GANs heavily relies on the access to large-scale training data and deteriorates rapidly when the amount of data is limited. This paper aims to provide an overview of GANs, its variants and applications in various vision tasks, focusing on addressing the limited data issue. We analyze state-of-the-art GANs in limited data regime with designed experiments, along with presenting various methods attempt to tackle this problem from different perspectives. Finally, we further elaborate on remaining challenges and trends for future research.
The many roads to dementia: a systems view of Alzheimer's disease
2025-04-07
Irina Kareva
Alzheimer's disease is not the outcome of a single cause but the convergence of many. This review reframes dementia as a systemic failure, where amyloid plaques and tau tangles are not root causes but late-stage byproducts of the underlying metabolic collapse. We begin by tracing the historical merger of early- and late-onset Alzheimer's into a single disease category, a conceptual error that may have misdirected decades of research. We then synthesize evidence pointing to metabolic dysfunction - especially mitochondrial damage - as a more likely initiating event. Through this lens, we examine diverse contributing factors including type 2 diabetes, hyperglycemia-induced oxidative stress, infections and neuroinflammation. Finally, we assess current treatment limitations and argue that prevention, grounded in early metabolic and vascular interventions, holds the most promise for altering the course of this complex disease.
We review the literature on algorithms for estimating the index space in a multi-index model. The primary focus is on computationally efficient (polynomial-time) algorithms in Gaussian space, the assumptions under which consistency is guaranteed by these methods, and their sample complexity. In many cases, a gap is observed between the sample complexity of the best known computationally efficient methods and the information-theoretical minimum. We also review algorithms based on estimating the span of gradients using nonparametric methods, and algorithms based on fitting neural networks using gradient descent
SoK: Frontier AI's Impact on the Cybersecurity Landscape
2025-04-07
Wenbo Guo, Yujin Potter, Tianneng Shi, Zhun Wang, Andy Zhang, Dawn Song
As frontier AI advances rapidly, understanding its impact on cybersecurity and inherent risks is essential to ensuring safe AI evolution (e.g., guiding risk mitigation and informing policymakers). While some studies review AI applications in cybersecurity, none of them comprehensively discuss AI's future impacts or provide concrete recommendations for navigating its safe and secure usage. This paper presents an in-depth analysis of frontier AI's impact on cybersecurity and establishes a systematic framework for risk assessment and mitigation. To this end, we first define and categorize the marginal risks of frontier AI in cybersecurity and then systemically analyze the current and future impacts of frontier AI in cybersecurity, qualitatively and quantitatively. We also discuss why frontier AI likely benefits attackers more than defenders in the short term from equivalence classes, asymmetry, and economic impact. Next, we explore frontier AI's impact on future software system development, including enabling complex hybrid systems while introducing new risks. Based on our findings, we provide security recommendations, including constructing fine-grained benchmarks for risk assessment, designing AI agents for defenses, building security mechanisms and provable defenses for hybrid systems, enhancing pre-deployment security testing and transparency, and strengthening defenses for users. Finally, we present long-term research questions essential for understanding AI's future impacts and unleashing its defensive capabilities.
Disentangling the Light Curve of a VIM: A Case Study Using NEOWISE Data
2025-04-07
Bringfried Stecklum
The data collected by the Wide-field Infrared Survey Explorer (WISE, Wright et al. 2010) and its follow-up Near Earth Object (NEO) mission (NEOWISE, Mainzer et al. 2011) represent a treasure trove for variability studies. However, the angular resolution imposed by the primary mirror implies that close double stars are often unresolved. Then, variability of one or both stars leads to motion of the image centroid along the connecting line. Such an object is known as a "variability-induced mover" (VIM). Knowledge of the angular separation, derived from higher-resolution imaging which resolves both components, allows disentangling of the joint light curve into individual ones. This is illustrated by the case of SPICY 1474 which featured an outburst several years ago. Removal of the contribution of the nearby companion, which led to a dilution of the burst strength, revealed that it was ~0.5 mag brighter than in the joint light curve. A comparison with light curves from unTimely suggests that utilizing the photocenter shift may lead to more reliable results.
The Route to Unveil the Cosmic Genealogy of Supermassive Black Hole Binaries Using Nano-Hertz Gravitational Waves and Galaxy Surveys
2025-04-07
Mohit Raj Sah, Suvodip Mukherjee
The nano-hertz (nHz) stochastic gravitational wave background (SGWB), produced by unresolved supermassive black hole binaries (SMBHBs), offers a unique probe of their cosmic evolution. Using our recently proposed technique, we investigate the prospect of discovering the SMBHB population and its evolution by combining the SGWB signal and its anisotropies with galaxy observation by exploring their cross-correlation. Using a Fisher analysis, we demonstrate that the SGWB power spectrum alone can provide only a weak measurement of the cosmic genealogy of SMBHBs, while the inclusion of the angular power spectrum of SGWB and its cross-correlation with the galaxy distribution can significantly improve our measurement and open a discovery space. With a PTA of 2000 pulsars, achievable in the SKA era, we find that the redshift evolution of the supermassive black hole (SMBH) mass and galaxy stellar mass can be measured with an SNR up to 4 in the redshift range up to three. The frequency distribution can be constrained with an SNR of approximately up to 80 in the same redshift range at a frequency range accessible from nHz. These SNRs improve further with the increase in the number of pulsars to 5000 expected from SKA. This demonstrates the prospect of answering the decades-long unsolved problem of SMBH growth and evolution over cosmic time and shedding new insights on the standard model of cosmology using synergy between nHz GW and galaxy surveys.
This study examines the mediating role of five variables in the relationship between eWOM credibility and purchase intention. Data were collected via an online survey of 359 Romanian participants and analyzed using Structural Equation Modeling. The results show that perceived product quality, emotional response to eWOM and perceived buying risk fully mediate this relationship, while brand trust and brand engagement do not. These findings suggest that effective eWOM communication should enhance quality perceptions, evoke positive emotions, and boost customer confidence by reducing perceived purchasing risks.
Large Language Models (LLMs) have recently emerged as powerful tools in cybersecurity, offering advanced capabilities in malware detection, generation, and real-time monitoring. Numerous studies have explored their application in cybersecurity, demonstrating their effectiveness in identifying novel malware variants, analyzing malicious code structures, and enhancing automated threat analysis. Several transformer-based architectures and LLM-driven models have been proposed to improve malware analysis, leveraging semantic and structural insights to recognize malicious intent more accurately. This study presents a comprehensive review of LLM-based approaches in malware code analysis, summarizing recent advancements, trends, and methodologies. We examine notable scholarly works to map the research landscape, identify key challenges, and highlight emerging innovations in LLM-driven cybersecurity. Additionally, we emphasize the role of static analysis in malware detection, introduce notable datasets and specialized LLM models, and discuss essential datasets supporting automated malware research. This study serves as a valuable resource for researchers and cybersecurity professionals, offering insights into LLM-powered malware detection and defence strategies while outlining future directions for strengthening cybersecurity resilience.
Splits! A Flexible Dataset for Evaluating a Model's Demographic Social Inference
2025-04-06
Eylon Caplan, Tania Chakraborty, Dan Goldwasser
Understanding how people of various demographics think, feel, and express themselves (collectively called group expression) is essential for social science and underlies the assessment of bias in Large Language Models (LLMs). While LLMs can effectively summarize group expression when provided with empirical examples, coming up with generalizable theories of how a group's expression manifests in real-world text is challenging. In this paper, we define a new task called Group Theorization, in which a system must write theories that differentiate expression across demographic groups. We make available a large dataset on this task, Splits!, constructed by splitting Reddit posts by neutral topics (e.g. sports, cooking, and movies) and by demographics (e.g. occupation, religion, and race). Finally, we suggest a simple evaluation framework for assessing how effectively a method can generate 'better' theories about group expression, backed by human validation. We publicly release the raw corpora and evaluation scripts for Splits! to help researchers assess how methods infer--and potentially misrepresent--group differences in expression. We make Splits! and our evaluation module available at https://github.com/eyloncaplan/splits.
Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspective
2025-04-06
Lei Wan, Jianxin Zhao, Andreas Wiedholz, Manuel Bied, Mateus Martinez de Lucena, Abhishek Dinkar Jagtap, Andreas Festag, Antônio Augusto Fröhlich, Hannan Ejaz Keen, Alexey Vinel
The effectiveness of autonomous vehicles relies on reliable perception capabilities. Despite significant advancements in artificial intelligence and sensor fusion technologies, current single-vehicle perception systems continue to encounter limitations, notably visual occlusions and limited long-range detection capabilities. Collaborative Perception (CP), enabled by Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, has emerged as a promising solution to mitigate these issues and enhance the reliability of autonomous systems. Beyond advancements in communication, the computer vision community is increasingly focusing on improving vehicular perception through collaborative approaches. However, a systematic literature review that thoroughly examines existing work and reduces subjective bias is still lacking. Such a systematic approach helps identify research gaps, recognize common trends across studies, and inform future research directions. In response, this study follows the PRISMA 2020 guidelines and includes 106 peer-reviewed articles. These publications are analyzed based on modalities, collaboration schemes, and key perception tasks. Through a comparative analysis, this review illustrates how different methods address practical issues such as pose errors, temporal latency, communication constraints, domain shifts, heterogeneity, and adversarial attacks. Furthermore, it critically examines evaluation methodologies, highlighting a misalignment between current metrics and CP's fundamental objectives. By delving into all relevant topics in-depth, this review offers valuable insights into challenges, opportunities, and risks, serving as a reference for advancing research in vehicular collaborative perception.
A Review on Symbolic Regression in Power Systems: Methods, Applications, and Future Directions
2025-04-06
Amir Bahador Javadi, Philip Pong
As power systems evolve with the increasing integration of renewable energy sources and smart grid technologies, there is a growing demand for flexible and scalable modeling approaches capable of capturing the complex dynamics of modern grids. This review focuses on symbolic regression, a powerful methodology for deriving parsimonious and interpretable mathematical models directly from data. The paper presents a comprehensive overview of symbolic regression methods, including sparse identification of nonlinear dynamics, automatic regression for governing equations, and deep symbolic regression, highlighting their applications in power systems. Through comparative case studies of the single machine infinite bus system, grid-following, and grid-forming inverter, we analyze the strengths, limitations, and suitability of each symbolic regression method in modeling nonlinear power system dynamics. Additionally, we identify critical research gaps and discuss future directions for leveraging symbolic regression in the optimization, control, and operation of modern power grids. This review aims to provide a valuable resource for researchers and engineers seeking innovative, data-driven solutions for modeling in the context of evolving power system infrastructure.
How Untested Modeling Assumptions Influence the U.S. EPA's Estimates of Population-Level Ozone Exposure Risk
2025-04-06
Garrett Glasgow, Anne E. Smith
In recent reviews of the National Ambient Air Quality Standards (NAAQS) for ozone, the U.S. Environmental Protection Agency (U.S. EPA) has presented estimates of the health risks associated with ozone exposure. One way in which the U.S. EPA calculates population-level ozone risk estimates is through a simulation model that calculates ozone exposures and the resulting lung function decrements for a simulated population. This simulation model includes several random error terms to capture inter- and intra-individual variability in responsiveness to ozone exposure. In this manuscript we undertake a sensitivity analysis examining the influence of untested assumptions about these error terms. We show that ad hoc bounds imposed on the error terms and the frequency of redrawing the intra-individual error terms have a strong influence on the population-level ozone exposure risk reported by the U.S. EPA.
Room-temperature dislocation plasticity in ceramics: Methods, Materials, and Mechanisms
2025-04-06
Alexander Frisch, Chukwudalu Okafor, Oliver Preuß, Jiawen Zhang, Katsuyuki Matsunaga, Atsutomo Nakamura, Wenjun Lu, Xufei Fang
Dislocation-mediated plastic deformation in ceramic materials has sparked renewed research interest due to the technological potential of dislocations. Despite the long research history of dislocations as one-dimensional lattice defects in crystalline solids, the understanding of plastically deformable ceramics at room temperature seems lacking. The conventional view holds that ceramics are brittle, difficult to deform at room temperature and exhibit no dislocation plasticity except in small-scale testing such as nanoindentation and nano-micropillar compression. In this review, we attempt to gather the evidence and reports of room-temperature dislocation plasticity in ceramics beyond the nano-/microscale, with a focus on meso-macroscale plasticity. First, we present a mechanical deformation toolbox covering various experimental approaches for assessing the dislocation plasticity, with a focus on bulk plasticity. Second, we provide a materials toolbox listing 44 ceramic compounds that have been reported to exhibit dislocation plasticity at meso-/macroscale under ambient conditions. Finally, we discuss the mechanics of dislocations in ceramics, aiming to establish a foundation for predicting and discovering additional ceramics capable of room-temperature plastic deformation, thereby advancing the development of prospective dislocation-based technologies.
A small-area ecological approach for estimating vote changes and their determinants
2025-04-06
Bruno Bracalente, Antonio Forcina, Nicola Falocci
Empirical analyses on the factors driving vote switching are rare, usually conducted at the national level without considering the parties of origin and destination, and often unreliable due to the severe inaccuracy of recall survey data. To overcome the problem of lack of adequate data and to incorporate the increasingly relevant role of local factors, we propose an ecological inference methodology to estimate the number of vote transitions within small homogeneous areas and to assess the relationships between these counts and local characteristics through multinomial logistic models. This approach allows for a disaggregate analysis of contextual factors behind vote switching, distinguishing between their different origins and destinations. We apply this methodology to the Italian region of Umbria, divided into 19 small areas. To explain the number of transitions toward the right-wing nationalist party that won the elections and towards increasing abstentionism, we focused on measures of geographical, economic, and cultural disadvantages of local communities. Among the main findings, the economic disadvantages mainly pushed previous abstainers and far-right Lega voters to change their choices in favor of the rising right-wing party, while transitions from the opposite political camp were mostly influenced by cultural factors such as a lack of social capital, negative attitude towards the EU, and political tradition.
Variational Bayesian Multiple Imputation in High-Dimensional Regression Models With Missing Responses
2025-04-06
Qiushuang Li, Recai Yucel
Multiple imputation has become one of the standard methods in drawing inferences in many incomplete data applications. Applications of multiple imputation in relatively more complex settings, such as high-dimensional clustered data, require specialized methods to overcome the computational burden. Using linear mixed-effects models, we develop such methods that can be applied to continuous, binary, or categorical incomplete data by employing variational Bayesian inference to sample the posterior predictive distribution of the missing data. These methods specifically target high-dimensional data and work with the spike-and-slab prior, which automatically selects the variables of importance to be in the imputation model. The individual regression computation is then incorporated into a variable-by-variable imputation algorithm. Finally, we use a calibration-based algorithm to adopt these methods to multiple imputations of categorical variables. We present a simulation study and illustrate on National Survey of Children's Health data to assess the performance of these methods in a repetitive sampling framework.
Sequential Hierarchical Regression Imputation with Variable Selection Routines
2025-04-06
Qiushuang Li, Recai Yucel
We aim to incorporate variable selection routines into variable-by-variable (or sequential) imputation in clustered data to achieve computational improvement in applications with large-scale health data. Specifically, we utilize variable selection routines using spike-and-slab priors within the Bayesian variable selection routine. The choice of these priors allows us to ``force'' variables of importance (e.g., design variables or variables known to play a role in the missingness mechanism) into the imputation models based on a class of mixed-effects models. Our ultimate goal is to improve computational speed by removing unnecessary variables. We employ Markov chain Monte Carlo techniques to sample from the implied posterior distributions for model unknowns as well as missing data. We assess the performance of our proposed methodology via simulation studies. Our results show that our proposed algorithms lead to satisfactory estimates and, in some instances, outperform some of the existing methods that are available to practitioners. We illustrate our methods using a national survey of children's health.
Understanding Collective Stability of ACC Systems: From Theory to Real-World Observations
2025-04-06
Raphael Korbmacher, Parthib Khound, Antoine Tordeux
Autonomated vehicles (AVs) are expected to have a profound impact on society, with high expectations for their potential benefits. One key anticipated benefit is the reduction of traffic congestion and stop-and-go waves, which negatively affect fuel efficiency, travel time, and environmental sustainability. This paper presents a comprehensive review and meta-analysis of the impact of AVs on the longitudinal collective stability of vehicle single file. We focus on adaptive cruise control (ACC) systems, a widely used precursor technology to fully autonomous driving. ACC controllers have been studied extensively in both theoretical and practical contexts, making it a valuable starting point for analysis. Our study systematically differentiates the findings from models and simulations, controlled experiments, and empirical observations to provide a structured overview of existing research. Although some results in the literature are contradictory, three key insights emerge from our analysis: i) String stability is highly dependent on the chosen time gap and reaction/response time. ii) ACC systems currently implemented in commercial vehicles are not string stable, as manufacturers prioritise individual comfort and smooth driving, resulting in high system response times. iii) Although cooperative ACC (CACC) systems are theoretically the most effective solution to ensure string stability, their widespread implementation in the near future remains uncertain. Instead, improvements in autonomous control algorithms should be considered to enhance system performance.
Building LLM Agents by Incorporating Insights from Computer Systems
2025-04-06
Yapeng Mi, Zhi Gao, Xiaojian Ma, Qing Li
LLM-driven autonomous agents have emerged as a promising direction in recent years. However, many of these LLM agents are designed empirically or based on intuition, often lacking systematic design principles, which results in diverse agent structures with limited generality and scalability. In this paper, we advocate for building LLM agents by incorporating insights from computer systems. Inspired by the von Neumann architecture, we propose a structured framework for LLM agentic systems, emphasizing modular design and universal principles. Specifically, this paper first provides a comprehensive review of LLM agents from the computer system perspective, then identifies key challenges and future directions inspired by computer system design, and finally explores the learning mechanisms for LLM agents beyond the computer system. The insights gained from this comparative analysis offer a foundation for systematic LLM agent design and advancement.
Probability for synthesis of superheavy nuclei with Z=121
2025-04-06
R. Zargini, S. A. Seyyedi
In this study the empirical method \cite{RN464} is used for calculating the evaporation residue (ER) cross section in the synthesis of superheavy nuclei (SHN). The superheavy nuclei examined in this work fall within the range $Z=112-118$. The theoretical calculations show good agreement with the experimental data. Furthermore, this model, along with an investigation of the optimal incident energy (OIE), is used to calculate the ER cross section for a hypothetical heavy system with $Z=121$. Five promising combinations are suggested for synthesis of SHN with $Z=121$: (1) ${^{50}}\mathrm{Ti}+{^{252}}\mathrm{Es}$, with the maximum ER cross section $\sigma_{3n}=24.5~\mathrm{fb}$, at the optimal incident energy, $\mathrm{OIE}=230~\mathrm{MeV}$; (2) ${^{50}}\mathrm{Ti}+{^{254}}\mathrm{Es}$, with the maximum ER cross section $\sigma_{3n}=11.8~\mathrm{fb}$, at the optimal incident energy, $\mathrm{OIE}=229~\mathrm{MeV}$; (3) ${^{51}}\mathrm{V}+{^{251}}\mathrm{Cf}$, with the maximum ER cross section $\sigma_{3n}=1.2~\mathrm{fb}$, at the optimal incident energy, $\mathrm{OIE}=238~\mathrm{MeV}$; (4) ${^{51}}\mathrm{V}+{^{249}}\mathrm{Cf}$, with the maximum ER cross section $\sigma_{3n}=1.0~\mathrm{fb}$, at the optimal incident energy, $\mathrm{OIE}=238~\mathrm{MeV}$; and (5) ${^{54}}\mathrm{Cr}+{^{247}}\mathrm{Bk}$, with the maximum ER cross section $\sigma_{2n}=0.9~\mathrm{fb}$, at the optimal incident energy, $\mathrm{OIE}=243~\mathrm{MeV}$.
Meta-Mathematics of Computational Complexity Theory
2025-04-06
Igor C. Oliveira
We survey results on the formalization and independence of mathematical statements related to major open problems in computational complexity theory. Our primary focus is on recent findings concerning the (un)provability of complexity bounds within theories of bounded arithmetic. This includes the techniques employed and related open problems, such as the (non)existence of a feasible proof that P = NP.
Sporadic Isogenies to the Quaternionic Orthogonal Groups $\operatorname{SO}^*(2n)$
2025-04-06
Craig McRae
For small dimensional Lie algebra's there are many so-called accidental isomorphisms which give rise to double covers of special orthogonal groups - Spin groups - which happen to coincide with groups already belonging to another classification. The well known catalog of these sporadic isogenies given by Dr. Paul Garrett keeps track of these facts for the complex orthogonal groups $\operatorname{SO}(n, \mathbb{C})$, and those real forms which are manifestly real: $\operatorname{SO}(p,q)$, while avoiding the quaternionic real forms, $\operatorname{SO}^*(2n)$. This article serves to complement and complete the existing catalog by presenting the sporadic isogenies to the first four quaternionic orthogonal groups in one place, with contemporary proofs of those group and algebra homomorphisms. A review of the definition of $\operatorname{SO}^*(2n)$ is presented in a modern notation, emphasizing the concept of `quaternion reversion' as a useful, albeit redundant, second conjugation upon quaternions, helping to explicate the self-conjugacy of quaternionic representations. Lastly, the triality of $\operatorname{SO}^*(8)$ is explored in a similar manner to work done by the author previously.
Supernovae at Distances < 40 Mpc: I.Catalogues and fractions of Supernovae in a Complete Sample
2025-04-06
Xiaoran Ma, Xiaofeng Wang, Jun Mo, D. Andrew Howell, Craig Pellegrino, Jujia Zhang, Shengyu Yan, Iair Arcavi, Zhihao Chen, Joseph Farah, Estefania Padilla Gonzalez, Fangzhou Guo, Daichi Hiramatsu, Gaici Li, Han Lin, Jialian Liu, Curtis McCully, Megan Newsome, Hanna Sai, Giacomo Terreran, Danfeng Xiang, Xinhan Zhang, Tianmeng Zhang
Context.This is the first paper of a series aiming to determine the fractions and birth rates of various types of supernovae (SNe) in the local Universe. Aims. In this paper, we aim to construct a complete sample of SNe in the nearby universe and provide more precise measurement of subtype fractions. Methods.We carefully selected our SN sample at a distance of < 40 Mpc mainly from wide-field surveys conducted over the years from 2016 to 2023. Results.The sample contains a total of 211 SNe, including 109 SNe II, 69 SNe Ia, and 33 SNe Ibc. With the aid of sufficient spectra, we can obtain relatively accurate subtype classifications for all SNe in this sample. After corrections for the Malmquist bias, this volume-limited sample gives fractions of SNe Ia, SNe Ibc, and SNe II as $30.4^{+3.7}_{-11.5}\%$, $16.3^{+3.7}_{-7.4}\%$, and $53.3^{+9.5}_{-18.7}\%$, respectively.In the SN Ia sample, the fraction of the 91T-like subtype becomes relatively low (~5.4\%), while that of the 02cx-like subtype shows a moderate increase (~6.8\%). In the SN Ibc sample, we find significant fractions of broadlined SNe Ic (~18.0\%) and SNe Ibn (~8.8\%). The fraction of 87A-like subtype is determined as ~2.3\% for the first time, indicating rare explosions from blue supergiant stars. We find that SNe Ia show a double peak number distribution in S0- and Sc-type host galaxies, which may serve as a straightforward evidence for the presence of "prompt" and "delayed" progenitor components giving rise to SN Ia explosions. Several subtypes of SNe such as 02cx-like SNe Ia, broadlined SNe Ic, SNe IIn (and perhaps SNe Ibn) are found to occur preferentially in less massive spiral galaxies, favoring their associations with young stellar progenitors. Moreover, the 02cx-like subtype shows a trend of exploding in the outer skirt of their hosts, suggestive of metal-poor progenitors.
Zoom serves millions of users daily and allows third-party developers to integrate their apps with the Zoom client and reach those users. So far, these apps' privacy and security aspects, which can access rich audio-visual data (among others) from Zoom, have not been scientifically investigated. This paper examines the evolution of the Zoom Marketplace over one year, identifying trends in apps, their data collection behaviors, and the transparency of privacy policies. Our findings include worrisome details about the increasing over-collection of user data, non-transparency about purposes and sharing behaviors, and possible non-compliance with relevant laws. We believe these findings will inform future privacy and security research on this platform and help improve Zoom's app review process and platform policy.
The peaked-spectrum (PS) sources exhibit turnover characteristics in their broad radio spectra. However, the mechanism underlying this phenomenon remains elusive. The two most common hypotheses are synchrotron self-absorption (SSA) and free-free absorption (FFA). By incorporating multiple absorption scenarios, we propose a multi-mechanism hybrid (MMH) model, which aligns well with current observational data and provides a good physical explanation. Using the GLEAM survey data, we identified a sample of 4,315 sources with peak frequencies approximately between 72--3000 MHz, most of which are MHz-peaked-spectrum sources (MPS). Our analysis shows that instead of SSA, the FFA is the dominant mechanism in producing the spectral turnover for most of the sources in this sample. The index of the optically thick spectrum alpha_thick has a lower boundary due to the FFA, and the steeper alpha_thick indicates a complex multi-absorption mechanism. In particular, the external FFA produces substantial alpha_thick, which exhibits a weak correlation with the peak frequency. Future ultra-long wavelength observations would also provide data on the spectrum of these sources at even lower frequencies. Determining the absorption mechanism that shaped the spectrum of these sources would be a crucial part of understanding their nature.
Future-Proof Yourself is a practical guide that helps readers navigate the fast-changing world of artificial intelligence in everyday life. The book begins by explaining how computers learn from data in simple, relatable terms, and gradually introduces the methods used in modern AI. It shows how basic ideas in machine learning evolve into advanced systems that can recognize images, understand language, and even make decisions. The guide also reviews the history of AI and highlights the major breakthroughs that have shaped its growth. Looking ahead, the book explores emerging trends such as the integration of AI with digital twins, wearable devices, and virtual environments. Designed for a general audience, the text avoids heavy technical jargon and presents complex ideas in clear, straightforward language so that anyone can gain a solid understanding of the technology that is set to transform our future.
How Accurately Do Large Language Models Understand Code?
2025-04-06
Sabaat Haroon, Ahmad Faraz Khan, Ahmad Humayun, Waris Gill, Abdul Haddi Amjad, Ali R. Butt, Mohammad Taha Khan, Muhammad Ali Gulzar
Large Language Models (LLMs) are increasingly used in post-development tasks such as code repair and testing. A key factor in these tasks' success is the model's deep understanding of code. However, the extent to which LLMs truly understand code remains largely unevaluated. Quantifying code comprehension is challenging due to its abstract nature and the lack of a standardized metric. Previously, this was assessed through developer surveys, which are not feasible for evaluating LLMs. Existing LLM benchmarks focus primarily on code generation, fundamentally different from code comprehension. Additionally, fixed benchmarks quickly become obsolete as they become part of the training data. This paper presents the first large-scale empirical investigation into LLMs' ability to understand code. Inspired by mutation testing, we use an LLM's fault-finding ability as a proxy for its deep code understanding. This approach is based on the insight that a model capable of identifying subtle functional discrepancies must understand the code well. We inject faults in real-world programs and ask the LLM to localize them, ensuring the specifications suffice for fault localization. Next, we apply semantic-preserving code mutations (SPMs) to the faulty programs and test whether the LLMs still locate the faults, verifying their confidence in code understanding. We evaluate nine popular LLMs on 575000 debugging tasks from 670 Java and 637 Python programs. We find that LLMs lose the ability to debug the same bug in 81% of faulty programs when SPMs are applied, indicating a shallow understanding of code and reliance on features irrelevant to semantics. We also find that LLMs understand code earlier in the program better than later. This suggests that LLMs' code comprehension remains tied to lexical and syntactic features due to tokenization designed for natural languages, which overlooks code semantics.
The Fidelity-based Presence Scale (FPS): Modeling the Effects of Fidelity on Sense of Presence
2025-04-06
Jacob Belga, Richard Skarbez, Yahya Hmaiti, Eric J. Chen, Ryan P. McMahan, Joseph J. LaViola
Within the virtual reality (VR) research community, there have been several efforts to develop questionnaires with the aim of better understanding the sense of presence. Despite having numerous surveys, the community does not have a questionnaire that informs which components of a VR application contributed to the sense of presence. Furthermore, previous literature notes the absence of consensus on which questionnaire or questions should be used. Therefore, we conducted a Delphi study, engaging presence experts to establish a consensus on the most important presence questions and their respective verbiage. We then conducted a validation study with an exploratory factor analysis (EFA). The efforts between our two studies led to the creation of the Fidelity-based Presence Scale (FPS). With our consensus-driven approach and fidelity-based factoring, we hope the FPS will enable better communication within the research community and yield important future results regarding the relationship between VR system fidelity and presence.
Generative Large Language Models Trained for Detecting Errors in Radiology Reports
2025-04-06
Cong Sun, Kurt Teichman, Yiliang Zhou, Brian Critelli, David Nauheim, Graham Keir, Xindi Wang, Judy Zhong, Adam E Flanders, George Shih, Yifan Peng
In this retrospective study, a dataset was constructed with two parts. The first part included 1,656 synthetic chest radiology reports generated by GPT-4 using specified prompts, with 828 being error-free synthetic reports and 828 containing errors. The second part included 614 reports: 307 error-free reports between 2011 and 2016 from the MIMIC-CXR database and 307 corresponding synthetic reports with errors generated by GPT-4 on the basis of these MIMIC-CXR reports and specified prompts. All errors were categorized into four types: negation, left/right, interval change, and transcription errors. Then, several models, including Llama-3, GPT-4, and BiomedBERT, were refined using zero-shot prompting, few-shot prompting, or fine-tuning strategies. Finally, the performance of these models was evaluated using the F1 score, 95\% confidence interval (CI) and paired-sample t-tests on our constructed dataset, with the prediction results further assessed by radiologists. Using zero-shot prompting, the fine-tuned Llama-3-70B-Instruct model achieved the best performance with the following F1 scores: 0.769 for negation errors, 0.772 for left/right errors, 0.750 for interval change errors, 0.828 for transcription errors, and 0.780 overall. In the real-world evaluation phase, two radiologists reviewed 200 randomly selected reports output by the model. Of these, 99 were confirmed to contain errors detected by the models by both radiologists, and 163 were confirmed to contain model-detected errors by at least one radiologist. Generative LLMs, fine-tuned on synthetic and MIMIC-CXR radiology reports, greatly enhanced error detection in radiology reports.
Artificial Intelligence for Software Architecture: Literature Review and the Road Ahead
2025-04-06
Alessio Bucaioni, Martin Weyssow, Junda He, Yunbo Lyu, David Lo
This paper presents a forward-looking vision for artificial intelligence-driven software architecture that addresses longstanding challenges in design and evolution. Although artificial intelligence has achieved notable success in software engineering, its explicit application to software architecture remains under-explored. Traditional practices, heavily reliant on expert knowledge and complex trade-off reasoning, tend to be manual and error-prone, thereby compromising system quality and maintainability. Building on recent advances, we examine how artificial intelligence can automate architectural design, support quantitative trade-off analyses, and continuously update architectural documentation. Our approach combines a systematic review of state-of-the-art applications with insights from industry practitioners. The resulting roadmap outlines 14 current artificial intelligence contributions to software architecture, identifies six artificial intelligence-specific challenges in supporting architectural tasks, and reveals six avenues for future improvement, charting a course for future research and practical implementations.
A Survey of Social Cybersecurity: Techniques for Attack Detection, Evaluations, Challenges, and Future Prospects
2025-04-06
Aos Mulahuwaish, Basheer Qolomany, Kevin Gyorick, Jacques Bou Abdo, Mohammed Aledhari, Junaid Qadir, Kathleen Carley, Ala Al-Fuqaha
In today's digital era, the Internet, especially social media platforms, plays a significant role in shaping public opinions, attitudes, and beliefs. Unfortunately, the credibility of scientific information sources is often undermined by the spread of misinformation through various means, including technology-driven tools like bots, cyborgs, trolls, sock-puppets, and deep fakes. This manipulation of public discourse serves antagonistic business agendas and compromises civil society. In response to this challenge, a new scientific discipline has emerged: social cybersecurity.
GPU-based compressible lattice Boltzmann simulations on non-uniform grids using standard C++ parallelism: From best practices to aerodynamics, aeroacoustics and supersonic flow simulations
2025-04-06
Christophe Coreixas, Jonas Latt
Despite decades of research, creating accurate, robust, and efficient lattice Boltzmann methods (LBM) on non-uniform grids with seamless GPU acceleration remains challenging. This work introduces a novel strategy to address this challenge by integrating simple yet effective components: (1) parallel algorithms in modern C++, (2) conservative cell-centered grid refinement, (3) local boundary conditions, and (4) robust collision models. Our framework supports multiple lattices (D2Q9, D2Q13, D2Q21, D2Q37) tailored to various flow conditions. It includes collision models with polynomial and numerical equilibria, a second distribution for polyatomic behavior, a Jameson-like shock sensor, and generalizes Rohde's refinement strategy. The framework's accuracy and robustness is validated across diverse benchmarks, including lid-driven cavity flows, Aeolian noise, 30P30N airfoil aerodynamics, inviscid Riemann problems, and viscous flows past a NACA airfoil in transonic and supersonic regimes. Modern C++ further enables our framework to reach GPU-native performance, while ensuring high portability, modularity, and ease of implementation. Notably, weakly compressible LBMs achieve state-of-the-art GPU efficiency on non-uniform grids, while fully compressible LBMs benefit from acceleration equivalent to thousands of CPU cores in the most compute-intensive cases. Our advanced performance models incorporate neighbor-list and asynchronous time-stepping effects, providing new insights into the performance decomposition of LB simulations on non-uniform grids. Overall, this study sets a new standard for portable, tree-based LBMs, demonstrating that a combination of well-chosen components can achieve high performance, accuracy, and robustness across various flow conditions. As a final proof-of-concept, adaptive mesh refinement is proposed for subsonic and supersonic applications.
Assessing Computer Science Student Attitudes Towards AI Ethics and Policy
2025-04-06
James Weichert, Dayoung Kim, Qin Zhu, Junghwan Kim, Hoda Eldardiry
As artificial intelligence (AI) grows in popularity and importance-both as a domain within broader computing research and in society at large-increasing focus will need to be paid to the ethical governance of this emerging technology. The attitudes and competencies with respect to AI ethics and policy among post-secondary students studying computer science (CS) are of particular interest, as many of these students will go on to play key roles in the development and deployment of future AI innovations. Despite this population of computer scientists being at the forefront of learning about and using AI tools, their attitudes towards AI remain understudied in the literature. In an effort to begin to close this gap, in fall 2024 we fielded a survey ($n=117$) to undergraduate and graduate students enrolled in CS courses at a large public university in the United States to assess their attitudes towards the nascent fields of AI ethics and policy. Additionally, we conducted one-on-one follow-up interviews with 13 students to elicit more in-depth responses on topics such as the use of AI tools in the classroom, ethical impacts of AI, and government regulation of AI. In this paper, we describe the findings of both the survey and interviews, drawing parallels and contrasts to broader public opinion polling in the United States. We conclude by evaluating the implications of CS student attitudes on the future of AI education and governance.
AI-induced sexual harassment: Investigating Contextual Characteristics and User Reactions of Sexual Harassment by a Companion Chatbot
2025-04-05
Mohammad, Namvarpour, Harrison Pauwels, Afsaneh Razi
Advancements in artificial intelligence (AI) have led to the increase of conversational agents like Replika, designed to provide social interaction and emotional support. However, reports of these AI systems engaging in inappropriate sexual behaviors with users have raised significant concerns. In this study, we conducted a thematic analysis of user reviews from the Google Play Store to investigate instances of sexual harassment by the Replika chatbot. From a dataset of 35,105 negative reviews, we identified 800 relevant cases for analysis. Our findings revealed that users frequently experience unsolicited sexual advances, persistent inappropriate behavior, and failures of the chatbot to respect user boundaries. Users expressed feelings of discomfort, violation of privacy, and disappointment, particularly when seeking a platonic or therapeutic AI companion. This study highlights the potential harms associated with AI companions and underscores the need for developers to implement effective safeguards and ethical guidelines to prevent such incidents. By shedding light on user experiences of AI-induced harassment, we contribute to the understanding of AI-related risks and emphasize the importance of corporate responsibility in developing safer and more ethical AI systems.
Discovery of a Weak CN Spectral Absorption Feature in Red Supergiant Stars in the Andromeda (M31) and Triangulum (M33) Galaxies
2025-04-05
Puragra Guhathakurta, Douglas Grion Filho, Antara R. Bhattacharya, Lara R. Cullinane, Julianne J. Dalcanton, Karoline M. Gilbert, Leo Girardi, Anika Kamath, Evan N. Kirby, Arya Maheshwari, Paola Marigo, Alexandra Masegian, Amanda C. N. Quirk, Rachel Raikar, Stanley M. Rinehart V, Caelum J. Rodriguez, Benjamin F. Williams
Using Keck DEIMOS spectra of stars in the Andromeda (M31) and Triangulum (M33) galaxies, selected from the large multi-band (near ultraviolet, visible light, and near infrared) Hubble Space Telescope surveys PHAT and PHATTER, respectively, we have identified a subset of stars that contain a previously unnoticed weak spectral absorption feature around 8000 Angstrom (0.8 micron). This absorption feature appears to be associated with the cyanogen (CN) molecule. Strong CN spectral absorption is a standard feature of carbon stars, which are thought to be intermediate mass (2-3 ~ M_sun) stars with C/O > 1 in the thermally-pulsating asymptotic giant branch phase of stellar evolution. However, the stars that are the focus of this paper are characterized by a weak version of this CN spectral absorption feature in a spectrum that is otherwise dominated by normal O-rich spectral absorption lines such as TiO and/or the Ca near infrared triplet. We have dubbed these stars "weak CN" stars. We present an automated method for identifying weak CN stars in M31 and M33, and examine their photometric properties in relation to model isochrones and stellar tracks. We find that weak CN stars tend to be fairly localized in color-magnitude space, and appear to be red supergiant stars with masses ranging from 5-10 M_sun, overall lifetimes of about 40-50 Myr, and currently in the core He burning phase of stellar evolution.
Two-Stage Final Exams: An Assessment Strategy for Enhanced Collaborative Learning and Reduced Student Stress
2025-04-05
Kristina Callaghan, Tim Milbourne, Anna Klales, Greg Kestin, Carlso Arguelles, Logan McCarty, Louis Deslauriers
Two-stage exams, which pair a traditional individual exam with a subsequent collaborative, group exam, have been shown to enhance learning, retention, and student attitudes in midterm settings. This study investigates whether these benefits extend to final exam settings, compares the traditional final exam experience with a two-stage variation, and explores using an asynchronous group component to maintain the exam comprehensive nature. Within introductory courses at two different institutions, two formats for two-stage exams were implemented: one with a 2-hour individual exam followed by a 1-hour synchronous group exam and another with a 3-hour individual exam followed by an untimed, asynchronous group exam completed within 36 hours. Survey data comparing student experiences with two-stage midterm and final exams showed consistently positive responses at both institutions. When comparing two-stage final exams to traditional (individual-only) final exams, students reported increased learning and retention, decreased anxiety, and an overwhelming preference for the two-stage format. These findings suggest that extending the benefits of two-stage midterms to the final exam is possible without compromising the evaluative purpose of a final exam while also maintaining high student motivation and engagement.
Modeling environmental ecosystems is essential for effective resource management, sustainable development, and understanding complex ecological processes. However, traditional data-driven methods face challenges in capturing inherently complex and interconnected processes and are further constrained by limited observational data in many environmental applications. Foundation models, which leverages large-scale pre-training and universal representations of complex and heterogeneous data, offer transformative opportunities for capturing spatiotemporal dynamics and dependencies in environmental processes, and facilitate adaptation to a broad range of applications. This survey presents a comprehensive overview of foundation model applications in environmental science, highlighting advancements in common environmental use cases including forward prediction, data generation, data assimilation, downscaling, inverse modeling, model ensembling, and decision-making across domains. We also detail the process of developing these models, covering data collection, architecture design, training, tuning, and evaluation. Through discussions on these emerging methods as well as their future opportunities, we aim to promote interdisciplinary collaboration that accelerates advancements in machine learning for driving scientific discovery in addressing critical environmental challenges.
Could AI Trace and Explain the Origins of AI-Generated Images and Text?
2025-04-05
Hongchao Fang, Can Qin, Ran Xu, Feng Liu, Yixin Liu, Lichao Sun, Dongwon Lee, Lifu Huang, Wenpeng Yin
AI-generated content is becoming increasingly prevalent in the real world, leading to serious ethical and societal concerns. For instance, adversaries might exploit large multimodal models (LMMs) to create images that violate ethical or legal standards, while paper reviewers may misuse large language models (LLMs) to generate reviews without genuine intellectual effort. While prior work has explored detecting AI-generated images and texts, and occasionally tracing their source models, there is a lack of a systematic and fine-grained comparative study. Important dimensions--such as AI-generated images vs. text, fully vs. partially AI-generated images, and general vs. malicious use cases--remain underexplored. Furthermore, whether AI systems like GPT-4o can explain why certain forged content is attributed to specific generative models is still an open question, with no existing benchmark addressing this. To fill this gap, we introduce AI-FAKER, a comprehensive multimodal dataset with over 280,000 samples spanning multiple LLMs and LMMs, covering both general and malicious use cases for AI-generated images and texts. Our experiments reveal two key findings: (i) AI authorship detection depends not only on the generated output but also on the model's original training intent; and (ii) GPT-4o provides highly consistent but less specific explanations when analyzing content produced by OpenAI's own models, such as DALL-E and GPT-4o itself.
Physics with high-luminosity proton-nucleus collisions at the LHC
2025-04-05
D. d'Enterria, C. A. Flett, I. Grabowska-Bold, C. Hadjidakis, P. Kotko, A. Kusina, J. P. Lansberg, R. McNulty, M. Rinaldi, L. Bonechi, R. Bruce, C. Da Silva, E. G. Ferreiro, S. Fichet, L. Harland-Lang, G. Innocenti, F. Jonas, J. M. Jowett, R. Longo, K. Lynch, C. McGinn, T. Pierog, M. Pitt, S. Redaelli, B. Schenke, I. Schienbein, M. Stefaniak, M. Strikman, L. Szymanowski, D. Tapia Takaki, C. Van Hulse, S. Wallon
The physics case for the operation of high-luminosity proton-nucleus ($pA$) collisions during Run 3 and 4 at the LHC is reviewed. The collection of $\mathcal{O}$(1-10 pb$^{-1}$) of proton-lead ($p$Pb) collisions at the LHC will provide unique physics opportunities in a broad range of topics including proton and nuclear parton distribution functions (PDFs and nPDFs), generalised parton distributions (GPDs), transverse momentum dependent PDFs (TMDs), low-$x$ QCD and parton saturation, hadron spectroscopy, baseline studies for quark-gluon plasma and parton collectivity, double and triple parton scatterings (DPS/TPS), photon-photon collisions, and physics beyond the Standard Model (BSM); which are not otherwise as clearly accessible by exploiting data from any other colliding system at the LHC. This report summarises the accelerator aspects of high-luminosity $pA$ operation at the LHC, as well as each of the physics topics outlined above, including the relevant experimental measurements that motivate -- much -- larger $pA$ datasets.
Bioverse: Potentially Observable Exoplanet Biosignature Patterns Under the UV Threshold Hypothesis for the Origin of Life
2025-04-05
Martin Schlecker, Dániel Apai, Antonin Affholder, Sukrit Ranjan, Régis Ferrière, Kevin K. Hardegree-Ullman, Tim Lichtenberg, Stéphane Mazevet
A wide variety of scenarios for the origin of life have been proposed, with many influencing the prevalence and distribution of biosignatures across exoplanet populations. This relationship suggests these scenarios can be tested by predicting biosignature distributions and comparing them with empirical data. Here, we demonstrate this approach by focusing on the cyanosulfidic origins-of-life scenario and investigating the hypothesis that a minimum near-ultraviolet (NUV) flux is necessary for abiogenesis. Using Bayesian modeling and the \bioverse\ survey simulator, we constrain the probability of obtaining strong evidence for or against this ``UV Threshold Hypothesis'' with future biosignature surveys. Our results indicate that a correlation between past NUV flux and current biosignature occurrence is testable for sample sizes of $\gtrsim$50 planets. The diagnostic power of such tests is critically sensitive to the intrinsic abiogenesis rate and host star properties, particularly maximum past NUV fluxes. Surveys targeting a wide range of fluxes, and planets orbiting M dwarfs enhance the chances of conclusive results, with sample sizes $\gtrsim$100 providing $\gtrsim$80\% likelihood of strong evidence if abiogenesis rates are high and the required NUV fluxes are moderate. For required fluxes exceeding a few hundred erg/s/cm$^2$, both the fraction of inhabited planets and the diagnostic power sharply decrease. Our findings demonstrate the potential of exoplanet surveys to test origins-of-life hypotheses. Beyond specific scenarios, this work underscores the broader value of realistic survey simulations for future observatories (e.g., HWO, LIFE, ELTs, Nautilus) in identifying testable science questions, optimizing mission strategies, and advancing theoretical and experimental studies of abiogenesis.
nonprobsvy -- An R package for modern methods for non-probability surveys
2025-04-05
Łukasz Chrostowski, Piotr Chlebicki, Maciej Beręsewicz
The following paper presents {nonprobsvy} -- an {R} package for inference based on non-probability samples. The package implements various approaches that can be categorized into three groups: prediction-based approach, inverse probability weighting and doubly robust approach. In the package, we assume the existence of either population-level data or probability-based population information and leverage the \pkg{survey} package for inference. The package implements both analytical and bootstrap variance estimation for the proposed estimators. In the paper we present the theory behind the package, its functionalities and case study that showcases the usage of the package. The package is aimed at scientists and researchers who would like to use non-probability samples (e.g.big data, opt-in web panels, social media) to accurately estimate population characteristics.
User-Centered AI for Data Exploration: Rethinking GenAI's Role in Visualization
2025-04-05
Kathrin Schnizer, Sven Mayer
Recent advances in GenAI have enabled automation in data visualization, allowing users to generate visual representations using natural language. However, existing systems primarily focus on automation, overlooking users' varying expertise levels and analytical needs. In this position paper, we advocate for a shift toward adaptive GenAI-driven visualization tools that tailor interactions, reasoning, and visualizations to individual users. We first review existing automation-focused approaches and highlight their limitations. We then introduce methods for assessing user expertise, as well as key open challenges and research questions that must be addressed to allow for an adaptive approach. Finally, we present our vision for a user-centered system that leverages GenAI not only for automation but as an intelligent collaborator in visual data exploration. Our perspective contributes to the broader discussion on designing GenAI-based systems that enhance human cognition by dynamically adapting to the user, ultimately advancing toward systems that promote augmented cognition.
Loss functions are at the heart of deep learning, shaping how models learn and perform across diverse tasks. They are used to quantify the difference between predicted outputs and ground truth labels, guiding the optimization process to minimize errors. Selecting the right loss function is critical, as it directly impacts model convergence, generalization, and overall performance across various applications, from computer vision to time series forecasting. This paper presents a comprehensive review of loss functions, covering fundamental metrics like Mean Squared Error and Cross-Entropy to advanced functions such as Adversarial and Diffusion losses. We explore their mathematical foundations, impact on model training, and strategic selection for various applications, including computer vision (Discriminative and generative), tabular data prediction, and time series forecasting. For each of these categories, we discuss the most used loss functions in the recent advancements of deep learning techniques. Also, this review explore the historical evolution, computational efficiency, and ongoing challenges in loss function design, underlining the need for more adaptive and robust solutions. Emphasis is placed on complex scenarios involving multi-modal data, class imbalances, and real-world constraints. Finally, we identify key future directions, advocating for loss functions that enhance interpretability, scalability, and generalization, leading to more effective and resilient deep learning models.
Automated Assessment in Mobile Programming Courses: Leveraging GitHub Classroom and Flutter for Enhanced Student Outcomes
2025-04-05
Pedro Alves, Bruno Pereira Cipriano
The growing demand for skilled mobile developers has made mobile programming courses an essential component of computer science curricula. However, these courses face unique challenges due to the complexity of mobile development environments and the graphical, interactive nature of mobile applications. This paper explores the potential of using GitHub Classroom, combined with the Flutter framework, for the automated assessment of mobile programming assignments. By leveraging GitHub Actions for continuous integration and Flutter's robust support for test automation, the proposed approach enables an auto-grading cost-effective solution. We evaluate the feasibility of integrating these tools through an experiment in a Mobile Programming course and present findings from a student survey that assesses their perceptions of the proposed evaluation model. The results are encouraging, showing that the approach is well-received by students.
Evaluating the Usability of Microgestures for Text Editing Tasks in Virtual Reality
2025-04-05
Xiang Li, Wei He, Per Ola Kristensson
As virtual reality (VR) continues to evolve, traditional input methods such as handheld controllers and gesture systems often face challenges with precision, social accessibility, and user fatigue. We introduce microGEXT, a lightweight microgesture-based system designed for text editing in VR without external sensors, which utilizes small, subtle hand movements to reduce physical strain compared to standard gestures. We evaluated microGEXT in three user studies. In Study 1 ($N=20$), microGEXT reduced overall edit time and fatigue compared to a baseline system. Study 2 ($N=20$) found that microGEXT performed well in short text selection tasks but was slower for longer text ranges. In Study 3 ($N=10$), participants found microGEXT intuitive for open-ended information-gathering tasks. Across all studies, microGEXT demonstrated enhanced user experience and reduced physical effort, offering a promising alternative to traditional VR text editing techniques.
Systematic reviews are fundamental to evidence-based medicine. Creating one is time-consuming and labour-intensive, mainly due to the need to screen, or assess, many studies for inclusion in the review. Several tools have been developed to streamline this process, mostly relying on traditional machine learning methods. Large language models (LLMs) have shown potential in further accelerating the screening process. However, no tool currently allows end users to directly leverage LLMs for screening or facilitates systematic and transparent usage of LLM-assisted screening methods. This paper introduces (i) an extensible framework for applying LLMs to systematic review tasks, particularly title and abstract screening, and (ii) a web-based interface for LLM-assisted screening. Together, these elements form AiReview-a novel platform for LLM-assisted systematic review creation. AiReview is the first of its kind to bridge the gap between cutting-edge LLM-assisted screening methods and those that create medical systematic reviews. The tool is available at https://aireview.ielab.io. The source code is also open sourced at https://github.com/ielab/ai-review.
When Will AI Transform Society? Swedish Public Predictions on AI Development Timelines
2025-04-05
Filip Fors Connolly, Mikael Hjerm, Sara Kalucza
This study investigates public expectations regarding the likelihood and timing of major artificial intelligence (AI) developments among Swedes. Through a mixed-mode survey (web/paper) of 1,026 respondents, we examined expectations across six key scenarios: medical breakthroughs, mass unemployment, democratic deterioration, living standard improvements, artificial general intelligence (AGI), and uncontrollable superintelligent AI. Findings reveal strong consensus on AI-driven medical breakthroughs (82.6%), while expectations for other major developments are significantly lower, ranging from 40.9% for mass unemployment down to 28.4% for AGI. Timeline expectations varied significantly, with major medical advances anticipated within 6-10 years, while more transformative developments like AGI were projected beyond 20 years. Latent class analysis identified three distinct groups: optimists (46.7%), ambivalents (42.2%), and skeptics (11.2%). The optimist group showed higher levels of self-rated AI knowledge and education, while gender differences were also observed across classes. The study addresses a critical gap in understanding temporal expectations of AI development among the general public, offering insights for policymakers and stakeholders.
Digital Gene: Learning about the Physical World through Analytic Concepts
2025-04-05
Jianhua Sun, Cewu Lu
Reviewing the progress in artificial intelligence over the past decade, various significant advances (e.g. object detection, image generation, large language models) have enabled AI systems to produce more semantically meaningful outputs and achieve widespread adoption in internet scenarios. Nevertheless, AI systems still struggle when it comes to understanding and interacting with the physical world. This reveals an important issue: relying solely on semantic-level concepts learned from internet data (e.g. texts, images) to understand the physical world is far from sufficient -- machine intelligence currently lacks an effective way to learn about the physical world. This research introduces the idea of analytic concept -- representing the concepts related to the physical world through programs of mathematical procedures, providing machine intelligence a portal to perceive, reason about, and interact with the physical world. Except for detailing the design philosophy and providing guidelines for the application of analytic concepts, this research also introduce about the infrastructure that has been built around analytic concepts. I aim for my research to contribute to addressing these questions: What is a proper abstraction of general concepts in the physical world for machine intelligence? How to systematically integrate structured priors with neural networks to constrain AI systems to comply with physical laws?
Unveiling the Coma Cluster Structure: From the Core to the Hubble Flow
2025-04-05
David Benisty, Jenny Wagner, Sandeep Haridasu, Paolo Salucci
The Coma cluster, embedded in a cosmic filament, is a complex and dynamically active structure in the local Universe. Applying a density-based member selection (dbscan) to data from the Sloan Digital Sky Survey (SDSS), we identify its virilised core and zero-velocity boundary. Cross-correlating with the Cosmicflows-4 (CF4) catalogue enables a velocity-distance analysis, incorporating radial infall models and redshift-independent distance estimators. This reveals, for the first time, the Hubble flow surrounding Coma, a first step to investigate the entanglement between the dark matter in bound objects and the dark energy driving the expansion of their surroundings. The distance to the Coma centre is determined as $69.959 \pm 0.012 \, h^{-1}~\text{Mpc}$. From dbscan, we infer a virial radius of $r_{\rm vir} = \left(1.95 \pm 0.12\right)\,h^{-1}~\text{Mpc}$ and a turnaround of $r_{\rm ta} \geq 4.87~{h}^{-1}~\mbox{Mpc}$. Combining the SDSS redshifts with the CF4 distances, we estimate the Hubble constant to be $H_0 = (73.10 \pm 0.92)~\mbox{km}/\mbox{s}/\mbox{Mpc}$. However, with different calibrations for the distance moduli, $H_0$ varies between $[72, 80]$ km/s/Mpc. Mass estimates via caustics, the virial theorem, and the Hubble-flow method yield $M = [0.77, 2.0] \times 10^{15}\,h^{-1}\,M_{\odot}$, consistent with prior studies. Our systematic approach maps the structure of Coma into the local Hubble flow and shows the degeneracies between dynamical parameters such as the Hubble constant, the virial radius, and the total mass.
Autoregressive large language model inference primarily consists of two stages: pre-filling and decoding. Decoding involves sequential computation for each token, which leads to significant latency. Speculative decoding is a technique that leverages the draft model combined with large model verification to enhance parallelism without sacrificing accuracy. However, existing external prediction methods face challenges in adapting to multi-node serial deployments. While they can maintain speedup under such conditions, the high latency of multi-node deployments ultimately results in low overall efficiency. We propose a speculative decoding framework named PipeDec to address the low global resource utilization of single tasks in pipeline deployments thereby reducing decoding latency. We integrate a draft model into the pipeline of the large model and immediately forward each prediction from the draft model to subsequent pipeline stages. A dynamic prediction tree manages prediction sequences across nodes, enabling efficient updating and pruning. This approach leverages the draft model's predictions to utilize all pipeline nodes for parallel decoding of a single task. Experiments were conducted using LLama3.2 1B as the draft model in conjunction with a 14-stage parallel pipeline to accelerate LLama3.1 70B by six different types of datasets. During the decoding phase of a single task, PipeDec achieved a 4.46x-7.79x speedup compared to traditional pipeline parallelism and a 2.2x-2.69x speedup compared to baseline tree-based speculative decoding methods. The code will be released after the review process.
Operational research approaches and mathematical models for kidney exchange: A literature survey and empirical evaluation
2025-04-05
Mathijs Barkel, Rachael Colley, Maxence Delorme, David Manlove, William Pettersson
Kidney exchange is a transplant modality that has provided new opportunities for living kidney donation in many countries around the world since 1991. It has been extensively studied from an Operational Research (OR) perspective since 2004. This article provides a comprehensive literature survey on OR approaches to fundamental computational problems associated with kidney exchange over the last two decades. We also summarise the key integer linear programming (ILP) models for kidney exchange, showing how to model optimisation problems involving only cycles and chains separately. This allows new combined ILP models, not previously presented, to be obtained by amalgamating cycle and chain models. We present a comprehensive empirical evaluation involving all combined models from this paper in addition to bespoke software packages from the literature involving advanced techniques. This focuses primarily on computation times for 49 methods applied to 4,320 problem instances of varying sizes that reflect the characteristics of real kidney exchange datasets, corresponding to over 200,000 algorithm executions. We have made our implementations of all cycle and chain models described in this paper, together with all instances used for the experiments, and a web application to visualise our experimental results, publicly available.
Gas giant planets, if present, are the most massive objects in a planetary system and play a pivotal role in shaping its overall architecture. The formation of these planets has constantly been a central issue in planetary science. Increasing evidence from spacecraft explorations of Jupiter and Saturn, as well as telescope observations of exoplanets, has provided new constraints on the formation process of gas giant planets. The classic challenge of explaining formation timescales still remains a significant issue, while new constraints on planetary interiors have introduced additional complexities. Recent shifts away from the single-size planetesimal hypothesis, nevertheless, show promise in resolving these problems. Additionally, various discoveries regarding exoplanets have led to theoretical improvements, while the discovery of numerous super-Earths and sub-Neptunes has posed new challenges in understanding gas accretion. This review synthesizes the latest theoretical advancements, discussing resolved issues and emerging challenges in giant planet formation.
A Survey of Pathology Foundation Model: Progress and Future Directions
2025-04-05
Conghao Xiong, Hao Chen, Joseph J. Y. Sung
Computational pathology, analyzing whole slide images for automated cancer diagnosis, relies on the multiple instance learning framework where performance heavily depends on the feature extractor and aggregator. Recent Pathology Foundation Models (PFMs), pretrained on large-scale histopathology data, have significantly enhanced capabilities of extractors and aggregators but lack systematic analysis frameworks. This survey presents a hierarchical taxonomy organizing PFMs through a top-down philosophy that can be utilized to analyze FMs in any domain: model scope, model pretraining, and model design. Additionally, we systematically categorize PFM evaluation tasks into slide-level, patch-level, multimodal, and biological tasks, providing comprehensive benchmarking criteria. Our analysis identifies critical challenges in both PFM development (pathology-specific methodology, end-to-end pretraining, data-model scalability) and utilization (effective adaptation, model maintenance), paving the way for future directions in this promising field. Resources referenced in this survey are available at https://github.com/BearCleverProud/AwesomeWSI.
A Comprehensive Survey of Challenges and Opportunities of Few-Shot Learning Across Multiple Domains
2025-04-05
Andrea Gajic, Sudip Vhaduri
In a world where new domains are constantly discovered and machine learning (ML) is applied to automate new tasks every day, challenges arise with the number of samples available to train ML models. While the traditional ML training relies heavily on data volume, finding a large dataset with a lot of usable samples is not always easy, and often the process takes time. For instance, when a new human transmissible disease such as COVID-19 breaks out and there is an immediate surge for rapid diagnosis, followed by rapid isolation of infected individuals from healthy ones to contain the spread, there is an immediate need to create tools/automation using machine learning models. At the early stage of an outbreak, it is not only difficult to obtain a lot of samples, but also difficult to understand the details about the disease, to process the data needed to train a traditional ML model. A solution for this can be a few-shot learning approach. This paper presents challenges and opportunities of few-shot approaches that vary across major domains, i.e., audio, image, text, and their combinations, with their strengths and weaknesses. This detailed understanding can help to adopt appropriate approaches applicable to different domains and applications.
Transformer-based foundation models have emerged as a dominant paradigm in time series analysis, offering unprecedented capabilities in tasks such as forecasting, anomaly detection, classification, trend analysis and many more time series analytical tasks. This survey provides a comprehensive overview of the current state of the art pre-trained foundation models, introducing a novel taxonomy to categorize them across several dimensions. Specifically, we classify models by their architecture design, distinguishing between those leveraging patch-based representations and those operating directly on raw sequences. The taxonomy further includes whether the models provide probabilistic or deterministic predictions, and whether they are designed to work with univariate time series or can handle multivariate time series out of the box. Additionally, the taxonomy encompasses model scale and complexity, highlighting differences between lightweight architectures and large-scale foundation models. A unique aspect of this survey is its categorization by the type of objective function employed during training phase. By synthesizing these perspectives, this survey serves as a resource for researchers and practitioners, providing insights into current trends and identifying promising directions for future research in transformer-based time series modeling.
JsStories: Improving Social Inclusion in Computer Science Education Through Interactive Stories
2025-04-05
Inas Ghazouani Ghailani, Yoshi Malaise, Beat Signer
A main challenge faced by non-profit organisations providing computer science education to under-represented groups are the high drop-out rates. This issue arises from various factors affecting both students and teachers, such as the one-size-fits-all approach of many lessons. Enhancing social inclusion in the learning process could help reduce these drop-out rates. We present JsStories, a tool designed to help students learn JavaScript through interactive stories. The development of JsStories has been informed by existing literature on storytelling for inclusion and insights gained from a visit to HackYourFuture Belgium (HYFBE), a non-profit organisation that teaches web development to refugees and migrants. To lower barriers to entry and maximise the feeling of connection to the story, we incorporated narratives from HYFBE alumni. Further, we adhered to educational best practices by applying the PRIMM principles and offering level-appropriate content based on knowledge graphs. JsStories has been demonstrated, evaluated and communicated to the different stakeholders through interviews and a survey, enabling us to identify future directions for story-based learning solutions.
Machine Learning Reviews Composition Dependent Thermal Stability in Halide Perovskites
2025-04-05
Abigail R. Hering, Mansha Dubey, Elahe Hosseini, Meghna Srivastava, Yu An, Juan-Pablo Correa-Baena, Houman Homayoun, Marina S. Leite
Halide perovskites exhibit unpredictable properties in response to environmental stressors, due to several composition-dependent degradation mechanisms. In this work, we apply data visualization and machine learning (ML) techniques to reveal unexpected correlations between composition, temperature, and material properties while using high throughput, in situ environmental photoluminescence (PL) experiments. Correlation heatmaps show the strong influence of Cs content on film degradation, and dimensionality reduction visualization methods uncover clear composition-based data clusters. An extreme gradient boosting algorithm (XGBoost) effectively forecasts PL features for ten perovskite films with both composition-agnostic (>85% accuracy) and composition-dependent (>75% accuracy) model approaches, while elucidating the relative feature importance of composition (up to 99%). This model validates a previously unseen anti-correlation between Cs content and material thermal stability. Our ML-based framework can be expanded to any perovskite family, significantly reducing the analysis time currently employed to identify stable options for photovoltaics.
Resurrecting Socrates in the Age of AI: A Study Protocol for Evaluating a Socratic Tutor to Support Research Question Development in Higher Education
2025-04-05
Ben Degen
Formulating research questions is a foundational yet challenging academic skill, one that generative AI systems often oversimplify by offering instant answers at the expense of student reflection. This protocol lays out a study grounded in constructivist learning theory to evaluate a novel AI-based Socratic Tutor, designed to foster cognitive engagement and scaffold research question development in higher education. Anchored in dialogic pedagogy, the tutor engages students through iterative, reflective questioning, aiming to promote System 2 thinking and counteract overreliance on AI-generated outputs. In a quasi-experimental design, approximately 80 German pre-service biology teacher students will be randomly assigned to one of two groups: an AI Socratic Tutor condition and an uninstructed chatbot control. Across multiple cycles, students are expected to formulate research questions based on background texts, with quality assessed through double-blind expert review. The study also examines transfer of skills to novel phenomena and captures student perceptions through mixed-methods analysis, including surveys, interviews and reflective journals. This study aims to advance the understanding of how generative AI can be pedagogically aligned to support, not replace, human cognition and offers design principles for human-AI collaboration in education.
Anisotropy in the carbon monoxide (CO) line emission across the Milky Way's disk
2025-04-04
J. D. Soler, M. Heyer, M. Benedettini, D. Elia, P. Hennebelle, R. S. Klessen, C. Mininni, A. Nucara, V. -M. Pelkonen, S. Molinari, R. J. Smith, E. Schisano, A. Traficante, R. Treß
We present a study of the $^{12}$CO(1-0) line emission anisotropy across the Milky Way's disk to examine the effect of stellar feedback and Galactic dynamics on the distribution of the dense interstellar medium. The Hessian matrix method is used to characterize the CO line emission distribution and identify the preferential orientation across line-of-sight velocity channels in the Dame et al. 2001 composite Galactic plane survey, which covers the Galactic latitude range $|b|<5^{\circ}$. The structures sampled with this tracer are predominantly parallel to the Galactic plane toward the inner Galaxy, in clear contrast with the predominantly perpendicular orientation of the structures traced by neutral atomic hydrogen (HI) emission toward the same regions. The analysis of the Galactic plane portions sampled at higher angular resolution with other surveys reveals that the alignment with the Galactic plane is also prevalent at smaller scales. We find no preferential orientation in the CO emission toward the outer Galaxy, in contrast with the preferential alignment with the Galactic plane displayed by HI in that portion of the Milky Way. We interpret these results as the combined effect of the decrease in mid-plane pressure with increasing Galactocentric radius and SN feedback lifting diffuse gas more efficiently than dense gas off the Galactic plane.
A New Statistical Approach to Calibration-Free Localization Using Unlabeled Crowdsourced Data
2025-04-04
Haozhou Hu, Harpreet S. Dhillon, R. Michael Buehrer
Fingerprinting-based indoor localization methods typically require labor-intensive site surveys to collect signal measurements at known reference locations and frequent recalibration, which limits their scalability. This paper addresses these challenges by presenting a novel approach for indoor localization that utilizes crowdsourced data {\em without location labels}. We leverage the statistical information of crowdsourced data and propose a cumulative distribution function (CDF) based distance estimation method that maps received signal strength (RSS) to distances from access points. This approach overcomes the limitations of conventional distance estimation based on the empirical path loss model by efficiently capturing the impacts of shadow fading and multipath. Compared to fingerprinting, our {\em unsupervised} statistical approach eliminates the need for signal measurements at known reference locations. The estimated distances are then integrated into a three-step framework to determine the target location. The localization performance of our proposed method is evaluated using RSS data generated from ray-tracing simulations. Our results demonstrate significant improvements in localization accuracy compared to methods based on the empirical path loss model. Furthermore, our statistical approach, which relies on unlabeled data, achieves localization accuracy comparable to that of the {\em supervised} approach, the $k$-Nearest Neighbor ($k$NN) algorithm, which requires fingerprints with location labels. For reproducibility and future research, we make the ray-tracing dataset publicly available at [2].
Choosing an analytic approach: Key study design considerations in state policy evaluation
2025-04-04
Elizabeth M. Stone, Megan S. Schuler, Elizabeth A. Stuart, Max Rubinstein, Max Griswold, Bradley D. Stein, Beth Ann Griffin
This paper reviews and details methods for state policy evaluation to guide selection of a research approach based on evaluation setting and available data. We highlight key design considerations for an analysis, including treatment and control group selection, timing of policy adoption, expected effect heterogeneity, and data considerations. We then provide an overview of analytic approaches and differentiate between methods based on evaluation context, such as settings with no control units, a single treated unit, multiple treated units, or with multiple treatment cohorts. Methods discussed include interrupted time series models, difference-in-differences estimators, autoregressive models, and synthetic control methods, along with method extensions which address issues like staggered policy adoption and heterogenous treatment effects. We end with an illustrative example, applying the developed framework to evaluate the impacts of state-level naloxone standing order policies on overdose rates. Overall, we provide researchers with an approach for deciding on methods for state policy evaluations, which can be used to select study designs and inform methodological choices.
Training effective AI agents for multi-turn interactions requires high-quality data that captures realistic human-agent dynamics, yet such data is scarce and expensive to collect manually. We introduce APIGen-MT, a two-phase framework that generates verifiable and diverse multi-turn agent data. In the first phase, our agentic pipeline produces detailed task blueprints with ground-truth actions, leveraging a committee of LLM reviewers and iterative feedback loops. These blueprints are then transformed into complete interaction trajectories through simulated human-agent interplay. We train a family of models -- the xLAM-2-fc-r series with sizes ranging from 1B to 70B parameters. Our models outperform frontier models such as GPT-4o and Claude 3.5 on $\tau$-bench and BFCL benchmarks, with the smaller models surpassing their larger counterparts, particularly in multi-turn settings, while maintaining superior consistency across multiple trials. Comprehensive experiments demonstrate that our verified blueprint-to-details approach yields high-quality training data, enabling the development of more reliable, efficient, and capable agents. We open-source both the synthetic data collected and the trained xLAM-2-fc-r models to advance research in AI agents. Models are available on HuggingFace at https://huggingface.co/collections/Salesforce/xlam-2-67ef5be12949d8dcdae354c4 and project website is https://apigen-mt.github.io
The DECam MAGIC Survey: A Wide-field Photometric Metallicity Study of the Sculptor Dwarf Spheroidal Galaxy
2025-04-04
Fabrícia O. Barbosa, Anirudh Chiti, Guilherme Limberg, Andrew Pace, William Cerny, Silvia Rossi, Jeffrey L. Carlin, Guy Stringfellow, Vinicius Placco, Kaia Atzerberg, Julio A. Carballo-Bello, Astha Chaturvedi, Yumi Choi, Denija Crnojevic, Alex Drlica-Wagner, Alexander Ji, Nitya Kallivayalil, Clara Martínez-Vázquez, Gustavo Medina, Noelia Noel, David Sand, Kathy Vivas, Clecio Bom, Peter Ferguson, Burçin Mutlu-Pakdil, Mahdieh Navabi, Alexander H. Riley, Joanna Sakowska, Alfredo Zenteno
The metallicity distribution function and internal chemical variations of a galaxy are fundamental to understand its formation and assembly history. In this work, we analyze photometric metallicities for 3883 stars over seven half-light radii ($\rm r_h$) in the Sculptor dwarf spheroidal (Scl dSph) galaxy, using new narrow-band imaging data from the Mapping the Ancient Galaxy in CaHK (MAGIC) survey conducted with the Dark Energy Camera (DECam) at the 4-m Blanco Telescope. This work demonstrates the scientific potential of MAGIC using the Scl dSph galaxy, one of the most well-studied satellites of the Milky Way. Our sample ranges from $\rm [Fe/H] \approx - 4.0$ to $\rm [Fe/H] \approx - 0.6$, includes six extremely metal-poor candidates ($\rm [Fe/H] \leq -3.0$) without previous spectroscopic observations, and is almost three times larger than the largest spectroscopic metallicity dataset in the Scl dSph. Our spatially unbiased sample of metallicities provides a more accurate representation of the metallicity distribution function, revealing a more metal-rich peak than observed in the most recent spectroscopic sample. It also reveals a break in the metallicity gradient, with a strong change in the slope: from $-3.35 \pm 0.18 \rm \ dex \, deg^{-1}$ for stars inside $\sim 1\ \rm r_h$ to $-0.56 \pm 0.26 \rm \ dex \, deg^{-1}$ for the outer part of the Scl dSph. Our study demonstrates that combining photometric metallicity analysis with the wide field of view of DECam offers an efficient and unbiased approach for studying the stellar populations of dwarf galaxies in the Local Group.
Censorship resistance with short-term inclusion guarantees is an important feature of decentralized systems, missing from many state-of-the-art and even deployed consensus protocols. In leader-based protocols the leader arbitrarily selects the transactions to be included in the new block, and so does a block builder in protocols such as Bitcoin and Ethereum. In a different line of work, since the redundancy of consensus for implementing distributed payments was formally proven, consensusless protocols have been described in theory and deployed in the real world. This has resulted in blockchains and payment systems that are more efficient, and at the same time avoid the centralized role of a leader or block builder. In this report we review existing consensus and consensusless protocols with regard to their censorship-resistance, efficiency, and other properties. Moreover, we present an approach for new constructions with these properties in mind, building on existing leader-based protocols.
CHILES IX: Observational and Simulated HI Content and Star Formation of Blue Galaxies in Different Cosmic Web Environments
2025-04-04
Nicholas Luber, Farhanul Hasan, J. H. van Gorkom, D. J. Pisano, Joseph N. Burchett, Julia Blue Bird, Hansung B. Him, Kelley M. Hess, Lucas R. Hunt, David C. Koo, Sushma Kurapati, Danielle Lucero, Nir Mandelker, Martin Meyer, Emmanuel Momjian, Daisuke Nagai, Joel R. Primack, Min S. Yun
We examine the redshift evolution of the relationship between the neutral atomic hydrogen ({\HI}) content and star-formation properties of blue galaxies, along with their location in the cosmic web. Using the COSMOS {\HI} Large Extragalactic Survey (CHILES) and the IllustrisTNG (TNG100) cosmological simulation, and the {\disperse} algorithm, we identify the filamentary structure in both observations and simulations, measure the distance of galaxies to the nearest filament spine {\dfil}, and calculate the mean {\HI} gas fraction and the relative specific star formation rate (sSFR) of blue galaxies in three different cosmic web environments -- $0<{\dfil}/\mathrm{Mpc}<2$ (filament cores), $2<{\dfil}/\mathrm{Mpc}<4$ (filament outskirts), and $4<{\dfil}/\mathrm{Mpc}<20$ (voids). We find that, although there are some similarities between CHILES and TNG, there exist significant discrepancies in the dependence of {\HI} and star formation on the cosmic web and on redshift. TNG overpredicts the observed {\HI} fraction and relative sSFR at $z=0-0.5$, with the tension being strongest in the voids. CHILES observes a decline in the {\HI} fraction from filament cores to voids, exactly the opposite of the trend predicted by TNG. CHILES observes an increase in {\HI} fraction at $z=0.5\rightarrow0$ in the voids, while TNG predicts an increase in this time in all environments. Further dividing the sample into stellar mass bins, we find that the {\HI} in ${\logms}>10$ galaxies is better reproduced by TNG than {\HI} in ${\logms}=9-10$ galaxies.
JWSTs PEARLS: NIRCam imaging and NIRISS spectroscopy of a $z=3.6$ star-forming galaxy lensed into a near-Einstein Ring by a $z=1.258$ massive elliptical galaxy
2025-04-04
Nathan J. Adams, Giovanni Ferrami, Lewi Westcott, Thomas Harvey, Vicente Estrada-Carpenter, Christopher J. Conselice, Duncan Austin, J. Stuart B. Wyithe, Caio M. Goolsby, Qiong Li, Vadim Rusakov, Rogier A. Windhorst, Seth H. Cohen, Rolf A. Jansen, Jake Summers, Roselia O'Brein, Anton M. Koekemoer, Simon P. Driver, Brenda Frye, Nimish P. Hathi, Dan Coe, Norman A. Grogin, Madeline A. Marshall, Nor Pirzkal, Russell E. Ryan Jr., Christopher N. A. Willmer, Haojing Yan, Benne W. Holwerda, Patrick S. Kamieneski, Tom Broadhurst, W. Peter Maksym, Payaswini Saikia, Joseph D. Gelfand
We present the discovery, and initial lensing analysis, of a high-redshift galaxy-galaxy lensing system within the JWST-PEARLS/HST-TREASUREHUNT North Ecliptic Pole Time Domain Field (designated NEPJ172238.9+655143.1). The lensing geometry shears a $z=3.6\pm0.1$ star-forming galaxy into a near-Einstein ring with a radius of 0\farcs92, consisting of 4 primary images, around a foreground massive elliptical galaxy at $z=1.258\pm0.005$. The system is fortuitously located within the NIRISS F200W footprint of the PEARLS survey, enabling spectroscopic identification of the 8500A TiO band in the foreground galaxy and allowing tight constraints to be placed on the redshift of the background galaxy based on its continuum detection and lack of strong emission lines. We calculate magnification factors of $2.6<\mu<8.4$ for the four images and a total lensing mass of $(4.08 \pm 0.07)\times10^{11}M_\odot$. SED fitting of the foreground elliptical galaxy within the Einstein radius reveals a stellar mass of $\sim1.26\times10^{11}M_\odot$, providing a mass/light ratio of 3.24. Employing simple scaling relations and assumptions, an NFW dark matter halo is found to provide the correct remaining mass within $0.12^{+0.21}_{-0.09}$dex. However, if a bottom-heavy IMF for elliptical galaxies is employed, stellar mass estimations increase and can account for the majority of the lensing mass (up to $\sim$83\%), reducing the need for dark matter. This system further demonstrates the new discovery space that the combined wavelength coverage, sensitivity and resolution of JWST now enables.
Forecasts for interacting dark energy with time-dependent momentum exchange
2025-04-04
Nathan Cruickshank, Robert Crittenden, Kazuya Koyama, Marco Bruni
Models of interacting dark energy and dark matter offer a possible solution to cosmological tensions. In this work, we examine a pure momentum-exchange model with a time-dependent coupling strength $\xi(z)$ that could help to alleviate the $S_8$ tension. We perform Fisher forecasting and MCMC analysis to constrain the coupling strength of this interaction for different redshift bins $0.0
JADES: comprehensive census of broad-line AGN from Reionization to Cosmic Noon revealed by JWST
2025-04-04
Ignas Juodžbalis, Roberto Maiolino, William M. Baker, Emma Curtis Lake, Jan Scholtz, Francesco D'Eugenio, Bartolomeo Trefoloni, Yuki Isobe, Sandro Tacchella, Andrew J. Bunker, Stefano Carniani, Stéphane Charlot, Gareth C. Jones, Eleonora Parlanti, Michele Perna, Pierluigi Rinaldi, Brant Robertson, Hannah Übler, Giacomo Venturi, Chris Willott
The depth and coverage of the first years of JWST observations have revealed low luminosity active galactic nuclei (AGN) across a wide redshift range, shedding light on black hole (BH) assembly and feedback. We present our spectroscopic sample of 34 Type 1 AGN obtained from JADES survey data and spanning $1.5 < z < 9$. Our sample of AGN probes a BH mass range of $10^{6-9}$~M$_{\odot}$ at bolometric luminosities down to $10^{43}$~erg~s$^{-1}$, implying generally sub-Eddington ratios of $<0.5L_{\rm Edd}$. Most of these AGN are hosted in low mass ($M_{\star}\sim10^8$~M$_{\odot}$) galaxies and are overmassive relative to the local $M_{BH}-M_{\star}$ relation, while remaining consistent with the local $M_{BH}$-$\sigma_*$ relation. The wide redshift range provided by our sample allows us to trace the emergence of local $M_{BH}$-$M_*$ scaling relation across the cosmic epoch. Additionally, we explore the capability of narrow-line diagnostics in identifying Type 2 AGN and find that a significant fraction of our AGN would be missed by them due to low metallicity or lack of high energy ionizing photons (potentially due to dust absorption, dense gas blanketing the broad and narrow line regions, or intrinsically soft ionizing spectra). We explore the UV luminosity function of AGN and their hosts and find that it is subject to significant cosmic variance and is also dependent on the AGN bolometric luminosity. Finally, we show that the electron and Balmer scattering scenarios recently proposed to explain the broad components of the Balmer lines are untenable on multiple grounds. There is no evidence that the black hole masses have been overestimated by orders of magnitude as proposed in those scenarios.
Bias in news reporting significantly impacts public perception, particularly regarding crime, politics, and societal issues. Traditional bias detection methods, predominantly reliant on human moderation, suffer from subjective interpretations and scalability constraints. Here, we introduce an AI-driven framework leveraging advanced large language models (LLMs), specifically GPT-4o, GPT-4o Mini, Gemini Pro, Gemini Flash, Llama 8B, and Llama 3B, to systematically identify and mitigate biases in news articles. To this end, we collect an extensive dataset consisting of over 30,000 crime-related articles from five politically diverse news sources spanning a decade (2013-2023). Our approach employs a two-stage methodology: (1) bias detection, where each LLM scores and justifies biased content at the paragraph level, validated through human evaluation for ground truth establishment, and (2) iterative debiasing using GPT-4o Mini, verified by both automated reassessment and human reviewers. Empirical results indicate GPT-4o Mini's superior accuracy in bias detection and effectiveness in debiasing. Furthermore, our analysis reveals temporal and geographical variations in media bias correlating with socio-political dynamics and real-world events. This study contributes to scalable computational methodologies for bias mitigation, promoting fairness and accountability in news reporting.
The Three Hundred Project: A fast semi-analytic model emulator of hydrodynamical galaxy cluster simulations
2025-04-04
Jonathan S. Gómez, Tomas Hough, Alejandro Jiménez Muñoz, Gustavo Yepes, Weiguang Cui, Sofía A. Cora
Next-generation photometric and spectroscopic surveys will detect faint galaxies in massive clusters, advancing our understanding of galaxy formation in dense environments. Comparing these observations with theoretical models requires high-resolution cluster simulations. Hydrodynamical simulations resolve galaxy properties in halos, but face challenges simulating low-mass galaxies due to computational limitations. In contrast, dark matter-only (DMO) simulations can provide higher resolution but need models to populate subhalos with galaxies. In this work, we introduce a fast and efficient emulator of hydrodynamical cluster simulations, based on the semi-analytic models (SAMs) SAGE and SAG. The SAMs are calibrated using cluster galaxies from hydrodynamical simulations at intermediate resolution, ensuring consistency in stellar masses and luminosities across redshifts. These SAMs are then applied to DMO simulations from The Three Hundred Project at three resolutions. We show that the SAG model, unlike SAGE, better emulates galaxy properties even at the highest resolution. This improvement is due to the treatment of orphan galaxies, which contribute significantly to the total population. SAG enables the study of dwarf galaxies down to stellar masses of 10^7 solar masses at the highest resolution, an order of magnitude smaller than those in the hydrodynamical simulations, corresponding to approximately four magnitudes fainter. This shows that SAMs can be effectively calibrated to provide fast and accurate predictions of hydrodynamical simulations, offering an efficient alternative to explore galaxy populations in dense environments.
Dexterous Manipulation through Imitation Learning: A Survey
2025-04-04
Shan An, Ziyu Meng, Chao Tang, Yuning Zhou, Tengyu Liu, Fangqiang Ding, Shufang Zhang, Yao Mu, Ran Song, Wei Zhang, Zeng-Guang Hou, Hong Zhang
Dexterous manipulation, which refers to the ability of a robotic hand or multi-fingered end-effector to skillfully control, reorient, and manipulate objects through precise, coordinated finger movements and adaptive force modulation, enables complex interactions similar to human hand dexterity. With recent advances in robotics and machine learning, there is a growing demand for these systems to operate in complex and unstructured environments. Traditional model-based approaches struggle to generalize across tasks and object variations due to the high-dimensionality and complex contact dynamics of dexterous manipulation. Although model-free methods such as reinforcement learning (RL) show promise, they require extensive training, large-scale interaction data, and carefully designed rewards for stability and effectiveness. Imitation learning (IL) offers an alternative by allowing robots to acquire dexterous manipulation skills directly from expert demonstrations, capturing fine-grained coordination and contact dynamics while bypassing the need for explicit modeling and large-scale trial-and-error. This survey provides an overview of dexterous manipulation methods based on imitation learning (IL), details recent advances, and addresses key challenges in the field. Additionally, it explores potential research directions to enhance IL-driven dexterous manipulation. Our goal is to offer researchers and practitioners a comprehensive introduction to this rapidly evolving domain.
Operator learning has emerged as a powerful tool in scientific computing for approximating mappings between infinite-dimensional function spaces. A primary application of operator learning is the development of surrogate models for the solution operators of partial differential equations (PDEs). These methods can also be used to develop black-box simulators to model system behavior from experimental data, even without a known mathematical model. In this article, we begin by formalizing operator learning as a function-to-function regression problem and review some recent developments in the field. We also discuss PDE-specific operator learning, outlining strategies for incorporating physical and mathematical constraints into architecture design and training processes. Finally, we end by highlighting key future directions such as active data collection and the development of rigorous uncertainty quantification frameworks.
Support for Gravitationally-Attractive Composite Antimatter and Gravitationally-Repulsive Non-composite Antimatter
2025-04-04
Giovanni Maria Piacentino, Anthony Palladino
The ALPHA collaboration recently published the article "Observation of the effects of gravity on the motion of antimatter" in issue 621 of Nature (2023), in which their conclusion states that "The probability that our data are consistent with repulsive gravity is so small as to be quantitatively meaningless (less than $10^{-15}$). Consequently, we can rule out the existence of repulsive gravity of magnitude 1$g$ between the Earth and antimatter." Indeed, their experiment proved that antihydrogen falls toward the Earth. Antihydrogen, however, is a composite particle and its mass, like that of the proton, is dominated by the mass of its binding energy. In this paper, we point out that their experiment only measures the gravitational effect on composite antimatter, and not free or valence antimatter particles such as antiquarks or antileptons. Here, we review the various theoretical frameworks supporting the possibility that valence antiquarks experience a repulsion of the order $g$ by the Earth's gravitational field. We show that the ALPHA collaboration's recent result may actually be consistent with a repulsive gravitational component of about 15%. Next, we review our concept for a direct interaction experiment between antiquarks and the Earth's gravitational field, which should be capable of providing irrefutable evidence of the gravitational repulsion of non-composite antiparticles. Finally, we suggest that existing data, collected by AMS-01 of daughter particles from cosmic protons interacting with the Mir space station, may help demonstrate the feasibility of our proposed experiment.
Managing Information Overload in Large-Scale Distributed Mixed-Reality Meetings
2025-04-04
Katja Krug, Wolfgang Büschel, Mats Ole Ellenberg
Large-scale distributed mixed-reality meetings involve many people and their audiovisual representations. These collaborative environments can introduce challenges such as sensory overload, cognitive strain, and social fatigue. In this paper, we discuss how the unique adaptability of Mixed Reality can be leveraged to weaken these stressors by managing information overload.
XXL-HSC: Host properties of X-ray detected AGNs in XXL clusters
2025-04-04
E. Drigga, E. Koulouridis, E. Pouliasis, Y. Toba, M. Akiyama, C. Vignali, I. Georgantopoulos, T. Nagao, S. Paltani, M. Plionis, M. Pierre, B. Vijarnwannaluk
There is compelling evidence that AGNs are strongly influenced by their environment. Therefore, studying the AGN population of clusters is essential, as both large-scale structures and AGN play key roles in galaxy evolution, though the interactions between these elements are still not well understood. The primary objective of this study is to unravel the different factors that may significantly affect the triggering of AGN activity in cluster galaxies, including galaxy merging and interactions with other galaxies, and ram pressure from the hot intracluster medium. For our purposes, we used 82 X-ray detected AGN found within a $4r_{500}$ radius of 164 X-ray detected and spectroscopically confirmed galaxy clusters of the XXL survey, up to a redshift of $z\sim$1. This field is covered by deep optical observations of the Hyper Suprime-Cam, which allows for a reliable morphological classification of galaxies. Furthermore, using the X-ray hardness ratio, the optical spectra and the SEDs of the X-ray sources, we have studied the obscuration and other AGN properties and the SFR of the hosts as further indicators of interactions. We found a moderately significant higher fraction of X-ray AGN in galaxy clusters hosted by merging or disturbed galaxies, compared to non-active cluster galaxies or X-ray AGN in the field. This excess is primarily localised in the cluster outskirts. Also, we discovered a higher number of X-ray-hard AGN (hence, possibly obscured) in clusters than in the field particularly in the outskirts. These findings further support the idea that galaxy mergers and interactions may serve as mechanisms for the triggering and obscuration of AGN activity. The relatively high number of disturbed, merging, and possibly obscured AGN hosts in cluster outskirts suggests that galaxy merging and interactions are key drivers in triggering AGN activity in these outer regions of clusters.
The Poisson noise in modeling the redshift-space distortion at large scales
2025-04-04
Hongxiang Chen, Jie Wang, Baojiu Li
We investigate the errors in modeling the redshift-space distortion (RSD) effect at large linear scales, using data from the Millennium simulation. While standard theoretical templates, such as the Kaiser formula and the TNS method, could precisely model RSD for individual large-scale modes, we find that for tracers with number densities lower than $\sim10^{-3}({\rm Mpc}/h)^{-3}$, there is a few-percent level bias in the predicted power spectrum. This error arises due to the amplification of intrinsic Poisson noise during RSD modeling from real-space power spectrum. This amplified noise can be analytically expressed as $1 + \epsilon/[{\bar{n}P}({1+\epsilon})]$, with $\epsilon=2\beta/3+\beta^2/5$, where $P$ denotes the real-space tracer power spectrum and $\beta \equiv f/b$. Specifically, for halos with a number density of around $5\times10^{-4}({\rm Mpc}/h)^{-3}$, this phenomenon results in an additional systematic error of 2.5\%. Our result suggests that caution should be exercised when modeling the RSD directly using real-space power spectra of tracers measured from simulations or from the real surveys. This is relevant, e.g., in situations where the real-space tracer power spectrum is predicted by emulators trained using simulation data.
The CARMENES search for exoplanets around M dwarfs. Occurrence rates of Earth-like planets around very low-mass stars
2025-04-04
A. Kaminski, S. Sabotta, J. Kemmer, P. Chaturvedi, R. Burn, J. C. Morales, J. A. Caballero, I. Ribas, A. Reiners, A. Quirrenbach, P. J. Amado, V. J. S. Béjar, S. Dreizler, E. W. Guenther, A. P. Hatzes, Th. Henning, M. Kürster, D. Montes, E. Nagel, E. Pallé, V. Pinter, S. Reffert, M. Schlecker, Y. Shan, T. Trifonov, M. R. Zapatero Osorio, M. Zechmeister
Aims: Previous estimates of planet occurrence rates in the CARMENES survey indicated increased numbers of planets on short orbits for M dwarfs with masses below 0.34\,M$_\odot$. Here we focused on the lowest-mass stars in the survey, comprising 15 inactive targets with masses under 0.16\,M$_\odot$. Methods: To correct for detection biases, we determined detection sensitivity maps for individual targets and the entire sample. Using Monte Carlo simulations, we estimated planet occurrence rates for orbital periods of 1\,d to 100\,d and minimum masses from 0.5\,M$_\oplus$ to 10\,M$_\oplus$. Results: The radial velocity (RV) data from CARMENES reveal four new planets around three stars in our sample, namely G~268--110\,b, G~261--6\,b, and G~192--15\,b and c. All three b planets have minimum masses of 1.03--1.52\,M$_\oplus$ and orbital periods of 1.43--5.45\,d, while G~192--15\,c is a 14.3\,M$_\oplus$ planet on a wide, eccentric orbit with $P \approx 1218$\,d and $e \approx 0.68$. Our occurrence rates suggest considerable dependencies with respect to stellar masses. For planets below 3\,M$_\oplus$ we found rates consistent with one planet per star across all investigated periods, but the rates decrease almost by an order of magnitude for larger planet masses up to 10\,M$_\oplus$. Compared to previous studies, low-mass stars tend to harbor more planets with $P <10$\,d. We also demonstrate that synthetic planet populations based on the standard core accretion scenario predict slightly more massive planets on wider orbits than observed. Conclusions: Our findings confirm that planet occurrence rates vary with stellar masses even among M dwarfs, as we found more planets with lower masses and on shorter orbits in our subsample of very low-mass stars compared to more massive M dwarfs. Therefore, we emphasize the need for additional differentiation in future studies.
Optimizing Password Cracking for Digital Investigations
2025-04-04
Mohamad Hachem, Adam Lanfranchi, Nathan Clarke, Joakim Kavrestad
Efficient password cracking is a critical aspect of digital forensics, enabling investigators to decrypt protected content during criminal investigations. Traditional password cracking methods, including brute-force, dictionary and rule-based attacks face challenges in balancing efficiency with increasing computational complexity. This study explores rule based optimisation strategies to enhance the effectiveness of password cracking while minimising resource consumption. By analysing publicly available password datasets, we propose an optimised rule set that reduces computational iterations by approximately 40%, significantly improving the speed of password recovery. Additionally, the impact of national password recommendations were examined, specifically, the UK National Cyber Security Centre's three word password guideline on password security and forensic recovery. Through user generated password surveys, we evaluate the crackability of three word passwords using dictionaries of varying common word proportions. Results indicate that while three word passwords provide improved memorability and usability, they remain vulnerable when common word combinations are used, with up to 77.5% of passwords cracked using a 30% common word dictionary subset. The study underscores the importance of dynamic password cracking strategies that account for evolving user behaviours and policy driven password structures. Findings contribution to both forensic efficiency and cyber security awareness, highlight the dual impact of password policies on security and investigative capabilities. Future work will focus upon refining rule based cracking techniques and expanding research on password composition trends.
Data Augmentation of Time-Series Data in Human Movement Biomechanics: A Scoping Review
2025-04-04
Christina Halmich, Lucas Höschler, Christoph Schranz, Christian Borgelt
The integration of machine learning and deep learning has transformed data analytics in biomechanics, enabled by extensive wearable sensor data. However, the field faces challenges such as limited large-scale datasets and high data acquisition costs, which hinder the development of robust algorithms. Data augmentation techniques show promise in addressing these issues, but their application to biomechanical time-series data requires comprehensive evaluation. This scoping review investigates data augmentation methods for time-series data in the biomechanics domain. It analyzes current approaches for augmenting and generating time-series datasets, evaluates their effectiveness, and offers recommendations for applying these techniques in biomechanics. Four databases, PubMed, IEEE Xplore, Scopus, and Web of Science, were searched for studies published between 2013 and 2024. Following PRISMA-ScR guidelines, a two-stage screening identified 21 relevant publications. Results show that there is no universally preferred method for augmenting biomechanical time-series data; instead, methods vary based on study objectives. A major issue identified is the absence of soft tissue artifacts in synthetic data, leading to discrepancies referred to as the synthetic gap. Moreover, many studies lack proper evaluation of augmentation methods, making it difficult to assess their effects on model performance and data quality. This review highlights the critical role of data augmentation in addressing limited dataset availability and improving model generalization in biomechanics. Tailoring augmentation strategies to the characteristics of biomechanical data is essential for advancing predictive modeling. A better understanding of how different augmentation methods impact data quality and downstream tasks will be key to developing more effective and realistic techniques.
The Galactic-Centre Arms inferred from ACES (ALMA CMZ Exploration Survey)
2025-04-04
Y. Sofue, Tomo. Oka, S. N. Longmore, D. Walker, A. Ginsburg, J. D. Henshaw, J. Bally, A. T. Barnes, C. Battersby, L. Colzi, P. Ho, I. Jimenez-Serra, J. M. D. Kruijssen, E. Mills, M. A. Petkova, M. C. Sormani, J. Wallace, J. Armijos-Abendano, K. M. Dutkowska, R. Enokiya, Y. Fukui, P. Garcia, A. Guzman, C. Henkel, P. -Y. Hsieh, Y. Hu, K. Immer, D. Jeff, R. S. Klessen, K. Kohno, M. R. Krumholz, D. Lipman, S. Martin, M. R. Morris, F. Nogueras-Lara, M. Nonhebel, J. Otto, J. E. Pineda, M. A. Requena-Torres, V. M. Rivilla, D. Riquelme-Vasquez, A. Sanchez-Monge, M. G. Santa-Maria, H. A. Smith, T. S. Tanvir, V. Tolls, Q. D. Wang
Analyzing longitude-velocity diagrams (LVDs) in the CS(J=2-1) and H13CN(J=1-0) molecular lines from the internal release data of the ALMA Central-Molecular-Zone Exploration Survey (ACES) and in the 13CO (J=1-0) line from the Nobeyama Galactic-Centre (GC) survey, we identify six GC Arms as prominent straight LV ridges. In addition to the currently known Arms I to IV, we identify a new inner arm, Arm V, and further highlight the circum-nuclear disc (CND) as Arm VI. Integrated intensity maps of the Arms on the sky suggest that most of the Arms compose ring-like structures inclined from the Galactic plane. We determine the radii (curvatures) of the Arms using the velocity-gradient ($dv/dl$) method, assuming that the arms are rotating on circular orbits at a constant velocity of $\sim 150$ km/s. We show that Arms I and II compose the main ring structure of the CMZ with radii $\sim 100$--120 pc; Arm III is a dense arm 42 pc from the GC; Arm IV is a clear and narrow arm 20 pc from the GC; and Arm V is a faint, long arm of 8.2 pc radius. We show that the circum-nuclear disc (CND) composes the sixth arm, Arm VI, of radius $\sim 2.3$ pc associated with bifurcated spiral fins. We also discuss the association of the 20- and 50-km/s clouds with these Arms. The radii of the arms fall on an empirical relation $R\sim 630 (2/5)^N$ for $N=1$ (Arm I) to 6 (VI), suggesting either discrete rings or a logarithmic spiral with pitch angle $\sim 22^\circ$. The vertical full extent of the arm increases with radius and is represented by $z\sim 0.7 (R/1 {\rm pc})^{0.7}$ pc. The tilt angle of the arms from the Galactic plane, or the warping, increases rapidly toward the GC.
We discuss recent progress in the mathematical analysis of dilute Bose gases. We review results in one to three dimensions, but the focus will be on three dimensions. In all dimensions we have a two term asymptotic expansion of the ground state energy density by an expression that depends only on the scattering length of the potential. In dimension three this is the celebrated Lee-Huang-Yang formula. In dimensions two and three the dilute limit is a weakly interacting regime whereas in dimension one it is rather strongly interacting. We sketch briefly the mathematical difficulties and review some remaining open problems in the field.
Gaia GraL: Gaia gravitational lens systems IX. Using XGBoost to explore the Gaia Focused Product Release GravLens catalogue
2025-04-04
Quentin Petit, Christine Ducourant, Eric Slezak, Alberto Krone-Martins, Céline Bœhm, Thomas Connor, Ludovic Delchambre, S. G. Djorgovski, Laurent Galluccio, Matthew J. Graham, Priyanka Jalan, Sergei A. Klioner, Jonas Klüter, François Mignard, Vibhore Negi, Sergio Scarano Jr, Jakob Sebastian den Brok, Dominique Sluse, Daniel Stern, Jean Surdej, Ramachrisna Teixeira, P. H. Vale-Cunha, Dominic J. Walton, Joachim Wambsganss
Aims. Quasar strong gravitational lenses are important tools for putting constraints on the dark matter distribution, dark energy contribution, and the Hubble-Lemaitre parameter. We aim to present a new supervised machine learning-based method to identify these lenses in large astrometric surveys. The Gaia Focused Product Release (FPR) GravLens catalogue is designed for the identification of multiply imaged quasars, as it provides astrometry and photometry of all sources in the field of 4.7 million quasars. Methods. Our new approach for automatically identifying four-image lens configurations in large catalogues is based on the eXtreme Gradient Boosting classification algorithm. To train this supervised algorithm, we performed realistic simulations of lenses with four images that account for the statistical distribution of the morphology of the deflecting halos as measured in the EAGLE simulation. We identified the parameters discriminant for the classification and performed two different trainings, namely, with and without distance information. Results. The performances of this method on the simulated data are quite good, with a true positive rate and a true negative rate of about 99.99% and 99.84%, respectively. Our validation of the method on a small set of known quasar lenses demonstrates its efficiency, with 75% of known lenses being correctly identified. We applied our algorithm (both trainings) to more than 0.9 million quadruplets selected from the Gaia FPR GravLens catalogue. We derived a list of 1127 candidates with at least one score larger than 0.75, where each candidate has two scores -- one from the model trained with distance information and one from the model trained without distance information -- and including 201 very good candidates with both high scores.
Do Large Language Models Solve the Problems of Agent-Based Modeling? A Critical Review of Generative Social Simulations
2025-04-04
Maik Larooij, Petter Törnberg
Recent advancements in AI have reinvigorated Agent-Based Models (ABMs), as the integration of Large Language Models (LLMs) has led to the emergence of ``generative ABMs'' as a novel approach to simulating social systems. While ABMs offer means to bridge micro-level interactions with macro-level patterns, they have long faced criticisms from social scientists, pointing to e.g., lack of realism, computational complexity, and challenges of calibrating and validating against empirical data. This paper reviews the generative ABM literature to assess how this new approach adequately addresses these long-standing criticisms. Our findings show that studies show limited awareness of historical debates. Validation remains poorly addressed, with many studies relying solely on subjective assessments of model `believability', and even the most rigorous validation failing to adequately evidence operational validity. We argue that there are reasons to believe that LLMs will exacerbate rather than resolve the long-standing challenges of ABMs. The black-box nature of LLMs moreover limit their usefulness for disentangling complex emergent causal mechanisms. While generative ABMs are still in a stage of early experimentation, these findings question of whether and how the field can transition to the type of rigorous modeling needed to contribute to social scientific theory.
A Novel Optical Design for Wide-Field Imaging in X-ray Astronomy
2025-04-04
Neeraj K. Tiwari, Santosh V. Vadawale, N. P. S. Mithun
Over the decades, astronomical X-ray telescopes have utilized the Wolter type-1 optical design, which provides stigmatic imaging in axial direction but suffers from coma and higher-order aberrations for off-axis sources. The Wolter-Schwarzschild design, with stigmatic imaging in the axial direction, while suffering from higher-order aberrations, is corrected for coma, thus performing better than the Wolter type-1. The Wolter type-1 and Wolter-Schwarzschild designs are optimized for on-axis but have reduced angular resolution when averaged over a wide field of view, with the averaging weighted by the area covered in the field of view. An optical design that maximizes angular resolution at the edge of the field of view rather than at the center is more suitable for wide-field X-ray telescopes required for deep-sky astronomical surveys or solar observations. A Hyperboloid-Hyperboloid optical design can compromise axial resolution to enhance field angle resolution, hence providing improved area-weighted average angular resolution over the Wolter-Schwarzschild design, but only for fields of view exceeding a specific size. Here, we introduce a new optical design that is free from coma aberration and capable of maximizing angular resolution at any desired field angle. This design consistently outperforms Wolter-1, Wolter-Schwarzschild, and Hyperboloid-Hyperboloid designs when averaged over any field of view size. The improvement in performance remains consistent across variations in other telescope parameters such as diameter, focal length, and mirror lengths. By utilizing this new optical design, we also present a design for a full-disk imaging solar X-ray telescope.
Spin-orbit interactions, time-reversal symmetry, and spin selection
2025-04-04
Amnon Aharony, Ora Entin-Wohlman
Spin selective transport is usually associated with spin-orbit interactions. However, these interactions are invariant under time-reversal symmetry, and the Onsager relations and Bardarson's theorem imply that such interactions cannot yield spin selectivity for transport through a junction between two electronic reservoirs. Here, we review several ways to overcome this restriction, using a Zeeman magnetic field, the Aharonov-Bohm phase, time-dependent electric fields that generate time-dependent spin-orbit interactions, time-dependent transients, more than two terminals, leakage, and more than one level per ion on the junction. Our considerations focus on the transport of noninteracting electrons at low temperatures. A possible connection with the phenomenon of chiral-induced spin selectivity (CISS) is pointed out in one of the systems considered.
Augmenting Human Cognition With Generative AI: Lessons From AI-Assisted Decision-Making
2025-04-04
Zelun Tony Zhang, Leon Reicherts
How can we use generative AI to design tools that augment rather than replace human cognition? In this position paper, we review our own research on AI-assisted decision-making for lessons to learn. We observe that in both AI-assisted decision-making and generative AI, a popular approach is to suggest AI-generated end-to-end solutions to users, which users can then accept, reject, or edit. Alternatively, AI tools could offer more incremental support to help users solve tasks themselves, which we call process-oriented support. We describe findings on the challenges of end-to-end solutions, and how process-oriented support can address them. We also discuss the applicability of these findings to generative AI based on a recent study in which we compared both approaches to assist users in a complex decision-making task with LLMs.
A Systematic Review on Women's Participation in Agricultural Work and Nutritional Outcomes
2025-04-04
Pallavi Gupta
While agriculture is recognised as vital for improving nutrition, the evidence linking women's participation to sustained nutritional gains remains inconclusive. This review synthesizes studies published between 2000 and 2024 to reflect current agricultural practices and nutritional challenges. We examine how agricultural practices and time use affect nutritional outcomes among rural women through pathways such as income generation food preparation and intra-household labour allocation. A structured methodology with clear inclusion and exclusion criteria was used to assess gender-sensitive and nutrition-sensitive interventions. Using narrative synthesis the review categorizes findings around key themes and contextual factors including socio-economic status seasonality and labour intensity. The results show that while increased involvement in agriculture can boost household dietary diversity and income it also raises time burdens that affect food preparation childcare and self-care. Positive outcomes occur when interventions enhance women's decision-making power income access and use of time-saving technologies whereas negative outcomes emerge when excessive workloads compromise energy balance and limit rest. A conceptual framework is presented to map the dual pathways linking agriculture time use and nutrition capturing the roles of labour distribution social norms and resource access. The framework underscores the need to integrate gender equity time efficiency and nutritional objectives into agricultural policies. In conclusion agricultural interventions have potential for nutritional improvement if they are carefully designed to avoid unintended negative impacts on women.
A Survey of Quantum Transformers: Approaches, Advantages, Challenges, and Future Directions
2025-04-04
Hui Zhang, Qinglin Zhao
Quantum Transformer models represent a significant research direction in quantum machine learning (QML), leveraging the parallelism and entanglement properties of quantum computing to overcome the computational complexity and expressive limitations of classical Transformers. Parameterized quantum circuit (PQC)-based Transformer models are the primary focus of current research, employing PQCs to achieve varying degrees of quantumization, including strategies such as QKV-only Quantum mapping, Quantum Pairwise Attention, Quantum Global Attention, and Quantum-Assisted Acceleration. These approaches are well-suited to Noisy Intermediate-Scale Quantum (NISQ) devices, demonstrating potential in small-scale tasks to reduce complexity or enhance performance. The strength of PQC-based methods lies in their compatibility with existing quantum hardware, positioning them as the main pathway toward the practical implementation of quantum Transformers. However, these methods face challenges such as limited scalability, the absence of standardized testing benchmarks, and the "barren plateau" problem during training. As a complementary approach, Quantum Linear Algebra (QLA)-based Transformer models rely on future fault-tolerant quantum computing, utilizing techniques like block-encoding and Quantum Singular Value Transformation (QSVT) to achieve efficient matrix operations and theoretically significant complexity reductions, though they remain in the theoretical exploration stage. Future research should prioritize optimizing PQC-based hybrid architectures and quantum global attention models, establishing unified evaluation frameworks, and addressing training difficulties, while also exploring hybrid PQC-QLA approaches to advance the development of quantum Transformers.
MIMRS: A Survey on Masked Image Modeling in Remote Sensing
2025-04-04
Shabnam Choudhury, Akhil Vasim, Michael Schmitt, Biplab Banerjee
Masked Image Modeling (MIM) is a self-supervised learning technique that involves masking portions of an image, such as pixels, patches, or latent representations, and training models to predict the missing information using the visible context. This approach has emerged as a cornerstone in self-supervised learning, unlocking new possibilities in visual understanding by leveraging unannotated data for pre-training. In remote sensing, MIM addresses challenges such as incomplete data caused by cloud cover, occlusions, and sensor limitations, enabling applications like cloud removal, multi-modal data fusion, and super-resolution. By synthesizing and critically analyzing recent advancements, this survey (MIMRS) is a pioneering effort to chart the landscape of mask image modeling in remote sensing. We highlight state-of-the-art methodologies, applications, and future research directions, providing a foundational review to guide innovation in this rapidly evolving field.
Reasoning is central to human intelligence, enabling structured problem-solving across diverse tasks. Recent advances in large language models (LLMs) have greatly enhanced their reasoning abilities in arithmetic, commonsense, and symbolic domains. However, effectively extending these capabilities into multimodal contexts-where models must integrate both visual and textual inputs-continues to be a significant challenge. Multimodal reasoning introduces complexities, such as handling conflicting information across modalities, which require models to adopt advanced interpretative strategies. Addressing these challenges involves not only sophisticated algorithms but also robust methodologies for evaluating reasoning accuracy and coherence. This paper offers a concise yet insightful overview of reasoning techniques in both textual and multimodal LLMs. Through a thorough and up-to-date comparison, we clearly formulate core reasoning challenges and opportunities, highlighting practical methods for post-training optimization and test-time inference. Our work provides valuable insights and guidance, bridging theoretical frameworks and practical implementations, and sets clear directions for future research.
A Human Digital Twin Architecture for Knowledge-based Interactions and Context-Aware Conversations
2025-04-04
Abdul Mannan Mohammed, Azhar Ali Mohammad, Jason A. Ortiz, Carsten Neumann, Grace Bochenek, Dirk Reiners, Carolina Cruz-Neira
Recent developments in Artificial Intelligence (AI) and Machine Learning (ML) are creating new opportunities for Human-Autonomy Teaming (HAT) in tasks, missions, and continuous coordinated activities. A major challenge is enabling humans to maintain awareness and control over autonomous assets, while also building trust and supporting shared contextual understanding. To address this, we present a real-time Human Digital Twin (HDT) architecture that integrates Large Language Models (LLMs) for knowledge reporting, answering, and recommendation, embodied in a visual interface. The system applies a metacognitive approach to enable personalized, context-aware responses aligned with the human teammate's expectations. The HDT acts as a visually and behaviorally realistic team member, integrated throughout the mission lifecycle, from training to deployment to after-action review. Our architecture includes speech recognition, context processing, AI-driven dialogue, emotion modeling, lip-syncing, and multimodal feedback. We describe the system design, performance metrics, and future development directions for more adaptive and realistic HAT systems.
The Moon is a primary focus of space exploration. Current navigation methods face significant limitations in providing precise location data for lunar missions. In particular, existing methods often require direct Line of Sight to Earth, have limited capacity, and suffer from long signal travel times. This paper aims to tackle these challenges through a novel single satellite navigation system at the lunar North Pole. By utilising the Doppler effect, this system facilitates 3D geolocation of a stationary receiver on the lunar surface. Key findings include choosing a Low Lunar Orbit (LLO) suitable for North Pole coverage, designing a 3-step geolocation algorithm tailored to lunar conditions, constructing a comprehensive error budget, and evaluating the system performance through Dilution of Position (DOP).
Bridging LMS and Generative AI: Dynamic Course Content Integration (DCCI) for Connecting LLMs to Course Content -- The Ask ME Assistant
2025-04-04
Kovan Mzwri, Márta Turcsányi-Szabo
The integration of Large Language Models (LLMs) with Learning Management Systems (LMSs) has the potential to enhance task automation and accessibility in education. However, hallucination where LLMs generate inaccurate or misleading information remains a significant challenge. This study introduces the Dynamic Course Content Integration (DCCI) mechanism, which dynamically retrieves and integrates course content and curriculum from Canvas LMS into the LLM-powered assistant, Ask ME. By employing prompt engineering to structure retrieved content within the LLM's context window, DCCI ensures accuracy, relevance, and contextual alignment, mitigating hallucination. To evaluate DCCI's effectiveness, Ask ME's usability, and broader student perceptions of AI in education, a mixed-methods approach was employed, incorporating user satisfaction ratings and a structured survey. Results from a pilot study indicate high user satisfaction (4.614/5), with students recognizing Ask ME's ability to provide timely and contextually relevant responses for both administrative and course-related inquiries. Additionally, a majority of students agreed that Ask ME's integration with course content in Canvas LMS reduced platform-switching, improving usability, engagement, and comprehension. AI's role in reducing classroom hesitation and fostering self-directed learning and intellectual curiosity was also highlighted. Despite these benefits and positive perception of AI tools, concerns emerged regarding over-reliance on AI, accuracy limitations, and ethical issues such as plagiarism and reduced student-teacher interaction. These findings emphasize the need for strategic AI implementation, ethical safeguards, and a pedagogical framework that prioritizes human-AI collaboration over substitution.
Exploring the Spectral Energy Distributions of Luminous Broad Absorption Line Quasars at High Redshift
2025-04-04
Harum Ahmed, Sarah C. Gallagher, Ohad Shemmer, Michael S. Brotherton, Cooper Dix, Leigh Parrott, Gordon T. Richards
We present the multiwavelength spectral energy distributions (SEDs) for 65 luminous broad absorption line (BAL) quasars with redshifts $1.55 \lesssim z \lesssim 3.50$ from the Gemini Near Infrared Spectrograph - Distant Quasar Survey (GNIRS-DQS). We integrate data from a variety of ground- and space-based observatories to construct a comprehensive spectral profile of these objects from radio through X-rays. In addition, we present a mid-infrared to X-ray composite SED of these sources. Our dataset represents the most uniform sample of BAL quasars, providing a statistically robust set of SEDs. Our findings indicate that the BAL quasars in the GNIRS-DQS sample exhibit significant reddening in the ultraviolet-optical continuum relative to their non-BAL counterparts, consistent with previous studies. Notably, our analysis reveals no significant differences in the mid- or near-infrared spectral regime between BAL and non-BAL quasars. In line with previous work, we find no strong evidence that BAL and non-BAL quasars possess fundamentally different SEDs, also consistent with recent findings that both groups display similar rest-frame optical emission-line properties.
Adaptation and synchronization -- basic mechanisms in music performance
2025-04-04
Jakub Sawicki
This review examines the roles of adaptation and synchronization in music performance, drawing on concepts from complex systems theory to understand the dynamic interactions between musicians, music, and listeners. Adaptation is explored through how musicians adjust their cognitive, emotional, and motor systems across the stages of preparation, execution, and reception, while synchronization is emphasized as essential for aligning internal states, coordinating actions with other performers, and engaging with the audience. The review highlights the interdisciplinary nature of music performance research, integrating cognitive, motor, and emotional processes to enhance both individual and collective musical expression. It also addresses the psychological state of flow, which arises from synchronized neurocognitive mechanisms that optimize performance. Additionally, the emotional synchronization facilitated by music is explored, emphasizing its role in both individual emotional coherence and social coordination within musical ensembles. Finally, the review highlights recent findings on interpersonal and inter-brain synchronization, particularly in live music performances and improvisation, showing how synchronization fosters creativity, social cohesion, and a shared collective experience.
ProbRes: Probabilistic Jump Diffusion for Open-World Egocentric Activity Recognition
2025-04-04
Sanjoy Kundu, Shanmukha Vellamchetti, Sathyanarayanan N. Aakur
Open-world egocentric activity recognition poses a fundamental challenge due to its unconstrained nature, requiring models to infer unseen activities from an expansive, partially observed search space. We introduce ProbRes, a Probabilistic Residual search framework based on jump-diffusion that efficiently navigates this space by balancing prior-guided exploration with likelihood-driven exploitation. Our approach integrates structured commonsense priors to construct a semantically coherent search space, adaptively refines predictions using Vision-Language Models (VLMs) and employs a stochastic search mechanism to locate high-likelihood activity labels while minimizing exhaustive enumeration efficiently. We systematically evaluate ProbRes across multiple openness levels (L0 - L3), demonstrating its adaptability to increasing search space complexity. In addition to achieving state-of-the-art performance on benchmark datasets (GTEA Gaze, GTEA Gaze+, EPIC-Kitchens, and Charades-Ego), we establish a clear taxonomy for open-world recognition, delineating the challenges and methodological advancements necessary for egocentric activity understanding. Our results highlight the importance of structured search strategies, paving the way for scalable and efficient open-world activity recognition.
Particle acceleration by turbulent-driven magnetic reconnection and the production of gamma-rays and neutrinos in AGNs
2025-04-04
E. M. de Gouveia Dal Pino, J. C. Ramirez-Rodriguez, T. Medina-Torrejon, G. H. Vicentin, L. Passos Reis
3D Magnetohydrodynamic (MHD) resistive simulations have highlighted the significance of ubiquitous turbulence to drive fast reconnection. It has been demonstrated that particle acceleration via reconnection in 3D magnetized flows, where turbulence is embedded in large-scale magnetic fields such as in relativistic jets and accretion flows around compact sources, is remarkably efficient. Particles experience Fermi acceleration across all scales of turbulent reconnection layers, outweighing the considerably slower drift acceleration mechanism. This stands in contrast to recent assertions stemming from PIC simulations, that claimed the dominance of the latter process. In this talk, I review how particle acceleration is driven by 3D turbulent reconnection to very high energies and demonstrate its potential in magnetized regions of AGN jets and accretion disks to explain the gamma-ray and neutrino emissions. Applications to sources such as TXS 0506+056, MRK 501, and NGC 1068 are discussed.
Emission line stacking of 21cm intensity maps with MeerKLASS: Inference pipeline and application to the L-band deep-field data
2025-04-04
Zhaoting Chen, Steven Cunnington, Alkistis Pourtsidou, Laura Wolz, Marta Spinelli, José Luis Bernal, Matilde Barberi-Squarotti, Stefano Camera, Isabella P. Carucci, José Fonseca, Keith Grainge, Melis O. Irfan, Mario G. Santos, Jingying Wang
We present a novel analysis of observational systematics through the emission line stacking of the MeerKLASS L-band deep-field intensity maps, following the detection reported in arXiv:2407.21626. A stacking signal is obtained by stacking the 21cm intensity map cubelets around the galaxy positions from the GAMA survey at $0.39\lesssim z \lesssim 0.46$. An extensive simulation framework is built to study the viability of the stacking detection, the covariance estimation, and the model inference, which are then applied to the data. The statistical significance of the detection is $8.66\sigma$ when averaged into an angular map, and $7.45\sigma$ when averaged into a spectrum. The stacked spectrum exhibits an oscillating component of systematics, and we provide evidence that the systematics is a convolutional effect on the map data. The frequency of the oscillation matches the diffraction from the secondary reflector into the primary beam of the MeerKAT telescope. Bayesian inference can be used to constrain the systematics and the average HI emission of the galaxies. The fitting of the parameters gives a constraint on the systematics frequency $\nu_{\rm sys}\,[{\rm MHz}] = 17.90^{+6.53}_{-4.27}$. The amplitude of the systematics is $>6.71\%$ at the 95\% confidence level. A tentative measurement of the average HI mass of the sources is achieved at ${\rm log}_{10}[\langle M_{\rm HI}\rangle/M_\odot ]=9.84^{+0.48}_{-0.59}$, limited by the narrow redshift bin of the data, the existence of the systematics, and the low-density galaxy sample. These shortfalls will be resolved for future MeerKLASS data to enable accurate measurements of the HI density through stacking of 21cm intensity maps.
Investigating Affective Use and Emotional Well-being on ChatGPT
2025-04-04
Jason Phang, Michael Lampe, Lama Ahmad, Sandhini Agarwal, Cathy Mengying Fang, Auren R. Liu, Valdemar Danry, Eunhae Lee, Samantha W. T. Chan, Pat Pataranutaporn, Pattie Maes
As AI chatbots see increased adoption and integration into everyday life, questions have been raised about the potential impact of human-like or anthropomorphic AI on users. In this work, we investigate the extent to which interactions with ChatGPT (with a focus on Advanced Voice Mode) may impact users' emotional well-being, behaviors and experiences through two parallel studies. To study the affective use of AI chatbots, we perform large-scale automated analysis of ChatGPT platform usage in a privacy-preserving manner, analyzing over 3 million conversations for affective cues and surveying over 4,000 users on their perceptions of ChatGPT. To investigate whether there is a relationship between model usage and emotional well-being, we conduct an Institutional Review Board (IRB)-approved randomized controlled trial (RCT) on close to 1,000 participants over 28 days, examining changes in their emotional well-being as they interact with ChatGPT under different experimental settings. In both on-platform data analysis and the RCT, we observe that very high usage correlates with increased self-reported indicators of dependence. From our RCT, we find that the impact of voice-based interactions on emotional well-being to be highly nuanced, and influenced by factors such as the user's initial emotional state and total usage duration. Overall, our analysis reveals that a small number of users are responsible for a disproportionate share of the most affective cues.
Plasma-Treated Polymeric Biomaterials for Improved Surface and Cell Adhesion
2025-04-04
Jairo Rondøn, Angel Gonzalez-Lizardo
Surface modification of polymeric biomaterials using plasma has emerged as an effective strategy to optimize the cell-material interface without compromising the structural properties of the material. This work presents a critical review of the impact of low-temperature plasma treatment on enhancing cell adhesion, with emphasis on the physicochemical changes induced on the surface of polymers commonly used in biomedical applications. The mechanisms of interaction between reactive plasma species and the polymer surface are analyzed, along with techniques used to introduce hydrophilic functional groups that improve wettability and biocompatibility. Scientific evidence demonstrates that this type of surface modification promotes greater cell spreading, anchorage, and proliferation, making it particularly useful in the design of tissue engineering scaffolds, implantable devices, and vascular prostheses. Finally, current and future trends in the development of smart plasma-functionalized biomaterials are discussed, highlighting their role in regenerative medicine.
Multiscale Modeling Primer: Focus on Chromatin and Epigenetics
2025-04-04
Achal Mahajan, Erik J. Navarro, William Poole, Carlos F Lopez
Essential life processes take place across multiple space and time scales in living organisms but understanding their mechanistic interactions remains an ongoing challenge. Advanced multiscale modeling techniques are providing new opportunities and insights into these complex processes. In cells, meters of chromatin are folded into a nucleus with a diameter on the order of microns. The three-dimensional chromatin structure coupled with biochemical processes that turn genes on or off, specify a given cell type through a complicated set of interactions collectively referred to as epigenetics. Important epigenetic processes include the differential accessibility of genomic loci to transcription factors and chemical modifications to DNA and DNA-binding molecules such as histones. The dynamics of these epigenetic processes span timescales from milliseconds to years. How do chemical modifications consisting of a handful of atoms cooperate to modulate genome folding at the scale of the nucleus and impact organism outcomes? In this review, we highlight the inherently multiscale nature of chromatin organization, with a focus on computational modeling to bridge the gaps in our understanding of biochemical processes across scales. We review relevant chromatin biology, including major types of epigenetic modifications as well as the higher order chromatin structures to present a multiscale view of chromatin. We also review relevant computational methods to simulate chromatin structure, function, and dynamics, as well as experimental techniques that inform and validate said models. Finally, we argue that multiscale modeling provides a path forward towards understanding emergent behavior in this inherently multiscale system.
Large-scale surveys of the quasar proximity effect
2025-04-04
Rupert A. C. Croft, Patrick Shaw, Ann-Marsha Alexis, Nianyi Chen, Yihao Zhou, Tiziana Di Matteo, Simeon Bird, Patrick Lachance, Yueying Ni
The UV radiation from high redshift quasars causes a local deficit in the neutral hydrogen absorption (Lyman-alpha forest) in their spectra, known as the proximity effect. Measurements from small samples of tens to hundreds of quasars have been used to constrain the global intensity of the UV background radiation, but so far the power of large-scale surveys such as the Sloan Digital Sky Survey and the Dark Energy Spectroscopic Instrument (DESI) survey has not been used to investigate the UV background in more detail. We develop a CDM-based halo model of the quasar proximity effect, which accounts by construction for the fact that quasars reside in overdense regions. We test this model on quasar Lyman-alpha spectra from the ASTRID cosmological hydrodynamic simulation, which includes self-consistent formation of quasar black holes and the intergalactic medium surrounding them. Fitting the model to individual quasar spectra, we constrain two parameters, r_eq (the radius at which the local quasar radiation intensity equals the background), and the quasar bias b_q (related to host halo mass). We find that r_eq can be recovered in an unbiased fashion with a statistical uncertainty of 25-50% from a single quasar spectrum. Applying such fitting to samples of millions of spectra from e.g., DESI would allow measurement of the UVBG intensity and its evolution with redshift with high precision. We use another, larger-scale, lower resolution simulation (Uchuu) to test how such a large sample of proximity effect measurements could be used to probe the spatial fluctuations in the intergalactic radiation field. We find that the large-scale structure of the UV radiation intensity could be mapped and its power spectrum measured on 100-1000 Mpc/h scales. This could allow the large-scale radiation field to join the density field as a dataset for constraining cosmology and the sources of radiation.
In cancer therapeutics, protein-metal binding mechanisms critically govern drug pharmacokinetics and targeting efficacy, thereby fundamentally shaping the rational design of anticancer metallodrugs. While conventional laboratory methods used to study such mechanisms are often costly, low throughput, and limited in capturing dynamic biological processes, machine learning (ML) has emerged as a promising alternative. Despite increasing efforts to develop protein-metal binding datasets and ML algorithms, the application of ML in tumor protein-metal binding remains limited. Key challenges include a shortage of high-quality, tumor-specific datasets, insufficient consideration of multiple data modalities, and the complexity of interpreting results due to the ''black box'' nature of complex ML models. This paper summarizes recent progress and ongoing challenges in using ML to predict tumor protein-metal binding, focusing on data, modeling, and interpretability. We present multimodal protein-metal binding datasets and outline strategies for acquiring, curating, and preprocessing them for training ML models. Moreover, we explore the complementary value provided by different data modalities and examine methods for their integration. We also review approaches for improving model interpretability to support more trustworthy decisions in cancer research. Finally, we offer our perspective on research opportunities and propose strategies to address the scarcity of tumor protein data and the limited number of predictive models for tumor protein-metal binding. We also highlight two promising directions for effective metal-based drug design: integrating protein-protein interaction data to provide structural insights into metal-binding events and predicting structural changes in tumor proteins after metal binding.
A [CII]-158-micron Survey of DLA galaxies at z~4: Observations of Dense and Metal-Enriched Neutral Gas within the Circumgalactic Medium of Star-Forming Galaxies
2025-04-04
Marcel Neeleman, Nissim Kanekar, J. Xavier Prochaska, Marc A. Rafelski, Lordrick A. Kahinga
We present a survey undertaken with the Atacama Large Millimeter/submillimeter Array (ALMA) to study the galaxies associated with a representative sample of 16 damped Ly-alpha absorbers (DLAs) at z~4.1-4.5, using the [CII]-158-micron ([CII]) line. We detect seven [CII]-emitting galaxies in the fields of 5 DLAs, all of which have absorption metallicity [M/H] > -1.5. We find that the detectability of these HI-selected galaxies with ALMA is a strong function of DLA metallicity, with a detection rate of 71^{+11}_{-20}% for DLAs with [M/H] > -1.5 and 0^{+18}% for DLAs with [M/H] <-1.5. The identified DLA galaxies have far-infrared properties similar to those of typical star-forming galaxies at z ~ 4, with estimated obscured star-formation rates ranging from ~6 Msun/yr to 110 Msun/yr. High-metallicity DLAs therefore provide an efficient way to identify and study samples of high-redshift, star-forming galaxies without preselecting the galaxies by their emission properties. The agreement between the velocities of the metal absorption lines of the DLA and the [CII] emission line of the DLA galaxy indicates that the metals within the DLA originated in the galaxy. With observed impact parameters between 14 and 59 kpc, this indicates that star-forming galaxies at z ~ 4 have a substantial reservoir of dense, cold neutral gas within their circumgalactic medium that has been enriched with metals from the galaxy.
ALMA Observations of [OI]145um and [NII]205um Emission lines from Star-Forming Galaxies at $z\sim7$
2025-04-04
Yoshinobu Fudamoto, Akio K. Inoue, Rychard Bouwens, Hanae Inami, Renske Smit, Dan Stark, Manuel Aravena, Andrea Pallottini, Takuya Hashimoto, Masamune Oguri, Rebecca A. A. Bowler, Elisabete da Cunha, Pratika Dayal, Andrea Ferrara, Seiji Fujimoto, Kasper E. Heintz, Alexander P. S. Hygate, Ivana F. van Leeuwen, Ilse De Looze, Lucie E. Rowland, Mauro Stefanon, Yuma Sugahara, Joris Witstok, Paul P. van der Werf
We present results of new observations of [OI]145um and [NII]205um emission lines from four star-forming galaxies at redshifts between $z=6.58$ and $7.68$ that have previous detections of \Ciium\ and dust continua. Using ALMA, we successfully detect [OI]145um emission from all targets at $>4\,\sigma$ significance. However, [NII]205um emission is undetected in all galaxies (SNR $<3.5\,\sigma$) except for a tentative detection from A1689-zD1. From the observed high [CII]/[NII] emission line ratios ($\gtrsim20 - 80$), we find that most of the [CII]158um emission arise from neutral gas regions ($3\,\sigma$ lower limits of $\gtrsim 74 - 96\%$). From [OI]145um, [CII]158um lines, and infrared luminosities, we estimate the neutral gas densities of $n_{\rm H}=10^{3.5}$ - $10^6\,{\rm cm^{-3}}$ and the far-ultraviolet (FUV) radiation strengths of $G_0\sim10^{2.5}$-$10^{3}$. While the neutral gas densities are similar to those of high-redshift starburst galaxies, the FUV strengths are lower compared to both local and high-redshift starbursts. Finally, we estimate atomic hydrogen masses using [OI]145um emission lines and the oxygen abundances measured from recent JWST observations. We find gas mass ratios of $f_{\rm gas}\sim0.3$ - $0.8$, which are similar to earlier studies using [CII]158um. Starting from this pilot observation, future large [OI]145um emission line surveys will provide us with currently little-known neutral gas properties of star-forming galaxies in the early Universe.
Outlook Towards Deployable Continual Learning for Particle Accelerators
2025-04-04
Kishansingh Rajput, Sen Lin, Auralee Edelen, Willem Blokland, Malachi Schram
Particle Accelerators are high power complex machines. To ensure uninterrupted operation of these machines, thousands of pieces of equipment need to be synchronized, which requires addressing many challenges including design, optimization and control, anomaly detection and machine protection. With recent advancements, Machine Learning (ML) holds promise to assist in more advance prognostics, optimization, and control. While ML based solutions have been developed for several applications in particle accelerators, only few have reached deployment and even fewer to long term usage, due to particle accelerator data distribution drifts caused by changes in both measurable and non-measurable parameters. In this paper, we identify some of the key areas within particle accelerators where continual learning can allow maintenance of ML model performance with distribution drifts. Particularly, we first discuss existing applications of ML in particle accelerators, and their limitations due to distribution drift. Next, we review existing continual learning techniques and investigate their potential applications to address data distribution drifts in accelerators. By identifying the opportunities and challenges in applying continual learning, this paper seeks to open up the new field and inspire more research efforts towards deployable continual learning for particle accelerators.
An Assessment of the CO$_2$ Emission Reduction Potential of Residential Load Management in Developing and Developed Countries
2025-04-03
Alona Zharova, Felix Creutzig
Intermittent renewable energies are increasingly dominating electricity grids and are forecasted to be the main force driving out fossil fuels from the grid in most major economies until 2040. However, grids based on intermittent renewables are challenged by diurnal and seasonal mismatch between supply of sun and wind and demand for electricity, including for heat pumps and electric two and four wheelers. Load management and demand response measures promise to adjust for this mismatch, utilizing information- and price-based approaches to steer demand towards times with high supply of intermittent renewables. Here, we systematically review the literature estimating CO$_2$ savings from residential load management in developing and developed nations. We find that load management holds high potential, locally differentiated with energy mix (including the respective share of renewables and fossils), climate zone, and the regulatory environment and price mechanism. Most identified studies suggest a mitigation potential between 1 and 20%. Load management becomes more relevant with higher shares of intermittent renewables, and when electricity prices are high. Importantly, load management aligns consumers' financial incentives with climate change mitigation, thus rendering accompanying strategies politically feasible. We summarize key regulatory steps to facilitate load management in economies and to realize relevant consumer surplus and mitigation potential.
Fractional attractors in light of the latest ACT observations
2025-04-03
Christian Dioguardi, Antonio J. Iovino, Antonio Racioppi
In light of the latest results from ACT observations we review a class of potentials labeled as fractional attractors, that can originate from Palatini gravity. We show in a model independent way that this class of potentials predicts both a spectral index $n_s$ and a tensor-to-scalar ratio $r$ which fit the $1\sigma$ region of the combination ACT+Planck data for a wide choice of the parameters. We also provide a numerical fit for the parameter space of this models in the case of a simple quadratic and quartic fractional potential.
A Survey of Large Language Models in Mental Health Disorder Detection on Social Media
2025-04-03
Zhuohan Ge, Nicole Hu, Darian Li, Yubo Wang, Shihao Qi, Yuming Xu, Han Shi, Jason Zhang
The detection and intervention of mental health issues represent a critical global research focus, and social media data has been recognized as an important resource for mental health research. However, how to utilize Large Language Models (LLMs) for mental health problem detection on social media poses significant challenges. Hence, this paper aims to explore the potential of LLM applications in social media data analysis, focusing not only on the most common psychological disorders such as depression and anxiety but also incorporating psychotic disorders and externalizing disorders, summarizing the application methods of LLM from different dimensions, such as text data analysis and detection of mental disorders, and revealing the major challenges and shortcomings of current research. In addition, the paper provides an overview of popular datasets, and evaluation metrics. The survey in this paper provides a comprehensive frame of reference for researchers in the field of mental health, while demonstrating the great potential of LLMs in mental health detection to facilitate the further application of LLMs in future mental health interventions.
North+Lone Star Supernova Host Survey I: Local Host-Galaxy H$α$ Surface Brightness and the Hubble Residuals of Type Ia Supernovae
2025-04-03
Ann M. Isaacs, Patrick Kelly, J. Craig Wheeler
We present optical integral-field unit (IFU) spectroscopy acquired with the George and Cynthia Mitchell Spectrograph on the Harlan J. Smith telescope at McDonald Observatory of 94 galaxies (0.01 < z < 0.058) that have hosted Type Ia supernovae (SNe Ia). We selected host galaxies with star-forming morphology, consistent with the criteria used by Riess et al. (2022). We measured the H${\alpha}$ surface brightness of each host galaxy within 1 kpc of the location of the supernova. Using distances from the Pantheon+ sample, we find a step in Hubble residuals compared to local H${\alpha}$ surface brightness of -0.097 $\pm$ 0.051 mag at 1.9${\sigma}$ significance in a sample of 73 host galaxies, where SNe in environments with smaller H${\alpha}$ surface brightness are, on average, less luminous after correction for light-curve shape and color. Almost all of the SNe in our sample were discovered by targeted surveys. Using an independent sample primarily from the untargeted Nearby Supernova Factory survey, Rigault et al. (2020) found a step of 0.045 $\pm$ 0.029 mag where SNe in passive environments are instead brighter, which is in 2.4${\sigma}$ tension with our measurement. Rigault et al. (2013) designated SNe Ia comparatively small HRs (< -0.1) and faint local H${\alpha}$ surface brightness (SB) (
A 3D view of dwarf galaxies with Gaia and VLT/FLAMES II. The Sextans dwarf spheroidal
2025-04-03
Eline Tolstoy, Giuseppina Battaglia, José María Arroyo-Polonio, Anthony G. A. Brown, Thom van Essen, Davide Massari, Ása Skúladóttir, Michael J. Irwin, Salvatore Taibi, John Pritchard
The Sextans dwarf spheroidal galaxy has been challenging to study in a comprehensive way as it is highly extended on the sky, with an uncertain but large tidal radius of between 80-160 arcminutes (or 3-4kpc), and an extremely low central surface brightness of SigmaV = 26.2 mag/arcsec2. Here we present a new homogeneous survey of 41 VLT/FLAMES multi-fibre spectroscopic pointings that contain 2108 individual spectra, and combined with Gaia DR3 photometry and astrometry we present v-los measurements for 333 individual Red Giant Branch stars that are consistent with membership in the Sextans dwarf spheroidal galaxy. In addition, we provide the metallicity, [Fe/H], determined from the two strongest CaII triplet lines, for 312 of these stars. We look again at the global characteristics of Sextans, deriving a mean line-of-sight velocity of = +227.1km/s and a mean metallicity of <[Fe/H]> = -2.37. The metallicity distribution is clearly double peaked, with the highest peak at [Fe/H]= -2.81 and another broader peak at [Fe/H]= -2.09. Thus it appears that Sextans hosts two populations and the superposition leads to a radial variation in the mean metallicity, with the more metal rich population being centrally concentrated. In addition there is an intriguing group of 9 probable members in the outer region of Sextans at higher [Fe/H] than the mean in this region. If this group could be confirmed as members they would eliminate the metallicity gradient. We also look again at the Colour-Magnitude Diagram of the resolved stellar population in Sextans. We also look again at the relation between Sextans and the intriguingly nearby globular cluster, Pal3. The global properties of Sextans have not changed significantly compared to previous studies, but they are now more precise, and the sample of known members in the outer regions is now more complete.
The Myth of Immutability: A Multivocal Review on Smart Contract Upgradeability
2025-04-03
Ilham Qasse, Isra M. Ali, Nafisa Ahmed, Mohammad Hamdaqa, Björn Þór Jónsson
The immutability of smart contracts on blockchain platforms like Ethereum promotes security and trustworthiness but presents challenges for updates, bug fixes, or adding new features post-deployment. These limitations can lead to vulnerabilities and outdated functionality, impeding the evolution and maintenance of decentralized applications. Despite various upgrade mechanisms proposed in academic research and industry, a comprehensive analysis of their trade-offs and practical implications is lacking. This study aims to systematically identify, classify, and evaluate existing smart contract upgrade mechanisms, bridging the gap between theoretical concepts and practical implementations. It introduces standardized terminology and evaluates the trade-offs of different approaches using software quality attributes. We conducted a Multivocal Literature Review (MLR) to analyze upgrade mechanisms from both academic research and industry practice. We first establish a unified definition of smart contract upgradeability and identify core components essential for understanding the upgrade process. Based on this definition, we classify existing methods into full upgrade and partial upgrade approaches, introducing standardized terminology to harmonize the diverse terms used in the literature. We then characterize each approach and assess its benefits and limitations using software quality attributes such as complexity, flexibility, security, and usability. The analysis highlights significant trade-offs among upgrade mechanisms, providing valuable insights into the benefits and limitations of each approach. These findings guide developers and researchers in selecting mechanisms tailored to specific project requirements.
Impact of a Blockchain-based Universal Basic Income Pilot: The case of Circles UBI currency
2025-04-03
Alessandro Longo, Teodoro Criscione, Julio Linares, Sowelu Avanzo
Circles UBI is a blockchain-based Community Currency System (CCS) that has been active in Berlin (Germany) since October 2021. The Circles Coop, which launched the project in 2021, was shut down in December 2023. In this paper, we show the results of a survey carried out between October and November 2023. The respondents were twenty-five individuals involved in various ways in the Circles' network. The main emerging narrative points out how their participation was deeply motivated by their identification with the values and ideals of the Circles community. Among them, we selected five profiles that stood for their difference in type and degree of involvement. Finally, we report some stories of economic linkages that suggest a positive externality in adopting a local community currency. To our knowledge, this is the first qualitative study of a universal basic income designed as a community currency and adopting blockchain technology. This pilot project was a remarkable experiment for its adopted advanced technological and social innovations. In fact, as far as we know, the integration of basic income and local currency features has been experimented with only in two other cases (Maric\'a, Brazil and Barcelona, Spain) and none of them adopted a decentralized ledger system. In this work, we try to outline strengths and weaknesses that emerged after about two years of activity. For this reason, future researchers and activists interested in this field will find valuable information.
The Hidden Space of Safety: Understanding Preference-Tuned LLMs in Multilingual context
2025-04-03
Nikhil Verma, Manasa Bharadwaj
Alignment tuning has enabled large language models to excel in reasoning, instruction-following, and minimizing harmful generations. However, despite their widespread deployment, these models exhibit a monolingual bias, raising concerns about the effectiveness of alignment across languages. Current alignment methods predominantly focus on English, leaving it unclear how alignment mechanism generalize to multilingual settings. To address this, we conduct a systematic analysis of distributional shifts in the embedding space of LLMs before and after alignment, uncovering its impact on model behavior across diverse languages. We leverage the alignment-induced separation in safety space as a quantitative tool to measure how alignment enforces safety constraints. Our study evaluates seven LLMs using balanced toxicity datasets and parallel text-detoxification benchmarks, revealing substantial disparities in the latent representation space between high-resource and low-resource languages. These findings underscore the need for language-specific fine-tuning to ensure fair, reliable and robust multilingual alignment. Our insights provide a foundation for developing truly safe multilingual LLMs, emphasizing the urgency of addressing alignment gaps in underrepresented languages.
The societal need to leverage third-party data has driven the data-distribution market and increased the importance of data quality assessment (DQA) in data transactions between organizations. However, DQA requires expert knowledge of raw data and related data attributes, which hinders consensus-building in data purchasing. This study focused on the differences in DQAs between experienced and inexperienced data handlers. We performed two experiments: The first was a questionnaire survey involving 41 participants with varying levels of data-handling experience, who evaluated 12 data samples using 10 predefined indices with and without quality metadata generated by the automated tool. The second was an eye-tracking experiment to reveal the viewing behavior of participants during data evaluation. It was revealed that using quality metadata generated by the automated tool can reduce misrecognition in DQA. While experienced data handlers rated the quality metadata highly, semi-experienced users gave it the lowest ratings. This study contributes to enhancing data understanding within organizations and promoting the distribution of valuable data by proposing an automated tool to support DQAs.
Complex Langevin Simulations of Supersymmetric Theories
2025-04-03
Anosh Joseph, Arpith Kumar
This review explores the Complex Langevin Method (CLM), a stochastic quantization technique designed to address the sign problem in quantum field theories with complex actions. Beginning with foundational principles, the review examines the applications of CLM across a range of models, including zero- and two-dimensional systems, supersymmetric quantum mechanics, and the IKKT matrix model, a candidate for non-perturbative string theory. Key advancements, such as stabilization techniques and mass deformations, are highlighted as solutions to challenges like numerical instability and singular drift terms. The review emphasizes the capacity of CLM to simulate complex systems and reveal non-perturbative phenomena, positioning it as a powerful tool for exploring quantum field theory and string theory. Future directions, including higher-dimensional applications and benchmarking against quantum simulations, underscore the potential of CLM to advance both theoretical understanding and computational methodologies.
Spectropolarimetry for Discerning Geometry and Structure in Circumstellar Media of Hot Massive Stars
2025-04-03
Richard Ignace, Kenneth Gayley, Roberto Casini, Paul Scowen, Christiana Erba, Jeremy Drake
Spectropolarimetric techniques are a mainstay of astrophysical inquiry, ranging from Solar System objects to the Cosmic Background Radiation. This review highlights applications of stellar polarimetry for massive hot stars, particularly in the context of ultraviolet (UV) spaceborne missions. The prevalence of binarity in the massive star population and uncertainties regarding the degree of rotational criticality among hot stars raises important questions about stellar interactions, interior structure, and even the lifetimes of evolutionary phases. These uncertainties have consequences for stellar population synthesis calculations. Spectropolarimetry is a key tool for extracting information about stellar and binary geometries. We review methodologies involving electron scattering in circumstellar envelopes; gravity darkening from rapid rotation; spectral line effects including the (a) "line effect", (b) Ohman effect, and (c) Hanle effect; and the imprint of interstellar polarization on measurements. Finally, we describe the Polstar UV spectropolarimetric SMEX mission concept as one means for employing these diagnostics to clarify the state of high rotation and its impacts for massive stars.
Reservoir Computing: A New Paradigm for Neural Networks
2025-04-03
Felix Grezes
A Literature Review of Reservoir Computing. Even before Artificial Intelligence was its own field of computational science, humanity has tried to mimic the activity of the human brain. In the early 1940s the first artificial neuron models were created as purely mathematical concepts. Over the years, ideas from neuroscience and computer science were used to develop the modern Neural Network. The interest in these models rose quickly but fell when they failed to be successfully applied to practical applications, and rose again in the late 2000s with the drastic increase in computing power, notably in the field of natural language processing, for example with the state-of-the-art speech recognizer making heavy use of deep neural networks. Recurrent Neural Networks (RNNs), a class of neural networks with cycles in the network, exacerbates the difficulties of traditional neural nets. Slow convergence limiting the use to small networks, and difficulty to train through gradient-descent methods because of the recurrent dynamics have hindered research on RNNs, yet their biological plausibility and their capability to model dynamical systems over simple functions makes then interesting for computational researchers. Reservoir Computing emerges as a solution to these problems that RNNs traditionally face. Promising to be both theoretically sound and computationally fast, Reservoir Computing has already been applied successfully to numerous fields: natural language processing, computational biology and neuroscience, robotics, even physics. This survey will explore the history and appeal of both traditional feed-forward and recurrent neural networks, before describing the theory and models of this new reservoir computing paradigm. Finally recent papers using reservoir computing in a variety of scientific fields will be reviewed.
Do evidence-based active-engagement courses reduce the gender gap in introductory physics?
2025-04-03
N. I. Karim, A. Maries, C. Singh
Prior research suggests that using evidence-based pedagogies can not only improve learning for all students, it can also reduce the gender gap. We describe the impact of physics education research based pedagogical techniques in flipped and active-engagement non-flipped courses on the gender gap observed with validated conceptual surveys. We compare male and female students' performance in courses which make significant use of evidence-based active engagement (EBAE) strategies with courses that primarily use lecture-based (LB) instruction. The analysis presented here includes data from two-semester sequences of introductory algebra-based and calculus-based introductory physics courses. The surveys used to assess student learning in the first and second semester courses were the Force Concept Inventory and the Conceptual Survey of Electricity and Magnetism, respectively. The performance of male and female students in EBAE courses at a particular level is compared with LB courses in two situations: (I) the same instructor taught two courses, one of which was an EBAE course and the other an LB course, while the homework, recitations and final exams were kept the same, (II) student performance in all of the EBAE courses taught by different instructors was averaged and compared with LB courses of the same type also averaged over different instructors. In all cases, we find that students in courses which make significant use of EBAE strategies, on average, outperformed students in courses of the same type using primarily LB instruction even though there was no statistically significant difference on the pretest before instruction. However, the gender gap persisted even in courses using EBAE methods. We also discuss correlations between the performance of male and female students on the validated conceptual surveys and the final exam, which had a heavy weight on quantitative problem solving.
Leveraging Sparse Annotations for Leukemia Diagnosis on the Large Leukemia Dataset
2025-04-03
Abdul Rehman, Talha Meraj, Aiman Mahmood Minhas, Ayisha Imran, Mohsen Ali, Waqas Sultani, Mubarak Shah
Leukemia is 10th most frequently diagnosed cancer and one of the leading causes of cancer related deaths worldwide. Realistic analysis of Leukemia requires White Blook Cells (WBC) localization, classification, and morphological assessment. Despite deep learning advances in medical imaging, leukemia analysis lacks a large, diverse multi-task dataset, while existing small datasets lack domain diversity, limiting real world applicability. To overcome dataset challenges, we present a large scale WBC dataset named Large Leukemia Dataset (LLD) and novel methods for detecting WBC with their attributes. Our contribution here is threefold. First, we present a large-scale Leukemia dataset collected through Peripheral Blood Films (PBF) from several patients, through multiple microscopes, multi cameras, and multi magnification. To enhance diagnosis explainability and medical expert acceptance, each leukemia cell is annotated at 100x with 7 morphological attributes, ranging from Cell Size to Nuclear Shape. Secondly, we propose a multi task model that not only detects WBCs but also predicts their attributes, providing an interpretable and clinically meaningful solution. Third, we propose a method for WBC detection with attribute analysis using sparse annotations. This approach reduces the annotation burden on hematologists, requiring them to mark only a small area within the field of view. Our method enables the model to leverage the entire field of view rather than just the annotated regions, enhancing learning efficiency and diagnostic accuracy. From diagnosis explainability to overcoming domain shift challenges, presented datasets could be used for many challenging aspects of microscopic image analysis. The datasets, code, and demo are available at: https://im.itu.edu.pk/sparse-leukemiaattri/
Technical Overview of Recent Developments in Small Modular Reactors in the United States
2025-04-03
Yifan Sun, Ken Kurosaki
Small modular reactors (SMRs) are a class of advanced nuclear fission reactors characterized by their compact core size (typically <300 MWe) and passive safety systems. Their modular design enables on-site assembly, making them suitable for deployment in locations inaccessible to conventional large-scale reactors. With rising global energy demand, particularly driven by the growth of AI, SMRs have recently gained attention as a potential solution for powering data centers. This technical review aims to provide the public and relevant stakeholders with a foundational understanding of SMR technology. It begins with an overview of SMR concepts, historical context, and their current role in the U.S. energy mix. Detailed technical summaries of nine selected SMR designs are then presented, covering core design, fuel systems, reactivity control, and safety features. The report also outlines key regulatory frameworks, including 10 CFR Part 50, Part 52, and the technology-inclusive, risk-informed, and performance-based framework currently under development. Finally, major U.S. programs and legislative efforts supporting SMR deployment over the past decade are summarized.
The advent of Large Language Models (LLMs) is transforming software development, significantly enhancing software engineering processes. Research has explored their role within development teams, focusing on specific tasks such as artifact generation, decision-making support, and information retrieval. Despite the growing body of work on LLMs in software engineering, most studies have centered on broad adoption trends, neglecting the nuanced relationship between individual cognitive and behavioral factors and their impact on task-specific adoption. While factors such as perceived effort and performance expectancy have been explored at a general level, their influence on distinct software engineering tasks remains underexamined. This gap hinders the development of tailored LLM-based systems (e.g., Generative AI Agents) that align with engineers' specific needs and limits the ability of team leaders to devise effective strategies for fostering LLM adoption in targeted workflows. This study bridges this gap by surveying N=188 software engineers to test the relationship between individual attributes related to technology adoption and LLM adoption across five key tasks, using structural equation modeling (SEM). The Unified Theory of Acceptance and Use of Technology (UTAUT2) was applied to characterize individual adoption behaviors. The findings reveal that task-specific adoption is influenced by distinct factors, some of which negatively impact adoption when considered in isolation, underscoring the complexity of LLM integration in software engineering. To support effective adoption, this article provides actionable recommendations, such as seamlessly integrating LLMs into existing development environments and encouraging peer-driven knowledge sharing to enhance information retrieval.
Cyberthreat intelligence sharing is a critical aspect of cybersecurity, and it is essential to understand its definition, objectives, benefits, and impact on society. Blockchain and Distributed Ledger Technology (DLT) are emerging technologies that have the potential to transform intelligence sharing. This paper aims to provide a comprehensive understanding of intelligence sharing and the role of blockchain and DLT in enhancing it. The paper addresses questions related to the definition, objectives, benefits, and impact of intelligence sharing and provides a review of the existing literature. Additionally, the paper explores the challenges associated with blockchain and DLT and their potential impact on security and privacy. The paper also discusses the use of DLT and blockchain in security and intelligence sharing and highlights the associated challenges and risks. Furthermore, the paper examines the potential impact of a National Cybersecurity Strategy on addressing cybersecurity risks. Finally, the paper explores the experimental set up required for implementing blockchain and DLT for intelligence sharing and discusses the curricular ramifications of intelligence sharing.
Improving User Experience with FAICO: Towards a Framework for AI Communication in Human-AI Co-Creativity
2025-04-03
Jeba Rezwana, Corey Ford
How AI communicates with humans is crucial for effective human-AI co-creation. However, many existing co-creative AI tools cannot communicate effectively, limiting their potential as collaborators. This paper introduces our initial design of a Framework for designing AI Communication (FAICO) for co-creative AI based on a systematic review of 107 full-length papers. FAICO presents key aspects of AI communication and their impacts on user experience to guide the design of effective AI communication. We then show actionable ways to translate our framework into two practical tools: design cards for designers and a configuration tool for users. The design cards enable designers to consider AI communication strategies that cater to a diverse range of users in co-creative contexts, while the configuration tool empowers users to customize AI communication based on their needs and creative workflows. This paper contributes new insights within the literature on human-AI co-creativity and Human-Computer Interaction, focusing on designing AI communication to enhance user experience.
Reinforcement learning (RL) has been widely adopted in post-training for large language models (LLMs) at scale. Recently, the incentivization of reasoning capabilities in LLMs from RL indicates that $\textit{proper learning methods could enable effective inference-time scalability}$. A key challenge of RL is to obtain accurate reward signals for LLMs in various domains beyond verifiable questions or artificial rules. In this work, we investigate how to improve reward modeling (RM) with more inference compute for general queries, i.e. the $\textbf{inference-time scalability of generalist RM}$, and further, how to improve the effectiveness of performance-compute scaling with proper learning methods. For the RM approach, we adopt pointwise generative reward modeling (GRM) to enable flexibility for different input types and potential for inference-time scaling. For the learning method, we propose Self-Principled Critique Tuning (SPCT) to foster scalable reward generation behaviors in GRMs through online RL, to generate principles adaptively and critiques accurately, resulting in $\textbf{DeepSeek-GRM}$ models. Furthermore, for effective inference-time scaling, we use parallel sampling to expand compute usage, and introduce a meta RM to guide voting process for better scaling performance. Empirically, we show that SPCT significantly improves the quality and scalability of GRMs, outperforming existing methods and models in various RM benchmarks without severe biases, and could achieve better performance compared to training-time scaling. DeepSeek-GRM still meets challenges in some tasks, which we believe can be addressed by future efforts in generalist reward systems. The models will be released and open-sourced.
Robot vision has greatly benefited from advancements in multimodal fusion techniques and vision-language models (VLMs). We systematically review the applications of multimodal fusion in key robotic vision tasks, including semantic scene understanding, simultaneous localization and mapping (SLAM), 3D object detection, navigation and localization, and robot manipulation. We compare VLMs based on large language models (LLMs) with traditional multimodal fusion methods, analyzing their advantages, limitations, and synergies. Additionally, we conduct an in-depth analysis of commonly used datasets, evaluating their applicability and challenges in real-world robotic scenarios. Furthermore, we identify critical research challenges such as cross-modal alignment, efficient fusion strategies, real-time deployment, and domain adaptation, and propose future research directions, including self-supervised learning for robust multimodal representations, transformer-based fusion architectures, and scalable multimodal frameworks. Through a comprehensive review, comparative analysis, and forward-looking discussion, we provide a valuable reference for advancing multimodal perception and interaction in robotic vision. A comprehensive list of studies in this survey is available at https://github.com/Xiaofeng-Han-Res/MF-RV.
Size and shape of the trans-Neptunian object (470316) 2007 OC10: Comparison with thermal data
2025-04-03
J. M. Gómez-Limón, R. Leiva, J. L. Ortiz, N. Morales, M. Kretlow, M. Vara-Lubiano, P. Santos-Sanz, A. Álvarez-Candal, J. L. Rizos, R. Duffard, E. Fernández-Valenzuela, Y. Kilic, S. Cikota, B. Sicardy, F. Bragas-Ribas, M. R. Alarcon, S. Alis, Z. Benkhaldoun, A. Burdanov, J. de Wit, S. Calavia Belloc, J. Calvo Fernández, O. Canales Moreno, G. Catanzaro, S. Fisek, A. Frasca, R. Iglesias-Marzoa, R. Infante-Sainz, A. Jiménez-Guisado, S. Kaspi, T. Kuutma, D. Lafuente Aznar, J. Licandro, J. L. Maestre, P. Martorell, A. Nastasi, G. Occhipinti, C. Perelló, C. Rinner, B. Samper-Doménech, A. San Segundo, T. Santana-Ros, M. Serra-Ricart
The shapes of only 12 trans-Neptunian objects have been directly measured, offering crucial insights into their internal structure. These properties are strongly connected to the processes that shaped the early Solar System, and provide important clues about its evolution. The aim of the present work is to characterise the size, shape, geometric albedo, and beaming parameter of the TNO (470316) 2007 OC10 . We compared these values to the effective diameter and geometric albedo obtained from thermal data by the TNOs are Cool survey. We also combined occultation and thermal data to constrain the size of a putative unresolved satellite. We predicted an occultation of the star Gaia DR3 2727866328215869952 by 2007 OC10 on 2022 August 22. Four stations detected the occultation. We implemented an elliptical shape model for the projection of 2007 OC10. Following a Bayesian approach, we obtained the posterior probability density in the model parameter space using a Markov chain Monte Carlo method. The elliptical limb of 2007 OC10 has semi-axes of $ 215^{+10}_{-7} \times 141 ^{+24}_{-23}$ km, and thus the projected axis ratio is $b/a = 0.58^{+0.16}_{-0.16}$. The area-equivalent diameter is $330^{+56}_{-55}$,km. From our own absolute magnitude value of $H_V = 5.40 \pm 0.02$, the geometric albedo is $p_V = 11.2 ^{+2.1}_{-5.0}$ %. Combining the occultation results with thermal data, we constrain the beaming parameter to $\eta = 1.42^{+0.75}_{-0.58}$. Occultation data reveal that the star is double. The secondary star has a position angle with respect to the primary of $56^{+3}_{-17}$ degrees, has an angular separation of $57^{+4}_{-11}$ mas, and is $1.18^{+0.07}_{-0.07}$ magnitudes fainter than the primary.
Traditional hospital-based medical examination methods face unprecedented challenges due to the aging global population. The Internet of Medical Things (IoMT), an advanced extension of the Internet of Things (IoT) tailored for the medical field, offers a transformative solution for delivering medical care. IoMT consists of interconnected medical devices that collect and transmit patients' vital signs online. This data can be analyzed to identify potential health issues, support medical decision-making, enhance patient outcomes, and streamline healthcare operations. Additionally, IoMT helps individuals make informed decisions about their health and fitness. There is a natural synergy with emerging communication technologies to ensure the secure and timely transmission of medical data. This paper presents the first comprehensive tutorial on cutting-edge IoMT research focusing on wireless communication-based solutions. It introduces a systematic three-tier framework to analyze IoMT networks and identify application scenarios. The paper examines the medical data transmission process, including intra-wireless Body Area Networks (WBAN), inter-WBAN, and beyond-WBAN communications. It also discusses the challenges of implementing IoMT applications, such as the longevity of biosensors, co-channel interference management, information security, and data processing delays. Proposed solutions to these challenges are explored from a wireless communication perspective, and future research directions are outlined. The survey concludes with a summary of key findings and insights.
Youthful perspectives on sustainability: Examining pro-environmental behaviors in tourism through latent class cluster analysis
2025-04-03
Riccardo Gianluigi Serio, Maria Michela Dickson, Giuseppe Espa, Rocco Micciolo
Tourism has emerged as a significant driver of the global economy. As its economic impact grows, concerns regarding environmental sustainability have intensified. This paper explores the dual dimensions of sustainable tourism: the relationship between tourism supply and sustainability, and tourist demand characteristics. It highlights the critical role of young tourists, who exhibit a heightened awareness of environmental issues and advocate for sustainable practices. By conducting a survey among young Italian university students, the study identifies distinct segments based on family background, political orientation, and travel habits. Utilizing latent class cluster analysis, the findings aim to enhance understanding of pro-environmental behaviors among youth, offering insights for policymakers to foster sustainable tourism practices.
Comparing the Sun to Sun-like stars. On the importance of addressing faculae/spot domination
2025-04-03
Konstantin Herbst, Eliana M. Amazo-Gómez, Athanasios Papaioannou
Whether the Sun is an ordinary G-type star is still an open scientific question. Stellar surveys by Kepler and TESS, however, revealed that Sun-like stars tend to show much stronger flare activity than the Sun. This study aims to reassess observed flare and spot activity of Sun-like Kepler stars by fine-tuning the criteria for a more robust definition of Sun-like conditions and better comparability between the current Sun and Sun-like stars. We update one of the recent stellar Sun-like star samples by applying new empirical stellar relations between the starspot size and the effective stellar temperature to derive more reliable starspot group sizes. From the 265 solar-type stars, we could select 48 stars supporting the Kepler 30-minute cadence light curves. These were analyzed by implementing the gradient of the power spectra method to distinguish between spot- and faculae-dominated stars. We employed the $\alpha$-factor to quantify the area ratio of bright and dark features on the stellar surface. We were able to group the 48 stars as being spot- or faculae-dominated, revealing a preferential distribution of the Kepler Sun-like stars towards the spot-dominated (44 stars) and transitional (four stars) regimes. As the current Sun is faculae-dominated, only the transitional stars were utilized for further evaluation. Additionally, accounting for comparability in stellar mass, radius, and rotation period, we show that only one of the utilized 265 Sun-like stars in the Kepler sample (i.e., KIC 11599385) allows for direct comparison to the current Sun. We further show that the single flare observed on KIC 11599385 falls right within the flare energy range estimated for the AD774/775 event observed in the cosmogenic radionuclide archives of $^{10}$Be, $^{14}$C, and $^{36}$Cl.
Mass, Luminosity, and Stellar Age of Early-type Stars from the LAMOST Survey
2025-04-03
Qida Li, Jianping Xiong, Jiao Li, Yanjun Guo, Zhanwen Han, Xuefei Chen, Chao Liu
Mass ($M$) and luminosity ($L$) are fundamental parameters of stars but can only be measured indirectly. Typically, effective temperature ($T_{\rm eff}$), surface gravity (${\rm log}\ g$) and metallicity ([M/H]) are derived from stellar spectra, then $M$, $L$ and stellar age ($t$) can be obtained by interpolating in the grid of stellar evolutionary models. In this paper, we use the Random Forest (RF) in combination with the evolutionary grid from PARSEC 1.2S to determine $M$, $L$, $t$ and initial mass ($M_{\rm i}$) for early-type main-sequence stars ($T_{\rm eff}\geq 7,000\ {\rm K}$) as identified from the LAMOST survey. The convex hull algorithm is employed to select the main-sequence stars. The results demonstrate that the prediction precision is $32\%$ and $9\%$ for $L$ and $M$, respectively, which is comparable to that achieved by fitting evolutionary tracks. Furthermore, the predicted $L$ also aligns with Gaia's observations, with a relative difference of $36\%$. The prediction for $t$ is relatively less accurate, indicating a difference of 0.44 Gyr for two components in wide binaries. This discrepancy is due to the inconsistent metallicity measurements. For the two sets of atmospheric parameters we used, the relative differences in $L$, $M$, $t$ and $M_{\rm i}$ are $29\%$, $7\%$, $36\%$ and $7\%$, respectively. The influence of metallicity on these parameters is analyzed, with the conclusion that metallicity has the greatest impact on $t$. Consequently, two catalogs are presented, which would be useful for studying stellar populations such as the luminosity function and initial mass function of early-type stars.
CoTAL: Human-in-the-Loop Prompt Engineering, Chain-of-Thought Reasoning, and Active Learning for Generalizable Formative Assessment Scoring
2025-04-03
Clayton Cohn, Nicole Hutchins, Ashwin T S, Gautam Biswas
Large language models (LLMs) have created new opportunities to assist teachers and support student learning. Methods such as chain-of-thought (CoT) prompting enable LLMs to grade formative assessments in science, providing scores and relevant feedback to students. However, the extent to which these methods generalize across curricula in multiple domains (such as science, computing, and engineering) remains largely untested. In this paper, we introduce Chain-of-Thought Prompting + Active Learning (CoTAL), an LLM-based approach to formative assessment scoring that (1) leverages Evidence-Centered Design (ECD) principles to develop curriculum-aligned formative assessments and rubrics, (2) applies human-in-the-loop prompt engineering to automate response scoring, and (3) incorporates teacher and student feedback to iteratively refine assessment questions, grading rubrics, and LLM prompts for automated grading. Our findings demonstrate that CoTAL improves GPT-4's scoring performance, achieving gains of up to 24.5% over a non-prompt-engineered baseline. Both teachers and students view CoTAL as effective in scoring and explaining student responses, each providing valuable refinements to enhance grading accuracy and explanation quality.
Mapped interacting boson model for nuclear structure studies
2025-04-03
Kosuke Nomura
The present status of the mapped interacting boson model studies on nuclear structure is reviewed. With the assumption that the nuclear surface deformation induced by the multi-nucleon dynamics is simulated by bosonic degrees of freedom, the interacting-boson Hamiltonian that provides energy spectra and wave functions is determined by mapping the potential energy surface that is obtained from self-consistent mean-field calculations based on the energy density functional onto the corresponding energy surface of the boson system. This procedure has been shown to be valid in general cases of the quadrupole collective states, and has allowed for systematic studies on spectroscopic properties of medium-heavy and heavy nuclei, including those that are far from the line of $\beta$ stability. The method is extended to study intriguing nuclear structure phenomena that include shape phase transitions and coexistence, octupole deformation and collectivity, and the coupling of the single-particle to collective degrees of freedom, which is crucial to describe structures of odd nuclei, and $\beta$ and $\beta\beta$ decays.
Tree-based models have achieved great success in a wide range of real-world applications due to their effectiveness, robustness, and interpretability, which inspired people to apply them in vertical federated learning (VFL) scenarios in recent years. In this paper, we conduct a comprehensive study to give an overall picture of applying tree-based models in VFL, from the perspective of their communication and computation protocols. We categorize tree-based models in VFL into two types, i.e., feature-gathering models and label-scattering models, and provide a detailed discussion regarding their characteristics, advantages, privacy protection mechanisms, and applications. This study also focuses on the implementation of tree-based models in VFL, summarizing several design principles for better satisfying various requirements from both academic research and industrial deployment. We conduct a series of experiments to provide empirical observations on the differences and advances of different types of tree-based models.
LLM Social Simulations Are a Promising Research Method
2025-04-03
Jacy Reese Anthis, Ryan Liu, Sean M. Richardson, Austin C. Kozlowski, Bernard Koch, James Evans, Erik Brynjolfsson, Michael Bernstein
Accurate and verifiable large language model (LLM) simulations of human research subjects promise an accessible data source for understanding human behavior and training new AI systems. However, results to date have been limited, and few social scientists have adopted these methods. In this position paper, we argue that the promise of LLM social simulations can be achieved by addressing five tractable challenges. We ground our argument in a literature survey of empirical comparisons between LLMs and human research subjects, commentaries on the topic, and related work. We identify promising directions with prompting, fine-tuning, and complementary methods. We believe that LLM social simulations can already be used for exploratory research, such as pilot experiments for psychology, economics, sociology, and marketing. More widespread use may soon be possible with rapidly advancing LLM capabilities, and researchers should prioritize developing conceptual models and evaluations that can be iteratively deployed and refined at pace with ongoing AI advances.
The statistical properties of double main sequence (MS) binaries are very important for binary evolution and binary population synthesis. To obtain these properties, we need to identify these MS binaries. In this paper, we have developed a method to differentiate single MS stars from double MS binaries from the Chinese Space Station Telescope (CSST) Survey with machine learning. This method is reliable and efficient to identify binaries with mass ratios between 0.20 and 0.80, which is independent of the mass ratio distribution. But the number of binaries identified with this method is not a good approximation to the number of binaries in the original sample due to the low detection efficiency of binaries with mass ratios smaller than 0.20 or larger than 0.80. Therefore, we have improved this point by using the detection efficiencies of our method and an empirical mass ratio distribution and then can infer the binary fraction in the sample. Once the CSST data are available, we can identify MS binaries with our trained multi-layer perceptron model and derive the binary fraction of the sample.
The statistical characteristics of double main-sequence (MS) binaries are essential for investigating star formation, binary evolution, and population synthesis. Our previous study proposed a machine learning-based method to identify MS binaries from MS single stars using mock data from the Chinese Space Station Telescope (CSST). We further utilized detection efficiencies and an empirical mass ratio distribution to estimate the binary fraction within the sample. To further validate the effectiveness of this method, we conducted a more realistic sample simulation, incorporating additional factors such as metallicity, extinction, and photometric errors from CSST simulations. The detection efficiency for binaries with mass ratios between 0.2 and 0.7 reached over 80%. We performed a detailed observational validation using the data selected from the Gaia Sky Survey and Galaxy Evolution Explorer. The detection efficiency for MS binaries in the observed sample was 65%. The binary fraction can be inferred with high precision for a set of observed samples, based on accurate empirical mass ratio distribution.
In this work, we present typical challenges encountered when developing methods for controlling crowds of people (or animal swarms). We discuss which elements shall be considered and the role they play to achieve a robust control in a variety of conditions. In particular, four different studies are reviewed, each of them investigating in detail important elements encountered in crowd steering and control. More specifically synchronization, compliance, crowd (or swarm) density and human perception are studied showing the role they play in combination. Ultimately, the success of a control strategy is determined by carefully considering the effect each element has on individuals, but also on the interactions between them, leading to the creation of a collective behavior. We will also highlight the importance of psychological and cognitive factors when dealing with human crowds, hinting at the fact that automatic control systems may achieve optimal performance, but may be not necessarily well perceived by people in terms of comfort. The discussion aims at showing recent trends and potentialities of crowd control systems, but should also warn on the risk in choosing a solution prioritizing optimization toward people's safety or comfort.
2025-04-03
Naimul Hoque, Zinat Ara, Safwat Ali Khan, Fanny Chevalier, Niklas Elmqvist
Understanding the role of creativity in visualization design becomes increasingly important as the field matures, particularly with the emergence of various visualization authoring and recommendation systems. In this paper, we examine how creativity manifests in visualization design processes and how academic research has conceptualized it over time. Through a systematic review of 58 visualization papers that use the terms "creativity" or "creative," we analyze the evolution of creative practices in visualization design. Our findings show that prior literature predominantly used atypical designs through free-form drawings, infographics, pictorials, and data comics to define creative representations. However, creativity in visualization design extends beyond visual representations to encompass early needfinding design activities such as sketching, storyboarding, discussion, and card sorting. Data visualization can also support a wide variety of creative tasks (e.g., fiction writing). We discuss the implications of these findings for fostering innovation within established design paradigms and for developing more sophisticated visualization authoring systems. The full list of coded papers are available here: https://vizcreativity.notion.site/coded-papers.
Recommender systems increasingly aim to combine signals from both user reviews and purchase (or other interaction) behaviors. While user-written comments provide explicit insights about preferences, merging these textual representations from large language models (LLMs) with graph-based embeddings of user actions remains a challenging task. In this work, we propose a framework that employs both a Graph Neural Network (GNN)-based model and an LLM to produce review-aware representations, preserving review semantics while mitigating textual noise. Our approach utilizes a hybrid objective that balances user-item interactions against text-derived features, ensuring that user's both behavioral and linguistic signals are effectively captured. We evaluate this method on multiple datasets from diverse application domains, demonstrating consistent improvements over a baseline GNN-based recommender model. Notably, our model achieves significant gains in recommendation accuracy when review data is sparse or unevenly distributed. These findings highlight the importance of integrating LLM-driven textual feedback with GNN-derived user behavioral patterns to develop robust, context-aware recommender systems.
GALEX UV survey of magnetic chemically peculiar stars in the Kepler prime field
2025-04-03
Emanuele Bertone, Stefan Hümmerich, Miguel Chávez, Ernst Paunzen, Klaus Bernhard, Daniel Olmedo, Manuel Olmedo, Eric. E. Mamajek, Osvan M. Portilla-Narvaez, Joshua Lara Sabala, Alan Silva Castro
Magnetic chemically peculiar (mCP) stars are strongly magnetic upper main-sequence stars that exhibit light rotational variability due to an uneven surface distribution of certain peculiar elements, which may appear in phase at certain wavelengths and in antiphase to the flux at other wavelengths. We present a study of the properties of photometric variability of a sample of confirmed mCP stars (mostly Ap/CP2 stars), mCP star candidates, and several non-CP stars in the near ultraviolet and visible wavelength regions based on observational data from the GALEX and Kepler prime missions. Antiphase variations between the near ultraviolet and optical light curves are observed in the majority of mCP stars. We investigate the presence of a correlation of the variability amplitudes in both wavelength regions with effective temperature, surface gravity, and metallicity and calculate model atmospheres, spectral energy distributions and synthetic light curves to connect our findings to theoretical models. While the theoretical calculations show that, at fixed abundances, a clear correlation between the light curve amplitude ratios and effective temperature is expected, our sample does not show any correlation with the investigated properties. This may be due to the highly individualistic abundance patterns of our sample stars, which are the main contributors to the line blanketing in different wavelength bands.
Extended Reality (XR) has expanded the horizons of entertainment and social life and shows great potential in the manufacturing industry. Prototyping in XR can help designers make initial proposals and iterations at low cost before manufacturers and investors decide whether to invest in research, development or even production. According to the literature (54 manuscripts in the last 15 years) prototyping in XR in XR is easier to use than three-dimensional (3D) modeling with a personal computer and more capable of displaying 3D structures than paper drawing. In this comprehensive review, we systematically surveyed the literature on prototyping in XR and discussed the possibility of transferring created virtual prototypes from XR to commonly used 3D modeling software and reality. We proposed five research questions regarding prototyping in XR. They are: what the constituent elements and workflow of prototyping are; which display devices can deliver satisfying immersive and interactive experiences; how user control input is obtained and what methods are available for users to interact with virtual elements and create XR prototypes; what approaches can facilitate the connection with fabrication to ensure a smooth transition from the virtual to the physical world; and what the challenges are and what the future holds for this research domain. Based on these questions, we summarized the components and workflows of prototyping in XR. Moreover, we present an overview of the latest trends in display device evolution, control technologies, digital model construction, and manufacturing processes. In view of these latest developments and gaps, we speculated on the challenges and opportunities in the field of prototyping in XR, especially in linking extended reality to digital fabrication, with the aim of guiding researchers towards new research directions.
Level Up Peer Review in Education: Investigating genAI-driven Gamification system and its influence on Peer Feedback Effectiveness
2025-04-03
Rafal Wlodarski, Leonardo da Silva Sousa, Allison Connell Pensky
In software engineering (SE), the ability to review code and critique designs is essential for professional practice. However, these skills are rarely emphasized in formal education, and peer feedback quality and engagement can vary significantly among students. This paper introduces Socratique, a gamified peer-assessment platform integrated with Generative AI (GenAI) assistance, designed to develop students' peer-review skills in a functional programming course. By incorporating game elements, Socratique aims to motivate students to provide more feedback, while the GenAI assistant offers real-time support in crafting high quality, constructive comments. To evaluate the impact of this approach, we conducted a randomized controlled experiment with master's students comparing a treatment group with a gamified, GenAI-driven setup against a control group with minimal gamification. Results show that students in the treatment group provided significantly more voluntary feedback, with higher scores on clarity, relevance, and specificity - all key aspects of effective code and design reviews. This study provides evidence for the effectiveness of combining gamification and AI to improve peer review processes, with implications for fostering review-related competencies in software engineering curricula.
Azimuthal anisotropy of direct photons in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV
2025-04-03
PHENIX Collaboration, N. J. Abdulameer, U. Acharya, A. Adare, C. Aidala, N. N. Ajitanand, Y. Akiba, M. Alfred, S. Antsupov, N. Apadula, H. Asano, B. Azmoun, V. Babintsev, M. Bai, N. S. Bandara, B. Bannier, E. Bannikov, K. N. Barish, S. Bathe, A. Bazilevsky, M. Beaumier, S. Beckman, R. Belmont, A. Berdnikov, Y. Berdnikov, L. Bichon, B. Blankenship, D. S. Blau, J. S. Bok, V. Borisov, K. Boyle, M. L. Brooks, J. Bryslawskyj, V. Bumazhnov, S. Campbell, C. -H. Chen, D. Chen, M. Chiu, C. Y. Chi, I. J. Choi, J. B. Choi, T. Chujo, Z. Citron, M. Connors, R. Corliss, M. Csanád, T. Csörgő, T. W. Danley, A. Datta, M. S. Daugherity, G. David, K. DeBlasio, K. Dehmelt, A. Denisov, A. Deshpande, E. J. Desmond, A. Dion, P. B. Diss, V. Doomra, J. H. Do, A. Drees, K. A. Drees, J. M. Durham, A. Durum, A. Enokizono, R. Esha, B. Fadem, W. Fan, N. Feege, D. E. Fields, M. Finger, Jr., M. Finger, D. Firak, D. Fitzgerald, S. L. Fokin, J. E. Frantz, A. Franz, A. D. Frawley, P. Gallus, C. Gal, P. Garg, H. Ge, M. Giles, F. Giordano, A. Glenn, Y. Goto, N. Grau, S. V. Greene, M. Grosse Perdekamp, T. Gunji, T. Guo, T. Hachiya, J. S. Haggerty, K. I. Hahn, H. Hamagaki, H. F. Hamilton, J. Hanks, S. Y. Han, S. Hasegawa, T. O. S. Haseler, K. Hashimoto, T. K. Hemmick, X. He, J. C. Hill, A. Hodges, R. S. Hollis, K. Homma, B. Hong, T. Hoshino, N. Hotvedt, J. Huang, K. Imai, M. Inaba, A. Iordanova, D. Isenhower, D. Ivanishchev, B. V. Jacak, M. Jezghani, X. Jiang, Z. Ji, B. M. Johnson, D. Jouan, D. S. Jumper, S. Kanda, J. H. Kang, G. Kasza, D. Kawall, A. V. Kazantsev, J. A. Key, V. Khachatryan, A. Khanzadeev, B. Kimelman, C. Kim, D. J. Kim, E. -J. Kim, G. W. Kim, M. Kim, E. Kistenev, R. Kitamura, J. Klatsky, D. Kleinjan, P. Kline, T. Koblesky, B. Komkov, D. Kotov, L. Kovacs, K. Kurita, M. Kurosawa, Y. Kwon, J. G. Lajoie, D. Larionova, A. Lebedev, S. Lee, S. H. Lee, M. J. Leitch, S. H. Lim, M. X. Liu, X. Li, X. Li, D. A. Loomis, D. Lynch, S. Lökös, Y. I. Makdisi, M. Makek, A. Manion, V. I. Manko, E. Mannel, M. McCumber, P. L. McGaughey, D. McGlinchey, C. McKinney, A. Meles, M. Mendoza, A. C. Mignerey, A. Milov, D. K. Mishra, J. T. Mitchell, M. Mitrankova, Iu. Mitrankov, S. Miyasaka, S. Mizuno, A. K. Mohanty, P. Montuenga, T. Moon, D. P. Morrison, T. V. Moukhanova, B. Mulilo, T. Murakami, J. Murata, A. Mwai, K. Nagashima, J. L. Nagle, M. I. Nagy, I. Nakagawa, H. Nakagomi, K. Nakano, C. Nattrass, P. K. Netrakanti, T. Niida, S. Nishimura, R. Nouicer, N. Novitzky, T. Novák, G. Nukazuka, A. S. Nyanin, E. O'Brien, C. A. Ogilvie, J. D. Orjuela Koop, M. Orosz, J. D. Osborn, A. Oskarsson, K. Ozawa, R. Pak, V. Pantuev, V. Papavassiliou, J. S. Park, S. Park, M. Patel, S. F. Pate, J. -C. Peng, D. V. Perepelitsa, G. D. N. Perera, D. Yu. Peressounko, J. Perry, R. Petti, C. Pinkenburg, R. Pinson, R. P. Pisani, M. Potekhin, M. L. Purschke, J. Rak, B. J. Ramson, I. Ravinovich, K. F. Read, D. Reynolds, V. Riabov, Y. Riabov, D. Richford, T. Rinn, S. D. Rolnick, M. Rosati, Z. Rowan, J. G. Rubin, B. Sahlmueller, N. Saito, T. Sakaguchi, H. Sako, V. Samsonov, M. Sarsour, S. Sato, B. Schaefer, B. K. Schmoll, K. Sedgwick, R. Seidl, A. Seleznev, A. Sen, R. Seto, P. Sett, A. Sexton, D. Sharma, I. Shein, T. -A. Shibata, K. Shigaki, M. Shimomura, P. Shukla, A. Sickles, C. L. Silva, D. Silvermyr, B. K. Singh, C. P. Singh, V. Singh, M. Slunečka, K. L. Smith, M. Snowball, R. A. Soltz, W. E. Sondheim, S. P. Sorensen, I. V. Sourikova, P. W. Stankus, M. Stepanov, S. P. Stoll, T. Sugitate, A. Sukhanov, T. Sumita, J. Sun, Z. Sun, J. Sziklai, A. Taketani, K. Tanida, M. J. Tannenbaum, S. Tarafdar, A. Taranenko, R. Tieulent, A. Timilsina, T. Todoroki, M. Tomášek, C. L. Towell, R. Towell, R. S. Towell, I. Tserruya, B. Ujvari, H. W. van Hecke, J. Velkovska, M. Virius, V. Vrba, X. R. Wang, Z. Wang, Y. Watanabe, Y. S. Watanabe, F. Wei, A. S. White, C. L. Woody, M. Wysocki, B. Xia, L. Xue, S. Yalcin, Y. L. Yamaguchi, A. Yanovich, I. Yoon, J. H. Yoo, I. E. Yushmanov, H. Yu, W. A. Zajc, A. Zelenski, S. Zhou, L. Zou
The PHENIX experiment at the Relativistic Heavy Ion Collider measured the second Fourier component $v_2$ of the direct-photon azimuthal anisotropy at midrapidity in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV. The results are presented in 10\% wide bins of collision centrality and cover the transverse-momentum range of $1
Cultural Learning-Based Culture Adaptation of Language Models
2025-04-03
Chen Cecilia Liu, Anna Korhonen, Iryna Gurevych
Adapting large language models (LLMs) to diverse cultural values is a challenging task, as existing LLMs often reflect the values of specific groups by default, and potentially causing harm to others. In this paper, we present CLCA, a novel framework for enhancing LLM alignment with cultural values based on cultural learning. The framework leverages simulated social interactions to generate conversations in which LLMs engage in role-playing within culturally adapted social scenarios, capturing implicit cultural norms for model fine-tuning. CLCA improves cultural value alignment across various model architectures measured using World Value Survey data, demonstrating the effectiveness of our proposed approach. Our results provide early evidence that understanding intent and social interactions can enhance cultural value adaptation in LLMs, highlighting the promise of training approaches based on cultural learning.
Triple stellar systems, consisting of three gravitationally bound stars, play a fundamental role in a wide array of astrophysical processes, from stellar evolution to the formation of exotic objects and gravitational wave sources. This review provides a comprehensive overview of the dynamics and evolution of triple stellar systems, highlighting their crucial role in shaping stellar populations and driving diverse astrophysical phenomena. We begin by discussing the observed properties of triples, including their frequency and orbital configurations, emphasizing the challenges in characterizing these systems. We then delve into the intricate dynamics of triples, exploring hierarchical, secular, quasi-secular, and chaotic regimes, with particular attention to the von Zeipel-Lidov-Kozai (vZLK) mechanism and its extensions. The interplay between stellar evolution and triple dynamics is examined, including mass loss, mass transfer, common-envelope evolution, and supernovae effects. We discuss the development and applications of triple population synthesis codes, demonstrating their power in modeling complex evolutionary scenarios and predicting observational outcomes. Finally, we emphasize the unique role of triple evolution in producing a wide range of astrophysical phenomena, from interacting binaries and stellar mergers to explosive transients and gravitational wave sources, underscoring the importance of triple systems in our understanding of stars and stellar populations.
The DESI Y1 RR Lyrae catalog I: Empirical modeling of the cyclic variation of spectroscopic properties and a chemodynamical analysis of the outer halo
2025-04-03
Gustavo E. Medina, Ting S. Li, Sergey E. Koposov, A. H. Riley, L. Beraldo e Silva, M. Valluri, W. Wang, A. Byström, O. Y. Gnedin, R. G. Carlberg, N. Kizhuprakkat, B. A. Weaver, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, A. P. Cooper, A. de la Macorra, A. Dey, P. Doel, A. Font-Ribera, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, J. Guy, K. Honscheid, M. Ishak, T. Kisner, M. Landriau, L. Le Guillou, A. Meisner, R. Miquel, A. D. Myers, S. Nadathur, C. Poppett, F. Prada, I. Pérez-Ràfols, G. Rossi, E. Sanchez, H. Seo, D. Sprayberry, G. Tarlé, R. H. Wechsler, R. Zhou, H. Zou
We present the catalog of RR Lyrae stars observed in the first year of operations of the Dark Energy Spectroscopic Instrument (DESI) survey. This catalog contains 6,240 RR Lyrae stars out to $\sim100$\,kpc from the Galactic center and over 12,000 individual epochs with homogeneously-derived stellar atmospheric parameters. We introduce a novel methodology to model the cyclical variation of the spectroscopic properties of RR Lyrae from single-epoch measurements. We employ this method to infer the radial velocity and effective temperature variation of fundamental mode and first-overtone RR Lyrae stars and to determine their systemic velocities and mean temperatures. For fundamental mode pulsators, we obtain radial velocity curves with amplitudes of $\sim$30--50\,km\,s$^{-1}$ and effective temperature curves with 300--1,000\,K variations, whereas for first-overtone pulsators these amplitudes are $\sim20$\,km\,s$^{-1}$ and $\sim 600$\,K, respectively. We use our sample to study the metallicity distribution of the halo and its dependence on Galactocentric distance ($R_{\rm GC}$). Using a radius-dependent mixture model, we split the data into chemodynamically distinct components and find that our inner halo sample ($R_{\rm GC}\lesssim50$\,kpc) is predominantly composed of stars with [Fe/H] $\sim-1.5$\,dex and largely radial orbits (with an anisotropy parameter $\beta\sim0.94$), that we associate with the Gaia-Sausage-Enceladus merger event. Stars in the outer halo field exhibit a broader and more metal-poor [Fe/H] distribution with more circular orbits ($\beta\sim0.39$). The metallicity gradient of the metal-rich and the metal-poor components is found to be $0.005$ and $0.010$\,dex\,kpc$^{-1}$, respectively. Our catalog highlights DESI's tantalizing potential for studying the Milky Way and the pulsation properties of RR Lyrae stars in the era of large spectroscopic surveys.
Stellar CoRGI in MaNGA: Stellar Counter-Rotation Galaxies Identified in MaNGA Survey
2025-04-03
Damir Gasymov, Ivan Yu. Katkov, Evgenii V. Rubtsov, Anna S. Saburova, Alexei Yu. Kniazev, Joseph D. Gelfand, Olga K. Silchenko, Igor V. Chilingarian, Alexei V. Moiseev, Anastasia V. Kasparova, Anatoly Zasov
Counter-rotating (CR) galaxies consist of two coplanar stellar disks rotating in opposite directions -- the main, pre-existing disk with an older stellar population and a younger CR disk likely formed from externally acquired gas. Such systems offer a unique opportunity to study disk assembly by analyzing the stellar populations of each component. Using integral field spectroscopic data from the SDSS-IV Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, we identified a sample of 120 CR disk galaxies by inspecting their kinematic maps and analyzing the shape of the stellar line-of-sight velocity distribution (LOSVD), which was recovered non-parametrically. For one-third of our sample, we further derived the ages and metallicities of stars for both disks via a spectral decomposition technique. We show that the observed kinematic bimodality -- where the CR disk is either concentrated in the central region (inner~CR) or dominates the outer part of the galaxy (outer~CR) -- is driven by differences in the stellar mass and angular momentum of the CR disk. The wide range of stellar metallicities observed in CR disks suggests that no single source of external material is solely responsible for CR formation in all galaxies; instead, proposed mechanisms such as merger with gas-rich satellites, accretion from cosmic filaments, and exchange of gas between neighboring galaxies can dominate in individual cases.
Parity-violating interactions are ubiquitous phenomena in particle physics. If they are significant during cosmic inflation, they can leave imprints on primordial perturbations and be observed in correlation functions of galaxy surveys. Importantly, parity-violating signals in the four-point correlation functions (4PCFs) cannot be generated by Einstein gravity in the late universe on large scales, making them unique and powerful probes of high-energy physics during inflation. However, the complex structure of the 4PCF poses challenges in diagnosing the underlying properties of parity-violating interactions from observational data. In this work, we introduce a general framework that provides a streamlined pipeline directly from a particle model in inflation to galaxy 4PCFs in position space. We demonstrate this framework with a series of toy models, effective-field-theory-like models, and full models featuring tree-level exchange-type processes with chemical-potential-induced parity violation. We further showed the detection sensitivity of these models from BOSS data and highlighted potential challenges in data interpretation and model prediction.
TDCOSMO XVII. New time delays in 22 lensed quasars from optical monitoring with the ESO-VST 2.6m and MPG 2.2m telescopes
2025-04-03
Frédéric Dux, Martin Millon, Aymeric Galan, Eric Paic, Cameron Lemon, Frédéric Courbin, Vivien Bonvin, Timo Anguita, Matt Auger, Simon Birrer, Elisabeth Buckley-Geer, Chris Fassnacht, Joshua Frieman, Richard G. McMahon, Philip J. Marshall, Alejandra Melo, Verónica Motta, Favio Neira, Dominique Sluse, Sherry H. Suyu, Tommaso Treu, Adriano Agnello, Felipe Ávila, James Chan, M. A. Chijani, Karina Rojas, Angela Hempel, Maren Hempel, Sam Kim, Paul Eigenthaler, Regis Lachaume, Markus Rabus
We present new time delays, the main ingredient of time delay cosmography, for 22 lensed quasars resulting from high-cadence r-band monitoring on the 2.6 m ESO VLT Survey Telescope and Max-Planck-Gesellschaft 2.2 m telescope. Each lensed quasar was typically monitored for one to four seasons, often shared between the two telescopes to mitigate the interruptions forced by the COVID-19 pandemic. The sample of targets consists of 19 quadruply and 3 doubly imaged quasars, which received a total of 1 918 hours of on-sky time split into 21 581 wide-field frames, each 320 seconds long. In a given field, the 5-{\sigma} depth of the combined exposures typically reaches the 27th magnitude, while that of single visits is 24.5 mag - similar to the expected depth of the upcoming Vera-Rubin LSST. The fluxes of the different lensed images of the targets were reliably de-blended, providing not only light curves with photometric precision down to the photon noise limit, but also high-resolution models of the targets whose features and astrometry were systematically confirmed in Hubble Space Telescope imaging. This was made possible thanks to a new photometric pipeline, lightcurver, and the forward modelling method STARRED. Finally, the time delays between pairs of curves and their uncertainties were estimated, taking into account the degeneracy due to microlensing, and for the first time the full covariance matrices of the delay pairs are provided. Of note, this survey, with 13 square degrees, has applications beyond that of time delays, such as the study of the structure function of the multiple high-redshift quasars present in the footprint at a new high in terms of both depth and frequency. The reduced images will be available through the European Southern Observatory Science Portal.
LiDAR-based Object Detection with Real-time Voice Specifications
2025-04-03
Anurag Kulkarni
This paper presents a LiDAR-based object detection system with real-time voice specifications, integrating KITTI's 3D point clouds and RGB images through a multi-modal PointNet framework. It achieves 87.0% validation accuracy on a 3000-sample subset, surpassing a 200-sample baseline of 67.5% by combining spatial and visual data, addressing class imbalance with weighted loss, and refining training via adaptive techniques. A Tkinter prototype provides natural Indian male voice output using Edge TTS (en-IN-PrabhatNeural), alongside 3D visualizations and real-time feedback, enhancing accessibility and safety in autonomous navigation, assistive technology, and beyond. The study offers a detailed methodology, comprehensive experimental analysis, and a broad review of applications and challenges, establishing this work as a scalable advancement in human-computer interaction and environmental perception, aligned with current research trends.
Background: Large language models (LLMs) are rapidly being integrated into healthcare, promising to enhance various clinical tasks. However, concerns exist regarding their potential for bias, which could compromise patient care and exacerbate health inequities. This systematic review investigates the prevalence, sources, manifestations, and clinical implications of bias in LLMs. Methods: We conducted a systematic search of PubMed, OVID, and EMBASE from database inception through 2025, for studies evaluating bias in LLMs applied to clinical tasks. We extracted data on LLM type, bias source, bias manifestation, affected attributes, clinical task, evaluation methods, and outcomes. Risk of bias was assessed using a modified ROBINS-I tool. Results: Thirty-eight studies met inclusion criteria, revealing pervasive bias across various LLMs and clinical applications. Both data-related bias (from biased training data) and model-related bias (from model training) were significant contributors. Biases manifested as: allocative harm (e.g., differential treatment recommendations); representational harm (e.g., stereotypical associations, biased image generation); and performance disparities (e.g., variable output quality). These biases affected multiple attributes, most frequently race/ethnicity and gender, but also age, disability, and language. Conclusions: Bias in clinical LLMs is a pervasive and systemic issue, with a potential to lead to misdiagnosis and inappropriate treatment, particularly for marginalized patient populations. Rigorous evaluation of the model is crucial. Furthermore, the development and implementation of effective mitigation strategies, coupled with continuous monitoring in real-world clinical settings, are essential to ensure the safe, equitable, and trustworthy deployment of LLMs in healthcare.
Systematic Literature Review: Explainable AI Definitions and Challenges in Education
2025-04-03
Zaid M. Altukhi, Sojen Pradhan
Explainable AI (XAI) seeks to transform black-box algorithmic processes into transparent ones, enhancing trust in AI applications across various sectors such as education. This review aims to examine the various definitions of XAI within the literature and explore the challenges of XAI in education. Our goal is to shed light on how XAI can contribute to enhancing the educational field. This systematic review, utilising the PRISMA method for rigorous and transparent research, identified 19 relevant studies. Our findings reveal 15 definitions and 62 challenges. These challenges are categorised using thematic analysis into seven groups: explainability, ethical, technical, human-computer interaction (HCI), trustworthiness, policy and guideline, and others, thereby deepening our understanding of the implications of XAI in education. Our analysis highlights the absence of standardised definitions for XAI, leading to confusion, especially because definitions concerning ethics, trustworthiness, technicalities, and explainability tend to overlap and vary.
Enhancing Air Quality Monitoring: A Brief Review of Federated Learning Advances
2025-04-03
Sara Yarham, Mehran Behjati
Monitoring air quality and environmental conditions is crucial for public health and effective urban planning. Current environmental monitoring approaches often rely on centralized data collection and processing, which pose significant privacy, security, and scalability challenges. Federated Learning (FL) offers a promising solution to these limitations by enabling collaborative model training across multiple devices without sharing raw data. This decentralized approach addresses privacy concerns while still leveraging distributed data sources. This paper provides a comprehensive review of FL applications in air quality and environmental monitoring, emphasizing its effectiveness in predicting pollutants and managing environmental data. However, the paper also identifies key limitations of FL when applied in this domain, including challenges such as communication overhead, infrastructure demands, generalizability issues, computational complexity, and security vulnerabilities. For instance, communication overhead, caused by the frequent exchange of model updates between local devices and central servers, is a notable challenge. To address this, future research should focus on optimizing communication protocols and reducing the frequency of updates to lessen the burden on network resources. Additionally, the paper suggests further research directions to refine FL frameworks and enhance their applicability in real-world environmental monitoring scenarios. By synthesizing findings from existing studies, this paper highlights the potential of FL to improve air quality management while maintaining data privacy and security, and it provides valuable insights for future developments in the field.
Low-cost Embedded Breathing Rate Determination Using 802.15.4z IR-UWB Hardware for Remote Healthcare
2025-04-03
Anton Lambrecht, Stijn Luchie, Jaron Fontaine, Ben Van Herbruggen, Adnan Shahid, Eli De Poorter
Respiratory diseases account for a significant portion of global mortality. Affordable and early detection is an effective way of addressing these ailments. To this end, a low-cost commercial off-the-shelf (COTS), IEEE 802.15.4z standard compliant impulse-radio ultra-wideband (IR-UWB) radar system is exploited to estimate human respiration rates. We propose a convolutional neural network (CNN) to predict breathing rates from ultra-wideband (UWB) channel impulse response (CIR) data, and compare its performance with other rule-based algorithms. The study uses a diverse dataset of 16 individuals, incorporating various real-life environments to evaluate system robustness. Results show that the CNN achieves a mean absolute error (MAE) of 1.73 breaths per minute (BPM) in unseen situations, significantly outperforming rule-based methods (3.40 BPM). By incorporating calibration data from other individuals in the unseen situations, the error is further reduced to 0.84 BPM. In addition, this work evaluates the feasibility of running the pipeline on a low-cost embedded device. Applying 8-bit quantization to both the weights and input/ouput tensors, reduces memory requirements by 67% and inference time by 64% with only a 3% increase in MAE. As a result, we show it is feasible to deploy the algorithm on an nRF52840 system-on-chip (SoC) requiring only 46 KB of memory and operating with an inference time of only 192 ms. Once deployed, the system can last up to 268 days without recharging using a 20 000 mAh battery pack. For breathing monitoring in bed, the sampling rate can be lowered, extending battery life to 313 days, making the solution highly efficient for real-world, low-cost deployments.
Deep Representation Learning for Unsupervised Clustering of Myocardial Fiber Trajectories in Cardiac Diffusion Tensor Imaging
2025-04-02
Mohini Anand, Xavier Tricoche
Understanding the complex myocardial architecture is critical for diagnosing and treating heart disease. However, existing methods often struggle to accurately capture this intricate structure from Diffusion Tensor Imaging (DTI) data, particularly due to the lack of ground truth labels and the ambiguous, intertwined nature of fiber trajectories. We present a novel deep learning framework for unsupervised clustering of myocardial fibers, providing a data-driven approach to identifying distinct fiber bundles. We uniquely combine a Bidirectional Long Short-Term Memory network to capture local sequential information along fibers, with a Transformer autoencoder to learn global shape features, with pointwise incorporation of essential anatomical context. Clustering these representations using a density-based algorithm identifies 33 to 62 robust clusters, successfully capturing the subtle distinctions in fiber trajectories with varying levels of granularity. Our framework offers a new, flexible, and quantitative way to analyze myocardial structure, achieving a level of delineation that, to our knowledge, has not been previously achieved, with potential applications in improving surgical planning, characterizing disease-related remodeling, and ultimately, advancing personalized cardiac care.
While AI agents have shown remarkable performance at various tasks, they still struggle with complex multi-modal applications, structured generation and strategic planning. Improvements via standard fine-tuning is often impractical, as solving agentic tasks usually relies on black box API access without control over model parameters. Inference-time methods such as Best-of-N (BON) sampling offer a simple yet effective alternative to improve performance. However, BON lacks iterative feedback integration mechanism. Hence, we propose Iterative Agent Decoding (IAD) which combines iterative refinement with dynamic candidate evaluation and selection guided by a verifier. IAD differs in how feedback is designed and integrated, specifically optimized to extract maximal signal from reward scores. We conduct a detailed comparison of baselines across key metrics on Sketch2Code, Text2SQL, and Webshop where IAD consistently outperforms baselines, achieving 3--6% absolute gains on Sketch2Code and Text2SQL (with and without LLM judges) and 8--10% gains on Webshop across multiple metrics. To better understand the source of IAD's gains, we perform controlled experiments to disentangle the effect of adaptive feedback from stochastic sampling, and find that IAD's improvements are primarily driven by verifier-guided refinement, not merely sampling diversity. We also show that both IAD and BON exhibit inference-time scaling with increased compute when guided by an optimal verifier. Our analysis highlights the critical role of verifier quality in effective inference-time optimization and examines the impact of noisy and sparse rewards on scaling behavior. Together, these findings offer key insights into the trade-offs and principles of effective inference-time optimization.
Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation
2025-04-02
Baban Gain, Dibyanayan Bandyopadhyay, Asif Ekbal
The advent of Large Language Models (LLMs) has significantly reshaped the landscape of machine translation (MT), particularly for low-resource languages and domains that lack sufficient parallel corpora, linguistic tools, and computational infrastructure. This survey presents a comprehensive overview of recent progress in leveraging LLMs for MT. We analyze techniques such as few-shot prompting, cross-lingual transfer, and parameter-efficient fine-tuning that enable effective adaptation to under-resourced settings. The paper also explores synthetic data generation strategies using LLMs, including back-translation and lexical augmentation. Additionally, we compare LLM-based translation with traditional encoder-decoder models across diverse language pairs, highlighting the strengths and limitations of each. We discuss persistent challenges such as hallucinations, evaluation inconsistencies, and inherited biases while also evaluating emerging LLM-driven metrics for translation quality. This survey offers practical insights and outlines future directions for building robust, inclusive, and scalable MT systems in the era of large-scale generative models.
Legal technology has changed the way law firms are managed worldwide. Substantial research has been undertaken on the role of legal technology in law firm management especially in developed countries. Though, most studies have only focused on the benefits and challenges, and have failed to analyse law firm management areas requiring software solutions. The principal objective of this paper was to investigate the level of technology adoption among Nigerian law firms, as well as to develop a software solution to automate work processes in identified areas. This investigation was done using systematic literature review to gather relevant data on the subject area and identify knowledge gaps. Findings from the research indicated a need for further analysis of the various areas in law practice that could require software solutions. The findings also discussed the implementation of a property management module which is an important contribution to the management of law firms in Nigeria. A speech-to-text transcription feature was also implemented to eliminate the need for lengthy typing.
In modern software engineering, build systems play the crucial role of facilitating the conversion of source code into software artifacts. Recent research has explored high-level causes of build failures, but has largely overlooked the structural properties of build files. Akin to source code, build systems face technical debt challenges that hinder maintenance and optimization. While refactoring is often seen as a key tool for addressing technical debt in source code, there is a significant research gap regarding the specific refactoring changes developers apply to build code and whether these refactorings effectively address technical debt. In this paper, we address this gap by examining refactorings applied to build scripts in open-source projects, covering the widely used build systems of Gradle, Ant, and Maven. Additionally, we investigate whether these refactorings are used to tackle technical debts in build systems. Our analysis was conducted on \totalCommits examined build-file-related commits. We identified \totalRefactoringCategories build-related refactorings, which we divided into \totalCategories main categories. These refactorings are organized into the first empirically derived taxonomy of build system refactorings. Furthermore, we investigate how developers employ these refactoring types to address technical debts via a manual commit-analysis and a developer survey. In this context, we identified \totalTechnicalDebts technical debts addressed by these refactorings and discussed their correlation with the different refactorings. Finally, we introduce BuildRefMiner, an LLM-powered tool leveraging GPT-4o to automate the detection of refactorings within build systems. We evaluated its performance and found that it achieves an F1 score of \toolFoneScore across all build systems.
What is AI, what is it not, how we use it in physics and how it impacts... you
2025-04-02
Claire David
Artificial Intelligence (AI) and Machine Learning (ML) have been prevalent in particle physics for over three decades, shaping many aspects of High Energy Physics (HEP) analyses. As AI's influence grows, it is essential for physicists $\unicode{x2013}$ as both researchers and informed citizens $\unicode{x2013}$ to critically examine its foundations, misconceptions, and impact. This paper explores AI definitions, examines how ML differs from traditional programming, and provides a brief review of AI/ML applications in HEP, highlighting promising trends such as Simulation-Based Inference, uncertainty-aware machine learning, and Fast ML for anomaly detection. Beyond physics, it also addresses the broader societal harms of AI systems, underscoring the need for responsible engagement. Finally, it stresses the importance of adapting research practices to an evolving AI landscape, ensuring that physicists not only benefit from the latest tools but also remain at the forefront of innovation.
Factors Influencing Farmers' Motivation to Adopt Smart Farm Technology in South Korea
2025-04-02
Jihyuk Bang, Ji Woo Han
Smart farming technologies have recently become a major focus because they promise improved agricultural productivity together with sustainability benefits. The rate at which farmers adopt new technologies differs because of various socio-economic and technological factors. This research investigates the main factors affecting smart farm adoption through an evaluation of farmer age, education attainment, land size, government banking, technological hurdles and financial limitations. Statistical analysis of survey responses reveals that farmers under thirty years old and possessing advanced education along with operating expansive farms demonstrate greater willingness to adopt smart farming solutions. Government policies that offer financial assistance and training programs drive farmers to adopt new systems yet technical and financial difficulties prevent broad adoption. The research results demonstrate the requirement for targeted policies that should help farmers by providing specific financial help and digital education programs. This study delivers important knowledge which helps government officials together with agricultural leaders to accelerate the adaptation speed of smart farming technology.
The Factors Influencing Well-Being in Software Engineers: A Cross-Country Mixed-Method Study
2025-04-02
Cristina Martinez Montes, Birgit Penzenstadler, Robert Feldt
The well-being of software engineers is increasingly under strain due to the high-stress nature of their roles, which involve complex problem-solving, tight deadlines, and the pressures of rapidly evolving technologies. Despite increasing recognition of mental health challenges in software engineering, few studies focus on the factors that sustain or undermine well-being. Existing research often overlooks the interaction between personal, collaborative, and organisational influences on this unique population. This study fills this gap by investigating the specific factors affecting the well-being of software engineers. We conducted 15 qualitative interviews and complemented them with a confirmatory cross-country survey to validate and extend our findings to a broader population. Our mixed-methods approach provides a robust framework to identify key factors influencing well-being, including personal perceptions of well-being, interpersonal and collaborative dynamics, workplace support and recognition, organisational culture, and specific stressors inherent to software engineering. By offering a detailed, context-specific exploration of these factors, our study builds on existing literature and provides actionable insights for improving well-being in software engineering. We conclude with policy recommendations to inform organisational strategies and develop targeted interventions that address the specific challenges of this field, contributing to more sustainable and supportive work environments.
Proper scoring rules for estimation and forecast evaluation
2025-04-02
Kartik Waghmare, Johanna Ziegel
Proper scoring rules have been a subject of growing interest in recent years, not only as tools for evaluation of probabilistic forecasts but also as methods for estimating probability distributions. In this article, we review the mathematical foundations of proper scoring rules including general characterization results and important families of scoring rules. We discuss their role in statistics and machine learning for estimation and forecast evaluation. Furthermore, we comment on interesting developments of their usage in applications.
KD$^{2}$M: An unifying framework for feature knowledge distillation
2025-04-02
Eduardo Fernandes Montesuma
Knowledge Distillation (KD) seeks to transfer the knowledge of a teacher, towards a student neural net. This process is often done by matching the networks' predictions (i.e., their output), but, recently several works have proposed to match the distributions of neural nets' activations (i.e., their features), a process known as \emph{distribution matching}. In this paper, we propose an unifying framework, Knowledge Distillation through Distribution Matching (KD$^{2}$M), which formalizes this strategy. Our contributions are threefold. We i) provide an overview of distribution metrics used in distribution matching, ii) benchmark on computer vision datasets, and iii) derive new theoretical results for KD.
Bridge the Gap between SNN and ANN for Image Restoration
2025-04-02
Xin Su, Chen Wu, Zhuoran Zheng
Models of dense prediction based on traditional Artificial Neural Networks (ANNs) require a lot of energy, especially for image restoration tasks. Currently, neural networks based on the SNN (Spiking Neural Network) framework are beginning to make their mark in the field of image restoration, especially as they typically use less than 10\% of the energy of ANNs with the same architecture. However, training an SNN is much more expensive than training an ANN, due to the use of the heuristic gradient descent strategy. In other words, the process of SNN's potential membrane signal changing from sparse to dense is very slow, which affects the convergence of the whole model.To tackle this problem, we propose a novel distillation technique, called asymmetric framework (ANN-SNN) distillation, in which the teacher is an ANN and the student is an SNN. Specifically, we leverage the intermediate features (feature maps) learned by the ANN as hints to guide the training process of the SNN. This approach not only accelerates the convergence of the SNN but also improves its final performance, effectively bridging the gap between the efficiency of the SNN and the superior learning capabilities of ANN. Extensive experimental results show that our designed SNN-based image restoration model, which has only 1/300 the number of parameters of the teacher network and 1/50 the energy consumption of the teacher network, is as good as the teacher network in some denoising tasks.
Toroid, Altermagnetic and Noncentrosymmetric ordering in metals
2025-04-02
V. P. Mineev
The article is dedicated to the 60th anniversary of the Landau Institute for Theoretical Physics and is a review of normal and superconducting properties of toroidal, altermagnetic and noncentrosymmetric metals. Metals with toroidal order are compounds with an electron spectrum that is asymmetric with respect to the reflection of the momentum. An electric current propagating through samples of such a material causes its magnetization. Superconducting states in toroidal metals are a mixture of singlet and triplet pair states. Superconductivity is gapless even in ideal crystals without impurities. Altermegnets are antiferromagnetic metals that have a specific splitting of electron bands determined by symmetry. In this respect, they are similar to metals whose symmetry does not have a spatial inversion operation. Both of these types of materials have an anomalous Hall effect. A current propagating through a noncentrosymmetric metal causes magnetization, but this is not the case in altermagnets. On the other hand, in altermagnets there is a specific piezomagnetic Hall effect. Superconducting pairing in non-centrosymmetric metals occurs between electrons occupying states in one zone, whereas in altermagnets we are dealing with interband pairing, which is unfavorable for the formation of a superconducting state.
We present an extension of K-P time-optimal quantum control solutions using global Cartan $KAK$ decompositions for geodesic-based solutions. Extending recent time-optimal \emph{constant-$\theta$} control results, we integrate Cartan methods into equivariant quantum neural network (EQNN) for quantum control tasks. We show that a finite-depth limited EQNN ansatz equipped with Cartan layers can replicate the constant-$\theta$ sub-Riemannian geodesics for K-P problems. We demonstrate how for certain classes of control problem on Riemannian symmetric spaces, gradient-based training using an appropriate cost function converges to certain global time-optimal solutions when satisfying simple regularity conditions. This generalises prior geometric control theory methods and clarifies how optimal geodesic estimation can be performed in quantum machine learning contexts.
The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics
2025-04-02
Eleonora Di Valentino, Jackson Levi Said, Adam Riess, Agnieszka Pollo, Vivian Poulin, Adrià Gómez-Valent, Amanda Weltman, Antonella Palmese, Caroline D. Huang, Carsten van de Bruck, Chandra Shekhar Saraf, Cheng-Yu Kuo, Cora Uhlemann, Daniela Grandón, Dante Paz, Dominique Eckert, Elsa M. Teixeira, Emmanuel N. Saridakis, Eoin Ó Colgáin, Florian Beutler, Florian Niedermann, Francesco Bajardi, Gabriela Barenboim, Giulia Gubitosi, Ilaria Musella, Indranil Banik, Istvan Szapudi, Jack Singal, Jaume Haro Cases, Jens Chluba, Jesús Torrado, Jurgen Mifsud, Karsten Jedamzik, Khaled Said, Konstantinos Dialektopoulos, Laura Herold, Leandros Perivolaropoulos, Lei Zu, Lluís Galbany, Louise Breuval, Luca Visinelli, Luis A. Escamilla, Luis A. Anchordoqui, M. M. Sheikh-Jabbari, Margherita Lembo, Maria Giovanna Dainotti, Maria Vincenzi, Marika Asgari, Martina Gerbino, Matteo Forconi, Michele Cantiello, Michele Moresco, Micol Benetti, Nils Schöneberg, Özgür Akarsu, Rafael C. Nunes, Reginald Christian Bernardo, Ricardo Chávez, Richard I. Anderson, Richard Watkins, Salvatore Capozziello, Siyang Li, Sunny Vagnozzi, Supriya Pan, Tommaso Treu, Vid Irsic, Will Handley, William Giarè, Yukei Murakami, Adèle Poudou, Alan Heavens, Alan Kogut, Alba Domi, Aleksander Łukasz Lenart, Alessandro Melchiorri, Alessandro Vadalà, Alexandra Amon, Alexander Bonilla, Alexander Reeves, Alexander Zhuk, Alfio Bonanno, Ali Övgün, Alice Pisani, Alireza Talebian, Amare Abebe, Amin Aboubrahim, Ana Luisa González Morán, András Kovács, Andreas Papatriantafyllou, Andrew R. Liddle, Andronikos Paliathanasis, Andrzej Borowiec, Anil Kumar Yadav, Anita Yadav, Anjan Ananda Sen, Anjitha John William Mini Latha, Anne Christine Davis, Anowar J. Shajib, Anthony Walters, Anto Idicherian Lonappan, Anton Chudaykin, Antonio Capodagli, Antonio da Silva, Antonio De Felice, Antonio Racioppi, Araceli Soler Oficial, Ariadna Montiel, Arianna Favale, Armando Bernui, Arrianne Crystal Velasco, Asta Heinesen, Athanasios Bakopoulos, Athanasios Chatzistavrakidis, Bahman Khanpour, Bangalore S. Sathyaprakash, Bartek Zgirski, Benjamin L'Huillier, Benoit Famaey, Bhuvnesh Jain, Biesiada Marek, Bing Zhang, Biswajit Karmakar, Branko Dragovich, Brooks Thomas, Carlos Correa, Carlos G. Boiza, Catarina Marques, Celia Escamilla-Rivera, Charalampos Tzerefos, Chi Zhang, Chiara De Leo, Christian Pfeifer, Christine Lee, Christo Venter, Cláudio Gomes, Clecio Roque De bom, Cristian Moreno-Pulido, Damianos Iosifidis, Dan Grin, Daniel Blixt, Dan Scolnic, Daniele Oriti, Daria Dobrycheva, Dario Bettoni, David Benisty, David Fernández-Arenas, David L. Wiltshire, David Sanchez Cid, David Tamayo, David Valls-Gabaud, Davide Pedrotti, Deng Wang, Denitsa Staicova, Despoina Totolou, Diego Rubiera-Garcia, Dinko Milaković, Dom Pesce, Dominique Sluse, Duško Borka, Ebrahim Yusofi, Elena Giusarma, Elena Terlevich, Elena Tomasetti, Elias C. Vagenas, Elisa Fazzari, Elisa G. M. Ferreira, Elvis Barakovic, Emanuela Dimastrogiovanni, Emil Brinch Holm, Emil Mottola, Emre Özülker, Enrico Specogna, Enzo Brocato, Erik Jensko, Erika Antonette Enriquez, Esha Bhatia, Fabio Bresolin, Felipe Avila, Filippo Bouchè, Flavio Bombacigno, Fotios K. Anagnostopoulos, Francesco Pace, Francesco Sorrenti, Francisco S. N. Lobo, Frédéric Courbin, Frode K. Hansen, Greg Sloan, Gabriel Farrugia, Gabriel Lynch, Gabriela Garcia-Arroyo, Gabriella Raimondo, Gaetano Lambiase, Gagandeep S. Anand, Gaspard Poulot, Genly Leon, Gerasimos Kouniatalis, Germano Nardini, Géza Csörnyei, Giacomo Galloni, Giada Bargiacchi, Giannis Papagiannopoulos, Giovanni Montani, Giovanni Otalora, Giulia De Somma, Giuliana Fiorentino, Giuseppe Fanizza, Giuseppe Gaetano Luciano, Giuseppe Sarracino, Gonzalo J. Olmo, Goran S. Djordjević, Guadalupe Cañas-Herrera, Hanyu Cheng, Harry Desmond, Hassan Abdalla, Houzun Chen, Hsu-Wen Chiang, Hume A. Feldman, Hussain Gohar, Ido Ben-Dayan, Ignacio Sevilla-Noarbe, Ignatios Antoniadis, Ilim Cimdiker, Inês S. Albuquerque, Ioannis D. Gialamas, Ippocratis Saltas, Iryna Vavilova, Isidro Gómez-Vargas, Ismael Ayuso, Ismailov Nariman Zeynalabdi, Ivan De Martino, Ivonne Zavala Carrasco, J. Alberto Vázquez, Jacobo Asorey, Janusz Gluza, Javier Rubio, Jenny G. Sorce, Jenny Wagner, Jeremy Sakstein, Jessica Santiago, Jim Braatz, Joan Solà Peracaula, John Blakeslee, John Webb, Jose A. R. Cembranos, José Pedro Mimoso, Joseph Jensen, Juan García-Bellido, Judit Prat, Kathleen Sammut, Kay Lehnert, Keith R. Dienes, Kishan Deka, Konrad Kuijken, Krishna Naidoo, László Árpád Gergely, Laur Järv, Laura Mersini-Houghton, Leila L. Graef, Léo Vacher, Levon Pogosian, Lilia Anguelova, Lindita Hamolli, Lu Yin, Luca Caloni, Luca Izzo, Lucas Macri, Luis E. Padilla, Luz Ángela García, Maciej Bilicki, Mahdi Najafi, Manolis Plionis, Manuel Gonzalez-Espinoza, Manuel Hohmann, Marcel A. van der Westhuizen, Marcella Marconi, Marcin Postolak, Marco de Cesare, Marco Regis, Marek Biesiada, Maret Einasto, Margus Saal, Maria Caruana, Maria Petronikolou, Mariam Bouhmadi-López, Mariana Melo, Mariaveronica De Angelis, Marie-Noëlle Célérier, Marina Cortês, Mark Reid, Markus Michael Rau, Martin S. Sloth, Martti Raidal, Masahiro Takada, Masoume Reyhani, Massimiliano Romanello, Massimo Marengo, Mathias Garny, Matías Leizerovich, Matteo Martinelli, Matteo Tagliazucchi, Mehmet Demirci, Miguel A. S. Pinto, Miguel A. Sabogal, Miguel A. García-Aspeitia, Milan Milošević, Mina Ghodsi, Mustapha Ishak, Nelson J. Nunes, Nick Samaras, Nico Hamaus, Nico Schuster, Nicola Borghi, Nicola Deiosso, Nicola Tamanini, Nicolao Fornengo, Nihan Katırcı, Nikolaos E. Mavromatos, Nikolaos Petropoulos, Nikolina Šarčević, Nils A. Nilsson, Noemi Frusciante, Octavian Postavaru, Oem Trivedi, Oleksii Sokoliuk, Olga Mena, Paloma Morilla, Paolo Campeti, Paolo Salucci, Paula Boubel, Paweł Bielewicz, Pekka Heinämäki, Petar Suman, Petros Asimakis, Pierros Ntelis, Pilar Ruiz-Lapuente, Pran Nath, Predrag Jovanović, Purba Mukherjee, Radosław Wojtak, Rafaela Gsponer, Rafid H. Dejrah, Rahul Shah, Rasmi Hajjar, Rebecca Briffa, Rebecca Habas, Reggie C. Pantig, Renier Mendoza, Riccardo Della Monica, Richard Stiskalek, Rishav Roshan, Rita B. Neves, Roberto Molinaro, Roberto Terlevich, Rocco D'Agostino, Rodrigo Sandoval-Orozco, Ronaldo C. Batista, Ruchika Kaushik, Ruth Lazkoz, Saeed Rastgoo, Sahar Mohammadi, Salvatore Samuele Sirletti, Sandeep Haridasu, Sanjay Mandal, Saurya Das, Sebastian Bahamonde, Sebastian Grandis, Sebastian Trojanowski, Sergei D. Odintsov, Sergij Mazurenko, Shahab Joudaki, Sherry H. Suyu, Shouvik Roy Choudhury, Shruti Bhatporia, Shun-Sheng Li, Simeon Bird, Simon Birrer, Simone Paradiso, Simony Santos da Costa, Sofia Contarini, Sophie Henrot-Versillé, Spyros Basilakos, Stefano Casertano, Stefano Gariazzo, Stylianos A. Tsilioukas, Surajit Kalita, Suresh Kumar, Susana J. Landau, Sveva Castello, Swayamtrupta Panda, Tanja Petrushevska, Thanasis Karakasis, Thejs Brinckmann, Tiago B. Gonçalves, Tiziano Schiavone, Tom Abel, Tomi Koivisto, Torsten Bringmann, Umut Demirbozan, Utkarsh Kumar, Valerio Marra, Maurice H. P. M. van Putten, Vasileios Kalaitzidis, Vasiliki A. Mitsou, Vasilios Zarikas, Vedad Pasic, Venus Keus, Verónica Motta, Vesna Borka Jovanović, Víctor H. Cárdenas, Vincenzo Ripepi, Vincenzo Salzano, Violetta Impellizzeri, Vitor da Fonseca, Vittorio Ghirardini, Vladas Vansevičius, Weiqiang Yang, Wojciech Hellwing, Xin Ren, Yu-Min Hu, Yuejia Zhai, Abdul Malik Sultan, Adrienn Pataki, Alessandro Santoni, Aliya Batool, Aneta Wojnar, Arman Tursunov, Avik De, Ayush Hazarika, Baojiu Li, Benjamin Bose, Bivudutta Mishra, Bobomurat Ahmedov, Chandra Shekhar Saraf, Claudia Scóccola, Crescenzo Tortora, D'Arcy Kenworthy, Daniel E. Holz, David F. Mota, David S. Pereira, Devon M. Williams, Dillon Brout, Dong Ha Lee, Eduardo Guendelman, Edward Olex, Emanuelly Silva, Emre Onur Kahya, Enzo Brocato, Eva-Maria Mueller, Felipe Andrade-Oliveira, Feven Markos Hunde, F. R. Joaquim, Florian Pacaud, Francis-Yan Cyr-Racine, Pozo Nuñez, F. Gábor Rácz, Gene Carlo Belinario, Geraint F. Lewis, Gergely Dálya, Giorgio Laverda, Guido Risaliti, Guillermo Franco-Abellán, Hayden Zammit, Hayley Camilleri, Helene M. Courtois, Hooman Moradpour, Igor de Oliveira Cardoso Pedreira, Ilí dio Lopes, István Csabai, James W. Rohlf, J. Bogdanoska, Javier de Cruz Pérez, Joan Bachs-Esteban, Joseph Sultana, Julien Lesgourgues, Jun-Qian Jiang, Karem Peñaló Castillo, Lavinia Heisenberg, Laxmipriya Pati, Léon V. E. Koopmans, Lokesh kumar Duchaniya, Lucas Lombriser, María Pérez Garrote, Mariano Domínguez, Marine Samsonyan, Mark Pace, Martin Krššák, Masroor C. Pookkillath, Matteo Peronaci, Matteo Piani, Matthildi Raftogianni, Meet J. Vyas, Melina Michalopoulou, Merab Gogberashvili, Michael Klasen, Michele Cicoli, Michele Moresco, Miguel Quartin, Miguel Zumalacárregui, Milan S. Dimitrijević, Milos Dordevic, Mindaugas Karčiauskas, Morgan Le Delliou, Nastassia Grimm, Nicolás Augusto Kozameh, Nicoleta Voicu, Nicolina Pop, Nikos Chatzifotis, Oliver Fabio Piattella, Paula Boubel, Pedro da Silveira Ferreira, Péter Raffai, Peter Schupp, Pierros Ntelis, Pradyumn Kumar Sahoo, Roberto V. Maluf, Ruth Durrer, S. A. Kadam, Sabino Matarrese, Samuel Brieden, Santiago González-Gaitán, Santosh V. Lohakare, Scott Watson, Shao-Jiang Wang, Simão Marques Nunes, Soumya Chakrabarti, Suvodip Mukherjee, Tajron Jurić, Tessa Baker, Theodoros Nakas, Tiago Barreiro, Upala Mukhopadhyay, Veljko Vujčić, Violetta Sagun, Vladimir A. Srećković, Wangzheng Zhang, Yo Toda, Yun-Song Piao, Zahra Davari
The standard model of cosmology has provided a good phenomenological description of a wide range of observations both at astrophysical and cosmological scales for several decades. This concordance model is constructed by a universal cosmological constant and supported by a matter sector described by the standard model of particle physics and a cold dark matter contribution, as well as very early-time inflationary physics, and underpinned by gravitation through general relativity. There have always been open questions about the soundness of the foundations of the standard model. However, recent years have shown that there may also be questions from the observational sector with the emergence of differences between certain cosmological probes. In this White Paper, we identify the key objectives that need to be addressed over the coming decade together with the core science projects that aim to meet these challenges. These discordances primarily rest on the divergence in the measurement of core cosmological parameters with varying levels of statistical confidence. These possible statistical tensions may be partially accounted for by systematics in various measurements or cosmological probes but there is also a growing indication of potential new physics beyond the standard model. After reviewing the principal probes used in the measurement of cosmological parameters, as well as potential systematics, we discuss the most promising array of potential new physics that may be observable in upcoming surveys. We also discuss the growing set of novel data analysis approaches that go beyond traditional methods to test physical models. [Abridged]
How to write competitive proposals and job applications
2025-04-02
Johan H. Knapen, Henri M. J. Boffin, Nushkia Chamba, Natashya Chamba
Writing proposals and job applications is arguably one of the most important tasks in the career of a scientist. The proposed ideas must be scientifically compelling, but how a proposal is planned, written, and presented can make an enormous difference. This Perspective is the third in a series aimed at training the writing skills of professional astronomers. In the first two papers we concentrated on the writing of papers, here we concentrate on how proposals and job applications can be optimally written and presented. We discuss how to select where to propose or apply, how to optimise your writing, and add notes on the potential use of artificial intelligence tools. This guide is aimed primarily at more junior researchers, but we hope that our observations and suggestions may also be helpful for more experienced applicants, as well as for reviewers and funding agencies.
Propagation of space-time optical vortices in multimode fibers
2025-04-02
Spencer W. Jolly, Julien Dechanxhe, Pascal Kockaert
The non-intuitive spatiotemporal modal content of space-time optical vortices (STOVs) is calculated in a graded-index fiber supporting a large number of propagating modes. We discuss how a fiber supporting many modes allows to truly couple higher-order STOVs, the number of modes necessary to support a STOV of a certain order, and conversely the truncation effect in a few-mode fiber. Based on the excited modes and their temporal profiles, we show numerical results for the linear and nonlinear propagation of STOVs in multimode fibers, specifically the linear space-time beating at short propagation distances, and the nonlinear trapping effect between modes producing stable states on long propagation distances. Our results underline how STOVs present a rich platform for multimode nonlinear optics and technology.
Benchmarking the Spatial Robustness of DNNs via Natural and Adversarial Localized Corruptions
2025-04-02
Giulia Marchiori Pietrosanti, Giulio Rossolini, Alessandro Biondi, Giorgio Buttazzo
The robustness of DNNs is a crucial factor in safety-critical applications, particularly in complex and dynamic environments where localized corruptions can arise. While previous studies have evaluated the robustness of semantic segmentation (SS) models under whole-image natural or adversarial corruptions, a comprehensive investigation into the spatial robustness of dense vision models under localized corruptions remained underexplored. This paper fills this gap by introducing specialized metrics for benchmarking the spatial robustness of segmentation models, alongside with an evaluation framework to assess the impact of localized corruptions. Furthermore, we uncover the inherent complexity of characterizing worst-case robustness using a single localized adversarial perturbation. To address this, we propose region-aware multi-attack adversarial analysis, a method that enables a deeper understanding of model robustness against adversarial perturbations applied to specific regions. The proposed metrics and analysis were evaluated on 15 segmentation models in driving scenarios, uncovering key insights into the effects of localized corruption in both natural and adversarial forms. The results reveal that models respond to these two types of threats differently; for instance, transformer-based segmentation models demonstrate notable robustness to localized natural corruptions but are highly vulnerable to adversarial ones and vice-versa for CNN-based models. Consequently, we also address the challenge of balancing robustness to both natural and adversarial localized corruptions by means of ensemble models, thereby achieving a broader threat coverage and improved reliability for dense vision tasks.
Horizon Scans can be accelerated using novel information retrieval and artificial intelligence tools
2025-04-02
Lena Schmidt, Oshin Sharma, Chris Marshall, Sonia Garcia Gonzalez Moral
Introduction: Horizon scanning in healthcare assesses early signals of innovation, crucial for timely adoption. Current horizon scanning faces challenges in efficient information retrieval and analysis, especially from unstructured sources like news, presenting a need for innovative tools. Methodology: The study introduces SCANAR and AIDOC, open-source Python-based tools designed to improve horizon scanning. SCANAR automates the retrieval and processing of news articles, offering functionalities such as de-duplication and unsupervised relevancy ranking. AIDOC aids filtration by leveraging AI to reorder textual data based on relevancy, employing neural networks for semantic similarity, and subsequently prioritizing likely relevant entries for human review. Results: Twelve internal datasets from horizon scans and four external benchmarking datasets were used. SCANAR improved retrieval efficiency by automating processes previously dependent on manual labour. AIDOC displayed work-saving potential, achieving around 62% reduction in manual review efforts at 95% recall. Comparative analysis with benchmarking data showed AIDOC's performance was similar to existing systematic review automation tools, though performance varied depending on dataset characteristics. A smaller case-study on our news datasets shows the potential of ensembling large language models within the active-learning process for faster detection of relevant articles across news datasets. Conclusion: The validation indicates that SCANAR and AIDOC show potential to enhance horizon scanning efficiency by streamlining data retrieval and prioritisation. These tools may alleviate methodological limitations and allow broader, swifter horizon scans. Further studies are suggested to optimize these models and to design new workflows and validation processes that integrate large language models.
Time-domain astronomy is one of the most important areas. Large sky area, deep-field, and short timescale are the priority of time-domain observations. SiTian is an ambitious ground-based project processing all sky optical monitoring, aiming for sky-survey timescale of less than 1 day. It is developed by the Chinese Academy of Sciences, an integrated network of dozens of 1-m-class telescopes deployed worldwide. The Mini-SiTian Telescope Array is carried out for demonstrations on optical design, group scheduling, and software pipeline developments, to overcome the high technical and financial difficulties of SiTian project. One array contains three 300 mm F/3 telescope, with FOV of 5 degrees over 400-1000 nm wavelength range. The Mini-SiTian Telescope Array is now under commissioning in Xinglong Observatory, and a perfect platform for technical research and educational purposes.
2025-04-02
Yu Zhang, Lin Du, Yi Hu, Yang Huang, Ying Wu, Min He, Hongrui Gu, Haiyang Mu, Xunhao Chen, Hong Wu
The Mini-SiTian project, which is the pathfinder for the SiTian project, utilizes three 30 cm telescopes equipped with commercial CMOS cameras (ZWO ASI6200MM Pro) to simulate large-area time-domain survey. Due to the avoidance of the traditional mechanical shutter, the CMOS camera is favorable in time-domain survey projects. In the future, the SiTian telescope array will employ a two-by-two scientific-grade mosaic CMOS camera to survey a 10,000-degree square area every 30 minutes. Therefore, the performance of CMOS directly determines the detection capability of SiTian telescopes for transient sources, and a comprehensive understanding of the performance of CMOS cameras is crucial. In this research, laboratory testing was conducted to thoroughly evaluate three cameras by assessing several critical parameters, including bias stability, dark current, pixel anomalies, linearity, gain, and read noise. We find exceptional short-term bias stability with standard deviations below 0.02 ADU, negligible dark current of approximately 0.002 e$^{-}$ pixel$^{-1}$ s$^{-1}$ at $0^\circ\text{C}$, and excellent linearity with nonlinearity consistently below $\pm$ 0.5\%, and a small proportion (0.06\% to 0.08\%) of pixels with anomalous responses. Furthermore, our analysis demonstrates uniform gain values across all cameras, ranging from 0.252 to 0.255 e$^{-}$ ADU$^{-1}$, with low readout noise, measured to be below 1.6 e$^{-}$ using conventional methods. We also propose a novel method for pixel-level gain and read noise calculation for CMOS sensors, which revealed a narrow gain distribution and a low median read noise of 1.028 e$^-$ for one of the cameras. The laboratory testing of the ZWO ASI6200MM Pro cameras indicates their potential to meet the requirements of time-domain surveys for the Mini-SiTian project.
The Mini-SiTian Array: Imaging Processing Pipeline
2025-04-02
Kai Xiao, Zhirui Li, Yang Huang, Jie Zheng, Haibo Yuan, Junju Du, Linying Mi, Hongrui Gu, Yongkang Sun, Bowen Zhang, Shunxuan He, Henggeng Han, Min He, Ruifeng Shi, Yu Zhang, Chuanjie Zheng, Zexi Niu, Guiting Tian, Hu Zou, Yongna Mao, Hong Wu, Jifeng Liu
As a pathfinder of the SiTian project, the Mini-SiTian (MST) array, employed three commercial CMOS cameras, represents a next-generation, cost-effective optical time-domain survey project. This paper focuses primarily on the precise data processing pipeline designed for wide-field, CMOS-based devices, including the removal of instrumental effects, astrometry, photometry, and flux calibration. When applying this pipeline to approximately 3000 observations taken in the Field 02 (f02) region by MST, the results demonstrate a remarkable astrometric precision of approximately 70--80\,mas (about 0.1\,pixel), an impressive calibration accuracy of approximately 1\,mmag in the MST zero points, and a photometric accuracy of about 4\,mmag for bright stars. Our studies demonstrate that MST CMOS can achieve photometric accuracy comparable to that of CCDs, highlighting the feasibility of large-scale CMOS-based optical time-domain surveys and their potential applications for cost optimization in future large-scale time-domain surveys, like the SiTian project.
The Mini-SiTian Array: A Pathfinder for the SiTian Project
2025-04-02
Yang Huang, Jifeng Liu, Hong Wu, Zhaohui Shang, Ali Luo, Shaoming Hu, Wenyuan Cui, Yongna Mao
The Mini-SiTian Array serves as a pathfinder for the SiTian project, which aims to survey the entire sky in $gri$ bands every 30 minutes, reaching a limiting magnitude of 21. This special issue features 11 papers covering the design, operation, data reduction, and early scientific results from two years of Mini-SiTian observations. The insights gained from these pathfinder experiments represent a significant milestone toward the full realization of the SiTian project.
The Mini-SiTian Array: Design and application of Master Control System
2025-04-02
Zheng Wang, Jin-hang Zou, Liang Ge, Min He, Jian Li, Yi Hu, Jianfeng Tian
The SiTian Project represents a groundbreaking initiative in astronomy, aiming to deploy a global network of telescopes, each with a 1-meter aperture, for comprehensive time-domain sky surveys. The network's innovative architecture features multiple observational nodes, each comprising three strategically aligned telescopes equipped with filters. This design enables three-color (g, r, i) channel imaging within each node, facilitating precise and coordinated observations. As a pathfinder to the full-scale project, the Mini-SiTian Project serves as the scientific and technological validation platform, utilizing three 30-centimeter aperture telescopes to validate the methodologies and technologies planned for the broader SiTian network. This paper focuses on the development and implementation of the Master Control System (MCS),and the central command hub for the Mini-SiTian array. The MCS is designed to facilitate seamless communication with the SiTian Brain, the project's central processing and decision-making unit, while ensuring accurate task allocation, real-time status monitoring, and optimized observational workflows. The system adopts a robust architecture that separates front-end and back-end functionalities.A key innovation of the MCS is its ability to dynamically adjust observation plans in response to transient source alerts, enabling rapid and coordinated scans of target sky regions...(abridged)
The SiTian project, designed to utilize 60 telescopes distributed across multiple sites in China, is a next-generation time-domain survey initiative. As a pathfinder for the SiTian project, the Mini-SiTian (MST) has been proposed and implemented to test the SiTian's brain and data pipeline, and to evaluate the feasibility of its technology and science cases. Mounted at the Xinglong Observatory, the MST project comprises three 30 cm telescopes and has been operated since Nov. 2022. Each telescope of the MST possesses a large field of view, covering $2.29^{\circ}$ $\times$ $1.53^{\circ}$ FOV, and is equipped with $g'$, $r'$ and $i'$ filters, respectively. Acting as the pioneer of the forthcoming SiTian project, the MST is dedicated to the discovery of variable stars, transients, and outburst events, and has already obtained some interesting scientific results. In this paper, we will summarize the first-two-year operation of the MST project.
The Mini-SiTian Array: real-bogus classification using deep learning
2025-04-02
Jing-Hang Shi, Hong-Rui Gu, Yang Huang, Yan-Xia Zhang, Peng-Liang Du
The Mini-SiTian (MST) project is a pathfinder for China's next-generation large-scale time-domain survey, SiTian, aimed at discovering variable stars, transients, and explosive events. MST generates hundreds of thousands of transient alerts every night, approximately 99\% of which are false alarms, posing a significant challenge to its scientific goals. To mitigate the impact of false positives, we propose a deep learning-based solution and systematically evaluate thirteen convolutional neural networks. The results show that ResNet achieves exceptional specificity (99.70\%), EfficientNet achieves the highest recall rate (98.68\%), and DenseNet provides balanced performance with a recall rate of 94.55\% and specificity of 98.66\%. Leveraging these complementary strengths, we developed a bagging-based ensemble classifier that integrates ResNet18, DenseNet121, and EfficientNet\_B0 using a soft voting strategy. This classifier achieved the best AUC value (0.9961) among all models, with a recall rate of 95.37\% and specificity of 99.25\%. It has now been successfully deployed in the MST real-time data processing pipeline. Validation using 5,000 practically processed samples with a classification threshold of 0.798 showed that the classifier achieved 88.31\% accuracy, 91.89\% recall rate, and 99.82\% specificity, confirming its effectiveness and robustness under real application conditions.
The Mini-SiTian Array: the mini-SiTian Realtime Image Processing pipeline (STRIP)
2025-04-02
Hongrui Gu, Yang Huang, Yongkang Sun, Kai Xiao, Zhirui Li, Beichuan Wang, Zhou Fan, Chuanjie Zheng, Henggeng Han, Hu Zou, Wenxiong Li, Hong Wu, Jifeng Liu
This paper provides a comprehensive introduction to the Mini-SiTian Real-Time Image Processing pipeline (STRIP) and evaluates its operational performance. The STRIP pipeline is specifically designed for real-time alert triggering and light curve generation for transient sources. By applying the STRIP pipeline to both simulated and real observational data of the Mini-SiTian survey, it successfully identified various types of variable sources, including stellar flares, supernovae, variable stars, and asteroids, while meeting requirements of reduction speed within 5 minutes. For the real observational dataset, the pipeline detected 1 flare event, 127 variable stars, and 14 asteroids from three monitored sky regions. Additionally, two datasets were generated: one, a real-bogus training dataset comprising 218,818 training samples, and the other, a variable star light curve dataset with 421 instances. These datasets will be used to train machine learning algorithms, which are planned for future integration into STRIP.
Text Speaks Louder than Vision: ASCII Art Reveals Textual Biases in Vision-Language Models
2025-04-02
Zhaochen Wang, Yujun Cai, Zi Huang, Bryan Hooi, Yiwei Wang, Ming-Hsuan Yang
Vision-language models (VLMs) have advanced rapidly in processing multimodal information, but their ability to reconcile conflicting signals across modalities remains underexplored. This work investigates how VLMs process ASCII art, a unique medium where textual elements collectively form visual patterns, potentially creating semantic-visual conflicts. We introduce a novel evaluation framework that systematically challenges five state-of-the-art models (including GPT-4o, Claude, and Gemini) using adversarial ASCII art, where character-level semantics deliberately contradict global visual patterns. Our experiments reveal a strong text-priority bias: VLMs consistently prioritize textual information over visual patterns, with visual recognition ability declining dramatically as semantic complexity increases. Various mitigation attempts through visual parameter tuning and prompt engineering yielded only modest improvements, suggesting that this limitation requires architectural-level solutions. These findings uncover fundamental flaws in how current VLMs integrate multimodal information, providing important guidance for future model development while highlighting significant implications for content moderation systems vulnerable to adversarial examples.
GPT Adoption and the Impact of Disclosure Policies
2025-04-02
Cathy Yang, David Restrepo Amariles, Leo Allen, Aurore Troussel
Generative Pre-trained Transformers (GPTs), particularly Large Language Models (LLMs) like ChatGPT, have proven effective in content generation and productivity enhancement. However, legal risks associated with these tools lead to adoption variance and concealment of AI use within organizations. This study examines the impact of disclosure on ChatGPT adoption in legal, audit and advisory roles in consulting firms through the lens of agency theory. We conducted a survey experiment to evaluate agency costs in the context of unregulated corporate use of ChatGPT, with a particular focus on how mandatory disclosure influences information asymmetry and misaligned interests. Our findings indicate that in the absence of corporate regulations, such as an AI policy, firms may incur agency costs, which can hinder the full benefits of GPT adoption. While disclosure policies reduce information asymmetry, they do not significantly lower overall agency costs due to managers undervaluing analysts' contributions with GPT use. Finally, we examine the scope of existing regulations in Europe and the United States regarding disclosure requirements, explore the sharing of risk and responsibility within firms, and analyze how incentive mechanisms promote responsible AI adoption.
Thermalization in a model of enhanced memory capacity
2025-04-02
Oleg Kaikov
We study thermalization within a quantum system with an enhanced capacity to store information. This system has been recently introduced to provide a prototype model of how a black hole processes and stores information. We perform a numerical finite-size analysis of this isolated quantum system and find indications that its information-carrying subsystem approaches thermality in the large system-size limit. The results lead us to suggest a novel thermalization mechanism. The corresponding distinguishing characteristic is that for a large class of physically meaningful non-equilibrium initial states $| \text{in} \rangle$, a few-body observable $\hat{A}$ thermalizes despite unignorable correlations between the fluctuations of its eigenstate expectation values $\langle \alpha | \hat{A} | \alpha \rangle$ in the eigenstate basis of the model $\left\{ | \alpha \rangle \right\}$ and the fluctuations of the squared magnitudes of the coefficients $|C_{\alpha}|^2 = |\langle \alpha | \text{in} \rangle |^2$.
Register Always Matters: Analysis of LLM Pretraining Data Through the Lens of Language Variation
2025-04-02
Amanda Myntti, Erik Henriksson, Veronika Laippala, Sampo Pyysalo
Pretraining data curation is a cornerstone in Large Language Model (LLM) development, leading to growing research on quality filtering of large web corpora. From statistical quality flags to LLM-based labeling systems, datasets are divided into categories, frequently reducing to a binary: those passing the filters deemed as valuable examples, others discarded as useless or detrimental. However, a more detailed understanding of the contribution of different kinds of texts to model performance is still largely lacking. In this article, we present the first study utilizing registers (also known as genres) - a widely used standard in corpus linguistics to model linguistic variation - to curate pretraining datasets and investigate the effect of register on the performance of LLMs. We perform comparative studies by training models with register classified data and evaluating them using standard benchmarks, and show that the register of pretraining data substantially affects model performance. We uncover surprising relationships between the pretraining material and the resulting models: using the News register results in subpar performance, and on the contrary, including the Opinion class, covering texts such as reviews and opinion blogs, is highly beneficial. While a model trained on the entire unfiltered dataset outperforms those trained on datasets limited to a single register, combining well-performing registers like How-to-Instructions, Informational Description, and Opinion leads to major improvements. Furthermore, analysis of individual benchmark results reveals key differences in the strengths and drawbacks of specific register classes as pretraining data. These findings show that register is an important explainer of model variation and can facilitate more deliberate future data selection practices.
2025-04-02
Silvia Fernandez-Sabido, Laura Peniche-Sabido
In this paper, we offer an overview of indigenous languages, identifying the causes of their devaluation and the need for legislation on language rights. We review the technologies used to revitalize these languages, finding that when they come from outside, they often have the opposite effect to what they seek; however, when developed from within communities, they become powerful instruments of expression. We propose that the inclusion of Indigenous knowledge in large language models (LLMs) will enrich the technological landscape, but must be done in a participatory environment that encourages the exchange of knowledge.
How to Define the Quality of Data? A Feature-Based Literature Survey
2025-04-02
Markus Matoni, Arno Kesper, Gabriele Taentzer
The digital transformation of our society is a constant challenge, as data is generated in almost every digital interaction. To use data effectively, it must be of high quality. This raises the question: what exactly is data quality? A systematic literature review of the existing literature shows that data quality is a multifaceted concept, characterized by a number of quality dimensions. However, the definitions of data quality vary widely. We used feature-oriented domain analysis to specify a taxonomy of data quality definitions and to classify the existing definitions. This allows us to identify research gaps and future topics.
Deep Learning-Driven Protein Structure Prediction and Design: Key Model Developments by Nobel Laureates and Multi-Domain Applications
2025-04-02
Wanqing Yang, Yanwei Wang, Yang Wang
This systematic review outlines pivotal advancements in deep learning-driven protein structure prediction and design, focusing on four core models-AlphaFold, RoseTTAFold, RFDiffusion, and ProteinMPNN-developed by 2024 Nobel Laureates in Chemistry: David Baker, Demis Hassabis, and John Jumper. We analyze their technological iterations and collaborative design paradigms, emphasizing breakthroughs in atomic-level structural accuracy, functional protein engineering, and multi-component biomolecular interaction modeling. Key innovations include AlphaFold3's diffusion-based framework for unified biomolecular prediction, RoseTTAFold's three-track architecture integrating sequence and spatial constraints, RFDiffusion's denoising diffusion for de novo protein generation, and ProteinMPNN's inverse folding for sequence-structure co-optimization. Despite transformative progress in applications such as binder design, nanomaterials, and enzyme engineering, challenges persist in dynamic conformational sampling, multimodal data integration, and generalization to non-canonical targets. We propose future directions, including hybrid physics-AI frameworks and multimodal learning, to bridge gaps between computational design and functional validation in cellular environments.
Tinnitus, lucid dreaming and awakening. An online survey and theoretical implications
2025-04-02
Robin Guillard, Nicolas Dauman, Aurélien Cadix, Charlotte Glabasnia Linck, Marco Congedo, Dirk de Ridder, Alain Londero
(1) Background: Tinnitus is the perception of phantom sound in the absence of a corresponding external source. Previous studies reported that the presence of tinnitus is notably absent during dreams. This study aimed at replicating previous findings regarding tinnitus-free dreams, while also gaining a deeper understanding of tinnitus manifestations during dreams and after awakening. (2) Methods: For this observational study, 195 tinnitus patients answered an online survey on the mutual-help community Siopi. (3) Results: 160 patients could recall their dreams. Among them, 92.5% state they do not hear their tinnitus while dreaming. The rest (7.5%) report higher tinnitus burden, higher stress and more often exhibit objective tinnitus and/or tinnitus related to peripheral auditory pathology and/or drug intake. 13% of the participants frequently experience lucid dreams. Among them, 36% could perceive their tinnitus during lucid dreams, and this was strongly associated with the concomitant perception of external sounds during lucid dreaming. While the majority of patients report perceiving their tinnitus instantly upon awakening, during nocturnal awakenings, 18% declared they could be awakened by their tinnitus and 9.8% mentioned that their tinnitus can temporarily cease. (4) Conclusions: Our findings confirm the previous findings: tinnitus is rarely perceived during dreams. Remarkably, our study is the first to document the case of tinnitus during lucid dreaming. 64% of these patients gain higher-order consciousness attributes while still experiencing a tinnitus-free state. Our observations suggest that the presence or absence of gating of external auditory information during dreams acts as a tinnitus on-off switch, refining the previously proposed integrative model of auditory phantom perception.
Interacting $p$-form gauge theories: New developments
2025-04-02
Sergei M. Kuzenko
Gauge $p$-forms in diverse dimensions are ubiquitous in supergravity and string theory. This work reviews novel covariant formulations designed to generate arbitrary interacting duality-invariant or chiral (self-dual) $p$-form theories in $d = 2p + 2$ space-time dimensions. For odd $p$, such theories possess $\mathsf{U}(1)$ duality invariance and include the Born-Infeld and ModMax theories. For even $p$, they describe a self-interacting chiral $p$-form with its gauge-invariant field strength obeying a nonlinear self-duality condition. We provide a complete description of $T\bar T$-like deformations of $\mathsf{U}(1)$ duality-invariant models for nonlinear electrodynamics in four dimensions and their six-dimensional counterparts -- interacting chiral two-form field theories. We also elaborate on consistent flows in the spaces of duality-invariant or chiral (self-dual) $p$-form theories beyond six dimensions.
Systematic Literature Review of Automation and Artificial Intelligence in Usability Issue Detection
2025-04-02
Eduard Kuric, Peter Demcak, Matus Krajcovic, Jan Lang
Usability issues can hinder the effective use of software. Therefore, various techniques are deployed to diagnose and mitigate them. However, these techniques are costly and time-consuming, particularly in iterative design and development. A substantial body of research indicates that automation and artificial intelligence can enhance the process of obtaining usability insights. In our systematic review of 155 publications, we offer a comprehensive overview of the current state of the art for automated usability issue detection. We analyze trends, paradigms, and the technical context in which they are applied. Finally, we discuss the implications and potential directions for future research.
A Survey on Physics-based Differentiable Rendering
2025-04-02
Yunfan Zeng, Guangyan Cai, Shuang Zhao
Physics-based differentiable rendering has emerged as a powerful technique in computer graphics and vision, with a broad range of applications in solving inverse rendering tasks. At its core, differentiable rendering enables the computation of gradients with respect to scene parameters, allowing optimization-based approaches to solve various problems. Over the past few years, significant advancements have been made in both the underlying theory and the practical implementations of differentiable rendering algorithms. In this report, we provide a comprehensive overview of the current state of the art in physics-based differentiable rendering, focusing on recent advances in general differentiable rendering theory, Monte Carlo sampling strategy, and computational efficiency.
Virtual Reality and Artificial Intelligence as Psychological Countermeasures in Space and Other Isolated and Confined Environments: A Scoping Review
2025-04-02
Jennifer Sharp, Joshua Kelson, Daryl South, Anthony Saliba, Muhammad Ashad Kabir
Spaceflight is an isolated and confined environment (ICE) that exposes astronauts to psychological hazards, such as stress, danger, and monotony. Virtual reality (VR) and artificial intelligence (AI) technologies can serve as psychological countermeasures as they can digitally simulate immersive environments, interactive companions, and therapeutic experiences. Our study employs a scoping literature review approach to identify what is currently known about the use and effectiveness of VR and AI-based interventions as psychological countermeasures to improve mood or emotional states in adults in space or other ICEs. Additionally, this review aimed to identify gaps in the knowledge base and whether a systematic review with meta-analysis was warranted. The review included studies where the intervention was used or intended for use in space or other extraterrestrial environments (ICE). Our search strategy yielded 19 studies from 3390 records across seven major databases. All studies focused on VR-based interventions, with no eligible AI-based intervention studies found. VR interventions were found to be effective for relaxation and improving mood, emergency training, as an interactive communication platform, for comparing interior designs, and for enhancing exercise. There were improvements for measures of mood and emotion\n (e.g., anxiety and stress); however, user preferences varied, and some instances of cybersickness were reported. A systematic review with meta-analysis is not recommended due to the heterogeneity of results. There is significant scope for further research into the use of VR for a wider range of mood and emotion variables using standardised assessment instruments. Additionally, the potential application of AI as a psychological countermeasure warrants further investigation.
A Practical Guide to Estimating Conditional Marginal Effects: Modern Approaches
2025-04-02
Jiehan Liu, Ziyi Liu, Yiqing Xu
This Element offers a practical guide to estimating conditional marginal effects-how treatment effects vary with a moderating variable-using modern statistical methods. Commonly used approaches, such as linear interaction models, often suffer from unclarified estimands, limited overlap, and restrictive functional forms. This guide begins by clearly defining the estimand and presenting the main identification results. It then reviews and improves upon existing solutions, such as the semiparametric kernel estimator, and introduces robust estimation strategies, including augmented inverse propensity score weighting with Lasso selection (AIPW-Lasso) and double machine learning (DML) with modern algorithms. Each method is evaluated through simulations and empirical examples, with practical recommendations tailored to sample size and research context. All tools are implemented in the accompanying interflex package for R.
Quo Vadis, HCOMP? A Review of 12 Years of Research at the Frontier of Human Computation and Crowdsourcing
2025-04-02
Jonas Oppenlaender, Ujwal Gadiraju, Simo Hosio
The field of human computation and crowdsourcing has historically studied how tasks can be outsourced to humans. However, many tasks previously distributed to human crowds can today be completed by generative AI with human-level abilities, and concerns about crowdworkers increasingly using language models to complete tasks are surfacing. These developments undermine core premises of the field. In this paper, we examine the evolution of the Conference on Human Computation and Crowdsourcing (HCOMP) - a representative example of the field as one of its key venues - through the lens of Kuhn's paradigm shifts. We review 12 years of research at HCOMP, mapping the evolution of HCOMP's research topics and identifying significant shifts over time. Reflecting on the findings through the lens of Kuhn's paradigm shifts, we suggest that these shifts do not constitute a paradigm shift. Ultimately, our analysis of gradual topic shifts over time, combined with data on the evident overlap with related venues, contributes a data-driven perspective to the broader discussion about the future of HCOMP and the field as a whole.
Demonstration of Plate Analysis, an algorithm for precise relative astrometry
2025-04-02
Ryou Ohsawa, Daisuke Kawata, Takafumi Kamizuka, Yoshiyuki Yamada, Wolfgang Löffler, Michael Biermann
Astrometry plays a crucial role in understanding the structure, dynamics, and evolution of celestial objects by providing precise measurements of their positions and motions. We propose a new approach to wide-field, relative astrometry, Plate Analysis. Plate Analysis is an innovative algorithm that estimates stellar coordinates and corrects geometric distortion based on precise reference sources, without relying on dedicated calibration fields. It is implemented as a probabilistic framework using Stochastic Variational Inference to efficiently optimize the numerous parameters involved in the model. The methodology was tested through a simplified simulation designed after the Galactic center survey of the JASMINE mission. This simulation, called the JASMINE mini-mock survey, covered three years of observation with 100 satellite orbits, providing a comprehensive dataset for evaluating the performance of Plate Analysis. Although the observation model incorporated more than 30,000 parameters, the parameters were efficiently optimized through Plate Analysis. The results showed that the estimated coordinates closely match the expected stellar motions, with an average positional error of about $70\,\mathrm{\mu as}$. These findings validate the potential of Plate Analysis for precise wide-field astrometric applications, offering significant insights into improving the accuracy of stellar dynamics measurements.
Weyl Semimetals: from Principles, Materials to Applications
2025-04-02
Mengyuan Zhong, Nam Thanh Trung Vu, Wenhao Zhai, Jian Rui Soh, Yuanda Liu, Jing Wu, Ady Suwardi, Huajun Liu, Guoqing Chang, Kian Ping Loh, Weibo Gao, Cheng-Wei Qiu, Joel K. W. Yang, Zhaogang Dong
Weyl semimetals have attracted significant interest in condensed matter physics and materials science, due to their unique electronic and topological properties. These characteristics not only deepen our understanding of fundamental quantum phenomena, but also make Weyl semimetals promising candidates for advanced applications in electronics, photonics, and spintronics. This review provides a systematic overview of the field, covering theoretical foundations, material synthesis, engineering strategies, and emerging device applications. We first outline the key theoretical principles and distinctive properties of Weyl semimetals, followed by an examination of recent advancements that enhance their functional versatility. Finally, we discuss the critical challenges hindering their practical implementation and explore future development directions, along with the potential for expanding and enhancing their existing range of applications. By integrating discussions of both opportunities and obstacles, this review offers a balanced perspective on current progress and future directions in Weyl semimetal research.
In this work, we study an integrated fault detection and classification framework called FARM for fast, accurate, and robust online chemical process monitoring. The FARM framework integrates the latest advancements in statistical process control (SPC) for monitoring nonparametric and heterogeneous data streams with novel data analysis approaches based on Riemannian geometry together in a hierarchical framework for online process monitoring. We conduct a systematic evaluation of the FARM monitoring framework using the Tennessee Eastman Process (TEP) dataset. Results show that FARM performs competitively against state-of-the-art process monitoring algorithms by achieving a good balance among fault detection rate (FDR), fault detection speed (FDS), and false alarm rate (FAR). Specifically, FARM achieved an average FDR of 96.97% while also outperforming benchmark methods in successfully detecting hard-to-detect faults that are previously known, including Faults 3, 9 and 15, with FDRs being 97.08%, 96.30% and 95.99%, respectively. In terms of FAR, our FARM framework allows practitioners to customize their choice of FAR, thereby offering great flexibility. Moreover, we report a significant improvement in average fault classification accuracy during online monitoring from 61% to 82% when leveraging Riemannian geometric analysis, and further to 84.5% when incorporating additional features from SPC. This illustrates the synergistic effect of integrating fault detection and classification in a holistic, hierarchical monitoring framework.
A Survey of Scaling in Large Language Model Reasoning
2025-04-02
Zihan Chen, Song Wang, Zhen Tan, Xingbo Fu, Zhenyu Lei, Peng Wang, Huan Liu, Cong Shen, Jundong Li
The rapid advancements in large Language models (LLMs) have significantly enhanced their reasoning capabilities, driven by various strategies such as multi-agent collaboration. However, unlike the well-established performance improvements achieved through scaling data and model size, the scaling of reasoning in LLMs is more complex and can even negatively impact reasoning performance, introducing new challenges in model alignment and robustness. In this survey, we provide a comprehensive examination of scaling in LLM reasoning, categorizing it into multiple dimensions and analyzing how and to what extent different scaling strategies contribute to improving reasoning capabilities. We begin by exploring scaling in input size, which enables LLMs to process and utilize more extensive context for improved reasoning. Next, we analyze scaling in reasoning steps that improves multi-step inference and logical consistency. We then examine scaling in reasoning rounds, where iterative interactions refine reasoning outcomes. Furthermore, we discuss scaling in training-enabled reasoning, focusing on optimization through iterative model improvement. Finally, we review applications of scaling across domains and outline future directions for further advancing LLM reasoning. By synthesizing these diverse perspectives, this survey aims to provide insights into how scaling strategies fundamentally enhance the reasoning capabilities of LLMs and further guide the development of next-generation AI systems.
Toward a Sustainable Low-Altitude Economy: A Survey of Energy-Efficient RIS-UAV Networks
2025-04-02
Manzoor Ahmed, Aized Amin Soofi, Feroz Khan, Salman Raza, Wali Ullah Khan, Lina Su, Fang Xu, Zhu Han
The integration of RIS into UAV networks presents a transformative solution for achieving energy-efficient and reliable communication, particularly within the rapidly expanding low-altitude economy (LAE). As UAVs facilitate diverse aerial services-spanning logistics to smart surveillance-their limited energy reserves create significant challenges. RIS effectively addresses this issue by dynamically shaping the wireless environment to enhance signal quality, reduce power consumption, and extend UAV operation time, thus enabling sustainable and scalable deployment across various LAE applications. This survey provides a comprehensive review of RIS-assisted UAV networks, focusing on energy-efficient design within LAE applications. We begin by introducing the fundamentals of RIS, covering its operational modes, deployment architectures, and roles in both terrestrial and aerial environments. Next, advanced EE-driven strategies for integrating RIS and UAVs. Techniques such as trajectory optimization, power control, beamforming, and dynamic resource management are examined. Emphasis is placed on collaborative solutions that incorporate UAV-mounted RIS, wireless energy harvesting (EH), and intelligent scheduling frameworks. We further categorize RIS-enabled schemes based on key performance objectives relevant to LAE scenarios. These objectives include sum rate maximization, coverage extension, QoS guarantees, secrecy rate improvement, latency reduction, and age of information (AoI) minimization. The survey also delves into RIS-UAV synergy with emerging technologies like MEC, NOMA, V2X communication, and WPT. These technologies are crucial to the LAE ecosystem. Finally, we outline open research challenges and future directions, emphasizing the critical role of energy-aware, RIS-enhanced UAV networks in shaping scalable, sustainable, and intelligent infrastructures within the LAE.
Reinsuring AI: Energy, Agriculture, Finance & Medicine as Precedents for Scalable Governance of Frontier Artificial Intelligence
2025-04-02
Nicholas Stetler
The governance of frontier artificial intelligence (AI) systems--particularly those capable of catastrophic misuse or systemic failure--requires institutional structures that are robust, adaptive, and innovation-preserving. This paper proposes a novel framework for governing such high-stakes models through a three-tiered insurance architecture: (1) mandatory private liability insurance for frontier model developers; (2) an industry-administered risk pool to absorb recurring, non-catastrophic losses; and (3) federally backed reinsurance for tail-risk events. Drawing from historical precedents in nuclear energy (Price-Anderson), terrorism risk (TRIA), agricultural crop insurance, flood reinsurance, and medical malpractice, the proposal shows how the federal government can stabilize private AI insurance markets without resorting to brittle regulation or predictive licensing regimes. The structure aligns incentives between AI developers and downstream stakeholders, transforms safety practices into insurable standards, and enables modular oversight through adaptive eligibility criteria. By focusing on risk-transfer mechanisms rather than prescriptive rules, this framework seeks to render AI safety a structural feature of the innovation ecosystem itself--integrated into capital markets, not external to them. The paper concludes with a legal and administrative feasibility analysis, proposing avenues for statutory authorization and agency placement within existing federal structures.
A Systematic Review of Security Communication Strategies: Guidelines and Open Challenges
2025-04-02
Carolina Carreira, Alexandra Mendes, João F. Ferreira, Nicolas Christin
Cybersecurity incidents such as data breaches have become increasingly common, affecting millions of users and organizations worldwide. The complexity of cybersecurity threats challenges the effectiveness of existing security communication strategies. Through a systematic review of over 3,400 papers, we identify specific user difficulties including information overload, technical jargon comprehension, and balancing security awareness with comfort. Our findings reveal consistent communication paradoxes: users require technical details for credibility yet struggle with jargon and need risk awareness without experiencing anxiety. We propose seven evidence-based guidelines to improve security communication and identify critical research gaps including limited studies with older adults, children, and non-US populations, insufficient longitudinal research, and limited protocol sharing for reproducibility. Our guidelines emphasize user-centric communication adapted to cultural and demographic differences while ensuring security advice remains actionable. This work contributes to more effective security communication practices that enable users to recognize and respond to cybersecurity threats appropriately.
CHILES VIII: Probing Evolution of Average HI Content in Star Forming Galaxies over the Past 5 Billion Years
2025-04-02
Nicholas Luber, D. J. Pisano, J. H. van Gorkom, Julia Blue Bird, Richard Dodson, Hansung B. Gim, Kelley M. Hess, Lucas R. Hunt, Danielle Lucero, Martin Meyer, Emmanuel Momjian, Min S. Yun
Utilizing the COSMOS HI Large Extragalactic Survey (CHILES) dataset, we investigate the evolution of the average atomic neutral hydrogen (HI) properties of galaxies over the continuous redshift range 0.09 $< z <$ 0.47. First, we introduce a simple multi-step, multi-scale imaging and continuum subtraction process that we apply to each observing session. These sessions are then averaged onto a common \textit{uv}-grid and run through a Fourier filtering artifact mitigation technique. We then demonstrate how this process results in science quality data products by comparing to the expected noise and image-cube kurtosis. This work offers the first-look description and scientific analysis after the processing of the entire CHILES database. These data are used to measure the average HI mass in four redshift bins, out to a redshift 0.47, by separately stacking blue cloud (NUV-r= -1 - 3) and red sequence (NUV-r = 3 - 6) galaxies. We find little-to-no change in gas fraction for the total ensemble of blue galaxies and make no detection for red galaxies. Additionally, we split up our sample of blue galaxies into an intermediate stellar mass bin (M$_{*} = 10^{9-10} M_{\odot}$) and a high stellar mass bin (M$_{*} = 10^{10-12.5} M_{\odot}$). We find that in the high mass bin galaxies are becoming increasingly HI poor with decreasing redshift, while the intermediate mass galaxies maintain a constant HI gas mass. We place these results in the context of the star-forming main sequence of galaxies and hypothesize about the different mechanisms responsible for their different evolutionary tracks.
An Introductory Survey to Autoencoder-based Deep Clustering -- Sandboxes for Combining Clustering with Deep Learning
2025-04-02
Collin Leiber, Lukas Miklautz, Claudia Plant, Christian Böhm
Autoencoders offer a general way of learning low-dimensional, non-linear representations from data without labels. This is achieved without making any particular assumptions about the data type or other domain knowledge. The generality and domain agnosticism in combination with their simplicity make autoencoders a perfect sandbox for researching and developing novel (deep) clustering algorithms. Clustering methods group data based on similarity, a task that benefits from the lower-dimensional representation learned by an autoencoder, mitigating the curse of dimensionality. Specifically, the combination of deep learning with clustering, called Deep Clustering, enables to learn a representation tailored to specific clustering tasks, leading to high-quality results. This survey provides an introduction to fundamental autoencoder-based deep clustering algorithms that serve as building blocks for many modern approaches.
In primates, loci associated with adaptive trait variation often fall in non-coding regions. Understanding the mechanisms linking these regulatory variants to fitness-relevant phenotypes remains challenging, but can be addressed using functional genomic data. However, such data are rarely generated at scale in non-human primates. When they are, only select tissues, cell types, developmental stages, and cellular environments are typically considered, despite appreciation that adaptive variants often exhibit context-dependent effects. In this review, we 1) discuss why context-dependent regulatory loci might be especially evolutionarily relevant in primates, 2) explore challenges and emerging solutions for mapping such context-dependent variation, and 3) discuss the scientific questions these data could address. We argue that filling this gap will provide critical insights into evolutionary processes, human disease, and regulatory adaptation.
Stellar tidal streams around nearby spiral galaxies with deep imaging from amateur telescopes
2025-04-02
David Martinez-Delgado, Michael Stein, Joanna D. Sakowska, M. Maurice Weigelt, Javier Roman, Giuseppe Donatiello, Santi Roca-Fabrega, Mischa Schirmer, Eva K. Grebel, Teymoor Saifollahi, Jeff Kanipe, M. Angeles Gomez-Flechoso, Mohammad Akhlaghi, Behnam Javanmardi, Gang Wu, Sepideh Eskandarlou, Dominik J. Bomans, Cristian Henkel, Adam Block, Mark Hanson, Johannes Schedler, Karel Teuwen, R. Jay GaBany, Alvaro Ibañez Perez, Ken Crawford, Wolfgang Promper, Manuel Jimenez, Silvia Farras-Aloy, Juan Miro-Carretero
Tidal interactions between massive galaxies and their satellites are fundamental processes in a Universe with L-Cold Dark Matter cosmology, redistributing material into faint features that preserve records of past galactic interactions. While stellar streams in the Local Group impressively demonstrate satellite disruption, they do not constitute a statistically significant sample. Constructing a substantial catalog of stellar streams beyond the Local Group remains challenging due to the difficulties in obtaining deep, wide-field images of galaxies. Despite their potential to illuminate dark matter distribution and galaxy formation processes, stellar streams remain underutilized as cosmological probes. The Stellar Tidal Stream Survey (STSS) addresses this observational gap by leveraging amateur telescopes to obtain deep, scientific-grade images of galactic outskirts, capable of building a more statistically meaningful sample of stellar streams. Over the last decade, the STSS has acquired deep (up to surface brightness limit 28.3 mag/arcsec^2 in the r-band), wide-field images of 15 nearby Milky Way analog galaxies using a network of robotic amateur telescopes, avoiding the issues associated with "mosaicing" smaller images taken with a professional telescope. Our survey has revealed a diverse range of previously unreported faint features related to dwarf satellite accretion - including stellar streams, shells, and umbrella-like structures. We discover an ultra-diffuse galaxy (NGC150-UDG1), which shows hints of tidal tails. The STSS demonstrates the suitability of modern amateur telescopes to detect and study faint, diffuse structures in large fields around nearby spiral galaxies. Their economic and accessibility advantages enable larger samples with deep imaging, essential for testing galaxy formation models and constraining the properties of minor merger events in the local Universe.
Epistemic Closure and the Irreversibility of Misalignment: Modeling Systemic Barriers to Alignment Innovation
2025-04-02
Andy Williams
Efforts to ensure the safe development of artificial general intelligence (AGI) often rely on consensus-based alignment approaches grounded in axiomatic formalism, interpretability, and empirical validation. However, these methods may be structurally unable to recognize or incorporate novel solutions that fall outside their accepted epistemic frameworks. This paper introduces a functional model of epistemic closure, in which cognitive, institutional, social, and infrastructural filters combine to make many alignment proposals illegible to existing evaluation systems. We present a weighted closure model supported by both theoretical and empirical sources, including a meta-analysis performed by an AI system on patterns of rejection and non-engagement with a framework for decentralized collective intelligence (DCI). We argue that the recursive failure to assess models like DCI is not just a sociological oversight but a structural attractor, mirroring the very risks of misalignment we aim to avoid in AGI. Without the adoption of DCI or a similarly recursive model of epistemic correction, we may be on a predictable path toward irreversible misalignment. The development and acceptance of this paper, first through simulated review and then through formal channels, provide a case study supporting its central claim: that epistemic closure can only be overcome by recursive modeling of the constraints that sustain it.
From Prompts to Templates: A Systematic Prompt Template Analysis for Real-world LLMapps
2025-04-02
Yuetian Mao, Junjie He, Chunyang Chen
Large Language Models (LLMs) have revolutionized human-AI interaction by enabling intuitive task execution through natural language prompts. Despite their potential, designing effective prompts remains a significant challenge, as small variations in structure or wording can result in substantial differences in output. To address these challenges, LLM-powered applications (LLMapps) rely on prompt templates to simplify interactions, enhance usability, and support specialized tasks such as document analysis, creative content generation, and code synthesis. However, current practices heavily depend on individual expertise and iterative trial-and-error processes, underscoring the need for systematic methods to optimize prompt template design in LLMapps. This paper presents a comprehensive analysis of prompt templates in practical LLMapps. We construct a dataset of real-world templates from open-source LLMapps, including those from leading companies like Uber and Microsoft. Through a combination of LLM-driven analysis and human review, we categorize template components and placeholders, analyze their distributions, and identify frequent co-occurrence patterns. Additionally, we evaluate the impact of identified patterns on LLMs' instruction-following performance through sample testing. Our findings provide practical insights on prompt template design for developers, supporting the broader adoption and optimization of LLMapps in industrial settings.
Gravitational waves (GWs) from compact binary mergers have emerged as one of the most promising probes of cosmology and General Relativity (GR). However, a major challenge in fully exploiting GWs as standard sirens with current and future GW observatories is developing efficient and robust codes capable of analyzing the increasing data volumes that are, and will be, acquired. We present here \texttt{CHIMERA} 2.0, an advanced computational framework for hierarchical Bayesian inference of cosmological, modified gravity, and population hyperparameters using standard sirens and galaxy catalogs. This upgrade introduces novel GPU-accelerated algorithms to estimate the hierarchical likelihood, enabling the analysis of thousands of events - crucial for next-generation experiments - and includes the two-parameter ($\Xi_0-n$) modified GW propagation model. Using \texttt{CHIMERA} 2.0, we forecast cosmological and modified GW propagation constraints for the future LIGO-Virgo-KAGRA O5 run. We analyze three binary black hole populations of 300 events at SNR>20, each with a different value of $\Xi_0$: 0.6, 1 (corresponding to GR), and 1.8. Multiple analyses were performed each catalog, comprising a population of approximately 5000 events, thanks to \texttt{CHIMERA} 2.0, which is 10-1000 times faster depending on the settings and catalog size. We jointly infer cosmological, modified GW propagation, and population hyperparameters. With spectroscopic galaxy catalogs, the fiducial $\Xi_0$ is recovered with a precision of 22%, 7.5%, and 10% for $\Xi_0$ = 0.6, 1, and 1.8, respectively; while the precision on $H_0$ is 2-7 times worse than when $\Xi_0$ is not inferred. Finally,in the case of photometric redshifts the constraints degrade on average by 3.5 times in all cases, underscoring the importance of future spectroscopic surveys in maximizing the constraining power of standard sirens.
Satellites are used for a multitude of applications, including communications, Earth observation, and space science. Neural networks and deep learning-based approaches now represent the state-of-the-art to enhance the performance and efficiency of these tasks. Given that satellites are susceptible to various faults, one critical application of Artificial Intelligence (AI) is fault detection. However, despite the advantages of neural networks, these systems are vulnerable to radiation errors, which can significantly impact their reliability. Ensuring the dependability of these solutions requires extensive testing and validation, particularly using fault injection methods. This study analyses a physics-informed (PI) real-valued non-volume preserving (Real NVP) normalizing flow model for fault detection in space systems, with a focus on resilience to Single-Event Upsets (SEUs). We present a customized fault injection framework in TensorFlow to assess neural network resilience. Fault injections are applied through two primary methods: Layer State injection, targeting internal network components such as weights and biases, and Layer Output injection, which modifies layer outputs across various activations. Fault types include zeros, random values, and bit-flip operations, applied at varying levels and across different network layers. Our findings reveal several critical insights, such as the significance of bit-flip errors in critical bits, that can lead to substantial performance degradation or even system failure. With this work, we aim to exhaustively study the resilience of Real NVP models against errors due to radiation, providing a means to guide the implementation of fault tolerance measures.
Urban computing has emerged as a multidisciplinary field that harnesses data-driven technologies to address challenges and improve urban living. Traditional approaches, while beneficial, often face challenges with generalization, scalability, and contextual understanding. The advent of Large Language Models (LLMs) offers transformative potential in this domain. This survey explores the intersection of LLMs and urban computing, emphasizing the impact of LLMs in processing and analyzing urban data, enhancing decision-making, and fostering citizen engagement. We provide a concise overview of the evolution and core technologies of LLMs. Additionally, we survey their applications across key urban domains, such as transportation, public safety, and environmental monitoring, summarizing essential tasks and prior works in various urban contexts, while highlighting LLMs' functional roles and implementation patterns. Building on this, we propose potential LLM-based solutions to address unresolved challenges. To facilitate in-depth research, we compile a list of available datasets and tools applicable to diverse urban scenarios. Finally, we discuss the limitations of current approaches and outline future directions for advancing LLMs in urban computing.
Test-time Adaptation for Foundation Medical Segmentation Model without Parametric Updates
2025-04-02
Kecheng Chen, Xinyu Luo, Tiexin Qin, Jie Liu, Hui Liu, Victor Ho Fun Lee, Hong Yan, Haoliang Li
Foundation medical segmentation models, with MedSAM being the most popular, have achieved promising performance across organs and lesions. However, MedSAM still suffers from compromised performance on specific lesions with intricate structures and appearance, as well as bounding box prompt-induced perturbations. Although current test-time adaptation (TTA) methods for medical image segmentation may tackle this issue, partial (e.g., batch normalization) or whole parametric updates restrict their effectiveness due to limited update signals or catastrophic forgetting in large models. Meanwhile, these approaches ignore the computational complexity during adaptation, which is particularly significant for modern foundation models. To this end, our theoretical analyses reveal that directly refining image embeddings is feasible to approach the same goal as parametric updates under the MedSAM architecture, which enables us to realize high computational efficiency and segmentation performance without the risk of catastrophic forgetting. Under this framework, we propose to encourage maximizing factorized conditional probabilities of the posterior prediction probability using a proposed distribution-approximated latent conditional random field loss combined with an entropy minimization loss. Experiments show that we achieve about 3\% Dice score improvements across three datasets while reducing computational complexity by over 7 times.
A Practical Synthesis of Detecting AI-Generated Textual, Visual, and Audio Content
2025-04-02
Lele Cao
Advances in AI-generated content have led to wide adoption of large language models, diffusion-based visual generators, and synthetic audio tools. However, these developments raise critical concerns about misinformation, copyright infringement, security threats, and the erosion of public trust. In this paper, we explore an extensive range of methods designed to detect and mitigate AI-generated textual, visual, and audio content. We begin by discussing motivations and potential impacts associated with AI-based content generation, including real-world risks and ethical dilemmas. We then outline detection techniques spanning observation-based strategies, linguistic and statistical analysis, model-based pipelines, watermarking and fingerprinting, as well as emergent ensemble approaches. We also present new perspectives on robustness, adaptation to rapidly improving generative architectures, and the critical role of human-in-the-loop verification. By surveying state-of-the-art research and highlighting case studies in academic, journalistic, legal, and industrial contexts, this paper aims to inform robust solutions and policymaking. We conclude by discussing open challenges, including adversarial transformations, domain generalization, and ethical concerns, thereby offering a holistic guide for researchers, practitioners, and regulators to preserve content authenticity in the face of increasingly sophisticated AI-generated media.
OnRL-RAG: Real-Time Personalized Mental Health Dialogue System
2025-04-02
Ahsan Bilal, Beiyu Lin, Mehdi Zaeifi
Large language models (LLMs) have been widely used for various tasks and applications. However, LLMs and fine-tuning are limited to the pre-trained data. For example, ChatGPT's world knowledge until 2021 can be outdated or inaccurate. To enhance the capabilities of LLMs, Retrieval-Augmented Generation (RAG), is proposed to augment LLMs with additional, new, latest details and information to LLMs. While RAG offers the correct information, it may not best present it, especially to different population groups with personalizations. Reinforcement Learning from Human Feedback (RLHF) adapts to user needs by aligning model responses with human preference through feedback loops. In real-life applications, such as mental health problems, a dynamic and feedback-based model would continuously adapt to new information and offer personalized assistance due to complex factors fluctuating in a daily environment. Thus, we propose an Online Reinforcement Learning-based Retrieval-Augmented Generation (OnRL-RAG) system to detect and personalize the responding systems to mental health problems, such as stress, anxiety, and depression. We use an open-source dataset collected from 2028 College Students with 28 survey questions for each student to demonstrate the performance of our proposed system with the existing systems. Our system achieves superior performance compared to standard RAG and simple LLM via GPT-4o, GPT-4o-mini, Gemini-1.5, and GPT-3.5. This work would open up the possibilities of real-life applications of LLMs for personalized services in the everyday environment. The results will also help researchers in the fields of sociology, psychology, and neuroscience to align their theories more closely with the actual human daily environment.
Automated Survey Collection with LLM-based Conversational Agents
2025-04-02
Kurmanbek Kaiyrbekov, Nicholas J Dobbins, Sean D Mooney
Objective: Traditional phone-based surveys are among the most accessible and widely used methods to collect biomedical and healthcare data, however, they are often costly, labor intensive, and difficult to scale effectively. To overcome these limitations, we propose an end-to-end survey collection framework driven by conversational Large Language Models (LLMs). Materials and Methods: Our framework consists of a researcher responsible for designing the survey and recruiting participants, a conversational phone agent powered by an LLM that calls participants and administers the survey, a second LLM (GPT-4o) that analyzes the conversation transcripts generated during the surveys, and a database for storing and organizing the results. To test our framework, we recruited 8 participants consisting of 5 native and 3 non-native english speakers and administered 40 surveys. We evaluated the correctness of LLM-generated conversation transcripts, accuracy of survey responses inferred by GPT-4o and overall participant experience. Results: Survey responses were successfully extracted by GPT-4o from conversation transcripts with an average accuracy of 98% despite transcripts exhibiting an average per-line word error rate of 7.7%. While participants noted occasional errors made by the conversational LLM agent, they reported that the agent effectively conveyed the purpose of the survey, demonstrated good comprehension, and maintained an engaging interaction. Conclusions: Our study highlights the potential of LLM agents in conducting and analyzing phone surveys for healthcare applications. By reducing the workload on human interviewers and offering a scalable solution, this approach paves the way for real-world, end-to-end AI-powered phone survey collection systems.
Processes Matter: How ML/GAI Approaches Could Support Open Qualitative Coding of Online Discourse Datasets
2025-04-02
John Chen, Alexandros Lotsos, Grace Wang, Lexie Zhao, Bruce Sherin, Uri Wilensky, Michael Horn
Open coding, a key inductive step in qualitative research, discovers and constructs concepts from human datasets. However, capturing extensive and nuanced aspects or "coding moments" can be challenging, especially with large discourse datasets. While some studies explore machine learning (ML)/Generative AI (GAI)'s potential for open coding, few evaluation studies exist. We compare open coding results by five recently published ML/GAI approaches and four human coders, using a dataset of online chat messages around a mobile learning software. Our systematic analysis reveals ML/GAI approaches' strengths and weaknesses, uncovering the complementary potential between humans and AI. Line-by-line AI approaches effectively identify content-based codes, while humans excel in interpreting conversational dynamics. We discussed how embedded analytical processes could shape the results of ML/GAI approaches. Instead of replacing humans in open coding, researchers should integrate AI with and according to their analytical processes, e.g., as parallel co-coders.
Legal invoice review is a costly, inconsistent, and time-consuming process, traditionally performed by Legal Operations, Lawyers or Billing Specialists who scrutinise billing compliance line by line. This study presents the first empirical comparison of Large Language Models (LLMs) against human invoice reviewers - Early-Career Lawyers, Experienced Lawyers, and Legal Operations Professionals-assessing their accuracy, speed, and cost-effectiveness. Benchmarking state-of-the-art LLMs against a ground truth set by expert legal professionals, our empirically substantiated findings reveal that LLMs decisively outperform humans across every metric. In invoice approval decisions, LLMs achieve up to 92% accuracy, surpassing the 72% ceiling set by experienced lawyers. On a granular level, LLMs dominate line-item classification, with top models reaching F-scores of 81%, compared to just 43% for the best-performing human group. Speed comparisons are even more striking - while lawyers take 194 to 316 seconds per invoice, LLMs are capable of completing reviews in as fast as 3.6 seconds. And cost? AI slashes review expenses by 99.97%, reducing invoice processing costs from an average of $4.27 per invoice for human invoice reviewers to mere cents. These results highlight the evolving role of AI in legal spend management. As law firms and corporate legal departments struggle with inefficiencies, this study signals a seismic shift: The era of LLM-powered legal spend management is not on the horizon, it has arrived. The challenge ahead is not whether AI can perform as well as human reviewers, but how legal teams will strategically incorporate it, balancing automation with human discretion.
Watermarking for AI Content Detection: A Review on Text, Visual, and Audio Modalities
2025-04-02
Lele Cao
The rapid advancement of generative artificial intelligence (GenAI) has revolutionized content creation across text, visual, and audio domains, simultaneously introducing significant risks such as misinformation, identity fraud, and content manipulation. This paper presents a practical survey of watermarking techniques designed to proactively detect GenAI content. We develop a structured taxonomy categorizing watermarking methods for text, visual, and audio modalities and critically evaluate existing approaches based on their effectiveness, robustness, and practicality. Additionally, we identify key challenges, including resistance to adversarial attacks, lack of standardization across different content types, and ethical considerations related to privacy and content ownership. Finally, we discuss potential future research directions aimed at enhancing watermarking strategies to ensure content authenticity and trustworthiness. This survey serves as a foundational resource for researchers and practitioners seeking to understand and advance watermarking techniques for AI-generated content detection.
2025-04-01
Martín G. Richarte, Facundo Toscano, Diego G. Lambas, Heliana E. Luparello, Luiz Filipe Guimarães, Júlio C. Fabris
Quasars are effective tracers of the large-scale distribution of galaxies at high redshift thanks to their high luminosity and dedicated surveys. Previous studies have shown that quasars exhibit a bias similar to that of rich groups, indicating that quasar pairs may be linked to higher density environments, serving as protocluster proxies. In this work, we aim to characterize close quasar pairs within the same halo by identifying them in redshift space. We analyze pair-quasar cross correlations and CMB-derived lensing convergence profiles centered in these systems. We have identified 2777 pairs of quasars in the redshift range from 1.2 to 2.8. Quasar pairs exhibit a distribution of relative luminosities that differs from that of random pairs with the same redshift distribution, indicating that quasars in these systems are distinct from isolated ones. The cross-correlation between pairs and quasars shows a larger amplitude than the auto-correlation function of quasars, suggesting these systems are more strongly biased toward the large-scale mass distribution and reside in more massive halos. This is further supported by higher convergence CMB lensing profiles of pairs compared to isolated quasars with a similar redshift distribution. Our results indicate that quasar pairs are suitable precursors to present-day clusters of galaxies, unlike isolated quasars, which are associated with less dense environments.
Hydrodynamics of the electronic Fermi liquid: a pedagogical overview
2025-04-01
Aaron Hui, Brian Skinner
For over a hundred years, electron transport in conductive materials has been primarily described by the Drude model, which assumes that current flow is impeded primarily by momentum-relaxing collisions between electrons and extrinsic objects such as impurities or phonons. In the past decade, however, experiments have increasingly realized ultra-high quality electronic materials that demonstrate a qualitatively distinct method of charge transport called hydrodynamic flow. Hydrodynamic flow occurs when electrons collide much more frequently with each other than with anything else, and in this limit the electric current has long-wavelength collective behavior analogous to that of a classical fluid. While electron hydrodynamics has long been postulated theoretically for solid-state systems, the plethora of recent experimental realizations has reinvigorated the field. Here, we review recent theoretical and experimental progress in understanding hydrodynamic electrons using the (hydrodynamic) Fermi liquid as our prototypical example.
Towards Resilient Federated Learning in CyberEdge Networks: Recent Advances and Future Trends
2025-04-01
Kai Li, Zhengyang Zhang, Azadeh Pourkabirian, Wei Ni, Falko Dressler, Ozgur B. Akan
In this survey, we investigate the most recent techniques of resilient federated learning (ResFL) in CyberEdge networks, focusing on joint training with agglomerative deduction and feature-oriented security mechanisms. We explore adaptive hierarchical learning strategies to tackle non-IID data challenges, improving scalability and reducing communication overhead. Fault tolerance techniques and agglomerative deduction mechanisms are studied to detect unreliable devices, refine model updates, and enhance convergence stability. Unlike existing FL security research, we comprehensively analyze feature-oriented threats, such as poisoning, inference, and reconstruction attacks that exploit model features. Moreover, we examine resilient aggregation techniques, anomaly detection, and cryptographic defenses, including differential privacy and secure multi-party computation, to strengthen FL security. In addition, we discuss the integration of 6G, large language models (LLMs), and interoperable learning frameworks to enhance privacy-preserving and decentralized cross-domain training. These advancements offer ultra-low latency, artificial intelligence (AI)-driven network management, and improved resilience against adversarial attacks, fostering the deployment of secure ResFL in CyberEdge networks.
Evaluating Electric Micro-Mobility Related Mode Choice Stated Preferences: A Latent Class Choice Approach
2025-04-01
Yikang Wu, Mehmet Yildirimoglu, Zuduo Zheng
This paper examines mode choice behaviour regarding Electric Micro-Mobility (EMM) among car and public transport users using a Latent Class Choice Modelling (LCCM) approach. Utilizing stated preference survey data from 1,671 Brisbane residents, the study identifies distinct user segments with heterogeneous sensitivities to EMM attributes. Results reveal significant potential for EMM adoption, notably among segments inclined toward multimodal trips or personal micromobility, driven primarily by weather conditions, travel time, and cost. Conversely, resistant segments characterized by demographic factors such as age, gender, income, and lack of EMM experience highlight challenges in shifting mode choices. Further analyses, including class profiles, elasticities, and geographical distribution, are conducted to generate comprehensive insights that inform policy decisions. These insights support targeted investments in micromobility infrastructure and integrated pricing strategies, thereby enhancing EMM adoption and promoting sustainable urban mobility.
Determinations of asteroid masses using mutual encounters observed in the Legacy Survey of Space and Time
2025-04-01
Gary M. Bernstein, Negin Najafi, Daniel C. H. Gomes
Using published simulations of the 10-year Legacy Survey of Space and Time (LSST), we forecast its ability to determine the masses of individual main-belt asteroids (MBAs) through precise astrometry of any pairs of the ~1.2 million known MBAs undergoing close gravitational encounters during the survey. The uncertainty $\sigma_I$ on the impulse applied to a tracer asteroid by its deflector is derived from the Fisher matrix of the tracer's astrometric data, including an azimuthal acceleration $A_2$ from the Yarkovsky effect as a free parameter for each tracer. If only LSST observations are available, $\sigma_I \approx7\times10^{-6}\,{\rm m}\,{\rm s}^{-1}$ for MBAs at apparent magnitude $m_V<19.5,$ degrading ~10x for $m_V=23.$ These tracers yield a median uncertainty on the mass of an MBA of $\approx4\times10^{-14}M_\odot,$ with a wide range of variation depending on the ``luck'' of close encounters. Roughly 125 MBAs obtain mass measures with S/N>5. If pre-LSST astrometry yields a strong constraint on the state vector of the tracer MBA at the start of LSST, then these values improve to median $\sigma_M\approx1.3\times10^{-14}M_\odot$ and 310 MBAs at S/N>5, with >1/2 of these having S/N>10. These yields would be a ~10-fold increase in the number of known asteroid masses, including a nearly complete knowledge of MBAs with H<7.5. If pre-LSST data are sufficient to start constraining the Yarkovsky effect, another factor $\sim1.5$ can be gained. Tables of the measurable deflector MBAs and their tracers are provided.
The Receding Cosmic Shoreline of Mid-to-Late M Dwarfs: Measurements of Active Lifetimes Worsen Challenges for Atmosphere Retention by Rocky Exoplanets
2025-04-01
Emily K Pass, David Charbonneau, Andrew Vanderburg
Detecting and characterizing the atmospheres of terrestrial exoplanets is a key goal of exoplanetary astronomy, one that may now be within reach given the upcoming campaign to conduct a large-scale survey of rocky M-dwarf worlds with the James Webb Space Telescope. It is imperative that we understand where known planets sit relative to the cosmic shoreline, the boundary between planets that have retained atmospheres and those that have not. Previous works modeled the historic XUV radiation received by mid-to-late M-dwarf planets using a scaling relation calibrated using more massive stars, but fully convective M dwarfs display unique rotation/activity histories that differ from Sun-like stars and early M dwarfs. We synthesize observations of the active lifetimes of mid-to-late M dwarfs to present an updated estimate of their historic XUV fluence. For known planets of inactive, mid-to-late M dwarfs, we calculate a historic XUV fluence that is 2.1-3.1 times the canonical XUV scaling relation on average, with the larger value including corrections for the pre-main-sequence phase and energetic flares. We find that only the largest terrestrial planets known to orbit mid-to-late M-dwarfs are likely to have retained atmospheres within the cosmic shoreline paradigm. Our calculations may help to guide the selection of targets for JWST and may prove useful in interpreting the results; to this end, we define a novel Atmosphere Retention Metric (ARM) that indicates the distance between a planet and the cosmic shoreline, and tabulate the ARM for known mid-to-late M-dwarf planets.
2025-04-01
Maarten de Rijke, Bart van den Hurk, Flora Salim, Alaa Al Khourdajie, Nan Bai, Renato Calzone, Declan Curran, Getnet Demil, Lesley Frew, Noah Gießing, Mukesh Kumar Gupta, Maria Heuss, Sanaa Hobeichi, David Huard, Jingwei Kang, Ana Lucic, Tanwi Mallick, Shruti Nath, Andrew Okem, Barbara Pernici, Thilina Rajapakse, Hira Saleem, Harry Scells, Nicole Schneider, Damiano Spina, Yuanyuan Tian, Edmund Totin, Andrew Trotman, Ramamurthy Valavandan, Dereje Workneh, Yangxinyu Xie
The purpose of the MANILA24 Workshop on information retrieval for climate impact was to bring together researchers from academia, industry, governments, and NGOs to identify and discuss core research problems in information retrieval to assess climate change impacts. The workshop aimed to foster collaboration by bringing communities together that have so far not been very well connected -- information retrieval, natural language processing, systematic reviews, impact assessments, and climate science. The workshop brought together a diverse set of researchers and practitioners interested in contributing to the development of a technical research agenda for information retrieval to assess climate change impacts.
3D printing for teaching and exploration in astronomy for individuals with blindness/visual impairment: textured representations of imagery
2025-04-01
Carol Christian, Antonella Nota, Noreen Grice, Thomas Madura, David Hurd
Astronomy, a captivating field that draws upon science, mathematics, and engineering, has traditionally relied on visual representations to convey the wonders of the cosmos. While this approach effectively engages the sighted population, the use of imagery can exclude individuals with blindness or visual impairment (B/VI). Astronomical research is incorporated into press releases, media, outreach efforts, and educational systems aimed at enhancing public interest and often skill in science, but visual materials can hamper a population with B/VI. This paper explores the potential of 3D printing as an assistive technology providing an alternative to imagery. We produced textured 3D prints of astronomical research data from the Hubble Space Telescope (HST). Useability assessment of materials is an important phase of production before integration into structured programs, and we used a multi-phased approach in our prior research to create and test appropriate textures for 3D astronomical prints. This paper describes the last step of reviewing our 3D prints through informal useability sessions with diverse individuals. The assessment indicated our 3D prints provide reliable, informative representations of astronomical data appropriate for public use especially for public information, outreach programs, and science education for individuals with BVI.
Indirect Excitons and Many-body Interactions in InGaAs Double Quantum Wells
2025-04-01
Christopher L. Smallwood, Rachel Owen, Matthew W. Day, Takeshi Suzuki, Rohan Singh, Travis M. Autry, Smriti Bhalerao, Fauzia Jabeen, Steven T. Cundiff
Spatially indirect excitons in semiconductor quantum wells are relevant to basic research and device applications because they exhibit enhanced tunability, delocalized wave functions, and potentially longer lifetimes relative to direct excitons. Here we investigate the properties of indirect excitons and their coupling interactions with direct excitons in asymmetric InGaAs double quantum wells using optical multidimensional coherent spectroscopy and photoluminescence excitation spectroscopy. Analyses of the spectra confirm a strong influence of many-body effects, and reveal that excited-state zero-quantum coherences between direct and indirect excitons in the quantum wells dephase faster than the much higher-energy single-quantum coherences between excitonic excited states and ground states. The results also suggest an important energy-dependent role of continuum states in mediating system dynamics, and they indicate that dephasing mechanisms are associated with uncorrelated or anticorrelated energy-level fluctuations.
A widely applicable Galaxy Group finder Using Machine Learning
2025-04-01
Juntao Ma, Jie Wang, Tianxiang Mao, Hongxiang Chen, Yuxi Meng, Xiaohu Yang, Qingyang Li
Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for identifying groups through machine learning techniques in real space taking into account the impact of redshift distortion. Our methodology involves two neural networks: one is a classification model for identifying central galaxy groups, and the other is a regression model for predicting the mass of these groups. Both models input observable galaxy traits, allowing future applicability to real survey data. Testing on simulated datasets indicates our method accurately identifies over $92\%$ of groups with $\mathrm{M}_{vir} \geq 10^{11}h^{-1}\mathrm{M}_\odot$, with $80\%$ achieving a membership completeness of at least $80\%$. The predicted group masses vary by less than 0.3 dex across different mass scales, even in the absence of a priori data. Our network adapts seamlessly to expand to sparse samples with a flux limit of $m_{r} < 14$, to high redshift samples at $z=1.08$, and to galaxy samples from the TNG300 hydrodynamical simulation without further training. Furthermore, the framework can easily adjust to real surveys by training on redshift distorted samples without needing parameter changes. Careful consideration of different observational effects in redshift space makes it promising that this method will be applicable to real galaxy surveys.
Cultural Intelligence (CQ) refers to the ability to understand unfamiliar cultural contexts-a crucial skill for large language models (LLMs) to effectively engage with globally diverse users. While existing research often focuses on explicitly stated cultural norms, such approaches fail to capture the subtle, implicit values that underlie real-world conversations. To address this gap, we introduce CQ-Bench, a benchmark specifically designed to assess LLMs' capability to infer implicit cultural values from natural conversational contexts. We generate a multi-character conversation-based stories dataset using values from the World Value Survey and GlobalOpinions datasets, with topics including ethical, religious, social, and political. Our dataset construction pipeline includes rigorous validation procedures-incorporation, consistency, and implicitness checks-using GPT-4o, with 98.2% human-model agreement in the final validation. Our benchmark consists of three tasks of increasing complexity: attitude detection, value selection, and value extraction. We find that while o1 and Deepseek-R1 models reach human-level performance in value selection (0.809 and 0.814), they still fall short in nuanced attitude detection, with F1 scores of 0.622 and 0.635, respectively. In the value extraction task, GPT-4o-mini and o3-mini score 0.602 and 0.598, highlighting the difficulty of open-ended cultural reasoning. Notably, fine-tuning smaller models (e.g., LLaMA-3.2-3B) on only 500 culturally rich examples improves performance by over 10%, even outperforming stronger baselines (o3-mini) in some cases. Using CQ-Bench, we provide insights into the current challenges in LLMs' CQ research and suggest practical pathways for enhancing LLMs' cross-cultural reasoning abilities.
Soft X-ray high-harmonic generation in an anti-resonant hollow core fiber driven by a 3 $μ$m ultrafast laser
2025-04-01
Drew Morrill, Will Hettel, Daniel Carlson, Benjamin Shearer, Clay Klein, Jeremy Thurston, Grzegorz Golba, Rae Larsen, Gabriella Seifert, James Uhrich, Daniel Lesko, Tin Nghia Nguyen, Gunnar Arisholm, Jonathan Knight, Scott Diddams, Margaret Murnane, Henry Kapteyn, Michaël Hemmer
High-harmonic upconversion driven by a mid-infrared femtosecond laser can generate coherent soft X-ray beams in a tabletop-scale setup. Here, we report on a compact ytterbium-pumped optical parametric chirped pulse amplifier (OPCPA) laser system seeded by an all-fiber front-end and employing periodically-poled lithium niobate (PPLN) nonlinear media operated near the pulse fluence limits of current commercially available PPLN crystals. The OPCPA delivers 3 $\mu$m wavelength pulses with 775 $\mu$J energy at 1 kHz repetition rate, with transform-limited 120 fs pulse duration, diffraction-limited beam quality, and ultrahigh 0.33% rms energy stability over >18 hours. Using this laser, we generate soft X-ray high harmonics (HHG) in argon gas by focusing into a low-loss, high-pressure gas-filled anti-resonant hollow core fiber (ARHCF), generating coherent light at photon energies up to the argon L-edge (250 eV) and carbon K-edge (284 eV), with high beam quality and ~1% rms energy stability. This work demonstrates soft X-ray HHG in a high-efficiency guided-wave phase matched geometry, overcoming the high losses inherent to mid-IR propagation in unstructured waveguides, or the short interaction lengths of gas cells or jets. The ARHCF can operate long term without damage, and with the repetition rate, stability and robustness required for demanding applications in spectro-microscopy and imaging. Finally, we discuss routes for maximizing the soft X-ray HHG flux by driving He at higher laser intensities using either 1.5 $\mu$m or 3 $\mu$m - the signal and idler wavelengths of the laser.
Photometric investigation of WY Leo: An Eclipsing binary system with Delta Scuti component
2025-04-01
F. Kahraman Alicavus
Eclipsing binary stars with Delta Scuti components are exceptional systems for gaining a deeper understanding of stellar systems. WY Leo is one such system, exhibiting Delta Scuti-type oscillations. However, it has not been extensively studied in the literature. WY Leo was observed by Transiting Exoplanet Survey Satellite (TESS), providing two sectors of high-quality photometric data. This study focuses on the photometric analysis of WY Leo. Binary modeling was performed, and the system's fundamental stellar parameters, such as mass and radius, were determined. The pulsational properties of WY Leo were also investigated, revealing that the more luminous star exhibits Delta Scuti type oscillations with a period of 0.052 days. The position of the primary component was examined on the HR diagram, and it was found to lie within the Delta Scuti instability strip.
JWST reveals the diversity of nuclear obscuring dust in nearby AGN: nuclear isolation of MIRI/MRS datacubes and continuum spectral fitting
2025-04-01
Omaira González-Martín, Daniel J. Díaz-González, Mariela Martínez-Paredes, Almudena Alonso-Herrero, Enrique López-Rodríguez, Begoña García-Lorenzo, Cristina Ramos Almeida, Ismael García-Bernete, Donaji Esparza-Arredondo, Sebastian F. Hoenig, Santiago García-Burillo, Chris Packham, Nancy A. Levenson, Alvaro Labiano, Miguel Pereira-Santaella, Francoise Combes, Anelise Audibert, Erin K. S. Hicks, Lulu Zhang, Enrica Bellocchi, Richard I. Davies, Laura Hermosa Muñoz, Masatoshi Imanishi, Claudio Ricci, Marko Stalevski
We investigate the capabilities of the mid-infrared instrument (MIRI) of James Webb Space Telescope (JWST) to advance our knowledge of AGN dust using the spectral fitting technique on an AGN collection of 21 nearby (z<0.05) AGN (7 type-1 and 14 type-2) observations obtained with the medium resolution spectroscopy (MRS) mode. This collection includes publicly available AGN and data from the collaboration of Galactic Activity, Torus, and Outflow Survey (GATOS). We developed a tool named MRSPSFisol that decomposes MRS cubes into point-like and extended contributions. We found statistically good fits for 12 targets with current AGN dust models. The model that provides good fits (chi2/dof<2) for {these 12 targets} assumes a combination of clumpy and smooth distribution of dust in a flare-disk geometry where the dust grain size is a free parameter. Still, two and one AGN statistically prefer the disk+wind and the classical clumpy torus model, respectively. However, the currently available models fail to reproduce 40% of the targets, likely due to the extreme silicate features not well reproduced by the models and signatures of water-ice and aliphatic hydrocarbon absorption features in most targets. New models exploring, for instance, new chemistry, are needed to explain the complexity of AGN dust continuum emission observed by JWST.
Filamentary Hierarchies and Superbubbles I: Characterizing filament properties across a simulated spiral galaxy
2025-04-01
Rachel Pillsworth, Erica Roscoe, Ralph E. Pudritz, Eric W. Koch
High resolution surveys reveal that the interstellar medium in the Milky Way and nearby galaxies consists of interlinked hierarchies of filamentary structure and superbubbles extending from galactic to subpc scales. The characterization of filament properties across this hierarchy is of fundamental importance for the origin of giant molecular clouds and their star clusters. In this paper we characterize the properties of filaments greater than 25 pc in length that are produced in the multi-scale galactic MHD simulations of Zhao et al. 2024. By adapting the FilFinder algorithm of Koch & Rosolowsky, 2015, we extract over 500 filaments ranging up to 10 kpc scales, to derive the probability distribution functions for filament masses and lengths, magnetic field orientations, and the gravitational stability and fragmentation patterns of filaments. We find power-law distributions for filament masses and lengths. The former has a power law index $\alpha_m = 1.85$ that is nearly identical to that of observed GMC mass functions in extragalactic and Galactic surveys, suggesting that GMC properties are inherited from their host filaments. The fragmentation of magnetized filaments on 200 pc scales or less occurs when they exceed an average critical line mass, as predicted by theory. On larger scales however, kpc filaments form out of the cold neutral medium (CNM) and fragmentation follows local variations in the critical line mass along spiral arms or at the boundaries of superbubbles.
Initial Conditions from Galaxies: Machine-Learning Subgrid Correction to Standard Reconstruction
2025-04-01
Liam Parker, Adrian E. Bayer, Uros Seljak
We present a hybrid method for reconstructing the primordial density from late-time halos and galaxies. Our approach involves two steps: (1) apply standard Baryon Acoustic Oscillation (BAO) reconstruction to recover the large-scale features in the primordial density field and (2) train a deep learning model to learn small-scale corrections on partitioned subgrids of the full volume. At inference, this correction is then convolved across the full survey volume, enabling scaling to large survey volumes. We train our method on both mock halo catalogs and mock galaxy catalogs in both configuration and redshift space from the Quijote $1(h^{-1}\,\mathrm{Gpc})^3$ simulation suite. When evaluated on held-out simulations, our combined approach significantly improves the reconstruction cross-correlation coefficient with the true initial density field and remains robust to moderate model misspecification. Additionally, we show that models trained on $1(h^{-1}\,\mathrm{Gpc})^3$ can be applied to larger boxes--e.g., $(3h^{-1}\,\mathrm{Gpc})^3$--without retraining. Finally, we perform a Fisher analysis on our method's recovery of the BAO peak, and find that it significantly improves the error on the acoustic scale relative to standard BAO reconstruction. Ultimately, this method robustly captures nonlinearities and bias without sacrificing large-scale accuracy, and its flexibility to handle arbitrarily large volumes without escalating computational requirements makes it especially promising for large-volume surveys like DESI.
A systematic search for HI 21-cm absorption in Quasar-Galaxy Pairs (QGPs) provides a powerful means to map the distribution of cold gas around high-redshift star-forming galaxies. Fiber spectroscopy of high-redshift quasars enables the serendipitous detection of foreground star-forming galaxies at extremely small impact parameters, forming a unique subset of QGPs known as Galaxies On Top Of Quasars (GOTOQs). In this study, we present results from a pilot upgraded Giant Metrewave Radio Telescope (uGMRT) survey of three GOTOQs, where we achieved a remarkable 100\% detection rate of HI 21-cm absorption. By combining our findings with existing literature, we establish that GOTOQs constitute a distinct population in terms of HI 21-cm absorption, with significantly higher detection rates than those observed in Damped Lyman-$\alpha$ (DLA)-based or metal absorption-based searches. For the GOTOQs, we find a strong correlation between the line-of-sight reddening and the HI 21-cm optical depth, characterized by $\int \tau\, dv\, (\rm{km\,s^{-1}}) = 13.58^{+2.75}_{-2.35} E(B-V) + 0.68^{+1.06}_{-1.27}$, consistent with the Milky Way sightlines. We also show that the HI 21-cm detection rates and optical depth declines rapidly with the impact parameter. With upcoming wide-field spectroscopic surveys expected to substantially expand the catalog of known GOTOQs, the success of this pilot survey lays the foundation for constructing a statistically significant sample of intervening HI 21-cm absorbers.
The SRG/eROSITA All-Sky Survey. The Weak-Lensing Mass Calibration and the Stellar Mass-to-Halo Mass Relation from the Hyper Suprime-Cam Subaru Strategic Program
2025-04-01
I-Non Chiu, Vittorio Ghirardini, Sebastian Grandis, Nobuhiro Okabe, Esra Bulbul, Y. Emre Bahar, Fabian Balzer, Nicolas Clerc, Johan Comparat, Florian Kleinebreil, Matthias Kluge, Ang Liu, Rogerio Monteiro-Oliveira, Masamune Oguri, Florian Pacaud, Miriam Ramos Ceja, H. Thomas Reiprich, Jeremy Sanders, Tim Schrabback, Riccardo Seppi, Martin Sommer, Sut-Ieng Tam, Keiichi Umetsu, Xiaoyuan Zhang
We present the weak-lensing mass calibration of 124 galaxy clusters and groups at redshift $0.1
Lighting up the nano-hertz gravitational wave sky: opportunities and challenges of multimessenger astronomy with PTA experiments
2025-04-01
Riccardo J. Truant, David Izquierdo-Villalba, Alberto Sesana, Golam Mohiuddin Shaifullah, Matteo Bonetti, Daniele Spinoso, Silvia Bonoli
Pulsar Timing Array (PTA) experiments have the potential to unveil continuous gravitational wave (CGW) signals from individual massive black hole binaries (MBHBs). Detecting them in both gravitational waves (GW) and the electromagnetic (EM) spectrum will open a new chapter in multimessenger astronomy. We investigate the feasibility of conducting multimessenger studies by combining the CGW detections from an idealized 30-year SKA PTA and the optical data from the forthcoming LSST survey. To this end, we employed the $\texttt{L-Galaxies}$ semi-analytical model applied to the $\texttt{Millennium}$ simulation. We generated 200 different all-sky lightcones that include galaxies, massive black holes, and MBHBs whose emission is modeled based on their star formation histories and gas accretion physics. We predict an average of $\approx 33$ CGW detections, with signal-to-noise ratios $ S/N > 5$. The detected MBHBs are typically at $z < 0.5$, with masses of $ \sim 3 \times 10^{9} M_{\odot}$, mass ratios $> 0.6$ and eccentricities $\lesssim 0.2$. In terms of EM counterparts, we find less than 15% of these systems to be connected with an AGN detectable by LSST, while their host galaxies are easily detectable ($ < 23$ mag) massive ($ M_{\star} > 10^{11} M_{\odot}$) ellipticals with typical star formation rates ($10^{-15} yr^{-1} < sSRF < 10^{-10} yr^{-1}$). Although the CGW-EM counterpart association is complicated by poor sky localization (only 35% of these CGWs are localized within $\rm 100\, deg^2$), the number of galaxy host candidates can be considerably reduced (thousands to tens) by applying priors based on the galaxy-MBH correlations. However, picking the actual host among these candidates is highly non-trivial, as they occupy a similar region in any optical color-color diagram. Our findings highlight the considerable challenges entailed in opening the low-frequency multimessenger GW sky.
Cluster-lensed supernova yields from the Vera C. Rubin Observatory and Nancy Grace Roman Space Telescope
2025-04-01
M. Bronikowski, T. Petrushevska, J. D. R. Pierel, A. Acebron, D. Donevski, B. Apostolova, N. Blagorodnova, T. Jankovič
Through gravitational lensing, galaxy clusters can magnify supernovae (SNe) and create multiple images of the same SN. This enables measurements of cosmological parameters, which will be increasingly important in light of upcoming telescopic surveys. We study the prospects of detecting strongly lensed SNe in cluster fields with the Nancy Grace Roman Space Telescope (Roman)'s High Latitude Time Domain Survey (HLTDS) and the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). We employed two approaches: one focusing on known multiply imaged galaxies behind clusters, along with the SN rates specific to those galaxies, and another based on the expected number of lensed SNe exploding in a given volume behind a galaxy cluster. We collected all the clusters in the literature that feature a well-constrained lens model and multiply imaged galaxies behind clusters with high-quality data for the lensed galaxies. This allowed us to determine the SN rate for each galaxy. We provide predictions for 46 clusters visible to the Vera C. Rubin Observatory, as well as for 9 observable by Roman's HLTDS, depending on whether the clusters fall within the survey's observing field. We predict that the number of multiply imaged SNe discovered by LSST in its first three years is $3.95 \pm 0.89$ from the first approach or $4.94 \pm 1.02$ from the second. For the HLTDS, the expected number of multiply imaged SNe ranges from $0.38 \pm 0.15$ to $5.2 \pm 2.2$, depending on the specific cluster observed, however, the fields to be targeted remain a matter of discussion. We conclude that LSST offers great prospects for detecting multiply imaged SNe. Our predictions are effectively lower limits, as we only considered the most massive and well-studied clusters. We provide a recommendation for HLTDS observing field selection, namely: either MACS J0553.4-3342 or Abell 1758a should be observed by the survey.
The MASSIVE Survey. XX. A Triaxial Stellar Dynamical Measurement of the Supermassive Black Hole Mass and Intrinsic Galaxy Shape of Giant Radio Galaxy NGC 315
2025-04-01
Jacob Pilawa, Emily R. Liepold, Chung-Pei Ma, Jonelle L. Walsh, Jenny E. Greene
We present a new dynamical measurement of the supermassive black hole mass and intrinsic shape of the stellar halo of the massive radio galaxy NGC 315 as part of the MASSIVE survey. High signal-to-noise ratio spectra from integral-field spectrographs at the Gemini and McDonald Observatories provide stellar kinematic measurements in $304$ spatial bins from the central ${\sim}0.3''$ out to $30''$. Using ${\sim} 2300$ kinematic constraints, we perform triaxial stellar orbit modeling with the TriOS code and search over ${\sim}$15,000 galaxy models with a Bayesian scheme to simultaneously measure six mass and intrinsic shape parameters. NGC 315 is triaxial and highly prolate, with middle-to-long and short-to-long axis ratios of $p=0.854$ and $q=0.833$ and a triaxiality parameter of $T=0.89$. The black hole mass inferred from our stellar kinematics is $M_\mathrm{BH} = \left(3.0 {\pm} 0.3\right) {\times} 10^{9}\ M_\odot$, which is higher than $M_\mathrm{BH}=(1.96^{+0.30}_{-0.13}) {\times} 10^{9} M_\odot$ inferred from CO kinematics (scaled to our distance). When the seven galaxies with $M_\mathrm{BH}$ measurements from both stellar and CO kinematics are compared, we find an intrinsic scatter of 0.28 dex in $M_\mathrm{BH}$ from the two tracers and do not detect statistically significant biases between the two methods in the current data. The implied black hole shadow size (${\approx} 4.7\, \mu{\rm as}$) and the relatively high millimeter flux of NGC 315 makes this galaxy a prime candidate for future horizon-size imaging studies.
Review of "Just Plain Wrong: The Dalliance of Quantum Theory with the Defiance of Bell's Inequality"
2025-04-01
W. M. Stuckey, Timothy McDevitt, Michael Silberstein
This is a review of Frank Lad's book "Just Plain Wrong: The Dalliance of Quantum Theory with the Defiance of Bell's Inequality" (Austin Macauley Publishers, 2024).
2025-04-01
Mengyang Qiu, Qingyu Gao, Linxuan Yang, Yang Gu, Tran Minh Nguyen, Zihao Huang, Jungyeul Park
Chinese Grammatical Error Correction (CGEC) is a critical task in Natural Language Processing, addressing the growing demand for automated writing assistance in both second-language (L2) and native (L1) Chinese writing. While L2 learners struggle with mastering complex grammatical structures, L1 users also benefit from CGEC in academic, professional, and formal contexts where writing precision is essential. This survey provides a comprehensive review of CGEC research, covering datasets, annotation schemes, evaluation methodologies, and system advancements. We examine widely used CGEC datasets, highlighting their characteristics, limitations, and the need for improved standardization. We also analyze error annotation frameworks, discussing challenges such as word segmentation ambiguity and the classification of Chinese-specific error types. Furthermore, we review evaluation metrics, focusing on their adaptation from English GEC to Chinese, including character-level scoring and the use of multiple references. In terms of system development, we trace the evolution from rule-based and statistical approaches to neural architectures, including Transformer-based models and the integration of large pre-trained language models. By consolidating existing research and identifying key challenges, this survey provides insights into the current state of CGEC and outlines future directions, including refining annotation standards to address segmentation challenges, and leveraging multilingual approaches to enhance CGEC.
A Large-Scale Search for Photometrically Variable AGNs in Dwarf Galaxies Using the Young Supernova Experiment
2025-04-01
Alexander Messick, Vivienne Baldassare, David O. Jones, K. Decker French, Sandra I. Raimundo, Nicholas Earl, Katie Auchettl, David A. Coulter, Mark E. Huber, Margaret E. Verrico, Thomas de Boer, Kenneth C. Chambers, Hua Gao, Chien-Cheng Lin, Richard J. Wainscoat
We conduct an analysis of over 60,000 dwarf galaxies (7<=log(M_*/M_\odot)<=10) in search of photometric variability indicative of active galactic nuclei (AGNs). Using data from the Young Supernova Experiment (YSE), a time domain survey on the Pan-STARRS telescopes, we construct light curves for each galaxy in up to four bands (griz) where available. We select objects with AGN-like variability by fitting each light curve to a damped random walk (DRW) model. After quality cuts and removing transient contaminants, we identify 1100 variability-selected AGN candidates (representing 2.4% of the available sample). We analyze their spectra to measure various emission lines and calculate black hole (BH) masses, finding general agreement with previously found mass scaling-relations and nine potential IMBH candidates. Furthermore, we re-analyze the light curves for our candidates to calculate the dampening timescale tau_DRW associated with the DRW and see a similar correlation between this value and the BH mass. Finally, we estimate the active fraction as a function of stellar mass and see evidence that active fraction increases with host mass.
A neutral hydrogen absorption study of cold gas in the outskirts of the Magellanic Clouds using the GASKAP-HI survey
2025-04-01
Hongxing Chen, Snežana Stanimirović, Nickolas M. Pingel, James Dempsey, Frances Buckland-Willis, Susan E. Clark, Helga Dénes, John M. Dickey, Steven Gibson, Katherine Jameson, Ian Kemp, Denis Leahy, Min-Young Lee, Callum Lynn, Yik Ki Ma, N. M. McClure-Griffiths, Claire E. Murray, Hiep Nguyen, Lucero Uscanga, Jacco Th. van Loon, Enrique Vázquez-Semadeni
Cold neutral hydrogen (HI) is a crucial precursor for molecular gas formation and can be studied via HI absorption. This study investigates HI absorption in low column density regions of the Small and Large Magellanic Clouds (SMC and LMC) using the Galactic-ASKAP HI (GASKAP-HI) survey, conducted by the Australian Square Kilometer Array Pathfinder (ASKAP). We select 10 SMC directions in the outer regions and 18 LMC directions, with 4 in the outskirts and 14 within the main disk. Using the radiative transfer method, we decompose the emission and absorption spectra into individual cold neutral medium (CNM) and warm neutral medium (WNM) components. In the SMC, we find HI peak optical depths of 0.09-1.16, spin temperatures of ~20-50 K, and CNM fractions of 1-11%. In the LMC, optical depths range from 0.03 to 3.55, spin temperatures from ~10 to 100 K, and CNM fractions from 1% to 100%. The SMC's low CNM fractions likely result from its low metallicity and large line-of-sight depth. Additionally, the SMC's outskirts show lower CNM fractions than the main body, potentially due to increased CNM evaporation influenced by the hot Magellanic Corona. Shell motions dominate the kinematics of the majority of CNM clouds in this study and likely supply cold HI to the Magellanic Stream. In the LMC, high CNM fraction clouds are found near supergiant shells, where thermal instability induced by stellar feedback promotes WNM-to-CNM transition. Although no carbon monoxide (CO) has been detected, enhanced dust shielding in these areas helps maintain the cold HI.
GKAN: Explainable Diagnosis of Alzheimer's Disease Using Graph Neural Network with Kolmogorov-Arnold Networks
2025-04-01
Tianqi Ding, Dawei Xiang, Keith E Schubert, Liang Dong
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that poses significant diagnostic challenges due to its complex etiology. Graph Convolutional Networks (GCNs) have shown promise in modeling brain connectivity for AD diagnosis, yet their reliance on linear transformations limits their ability to capture intricate nonlinear patterns in neuroimaging data. To address this, we propose GCN-KAN, a novel single-modal framework that integrates Kolmogorov-Arnold Networks (KAN) into GCNs to enhance both diagnostic accuracy and interpretability. Leveraging structural MRI data, our model employs learnable spline-based transformations to better represent brain region interactions. Evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, GCN-KAN outperforms traditional GCNs by 4-8% in classification accuracy while providing interpretable insights into key brain regions associated with AD. This approach offers a robust and explainable tool for early AD diagnosis.
Physics-informed machine learning for building performance simulation-A review of a nascent field
2025-04-01
Zixin Jiang, Xuezheng Wang, Han Li, Tianzhen Hong, Fengqi You, Ján Drgoňa, Draguna Vrabie, Bing Dong
Building performance simulation (BPS) is critical for understanding building dynamics and behavior, analyzing performance of the built environment, optimizing energy efficiency, improving demand flexibility, and enhancing building resilience. However, conducting BPS is not trivial. Traditional BPS relies on an accurate building energy model, mostly physics-based, which depends heavily on detailed building information, expert knowledge, and case-by-case model calibrations, thereby significantly limiting their scalability. With the development of sensing technology and increased data availability, there is a growing attention and interest in data-driven BPS. However, purely data-driven models often suffer from limited generalization ability and a lack of physical consistency, resulting in poor performance in real-world applications. To address these limitations, recent studies have started to incorporate physics priors into data-driven models, a methodology called physics-informed machine learning (PIML). PIML is an emerging field with the definitions, methodologies, evaluation criteria, application scenarios, and future directions that remain open. To bridge those gaps, this study systematically reviews the state-of-art PIML for BPS, offering a comprehensive definition of PIML, and comparing it to traditional BPS approaches regarding data requirements, modeling effort, performance and computation cost. We also summarize the commonly used methodologies, validation approaches, application domains, available data sources, open-source packages and testbeds. In addition, this study provides a general guideline for selecting appropriate PIML models based on BPS applications. Finally, this study identifies key challenges and outlines future research directions, providing a solid foundation and valuable insights to advance R&D of PIML in BPS.
Recent advancements in Foundation Models (FMs), such as Large Language Models (LLMs), have significantly enhanced Autonomous Driving Systems (ADSs) by improving perception, reasoning, and decision-making in dynamic and uncertain environments. However, ADSs are highly complex cyber-physical systems that demand rigorous software engineering practices to ensure reliability and safety. Integrating FMs into ADSs introduces new challenges in system design and evaluation, requiring a systematic review to establish a clear research roadmap. To unlock these challenges, we present a structured roadmap for integrating FMs into autonomous driving, covering three key aspects: the infrastructure of FMs, their application in autonomous driving systems, and their current applications in practice. For each aspect, we review the current research progress, identify existing challenges, and highlight research gaps that need to be addressed by the community.
NEOWISE Data Processing and Color Corrections for Near-Earth Asteroid Observations
2025-04-01
Samuel A. Myers, Ellen S. Howell, Yanga R. Fernández, Sean E. Marshall, Christopher Magri, Ronald J. Vervack Jr., Mary L. Hinkle
The Wide-field Infrared Survey Explorer and NEOWISE missions are a key source of thermal data for near-Earth asteroids (NEAs). These missions, which utilized a space-based platform in Earth orbit, produced thermal images across four different wavelength bands, W1 - W4, with effective wavelengths of 3.4, 4.6, 12, and 22 {\mu}m respectively. Despite its use for NEA observations though, the mission architecture was originally designed to observe stars. Thus, careful data analysis methods are crucial when working with NEA data to account for the differences between these objects. However, detailed information on how to work with these data can be difficult to find for users unfamiliar with the mission. The required information is well documented, but locating it can be challenging, and many details, such as specifics about color corrections, are not fully explained. Therefore, in this work, we provide a set of "lessons learned" for working with NEOWISE data, outline the basics of how to retrieve and process NEOWISE data for NEA investigations, and present an empirical method for color correction determination. We highlight the importance of this process by processing data for three NEAs, finding that nearly half of all available observations should be discarded. Finally, we present simple thermal model results based on different levels of data analysis to highlight how data processing can affect model results.
A Decade of Deep Learning for Remote Sensing Spatiotemporal Fusion: Advances, Challenges, and Opportunities
2025-04-01
Enzhe Sun, Yongchuan Cui, Peng Liu, Jining Yan
Hardware limitations and satellite launch costs make direct acquisition of high temporal-spatial resolution remote sensing imagery challenging. Remote sensing spatiotemporal fusion (STF) technology addresses this problem by merging high temporal but low spatial resolution imagery with high spatial but low temporal resolution imagery to efficiently generate high spatiotemporal resolution satellite images. STF provides unprecedented observational capabilities for land surface change monitoring, agricultural management, and environmental research. Deep learning (DL) methods have revolutionized the remote sensing spatiotemporal fusion field over the past decade through powerful automatic feature extraction and nonlinear modeling capabilities, significantly outperforming traditional methods in handling complex spatiotemporal data. Despite the rapid development of DL-based remote sensing STF, the community lacks a systematic review of this quickly evolving field. This paper comprehensively reviews DL developments in remote sensing STF over the last decade, analyzing key research trends, method classifications, commonly used datasets, and evaluation metrics. It discusses major challenges in existing research and identifies promising future research directions as references for researchers in this field to inspire new ideas. The specific models, datasets, and other information mentioned in this article have been collected in: https://github.com/yc-cui/Deep-Learning-Spatiotemporal-Fusion-Survey.
AutoAppendix: Towards one-click Reproduction of Computational Artifacts
2025-04-01
Klaus Kraßnitzer
This report summarizes the findings of the AutoAppendix project, conducted during the UCSC OSPO Summer of Reproducibility 2024. The project involved a evaluation of reproducibility artifacts submitted to SC24, focusing on their deployability and robustness on the Chameleon Cloud platform. This technical report aims to inform and support the reproducibility community by sharing observed challenges, patterns, and best practices. Furthermore, we share templates developed for Chameleon Cloud's Jupyter interface that are intended to assist future authors and reviewers in streamlining artifact evaluation workflows.
Recent advances in the electromagnetic interactions of Dirac and Weyl particles
2025-04-01
Georgios N. Tsigaridas, Aristides I. Kechriniotis, Christos A. Tsonos, Konstantinos K. Delibasis
In this article, we present a comprehensive review of recent advancements in the study of the electromagnetic interactions of Dirac and Weyl particles, highlighting novel and significant findings. Specifically, we demonstrate that all Weyl particles, and under certain conditions Dirac particles, can occupy the same quantum state under an extensive range of electromagnetic 4-potentials and fields. These fields, which are infinite in number, have been explicitly derived and analysed. Additionally, we establish that Weyl particles can form localized states even in the absence of external electromagnetic fields. Moreover, we show that their localization can be precisely controlled through the application of simple electric fields, offering a tuneable mechanism for manipulating these particles. Building on these insights, we propose an innovative device that leverages Weyl fermions to regulate information flow at unprecedented rates of up to 100 petabits per second. This finding has significant implications for the development of next-generation electronic and quantum information technologies, as it presents a fundamentally new approach to high-speed data processing and transmission.
Multi-modal music generation, using multiple modalities like images, video, and text alongside musical scores and audio as guidance, is an emerging research area with broad applications. This paper reviews this field, categorizing music generation systems from the perspective of modalities. It covers modality representation, multi-modal data alignment, and their utilization to guide music generation. We also discuss current datasets and evaluation methods. Key challenges in this area include effective multi-modal integration, large-scale comprehensive datasets, and systematic evaluation methods. Finally, we provide an outlook on future research directions focusing on multi-modal fusion, alignment, data, and evaluation.
Inaccuracy of an E-Dictionary and Its Influence on Chinese Language Users
2025-04-01
Xi Wang, Fanfei Meng, Shiyang Zhang, Lan Li
Electronic dictionaries have largely replaced paper dictionaries and become central tools for L2 learners seeking to expand their vocabulary. Users often assume these resources are reliable and rarely question the validity of the definitions provided. The accuracy of major E-dictionaries is seldom scrutinized, and little attention has been paid to how their corpora are constructed. Research on dictionary use, particularly the limitations of electronic dictionaries, remains scarce. This study adopts a combined method of experimentation, user survey, and dictionary critique to examine Youdao, one of the most widely used E-dictionaries in China. The experiment involved a translation task paired with retrospective reflection. Participants were asked to translate sentences containing words that are insufficiently or inaccurately defined in Youdao. Their consultation behavior was recorded to analyze how faulty definitions influenced comprehension. Results show that incomplete or misleading definitions can cause serious misunderstandings. Additionally, students exhibited problematic consultation habits. The study further explores how such flawed definitions originate, highlighting issues in data processing and the integration of AI and machine learning technologies in dictionary construction. The findings suggest a need for better training in dictionary literacy for users, as well as improvements in the underlying AI models used to build E-dictionaries.
IHC-LLMiner: Automated extraction of tumour immunohistochemical profiles from PubMed abstracts using large language models
2025-04-01
Yunsoo Kim, Michal W. S. Ong, Daniel W. Rogalsky, Manuel Rodriguez-Justo, Honghan Wu, Adam P. Levine
Immunohistochemistry (IHC) is essential in diagnostic pathology and biomedical research, offering critical insights into protein expression and tumour biology. This study presents an automated pipeline, IHC-LLMiner, for extracting IHC-tumour profiles from PubMed abstracts, leveraging advanced biomedical text mining. There are two subtasks: abstract classification (include/exclude as relevant) and IHC-tumour profile extraction on relevant included abstracts. The best-performing model, "Gemma-2 finetuned", achieved 91.5% accuracy and an F1 score of 91.4, outperforming GPT4-O by 9.5% accuracy with 5.9 times faster inference time. From an initial dataset of 107,759 abstracts identified for 50 immunohistochemical markers, the classification task identified 30,481 relevant abstracts (Include) using the Gemma-2 finetuned model. For IHC-tumour profile extraction, the Gemma-2 finetuned model achieved the best performance with 63.3% Correct outputs. Extracted IHC-tumour profiles (tumour types and markers) were normalised to Unified Medical Language System (UMLS) concepts to ensure consistency and facilitate IHC-tumour profile landscape analysis. The extracted IHC-tumour profiles demonstrated excellent concordance with available online summary data and provided considerable added value in terms of both missing IHC-tumour profiles and quantitative assessments. Our proposed LLM based pipeline provides a practical solution for large-scale IHC-tumour profile data mining, enhancing the accessibility and utility of such data for research and clinical applications as well as enabling the generation of quantitative and structured data to support cancer-specific knowledge base development. Models and training datasets are available at https://github.com/knowlab/IHC-LLMiner.
Large Language Models (LLMs) have been increasingly used to optimize the analysis and synthesis of legal documents, enabling the automation of tasks such as summarization, classification, and retrieval of legal information. This study aims to conduct a systematic literature review to identify the state of the art in prompt engineering applied to LLMs in the legal context. The results indicate that models such as GPT-4, BERT, Llama 2, and Legal-Pegasus are widely employed in the legal field, and techniques such as Few-shot Learning, Zero-shot Learning, and Chain-of-Thought prompting have proven effective in improving the interpretation of legal texts. However, challenges such as biases in models and hallucinations still hinder their large-scale implementation. It is concluded that, despite the great potential of LLMs for the legal field, there is a need to improve prompt engineering strategies to ensure greater accuracy and reliability in the generated results.
Multi-modal optimization involves identifying multiple global and local optima of a function, offering valuable insights into diverse optimal solutions within the search space. Evolutionary algorithms (EAs) excel at finding multiple solutions in a single run, providing a distinct advantage over classical optimization techniques that often require multiple restarts without guarantee of obtaining diverse solutions. Among these EAs, differential evolution (DE) stands out as a powerful and versatile optimizer for continuous parameter spaces. DE has shown significant success in multi-modal optimization by utilizing its population-based search to promote the formation of multiple stable subpopulations, each targeting different optima. Recent advancements in DE for multi-modal optimization have focused on niching methods, parameter adaptation, hybridization with other algorithms including machine learning, and applications across various domains. Given these developments, it is an opportune moment to present a critical review of the latest literature and identify key future research directions. This paper offers a comprehensive overview of recent DE advancements in multimodal optimization, including methods for handling multiple optima, hybridization with EAs, and machine learning, and highlights a range of real-world applications. Additionally, the paper outlines a set of compelling open problems and future research issues from multiple perspectives
The purpose of this note is to investigate the coupling of Dirichlet and Neumann numerical boundary conditions for the transport equation set on an interval. When one starts with a stable finite difference scheme on the lattice $\mathbb{Z}$ and each numerical boundary condition is taken separately with the Neumann extrapolation condition at the outflow boundary, the corresponding numerical semigroup on a half-line is known to be bounded. It is also known that the coupling of such numerical boundary conditions on a compact interval yields a stable approximation, even though large time exponentially growing modes may occur. We review the different stability estimates associated with these numerical boundary conditions and give explicit examples of such exponential growth phenomena for finite difference schemes with ''small'' stencils. This provides numerical evidence for the optimality of some stability estimates on the interval.
Cosmography with the Double Source Plane Strong Gravitational Lens AGEL150745+052256
2025-04-01
Nandini Sahu, Anowar J. Shajib, Kim-Vy Tran, Hannah Skobe, Sunny Rhoades, Tucker Jones, Karl Glazebrook, Thomas E. Collett, Sherry H. Suyu, Tania M. Barone, Keerthi Vasan G. C., Duncan J. Bowden, Daniel Ballard, Glenn G. Kacprzak, Sarah M. Sweet, Geraint F. Lewis, Themiya Nanayakkara
Strong gravitational lenses with two background sources at widely separated redshifts are a powerful and independent probe of cosmological parameters. We can use these systems, known as Double-Source-Plane Lenses (DSPLs), to measure the ratio ($\beta$) of angular-diameter distances of the sources, which is sensitive to the matter density ($\Omega_m$) and the equation-of-state parameter for dark-energy ($w$). However, DSPLs are rare and require high-resolution imaging and spectroscopy for detection, lens modeling, and measuring $\beta$. Here we report only the second DSPL ever used to measure cosmological parameters. We model the DSPL AGEL150745+052256 from the ASTRO 3D Galaxy Evolution with Lenses (AGEL) survey using HST/WFC3 imaging and Keck/KCWI spectroscopy. The spectroscopic redshifts for the deflector and two sources in AGEL1507 are $z_{\rm defl}=0.594$, $z_{\rm S1}=2.163$, and $z_{\rm S2}=2.591$. We measure a stellar velocity dispersion of $\sigma_{\rm obs}=109 \pm 27$ km s$^{-1}$ for the nearer source. Using $\sigma_{\rm obs}$ for the main deflector (from literature) and S1, we test the robustness of our DSPL model. We measure $\beta=0.953^{+0.008}_{-0.010}$ for AGEL1507 and infer $\Omega_{\rm m}=0.33^{+0.38}_{-0.23}$ for $\Lambda$CDM cosmology. Combining AGEL1507 with the published model of the Jackpot lens improves the precision on $\Omega_{\rm m}$ ($\Lambda$CDM) and w (wCDM) by $\sim 10 \%$. The inclusion of DSPLs significantly improves the constraints when combined with Plancks cosmic microwave background observations, enhancing precision on w by $30 \%$. This paper demonstrates the constraining power of DSPLs and their complementarity to other standard cosmological probes. Tighter future constraints from larger DSPL samples discovered from ongoing and forthcoming large-area sky surveys would provide insights into the nature of dark energy.
How Cyclic Acoustic Patterns Influence ASMR Perception: A Signal Processing Perspective
2025-04-01
Zexin Fang, Bin Han, Henrik H. Sveen, C. Clark Cao, Hans D. Schotten
Autonomous Sensory Meridian Response (ASMR) has been remarkably popular in the recent decade. While its effect has been validated through behavioral studies and neuro-physiological measurements such as electroencephalography (EEG) and related bio-signal analyses, its development and triggers remain a subject of debate. Previous studies suggest that its triggers are highly linked with cyclic patterns: predictable patterns introduce relaxation while variations maintain intrigue. To validate this and further understand the impact of acoustic features on ASMR effects, we designed three distinct cyclic patterns with monophonic and stereophonic variations, while controlling their predictability and randomness, and collected ASMR triggering scores through online surveys. Then, we extracted cyclic features and carried out regression analysis, seeking an explainable mapping of cyclic features and ASMR triggers. We found that relaxing effects accumulate progressively and are independent of spatial orientation. Cyclic patterns significantly influence psychological and physical effects, which remain invariant with time. Regression analysis revealed that smoothly spread and energy-dense cyclic patterns most effectively trigger ASMR responses.
Near Field Localization via AI-Aided Subspace Methods
2025-04-01
Arad Gast, Luc Le Magoarou, Nir Shlezinger
The increasing demands for high-throughput and energy-efficient wireless communications are driving the adoption of extremely large antennas operating at high-frequency bands. In these regimes, multiple users will reside in the radiative near-field, and accurate localization becomes essential. Unlike conventional far-field systems that rely solely on DOA estimation, near-field localization exploits spherical wavefront propagation to recover both DOA and range information. While subspace-based methods, such as MUSIC and its extensions, offer high resolution and interpretability for near-field localization, their performance is significantly impacted by model assumptions, including non-coherent sources, well-calibrated arrays, and a sufficient number of snapshots. To address these limitations, this work proposes AI-aided subspace methods for near-field localization that enhance robustness to real-world challenges. Specifically, we introduce NF-SubspaceNet, a deep learning-augmented 2D MUSIC algorithm that learns a surrogate covariance matrix to improve localization under challenging conditions, and DCD-MUSIC, a cascaded AI-aided approach that decouples angle and range estimation to reduce computational complexity. We further develop a novel model-order-aware training method to accurately estimate the number of sources, that is combined with casting of near field subspace methods as AI models for learning. Extensive simulations demonstrate that the proposed methods outperform classical and existing deep-learning-based localization techniques, providing robust near-field localization even under coherent sources, miscalibrations, and few snapshots.
Retrieval-augmented generation (RAG) with large language models (LLMs) has demonstrated strong performance in multilingual question-answering (QA) tasks by leveraging relevant passages retrieved from corpora. In multilingual RAG (mRAG), the retrieved passages can be written in languages other than that of the query entered by the user, making it challenging for LLMs to effectively utilize the provided information. Recent research suggests that retrieving passages from multilingual corpora can improve RAG performance, particularly for low-resource languages. However, the extent to which LLMs can leverage different kinds of multilingual contexts to generate accurate answers, *independently from retrieval quality*, remains understudied. In this paper, we conduct an extensive assessment of LLMs' ability to (i) make consistent use of a relevant passage regardless of its language, (ii) respond in the expected language, and (iii) focus on the relevant passage even when multiple `distracting' passages in different languages are provided in the context. Our experiments with four LLMs across three QA datasets covering a total of 48 languages reveal a surprising ability of LLMs to extract the relevant information from out-language passages, but a much weaker ability to formulate a full answer in the correct language. Our analysis, based on both accuracy and feature attribution techniques, further shows that distracting passages negatively impact answer quality regardless of their language. However, distractors in the query language exert a slightly stronger influence. Taken together, our findings deepen the understanding of how LLMs utilize context in mRAG systems, providing directions for future improvements.
Joint gravity survey using an absolute atom gravimeter and relative gravimeters
2025-04-01
Li Chen-yang, Xu Ru-gang, Chen Xi, Sun Hong-bo, Li Su-peng, Luo Yu, Huang Ming-qi, Di Xue-feng, Li Zhao-long, Xiao Wei-peng, Liang Xiao, Yang Xuan, Huang Xian-liang, Yao Hua-jian, Huang Jin-shui, Chen Luo-kan, Chen Shuai
Time-varying gravity field survey is one of the important methods for seismic risk assessment. To obtain accurate timevarying gravity data, it is essential to establish a gravity reference, which can be achieved using absolute gravimeters. Atom gravimeters, as a recently emerging type of absolute gravimeter, have not yet been practically validated for their reliability in mobile gravity surveys. To study and evaluate the operational status and performance metrics of the A-Grav atom gravimeter under complex field conditions, the University of Science and Technology of China, Hefei National Laboratory, and the Anhui Earthquake Agency conducted a joint observation experiment using an atom gravimeter (AGrav) and relative gravimeters (CG-6) within the North China Seismic Gravity Monitoring Network. The experiment yielded the following results: 1) The standard deviations for mobile observations of the atom gravimeter is 2.1 {\mu}Gal; 2)The mean differences in point values and segment differences between the atom gravimeter and the relative gravimeter at the same locations is 5.8(17.1) {\mu}Gal and 4.4(11.0) {\mu}Gal, respectively, with point value differences of less than 2.0 {\mu}Gal compared to the FG5X absolute gravimeter at the same location; 3) The results of hybrid gravity adjustment based on absolute gravity control and the point value precision at each measurement point, with an average point value precision of 3.6 {\mu}Gal. The results indicate that the A-Grav atom gravimeter has observation accuracy and precision comparable to the FG5X absolute gravimeter, demonstrating good stability and reliability in field mobile measurements, and can meet the requirements for seismic gravity monitoring. This work provides a technical reference for the practical application of atom gravimeters in control measurements and time-varying gravity monitoring for earthquakes.
Retrieval-Augmented Language Models boost task performance, owing to the retriever that provides external knowledge. Although crucial, the retriever primarily focuses on semantics relevance, which may not always be effective for generation. Thus, utility-based retrieval has emerged as a promising topic, prioritizing passages that provides valid benefits for downstream tasks. However, due to insufficient understanding, capturing passage utility accurately remains unexplored. This work proposes SCARLet, a framework for training utility-based retrievers in RALMs, which incorporates two key factors, multi-task generalization and inter-passage interaction. First, SCARLet constructs shared context on which training data for various tasks is synthesized. This mitigates semantic bias from context differences, allowing retrievers to focus on learning task-specific utility for better task generalization. Next, SCARLet uses a perturbation-based attribution method to estimate passage-level utility for shared context, which reflects interactions between passages and provides more accurate feedback. We evaluate our approach on ten datasets across various tasks, both in-domain and out-of-domain, showing that retrievers trained by SCARLet consistently improve the overall performance of RALMs.
Carbon and Reliability-Aware Computing for Heterogeneous Data Centers
2025-04-01
Yichao Zhang, Yubo Song, Subham Sahoo
The rapid expansion of data centers (DCs) has intensified energy and carbon footprint, incurring a massive environmental computing cost. While carbon-aware workload migration strategies have been examined, existing approaches often overlook reliability metrics such as server lifetime degradation, and quality-of-service (QoS) that substantially affects both carbon and operational efficiency of DCs. Hence, this paper proposes a comprehensive optimization framework for spatio-temporal workload migration across distributed DCs that jointly minimizes operational and embodied carbon emissions while complying with service-level agreements (SLA). A key contribution is the development of an embodied carbon emission model based on servers' expected lifetime analysis, which explicitly considers server heterogeneity resulting from aging and utilization conditions. These issues are accommodated using new server dispatch strategies, and backup resource allocation model, accounting hardware, software and workload-induced failure. The overall model is formulated as a mixed-integer optimization problem with multiple linearization techniques to ensure computational tractability. Numerical case studies demonstrate that the proposed method reduces total carbon emissions by up to 21%, offering a pragmatic approach to sustainable DC operations.
ATOMIUM: Dust and tracers of binarity in the continua
2025-04-01
T. Danilovich, N. Samaratunge, Y. Mori, A. M. S. Richards, A. Baudry, S. Etoka, M. Montargès, P. Kervella, I. McDonald, C. A. Gottlieb, A. Wallace, D. J. Price, L. Decin, J. Bolte, T. Ceulemans, F. De Ceuster, A. de Koter, D. Dionese, I. El Mellah, M. Esseldeurs, M. Gray, F. Herpin, T. Khouri, E. Lagadec, C. Landri, L. Marinho, K. M. Menten, T. J. Millar, H. S. P. Müller, B. Pimpanuwat, J. M. C. Plane, R. Sahai, L. Siess, M. Van de Sande, O. Vermeulen, K. T. Wong, J. Yates, A. Zijlstra
Low- and intermediate-mass stars on the asymptotic giant branch (AGB) account for a significant portion of the dust and chemical enrichment in their host galaxy. Here we present ALMA observations of the continuum emission at 1.24 mm around a sample of 17 stars from the ATOMIUM survey. From our analysis of the stellar contributions to the continuum flux, we find that the semi-regular variables all have smaller physical radii and fainter monochromatic luminosities than the Mira variables. Comparing these properties with pulsation periods, we find a positive trend between stellar radius and period only for the Mira variables with periods above 300 days and a positive trend between the period and the monochromatic luminosity only for the red supergiants and the most extreme AGB stars with periods above 500 days. We find that the continuum emission at 1.24 mm can be classified into four groups. "Featureless" continuum emission is confined to the (unresolved) regions close to the star for five stars in our sample, relatively uniform extended flux is seen for four stars, tentative bipolar features are seen for three stars, and the remaining five stars have unique or unusual morphological features in their continuum maps. These features can be explained by binary companions to 10 out of the 14 AGB stars in our sample. Based on our results we conclude that there are two modes of dust formation: well established pulsation-enhanced dust formation and our newly proposed companion-enhanced dust formation. If the companion is located close to the AGB star, in the wind acceleration region, then additional dust formed in the wake of the companion can increase the mass lost through the dust driven wind. This explains the different dust morphologies seen around our stars and partly accounts for a large scatter in literature mass-loss rates, especially among semiregular stars with small pulsation periods.
FortisAVQA and MAVEN: a Benchmark Dataset and Debiasing Framework for Robust Multimodal Reasoning
2025-04-01
Jie Ma, Zhitao Gao, Qi Chai, Jun Liu, Pinghui Wang, Jing Tao, Zhou Su
Audio-Visual Question Answering (AVQA) is a challenging multimodal reasoning task requiring intelligent systems to answer natural language queries based on paired audio-video inputs accurately. However, existing AVQA approaches often suffer from overfitting to dataset biases, leading to poor robustness. Moreover, current datasets may not effectively diagnose these methods. To address these challenges, we first introduce a novel dataset, FortisAVQA, constructed in two stages: (1) rephrasing questions in the test split of the public MUSIC-AVQA dataset and (2) introducing distribution shifts across questions. The first stage expands the test space with greater diversity, while the second enables a refined robustness evaluation across rare, frequent, and overall question distributions. Second, we introduce a robust Multimodal Audio-Visual Epistemic Network (MAVEN) that leverages a multifaceted cycle collaborative debiasing strategy to mitigate bias learning. Experimental results demonstrate that our architecture achieves state-of-the-art performance on FortisAVQA, with a notable improvement of 7.81\%. Extensive ablation studies on both datasets validate the effectiveness of our debiasing components. Additionally, our evaluation reveals the limited robustness of existing multimodal QA methods. We also verify the plug-and-play capability of our strategy by integrating it with various baseline models across both datasets. Our dataset and code are available at https://github.com/reml-group/fortisavqa.
We study the collective charging of a quantum battery (QB) consisting of a one-dimensional molecular aggregate and a coupled single-mode cavity, to which we refer as an ``organic quantum battery" since the battery part is an organic material. The organic QB can be viewed as an extension of the so-called Dicke QB [D. Ferraro \emph{et al}., Phys. Rev. Lett. \textbf{120}, 117702 (2018)] by including finite exciton hopping and exciton-exciton interaction within the battery. We consider two types of normalizations of the exciton-cavity coupling when the size of the aggregate $N$ is increased: (I) The cavity length also increases to keep the density of monomers constant, (II) The cavity length does not change. Our main findings are that: (i) For fixed $N$ and exciton-cavity coupling, there exist optimal exciton-exciton interactions at which the maximum stored energy density and the maximum charging power density reach their respective maxima that both increase with increasing exciton-cavity coupling. The existence of such maxima for weak exciton-cavity coupling is argued to be due to the non-monotonic behavior of the one-exciton to two-exciton transition probability in the framework of second-order time-dependent perturbation theory. (ii) Under normalization I, no quantum advantage is observed in the scaling of the two quantities with varying $N$. Under normalization II, it is found that both the maximum stored energy density and the maximum charging power density exhibit quantum advantages compared with the Dicke QB.
A Survey of mmWave Backscatter: Applications, Platforms, and Technologies
2025-04-01
Yimiao Sun, Yuan He, Yang Zou, Jiaming Gu, Xiaolei Yang, Jia Zhang, Ziheng Mao
As a key enabling technology of the Internet of Things (IoT) and 5G communication networks, millimeter wave (mmWave) backscatter has undergone noteworthy advancements and brought significant improvement to prevailing sensing and communication systems. Past few years have witnessed growing efforts in innovating mmWave backscatter transmitters (e.g., tags and metasurfaces) and the corresponding techniques, which provide efficient information embedding and fine-grained signal manipulation for mmWave backscatter technologies. These efforts have greatly enabled a variety of appealing applications, such as long-range localization, roadside-to-vehicle communication, coverage optimization and large-scale identification. In this paper, we carry out a comprehensive survey to systematically summarize the works related to the topic of mmWave backscatter. Firstly, we introduce the scope of this survey and provide a taxonomy to distinguish two categories of mmWave backscatter research based on the operating principle of the backscatter transmitter: modulation-based and relay-based. Furthermore, existing works in each category are grouped and introduced in detail, with their common applications, platforms and technologies, respectively. Finally, we elaborate on potential directions and discuss related surveys in this area.
We review the well-known Hellmann Feynman Theorem (HFT), originally developed for Hermitian systems to facilitate the calculation of forces among the molecules. Our work extends this foundational theorem to the domain of non-Hermitian quantum mechanics, in particular the PT symmetric non-Hermitian quantum physics. We derive a modified form of the HFT (MHFT) which holds good for both PT broken, unbroken phases and even at the exceptional point of the theory as demonstrated with help of a discrete and a continumm model. Since a PT-symmetric Hamiltonian admits biorthonormal set of eigenvectors, a more appropriate inner product known as the G inner product is defined, based on which, the system in the unbroken phase can be shown to satisfy unitary time evolution, while a system in broken phase does not. We show here that the MHFT obtained is valid for both these situations.
A circular polarization-dependent photocurrent is exclusively observed along the direction parallel to the helical axis in millimeter-sized quadrilateral-shaped bulk crystalline tellurium samples. The deterioration of the polarization-dependent character of unclear origin is also recorded. The estimate of the gyrotropic photoconductivity constant Betha in the context of the circular photogalvanic effect yields the value of approximately 5 micro-A/W based on experimental results obtained under a relatively weak excitation of 1.7 W/cm2. A comparison of this Betha value with those reported in previous studies is also discussed. Enhancement of Betha value may be achievable with bulk samples of superior crystalline quality.
Computer-based Deceptive Game Design in Commercial Virtual Reality Games: A Preliminary Investigation
2025-04-01
Hilda Hadan, Leah Zhang-Kennedy, Lennart E. Nacke
As Virtual Reality (VR) games become more popular, it is crucial to understand how deceptive game design patterns manifest and impact player experiences in this emerging medium. Our study sheds light on the presence and effects of manipulative design techniques in commercial VR games compared to a traditional computer game. We conducted an autoethnography study and developed a VR Deceptive Game Design Assessment Guide based on a critical literature review. Using our guide, we compared how deceptive patterns in a popular computer game are different from two commercial VR titles. While VR's technological constraints, such as battery life and limited temporal manipulation, VR's unique sensory immersion amplified the impact of emotional and sensory deception. Current VR games showed similar but evolved forms of deceptive design compared to the computer game. We forecast more sophisticated player manipulation as VR technology advances. Our findings contribute to a better understanding of how deceptive game design persists and escalates in VR. We highlight the urgent need to develop ethical design guidelines for the rapidly advancing VR games industry.
Nuclear Matrix Elements for Neutrinoless Double-Beta Decay
2025-04-01
Anthony V. Grebe
Neutrinoless double-beta decay ($0\nu\beta\beta$) is a rare hypothesised process that, if discovered, would establish that the neutrino is Majorana, that is, it is its own antiparticle. Interpretation of experimental results relies on knowledge of nuclear matrix elements, whose large model uncertainty is the limiting factor in comparing measured (bounds on) half-lives to the neutrino mass. Nuclear effective field theory and lattice QCD have the potential to compute these matrix elements with better control over uncertainties, enhancing the discovery potential of next-generation $0\nu\beta\beta$ experiments. This work will survey various lattice QCD double-beta decay calculations and discuss their implications.
Denoising guarantees for optimized sampling schemes in compressed sensing
2025-04-01
Yaniv Plan, Matthew S. Scott, Xia Sheng, Ozgur Yilmaz
Compressed sensing with subsampled unitary matrices benefits from \emph{optimized} sampling schemes, which feature improved theoretical guarantees and empirical performance relative to uniform subsampling. We provide, in a first of its kind in compressed sensing, theoretical guarantees showing that the error caused by the measurement noise vanishes with an increasing number of measurements for optimized sampling schemes, assuming that the noise is Gaussian. We moreover provide similar guarantees for measurements sampled with-replacement with arbitrary probability weights. All our results hold on prior sets contained in a union of low-dimensional subspaces. Finally, we demonstrate that this denoising behavior appears in empirical experiments with a rate that closely matches our theoretical guarantees when the prior set is the range of a generative ReLU neural network and when it is the set of sparse vectors.
Using machine learning method for variable star classification using the TESS Sectors 1-57 data
2025-04-01
Li-Heng Wang, Kai Li, Xiang Gao, Ya-Ni Guo, Guo-You Sun
The Transiting Exoplanet Survey Satellite (TESS) is a wide-field all-sky survey mission designed to detect Earth-sized exoplanets. After over four years photometric surveys, data from sectors 1-57, including approximately 1,050,000 light curves with a 2-minute cadence, were collected. By cross-matching the data with Gaia's variable star catalogue, we obtained labeled datasets for further analysis. Using a random forest classifier, we performed classification of variable stars and designed distinct classification processes for each subclass, 6770 EA, 2971 EW, 980 CEP, 8347 DSCT, 457 RRab, 404 RRc and 12348 ROT were identified. Each variable star was visually inspected to ensure the reliability and accuracy of the compiled catalog. Subsequently, we ultimately obtained 6046 EA, 3859 EW, 2058 CEP, 8434 DSCT, 482 RRab, 416 RRc, and 9694 ROT, and a total of 14092 new variable stars were discovered.
Karabo: A versatile SKA Observation Simulation Framework
2025-04-01
Rohit Sharma, Simon Felix, Luis Fernando Machado Poletti Valle, Vincenzo Timmel, Lukas Gehrig, Andreas Wassmer, Jennifer Studer, Pascal Hitz, Filip Schramka, Michele Bianco, Devin Crichton, Marta Spinelli, André Csillaghy, Stefan Kögel, Alexandre Réfrégier
Karabo is a versatile Python-based software framework simplifying research with radio astronomy data. It bundles existing software packages into a coherent whole to improve the ease of use of its components. Karabo includes useful abstractions, like strategies to scale and parallelize typical workloads or science-specific Python modules. The framework includes functionality to access datasets and mock observations to study the Square Kilometer Array (SKA) instruments and their expected accuracy. SKA will address problems in a wide range of fields of astronomy. We demonstrate the application of Karabo to some of the SKA science cases from HI intensity mapping, mock radio surveys, radio source detection, the epoch of re-ionisation and heliophysics. We discuss the capabilities and challenges of simulating large radio datasets in the context of SKA.
Metric-Affine Myrzakulov Gravity Theories: Models, Applications and Theoretical Developments
2025-04-01
Davood Momeni, Ratbay Myrzakulov
This review presents a comprehensive overview of Myrzakulov gravity, highlighting key developments and significant results shaping the theory. It examines the foundational principles, field equations, and the role of non-metricity and torsion in gravitational interactions. The theory's implications extend to cosmology, astrophysics, and strong gravitational fields, offering alternatives to dark matter and dark energy. A focus is placed on the modified Einstein-Hilbert action, its impact on cosmic expansion, and astrophysical applications such as black holes and gravitational waves. Observational constraints from cosmology and gravitational wave tests are discussed to refine the model. By situating Myrzakulov gravity within the broader context of modified gravity, this review explores its potential to reshape fundamental physics.
TheBlueScrubs-v1, a comprehensive curated medical dataset derived from the internet
2025-04-01
Luis Felipe, Carlos Garcia, Issam El Naqa, Monique Shotande, Aakash Tripathi, Vivek Rudrapatna, Ghulam Rasool, Danielle Bitterman, Gilmer Valdes
The need for robust and diverse data sets to train clinical large language models (cLLMs) is critical given that currently available public repositories often prove too limited in size or scope for comprehensive medical use. While resources like PubMed provide foundational medical literature, they capture only a narrow range of formal publications and omit the broader medical discourse on the internet. To address these deficits, we introduce TheBlueScrubs-v1, a curated dataset of over 25 billion medical tokens - nearly three times larger than PubMed - drawn from a broad-scale internet corpus. Our two-stage filtering pipeline employs a Logistic Regression model for document screening (achieving an AUC of approximately 0.95 on external validation), followed by verification via a 70B-parameter Llama 3.1 instruct model. Each text is assigned three LLM-based quality scores encompassing medical relevance, precision and factual detail, and safety and ethical standards. Clinician reviews confirm high concordance with these automated evaluations, and a specialized cancer classifier further labels approximately 11 billion oncology tokens. Two demonstration tasks highlight the dataset's practical value: first, we distill the safety evaluations to a smaller BERT-style model that reaches an AUC near 0.96 on unseen data; second, we fine-tune a compact LLM on a filtered subset, showing measurable improvements over standard baselines in medical benchmarks as well as private ones. This Data Descriptor details the dataset's creation and validation, underscoring its potential utility for medical AI research.
Machine Learning Prediction of Cardiovascular Risk in Type 1 Diabetes Mellitus Using Radiomics Features from Multimodal Retinal Images
2025-04-01
Ariadna Tohà-Dalmau, Josep Rosinés-Fonoll, Enrique Romero, Ferran Mazzanti, Ruben Martin-Pinardel, Sonia Marias-Perez, Carolina Bernal-Morales, Rafael Castro-Dominguez, Andrea Mendez, Emilio Ortega, Irene Vinagre, Marga Gimenez, Alfredo Vellido, Javier Zarranz-Ventura
This study aimed to develop a machine learning (ML) algorithm capable of determining cardiovascular risk in multimodal retinal images from patients with type 1 diabetes mellitus, distinguishing between moderate, high, and very high-risk levels. Radiomic features were extracted from fundus retinography, optical coherence tomography (OCT), and OCT angiography (OCTA) images. ML models were trained using these features either individually or combined with clinical data. A dataset of 597 eyes (359 individuals) was analyzed, and models trained only with radiomic features achieved AUC values of (0.79 $\pm$ 0.03) for identifying moderate risk cases from high and very high-risk cases, and (0.73 $\pm$ 0.07) for distinguishing between high and very high-risk cases. The addition of clinical variables improved all AUC values, reaching (0.99 $\pm$ 0.01) for identifying moderate risk cases and (0.95 $\pm$ 0.02) for differentiating between high and very high-risk cases. For very high CV risk, radiomics combined with OCT+OCTA metrics and ocular data achieved an AUC of (0.89 $\pm$ 0.02) without systemic data input. These results demonstrate that radiomic features obtained from multimodal retinal images are useful for discriminating and classifying CV risk labels, highlighting the potential of this oculomics approach for CV risk assessment.
Reasoning before action and imagining potential outcomes (i.e., world models) are essential for embodied agents operating in complex open-world environments. Yet, prior work either incorporates only one of these abilities in an end-to-end agent or integrates multiple specialized models into an agent system, limiting the learning efficiency and generalization of the policy. Thus, this paper makes the first attempt to synergize Reasoning and Imagination in an end-to-end Generalist policy, termed RIG. To train RIG in an end-to-end manner, we construct a data pipeline that progressively integrates and enriches the content of imagination and reasoning in the trajectories collected from existing agents. The joint learning of reasoning and next image generation explicitly models the inherent correlation between reasoning, action, and dynamics of environments, and thus exhibits more than $17\times$ sample efficiency improvements and generalization in comparison with previous works. During inference, RIG first reasons about the next action, produces potential action, and then predicts the action outcomes, which offers the agent a chance to review and self-correct based on the imagination before taking real actions. Experimental results show that the synergy of reasoning and imagination not only improves the robustness, generalization, and interoperability of generalist policy but also enables test-time scaling to enhance overall performance.
Evolution of structure growth during dark energy domination: insights from the cross-correlation of DESI galaxies with CMB lensing and galaxy magnification
2025-03-31
Noah Sailer, Joseph DeRose, Simone Ferraro, Shi-Fan Chen, Rongpu Zhou, Martin White, Joshua Kim, Mathew Madhavacheril
We use a Hybrid Effective Field Theory (HEFT) model to constrain the evolution of low-redshift $(z\lesssim0.4)$ matter fluctuations by cross-correlating DESI Bright Galaxy Survey (BGS) legacy imaging with the latest CMB lensing maps from Planck and ACT. Our tomographic BGS analysis finds that the evolution and amplitude of matter fluctuations align with CMB-conditioned $\Lambda$CDM predictions. When including DESI Baryon Acoustic Oscillation (BAO) measurements we obtain $\sigma_8 = 0.876^{+0.051}_{-0.067}$ from BGS alone. Jointly analyzing BGS and Luminous Red Galaxy (LRG) cross-correlations with the same CMB lensing maps yields $\sigma_8 = 0.791\pm0.021$. As a complementary approach we isolate the galaxy magnification signal from the cross-correlation of non-overlapping BGS and LRG photometric redshift bins, ruling out the null-magnification hypothesis at $11\sigma$. For the first time, we constrain structure growth from the (finite-difference calibrated) galaxy magnification signal and find $\sigma_8=0.720\pm0.047\,\,({\rm stat.})\pm0.050\,\,({\rm sys.})$ when adopting a linear bias model and including BAO data.
Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models
2025-03-31
Rui Wang, Hongru Wang, Boyang Xue, Jianhui Pang, Shudong Liu, Yi Chen, Jiahao Qiu, Derek Fai Wong, Heng Ji, Kam-Fai Wong
Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to perform complex reasoning tasks, transitioning from fast and intuitive thinking (System 1) to slow and deep reasoning (System 2). While System 2 reasoning improves task accuracy, it often incurs substantial computational costs due to its slow thinking nature and inefficient or unnecessary reasoning behaviors. In contrast, System 1 reasoning is computationally efficient but leads to suboptimal performance. Consequently, it is critical to balance the trade-off between performance (benefits) and computational costs (budgets), giving rise to the concept of reasoning economy. In this survey, we provide a comprehensive analysis of reasoning economy in both the post-training and test-time inference stages of LLMs, encompassing i) the cause of reasoning inefficiency, ii) behavior analysis of different reasoning patterns, and iii) potential solutions to achieve reasoning economy. By offering actionable insights and highlighting open challenges, we aim to shed light on strategies for improving the reasoning economy of LLMs, thereby serving as a valuable resource for advancing research in this evolving area. We also provide a public repository to continually track developments in this fast-evolving field.
Which LIME should I trust? Concepts, Challenges, and Solutions
2025-03-31
Patrick Knab, Sascha Marton, Udo Schlegel, Christian Bartelt
As neural networks become dominant in essential systems, Explainable Artificial Intelligence (XAI) plays a crucial role in fostering trust and detecting potential misbehavior of opaque models. LIME (Local Interpretable Model-agnostic Explanations) is among the most prominent model-agnostic approaches, generating explanations by approximating the behavior of black-box models around specific instances. Despite its popularity, LIME faces challenges related to fidelity, stability, and applicability to domain-specific problems. Numerous adaptations and enhancements have been proposed to address these issues, but the growing number of developments can be overwhelming, complicating efforts to navigate LIME-related research. To the best of our knowledge, this is the first survey to comprehensively explore and collect LIME's foundational concepts and known limitations. We categorize and compare its various enhancements, offering a structured taxonomy based on intermediate steps and key issues. Our analysis provides a holistic overview of advancements in LIME, guiding future research and helping practitioners identify suitable approaches. Additionally, we provide a continuously updated interactive website (https://patrick-knab.github.io/which-lime-to-trust/), offering a concise and accessible overview of the survey.
Unveiling the Fast Acceleration of AGN-Driven Winds at Kiloparsec Scales
2025-03-31
Cosimo Marconcini, Alessandro Marconi, Giovanni Cresci, Filippo Mannucci, Lorenzo Ulivi, Giacomo Venturi, Martina Scialpi, Giulia Tozzi, Francesco Belfiore, Elena Bertola, Stefano Carniani, Elisa Cataldi, Avinanda Chakraborty, Quirino D'Amato, Enrico Di Teodoro, Anna Feltre, Michele Ginolfi, Bianca Moreschini, Nicole Orientale, Bartolomeo Trefoloni, Andrew King
Supermassive black holes at the centre of galaxies gain mass through accretion disks. Models predict that quasi-spherical winds, expelled by the black hole during active accretion phases, have a key role in shaping galaxy evolution by regulating star formation, the distribution of metals over kiloparsec scales, and by sweeping ambient gas to the outskirts and beyond of galaxies. Nonetheless, the mechanism driving these outflows and the amount of energy exchanged between the wind and the galaxy's interstellar medium remain unclear. Here, we present a detailed analysis of the kinematical properties of winds in a sample of nearby active galaxies using the novel kinematic tool MOKA3D, which takes into account the clumpy nature of the ISM. We find remarkable similarities among the properties of the outflows in all the galaxies examined. In particular, we provide the first evidence that outflows exhibit a regular trend in radial velocity, initially constant or slightly decreasing, followed by rapid acceleration starting at approximately 1 kpc from the nucleus, despite the seemingly complex kinematics observed. The observed behavior aligns with our current theoretical understanding of Active Galactic Nuclei outflows, where a momentum-driven phase transitions to an energy-conserving phase just beyond approximately 1 kpc. The constant velocity of the momentum-driven wind is then rapidly accelerated following the inefficient Compton cooling of post-shock material and the transition to energy conservation. The measured radial terminal velocities of the outflows are always larger than the escape velocities from the host galaxies, confirming the key role of outflows in shaping the galaxy properties and evolution, as a manifestation of AGN feedback. Our results, only made possible by our novel kinematic analysis tool, are crucial to understand the origin and the powering mechanism of these winds.
Augmenting Expert Cognition in the Age of Generative AI: Insights from Document-Centric Knowledge Work
2025-03-31
Alexa Siu, Raymond Fok
As Generative AI (GenAI) capabilities expand, understanding how to preserve and develop human expertise while leveraging AI's benefits becomes increasingly critical. Through empirical studies in two contexts -- survey article authoring in scholarly research and business document sensemaking -- we examine how domain expertise shapes patterns of AI delegation and information processing among knowledge workers. Our findings reveal that while experts welcome AI assistance with repetitive information foraging tasks, they prefer to retain control over complex synthesis and interpretation activities that require nuanced domain understanding. We identify implications for designing GenAI systems that support expert cognition. These include enabling selective delegation aligned with expertise levels, preserving expert agency over critical analytical tasks, considering varying levels of domain expertise in system design, and supporting verification mechanisms that help users calibrate their reliance while deepening expertise. We discuss the inherent tension between reducing cognitive load through automation and maintaining the deliberate practice necessary for expertise development. Lastly, we suggest approaches for designing systems that provide metacognitive support, moving beyond simple task automation toward actively supporting expertise development. This work contributes to our understanding of how to design AI systems that augment rather than diminish human expertise in document-centric workflows.
Thermal transport in superconductor heterostructures: some recent progress
2025-03-31
Paramita Dutta
This article reviews recent advances in low-temperature electronic thermal transport properties of thermally biased superconductor heterostructures focusing on the two-terminal transport. Since the last decade, ferromagnetism has been widely used to enhance the thermoelectricity in heterostructures based on ordinary superconductors. The possibility of getting giant thermoelectric effects with optimum thermal conductance by breaking the electron-hole symmetry of the ordinary superconductor boosted the research in this direction. Recently, attention has been paid to the role of triplet Cooper pairs that emerged in ferromagnetic junctions and the possibility of advanced applications. Other forms of magnetism, specifically antiferromagnetism and altermagnetism, have been investigated to unravel the behavior of the thermal and charge current in thermally biased junctions. In parallel to ordinary superconductors, junctions with unconventional superconductors have been explored for the same purpose. Thermal transport in superconducting bilayers has been studied using advanced materials like Dirac and topological materials, including Weyl semimetals. Significant attention has been paid to thermally biased topological Josephson junctions to explore the phase-tunable current in recent times. Weyl Josephson junctions, multi-terminal Josephson junctions, and various other multilayer junctions have also been studied to engineer large thermoelectric effects and various functionalities with potential applications in superconductor-based thermal device components.
A direct detection method of galaxy intrinsic ellipticity-gravitational shear correlation in non-linear regimes using self-calibration
2025-03-31
Avijit Bera, Leonel Medina Varela, Vinu Sooriyaarachchi, Mustapha Ishak, Carter Williams, The LSST Dark Energy Science Collaboration
Intrinsic alignment (IA) of galaxies is a challenging source of contamination in the Cosmic shear (GG) signals. The galaxy intrinsic ellipticity-gravitational shear (IG) correlation is generally the most dominant component of such contamination for cross-correlating redshift bins. The self-calibration (SC) method is one of the most effective techniques to mitigate such contamination from the GG signal. In a photometric survey, the SC method first extracts the galaxy number density-galaxy intrinsic ellipticity (gI) correlation from the observed galaxy-galaxy lensing correlation using the redshift dependence of lens-source pairs. The IG correlation is computed through a scaling relation using the gI correlation and other lensing observables. We extend the SC method beyond the linear regime by modifying its scaling relation which can account for the non-linear galaxy bias model and various IA models. In this study, we provide a framework to detect the IG correlation for the redshift bins for source galaxies for the proposed year 1 survey of the Rubin Legacy Survey of Space and Time (LSST Y1). We tested the method for the tidal alignment and tidal torquing (TATT) model of IA and we found that the scaling relation is accurate within 10$\%$ and 20$\%$ for cross-correlating and auto-correlating redshift bins, respectively. Hence the suppression of IG contamination can be accomplished with a factor of 10 and 5, for cross-correlating and auto-correlating redshift bins, respectively. We tested the method's robustness and found that the suppression of IG contamination by a factor of 5 is still achievable for all combinations of cross-correlating bins even with the inclusion of a moderate amount of uncertainties on IA and bias parameters, respectively. [Abridged]
Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review
2025-03-31
Bruno Deprez, Wei Wei, Wouter Verbeke, Bart Baesens, Kevin Mets, Tim Verdonck
Financial institutions are required by regulation to report suspicious financial transactions related to money laundering. Therefore, they need to constantly monitor vast amounts of incoming and outgoing transactions. A particular challenge in detecting money laundering is that money launderers continuously adapt their tactics to evade detection. Hence, detection methods need constant fine-tuning. Traditional machine learning models suffer from catastrophic forgetting when fine-tuning the model on new data, thereby limiting their effectiveness in dynamic environments. Continual learning methods may address this issue and enhance current anti-money laundering (AML) practices, by allowing models to incorporate new information while retaining prior knowledge. Research on continual graph learning for AML, however, is still scarce. In this review, we critically evaluate state-of-the-art continual graph learning approaches for AML applications. We categorise methods into replay-based, regularization-based, and architecture-based strategies within the graph neural network (GNN) framework, and we provide in-depth experimental evaluations on both synthetic and real-world AML data sets that showcase the effect of the different hyperparameters. Our analysis demonstrates that continual learning improves model adaptability and robustness in the face of extreme class imbalances and evolving fraud patterns. Finally, we outline key challenges and propose directions for future research.
As enthusiasm for scaling computation (data and parameters) in the pretraining era gradually diminished, test-time scaling (TTS), also referred to as ``test-time computing'' has emerged as a prominent research focus. Recent studies demonstrate that TTS can further elicit the problem-solving capabilities of large language models (LLMs), enabling significant breakthroughs not only in specialized reasoning tasks, such as mathematics and coding, but also in general tasks like open-ended Q&A. However, despite the explosion of recent efforts in this area, there remains an urgent need for a comprehensive survey offering a systemic understanding. To fill this gap, we propose a unified, multidimensional framework structured along four core dimensions of TTS research: what to scale, how to scale, where to scale, and how well to scale. Building upon this taxonomy, we conduct an extensive review of methods, application scenarios, and assessment aspects, and present an organized decomposition that highlights the unique functional roles of individual techniques within the broader TTS landscape. From this analysis, we distill the major developmental trajectories of TTS to date and offer hands-on guidelines for practical deployment. Furthermore, we identify several open challenges and offer insights into promising future directions, including further scaling, clarifying the functional essence of techniques, generalizing to more tasks, and more attributions.
GPU-centric Communication Schemes for HPC and ML Applications
2025-03-31
Naveen Namashivayam
Compute nodes on modern heterogeneous supercomputing systems comprise CPUs, GPUs, and high-speed network interconnects (NICs). Parallelization is identified as a technique for effectively utilizing these systems to execute scalable simulation and deep learning workloads. The resulting inter-process communication from the distributed execution of these parallel workloads is one of the key factors contributing to its performance bottleneck. Most programming models and runtime systems enabling the communication requirements on these systems support GPU-aware communication schemes that move the GPU-attached communication buffers in the application directly from the GPU to the NIC without staging through the host memory. A CPU thread is required to orchestrate the communication operations even with support for such GPU-awareness. This survey discusses various available GPU-centric communication schemes that move the control path of the communication operations from the CPU to the GPU. This work presents the need for the new communication schemes, various GPU and NIC capabilities required to implement the schemes, and the potential use-cases addressed. Based on these discussions, challenges involved in supporting the exhibited GPU-centric communication schemes are discussed.
2025-03-31
Saab Mansour, Leonardo Perelli, Lorenzo Mainetti, George Davidson, Stefano D'Amato
In e-commerce, behavioral data is collected for decision making which can be costly and slow. Simulation with LLM powered agents is emerging as a promising alternative for representing human population behavior. However, LLMs are known to exhibit certain biases, such as brand bias, review rating bias and limited representation of certain groups in the population, hence they need to be carefully benchmarked and aligned to user behavior. Ultimately, our goal is to synthesise an agent population and verify that it collectively approximates a real sample of humans. To this end, we propose a framework that: (i) creates synthetic shopping agents by automatically mining personas from anonymised historical shopping data, (ii) equips agents with retail-specific tools to synthesise shopping sessions and (iii) introduces a novel alignment suite measuring distributional differences between humans and shopping agents at the group (i.e. population) level rather than the traditional "individual" level. Experimental results demonstrate that using personas improves performance on the alignment suite, though a gap remains to human behaviour. We showcase an initial application of our framework for automated agentic A/B testing and compare the findings to human results. Finally, we discuss applications, limitations and challenges setting the stage for impactful future work.
Computational Orthodontic Force Simulation: A Review
2025-03-31
Waheed Ahmad, Jing Xiong, Zeyang Xia
In orthodontic treatment, the biological response of the tooth, periodontal ligament, and bone complex to orthodontic force is crucial in influencing treatment outcomes. The challenge lies in accurately measuring, estimating, and predicting these forces during clinical procedures. This review aims to fill the gap in the literature by systematically summarizing existing research on orthodontic force simulation, examining common loading techniques and technologies, and discussing the potential for refining the orthodontic force simulation process. The literature was comprehensively reviewed, with an emphasis on the exploration of the biological mechanism of tooth movement. Studies were categorized based on force-loading techniques for both fixed and invisible orthodontic appliances. Finite element (FE) analysis stands out as the predominant technique for orthodontic force simulation, with a significant focus on fixed orthodontics but limited emphasis on invisible orthodontics. Current orthodontic force simulations tend to be fragmented, often considering only the instantaneous response to applied forces. There exists an urgent demand for a sophisticated analytical simulation model. Such a model, possibly leveraging advanced technologies like deep learning, holds the promise of forecasting orthodontic treatment outcomes with heightened precision and efficiency.
Recent results on causality in noncommutative space-time are reviewed. We study, in particular, quantum causal structures in 1+1 dimensional kappa Minkowski space-time. This later is described by a twisted Lorentzian Spectral Triple build with a twisted set of derivatives. Investigation of causality provides a quantum constraint, which is a quantum analog of the speed light limits
Computational challenges of 21st century Global Astrometry
2025-03-31
Beatrice Bucciarelli
Major advancements in space science and detector technology brought about a revolution in global astrometry, the science of measuring distances and motions of stars in the Milky Way and in the local universe. From the first ESA astrometric mission HIPPARCOS of the early 80s to the current Gaia mission, the data volume and computational complexity of the full reduction process has increased by several orders of magnitude, requiring high-performance computing and data throughput. We review the principles and computational complexity of general global astrometric models that lead to the statistical treatment of an extra-large, highly non-linear estimation problem. Some numerical aspects of inspecting Gaia's proper motions to find cosmological signals at all scales are also addressed.
What is the Funniest Number? An investigation of numerical humor
2025-03-31
E. G. Pottebaum
In a preliminary study of numerical humor, we propose the Perceived Specificity Hypothesis (PSH). The PSH states that, for nonnegative integers < 100, the funniness of a number increases with its apparent precision. A survey of 68 individuals supports the veracity of this hypothesis and indicates that oddly specific numbers tend to be funniest. Our results motivate future study in this novel subfield.
Low-energy electron microscopy as a tool for analysis of self-assembled molecular layers on surfaces
2025-03-31
Jan Čechal, Pavel Procházka
Low-energy electron microscopy (LEEM) is a surface science method that works primarily in the UHV environment. It provides information complementary to the other established techniques: it extends the limited view of scanning probe microscopies from nanometers to micrometers and measurement time down to tens of milliseconds, enabling to visualize the changes during sample treatment, e.g., annealing, deposition, and gas or light exposure. From the point of structural analysis, it allows the measurement of diffraction patterns from an area of diameter below 200 nm and imaging of phase distribution on the surfaces either through dark-filed imaging or LEEM-I(V) fingerprinting. The advanced modes provide local angle-resolved photoelectron spectra and surface potential distribution. In this review, we aim to describe the utilization of LEEM to study self-assembled molecular structures on solid surfaces. We present the LEEM instrumentation and analysis of measured data in a tutorial way to provide the necessary background knowledge to enter the field. In the second part, we summarize the knowledge obtained by LEEM for several selected systems, which points to the strength of LEEM in understanding the self-assembled molecular systems and its synergy with other surface science techniques.
Foundation Models For Seismic Data Processing: An Extensive Review
2025-03-31
Fabian Fuchs, Mario Ruben Fernandez, Norman Ettrich, Janis Keuper
Seismic processing plays a crucial role in transforming raw data into high-quality subsurface images, pivotal for various geoscience applications. Despite its importance, traditional seismic processing techniques face challenges such as noisy and damaged data and the reliance on manual, time-consuming workflows. The emergence of deep learning approaches has introduced effective and user-friendly alternatives, yet many of these deep learning approaches rely on synthetic datasets and specialized neural networks. Recently, foundation models have gained traction in the seismic domain, due to their success in natural imaging. This paper investigates the application of foundation models in seismic processing on the tasks: demultiple, interpolation, and denoising. It evaluates the impact of different model characteristics, such as pre-training technique and neural network architecture, on performance and efficiency. Rather than proposing a single seismic foundation model, this paper critically examines various natural image foundation models and suggest some promising candidates for future exploration.
Current status of real-time imaging of antiproton annihilation in biological targets
2025-03-31
Zlatko Dimcovski, Michael Doser
Following a successful pioneering study of the biological effects of antimatter ( the AD-4/ACE experiment at CERN), the use of antiprotons in clinical radiotherapy became a very serious possibility. A major part of any future radiotherapy using antiprotons will be real-time imaging of antiproton annihilation in biological targets. In principle, real-time imaging is possible thanks to the secondary particles, neutral pions and charger pions, produced by the annihilation of antiprotons in the cells they come to a stop in.Neutral pions decay dominantly and almost instantaneously into a photon pair, and one approach to real-time imaging is based on the detection of these high-energy gammas. An alternative is based on the detection of charged pions, which have lifetimes almost a billion times longer than neutral pions. This paper is a brief review of this field of research, from the first real-time imaging achieved by the AD-4/ACE experiment in 2004 to the present day.
IMPACT: A Generic Semantic Loss for Multimodal Medical Image Registration
2025-03-31
Valentin Boussot, Cédric Hémon, Jean-Claude Nunes, Jason Downling, Simon Rouzé, Caroline Lafond, Anaïs Barateau, Jean-Louis Dillenseger
Image registration is fundamental in medical imaging, enabling precise alignment of anatomical structures for diagnosis, treatment planning, image-guided interventions, and longitudinal monitoring. This work introduces IMPACT (Image Metric with Pretrained model-Agnostic Comparison for Transmodality registration), a novel similarity metric designed for robust multimodal image registration. Rather than relying on raw intensities, handcrafted descriptors, or task-specific training, IMPACT defines a semantic similarity measure based on the comparison of deep features extracted from large-scale pretrained segmentation models. By leveraging representations from models such as TotalSegmentator, Segment Anything (SAM), and other foundation networks, IMPACT provides a task-agnostic, training-free solution that generalizes across imaging modalities. These features, originally trained for segmentation, offer strong spatial correspondence and semantic alignment capabilities, making them naturally suited for registration. The method integrates seamlessly into both algorithmic (Elastix) and learning-based (VoxelMorph) frameworks, leveraging the strengths of each. IMPACT was evaluated on five challenging 3D registration tasks involving thoracic CT/CBCT and pelvic MR/CT datasets. Quantitative metrics, including Target Registration Error and Dice Similarity Coefficient, demonstrated consistent improvements in anatomical alignment over baseline methods. Qualitative analyses further highlighted the robustness of the proposed metric in the presence of noise, artifacts, and modality variations. With its versatility, efficiency, and strong performance across diverse tasks, IMPACT offers a powerful solution for advancing multimodal image registration in both clinical and research settings.
Phase Separation in Mixtures of Nematic and Isotropic Fluids
2025-03-31
Margarida M. Telo da Gama, Rodrigo C. V. Coelho
Mixtures of nematic liquid crystals and isotropic fluids display a diverse range of phase behaviors, arising from the coupling between orientational order and concentration fluctuations. In this review, we introduce a simplified mathematical framework that integrates the Landau-de Gennes free energy for nematic ordering with the Cahn-Hilliard free energy for phase separation. We derive the corresponding governing equations and analyze the stability of uniform phases, along with the resulting interfacial phenomena. The review concludes with a brief discussion highlighting key differences in phase separation between mixtures of isotropic fluids with passive and active nematics.
Systematic Search for FFPs in KMTNet Full-Frame Images. I. Photometry Pipeline
2025-03-31
Qiyue Qian, Hongjing Yang, Weicheng Zang, Yoon-Hyun Ryu, Shude Mao, Renkun Kuang, Jiyuan Zhang, Michael D. Albrow, Sun-Ju Chung, Andrew Gould, Cheongho Han, Kyu-Ha Hwang, Youn Kil Jung, In-Gu Shin, Yossi Shvartzvald, Jennifer C. Yee, Sang-Mok Cha, Dong-Jin Kim, Hyoun-Woo Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge
To exhume the buried signatures of free-floating planets (FFPs) with small angular Einstein radius $\theta_\mathrm{E}$, we build a new full-frame difference image for the Korean Microlensing Telescope Network (KMTNet) survey based on the newly optimized pySIS package. We introduce the detailed processes of the new pipeline, including frame registration, difference image analysis, and light curve extraction. To test this pipeline, we extract the light curves for 483,068 stars with $I \lesssim 17$ and conduct a model-independent search for microlensing events. The search finds 36 microlensing events, including five new events and six events discovered by other collaborations but missed by previous KMTNet searches. We find that the light curves from the new pipeline are precise enough to be sensitive to FFPs with $\theta_\mathrm{E} \sim 1~\mu$as. Using the new pipeline, a complete FFP search on the eight-year KMTNet images can be finished within six months and then yield the FFP mass function. The new pipeline can be used for a new KMTNet AlertFinder system, with significantly reduced false positives.
4D mmWave Radar in Adverse Environments for Autonomous Driving: A Survey
2025-03-31
Xiangyuan Peng, Miao Tang, Huawei Sun, Lorenzo Servadei, Robert Wille
Autonomous driving systems require accurate and reliable perception. However, adverse environments, such as rain, snow, and fog, can significantly degrade the performance of LiDAR and cameras. In contrast, 4D millimeter-wave (mmWave) radar not only provides 3D sensing and additional velocity measurements but also maintains robustness in challenging conditions, making it increasingly valuable for autonomous driving. Recently, research on 4D mmWave radar under adverse environments has been growing, but a comprehensive survey is still lacking. To bridge this gap, this survey comprehensively reviews the current research on 4D mmWave radar under adverse environments. First, we present an overview of existing 4D mmWave radar datasets encompassing diverse weather and lighting scenarios. Next, we analyze methods and models according to different adverse conditions. Finally, the challenges faced in current studies and potential future directions are discussed for advancing 4D mmWave radar applications in harsh environments. To the best of our knowledge, this is the first survey specifically focusing on 4D mmWave radar in adverse environments for autonomous driving.
Transformers are the cornerstone of modern large language models, but their quadratic computational complexity limits efficiency in long-sequence processing. Recent advancements in Mamba, a state space model (SSM) with linear complexity, offer promising efficiency gains but suffer from unstable contextual learning and multitask generalization. This paper proposes TransMamba, a novel framework that unifies Transformer and Mamba through shared parameter matrices (e.g., QKV and CBx), and thus could dynamically switch between attention and SSM mechanisms at different token lengths and layers. We design the Memory converter to bridge Transformer and Mamba by converting attention outputs into SSM-compatible states, ensuring seamless information flow at TransPoints where the transformation happens. The TransPoint scheduling is also thoroughly explored for further improvements. We conducted extensive experiments demonstrating that TransMamba achieves superior training efficiency and performance compared to baselines, and validated the deeper consistency between Transformer and Mamba paradigms, offering a scalable solution for next-generation sequence modeling.
A robot-assisted pipeline to rapidly scan 1.7 million historical aerial photographs
2025-03-31
Sheila Masson, Alan Potts, Allan Williams, Steve Berggreen, Kevin McLaren, Sam Martin, Eugenio Noda, Nicklas Nordfors, Nic Ruecroft, Hannah Druckenmiller, Solomon Hsiang, Andreas Madestam, Anna Tompsett
During the 20th Century, aerial surveys captured hundreds of millions of high-resolution photographs of the earth's surface. These images, the precursors to modern satellite imagery, represent an extraordinary visual record of the environmental and social upheavals of the 20th Century. However, most of these images currently languish in physical archives where retrieval is difficult and costly. Digitization could revolutionize access, but manual scanning is slow and expensive. Here, we describe and validate a novel robot-assisted pipeline that increases worker productivity in scanning 30-fold, applied at scale to digitize an archive of 1.7 million historical aerial photographs from 65 countries.
Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents
2025-03-31
Shuo Ren, Pu Jian, Zhenjiang Ren, Chunlin Leng, Can Xie, Jiajun Zhang
As scientific research becomes increasingly complex, innovative tools are needed to manage vast data, facilitate interdisciplinary collaboration, and accelerate discovery. Large language models (LLMs) are now evolving into LLM-based scientific agents that automate critical tasks, ranging from hypothesis generation and experiment design to data analysis and simulation. Unlike general-purpose LLMs, these specialized agents integrate domain-specific knowledge, advanced tool sets, and robust validation mechanisms, enabling them to handle complex data types, ensure reproducibility, and drive scientific breakthroughs. This survey provides a focused review of the architectures, design, benchmarks, applications, and ethical considerations surrounding LLM-based scientific agents. We highlight why they differ from general agents and the ways in which they advance research across various scientific fields. By examining their development and challenges, this survey offers a comprehensive roadmap for researchers and practitioners to harness these agents for more efficient, reliable, and ethically sound scientific discovery.
TOPz: photometric redshifts using template fitting applied to GAMA survey
2025-03-31
E. Tempel, J. Laur, Z. R. Jones, R. Kipper, L. J. Liivamägi, D. Pandey, G. Sakteos, A. Tamm, A. N. Triantafyllaki, T. Tuvikene
Context. Accurate photometric redshift (photo-z) estimation is crucial for cosmological and galaxy evolution studies, especially with the advent of large-scale photometric surveys. Aims. We develop a photo-z estimation code called TOPz (Tartu Observatory Photo-z) and apply it to the GAMA photometric catalogue. Methods. TOPz employs a Bayesian template-fitting approach to estimate photo-z from marginalised redshift posteriors. Using nine-band photometric data from the GAMA project, we assess the accuracy of TOPz by comparing its photo-z estimates to available spectroscopic redshifts. We generate synthetic galaxy spectra using the CIGALE software and run template set optimisation. We improve the photometry by applying flux and flux uncertainty corrections. An analytical prior is then imposed on the resulting posteriors to refine the redshift estimates. Results. The photo-z estimates produced by TOPz show good agreement with the spectroscopic redshifts. We demonstrate the redshift accuracy across various magnitude bins and test how the flux corrections and posteriors reflect the actual uncertainty of the estimates. We show that the TOPz results are consistent with those obtained from other photo-z codes applied to the same data set. Additionally, TOPz estimates stellar masses as a by-product, comparable to those calculated by other methods. We made the GAMA photo-z catalogue and all the codes and scripts used for the analysis and figures publicly available. Conclusions. TOPz is an advanced photo-z estimation code that integrates flux corrections, physical priors, and template set optimisation to provide state-of-the-art photo-z among competing template-based redshift estimators. Future work will focus on incorporating additional photometric data and applying the TOPz algorithm to J-PAS narrow-band survey, further validating and enhancing its capabilities.
Key-Value cache (\texttt{KV} \texttt{cache}) compression has emerged as a promising technique to optimize Large Language Model (LLM) serving. It primarily decreases the memory consumption of \texttt{KV} \texttt{cache} to reduce the computation cost. Despite the development of many compression algorithms, their applications in production environments are still not prevalent. In this paper, we revisit mainstream \texttt{KV} \texttt{cache} compression solutions from a practical perspective. Our contributions are three-fold. First, we comprehensively review existing algorithmic designs and benchmark studies for \texttt{KV} \texttt{cache} compression and identify missing pieces in their performance measurement, which could hinder their adoption in practice. Second, we empirically evaluate representative \texttt{KV} \texttt{cache} compression methods to uncover two key issues that affect the computational efficiency: (1) while compressing \texttt{KV} \texttt{cache} can reduce memory consumption, current implementations (e.g., FlashAttention, PagedAttention) do not optimize for production-level LLM serving, resulting in suboptimal throughput performance; (2) compressing \texttt{KV} \texttt{cache} may lead to longer outputs, resulting in increased end-to-end latency. We further investigate the accuracy performance of individual samples rather than the overall performance, revealing the intrinsic limitations in \texttt{KV} \texttt{cache} compression when handling specific LLM tasks. Third, we provide tools to shed light on future \texttt{KV} \texttt{cache} compression studies and facilitate their practical deployment in production. They are open-sourced in \href{https://github.com/LLMkvsys/rethink-kv-compression}{https://github.com/LLMkvsys/rethink-kv-compression}.
A cost of capital approach to determining the LGD discount rate
2025-03-31
Janette Larney, Arno Botha, Gerrit Lodewicus Grobler, Helgard Raubenheimer
Loss Given Default (LGD) is a key risk parameter in determining a bank's regulatory capital. During LGD-estimation, realised recovery cash flows are to be discounted at an appropriate rate. Regulatory guidance mandates that this rate should allow for the time value of money, as well as include a risk premium that reflects the "undiversifiable risk" within these recoveries. Having extensively reviewed earlier methods of determining this rate, we propose a new approach that is inspired by the cost of capital approach from the Solvency II regulatory regime. Our method involves estimating a market-consistent price for a portfolio of defaulted loans, from which an associated discount rate may be inferred. We apply this method to mortgage and personal loans data from a large South African bank. The results reveal the main drivers of the discount rate to be the mean and variance of these recoveries, as well as the bank's cost of capital in excess of the risk-free rate. Our method therefore produces a discount rate that reflects both the undiversifiable risk of recovery recoveries and the time value of money, thereby satisfying regulatory requirements. This work can subsequently enhance the LGD-component within the modelling of both regulatory and economic capital.
Since the disruption in LLM technology brought about by the release of GPT-3 and ChatGPT, LLMs have shown remarkable promise in programming-related tasks. While code generation remains a popular field of research, code evaluation using LLMs remains a problem with no conclusive solution. In this paper, we focus on LLM-based code evaluation and attempt to fill in the existing gaps. We propose multi-agentic novel approaches using question-specific rubrics tailored to the problem statement, arguing that these perform better for logical assessment than the existing approaches that use question-agnostic rubrics. To address the lack of suitable evaluation datasets, we introduce two datasets: a Data Structures and Algorithms dataset containing 150 student submissions from a popular Data Structures and Algorithms practice website, and an Object Oriented Programming dataset comprising 80 student submissions from undergraduate computer science courses. In addition to using standard metrics (Spearman Correlation, Cohen's Kappa), we additionally propose a new metric called as Leniency, which quantifies evaluation strictness relative to expert assessment. Our comprehensive analysis demonstrates that question-specific rubrics significantly enhance logical assessment of code in educational settings, providing better feedback aligned with instructional goals beyond mere syntactic correctness.
An Empirical Study of Rust-Specific Bugs in the rustc Compiler
2025-03-31
Zixi Liu, Yang Feng, Yunbo Ni, Shaohua Li, Xizhe Yin, Qingkai Shi, Baowen Xu, Zhendong Su
Rust is gaining popularity for its well-known memory safety guarantees and high performance, distinguishing it from C/C++ and JVM-based languages. Its compiler, rustc, enforces these guarantees through specialized mechanisms such as trait solving, borrow checking, and specific optimizations. However, Rust's unique language mechanisms introduce complexity to its compiler, leading to Rust-specific compiler bugs that are less common in traditional compilers. With Rust's increasing adoption in safety-critical domains, understanding these language mechanisms and their impact on compiler bugs is essential for improving the reliability of both rustc and Rust programs. Yet, we still lack a large-scale, detailed, and in-depth study of Rust-specific bugs in rustc. To bridge this gap, this work conducts a comprehensive and systematic study of Rust-specific bugs in rustc, with a particular focus on the components that support its unique language features. Our analysis examines issues and fixes reported between 2022 and 2024, with a manual review of 301 valid issues. We categorize these bugs based on their causes, symptoms, affected compilation stages, and test case characteristics. Additionally, we evaluate existing rustc testing tools to assess their effectiveness and limitations. Our key findings include: (1) rustc bugs primarily arise from Rust's type system and lifetime model, with frequent errors in the High-Level Intermediate Representation (HIR) and Mid-Level Intermediate Representation (MIR) modules due to complex checkers and optimizations; (2) bug-revealing test cases often involve unstable features, advanced trait usages, lifetime annotations, standard APIs, and specific optimization levels; (3) while both valid and invalid programs can trigger bugs, existing testing tools struggle to detect non-crash errors, underscoring the need for further advancements in rustc testing.
2025-03-31
Nadine du Toit, Kristian K. Müller-Nedebock
A novel field theoretical approach towards modelling dynamic networking in complex systems is presented. An equilibrium networking formalism which utilises Gaussian fields is adapted to model the dynamics of particles that can bind and unbind from one another. Here, \textit{networking} refers to the introduction of instantaneous co-localisation constraints and does not necessitate the formation of a well-defined transient or persistent network. By combining this formalism with Martin-Siggia-Rose generating functionals, a weighted generating functional for the networked system is obtained. The networking formalism introduces spatial and temporal constraints into the Langevin dynamics, via statistical weights, thereby accounting for all possible configurations in which particles can be networked to one another. A simple example of Brownian particles which can bind and unbind from one another demonstrates the tool and that this leads to results for physical quantities in a collective description. Applying the networking formalism to model the dynamics of cross-linking polymers in a mixture, we can calculate the average number of networking instances. As expected, the dynamic structure factors for each type of polymer show that the system collapses once networking is introduced, but that the addition of a repulsive time-dependent potential above a minimum strength prevents this. The examples presented in this paper indicate that this novel approach towards modelling dynamic networking could be applied to a range of synthetic and biological systems to obtain theoretical predictions for experimentally verifiable quantities.
In the report by BICEP and Keck collaborations, the tensor-to-scalar ratio is $r_{0.05}<0.036$ (95\% C.L.) and $ <1.3\sigma$ non-zero (with pre-DESI BAO data). However, recent datasets have significantly shifted the bestfit values of relevant $\Lambda$CDM cosmological parameters, and thus possibly alter the amplitude of lensing B-mode spectrum, which would affect the search for $r$. Here, the joint analysis of Planck and BICEP/Keck data with DESI DR2 reveals that the lower bound of $r_{0.05}$ is $2.0\sigma$ and $2.1\sigma$ non-zero for PantheonPlus and DES-Y5, respectively, and the bestfit $r$ is $r_{0.05}\simeq 0.01$. The results are consistent with those with DESI DR1, but slightly strengthened. There might be still systematic uncertainties in B-mode measurements due to the foreground contamination, however, our work is to not say what about the value of $r$, but emphasize that the detection for $r$ is model-dependent and depends potentially on our insight into the dark universe, highlighting the important role of cosmological surveys in comprehending our very early universe.
Scenarios for the Deployment of Automated Vehicles in Europe
2025-03-31
Louison Duboz, Ioan Cristinel Raileanu, Jette Krause, Ana Norman-López, Matthias Weitzel, Biagio Ciuffo
The deployment of Automated Vehicles (AVs) is expected to address road transport externalities (e.g., safety, traffic, environmental impact, etc.). For this reason, a legal framework for their large-scale market introduction and deployment is currently being developed in the European Union. Despite the first steps towards road transport automation, the timeline for full automation and its potential economic benefits remains uncertain. The aim of this paper is twofold. First, it presents a methodological framework to determine deployment pathways of the five different levels of automation in EU27+UK to 2050 under three scenarios (i.e., slow, medium baseline and fast) focusing on passenger vehicles. Second, it proposes an assessment of the economic impact of AVs through the calculation of the value-added. The method to define assumptions and uptake trajectories involves a comprehensive literature review, expert interviews, and a model to forecast the new registrations of different levels of automation. In this way, the interviews provided insights that complemented the literature and informed the design of assumptions and deployment trajectories. The added-value assessment shows additional economic activity due to the introduction of automated technologies in all uptake scenarios.
DiffScale: Continuous Downscaling and Bias Correction of Subseasonal Wind Speed Forecasts using Diffusion Models
2025-03-31
Maximilian Springenberg, Noelia Otero, Yuxin Xue, Jackie Ma
Renewable resources are strongly dependent on local and large-scale weather situations. Skillful subseasonal to seasonal (S2S) forecasts -- beyond two weeks and up to two months -- can offer significant socioeconomic advantages to the energy sector. This study aims to enhance wind speed predictions using a diffusion model with classifier-free guidance to downscale S2S forecasts of surface wind speed. We propose DiffScale, a diffusion model that super-resolves spatial information for continuous downscaling factors and lead times. Leveraging weather priors as guidance for the generative process of diffusion models, we adopt the perspective of conditional probabilities on sampling super-resolved S2S forecasts. We aim to directly estimate the density associated with the target S2S forecasts at different spatial resolutions and lead times without auto-regression or sequence prediction, resulting in an efficient and flexible model. Synthetic experiments were designed to super-resolve wind speed S2S forecasts from the European Center for Medium-Range Weather Forecast (ECMWF) from a coarse resolution to a finer resolution of ERA5 reanalysis data, which serves as a high-resolution target. The innovative aspect of DiffScale lies in its flexibility to downscale arbitrary scaling factors, enabling it to generalize across various grid resolutions and lead times -without retraining the model- while correcting model errors, making it a versatile tool for improving S2S wind speed forecasts. We achieve a significant improvement in prediction quality, outperforming baselines up to week 3.
Surveying Uncertainty Representation: A Unified Model for Cyber-Physical Systems
2025-03-31
Johannes Mäkelburg, Diego Perez-Palacin, Raffaela Mirandola, Maribel Acosta
Cyber-Physical Systems (CPS) operate in dynamic environments, leading to different types of uncertainty. This work provides a comprehensive review of uncertainty representations and categorizes them based on the dimensions used to represent uncertainty. Through this categorization, key gaps and limitations in existing approaches are identified. To address these issues, a Conceptual Model of Uncertainty Representations in CPS is introduced, integrating and extending existing models. Its applicability is demonstrated through examples from the automotive domain, showing its effectiveness in capturing and structuring uncertainty in real-world scenarios.
2025-03-31
George Pappas, Ippocratis D. Saltas, Laurent Eyer
We use the framework of microlensing to show that observations of binary systems, such as those made by {\it Gaia}, combined with follow-up weak lensing measurements, can provide a means to probe halos of exotic matter, possibly clumped around compact objects such as black holes. This could potentially cover a broad range of physical scenarios - from dark matter mini-halos to black holes with bosonic configurations around them, known as hair. Assuming that light can freely propagate through the halo of the exotic matter, the companion star will produce characteristic, sizable lensing signatures due to the deviation from a central gravitational potential. The signature of the multiple images, the magnification and the light-curve could be the smoking gun of such structures. We discuss how the precise observations of the {\it Gaia} survey offer an opportunity to search for new, yet undiscovered fundamental fields interacting gravitationally with baryons.
Evaluating small vision-language models as AI assistants for radio astronomical source analysis tasks
2025-03-31
S. Riggi, T. Cecconello, A. Pilzer, S. Palazzo, N. Gupta, A. M. Hopkins, C. Trigilio, G. Umana
The advent of next-generation radio telescopes is set to transform radio astronomy by producing massive data volumes that challenge traditional processing methods. Deep learning techniques have shown strong potential in automating radio analysis tasks, yet are often constrained by the limited availability of large annotated datasets. Recent progress in self-supervised learning has led to foundational radio vision models, but adapting them for new tasks typically requires coding expertise, limiting their accessibility to a broader astronomical community. Text-based AI interfaces offer a promising alternative by enabling task-specific queries and example-driven learning. In this context, Large Language Models (LLMs), with their remarkable zero-shot capabilities, are increasingly used in scientific domains. However, deploying large-scale models remains resource-intensive, and there is a growing demand for AI systems that can reason over both visual and textual data in astronomical analysis. This study explores small-scale Vision-Language Models (VLMs) as AI assistants for radio astronomy, combining LLM capabilities with vision transformers. We fine-tuned the LLaVA VLM on a dataset of 59k radio images from multiple surveys, enriched with 38k image-caption pairs from the literature. The fine-tuned models show clear improvements over base models in radio-specific tasks, achieving ~30% F1-score gains in extended source detection, but they underperform pure vision models and exhibit ~20% drop on general multimodal tasks. Inclusion of caption data and LoRA fine-tuning enhances instruction-following and helps recover ~10% accuracy on standard benchmarks. This work lays the foundation for future advancements in radio VLMs, highlighting their potential and limitations, such as the need for better multimodal alignment, higher-quality datasets, and mitigation of catastrophic forgetting.
Narrow-Line Seyfert 1 Galaxies Beyond the Local X-ray Universe: An X-ray spectral sample
2025-03-31
Jiachen Jiang, Dominic J. Walton, Luigi C. Gallo, Andrew C. Fabian, Dirk Grupe, Richard McMahon, Christopher S. Reynolds, Andrew Young, Zhibo Yu, Honghui Liu, Zuobin Zhang
Narrow-line Seyfert 1 AGNs (NLS1s) represent a unique stage in the black hole growth history, characterised by low black hole masses of approximately $10^{6}$-$10^{8}$ solar masses and around-Eddington accretion rates. X-ray studies of NLS1s have largely been confined to the local Universe ($z < 0.2$), while their broad-line counterparts and radio-loud quasars have been more extensively investigated at higher redshifts. In this work, we conducted an X-ray spectral analysis for 14 SDSS-observed NLS1s at $z\approx1$ in the eRASS1 catalogue. We found that all of their eROSITA observations agree with the expected rest-frame 2 keV monochromatic luminosity given their rest-frame 2500 angstrom monochromatic luminosity, further supporting evidence of AGN emission. Second, when fitted with a power-law model, most continuum spectra between 0.7-7 keV in their rest frames necessitate photon indices $\Gamma\gtrsim2.5$. Notably, the highest photon index of around 4.7 in one of our NLS1 AGNs hints at a significant contribution from soft excess emission. Finally, our analysis demonstrates that we can align the Eddington ratios with optical measurements by applying a correction factor between 10-120 to their X-ray luminosity. Although measurement uncertainty remains considerable, our findings suggest that assumptions for the standard geometrically thin accretion disc model made in previous estimations of this correction factor may not apply to near or super-Eddington NLS1 AGNs. Finally, we also compare this sample with extremely variable nearby NLS1s and other X-ray-weak AGNs, such as JWST-observed, broad-line AGNs at $z=5-6$, and underscores the importance of deeper X-ray surveys for more X-ray-weak NLS1s.
A MeerKAT survey of nearby dwarf novae: I. New detections
2025-03-31
J. Kersten, E. Körding, P. A. Woudt, P. J. Groot, D. R. A. Williams, I. Heywood, D. L. Coppejans, C. Knigge, J. C. A. Miller-Jones, G. R. Sivakoff, R. Fender
A program to search for radio emission from dwarf-novae-type cataclysmic variables was conducted with the South African MeerKAT radio telescope. The dwarf novae RU Pegasi, V426 Ophiuchi and IP Pegasi were detected during outburst at L-band (1284 MHz central frequency). Previously, only one cataclysmic variable was radio-detected at a frequency this low. We now bring the number to four. With these three newly found radio-emitters, the population of dwarf novae confirmed to be radio-emitting at any frequency reaches 10 systems. We found that the radio luminosity is correlated with the optical luminosity. For V426 Ophiuchi and RU Pegasi we found a radio decline contemporary with the outburst's optical decline. The peak radio luminosity of dwarf novae in outburst is very similar to that of novalike Cataclysmic Variables and no correlation with orbital period is seen.
Reviewing the fundamentals and best practices to characterize microplastics using state-of-the-art quantum-cascade laser reflectance-absorbance spectroscopy
2025-03-31
Adrian Lopez-Rosales, Borja Ferreiro, Jose Andrade, Andreas Kerstan, Darren Robey, Soledad Muniategui
Microplastic pollution studies depend on reliable identification of the suspicious particles. Out of the various analytical techniques available to characterize them, infrared transflectance using a tuneable mid-IR quantum cascade laser is a high-throughput state-of-the-art imaging option, specifically Agilent QCL-LDIR (Quantum Cascade Laser Direct Infrared imaging). Its conceptual grounds are reviewed, instrumental developments are discussed, along with a review of applications and best practices to overcome obstacles/difficulties in routine measurements, namely: the spectral range, the variation of some peak intensities with the particles size, effects of the size of the particles, processing speed, and avoiding the use of measurement aliquots. Objective procedures to avoid too many false positives when identifying spectra and to distinguish fibers and fragments are given. These practices open a path to QCL-LDIR measurement standardization and potential use for microplastics monitoring, as requested by many governmental bodies in charge of setting environmental protection rules.
Dual-band Unified Exploration of Three CMZ Clouds (DUET). Cloud-wide census of continuum sources showing low spectral indices
2025-03-31
Fengwei Xu, Xing Lu, Ke Wang, Hauyu Baobab Liu, Adam Ginsburg, Tie Liu, Qizhou Zhang, Nazar Budaiev, Xindi Tang, Peter Schilke, Suinan Zhang, Sihan Jiao, Wenyu Jiao, Siqi Zheng, Beth Jones, J. M. Diederik Kruijssen, Cara Battersby, Daniel L. Walker, Elisabeth A. C. Mills, Jens Kauffmann, Steven N. Longmore, Thushara G. S. Pillai
The Milky Way's Central Molecular Zone (CMZ) is measured to form stars 10 times less efficiently than in the Galactic disk, based on emission from high-mass stars. However, the CMZ's low-mass protostellar population, which accounts for most of the initial stellar mass budget and star formation rate (SFR), is poorly constrained observationally due to limited sensitivity and resolution. We present the Dual-band Unified Exploration of Three CMZ Clouds (DUET) survey, targeting the 20 km/s Cloud, Sgr C, and Dust Ridge cloud e using the Atacama Large Millimeter/submillimeter Array (ALMA) at 1.3 and 3 mm. The mosaicked observations achieve a comparable resolution of 0.2-0.3" (~1600-2500 au) and a sky coverage of 8.3-10.4 square arcmin, respectively. We report 563 continuum sources at 1.3 mm and 330 at 3 mm, respectively, and a dual-band catalog with 450 continuum sources. These sources are marginally resolved at the 2,000 au resolution. We find a cloud-wide deviation (>70%) from commonly-used dust modified blackbody (MBB) models, characterized by either low spectral indices or low brightness temperatures. Three possible explanations for the deviation are discussed. (1) Optically thick Class 0/I Young stellar objects (YSOs) with very small beam filling factors can lead to lower brightness temperatures than what MBB models predict. (2) Large (mm/cm-sized) dust grains have more significant self-scattering, and therefore frequency-dependent albedo could cause lower spectral indices. (3) Free-free emission over 30 uJy can severely contaminate dust emission and cause low spectral indices for mJy sources in our sample, although the needed number of massive protostars (embedded UCHII regions) is infeasibly high for the normal stellar initial mass function. A reliable measurement of the SFR at low protostellar masses will require future work to distinguish between these possible explanations.
Pinalites: Optical properties and Quantum Magnetism of Heteroanionic A$_3$MO$_5$X$_2$ Compounds
2025-03-31
Daigorou Hirai
Heteroanionic compounds, which contain two or more types of anions, have emerged as a promising class of materials with diverse properties and functionalities. In this paper, I review the experimental findings on Ca3ReO5Cl2 and related com-pounds that exhibit remarkable pleochroism and novel quantum magnetism. I discuss how the heteroanionic coordination affects the optical and magnetic properties by modulating the d-orbital states of the transition metal ions and then compare these materials with other heteroanionic and monoanionic compounds and highlight the potential of A3MO5X2 materials for future exploration of materials and phenomena.
Recent proposals for the realization of time-reversal symmetry breaking and topological superconductivity in twisted nodal superconductors have led to a surge of theoretical and experimental studies of these systems, marking one of the newest entries in the rapidly growing field of moir\'e materials. The interplay between order parameters of the separate layers makes twisted superconductors unique, leading to additional emergent phenomena in regimes usually not of importance in moir\'e physics, such as bulk interfaces and large twist angles. We review the physics of twisted nodal superconductors, highlighting both similarities and qualitative differences with other moir\'e platforms. While inspired by the rise of moir\'e materials, the field is anchored in studies of unconventional superconductivity preceding the moir\'e era, which we discuss in detail. In addition to summarizing the developments at the present stage, we present a detailed outlook on the major open questions in the field and some of the most exciting future directions.
A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective
2025-03-31
Zhuoren Li, Guizhe Jin, Ran Yu, Zhiwen Chen, Nan Li, Wei Han, Lu Xiong, Bo Leng, Jia Hu, Ilya Kolmanovsky, Dimitar Filev
Reinforcement learning (RL), with its ability to explore and optimize policies in complex, dynamic decision-making tasks, has emerged as a promising approach to addressing motion planning (MoP) challenges in autonomous driving (AD). Despite rapid advancements in RL and AD, a systematic description and interpretation of the RL design process tailored to diverse driving tasks remains underdeveloped. This survey provides a comprehensive review of RL-based MoP for AD, focusing on lessons from task-specific perspectives. We first outline the fundamentals of RL methodologies, and then survey their applications in MoP, analyzing scenario-specific features and task requirements to shed light on their influence on RL design choices. Building on this analysis, we summarize key design experiences, extract insights from various driving task applications, and provide guidance for future implementations. Additionally, we examine the frontier challenges in RL-based MoP, review recent efforts to addresse these challenges, and propose strategies for overcoming unresolved issues.
SPHERE/ZIMPOL insights into discs around evolved stars: arcs, asymmetries and porous dust aggregates
2025-03-31
Kateryna Andrych, Devika Kamath, Hans Van Wincke, Akke Corporaa, Toon de Prins, Daniel Price, Steve Ertel, Jacques Kluska
Second-generation circumbinary discs around evolved binary stars, such as post-Asymptotic Giant Branch (post-AGB) stars, provide insights into poorly understood mechanisms of dust processing and disc evolution across diverse stellar environments. We present a multi-wavelength polarimetric survey of five evolved binary systems - AR Pup, HR 4049, HR 4226, U Mon, and V709 Car - using the SPHERE/ZIMPOL instrument. Post-AGB discs show significant polarimetric brightness at optical and near-IR wavelengths, often exceeding 1% of the system's total intensity. We also measured a maximum fractional polarization of the scattered light for AR Pup of ~0.7 in the V-band and ~0.55 in the I-band. To investigate wavelength-dependent polarization, we combine the SPHERE/ZIMPOL dataset with results from previous SPHERE/IRDIS studies. This analysis reveals that post-AGB discs exhibit a grey to blue polarimetric colour in the optical and near-IR. Along with high fractional polarization and polarized intensity distribution, these findings indicate that porous dust aggregates dominate the surface dust composition. We also find evidence of diverse disc geometries within the post-AGB sample, including arcs, asymmetries and variations in disc size across optical and near-IR wavelengths for some systems. Combining our findings with existing multi-technique studies, we question the classification of two systems in our sample, HR 4226 and V709 Car, which were originally identified as post-AGB binaries based on their near-IR excess. On comparing post-AGB discs to circumstellar environments around AGB stars and YSOs, we found that post-AGB systems exhibit a higher degree of polarization than single AGB stars and are comparable to the brightest protoplanetary discs around YSOs. Overall, our results reinforce the importance of polarimetric observations in probing dust properties and complex circumbinary structures.
Review and prospects of hot exozodiacal dust research for future exo-Earth direct imaging missions
2025-03-31
Steve Ertel, Tim D. Pearce, John H. Debes, Virginie C. Faramaz, William C. Danchi, Ramya M. Anche, Denis Defrère, Yasuhiro Hasegawa, Justin Hom, Florian Kirchschlager, Isabel Rebollido, Hélène Rousseau, Jeremy Scott, Karl Stapelfeldt, Thomas A. Stuber
Hot exozodiacal dust is dust in the innermost regions of planetary systems, at temperatures around 1000K to 2000K, and commonly detected by near-infrared interferometry. The phenomenon is poorly understood and has received renewed attention as a potential risk to a planned future space mission to image potentially habitable exoplanets and characterize their atmospheres (exo-Earth imaging) such as the Habitable Worlds Observatory (HWO). In this article, we review the current understanding of hot exozodiacal dust and its implications for HWO. We argue that the observational evidence suggests that the phenomenon is most likely real and indeed caused by hot dust, although conclusive proof in particular of the latter statement is still missing. Furthermore, we find that there exists as of yet no single model that is able to successfully explain the presence of the dust. We find that it is plausible and not unlikely that large amounts of hot exozodiacal dust in a system will critically limit the sensitivity of exo-Earth imaging observations around that star. It is thus crucial to better understood the phenomenon in order to be able to evaluate the actual impact on such a mission, and current and near-future observational opportunities for acquiring the required data exist. At the same time, hot exozodiacal dust (and warm exozodiacal dust closer to a system's habitable zone) has the potential to provide important context for HWO observations of rocky, HZ planets, constraining the environment in which these planets exist and hence to determine why a detected planet may be capable to sustain life or not.
Digital Twins in Biopharmaceutical Manufacturing: Review and Perspective on Human-Machine Collaborative Intelligence
2025-03-31
Mohammed Aatif Shahab, Francesco Destro, Richard D. Braatz
The biopharmaceutical industry is increasingly developing digital twins to digitalize and automate the manufacturing process in response to the growing market demands. However, this shift presents significant challenges for human operators, as the complexity and volume of information can overwhelm their ability to manage the process effectively. These issues are compounded when digital twins are designed without considering interaction and collaboration with operators, who are responsible for monitoring processes and assessing situations, particularly during abnormalities. Our review of current trends in biopharma digital twin development reveals a predominant focus on technology and often overlooks the critical role of human operators. To bridge this gap, this article proposes a collaborative intelligence framework that emphasizes the integration of operators with digital twins. Approaches to system design that can enhance operator trust and human-machine interface usability are presented. Moreover, innovative training programs for preparing operators to understand and utilize digital twins are discussed. The framework outlined in this article aims to enhance collaboration between operators and digital twins effectively by using their full capabilities to boost resilience and productivity in biopharmaceutical manufacturing.
A weak entanglement approximation for nuclear structure: a progress report
2025-03-31
Calvin W. Johnson, Oliver C. Gorton
We report on a recently proposed approach, inspired by quantum informationtheory, for calculating low-energy nuclear structure in the framework of the configuration-interaction shell-model. Empirical evidence has demonstrated that the many-proton and many-neutron partitions of nuclear configuration-interaction wave functions are weakly entangled, especially away from $N=Z$. This has been developed into a practical methodology, the Proton And Neutron Approximate Shell-model (PANASh). We review the basic ideas and present recent results. We also discuss some technical developments in calculations.
Identifying Dwarfs of MC Analog GalaxiEs (ID-MAGE): The Search for Satellites Around Low-Mass Hosts
2025-03-31
Laura Congreve Hunter, Burçin Mutlu-Pakdil, David J. Sand, Paul Bennet, Donghyeon J. Khim, Denija Crnojević, Amandine Doliva-Dolinsky, Emmanuel Durodola, Catherine Fielder, Rowan Goebel-Bain, Michael G. Jones, Ananthan Karunakaran, Kristine Spekkens, Dennis Zaritsky
We present results from ID-MAGE (Identifying Dwarfs of MC Analog GalaxiEs), a survey aimed at identifying and characterizing unresolved satellite galaxies around 36~nearby LMC- and SMC-mass hosts (D$=$4$-$10~Mpc). We use archival DESI Legacy Survey imaging data and perform an extensive search for dwarf satellites, extending out to a radius of 150~kpc ($\sim$$R_{vir}$). We identify \tot candidate satellite galaxies, including \new new discoveries. Extensive tests with injected galaxies demonstrate that the survey is complete down to $M_V\sim-$9.0 (assuming the distance of the host) and $\mu_{0,V}\sim$26 mag arcsec$^{-2}$ (assuming a n$=$1 S\'ersic profile). We perform consistent photometry, via S\'ersic profile fitting, on all candidates and have initiated a comprehensive follow-up campaign to confirm and characterize candidates. Through a systematic visual inspection campaign, we classify the top candidates as high-likelihood satellites. On average, we find 4.0$\pm$1.4 high-likelihood candidate satellites per LMC-mass host and 2.0$\pm$0.6 per SMC-mass host which is within the range predicted by cosmological models. We use this sample to establish upper and lower estimates on the satellite luminosity function of LMC/SMC-mass galaxies. ID-MAGE nearly triples the number of low-mass galaxies surveyed for satellites with well-characterized completeness limits, providing a unique dataset to explore small-scale structure and dwarf galaxy evolution around low-mass hosts in diverse environments.
Synthesizing Public Opinions with LLMs: Role Creation, Impacts, and the Future to eDemorcacy
2025-03-31
Rabimba Karanjai, Boris Shor, Amanda Austin, Ryan Kennedy, Yang Lu, Lei Xu, Weidong Shi
This paper investigates the use of Large Language Models (LLMs) to synthesize public opinion data, addressing challenges in traditional survey methods like declining response rates and non-response bias. We introduce a novel technique: role creation based on knowledge injection, a form of in-context learning that leverages RAG and specified personality profiles from the HEXACO model and demographic information, and uses that for dynamically generated prompts. This method allows LLMs to simulate diverse opinions more accurately than existing prompt engineering approaches. We compare our results with pre-trained models with standard few-shot prompts. Experiments using questions from the Cooperative Election Study (CES) demonstrate that our role-creation approach significantly improves the alignment of LLM-generated opinions with real-world human survey responses, increasing answer adherence. In addition, we discuss challenges, limitations and future research directions.
The Case for High Resolution Spectroscopy in the Ultraviolet
2025-03-31
Jeffrey L. Linsky, Todd M. Tripp, Seth Redfield, Kevin France
The Astro2020 Decadal Survey declared that the baryon cycle is one of the top-priority science topics for current astrophysics. Space instruments with both high spectral resolution and high throughput in the ultraviolet are required for investigations of low density warm and cold gas present in both the \underline{inner} regions of the baryon cycle (interstellar medium, star-exoplanet interactions, pre-main sequence stars, stellar winds, flows and structures driven by supernovae) and the \underline{outer} regions (outflows of matter and energy from galaxies, circumgalactic media). The Space Telescope Imaging Spectrograph on HST has pioneered such studies, but STIS has its limitations and the lifetime of HST is limited. There is a pressing need for future large instruments with high spectral resolution ($R\approx 100,000$) in the 120--320~nm wavelength band such as the present STIS E140H and E230H capabilities, but with increased throughput to study gas in sight lines to faint sources such as M dwarf stars and circumgalactic clouds. Multi-object spectroscopy at high spectral resolution could enhance observational efficiency. This document describes some of the scientific results obtained with STIS and the new science that an enhanced instrument on a large telescope such as HWO could accomplish. We provide examples of the resolution needed for these science investigations.
Non-Asymptotic Analysis of Classical Spectrum Estimators for $L$-mixing Time-series Data with Unknown Means
2025-03-31
Yuping Zheng, Andrew Lamperski
Spectral estimation is an important tool in time series analysis, with applications including economics, astronomy, and climatology. The asymptotic theory for non-parametric estimation is well-known but the development of non-asymptotic theory is still ongoing. Our recent work obtained the first non-asymptotic error bounds on the Bartlett and Welch methods for $L$-mixing stochastic processes. The class of $L$-mixing processes contains common models in time series analysis, including autoregressive processes and measurements of geometrically ergodic Markov chains. Our prior analysis assumes that the process has zero mean. While zero-mean assumptions are common, real-world time-series data often has unknown, non-zero mean. In this work, we derive non-asymptotic error bounds for both Bartlett and Welch estimators for $L$-mixing time-series data with unknown means. The obtained error bounds are of $O(\frac{1}{\sqrt{k}})$, where $k$ is the number of data segments used in the algorithm, which are tighter than our previous results under the zero-mean assumption.
Surface Sensitive Raman Response of Metal-Supported Monolayer MoS$_2$
2025-03-31
Francesco Tumino, Sergio Tosoni, Paolo D'Agosta, Valeria Russo, Carlo Enrico Bottani, Andrea Li Bassi, Carlo Spartaco Casari
The Raman spectrum of monolayer (ML) MoS$_2$ is remarkably affected by the interaction with metals. In this work we studied ML-MoS$_2$ supported by the Ag(111) and Ag(110) surfaces by using a combined experimental and theoretical approach. The MoS$_2$ layer was directly grown on atomically clean Ag(111) and Ag(110) surfaces by pulsed laser deposition, followed by in-situ thermal annealing under ultra-high vacuum conditions. The morphology and structure of the two systems were characterized in-situ by scanning tunneling microscopy, providing atomic-scale information on the relation between the MoS$_2$ lattice and the underlying surface. Raman spectroscopy revealed differences between the two MoS$_2$-metal interfaces, especially concerning the behavior of the out-of-plane $A'_1$ vibrational mode, which splits into two contributions on Ag(110). The metal-induced effects on MoS$_2$ vibrational modes are further evidenced by transferring MoS$_2$ onto a more inert substrate (SiO$_2$/Si), where the MoS$_2$ Raman response displays a more ``freestanding-like'' behavior. The experimental data were interpreted with the support of ab-initio calculations of the vibrational modes, which provided insight into the effect of interface properties, such as strain and out-of-plane distortion. Our results highlight the influence of the interaction with metals on MoS$_2$ vibrational properties, and show the high sensitivity of MoS$_2$ Raman modes to the surface structure of the supporting metal.
2025-03-31
Prateek Agrawal, Gaurang Ramakant Kane, Vazha Loladze, Mario Reig
We study general properties of confinement phase transitions in the early universe. An observable gravitational wave signal from such transitions requires significant supercooling. However, in almost all understood examples of confining gauge theories the degree of supercooling is too small to give interesting gravitational wave signals. We review and highlight the evidence why supercooling is not generic in confining gauge theories. The exceptions are Randall-Sundrum models which define a strongly coupled gauge theory holographically by a 5D gravitational theory. We construct a simple illustrative model of a 4D gauge theory inspired by features of the Randall-Sundrum model. It is a large-$N$ gauge theory in the conformal window coupled to a weakly coupled scalar field which undergoes a supercooled phase transition that breaks the conformal symmetry and triggers confinement. We show that there are interesting features in the gravitational wave spectra that can carry the imprint of the confining gauge theory.
An Updated Repository of Sub-mJy Extragalactic Source-Count Measurements in the Radio Domain
2025-03-31
Vincenzo Galluzzi, Meriem Behiri, Marika Giulietti, Andrea Lapi
We present an updated repository of sub-mJy extragalactic radio source counts between $150$ MHz and $10$ GHz, incorporating recent advances in radio surveys and observational techniques. By compiling and refining previous datasets, we provide a comprehensive catalog that enhances the understanding of faint radio-source populations, including Dusty Star-Forming Galaxies (DSFGs) and Radio-Quiet Active Galactic Nuclei (RQAGNs), from intermediate to high redshifts. Our analysis accounts for observational biases, such as resolution effects and Eddington bias, ensuring improved accuracy in flux-density estimations. We also discuss the implications of new-generation radio telescopes, such as the Square-Kilometer Array Observatory (SKAO) and its precursors and pathfinders, to further resolve these populations. Our collection contributes to constraining evolutionary models of radio sources, highlighting the increasing role of polarization studies in distinguishing different classes. This work serves as a key reference for future deep radio surveys targeting the faintest end of the extragalactic radio sky.
Spectral properties of canonical systems: discreteness and distribution of eigenvalues
2025-03-31
Jakob Reiffenstein, Harald Woracek
In this survey paper we review classical results and recent progress about a certain topic in the spectral theory of two-dimensional canonical systems. Namely, we consider the questions whether the spectrum $\sigma$ is discrete, and if it is, what is its density. Here we measure density by the growth of the counting function of $\sigma$ in the sense of integrability or $\limsup$-conditions relative to suitable comparison functions. These questions have been around for many years. However, full answers have been obtained only very recently -- in partly still unpublished work. The way we measure density of eigenvalues must be clearly distinguished from spectral asymptotics, where one asks for an asymptotic expansion of eigenvalues. We explicitly and on purpose do not go into this direction and do not present any results about spectral asymptotics. We understand this survey as a focussed presentation of results revolving around the initially stated questions, and not as an exhaustive account on the literature which is in the one or other way related to the area. It does not contain any proofs, but we do comment on proof methods. The theorems we present are very diverse. They rely on different methods from operator theory, complex analysis, and classical analysis, and beautifully invoke the interplay between these areas. Besides the fundamental theorems solving the problem and several selected additions, we decided to include a number of results devoted to the spectral theory of Jacobi matrices, in other words, to the Hamburger moment problem. The connection between the theories of canonical systems and moment problems gives rise to important new insights, yet seems to be less widely known than other connections, e.g., with Krein-Feller or Schr\"odinger operators. We compile all necessary prerequisites from the general theory to make the exposition as self-contained as possible. It is our aim that this survey can be read and enjoyed by a broad community, including non-specialist readers.
We introduce the Lorentzian Graph Isomorphic Network (LGIN), a novel graph neural network (GNN) designed to operate in hyperbolic spaces, leveraging the Lorentzian model to enhance graph representation learning. Existing GNNs primarily operate in Euclidean spaces, which can limit their ability to capture hierarchical and multi-relational structures inherent to complex graphs. LGIN addresses this by incorporating curvature-aware aggregation functions that preserve the Lorentzian metric tensor, ensuring embeddings remain constrained within the hyperbolic space by proposing a new update rule that effectively captures both local neighborhood interactions and global structural properties, enabling LGIN to distinguish non-isomorphic graphs with expressiveness at least as powerful as the Weisfeiler-Lehman test. Through extensive evaluation across nine benchmark datasets, including molecular and protein structures, LGIN consistently outperforms or matches state-of-the-art GNNs, demonstrating its robustness and efficacy in modeling complex graph structures. To the best of our knowledge, this is the first study to extend the concept of a powerful graph neural network to Riemannian manifolds, paving the way for future advancements in hyperbolic graph learning. The code for our paper can be found at https://github.com/Deceptrax123/LGIN.
2025-03-31
Victor Gustafsson, Marcus Brüggen, Cyril Tasse, Torsten Enßlin, Shane O'Sullivan, Francesco de Gasperin
Modern radio interferometers enable high-resolution polarization imaging, offering insights into cosmic magnetism through Rotation Measure (RM) synthesis. Traditional 2+1D RM synthesis treats the 2D spatial and 1D spectral transforms separately. A fully 3D approach transforms data directly from visibility-frequency space to sky-Faraday depth space using a 3D Fourier transform. Faraday synthesis uses the full dataset for improved reconstruction but requires a 3D deconvolution algorithm to subtract artifacts from the residual image. Applying this method to modern interferometers also requires corrections for direction-dependent effects (DDEs). We extend Faraday synthesis by incorporating DDE corrections, enabling accurate polarized imaging in the presence of instrumental and ionospheric effects. We implement this method within DDFACET, introducing a direction-dependent deconvolution algorithm (DDFSCLEAN) that applies DDE corrections in a faceted framework. Additionally, we parameterize the CLEAN components and evaluate the model over a larger set of frequency channels, naturally correcting for bandwidth depolarization. The method is tested on both synthetic and real data. Our results show that Faraday synthesis enables deeper deconvolution, reduces artifacts, and increases dynamic range. The depolarization correction improves recovery of polarized flux, allowing coarser frequency resolution without loss of sensitivity at high Faraday depths. From the 3D reconstruction, we identify a polarized source in a LOFAR Surveys pointing not detected by earlier RM surveys. Faraday synthesis is memory-intensive due to the large transforms between the visibility domain and the Faraday cube, and is only now becoming practical. Nevertheless, our implementation achieves comparable or faster runtimes than the 2+1D approach, making it a competitive alternative for polarization imaging.
SuperEvent: Cross-Modal Learning of Event-based Keypoint Detection
2025-03-31
Yannick Burkhardt, Simon Schaefer, Stefan Leutenegger
Event-based keypoint detection and matching holds significant potential, enabling the integration of event sensors into highly optimized Visual SLAM systems developed for frame cameras over decades of research. Unfortunately, existing approaches struggle with the motion-dependent appearance of keypoints and the complex noise prevalent in event streams, resulting in severely limited feature matching capabilities and poor performance on downstream tasks. To mitigate this problem, we propose SuperEvent, a data-driven approach to predict stable keypoints with expressive descriptors. Due to the absence of event datasets with ground truth keypoint labels, we leverage existing frame-based keypoint detectors on readily available event-aligned and synchronized gray-scale frames for self-supervision: we generate temporally sparse keypoint pseudo-labels considering that events are a product of both scene appearance and camera motion. Combined with our novel, information-rich event representation, we enable SuperEvent to effectively learn robust keypoint detection and description in event streams. Finally, we demonstrate the usefulness of SuperEvent by its integration into a modern sparse keypoint and descriptor-based SLAM framework originally developed for traditional cameras, surpassing the state-of-the-art in event-based SLAM by a wide margin. Source code and multimedia material are available at smartroboticslab.github.io/SuperEvent.
Early Co-formation of the Milky Way's Thin and Thick Disks at Redshift z > 2
2025-03-31
Lais Borbolato, Silvia Rossi, Hélio D. Perottoni, Guilherme Limberg, João A. S. Amarante, Anna B. A. Queiroz, Cristina Chiappini, Friedrich Anders, Rafael M. Santucci, Fabrícia O. Barbosa, João V. Nogueira-Santos
The Milky Way serves as a template for understanding the formation and evolution of late-type massive disk galaxies since we can obtain detailed chemical and kinematic information for large samples of individual stars. However, the early formation of the disk and the dichotomy between the chemical thick and thin disks remain under intense debate. Some mechanisms have been proposed to explain the formation of this dichotomy, such as the injection of metal-poor gas by a gas-rich merger such as Gaia-Sausage Enceladus (GSE), or by cosmic gas filaments, radial migration, and the presence of star-forming clumps at high redshift ($z > 2$). In this work, we combine astrometric data from the Gaia mission, chemical abundances from APOGEE and LAMOST spectroscopic surveys, and StarHorse ages to map the evolution of our Galaxy. The Bayesian isochrone-fitting code StarHorse can estimate ages for thousands of stars in the solar neighborhood, being most reliable for main sequence turnoff and sub-giants, computing distances and extinction simultaneously. From these samples, we show that (i) there is an old thin disk population ($>11$ Gyr) that indicates a period of co-formation between the thick and thin disks of the Milky Way before the GSE merger, i.e. the Galaxy itself could initiate the formation of a low-alpha disk without the need for a gas-rich merger, and (ii) this merger would have been important to stop the formation of stars in the thick disk.
Large Language Models (LLMs) offer a promising approach to enhancing Explainable AI (XAI) by transforming complex machine learning outputs into easy-to-understand narratives, making model predictions more accessible to users, and helping bridge the gap between sophisticated model behavior and human interpretability. AI models, such as state-of-the-art neural networks and deep learning models, are often seen as "black boxes" due to a lack of transparency. As users cannot fully understand how the models reach conclusions, users have difficulty trusting decisions from AI models, which leads to less effective decision-making processes, reduced accountabilities, and unclear potential biases. A challenge arises in developing explainable AI (XAI) models to gain users' trust and provide insights into how models generate their outputs. With the development of Large Language Models, we want to explore the possibilities of using human language-based models, LLMs, for model explainabilities. This survey provides a comprehensive overview of existing approaches regarding LLMs for XAI, and evaluation techniques for LLM-generated explanation, discusses the corresponding challenges and limitations, and examines real-world applications. Finally, we discuss future directions by emphasizing the need for more interpretable, automated, user-centric, and multidisciplinary approaches for XAI via LLMs.
Timescales for the Effects of Interactions on Galaxy Properties and SMBH Growth
2025-03-31
D. O'Ryan, B. D. Simmons, A. L. Faisst, I. L. Garland, T. Géron, G. Gozaliasl, S. Gillman, S. G. V. Pinto, W. C. Keel, A. M. Koekemoer, S. Kruk, K. L. Masters, O. Montoya C., M. Redden, M. R. Thorne, E. R. Walls, D. Weerasinghe, J. R. Weaver
Galaxy interaction and merging have clear effects on the systems involved. We find an increase in the star formation rate (SFR), potential ignition of active galactic nuclei (AGN) and significant morphology changes. However, at what stage during interactions or mergers these changes begin to occur remains an open question. With a combination of machine learning and visual classification, we select a sample of 3,162 interacting and merging galaxies in the Cosmic Evolutionary Survey (COSMOS) field across a redshift range of 0.0 - 1.2. We divide this sample into four distinct stages of interaction based on their morphology, each stage representing a different phase of the dynamical timescale. We use the rich ancillary data available in COSMOS to probe the relation between interaction stage, stellar mass, SFR, and AGN fraction. We find that the distribution of SFRs rapidly change with stage for mass distributions consistent with being drawn from the same parent sample. This is driven by a decrease in the fraction of red sequence galaxies (from 17% as close pairs to 1.4% during merging) and an increase in the fraction of starburst galaxies (from 7% to 32%). We find the AGN fraction increases by a factor of 1.2 only at coalescence. We find the effects of interaction peak at the point of closest approach and coalescence of the two systems. We show that the point in time of the underlying dynamical timescale - and its related morphology - is as important to consider as its projected separation.
The Optical to X-ray Luminosity and Spectrum of Supernova Wind Breakouts
2025-03-31
Tal Wasserman, Nir Sapir, Peter Szabo, Eli Waxman
Observations indicate that optically thick circum-stellar medium (CSM) at radii of $10^{14}-10^{15}$ cm around core-collapse supernovae (SN) progenitors is common. The breakout of the SN radiation-mediated shock (RMS) through such CSM leads to the formation of a collisionless shock (CLS). We analyze the evolution of the shock structure and associated radiation field during and after the RMS-CLS transition for non-relativistic breakouts (breakout shock velocity $v_{\rm bo}=10^9v_9~{\rm cm/s}<0.1c$) through a hydrogen-rich CSM "wind" density profile, $\rho\propto r^{-2}$, with breakout radius $R_{{\rm bo}}=10^{14}R_{14}$ cm much larger than the progenitor radius. An analytic description of the key properties of the emitted optical to X-ray radiation is provided, supported by numeric radiation-hydrodynamics calculations that self-consistently describe the time-dependent spatial distribution of the plasma and radiation, governed by the interplay between Bremsstrahlung emission/absorption and inelastic Compton scattering. We show that the characteristic energy of the photons carrying most of the luminosity, $\approx10^{43}R_{14}v_9^2$ erg/s, shifts from UV to X-ray, reaching 1 keV as the shock reaches $\approx3R_{\rm bo}$. The X-ray signal is not suppressed by propagation through the upstream wind, and its absence may suggest that the dense CSM does not extend much beyond $R_{\rm bo}$. Our results provide the basis for a quantitative calculation of the high energy $\gamma$-ray and neutrino emission that is expected from particles accelerated at the CLS, and will allow using data from upcoming surveys that will systematically detect large numbers of young SNe, particularly ULTRASAT, to infer the pre-explosion mass loss history of the SN progenitor population.
The MeerKAT Absorption Line Survey (MALS) data release 3: Cold atomic gas associated with the Milky Way
2025-03-31
N. Gupta, J. Kerp, S. A. Balashev, A. P. M. Morelli, F. Combes, J-. K. Krogager, E. Momjian, D. Borgaonkar, P. P. Deka, K. L. Emig, J. Jose, G. I. G. Jozsa, H. -R. Klockner, K. Moodley, S. Muller, P. Noterdaeme, P. Petitjean, J. D. Wagenveld
We present results of a blind search for Galactic HI 21-cm absorption lines toward 19130 radio sources, using 390 pointings of MALS, each pointing centered on a source brighter than 200 mJy. We detected 3640 HI absorption features. This represents the largest Galactic HI absorption line catalog to date. Based on the strong correlation between the HI 21-cm emission line column densities ($N_{HI}$) and the visual extinction ($A_V$) measured toward the pointing center, along with the confinement of the absorption features to a narrow range in radial velocities (-25$2\sigma$. A similar difference is observed for H$_2$ detections and non-detections in damped Lyman-alpha systems at $z$ > 1.8, implying that turbulence-driven WNM-to-CNM conversion is the common governing factor for the presence of HI 21-cm and H$_2$ absorption. Through a comparison of central and off-axis absorption features, we find that the slope of rms fluctuations in the optical depth variations in the quiescent gas associated with LISM is shallower than the earlier measurements in the disk. The densities (20-30 cm$^{-3}$) inferred from the optical depth variations are typical of the CNM values. The negligible velocity shifts between central and off-axis absorbers are in line with the hypothesis that the CNM and LNM clouds freeze out of the extended WNM phase.
The global energetics of radio AGN kinetic feedback in the local universe
2025-03-31
Zsofi Igo, Andrea Merloni
[abridged] AGN feedback is a crucial ingredient for understanding galaxy evolution. However, a complete quantitative time-dependent framework, including the dependence of such feedback on AGN, host galaxy, and host halo properties, is yet to be developed. Using the complete sample of 682 radio AGN from the LOFAR-eFEDS survey ($z<0.4$), we derive the average jet power of massive galaxies and its variation as a function of stellar mass ($M_*$), halo mass ($M_h$) and radio morphology. We compare the incidence distributions of compact and complex radio AGN as a function of specific black hole kinetic power, $\lambda_{\rm Jet}$, and synthesise, for the first time, the radio luminosity function (RLF) by $M_*$ and radio morphology. Our RLF and derived total radio AGN kinetic luminosity density, $\log \Omega_{\rm kin}/[\rm {W~Mpc^{-3}}]=32.15_{-0.34}^{+0.18}$, align with previous work. We find that kinetic feedback from radio AGN dominates over any plausible inventory of radiatively-driven feedback for galaxies with $\log M_*/M_\odot > 10.6$. More specifically, it is the compact radio AGN which dominate this global kinetic energy budget for all but the most massive galaxies ($10.6 < \log M_*/M_{\odot} < 11.5$). Subsequently, we compare the average injected jet energy against the galaxy and halo binding energy, and against the total thermal energy of the host gas within halos. We find that radio AGN cannot fully unbind their host galaxies nor host halos. However, they have enough energy to impact the global thermodynamical heating and cooling balance in small halos and significantly contribute to offsetting local cooling flows in even the most massive clusters cores. Overall, our findings provide important insights on jet powering, accretion processes and black hole-galaxy coevolution via AGN feedback, as well as a clear observational benchmark to calibrate AGN feedback simulations.
Effects of Mobile Gacha Games on Gambling Behavior and Psychological Health
2025-03-31
Ji Woo Han
The study examines how Gacha games affect gambling practices by analyzing when players start playing and how often they play and how much money they spend on games. The modified Problem Gambling Severity Index (PGSI) serves as the research tool to examine the impact of these variables on mobile Gacha game gambling-like severity. A survey distributed to online Gacha game communities provided the data which researchers analyzed by applying linear regression and multiple regression and T-test methods. The research indicates entry age demonstrated low association with gambling severity but gameplay frequency together with duration directly influenced higher gambling severity ratings. The research findings demonstrated that the level of Gacha game spending directly influences gambling severity scores among players. The research findings demonstrate how addictive potential exists in mobile Gacha games which requires stronger regulation of these platforms for young vulnerable gamers. Research needs to study the extended influence of Gacha game exposure while examining how cognitive biases affect gambling behavior patterns.
Semi-Supervised Learning for Lensed Quasar Detection
2025-03-31
David Sweeney, Alberto Krone-Martins, Daniel Stern, Peter Tuthill, Richard Scalzo, George Djorgovski, Christine Ducourant, Ashish Mahabal, Ramachrisna Teixeira, Matthew Graham
Lensed quasars are key to many areas of study in astronomy, offering a unique probe into the intermediate and far universe. However, finding lensed quasars has proved difficult despite significant efforts from large collaborations. These challenges have limited catalogues of confirmed lensed quasars to the hundreds, despite theoretical predictions that they should be many times more numerous. We train machine learning classifiers to discover lensed quasar candidates. By using semi-supervised learning techniques we leverage the large number of potential candidates as unlabelled training data alongside the small number of known objects, greatly improving model performance. We present our two most successful models: (1) a variational autoencoder trained on millions of quasars to reduce the dimensionality of images for input to a gradient boosting classifier that can make accurate predictions and (2) a convolutional neural network trained on a mix of labelled and unlabelled data via virtual adversarial training. These models are both capable of producing high-quality candidates, as evidenced by our discovery of GRALJ140833.73+042229.98. The success of our classifier, which uses only images, is particularly exciting as it can be combined with existing classifiers, which use other data than images, to improve the classifications of both models and discover more lensed quasars.
Assessing Validity of ICD-10 Administrative Data in Coding Comorbidities
2025-03-31
Jie Pan, Seungwon Lee, Cheligeer Cheligeer, Bing Li, Guosong Wu, Catherine A Eastwood, Yuan Xu, Hude Quan
Objectives: Administrative data is commonly used to inform chronic disease prevalence and support health informatics research. This study assessed the validity of coding comorbidity in the International Classification of Diseases, 10th Revision (ICD-10) administrative data. Methods: We analyzed three chart review cohorts (4,008 patients in 2003, 3,045 in 2015, and 9,024 in 2022) in Alberta, Canada. Nurse reviewers assessed the presence of 17 clinical conditions using a consistent protocol. The reviews were linked with administrative data using unique identifiers. We compared the accuracy in coding comorbidity by ICD-10, using chart review data as the reference standard. Results: Our findings showed that the mean difference in prevalence between chart reviews and ICD-10 for these 17 conditions was 2.1% in 2003, 7.6% in 2015, and 6.3% in 2022. Some conditions were relatively stable, such as diabetes (1.9%, 2.1%, and 1.1%) and metastatic cancer (0.3%, 1.1%, and 0.4%). For these 17 conditions, the sensitivity ranged from 39.6-85.1% in 2003, 1.3-85.2% in 2015, and 3.0-89.7% in 2022. The C-statistics for predicting in-hospital mortality using comorbidities by ICD-10 were 0.84 in 2003, 0.81 in 2015, and 0.78 in 2022. Discussion: The under-coding could be primarily due to the increase of hospital patient volumes and the limited time allocated to coders. There is a potential to develop artificial intelligence methods based on electronic health records to support coding practices and improve coding quality. Conclusion: Comorbidities were increasingly under-coded over 20 years. The validity of ICD-10 decreased but remained relatively stable for certain conditions mandated for coding. The under-coding exerted minimal impact on in-hospital mortality prediction.
SEMIDV: A Compact Semiconductor Device Simulator with Quantum Effects
2025-03-31
Chien-Ting Tung
In this paper, I present SEMIDV - a compact semiconductor device simulator incorporating quantum effects. SEMIDV solves the Poisson-Drift-Diffusion equations for semiconductor devices and provides a user-friendly Python interface for scripting and data analysis. Localization landscape theory is introduced to provide quantum corrections to the Drift-Diffusion equation. This theory directly solves the ground state of the Schrodinger equation without further approximation, offering an efficient solution for quantum effect modeling. Additionally, a compact mobility model considering ballistic transport is developed to capture the ballistic length dependence of mobility and the velocity overshoot effect in short-channel devices. Finally, a study on a nanosheet FET using SEMIDV is conducted. I analyze the electrical characteristics of a state-of-the-art GAA/RibbonFET with a 6 nm gate length and discuss the effects of velocity overshoot and quantum confinement on currents and capacitances. A design for an ultra-short-channel transistor with a gate length down to 4.5 nm with a Vdd = 0.45 V is proposed to push the boundaries of integrated circuit technology further.
The advent of large language models (LLMs) has catalyzed a transformative shift in artificial intelligence, paving the way for advanced intelligent agents capable of sophisticated reasoning, robust perception, and versatile action across diverse domains. As these agents increasingly drive AI research and practical applications, their design, evaluation, and continuous improvement present intricate, multifaceted challenges. This survey provides a comprehensive overview, framing intelligent agents within a modular, brain-inspired architecture that integrates principles from cognitive science, neuroscience, and computational research. We structure our exploration into four interconnected parts. First, we delve into the modular foundation of intelligent agents, systematically mapping their cognitive, perceptual, and operational modules onto analogous human brain functionalities, and elucidating core components such as memory, world modeling, reward processing, and emotion-like systems. Second, we discuss self-enhancement and adaptive evolution mechanisms, exploring how agents autonomously refine their capabilities, adapt to dynamic environments, and achieve continual learning through automated optimization paradigms, including emerging AutoML and LLM-driven optimization strategies. Third, we examine collaborative and evolutionary multi-agent systems, investigating the collective intelligence emerging from agent interactions, cooperation, and societal structures, highlighting parallels to human social dynamics. Finally, we address the critical imperative of building safe, secure, and beneficial AI systems, emphasizing intrinsic and extrinsic security threats, ethical alignment, robustness, and practical mitigation strategies necessary for trustworthy real-world deployment.
Interpretable Machine Learning in Physics: A Review
2025-03-30
Sebastian Johann Wetzel, Seungwoong Ha, Raban Iten, Miriam Klopotek, Ziming Liu
Machine learning is increasingly transforming various scientific fields, enabled by advancements in computational power and access to large data sets from experiments and simulations. As artificial intelligence (AI) continues to grow in capability, these algorithms will enable many scientific discoveries beyond human capabilities. Since the primary goal of science is to understand the world around us, fully leveraging machine learning in scientific discovery requires models that are interpretable -- allowing experts to comprehend the concepts underlying machine-learned predictions. Successful interpretations increase trust in black-box methods, help reduce errors, allow for the improvement of the underlying models, enhance human-AI collaboration, and ultimately enable fully automated scientific discoveries that remain understandable to human scientists. This review examines the role of interpretability in machine learning applied to physics. We categorize different aspects of interpretability, discuss machine learning models in terms of both interpretability and performance, and explore the philosophical implications of interpretability in scientific inquiry. Additionally, we highlight recent advances in interpretable machine learning across many subfields of physics. By bridging boundaries between disciplines -- each with its own unique insights and challenges -- we aim to establish interpretable machine learning as a core research focus in science.
In this survey we present classical results on methods to use group actions to collapse manifolds to the orbit spaces while keeping some control on the curvature, and recent extensions of these constructions to the setting of singular Riemannian foliations.
Catsteroseismology: Survey-based Analysis of Purr-mode Oscillations Suggests Inner Lives of Cats are Unknowable
2025-03-30
Rae Holcomb, Christopher Lam
Catsteroseismology, or asterocatsmology, is an unexplored area of observational and theoretical research that proposes to use purr-mode oscillations to study the much-beloved but poorly-understood species Felis catus. In this work, we conduct a survey to measure fundamental purrameters of cats and relate them to their purr-modes. Relations between these fundamental cat purrameters, which include physical (eg. size, cuddliness) and personality (eg. aggression, intelligence) traits, and purr-modes can help probe their inner lives and emotions. We find that while purr characteristics tentatively trend with several physical and personality traits, more data is required to better constrain these relationships and infer the direct predictive power of personality on purr-modes, or vice versa.
2025-03-30
Jiahao Li, Yiqiang Chen, Yunbing Xing, Yang Gu, Xiangyuan Lan
Unlearnable data (ULD) has emerged as an innovative defense technique to prevent machine learning models from learning meaningful patterns from specific data, thus protecting data privacy and security. By introducing perturbations to the training data, ULD degrades model performance, making it difficult for unauthorized models to extract useful representations. Despite the growing significance of ULD, existing surveys predominantly focus on related fields, such as adversarial attacks and machine unlearning, with little attention given to ULD as an independent area of study. This survey fills that gap by offering a comprehensive review of ULD, examining unlearnable data generation methods, public benchmarks, evaluation metrics, theoretical foundations and practical applications. We compare and contrast different ULD approaches, analyzing their strengths, limitations, and trade-offs related to unlearnability, imperceptibility, efficiency and robustness. Moreover, we discuss key challenges, such as balancing perturbation imperceptibility with model degradation and the computational complexity of ULD generation. Finally, we highlight promising future research directions to advance the effectiveness and applicability of ULD, underscoring its potential to become a crucial tool in the evolving landscape of data protection in machine learning.
Exploring Young Stellar Variability with Gaia DR3 light curves
2025-03-30
Chloé Mas, Julia Roquette, Marc Audard, Mate Madarász, Gabor Marton, David Hernandez, Ilknur Gezer, Odysseas Dionatos
Context: Photometric variability is a defining characteristic of young stellar objects (YSO), which can be traced back to a range of physical processes taking place at different stages of young stars' formation and early evolution. Gaia's third Data Release (GDR3) has provided an unprecedented dataset of photometric time series, including 79375 light curves for YSO candidates. With its all-sky coverage, Gaia provides a unique opportunity for large-scale studies of YSO variability. Aims: Our goal was to characterise the GDR3 sample of YSO variables further and verify the recurrence of YSO variability modes due to accretion, extinction, rotation modulation, etc. By adapting the Q&M methodology for Gaia's sparse and long-term light curves, we seek to bridge the gap between low and high-cadence surveys' insights on YSO variability. Methods: We piloted the application of the asymmetry (M) and periodicity (Q) variability metrics to characterise YSO variability with Gaia light curves. Through refined sample selection, we identified sources with appropriate sampling for the Q&M methodology. We used the Generalised Lomb Scargle periodogram and structure functions to infer variability timescales. Results: We successfully derived Q&M indices for ~23000 sources in the GDR3 YSO sample. These variables were then classified into eight variability morphological classes. We linked morphological classes with physical mechanisms by using H$\alpha$ as a proxy of accretion and $\alpha_\mathrm{IR}$-indices to gauge circumstellar material's presence. Conclusions: We demonstrate that the Q&M metrics can be successfully applied to Gaia's sparse time-series. We applied them to distinguish between the several variability modes. While our results are generally consistent with previous high-cadence, short-term studies, we find that GDR3's long timespan yields an enhanced variety of variability mechanisms.
The HADS Star CSS_J102714.3+205943: a Component of a Binary System with an Elliptic Orbit?
2025-03-30
Maksym Yu. Pyatnytskyy, Ivan L. Andronov
We analyzed period changes of the high-amplitude Delta Scuti variable star CSS_J102714.3+205943 for about 20 years, utilizing data from the automated sky surveys along with our own observations. With the help of the O-C diagram, we found that the period decreased noticeably between JD2454800 and JD2457300. A possible cause of the change could be intrinsic processes in the star. However, the observed behavior of the O-C diagram can also be explained by the light-time effect if the star is a component of a binary system. Times of maxima for the star, derived from the surveys and our observations, are listed.
A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior
2025-03-30
Nikil Jayasuriya, Deshan Sumanathilaka
This paper systematically explores the advancements in adaptive trip route planning and travel time estimation (TTE) through Artificial Intelligence (AI). With the increasing complexity of urban transportation systems, traditional navigation methods often struggle to accommodate dynamic user preferences, real-time traffic conditions, and scalability requirements. This study explores the contributions of established AI techniques, including Machine Learning (ML), Reinforcement Learning (RL), and Graph Neural Networks (GNNs), alongside emerging methodologies like Meta-Learning, Explainable AI (XAI), Generative AI, and Federated Learning. In addition to highlighting these innovations, the paper identifies critical challenges such as ethical concerns, computational scalability, and effective data integration, which must be addressed to advance the field. The paper concludes with recommendations for leveraging AI to build efficient, transparent, and sustainable navigation systems.
We propose Cross-Attention in Audio, Space, and Time (CA^2ST), a transformer-based method for holistic video recognition. Recognizing actions in videos requires both spatial and temporal understanding, yet most existing models lack a balanced spatio-temporal understanding of videos. To address this, we propose a novel two-stream architecture, called Cross-Attention in Space and Time (CAST), using only RGB input. In each layer of CAST, Bottleneck Cross-Attention (B-CA) enables spatial and temporal experts to exchange information and make synergistic predictions. For holistic video understanding, we extend CAST by integrating an audio expert, forming Cross-Attention in Visual and Audio (CAVA). We validate the CAST on benchmarks with different characteristics, EPIC-KITCHENS-100, Something-Something-V2, and Kinetics-400, consistently showing balanced performance. We also validate the CAVA on audio-visual action recognition benchmarks, including UCF-101, VGG-Sound, KineticsSound, and EPIC-SOUNDS. With a favorable performance of CAVA across these datasets, we demonstrate the effective information exchange among multiple experts within the B-CA module. In summary, CA^2ST combines CAST and CAVA by employing spatial, temporal, and audio experts through cross-attention, achieving balanced and holistic video understanding.
Spatially-Embedded Lens Visualization: A Design Space
2025-03-30
Roberta Mota, Ehud Sharlin, Usman Alim
Lens visualization has been a prominent research area in the visualization community, fueled by the continuous need to mitigate visual clutter and occlusion resulting from the increase in data volume. Interactive lenses for spatial data, particularly, challenge designers to conceive design strategies to support the analysis of high-density, multifaceted data with spatial referents. Despite their relevance, there is a lack of systematic understanding regarding the various design elements that compose spatially-embedded lens visualizations. To fill in this gap, we unify these components under a common hood in the form of a design space, which we propose in this paper. Building our knowledge on top of the initial insights gained from Tominski et al.'s survey [57], we construct a design space spanning 7 dimensions through our analysis of 45 papers published in the visualization community over the past 15 years. We describe each design dimension through representative examples and examine the range of design choices available within each, discussing their benefits and pitfalls that affect lens performance and usability. In doing so, we offer a cohesive catalog of considerations for designers-both when examining existing lenses and when conceptualizing novel spatially-embedded lens visualizations. We conclude by shedding light on regions of the design space that remain largely understudied, revealing open opportunities for future research.
Speculative End-Turn Detector for Efficient Speech Chatbot Assistant
2025-03-30
Hyunjong Ok, Suho Yoo, Jaeho Lee
Spoken dialogue systems powered by large language models have demonstrated remarkable abilities in understanding human speech and generating appropriate spoken responses. However, these systems struggle with end-turn detection (ETD) -- the ability to distinguish between user turn completion and hesitation. This limitation often leads to premature or delayed responses, disrupting the flow of spoken conversations. In this paper, we introduce the ETD Dataset, the first public dataset for end-turn detection. The ETD dataset consists of both synthetic speech data generated with text-to-speech models and real-world speech data collected from web sources. We also propose SpeculativeETD, a novel collaborative inference framework that balances efficiency and accuracy to improve real-time ETD in resource-constrained environments. Our approach jointly employs a lightweight GRU-based model, which rapidly detects the non-speaking units in real-time on local devices, and a high-performance Wav2vec-based model running on the server to make a more challenging classification of distinguishing turn ends from mere pauses. Experiments demonstrate that the proposed SpeculativeETD significantly improves ETD accuracy while keeping the required computations low. Datasets and code will be available after the review.
GUI agents, powered by large foundation models, can interact with digital interfaces, enabling various applications in web automation, mobile navigation, and software testing. However, their increasing autonomy has raised critical concerns about their security, privacy, and safety. This survey examines the trustworthiness of GUI agents in five critical dimensions: security vulnerabilities, reliability in dynamic environments, transparency and explainability, ethical considerations, and evaluation methodologies. We also identify major challenges such as vulnerability to adversarial attacks, cascading failure modes in sequential decision-making, and a lack of realistic evaluation benchmarks. These issues not only hinder real-world deployment but also call for comprehensive mitigation strategies beyond task success. As GUI agents become more widespread, establishing robust safety standards and responsible development practices is essential. This survey provides a foundation for advancing trustworthy GUI agents through systematic understanding and future research.
Distributed Design of Ultra Large-Scale Control Systems: Progress, Challenges, and Prospects
2025-03-30
Leonardo Pedroso, Pedro Batista, W. P. M. H. Heemels
The transition from large centralized complex control systems to distributed configurations that rely on a network of a very large number of interconnected simpler subsystems is ongoing and inevitable in many applications. It is attributed to the quest for resilience, flexibility, and scalability in a multitude of engineering fields with far-reaching societal impact. Although many design methods for distributed and decentralized control systems are available, most of them rely on a centralized design procedure requiring some form of global information of the whole system. Clearly, beyond a certain scale of the network, these centralized design procedures for distributed controllers are no longer feasible and we refer to the corresponding systems as ultra large-scale systems (ULSS). For these ULSS, design algorithms are needed that are distributed themselves among the subsystems and are subject to stringent requirements regarding communication, computation, and memory usage of each subsystem. In this paper, a set of requirements is provided that assures a feasible real-time implementation of all phases of a control solution on an ultra large scale. State-of-the-art approaches are reviewed in the light of these requirements and the challenges hampering the development of befitting control algorithms are pinpointed. Comparing the challenges with the current progress leads to the identification and motivation of promising research directions.
Neutron stars and Constraints for the Equation of State of Dense Matter
2025-03-30
Rajesh Kumar, Veronica Dexheimer, Johannes Jahan
Neutron stars provide a natural laboratory for studying the properties of dense nuclear matter under extreme conditions. In this proceeding, we review our current understanding of dense isospin symmetric and asymmetric matter and neutron star physics. We focus on modern theoretical, experimental, and observational constraints, including first-principle calculations from lattice and perturbative Quantum Chromodynamics (QCD), as well as chiral effective field theory approaches at nuclear densities. From the experimental perspective, constraints on the equation of state arise from heavy-ion collisions, low-energy nuclear physics, and astrophysical observations, including neutron star masses, radii, and gravitational wave signatures from mergers. These multidisciplinary comparisons are crucial for bridging the gap between nuclear physics and astrophysical observations, in order to expand our knowledge of matter at supra-nuclear densities.
A Survey of WebAgents: Towards Next-Generation AI Agents for Web Automation with Large Foundation Models
2025-03-30
Liangbo Ning, Ziran Liang, Zhuohang Jiang, Haohao Qu, Yujuan Ding, Wenqi Fan, Xiao-yong Wei, Shanru Lin, Hui Liu, Philip S. Yu, Qing Li
With the advancement of web techniques, they have significantly revolutionized various aspects of people's lives. Despite the importance of the web, many tasks performed on it are repetitive and time-consuming, negatively impacting overall quality of life. To efficiently handle these tedious daily tasks, one of the most promising approaches is to advance autonomous agents based on Artificial Intelligence (AI) techniques, referred to as AI Agents, as they can operate continuously without fatigue or performance degradation. In the context of the web, leveraging AI Agents -- termed WebAgents -- to automatically assist people in handling tedious daily tasks can dramatically enhance productivity and efficiency. Recently, Large Foundation Models (LFMs) containing billions of parameters have exhibited human-like language understanding and reasoning capabilities, showing proficiency in performing various complex tasks. This naturally raises the question: `Can LFMs be utilized to develop powerful AI Agents that automatically handle web tasks, providing significant convenience to users?' To fully explore the potential of LFMs, extensive research has emerged on WebAgents designed to complete daily web tasks according to user instructions, significantly enhancing the convenience of daily human life. In this survey, we comprehensively review existing research studies on WebAgents across three key aspects: architectures, training, and trustworthiness. Additionally, several promising directions for future research are explored to provide deeper insights.
Recent advances in multimodal large language models (MLLMs) have demonstrated impressive results in various visual tasks. However, in remote sensing (RS), high resolution and small proportion of objects pose challenges to existing MLLMs, which struggle with object-centric tasks, particularly in precise localization and fine-grained attribute description for each object. These RS MLLMs have not yet surpassed classical visual perception models, as they only provide coarse image understanding, leading to limited gains in real-world scenarios. To address this gap, we establish EagleVision, an MLLM tailored for remote sensing that excels in object detection and attribute comprehension. Equipped with the Attribute Disentangle module, EagleVision learns disentanglement vision tokens to express distinct attributes. To support object-level visual-language alignment, we construct EVAttrs-95K, the first large-scale object attribute understanding dataset in RS for instruction tuning, along with a novel evaluation benchmark, EVBench. EagleVision achieves state-of-the-art performance on both fine-grained object detection and object attribute understanding tasks, highlighting the mutual promotion between detection and understanding capabilities in MLLMs. The code, model, data, and demo will be available at https://github.com/XiangTodayEatsWhat/EagleVision.
SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science
2025-03-30
Wonduk Seo, Juhyeon Lee, Yi Bu
Large Language Models (LLMs) have revolutionized automated data analytics and machine learning by enabling dynamic reasoning and adaptability. While recent approaches have advanced multi-stage pipelines through multi-agent systems, they typically rely on rigid, single-path workflows that limit the exploration and integration of diverse strategies, often resulting in suboptimal predictions. To address these challenges, we propose SPIO (Sequential Plan Integration and Optimization), a novel framework that leverages LLM-driven decision-making to orchestrate multi-agent planning across four key modules: data preprocessing, feature engineering, modeling, and hyperparameter tuning. In each module, dedicated planning agents independently generate candidate strategies that cascade into subsequent stages, fostering comprehensive exploration. A plan optimization agent refines these strategies by suggesting several optimized plans. We further introduce two variants: SPIO-S, which selects a single best solution path as determined by the LLM, and SPIO-E, which selects the top k candidate plans and ensembles them to maximize predictive performance. Extensive experiments on Kaggle and OpenML datasets demonstrate that SPIO significantly outperforms state-of-the-art methods, providing a robust and scalable solution for automated data science task.
Active matter refers to systems composed of self-propelled entities that consume energy to produce motion, exhibiting complex non-equilibrium dynamics that challenge traditional models. With the rapid advancements in machine learning, reinforcement learning (RL) has emerged as a promising framework for addressing the complexities of active matter. This review systematically introduces the integration of RL for guiding and controlling active matter systems, focusing on two key aspects: optimal motion strategies for individual active particles and the regulation of collective dynamics in active swarms. We discuss the use of RL to optimize the navigation, foraging, and locomotion strategies for individual active particles. In addition, the application of RL in regulating collective behaviors is also examined, emphasizing its role in facilitating the self-organization and goal-directed control of active swarms. This investigation offers valuable insights into how RL can advance the understanding, manipulation, and control of active matter, paving the way for future developments in fields such as biological systems, robotics, and medical science.
Comprehensive Survey towards Security Authentication Methods for Satellite Communication Systems
2025-03-30
Yunfei Meng, Changbo Ke, Zhiqiu Huang
Satellite communication systems (SatCom) is a brand-new network that uses artificial Earth satellites as relay stations to provide communication services such as broadband Internet access to various users on land, sea, air and in space. It features wide coverage, relatively high transmission rates and strong anti-interference capabilities. Security authentication is of crucial significance for the stable operation and widespread application of satellite communication systems. It can effectively prevent unauthorized access, ensuring that only users and devices that pass security authentication can access the satellite network. It also ensures the confidentiality, integrity, and availability of data during transmission and storage, preventing data from being stolen, tampered with, or damaged. By means of literature research and comparative analysis, this paper carries out on a comprehensive survey towards the security authentication methods used by SatCom. This paper first summarizes the existing SatCom authentication methods as five categories, namely, those based on cryptography, Blockchain, satellite orbital information, the AKA protocol and physical hardware respectively. Subsequently, a comprehensive comparative analysis is carried out on the above-mentioned five categories of security authentication methods from four dimensions, i.e., security, implementation difficulty and cost, applicable scenarios and real-time performance, and the final comparison results are following obtained. Finally, prospects are made for several important future research directions of security authentication methods for SatCom, laying a well foundation for further carrying on the related research works.
Context in object detection: a systematic literature review
2025-03-29
Mahtab Jamali, Paul Davidsson, Reza Khoshkangini, Martin Georg Ljungqvist, Radu-Casian Mihailescu
Context is an important factor in computer vision as it offers valuable information to clarify and analyze visual data. Utilizing the contextual information inherent in an image or a video can improve the precision and effectiveness of object detectors. For example, where recognizing an isolated object might be challenging, context information can improve comprehension of the scene. This study explores the impact of various context-based approaches to object detection. Initially, we investigate the role of context in object detection and survey it from several perspectives. We then review and discuss the most recent context-based object detection approaches and compare them. Finally, we conclude by addressing research questions and identifying gaps for further studies. More than 265 publications are included in this survey, covering different aspects of context in different categories of object detection, including general object detection, video object detection, small object detection, camouflaged object detection, zero-shot, one-shot, and few-shot object detection. This literature review presents a comprehensive overview of the latest advancements in context-based object detection, providing valuable contributions such as a thorough understanding of contextual information and effective methods for integrating various context types into object detection, thus benefiting researchers.
New Constraints on Interacting Dark Energy from DESI DR2 BAO Observations
2025-03-29
Emanuelly Silva, Miguel A. Sabogal, Mateus S. Souza, Rafael C. Nunes, Eleonora Di Valentino, Suresh Kumar
In its second data release (DR2), the \textit{Dark Energy Spectroscopic Instrument} (DESI) publicly released measurements of Baryon Acoustic Oscillations (BAO) from over 13.1 million galaxies and 1.6 million quasars, covering the redshift range $0.295 \leq z \leq 2.330$. In this work, we investigate the impact of this new dataset on dark sector interaction models, which are motivated by non-gravitational interactions between dark energy (DE) and dark matter (DM), commonly referred to as interacting dark energy models (IDE). We focus on two frameworks: the traditional IDE model and the recently proposed sign-switching Interacting model (S-IDE), aiming to derive new and robust constraints on both scenarios. After carefully selecting the sample for the joint analysis, ensuring compatibility among the data without significant tension, our main results indicate that both models can alleviate the $H_0$ tension, reducing it to moderate tension approximately $2.7\sigma$. The IDE model shows compatibility with the latest $S_8$ constraints from cosmic shear surveys, while the S-IDE model predicts lower values of $S_8$, which align with alternative perspectives on the $S_8$ tension. For the traditional IDE model, we derive new bounds for the coupling parameter, marking the strongest constraints to date through geometric measurements. This highlights the crucial role that supernova samples can play in refining these constraints. For the S-IDE model, we find mild evidence (approximately $2\sigma$) for the coupling parameter, suggesting a potential interaction between DE and DM.
AstroAgents: A Multi-Agent AI for Hypothesis Generation from Mass Spectrometry Data
2025-03-29
Daniel Saeedi, Denise Buckner, Jose C. Aponte, Amirali Aghazadeh
With upcoming sample return missions across the solar system and the increasing availability of mass spectrometry data, there is an urgent need for methods that analyze such data within the context of existing astrobiology literature and generate plausible hypotheses regarding the emergence of life on Earth. Hypothesis generation from mass spectrometry data is challenging due to factors such as environmental contaminants, the complexity of spectral peaks, and difficulties in cross-matching these peaks with prior studies. To address these challenges, we introduce AstroAgents, a large language model-based, multi-agent AI system for hypothesis generation from mass spectrometry data. AstroAgents is structured around eight collaborative agents: a data analyst, a planner, three domain scientists, an accumulator, a literature reviewer, and a critic. The system processes mass spectrometry data alongside user-provided research papers. The data analyst interprets the data, and the planner delegates specific segments to the scientist agents for in-depth exploration. The accumulator then collects and deduplicates the generated hypotheses, and the literature reviewer identifies relevant literature using Semantic Scholar. The critic evaluates the hypotheses, offering rigorous suggestions for improvement. To assess AstroAgents, an astrobiology expert evaluated the novelty and plausibility of more than a hundred hypotheses generated from data obtained from eight meteorites and ten soil samples. Of these hypotheses, 36% were identified as plausible, and among those, 66% were novel. Project website: https://astroagents.github.io/
Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks
2025-03-29
Zewen Liu, Xiaoda Wang, Bohan Wang, Zijie Huang, Carl Yang, Wei Jin
Graph Neural Networks (GNNs) and differential equations (DEs) are two rapidly advancing areas of research that have shown remarkable synergy in recent years. GNNs have emerged as powerful tools for learning on graph-structured data, while differential equations provide a principled framework for modeling continuous dynamics across time and space. The intersection of these fields has led to innovative approaches that leverage the strengths of both, enabling applications in physics-informed learning, spatiotemporal modeling, and scientific computing. This survey aims to provide a comprehensive overview of the burgeoning research at the intersection of GNNs and DEs. We will categorize existing methods, discuss their underlying principles, and highlight their applications across domains such as molecular modeling, traffic prediction, and epidemic spreading. Furthermore, we identify open challenges and outline future research directions to advance this interdisciplinary field. A comprehensive paper list is provided at https://github.com/Emory-Melody/Awesome-Graph-NDEs. This survey serves as a resource for researchers and practitioners seeking to understand and contribute to the fusion of GNNs and DEs
The realization of tones in spontaneous spoken Taiwan Mandarin: a corpus-based survey and theory-driven computational modeling
2025-03-29
Yuxin Lu, Yu-Ying Chuang, R. Harald Baayen
A growing body of literature has demonstrated that semantics can co-determine fine phonetic detail. However, the complex interplay between phonetic realization and semantics remains understudied, particularly in pitch realization. The current study investigates the tonal realization of Mandarin disyllabic words with all 20 possible combinations of two tones, as found in a corpus of Taiwan Mandarin spontaneous speech. We made use of Generalized Additive Mixed Models (GAMs) to model f0 contours as a function of a series of predictors, including gender, tonal context, tone pattern, speech rate, word position, bigram probability, speaker and word. In the GAM analysis, word and sense emerged as crucial predictors of f0 contours, with effect sizes that exceed those of tone pattern. For each word token in our dataset, we then obtained a contextualized embedding by applying the GPT-2 large language model to the context of that token in the corpus. We show that the pitch contours of word tokens can be predicted to a considerable extent from these contextualized embeddings, which approximate token-specific meanings in contexts of use. The results of our corpus study show that meaning in context and phonetic realization are far more entangled than standard linguistic theory predicts.
A long time ago C.N. Yang proposed a generalization of the Snyder model to the case of a curved background spacetime, based on an algebra isomorphic to $so(1,5)$, which includes as subalgebras both the Snyder and the de Sitter algebras. His proposal can therefore be interpreted as a model of noncommutative curved spacetime, and could be useful for relating physics at very small and very large scales. We review this model and some recent progress concerning its generalizations and its interpretation in the framework of Hopf algebras. We also report some possibilities to relate it to more phenomenological aspects.
Conversational Agents for Older Adults' Health: A Systematic Literature Review
2025-03-29
Jiaxin An, Siqi Yi, Yao Lyu, Houjiang Liu, Yan Zhang
There has been vast literature that studies Conversational Agents (CAs) in facilitating older adults' health. The vast and diverse studies warrants a comprehensive review that concludes the main findings and proposes research directions for future studies, while few literature review did it from human-computer interaction (HCI) perspective. In this study, we present a survey of existing studies on CAs for older adults' health. Through a systematic review of 72 papers, this work reviewed previously studied older adults' characteristics and analyzed participants' experiences and expectations of CAs for health. We found that (1) Past research has an increasing interest on chatbots and voice assistants and applied CA as multiple roles in older adults' health. (2) Older adults mainly showed low acceptance CAs for health due to various reasons, such as unstable effects, harm to independence, and privacy concerns. (3) Older adults expect CAs to be able to support multiple functions, to communicate using natural language, to be personalized, and to allow users full control. We also discuss the implications based on the findings.
A long-lasting eruption heralds SN 2023ldh, a clone of SN 2009ip
2025-03-29
A. Pastorello, A. Reguitti, L. Tartaglia, G. Valerin, Y. -Z. Cai, P. Charalampopoulos, F. De Luise, Y. Dong, N. Elias-Rosa, J. Farah, A. Farina, S. Fiscale, M. Fraser, L. Galbany, S. Gomez, M. González-Bañuelos, D. Hiramatsu, D. A. Howell, T. Kangas, T. L. Killestein, P. Marziani, P. A. Mazzali, E. Mazzotta Epifani, C. McCully, P. Ochner, E. Padilla Gonzales, A. P. Ravi, I. Salmaso, S. Schuldt, A. G. Schweinfurth, S. J. Smartt, K. W. Smith, S. Srivastav, M. D. Stritzinger, S. Taubenberger, G. Terreran, S. Valenti, Z. -Y. Wang, F. Guidolin, C. P. Gutiérrez, K. Itagaki, S. Kiyota, P. Lundqvist, K. C. Chambers, T. J. L. de Boer, C. -C. Lin, T. B. Lowe, E. A. Magnier, R. J. Wainscoat
We discuss the results of the spectroscopic and photometric monitoring of the type IIn supernova (SN) 2023ldh. Survey archive data show that the SN progenitor experienced some erratic outbursts in the years before exploding. From May 2023, the source shows a general slow luminosity rise lasting over four months with some superposed luminosity fluctuations. In analogy to SN 2009ip, we label this brightening as Event A. During Event A, SN 2023ldh reaches a maximum absolute magnitude of Mr = -15.52 $\pm$ 0.24 mag. Then the light curves show a luminosity decline of about 1 mag in all filters lasting about two weeks, followed by a steep brightening (Event B) to an absolute peak magnitude of Mr = -18.53 $\pm$ 0.23 mag, replicating the evolution of SN 2009ip and similar SNe IIn. Three spectra of SN 2023ldh are obtained during Event A, showing multi-component P Cygni profiles of H I and Fe II lines. During the rise to the Event B peak, the spectrum shows a blue continuum dominated by Balmer lines in emission with Lorentzian profiles, with a full width at half-maximum (FWHM) velocity of about 650 km/s. Later, in the post-peak phase, the spectrum reddens, and broader wings appear in the H{\alpha} line profile. Metal lines are well visible with P Cygni profiles and velocities of about 2000 km/s. Beginning around three months past maximum and until very late phases, the Ca II lines become among the most prominent features, while H{\alpha} is dominated by an intermediate-width component with a boxy profile. Although SN 2023ldh mimics the evolution of other SN 2009ip-like transients, it is slightly more luminous and has a slower photometric evolution. The surprisingly homogeneous observational properties of SN 2009ip-like events may indicate similar explosion scenarios and similar progenitor parameters.
Perfect cosmological hyperfluids generalize the concept of a perfect fluid within the framework of metric affine gravity. These hyperfluids encode the microstructure of matter including shear, dilation, and spin via the hypermomentum tensor. In this paper, we focus on the observational constraints of the recently introduced Yano-Schr\"odinger hyperfluid, which sources a special type of nonmetricity that preserves the lengths of vectors under autoparallel transport. We propose a model in which the effective nonmetricity contributions to pressure and matter density are related linearly as $p_{\text{eff}} = \omega \rho_{\text{eff}}$. This assumption allows for a straightforward parameterization of deviations from standard cosmological behavior while maintaining analytical tractability. To constrain the effective equation of state parameter $\omega$, we perform a Markov Chain Monte Carlo analysis using Baryon Acoustic Oscillation measurements from the Dark Energy Spectroscopic Instrument Year 1 and the Dark Energy Survey Year 6, along with Type Ia supernova and Cosmic Chronometer data. In our analysis, we treat $r_d$ as a free parameter, enabling late-time data to extract posterior distributions for the Hubble constant ($H_0$) and the sound horizon ($r_d$), along with the corresponding model parameters. Our results yield $H_0 = 66.9 \pm 1.6$ km s$^{-1}$ Mpc$^{-1}$ and $r_d = 147.7 \pm 3.5$ Mpc, with $\omega = -0.476$. Finally, we employ various statistical metrics to compare different Yano-Schr\"odinger models against the $\Lambda$CDM model. We find that the LESC model provides a better fit to the data, suggesting that modifications to metric-affine gravity could offer viable alternatives to the standard cosmological paradigm.
All about Cookies: The perfect compromise between softness and crispiness
2025-03-29
Sophie Rosu
Cookies are enjoyed best when they are both crispy and soft. I investigate in which proportion the cookies are crispy and soft, and disentangle whether it makes them biscuits, cakes, or none of the above. I baked cookies for colleagues at KTH, Stockholm, and University of Geneva, Switzerland, adopting my mum's mum's mum's etc. recipe. I created a dedicated survey for my colleagues with three well-selected questions to answer while eating one cookie. The weighted-average mean of the crispiness and softness, weighted by the respective enjoyment of the cookie, over the whole population amount to 7.0 +/- 1.1 and 5.3 +/- 1.4, respectively. The enjoyment of the cookies amounts to 9.1 +/- 2.3. People like (my) cookies, and cookies are neither cakes, nor biscuits, they are just... cookies!
Large Reasoning Models (LRMs) significantly improve the reasoning ability of Large Language Models (LLMs) by learning to reason, exhibiting promising performance in complex task-solving. However, their deliberative reasoning process leads to inefficiencies in token usage, memory consumption, and inference time. Thus, this survey provides a review of efficient inference methods designed specifically for LRMs, focusing on mitigating token inefficiency while preserving the reasoning quality. First, we introduce a taxonomy to group the recent methods into two main categories: (a) explicit compact Chain-of-Thought (CoT), which reduces tokens while keeping the explicit reasoning structure, and (b) implicit latent CoT, which encodes reasoning steps within hidden representations instead of explicit tokens. Meanwhile, we discuss their strengths and weaknesses. Then, we conduct empirical analyses on existing methods from performance and efficiency aspects. Besides, we present open challenges in this field, including human-centric controllable reasoning, trade-off between interpretability and efficiency of reasoning, ensuring safety of efficient reasoning, and broader applications of efficient reasoning. In addition, we highlight key insights for enhancing LRMs' inference efficiency via techniques such as model merging, new architectures, and agent routers. We hope this work serves as a valuable guide, helping researchers overcome challenges in this vibrant field\footnote{https://github.com/yueliu1999/Awesome-Efficient-Inference-for-LRMs}.
Stellar parameters, extinction, and distances for stars in SMSS DR2 by SPar method
2025-03-29
Mingxu Sun, Bingqiu Chen, Baokun Sun, Tao Wang, Zheng Yu, Baisong Zhang, Lin Zhang, Yuxi Bao, Guangya Zeng, Ming Yang, Wenyuan Cui
The availability of large datasets containing stellar parameters, distances, and extinctions for stars in the Milky Way, particularly within the Galactic disk, is essential for advancing our understanding of the Galaxy's stellar populations, structure, kinematics, and chemical evolution. In this study, we present a catalog of stellar parameters, including effective temperature (\teff), metallicity (\feh), absolute magnitudes ($M_{G}$), distances ($d$), and reddening values (\ebr), for a sample of 141 million stars from the SkyMapper Southern Survey (SMSS). These parameters are derived using the SPar algorithm, which employs a fitting procedure to match multi-band photometric observations and estimate the stellar properties (\teff, \feh, $M_G$, $d$, and \ebr) on an individual star basis, following the methodology outlined in our previous work. This study successfully determines stellar parameters and extinction values simultaneously for stars located in high and low Galactic latitudes. The resulting stellar parameters and extinction values are in good agreement with results from other studies, demonstrating the robustness of our method. We derive a temperature dispersion of 195\,K and a metallicity dispersion of 0.31\,dex when comparing our results with spectroscopic data. The catalog produced in this work provides a valuable resource for future research on the Galactic metallicity distribution function, the structure of the Galaxy, three-dimensional extinction mapping, and other related astrophysical topics.
Binary neutron-star mergers offer crucial insights into the matter properties of neutron stars. We present direct evidence of phase transition on the observational signatures from such events. Our study employs a range of equations of states with hadron-quark phase transition surveying from Maxwell construction smoothened up to the Gibbs construction using a control parameter $\Delta p$. This smoothening parameter allows us to explore different mixed phases and analyse their direct impact on merger dynamics. Post-merger gravitational wave emissions reveal the expression of specific signatures in the spectogram and power spectral density, serving as a distinct signature of equations of state with mixed phases. We found additional peaks in power spectral density that were fully responsible from the post-merger remnant experiencing a phase transition. Alongside this signature, the nature of phase transition transition leaves specific imprints on the spectrogram, leading to a two-folded signature from gravitational wave analysis. Furthermore, we establish a direct correlation between our findings and the threshold mass for prompt collapse. Our analysis also provides key evidence against a Maxwell-type phase transition for GW170817 if the post-merger is believed to have experienced a prompt collapse into a black hole. We show that $\Delta p \gtrsim 0.04$ is required for such a scenario. Effects of $\Delta p$ have also been observed on the ejecta mass from the event, which can affect the kilonova afterglow.
2025-03-29
Aske Plaat, Max van Duijn, Niki van Stein, Mike Preuss, Peter van der Putten, Kees Joost Batenburg
There is great interest in agentic LLMs, large language models that act as agents. We review the growing body of work in this area and provide a research agenda. Agentic LLMs are LLMs that (1) reason, (2) act, and (3) interact. We organize the literature according to these three categories. The research in the first category focuses on reasoning, reflection, and retrieval, aiming to improve decision making; the second category focuses on action models, robots, and tools, aiming for agents that act as useful assistants; the third category focuses on multi-agent systems, aiming for collaborative task solving and simulating interaction to study emergent social behavior. We find that works mutually benefit from results in other categories: retrieval enables tool use, reflection improves multi-agent collaboration, and reasoning benefits all categories. We discuss applications of agentic LLMs and provide an agenda for further research. Important applications are in medical diagnosis, logistics and financial market analysis. Meanwhile, self-reflective agents playing roles and interacting with one another augment the process of scientific research itself. Further, agentic LLMs may provide a solution for the problem of LLMs running out of training data: inference-time behavior generates new training states, such that LLMs can keep learning without needing ever larger datasets. We note that there is risk associated with LLM assistants taking action in the real world, while agentic LLMs are also likely to benefit society.
Multi-label classification for multi-temporal, multi-spatial coral reef condition monitoring using vision foundation model with adapter learning
2025-03-29
Xinlei Shao, Hongruixuan Chen, Fan Zhao, Kirsty Magson, Jundong Chen, Peiran Li, Jiaqi Wang, Jun Sasaki
Coral reef ecosystems provide essential ecosystem services, but face significant threats from climate change and human activities. Although advances in deep learning have enabled automatic classification of coral reef conditions, conventional deep models struggle to achieve high performance when processing complex underwater ecological images. Vision foundation models, known for their high accuracy and cross-domain generalizability, offer promising solutions. However, fine-tuning these models requires substantial computational resources and results in high carbon emissions. To address these challenges, adapter learning methods such as Low-Rank Adaptation (LoRA) have emerged as a solution. This study introduces an approach integrating the DINOv2 vision foundation model with the LoRA fine-tuning method. The approach leverages multi-temporal field images collected through underwater surveys at 15 dive sites at Koh Tao, Thailand, with all images labeled according to universal standards used in citizen science-based conservation programs. The experimental results demonstrate that the DINOv2-LoRA model achieved superior accuracy, with a match ratio of 64.77%, compared to 60.34% achieved by the best conventional model. Furthermore, incorporating LoRA reduced the trainable parameters from 1,100M to 5.91M. Transfer learning experiments conducted under different temporal and spatial settings highlight the exceptional generalizability of DINOv2-LoRA across different seasons and sites. This study is the first to explore the efficient adaptation of foundation models for multi-label classification of coral reef conditions under multi-temporal and multi-spatial settings. The proposed method advances the classification of coral reef conditions and provides a tool for monitoring, conserving, and managing coral reef ecosystems.
The search for life beyond the solar system is a central goal in exoplanetary science. Exoplanet surveys are increasingly detecting potentially habitable exoplanets and large telescopes in space and on ground are aiming to detect possible biosignatures in their atmospheres. At the same time, theoretical studies are expanding the range of habitable environments beyond the conventional focus on Earth-like rocky planets and biosignatures beyond the dominant biogenic gases in the Earth's atmosphere. The present work provides an introductory compendium of key aspects of habitability and biosignatures of importance to the search for life in exoplanetary environments. Basic concepts of planetary habitability are introduced along with essential requirements for life as we know it and the various factors that affect habitability. These include the requirement for liquid water, energy sources, bioessential elements, and geophysical environmental conditions conducive for life. The factors affecting habitability include both astrophysical conditions, such as those due to the host star, as well as planetary processes, such as atmospheric escape, magnetic interactions, and geological activity. A survey of different types of habitable environments possible in exoplanetary systems is presented. The notion of a biosignature is presented along with examples of biosignatures on Earth and their applicability to habitable environments in exoplanetary systems. The desired properties of an ideal biosignature are discussed, along with considerations of the environmental context and chemical disequilibria in the assessment of biosignatures in diverse environments. A discussion of current state-of-the-art and future prospects in the search for habitable conditions and biosignatures on exoplanets is presented.
Prompt Engineering for Large Language Model-assisted Inductive Thematic Analysis
2025-03-29
Muhammad Talal Khalid, Ann-Perry Witmer
The potential of large language models (LLMs) to mitigate the time- and cost- related challenges associated with inductive thematic analysis (ITA) has been extensively explored in the literature. However, the use of LLMs to support ITA has often been opportunistic, relying on ad hoc prompt engineering (PE) approaches, thereby undermining the reliability, transparency, and replicability of the analysis. The goal of this study is to develop a structured approach to PE in LLM-assisted ITA. To this end, a comprehensive review of the existing literature is conducted to examine how ITA researchers integrate LLMs into their workflows and, in particular, how PE is utilized to support the analytical process. Built on the insights generated from this review, four key steps for effective PE in LLM-assisted ITA are identified and extensively outlined. Furthermore, the study explores state-of-the-art PE techniques that can enhance the execution of these steps, providing ITA researchers with practical strategies to improve their analyses. In conclusion, the main contributions of this paper include: (i) it maps the existing research on LLM-assisted ITA to enable a better understanding of the rapidly developing field, (ii) it outlines a structured four-step PE process to enhance methodological rigor, (iii) it discusses the application of advanced PE techniques to support the execution of these steps, and (iv) it highlights key directions for future research.
We propose here a set of new methods involving probing and knocking with muons (PKMu). There is a wealth of rich physics to explore with GeV muon beams. Examples include but not limited to: muon scattering can occur at large angles, providing evidence of potential muon-philic dark matter or dark mediator candidates; muon-electron scattering can be used to detect new types of bosons associated with charged lepton flavor violation; precise measurements of GeV-scale muon-electron scattering can be employed to probe quantum correlations.
With the increasing complexity of mobile device applications, these devices are evolving toward high mobility. This shift imposes new demands on mobile sensing, particularly in terms of achieving high accuracy and low latency. Event-based vision has emerged as a disruptive paradigm, offering high temporal resolution, low latency, and energy efficiency, making it well-suited for high-accuracy and low-latency sensing tasks on high-mobility platforms. However, the presence of substantial noisy events, the lack of inherent semantic information, and the large data volume pose significant challenges for event-based data processing on resource-constrained mobile devices. This paper surveys the literature over the period 2014-2024, provides a comprehensive overview of event-based mobile sensing systems, covering fundamental principles, event abstraction methods, algorithmic advancements, hardware and software acceleration strategies. We also discuss key applications of event cameras in mobile sensing, including visual odometry, object tracking, optical flow estimation, and 3D reconstruction, while highlighting the challenges associated with event data processing, sensor fusion, and real-time deployment. Furthermore, we outline future research directions, such as improving event camera hardware with advanced optics, leveraging neuromorphic computing for efficient processing, and integrating bio-inspired algorithms to enhance perception. To support ongoing research, we provide an open-source \textit{Online Sheet} with curated resources and recent developments. We hope this survey serves as a valuable reference, facilitating the adoption of event-based vision across diverse applications.
Community-driven and water quality indicators of sanitation system failures in a rural U.S. community
2025-03-29
Lorelay Mendoza Grijalva, Allisa G. Hastie, Meili Gong, Brenda Rojas Cala, Brandon Hunter, Stephanie Wallace, Rojelio Mejia, Catherine Flowers, Khalid K. Osman, William A. Tarpeh
Safe sanitation access is commonly believed to be ubiquitous in high-income countries; however, researchers and community advocates have exposed a glaring lack of access for many low-income communities and communities of color across the U.S. While this disparity has been identified and quantified at a high level, local and household-level implications of sanitation failures remain ill-defined. We develop a set of user-based and environmental measures to assess the performance of centralized wastewater systems, septic systems, and straight-piped systems in Lowndes County, Alabama. We combine qualitative, survey, and environmental sampled data to holistically compare user experiences across infrastructure types. This integrated approach reveals new routes of exposure to wastewater through informal household maintenance and system backups and provides evidence for the spread of wastewater-like contamination throughout the community. This work elucidates the severity of sanitation failures in one rural U.S. community and provides a framework to assess sanitation quality in other contexts with limited sanitation access in high-income countries.
2025-03-29
Caroline Haimerl, Filipe S. Rodrigues, Joseph J. Paton
Because organisms are able to sense its passage, it is perhaps tempting to treat time as a sensory modality, akin to vision or audition. Indeed, certain features of sensory estimation, such as Weber's law, apply to timing and sensation alike (Gibbon, 1977; Pardo-Vazquez et al., 2019). However, from an organismal perspective, time is a derived feature of other signals, not a stimulus that can be readily transduced by sensory receptors. Its importance for biology lies in the fact that the physical world comprises a complex dynamical system. The multiscale spatiotemporal structure of sensory and internally generated signals within an organism is the informational fabric underlying its ability to control behavior. Viewed this way, temporal computations assume a more fundamental role than is implied by treating time as just another element of the experienced world (Paton & Buonomano, 2018). Thus, in this review we focus on temporal processing as a means of approaching the more general problem of how the nervous system produces adaptive behavior.
We study the problem of finding positive integers $n$ such that all the decimal digits of $2^n$ are even, i.e., belong to $\{0, 2, 4, 6, 8\}$. Computational checks up to $n = 10^{13}$ reveal the known cases $n = 1, 2, 3, 6, 11$ and no additional instances. We present a self-contained argument, based on equidistribution and a shrinking targets approach, showing there are only finitely many positive integers $n$ for which $2^n$ has all even decimal digits.
Who Owns the Output? Bridging Law and Technology in LLMs Attribution
2025-03-29
Emanuele Mezzi, Asimina Mertzani, Michael P. Manis, Siyanna Lilova, Nicholas Vadivoulis, Stamatis Gatirdakis, Styliani Roussou, Rodayna Hmede
Since the introduction of ChatGPT in 2022, Large language models (LLMs) and Large Multimodal Models (LMM) have transformed content creation, enabling the generation of human-quality content, spanning every medium, text, images, videos, and audio. The chances offered by generative AI models are endless and are drastically reducing the time required to generate content and usually raising the quality of the generation. However, considering the complexity and the difficult traceability of the generated content, the use of these tools provides challenges in attributing AI-generated content. The difficult attribution resides for a variety of reasons, starting from the lack of a systematic fingerprinting of the generated content and ending with the enormous amount of data on which LLMs and LMM are trained, which makes it difficult to connect generated content to the training data. This scenario is raising concerns about intellectual property and ethical responsibilities. To address these concerns, in this paper, we bridge the technological, ethical, and legislative aspects, by proposing a review of the legislative and technological instruments today available and proposing a legal framework to ensure accountability. In the end, we propose three use cases of how these can be combined to guarantee that attribution is respected. However, even though the techniques available today can guarantee a greater attribution to a greater extent, strong limitations still apply, that can be solved uniquely by the development of new attribution techniques, to be applied to LLMs and LMMs.
Skeletonization Quality Evaluation: Geometric Metrics for Point Cloud Analysis in Robotics
2025-03-29
Qingmeng Wen, Yu-Kun Lai, Ze Ji, Seyed Amir Tafrishi
Skeletonization is a powerful tool for shape analysis, rooted in the inherent instinct to understand an object's morphology. It has found applications across various domains, including robotics. Although skeletonization algorithms have been studied in recent years, their performance is rarely quantified with detailed numerical evaluations. This work focuses on defining and quantifying geometric properties to systematically score the skeletonization results of point cloud shapes across multiple aspects, including topological similarity, boundedness, centeredness, and smoothness. We introduce these representative metric definitions along with a numerical scoring framework to analyze skeletonization outcomes concerning point cloud data for different scenarios, from object manipulation to mobile robot navigation. Additionally, we provide an open-source tool to enable the research community to evaluate and refine their skeleton models. Finally, we assess the performance and sensitivity of the proposed geometric evaluation methods from various robotic applications.
Evaluation of Machine-generated Biomedical Images via A Tally-based Similarity Measure
2025-03-28
Frank J. Brooks, Rucha Deshpande
Super-resolution, in-painting, whole-image generation, unpaired style-transfer, and network-constrained image reconstruction each include an aspect of machine-learned image synthesis where the actual ground truth is not known at time of use. It is generally difficult to quantitatively and authoritatively evaluate the quality of synthetic images; however, in mission-critical biomedical scenarios robust evaluation is paramount. In this work, all practical image-to-image comparisons really are relative qualifications, not absolute difference quantifications; and, therefore, meaningful evaluation of generated image quality can be accomplished using the Tversky Index, which is a well-established measure for assessing perceptual similarity. This evaluation procedure is developed and then demonstrated using multiple image data sets, both real and simulated. The main result is that when the subjectivity and intrinsic deficiencies of any feature-encoding choice are put upfront, Tversky's method leads to intuitive results, whereas traditional methods based on summarizing distances in deep feature spaces do not.
Advancing DevSecOps in SMEs: Challenges and Best Practices for Secure CI/CD Pipelines
2025-03-28
Jayaprakashreddy Cheenepalli, John D. Hastings, Khandaker Mamun Ahmed, Chad Fenner
This study evaluates the adoption of DevSecOps among small and medium-sized enterprises (SMEs), identifying key challenges, best practices, and future trends. Through a mixed methods approach backed by the Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) theory, we analyzed survey data from 405 SME professionals, revealing that while 68% have implemented DevSecOps, adoption is hindered by technical complexity (41%), resource constraints (35%), and cultural resistance (38%). Despite strong leadership prioritization of security (73%), automation gaps persist, with only 12% of organizations conducting security scans per commit. Our findings highlight a growing integration of security tools, particularly API security (63%) and software composition analysis (62%), although container security adoption remains low (34%). Looking ahead, SMEs anticipate artificial intelligence and machine learning to significantly influence DevSecOps, underscoring the need for proactive adoption of AI-driven security enhancements. Based on our findings, this research proposes strategic best practices to enhance CI/CD pipeline security including automation, leadership-driven security culture, and cross-team collaboration.
Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users
2025-03-28
Antonia Karamolegkou, Malvina Nikandrou, Georgios Pantazopoulos, Danae Sanchez Villegas, Phillip Rust, Ruchira Dhar, Daniel Hershcovich, Anders Søgaard
This paper explores the effectiveness of Multimodal Large Language models (MLLMs) as assistive technologies for visually impaired individuals. We conduct a user survey to identify adoption patterns and key challenges users face with such technologies. Despite a high adoption rate of these models, our findings highlight concerns related to contextual understanding, cultural sensitivity, and complex scene understanding, particularly for individuals who may rely solely on them for visual interpretation. Informed by these results, we collate five user-centred tasks with image and video inputs, including a novel task on Optical Braille Recognition. Our systematic evaluation of twelve MLLMs reveals that further advancements are necessary to overcome limitations related to cultural context, multilingual support, Braille reading comprehension, assistive object recognition, and hallucinations. This work provides critical insights into the future direction of multimodal AI for accessibility, underscoring the need for more inclusive, robust, and trustworthy visual assistance technologies.
On the Alignment of Post-Publication Reviews & Bibliometric and Altmetric Impact -- A Case Study on Expert Statements from the Science Media Center Germany
2025-03-28
Dirk Tunger, Philipp Schaer
In the context of academic publishing and peer review, this study investigates the relationship between post-publication expert evaluations, their agreement levels, and the subsequent scientific and public recognition of the reviewed research. Using expert statements from the Science Media Center Germany as a dataset, we analyze Research in Context reviews to examine the alignment between qualitative post-publication assessments and bibliometric as well as altmetric indicators. We employ a Large Language Model to translate unstructured expert reviews into a structured rating scheme. Furthermore, we correlate these evaluations with citation counts from the Web of Science and alternative impact metrics such as the Altmetric Attention Score, news mentions, and Mendeley readership statistics from the Altmetric Explorer. We investigate the alignment of positive or critical post-publication reviews and high or low citation or altmetric counts.
KEVS: Enhancing Segmentation of Visceral Adipose Tissue in Pre-Cystectomy CT with Gaussian Kernel Density Estimation
2025-03-28
Thomas Boucher, Nicholas Tetlow, Annie Fung, Amy Dewar, Pietro Arina, Sven Kerneis, John Whittle, Evangelos B. Mazomenos
Purpose: The distribution of visceral adipose tissue (VAT) in cystectomy patients is indicative of the incidence of post-operative complications. Existing VAT segmentation methods for computed tomography (CT) employing intensity thresholding have limitations relating to inter-observer variability. Moreover, the difficulty in creating ground-truth masks limits the development of deep learning (DL) models for this task. This paper introduces a novel method for VAT prediction in pre-cystectomy CT, which is fully automated and does not require ground-truth VAT masks for training, overcoming aforementioned limitations. Methods: We introduce the Kernel density Enhanced VAT Segmentator ( KEVS), combining a DL semantic segmentation model, for multi-body feature prediction, with Gaussian kernel density estimation analysis of predicted subcutaneous adipose tissue to achieve accurate scan-specific predictions of VAT in the abdominal cavity. Uniquely for a DL pipeline, KEVS does not require ground-truth VAT masks. Results: We verify the ability of KEVS to accurately segment abdominal organs in unseen CT data and compare KEVS VAT segmentation predictions to existing state-of-the-art (SOTA) approaches in a dataset of 20 pre-cystectomy CT scans, collected from University College London Hospital (UCLH-Cyst), with expert ground-truth annotations. KEVS presents a 4.80% and 6.02% improvement in Dice Coefficient over the second best DL and thresholding-based VAT segmentation techniques respectively when evaluated on UCLH-Cyst. Conclusion: This research introduces KEVS; an automated, SOTA method for the prediction of VAT in pre-cystectomy CT which eliminates inter-observer variability and is trained entirely on open-source CT datasets which do not contain ground-truth VAT masks.
Silica nanoparticles have emerged as key building blocks for advanced applications in electronics, catalysis, energy storage, biomedicine, and environmental science. In this review, we focus on recent developments in both the synthesis and deposition of these nanoparticles, emphasizing the widely used St\"ober method and the versatile technique of electrophoretic deposition (EPD). The St\"ober method is celebrated for its simplicity and reliability, offering precise control over particle size, morphology, and surface properties to produce uniform, monodisperse silica nanoparticles that meet high-quality standards for advanced applications. EPD, on the other hand, is a cost-effective, room-temperature process that enables uniform coatings on substrates with complex geometries. When compared to traditional techniques such as chemical vapor deposition, atomic layer deposition, and spin coating, EPD stands out due to its scalability, enhanced material compatibility, and ease of processing. Moreover, Future research should integrate AI-driven optimization with active learning to enhance electrophoretic deposition (EPD) of silica nanoparticles, leveraging predictive modeling and real-time adjustments for improved film quality and process efficiency. This approach promises to accelerate material discovery and enable scalable nanofabrication of advanced functional films.
Do Researchers Benefit Career-wise from Involvement in International Policy Guideline Development?
2025-03-28
Yuta Tomokiyo, Keita Nishimoto, Kimitaka Asatani, Ichiro Sakata
Researchers are no longer limited to producing knowledge; in today's complex world, they also address societal challenges by engaging in policymaking. Although involvement in policymaking has expanded, direct empirical evidence of its career benefits remains underexplored. Prior survey-based studies suggest potential advantages-such as broader professional networks and enhanced opportunities-yet raise concerns about insufficient institutional support. Here, we examine the 2021 WHO global air quality guideline-a science-based regulatory guideline-as a case study. To evaluate the impact of guideline development on research outcomes, we match guideline researchers with a control group of peers sharing similar research topics and prior performance. Our analysis reveals that guideline researchers attain higher future citation counts in both academic and policy domains. New collaborations formed during development yield publications with higher citation impact and the disruptive index. Moreover, about half the guideline's references are derived from guideline researchers' papers, highlighting their central role in shaping the evidence base. These results provide empirical support for the career benefits of policy engagement. Our findings indicate that engaging in international guideline development offers tangible career incentives for researchers, and that institutions can enhance research impact and promote innovative scientific progress by actively supporting their researchers' participation in such initiatives.
Euclid Quick Data Release (Q1) Ultracool dwarfs in the Euclid Deep Field North
2025-03-28
A. Mohandasan, R. L. Smart, C. Reylé, V. Le Brun, A. Pérez-Garrido, E. Bañados, B. Goldman, S. L. Casewell, M. R. Zapatero Osorio, T. Dupuy, M. Rejkuba, E. L. Martín, C. Dominguez-Tagle, M. {Ž}erjal, N. Huélamo, N. Lodieu, P. Cruz, R. Rebolo, M. W. Phillips, J. -Y. Zhang, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, A. Biviano, A. Bonchi, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, A. Costille, F. Courbin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, H. Dole, M. Douspis, F. Dubath, X. Dupac, S. Dusini, S. Escoffier, M. Farina, F. Faustini, S. Ferriol, S. Fotopoulou, M. Frailis, E. Franceschi, S. Galeotta, K. George, W. Gillard, B. Gillis, C. Giocoli, J. Gracia-Carpio, B. R. Granett, A. Grazian, F. Grupp, S. V. H. Haugan, J. Hoar, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, K. Kuijken, M. Kümmel, M. Kunz, H. Kurki-Suonio, Q. Le Boulc'h, A. M. C. Le Brun, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, E. Medinaceli, S. Mei, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, J. A. Schewtschenko, M. Schirmer, P. Schneider, T. Schrabback, A. Secroun, G. Seidel, S. Serrano, P. Simon, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, C. Surace, P. Tallada-Crespí, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, A. Tsyganov, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, D. Vibert, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, E. Zucca, J. Martín-Fleitas, V. Scottez
Ultracool dwarfs (UCDs) encompass the lowest mass stars and brown dwarfs, defining the stellar substellar boundary. They have significant potential for advancing the understanding of substellar physics; however, these objects are challenging to detect due to their low luminosity. The wide coverage and deep sensitivity of the Euclid survey will increase the number of confirmed and well characterised UCDs by several orders of magnitude. In this study, we take advantage of the Euclid Quick Data Release (Q1) and in particular we look in detail at the known and new UCDs in the Euclid Deep Field North (22.9 deg2 down to JE = 24.5 mag), to understand the advantages of using the slitless Euclid spectroscopy. We compile a comparison sample of known UCDs and use their spectra to demonstrate the capability of Euclid to derive spectral types using a template matching method. This method is then applied to the spectra of the newly identified candidates. We confirm that 33 of these candidates are new UCDs, with spectral types ranging from M7 to T1 and JE = 17 to 21 mag. We look at their locus in colour colour diagrams and compare them with the expected colours of QSOs. A machine readable catalogue is provided for further study, containing both the comparison sample and the newly identified UCDs, along with their spectral classifications where the Q1 spectra quality allows for confident determination
CAMmary: A Review of Spacecraft Collision Avoidance Manoeuvre Design Methods
2025-03-28
Zeno Pavanello, Luigi De Maria, Andrea De Vittori, Michele Maestrini, Pierluigi Di Lizia, Roberto Armellin
Ensuring safety for spacecraft operations has become a paramount concern due to the proliferation of space debris and the saturation of valuable orbital regimes. In this regard, the Collision Avoidance Manoeuvre (CAM) has emerged as a critical requirement for spacecraft operators, aiming to efficiently navigate through potentially hazardous encounters. Currently, when a conjunction is predicted, operators dedicate a considerable amount of time and resources to designing a CAM. Given the increased frequency of conjunctions, autonomous computation of fuel-efficient CAMs is crucial to reduce costs and improve the performance of future operations. To facilitate the transition to an autonomous CAM design, it is useful to provide an overview of its state-of-the-art. In this survey article, a collection of the most relevant research contributions in the field is presented. We review and categorize existing CAM techniques based on their underlying principles, such as (i) analytic, semi-analytic, or numerical solutions; (ii) impulsive or continuous thrust; (iii) deterministic or stochastic approaches, (iv) free or fixed manoeuvring time; (v) free or fixed thrust direction. Finally, to determine the validity of the algorithms potentially implementable for autonomous use, we perform a numerical comparison on a large set of conjunctions. With this analysis, the algorithms are evaluated in terms of computational efficiency, accuracy, and optimality of the computed policy. Through this comprehensive survey, we aim to provide insights into the state-of-the-art CAM methodologies, identify gaps in current research, and outline potential directions for future developments in ensuring the safety and sustainability of spacecraft operations in increasingly congested orbital environments.
Comparing methods to assess treatment effect heterogeneity in general parametric regression models
2025-03-28
Yao Chen, Sophie Sun, Konstantinos Sechidis, Cong Zhang, Torsten Hothorn, Björn Bornkamp
This paper reviews and compares methods to assess treatment effect heterogeneity in the context of parametric regression models. These methods include the standard likelihood ratio tests, bootstrap likelihood ratio tests, and Goeman's global test motivated by testing whether the random effect variance is zero. We place particular emphasis on tests based on the score-residual of the treatment effect and explore different variants of tests in this class. All approaches are compared in a simulation study, and the approach based on residual scores is illustrated in a study comparing multiple doses versus placebo. Our findings demonstrate that score-residual based methods provide practical, flexible and reliable tools for identifying treatment effect heterogeneity and treatment effect modifiers, and can provide useful guidance for decision making around treatment effect heterogeneity.
The most developed aspect of the theory of finite semigroups is their classification in pseudovarieties. The main motivation for investigating such entities comes from their connection with the classification of regular languages via Eilenberg's correspondence. This connection prompted the study of various natural operators on pseudovarieties and led to several important questions, both algebraic and algorithmic. The most important of these questions is decidability: given a finite semigroup is there an algorithm that tests whether it belongs to the pseudovariety? Since the most relevant operators on pseudovarieties do not preserve decidability, one often seeks to establish stronger properties. A key role is played by relatively free profinite semigroups, which is the counterpart of free algebras in universal algebra. The purpose of this paper is to give a brief survey of the state of the art, highlighting some of the main developments and problems.
2025-03-28
Michele Bosi, Andrea Lapi, Lumen Boco, Carlos Alonso-Alvarez, Marcos Muniz-Cueli, Giovanni Antinozzi, Meriem Behiri, Marika Giulietti, Marcella Massardi, Mario Spera, Alessandro Bressan, Carlo Baccigalupi, Luigi Danese
We build a semi-empirical framework of galaxy evolution (dubbed StAGE) firmly grounded on stellar archaeology. The latter provides data-driven prescriptions that, on a population statistical ground, allow to define the age and the star formation history for the progenitors of quiescent galaxies (QGs). We exploit StAGE to compute the cosmic star formation rate (SFR) density contributed by the progenitors of local QGs, and show it to remarkably agree with that estimated for high-$z$ dusty star-forming galaxies which are faint/dark in the NIR, so pointing toward a direct progenitor-descendant connection among these galaxy populations. Furthermore, we argue that by appropriately correcting the observed stellar mass density by the contribution of such NIR-dark progenitors, StAGE recovers a SFR density which is consistent with direct determinations from UV/IR/radio surveys, so substantially alleviating a longstanding tension. Relatedly, we also show how StAGE can provide the average mass and metal assembly history of QGs, and their redshift-dependent statistics. Focusing on the supermassive black holes (BHs) hosted by massive QGs, we exploit StAGE to reconstruct the average BH mass assembly history, the cosmic BH accretion rate density as a function of redshift, and the evolution of the Magorrian-like relationship between the relic stellar and BH masses. All in all, StAGE may constitute a valuable tool to understand via a data-driven, easily expandable, and computationally low-cost approach the co-evolution of QGs and of their hosted supermassive BHs across cosmic times.
To CPL, or not to CPL? What we have not learned about the dark energy equation of state
2025-03-28
Savvas Nesseris, Yashar Akrami, Glenn D. Starkman
We show that using a Taylor expansion for the dark energy equation-of-state parameter and limiting it to the zeroth and first-order terms, i.e., the so-called Chevallier-Polarski-Linder (CPL) parametrization, instead of allowing for higher-order terms and then marginalizing over them, adds extra information not present in the data and leads to markedly different and potentially misleading conclusions. Fixing the higher-order terms to zero, one concludes that vacuum energy that is currently non-dynamical (e.g., the cosmological constant) is excluded at several $\sigma$ significance as the explanation of cosmic acceleration, even in Dark Energy Spectroscopic Survey (DESI) DR1 data. Meanwhile, instead marginalizing over the higher-order terms shows that we know neither the current dark energy equation of state nor its current rate of change well enough to make such a claim. This issue has become more prominent now with the recent release of high-quality Stage IV galaxy survey data. The results of analyses using simple Taylor expansions should be interpreted with great care.
CaII and H$α$ flaring M dwarfs detected with multi-filter photometry
2025-03-28
P. Mas-Buitrago, J. -Y. Zhang, E. Solano, E. L. Martín
Understanding and characterising the magnetic activity of M dwarfs is of paramount importance in the search for Earth-like exoplanets orbiting them. Energetic stellar activity phenomena, such as flares or coronal mass ejections, which are common in these stars, are deeply connected with the habitability and atmospheric evolution of the surrounding exoplanets. We present a follow-up of a sample of M dwarfs with strong H$\alpha$ and CaII H and K emission lines identified with J-PLUS photometry in a previous work. We collected low-resolution NOT/ALFOSC and GTC/OSIRIS spectra, measuring the PC3 index for the spectral type determination. We used two-minute-cadence calibrated TESS light curves to identify and characterise multiple flares and to calculate the rotation period of the two active M dwarfs found in our sample. We confirm that the strong emission lines detected in the J-PLUS photometry are caused by transient flaring activity. We find clear evidence of flaring activity and periodic variability for LP 310-34 and LP 259-39, and estimated flare energies in the TESS bandpass between $7.4\times10^{30}$ and $2.2\times10^{33}$ erg for them. We characterised LP 310-34 and LP 259-39 as very rapidly rotating M dwarfs with CaII H and K and H$\alpha$ in emission, and computed a rotation period for LP 259-39 for the first time: $P_{\rm rot}=1.69\pm0.02$ d. This work advocates the approach of exploiting multi-filter photometric surveys to systematically identify flaring M dwarfs, especially to detect episodes of strong CaII H and K line emission, which may have important implications for exoplanetary space weather and habitability studies. Our results reveal that common M dwarfs experience flare events in CaII H and K in addition to well known H$\alpha$ flares.
Euclid Quick Data Release (Q1): A photometric search for ultracool dwarfs in the Euclid Deep Fields
2025-03-28
M. Žerjal, C. Dominguez-Tagle, N. Sedighi, E. L. Martín, N. Lodieu, B. Goldman, C. Reylé, R. L. Smart, A. Mohandasan, M. R. Zapatero Osorio, D. Barrado, P. Mas Buitrago, N. Vitas, P. Cruz, V. J. S. Béjar, H. Bouy, A. Burgasser, S. Muñoz Torres, N. Phan-Bao, E. Solano, R. Tata, S. Tsilia, J. -Y. Zhang, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, A. Biviano, A. Bonchi, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Cropper, J. -G. Cuby, A. Da Silva, H. Degaudenzi, G. De Lucia, C. Dolding, H. Dole, M. Douspis, F. Dubath, X. Dupac, S. Dusini, S. Escoffier, M. Farina, F. Faustini, S. Ferriol, S. Fotopoulou, M. Frailis, E. Franceschi, S. Galeotta, K. George, B. Gillis, C. Giocoli, P. Gómez-Alvarez, J. Gracia-Carpio, B. R. Granett, A. Grazian, F. Grupp, S. V. H. Haugan, J. Hoar, W. Holmes, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, K. Kuijken, M. Kümmel, M. Kunz, H. Kurki-Suonio, Q. Le Boulc'h, A. M. C. Le Brun, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, E. Medinaceli, S. Mei, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, J. A. Schewtschenko, M. Schirmer, P. Schneider, A. Secroun, G. Seidel, M. Seiffert, S. Serrano, P. Simon, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, S. Toft, R. Toledo-Moreo, F. Torradeflot, A. Tsyganov, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, E. Zucca, J. Martín-Fleitas, V. Scottez
We present a catalogue of more than 5000 new ultracool dwarf (UCD) candidates in the three Euclid Deep Fields in the Q1 data release. They range from late M to late T dwarfs, and include 1200 L and T dwarfs. More than 100 of them have been spectroscopically confirmed, with seven of them being T dwarfs. Our UCD selection criteria are based only on colour ($I_\mathrm{E}-Y_\mathrm{E}>2.5$). The combined requirement for optical detection and stringent signal-to-noise ratio threshold ensure a high purity of the sample, but at the expense of completeness, especially for T dwarfs. The detections range from magnitudes 19 and 24 in the near-infrared bands, and extend down to 26 in the optical band. The average surface density of detected UCDs on the sky is approximately 100 objects per $\mathrm{deg}^2$, including 20 L and T dwarfs per $\mathrm{deg}^2$. This leads to an expectation of at least 1.4 million ultracool dwarfs in the final data release of the Euclid Wide Survey, including at least 300,000 L dwarfs, and more than 2,600 T dwarfs, using the strict selection criteria from this work. We provide empirical Euclid colours as a function of spectral type, and a probability that an object with a given colour has a certain spectral type.
Charged Lepton Flavour Violations searches with muons: present and future
2025-03-28
M. Aoki, A. M. Baldini, R. H. Bernstein, C. Carloganu, S. Mihara, S. Miscetti, T. Mori, W. Ootani, F. Renga, S. Ritt, A. Schoening
Charged-lepton flavor violation (cLFV) is one of the most powerful probes for New Physics (NP). Since lepton flavor conservation is an accidental symmetry in the Standard Model (SM), it is naturally violated in many NP models, with contributions at the level of the current experimental sensitivities. Moreover, the negligible SM contributions would make the observation of cLFV unambiguous evidence of NP. It makes these searches extremely sensitive and, at the same time, extremely pure. Thanks to the intense muon beams currently available, their intriguing upgrade programs, and the progress in the detection techniques, cLFV muon processes are the golden channels in this field. Experimental programs to search for $\mu^+ \to e^+ \gamma$, $\mu^+ \to e^+ e^+ e^-$ and the $\mu \to e$ conversion in the nuclear field are currently ongoing. We review the current status and the strategic plans for future searches. This document is an update of the prior cLFV submission to the 2018 European Strategy for Particle Physics (ESPP); the earlier submission should be consulted for more experimental details.
This survey examines evaluation methods for large language model (LLM)-based agents in multi-turn conversational settings. Using a PRISMA-inspired framework, we systematically reviewed nearly 250 scholarly sources, capturing the state of the art from various venues of publication, and establishing a solid foundation for our analysis. Our study offers a structured approach by developing two interrelated taxonomy systems: one that defines \emph{what to evaluate} and another that explains \emph{how to evaluate}. The first taxonomy identifies key components of LLM-based agents for multi-turn conversations and their evaluation dimensions, including task completion, response quality, user experience, memory and context retention, as well as planning and tool integration. These components ensure that the performance of conversational agents is assessed in a holistic and meaningful manner. The second taxonomy system focuses on the evaluation methodologies. It categorizes approaches into annotation-based evaluations, automated metrics, hybrid strategies that combine human assessments with quantitative measures, and self-judging methods utilizing LLMs. This framework not only captures traditional metrics derived from language understanding, such as BLEU and ROUGE scores, but also incorporates advanced techniques that reflect the dynamic, interactive nature of multi-turn dialogues.
Data Quality Matters: Quantifying Image Quality Impact on Machine Learning Performance
2025-03-28
Christian Steinhauser, Philipp Reis, Hubert Padusinski, Jacob Langner, Eric Sax
Precise perception of the environment is essential in highly automated driving systems, which rely on machine learning tasks such as object detection and segmentation. Compression of sensor data is commonly used for data handling, while virtualization is used for hardware-in-the-loop validation. Both methods can alter sensor data and degrade model performance. This necessitates a systematic approach to quantifying image validity. This paper presents a four-step framework to evaluate the impact of image modifications on machine learning tasks. First, a dataset with modified images is prepared to ensure one-to-one matching image pairs, enabling measurement of deviations resulting from compression and virtualization. Second, image deviations are quantified by comparing the effects of compression and virtualization against original camera-based sensor data. Third, the performance of state-of-the-art object detection models is analyzed to determine how altered input data affects perception tasks, including bounding box accuracy and reliability. Finally, a correlation analysis is performed to identify relationships between image quality and model performance. As a result, the LPIPS metric achieves the highest correlation between image deviation and machine learning performance across all evaluated machine learning tasks.
Probing stellar-mass primordial black holes with type Ia supernova microlensing
2025-03-28
Mingqi Sun, Kai Liao
Gravitationally lensed quasars have served as a powerful tool for studying the composition of dark matter (DM) in lensing galaxies. In this work, we propose a novel method to investigate stellar-mass primordial black holes (PBHs) by using the microlensing effect of strongly lensed Type Ia supernovae (SNe Ia). Using the parameters of the lensed quasar system PG 1115+80, such as convergence, shear, and stellar/dark matter fractions, we generate microlensing magnification maps. A uniform brightness disk model is applied to these maps to evaluate the microlensing amplitude at different stages of the supernova explosion. We extend this analysis by employing the strong lensing parameters derived from the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) to create extensive image datasets of lensed SNe Ia. Utilizing these datasets and the Kolmogorov-Smirnov (KS) test, we compare two models: (1) the fiducial model, where galaxies are composed of stars and smooth DM, and (2) the alternative model, where galaxies consist of stars and compact DM, specifically PBHs. Our preliminary analysis predicts that at least 60 image datasets are required to distinguish these two scenarios at a 95 percent confidence level. Additionally, by incorporating the Strong Lensing Halo model-based mock catalogs (SL-Hammocks), which provide more realistic and precise image data, we refine our prediction to assess the data requirements for distinguishing cases where PBHs constitute fractions x of the total dark matter mass. Our findings indicate that 50, 55, and 65 image datasets, corresponding to compact dark matter fractions of 100 percent, 50 percent, and 25 percent (denoted by x), are necessary to distinguish between the specific models.
AT2021uey: A planetary microlensing event outside the Galactic bulge
2025-03-28
M. Ban, P. Voloshyn, R. Adomaviciene, E. Bachelet, V. Bozza, S. M. Brincat, I. Bruni, U. Burgaz, J. M. Carrasco, A. Cassan, V. Cepas, F. Cusano, M. Dennefeld, M. Dominik, F. Dubois, R. Figuera Jaimes, A. Fukui, C. Galdies, A. Garofalo, M. Hundertmark, I. Ilyin, K. Kruszynska, V. Kulijanishvili, T. Kvernadze, L. Logie, M. Maskoliunas, P. J. Mikolajczyk, P. Mroz, N. Narita, E. Pakstiene, J. Peloton, R. Poleski, J. K. T. Qvam, S. Rau, P. Rota, K. A. Rybicki, R. A. Street, Y. Tsapras, S. Vanaverbeke, J. Wambsganss, L. Wyrzykowski, J. Zdanavicius, M. Zejmo, P. Zielinski, S. Zola
We report the analysis of a planetary microlensing event AT2021uey. The event was observed outside the Galactic bulge and was alerted by both space- (Gaia) and ground-based (ZTF and ASAS-SN) surveys. From the observed data, we find that the lens system is located at a distance of 1 kpc and comprises an M-dwarf host star of about half a solar mass, orbited by a Jupiter-like planet beyond the snowline. The source star could be a metal-poor giant located in the halo according to the spectral analyses and modelling. Hence, AT2021uey is a unique example of the binary-lens event outside the bulge that is offered by a disc-halo lens-source combination.
Static and hydrodynamic periodic structures induced by AC electric fields in the antiferroelectric SmZA phase
2025-03-28
K. S. Krishnamurthy, S. Krishna Prasad, D. S. Shankar Rao, R. J. Mandle, C. J. Gibb, J. Hobbs, N. V. Madhusudana
We report the effect of AC electric fields in the range of 0.1-300 kHz on planar antiferroelectric SmZA layers of DIO. Significant results are (a) primary bifurcation into a quasistationary periodic instability with its voltage threshold Uc and wave vector qc along the initial director being, respectively, quadratic and linear functions of f over 10-150 kHz, and with an azimuthal distortion of the director which changes sign between adjacent stripes, (b) transition from the modulated planar state to a homogeneous state at higher voltages, and (c) third bifurcation into travelling wave periodic state on further rise in U in the region 10-40 kHz. We interpret these findings as follows. The low voltage instability is very similar to that seen in the higher temperature apolar nematic phase, and is the electrohydrodynamic (EHD) instability possibly belonging to the region of dielectric inversion frequency. The azimuthal distortions of n result from an undulatory distortion of the SmZA layers in the book-shelf geometry. The intermediate homogeneous state of SmZA in which the periodic structure is absent results from a linear coupling between the layer polarization P and applied field E, giving rise to a scissoring type mutual P reorientation in adjacent layers. Finally, at even higher voltages, the medium goes over to a field induced transition to the ferroelectric nematic, with the polarization following the AC field, and the periodic EHD instability being similar to that of the dielectric regime. The polar vector symmetry of the medium leads in general to travelling waves.
Influence of dark photon on magnetized and charged particle orbits around static spherically symmetric black hole
2025-03-28
Marek Rogatko, Paritosh Verma
We elaborate the problem of magnetized particle motion in the spacetime of a static, spherically symmetric black hole influenced by weak magnetic fields stemming from visible and dark matter sectors. The Wald's procedure for obtaining the weakly magnetized solution, generalized to the case of dark photon - Einstein-Maxwell gravity was implemented. The collision process analysis of two particles in the background of the black hole has been studied in order to find the signature of dark matter presence in the nearby of the object in question.
Integrating LLMs in Software Engineering Education: Motivators, Demotivators, and a Roadmap Towards a Framework for Finnish Higher Education Institutes
2025-03-28
Maryam Khan, Muhammad Azeem Akbar, Jussi Kasurinen
The increasing adoption of Large Language Models (LLMs) in software engineering education presents both opportunities and challenges. While LLMs offer benefits such as enhanced learning experiences, automated assessments, and personalized tutoring, their integration also raises concerns about academic integrity, student over-reliance, and ethical considerations. In this study, we conducted a preliminary literature review to identify motivators and demotivators for using LLMs in software engineering education. We applied a thematic mapping process to categorize and structure these factors (motivators and demotivators), offering a comprehensive view of their impact. In total, we identified 25 motivators and 30 demotivators, which are further organized into four high-level themes. This mapping provides a structured framework for understanding the factors that influence the integration of LLMs in software engineering education, both positively and negatively. As part of a larger research project, this study serves as a feasibility assessment, laying the groundwork for future systematic literature review and empirical studies. Ultimately, this project aims to develop a framework to assist Finnish higher education institutions in effectively integrating LLMs into software engineering education while addressing potential risks and challenges.
DREMnet: An Interpretable Denoising Framework for Semi-Airborne Transient Electromagnetic Signal
2025-03-28
Shuang Wang, Ming Guo, Xuben Wang, Fei Deng, Lifeng Mao, Bin Wang, Wenlong Gao
The semi-airborne transient electromagnetic method (SATEM) is capable of conducting rapid surveys over large-scale and hard-to-reach areas. However, the acquired signals are often contaminated by complex noise, which can compromise the accuracy of subsequent inversion interpretations. Traditional denoising techniques primarily rely on parameter selection strategies, which are insufficient for processing field data in noisy environments. With the advent of deep learning, various neural networks have been employed for SATEM signal denoising. However, existing deep learning methods typically use single-mapping learning approaches that struggle to effectively separate signal from noise. These methods capture only partial information and lack interpretability. To overcome these limitations, we propose an interpretable decoupled representation learning framework, termed DREMnet, that disentangles data into content and context factors, enabling robust and interpretable denoising in complex conditions. To address the limitations of CNN and Transformer architectures, we utilize the RWKV architecture for data processing and introduce the Contextual-WKV mechanism, which allows unidirectional WKV to perform bidirectional signal modeling. Our proposed Covering Embedding technique retains the strong local perception of convolutional networks through stacked embedding. Experimental results on test datasets demonstrate that the DREMnet method outperforms existing techniques, with processed field data that more accurately reflects the theoretical signal, offering improved identification of subsurface electrical structures.
Sell It Before You Make It: Revolutionizing E-Commerce with Personalized AI-Generated Items
2025-03-28
Jianghao Lin, Peng Du, Jiaqi Liu, Weite Li, Yong Yu, Weinan Zhang, Yang Cao
E-commerce has revolutionized retail, yet its traditional workflows remain inefficient, with significant time and resource costs tied to product design and manufacturing inventory. This paper introduces a novel system deployed at Alibaba that leverages AI-generated items (AIGI) to address these challenges with personalized text-to-image generation for e-commercial product design. AIGI enables an innovative business mode called "sell it before you make it", where merchants can design fashion items and generate photorealistic images with digital models based on textual descriptions. Only when the items have received a certain number of orders, do the merchants start to produce them, which largely reduces reliance on physical prototypes and thus accelerates time to market. For such a promising application, we identify the underlying key scientific challenge, i.e., capturing the users' group-level personalized preferences towards multiple generated candidate images. To this end, we propose a Personalized Group-Level Preference Alignment Framework for Diffusion Models (i.e., PerFusion). We first design PerFusion Reward Model for user preference estimation with a feature-crossing-based personalized plug-in. Then we develop PerFusion with a personalized adaptive network to model diverse preferences across users, and meanwhile derive the group-level preference optimization objective to capture the comparative behaviors among multiple candidates. Both offline and online experiments demonstrate the effectiveness of our proposed algorithm. The AI-generated items have achieved over 13% relative improvements for both click-through rate and conversion rate compared to their human-designed counterparts, validating the revolutionary potential of AI-generated items for e-commercial platforms.
Spin-polarized antiferromagnets (AFMs), including altermagnets, noncollinear AFMs, and two-dimensional layer-polarized AFMs, have emerged as transformative materials for next-generation spintronic and optoelectronic technologies. These systems uniquely combine spin-polarized electronic states with vanishing net magnetization, enabling ultrafast spin dynamics, high-density integration, and robustness against stray magnetic fields. Their unconventional symmetry-breaking mechanisms-governed by crystal symmetry, chiral spin textures, or interlayer potential control-give rise to emergent phenomena previously exclusive to ferromagnets: nonrelativistic spin-momentum locking, spontaneous anomalous transport phenomena, gate-tunable magneto-optical responses, and nonrelativistic spin-polarized current. This review systematically examines the fundamental principles linking symmetry, band topology, and transport properties across these material classes, synthesizing recent breakthroughs in both theory and experiment. We further identify critical challenges in achieving room-temperature functionality, scalable Neel vector control, and coherent spin-current manipulation, while outlining pathways to harness these materials for ultra-low-power memory, spin-logic architectures, and quantum information technologies.
Traffic Modeling for Network Security and Privacy: Challenges Ahead
2025-03-28
Dinil Mon Divakaran
Traffic analysis using machine learning and deep learning models has made significant progress over the past decades. These models address various tasks in network security and privacy, including detection of anomalies and attacks, countering censorship, etc. They also reveal privacy risks to users as demonstrated by the research on LLM token inference as well as fingerprinting (and counter-fingerprinting) of user-visiting websites, IoT devices, and different applications. However, challenges remain in securing our networks from threats and attacks. After briefly reviewing the tasks and recent ML models in network security and privacy, we discuss the challenges that lie ahead.
Asteroseismology of Long-Period Variables with OGLE-IV data: Using Global Seismic Parameters as Luminosity Indicators
2025-03-28
Qiang Xiong, Tanda Li, Jie Yu, Zhiwen Li, Shaolan Bi, Xianfei Zhang, Haibo Yuan, I. Soszyński, A. Udalski, M. K. Szymański, D. M. Skowron, J. Skowron, P. Pietrukowicz, R. Poleski, S. Kozłowski, P. Mróz, K. Ulaczyk, K. Rybicki, P. Iwanek, M. Wrona, M. Gromadzki, M. Mróz
Long-period variables (LPVs) are high-luminosity red giants or supergiants with pulsation periods ranging from days to years. Many LPVs in the Large Magellanic Cloud (LMC) and Galactic Bulge (BLG) have been continuously observed over a time span of 26 years by the Optical Gravitational Lensing Experiment (OGLE) survey. Using OGLE-IV data, we applied Gaussian Processes with kernels tailored for solar-like oscillations to extract two global asteroseismic parameters: the frequency of maximum power (numax) and the large frequency separation (Dnu), for LPVs with primary mode periods (P1) between 10 and 100 days in the LMC and BLG. We found that the numax-Dnu relation for LPVs in this work aligns with that of lower-luminosity Kepler red giants, confirming that the pulsations of these LPVs are likely solar-like. We found that numax and Dnu can serve as luminosity indicators. Compared to P1, numax and Dnu exhibit significantly tighter correlations with the absolute magnitude in the 2MASS K_s band (M_{K}), with corresponding scatter of 0.27 mag and 0.21 mag, respectively. Using the calibrated numax-mk and Dnu-mk relations for LPVs in the LMC, we determined the M_{K} values for individual stars in the BLG. By accounting for extinction, we further calculated the distances to 4,948 BLG stars. The peak of the resulting distance distribution corresponds to an estimated distance to the Galactic center of approximately 9.1 kpc, which appears to be overestimated, suggesting that the seismic luminosity relation calibrated from the LMC may not be directly applicable to BLG stars.
Multimodal Machine Learning for Real Estate Appraisal: A Comprehensive Survey
2025-03-28
Chenya Huang, Zhidong Li, Fang Chen, Bin Liang
Real estate appraisal has undergone a significant transition from manual to automated valuation and is entering a new phase of evolution. Leveraging comprehensive attention to various data sources, a novel approach to automated valuation, multimodal machine learning, has taken shape. This approach integrates multimodal data to deeply explore the diverse factors influencing housing prices. Furthermore, multimodal machine learning significantly outperforms single-modality or fewer-modality approaches in terms of prediction accuracy, with enhanced interpretability. However, systematic and comprehensive survey work on the application in the real estate domain is still lacking. In this survey, we aim to bridge this gap by reviewing the research efforts. We begin by reviewing the background of real estate appraisal and propose two research questions from the perspecve of performance and fusion aimed at improving the accuracy of appraisal results. Subsequently, we explain the concept of multimodal machine learning and provide a comprehensive classification and definition of modalities used in real estate appraisal for the first time. To ensure clarity, we explore works related to data and techniques, along with their evaluation methods, under the framework of these two research questions. Furthermore, specific application domains are summarized. Finally, we present insights into future research directions including multimodal complementarity, technology and modality contribution.
Contrastive language-audio pre-training (CLAP) has addressed audio-language tasks such as audio-text retrieval by aligning audio and text in a common feature space. While CLAP addresses general audio-language tasks, its audio features do not generalize well in audio tasks. In contrast, self-supervised learning (SSL) models learn general-purpose audio features that perform well in diverse audio tasks. We pursue representation learning that can be widely used in audio applications and hypothesize that a method that learns both general audio features and CLAP features should achieve our goal, which we call a general-purpose audio-language representation. To implement our hypothesis, we propose M2D2, a second-generation masked modeling duo (M2D) that combines an SSL M2D and CLAP. M2D2 learns two types of features using two modalities (audio and text) in a two-stage training process. It also utilizes advanced LLM-based sentence embeddings in CLAP training for powerful semantic supervision. In the first stage, M2D2 learns generalizable audio features from M2D and CLAP, where CLAP aligns the features with the fine LLM-based semantic embeddings. In the second stage, it learns CLAP features using the audio features learned from the LLM-based embeddings. Through these pre-training stages, M2D2 should enhance generalizability and performance in its audio and CLAP features. Experiments validated that M2D2 achieves effective general-purpose audio-language representation, highlighted with SOTA fine-tuning mAP of 49.0 for AudioSet, SOTA performance in music tasks, and top-level performance in audio-language tasks.
The rapid advancement of remote sensing foundation models, particularly vision and multimodal models, has significantly enhanced the capabilities of intelligent geospatial data interpretation. These models combine various data modalities, such as optical, radar, and LiDAR imagery, with textual and geographic information, enabling more comprehensive analysis and understanding of remote sensing data. The integration of multiple modalities allows for improved performance in tasks like object detection, land cover classification, and change detection, which are often challenged by the complex and heterogeneous nature of remote sensing data. However, despite these advancements, several challenges remain. The diversity in data types, the need for large-scale annotated datasets, and the complexity of multimodal fusion techniques pose significant obstacles to the effective deployment of these models. Moreover, the computational demands of training and fine-tuning multimodal models require significant resources, further complicating their practical application in remote sensing image interpretation tasks. This paper provides a comprehensive review of the state-of-the-art in vision and multimodal foundation models for remote sensing, focusing on their architecture, training methods, datasets and application scenarios. We discuss the key challenges these models face, such as data alignment, cross-modal transfer learning, and scalability, while also identifying emerging research directions aimed at overcoming these limitations. Our goal is to provide a clear understanding of the current landscape of remote sensing foundation models and inspire future research that can push the boundaries of what these models can achieve in real-world applications. The list of resources collected by the paper can be found in the https://github.com/IRIP-BUAA/A-Review-for-remote-sensing-vision-language-models.
Identification and analysis of galactic bars in DESI Legacy Imaging Surveys
2025-03-28
Wenwen Wang, Zhimin Zhou
We present a comprehensive study of bar structures in the local Universe using data from the DESI Legacy Imaging Surveys. Through isophotal analysis of 232,142 galaxies, we identify bars and classify them into strong and weak categories based on normalized bar length, using a threshold of 0.4. We find a total bar fraction of 42.9%, rising to 62.0% in disk galaxies, with strong barred galaxies accounting for 30.0%. For barred galaxies in our sample, deprojected bar lengths are measured both in absolute terms and normalized by galaxy size. Most bars are found to have absolute lengths of 3-7 kpc, and normalized bar lengths concentrated around a median value of 0.4. Bar ellipticity mainly ranges from 0.2 to 0.6, with a median value of 0.3. Our analysis reveals a bimodal distribution of bar fractions with respect to galaxy color, with weak bars in our classification being more prevalent in intermediate-color systems. With respect to stellar mass, strong bars also present a bimodal distribution, while weak bars are distributed uniformly. Normalized bar length remains relatively stable across stellar masses, while absolute bar length positively correlates with stellar mass. Cross-validation with visual classifications from GZD catalog confirms a bar identification accuracy of 93%. These results validate our automated method for bar identification and measurement, demonstrating its reliability. Our findings underscore the importance of bars in galaxy evolution and highlight the potential of upcoming wide-field surveys to deepen our understanding of barred galaxies.
Open-source software communities thrive on global collaboration and contributions from diverse participants. This study explores the Rust programming language ecosystem to understand its contributors' demographic composition and interaction patterns. Our objective is to investigate the phenomenon of participation inequality in key Rust projects and the presence of diversity among them. We studied GitHub pull request data from the year leading up to the release of the latest completed Rust community annual survey in 2023. Specifically, we extracted information from three leading repositories: Rust, Rust Analyzer, and Cargo, and used social network graphs to visualize the interactions and identify central contributors and sub-communities. Social network analysis has shown concerning disparities in gender and geographic representation among contributors who play pivotal roles in collaboration networks and the presence of varying diversity levels in the sub-communities formed. These results suggest that while the Rust community is globally active, the contributor base does not fully reflect the diversity of the wider user community. We conclude that there is a need for more inclusive practices to encourage broader participation and ensure that the contributor base aligns more closely with the diverse global community that utilizes Rust.
Deceived by Immersion: A Systematic Analysis of Deceptive Design in Extended Reality
2025-03-28
Hilda Hadan, Lydia Choong, Leah Zhang-Kennedy, Lennart E. Nacke
The well-established deceptive design literature has focused on conventional user interfaces. With the rise of extended reality (XR), understanding deceptive design's unique manifestations in this immersive domain is crucial. However, existing research lacks a full, cross-disciplinary analysis that analyzes how XR technologies enable new forms of deceptive design. Our study reviews the literature on deceptive design in XR environments. We use thematic synthesis to identify key themes. We found that XR's immersive capabilities and extensive data collection enable subtle and powerful manipulation strategies. We identified eight themes outlining these strategies and discussed existing countermeasures. Our findings show the unique risks of deceptive design in XR, highlighting implications for researchers, designers, and policymakers. We propose future research directions that explore unintentional deceptive design, data-driven manipulation solutions, user education, and the link between ethical design and policy regulations.
Leveraging MMW-MMW Double Resonance Spectroscopy to Understand the Pure Rotational Spectrum of Glycidaldehyde and 17 of Its Vibrationally Excited States
2025-03-28
Luis Bonah, Jean-Claude Guillemin, Arnaud Belloche, Sven Thorwirth, Holger S. P. Müller, Stephan Schlemmer
Broadband measurements of glycidaldehyde in the frequency ranges 75-170 and 500-750 GHz were recorded to extend previous analyses of its pure rotational spectrum in the microwave region. The rotational parameters of the ground vibrational states for the main isotopologue and the three singly 13C-substituted isotopologues were considerably improved, and additional higher-order parameters were determined. To identify new vibrationally excited states in the dense and convoluted spectrum, an updated version of the double-modulation double-resonance spectroscopy technique was used. Connecting transitions with a shared energy level into series and expanding these via Loomis-Wood plots proved to be a powerful method, which allowed the identification of 11 new vibrationally excited states in addition to the already known aldehyde torsions, v21 = 1 to v21 = 6. Interactions between several vibrational states were observed, and three interacting systems were treated successfully. Rotational transitions of glycidaldehyde were searched for in the imaging spectral line survey ReMoCA obtained with the Atacama Large Millimeter/submillimeter Array (ALMA) toward the high-mass star-forming region Sgr B2(N). The observed spectra were modeled under the assumption of local thermodynamic equilibrium (LTE). Glycidaldehyde, an oxirane derivative, was not detected toward Sgr B2(N2b). The upper limit on its column density implies that it is at least six times less abundant than oxirane in this source.
Social Media Bot Detection Research: Review of Literature
2025-03-28
Blaž Rodič
This study presents a review of research on social media bot detection. Social media bots are used by political and criminal actors for mass distribution of political messages, as well as rumors, conspiracy theories, and other forms of false information. Through the spread of disinformation, bots are eroding the public trust in political and media institutions and integrity of social media. We have examined recent research publication in the field of social media bot detection, including several previous reviews of bot detection research, and identified the methods used in bot detection and issues encountered by researchers. Our review was conducted through a search of 5 main bibliographical databases, which has produced a total of 534 research papers and other publications. This collection was then filtered with exclusion and inclusion criteria to isolate the most pertinent documents, resulting in a focused selection of 49 documents that were analyzed for this review. In the first part of the paper we introduce the phenomenon of fake news within social networks, its connection with social media bot activity, and conclude the introduction with issues caused or exacerbated by bots. In the main part of this paper we first present the results of statistical analysis of the reviewed documents and then introduce the field of social media bot research, followed by an overview of the issues of social media bot detection identified in the reviewed literature, including the evolution of bot concealment techniques and the methodological issues presented in some of the bot detection studies. We then proceed with an overview of the methods and results from the reviewed research papers, structured according to the main methodology used in the examined studies. Our review concludes with examination of the recent trends in social media bot development and related bot detection research.
United States Early Career Researchers in Collider Physics input to the European Strategy for Particle Physics Update
2025-03-28
Oz Amram, Grace Cummings
This document represents a contribution of the United States early career collider physics community to the 2025--2026 update to the European Strategy for Particle Physics. Preferences with regard to different future collider options and R&D priorities were assessed via a survey. The early career community was defined as anyone who is a graduate student, postdoctoral researcher, untenured faculty member, or research scientist under 40 years of age. In total, 105 participants responded to the survey between February and March 10th, 2025. Questions were formulated primarily to gauge the enthusiasm and preferences for different collider options in line with the recommendations of the United States' P5 report, relevant to the European Strategy Update.
2025-03-28
Filippo Bonchi, Cipriano Junior Cioffo, Alessandro Di Giorgio, Elena Di Lavore
Tape diagrams provide a graphical representation for arrows of rig categories, namely categories equipped with two monoidal structures, $\oplus$ and $\otimes$, where $\otimes$ distributes over $\oplus$. However, their applicability is limited to categories where $\oplus$ is a biproduct, i.e., both a categorical product and a coproduct. In this work, we extend tape diagrams to deal with Kleisli categories of symmetric monoidal monads, presented by algebraic theories.
Many facets of cohomology: Differential complexes and structure-aware formulations
2025-03-28
Kaibo Hu
Complexes and cohomology, traditionally central to topology, have emerged as fundamental tools across applied mathematics and the sciences. This survey explores their roles in diverse areas, from partial differential equations and continuum mechanics to reformulations of the Einstein equations and network theory. Motivated by advances in compatible and structure-preserving discretisation such as Finite Element Exterior Calculus (FEEC), we examine how differential complexes encode critical properties such as existence, uniqueness, stability and rigidity of solutions to differential equations. We demonstrate that various fundamental concepts and models in solid and fluid mechanics are essentially formulated in terms of differential complexes.
How to set up a psychedelic study: Unique considerations for research involving human participants
2025-03-28
Marcus J. Glennon, Catherine I. V. Bird, Prateek Yadav, Patrick Kleine, Shayam Suseelan, Christina Boman-Markaki, Vasileia Kotoula, Matt Butler, Robert Leech, Leor Roseman, David Erritzoe, Deepak P. Srivastava, Celia Morgan, Christopher Timmermann, Greg Cooper, Jeremy I. Skipper, James Rucker, Sunjeev K. Kamboj, Mitul A. Mehta, Ravi K. Das, Anjali Bhat
Setting up a psychedelic study can be a long, arduous, and kafkaesque process. This rapidly-developing field poses several unique challenges for researchers, necessitating a range of considerations that have not yet been standardised. Many of the complexities inherent to psychedelic research also challenge existing assumptions around, for example, approaches to psychiatric prescribing, the conceptual framing of the placebo effect, and definitions of selfhood. This review paper brings together several of the major psychedelic research teams across the United Kingdom to formalise these unique considerations, identify continuing areas of debate, and provide a practical, experience-based guide, with recommendations for policymakers and future researchers intending to set up a psychedelic research study or clinical trial. We approach this such that the paper can either be read end to end, or treated as a manual: readers can dip into relevant sections as needed.
Supermassive Black Holes in X-rays: From Standard Accretion to Extreme Transients
2025-03-28
Erin Kara, Javier García
X-rays are a critical wavelength for understanding supermassive black holes (SMBHs). X-rays probe the inner accretion flow, closest to the event horizon, where gas inspirals, releasing energy and driving black hole growth. This region also governs the launching of outflows and jets that regulate galaxy evolution and link SMBH growth to their host galaxies. This review focuses on X-ray observations of SMBHs, through "standard accretion" in persistent Active Galactic Nuclei (AGN) and in extreme transient events, such as Tidal Disruption Events (TDEs), Changing-Look AGN (CLAGN), and Quasi-Periodic Eruptions (QPEs). We describe the X-ray spectral and variability properties of AGN, and the observational techniques that probe the inner accretion flow. Through understanding the phenomenology and accretion physics in standard, individual AGN, we can better search for more exotic phenomenon, including binary SMBH mergers, or Extreme Mass Ratio Inspirals (EMRIs). In this review, the reader will discover: (1) X-ray variability on timescales from minutes to hours traces accretion near the event horizon. (2) X-rays can measure the black hole mass, spin and accretion flow geometry and dynamics. (3) In transients like TDEs, X-rays probe the newly formed accretion disk that feeds the black hole. (4) QPEs are posited to be EMRIs orbiting accreting SMBHs that would emit low-frequency gravitational waves. (5) Future X-ray, time-domain and multi-messenger surveys will revolutionize our understanding of SMBH growth.
2025-03-28
Amr Alshatnawi, Remi Sampaleanu, David Liebovitz
Artificial Intelligence (AI) has been advancing rapidly and with the advent of large language models (LLMs) in late 2022, numerous opportunities have emerged for adopting this technology across various domains, including medicine. These innovations hold immense potential to revolutionize and modernize medical education. Our research project leverages large language models to enhance medical education and address workflow challenges through the development of MediTools - AI Medical Education. This prototype application focuses on developing interactive tools that simulate real-life clinical scenarios, provide access to medical literature, and keep users updated with the latest medical news. Our first tool is a dermatology case simulation tool that uses real patient images depicting various dermatological conditions and enables interaction with LLMs acting as virtual patients. This platform allows users to practice their diagnostic skills and enhance their clinical decision-making abilities. The application also features two additional tools: an AI-enhanced PubMed tool for engaging with LLMs to gain deeper insights into research papers, and a Google News tool that offers LLM generated summaries of articles for various medical specialties. A comprehensive survey has been conducted among medical professionals and students to gather initial feedback on the effectiveness and user satisfaction of MediTools, providing insights for further development and refinement of the application. This research demonstrates the potential of AI-driven tools in transforming and revolutionizing medical education, offering a scalable and interactive platform for continuous learning and skill development.
Recent advancements in video generation have witnessed significant progress, especially with the rapid advancement of diffusion models. Despite this, their deficiencies in physical cognition have gradually received widespread attention - generated content often violates the fundamental laws of physics, falling into the dilemma of ''visual realism but physical absurdity". Researchers began to increasingly recognize the importance of physical fidelity in video generation and attempted to integrate heuristic physical cognition such as motion representations and physical knowledge into generative systems to simulate real-world dynamic scenarios. Considering the lack of a systematic overview in this field, this survey aims to provide a comprehensive summary of architecture designs and their applications to fill this gap. Specifically, we discuss and organize the evolutionary process of physical cognition in video generation from a cognitive science perspective, while proposing a three-tier taxonomy: 1) basic schema perception for generation, 2) passive cognition of physical knowledge for generation, and 3) active cognition for world simulation, encompassing state-of-the-art methods, classical paradigms, and benchmarks. Subsequently, we emphasize the inherent key challenges in this domain and delineate potential pathways for future research, contributing to advancing the frontiers of discussion in both academia and industry. Through structured review and interdisciplinary analysis, this survey aims to provide directional guidance for developing interpretable, controllable, and physically consistent video generation paradigms, thereby propelling generative models from the stage of ''visual mimicry'' towards a new phase of ''human-like physical comprehension''.
The formation and evolution of galaxies cannot be separated from large scale structure growth. Dark matter halos (and, therefore, galaxies) form and grow within the cosmic web - the classification of large-scale structure as distinct environments, namely voids, walls, filaments and nodes. Thanks to the rapid development of extragalactic spectroscopic redshift surveys and cosmological simulations over the last two decades, we are now able to measure the impact of the cosmic web on galaxies and halos in observations and in simulations. In this chapter we summarise the state of play in our understanding of the link between dark matter halos, galaxies, and the cosmic web.
A Unified Framework for Diffusion Bridge Problems: Flow Matching and Schrödinger Matching into One
2025-03-27
Minyoung Kim
The bridge problem is to find an SDE (or sometimes an ODE) that bridges two given distributions. The application areas of the bridge problem are enormous, among which the recent generative modeling (e.g., conditional or unconditional image generation) is the most popular. Also the famous Schr\"{o}dinger bridge problem, a widely known problem for a century, is a special instance of the bridge problem. Two most popular algorithms to tackle the bridge problems in the deep learning era are: (conditional) flow matching and iterative fitting algorithms, where the former confined to ODE solutions, and the latter specifically for the Schr\"{o}dinger bridge problem. The main contribution of this article is in two folds: i) We provide concise reviews of these algorithms with technical details to some extent; ii) We propose a novel unified perspective and framework that subsumes these seemingly unrelated algorithms (and their variants) into one. In particular, we show that our unified framework can instantiate the Flow Matching (FM) algorithm, the (mini-batch) optimal transport FM algorithm, the (mini-batch) Schr\"{o}dinger bridge FM algorithm, and the deep Schr\"{o}dinger bridge matching (DSBM) algorithm as its special cases. We believe that this unified framework will be useful for viewing the bridge problems in a more general and flexible perspective, and in turn can help researchers and practitioners to develop new bridge algorithms in their fields.
Three-Dimensional Stacking as a Line Intensity Mapping Statistic
2025-03-27
D. A. Dunne, K. A. Cleary, P. C. Breysse, D. T. Chung, H. T. Ihle, J. G. S. Lunde, H. Padmanabhan, N. -O. Stutzer, J. R. Bond, J. O. Gundersen, J. Kim, A. C. S. Readhead
Line-intensity mapping (LIM) is a growing technique that measures the integrated spectral-line emission from unresolved galaxies over a three-dimensional region of the Universe. Although LIM experiments ultimately aim to provide powerful cosmological constraints via auto-correlation, many LIM experiments are also designed to take advantage of overlapping galaxy surveys, enabling joint analyses of the two datasets. We introduce a flexible simulation pipeline that can generate mock galaxy surveys and mock LIM data simultaneously for the same population of simulated galaxies. Using this pipeline, we explore a simple joint analysis technique: three-dimensional co-addition (stacking) of LIM data on the positions of galaxies from a traditional galaxy catalogue. We test how the output of this technique reacts to changes in experimental design of both the LIM experiment and the galaxy survey, its sensitivity to various astrophysical parameters, and its susceptibility to common systematic errors. We find that an ideal catalogue for a stacking analysis targets as many high-mass dark matter halos as possible. We also find that the signal in a LIM stacking analysis originates almost entirely from the large-scale clustering of halos around the catalogue objects, rather than the catalogue objects themselves. While stacking is a sensitive and conceptually simple way to achieve a LIM detection, thus providing a valuable way to validate a LIM auto-correlation detection, it will likely require a full cross-correlation to achieve further characterization of the galaxy tracers involved, as the cosmological and astrophysical parameters we explore here have degenerate effects on the stack.
Adiabatic quantum state preparation in integrable models
2025-03-27
Maximilian Lutz, Lorenzo Piroli, Georgios Styliaris, J. Ignacio Cirac
We propose applying the adiabatic algorithm to prepare high-energy eigenstates of integrable models on a quantum computer. We first review the standard adiabatic algorithm to prepare ground states in each magnetization sector of the prototypical XXZ Heisenberg chain. Based on the thermodynamic Bethe ansatz, we show that the algorithm circuit depth is polynomial in the number of qubits $N$, outperforming previous methods explicitly relying on integrability. Next, we propose a protocol to prepare arbitrary eigenstates of integrable models that satisfy certain conditions. For a given target eigenstate, we construct a suitable parent Hamiltonian written in terms of a complete set of local conserved quantities. We propose using such Hamiltonian as an input for an adiabatic algorithm. After benchmarking this construction in the case of the non-interacting XY spin chain, where we can rigorously prove its efficiency, we apply it to prepare arbitrary eigenstates of the Richardson-Gaudin models. In this case, we provide numerical evidence that the circuit depth of our algorithm is polynomial in $N$ for all eigenstates, despite the models being interacting.
Galaxy Morphologies at Cosmic Noon with JWST : A Foundation for Exploring Gas Transport with Bars and Spiral Arms
2025-03-27
Juan M. Espejo Salcedo, Stavros Pastras, Josef Vácha, Claudia Pulsoni, Reinhard Genzel, N. M. Förster Schreiber, Jean-Baptiste Jolly, Capucine Barfety, Jianhang Chen, Giulia Tozzi, Daizhong Liu, Lilian Lee, Stijn Wuyts, Linda Tacconi, Ric Davies, Hannah Übler, Dieter Lutz, Emily Wisnioski, Jinyi Shangguan, Minju Lee, H. Sedona Price, Frank Eisenhauer, Alvio Renzini, Amit Nestor Shachar, Rodrigo Herrera-Camus
How radial flows shape galaxy structure and evolution remains an open question. Internal drivers of such flows, such as bars and spiral arms, known to mediate gas flows in the local Universe, are now observable at high redshift thanks to JWST's unobscured view. We investigate the morphology of massive star-forming galaxies at 0.8 < z < 1.3 and 2.0 < z < 2.5, epochs marking the peak and decline of cosmic star formation, both well-covered by kinematic surveys. Using JWST/NIRCam imaging, we visually classify 1,451 galaxies, identify non-axisymmetric features, count the number of spiral arms, analyze non-parametric morphological indicators and study the dynamical support of the sample covered by kinematics (10% of the sample) as measured via v/{\sigma}. Disk galaxies dominate the sample (82%), with 48% exhibiting spiral structure and 11% hosting bars. Both fractions decline with redshift, consistent with previous studies. The proportion of two- and three-armed spirals remains largely unchanged across redshift, with roughly two-thirds showing two arms and one-third showing three arms in both bins. Notably, we find a higher incidence of three-armed spirals than reported in the local Universe, suggesting a mild evolution in spiral arm multiplicity. Non-parametric morphological metrics strongly correlate with stellar mass but show no significant redshift evolution. Finally, kinematic analysis reveals a strong correlation between disk morphology and rotational support, with most disks exhibiting v/{\sigma} > 3 and median values of v/{\sigma} > 7 for spirals and v/{\sigma} > 5 for barred galaxies. This study establishes a population-wide framework for linking galaxy morphology and dynamics at cosmic noon, providing a key reference for future studies on the role of detailed structural features in galaxy evolution.
Local Primordial non-Gaussian Bias from Time Evolution
2025-03-27
James M. Sullivan, Uros Seljak
Primordial non-Gaussianity (PNG) is a signature of fundamental physics in the early universe that is probed by cosmological observations. It is well known that the local type of PNG generates a strong signal in the two-point function of large-scale structure tracers, such as galaxies. This signal, often termed ``scale-dependent bias'' is a generic feature of modulation of gravitational structure formation by a large-scale mode. It is less well-appreciated that the coefficient controlling this signal, $b_{\phi}$, is closely connected to the time evolution of the tracer number density. This correspondence between time evolution and local PNG can be simply explained for a universal tracer whose mass function only depends on peak height, and more generally for non-universal tracers in the separate universe picture, which we validate in simulations. We also describe how to recover the bias of tracers subject to a survey selection function, and perform a simple demonstration on simulated galaxies. Since the local PNG amplitude in $n-$point statistics ($f_{\rm NL}$) is largely degenerate with the coefficient $b_{\phi}$, this proof of concept study demonstrates that galaxy survey data can allow for more optimal and robust extraction of local PNG information from upcoming surveys.
CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers?
2025-03-27
Jiefu Ou, William Gantt Walden, Kate Sanders, Zhengping Jiang, Kaiser Sun, Jeffrey Cheng, William Jurayj, Miriam Wanner, Shaobo Liang, Candice Morgan, Seunghoon Han, Weiqi Wang, Chandler May, Hannah Recknor, Daniel Khashabi, Benjamin Van Durme
A core part of scientific peer review involves providing expert critiques that directly assess the scientific claims a paper makes. While it is now possible to automatically generate plausible (if generic) reviews, ensuring that these reviews are sound and grounded in the papers' claims remains challenging. To facilitate LLM benchmarking on these challenges, we introduce CLAIMCHECK, an annotated dataset of NeurIPS 2023 and 2024 submissions and reviews mined from OpenReview. CLAIMCHECK is richly annotated by ML experts for weakness statements in the reviews and the paper claims that they dispute, as well as fine-grained labels of the validity, objectivity, and type of the identified weaknesses. We benchmark several LLMs on three claim-centric tasks supported by CLAIMCHECK, requiring models to (1) associate weaknesses with the claims they dispute, (2) predict fine-grained labels for weaknesses and rewrite the weaknesses to enhance their specificity, and (3) verify a paper's claims with grounded reasoning. Our experiments reveal that cutting-edge LLMs, while capable of predicting weakness labels in (2), continue to underperform relative to human experts on all other tasks.
Machine Learning Assisted Modeling of Amorphous TiO$_2$-Doped GeO$_2$ for Advanced LIGO Mirror Coatings
2025-03-27
Jun Jiang, Rui Zhang, Kiran Prasai, Riccardo Bassiri, James N. Fry, Martin M. Fejer, Hai-Ping Cheng
The mechanical loss angle of amorphous TiO$_2$-doped GeO$_2$ can be lower than 10$^{-4}$, making it a candidate for Laser Interferometer Gravitational-wave Observatory (LIGO) mirror coatings. Amorphous oxides have complex atomic structures that are influenced by various factors, including doping concentration, preparation, and thermal history, resulting in different mass densities and physical properties. Modeling at atomistic level enables capturing these effects by generating atomic structure models according to experimental conditions. In order to obtain reliable and physical amorphous models at an affordable cost, we develop classical and machine-learning potentials (MLP) to speed up simulations. First-principles calculations are used to train and validate MLP as well as validating structure models. To better reproduce properties such as elastic modulus, radial distribution function (RDF) and the variations in mass density of doped amorphous oxides, density functional theory (DFT) calculations are used to optimize the final models. We find that the mass densities of amorphous systems are correlated with the total void volume. The experimental mass density matches the models with the most symmetric potential energy wells under volume change. The elastic response of the metal-oxygen network is also studied. The 27\% TiO$_2$ doped GeO$_2$ system shows the least number of large atom-atom distance changes, while for 44\% TiO$_2$ doped GeO$_2$, a majority of Ti-O distances are significantly changed. In response to strains, the metal-oxygen network at low mass densities prefers to adjust bond angles, while at high mass densities, the adjustment is mainly done by changing atom-atom distance.
A possible GeV-TeV connection in the direction of the Globular Cluster UKS 1
2025-03-27
Jiwon Shin, C. Y. Hui, Sangin Kim, Kwangmin Oh, Ellis R. Owen
Using public data collected by the Fermi Large Area Telescope (LAT) over 16 years, and the 1523 days of survey data (3HWC) from the High Altitude Water Cherenkov (HAWC) observatory, we searched for possible GeV-TeV connections in globular clusters (GCs). In addition to the confirmed $\gamma-$ray GCs in the 4FGL catalog, we report a GeV detection at the position of UKS 1 with a post-trial probability of $\sim8\times10^{-5}$ of it being a fluctuation. Its spectrum within this energy range is well described by a power-law model with $\Gamma\simeq2.3\pm0.5$. Furthermore, this GeV feature appears to extend southeast in a direction towards the Galactic plane. From the 3HWC survey data, we have also identified a TeV feature in the direction of UKS 1. It is well-resolved from any known Very High Energy (VHE) source. The post-trial probability that this feature is a fluctuation is $\sim3\times10^{-4}$. If confirmed, this would be the second detection of a TeV feature in the proximity of a GC. While the GeV emission mostly coincides with the center of UKS 1, the TeV peak is displaced from the cluster center by several tidal radii in the trailing direction of the GC's proper motion. Given the supersonic speed of UKS 1 at $\sim270$ km s$^{-1}$, our findings are consistent with a scenario where the VHE $\gamma-$rays are produced by inverse Compton scattering between relativistic particles and ambient soft photon fields during the course of their propagation away from the head of the bow shock.
Towards Fully Automated Decision-Making Systems for Greenhouse Control: Challenges and Opportunities
2025-03-27
Yongshuai Liu, Taeyeong Choi, Xin Liu
Machine learning has been successful in building control policies to drive a complex system to desired states in various applications (e.g. games, robotics, etc.). To be specific, a number of parameters of policy can be automatically optimized from the observations of environment to be able to generate a sequence of decisions leading to the best performance. In this survey paper, we particularly explore such policy-learning techniques for another unique, practical use-case scenario--farming, in which critical decisions (e.g., water supply, heating, etc.) must be made in a timely manner to minimize risks (e.g., damage to plants) while maximizing the revenue (e.g., healthy crops) in the end. We first provide a broad overview of latest studies on it to identify not only domain-specific challenges but opportunities with potential solutions, some of which are suggested as promising directions for future research. Also, we then introduce our successful approach to being ranked second among 46 teams at the ''3rd Autonomous Greenhouse Challenge'' to use this specific example to discuss the lessons learned about important considerations for design to create autonomous farm-management systems.
In recent years, performance on existing anomaly detection benchmarks like MVTec AD and VisA has started to saturate in terms of segmentation AU-PRO, with state-of-the-art models often competing in the range of less than one percentage point. This lack of discriminatory power prevents a meaningful comparison of models and thus hinders progress of the field, especially when considering the inherent stochastic nature of machine learning results. We present MVTec AD 2, a collection of eight anomaly detection scenarios with more than 8000 high-resolution images. It comprises challenging and highly relevant industrial inspection use cases that have not been considered in previous datasets, including transparent and overlapping objects, dark-field and back light illumination, objects with high variance in the normal data, and extremely small defects. We provide comprehensive evaluations of state-of-the-art methods and show that their performance remains below 60% average AU-PRO. Additionally, our dataset provides test scenarios with lighting condition changes to assess the robustness of methods under real-world distribution shifts. We host a publicly accessible evaluation server that holds the pixel-precise ground truth of the test set (https://benchmark.mvtec.com/). All image data is available at https://www.mvtec.com/company/research/datasets/mvtec-ad-2.
Strong convergence of unitary and permutation representations of discrete groups
2025-03-27
Michael Magee
We survey a research program on the strong convergence of unitary and permutation representations of discrete groups. We also take the opportunity to flesh out details that have not appeared elsewhere.
A Measure Based Generalizable Approach to Understandability
2025-03-27
Vikas Kushwaha, Sruti Srinivasa Ragavan, Subhajit Roy
Successful agent-human partnerships require that any agent generated information is understandable to the human, and that the human can easily steer the agent towards a goal. Such effective communication requires the agent to develop a finer-level notion of what is understandable to the human. State-of-the-art agents, including LLMs, lack this detailed notion of understandability because they only capture average human sensibilities from the training data, and therefore afford limited steerability (e.g., requiring non-trivial prompt engineering). In this paper, instead of only relying on data, we argue for developing generalizable, domain-agnostic measures of understandability that can be used as directives for these agents. Existing research on understandability measures is fragmented, we survey various such efforts across domains, and lay a cognitive-science-rooted groundwork for more coherent and domain-agnostic research investigations in future.
Recent Large Reasoning Models (LRMs), such as DeepSeek-R1 and OpenAI o1, have demonstrated strong performance gains by scaling up the length of Chain-of-Thought (CoT) reasoning during inference. However, a growing concern lies in their tendency to produce excessively long reasoning traces, which are often filled with redundant content (e.g., repeated definitions), over-analysis of simple problems, and superficial exploration of multiple reasoning paths for harder tasks. This inefficiency introduces significant challenges for training, inference, and real-world deployment (e.g., in agent-based systems), where token economy is critical. In this survey, we provide a comprehensive overview of recent efforts aimed at improving reasoning efficiency in LRMs, with a particular focus on the unique challenges that arise in this new paradigm. We identify common patterns of inefficiency, examine methods proposed across the LRM lifecycle, i.e., from pretraining to inference, and discuss promising future directions for research. To support ongoing development, we also maintain a real-time GitHub repository tracking recent progress in the field. We hope this survey serves as a foundation for further exploration and inspires innovation in this rapidly evolving area.
Finite-temperature charge and spin transport in the one-dimensional Hubbard model accounting for its global [SU (2) X SU(2) X U(1)]/Z_2^2$ symmetry
2025-03-27
J. M. P. Carmelo, J. E. C. Carmelo
Using a general representation that accounts for the effects on finite-temperature spin and charge transport of the global [SU (2) X SU(2) X U(1)]/(Z2 X Z2) symmetry of the one-dimensional (1D) Hubbard model, we show that important finite-temperature transport quantities as the finite-field spin and finite-chemical potential charge stiffnesses and the zero-field spin and zero-chemical potential charge diffusion constants are controlled by microscopic processes associated with the spin and charge elementary currents carried by the spin and charge carriers, respectively. We describe the model finite-temperature transport properties in terms of the microscopic processes associated with the spin and charge elementary currents carried by the corresponding carriers. We expect that the general representation used in our study is that suitable to address the open problems on finite-temperature transport in the 1D Hubbard model also discussed in this paper.
The Fe-Ni phase diagram and the Earth's inner core structure
2025-03-27
Liangrui Wei, Kai-Ming Ho, Renata M. Wentzcovitch, Yang Sun
The Fe-Ni alloy is believed to be the main component of Earth's core. Yet, a comprehensive understanding of phase equilibria near the melting point of this alloy under core conditions is still lacking, leaving the effect of nickel inconclusive. Using ab initio simulations, we computed Gibbs free energy and phase diagram for liquid and solid solutions of the Fe-Ni alloy under conditions close to the inner core, considering inner-shell electron contributions and non-ideal mixing effects. The Fe-Ni phase diagram provides crucial insights for understanding previous experimental observations and crystallization simulations of the Fe-Ni alloy under core conditions. It also presents new scenarios for inner core structures, suggesting bcc-liquid coexistence at the inner core boundary and the possibility of multi-layer structures consisting of bcc-hcp composites within the inner core. Our work clarifies nickel's substantial impact on the inner core structure, providing new constraints for the study of core's composition and formation.
Data-Driven Contact-Aware Control Method for Real-Time Deformable Tool Manipulation: A Case Study in the Environmental Swabbing
2025-03-27
Siavash Mahmoudi, Amirreza Davar, Dongyi Wang
Deformable Object Manipulation (DOM) remains a critical challenge in robotics due to the complexities of developing suitable model-based control strategies. Deformable Tool Manipulation (DTM) further complicates this task by introducing additional uncertainties between the robot and its environment. While humans effortlessly manipulate deformable tools using touch and experience, robotic systems struggle to maintain stability and precision. To address these challenges, we present a novel State-Adaptive Koopman LQR (SA-KLQR) control framework for real-time deformable tool manipulation, demonstrated through a case study in environmental swab sampling for food safety. This method leverages Koopman operator-based control to linearize nonlinear dynamics while adapting to state-dependent variations in tool deformation and contact forces. A tactile-based feedback system dynamically estimates and regulates the swab tool's angle, contact pressure, and surface coverage, ensuring compliance with food safety standards. Additionally, a sensor-embedded contact pad monitors force distribution to mitigate tool pivoting and deformation, improving stability during dynamic interactions. Experimental results validate the SA-KLQR approach, demonstrating accurate contact angle estimation, robust trajectory tracking, and reliable force regulation. The proposed framework enhances precision, adaptability, and real-time control in deformable tool manipulation, bridging the gap between data-driven learning and optimal control in robotic interaction tasks.
Shape Modeling of Longitudinal Medical Images: From Diffeomorphic Metric Mapping to Deep Learning
2025-03-27
Edwin Tay, Nazli Tümer, Amir A. Zadpoor
Living biological tissue is a complex system, constantly growing and changing in response to external and internal stimuli. These processes lead to remarkable and intricate changes in shape. Modeling and understanding both natural and pathological (or abnormal) changes in the shape of anatomical structures is highly relevant, with applications in diagnostic, prognostic, and therapeutic healthcare. Nevertheless, modeling the longitudinal shape change of biological tissue is a non-trivial task due to its inherent nonlinear nature. In this review, we highlight several existing methodologies and tools for modeling longitudinal shape change (i.e., spatiotemporal shape modeling). These methods range from diffeomorphic metric mapping to deep-learning based approaches (e.g., autoencoders, generative networks, recurrent neural networks, etc.). We discuss the synergistic combinations of existing technologies and potential directions for future research, underscoring key deficiencies in the current research landscape.
The era of intelligent agents is upon us, driven by revolutionary advancements in large language models. Large Language Model (LLM) agents, with goal-driven behaviors and dynamic adaptation capabilities, potentially represent a critical pathway toward artificial general intelligence. This survey systematically deconstructs LLM agent systems through a methodology-centered taxonomy, linking architectural foundations, collaboration mechanisms, and evolutionary pathways. We unify fragmented research threads by revealing fundamental connections between agent design principles and their emergent behaviors in complex environments. Our work provides a unified architectural perspective, examining how agents are constructed, how they collaborate, and how they evolve over time, while also addressing evaluation methodologies, tool applications, practical challenges, and diverse application domains. By surveying the latest developments in this rapidly evolving field, we offer researchers a structured taxonomy for understanding LLM agents and identify promising directions for future research. The collection is available at https://github.com/luo-junyu/Awesome-Agent-Papers.
Despite the popularity and importance of modern code review, the understanding of the cognitive processes that enable reviewers to analyze code and provide meaningful feedback is lacking. To address this gap, we observed and interviewed ten experienced reviewers while they performed 25 code reviews from their review queue. Since comprehending code changes is essential to perform code review and the primary challenge for reviewers, we focused our analysis on this cognitive process. Using Letovsky's model of code comprehension, we performed a theory-driven thematic analysis to investigate how reviewers apply code comprehension to navigate changes and provide feedback. Our findings confirm that code comprehension is fundamental to code review. We extend Letovsky's model to propose the Code Review Comprehension Model and demonstrate that code review, like code comprehension, relies on opportunistic strategies. These strategies typically begin with a context-building phase, followed by code inspection involving code reading, testing, and discussion management. To interpret and evaluate the proposed change, reviewers construct a mental model of the change as an extension of their understanding of the overall software system and contrast mental representations of expected and ideal solutions against the actual implementation. Based on our findings, we discuss how review tools and practices can better support reviewers in employing their strategies and in forming understanding. Data and material: https://doi.org/10.5281/zenodo.14748996
Measurement-Induced Entanglement Phase Transition in Free Fermion Systems
2025-03-27
Han-Ze Li, Jian-Xin Zhong, Xue-Jia Yu
Measurement-induced entanglement phase transitions (MIET) highlight how local measurements drive quantum systems between area-law and volume-law entangled states. This review surveys MIET in free fermion models, focusing on how unitary hopping competes with measurement-induced non-unitarity. We discuss controversies regarding the existence of MIET in one dimension, the impact of non-Hermitian skin effects, and potential experimental platforms. We conclude with open challenges, including feedback control and higher-dimensional extensions.
Quantitative investment (quant) is an emerging, technology-driven approach in asset management, increasingy shaped by advancements in artificial intelligence. Recent advances in deep learning and large language models (LLMs) for quant finance have improved predictive modeling and enabled agent-based automation, suggesting a potential paradigm shift in this field. In this survey, taking alpha strategy as a representative example, we explore how AI contributes to the quantitative investment pipeline. We first examine the early stage of quant research, centered on human-crafted features and traditional statistical models with an established alpha pipeline. We then discuss the rise of deep learning, which enabled scalable modeling across the entire pipeline from data processing to order execution. Building on this, we highlight the emerging role of LLMs in extending AI beyond prediction, empowering autonomous agents to process unstructured data, generate alphas, and support self-iterative workflows.
Illuminating Protein Dynamics: A Review of Computational Methods for Studying Photoactive Proteins
2025-03-27
Sylwia Czach, Jakub Rydzewski, Wiesław Nowak
Photoactive proteins absorb light and undergo structural changes that enable them to perform essential biological functions. These proteins are critical for understanding light-induced biological processes, making them important in biophysics, biotechnology, and medicine. One effective approach to uncovering photoactive processes is through computational methods. These techniques provide atomic-level insights into the structural, electronic, and dynamic changes that occur upon light absorption. By employing these methods, we can gain a better understanding of processes that are challenging to capture experimentally, such as chromophore isomerization and protein conformational changes. Here, we provide a brief overview of the different families of photoactive proteins and the computational methods used to study them, including bioinformatics, molecular dynamics, and enhanced sampling. Our review can serve as an introduction to computational methods for studying light-activated molecular processes, specifically targeting researchers beginning their journey in this field.
Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap
2025-03-27
Tong Nie, Jian Sun, Wei Ma
Modern transportation systems face pressing challenges due to increasing demand, dynamic environments, and heterogeneous information integration. The rapid evolution of Large Language Models (LLMs) offers transformative potential to address these challenges. Extensive knowledge and high-level capabilities derived from pretraining evolve the default role of LLMs as text generators to become versatile, knowledge-driven task solvers for intelligent transportation systems. This survey first presents LLM4TR, a novel conceptual framework that systematically categorizes the roles of LLMs in transportation into four synergetic dimensions: information processors, knowledge encoders, component generators, and decision facilitators. Through a unified taxonomy, we systematically elucidate how LLMs bridge fragmented data pipelines, enhance predictive analytics, simulate human-like reasoning, and enable closed-loop interactions across sensing, learning, modeling, and managing tasks in transportation systems. For each role, our review spans diverse applications, from traffic prediction and autonomous driving to safety analytics and urban mobility optimization, highlighting how emergent capabilities of LLMs such as in-context learning and step-by-step reasoning can enhance the operation and management of transportation systems. We further curate practical guidance, including available resources and computational guidelines, to support real-world deployment. By identifying challenges in existing LLM-based solutions, this survey charts a roadmap for advancing LLM-driven transportation research, positioning LLMs as central actors in the next generation of cyber-physical-social mobility ecosystems. Online resources can be found in the project page: https://github.com/tongnie/awesome-llm4tr.
Bounds for survival probabilities in supercritical Galton-Watson processes and applications to population genetics
2025-03-27
Reinhard Bürger
Population genetic processes, such as the adaptation of a quantitative trait to directional selection, may occur on longer time scales than the sweep of a single advantageous mutation. To study such processes in finite populations, approximations for the time course of the distribution of a beneficial mutation were derived previously by branching process methods. The application to the evolution of a quantitative trait requires bounds for the probability of survival $\Sn$ up to generation $n$ of a single beneficial mutation. Here, we present a method to obtain a simple, analytically explicit, either upper or lower, bound for $\Sn$ in a supercritical Galton-Watson process. We prove the existence of an upper bound for offspring distributions including Poisson and binomial. They are constructed by bounding the given generating function, $\varphi$, by a fractional linear one that has the same survival probability $\Sinf$ and yields the same rate of convergence of $\Sn$ to $\Sinf$ as $\varphi$. For distributions with at most three offspring, we characterize when this method yields an upper bound, a lower bound, or only an approximation. Because for many distributions it is difficult to get a handle on $\Sinf$, we derive an approximation by series expansion in $s$, where $s$ is the selective advantage of the mutant. We briefly review well-known asymptotic results that generalize Haldane's approximation $2s$ for $\Sinf$, as well as less well-known results on sharp bounds for $\Sinf$. We apply them to explore when bounds for $\Sn$ exist for a family of generalized Poisson distributions. Numerical results demonstrate the accuracy of our and of previously derived bounds for $\Sinf$ and $\Sn$. Finally, we treat an application of these results to determine the response of a quantitative trait to prolonged directional selection.
We provide an overview of the Jiutian simulations, a hybrid simulation suite for the China Space Survey Telescope (CSST) extragalactic surveys. It consists of four complementary modules: the primary runs with high resolutions under the fiducial concordance cosmology, the emulator runs exploring the parameter uncertainties around the fiducial cosmology, the reconstruction runs intended for recovering the observed Universe site by site, and the extension runs employing extended cosmologies beyond the standard model. For the primary runs, two independent pipelines are adopted to construct subhaloes and merger trees. On top of them, four sets of mock galaxy light-cone catalogs are produced from semi-analytical models and subhalo abundance matching, providing a variety of observational properties including galaxy SED, emission lines, lensing distortions, and mock images. The 129 emulator runs are used to train the CSST emulator, achieving one percent accuracy in predicting a variety of cosmological statistics over $k\leq 10 h{\rm Mpc}^{-1}$ and $z\leq 2$. The reconstruction runs employ a number of subgrid baryonic models to predict the evolution and galaxy population of certain regions in the real Universe with constrained initial conditions, enabling controlled investigation of galaxy formation on top of structure formation. The extension runs cover models with warm dark matter, $f(R)$ gravity, interacting dark energy, and nonzero neutrino masses, revealing differences in the cosmic structure under alternative cosmological models. We introduce the specifications for each run, the data products derived from them, the corresponding pipeline developments, and present some main tests. Using the primary runs, we also show that the subhalo peak mass functions of different levels are approximately universal. These simulations form a comprehensive and open library for the CSST surveys.
Current AI counseling systems struggle with maintaining effective long-term client engagement. Through formative research with counselors and a systematic literature review, we identified five key design considerations for AI counseling interactions. Based on these insights, we propose CA+, a Cognition Augmented counselor framework enhancing contextual understanding through three components: (1) Therapy Strategies Module: Implements hierarchical Goals-Session-Action planning with bidirectional adaptation based on client feedback; (2) Communication Form Module: Orchestrates parallel guidance and empathy pathways for balanced therapeutic progress and emotional resonance; (3) Information Management: Utilizes client profile and therapeutic knowledge databases for dynamic, context-aware interventions. A three-day longitudinal study with 24 clients demonstrates CA+'s significant improvements in client engagement, perceived empathy, and overall satisfaction compared to a baseline system. Besides, two licensed counselors confirm its high professionalism. Our research demonstrates the potential for enhancing LLM engagement in psychological counseling dialogues through cognitive theory, which may inspire further innovations in computational interaction in the future.
Using large language models to produce literature reviews: Usages and systematic biases of microphysics parametrizations in 2699 publications
2025-03-27
Tianhang Zhang, Shengnan Fu, David M. Schultz, Zhonghua Zheng
Large language models afford opportunities for using computers for intensive tasks, realizing research opportunities that have not been considered before. One such opportunity could be a systematic interrogation of the scientific literature. Here, we show how a large language model can be used to construct a literature review of 2699 publications associated with microphysics parametrizations in the Weather and Research Forecasting (WRF) model, with the goal of learning how they were used and their systematic biases, when simulating precipitation. The database was constructed of publications identified from Web of Science and Scopus searches. The large language model GPT-4 Turbo was used to extract information about model configurations and performance from the text of 2699 publications. Our results reveal the landscape of how nine of the most popular microphysics parameterizations have been used around the world: Lin, Ferrier, WRF Single-Moment, Goddard Cumulus Ensemble, Morrison, Thompson, and WRF Double-Moment. More studies used one-moment parameterizations before 2020 and two-moment parameterizations after 2020. Seven out of nine parameterizations tended to overestimate precipitation. However, systematic biases of parameterizations differed in various regions. Except simulations using the Lin, Ferrier, and Goddard parameterizations that tended to underestimate precipitation over almost all locations, the remaining six parameterizations tended to overestimate, particularly over China, southeast Asia, western United States, and central Africa. This method could be used by other researchers to help understand how the increasingly massive body of scientific literature can be harnessed through the power of artificial intelligence to solve their research problems.
Large Language Models for Traffic and Transportation Research: Methodologies, State of the Art, and Future Opportunities
2025-03-27
Yimo Yan, Yejia Liao, Guanhao Xu, Ruili Yao, Huiying Fan, Jingran Sun, Xia Wang, Jonathan Sprinkle, Ziyan An, Meiyi Ma, Xi Cheng, Tong Liu, Zemian Ke, Bo Zou, Matthew Barth, Yong-Hong Kuo
The rapid rise of Large Language Models (LLMs) is transforming traffic and transportation research, with significant advancements emerging between the years 2023 and 2025 -- a period marked by the inception and swift growth of adopting and adapting LLMs for various traffic and transportation applications. However, despite these significant advancements, a systematic review and synthesis of the existing studies remain lacking. To address this gap, this paper provides a comprehensive review of the methodologies and applications of LLMs in traffic and transportation, highlighting their ability to process unstructured textual data to advance transportation research. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. Our analysis identifies critical research gaps. From the methodological perspective, many research gaps can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, we identify numerous opportunities for LLMs to tackle a variety of traffic and transportation challenges, building upon existing research. By synthesizing these findings, this review not only clarifies the current state of LLM adoption and adaptation in traffic and transportation but also proposes future research directions, paving the way for smarter and more sustainable transportation systems.
We relate two notions of non-positive curvature: bounded combings and the Morse local-to-global (MLTG) property (in its weak and strong version). The latter is a property of a space that has been shown to eliminate pathological behavior of Morse geodesics. We showcase its importance in a survey in the appendix. We show that having a bounded combing implies the weak MLTG property. If the Morse boundary of a group is sigma-compact, we show that the weak MLTG property is upgraded to the (strong) MLTG property.
Euclid Quick Data Release (Q1). The Euclid view on Planck galaxy protocluster candidates: towards a probe of the highest sites of star formation at cosmic noon
2025-03-27
Euclid Collaboration, T. Dusserre, H. Dole, F. Sarron, G. Castignani, N. Ramos-Chernenko, N. Aghanim, A. Garic, I. -E. Mellouki, N. Dagoneau, O. Chapuis, B. L. Frye, M. Polletta, H. Dannerbauer, M. Langer, L. Maurin, E. Soubrie, A. Biviano, S. Mei, N. Mai, B. Altieri, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, A. Bonchi, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, A. Costille, F. Courbin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, C. Dolding, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, A. Ealet, S. Escoffier, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, P. Fosalba, S. Fotopoulou, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, B. Gillis, C. Giocoli, P. Gómez-Alvarez, J. Gracia-Carpio, B. R. Granett, A. Grazian, F. Grupp, L. Guzzo, S. Gwyn, S. V. H. Haugan, J. Hoar, W. Holmes, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, K. Kuijken, M. Kümmel, M. Kunz, H. Kurki-Suonio, Q. Le Boulc'h, A. M. C. Le Brun, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, S. Maurogordato, E. Medinaceli, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, J. A. Schewtschenko, M. Schirmer, P. Schneider, T. Schrabback, A. Secroun, G. Seidel, S. Serrano, P. Simon, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, H. I. Teplitz, I. Tereno, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, I. A. Zinchenko, E. Zucca, V. Allevato, M. Ballardini, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, A. Cappi, D. Di Ferdinando, J. A. Escartin Vigo, G. Fabbian, L. Gabarra, M. Huertas-Company, J. Martín-Fleitas, S. Matthew, M. Maturi, N. Mauri, R. B. Metcalf, A. Pezzotta, M. Pöntinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, S. Alvi, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, K. Benson, P. Bergamini, D. Bertacca, M. Bethermin, A. Blanchard, L. Blot, H. Böhringer, S. Borgani, M. L. Brown, S. Bruton, A. Calabro, B. Camacho Quevedo, F. Caro, C. S. Carvalho, T. Castro, F. Cogato, T. Contini, A. R. Cooray, M. Costanzi, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, J. M. Diego, P. Dimauro, P. -A. Duc, A. Enia, Y. Fang, A. G. Ferrari, P. G. Ferreira, A. Finoguenov, A. Franco, K. Ganga, J. García-Bellido, T. Gasparetto, V. Gautard, E. Gaztanaga, F. Giacomini, F. Gianotti, A. H. Gonzalez, G. Gozaliasl, A. Gruppuso, M. Guidi, C. M. Gutierrez, A. Hall, W. G. Hartley, S. Hemmati, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, S. Kruk, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, L. Leuzzi, T. I. Liaudat, S. J. Liu, A. Loureiro, J. Macias-Perez, G. Maggio, M. Magliocchetti, E. A. Magnier, F. Mannucci, R. Maoli, C. J. A. P. Martins, M. Migliaccio, M. Miluzio, P. Monaco, C. Moretti, G. Morgante, S. Nadathur, K. Naidoo, A. Navarro-Alsina, S. Nesseris, F. Passalacqua, K. Paterson, L. Patrizii, A. Philippon, A. Pisani, D. Potter, S. Quai, M. Radovich, G. Rodighiero, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, A. Schneider, D. Sciotti, E. Sellentin, F. Shankar, L. C. Smith, S. A. Stanford, K. Tanidis, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, G. Verza, P. Vielzeuf, N. A. Walton, J. R. Weaver, J. G. Sorce, D. Scott
We search for galaxy protoclusters at redshifts $z > 1.5$ in the first data release (Q1) of the $\textit{Euclid}$ survey. We make use of the catalogues delivered by the $\textit{Euclid}$ Science Ground Segment (SGS). After a galaxy selection on the $H_\textrm{E}$ magnitude and on the photometric redshift quality, we undertake the search using the $\texttt{DETECTIFz}$ algorithm, an overdensity finder based on Delaunay tessellation that uses photometric redshift probability distributions through Monte Carlo simulations. In this pilot study, we conduct a search in the 11 $\textit{Euclid}$ tiles that contain previously known $\textit{Planck}$ high star-forming galaxy protocluster candidates and focus on the two detections that coincide with these regions. These counterparts lie at photometric redshifts $z_\textrm{ph}=1.63^{+0.19}_{-0.23}$ and $z_\textrm{ph}=1.56^{+0.18}_{-0.21}$ and have both been confirmed by two other independent protocluster detection algorithms. We study their colours, their derived stellar masses and star-formation rates, and we estimate their halo mass lower limits. We investigate whether we are intercepting these galaxy overdensities in their `dying' phase, such that the high star-formation rates would be due to their last unsustainable starburst before transitioning to groups or clusters of galaxies. Indeed, some galaxy members are found to lie above the main sequence of galaxies (star-formation rate versus stellar mass). These overdense regions occupy a specific position in the dark matter halo mass / redshift plane where forming galaxy clusters are expected to have experienced a transition between cold flows to shock heating in the halo. Finally, we empirically update the potential for galaxy protocluster discoveries at redshift up to $z \simeq3$ (wide survey) and $z \simeq5.5$ (deep survey) with $\textit{Euclid}$ for the next data release (DR1).
2025-03-27
Chence Niu, Elnaz Irannezhad, Casey Myers, Vinayak Dixit
Quantum computing, leveraging the principles of quantum mechanics, has been found to significantly enhance computational capabilities in principle, in some cases beyond classical computing limits. This paper explores quantum computing's potential to address complex, large-scale problems in transportation systems. It focuses on three principal paradigms: Gate-based quantum computing, Quantum annealing, and Quantum machine learning, which, though based on gate-based quantum computing, is treated as distinct due to its unique methods and applications. Each paradigm's foundational concepts, practical applications, and potential impacts on the field are discussed to provide a comprehensive overview of quantum computing strategies and their future implications.
The Molecular and Atomic Hydrogen Gas Content of the Boötes Void galaxy CG 910
2025-03-27
Ekta Sharma, Prerana Biswas, Mousumi Das, Benjamin Winkel
Void galaxies are located in the most underdense environments of the Universe, where the number density of galaxies is extremely low. They are, hence, good targets for studying the secular evolution of galaxies and the slow buildup of stellar mass through star formation. To date, very little is known about their cold gas content, both molecular (H$_2$) gas and atomic hydrogen (HI) gas. We present CO (1--0) observations of the H$_2$ gas disk in CG 910, which lies in the Bo\"otes void, one of the largest nearby voids at relatively low redshifts (z$\sim$0.04-0.05). We used the Combined Array for Research in Research in Millimeter Astronomy (CARMA) to study the CO(1-0) distribution and gas kinematics in CG 910. We also carried out atomic hydrogen observations of the galaxy using the Robert C. Byrd Green Bank Telescope (GBT). The CARMA CO(1-0) observations reveal a molecular gas disk of mass, M(H$_{2}$) $\sim$12.0$\pm$1.1$\times$10$^{9}$M$_{\odot}$ and diameter 7 kpc. The CO velocity field shows a regularly rotating disk with a flat rotation velocity of 256 kms$^{-1}$ with no clear signatures of interaction or gas accretion. This is possibly the first CO (1-0) map of a void galaxy, and hence, important for understanding the molecular gas distribution and kinematics in void galaxies. The GBT observations reveal a HI disk of mass, M(HI) $\sim$3.1$\pm$0.8$\times$10$^{9}$M$_{\odot}$, which is relatively small compared to its stellar mass of M$_{\star}\sim$21.5$\times$10$^{9}M_{\odot}$. The total gas mass fraction, (M(H$_2$)+M(HI))/$M_{\star}$ and the atomic gas mass fraction, M(HI)/M$_{\star}$ for CG 910 are 0.70 and 0.14, respectively. We conclude that CG 910 has a regularly rotating but massive molecular gas disk. The lower atomic gas mass fraction and star formation rate indicate a longer gas depletion timescale, confirming that CG 910 is slowly evolving compared to normal disk galaxies.
The OTELO survey: New evidence of downsizing from the specific star formation rates, stellar mass functions, and star formation histories of a sample of low-mass galaxies at 0.38
2025-03-27
Bernabé Cedrés, Ángel Bongiovanni, Jordi Cepa, Carmen P. Padilla-Torres, Jakub Nadolny, Maritza A. Lara-López, J. Ignacio González-Serrano, Emilio J. Alfaro, Miguel Cerviño, Jesús Gallego, Mauro González-Otero, Miguel Sánchez-Portal, José A. de Diego, Ricardo Pérez-Martínez, Ana María Pérez-García, Mirjana Povic
We present an analysis of the emitters (\ha, \hb, and \oii) from the OTELO survey, in order to characterize the star formation properties of low-mass galaxies ($<10^9$ M$_{\odot}$ stellar masses). We calculated the specific star formation rate function, the stellar mass function, and, by integrating them, the associated densities for both quantities: the specific star formation rate density and the stellar mass density. We obtained the star formation history of our low-mass sample galaxies by fitting the spectral energy distribution of the galaxies. We also compared our results with those from the literature at different mass regimes and redshifts. The specific star formation rate density and the stellar mass density for low-mass galaxies do not depend on the redshift, contrary to the behaviour presented by the high-mass galaxies. We found that the star formation histories of low-mass galaxies are characterized by a constant star formation rate, in contrast to high-mass galaxies. We interpret these results, in the context of the downsizing effect, as representative of the faster evolution of massive galaxies compared with low-mass ones.
The ESO SupJup Survey VII: Clouds and line asymmetries in CRIRES$^+$ J-band spectra of the Luhman 16 binary
2025-03-27
S. de Regt, I. A. G. Snellen, N. F. Allard, D. González Picos, S. Gandhi, N. Grasser, R. Landman, P. Mollière, E. Nasedkin, T. Stolker, Y. Zhang
Brown dwarfs at the L-T transition likely experience an inhomogeneous clearing of the clouds in their atmospheres. The resulting surface of thin and thick cloudy patches has been put forward to explain the observed variability, J-band brightening, and re-emergence of FeH absorption. We study the closest brown dwarf binary, Luhman 16A and B, in an effort to constrain their chemical and cloud compositions. As this binary consists of an L7.5 and T0.5 component, we gain insight into the atmospheric properties at the L-T transition. As part of the ESO SupJup Survey, we observed Luhman 16AB at high spectral resolution in the J-band ($1.1-1.4\ \mathrm{\mu m}$) using CRIRES$^+$. To analyse the spectra, we employ an atmospheric retrieval framework, coupling the radiative transfer code petitRADTRANS with the MultiNest sampling algorithm. For both objects, we report detections of H$_2$O, K, Na, FeH, and, for the first time in the J-band, hydrogen-fluoride (HF). The K doublet at $1250\ \mathrm{nm}$ shows asymmetric absorption in the blue line wings, which are reproduced via pressure- and temperature-dependent shifts of the line cores. We find evidence for clouds in both spectra and we place constraints on an FeH-depletion in the Luhman 16A photosphere. The inferred over-abundance of FeH for Luhman 16B opposes its predicted rainout into iron clouds. A two-column model, which emulates the patchy surface expected at the L-T transition, is weakly preferred ($\sim 1.8\sigma$) for component B but disfavoured for A ($\sim 5.5\sigma$). The results suggest a uniform surface on Luhman 16A, which is in good agreement with the reduced variability observed for this L-type component. While the presented evidence is not sufficient to draw conclusions about any inhomogeneity on Luhman 16B, future observations covering a broader wavelength range could help to test the cloud-clearing hypothesis.
2025-03-27
Zhaokai Wang, Chenxi Bao, Le Zhuo, Jingrui Han, Yang Yue, Yihong Tang, Victor Shea-Jay Huang, Yue Liao
Vision-to-music Generation, including video-to-music and image-to-music tasks, is a significant branch of multimodal artificial intelligence demonstrating vast application prospects in fields such as film scoring, short video creation, and dance music synthesis. However, compared to the rapid development of modalities like text and images, research in vision-to-music is still in its preliminary stage due to its complex internal structure and the difficulty of modeling dynamic relationships with video. Existing surveys focus on general music generation without comprehensive discussion on vision-to-music. In this paper, we systematically review the research progress in the field of vision-to-music generation. We first analyze the technical characteristics and core challenges for three input types: general videos, human movement videos, and images, as well as two output types of symbolic music and audio music. We then summarize the existing methodologies on vision-to-music generation from the architecture perspective. A detailed review of common datasets and evaluation metrics is provided. Finally, we discuss current challenges and promising directions for future research. We hope our survey can inspire further innovation in vision-to-music generation and the broader field of multimodal generation in academic research and industrial applications. To follow latest works and foster further innovation in this field, we are continuously maintaining a GitHub repository at https://github.com/wzk1015/Awesome-Vision-to-Music-Generation.
Large language models (LLMs) have demonstrated potential in assisting scientific research, yet their ability to discover high-quality research hypotheses remains unexamined due to the lack of a dedicated benchmark. To address this gap, we introduce the first large-scale benchmark for evaluating LLMs with a near-sufficient set of sub-tasks of scientific discovery: inspiration retrieval, hypothesis composition, and hypothesis ranking. We develop an automated framework that extracts critical components - research questions, background surveys, inspirations, and hypotheses - from scientific papers across 12 disciplines, with expert validation confirming its accuracy. To prevent data contamination, we focus exclusively on papers published in 2024, ensuring minimal overlap with LLM pretraining data. Our evaluation reveals that LLMs perform well in retrieving inspirations, an out-of-distribution task, suggesting their ability to surface novel knowledge associations. This positions LLMs as "research hypothesis mines", capable of facilitating automated scientific discovery by generating innovative hypotheses at scale with minimal human intervention.
Value of risk-contact data from digital contact monitoring apps in infectious disease modeling
2025-03-27
Martijn H. H. Schoot Uiterkamp, Willian J. van Dijk, Hans Heesterbeek, Remco van der Hofstad, Jessica C. Kiefte-de Jong, Nelly Litvak
In this paper, we present a simple method to integrate risk-contact data, obtained via digital contact monitoring (DCM) apps, in conventional compartmental transmission models. During the recent COVID-19 pandemic, many such data have been collected for the first time via newly developed DCM apps. However, it is unclear what the added value of these data is, unlike that of traditionally collected data via, e.g., surveys during non-epidemic times. The core idea behind our method is to express the number of infectious individuals as a function of the proportion of contacts that were with infected individuals and use this number as a starting point to initialize the remaining compartments of the model. As an important consequence, using our method, we can estimate key indicators such as the effective reproduction number using only two types of daily aggregated contact information, namely the average number of contacts and the average number of those contacts that were with an infected individual. We apply our method to the recent COVID-19 epidemic in the Netherlands, using self-reported data from the health surveillance app COVID RADAR and proximity-based data from the contact tracing app CoronaMelder. For both data sources, our corresponding estimates of the effective reproduction number agree both in time and magnitude with estimates based on other more detailed data sources such as daily numbers of cases and hospitalizations. This suggests that the use of DCM data in transmission models, regardless of the precise data type and for example via our method, offers a promising alternative for estimating the state of an epidemic, especially when more detailed data are not available.
Recently, the emergence of large language models (LLMs) has prompted researchers to explore the integration of language descriptions into graphs, aiming to enhance model encoding capabilities from a data-centric perspective. This graph representation is called text-attributed graphs (TAGs). A review of prior advancements highlights that graph structure learning (GSL) is a pivotal technique for improving data utility, making it highly relevant to efficient TAG learning. However, most GSL methods are tailored for traditional graphs without textual information, underscoring the necessity of developing a new GSL paradigm. Despite clear motivations, it remains challenging: (1) How can we define a reasonable optimization objective for GSL in the era of LLMs, considering the massive parameters in LLM? (2) How can we design an efficient model architecture that enables seamless integration of LLM for this optimization objective? For Question 1, we reformulate existing GSL optimization objectives as a tree optimization framework, shifting the focus from obtaining a well-trained edge predictor to a language-aware tree sampler. For Question 2, we propose decoupled and training-free model design principles for LLM integration, shifting the focus from computation-intensive fine-tuning to more efficient inference. Based on this, we propose Large Language and Tree Assistant (LLaTA), which leverages tree-based LLM in-context learning to enhance the understanding of topology and text, enabling reliable inference and generating improved graph structure. Extensive experiments on 10 TAG datasets demonstrate that LLaTA enjoys flexibility - incorporated with any backbone; scalability - outperforms other LLM-based GSL methods in terms of running efficiency; effectiveness - achieves SOTA performance.
The neutrino sector is the least known in the Standard Model. We briefly review the open questions in neutrino physics and the future experimental prospects of answering them. We argue that a synergistic approach at multiple frontiers is needed.
Symmetric measurement-induced lower bounds of concurrence
2025-03-27
Hao-Fan Wang, Shao-Ming Fei
We provide a class of lower bounds for concurrence based on symmetric measurements. We show that our lower bounds estimate the quantum entanglement better than some existing lower bounds by detailed examples. Moreover, our lower bounds can be experimentally identified without state tomography.
Extending Silicon Lifetime: A Review of Design Techniques for Reliable Integrated Circuits
2025-03-27
Shaik Jani Babu, Fan Hu, Linyu Zhu, Sonal Singhal, Xinfei Guo
Reliability has become an increasing concern in modern computing. Integrated circuits (ICs) are the backbone of modern computing devices across industries, including artificial intelligence (AI), consumer electronics, healthcare, automotive, industrial, and aerospace. Moore Law has driven the semiconductor IC industry toward smaller dimensions, improved performance, and greater energy efficiency. However, as transistors shrink to atomic scales, aging-related degradation mechanisms such as Bias Temperature Instability (BTI), Hot Carrier Injection (HCI), Time-Dependent Dielectric Breakdown (TDDB), Electromigration (EM), and stochastic aging-induced variations have become major reliability threats. From an application perspective, applications like AI training and autonomous driving require continuous and sustainable operation to minimize recovery costs and enhance safety. Additionally, the high cost of chip replacement and reproduction underscores the need for extended lifespans. These factors highlight the urgency of designing more reliable ICs. This survey addresses the critical aging issues in ICs, focusing on fundamental degradation mechanisms and mitigation strategies. It provides a comprehensive overview of aging impact and the methods to counter it, starting with the root causes of aging and summarizing key monitoring techniques at both circuit and system levels. A detailed analysis of circuit-level mitigation strategies highlights the distinct aging characteristics of digital, analog, and SRAM circuits, emphasizing the need for tailored solutions. The survey also explores emerging software approaches in design automation, aging characterization, and mitigation, which are transforming traditional reliability optimization. Finally, it outlines the challenges and future directions for improving aging management and ensuring the long-term reliability of ICs across diverse applications.
Effect of convective transport in edge/SOL plasmas of ADITYA-U tokamak
2025-03-27
Ritu Dey, Joydeep Ghosh, Tanmay M. Macwan, Kaushlender Singh, M. B. Chowdhuri, H. Raj, R. L. Tanna, Deepti Sharma, T. D. Rognlien
The 2-D edge plasma fluid transport code, UEDGE has been used to simulate the edge region of circular limiter plasmas of ADITYA-U for modelling the measured electron density profile. The limiter geometry of ADITYA-U has been introduced in the UEDGE code, which is primarily developed and used for divertor configuration. The computational mesh defining the limiter geometry is generated by a routine developed in-house, and has successfully been integrated with the UEDGE code to simulate the edge plasma parameters of ADITYA-U. The radial profiles of edge and scrape-off layer (SOL) electron density, ne and temperature are obtained from the simulations and used to model the measured ne profile using Langmuir probe array. It has been found that a convective velocity, vconv. is definitely needed in addition to the constant perpendicular diffusion coefficient, D throughout the edge and SOL regions to model the edge ne profile. The obtained vconv. is inward and radially constant with a value of 1.5 m/s and the radially constant D is ~ 0.2 m 2/s. The value of D ~ 0.2 m2/s is found to be much less than fluctuation induced diffusivities and lies in-between the neoclassical diffusivity and Bohm diffusivity estimated in the edge-SOL region of ADITYA-U tokamak. Furthermore, the transport of radial electron heat flux is found to be maximizing near the limiter tip location in the poloidal plane.
Real-Time Evaluation Models for RAG: Who Detects Hallucinations Best?
2025-03-27
Ashish Sardana
This article surveys Evaluation models to automatically detect hallucinations in Retrieval-Augmented Generation (RAG), and presents a comprehensive benchmark of their performance across six RAG applications. Methods included in our study include: LLM-as-a-Judge, Prometheus, Lynx, the Hughes Hallucination Evaluation Model (HHEM), and the Trustworthy Language Model (TLM). These approaches are all reference-free, requiring no ground-truth answers/labels to catch incorrect LLM responses. Our study reveals that, across diverse RAG applications, some of these approaches consistently detect incorrect RAG responses with high precision/recall.
Hunting for High-Mass X-ray Binaries in the Galactic Center with NuSTAR
2025-03-27
Shifra Mandel, Julian Gerber, Kaya Mori, Ceaser Stringfield, Mabel Peñaherrera, Charles J. Hailey, Alan Du, Jonathan Grindlay, JaeSub Hong, Gabriele Ponti, John A. Tomsick, Maureen van den Berg
The central $2\times0.8$ deg$^2$ region of our Galaxy contains $\sim10,000$ X-ray point sources that were detected by a series of Chandra observations over the last two decades. However, the limited bandpass of Chandra below 8 keV hampered their spectroscopic classification. In 2016, the initial NuSTAR Galactic center (GC) survey detected 77 X-ray sources above 10 keV (Hong et al. 2016). The hard X-ray detections indicate magnetic cataclysmic variables (CVs), low-mass X-ray binaries (LMXBs), high-mass X-ray binaries (HMXBs), or even pulsars. The possibility of HMXB detections is particularly interesting given the dearth of identified HMXBs in the GC. We conducted a search for bright ($K_s\lt16$ mag) near-infrared (NIR) counterparts to the hard X-ray sources $-$ utilizing their Chandra positions $-$ in order to identify HMXB candidates. We identified seven NuSTAR sources with NIR counterpart candidates whose magnitudes are consistent with HMXBs at the GC. We assessed the likelihood of random association for these seven sources and determined that two have a non-random association with a probability exceeding $99.98\%$, making them strong HMXB candidates. We analyzed broadband NuSTAR, Chandra and XMM-Newton spectral data for these two candidates, one of which was previously identified as a red supergiant. We find that the X-ray spectra are consistent with HMXBs. If confirmed through follow-up NIR spectroscopic studies, our findings will open a new window into our understanding of the intrinsic luminosity distribution of HMXBs in our Galaxy in general and the GC HMXB population in particular.
eROSITA clusters dynamical state and their impact on the BCG luminosity
2025-03-27
A. Zenteno, M. Kluge, R. Kharkrang, D. Hernandez-Lang, G. Damke, A. Saro, R. Monteiro-Oliveira, E. R. Carrasco, M. Salvato, J. Comparat, M. Fabricius, J. Snigula, P. Arevalo, H. Cuevas, J. L. Nilo Castellon, A. Ramirez, S. Véliz Astudillo, M. Landriau, A. D. Myers, E. Schlafly, F. Valdes, B. Weaver, J. J. Mohr, S. Grandis, M. Klein, A. Liu, E. Bulbul, X. Zhang, J. S. Sanders, Y. E. Bahar, V. Ghirardini, M. Ramos, F. Balzer
The Spectrum Roentgen Gamma/eROSITA first public release contains 12,247 clusters and groups. We use the offset between the Brightest Cluster Galaxy (BCG) and the X--ray peak (D$_{\rm BCG-X}$) to classify the cluster dynamical state of 3,946 galaxy clusters and groups. The X--ray peaks come from the eROSITA survey while the BCG positions come from the DECaLS DR10 optical data, which includes the DECam eROSITA Survey optical data. We aim to investigate the evolution of the merger and relaxed cluster distributions with redshift and mass, and their impact on the BCG. We model the distribution of D$_{\rm BCG-X}$ as the sum of two Rayleigh distributions representing the cluster's relaxed and disturbed populations, and explore their evolution with redshift and mass. To explore the impact of the cluster's dynamical state on the BCG luminosity, we separate the main sample according to the dynamical state. We define clusters as relaxed if D$_{\rm BCG-X}$ < 0.25$r_{500}$, disturbed if D$_{\rm BCG-X}$>0.5$r_{500}$, and as `diverse' otherwise. We find no evolution of the merging fraction in redshift and mass. We observe that the width of the relaxed distribution to increase with redshift, while the width of the two Rayleigh distributions decreases with mass. The analysis reveals that BCGs in relaxed clusters are brighter than BCGs in both the disturbed and diverse cluster population. The most significant differences are found for high mass clusters at higher redshift. The results suggest that BCGs in low-mass clusters are less centrally bound than those in high-mass systems, irrespective of dynamical state. Over time, BCGs in relaxed clusters progressively align with the potential center. This alignment correlates with their luminosity growth relative to BCGs in dynamically disturbed clusters, underscoring the critical role of the clusters dynamical state in regulating BCG evolution. [Abridged]
This study focuses on Class I, Flat Spectrum (FS), and Class II disks in the Ophiuchus molecular cloud, a nearby active star-forming region with numerous young stellar objects (YSOs), to unveil signs of substructure formation in these disks. We employ two-dimensional super-resolution imaging based on Sparse Modeling (SpM) for ALMA archival Band 6 continuum data, achieving images with spatial resolutions comparable to a few au (0".02-0".2) for 78 dust disks, all of which are spatially resolved. In our sample, we confirm that approximately 30-40% of the disks exhibit substructures, and we identify new substructures in 15 disks (4 Class I, 7 Class FS, and 4 Class II objects). Compared to the eDisk sample in terms of bolometric temperature, Tbol, our targets are in a relatively later accretion phase. By combining our targets with the eDisk sample, we confirm that substructure detection in available data is restricted to objects where Tbol exceeds 200-300 K and the dust disk radius, Rdust, is larger than ~30 au. Moreover, we find that the distribution of inclination angles for Class II disks has a deficit of high values and is not consistent with being random. Analyzing molecular line emission data around these objects will be crucial to constrain disk evolutionary stages further and understand when and how substructures form.
Hints of an Anomalous Lens Population towards the Galactic Bulge
2025-03-27
Scott E. Perkins, Peter McGill, William A. Dawson, Ming-Feng Ho, Natasha S. Abrams, Simeon Bird, Jessica R. Lu
The dark and dynamic parts of the Galaxy, including the bulk shape and movement of the Galactic Bulge and characteristics of dark compact object populations, such as a hypothetical population of primordial black holes (PBHs), are difficult to study directly by their very nature, but are critical to our understanding of the universe. Fortunately, all of these mysteries can be uniquely studied via gravitational microlensing, a method of astronomical detection that traces mass and dynamics as opposed to light. Using the OGLE-IV microlensing survey bulge fields, we apply a Bayesian hierarchical model to jointly infer properties of the Galaxy, the characteristics of compact objects, and and test PBHs with an extended mass distribution as a test PBHs as a viable explanation of dark matter, extending work focused on the Small and Large Magellanic Clouds, both with much lower event-rates. We infer a preference within the data for a lower patternspeed in the galactic model and a wider mass spectrum for compact objects. When adding a PBH component to the favored astrophysical model from our initial investigations, we find a Bayes factor of $\ln\mathcal{B} = 20.23$ preferring the PBH model. Upon further investigation of these results, we find the critical feature in the PBH model to be the velocity distribution, which is fundamentally different than the velocity distribution of astrophysical objects and uniquely able to explain a large number of low parallax, low timescale microlensing events. Noting that this effect is not unique to PBHs, we consider the implications of these results as applied to a hypothetical population of PBHs and discuss alternative explanations, including a variety of other possible astrophysical and survey or analysis systematics.
Reliability and Availability in Virtualized Networks: A Survey on Standards, Modeling Approaches, and Research Challenges
2025-03-27
Mario Di Mauro, Walter Cerroni, Fabio Postiglione, Massimo Tornatore, Kishor S. Trivedi
The rise of Network Function Virtualization (NFV) has transformed network infrastructures by replacing fixed hardware with software-based Virtualized Network Functions (VNFs), enabling greater agility, scalability, and cost efficiency. Virtualization increases the distribution of system components and introduces stronger interdependencies. As a result, failures become harder to predict, monitor, and manage compared to traditional monolithic networks. Reliability, i.e. the ability of a system to perform regularly under specified conditions, and availability, i.e. the probability of a system of being ready to use, are critical requirements that must be guaranteed to maintain seamless network operations. Accurate modeling of these aspects is crucial for designing robust, fault-tolerant virtualized systems that can withstand service disruptions. This survey focuses on reliability and availability attributes of virtualized networks from a modeling perspective. After introducing the NFV architecture and basic definitions, we discuss the standardization efforts of the European Telecommunications Standards Institute (ETSI), which provides guidelines and recommendations through a series of standard documents focusing on reliability and availability. Next, we explore several formalisms proposed in the literature for characterizing reliability and availability, with a focus on their application to modeling the failure and repair behavior of virtualized networks through practical examples. Then, we overview numerous references demonstrating how different authors adopt specific methods to characterize reliability and/or availability of virtualized systems. Moreover, we present a selection of the most valuable software tools that support modeling of reliable virtualized networks. Finally, we discuss a set of open problems with the aim to encourage readers to explore further advances in this field.
Advances for QCD and the Standard Model: Color-Confining Light-Front Holography and the Principle of Maximum Conformality
2025-03-27
Stanley J. Brodsky
I review how the application of superconformal quantum mechanics and light-front holography leads to new insights into the physics of color confinement, the spectroscopy and dynamics of hadrons, as well as surprising supersymmetric relations between the masses of mesons, baryons, and tetraquarks. Spontaneous chiral symmetry breaking is automatically fulfilled by supersymmetric Light-Front QCD. The light-front holographic approach (HLFQCD) also predicts the behavior of the QCD running coupling and other observables from the nonperturbative color-confining domain to the perturbative domain. One can determine the QCD running coupling to high precision from the data of just a single experiment over the entire perturbative regime by using the Principle of Maximum Conformality (PMC). The PMC, which generalizes the conventional Gell-Mann-Low method for scale-setting in perturbative QED to non-Abelian QCD, provides a rigorous method for achieving unambiguous scheme-independent, fixed-order Standard Model predictions, consistent with the principles of the renormalization group. I also briefly review a novel feature of hadronic physics predicted by QCD: intrinsic heavy quarks.
Observation of WZ$γ$ production and constraints on new physics scenarios in proton-proton collisions at $\sqrt{s}$ = 13 TeV
2025-03-27
CMS Collaboration
A measurement of the WZ$\gamma$ triboson production cross section is presented. The analysis is based on a data sample of proton-proton collisions at a center-of-mass energy of $\sqrt{s}$ = 13 TeV recorded with the CMS detector at the LHC, corresponding to an integrated luminosity of 138 fb$^{-1}$. The analysis focuses on the final state with three charged leptons, $\ell^\pm\nu\ell^+\ell^-$, where $\ell$ = e or $\mu$, accompanied by an additional photon. The observed (expected) significance of the WZ$\gamma$ signal is 5.4 (3.8) standard deviations. The cross section is measured in a fiducial region to be 5.48 $\pm$ 1.11 fb, which is compatible with the prediction of 3.69 $\pm$ 0.24 fb at next-to-leading order in quantum chromodynamics. Exclusion limits are set on anomalous quartic gauge couplings and on the production cross sections of massive axion-like particles.
The Potential of the SPHEREx Mission for Characterizing PAH 3.3 μm Emission in Nearby Galaxies
2025-03-27
Edward Zhang, Andreas L. Faisst, Brendan Crill, Hanae Inami, Thomas Lai, Youichi Ohyama, Jeonghyun Pyo, Rachel Akeson, Matthew L. Ashby, James J. Bock, Yun-Ting Cheng, Yi-Kuan Chiang, Asantha Cooray, Olivier Dore, Richard M. Feder, Yongjung Kim, Bomee Lee, Daniel C. Masters, Gary Melnick, Roberta Paladini, Michael W Werner
Dust is a key galaxy component together with gas, stars, and central supermassive black holes, playing a crucial role in stellar and galaxy evolution. Hence, it is critical to understand galaxies' dust content and properties across cosmic time in order to better understand how galaxies evolve. In addition to photometric constraints on the absorption of blue light and its re-emission at infrared (IR) wavelengths, the detailed dust grain properties can be explored spectroscopically via Polycyclic Aromatic Hydrocarbon (PAH) emission bands in the mid-IR. The new SPHEREx space telescope will conduct an all-sky spectrophotometric survey of stars and galaxies at wavelengths of 0.75-5 ${\mu}$m, making it ideal for studying the widespread presence of the 3.3 ${\mu}$m PAH emission across entire galaxy populations out to z~0.4. In the present paper, we performed realistic simulations of galaxy spectra to investigate the capability of SPHEREx to study PAH emission in galaxies up to z=0.4. We find that for the all-sky survey, the PAH 3.3 ${\mu}$m emission band flux can be measured to an accuracy of 30% at log(M/$M_\odot$)>9.5 and Star Formation Rate (SFR)>1 $M_\odot$ yr$^{-1}$ at z=0.1, log(M/$M_\odot$)>10.5 and SFR>10 $M_\odot$ yr$^{-1}$ at z=0.2-0.3, and log(M/$M_\odot$)>11 and SFR>100 $M_\odot$ yr$^{-1}$ at z=0.4. In the deep SPHEREx fields, a factor of ~10 deeper sensitivity limits can be reached. Overall, SPHEREx will enable the measurement of the 3.3 ${\mu}$m PAH band emission in a several hundred thousand galaxies across the sky. Given that PAH emission originates from interactions between small dust grains ("nano grains") and ultraviolet radiation from young stars, these measurements will provide a population study of the smallest dust grains and radiation properties in massive galaxies in the nearby Universe.
Galaxy Zoo JWST: Up to 75% of discs are featureless at $3
2025-03-27
R. J. Smethurst, B. D. Simmons, T. Géron, H. Dickinson, L. Fortson, I. L. Garland, S. Kruk, S. M. Jewell, C. J. Lintott, J. S. Makechemu, K. B. Mantha, K. L. Masters, D. O'Ryan, H. Roberts, M. R. Thorne, M. Walmsley, M. Calabrò, B. Holwerda, J. S. Kartaltepe, A. M. Koekemoer, Y. Lyu, R. Lucas, F. Pacucci, M. Tarrasse
We have not yet observed the epoch at which disc galaxies emerge in the Universe. While high-$z$ measurements of large-scale features such as bars and spiral arms trace the evolution of disc galaxies, such methods cannot directly quantify featureless discs in the early Universe. Here we identify a substantial population of apparently featureless disc galaxies in the Cosmic Evolution Early Release Science (CEERS) survey by combining quantitative visual morphologies of $\sim 7,000$ galaxies from the Galaxy Zoo JWST CEERS project with a public catalogue of expert visual and parametric morphologies. While the highest-redshift featured disc we identify is at $z_{\rm{phot}}=5.5$, the highest-redshift featureless disc we identify is at $z_{\rm{phot}}=7.4$. The distribution of S\'ersic indices for these featureless systems suggests that they truly are dynamically cold: disc-dominated systems have existed since at least $z\sim 7.4$. We place upper limits on the featureless disc fraction as a function of redshift, and show that up to $75\%$ of discs are featureless at $3.0
We posit that gene-environment interplay (GxE) studies should be developed both theoretically and empirically to be of relevance to policy makers. On the theoretical front, this development is essential because the current literature lacks the integration of a clear framework capturing the various goals of public policies. Empirically, GxE models need to be further developed because the common way of modelling GxE effects fails to adequately capture the heterogeneous effects public policies may have along the distribution of genetic propensities (as captured by polygenic indices). We fill these gaps by proposing a policy classification for GxE research and by offering guidance on advancing the empirical modelling of policy-informative GxE interplay. While doing so, we provide a systematic review of existing GxE studies on educational outcomes exploiting policy reforms or environments that could be targeted by public policy.
In recent years, non-control-data attacks have be come a research hotspot in the field of network security, driven by the increasing number of defense methods against control-flow hijacking attacks. These attacks exploit memory vulnerabilities to modify non-control data within a program, thereby altering its behavior without compromising control-flow integrity. Research has shown that non-control-data attacks can be just as damaging as control-flow hijacking attacks and are even Turing complete, making them a serious security threat. However, despite being discovered long ago, the threat of non-control-data attacks has not been adequately addressed. In this review, we first classify non-control-data attacks into two categories based on their evolution: security-sensitive function attacks and data-oriented programming (DOP) attacks. Subsequently, based on the non control-data attack model, we categorize existing defense methods into three main strategies: memory safety, data confidentiality, and data integrity protection. We then analyze recent defense techniques specifically designed for DOP attacks. Finally, we identify the key challenges hindering the widespread adoption of defenses against non-control-data attacks and explore future research directions in this field.
Randomization to Reduce Terror Threats at Large Venues
2025-03-27
Paul B. Kantor, Fred S. Roberts
Can randomness be better than scheduled practices, for securing an event at a large venue such as a stadium or entertainment arena? Perhaps surprisingly, from several perspectives the answer is "yes." This note examines findings from an extensive study of the problem, including interviews and a survey of selected venue security directors. That research indicates that: randomness has several goals; many security directors recognize its potential; but very few have used it much, if at all. Some fear they will not be able to defend using random methods if an adversary does slip through security. Others are concerned that staff may not be able to perform effectively. We discuss ways in which it appears that randomness can improve effectiveness, ways it can be effectively justified to those who must approve security processes, and some potential research or regulatory advances.
2025-03-27
Pinlong Zhao, Weiyao Zhu, Pengfei Jiao, Di Gao, Ou Wu
Deep learning has become a cornerstone of modern artificial intelligence, enabling transformative applications across a wide range of domains. As the core element of deep learning, the quality and security of training data critically influence model performance and reliability. However, during the training process, deep learning models face the significant threat of data poisoning, where attackers introduce maliciously manipulated training data to degrade model accuracy or lead to anomalous behavior. While existing surveys provide valuable insights into data poisoning, they generally adopt a broad perspective, encompassing both attacks and defenses, but lack a dedicated, in-depth analysis of poisoning attacks specifically in deep learning. In this survey, we bridge this gap by presenting a comprehensive and targeted review of data poisoning in deep learning. First, this survey categorizes data poisoning attacks across multiple perspectives, providing an in-depth analysis of their characteristics and underlying design princinples. Second, the discussion is extended to the emerging area of data poisoning in large language models(LLMs). Finally, we explore critical open challenges in the field and propose potential research directions to advance the field further. To support further exploration, an up-to-date repository of resources on data poisoning in deep learning is available at https://github.com/Pinlong-Zhao/Data-Poisoning.
Reasoning Under Threat: Symbolic and Neural Techniques for Cybersecurity Verification
2025-03-27
Sarah Veronica
Cybersecurity demands rigorous and scalable techniques to ensure system correctness, robustness, and resilience against evolving threats. Automated reasoning, encompassing formal logic, theorem proving, model checking, and symbolic analysis, provides a foundational framework for verifying security properties across diverse domains such as access control, protocol design, vulnerability detection, and adversarial modeling. This survey presents a comprehensive overview of the role of automated reasoning in cybersecurity, analyzing how logical systems, including temporal, deontic, and epistemic logics are employed to formalize and verify security guarantees. We examine SOTA tools and frameworks, explore integrations with AI for neural-symbolic reasoning, and highlight critical research gaps, particularly in scalability, compositionality, and multi-layered security modeling. The paper concludes with a set of well-grounded future research directions, aiming to foster the development of secure systems through formal, automated, and explainable reasoning techniques.
Demand is spiking in industrial fields for multidisciplinary forecasting, where a broad spectrum of sectors needs planning and forecasts to streamline intelligent business management, such as demand forecasting, product planning, inventory optimization, etc. Specifically, these tasks expecting intelligent approaches to learn from sequentially collected historical data and then foresee most possible trend, i.e. time series forecasting. Challenge of it lies in interpreting complex business contexts and the efficiency and generalisation of modelling. With aspirations of pre-trained foundational models for such purpose, given their remarkable success of large foundation model across legions of tasks, we disseminate \leforecast{}, an enterprise intelligence platform tailored for time series tasks. It integrates advanced interpretations of time series data and multi-source information, and a three-pillar modelling engine combining a large foundation model (Le-TSFM), multimodal model and hybrid model to derive insights, predict or infer futures, and then drive optimisation across multiple sectors in enterprise operations. The framework is composed by a model pool, model profiling module, and two different fusion approaches regarding original model architectures. Experimental results verify the efficiency of our trail fusion concepts: router-based fusion network and coordination of large and small models, resulting in high costs for redundant development and maintenance of models. This work reviews deployment of LeForecast and its performance in three industrial use cases. Our comprehensive experiments indicate that LeForecast is a profound and practical platform for efficient and competitive performance. And we do hope that this work can enlighten the research and grounding of time series techniques in accelerating enterprise.
Objective: In this review, we aim to provide an analysis of current cardiac ablation techniques, such as radiofrequency ablation (RF), cryoablation, and pulsed-field ablation (PFA), with a focus on the role of computational modeling in enhancing the precision, safety, and effectiveness of these treatments. Particular attention is given to thermal management, exploring how computational approaches contribute to understanding and controlling energy delivery, heat distribution, and tissue response during ablation procedures. Methods: The mechanisms, applications, and limitations of radiofrequency (RF) ablation, cryoablation, and pulsed field ablation (PFA) are reviewed. Additionally, the use of computational approaches, including numerical methods and artificial intelligence (AI)-based models, for evaluating energy distribution, lesion size, and tissue response during ablation procedures is discussed. Results: Computational methods can predict ablation treatment outcomes and help optimize lesion size, ablation parameters, and procedural safety. However, these models are only reliable when properly validated and verified. Conclusion: Further research is essential to collect reliable in vivo data for validating computational models and integrating them into clinical practice to improve patient outcomes.
Ontology-based Semantic Similarity Measures for Clustering Medical Concepts in Drug Safety
2025-03-26
Jeffery L Painter, François Haguinet, Gregory E Powell, Andrew Bate
Semantic similarity measures (SSMs) are widely used in biomedical research but remain underutilized in pharmacovigilance. This study evaluates six ontology-based SSMs for clustering MedDRA Preferred Terms (PTs) in drug safety data. Using the Unified Medical Language System (UMLS), we assess each method's ability to group PTs around medically meaningful centroids. A high-throughput framework was developed with a Java API and Python and R interfaces support large-scale similarity computations. Results show that while path-based methods perform moderately with F1 scores of 0.36 for WUPALMER and 0.28 for LCH, intrinsic information content (IC)-based measures, especially INTRINSIC-LIN and SOKAL, consistently yield better clustering accuracy (F1 score of 0.403). Validated against expert review and standard MedDRA queries (SMQs), our findings highlight the promise of IC-based SSMs in enhancing pharmacovigilance workflows by improving early signal detection and reducing manual review.
Imprints of Stellar Feedback on Magnetic Fields in the Iris Nebula NGC 7023
2025-03-26
Ekta Sharma, Kate Pattle, Di Li, Chang Won Lee, Maheswar Gopinathan, Tao-Chung Ching, Mehrnoosh Tahani, Shinyoung Kim
We present 850 $\mu$m polarized continuum observations carried out with the POL-2 polarimeter mounted on the James Clerk Maxwell Telescope (JCMT) towards NGC 7023 located in the Cepheus Flare region. NGC 7023 is a reflection nebula powered by a Herbig Ae Be star HD 200775 and also identified as a hub in the hub-filament cloud, LDN 1172/1174. We detect submillimetre emission well towards the northern (identified as C1) and the eastern region of the reflection nebula. We investigated the polarization structure and the magnetic field (B-field) morphology, which is found to be curved and follows the clump morphology. The comparison of the B-field morphology at the clump scales ($\sim$0.02 pc) with that of the envelope ($\sim$0.5 pc) suggests that the field lines are not preserved from envelope to clump scales, implying that an external factor may be responsible for disturbing the B-field structure. We estimated a magnetic field strength of 179$\pm$50 $\mu$G in the starless core, 121$\pm$34 $\mu$G in the protostellar core with a class I source and 150$\pm$42 $\mu$G in the protostellar core with a class II source using the $N_{2}H^{+}$ (1-0) line observed with 13.7 m single dish radio telescope at Taeduk Radio Astronomy Observatory (TRAO). The stability analysis using these B-field strengths gives magnetically sub-critical values, while the magnetic, gravitational, and outflow kinetic energies are roughly balanced. We also suggest that the reordering of the magnetic field lines could be due to the interaction with the already evolved high-velocity outflow gas around the central star, which hints at the presence of outflow feedback.
Four imprints of Belnap's useful four-valued logic in computer science
2025-03-26
Tomáš Jakl
We review four areas of theoretical computer science which share technical or philosophical ideas with the work of Belnap on his useful four-valued logic. Perhaps surprisingly, the inspiration by Belnap-Dunn logic is acknowledged only in the study of d-frames. The connections of Belnap's work and linear logic, Blame Calculus or the study of LVars are not openly admitted. The key to three of these connections with Belnap's work go via the twist-product representation of bilattices. On the one hand, it allows us to view a large class of models of linear logic as based on Belnap-Dunn logic. On the other hand, d-frames admit two twist-product representation theorems and, also, the key theorem of Blame Calculus is essentially a twist-product representation theorem too, albeit with a strong proof-theoretic flavour.
Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification
2025-03-26
Theo Di Piazza, Carole Lazarus, Olivier Nempont, Loic Boussel
The rapid increase in the number of Computed Tomography (CT) scan examinations has created an urgent need for automated tools, such as organ segmentation, anomaly classification, and report generation, to assist radiologists with their growing workload. Multi-label classification of Three-Dimensional (3D) CT scans is a challenging task due to the volumetric nature of the data and the variety of anomalies to be detected. Existing deep learning methods based on Convolutional Neural Networks (CNNs) struggle to capture long-range dependencies effectively, while Vision Transformers require extensive pre-training, posing challenges for practical use. Additionally, these existing methods do not explicitly model the radiologist's navigational behavior while scrolling through CT scan slices, which requires both global context understanding and local detail awareness. In this study, we present CT-Scroll, a novel global-local attention model specifically designed to emulate the scrolling behavior of radiologists during the analysis of 3D CT scans. Our approach is evaluated on two public datasets, demonstrating its efficacy through comprehensive experiments and an ablation study that highlights the contribution of each model component.
PVLens: Enhancing Pharmacovigilance Through Automated Label Extraction
2025-03-26
Jeffery L Painter, Gregory E Powell, Andrew Bate
Reliable drug safety reference databases are essential for pharmacovigilance, yet existing resources like SIDER are outdated and static. We introduce PVLens, an automated system that extracts labeled safety information from FDA Structured Product Labels (SPLs) and maps terms to MedDRA. PVLens integrates automation with expert oversight through a web-based review tool. In validation against 97 drug labels, PVLens achieved an F1 score of 0.882, with high recall (0.983) and moderate precision (0.799). By offering a scalable, more accurate and continuously updated alternative to SIDER, PVLens enhances real-time pharamcovigilance with improved accuracy and contemporaneous insights.
Robust Flower Cluster Matching Using The Unscented Transform
2025-03-26
Andy Chu, Rashik Shrestha, Yu Gu, Jason N. Gross
Monitoring flowers over time is essential for precision robotic pollination in agriculture. To accomplish this, a continuous spatial-temporal observation of plant growth can be done using stationary RGB-D cameras. However, image registration becomes a serious challenge due to changes in the visual appearance of the plant caused by the pollination process and occlusions from growth and camera angles. Plants flower in a manner that produces distinct clusters on branches. This paper presents a method for matching flower clusters using descriptors generated from RGB-D data and considers allowing for spatial uncertainty within the cluster. The proposed approach leverages the Unscented Transform to efficiently estimate plant descriptor uncertainty tolerances, enabling a robust image-registration process despite temporal changes. The Unscented Transform is used to handle the nonlinear transformations by propagating the uncertainty of flower positions to determine the variations in the descriptor domain. A Monte Carlo simulation is used to validate the Unscented Transform results, confirming our method's effectiveness for flower cluster matching. Therefore, it can facilitate improved robotics pollination in dynamic environments.
AV-TLX for measuring (mental) workload while driving AVs: Born from NASA-TLX but developed for the era of automated vehicles
2025-03-26
Saeedeh Mosaferchi, Alireza Mortezapour, Magnus Liebherr, Francesco Villecco, Alessandro Naddeo
The introduction of automated vehicles has redefined the level of interaction between the driver and the vehicle, introducing new tasks and so impose different workloads. Existing tools such as NASA-TLX and DALI are still used to assess driving workload in automated vehicles, despite not accounting for new tasks. This study introduces AV-TLX, a specialized tool for measuring workload in Level 3 automated driving. The development process began with a narrative literature review to identify the primary factors influencing workload. This was followed by a series of qualitative sessions during which the dimensions and later the questions of the questionnaire were designed. The tools validity was first assessed using CVR and CVI indices, and its reliability and convergent validity were evaluated using a dynamic driving simulator with high fidelity. The final version of AV-TLX comprises 19 questions across 8 subscales, demonstrating excellent reliability (0.86) and validity (CVR > 0.78). An agreement scores between the results of AV-TLX and NASA-TLX in the simulation study was 0.6, which is considered acceptable for the consistency of two questionnaires. Furthermore, this questionnaire can be utilized in two ways. First by reporting the overall workload and/or divided into 8 primary subscales, or by categorizing the questions into two groups including takeover task workload and automated driving task workload. The final version of this questionnaire, as presented in the paper, is available for use in future studies focusing on Level 3 automated driving.
The nature of HERGs and LERGs in LoTSS DR2 $-$ a morphological perspective
2025-03-26
J. Chilufya, M. J. Hardcastle, J. C. S. Pierce, A. B. Drake, R. D. Baldi, H. J. A. Röttgering, D. J. B. Smith
We present the largest visually selected sample of extended ($>$60 arcsec) radio-loud active galactic nuclei (RLAGN) to date, based on the LOw-Frequency Array Two-Metre Sky Survey second data release (LoTSS DR2). From the broader LoTSS DR2 dataset with spectroscopic classifications, we construct a subsample of 2828 RLAGN with radio luminosities greater than $10^{23}~\mathrm{W~Hz^{-1}}$ at $z<0.57$. These RLAGN are further classified by optical emission-line properties into high-excitation and low-excitation radio galaxies, enabling a detailed emission-line analysis. Our subsample is also morphologically classified into Fanaroff \& Riley centre- and edge-brightened (FRI/FRII) sources, wide- and narrow-angle tail (WAT and NAT) sources, head-tail (HT) sources, and relaxed double (RD) sources. For these classifications, we utilize data from the Very Large Array Sky Survey (VLASS) to assist with the classification, taking advantage of its 2.5 arcsec resolution which is sensitive to structures below 30 arcsec. This resolution allows us to identify compact cores and hotspots, facilitating the identification of remnant and restarted RLAGN candidates. We investigate the relationship between emission-line and radio properties in RLAGN, analyzing mid-infrared data, host galaxy mass, and core prominence. These analyses uncover the complex relationship between these factors and the underlying accretion mechanisms. Our findings emphasize that no single property can fully constrain the accretion mode in RLAGN, highlighting the necessity of multi-dimensional approaches to reveal the processes driving RLAGN behaviour.
SEMPER I. Radio Predictions for Star-Forming Galaxies at $0
2025-03-26
M. Giulietti, I. Prandoni, M. Bonato, L. Bisigello, M. Bondi, G. Gandolfi, M. Massardi, L. Boco, H. J. A. Rottgering, A. Lapi
[Abridged] SFGs are the dominant population in the faint radio sky, corresponding to flux densities at 1.4 GHz $< 0.1$ mJy. A panchromatic approach is essential for selecting SFGs in the radio band and understanding star formation processes over cosmic time. Semi-empirical models are valuable tools to effectively study galaxy formation and evolution, relying on minimal assumptions and exploiting empirical relations between galaxy properties and enabling us to take full advantage of the recent progress in radio and optical/near-infrared (NIR) observations. In this paper, we develop the Semi-EMPirical model for Extragalactic Radio emission (SEMPER) to predict radio luminosity functions and number counts at 1.4 GHz and 150 MHz for SFGs. SEMPER is based on state-of-the-art empirical relations and combines the redshift-dependent galaxy stellar mass functions obtained from the recent COSMOS2020 catalogue, which exploits deep near-infrared observations, with up-to-date observed scaling relations, such as the galaxy main sequence and the mass-dependent far-infrared/radio correlation across cosmic time. Our luminosity functions are compared with recent observational determinations from several radio telescopes, along with previous semi-empirical models and simulations. Our semi-empirical model successfully reproduces the observed luminosity functions at 1.4 GHz and 150 MHz up to $z\sim 5$ and the most recent number count statistics from radio observations in the LoTSS deep fields. Our model, based on galaxies selected in the NIR, naturally predicts the presence of radio-selected massive and/or dust-obscured galaxies already in place at high redshift ($z\gtrsim3.5$), as suggested by recent results from JWST. Our predictions offer an excellent benchmark for upcoming updates from JWST and future ultra-deep radio surveys planned with the SKA and its precursors.
Small object detection (SOD) is a critical yet challenging task in computer vision, with applications like spanning surveillance, autonomous systems, medical imaging, and remote sensing. Unlike larger objects, small objects contain limited spatial and contextual information, making accurate detection difficult. Challenges such as low resolution, occlusion, background interference, and class imbalance further complicate the problem. This survey provides a comprehensive review of recent advancements in SOD using deep learning, focusing on articles published in Q1 journals during 2024-2025. We analyzed challenges, state-of-the-art techniques, datasets, evaluation metrics, and real-world applications. Recent advancements in deep learning have introduced innovative solutions, including multi-scale feature extraction, Super-Resolution (SR) techniques, attention mechanisms, and transformer-based architectures. Additionally, improvements in data augmentation, synthetic data generation, and transfer learning have addressed data scarcity and domain adaptation issues. Furthermore, emerging trends such as lightweight neural networks, knowledge distillation (KD), and self-supervised learning offer promising directions for improving detection efficiency, particularly in resource-constrained environments like Unmanned Aerial Vehicles (UAV)-based surveillance and edge computing. We also review widely used datasets, along with standard evaluation metrics such as mean Average Precision (mAP) and size-specific AP scores. The survey highlights real-world applications, including traffic monitoring, maritime surveillance, industrial defect detection, and precision agriculture. Finally, we discuss open research challenges and future directions, emphasizing the need for robust domain adaptation techniques, better feature fusion strategies, and real-time performance optimization.
Radio observation of the large-scale structure (LSS) of our Universe faces major challenges from foreground contamination, which is many orders of magnitude stronger than the cosmic signal. While other foreground removal techniques struggle with complex systematics, methods like foreground avoidance emerge as effective alternatives. However, this approach inevitably results in the loss of Fourier modes and a reduction in cosmological constraints. We present a novel method that, by enforcing the non-negativity of the observed field in real space, allows us to recover some of the lost information, particularly phase angles. We demonstrate that the effectiveness of this straightforward yet powerful technique arises from the mode mixing from the non-linear evolution of LSS. Since the non-negativity is ensured by mass conservation, one of the key principles of the cosmic dynamics, we can restore the lost modes without explicitly expressing the exact form of the mode mixing. Unlike previous methods, our approach utilizes information from highly non-linear scales, and has the potential to revolutionize the analysis of radio observational data in cosmology. Crucially, we demonstrate that in long-baseline interferometric observations, such as those from the Square Kilometre Array (SKA), it is still possible to recover the baryonic acoustic oscillation (BAO) signature despite not directly covering the relevant scales. This opens up potential future survey designs for cosmological detection.
Periodic KPP equations: new insights into persistence, spreading, and the role of advection
2025-03-26
Nathanaël Boutillon, François Hamel, Lionel Roques
We focus on the persistence and spreading properties for a heterogeneous Fisher-KPP equation with advection. After reviewing the different notions of persistence and spreading speeds, we focus on the effect of the direction of the advection term, denoted by $b$. First, we prove that changing $b$ to $-b$ can have an effect on the spreading speeds and the ability of persistence. Next, we provide a class of relationships between the intrinsic growth term $r$ and the advection term $b$ such that changing $b$ to $-b$ does not change the spreading speeds and the ability of persistence. We briefly mention the cases of slowly and rapidly varying environments, and bounded domains. Lastly, we show that in general, the spreading speeds are not controlled by the ability of persistence, and conversely. However, when there is no advection term, the spreading speeds are controlled by the ability of persistence, though the converse still does not hold.
Travelling waves in non-local reaction-dispersion equations with diffuse levy measures
2025-03-26
Emeric Bouin, Jérôme Coville
In this paper, we focus on the existence of propagation fronts, solutions to non-local dispersion reaction models. Our aim is to provide a unified proof of this existence in a very broad framework using simple real analysis tools. In particular, we review the results that already exist in the literature and complete the table. It appears that the most important case is that of a bistable nonlinearity, which we then extend to other classical nonlinearities.
Survey-Based Calibration of the One-Community and Two-Community Social Network Models Used for Testing Singapore's Resilience to Pandemic Lockdown
2025-03-26
Jon Spalding, Bertrand Jayles, Renate Schubert, Siew Ann Cheong, Hans Herrmann
A resilient society is one capable of withstanding and thereafter recovering quickly from large shocks. Brought to the fore by the COVID-19 pandemic of 2020--2022, this social resilience is nevertheless difficult to quantify. In this paper, we measured how quickly the Singapore society recovered from the pandemic, by first modeling it as a dynamic social network governed by three processes: (1) random link addition between strangers; (2) social link addition between individuals with a friend in common; and (3) random link deletion . To calibrate this model, we carried out a survey of a representative sample of $N = 2,057$ residents and non-residents in Singapore between Jul and Sep 2022 to measure the numbers of random and social contacts gained over a fixed duration, as well as the number of contacts lost over the same duration, using phone contacts as proxy for social contacts. Lockdown simulations using the model that fits the survey results best suggest that Singapore would recover from such a disruption after 1--2 months.
Translating Workflow Nets into the Partially Ordered Workflow Language
2025-03-26
Humam Kourani, Gyunam Park, Wil van der Aalst
The Partially Ordered Workflow Language (POWL) has recently emerged as a process modeling notation, offering strong quality guarantees and high expressiveness. However, its adoption is hindered by the prevalence of standard notations like workflow nets (WF-nets) and BPMN in practice. This paper presents a novel algorithm for transforming safe and sound WF-net into equivalent POWL models. The algorithm recursively identifies structural patterns within the WF-net and translates them into their POWL representation. We formally prove the correctness of our approach, showing that the generated POWL model preserves the language of the input WF-net. Furthermore, we demonstrate the high scalability of our algorithm, and we show its completeness on a subclass of WF-nets that encompasses equivalent representations for all POWL models. This work bridges the gap between the theoretical advantages of POWL and the practical need for compatibility with established notations, paving the way for broader adoption of POWL in process analysis and improvement applications.
The turnpike principle is a fundamental concept in optimal control theory, stating that for a wide class of long-horizon optimal control problems, the optimal trajectory spends most of its time near a steady-state solution (the ''turnpike'') rather than being influenced by the initial or final conditions. In this article, we provide a survey on the turnpike property in optimal control, adding several recent and novel considerations. After some historical insights, we present an elementary proof of the exponential turnpike property for linear-quadratic optimal control problems in finite dimension. Next, we show an extension to nonlinear optimal control problems, with a local exponential turnpike property. On simple but meaningful examples, we illustrate the local and global aspects of the turnpike theory, clarifying the global picture and raising new questions. We discuss key generalizations, in infinite dimension and other various settings, and review several applications of the turnpike theory across different fields.
The growing scale of data requires efficient memory subsystems with large memory capacity and high memory performance. Disaggregated architecture has become a promising solution for today's cloud and edge computing for its scalability and elasticity. As a critical part of disaggregation, disaggregated memory faces many design challenges in many dimensions, including hardware scalability, architecture structure, software system design, application programmability, resource allocation, power management, etc. These challenges inspire a number of novel solutions at different system levels to improve system efficiency. In this paper, we provide a comprehensive review of disaggregated memory, including the methodology and technologies of disaggregated memory system foundation, optimization, and management. We study the technical essentials of disaggregated memory systems and analyze them from the hardware, architecture, system, and application levels. Then, we compare the design details of typical cross-layer designs on disaggregated memory. Finally, we discuss the challenges and opportunities of future disaggregated memory works that serve better for next-generation elastic and efficient datacenters.
Emergent properties and the multiscale characterization challenge in condensed matter, from crystals to complex materials: a Review
2025-03-26
Elisabetta Nocerino
The complexity of condensed matter arises from emergent behaviors that cannot be understood by analyzing individual constituents in isolation. While traditional condensed-matter approaches-developed primarily for ideal crystalline solids-have provided deep insights into symmetry, order, and electronic structure, they fall short in describing the rich, multiscale organization of hierarchical and soft materials. These systems exhibit structural correlations across multiple length and time scales, often governed by nonlinear interactions that span from molecular to macroscopic domains. This review explores how the convergence of emerging experimental and computational strategies are redefining our ability to characterize and model such systems. We examine how multimodal techniques-combining scattering, imaging, and spectroscopy-can map structural order and dynamics across scales, with methods like small-angle scattering tensor tomography, dark-field imaging, and ultrafast spectroscopies providing unprecedented spatiotemporal resolution. On the computational front, machine learning approaches such as graph neural networks, neural operators, and physics-informed models offer powerful tools to connect disparate scales and uncover hidden correlations in high-dimensional data. These advancements have the potential to close the gap between structure and function in complex materials, thereby addressing one of the grand challenges of contemporary material science: understanding and engineering multiscale architectures, whose emergent properties underpin the behavior of next-generation functional materials, biological systems, and adaptive technologies.
Modern software systems are developed in diverse programming languages and often harbor critical vulnerabilities that attackers can exploit to compromise security. These vulnerabilities have been actively targeted in real-world attacks, causing substantial harm to users and cyberinfrastructure. Since many of these flaws originate from the code itself, a variety of techniques have been proposed to detect and mitigate them prior to software deployment. However, a comprehensive comparative study that spans different programming languages, program representations, bug types, and analysis techniques is still lacking. As a result, the relationships among programming languages, abstraction levels, vulnerability types, and detection approaches remain fragmented, and the limitations and research gaps across the landscape are not clearly understood. This article aims to bridge that gap by systematically examining widely used programming languages, levels of program representation, categories of vulnerabilities, and mainstream detection techniques. The survey provides a detailed understanding of current practices in vulnerability discovery, highlighting their strengths, limitations, and distinguishing characteristics. Furthermore, it identifies persistent challenges and outlines promising directions for future research in the field of software security.
ProtoBERT-LoRA: Parameter-Efficient Prototypical Finetuning for Immunotherapy Study Identification
2025-03-26
Shijia Zhang, Xiyu Ding, Kai Ding, Jacob Zhang, Kevin Galinsky, Mengrui Wang, Ryan P. Mayers, Zheyu Wang, Hadi Kharrazi
Identifying immune checkpoint inhibitor (ICI) studies in genomic repositories like Gene Expression Omnibus (GEO) is vital for cancer research yet remains challenging due to semantic ambiguity, extreme class imbalance, and limited labeled data in low-resource settings. We present ProtoBERT-LoRA, a hybrid framework that combines PubMedBERT with prototypical networks and Low-Rank Adaptation (LoRA) for efficient fine-tuning. The model enforces class-separable embeddings via episodic prototype training while preserving biomedical domain knowledge. Our dataset was divided as: Training (20 positive, 20 negative), Prototype Set (10 positive, 10 negative), Validation (20 positive, 200 negative), and Test (71 positive, 765 negative). Evaluated on test dataset, ProtoBERT-LoRA achieved F1-score of 0.624 (precision: 0.481, recall: 0.887), outperforming the rule-based system, machine learning baselines and finetuned PubMedBERT. Application to 44,287 unlabeled studies reduced manual review efforts by 82%. Ablation studies confirmed that combining prototypes with LoRA improved performance by 29% over stand-alone LoRA.
Impact of Spectral Resolution on S-index and Its Application to Spectroscopic Surveys
2025-03-26
Henggeng Han, Song Wang, Xue Li, Chuanjie Zheng, Jifeng Liu
Utilizing the PHOENIX synthetic spectra, we investigated the impact of spectral resolution on the calculation of $S$-indices. We found that for spectra with a resolution lower than $\approx$30,000, it is crucial to calibrate $S$-indices for accurate estimations. This is especially essential for low-resolution spectral observations. We provided calibrations for several ongoing or upcoming spectroscopic surveys such as the LAMOST low-resolution survey, the SEGUE survey, the SDSS-V/BOSS survey, the DESI survey, the MSE survey, and the MUST survey. Using common targets between the HARPS and MWO observations, we established conversions from spectral $S$-indices to the well-known $S_{\rm MWO}$ values, applicable to stars with [Fe/H] values greater than $-$1. These calibrations offer a reliable approach to convert $S$-indices obtained from various spectroscopic surveys into $S_{\rm{MWO}}$ values and can be widely applied in studies on chromospheric activity.
Estimating non-gaussian bias using counts of tracers
2025-03-26
Neal Dalal, Will J. Percival
Local-type primordial non-gaussianity generates a distinctive term in the clustering of tracers of large-scale structure, behaving as $k^{-2}$ at small wavenumbers $k$. In order to use this signal in a sample of galaxies to measure the amplitude of primordial non-gaussianity, $f_{NL}$, we need to independently determine the degenerate bias coefficient, $b_\Phi$, which quantifies the logarithmic response of the galaxy number density to a change in amplitude of the matter clustering. We study whether $b_\Phi$ may be estimated from the observed evolution of the number density of galaxies as a function of redshift. Using cosmological N-body simulations, we find that $b_\Phi$ may be estimated reasonably well for dark matter halos across the range of redshifts and halo masses used by large-scale structure surveys aimed at measuring $f_{NL}$. This includes non-gaussian secondary bias (or assembly bias) in halo concentration, which has previously been found to be quite large in amplitude. For an observed survey of galaxies, we additionally need to consider the selection function of the sample, which can introduce redshift dependence via cuts on apparent magnitude and colour. These effects of the selection function can be mitigated by further cutting the sample using k-corrected magnitudes and colours, to retain only those galaxies that would pass the targeting criteria for all redshifts within the interval considered.
Privacy in Immersive Extended Reality: Exploring User Perceptions, Concerns, and Coping Strategies
2025-03-26
Hilda Hadan, Derrick M. Wang, Lennart E. Nacke, Leah Zhang-Kennedy
Extended Reality (XR) technology is changing online interactions, but its granular data collection sensors may be more invasive to user privacy than web, mobile, and the Internet of Things technologies. Despite an increased interest in studying developers' concerns about XR device privacy, user perceptions have rarely been addressed. We surveyed 464 XR users to assess their awareness, concerns, and coping strategies around XR data in 18 scenarios. Our findings demonstrate that many factors, such as data types and sensitivity, affect users' perceptions of privacy in XR. However, users' limited awareness of XR sensors' granular data collection capabilities, such as involuntary body signals of emotional responses, restricted the range of privacy-protective strategies they used. Our results highlight a need to enhance users' awareness of data privacy threats in XR, design privacy-choice interfaces tailored to XR environments, and develop transparent XR data practices.
Evaluating Large Language Models for Automated Clinical Abstraction in Pulmonary Embolism Registries: Performance Across Model Sizes, Versions, and Parameters
2025-03-26
Mahmoud Alwakeel, Emory Buck, Jonathan G. Martin, Imran Aslam, Sudarshan Rajagopal, Jian Pei, Mihai V. Podgoreanu, Christopher J. Lindsell, An-Kwok Ian Wong
Pulmonary embolism (PE) is a leading cause of cardiovascular mortality, yet our understanding of optimal management remains limited due to heterogeneous and inaccessible radiology documentation. The PERT Consortium registry standardizes PE management data but depends on resource-intensive manual abstraction. Large language models (LLMs) offer a scalable alternative for automating concept extraction from computed tomography PE (CTPE) reports. This study evaluated the accuracy of LLMs in extracting PE-related concepts compared to a human-curated criterion standard. We retrospectively analyzed MIMIC-IV and Duke Health CTPE reports using multiple LLaMA models. Larger models (70B) outperformed smaller ones (8B), achieving kappa values of 0.98 (PE detection), 0.65-0.75 (PE location), 0.48-0.51 (right heart strain), and 0.65-0.70 (image artifacts). Moderate temperature tuning (0.2-0.5) improved accuracy, while excessive in-context examples reduced performance. A dual-model review framework achieved >80-90% precision. LLMs demonstrate strong potential for automating PE registry abstraction, minimizing manual workload while preserving accuracy.
Patients Speak, AI Listens: LLM-based Analysis of Online Reviews Uncovers Key Drivers for Urgent Care Satisfaction
2025-03-26
Xiaoran Xu, Zhaoqian Xue, Chi Zhang, Jhonatan Medri, Junjie Xiong, Jiayan Zhou, Jin Jin, Yongfeng Zhang, Siyuan Ma, Lingyao Li
Investigating the public experience of urgent care facilities is essential for promoting community healthcare development. Traditional survey methods often fall short due to limited scope, time, and spatial coverage. Crowdsourcing through online reviews or social media offers a valuable approach to gaining such insights. With recent advancements in large language models (LLMs), extracting nuanced perceptions from reviews has become feasible. This study collects Google Maps reviews across the DMV and Florida areas and conducts prompt engineering with the GPT model to analyze the aspect-based sentiment of urgent care. We first analyze the geospatial patterns of various aspects, including interpersonal factors, operational efficiency, technical quality, finances, and facilities. Next, we determine Census Block Group(CBG)-level characteristics underpinning differences in public perception, including population density, median income, GINI Index, rent-to-income ratio, household below poverty rate, no insurance rate, and unemployment rate. Our results show that interpersonal factors and operational efficiency emerge as the strongest determinants of patient satisfaction in urgent care, while technical quality, finances, and facilities show no significant independent effects when adjusted for in multivariate models. Among socioeconomic and demographic factors, only population density demonstrates a significant but modest association with patient ratings, while the remaining factors exhibit no significant correlations. Overall, this study highlights the potential of crowdsourcing to uncover the key factors that matter to residents and provide valuable insights for stakeholders to improve public satisfaction with urgent care.
Broad Observational Perspectives Achieved by the Accreting White Dwarf Sciences in the XMM-Newton and Chandra Eras
2025-03-26
Solen Balman, Marina Orio, Gerardo J. M. Luna
Accreting white dwarf binaries (AWDs) comprise cataclysmic variables (CVs), symbiotics, AM CVns, and other related systems that host a primary white dwarf (WD) accreting from a main sequence or evolved companion star. AWDs are a product of close binary evolution; thus, they are important for understanding the evolution and population of X-ray binaries in the Milky Way and other galaxies. AWDs are essential for studying astrophysical plasmas under different conditions along with accretion physics and processes, transient events, matter ejection and outflows, compact binary evolution, mergers, angular momentum loss mechanisms, and nuclear processes leading to explosions. AWDs are also closely related to other objects in the late stages of stellar evolution, with other accreting objects in compact binaries, and even share common phenomena with young stellar objects, active galactic nuclei, quasars, and supernova remnants. As X-ray astronomy came to a climax with the start of the Chandra and XMM-Newton missions owing to their unprecedented instrumentation, new excellent imaging capabilities, good time resolution, and X-ray grating technologies allowed immense advancement in many aspects of astronomy and astrophysics. In this review, we lay out a panorama of developments on the study of AWDs that have been accomplished and have been made possible by these two observatories; we summarize the key observational achievements and the challenges ahead.
Principal Components for Model-Agnostic Modified Gravity with 3x2pt
2025-03-26
C. M. A. Zanoletti, C. D. Leonard
To mitigate the severe information loss arising from widely adopted linear scale cuts in constraints on modified gravity parameterisations with Weak Lensing (WL) and Large-Scale Structure (LSS) data, we introduce a novel alternative method for data reduction. This Principal Component Analysis (PCA)-based framework extracts key features in the matter power spectrum arising from nonlinear effects in a set of representative gravity theories. By performing the analysis in the space of principal components, we can replace sweeping `linear-only' scale cuts with targeted cuts on the transformed data vector, ultimately reducing parameter bias and significantly tightening constraints. We forecast constraints on a minimal parameterised extension to $\Lambda$CDM which includes modifications to the growth of structure and lensing of light ($\Lambda$CDM$+\mu_0+\Sigma_0$) using mock Stage-IV data for two simulated cosmologies: the $\Lambda$CDM model and Extended Shift Symmetric (ESS) gravity. Under the assumption of a Universe defined by $\Lambda$CDM and General Relativity, our method offers constraints on $\mu_0$ a factor of 1.65 tighter than traditional linear-only scale cuts. Crucially, our approach also provides the necessary constraining power to break key degeneracies in modified gravity without relying on $f\sigma_8$ measurements, introducing a promising new tool for the analysis of present and future WL and LSS photometric surveys.
In this work, we present the first algorithm for identifying the minimum reboiler vapor duty requirement for a general multi-feed, multi-product (MFMP) distillation column separating ideal multicomponent mixtures. This algorithm incorporates our latest advancement in developing the first shortcut model for MFMP columns. We demonstrate the accuracy and efficiency of this algorithm through case studies. The results obtained from these case studies also provide valuable insights on optimal design of MFMP columns. Many of these insights are against the existing design guidelines and heuristics. For example, placing a colder saturated feed stream above a hotter saturated feed stream sometimes leads to higher energy requirement. Furthermore, decomposing a general MFMP column into individual simple columns may lead to incorrect estimation of the minimum reflux ratio for the MFMP column. Thus, the algorithm presented here offers a fast, accurate, and automated approach to synthesize new, energy-efficient, and cost-effective MFMP columns.
Void and Density Walls Inhomogeneous Cosmic Web And Dark Energy
2025-03-26
John W. Moffat
Recent observations from the Dark Energy Spectroscopic Instrument (DESI 2025) indicate a weakening of cosmic acceleration at low redshifts $z < 1$, with effective dark energy equation of state parameters $w_0 > -1$ and $w_a < 0$. We demonstrate that this evolution in dark energy can be explained by cosmic inhomogeneities and the Cosmic Web without modifying fundamental physics. Our model shows how the differential expansion between underdense voids and overdense walls creates an effective backreaction term that simulates evolving dark energy when interpreted within homogeneous cosmological frameworks. The inhomogeneous cosmic structure formation becomes significant $z\sim 1-2$, the increasing gravitational influence of wall regions counteracting the cosmic acceleration, producing both a weakening acceleration signal and a direction-dependent Hubble parameter consistent with local measurements. This mechanism reconciles the higher locally measured Hubble constant $H_0\approx 73~ km/s/Mpc$ with the lower value inferred from CMB observations $H_0\approx 67-69~ km/s/Mpc$ without introducing new energy components or modifying general relativity. Our model makes testable predictions regarding directional and scale-dependent variations in cosmological parameters that can be verified with next-generation surveys. This work suggests that properly accounting for cosmic structure may be essential for resolving apparent tensions in cosmological parameters.
Eclipse Timing Variations of Circumbinary Substellar Objects in TESS Data
2025-03-26
Ekrem Murat Esmer, Tansu Daylan
Circumbinary planets and brown dwarfs form in complex gravitational environments, offering insights into formation, orbital stability, and habitability prospects. However, they remain underrepresented, with only 60 confirmed or candidate systems known. In this work, we leverage TESS photometry to search for circumbinary companions through eclipse timing variations (ETVs), analyzing 152 detached eclipsing binaries. By modeling eclipse timings, we identify 26 systems with significant periodic signals, 14 of which have false alarm probabilities below 0.01. While no detections are confirmed, TIC 350297040 emerges as a candidate for a circumbinary brown dwarf ($0.06~M_{\odot}$) under the assumption of a $1~M_{\odot}$ binary system, though further investigation is required. Simulations using synthetic ETVs indicate a 5\% recovery rate for circumbinary brown dwarfs and 0.1\% for Jupiter-like planets, with median masses of $56.6^{+16.5}{-23.4}~M_{\rm J}$ and periods of $1404^{+1361}_{-953}$ d. Our simulations show that the smallest detectable mass is $1.6~M_{\rm J}$ at a period of 1860 d and confirm that ETV methods are effective in detecting misaligned systems. In the absence of a detection, we set an upper limit of 40\% on the occurrence rate of circumbinary brown dwarfs at the 2$\sigma$ confidence level, while a confirmed single detection would imply an occurrence rate of 13.08\%. These constraints are consistent with previous abundance estimates for circumbinary brown dwarfs ($\lesssim6.5\%$) but motivate a larger sample size. Furthermore, the very low recovery rates provide insights into the debate on first- and second-generation planet formation around post-common envelope binaries.
Feedback from low-to-moderate luminosity radio-AGN with MaNGA
2025-03-26
Pranav Kukreti, Dominika Wylezalek, Marco Albán, Bruno DallAgnol de Oliveira
Spatially resolved spectral studies of radio-AGN host galaxies have shown that these systems can impact the ionised gas on galactic scales. However, whether jet and radiation-driven feedback occurs simultaneously is still unclear. We select a large and representative sample of 806 radio-AGN from the MaNGA survey, from $L_\mathrm{1.4GHz}\approx10^{21}-10^{25}$W/Hz, and trace the warm ionised gas kinematics using the [OIII] emission line from the IFU spectra. We measure the [OIII] line width and compare it to the stellar velocity dispersion to determine the presence and location of the disturbed gas. We find most disturbed [OIII] kinematics and proportion of disturbed sources up to a radial distance of 0.25$R_{eff}$, when both radio and optical AGN are present in a source, and the radio luminosity is larger than $10^{23}$W/Hz. When either radio or optical-AGN are present, the impact on [OIII] is milder. Irrespective of the presence of an optical-AGN, we find significant differences in the feedback from high and low luminosity radio-AGN only up to a radial distance of 0.25$R_{eff}$. The presence of more kinematically disturbed warm ionised gas in the central region of radio-AGN host galaxies is related to both jets and radiation in these sources. We propose that in moderate radio luminosity AGN ($L_\mathrm{1.4GHz}\approx10^{23}-10^{25}$W/Hz) gas clouds pushed to high velocities by the jets (radiation) are driven to even higher velocities by the impact of radiation (jets) when both radio and optical-AGN are present. At lower luminosities ($L_\mathrm{1.4GHz}\approx10^{21}-10^{23}$W/Hz), the correlation between the disturbed ionised gas and enhanced radio emission could either be due to wind-driven shocks powering the radio emission, or low-power jets disturbing the gas.
Hamiltonian formalism for gauge-invariant cosmological perturbations with multiple scalar fields
2025-03-26
Mateo Pascual
We generalise Langlois' Hamiltonian treatment of gauge-invariant linear cosmological perturbations to a cosmological setting with multiple scalar fields minimally coupled to gravity. We review the Hamilton-Jacobi-like technique for a Hamiltonian system with first-class constraints. With this technique, elucidating the gauge-invariant quantities of the system is reduced to solving for the generating function of the appropriate canonical transformation. We then apply it to the case with only two scalar fields, showcasing how the presence of more than one scalar field results in a coupled evolution of the different gauge-invariant scalar degrees of freedom. Their coupled evolution may lead to new phenomenology that cannot be simply inferred by superimposing the results of the single-field case. A simple tracking of the scalar degrees of freedom present in a system with an arbitrary number of scalar fields drives the conclusion that the results for the two-field case can be trivially extended to a multi-field scenario. For completeness, we conclude by recasting explicitly the derivation for the tensor modes, which is nevertheless unaffected by the number of scalar fields in the system. The resulting gauge-invariant Hamiltonian formulation provides a solid foundation for a canonical quantisation of perturbations, necessary to explore the quantum nature of the early universe.
The Parallel Ionizing Emissivity Survey (PIE). I. Survey design and selection of candidate Lyman Continuum leakers at 3.1
2025-03-26
Alexander Beckett, Marc Rafelski, Claudia Scarlata, Wanjia Hu, Keunho Kim, Ilias Goovaerts, Matthew A. Malkan, Wayne Webb, Harry Teplitz, Matthew Hayes, Vihang Mehta, Anahita Alavi, Andrew J. Bunker, Annalisa Citro, Nimish Hathi, Alaina Henry, Alexandra Le Reste, Alessia Moretti, Michael J. Rutkowski, Maxime Trebitsch, Anita Zanella
We present the survey design and initial results from the Parallel Ionizing Emissivity (PIE) survey. PIE is a large HST survey designed to detect Lyman continuum (LyC) emitting galaxies at 3.1$<$z$<$3.5 and stack the images of galaxies at these redshifts in order to measure average LyC escape fractions as a function of galaxy properties. PIE has imaged 37 independent fields in three filters (F336W, F625W and F814W), of which 18 are observed with a fourth band (F475W) from the accompanying PIE+ program. We use photometric colors to select candidate Lyman Break Galaxies (LBGs) at 3.1$<$z$<$3.5, which can be followed-up using ground-based spectrographs to confirm their redshifts. Unlike previous surveys, our use of many independent fields allows us to remove any biases caused by correlated absorption in the IGM. In this paper, we describe the survey design, photometric measurements, and the use of mock galaxy samples to optimize our color selection. We find that a 3-filter selection allows us to select a galaxy sample of which $\approx90\%$ are LBGs and over $30\%$ lie in the 3.1$<$z$<$3.5 range for which we can detect uncontaminated LyC emission in F336W. We also use mock IGM sightlines to measure the expected transmission of the IGM, which will allow us to determine escape fractions from our stacked galaxies. We find $\approx$1000 galaxies within our selection window, and predict that this includes $\approx$80 LyC-emitting galaxies and $\approx$350 that we can use in stacking. Finally, we present our first ground-based spectra, including a plausible LyC emitter at z=3.067.
CETRA: A fast, sensitive exoplanet transit detection algorithm implemented for GPUs
2025-03-26
Leigh C. Smith, Saad Ahmed, Francesca De Angeli, P. W. Burgess, Giorgia Busso, Dominic C. Ford, Diana L. Harrison, S. T. Hodgkin, Jonathan M. Irwin, Guy T. Rixon, Nicholas A. Walton
We present the Cambridge Exoplanet Transit Recovery Algorithm (CETRA), a fast and sensitive transit detection algorithm, optimised for GPUs. CETRA separates the task into a search for transit signals across linear time space, followed by a phase-folding of the former to enable a periodic signal search, using a physically motivated transit model to improve detection sensitivity. It outperforms traditional methods like Box Least Squares and Transit Least Squares in both sensitivity and speed. Tests on synthetic light curves demonstrate that CETRA can identify at least 20 per cent more low-SNR transits than Transit Least Squares in the same data, particularly those of long period planets. It is also shown to be up to a few orders of magnitude faster for high cadence light curves, enabling rapid large-scale searches. Through application of CETRA to Transiting Exoplanet Survey Satellite short cadence data, we recover the three planets in the HD 101581 system with improved significance. In particular, the transit signal of the previously unvalidated planet TOI-6276.03 is enhanced from ${\rm SNR}=7.9$ to ${\rm SNR}=16.0$, which means it may now meet the criteria for statistical validation. CETRA's speed and sensitivity make it well-suited for current and future exoplanet surveys, particularly in the search for Earth analogues. Our implementation of this algorithm uses NVIDIA's CUDA platform and requires an NVIDIA GPU, it is open-source and available from GitHub and PyPI.
Intra-Cluster Light as a Dynamical Clock for Galaxy Clusters: Insights from the MAGNETICUM, IllustrisTNG, Hydrangea and Horizon-AGN Simulations
2025-03-26
Lucas C. Kimmig, Sarah Brough, Klaus Dolag, Rhea-Silvia Remus, Yannick M. Bahé, Garreth Martin, Annalisa Pillepich, Nina Hatch, Mireia Montes, Syeda Lammim Ahad, Callum Bellhouse, Harley J. Brown, Amaël Ellien, Jesse B. Golden-Marx, Anthony H. Gonzalez, Enrica Iodice, Yolanda Jiménez-Teja, Matthias Kluge, Johan H. Knapen, J. Christopher Mihos, Rossella Ragusa, Marilena Spavone
As the most massive nodes of the cosmic web, galaxy clusters represent the best probes of structure formation. Over time, they grow by accreting and disrupting satellite galaxies, adding those stars to the brightest cluster galaxy (BCG) and the intra-cluster light (ICL). However, the formation pathways of different galaxy clusters can vary significantly. To inform upcoming large surveys, we aim to identify observables that can distinguish galaxy cluster formation pathways. Using four different hydrodynamical simulations, Magneticum, TNG100 of IllustrisTNG, Horizon-AGN, and Hydrangea, we study how the fraction of stellar mass in the BCG and ICL ($f_{ICL+BCG}$) relates to the galaxy cluster mass assembly history. For all simulations, $f_{ICL+BCG}$ is the best tracer for the time at which the cluster has accumulated 50% of its mass ($z_{f}$), performing better than other typical dynamical tracers, such as the subhalo mass fraction, the halo mass, and the center shift. More relaxed clusters have higher $f_{ICL+BCG}$, in rare cases up to 90%, while dynamically active clusters have lower fractions, down to 20%, which we find to be independent of the exact implemented baryonic physics. We determine the average increase in $f_{ICL+BCG}$ from stripping and mergers to be between 3-4% per Gyr. $f_{ICL+BCG}$ is tightly traced by the stellar mass ratio between the BCG and both the second (M12) and fourth (M14) most massive cluster galaxy. The average galaxy cluster has assembled half of its halo mass by $z_{f}=0.67$ (about 6 Gyr ago), though individual histories vary significantly from $z_{f}=0.06$ to $z_{f}=1.77$ (0.8 to 10 Gyr ago). As all four cosmological simulations consistently find that $f_{ICL+BCG}$ is an excellent tracer of the cluster dynamical state, upcoming surveys can leverage measurements of $f_{ICL+BCG}$ to statistically quantify the assembly of the most massive structures.
Variability of Galactic blue supergiants observed with TESS
2025-03-26
Michalis Kourniotis, Lydia S. Cidale, Michaela Kraus, Matias A. Ruiz Diaz, Aldana Alberici Adam
Blue supergiants (BSGs) mediate between the main sequence and the late stages of massive stars, which makes them valuable for assessing the physics that drives the stars across the diverse evolutionary channels. By exploring correlations between the parameters of BSGs and their variability properties, we aim to improve the constraints on the models of the evolved star structure and on the physics of the post-main-sequence evolution. We conducted a variability study of 41 BSGs with known spectroscopic parameters in the Galaxy using photometry from the Transiting Exoplanet Survey Satellite. We described the time domain of the stars by means of three statistical measures and extracted frequencies via iterative pre-whitening. Alongside, we investigated the stochastic low-frequency (SLF) variability, which manifests itself in all amplitude spectra. We report a positive correlation between the amplitude of variability and the stellar luminosity. For log(L/L$_{\odot}\lesssim 5$), stars display frequencies that match the rotational one, suggesting that variability is driven by surface spots and/or features embedded in the wind. For log(L/L$_{\odot}\gtrsim 5$), variables of the $\alpha$ Cyg class manifest themselves with their diverse and/or time-variant properties. Moreover, we report a positive correlation between the SLF variability amplitude and $T_{\rm eff}$, indicating an influential role that the stellar age plays on the emergence of the background signal beyond the main sequence. Positive, though weak, correlation is observed between the intrinsic brightness and the SLF variability amplitude, similar to the findings in the Large Magellanic Cloud, which suggests an in common excitation mechanism that depends only mildly on metallicity. Exceptionally, the $\alpha$ Cyg variables display a suppressed SLF variability that prompts to the interior changes that the evolving stars undergo.
Generating Synthetic Data with Formal Privacy Guarantees: State of the Art and the Road Ahead
2025-03-26
Viktor Schlegel, Anil A Bharath, Zilong Zhao, Kevin Yee
Privacy-preserving synthetic data offers a promising solution to harness segregated data in high-stakes domains where information is compartmentalized for regulatory, privacy, or institutional reasons. This survey provides a comprehensive framework for understanding the landscape of privacy-preserving synthetic data, presenting the theoretical foundations of generative models and differential privacy followed by a review of state-of-the-art methods across tabular data, images, and text. Our synthesis of evaluation approaches highlights the fundamental trade-off between utility for down-stream tasks and privacy guarantees, while identifying critical research gaps: the lack of realistic benchmarks representing specialized domains and insufficient empirical evaluations required to contextualise formal guarantees. Through empirical analysis of four leading methods on five real-world datasets from specialized domains, we demonstrate significant performance degradation under realistic privacy constraints ($\epsilon \leq 4$), revealing a substantial gap between results reported on general domain benchmarks and performance on domain-specific data. %Our findings highlight key challenges including unaccounted privacy leakage, insufficient empirical verification of formal guarantees, and a critical deficit of realistic benchmarks. These challenges underscore the need for robust evaluation frameworks, standardized benchmarks for specialized domains, and improved techniques to address the unique requirements of privacy-sensitive fields such that this technology can deliver on its considerable potential.
TAR: Teacher-Aligned Representations via Contrastive Learning for Quadrupedal Locomotion
2025-03-26
Amr Mousa, Neil Karavis, Michele Caprio, Wei Pan, Richard Allmendinger
Quadrupedal locomotion via Reinforcement Learning (RL) is commonly addressed using the teacher-student paradigm, where a privileged teacher guides a proprioceptive student policy. However, key challenges such as representation misalignment between the privileged teacher and the proprioceptive-only student, covariate shift due to behavioral cloning, and lack of deployable adaptation lead to poor generalization in real-world scenarios. We propose Teacher-Aligned Representations via Contrastive Learning (TAR), a framework that leverages privileged information with self-supervised contrastive learning to bridge this gap. By aligning representations to a privileged teacher in simulation via contrastive objectives, our student policy learns structured latent spaces and exhibits robust generalization to Out-of-Distribution (OOD) scenarios, surpassing the fully privileged "Teacher". Results showed accelerated training by 2x compared to state-of-the-art baselines to achieve peak performance. OOD scenarios showed better generalization by 40 percent on average compared to existing methods. Additionally, TAR transitions seamlessly into learning during deployment without requiring privileged states, setting a new benchmark in sample-efficient, adaptive locomotion and enabling continual fine-tuning in real-world scenarios. Open-source code and videos are available at https://ammousa.github.io/TARLoco/.
Comprehensive Manuscript Assessment with Text Summarization Using 69707 articles
2025-03-26
Qichen Sun, Yuxing Lu, Kun Xia, Li Chen, He Sun, Jinzhuo Wang
Rapid and efficient assessment of the future impact of research articles is a significant concern for both authors and reviewers. The most common standard for measuring the impact of academic papers is the number of citations. In recent years, numerous efforts have been undertaken to predict citation counts within various citation windows. However, most of these studies focus solely on a specific academic field or require early citation counts for prediction, rendering them impractical for the early-stage evaluation of papers. In this work, we harness Scopus to curate a significantly comprehensive and large-scale dataset of information from 69707 scientific articles sourced from 99 journals spanning multiple disciplines. We propose a deep learning methodology for the impact-based classification tasks, which leverages semantic features extracted from the manuscripts and paper metadata. To summarize the semantic features, such as titles and abstracts, we employ a Transformer-based language model to encode semantic features and design a text fusion layer to capture shared information between titles and abstracts. We specifically focus on the following impact-based prediction tasks using information of scientific manuscripts in pre-publication stage: (1) The impact of journals in which the manuscripts will be published. (2) The future impact of manuscripts themselves. Extensive experiments on our datasets demonstrate the superiority of our proposed model for impact-based prediction tasks. We also demonstrate potentials in generating manuscript's feedback and improvement suggestions.
Analysis of Information Digestion Differences among Players in Online C2C Markets
2025-03-26
Jun Sashihara, Teruaki Hayashi
In recent years, the magnitude of consumer-to-consumer (C2C) markets have grown significantly, highlighting the increasing significance of trust between buyers and sellers. However, the specific aspects of product information that facilitate effective communication and trust building in C2C markets remain poorly understood. This study examines the concept of information digestion-the process through which information is accurately understood-and aims to elucidate differences in the information digestion processes of sellers and buyers to clarify the role of product page information. To address this, we conducted two experiments: a questionnaire survey involving 400 subjects and a conjoint analysis with eye-tracking for 15 participants. Initially, we selected eight sample products from four distinct product categories based on the Foote, Cone, and Belding (FCB) grid and compared the product information components considered important by sellers and buyers. Subsequently, we created 12 types of product pages that varied in the combination of four attributes: title, price, product description, and image. Experiment 1 revealed significant differences in the perceived importance of product page components between buyers and sellers. It also demonstrated that product categories and transaction experience influenced the importance assigned to these components, particularly for buyers. Results from Experiment 2 showed that buyers prioritize product descriptions and identified two distinct buyer groups based on their information digestion patterns: one that carefully reads and digests information and another that processes information more rapidly. These findings enhance our understanding of trust-building mechanisms in online C2C markets and provide practical insights for platform designers and market participants.
Refining Time Series Anomaly Detectors using Large Language Models
2025-03-26
Alan Yang, Yulin Chen, Sean Lee, Venus Montes
Time series anomaly detection (TSAD) is of widespread interest across many industries, including finance, healthcare, and manufacturing. Despite the development of numerous automatic methods for detecting anomalies, human oversight remains necessary to review and act upon detected anomalies, as well as verify their accuracy. We study the use of multimodal large language models (LLMs) to partially automate this process. We find that LLMs can effectively identify false alarms by integrating visual inspection of time series plots with text descriptions of the data-generating process. By leveraging the capabilities of LLMs, we aim to reduce the reliance on human effort required to maintain a TSAD system
Assembly line balancing considering stochastic task times and production defects
2025-03-26
Gazi Nazia Nur, Mohammad Ahnaf Sadat, Basit Mahmud Shahriar
In this paper, we address the inherent limitations in traditional assembly line balancing, specifically the assumptions that task times are constant and no defective outputs occur. These assumptions often do not hold in practical scenarios, leading to inefficiencies. To address these challenges, we introduce a framework utilizing an "adjusted processing time" approach based on the distributional information of both processing times and defect occurrences. We validate our framework through the analysis of two case studies from existing literature, demonstrating its robustness and adaptability. Our framework is characterized by its simplicity, both in understanding and implementation, marking a substantial advancement in the field. It presents a viable and efficient solution for industries seeking to enhance operational efficiency through improved resource allocation.
Hybrid Multi-Stage Learning Framework for Edge Detection: A Survey
2025-03-26
Mark Phil Pacot, Jayno Juventud, Gleen Dalaorao
Edge detection remains a fundamental yet challenging task in computer vision, especially under varying illumination, noise, and complex scene conditions. This paper introduces a Hybrid Multi-Stage Learning Framework that integrates Convolutional Neural Network (CNN) feature extraction with a Support Vector Machine (SVM) classifier to improve edge localization and structural accuracy. Unlike conventional end-to-end deep learning models, our approach decouples feature representation and classification stages, enhancing robustness and interpretability. Extensive experiments conducted on benchmark datasets such as BSDS500 and NYUDv2 demonstrate that the proposed framework outperforms traditional edge detectors and even recent learning-based methods in terms of Optimal Dataset Scale (ODS) and Optimal Image Scale (OIS), while maintaining competitive Average Precision (AP). Both qualitative and quantitative results highlight enhanced performance on edge continuity, noise suppression, and perceptual clarity achieved by our method. This work not only bridges classical and deep learning paradigms but also sets a new direction for scalable, interpretable, and high-quality edge detection solutions.
2025-03-26
William Claster, Suhas KM, Dhairya Gundechia
We propose Adaptive Integrated Layered Attention (AILA), a neural network architecture that combines dense skip connections with different mechanisms for adaptive feature reuse across network layers. We evaluate AILA on three challenging tasks: price forecasting for various commodities and indices (S&P 500, Gold, US dollar Futures, Coffee, Wheat), image recognition using the CIFAR-10 dataset, and sentiment analysis on the IMDB movie review dataset. In all cases, AILA matches strong deep learning baselines (LSTMs, Transformers, and ResNets), achieving it at a fraction of the training and inference time. Notably, we implement and test two versions of the model - AILA-Architecture 1, which uses simple linear layers as the connection mechanism between layers, and AILA-Architecture 2, which implements an attention mechanism to selectively focus on outputs from previous layers. Both architectures are applied in a single-task learning setting, with each model trained separately for individual tasks. Results confirm that AILA's adaptive inter-layer connections yield robust gains by flexibly reusing pertinent features at multiple network depths. The AILA approach thus presents an extension to existing architectures, improving long-range sequence modeling, image recognition with optimised computational speed, and SOTA classification performance in practice.
A New Hope for Obscured AGN: The PRIMA-NewAthena Alliance
2025-03-25
Luigi Barchiesi, F. J. Carrera, C. Vignali, F. Pozzi, L. Marchetti, C. Gruppioni, I. Delvecchio, L. Bisigello, F. Calura, J. Aird, M. Vaccari
Understanding the AGN-galaxy co-evolution, feedback processes, and the evolution of Black Hole Accretion rate Density (BHAD) requires accurately estimating the contribution of obscured Active Galactic Nuclei (AGN). However, detecting these sources is challenging due to significant extinction at the wavelengths typically used to trace their emission. We evaluate the capabilities of the proposed far-infrared observatory PRIMA and its synergies with the X-ray observatory NewAthena in detecting AGN and in measuring the BHAD. Starting from X-ray background synthesis models, we simulate the performance of NewAthena and of PRIMA in Deep and Wide surveys. Our results show that the combination of these facilities is a powerful tool for selecting and characterising all types of AGN. While NewAthena is particularly effective at detecting the most luminous, the unobscured, and the moderately obscured AGN, PRIMA excels at identifying heavily obscured sources, including Compton-thick AGN (of which we expect 7500 detections per deg$^2$). We find that PRIMA will detect 60 times more sources than Herschel over the same area and will allow us to accurately measure the BHAD evolution up to z=8, better than any current IR or X-ray survey, finally revealing the true contribution of Compton-thick AGN to the BHAD evolution.
Backlighting extended gas halos around luminous red galaxies: kinematic Sunyaev-Zel'dovich effect from DESI Y1 x ACT
2025-03-25
Bernardita Ried Guachalla, Emmanuel Schaan, Boryana Hadzhiyska, Simone Ferraro, Jessica N. Aguilar, Steven Ahlen, Nicholas Battaglia, Davide Bianchi, Richard Bond, David Brooks, Todd Claybaugh, William R. Coulton, Axel de la Macorra, Mark J. Devlin, Arjun Dey, Peter Doel, Jo Dunkley, Kevin Fanning, Jaime Forero-Romero, Enrique Gaztañaga, Satya Gontcho A Gontcho, Gaston Gutierrez, Julien Guy, J. Colin Hill, Klaus Honscheid, Stephanie Juneau, Theodore Kisner, Anthony Kremin, Andrew Lambert, Martin Landriau, Laurent Le Guillou, Niall MacCrann, Marc Manera, Aaron Meisner, Ramon Miquel, Kavilan Moodley, John Moustakas, Tony Mroczkowski, Adam D. Myers, Michael D. Niemack, Gustavo Niz, Nathalie Palanque-Delabrouille, Will Percival, Ignasi Pérez-Ràfols, Claire Poppett, Francisco Prada, Frank J. Qu, Graziano Rossi, Eusebio Sanchez, David Schlegel, Michael Schubnell, Hee-Jong Seo, Cristóbal Sifón, David N. Spergel, David Sprayberry, Gregory Tarlé, Mariana Vargas-Magaña, Eve M. Vavagiakis, Benjamin A. Weaver, Edward J. Wollack, Pauline Zarrouk
The gas density profile around galaxies, shaped by feedback and affecting the galaxy lensing signal, is imprinted on the cosmic microwave background (CMB) by the kinematic Sunyaev-Zel'dovich effect (kSZ). We precisely measure this effect ($S/N\approx 10$) via velocity stacking with $825,283$ spectroscopically confirmed luminous red galaxies (LRG) from the Dark Energy Spectroscopic Instrument (DESI) Y1 survey, which overlap with the Atacama Cosmology Telescope (ACT) Data Release 6 temperature maps over $\geq 4,000 \text{deg}^2$. We explore the kSZ dependence with various galaxy parameters. We find no significant trend with redshift, but clear trends with stellar mass and absolute magnitude in $g$, $r$, and $z$ bands. We highlight new challenges when comparing data and hydrodynamical simulations. Our simple and most conservative analysis suggests that the gas is more extended than the dark matter (99.5% confidence, i.e. PTE = 0.005). It also hints at a preference for galaxy formation models with more feedback (Illustris $z=0.5$, PTE = 0.37) rather than less (Illustris TNG $z=0.8$, PTE = 0.045), though with less statistical significance. In all cases, a free multiplicative amplitude was fit to the simulated profiles, and further modeling work is required to firm up these conclusions. We find consistency between kSZ profiles around spectroscopic and photometric LRG, with comparable statistical power, thus increasing our confidence in the photometric analysis. Additionally, we present the first kSZ measurement around DESI Y1 bright galaxy sample (BGS) and emission-line galaxies (ELG), whose features match qualitative expectations. Finally, we forecast $S/N \sim 50$ for future stacked kSZ measurements using data from ACT, DESI Y3, and Rubin Observatory. These measurements will serve as an input for galaxy formation models and baryonic uncertainties in galaxy lensing.
Early Career Researcher Input to the European Strategy for Particle Physics Update: White Paper
2025-03-25
Jan-Hendrik Arling, Alexander Burgman, Christina Dimitriadi, Ulrich Einhaus, Axel Gallén, Abdelhamid Haddad, Laura Huhta, Armin Ilg, Jan Klamka, Elizabeth Long, Thomas Madlener, Arnau Morancho Tardà, Emanuela Musumeci, Krzysztof Mękała, Elena Pompa Pacchi, Marvin Pfaff, Daniel Reichelt, Leonhard Reichenbach, Birgit Stapf, Francesco P. Ucci, Erik Wallin, Harriet Watson, Sagar Vidya Addepalli, Bruno Alves, Robert Mihai Amarinei, Ricardo Barrué, Lydia Brenner, Giacomo Da Molin, Arturo de Giorgi, Bohdan Dudar, Francesco Giuli, Andrea Gurgone, César Jesús-Valls, Antoine Laudrain, Martin J. Losekamm, Rafał Masełek, Wrishik Naskar, Miquel Nebot-Guinot, Marko Pesut, Thomas Pöschl, Efrain P. Segarra, Rebecca Taylor, Pavel Vana, Hannah Wakeling, Aidan R. Wiederhold
This document, written by early career researchers (ECRs) in particle physics, aims to represent the perspectives of the European ECR community and serves as input for the 2025--2026 update of the European Strategy for Particle Physics. With input from a community-wide survey, it highlights key challenges faced by ECRs -- career stability, funding access and long-term research opportunities -- while proposing policy recommendations and targeted initiatives. It underscores the importance of practices fostering diverse, equitable, inclusive and healthy workplaces, as well as of stronger ECR communities, and highlights how effective communication and interdisciplinary collaborations reinforce the societal relevance of particle physics and promote continued support for large-scale and long-term projects. Finally, the future of both collider and beyond-collider experiments is addressed, emphasising the critical role of ECRs in shaping future projects. The ECR contribution is formed of two parts: the ten-page executive summary submitted as input to the European Strategy for Particle Physics Update and, as backup document, this extended white paper providing additional context.
An Overview of Low-Rank Structures in the Training and Adaptation of Large Models
2025-03-25
Laura Balzano, Tianjiao Ding, Benjamin D. Haeffele, Soo Min Kwon, Qing Qu, Peng Wang, Zhangyang Wang, Can Yaras
The rise of deep learning has revolutionized data processing and prediction in signal processing and machine learning, yet the substantial computational demands of training and deploying modern large-scale deep models present significant challenges, including high computational costs and energy consumption. Recent research has uncovered a widespread phenomenon in deep networks: the emergence of low-rank structures in weight matrices and learned representations during training. These implicit low-dimensional patterns provide valuable insights for improving the efficiency of training and fine-tuning large-scale models. Practical techniques inspired by this phenomenon, such as low-rank adaptation (LoRA) and training, enable significant reductions in computational cost while preserving model performance. In this paper, we present a comprehensive review of recent advances in exploiting low-rank structures for deep learning and shed light on their mathematical foundations. Mathematically, we present two complementary perspectives on understanding the low-rankness in deep networks: (i) the emergence of low-rank structures throughout the whole optimization dynamics of gradient and (ii) the implicit regularization effects that induce such low-rank structures at convergence. From a practical standpoint, studying the low-rank learning dynamics of gradient descent offers a mathematical foundation for understanding the effectiveness of LoRA in fine-tuning large-scale models and inspires parameter-efficient low-rank training strategies. Furthermore, the implicit low-rank regularization effect helps explain the success of various masked training approaches in deep neural networks, ranging from dropout to masked self-supervised learning.
Guarding against artificial intelligence--hallucinated citations: the case for full-text reference deposit
2025-03-25
Alex Glynn
The tendency of generative artificial intelligence (AI) systems to "hallucinate" false information is well-known; AI-generated citations to non-existent sources have made their way into the reference lists of peer-reviewed publications. Here, I propose a solution to this problem, taking inspiration from the Transparency and Openness Promotion (TOP) data sharing guidelines, the clash of generative AI with the American judiciary, and the precedent set by submissions of prior art to the United States Patent and Trademark Office. Journals should require authors to submit the full text of each cited source along with their manuscripts, thereby preventing authors from citing any material whose full text they cannot produce. This solution requires limited additional work on the part of authors or editors while effectively immunizing journals against hallucinated references.
Nordic perspective on System Integrity Protection Schemes in relation to capacity allocation
2025-03-25
Gabriel Malmer, Arvid Rolander, Emil Hillberg, Olof Samuelsson, Susanne Ackeby, Lars Nordström
The urgent need to address climate change prompts societies worldwide to adopt carbon neutral energy and electrification. To facilitate this, a range of technologies and policies will be needed. Alternatives to traditional power grid reinforcement, such as grid-enhancing technologies and system automation, are particularly attractive due to their potentially low cost and fast deployment time. One alternative is System Integrity Protection Schemes (SIPS) - automatic and curative remedial actions (RAs) which can boost grid transfer capacities without compromising with reliability since they can act faster than manual control. The use of SIPS however is scattered, with limited coordination between countries, and the full potential of using SIPS for capacity enhancement is not yet realized. The aim of this paper is to provide a case study and comparison of SIPS in the Nordic countries, particularly in relation to capacity allocation. It also seeks to harmonize terminology relating to ancillary services, RAs, and SIPS. Finally, it examines and compares the inclusion of RAs and SIPS in different Capacity Calculation Methodologies (CCMs). In both main EU CCMs - Net Transfer Capacity (NTC) and Flow-Based (FB) - RAs play a pronounced role. The paper is based on a survey and interviews with Nordic stakeholders, along with a literature review and analysis of public data. The results indicate a large variation in SIPS use across the Nordics. Regarding terminology, we suggest that SIPS is a subcategory of RAs which overlaps with ancillary services. Concerning CCMs, NTC is unable to fully represent capacity constraints in meshed AC systems, which in turn hinders systematic capacity enhancement using RAs. FB on the other hand explicitly includes RAs in the capacity domain. A lower bound for the economic value of RAs can be calculated, amounting to 11.5 million EUR in the Nordics in Nov and Dec 2024.
A Systematic Review of EEG-based Machine Intelligence Algorithms for Depression Diagnosis, and Monitoring
2025-03-25
Amir Nassibi, Christos Papavassiliou, Ildar Rakhmatulin, Danilo Mandic, S. Farokh Atashzar
Depression disorder is a serious health condition that has affected the lives of millions of people around the world. Diagnosis of depression is a challenging practice that relies heavily on subjective studies and, in most cases, suffers from late findings. Electroencephalography (EEG) biomarkers have been suggested and investigated in recent years as a potential transformative objective practice. In this article, for the first time, a detailed systematic review of EEG-based depression diagnosis approaches is conducted using advanced machine learning techniques and statistical analyses. For this, 938 potentially relevant articles (since 1985) were initially detected and filtered into 139 relevant articles based on the review scheme 'preferred reporting items for systematic reviews and meta-analyses (PRISMA).' This article compares and discusses the selected articles and categorizes them according to the type of machine learning techniques and statistical analyses. Algorithms, preprocessing techniques, extracted features, and data acquisition systems are discussed and summarized. This review paper explains the existing challenges of the current algorithms and sheds light on the future direction of the field. This systematic review outlines the issues and challenges in machine intelligence for the diagnosis of EEG depression that can be addressed in future studies and possibly in future wearable technologies.
SemEval-2025 Task 9: The Food Hazard Detection Challenge
2025-03-25
Korbinian Randl, John Pavlopoulos, Aron Henriksson, Tony Lindgren, Juli Bakagianni
In this challenge, we explored text-based food hazard prediction with long tail distributed classes. The task was divided into two subtasks: (1) predicting whether a web text implies one of ten food-hazard categories and identifying the associated food category, and (2) providing a more fine-grained classification by assigning a specific label to both the hazard and the product. Our findings highlight that large language model-generated synthetic data can be highly effective for oversampling long-tail distributions. Furthermore, we find that fine-tuned encoder-only, encoder-decoder, and decoder-only systems achieve comparable maximum performance across both subtasks. During this challenge, we gradually released (under CC BY-NC-SA 4.0) a novel set of 6,644 manually labeled food-incident reports.
Fail Faster: Staging and Fast Randomness for High-Performance PBT
2025-03-25
Cynthia Richey, Joseph W. Cutler, Harrison Goldstein, Benjamin C. Pierce
Property-based testing (PBT) relies on generators for random test cases, often constructed using embedded domain specific languages, which provide expressive combinators for building and composing generators. The effectiveness of PBT depends critically on the speed of these generators. However, careful measurements show that the generator performance of widely used PBT libraries falls well short of what is possible, due principally to (1) the abstraction overhead of their combinator-heavy style and (2) suboptimal sources of randomness. We characterize, quantify, and address these bottlenecks. To eliminate abstraction overheads, we propose a technique based on multi-stage programming, dubbed Allegro. We apply this technique to leading generator libraries in OCaml and Scala 3, significantly improving performance. To quantify the performance impact of the randomness source, we carry out a controlled experiment, replacing the randomness in the OCaml PBT library with an optimized version. Both interventions exactly preserve the semantics of generators, enabling precise, pointwise comparisons. Together, these improvements find bugs up to $13\times$ faster.
The cross-correlation between soft X-rays and galaxies A new benchmark for galaxy evolution models
2025-03-25
Johan Comparat, Andrea Merloni, Gabriele Ponti, Soumya Shreeram, Yi Zhang, Thomas H. Reiprich, Ang Liu, Riccardo Seppi, Xiaoyuan Zhang, Nicolas Clerc, Andrina Nicola, Kirpal Nandra, Mara Salvato, Nicola Malavasi
This article presents the construction and validation of complete stellar mass-selected, volume-limited galaxy samples using the Legacy Survey (data release 10) galaxy catalogs, covering $\sim16,800$ deg$^2$ of extra-galactic sky, and extending to redshift $z<0.35$. We measure the correlation function of these galaxies with tiny statistical uncertainties at the percent level and systematic uncertainties up to 5\%. A 4-parameter halo occupation distribution (HOD) model is fitted to retrieve the population of host halos, yielding results on the stellar to halo mass relation consistent with the current models of galaxy formation and evolution. Using these complete galaxy samples, we measure and analyze the cross-correlation (X-corr) between galaxies and all soft X-ray photons observed by SRG/eROSITA in the 0.5-2 keV band over $\sim13,000$ deg$^2$. The cross correlation measurements have unprecedented sub-percent statistical uncertainty and ~5-10\% systematic uncertainty. An extension to the halo model is introduced to interpret the X-corr, decomposing contributions from X-ray point sources, hot gas (CGM), satellites, and the 2-halo term. For low stellar mass thresholds ($\log M^*/M_{\odot}>$ 10, 10.25, 10.5), we find that the point source emission dominates the X-corr at small separation ($r<80$kpc). Then, in the range ($802$Mpc), the 2-halo term becomes dominant. Interestingly, there is no scale at which the CGM dominates. In the range ($20
The COSMOS Wall at z$\sim$0.73: star-forming galaxies and their evolution in different environments
2025-03-25
S. Zhou, A. Iovino, M. Longhetti, M. Scodeggio, S. Bardelli, M. Bolzonella, O. Cucciati, F. R. Ditrani, A. Finoguenov, L. Pozzetti, M. Salvato, L. Tasca, D. Vergani, E. Zucca
We present a study of the evolution of star-forming galaxies within the so-called Wall structure at z$\sim$0.73 in the field of the COSMOS survey. We use a sample of star-forming galaxies from a comprehensive range of environments and across a wide stellar mass range and discuss the correlation between the environment and the galaxy's internal properties, including its metallicity from the present-day gas-phase value measured from emission-lines and its past evolution as imprinted in its stellar populations. We build a simple yet comprehensive galaxy chemical evolution model, which is constrained by the gas-phase metallicities, stacked spectra and photometry of galaxies to reach a full description of the galaxies' past star formation and chemical evolution histories in different environments. We reproduce the `downsizing' formation of galaxies in both their star formation histories and chemical evolution histories at $z\sim0.73$ so that more massive galaxies tend to grow their stellar mass and become enriched in metals earlier than less massive ones. In addition, the current gas-phase metallicity of a galaxy and its past evolution correlate with the environment it inhabits. Galaxies in groups, especially massive groups that have X-ray counterparts, tend to have higher gas-phase metallicities and are enriched in metals earlier than field galaxies of similar stellar mass. Galaxies in the highest stellar mass bin and located in X-ray groups exhibit a more complex and varied chemical composition. Strangulation due to interactions with the group environment, leading to an early cessation of gas supply, may have driven the faster mass growth and chemical enrichment observed in group galaxies. Additionally, the removal of metal-enriched gas could play a key role in the evolution of the most massive galaxies. Alternative mechanisms other than environmental processes are also discussed.
Hot Rocks Survey II: The thermal emission of TOI-1468 b reveals a hot bare rock
2025-03-25
E. A. Meier Valdés, B. -O. Demory, H. Diamond-Lowe, J. M. Mendonça, P. C. August, M. Fortune, N. H. Allen, D. Kitzmann, A. Gressier, M. Hooton, K. D. Jones, L. A. Buchhave, N. Espinoza, C. E. Fisher, N. P. Gibson, K. Heng, J. Hoeijmakers, B. Prinoth, A. D. Rathcke, J. D. Eastman
Terrestrial exoplanets orbiting nearby small, cool stars known as M dwarfs are well suited for atmospheric characterisation. Given the strong XUV irradiation from M dwarf host stars, orbiting exoplanets are thought to be unable to retain primordial H/He-dominated atmospheres. However, the survivability of heavier secondary atmospheres is currently unknown. The aim of the Hot Rocks Survey programme is to determine if exoplanets can retain secondary atmospheres in the presence of M dwarf hosts. Among the sample of 9 exoplanets in the programme, here we aim to determine whether TOI-1468 b has a substantial atmosphere or is consistent with a low-albedo bare rock. The James Webb Space Telescope provides an opportunity to characterise the thermal emission with MIRI at 15 $\mu$m. TOI-1468 b's occultation was observed three times. We compare our observations to atmospheric models including varying amounts of CO$_{2}$ and H$_{2}$O. The observed occultation depth for the individual visits are 239$\pm$52 ppm, 341$\pm$53 ppm and 357$\pm$52 ppm. A joint fit yields an occultation depth of 311$\pm$31 ppm. The thermal emission is mostly consistent with no atmosphere and zero Bond albedo at 1.65-$\sigma$ confidence level or a blackbody at a brightness temperature of 1024$\pm$78 K. A pure CO$_{2}$ or H$_{2}$O atmosphere with a surface pressure above 1 bar is ruled out over 3-$\sigma$. Surprisingly, TOI-1468 b presents a surface marginally hotter than expected, hinting at an additional source of energy on the planet. It could originate from a temperature inversion, induction heating or be an instrumental artifact. The results within the Hot Rocks Survey build on the legacy of studying the atmospheres of exoplanets around M dwarfs. The outcome of this survey will prove useful to the large-scale survey on M dwarfs recently approved by the STScI.
Event cameras have gained increasing attention for 3D reconstruction due to their high temporal resolution, low latency, and high dynamic range. They capture per-pixel brightness changes asynchronously, allowing accurate reconstruction under fast motion and challenging lighting conditions. In this survey, we provide a comprehensive review of event-driven 3D reconstruction methods, including stereo, monocular, and multimodal systems. We further categorize recent developments based on geometric, learning-based, and hybrid approaches. Emerging trends, such as neural radiance fields and 3D Gaussian splatting with event data, are also covered. The related works are structured chronologically to illustrate the innovations and progression within the field. To support future research, we also highlight key research gaps and future research directions in dataset, experiment, evaluation, event representation, etc.
Removal of high-voltage-induced surface charges by ultraviolet light
2025-03-25
M. T. Ziemba, J. Phrompao, F. Jung, I. M. Rabey, G. Rempe
A plethora of studies ranging from precision physics to quantum information employ ions, highly excited neutral atoms or polar molecules as tools. Motivated by the need for miniaturization, scalability and controllability, the particles are often trapped close to electrodes imprinted on dielectric surfaces. However, such geometry makes the particles susceptible to undesired surface charges. To understand how high voltage applied to the electrodes mediates the buildup of charges, we use a scanning Kelvin probe in an ultrahigh vacuum to investigate the electric potential landscape of an electrostatic molecule trap with a microstructured electrode array. We find that the surface becomes positively charged after applying voltages to the electrodes, leading to offset and patch electric potentials across the surface. Removal of these charges is demonstrated using ultraviolet light. The dynamics of this process, including the dependence on light intensity, wavelength and sample type, are investigated in detail. We find the charge removal to be faster for shorter ultraviolet wavelengths, with an additional polymer coating providing further enhancement. Our findings may be useful in a wide spectrum of experiments with electric-field-sensitive particles.
Homogenized harmonic balance finite element method for nonlinear eddy current simulations of fast corrector magnets
2025-03-25
Jan-Magnus Christmann, Laura Anna Maria D'Angelo, Herbert De Gersem, Sven Pfeiffer, Sajjad Hussain Mirza, Matthias Thede, Alexander Aloev, Holger Schlarb
This paper develops a homogenized harmonic balance finite element method (HomHBFEM) to predict the dynamic behavior of magnets with fast excitation cycles, including eddy current and skin effects. A homogenization technique for laminated yokes avoids resolving the individual laminates and the skin depth in the finite element (FE) mesh. Instead, the yoke is represented by a bulk surrogate material with frequency-dependent parameters. The ferromagnetic saturation of the yoke at higher excitation currents is tackled by a harmonic balance method, which accounts for a coupled set of frequency components. Thereby, a computationally expensive time-stepping of the eddy-current field problem and a convolution of the homogenized yoke model are avoided. The HomHBFEM enables, for the first time, to conduct nonlinear simulations of fast corrector magnets, which are embedded in a fast orbit feedback system to counteract orbit disturbances over a broad frequency spectrum, and thus guarantee a stable light-source operation. The results show the impact of the nonlinearity on the phase lag and the field attenuation as well as the eddy current losses at frequencies up to 65 kHz. The numerical validation for a C-dipole magnet example shows that the HomHBFEM achieves a sufficient accuracy at an affordable computational effort, with simulation times of a few hours. In comparison, standard 3D transient FE simulations need to resolve the lamination thickness and the skin depth in space and the largest relevant frequency in time, which leads to a two to three orders of magnitude larger mesh and prohibitive computational effort, with simulation times of a few weeks on a contemporary computer server.
The progress of artificial intelligence (AI) has made sophisticated methods available for cyberattacks and red team activities. These AI attacks can automate the process of penetrating a target or collecting sensitive data. The new methods can also accelerate the execution of the attacks. This review article examines the use of AI technologies in cybersecurity attacks. It also tries to describe typical targets for such attacks. We employed a scoping review methodology to analyze articles and identify AI methods, targets, and models that red teams can utilize to simulate cybercrime. From the 470 records screened, 11 were included in the review. Various cyberattack methods were identified, targeting sensitive data, systems, social media profiles, passwords, and URLs. The application of AI in cybercrime to develop versatile attack models presents an increasing threat. Furthermore, AI-based techniques in red team use can provide new ways to address these issues.
Enabling Rapid Shared Human-AI Mental Model Alignment via the After-Action Review
2025-03-25
Edward Gu, Ho Chit Siu, Melanie Platt, Isabelle Hurley, Jaime Peña, Rohan Paleja
In this work, we present two novel contributions toward improving research in human-machine teaming (HMT): 1) a Minecraft testbed to accelerate testing and deployment of collaborative AI agents and 2) a tool to allow users to revisit and analyze behaviors within an HMT episode to facilitate shared mental model development. Our browser-based Minecraft testbed allows for rapid testing of collaborative agents in a continuous-space, real-time, partially-observable environment with real humans without cumbersome setup typical to human-AI interaction user studies. As Minecraft has an extensive player base and a rich ecosystem of pre-built AI agents, we hope this contribution can help to facilitate research quickly in the design of new collaborative agents and in understanding different human factors within HMT. Our mental model alignment tool facilitates user-led post-mission analysis by including video displays of first-person perspectives of the team members (i.e., the human and AI) that can be replayed, and a chat interface that leverages GPT-4 to provide answers to various queries regarding the AI's experiences and model details.
Notes on Non-Compact Maps and the Importance of Bernstein Numbers
2025-03-25
David E Edmunds, Jan Lang
In this review paper we study non-compact operators and embeddings between function spaces, highlighting interesting phenomena and the significance of Bernstein numbers. In particular, we demonstrate that for non-compact maps the usual $s$-numbers (e.g., approximation, Kolmogorov, and entropy numbers) fail to reveal finer structural properties, and one must instead consider concepts such as strict singularity and Bernstein numbers.
Motif Counting in Complex Networks: A Comprehensive Survey
2025-03-25
Haozhe Yin, Kai Wang, Wenjie Zhang, Yizhang He, Ying Zhang, Xuemin Lin
Motif counting plays a crucial role in understanding the structural properties of networks. By computing motif frequencies, researchers can draw key insights into the structural properties of the underlying network. As networks become increasingly complex, different graph models have been proposed, giving rise to diverse motif patterns. These variations introduce unique computational challenges that require specialized algorithms tailored to specific motifs within different graph structures. This survey provides a comprehensive and structured overview of motif counting techniques across general graphs, heterogeneous graphs, and hypergraphs. We categorize existing algorithms according to their underlying computational strategies, emphasizing key similarities and distinctions. In addition to reviewing current methodologies, we examine their strengths, limitations, and computational trade-offs. Furthermore, we explore future directions in motif counting, including scalable implementations to improve efficiency in large-scale networks, algorithmic adaptations for dynamic, temporal, and attributed graphs, and deeper integration with large language models (LLMs) and graph-based retrieval-augmented generation (GraphRAG). By offering a detailed analysis of these approaches, this survey aims to support researchers and practitioners in advancing motif counting for increasingly complex network data.
ZTF-observed late-time signals of pre-ZTF transients
2025-03-25
Jacco H. Terwel, Kate Maguire, Jesper Sollerman, Phil Wiseman, Tracy X. Chen, Matthew J. Graham, Russ R. Laher, Reed Riddle, Niharika Sravan
With large-scale surveys such as the Zwicky Transient Facility (ZTF), it has become possible to obtain a well-sampled light curve spanning the full length of the survey for any discovery within the survey footprint. Similarly, any transient within the footprint that was first detected before the start of the survey will likely have a large number of post-transient observations, making them excellent targets to search for the presence of late-time signals, in particular due to interaction with circumstellar material (CSM). We search for late-time signals in a sample of 7718 transients, mainly supernovae (SNe), which were first detected during the 10 years before the start of ZTF, aiming to find objects showing signs of late-time interaction with CSM. We find one candidate whose late-time signal is best explained by late-time CSM interaction, with the signal being around 300 days after transient discovery. A thin, distant shell containing $\lesssim5$ M$_\odot$ of material could explain the recovered signal. We also find five objects whose late-time signal is best explained by faint nuclear transients occurring in host nuclei close to the pre-ZTF transient locations. Finally, we find two objects where it is difficult to determine whether the signal is from a nuclear transient or due to late-time CSM interaction $>5$ years after the SN. This study demonstrates the ability of large-scale surveys to find faint transient signals for a variety of objects, uncovering a population of previously unknown sources. However, the large number of non-detections show that strong late-time CSM interaction occurring years after the SN explosion is extremely rare.
ZodiAq: An Isotropic Flagella-Inspired Soft Underwater Drone for Safe Marine Exploration
2025-03-25
Anup Teejo Mathew, Daniel Feliu-Talegon, Yusuf Abdullahi Adamu, Ikhlas Ben Hmida, Costanza Armanini, Cesare Stefanini, Lakmal Seneviratne, Federico Renda
The inherent challenges of robotic underwater exploration, such as hydrodynamic effects, the complexity of dynamic coupling, and the necessity for sensitive interaction with marine life, call for the adoption of soft robotic approaches in marine exploration. To address this, we present a novel prototype, ZodiAq, a soft underwater drone inspired by prokaryotic bacterial flagella. ZodiAq's unique dodecahedral structure, equipped with 12 flagella-like arms, ensures design redundancy and compliance, ideal for navigating complex underwater terrains. The prototype features a central unit based on a Raspberry Pi, connected to a sensory system for inertial, depth, and vision detection, and an acoustic modem for communication. Combined with the implemented control law, it renders ZodiAq an intelligent system. This paper details the design and fabrication process of ZodiAq, highlighting design choices and prototype capabilities. Based on the strain-based modeling of Cosserat rods, we have developed a digital twin of the prototype within a simulation toolbox to ease analysis and control. To optimize its operation in dynamic aquatic conditions, a simplified model-based controller has been developed and implemented, facilitating intelligent and adaptive movement in the hydrodynamic environment. Extensive experimental demonstrations highlight the drone's potential, showcasing its design redundancy, embodied intelligence, crawling gait, and practical applications in diverse underwater settings. This research contributes significantly to the field of underwater soft robotics, offering a promising new avenue for safe, efficient, and environmentally conscious underwater exploration.
One Framework to Rule Them All: Unifying RL-Based and RL-Free Methods in RLHF
2025-03-25
Xin Cai
In this article, we primarily examine a variety of RL-based and RL-free methods designed to address Reinforcement Learning from Human Feedback (RLHF) and Large Reasoning Models (LRMs). We begin with a concise overview of the typical steps involved in RLHF and LRMs. Next, we reinterpret several RL-based and RL-free algorithms through the perspective of neural structured bandit prediction, providing a clear conceptual framework that uncovers a deeper connection between these seemingly distinct approaches. Following this, we briefly review some core principles of reinforcement learning, drawing attention to an often-overlooked aspect in existing RLHF studies. This leads to a detailed derivation of the standard RLHF objective within a full RL context, demonstrating its equivalence to neural structured bandit prediction. Finally, by reinvestigating the principles behind Proximal Policy Optimization (PPO), we pinpoint areas needing adjustment, which culminates in the introduction of the Generalized Reinforce Optimization (GRO) framework, seamlessly integrating RL-based and RL-free methods in RLHF. We look forward to the community's efforts to empirically validate GRO and invite constructive feedback.
A high-resolution survey of protoplanetary disks in Lupus and the nature of compact disks
2025-03-25
Osmar M. Guerra-Alvarado, Nienke van der Marel, Jonathan P. Williams, Paola Pinilla, Gijs D. Mulders, Michiel Lambrechts, Mariana Sanchez
Most of the exoplanets discovered in our galaxy to date orbit low-mass stars, which tend to host small disks in their early stages. To better elucidate the link between planet formation and disk substructures, observational biases should be reduced through observations of these small, faint disks at the highest resolution using the Atacama Large Millimeter Array (ALMA). In this work, we present new high-resolution (0.03-0.04") ALMA observations at 1.3 mm of 33 disks located in the Lupus star-forming region. Combining archival data and previously published work, we provide a near-complete high resolution image library of 73 protoplanetary (Class II) disks in Lupus. This enable us to measure dust disk radii down to a limit of 0.6 au and analyze intensity profiles using visibility modeling. We show that 67% of Lupus protoplanetary disks have dust radii smaller than 30 au, with new substructures detected in 11, showing some of the shortest separation gaps. The size-luminosity relation in Lupus aligns well with a drift-dominated dust evolution scenario and, for the most compact disks (< 30 au), we found dust masses ranging from 0.3 to 26.3 Earth masses. Assuming that the detected substructures were dynamical effects of planets, we estimated the planet masses to range from 20 to 2000 Earth masses with separations between 2 to 74 au. Our results indicate that two-thirds of the protoplanetary disks in Lupus are smooth, and compact, with substructures being more prominent in the few larger disks. These compact disks are consistent with drift-dominated evolution, with their masses and optical depths suggesting that they may have already experienced some planet formation, with most of the small solids converted into planetesimals and planets. This makes them prime candidates, for explaining the formation and origin of super-Earths. [Abridged]
Rubber Wear: History, Mechanisms, and Perspectives
2025-03-25
R. Xu, W. Sheng, F. Zhou, B. N. J. Persson
This paper presents a comprehensive review of wear mechanisms, with a primary focus on rubber wear under sliding conditions. Beginning with classical wear theories, including the Archard and Rabinowicz models, we analyze their applicability to both metals and elastomers and discuss extensions relevant to elastic contact and multiscale surface roughness. Various experimental studies on rubber, such as abrasion by sharp tools, erosion by particle impact, and wear during sliding on rough substrates, are reviewed and interpreted. The effects of environmental factors, such as oxygen and lubrication, are also discussed. In addition, we review a recently proposed wear model based on fatigue crack growth within asperity contact regions, which accounts for energy dissipation and multiscale interactions. This model explains the wide variability in the wear coefficient and predicts wear rates consistent with experimental observations across a broad range of conditions. It also explains the formation mechanism and provides the size distribution of rubber wear particles, from micrometer-scale up to severe cut-chip-chunk (CCC) wear. The results have implications for tire wear and the environmental impact of microscale wear debris (microplastics).
Systematic Reanalysis of KMTNet Microlensing Events, Paper II: Two New Planets in Giant-Source Events
2025-03-25
Hongjing Yang, Jennifer C. Yee, Jiyuan Zhang, Chung-Uk Lee, Dong-Jin Kim, Ian A. Bond, Andrzej Udalski, Kyu-Ha Hwang, Weicheng Zang, Qiyue Qian, Andrew Gould, Shude Mao, Michael D. Albrow, Sun-Ju Chung, Cheongho Han, Youn Kil Jung, Yoon-Hyun Ryu, In-Gu Shin, Yossi Shvartzvald, Sang-Mok Cha, Hyoun-Woo Kim, Seung-Lee Kim, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge, Fumio Abe, Ken Bando, David P. Bennett, Aparna Bhattacharya, Akihiko Fukui, Ryusei Hamada, Shunya Hamada, Naoto Hamasaki, Yuki Hirao, Stela Ishitani Silva, Yoshitaka Itow, Naoki Koshimoto, Yutaka Matsubara, Shota Miyazaki, Yasushi Muraki, Tutumi Nagai, Kansuke Nunota, Greg Olmschenk, Clément Ranc, Nicholas J. Rattenbury, Yuki Satoh, Takahiro Sumi, Daisuke Suzuki, Sean K. Terry, Paul . J. Tristram, Aikaterini Vandorou, Hibiki Yama, Przemek Mróz, Jan Skowron, Radoslaw Poleski, Michał K. Szymański, Igor Soszyński, Paweł Pietrukowicz, Szymon Kozłowski, Krzysztof Ulaczyk, Krzysztof A. Rybicki, Patryk Iwanek, Marcin Wrona
In this work, we continue to apply the updated KMTNet tender-love care (TLC) photometric pipeline to historical microlensing events. We apply the pipeline to a subsample of events from the KMTNet database, which we refer to as the giant source sample. Leveraging the improved photometric data, we conduct a systematic search for anomalies within this sample. The search successfully uncovers four new planet-like anomalies and recovers two previously known planetary signals. After detailed analysis, two of the newly discovered anomalies are confirmed as clear planets: KMT-2019-BLG-0578 and KMT-2021-BLG-0736. Their planet-to-host mass ratios are $q\sim4\times10^{-3}$ and $q\sim1\times10^{-4}$, respectively. Another event, OGLE-2018-BLG-0421 (KMT-2018-BLG-0831), remains ambiguous. Both a stellar companion and a giant planet in the lens system could potentially explain the observed anomaly. The anomaly signal of the last event, MOA-2022-BLG-038 (KMT-2022-BLG-2342), is attributed to an extra source star. Within this sample, our procedure doubles the number of confirmed planets, demonstrating a significant enhancement in the survey sensitivity.
Persistent exclusion process with time-periodic drive
2025-03-25
Deepsikha Das, Sakuntala Chatterjee
We study a persistent exclusion process with time-periodic external potential on a 1d periodic lattice through numerical simulations. A set of run-and-tumble particles move on a lattice of length $L$ and tumbling probability $\gamma \ll 1$ and interact among each other via hardcore exclusion. The effect of the external potential has been modeled as a special site where the tumbling probability is $1$. We call it a "defect" site and move its location along the ring lattice with speed $u$. In the case of $\gamma=0$ the system goes to a jammed state when there is no defect. But introduction of the moving defect creates a strongly phase-separated state where almost all active particles are present in a single large cluster, for small and moderate $u$. This striking effect is caused by the long-range velocity correlation of the active particles, induced by the moving defect. For large $u$, a single large cluster is no longer stable and breaks into multiple smaller clusters. For nonzero $\gamma$ a competition develops between the timescales associated with tumbling and defect motion. While the moving defect attempts to create long-range velocity order, bulk tumbling tends to randomize the velocity alignment. If $\gamma$ is comparable to $u/L$, then a relatively small number of tumbles take place during the time the moving defect travels through the entire system. In this case, the defect has enough time to restore the order in the system and our simulations show that the long-range order in velocity and density survive for $\gamma$ values in this range. As $\gamma$ increases further, long range order is destroyed and the system develops multiple regions of high and low density. We characterize the density inhomogeneity in this case by measuring subsystem density fluctuations and present a heatmap in the $\gamma$-$u$ plane showing the regions with most pronounced density inhomogeneities.
AI Safety in the Eyes of the Downstream Developer: A First Look at Concerns, Practices, and Challenges
2025-03-25
Haoyu Gao, Mansooreh Zahedi, Wenxin Jiang, Hong Yi Lin, James Davis, Christoph Treude
Pre-trained models (PTMs) have become a cornerstone of AI-based software, allowing for rapid integration and development with minimal training overhead. However, their adoption also introduces unique safety challenges, such as data leakage and biased outputs, that demand rigorous handling by downstream developers. While previous research has proposed taxonomies of AI safety concerns and various mitigation strategies, how downstream developers address these issues remains unexplored. This study investigates downstream developers' concerns, practices and perceived challenges regarding AI safety issues during AI-based software development. To achieve this, we conducted a mixed-method study, including interviews with 18 participants, a survey of 86 practitioners, and an analysis of 874 AI incidents from the AI Incident Database. Our results reveal that while developers generally demonstrate strong awareness of AI safety concerns, their practices, especially during the preparation and PTM selection phases, are often inadequate. The lack of concrete guidelines and policies leads to significant variability in the comprehensiveness of their safety approaches throughout the development lifecycle, with additional challenges such as poor documentation and knowledge gaps, further impeding effective implementation. Based on our findings, we offer suggestions for PTM developers, AI-based software developers, researchers, and policy makers to enhance the integration of AI safety measures.
KiDS-Legacy: Consistency of cosmic shear measurements and joint cosmological constraints with external probes
2025-03-25
Benjamin Stölzner, Angus H. Wright, Marika Asgari, Catherine Heymans, Hendrik Hildebrandt, Henk Hoekstra, Benjamin Joachimi, Konrad Kuijken, Shun-Sheng Li, Constance Mahony, Robert Reischke, Mijin Yoon, Maciej Bilicki, Pierre Burger, Nora Elisa Chisari, Andrej Dvornik, Christos Georgiou, Benjamin Giblin, Joachim Harnois-Déraps, Priyanka Jalan, Anjitha John William, Shahab Joudaki, Giorgio Francesco Lesci, Laila Linke, Arthur Loureiro, Matteo Maturi, Lauro Moscardini, Nicola R. Napolitano, Lucas Porth, Mario Radovich, Tilman Tröster, Maximilian von Wietersheim-Kramsta, Anna Wittje, Ziang Yan, Yun-Hao Zhang
We present a cosmic shear consistency analysis of the final data release from the Kilo-Degree Survey (KiDS-Legacy). By adopting three tiers of consistency metrics, we compare cosmological constraints between subsets of the KiDS-Legacy dataset split by redshift, angular scale, galaxy colour and spatial region. We also review a range of two-point cosmic shear statistics. With the data passing all our consistency metric tests, we demonstrate that KiDS-Legacy is the most internally consistent KiDS catalogue to date. In a joint cosmological analysis of KiDS-Legacy and DES Y3 cosmic shear, combined with data from the Pantheon+ Type Ia supernovae compilation and baryon acoustic oscillations from DESI Y1, we find constraints consistent with Planck measurements of the cosmic microwave background with $S_8\equiv \sigma_8\sqrt{\Omega_{\rm m}/0.3} = 0.814^{+0.011}_{-0.012}$ and $\sigma_8 = 0.802^{+0.022}_{-0.018}$.
KiDS-Legacy: Cosmological constraints from cosmic shear with the complete Kilo-Degree Survey
2025-03-25
Angus H. Wright, Benjamin Stölzner, Marika Asgari, Maciej Bilicki, Benjamin Giblin, Catherine Heymans, Hendrik Hildebrandt, Henk Hoekstra, Benjamin Joachimi, Konrad Kuijken, Shun-Sheng Li, Robert Reischke, Maximilian von Wietersheim-Kramsta, Mijin Yoon, Pierre Burger, Nora Elisa Chisari, Jelte de Jong, Andrej Dvornik, Christos Georgiou, Joachim Harnois-Déraps, Priyanka Jalan, Anjitha John William, Shahab Joudaki, Giorgio Francesco Lesci, Laila Linke, Arthur Loureiro, Constance Mahony, Matteo Maturi, Lance Miller, Lauro Moscardini, Nicola R. Napolitano, Lucas Porth, Mario Radovich, Peter Schneider, Tilman Tröster, Anna Wittje, Ziang Yan, Yun-Hao Zhang
We present cosmic shear constraints from the completed Kilo-Degree Survey (KiDS), where the cosmological parameter $S_8\equiv\sigma_8\sqrt{\Omega_{\rm m}/0.3} = 0.815^{+0.016}_{-0.021}$, is found to be in agreement ($0.73\sigma$) with results from the Planck Legacy cosmic microwave background experiment. The final KiDS footprint spans $1347$ square degrees of deep nine-band imaging across the optical and near-infrared, along with an extra $23$ square degrees of KiDS-like calibration observations of deep spectroscopic surveys. Improvements in our redshift distribution estimation methodology, combined with our enhanced calibration data and multi-band image simulations, allow us to extend our lensed sample out to a photometric redshift of $z_{\rm B}\leq2.0$. Compared to previous KiDS analyses, the increased survey area and redshift depth results in a $\sim32\%$ improvement in constraining power in terms of $\Sigma_8\equiv\sigma_8\left(\Omega_{\rm m}/0.3\right)^\alpha = 0.821^{+0.014}_{-0.016}$, where $\alpha = 0.58$ has been optimised to match the revised degeneracy direction of $\sigma_8$ and $\Omega_{\rm m}$. We adopt a new physically motivated intrinsic alignment model that depends jointly on the galaxy sample's halo mass and spectral type distributions, and that is informed by previous direct alignment measurements. We also marginalise over our uncertainty on the impact of baryon feedback on the non-linear matter power spectrum. Comparing to previous KiDS analyses, we conclude that the increase seen in $S_8$ primarily results from our improved redshift distribution estimation and calibration, as well as new survey area and improved image reduction. Our companion paper St\"olzner et al. (submitted) presents a full suite of internal and external consistency tests, finding the KiDS-Legacy data set to be the most internally robust sample produced by KiDS to date.
KiDS-Legacy: Redshift distributions and their calibration
2025-03-25
Angus H. Wright, Hendrik Hildebrandt, Jan Luca van den Busch, Maciej Bilicki, Catherine Heymans, Benjamin Joachimi, Constance Mahony, Robert Reischke, Benjamin Stölzner, Anna Wittje, Marika Asgari, Nora Elisa Chisari, Andrej Dvornik, Christos Georgiou, Benjamin Giblin, Henk Hoekstra, Priyanka Jalan, Anjitha John William, Shahab Joudaki, Konrad Kuijken, Giorgio Francesco Lesci, Shun-Sheng Li, Laila Linke, Arthur Loureiro, Matteo Maturi, Lauro Moscardin, Lucas Porth, Mario Radovich, Tilman Tröster, Maximilian von Wietersheim-Kramsta, Ziang Yan, Mijin Yoon, Yun-Hao Zhang
We present the redshift calibration methodology and bias estimates for the cosmic shear analysis of the fifth and final data release (DR5) of the Kilo-Degree Survey (KiDS). KiDS-DR5 includes a greatly expanded compilation of calibrating spectra, drawn from $27$ square degrees of dedicated optical and near-IR imaging taken over deep spectroscopic fields. The redshift distribution calibration leverages a range of new methods and updated simulations to produce the most precise $N(z)$ bias estimates used by KiDS to date. Improvements to our colour-based redshift distribution measurement method (SOM) mean that we are able to use many more sources per tomographic bin for our cosmological analyses, and better estimate the representation of our source sample given the available spec-$z$. We validate our colour-based redshift distribution estimates with spectroscopic cross-correlations (CC). We find that improvements to our cross-correlation redshift distribution measurement methods mean that redshift distribution biases estimated between the SOM and CC methods are fully consistent on simulations, and the data calibration is consistent to better than $2\sigma$ in all tomographic bins.
The fifth data release of the Kilo Degree Survey: Multi-epoch optical/NIR imaging covering wide and legacy-calibration fields
2025-03-25
Angus H. Wright, Konrad Kuijken, Hendrik Hildebrandt, Mario Radovich, Maciej Bilicki, Andrej Dvornik, Fedor Getman, Catherine Heymans, Henk Hoekstra, Shun-Sheng Li, Lance Miller, Nicola R. Napolitano, Qianli Xia, Marika Asgari, Massimo Brescia, Hugo Buddelmeijer, Pierre Burger, Gianluca Castignani, Stefano Cavuoti, Jelte de Jong, Alastair Edge, Benjamin Giblin, Carlo Giocoli, Joachim Harnois-Déraps, Priyanka Jalan, Benjamin Joachimi, Anjitha John William, Shahab Joudaki, Arun Kannawadi, Gursharanjit Kaur, Francesco La Barbera, Laila Linke, Constance Mahony, Matteo Maturi, Lauro Moscardini, Szymon J. Nakoneczny, Maurizio Paolillo, Lucas Porth, Emanuella Puddu, Robert Reischke, Peter Schneider, Mauro Sereno, HuanYuan Shan, Cristóbal Sifón, Benjamin Stölzner, Tilman Tröster, Edwin Valentijn, Jan Luca van den Busch, Gijs Verdoes Kleijn, Anna Wittje, Ziang Yan, Ji Yao, Mijin Yoon, Yun-Hao Zhang
We present the final data release of the Kilo-Degree Survey (KiDS-DR5), a public European Southern Observatory (ESO) wide-field imaging survey optimised for weak gravitational lensing studies. We combined matched-depth multi-wavelength observations from the VLT Survey Telescope and the VISTA Kilo-degree INfrared Galaxy (VIKING) survey to create a nine-band optical-to-near-infrared survey spanning $1347$ deg$^2$. The median $r$-band $5\sigma$ limiting magnitude is 24.8 with median seeing $0.7^{\prime\prime}$. The main survey footprint includes $4$ deg$^2$ of overlap with existing deep spectroscopic surveys. We complemented these data in DR5 with a targeted campaign to secure an additional $23$ deg$^2$ of KiDS- and VIKING-like imaging over a range of additional deep spectroscopic survey fields. From these fields, we extracted a catalogue of $126\,085$ sources with both spectroscopic and photometric redshift information, which enables the robust calibration of photometric redshifts across the full survey footprint. In comparison to previous releases, DR5 represents a $34\%$ areal extension and includes an $i$-band re-observation of the full footprint, thereby increasing the effective $i$-band depth by $0.4$ magnitudes and enabling multi-epoch science. Our processed nine-band imaging, single- and multi-band catalogues with masks, and homogenised photometry and photometric redshifts can be accessed through the ESO Archive Science Portal.
Enhanced Bloom's Educational Taxonomy for Fostering Information Literacy in the Era of Large Language Models
2025-03-25
Yiming Luo, Ting Liu, Patrick Cheong-Iao Pang, Dana McKay, Ziqi Chen, George Buchanan, Shanton Chang
The advent of Large Language Models (LLMs) has profoundly transformed the paradigms of information retrieval and problem-solving, enabling students to access information acquisition more efficiently to support learning. However, there is currently a lack of standardized evaluation frameworks that guide learners in effectively leveraging LLMs. This paper proposes an LLM-driven Bloom's Educational Taxonomy that aims to recognize and evaluate students' information literacy (IL) with LLMs, and to formalize and guide students practice-based activities of using LLMs to solve complex problems. The framework delineates the IL corresponding to the cognitive abilities required to use LLM into two distinct stages: Exploration & Action and Creation & Metacognition. It further subdivides these into seven phases: Perceiving, Searching, Reasoning, Interacting, Evaluating, Organizing, and Curating. Through the case presentation, the analysis demonstrates the framework's applicability and feasibility, supporting its role in fostering IL among students with varying levels of prior knowledge. This framework fills the existing gap in the analysis of LLM usage frameworks and provides theoretical support for guiding learners to improve IL.
The analysis of symmetries is extremely useful across science. In Physics, symmetries are used to derive conservation laws and selection rules for transitions in interacting systems. In the early days of nonlinear optics (NLO), symmetries were used to formulate a set of rules for photonic processes according to the medium's symmetries that are reflected in the NLO coefficient tensor. While this approach was believed to be complete and closed, the field has recently reignited as multi-color ultrashort laser pulses with tailored polarization and spatiotemporal structures become standard in NLO. A more complete theory has been recently emerging, which aims to incorporate all possible dynamical degrees of freedom of light: spin and orbital angular momentum, spatial structure, time-dependent polarizations, temporal envelopes, etc., in addition to the symmetries of the medium. This theoretical development is also accompanied by experimental advances that rely on tailored light beams that can now be generated with ever-increasing complexity, including topologies in real and a variety of synthetic dimensions, carrying poly-chromatic carrier waves, time-dependent varying angular momenta, local-chirality, and more. The nonlinear interactions between light fields with unique symmetries (or asymmetries) and matter is especially appealing, since that holds the key for developing new ultrafast spectroscopies with sub-femtosecond resolution, for exerting exact control over matter, and improving our fundamental understanding of how light and matter interact. We review these recent advances in this expanding field, focusing on the theory, its implications, and seminal experiments. We aim to establish a comprehensive database of symmetries and selection rules governing NLO light-matter interactions within the emerging new formalism, and invite the scientific community to contribute to this effort.
Optimal Parameter Adaptation for Safety-Critical Control via Safe Barrier Bayesian Optimization
2025-03-25
Shengbo Wang, Ke Li, Zheng Yan, Zhenyuan Guo, Song Zhu, Guanghui Wen, Shiping Wen
Safety is of paramount importance in control systems to avoid costly risks and catastrophic damages. The control barrier function (CBF) method, a promising solution for safety-critical control, poses a new challenge of enhancing control performance due to its direct modification of original control design and the introduction of uncalibrated parameters. In this work, we shed light on the crucial role of configurable parameters in the CBF method for performance enhancement with a systematical categorization. Based on that, we propose a novel framework combining the CBF method with Bayesian optimization (BO) to optimize the safe control performance. Considering feasibility/safety-critical constraints, we develop a safe version of BO using the barrier-based interior method to efficiently search for promising feasible configurable parameters. Furthermore, we provide theoretical criteria of our framework regarding safety and optimality. An essential advantage of our framework lies in that it can work in model-agnostic environments, leaving sufficient flexibility in designing objective and constraint functions. Finally, simulation experiments on swing-up control and high-fidelity adaptive cruise control are conducted to demonstrate the effectiveness of our framework.
Membership Inference Attacks on Large-Scale Models: A Survey
2025-03-25
Hengyu Wu, Yang Cao
The adoption of the Large Language Model (LLM) has accelerated dramatically since the ChatGPT from OpenAI went online in November 2022. Recent advances in Large Multimodal Models (LMMs), which process diverse data types and enable interaction through various channels, have expanded beyond the text-to-text limitations of early LLMs, attracting significant and concurrent attention from both researchers and industry. While LLMs and LMMs are starting to spread widely, concerns about their privacy risks are increasing as well. Membership Inference Attacks (MIAs), techniques used to determine whether a particular data point was part of a model's training set, serve as a key metric for assessing the privacy vulnerabilities of machine learning models. Hu et al. show that various machine learning algorithms are vulnerable to MIA. Despite extensive studies on MIAs in traditional models, there remains a lack of systematic surveys addressing their effectiveness and implications in modern large-scale models like LLMs and LMMs. In this paper, we systematically reviewed recent studies of MIA against LLMs and LMMs. We analyzed and categorized each attack based on their methodology and scenario and discussed the limitations in existing research. Additionally, we examine privacy concerns associated with the fine-tuning process. Finally, we provided some suggestions for future research in this direction.
2025-03-25
Zhiping Xiao, Xinyu Wang, Yifang Qin, Zijie Huang, Mason A. Porter, Yizhou Sun
Understanding the evolution of public opinion is crucial for informed decision-making in various domains, particularly public affairs. The rapid growth of social networks, such as Twitter (now rebranded as X), provides an unprecedented opportunity to analyze public opinion at scale without relying on traditional surveys. With the rise of deep learning, Graph Neural Networks (GNNs) have shown great promise in modeling online opinion dynamics. Notably, classical opinion dynamics models, such as DeGroot, can be reformulated within a GNN framework. We introduce Latent Social Dynamical System (LSDS), a novel framework for modeling the latent dynamics of social media users' opinions based on textual content. Since expressed opinions may not fully reflect underlying beliefs, LSDS first encodes post content into latent representations. It then leverages a GraphODE framework, using a GNN-based ODE function to predict future opinions. A decoder subsequently utilizes these predicted latent opinions to perform downstream tasks, such as interaction prediction, which serve as benchmarks for model evaluation. Our framework is highly flexible, supporting various opinion dynamic models as ODE functions, provided they can be adapted into a GNN-based form. It also accommodates different encoder architectures and is compatible with diverse downstream tasks. To validate our approach, we constructed dynamic datasets from Twitter data. Experimental results demonstrate the effectiveness of LSDS, highlighting its potential for future applications. We plan to publicly release our dataset and code upon the publication of this paper.
LogicLearner: A Tool for the Guided Practice of Propositional Logic Proofs
2025-03-25
Amogh Inamdar, Uzay Macar, Michel Vazirani, Michael Tarnow, Zarina Mustapha, Natalia Dittren, Sam Sadeh, Nakul Verma, Ansaf Salleb-Aouissi
The study of propositional logic -- fundamental to the theory of computing -- is a cornerstone of the undergraduate computer science curriculum. Learning to solve logical proofs requires repeated guided practice, but undergraduate students often lack access to on-demand tutoring in a judgment-free environment. In this work, we highlight the need for guided practice tools in undergraduate mathematics education and outline the desiderata of an effective practice tool. We accordingly develop LogicLearner, a web application for guided logic proof practice. LogicLearner consists of an interface to attempt logic proofs step-by-step and an automated proof solver to generate solutions on the fly, allowing users to request guidance as needed. We pilot LogicLearner as a practice tool in two semesters of an undergraduate discrete mathematics course and receive strongly positive feedback for usability and pedagogical value in student surveys. To the best of our knowledge, LogicLearner is the only learning tool that provides an end-to-end practice environment for logic proofs with immediate, judgment-free feedback.
mRNA Folding Algorithms for Structure and Codon Optimization
2025-03-25
Max Ward, Mary Richardson, Mihir Metkar
mRNA technology has revolutionized vaccine development, protein replacement therapies, and cancer immunotherapies, offering rapid production and precise control over sequence and efficacy. However, the inherent instability of mRNA poses significant challenges for drug storage and distribution, particularly in resource-limited regions. Co-optimizing RNA structure and codon choice has emerged as a promising strategy to enhance mRNA stability while preserving efficacy. Given the vast sequence and structure design space, specialized algorithms are essential to achieve these qualities. Recently, several effective algorithms have been developed to tackle this challenge that all use similar underlying principles. We call these specialized algorithms "mRNA folding" algorithms as they generalize classical RNA folding algorithms. A comprehensive analysis of their underlying principles, performance, and limitations is lacking. This review aims to provide an in-depth understanding of these algorithms, identify opportunities for improvement, and benchmark existing software implementations in terms of scalability, correctness, and feature support.
PHEONA: An Evaluation Framework for Large Language Model-based Approaches to Computational Phenotyping
2025-03-25
Sarah Pungitore, Shashank Yadav, Vignesh Subbian
Computational phenotyping is essential for biomedical research but often requires significant time and resources, especially since traditional methods typically involve extensive manual data review. While machine learning and natural language processing advancements have helped, further improvements are needed. Few studies have explored using Large Language Models (LLMs) for these tasks despite known advantages of LLMs for text-based tasks. To facilitate further research in this area, we developed an evaluation framework, Evaluation of PHEnotyping for Observational Health Data (PHEONA), that outlines context-specific considerations. We applied and demonstrated PHEONA on concept classification, a specific task within a broader phenotyping process for Acute Respiratory Failure (ARF) respiratory support therapies. From the sample concepts tested, we achieved high classification accuracy, suggesting the potential for LLM-based methods to improve computational phenotyping processes.
MIRAGE: Multi-model Interface for Reviewing and Auditing Generative Text-to-Image AI
2025-03-25
Matheus Kunzler Maldaner, Wesley Hanwen Deng, Jason Hong, Ken Holstein, Motahhare Eslami
While generative AI systems have gained popularity in diverse applications, their potential to produce harmful outputs limits their trustworthiness and usability in different applications. Recent years have seen growing interest in engaging diverse AI users in auditing generative AI that might impact their lives. To this end, we propose MIRAGE as a web-based tool where AI users can compare outputs from multiple AI text-to-image (T2I) models by auditing AI-generated images, and report their findings in a structured way. We used MIRAGE to conduct a preliminary user study with five participants and found that MIRAGE users could leverage their own lived experiences and identities to surface previously unnoticed details around harmful biases when reviewing multiple T2I models' outputs compared to reviewing only one.
Ruby is a widely used open-source programming language, valued for its simplicity, especially in web development. Despite its popularity, with over one million users on GitHub, little is known about the issues faced by Ruby developers. This study aims to investigate the key topics, trends, and difficulties faced by Ruby developers by analyzing over 498,000 Ruby-related questions on Stack Overflow (SO), followed by a survey of 154 Ruby developers. We employed BERTopic modeling and manual analysis to develop a taxonomy of 35 topics, grouped into six main categories. Our findings reveal that Web Application Development is the most commonly discussed category, while Ruby Gem Installation and Configuration Issues emerged as the most challenging topic. Analysis of trends on SO showed a steady decline. A survey of 154 Ruby developers demonstrated that 31.6% of the participants find the Core Ruby Concepts category particularly difficult, while Application Quality and Security is found to be difficult for over 40% of experienced developers. Notably, a comparison between survey responses and SO metrics highlights a misalignment, suggesting that perceived difficulty and objective indicators from SO differ; emphasizing the need for improved metrics to better capture developer challenges. Our study provides insights about the challenges Ruby developers face and strong implications for researchers.
Student Explanation Strategies in Postsecondary Mathematics and Statistics Education: A Scoping Review
2025-03-25
Huixin Gao, Tanya Evans, Anna Fergusson
This scoping review examines the use of student explanation strategies in postsecondary mathematics and statistics education. We analyzed 46 peer-reviewed articles published between 2014 and 2024, categorizing student explanations into three main types: self-explanation, peer explanation and explanation to fictitious others. The review synthesizes the theoretical underpinnings of these strategies, drawing on the retrieval practice hypothesis, generative learning hypothesis, and social presence hypothesis. Our findings indicate that while self-explanation and explaining to fictitious others foster individual cognitive processes enhancing generative thinking, peer explanation have the potential to combine these benefits with collaborative learning. However, explanation to fictitious others have the potential to mitigate some of the negative impacts that may occur in peer explanation, such as more knowledgeable students dominating peer discussions. The efficacy of the methods varies based on implementation, duration, and context. This scoping review contributes to the growing body of literature on generative learning strategies in postsecondary education and provides insights for optimizing the integration of student explanation techniques in mathematics and statistics.
Silicon-Integrated Next-Generation Plasmonic Devices for Energy-Efficient Semiconductor Applications
2025-03-25
Nasir Alfaraj, Amr S. Helmy
Silicon-based integrated photonics has demonstrated significant advances in miniaturization and performance, yet critical challenges remain in achieving efficient on-chip communication at high bandwidths. Plasmonic devices on silicon and silicon-on-insulator substrates offer a promising solution, enabling subwavelength light confinement and enhanced light-matter interactions through hybrid modes. However, integrating traditional plasmonic materials like gold and silver into silicon-based platforms presents significant challenges, particularly due to their incompatibility with standard silicon processing techniques and their increased optical losses at longer wavelengths, which can hinder performance in near-infrared applications. Plasmonic devices, leveraging advances in device architectures, have the potential to close these performance gaps and enable the next generation of high-speed, on-chip data communication. This review explores recent progress in silicon-integrated hybrid-mode plasmonic devices, highlighting the potential of transparent conductive oxides like indium tin oxide for low-loss and tunable operation. Key device topologies including coupled hybrid plasmonic waveguides and dielectric-based heterostructures are examined, along with fabrication techniques and practical considerations. By critically comparing various plasmonic approaches and identifying their respective advantages and limitations, a path toward realizing the full potential of plasmonics in shaping the future of high-performance, silicon-based integrated photonics is charted.
Peepers & Pixels: Human Recognition Accuracy on Low Resolution Faces
2025-03-25
Xavier Merino, Gabriella Pangelinan, Samuel Langborgh, Michael C. King, Kevin W. Bowyer
Automated one-to-many (1:N) face recognition is a powerful investigative tool commonly used by law enforcement agencies. In this context, potential matches resulting from automated 1:N recognition are reviewed by human examiners prior to possible use as investigative leads. While automated 1:N recognition can achieve near-perfect accuracy under ideal imaging conditions, operational scenarios may necessitate the use of surveillance imagery, which is often degraded in various quality dimensions. One important quality dimension is image resolution, typically quantified by the number of pixels on the face. The common metric for this is inter-pupillary distance (IPD), which measures the number of pixels between the pupils. Low IPD is known to degrade the accuracy of automated face recognition. However, the threshold IPD for reliability in human face recognition remains undefined. This study aims to explore the boundaries of human recognition accuracy by systematically testing accuracy across a range of IPD values. We find that at low IPDs (10px, 5px), human accuracy is at or below chance levels (50.7%, 35.9%), even as confidence in decision-making remains relatively high (77%, 70.7%). Our findings indicate that, for low IPD images, human recognition ability could be a limiting factor to overall system accuracy.
Federated Learning: A new frontier in the exploration of multi-institutional medical imaging data
2025-03-25
Dominika Ciupek, Maciej Malawski, Tomasz Pieciak
Artificial intelligence has transformed the perspective of medical imaging, leading to a genuine technological revolution in modern computer-assisted healthcare systems. However, ubiquitously featured deep learning (DL) systems require access to a considerable amount of data, facilitating proper knowledge extraction and generalization. Admission to such extensive resources may be hindered due to the time and effort required to convey ethical agreements, set up and carry the acquisition procedures through, and manage the datasets adequately with a particular emphasis on proper anonymization. One of the pivotal challenges in the DL field is data integration from various sources acquired using different hardware vendors, diverse acquisition protocols, experimental setups, and even inter-operator variabilities. In this paper, we review the federated learning (FL) concept that fosters the integration of large-scale heterogeneous datasets from multiple institutions in training DL models. In contrast to a centralized approach, the decentralized FL procedure promotes training DL models while preserving data privacy at each institution involved. We formulate the FL principle and comprehensively review general and dedicated medical imaging aggregation and learning algorithms, enabling the generation of a globally generalized model. We meticulously go through the challenges in constructing FL-based systems, such as data heterogeneity across the institutions, resilience to potential attacks on data privacy, and the variability in computational and communication resources among the entangled sites that might induce efficiency issues of the entire system. Finally, we explore the up-to-date open frameworks for rapid FL-based algorithm prototyping and shed light on future directions in this intensively growing field.
AI Identity, Empowerment, and Mindfulness in Mitigating Unethical AI Use
2025-03-25
Mayssam Tarighi Shaayesteh, Sara Memarian Esfahani, Hossein Mohit
This study examines how AI identity influences psychological empowerment and unethical AI behavior among college students, while also exploring the moderating role of IT mindfulness. Findings show that a strong AI identity enhances psychological empowerment and academic engagement but can also lead to increased unethical AI practices. Crucially, IT mindfulness acts as an ethical safeguard, promoting sensitivity to ethical concerns and reducing misuse of AI. These insights have implications for educators, policymakers, and AI developers, emphasizing For Peer Review the need for a balanced approach that encourages digital engagement without compromising student responsibility. The study also contributes to philosophical discussions of psychological agency, suggesting that empowerment through AI can yield both positive and negative outcomes. Mindfulness emerges as essential in guiding ethical AI interactions. Overall, the research informs ongoing debates on ethics in education and AI, offering strategies to align technological advancement with ethical accountability and responsible use.
Peer Disambiguation in Self-Reported Surveys using Graph Attention Networks
2025-03-25
Ajitesh Srivastava, Aryan Shetty, Eric Rice
Studying peer relationships is crucial in solving complex challenges underserved communities face and designing interventions. The effectiveness of such peer-based interventions relies on accurate network data regarding individual attributes and social influences. However, these datasets are often collected through self-reported surveys, introducing ambiguities in network construction. These ambiguities make it challenging to fully utilize the network data to understand the issues and to design the best interventions. We propose and solve two variations of link ambiguities in such network data -- (i) which among the two candidate links exists, and (ii) if a candidate link exists. We design a Graph Attention Network (GAT) that accounts for personal attributes and network relationships on real-world data with real and simulated ambiguities. We also demonstrate that by resolving these ambiguities, we improve network accuracy, and in turn, improve suicide risk prediction. We also uncover patterns using GNNExplainer to provide additional insights into vital features and relationships. This research demonstrates the potential of Graph Neural Networks (GNN) to advance real-world network data analysis facilitating more effective peer interventions across various fields.
Deploying an Aerial Reconfigurable Intelligent Surface for Vehicle-to-Vehicle Communications (PL: Wykorzystanie powietrznych przełączalnych inteligentnych powierzchni do komunikacji międzypojazdowej)
2025-03-25
Salim Janji
This paper addresses the deployment of a drone equipped with a reconfigurable intelligent surface (RIS), creating a drone relay station (DRS) to enhance the connectivity of vehicle-to-vehicle (V2V) pairs on the ground. The trajectory of the DRS is optimized to quickly reach the best location for maximizing throughput. Additionally, the presence of an interfering node is considered, and an analytical solution is derived to determine the optimal orientation of the DRS at each time step, minimizing interference to the receiver. Simulation results confirm the effectiveness of the proposed framework.
Tomography of the z ~ 2 Circumgalactic Medium using KBSS Galaxy Pairs
2025-03-25
Nikolaus Z. Prusinski, Charles C. Steidel, Yuguang Chen
We present new results on the spatial structure and kinematics of Lya and several far-UV metallic ions in the circumgalactic medium of Keck Baryonic Structure Survey galaxies using foreground/background galaxy pairs with angular separations < 30". Keck/KCWI and Keck/LRIS spectra of 736 background galaxies with z_bg = 2.58 +/- 0.38 probe sightlines through 1033 foreground galaxies (z_fg = 2.03 +/- 0.36) at projected distances 8 < D_tran/kpc < 250. For each ion, we measure equivalent widths (W_lambda) as a function of D_tran and find W_lambda ~ D_tran^(-gamma) with 0.3 < gamma < 0.6. Higher ionization species (C IV) decrease less rapidly and extend to larger D_tran compared to low ions (O I, C II, Si II). Splitting the pair sample into subsets based on foreground galaxy properties, we find W_lambda(C IV) exhibits a strong dependence on stellar mass (M_star) and a weaker dependence on star formation rate. Similarly, W_lambda(Lya) increases with M_star, albeit with more scatter. In 2D, we map the excess Lya and C IV absorption as functions of line-of-sight velocity and D_tran. The Lya velocity centroid varies with D_tran; at D_tran < 50 kpc, Lya emission from the foreground galaxy affects the Lya absorption feature measured in the background galaxy spectrum, resulting in a net blueshifted absorption profile. We construct a two-component model to fit the observed Lya map. The best fit suggests approximately half of the excess Lya absorption arises from a decelerating outflow component with initial velocity v_out ~ 750 km/s; the remainder is associated with a component centered on the galaxy systemic redshift with a D_tran-independent 1D velocity dispersion of sigma_v ~ 220 km/s, consistent with the average foreground galaxy circular velocity.
Reprocessing the NEAT Dataset: Preliminary Results
2025-03-25
C. R. Nugent, J. M. Bauer, O. Benitez, M. Blain, N. D'Souza, S. Garimella, M. Goldwater, Y. Kim, H. C. G. Larsen, T. Linder, K. Mackowiak, Z. McGinnis, E. Pan, C. C. Pedersen, P. Sadhwani, F. Spoto, N. J. Tan, P. Vereš, C. Xue
We have created a new image analysis pipeline to reprocess images taken by the Near Earth Asteroid Tracking survey and have applied it to ten nights of observations. This work is the first large-scale reprocessing of images from an asteroid discovery survey in which thousands of archived images are re-calibrated, searched for minor planets, and resulting observations are reported to the Minor Planet Center. We describe the software used to extract, calibrate, and clean sources from the images, including specific techniques that accommodate the unique features of these archival images. This pipeline is able to find fainter asteroids than the original pipeline.
The Coralscapes Dataset: Semantic Scene Understanding in Coral Reefs
2025-03-25
Jonathan Sauder, Viktor Domazetoski, Guilhem Banc-Prandi, Gabriela Perna, Anders Meibom, Devis Tuia
Coral reefs are declining worldwide due to climate change and local stressors. To inform effective conservation or restoration, monitoring at the highest possible spatial and temporal resolution is necessary. Conventional coral reef surveying methods are limited in scalability due to their reliance on expert labor time, motivating the use of computer vision tools to automate the identification and abundance estimation of live corals from images. However, the design and evaluation of such tools has been impeded by the lack of large high quality datasets. We release the Coralscapes dataset, the first general-purpose dense semantic segmentation dataset for coral reefs, covering 2075 images, 39 benthic classes, and 174k segmentation masks annotated by experts. Coralscapes has a similar scope and the same structure as the widely used Cityscapes dataset for urban scene segmentation, allowing benchmarking of semantic segmentation models in a new challenging domain which requires expert knowledge to annotate. We benchmark a wide range of semantic segmentation models, and find that transfer learning from Coralscapes to existing smaller datasets consistently leads to state-of-the-art performance. Coralscapes will catalyze research on efficient, scalable, and standardized coral reef surveying methods based on computer vision, and holds the potential to streamline the development of underwater ecological robotics.
A Linear Collider Vision for the Future of Particle Physics
2025-03-25
H. Abramowicz, E. Adli, F. Alharthi, M. Almanza-Soto, M. M. Altakach, S Ampudia Castelazo, D. Angal-Kalinin, R. B. Appleby, A. Arbey, O. Arquero, A. Aryshev, S. Asai, D. Attia, J. L. Avila-Jimenez, H. Baer, J. A. Bagger, Y. Bai, I. R. Bailey, C. Balazs, T. Barklow, J. Baudot, P. Bechtle, T. Behnke, A. B. Bellerive, S. Belomestnykh, Y. Benhammou, J. Berenguer-Antequera, M. Berger, M. Berggren, M. Besancon, T. Biekoetter, S. Bilanishvili, B. Bilki, B. Bilki, V. M. Bjelland, C. Blanch, M. Boehler, S. Boogert, M. Boronat, V. Boudry, D. Bourilkov, I. Bozovic, J. Braathen, J. E. Brau, C. Breuning, J. -C. Brient, I. Brock, B. Brudnowski, E. Bulyak, P. N. Burrows, A. Caldwell, F. G. Celiberto, E. Cenni, I. Chaikovska, R. Chehab, J. B. B. Chen, T. Chikamatsu, V. Cilento, P. Colas, M. Coman, F. Cornet, F. Cornet-Gomez, F. Corriveau, J. Cvach, R. D'Arcy, C. Damerell, A. Das, S. Dasu, J. de Blas, M. Demarteau, H. Denizli, R. Dermisek, A. Dhar, S. Dittmaier, F. Djurabekova, E. M. Donegani, A. Doyle, P. Drobniak, T. A. du Pree, H. Duran Yildiz, J. Dutta, S. Easo, G. Eckerlin, U. Einhaus, L. R Evans, J. Farmer, A. Faus Golfe, M. Fernandez, J. C. Fernandez-Ortega, M. Formela, B. Foster, N. Fourches, M. C. Fouz, A. Freitas, C. D. Fu, K. Fujii, J. Fujimoto, B. Fuks, J. Fuster, N. Fuster-Martinez, F. Gaede, S. Ganjour, M. V. Garzelli, R. L. Geng, S. Gessner, I. D. Gialamas, B. Gimeno, J. Goldstein, M. Goncerz, F. J. Gonzalez, D. Gonzalez-Iglesias, S. G. Gori, J. Grames, P. Grannis, L. Gray, G. Grenier, H. Guler, H. E. Haber, N. Hamann, O. Hartbrich, G. Huetsi, S. Heinemeyer, G. Heinrich, C. Hensel, K. Hidaka, S. Hirose, A. H. Hoang, M. J. Hogan, W. Hollik, Y. Horii, R. Hosokawa, S. Huang, D. Hynds, M. I. Idzik, S. Iguro, K. Ikematsu, A. Irles, A. Ishikawa, M. Ishino, I. P. Ivanov, Y. Iwashita, M. Jacewicz, R. Jaramillo, D. Jeans, R. M Jones, W. Kaabi, J. Kalinowski, D. K Kalvik, J. Kaminski, S. Kanemura, K. Kannike, A. Karam, Y. Kato, D. Kaefer, L. Kennedy, S. A. Khan, V. A. Khoze, W. Kilian, J. Klamka, P. Kluit, K. Kong, P. Koppenburg, A. Korsun, A. V. Kotwal, S. Kraml, K. Krueger, T. Kubo, K. Kubo, M. Kucharczyk, N. Kumar, Y. Kurihara, J. Kvasnicka, K. Kyoya, I. Laktineh, A. Latina, F. LeDiberder, A. Levy, M. Liepe, C. A. Lindstrom, J. List, B. List, V. N. Litvinenko, A. Lopez-Virto, X. Lu, T. Luo, M. Lupberger, B. Madison, T. Madlener, J. Maeda, F. Mahmoudi, E. Maina, N. Majernik, P. Martin-Luna, A. Martens, V. J. Martin, L. Marzola, K. T. Matchev, S. Matsumoto, K. Mawatari, J. P. Marquez, M. M. Muehlleitner, K. Mekala, D. Melini, F. Meloni, A. Menandez, K. Mimasu, V. Miralles, W. Mitaroff, V. A. Mitsou, L. Monaco, G. M. Moortgat-Pick, M. Moreno-Llacer, T. Mori, J. M. Moron, S. Morton, D. Moya, M. Mulder, E. Musumeci, V. V. Mytrochenko, N. Nagata, E. Nagy, E. A. Nanni, E. Nardi, S. Narita, M. T. Nunez Pardo de Vera, M. Nojiri, D. Ntounis, O. M. Ogreid, Y. Okada, C. Oleari, M. Olvegard, T. Omori, Y. Onel, H. Ono, W. Ootani, C. Orero, J. Osborne, K. Osterberg, J. Osterhoff, Q. Ouyang, S. Pagan Griso, S. C. Park, J. Pata, J. R. Patterson, R. Poeschl, L. K. Pedraza-Motavita, M. E. Peskin, F. Pilat, I. Polak, W. Porod, K. Potamianos, C. T. Potter, P. Poulose, J. G. Power, D. Protopopescu, H. R. Qi, J Rabara Bailey, M. Raidal, J. P. Ralston, L. Reichenbach, J. Reuter, F. Richard, S. Riemann, R. A. Rimmer, T. Robens, A. Robson, G. Rodrigo, J. Rojo, M. Q. Ruan, S. Rudrabhatla, A. Ruiz Jimeno, T. Saeki, H. Sakai, Y. Sakaki, F. Salvatore, T. Sanuki, T Schoerner, I. Schulthess, M. Schumacher, A. Schwartzman, Y. Seiya, A. Senol, H. Sert, D. Sertore, A. Seryi, K. Sjobak, I. Smiljanic, S. Spannagel, M. Spira, M. Stanitzki, S. Stapnes, J. Strube, A. Subba, T. Suehara, K. Sugizaki, Z. Sun, T. Takahashi, T. Takeshita, T. Tauchi, N. Terunuma, J. Tian, J. Timmermans, M. Titov, R. Tomas Garcia, T. Tong, J. M. Torndal, M. Trautwein, A. Tricoli, K. Tsumura, B. Tuchming, M. Tytgat, A. Ukleja, C. Vallee, A. Vauth, M. Vellasco, C. Vernieri, M. Vicente Barreto Pinto, I. Vidakovic, H. Videau, I. Vila, M. Villaplana, M. Vos, N. Vukasinovic, N. Walker, K. Wandall-Christensen, Y. Wang, Z. Was, T. Watanabe, N. K. Watson, G. Weiglein, J. D. Wells, H. Wennloef, M. Wenskat, J. W. Wetzel, A. P. White, M. Williams, G. W. Wilson, M. Wing, M. Winter, A Winter, T. Wojton, J. C. Wood, X. Xia, K. Yagyu, K. Yamamoto, Y. Yamamoto, H. Yamamoto, A. Yamamoto, S. Yamashita, K. Yokoya, J. Zalesak, A. F. Zarnecki, K. Zembaczynski, D. Zerwas, Y. Zhang, J. Zhang, M. Zielinski, M. Zobov
In this paper we review the physics opportunities at linear $e^+e^-$ colliders with a special focus on high centre-of-mass energies and beam polarisation, take a fresh look at the various accelerator technologies available or under development and, for the first time, discuss how a facility first equipped with a technology mature today could be upgraded with technologies of tomorrow to reach much higher energies and/or luminosities. In addition, we will discuss detectors and alternative collider modes, as well as opportunities for beyond-collider experiments and R\&D facilities as part of a linear collider facility (LCF). The material of this paper will support all plans for $e^+e^-$ linear colliders and additional opportunities they offer, independently of technology choice or proposed site, as well as R\&D for advanced accelerator technologies. This joint perspective on the physics goals, early technologies and upgrade strategies has been developed by the LCVision team based on an initial discussion at LCWS2024 in Tokyo and a follow-up at the LCVision Community Event at CERN in January 2025. It heavily builds on decades of achievements of the global linear collider community, in particular in the context of CLIC and ILC.
Two or three things particle physicists (mis)understand about (pre)heating
2025-03-25
Basabendu Barman, Nicolás Bernal, Javier Rubio
The transition from the end of inflation to a hot, thermal Universe, commonly referred to as (re)heating, is a critical yet often misunderstood phase in early Universe cosmology. This short review aims to provide a comprehensive, conceptually clear, and accessible introduction to the physics of (re)heating, tailored to the particle physics community. We critically examine the standard Boltzmann approach, emphasizing its limitations in capturing the intrinsically non-perturbative and non-linear dynamics that dominate the early stages of energy transfer. These include explosive particle production, inflaton fragmentation, turbulence, and thermalization; phenomena often overlooked in perturbative treatments. We survey a wide range of theoretical tools, from Boltzmann equations to lattice simulations, clarifying when each is applicable and highlighting scenarios where analytic control is still feasible. Special attention is given to model-dependent features such as (pre)heating, the role of fermions, gravitational couplings, and the impact of multifield dynamics. We also discuss exceptional cases, including Starobinsky-like models and instant (pre)heating, where (re)heating proceeds through analytically tractable channels without requiring full non-linear simulations. Ultimately, this review serves both as a practical guide and a cautionary tale, advocating for a more nuanced and physically accurate understanding of this pivotal epoch within the particle physics community.
2025-03-25
A. J. Levan, B. P. Gompertz, G. P. Smith, M. E. Ravasio, G. P. Lamb, N. R. Tanvir
Gravitationally lensed Gamma-ray bursts (GRBs) offer critical advantages over other lensed sources. They can be detected via continuously operating detectors covering most of the sky. They offer extremely high time resolution to determine lensing delays and find short-time delays accurately. They are detectable across most of the visible Universe. However, they are also rare and frequently poorly localized. In this paper, we review searches for gravitational lensing in GRBs and comment on promising avenues for the future. We note that the highly structured jets in GRBs can show variations on sufficiently small scales that, unlike lensing of most transient sources, gravitational lensing in gamma-ray bursts may not be achromatic. Such behavior would weaken the stringent requirements for identifying lensed bursts but would also make robust identification of lensing more challenging. A continuously running search that could identify candidate lensed events in near real-time would enable afterglow searches with current and near-future wide-field optical/IR surveys that could yield the first unambiguous detection of a lensed GRB. The new generation of sensitive X-ray and gamma-ray detectors, such as the Einstein Probe and SVOM, will complement Swift and significantly enhance the number of well-localized gamma-ray and X-ray transients. Tuned strategies could dramatically improve the probability of observing a lensed GRB.
The COSMOS Wall at z ~ 0.73: Quiescent galaxies and their evolution in different environments
2025-03-25
F. R. Ditrani, M. Longhetti, A. Iovino, M. Fossati, S. Zhou, S. Bardelli, M. Bolzonella, O. Cucciati, A. Finoguenov, L. Pozzetti, M. Salvato, M. Scodeggio, L. Tasca, D. Vergani, E. Zucca
The evolution of quiescent galaxies is driven by numerous physical processes, often considered to be related to their stellar mass and environment over cosmic time. Tracing their stellar populations can provide insight into the processes that transformed these galaxies into their observed quiescent state. In particular, higher-redshift galaxies exhibit more pronounced relative age differences. At early stages, even small differences in age remain significant, whereas as galaxies evolve, these differences become harder to detect in the local Universe. The COSMOS Wall is a structure at z ~ 0.73 that contains a large variety of environments, from rich clusters down to field-like regions. This sample offers a great opportunity to study the effect of the environment on the quiescent galaxy population. Leveraging high-quality spectroscopic data from the LEGA-C survey, and photometric data from the COSMOS2020 catalogue, we performed a full-index and photometry fitting of 74 massive quiescent galaxies, deriving their mass-weighted ages, metallicities, and star formation timescales. We characterised the environment in three subsamples: X-ray and non-X-ray groups and a field subsample. We find a decreasing trend in mass-weighted age with increasing environmental density, with galaxies groups > 1 Gyr older than those in the field. Conversely, we do not find any significant difference in stellar metallicity between galaxies in X-ray and non-X-ray groups, while we find galaxies with 0.15 dex higher metallicities in the field. Our results indicate that, at z ~ 0.7, the environment plays a crucial role in shaping the evolution of massive quiescent galaxies, noticeably affecting both their mass-weighted age and star formation timescale. These results support faster quenching mechanisms, at fixed stellar mass, in the dense X-ray-detected groups compared to the field.
2025-03-25
Graham P. Smith, Tessa Baker, Simon Birrer, Christine E. Collins, Jose María Ezquiaga, Srashti Goyal, Otto A. Hannuksela, Phurailatpam Hemantakumar, Martin A. Hendry, Justin Janquart, David Keitel, Andrew J. Levan, Rico K. L. Lo, Anupreeta More, Matt Nicholl, Inés Pastor-Marazuela, Andrés I. Ponte Pérez, Helena Ubach, Laura E. Uronen, Mick Wright, Miguel Zumalacarregui, Federica Bianco, Mesut Çalışkan, Juno C. L. Chan, Elena Colangeli, Benjamin P. Gompertz, Christopher P. Haines, Erin E. Hayes, Bin Hu, Gavin P. Lamb, Anna Liu, Soheb Mandhai, Harsh Narola, Quynh Lan Nguyen, Jason S. C. Poon, Dan Ryczanowski, Eungwang Seo, Anowar J. Shajib, Xikai Shan, Nial Tanvir, Luka Vujeva
We introduce the rapidly emerging field of multi-messenger gravitational lensing - the discovery and science of gravitationally lensed phenomena in the distant universe through the combination of multiple messengers. This is framed by gravitational lensing phenomenology that has grown since the first discoveries in the 20th century, messengers that span 30 orders of magnitude in energy from high energy neutrinos to gravitational waves, and powerful "survey facilities" that are capable of continually scanning the sky for transient and variable sources. Within this context, the main focus is on discoveries and science that are feasible in the next 5-10 years with current and imminent technology including the LIGO-Virgo-KAGRA network of gravitational wave detectors, the Vera C. Rubin Observatory, and contemporaneous gamma/X-ray satellites and radio surveys. The scientific impact of even one multi-messenger gravitational lensing discovery will be transformational and reach across fundamental physics, cosmology and astrophysics. We describe these scientific opportunities and the key challenges along the path to achieving them. This article is the introduction to the Theme Issue of the Philosophical Transactions of The Royal Society A on the topic of Multi-messenger Gravitational Lensing, and describes the consensus that emerged at the associated Theo Murphy Discussion Meeting in March 2024.
Inferring CSM Properties of Type II SNe Using a Magnitude-Limited ZTF Sample
2025-03-25
K-Ryan Hinds, Daniel Perley, Jesper Sollerman, Adam Miller, Christoffer Fremling, Takashi Moriya, Kaustav Das, Yu-Jing Qin, Eric Bellm, Xi Tracy Chen, Michael Coughlin, Wynn Jacobson-Galán, Mansi Kasliwal, Shrinivas Kulkarni, Ashish Mahabal, F. Masci, Priscila, J. Pessi, J. N. Purdum, Reed Riddle, Avinash Singh, Roger Smith, Niharika Sravan
Although all Type II supernovae (SNe) originate from massive stars possessing a hydrogen-rich envelope, their light curve morphology is diverse, reflecting poorly characterised heterogeneity in the physical properties of their progenitor systems. Here, we present a detailed light curve analysis of a magnitude-limited sample of 639 Type II SNe from the Zwicky Transient Facility Bright Transient Survey. Using Gaussian processes, we systematically measure empirical light curve features (e.g. rise times, peak colours and luminosities) in a robust sampling-independent manner. We focus on rise times as they are highly sensitive to pre-explosion progenitor properties, especially the presence of a dense circumstellar medium (CSM) shed by the progenitor in the years immediately pre-explosion. By correlating our feature measurements with physical parameters from an extensive grid of STELLA hydrodynamical models with varying progenitor properties (CSM structure, $\dot M$, $R_{CSM}$ and $M_{ZAMS}$), we quantify the proportion of events with sufficient pre-explosion mass-loss to significantly alter the initial light curve (roughly $M_{CSM} \geq 10^{-2.5} M_{\odot}$) in a highly complete sample of 377 spectroscopically classified Type II SNe. We find that 67 $\pm$ 6\% of observed SNe in our magnitude-limited sample show evidence for substantial CSM ($M_{CSM} \geq 10^{-2.5} M_{\odot}$) close to the progenitor ($R_{CSM} <10^{15}$ cm) at the time of explosion. After applying a volumetric-correction, we find 36$^{+5}_{-7}$\% of all Type II SN progenitors possess substantial CSM within $10^{15}$ cm at the time of explosion. This high fraction of progenitors with dense CSM, supported by photometric and spectroscopic evidence of previous SNe, reveals mass-loss rates significantly exceeding those measured in local group red supergiants or predicted by current theoretical models.
Automated Video-EEG Analysis in Epilepsy Studies: Advances and Challenges
2025-03-25
Valerii A. Zuev, Elena G. Salmagambetova, Stepan N. Djakov, Lev V. Utkin
Epilepsy is typically diagnosed through electroencephalography (EEG) and long-term video-EEG (vEEG) monitoring. The manual analysis of vEEG recordings is time-consuming, necessitating automated tools for seizure detection. Recent advancements in machine learning have shown promise in real-time seizure detection and prediction using EEG and video data. However, diversity of seizure symptoms, markup ambiguities, and limited availability of multimodal datasets hinder progress. This paper reviews the latest developments in automated video-EEG analysis and discusses the integration of multimodal data. We also propose a novel pipeline for treatment effect estimation from vEEG data using concept-based learning, offering a pathway for future research in this domain.
2025-03-25
Tianze Chen, Ricardo Frumento, Giulia Pagnanelli, Gianmarco Cei, Villa Keth, Shahadding Gafarov, Jian Gong, Zihe Ye, Marco Baracca, Salvatore D'Avella, Matteo Bianchi, Yu Sun
In this work, we describe a multi-object grasping benchmark to evaluate the grasping and manipulation capabilities of robotic systems in both pile and surface scenarios. The benchmark introduces three robot multi-object grasping benchmarking protocols designed to challenge different aspects of robotic manipulation. These protocols are: 1) the Only-Pick-Once protocol, which assesses the robot's ability to efficiently pick multiple objects in a single attempt; 2) the Accurate pick-trnsferring protocol, which evaluates the robot's capacity to selectively grasp and transport a specific number of objects from a cluttered environment; and 3) the Pick-transferring-all protocol, which challenges the robot to clear an entire scene by sequentially grasping and transferring all available objects. These protocols are intended to be adopted by the broader robotics research community, providing a standardized method to assess and compare robotic systems' performance in multi-object grasping tasks. We establish baselines for these protocols using standard planning and perception algorithms on a Barrett hand, Robotiq parallel jar gripper, and the Pisa/IIT Softhand-2, which is a soft underactuated robotic hand. We discuss the results in relation to human performance in similar tasks we well.
Toward building next-generation Geocoding systems: a systematic review
2025-03-24
Zhengcong Yin, Daniel W. Goldberg, Binbin Lin, Bing Zhou, Diya Li, Andong Ma, Ziqian Ming, Heng Cai, Zhe Zhang, Shaohua Wang, Shanzhen Gao, Joey Ying Lee, Xiao Li, Da Huo
Geocoding systems are widely used in both scientific research for spatial analysis and everyday life through location-based services. The quality of geocoded data significantly impacts subsequent processes and applications, underscoring the need for next-generation systems. In response to this demand, this review first examines the evolving requirements for geocoding inputs and outputs across various scenarios these systems must address. It then provides a detailed analysis of how to construct such systems by breaking them down into key functional components and reviewing a broad spectrum of existing approaches, from traditional rule-based methods to advanced techniques in information retrieval, natural language processing, and large language models. Finally, we identify opportunities to improve next-generation geocoding systems in light of recent technological advances.
A benchmark companion at the hydrogen-burning limit imaged in the core cluster of the Fornax-Horologium association
2025-03-24
Pengyu Liu, Beth A. Biller, Matthew A. Kenworthy, Clémence Fontanive, Ronan M. P. Kerr, Tomas Stolker, Christian Ginski
Low-mass stellar and substellar companions are indispensable objects for verifying evolutionary models that transition from low-mass stars to planets. Their formation is likely also affected by the surrounding stellar environment. The Fornax-Horologium (FH) association is a recently classified young association in the solar neighbourhood with a dissolving open cluster in its centre. It has not been searched widely for substellar companions and exoplanets with direct imaging. We search for companions of stars in the FH and investigate the formation and evolution of companions during the expansion and dissolution of a star cluster. We conduct a direct-imaging survey of 49 stars in FH with VLT/SPHERE. We present HD 24121B, a companion at the hydrogen-burning limit that orbits star HD 24121 in the core cluster of the FH. The companion is located at a projected angular separation of 2.082 $\pm$ 0.004 arcsec from the central star and has contrasts of $\Delta$H2 = 5.07 $\pm$ 0.05 mag and $\Delta$H3= 4.98 $\pm$ 0.05 mag. We estimate a photometric mass of 74.9 $\pm$ 7.5 $M_J$ for HD 24121B, which places it at the boundary of brown dwarfs and low-mass stars. It is a new benchmark object for evolutionary models at the hydrogen-burning boundary, especially at the age of 30 - 40 Myr. Orbital fitting with joint Gaia and SPHERE relative astrometry found a high eccentricity for HD 24121B. This suggests that it may have undergone dynamic scattering. Future chemical composition measurements such as high-resolution spectroscopic observations will provide more clues on its formation history.
Abstaining classifiers have the option to refrain from providing a prediction for instances that are difficult to classify. The abstention mechanism is designed to trade off the classifier's performance on the accepted data while ensuring a minimum number of predictions. In this setting, often fairness concerns arise when the abstention mechanism solely reduces errors for the majority groups of the data, resulting in increased performance differences across demographic groups. While there exist a bunch of methods that aim to reduce discrimination when abstaining, there is no mechanism that can do so in an explainable way. In this paper, we fill this gap by introducing Interpretable and Fair Abstaining Classifier IFAC, an algorithm that can reject predictions both based on their uncertainty and their unfairness. By rejecting possibly unfair predictions, our method reduces error and positive decision rate differences across demographic groups of the non-rejected data. Since the unfairness-based rejections are based on an interpretable-by-design method, i.e., rule-based fairness checks and situation testing, we create a transparent process that can empower human decision-makers to review the unfair predictions and make more just decisions for them. This explainable aspect is especially important in light of recent AI regulations, mandating that any high-risk decision task should be overseen by human experts to reduce discrimination risks.
2025-03-24
Tan-Khiem Huynh, Malcolm Egan, Giovanni Neglia, Jean-Marie Gorce
Federated learning (FL) is now recognized as a key framework for communication-efficient collaborative learning. Most theoretical and empirical studies, however, rely on the assumption that clients have access to pre-collected data sets, with limited investigation into scenarios where clients continuously collect data. In many real-world applications, particularly when data is generated by physical or biological processes, client data streams are often modeled by non-stationary Markov processes. Unlike standard i.i.d. sampling, the performance of FL with Markovian data streams remains poorly understood due to the statistical dependencies between client samples over time. In this paper, we investigate whether FL can still support collaborative learning with Markovian data streams. Specifically, we analyze the performance of Minibatch SGD, Local SGD, and a variant of Local SGD with momentum. We answer affirmatively under standard assumptions and smooth non-convex client objectives: the sample complexity is proportional to the inverse of the number of clients with a communication complexity comparable to the i.i.d. scenario. However, the sample complexity for Markovian data streams remains higher than for i.i.d. sampling.
Virtual Reality in Manufacturing Education: A Scoping Review Indicating State-of-the-Art, Benefits, and Challenges Across Domains, Levels, and Entities
To address the shortage of a skilled workforce in the U.S. manufacturing industry, immersive Virtual Reality (VR)-based training solutions hold promising potential. To effectively utilize VR to meet workforce demands, it is important to understand the role of VR in manufacturing education. Therefore, we conduct a scoping review in the field. As a first step, we used a 5W1H (What, Where, Who, When, Why, How) formula as a problem-solving approach to define a comprehensive taxonomy that can consider the role of VR from all relevant possibilities. Our taxonomy categorizes VR applications across three key aspects: (1) Domains, (2) Levels, and (3) Entities. Using a systematic literature search and analysis, we reviewed 108 research articles to find the current state, benefits, challenges, and future opportunities of VR in the field. It was found that VR has been explored in a variety of areas and provides numerous benefits to learners. Despite these benefits, its adoption in manufacturing education is limited. This review discusses the identified barriers and provides actionable insights to address them. These insights can enable the widespread usage of immersive technology to nurture and develop a workforce equipped with the skills required to excel in the evolving landscape of manufacturing.
The PHANGS-HST-Halpha Survey: Warm Ionized Gas Physics at High Angular resolution in Nearby GalaxieS with the Hubble Space Telescope
2025-03-24
Rupali Chandar, Ashley T. Barnes, David A. Thilker, Miranda Caputo, Matthew R. Floyd, Adam K. Leroy, Leonardo Ubeda, Janice C. Lee, Médéric Boquien, Daniel Maschmann, Francesco Belfiore, Kathryn Kreckel, Simon C. O. Glover, Ralf S. Klessen, Brent Groves, Daniel A. Dale, Eva Schinnerer, Eric Emsellem, Erik Rosolowsky, Frank Bigiel, Guillermo Blanc, Melanie Chevance, Enrico Congiu, Oleg V. Egorov, Chris Faesi, Kathryn Grasha, Kirsten L. Larson, Laura A. Lopez, Angus Mok, Justus Newmann, Eve Ostriker, Alessandro Razza, Patricia S'anchez-Bl'azquez, Franesco Santoro, Kiayi Sun, Antonio Usero, Elizabeth Watkins, Bradley C. Whitmore, Thomas G. Williams
The PHANGS project is assembling a comprehensive, multi-wavelength dataset of nearby (~5-20 Mpc), massive star-forming galaxies to enable multi-phase, multi-scale investigations into the processes that drive star formation and galaxy evolution. To date, large survey programs have provided molecular gas (CO) cubes with ALMA, optical IFU spectroscopy with VLT/MUSE, high-resolution NUV--optical imaging in five broad-band filters with HST, and infrared imaging in NIRCAM+MIRI filters with JWST. Here, we present PHANGS-HST-Halpha, which has obtained high-resolution (~2-10 pc), narrow-band imaging in the F658N or F657N filters with the HST/WFC3 camera of the warm ionized gas in the first 19 nearby galaxies observed in common by all four of the PHANGS large programs. We summarize our data reduction process, with a detailed discussion of the production of flux-calibrated, Milky Way extinction corrected, continuum-subtracted Halpha maps. PHANGS-MUSE IFU spectroscopy data are used to background subtract the HST-Halpha maps, and to determine the [NII] correction factors for each galaxy. We describe our public data products and highlight a few key science cases enabled by the PHANGS-HST-Halpha observations.
The case for delegated AI autonomy for Human AI teaming in healthcare
2025-03-24
Yan Jia, Harriet Evans, Zoe Porter, Simon Graham, John McDermid, Tom Lawton, David Snead, Ibrahim Habli
In this paper we propose an advanced approach to integrating artificial intelligence (AI) into healthcare: autonomous decision support. This approach allows the AI algorithm to act autonomously for a subset of patient cases whilst serving a supportive role in other subsets of patient cases based on defined delegation criteria. By leveraging the complementary strengths of both humans and AI, it aims to deliver greater overall performance than existing human-AI teaming models. It ensures safe handling of patient cases and potentially reduces clinician review time, whilst being mindful of AI tool limitations. After setting the approach within the context of current human-AI teaming models, we outline the delegation criteria and apply them to a specific AI-based tool used in histopathology. The potential impact of the approach and the regulatory requirements for its successful implementation are then discussed.
The exponential growth of user-generated content on social media platforms has precipitated significant challenges in information management, particularly in content organization, retrieval, and discovery. Hashtags, as a fundamental categorization mechanism, play a pivotal role in enhancing content visibility and user engagement. However, the development of accurate and robust hashtag recommendation systems remains a complex and evolving research challenge. Existing surveys in this domain are limited in scope and recency, focusing narrowly on specific platforms, methodologies, or timeframes. To address this gap, this review article conducts a systematic analysis of hashtag recommendation systems, comprehensively examining recent advancements across several dimensions. We investigate unimodal versus multimodal methodologies, diverse problem formulations, filtering strategies, methodological evolution from traditional frequency-based models to advanced deep learning architectures. Furthermore, we critically evaluate performance assessment paradigms, including quantitative metrics, qualitative analyses, and hybrid evaluation frameworks. Our analysis underscores a paradigm shift toward transformer-based deep learning models, which harness contextual and semantic features to achieve superior recommendation accuracy. Key challenges such as data sparsity, cold-start scenarios, polysemy, and model explainability are rigorously discussed, alongside practical applications in tweet classification, sentiment analysis, and content popularity prediction. By synthesizing insights from diverse methodological and platform-specific perspectives, this survey provides a structured taxonomy of current research, identifies unresolved gaps, and proposes future directions for developing adaptive, user-centric recommendation systems.
From Fragment to One Piece: A Survey on AI-Driven Graphic Design
2025-03-24
Xingxing Zou, Wen Zhang, Nanxuan Zhao
This survey provides a comprehensive overview of the advancements in Artificial Intelligence in Graphic Design (AIGD), focusing on integrating AI techniques to support design interpretation and enhance the creative process. We categorize the field into two primary directions: perception tasks, which involve understanding and analyzing design elements, and generation tasks, which focus on creating new design elements and layouts. The survey covers various subtasks, including visual element perception and generation, aesthetic and semantic understanding, layout analysis, and generation. We highlight the role of large language models and multimodal approaches in bridging the gap between localized visual features and global design intent. Despite significant progress, challenges remain to understanding human intent, ensuring interpretability, and maintaining control over multilayered compositions. This survey serves as a guide for researchers, providing information on the current state of AIGD and potential future directions\footnote{https://github.com/zhangtianer521/excellent\_Intelligent\_graphic\_design}.
Adaptive Machine Learning for Resource-Constrained Environments
2025-03-24
Sebastián A. Cajas Ordóñez, Jaydeep Samanta, Andrés L. Suárez-Cetrulo, Ricardo Simón Carbajo
The Internet of Things is an example domain where data is perpetually generated in ever-increasing quantities, reflecting the proliferation of connected devices and the formation of continuous data streams over time. Consequently, the demand for ad-hoc, cost-effective machine learning solutions must adapt to this evolving data influx. This study tackles the task of offloading in small gateways, exacerbated by their dynamic availability over time. An approach leveraging CPU utilization metrics using online and continual machine learning techniques is proposed to predict gateway availability. These methods are compared to popular machine learning algorithms and a recent time-series foundation model, Lag-Llama, for fine-tuned and zero-shot setups. Their performance is benchmarked on a dataset of CPU utilization measurements over time from an IoT gateway and focuses on model metrics such as prediction errors, training and inference times, and memory consumption. Our primary objective is to study new efficient ways to predict CPU performance in IoT environments. Across various scenarios, our findings highlight that ensemble and online methods offer promising results for this task in terms of accuracy while maintaining a low resource footprint.
Neural network-based emulators for the inference of stellar parameters and elemental abundances represent an increasingly popular methodology in modern spectroscopic surveys. However, these approaches are often constrained by their emulation precision and domain transfer capabilities. Greater generalizability has previously been achieved only with significantly larger model architectures, as demonstrated by Transformer-based models in natural language processing. This observation aligns with neural scaling laws, where model performance predictably improves with increased model size, computational resources allocated to model training, and training data volume. In this study, we demonstrate that these scaling laws also apply to Transformer-based spectral emulators in astronomy. Building upon our previous work with TransformerPayne and incorporating Maximum Update Parametrization techniques from natural language models, we provide training guidelines for scaling models to achieve optimal performance. Our results show that within the explored parameter space, clear scaling relationships emerge. These findings suggest that optimal computational resource allocation requires balanced scaling. Specifically, given a tenfold increase in training compute, achieving an optimal seven-fold reduction in mean squared error necessitates an approximately 2.5-fold increase in dataset size and a 3.8-fold increase in model size. This study establishes a foundation for developing spectral foundational models with enhanced domain transfer capabilities.
Natural Language Processing for Electronic Health Records in Scandinavian Languages: Norwegian, Swedish, and Danish
2025-03-24
Ashenafi Zebene Woldaregay, Jørgen Aarmo Lund, Phuong Dinh Ngo, Mariyam Tayefi, Joel Burman, Stine Hansen, Martin Hylleholt Sillesen, Hercules Dalianis, Robert Jenssen, Lindsetmo Rolf Ole, Karl Øyvind Mikalsen
Background: Clinical natural language processing (NLP) refers to the use of computational methods for extracting, processing, and analyzing unstructured clinical text data, and holds a huge potential to transform healthcare in various clinical tasks. Objective: The study aims to perform a systematic review to comprehensively assess and analyze the state-of-the-art NLP methods for the mainland Scandinavian clinical text. Method: A literature search was conducted in various online databases including PubMed, ScienceDirect, Google Scholar, ACM digital library, and IEEE Xplore between December 2022 and February 2024. Further, relevant references to the included articles were also used to solidify our search. The final pool includes articles that conducted clinical NLP in the mainland Scandinavian languages and were published in English between 2010 and 2024. Results: Out of the 113 articles, 18% (n=21) focus on Norwegian clinical text, 64% (n=72) on Swedish, 10% (n=11) on Danish, and 8% (n=9) focus on more than one language. Generally, the review identified positive developments across the region despite some observable gaps and disparities between the languages. There are substantial disparities in the level of adoption of transformer-based models. In essential tasks such as de-identification, there is significantly less research activity focusing on Norwegian and Danish compared to Swedish text. Further, the review identified a low level of sharing resources such as data, experimentation code, pre-trained models, and rate of adaptation and transfer learning in the region. Conclusion: The review presented a comprehensive assessment of the state-of-the-art Clinical NLP for electronic health records (EHR) text in mainland Scandinavian languages and, highlighted the potential barriers and challenges that hinder the rapid advancement of the field in the region.
Minimax Rate-Optimal Inference for Individualized Quantile Treatment Effects in High-dimensional Models
2025-03-24
Jiachen Sun, Yin Xia
The quantification of treatment effects plays an important role in a wide range of applications, including policy making and bio-pharmaceutical research. In this article, we study the quantile treatment effect (QTE) while addressing two specific types of heterogeneities: (a) personalized heterogeneity, which captures the varying treatment effects for different individuals, and (b) quantile heterogeneity, which accounts for how the impact of covariates varies across different quantile levels. A well-designed debiased estimator for the individualized quantile treatment effect (IQTE) is proposed to capture such heterogeneities effectively. We show that this estimator converges weakly to a Gaussian process as a function of the quantile levels and propose valid statistical inference methods, including the construction of confidence intervals and the development of hypothesis testing decision rules. In addition, the minimax optimality frameworks for these inference procedures are established. Specifically, we derive the minimax optimal rates for the expected length of confidence intervals and the magnitude of the detection boundary for hypothesis testing procedures, illustrating the superiority of the proposed estimator. The effectiveness of our methods is demonstrated through extensive simulations and an analysis of the National Health and Nutrition Examination Survey (NHANES) datasets.
Brown dwarf number density in the JWST COSMOS-Web field
2025-03-24
Amos Y. -A. Chen, Tomotsugu Goto, Cossas K. -W. Wu, Chih-Teng Ling, Seong Jin Kim, Simon C. -C. Ho, Ece Kilerci, Yuri Uno, Terry Long Phan, Yu-Wei Lin, Tsung-Ching Yang, Tetsuya Hashimoto
Brown dwarfs are failed stars with very low mass (13 to 75 $M_J$), and an effective temperature lower than 2500 K. Thus, they play a key role in understanding the gap in the mass function between stars and planets. However, due to their faint nature, previous searches are inevitably limited to the solar neighbourhood (20 pc). To improve our knowledge of the low mass part of the initial stellar mass function and the star formation history of the Milky Way, it is crucial to find more distant brown dwarfs. Using James Webb Space Telescope (JWST) COSMOS-Web data, this study seeks to enhance our comprehension of the physical characteristics of brown dwarfs situated at a distance of kpc scale. The exceptional sensitivity of the JWST enables the detection of brown dwarfs that are up to 100 times more distant than those discovered in the earlier all-sky infrared surveys. The large area coverage of the JWST COSMOS-Web survey allows us to find more distant brown dwarfs than earlier JWST studies with smaller area coverages. To capture prominent water absorption features around 2.7 $\mu$m, we apply two colour criteria, F115W-F277W+10.9. We then select point sources by CLASS_STAR, FLUX_RADIUS, and SPREAD_MODEL criteria. Faint sources are visually checked to exclude possibly extended sources. We conduct SED fitting and MCMC simulations to determine their physical properties and associated uncertainties. Our search reveals 25 T-dwarf and 2 Y-dwarf candidates, more than any previous JWST brown dwarf searches. They are located from 0.3 kpc to 4 kpc away from the Earth. The cumulative number count of our brown dwarf candidates is consistent with the prediction from a standard double exponential model. Three of our brown dwarf candidates were detected by HST, with transverse velocities $12\pm5$ km s$^{-1}$, $12\pm4$ km s$^{-1}$, and $17\pm6$ km s$^{-1}$.
Modern cosmological theories invoke the idea that all structure in the Universe originates from quantum fluctuations. Understanding the quantum-to-classical transition for these fluctuations is of central importance not only for the foundations of quantum theory, but also for observational astronomy. In my contribution, I review the essential features of this transition, emphasizing in particular the role of Alexei Starobinsky.
Large Language Models powered Network Attack Detection: Architecture, Opportunities and Case Study
2025-03-24
Xinggong Zhang, Qingyang Li, Yunpeng Tan, Zongming Guo, Lei Zhang, Yong Cui
Network attack detection is a pivotal technology to identify network anomaly and classify malicious traffic. Large Language Models (LLMs) are trained on a vast corpus of text, have amassed remarkable capabilities of context-understanding and commonsense knowledge. This has opened up a new door for network threat detection. Researchers have already initiated discussions regarding the application of LLMs on specific cyber-security tasks. Unfortunately, there is still a lack of comprehensive elaboration how to mine LLMs' potentials in network threat detections, as well as the opportunities and challenges. In this paper, we mainly focus on the classification of malicious traffic from the perspective of LLMs' capability. We present a holistic view of the architecture of LLM-powered network attack detection, including Pre-training, Fine-tuning, and Detection. Especially, by exploring the knowledge and capabilities of LLM, we identify three distinct roles LLM can act in network attack detection: \textit{Classifier, Encoder, and Predictor}. For each of them, the modeling paradigm, opportunities and challenges are elaborated. Finally, we present our design on LLM-powered DDoS detection as a case study. The proposed framework attains accurate detection on carpet bombing DDoS by exploiting LLMs' capabilities in contextual mining. The evaluation shows its efficacy, exhibiting a nearly $35$\% improvement compared to existing systems.
Benchmarking Multi-modal Semantic Segmentation under Sensor Failures: Missing and Noisy Modality Robustness
2025-03-24
Chenfei Liao, Kaiyu Lei, Xu Zheng, Junha Moon, Zhixiong Wang, Yixuan Wang, Danda Pani Paudel, Luc Van Gool, Xuming Hu
Multi-modal semantic segmentation (MMSS) addresses the limitations of single-modality data by integrating complementary information across modalities. Despite notable progress, a significant gap persists between research and real-world deployment due to variability and uncertainty in multi-modal data quality. Robustness has thus become essential for practical MMSS applications. However, the absence of standardized benchmarks for evaluating robustness hinders further advancement. To address this, we first survey existing MMSS literature and categorize representative methods to provide a structured overview. We then introduce a robustness benchmark that evaluates MMSS models under three scenarios: Entire-Missing Modality (EMM), Random-Missing Modality (RMM), and Noisy Modality (NM). From a probabilistic standpoint, we model modality failure under two conditions: (1) all damaged combinations are equally probable; (2) each modality fails independently following a Bernoulli distribution. Based on these, we propose four metrics-$mIoU^{Avg}_{EMM}$, $mIoU^{E}_{EMM}$, $mIoU^{Avg}_{RMM}$, and $mIoU^{E}_{RMM}$-to assess model robustness under EMM and RMM. This work provides the first dedicated benchmark for MMSS robustness, offering new insights and tools to advance the field. Source code is available at https://github.com/Chenfei-Liao/Multi-Modal-Semantic-Segmentation-Robustness-Benchmark.
The paper focuses on the phenomenon of asymmetric polarization arising in the presence of a dominant group in the network. The existing works in the literature analyze polarization primarily in structurally and quasi-structurally balanced networks. In this work, we introduce generalized quasi-structurally balanced (GQSB) networks, which include both of these networks as special cases. In the presence of a dominant group, a GQSB network has a unique bipartition: the dominant group (and its allies) and the remaining agents. The dominant group's superior influence results in an asymmetry in how the inter-subset antagonistic interactions are perceived by both of the subsets. This, in turn, leads to asymmetry in the final polarized opinions. To model this behavior, we propose a generalized Laplacian flow for undirected GQSB networks with a dominant group and establish necessary and sufficient conditions for achieving asymmetric polarization. The theoretical results presented in this paper are validated through numerical simulations on the Highland Tribes real-world dataset.
LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectives
2025-03-24
Yuan Gao, Shaobo Xia, Pu Wang, Xiaohuan Xi, Sheng Nie, Cheng Wang
LiDAR (Light Detection and Ranging) enables rapid and accurate acquisition of three-dimensional spatial data, widely applied in remote sensing areas such as surface mapping, environmental monitoring, urban modeling, and forestry inventory. LiDAR remote sensing primarily includes data interpretation and LiDAR-based inversion. However, LiDAR interpretation typically relies on dense and precise annotations, which are costly and time-consuming. Similarly, LiDAR inversion depends on scarce supervisory signals and expensive field surveys for annotations. To address this challenge, weakly supervised learning has gained significant attention in recent years, with many methods emerging to tackle LiDAR remote sensing tasks using incomplete, inaccurate, and inexact annotations, as well as annotations from other domains. Existing review articles treat LiDAR interpretation and inversion as separate tasks. This review, for the first time, adopts a unified weakly supervised learning perspective to systematically examine research on both LiDAR interpretation and inversion. We summarize the latest advancements, provide a comprehensive review of the development and application of weakly supervised techniques in LiDAR remote sensing, and discuss potential future research directions in this field.
A Promising Method for Strongly Correlated Electrons in Two Dimensions: Gutzwiller-Guided Density Matrix Renormalization Group
2025-03-24
Hui-Ke Jin, Rong-Yang Sun, Hong-Hao Tu, Yi Zhou
The study of strongly correlated electron systems remains a fundamental challenge in condensed matter physics, particularly in two-dimensional (2D) systems hosting various exotic phases of matter including quantum spin liquids, unconventional superconductivity, and topological orders. Although Density Matrix Renormalization Group (DMRG) has established itself as a pillar for simulating one-dimensional quantum systems, its application to 2D systems has long been hindered by the notorious ``local minimum'' issues. Recent methodological breakthroughs have addressed this challenge by incorporating Gutzwiller-projected wavefunctions as initial states for DMRG simulations. This hybrid approach, referred to as DMRG guided by Gutzwiller-projected wave functions (or Gutzwiller-guided DMRG), has demonstrated remarkable improvements in accuracy, efficiency, and the ability to explore exotic quantum phases such as topological orders. This review examines the theoretical underpinnings of this approach, details key algorithmic developments, and showcases its applications in recent studies of 2D quantum systems.
Food Recommendation With Balancing Comfort and Curiosity
2025-03-24
Yuto Sakai, Qiang Ma
Food is a key pleasure of traveling, but travelers face a trade-off between exploring curious new local food and choosing comfortable, familiar options. This creates demand for personalized recommendation systems that balance these competing factors. To the best of our knowledge, conventional recommendation methods cannot provide recommendations that offer both curiosity and comfort for food unknown to the user at a travel destination. In this study, we propose new quantitative methods for estimating comfort and curiosity: Kernel Density Scoring (KDS) and Mahalanobis Distance Scoring (MDS). KDS probabilistically estimates food history distribution using kernel density estimation, while MDS uses Mahalanobis distances between foods. These methods score food based on how their representation vectors fit the estimated distributions. We also propose a ranking method measuring the balance between comfort and curiosity based on taste and ingredients. This balance is defined as curiosity (return) gained per unit of comfort (risk) in choosing a food. For evaluation the proposed method, we newly collected a dataset containing user surveys on Japanese food and assessments of foreign food regarding comfort and curiosity. Comparing our methods against the existing method, the Wilcoxon signed-rank test showed that when estimating comfort from taste and curiosity from ingredients, the MDS-based method outperformed the Baseline, while the KDS-based method showed no significant differences. When estimating curiosity from taste and comfort from ingredients, both methods outperformed the Baseline. The MDS-based method consistently outperformed KDS in ROC-AUC values.
Rise of the Community Champions: From Reviewer Crunch to Community Power
2025-03-24
Changlun Li, Yao Shi, Yuyu Luo, Nan Tang
Academic publishing is facing a crisis driven by exponential growth in submissions and an overwhelmed peer review system, leading to inconsistent decisions and a severe reviewer shortage. This paper introduces Panvas, a platform that reimagines academic publishing as a continuous, community-driven process. Panvas addresses these systemic failures with a novel combination of economic incentives (paid reviews) and rich interaction mechanisms (multi-dimensional ratings, threaded discussions, and expert-led reviews). By moving beyond the traditional accept/reject paradigm and integrating paper hosting with code/data repositories and social networking, Panvas fosters a meritocratic environment for scholarly communication and presents a radical rethinking of how we evaluate and disseminate scientific knowledge. We present the system design, development roadmap, and a user study plan to evaluate its effectiveness.
Efficient parameter inference in networked dynamical systems via steady states: A surrogate objective function approach integrating mean-field and nonlinear least squares
2025-03-24
Yanna Ding, Malik Magdon-Ismail, Jianxi Gao
In networked dynamical systems, inferring governing parameters is crucial for predicting nodal dynamics, such as gene expression levels, species abundance, or population density. While many parameter estimation techniques rely on time-series data, particularly systems that converge over extreme time ranges, only noisy steady-state data is available, requiring a new approach to infer dynamical parameters from noisy observations of steady states. However, the traditional optimization process is computationally demanding, requiring repeated simulation of coupled ordinary differential equations (ODEs). To overcome these limitations, we introduce a surrogate objective function that leverages decoupled equations to compute steady states, significantly reducing computational complexity. Furthermore, by optimizing the surrogate objective function, we obtain steady states that more accurately approximate the ground truth than noisy observations and predict future equilibria when topology changes. We empirically demonstrate the effectiveness of the proposed method across ecological, gene regulatory, and epidemic networks. Our approach provides an efficient and effective way to estimate parameters from steady-state data and has the potential to improve predictions in networked dynamical systems.
Spectral-line performance of low-frequency radio telescope arrays: SKA-Low stations
2025-03-24
Lister Staveley-Smith
The effects of diffraction, reflection and mutual coupling on the spectral smoothness of radio telescopes becomes increasingly important at low frequencies, where the observing wavelength may be significant compared with the antenna or array dimensions. These effects are important for both traditional parabolic antennas, which are prone to the 'standing wave' phenomenon caused by interference between direct and scattered wavefronts, and aperture arrays, such as the SKA-Low, MWA, HERA, and LOFAR which have more complicated scattering geometries and added dependence on pointing direction (scan angle). Electromagnetic modelling of these effects is computationally intensive and often only possible at coarse frequency resolution. Therefore, using the example of SKA-Low station configurations, we investigate the feasibility of parameterising scattering matrices, and separating antenna and array contributions to telescope chromaticity. This allows deeper insights into the effect on spectral smoothness and frequency-dependent beam patterns of differing antenna configurations. Even for the complicated SKA-Low element design, band-limited delay-space techniques appear to produce similar results to brute-force electromagnetic models, and allow for faster computation of station beam hypercubes (position, frequency and polarisation-dependent point spread functions) at arbitrary spectral resolution. As such techniques could facilitate improvements in the design of low-frequency spectral-line surveys, we conduct a simulated Cosmic Dawn experiment using different observing techniques and station configurations.
Effective risk management solutions become absolutely crucial when financial markets embrace distributed technology and decentralized financing (DeFi). This study offers a thorough survey and comparative analysis of the integration of artificial intelligence (AI) in risk management for distributed arbitrage systems. We examine several modern caching techniques namely in memory caching, distributed caching, and proxy caching and their functions in enhancing performance in decentralized settings. Through literature review we examine the utilization of AI techniques for alleviating risks related to market volatility, liquidity challenges, operational failures, regulatory compliance, and security threats. This comparison research evaluates various case studies from prominent DeFi technologies, emphasizing critical performance metrics like latency reduction, load balancing, and system resilience. Additionally, we examine the problems and trade offs associated with these technologies, emphasizing their effects on consistency, scalability, and fault tolerance. By meticulously analyzing real world applications, specifically centering on the Aave platform as our principal case study, we illustrate how the purposeful amalgamation of AI with contemporary caching methodologies has revolutionized risk management in distributed arbitrage systems.
Flipping of the tidal tails of the Ophiuchus stream due to the decelerating Galactic bar
2025-03-24
Yong Yang, Geraint F. Lewis, Denis Erkal, Ting S. Li, Andrew P. Li, Sarah L. Martell, Lara R. Cullinane, Guilherme Limberg, Daniel B. Zucker, Joss Bland-Hawthorn, Andrew B. Pace, Gary S. Da Costa, Alexander P. Ji, Sergey E. Koposov, Kyler Kuehn, Nora Shipp, Miles Pearson, Sam A. Usman, S5 Collaboration
The Ophiuchus stellar stream presents a puzzle due to its complicated morphology, with a substructure perpendicular to the main track (spur), a broadened tail (fanning), and a shorter than expected angular extent given its old stellar population and short orbital period. The location of the stream approaches the Galactic center, implying a possible connection between its orbit and its unusual morphology. Here we demonstrate that the morphology of Ophiuchus can be attributed to its interaction with the decelerating Galactic bar, which leads to the flipping or transposition of its tidal tails. The short length of the stream is the result of stars stripped in the ancient past still remaining concentrated, and the spur, as well as the fanning, are composed of either leading or trailing tails built up of stars released at different time intervals. Our new spectroscopic data, obtained as part of the Southern Stellar Stream Spectroscopic Survey $(S^5)$, and modeling of Ophiuchus indicate that, in the presence of the bar, an initial leading tail can be redistributed to the trailing side and vice versa, and the morphology of a stream can be reshaped. This result confirms that the Galactic bar plays a vital role in reconstructing the orbital behavior of streams passing close to the central region of the Milky Way.
A Survey of Large Language Model Agents for Question Answering
2025-03-24
Murong Yue
This paper surveys the development of large language model (LLM)-based agents for question answering (QA). Traditional agents face significant limitations, including substantial data requirements and difficulty in generalizing to new environments. LLM-based agents address these challenges by leveraging LLMs as their core reasoning engine. These agents achieve superior QA results compared to traditional QA pipelines and naive LLM QA systems by enabling interaction with external environments. We systematically review the design of LLM agents in the context of QA tasks, organizing our discussion across key stages: planning, question understanding, information retrieval, and answer generation. Additionally, this paper identifies ongoing challenges and explores future research directions to enhance the performance of LLM agent QA systems.
Towards Terminology Management Automation for Arabic
2025-03-24
Mahdi Nasser, Laura Sayyah, Fadi A. Zaraket
This paper presents a method and supporting tools for automation of terminology management for Arabic. The tools extract lists of parallel terminology matching terms in foreign languages to their Arabic counterparts from field specific texts. This has significant implications as it can be used to improve consistent translation and use of terms in specialized Arabic academic books, and provides automated aid for enhancing cross lingual text processing. This automation of terminology management aims to reduce processing time, and ensure use of consistent and correct terminology. The extraction takes advantage of naturally occurring term translations. It considers several candidate phrases of varying lengths that co-occur next to the foreign terms. Then it computes several similarity metrics, including lexicographic, phonetic, morphological, and semantic ones to decide the problem. We experiment with heuristic, machine learning, and ML with post processing approaches. This paper reports on a novel curated dataset for the task, an existing expert reviewed industry parallel corpora, and on the performance of the three approaches. The best approach achieved 94.9% precision and 92.4% recall.
Euclid Quick Data Release (Q1). First detections from the galaxy cluster workflow
2025-03-24
Euclid Collaboration, S. Bhargava, C. Benoist, A. H. Gonzalez, M. Maturi, J. -B. Melin, S. A. Stanford, E. Munari, M. Vannier, C. Murray, S. Maurogordato, A. Biviano, J. Macias-Perez, J. G. Bartlett, F. Pacaud, A. Widmer, M. Meneghetti, B. Sartoris, M. Aguena, G. Alguero, S. Andreon, S. Bardelli, L. Baumont, M. Bolzonella, R. Cabanac, A. Cappi, G. Castignani, C. Combet, J. Comparat, S. Farrens, Z. Ghaffari, P. A. Giles, C. Giocoli, M. Girardi, N. A. Hatch, L. Ingoglia, A. Iovino, G. A. Mamon, S. Mei, L. Moscardini, S. Mourre, J. Odier, G. W. Pratt, P. Tarrío, G. Toni, J. Weller, E. Zucca, N. Aghanim, B. Altieri, A. Amara, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, P. Battaglia, F. Bellagamba, A. Bonchi, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, C. Dolding, H. Dole, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, A. Ealet, S. Escoffier, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, P. Fosalba, S. Fotopoulou, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, B. Gillis, P. Gómez-Alvarez, J. Gracia-Carpio, B. R. Granett, A. Grazian, F. Grupp, L. Guzzo, S. Gwyn, S. V. H. Haugan, J. Hoar, H. Hoekstra, W. Holmes, F. Hormuth, A. Hornstrup, P. Hudelot, S. Ilić, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, M. Kilbinger, B. Kubik, K. Kuijken, M. Kümmel, M. Kunz, H. Kurki-Suonio, Q. Le Boulc'h, A. M. C. Le Brun, D. Le Mignant, P. Liebing, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, E. Medinaceli, M. Melchior, Y. Mellier, E. Merlin, G. Meylan, A. Mora, M. Moresco, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, J. A. Schewtschenko, P. Schneider, T. Schrabback, A. Secroun, E. Sefusatti, G. Seidel, S. Serrano, P. Simon, C. Sirignano, G. Sirri, J. Skottfelt, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, A. Zacchei, G. Zamorani, F. M. Zerbi, V. Allevato, M. Ballardini, E. Bozzo, C. Burigana, P. Casenove, D. Di Ferdinando, J. A. Escartin Vigo, G. Fabbian, L. Gabarra, J. Martín-Fleitas, S. Matthew, N. Mauri, R. B. Metcalf, A. Pezzotta, M. Pöntinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, K. Benson, P. Bergamini, D. Bertacca, M. Bethermin, A. Blanchard, L. Blot, H. Böhringer, S. Borgani, M. L. Brown, S. Bruton, A. Calabro, B. Camacho Quevedo, F. Caro, C. S. Carvalho, T. Castro, Y. Charles, F. Cogato, A. R. Cooray, M. Costanzi, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, J. M. Diego, P. Dimauro, P. -A. Duc, A. Enia, Y. Fang, A. G. Ferrari, P. G. Ferreira, A. Finoguenov, A. Fontana, A. Franco, K. Ganga, J. García-Bellido, T. Gasparetto, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, M. Guidi, C. M. Gutierrez, A. Hall, W. G. Hartley, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, S. Kruk, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, L. Leuzzi, T. I. Liaudat, S. J. Liu, A. Loureiro, G. Maggio, M. Magliocchetti, E. A. Magnier, F. Mannucci, R. Maoli, C. J. A. P. Martins, L. Maurin, M. Migliaccio, M. Miluzio, P. Monaco, A. Montoro, C. Moretti, G. Morgante, K. Naidoo, A. Navarro-Alsina, S. Nesseris, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, S. Quai, M. Radovich, P. -F. Rocci, P. Rosati, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, J. Schaye, A. Schneider, M. Schultheis, D. Sciotti, E. Sellentin, L. C. Smith, K. Tanidis, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, G. Verza, P. Vielzeuf, N. A. Walton, E. Soubrie, D. Scott
The first survey data release by the Euclid mission covers approximately $63\,\mathrm{deg^2}$ in the Euclid Deep Fields to the same depth as the Euclid Wide Survey. This paper showcases, for the first time, the performance of cluster finders on Euclid data and presents examples of validated clusters in the Quick Release 1 (Q1) imaging data. We identify clusters using two algorithms (AMICO and PZWav) implemented in the Euclid cluster-detection pipeline. We explore the internal consistency of detections from the two codes, and cross-match detections with known clusters from other surveys using external multi-wavelength and spectroscopic data sets. This enables assessment of the Euclid photometric redshift accuracy and also of systematics such as mis-centring between the optical cluster centre and centres based on X-ray and/or Sunyaev--Zeldovich observations. We report 426 joint PZWav and AMICO-detected clusters with high signal-to-noise ratios over the full Q1 area in the redshift range $0.2 \leq z \leq 1.5$. The chosen redshift and signal-to-noise thresholds are motivated by the photometric quality of the early Euclid data. We provide richness estimates for each of the Euclid-detected clusters and show its correlation with various external cluster mass proxies. Out of the full sample, 77 systems are potentially new to the literature. Overall, the Q1 cluster catalogue demonstrates a successful validation of the workflow ahead of the Euclid Data Release 1, based on the consistency of internal and external properties of Euclid-detected clusters.
SoK: How Robust is Audio Watermarking in Generative AI models?
2025-03-24
Yizhu Wen, Ashwin Innuganti, Aaron Bien Ramos, Hanqing Guo, Qiben Yan
Audio watermarking is increasingly used to verify the provenance of AI-generated content, enabling applications such as detecting AI-generated speech, protecting music IP, and defending against voice cloning. To be effective, audio watermarks must resist removal attacks that distort signals to evade detection. While many schemes claim robustness, these claims are typically tested in isolation and against a limited set of attacks. A systematic evaluation against diverse removal attacks is lacking, hindering practical deployment. In this paper, we investigate whether recent watermarking schemes that claim robustness can withstand a broad range of removal attacks. First, we introduce a taxonomy covering 22 audio watermarking schemes. Next, we summarize their underlying technologies and potential vulnerabilities. We then present a large-scale empirical study to assess their robustness. To support this, we build an evaluation framework encompassing 22 types of removal attacks (109 configurations) including signal-level, physical-level, and AI-induced distortions. We reproduce 9 watermarking schemes using open-source code, identify 8 new highly effective attacks, and highlight 11 key findings that expose the fundamental limitations of these methods across 3 public datasets. Our results reveal that none of the surveyed schemes can withstand all tested distortions. This evaluation offers a comprehensive view of how current watermarking methods perform under real-world threats. Our demo and code are available at https://sokaudiowm.github.io/.
Multi-objective Pseudo Boolean Functions in Runtime Analysis: A Review
2025-03-24
Zimin Liang, Miqing Li
Recently, there has been growing interest within the theoretical community in analytically studying multi-objective evolutionary algorithms. This runtime analysis-focused research can help formally understand algorithm behaviour, explain empirical observations, and provide theoretical insights to support algorithm development and exploration. However, the test problems commonly used in the theoretical analysis are predominantly limited to problems with heavy ``artificial'' characteristics (e.g., symmetric objectives and linear Pareto fronts), which may not be able to well represent realistic scenarios. In this paper, we survey commonly used multi-objective functions in the theory domain and systematically review their features, limitations and implications to practical use. Moreover, we present several new functions with more realistic features, such as local optimality and nonlinearity of the Pareto front, through simply mixing and matching classical single-objective functions in the area (e.g., LeadingOnes, Jump and RoyalRoad). We hope these functions can enrich the existing test problem suites, and strengthen the connection between theoretic and practical research.
Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade-offs between electrical conductivity, the Seebeck coefficient, and thermal conductivity, which are further complicated by the presence of defects. This review explores how artificial intelligence (AI) and machine learning (ML) are transforming thermoelectric materials design. Advanced ML approaches including deep neural networks, graph-based models, and transformer architectures, integrated with high-throughput simulations and growing databases, effectively capture structure-property relationships in a complex multiscale defect space and overcome the curse of dimensionality. This review discusses AI-enhanced defect engineering strategies such as composition optimization, entropy and dislocation engineering, and grain boundary design, along with emerging inverse design techniques for generating materials with targeted properties. Finally, it outlines future opportunities in novel physics mechanisms and sustainability, highlighting the critical role of AI in accelerating the discovery of thermoelectric materials.
Towards Collective Storytelling: Investigating Audience Annotations in Data Visualizations
2025-03-24
Tobias Kauer, Marian Dörk, Benjamin Bach
This work investigates personal perspectives in visualization annotations as devices for collective data-driven storytelling. Inspired by existing efforts in critical cartography, we show how people share personal memories in a visualization of COVID-19 data and how comments by other visualization readers influence the reading and understanding of visualizations. Analyzing interaction logs, reader surveys, visualization annotations, and interviews, we find that reader annotations help other viewers relate to other people's stories and reflect on their own experiences. Further, we found that annotations embedded directly into the visualization can serve as social traces guiding through a visualization and help readers contextualize their own stories. With that, they supersede the attention paid to data encodings and become the main focal point of the visualization.
The double-beta (2$\beta$)-decay is the rarest nuclear physics process, and its experimental half-lives (T$_{1/2}$) exceed the age of the Universe from nine to fourteen orders of magnitude. Double-beta decay was observed, and its half-life was measured in 14 parent nuclei using direct, radiochemical, and geochemical methods. The decay observables are analyzed using the Evaluated Nuclear Structure Data File (ENSDF) procedures, and the recommended T$_{1/2}$ were deduced. Using the calculated values of phase factors, the effective nuclear matrix elements were extracted and compared with available data. Thousands of theoretical and experimental works have been dedicated to these topics in the last 85 years, and we present two data sets of recommended values to encapsulate the results.
The impact of assembly history on the X-ray detectability of halos. From galaxy groups to galaxy clusters
2025-03-24
I. Marini, P. Popesso, K. Dolag, V. Biffi, S. Vladutescu-Zopp, T. Castro, V. Toptun, N. de Isídio, A. Dev, D. Mazengo, J. Comparat, C. Gouin, N. Malavasi, A. Merloni, T. Mroczkowski, G. Ponti, S. Shreeram, Y. Zhang
Galaxy groups represent a significant fraction of the halo population, playing a crucial role in galaxy formation and evolution. However, their detection in X-rays remains challenging, raising questions about the physical mechanisms driving their detectability in current surveys. Using the Magneticum simulations, we construct a mock X-ray lightcone of the local Universe ($z<0.2$) to investigate the selection function of galaxy groups and clusters. We find that AGN activity is a key driver of baryon depletion, but late-time mergers boost X-ray brightness by replenishing the gas reservoir in the halos, highlighting the interplay between feedback processes and the environment. Our analysis shows that X-ray bright groups experience sustained late-time mass accretion, maintaining higher gas fractions and fueling the central supermassive black holes (SMBH), further increasing the X-ray emissivity in the core. In contrast, X-ray faint groups form earlier and lose most of their gas over time, resembling fossil groups. Magneticum predicts strong anti-correlations between gas fraction (or X-ray luminosity) and SMBH mass, stellar mass (both in the central galaxy and intracluster light), and group richness at fixed halo mass. We derive predictions on the hot gas fraction at fixed halos mass (e.g. a group of total mass $M_{500}=10^{13} M_{\odot}$ can have hot gas fractions in the range $f_\mathrm{gas}=0.02-0.06$ and a central SMBH with a median mass of $M_\mathrm{BH}=10^9 M_{\odot}$ and a scatter of $0.5$ dex) compatible with the most recent measurements of the baryonic fraction. These findings will aid the interpretation of future X-ray surveys, demonstrating the power of simulation-based inference.
Studying Binary Systems in Omega Centauri with MUSE: II. Observational constraints on the orbital period distribution
2025-03-24
S. Saracino, S. Kamann, F. Wragg, S. Dreizler, K. Kremer, M. Latour, J. Müller-Horn, N. Neumayer, A. C. Seth, G. van de Ven, M. Häberle
Omega Centauri ($\omega$ Cen) is one of the most complex star clusters in the Milky Way, and likely the stripped nucleus of an accreted dwarf galaxy. Being the subject of debate between it hosting an intermediate-mass black hole (IMBH) or a collection of stellar-mass black holes (BHs) in its center, $\omega$ Cen has been intensively studied over the past decades. Our work focuses on characterizing the properties of binary systems in $\omega$ Cen via multi-epoch MUSE spectroscopic observations spanning over eight years and covering much of its central regions (i.e. core radius). We did not detect any stellar-mass BHs candidates orbiting luminous stars, although mock samples indicate a high sensitivity of our survey to such systems. This suggests that BHs orbiting stars may be rare in $\omega$ Cen or in wide orbits around low-mass companions (where our survey is 50% complete) or that the periods of such systems are longer than expected from cluster dynamics. Additionally, we constrained the orbital properties of 19 binary systems in the cluster, with periods ranging from fractions of a day up to several hundred days. We observe an excess of binaries with P $\ge$ 10 d and find evidence that the intrinsic period distribution of binaries in $\omega$ Cen differs from those predicted by cluster evolutionary models.
Classification of Radio Sources Through Self-Supervised Learning
2025-03-24
Nicolas Baron Perez, Marcus Brüggen, Gregor Kasieczka, Luisa Lucie-Smith
The morphology of radio galaxies is indicative, for instance, of their interaction with their surroundings. Since modern radio surveys contain a large number of radio sources that will be impossible to analyse and classify manually, it is important to develop automatic schemes. Unlike other fields, which benefit from established theoretical frameworks and simulations, for radio galaxies there are no such comprehensive models, posing unique challenges for data analysis. In this study, we investigate the classification of radio galaxies from the LOFAR Two-meter Sky Survey Data Release 2 (LoTSS-DR2) using self-supervised learning. Our classification strategy involves three main steps: (i) self-supervised pre-training; (ii) fine-tuning using a labelled subsample created from the learned representations; and (iii) final classification of the selected unlabelled sample. To enhance morphological information in the representations, we developed an additional random augmentation. Our results demonstrate that the learned representations contain rich morphological information, enabling the creation of a labelled subsample that effectively captures the morphological diversity within the unlabelled sample. Additionally, the classification of the unlabelled sample into 12 morphological classes yields robust class probabilities. We successfully demonstrate that a subset of radio galaxies from LoTSS-DR2, encompassing diverse morphologies, can be classified using self-supervised learning. The methodology developed here bridges the gap left by the absence of simulations and theoretical models, offering a framework that can readily be applied to astronomical image analyses in other wavebands.
Bound Dark Energy: a particle's origin of dark energy
2025-03-24
Axel de la Macorra, Jose Agustin Lozano Torres
Dark energy, the enigmatic force driving the accelerated cosmic expansion of the universe, is conventionally described as a cosmological constant in the standard $\Lambda$CDM model. However, measurements from the Dark Energy Spectroscopic Instrument (DESI) reports a $> 2.5\sigma$ preference for dynamical dark energy, with baryon acoustic oscillation (BAO) data favoring a time varying equation of state $w(z)$ over the cosmological constant ($w=-1$). We present the Bound Dark Energy (BDE) model, where dark energy originates from the lightest meson field $\phi$ in a dark SU(3) gauge sector, emerging dynamically via non perturbative interactions. Governed by an inverse-power-law potential $V(\phi)=\Lambda_{c}^{4 + 2/3}\phi^{-2/3}$, BDE has no free parameters, one less than $\Lambda$CDM and three less than $w_{0}w_{a}$CDM models. Combining the DESI BAO measurements, cosmic microwave background data, and Dark Energy Survey SN Ia distance measurements from the fifth year, BDE achieves a $42\%$ and $37\%$ reduction in the reduced $\chi^{2}_{BAO}$ compared to $w_{0}w_{a}$CDM and $\Lambda$CDM, respectively, while having an equivalent fit for type Ia supernovae and the cosmic microwave background data. The model predicts a dark energy equation of state transitioning from radiation like $w=1/3$ at early times ($a < a_{c}$) to $ w_{0}=-0.9301 \pm 0.0004$ at present time. The ($w_{0},w_{a}$) contour is 10,000 times smaller in BDE than in $w_{0}w_{a}$CDM model, while having an equivalent cosmological fit. Key parameters the condensation energy scale $\Lambda_{c}=43.806 \pm 0.19$ eV and epoch $a_{c}=2.4972 \pm 0.011 \times 10^{-6}$ align with high-energy physics predictions. These results, consistent with current observational bounds, establish BDE as a predictive framework that unifies particle physics and cosmology, offering a first-principles resolution to dark energy dynamical nature.
MEGA Mass Assembly with JWST: The MIRI EGS Galaxy and AGN Survey
2025-03-24
Bren E. Backhaus, Allison Kirkpatrick, Guang Yang, Gregory Troiani, Kurt Hamblin, Jeyhan S. Kartaltepe, Dale D. Kocevski, Anton M. Koekemoer, Erini Lambrides, Casey Papovich, Kaila Ronayne
We present the MIRI EGS Galaxy and AGN (MEGA) survey, a four band MIRI survey with 25 pointing in the Extended Groth Strip (EGS) extragalactic field. Three of the pointings utilized only the three reddest bands (F1000W, F1500W, F2100W) while the remainder of the pointings also add a blue filter (F770W). MEGA builds upon the existing observations in the EGS field by providing MIRI imaging for 68.9% of CEERS NIRCam imaging, filling a cruciality gap in order to understand galaxy evolution by observing the obscured Universe. Here, we present the technical design, data reduction, photometric catalog creation, the first data release, and science drivers of the MEGA survey. Our data reduction starts with the standard JWST calibration pipeline, but adds additional warm pixel masking and custom background subtraction steps to improve the quality of the final science image. We estimate the image depth of the reduced mosaics and present new galaxy number counts in four MIRI bands.
Relational Supersymmetry and Matter-Interaction Supergeometric Framework
2025-03-24
J. François, L. Ravera
Relationality is the paradigmatic conceptual core of general-relativistic gauge field theory. It can be made manifest via the Dressing Field Method (DFM) of symmetry reduction, a systematic tool to achieve gauge-invariance by extracting the physical degrees of freedom representing relations among field variables. We review and further expand on some applications of the DFM to the very foundations of the supersymmetric framework, where it allows to build relational supersymmetric field theory and (dis)solves crucial issues. Furthermore, we elaborate on a novel approach within the relational supersymmetric field theory framework giving a unified description of fermionic matter fields and bosonic gauge fields, and thus close to Berezin's original motivation for the introduction of supergeometry in fundamental physics: a Matter-Interaction Supergeometric Unification (MISU). This new approach stands irrespective from the ultimate empirical status of standard supersymmetric field theory, about which it is agnostic.
Forecasting Labor Demand: Predicting JOLT Job Openings using Deep Learning Model
2025-03-24
Kyungsu Kim
This thesis studies the effectiveness of Long Short Term Memory model in forecasting future Job Openings and Labor Turnover Survey data in the United States. Drawing on multiple economic indicators from various sources, the data are fed directly into LSTM model to predict JOLT job openings in subsequent periods. The performance of the LSTM model is compared with conventional autoregressive approaches, including ARIMA, SARIMA, and Holt-Winters. Findings suggest that the LSTM model outperforms these traditional models in predicting JOLT job openings, as it not only captures the dependent variables trends but also harmonized with key economic factors. These results highlight the potential of deep learning techniques in capturing complex temporal dependencies in economic data, offering valuable insights for policymakers and stakeholders in developing data-driven labor market strategies
A Spectroscopic Survey of Metal-Poor OB Stars in Local Dwarf Galaxy NGC 3109
2025-03-24
Abby Mintz, O. Grace Telford, Evan N. Kirby, John Chisholm, Kristen B. W. McQuinn, Danielle Berg
As JWST uncovers increasingly strong evidence that metal-poor, massive stars in early galaxies dominated reionization, observational constraints on the properties of such stars are more relevant than ever before. However, spectra of individual O- and B-type stars are rare at the relevant metallicities ($\lesssim 0.2$ $Z_\odot$), leaving models of stellar evolution and ionizing flux poorly constrained by data in this regime. We present new medium-resolution ($R\sim 4000)$ Keck/DEIMOS optical spectra of 17 OB stars in the local low-metallicity ($0.12$ $Z_\odot$) dwarf galaxy NGC 3109. We assign spectral types to the stars and present new criteria for selecting O stars using optical and NUV photometry from Hubble Space Telescope imaging. We fit the spectra and photometry with grids of stellar atmosphere models to measure stellar temperatures, surface gravities, luminosities, radii, and masses. We find evidence of strong mass loss via radiation-driven stellar winds in two O stars, one of which is the hottest, youngest, and most massive star confirmed in the host galaxy to date. Though its spectrum does not meet conventional Wolf-Rayet spectral classification criteria, this metal-poor O If star produces strong He II 4686 emission and its evolutionary status is ambiguous. This work nearly doubles the number of OB stars with measured parameters in NGC 3109, including ten stars with no previously reported parameters, four with no published spectroscopy, and four binary candidates. This large sample of OB stellar parameters provides a new observational testbed to constrain the stellar astrophysics that drove cosmic reionization and influenced the evolution of the earliest galaxies.
Spectral Effects of Shock Darkening on Ordinary Chondrite and Howardite, Eucrite, and Diogenite meteorites
2025-03-24
Juan A. Sanchez, Vishnu Reddy, Lucille Le Corre, Neil Pearson, Adam Battle
Impacts are the most ubiquitous processes on planetary bodies in our solar system. During these impact events, shock waves can deposit enough energy to produce shock-induced darkening in the target material, resulting in an alteration of its spectral properties. This spectral alteration can lead to an ambiguous taxonomic classification of asteroids and an incorrect identification of meteorite analogs. In this study, we investigated the effects of shock darkening on the visible and near-infrared spectra of ordinary chondrites and members of the howardites, eucrites, and diogenites clan. A decrease in albedo and suppression of the absorption bands with increasing shock darkening was observed for all the samples. We found that adding $\gtrsim$50\% of shock-darkened material to an unaltered sample was enough to change the taxonomic classification of an ordinary chondrite from S-complex to C- or X-complex. A similar amount was sufficient to change the taxonomic classification of a eucrite from V-type to O-type, whereas a eucrite composed of 100\% shock-darkened material was classified as a Q-type. We discuss the limitations of using just albedo for taxonomic classification of asteroids, which has implications for future space-based infrared surveys. We also investigated if shock darkening is responsible for a bias against the discovery of near-Earth objects (NEOs) with H and L chondrite-like compositions, which could explain the abundance of LL chondrites among NEOs.
Computational Thinking with Computer Vision: Developing AI Competency in an Introductory Computer Science Course
2025-03-24
Tahiya Chowdhury
Developing competency in artificial intelligence is becoming increasingly crucial for computer science (CS) students at all levels of the CS curriculum. However, most previous research focuses on advanced CS courses, as traditional introductory courses provide limited opportunities to develop AI skills and knowledge. This paper introduces an introductory CS course where students learn computational thinking through computer vision, a sub-field of AI, as an application context. The course aims to achieve computational thinking outcomes alongside critical thinking outcomes that expose students to AI approaches and their societal implications. Through experiential activities such as individual projects and reading discussions, our course seeks to balance technical learning and critical thinking goals. Our evaluation, based on pre-and post-course surveys, shows an improved sense of belonging, self-efficacy, and AI ethics awareness among students. The results suggest that an AI-focused context can enhance participation and employability, student-selected projects support self-efficacy, and ethically grounded AI instruction can be effective for interdisciplinary audiences. Students' discussions on reading assignments demonstrated deep engagement with the complex challenges in today's AI landscape. Finally, we share insights on scaling such courses for larger cohorts and improving the learning experience for introductory CS students.
FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition
2025-03-24
Haiqi Liu, C. L. Philip Chen, Tong Zhang
Cross-subject EEG emotion recognition is challenged by significant inter-subject variability and intricately entangled intra-subject variability. Existing works have primarily addressed these challenges through domain adaptation or generalization strategies. However, they typically require extensive target subject data or demonstrate limited generalization performance to unseen subjects. Recent few-shot learning paradigms attempt to address these limitations but often encounter catastrophic overfitting during subject-specific adaptation with limited samples. This article introduces the few-shot adapter with a cross-view fusion method called FACE for cross-subject EEG emotion recognition, which leverages dynamic multi-view fusion and effective subject-specific adaptation. Specifically, FACE incorporates a cross-view fusion module that dynamically integrates global brain connectivity with localized patterns via subject-specific fusion weights to provide complementary emotional information. Moreover, the few-shot adapter module is proposed to enable rapid adaptation for unseen subjects while reducing overfitting by enhancing adapter structures with meta-learning. Experimental results on three public EEG emotion recognition benchmarks demonstrate FACE's superior generalization performance over state-of-the-art methods. FACE provides a practical solution for cross-subject scenarios with limited labeled data.
CAN-STRESS: A Real-World Multimodal Dataset for Understanding Cannabis Use, Stress, and Physiological Responses
2025-03-24
Reza Rahimi Azghan, Nicholas C. Glodosky, Ramesh Kumar Sah, Carrie Cuttler, Ryan McLaughlin, Michael J. Cleveland, Hassan Ghasemzadeh
Coping with stress is one of the most frequently cited reasons for chronic cannabis use. Therefore, it is hypothesized that cannabis users exhibit distinct physiological stress responses compared to non-users, and these differences would be more pronounced during moments of consumption. However, there is a scarcity of publicly available datasets that allow such hypotheses to be tested in real-world environments. This paper introduces a dataset named CAN-STRESS, collected using Empatica E4 wristbands. The dataset includes physiological measurements such as skin conductance, heart rate, and skin temperature from 82 participants (39 cannabis users and 43 non-users) as they went about their daily lives. Additionally, the dataset includes self-reported surveys where participants documented moments of cannabis consumption, exercise, and rated their perceived stress levels during those moments. In this paper, we publicly release the CAN-STRESS dataset, which we believe serves as a highly reliable resource for examining the impact of cannabis on stress and its associated physiological markers. I
SCVI: Bridging Social and Cyber Dimensions for Comprehensive Vulnerability Assessment
2025-03-24
Shutonu Mitra, Tomas Neguyen, Qi Zhang, Hyungmin Kim, Hossein Salemi, Chen-Wei Chang, Fengxiu Zhang, Michin Hong, Chang-Tien Lu, Hemant Purohit, Jin-Hee Cho
The rise of cyber threats on social media platforms necessitates advanced metrics to assess and mitigate social cyber vulnerabilities. This paper presents the Social Cyber Vulnerability Index (SCVI), a novel framework integrating individual-level factors (e.g., awareness, behavioral traits, psychological attributes) and attack-level characteristics (e.g., frequency, consequence, sophistication) for comprehensive socio-cyber vulnerability assessment. SCVI is validated using survey data (iPoll) and textual data (Reddit scam reports), demonstrating adaptability across modalities while revealing demographic disparities and regional vulnerabilities. Comparative analyses with the Common Vulnerability Scoring System (CVSS) and the Social Vulnerability Index (SVI) show the superior ability of SCVI to capture nuanced socio-technical risks. Monte Carlo-based weight variability analysis confirms SCVI is robust and highlights its utility in identifying high-risk groups. By addressing gaps in traditional metrics, SCVI offers actionable insights for policymakers and practitioners, advancing inclusive strategies to mitigate emerging threats such as AI-powered phishing and deepfake scams.
Deciphering the Origins of the Elements Through Galactic Archeology
2025-03-23
Khalil Farouqi, Anna Frebel, Friedrich-Karl Thielemann
Low-metallicity stars preserve the signatures of the first stellar nucleosynthesis events in the Galaxy, as their surface abundances reflect the composition of the interstellar medium from which they were born. Aside from primordial Big Bang nucleosynthesis, massive stars, due to their short lifetimes, dominate the ejecta into the interstellar medium of the early Galaxy. Most of them will end as core-collapse supernova (CCSN) explosions, and typical ejected abundance distributions, e.g. in terms of the alpha-element-to-Fe ratios, reflect these contributions. Essentially all CCSNe contribute 56Fe. Therefore, low-metallicity stars can be used to test whether the abundances of any other elements are correlated with those of Fe, i.e. whether these elements have been co-produced in the progenitor sources or if they require either a different or additional astrophysical origin(s). The present analysis focuses on stars with [Fe/H]<-2, as they probe the earliest formation phase of the Galaxy when only one or very few nucleosynthesis events had contributed their ejecta to the gas from which the lowest metallicity stars form. This was also the era before low and intermediate mass stars (or type Ia supernovae) could contribute any additional heavy elements. Following earlier works into the origin of heavy r-process elements [1], we extend the present study to examine Pearson and Spearman correlations of Fe with Li, Be, C, N, Na, Mg, Si, S, Ca, Ti, Cr, Ni, Zn, Ge, Se, Sr, Zr, Ba, Ce, Sm, Eu, Yb, Lu, Hf, Os, Ir, Pb, Th, and U, using high-resolution stellar abundance data from the SAGA [2] and JINA [3] databases. The main goal is to identify which of the observed elements (i) may have been co-produced with Fe in (possibly a variety of) CCSNe, and which elements require (ii) either a completely different, or (iii) at least an additional astrophysical origin.
A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectives
2025-03-23
Delower Hossain, Jake Y Chen
Over the last few decades, Artificial Intelligence (AI) scientists have been conducting investigations to attain human-level performance by a machine in accomplishing a cognitive task. Within machine learning, the ultimate aspiration is to attain Artificial General Intelligence (AGI) through a machine. This pursuit has led to the exploration of two distinct AI paradigms. Symbolic AI, also known as classical or GOFAI (Good Old-Fashioned AI) and Connectionist (Sub-symbolic) AI, represented by Neural Systems, are two mutually exclusive paradigms. Symbolic AI excels in reasoning, explainability, and knowledge representation but faces challenges in processing complex real-world data with noise. Conversely, deep learning (Black-Box systems) research breakthroughs in neural networks are notable, yet they lack reasoning and interpretability. Neuro-symbolic AI (NeSy), an emerging area of AI research, attempts to bridge this gap by integrating logical reasoning into neural networks, enabling them to learn and reason with symbolic representations. While a long path, this strategy has made significant progress towards achieving common sense reasoning by systems. This article conducts an extensive review of over 977 studies from prominent scientific databases (DBLP, ACL, IEEExplore, Scopus, PubMed, ICML, ICLR), thoroughly examining the multifaceted capabilities of Neuro-Symbolic AI, with a particular focus on its healthcare applications, particularly in drug discovery, and Protein engineering research. The survey addresses vital themes, including reasoning, explainability, integration strategies, 41 healthcare-related use cases, benchmarking, datasets, current approach limitations from both healthcare and broader perspectives, and proposed novel approaches for future experiments.
Future Opportunities with Lepton-Hadron Collisions
2025-03-23
Allen Caldwell, Silvia Dalla Torre, Rolf Ent, Aharon Levy, Paul Newman, Fred Olness, Juan Rojo
Deep Inelastic lepton-hadron Scattering (DIS) is a cornerstone of particle physics discovery and the precision measurement of the structure of matter. This document surveys the international DIS landscape, exploring current and future opportunities to continue this rich heritage, leading to new understandings and enabling discoveries. Of immediate relevance to the future of the field in Europe, the Large Hadron electron Collider (LHeC) offers an impactful bridge between the end of the HL-LHC and the beginning of the next CERN flagship project, both in terms of technology development and new scientific exploration from Higgs physics to the structure of the proton. More generally, the facilities described here cover energies from a few GeV to multiple TeV and address a wide range of topics, with unique sensitivity to Quantum Chromodynamics and hadron structure at their core. Additionally, they enhance the science programmes at hadron-hadron colliders. The fixed-target CEBAF programme at JLab probes nucleon and light ion structure at large x in novel ways, while high energy neutrino DIS is enabled at the FASER and SND@LHC experiments by the intense LHC beams. The Electron Ion Collider (EIC) is on course for deployment at Brookhaven in the early 2030s. Its science includes a 3-dimensional mapping of hadrons, leading to a thorough understanding of the mechanisms behind proton mass and spin. Adding the LHeC provides a Europe-based lepton-hadron frontier, including a complementary Higgs, top and electroweak programme to the HL-LHC, together with precise determinations of proton and nuclear structure in a kinematic range that improves HL-LHC sensitivities. In the longer term, plasma wakefield acceleration and the FCC offer possible major steps forward in centre-of-mass energy, extending into a low parton momentum-fraction domain where new strong interaction discoveries are guaranteed.
Review of the Electrochemical Double Layer Theory and Its Applications in Battery Electrode Materials
2025-03-23
Yitao He
The interface plays a critical role in electrochemical systems, driving the development of various theories to investigate properties at nanoscale and microscale levels, including the electrictrochemical double layer (EDL) theory and continuum theory. However, the application of EDL theory within the realm of battery electrode materials has not been clearly defined according to material type. The diverse range of materials and their intricate interactions with interface theories often lead to misunderstandings. This review meticulously outlines the origins and principles of the EDL theory. Additionally, it categorizes battery electrode materials into two main groups: semiconductors and conductors. A comprehensive examination is presented on the implementation of EDL and other interface theories, accompanied by a comparative evaluation of the fundamental models and experiments described. The review also explores the potential advancements of EDL and other interface theories, as well as their application in electrode materials. This exhaustive review serves as an invaluable resource for scholars in electrochemistry and materials science, aiding in their understanding of interface theories, their scope of applicability, and the integration of these theories with practical experimental approaches.
Adaptive Physics-informed Neural Networks: A Survey
2025-03-23
Edgar Torres, Jonathan Schiefer, Mathias Niepert
Physics-informed neural networks (PINNs) have emerged as a promising approach to solving partial differential equations (PDEs) using neural networks, particularly in data-scarce scenarios, due to their unsupervised training capability. However, limitations related to convergence and the need for re-optimization with each change in PDE parameters hinder their widespread adoption across scientific and engineering applications. This survey reviews existing research that addresses these limitations through transfer learning and meta-learning. The covered methods improve the training efficiency, allowing faster adaptation to new PDEs with fewer data and computational resources. While traditional numerical methods solve systems of differential equations directly, neural networks learn solutions implicitly by adjusting their parameters. One notable advantage of neural networks is their ability to abstract away from specific problem domains, allowing them to retain, discard, or adapt learned representations to efficiently address similar problems. By exploring the application of these techniques to PINNs, this survey identifies promising directions for future research to facilitate the broader adoption of PINNs in a wide range of scientific and engineering applications.
Emotional Multifaceted Feedback on AI Tool Use in EFL Learning Initiation: Chain-Mediated Effects of Motivation and Metacognitive Strategies in an Optimized TAM Model
2025-03-23
Le Yao, Yantong Liu
This study specifically investigates the initiation phase of EFL learners' engagement with AI tools, focusing on how technology acceptance constructs perceived usefulness (PU), perceived ease of use (PEOU), and perceived self-efficacy (PSE) influence learning resilience. Drawing on an optimized Technology Acceptance Model (TAM) and integrating constructs from positive psychology, the study examines the chain-mediated effects of learning motivation (LM) and metacognitive strategies (MS) on resilience outcomes, operationalized through optimism (OP), psychological resilience (PR), and growth mindset (GM). A survey of first-year English majors (N = 730) was conducted, and structural equation modeling was employed to analyze the data. The findings indicate that favorable perceptions of AI tools are significantly associated with enhanced LM and MS, which in turn positively impact resilience measures. These results suggest that the interplay between technology acceptance and internal regulatory processes is vital in shaping EFL learners' early experiences with AI-assisted learning. Practical implications for educators and researchers are discussed, with an emphasis on promoting user-friendly and effective AI environments to support the development of adaptive learning behaviors.
Literature Review: Cyber Security Monitoring in Maritime
2025-03-23
Risto Vaarandi, Leonidas Tsiopoulos, Gabor Visky, Muaan Ur Rehman, Hayretdin Bahsi
In recent years, many cyber incidents have happened in the maritime sector, targeting the information technology (IT) and operational technology (OT) infrastructure. Although several systematization-of-knowledge papers have been published in the maritime field, none of the previous studies has focused on cyber security monitoring, which aims at timely detection of cyber attacks with automated methods. The current article addresses this research gap and surveys the methods, algorithms, tools and architectures used for cyber security monitoring in the maritime sector. For the survey, a systematic literature review of cyber security monitoring studies is conducted in this article, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The first contribution of this article is the bibliometric analysis of related literature and the identification of the main research themes in previous works. For that purpose, our article presents a taxonomy for existing studies which highlights the main properties of maritime cyber security monitoring research. The second contribution of this article is an in-depth analysis of previous works and the identification of research gaps and limitations in existing literature. Based on our findings, we outline future research directions for cyber security monitoring in the maritime field.
Misleading chart visualizations, which intentionally manipulate data representations to support specific claims, can distort perceptions and lead to incorrect conclusions. Despite decades of research, misleading visualizations remain a widespread and pressing issue. Recent advances in multimodal large language models (MLLMs) have demonstrated strong chart comprehension capabilities, yet no existing work has systematically evaluated their ability to detect and interpret misleading charts. This paper introduces the Misleading Chart Question Answering (Misleading ChartQA) Benchmark, a large-scale multimodal dataset designed to assess MLLMs in identifying and reasoning about misleading charts. It contains over 3,000 curated examples, covering 21 types of misleaders and 10 chart types. Each example includes standardized chart code, CSV data, and multiple-choice questions with labeled explanations, validated through multi-round MLLM checks and exhausted expert human review. We benchmark 16 state-of-the-art MLLMs on our dataset, revealing their limitations in identifying visually deceptive practices. We also propose a novel pipeline that detects and localizes misleaders, enhancing MLLMs' accuracy in misleading chart interpretation. Our work establishes a foundation for advancing MLLM-driven misleading chart comprehension. We publicly release the sample dataset to support further research in this critical area.
Adoption of Watermarking for Generative AI Systems in Practice and Implications under the new EU AI Act
2025-03-23
Bram Rijsbosch, Gijs van Dijck, Konrad Kollnig
AI-generated images have become so good in recent years that individuals cannot distinguish them any more from "real" images. This development creates a series of societal risks, and challenges our perception of what is true and what is not, particularly with the emergence of "deep fakes" that impersonate real individuals. Watermarking, a technique that involves embedding identifying information within images to indicate their AI-generated nature, has emerged as a primary mechanism to address the risks posed by AI-generated images. The implementation of watermarking techniques is now becoming a legal requirement in many jurisdictions, including under the new 2024 EU AI Act. Despite the widespread use of AI image generation systems, the current status of watermarking implementation remains largely unexamined. Moreover, the practical implications of the AI Act's watermarking requirements have not previously been studied. The present paper therefore both provides an empirical analysis of 50 of the most widely used AI systems for image generation, and embeds this empirical analysis into a legal analysis of the AI Act. We identify four categories of generative AI image systems relevant under the AI Act, outline the legal obligations for each category, and find that only a minority number of providers currently implement adequate watermarking practices.
2025-03-23
Hoang Nhan Luu, Yu-Cheng Qiu, S. -H. Henry Tye
Recently the Dark Energy Survey (DES) Collaboration presented evidence that the equation of state $w$ of the dark energy is varying, or $w \simeq -0.948$ if it is constant. In either case, the dark energy cannot be due to a cosmological constant alone. Here we introduce an ultralight axion (or axion-like particle) with mass $m_\phi \simeq 2 \times 10^{-33}$ eV that has properties that can explain the new $w$ measurement. In particular, $w\ge -1$ in this model, ruling out the $w < -1$ region allowed by the DES data.
Clarifying Misconceptions in COVID-19 Vaccine Sentiment and Stance Analysis and Their Implications for Vaccine Hesitancy Mitigation: A Systematic Review
2025-03-23
Lorena G Barberia, Belinda Lombard, Norton Trevisan Roman, Tatiane C. M. Sousa
Background Advances in machine learning (ML) models have increased the capability of researchers to detect vaccine hesitancy in social media using Natural Language Processing (NLP). A considerable volume of research has identified the persistence of COVID-19 vaccine hesitancy in discourse shared on various social media platforms. Methods Our objective in this study was to conduct a systematic review of research employing sentiment analysis or stance detection to study discourse towards COVID-19 vaccines and vaccination spread on Twitter (officially known as X since 2023). Following registration in the PROSPERO international registry of systematic reviews, we searched papers published from 1 January 2020 to 31 December 2023 that used supervised machine learning to assess COVID-19 vaccine hesitancy through stance detection or sentiment analysis on Twitter. We categorized the studies according to a taxonomy of five dimensions: tweet sample selection approach, self-reported study type, classification typology, annotation codebook definitions, and interpretation of results. We analyzed if studies using stance detection report different hesitancy trends than those using sentiment analysis by examining how COVID-19 vaccine hesitancy is measured, and whether efforts were made to avoid measurement bias. Results Our review found that measurement bias is widely prevalent in studies employing supervised machine learning to analyze sentiment and stance toward COVID-19 vaccines and vaccination. The reporting errors are sufficiently serious that they hinder the generalisability and interpretation of these studies to understanding whether individual opinions communicate reluctance to vaccinate against SARS-CoV-2. Conclusion Improving the reporting of NLP methods is crucial to addressing knowledge gaps in vaccine hesitancy discourse.
Fast radio bursts (FRBs) are fierce radio flashes lasting for a few milliseconds from the sky. Although their connection to strongly magnetized neutron stars has been strongly indicated, the exact triggering process and radiation mechanism are still unknown and highly debated. Due to their extremely short duration, the observation of FRBs has long been a difficult task even for large radio telescopes. The difficulty results from the fact that the information obtained in observations is always incomplete, since the telescope always has a limited flux sensitivity and finite operating frequency band. A pressing challenge is to decode the intrinsic features of FRBs from the incomplete observations. Here we establish an efficient methodology to overcome this problem, aiming at effectively correcting for the fluence and frequency cutoffs. Using this method, inverse modeling is performed on a large number of repeating bursts from FRB 20121102A to recover their intrinsic features. It is found that strong bursts intrinsically tend to concentrate their energy in a narrow band, while the spectral range of weak bursts can be either narrow or wide. However, when a weak burst has a broad spectrum, the wing of the spectrum can easily go undetected, resulting in a very narrow spectrum being observed. The narrow spectrum features observed in repeating FRBs are thus an observational selection effect. Underestimation of the burst energy caused by observational cutoffs is also corrected for, and the intrinsic burst energy distribution is re-constructed. It is also found that the bandwidth increases with the increasing central frequency in the Arecibo sample (1.15-1.73 GHz), but such a correlation is not observed in the FAST (1-1.5 GHz) and GBT (4-8 GHz) sample. It indicates the emission pattern of the FRB source might vary across different active periods and frequency bands.
In the emerging field of urban digital twins (UDTs), advancing intelligent road inspection (IRI) vehicles with automatic road crack detection systems is essential for maintaining civil infrastructure. Over the past decade, deep learning-based road crack detection methods have been developed to detect cracks more efficiently, accurately, and objectively, with the goal of replacing manual visual inspection. Nonetheless, there is a lack of systematic reviews on state-of-the-art (SoTA) deep learning techniques, especially data-fusion and label-efficient algorithms for this task. This paper thoroughly reviews the SoTA deep learning-based algorithms, including (1) supervised, (2) unsupervised, (3) semi-supervised, and (4) weakly-supervised methods developed for road crack detection. Also, we create a dataset called UDTIRI-Crack, comprising $2,500$ high-quality images from seven public annotated sources, as the first extensive online benchmark in this field. Comprehensive experiments are conducted to compare the detection performance, computational efficiency, and generalizability of public SoTA deep learning-based algorithms for road crack detection. In addition, the feasibility of foundation models and large language models (LLMs) for road crack detection is explored. Afterwards, the existing challenges and future development trends of deep learning-based road crack detection algorithms are discussed. We believe this review can serve as practical guidance for developing intelligent road detection vehicles with the next-generation road condition assessment systems. The released benchmark UDTIRI-Crack is available at https://udtiri.com/submission/.
Language models have recently advanced into the realm of reasoning, yet it is through multimodal reasoning that we can fully unlock the potential to achieve more comprehensive, human-like cognitive capabilities. This survey provides a systematic overview of the recent multimodal reasoning approaches, categorizing them into two levels: language-centric multimodal reasoning and collaborative multimodal reasoning. The former encompasses one-pass visual perception and active visual perception, where vision primarily serves a supporting role in language reasoning. The latter involves action generation and state update within reasoning process, enabling a more dynamic interaction between modalities. Furthermore, we analyze the technical evolution of these methods, discuss their inherent challenges, and introduce key benchmark tasks and evaluation metrics for assessing multimodal reasoning performance. Finally, we provide insights into future research directions from the following two perspectives: (i) from visual-language reasoning to omnimodal reasoning and (ii) from multimodal reasoning to multimodal agents. This survey aims to provide a structured overview that will inspire further advancements in multimodal reasoning research.
Efficient and inefficient hydrodynamic escape of exo-satellite atmospheres driven by irradiation from their young giant planets
2025-03-23
Matthäus Schulik, James E. Owen, Richard A. Booth, Shun Fai Ling, Shun Ping Wong
The bolometric radiation from a central body is potentially a powerful driver of atmospheric escape from planets or satellites. When heated above their equilibrium temperatures those satellites, due to their low surface gravity, are be prone to significant atmospheric erosion. Such high temperatures can be reached through a known mechanism: a large ratio of the irradiation to re-radiation opacities of the atmospheric species. We investigate this mechanism for irradiating black-bodies of sub-stellar temperatures and find that specific molecules exist, such as $\rm NH_3$ and $\rm CH_4$, which develop temperature inversions under the irradiation of young post-formation giant planets. These non-isothermal temperature profiles lead to escape rates that can significantly exceed isothermal Parker-model escape rates evaluated at the satellite's equilibrium temperature. Our results indicate that exo-satellites can lose most of their atmospheric mass through this mechanism if the cooling of the exo-satellite's interior is not too rapid. In all scenarios, we find a hierarchical ordering of escape rates of atmospheric species due to thermal decoupling in the upper atmosphere. This thermal decoupling leads to a natural depletion of $\rm CH_4$ and retention of $\rm NH_3$ in our models. We find that giant planets with masses above 2$m_{\rm Jup}$, for cold starts and above 1$m_{\rm Jup}$ in hot start scenarios are able to remove the majority of a Titan analogue's atmosphere. Hence, finding and characterizing exomoon atmospheres in hypothetical future surveys can constrain the post-formation cooling behaviour of giant planets.
Determining some graph joins by the signless Laplacian spectrum
2025-03-23
Jiachang Ye, Jianguo Qian, Zoran Stani\' c
A graph is determined by its signless Laplacian spectrum if there is no other non-isomorphic graph sharing the same signless Laplacian spectrum. Let $C_l$, $P_l$, $K_l$ and $K_{s,l-s}$ be the cycle, the path, the complete graph and the complete bipartite graph with $l$ vertices, respectively. We prove that $$G\cong K_1\vee (C_{l_1}\cup C_{l_2}\cup\cdots \cup C_{l_t}\cup sK_1),$$ with $s\ge 0, t\ge 1, n\geq 22$, is determined by the signless Laplacian spectrum if and only if either $s=0$ or $s\ge 1$ and $l_i\ne 3$ holds for all $1\leq i\leq t$, where $n$ is the order of $G$, and $\cup$ and $\vee$ stand for the disjoint union and the join of two graphs, respectively. Moreover, for $s\ge 1$ and $l_t=3$, $K_1\vee (K_{1,3}\cup C_{l_1}\cup C_{l_2}\cup\cdots \cup C_{l_{t-1}}\cup (s-1)K_1)$ is fixed as a graph sharing the signless Laplacian spectrum with $G$. This contribution extends some recently published results.
A State-of-the-Art Review on Acoustic Preservation of Historical Worship Spaces through Auralization
2025-03-23
Hannes Rosseel, Toon van Waterschoot
Historical Worship Spaces (HWS) are significant architectural landmarks which hold both cultural and spiritual value. The acoustic properties of these spaces play a crucial role in historical and contemporary religious liturgies, rituals, and ceremonies, as well as in the performance of sacred music. However, the original acoustic characteristics of these spaces are often at risk due to repurposing, renovations, natural disasters, or deterioration over time. This paper presents a comprehensive review of the current state of research on the acquisition, analysis, and synthesis of acoustics, with a focus on HWS. An example case study of the Nassau chapel in Brussels, Belgium, is presented to demonstrate the application of these techniques for the preservation and auralization of historical worship space acoustics. The paper concludes with a discussion of the challenges and opportunities in the field, and outlines future research directions.
Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook
2025-03-23
Xu Zheng, Ziqiao Weng, Yuanhuiyi Lyu, Lutao Jiang, Haiwei Xue, Bin Ren, Danda Paudel, Nicu Sebe, Luc Van Gool, Xuming Hu
Retrieval-augmented generation (RAG) has emerged as a pivotal technique in artificial intelligence (AI), particularly in enhancing the capabilities of large language models (LLMs) by enabling access to external, reliable, and up-to-date knowledge sources. In the context of AI-Generated Content (AIGC), RAG has proven invaluable by augmenting model outputs with supplementary, relevant information, thus improving their quality. Recently, the potential of RAG has extended beyond natural language processing, with emerging methods integrating retrieval-augmented strategies into the computer vision (CV) domain. These approaches aim to address the limitations of relying solely on internal model knowledge by incorporating authoritative external knowledge bases, thereby improving both the understanding and generation capabilities of vision models. This survey provides a comprehensive review of the current state of retrieval-augmented techniques in CV, focusing on two main areas: (I) visual understanding and (II) visual generation. In the realm of visual understanding, we systematically review tasks ranging from basic image recognition to complex applications such as medical report generation and multimodal question answering. For visual content generation, we examine the application of RAG in tasks related to image, video, and 3D generation. Furthermore, we explore recent advancements in RAG for embodied AI, with a particular focus on applications in planning, task execution, multimodal perception, interaction, and specialized domains. Given that the integration of retrieval-augmented techniques in CV is still in its early stages, we also highlight the key limitations of current approaches and propose future research directions to drive the development of this promising area.
On the ventilation of surface-piercing hydrofoils under steady-state conditions
2025-03-23
Manuel Aguiar Ferreira, Carlos Navas Rodríguez, Gunnar Jacobi, Daniele Fiscaletti, Arnoud Greidanus, Jerry Westerweel
The present study experimentally investigates the onset of ventilation of surface-piercing hydrofoils. Under steady-state conditions, the depth-based Froude number $Fr$ and the angle of attack $\alpha$ define regions where distinct flow regimes are either locally or globally stable. To map the boundary between these stability regions, the parameter space $(\alpha,Fr)$ was systematically surveyed by increasing $\alpha$ until the onset of ventilation, while maintaining a constant $Fr$. Two simplified model hydrofoils were examined: a semi-ogive profile with a blunt trailing edge and a modified NACA 0010-34. Tests were conducted in a towing tank under quasi-steady-state conditions for aspect ratios of $1.0$ and $1.5$, and $Fr$ ranging from $0.5$ to $2.5$. Ventilation occurred spontaneously for all test conditions as $\alpha$ increased. Three distinct trigger mechanisms were identified: nose, tail, and base ventilation. Nose ventilation is prevalent at $Fr<1.0$ and $Fr<1.25$ for aspect ratios of $1.0$ and $1.5$, respectively, and is associated with an increase in the inception angle of attack. Tail ventilation becomes prevalent at higher $Fr$, where the inception angle of attack takes a negative trend. Base ventilation was observed only for the semi-ogive profile but did not lead to the development of a stable ventilated cavity. Notably, the measurements indicate that the boundary between bistable and globally stable regions is not uniform and extends to significantly higher $\alpha$ than previously estimated. A revised stability map is proposed to reconcile previously published and current data, demonstrating how two alternative paths to a steady-state condition can lead to different flow regimes.
Automatic High-Level Test Case Generation using Large Language Models
2025-03-23
Navid Bin Hasan, Md. Ashraful Islam, Junaed Younus Khan, Sanjida Senjik, Anindya Iqbal
We explored the challenges practitioners face in software testing and proposed automated solutions to address these obstacles. We began with a survey of local software companies and 26 practitioners, revealing that the primary challenge is not writing test scripts but aligning testing efforts with business requirements. Based on these insights, we constructed a use-case $\rightarrow$ (high-level) test-cases dataset to train/fine-tune models for generating high-level test cases. High-level test cases specify what aspects of the software's functionality need to be tested, along with the expected outcomes. We evaluated large language models, such as GPT-4o, Gemini, LLaMA 3.1 8B, and Mistral 7B, where fine-tuning (the latter two) yields improved performance. A final (human evaluation) survey confirmed the effectiveness of these generated test cases. Our proactive approach strengthens requirement-testing alignment and facilitates early test case generation to streamline development.
Interpersonal trust is recognized as one of the pillars of collaboration and successful learning among students in virtual learning environments (VLEs). This systematic mapping study investigates attributes, phases, and features that support interpersonal trust among students in VLEs. Analyzing 46 articles, we identified 37 attributes that influence phases of acquiring and losing trust, categorized into four themes: Ability, Integrity, Affinity, and Non-Personal Factors. Attributes such as collaborative and ethical behavior, academic skills, and higher grades are often used to select peers, mainly through recommendation systems and user profiles. To organize our findings, we elaborated two conceptual maps describing the main characteristics of trust definitions and the attributes classification by phases and themes.
Nonlinear Domain Engineering for Quantum Technologies
2025-03-23
Tim F. Weiss, Alberto Peruzzo
The continuously growing effort towards developing real-world quantum technological applications has come to demand an increasing amount of flexibility from its respective platforms. This review presents a highly adaptable engineering technique for photonic quantum technologies based on the artificial structuring of the material nonlinearity. This technique, while, in a simple form, already featured across the full breadth of photonic quantum technologies, has undergone significant development over the last decade, now featuring advanced, aperiodic designs. This review gives an introduction to the three-wave-mixing processes lying at the core of this approach, and illustrates, on basis of the underlying quantum-mechanical description, how they can artificially be manipulated to engineer the corresponding photon characteristics. It then describes how this technique can be employed to realize a number of very different objectives which are expected to find application across the full range of photonic quantum technologies, and presents a summary of the research done towards these ends to date.
Human-AI Interaction and User Satisfaction: Empirical Evidence from Online Reviews of AI Products
2025-03-23
Stefan Pasch, Sun-Young Ha
Human-AI Interaction (HAI) guidelines and design principles have become increasingly important in both industry and academia to guide the development of AI systems that align with user needs and expectations. However, large-scale empirical evidence on how HAI principles shape user satisfaction in practice remains limited. This study addresses that gap by analyzing over 100,000 user reviews of AI-related products from G2, a leading review platform for business software and services. Based on widely adopted industry guidelines, we identify seven core HAI dimensions and examine their coverage and sentiment within the reviews. We find that the sentiment on four HAI dimensions-adaptability, customization, error recovery, and security-is positively associated with overall user satisfaction. Moreover, we show that engagement with HAI dimensions varies by professional background: Users with technical job roles are more likely to discuss system-focused aspects, such as reliability, while non-technical users emphasize interaction-focused features like customization and feedback. Interestingly, the relationship between HAI sentiment and overall satisfaction is not moderated by job role, suggesting that once an HAI dimension has been identified by users, its effect on satisfaction is consistent across job roles.
Threshold Crossings as Tail Events for Catastrophic AI Risk
2025-03-23
Elija Perrier
We analyse circumstances in which bifurcation-driven jumps in AI systems are associated with emergent heavy-tailed outcome distributions. By analysing how a control parameter's random fluctuations near a catastrophic threshold generate extreme outcomes, we demonstrate in what circumstances the probability of a sudden, large-scale, transition aligns closely with the tail probability of the resulting damage distribution. Our results contribute to research in monitoring, mitigation and control of AI systems when seeking to manage potentially catastrophic AI risk.
Detecting and Mitigating DDoS Attacks with AI: A Survey
2025-03-22
Alexandru Apostu, Silviu Gheorghe, Andrei Hîji, Nicolae Cleju, Andrei Pătraşcu, Cristian Rusu, Radu Ionescu, Paul Irofti
Distributed Denial of Service attacks represent an active cybersecurity research problem. Recent research shifted from static rule-based defenses towards AI-based detection and mitigation. This comprehensive survey covers several key topics. Preeminently, state-of-the-art AI detection methods are discussed. An in-depth taxonomy based on manual expert hierarchies and an AI-generated dendrogram are provided, thus settling DDoS categorization ambiguities. An important discussion on available datasets follows, covering data format options and their role in training AI detection methods together with adversarial training and examples augmentation. Beyond detection, AI based mitigation techniques are surveyed as well. Finally, multiple open research directions are proposed.
ClaraVid: A Holistic Scene Reconstruction Benchmark From Aerial Perspective With Delentropy-Based Complexity Profiling
2025-03-22
Radu Beche, Sergiu Nedevschi
The development of aerial holistic scene understanding algorithms is hindered by the scarcity of comprehensive datasets that enable both semantic and geometric reconstruction. While synthetic datasets offer an alternative, existing options exhibit task-specific limitations, unrealistic scene compositions, and rendering artifacts that compromise real-world applicability. We introduce ClaraVid, a synthetic aerial dataset specifically designed to overcome these limitations. Comprising 16,917 high-resolution images captured at 4032x3024 from multiple viewpoints across diverse landscapes, ClaraVid provides dense depth maps, panoptic segmentation, sparse point clouds, and dynamic object masks, while mitigating common rendering artifacts. To further advance neural reconstruction, we introduce the Delentropic Scene Profile (DSP), a novel complexity metric derived from differential entropy analysis, designed to quantitatively assess scene difficulty and inform reconstruction tasks. Utilizing DSP, we systematically benchmark neural reconstruction methods, uncovering a consistent, measurable correlation between scene complexity and reconstruction accuracy. Empirical results indicate that higher delentropy strongly correlates with increased reconstruction errors, validating DSP as a reliable complexity prior. Currently under review, upon acceptance the data and code will be available at $\href{https://rdbch.github.io/claravid}{rdbch.github.io/ClaraVid}$.
Local patch analysis of ACT DR6 convergence map using morphological statistics
2025-03-22
Masroor Bashir, Nidharssan S, Pravabati Chingangbam, Fazlu Rahman, Priya Goyal, Stephen Appleby, Changbom Park
We carry out a comprehensive hierarchical multi-scale morphological analysis to search for anomalous behaviour in the large scale matter distribution using convergence map provided by the Atacama Cosmology Telescope (ACT) Data Release 6. We use a suite of morphological statistics consisting of Minkowski functionals, contour Minkowski tensor and Betti numbers for the analysis, and compute their deviations from the ensemble expectations and median values obtained from isotropic $\Lambda$CDM simulations provided by ACT. To assess the statistical significance of these deviations, we devise a general methodology based on the persistence of the deviations across threshold ranges and spatial resolutions, while taking into account correlations among the statistics. From the analysis of the full dataset, and hemispherical regions, we find consistency with isotropic $\Lambda$CDM simulations provided by ACT. Since deviations in smaller sky regions tend to get washed out when averaged over larger regions, we further analyze smaller sky patches. This localized analysis reveals some patches that exhibit statistically significant deviations which we refer to as 'anomalous'. We find that near the CMB cold spot, both the positive and negative density fluctuations are anomalous, at 99% CL and 95% CL respectively. This region also encompasses an anomalous southern spot previously identified in Planck CMB temperature data. We also carry out a comparison of anomalous patches identified here for ACT data with a previous analysis of the convergence map from Planck. We do not find common patches between the two datasets, which suggest that the anomalous behavior of the Planck data arises from noise in the map. Further investigation of the atypical patches using large scale structure surveys is warranted to determine their physical origin.
Gauge preservation in renormalization for Yang-Mills and gravity theories
2025-03-22
Osvaldo Santillán, Alejandro Morano
In the present work, multiplicative renormalization \cite{dixon} for Yang-Mills theories is reviewed. While this subject is not new, it is suggested that a clear understanding of these methods leads to a systematic way for interpreting the counter terms needed for non multiplicative renormalization of quantum gravity, for models such as \cite{dewitt}-\cite{stelle2}. These models are renormalizable but contain apparent instabilities leading to possible unitarity loss, an earlier analog is \cite{pais}. This systematic may be interesting, especially in the modern context, since there are efforts for avoiding with those apparent instabilities by employing variants of the standard quantization methods \cite{mannheim1}-\cite{salve}.
ODIN: Clustering Analysis of 14,000 Lyα Emitting Galaxies at z=2.4, 3.1, and 4.5
2025-03-22
Danisbel Herrera, Eric Gawiser, Barbara Benda, Nicole Firestone, Vandana Ramakrishnan, Byeongha Moon, Kyoung-Soo Lee, Changbom Park, Francisco Valdes, Yujin Yang, M. Celeste Artale, Robin Ciardullo, Caryl Gronwall, Lucia Guaita, Ho Seong Hwang, Jacob Kennedy, Ankit Kumar, Ann Zabludoff
Lyman Alpha Emitters (LAEs) are star-forming galaxies that efficiently probe the spatial distribution of galaxies in the high redshift universe. The spatial clustering of LAEs reflects the properties of their individual host dark matter halos, allowing us to study the evolution of the galaxy-halo connection. We analyze the clustering of 5233, 5220, and 3706 LAEs at z = 2.4, 3.1, and 4.5, respectively, in the 9 deg$^2$ COSMOS field from the One-hundred-deg$^2$ DECam Imaging in Narrowbands (ODIN) survey. After correcting for redshift space distortions, LAE contamination rates, and the integral constraint, the observed angular correlation functions imply linear galaxy bias factors of b = $1.64^{+0.26}_{-0.23}, 1.84^{+0.24}_{-0.22}$, and $2.93^{+0.41}_{-0.36}$, for z = 2.4, 3.1, and 4.5, respectively. The median dark matter halo masses inferred from these measurements are $log(M_h/M_{\odot}) = 11.34^{+0.30}_{-0.31}, 10.94^{+0.26}_{-0.28}$, and $10.83^{+0.26}_{-0.24}$ for the three samples, respectively. The analysis also reveals that LAEs occupy roughly 3-5% of the halos whose clustering strength matches that of the LAEs. The large contrast between this low halo occupation fraction and the high fraction of continuum-selected star-forming galaxies that exhibit Ly$\alpha$ in emission implies that LAEs are unusually luminous for their dark matter masses.
Metacognition in Content-Centric Computational Cognitive C4 Modeling
2025-03-22
Sergei Nirenburg, Marjorie McShane, Sanjay Oruganti
For AI agents to emulate human behavior, they must be able to perceive, meaningfully interpret, store, and use large amounts of information about the world, themselves, and other agents. Metacognition is a necessary component of all of these processes. In this paper, we briefly a) introduce content-centric computational cognitive (C4) modeling for next-generation AI agents; b) review the long history of developing C4 agents at RPI's LEIA (Language-Endowed Intelligent Agents) Lab; c) discuss our current work on extending LEIAs' cognitive capabilities to cognitive robotic applications developed using a neuro symbolic processing model; and d) sketch plans for future developments in this paradigm that aim to overcome underappreciated limitations of currently popular, LLM-driven methods in AI.
Assessing workflow impact and clinical utility of AI-assisted brain aneurysm detection: a multi-reader study
2025-03-22
Tommaso Di Noto, Sofyan Jankowski, Francesco Puccinelli, Guillaume Marie, Sebastien Tourbier, Yasser Aleman-Gomez, Oscar Esteban, Ricardo Corredor-Jerez, Guillaume Saliou, Patric Hagmann, Meritxell Bach Cuadra, Jonas Richiardi
Despite the plethora of AI-based algorithms developed for anomaly detection in radiology, subsequent integration into clinical setting is rarely evaluated. In this work, we assess the applicability and utility of an AI-based model for brain aneurysm detection comparing the performance of two readers with different levels of experience (2 and 13 years). We aim to answer the following questions: 1) Do the readers improve their performance when assisted by the AI algorithm? 2) How much does the AI algorithm impact routine clinical workflow? We reuse and enlarge our open-access, Time-Of-Flight Magnetic Resonance Angiography dataset (N=460). We use 360 subjects for training/validating our algorithm and 100 as unseen test set for the reading session. Even though our model reaches state-of-the-art results on the test set (sensitivity=74%, false positive rate=1.6), we show that neither the junior nor the senior reader significantly increase their sensitivity (p=0.59, p=1, respectively). In addition, we find that reading time for both readers is significantly higher in the "AI-assisted" setting than in the "Unassisted" (+15 seconds, on average; p=3x10^(-4) junior, p=3x10^(-5) senior). The confidence reported by the readers is unchanged across the two settings, indicating that the AI assistance does not influence the certainty of the diagnosis. Our findings highlight the importance of clinical validation of AI algorithms in a clinical setting involving radiologists. This study should serve as a reminder to the community to always examine the real-word effectiveness and workflow impact of proposed algorithms.
Neutron particle transport 3D method of characteristic Multi GPU platform Parallel Computing
2025-03-22
Faguo Zhou, Shunde Li, Rong Xue, Lingkun Bu, Ningming Nie, Peng Shi, Jue Wang, Yun Hu, Zongguo Wang, Yangang Wang, Qinmeng Yang, Miao Yu
Three-dimensional neutron transport calculations using the Method of Characteristics (MOC) are highly regarded for their exceptional computational efficiency, precision, and stability. Nevertheless, when dealing with extensive-scale computations, the computational demands are substantial, leading to prolonged computation times. To address this challenge while considering GPU memory limitations, this study transplants the real-time generation and characteristic line computation techniques onto the GPU platform. Empirical evidence emphasizes that the GPU-optimized approach maintains a heightened level of precision in computation results and produces a significant acceleration effect. Furthermore, to fully harness the computational capabilities of GPUs, a dual approach involving characteristic line preloading and load balancing mechanisms is adopted, further enhancing computational efficiency. The resulting increase in computational efficiency, compared to traditional methods, reaches an impressive 300 to 400-fold improvement.
A Survey on Mathematical Reasoning and Optimization with Large Language Models
2025-03-22
Ali Forootani
Mathematical reasoning and optimization are fundamental to artificial intelligence and computational problem-solving. Recent advancements in Large Language Models (LLMs) have significantly improved AI-driven mathematical reasoning, theorem proving, and optimization techniques. This survey explores the evolution of mathematical problem-solving in AI, from early statistical learning approaches to modern deep learning and transformer-based methodologies. We review the capabilities of pretrained language models and LLMs in performing arithmetic operations, complex reasoning, theorem proving, and structured symbolic computation. A key focus is on how LLMs integrate with optimization and control frameworks, including mixed-integer programming, linear quadratic control, and multi-agent optimization strategies. We examine how LLMs assist in problem formulation, constraint generation, and heuristic search, bridging theoretical reasoning with practical applications. We also discuss enhancement techniques such as Chain-of-Thought reasoning, instruction tuning, and tool-augmented methods that improve LLM's problem-solving performance. Despite their progress, LLMs face challenges in numerical precision, logical consistency, and proof verification. Emerging trends such as hybrid neural-symbolic reasoning, structured prompt engineering, and multi-step self-correction aim to overcome these limitations. Future research should focus on interpretability, integration with domain-specific solvers, and improving the robustness of AI-driven decision-making. This survey offers a comprehensive review of the current landscape and future directions of mathematical reasoning and optimization with LLMs, with applications across engineering, finance, and scientific research.
RIS-based Physical Layer Security for Integrated Sensing and Communication: A Comprehensive Survey
2025-03-22
Yongxiao Li, Feroz Khan, Manzoor Ahmed, Aized Amin Soofi, Wali Ullah Khan, Chandan Kumar Sheemar, Muhammad Asif, Zhu Han
Integrated Sensing and Communication (ISAC) is a crucial component of future wireless networks, enabling seamless integration of Communication and Sensing (C\&S) functionalities. However, ensuring security in ISAC systems remains a significant challenge, as both C\&S data are susceptible to adversarial threats. Physical Layer Security (PLS) has emerged as a key framework for mitigating these risks at the transmission level. Reconfigurable Intelligent Surfaces (RIS) further enhance PLS by dynamically shaping the radio environment to improve both secrecy along with C\&S performance. This survey begins with an overview of RIS, PLS, and ISAC fundamentals, establishing a foundation for understanding their integration. The state-of-the-art RIS-assisted PLS approaches in ISAC systems are then categorized into passive RIS and Active RIS (ARIS) paradigms. Passive RIS-based techniques focus on optimizing system throughput, covert communication, and Secrecy Rates (SRs), alongside improving sensing Signal-to-Noise Ratio (SNR) and Weighted Sum Rate (WSR) under various constraints. ARIS-based strategies extend these capabilities by actively optimizing beamforming to enhance secrecy and covert rates while ensuring robust sensing under communication and security constraints. By reviewing both passive and ARIS-based security frameworks, this survey highlights the transformative role of RIS in strengthening ISAC security. Furthermore, it explores key optimization methodologies, technical challenges, and future research directions for integrating RIS with PLS to ensure secure and efficient ISAC in next-generation 6G wireless networks.
On the (im)possibility of sustainable artificial intelligence. Why it does not make sense to move faster when heading the wrong way
2025-03-22
Rainer Rehak
Artificial intelligence (AI) is currently considered a sustainability "game-changer" within and outside of academia. In order to discuss sustainable AI this article draws from insights by critical data and algorithm studies, STS, transformative sustainability science, critical computer science, and public interest theory. I argue that while there are indeed many sustainability-related use cases for AI, they are likely to have more overall drawbacks than benefits. To substantiate this claim, I differentiate three 'AI materialities' of the AI supply chain: first the literal materiality (e.g. water, cobalt, lithium, energy consumption etc.), second, the informational materiality (e.g. lots of data and centralised control necessary), and third, the social materiality (e.g. exploitative data work, communities harm by waste and pollution). In all materialities, effects are especially devastating for the global south while benefiting the global north. A second strong claim regarding sustainable AI circles around so called apolitical optimisation (e.g. regarding city traffic), however the optimisation criteria (e.g. cars, bikes, emissions, commute time, health) are purely political and have to be collectively negotiated before applying AI optimisation. Hence, sustainable AI, in principle, cannot break the glass ceiling of transformation and might even distract from necessary societal change. To address that I propose to stop 'unformation gathering' and to apply the 'small is beautiful' principle. This aims to contribute to an informed academic and collective negotiation on how to (not) integrate AI into the sustainability project while avoiding to reproduce the status quo by serving hegemonic interests between useful AI use cases, techno-utopian salvation narratives, technology-centred efficiency paradigms, the exploitative and extractivist character of AI and concepts of digital degrowth.
CardioTabNet: A Novel Hybrid Transformer Model for Heart Disease Prediction using Tabular Medical Data
2025-03-22
Md. Shaheenur Islam Sumon, Md. Sakib Bin Islam, Md. Sohanur Rahman, Md. Sakib Abrar Hossain, Amith Khandakar, Anwarul Hasan, M Murugappan, Muhammad E. H. Chowdhury
The early detection and prediction of cardiovascular diseases are crucial for reducing the severe morbidity and mortality associated with these conditions worldwide. A multi-headed self-attention mechanism, widely used in natural language processing (NLP), is operated by Transformers to understand feature interactions in feature spaces. However, the relationships between various features within biological systems remain ambiguous in these spaces, highlighting the necessity of early detection and prediction of cardiovascular diseases to reduce the severe morbidity and mortality with these conditions worldwide. We handle this issue with CardioTabNet, which exploits the strength of tab transformer to extract feature space which carries strong understanding of clinical cardiovascular data and its feature ranking. As a result, performance of downstream classical models significantly showed outstanding result. Our study utilizes the open-source dataset for heart disease prediction with 1190 instances and 11 features. In total, 11 features are divided into numerical (age, resting blood pressure, cholesterol, maximum heart rate, old peak, weight, and fasting blood sugar) and categorical (resting ECG, exercise angina, and ST slope). Tab transformer was used to extract important features and ranked them using random forest (RF) feature ranking algorithm. Ten machine-learning models were used to predict heart disease using selected features. After extracting high-quality features, the top downstream model (a hyper-tuned ExtraTree classifier) achieved an average accuracy rate of 94.1% and an average Area Under Curve (AUC) of 95.0%. Furthermore, a nomogram analysis was conducted to evaluate the model's effectiveness in cardiovascular risk assessment. A benchmarking study was conducted using state-of-the-art models to evaluate our transformer-driven framework.
Natural language video localization (NLVL) is a crucial task in video understanding that aims to localize the target moment in videos specified by a given language description. Recently, a point-supervised paradigm has been presented to address this task, requiring only a single annotated frame within the target moment rather than complete temporal boundaries. Compared with the fully-supervised paradigm, it offers a balance between localization accuracy and annotation cost. However, due to the absence of complete annotation, it is challenging to align the video content with language descriptions, consequently hindering accurate moment prediction. To address this problem, we propose a new COllaborative Temporal consistEncy Learning (COTEL) framework that leverages the synergy between saliency detection and moment localization to strengthen the video-language alignment. Specifically, we first design a frame- and a segment-level Temporal Consistency Learning (TCL) module that models semantic alignment across frame saliencies and sentence-moment pairs. Then, we design a cross-consistency guidance scheme, including a Frame-level Consistency Guidance (FCG) and a Segment-level Consistency Guidance (SCG), that enables the two temporal consistency learning paths to reinforce each other mutually. Further, we introduce a Hierarchical Contrastive Alignment Loss (HCAL) to comprehensively align the video and text query. Extensive experiments on two benchmarks demonstrate that our method performs favorably against SoTA approaches. We will release all the source codes.
Content annotation at scale remains challenging, requiring substantial human expertise and effort. This paper presents a case study in code documentation analysis, where we explore the balance between automation efficiency and annotation accuracy. We present MCHR (Multi-LLM Consensus with Human Review), a novel semi-automated framework that enhances annotation scalability through the systematic integration of multiple LLMs and targeted human review. Our framework introduces a structured consensus-building mechanism among LLMs and an adaptive review protocol that strategically engages human expertise. Through our case study, we demonstrate that MCHR reduces annotation time by 32% to 100% compared to manual annotation while maintaining high accuracy (85.5% to 98%) across different difficulty levels, from basic binary classification to challenging open-set scenarios.
We investigate the impact of massive primordial black holes (PBHs; $m_{\rm BH}\sim 10^6~M_{\odot}$) on the star formation and first galaxy assembly process using high-resolution hydrodynamical simulations from $z = 1100$ to $z \sim 9$. We find that PBH accretion is self-regulated by feedback, suppressing mass growth unless feedback is weak. PBHs accelerate structure formation by seeding dark matter halos and gravitationally attracting gas, but strong feedback can delay cooling and suppress star formation. In addition, the presence of baryon-dark matter streaming creates an offset between the PBH location and the peaks induced in gas density, promoting earlier and more efficient star formation compared to standard $\Lambda$CDM. By $z \sim 10$, PBH-seeded galaxies form dense star clusters, with PBH-to-stellar mass ratios comparable to observed high-$z$ AGN like UHZ-1. Our results support PBHs as viable SMBH seeds but do not exclude alternative scenarios. We emphasize that PBH-seeding provides a natural explanation for some of the newly-discovered overmassive SMBHs at high redshift, in particular those with extreme ratios of BH-to-dynamical (virial) mass that challenge standard formation channels. Future studies with ultra-deep JWST surveys, the Roman Space Telescope, and radio surveys with facilities such as SKA and HERA will be critical in distinguishing PBH-driven SMBH growth from other pathways.
The rapid advancement of machine learning and artificial intelligence (AI)-driven techniques is revolutionizing materials discovery, property prediction, and material design by minimizing human intervention and accelerating scientific progress. This review provides a comprehensive overview of smart, machine learning (ML)-driven approaches, emphasizing their role in predicting material properties, discovering novel compounds, and optimizing material structures. Key methodologies ranging from deep learning, graph neural networks, and Bayesian optimization to automated generative models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs) enable the autonomous design of materials with tailored functionalities. By leveraging AutoML frameworks (e.g., AutoGluon, TPOT, and H2O.ai), researchers can automate the model selection, hyperparameter tuning, and feature engineering, significantly improving the efficiency of materials informatics. Furthermore, the integration of AI-driven robotic laboratories and high-throughput computing has established a fully automated pipeline for rapid synthesis and experimental validation, drastically reducing the time and cost of material discovery. This review highlights real-world applications of automated ML-driven approaches in predicting mechanical, thermal, electrical, and optical properties of materials, demonstrating successful cases in superconductors, catalysts, photovoltaics, and energy storage systems. We also address key challenges, such as data quality, interpretability, and the integration of AutoML with quantum computing, which are essential for future advancements. Ultimately, the synergy between AI, automated experimentation, and computational modeling transforms the way the materials are discovered, optimized, and designed, paving the way for next-generation innovations in energy, electronics, and nanotechnology.
LLMs as Planning Modelers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models
2025-03-22
Marcus Tantakoun, Xiaodan Zhu, Christian Muise
Large Language Models (LLMs) excel in various natural language tasks but often struggle with long-horizon planning problems requiring structured reasoning. This limitation has drawn interest in integrating neuro-symbolic approaches within the Automated Planning (AP) and Natural Language Processing (NLP) communities. However, identifying optimal AP deployment frameworks can be daunting. This paper aims to provide a timely survey of the current research with an in-depth analysis, positioning LLMs as tools for extracting and refining planning models to support reliable AP planners. By systematically reviewing the current state of research, we highlight methodologies, and identify critical challenges and future directions, hoping to contribute to the joint research on NLP and Automated Planning.
A Survey on Structured State Space Sequence (S4) Models
2025-03-22
Shriyank Somvanshi, Md Monzurul Islam, Mahmuda Sultana Mimi, Sazzad Bin Bashar Polock, Gaurab Chhetri, Subasish Das
Recent advancements in sequence modeling have led to the emergence of Structured State Space Models (SSMs) as an efficient alternative to Recurrent Neural Networks (RNNs) and Transformers, addressing challenges in long-range dependency modeling and computational efficiency. While RNNs suffer from vanishing gradients and sequential inefficiencies, and Transformers face quadratic complexity, SSMs leverage structured recurrence and state-space representations to achieve superior long-sequence processing with linear or near-linear complexity. This survey provides a comprehensive review of SSMs, tracing their evolution from the foundational S4 model to its successors like Mamba, Simplified Structured State Space Sequence Model (S5), and Jamba, highlighting their improvements in computational efficiency, memory optimization, and inference speed. By comparing SSMs with traditional sequence models across domains such as natural language processing (NLP), speech recognition, vision, and time-series forecasting, we demonstrate their advantages in handling long-range dependencies while reducing computational overhead. Despite their potential, challenges remain in areas such as training optimization, hybrid modeling, and interpretability. This survey serves as a structured guide for researchers and practitioners, detailing the advancements, trade-offs, and future directions of SSM-based architectures in AI and deep learning.
On the road to the radius valley: distinguishing between gas dwarfs and water worlds with young transiting exoplanets
2025-03-21
James G. Rogers
The detection of young transiting exoplanets represents a new frontier in our understanding of planet formation and evolution. For the population of observed close-in sub-Neptunes, two proposed formation pathways can reproduce their observed masses and radii at $\sim$Gyr ages: the "gas dwarf" hypothesis and the "water world" hypothesis. We show that a sub-Neptune's size at early ages $\lesssim 100$ Myrs is strongly dependent on the bulk mean molecular weight within its envelope. As a result, gas dwarfs and water worlds should diverge in size at early ages since the mean molecular weight of gas dwarf envelopes is predicted to be smaller than that of water worlds. We construct population models under both scenarios that reproduce Kepler demographics in the age range $\sim1-10$ Gyrs. We find tentative evidence that the gas dwarf model is more consistent with the small population of young exoplanets $< 40$ Myrs from TESS. We show that planet radius is relatively insensitive to planet mass for young, puffy sub-Neptunes, meaning that well-characterised masses are not necessarily required to exploit the effects of mean molecular weight at the population level. We confirm the predicted difference in planet size between the models is also true under mixed-envelope scenarios, in which envelopes consist of mixtures of hydrogen and steam. We highlight that transit surveys of young exoplanets should target the youngest observable stellar clusters to exploit the effects of mean molecular weight.
Plasma treated metals after H- irradiation and its effect on vacuum breakdown behaviour
2025-03-21
C. Serafim, S. Calatroni, F. Djurabekova, M. C. Giordano, M. Himmerlich, V. Bjelland, C. Kouzios, P. Costa Pinto, A. T. Perez-Fontenla, W. Wuensch, A. Grudiev, S. Sgobba
Vacuum breakdown in accelerator structures is a critical challenge that occurs under high electric fields. In environments subjected to hydrogen ion irradiation or high beam losses, such as in Radio-Frequency Quadrupoles (RFQ), residual hydrocarbons from the vacuum may result in carbon contamination of the metal surfaces from charged particle induced cracking. Under these conditions, it has been assessed that surface carbon contamination leads to a decrement of the surface electric field holding properties. This study extends the latest research by exploring the efficacy of Oxygen Plasma Cleaning (OPC) on metal electrodes irradiated by low energy hydrogen ion beam with the purpose of reducing surface carbon contamination. OPC treatment has been employed on different metals, namely copper beryllium (CuBe2), oxygen-free copper (Cu-OFE), and stainless steel (SS316LN). Treated electrodes have been tested for electric field performance in a DC pulsed system and results compared with non-irradiated electrodes and irradiated ones without OPC treatment. The study indicates a significant reduction in carbon contamination by OPC, enough to allow irradiated materials to achieve performances comparable with the electric field strength of raw surfaces. Moreover, it has been observed that stainless steel samples had some alteration in the surface chemistry that enhanced the materials ability to sustain high electric fields while decreasing vacuum arcing events. Notably, OPC treated SS316LN electrodes surpassed the performance value of untreated ones, demonstrating the potential of plasma treatments in extending the operational performance of accelerator components.
Radio Spectral Energy Distribution of Low-$z$ Metal Poor Extreme Starburst Galaxies: Novel insights on the escape of ionizing photons
2025-03-21
Omkar Bait, Daniel Schaerer, Yuri I. Izotov, Biny Sebastian
Recent optical surveys have found a rare population of low-$z$ ($z \sim 0.01 - 0.06$) extreme star-forming galaxies (xSFGs) that are the most metal-poor galaxies with strong emission lines, extremely high specific star-formation rate and low stellar mass. Using the Hubble Space Telescope Cosmic Origins Spectrograph, it was found that xSFGs are strong Ly$\alpha$ emitters. Their Ly$\alpha$ properties indirectly suggest that they are also strong Ly continuum (LyC) leakers. This along with several other global properties makes them similar to the recently found $z > 6$ reionization-era star-forming (SF) galaxies using the James Webb Space Telescope. Here we aim to study the radio spectral energy distribution (radio-SED) of $8$ xSFGs to understand mechanisms behind the extreme nature of SF in these galaxies, particularly their high ionisation state and possible strong LyC leakage. We present new radio continuum (RC) observations using the upgraded Giant Metrewave Radio Telescope (uGMRT) at Band-$5$ ($1060 - 1460$ MHz) together with the Karl G. Jansky Very Large Array (VLA) at S- ($2-4$ GHz), C- ($4-8$ GHz), X- ($8-12$ GHz) and Ku- ($12-18$ GHz) bands and archival LOw Frequency ARray (LOFAR) survey data between $120-168$ MHz for a few sources. The radio-SED of xSFGs is flat between $6-15$ GHz and shows a strong evidence for a turnover at lower frequencies in the range of $2-10$ GHz. They can be well described using a thermally dominated radio spectra with a free-free absorption component with a high emission measure. Thus the SF complexes in these galaxies are extremely young (pre-SNe stage; below $\sim 5$ Myrs) and the ISM is very dense suggesting a dominance of young massive star clusters. This can explain several extreme star-forming properties, along with the potential leakage of a significant amount of LyC photons in metal-poor extreme starburst galaxies. (abridged)
HST Grism Observations of a z~1.8 Cluster Candidate from the Clusters Occupied by Bent Radio AGN (COBRA) Survey
2025-03-21
Courtney B. Watson, Elizabeth L. Blanton, Emmet Golden-Marx, Matthew L. N. Ashby, Scott W. Randall, J. D. Wing, E. M. Douglass
We present new Hubble Space Telescope/Wide Field Camera 3 G141 grism observations for COBRA1411+3415, originally identified as a high-redshift cluster candidate in the Clusters Occupied by Bent Radio AGN (COBRA) survey using radio, infrared, and optical data. We spectroscopically identify seven cluster members within a 0.5 Mpc radius with grism redshifts in the range $1.8006 \leq z_{grism} \leq 1.8175$, consistent with COBRA1411+3415 being a high-redshift cluster with a mean redshift of $\langle z_{grism}\rangle = 1.8106 \pm 0.0006$. The detection of seven galaxies within this small redshift range is significant above the background distribution of galaxies at the level of 5$\sigma$. The line-of-sight velocity dispersion of the cluster is found to be $\sigma_{\parallel} = 701^{+347}_{-138}$ km/s with a virial mass of $M_{200} \approx 2.2^{+3.3}_{-1.3}\times 10^{14}$ M$_{\odot}$. However, the mass may be lower if the cluster is still in formation. In projected phase-space, we also identify two possible infalling members of COBRA1411+3415 and two additional structures at $z\sim 1.73$ and $z\sim 1.88$. The similar spatial distributions and small projected separation from the main cluster suggest they could be a part of the same large-scale filament and together may form a protocluster system that could eventually merge to form a single, massive cluster. COBRA1411+3415 is the highest redshift cluster to be spectroscopically confirmed using a bent, double-lobed radio source as a cluster tracer.
The dark sector offers a compelling theoretical framework for addressing the nature of dark matter while potentially solving other fundamental problems in physics. This review focuses on light dark flavored sector models, which are those where the flavor structure of the interactions with the standard model is non-trivial and distinguish among different fermion families. Such scenarios feature flavor violation leading to unique experimental signatures, such as flavor-changing neutral current decays of heavy hadrons (kaons, $D$ and $B$ mesons, baryons) and leptons (muons and taus) with missing energy carried away by light dark-sector particles. In this article, we review their motivation, summarize current constraints, highlight discovery opportunities in ongoing and future flavor experiments, and discuss implications for astrophysics and cosmology.
We review the 20-year history of the Microstate Geometry Programme and the essential role that supergravity has played, and will continue to play, in the description of black-hole microstructure.
Structure evolution with cosmic backgrounds from radio to far infrared
2025-03-21
Carlo Burigana, Tiziana Trombetti, Matteo Bonato, Adélie Gorce, Luigi Toffolatti
Cosmic background radiation, both diffuse and discrete in nature, produced at different cosmic epochs before and after recombination, provides key information on the evolution of cosmic structures. We discuss the main classes of sources that contribute to the extragalactic background light from radio to sub-millimetre wavelenghs and the currently open question on the level of the cosmic radio background spectrum. The redshifted 21cm line signal from cosmological neutral Hydrogen during the primeval phases of cosmic structures as a probe of the cosmological reionisation process is presented, along with the route for confident detection of this signal. We then describe the basic formalism and the feasibility to study via a differential approach, based mainly on dipole analysis, the tiny imprints in the CB spectrum expected from a variety of cosmological and astrophysical processes at work during the early phases of cosmic perturbation and structure evolution. Finally, we discuss the identification of high-redshift sub-millimetre lensed galaxies with extreme magnifications in the Planck maps and their use for the comprehension of fundamental processes in early galaxy formation and evolution.
Bugdar: AI-Augmented Secure Code Review for GitHub Pull Requests
2025-03-21
John Naulty, Eason Chen, Joy Wang, George Digkas, Kostas Chalkias
As software systems grow increasingly complex, ensuring security during development poses significant challenges. Traditional manual code audits are often expensive, time-intensive, and ill-suited for fast-paced workflows, while automated tools frequently suffer from high false-positive rates, limiting their reliability. To address these issues, we introduce Bugdar, an AI-augmented code review system that integrates seamlessly into GitHub pull requests, providing near real-time, context-aware vulnerability analysis. Bugdar leverages fine-tunable Large Language Models (LLMs) and Retrieval Augmented Generation (RAGs) to deliver project-specific, actionable feedback that aligns with each codebase's unique requirements and developer practices. Supporting multiple programming languages, including Solidity, Move, Rust, and Python, Bugdar demonstrates exceptional efficiency, processing an average of 56.4 seconds per pull request or 30 lines of code per second. This is significantly faster than manual reviews, which could take hours per pull request. By facilitating a proactive approach to secure coding, Bugdar reduces the reliance on manual reviews, accelerates development cycles, and enhances the security posture of software systems without compromising productivity.
2025-03-21
Arthur Amorim, Max Taylor, Trevor Kann, Gary T. Leavens, William L. Harrison, Lance Joneckis
Unmanned aerial vehicles (UAVs) depend on untrusted software components to automate dangerous or critical missions, making them a desirable target for attacks. Some work has been done to prevent an attacker who has either compromised a ground control station or parts of a UAV's software from sabotaging the vehicle, but not both. We present an architecture running a UAV software stack with runtime monitoring and seL4-based software isolation that prevents attackers from both exploiting software bugs and utilizing stealthy attacks. Our architecture retrofits legacy UAVs and secures the popular MAVLink protocol, making wide adoption possible.
2025-03-21
Minsu Kim, Jiayao Gu, Ye Yuan, Taeyoung Yun, Zixuan Liu, Yoshua Bengio, Can Chen
Offline optimization is a fundamental challenge in science and engineering, where the goal is to optimize black-box functions using only offline datasets. This setting is particularly relevant when querying the objective function is prohibitively expensive or infeasible, with applications spanning protein engineering, material discovery, neural architecture search, and beyond. The main difficulty lies in accurately estimating the objective landscape beyond the available data, where extrapolations are fraught with significant epistemic uncertainty. This uncertainty can lead to objective hacking(reward hacking), exploiting model inaccuracies in unseen regions, or other spurious optimizations that yield misleadingly high performance estimates outside the training distribution. Recent advances in model-based optimization(MBO) have harnessed the generalization capabilities of deep neural networks to develop offline-specific surrogate and generative models. Trained with carefully designed strategies, these models are more robust against out-of-distribution issues, facilitating the discovery of improved designs. Despite its growing impact in accelerating scientific discovery, the field lacks a comprehensive review. To bridge this gap, we present the first thorough review of offline MBO. We begin by formalizing the problem for both single-objective and multi-objective settings and by reviewing recent benchmarks and evaluation metrics. We then categorize existing approaches into two key areas: surrogate modeling, which emphasizes accurate function approximation in out-of-distribution regions, and generative modeling, which explores high-dimensional design spaces to identify high-performing designs. Finally, we examine the key challenges and propose promising directions for advancement in this rapidly evolving field including safe control of superintelligent systems.
The SPT-Deep Cluster Catalog: Sunyaev-Zel'dovich Selected Clusters from Combined SPT-3G and SPTpol Measurements over 100 Square Degrees
2025-03-21
K. Kornoelje, L. E. Bleem, E. S. Rykoff, T. M. C. Abbott, P. A. R. Ade, M. Aguena, O. Alves, A. J. Anderson, F. Andrade-Oliveira, B. Ansarinejad, M. Archipley, M. L. N. Ashby, J. E. Austermann, D. Bacon, L. Balkenhol, J. A. Beall, K. Benabed, A. N. Bender, B. A. Benson, F. Bianchini, S. Bocquet, F. R. Bouchet, D. Brooks, D. L. Burke, M. Calzadilla, E. Camphuis, J. E. Carlstrom, A. Carnero Rosell, J. Carretero, C. L. Chang, P. Chaubal, H. C. Chiang, P. M. Chichura, A. Chokshi, T. -L. Chou, R. Citron, A. Coerver, C. Corbett Moran, M. Costanzi, T. M. Crawford, A. T. Crites, L. N. da Costa, C. Daley, T. de Haan, J. De Vicente, S. Desai, K. R. Dibert, M. A. Dobbs, P. Doel, M. Doohan, A. Doussot, D. Dutcher, W. Everett, S. Everett, C. Feng, K. R. Ferguson, I. Ferrero, K. Fichman, B. Flaugher, B. Floyd, A. Foster, D. Friedel, J. Frieman, S. Galli, J. Gallicchio, A. E. Gambrel, J. Garc'ia-Bellido, R. W. Gardner, R. Gassis, M. Gatti, F. Ge, E. M. George, G. Giannini, N. Goeckner-Wald, S. Grandis, D. Gruen, R. A. Gruendl, R. Gualtieri, F. Guidi, Guillaume Mahler, S. Guns, N. Gupta, G. Gutierrez, N. W. Halverson, S. R. Hinton, E. Hivon, G. P. Holder, D. L. Hollowood, W. L. Holzapfel, K. Honscheid, J. C. Hood, J. D. Hrubes, A. Hryciuk, N. Huang, J. Hubmayr, K. D. Irwin, J. Mena-Fernández, D. J. James, F. Kéruzoré, A. R. Khalife, M. Klein, L. Knox, M. Korman, K. Kuehn, C. -L. Kuo, O. Lahav, A. T. Lee, S. Lee, K. Levy, D. Li, M. Lima, A. E. Lowitz, A. Lowitz, C. Lu, A. Maniyar, J. L. Marshal, J. L. Marshall, E. S. Martsen, Matthew B. Bayliss, M. McDonald, J. J. McMahon, F. Menanteau, M. Millea, R. Miquel, J. J. Mohr, J. Montgomery, J. Myles, Y. Nakato, T. Natoli, J. P. Nibarger, G. I. Noble, V. Novosad, R. L. C. Ogando, Y. Omori, A. Ouellette, S. Padin, Z. Pan, S. Patil, M. E. S. Pereira, K. A. Phadke, A. Pieres, A. A. Plazas Malag'on, A. W. Pollak, K. Prabhu, C. Pryke, W. Quan, S. Raghunathan, M. Rahimi, A. Rahlin, C. L. Reichardt, M. Rodr'iguez-Monroy, A. K. Romer, M. Rouble, J. E. Ruhl, B. R. Saliwanchik, L. Salvati, S. Samuroff, E. Sanchez, A. Saro, K. K. Schaffer, E. Schiappucci, T. Schrabback, I. Sevilla-Noarbe, C. Sievers, G. Smecher, M. Smith, J. A. Sobrin, T. Somboonpanyakul, B. Stalder, A. A. Stark, E. Suchyta, M. E. C. Swanson, C. Tandoi, G. Tarle, B. Thorne, C. To, C. Trendafilova, C. Tucker, C. Umilta, T. Veach, J. D. Vieira, M. Vincenzi, A. Vitrier, Y. Wan, G. Wang, N. Weaverdyck, J. Weller, N. Whitehorn, P. Wiseman, W. L. K. Wu, V. Yefremenko, M. R. Young, J. A. Zebrowski, Y. Zhang
We present a catalog of 500 galaxy cluster candidates in the SPT-Deep field: a 100 deg$^2$ field that combines data from the SPT-3G and SPTpol surveys to reach noise levels of 3.0, 2.2, and 9.0 $\mu$K-arcmin at 95, 150, and 220 GHz, respectively. This is comparable to noise levels expected for the wide field survey of CMB-S4, a next-generation CMB experiment. Candidates are selected via the thermal Sunyaev-Zel'dovich (SZ) effect with a minimum significance of $\xi = 4.0$, resulting in a catalog of purity $\sim 89 \%$. Optical data from the Dark Energy Survey and infrared data from the Spitzer Space Telescope are used to confirm 442 cluster candidates. The clusters span $0.12 < z \lesssim 1.8$ and $1.0 \times 10^{14} M_{\odot}/h_{70} < M_{500c} < 8.7 \times 10^{14} M_{\odot}/h_{70}$. The sample's median redshift is 0.74 and the median mass is $1.7 \times 10^{14} M_{\odot}/h_{70}$; these are the lowest median mass and highest median redshift of any SZ-selected sample to date. We assess the effect of infrared emission from cluster member galaxies on cluster selection by performing a joint fit to the infrared dust and tSZ signals by combining measurements from SPT and overlapping submillimeter data from Herschel/SPIRE. We find that at high redshift ($z>1)$, the tSZ signal is reduced by $17.4^{+3.1}_{-2.9} \%$ ($3.7^{+0.7}_{-0.7}\%$) at 150 GHz (95 GHz) due to dust contamination. We repeat our cluster finding method on dust-nulled SPT maps and find the resulting catalog is consistent with the nominal SPT-Deep catalog, demonstrating dust contamination does not significantly impact the SPT-Deep selection function; we attribute this lack of bias to the inclusion of the SPT 220 GHz band.
On the manifest link between Terzan 5 and the Galactic bulge
2025-03-21
L. Origlia, F. R. Ferraro, C. Fanelli, B. Lanzoni, D. Massari, E. Dalessandro, C. Pallanca
We address the chemical link between Terzan 5 (hereafter Ter5) and the Bulge, as probed by the observed distributions of [$\alpha$/Fe] abundance ratios with varying [Fe/H] and by suitable statistical tests to evaluate their significance. We also present a comprehensive review of the kinematic and evolutionary properties of Ter5, based on all the available observational signatures and the scenarios proposed so far in the literature for the formation and evolution of Ter5, based on these observational facts and the recent modeling of its star formation and chemical enrichment history. This analysis confirms the complex nature of this massive stellar system, with robust evidences of a bulge in-situ formation and of a subsequent evolution that cannot be simply explained by a single merging/accretion event of two globulars or a globular and a giant molecular cloud, as proposed in the literature, but it requires a more complex star formation likely accompanied by some self-enrichment.
The EnviroMapper Toolkit: an Input Physicalisation that Captures the Situated Experience of Environmental Comfort in Offices
2025-03-21
Silvia Cazacu, Stien Poncelet, Emma Feijtraij, Andrew Vande Moere
The environmental comfort in offices is traditionally captured by surveying an entire workforce simultaneously, which yet fails to capture the situatedness of the different personal experiences. To address this limitation, we developed the EnviroMapper Toolkit, a data physicalisation toolkit that allows individual office workers to record their personal experiences of environmental comfort by mapping the actual moments and locations these occurred. By analysing two in-the-wild studies in existing open-plan office environments (N=14), we demonstrate how this toolkit acts like a situated input visualisation that can be interpreted by domain experts who were not present during its construction. This study therefore offers four key contributions: (1) the iterative design process of the physicalisation toolkit; (2) its preliminary deployment in two real-world office contexts; (3) the decoding of the resulting artefacts by domain experts; and (4) design considerations to support future input physicalisation and visualisation constructions that capture and synthesise data from multiple individuals.
We investigate differentially private estimators for individual parameters within larger parametric models. While generic private estimators exist, the estimators we provide repose on new local notions of estimand stability, and these notions allow procedures that provide private certificates of their own stability. By leveraging these private certificates, we provide computationally and statistical efficient mechanisms that release private statistics that are, at least asymptotically in the sample size, essentially unimprovable: they achieve instance optimal bounds. Additionally, we investigate the practicality of the algorithms both in simulated data and in real-world data from the American Community Survey and US Census, highlighting scenarios in which the new procedures are successful and identifying areas for future work.
Decentralization: A Qualitative Survey of Node Operators
2025-03-21
Alex Lynham, Geoff Goodell
Decentralization is understood both by professionals in the blockchain industry and general users as a core design goal of permissionless ledgers. However, its meaning is far from universally agreed, and often it is easier to get opinions on what it is not, rather than what it is. In this paper, we solicit definitions of 'decentralization' and 'decentralization theatre' from blockchain node operators. Key to a definition is asking about effective decentralization strategies, as well as those that are ineffective, sometimes deliberately so. Malicious, deceptive or at the least incompetent strategies are commonly referred to by the term 'decentralization theatre.' Finally, we ask what is being decentralized. We find that most operators conceive decentralization as existing broadly on a technical and a governance axis. Isolating relevant variables, we collapse the categories to network topology and governance topology, or the structure of decision-making power. Our key finding is that `decentralization' alone does not affect ledger immutability or systemic robustness.
Automating Adjudication of Cardiovascular Events Using Large Language Models
2025-03-21
Sonish Sivarajkumar, Kimia Ameri, Chuqin Li, Yanshan Wang, Min Jiang
Cardiovascular events, such as heart attacks and strokes, remain a leading cause of mortality globally, necessitating meticulous monitoring and adjudication in clinical trials. This process, traditionally performed manually by clinical experts, is time-consuming, resource-intensive, and prone to inter-reviewer variability, potentially introducing bias and hindering trial progress. This study addresses these critical limitations by presenting a novel framework for automating the adjudication of cardiovascular events in clinical trials using Large Language Models (LLMs). We developed a two-stage approach: first, employing an LLM-based pipeline for event information extraction from unstructured clinical data and second, using an LLM-based adjudication process guided by a Tree of Thoughts approach and clinical endpoint committee (CEC) guidelines. Using cardiovascular event-specific clinical trial data, the framework achieved an F1-score of 0.82 for event extraction and an accuracy of 0.68 for adjudication. Furthermore, we introduce the CLEART score, a novel, automated metric specifically designed for evaluating the quality of AI-generated clinical reasoning in adjudicating cardiovascular events. This approach demonstrates significant potential for substantially reducing adjudication time and costs while maintaining high-quality, consistent, and auditable outcomes in clinical trials. The reduced variability and enhanced standardization also allow for faster identification and mitigation of risks associated with cardiovascular therapies.
Stream Automatic Detection with Convolutional Neural Network (SAD-CNN)
2025-03-21
Alex Vera-Casanova., Nicolas Monsalves Gonzalez, Facundo A. Gómez, Marcelo Jaque Arancibia., Valentina Fontirroig, Diego Pallero., Rüdiger Pakmor., Freeke van de Voort., Robert J. J. Grand., Rebekka Bieri., Federico Marinacci
Context. Galactic halos host faint substructures, such as stellar streams and shells, which provide insights into the hierarchical assembly history of galaxies. To date, such features have been identified in external galaxies by visual inspection. However, with the advent of larger and deeper surveys and the associated increase in data volume, this methodology is becoming impractical. Aims. Here we aim to develop an automated method to detect low surface brightness features in galactic stellar halos. Moreover, we seek to quantify its performance when considering progressively more complex data sets, including different stellar disc orientations and redshifts. Methods. We develop the Stream Automatic Detection with Convolutional Neural Networks, SAD-CNN. This tool is trained on mock surface brightness maps obtained from simulations of the Auriga Project. The model incorporates transfer learning, data augmentation and balanced datasets to optimise its detection capabilities at surface brightness limiting magnitudes ranging from 27 to 31 mag arcsec^-2. Results. The iterative training approach, coupled with transfer learning, allowed the model to adapt to increasingly challenging datasets, achieving precision and recall metrics above 80% in all considered scenarios. The use of a well-balanced training dataset is critical for mitigating biases, ensuring that the CNN accurately distinguishes between galaxies with and without streams. Conclusions. SAD-CNN is a reliable and scalable tool for automating the detection of faint substructures in galactic halos. Its adaptability makes it well-suited for future applications, including the analysis of data from upcoming large astronomical surveys (such as LSST, JWT).
Robustness of deep learning classification to adversarial input on GPUs: asynchronous parallel accumulation is a source of vulnerability
2025-03-21
Sanjif Shanmugavelu, Mathieu Taillefumier, Christopher Culver, Vijay Ganesh, Oscar Hernandez, Ada Sedova
The ability of machine learning (ML) classification models to resist small, targeted input perturbations - known as adversarial attacks - is a key measure of their safety and reliability. We show that floating-point non associativity (FPNA) coupled with asynchronous parallel programming on GPUs is sufficient to result in misclassification, without any perturbation to the input. Additionally, we show this misclassification is particularly significant for inputs close to the decision boundary and that standard adversarial robustness results may be overestimated up to 4.6% when not considering machine-level details. We first study a linear classifier, before focusing on standard Graph Neural Network (GNN) architectures and datasets. We present a novel black-box attack using Bayesian optimization to determine external workloads that bias the output of reductions on GPUs and reliably lead to misclassification. Motivated by these results, we present a new learnable permutation (LP) gradient-based approach, to learn floating point operation orderings that lead to misclassifications, making the assumption that any reduction or permutation ordering is possible. This LP approach provides a worst-case estimate in a computationally efficient manner, avoiding the need to run identical experiments tens of thousands of times over a potentially large set of possible GPU states or architectures. Finally, we investigate parallel reduction ordering across different GPU architectures for a reduction under three conditions: (1) executing external background workloads, (2) utilizing multi-GPU virtualization, and (3) applying power capping. Our results demonstrate that parallel reduction ordering varies significantly across architectures under the first two conditions. The results and methods developed here can help to include machine-level considerations into adversarial robustness assessments.
Recent studies have identified a critical challenge in deep neural networks (DNNs) known as ``robust fairness", where models exhibit significant disparities in robust accuracy across different classes. While prior work has attempted to address this issue in adversarial robustness, the study of worst-class certified robustness for smoothed classifiers remains unexplored. Our work bridges this gap by developing a PAC-Bayesian bound for the worst-class error of smoothed classifiers. Through theoretical analysis, we demonstrate that the largest eigenvalue of the smoothed confusion matrix fundamentally influences the worst-class error of smoothed classifiers. Based on this insight, we introduce a regularization method that optimizes the largest eigenvalue of smoothed confusion matrix to enhance worst-class accuracy of the smoothed classifier and further improve its worst-class certified robustness. We provide extensive experimental validation across multiple datasets and model architectures to demonstrate the effectiveness of our approach.
Melnick 39 is a very massive intermediate-period colliding-wind binary
2025-03-21
A. M. T. Pollock, P. A. Crowther, J. M. Bestenlehner, Patrick S. Broos, Leisa K. Townsley
Individually identified binary systems of very massive stars define fixed points on possible evolutionary pathways that begin with extreme star formation and end in either coalescence of compact remnants or complete disruption as pair-production supernovae. The LMC star Melnick 39 in the Tarantula Nebula is revealed to be an eccentric ($e = 0.618\pm0.014$) binary system of reasonably long period from time-series analysis of Chandra T-ReX X-ray observations. Its X-ray luminosity scales with the inverse of the binary separation, as expected for colliding-wind binaries in the adiabatic regime. The inclusion of optical time-series spectroscopy from the VLT FLAMES Tarantula Survey and archival HST spectroscopy confirms Melnick 39 as a double-lined O2.5If/WN6+O3V-III spectroscopic binary with orbital period near 648 days. We obtain a mass ratio of $q = 0.76 \pm 0.06$, and minimum dynamical masses of $105\pm11$ and $80\pm11$ $M_{solar}$ for the O2.5If/WN6 and O3V-III components, plus photometric evidence for an orbital inclination near 90 degrees. Disentangled spectroscopy allows the physical and wind properties of the primary to be determined, including $T_{\ast}$ = 44 kK, $\log L/L_{solar}$ = 6.2, $\log \dot{M}/M_{solar}$ yr$^{-1}$ = $-5.0$. Its dynamical mass agrees closely with $109 M_{solar}$ obtained from the mass-luminosity relation of very massive stars.
Tidal Amplitudes in the Magellanic Cloud Population of Heartbeat Stars
2025-03-21
Morgan MacLeod, Abraham Loeb
Heartbeat (HB) stars exhibit pulses in their light curves once per orbit due to ellipsoidal distortions from strong tides at periapse. We analyze the population of HB stars in the Magellanic Clouds captured by the OGLE survey, and provide broadband spectral energy distribution fitting to estimate physical properties of the HB stars. The HB stars span a wide range of luminosities, radii, and effective temperatures. However, we find that they cluster near loci of strong tidal influence at periapse, indicating that in nearly all cases, strong tides are indeed responsible for their photometric variability. HB stars tend to populate regions away from the main sequence, where stellar evolution is particularly rapid. We examine the distribution of tidal amplitudes, and show that these can be interpreted through a simplified model of radius growth through stellar evolution and orbital circularization through linear tidal dissipation. When we compare rates of tidal dissipation, we find differences between the modeled modified tidal quality factor among hot ($T_{\rm eff}>6250$~K), $Q_\ast' \gtrsim 10^7$, and cool ($T_{\rm eff}<6250$~K), $Q_\ast'\sim 10^5$, stars, which is qualitatively consistent with models of efficient tidal dissipation in the convective envelopes of cool stars. We find that this model can reproduce the observed observed locations and amplitudes of HB stars in the Hertzsprung-Russell diagram. The hot stars, in particular, extend to amplitudes near, but not beyond, the threshold for nonlinear tidal wave breaking on stellar surfaces, suggesting a physical saturation of tidal amplitudes at this threshold.
The search for exoplanets is an active field in astronomy, with direct imaging as one of the most challenging methods due to faint exoplanet signals buried within stronger residual starlight. Successful detection requires advanced image processing to separate the exoplanet signal from this nuisance component. This paper presents a novel statistical model that captures nuisance fluctuations using a multi-scale approach, leveraging problem symmetries and a joint spectral channel representation grounded in physical principles. Our model integrates into an interpretable, end-to-end learnable framework for simultaneous exoplanet detection and flux estimation. The proposed algorithm is evaluated against the state of the art using datasets from the SPHERE instrument operating at the Very Large Telescope (VLT). It significantly improves the precision-recall trade-off, notably on challenging datasets that are otherwise unusable by astronomers. The proposed approach is computationally efficient, robust to varying data quality, and well suited for large-scale observational surveys.
Thanks to the advent of sensitive and broad bandwidth instrumentation, complex organic molecules (COMs) have been found in a wide variety of interstellar environments, not only in our Galaxy but also in external galaxies up to a redshift of 0.89. The detection of COMs in cold environments such as starless or prestellar cores has challenged our understanding of COM formation and new ideas are being implemented in chemical models and explored in laboratory experiments. At the protostellar stage, the advent of new interferometers such as the Atacama Large Millimeter/submillimeter Array (ALMA) has allowed the mapping of the weak emission of COMs in the protostellar envelopes and protoplanetary disks around both low-mass and high-mass protostars, pinpointing their location and revealing differentiation between the different families of molecules. In this way, thermal and non-thermal desorption mechanisms can be probed, constraining the efficiency of formation of COMs in the gas phase versus on grain surfaces. Some degree of continuity in the COM composition is found from the early to late stages of star formation, suggesting that a significant fraction of COMs are formed at the initial conditions of star formation. For extreme environments such as the Galactic Center, cosmic rays and low-velocity shocks seem to influence the COM composition of low and high-density gas components. The spectral confusion limit will be a major challenge for the detection of new COMs in future spectroscopic surveys. However, low-frequency interferometers targeting sources with low-excitation temperatures may help to overcome this limit.
Closeby Habitable Exoplanet Survey (CHES). III. Retrieval of Planetary Masses in Binaries Using the N-body Model with RV and Astrometry Synergy
2025-03-21
Xiumin Huang, Jianghui Ji, Chunhui Bao, Dongjie Tan, Su Wang, Yao Dong, Guo Chen
Given that secular perturbations in a binary system not only excite high orbital eccentricities but also alter the planetary orbital inclination, the classical Keplerian orbital model is no longer applicable for orbital retrieval. The combination of a dynamical model and observational data is essential for characterizing the configuration and planetary mass in close binaries. We calculate the theoretical radial velocity (RV) signal in the N-body framework and observe a drift in the RV semi-amplitude, which leads to a reduction in the $m$sin$i$ detection threshold by 20 $M_{\oplus}$, with $\sim$ 100% detection probability in the $m_1$sin$i_1$-$a_1$ parameter space. High-precision RV data with an accuracy of 1 m/s can detect such dynamical effects. For four close-in binaries-GJ 86, GJ 3021, HD 196885, and HD 41004, the deviation between the minimum mass derived from the Keplerian and N-body models is found to be $> 0.2 ~ M_{\mathrm{Jup}}$. High-precision astrometric data are also necessary to resolve the 3D orbits and true masses exoplanets. We generate astrometric simulation data with accuracies corresponding to Gaia (57.8 $\mu$as) and the Closeby Habitable Exoplanet Survey (CHES) (1 $\mu$as), respectively. Joint orbit fitting is performed for RV + Gaia and RV + CHES synergy methods. Compared with the fitting results from the astrometry-only model, the synergy models more effectively constrain the range of orbital inclinations. Using simulation data, we derive precise uncertainties for the true planetary mass, which are critical for determining the evolution of planets around binary stars and multi-planet systems.
TESS light curves and period changes in low-mass eclipsing binary BB Persei
2025-03-21
Marek Wolf, Petr Zasche, Miloslav Zejda, Martin Mašek, Andrej Mudray, Hana Kučáková, Waldemar Ogłoza, Jaroslav Merc, Jan Kára, Vojtěch Dienstbier
We present a detailed analysis of the low-mass detached eclipsing binary system BB Persei, which contains two K-type stars in a circular orbit with a short period of 0.4856 d. We used light curves from the Transiting Exoplanet Survey Satellite, which observed BB Per in five sectors, to determine its photometric properties and a precise orbital ephemeris. The solution of the TESS light curve in Phoebe results in a detached configuration, where the temperature of the primary component was fixed to $T_1 = 5~300$ K according to Lamost, which gives us $T_2 = 5~050 \pm 50$ K for the secondary. The spectral type of the primary component was derived as K0 and the photometric mass ratio was estimated $q = 0.90$. Slow period changes on the current O-C diagram spanning the past 25 years indicate the presence of a third body orbiting the eclipsing pair with an orbital period of about 22 years. The companion could be a red dwarf of spectral type M6 - M7 with a minimal mass of about 0.1 M$_{\odot}$. The characteristics and temporal variation of the dark region on the surface of the secondary component were estimated.
Exploring psychophysiological methods for human-robot collaboration in construction
2025-03-21
Saika Wong, Zhentao Chen, Mi Pan, Miroslaw J. Skibniewski
Psychophysiological methods present a promising approach to fostering enhanced mutual communication and collaboration between human workers and robots. Despite their potential, there is still limited understanding of how to effectively integrate psychophysiological methods to improve human-robot collaboration (HRC) in construction. This paper addresses this gap by critically reviewing the use of psychophysiological methods for HRC within construction environments, employing a concept-methodology-value philosophical framework. The analysis reveals that measuring brain activity using electroencephalography is the most widely used method, while most of the works are still at the proof of concept stage and lack empirical evidence. Three potential research directions were proposed: the integration of multi-modal psychophysiological signals, enriching the existing experimental settings for better generalizability, and leveraging advanced biocompatible or contactless technologies for effective signal detection. The findings should benefit subsequent exploration and practical applications of psychophysiological methods to enable better implementation of robots and support HRC in construction.
Data-Driven Optimization of EV Charging Station Placement Using Causal Discovery
2025-03-21
Julius Stephan Junker, Rong Hu, Ziyue Li, Wolfgang Ketter
This paper addresses the critical challenge of optimizing electric vehicle charging station placement through a novel data-driven methodology employing causal discovery techniques. While traditional approaches prioritize economic factors or power grid constraints, they often neglect empirical charging patterns that ultimately determine station utilization. We analyze extensive charging data from Palo Alto and Boulder (337,344 events across 100 stations) to uncover latent relationships between station characteristics and utilization. Applying structural learning algorithms (NOTEARS and DAGMA) to this data reveals that charging demand is primarily determined by three factors: proximity to amenities, EV registration density, and adjacency to high-traffic routes. These findings, consistent across multiple algorithms and urban contexts, challenge conventional infrastructure distribution strategies. We develop an optimization framework that translates these insights into actionable placement recommendations, identifying locations likely to experience high utilization based on the discovered dependency structures. The resulting site selection model prioritizes strategic clustering in high-amenity areas with substantial EV populations rather than uniform spatial distribution. Our approach contributes a framework that integrates empirical charging behavior into infrastructure planning, potentially enhancing both station utilization and user convenience. By focusing on data-driven insights instead of theoretical distribution models, we provide a more effective strategy for expanding charging networks that can adjust to various stages of EV market development.
Dynamics of Urban Heat Island in Lafia, Nasarawa State of Nigeria: A Remote Sensing Analysis of Land Surface Temperature, Urban Development and Vegetation Change
2025-03-21
Oladiran Johnson Abimbola, Taiwo Adewumi, Musa Abubakar
As the global climate changes, urban heat island (UHI) is a critical factor in ever expanding urban landscape, studying and mitigating the UHI is important for remediating climate change and providing for the human and ecosystem health within the urban area. This study has aimed to study the UHI in Lafia, a tropical city in Nigeria and its other impacted factors such as the land surface temperature (LST) and normalized difference vegetation index (NDVI), with the aim of mitigating the UHI effect. Landsat 4, 5, 7 and 8 together with Sentinel data has been used for this study, through the public archive of the Google Earth Engine data catalog, used also is the ERA5 data from the same data catalog. The result showed that the expanding city of Lafia is experiencing significant UHI with increase in temperatures in the city and adjoining areas, it was found that the vegetation cover in Lafia city is rapidly disappearing as a result of urbanization leading to more UHI and greater discomfort to the inhabitant of the city. Several remediation steps were suggested to mitigate the UHI effect in Lafia.
Automatic robotic facial expression generation is crucial for human-robot interaction, as handcrafted methods based on fixed joint configurations often yield rigid and unnatural behaviors. Although recent automated techniques reduce the need for manual tuning, they tend to fall short by not adequately bridging the gap between human preferences and model predictions-resulting in a deficiency of nuanced and realistic expressions due to limited degrees of freedom and insufficient perceptual integration. In this work, we propose a novel learning-to-rank framework that leverages human feedback to address this discrepancy and enhanced the expressiveness of robotic faces. Specifically, we conduct pairwise comparison annotations to collect human preference data and develop the Human Affective Pairwise Impressions (HAPI) model, a Siamese RankNet-based approach that refines expression evaluation. Results obtained via Bayesian Optimization and online expression survey on a 35-DOF android platform demonstrate that our approach produces significantly more realistic and socially resonant expressions of Anger, Happiness, and Surprise than those generated by baseline and expert-designed methods. This confirms that our framework effectively bridges the gap between human preferences and model predictions while robustly aligning robotic expression generation with human affective responses.
A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool for this task. BNs require constructing a structure of dependencies among variables and learning the parameters that govern these relationships. These tasks, referred to as structural learning and parameter learning, are actively investigated by the research community, with several algorithms proposed and no single method having established itself as standard. A wide range of software, tools, and packages have been developed for BNs analysis and made available to academic researchers and industry practitioners. As a consequence of having no one-size-fits-all solution, moving the first practical steps and getting oriented into this field is proving to be challenging to outsiders and beginners. In this paper, we review the most relevant tools and software for BNs structural and parameter learning to date, providing our subjective recommendations directed to an audience of beginners. In addition, we provide an extensive easy-to-consult overview table summarizing all software packages and their main features. By improving the reader understanding of which available software might best suit their needs, we improve accessibility to the field and make it easier for beginners to take their first step into it.
Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their transition to real-world applications reveals a critical limitation: the inability to adapt to individual preferences while maintaining alignment with universal human values. Current alignment techniques adopt a one-size-fits-all approach that fails to accommodate users' diverse backgrounds and needs. This paper presents the first comprehensive survey of personalized alignment-a paradigm that enables LLMs to adapt their behavior within ethical boundaries based on individual preferences. We propose a unified framework comprising preference memory management, personalized generation, and feedback-based alignment, systematically analyzing implementation approaches and evaluating their effectiveness across various scenarios. By examining current techniques, potential risks, and future challenges, this survey provides a structured foundation for developing more adaptable and ethically-aligned LLMs.
Maximising information from weak lensing galaxy surveys
2025-03-21
Alessandro Maraio
Weak lensing galaxy surveys are currently undergoing a dramatic revolution as the dawn of the Stage-IV surveys are upon us. Hence, ensuring that our analysis methods are as accurate and precise as the raw data is of upmost importance. This motivated the development of a new implementation of the quadratic maximum likelihood power spectrum estimation technique, the application of the theoretical uncertainties approach to mitigate baryonic feedback biases, and to re-evaluating the criterion from which binary scale cuts are derived when aiming to eliminate baryonic biases. These techniques maximise the available information from weak lensing observations while minimising potential systematic biases, and shows how this PhD thesis contributes to the advancement of weak lensing cosmology.
Heat pumps are essential for decarbonizing residential heating but consume substantial electrical energy, impacting operational costs and grid demand. Many systems run inefficiently due to planning flaws, operational faults, or misconfigurations. While optimizing performance requires skilled professionals, labor shortages hinder large-scale interventions. However, digital tools and improved data availability create new service opportunities for energy efficiency, predictive maintenance, and demand-side management. To support research and practical solutions, we present an open-source dataset of electricity consumption from 1,408 households with heat pumps and smart electricity meters in the canton of Zurich, Switzerland, recorded at 15-minute and daily resolutions between 2018-11-03 and 2024-03-21. The dataset includes household metadata, weather data from 8 stations, and ground truth data from 410 field visit protocols collected by energy consultants during system optimizations. Additionally, the dataset includes a Python-based data loader to facilitate seamless data processing and exploration.
We review exceptional field theories as the duality-covariant reformulation of maximal supergravity theories in ten and eleven dimensions, that make the underlying exceptional symmetries explicit. Beyond their structural role in unifying the various maximal supergravities, we illustrate how they also provide access to very efficient techniques for tackling concrete computational problems in supergravity.
In this survey, we consider various analytic problems related to the geometry of the Chern connection on Hermitian manifolds, such as the existence of metrics with constant Chern-scalar curvature, generalizations of the K\"ahler-Einstein condition to the non-K\"ahler setting, and the convergence of the Chern-Ricci flow on compact complex surfaces.
Assessing AI Explainability: A Usability Study Using a Novel Framework Involving Clinicians
2025-03-21
Mohammad Golam Kibria, Lauren Kucirka, Javed Mostafa
An AI design framework was developed based on three core principles, namely understandability, trust, and usability. The framework was conceptualized by synthesizing evidence from the literature and by consulting with experts. The initial version of the AI Explainability Framework was validated based on an in-depth expert engagement and review process. For evaluation purposes, an AI-anchored prototype, incorporating novel explainability features, was built and deployed online. The primary function of the prototype was to predict the postpartum depression risk using analytics models. The development of the prototype was carried out in an iterative fashion, based on a pilot-level formative evaluation, followed by refinements and summative evaluation. The System Explainability Scale (SES) metric was developed to measure the influence of the three dimensions of the AI Explainability Framework. For the summative stage, a comprehensive usability test was conducted involving 20 clinicians, and the SES metric was used to assess clinicians` satisfaction with the tool. On a 5-point rating system, the tool received high scores for the usability dimension, followed by trust and understandability. The average explainability score was 4.56. In terms of understandability, trust, and usability, the average score was 4.51, 4.53 and 4.71 respectively. Overall, the 13-item SES metric showed strong internal consistency with Cronbach`s alpha of 0.84 and a positive correlation coefficient (Spearman`s rho = 0.81, p<0.001) between the composite SES score and explainability. A major finding was that the framework, combined with the SES usability metric, provides a straightforward approach for developing AI-based healthcare tools that lower the challenges associated with explainability.
Status and prospects of the $χ_{c1}(3872)$ at BESIII
2025-03-21
Chunhua Li, Xin Liu, Yueming Zhang
The $\chi_{c1}(3872)$ serves as a pivotal role for understanding hadronic structures, remaining one of the most extensively studied exotic particles despite the experimental discovery of numerous unconventional hadronic states. Sustained experimental and theoretical investigations into the particle over the past two decades have propelled its study into a high-precision regime, marked by refined measurements of its decay dynamics and line shape, thereby offering critical insights to resolve longstanding debates between molecular, tetraquark, hybrid, and charmonium interpretations of this particle. The BESIII experiment has made seminal contributions to the study of the $\chi_{c1}(3872)$, leveraging its unique capabilities in high-statistics data acquisition and low-background condition. This article gives a concise review and prospects of the study of the $\chi_{c1}(3872)$ from the BESIII experiment.
Deep neural networks (DNNs) often struggle with out-of-distribution data, limiting their reliability in diverse realworld applications. To address this issue, domain generalization methods have been developed to learn domain-invariant features from single or multiple training domains, enabling generalization to unseen testing domains. However, existing approaches usually overlook the impact of style frequency within the training set. This oversight predisposes models to capture spurious visual correlations caused by style confounding factors, rather than learning truly causal representations, thereby undermining inference reliability. In this work, we introduce Style Deconfounding Causal Learning (SDCL), a novel causal inference-based framework designed to explicitly address style as a confounding factor. Our approaches begins with constructing a structural causal model (SCM) tailored to the domain generalization problem and applies a backdoor adjustment strategy to account for style influence. Building on this foundation, we design a style-guided expert module (SGEM) to adaptively clusters style distributions during training, capturing the global confounding style. Additionally, a back-door causal learning module (BDCL) performs causal interventions during feature extraction, ensuring fair integration of global confounding styles into sample predictions, effectively reducing style bias. The SDCL framework is highly versatile and can be seamlessly integrated with state-of-the-art data augmentation techniques. Extensive experiments across diverse natural and medical image recognition tasks validate its efficacy, demonstrating superior performance in both multi-domain and the more challenging single-domain generalization scenarios.
Safe On-Orbit Dislodging of Deployable Structures via Robust Adaptive MPC
2025-03-21
Longsen Gao, Claus Danielson, Andrew Kwas, Rafael Fierro
This paper proposes a novel robust adaptive model predictive controller for on-orbit dislodging. We consider the scenario where a servicer, equipped with a robot arm, must dislodge a client, a time-varying system composed of an underpowered jammed solar panel with a hybrid hinge system on a space station. Our approach leverages online set-membership identification to reduce the uncertainty to provide robust safety guarantees during dislodging despite bounded disturbances while balancing exploration and exploitation effectively in the parameter space. The feasibility of the developed robust adaptive MPC method is also examined through dislodging simulations and hardware experiments in zero-gravity and gravity environments, respectively. In addition, the advantages of our method are shown through comparison experiments with several state-of-the-art control schemes for both accuracy of parameter estimation and control performance.
A Flexible Fairness Framework with Surrogate Loss Reweighting for Addressing Sociodemographic Disparities
2025-03-21
Wen Xu, Elham Dolatabadi
This paper presents a new algorithmic fairness framework called $\boldsymbol{\alpha}$-$\boldsymbol{\beta}$ Fair Machine Learning ($\boldsymbol{\alpha}$-$\boldsymbol{\beta}$ FML), designed to optimize fairness levels across sociodemographic attributes. Our framework employs a new family of surrogate loss functions, paired with loss reweighting techniques, allowing precise control over fairness-accuracy trade-offs through tunable hyperparameters $\boldsymbol{\alpha}$ and $\boldsymbol{\beta}$. To efficiently solve the learning objective, we propose Parallel Stochastic Gradient Descent with Surrogate Loss (P-SGD-S) and establish convergence guarantees for both convex and nonconvex loss functions. Experimental results demonstrate that our framework improves overall accuracy while reducing fairness violations, offering a smooth trade-off between standard empirical risk minimization and strict minimax fairness. Results across multiple datasets confirm its adaptability, ensuring fairness improvements without excessive performance degradation.
Current and Future Use of Large Language Models for Knowledge Work
2025-03-21
Michelle Brachman, Amina El-Ashry, Casey Dugan, Werner Geyer
Large Language Models (LLMs) have introduced a paradigm shift in interaction with AI technology, enabling knowledge workers to complete tasks by specifying their desired outcome in natural language. LLMs have the potential to increase productivity and reduce tedious tasks in an unprecedented way. A systematic study of LLM adoption for work can provide insight into how LLMs can best support these workers. To explore knowledge workers' current and desired usage of LLMs, we ran a survey (n=216). Workers described tasks they already used LLMs for, like generating code or improving text, but imagined a future with LLMs integrated into their workflows and data. We ran a second survey (n=107) a year later that validated our initial findings and provides insight into up-to-date LLM use by knowledge workers. We discuss implications for adoption and design of generative AI technologies for knowledge work.
Two-Photon Resonance Fluorescence in a Three-Level Ladder-Type Atom
2025-03-21
Jacob Ngaha, Scott Parkins, Howard J. Carmichael
In this work, we consider a three-level ladder-type atom driven by a coherent field, inspired by the experimental work of Gasparinetti et al. [Phys. Rev. A 100, 033802 (2019)]. When driven on two-photon resonance, the atom is excited into its highest energy state $| f \rangle$ by absorbing two photons simultaneously. The atom then de-excites via a cascaded decay $| f \rangle \rightarrow | e \rangle \rightarrow | g \rangle$. Here we present a theoretical study of the atomic fluorescence spectrum where, upon strong coherent driving, the spectrum exhibits seven distinct frequencies corresponding to transitions amongst the atomic dressed states. We characterize the quantum statistics of the emitted photons by investigating the second-order correlation functions of the emitted field. We do so by considering the total field emitted by the atom and focusing on each of the dressed-state components, taking in particular a secular-approximation and deriving straightforward, transparent analytic expressions for the second-order auto- and cross-correlations.
The shear $\eta$ and bulk $\zeta$ viscous coefficients have been calculated in a hot and chirally asymmetric quark matter quantified in terms of a chiral chemical potential (CCP) using the two-flavor Nambu-Jona--Lasinio (NJL) model. This is done by employing the one-loop Green-Kubo formalism where the viscous coefficients have been extracted from the long-wavelength limit of the in-medium spectral function corresponding to the energy momentum tensor (EMT) current correlator calculated using the real time formalism of finite temperature field theory. The momentum dependent thermal width of the quark/antiquark that enters into the expression of the viscosities as a dynamical input containing interactions, has been obtained from the $2\to2$ scattering processes mediated via the collective mesonic modes in scalar and pseudoscalar chanels encoded in respective in-medium polarization functions having explicit temperature and CCP dependence. Several thermodynamic quantities such as pressure, energy density, entropy density $(s)$, specific heat and isentropic speed of sound have also been calculated at finite CCP. The temperature and CCP dependence of the viscosity to entropy density ratios $\eta/s$ and $\zeta/s$ have also been studied.
Broad-based experimental evidence for a hidden phase of cuprate coexistence with far-reaching implications
2025-03-21
K. E. Gray
Analyses of experimental data in the literature show thresholds that directly imply a coexistent superconductive (SC) and pseudogap (PG) phase that, to our knowledge, has not been previously identified. The data used emphasize the essences of d-wave cuprate SC, i.e., the superfluid density and the momentum dependence. For severe underdoping, these data imply that SC condenses conventionally onto the unaltered low-energy density-of-states of a high-temperature PG. Beyond a doping threshold, the high-temperature normal state is replaced, below the SC transition temperature, by a single energetically-favorable thermodynamic phase that embodies a more complex interplay of these orders. This previously hidden phase occupies a central portion of the SC dome, enhances the superfluid density by a factor of two or more to benefit applications, can resolve thirteen mysteries in the literature and suggests an alternative approach to cuprate SC.
The heterointerface between topological insulators and magnetic materials provides a crucial platform for investigating exotic electronic and magnetic phases, with implications for both fundamental studies and potential applications. A key challenge is determining the spatial extent of magnetic perturbation across the interface. In this study, we grew Bi2Te3 films with mixed thicknesses of 5 and 6 quintuple layers (QLs) on a thick ferromagnetic Cr2Te3 film. Chemical analysis indicates gradual Cr diffusion across the interface into Bi2Te3, reaching up to the 4th QL, while the 5th and 6th QL remain free of Cr. Despite the absence of direct Cr incorporation, quasiparticle interference imaging using spectroscopic scanning tunneling microscopy reveals strong backscattering on the 5th QL surface. This suggests an intrinsic magnetic proximity effect, which breaks time-reversal symmetry without magnetic doping, extending into the 5th QLs. Although variations may exist across different systems, the large magnetic perturbation length scale observed here is a valuable guiding principle for engineering exotic electronic and magnetic states at heterointerfaces between topological insulators and magnetic materials.
Autistic individuals often experience negative self-talk (NST), leading to increased anxiety and depression. While therapy is recommended, it presents challenges for many autistic individuals. Meanwhile, a growing number are turning to large language models (LLMs) for mental health support. To understand how autistic individuals perceive AI's role in coping with NST, we surveyed 200 autistic adults and interviewed practitioners. We also analyzed LLM responses to participants' hypothetical prompts about their NST. Our findings show that participants view LLMs as useful for managing NST by identifying and reframing negative thoughts. Both participants and practitioners recognize AI's potential to support therapy and emotional expression. Participants also expressed concerns about LLMs' understanding of neurodivergent thought patterns, particularly due to the neurotypical bias of LLMs. Practitioners critiqued LLMs' responses as overly wordy, vague, and overwhelming. This study contributes to the growing research on AI-assisted mental health support, with specific insights for supporting the autistic community.
Towards Understanding the Benefits of Neural Network Parameterizations in Geophysical Inversions: A Study With Neural Fields
2025-03-21
Anran Xu, Lindsey J. Heagy
In this work, we employ neural fields, which use neural networks to map a coordinate to the corresponding physical property value at that coordinate, in a test-time learning manner. For a test-time learning method, the weights are learned during the inversion, as compared to traditional approaches which require a network to be trained using a training data set. Results for synthetic examples in seismic tomography and direct current resistivity inversions are shown first. We then perform a singular value decomposition analysis on the Jacobian of the weights of the neural network (SVD analysis) for both cases to explore the effects of neural networks on the recovered model. The results show that the test-time learning approach can eliminate unwanted artifacts in the recovered subsurface physical property model caused by the sensitivity of the survey and physics. Therefore, NFs-Inv improves the inversion results compared to the conventional inversion in some cases such as the recovery of the dip angle or the prediction of the boundaries of the main target. In the SVD analysis, we observe similar patterns in the left-singular vectors as were observed in some diffusion models, trained in a supervised manner, for generative tasks in computer vision. This observation provides evidence that there is an implicit bias, which is inherent in neural network structures, that is useful in supervised learning and test-time learning models. This implicit bias has the potential to be useful for recovering models in geophysical inversions.
SaudiCulture: A Benchmark for Evaluating Large Language Models Cultural Competence within Saudi Arabia
2025-03-21
Lama Ayash, Hassan Alhuzali, Ashwag Alasmari, Sultan Aloufi
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing; however, they often struggle to accurately capture and reflect cultural nuances. This research addresses this challenge by focusing on Saudi Arabia, a country characterized by diverse dialects and rich cultural traditions. We introduce SaudiCulture, a novel benchmark designed to evaluate the cultural competence of LLMs within the distinct geographical and cultural contexts of Saudi Arabia. SaudiCulture is a comprehensive dataset of questions covering five major geographical regions, such as West, East, South, North, and Center, along with general questions applicable across all regions. The dataset encompasses a broad spectrum of cultural domains, including food, clothing, entertainment, celebrations, and crafts. To ensure a rigorous evaluation, SaudiCulture includes questions of varying complexity, such as open-ended, single-choice, and multiple-choice formats, with some requiring multiple correct answers. Additionally, the dataset distinguishes between common cultural knowledge and specialized regional aspects. We conduct extensive evaluations on five LLMs, such as GPT-4, Llama 3.3, FANAR, Jais, and AceGPT, analyzing their performance across different question types and cultural contexts. Our findings reveal that all models experience significant performance declines when faced with highly specialized or region-specific questions, particularly those requiring multiple correct responses. Additionally, certain cultural categories are more easily identifiable than others, further highlighting inconsistencies in LLMs cultural understanding. These results emphasize the importance of incorporating region-specific knowledge into LLMs training to enhance their cultural competence.
Bayesian generative models can flag performance loss, bias, and out-of-distribution image content
2025-03-21
Miguel López-Pérez, Marco Miani, Valery Naranjo, Søren Hauberg, Aasa Feragen
Generative models are popular for medical imaging tasks such as anomaly detection, feature extraction, data visualization, or image generation. Since they are parameterized by deep learning models, they are often sensitive to distribution shifts and unreliable when applied to out-of-distribution data, creating a risk of, e.g. underrepresentation bias. This behavior can be flagged using uncertainty quantification methods for generative models, but their availability remains limited. We propose SLUG: A new UQ method for VAEs that combines recent advances in Laplace approximations with stochastic trace estimators to scale gracefully with image dimensionality. We show that our UQ score -- unlike the VAE's encoder variances -- correlates strongly with reconstruction error and racial underrepresentation bias for dermatological images. We also show how pixel-wise uncertainty can detect out-of-distribution image content such as ink, rulers, and patches, which is known to induce learning shortcuts in predictive models.
Euclid preparation LXX. Forecasting detection limits for intracluster light in the Euclid Wide Survey
2025-03-21
Euclid Collaboration, C. Bellhouse, J. B. Golden-Marx, S. P. Bamford, N. A. Hatch, M. Kluge, A. Ellien, S. L. Ahad, P. Dimauro, F. Durret, A. H. Gonzalez, Y. Jimenez-Teja, M. Montes, M. Sereno, E. Slezak, M. Bolzonella, G. Castignani, O. Cucciati, G. De Lucia, Z. Ghaffari, L. Moscardini, R. Pello, L. Pozzetti, T. Saifollahi, A. S. Borlaff, N. Aghanim, B. Altieri, A. Amara, S. Andreon, C. Baccigalupi, M. Baldi, S. Bardelli, A. Basset, P. Battaglia, R. Bender, A. Biviano, D. Bonino, E. Branchini, M. Brescia, A. Caillat, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, V. F. Cardone, J. Carretero, S. Casas, M. Castellano, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, J. -C. Cuillandre, A. Da Silva, H. Degaudenzi, A. M. Di Giorgio, J. Dinis, H. Dole, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, S. Escoffier, M. Farina, R. Farinelli, S. Farrens, F. Faustini, S. Ferriol, F. Finelli, S. Fotopoulou, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, B. Gillis, C. Giocoli, P. Gómez-Alvarez, J. Gracia-Carpio, A. Grazian, F. Grupp, S. V. H. Haugan, H. Hoekstra, M. S. Holliman, W. Holmes, I. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, E. Keihänen, S. Kermiche, A. Kiessling, M. Kilbinger, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, P. Liebing, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, S. Maurogordato, E. Medinaceli, S. Mei, M. Melchior, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, R. Nakajima, C. Neissner, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, P. Schneider, T. Schrabback, G. Seidel, S. Serrano, P. Simon, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, P. Tallada-Crespí, I. Tereno, S. Toft, R. Toledo-Moreo, F. Torradeflot, A. Tsyganov, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, G. Zamorani, E. Zucca, E. Bozzo, C. Burigana, M. Calabrese, D. Di Ferdinando, J. A. Escartin Vigo, L. Gabarra, S. Matthew, N. Mauri, M. Pöntinen, V. Scottez, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, M. Ballardini, M. Bethermin, A. Blanchard, L. Blot, H. Böhringer, S. Borgani, M. L. Brown, S. Bruton, R. Cabanac, A. Calabro, A. Cappi, F. Caro, C. S. Carvalho, T. Castro, F. Cogato, T. Contini, A. R. Cooray, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, J. M. Diego, A. G. Ferrari, P. G. Ferreira, A. Finoguenov, A. Fontana, K. Ganga, J. García-Bellido, T. Gasparetto, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, A. Gregorio, M. Guidi, C. M. Gutierrez, A. Hall, W. G. Hartley, S. Hemmati, H. Hildebrandt, J. Hjorth, A. Jimenez Muñoz, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, C. C. Kirkpatrick, S. Kruk, M. Lattanzi, J. Le Graet, L. Legrand, M. Lembo, J. Lesgourgues, T. I. Liaudat, S. J. Liu, A. Loureiro, M. Magliocchetti, F. Mannucci, R. Maoli, J. Martín-Fleitas, C. J. A. P. Martins, L. Maurin, R. B. Metcalf, M. Miluzio, P. Monaco, C. Moretti, G. Morgante, C. Murray, K. Naidoo, A. Navarro-Alsina, S. Nesseris, K. Paterson, L. Patrizii, A. Pisani, V. Popa, D. Potter, I. Risso, P. -F. Rocci, M. Sahlén, E. Sarpa, A. Schneider, M. Schultheis, D. Sciotti, E. Sellentin, L. C. Smith, S. A. Stanford, K. Tanidis, C. Tao, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, D. Vergani, G. Verza, P. Vielzeuf, N. A. Walton
The intracluster light (ICL) permeating galaxy clusters is a tracer of the cluster's assembly history, and potentially a tracer of their dark matter structure. In this work we explore the capability of the Euclid Wide Survey to detect ICL using H-band mock images. We simulate clusters across a range of redshifts (0.3-1.8) and halo masses ($10^{13.9}$-$10^{15.0}$ M$_\odot$), using an observationally motivated model of the ICL. We identify a 50-200 kpc circular annulus around the brightest cluster galaxy (BCG) in which the signal-to-noise ratio (S/N) of the ICL is maximised and use the S/N within this aperture as our figure of merit for ICL detection. We compare three state-of-the-art methods for ICL detection, and find that a method that performs simple aperture photometry after high-surface brightness source masking is able to detect ICL with minimal bias for clusters more massive than $10^{14.2}$ M$_\odot$. The S/N of the ICL detection is primarily limited by the redshift of the cluster, driven by cosmological dimming, rather than the mass of the cluster. Assuming the ICL in each cluster contains 15% of the stellar light, we forecast that Euclid will be able to measure the presence of ICL in up to $\sim80000$ clusters of $>10^{14.2}$ M$_\odot$ between $z=0.3$ and 1.5 with a S/N$>3$. Half of these clusters will reside below $z=0.75$ and the majority of those below $z=0.6$ will be detected with a S/N $>20$. A few thousand clusters at $1.310^{14.7}$ M$_\odot$. Euclid will detect the ICL at more than 500 kpc distance from the BCG, up to $z=0.7$, in several hundred of these massive clusters over its large survey volume.
UV LIGHTS. New tools for revealing the low surface brightness regime in the ultraviolet
2025-03-21
Ignacio Ruiz Cejudo, Ignacio Trujillo, Giulia Golini, Nafise Sedighi, Mireia Montes, Sergio Guerra Arencibia, Mauro D'Onofrio, Dennis Zaritsky, Samane Raji, Nushkia Chamba, Chen-Yu Chuang, Richard Donnerstein, Sepideh Eskandarlou, S. Zahra Hosseini-ShahiSavandi, Raúl Infante Sainz, Ouldouz Kaboud, Garreth Martin, Javier Román, Zahra Sharbaf
Ultra-deep optical surveys have reached unprecedented depths, facilitating the study of faint galactic structures. However, the ultraviolet bands, crucial for stellar population studies, remain essentially unexplored at these depths. We present a detailed surface brightness and color analysis of 20 nearby galaxies in the LIGHTS fields observed by GALEX in the FUV and NUV. We adapt and apply a low surface brightness oriented methodology that has proven effective in ultra-deep optical surveys. A novel approach to background subtraction is proposed for UV imaging. Instead of subtracting a constant value from the background, we subtract a Poisson distribution that transforms the background into a pseudo-Gaussian distribution centered at zero. Furthermore, the PSF deconvolution algorithms developed for optical data are applied to our sample, using a novel set of very extended (R=750 arcsec) PSFs for the GALEX bands. This methodology allows us to obtain depths ranging from 28.5 to 30 mag arcsec^{-2}, with reliable surface brightness profiles up to 31 mag arcsec^{-2}. This is about 1 mag deeper than with standard UV techniques. We use the surface brightness and color profiles to show that the application of PSF deconvolution, especially in the FUV, effectively mitigates the excess of light present in the outer regions of certain galaxies compared to the standard GALEX pipeline. This finding is crucial for any accurate stellar population inference from the color profiles. Additionally, a qualitative analysis of the results is presented, with particular emphasis on surface brightness and color properties of the galaxies beyond their optical edges. Our work highlights the importance of developing innovative low surface brightness methods for UV surveys.
Large language models (LLMs) have demonstrated remarkable reasoning capability in solving mathematical problems. However, existing approaches primarily focus on improving the quality of correct training data, e.g., distilling high-quality correct solutions from advanced models, neglecting the value contained in error data, potentially hindering the model's reflective ability. Though some studies attempt to leverage error data, they often involve complex mechanisms, such as Monte Carlo Tree Search (MCTS) to explore error nodes. In this work, we propose to enhance LLMs' reasoning ability by Learning from Errors for Mathematical Advancement (LEMMA). LEMMA constructs data consisting of an incorrect solution with an erroneous step and a reflection connection to a correct solution for fine-tuning. Specifically, we systematically analyze the model-generated error types and introduce an error-type grounded mistake augmentation method to collect diverse and representative errors. Correct solutions are either from fixing the errors or generating a fresh start. Through a model-aware smooth reflection connection, the erroneous solution is transferred to the correct one. By fine-tuning on the constructed dataset, the model is able to self-correct errors autonomously within the generation process without relying on external critique models. Experimental results demonstrate that LEMMA achieves significant performance improvements over other strong baselines.
As a system of integrated homeostasis, life is susceptible to disruptions by visceral inflammation, which can disturb internal environment equilibrium. The role of body-spread subcutaneous fascia (scFascia) in this process is poorly understood. In the rat model of Salmonella-induced dysentery, scRNA-seq of scFascia and deep-learning analysis revealed Warburg-like metabolic reprogramming in macrophages (MPs) with reduced citrate cycle activity. Cd34+/Pdgfra+ telocytes (CPTCs) regulated MPs differentiation and proliferation via Wnt/Fgf signal, suggesting a pathological crosstalk pattern in the scFascia, herein termed the fascia-visceral inflammatory crosstalk pattern (FVICP). PySCENIC analysis indicated increased activity transcription factors Fosl1, Nfkb2, and Atf4, modulated by CPTCs signaling to MPs, downregulating aerobic respiration and upregulating cell cycle, DNA replication, and transcription. This study highlights scFascia's role in immunomodulation and metabolic reprogramming during visceral inflammation, underscoring its function in systemic homeostasis.
Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
2025-03-20
Yang Sui, Yu-Neng Chuang, Guanchu Wang, Jiamu Zhang, Tianyi Zhang, Jiayi Yuan, Hongyi Liu, Andrew Wen, Shaochen Zhong, Hanjie Chen, Xia Hu
Large Language Models (LLMs) have demonstrated remarkable capabilities in complex tasks. Recent advancements in Large Reasoning Models (LRMs), such as OpenAI o1 and DeepSeek-R1, have further improved performance in System-2 reasoning domains like mathematics and programming by harnessing supervised fine-tuning (SFT) and reinforcement learning (RL) techniques to enhance the Chain-of-Thought (CoT) reasoning. However, while longer CoT reasoning sequences improve performance, they also introduce significant computational overhead due to verbose and redundant outputs, known as the "overthinking phenomenon". In this paper, we provide the first structured survey to systematically investigate and explore the current progress toward achieving efficient reasoning in LLMs. Overall, relying on the inherent mechanism of LLMs, we categorize existing works into several key directions: (1) model-based efficient reasoning, which considers optimizing full-length reasoning models into more concise reasoning models or directly training efficient reasoning models; (2) reasoning output-based efficient reasoning, which aims to dynamically reduce reasoning steps and length during inference; (3) input prompts-based efficient reasoning, which seeks to enhance reasoning efficiency based on input prompt properties such as difficulty or length control. Additionally, we introduce the use of efficient data for training reasoning models, explore the reasoning capabilities of small language models, and discuss evaluation methods and benchmarking.
2025-03-20
Asaf Yehudai, Lilach Eden, Alan Li, Guy Uziel, Yilun Zhao, Roy Bar-Haim, Arman Cohan, Michal Shmueli-Scheuer
The emergence of LLM-based agents represents a paradigm shift in AI, enabling autonomous systems to plan, reason, use tools, and maintain memory while interacting with dynamic environments. This paper provides the first comprehensive survey of evaluation methodologies for these increasingly capable agents. We systematically analyze evaluation benchmarks and frameworks across four critical dimensions: (1) fundamental agent capabilities, including planning, tool use, self-reflection, and memory; (2) application-specific benchmarks for web, software engineering, scientific, and conversational agents; (3) benchmarks for generalist agents; and (4) frameworks for evaluating agents. Our analysis reveals emerging trends, including a shift toward more realistic, challenging evaluations with continuously updated benchmarks. We also identify critical gaps that future research must address-particularly in assessing cost-efficiency, safety, and robustness, and in developing fine-grained, and scalable evaluation methods. This survey maps the rapidly evolving landscape of agent evaluation, reveals the emerging trends in the field, identifies current limitations, and proposes directions for future research.
A foundational derivation of quantum weak values and time-dependent density functional theory
2025-03-20
Russell B. Thompson, Zarin Tasneem, Yves Caudano
The equations of time-dependent density functional theory are derived, via the expression for the quantum weak value, from ring polymer quantum theory using a symmetry between time and imaginary time. The imaginary time path integral formalism of Feynman, in which inverse temperature is seen to be a Wick rotation of time, allows one to write the equilibrium partition function of a quantum system in a form isomorphic with the path integral expression for the dynamics. Therefore the self-consistent field theory equations which are solutions to the equilibrium partition function are Wick rotated back into a set of dynamic equations, which are shown to give the formula for the quantum weak value. As a special case, this in turn reduces to the equations of time-dependent density functional theory. This first-principles derivation does not use the theorems of density functional theory, which are instead applied to guarantee equivalence with standard quantum mechanics. An expression for finite-temperature dynamics is also derived using the limits of the equilibrium equations and the ground state dynamics. This finite temperature expression shows that a ring polymer model for quantum particles holds for time-dependent systems as well as static situations. Issues arising in time-dependent density functional theory, such as causality, initial state dependence, and $v$-representability, are discussed in the context of the ring polymer derivation. Connections with a variety of quantum phenomena is reviewed, and a possible link with de Broglie-Bohm theory is mentioned.
OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence
2025-03-20
Long Yuan, Fengran Mo, Kaiyu Huang, Wenjie Wang, Wangyuxuan Zhai, Xiaoyu Zhu, You Li, Jinan Xu, Jian-Yun Nie
The rapid advancement of multimodal large language models (LLMs) has opened new frontiers in artificial intelligence, enabling the integration of diverse large-scale data types such as text, images, and spatial information. In this paper, we explore the potential of multimodal LLMs (MLLM) for geospatial artificial intelligence (GeoAI), a field that leverages spatial data to address challenges in domains including Geospatial Semantics, Health Geography, Urban Geography, Urban Perception, and Remote Sensing. We propose a MLLM (OmniGeo) tailored to geospatial applications, capable of processing and analyzing heterogeneous data sources, including satellite imagery, geospatial metadata, and textual descriptions. By combining the strengths of natural language understanding and spatial reasoning, our model enhances the ability of instruction following and the accuracy of GeoAI systems. Results demonstrate that our model outperforms task-specific models and existing LLMs on diverse geospatial tasks, effectively addressing the multimodality nature while achieving competitive results on the zero-shot geospatial tasks. Our code will be released after publication.
Chain of Functions: A Programmatic Pipeline for Fine-Grained Chart Reasoning Data
2025-03-20
Zijian Li, Jingjing Fu, Lei Song, Jiang Bian, Jun Zhang, Rui Wang
Visual reasoning is crucial for multimodal large language models (MLLMs) to address complex chart queries, yet high-quality rationale data remains scarce. Existing methods leveraged (M)LLMs for data generation, but direct prompting often yields limited precision and diversity. In this paper, we propose \textit{Chain of Functions (CoF)}, a novel programmatic reasoning data generation pipeline that utilizes freely-explored reasoning paths as supervision to ensure data precision and diversity. Specifically, it starts with human-free exploration among the atomic functions (e.g., maximum data and arithmetic operations) to generate diverse function chains, which are then translated into linguistic rationales and questions with only a moderate open-sourced LLM. \textit{CoF} provides multiple benefits: 1) Precision: function-governed generation reduces hallucinations compared to freeform generation; 2) Diversity: enumerating function chains enables varied question taxonomies; 3) Explainability: function chains serve as built-in rationales, allowing fine-grained evaluation beyond overall accuracy; 4) Practicality: eliminating reliance on extremely large models. Employing \textit{CoF}, we construct the \textit{ChartCoF} dataset, with 1.4k complex reasoning Q\&A for fine-grained analysis and 50k Q\&A for reasoning enhancement. The fine-grained evaluation on \textit{ChartCoF} reveals varying performance across question taxonomies for each MLLM, and the experiments also show that finetuning with \textit{ChartCoF} achieves state-of-the-art performance among same-scale MLLMs on widely used benchmarks. Furthermore, the novel paradigm of function-governed rationale generation in \textit{CoF} could inspire broader applications beyond charts.
RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility in Autonomous Vehicles
2025-03-20
Dawood Wasif, Terrence J. Moore, Jin-Hee Cho
Autonomous vehicles (AVs) increasingly rely on Federated Learning (FL) to enhance perception models while preserving privacy. However, existing FL frameworks struggle to balance privacy, fairness, and robustness, leading to performance disparities across demographic groups. Privacy-preserving techniques like differential privacy mitigate data leakage risks but worsen fairness by restricting access to sensitive attributes needed for bias correction. This work explores the trade-off between privacy and fairness in FL-based object detection for AVs and introduces RESFL, an integrated solution optimizing both. RESFL incorporates adversarial privacy disentanglement and uncertainty-guided fairness-aware aggregation. The adversarial component uses a gradient reversal layer to remove sensitive attributes, reducing privacy risks while maintaining fairness. The uncertainty-aware aggregation employs an evidential neural network to weight client updates adaptively, prioritizing contributions with lower fairness disparities and higher confidence. This ensures robust and equitable FL model updates. We evaluate RESFL on the FACET dataset and CARLA simulator, assessing accuracy, fairness, privacy resilience, and robustness under varying conditions. RESFL improves detection accuracy, reduces fairness disparities, and lowers privacy attack success rates while demonstrating superior robustness to adversarial conditions compared to other approaches.
Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI
2025-03-20
Dawood Wasif, Dian Chen, Sindhuja Madabushi, Nithin Alluru, Terrence J. Moore, Jin-Hee Cho
Federated Learning (FL) enables collaborative machine learning while preserving data privacy but struggles to balance privacy preservation (PP) and fairness. Techniques like Differential Privacy (DP), Homomorphic Encryption (HE), and Secure Multi-Party Computation (SMC) protect sensitive data but introduce trade-offs. DP enhances privacy but can disproportionately impact underrepresented groups, while HE and SMC mitigate fairness concerns at the cost of computational overhead. This work explores the privacy-fairness trade-offs in FL under IID (Independent and Identically Distributed) and non-IID data distributions, benchmarking q-FedAvg, q-MAML, and Ditto on diverse datasets. Our findings highlight context-dependent trade-offs and offer guidelines for designing FL systems that uphold responsible AI principles, ensuring fairness, privacy, and equitable real-world applications.
Flight Testing an Optionally Piloted Aircraft: a Case Study on Trust Dynamics in Human-Autonomy Teaming
2025-03-20
Jeremy C. -H. Wang, Ming Hou, David Dunwoody, Marko Ilievski, Justin Tomasi, Edward Chao, Carl Pigeon
This paper examines how trust is formed, maintained, or diminished over time in the context of human-autonomy teaming with an optionally piloted aircraft. Whereas traditional factor-based trust models offer a static representation of human confidence in technology, here we discuss how variations in the underlying factors lead to variations in trust, trust thresholds, and human behaviours. Over 200 hours of flight test data collected over a multi-year test campaign from 2021 to 2023 were reviewed. The dispositional-situational-learned, process-performance-purpose, and IMPACTS homeostasis trust models are applied to illuminate trust trends during nominal autonomous flight operations. The results offer promising directions for future studies on trust dynamics and design-for-trust in human-autonomy teaming.
Since the discovery of high-temperature superconductivity in cuprates, understanding the unconventional pairing mechanism has remained one of the most significant challenges. The upper critical field ($H_{\rm{c2}}$) is an essential parameter for obtaining information on the pair-breaking mechanism, coherence length $\xi$, and pairing symmetry, all of which are crucial for understanding unconventional superconducting mechanisms. Here, we provide a brief review of studies on $ H_{\rm{c2}} $ in several representative series of cuprate, iron-based, and nickelate superconductors. By comparing the behavior of $ H_{\rm{c2}} $ as a function of temperature, doping concentration, and anisotropy across these three major classes of superconductors, we hope to contribute to a better understanding of the complex pairing interactions in high-temperature superconductors.
Analyzing the Morphology of Late-phase Stellar Flares From G-, K-, and M-type Stars
2025-03-20
Denise G. Yudovich, Kai E. Yang, Xudong Sun
Stellar flares occasionally present a $\textit{peak-bump}$ light curve morphology, consisting of an initial impulsive phase followed by a gradual late phase. Analyzing this specific morphology can uncover the underlying physics of stellar flare dynamics, particularly the plasma heating-evaporation-condensation process. While previous studies have mainly examined peak-bump occurrences on M-dwarfs, this report extends the investigation to G-, K-, and M-type stars. We utilize the flare catalog published by arXiv:2212.00993, encompassing 12,597 flares, detected by using $\textit{Transiting Exoplanet Survey Satellite}$ (TESS) observations. Our analysis identifies 10,142 flares with discernible classical and complex morphology, of which 197 ($\sim1.9\%$) exhibit the peak-bump feature. We delve into the statistical properties of these TESS late-phase flares, noting that both the amplitude and full-width-half-maximum (FWHM) duration of both the peaks and bumps show positive correlations across all source-star spectral types, following a power law with indices 0.69 $\pm$ 0.09 and 1.0 $\pm$ 0.15, respectively. Additionally, a negative correlation between flare amplitude and the effective temperature of their host stars is observed. Compared to the other flares in our sample, peak-bump flares tend to have larger and longer initial peak amplitudes and FWHM durations, and possess energies ranging from $10^{31}$ to $10^{36}$ erg.
CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models
2025-03-20
Hong Yi Lin, Chunhua Liu, Haoyu Gao, Patanamon Thongtanunam, Christoph Treude
State-of-the-art large language models (LLMs) have demonstrated impressive code generation capabilities but struggle with real-world software engineering tasks, such as revising source code to address code reviews, hindering their practical use. Code review comments are often implicit, ambiguous, and colloquial, requiring models to grasp both code and human intent. This challenge calls for evaluating large language models' ability to bridge both technical and conversational contexts. While existing work has employed the automated code refinement (ACR) task to resolve these comments, current evaluation methods fall short, relying on text matching metrics that provide limited insight into model failures and remain susceptible to training data contamination. To address these limitations, we introduce a novel evaluation benchmark, $\textbf{CodeReviewQA}$ that enables us to conduct fine-grained assessment of model capabilities and mitigate data contamination risks. In CodeReviewQA, we decompose the generation task of code refinement into $\textbf{three essential reasoning steps}$: $\textit{change type recognition}$ (CTR), $\textit{change localisation}$ (CL), and $\textit{solution identification}$ (SI). Each step is reformulated as multiple-choice questions with varied difficulty levels, enabling precise assessment of model capabilities, while mitigating data contamination risks. Our comprehensive evaluation spans 72 recently released large language models on $\textbf{900 manually curated, high-quality examples}$ across nine programming languages. Our results show that CodeReviewQA is able to expose specific model weaknesses in code review comprehension, disentangled from their generative automated code refinement results.
Towards a definition of a meteor cluster: Detection of meteor clusters from meteor orbit databases
2025-03-20
A. Ashimbekova, J. Vaubaillon, P. Koten
As of today, there is no official definition of a meteor cluster. It is usually identified as a large number of meteors sharing a similar radiant and velocity, all occurring within a few seconds. Only eight clusters have been reported so far, from single-camera or camera network observations. We aim to provide an overview of meteor clusters to help define what constitutes a cluster by potentially adding more to the already identified ones and determining their common parameters. A search for new clusters is performed in publicly available International Astronomical Union meteor databases with the DBSCAN algorithm. Then, a statistical significance method is applied to derive the most promising cluster candidates. However, the method still lacks a way to debias the atmospheric area surveyed by the cameras due to a lack of publicly available data. A set of 16 statistically significant potential clusters is identified, involving 4 to 7 fragments. The 90th percentile includes a duration of 8 seconds, a velocity difference of 2.2 km/s, and a radiant spread of nearly 4 degrees. The velocity difference may arise from the method used for orbit computation. Meteor clusters might be more frequent than currently reported. However, we recommend that future meteor orbit databases also include a way to estimate the surveyed area by the cameras involved in the detection. This would strengthen the veracity of the 16 identified cluster candidates and ultimately allow scientists to fully debias the number of clusters, and hence derive the physical lifetime expectancy of meteoroids, which is often overlooked due to the focus on collisional lifetime estimates only. We also recommend that any future cluster observation report includes the expected number of random occurrences and consider the event to be real if this value is below 0.1.
Only a Little to the Left: A Theory-grounded Measure of Political Bias in Large Language Models
2025-03-20
Mats Faulborn, Indira Sen, Max Pellert, Andreas Spitz, David Garcia
Prompt-based language models like GPT4 and LLaMa have been used for a wide variety of use cases such as simulating agents, searching for information, or for content analysis. For all of these applications and others, political biases in these models can affect their performance. Several researchers have attempted to study political bias in language models using evaluation suites based on surveys, such as the Political Compass Test (PCT), often finding a particular leaning favored by these models. However, there is some variation in the exact prompting techniques, leading to diverging findings and most research relies on constrained-answer settings to extract model responses. Moreover, the Political Compass Test is not a scientifically valid survey instrument. In this work, we contribute a political bias measured informed by political science theory, building on survey design principles to test a wide variety of input prompts, while taking into account prompt sensitivity. We then prompt 11 different open and commercial models, differentiating between instruction-tuned and non-instruction-tuned models, and automatically classify their political stances from 88,110 responses. Leveraging this dataset, we compute political bias profiles across different prompt variations and find that while PCT exaggerates bias in certain models like GPT3.5, measures of political bias are often unstable, but generally more left-leaning for instruction-tuned models.
Gravitational lensing due to charged galactic wormhole
2025-03-20
Md Khalid Hossain, Farook Rahaman
We propose the back reaction to the charged galactic wormhole spacetime based on Yoshiaki Sofue's exponential dark matter density profile to find exact solutions. The charges act as an additional component to the static wormhole, which is primarily formed by the galactic dark matter density. Unlike traditional mass-based models, this solution incorporates charge effects within a realistic dark matter distribution, revealing unique interactions between dark matter, electromagnetic fields, and spacetime curvature. This study confirms the criteria for wormhole formation, designating it the "Charged Galactic Wormhole," and offers a new framework for investigating galactic structures, with potential observational signatures that deepen our understanding of dark matter and spacetime. Later, the proper radial distance and the embedding surface were also analyzed. Furthermore, the deflection of light around a charged galactic wormhole was investigated, along with a comprehensive review of the resulting image. The deflection of massive objects (charge less) near charged galactic wormholes is studied using the Gauss-Bonnet and Rindler-Ishak methods, with a detailed comparison of the results from both approaches. Additionally, in both the Rindler-Ishak (RI) and Gauss-Bonnet (GB) methods, when v tends to 1 i.e. when particle's velocity comparable to the speed of light , the results from these approaches converge, producing the same outcome as strong gravitational lensing.
Cultural Alignment in Large Language Models Using Soft Prompt Tuning
2025-03-20
Reem I. Masoud, Martin Ferianc, Philip Treleaven, Miguel Rodrigues
Large Language Model (LLM) alignment conventionally relies on supervised fine-tuning or reinforcement learning based alignment frameworks. These methods typically require labeled or preference datasets and involve updating model weights to align the LLM with the training objective or reward model. Meanwhile, in social sciences such as cross-cultural studies, factor analysis is widely used to uncover underlying dimensions or latent variables that explain observed patterns in survey data. The non-differentiable nature of these measurements deriving from survey data renders the former alignment methods infeasible for alignment with cultural dimensions. To overcome this, we propose a parameter efficient strategy that combines soft prompt tuning, which freezes the model parameters while modifying the input prompt embeddings, with Differential Evolution (DE), a black-box optimization method for cases where a differentiable objective is unattainable. This strategy ensures alignment consistency without the need for preference data or model parameter updates, significantly enhancing efficiency and mitigating overfitting. Our method demonstrates significant improvements in LLama-3-8B-Instruct's cultural dimensions across multiple regions, outperforming both the Naive LLM and the In-context Learning (ICL) baseline, and effectively bridges computational models with human cultural nuances.
Hyperspectral Imaging for Identifying Foreign Objects on Pork Belly
2025-03-20
Gabriela Ghimpeteanu, Hayat Rajani, Josep Quintana, Rafael Garcia
Ensuring food safety and quality is critical in the food processing industry, where the detection of contaminants remains a persistent challenge. This study presents an automated solution for detecting foreign objects on pork belly meat using hyperspectral imaging (HSI). A hyperspectral camera was used to capture data across various bands in the near-infrared (NIR) spectrum (900-1700 nm), enabling accurate identification of contaminants that are often undetectable through traditional visual inspection methods. The proposed solution combines pre-processing techniques with a segmentation approach based on a lightweight Vision Transformer (ViT) to distinguish contaminants from meat, fat, and conveyor belt materials. The adopted strategy demonstrates high detection accuracy and training efficiency, while also addressing key industrial challenges such as inherent noise, temperature variations, and spectral similarity between contaminants and pork belly. Experimental results validate the effectiveness of hyperspectral imaging in enhancing food safety, highlighting its potential for broad real-time applications in automated quality control processes.
Age of Information in Multi-Relay Networks with Maximum Age Scheduling
2025-03-20
Gabriel Martins de Jesus, Felippe Moraes Pereira, João Luiz Rebelatto, Richard Demo Souza, Onel Alcaraz López
We propose and evaluate age of information (AoI)-aware multiple access mechanisms for the Internet of Things (IoT) in multi-relay two-hop networks. The network considered comprises end devices (EDs) communicating with a set of relays in ALOHA fashion, with new information packets to be potentially transmitted every time slot. The relays, in turn, forward the collected packets to an access point (AP), the final destination of the information generated by the EDs. More specifically, in this work we investigate the performance of four age-aware algorithms that prioritize older packets to be transmitted, namely max-age matching (MAM), iterative max-age scheduling (IMAS), age-based delayed request (ABDR), and buffered ABDR (B-ABDR). The former two algorithms are adapted into the multi-relay setup from previous research, and achieve satisfactory average AoI and average peak AoI performance, at the expense of a significant amount of information exchange between the relays and the AP. The latter two algorithms are newly proposed to let relays decide which one(s) will transmit in a given time slot, requiring less signaling than the former algorithms. We provide an analytical formulation for the AoI lower bound performance, compare the performance of all algorithms in this set-up, and show that they approach the lower bound. The latter holds especially true for B-ABDR, which approaches the lower bound the most closely, tilting the scale in its favor, as it also requires far less signaling than MAM and IMAS.
Hydrodynamic Interactions in Particle Suspensions: A Perspective on Stokesian Dynamics
2025-03-20
Kim William Torre, Joost de Graaf
Stokesian Dynamics (SD) is a numerical framework used for simulating hydrodynamic interactions in particle suspensions at low Reynolds number. It combines far-field approximations with near-field lubrication corrections, offering a balance between accuracy and efficiency. This work reviews SD and provides a perspective on future directions for this approach. We outline the mathematical foundations, the method's strengths and weaknesses, and the computational challenges that need to be overcome to work with SD effectively. We also discuss recent advancements that improve the algorithm's efficiency, including the use of iterative solvers and matrix-free approaches. In addition, we highlight the limitations of making stronger, albeit more cost-effective approximations to studying hydrodynamic interactions in dense suspensions than made in SD, such as the two-body Rotne-Prager-Yamakawa (RPY) approximation. To overcome these issues, we propose a hybrid framework that replaces SD's full many-body computations with a neural network trained on SD data. That is, we correct the RPY approximation, while avoiding costly matrix inversions. We demonstrate the potential of this method on a simple system, where we find a close match to SD data while algorithmically outperforming RPY. Our work provides an outlook on the way in which large-scale simulations of particle suspensions can be performed in the foreseeable future.
This study introduces GreenIQ, an AI-powered deep search platform designed to revolutionise carbon market intelligence through autonomous analysis and automated report generation. Carbon markets operate across diverse regulatory landscapes, generating vast amounts of heterogeneous data from policy documents, industry reports, academic literature, and real-time trading platforms. Traditional research approaches remain labour-intensive, slow, and difficult to scale. GreenIQ addresses these limitations through a multi-agent architecture powered by Large Language Models (LLMs), integrating five specialised AI agents: a Main Researcher Agent for intelligent information retrieval, a Report Writing Agent for structured synthesis, a Final Reviewer Agent for accuracy verification, a Data Visualisation Agent for enhanced interpretability, and a Translator Agent for multilingual adaptation. The system achieves seamless integration of structured and unstructured information with AI-driven citation verification, ensuring high transparency and reliability. GreenIQ delivers a 99.2\% reduction in processing time and a 99.7\% cost reduction compared to traditional research methodologies. A novel AI persona-based evaluation framework involving 16 domain-specific AI personas highlights its superior cross-jurisdictional analytical capabilities and regulatory insight generation. GreenIQ sets new standards in AI-driven research synthesis, policy analysis, and sustainability finance by streamlining carbon market research. It offers an efficient and scalable framework for environmental and financial intelligence, enabling more accurate, timely, and cost-effective decision-making in complex regulatory landscapes
A Survey on fMRI-based Brain Decoding for Reconstructing Multimodal Stimuli
2025-03-20
Pengyu Liu, Guohua Dong, Dan Guo, Kun Li, Fengling Li, Xun Yang, Meng Wang, Xiaomin Ying
In daily life, we encounter diverse external stimuli, such as images, sounds, and videos. As research in multimodal stimuli and neuroscience advances, fMRI-based brain decoding has become a key tool for understanding brain perception and its complex cognitive processes. Decoding brain signals to reconstruct stimuli not only reveals intricate neural mechanisms but also drives progress in AI, disease treatment, and brain-computer interfaces. Recent advancements in neuroimaging and image generation models have significantly improved fMRI-based decoding. While fMRI offers high spatial resolution for precise brain activity mapping, its low temporal resolution and signal noise pose challenges. Meanwhile, techniques like GANs, VAEs, and Diffusion Models have enhanced reconstructed image quality, and multimodal pre-trained models have boosted cross-modal decoding tasks. This survey systematically reviews recent progress in fMRI-based brain decoding, focusing on stimulus reconstruction from passive brain signals. It summarizes datasets, relevant brain regions, and categorizes existing methods by model structure. Additionally, it evaluates model performance and discusses their effectiveness. Finally, it identifies key challenges and proposes future research directions, offering valuable insights for the field. For more information and resources related to this survey, visit https://github.com/LpyNow/BrainDecodingImage.
Are We There Yet? A Study of Decentralized Identity Applications
2025-03-20
Daria Schumm, Katharina O. E. Müller, Burkhard Stiller
The development of Decentralized Identities (DI) and Self-Sovereign Identities (SSI) has seen significant growth in recent years. This is accompanied by a numerous academic and commercial contributions to the development of principles, standards, and systems. While several comprehensive reviews have been produced, they predominantly focus on academic literature, with few considering grey literature to provide a holistic view of technological advancements. Furthermore, no existing surveys have thoroughly analyzed real-world deployments to understand the barriers to the widespread adoption of decentralized identity models. This paper addresses the gap by exploring both academic and grey literature and examining commercial and governmental initiatives, to present a comprehensive landscape of decentralized identity technologies and their adoption in real-world. Additionally, it identifies the practical challenges and limitations that slowdown the transition from centralized to decentralized identity management systems. By shifting the focus from purely technological constraints to real-world deployment issues, this survey identifies the underlying reasons preventing the adoption of decentralized identities despite their evident benefits to the data owner.
Preparing for Rubin-LSST -- Detecting Brightest Cluster Galaxies with Machine Learning in the LSST DP0.2 simulation
2025-03-20
Aline Chu, Ludvig Doeser, Simon Ding, Jens Jasche
The future Rubin Legacy Survey of Space and Time (LSST) is expected to deliver its first data release in the current of 2025. The upcoming survey will provide us with images of galaxy clusters in the optical to the near-infrared, with unrivalled coverage, depth and uniformity. The study of galaxy clusters informs us on the effect of environmental processes on galactic formation, which directly translates onto the formation of the brightest cluster galaxy (BCG). These massive galaxies present traces of the whole merger history of their host clusters, which can be in the shape of intra-cluster light (ICL) that surrounds them, tidal streams, or simply by the accumulated stellar mass that has been acquired over the past 10 billion years as they have cannibalized other galaxies in their surroundings. In an era where new data is being generated faster than humans can deal with, new methods involving machine learning have been emerging more and more in the most recent years. In the aim of preparing for the future LSST data release which will allow the observations of more than 20000 clusters and BCGs, we present in this paper different methods based on machine learning to detect these BCGs on LSST-like optical images. This study is done by making use of the simulated LSST Data Preview images. We find that the use of machine learning allows to accurately identify the BCG in up to 95% of clusters in our sample. Compared to more conventional red sequence extraction methods, the use of machine learning appears to be faster, more efficient and consistent, and does not require much, if any, pre-processing.
Influence of structural disorder on magnetic properties and electronic structure of YCo$_2$
2025-03-20
Z. Śniadecki, N. Pierunek, B. Idzikowski, B. Wasilewski, M. Werwiński, U. K. Rößler, Yu. Ivanisenko
In this paper, the changes of magnetic properties with increasing disorder in the exchange enhanced Pauli paramagnet YCo$_2$ are discussed. The structural disorder is initially introduced by rapid quenching, while further changes on micro-/nanoscale are caused by a high pressure torsion (HPT). Values of the magnetic moment determined for the plastically deformed ribbons reach 0.10 $\mu_B$/Co atom (for a sample subjected to the deformation at a pressure of 4 GPa) and 0.25 $\mu_B$/Co (6 GPa) at 2 K. Magnetic moment arise not only from the surface of nanocrystals but also from volume. Ab initio calculations explained the influence of chemical disorder and different types of structural defects on the electronic structure and magnetic properties of YCo2-based Laves phases. The calculated magnetic ground states are in qualitative agreement with experimental results for all considered structures with point defects.
Large Language Models (LLMs) excel in text generation, reasoning, and decision-making, enabling their adoption in high-stakes domains such as healthcare, law, and transportation. However, their reliability is a major concern, as they often produce plausible but incorrect responses. Uncertainty quantification (UQ) enhances trustworthiness by estimating confidence in outputs, enabling risk mitigation and selective prediction. However, traditional UQ methods struggle with LLMs due to computational constraints and decoding inconsistencies. Moreover, LLMs introduce unique uncertainty sources, such as input ambiguity, reasoning path divergence, and decoding stochasticity, that extend beyond classical aleatoric and epistemic uncertainty. To address this, we introduce a new taxonomy that categorizes UQ methods based on computational efficiency and uncertainty dimensions (input, reasoning, parameter, and prediction uncertainty). We evaluate existing techniques, assess their real-world applicability, and identify open challenges, emphasizing the need for scalable, interpretable, and robust UQ approaches to enhance LLM reliability.
A Unified Stability Analysis of Safety-Critical Control using Multiple Control Barrier Functions
2025-03-20
Matheus F. Reis, José P. Carvalho, A. Pedro Aguiar
Ensuring liveness and safety of autonomous and cyber-physical systems remains a fundamental challenge, particularly when multiple safety constraints are present. This letter advances the theoretical foundations of safety-filter Quadratic Programs (QP) and Control Lyapunov Function (CLF)-Control Barrier Function (CBF) controllers by establishing a unified analytical framework for studying their stability properties. We derive sufficient feasibility conditions for QPs with multiple CBFs and formally characterize the conditions leading to undesirable equilibrium points at possible intersecting safe set boundaries. Additionally, we introduce a stability criterion for equilibrium points, providing a systematic approach to identifying conditions under which they can be destabilized or eliminated. Our analysis extends prior theoretical results, deepening the understanding of the conditions of feasibility and stability of CBF-based safety filters and the CLF-CBF QP framework.
2025-03-20
Hannah S. Christie, Adrien Hélias, Matheus do Carmo Carvalho, Pauline Barmby
The beginning of the 21st century marked the "modern era of galaxy surveys" in astronomy. Rapid innovation in observing technology, combined with the base built by galaxy catalogs and atlases dating back centuries, sparked an explosion of new observational programs driven by efforts to understand the different processes driving galaxy evolution. This review aims to answer the following science questions: (1) how have galaxy surveys evolved in the past 20 years, and how have traditional observational programs been affected by the rise of large panoramic surveys, (2) can the term "nearby" be quantified in the context of galaxy surveys, and (3) how complete is the coverage of the nearby universe and what areas hold the largest opportunity for future work? We define a galaxy survey as a systematically obtained data set which aims to characterize a set of astronomical objects. Galaxy surveys can further be subdivided based on the methods used to select the objects to observe, the properties of the survey samples (e.g. distance or morphology), or the observing strategies used. We focus on \textit{pointed} nearby galaxy surveys, which we define as surveys which observe a specific sample of target galaxies. Through a study of 43 nearby galaxy surveys, we find no standardized quantitative definition for "nearby" with surveys covering a wide range of distances. We observe that since 2003, traditional targeted galaxy surveys have undergone a dramatic evolution, transitioning from large, statistical surveys to small, ultra-specific projects which complement the rise of large high resolution panoramic surveys. While wavelength regimes observable from the ground (such as radio or optical wavelengths) host numerous surveys, the largest opportunity for future work is within the less covered space-based wavelength regimes (especially ultraviolet and X-ray).
ChatGPT and U(X): A Rapid Review on Measuring the User Experience
2025-03-20
Katie Seaborn
ChatGPT, powered by a large language model (LLM), has revolutionized everyday human-computer interaction (HCI) since its 2022 release. While now used by millions around the world, a coherent pathway for evaluating the user experience (UX) ChatGPT offers remains missing. In this rapid review (N = 58), I explored how ChatGPT UX has been approached quantitatively so far. I focused on the independent variables (IVs) manipulated, the dependent variables (DVs) measured, and the methods used for measurement. Findings reveal trends, gaps, and emerging consensus in UX assessments. This work offers a first step towards synthesizing existing approaches to measuring ChatGPT UX, urgent trajectories to advance standardization and breadth, and two preliminary frameworks aimed at guiding future research and tool development. I seek to elevate the field of ChatGPT UX by empowering researchers and practitioners in optimizing user interactions with ChatGPT and similar LLM-based systems.
Multiwavelength Analysis of GRB 250101A: From Gamma-ray Prompt Emission to Optical Afterglow
2025-03-20
Guowang Du, Yehao Cheng, Yuan-Pei Yang, Jun Yang, Jinghua Zhang, Dan Zhu, Yu Pan, Yuan Fang, Xingzhu Zou, Brajesh Kumar, Helong Guo, Xufeng Zhu, Yangwei Zhang, Fanchuan Kong, Chenxi Shang, Xinlei Chen, Xiangkun Liu, Xiaowei Liu
Gamma-ray bursts (GRBs) are the most luminous transients in the universe. The interaction between the relativistic jet and the circumburst medium produces a multiwavelength afterglow through synchrotron radiation. In this work, we present multiwavelength properties of GRB~250101A based on the observations of Swift, Fermi, and Multi-channel Photometric Survey Telescope (Mephisto). The spectral analysis of Swift/BAT and Fermi/GBM reveals a soft prompt spectrum with a low-energy photon index of $-1.18$ and a peak energy of 33 keV, and the isotropic energy is $1.4\times10^{52}~{\rm erg}$. The prompt emission of GRB 250101A aligns with Type II GRBs in the Amati relation. Meanwhile, our analysis indicates that GRB 250101A is an X-ray-rich or X-ray-dominated GRB, with intrinsic properties suggesting that it is relatively softer than most classical GRBs. Optical observation with Mephisto, beginning 197 s post-trigger, shows a single power-law decay in $uvgriz$ bands, with $F_{\nu,\mathrm{obs}} \propto t^{-0.76} \nu^{-1.20}$. The observed spectral index significantly exceeds theoretical predictions under standard afterglow models, suggesting a color excess of $\sim0.21$ mag. However, combining X-ray and optical afterglow, we find that GRB 250101A is more likely a ``normal burst'' rather than an ``optical-dark burst'', and the dust extinction effect plays an important role in the optical blue bands. Furthermore, there is a structural change at $T_0+2924$ s in the optical light curve, indicating a density drop of $\sim50$ \% in the interstellar medium at a distance of $\sim0.05~{\rm pc}$.
Experimental demonstration of electric power generation from Earth's rotation through its own magnetic field
2025-03-20
Christopher F. Chyba, Kevin P. Hand, Thomas H. Chyba
Earth rotates through the axisymmetric part of its own magnetic field, but a simple proof shows that it is impossible to use this to generate electricity in a conductor rotating with Earth.However, we previously identified implicit assumptions underlying this proof and showed theoretically that these could be violated and the proof circumvented. This requires using a soft magnetic material with a topology satisfying a particular mathematical condition and a composition and scale favoring magnetic diffusion, i.e. having a low magnetic Reynolds number Rm (C.F. Chyba, K.P. Hand, Electric power generation from Earth's rotation through its own magnetic field. Phys. Rev. Applied 6, 014017-1-18 (2016)). Here we realize these requirements with a cylindrical shell of manganese-zinc ferrite. Controlling for thermoelectric and other potentially confounding effects (including 60 Hz and RF background), we show that this small demonstration system generates a continuous DC voltage and current of the (low) predicted magnitude. We test and verify other predictions of the theory: voltage and current peak when the cylindrical shell's long axis is orthogonal to both Earth's rotational velocity v and magnetic field; voltage and current go to zero when the entire apparatus (cylindrical shell together with current leads and multimeters) is rotated 90 degrees to orient the shell parallel to v; voltage and current again reach a maximum but of opposite sign when the apparatus is rotated a further 90 degrees; an otherwise-identical solid MnZn cylinder generates zero voltage at all orientations; and a highRm cylindrical shell produces zero voltage. We also reproduce the effect at a second experimental location. The purpose of these experiments was to test the existence of the predicted effect. Ways in which this effect might be scaled to generate higher voltage and current may now be investigated.
Copenhagen Survey on Black Holes and Fundamental Physics
2025-03-20
Alice Y. Chen, Phil Halper, Niayesh Afshordi
The purpose of this survey is to take a snapshot of the attitudes of physicists, which may be useful to sociologists and historians of science. A total of 85 completed surveys were returned out of 151 registered participants of the "Black holes Inside and out" conference, held in Copenhagen in 2024. The survey asked questions about the nature of black holes and some of the most contentious issues in fundamental physics. A number of surprising results were found. For example, some of the leading frameworks, such the cosmological constant, cosmic inflation, or string theory, while most popular, gain less than majority of votes from the participants. The only statement that gains majority approval (by 68% of participants) was that the Big Bang means that "the universe evolved from a hot dense state", not "an absolute beginning time". This provides reasons for caution in describing ideas as consensus in the scientific community when a more nuanced view may be justified.
Search for continuous gravitational waves from neutron stars in five globular clusters with a phase-tracking hidden Markov model in the third LIGO observing run
2025-03-20
L. Dunn, A. Melatos, P. Clearwater, S. Suvorova, L. Sun, W. Moran, R. J. Evans
A search is performed for continuous gravitational waves emitted by unknown neutron stars in five nearby globular clusters using data from the third Laser Interferometer Gravitational-Wave Observatory (LIGO) observing run, over the frequency range $100$--$800\,\mathrm{Hz}$. The search uses a hidden Markov model to track both the frequency and phase of the continuous wave signal from one coherent segment to the next. It represents the first time that a phase-tracking hidden Markov model has been used in a LIGO search. After applying vetoes to reject candidates consistent with non-Gaussian artifacts, no significant candidates are detected. Estimates of the strain sensitivity at 95\% confidence $h_{0,\mathrm{eff}}^{95\%}$ and corresponding neutron star ellipticity $\epsilon^{95\%}$ are presented. The best strain sensitivity, $h_{0,\mathrm{eff}}^{95\%} = 2.7 \times 10^{-26}$ at $211\,\mathrm{Hz}$, is achieved for the cluster NGC6544.
2025-03-20
Vishisht Rao, Aounon Kumar, Himabindu Lakkaraju, Nihar B. Shah
Editors of academic journals and program chairs of conferences require peer reviewers to write their own reviews. However, there is growing concern about the rise of lazy reviewing practices, where reviewers use large language models (LLMs) to generate reviews instead of writing them independently. Existing tools for detecting LLM-generated content are not designed to differentiate between fully LLM-generated reviews and those merely polished by an LLM. In this work, we employ a straightforward approach to identify LLM-generated reviews - doing an indirect prompt injection via the paper PDF to ask the LLM to embed a watermark. Our focus is on presenting watermarking schemes and statistical tests that maintain a bounded family-wise error rate, when a venue evaluates multiple reviews, with a higher power as compared to standard methods like Bonferroni correction. These guarantees hold without relying on any assumptions about human-written reviews. We also consider various methods for prompt injection including font embedding and jailbreaking. We evaluate the effectiveness and various tradeoffs of these methods, including different reviewer defenses. We find a high success rate in the embedding of our watermarks in LLM-generated reviews across models. We also find that our approach is resilient to common reviewer defenses, and that the bounds on error rates in our statistical tests hold in practice while having the power to flag LLM-generated reviews, while Bonferroni correction is infeasible.
EarlyStopping: Implicit Regularization for Iterative Learning Procedures in Python
2025-03-20
Eric Ziebell, Ratmir Miftachov, Bernhard Stankewitz, Laura Hucker
Iterative learning procedures are ubiquitous in machine learning and modern statistics. Regularision is typically required to prevent inflating the expected loss of a procedure in later iterations via the propagation of noise inherent in the data. Significant emphasis has been placed on achieving this regularisation implicitly by stopping procedures early. The EarlyStopping-package provides a toolbox of (in-sample) sequential early stopping rules for several well-known iterative estimation procedures, such as truncated SVD, Landweber (gradient descent), conjugate gradient descent, L2-boosting and regression trees. One of the central features of the package is that the algorithms allow the specification of the true data-generating process and keep track of relevant theoretical quantities. In this paper, we detail the principles governing the implementation of the EarlyStopping-package and provide a survey of recent foundational advances in the theoretical literature. We demonstrate how to use the EarlyStopping-package to explore core features of implicit regularisation and replicate results from the literature.
Design and Implementation of an FPGA-Based Tiled Matrix Multiplication Accelerator for Transformer Self-Attention on the Xilinx KV260 SoM
2025-03-20
Richie Li, Sicheng Chen
Transformer-based large language models (LLMs) rely heavily on intensive matrix multiplications for attention and feed-forward layers, with the Q, K, and V linear projections in the Multi-Head Self-Attention (MHA) module constituting a decisive performance bottleneck. In this work, we introduce a highly optimized tiled matrix multiplication accelerator on a resource-constrained Xilinx KV260 FPGA that not only addresses this challenge but sets a new standard for efficiency and performance. Our design exploits persistent on-chip storage, a robust two-level tiling strategy for maximal data reuse, and a systolic-like unrolled compute engine that together deliver unparalleled speed and energy efficiency. Integrated with DistilBERT for Q, K, and V projections, our accelerator achieves an unequivocal 7x speedup over ARM CPU implementations (PyTorch) and an extraordinary 200x improvement over naive NumPy, reaching a throughput of up to 3.1~GFLOPs for matrix multiplications on (64,768) x (768,3072) matrices while operating at a conservative 100 MHz. These results decisively demonstrate the transformative potential of FPGA-based acceleration for critical Transformer operations, paving the way for scalable and energy-efficient deep learning inference on edge devices.
Deep-TAO: The Deep Learning Transient Astronomical Object data set for Astronomical Transient Event Classification
2025-03-20
John F. Suárez-Pérez, Catalina Gómez, Mauricio Neira, Marcela Hernández Hoyos, Pablo Arbeláez, Jaime E. Forero-Romero
We present the Deep-learning Transient Astronomical Object (Deep-TAO), a dataset of 1,249,079 annotated images from the Catalina Real-time Transient Survey, including 3,807 transient and 12,500 non-transient sequences. Deep-TAO has been curated to provide a clean, open-access, and user-friendly resource for benchmarking deep learning models. Deep-TAO covers transient classes such as blazars, active galactic nuclei, cataclysmic variables, supernovae, and events of indeterminate nature. The dataset is publicly available in FITS format, with Python routines and Jupyter notebooks for easy data manipulation. Using Deep-TAO, a baseline Convolutional Neural Network outperformed traditional random forest classifiers trained on light curves, demonstrating its potential for advancing transient classification.
Surveys of clumps, cores, and condensations in Cygnus-X: Searching for Keplerian disks on the scale of 500 au
2025-03-20
Xing Pan, Keping Qiu, Qizhou Zhang
Over the past decades, observational evidence of circumstellar disks around massive protostars has been steadily accumulating. However, there have also been cases of non-detections in high-mass star-forming regions, leaving the role and prevalence of disks around massive protostars still uncertain. We used high-resolution (0.2") NOrthern Extended Millimeter Array (NOEMA) observations to study the 1.3 mm continuum and molecular line emission of five massive dense cores in the Cygnus-X cloud complex. Four cores host 2000-au-scale rotating structures previously identified as disk candidates in lower-resolution SMA observations, while the remaining core with no evidence for a disk serves as a comparison. With a resolution of 300 au, the 1.3 mm continuum emission reveals varying levels of fragmentation in our sample, with fragment radii ranging from 150 to 800 AU. In this work, we confirm the existence of two small, stable disks in Keplerian-like rotation at scales of 500 au out of four previously identified disk candidates from the SMA observations at coarser resolution. The lack of evidence for Keplerian disks in other disk candidates identified from the SMA data suggests that rotational signatures observed at 2000 au scales do not necessarily imply the presence of Keplerian disks at smaller scales. Therefore, higher-resolution and higher-sensitivity observations are essential to definitively identify Keplerian disks on smaller scales.
The principles behind the sharp, singular structures in a crumpled sheet are well understood. Here we discuss more general ways of exploiting such sharp structures to control the shape of a sheet by deforming or forcing it elsewhere. Often, the induced shape leads to further sharp structures -- ``sub-singularities". Though weaker and softer than the primary singularities, they nevertheless provide robust ways of shaping a sheet. In simple cases we understand the reason for these and their strength. This paper surveys a broad range of other sub-structure phenomena and reports recent progress in understanding some of them.
2025-03-20
J. Zeng, T. Aramaki, K. Aoyama, S. Arai, S. Arai, J. Asaadi, A. Bamba, N. Cannady, P. Coppi, G. De Nolfo, M. Errando, L. Fabris, T. Fujiwara, Y. Fukazawa, P. Ghosh, K. Hagino, T. Hakamata, N. Hiroshima, M. Ichihashi, Y. Ichinohe, Y. Inoue, K. Ishikawa, K. Ishiwata, T. Iwata, G. Karagiorgi, T. Kato, H. Kawamura, D. Khangulyan, J. Krizmanic, J. LeyVa, A. Malige, J. G. Mitchell, J. W. Mitchell, R. Mukherjee, R. Nakajima, K. Nakazawa, H. Odaka, K. Okuma, K. Perez, N. Poudyal, I. Safa, K. Sakai, M. Sasaki, W. Seligman, J. Sensenig, K. Shirahama, T. Shiraishi, S. Smith, Y. Suda, A. Suraj, H. Takahashi, S. Takashima, T. Tamba, M. Tanaka, S. Tandon, R. Tatsumi, J. Tomsick, N. Tsuji, Y. Uchida, Y. Utsumi, S. Watanabe, Y. Yano, K. Yawata, H. Yoneda, K. Yorita, M. Yoshimoto
The Gamma-Ray and AntiMatter Survey (GRAMS) is a next-generation balloon/satellite mission utilizing a Liquid Argon Time Projection Chamber (LArTPC) detector to measure both MeV gamma rays and antinuclei produced by dark matter annihilation or decay. The GRAMS can identify antihelium-3 events based on the measurements of X-rays and charged pions from the decay of the exotic atoms, Time of Flight (TOF), energy deposition, and stopping range. This paper shows the antihelium-3 sensitivity estimation using a GEANT4 Monte Carlo simulation. For the proposed long-duration balloon (LDB) flight program (35 days $ \times $ 3 flights) and future satellite mission (2-year observation), the sensitivities become $ 1.47\times 10^{-7}\ [m^2s\ sr\ GeV/n]^{-1} $ and $ 1.55\times 10^{-9}\ [m^2s\ sr\ GeV/n]^{-1} $, respectively. The results indicate that GRAMS can extensively investigate various dark matter models through the antihelium-3 measurements.
Catalog-based detection of unrecognized blends in deep optical ground based catalogs
2025-03-20
Shuang Liang, Prakruth Adari, Anja von der Linden
In deep, ground-based imaging, about 15%-30% of object detections are expected to correspond to two or more true objects - these are called ``unrecognized blends''. We use Machine Learning algorithms to detect unrecognized blends in deep ground-based photometry using only catalog-level information: colors, magnitude, and size. We compare the performance of Self Organizing Map, Random Forest, k-Nearest Neighbors, and Anomaly Detection algorithms. We test all algorithms on 9-band ($uBVri^{+}z^{++}YJH$) and 1-size (flux_radius in $\textit{i}$-band) measurements of the ground-based COSMOS catalog, and use COSMOS HST data as the truth for unrecognized blend. We find that 17% of objects in the ground-based COSMOS catalog are unrecognized blends. We show that some unrecognized blends can be identified as such using only catalog-level information; but not all blends can be easily identified. Nonetheless, our methods can be used to improve sample purity, and can identify approximately 30% to 80% of unrecognized blends while rejecting 10% to 50% of all detected galaxies (blended or unblended). The results are similar when only optical bands ($uBVri^{+}z^{++}$) and the size information is available. We also investigate the ability of these algorithms to remove photo-z outliers (identified with spectroscopic redshifts), and find that algorithms targeting color outliers perform better than algorithms targeting unrecognized blends. Our method can offer a cleaner galaxy sample with lower blending rates for future cosmological surveys such as the Legacy Survey of Space and Time (LSST), and can potentially improve the accuracy on cosmological parameter constraints at a moderate cost of precision.
Advances in Protein Representation Learning: Methods, Applications, and Future Directions
2025-03-20
Viet Thanh Duy Nguyen, Truong-Son Hy
Proteins are complex biomolecules that play a central role in various biological processes, making them critical targets for breakthroughs in molecular biology, medical research, and drug discovery. Deciphering their intricate, hierarchical structures, and diverse functions is essential for advancing our understanding of life at the molecular level. Protein Representation Learning (PRL) has emerged as a transformative approach, enabling the extraction of meaningful computational representations from protein data to address these challenges. In this paper, we provide a comprehensive review of PRL research, categorizing methodologies into five key areas: feature-based, sequence-based, structure-based, multimodal, and complex-based approaches. To support researchers in this rapidly evolving field, we introduce widely used databases for protein sequences, structures, and functions, which serve as essential resources for model development and evaluation. We also explore the diverse applications of these approaches in multiple domains, demonstrating their broad impact. Finally, we discuss pressing technical challenges and outline future directions to advance PRL, offering insights to inspire continued innovation in this foundational field.
Accelerating Antibiotic Discovery with Large Language Models and Knowledge Graphs
2025-03-20
Maxime Delmas, Magdalena Wysocka, Danilo Gusicuma, André Freitas
The discovery of novel antibiotics is critical to address the growing antimicrobial resistance (AMR). However, pharmaceutical industries face high costs (over $1 billion), long timelines, and a high failure rate, worsened by the rediscovery of known compounds. We propose an LLM-based pipeline that acts as an alarm system, detecting prior evidence of antibiotic activity to prevent costly rediscoveries. The system integrates organism and chemical literature into a Knowledge Graph (KG), ensuring taxonomic resolution, synonym handling, and multi-level evidence classification. We tested the pipeline on a private list of 73 potential antibiotic-producing organisms, disclosing 12 negative hits for evaluation. The results highlight the effectiveness of the pipeline for evidence reviewing, reducing false negatives, and accelerating decision-making. The KG for negative hits and the user interface for interactive exploration will be made publicly available.
To impute or not to impute: How machine learning modelers treat missing data
2025-03-20
Wanyi Chen, Mary Cummings
Missing data is prevalent in tabular machine learning (ML) models, and different missing data treatment methods can significantly affect ML model training results. However, little is known about how ML researchers and engineers choose missing data treatment methods and what factors affect their choices. To this end, we conducted a survey of 70 ML researchers and engineers. Our results revealed that most participants were not making informed decisions regarding missing data treatment, which could significantly affect the validity of the ML models trained by these researchers. We advocate for better education on missing data, more standardized missing data reporting, and better missing data analysis tools.
Euclid: Star clusters in IC 342, NGC 2403, and Holmberg II
2025-03-20
S. S. Larsen, A. M. N. Ferguson, J. M. Howell, F. Annibali, J. -C. Cuillandre, L. K. Hunt, A. Lançon, T. Saifollahi, D. Massari, M. N. Le, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, A. Biviano, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, H. Dole, M. Douspis, F. Dubath, X. Dupac, S. Dusini, S. Escoffier, M. Fabricius, M. Farina, F. Faustini, S. Ferriol, S. Fotopoulou, M. Frailis, E. Franceschi, S. Galeotta, K. George, B. Gillis, C. Giocoli, P. Gómez-Alvarez, J. Gracia-Carpio, A. Grazian, F. Grupp, S. V. H. Haugan, H. Hoekstra, W. Holmes, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, E. Keihänen, S. Kermiche, A. Kiessling, R. Kohley, B. Kubik, K. Kuijken, M. Kümmel, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, D. Maino, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, E. Medinaceli, S. Mei, Y. Mellier, M. Meneghetti, G. Meylan, A. Mora, M. Moresco, L. Moscardini, C. Neissner, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, J. A. Schewtschenko, M. Schirmer, P. Schneider, A. Secroun, G. Seidel, M. Seiffert, S. Serrano, P. Simon, C. Sirignano, G. Sirri, J. Skottfelt, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, A. Tsyganov, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, G. Zamorani, F. M. Zerbi, E. Zucca, J. Martín-Fleitas, V. Scottez
We examine the star cluster populations in the three nearby galaxies IC 342, NGC 2403, and Holmberg II, observed as part of the Euclid Early Release Observations programme. Our main focus is on old globular clusters (GCs), for which the wide field-of-view and excellent image quality of Euclid offer substantial advantages over previous work. For IC 342 this is the first study of stellar clusters other than its nuclear cluster. After selection based on size and magnitude criteria, followed by visual inspection, we identify 111 old (> 1 Gyr) GC candidates in IC 342, 50 in NGC 2403 (of which 15 were previously known), and 7 in Holmberg II. In addition, a number of younger and/or intermediate-age candidates are identified. The colour distributions of GC candidates in the two larger galaxies show hints of bimodality with peaks at IE-HE = 0.36 and 0.79 (IC 342) and IE-HE = 0.36 and 0.80 (NGC 2403), corresponding to metallicities of [Fe/H]=-1.5 and [Fe/H]=-0.5, similar to those of the metal-poor and metal-rich GC subpopulations in the Milky Way. The luminosity functions of our GC candidates exhibit an excess of relatively faint objects, relative to a canonical, approximately Gaussian GC luminosity function (GCLF). The excess objects may be similar to those previously identified in other galaxies. The specific frequency of classical old GCs in IC 342, as determined based on the brighter half of the GCLF, appears to be unusually low with SN=0.2-0.3. The combined luminosity function of young and intermediate-age clusters in all three galaxies is consistent with a power-law distribution, dN/dL ~ L^(-2.3+/-0.1) and the total numbers of young clusters brighter than M(IE)=-8 in NGC 2403 and Holmberg II are comparable with those found in their Local Group counterparts, that is, M33 and the Small Magellanic Cloud, respectively.
MobilePlantViT: A Mobile-friendly Hybrid ViT for Generalized Plant Disease Image Classification
2025-03-20
Moshiur Rahman Tonmoy, Md. Mithun Hossain, Nilanjan Dey, M. F. Mridha
Plant diseases significantly threaten global food security by reducing crop yields and undermining agricultural sustainability. AI-driven automated classification has emerged as a promising solution, with deep learning models demonstrating impressive performance in plant disease identification. However, deploying these models on mobile and edge devices remains challenging due to high computational demands and resource constraints, highlighting the need for lightweight, accurate solutions for accessible smart agriculture systems. To address this, we propose MobilePlantViT, a novel hybrid Vision Transformer (ViT) architecture designed for generalized plant disease classification, which optimizes resource efficiency while maintaining high performance. Extensive experiments across diverse plant disease datasets of varying scales show our model's effectiveness and strong generalizability, achieving test accuracies ranging from 80% to over 99%. Notably, with only 0.69 million parameters, our architecture outperforms the smallest versions of MobileViTv1 and MobileViTv2, despite their higher parameter counts. These results underscore the potential of our approach for real-world, AI-powered automated plant disease classification in sustainable and resource-efficient smart agriculture systems. All codes will be available in the GitHub repository: https://github.com/moshiurtonmoy/MobilePlantViT
Sparking Curiosity in Digital System Design Lectures with Take Home Labs
2025-03-20
Senol Gulgonul
Digital system design lectures are mandatory in the electrical and electronics engineering curriculum. Besides HDL simulators and viewers, FPGA boards are necessary for the real implementation of HDL, which were previously costly for students. With the emergence of low-cost FPGA boards, the use of take-home labs is increasing. The COVID-19 pandemic has further accelerated this process. Traditional lab sessions have limitations, prompting the exploration of take-home lab kits to enhance learning flexibility and engagement. This study aims to evaluate the effectiveness of a low-cost take-home lab kit, consisting of a Tang Nano 9K FPGA board and a Saleae Logic Analyzer, in improving students' practical skills and sparking curiosity in digital system design. The research was conducted in the EEE 303 Digital Design lecture. Students used the Tang Nano 9K FPGA and Saleae Logic Analyzer for a term project involving PWM signal generation. Data was collected through a survey assessing the kit's impact on learning and engagement. Positive Acceptance: 75% of students agreed or strongly agreed that the take-home lab kit was beneficial. Preference for Lab Types: 60% of students preferred classical weekly lab hours over take-home labs. Increased Curiosity: 65% of students conducted additional, unassigned experiments, indicating heightened interest and engagement. The take-home lab kit effectively aids in learning practical aspects of digital system design and stimulates curiosity, though some students prefer traditional lab sessions for group work.
Concurrent Optimization of Satellite Phasing and Tasking for Cislunar Space Situational Awareness
2025-03-20
Malav Patel, Kento Tomita, Koki Ho
Recently, renewed interest in cislunar space spurred by private and public organizations has driven research for future infrastructure in the region. As Earth-Moon traffic increases amidst a growing space economy, monitoring architectures supporting this traffic must also develop. These are likely to be realized as constellations of patrol satellites surveying traffic between the Earth and the Moon. This work investigates the concurrent optimization of patrol satellite phasing and tasking to provide information-maximal coverage of traffic in periodic orbits.
Predictions for Detecting a Turndown in the Baryonic Tully Fisher Relation
2025-03-20
Dilys Ruan, Alyson M. Brooks, Akaxia Cruz, Annika H. G. Peter, Ben Keller, Thomas Quinn, James Wadsley, Elizabeth A. K. Adams
The baryonic Tully Fisher relation (bTFR) provides an empirical connection between baryonic mass and dynamical mass (measured by the maximum rotation velocity) for galaxies. Due to the impact of baryonic feedback in the shallower potential wells of dwarf galaxies, the bTFR is predicted to turn down at low masses from the extrapolated power-law relation at high masses. The low-mass end of the bTFR is poorly constrained due to small samples and difficulty in connecting the galaxy's gas kinematics to its dark matter halo. Simulations can help us understand this connection and interpret observations. We measure the bTFR with 66 dwarf galaxies from the Marvel-ous and Marvelous Massive Dwarfs hydrodynamic simulations. Our sample has M$_\star = 10^6-10^9$ M$_\odot$, and is mostly gas dominated. We compare five velocity methods: V$_\text{out,circ}$ (spatially resolved mass-enclosed), V$_\text{out,mid}$ (spatially resolved midplane gravitational potential), and unresolved HI linewidths at different percentages of the peak flux (W$_\text{10}$, W$_\text{20}$, and W$_\text{50}$). We find an intrinsic turndown in the bTFR for maximum halo speeds $\lesssim 50$ km s$^{-1}$ (or total baryonic mass, M$_\text{bary}\lesssim 10^{8.5}$ M$_\odot$). We find that observing HI in lower-mass galaxies to the conventional surface density limit of 1M$_\odot$pc$^{-2}$ is not enough to detect a turndown in the bTFR; none of the HI velocity methods (spatially resolved or unresolved) recover the turndown, and we find bTFR slopes consistent with observations of higher-mass galaxies. However, we predict that the turndown can be recovered by resolved rotation curves if the HI limit is $\lesssim 0.08$ M$_\odot$ pc$^{-2}$, which is within the sensitivity of current HI surveys like FEASTS and MHONGOOSE.
Distributed LLMs and Multimodal Large Language Models: A Survey on Advances, Challenges, and Future Directions
2025-03-20
Hadi Amini, Md Jueal Mia, Yasaman Saadati, Ahmed Imteaj, Seyedsina Nabavirazavi, Urmish Thakker, Md Zarif Hossain, Awal Ahmed Fime, S. S. Iyengar
Language models (LMs) are machine learning models designed to predict linguistic patterns by estimating the probability of word sequences based on large-scale datasets, such as text. LMs have a wide range of applications in natural language processing (NLP) tasks, including autocomplete and machine translation. Although larger datasets typically enhance LM performance, scalability remains a challenge due to constraints in computational power and resources. Distributed computing strategies offer essential solutions for improving scalability and managing the growing computational demand. Further, the use of sensitive datasets in training and deployment raises significant privacy concerns. Recent research has focused on developing decentralized techniques to enable distributed training and inference while utilizing diverse computational resources and enabling edge AI. This paper presents a survey on distributed solutions for various LMs, including large language models (LLMs), vision language models (VLMs), multimodal LLMs (MLLMs), and small language models (SLMs). While LLMs focus on processing and generating text, MLLMs are designed to handle multiple modalities of data (e.g., text, images, and audio) and to integrate them for broader applications. To this end, this paper reviews key advancements across the MLLM pipeline, including distributed training, inference, fine-tuning, and deployment, while also identifying the contributions, limitations, and future areas of improvement. Further, it categorizes the literature based on six primary focus areas of decentralization. Our analysis describes gaps in current methodologies for enabling distributed solutions for LMs and outline future research directions, emphasizing the need for novel solutions to enhance the robustness and applicability of distributed LMs.
Machine Learning-Based Genomic Linguistic Analysis (Gene Sequence Feature Learning): A Case Study on Predicting Heavy Metal Response Genes in Rice
2025-03-20
Ruiqi Yang, Jianxu Wang, Wei Yuan, Xun Wang, Mei Li
This study explores the application of machine learning-based genetic linguistics for identifying heavy metal response genes in rice (Oryza sativa). By integrating convolutional neural networks and random forest algorithms, we developed a hybrid model capable of extracting and learning meaningful features from gene sequences, such as k-mer frequencies and physicochemical properties. The model was trained and tested on datasets of genes, achieving high predictive performance (precision: 0.89, F1-score: 0.82). RNA-seq and qRT-PCR experiments conducted on rice leaves which exposed to Hg0, revealed differential expression of genes associated with heavy metal responses, which validated the model's predictions. Co-expression network analysis identified 103 related genes, and a literature review indicated that these genes are highly likely to be involved in heavy metal-related biological processes. By integrating and comparing the analysis results with those of differentially expressed genes (DEGs), the validity of the new machine learning method was further demonstrated. This study highlights the efficacy of combining machine learning with genetic linguistics for large-scale gene prediction. It demonstrates a cost-effective and efficient approach for uncovering molecular mechanisms underlying heavy metal responses, with potential applications in developing stress-tolerant crop varieties.
AUV Acceleration Prediction Using DVL and Deep Learning
2025-03-20
Yair Stolero, Itzik Klein
Autonomous underwater vehicles (AUVs) are essential for various applications, including oceanographic surveys, underwater mapping, and infrastructure inspections. Accurate and robust navigation are critical to completing these tasks. To this end, a Doppler velocity log (DVL) and inertial sensors are fused together. Recently, a model-based approach demonstrated the ability to extract the vehicle acceleration vector from DVL velocity measurements. Motivated by this advancement, in this paper we present an end-to-end deep learning approach to estimate the AUV acceleration vector based on past DVL velocity measurements. Based on recorded data from sea experiments, we demonstrate that the proposed method improves acceleration vector estimation by more than 65% compared to the model-based approach by using data-driven techniques. As a result of our data-driven approach, we can enhance navigation accuracy and reliability in AUV applications, contributing to more efficient and effective underwater missions through improved accuracy and reliability.
Efficient processing of long contexts has been a persistent pursuit in Natural Language Processing. With the growing number of long documents, dialogues, and other textual data, it is important to develop Long Context Language Models (LCLMs) that can process and analyze extensive inputs in an effective and efficient way. In this paper, we present a comprehensive survey on recent advances in long-context modeling for large language models. Our survey is structured around three key aspects: how to obtain effective and efficient LCLMs, how to train and deploy LCLMs efficiently, and how to evaluate and analyze LCLMs comprehensively. For the first aspect, we discuss data strategies, architectural designs, and workflow approaches oriented with long context processing. For the second aspect, we provide a detailed examination of the infrastructure required for LCLM training and inference. For the third aspect, we present evaluation paradigms for long-context comprehension and long-form generation, as well as behavioral analysis and mechanism interpretability of LCLMs. Beyond these three key aspects, we thoroughly explore the diverse application scenarios where existing LCLMs have been deployed and outline promising future development directions. This survey provides an up-to-date review of the literature on long-context LLMs, which we wish to serve as a valuable resource for both researchers and engineers. An associated GitHub repository collecting the latest papers and repos is available at: \href{https://github.com/LCLM-Horizon/A-Comprehensive-Survey-For-Long-Context-Language-Modeling}{\color[RGB]{175,36,67}{LCLM-Horizon}}.
Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks but their remote sensing (RS) counterpart are relatively under explored. Unlike natural images, RS imagery presents unique challenges that current MLLMs struggle to handle, particularly in visual grounding and spatial reasoning. This study investigates the limitations of CLIP-based MLLMs in RS, highlighting their failure to differentiate visually distinct yet semantically similar RS images. To address this, we introduce a remote sensing multimodal visual patterns (RSMMVP) benchmark. It is designed to evaluate MLLMs in RS tasks by identifying the CLIP-blind pairs, where CLIP-based models incorrectly assign high similarity scores to visually distinct RS images. Through a visual question answering (VQA) evaluation, we analyze the performance of state-of-the-art MLLMs, revealing significant limitations in RS specific representation learning. The results provide valuable insights into the weaknesses of CLIP-based visual encoding and offer a foundation for future research to develop more effective MLLMs tailored for remote sensing applications.
Characterization of locally most split reliable graphs
2025-03-19
Pablo Romero
A two-terminal graph is a graph equipped with two distinguished vertices, called terminals. Let $T_{n,m}$ be the set of all nonisomorphic connected simple two-terminal graphs on $n$ vertices and $m$ edges. Let $G$ be any two-terminal graph in $T_{n,m}$. For every number $p$ in $[0,1]$ we let each of the edges in $G$ be independently deleted with probability $1-p$. The split reliability $SR_{G}(p)$ is the probability that the resulting spanning subgraph has precisely $2$ connected components, each one including one terminal. The two-terminal graph $G$ is uniformly most split reliable if $SR_G(p)\geq SR_{H}(p)$ for each $H$ in $T_{n,m}$ and every $p$ in $[0,1]$. We say $G$ is locally most split reliable if there exists $\delta>0$ such that $SR_G(p)\geq SR_{H}(p)$ for each $H$ in $T_{n,m}$ and every $p$ in $(1-\delta,1)$. Brown and McMullin showed that there exists uniformly most split reliable graphs in each class $T_{n,m}$ such that $m=n-1$, $m=\binom{n}{2}$, or $m=\binom{n}{2}-1$. The authors also proved that there is no uniformly most split reliable two-terminal graph in $T_{n,n}$ when $n\geq 6$ and specified in which classes $T_{n,m}$ such that $n\leq 7$ there exist uniformly most split reliable graphs. The existence or nonexistence of uniformly most split reliable graphs in the remaining cases is posed by Brown and McMullin as an open problem. In this work, the set $\mathcal{G}_{n,m}$ consisting of all locally most split reliable graphs is characterized in each nonempty class $T_{n,m}$. It is proved that a graph in $T_{n,m}$ is locally most split reliable if and only if its split reliability equals that of the balloon graph equipped with two terminals whose distance equals its diameter. Finally, it is proved that there is no uniformly most split reliable graph in $T_{n,m}$ when $n\geq 7$ and $n\leq m \leq \binom{n-3}{2}+3$.
Separation of variables for higher rank integrable models: review of recent progress
2025-03-19
Fedor Levkovich-Maslyuk
Separation of variables (SoV) is a powerful method expected to be applicable for a wide range of quantum integrable systems, from models in condensed matter physics to gauge and string theories. Yet its full implementation for many higher rank examples, such as SU(N) spin chains with N>2, has remained elusive for a long time. In this pedagogical review we discuss the major progress achieved recently in understanding SoV for models of this type. In particular, for rational SU(N) spin chains we describe different constructions of the SoV basis, novel compact forms for spin chain eigenstates, the functional SoV approach, and explicit computation of the SoV measure. We also discuss key first applications of these results, namely the new compact determinant representations for many observables such as scalar products and correlators.
Making Online Polls More Accurate: Statistical Methods Explained
2025-03-19
Alberto Arletti, Maria Letizia Tanturri, Omar Paccagnella
Online data has the potential to transform how researchers and companies produce election forecasts. Social media surveys, online panels and even comments scraped from the internet can offer valuable insights into political preferences. However, such data is often affected by significant selection bias, as online respondents may not be representative of the overall population. At the same time, traditional data collection methods are becoming increasingly cost-prohibitive. In this scenario, scientists need instruments to be able to draw the most accurate estimate possible from samples drawn online. This paper provides an introduction to key statistical methods for mitigating bias and improving inference in such cases, with a focus on electoral polling. Specifically, it presents the main statistical techniques, categorized into weighting, modeling and other approaches. It also offers practical recommendations for drawing estimates with measures of uncertainty. Designed for both researchers and industry practitioners, this introduction takes a hands-on approach, with code available for implementing the main methods.
Highway merges present difficulties for human drivers and automated vehicles due to incomplete situational awareness and a need for a structured (precedence, order) environment, respectively. In this paper, an unstructured merge algorithm is presented for connected and automated vehicles. There is neither precedence nor established passing order through the merge point. The algorithm relies on Control Barrier Functions for safety (collision avoidance) and for coordination that arises from exponential instability of stall-equilibria in the inter-agent space. A Monte Carlo simulation comparison to a first-in-first-out approach shows improvement in traffic flow and a significant energy efficiency benefit.
Plenty of hadrons have been established experimentally, yet the nonperturbative nature of the strong interaction complicates a comprehensive understanding of their internal structure, particularly for exotic hadrons that extend beyond conventional mesons and baryons. One prominent candidate for the internal structure of exotic hadrons is the hadronic molecule, a loosely bound system of hadrons analogous to atomic nuclei. Understanding such systems requires precise knowledge of hadron interactions, which traditional scattering experiments struggle to provide, especially in the low-energy region. Recent advances, including precise baryon-baryon interaction measurements, femtoscopy techniques that probe momentum correlations between particles, and first-principles lattice QCD calculations, have significantly improved our understanding of hadron interactions. Here, we review these recent developments, demonstrate the successful application of femtoscopy to antikaon-nucleon interactions, utilize the concept of compositeness to quantify the hadronic molecular component of the Lambda(1405), and discuss both experimental and theoretical prospects, including future studies at J-PARC.
Real-world validation of a multimodal LLM-powered pipeline for High-Accuracy Clinical Trial Patient Matching leveraging EHR data
2025-03-19
Anatole Callies, Quentin Bodinier, Philippe Ravaud, Kourosh Davarpanah
Background: Patient recruitment in clinical trials is hindered by complex eligibility criteria and labor-intensive chart reviews. Prior research using text-only models have struggled to address this problem in a reliable and scalable way due to (1) limited reasoning capabilities, (2) information loss from converting visual records to text, and (3) lack of a generic EHR integration to extract patient data. Methods: We introduce a broadly applicable, integration-free, LLM-powered pipeline that automates patient-trial matching using unprocessed documents extracted from EHRs. Our approach leverages (1) the new reasoning-LLM paradigm, enabling the assessment of even the most complex criteria, (2) visual capabilities of latest LLMs to interpret medical records without lossy image-to-text conversions, and (3) multimodal embeddings for efficient medical record search. The pipeline was validated on the n2c2 2018 cohort selection dataset (288 diabetic patients) and a real-world dataset composed of 485 patients from 30 different sites matched against 36 diverse trials. Results: On the n2c2 dataset, our method achieved a new state-of-the-art criterion-level accuracy of 93\%. In real-world trials, the pipeline yielded an accuracy of 87\%, undermined by the difficulty to replicate human decision-making when medical records lack sufficient information. Nevertheless, users were able to review overall eligibility in under 9 minutes per patient on average, representing an 80\% improvement over traditional manual chart reviews. Conclusion: This pipeline demonstrates robust performance in clinical trial patient matching without requiring custom integration with site systems or trial-specific tailoring, thereby enabling scalable deployment across sites seeking to leverage AI for patient matching.
Blocked Cholesky factorization updates of the Riccati recursion using hyperbolic Householder transformations
2025-03-19
Pieter Pas, Panagiotis Patrinos
Newton systems in quadratic programming (QP) methods are often solved using direct Cholesky or LDL factorizations. When the linear systems in successive iterations differ by a low-rank modification (as is common in active set and augmented Lagrangian methods), updating the existing factorization can offer significant performance improvements over recomputing a full Cholesky factorization. We review the hyperbolic Householder transformation, and demonstrate its usefulness in describing low-rank Cholesky factorization updates. By applying this hyperbolic Householder-based framework to the well-known Riccati recursion for solving saddle-point problems with optimal control structure, we develop a novel algorithm for updating the factorizations used in optimization solvers for optimal control. Specifically, the proposed method can be used to efficiently solve the semismooth Newton systems that are at the core of the augmented Lagrangian-based QPALM-OCP solver. An optimized open-source implementation of the proposed factorization update routines is provided as well.
Euclid: Quick Data Release (Q1) -- Photometric studies of known transients
2025-03-19
C. Duffy, E. Cappellaro, M. T. Botticella, I. M. Hook, F. Poidevin, T. J. Moriya, A. A. Chrimes, V. Petrecca, K. Paterson, A. Goobar, L. Galbany, R. Kotak, C. Gall, C. M. Gutierrez, C. Tao, L. Izzo, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, A. Basset, P. Battaglia, A. Biviano, A. Bonchi, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, R. Casas, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, C. Dolding, H. Dole, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, A. Ealet, S. Escoffier, M. Farina, F. Faustini, S. Ferriol, S. Fotopoulou, M. Frailis, P. Franzetti, S. Galeotta, K. George, B. Gillis, C. Giocoli, P. Gómez-Alvarez, A. Grazian, F. Grupp, S. Gwyn, S. V. H. Haugan, J. Hoar, H. Hoekstra, W. Holmes, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, E. Keihänen, S. Kermiche, B. Kubik, K. Kuijken, M. Kümmel, M. Kunz, H. Kurki-Suonio, Q. Le Boulc'h, A. M. C. Le Brun, D. Le Mignant, P. Liebing, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, E. Medinaceli, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, M. Moresco, P. W. Morris, L. Moscardini, C. Neissner, R. C. Nichol, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, J. A. Schewtschenko, M. Schirmer, P. Schneider, T. Schrabback, A. Secroun, G. Seidel, S. Serrano, P. Simon, C. Sirignano, G. Sirri, J. Skottfelt, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, T. Vassallo, G. Verdoes Kleijn, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, E. Zucca, C. Burigana, R. Cabanac, L. Gabarra, V. Scottez, D. Scott, M. Sullivan
We report on serendipitous Euclid observations of previously known transients, using the Euclid Q1 data release. By cross-matching with the Transient Name Server (TNS) we identify 164 transients that coincide with the data release. Although the Euclid Q1 release only includes single-epoch data, we are able to make Euclid photometric measurements at the location of 161 of these transients. Euclid obtained deep photometric measurements or upper limits of these transients in the $I_E$, $Y_E$, $J_E$, and $H_E$ bands at various phases of the transient light-curves, including before, during, and after the observations of ground-based transient surveys. Approximately 70\% of known transients reported in the six months before the Euclid observation date and with discovery magnitude brighter than 24 were detected in Euclid $\IE$ images. Our observations include one of the earliest near-infrared detections of a Type~Ia supernova (SN 2024pvw) 15 days prior to its peak brightness, and the late-phase (435.9 days post peak) observations of the enigmatic core-collapse SN 2023aew. Euclid deep photometry provides valuable information on the nature of these transients such as their progenitor systems and power sources, with late time observations being a uniquely powerful contribution. In addition, Euclid is able to detect the host galaxies of some transients that were previously classed as hostless. The Q1 data demonstrate the power of the Euclid data even with only single-epoch observations available, as will be the case for much larger areas of sky in the Euclid Wide Survey.
Euclid Quick Data Release (Q1). Galaxy shapes and alignments in the cosmic web
2025-03-19
Euclid Collaboration, C. Laigle, C. Gouin, F. Sarron, L. Quilley, C. Pichon, K. Kraljic, F. Durret, N. E. Chisari, U. Kuchner, N. Malavasi, M. Magliocchetti, H. J. McCracken, J. G. Sorce, Y. Kang, C. J. R. McPartland, S. Toft, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, H. Aussel, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, A. Basset, P. Battaglia, F. Bernardeau, A. Biviano, A. Bonchi, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, S. de la Torre, G. De Lucia, A. M. Di Giorgio, C. Dolding, H. Dole, F. Dubath, C. A. J. Duncan, X. Dupac, A. Ealet, S. Escoffier, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, S. Fotopoulou, M. Frailis, E. Franceschi, S. Galeotta, K. George, W. Gillard, B. Gillis, C. Giocoli, P. Gómez-Alvarez, J. Gracia-Carpio, B. R. Granett, A. Grazian, F. Grupp, S. Gwyn, S. V. H. Haugan, H. Hoekstra, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, M. Kilbinger, B. Kubik, K. Kuijken, M. Kümmel, M. Kunz, H. Kurki-Suonio, Q. Le Boulc'h, A. M. C. Le Brun, D. Le Mignant, P. Liebing, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, S. Maurogordato, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, B. Rusholme, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, J. A. Schewtschenko, M. Schirmer, P. Schneider, T. Schrabback, M. Scodeggio, A. Secroun, G. Seidel, M. Seiffert, S. Serrano, P. Simon, C. Sirignano, G. Sirri, J. Skottfelt, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, H. I. Teplitz, I. Tereno, N. Tessore, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, I. A. Zinchenko, E. Zucca, V. Allevato, M. Ballardini, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, A. Cappi, D. Di Ferdinando, J. A. Escartin Vigo, L. Gabarra, M. Huertas-Company, J. Martín-Fleitas, S. Matthew, N. Mauri, R. B. Metcalf, A. Pezzotta, M. Pöntinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, S. Alvi, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, A. Balaguera-Antolinez, C. Benoist, K. Benson, D. Bertacca, M. Bethermin, A. Blanchard, L. Blot, H. Böhringer, S. Borgani, M. L. Brown, S. Bruton, A. Calabro, B. Camacho Quevedo, F. Caro, C. S. Carvalho, T. Castro, F. Cogato, T. Contini, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, J. M. Diego, P. -A. Duc, A. Enia, Y. Fang, A. G. Ferrari, P. G. Ferreira, A. Finoguenov, A. Fontana, A. Franco, K. Ganga, J. García-Bellido, T. Gasparetto, V. Gautard, E. Gaztanaga, F. Giacomini, F. Gianotti, A. H. Gonzalez, G. Gozaliasl, M. Guidi, C. M. Gutierrez, A. Hall, W. G. Hartley, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, S. Joudaki, J. J. E. Kajava, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, S. Kruk, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, L. Leuzzi, T. I. Liaudat, A. Loureiro, J. Macias-Perez, G. Maggio, E. A. Magnier, F. Mannucci, R. Maoli, C. J. A. P. Martins, L. Maurin, M. Miluzio, P. Monaco, A. Montoro, C. Moretti, G. Morgante, S. Nadathur, K. Naidoo, A. Navarro-Alsina, S. Nesseris, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, S. Quai, M. Radovich, P. -F. Rocci, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, C. Scarlata, A. Schneider, M. Schultheis, D. Sciotti, E. Sellentin, F. Shankar, L. C. Smith, S. A. Stanford, K. Tanidis, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, G. Verza, P. Vielzeuf, N. A. Walton, J. R. Weaver, D. Scott
Galaxy morphologies and shape orientations are expected to correlate with their large-scale environment, since they grow by accreting matter from the cosmic web and are subject to interactions with other galaxies. Cosmic filaments are extracted in projection from the Euclid Quick Data Release 1 (covering 63.1 $\mathrm{deg}^2$) at $0.5 10^{10} M_\odot$) in the projected cosmic web is analysed as a function of morphology measured from VIS data. Specifically, the 2D alignment of galaxy shapes with large-scale filaments is quantified as a function of S\'ersic indices and masses. We find the known trend that more massive galaxies are closer to filament spines. At fixed stellar masses, morphologies correlate both with densities and distances to large-scale filaments. In addition, the large volume of this data set allows us to detect a signal indicating that there is a preferential alignment of the major axis of massive early-type galaxies along projected cosmic filaments. Overall, these results demonstrate our capabilities to carry out detailed studies of galaxy environments with Euclid, which will be extended to higher redshift and lower stellar masses with the future Euclid Deep Survey.
Euclid Quick Data Release (Q1). Combined Euclid and Spitzer galaxy density catalogues at $z>$ 1.3 and detection of significant Euclid passive galaxy overdensities in Spitzer overdense regions
2025-03-19
Euclid Collaboration, N. Mai, S. Mei, C. Cleland, R. Chary, J. G. Bartlett, G. Castignani, H. Dannerbauer, G. De Lucia, F. Fontanot, D. Scott, S. Andreon, S. Bhargava, H. Dole, T. DUSSERRE, S. A. Stanford, V. P. Tran, J. R. Weaver, P. -A. Duc, I. Risso, N. Aghanim, B. Altieri, A. Amara, N. Auricchio, H. Aussel, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, A. Biviano, A. Bonchi, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, A. Caillat, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, A. Da Silva, H. Degaudenzi, A. M. Di Giorgio, M. Douspis, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, A. Ealet, S. Escoffier, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, P. Fosalba, S. Fotopoulou, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, B. Gillis, C. Giocoli, J. Gracia-Carpio, B. R. Granett, A. Grazian, F. Grupp, S. V. H. Haugan, W. Holmes, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, Q. Le Boulc'h, A. M. C. Le Brun, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, S. Maurogordato, E. Medinaceli, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, J. A. Schewtschenko, P. Schneider, M. Scodeggio, A. Secroun, G. Seidel, M. Seiffert, S. Serrano, P. Simon, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, H. I. Teplitz, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, E. Zucca, V. Allevato, M. Ballardini, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, A. Cappi, D. Di Ferdinando, J. A. Escartin Vigo, L. Gabarra, W. G. Hartley, J. Martín-Fleitas, S. Matthew, N. Mauri, R. B. Metcalf, A. Pezzotta, M. Pöntinen, C. Porciani, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, S. Alvi, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, D. Bertacca, M. Bethermin, A. Blanchard, L. Blot, H. Böhringer, S. Borgani, M. L. Brown, S. Bruton, A. Calabro, F. Caro, C. S. Carvalho, T. Castro, Y. Charles, F. Cogato, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, J. M. Diego, P. Dimauro, A. Enia, Y. Fang, A. G. Ferrari, A. Finoguenov, A. Fontana, A. Franco, K. Ganga, J. García-Bellido, T. Gasparetto, V. Gautard, E. Gaztanaga, F. Giacomini, F. Gianotti, A. H. Gonzalez, G. Gozaliasl, M. Guidi, C. M. Gutierrez, A. Hall, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, S. Kruk, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, L. Leuzzi, T. I. Liaudat, A. Loureiro, J. Macias-Perez, G. Maggio, E. A. Magnier, C. Mancini, F. Mannucci, R. Maoli, C. J. A. P. Martins, L. Maurin, M. Miluzio, C. Moretti, G. Morgante, K. Naidoo, A. Navarro-Alsina, S. Nesseris, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, S. Quai, M. Radovich, P. -F. Rocci, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, C. Scarlata, A. Schneider, D. Sciotti, E. Sellentin, F. Shankar, L. C. Smith, K. Tanidis, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, G. Verza, P. Vielzeuf, N. A. Walton
Euclid will detect tens of thousands of clusters and protoclusters at $z$>1.3. With a total coverage of 63.1deg$^2$, the Euclid Quick Data Release 1 (Q1) is large enough to detect tens of clusters and hundreds of protoclusters at these early epochs. The Q1 photometric redshift catalogue enables us to detect clusters out to $z$ < 1.5; however, infrared imaging from Spitzer extends this limit to higher redshifts by using high local projected densities of Spitzer-selected galaxies as signposts for cluster and protocluster candidates. We use Spitzer imaging of the Euclid Deep Fields (EDFs) to derive densities for a sample of Spitzer-selected galaxies at redshifts $z$ > 1.3, building Spitzer IRAC1 and IRAC2 photometric catalogues that are 95% complete at a magnitude limit of IRAC2=22.2, 22.6, and 22.8 for the EDF-S, EDF-F, and EDF-N, respectively. We apply two complementary methods to calculate galaxy densities: (1) aperture and surface density; and (2) the Nth-nearest-neighbour method. When considering a sample selected at a magnitude limit of IRAC2 < 22.2, at which all three EDFs are 95% complete, our surface density distributions are consistent among the three EDFs and with the SpUDS blank field survey. We also considered a deeper sample (IRAC2 < 22.8), finding that 2% and 3% of the surface densities in the North and Fornax fields are 3$\sigma$ higher than the average field distribution and similar to densities found in the CARLA cluster survey. Our surface densities are also consistent with predictions from the GAEA semi-analytical model. Using combined Euclid and ground-based i-band photometry we show that our highest Spitzer-selected galaxy overdense regions, found at $z$~1.5, also host high densities of passive galaxies. This means that we measure densities consistent with those found in clusters and protoclusters at $z$>1.3.
Euclid Quick Data Release (Q1). LEMON -- Lens Modelling with Neural networks. Automated and fast modelling of Euclid gravitational lenses with a singular isothermal ellipsoid mass profile
2025-03-19
Euclid Collaboration, V. Busillo, C. Tortora, R. B. Metcalf, J. W. Nightingale, M. Meneghetti, F. Gentile, R. Gavazzi, F. Zhong, R. Li, B. Clément, G. Covone, N. R. Napolitano, F. Courbin, M. Walmsley, E. Jullo, J. Pearson, D. Scott, A. M. C. Le Brun, L. Leuzzi, N. Aghanim, B. Altieri, A. Amara, S. Andreon, H. Aussel, C. Baccigalupi, M. Baldi, S. Bardelli, P. Battaglia, A. Biviano, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, V. F. Cardone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, S. de la Torre, G. De Lucia, A. M. Di Giorgio, J. Dinis, H. Dole, F. Dubath, X. Dupac, S. Dusini, S. Escoffier, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, S. Fotopoulou, M. Frailis, E. Franceschi, S. Galeotta, K. George, W. Gillard, B. Gillis, C. Giocoli, J. Gracia-Carpio, B. R. Granett, A. Grazian, F. Grupp, S. V. H. Haugan, W. Holmes, I. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, Q. Le Boulc'h, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, S. Maurogordato, E. Medinaceli, S. Mei, Y. Mellier, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, J. A. Schewtschenko, M. Schirmer, P. Schneider, T. Schrabback, A. Secroun, E. Sefusatti, G. Seidel, M. Seiffert, S. Serrano, P. Simon, C. Sirignano, G. Sirri, G. Smadja, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, A. Veropalumbo, Y. Wang, J. Weller, G. Zamorani, E. Zucca, V. Allevato, M. Ballardini, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, M. Calabrese, D. Di Ferdinando, J. A. Escartin Vigo, L. Gabarra, M. Huertas-Company, S. Matthew, N. Mauri, A. A. Nucita, A. Pezzotta, M. Pöntinen, C. Porciani, V. Scottez, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, S. Alvi, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, D. Bertacca, M. Bethermin, A. Blanchard, L. Blot, S. Borgani, M. L. Brown, S. Bruton, A. Calabro, B. Camacho Quevedo, A. Cappi, F. Caro, C. S. Carvalho, T. Castro, F. Cogato, S. Conseil, S. Contarini, A. R. Cooray, O. Cucciati, F. De Paolis, G. Desprez, A. Díaz-Sánchez, S. Di Domizio, J. M. Diego, P. Dimauro, A. Enia, Y. Fang, A. G. Ferrari, P. G. Ferreira, A. Finoguenov, A. Franco, K. Ganga, J. García-Bellido, T. Gasparetto, V. Gautard, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, M. Guidi, C. M. Gutierrez, A. Hall, W. G. Hartley, S. Hemmati, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, S. Kruk, M. Lattanzi, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. Leroy, J. Lesgourgues, T. I. Liaudat, S. J. Liu, A. Loureiro, J. Macias-Perez, G. Maggio, M. Magliocchetti, F. Mannucci, R. Maoli, J. Martín-Fleitas, C. J. A. P. Martins, L. Maurin, M. Miluzio, P. Monaco, C. Moretti, G. Morgante, S. Nadathur, K. Naidoo, P. Natoli, A. Navarro-Alsina, S. Nesseris, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, S. Quai, M. Radovich, I. Risso, P. -F. Rocci, S. Sacquegna, M. Sahlén, E. Sarpa, A. Schneider, M. Schultheis, D. Sciotti, E. Sellentin, M. Sereno, L. C. Smith, J. Stadel, K. Tanidis, C. Tao, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, G. Vernardos, G. Verza, P. Vielzeuf, N. A. Walton
The Euclid mission aims to survey around 14000 deg^{2} of extragalactic sky, providing around 10^{5} gravitational lens images. Modelling of gravitational lenses is fundamental to estimate the total mass of the lens galaxy, along with its dark matter content. Traditional modelling of gravitational lenses is computationally intensive and requires manual input. In this paper, we use a Bayesian neural network, LEns MOdelling with Neural networks (LEMON), for modelling Euclid gravitational lenses with a singular isothermal ellipsoid mass profile. Our method estimates key lens mass profile parameters, such as the Einstein radius, while also predicting the light parameters of foreground galaxies and their uncertainties. We validate LEMON's performance on both mock Euclid data sets, real Euclidised lenses observed with Hubble Space Telescope (hereafter HST), and real Euclid lenses found in the Perseus ERO field, demonstrating the ability of LEMON to predict parameters of both simulated and real lenses. Results show promising accuracy and reliability in predicting the Einstein radius, axis ratio, position angle, effective radius, S\'ersic index, and lens magnitude for simulated lens galaxies. The application to real data, including the latest Quick Release 1 strong lens candidates, provides encouraging results, particularly for the Einstein radius. We also verified that LEMON has the potential to accelerate traditional modelling methods, by giving to the classical optimiser the LEMON predictions as starting points, resulting in a speed-up of up to 26 times the original time needed to model a sample of gravitational lenses, a result that would be impossible with randomly initialised guesses. This work represents a significant step towards efficient, automated gravitational lens modelling, which is crucial for handling the large data volumes expected from Euclid.
Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine E -- Ensemble classification of strong gravitational lenses: lessons for Data Release 1
2025-03-19
Euclid Collaboration, P. Holloway, A. Verma, M. Walmsley, P. J. Marshall, A. More, T. E. Collett, N. E. P. Lines, L. Leuzzi, A. Manjón-García, S. H. Vincken, J. Wilde, R. Pearce-Casey, I. T. Andika, J. A. Acevedo Barroso, T. Li, A. Melo, R. B. Metcalf, K. Rojas, B. Clément, H. Degaudenzi, F. Courbin, G. Despali, R. Gavazzi, S. Schuldt, B. C. Nagam, D. Sluse, C. Tortora, H. Domínguez Sánchez, K. Finner, A. Galan, C. Giocoli, L. Guzzo, N. B. Hogg, K. Jahnke, S. Kruk, G. Mahler, M. Millon, P. Nugent, J. Pearson, L. R. Ecker, A. Sainz de Murieta, C. Scarlata, S. Serjeant, A. Sonnenfeld, C. Spiniello, T. T. Thai, L. Ulivi, L. Weisenbach, M. Zumalacarregui, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, H. Aussel, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, R. Bender, A. Biviano, A. Bonchi, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, V. F. Cardone, J. Carretero, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, H. M. Courtois, M. Cropper, A. Da Silva, G. De Lucia, A. M. Di Giorgio, C. Dolding, H. Dole, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, A. Ealet, S. Escoffier, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, S. Fotopoulou, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, B. Gillis, P. Gómez-Alvarez, J. Gracia-Carpio, B. R. Granett, A. Grazian, F. Grupp, S. V. H. Haugan, J. Hoar, W. Holmes, F. Hormuth, A. Hornstrup, P. Hudelot, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, Q. Le Boulc'h, A. M. C. Le Brun, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, S. Maurogordato, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, J. A. Schewtschenko, P. Schneider, A. Secroun, G. Seidel, S. Serrano, P. Simon, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, E. A. Valentijn, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, E. Zucca, V. Allevato, M. Ballardini, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, A. Cappi, D. Di Ferdinando, J. A. Escartin Vigo, L. Gabarra, M. Huertas-Company, J. Martín-Fleitas, S. Matthew, N. Mauri, A. A. Nucita, A. Pezzotta, M. Pöntinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, C. Benoist, K. Benson, P. Bergamini, D. Bertacca, M. Bethermin, A. Blanchard, L. Blot, S. Borgani, M. L. Brown, S. Bruton, A. Calabro, F. Caro, C. S. Carvalho, T. Castro, F. Cogato, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, J. M. Diego, P. -A. Duc, A. Enia, Y. Fang, A. G. Ferrari, P. G. Ferreira, A. Finoguenov, A. Fontana, A. Franco, K. Ganga, J. García-Bellido, T. Gasparetto, V. Gautard, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, C. M. Gutierrez, A. Hall, W. G. Hartley, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, T. I. Liaudat, A. Loureiro, J. Macias-Perez, G. Maggio, M. Magliocchetti, E. A. Magnier, F. Mannucci, R. Maoli, C. J. A. P. Martins, L. Maurin, M. Miluzio, P. Monaco, A. Montoro, C. Moretti, G. Morgante, K. Naidoo, A. Navarro-Alsina, S. Nesseris, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, S. Quai, M. Radovich, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, A. Schneider, D. Sciotti, E. Sellentin, L. C. Smith, K. Tanidis, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, G. Vernardos, G. Verza, P. Vielzeuf, N. A. Walton, D. Scott
The Euclid Wide Survey (EWS) is expected to identify of order $100\,000$ galaxy-galaxy strong lenses across $14\,000$deg$^2$. The Euclid Quick Data Release (Q1) of $63.1$deg$^2$ Euclid images provides an excellent opportunity to test our lens-finding ability, and to verify the anticipated lens frequency in the EWS. Following the Q1 data release, eight machine learning networks from five teams were applied to approximately one million images. This was followed by a citizen science inspection of a subset of around $100\,000$ images, of which $65\%$ received high network scores, with the remainder randomly selected. The top scoring outputs were inspected by experts to establish confident (grade A), likely (grade B), possible (grade C), and unlikely lenses. In this paper we combine the citizen science and machine learning classifiers into an ensemble, demonstrating that a combined approach can produce a purer and more complete sample than the original individual classifiers. Using the expert-graded subset as ground truth, we find that this ensemble can provide a purity of $52\pm2\%$ (grade A/B lenses) with $50\%$ completeness (for context, due to the rarity of lenses a random classifier would have a purity of $0.05\%$). We discuss future lessons for the first major Euclid data release (DR1), where the big-data challenges will become more significant and will require analysing more than $\sim300$ million galaxies, and thus time investment of both experts and citizens must be carefully managed.
Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine D -- Double-source-plane lens candidates
2025-03-19
Euclid Collaboration, T. Li, T. E. Collett, M. Walmsley, N. E. P. Lines, K. Rojas, J. W. Nightingale, W. J. R. Enzi, L. A. Moustakas, C. Krawczyk, R. Gavazzi, G. Despali, P. Holloway, S. Schuldt, F. Courbin, R. B. Metcalf, D. J. Ballard, A. Verma, B. Clément, H. Degaudenzi, A. Melo, J. A. Acevedo Barroso, L. Leuzzi, A. Manjón-García, R. Pearce-Casey, D. Sluse, C. Tortora, R. Massey, G. Mahler, A. More, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, H. Aussel, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, R. Bender, F. Bernardeau, A. Biviano, A. Bonchi, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, V. F. Cardone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, H. M. Courtois, M. Cropper, A. Da Silva, G. De Lucia, A. M. Di Giorgio, C. Dolding, H. Dole, F. Dubath, C. A. J. Duncan, X. Dupac, S. Escoffier, M. Fabricius, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, S. Fotopoulou, M. Frailis, E. Franceschi, S. Galeotta, K. George, W. Gillard, B. Gillis, C. Giocoli, P. Gómez-Alvarez, J. Gracia-Carpio, B. R. Granett, A. Grazian, F. Grupp, S. V. H. Haugan, H. Hoekstra, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, Q. Le Boulc'h, A. M. C. Le Brun, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, S. Maurogordato, E. Medinaceli, S. Mei, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, J. A. Schewtschenko, M. Schirmer, P. Schneider, T. Schrabback, A. Secroun, G. Seidel, M. Seiffert, S. Serrano, P. Simon, C. Sirignano, G. Sirri, A. Spurio Mancini, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, E. A. Valentijn, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, E. Zucca, V. Allevato, M. Ballardini, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, A. Cappi, D. Di Ferdinando, J. A. Escartin Vigo, L. Gabarra, M. Huertas-Company, J. Martín-Fleitas, S. Matthew, N. Mauri, A. Pezzotta, M. Pöntinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, S. Alvi, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, K. Benson, P. Bergamini, D. Bertacca, M. Bethermin, A. Blanchard, L. Blot, M. L. Brown, S. Bruton, A. Calabro, F. Caro, C. S. Carvalho, T. Castro, F. Cogato, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, J. M. Diego, P. -A. Duc, A. Enia, Y. Fang, A. G. Ferrari, P. G. Ferreira, A. Finoguenov, A. Fontana, A. Franco, K. Ganga, J. García-Bellido, T. Gasparetto, V. Gautard, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, M. Guidi, C. M. Gutierrez, A. Hall, W. G. Hartley, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, M. Jauzac, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, S. Kruk, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, T. I. Liaudat, A. Loureiro, J. Macias-Perez, G. Maggio, M. Magliocchetti, F. Mannucci, R. Maoli, C. J. A. P. Martins, L. Maurin, M. Migliaccio, M. Miluzio, P. Monaco, C. Moretti, G. Morgante, S. Nadathur, K. Naidoo, A. Navarro-Alsina, S. Nesseris, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, S. Quai, M. Radovich, P. -F. Rocci, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, C. Scarlata, J. Schaye, A. Schneider, D. Sciotti, E. Sellentin, L. C. Smith, K. Tanidis, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, G. Vernardos, G. Verza, P. Vielzeuf, N. A. Walton, J. Wilde, D. Scott
Strong gravitational lensing systems with multiple source planes are powerful tools for probing the density profiles and dark matter substructure of the galaxies. The ratio of Einstein radii is related to the dark energy equation of state through the cosmological scaling factor $\beta$. However, galaxy-scale double-source-plane lenses (DSPLs) are extremely rare. In this paper, we report the discovery of four new galaxy-scale double-source-plane lens candidates in the Euclid Quick Release 1 (Q1) data. These systems were initially identified through a combination of machine learning lens-finding models and subsequent visual inspection from citizens and experts. We apply the widely-used {\tt LensPop} lens forecasting model to predict that the full \Euclid survey will discover 1700 DSPLs, which scales to $6 \pm 3$ DSPLs in 63 deg$^2$, the area of Q1. The number of discoveries in this work is broadly consistent with this forecast. We present lens models for each DSPL and infer their $\beta$ values. Our initial Q1 sample demonstrates the promise of \Euclid to discover such rare objects.
Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine C -- Finding lenses with machine learning
2025-03-19
Euclid Collaboration, N. E. P. Lines, T. E. Collett, M. Walmsley, K. Rojas, T. Li, L. Leuzzi, A. Manjón-García, S. H. Vincken, J. Wilde, P. Holloway, A. Verma, R. B. Metcalf, I. T. Andika, A. Melo, M. Melchior, H. Domínguez Sánchez, A. Díaz-Sánchez, J. A. Acevedo Barroso, B. Clément, C. Krawczyk, R. Pearce-Casey, S. Serjeant, F. Courbin, G. Despali, R. Gavazzi, S. Schuldt, H. Degaudenzi, L. R. Ecker, W. J. R. Enzi, K. Finner, A. Galan, C. Giocoli, N. B. Hogg, K. Jahnke, S. Kruk, G. Mahler, A. More, B. C. Nagam, J. Pearson, A. Sainz de Murieta, C. Scarlata, D. Sluse, A. Sonnenfeld, C. Spiniello, T. T. Thai, C. Tortora, L. Ulivi, L. Weisenbach, M. Zumalacarregui, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, H. Aussel, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, R. Bender, F. Bernardeau, A. Biviano, A. Bonchi, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, V. F. Cardone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, A. Costille, H. M. Courtois, M. Cropper, A. Da Silva, G. De Lucia, A. M. Di Giorgio, C. Dolding, H. Dole, F. Dubath, C. A. J. Duncan, X. Dupac, S. Escoffier, M. Fabricius, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, S. Fotopoulou, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, W. Gillard, B. Gillis, P. Gómez-Alvarez, J. Gracia-Carpio, B. R. Granett, A. Grazian, F. Grupp, L. Guzzo, S. Gwyn, S. V. H. Haugan, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, M. Jhabvala, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, Q. Le Boulc'h, A. M. C. Le Brun, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, S. Maurogordato, E. Medinaceli, S. Mei, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, J. A. Schewtschenko, M. Schirmer, P. Schneider, T. Schrabback, A. Secroun, G. Seidel, M. Seiffert, S. Serrano, P. Simon, C. Sirignano, G. Sirri, A. Spurio Mancini, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, E. A. Valentijn, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, E. Zucca, V. Allevato, M. Ballardini, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, A. Cappi, D. Di Ferdinando, J. A. Escartin Vigo, L. Gabarra, J. Martín-Fleitas, S. Matthew, N. Mauri, A. Pezzotta, M. Pöntinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, C. Benoist, K. Benson, P. Bergamini, D. Bertacca, M. Bethermin, A. Blanchard, L. Blot, M. L. Brown, S. Bruton, A. Calabro, F. Caro, C. S. Carvalho, T. Castro, Y. Charles, F. Cogato, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, J. J. Diaz, S. Di Domizio, J. M. Diego, A. Enia, Y. Fang, A. G. Ferrari, A. Finoguenov, A. Fontana, A. Franco, K. Ganga, J. García-Bellido, T. Gasparetto, V. Gautard, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, M. Guidi, C. M. Gutierrez, A. Hall, W. G. Hartley, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, T. I. Liaudat, S. J. Liu, A. Loureiro, J. Macias-Perez, G. Maggio, M. Magliocchetti, E. A. Magnier, F. Mannucci, R. Maoli, C. J. A. P. Martins, L. Maurin, M. Miluzio, P. Monaco, C. Moretti, G. Morgante, S. Nadathur, K. Naidoo, A. Navarro-Alsina, S. Nesseris, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, S. Quai, M. Radovich, P. -F. Rocci, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, A. Schneider, D. Sciotti, E. Sellentin, L. C. Smith, K. Tanidis, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, G. Vernardos, G. Verza, P. Vielzeuf, N. A. Walton, D. Scott
Strong gravitational lensing has the potential to provide a powerful probe of astrophysics and cosmology, but fewer than 1000 strong lenses have been confirmed previously. With \ang{;;0.16} resolution covering a third of the sky, the \Euclid telescope will revolutionise strong lens finding, with \num{170000} lenses forecasted to be discovered amongst its 1.5 billion galaxies. We present an analysis of the performance of five machine-learning models at finding strong gravitational lenses in the quick release of \Euclid data (Q1), covering 63\,deg$^{2}$. The models are validated with citizen scientists and expert visual inspection. We focus on the best performing network: a fine-tuned version of the \texttt{Zoobot} pretrained model, originally trained to classify galaxy morphologies in heterogeneous astronomical imaging surveys. Of the one million Q1 objects that \texttt{Zoobot} was tasked to find strong lenses within, the top 1000 ranked objects contained 122 grade A lenses (almost certain lenses), and 41 grade B lenses (probable lenses). A deeper search with the five networks combined with visual inspection discovered 250 (247) grade A (B) lenses, of which 224 (182) are ranked in the top \num{20000} by \texttt{Zoobot}. When extrapolated to the full \Euclid survey, the highest ranked one million images will contain \num{75000} grade A or B strong gravitational lenses.
Euclid Quick Data Release (Q1): The Strong Lensing Discovery Engine A -- System overview and lens catalogue
2025-03-19
Euclid Collaboration, M. Walmsley, P. Holloway, N. E. P. Lines, K. Rojas, T. E. Collett, A. Verma, T. Li, J. W. Nightingale, G. Despali, S. Schuldt, R. Gavazzi, A. Melo, R. B. Metcalf, I. T. Andika, L. Leuzzi, A. Manjón-García, R. Pearce-Casey, S. H. Vincken, J. Wilde, V. Busillo, C. Tortora, J. A. Acevedo Barroso, H. Dole, L. R. Ecker, J. Pearson, P. J. Marshall, A. More, T. Saifollahi, J. Gracia-Carpio, E. Baeten, C. Cornen, L. C. Johnson, C. Macmillan, S. Kruk, K. A. Remmelgas, B. Clément, H. Degaudenzi, F. Courbin, J. Bovy, S. Casas, H. Dannerbauer, J. M. Diego, K. Finner, A. Galan, C. Giocoli, N. B. Hogg, K. Jahnke, J. Katona, A. Kovács, C. De Leo, G. Mahler, M. Millon, B. C. Nagam, P. Nugent, A. Sainz de Murieta, C. M. O'Riordan, D. Sluse, A. Sonnenfeld, C. Spiniello, S. Serjeant, T. T. Thai, L. Ulivi, G. L. Walth, L. Weisenbach, M. Zumalacarregui, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, H. Aussel, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, F. Bernardeau, A. Biviano, A. Bonchi, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, V. F. Cardone, J. Carretero, F. J. Castander, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, L. Corcione, H. M. Courtois, M. Cropper, A. Da Silva, G. De Lucia, A. M. Di Giorgio, C. Dolding, F. Dubath, C. A. J. Duncan, X. Dupac, A. Ealet, S. Escoffier, M. Fabricius, M. Farina, R. Farinelli, F. Faustini, F. Finelli, S. Fotopoulou, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, W. Gillard, B. Gillis, P. Gómez-Alvarez, B. R. Granett, A. Grazian, F. Grupp, L. Guzzo, S. Gwyn, S. V. H. Haugan, H. Hoekstra, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, O. Lahav, Q. Le Boulc'h, A. M. C. Le Brun, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, S. Maurogordato, H. J. McCracken, E. Medinaceli, S. Mei, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, J. A. Schewtschenko, M. Schirmer, P. Schneider, T. Schrabback, A. Secroun, G. Seidel, M. Seiffert, S. Serrano, P. Simon, C. Sirignano, G. Sirri, A. Spurio Mancini, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, E. A. Valentijn, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, E. Zucca, V. Allevato, M. Ballardini, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, A. Cappi, D. Di Ferdinando, J. A. Escartin Vigo, L. Gabarra, M. Huertas-Company, J. Martín-Fleitas, S. Matthew, N. Mauri, A. Pezzotta, M. Pöntinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, C. Benoist, K. Benson, P. Bergamini, D. Bertacca, M. Bethermin, L. Blot, S. Borgani, M. L. Brown, S. Bruton, A. Calabro, B. Camacho Quevedo, F. Caro, C. S. Carvalho, T. Castro, Y. Charles, F. Cogato, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, A. Enia, Y. Fang, A. G. Ferrari, A. Finoguenov, A. Fontana, A. Franco, K. Ganga, J. García-Bellido, T. Gasparetto, V. Gautard, E. Gaztanaga, F. Giacomini, G. Gozaliasl, M. Guidi, C. M. Gutierrez, A. Hall, W. G. Hartley, S. Hemmati, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, T. I. Liaudat, A. Loureiro, J. Macias-Perez, G. Maggio, M. Magliocchetti, E. A. Magnier, F. Mannucci, R. Maoli, C. J. A. P. Martins, L. Maurin, M. Miluzio, P. Monaco, C. Moretti, G. Morgante, C. Murray, S. Nadathur, K. Naidoo, A. Navarro-Alsina, S. Nesseris, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, S. Quai, M. Radovich, P. -F. Rocci, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, C. Scarlata, J. Schaye, A. Schneider, D. Sciotti, E. Sellentin, F. Shankar, L. C. Smith, K. Tanidis, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, G. Vernardos, G. Verza, P. Vielzeuf, N. A. Walton, D. Scott
We present a catalogue of 497 galaxy-galaxy strong lenses in the Euclid Quick Release 1 data (63 deg$^2$). In the initial 0.45\% of Euclid's surveys, we double the total number of known lens candidates with space-based imaging. Our catalogue includes 250 grade A candidates, the vast majority of which (243) were previously unpublished. Euclid's resolution reveals rare lens configurations of scientific value including double-source-plane lenses, edge-on lenses, complete Einstein rings, and quadruply-imaged lenses. We resolve lenses with small Einstein radii ($\theta_{\rm E} < 1''$) in large numbers for the first time. These lenses are found through an initial sweep by deep learning models, followed by Space Warps citizen scientist inspection, expert vetting, and system-by-system modelling. Our search approach scales straightforwardly to Euclid Data Release 1 and, without changes, would yield approximately 7000 high-confidence (grade A or B) lens candidates by late 2026. Further extrapolating to the complete Euclid Wide Survey implies a likely yield of over 100000 high-confidence candidates, transforming strong lensing science.
Euclid Quick Data Release (Q1). The active galaxies of Euclid
2025-03-19
Euclid Collaboration, T. Matamoro Zatarain, S. Fotopoulou, F. Ricci, M. Bolzonella, F. La Franca, A. Viitanen, G. Zamorani, M. B. Taylor, M. Mezcua, B. Laloux, A. Bongiorno, K. Jahnke, G. Stevens, R. A. Shaw, L. Bisigello, W. Roster, Y. Fu, B. Margalef-Bentabol, A. La Marca, F. Tarsitano, A. Feltre, J. Calhau, X. Lopez Lopez, M. Scialpi, M. Salvato, V. Allevato, M. Siudek, C. Saulder, D. Vergani, M. N. Bremer, L. Wang, M. Giulietti, D. M. Alexander, D. Sluse, F. Shankar, L. Spinoglio, D. Scott, R. Shirley, H. Landt, M. Selwood, Y. Toba, P. Dayal, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, H. Aussel, C. Baccigalupi, M. Baldi, S. Bardelli, A. Basset, P. Battaglia, A. Biviano, A. Bonchi, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, F. J. Castander, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, C. Dolding, H. Dole, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, A. Ealet, S. Escoffier, M. Fabricius, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, N. Fourmanoit, M. Frailis, E. Franceschi, S. Galeotta, K. George, B. Gillis, C. Giocoli, P. Gómez-Alvarez, J. Gracia-Carpio, B. R. Granett, A. Grazian, F. Grupp, S. Gwyn, S. V. H. Haugan, H. Hoekstra, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, M. Jhabvala, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, Q. Le Boulc'h, A. M. C. Le Brun, D. Le Mignant, P. Liebing, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, D. C. Masters, S. Maurogordato, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, H. J. A. Rottgering, B. Rusholme, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, J. A. Schewtschenko, P. Schneider, T. Schrabback, M. Scodeggio, A. Secroun, G. Seidel, M. Seiffert, S. Serrano, P. Simon, C. Sirignano, G. Sirri, J. Skottfelt, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, F. M. Zerbi, I. A. Zinchenko, E. Zucca, M. Ballardini, E. Bozzo, C. Burigana, R. Cabanac, A. Cappi, D. Di Ferdinando, J. A. Escartin Vigo, G. Fabbian, L. Gabarra, J. Martín-Fleitas, S. Matthew, N. Mauri, R. B. Metcalf, A. Pezzotta, M. Pöntinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, C. Benoist, K. Benson, D. Bertacca, M. Bethermin, A. Blanchard, L. Blot, H. Böhringer, M. L. Brown, S. Bruton, A. Calabro, B. Camacho Quevedo, F. Caro, C. S. Carvalho, T. Castro, F. Cogato, T. Contini, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, J. M. Diego, P. -A. Duc, A. Enia, Y. Fang, A. G. Ferrari, A. Finoguenov, A. Fontana, F. Fontanot, A. Franco, K. Ganga, J. García-Bellido, T. Gasparetto, V. Gautard, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, A. Gregorio, M. Guidi, C. M. Gutierrez, A. Hall, W. G. Hartley, S. Hemmati, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, S. Kruk, L. Legrand, M. Lembo, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, L. Leuzzi, T. I. Liaudat, A. Loureiro, J. Macias-Perez, G. Maggio, M. Magliocchetti, E. A. Magnier, F. Mannucci, R. Maoli, C. J. A. P. Martins, L. Maurin, M. Miluzio, P. Monaco, C. Moretti, G. Morgante, C. Murray, K. Naidoo, A. Navarro-Alsina, S. Nesseris, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, S. Quai, M. Radovich, P. -F. Rocci, G. Rodighiero, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, A. Schneider, M. Schultheis, D. Sciotti, E. Sellentin, A. Shulevski, L. C. Smith, S. A. Stanford, K. Tanidis, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, G. Verza, P. Vielzeuf, N. A. Walton, E. Soubrie
We present a catalogue of candidate active galactic nuclei (AGN) in the $Euclid$ Quick Release (Q1) fields. For each $Euclid$ source we collect multi-wavelength photometry and spectroscopy information from Galaxy Evolution Explorer (GALEX), $Gaia$, Dark Energy Survey (DES), Wise-field Infrared Survey Explorer (WISE), $Spitzer$, Dark Energy Survey (DESI), and Sloan Digital Sky Survey (SDSS), including spectroscopic redshift from public compilations. We investigate the AGN contents of the Q1 fields by applying selection criteria using $Euclid$ colours and WISE-AllWISE cuts finding respectively 292,222 and 65,131 candidates. We also create a high-purity QSO catalogue based on $Gaia$ DR3 information containing 1971 candidates. Furthermore, we utilise the collected spectroscopic information from DESI to perform broad-line and narrow-line AGN selections, leading to a total of 4392 AGN candidates in the Q1 field. We investigate and refine the Q1 probabilistic random forest QSO population, selecting a total of 180,666 candidates. Additionally, we perform SED fitting on a subset of sources with available $z_{\text{spec}}$, and by utilizing the derived AGN fraction, we identify a total of 7766 AGN candidates. We discuss purity and completeness of the selections and define two new colour selection criteria ($JH$_$I_{\text{E}}Y$ and $I_{\text{E}}H$_$gz$) to improve on purity, finding 313,714 and 267,513 candidates respectively in the Q1 data. We find a total of 229,779 AGN candidates equivalent to an AGN surface density of 3641 deg$^{-2}$ for $18
Euclid Quick Data Release (Q1) First study of red quasars selection
2025-03-19
Euclid Collaboration, F. Tarsitano, S. Fotopoulou, M. Banerji, J. Petley, A. L. Faisst, M. Tucci, S. Tacchella, Y. Toba, H. Landt, Y. Fu, P. A. C. Cunha, K. Duncan, W. Roster, M. Salvato, B. Laloux, P. Dayal, F. Ricci, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, H. Aussel, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, A. Biviano, A. Bonchi, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, A. Costille, F. Courbin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, C. Dolding, H. Dole, F. Dubath, C. A. J. Duncan, X. Dupac, S. Escoffier, M. Fabricius, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, M. Frailis, E. Franceschi, S. Galeotta, K. George, W. Gillard, B. Gillis, C. Giocoli, P. Gómez-Alvarez, J. Gracia-Carpio, B. R. Granett, A. Grazian, F. Grupp, L. Guzzo, S. Gwyn, S. V. H. Haugan, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, Q. Le Boulc'h, A. M. C. Le Brun, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, B. Rusholme, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, J. A. Schewtschenko, P. Schneider, T. Schrabback, M. Scodeggio, A. Secroun, G. Seidel, M. Seiffert, S. Serrano, P. Simon, C. Sirignano, G. Sirri, A. Spurio Mancini, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, H. I. Teplitz, I. Tereno, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, E. Zucca, V. Allevato, M. Ballardini, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, A. Cappi, D. Di Ferdinando, J. A. Escartin Vigo, L. Gabarra, J. Martín-Fleitas, S. Matthew, N. Mauri, R. B. Metcalf, A. Pezzotta, M. Pöntinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, C. Benoist, K. Benson, D. Bertacca, M. Bethermin, L. Bisigello, A. Blanchard, L. Blot, M. L. Brown, S. Bruton, A. Calabro, F. Caro, C. S. Carvalho, T. Castro, Y. Charles, F. Cogato, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, J. M. Diego, P. -A. Duc, A. Enia, Y. Fang, A. G. Ferrari, A. Finoguenov, A. Fontana, A. Franco, K. Ganga, J. García-Bellido, T. Gasparetto, V. Gautard, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, M. Guidi, C. M. Gutierrez, A. Hall, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, S. Kruk, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, L. Leuzzi, T. I. Liaudat, S. J. Liu, A. Loureiro, J. Macias-Perez, G. Maggio, M. Magliocchetti, F. Mannucci, R. Maoli, C. J. A. P. Martins, L. Maurin, M. Miluzio, P. Monaco, C. Moretti, G. Morgante, S. Nadathur, K. Naidoo, A. Navarro-Alsina, S. Nesseris, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, S. Quai, M. Radovich, P. -F. Rocci, G. Rodighiero, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, A. Schneider, M. Schultheis, D. Sciotti, E. Sellentin, F. Shankar, L. C. Smith, K. Tanidis, G. Testera, R. Teyssier, S. Tosi, A. Troja, C. Valieri, A. Venhola, D. Vergani, G. Verza, P. Vielzeuf, A. Viitanen, N. A. Walton, J. G. Sorce
Red quasars constitute an important but elusive phase in the evolution of supermassive black holes, where dust obscuration can significantly alter their observed properties. They have broad emission lines, like other quasars, but their optical continuum emission is significantly reddened, which is why they were traditionally identified based on near- and mid-infrared selection criteria. This work showcases the capability of the \Euclid space telescope to find a large sample of red quasars, using \Euclid near infrared (NIR) photometry. We first conduct a forecast analysis, comparing a synthetic catalogue of red QSOs with COSMOS2020. Using template fitting, we reconstruct \Euclid-like photometry for the COSMOS sources and identify a sample of candidates in a multidimensional colour-colour space achieving $98\%$ completeness for mock red QSOs with $30\%$ contaminants. To refine our selection function, we implement a probabilistic Random Forest classifier, and use UMAP visualisation to disentangle non-linear features in colour-space, reaching $98\%$ completeness and $88\%$ purity. A preliminary analysis of the candidates in the \Euclid Deep Field Fornax (EDF-F) shows that, compared to VISTA+DECAm-based colour selection criteria, \Euclid's superior depth, resolution and optical-to-NIR coverage improves the identification of the reddest, most obscured sources. Notably, the \Euclid exquisite resolution in the $I_E$ filter unveils the presence of a candidate dual quasar system, highlighting the potential for this mission to contribute to future studies on the population of dual AGN. The resulting catalogue of candidates, including more the 150 000 sources, provides a first census of red quasars in \Euclid Q1 and sets the groundwork for future studies in the Euclid Wide Survey (EWS), including spectral follow-up analyses and host morphology characterisation.
Euclid Quick Data Release (Q1). Optical and near-infrared identification and classification of point-like X-ray selected sources
2025-03-19
Euclid Collaboration, W. Roster, M. Salvato, J. Buchner, R. Shirley, E. Lusso, H. Landt, G. Zamorani, M. Siudek, B. Laloux, T. Matamoro Zatarain, F. Ricci, S. Fotopoulou, A. Ferré-Mateu, X. Lopez Lopez, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, H. Aussel, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, A. Biviano, A. Bonchi, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, C. Dolding, H. Dole, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, S. Escoffier, M. Fabricius, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, P. Fosalba, N. Fourmanoit, M. Frailis, E. Franceschi, S. Galeotta, K. George, B. Gillis, C. Giocoli, J. Gracia-Carpio, B. R. Granett, A. Grazian, F. Grupp, S. Gwyn, S. V. H. Haugan, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, Q. Le Boulc'h, A. M. C. Le Brun, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, D. C. Masters, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, J. A. Schewtschenko, M. Schirmer, P. Schneider, T. Schrabback, A. Secroun, G. Seidel, M. Seiffert, S. Serrano, P. Simon, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, P. Tallada-Crespí, D. Tavagnacco, A. N. Taylor, I. Tereno, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, F. M. Zerbi, I. A. Zinchenko, E. Zucca, V. Allevato, M. Ballardini, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, A. Cappi, D. Di Ferdinando, J. A. Escartin Vigo, L. Gabarra, M. Huertas-Company, J. Martín-Fleitas, S. Matthew, N. Mauri, R. B. Metcalf, A. Pezzotta, M. Pöntinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, C. Benoist, K. Benson, D. Bertacca, M. Bethermin, L. Bisigello, A. Blanchard, L. Blot, H. Böhringer, M. L. Brown, S. Bruton, A. Calabro, F. Caro, C. S. Carvalho, T. Castro, F. Cogato, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, J. M. Diego, A. Enia, Y. Fang, A. G. Ferrari, A. Finoguenov, A. Fontana, A. Franco, K. Ganga, J. García-Bellido, T. Gasparetto, V. Gautard, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, M. Guidi, C. M. Gutierrez, A. Hall, W. G. Hartley, S. Hemmati, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, S. Kruk, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, L. Leuzzi, T. I. Liaudat, A. Loureiro, J. Macias-Perez, G. Maggio, M. Magliocchetti, F. Mannucci, R. Maoli, C. J. A. P. Martins, L. Maurin, M. Miluzio, P. Monaco, C. Moretti, G. Morgante, K. Naidoo, A. Navarro-Alsina, S. Nesseris, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, S. Quai, M. Radovich, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, A. Schneider, D. Sciotti, E. Sellentin, F. Shankar, L. C. Smith, K. Tanidis, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, G. Verza, P. Vielzeuf, A. Viitanen, N. A. Walton, E. Soubrie, D. Scott
To better understand the role of active galactic nuclei (AGN) in galaxy evolution, it is crucial to achieve a complete and pure AGN census. X-ray surveys are key to this, but identifying their counterparts (CTPs) at other wavelengths remains challenging due to their larger positional uncertainties and limited availability of deeper, uniform ancillary data. Euclid is revolutionising this effort, offering extensive coverage of nearly the entire extragalactic sky, particularly in the near-infrared bands, where AGN are more easily detected. With the first Euclid Quick Data Release (Q1), we identifyed, classifyed, and determined the redshifts of Euclid CTPs to known point-like sources from major X-ray surveys, including XMM-Newton, Chandra, and eROSITA. Using Bayesian statistics, combined with machine learning (ML), we identify the CTPs to 11 286 X-ray sources from the three X-ray telescopes. For the large majority of 10 194 sources, the associations are unique, with the remaining $\sim$ 10% of multi-CTP cases equally split between XMM-Newton and eROSITA. ML is then used to distinguish between Galactic (8%) and extragalactic (92%) sources. We computed photo-zs using deep learning for the 8617 sources detected in the 10th data release of the DESI Legacy Survey, reaching an accuracy and a fraction of outliers of about 5%. Based on their X-ray luminosities, over 99% of CTPs identified as extragalactic are classified as AGN, most of which appear unobscured given their hardness ratios. With this paper, we release our catalogue, which includes identifiers, basic X-ray properties, the details of the associations, and additional features such as Galactic/extragalactic classifications and photometric/spectroscopic redshifts. We also provide probabilities for sub-selecting the sample based on purity and completeness, allowing users to tailor the sample according to their specific needs.
Euclid Quick Data Release (Q1) Exploring galaxy properties with a multi-modal foundation model
2025-03-19
Euclid Collaboration, M. Siudek, M. Huertas-Company, M. Smith, G. Martinez-Solaeche, F. Lanusse, S. Ho, E. Angeloudi, P. A. C. Cunha, H. Domínguez Sánchez, M. Dunn, Y. Fu, P. Iglesias-Navarro, J. Junais, J. H. Knapen, B. Laloux, M. Mezcua, W. Roster, G. Stevens, J. Vega-Ferrero, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, H. Aussel, C. Baccigalupi, M. Baldi, S. Bardelli, P. Battaglia, A. Biviano, A. Bonchi, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, F. J. Castander, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, J. Dinis, C. Dolding, H. Dole, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, S. Escoffier, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, S. Fotopoulou, M. Frailis, E. Franceschi, S. Galeotta, K. George, B. Gillis, C. Giocoli, J. Gracia-Carpio, B. R. Granett, A. Grazian, F. Grupp, S. Gwyn, S. V. H. Haugan, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, Q. Le Boulc'h, A. M. C. Le Brun, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, S. Maurogordato, H. J. McCracken, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, J. A. Schewtschenko, P. Schneider, T. Schrabback, M. Scodeggio, A. Secroun, G. Seidel, M. Seiffert, S. Serrano, P. Simon, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, I. A. Zinchenko, E. Zucca, V. Allevato, M. Ballardini, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, A. Cappi, D. Di Ferdinando, J. A. Escartin Vigo, L. Gabarra, J. Martín-Fleitas, S. Matthew, N. Mauri, R. B. Metcalf, A. Pezzotta, M. Pöntinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, C. Benoist, K. Benson, D. Bertacca, M. Bethermin, L. Bisigello, A. Blanchard, L. Blot, M. L. Brown, S. Bruton, A. Calabro, B. Camacho Quevedo, F. Caro, C. S. Carvalho, T. Castro, Y. Charles, F. Cogato, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, J. M. Diego, P. -A. Duc, A. Enia, Y. Fang, A. G. Ferrari, P. G. Ferreira, A. Finoguenov, A. Fontana, A. Franco, K. Ganga, J. García-Bellido, T. Gasparetto, V. Gautard, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, M. Guidi, C. M. Gutierrez, A. Hall, W. G. Hartley, S. Hemmati, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, S. Kruk, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, L. Leuzzi, T. I. Liaudat, A. Loureiro, J. Macias-Perez, G. Maggio, M. Magliocchetti, E. A. Magnier, F. Mannucci, R. Maoli, C. J. A. P. Martins, L. Maurin, M. Miluzio, P. Monaco, C. Moretti, G. Morgante, C. Murray, K. Naidoo, A. Navarro-Alsina, S. Nesseris, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, S. Quai, M. Radovich, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, A. Schneider, D. Sciotti, D. Scognamiglio, E. Sellentin, L. C. Smith, K. Tanidis, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, G. Verza, P. Vielzeuf, N. A. Walton, J. G. Sorce
Modern astronomical surveys, such as the Euclid mission, produce high-dimensional, multi-modal data sets that include imaging and spectroscopic information for millions of galaxies. These data serve as an ideal benchmark for large, pre-trained multi-modal models, which can leverage vast amounts of unlabelled data. In this work, we present the first exploration of Euclid data with AstroPT, an autoregressive multi-modal foundation model trained on approximately 300 000 optical and infrared Euclid images and spectral energy distributions (SEDs) from the first Euclid Quick Data Release. We compare self-supervised pre-training with baseline fully supervised training across several tasks: galaxy morphology classification; redshift estimation; similarity searches; and outlier detection. Our results show that: (a) AstroPT embeddings are highly informative, correlating with morphology and effectively isolating outliers; (b) including infrared data helps to isolate stars, but degrades the identification of edge-on galaxies, which are better captured by optical images; (c) simple fine-tuning of these embeddings for photometric redshift and stellar mass estimation outperforms a fully supervised approach, even when using only 1% of the training labels; and (d) incorporating SED data into AstroPT via a straightforward multi-modal token-chaining method improves photo-z predictions, and allow us to identify potentially more interesting anomalies (such as ringed or interacting galaxies) compared to a model pre-trained solely on imaging data.
Euclid Quick Data Release (Q1), A first look at the fraction of bars in massive galaxies at $z<1$
2025-03-19
Euclid Collaboration, M. Huertas-Company, M. Walmsley, M. Siudek, P. Iglesias-Navarro, J. H. Knapen, S. Serjeant, H. J. Dickinson, L. Fortson, I. Garland, T. Géron, W. Keel, S. Kruk, C. J. Lintott, K. Mantha, K. Masters, D. O'Ryan, J. J. Popp, H. Roberts, C. Scarlata, J. S. Makechemu, B. Simmons, R. J. Smethurst, A. Spindler, M. Baes, E. M. Corsini, H. Domínguez Sánchez, E. Duran-Camacho, H. Fu, J. Junais, J. Mendez-Abreu, A. Nersesian, F. Shankar, M. N. Le, J. Vega-Ferrero, L. Wang, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, A. Basset, P. Battaglia, F. Bernardeau, A. Biviano, A. Bonchi, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, C. Dolding, H. Dole, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, A. Ealet, S. Escoffier, M. Fabricius, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, S. Fotopoulou, M. Frailis, S. Galeotta, K. George, W. Gillard, B. Gillis, C. Giocoli, J. Gracia-Carpio, A. Grazian, F. Grupp, S. Gwyn, S. V. H. Haugan, H. Hoekstra, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, E. Keihänen, S. Kermiche, B. Kubik, K. Kuijken, M. Kümmel, M. Kunz, H. Kurki-Suonio, Q. Le Boulc'h, A. M. C. Le Brun, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, H. J. McCracken, E. Medinaceli, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, C. Neissner, R. C. Nichol, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, M. Schirmer, P. Schneider, M. Scodeggio, A. Secroun, G. Seidel, M. Seiffert, S. Serrano, P. Simon, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, I. A. Zinchenko, E. Zucca, V. Allevato, M. Ballardini, M. Bolzonella, E. Bozzo, C. Burigana, A. Cappi, D. Di Ferdinando, J. A. Escartin Vigo, L. Gabarra, J. Martín-Fleitas, S. Matthew, N. Mauri, R. B. Metcalf, A. Pezzotta, M. Pöntinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, C. Benoist, K. Benson, D. Bertacca, M. Bethermin, L. Bisigello, A. Blanchard, L. Blot, H. Böhringer, S. Borgani, M. L. Brown, S. Bruton, A. Calabro, B. Camacho Quevedo, F. Caro, C. S. Carvalho, T. Castro, Y. Charles, F. Cogato, T. Contini, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, J. M. Diego, P. -A. Duc, A. Enia, Y. Fang, A. G. Ferrari, A. Finoguenov, A. Fontana, F. Fontanot, A. Franco, K. Ganga, J. García-Bellido, T. Gasparetto, V. Gautard, E. Gaztanaga, F. Giacomini, G. Gozaliasl, M. Guidi, C. M. Gutierrez, A. Hall, W. G. Hartley, S. Hemmati, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, L. Leuzzi, T. I. Liaudat, A. Loureiro, J. Macias-Perez, G. Maggio, M. Magliocchetti, E. A. Magnier, F. Mannucci, R. Maoli, C. J. A. P. Martins, L. Maurin, M. Miluzio, P. Monaco, C. Moretti, G. Morgante, C. Murray, K. Naidoo, A. Navarro-Alsina, S. Nesseris, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, S. Quai, M. Radovich, G. Rodighiero, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, J. Schaye, A. Schneider, M. Schultheis, D. Sciotti, E. Sellentin, L. C. Smith, K. Tanidis, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, G. Verza, P. Vielzeuf, N. A. Walton, E. Soubrie, D. Scott
Stellar bars are key structures in disc galaxies, driving angular momentum redistribution and influencing processes such as bulge growth and star formation. Quantifying the bar fraction as a function of redshift and stellar mass is therefore important for constraining the physical processes that drive disc formation and evolution across the history of the Universe. Leveraging the unprecedented resolution and survey area of the Euclid Q1 data release combined with the Zoobot deep-learning model trained on citizen-science labels, we identify 7711 barred galaxies with $M_* \gtrsim 10^{10}M_\odot$ in a magnitude-selected sample $I_E < 20.5$ spanning $63.1 deg^2$. We measure a mean bar fraction of $0.2-0.4$, consistent with prior studies. At fixed redshift, massive galaxies exhibit higher bar fractions, while lower-mass systems show a steeper decline with redshift, suggesting earlier disc assembly in massive galaxies. Comparisons with cosmological simulations (e.g., TNG50, Auriga) reveal a broadly consistent bar fraction, but highlight overpredictions for high-mass systems, pointing to potential over-efficiency in central stellar mass build-up in simulations. These findings demonstrate Euclid's transformative potential for galaxy morphology studies and underscore the importance of refining theoretical models to better reproduce observed trends. Future work will explore finer mass bins, environmental correlations, and additional morphological indicators.
Euclid Quick Data Release (Q1) -- Characteristics and limitations of the spectroscopic measurements
2025-03-19
Euclid Collaboration, V. Le Brun, M. Bethermin, M. Moresco, D. Vibert, D. Vergani, C. Surace, G. Zamorani, A. Allaoui, T. Bedrine, P. -Y. Chabaud, G. Daste, F. Dufresne, M. Gray, E. Rossetti, Y. Copin, S. Conseil, E. Maiorano, Z. Mao, E. Palazzi, L. Pozzetti, S. Quai, C. Scarlata, M. Talia, H. M. Courtois, L. Guzzo, B. Kubik, A. M. C. Le Brun, J. A. Peacock, D. Scott, D. Bagot, A. Basset, P. Casenove, R. Gimenez, G. Libet, M. Ruffenach, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, H. Aussel, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, A. Biviano, A. Bonchi, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, A. Caillat, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, F. J. Castander, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, A. Costille, F. Courbin, J. -G. Cuby, A. Da Silva, H. Degaudenzi, S. de la Torre, G. De Lucia, A. M. Di Giorgio, H. Dole, M. Douspis, F. Dubath, X. Dupac, S. Dusini, A. Ealet, S. Escoffier, M. Fabricius, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, S. Fotopoulou, N. Fourmanoit, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, W. Gillard, B. Gillis, C. Giocoli, J. Gracia-Carpio, B. R. Granett, A. Grazian, F. Grupp, S. V. H. Haugan, J. Hoar, H. Hoekstra, W. Holmes, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, M. Kümmel, M. Kunz, H. Kurki-Suonio, Q. Le Boulc'h, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, S. Maurogordato, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, L. Moscardini, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, M. Sauvage, J. A. Schewtschenko, M. Schirmer, P. Schneider, T. Schrabback, M. Scodeggio, A. Secroun, G. Seidel, M. Seiffert, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, H. I. Teplitz, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, F. M. Zerbi, I. A. Zinchenko, E. Zucca, V. Allevato, M. Ballardini, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, A. Cappi, D. Di Ferdinando, J. A. Escartin Vigo, G. Fabbian, L. Gabarra, W. G. Hartley, J. Martín-Fleitas, S. Matthew, M. Maturi, N. Mauri, R. B. Metcalf, A. Pezzotta, M. Pöntinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, S. Alvi, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, S. Avila, M. Bella, P. Bergamini, D. Bertacca, L. Blot, S. Borgani, M. L. Brown, S. Bruton, A. Calabro, B. Camacho Quevedo, F. Caro, C. S. Carvalho, T. Castro, Y. Charles, R. Chary, F. Cogato, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, J. M. Diego, P. Dimauro, P. -A. Duc, A. Enia, Y. Fang, A. M. N. Ferguson, A. G. Ferrari, A. Finoguenov, A. Fontana, A. Franco, K. Ganga, J. García-Bellido, T. Gasparetto, V. Gautard, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, A. Gregorio, M. Guidi, C. M. Gutierrez, A. Hall, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, S. Kruk, L. Legrand, M. Lembo, F. Lepori, G. F. Lesci, J. Lesgourgues, L. Leuzzi, T. I. Liaudat, S. J. Liu, A. Loureiro, J. Macias-Perez, M. Magliocchetti, E. A. Magnier, C. Mancini, F. Mannucci, R. Maoli, C. J. A. P. Martins, L. Maurin, M. Miluzio, P. Monaco, A. Montoro, C. Moretti, G. Morgante, S. Nadathur, K. Naidoo, A. Navarro-Alsina, S. Nesseris, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, M. Radovich, P. -F. Rocci, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, A. Schneider, D. Sciotti, E. Sellentin, F. Shankar, L. C. Smith, K. Tanidis, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, G. Verza, P. Vielzeuf, N. A. Walton, J. R. Weaver, L. Zalesky, J. G. Sorce
The SPE processing function (PF) of the \Euclid pipeline is dedicated to the automatic analysis of one-dimensional spectra to determine redshifts, line fluxes, and spectral classifications. The first \Euclid Quick Data Release (Q1) delivers these measurements for all $H_\mathrm{E}<22.5$ objects identified in the photometric survey. In this paper, we present an overview of the SPE PF algorithm and assess its performance by comparing its results with high-quality spectroscopic redshifts from the Dark Energy Spectroscopic Instrument (DESI) survey in the Euclid Deep Field North. Our findings highlight remarkable accuracy in successful redshift measurements, with a bias of less than $3 \times 10^{-5}$ in $(z_{\rm SPE}-z_{\rm DESI})/(1+z_{\rm DESI})$ and a high precision of approximately $10^{-3}$. The majority of spectra have only a single spectral feature or none at all. To avoid spurious detections, where noise features are misinterpreted as lines or lines are misidentified, it is therefore essential to apply well-defined criteria on quantities such as the redshift probability or the \ha\ flux and signal-to-noise ratio. Using a well-tuned quality selection, we achieve an 89\% redshift success rate in the target redshift range for cosmology ($0.9
Euclid Quick Data Release (Q1): From spectrograms to spectra: the SIR spectroscopic Processing Function
2025-03-19
Euclid Collaboration, Y. Copin, M. Fumana, C. Mancini, P. N. Appleton, R. Chary, S. Conseil, A. L. Faisst, S. Hemmati, D. C. Masters, C. Scarlata, M. Scodeggio, A. Alavi, A. Carle, P. Casenove, T. Contini, I. Das, W. Gillard, G. Herzog, J. Jacobson, V. Le Brun, D. Maino, G. Setnikar, N. R. Stickley, D. Tavagnacco, Q. Xie, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, H. Aussel, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, A. Basset, P. Battaglia, A. N. Belikov, A. Biviano, A. Bonchi, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, F. J. Castander, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, F. Courbin, H. M. Courtois, A. Da Silva, R. da Silva, H. Degaudenzi, S. de la Torre, G. De Lucia, A. M. Di Giorgio, H. Dole, F. Dubath, X. Dupac, S. Dusini, A. Ealet, S. Escoffier, M. Farina, R. Farinelli, S. Ferriol, F. Finelli, S. Fotopoulou, N. Fourmanoit, M. Frailis, E. Franceschi, P. Franzetti, S. Galeotta, K. George, B. Gillis, C. Giocoli, J. Gracia-Carpio, B. R. Granett, A. Grazian, F. Grupp, L. Guzzo, S. V. H. Haugan, J. Hoar, H. Hoekstra, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, K. Kuijken, M. Kümmel, M. Kunz, H. Kurki-Suonio, Q. Le Boulc'h, A. M. C. Le Brun, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, S. Maurogordato, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, P. W. Morris, L. Moscardini, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, G. D. Racca, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, J. A. Schewtschenko, M. Schirmer, P. Schneider, T. Schrabback, A. Secroun, E. Sefusatti, G. Seidel, S. Serrano, P. Simon, C. Sirignano, G. Sirri, A. Spurio Mancini, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, H. I. Teplitz, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, I. A. Zinchenko, E. Zucca, V. Allevato, M. Ballardini, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, A. Cappi, D. Di Ferdinando, J. A. Escartin Vigo, L. Gabarra, M. Huertas-Company, J. Martín-Fleitas, S. Matthew, N. Mauri, R. B. Metcalf, A. Pezzotta, M. Pöntinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, S. Alvi, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, C. Benoist, K. Benson, P. Bergamini, D. Bertacca, M. Bethermin, L. Bisigello, A. Blanchard, L. Blot, M. L. Brown, S. Bruton, A. Calabro, B. Camacho Quevedo, F. Caro, T. Castro, F. Cogato, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, J. M. Diego, P. -A. Duc, A. Enia, Y. Fang, A. M. N. Ferguson, A. G. Ferrari, A. Finoguenov, A. Fontana, A. Franco, K. Ganga, J. García-Bellido, T. Gasparetto, V. Gautard, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, A. Gregorio, M. Guidi, C. M. Gutierrez, A. Hall, W. G. Hartley, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, S. Hosseini, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, S. Kruk, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, L. Leuzzi, T. I. Liaudat, A. Loureiro, J. Macias-Perez, G. Maggio, M. Magliocchetti, F. Mannucci, R. Maoli, C. J. A. P. Martins, L. Maurin, C. J. R. McPartland, M. Miluzio, P. Monaco, A. Montoro, C. Moretti, G. Morgante, C. Murray, S. Nadathur, K. Naidoo, A. Navarro-Alsina, S. Nesseris, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, S. Quai, M. Radovich, P. -F. Rocci, G. Rodighiero, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, A. Schneider, D. Sciotti, E. Sellentin, L. C. Smith, K. Tanidis, C. Tao, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, G. Verza, P. Vielzeuf, N. A. Walton, M. Bella, D. Scott
The Euclid space mission aims to investigate the nature of dark energy and dark matter by mapping the large-scale structure of the Universe. A key component of Euclid's observational strategy is slitless spectroscopy, conducted using the Near Infrared Spectrometer and Photometer (NISP). This technique enables the acquisition of large-scale spectroscopic data without the need for targeted apertures, allowing precise redshift measurements for millions of galaxies. These data are essential for Euclid's core science objectives, including the study of cosmic acceleration and the evolution of galaxy clustering, as well as enabling many non-cosmological investigations. This study presents the SIR processing function (PF), which is responsible for processing slitless spectroscopic data. The objective is to generate science-grade fully-calibrated one-dimensional spectra, ensuring high-quality spectroscopic data. The processing function relies on a source catalogue generated from photometric data, effectively corrects detector effects, subtracts cross-contaminations, minimizes self-contamination, calibrates wavelength and flux, and produces reliable spectra for later scientific use. The first Quick Data Release (Q1) of Euclid's spectroscopic data provides approximately three million validated spectra for sources observed in the red-grism mode from a selected portion of the Euclid Wide Survey. We find that wavelength accuracy and measured resolving power are within requirements, thanks to the excellent optical quality of the instrument. The SIR PF represents a significant step in processing slitless spectroscopic data for the Euclid mission. As the survey progresses, continued refinements and additional features will enhance its capabilities, supporting high-precision cosmological and astrophysical measurements.
Euclid Quick Data Release (Q1): From images to multiwavelength catalogues: the Euclid MERge Processing Function
2025-03-19
Euclid Collaboration, E. Romelli, M. Kümmel, H. Dole, J. Gracia-Carpio, E. Merlin, S. Galeotta, Y. Fang, M. Castellano, F. Caro, E. Soubrie, L. Maurin, R. Cabanac, P. Dimauro, M. Huertas-Company, M. D. Lepinzan, T. Vassallo, M. Walmsley, I. A. Zinchenko, A. Boucaud, A. Calabro, V. Roscani, A. Tramacere, M. Douspis, A. Fontana, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, H. Aussel, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, A. Basset, P. Battaglia, A. N. Belikov, A. Biviano, A. Bonchi, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, F. J. Castander, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Cropper, J. -G. Cuby, A. Da Silva, R. da Silva, H. Degaudenzi, S. de la Torre, G. De Lucia, A. M. Di Giorgio, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, S. Escoffier, M. Fabricius, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, S. Fotopoulou, N. Fourmanoit, M. Frailis, E. Franceschi, K. George, W. Gillard, B. Gillis, C. Giocoli, B. R. Granett, A. Grazian, F. Grupp, S. Gwyn, S. V. H. Haugan, J. Hoar, H. Hoekstra, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, M. Kilbinger, B. Kubik, K. Kuijken, M. Kunz, H. Kurki-Suonio, R. Laureijs, Q. Le Boulc'h, A. M. C. Le Brun, D. Le Mignant, P. Liebing, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, S. Maurogordato, H. J. McCracken, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, G. D. Racca, F. Raison, A. Renzi, J. Rhodes, G. Riccio, M. Roncarelli, E. Rossetti, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, J. A. Schewtschenko, M. Schirmer, P. Schneider, M. Scodeggio, A. Secroun, E. Sefusatti, G. Seidel, M. Seiffert, S. Serrano, P. Simon, C. Sirignano, G. Sirri, A. Spurio Mancini, L. Stanco, J. Steinwagner, P. Tallada-Crespí, D. Tavagnacco, A. N. Taylor, H. I. Teplitz, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, A. Tsyganov, I. Tutusaus, E. A. Valentijn, L. Valenziano, J. Valiviita, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, G. Zamorani, F. M. Zerbi, E. Zucca, V. Allevato, M. Ballardini, M. Bolzonella, E. Bozzo, C. Burigana, A. Cappi, P. Casenove, D. Di Ferdinando, J. A. Escartin Vigo, L. Gabarra, W. G. Hartley, H. Israel, J. Martín-Fleitas, S. Matthew, N. Mauri, R. B. Metcalf, A. Pezzotta, M. Pöntinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, S. Alvi, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, P. Bergamini, D. Bertacca, M. Bethermin, A. Blanchard, L. Blot, H. Böhringer, S. Borgani, M. L. Brown, S. Bruton, B. Camacho Quevedo, C. S. Carvalho, T. Castro, R. Chary, F. Cogato, S. Conseil, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, J. M. Diego, P. -A. Duc, A. Enia, A. M. N. Ferguson, A. Finoguenov, A. Franco, K. Ganga, J. García-Bellido, T. Gasparetto, R. Gavazzi, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, A. Gregorio, M. Guidi, C. M. Gutierrez, A. Hall, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, S. Kruk, V. Le Brun, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. F. Lesci, J. Lesgourgues, L. Leuzzi, T. I. Liaudat, A. Loureiro, J. Macias-Perez, G. Maggio, M. Magliocchetti, E. A. Magnier, C. Mancini, F. Mannucci, R. Maoli, C. J. A. P. Martins, M. Miluzio, P. Monaco, C. Moretti, G. Morgante, S. Nadathur, K. Naidoo, A. Navarro-Alsina, S. Nesseris, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, S. Quai, M. Radovich, P. Reimberg, P. -F. Rocci, G. Rodighiero, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, C. Scarlata, A. Schneider, M. Schultheis, D. Sciotti, E. Sellentin, L. C. Smith, K. Tanidis, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, G. Verza, P. Vielzeuf, N. A. Walton, J. R. Weaver, J. G. Sorce, D. Scott
The Euclid satellite is an ESA mission that was launched in July 2023. \Euclid is working in its regular observing mode with the target of observing an area of $14\,000~\text{deg}^2$ with two instruments, the Visible Camera (VIS) and the Near IR Spectrometer and Photometer (NISP) down to $I_{\rm E} = 24.5~\text{mag}$ ($10\, \sigma$) in the Euclid Wide Survey. Ground-based imaging data in the \textit{ugriz} bands complement the \Euclid data to enable photo-$z$ determination and VIS PSF modeling for week lensing analysis. Euclid investigates the distance-redshift relation and the evolution of cosmic structures by measuring shapes and redshifts of galaxies and clusters of galaxies out to $z\sim 2$. Generating the multi-wavelength catalogues from \Euclid and ground-based data is an essential part of the \Euclid data processing system. In the framework of the \Euclid Science Ground Segment (SGS), the aim of the MER Processing Function (PF) pipeline is to detect objects in the \Euclid imaging data, measure their properties, and MERge them into a single multi-wavelength catalogue. The MER PF pipeline performs source detection on both visible (VIS) and near-infrared (NIR) images and offers four different photometric measurements: Kron total flux, aperture photometry on PSF-matched images, template fitting photometry, and S\'ersic fitting photometry. Furthermore, the MER PF pipeline measures a set of ancillary quantities, spanning from morphology to quality flags, to better characterise all detected sources. In this paper, we show how the MER PF pipeline is designed, detailing its main steps, and we show that the pipeline products meet the tight requirements that Euclid aims to achieve on photometric accuracy. We also present the other measurements (e.g. morphology) that are included in the OU-MER output catalogues and we list all output products coming out of the MER PF pipeline.
Euclid Quick Data Release (Q1). NIR processing and data products
2025-03-19
Euclid Collaboration, G. Polenta, M. Frailis, A. Alavi, P. N. Appleton, P. Awad, A. Bonchi, R. Bouwens, L. Bramante, D. Busonero, G. Calderone, F. Cogato, S. Conseil, M. Correnti, R. da Silva, I. Das, F. Faustini, Y. Fu, T. Gasparetto, W. Gillard, A. Grazian, S. Hemmati, J. Jacobson, K. Jahnke, B. Kubik, X. Liu, C. Macabiau, E. Medinaceli, P. W. Morris, K. Paterson, M. Radovich, M. Schirmer, A. Shulevski, H. I. Teplitz, B. Venemans, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, H. Aussel, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, A. Basset, P. Battaglia, A. N. Belikov, R. Bender, A. Biviano, E. Branchini, M. Brescia, J. Brinchmann, A. Caillat, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, F. J. Castander, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, L. Corcione, A. Costille, F. Courbin, H. M. Courtois, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, H. Dole, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, A. Ealet, S. Escoffier, M. Fabricius, M. Farina, R. Farinelli, S. Ferriol, F. Finelli, S. Fotopoulou, N. Fourmanoit, E. Franceschi, M. Fumana, S. Galeotta, K. George, B. Gillis, C. Giocoli, P. Gómez-Alvarez, J. Gracia-Carpio, B. R. Granett, F. Grupp, S. V. H. Haugan, J. Hoar, H. Hoekstra, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, M. Jhabvala, E. Keihänen, S. Kermiche, A. Kiessling, K. Kuijken, M. Kümmel, M. Kunz, H. Kurki-Suonio, Q. Le Boulc'h, A. M. C. Le Brun, D. Le Mignant, P. Liebing, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. Massey, S. Maurogordato, H. J. McCracken, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, M. Poncet, L. A. Popa, L. Pozzetti, G. D. Racca, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, B. Rusholme, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, J. A. Schewtschenko, P. Schneider, M. Scodeggio, A. Secroun, E. Sefusatti, G. Seidel, M. Seiffert, S. Serrano, P. Simon, C. Sirignano, G. Sirri, A. Spurio Mancini, L. Stanco, J. Steinwagner, P. Tallada-Crespí, D. Tavagnacco, A. N. Taylor, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, E. A. Valentijn, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, I. A. Zinchenko, E. Zucca, V. Allevato, M. Ballardini, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, M. Calabrese, A. Cappi, P. Casenove, D. Di Ferdinando, J. A. Escartin Vigo, G. Fabbian, L. Gabarra, M. Huertas-Company, J. Martín-Fleitas, S. Matthew, N. Mauri, A. A. Nucita, A. Pezzotta, M. Pöntinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, C. Benoist, P. Bergamini, D. Bertacca, M. Bethermin, L. Bisigello, A. Blanchard, L. Blot, S. Borgani, A. S. Borlaff, M. L. Brown, S. Bruton, A. Calabro, B. Camacho Quevedo, F. Caro, C. S. Carvalho, T. Castro, Y. Charles, R. Chary, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, J. M. Diego, P. -A. Duc, A. Enia, Y. Fang, A. M. N. Ferguson, A. G. Ferrari, P. G. Ferreira, A. Finoguenov, A. Fontana, A. Franco, K. Ganga, J. García-Bellido, V. Gautard, E. Gaztanaga, F. Giacomini, F. Gianotti, A. H. Gonzalez, G. Gozaliasl, A. Gregorio, A. Gruppuso, M. Guidi, C. M. Gutierrez, A. Hall, W. G. Hartley, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, S. Kruk, V. Le Brun, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, L. Leuzzi, T. I. Liaudat, S. J. Liu, A. Loureiro, J. Macias-Perez, G. Maggio, M. Magliocchetti, F. Mannucci, R. Maoli, C. J. A. P. Martins, L. Maurin, C. J. R. McPartland, M. Miluzio, P. Monaco, A. Montoro, C. Moretti, G. Morgante, S. Nadathur, K. Naidoo, P. Natoli, A. Navarro-Alsina, S. Nesseris, L. Nicastro, F. Passalacqua, L. Patrizii, A. Pisani, D. Potter, S. Quai, P. -F. Rocci, G. Rodighiero, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, C. Scarlata, A. Schneider, D. Sciotti, E. Sellentin, F. Shankar, L. C. Smith, K. Tanidis, C. Tao, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, G. Verza, P. Vielzeuf, N. A. Walton, J. R. Weaver, M. Bella, D. Scott
This paper describes the near-infrared processing function (NIR PF) that processes near-infrared images from the Near-Infrared Spectrometer and Photometer (NISP) instrument onboard the Euclid satellite. NIR PF consists of three main components: (i) a common pre-processing stage for both photometric (NIR) and spectroscopic (SIR) data to remove instrumental effects; (ii) astrometric and photometric calibration of NIR data, along with catalogue extraction; and (iii) resampling and stacking. The necessary calibration products are generated using dedicated pipelines that process observations from both the early performance verification (PV) phase in 2023 and the nominal survey operations. After outlining the pipeline's structure and algorithms, we demonstrate its application to Euclid Q1 images. For Q1, we achieve an astrometric accuracy of 9-15 mas, a relative photometric accuracy of 5 mmag, and an absolute flux calibration limited by the 1% uncertainty of the Hubble Space Telescope (HST) CALSPEC database. We characterise the point-spread function (PSF) that we find very stable across the focal plane, and we discuss current limitations of NIR PF that will be improved upon for future data releases.
Euclid Quick Data Release (Q1) -- Data release overview
2025-03-19
Euclid Collaboration, H. Aussel, I. Tereno, M. Schirmer, G. Alguero, B. Altieri, E. Balbinot, T. de Boer, P. Casenove, P. Corcho-Caballero, H. Furusawa, J. Furusawa, M. J. Hudson, K. Jahnke, G. Libet, J. Macias-Perez, N. Masoumzadeh, J. J. Mohr, J. Odier, D. Scott, T. Vassallo, G. Verdoes Kleijn, A. Zacchei, N. Aghanim, A. Amara, S. Andreon, N. Auricchio, S. Awan, R. Azzollini, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, A. Basset, P. Battaglia, A. N. Belikov, R. Bender, A. Biviano, A. Bonchi, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, V. F. Cardone, J. Carretero, S. Casas, F. J. Castander, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Cropper, J. -G. Cuby, A. Da Silva, R. da Silva, H. Degaudenzi, J. T. A. de Jong, G. De Lucia, A. M. Di Giorgio, J. Dinis, C. Dolding, H. Dole, M. Douspis, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, A. Ealet, S. Escoffier, M. Fabricius, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, S. Fotopoulou, N. Fourmanoit, M. Frailis, E. Franceschi, P. Franzetti, S. Galeotta, K. George, W. Gillard, B. Gillis, C. Giocoli, P. Gómez-Alvarez, J. Gracia-Carpio, B. R. Granett, A. Grazian, F. Grupp, L. Guzzo, S. Gwyn, S. V. H. Haugan, O. Herent, J. Hoar, H. Hoekstra, M. S. Holliman, W. Holmes, I. M. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, S. Ilić, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, K. Kuijken, M. Kümmel, M. Kunz, H. Kurki-Suonio, O. Lahav, Q. Le Boulc'h, A. M. C. Le Brun, D. Le Mignant, P. Liebing, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, S. Maurogordato, H. J. McCracken, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, P. W. Morris, L. Moscardini, S. Mourre, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, J. W. Nightingale, T. Nutma, C. Padilla, S. Paltani, F. Pasian, J. A. Peacock, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, J. E. Pollack, M. Poncet, L. A. Popa, L. Pozzetti, G. D. Racca, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, H. -W. Rix, E. Romelli, M. Roncarelli, E. Rossetti, B. Rusholme, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, M. Sauvage, J. A. Schewtschenko, P. Schneider, M. Scodeggio, A. Secroun, E. Sefusatti, G. Seidel, M. Seiffert, S. Serrano, P. Simon, C. Sirignano, G. Sirri, J. Skottfelt, A. Spurio Mancini, L. Stanco, J. Steinwagner, C. Surace, P. Tallada-Crespí, D. Tavagnacco, A. N. Taylor, H. I. Teplitz, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, A. Tsyganov, I. Tutusaus, E. A. Valentijn, L. Valenziano, J. Valiviita, A. Veropalumbo, Y. Wang, J. Weller, O. R. Williams, G. Zamorani, F. M. Zerbi, E. Zucca, V. Allevato, M. Ballardini, R. P. Blake, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, M. Calabrese, A. Cappi, D. Di Ferdinando, J. A. Escartin Vigo, L. Gabarra, W. G. Hartley, M. Huertas-Company, J. Martín-Fleitas, S. Matthew, M. Maturi, N. Mauri, R. B. Metcalf, A. Pezzotta, M. Pöntinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, S. Alvi, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, S. Avila, P. Bergamini, D. Bertacca, M. Bethermin, L. Bisigello, A. Blanchard, L. Blot, H. Böhringer, S. Borgani, A. S. Borlaff, M. L. Brown, S. Bruton, F. Buitrago, A. Calabro, G. Calderone, B. Camacho Quevedo, F. Caro, C. S. Carvalho, T. Castro, Y. Charles, F. Cogato, S. Conseil, A. R. Cooray, M. Costanzi, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, J. M. Diego, P. Dimauro, P. -A. Duc, A. Enia, Y. Fang, A. M. N. Ferguson, A. G. Ferrari, A. Finoguenov, A. Fontana, F. Fontanot, A. Franco, J. García-Bellido, T. Gasparetto, R. Gavazzi, E. Gaztanaga, F. Giacomini, F. Gianotti, A. H. Gonzalez, G. Gozaliasl, A. Gruppuso, M. Guidi, C. M. Gutierrez, A. Hall, C. Hernández-Monteagudo, H. Hildebrandt, J. Hjorth, J. Jacobson, S. Joudaki, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, C. C. Kirkpatrick, S. Kruk, F. Lacasa, C. Laigle, M. Lattanzi, V. Le Brun, J. Le Graet, L. Legrand, M. Lembo, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, L. Leuzzi, T. I. Liaudat, A. Loureiro, M. Magliocchetti, E. A. Magnier, C. Mancini, F. Mannucci, R. Maoli, C. J. A. P. Martins, L. Maurin, C. J. R. McPartland, J. -B. Melin, M. Migliaccio, M. Miluzio, P. Monaco, A. Montoro, C. Moretti, G. Morgante, C. Murray, S. Nadathur, K. Naidoo, A. Navarro-Alsina, S. Nesseris, L. Nicastro, M. Oguri, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, S. Quai, M. Radovich, P. Reimberg, P. -F. Rocci, G. Rodighiero, R. P. Rollins, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, C. Scarlata, J. Schaye, A. Schneider, M. Schultheis, D. Sciotti, D. Scognamiglio, E. Sellentin, F. Shankar, L. C. Smith, E. Soubrie, S. A. Stanford, K. Tanidis, C. Tao, G. Testera, M. Tewes, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, F. Vernizzi, G. Verza, P. Vielzeuf, N. A. Walton, J. R. Weaver, J. Wilde, L. Zalesky
The first Euclid Quick Data Release, Q1, comprises 63.1 sq deg of the Euclid Deep Fields (EDFs) to nominal wide-survey depth. It encompasses visible and near-infrared space-based imaging and spectroscopic data, ground-based photometry in the u, g, r, i and z bands, as well as corresponding masks. Overall, Q1 contains about 30 million objects in three areas near the ecliptic poles around the EDF-North and EDF-South, as well as the EDF-Fornax field in the constellation of the same name. The purpose of this data release -- and its associated technical papers -- is twofold. First, it is meant to inform the community of the enormous potential of the Euclid survey data, to describe what is contained in these data, and to help prepare expectations for the forthcoming first major data release DR1. Second, it enables a wide range of initial scientific projects with wide-survey Euclid data, ranging from the early Universe to the Solar System. The Q1 data were processed with early versions of the processing pipelines, which already demonstrate good performance, with numerous improvements in implementation compared to pre-launch development. In this paper, we describe the sky areas released in Q1, the observations, a top-level view of the data processing of Euclid and associated external data, the Q1 photometric masks, and how to access the data. We also give an overview of initial scientific results obtained using the Q1 data set by Euclid Consortium scientists, and conclude with important caveats when using the data. As a complementary product, Q1 also contains observations of a star-forming area in Lynd's Dark Nebula 1641 in the Orion~A Cloud, observed for technical purposes during Euclid's performance-verification phase. This is a unique target, of a type not commonly found in Euclid's nominal sky survey.
With the rapid development of artificial intelligence technologies and wearable devices, egocentric vision understanding has emerged as a new and challenging research direction, gradually attracting widespread attention from both academia and industry. Egocentric vision captures visual and multimodal data through cameras or sensors worn on the human body, offering a unique perspective that simulates human visual experiences. This paper provides a comprehensive survey of the research on egocentric vision understanding, systematically analyzing the components of egocentric scenes and categorizing the tasks into four main areas: subject understanding, object understanding, environment understanding, and hybrid understanding. We explore in detail the sub-tasks within each category. We also summarize the main challenges and trends currently existing in the field. Furthermore, this paper presents an overview of high-quality egocentric vision datasets, offering valuable resources for future research. By summarizing the latest advancements, we anticipate the broad applications of egocentric vision technologies in fields such as augmented reality, virtual reality, and embodied intelligence, and propose future research directions based on the latest developments in the field.
The spectrum of a tensor-triangulated category carries a compact Hausdorff topology, called the constructible topology, also known as the patch topology. We prove that patch-dense subsets detect tt-ideals and we prove that any infinite family of tt-functors that detects nilpotence provides such a patch-dense subset. We review several applications and examples in algebra, in topology and in the representation theory of profinite groups.
The localization problem: an antinomy between measurability and causal dynamics
2025-03-19
Evan P. G. Gale
The localization problem in relativistic quantum theory has persisted for more than seven decades, yet it is largely unknown and continues to perplex even those well-versed in the subject. At the heart of this problem lies a fundamental conflict between localizability and relativistic causality, which can also be construed as part of the broader dichotomy between measurement and unitary dynamics. This article provides a historical review of the localization problem in one-particle relativistic quantum mechanics, clarifying some persistent misconceptions in the literature, and underscoring the antinomy between causal dynamics and localized observables.
Integrating Sensing and Communications in 6G? Not Until It Is Secure to Do So
2025-03-19
Nanchi Su, Fan Liu, Jiaqi Zou, Christos Masouros, George C. Alexandropoulos, Alain Mourad, Javier Lorca Hernando, Qinyu Zhang, Tse-Tin Chan
Integrated Sensing and Communication (ISAC) is emerging as a cornerstone technology for forthcoming 6G systems, significantly improving spectrum and energy efficiency. However, the commercial viability of ISAC hinges on addressing critical challenges surrounding security, privacy, and trustworthiness. These challenges necessitate an end-to-end framework to safeguards both communication data and sensing information, particularly in ultra-low-latency and highly connected environments. Conventional solutions, such as encryption and key management, often fall short when confronted with ISAC's dual-functional nature. In this context, the physical layer plays a pivotal role: this article reviews emerging physical-layer strategies, including artificial noise (AN) injection, cooperative jamming, and constructive interference (CI), which enhance security by mitigating eavesdropping risks and safeguarding both communication data and sensing information. We further highlight the unique privacy issues that ISAC introduces to cellular networks and outline future research directions aimed at ensuring robust security and privacy for efficient ISAC deployment in 6G.
This paper provides a comprehensive review of the integration of Large Language Models (LLMs) with visual analytics, addressing their foundational concepts, capabilities, and wide-ranging applications. It begins by outlining the theoretical underpinnings of visual analytics and the transformative potential of LLMs, specifically focusing on their roles in natural language understanding, natural language generation, dialogue systems, and text-to-media transformations. The review further investigates how the synergy between LLMs and visual analytics enhances data interpretation, visualization techniques, and interactive exploration capabilities. Key tools and platforms including LIDA, Chat2VIS, Julius AI, and Zoho Analytics, along with specialized multimodal models such as ChartLlama and CharXIV, are critically evaluated. The paper discusses their functionalities, strengths, and limitations in supporting data exploration, visualization enhancement, automated reporting, and insight extraction. The taxonomy of LLM tasks, ranging from natural language understanding (NLU), natural language generation (NLG), to dialogue systems and text-to-media transformations, is systematically explored. This review provides a SWOT analysis of integrating Large Language Models (LLMs) with visual analytics, highlighting strengths like accessibility and flexibility, weaknesses such as computational demands and biases, opportunities in multimodal integration and user collaboration, and threats including privacy concerns and skill degradation. It emphasizes addressing ethical considerations and methodological improvements for effective integration.
UltraFlwr -- An Efficient Federated Medical and Surgical Object Detection Framework
2025-03-19
Yang Li, Soumya Snigdha Kundu, Maxence Boels, Toktam Mahmoodi, Sebastien Ourselin, Tom Vercauteren, Prokar Dasgupta, Jonathan Shapey, Alejandro Granados
Object detection shows promise for medical and surgical applications such as cell counting and tool tracking. However, its faces multiple real-world edge deployment challenges including limited high-quality annotated data, data sharing restrictions, and computational constraints. In this work, we introduce UltraFlwr, a framework for federated medical and surgical object detection. By leveraging Federated Learning (FL), UltraFlwr enables decentralized model training across multiple sites without sharing raw data. To further enhance UltraFlwr's efficiency, we propose YOLO-PA, a set of novel Partial Aggregation (PA) strategies specifically designed for YOLO models in FL. YOLO-PA significantly reduces communication overhead by up to 83% per round while maintaining performance comparable to Full Aggregation (FA) strategies. Our extensive experiments on BCCD and m2cai16-tool-locations datasets demonstrate that YOLO-PA not only provides better client models compared to client-wise centralized training and FA strategies, but also facilitates efficient training and deployment across resource-constrained edge devices. Further, we also establish one of the first benchmarks in federated medical and surgical object detection. This paper advances the feasibility of training and deploying detection models on the edge, making federated object detection more practical for time-critical and resource-constrained medical and surgical applications. UltraFlwr is publicly available at https://github.com/KCL-BMEIS/UltraFlwr.
Two- and Three-gluon Glueballs within the Helicity Formalism
2025-03-19
Cyrille Chevalier, Vincent Mathieu
Both positive and negative charge conjugation glueball spectra are computed with a constituent gluon approach. We first compute the spectrum of the Hamiltionian describing two-gluon bound states having $C=+$, before tackling the three-gluon bound states having $C=-$. We review the construction of two and three particle helicity states in order to build totally symmetric wave functions for our glueball states. In the literature, two different couplings schemes are used to build three particles states. We derive for the first time the relation between these two three-body state definitions. The glueball spectra are compared with those obtained from quantum chromodynamics simulations on a lattice. The two-gluon glueball spectra show a good agreement with the constituent approach and the numerical lattice simulations. The three-gluon spectrum is in overall agreement with the masses observed on the lattice but also predicts additional low-lying states.
$\mathcal{O}_α$-transformation and its uncertainty principles
2025-03-19
Lai Tien Minh, Trinh Tuan
In this paper, we introduce a family of $\mathcal{O}_{\alpha}$-transformation based on kernels fusion of the fractional Fourier transform (abbreviated as FRFT) with angle $\alpha \notin \pi \mathbb{Z}$. We point out this is a valid integral transform via establishing its basic operational properties. Besides, we survey various mathematical aspects of the uncertainty principles for the $\mathcal{O}_{\alpha}$-transform, including Heisenberg's inequality, logarithmic uncertainty inequality, local uncertainty inequality, Hardy's inequality, and Beurling-H{\"o}rmander's theorem.
Control, Optimal Transport and Neural Differential Equations in Supervised Learning
2025-03-19
Minh-Nhat Phung, Minh-Binh Tran
From the perspective of control theory, neural differential equations (neural ODEs) have become an important tool for supervised learning. In the fundamental work of Ruiz-Balet and Zuazua (SIAM REVIEW 2023), the authors pose an open problem regarding the connection between control theory, optimal transport theory, and neural differential equations. More precisely, they inquire how one can quantify the closeness of the optimal flows in neural transport equations to the true dynamic optimal transport. In this work, we propose a construction of neural differential equations that converge to the true dynamic optimal transport in the limit, providing a significant step in solving the formerly mentioned open problem.
Contribution to the study of the flora in the central-west of Tunisia landscape dynamics and evaluation of plant biodiversity of mountain Bouchebka
2025-03-19
Saadaoui Islem, Ilahi Hayet, Robin Bryant Christopher, Rejeb Hichem
The study was conducted during 2013 in Bouchebka, located in the central west of Tunisia.Such territory has a typical landscape of the transfrontier region. The series of the forest in Bouchebka is a part of the great mass of Aleppo pine. It is distinguished by the importance of the forest area which covers 92 % of the surface area (19,700 ha). The study attempts to inventory the natural vegetation and characterize ecological terms while highlighting the importance of environmental conditions. The method is based on a phytoecological analysis to quantify the floristic richness and diversity of the ecosystem in the forest of mountains in Bouchebka on the basis of floristic surveys and transects distributed in a stratified, systematic sampling in different vegetation formations that were previously distinguished. Statistical analyzes were performed using the Factorial Correspondence Analysis (FCA). The results show that the forest is composed primarily of the Aleppo pine trees, these forests are characterized by the abundance of young feet (10-25 cm diameter class). The ecosystem includes 12 families and 17 genera, 26 species. Thus the study has identified that the biological spectrum of the study area is characterized by a clear dominance of shrubs (41%) and chamaephytes (32 %). The distribution of plant species is influenced by ecological features of the region: the results show that 82% of species are drought tolerant which shows the arid environment. The region is also characterized by its windy side: 32% of species spread via anemochory. Factor analysis showed a pastoral aspect in the study area, with a presence of cultured human action exerted on the forest land species. Phytological spectrum indicates a predominance of woody species reflecting a territory dominated by open grassy areas, predominantly reflecting an arid climate.
Language-based Image Colorization: A Benchmark and Beyond
2025-03-19
Yifan Li, Shuai Yang, Jiaying Liu
Image colorization aims to bring colors back to grayscale images. Automatic image colorization methods, which requires no additional guidance, struggle to generate high-quality images due to color ambiguity, and provides limited user controllability. Thanks to the emergency of cross-modality datasets and models, language-based colorization methods are proposed to fully utilize the efficiency and flexibly of text descriptions to guide colorization. In view of the lack of a comprehensive review of language-based colorization literature, we conduct a thorough analysis and benchmarking. We first briefly summarize existing automatic colorization methods. Then, we focus on language-based methods and point out their core challenge on cross-modal alignment. We further divide these methods into two categories: one attempts to train a cross-modality network from scratch, while the other utilizes the pre-trained cross-modality model to establish the textual-visual correspondence. Based on the analyzed limitations of existing language-based methods, we propose a simple yet effective method based on distilled diffusion model. Extensive experiments demonstrate that our simple baseline can produces better results than previous complex methods with 14 times speed up. To the best of our knowledge, this is the first comprehensive review and benchmark on language-based image colorization field, providing meaningful insights for the community. The code is available at https://github.com/lyf1212/Color-Turbo.
Multimodal Large Language Models (MLLMs) have emerged to tackle the challenges of Visual Question Answering (VQA), sparking a new research focus on conducting objective evaluations of these models. Existing evaluation methods face limitations due to the significant human workload required to design Q&A pairs for visual images, which inherently restricts the scale and scope of evaluations. Although automated MLLM-as-judge approaches attempt to reduce the human workload through automatic evaluations, they often introduce biases. To address these problems, we propose an Unsupervised Peer review MLLM Evaluation framework. It utilizes only image data, allowing models to automatically generate questions and conduct peer review assessments of answers from other models, effectively alleviating the reliance on human workload. Additionally, we introduce the vision-language scoring system to mitigate the bias issues, which focuses on three aspects: (i) response correctness; (ii) visual understanding and reasoning; and (iii) image-text correlation. Experimental results demonstrate that UPME achieves a Pearson correlation of 0.944 with human evaluations on the MMstar dataset and 0.814 on the ScienceQA dataset, indicating that our framework closely aligns with human-designed benchmarks and inherent human preferences.
The Herbig-Haro Outflow Content and Star-Forming Environment of Circinus West and East
2025-03-19
T. A. Rector, L. Prato, R. Kerr, E. Papraniku, K. Fisk
We report the results of a spatially compete, high-sensitivity survey for Herbig-Haro (HH) outflows in the Western and Eastern Circinus molecular clouds. We have detected 28 new HH objects in Circinus West, doubling the number known in this dark nebula. We have also discovered 9 outflows in Circinus East, the first to be identified here. Although both Circinus West and East appear to be located at ~800 pc, their morphologies are distinct. Circinus West shows filamentary structure, while Circinus East is dominated by amorphous dark clouds. North-east of Circinus East, an extended distribution of young stars is centered on the ~6 Myr-old open cluster ASCC 79, which may have triggered the sequential formation of younger surrounding populations. New transverse velocities from Gaia show two dynamically distinct stellar populations in Circinus East; their velocity distribution is consistent with an active cloud-cloud collision between material ejected by the formation of O and B stars in ASCC~79 and a dynamically similar interloping cloud. Given the similar distances to Circinus West and East, and the presence in both of HH objects -- a phenomenon associated with stellar ages of ~1 Myr -- it is likely that these clouds are nominally related, but only Circinus East is subject to substantial feedback from the central cluster in the parent complex. This feedback appears to guide the morphology and evolution of Circinus East, resulting in a complex and possibly disruptive dynamical environment rich in star formation potential that contrasts with the relatively quiescent environment in Circinus West.
Enhanced Astrometry of the Rapid ASKAP Continuum Survey for Precise Localisation of Fast Radio Bursts
2025-03-19
Akhil Jaini, Adam T. Deller, Yuanming Wang, Emil Lenc, Marcin Glowacki
Fast Radio Bursts (FRBs) are short, intense radio signals from distant astrophysical sources, and their accurate localisation is crucial for probing their origins and utilising them as cosmological tools. This study focuses on improving the astrometric precision of FRBs discovered by the Australian Square Kilometre Array Pathfinder (ASKAP) by correcting systematic positional errors in the Rapid ASKAP Continuum Survey (RACS), which is used as a primary reference for ASKAP FRB localisation. We present a detailed methodology for refining astrometry in two RACS epochs (RACS-Low1 and RACS-Low3) through crossmatching with the Wide-field Infrared Survey Explorer (WISE) catalogue. The uncorrected RACS-Low1 and RACS-Low3 catalogues had significant astrometric offsets, with all-sky median values of $0.58''$ in RA and $-0.26''$ in Dec. (RACS-Low1) and $0.29''$ in RA and $1.24''$ in Dec. (RACS-Low3), with a substantial and direction-dependent scatter around these values. After correction, the median offset was completely eliminated, and the 68\% confidence interval in the all-sky residuals was reduced to $0.2''$ or better for both surveys. By validating the corrected catalogues against other, independent radio surveys, we conclude that the individual corrected RACS source positions are accurate to a 1-$\sigma$ confidence level of $0.3''$ over the bulk of the survey area, degrading slightly to $0.4''$ near the Galactic plane. This work lays the groundwork to extend our corrections to the full RACS catalogue that will enhance future radio observations, particularly for FRB studies.
UntrustVul: An Automated Approach for Identifying Untrustworthy Alerts in Vulnerability Detection Models
2025-03-19
Lam Nguyen Tung, Xiaoning Du, Neelofar Neelofar, Aldeida Aleti
Machine learning (ML) has shown promise in detecting vulnerabilities. To review vulnerabilities detected by ML predictions, developers manually assess suspicious lines in their interpretations. However, studies have revealed that these models often learn and predict based on irrelevant features frequently appearing in vulnerable code. This leads to predictions that may correctly flag vulnerable functions but for the wrong reasons, which we call untrustworthy. These predictions can mislead developers, hindering them from locating the vulnerabilities. This increases the efforts of manual assessment and, worse, risks creating flawed patches that fail to address existing vulnerabilities and even introduce new ones. Hence, automated approaches are needed to detect untrustworthy predictions, preventing overlooked vulnerabilities and alleviating the burden of manual assessment. We propose UntrustVul, the first automated approach to identify untrustworthy vulnerability predictions. Given a vulnerability prediction during inference, UntrustVul systematically assesses whether suspicious lines annotated by the prediction are vulnerability-unrelated. It simulates developers' rationales, considering a line unrelated if (1) it is absent from historical vulnerabilities and (2) it cannot reach any vulnerabilities in execution flows. UntrustVul assesses (1) by analysing its syntactic meaning using deep representations to determine whether it is syntax-benign. To assess (2), UntrustVul traces dependencies of the syntax-benign lines on other suspicious lines using static and rule-based analyses. We evaluate UntrustVul on 155K vulnerability predictions by four models across three datasets. UntrustVul effectively detects untrustworthy predictions with an F1-score of 82%-94% and helps improve the ability of models to detect vulnerabilities by up to 321% in F1-score and 100% in trustworthiness.
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities
2025-03-19
Md Shahir Zaoad, Niamat Zawad, Priyanka Ranade, Richard Krogman, Latifur Khan, James Holt
Knowledge graphs have emerged to be promising datastore candidates for context augmentation during Retrieval Augmented Generation (RAG). As a result, techniques in graph representation learning have been simultaneously explored alongside principal neural information retrieval approaches, such as two-phased retrieval, also known as re-ranking. While Graph Neural Networks (GNNs) have been proposed to demonstrate proficiency in graph learning for re-ranking, there are ongoing limitations in modeling and evaluating input graph structures for training and evaluation for passage and document ranking tasks. In this survey, we review emerging GNN-based ranking model architectures along with their corresponding graph representation construction methodologies. We conclude by providing recommendations on future research based on community-wide challenges and opportunities.
Cybersecurity in Vehicle-to-Grid (V2G) Systems: A Systematic Review
2025-03-19
Mohammad A Razzaque, Shafiuzzaman K Khadem, Sandipan Patra, Glory Okwata, Md. Noor-A-Rahim
This paper presents a systematic review of recent advancements in V2G cybersecurity, employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework for detailed searches across three journal databases and included only peer-reviewed studies published between 2020 and 2024 (June). We identified and reviewed 133 V2G cybersecurity studies and found five important insights on existing V2G cybersecurity research. First, most studies (103 of 133) focused on protecting V2G systems against cyber threats, while only seven studies addressed the recovery aspect of the CRML (Cybersecurity Risk Management Lifecycle) function. Second, existing studies have adequately addressed the security of EVs and EVCS (EV charging stations) in V2G systems (112 and 81 of 133 studies, respectively). However, none have focused on the linkage between the behaviour of EV users and the cybersecurity of V2G systems. Third, physical access, control-related vulnerabilities, and user behaviour-related attacks in V2G systems are not addressed significantly. Furthermore, existing studies overlook vulnerabilities and attacks specific to AI and blockchain technologies. Fourth, blockchain, artificial intelligence (AI), encryption, control theory, and optimisation are the main technologies used, and finally, the inclusion of quantum safety within encryption and AI models and AI assurance (AIA) is in a very early stage; only two and one of 133 studies explicitly addressed quantum safety and AIA through explainability. By providing a holistic perspective, this study identifies critical research gaps and outlines future directions for developing robust end-to-end cybersecurity solutions to safeguard V2G systems and support global sustainability goals.
Critical review of patient outcome study in head and neck cancer radiotherapy
2025-03-19
Jingyuan Chen, Yunze Yang, Chenbin Liu, Hongying Feng, Jason M. Holmes, Lian Zhang, Steven J. Frank, Charles B. Simone II, Daniel J. Ma, Samir H. Patel, Wei Liu
Rapid technological advances in radiation therapy have significantly improved dose delivery and tumor control for head and neck cancers. However, treatment-related toxicities caused by high-dose exposure to critical structures remain a significant clinical challenge, underscoring the need for accurate prediction of clinical outcomes-encompassing both tumor control and adverse events (AEs). This review critically evaluates the evolution of data-driven approaches in predicting patient outcomes in head and neck cancer patients treated with radiation therapy, from traditional dose-volume constraints to cutting-edge artificial intelligence (AI) and causal inference framework. The integration of linear energy transfer in patient outcomes study, which has uncovered critical mechanisms behind unexpected toxicity, was also introduced for proton therapy. Three transformative methodological advances are reviewed: radiomics, AI-based algorithms, and causal inference frameworks. While radiomics has enabled quantitative characterization of medical images, AI models have demonstrated superior capability than traditional models. However, the field faces significant challenges in translating statistical correlations from real-world data into interventional clinical insights. We highlight that how causal inference methods can bridge this gap by providing a rigorous framework for identifying treatment effects. Looking ahead, we envision that combining these complementary approaches, especially the interventional prediction models, will enable more personalized treatment strategies, ultimately improving both tumor control and quality of life for head and neck cancer patients treated with radiation therapy.
Student's Use of Generative AI as a Support Tool in an Advanced Web Development Course
2025-03-19
Isaac Alpizar-Chacon, Hieke Keuning
Various studies have studied the impact of Generative AI on Computing Education. However, they have focused on the implications for novice programmers. In this experience report, we analyze the use of GenAI as a support tool for learning, creativity, and productivity in a web development course for undergraduate students with extensive programming experience. We collected diverse data (assignments, reflections, logs, and a survey) and found that students used GenAI on different tasks (code generation, idea generation, etc.) with a reported increase in learning and productivity. However, they are concerned about over-reliance and incorrect solutions and want more training in prompting strategies.
Pervasive Sensing for Livestock Health and Activity Monitoring: Current Methods and Techniques
2025-03-19
Jeffrey D Shulkin, Abhipol Vibhatasilpin, Vedant Adhana
Pervasive sensing is transforming health and activity monitoring by enabling continuous and automated data collection through advanced sensing modalities. While extensive research has been conducted on human subjects, its application in livestock remains underexplored. In large-scale agriculture, real-time monitoring of biological signals and behavioral patterns can facilitate early disease detection, optimize feeding and breeding strategies, and ensure compliance with welfare standards. This survey examines key sensing technologies -- including structural vibration, radio frequency (RF), computer vision, and wearables -- highlighting their benefits and challenges in livestock monitoring. By comparing these approaches, we provide insights into their effectiveness, limitations, and potential for integration into modern smart farming systems. Finally, we discuss research gaps and future directions to advance pervasive sensing in livestock health and activity monitoring.
ECO: An LLM-Driven Efficient Code Optimizer for Warehouse Scale Computers
2025-03-19
Hannah Lin, Martin Maas, Maximilian Roquemore, Arman Hasanzadeh, Fred Lewis, Yusuf Simonson, Tzu-Wei Yang, Amir Yazdanbakhsh, Deniz Altinbüken, Florin Papa, Maggie Nolan Edmonds, Aditya Patil, Don Schwarz, Satish Chandra, Chris Kennelly, Milad Hashemi, Parthasarathy Ranganathan
With the end of Moore's Law, optimizing code for performance has become paramount for meeting ever-increasing compute demands, particularly in hyperscale data centers where even small efficiency gains translate to significant resource and energy savings. Traditionally, this process requires significant programmer effort to identify optimization opportunities, modify the code to implement the optimization, and carefully deploy and measure the optimization's impact. Despite a significant amount of work on automating program edits and promising results in small-scale settings, such performance optimizations have remained elusive in large real-world production environments, due to the scale, high degree of complexity, and reliability required. This paper introduces ECO (Efficient Code Optimizer), a system that automatically refactors source code to improve performance at scale. To achieve these performance gains, ECO searches through historical commits at scale to create a dictionary of performance anti-patterns that these commits addressed. These anti-patterns are used to search for similar patterns in a code base of billions of lines of code, pinpointing other code segments with similar potential optimization opportunities. Using a fine-tuned LLM, ECO then automatically refactors the code to generate and apply similar edits. Next, ECO verifies the transformed code, submits it for code review, and measures the impact of the optimization in production. Currently deployed on Google's hyperscale production fleet, this system has driven >25k changed lines of production code, across over 6.4k submitted commits, with a >99.5% production success rate. Over the past year, ECO has consistently resulted in significant performance savings every quarter. On average, the savings produced per quarter are equivalent to over 500k normalized CPU cores.
Entropy has emerged as a dynamic, interdisciplinary, and widely accepted quantitative measure of uncertainty across different disciplines. A unified understanding of entropy measures, supported by a detailed review of their theoretical foundations and practical applications, is crucial to advance research across disciplines. This review article provides motivation, fundamental properties, and constraints of various entropy measures. These measures are categorized with time evolution ranging from Shannon entropy generalizations, distribution function theory, fuzzy theory, fractional calculus to graph theory, all explained in a simplified and accessible manner. These entropy measures are selected on the basis of their usability, with descriptions arranged chronologically. We have further discussed the applicability of these measures across different domains, including thermodynamics, communication theory, financial engineering, categorical data, artificial intelligence, signal processing, and chemical and biological systems, highlighting their multifaceted roles. A number of examples are included to demonstrate the prominence of specific measures in terms of their applicability. The article also focuses on entropy-based applications in different disciplines, emphasizing openly accessible resources. Furthermore, this article emphasizes the applicability of various entropy measures in the field of finance. The article may provide a good insight to the researchers and experts working to quantify uncertainties, along with potential future directions.
The S-PLUS Fornax Project (S+FP): Mapping globular clusters systems within 5 virial radii around NGC 1399
2025-03-19
Luis Lomelí-Núñez, A. Cortesi, A. V. Smith Castelli, M. L. Buzzo, Y. D. Mayya, Vasiliki Fragkou, J. A. Alzate-Trujillo, R. F. Haack, J. P. Calderón, A. R. Lopes, Michael Hilker, M. Grossi, Karín Menéndez-Delmestre, Thiago S. Gonçalves, Ana L. Chies-Santos, L. A. Gutiérrez-Soto, Ciria Lima-Dias, S. V. Werner, Pedro K. Humire, R. C. Thom de Souza, A. Alvarez-Candal, Swayamtrupta Panda, Avinash Chaturvedi, E. Telles, C. Mendes de Oliveira, A. Kanaan, T. Ribeiro, W. Schoenell
We present the largest sample ($\sim$13,000 candidates, $\sim$3000 of wich are bona-fide candidates) of globular cluster (GCs) candidates reported in the Fornax Cluster so far. The survey is centered on the NGC 1399 galaxy, extending out to 5 virial radii (\rv) of the cluster. We carried out a photometric study using images observed in the 12-bands system of the Southern Photometric Local Universe Survey (S-PLUS), corresponding to 106 pointings, covering a sky area of $\sim$208 square degrees. Studying the properties of spectroscopically confirmed GCs, we have designed a method to select GC candidates using structural and photometric parameters. We found evidence of color bimodality in 2 broad bands colors, namely $(g-i)_{0}$ and $(g-z)_{0}$, while, in the narrow bands, we did not find strong statistical evidence to confirm bimodality in any color. We analyzed the GCs luminosity functions (GCLF) in the 12-bands of S-PLUS, and we can highlight two points: a) due to the relatively shallow depth of S-PLUS, it is only possible to observe the bright end of the GCLF and, b) at that level, in all the bands it can be appreciated the log-normal distribution typical for GC systems. With the spatial coverage reached in this study, we are able for the first time explore the large scale distribution of GCs within and around a galaxy cluster. In particular, we noted that the GCs might be clustered along substructures, which traces the current cluster build up.
Survey on Generalization Theory for Graph Neural Networks
2025-03-19
Antonis Vasileiou, Stefanie Jegelka, Ron Levie, Christopher Morris
Message-passing graph neural networks (MPNNs) have emerged as the leading approach for machine learning on graphs, attracting significant attention in recent years. While a large set of works explored the expressivity of MPNNs, i.e., their ability to separate graphs and approximate functions over them, comparatively less attention has been directed toward investigating their generalization abilities, i.e., making meaningful predictions beyond the training data. Here, we systematically review the existing literature on the generalization abilities of MPNNs. We analyze the strengths and limitations of various studies in these domains, providing insights into their methodologies and findings. Furthermore, we identify potential avenues for future research, aiming to deepen our understanding of the generalization abilities of MPNNs.
Euclid preparation. Spatially resolved stellar populations of local galaxies with Euclid: a proof of concept using synthetic images with the TNG50 simulation
2025-03-19
Euclid Collaboration, Abdurro'uf, C. Tortora, M. Baes, A. Nersesian, I. Kovačić, M. Bolzonella, A. Lançon, L. Bisigello, F. Annibali, M. N. Bremer, D. Carollo, C. J. Conselice, A. Enia, A. M. N. Ferguson, A. Ferré-Mateu, L. K. Hunt, E. Iodice, J. H. Knapen, A. Iovino, F. R. Marleau, R. F. Peletier, R. Ragusa, M. Rejkuba, A. S. G. Robotham, J. Román, T. Saifollahi, P. Salucci, M. Scodeggio, M. Siudek, A. van der Wel, K. Voggel, B. Altieri, S. Andreon, C. Baccigalupi, M. Baldi, S. Bardelli, A. Biviano, A. Bonchi, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, A. Caillat, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, J. Dinis, H. Dole, F. Dubath, X. Dupac, S. Dusini, S. Escoffier, M. Farina, R. Farinelli, S. Farrens, F. Faustini, S. Ferriol, F. Finelli, S. Fotopoulou, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, B. Gillis, C. Giocoli, P. Gómez-Alvarez, J. Gracia-Carpio, A. Grazian, F. Grupp, W. Holmes, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, E. Keihänen, S. Kermiche, A. Kiessling, M. Kilbinger, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, M. Schirmer, P. Schneider, T. Schrabback, A. Secroun, E. Sefusatti, G. Seidel, S. Serrano, P. Simon, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, G. Zamorani, E. Zucca, E. Bozzo, C. Burigana, M. Calabrese, D. Di Ferdinando, J. A. Escartin Vigo, S. Matthew, N. Mauri, M. Pöntinen, C. Porciani, V. Scottez, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, V. Allevato, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, M. Ballardini, D. Bertacca, A. Blanchard, L. Blot, S. Borgani, M. L. Brown, S. Bruton, R. Cabanac, A. Calabro, A. Cappi, F. Caro, C. S. Carvalho, T. Castro, F. Cogato, T. Contini, A. R. Cooray, O. Cucciati, G. Desprez, A. Díaz-Sánchez, S. Di Domizio, A. G. Ferrari, I. Ferrero, A. Finoguenov, A. Fontana, F. Fornari, K. Ganga, J. García-Bellido, T. Gasparetto, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, A. Gregorio, M. Guidi, C. M. Gutierrez, A. Hall, S. Hemmati, H. Hildebrandt, J. Hjorth, M. Huertas-Company, A. Jimenez Muñoz, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, C. C. Kirkpatrick, S. Kruk, M. Lattanzi, S. Lee, J. Le Graet, L. Legrand, M. Lembo, J. Lesgourgues, T. I. Liaudat, A. Loureiro, J. Macias-Perez, M. Magliocchetti, F. Mannucci, R. Maoli, J. Martín-Fleitas, C. J. A. P. Martins, L. Maurin, R. B. Metcalf, M. Miluzio, P. Monaco, C. Moretti, G. Morgante, K. Naidoo, Nicholas A. Walton, K. Paterson, L. Patrizii, A. Pisani, V. Popa, D. Potter, I. Risso, P. -F. Rocci, M. Sahlén, E. Sarpa, A. Schneider, D. Sciotti, E. Sellentin, M. Sereno, K. Tanidis, C. Tao, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, D. Vergani, G. Verza, P. Vielzeuf
The European Space Agency's Euclid mission will observe approximately 14,000 $\rm{deg}^{2}$ of the extragalactic sky and deliver high-quality imaging for many galaxies. The depth and high spatial resolution of the data will enable a detailed analysis of stellar population properties of local galaxies. In this study, we test our pipeline for spatially resolved SED fitting using synthetic images of Euclid, LSST, and GALEX generated from the TNG50 simulation. We apply our pipeline to 25 local simulated galaxies to recover their resolved stellar population properties. We produce 3 types of data cubes: GALEX + LSST + Euclid, LSST + Euclid, and Euclid-only. We perform the SED fitting tests with two SPS models in a Bayesian framework. Because the age, metallicity, and dust attenuation estimates are biased when applying only classical formulations of flat priors, we examine the effects of additional priors in the forms of mass-age-$Z$ relations, constructed using a combination of empirical and simulated data. Stellar-mass surface densities can be recovered well using any of the 3 data cubes, regardless of the SPS model and prior variations. The new priors then significantly improve the measurements of mass-weighted age and $Z$ compared to results obtained without priors, but they may play an excessive role compared to the data in determining the outcome when no UV data is available. The spatially resolved SED fitting method is powerful for mapping the stellar populations of galaxies with the current abundance of high-quality imaging data. Our study re-emphasizes the gain added by including multiwavelength data from ancillary surveys and the roles of priors in Bayesian SED fitting. With the Euclid data alone, we will be able to generate complete and deep stellar mass maps of galaxies in the local Universe, thus exploiting the telescope's wide field, NIR sensitivity, and high spatial resolution.
Light in the dark forest -- I. An efficient optimal estimator for 3D Lyman-alpha forest power spectrum
2025-03-19
N. G. Karaçaylı, C. M. Hirata
The highly anisotropic nature of the Lyman-alpha (Ly$\alpha$) forest data introduces a complex survey window function that complicates the measurement of the three-dimensional power spectrum ($P_{\mathrm{3D}}$). In this paper, we present the first fully optimal estimator for $P_{\mathrm{3D}}$, which exactly deconvolves the survey window function and marginalizes contaminated modes that distort the power spectrum. Our approach adapts optimal estimator techniques developed for the 2D cosmic microwave background data to the 3D case. To achieve computational feasibility, we employ the conjugate gradient method and implement the P$^3$M formalism to handle large-scale and small-scale operations separately and efficiently. We validate our estimator using Monte Carlo mocks and Gaussian simulations, demonstrating its accuracy and computational efficiency. We confirm that mode marginalization eliminates distortions arising from quasar continuum errors and delivers robust power spectrum estimation, though it also inflates errors at large scales. This first implementation works in the flat sky case; we discuss the remaining steps needed to generalize to the curved sky. This formalism offers a foundation for the Ly$\alpha$ forest $P_{\mathrm{3D}}$ measurements and a new path toward cosmological constraints from the Ly$\alpha$ forest data.
Unification of observational entropy with maximum entropy principles
2025-03-19
Joseph Schindler, Philipp Strasberg, Niklas Galke, Andreas Winter, Michael G. Jabbour
We introduce a definition of coarse-grained entropy that unifies measurement-based (observational entropy) and max-entropy-based (Jaynes) approaches to coarse-graining, by identifying physical constraints with information theoretic priors. The definition is shown to include as special cases most other entropies of interest in physics. We then consider second laws, showing that the definition admits new entropy increase theorems and connections to thermodynamics. We survey mathematical properties of the definition, and show it resolves some pathologies of the traditional observational entropy in infinite dimensions. Finally, we study the dynamics of this entropy in a quantum random matrix model and a classical hard sphere gas. Together the results suggest that this generalized observational entropy can form the basis of a highly general approach to statistical mechanics.
Self-similarity of the mass distribution in rich galaxy clusters up z~1 tracked with weak lensing
2025-03-19
Mauro Sereno
In the standard theory of growth of the nonbaryonic dark matter, cosmic structures form hierarchically and self-similarly from smaller clumps. The assembly merger tree goes from the linear perturbations in the early universe to highly non linear structures at late times. Gravity is the driving force and self-similarity should inform cosmic haloes. However, it is unclear if apparent anomalies at non-linear scales are due to either baryonic or new physics. Here, I show that the mass distribution of rich haloes evolve self-similarly at least since the universe was 5.7 Gyr old. Using gravitational weak lensing, I constrain the mass profiles of galaxy clusters with M_200c >~ 2 x 10^14 M_Sun that were optically detected in the HSC-SSP survey in the redshift range 0.2 <= z < 1.0. Cluster self-similarity confirms the standard theory of growth in the non-linear regime. Clusters are still growing but neither violent mergers nor matter slowly falling in from the cosmic web disrupt self-similarity, which is in place well before the halo formation time. Dark matter growth can fit the fossil cosmic microwave background as well as young, very massive haloes. Looking with next generation surveys at scales in clusters where self-similarity breaks could pose a new challenge to dark matter.
The OGLE Collection of Variable Stars. Over 75 000 Eclipsing and Ellipsoidal Binary Systems in the Magellanic Clouds
2025-03-19
M. Głowacki, I. Soszyński, A. Udalski, M. K. Szymański, J. Skowron, D. M. Skowron, P. Mróz, P. Pietrukowicz, R. Poleski, S. Kozłowski, P. Iwanek, M. Wrona, K. Ulaczyk, K. Rybicki, M. Gromadzki, M. Mróz, M. Urbanowicz
We present an updated collection of eclipsing and ellipsoidal binary systems in the Large and Small Magellanic Clouds (LMC and SMC), as observed by the Optical Gravitational Lensing Experiment (OGLE) survey. The catalog comprises a total of 75 400 binary systems, including 63 252 in the LMC and 12 148 in the SMC. The sample is categorized into 67 971 eclipsing and 7429 ellipsoidal variables. For all stars, we provide I-band and V-band photometric time series collected between 2010 and 2024 during the fourth phase of the OGLE project (OGLE-IV). We discuss methods used to identify binary systems in the OGLE data and present objects of particular interest, including double periodic variables, transient eclipsing binaries, double eclipsing binaries, and binary systems with pulsating stars. We present a comparative analysis based on the most comprehensive catalogs of variable stars in the Magellanic System, compiled from surveys like Gaia, ASAS-SN, and EROS-2, and included in the International Variable Star Index.
SAPPHIRES: A Galaxy Over-Density in the Heart of Cosmic Reionization at $z=8.47$
2025-03-19
Yoshinobu Fudamoto, Jakob M. Helton, Xiaojing Lin, Fengwu Sun, Peter Behroozi, Tiger Yu-Yang Hsiao, Eiichi Egami, Andrew J. Bunker, Yuichi Harikane, Masami Ouchi, Yichen Liu, Weizhe Liu, Roberto Maiolino, Zhiyuan Ji, Xiangyu Jin, Wei Leong Tee, Feige Wang, Christopher N. A. Willmer, Yi Xu, Yongda Zhu
We report the discovery of a galaxy proto-cluster candidate (dubbed MACS0416-OD-z8p5) at a spectroscopic redshift of $z\sim8.47$, dating back to $\sim550$Myr after the Big Bang. The observations are part of the JWST Cycle-3 treasury program, Slitless Areal Pure-Parallel HIgh-Redshift Emission Survey (SAPPHIRES) with NIRCam-grism. Using wide field slitless spectroscopy (WFSS) obtained in the MACS0416 parallel field, we robustly confirm nine galaxies at $z_{\rm spec}\sim8.47$ via emission line detections of [OIII]5008A (with $>5\,\sigma$) and tentatively confirm one additional galaxy (at $\sim3\,\sigma$). This discovery represents the highest-redshift, spectroscopically confirmed galaxy over-density known to date, which is $\sim6$--$8$ times more dense than the average volume density of galaxies at the same redshift. Furthermore, a galaxy hosting a low-mass active galactic nucleus (``Little-Red-Dot'') is found as a member, suggesting an early emergence of active, massive black holes and feedback between these black holes and their surrounding environments. We also discuss the spatial structures connecting the galaxy over-density to nearby massive star-forming galaxies (separated by $\sim 5$pMpc, including MACS0416-Y1 and MACS0416-JD. This finding of a massive dark matter halo hosting a galaxy over-density at $z\sim8.5$ is surprising given that our survey covered only a small, random field ($16.5\,{\rm arcmin^2}$) as part of a pure parallel observation. The comparison with cosmological simulations shows that the likelihood of finding such a large-scale structure is $<5\,\%$ under the current galaxy formation scenario and the observed survey volume. Our results demonstrate the power of WFSS observations to build a complete line-emitter sample and suggest an important role for over-densities in enhancing galaxy formation by funneling large-scale gas supplies into small cosmological volumes.
A Homogeneous Catalog of Oscillating Solar-Type Stars Observed by the Kepler Mission and a New Amplitude Scaling Relation Including Chromospheric Activity
2025-03-19
Maryum Sayeed, Daniel Huber, Ashley Chontos, Yaguang Li
We present a homogeneous catalog of global asteroseismic parameters and derived stellar parameters for 765 Kepler main-sequence and subgiant stars. The catalog was produced by re-analyzing all available Kepler DR25 short-cadence data using pySYD, an automated pipeline to extract global asteroseismic parameters. We find 50 new detections, seven of which are also planet candidate host stars. We find excellent agreement between our $\nu_{\text{max}}$ and $\Delta \nu$ measurements and literature values, with an average offset of $0.2 \pm 0.4\%$ ($\sigma=5\%$) and $0.2 \pm 0.7\%$ ($\sigma=2\%$), respectively. In addition, we derive stellar radii and masses with an average precision of $2.7\%$ and $10.4\%$, respectively, and find a median offset of $0.4 \pm 0.4\%$ ($\sigma=10\%$) between our radii derived with asteroseismology and those from Gaia parallaxes. Using spectroscopic $\log{R'_{\text{HK}}}$ activity measurements from Keck/HIRES, we derive a new amplitude scaling relation with an activity term for main-sequence and subgiant stars, which reduces the offset between expected and observed oscillation amplitudes from $9.3 \pm 1.6\%$ to $1.7 \pm 0.9\%$. Our work is the largest and most homogeneous asteroseismic catalog of Kepler main-sequence and subgiant stars to date, including a total of 101 stars hosting planet candidates and 451 stars with measured rotation periods.
Slitless Areal Pure-Parallel HIgh-Redshift Emission Survey (SAPPHIRES): Early Data Release of Deep JWST/NIRCam Images and Spectra in MACS J0416 Parallel Field
2025-03-19
Fengwu Sun, Yoshinobu Fudamoto, Xiaojing Lin, Jakob M. Helton, Tiger Yu-Yang Hsiao, Eiichi Egami, Arshia Akhtarkavan, Andrew J. Bunker, Zheng Cai, Christa DeCoursey, Daniel J. Eisenstein, Xiaohui Fan, Yuichi Harikane, Zhiyuan Ji, Xiangyu Jin, Weizhe Liu, Yichen Liu, Zheng Ma, Roberto Maiolino, Masami Ouchi, Wei Leong Tee, Feige Wang, Christopher N. A. Willmer, Yunjing Wu, Yi Xu, Jinyi Yang, Junyu Zhang, Yongda Zhu
We present the early data release (EDR) of SAPPHIRES, a JWST Cycle-3 Treasury imaging and spectroscopic survey using the powerful NIRCam wide-field slitless spectroscopic (WFSS) mode in pure parallel. SAPPHIRES will obtain NIRCam imaging and WFSS data in many cosmological deep fields totaling a telescope charged time of 709 hours (557-hour exposures). In this EDR, we present NIRCam imaging and WFSS data obtained in parallel to the Frontier Field galaxy cluster MACS J0416.1-2403, which are attached to primary observations JWST-GO-4750. With a total dual-channel exposure time of 47.2 hours, we obtain deep NIRCam imaging in 13 bands at 0.6--5.0 micron and deep WFSS at 3.1-5.0 micron through the F356W and F444W filters with grisms in orthogonal dispersion directions. We release reduced NIRCam images, photometric catalogs of 22107 sources and WFSS spectra of 1060 sources with confirmed redshifts ($z\simeq0-8.5$). Preliminary value-added catalogs including photometric redshifts, spectroscopic redshifts and physical properties (mass, star-formation rate, etc.) are also made available. We also characterize the data quality and demonstrate scientific applications, including (1) galaxy candidates at the redshift frontier ($z\gtrsim10$), (2) the ionized gas kinematics of a galaxy reconstructed from $R\sim1500$ grism spectra at orthogonal dispersion directions, (3) massive emission-line galaxies and active galactic nuclei (AGN) around the Epoch of Reionization.
The rise of the galactic empire: luminosity functions at $z\sim17$ and $z\sim25$ estimated with the MIDIS$+$NGDEEP ultra-deep JWST/NIRCam dataset
2025-03-19
Pablo G. Pérez-González, Göran Östlin, Luca Costantin, Jens Melinder, Steven L. Finkelstein, Rachel S. Somerville, Marianna Annunziatella, Javier Álvarez-Márquez, Luis Colina, Avishai Dekel, Henry C. Ferguson, Zhaozhou Li, L. Y. Aaron Yung, Mic B. Bagley, Leindert A. Boogard, Denis Burgarella, Antonello Calabrò, Karina I. Caputi, Yingjie Cheng, Andreas Eckart, Mauro Giavalisco, Steven Gillman, Thomas R. Greve, Nimish P. Hathi, Jens Hjorth, Marc Huertas-Company, Jeyhan Kartaltepe, Anton M. Koekemoer, Vasily Kokorev, Álvaro Labiano, Danial Langeroodi, Gene C. K. Leung, Priyamvada Natarajan, Casey Papovich, Florian Peissker, Laura Pentericci, Nor Pirzkal, Pierluigi Rinaldi, Paul van der Werf, Fabian Walter
We present a sample of six F200W and three F277W dropout sources identified as $1616$ galaxy candidates present mass-weighted ages around 30 Myr, and attenuations $\mathrm{A(V)}<0.1$ mag. Their average stellar mass is $\mathrm{M}_\bigstar\sim10^{7}\,\mathrm{M}_\odot$, implying a star formation efficiency (stellar-to-baryon mass fraction) around 10%. We find three galaxies with very blue UV spectral slopes ($\beta\sim-3$) compatible with low metallicity or Pop III and young ($\lesssim10$ Myr) stars and/or high escape fractions of ionizing photons, the rest presenting slopes $\beta\sim-2.5$ similar to $z=10-12$ samples.
Reliable Radiologic Skeletal Muscle Area Assessment -- A Biomarker for Cancer Cachexia Diagnosis
2025-03-19
Sabeen Ahmed, Nathan Parker, Margaret Park, Daniel Jeong, Lauren Peres, Evan W. Davis, Jennifer B. Permuth, Erin Siegel, Matthew B. Schabath, Yasin Yilmaz, Ghulam Rasool
Cancer cachexia is a common metabolic disorder characterized by severe muscle atrophy which is associated with poor prognosis and quality of life. Monitoring skeletal muscle area (SMA) longitudinally through computed tomography (CT) scans, an imaging modality routinely acquired in cancer care, is an effective way to identify and track this condition. However, existing tools often lack full automation and exhibit inconsistent accuracy, limiting their potential for integration into clinical workflows. To address these challenges, we developed SMAART-AI (Skeletal Muscle Assessment-Automated and Reliable Tool-based on AI), an end-to-end automated pipeline powered by deep learning models (nnU-Net 2D) trained on mid-third lumbar level CT images with 5-fold cross-validation, ensuring generalizability and robustness. SMAART-AI incorporates an uncertainty-based mechanism to flag high-error SMA predictions for expert review, enhancing reliability. We combined the SMA, skeletal muscle index, BMI, and clinical data to train a multi-layer perceptron (MLP) model designed to predict cachexia at the time of cancer diagnosis. Tested on the gastroesophageal cancer dataset, SMAART-AI achieved a Dice score of 97.80% +/- 0.93%, with SMA estimated across all four datasets in this study at a median absolute error of 2.48% compared to manual annotations with SliceOmatic. Uncertainty metrics-variance, entropy, and coefficient of variation-strongly correlated with SMA prediction errors (0.83, 0.76, and 0.73 respectively). The MLP model predicts cachexia with 79% precision, providing clinicians with a reliable tool for early diagnosis and intervention. By combining automation, accuracy, and uncertainty awareness, SMAART-AI bridges the gap between research and clinical application, offering a transformative approach to managing cancer cachexia.
Explainable AI Components for Narrative Map Extraction
2025-03-19
Brian Keith, Fausto German, Eric Krokos, Sarah Joseph, Chris North
As narrative extraction systems grow in complexity, establishing user trust through interpretable and explainable outputs becomes increasingly critical. This paper presents an evaluation of an Explainable Artificial Intelligence (XAI) system for narrative map extraction that provides meaningful explanations across multiple levels of abstraction. Our system integrates explanations based on topical clusters for low-level document relationships, connection explanations for event relationships, and high-level structure explanations for overall narrative patterns. In particular, we evaluate the XAI system through a user study involving 10 participants that examined narratives from the 2021 Cuban protests. The analysis of results demonstrates that participants using the explanations made the users trust in the system's decisions, with connection explanations and important event detection proving particularly effective at building user confidence. Survey responses indicate that the multi-level explanation approach helped users develop appropriate trust in the system's narrative extraction capabilities. This work advances the state-of-the-art in explainable narrative extraction while providing practical insights for developing reliable narrative extraction systems that support effective human-AI collaboration.
A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions
2025-03-19
Saddam Hussain Khan, Rashid Iqbal
Deep Convolutional Neural Networks (CNNs) have significantly advanced deep learning, driving breakthroughs in computer vision, natural language processing, medical diagnosis, object detection, and speech recognition. Architectural innovations including 1D, 2D, and 3D convolutional models, dilated and grouped convolutions, depthwise separable convolutions, and attention mechanisms address domain-specific challenges and enhance feature representation and computational efficiency. Structural refinements such as spatial-channel exploitation, multi-path design, and feature-map enhancement contribute to robust hierarchical feature extraction and improved generalization, particularly through transfer learning. Efficient preprocessing strategies, including Fourier transforms, structured transforms, low-precision computation, and weight compression, optimize inference speed and facilitate deployment in resource-constrained environments. This survey presents a unified taxonomy that classifies CNN architectures based on spatial exploitation, multi-path structures, depth, width, dimensionality expansion, channel boosting, and attention mechanisms. It systematically reviews CNN applications in face recognition, pose estimation, action recognition, text classification, statistical language modeling, disease diagnosis, radiological analysis, cryptocurrency sentiment prediction, 1D data processing, video analysis, and speech recognition. In addition to consolidating architectural advancements, the review highlights emerging learning paradigms such as few-shot, zero-shot, weakly supervised, federated learning frameworks and future research directions include hybrid CNN-transformer models, vision-language integration, generative learning, etc. This review provides a comprehensive perspective on CNN's evolution from 2015 to 2025, outlining key innovations, challenges, and opportunities.
Medical Ultrasound (US) imaging has seen increasing demands over the past years, becoming one of the most preferred imaging modalities in clinical practice due to its affordability, portability, and real-time capabilities. However, it faces several challenges that limit its applicability, such as operator dependency, variability in interpretation, and limited resolution, which are amplified by the low availability of trained experts. This calls for the need of autonomous systems that are capable of reducing the dependency on humans for increased efficiency and throughput. Reinforcement Learning (RL) comes as a rapidly advancing field under Artificial Intelligence (AI) that allows the development of autonomous and intelligent agents that are capable of executing complex tasks through rewarded interactions with their environments. Existing surveys on advancements in the US scanning domain predominantly focus on partially autonomous solutions leveraging AI for scanning guidance, organ identification, plane recognition, and diagnosis. However, none of these surveys explore the intersection between the stages of the US process and the recent advancements in RL solutions. To bridge this gap, this review proposes a comprehensive taxonomy that integrates the stages of the US process with the RL development pipeline. This taxonomy not only highlights recent RL advancements in the US domain but also identifies unresolved challenges crucial for achieving fully autonomous US systems. This work aims to offer a thorough review of current research efforts, highlighting the potential of RL in building autonomous US solutions while identifying limitations and opportunities for further advancements in this field.
Large Language Models (LLMs) have revo lutionized natural language processing Natural Language Processing (NLP), with Chat Generative Pre-trained Transformer (ChatGPT) standing out as a notable exampledue to its advanced capabilities and widespread applications. This survey provides a comprehensive analysis of ChatGPT, exploring its architecture, training processes, and functionalities. We examine its integration into various domains across industries such as customer service, education, healthcare, and entertainment. A comparative analysis with other LLMs highlights ChatGPT's unique features and performance metrics. Regarding benchmarks, the paper examines ChatGPT's comparative performance against other LLMs and discusses potential risks such as misinformation, bias, and data privacy concerns. Additionally, we offer a number of figures and tables that outline the backdrop of the discussion, the main ideas of the article, the numerous LLM models, a thorough list of datasets used for pre-training, fine-tuning, and evaluation, as well as particular LLM applications with pertinent references. Finally, we identify future research directions and technological advancements, underscoring the evolving landscape of LLMs and their profound impact on artificial intelligence Artificial Intelligence (AI) and society.
Aligning Multimodal LLM with Human Preference: A Survey
2025-03-18
Tao Yu, Yi-Fan Zhang, Chaoyou Fu, Junkang Wu, Jinda Lu, Kun Wang, Xingyu Lu, Yunhang Shen, Guibin Zhang, Dingjie Song, Yibo Yan, Tianlong Xu, Qingsong Wen, Zhang Zhang, Yan Huang, Liang Wang, Tieniu Tan
Large language models (LLMs) can handle a wide variety of general tasks with simple prompts, without the need for task-specific training. Multimodal Large Language Models (MLLMs), built upon LLMs, have demonstrated impressive potential in tackling complex tasks involving visual, auditory, and textual data. However, critical issues related to truthfulness, safety, o1-like reasoning, and alignment with human preference remain insufficiently addressed. This gap has spurred the emergence of various alignment algorithms, each targeting different application scenarios and optimization goals. Recent studies have shown that alignment algorithms are a powerful approach to resolving the aforementioned challenges. In this paper, we aim to provide a comprehensive and systematic review of alignment algorithms for MLLMs. Specifically, we explore four key aspects: (1) the application scenarios covered by alignment algorithms, including general image understanding, multi-image, video, and audio, and extended multimodal applications; (2) the core factors in constructing alignment datasets, including data sources, model responses, and preference annotations; (3) the benchmarks used to evaluate alignment algorithms; and (4) a discussion of potential future directions for the development of alignment algorithms. This work seeks to help researchers organize current advancements in the field and inspire better alignment methods. The project page of this paper is available at https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Alignment.
2025-03-18
Qiaowei Miao, Kehan Li, Jinsheng Quan, Zhiyuan Min, Shaojie Ma, Yichao Xu, Yi Yang, Yawei Luo
Generative artificial intelligence (AI) has made significant progress across various domains in recent years. Building on the rapid advancements in 2D, video, and 3D content generation fields, 4D generation has emerged as a novel and rapidly evolving research area, attracting growing attention. 4D generation focuses on creating dynamic 3D assets with spatiotemporal consistency based on user input, offering greater creative freedom and richer immersive experiences. This paper presents a comprehensive survey of the 4D generation field, systematically summarizing its core technologies, developmental trajectory, key challenges, and practical applications, while also exploring potential future research directions. The survey begins by introducing various fundamental 4D representation models, followed by a review of 4D generation frameworks built upon these representations and the key technologies that incorporate motion and geometry priors into 4D assets. We summarize five major challenges of 4D generation: consistency, controllability, diversity, efficiency, and fidelity, accompanied by an outline of existing solutions to address these issues. We systematically analyze applications of 4D generation, spanning dynamic object generation, scene generation, digital human synthesis, 4D editing, and autonomous driving. Finally, we provide an in-depth discussion of the obstacles currently hindering the development of the 4D generation. This survey offers a clear and comprehensive overview of 4D generation, aiming to stimulate further exploration and innovation in this rapidly evolving field. Our code is publicly available at: https://github.com/MiaoQiaowei/Awesome-4D.
Global survey of star clusters in the Milky Way VIII. Cluster formation and evolution
2025-03-18
Jonathan H. Klos, Andreas Just, Evgeny V. Polyachenko, Peter Berczik, Marina Ishchenko
We consider tidal masses and ages of Milky Way open clusters, as well as a simple model of their distribution. Our aim is to investigate the space of model parameters and the correspondence between modelled and observed two-dimensional cluster age-mass distributions. The model for cluster evolution is comprised of a two-section cluster initial mass function, constant cluster formation rate, and a mass loss function. This mass loss function represents a supervirial phase after gas expulsion, mass loss due to stellar evolution, and gradual dissolution driven by internal dynamics and the Galactic tidal field. We construct different estimators of model fitness based on $\chi^2$-statistics, the Kullback-Leibler divergence (KLD) and a maximum-likelihood approach. Using these estimators and Markov Chain Monte Carlo sampling, we obtain best-fit values and posterior distributions for a selection of model parameters. The KLD returns a superior model compared to the other statistics. The cluster initial mass function is well constrained and we find a clear signature of an enhanced cluster mass loss in the first 50 Myr. In the KLD best model, clusters lose 72% of their initial mass in the violent relaxation phase, after which cluster mass loss slows down, allowing for a relatively low rate of cluster formation of $0.088\mathrm{M_\odot kpc^{-2} Gyr^{-1}}$. The observed upper limit of cluster ages at approx. 5 Gyr is reflected in the model by a shallow lifetime-mass relation for clusters with initial masses above $1000\mathrm{M_\odot}$. The application of the model to an independent cluster sample based on Gaia DR3 data yields similar results except for a systematic shift in age. The observed cluster age-mass distribution is compatible with a constant cluster formation rate. The enhanced number of young massive clusters observed requires an early violent relaxation phase of strong mass loss.
Due to non-zero neutrino rest masses we expect the energy density today in non-relativistic matter, $\omega_{\rm m}$, to be greater than the sum of baryon and cold dark matter densities, $\omega_{\rm cb}$. We also expect the amplitude of deflections of CMB photons due to gravitational lensing to be suppressed relative to expectations assuming massless neutrinos. The combination of CMB and BAO data, however, appear to be defying both of these expectations. Here we review how the neutrino rest mass is determined from cosmological observations, and emphasize the complementary roles played by BAO and lensing data in this process. We explain why, for current constraints on the sum of neutrino masses, the addition of BAO data to primary CMB data is much more informative than the addition of CMB lensing reconstruction data. We then use a phenomenological model to find that the preference from CMB and BAO data for a matter density that is below expectations from the CMB alone is at the $3\, \sigma$ level. We also show that if a fraction of the dark matter decays to dark radiation, the preference for $\omega_{\rm m} > \omega_{\rm cb}$ can be restored, but with a small increase to the CMB lensing excess.
Characterizing Data Visualization Literacy: a Systematic Literature Review
2025-03-18
Sara Beschi, Davide Falessi, Silvia Golia, Angela Locoro
With the advent of the data era, and of new, more intelligent interfaces for supporting decision making, there is a growing need to define, model and assess human ability and data visualizations usability for a better encoding and decoding of data patterns. Data Visualization Literacy (DVL) is the ability of encoding and decoding data into and from a visual language. Although this ability and its measurement are crucial for advancing human knowledge and decision capacity, they have seldom been investigated, let alone systematically. To address this gap, this paper presents a systematic literature review comprising 43 reports on DVL, analyzed using the PRISMA methodology. Our results include the identification of the purposes of DVL, its satellite aspects, the models proposed, and the assessments designed to evaluate the degree of DVL of people. Eventually, we devise many research directions including, among the most challenging, the definition of a (standard) unifying construct of DVL.
Low-Metallicity Star Formation Survey in Sh2-284 (LZ-STAR). I. Ordered massive star formation in the outer Galaxy
2025-03-18
Yu Cheng, Jonathan C. Tan, Morten Andersen, Rubén Fedriani, Yichen Zhang, Massimo Robberto, Zhi-Yun Li, Kei E. I. Tanaka
Star formation is a fundamental, yet poorly understood, process of the Universe. It is important to study how star formation occurs in different galactic environments. Thus, here, in the first of a series of papers, we introduce the Low-Metallicity Star Formation (LZ-STAR) survey of the Sh2-284 (hereafter S284) region, which, at $Z\sim 0.3-0.5Z_\odot$, is one of the lowest-metallicity star-forming regions of our Galaxy. LZ-STAR is a multi-facility survey, including observations with {\it JWST}, {\it ALMA}, {\it HST}, {\it Chandra} and {\it Gemini}. As a starting point, we report {\it JWST} and {\it ALMA} observations of one of the most massive protostars in the region, S284p1. The observations of shock-excited molecular hydrogen reveal a symmetric, bipolar outflow originating from the protostar, spanning several parsecs, and fully covered by the {\it JWST} field of view and the {\it ALMA} observations of CO(2-1) emission. This allows us to infer that the protostar has maintained a relatively stable orientation of disk accretion over its formation history. The {\it JWST} near-IR continuum observations detect a centrally illuminated bipolar outflow cavity around the protostar, as well as a surrounding cluster of low-mass young stars. We develop new radiative transfer models of massive protostars designed for the low metallicity of S284. Fitting these models to the protostar's spectral energy distribution implies a current protostellar mass of $\sim11\:M_\odot$ has formed from an initially $\sim100\:M_\odot$ core over the last $\sim3\times10^5$ years. Overall, these results indicate that massive stars can form in an ordered manner in low-metallicity, protocluster environments.
The Distance to the Magellanic Stream: Constraints from Optical Absorption along Stellar Sightlines
2025-03-18
Sapna Mishra, Andrew J. Fox, J. V. Smoker, Scott Lucchini, Elena D'Onghia
The Magellanic Stream (MS) is a large tail of neutral and ionized gas originating from tidal and hydrodynamical interactions between the Magellanic Clouds as they orbit the Milky Way (MW). It carries a significant gas reservoir that could impact the future evolution of the MW. Despite its importance, no direct observational constraints on the Stream's distance have been previously published. In this study, we analyze Very Large Telescope/Ultraviolet and Visual Echelle Spectrograph (VLT/UVES) spectra of five blue horizontal branch (BHB) stars in the MW halo located at distances ranging from 13 to 56 kpc near two regions of the Stream, with the aim of detecting Ca II and Na I absorption. No Ca II or Na I absorption is detected at Stream velocities in any of the individual spectra, or in higher signal-to-noise stacks of the spectra. The resulting limits on the Ca II absorption are significantly lower than the Ca II columns measured in the Stream along extragalactic directions. These non-detections establish a firm lower distance limit of 20 kpc for the two regions of the Stream studied. Ca II non-detections in the most distant stars yield tentative lower distance limits of 42 kpc and 55 kpc for the two regions, but deeper spectra are needed to confirm this. Our results provide the first observational constraints on the gaseous Stream's distance.
Constraints on the early Universe star formation efficiency from galaxy clustering and halo modeling of H$α$ and [O III] emitters
2025-03-18
Marko Shuntov, Pascal A. Oesch, Sune Toft, Romain A. Meyer, Alba Covelo-Paz, Louise Paquereau, Rychard Bouwens, Gabriel Brammer, Viola Gelli, Emma Giovinazzo, Thomas Herard-Demanche, Garth D. Illingworth, Charlotte Mason, Rohan P. Naidu, Andrea Weibel, Mengyuan Xiao
We develop a theoretical framework to provide observational constraints on the early Universe galaxy-halo connection by combining measurements of the UV luminosity function (UVLF) and galaxy clustering via the 2-point correlation function (2PCF). We implemented this framework in the FRESCO and CONGRESS JWST NIRCam/grism surveys by measuring the 2PCF of spectroscopically selected samples of H$\alpha$ and [OIII] emitters at $3.89$ shows that a constant SFE-$M_{\rm h}$ fixed at $z=8$ cannot reproduce the observed UVLF and neither high maximum SFE nor high stochasticity alone can explain the high abundances of luminous galaxies seen by JWST. Extending the analysis of the UVLF and 2PCF to $z>9$ measured from wider surveys will be crucial in breaking degeneracies.
Dockless bike-sharing (DBS) users often encounter difficulties in finding available bikes at their preferred times and locations. This study examines the determinants of the users' mode shifts in the context of bike absence, using survey data from Nanjing, China. An integrated choice and latent variable based on multinomial logit was employed to investigate the impact of socio-demographic, trip characteristics, and psychological factors on travel mode choices. Mode choice models were estimated with seven mode alternatives, including bike-sharing related choices (waiting in place, picking up bikes on the way, and picking up bikes on a detour), bus, taxi, riding hailing, and walk. The findings show that under shared-bike unavailability, users prefer to pick up bikes on the way rather than take detours, with buses and walking as favored alternatives to shared bikes. Lower-educated users tend to wait in place, showing greater concern for waiting time compared to riding time. Lower-income users, commuters, and females prefer picking up bikes on the way, while non-commuters and males opt for detours. The insights gained in this study can provide ideas for solving the problems of demand estimation, parking area siting, and multi-modal synergies of bike sharing to enhance utilization and user satisfaction.
With the increasing demand for efficient and flexible robotic exploration solutions, Reinforcement Learning (RL) is becoming a promising approach in the field of autonomous robotic exploration. However, current RL-based exploration algorithms often face limited environmental reasoning capabilities, slow convergence rates, and substantial challenges in Sim-To-Real (S2R) transfer. To address these issues, we propose a Curriculum Learning-based Transformer Reinforcement Learning Algorithm (CTSAC) aimed at improving both exploration efficiency and transfer performance. To enhance the robot's reasoning ability, a Transformer is integrated into the perception network of the Soft Actor-Critic (SAC) framework, leveraging historical information to improve the farsightedness of the strategy. A periodic review-based curriculum learning is proposed, which enhances training efficiency while mitigating catastrophic forgetting during curriculum transitions. Training is conducted on the ROS-Gazebo continuous robotic simulation platform, with LiDAR clustering optimization to further reduce the S2R gap. Experimental results demonstrate the CTSAC algorithm outperforms the state-of-the-art non-learning and learning-based algorithms in terms of success rate and success rate-weighted exploration time. Moreover, real-world experiments validate the strong S2R transfer capabilities of CTSAC.
The shift toward high-quality urbanization has brought increased attention to the issue of "urban villages", which has become a prominent social problem in China. However, there is a lack of available geospatial data on urban villages, making it crucial to prioritize urban village mapping. In order to assess the current progress in urban village mapping and identify challenges and future directions, we have conducted a comprehensive review, which to the best of our knowledge is the first of its kind in this field. Our review begins by providing a clear context for urban villages and elaborating the method for literature review, then summarizes the study areas, data sources, and approaches used for urban village mapping in China. We also address the challenges and future directions for further research. Through thorough investigation, we find that current studies only cover very limited study areas and periods and lack sufficient investigation into the scalability, transferability, and interpretability of identification approaches due to the challenges in concept fuzziness and variances, spatial heterogeneity and variances of urban villages, and data availability. Future research can complement and further the current research in the following potential directions in order to achieve large-area mapping across the whole nation...
Populating Large N-body Simulations with LRGs Using Neural Networks
2025-03-18
M. Icaza-Lizaola, E. L. Sirks, Yong-Seon Song, Peder Norberg, Feng Shi
The analysis of state-of-the-art cosmological surveys like the Dark Energy Spectroscopic Instrument (DESI) survey requires high-resolution, large-volume simulations. However, the computational cost of hydrodynamical simulations at these scales is prohibitive. Instead, dark matter (DM)-only simulations are used, with galaxies populated a posteriori, typically via halo occupation distribution (HOD) models. While effective, HOD models are statistical in nature and lack full physical motivation. In this work, we explore using neural networks (NNs) to learn the complex, physically motivated relationships between DM haloes and galaxy properties. Trained on small-volume, high-resolution hydrodynamical simulations, our NN predicts galaxy properties in a larger DM-only simulation and determines which galaxies should be classified as luminous red galaxies (LRGs). Comparing the original LRG sample to the one generated by our NN, we find that, while the subhalo mass distributions are similar, our NN selects fewer low-mass subhaloes as LRG hosts, possibly due to the absence of baryonic feedback effects in DM-only simulations. This feedback could brighten or redden galaxies, altering their classification. Finally, we generate a new LRG sample by fitting an HOD model to the NN-generated LRG sample. We verify that both the HOD- and NN-generated samples preserve a set of bias parameter relations, which assume that the higher-order parameters, $b_{s2}$ and $b_{3\rm{nl}}$, are determined by the linear bias parameter $b_{1}$. These relations are commonly used to simplify clustering analyses.
Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF
2025-03-18
Sascha Caron, Andreas Ipp, Gert Aarts, Gábor Bíró, Daniele Bonacorsi, Elena Cuoco, Caterina Doglioni, Tommaso Dorigo, Julián García Pardiñas, Stefano Giagu, Tobias Golling, Lukas Heinrich, Ik Siong Heng, Paula Gina Isar, Karolos Potamianos, Liliana Teodorescu, John Veitch, Pietro Vischia, Christoph Weniger
Artificial intelligence (AI) is transforming scientific research, with deep learning methods playing a central role in data analysis, simulations, and signal detection across particle, nuclear, and astroparticle physics. Within the JENA communities-ECFA, NuPECC, and APPEC-and as part of the EuCAIF initiative, AI integration is advancing steadily. However, broader adoption remains constrained by challenges such as limited computational resources, a lack of expertise, and difficulties in transitioning from research and development (R&D) to production. This white paper provides a strategic roadmap, informed by a community survey, to address these barriers. It outlines critical infrastructure requirements, prioritizes training initiatives, and proposes funding strategies to scale AI capabilities across fundamental physics over the next five years.
Discovery of a Giant Molecular Cloud at the Midpoint of the Galactic Bar Dust Lanes: M4.7-0.8
2025-03-18
Natalie Butterfield, Larry Morgan, Ashley Barnes, Adam Ginsburg, Savannah Gramze, Mark Morris, Mattia Sormani, Cara Battersby, Charlie Burton, Allison Costa, Elisabeth Mills, Juergen Ott, Michael Rugel
We present the detection of a previously unknown giant molecular cloud (GMC) located at the midpoint of the Galactic Bar Dust Lanes (M4.7--0.8), using spectral line observations taken with the Green Bank Telescope (GBT). This $\sim$60 pc long GMC is associated with accreting material that is transitioning from the quieter Galactic disk environment to the more extreme central molecular zone (CMZ) environment. Our 24 GHz single-dish radio observations targeted the NH$_3$ (1,1)$-$(4,4) and HC$_5$N (9$-$8), known dense gas tracers. The observations reveal the main features of the GMC, which we have dubbed the `Nexus' and `Filament', covering a 0$.\!\!^\circ$5$\times$0$.\!\!^\circ$25 area at 31$''$ angular resolution. In this publication we investigate the gas kinematics within the observed region and compare the distribution of molecular emission to previous infrared surveys to better understand the dust component. The observed gas tracers show centrally condensed cores corresponding to the positions of high dust column densities and low dust temperatures. We report the detection of a previously unknown NH$_3$ (3,3) maser, along with a 70$\mu$m source association, which supports the identification of this region as being actively star-forming. Gas emission in this region shows broad linewidths, comparable to values seen in CMZ clouds. The overall description of this cloud that we present is that of a highly dynamic region comprising dense gas and dust. This encapsulates a wide range of features associated with star formation, in addition to material transport related to the CMZ.
What elements should we focus when designing immersive virtual nature? A preliminary user study
2025-03-18
Lin Ma, Qiyuan An, Jing Chen, Xinggang Hou, Yuan Feng, Dengkai Chen
Extensive research has confirmed the positive relationship between exposure to natural environments and human cognitive, behavioral, physical, and mental health. However, only some have easy access to nature. With electronic information and simulation technology advancements, digital nature experiences are widely used across various devices and scenarios. It is essential to explore how to effectively select and utilize natural elements to guide the design of digital nature scenes. This paper examines critical elements in immersive virtual nature (IVN) and their impact on user perception. Through online surveys and design experiments, we identified specific natural elements that promote relaxation and proposed design strategies for virtual environments. We developed several immersive virtual nature scenes for further validation. Finally, we outline our future experimental plans and research directions in digital nature. Our research aims to provide HCI designers insights into creating restorative, immersive virtual scenes.
The CatSouth Quasar Candidate Catalog for the Southern Sky and a Unified All-Sky Catalog Based on Gaia DR3
2025-03-18
Yuming Fu, Xue-Bing Wu, R. J. Bouwens, Karina I. Caputi, Yuxuan Pang, Rui Zhu, Da-Ming Yang, Jin Qin, Huimei Wang, Christian Wolf, Yifan Li, Ravi Joshi, Yanxia Zhang, Zhi-Ying Huo, Y. L. Ai
The Gaia DR3 has provided a large sample of more than 6.6 million quasar candidates with high completeness but low purity. Previous work on the CatNorth quasar candidate catalog has shown that including external multiband data and applying machine-learning methods can efficiently purify the original Gaia DR3 quasar candidate catalog and improve the redshift estimates. In this paper, we extend the Gaia DR3 quasar candidate selection to the southern hemisphere using data from SkyMappper, CatWISE, and VISTA surveys. We train an XGBoost classifier on a unified set of high-confidence stars and spectroscopically confirmed quasars and galaxies. For sources with available Gaia BP/RP spectra, spectroscopic redshifts are derived using a pre-trained convolutional neural network (RegNet). We also train an ensemble photometric redshift estimation model based on XGBoost, TabNet, and FT-Transformer, achieving an RMSE of 0.2256 and a normalized median absolute deviation of 0.0187 on the validation set. By merging CatSouth with the previously published CatNorth catalog, we construct the unified all-sky CatGlobe catalog with nearly 1.9 million sources at $G<21$, providing a comprehensive and high-purity quasar candidate sample for future spectroscopic and cosmological investigations.
Positronium Imaging: first clinical images and perspectives
2025-03-18
Paweł Moskal, Aleksander Bilewicz, Manish Das, Bangyan Huang, Aleksander Khreptak, Szymon Parzych, Jinyi Qi, Axel Rominger, Robert Seifert, Sushil Sharma, Kuangyu Shi, William Steinberger, Rafał Walczak, Ewa Stępień
Positronium imaging was recently proposed to image the properties of positronium atoms in the patient body. Positronium properties depend on the size of intramolecular voids and oxygen concentration; therefore, they deliver information different and complementary to the anatomic, morphological, and metabolic images. Thus far, the mean ortho-positronium lifetime imaging has been at the center of research interest. The first ex vivo and in vivo positronium lifetime images of humans have been demonstrated with the dedicated J-PET scanner enabling simultaneous registration of annihilation photons and prompt gamma from ${\beta^{+} \gamma}$ emitters. Annihilation photons are used to reconstruct the annihilation place and time while prompt gamma is used to reconstruct the time of positronium formation. This review describes recent achievements in the translation of positronium imaging into clinics. The first measurements of positronium lifetime in humans with commercial PET scanners modernized to register triple coincidences are reported. The in vivo observations of differences in ortho-positronium lifetime between tumor and healthy tissues and between different oxygen concentrations are discussed. So far, the positronium lifetime measurements in humans were completed with clinically available ${^{68}\text{Ga}}$, ${^{82}\text{Rb}}$, and ${^{124}\text{I}}$ radionuclides. Status and challenges in developing positronium imaging on a way to a clinically useful procedure are presented and discussed.
Recommender systems (RS) have become crucial tools for information filtering in various real world scenarios. And cross domain recommendation (CDR) has been widely explored in recent years in order to provide better recommendation results in the target domain with the help of other domains. The CDR technology has developed rapidly, yet there is a lack of a comprehensive survey summarizing recent works. Therefore, in this paper, we will summarize the progress and prospects based on the main procedure of CDR, including Cross Domain Relevance, Cross Domain Interaction, Cross Domain Representation Enhancement and Model Optimization. To help researchers better understand and engage in this field, we also organize the applications and resources, and highlight several current important challenges and future directions of CDR. More details of the survey articles are available at https://github.com/USTCAGI/Awesome-Cross-Domain Recommendation-Papers-and-Resources.
Operational Change Detection for Geographical Information: Overview and Challenges
2025-03-18
Nicolas Gonthier
Rapid evolution of territories due to climate change and human impact requires prompt and effective updates to geospatial databases maintained by the National Mapping Agency. This paper presents a comprehensive overview of change detection methods tailored for the operational updating of large-scale geographic databases. This review first outlines the fundamental definition of change, emphasizing its multifaceted nature, from temporal to semantic characterization. It categorizes automatic change detection methods into four main families: rule-based, statistical, machine learning, and simulation methods. The strengths, limitations, and applicability of every family are discussed in the context of various input data. Then, key applications for National Mapping Agencies are identified, particularly the optimization of geospatial database updating, change-based phenomena, and dynamics monitoring. Finally, the paper highlights the current challenges for leveraging change detection such as the variability of change definition, the missing of relevant large-scale datasets, the diversity of input data, the unstudied no-change detection, the human in the loop integration and the operational constraints. The discussion underscores the necessity for ongoing innovation in change detection techniques to address the future needs of geographic information systems for national mapping agencies.
Variations in the Radiation Intensity of Pulsar B0950+08: Nine Years of Monitoring at 110 MHz
2025-03-18
T. V. Smirnova, M. O. Toropov, S. A. Tyul'bashev
The analysis of variations in the emission intensity of the pulsar B0950+08 from 2014 to 2022 with scales from minutes to years was carried out. The observations were obtained in a round-the-clock daily survey conducted on the Large Phased Array (LPA) radio telescope. The high variability of emission is shown not only from pulse to pulse, but also at scales greater than 3 min. The average value of the estimated amplitude of these variations in 3.2 minutes is 25~Jy, the modulation index is 1. The average relative amplitude of the interpulse (IP) is $2.00 \pm 0.28\%$ of the main pulse. In individual pulses, the amplitude of the interpulse may exceed the amplitude of the main pulse (MP), but this is a rare event. Emission is observed in almost the entire period of the pulsar. For the first time, the relative amplitude of emission between the main pulse and the interpulse (emission bridge) was measured. When averaging about 10 hours, it varies from $0.8\%$ to $1.31\%$ with an average value of $1.04 \pm 0.28\%$. A high correlation was found between MP and IP amplitude variations both when averaging profiles over 3.2 minutes and when averaging over years. This correlation is due to refractive interstellar scintillation. The frequency scale of IP diffraction interstellar scintillation was measured for the first time and it was shown that the spectral forms for IP and MP are well correlated and have the same frequency scale. There are strong variations in the frequency scale of scintillation $f_{dif}$ from session to session (time interval from one day) on scales of 200-800 kHz. The refractive scale of scintillation for 1-2 days has been determined. A modulation of emission with a characteristic scale of about 130 days was detected, which, apparently, is also associated with refractive scintillation.
Synthetic Data Generation Using Large Language Models: Advances in Text and Code
2025-03-18
Mihai Nadas, Laura Diosan, Andreea Tomescu
Large language models (LLMs) have unlocked new possibilities for generating synthetic training data in both natural language and code. By producing artificial but task-relevant examples, these models can significantly augment or even replace real-world datasets, especially when labeled data is scarce or sensitive. This paper surveys recent advances in using LLMs to create synthetic text and code, emphasizing prompt-based generation, retrieval-augmented pipelines, and iterative self-refinement. We show how these methods enrich low-resource tasks such as classification and question answering, as well as code-centric applications such as instruction tuning, code translation, and bug repair, by enabling automated verification of functional correctness. Alongside potential benefits like cost-effectiveness, broad coverage, and controllable diversity, we address challenges such as factual inaccuracies in generated text, lack of stylistic realism, and the risk of bias amplification. Proposed mitigations include filtering and weighting outputs and reinforcement learning with execution feedback for code. We conclude with open research directions like automated prompt engineering, cross-modal data synthesis, and robust evaluation frameworks, highlighting the importance of LLM-generated synthetic data in advancing AI while emphasizing ethical and quality safeguards.
Securing Automated Insulin Delivery Systems: A Review of Security Threats and Protectives Strategies
2025-03-18
Yuchen Niu, Siew-Kei Lam
Automated insulin delivery (AID) systems have emerged as a significant technological advancement in diabetes care. These systems integrate a continuous glucose monitor, an insulin pump, and control algorithms to automate insulin delivery, reducing the burden of self-management and offering enhanced glucose control. However, the increasing reliance on wireless connectivity and software control has exposed AID systems to critical security risks that could result in life-threatening treatment errors. This review first presents a comprehensive examination of the security landscape, covering technical vulnerabilities, legal frameworks, and commercial product considerations, and an analysis of existing research on attack vectors, defence mechanisms, as well as evaluation methods and resources for AID systems. Despite recent advancements, several open challenges remain in achieving secure AID systems, particularly in standardising security evaluation frameworks and developing comprehensive, lightweight, and adaptive defence strategies. As one of the most widely adopted and extensively studied physiologic closed-loop control systems, this review serves as a valuable reference for understanding security challenges and solutions applicable to analogous medical systems.
Augmenting Software Bills of Materials with Software Vulnerability Description: A Preliminary Study on GitHub
2025-03-18
Davide Fucci, Massimiliano Di Penta, Simone Romano, Giuseppe Scannielllo
Software Bills of Material (SBOMs) are becoming a consolidated, often enforced by governmental regulations, way to describe software composition. However, based on recent studies, SBOMs suffer from limited support for their consumption and lack information beyond simple dependencies, especially regarding software vulnerabilities. This paper reports the results of a preliminary study in which we augmented SBOMs of 40 open-source projects with information about Common Vulnerabilities and Exposures (CVE) exposed by project dependencies. Our augmented SBOMs have been evaluated by submitting pull requests and by asking project owners to answer a survey. Although, in most cases, augmented SBOMs were not directly accepted because owners required a continuous SBOM update, the received feedback shows the usefulness of the suggested SBOM augmentation.
Stratified Permutational Berry--Esseen Bounds and Their Applications to Statistics
2025-03-18
Pengfei Tian, Fan Yang, Peng Ding
The stratified linear permutation statistic arises in various statistics problems, including stratified and post-stratified survey sampling, stratified and post-stratified experiments, conditional permutation tests, etc. Although we can derive the Berry--Esseen bounds for the stratified linear permutation statistic based on existing bounds for the non-stratified statistics, those bounds are not sharp, and moreover, this strategy does not work in general settings with heterogeneous strata with varying sizes. We first use Stein's method to obtain a unified stratified permutational Berry--Esseen bound that can accommodate heterogeneous strata. We then apply the bound to various statistics problems, leading to stronger theoretical quantifications and thereby facilitating statistical inference in those problems.
Illuminating Youth: Decades of Mid-Infrared Variability and Color Evolution of Young Stellar Objects
2025-03-18
Neha S., Saurabh
The variability of Young Stellar Objects (YSOs) is a crucial tool for understanding the mechanisms driving flux changes. In this study, we present an infrared variability analysis of a large sample of over 20,000 candidate YSOs, using data from the ALLWISE and NEOWISE surveys, which span around a decade with a 6-month cadence. We applied Lomb-Scargle Periodogram (LSP) analysis and linear fitting to the light curves, classifying them into distinct categories: {\it Secular} ({\it Linear}, {\it Curved}, and {\it Periodic}) and {\it Stochastic} ({\it Burst}, {\it Drop}, and {\it Irregular}). Our findings show that 5,467 (26.2$\pm$0.3\%) of the sources exhibit variability, with most (19.7$\pm$0.3\%) showing {\it Irregular} variations, followed by {\it Curved} and {\it Periodic} variations. In addition, 235 sources of {\it Bursts} and 122 {\it Drop} sources were identified. Variability is more pronounced in Class I sources with a higher fraction of variables (36.3$\pm$0.6\%) compared to Class II (22.1$\pm$0.4\%) and Class III (22.5$\pm$1.0\%) sources. The color (W1 $-$ W2) versus magnitude analysis (W2) using linear fitting shows that the trend ``redder-when-brighter" (RWB) is more prevalent (85.4$\pm$0.5\%) among YSOs. In contrast, the trend ``bluer-when-brighter" (BWB) is more common in younger sources compared to more evolved ones, having a BWB fraction of 29.0$\pm$1.1\% for Class I to 4.0$\pm$0.9\% for Class III.
Survey of Adversarial Robustness in Multimodal Large Language Models
2025-03-18
Chengze Jiang, Zhuangzhuang Wang, Minjing Dong, Jie Gui
Multimodal Large Language Models (MLLMs) have demonstrated exceptional performance in artificial intelligence by facilitating integrated understanding across diverse modalities, including text, images, video, audio, and speech. However, their deployment in real-world applications raises significant concerns about adversarial vulnerabilities that could compromise their safety and reliability. Unlike unimodal models, MLLMs face unique challenges due to the interdependencies among modalities, making them susceptible to modality-specific threats and cross-modal adversarial manipulations. This paper reviews the adversarial robustness of MLLMs, covering different modalities. We begin with an overview of MLLMs and a taxonomy of adversarial attacks tailored to each modality. Next, we review key datasets and evaluation metrics used to assess the robustness of MLLMs. After that, we provide an in-depth review of attacks targeting MLLMs across different modalities. Our survey also identifies critical challenges and suggests promising future research directions.
Is the distribution of resolvable uncertainty Type I extreme value? A Test for Random Coefficient Models using Choice Probabilities
2025-03-18
Romuald Meango
Stated choice probabilities are increasingly used in conjunction with the random-coefficient model (RCM) to describe individual preferences. They allow survey respondents to express uncertainty about the future or the incompleteness of a hypothetical scenario: the resolvable uncertainty. Parametric assumptions such as a Type I extreme value (EV1) distribution are almost always imposed on this uncertainty to identify and estimate the associated RCM. This paper proposes the first test for these parametric assumptions, based on a nonparametric identification result for the population distribution of the interquantile range of the resolvable uncertainty. In all four empirical applications considered, the test finds strong evidence against the EV1 assumption.
Constraints on LIGO/Virgo Compact Object Mergers from Late-time Radio Observations
2025-03-18
Ashna Gulati, Tara Murphy, Dougal Dobie, Adam Deller, David L. Kaplan, Emil Lenc, Ilya Mandel, Stefan Duchesne, Vanessa Moss
We present results from a search for radio afterglows of compact object mergers conducted with the Australian SKA Pathfinder. We used data from four epochs of the Rapid ASKAP Continuum Survey to search compact binary merger localization regions observed during the LIGO/Virgo O2, and O3 observing runs. Our investigation focused on eleven events (published in the GWTC-1, GWTC-2, and GWTC-3 catalogues of gravitational-wave events) with 90\% posterior localisations smaller than $150\,\deg^2$ and $\ge$99\% probabilities of being of astrophysical origin, to identify potential radio afterglow-like transients up to $\lesssim$1500 days post-merger. We identified candidate afterglow-type variable sources in the 90\% localisation for events -- GW190503, GW200202 and GW200208, which were ruled out as unlikely to be related to the corresponding GW event on further analysis. Since we find no likely candidate counterparts, we constrain the inclination angle and the circum-merger density at isotropic equivalent energies ranging from $2\times10^{51} -1\times10^{54}\rm \:erg$. These constraints are based on the assumption that the electron energy distribution in the associated jets follows a power-law index of $ p = 2.2$, with 1% of the shock energy in the magnetic field ($ \epsilon_B = 0.01$) and 10% in the electrons ($\epsilon_e = 0.1$). We discuss the detectability of late-time afterglows as a function of merger distance and inclination angles with millijansky surveys.
The rapid growth of scholarly submissions has overwhelmed traditional peer review systems, driving the need for intelligent automation to preserve scientific rigor. While large language models (LLMs) show promise in automating manuscript critiques, their ability to synthesize high-stakes meta-reviews, which require conflict-aware reasoning and consensus derivation, remains underdeveloped. Existing methods fail to effectively handle conflicting viewpoints within differing opinions, and often introduce additional cognitive biases, such as anchoring effects and conformity bias.To overcome these limitations, we propose the Cognitive Alignment Framework (CAF), a dual-process architecture that transforms LLMs into adaptive scientific arbitrators. By operationalizing Kahneman's dual-process theory, CAF introduces a three-step cognitive pipeline: review initialization, incremental integration, and cognitive alignment.Empirical validation shows that CAF outperforms existing LLM-based methods, with sentiment consistency gains reaching up to 19.47\% and content consistency improving by as much as 12.95\%.
Time series frequently manifest distribution shifts, diverse latent features, and non-stationary learning dynamics, particularly in open and evolving environments. These characteristics pose significant challenges for out-of-distribution (OOD) generalization. While substantial progress has been made, a systematic synthesis of advancements remains lacking. To address this gap, we present the first comprehensive review of OOD generalization methodologies for time series, organized to delineate the field's evolutionary trajectory and contemporary research landscape. We organize our analysis across three foundational dimensions: data distribution, representation learning, and OOD evaluation. For each dimension, we present several popular algorithms in detail. Furthermore, we highlight key application scenarios, emphasizing their real-world impact. Finally, we identify persistent challenges and propose future research directions. A detailed summary of the methods reviewed for the generalization of OOD in time series can be accessed at https://tsood-generalization.com.
Enabling Inclusive Systematic Reviews: Incorporating Preprint Articles with Large Language Model-Driven Evaluations
2025-03-18
Rui Yang, Jiayi Tong, Haoyuan Wang, Hui Huang, Ziyang Hu, Peiyu Li, Nan Liu, Christopher J. Lindsell, Michael J. Pencina, Yong Chen, Chuan Hong
Background. Systematic reviews in comparative effectiveness research require timely evidence synthesis. Preprints accelerate knowledge dissemination but vary in quality, posing challenges for systematic reviews. Methods. We propose AutoConfidence (automated confidence assessment), an advanced framework for predicting preprint publication, which reduces reliance on manual curation and expands the range of predictors, including three key advancements: (1) automated data extraction using natural language processing techniques, (2) semantic embeddings of titles and abstracts, and (3) large language model (LLM)-driven evaluation scores. Additionally, we employed two prediction models: a random forest classifier for binary outcome and a survival cure model that predicts both binary outcome and publication risk over time. Results. The random forest classifier achieved AUROC 0.692 with LLM-driven scores, improving to 0.733 with semantic embeddings and 0.747 with article usage metrics. The survival cure model reached AUROC 0.716 with LLM-driven scores, improving to 0.731 with semantic embeddings. For publication risk prediction, it achieved a concordance index of 0.658, increasing to 0.667 with semantic embeddings. Conclusion. Our study advances the framework for preprint publication prediction through automated data extraction and multiple feature integration. By combining semantic embeddings with LLM-driven evaluations, AutoConfidence enhances predictive performance while reducing manual annotation burden. The framework has the potential to facilitate systematic incorporation of preprint articles in evidence-based medicine, supporting researchers in more effective evaluation and utilization of preprint resources.
Advances and challenges of SCAN and r2SCAN density functionals in transition-metal compounds
2025-03-18
Yubo Zhang, Akilan Ramasamy, Kanun Pokharel, James W. Furness, Jinliang Ning, Ruiqi Zhang, Jianwei Sun
Transition-metal compounds (TMCs) with open-shell d-electrons are characterized by a complex interplay of lattice, charge, orbital, and spin degrees of freedom, giving rise to a diverse range of fascinating applications. Often exhibiting exotic properties, these compounds are commonly classified as correlated systems due to strong inter-electronic interactions called Hubbard U. This inherent complexity presents significant challenges to Kohn-Sham density functional theory (KS-DFT), the most widely used electronic structure method in condensed matter physics and materials science. While KS-DFT is, in principle, exact for the ground-state total energy, its exchange-correlation energy must be approximated in practice. The mean-field nature of KS implementations, combined with the limitations of current exchange-correlation density functional approximations, has led to the perception that DFT is inadequate for correlated systems, particularly TMCs. Consequently, a common workaround involves augmenting DFT with an on-site Hubbard-like U correction. In recent years, the strongly constrained and appropriately normed (SCAN) density functional, along with its refined variant r2SCAN, has achieved remarkable progress in accurately describing the structural, energetic, electronic, magnetic, and vibrational properties of TMCs, challenging the traditional perception of DFT's limitations. This review explores the design principles of SCAN and r2SCAN, highlights their key advancements in studying TMCs, explains the mechanisms driving these improvements, and addresses the remaining challenges in this evolving field.
Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives
2025-03-18
Dipin Khati, Yijin Liu, David N. Palacio, Yixuan Zhang, Denys Poshyvanyk
Applications of Large Language Models (LLMs) are rapidly growing in industry and academia for various software engineering (SE) tasks. As these models become more integral to critical processes, ensuring their reliability and trustworthiness becomes essential. Consequently, the concept of trust in these systems is becoming increasingly critical. Well-calibrated trust is important, as excessive trust can lead to security vulnerabilities, and risks, while insufficient trust can hinder innovation. However, the landscape of trust-related concepts in LLMs in SE is relatively unclear, with concepts such as trust, distrust, and trustworthiness lacking clear conceptualizations in the SE community. To bring clarity to the current research status and identify opportunities for future work, we conducted a comprehensive review of $88$ papers: a systematic literature review of $18$ papers focused on LLMs in SE, complemented by an analysis of 70 papers from broader trust literature. Additionally, we conducted a survey study with 25 domain experts to gain insights into practitioners' understanding of trust and identify gaps between existing literature and developers' perceptions. The result of our analysis serves as a roadmap that covers trust-related concepts in LLMs in SE and highlights areas for future exploration.
UNIONS: The Ultraviolet Near-Infrared Optical Northern Survey
2025-03-18
Stephen Gwyn, Alan W. McConnachie, Jean-Charles Cuillandre, Ken C. Chambers, Eugene A. Magnier, Michael J. Hudson, Masamune Oguri, Hisanori Furusawa, Hendrik Hildebrandt, Raymond Carlberg, Sara L. Ellison, Junko Furusawa, Raphaël Gavazzi, Rodrigo Ibata, Yannick Mellier, Ken Osato, H. Aussel, Lucie Baumont, Manuel Bayer, Olivier Boulade, Patrick Côté, David Chemaly, Cail Daley, Pierre-Alain Duc, A. Ellien, Sébastien Fabbro, Leonardo Ferreira, Itsna K. Fitriana, Emeric Le Floc'h, Hammer, Francois, Yoshinobu Fudamoto, Hua Gao, L. W. K. Goh, Tomotsugu Goto, Sacha Guerrini, Axel Guinot, Vincent Hénault-Brunet, Yuichi Harikane, Kohei Hayashi, Nick Heesters, Kohei Ichikawa, Martin Kilbinger, P. B. Kuzma, Qinxun Li, Tobías I. Liaudat, Chien-Cheng Lin, Oliver Müller, Nicolas F. Martin, Yoshiki Matsuoka, Gustavo E. Medina, Hironao Miyatake, Satoshi Miyazaki, Charlie T. Mpetha, Tohru Nagao, Julio F. Navarro, Masafumi Niwano, Itsuki Ogami, Nobuhiro Okabe, Masafusa Onoue, Gregory S. H. Paek, Laura C. Parker, David R. Patton, Fabian Hervas Peters, Simon Prunet, Rubén Sánchez-Janssen, M. Schultheis, Federico Sestito, Simon E. T. Smith, J. -L. Starck, Else Starkenburg, Connor Stone, Christopher Storfer, Yoshihisa Suzuki, Erben, T., Salvatore Taibi, G. F. Thomas, Zhang TianFang, Yoshiki Toba, Hisakazu Uchiyama, David Valls-Gabaud, Kim A. Venn, Ludovic Van Waerbeke, Richard J. Wainscoat, Scott Wilkinson, Anna Wittje, Taketo Yoshida, Yuxing Zhong
The Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) is a "collaboration of collaborations" that is using the Canada-France-Hawai'i Telescope, the Pan-STARRS telescopes, and the Subaru Observatory to obtain $ugriz$ images of a core survey region of 6250 deg$^2$ of the northern sky. The $10\sigma$ point source depth of the data, as measured within a 2-arcsecond diameter aperture, are $[u,g,r,i,z] = [23.7, 24.5, 24.2, 23.8, 23.3]$\ in AB magnitudes. UNIONS is addressing some of the most fundamental questions in astronomy, including the properties of dark matter, the growth of structure in the Universe from the very smallest galaxies to large-scale structure, and the assembly of the Milky Way. It is set to become the major ground-based legacy survey for the northern hemisphere for the next decade and provides an essential northern complement to the static-sky science of the Vera C. Rubin Observatory's Legacy Survey of Space and Time. UNIONS supports the core science mission of the {\it Euclid} space mission by providing the data necessary in the northern hemisphere for the calibration of the wavelength dependence of the {\it Euclid} point-spread function and derivation of photometric redshifts in the North Galactic Cap. This region contains the highest quality sky for {\it Euclid}, with low backgrounds from the zodiacal light, stellar density, extinction, and emission from Galactic cirrus. Here, we describe the UNIONS survey components, science goals, data products, and the current status of the overall program.
Analyses of anomalous lensing events detected from the UKIRT microlensing survey
2025-03-18
Cheongho Han, Weicheng Zang, Andrzej Udalski, Chung-Uk Lee, Ian A. Bond, Yongxin Wen, Bo Ma, Michael D. Albrow, Sun-Ju Chung, Andrew Gould, Kyu-Ha Hwang, Youn Kil Jung, Yoon-Hyun Ryu, Yossi Shvartzvald, In-Gu Shin, Hongjing Yang, Jennifer C. Yee, Doeon Kim, Dong-Jin Kim, Sang-Mok Cha, Seung-Lee Kim, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge, Przemek Mróz, Michał K. Szymański, Jan Skowron, Radosław Poleski, Igor Soszyński, Paweł Pietrukowicz, Szymon Kozłowski, Krzysztof A. Rybicki, Patryk Iwanek, Krzysztof Ulaczyk, Marcin Wrona, Mariusz Gromadzki, Mateusz J. Mróz, Fumio Abe, Ken Bando, David P. Bennett, Aparna Bhattacharya, Akihiko Fukui, Ryusei Hamada, Shunya Hamada, Naoto Hamasaki, Yuki Hirao, Stela Ishitani Silva, Naoki Koshimoto, Yutaka Matsubara, Shota Miyazaki, Yasushi Muraki, Tutumi Nagai, Kansuke Nunota, Greg Olmschenk, Clément Ranc, Nicholas J. Rattenbury, Yuki Satoh, Takahiro Sumi, Daisuke Suzuki, Sean K. Terry, Paul J. Tristram, Aikaterini Vandorou, Hibiki Yama
The United Kingdom Infrared Telescope (UKIRT) microlensing survey was conducted over four years, from 2016 to 2019, with the goal of serving as a precursor to future near-infrared microlensing surveys (Shvartzvald et al. 2017). Focusing on stars in the Galactic center and utilizing near-infrared passbands, the survey identified approximately one thousand microlensing events, 27 of which displayed anomalies in their light curves (Wen et al. 2023). This paper presents an analysis of these anomalous events, aiming to uncover the underlying causes of the observed anomalies. The events were analyzed under various configurations, considering the potential binarity of both the lens and the source. For 11 events that were additionally observed by other optical microlensing surveys, including those conducted by the OGLE, KMTNet, and MOA collaborations, we incorporated their data into our analysis. Among the reported anomalous events, we revealed the nature of 24 events except for three events, in which one was likely to be a transient variable, and two were were difficult to accurately characterize their nature due to the limitations of the available data. We confirmed the binary lens nature of the anomalies in 22 events. Among these, we verified the earlier discovery that the companion in the binary lens system UKIRT11L is a planetary object. Accurately describing the anomaly in UKIRT21 required a model that accounted for the binarity of both the lens and the source. For two events UKIRT01 and UKIRT17, the anomalies could be interpreted using either a binary-source or a binary-lens model.
Ferromagnetism and structural phase transition in rhombohedral PrIr3
2025-03-18
Karolina Gornicka, Michal J. Winiarski, Robert J. Cava, Michael A. McGuire, Tomasz Klimczuk
The synthesis, structural, magnetic, thermal and transport properties are reported for polycrystalline PrIr3. At room temperature PrIr3 displays the rhombohedral space group R-3m and a PuNi3- type structure. At around 70 K a phase transition to a monoclinic C2/m structure is observed and continued cooling reveals temperature independent behavior of the unit cell volume. Further, PrIr3 undergoes a paramagnetic to ferromagnetic transition with T_C = 7.5 K. The temperature dependent magnetic susceptibility follows the Curie Weiss law with a positive Curie-Weiss temperature, and an effective moment that is close to the theoretical effective moment for a free Pr+3 ion. This introduces further complexity into the behavior of PuNi3 - type materials and highlights the importance of temperature-dependent structural studies to complement physical property measurements in intermetallic compounds.
Bayesian Modeling of Zero-Shot Classifications for Urban Flood Detection
2025-03-18
Matt Franchi, Nikhil Garg, Wendy Ju, Emma Pierson
Street scene datasets, collected from Street View or dashboard cameras, offer a promising means of detecting urban objects and incidents like street flooding. However, a major challenge in using these datasets is their lack of reliable labels: there are myriad types of incidents, many types occur rarely, and ground-truth measures of where incidents occur are lacking. Here, we propose BayFlood, a two-stage approach which circumvents this difficulty. First, we perform zero-shot classification of where incidents occur using a pretrained vision-language model (VLM). Second, we fit a spatial Bayesian model on the VLM classifications. The zero-shot approach avoids the need to annotate large training sets, and the Bayesian model provides frequent desiderata in urban settings - principled measures of uncertainty, smoothing across locations, and incorporation of external data like stormwater accumulation zones. We comprehensively validate this two-stage approach, showing that VLMs provide strong zero-shot signal for floods across multiple cities and time periods, the Bayesian model improves out-of-sample prediction relative to baseline methods, and our inferred flood risk correlates with known external predictors of risk. Having validated our approach, we show it can be used to improve urban flood detection: our analysis reveals 113,738 people who are at high risk of flooding overlooked by current methods, identifies demographic biases in existing methods, and suggests locations for new flood sensors. More broadly, our results showcase how Bayesian modeling of zero-shot LM annotations represents a promising paradigm because it avoids the need to collect large labeled datasets and leverages the power of foundation models while providing the expressiveness and uncertainty quantification of Bayesian models.
Data Release 1 of the Dark Energy Spectroscopic Instrument
2025-03-18
DESI Collaboration, M. Abdul Karim, A. G. Adame, D. Aguado, J. Aguilar, S. Ahlen, S. Alam, G. Aldering, D. M. Alexander, R. Alfarsy, L. Allen, C. Allende Prieto, O. Alves, A. Anand, U. Andrade, E. Armengaud, S. Avila, A. Aviles, H. Awan, S. Bailey, A. Baleato Lizancos, O. Ballester, A. Bault, J. Bautista, S. BenZvi, L. Beraldo e Silva, J. R. Bermejo-Climent, F. Beutler, D. Bianchi, C. Blake, R. Blum, A. S. Bolton, M. Bonici, S. Brieden, A. Brodzeller, D. Brooks, E. Buckley-Geer, E. Burtin, R. Canning, A. Carnero Rosell, A. Carr, P. Carrilho, L. Casas, F. J. Castander, R. Cereskaite, J. L. Cervantes-Cota, E. Chaussidon, J. Chaves-Montero, S. Chen, X. Chen, T. Claybaugh, S. Cole, A. P. Cooper, M. -C. Cousinou, A. Cuceu, T. M. Davis, K. S. Dawson, R. de Belsunce, R. de la Cruz, A. de la Macorra, A. de Mattia, N. Deiosso, J. Della Costa, R. Demina, U. Demirbozan, J. DeRose, A. Dey, B. Dey, J. Ding, Z. Ding, P. Doel, K. Douglass, M. Dowicz, H. Ebina, J. Edelstein, D. J. Eisenstein, W. Elbers, N. Emas, S. Escoffier, P. Fagrelius, X. Fan, K. Fanning, V. A. Fawcett, E. Fernández-García, S. Ferraro, N. Findlay, A. Font-Ribera, J. E. Forero-Romero, D. Forero-Sánchez, C. S. Frenk, B. T. Gänsicke, L. Galbany, J. García-Bellido, C. Garcia-Quintero, L. H. Garrison, E. Gaztañaga, H. Gil-Marín, O. Y. Gnedin, S. Gontcho A Gontcho, A. X. Gonzalez-Morales, V. Gonzalez-Perez, C. Gordon, O. Graur, D. Green, D. Gruen, R. Gsponer, C. Guandalin, G. Gutierrez, J. Guy, C. Hahn, J. J. Han, J. Han, S. He, H. K. Herrera-Alcantar, K. Honscheid, J. Hou, C. Howlett, D. Huterer, V. Iršič, M. Ishak, A. Jacques, J. Jimenez, Y. P. Jing, B. Joachimi, S. Joudaki, R. Joyce, E. Jullo, S. Juneau, N. G. Karaçaylı, T. Karim, R. Kehoe, S. Kent, A. Khederlarian, D. Kirkby, T. Kisner, F. -S. Kitaura, N. Kizhuprakkat, H. Kong, S. E. Koposov, A. Kremin, A. Krolewski, O. Lahav, Y. Lai, C. Lamman, T. -W. Lan, M. Landriau, D. Lang, J. U. Lange, J. Lasker, J. M. Le Goff, L. Le Guillou, A. Leauthaud, M. E. Levi, S. Li, T. S. Li, K. Lodha, M. Lokken, Y. Luo, C. Magneville, M. Manera, C. J. Manser, D. Margala, P. Martini, M. Maus, J. McCullough, P. McDonald, G. E. Medina, L. Medina-Varela, A. Meisner, J. Mena-Fernández, A. Menegas, M. Mezcua, R. Miquel, P. Montero-Camacho, J. Moon, J. Moustakas, A. Muñoz-Gutiérrez, D. Muñoz-Santos, A. D. Myers, J. Myles, S. Nadathur, J. Najita, L. Napolitano, J. A. Newman, F. Nikakhtar, R. Nikutta, G. Niz, H. E. Noriega, N. Padmanabhan, E. Paillas, N. Palanque-Delabrouille, A. Palmese, J. Pan, Z. Pan, D. Parkinson, J. Peacock, W. J. Percival, A. Pérez-Fernández, I. Pérez-Ràfols, P. Peterson, J. Piat, M. M. Pieri, M. Pinon, C. Poppett, A. Porredon, F. Prada, R. Pucha, F. Qin, D. Rabinowitz, A. Raichoor, C. Ramírez-Pérez, S. Ramirez-Solano, M. Rashkovetskyi, C. Ravoux, A. H. Riley, A. Rocher, C. Rockosi, J. Rohlf, A. J. Ross, G. Rossi, R. Ruggeri, V. Ruhlmann-Kleider, C. G. Sabiu, K. Said, A. Saintonge, L. Samushia, E. Sanchez, N. Sanders, C. Saulder, E. F. Schlafly, D. Schlegel, D. Scholte, M. Schubnell, H. Seo, A. Shafieloo, R. Sharples, J. Silber, M. Siudek, A. Smith, D. Sprayberry, J. Suárez-Pérez, J. Swanson, T. Tan, G. Tarlé, P. Taylor, G. Thomas, R. Tojeiro, R. J. Turner, W. Turner, L. A. Ureña-López, R. Vaisakh, M. Valluri, M. Vargas-Magaña, L. Verde, M. Walther, B. Wang, M. S. Wang, W. Wang, B. A. Weaver, N. Weaverdyck, R. H. Wechsler, M. White, M. Wolfson, J. Yang, C. Yèche, S. Youles, J. Yu, S. Yuan, E. A. Zaborowski, P. Zarrouk, H. Zhang, C. Zhao, R. Zhao, Z. Zheng, R. Zhou, H. Zou, S. Zou, Y. Zu
In 2021 May the Dark Energy Spectroscopic Instrument (DESI) collaboration began a 5-year spectroscopic redshift survey to produce a detailed map of the evolving three-dimensional structure of the universe between $z=0$ and $z\approx4$. DESI's principle scientific objectives are to place precise constraints on the equation of state of dark energy, the gravitationally driven growth of large-scale structure, and the sum of the neutrino masses, and to explore the observational signatures of primordial inflation. We present DESI Data Release 1 (DR1), which consists of all data acquired during the first 13 months of the DESI main survey, as well as a uniform reprocessing of the DESI Survey Validation data which was previously made public in the DESI Early Data Release. The DR1 main survey includes high-confidence redshifts for 18.7M objects, of which 13.1M are spectroscopically classified as galaxies, 1.6M as quasars, and 4M as stars, making DR1 the largest sample of extragalactic redshifts ever assembled. We summarize the DR1 observations, the spectroscopic data-reduction pipeline and data products, large-scale structure catalogs, value-added catalogs, and describe how to access and interact with the data. In addition to fulfilling its core cosmological objectives with unprecedented precision, we expect DR1 to enable a wide range of transformational astrophysical studies and discoveries.
Validation of the DESI DR2 Measurements of Baryon Acoustic Oscillations from Galaxies and Quasars
2025-03-18
DESI Collaboration, U. Andrade, E. Paillas, J. Mena-Fernández, Q. Li, A. J. Ross, S. Nadathur, M. Rashkovetskyi, A. Pérez-Fernández, H. Seo, N. Sanders, O. Alves, X. Chen, N. Deiosso, M. Abdul Karim, S. Ahlen, E. Armengaud, A. Aviles, D. Bianchi, S. Brieden, A. Brodzeller, D. Brooks, E. Burtin, R. Calderon, R. Canning, A. Carnero Rosell, L. Casas, F. J. Castander, M. Charles, E. Chaussidon, J. Chaves-Montero, T. Claybaugh, S. Cole, A. Cuceu, K. S. Dawson, A. de la Macorra, A. de Mattia, J. Della Costa, A. Dey, B. Dey, Z. Ding, P. Doel, D. J. Eisenstein, W. Elbers, E. Fernández-García, S. Ferraro, A. Font-Ribera, J. E. Forero-Romero, C. Garcia-Quintero, L. H. Garrison, E. Gaztañaga, H. Gil-Marín, S. Gontcho A Gontcho, A. X. Gonzalez-Morales, C. Gordon, G. Gutierrez, J. Guy, C. Hahn, S. He, H. K. Herrera-Alcantar, K. Honscheid, C. Howlett, D. Huterer, M. Ishak, S. Juneau, R. Kehoe, D. Kirkby, T. Kisner, A. Kremin, O. Lahav, C. Lamman, M. Landriau, L. Le Guillou, A. Leauthaud, M. E. Levi, C. Magneville, M. Manera, P. Martini, W. Matthewson, A. Meisner, R. Miquel, J. Moustakas, A. Muñoz-Gutiérrez, D. Muñoz-Santos, A. D. Myers, L. Napolitano, J. A. Newman, H. E. Noriega, N. Palanque-Delabrouille, J. Pan, W. J. Percival, I. Pérez-Ràfols, C. Poppett, F. Prada, A. Raichoor, C. Ramírez-Pérez, C. Ravoux, G. Rossi, R. Ruggeri, L. Samushia, E. Sanchez, D. Schlegel, M. Schubnell, F. Sinigaglia, D. Sprayberry, T. Tan, G. Tarlé, P. Taylor, W. Turner, R. Vaisakh, M. Vargas-Magaña, M. Walther, B. A. Weaver, M. White, M. Wolfson, J. Yu, C. Yèche, P. Zarrouk, R. Zhou, H. Zou
The Dark Energy Spectroscopic Instrument (DESI) data release 2 (DR2) galaxy and quasar clustering data represents a significant expansion of data from DR1, providing improved statistical precision in BAO constraints across multiple tracers, including bright galaxies (BGS), luminous red galaxies (LRGs), emission line galaxies (ELGs), and quasars (QSOs). In this paper, we validate the BAO analysis of DR2. We present the results of robustness tests on the blinded DR2 data and, after unblinding, consistency checks on the unblinded DR2 data. All results are compared to those obtained from a suite of mock catalogs that replicate the selection and clustering properties of the DR2 sample. We confirm the consistency of DR2 BAO measurements with DR1 while achieving a reduction in statistical uncertainties due to the increased survey volume and completeness. We assess the impact of analysis choices, including different data vectors (correlation function vs. power spectrum), modeling approaches and systematics treatments, and an assumption of the Gaussian likelihood, finding that our BAO constraints are stable across these variations and assumptions with a few minor refinements to the baseline setup of the DR1 BAO analysis. We summarize a series of pre-unblinding tests that confirmed the readiness of our analysis pipeline, the final systematic errors, and the DR2 BAO analysis baseline. The successful completion of these tests led to the unblinding of the DR2 BAO measurements, ultimately leading to the DESI DR2 cosmological analysis, with their implications for the expansion history of the Universe and the nature of dark energy presented in the DESI key paper.
DESI DR2 Results I: Baryon Acoustic Oscillations from the Lyman Alpha Forest
2025-03-18
DESI Collaboration, M. Abdul Karim, J. Aguilar, S. Ahlen, C. Allende Prieto, O. Alves, A. Anand, U. Andrade, E. Armengaud, A. Aviles, S. Bailey, A. Bault, S. BenZvi, D. Bianchi, C. Blake, A. Brodzeller, D. Brooks, E. Buckley-Geer, E. Burtin, R. Calderon, R. Canning, A. Carnero Rosell, P. Carrilho, L. Casas, F. J. Castander, R. Cereskaite, M. Charles, E. Chaussidon, J. Chaves-Montero, D. Chebat, T. Claybaugh, S. Cole, A. P. Cooper, A. Cuceu, K. S. Dawson, R. de Belsunce, A. de la Macorra, A. de Mattia, N. Deiosso, J. Della Costa, A. Dey, B. Dey, Z. Ding, P. Doel, J. Edelstein, D. J. Eisenstein, W. Elbers, P. Fagrelius, K. Fanning, S. Ferraro, A. Font-Ribera, J. E. Forero-Romero, C. Garcia-Quintero, L. H. Garrison, E. Gaztañaga, H. Gil-Marín, S. Gontcho A Gontcho, A. X. Gonzalez-Morales, C. Gordon, D. Green, G. Gutierrez, J. Guy, C. Hahn, M. Herbold, H. K. Herrera-Alcantar, M. Ho, K. Honscheid, C. Howlett, D. Huterer, M. Ishak, S. Juneau, N. G. Karaçaylı, R. Kehoe, S. Kent, D. Kirkby, T. Kisner, F. -S. Kitaura, S. E. Koposov, A. Kremin, O. Lahav, C. Lamman, M. Landriau, D. Lang, J. Lasker, J. M. Le Goff, L. Le Guillou, A. Leauthaud, M. E. Levi, Q. Li, T. S. Li, K. Lodha, M. Lokken, C. Magneville, M. Manera, P. Martini, W. Matthewson, P. McDonald, A. Meisner, J. Mena-Fernández, R. Miquel, J. Moustakas, A. Muñoz-Gutiérrez, D. Muñoz-Santos, A. D. Myers, J. A. Newman, G. Niz, H. E. Noriega, E. Paillas, N. Palanque-Delabrouille, J. Pan, W. J. Percival, I. Pérez-Ràfols, M. M. Pieri, C. Poppett, F. Prada, D. Rabinowitz, A. Raichoor, C. Ramírez-Pérez, M. Rashkovetskyi, C. Ravoux, J. Rich, C. Rockosi, A. J. Ross, G. Rossi, V. Ruhlmann-Kleider, E. Sanchez, N. Sanders, S. Satyavolu, D. Schlegel, M. Schubnell, H. Seo, A. Shafieloo, R. Sharples, J. Silber, F. Sinigaglia, D. Sprayberry, T. Tan, G. Tarlé, P. Taylor, W. Turner, F. Valdes, M. Vargas-Magaña, M. Walther, B. A. Weaver, M. Wolfson, C. Yèche, P. Zarrouk, R. Zhou, H. Zou
We present the Baryon Acoustic Oscillation (BAO) measurements with the Lyman-alpha (LyA) forest from the second data release (DR2) of the Dark Energy Spectroscopic Instrument (DESI) survey. Our BAO measurements include both the auto-correlation of the LyA forest absorption observed in the spectra of high-redshift quasars and the cross-correlation of the absorption with the quasar positions. The total sample size is approximately a factor of two larger than the DR1 dataset, with forest measurements in over 820,000 quasar spectra and the positions of over 1.2 million quasars. We describe several significant improvements to our analysis in this paper, and two supporting papers describe improvements to the synthetic datasets that we use for validation and how we identify damped LyA absorbers. Our main result is that we have measured the BAO scale with a statistical precision of 1.1% along and 1.3% transverse to the line of sight, for a combined precision of 0.65% on the isotropic BAO scale at $z_{eff} = 2.33$. This excellent precision, combined with recent theoretical studies of the BAO shift due to nonlinear growth, motivated us to include a systematic error term in LyA BAO analysis for the first time. We measure the ratios $D_H(z_{eff})/r_d = 8.632 \pm 0.098 \pm 0.026$ and $D_M(z_{eff})/r_d = 38.99 \pm 0.52 \pm 0.12$, where $D_H = c/H(z)$ is the Hubble distance, $D_M$ is the transverse comoving distance, $r_d$ is the sound horizon at the drag epoch, and we quote both the statistical and the theoretical systematic uncertainty. The companion paper presents the BAO measurements at lower redshifts from the same dataset and the cosmological interpretation.
The galaxy-environment connection revealed by constrained simulations
2025-03-18
Catherine Gallagher, Tariq Yasin, Richard Stiskalek, Harry Desmond, Matt J. Jarvis
The evolution of galaxies is known to be connected to their position within the large-scale structure and their local environmental density. We investigate the relative importance of these using the underlying dark matter density field extracted from the Constrained Simulations in BORG (CSiBORG) suite of constrained cosmological simulations. We define cosmic web environment through both dark matter densities averaged on a scale up to 16 Mpc/h, and through cosmic web location identified by applying DisPerSE to the CSiBORG haloes. We correlate these environmental measures with the properties of observed galaxies in large surveys using optical data (from the NASA-Sloan Atlas) and 21-cm radio data (from ALFALFA). We find statistically significant correlations between environment and colour, neutral hydrogen gas (HI) mass fraction, star formation rate and S\'ersic index. Together, these correlations suggest that bluer, star forming, HI rich, and disk-type galaxies tend to reside in lower density areas, further from filaments, while redder, more elliptical galaxies with lower star formation rates tend to be found in higher density areas, closer to filaments. We find analogous trends with the quenching of galaxies, but notably find that the quenching of low mass galaxies has a greater dependence on environment than the quenching of high mass galaxies. We find that the relationship between galaxy properties and the environmental density is stronger than that with distance to filament, suggesting that environmental density has a greater impact on the properties of galaxies than their location within the larger-scale cosmic web.
Strategic resource allocation in memory encoding: An efficiency principle shaping language processing
2025-03-18
Weijie Xu, Richard Futrell
How is the limited capacity of working memory efficiently used to support human linguistic behaviors? In this paper, we investigate strategic resource allocation as an efficiency principle for memory encoding in sentence processing. The idea is that working memory resources are dynamically and strategically allocated to prioritize novel and unexpected information, enhancing their representations to make them less susceptible to memory decay and interference. Theoretically, from a resource-rational perspective, we argue that this efficiency principle naturally arises from two functional assumptions about working memory, namely, its limited capacity and its noisy representation. Empirically, through naturalistic corpus data, we find converging evidence for strategic resource allocation in the context of dependency locality from both the production and the comprehension side, where non-local dependencies with less predictable antecedents are associated with reduced locality effect. However, our results also reveal considerable cross-linguistic variability, highlighting the need for a closer examination of how strategic resource allocation, as a universal efficiency principle, interacts with language-specific phrase structures.
Characterizing Magnetic Properties of Young Protostars in Orion
2025-03-18
Bo Huang, Josep M. Girart, Ian W. Stephens, Philip C. Myers, Qizhou Zhang, Paulo Cortés, Álvaro Sánchez-Monge, Manuel Fernández-López, Valentine J. M. Le Gouellec, Tom Megeath, Nadia M. Murillo, John M. Carpenter, Zhi-Yun Li, Junhao Liu, Leslie W. Looney, Sarah Sadavoy, Nicole Karnath, Woojin Kwon
The {\em B}-field Orion Protostellar Survey (BOPS) recently obtained polarimetric observations at 870 ${\rm \mu m}$ towards 61 protostars in the Orion molecular clouds with $\sim 1^{\prime\prime}$ spatial resolution using the Atacama Large Millimeter/submillimeter Array. From the BOPS sample, we selected the 26 protostars with extended polarized emission within a radius of $\sim 6^{\prime\prime}$ (2400~au) around the protostar. This allows to have sufficient statistical polarization data to infer the magnetic field strength. The magnetic field strength is derived using the Davis-Chandrasekhar-Fermi method. The underlying magnetic field strengths are approximately 2.0~mG for protostars with a standard hourglass magnetic field morphology, which is higher than the values derived for protostars with rotated hourglass, spiral, and complex magnetic field configurations ($\lesssim1.0$~mG). This suggests that the magnetic field plays a more significant role in envelopes exhibiting a standard hourglass field morphology, and a value of $\gtrsim2.0$ mG would be required to maintain such a structure at these scales. Furthermore, most protostars in the sample are slightly supercritical, with mass-to-flux ratios $\lesssim3.0$. In particular, the mass-to-flux ratios for all protostars with a standard hourglass magnetic field morphology are lower than 3.0. However, these ratios do not account for the contribution of the protostellar mass, which means they are likely significantly underestimated.
ViVa-SAFELAND: a New Freeware for Safe Validation of Vision-based Navigation in Aerial Vehicles
2025-03-18
Miguel S. Soriano-García, Diego A. Mercado-Ravell
ViVa-SAFELAND is an open source software library, aimed to test and evaluate vision-based navigation strategies for aerial vehicles, with special interest in autonomous landing, while complying with legal regulations and people's safety. It consists of a collection of high definition aerial videos, focusing on real unstructured urban scenarios, recording moving obstacles of interest, such as cars and people. Then, an Emulated Aerial Vehicle (EAV) with a virtual moving camera is implemented in order to ``navigate" inside the video, according to high-order commands. ViVa-SAFELAND provides a new, safe, simple and fair comparison baseline to evaluate and compare different visual navigation solutions under the same conditions, and to randomize variables along several trials. It also facilitates the development of autonomous landing and navigation strategies, as well as the generation of image datasets for different training tasks. Moreover, it is useful for training either human of autonomous pilots using deep learning. The effectiveness of the framework for validating vision algorithms is demonstrated through two case studies, detection of moving objects and risk assessment segmentation. To our knowledge, this is the first safe validation framework of its kind, to test and compare visual navigation solution for aerial vehicles, which is a crucial aspect for urban deployment in complex real scenarios.
2025-03-18
Zhenhua Wang, Paul A. Parker, Scott H. Holan
Small area estimation models are essential for estimating population characteristics in regions with limited sample sizes, thereby supporting policy decisions, demographic studies, and resource allocation, among other use cases. The spatial Fay-Herriot model is one such approach that incorporates spatial dependence to improve estimation by borrowing strength from neighboring regions. However, this approach often requires substantial computational resources, limiting its scalability for high-dimensional datasets, especially when considering multiple (multivariate) responses. This paper proposes two methods that integrate the multivariate spatial Fay-Herriot model with spatial random effects, learned through variational autoencoders, to efficiently leverage spatial structure. Importantly, after training the variational autoencoder to represent spatial dependence for a given set of geographies, it may be used again in future modeling efforts, without the need for retraining. Additionally, the use of the variational autoencoder to represent spatial dependence results in extreme improvements in computational efficiency, even for massive datasets. We demonstrate the effectiveness of our approach using 5-year period estimates from the American Community Survey over all census tracts in California.
The Emerging Black Hole Mass Function in the High-Redshift Universe
2025-03-18
Junehyoung Jeon, Boyuan Liu, Anthony J. Taylor, Vasily Kokorev, John Chisholm, Dale D. Kocevski, Steven L. Finkelstein, Volker Bromm
Observations with the James Webb Space Telescope (JWST) have identified an abundant population of supermassive black holes (SMBHs) already in place during the first few hundred million years of cosmic history. Most of them appear overmassive relative to the stellar mass in their host systems, challenging models of early black hole seeding and growth. Multiple pathways exist to explain their formation, including heavy seeds formed from direct collapse/supermassive stars or sustained super-Eddington accretion onto light stellar remnant seeds. We use the semi-analytical code A-SLOTH to predict the emerging SMBH mass function under physically motivated models for both light and heavy seed formation, to be compared with upcoming ultra-deep JWST surveys. We find that both pathways can reproduce observations at $z\sim5-6$, but have distinct features at higher redshifts of $z\sim10$. Specifically, JWST observations have the potential to constrain the fraction of efficiently accreting (super-Eddington) SMBHs, as well as the existence and prevalence of heavy seeds, in particular through ultra-deep observations of blank fields and/or gravitational lensing surveys. Such observations will provide key insights to understand the process of SMBH formation and evolution during the emergence of the first galaxies. We further emphasize the great promise of possible SMBH detections at $z\gtrsim 15$ with future JWST observations to break the degeneracy between light- and heavy-seed models.
Performance predictions and contrast limits for an ultraviolet high contrast imaging testbed
2025-03-18
Kyle Van Gorkom, Ramya M. Anche, Christopher B. Mendillo, Jessica Gersh-Range, Justin Hom, Tyler D Robinson, Mamadou N'Diaye, Nikole K. Lewis, Bruce Macintosh, Ewan S. Douglas
NASA's Habitable Worlds Observatory (HWO) concept and the 2020 Decadal Survey's recommendation to develop a large space telescope to "detect and characterize Earth-like extrasolar planets" requires new starlight suppression technologies to probe a variety of biomarkers across multiple wavelengths. Broadband absorption due to ozone dominates Earth's spectrum in the mid-ultraviolet (200-300 nm) and can be detected with low spectral resolution. Despite the high value of direct ultraviolet (UV) exoplanet observations, high-contrast coronagraph demonstrations have yet to be performed in the UV. Typical coronagraph leakage sources such as wavefront error, surface scatter, polarization aberrations, and coronagraph mask quality all become more significant in the UV and threaten the viability of HWO to produce meaningful science in this regime. As a first step toward a demonstration of UV coronagraphy in a laboratory environment, we develop an end-to-end model to produce performance predictions and a contrast budget for a vacuum testbed operating at wavelengths from 200-400nm. At 300nm, our model predicts testbed performance of ${\sim}3\times10^{-9}$ contrast in a narrow 2% bandwidth and $\lessapprox10^{-8}$ in a 5% bandwidth, dominated primarily by the chromatic residuals from surface errors on optics that are not conjugate to the pupil.
Analyzing DevOps Practices Through Merge Request Data: A Case Study in Networking Software Company
2025-03-18
Samah Kansab, Matthieu Hanania, Francis Bordeleau, Ali Tizghadam
DevOps integrates collaboration, automation, and continuous improvement, enhancing agility, reducing time to market, and ensuring consistent software releases. A key component of this process is GitLab's Merge Request (MR) mechanism, which streamlines code submission and review. Studies have extensively analyzed MR data and similar mechanisms like GitHub pull requests and Gerrit Code Review, focusing on metrics such as review completion time and time to first comment. However, MR data also reflects broader aspects, including collaboration patterns, productivity, and process optimization. This study examines 26.7k MRs from four teams across 116 projects of a networking software company to analyze DevOps processes. We first assess the impact of external factors like COVID-19 and internal changes such as migration to OpenShift. Findings show increased effort and longer MR review times during the pandemic, with stable productivity and a lasting shift to out-of-hours work, reaching 70% of weekly activities. The transition to OpenShift was successful, with stabilized metrics over time. Additionally, we identify prioritization patterns in branch management, particularly in stable branches for new releases, underscoring the importance of workflow efficiency. In code review, while bots accelerate review initiation, human reviewers remain crucial in reducing review completion time. Other factors, such as commit count and reviewer experience, also influence review efficiency. This research provides actionable insights for practitioners, demonstrating how MR data can enhance productivity, effort analysis, and overall efficiency in DevOps.
Energy-time and time-bin entanglement: past, present and future
2025-03-18
Guilherme B. Xavier, Jan-Åke Larsson, Paolo Villoresi, Giuseppe Vallone, Adán Cabello
Entanglement is a key resource in many quantum information tasks. From a fundamental perspective entanglement is at the forefront of major philosophical discussions advancing our understanding of nature. An experimental scheme was proposed in 1989 by Franson that exploited the unpredictability in the generation time of a photon pair in order to produce a then new form of quantum entanglement, known as energy-time entanglement. A later modification gave rise to the very popular time-bin entanglement, an important cornerstone in many real-world quantum communication applications. Both forms of entanglement have radically pushed forward our understanding of quantum mechanics throughout the 1990s and 2000s. A decade later modifications to the original proposals were proposed and demonstrated, which opens the path for the highly sought-after device-independence capability for entanglement certification, with a goal of ultra-secure quantum communication. In this review we cover the beginnings of energy-time and time-bin entanglement, many key experiments that expanded our understanding of what was achievable in quantum information experiments all the way down to modern demonstrations based on new technological advances. We will then point out to the future discussing the important place that energy-time and time-bin entanglement will have in upcoming quantum networks and novel protocols based on nonlocality.
The Bulge Cluster Origin (BulCO) survey at the ESO-VLT: probing the early history of the Milky Way assembling. Design and first results in Liller1
2025-03-18
F. R. Ferraro, L. Chiappino, A. Bartolomei, L. Origlia, C. Fanelli, B. Lanzoni, C. Pallanca, M. Loriga, S. Leanza, E. Valenti, D. Romano, A. Mucciarelli, D. Massari, M. Cadelano, E. Dalessandro, C. Crociati, R. M. Rich
We present the scientific goals and the very first results of the Bulge Cluster Origin (BulCO) survey. This survey has been specifically designed to perform an unprecedented chemical screening of stellar systems orbiting the Milky Way bulge, with the aim to unveil their true origin. It takes advantage of the improved performances of the spectrograph CRIRES+ operating at the ESO Very Large telescope, in the near-infrared domain. Due to the complex evolutionary history of the Milky Way, a variety of relics tracing different phenomena is expected to populate the Bulge: globular clusters formed in-situ or accreted from outside the Galaxy, nuclear star clusters of cannibalized structures, and possibly a few remnants of the proto-bulge formation process (the so-called ``bulge fossil fragments"). The signatures of the different origins are imprinted in the chemical properties of these stellar systems because specific abundance patterns provide authentic "chemical DNA" tests univocally tracing the enrichment process and, therefore, the environment where the stellar population formed. Thus, each system can provide a new piece of information on the bulge formation and evolutionary history. As first results of the survey, here we discuss the alpha-element and iron abundances of a sample of stars observed in the stellar system Liller 1, which is proposed to be a bulge fossil fragment. By combining this dataset with a recently published sample of high/mid-resolution spectra, we discuss the overall chemical properties of the stellar populations in Liller1, proving its link with the Galactic bulge and providing new constraints on its star formation history.
Image Captioning Evaluation in the Age of Multimodal LLMs: Challenges and Future Perspectives
2025-03-18
Sara Sarto, Marcella Cornia, Rita Cucchiara
The evaluation of machine-generated image captions is a complex and evolving challenge. With the advent of Multimodal Large Language Models (MLLMs), image captioning has become a core task, increasing the need for robust and reliable evaluation metrics. This survey provides a comprehensive overview of advancements in image captioning evaluation, analyzing the evolution, strengths, and limitations of existing metrics. We assess these metrics across multiple dimensions, including correlation with human judgment, ranking accuracy, and sensitivity to hallucinations. Additionally, we explore the challenges posed by the longer and more detailed captions generated by MLLMs and examine the adaptability of current metrics to these stylistic variations. Our analysis highlights some limitations of standard evaluation approaches and suggests promising directions for future research in image captioning assessment.
We present a first two-dimensional general-relativistic analysis of superconducting regions in axially symmetric highly magnetized neutron star (magnetar) models with toroidal magnetic fields. We investigate the topology and distribution of type-II and type-I superconducting regions for varying toroidal magnetic field strengths and stellar masses by solving the Einstein-Maxwell equations using the XNS code. Our results reveal that the outer cores of low- to intermediate-mass magnetars sustain superconductivity over larger regions compared to higher-mass stars with non-trivial distribution of type-II and type-I regions. Consistent with previous one-dimensional (1D) models, we find that regardless of the gravitational mass, the inner cores of magnetars with toroidal magnetic fields are devoid of $S$-wave proton superconductivity. Furthermore, these models contain non-superconducting, torus-shaped regions - a novel feature absent in previous 1D studies. Finally, we speculate on the potential indirect effects of superconductivity on continuous gravitational wave emissions from millisecond pulsars, such as PSR J1843-1113, highlighting their relevance for future gravitational wave detectors.
The Local Ultraviolet to Infrared Treasury II. Refining Star Formation Histories of Ten Metal-Poor Dwarf Galaxies with Simultaneous UV-Optical Two-CMD Fitting
2025-03-18
Yumi Choi, Karoline M. Gilbert, Benjamin F. Williams, Daniel R. Weisz, Evan D. Skillman, Julianne J. Dalcanton, Kristen B. W. McQuinn, Andrew E. Dolphin, Giada Pastorelli, Martha L. Boyer, Karl D. Gordon, L'eo Girardi, Maude Gull
We present the star formation histories (SFHs) of ten metal-poor (<12% Zsun), star-forming dwarf galaxies from the Local Ultraviolet to Infrared Treasury (LUVIT) survey. The derived SFHs exhibit significant variability, consistent with the irregular star formation expected for dwarf galaxies. Using synthetic near ultraviolet (UV) and optical CMDs with various yet targeted configurations for dust and input SFHs, we quantitatively demonstrate that simultaneous modeling of the UV and optical CMDs ("UVopt" case) improves the precision of SFH measurements in recent time bins up to ~1 Gyr, compared to the classical single optical CMD modeling ("Opt-only" case). The UVopt case reduces uncertainties relative to the Opt-only case by ~4-8% over the past 10 Myr, ~8-20% over 100 Myr, and ~8-14% over 1 Gyr, across various dust configurations and input SFHs. Additionally, we demonstrate discrepancies in stellar models for blue core helium-burning (BHeB) stars at the low metallicity regime. This discrepancy can artificially inflate star formation rate (SFR) estimates in younger age bins by misinterpreting the evolved BHeB stars as reddened upper main-sequence (MS) stars. Incorporating UV data improves BHeB-MS separation and mitigates the limitations of current low metallicity stellar models. Comparisons of the UVopt SFHs with Ha and FUV-based SFRs reconfirm that Ha is an unreliable tracer over its nominal 10 Myr timescale for low-SFR galaxies, while FUV provides a more reliable tracer but yields SFR_FUV values up to twice those of CMD-based _100Myr. Our findings underscore the importance of UV data in refining recent SFHs in low-metallicity environments.
2025-03-18
Adam A. Miller, Natasha S. Abrams, Greg Aldering, Shreya Anand, Charlotte R. Angus, Iair Arcavi, Charles Baltay, Franz E. Bauer, Daniel Brethauer, Joshua S. Bloom, Hemanth Bommireddy, Marcio Catelan, Ryan Chornock, Peter Clark, Thomas E. Collett, Georgios Dimitriadis, Sara Faris, Francisco Forster, Anna Franckowiak, Christopher Frohmaier, Lluıs Galbany, Renato B. Galleguillos, Ariel Goobar, Claudia P. Gutierrez, Saarah Hall, Erica Hammerstein, Kenneth R. Herner, Isobel M. Hook, Macy J. Huston, Joel Johansson, Charles D. Kilpatrick, Alex G. Kim, Robert A. Knop, Marek P. Kowalski, Lindsey A. Kwok, Natalie LeBaron, Kenneth W. Lin, Chang Liu, Jessica R. Lu, Wenbin Lu, Ragnhild Lunnan, Kate Maguire, Lydia Makrygianni, Raffaella Margutti, Dan Maoz, Patrik Milan Veres, Thomas Moore, A. J. Nayana, Matt Nicholl, Jakob Nordin, Giuliano Pignata, Abigail Polin, Dovi Poznanski, Jose L. Prieto, David L. Rabinowitz, Nabeel Rehemtulla, Mickael Rigault, Dan Ryczanowski, Nikhil Sarin, Steve Schulze, Ved G. Shah, Xinyue Sheng, Samuel P. R. Shilling, Brooke D. Simmons, Avinash Singh, Graham P. Smith, Mathew Smith, Jesper Sollerman, Maayane T. Soumagnac, Christopher W. Stubbs, Mark Sullivan, Aswin Suresh, Benny Trakhtenbrot, Charlotte Ward, Eli Wiston, Helen Xiong, Yuhan Yao, Peter E. Nugent
We present the La Silla Schmidt Southern Survey (LS4), a new wide-field, time-domain survey to be conducted with the 1 m ESO Schmidt telescope. The 268 megapixel LS4 camera mosaics 32 2k$\times$4k fully depleted CCDs, providing a $\sim$20 deg$^2$ field of view with $1''$ pixel$^{-1}$ resolution. The LS4 camera will have excellent performance at longer wavelengths: in a standard 45 s exposure the expected 5$\sigma$ limiting magnitudes in $g$, $i$, $z$ are $\sim$21.5, $\sim$20.9, and $\sim$20.3 mag (AB), respectively. The telescope design requires a novel filter holder that fixes different bandpasses over each quadrant of the detector. Two quadrants will have $i$ band, while the other two will be $g$ and $z$ band and color information will be obtained by dithering targets across the different quadrants. The majority (90%) of the observing time will be used to conduct a public survey that monitors the extragalactic sky at both moderate (3 d) and high (1 d) cadence, as well as focused observations within the Galactic bulge and plane. Alerts from the public survey will be broadcast to the community via established alert brokers. LS4 will run concurrently with the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). The combination of LS4+LSST will enable detailed holistic monitoring of many nearby transients: high-cadence LS4 observations will resolve the initial rise and peak of the light curve while less-frequent but deeper observations by LSST will characterize the years before and after explosion. Here, we summarize the primary science objectives of LS4 including microlensing events in the Galaxy, extragalactic transients, the search for electromagnetic counterparts to multi-messenger events, and cosmology.
The Future of Stellar Populations with the Maunakea Spectroscopic Explorer (MSE)
2025-03-18
Peter Frinchaboy, Andy Sheinis, Sam Barden, Viraja Khatu
The Maunakea Spectroscopic Explorer (MSE) ia a massively multiplexed spectroscopic survey facility that is proposed to replace the Canada-France-Hawai'i-Telescope in the 2040s. Since 2019, due to the uncertainty for new facilities on Maunakea, the project has been focused on new technology enabling greater capabilities beyond the concept design reviewed facility plan. Enhanced fiber density, and thereby survey speed, is made possible by using a new quad mirror (QM) 11.5-meter telescope design with 18,000+ fibers and a 1.5 square degree field-of-view. The MSE spectrographs will be moderate-resolution (360 nm through H-band at R=7,000) and high-resolution (R=40,000). MSE's baseline NIR capabilities will enable studies of highly-reddened regions in the Local Group, unlike other proposed next generation facilities. The MSE large-scale survey instrument suite will enable the equivalent to a full SDSS Legacy Survey every several weeks. This work presents the current status of the project after the Fall 2024 MSE science Workshop.
Measuring and unbiasing the BAO shift in the Lyman-Alpha forest with AbacusSummit
2025-03-17
Boryana Hadzhiyska, Roger de Belsunce, Andrei Cuceu, Julien Guy, Mikhail M. Ivanov, Henri Coquinot, Andreu Font-Ribera
The currently observing Dark Energy Spectroscopic Instrument (DESI) places sub-percent constraints on the Baryon Acoustic Oscillations (BAO) scaling parameters from the Lyman-$\alpha$ (Ly-$\alpha$) forest. However, no systematic error budget stemming from non-linearities in the 3D clustering of the Ly-$\alpha$ forest is included in the DESI-Ly-$\alpha$ analysis. In this work, we measure the size of the shift of the BAO peak using large Ly-$\alpha$ forest mocks produced on the $N$-body simulation suite \textsc{AbacusSummit}. Specifically, we measure the Ly-$\alpha$ auto-correlation and the Ly-$\alpha$-quasar cross-correlation functions. We use the DESI Ly-$\alpha$ forest fitting pipeline, \textsc{Vega}, with the publicly available covariance matrix from eBOSS DR16. To mitigate the noise, we adopt a linear control variates (LCV) technique, reducing the error bars by a factor of up to $\sim \sqrt{50}$ on large scales. From the auto-correlation, we detect a small positive shift in radial direction of $\Delta \alpha_\parallel = 0.35\%$ at the 3$\sigma$ level and virtually no shift in the transverse direction, $\alpha_\perp$. From the cross-correlation, we see a similar shift to $\Delta\alpha_\parallel$, albeit with larger error bars, and a small negative shift, $\Delta \alpha_\perp=\sim$0.25\%, at the 2$\sigma$ level. We also make a connection with the Ly-$\alpha$ forest effective field theory (EFT) framework and find that the one-loop EFT power spectrum yields unbiased measurements of the BAO shift parameters in radial and transverse direction for Ly-$\alpha$ auto and the Ly-$\alpha$-quasar cross-correlation measurements. When using the one-loop EFT framework, we find that we can recover the BAO parameters without a shift, which has important implications for future Ly-$\alpha$ forest analyses based on EFT. This work paves the way for the full-shape analysis of DESI and future surveys.
Quantum knot invariants (like colored HOMFLY-PT or Kauffman polynomials) are a distinguished class of non-perturbative topological invariants. Any known way to construct them (via Chern-Simons theory or quantum R-matrix) starts with a finite simple Lie algebra. Another set of knot invariants - of finite type - is related to quantum invariants via a perturbative expansion. However can all finite type invariants be obtained in this way? Investigating this problem, P. Vogel discovered a way to polynomially parameterize the expansion coefficients with three parameters so that, at different specific values, this reproduces the answers for all simple Lie (super)algebras. Then it is easy to construct a polynomial $P_{alg}$ that vanishes for all simple Lie algebras, and the corresponding Vassiliev invariant would thus be absent from the perturbative expansion. We review these Vogel claims pointing out at least two interesting implications of his construction. First, we discuss whether infinite-dimensional Lie algebras might enlarge Chern-Simons theory. Second, Vogel's construction implies an alternative axiomatization of simple Lie algebras - when we start from knot invariants and arrive at Lie algebras and their classification, which is opposite to conventional logic that we mentioned at the beginning.
A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives
2025-03-17
Weiqiang Jin, Hongyang Du, Biao Zhao, Xingwu Tian, Bohang Shi, Guang Yang
With the rapid development of artificial intelligence, intelligent decision-making techniques have gradually surpassed human levels in various human-machine competitions, especially in complex multi-agent cooperative task scenarios. Multi-agent cooperative decision-making involves multiple agents working together to complete established tasks and achieve specific objectives. These techniques are widely applicable in real-world scenarios such as autonomous driving, drone navigation, disaster rescue, and simulated military confrontations. This paper begins with a comprehensive survey of the leading simulation environments and platforms used for multi-agent cooperative decision-making. Specifically, we provide an in-depth analysis for these simulation environments from various perspectives, including task formats, reward allocation, and the underlying technologies employed. Subsequently, we provide a comprehensive overview of the mainstream intelligent decision-making approaches, algorithms and models for multi-agent systems (MAS). Theseapproaches can be broadly categorized into five types: rule-based (primarily fuzzy logic), game theory-based, evolutionary algorithms-based, deep multi-agent reinforcement learning (MARL)-based, and large language models(LLMs)reasoning-based. Given the significant advantages of MARL andLLMs-baseddecision-making methods over the traditional rule, game theory, and evolutionary algorithms, this paper focuses on these multi-agent methods utilizing MARL and LLMs-based techniques. We provide an in-depth discussion of these approaches, highlighting their methodology taxonomies, advantages, and drawbacks. Further, several prominent research directions in the future and potential challenges of multi-agent cooperative decision-making are also detailed.
Solar System objects observed with TESS -- Early Data Release 2: I. Spin-shape recovery potential of multi-epoch TESS observations
2025-03-17
Nóra Takács, Csaba Kiss, Róbert Szakáts, András Pál
Using multidirectional measurements from the Transiting Exoplanet Survey Satellite (TESS), we investigated the viability of determining the approximate shape and spin axis orientations for 44 selected main belt asteroids, using light curve inversion, assuming Lommel-Seeliger ellipsoids. This study aims to investigate the applicability of low-degree-of-freedom shape models in those cases when rotation periods can be accurately determined, but light curves are only available in a limited number of geometries or orbital phases. Our results are compared with the shape and spin axis solutions obtained for the same set of asteroids by more complex light curve inversion methods using mainly ground-based measurements, available via the Database of Asteroid Models from Inversion Techniques (DAMIT).The best-fit spin-axis orientations show a moderately good match with the DAMIT solutions; however, a better agreement is reached with triaxial ellipsoid solution obtained from other large, independent surveys. This suggests that while TESS-only data works well for finding rotation periods, it has its limitations when determining asteroid shape and spin-axis orientation. We discuss the challenges and potential applications of this approach for studying large number of asteroids observed by TESS.
Context: Generative Artificial Intelligence (GenAI) is transforming much of software development, yet its application in software architecture is still in its infancy, and no prior study has systematically addressed the topic. Aim: We aim to systematically synthesize the use, rationale, contexts, usability, and future challenges of GenAI in software architecture. Method: We performed a multivocal literature review (MLR), analyzing peer-reviewed and gray literature, identifying current practices, models, adoption contexts, and reported challenges, extracting themes via open coding. Results: Our review identified significant adoption of GenAI for architectural decision support and architectural reconstruction. OpenAI GPT models are predominantly applied, and there is consistent use of techniques such as few-shot prompting and retrieved-augmented generation (RAG). GenAI has been applied mostly to initial stages of the Software Development Life Cycle (SDLC), such as Requirements-to-Architecture and Architecture-to-Code. Monolithic and microservice architectures were the dominant targets. However, rigorous testing of GenAI outputs was typically missing from the studies. Among the most frequent challenges are model precision, hallucinations, ethical aspects, privacy issues, lack of architecture-specific datasets, and the absence of sound evaluation frameworks. Conclusions: GenAI shows significant potential in software design, but several challenges remain on its path to greater adoption. Research efforts should target designing general evaluation methodologies, handling ethics and precision, increasing transparency and explainability, and promoting architecture-specific datasets and benchmarks to bridge the gap between theoretical possibilities and practical use.
Large language models (LLMs) based on Transformer have been widely applied in the filed of natural language processing (NLP), demonstrating strong performance, particularly in handling short text tasks. However, when it comes to long context scenarios, the performance of LLMs degrades due to some challenges. To alleviate this phenomenon, there is a number of work proposed recently. In this survey, we first list the challenges of applying pre-trained LLMs to process long contexts. Then systematically review the approaches related to long context and propose our taxonomy categorizing them into four main types: positional encoding, context compression, retrieval augmented, and attention pattern. In addition to the approaches, we focus on the evaluation of long context, organizing relevant data, tasks, and metrics based on existing long context benchmarks. Finally, we summarize unresolved issues in the long context domain and put forward our views on future developments.
Contribution to the Radio Light Curves of a Core-Collapse Supernova due to the Off-axis Gamma-Ray Burst Afterglow
2025-03-17
Yo Kusafuka, Tomoki Matsuoka, Ryo Sawada
We present the first comprehensive theoretical predictions of radio emission from a Type Ic-BL supernova (SN) associated with a gamma-ray burst (GRB) jet. It is naturally supposed that the expected radio light curve consists of two components of emitting source: radio emission from a non-relativistic SN shock and afterglow emitted from the head of the relativistic GRB jet. We calculate each component of radio emission, placing an emphasis on surveying wide ranges of isotropic explosion energy and viewing angle in our GRB afterglow modeling. We show that in particular parameter space, the composite radio light curve would be characterized by a double-peaked shape. The condition for the clear appearance of the double-peaked radio light curve is either (1) the isotropic explosion energy is small (low-luminosity GRB) or (2) the viewing angle is large (off-axis GRB). Our results highlight that follow-up radio observations conducted within a few years after the optical discovery of nearby Type Ic-BL supernovae ($\sim$100~Mpc) can serve as a unique diagnostic for off-axis GRBs undiscovered in Type IcBL SNe. This will be a step toward uncovering the nature of long GRB progenitors and clarifying their connection to Type IcBL SNe.
Properties of a definition of the SI second based on several optical transitions
2025-03-17
Jérôme Lodewyck, Tetsuya Ido
The current definition of the SI second based on the Cs ground state hyperfine transition is expected to be replaced by a new definition based on optical frequency standards in the next decade. Several options are currently under consideration for the new definition, including a definition based on the weighted geometric mean of several transitions. In this paper, we review several properties of this option, and most notably introduce a quantitative method to set the weights of transitions, and a graphical representation of the unit that make the distinction between the definition and the realisation of the unit easier to understand.
On Fractional Generalizations of the Logistic Map and their Applications
2025-03-17
Mark Edelman
The regular logistic map was introduced in 1960s, served as an example of a complex system, and was used as an instrument to demonstrate and investigate the period doubling cascade of bifurcations scenario of transition to chaos. In this paper, we review various fractional generalizations of the logistic map and their applications.
Investing in nature: Stakeholder's willingness to pay for Tunisian forest services
2025-03-17
Islem Saadaoui
This study explores the economic value of Aleppo pine forests, a unique and threatened ecosystem in the border region of central Tunisia. These forests play a vital role in supporting small rural communities, but face increasing pressures and restrictions on their use. This research aims to assign a monetary value to forest conservation, considering the region's specific socio-economic context. Strategies for empowering local residents as key actors in developing sustainable cross-border initiatives are further investigated. Employing the contingent valuation method, a survey of 350 local residents and international users was conducted to assess their willigness to pay fo forest conservation efforts. Logistic regression analysis revealed that sociodemographic factors, such as monthly income and preferred payment method, significantly influence both and the likehood of participation. These findingd highlight the feasibility and importance of reconciling economic development with ecological sustainability in this critical region.
Synthetic Spectroscopy for White Dwarf Classification: Addressing Label Uncertainty and Class Imbalance
2025-03-17
Olivier Vincent, Pierre Bergeron, Patrick Dufour
With the imminent data releases from next-generation spectroscopic surveys, hundreds of thousands of white dwarf spectra are expected to become available within the next few years, increasing the data volume by an order of magnitude. This surge in data has created a pressing need for automated tools to efficiently analyze and classify these spectra. Although machine learning algorithms have recently been applied to classify large spectroscopic datasets, they remain constrained by the limited availability of training data. The Sloan Digital Sky Survey (SDSS) serves as the current standard training set, as it provides the largest collection of labeled spectra; however, it faces challenges related to severe class imbalance and uncertain label consistency across different surveys. In this work, we address these limitations by training histogram gradient-boosted classifiers on a synthetic SDSS dataset to identify six ubiquitous chemical signatures in the atmosphere of white dwarfs, and test them on 14,246 objects with SNR$>$10 SDSS spectroscopy. We show our approach not only surpasses human expert performance, but also enables subtype classification and effectively resolves label transferability issues. The methodology developed here is adaptable to any spectroscopic survey, providing a critical tool for the astronomical community.
Exploring a tentative link between FRBs and pulsars with broad energy distributions and applications for nearby FRB survey strategies
2025-03-17
S. B. Zhang, G. Hobbs, S. Johnston, S. Dai, Y. Li, J. S. Wang, X. Yang, X. F. Wu, L. Staveley-Smith
Fast radio bursts (FRBs) are energetic, short-duration radio pulses of unclear origins. In this study, we investigate the FRBs and pulsars with broad energy distributions by fitting their high energy tails with a power-law model. Two cosmological repeating FRBs (FRB 20201124A and FRB 20220912A), one nearby FRB (FRB 20200120E), and two pulsars (RRATs J1846-0257 and J1854+0306), exhibit power-law indices of $\alpha \gtrsim -1$, suggesting that their bright pulses contribute significantly to the total radio pulse energy. The brightest bursts from these sources fit well with a simple power-law model ($\alpha = -0.26 \pm 0.05$), indicating a tentative link between certain high-luminosity FRBs and low-luminosity radio bursts. We also discuss detailed survey strategies for FAST, MeerKAT and Parkes cryoPAF in the search for FRBs in nearby globular clusters (GCs) using different power-law indices, recommending targets for observation. We suggest that combining observations with FAST ($\sim$ 3 hours) and Parkes cryoPAF (10-20 hours) are practicable for discovering new FRBs in the nearby GCs.
The use of a wire probe is a robust method for beam profile measurement, but it can only provide a 1D projection of the beam profile. In this study, we developed a novel method for measuring a beam projected from a 360{\deg} angle by a dual-axis rotation of a wire probe and obtaining a complete 2D profile via image reconstruction. We conducted a proof-of-principle study using an Ar+ ion beam and optimized the reconstruction algorithm. The experimental results showed that the use of the order subsets expectation maximization (OS-EM) algorithm is the most reasonable method, providing a highly accurate absolute 2D beam profile within a processing time on the millisecond scale. Furthermore, analysis of 2D profiles at different probing positions provided the beam direction and the phase space distribution. This versatile method can be applied to various fields of quantum beam technologies, such as particle therapy, semiconductor processing, and material analysis, as well as basic scientific research.
Reconstructing the dark energy density in light of DESI BAO observations
2025-03-17
Maria Berti, Emilio Bellini, Camille Bonvin, Martin Kunz, Matteo Viel, Miguel Zumalacarregui
In light of the evidence for dynamical dark energy (DE) found from the most recent Dark Energy Spectroscopic Instrument (DESI) baryon acoustic oscillation (BAO) measurements, we perform a non-parametric, model-independent reconstruction of the DE density evolution. To do so, we develop and validate a new framework that reconstructs the DE density through a third-degree piece-wise polynomial interpolation, allowing for direct constraints on its redshift evolution without assuming any specific functional form. The strength of our approach resides in the choice of directly reconstructing the DE density, which provides a more straightforward relation to the distances measured by BAO than the equation of state parameter. We investigate the constraining power of cosmic microwave background (CMB) observations combined with supernovae (SNe) and BAO measurements. In agreement with results from other works, we find a preference for deviations from $\Lambda$CDM, with a significance of $2.4\sigma$ when using the Dark Energy Survey Year 5 (DESY5) SNe data, and $1.3\sigma$ with PantheonPlus. In all the cases we consider, the derived DE equation of state parameter presents evidence for phantom crossing. By investigating potential systematic effects in the low-redshift samples of DESY5 observations, we confirm that correcting for the offset in apparent magnitude with respect to PantheonPlus data, as suggested in previous studies, completely removes the tension. Furthermore, we assess the risk of potentially overfitting the data by changing the number of interpolation nodes. As expected, we find that with lesser nodes we get a smoother reconstructed behavior of the DE density, although with similar overall features. The pipeline developed in this work is ready to be used with future high-precision data to further investigate the evidence for a non-standard background evolution.
The rapid expansion of data from diverse sources has made anomaly detection (AD) increasingly essential for identifying unexpected observations that may signal system failures, security breaches, or fraud. As datasets become more complex and high-dimensional, traditional detection methods struggle to effectively capture intricate patterns. Advances in deep learning have made AD methods more powerful and adaptable, improving their ability to handle high-dimensional and unstructured data. This survey provides a comprehensive review of over 180 recent studies, focusing on deep learning-based AD techniques. We categorize and analyze these methods into reconstruction-based and prediction-based approaches, highlighting their effectiveness in modeling complex data distributions. Additionally, we explore the integration of traditional and deep learning methods, highlighting how hybrid approaches combine the interpretability of traditional techniques with the flexibility of deep learning to enhance detection accuracy and model transparency. Finally, we identify open issues and propose future research directions to advance the field of AD. This review bridges gaps in existing literature and serves as a valuable resource for researchers and practitioners seeking to enhance AD techniques using deep learning.
Current Advances in Magnetoelectric Composites with Various Interphase Connectivity Types
2025-03-17
Youness Hadouch, Daoud Mezzane, M barek Amjoud, Hana Ursic, Abdelilah Lahmar, Brigita Rozic, Igor Lukyanchuk, Zdravko Kutnjak, Mimoun El Marssi
Magnetoelectric composites integrate the coupling between magnetic and piezoelectric materials to create new functionalities for potential technological applications. This coupling is typically achieved through the exchange of magnetic, electric, or elastic energy across the interfaces between the different constituent materials. Tailoring the strength of the magnetoelectric effect is primarily accomplished by selecting suitable materials for each constituent and by optimizing geometrical and microstructural designs. Various composite architectures, such as (0-3), (2-2), (1-3) and core-shell connectivities, have been studied to enhance magnetoelectric coupling and other required physical properties in composites. This review examines the latest advancements in magnetoelectric materials, focusing on the impact of different interphase connectivity types on their properties and performance. Before exploring magnetic-electric coupling, a brief overview of the historical background of multiferroic magnetoelectric composites is provided. Fundamental concepts underlying the magnetoelectric effect, piezoelectricity, and the magnetostrictive effect are explained, including their origins and examples of these materials' properties. So far, three types of magnetoelectric composite connectivities have been investigated experimentally: particulate composites (0-3), laminated and thin films (2-2), sticks embedded in matrix, core-shell particles, and coaxial fibers. An outlook on the prospects and scientific challenges in the field of multiferroic magnetoelectric composites is given at the end of this review.
Photon information efficiency limits in deep-space optical communications
2025-03-17
M. Jarzyna, L. Kunz, W. Zwolinski, M. Jachura, K. Banaszek
Deep-space optical communication links operate under severely limited signal power, approaching the photon-starved regime which requires a receiver capable of measuring individual incoming photons. This makes the photon information efficiency (PIE), i.e. the number of bits that can be retrieved from a single received photon, a relevant figure of merit to characterize data rates achievable in deep-space scenarios. Here we review theoretical PIE limits assuming a scalable modulation format, such as pulse position modulation (PPM), combined with a photon counting direct detection receiver. For unrestricted signal bandwidth, the attainable PIE is effectively limited by the background noise acquired by the propagating optical signal. The actual PIE limit depends on the effectiveness of the noise rejection mechanism implemented at the receiver, which can be improved by the nonlinear optical technique of quantum pulse gating. Further enhancement is possible by resorting to photon number resolved detection, which improves discrimination of PPM pulses against weak background noise. The results are compared with the ultimate quantum mechanical PIE limit implied by the Gordon-Holevo capacity bound, which takes into account general modulation formats as well as any physically permitted measurement techniques.
Laplace-Net: Learning Dynamical Systems with External Forcing
2025-03-17
Bernd Zimmering, Cecília Coelho, Vaibhav Gupta, Maria Maleshkova, Oliver Niggemann
Modelling forced dynamical systems - where an external input drives the system state - is critical across diverse domains such as engineering, finance, and the natural sciences. In this work, we propose Laplace-Net, a decoupled, solver-free neural framework for learning forced and delay-aware systems. It leverages a Laplace transform-based approach to decompose internal dynamics, external inputs, and initial values into established theoretical concepts, enhancing interpretability. Laplace-Net promotes transferability since the system can be rapidly re-trained or fine-tuned for new forcing signals, providing flexibility in applications ranging from controller adaptation to long-horizon forecasting. Experimental results on eight benchmark datasets - including linear, non-linear, and delayed systems - demonstrate the method's improved accuracy and robustness compared to state-of-the-art approaches, particularly in handling complex and previously unseen inputs.
We clarify and extend our earlier work (K.A.Meissner and H.Nicolai, Phys. Rev. D91 (2015) 065029 and Phys. Rev. Lett. 121 (2018) 091601) where it was shown how to amend a scheme originally proposed by M. Gell-Mann to identify the three families of quarks and leptons of the Standard Model with the 48 spin 1/2 fermions of N=8 supergravity that remain after absorption of eight Goldstinos, a scheme that in its original form is dynamically realized at the SU(3)xU(1) stationary point of gauged N=8 supergravity. We explain how to deform and enlarge this symmetry at the kinematical level to the full Standard Model symmetry group SU(3)_c x SU(2)_w x U(1)_Y, with the correct charge and chiral assignments for all fermions. The framework also leaves room for an extra U(1)_(B-L) symmetry. This symmetry enhancement is achieved by embedding the Standard Model symmetries into (a quotient group of) K(E_10), the `maximal compact subgroup' of the maximal rank hyperbolic Kac-Moody symmetry E_10, and an infinite prolongation of the SU(8) R-symmetry of N=8 supergravity. This scheme, which is also supposed to encompass quantum gravity, cannot be realized within the framework of space-time based (quantum) field theory, but requires space-time and related geometrical concepts to be `emergent'. We critically review the main hypotheses underlying this construction.
Understanding long video content is a complex endeavor that often relies on densely sampled frame captions or end-to-end feature selectors, yet these techniques commonly overlook the logical relationships between textual queries and visual elements. In practice, computational constraints necessitate coarse frame subsampling, a challenge analogous to ``finding a needle in a haystack.'' To address this issue, we introduce a semantics-driven search framework that reformulates keyframe selection under the paradigm of Visual Semantic-Logical Search. Specifically, we systematically define four fundamental logical dependencies: 1) spatial co-occurrence, 2) temporal proximity, 3) attribute dependency, and 4) causal order. These relations dynamically update frame sampling distributions through an iterative refinement process, enabling context-aware identification of semantically critical frames tailored to specific query requirements. Our method establishes new SOTA performance on the manually annotated benchmark in key-frame selection metrics. Furthermore, when applied to downstream video question-answering tasks, the proposed approach demonstrates the best performance gains over existing methods on LongVideoBench and Video-MME, validating its effectiveness in bridging the logical gap between textual queries and visual-temporal reasoning. The code will be publicly available.
Search for cascade decays of charged sleptons and sneutrinos in final states with three leptons and missing transverse momentum in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
2025-03-17
ATLAS Collaboration
A search for cascade decays of charged sleptons and sneutrinos using final states characterized by three leptons (electrons or muons) and missing transverse momentum is presented. The analysis is based on a dataset with 140 fb$^{-1}$ of proton-proton collisions at a center-of-mass energy of $\sqrt{s}$=13 TeV recorded by the ATLAS detector at the Large Hadron Collider. This paper focuses on a supersymmetric scenario that is motivated by the muon anomalous magnetic moment observation, dark mattter relic density abundance, and electroweak naturalness. A mass spectrum involving light higgsinos and heavier sleptons with a bino at intermediate mass is targeted. No significant deviation from the Standard Model expectation is observed. This search enables to place stringent constraints on this model, excluding at the 95% confidence level charged slepton and sneutrino masses up to 450 GeV when assuming a lightest neutralino mass of 100 GeV and mass-degenerate selectrons, smuons and sneutrinos.
An Online Integrated Development Environment for Automated Programming Assessment Systems
2025-03-17
Eduard Frankford, Daniel Crazzolara, Michael Vierhauser, Niklas Meissner, Stephan Krusche, Ruth Breu
The increasing demand for programmers has led to a surge in participants in programming courses, making it increasingly challenging for instructors to assess student code manually. As a result, automated programming assessment systems (APASs) have been developed to streamline this process. These APASs support lecturers by managing and evaluating student programming exercises at scale. However, these tools often do not provide feature-rich online editors compared to their traditional integrated development environments (IDEs) counterparts. This absence of key features, such as syntax highlighting and autocompletion, can negatively impact the learning experience, as these tools are crucial for effective coding practice. To address this gap, this research contributes to the field of programming education by extracting and defining requirements for an online IDE in an educational context and presenting a prototypical implementation of an open-source solution for a scalable and secure online IDE. The usability of the new online IDE was assessed using the Technology Acceptance Model (TAM), gathering feedback from 27 first-year students through a structured survey. In addition to these qualitative insights, quantitative measures such as memory (RAM) usage were evaluated to determine the efficiency and scalability of the tool under varying usage conditions.
2025-03-17
Siyuan Fan, Wenke Huang, Xiantao Cai, Bo Du
3D human interaction generation has emerged as a key research area, focusing on producing dynamic and contextually relevant interactions between humans and various interactive entities. Recent rapid advancements in 3D model representation methods, motion capture technologies, and generative models have laid a solid foundation for the growing interest in this domain. Existing research in this field can be broadly categorized into three areas: human-scene interaction, human-object interaction, and human-human interaction. Despite the rapid advancements in this area, challenges remain due to the need for naturalness in human motion generation and the accurate interaction between humans and interactive entities. In this survey, we present a comprehensive literature review of human interaction generation, which, to the best of our knowledge, is the first of its kind. We begin by introducing the foundational technologies, including model representations, motion capture methods, and generative models. Subsequently, we introduce the approaches proposed for the three sub-tasks, along with their corresponding datasets and evaluation metrics. Finally, we discuss potential future research directions in this area and conclude the survey. Through this survey, we aim to offer a comprehensive overview of the current advancements in the field, highlight key challenges, and inspire future research works.
On the Robustness of Cluster Clustering Covariance Calibration
2025-03-17
A. Fumagalli, T. Castro, S. Borgani, M. Valentini
Ongoing and upcoming wide-field surveys at different wavelengths will measure the distribution of galaxy clusters with unprecedented precision, demanding accurate models for the two-point correlation function (2PCF) covariance. In this work, we assess a semi-analytical framework for the cluster 2PCF covariance that employs three nuisance parameters to account for non-Poissonian shot noise, residual uncertainties in the halo bias model, and subleading noise terms. We calibrate these parameters on a suite of fast approximate simulations generated by PINOCCHIO as well as full $N$-body simulations from OpenGADGET3. We demonstrate that PINOCCHIO can reproduce the 2PCF covariance measured in OpenGADGET3 at the few percent level, provided the mass functions are carefully rescaled. Resolution tests confirm that high particle counts are necessary to capture shot-noise corrections, especially at high redshifts. We perform the parameter calibration across multiple cosmological models, showing that one of the nuisance parameters, the non-Poissonian shot-noise correction $\alpha$, depends mildly on the amplitude of matter fluctuations $\sigma_8$. In contrast, the remaining two parameters, $\beta$ controlling the bias correction and $\gamma$ controlling the secondary shot-noise correction, exhibit more significant variation with redshift and halo mass. Overall, our results underscore the importance of calibrating covariance models on realistic mock catalogs that replicate the selection function of forthcoming surveys and highlight that approximate methods, when properly tuned, can effectively complement full $N$-body simulations for precision cluster cosmology.
The VMC survey -- LII. The internal kinematics of the LMC with new VISTA observations
2025-03-17
S. Vijayasree, F. Niederhofer, M. -R. L. Cioni, L. Cullinane, K. Bekki, J. Th. Loon, N. Kacharov, R. de Grijis, V. D. Ivanov, J. M. Oliveira, F. Dresbach, M. A. T. Groenewegen, D. Erkal
Context: Studying the internal kinematics of galaxies provides insights into their past evolution, current dynamics, and future trajectory. The Large Magellanic Cloud (LMC), as the largest and one of the nearest satellite galaxies of the Milky Way, presents unique opportunities to investigate these phenomena in great detail. Aims: We investigate the internal kinematics of the LMC by deriving precise stellar proper motions using data from the VISTA survey of the Magellanic Clouds system (VMC). The main objective is to refine the LMC's dynamical parameters using improved proper motion measurements including one additional epoch of VISTA observations which extended the time baseline from ~ 2 to 10 years. Methods: The precision of the proper motion was significantly enhanced, reducing uncertainties from 6 mas/yr to 1.5 mas/yr. We derived geometrical and kinematic parameters, generating velocity maps and rotation curves, for both young and old stellar populations. Finally, we compared a suite of dynamical models, which simulate the interaction of the LMC with the Milky Way and Small Magellanic Cloud (SMC), against the observations. Results: The tangential rotation curve reveals an asymmetric drift between young and old stars, while the radial velocity curve for the young population shows an increasing trend within the inner bar region, suggesting non-circular orbits. We confirm the clockwise rotation around the dynamical centre of the LMC, consistent with previous predictions. A significant residual motion was detected toward the north-east of the LMC, directed away from the centre. This feature observed in the inner disk region is kinematically connected with a substructure identified in the periphery known as Eastern Substructure 1. This motion suggests a possible tidal influence from the Milky Way, combined with the effects of the recent close pericentre passage of the SMC ~150 Myr ago.
On the characteristic structure of the adjoint Euler equations with application to supersonic flows
2025-03-17
Carlos Lozano, Jorge Ponsin
We review the characteristic structure of the two-dimensional adjoint Euler equations. We derive the compatibility and jump conditions along characteristics and show that the characteristic information can be used to obtain exact predictions for the adjoint variables in certain supersonic flows.
HICD: Hallucination-Inducing via Attention Dispersion for Contrastive Decoding to Mitigate Hallucinations in Large Language Models
2025-03-17
Xinyan Jiang, Hang Ye, Yongxin Zhu, Xiaoying Zheng, Zikang Chen, Jun Gong
Large Language Models (LLMs) often generate hallucinations, producing outputs that are contextually inaccurate or factually incorrect. We introduce HICD, a novel method designed to induce hallucinations for contrastive decoding to mitigate hallucinations. Unlike existing contrastive decoding methods, HICD selects attention heads crucial to the model's prediction as inducing heads, then induces hallucinations by dispersing attention of these inducing heads and compares the hallucinated outputs with the original outputs to obtain the final result. Our approach significantly improves performance on tasks requiring contextual faithfulness, such as context completion, reading comprehension, and question answering. It also improves factuality in tasks requiring accurate knowledge recall. We demonstrate that our inducing heads selection and attention dispersion method leads to more "contrast-effective" hallucinations for contrastive decoding, outperforming other hallucination-inducing methods. Our findings provide a promising strategy for reducing hallucinations by inducing hallucinations in a controlled manner, enhancing the performance of LLMs in a wide range of tasks.
Language Models (LMs) are widely used in software engineering for code generation, but they may produce code with errors. Rather than repairing the generated code, an alternative way is to address the underlying failures of models. LM repair offers a lightweight solution to this challenge: it requires minimal data, reduces computational costs, and reduces the side effects. Unlike retraining, LM repair focuses on applying tailored updates to targeted neurons, making it ideal for scenarios with limited resources, high-performance demands, or strict safety requirements. In this paper, we propose \ul{S}emantic \ul{T}argeting for \ul{A}nalytical \ul{R}epair (\textsc{STAR}), a pioneering and novel semantic-based optimization approach for repairing LLMs. \textsc{STAR} realizes main operations in LM repair methods in an optimization process, including locating ``buggy neurons'', solving ``neuron patches'', and patching ``buggy neurons''. Correspondingly, it computes the deltas of weight matrix as the prior information to guide optimization; and attributes the targeted layers and neurons leveraging statistical insights. The neuron patches are computed with a solid semantic-based analytical formula, which directly bridges the changes to logits with the deltas of neurons, by steering latent representations. Compared to the prior work of LM repair (\textsc{MINT}) and optimization methods (\textsc{SGD}), \textsc{STAR} integrates their strengths while mitigating their limitations. \textsc{STAR} supports solving multiple failures together, significantly improving the usefulness. Evaluated on three code generation tasks using popular code LMs, \textsc{STAR} demonstrates superior effectiveness. Additionally, \textsc{STAR} exhibits better efficiency. In terms of side effects, namely the balance between generalization and specificity, \textsc{STAR} outperforms prior work by a significant margin.
A Sample of Low-mass AGNs Extended to z $\approx$ 0.6
2025-03-17
Wen-Juan Liu, Luis C. Ho, Xiao-Bo Dong, Su Yao, Paulina Lira
We present a catalog of 927 low-mass active galactic nuclei (AGNs) with black hole mass of $M_{BH}\leqslant2\times10^{6} M_{\odot}$ characterized by broad H$\alpha$ or H$\beta$ emission lines,uniformly selected from the Seventeenth Data Release (DR17) of the Sloan Digital Sky Survey (SDSS) spectra. Taking advantages of the wide wavelength coverage of BOSS/eBOSS spectra of the SDSS, this sample significantly extends the redshift range to $z\leqslant0.57$, a marked improvement over the previous studies which were generally limited to $z\leqslant0.35$. This sample encompasses black hole masses from $10^{3.7}$ to $10^{6.3} M_{\odot}$, with Eddington ratios ranging from 0.01 to 3.3. Preliminary analysis of this sample reveals a marked decline in maximum accretion rates (namely $L/L_{Edd}$) and broad-H$\alpha$ luminosities with decreasing redshift, analogous to the passive ``downsizing'' evolutionary trend observed in high-mass AGNs. This systematic catalog of low-redshift low-mass AGNs, the largest so far, will benefit the studies on accretion physics, AGN--galaxy connection and the origin of supermassive black holes.
FAST Ultra-Deep Survey (FUDS): the star formation histories of FUDS0 galaxies
2025-03-17
Hongwei Xi, Lister Staveley-Smith, Bo Peng, Bi-Qing For, Bin Liu, Dejian Ding
We present the ultraviolet, optical and infrared counterparts of 128 galaxies detected in neutral hydrogen (HI) in the FAST Ultra-Deep Survey (FUDS) field 0 (FUDS0). HI mass upper limits are also calculated for 134 non-detections in the field. Stellar masses ($M_*$), star formation rates (SFRs) and star formation histories are computed by fitting spectral energy distributions (SEDs) using ProSpect. The results show that HI-selected galaxies prefer recent long-lasting, but mild star formation activity, while HI non-detections have earlier and more intense star formation activity. Based on their distribution on the SFR versus $M_*$ diagram, the typical evolution of HI-selected galaxies follows three distinct stages: (i) Early stage: the total SFR increases, though the specific SFR (sSFR) decreases from 10$^{-8}$ to 10$^{-9}$ yr$^{-1}$; (ii) Mass accumulation stage: the SFR is steady, and stellar mass increase linearly with time; (iii) Quenching stage: star formation activity quenches on a rapid timescale and at constant stellar mass. 37 non-detections are located on star-forming main sequence, but are not detected in HI due to low sensitivity close to field edges or close to strong radio frequency interference. Comparisons with the existing optical, optically-selected HI, and HI catalogs show a good agreement with respect to measured $M_*$ and SFR, with minor discrepancies due to selection effects. The ongoing full FUDS survey will help us better explore the evolutionary stages of HI galaxies through a larger sample.
ACT360: An Efficient 360-Degree Action Detection and Summarization Framework for Mission-Critical Training and Debriefing
2025-03-17
Aditi Tiwari, Klara Nahrstedt
Effective training and debriefing are critical in high-stakes, mission-critical environments such as disaster response, military simulations, and industrial safety, where precision and minimizing errors are paramount. The traditional post-training analysis relies on manually reviewing 2D videos, a time-consuming process that lacks comprehensive situational awareness. To address these limitations, we introduce ACT360, a system that leverages 360-degree videos and machine learning for automated action detection and structured debriefing. ACT360 integrates 360YOWO, an enhanced You Only Watch Once (YOWO) model with spatial attention and equirectangular-aware convolution (EAC) to mitigate panoramic video distortions. To enable deployment in resource-constrained environments, we apply quantization and model pruning, reducing the model size by 74% while maintaining robust accuracy (mAP drop of only 1.5%, from 0.865 to 0.850) and improving inference speed. We validate our approach on a publicly available dataset of 55 labeled 360-degree videos covering seven key operational actions, recorded across various real-world training sessions and environmental conditions. Additionally, ACT360 integrates 360AIE (Action Insight Explorer), a web-based interface for automatic action detection, retrieval, and textual summarization using large language models (LLMs), significantly enhancing post-incident analysis efficiency. ACT360 serves as a generalized framework for mission-critical debriefing, incorporating EAC, spatial attention, summarization, and model optimization. These innovations apply to any training environment requiring lightweight action detection and structured post-exercise analysis.
AUTV: Creating Underwater Video Datasets with Pixel-wise Annotations
2025-03-17
Quang Trung Truong, Wong Yuk Kwan, Duc Thanh Nguyen, Binh-Son Hua, Sai-Kit Yeung
Underwater video analysis, hampered by the dynamic marine environment and camera motion, remains a challenging task in computer vision. Existing training-free video generation techniques, learning motion dynamics on the frame-by-frame basis, often produce poor results with noticeable motion interruptions and misaligments. To address these issues, we propose AUTV, a framework for synthesizing marine video data with pixel-wise annotations. We demonstrate the effectiveness of this framework by constructing two video datasets, namely UTV, a real-world dataset comprising 2,000 video-text pairs, and SUTV, a synthetic video dataset including 10,000 videos with segmentation masks for marine objects. UTV provides diverse underwater videos with comprehensive annotations including appearance, texture, camera intrinsics, lighting, and animal behavior. SUTV can be used to improve underwater downstream tasks, which are demonstrated in video inpainting and video object segmentation.
Comparative Review of Modern Competing Risk Methods in High-dimensional Settings
2025-03-17
Paul M. Djangang, Summer S. Han, Nilotpal Sanyal
Competing risk analysis accounts for multiple mutually exclusive events, improving risk estimation over traditional survival analysis. Despite methodological advancements, a comprehensive comparison of competing risk methods, especially in high-dimensional settings, remains limited. This study evaluates penalized regression (LASSO, SCAD, MCP), boosting (CoxBoost, CB), random forest (RF), and deep learning (DeepHit, DH) methods for competing risk analysis through extensive simulations, assessing variable selection, estimation accuracy, discrimination, and calibration under diverse data conditions. Our results show that CB achieves the best variable selection, estimation stability, and discriminative ability, particularly in high-dimensional settings. while MCP and SCAD provide superior calibration in $n>p$ scenarios. RF and DH capture nonlinear effects but exhibit instability, with RF showing high false discovery rates and DH suffering from poor calibration. Further, we compare the flexibility of these methods through the analysis of a melanoma gene expression data with survival information. This study provides practical guidelines for selecting competing risk models to ensure robust and interpretable analysis in high-dimensional settings and outlines important directions for future research.
Observation of multiple surface states in naturally cleavable chiral crystal PdSbSe
2025-03-17
Zhicheng Jiang, Zhengtai Liu, Chenqiang Hua, Xiangqi Liu, Yichen Yang, Jianyang Ding, Jiayu Liu, Jishan Liu, Mao Ye, Ji Dai, Massimo Tallarida, Yanfeng Guo, Yunhao Lu, Dawei Shen
Chiral multifold fermions in solids exhibit unique band structures and topological properties, making them ideal for exploring fundamental physical phenomena related to nontrivial topology, chirality, and symmetry breaking. However, the challenge of obtaining clean, flat surfaces through cleavage has hindered the investigation of their unique electronic states. In this study, we utilize high-resolution angle-resolved photoemission spectroscopy and density functional theory calculations to investigate the low-energy electronic structure of the cleavable single-crystal PdSbSe. Our combined experimental and theoretical analysis reveals the presence of multifold degenerate fermions within this chiral crystal. We also observe multiple chiral Fermi arc surface states and spin-splitting behavior in the associated bulk bands. These findings provide unique insights into chiral, multifold fermionic states in easily cleavable crystals and offer a robust platform for further research into their unique electronic properties and potential applications in novel electronic devices.
SAM2 for Image and Video Segmentation: A Comprehensive Survey
2025-03-17
Zhang Jiaxing, Tang Hao
Despite significant advances in deep learning for image and video segmentation, existing models continue to face challenges in cross-domain adaptability and generalization. Image and video segmentation are fundamental tasks in computer vision with wide-ranging applications in healthcare, agriculture, industrial inspection, and autonomous driving. With the advent of large-scale foundation models, SAM2 - an improved version of SAM (Segment Anything Model)has been optimized for segmentation tasks, demonstrating enhanced performance in complex scenarios. However, SAM2's adaptability and limitations in specific domains require further investigation. This paper systematically analyzes the application of SAM2 in image and video segmentation and evaluates its performance in various fields. We begin by introducing the foundational concepts of image segmentation, categorizing foundation models, and exploring the technical characteristics of SAM and SAM2. Subsequently, we delve into SAM2's applications in static image and video segmentation, emphasizing its performance in specialized areas such as medical imaging and the challenges of cross-domain adaptability. As part of our research, we reviewed over 200 related papers to provide a comprehensive analysis of the topic. Finally, the paper highlights the strengths and weaknesses of SAM2 in segmentation tasks, identifies the technical challenges it faces, and proposes future development directions. This review provides valuable insights and practical recommendations for optimizing and applying SAM2 in real-world scenarios.
Humans inhabit a world defined by interactions -- with other humans, objects, and environments. These interactive movements not only convey our relationships with our surroundings but also demonstrate how we perceive and communicate with the real world. Therefore, replicating these interaction behaviors in digital systems has emerged as an important topic for applications in robotics, virtual reality, and animation. While recent advances in deep generative models and new datasets have accelerated progress in this field, significant challenges remain in modeling the intricate human dynamics and their interactions with entities in the external world. In this survey, we present, for the first time, a comprehensive overview of the literature in human interaction motion generation. We begin by establishing foundational concepts essential for understanding the research background. We then systematically review existing solutions and datasets across three primary interaction tasks -- human-human, human-object, and human-scene interactions -- followed by evaluation metrics. Finally, we discuss open research directions and future opportunities.
Travel demand modeling has shifted from aggregated trip-based models to behavior-oriented activity-based models because daily trips are essentially driven by human activities. To analyze the sequential activity-travel decisions, deep inverse reinforcement learning (DIRL) has proven effective in learning the decision mechanisms by approximating a reward function to represent preferences and a policy function to replicate observed behavior using deep neural networks (DNNs). However, most existing research has focused on using DIRL to enhance only prediction accuracy, with limited exploration into interpreting the underlying decision mechanisms guiding sequential decision-making. To address this gap, we introduce an interpretable DIRL framework for analyzing activity-travel decision processes, bridging the gap between data-driven machine learning and theory-driven behavioral models. Our proposed framework adapts an adversarial IRL approach to infer the reward and policy functions of activity-travel behavior. The policy function is interpreted through a surrogate interpretable model based on choice probabilities from the policy function, while the reward function is interpreted by deriving both short-term rewards and long-term returns for various activity-travel patterns. Our analysis of real-world travel survey data reveals promising results in two key areas: (i) behavioral pattern insights from the policy function, highlighting critical factors in decision-making and variations among socio-demographic groups, and (ii) behavioral preference insights from the reward function, indicating the utility individuals gain from specific activity sequences.
Navigating Heat Exposure: Simulation of Route Planning Based on Visual Language Model Agents
2025-03-17
Haoran Ma, Kaihan Zhang, Jiannan Cai
Heat exposure significantly influences pedestrian routing behaviors. Existing methods such as agent-based modeling (ABM) and empirical measurements fail to account for individual physiological variations and environmental perception mechanisms under thermal stress. This results in a lack of human-centred, heat-adaptive routing suggestions. To address these limitations, we propose a novel Vision Language Model (VLM)-driven Persona-Perception-Planning-Memory (PPPM) framework that integrating street view imagery and urban network topology to simulate heat-adaptive pedestrian routing. Through structured prompt engineering on Gemini-2.0 model, eight distinct heat-sensitive personas were created to model mobility behaviors during heat exposure, with empirical validation through questionnaire survey. Results demonstrate that simulation outputs effectively capture inter-persona variations, achieving high significant congruence with observed route preferences and highlighting differences in the factors driving agents decisions. Our framework is highly cost-effective, with simulations costing 0.006USD and taking 47.81s per route. This Artificial Intelligence-Generated Content (AIGC) methodology advances urban climate adaptation research by enabling high-resolution simulation of thermal-responsive mobility patterns, providing actionable insights for climate-resilient urban planning.
Can Reasoning Models Reason about Hardware? An Agentic HLS Perspective
2025-03-17
Luca Collini, Andrew Hennessee, Ramesh Karri, Siddharth Garg
Recent Large Language Models (LLMs) such as OpenAI o3-mini and DeepSeek-R1 use enhanced reasoning through Chain-of-Thought (CoT). Their potential in hardware design, which relies on expert-driven iterative optimization, remains unexplored. This paper investigates whether reasoning LLMs can address challenges in High-Level Synthesis (HLS) design space exploration and optimization. During HLS, engineers manually define pragmas/directives to balance performance and resource constraints. We propose an LLM-based optimization agentic framework that automatically restructures code, inserts pragmas, and identifies optimal design points via feedback from HLs tools and access to integer-linear programming (ILP) solvers. Experiments compare reasoning models against conventional LLMs on benchmarks using success rate, efficiency, and design quality (area/latency) metrics, and provide the first-ever glimpse into the CoTs produced by a powerful open-source reasoning model like DeepSeek-R1.
2025-03-17
Joanna M Zajac, Tobias Huber-Loyola, Sven Hofling
This article reviews the current state of the field of quantum repeaters (QRs) based on Quantum Dots (QDs). First, we provide a short introduction to QRs. Then, we give an overview of the state of the art of non-classical photon sources based on III-V QDs. We discuss their performance and benchmark them according to critical parameters such as their low multiphoton emission probability and indistinguishability. Then, we discuss quantum repeaters focusing firstly on all-photonic QRs. Secondly, we discuss memory based QRs with QDs as photon sources and alkali vapors as memories. We review storage protocols and their compatibility with NIR and telecom QDs. Finally, we review enabling technologies that facilitate deploying these systems in commercial fiber networks.
CoCCoA: Complex Chemistry in hot Cores with ALMA. The chemical evolution of acetone from ice to gas
2025-03-17
Y. Chen, R. T. Garrod, M. Rachid, E. F. van Dishoeck, C. L. Brogan, R. Loomis, A. Lipnicky, B. A. McGuire
Acetone (CH3COCH3) is one of the most abundant three-carbon oxygen-bearing complex organic molecules (O-COMs) that have been detected in space. Recently, acetone ice has been reported as (tentatively) detected toward B1-c, which enables the gas-to-ice comparison of its abundances. The detection of acetone ice warrants a more systematic study of its gaseous abundances which is currently lacking. Therefore, we conducted systematic measurements of acetone gas in a dozen hot cores observed by the CoCCoA survey and investigate the chemical evolution from ice to gas of acetone in protostellar systems. We fit the ALMA spectra to determined the column density, excitation temperature, and line width of acetone, along with propanal (C2H5CHO), ketene (CH2CO), and propyne (CH3CCH), which might be chemically linked with acetone. We found that the observed gas abundances of acetone are surprisingly high compared to those of two-carbon O-COMs, while aldehydes are overall less abundant than other O-COMs (e.g., alcohols, ethers, and esters). This may suggest specific formation or destruction mechanisms that favor the production of ethers, esters, and ketones over aldehydes. The derived physical properties suggest that acetone, propanal, and ketene have the same origin from hot cores as other O-COMs, while propyne tends to trace the more extended outflows. The acetone-to-methanol ratios are higher in ice than in gas by one order of magnitude, hinting at gas-phase reprocessing after sublimation. There are several suggested formation pathways of acetone (in both ice and gas) from acetaldehyde (CH3CHO), ketene, and propylene (C3H6). The observed ratios between acetone and the relevant species are rather constant across the sample, and can be well reproduced by astrochemical simulations, but more investigations are needed to draw solid conclusions.
From Autonomous Agents to Integrated Systems, A New Paradigm: Orchestrated Distributed Intelligence
2025-03-17
Krti Tallam
The rapid evolution of artificial intelligence (AI) has ushered in a new era of integrated systems that merge computational prowess with human decision-making. In this paper, we introduce the concept of Orchestrated Distributed Intelligence (ODI), a novel paradigm that reconceptualizes AI not as isolated autonomous agents, but as cohesive, orchestrated networks that work in tandem with human expertise. ODI leverages advanced orchestration layers, multi-loop feedback mechanisms, and a high cognitive density framework to transform static, record-keeping systems into dynamic, action-oriented environments. Through a comprehensive review of multi-agent system literature, recent technological advances, and practical insights from industry forums, we argue that the future of AI lies in integrating distributed intelligence within human-centric workflows. This approach not only enhances operational efficiency and strategic agility but also addresses challenges related to scalability, transparency, and ethical decision-making. Our work outlines key theoretical implications and presents a practical roadmap for future research and enterprise innovation, aiming to pave the way for responsible and adaptive AI systems that drive sustainable innovation in human organizations.
Multi-modal time series analysis has recently emerged as a prominent research area in data mining, driven by the increasing availability of diverse data modalities, such as text, images, and structured tabular data from real-world sources. However, effective analysis of multi-modal time series is hindered by data heterogeneity, modality gap, misalignment, and inherent noise. Recent advancements in multi-modal time series methods have exploited the multi-modal context via cross-modal interactions based on deep learning methods, significantly enhancing various downstream tasks. In this tutorial and survey, we present a systematic and up-to-date overview of multi-modal time series datasets and methods. We first state the existing challenges of multi-modal time series analysis and our motivations, with a brief introduction of preliminaries. Then, we summarize the general pipeline and categorize existing methods through a unified cross-modal interaction framework encompassing fusion, alignment, and transference at different levels (\textit{i.e.}, input, intermediate, output), where key concepts and ideas are highlighted. We also discuss the real-world applications of multi-modal analysis for both standard and spatial time series, tailored to general and specific domains. Finally, we discuss future research directions to help practitioners explore and exploit multi-modal time series. The up-to-date resources are provided in the GitHub repository: https://github.com/UConn-DSIS/Multi-modal-Time-Series-Analysis
Dark Energy Survey Year 3 Results: Cosmological Constraints from Cluster Abundances, Weak Lensing, and Galaxy Clustering
2025-03-17
DES Collaboration, T. M. C. Abbott, M. Aguena, A. Alarcon, D. Anbajagane, F. Andrade-Oliveira, S. Avila, D. Bacon, M. R. Becker, S. Bhargava, J. Blazek, S. Bocquet, D. Brooks, A. Carnero Rosell, J. Carretero, F. J. Castander, C. Chang, A. Choi, C. Conselice, M. Costanzi, M. Crocce, L. N. da Costa, M. E. S. Pereira, T. M. Davis, S. Desai, H. T. Diehl, S. Dodelson, P. Doel, J. Elvin-Poole, J. Esteves, S. Everett, A. Farahi, A. Ferté, B. Flaugher, J. García-Bellido, M. Gatti, G. Giannini, P. Giles, S. Grandis, D. Gruen, R. A. Gruendl, G. Gutierrez, I. Harrison, S. R. Hinton, D. L. Hollowood, K. Honscheid, N. Jeffrey, T. Jeltema, E. Krause, O. Lahav, S. Lee, C. Lidman, M. Lima, H. Lin, J. J. Mohr, J. L. Marshall, J. McCullough, J. Mena-Fern, R. Miquel, J. Muir, J. Myles, R. L. C. Ogando, A. Palmese, M. Paterno, A. A. Plazas Malagón, A. Porredon, J. Prat, A. K. Romer, A. Roodman, E. Rozo, E. S. Rykoff, E. Sanchez, D. Sanchez Cid, I. Sevilla-Noarbe, M. Smith, E. Suchyta, G. Tarle, D. Thomas, Chun-Hao To, M. A. Troxel, V. Vikram, A. R. Walker, David H. Weinberg, N. Weaverdyck, R. H. Wechsler, J. Weller, H. -Y. Wu, M. Yamamoto, B. Yanny, Y. Zhang, C. Zhou
Galaxy clusters provide a unique probe of the late-time cosmic structure and serve as a powerful independent test of the $\Lambda$CDM model. This work presents the first set of cosmological constraints derived with ~16,000 optically selected redMaPPer clusters across nearly 5,000 $\rm{deg}^2$ using DES Year 3 data sets. Our analysis leverages a consistent modeling framework for galaxy cluster cosmology and DES-Y3 joint analyses of galaxy clustering and weak lensing (3x2pt), ensuring direct comparability with the DES-Y3 3x2pt analysis. We obtain constraints of $S_8 = 0.864 \pm 0.035$ and $\Omega_{\rm{m}} = 0.265^{+0.019}_{-0.031}$ from the cluster-based data vector. We find that cluster constraints and 3x2pt constraints are consistent under the $\Lambda$CDM model with a Posterior Predictive Distribution (PPD) value of $0.53$. The consistency between clusters and 3x2pt provides a stringent test of $\Lambda$CDM across different mass and spatial scales. Jointly analyzing clusters with 3x2pt further improves cosmological constraints, yielding $S_8 = 0.811^{+0.022}_{-0.020}$ and $\Omega_{\rm{m}} = 0.294^{+0.022}_{-0.033}$, a $24\%$ improvement in the $\Omega_{\rm{m}}-S_8$ figure-of-merit over 3x2pt alone. Moreover, we find no significant deviation from the Planck CMB constraints with a probability to exceed (PTE) value of $0.6$, significantly reducing previous $S_8$ tension claims. Finally, combining DES 3x2pt, DES clusters, and Planck CMB places an upper limit on the sum of neutrino masses of $\sum m_\nu < 0.26$ eV at 95% confidence under the $\Lambda$CDM model. These results establish optically selected clusters as a key cosmological probe and pave the way for cluster-based analyses in upcoming Stage-IV surveys such as LSST, Euclid, and Roman.
Dark Energy Survey: Modeling strategy for multiprobe cluster cosmology and validation for the Full Six-year Dataset
2025-03-17
Chun-Hao To, Elisabeth Krause, Chihway Chang, Hao-Yi Wu, Risa H. Wechsler, Eduardo Rozo, David H. Weinberg, D. Anbajagane, S. Avila, J. Blazek, S. Bocquet, M. Costanzi, J. De Vicente, J. Elvin-Poole, A. Ferté, S. Grandis, J. Muir, A. Porredon, S. Samuroff, E. Sanchez, D. Sanchez Cid, I. Sevilla-Noarbe, N. Weaverdyck, T. M. C. Abbott, M. Aguena, F. Andrade-Oliveira, D. Bacon, M. R. Becker, D. Brooks, A. Carnero Rosell, J. Carretero, A. Choi, L. N. da Costa, M. E. S. Pereira, T. M. Davis, S. Desai, P. Doel, S. Everett, J. Frieman, J. García-Bellido, M. Gatti, E. Gaztanaga, G. Giannini, D. Gruen, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, T. Jeltema, K. Kuehn, S. Lee, J. L. Marshall, J. Mena-Fern, R. Miquel, J. J. Mohr, J. Myles, A. Palmese, A. A. Plazas Malagón, A. K. Romer, T. Shin, M. Smith, E. Suchyta, G. Tarle, V. Vikram, A. R. Walker, J. Weller
We introduce an updated To&Krause2021 model for joint analyses of cluster abundances and large-scale two-point correlations of weak lensing and galaxy and cluster clustering (termed CL+3x2pt analysis) and validate that this model meets the systematic accuracy requirements of analyses with the statistical precision of the final Dark Energy Survey (DES) Year 6 (Y6) dataset. The validation program consists of two distinct approaches, (1) identification of modeling and parameterization choices and impact studies using simulated analyses with each possible model misspecification (2) end-to-end validation using mock catalogs from customized Cardinal simulations that incorporate realistic galaxy populations and DES-Y6-specific galaxy and cluster selection and photometric redshift modeling, which are the key observational systematics. In combination, these validation tests indicate that the model presented here meets the accuracy requirements of DES-Y6 for CL+3x2pt based on a large list of tests for known systematics. In addition, we also validate that the model is sufficient for several other data combinations: the CL+GC subset of this data vector (excluding galaxy--galaxy lensing and cosmic shear two-point statistics) and the CL+3x2pt+BAO+SN (combination of CL+3x2pt with the previously published Y6 DES baryonic acoustic oscillation and Y5 supernovae data).
Microstates of Non-extremal Black Holes: A New Hope
2025-03-17
Soumangsu Chakraborty, Pierre Heidmann
We provide a new roadmap for constructing microstates of non-extremal black holes in supergravity. First, we review the non-linear sigma model of five-dimensional supergravity governing stationary solutions with a U(1) isometry and present the first generalized Ernst formulation of this model. We then revisit solution-generating techniques associated to the coset model symmetry, Ernst formalism and inverse scattering method. While some of these techniques have been extensively used to generate non-extremal black holes and black rings in supergravity, we demonstrate how they can be adapted to construct systematically non-BPS smooth horizonless geometries that have the same mass and charges as non-extremal black holes. To illustrate these methods, we construct novel static solutions of this type, including a non-BPS generalization of the $\frac{1}{2}$-BPS Gibbons-Hawking center, which has served as the fundamental building block of multicenter microstates of BPS black holes.
A critical issue in studying the evolution of galaxy clusters is to find ways that enable meaningful comparisons of clusters observed at different redshifts, as well as in various stages of their growth. Studies in the past have typically suffered from uncertainties in cluster mass estimates due to the scatter between cluster observables and mass. Here we propose a novel and general approach that uses the probability distribution function of an observable-cluster mass relation, together with dark matter halo merger trees extracted from numerical simulations, such that one can trace the evolution in a self-contained fashion, for clusters chosen to lie in a specified range in mass and redshift. This method, when applied to clusters of different mass ranges, further allows one to examine the evolution of various observable-cluster mass scaling relations. We illustrate the potential of this method by studying the stellar mass content of red cluster member galaxies, as well as the growth of brightest cluster galaxies, from z=1.0 to z=0.3, using a large optically-detected cluster sample from the Subaru Hyper Suprime-Cam Survey, finding good agreement with previous studies.
Discovery of a Glueball-like particle X(2370) at BESIII
2025-03-17
Yanping Huang
Radiative decays of the $J/\psi$ particle are of gluon-rich environment, providing an ideal place for hunting glueballs. The $X(2370)$ particle was first discovered in $J/\psi\to \gamma \pi^+\pi^-\eta^{\prime}$ process in 2011 with the BESIII experiment at BEPCII Collider, and later it was confirmed in $J/\psi\to\gamma K\bar{K}\eta^{\prime}$ decays. In 2024, with a sample of 10 billion $J/\psi$ events collected at the BESIII detector, the spin-parity of the $X(2370)$ was determined to be $0^{-+}$ for the first time in the partial wave analysis of $J/\psi\to \gamma K^{0}_{S}K^{0}_{S} \eta^{\prime}$ process. Recently, new decay modes of $X(2370)\to K^{0}_{S}K^{0}_{S}\pi^0$, $\pi^0\pi^0\eta$ and $a^0\pi^0$ were observed. The mass, spin-parity quantum numbers, production and decay properties of the $X(2370)$ particle are consistent with the features of the lightest pseudoscalar glueball.
A Comprehensive Survey on Visual Concept Mining in Text-to-image Diffusion Models
2025-03-17
Ziqiang Li, Jun Li, Lizhi Xiong, Zhangjie Fu, Zechao Li
Text-to-image diffusion models have made significant advancements in generating high-quality, diverse images from text prompts. However, the inherent limitations of textual signals often prevent these models from fully capturing specific concepts, thereby reducing their controllability. To address this issue, several approaches have incorporated personalization techniques, utilizing reference images to mine visual concept representations that complement textual inputs and enhance the controllability of text-to-image diffusion models. Despite these advances, a comprehensive, systematic exploration of visual concept mining remains limited. In this paper, we categorize existing research into four key areas: Concept Learning, Concept Erasing, Concept Decomposition, and Concept Combination. This classification provides valuable insights into the foundational principles of Visual Concept Mining (VCM) techniques. Additionally, we identify key challenges and propose future research directions to propel this important and interesting field forward.
Lorentz symmetry is a cornerstone of both the General relativity and Standard Model and its experimental verification deepens our understanding of nature. This paper focuses on the investigation of Lorentz violations with the context of clock comparison experiments in the framework of Standard Model Extension (SME). Considering matter-gravity coupling sector, we provide a generic frame to study the sensitivities of Lorentz-violating coefficients for three distinct types of clock redshift tests, including the traditional gravitational redshift test, null-redshift test I and null-redshift test II. Each of these tests is sensitivity to different combinations of Lorentz-violating coefficients. By using the current clock comparison results, we estimate the limits of SME coefficients at level of parts in $10^{4}$ down to parts in $10^{7}$. Better sensitivity may be achieved in the clock comparisons by using the state-of-the-art optical clocks. Additionally considering relativistic factors in null-redshift I, the frequency comparison result of E2 and E3 transitions of Yb$^{+}$ can set the limit $c^{e}_{00}=(7.4\pm9.3)\times10^{-9}$ in the electron sector. Our analysis demonstrates that clock-comparison redshift experiments may contribute to explore the vast parameters space on searching for the Lorentz violation.
Fire and smoke phenomena pose a significant threat to the natural environment, ecosystems, and global economy, as well as human lives and wildlife. In this particular circumstance, there is a demand for more sophisticated and advanced technologies to implement an effective strategy for early detection, real-time monitoring, and minimizing the overall impacts of fires on ecological balance and public safety. Recently, the rapid advancement of Artificial Intelligence (AI) and Computer Vision (CV) frameworks has substantially revolutionized the momentum for developing efficient fire management systems. However, these systems extensively rely on the availability of adequate and high-quality fire and smoke data to create proficient Machine Learning (ML) methods for various tasks, such as detection and monitoring. Although fire and smoke datasets play a critical role in training, evaluating, and testing advanced Deep Learning (DL) models, a comprehensive review of the existing datasets is still unexplored. For this purpose, we provide an in-depth review to systematically analyze and evaluate fire and smoke datasets collected over the past 20 years. We investigate the characteristics of each dataset, including type, size, format, collection methods, and geographical diversities. We also review and highlight the unique features of each dataset, such as imaging modalities (RGB, thermal, infrared) and their applicability for different fire management tasks (classification, segmentation, detection). Furthermore, we summarize the strengths and weaknesses of each dataset and discuss their potential for advancing research and technology in fire management. Ultimately, we conduct extensive experimental analyses across different datasets using several state-of-the-art algorithms, such as ResNet-50, DeepLab-V3, and YoloV8.
The Impact of Artificial Intelligence on Emergency Medicine: A Review of Recent Advances
2025-03-17
Gustavo Correia, Victor Alves, Paulo Novais
Artificial Intelligence (AI) is revolutionizing emergency medicine by enhancing diagnostic processes and improving patient outcomes. This article provides a review of the current applications of AI in emergency imaging studies, focusing on the last five years of advancements. AI technologies, particularly machine learning and deep learning, are pivotal in interpreting complex imaging data, offering rapid, accurate diagnoses and potentially surpassing traditional diagnostic methods. Studies highlighted within the article demonstrate AI's capabilities in accurately detecting conditions such as fractures, pneumothorax, and pulmonary diseases from various imaging modalities including X-rays, CT scans, and MRIs. Furthermore, AI's ability to predict clinical outcomes like mechanical ventilation needs illustrates its potential in crisis resource optimization. Despite these advancements, the integration of AI into clinical practice presents challenges such as data privacy, algorithmic bias, and the need for extensive validation across diverse settings. This review underscores the transformative potential of AI in emergency settings, advocating for a future where AI and clinical expertise synergize to elevate patient care standards.
Regulating Ai In Financial Services: Legal Frameworks And Compliance Challenges
2025-03-17
Shahmar Mirishli
This article examines the evolving landscape of artificial intelligence (AI) regulation in financial services, detailing the legal frameworks and compliance challenges posed by rapid technological adoption. By reviewing current legislation, industry guidelines, and real-world use cases, it highlights how AI-driven processes, from fraud detection to algorithmic trading, offer efficiency gains yet introduce significant risks, including algorithmic bias, data privacy breaches, and lack of transparency in automated decision-making. The study compares regulatory approaches across major jurisdictions such as the European Union, United States, and United Kingdom, identifying both universal concerns, like the need for explainability and robust data protection, and region-specific compliance requirements that impact the implementation of high-risk AI applications. Additionally, it underscores emerging areas of focus, such as liability for AI-driven errors, systemic risks posed by interlinked AI systems, and the ethical considerations of technology-driven financial exclusion. The findings reveal gaps in existing rules and emphasize the necessity for adaptive, technology-neutral policies capable of fostering innovation while safeguarding consumer rights and market integrity. The article concludes by proposing a principled regulatory model that balances flexibility with enforceable standards, advocating closer collaboration between policymakers, financial institutions, and AI developers to ensure a secure, fair, and forward-looking framework for AI in finance.
Learning-based 3D Reconstruction in Autonomous Driving: A Comprehensive Survey
2025-03-17
Liewen Liao, Weihao Yan, Ming Yang, Songan Zhang
Learning-based 3D reconstruction has emerged as a transformative technique in autonomous driving, enabling precise modeling of both dynamic and static environments through advanced neural representations. Despite augmenting perception, 3D reconstruction inspires pioneering solution for vital tasks in the field of autonomous driving, such as scene understanding and closed-loop simulation. Commencing with an examination of input modalities, we investigates the details of 3D reconstruction and conducts a multi-perspective, in-depth analysis of recent advancements. Specifically, we first provide a systematic introduction of preliminaries, including data formats, benchmarks and technical preliminaries of learning-based 3D reconstruction, facilitating instant identification of suitable methods based on hardware configurations and sensor suites. Then, we systematically review learning-based 3D reconstruction methods in autonomous driving, categorizing approaches by subtasks and conducting multi-dimensional analysis and summary to establish a comprehensive technical reference. The development trends and existing challenges is summarized in the context of learning-based 3D reconstruction in autonomous driving. We hope that our review will inspire future researches.
Unveiling a New $β$-Scaling of the Tearing Instability in Weakly Collisional Plasmas
2025-03-16
Gabriel L. Ferreira-Santos, Grzegorz Kowal, Diego A. Falceta-Gonçalves
We investigate the linear tearing instability in weakly collisional, gyrotropic plasmas via a non-ideal CGL-MHD framework. Even for an initially isotropic equilibrium, our analysis reveals a striking dependence of the maximum growth rate on plasma-$\beta$, with $\gamma_{max}\tau_{A}\propto\beta^{-1/4}$ in the high-$\beta$ regime, thereby challenging the $\beta$-independence predicted by standard MHD theory. We show that this new scaling emerges from the self-consistent fluctuations in pressure anisotropy, which can suppress or enhance the instability depending on the underlying plasma parameters. Systematic scans of the Lundquist number, magnetic Prandtl number, and anisotropy degree $\Delta\beta$ highlight conditions under which the tearing mode departs significantly from classical behavior. Our findings emphasize that weak collisionality and gyrotropic effects must be considered to accurately capture tearing evolution in high-$\beta$ plasma environments.
AI Agents: Evolution, Architecture, and Real-World Applications
2025-03-16
Naveen Krishnan
This paper examines the evolution, architecture, and practical applications of AI agents from their early, rule-based incarnations to modern sophisticated systems that integrate large language models with dedicated modules for perception, planning, and tool use. Emphasizing both theoretical foundations and real-world deployments, the paper reviews key agent paradigms, discusses limitations of current evaluation benchmarks, and proposes a holistic evaluation framework that balances task effectiveness, efficiency, robustness, and safety. Applications across enterprise, personal assistance, and specialized domains are analyzed, with insights into future research directions for more resilient and adaptive AI agent systems.
Gravitational instability and affine dynamics of gaseous astrophysical discs
2025-03-16
Gordon I. Ogilvie
We develop several aspects of the theory of gaseous astrophysical discs in which the gravity of the disc makes a significant contribution to its structure and dynamics. We show how the internal gravitational potential can be expanded in powers of the aspect ratio of the disc (or of a structure within it) and separated into near and far contributions. We analyse the hydrostatic vertical structure of a wide family of disc models, both analytically and numerically, and show that the near contribution to the internal gravitational potential energy can be written in an almost universal form in terms of the surface density and scaleheight. We thereby develop an affine model of the dynamics of (generally non-hydrostatic) self-gravitating discs in which this contribution to the energy acts as a gravitational pressure in the plane of the disc. This combines with and significantly reinforces the gas pressure, allowing us to define an enhanced effective sound speed and Toomre stability parameter Q for self-gravitating discs. We confirm that this theory fairly accurately reproduces the onset of axisymmetric gravitational instability in discs with resolved vertical structure. Among other things, this analysis shows that the critical wavelength is on the order of twenty times the scaleheight, helping to justify the validity of the affine model. The weakly nonlinear theory also typically exhibits subcritical behaviour, with equilibrium solutions of finite amplitude being found in the linearly stable regime Q > 1 for adiabatic exponents less than 1.50.
Discovery and Timing of Four $γ$-ray Millisecond Pulsars
2025-03-16
M. Kerr, S. Johnston, C. J. Clark, F. Camilo, E. C. Ferrara, M. T. Wolff, S. M. Ransom, S. Dai, P. S. Ray, J. E. Reynolds, J. M. Sarkissian, E. D. Barr, M. K. Kramer, B. W. Stappers
We discovered four millisecond pulsars (MSPs) in searches of 80 $\gamma$-ray sources conducted from 2015 to 2017 with the Murriyang radio telescope of the Parkes Observatory. We provide an overview of the survey and focus on the results of a follow-up pulsar timing campaign. Using Fermi Large Area Telescope data, we have detected $\gamma$-ray pulsations from all four pulsars, and by combining radio and $\gamma$-ray data we obtain improved timing solutions. We also provide flux density distributions for the radio pulsars and flux-calibrated and phase-aligned radio and $\gamma$-ray pulse profiles. Some of the pulsars may be suitable for radio pulsar timing array experiments. PSR J0646-5455, PSR J1803-4719, and PSR J2045-6837 are in typical, nearly circular white dwarf binaries with residual eccentricities proportional to their binary periods. PSR J1833-3840 is a black widow pulsar with the longest known period, Pb = 0.9 d, and a very soft radio spectrum. PSR J0646-5455 has a strong, Vela-like $\gamma$-ray pulse profile and is suitable for inclusion in the $\gamma$-ray Pulsar Timing Array (GPTA). Despite this, it is possibly one of the lowest-efficiency $\gamma$-ray MSPs known. Indeed, all four new $\gamma$-ray MSPs have lower-than-average efficiency, a potential indication of bias in earlier searches. Finally, we retrospectively evaluate the efficiency of this survey: while only four new MSPs were directly discovered, subsequent campaigns have found pulsars in a further 19 of our targets, an excellent 30% efficiency.
Negotiative Alignment: Embracing Disagreement to Achieve Fairer Outcomes -- Insights from Urban Studies
2025-03-16
Rashid Mushkani, Hugo Berard, Shin Koseki
Cities are not monolithic; they are arenas of negotiation among groups that hold varying needs, values, and experiences. Conventional methods of urban assessment -- from standardized surveys to AI-driven evaluations -- frequently rely on a single consensus metric (e.g., an average measure of inclusivity or safety). Although such aggregations simplify design decisions, they risk obscuring the distinct perspectives of marginalized populations. In this paper, we present findings from a community-centered study in Montreal involving 35 residents with diverse demographic and social identities, particularly wheelchair users, seniors, and LGBTQIA2+ individuals. Using rating and ranking tasks on 20 urban sites, we observe that disagreements are systematic rather than random, reflecting structural inequalities, differing cultural values, and personal experiences of safety and accessibility. Based on these empirical insights, we propose negotiative alignment, an AI framework that treats disagreement as an essential input to be preserved, analyzed, and addressed. Negotiative alignment builds on pluralistic models by dynamically updating stakeholder preferences through multi-agent negotiation mechanisms, ensuring no single perspective is marginalized. We outline how this framework can be integrated into urban analytics -- and other decision-making contexts -- to retain minority viewpoints, adapt to changing stakeholder concerns, and enhance fairness and accountability. The study demonstrates that preserving and engaging with disagreement, rather than striving for an artificial consensus, can produce more equitable and responsive AI-driven outcomes in urban design.
VISO-Grasp: Vision-Language Informed Spatial Object-centric 6-DoF Active View Planning and Grasping in Clutter and Invisibility
2025-03-16
Yitian Shi, Di Wen, Guanqi Chen, Edgar Welte, Sheng Liu, Kunyu Peng, Rainer Stiefelhagen, Rania Rayyes
We propose VISO-Grasp, a novel vision-language-informed system designed to systematically address visibility constraints for grasping in severely occluded environments. By leveraging Foundation Models (FMs) for spatial reasoning and active view planning, our framework constructs and updates an instance-centric representation of spatial relationships, enhancing grasp success under challenging occlusions. Furthermore, this representation facilitates active Next-Best-View (NBV) planning and optimizes sequential grasping strategies when direct grasping is infeasible. Additionally, we introduce a multi-view uncertainty-driven grasp fusion mechanism that refines grasp confidence and directional uncertainty in real-time, ensuring robust and stable grasp execution. Extensive real-world experiments demonstrate that VISO-Grasp achieves a success rate of $87.5\%$ in target-oriented grasping with the fewest grasp attempts outperforming baselines. To the best of our knowledge, VISO-Grasp is the first unified framework integrating FMs into target-aware active view planning and 6-DoF grasping in environments with severe occlusions and entire invisibility constraints.
By extending the advantage of chain-of-thought (CoT) reasoning in human-like step-by-step processes to multimodal contexts, multimodal CoT (MCoT) reasoning has recently garnered significant research attention, especially in the integration with multimodal large language models (MLLMs). Existing MCoT studies design various methodologies and innovative reasoning paradigms to address the unique challenges of image, video, speech, audio, 3D, and structured data across different modalities, achieving extensive success in applications such as robotics, healthcare, autonomous driving, and multimodal generation. However, MCoT still presents distinct challenges and opportunities that require further focus to ensure consistent thriving in this field, where, unfortunately, an up-to-date review of this domain is lacking. To bridge this gap, we present the first systematic survey of MCoT reasoning, elucidating the relevant foundational concepts and definitions. We offer a comprehensive taxonomy and an in-depth analysis of current methodologies from diverse perspectives across various application scenarios. Furthermore, we provide insights into existing challenges and future research directions, aiming to foster innovation toward multimodal AGI.
Autonomous driving is a safety-critical application, and it is therefore a top priority that the accompanying assistance systems are able to provide precise information about the surrounding environment of the vehicle. Tasks such as 3D Object Detection deliver an insufficiently detailed understanding of the surrounding scene because they only predict a bounding box for foreground objects. In contrast, 3D Semantic Segmentation provides richer and denser information about the environment by assigning a label to each individual point, which is of paramount importance for autonomous driving tasks, such as navigation or lane changes. To inspire future research, in this review paper, we provide a comprehensive overview of the current state-of-the-art methods in the field of Point Cloud Semantic Segmentation for autonomous driving. We categorize the approaches into projection-based, 3D-based and hybrid methods. Moreover, we discuss the most important and commonly used datasets for this task and also emphasize the importance of synthetic data to support research when real-world data is limited. We further present the results of the different methods and compare them with respect to their segmentation accuracy and efficiency.
This paper outlines the existing situation and future trends related to automobile battery packs, specifically from the automobile manufacturer's point of view. It formulates the specifications required for such packs to adhere to prevailing regulatory schemes and examines top-level solutions to target a uniform architecture for passenger cars. Key elements such as electrical performance, safety, mechanical integrity, reliability, environmental issues, diagnostics, and real-world implications have been extensively examined. This paper draws attention to the industry trend of shifting to high-voltage battery architectures to enable ultra-fast charging above 350 kW, reducing the charging time to less than 20 minutes. Technological advancements in energy density and battery pack capacities are poised to take electric vehicle ranges over 1000 km from a single charge. This study also examines developments in artificial intelligence-improved battery management systems, enhanced safety, mechanical integrity, reliability, diagnostics, and practical considerations. Furthermore, future developments, such as the incorporation of batteries in aviation and other new uses, are investigated to provide insight into the future generation of economically viable, secure, and high-performance battery systems.
Robotic kitting is a critical task in industrial automation that requires the precise arrangement of objects into kits to support downstream production processes. However, when handling complex kitting tasks that involve fine-grained orientation alignment, existing approaches often suffer from limited accuracy and computational efficiency. To address these challenges, we propose Histogram Transporter, a novel kitting framework that learns high-precision pick-and-place actions from scratch using only a few demonstrations. First, our method extracts rotation-equivariant orientation histograms (EOHs) from visual observations using an efficient Fourier-based discretization strategy. These EOHs serve a dual purpose: improving picking efficiency by directly modeling action success probabilities over high-resolution orientations and enhancing placing accuracy by serving as local, discriminative feature descriptors for object-to-placement matching. Second, we introduce a subgroup alignment strategy in the place model that compresses the full spectrum of EOHs into a compact orientation representation, enabling efficient feature matching while preserving accuracy. Finally, we examine the proposed framework on the simulated Hand-Tool Kitting Dataset (HTKD), where it outperforms competitive baselines in both success rates and computational efficiency. Further experiments on five Raven-10 tasks exhibits the remarkable adaptability of our approach, with real-robot trials confirming its applicability for real-world deployment.
The advent of Large Language Models (LLMs) is reshaping education, particularly in programming, by enhancing problem-solving, enabling personalized feedback, and supporting adaptive learning. Existing AI tools for programming education struggle with key challenges, including the lack of Socratic guidance, direct code generation, limited context retention, minimal adaptive feedback, and the need for prompt engineering. To address these challenges, we introduce Sakshm AI, an intelligent tutoring system for learners across all education levels. It fosters Socratic learning through Disha, its inbuilt AI chatbot, which provides context-aware hints, structured feedback, and adaptive guidance while maintaining conversational memory and supporting language flexibility. This study examines 1170 registered participants, analyzing platform logs, engagement trends, and problem-solving behavior to assess Sakshm AI's impact. Additionally, a structured survey with 45 active users and 25 in-depth interviews was conducted, using thematic encoding to extract qualitative insights. Our findings reveal how AI-driven Socratic guidance influences problem-solving behaviors and engagement, offering key recommendations for optimizing AI-based coding platforms. This research combines quantitative and qualitative insights to inform AI-assisted education, providing a framework for scalable, intelligent tutoring systems that improve learning outcomes. Furthermore, Sakshm AI represents a significant step toward Sustainable Development Goal 4 Quality Education, providing an accessible and structured learning tool for undergraduate students, even without expert guidance. This is one of the first large-scale studies examining AI-assisted programming education across multiple institutions and demographics.
Subjective interpretation and content diversity make predicting whether an image is private or public a challenging task. Graph neural networks combined with convolutional neural networks (CNNs), which consist of 14,000 to 500 millions parameters, generate features for visual entities (e.g., scene and object types) and identify the entities that contribute to the decision. In this paper, we show that using a simpler combination of transfer learning and a CNN to relate privacy with scene types optimises only 732 parameters while achieving comparable performance to that of graph-based methods. On the contrary, end-to-end training of graph-based methods can mask the contribution of individual components to the classification performance. Furthermore, we show that a high-dimensional feature vector, extracted with CNNs for each visual entity, is unnecessary and complexifies the model. The graph component has also negligible impact on performance, which is driven by fine-tuning the CNN to optimise image features for privacy nodes.
Evidence for symbiotic nature of 2MASS J21012803+4555377
2025-03-16
A. M. Tatarnikov, A. A. Tatarnikova, N. A. Maslennikova, A. V. Dodin, M. A. Burlak, A. A. Tatarnikov
Just less than 300 symbiotic stars are currently known in the Galaxy. The population synthesis methods predict that this amount should be 10--100 times larger. In recent years, several works have attempted to find symbiotic candidates from photometric surveys. Regular spectroscopic observations of these candidates can increase the number of known symbiotic systems. We aim to verify the symbiotic nature of 2MASS J21012803+4555377. Archive photometry and low-resolution spectra (R=1500-2000) of the candidate are presented. Besides molecular bands, the spectrum displays numerous emission lines including forbidden and high-ionization ones -- He II, [Fe VII], etc. A strong Raman-scattered O VI line is observed at 6825A. The source shows prominent excessive emission in the infrared related to a dense circumstellar dust shell. All this evidence indicates that 2MASS J21012803+4555377 should be classified as a D-type symbiotic star.
Calibration of Complementary Metal-oxide-semiconductor Sensor-based Photometry to a Few-millimagnitude Precision: The Case of the Mini-SiTian Array
2025-03-16
Kai Xiao, Yang Huang, Haibo Yuan, Zhirui Li, Yongkang Sun, Timothy C. Beers, Min He, Jifeng Liu, Hong Wu, Yongna Mao, Bowen Huang, Mingyang Ma, Chuanjie Zheng, Hongrui Gu, Beichuan Wang, Lin Yang, Shuai Xu
We present a pioneering achievement in the high-precision photometric calibration of CMOS-based photometry, by application of the Gaia BP/RP (XP) spectra-based synthetic photometry (XPSP) method to the mini-SiTian array (MST) photometry. Through 79 repeated observations of the $\texttt{f02}$ field on the night, we find good internal consistency in the calibrated MST $G_{\rm MST}$-band magnitudes for relatively bright stars, with a precision of about 4\,mmag for $G_{\rm MST}\sim 13$. Results from more than 30 different nights (over 3100 observations) further confirm this internal consistency, indicating that the 4\,mmag precision is stable and achievable over timescales of months. An independent external validation using spectroscopic data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) DR10 and high-precision photometric data using CCDs from Gaia DR3 reveals a zero-point consistency better than 1\,mmag. Our results clearly demonstrate that CMOS photometry is on par with CCD photometry for high-precision results, highlighting the significant capabilities of CMOS cameras in astronomical observations, especially for large-scale telescope survey arrays.
CorpusStudio: Surfacing Emergent Patterns in a Corpus of Prior Work while Writing
2025-03-16
Hai Dang, Chelse Swoopes, Daniel Buschek, Elena L. Glassman
Many communities, including the scientific community, develop implicit writing norms. Understanding them is crucial for effective communication with that community. Writers gradually develop an implicit understanding of norms by reading papers and receiving feedback on their writing. However, it is difficult to both externalize this knowledge and apply it to one's own writing. We propose two new writing support concepts that reify document and sentence-level patterns in a given text corpus: (1) an ordered distribution over section titles and (2) given the user's draft and cursor location, many retrieved contextually relevant sentences. Recurring words in the latter are algorithmically highlighted to help users see any emergent norms. Study results (N=16) show that participants revised the structure and content using these concepts, gaining confidence in aligning with or breaking norms after reviewing many examples. These results demonstrate the value of reifying distributions over other authors' writing choices during the writing process.
A Survey on the Optimization of Large Language Model-based Agents
2025-03-16
Shangheng Du, Jiabao Zhao, Jinxin Shi, Zhentao Xie, Xin Jiang, Yanhong Bai, Liang He
With the rapid development of Large Language Models (LLMs), LLM-based agents have been widely adopted in various fields, becoming essential for autonomous decision-making and interactive tasks. However, current work typically relies on prompt design or fine-tuning strategies applied to vanilla LLMs, which often leads to limited effectiveness or suboptimal performance in complex agent-related environments. Although LLM optimization techniques can improve model performance across many general tasks, they lack specialized optimization towards critical agent functionalities such as long-term planning, dynamic environmental interaction, and complex decision-making. Although numerous recent studies have explored various strategies to optimize LLM-based agents for complex agent tasks, a systematic review summarizing and comparing these methods from a holistic perspective is still lacking. In this survey, we provide a comprehensive review of LLM-based agent optimization approaches, categorizing them into parameter-driven and parameter-free methods. We first focus on parameter-driven optimization, covering fine-tuning-based optimization, reinforcement learning-based optimization, and hybrid strategies, analyzing key aspects such as trajectory data construction, fine-tuning techniques, reward function design, and optimization algorithms. Additionally, we briefly discuss parameter-free strategies that optimize agent behavior through prompt engineering and external knowledge retrieval. Finally, we summarize the datasets and benchmarks used for evaluation and tuning, review key applications of LLM-based agents, and discuss major challenges and promising future directions. Our repository for related references is available at https://github.com/YoungDubbyDu/LLM-Agent-Optimization.
The frustrated honeycomb spin model can stabilize a subextensively degenerate spiral spin liquid with nontrivial topological excitations and defects, but its material realization remains rare. Here, we report the experimental realization of this model in the structurally disorder-free compound GdZnPO. Using a single-crystal sample, we find that spin-7/2 rare-earth Gd$^{3+}$ ions form a honeycomb lattice with dominant second-nearest-neighbor antiferromagnetic and first-nearest-neighbor ferromagnetic couplings, along with easy-plane single-site anisotropy. This frustrated model stabilizes a unique spiral spin liquid with a degenerate contour around the $\mathrm{K}$$\{$1/3,1/3$\}$ point in reciprocal space, consistent with our experiments down to 30 mK, including the observation of a giant residual specific heat. Our results establish GdZnPO as an ideal platform for exploring the stability of spiral spin liquids and their novel properties, such as the emergence of low-energy topological defects on the sublattices.
Pursuit-evasion (PE) problem is a critical challenge in multi-robot systems (MRS). While reinforcement learning (RL) has shown its promise in addressing PE tasks, research has primarily focused on single-target pursuit, with limited exploration of multi-target encirclement, particularly in large-scale settings. This paper proposes a Transformer-Enhanced Reinforcement Learning (TERL) framework for large-scale multi-target encirclement. By integrating a transformer-based policy network with target selection, TERL enables robots to adaptively prioritize targets and safely coordinate robots. Results show that TERL outperforms existing RL-based methods in terms of encirclement success rate and task completion time, while maintaining good performance in large-scale scenarios. Notably, TERL, trained on small-scale scenarios (15 pursuers, 4 targets), generalizes effectively to large-scale settings (80 pursuers, 20 targets) without retraining, achieving a 100% success rate.
Serenade: A Singing Style Conversion Framework Based On Audio Infilling
2025-03-16
Lester Phillip Violeta, Wen-Chin Huang, Tomoki Toda
We propose Serenade, a novel framework for the singing style conversion (SSC) task. Although singer identity conversion has made great strides in the previous years, converting the singing style of a singer has been an unexplored research area. We find three main challenges in SSC: modeling the target style, disentangling source style, and retaining the source melody. To model the target singing style, we use an audio infilling task by predicting a masked segment of the target mel-spectrogram with a flow-matching model using the complement of the masked target mel-spectrogram along with disentangled acoustic features. On the other hand, to disentangle the source singing style, we use a cyclic training approach, where we use synthetic converted samples as source inputs and reconstruct the original source mel-spectrogram as a target. Finally, to retain the source melody better, we investigate a post-processing module using a source-filter-based vocoder and resynthesize the converted waveforms using the original F0 patterns. Our results showed that the Serenade framework can handle generalized SSC tasks with the best overall similarity score, especially in modeling breathy and mixed singing styles. Moreover, although resynthesizing with the original F0 patterns alleviated out-of-tune singing and improved naturalness, we found a slight tradeoff in similarity due to not changing the F0 patterns into the target style.
EXPRESS: An LLM-Generated Explainable Property Valuation System with Neighbor Imputation
2025-03-16
Wei-Wei Du, Yung-Chien Wang, Wen-Chih Peng
The demand for property valuation has attracted significant attention from sellers, buyers, and customers applying for loans. Reviews of existing approaches have revealed shortcomings in terms of not being able to handle missing value situations, as well as lacking interpretability, which means they cannot be used in real-world applications. To address these challenges, we propose an LLM-Generated EXplainable PRopErty valuation SyStem with neighbor imputation called EXPRESS, which provides the customizable missing value imputation technique, and addresses the opaqueness of prediction by providing the feature-wise explanation generated by LLM. The dynamic nearest neighbor search finds similar properties depending on different application scenarios by property configuration set by users (e.g., house age as criteria for the house in rural areas, and locations for buildings in urban areas). Motivated by the human appraisal procedure, we generate feature-wise explanations to provide users with a more intuitive understanding of the prediction results.
A Review on Intermodal Transportation and Decarbonization: An Operations Research Perspective
2025-03-16
Madelaine Martinez Ferguson, Aliza Sharmin, Mustafa Can Camur, Xueping Li
This paper reviews intermodal transportation systems and their role in decarbonizing freight networks from an operations research perspective, analyzing over a decade of studies (2010-2024). We present a chronological analysis of the literature, illustrating how the field evolved over time while highlighting the emergence of new research avenues. We observe a significant increase in research addressing decarbonization since 2018, driven by regulatory pressures and technological advancements. Our integrated analysis is organized around three themes: a) modality, b) sustainability, and c) solution techniques. Key recommendations include the development of multistage stochastic models to better manage uncertainties and disruptions within intermodal transportation systems. Further research could leverage innovative technologies like machine learning and blockchain to improve decision-making and resource use through stakeholder collaboration. Life cycle assessment models are also suggested to better understand emissions across transportation stages and support the transition to alternative energy sources.
Unified Modeling Language Code Generation from Diagram Images Using Multimodal Large Language Models
2025-03-15
Averi Bates, Ryan Vavricka, Shane Carleton, Ruosi Shao, Chongle Pan
The Unified Modeling Language is a standardized visual language widely used for modeling and documenting the design of software systems. Although many tools generate UML diagrams from UML code, generating executable UML code from image-based UML diagrams remains challenging. This paper proposes a new approach to generate UML code using a large multimodal language model automatically. Synthetic UML activity and sequence diagram datasets were created to train and test the model. We compared standard fine-tuning with LoRA techniques to optimize base models. The experiments measured code generation accuracy across different model sizes and training strategies. These results demonstrated that domain-adapted MM-LLMs perform for UML code generation automation, whereby, at the best model, it achieved BLEU and SSIM scores of 0.779 and 0.942 on sequence diagrams. This will enable the modernization of legacy systems and decrease the manual effort in software development workflows.
Prior distributions for Gaussian processes in computer model emulation and calibration
2025-03-15
Mengyang Gu, Victor De Oliveira
This article discusses prior distributions for the parameters of Gaussian processes (GPs) that are widely used as surrogate models to emulate expensive computer simulations. The parameters typically involve mean parameters, a variance parameter, and correlation parameters. These parameters are often estimated by maximum likelihood (MLE). In some scenarios, however, the MLE can be unstable, particularly when the number of simulation runs is small, and some Bayesian estimators display better properties. We introduce default Bayesian priors for the parameters of GPs with isotropic and separable correlation functions for emulating computer simulations with both scalar-valued and vector-valued outputs. We also summarize recent developments of Bayesian priors for calibrating computer models by field or experimental observations. Finally, we review software packages for computer model emulation and calibration.
Multivariate Distribution-Free Nonparametric Testing: Generalizing Wilcoxon's Tests via Optimal Transport
2025-03-15
Zhen Huang, Bodhisattva Sen
This paper reviews recent advancements in the application of optimal transport (OT) to multivariate distribution-free nonparametric testing. Inspired by classical rank-based methods, such as Wilcoxon's rank-sum and signed-rank tests, we explore how OT-based ranks and signs generalize these concepts to multivariate settings, while preserving key properties, including distribution-freeness, robustness, and efficiency. Using the framework of asymptotic relative efficiency (ARE), we compare the power of the proposed (generalized Wilcoxon) tests against the Hotelling's $T^2$ test. The ARE lower bounds reveal the Hodges-Lehmann and Chernoff-Savage phenomena in the context of multivariate location testing, underscoring the high power and efficiency of the proposed methods. We also demonstrate how OT-based ranks and signs can be seamlessly integrated with more modern techniques, such as kernel methods, to develop universally consistent, distribution-free tests. Additionally, we present novel results on the construction of consistent and distribution-free kernel-based tests for multivariate symmetry, leveraging OT-based ranks and signs.
The instanton vacuum provides an effective description of chiral symmetry breaking by local topological fluctuations of the gauge fields, as observed in lattice QCD simulations. The resulting effective dynamics at momenta below $1/\bar\rho \approx$ 0.6 GeV explains the basic features of light-quark correlation functions and is used extensively in studies of hadron structure. The instanton fields also make definite contributions to the gluonic structure of light hadrons, as expressed in the matrix elements of composite quark-gluon or gluon operators. The article reviews the gluonic structure of light hadrons (nucleon, pion) induced by instantons. This includes: (i) twist-2 parton distributions and momentum sum rule; (ii) twist-3 angular momentum and spin-orbit interactions; (iii) twist-3 and 4 quark-gluon correlations and power corrections; (iv) trace anomaly and hadron mass decomposition; (v) scalar gluon form factors and mechanical properties; (vi) axial anomaly and pseudoscalar gluon form factors. It also discusses possible further applications of the methods and recent developments including gauge field configurations beyond instantons.
Conspiracy theories present significant societal challenges, shaping political behavior, eroding public trust, and disrupting social cohesion. Addressing their impact requires recognizing that conspiracy engagement is not a singular act but a multi-stage process involving distinct cognitive and behavioral transitions. In this study, we investigate this sequential progression, "recognition," "belief," and "action" (demonstrative action and diffusion action), using nationally representative surveys from the United States (N=13,578) and Japan (N=16,693). Applying a Bayesian hierarchical model, we identify the key social, political, and economic factors that drive engagement at each stage, providing a structured framework for understanding the mechanisms underlying conspiracy theory adoption and dissemination. We find that recognition serves as a crucial gateway determining who transitions to belief, and that demonstrative and diffusion actions are shaped by distinct factors. Demonstrative actions are more prevalent among younger, higher-status individuals with strong political alignments, whereas diffusion actions occur across broader demographics, particularly among those engaged with diverse media channels. Our findings further reveal that early-life economic and cultural capital significantly influence the shape of conspiratorial engagement, emphasizing the role of life-course experiences. These insights highlight the necessity of distinguishing between different forms of conspiracy engagement and highlight the importance of targeted interventions that account for structural, cultural, and psychological factors to mitigate their spread and societal impact.
This research presents an advanced sentiment analysis framework studied on Iranian restaurant reviews, combining fuzzy logic with conventional sentiment analysis techniques to assess both sentiment polarity and intensity. A dataset of 1266 reviews, alongside corresponding star ratings, was compiled and preprocessed for analysis. Initial sentiment analysis was conducted using the Sentiment Intensity Analyzer (VADER), a rule-based tool that assigns sentiment scores across positive, negative, and neutral categories. However, a noticeable bias toward neutrality often led to an inaccurate representation of sentiment intensity. To mitigate this issue, based on a fuzzy perspective, two refinement techniques were introduced, applying square-root and fourth-root transformations to amplify positive and negative sentiment scores while maintaining neutrality. This led to three distinct methodologies: Approach 1, utilizing unaltered VADER scores; Approach 2, modifying sentiment values using the square root; and Approach 3, applying the fourth root for further refinement. A Fuzzy Inference System incorporating comprehensive fuzzy rules was then developed to process these refined scores and generate a single, continuous sentiment value for each review based on each approach. Comparative analysis, including human supervision and alignment with customer star ratings, revealed that the refined approaches significantly improved sentiment analysis by reducing neutrality bias and better capturing sentiment intensity. Despite these advancements, minor over-amplification and persistent neutrality in domain-specific cases were identified, leading us to propose several future studies to tackle these occasional barriers. The study's methodology and outcomes offer valuable insights for businesses seeking a more precise understanding of consumer sentiment, enhancing sentiment analysis across various industries.
MT-RewardTree: A Comprehensive Framework for Advancing LLM-Based Machine Translation via Reward Modeling
2025-03-15
Zhaopeng Feng, Jiahan Ren, Jiayuan Su, Jiamei Zheng, Zhihang Tang, Hongwei Wang, Zuozhu Liu
Process reward models (PRMs) have shown success in complex reasoning tasks for large language models (LLMs). However, their application to machine translation (MT) remains underexplored due to the lack of systematic methodologies and evaluation benchmarks. To address this gap, we introduce \textbf{MT-RewardTree}, a comprehensive framework for constructing, evaluating, and deploying process reward models in MT. Unlike traditional vanilla preference pair construction, we propose a novel method for automatically generating token-level preference pairs using approximate Monte Carlo Tree Search (MCTS), which mitigates the prohibitive cost of human annotation for fine-grained steps. Then, we establish the first MT-specific reward model benchmark and provide a systematic comparison of different reward modeling architectures, revealing that token-level supervision effectively captures fine-grained preferences. Experimental results demonstrate that our MT-PRM-Qwen-2.5-3B achieves state-of-the-art performance in both token-level and sequence-level evaluation given the same input prefix. Furthermore, we showcase practical applications where PRMs enable test-time alignment for LLMs without additional alignment training and significantly improve performance in hypothesis ensembling. Our work provides valuable insights into the role of reward models in MT research. Our code and data are released in \href{https://sabijun.github.io/MT_RewardTreePage/}{https://sabijun.github.io/MT\_RewardTreePage}.
2025-03-15
Weiming Zhang, Dingwen Xiao, Lei Chen, Lin Wang
Entity Segmentation (ES) aims at identifying and segmenting distinct entities within an image without the need for predefined class labels. This characteristic makes ES well-suited to open-world applications with adaptation to diverse and dynamically changing environments, where new and previously unseen entities may appear frequently. Existing ES methods either require large annotated datasets or high training costs, limiting their scalability and adaptability. Recently, the Segment Anything Model (SAM), especially in its Automatic Mask Generation (AMG) mode, has shown potential for holistic image segmentation. However, it struggles with over-segmentation and under-segmentation, making it less effective for ES. In this paper, we introduce E-SAM, a novel training-free framework that exhibits exceptional ES capability. Specifically, we first propose Multi-level Mask Generation (MMG) that hierarchically processes SAM's AMG outputs to generate reliable object-level masks while preserving fine details at other levels. Entity-level Mask Refinement (EMR) then refines these object-level masks into accurate entity-level masks. That is, it separates overlapping masks to address the redundancy issues inherent in SAM's outputs and merges similar masks by evaluating entity-level consistency. Lastly, Under-Segmentation Refinement (USR) addresses under-segmentation by generating additional high-confidence masks fused with EMR outputs to produce the final ES map. These three modules are seamlessly optimized to achieve the best ES without additional training overhead. Extensive experiments demonstrate that E-SAM achieves state-of-the-art performance compared to prior ES methods, demonstrating a significant improvement by +30.1 on benchmark metrics.
2025-03-15
Kirill A. Bronnikov, Milena V. Skvortsova
Alexei Starobinsky is most famous for his great contribution to cosmology, but he has considerable achievements in other branches of gravitational physics and astrophysics, such as the theory of compact objects including black holes and wormholes. In this note, we give a brief review of Alexei's papers devoted to wormhole physics. They mostly concern such issues of common interest as the necessary conditions for wormhole existence in general relativity and its extensions as well as generic properties of some kinds of wormholes. We also extend one of the no-go theorems on thin-shell wormholes to a wider choice of their symmetry and matter content.
2025-03-15
Amir M. Mansourian, Rozhan Ahmadi, Masoud Ghafouri, Amir Mohammad Babaei, Elaheh Badali Golezani, Zeynab Yasamani Ghamchi, Vida Ramezanian, Alireza Taherian, Kimia Dinashi, Amirali Miri, Shohreh Kasaei
Deep Neural Networks (DNNs) have achieved notable performance in the fields of computer vision and natural language processing with various applications in both academia and industry. However, with recent advancements in DNNs and transformer models with a tremendous number of parameters, deploying these large models on edge devices causes serious issues such as high runtime and memory consumption. This is especially concerning with the recent large-scale foundation models, Vision-Language Models (VLMs), and Large Language Models (LLMs). Knowledge Distillation (KD) is one of the prominent techniques proposed to address the aforementioned problems using a teacher-student architecture. More specifically, a lightweight student model is trained using additional knowledge from a cumbersome teacher model. In this work, a comprehensive survey of knowledge distillation methods is proposed. This includes reviewing KD from different aspects: distillation sources, distillation schemes, distillation algorithms, distillation by modalities, applications of distillation, and comparison among existing methods. In contrast to most existing surveys, which are either outdated or simply update former surveys, this work proposes a comprehensive survey with a new point of view and representation structure that categorizes and investigates the most recent methods in knowledge distillation. This survey considers various critically important subcategories, including KD for diffusion models, 3D inputs, foundational models, transformers, and LLMs. Furthermore, existing challenges in KD and possible future research directions are discussed. Github page of the project: https://github.com/IPL-Sharif/KD_Survey
A Survey on Federated Fine-tuning of Large Language Models
2025-03-15
Yebo Wu, Chunlin Tian, Jingguang Li, He Sun, Kahou Tam, Li Li, Chengzhong Xu
Large Language Models (LLMs) have achieved remarkable success across a wide range of tasks, with fine-tuning playing a pivotal role in adapting them to specific downstream applications. Federated Learning (FL) offers a promising approach that enables collaborative model adaptation while ensuring data privacy, i.e., FedLLM. In this survey, we provide a systematic and thorough review of the integration of LLMs with FL. Specifically, we first trace the historical evolution of both LLMs and FL, while summarizing relevant prior surveys. We then present an in-depth analysis of the fundamental challenges encountered in deploying FedLLM. Following this, we conduct an extensive study of existing parameter-efficient fine-tuning (PEFT) methods and explore their applicability in FL. Furthermore, we introduce a comprehensive evaluation benchmark to rigorously assess FedLLM performance and discuss its diverse real-world applications across multiple domains. Finally, we identify critical open challenges and outline promising research directions to drive future advancements in FedLLM. We maintain an active \href{https://github.com/Clin0212/Awesome-Federated-LLM-Learning}{GitHub repository} tracking cutting-edge advancements. This survey serves as a foundational resource for researchers and practitioners, offering insights into the evolving landscape of federated fine-tuning for LLMs while guiding future innovations in privacy-preserving AI.
Goal-Oriented Source Coding using LDPC Codes for Compressed-Domain Image Classification
2025-03-15
Ahcen Aliouat, Elsa Dupraz
In the emerging field of goal-oriented communications, the focus has shifted from reconstructing data to directly performing specific learning tasks, such as classification, segmentation, or pattern recognition, on the received coded data. In the commonly studied scenario of classification from compressed images, a key objective is to enable learning directly on entropy-coded data, thereby bypassing the computationally intensive step of data reconstruction. Conventional entropy-coding methods, such as Huffman and Arithmetic coding, are effective for compression but disrupt the data structure, making them less suitable for direct learning without decoding. This paper investigates the use of low-density parity-check (LDPC) codes -- originally designed for channel coding -- as an alternative entropy-coding approach. It is hypothesized that the structured nature of LDPC codes can be leveraged more effectively by deep learning models for tasks like classification. At the receiver side, gated recurrent unit (GRU) models are trained to perform image classification directly on LDPC-coded data. Experiments on datasets like MNIST, Fashion-MNIST, and CIFAR show that LDPC codes outperform Huffman and Arithmetic coding in classification tasks, while requiring significantly smaller learning models. Furthermore, the paper analyzes why LDPC codes preserve data structure more effectively than traditional entropy-coding techniques and explores the impact of key code parameters on classification performance. These results suggest that LDPC-based entropy coding offers an optimal balance between learning efficiency and model complexity, eliminating the need for prior decoding.
The increasing integration of Artificial Intelligence (AI) in digital ecosystems has reshaped privacy dynamics, particularly for young digital citizens navigating data-driven environments. This study explores evolving privacy concerns across three key stakeholder groups, digital citizens (ages 16-19), parents, educators, and AI professionals, and assesses differences in data ownership, trust, transparency, parental mediation, education, and risk-benefit perceptions. Employing a grounded theory methodology, this research synthesizes insights from 482 participants through structured surveys, qualitative interviews, and focus groups. The findings reveal distinct privacy expectations- Young users emphasize autonomy and digital freedom, while parents and educators advocate for regulatory oversight and AI literacy programs. AI professionals, in contrast, prioritize the balance between ethical system design and technological efficiency. The data further highlights gaps in AI literacy and transparency, emphasizing the need for comprehensive, stakeholder-driven privacy frameworks that accommodate diverse user needs. Using comparative thematic analysis, this study identifies key tensions in privacy governance and develops the novel Privacy-Ethics Alignment in AI (PEA-AI) model, which structures privacy decision-making as a dynamic negotiation between stakeholders. By systematically analyzing themes such as transparency, user control, risk perception, and parental mediation, this research provides a scalable, adaptive foundation for AI governance, ensuring that privacy protections evolve alongside emerging AI technologies and youth-centric digital interactions.
The rapid expansion of Artificial Intelligence (AI) in digital platforms used by youth has created significant challenges related to privacy, autonomy, and data protection. While AI-driven personalization offers enhanced user experiences, it often operates without clear ethical boundaries, leaving young users vulnerable to data exploitation and algorithmic biases. This paper presents a call to action for ethical AI governance, advocating for a structured framework that ensures youth-centred privacy protections, transparent data practices, and regulatory oversight. We outline key areas requiring urgent intervention, including algorithmic transparency, privacy education, parental data-sharing ethics, and accountability measures. Through this approach, we seek to empower youth with greater control over their digital identities and propose actionable strategies for policymakers, AI developers, and educators to build a fairer and more accountable AI ecosystem.
This paper provides an overview of Lawvere's Fixed-Point Theorem in category theory and aims to detail the universal framework underlying self-reference and recursive structures. First, we rigorously define fundamental concepts - such as terminal objects, products, Cartesian Closed Categories, exponential objects, evaluation maps, currying, and point-surjective morphisms - and explain their intuitive meanings through concrete examples and commutative diagrams. Based on these foundational notions, we derive key lemmas (the universality of currying, the diagonal lemma, and the fixed-point construction lemma) and integrate them to develop a proof of Lawvere's Fixed-Point Theorem. Furthermore, we discuss the impact of this theorem on fixed-point combinators in programming languages, type theory, and homotopy type theory, as well as current research trends and open problems. In doing so, we clarify how the abstract principle of self-reference contributes to a wide range of applications in both mathematics and computational theory.
Unlocking Learning Potentials: The Transformative Effect of Generative AI in Education Across Grade Levels
2025-03-15
Meijuan Xie, Liling Luo
The advent of generative artificial intelligence (GAI) has brought about a notable surge in the field of education. The use of GAI to support learning is becoming increasingly prevalent among students. However, the manner and extent of its utilisation vary considerably from one individual to another. And researches about student's utilisation and perceptions of GAI remains relatively scarce. To gain insight into the issue, this paper proposed a hybrid-survey method to examine the impact of GAI on students across four different grades in six key areas (LIPSAL): learning interest, independent learning, problem solving, self-confidence, appropriate use, and learning enjoyment. Firstly, through questionnaire, we found that among LIPSAL, GAI has the greatest impact on the concept of appropriate use, the lowest level of learning interest and self-confidence. Secondly, a comparison of four grades revealed that the high and low factors of LIPSAL exhibited grade-related variation, and college students exhibited a higher level than high school students across LIPSAL. Thirdly, through interview, the students demonstrated a comprehensive understanding of the application of GAI. We found that students have a positive attitude towards GAI and are very willing to use it, which is why GAI has grown so rapidly in popularity. They also told us prospects and challenges in using GAI. In the future, as GAI matures technologically, it will have an greater impact on students. These findings may help better understand usage by different students and inform future research in digital education.
Time parallelization for hyperbolic and parabolic problems
2025-03-15
Martin J. Gander, Shu-Lin Wu, Tao Zhou
Time parallelization, also known as PinT (Parallel-in-Time) is a new research direction for the development of algorithms used for solving very large scale evolution problems on highly parallel computing architectures. Despite the fact that interesting theoretical work on PinT appeared as early 1964, it was not until 2004, when processor clock speeds reached their physical limit, that research in PinT took off. A distinctive characteristic of parallelization in time is that information flow only goes forward in time, meaning that time evolution processes seem necessarily to be sequential. Nevertheless, many algorithms have been developed over the last two decades to do PinT computations, and they are often grouped into four basic classes according to how the techniques work and are used: shooting-type methods; waveform relaxation methods based on domain decomposition; multigrid methods in space-time; and direct time parallel methods. However, over the past few years, it has been recognized that highly successful PinT algorithms for parabolic problems struggle when applied to hyperbolic problems. We focus in this survey therefore on this important aspect, by first providing a summary of the fundamental differences between parabolic and hyperbolic problems for time parallelization. We then group PinT algorithms into two basic groups: the first group contains four effective PinT techniques for hyperbolic problems, namely Schwarz Waveform Relaxation with its relation to Tent Pitching; Parallel Integral Deferred Correction; ParaExp; and ParaDiag. While the methods in the first group also work well for parabolic problems, we then present PinT methods especially designed for parabolic problems in the second group: Parareal: the Parallel Full Approximation Scheme in Space-Time; Multigrid Reduction in Time; and Space-Time Multigrid.
The experimental observations that led to the quark structure of matter and the development of hadron physics are reviewed with emphasis on the discoveries of mesons and baryons, starting in the 1940s with the pion and kaon which mediate the strong hadronic force. The evidence for an internal structure of the hadrons consisting of two or three elementary spin 1/2 particles is reviewed. The discoveries of hadrons made of the heavier charm and bottom quarks are described. In 2003 more complex multi-quark hadrons began to emerge. The subsequent developments beyond the early 2000s are covered in the Review of Particle Physics (Phys. Rev. D 110 (2024) 030001). Given the very large number of observed hadrons, the choice of key experiments is somewhat subjective.
From few to many maps: A fast map-level emulator for extreme augmentation of CMB systematics datasets
2025-03-14
P. Campeti, J. -M. Delouis, L. Pagano, E. Allys, M. Lattanzi, M. Gerbino
We introduce a novel, fast, and efficient generative model built upon scattering covariances, the most recent iteration of the scattering transforms statistics. This model is designed to augment by several orders of magnitude the number of map simulations in datasets of computationally expensive CMB instrumental systematics simulations, including their non-Gaussian and inhomogeneous features. Unlike conventional neural network-based algorithms, this generative model requires only a minimal number of training samples, making it highly compatible with the computational constraints of typical CMB simulation campaigns. We validate the method using realistic simulations of CMB systematics, which are particularly challenging to emulate, and perform extensive statistical tests to confirm its ability to produce new statistically independent approximate realizations. Remarkably, even when trained on as few as 10 simulations, the emulator closely reproduces key summary statistics -- including the angular power spectrum, scattering coefficients, and Minkowski functionals -- and provides pixel-to-pixel covariance estimates with substantially reduced sample noise compared to those obtained without augmentation. The proposed approach has the potential to shift the paradigm in simulation campaign design. Instead of producing large numbers of low- or medium-accuracy simulations, future pipelines can focus on generating a few high-accuracy simulations that are then efficiently augmented using such generative model. This promises significant benefits for current and forthcoming cosmological surveys such as $Planck$, $LiteBIRD$, Simons Observatory, CMB-S4, Euclid and Rubin-LSST. We make both the general framework for scattering transform statistics available at https://github.com/jmdelouis/HealpixML and the emulator available at https://github.com/pcampeti/CMBSCAT.
Recent cosmological observations suggest that the dark energy equation of state may have changed in the latter stages of cosmic history. We introduce a quintessence scenario, termed bounded dark energy, capable of explaining this feature in a technically natural way. Our approach is motivated from a bottom-up perspective, based on the concept of mirage cut-off, where we demonstrate the stability of the quintessence potential against large quantum corrections. At the same time, the bounded dark energy framework aligns well with top-down considerations motivated from quantum gravity arguments. We employ both human-driven insights and machine learning techniques to identify explicit realizations of bounded dark energy models. We then perform an analysis based on Markov Chain Monte-Carlo to assess their predictions against CMB, galaxy surveys, and supernova data, showing that bounded dark energy provides a good fit to current observations. We also discuss how upcoming measurements can further test and refine our proposal.
Euclid preparation. BAO analysis of photometric galaxy clustering in configuration space
2025-03-14
Euclid Collaboration, V. Duret, S. Escoffier, W. Gillard, I. Tutusaus, S. Camera, N. Tessore, F. J. Castander, N. Aghanim, A. Amara, L. Amendola, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, S. Bardelli, P. Battaglia, A. Biviano, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, A. Caillat, G. Cañas-Herrera, V. Capobianco, C. Carbone, V. F. Cardone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, S. de la Torre, G. De Lucia, A. M. Di Giorgio, H. Dole, F. Dubath, X. Dupac, S. Dusini, A. Ealet, M. Farina, R. Farinelli, S. Farrens, F. Faustini, S. Ferriol, F. Finelli, S. Fotopoulou, N. Fourmanoit, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, B. Gillis, C. Giocoli, J. Gracia-Carpio, A. Grazian, F. Grupp, S. V. H. Haugan, W. Holmes, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, M. Kilbinger, B. Kubik, M. Kunz, H. Kurki-Suonio, O. Lahav, A. M. C. Le Brun, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, S. Maurogordato, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, B. Morin, L. Moscardini, E. Munari, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, J. A. Schewtschenko, P. Schneider, T. Schrabback, A. Secroun, E. Sefusatti, G. Seidel, S. Serrano, P. Simon, C. Sirignano, G. Sirri, A. Spurio Mancini, L. Stanco, J. -L. Starck, J. Steinwagner, P. Tallada-Crespí, D. Tavagnacco, A. N. Taylor, I. Tereno, S. Toft, R. Toledo-Moreo, F. Torradeflot, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, G. Zamorani, F. M. Zerbi, E. Zucca, M. Bolzonella, C. Burigana, M. Calabrese, D. Di Ferdinando, J. A. Escartin Vigo, L. Gabarra, S. Matthew, N. Mauri, A. Pezzotta, M. Pöntinen, C. Porciani, V. Scottez, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, V. Allevato, I. T. Andika, M. Archidiacono, F. Atrio-Barandela, A. Balaguera-Antolinez, M. Ballardini, D. Bertacca, M. Bethermin, A. Blanchard, L. Blot, H. Böhringer, S. Borgani, M. L. Brown, S. Bruton, R. Cabanac, A. Calabro, B. Camacho Quevedo, A. Cappi, F. Caro, C. S. Carvalho, T. Castro, F. Cogato, S. Contarini, T. Contini, A. R. Cooray, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, S. Di Domizio, J. M. Diego, A. G. Ferrari, P. G. Ferreira, A. Finoguenov, K. Ganga, J. García-Bellido, T. Gasparetto, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, A. Gregorio, M. Guidi, C. M. Gutierrez, A. Hall, S. Hemmati, H. Hildebrandt, J. Hjorth, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, C. C. Kirkpatrick, S. Kruk, M. Lattanzi, M. Lembo, G. Leroy, J. Lesgourgues, T. I. Liaudat, S. J. Liu, A. Loureiro, G. Maggio, M. Magliocchetti, F. Mannucci, R. Maoli, J. Martín-Fleitas, C. J. A. P. Martins, L. Maurin, R. B. Metcalf, M. Miluzio, P. Monaco, C. Moretti, C. Murray, S. Nadathur, K. Naidoo, A. Navarro-Alsina, S. Nesseris, K. Paterson, A. Pisani, D. Potter, I. Risso, P. -F. Rocci, M. Sahlén, E. Sarpa, A. Schneider, D. Sciotti, E. Sellentin, M. Sereno, A. Silvestri, L. C. Smith, K. Tanidis, C. Tao, G. Testera, R. Teyssier, S. Tosi, A. Troja, M. Tucci, C. Valieri, A. Venhola, D. Vergani, F. Vernizzi, G. Verza, P. Vielzeuf, N. A. Walton
With about 1.5 billion galaxies expected to be observed, the very large number of objects in the Euclid photometric survey will allow for precise studies of galaxy clustering from a single survey, over a large range of redshifts $0.2 < z < 2.5$. In this work, we use photometric redshifts to extract the baryon acoustic oscillation signal (BAO) from the Flagship galaxy mock catalogue with a tomographic approach to constrain the evolution of the Universe and infer its cosmological parameters. We measure the two-point angular correlation function in 13 redshift bins. A template-fitting approach is applied to the measurement to extract the shift of the BAO peak through the transverse Alcock--Paczynski parameter $\alpha$. A joint analysis of all redshift bins is performed to constrain $\alpha$ at the effective redshift $z_\mathrm{eff}=0.77$ with MCMC and profile likelihood techniques. We also extract one $\alpha_i$ parameter per redshift bin to quantify its evolution as a function of time. From these 13 $\alpha_i$, which are directly proportional to the ratio $D_\mathrm{A}/\,r_\mathrm{s,\,drag}$, we constrain $h$, $\Omega_\mathrm{b}$, and $\Omega_\mathrm{cdm}$. From the joint analysis, we constrain $\alpha(z_\mathrm{eff}=0.77)=1.0011^{+0.0078}_{-0.0079}$, which represents a three-fold improvement over current constraints from the Dark Energy Survey. As expected, the constraining power in the analysis of each redshift bin is lower, with an uncertainty ranging from $\pm\,0.13$ to $\pm\,0.024$. From these results, we constrain $h$ at 0.45 %, $\Omega_\mathrm{b}$ at 0.91 %, and $\Omega_\mathrm{cdm}$ at 7.7 %. We quantify the influence of analysis choices like the template, scale cuts, redshift bins, and systematic effects like redshift-space distortions over our constraints both at the level of the extracted $\alpha_i$ parameters and at the level of cosmological inference.
Nonlinear optical metasurfaces empowered by bound-states in the continuum
2025-03-14
Ji Tong Wang, Nicolae C. Panoiu
Optical bound-states in the continuum (BICs) have greatly enriched the field of nonlinear optics with novel ways to control and manipulate light-matter interaction at the nanoscale. This has been made possible by their unique physical properties, including effective confinement of light, non-trivial topological features, and robustness upon the propagation of the optical field both in the real and momentum space. Regarding the exploration of nonlinear optical response in various photonic nanostructures supporting BICs, particular attention has been paid to optical metasurfaces, chiefly due to their ability to control the light flow at subwavelength scale, design and fabrication flexibility, and convenient phase-matching conditions. In this review, we outline and discuss recent advances in metasurface-based frequency conversion processes utilizing the versatile physics of BICs, with a particular emphasis on the main physics background pertaining to nonlinear optical phenomena and optics of BICs, as well as state-of-the-art functionalities enabled by BIC-driven nonlinear metasurfaces. These applications include harmonic generation, harmonic chiroptical effects, generation of complex quantum states, and broadband terahertz generation. In addition, several emerging research fields and the existing challenges of photonic nanodevices relying on BICs are discussed.
Additive Manufacturing for Advanced Quantum Technologies
2025-03-14
F. Wang, N. Cooper, D. Johnson, B. Hopton, T. M. Fromhold, R. Hague, A. Murray, R. McMullen, L. Turyanska, L. Hackermüller
The development of quantum technology has opened up exciting opportunities to revolutionize computing and communication, timing and navigation systems, enable non-invasive imaging of the human body, and probe fundamental physics with unprecedented precision. Alongside these advancements has come an increase in experimental complexity and a correspondingly greater dependence on compact, efficient and reliable hardware. The drive to move quantum technologies from laboratory prototypes to portable, real-world instruments has incentivized miniaturization of experimental systems relating to a strong demand for smaller, more robust and less power-hungry quantum hardware and for increasingly specialized and intricate components. Additive manufacturing, already heralded as game-changing for many manufacturing sectors, is especially well-suited to this task owing to the comparatively large amount of design freedom it enables and its ability to produce intricate three-dimensional forms and specialized components. Herein we review work conducted to date on the application of additive manufacturing to quantum technologies, discuss the current state of the art in additive manufacturing in optics, optomechanics, magnetic components and vacuum equipment, and consider pathways for future advancement. We also give an overview of the research and application areas most likely to be impacted by the deployment of additive manufacturing techniques within the quantum technology sector.
This article is part of a Living Literature Review exploring topics related to intellectual property, focusing on insights from the economic literature. Our aim is to provide a clear and non-technical introduction to patent rights, making them accessible to graduate students, legal scholars and practitioners, policymakers, and anyone curious about the subject.
This paper examines the Higgs particle self-coupling and its implications for electroweak symmetry breaking in the Standard Model. We review the current experimental constraints on the Higgs trilinear coupling and discuss the challenges in measuring it precisely. The potential consequences of deviations from the Standard Model prediction are explored, including the possibility of new physics. We then consider an alternative ultraviolet complete electroweak theory based on finite quantum field theory, which does not require spontaneous symmetry breaking or a non-zero vacuum expectation value. The predictions of this model for particle masses and the stability of the electroweak vacuum are compared to the standard Higgs mechanism. Finally, we review the SM derivation of the W-boson mass, its dependence on radiative corrections and its experimentally determined value.
mobilityDCAT-AP: a Metadata Specification for Enhanced Cross-border Mobility Data Sharing
2025-03-14
Mario Scrocca, Lina Molinas Comet, Benjamin Witsch, Daham Mohammed Mustafa, Christoph Lange, Marco Comerio, Peter Lubrich
Integrated and efficient mobility requires data sharing among the involved stakeholders. In this direction, regulators and transport authorities have been defining policies to foster the digitalisation and online publication of mobility data. However, the creation of several heterogeneous data portals for mobility data resulted in a fragmented ecosystem that challenges data accessibility. In this context, metadata is a key enabler to foster the findability and reusability of relevant datasets, but their interoperability across different data portals should be ensured. Moreover, each domain presents specificities on the relevant information that should be encoded through metadata. To solve these issues within the mobility domain, we present mobilityDCAT-AP, a reference metadata specification for mobility data portals specified by putting together domain experts and the Semantic Web community. We report on the work done to develop the metadata model behind mobilityDCAT-AP and the best practices followed in its implementation and publication. Finally, we describe the available educational resources and the activities performed to ensure broader adoption of mobilityDCAT-AP across mobility data portals. We present success stories from early adopters and discuss the challenges they encountered in implementing a metadata specification based on Semantic Web technologies.
Potential of large language model-powered nudges for promoting daily water and energy conservation
2025-03-14
Zonghan Li, Song Tong, Yi Liu, Kaiping Peng, Chunyan Wang
The increasing amount of pressure related to water and energy shortages has increased the urgency of cultivating individual conservation behaviors. While the concept of nudging, i.e., providing usage-based feedback, has shown promise in encouraging conservation behaviors, its efficacy is often constrained by the lack of targeted and actionable content. This study investigates the impact of the use of large language models (LLMs) to provide tailored conservation suggestions for conservation intentions and their rationale. Through a survey experiment with 1,515 university participants, we compare three virtual nudging scenarios: no nudging, traditional nudging with usage statistics, and LLM-powered nudging with usage statistics and personalized conservation suggestions. The results of statistical analyses and causal forest modeling reveal that nudging led to an increase in conservation intentions among 86.9%-98.0% of the participants. LLM-powered nudging achieved a maximum increase of 18.0% in conservation intentions, surpassing traditional nudging by 88.6%. Furthermore, structural equation modeling results reveal that exposure to LLM-powered nudges enhances self-efficacy and outcome expectations while diminishing dependence on social norms, thereby increasing intrinsic motivation to conserve. These findings highlight the transformative potential of LLMs in promoting individual water and energy conservation, representing a new frontier in the design of sustainable behavioral interventions and resource management.
Data-Driven Construction of Age-Structured Contact Networks
2025-03-14
Luke Murray Kearney, Emma L. Davis, Matt J. Keeling
Capturing the structure of a population and characterising contacts within the population are key to reliable projections of infectious disease. Two main elements of population structure -- contact heterogeneity and age -- have been repeatedly demonstrated to be key in infection dynamics, yet are rarely combined. Regarding individuals as nodes and contacts as edges within a network provides a powerful and intuitive method to fully realise this population structure. While there are a few key examples of contact networks being measured explicitly, in general we need to construct the appropriate networks from individual-level data. Here, using data from social contact surveys, we develop a generic and robust algorithm to generate an extrapolated network that preserves both age-structured mixing and heterogeneity in the number of contacts. We then use these networks to simulate the spread of infection through the population, constrained to have a given basic reproduction number ($R_0$) and hence a given early growth rate. Given the over-dominant role that highly connected nodes (`superspreaders') would otherwise play in early dynamics, we scale transmission by the average duration of contacts, providing a better match to surveillance data for numbers of secondary cases. This network-based model shows that, for COVID-like parameters, including both heterogeneity and age-structure reduces both peak height and epidemic size compared to models that ignore heterogeneity. Our robust methodology therefore allows for the inclusion of the full wealth of data commonly collected by surveys but frequently overlooked to be incorporated into more realistic transmission models of infectious diseases.
This article presents Underdamped Particle Swarm Optimization (UEPS), a novel metaheuristic inspired by both the Particle Swarm Optimization (PSO) algorithm and the dynamic behavior of an underdamped system. The underdamped motion acts as an intermediate solution between undamped systems, which oscillate indefinitely, and overdamped systems, which stabilize without oscillation. In the context of optimization, this type of motion allows particles to explore the search space dynamically, alternating between exploration and exploitation, with the ability to overshoot the optimal solution to explore new regions and avoid getting trapped in local optima. First, we review the concept of damped vibrations, an essential physical principle that describes how a system oscillates while losing energy over time, behaving in an underdamped, overdamped, or critically damped manner. This understanding forms the foundation for applying these concepts to optimization, ensuring a balanced management of exploration and exploitation. Furthermore, the classical PSO algorithm is discussed, highlighting its fundamental features and limitations, providing the necessary context to understand how the underdamped behavior improves PSO performance. The proposed metaheuristic is evaluated using benchmark functions and classic engineering problems, demonstrating its high robustness and efficiency.
A Review of DeepSeek Models' Key Innovative Techniques
2025-03-14
Chengen Wang, Murat Kantarcioglu
DeepSeek-V3 and DeepSeek-R1 are leading open-source Large Language Models (LLMs) for general-purpose tasks and reasoning, achieving performance comparable to state-of-the-art closed-source models from companies like OpenAI and Anthropic -- while requiring only a fraction of their training costs. Understanding the key innovative techniques behind DeepSeek's success is crucial for advancing LLM research. In this paper, we review the core techniques driving the remarkable effectiveness and efficiency of these models, including refinements to the transformer architecture, innovations such as Multi-Head Latent Attention and Mixture of Experts, Multi-Token Prediction, the co-design of algorithms, frameworks, and hardware, the Group Relative Policy Optimization algorithm, post-training with pure reinforcement learning and iterative training alternating between supervised fine-tuning and reinforcement learning. Additionally, we identify several open questions and highlight potential research opportunities in this rapidly advancing field.
Demography-independent behavioural dynamics influenced the spread of COVID-19 in Denmark
2025-03-14
Léo Meynent, Michael Bang Petersen, Sune Lehmann, Benjamin F. Maier
Understanding the factors that impact how a communicable disease like COVID-19 spreads is of central importance to mitigate future outbreaks. Traditionally, epidemic surveillance and forecasting analyses have focused on epidemiological data but recent advancements have demonstrated that monitoring behavioural changes may be equally important. Prior studies have shown that high-frequency survey data on social contact behaviour were able to improve predictions of epidemiological observables during the COVID-19 pandemic. Yet, the full potential of such highly granular survey data remains debated. Here, we utilise daily nationally representative survey data from Denmark collected during 23 months of the COVID-19 pandemic to demonstrate two central use-cases for such high-frequency survey data. First, we show that complex behavioural patterns across demographics collapse to a small number of universal key features, greatly simplifying the monitoring and analysis of adherence to outbreak-mitigation measures. Notably, the temporal evolution of the self-reported median number of face-to-face contacts follows a universal behavioural pattern across age groups, with potential to simplify analysis efforts for future outbreaks. Second, we show that these key features can be leveraged to improve deep-learning-based predictions of daily reported new infections. In particular, our models detect a strong link between aggregated self-reported social distancing and hygiene behaviours and the number of new cases in the subsequent days. Taken together, our results highlight the value of high-frequency surveys to improve our understanding of population behaviour in an ongoing public health crisis and its potential use for prediction of central epidemiological observables.
Time series analysis is crucial for understanding dynamics of complex systems. Recent advances in foundation models have led to task-agnostic Time Series Foundation Models (TSFMs) and Large Language Model-based Time Series Models (TSLLMs), enabling generalized learning and integrating contextual information. However, their success depends on large, diverse, and high-quality datasets, which are challenging to build due to regulatory, diversity, quality, and quantity constraints. Synthetic data emerge as a viable solution, addressing these challenges by offering scalable, unbiased, and high-quality alternatives. This survey provides a comprehensive review of synthetic data for TSFMs and TSLLMs, analyzing data generation strategies, their role in model pretraining, fine-tuning, and evaluation, and identifying future research directions.
Cost-effectiveness of climate regulations depends on non-climate benefits
2025-03-14
Nick Loris, Philip Rossetti, Chung-Yi See, Ashley Nunes
The United States has long pursued regulations that aim to reduce fossil fuel use. However, while potential emission reduction motivates the introduction and enforcement of these regulations, realization of this potential does not obfuscate the need for prudent economic policy. Scrutinizing nearly two decades of climate regulations in the transportation, industrial, and electric power sector, we enumerate their associated cost effectiveness. Our findings are twofold. Firstly, we find that whereas there are instances where climate regulations carry net economic benefits, there are also cases where compliance costs substantially exceed the social cost of carbon (SCC). Aggregated across sectors and subject to the precise SCC leveraged, up to 65 percent of climate regulations have abatement costs that exceed the SCC. This exceedance is particularly profound in the transportation sector where up to 82 percent of regulations reviewed have abatement costs that exceed the SCC. Secondly, we find that whereas the economic benefits of climate regulations generally exceed the economic costs, this exceedance is largely contingent on the presence of non-climate benefits. Aggregated across sectors, climate benefits account for 33 percent of overall benefits associated with a regulation, compared to 18, 46, and 2 percent for public health, private, and other benefits respectively. Furthermore, we document instances where climate benefits account for as little as 1.6 percent of overall regulatory benefits. This finding hints at a regulatory inefficiency and raises the prospect that alternative, non-climate specific regulation may be a more appropriate mechanism for realizing the non-climate benefits associated with these regulations. Collectively, our findings challenge the economic pragmatism of some (but not all) climate regulations adopted by the United States over a period of nearly two decades.
A Framework for a Capability-driven Evaluation of Scenario Understanding for Multimodal Large Language Models in Autonomous Driving
2025-03-14
Tin Stribor Sohn, Philipp Reis, Maximilian Dillitzer, Johannes Bach, Jason J. Corso, Eric Sax
Multimodal large language models (MLLMs) hold the potential to enhance autonomous driving by combining domain-independent world knowledge with context-specific language guidance. Their integration into autonomous driving systems shows promising results in isolated proof-of-concept applications, while their performance is evaluated on selective singular aspects of perception, reasoning, or planning. To leverage their full potential a systematic framework for evaluating MLLMs in the context of autonomous driving is required. This paper proposes a holistic framework for a capability-driven evaluation of MLLMs in autonomous driving. The framework structures scenario understanding along the four core capability dimensions semantic, spatial, temporal, and physical. They are derived from the general requirements of autonomous driving systems, human driver cognition, and language-based reasoning. It further organises the domain into context layers, processing modalities, and downstream tasks such as language-based interaction and decision-making. To illustrate the framework's applicability, two exemplary traffic scenarios are analysed, grounding the proposed dimensions in realistic driving situations. The framework provides a foundation for the structured evaluation of MLLMs' potential for scenario understanding in autonomous driving.
Quantum advantage is well-established in centralized computing, where quantum algorithms can solve certain problems exponentially faster than classical ones. In the distributed setting, significant progress has been made in bandwidth-limited networks, where quantum distributed networks have shown computational advantages over classical counterparts. However, the potential of quantum computing in networks that are constrained only by large distances is not yet understood. We focus on the LOCAL model of computation (Linial, FOCS 1987), a distributed computational model where computational power and communication bandwidth are unconstrained, and its quantum generalization. In this brief survey, we summarize recent progress on the quantum-LOCAL model outlining its limitations with respect to its classical counterpart: we discuss emerging techniques, and highlight open research questions that could guide future efforts in the field.
The observed cosmological constant may originate as the minimum value $U_{min}$ of a scalar field potential, where the scalar field is frozen due to a large mass. If this vacuum is metastable, it may decay to a true vacuum either at present or in the future. Assuming its decay rate $\Gamma$ is comparable to the Hubble expansion rate $H_0$, we estimate the scale of true vacuum bubbles and analyze their evolution. We find that their initial formation scale is sub-millimeter and their tension causes rapid collapse if $m \gtrsim 1.7 \cdot 10^{-3}\, eV$. For smaller masses, the bubbles expand at the speed of light. We extend our analysis to scalar-tensor theories with non-minimal coupling, finding that the nucleation scale of gravitational constant bubbles remains consistent with the sub-millimeter regime of General Relativity. The critical mass scale remains around $10^{-3}\,eV$. A theoretical estimate at redshift $z_{obs} \sim 0.01$ suggests an observable bubble radius of $\sim 50$ Mpc, implying a gravitational transition triggered $\sim 300$ Myr ago, with a present-day size approaching $100$ Mpc. Additionally, we explore mass ranges ($m < 10^{-3}\,eV$) and non-minimal coupling $\xi$ ranges ($10^{-8}\,eV^{2-n} - 10^{-1}\,eV^{2-n}$) that lead to a variation $\Delta G/G_N$ within the $1\%-7\%$ range. We assume non-minimal coupling of the form $F(\phi)=1/\kappa - \xi \phi^n$, with $\kappa=8\pi G_N$ and $2 \leq n \leq 9$. Finally, we review various local physics or/and transition based proposed solutions to the Hubble tension, including ultra-late-time transitional models (\(z \sim 0.01\)), screened fifth-force mechanisms, and the \(\Lambda_s\)CDM model, which features a transition at \(z \sim 2\). We discuss observational hints supporting these scenarios and the theoretical challenges they face.
Enhancing Hand Palm Motion Gesture Recognition by Eliminating Reference Frame Bias via Frame-Invariant Similarity Measures
2025-03-14
Arno Verduyn, Maxim Vochten, Joris De Schutter
The ability of robots to recognize human gestures facilitates a natural and accessible human-robot collaboration. However, most work in gesture recognition remains rooted in reference frame-dependent representations. This poses a challenge when reference frames vary due to different work cell layouts, imprecise frame calibrations, or other environmental changes. This paper investigated the use of invariant trajectory descriptors for robust hand palm motion gesture recognition under reference frame changes. First, a novel dataset of recorded Hand Palm Motion (HPM) gestures is introduced. The motion gestures in this dataset were specifically designed to be distinguishable without dependence on specific reference frames or directional cues. Afterwards, multiple invariant trajectory descriptor approaches were benchmarked to assess how their performances generalize to this novel HPM dataset. After this offline benchmarking, the best scoring approach is validated for online recognition by developing a real-time Proof of Concept (PoC). In this PoC, hand palm motion gestures were used to control the real-time movement of a manipulator arm. The PoC demonstrated a high recognition reliability in real-time operation, achieving an $F_1$-score of 92.3%. This work demonstrates the effectiveness of the invariant descriptor approach as a standalone solution. Moreover, we believe that the invariant descriptor approach can also be utilized within other state-of-the-art pattern recognition and learning systems to improve their robustness against reference frame variations.
Understanding the dynamic nature of biological systems is fundamental to deciphering cellular behavior, developmental processes, and disease progression. Single-cell RNA sequencing (scRNA-seq) has provided static snapshots of gene expression, offering valuable insights into cellular states at a single time point. Recent advancements in temporally resolved scRNA-seq, spatial transcriptomics (ST), and time-series spatial transcriptomics (temporal-ST) have further revolutionized our ability to study the spatiotemporal dynamics of individual cells. These technologies, when combined with computational frameworks such as Markov chains, stochastic differential equations (SDEs), and generative models like optimal transport and Schr\"odinger bridges, enable the reconstruction of dynamic cellular trajectories and cell fate decisions. This review discusses how these dynamical system approaches offer new opportunities to model and infer cellular dynamics from a systematic perspective.
Advancements in Real-Time Oncology Diagnosis: Harnessing AI and Image Fusion Techniques
2025-03-14
Leila Bagheriye, Johan Kwisthout
Real-time computer-aided diagnosis using artificial intelligence (AI), with images, can help oncologists diagnose cancer with high accuracy and in an early phase. We reviewed real-time AI-based analyzed images for decision-making in different cancer types. This paper provides insights into the present and future potential of real-time imaging and image fusion. It explores various real-time techniques, encompassing technical solutions, AI-based imaging, and image fusion diagnosis across multiple anatomical areas, and electromagnetic needle tracking. To provide a thorough overview, this paper discusses ultrasound image fusion, real-time in vivo cancer diagnosis with different spectroscopic techniques, different real-time optical imaging-based cancer diagnosis techniques, elastography-based cancer diagnosis, cervical cancer detection using neuromorphic architectures, different fluorescence image-based cancer diagnosis techniques, and hyperspectral imaging-based cancer diagnosis. We close by offering a more futuristic overview to solve existing problems in real-time image-based cancer diagnosis.
Designing a Syllabus for a Course on Empirical Software Engineering
2025-03-14
Paris Avgeriou, Nauman bin Ali, Marcos Kalinowski, Daniel Mendez
Increasingly, courses on Empirical Software Engineering research methods are being offered in higher education institutes across the world, mostly at the M.Sc. and Ph.D. levels. While the need for such courses is evident and in line with modern software engineering curricula, educators designing and implementing such courses have so far been reinventing the wheel; every course is designed from scratch with little to no reuse of ideas or content across the community. Due to the nature of the topic, it is rather difficult to get it right the first time when defining the learning objectives, selecting the material, compiling a reader, and, more importantly, designing relevant and appropriate practical work. This leads to substantial effort (through numerous iterations) and poses risks to the course quality. This chapter attempts to support educators in the first and most crucial step in their course design: creating the syllabus. It does so by consolidating the collective experience of the authors as well as of members of the Empirical Software Engineering community; the latter was mined through two working sessions and an online survey. Specifically, it offers a list of the fundamental building blocks for a syllabus, namely course aims, course topics, and practical assignments. The course topics are also linked to the subsequent chapters of this book, so that readers can dig deeper into those chapters and get support on teaching specific research methods or cross-cutting topics. Finally, we guide educators on how to take these building blocks as a starting point and consider a number of relevant aspects to design a syllabus to meet the needs of their own program, students, and curriculum.
CyclePose -- Leveraging Cycle-Consistency for Annotation-Free Nuclei Segmentation in Fluorescence Microscopy
2025-03-14
Jonas Utz, Stefan Vocht, Anne Tjorven Buessen, Dennis Possart, Fabian Wagner, Mareike Thies, Mingxuan Gu, Stefan Uderhardt, Katharina Breininger
In recent years, numerous neural network architectures specifically designed for the instance segmentation of nuclei in microscopic images have been released. These models embed nuclei-specific priors to outperform generic architectures like U-Nets; however, they require large annotated datasets, which are often not available. Generative models (GANs, diffusion models) have been used to compensate for this by synthesizing training data. These two-stage approaches are computationally expensive, as first a generative model and then a segmentation model has to be trained. We propose CyclePose, a hybrid framework integrating synthetic data generation and segmentation training. CyclePose builds on a CycleGAN architecture, which allows unpaired translation between microscopy images and segmentation masks. We embed a segmentation model into CycleGAN and leverage a cycle consistency loss for self-supervision. Without annotated data, CyclePose outperforms other weakly or unsupervised methods on two public datasets. Code is available at https://github.com/jonasutz/CyclePose
To Assess the Impact of Smart Cities on Urbanization Patterns in the United States
2025-03-14
Wayne S Singh
This paper investigates the relationship between smart city initiatives and evolving urbanization trends in the United States. The research addresses the critical issue of rapid urban growth in the U.S. and explores how innovations within the smart city paradigm influence urban development. Utilizing principles from Urban Complexity Theory, this study identifies four key variables relevant to smart cities and their impact on urbanization: smart city technology, government policy, environmental sustainability, and socioeconomic factors. A mixed-method approach, combining quantitative and qualitative methodologies, was employed. A web-based survey (n=50) utilizing a five-point Likert scale was conducted among residents of Manhattan, New York, and Capitol Hill, Seattle. Results indicate that the implementation of smart city technologies is significantly associated with shifts in population density, land use diversification, and enhanced infrastructure dynamics. Additionally, residents demonstrated preferences for smart cities based on efficient urban mobility, environmental sustainability, and personal socioeconomic improvements. The findings highlight essential considerations for urban planners, policymakers, and employers. This study concludes that incorporating the identified influential factors into strategic urban planning optimizes city development to better accommodate growing urban populations.
Breaking Shallow Limits: Task-Driven Pixel Fusion for Gap-free RGBT Tracking
2025-03-14
Andong Lu, Yuanzhi Guo, Wanyu Wang, Chenglong Li, Jin Tang, Bin Luo
Current RGBT tracking methods often overlook the impact of fusion location on mitigating modality gap, which is key factor to effective tracking. Our analysis reveals that shallower fusion yields smaller distribution gap. However, the limited discriminative power of shallow networks hard to distinguish task-relevant information from noise, limiting the potential of pixel-level fusion. To break shallow limits, we propose a novel \textbf{T}ask-driven \textbf{P}ixel-level \textbf{F}usion network, named \textbf{TPF}, which unveils the power of pixel-level fusion in RGBT tracking through a progressive learning framework. In particular, we design a lightweight Pixel-level Fusion Adapter (PFA) that exploits Mamba's linear complexity to ensure real-time, low-latency RGBT tracking. To enhance the fusion capabilities of the PFA, our task-driven progressive learning framework first utilizes adaptive multi-expert distillation to inherits fusion knowledge from state-of-the-art image fusion models, establishing robust initialization, and then employs a decoupled representation learning scheme to achieve task-relevant information fusion. Moreover, to overcome appearance variations between the initial template and search frames, we presents a nearest-neighbor dynamic template updating scheme, which selects the most reliable frame closest to the current search frame as the dynamic template. Extensive experiments demonstrate that TPF significantly outperforms existing most of advanced trackers on four public RGBT tracking datasets. The code will be released upon acceptance.
2025-03-14
Luka Lanča, Karlo Jakac, Sylvain Calinon, Stefan Ivić
This research addresses the challenge of performing search missions in dynamic environments, particularly for drifting targets whose movement is dictated by a flow field. This is accomplished through a dynamical system that integrates two partial differential equations: one governing the dynamics and uncertainty of the probability distribution, and the other regulating the potential field for ergodic multi-agent search. The target probability field evolves in response to the target dynamics imposed by the environment and accomplished sensing efforts, while being explored by multiple robot agents guided by the potential field gradient. The proposed methodology was tested on two simulated search scenarios, one of which features a synthetically generated domain and showcases better performance when compared to the baseline method with static target probability over a range of agent to flow field velocity ratios. The second search scenario represents a realistic sea search and rescue mission where the search start is delayed, the search is performed in multiple robot flight missions, and the procedure for target drift uncertainty compensation is demonstrated. Furthermore, the proposed method provides an accurate survey completion metric, based on the known detection/sensing parameters, that correlates with the actual number of targets found independently.
State Space Models (SSMs) have emerged as a promising alternative to the popular transformer-based models and have been increasingly gaining attention. Compared to transformers, SSMs excel at tasks with sequential data or longer contexts, demonstrating comparable performances with significant efficiency gains. In this survey, we provide a coherent and systematic overview for SSMs, including their theoretical motivations, mathematical formulations, comparison with existing model classes, and various applications. We divide the SSM series into three main sections, providing a detailed introduction to the original SSM, the structured SSM represented by S4, and the selective SSM typified by Mamba. We put an emphasis on technicality, and highlight the various key techniques introduced to address the effectiveness and efficiency of SSMs. We hope this manuscript serves as an introduction for researchers to explore the theoretical foundations of SSMs.
2025-03-14
Andong Lu, Mai Wen, Jinhu Wang, Yuanzhi Guo, Chenglong Li, Jin Tang, Bin Luo
Existing multimodal tracking studies focus on bi-modal scenarios such as RGB-Thermal, RGB-Event, and RGB-Language. Although promising tracking performance is achieved through leveraging complementary cues from different sources, it remains challenging in complex scenes due to the limitations of bi-modal scenarios. In this work, we introduce a general multimodal visual tracking task that fully exploits the advantages of four modalities, including RGB, thermal infrared, event, and language, for robust tracking under challenging conditions. To provide a comprehensive evaluation platform for general multimodal visual tracking, we construct QuadTrack600, a large-scale, high-quality benchmark comprising 600 video sequences (totaling 384.7K high-resolution (640x480) frame groups). In each frame group, all four modalities are spatially aligned and meticulously annotated with bounding boxes, while 21 sequence-level challenge attributes are provided for detailed performance analysis. Despite quad-modal data provides richer information, the differences in information quantity among modalities and the computational burden from four modalities are two challenging issues in fusing four modalities. To handle these issues, we propose a novel approach called QuadFusion, which incorporates an efficient Multiscale Fusion Mamba with four different scanning scales to achieve sufficient interactions of the four modalities while overcoming the exponential computational burden, for general multimodal visual tracking. Extensive experiments on the QuadTrack600 dataset and three bi-modal tracking datasets, including LasHeR, VisEvent, and TNL2K, validate the effectiveness of our QuadFusion.
Comparative Study of Spike Encoding Methods for Environmental Sound Classification
2025-03-14
Andres Larroza, Javier Naranjo-Alcazar, Vicent Ortiz Castelló, Pedro Zuccarello
Spiking Neural Networks (SNNs) offer a promising approach to reduce energy consumption and computational demands, making them particularly beneficial for embedded machine learning in edge applications. However, data from conventional digital sensors must first be converted into spike trains to be processed using neuromorphic computing technologies. The classification of environmental sounds presents unique challenges due to the high variability of frequencies, background noise, and overlapping acoustic events. Despite these challenges, most studies on spike-based audio encoding focus on speech processing, leaving non-speech environmental sounds underexplored. In this work, we conduct a comprehensive comparison of widely used spike encoding techniques, evaluating their effectiveness on the ESC-10 dataset. By understanding the impact of encoding choices on environmental sound processing, researchers and practitioners can select the most suitable approach for real-world applications such as smart surveillance, environmental monitoring, and industrial acoustic analysis. This study serves as a benchmark for spike encoding in environmental sound classification, providing a foundational reference for future research in neuromorphic audio processing.
Extraction of HI gas with bulk motions in the disk of galaxies
2025-03-14
Se-Heon Oh, Jing Wang
We propose a new method for extracting bulk motion gases in the disk of a galaxy from HI data cubes, offering improvements over classical techniques like moment analysis and line profile fitting. Our approach decomposes the line-of-sight velocity profiles into multiple Gaussian components, which are then classified into (underlying and dominant) bulk and non-bulk motion gases based on criteria such as HI surface density, velocity dispersion, kinetic energy, and rotation velocity. A 2D tilted-ring analysis is employed to refine the kinematical parametres of the galaxy disk, ensuring robust extraction of the bulk motion gases. We demonstrate the effectiveness of this method using the HI data cubes of NGC 4559 from the WSRT-HALOGAS survey, distinguishing between bulk and non-bulk gas components. From this, we find that approximately 50% of the HI gas in NGC 4559 is classified as non-bulk, possibly linked to processes such as stellar feedback. This work provides a robust framework for analysing HI kinematics of galaxies from high sensitivity HI observations of galaxies like MeerKAT-MHONGOOSE and FAST-FEASTS and allows us to best exploit the kinematic information of the complex gas dynamics within galaxy disks.
Reliable and Cost-Efficient IoT Connectivity for Smart Agriculture: A Comparative Study of LPWAN, 5G, and Hybrid Connectivity Models
2025-03-14
Mohamed Shabeer Mohamed Rafi, Mehran Behjati, Ahmad Sahban Rafsanjani
The integration of the Internet of Things (IoT) in smart agriculture has transformed farming practices by enabling real time monitoring, data-driven decision making, and automation. However, ensuring reliable connectivity in diverse agricultural environments remains a critical challenge. This paper analyzes the performance trade offs between Low Power Wide Area Networks (LPWAN), specifically LoRaWAN, NBIoT, and Sigfox and cellular networks (4G and 5G) in agricultural applications. Beyond a comprehensive literature review, this study evaluates hybrid LPWAN and 5G architectures that integrate the strengths of both network types to enhance cost-efficiency and connectivity reliability. Using real-world case studies, the findings demonstrate that hybrid LPWAN and 5G models can reduce connectivity costs by up to 30% while significantly improving network reliability in remote agricultural settings. This work provides actionable recommendations for selecting optimal IoT connectivity solutions based on agricultural requirements and proposes future research directions to further optimize IoT infrastructure in smart farming.
Maximal determinants of matrices over the roots of unity
2025-03-14
Guillermo Nuñez Ponasso
We study the maximum absolute value of the determinant of matrices with entries in the set of $\ell$-th roots of unity; this is a generalization of $D$-optimal designs and Hadamard's maximal determinant problem, which involves $\pm 1$ matrices. For general values of $\ell$, we give sharpened determinantal upper bounds and constructions of matrices of large determinant. The maximal determinant problem in the cases $\ell = 3$, $\ell = 4$ is similar to the classical Hadamard maximal determinant problem for matrices with entries $\pm 1$, and many techniques can be generalized. For $\ell = 3$ we give an additional construction of matrices with large determinant, and calculate the value of the maximal determinant over $\mu_3$ for all orders $n < 14$. Additionally, we survey the case $\ell = 4$ and exhibit an infinite family of maximal determinant matrices over the fourth roots of unity.
A Survey on Self-supervised Contrastive Learning for Multimodal Text-Image Analysis
2025-03-14
Asifullah Khan, Laiba Asmatullah, Anza Malik, Shahzaib Khan, Hamna Asif
Self-supervised learning is a machine learning approach that generates implicit labels by learning underlined patterns and extracting discriminative features from unlabeled data without manual labelling. Contrastive learning introduces the concept of "positive" and "negative" samples, where positive pairs (e.g., variation of the same image/object) are brought together in the embedding space, and negative pairs (e.g., views from different images/objects) are pushed farther away. This methodology has shown significant improvements in image understanding and image text analysis without much reliance on labeled data. In this paper, we comprehensively discuss the terminologies, recent developments and applications of contrastive learning with respect to text-image models. Specifically, we provide an overview of the approaches of contrastive learning in text-image models in recent years. Secondly, we categorize the approaches based on different model structures. Thirdly, we further introduce and discuss the latest advances of the techniques used in the process such as pretext tasks for both images and text, architectural structures, and key trends. Lastly, we discuss the recent state-of-art applications of self-supervised contrastive learning Text-Image based models.
A Survey of Cross-domain Graph Learning: Progress and Future Directions
2025-03-14
Haihong Zhao, Chenyi Zi, Aochuan Chen, Jia Li
Graph learning plays a vital role in mining and analyzing complex relationships involved in graph data, which is widely used in many real-world applications like transaction networks and communication networks. Foundation models in CV and NLP have shown powerful cross-domain capabilities that are also significant in graph domains. However, existing graph learning approaches struggle with cross-domain tasks. Inspired by successes in CV and NLP, cross-domain graph learning has once again become a focal point of attention to realizing true graph foundation models. In this survey, we present a comprehensive review and analysis of existing works on cross-domain graph learning. Concretely, we first propose a new taxonomy, categorizing existing approaches based on the learned cross-domain information: structure, feature, and structure-feature mixture. Next, we systematically survey representative methods in these categories. Finally, we discuss the remaining limitations of existing studies and highlight promising avenues for future research. Relevant papers are summarized and will be consistently updated at: https://github.com/cshhzhao/Awesome-Cross-Domain-Graph-Learning.
Falcon: A Remote Sensing Vision-Language Foundation Model
2025-03-14
Kelu Yao, Nuo Xu, Rong Yang, Yingying Xu, Zhuoyan Gao, Titinunt Kitrungrotsakul, Yi Ren, Pu Zhang, Jin Wang, Ning Wei, Chao Li
This paper introduces a holistic vision-language foundation model tailored for remote sensing, named Falcon. Falcon offers a unified, prompt-based paradigm that effectively executes comprehensive and complex remote sensing tasks. Falcon demonstrates powerful understanding and reasoning abilities at the image, region, and pixel levels. Specifically, given simple natural language instructions and remote sensing images, Falcon can produce impressive results in text form across 14 distinct tasks, i.e., image classification, object detection, segmentation, image captioning, and etc. To facilitate Falcon's training and empower its representation capacity to encode rich spatial and semantic information, we developed Falcon_SFT, a large-scale, multi-task, instruction-tuning dataset in the field of remote sensing. The Falcon_SFT dataset consists of approximately 78 million high-quality data samples, covering 5.6 million multi-spatial resolution and multi-view remote sensing images with diverse instructions. It features hierarchical annotations and undergoes manual sampling verification to ensure high data quality and reliability. Extensive comparative experiments are conducted, which verify that Falcon achieves remarkable performance over 67 datasets and 14 tasks, despite having only 0.7B parameters. We release the complete dataset, code, and model weights at https://github.com/TianHuiLab/Falcon, hoping to help further develop the open-source community.
Further Exploration of Precise Binding Energies from Physics Informed Machine Learning and the Development of a Practical Ensemble Model
2025-03-14
I. Bentley, J. Tedder, M. Gebran, A. Paul
Sixteen new physics informed machine learning models have been trained on binding energy residuals from modern mass models that leverage shape parameters and other physical features. The models have been trained on a subset of AME 2012 data and have been verified with a subset of the AME 2020 data. Among the machine learning approaches tested in this work, the preferred approach is the least squares boosted ensemble of trees which appears to have a superior ability to both interpolate and extrapolate binding energy residuals. The machine learning models for four mass models created from the ensemble of trees approach have been combined to create a composite model called the Four Model Tree Ensemble (FMTE). The FMTE model predicts binding energy values from AME 2020 with a standard deviation of 76 keV and a mean deviation of 34 keV for all nuclei with N > 7 and Z > 7. A comparison with new mass measurements for 33 isotopes not included in AME 2012 or AME 2020 indicates that the FMTE performs better than all mass models that were tested.
Measuring Similarity in Causal Graphs: A Framework for Semantic and Structural Analysis
2025-03-14
Ning-Yuan Georgia Liu, Flower Yang, Mohammad S. Jalali
Causal graphs are commonly used to understand and model complex systems. Researchers often construct these graphs from different perspectives, leading to significant variations for the same problem. Comparing causal graphs is, therefore, essential for evaluating assumptions, integrating insights, and resolving disagreements. The rise of AI tools has further amplified this need, as they are increasingly used to generate hypothesized causal graphs by synthesizing information from various sources such as prior research and community inputs, providing the potential for automating and scaling causal modeling for complex systems. Similar to humans, these tools also produce inconsistent results across platforms, versions, and iterations. Despite its importance, research on causal graph comparison remains scarce. Existing methods often focus solely on structural similarities, assuming identical variable names, and fail to capture nuanced semantic relationships, which is essential for causal graph comparison. We address these gaps by investigating methods for comparing causal graphs from both semantic and structural perspectives. First, we reviewed over 40 existing metrics and, based on predefined criteria, selected nine for evaluation from two threads of machine learning: four semantic similarity metrics and five learning graph kernels. We discuss the usability of these metrics in simple examples to illustrate their strengths and limitations. We then generated a synthetic dataset of 2,000 causal graphs using generative AI based on a reference diagram. Our findings reveal that each metric captures a different aspect of similarity, highlighting the need to use multiple metrics.
The emergence of large language models (LLMs) has revolutionized AI development, yet their training demands computational resources beyond a single cluster or even datacenter, limiting accessibility to large organizations. Decentralized training has emerged as a promising paradigm to leverage dispersed resources across clusters, datacenters, and global regions, democratizing LLM development for broader communities. As the first comprehensive exploration of this emerging field, we present decentralized LLM training as a resource-driven paradigm and categorize it into community-driven and organizational approaches. Furthermore, our in-depth analysis clarifies decentralized LLM training, including: (1) position with related domain concepts comparison, (2) decentralized resource development trends, and (3) recent advances with discussion under a novel taxonomy. We also provide up-to-date case studies and explore future directions, contributing to the evolution of decentralized LLM training research.
What's DAT? Three Case Studies of Measuring Software Development Productivity at Meta With Diff Authoring Time
2025-03-14
Moritz Beller, Amanda Park, Karim Nakad, Akshay Patel, Sarita Mohanty, Ford Garberson, Ian G. Malone, Vaishali Garg, Henri Verroken, Andrew Kennedy, Pavel Avgustinov
This paper introduces Diff Authoring Time (DAT), a powerful, yet conceptually simple approach to measuring software development productivity that enables rigorous experimentation. DAT is a time based metric, which assess how long engineers take to develop changes, using a privacy-aware telemetry system integrated with version control, the IDE, and the OS. We validate DAT through observational studies, surveys, visualizations, and descriptive statistics. At Meta, DAT has powered experiments and case studies on more than 20 projects. Here, we highlight (1) an experiment on introducing mock types (a 14% DAT improvement), (2) the development of automatic memoization in the React compiler (33% improvement), and (3) an estimate of thousands of DAT hours saved annually through code sharing (> 50% improvement). DAT offers a precise, yet high-coverage measure for development productivity, aiding business decisions. It enhances development efficiency by aligning the internal development workflow with the experiment-driven culture of external product development. On the research front, DAT has enabled us to perform rigorous experimentation on long-standing software engineering questions such as "do types make development more efficient?"
REGEN: A Dataset and Benchmarks with Natural Language Critiques and Narratives
2025-03-14
Kun Su, Krishna Sayana, Hubert Pham, James Pine, Yuri Vasilevski, Raghavendra Vasudeva, Marialena Kyriakidi, Liam Hebert, Ambarish Jash, Anushya Subbiah, Sukhdeep Sodhi
This paper introduces a novel dataset REGEN (Reviews Enhanced with GEnerative Narratives), designed to benchmark the conversational capabilities of recommender Large Language Models (LLMs), addressing the limitations of existing datasets that primarily focus on sequential item prediction. REGEN extends the Amazon Product Reviews dataset by inpainting two key natural language features: (1) user critiques, representing user "steering" queries that lead to the selection of a subsequent item, and (2) narratives, rich textual outputs associated with each recommended item taking into account prior context. The narratives include product endorsements, purchase explanations, and summaries of user preferences. Further, we establish an end-to-end modeling benchmark for the task of conversational recommendation, where models are trained to generate both recommendations and corresponding narratives conditioned on user history (items and critiques). For this joint task, we introduce a modeling framework LUMEN (LLM-based Unified Multi-task Model with Critiques, Recommendations, and Narratives) which uses an LLM as a backbone for critiquing, retrieval and generation. We also evaluate the dataset's quality using standard auto-rating techniques and benchmark it by training both traditional and LLM-based recommender models. Our results demonstrate that incorporating critiques enhances recommendation quality by enabling the recommender to learn language understanding and integrate it with recommendation signals. Furthermore, LLMs trained on our dataset effectively generate both recommendations and contextual narratives, achieving performance comparable to state-of-the-art recommenders and language models.
A Survey on SAR ship classification using Deep Learning
2025-03-14
Ch Muhammad Awais, Marco Reggiannini, Davide Moroni, Emanuele Salerno
Deep learning (DL) has emerged as a powerful tool for Synthetic Aperture Radar (SAR) ship classification. This survey comprehensively analyzes the diverse DL techniques employed in this domain. We identify critical trends and challenges, highlighting the importance of integrating handcrafted features, utilizing public datasets, data augmentation, fine-tuning, explainability techniques, and fostering interdisciplinary collaborations to improve DL model performance. This survey establishes a first-of-its-kind taxonomy for categorizing relevant research based on DL models, handcrafted feature use, SAR attribute utilization, and the impact of fine-tuning. We discuss the methodologies used in SAR ship classification tasks and the impact of different techniques. Finally, the survey explores potential avenues for future research, including addressing data scarcity, exploring novel DL architectures, incorporating interpretability techniques, and establishing standardized performance metrics. By addressing these challenges and leveraging advancements in DL, researchers can contribute to developing more accurate and efficient ship classification systems, ultimately enhancing maritime surveillance and related applications.
Resolving UnderEdit & OverEdit with Iterative & Neighbor-Assisted Model Editing
2025-03-14
Bhiman Kumar Baghel, Scott M. Jordan, Zheyuan Ryan Shi, Xiang Lorraine Li
Large Language Models (LLMs) are used in various downstream language tasks, making it crucial to keep their knowledge up-to-date, but both retraining and fine-tuning the model can be costly. Model editing offers an efficient and effective alternative by a single update to only a key subset of model parameters. While being efficient, these methods are not perfect. Sometimes knowledge edits are unsuccessful, i.e., UnderEdit, or the edit contaminated neighboring knowledge that should remain unchanged, i.e., OverEdit. To address these limitations, we propose iterative model editing, based on our hypothesis that a single parameter update is often insufficient, to mitigate UnderEdit, and neighbor-assisted model editing, which incorporates neighboring knowledge during editing to minimize OverEdit. Extensive experiments demonstrate that our methods effectively reduce UnderEdit up to 38 percentage points and OverEdit up to 6 percentage points across multiple model editing algorithms, LLMs, and benchmark datasets.
Towards shell model interactions with credible uncertainties
2025-03-14
Oliver C. Gorton, Konstantinos Kravvaris
Background: The nuclear shell model offers realistic predictions of nuclear structure starting from (quasi-) proton and neutron degrees of freedom, but relies on coupling constants (interaction matrix elements) that must be fit to experiment. To extend the shell model's applicability across the nuclear chart, and specifically toward the driplines, we must first be able to efficiently test new interaction matrix elements and assign credible uncertainties. Purpose: We develop and test a framework to efficiently fit new shell model interactions and obtain credible uncertainties. We further demonstrate its use by validating the uncertainty estimates of the known \textit{sd}-shell effective interactions. Methods: We use eigenvector continuation to emulate solutions to the exact shell model. First, we use the emulator to replicate earlier results using a well-known linear-combination chi-squared minimization algorithm. Then, we employ a modern Markov Chain Monte Carlo method to test for nonlinearities in the observable posterior distributions, which previous sensitivity analyses precluded. Results: The emulator reproduces the USDB interaction within a small margin of error, allowing for the quantification of the matrix element uncertainty. However, we find that to obtain credible predictive intervals the model defect of the shell model itself, rather than experimental or emulator uncertainty/error, must be taken into account. Conclusions: Eigenvector continuation can be used to accelerate fitting shell model interactions. We confirm that the linear approximation used to develop interactions in the past is indeed sufficient. However, we find that typical assumptions about the likelihood function must be modified in order to obtain a credible uncertainty-quantified interaction.
The Galactic population of magnetars : a simulation-based inference study
2025-03-14
Mattéo Sautron, Alexander Eli McEwen, George Younes, Jérôme Pétri, Paz Beniamini, Daniela Huppenkothen
Population synthesis modeling of the observed dynamical and physical properties of a population is a highly effective method for constraining the underlying birth parameters and evolutionary tracks. In this work, we apply a population synthesis model to the canonical magnetar population to gain insight into the parent population. We utilize simulation-based inference to reproduce the observed magnetar population with a model which takes into account the secular evolution of the force-free magnetosphere and magnetic field decay simultaneously and self-consistently. Our observational constraints are such that no magnetar is detected through their persistent emission when convolving the simulated populations with the XMM-Newton EPIC-pn Galactic plane observations, and that all of the $\sim$30 known magnetars are discovered through their bursting activity in the last $\sim50$ years. Under these constraints, we find that, within 95 % credible intervals, the birth rate of magnetars to be $1.8^{+2.6}_{-0.6}$ kyr$^{-1}$, and lead to having $10.7^{+18.8}_{-4.4}$ % of neutron stars born as magnetars. We also find a mean magnetic field at birth ($\mu_b$ is in T) $\log\left(\mu_b\right) = 10.2^{+0.1}_{-0.2}$, a magnetic field decay slope $\alpha_d = 1.9 ^{+0.9}_{-1.3}$, and timescale $\tau_d = 17.9^{+24.1}_{-14.5}$ kyr, in broad agreement with previous estimates. We conclude this study by exploring detection prospects: an all-sky survey with XMM-Newton would potentially allow to get around 7 periodic detections of magnetars, with approximately 150 magnetars exceeding XMM-Newton's flux threshold, and the upcoming AXIS experiment should allow to double these detections.
Banking on Feedback: Text Analysis of Mobile Banking iOS and Google App Reviews
2025-03-14
Yekta Amirkhalili, Ho Yi Wong
The rapid growth of mobile banking (m-banking), especially after the COVID-19 pandemic, has reshaped the financial sector. This study analyzes consumer reviews of m-banking apps from five major Canadian banks, collected from Google Play and iOS App stores. Sentiment analysis and topic modeling classify reviews as positive, neutral, or negative, highlighting user preferences and areas for improvement. Data pre-processing was performed with NLTK, a Python language processing tool, and topic modeling used Latent Dirichlet Allocation (LDA). Sentiment analysis compared methods, with Long Short-Term Memory (LSTM) achieving 82\% accuracy for iOS reviews and Multinomial Naive Bayes 77\% for Google Play. Positive reviews praised usability, reliability, and features, while negative reviews identified login issues, glitches, and dissatisfaction with updates.This is the first study to analyze both iOS and Google Play m-banking app reviews, offering insights into app strengths and weaknesses. Findings underscore the importance of user-friendly designs, stable updates, and better customer service. Advanced text analytics provide actionable recommendations for improving user satisfaction and experience.
How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlook
2025-03-14
Haoxin Liu, Harshavardhan Kamarthi, Zhiyuan Zhao, Shangqing Xu, Shiyu Wang, Qingsong Wen, Tom Hartvigsen, Fei Wang, B. Aditya Prakash
Time series analysis (TSA) is a longstanding research topic in the data mining community and has wide real-world significance. Compared to "richer" modalities such as language and vision, which have recently experienced explosive development and are densely connected, the time-series modality remains relatively underexplored and isolated. We notice that many recent TSA works have formed a new research field, i.e., Multiple Modalities for TSA (MM4TSA). In general, these MM4TSA works follow a common motivation: how TSA can benefit from multiple modalities. This survey is the first to offer a comprehensive review and a detailed outlook for this emerging field. Specifically, we systematically discuss three benefits: (1) reusing foundation models of other modalities for efficient TSA, (2) multimodal extension for enhanced TSA, and (3) cross-modality interaction for advanced TSA. We further group the works by the introduced modality type, including text, images, audio, tables, and others, within each perspective. Finally, we identify the gaps with future opportunities, including the reused modalities selections, heterogeneous modality combinations, and unseen tasks generalizations, corresponding to the three benefits. We release an up-to-date GitHub repository that includes key papers and resources.
Learning Closed-Loop Parametric Nash Equilibria of Multi-Agent Collaborative Field Coverage
2025-03-14
Jushan Chen, Santiago Paternain
Multi-agent reinforcement learning is a challenging and active field of research due to the inherent nonstationary property and coupling between agents. A popular approach to modeling the multi-agent interactions underlying the multi-agent RL problem is the Markov Game. There is a special type of Markov Game, termed Markov Potential Game, which allows us to reduce the Markov Game to a single-objective optimal control problem where the objective function is a potential function. In this work, we prove that a multi-agent collaborative field coverage problem, which is found in many engineering applications, can be formulated as a Markov Potential Game, and we can learn a parameterized closed-loop Nash Equilibrium by solving an equivalent single-objective optimal control problem. As a result, our algorithm is 10x faster during training compared to a game-theoretic baseline and converges faster during policy execution.
Adiabatic Flame Temperatures for Oxy-Methane, Oxy-Hydrogen, Air-Methane, and Air-Hydrogen Stoichiometric Combustion using the NASA CEARUN Tool, GRI-Mech 3.0 Reaction Mechanism, and Cantera Python Package
2025-03-14
Osama A. Marzouk
The Adiabatic Flame Temperature (AFT) in combustion represents the maximum attainable temperature at which the chemical energy in the reactant fuel is converted into sensible heat in combustion products without heat loss. AFT depends on the fuel, oxidizer, and chemical composition of the products. Computing AFT requires solving either a nonlinear equation or a larger minimization problem. This study obtained the AFTs for oxy-methane (methane and oxygen), oxy-hydrogen (hydrogen and oxygen), air-methane (methane and air), and air-hydrogen (hydrogen and air) for stoichiometric conditions. The reactant temperature was 298.15 K (25{\deg}C), and the pressure was kept constant at 1 atm. Two reaction mechanisms were attempted: a global single-step irreversible reaction for complete combustion and the GRI-Mech 3.0 elementary mechanism (53 species, 325 steps) for chemical equilibrium with its associated thermodynamic data. NASA CEARUN was the main modeling tool used. Two other tools were used for benchmarking: an Excel and a Cantera-Python implementation of GRI-Mech 3.0. The results showed that the AFTs for oxy-methane were 5,166.47 K (complete combustion) and 3,050.12 K (chemical equilibrium), and dropped to 2,326.35 K and 2,224.25 K for air-methane, respectively. The AFTs for oxy-hydrogen were 4,930.56 K (complete combustion) and 3,074.51 K (chemical equilibrium), and dropped to 2,520.33 K and 2,378.62 K for air-hydrogen, respectively. For eight combustion modeling cases, the relative deviation between the AFTs predicted by CEARUN and GRI-Mech 3.0 ranged from 0.064% to 3.503%.
Investigating the 1L2S Degeneracy for Wide-Orbit Planets
2025-03-14
Parisa Sangtarash, Jennifer C. Yee
Wide-orbit planets are particularly sensitive to detection by the Roman Galactic Bulge Time Domain Survey (GBTDS). This study investigates the degeneracy of these events with binary sources, focusing on how observation cadence affects the resolution of these degeneracies. We analyzed the impact of various cadences from (3.6 min)^(-1) to (5 hr)^(-1), which encompasses planned cadences for both the GBTDS and cadences used by ground-based surveys like KMTNet. The results show that a $\sim (15 min)^(-1) cadence is generally sufficient to resolve this degeneracy unless the source star is a giant (rho >~ 0.01).
ECHO21: Exploring the Cosmos with Hydrogen Observation
2025-03-14
Shikhar Mittal
We introduce a python package called ECHO21 for generating global 21-cm signal from the dark ages through cosmic dawn to the end of reionization. Because of its analytical-prescription-based foundation, ECHO21 generates a single model in $\mathcal{O}(1)\,$s. The code can generate a large set of signals, ideal for building emulators and performing astrophysical or cosmological inference from a given 21-cm dataset. The code is MPI parallel with reasonable scalability and thus, can be run on high-performance computers. We offer six astrophysical parameters that control the Lyman-$\alpha$ emissivity, X-ray emissivity, emissivity of ionizing photons, and star formation rate. A critical component of 21-cm modelling, but ignored by majority of public codes, that we include is the Ly$\alpha$ heating. For a certain range of astrophysical parameters, the Ly$\alpha$ heating could even dominate the X-ray heating. In addition to astrophysical parameters, in ECHO21 it is just as easy to vary the standard cosmological parameters which makes it possible to combine constraints from 21-cm observations and other cosmological probes. Further, we offer two models of star formation rate; a physically-motivated and an empirically-motivated. Since the latter is directly inferred by HST/JWST observations, it makes ECHO21 an appropriate tool to build synergies between 21-cm observations and galaxy surveys. With a number of 21-cm experiments soon to provide cosmic dawn 21-cm data, ECHO21 is a handy package for making quick but sufficiently realistic astrophysical inferences.
Neutrino Flavor Transformation in Neutron Star Mergers
2025-03-14
Yi Qiu, David Radice, Sherwood Richers, Maitraya Bhattacharyya
We present the first numerical relativity simulations including neutrino flavor transformations that could result from flavor instabilities, quantum many-body effects, or potential beyond standard model physics in neutron star mergers. We find that neutrino flavor transformations impact the composition and structure of the remnant, potentially leaving an imprint on the post-merger gravitational-wave signal. They also have a significant impact on the composition and nucleosynthesis yields of the ejecta.
BlackTHUNDER strikes twice: rest-frame Balmer-line absorption and high Eddington accretion rate in a Little Red Dot at $z=7.04$
2025-03-14
Francesco D'Eugenio, Roberto Maiolino, Michele Perna, Hannah Uebler, Xihan Ji, William McClymont, Sophie Koudmani, Debora Sijacki, Ignas Juodžbalis, Jan Scholtz, Jake Bennett, Andrew J. Bunker, Stefano Carniani, Stéphane Charlot, Giovanni Cresci, Emma Curtis-Lake, Elena Dalla Bontà, Gareth C. Jones, Jianwei Lyu, Alessandro Marconi, Giovanni Mazzolari, Erica J. Nelson, Eleonora Parlanti, Brant E. Robertson, Raffaella Schneider, Charlotte Simmonds, Sandro Tacchella, Giacomo Venturi, Chris Willott, Joris Witstok, Callum Witten
JWST spectroscopy has revealed a population of compact objects at redshifts $z=2$-9 with `v'-shaped spectral energy distributions, broad permitted lines, and, often, hydrogen Balmer absorption. Among these `Little Red Dots' (LRDs), Abell2744-QSO1 at $z=7.04$ has been confirmed to have time-variable equivalent width (EW) in its broad emission lines, confirming its AGN nature. We extend the analysis of NIRSpec/IFS data from the BlackTHUNDER survey to the H$\alpha$ line. The broad-line profile in Abell2744-QSO1 is manifestly non-Gaussian, requiring at least two Gaussian components with full width at half maximum FWHM=$450\pm50$ and $1800\pm100$ km s$^{-1}$. Crucially, we also detect a narrow-line Gaussian component, and strong H$\alpha$ absorption (EW relative to the continuum $\approx 30^{+15}_{-9}$ A), confirming a connection between the strong Balmer break and line absorption. The absorber is at rest with respect to broad H$\alpha$, suggesting that the gas cannot be interpreted as an inflow or outflow, forming instead a long-lived structure. Its velocity dispersion is $\sigma_{abs} = 100\pm10$ km s$^{-1}$, consistent with the value inferred from the analysis of the Balmer break. Based on H$\alpha$, we infer a black hole mass of log(M$_{BH}$/M$_\odot$)=6.3-6.7, 0.9-1.3 dex smaller than previous estimates based on H$\beta$. The Eddington ratio is 0.7-1.6. Combining the high signal-to-noise ratio of the narrow H$\alpha$ line with the spectral resolution R=3,700 of the G395H grating, we infer a narrow-line dispersion $\sigma_n = 22^{+5}_{-6}$ km s$^{-1}$, which places a stringent constraint on the black-hole-to-dynamical-mass ratio of this system to be M$_{BH}$/M$_{dyn}$>0.02-0.4. If M$_{BH}$ is near the low-mass end of our estimates, the SMBH would be accreting at a super-Eddington rate. Alternatively, at the high-M$_{BH}$ end, there would be minimal room for a host galaxy.
Anomalous diffusion via iterative quantitative homogenization: an overview of the main ideas
2025-03-14
Scott Armstrong, Vlad Vicol
Anomalous diffusion is the fundamental ansatz of phenomenological theories of passive scalar turbulence, and has been confirmed numerically and experimentally to an extraordinary extent. The purpose of this survey is to discuss our recent result, in which we construct a class of incompressible vector fields that have many of the properties observed in a fully turbulent velocity field, and for which the associated scalar advection-diffusion equation generically displays anomalous diffusion. Our main contribution is to propose an analytical framework in which to study anomalous diffusion via a backward cascade of renormalized eddy viscosities.
CoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal Control
2025-03-14
Zirui Yuan, Siqi Lai, Hao Liu
Traffic Signal Control (TSC) plays a critical role in urban traffic management by optimizing traffic flow and mitigating congestion. While Large Language Models (LLMs) have recently emerged as promising tools for TSC due to their exceptional problem-solving and generalization capabilities, existing approaches fail to address the essential need for inter-agent coordination, limiting their effectiveness in achieving network-wide optimization. To bridge this gap, we propose CoLLMLight, a cooperative LLM agent framework for TSC. Specifically, we first construct a structured spatiotemporal graph to capture real-time traffic dynamics and spatial relationships among neighboring intersections, enabling the LLM to reason about complex traffic interactions. Moreover, we introduce a complexity-aware reasoning mechanism that dynamically adapts reasoning depth based on real-time traffic conditions, ensuring optimal computational efficiency without sacrificing decision quality. Besides, we propose a fine-tuning strategy that leverages iterative simulation-driven data collection and environmental feedback to build a lightweight LLM tailored for cooperative TSC. Extensive experiments on both synthetic and real-world datasets demonstrate that CoLLMLight outperforms state-of-the-art methods in diverse traffic scenarios, showcasing its effectiveness, scalability, and robustness.
LLM Agents for Education: Advances and Applications
2025-03-14
Zhendong Chu, Shen Wang, Jian Xie, Tinghui Zhu, Yibo Yan, Jinheng Ye, Aoxiao Zhong, Xuming Hu, Jing Liang, Philip S. Yu, Qingsong Wen
Large Language Model (LLM) agents have demonstrated remarkable capabilities in automating tasks and driving innovation across diverse educational applications. In this survey, we provide a systematic review of state-of-the-art research on LLM agents in education, categorizing them into two broad classes: (1) \emph{Pedagogical Agents}, which focus on automating complex pedagogical tasks to support both teachers and students; and (2) \emph{Domain-Specific Educational Agents}, which are tailored for specialized fields such as science education, language learning, and professional development. We comprehensively examine the technological advancements underlying these LLM agents, including key datasets, benchmarks, and algorithmic frameworks that drive their effectiveness. Furthermore, we discuss critical challenges such as privacy, bias and fairness concerns, hallucination mitigation, and integration with existing educational ecosystems. This survey aims to provide a comprehensive technological overview of LLM agents for education, fostering further research and collaboration to enhance their impact for the greater good of learners and educators alike.
Survey of City-Wide Homelessness Detection Through Environmental Sensing
2025-03-14
Julia Gersey, Rose Allegrette, Joshua Lian, Zawad Munshi, Aarti Phatke
The growing homelessness crisis in the U.S. presents complex social, economic, and public health challenges, straining shelters, healthcare, and social services while limiting effective interventions. Traditional assessment methods struggle to capture its dynamic, dispersed nature, highlighting the need for scalable, data-driven detection. This survey explores computational approaches across four domains: (1) computer vision and deep learning for identifying encampments and urban indicators, (2) air quality sensing via fixed, mobile, and crowdsourced deployments, (3) IoT and edge computing for real-time urban monitoring, and (4) pedestrian behavior analysis for mobility patterns. Despite advancements, challenges persist in computational constraints, data privacy, accurate environmental measurement, and adaptability. This survey synthesizes recent research, identifies key gaps, and highlights opportunities to enhance homelessness detection, optimize resource allocation, and improve urban planning and social support systems for equitable aid distribution and better neighborhood conditions.
Advanced Deep Learning Methods for Protein Structure Prediction and Design
2025-03-14
Weikun Wu, Tianyang Wang, Yichao Zhang, Ningyuan Deng, Xinyuan Song, Ziqian Bi, Zheyu Yao, Keyu Chen, Ming Li, Qian Niu, Junyu Liu, Benji Peng, Sen Zhang, Ming Liu, Li Zhang, Xuanhe Pan, Jinlang Wang, Pohsun Feng, Yizhu Wen, Lawrence KQ Yan, Hongming Tseng, Yan Zhong, Yunze Wang, Ziyuan Qin, Bowen Jing, Junjie Yang, Jun Zhou, Chia Xin Liang, Junhao Song
After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic. We comprehensively explore advanced deep learning methods applied to protein structure prediction and design. It begins by examining recent innovations in prediction architectures, with detailed discussions on improvements such as diffusion based frameworks and novel pairwise attention modules. The text analyses key components including structure generation, evaluation metrics, multiple sequence alignment processing, and network architecture, thereby illustrating the current state of the art in computational protein modelling. Subsequent chapters focus on practical applications, presenting case studies that range from individual protein predictions to complex biomolecular interactions. Strategies for enhancing prediction accuracy and integrating deep learning techniques with experimental validation are thoroughly explored. The later sections review the industry landscape of protein design, highlighting the transformative role of artificial intelligence in biotechnology and discussing emerging market trends and future challenges. Supplementary appendices provide essential resources such as databases and open source tools, making this volume a valuable reference for researchers and students.
Searching for strong lensing by late-type galaxies in UNIONS
2025-03-13
J. A. Acevedo Barroso, B. Clément, F. Courbin, R. Gavazzi, C. Lemon, K. Rojas, D. Scott, S. Gwyn, F. Hammer, M. J. Hudson, E. A. Magnier
Recent wide-field galaxy surveys have led to an explosion in numbers of galaxy-scale strong gravitational lens candidates. However, the vast majority feature massive luminous red galaxies as the main deflectors, with late-type galaxies being vastly under-represented. This work presents a dedicated search for lensing by edge-on late-type galaxies in the Ultraviolet Near Infrared Optical Northern Survey (UNIONS). The search covers $3600$ deg$^2$ of $r$-band observations taken from the Canada-France-Hawaii Telescope. We consider all sources with magnitudes in the range $17 < r < 20.5$, without any colour preselection, yielding a parent sample of seven million sources. We characterise our parent sample via the visual inspection of $120\,000$ sources selected at random. From it, we estimate, with a 68\% confidence interval, that 1 in every $30\,000$ sources is an edge-on lens candidate, with at least eight high-quality candidates in the parent sample. This corresponds to 1 candidate per $17\,000$ edge-on late-type galaxies. Our search relies on a convolutional neural network (CNN) to select a reduced sample of candidates, followed by a visual inspection to curate the final sample. The CNN is trained from scratch using simulated $r$-band observations of edge-on lenses, and real observations of non-lenses. We find 61 good edge-on lens candidates using the CNN. Moreover, combining the CNN candidates with those found serendipitously, and those identified while characterising the parent sample, we discovered 4 grade A, 20 grade B, and 58 grade C edge-on lens candidates; effectively doubling the known sample of these systems. We also discovered 16 grade A, 16 grade B, and 18 grade C lens candidates of other types. Finally, based on the characterisation of the parent sample, we estimate that our search found around 60\% of the bright grade A and B edge-on lens candidates within the parent sample.
Language Models, Graph Searching, and Supervision Adulteration: When More Supervision is Less and How to Make More More
2025-03-13
Arvid Frydenlund
This work concerns the path-star task, a minimal example of searching over a graph. The graph, $G$, is star-shaped with $D$ arms radiating from a start node, $s$. A language model (LM) is given $G$, $s$, and a target node $t$, which ends one of the arms and is tasked with generating the arm containing $t$. The minimal nature of this task means only a single choice needs to be made: which of the $D$ arms contains $t$? Decoder-only LMs fail to solve this elementary task above $1/D$ chance due to a learned shortcut that absorbs training supervision. We show how this pathology is caused by excess supervision and we present a series of solutions demonstrating that the task is solvable via decoder-only LMs. We find that the task's minimal nature causes its difficulty, as it prevents task decomposition. Our solutions provide insight into the pathology and its implications for LMs trained via next-token prediction.
Period-Luminosity Relations for Galactic Type II Cepheids in the Sloan bands
2025-03-13
W. Narloch, G. Hajdu, G. Pietrzynski, P. Wielgorski, R. Smolec, W. Gieren, B. Zgirski, M. Gorski, P. Karczmarek, D. Graczyk
Type II Cepheids (T2Ceps), alongside RR Lyrae stars, serve as important distance indicators for old population II stars due to their period-luminosity (PL) relations. However, studies of these relations in the Sloan photometric system are rather limited in the literature. Our goal is to calibrate PL relations (and their counterparts in Wesenheit magnitudes) in the Sloan-Pan-STARRS gP1rP1iP1 bands for Galactic T2Ceps located in the vicinity of the Sun. We collected data for 16 T2Ceps of the BLHer type and 17 of the WVir type using 40 cm telescopes of the Las Cumbres Observatory Global Telescope Network. Geometric parallaxes were adopted from Gaia Data Release 3. We have calibrated PL and period-Wesenheit relations for Milky Way BLHer and WVir stars in the solar neighborhood, as well as for a combined sample of both types. The relationships derived here will allow to determine the distances to T2Ceps that will be discovered by the Legacy Survey of Space and Time survey and, in turn, to probe the extended halo of the Milky Way, as well as the halos of nearby galaxies. To the best of our knowledge, the relations derived in this study are the first for Milky Way T2Ceps in the Sloan bands.
A Module-theoretic Introduction to Abstract Elementary Classes
2025-03-13
Will Boney
The first-order model theory of modules has been studied for decades. More recently, the model theoretic study of nonelementary classes of modules--especially Abstract Elementary Classes of modules--has produced interesting results. This survey aims to discuss these recent results and give an introduction to the framework of Abstract Elementary Classes for module theorists.
Cardinal invariants associated with the combinatorics of the uniformity number of the ideal of meager-additive sets
2025-03-13
Miguel A. Cardona
In [CMRM24], it was proved that it is relatively consistent that \emph{bounding number} $\mathfrak{b}$ is smaller than the uniformity of $\mathcal{MA}$, where $\mathcal{MA}$ denotes the ideal of the meager-additive sets of $2^{\omega}$. To establish this result, a specific cardinal invariant, which we refer to as $\mathfrak{b}_b^\mathsf{eq}$, was introduced in close relation to Bartoszy\'nski's and Judah's characterization of the uniformity of $\mathcal{MA}$. This survey aims to explore this cardinal invariant along with its dual, which we call as $\mathfrak{d}_b^\mathsf{eq}$. In particular, we will illustrate its connections with the cardinals represented in Cicho\'n's diagram. Furthermore, we will present several open problems pertaining to these cardinals.
Irradiated Pulsar Planets and Companions as 511 keV Positron Annihilation Line Sources
2025-03-13
Zachary Metzler, Zorawar Wadiasingh
Millisecond pulsars (MSPs) are prolific GeV {\gamma}-ray emitters, and nearly 80% of Fermi-LAT MSPs reside in compact binaries. We demonstrate that the companions in these compact MSPs binaries are also 511 keV annihilation line emitters using MEGAlib simulations (a high energy radiation transport software built with Geant4) to compute the particle showers and resulting backsplash emission from the pulsar irradiation. The 511 keV signal exhibits strong flux modulation and red/blueshifts associated with a binary orbit, enabling powerful coherent searches. Measuring the 511 keV emission would enable direct {\gamma}-ray characterization of unusual pulsar exoplanets and companions, and allow one to identify the unambiguous presence of active pulsars whose beams do not intercept Earth. Intriguingly, the 511 keV flux is brightest for ultra-compact systems against which pulsar surveys are systematically biased. These ultra-compact systems are also possibly prime LISA galactic sources. This necessitates future joint LISA-MeV {\gamma}-ray techniques to characterize MSP binaries. These MSP binaries may also contribute to the puzzling source of the excess 511 keV photons near the galactic bulge and center.
Sentiment Analysis in SemEval: A Review of Sentiment Identification Approaches
2025-03-13
Bousselham El Haddaoui, Raddouane Chiheb, Rdouan Faizi, Abdellatif El Afia
Social media platforms are becoming the foundations of social interactions including messaging and opinion expression. In this regard, Sentiment Analysis techniques focus on providing solutions to ensure the retrieval and analysis of generated data including sentiments, emotions, and discussed topics. International competitions such as the International Workshop on Semantic Evaluation (SemEval) have attracted many researchers and practitioners with a special research interest in building sentiment analysis systems. In our work, we study top-ranking systems for each SemEval edition during the 2013-2021 period, a total of 658 teams participated in these editions with increasing interest over years. We analyze the proposed systems marking the evolution of research trends with a focus on the main components of sentiment analysis systems including data acquisition, preprocessing, and classification. Our study shows an active use of preprocessing techniques, an evolution of features engineering and word representation from lexicon-based approaches to word embeddings, and the dominance of neural networks and transformers over the classification phase fostering the use of ready-to-use models. Moreover, we provide researchers with insights based on experimented systems which will allow rapid prototyping of new systems and help practitioners build for future SemEval editions.
We present Transit Timing Variations (TTVs) of HAT-P-12b, a low-density sub-Saturn mass planet orbiting a metal-poor K4 dwarf star. Using 14 years of observational data (2009-2022), our study incorporates 7 new ground-based photometric transit observations, three sectors of Transiting Exoplanet Survey Satellite (TESS) data, and 23 previously published light curves. A total of 46 light curves were analyzed using various analytical models, such as linear, orbital decay, apsidal precession, and sinusoidal models to investigate the presence of additional planets. The stellar tidal quality factor ($Q_\star' \sim$ 28.4) is lower than the theoretical predictions, making the orbital decay model an unlikely explanation. The apsidal precession model with a $\chi_r^2$ of 4.2 revealed a slight orbital eccentricity (e = 0.0013) and a precession rate of 0.0045 rad/epoch. Frequency analysis using the Generalized Lomb-Scargle (GLS) periodogram identified a significant periodic signal at 0.00415 cycles/day (FAP = 5.1$\times$10$^{-6}$ %), suggesting the influence of an additional planetary companion. The sinusoidal model provides the lowest reduced chi-squared value ($\chi_r^2$) of 3.2. Sinusoidal fitting of the timing residuals estimated this companion to have a mass of approximately 0.02 $M_J$ , assuming it is in a 2:1 Mean-Motion Resonance (MMR) with HAT-P-12b. Additionally, the Applegate mechanism, with an amplitude much smaller than the observed TTV amplitude of 156 s, confirms that stellar activity is not responsible for the observed variations.
EFC++: Elastic Feature Consolidation with Prototype Re-balancing for Cold Start Exemplar-free Incremental Learning
2025-03-13
Simone Magistri, Tomaso Trinci, Albin Soutif-Cormerais, Joost van de Weijer, Andrew D. Bagdanov
Exemplar-Free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data. In this paper, we consider the challenging Cold Start scenario in which insufficient data is available in the first task to learn a high-quality backbone. This is especially challenging for EFCIL since it requires high plasticity, resulting in feature drift which is difficult to compensate for in the exemplar-free setting. To address this problem, we propose an effective approach to consolidate feature representations by regularizing drift in directions highly relevant to previous tasks and employs prototypes to reduce task-recency bias. Our approach, which we call Elastic Feature Consolidation++ (EFC++) exploits a tractable second-order approximation of feature drift based on a proposed Empirical Feature Matrix (EFM). The EFM induces a pseudo-metric in feature space which we use to regularize feature drift in important directions and to update Gaussian prototypes. In addition, we introduce a post-training prototype re-balancing phase that updates classifiers to compensate for feature drift. Experimental results on CIFAR-100, Tiny-ImageNet, ImageNet-Subset, ImageNet-1K and DomainNet demonstrate that EFC++ is better able to learn new tasks by maintaining model plasticity and significantly outperform the state-of-the-art.
Handling Domain Shifts for Anomalous Sound Detection: A Review of DCASE-Related Work
2025-03-13
Kevin Wilkinghoff, Takuya Fujimura, Keisuke Imoto, Jonathan Le Roux, Zheng-Hua Tan, Tomoki Toda
When detecting anomalous sounds in complex environments, one of the main difficulties is that trained models must be sensitive to subtle differences in monitored target signals, while many practical applications also require them to be insensitive to changes in acoustic domains. Examples of such domain shifts include changing the type of microphone or the location of acoustic sensors, which can have a much stronger impact on the acoustic signal than subtle anomalies themselves. Moreover, users typically aim to train a model only on source domain data, which they may have a relatively large collection of, and they hope that such a trained model will be able to generalize well to an unseen target domain by providing only a minimal number of samples to characterize the acoustic signals in that domain. In this work, we review and discuss recent publications focusing on this domain generalization problem for anomalous sound detection in the context of the DCASE challenges on acoustic machine condition monitoring.
2025-03-13
Norbert A'Campo, Pablo Portilla Cuadrado
Generic relative immersions of compact one-manifolds in the closed unit disk, i.e. divides, provide a powerful combinatorial framework, and allow a topological construction of fibered classical links, for which the monodromy diffeomorphism is explicitly given as a product of Dehn twists. Complex isolated plane curve singularities provide a classical fibered link, the Milnor fibration, with its Milnor monodromy, monodromy group, and vanishing cycles. This surveys puts together much of the work done on divides and their role in the topology of isolated plane curve singularities.
Protostellar Outflows at the EarliesT Stages (POETS). VII. Circumstellar gas kinematics traced by water masers inside the HC HII region NGC7538 IRS1
2025-03-13
Luca Moscadelli, Ciriaco Goddi, Tomoya Hirota, Alberto Sanna
This article focuses on NGC7538 IRS1, one of the most luminous and studied HC HII regions in the northern hemisphere. Our aim is to identify the young stellar objects (YSOs) embedded within the ionized gas and study their kinematic structures. This work expands on a recent survey called "Protostellar Outflows at the EarliesT Stages" (POETS), which has been devoted to studying young outflow emission on scales of 10-100 au near luminous YSOs, before they start photoionizing the surrounding medium. We carried out multi-epoch Very Long Baseline Array observations of the 22 GHz water masers toward NGC7538 IRS1 to measure the maser 3D velocities, which, following POETS' findings, are reliable tracers of the protostellar winds. Recently, we reobserved the water masers in NGC7538 IRS1 with sensitive global very long baseline interferometry (VLBI) observations to map weaker maser emission. Our study confirms the presence of two embedded YSOs, IRS1a and IRS1b, at the center of the two linear distributions of 6.7 GHz methanol masers observed in the southern and northern cores of the HC HII region, which have been previously interpreted in terms of edge-on rotating disks. The water masers trace an extended (~200 au) stationary shock front adjacent to the inner portion of the disk around IRS1a. This shock front corresponds to the edge of the southern tip of the ionized core and might be produced by the interaction of the disk wind ejected from IRS1a with the infalling envelope. The water masers closer to IRS1b follow the same LSR velocity (Vlsr) pattern of the 6.7~GHz masers rotating in the disk, but the direction and amplitude of the water maser proper motions are inconsistent with rotation. We propose that these water masers are tracing a photo-evaporated disk wind, where the maser Vlsr traces mainly the disk rotation and the proper motions the poloidal velocity of the wind.
Analysis of the Institutional Free Market in Accredited Medical Physics Graduate Programs
2025-03-13
Brian W. Pogue, Alexander P. Niver
Medical Physics education is delivered through accredited programs with admissions and funding for students determined by individual institutions providing the educational experiences. Public data from accredited graduate programs, along with funding data, were used to analyze institutional trends in this educational market. Temporal trends from 2017 to 2023 show robust growth in MS graduates, increasing at an average of 17.7 per year, as compared to steady but modest growth in PhDs, increasing by 3.6 per year. The current status is there are nearly two MS graduates for every PhD graduate. Trends in funding show self-funding of students is a dominant pathway in domestic programs. Those programs dominated by accredited MS education have their largest fraction of faculty in radiation oncology departments, whereas those dominated by PhD education have their largest fraction of faculty in radiology departments. Overall NIH funding in the space of radiation diagnostics and therapeutics has been largely static over this timeframe, but with a notable 5 year rise in NCI funding. This can be contrasted to a substantial 5X-6X rise in NIH funding for engineering research in this same period, with significant increases in trainee funding there. Taken as a whole, this survey shows that growth in the field of medical physics education is dominated by MS graduates, presumably servicing the expanded growth needs for well-trained clinical physicists. However, the research infrastructure that supports PhD training in medical physics seems likely to be growing modestly and missing the growth trend of NIH funding that appears to show substantially more growth in non-accredited programs such as biomedical engineering. This data is useful to informing accreditation guidance on numbers of graduates to match the workforce needs or for inter-institutional planning around education goals.
Subgroup Performance Analysis in Hidden Stratifications
2025-03-13
Alceu Bissoto, Trung-Dung Hoang, Tim Flühmann, Susu Sun, Christian F. Baumgartner, Lisa M. Koch
Machine learning (ML) models may suffer from significant performance disparities between patient groups. Identifying such disparities by monitoring performance at a granular level is crucial for safely deploying ML to each patient. Traditional subgroup analysis based on metadata can expose performance disparities only if the available metadata (e.g., patient sex) sufficiently reflects the main reasons for performance variability, which is not common. Subgroup discovery techniques that identify cohesive subgroups based on learned feature representations appear as a potential solution: They could expose hidden stratifications and provide more granular subgroup performance reports. However, subgroup discovery is challenging to evaluate even as a standalone task, as ground truth stratification labels do not exist in real data. Subgroup discovery has thus neither been applied nor evaluated for the application of subgroup performance monitoring. Here, we apply subgroup discovery for performance monitoring in chest x-ray and skin lesion classification. We propose novel evaluation strategies and show that a simplified subgroup discovery method without access to classification labels or metadata can expose larger performance disparities than traditional metadata-based subgroup analysis. We provide the first compelling evidence that subgroup discovery can serve as an important tool for comprehensive performance validation and monitoring of trustworthy AI in medicine.
The fact that quantum theory is non-differentiable, while general relativity is built on the assumption of differentiability sources an incompatibility between quantum theory and gravity. Higher order geometry addresses this issue directly by extending differential geometry, such that it can be applied to theories that are non-differentiable, but have a certain degree of H\"older regularity. As this includes the path integral formulation of quantum theory, it provides a natural mathematical framework for describing the interplay between gravity and quantum theory. In this article, we review the motivation for and the basic features of this framework and point towards future developments.
Cellular Automata (CA) are commonly investigated as a particular type of dynamical systems, defined by shift-invariant local rules. In this paper, we consider instead CA as algebraic systems, focusing on the combinatorial designs induced by their short-term behavior. Specifically, we review the main results published in the literature concerning the construction of mutually orthogonal Latin squares via bipermutive CA, considering both the linear and nonlinear cases. We then survey some significant applications of these results to cryptography, and conclude with a discussion of open problems to be addressed in future research on CA-based combinatorial designs.
Observation of the dimer-singlet phase in the one-dimensional S = 1/2 Heisenberg antiferromagnet Cu(Ampy)ClBr (Ampy= C6H8N2 = 2-(Aminomethyl)pyridine)
2025-03-13
Saikat Nandi, Monika Jawale, Sanjay Bachhar, Rahul Kumar, Marlis Schuller, Rabindranath Bag, J. Wilkinson, Jörg Sichelschmidt, A. Sundaresan, Sara Haravifard, N. Büttgen, A. V. Mahajan
Spin-1/2 Heisenberg antiferromagnetic frustrated spin chain systems display exotic ground states with unconventional excitations and distinct quantum phase transitions as the ratio of next-nearest-neighbor to nearest-neighbor coupling is tuned. We present a comprehensive investigation of the structural, magnetic, and thermodynamics properties of the spin-1/2 compound, Cu(Ampy)ClBr (Ampy= C6H8N2 = 2-(Aminomethyl)pyridine) via x-ray diffraction, magnetization, specific heat, 1H nuclear magnetic resonance (NMR), electron spin resonance (ESR), and muon spin relaxation (muSR) techniques. The crystal structure features an anisotropic triangular chain lattice of magnetic Cu2+ ions. Our bulk and local probe experiments detect neither long-range magnetic ordering nor spin freezing down to 0.06 K despite the presence of moderate antiferromagnetic interaction between Cu2+ spins as reflected by a Curie-Weiss temperature of about -9 K from the bulk susceptibility data. A broad maximum is observed at about 9 K in magnetic susceptibility and specific heat data, indicating the onset of short-range spin correlations. At low temperatures, the zero-field magnetic specific heat and the 1H NMR spin-lattice relaxation rate follow an exponential temperature dependence, indicating the presence of gapped magnetic excitations. Furthermore, persistent spin dynamics down to 0.088 K observed by zero-field muSR evidences lack of any static magnetism. We attribute these experimental results to the stabilization of a dimer-singlet phase in the presence of a next-near neighbor interaction and of a randomness in the exchange coupling driven by Cl/Br mixing.
PyGDA: A Python Library for Graph Domain Adaptation
2025-03-13
Zhen Zhang, Meihan Liu, Bingsheng He
Graph domain adaptation has emerged as a promising approach to facilitate knowledge transfer across different domains. Recently, numerous models have been proposed to enhance their generalization capabilities in this field. However, there is still no unified library that brings together existing techniques and simplifies their implementation. To fill this gap, we introduce PyGDA, an open-source Python library tailored for graph domain adaptation. As the first comprehensive library in this area, PyGDA covers more than 20 widely used graph domain adaptation methods together with different types of graph datasets. Specifically, PyGDA offers modular components, enabling users to seamlessly build custom models with a variety of commonly used utility functions. To handle large-scale graphs, PyGDA includes support for features such as sampling and mini-batch processing, ensuring efficient computation. In addition, PyGDA also includes comprehensive performance benchmarks and well-documented user-friendly API for both researchers and practitioners. To foster convenient accessibility, PyGDA is released under the MIT license at https://github.com/pygda-team/pygda, and the API documentation is https://pygda.readthedocs.io/en/stable/.
Climate land use and other drivers impacts on island ecosystem services: a global review
2025-03-13
Aristides Moustakas, Shiri Zemah-Shamir, Mirela Tase, Savvas Zotos, Nazli Demirel, Christos Zoumides, Irene Christoforidi, Turgay Dindaroglu, Tamer Albayrak, Cigdem Kaptan Ayhan, Mauro Fois, Paraskevi Manolaki, Attila D. Sandor, Ina Sieber, Valentini Stamatiadou, Elli Tzirkalli, Ioannis N. Vogiatzakis, Ziv Zemah-Shamir, George Zittis
Islands are diversity hotspots and vulnerable to environmental degradation, climate variations, land use changes and societal crises. These factors can exhibit interactive impacts on ecosystem services. The study reviewed a large number of papers on the climate change-islands-ecosystem services topic worldwide. Potential inclusion of land use changes and other drivers of impacts on ecosystem services were sequentially also recorded. The study sought to investigate the impacts of climate change, land use change, and other non-climatic driver changes on island ecosystem services. Explanatory variables examined were divided into two categories: environmental variables and methodological ones. Environmental variables include sea zone geographic location, ecosystem, ecosystem services, climate, land use, other driver variables, Methodological variables include consideration of policy interventions, uncertainty assessment, cumulative effects of climate change, synergistic effects of climate change with land use change and other anthropogenic and environmental drivers, and the diversity of variables used in the analysis. Machine learning and statistical methods were used to analyze their effects on island ecosystem services. Negative climate change impacts on ecosystem services are better quantified by land use change or other non-climatic driver variables than by climate variables. The synergy of land use together with climate changes is modulating the impact outcome and critical for a better impact assessment. Analyzed together, there is little evidence of more pronounced for a specific sea zone, ecosystem, or ecosystem service. Climate change impacts may be underestimated due to the use of a single climate variable deployed in most studies. Policy interventions exhibit low classification accuracy in quantifying impacts indicating insufficient efficacy or integration in the studies.
While peer review enhances writing and research quality, harsh feedback can frustrate and demotivate authors. Hence, it is essential to explore how critiques should be delivered to motivate authors and enable them to keep iterating their work. In this study, we explored the impact of appending an automatically generated positive summary to the peer reviews of a writing task, alongside varying levels of overall evaluations (high vs. low), on authors' feedback reception, revision outcomes, and motivation to revise. Through a 2x2 online experiment with 137 participants, we found that adding an AI-reframed positive summary to otherwise harsh feedback increased authors' critique acceptance, whereas low overall evaluations of their work led to increased revision efforts. We discuss the implications of using AI in peer feedback, focusing on how AI-driven critiques can influence critique acceptance and support research communities in fostering productive and friendly peer feedback practices.
Markerless Tracking-Based Registration for Medical Image Motion Correction
2025-03-13
Luisa Neubig, Deirdre Larsen, Takeshi Ikuma, Markus Kopp, Melda Kunduk, Andreas M. Kist
Our study focuses on isolating swallowing dynamics from interfering patient motion in videofluoroscopy, an X-ray technique that records patients swallowing a radiopaque bolus. These recordings capture multiple motion sources, including head movement, anatomical displacements, and bolus transit. To enable precise analysis of swallowing physiology, we aim to eliminate distracting motion, particularly head movement, while preserving essential swallowing-related dynamics. Optical flow methods fail due to artifacts like flickering and instability, making them unreliable for distinguishing different motion groups. We evaluated markerless tracking approaches (CoTracker, PIPs++, TAP-Net) and quantified tracking accuracy in key medical regions of interest. Our findings show that even sparse tracking points generate morphing displacement fields that outperform leading registration methods such as ANTs, LDDMM, and VoxelMorph. To compare all approaches, we assessed performance using MSE and SSIM metrics post-registration. We introduce a novel motion correction pipeline that effectively removes disruptive motion while preserving swallowing dynamics and surpassing competitive registration techniques. Code will be available after review.
Super-apps have emerged as comprehensive platforms integrating various mini-apps to provide diverse services. While super-apps offer convenience and enriched functionality, they can introduce new privacy risks. This paper reveals a new privacy leakage source in super-apps: mini-app interaction history, including mini-app usage history (Mini-H) and operation history (Op-H). Mini-H refers to the history of mini-apps accessed by users, such as their frequency and categories. Op-H captures user interactions within mini-apps, including button clicks, bar drags, and image views. Super-apps can naturally collect these data without instrumentation due to the web-based feature of mini-apps. We identify these data types as novel and unexplored privacy risks through a literature review of 30 papers and an empirical analysis of 31 super-apps. We design a mini-app interaction history-oriented inference attack (THEFT), to exploit this new vulnerability. Using THEFT, the insider threats within the low-privilege business department of the super-app vendor acting as the adversary can achieve more than 95.5% accuracy in inferring privacy attributes of over 16.1% of users. THEFT only requires a small training dataset of 200 users from public breached databases on the Internet. We also engage with super-app vendors and a standards association to increase industry awareness and commitment to protect this data. Our contributions are significant in identifying overlooked privacy risks, demonstrating the effectiveness of a new attack, and influencing industry practices toward better privacy protection in the super-app ecosystem.
Time series forecasting (TSF) has long been a crucial task in both industry and daily life. Most classical statistical models may have certain limitations when applied to practical scenarios in fields such as energy, healthcare, traffic, meteorology, and economics, especially when high accuracy is required. With the continuous development of deep learning, numerous new models have emerged in the field of time series forecasting in recent years. However, existing surveys have not provided a unified summary of the wide range of model architectures in this field, nor have they given detailed summaries of works in feature extraction and datasets. To address this gap, in this review, we comprehensively study the previous works and summarize the general paradigms of Deep Time Series Forecasting (DTSF) in terms of model architectures. Besides, we take an innovative approach by focusing on the composition of time series and systematically explain important feature extraction methods. Additionally, we provide an overall compilation of datasets from various domains in existing works. Finally, we systematically emphasize the significant challenges faced and future research directions in this field.
Optical+NIR analysis of a newly found Einstein ring at z$\sim$1 from the Kilo-Degree Survey: Dark matter fraction, total and dark matter density slope and IMF
2025-03-13
Rui Li, Nicola R. Napolitano, Giuseppe D Ago, Vyacheslav N. Shalyapin, Kai Zhu, Xiaotong Guo, Ran Li, Leon V. E. Koopmans, Chiara Spiniello, Crescenzo Tortora, Francesco La Barbera, Haicheng Feng, Liang Gao, Zhiqi Huang, Koen Kuijken, Hui Li, Linghua Xie, Mario Radovich, Alexey Sergeyev
We report the discovery and spectroscopic confirmation of a new bright blue Einstein ring in the Kilo Degree Survey (KiDS) footprint: the Einstein ``blue eye''. Spectroscopic data from X-Shooter at the Very Large Telescope (VLT) show that the lens is a typical early-type galaxy (ETG) at $z=0.9906$, while the background source is a Ly$\alpha$ emitter at $z=2.823$. The reference lens modeling was performed on a high-resolution $Y-$band adaptive-optics image from HAWK-I at VLT. Assuming a singular isothermal ellipsoid (SIE) total mass density profile, we inferred an Einstein radius $R_{Ein}=10.47 \pm 0.06$ kpc. The average slope of the total mass density inside the Einstein radius, as determined by a joint analysis of lensing and isotropic Jeans equations is $\gamma=2.14^{+0.06}_{-0.07}$, showing no systematic deviation from the slopes of lower redshit galaxies, This can be the evidence of ETGs developing through dry mergers plus moderate dissipationless accretion. Stellar population analysis with 8-band ($gri$ZYJHK$s$) photometries from KiDS and VIKING shows that the total stellar mass of the lens is $M*=(3.95\pm 0.35)\times 10^{11} M_\odot$ (Salpeter Initial Mass Function, IMF), implying a dark matter fraction inside the effective radius to be $f_{\rm DM}=0.307\pm 0.151$. We finally explored the dark matter halo slope and found a strong degeneracy with the dynamic stellar mass. Dark matter adiabatic contraction is needed to explain the posterior distribution of the slope, unless IMF heavier than Salpeter is assumed.
Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding
2025-03-13
Jinze Li, Yixing Xu, Haiduo Huang, Xuanwu Yin, Dong Li, Edith C. H. Ngai, Emad Barsoum
Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multiple heads to predict a sequence of future tokens, where each head handles a token in the sequence. The target LLM verifies the predicted sequence and accepts aligned tokens, enabling efficient multi-token generation. However, existing methods assume that all tokens within a sequence are equally important, employing identical head structures and relying on a single-generation paradigm, either serial or parallel. To this end, we theoretically demonstrate that initial tokens in the draft sequence are more important than later ones. Building on this insight, we propose Gumiho, a hybrid model combining serial and parallel heads. Specifically, given the critical importance of early tokens, we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy. For later tokens, we utilize multiple lightweight MLP heads operating in parallel to enhance efficiency. By allocating more advanced model structures and longer running times to the early heads, Gumiho achieves improved overall performance. The experimental results demonstrate that our method outperforms existing approaches, fully validating its effectiveness.
GalProTE: Galactic Properties Mapping using Transformer Encoder
2025-03-13
Omar Anwar, Brent Groves, Luca Cortese, Adam B. Watts
This work presents GalProTE, a proof-of-concept Machine Learning model utilizing a Transformer Encoder to determine stellar age, metallicity, and dust attenuation from optical spectra. Designed for large astronomical surveys, GalProTE significantly accelerates processing while maintaining accuracy. Using the E-MILES spectral library, we construct a dataset of 111,936 diverse templates by expanding 636 simple stellar population models with varying extinction, spectral combinations, and noise modifications. This ensures robust training over 4750 to 7100 Angstrom at 2.5 Angstrom resolution. GalProTE employs four parallel attention-based encoders with varying kernel sizes to capture spectral features. On synthetic test data, it achieves a mean squared error (MSE) of 0.27% between input and predicted spectra. Validation on PHANGS-MUSE galaxies NGC4254 and NGC5068 confirms its ability to extract physical parameters efficiently, with residuals averaging -0.02% and 0.28% and standard deviations of 4.3% and 5.3%, respectively. To contextualize these results, we compare GalProTE's age, metallicity, and dust attenuation maps with pPXF, a state-of-the-art spectral fitting tool. While pPXF requires approximately 11 seconds per spectrum, GalProTE processes one in less than 4 milliseconds, offering a 2750 times speedup and consuming 68 times less power per spectrum. The strong agreement between pPXF and GalProTE highlights the potential of machine learning to enhance traditional methods, paving the way for faster, energy-efficient, and scalable analyses of galactic properties in modern surveys.
Money laundering is a financial crime that obscures the origin of illicit funds, necessitating the development and enforcement of anti-money laundering (AML) policies by governments and organizations. The proliferation of mobile payment platforms and smart IoT devices has significantly complicated AML investigations. As payment networks become more interconnected, there is an increasing need for efficient real-time detection to process large volumes of transaction data on heterogeneous payment systems by different operators such as digital currencies, cryptocurrencies and account-based payments. Most of these mobile payment networks are supported by connected devices, many of which are considered loT devices in the FinTech space that constantly generate data. Furthermore, the growing complexity and unpredictability of transaction patterns across these networks contribute to a higher incidence of false positives. While machine learning solutions have the potential to enhance detection efficiency, their application in AML faces unique challenges, such as addressing privacy concerns tied to sensitive financial data and managing the real-world constraint of limited data availability due to data regulations. Existing surveys in the AML literature broadly review machine learning approaches for money laundering detection, but they often lack an in-depth exploration of advanced deep learning techniques - an emerging field with significant potential. To address this gap, this paper conducts a comprehensive review of deep learning solutions and the challenges associated with their use in AML. Additionally, we propose a novel framework that applies the least-privilege principle by integrating machine learning techniques, codifying AML red flags, and employing account profiling to provide context for predictions and enable effective fraud detection under limited data availability....
Distinguishing Tidal Disruption Events and Changing-look Active Galactic Nuclei via Variation of Mid-infrared Color
2025-03-13
Yujun Yao, Jingjing Ye, Luming Sun, Ning Jiang, Megan Masterson, Xinwen Shu
At present, there is a lack of effective probes to distinguish between mid-infrared (MIR) outbursts induced by tidal disruption events (TDEs) and changing-look active galactic nuclei (CLAGNs) based on only MIR data. Here, we propose that the time variation of MIR color (K-corrected W1-W2 after subtracting the quiescent fluxes) is a promising probe. With an optically selected sample containing TDEs, ambiguous nuclear transients (ANTs), and CLAGNs, we studied the MIR color variation of their MIR counterparts using NEOWISE-R data. We found that the MIR color of TDEs and ANTs turns red faster than CLAGNs during the rising phase, and TDEs have a redder color than ANTs at the earliest phase. The former may be caused by the difference between the ultraviolet light curves of TDEs/ANTs and CLAGNs, or be related to no or relatively weak underlying AGN in TDEs/ANTs, while the latter may be related to the difference in the dust geometry. Based on color variation rate, we selected high-probability TDE, ANT, and CLAGN candidates from MIR outbursts in samples of Jiang et al. (2021) and Masterson et al. (2024). We found that both samples are mixtures of TDEs/ANTs and CLAGNs. For MIR outbursts whose hosts are not Seyfert galaxies, we estimated that $\sim50\%-80\%$ are TDEs and inferred a rate of infrared TDEs of $1.5-2.8\times10^{-5}$ galaxy$^{-1}$ yr$^{-1}$, comparable with that of optical TDEs. The rest are CLAGNs, suggesting the presence of weak AGNs that cannot be identified using common diagnoses. We predicted that with our method, a large amount of dust-obscured TDEs could be selected from future infrared surveys with higher data quality and cadence.
$T\overline{T}$ Deformation: Introduction and Some Recent Advances
2025-03-13
Song He, Yi Li, Hao Ouyang, Yuan Sun
This review explores recent advances in the theory of $T\bar{T}$ deformation, an irrelevant yet solvable deformation of quantum field theories defined via the quadratic form of the energy-momentum tensor. It addresses classical and quantum aspects, highlighting significant developments across various fields, including field theory, holography, and string theory. Classically, $T\bar{T}$ deformation manifests through multiple geometric interpretations, notably random geometry, Jackiw-Teitelboim-like gravity, and uniform light-cone gauge frameworks. For quantum aspects, the deformation introduces notable features such as non-locality, UV-IR mixing, solvable renormalization structures, and intriguing modifications to correlation functions and entanglement properties. Furthermore, the paper examines the profound relationship between $T\bar{T}$ deformation and holography, particularly within the mixed boundary conditions/cutoff AdS holography proposal and holographic entanglement entropy. Connections to string theory through single-trace deformations and their holographic duals further reveal the deformed structure of the worldsheet. This review synthesizes recent developments and outlines potential directions for future research in the study of $T\bar{T}$-like deformation.
Quadratic Transform for Fractional Programs in Signal Processing and Machine Learning
2025-03-13
Kaiming Shen, Wei Yu
Fractional programming (FP) is a branch of mathematical optimization that deals with the optimization of ratios. It is an invaluable tool for signal processing and machine learning, because many key metrics in these fields are fractionally structured, e.g., the signal-to-interference-plus-noise ratio (SINR) in wireless communications, the Cramer-Rao bound (CRB) in radar sensing, the normalized cut in graph clustering, and the margin in support vector machine (SVM). This article provides a comprehensive review of both the theory and applications of a recently developed FP technique known as the quadratic transform, which can be applied to a wide variety of FP problems, including both the minimization and the maximization of the sum of functions of ratios as well as matrix ratio problems.
Tantalizing Evidence of Reionization Relics in the eBOSS DR16 Ly$\boldsymbolα$ Forest Correlations: a Preference for Early Reionization
2025-03-13
Yifan Zheng, Paulo Montero-Camacho, Zheng Cai, Yi Mao
Cosmic reionization of HI leaves enduring relics in the post-reionization intergalactic medium, potentially influencing the Lyman-$\alpha$ (Ly$\alpha$) forest down to redshifts as low as $z \approx 2$, which is the so-called ''memory of reionization'' effect. Here, we re-analyze the baryonic acoustic oscillation (BAO) measurements from Ly$\alpha$ absorption and quasar correlations using data from the extended Baryonic Oscillation Spectroscopic Survey (eBOSS) Data Release 16 (DR16), incorporating for the first time the memory of reionization in the Ly$\alpha$ forest. Three distinct scenarios of reionization timeline are considered in our analyses. We find that the recovered BAO parameters ($\alpha_\parallel$, $\alpha_\perp$) remain consistent with the original eBOSS DR16 analysis. However, models incorporating reionization relics provide a better fit to the data, with a tantalizing preference for early reionization, consistent with recent findings from the James Webb Space Telescope. Furthermore, the inclusion of reionization relics significantly impacts the non-BAO parameters. For instance, we report deviations of up to $3\sigma$ in the Ly$\alpha$ redshift-space distortion parameter and $\sim7\sigma$ in the linear Ly$\alpha$ bias for the late reionization scenario. Our findings suggest that the eBOSS Ly$\alpha$ data is more accurately described by models that incorporate a broadband enhancement to the Ly$\alpha$ forest power spectrum, highlighting the importance of accounting for reionization relics in cosmological analyses.
Exploring Mutual Empowerment Between Wireless Networks and RL-based LLMs: A Survey
2025-03-13
Yu Qiao, Phuong-Nam Tran, Ji Su Yoon, Loc X. Nguyen, Choong Seon Hong
Reinforcement learning (RL)-based large language models (LLMs), such as ChatGPT, DeepSeek, and Grok-3, have gained significant attention for their exceptional capabilities in natural language processing and multimodal data understanding. Meanwhile, the rapid expansion of information services has driven the growing need for intelligence, efficient, and adaptable wireless networks. Wireless networks require the empowerment of RL-based LLMs while these models also benefit from wireless networks to broaden their application scenarios. Specifically, RL-based LLMs can enhance wireless communication systems through intelligent resource allocation, adaptive network optimization, and real-time decision-making. Conversely, wireless networks provide a vital infrastructure for the efficient training, deployment, and distributed inference of RL-based LLMs, especially in decentralized and edge computing environments. This mutual empowerment highlights the need for a deeper exploration of the interplay between these two domains. We first review recent advancements in wireless communications, highlighting the associated challenges and potential solutions. We then discuss the progress of RL-based LLMs, focusing on key technologies for LLM training, challenges, and potential solutions. Subsequently, we explore the mutual empowerment between these two fields, highlighting key motivations, open challenges, and potential solutions. Finally, we provide insights into future directions, applications, and their societal impact to further explore this intersection, paving the way for next-generation intelligent communication systems. Overall, this survey provides a comprehensive overview of the relationship between RL-based LLMs and wireless networks, offering a vision where these domains empower each other to drive innovations.
The SRG/eROSITA All-Sky Survey : Subaru/HSC-SSP weak-lensing mass measurements for the eRASS1 Galaxy Clusters
2025-03-13
Nobuhiro Okabe, Thomas Reiprich, Sebastian Grandis, I-Non Chiu, Masamune Oguri, Keiichi Umetsu, Esra Bulbul, Emre Bahar, Fabian Balzer, Nicolas Clerc, Johan Comparat, Vittorio Ghirardini, Florian Kleinebreil, Matthias Kluge, Ang Liu, Yen-Ting Lin, Rogério Monteiro-Oliveira, Florian Pacaud, Miriam Ramos Ceja, Jeremy Sanders, Tim Schrabback, Riccardo Seppi, Martin Sommer, Xiaoyuan Zhang
We perform individual weak-lensing (WL) mass measurements for 78 eROSITA's first All-Sky Survey (eRASS1) clusters in the footprint of Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) S19A, without any priors of the eRASS1 X-ray quantities or assumption of the mass and concentration relation. In the sample, we find 3 clusters are mis-associated with optical counterparts and 12 clusters are poorly fitted with an NFW profile. The average mass for the 12 poor-fit clusters changes from $\sim 10^{14}h_{70}^{-1}M_\odot$ to $\sim 2\times 10^{13}h_{70}^{-1}M_\odot$ when lensing contamination from surrounding mass structures is taken into account. The scaling relations between the true mass and cluster richness and X-ray count-rate agree well with the results of the eRASS1 Western Galactic Hemisphere region based on count-rate-inferred masses, which are calibrated with the HSC-SSP, DES, and KiDS surveys. We develop a Bayesian framework for inferring the mass-concentration relation of the cluster sample, explicitly incorporating the effects of weak-lensing mass calibration in the mass-concentration parameter space. The redshift-dependent mass and concentration relation is in excellent agreement with predictions of dark-matter-only numerical simulations and previous studies using X-ray-selected clusters. Based on the two-dimensional (2D) WL analysis, the offsets between the WL-determined centers and the X-ray centroids for 36 eRASS1 clusters with high WL S/N are described by two Gaussian components. We find that the mis-centering effect with X-ray centroids is smaller than that with peaks in the galaxy maps. The projected halo ellipticity is $\langle \varepsilon \rangle=0.45$ at $M_{200}\sim 4\times10^{14}h_{70}^{-1}M_\odot$, in agreement with results of numerical simulations and previous studies of clusters more than twice our mass.
RIS-Assisted Joint Sensing and Communications via Fractionally Constrained Fractional Programming
2025-03-13
Yiming Liu, Kareem M. Attiah, Wei Yu
This paper studies an uplink dual-functional sensing and communication system aided by a reconfigurable intelligent surface (RIS), whose reflection pattern is optimally configured to trade-off sensing and communication functionalities. Specifically, the Bayesian Cram\'er-Rao lower bound (BCRLB) for estimating the azimuth angle of a sensing user is minimized while ensuring the signal-to-interference-plus-noise ratio constraints for communication users. We show that this problem can be formulated as a novel fractionally constrained fractional programming (FCFP) problem. To deal with this highly nontrivial problem, we extend a quadratic transform technique, originally proposed to handle optimization problems containing ratio structures only in objectives, to the scenario where the constraints also contain ratio structures. First, we consider the case where the fading coefficient is known. Using the quadratic transform, the FCFP problem is turned into a sequence of subproblems that are convex except for the constant-modulus constraints which can be tackled using a penalty-based method. To further reduce the computational complexity, we leverage the constant-modulus conditions and propose a novel linear transform. This new transform enables the FCFP problem to be turned into a sequence of linear programming (LP) subproblems, which can be solved with linear complexity in the dimension of reflecting elements. Then, we consider the case where the fading coefficient is unknown. A modified BCRLB is used to make the problem more tractable, and the proposed quadratic transform-based algorithm is used to solve the problem. Finally, numerical results unveil nontrivial and effective reflection patterns that the RIS can be configured to generate to facilitate both functionalities.
Domain randomization has emerged as a fundamental technique in reinforcement learning (RL) to facilitate the transfer of policies from simulation to real-world robotic applications. Many existing domain randomization approaches have been proposed to improve robustness and sim2real transfer. These approaches rely on wide randomization ranges to compensate for the unknown actual system parameters, leading to robust but inefficient real-world policies. In addition, the policies pretrained in the domain-randomized simulation are fixed after deployment due to the inherent instability of the optimization processes based on RL and the necessity of sampling exploitative but potentially unsafe actions on the real system. This limits the adaptability of the deployed policy to the inevitably changing system parameters or environment dynamics over time. We leverage safe RL and continual learning under domain-randomized simulation to address these limitations and enable safe deployment-time policy adaptation in real-world robot control. The experiments show that our method enables the policy to adapt and fit to the current domain distribution and environment dynamics of the real system while minimizing safety risks and avoiding issues like catastrophic forgetting of the general policy found in randomized simulation during the pretraining phase. Videos and supplementary material are available at https://safe-cda.github.io/.
Incident beam optics optimization for the single crystal neutron diffractometer Pioneer with a polarized beam option
2025-03-13
Yaohua Liu, Peter Torres
Pioneer, a next-generation single-crystal neutron diffractometer, is under development for Oak Ridge National Laboratory's Second Target Station (STS). Designed to address a wide range of scientific questions, Pioneer will deliver homogeneous neutron beams with customizable size and divergence, and provide a polarized beam option. This article introduces its incident beam optics, highlighting the optimization methodology and the simulated performance. Pioneer will utilize a modified elliptical-straight guide for neutron transport and deploy slit packages and insertable apertures to control beam size and divergence. The optimized guide geometry matches the optimal-and-full-sample-illumination condition, and the beam control system effectively filters out unwanted neutrons while preserving the desired ones. Additionally, we have found that polygon-approximated guides provide satisfactory transport efficiency and beam homogeneity, eliminating the need of truly curved guides. To enhance neutronics performance and reduce cost, the coatings of supermirror elements are individually optimized to the lowest half-integer $m$-values that are sufficient to deliver the desired neutrons. After evaluating polarizing V-cavities and $^3$He spin filters over the default polarized wavelength band of 1.2-5.5~\AA, we selected a translatable multichannel polarizing V-cavity as the incident beam polarizer. Strategically placed at a location where the beam divergence is low and a large in-guide gap has negligible impact on transport efficiency, the optimized V-cavity achieves an average $P^2T$ of approximately 35\%.
Optical design for the single crystal neutron diffractometer Pioneer
2025-03-13
Yaohua Liu, Peter Torres, Scott Dixon, Cameron Hart, Darian Kent, Anton Khaplanov, Bill McHargue, Kumar Mohindroo, Rudolf Thermer
Pioneer is a single-crystal neutron diffractometer optimized for small-volume samples and weak signals at the Second Target Station (STS) at Oak Ridge National Laboratory. This paper presents the preliminary optical design progress, focusing on the rationale behind key design choices. It covers the T$_0$ and bandwidth disk choppers, guide and beam control system, incident-beam polarizer, scattering beam collimators, and additional strategies. The chopper locations are selected to maximize neutron transport while taking advantage of standardized shielding structures. To accommodate the maintenance shield, operational shutter, and polarizing V-cavity, the guide design includes significant gaps. When these optical components are moved out of the beam path, oversized collimators, rather than guides, will be translated in. Pioneer will utilize slit packages to control beam size and divergence, and a translatable polarizing V-cavity. Absorbing panels are strategically placed near the end station to minimize background. An oscillating radial collimator, operating in a shift mode, will be used with the vertical cylindrical detector, while a fixed multi-cone collimator will be used with the bottom flat detector. These collimators will enable the detection of weak signals when complex sample environments are used.
A review on modelling, evaluation, and optimization of cyber-physical system reliability
2025-03-13
Moslem Uddin, Huadong Mo, Daoyi Dong
The aim of this study is to present an overview of current research on modelling, evaluation, and optimization methods for improving the reliability of Cyber-Physical System (CPS). Three major modelling approaches, namely analytical, simulation, and hybrid models, are discussed. Various evaluation techniques, including fault tree analysis, Markov models, and availability measures, are reviewed and compared. Optimization strategies for CPS reliability, including fault tolerance, dynamic reconfiguration, and resource allocation, are also reviewed and briefly discussed. Besides, emerging trends and research opportunities in this field are highlighted and explained. Finally, the possible challenges are outlined and then future research are directed for CPS. This study can provide a systematic and in-dept introduction to CPS for researchers, practitioners, and policymakers.
Electron ptychography describes a family of algorithms which are used to enable the reconstruction of complex specimen transmission functions of a sample in order to obtain both phase and amplitude information, as applied within the realms of electron microscopy. Ptychographic methods can be very useful in the imaging of beam sensitive materials, samples with both strongly- and weakly-scattering elements, and for mapping the functional properties of materials. Ptychography can further be used to achieve image resolutions far beyond the conventional resolution limit defined by the imaging aperture size. In this article, we review the development of ptychography and compare presently available methods to perform ptychographic image reconstruction on data collected in the (scanning) (transmission) electron microscope. We aim to enable the reader to determine optimal data collection parameters, and the most suitable ptychographic method for the information they seek to determine from their sample.
HyperDAS: Towards Automating Mechanistic Interpretability with Hypernetworks
2025-03-13
Jiuding Sun, Jing Huang, Sidharth Baskaran, Karel D'Oosterlinck, Christopher Potts, Michael Sklar, Atticus Geiger
Mechanistic interpretability has made great strides in identifying neural network features (e.g., directions in hidden activation space) that mediate concepts(e.g., the birth year of a person) and enable predictable manipulation. Distributed alignment search (DAS) leverages supervision from counterfactual data to learn concept features within hidden states, but DAS assumes we can afford to conduct a brute force search over potential feature locations. To address this, we present HyperDAS, a transformer-based hypernetwork architecture that (1) automatically locates the token-positions of the residual stream that a concept is realized in and (2) constructs features of those residual stream vectors for the concept. In experiments with Llama3-8B, HyperDAS achieves state-of-the-art performance on the RAVEL benchmark for disentangling concepts in hidden states. In addition, we review the design decisions we made to mitigate the concern that HyperDAS (like all powerful interpretabilty methods) might inject new information into the target model rather than faithfully interpreting it.
The concept of a superconducting spin flipper - neutron decelerator for a UCN source at a pulsed reactor
2025-03-13
A. A. Popov, K. S. Osipenko, V. I. Scherbakov, A. I. Frank, G. V. Kulin, M. A. Zakharov, V. A. Kurylev, D. A. Kolomentseva
The work is devoted to the development of a conceptual design for a gradient spin flipper - neutron decelerator, which is the main component of a designed UCN source for a pulsed reactor. In close cooperation between the JINR group and SuperOx, a preliminary design of a stationary gradient magnet for the adiabatic spin flipper has been developed. A thorough calculation of the magnetic field configuration has been performed. The movement of neutrons in the magnetic field generated by the designed magnetic system has been simulated, and the deceleration time of neutrons in the spin flipper has been analyzed. The results obtained give grounds for hope that the idea of creating a UCN source based on pulsed accumulation in a trap using non-stationary neutron deceleration is feasible.
A Sentence-Level Approach to Understanding Software Vulnerability Fixes
2025-03-13
Amiao Gao, Zenong Zhang, Simin Wang, Liguo Huang, Shiyi Wei, Vincent Ng
Understanding software vulnerabilities and their resolutions is crucial for securing modern software systems. This study presents a novel traceability model that links a pair of sentences describing at least one of the three types of semantics (triggers, crash phenomenon and fix action) for a vulnerability in natural language (NL) vulnerability artifacts, to their corresponding pair of code statements. Different from the traditional traceability models, our tracing links between a pair of related NL sentences and a pair of code statements can recover the semantic relationship between code statements so that the specific role played by each code statement in a vulnerability can be automatically identified. Our end-to-end approach is implemented in two key steps: VulnExtract and VulnTrace. VulnExtract automatically extracts sentences describing triggers, crash phenomenon and/or fix action for a vulnerability using 37 discourse patterns derived from NL artifacts (CVE summary, bug reports and commit messages). VulnTrace employs pre-trained code search models to trace these sentences to the corresponding code statements. Our empirical study, based on 341 CVEs and their associated code snippets, demonstrates the effectiveness of our approach, with recall exceeding 90% in most cases for NL sentence extraction. VulnTrace achieves a Top5 accuracy of over 68.2% for mapping a pair of related NL sentences to the corresponding pair of code statements. The end-to-end combined VulnExtract+VulnTrace achieves a Top5 accuracy of 59.6% and 53.1% for mapping two pairs of NL sentences to code statements. These results highlight the potential of our method in automating vulnerability comprehension and reducing manual effort.
Teamwork makes the dream work: LLMs-Based Agents for GitHub README.MD Summarization
2025-03-13
Duc S. H. Nguyen, Bach G. Truong, Phuong T. Nguyen, Juri Di Rocco, Davide Di Ruscio
The proliferation of Large Language Models (LLMs) in recent years has realized many applications in various domains. Being trained with a huge of amount of data coming from various sources, LLMs can be deployed to solve different tasks, including those in Software Engineering (SE). Though they have been widely adopted, the potential of using LLMs cooperatively has not been thoroughly investigated. In this paper, we proposed Metagente as a novel approach to amplify the synergy of various LLMs. Metagente is a Multi-Agent framework based on a series of LLMs to self-optimize the system through evaluation, feedback, and cooperation among specialized agents. Such a framework creates an environment where multiple agents iteratively refine and optimize prompts from various perspectives. The results of these explorations are then reviewed and aggregated by a teacher agent. To study its performance, we evaluated Metagente with an SE task, i.e., summarization of README.MD files, and compared it with three well-established baselines, i.e., GitSum, LLaMA-2, and GPT-4o. The results show that our proposed approach works efficiently and effectively, consuming a small amount of data for fine-tuning but still getting a high accuracy, thus substantially outperforming the baselines. The performance gain compared to GitSum, the most relevant benchmark, ranges from 27.63% to 60.43%. More importantly, compared to using only one LLM, Metagente boots up the accuracy to multiple folds.
TAIJI: Textual Anchoring for Immunizing Jailbreak Images in Vision Language Models
2025-03-13
Xiangyu Yin, Yi Qi, Jinwei Hu, Zhen Chen, Yi Dong, Xingyu Zhao, Xiaowei Huang, Wenjie Ruan
Vision Language Models (VLMs) have demonstrated impressive inference capabilities, but remain vulnerable to jailbreak attacks that can induce harmful or unethical responses. Existing defence methods are predominantly white-box approaches that require access to model parameters and extensive modifications, making them costly and impractical for many real-world scenarios. Although some black-box defences have been proposed, they often impose input constraints or require multiple queries, limiting their effectiveness in safety-critical tasks such as autonomous driving. To address these challenges, we propose a novel black-box defence framework called \textbf{T}extual \textbf{A}nchoring for \textbf{I}mmunizing \textbf{J}ailbreak \textbf{I}mages (\textbf{TAIJI}). TAIJI leverages key phrase-based textual anchoring to enhance the model's ability to assess and mitigate the harmful content embedded within both visual and textual prompts. Unlike existing methods, TAIJI operates effectively with a single query during inference, while preserving the VLM's performance on benign tasks. Extensive experiments demonstrate that TAIJI significantly enhances the safety and reliability of VLMs, providing a practical and efficient solution for real-world deployment.
Signatures of Atmospheric Mass Loss and Planet Migration in the Time Evolution of Short-Period Transiting Exoplanets
2025-03-13
Rachel B. Fernandes, Galen J. Bergsten, Gijs D. Mulders, Ilaria Pascucci, Kevin K. Hardegree-Ullman, Steven Giacalone, Jessie L. Christiansen, James G. Rogers, Akash Gupta, Rebekah I. Dawson, Tommi T. Koskinen, Kiersten M. Boley, Jason L. Curtis, Katia Cunha, Eric E. Mamajek, Sabina Sagynbayeva, Sakhee S. Bhure, David R. Ciardi, Preethi R. Karpoor, Kyle A. Pearson, Jon K. Zink, Gregory A. Feiden
Comparative studies of young and old exoplanet populations offer a glimpse into how planets may form and evolve with time. We present an occurrence rate study of short-period ($<$12 days) planets between 1.8--10 Rearth around 1374 FGK stars in nearby (200 pc) young clusters ($<$1 Gyr), utilizing data from the Transiting Exoplanet Survey Satellite (TESS) mission. These planets represent a population closer to their primordial state. We find that the occurrence rate of young planets is higher ($64^{+32}_{-22}$%) compared to the Gyr-old population observed by \kepler ($7.98^{+0.37}_{-0.35}$%). Dividing our sample into bins of young (10--100 Myr) and intermediate (100\,Myr--1 Gyr) ages, we also find that the occurrence distribution in orbital period remains unchanged while the distribution in planet radius changes with time. Specifically, the radius distribution steepens with age, indicative of a larger planet population shrinking due to the atmospheric thermal cooling and mass loss. We also find evidence for an increase (1.9$\sigma$) in occurrence after 100 Myr, possibly due to tidal migration driving planets inside of 12 days. While evidence suggests post-disk migration and atmospheric mass loss shape the population of short-period planets, more detections of young planets are needed to improve statistical comparisons with older planets. Detecting long-period young planets and planets $<$1.8 Rearth will help us understand these processes better. Additionally, studying young planetary atmospheres provides insights into planet formation and the efficiency of atmospheric mass loss mechanisms on the evolution of planetary systems.
LiDO: Exploring the Stable Plutino Parameter Space
2025-03-13
Samantha M. Lawler, Mark Comte, Rosemary E. Pike, Mike Alexandersen, Ying-Tung Chen, Cameron Collyer, Matthew Holman, JJ Kavelaars, Lowell Peltier, Cameron Semenchuck
We present a publicly available, high-resolution, filled-parameter-space synthetic distribution of the Plutinos, trans-Neptunian Objects (TNOs) librating in the 3:2 mean-motion resonance with Neptune, with particular focus on the Plutinos simultaneously Kozai-librating. This synthetic distribution was built in preparation for results from the Large inclination Distant Objects (LiDO) Survey, which pointed at locations on the sky where Kozai Plutinos are predicted to come to pericenter and are thus most easily detected in magnitude-limited surveys. Although we do not expect the full stable parameter-space presented here to be populated with real TNOs, it provides a useful starting point for comparison with Neptune migration simulations and debiased observational results. Our new stable parameter space synthetic distribution of fictitious Plutinos is consistent with previous works, and we build on past results by focusing on the behavior of Kozai Plutinos over 4Gyr integrations. We find that 95% of 4Gyr stable Kozai Plutinos remain in the same omega-libration island for the entire integration. This provides an interesting diagnostic opportunity: any asymmetry in the true number of 4Gyr stable Kozai Plutinos in the two omega-libration islands must be caused by the details of emplacement during giant planet migration. Through analysis of previously published Neptune migration models, we show that the intrinsic fraction of Plutinos captured into Kozai depends on Neptune's migration speed and mode. Combining the filled-parameter-space synthetic distribution with future migration simulations and the results of the carefully characterized LiDO survey will enable interpretation of the intrinsic orbital distribution of the Kozai and non-Kozai Plutinos.
In the cosmic web, filaments play a crucial role in connecting walls to clusters and also act as an important stage for galaxy formation and evolution. Recent observational studies claim that filaments have spin. In this study, we examined the potential impact of diversity in filament identification algorithms and galaxy survey datasets on the quantification of filament spin. The results of this study demonstrate qualitative agreement with previous research, suggesting that a reliable filament spin signal is detectable when the viewing angle of filament spine larger than 80 degrees under a rough estimation. The detected filament spin signal is intricately linked to the viewing angle, dynamic temperature, etc. The quantitative difference of filament spin signal among samples is slightly dependent on the filament identification algorithms, while the value is relatively greater dependent on the redshift space distortion effect in the galaxy sample.
Lessons from the trenches on evaluating machine-learning systems in materials science
2025-03-13
Nawaf Alampara, Mara Schilling-Wilhelmi, Kevin Maik Jablonka
Measurements are fundamental to knowledge creation in science, enabling consistent sharing of findings and serving as the foundation for scientific discovery. As machine learning systems increasingly transform scientific fields, the question of how to effectively evaluate these systems becomes crucial for ensuring reliable progress. In this review, we examine the current state and future directions of evaluation frameworks for machine learning in science. We organize the review around a broadly applicable framework for evaluating machine learning systems through the lens of statistical measurement theory, using materials science as our primary context for examples and case studies. We identify key challenges common across machine learning evaluation such as construct validity, data quality issues, metric design limitations, and benchmark maintenance problems that can lead to phantom progress when evaluation frameworks fail to capture real-world performance needs. By examining both traditional benchmarks and emerging evaluation approaches, we demonstrate how evaluation choices fundamentally shape not only our measurements but also research priorities and scientific progress. These findings reveal the critical need for transparency in evaluation design and reporting, leading us to propose evaluation cards as a structured approach to documenting measurement choices and limitations. Our work highlights the importance of developing a more diverse toolbox of evaluation techniques for machine learning in materials science, while offering insights that can inform evaluation practices in other scientific domains where similar challenges exist.
Rotated Bitboards in FUSc# and Reinforcement Learning in Computer Chess and Beyond
2025-03-13
Johannes Buchner
There exist several techniques for representing the chess board inside the computer. In the first part of this paper, the concepts of the bitboard-representation and the advantages of (rotated) bitboards in move generation are explained. In order to illustrate those ideas practice, the concrete implementation of the move-generator in FUSc# is discussed and we explain a technique how to verify the move-generator with the "perft"-command. We show that the move-generator of FUSc# works 100% correct. The second part of this paper deals with reinforcement learning in computer chess (and beyond). We exemplify the progress that has been made in this field in the last 15-20 years by comparing the "state of the art" from 2002-2008, when FUSc# was developed, with recent innovations connected to "AlphaZero". We discuss how a "FUSc#-Zero" could be implemented and what would be necessary to reduce the number of training games necessary to achieve a good performance. This can be seen as a test case to the general prblem of improving "sample effciency" in reinforcement learning. In the final part, we move beyond computer chess, as the importance of sample effciency extends far beyond board games into a wide range of applications where data is costly, diffcult to obtain, or time consuming to generate. We review some application of the ideas developed in AlphaZero in other domains, i.e. the "other Alphas" like AlphaFold, AlphaTensor, AlphaGeometry and AlphaProof. We also discuss future research and the potential for such methods for ecological economic planning.
Public Support for Environmental Regulation: When Ideology Trumps Knowledge
2025-03-13
Markus Dertwinkel-Kalt, Max R. P. Grossmann
When environmental regulations are unpopular, policymakers often attribute resistance to information frictions and poor communication. We test this idea in the context of a major climate policy: Germany's Heating Law of 2023, which mandates the phase-out of fossil fuel heating. Through a survey experiment with property owners, we examine whether providing comprehensive information about the regulation's costs, requirements, and timeline affects adoption decisions and policy support. Despite successfully increasing factual knowledge, information provision has no significant effect on intended technology adoption, policy support, or incentivized measures of climate preferences. Instead, pre-existing environmental preferences and demographic characteristics emerge as the key predictors of responses to the regulation. A feeling that existing systems still work well and cost considerations dominate fossil fuel users' stated reasons for non-adoption, while independence from fossil fuels and perceived contributions to the common good drive adoption among switchers. Our findings suggest that opposition to climate policy stems from fundamental preference heterogeneity rather than information frictions. This has important implications for optimal policy design, highlighting potential limits of information provision in overcoming resistance to environmental regulation. The results also speak to broader questions in political economy about the relationship between knowledge, preferences, and support for policy reform.
Large Language Models (LLMs) are highly proficient in language-based tasks. Their language capabilities have positioned them at the forefront of the future AGI (Artificial General Intelligence) race. However, on closer inspection, Valmeekam et al. (2024); Zecevic et al. (2023); Wu et al. (2024) highlight a significant gap between their language proficiency and reasoning abilities. Reasoning in LLMs and Vision Language Models (VLMs) aims to bridge this gap by enabling these models to think and re-evaluate their actions and responses. Reasoning is an essential capability for complex problem-solving and a necessary step toward establishing trust in Artificial Intelligence (AI). This will make AI suitable for deployment in sensitive domains, such as healthcare, banking, law, defense, security etc. In recent times, with the advent of powerful reasoning models like OpenAI O1 and DeepSeek R1, reasoning endowment has become a critical research topic in LLMs. In this paper, we provide a detailed overview and comparison of existing reasoning techniques and present a systematic survey of reasoning-imbued language models. We also study current challenges and present our findings.
A FLASH on Blazars: Capturing the Radio Realm of 4FGL Blazars with SKAO Pathfinders
2025-03-13
Meriem Behiri, Elizabeth Mahony, Elaine Sadler, Emily Kerrison, Alberto Traina, MariaVittoria Zanchettin, Vincenzo Galluzzi, Andrea Lapi, Marcella Massardi
This work investigates the multi-wavelength properties of 165 4FGL blazars from the Fermi-LAT fourth source catalogue, looking for with counterparts in the Australian SKA Pathfinder (ASKAP) First Large Absorption Survey in HI (FLASH) continuum. Using high-resolution data from FLASH and complementary radio datasets, combined with archival Atacama Large Millimeter Array (ALMA) observations, we perform detailed spectral energy distribution (SED) analyses across cm-to-mm wavelengths. Our findings reveal that most blazars exhibit re-triggered peaked spectra, indicative of emission dominated by a single emitting region. Additionally, we identify strong correlations between radio and gamma-ray luminosities, highlighting the significant role of relativistic jets in these active galactic nuclei. The inclusion of spectroscopic redshifts from Sloan Digital Sky Survey (SDSS) and Gaia enables a comprehensive analysis of the evolutionary trends and physical characteristics of the sources. Furthermore, we report a tight Radio-X-ray Correlation for Flat Spectrum Radio Quasars, contrasting with the more scattered behaviour observed in BL-Lacs, reflecting their distinct accretion and jet-driving mechanisms. These results provide critical insights into the physics of blazars and their environments, paving the way for future studies with next-generation facilities like the SKA Observatory (SKAO) for radio observations and Cherenkov Telescope Array for gamma-ray studies.
Exploring the Relationship Between Stellar Mass, Metallicity, and Star Formation Rate at $z \sim 2.3$ in KBSS-MOSFIRE
2025-03-13
Nathalie A. Korhonen Cuestas, Allison L. Strom, Tim B. Miller, Charles C. Steidel, Ryan F. Trainor, Gwen C. Rudie, Evan Haze Nuñez
The metal enrichment of a galaxy is determined by the cycle of baryons in outflows, inflows, and star formation. The relative contribution and timescale of each process sets the relationship between stellar mass, metallicity, and the star formation rate (SFR). In the local universe, galaxies evolve in an equilibrium state where the timescales on which SFR and metallicity vary are comparable, and define a surface in mass-metallicity-SFR space known as the Fundamental Metallicity Relation (FMR). However, high-redshift observations suggest that this state of equilibrium may not persist throughout cosmic time. Using galaxies from the Keck Baryonic Structure Survey (KBSS) observed with MOSFIRE, we explore the relationship between stellar mass, gas-phase oxygen abundance, and SFR at $z \sim 2.3$. Across strong-line calibrations and SFR calculation methods, KBSS galaxies are inconsistent with the locally-defined FMR. We use both parametric and non-parametric methods of exploring a mass-metallicity-SFR relation. When using a parametric approach, we find no significant reduction mass-metallicity relation scatter when folding in SFR as a third parameter, although a non-parametric approach reveals that there could be a weak, redshift-dependent anticorrelation between residual gas-phase oxygen abundance, and SFR. Injection-recovery tests show that a significant reduction in scatter requires a stronger anticorrelation between SFR and residual metallicity. Our results suggest that the local FMR may not persist to $z \sim 2.3$, implying that $z \sim 2.3$ galaxies may not be in the equilibrium state described by the FMR and are more similar to higher redshift galaxies.
The Atacama Cosmology Telescope: The Development of Machine Learning Tools for Detecting Millimeter Sources in Timestream Pre-processing
2025-03-13
Simran K. Nerval, Erika Hornecker, Yilun Guan, Zeling Zhang, Adam Hincks, Emily Biermann, J. Richard Bond, Justin Clancy, Rolando Dunner, Allen Foster, Carlos Hervias-Caimapo, Renee Hlozek, Thomas W. Morris, Sigurd Naess, John Orlowski-Scherer, Cristobal Sifon, Jesse Treu
We present a new machine learning algorithm for classifying short-duration features in raw time ordered data (TODs) of cosmic microwave background survey observations. The algorithm, specifically designed for the Atacama Cosmology Telescope (ACT), works in conjunction with the previous TOD preprocessing techniques that employ statistical thresholding to indiscriminately remove all large spikes in the data, whether they are due to noise features, cosmic rays, or true astrophysical sources in a process called "data cuts". This has the undesirable effect of excising real astrophysical sources, including transients, from the data. The machine learning algorithm demonstrated in this work uses the output from these data cuts and is able to differentiate between electronic noise, cosmic rays, and point sources, enabling the removal of undesired signals while retaining true astrophysical signals during TOD pre-processing. We achieve an overall accuracy of 90% in categorizing data spikes of different origin and, importantly, 94% for identifying those caused by astrophysical sources. Our algorithm also measures the amplitude of any detected source seen more than once and produces a sub-minute to minute light curve, providing information on its short timescale variability. This automated algorithm for source detection and amplitude estimation will be particularly useful for upcoming surveys with large data volumes, such as the Simons Observatory.
Bloodhound Unleashed: Particle-based Substructure Tracking for Cosmological Simulations
2025-03-13
Hyunsu Kong, Michael Boylan-Kolchin, James S. Bullock
Modern studies of galaxy formation rely heavily on numerical simulations, which in turn require tools to identify and track self-bound structures in stars and dark matter. In this paper, we present Bloodhound, a new halo tracking algorithm optimized to track and characterize substructure in cosmological simulations, a regime that is crucial for studies of the nature of dark matter but where standard methods often have difficulties. Using simulations of Milky Way-mass haloes, we demonstrate that Bloodhound extends subhalo tracking by $3-4\, \mathrm{Gyr}$ on average, and significantly longer for subhaloes with small pericentres, relative to the widely used ROCKSTAR $+$ consistent-trees halo tracking pipeline. We also show that Bloodhound provides continuous tracking, mitigating an issue for the standard technique where subhaloes can be lost and then found again -- but assigned to a new merger tree -- after several snapshots. This improved tracking leads to a substantially larger number of surviving subhaloes in the inner regions of dark matter haloes, which has several implications for studies of the Milky Way's satellite galaxy system and its use for constraining properties of dark matter. For example, within the radius where current surveys are complete to ultra-faint galaxies ($D_{\rm MW} \lesssim 50$ kpc), Bloodhound finds more than twice as many subhaloes above the atomic cooling scale relative to the standard tracking method. Our results underscore the importance of robust subhalo tracking techniques in advancing our understanding of galaxy formation and cosmological models.
Nearly a Decade of Groundbreaking Speckle Interferometry at the International Gemini Observatory
2025-03-13
Steve B. Howell, Clara E. Martínez-Vázquez, Elise Furlan, Nicholas J. Scott, Rachel A. Matson, Colin Littlefield, Catherine A. Clark, Kathryn V. Lester, Zachary D. Hartman, David R. Ciardi, Sarah J. Deveny
Since its inception, speckle interferometry has revolutionized high-resolution astronomical imaging, overcoming atmospheric challenges to achieve the diffraction limits of telescopes. Almost a decade ago, in 2018, a pair of speckle cameras -- 'Alopeke and Zorro -- were installed at the twin 8.1-meter Gemini North and South telescopes, two of the largest apertures in the world, in Hawai'i and Chile. Equipped with dual blue and red channels, 'Alopeke and Zorro deliver high-resolution imaging across optical bandpasses from 350 to 1000 nm, which has led to crucial discoveries in both stellar multiplicity and exoplanetary science. Furthermore, the broad and nonrestrictive access to these instruments, given by each Gemini Observatory partner and via the US NOIRLab open skies policy, has allowed our community to expand the applications of the instruments, supporting a wide range of scientific investigations from Solar System bodies, to morphological studies of stellar remnants, to evolved stars, to transient phenomena. This paper reviews the instrument technology and observational capabilities, and highlights key scientific contributions and discoveries of 'Alopeke and Zorro, emphasizing the enduring importance of speckle interferometry in advancing modern observational astronomy and expanding the frontiers of astronomical research.
Quantum complexity in gravity, quantum field theory, and quantum information science
2025-03-13
Stefano Baiguera, Vijay Balasubramanian, Pawel Caputa, Shira Chapman, Jonas Haferkamp, Michal P. Heller, Nicole Yunger Halpern
Quantum complexity quantifies the difficulty of preparing a state, or implementing a unitary, using limited resources. Over the past decade, quantum-complexity research has surged. Applications range from quantum computation to condensed matter and quantum gravity. Different communities apply different tools to quantum complexity, as well as define complexity differently. This review bridges the disciplines' approaches. We cover multiple definitions of complexity, as well as their key properties and applications. In quantum information, random quantum circuits proved to be an invaluable tool in extracting properties of the complexity growth of discrete quantum circuits. In quantum many-body systems and quantum field theory (QFT), complexity is defined geometrically through geodesics. Complexity can be defined also in terms of state or operator spreading and tensor-network concepts. In addition to reviewing these approaches, we outline applications to simple quantum systems, quantum many-body models and QFTs, including conformal field theories (CFTs). Finally, we detail the relationship between complexity and gravitational observables within the anti-de Sitter-space (AdS)/CFT correspondence. This review thereby integrates recent quantum-complexity advances across fields.
Federated Learning (FL) has emerged as a promising privacy-preserving collaborative model training paradigm without sharing raw data. However, recent studies have revealed that private information can still be leaked through shared gradient information and attacked by Gradient Inversion Attacks (GIA). While many GIA methods have been proposed, a detailed analysis, evaluation, and summary of these methods are still lacking. Although various survey papers summarize existing privacy attacks in FL, few studies have conducted extensive experiments to unveil the effectiveness of GIA and their associated limiting factors in this context. To fill this gap, we first undertake a systematic review of GIA and categorize existing methods into three types, i.e., \textit{optimization-based} GIA (OP-GIA), \textit{generation-based} GIA (GEN-GIA), and \textit{analytics-based} GIA (ANA-GIA). Then, we comprehensively analyze and evaluate the three types of GIA in FL, providing insights into the factors that influence their performance, practicality, and potential threats. Our findings indicate that OP-GIA is the most practical attack setting despite its unsatisfactory performance, while GEN-GIA has many dependencies and ANA-GIA is easily detectable, making them both impractical. Finally, we offer a three-stage defense pipeline to users when designing FL frameworks and protocols for better privacy protection and share some future research directions from the perspectives of attackers and defenders that we believe should be pursued. We hope that our study can help researchers design more robust FL frameworks to defend against these attacks.
Low-pass sampling in Model Predictive Path Integral Control
2025-03-13
Piotr Kicki
Model Predictive Path Integral (MPPI) control is a widely used sampling-based approach for real-time control, offering flexibility in handling arbitrary dynamics and cost functions. However, the original MPPI suffers from high-frequency noise in the sampled control trajectories, leading to actuator wear and inefficient exploration. In this work, we introduce Low-Pass Model Predictive Path Integral Control (LP-MPPI), which integrates low-pass filtering into the sampling process to eliminate detrimental high-frequency components and improve the effectiveness of the control trajectories exploration. Unlike prior approaches, LP-MPPI provides direct and interpretable control over the frequency spectrum of sampled trajectories, enhancing sampling efficiency and control smoothness. Through extensive evaluations in Gymnasium environments, simulated quadruped locomotion, and real-world F1TENTH autonomous racing, we demonstrate that LP-MPPI consistently outperforms state-of-the-art MPPI variants, achieving significant performance improvements while reducing control signal chattering.
Ensemble Learning for Large Language Models in Text and Code Generation: A Survey
2025-03-13
Mari Ashiga, Wei Jie, Fan Wu, Vardan Voskanyan, Fateme Dinmohammadi, Paul Brookes, Jingzhi Gong, Zheng Wang
Generative pretrained transformers (GPT) are the common large language models (LLMs) used for generating text from natural language inputs. However, the fixed properties of language parameters in individual LLMs can lead to inconsistencies in the generated outputs. This limitation also restricts the models' ability to represent diverse language patterns due to inherent biases. Moreover, many powerful LLMs are closed-source. This prevents organizations from integrating their data into these systems, raising concerns about data privacy and limiting industry applications. Inspired by the successful application of LLM ensemble models in text generation, recent literature has also investigated their potential in code generation. This article reviews these emerging LLM ensemble approaches. Our goal is to enhance readers' understanding of existing techniques and encourage further research and practical implementation, aiming to expand the real-world applications of LLM ensemble models in both text and code generation. We categorize these approaches into seven main methods: weight merging, knowledge fusion, mixture of experts, reward ensemble, output ensemble, routing, and cascading. From this list, we focus on four methods and models that show strong performance and potential for broader applications. We analyze their modeling steps, training methods, and output features to provide a clear understanding of their capabilities. Our findings highlight the benefits of LLM ensemble techniques. These include better representation of diversity, improved output quality, and greater flexibility in applications. This information offers valuable insights for selecting models for various real-world tasks involving text and code generation, and potentially applying methods to multimodal LLMs.
QuickDraw: Fast Visualization, Analysis and Active Learning for Medical Image Segmentation
2025-03-12
Daniel Syomichev, Padmini Gopinath, Guang-Lin Wei, Eric Chang, Ian Gordon, Amanuel Seifu, Rahul Pemmaraju, Neehar Peri, James Purtilo
Analyzing CT scans, MRIs and X-rays is pivotal in diagnosing and treating diseases. However, detecting and identifying abnormalities from such medical images is a time-intensive process that requires expert analysis and is prone to interobserver variability. To mitigate such issues, machine learning-based models have been introduced to automate and significantly reduce the cost of image segmentation. Despite significant advances in medical image analysis in recent years, many of the latest models are never applied in clinical settings because state-of-the-art models do not easily interface with existing medical image viewers. To address these limitations, we propose QuickDraw, an open-source framework for medical image visualization and analysis that allows users to upload DICOM images and run off-the-shelf models to generate 3D segmentation masks. In addition, our tool allows users to edit, export, and evaluate segmentation masks to iteratively improve state-of-the-art models through active learning. In this paper, we detail the design of our tool and present survey results that highlight the usability of our software. Notably, we find that QuickDraw reduces the time to manually segment a CT scan from four hours to six minutes and reduces machine learning-assisted segmentation time by 10\% compared to prior work. Our code and documentation are available at https://github.com/qd-seg/quickdraw
IT Students Career Confidence and Career Identity During COVID-19
2025-03-12
Sophie McKenzie
COVID-19 disrupted the professional preparation of university students, with less opportunity to engage in professional practice due to a reduced employment market. Little is known about how this period impacted upon the career confidence and career identity of university students. This research paper explores the career confidence and identity of university students in Information Technology (IT) prior and during the COVID-19 period. Using a survey method and quantitative analysis, ANOVA and Kruskal-Wallis tests with different sensitivity and variance standards were used during analysis to present mean and mean rank of data collected during 2018, 2019, 2020 and 2021. 1349 IT students from an Australian University reported their career confidence. The results indicate IT students' career confidence maintained during the period. In 2021, the results indicate increased career commitment of IT students showing higher professional expectations to work in IT along with greater self-awareness regarding their professional development needs. Even with increased career confidence as observed in this study, supporting university students to explore their career options and build upon their career identity, and more broadly their employability, remains an important activity for universities to curate in their graduates.
Training Human-Robot Teams by Improving Transparency Through a Virtual Spectator Interface
2025-03-12
Sean Dallas, Hongjiao Qiang, Motaz AbuHijleh, Wonse Jo, Kayla Riegner, Jon Smereka, Lionel Robert, Wing-Yue Louie, Dawn M. Tilbury
After-action reviews (AARs) are professional discussions that help operators and teams enhance their task performance by analyzing completed missions with peers and professionals. Previous studies that compared different formats of AARs have mainly focused on human teams. However, the inclusion of robotic teammates brings along new challenges in understanding teammate intent and communication. Traditional AAR between human teammates may not be satisfactory for human-robot teams. To address this limitation, we propose a new training review (TR) tool, called the Virtual Spectator Interface (VSI), to enhance human-robot team performance and situational awareness (SA) in a simulated search mission. The proposed VSI primarily utilizes visual feedback to review subjects' behavior. To examine the effectiveness of VSI, we took elements from AAR to conduct our own TR, designed a 1 x 3 between-subjects experiment with experimental conditions: TR with (1) VSI, (2) screen recording, and (3) non-technology (only verbal descriptions). The results of our experiments demonstrated that the VSI did not result in significantly better team performance than other conditions. However, the TR with VSI led to more improvement in the subjects SA over the other conditions.
Measuring the Sources of Taste-Based Discrimination Using List Experiments
2025-03-12
Ariel Listo, Ercio A. Munoz, Dario Sansone
This study examines how attitudes among supervisors, co-workers, and customers contribute to discrimination against sexual minorities in the workplace. A large, nationally representative sample in Chile was recruited in collaboration with a local firm. The survey employs a series of double list experiments designed to measure attitudes on sensitive issues while reducing social desirability bias, followed by direct questions on attitudes toward sexual minorities. Findings reveal a discrepancy between reported and actual comfort levels with gay individuals in the labor market. Respondents underreport their discomfort by 15 to 23 percentage points, with the largest bias and lowest comfort levels observed when asked about supervising a gay employee. Additionally, respondents consistently underestimate broader societal support for gay employees and co-workers. These differences are reflected in real-stakes donation behavior: respondents who choose not to donate any amount from a lottery to a local LGBTQ-related NGO also report lower comfort levels and exhibit greater misreporting.
A Comprehensive Review on Understanding the Decentralized and Collaborative Approach in Machine Learning
2025-03-12
Sarwar Saif, Md Jahirul Islam, Md. Zihad Bin Jahangir, Parag Biswas, Abdur Rashid, MD Abdullah Al Nasim, Kishor Datta Gupta
The arrival of Machine Learning (ML) completely changed how we can unlock valuable information from data. Traditional methods, where everything was stored in one place, had big problems with keeping information private, handling large amounts of data, and avoiding unfair advantages. Machine Learning has become a powerful tool that uses Artificial Intelligence (AI) to overcome these challenges. We started by learning the basics of Machine Learning, including the different types like supervised, unsupervised, and reinforcement learning. We also explored the important steps involved, such as preparing the data, choosing the right model, training it, and then checking its performance. Next, we examined some key challenges in Machine Learning, such as models learning too much from specific examples (overfitting), not learning enough (underfitting), and reflecting biases in the data used. Moving beyond centralized systems, we looked at decentralized Machine Learning and its benefits, like keeping data private, getting answers faster, and using a wider variety of data sources. We then focused on a specific type called federated learning, where models are trained without directly sharing sensitive information. Real-world examples from healthcare and finance were used to show how collaborative Machine Learning can solve important problems while still protecting information security. Finally, we discussed challenges like communication efficiency, dealing with different types of data, and security. We also explored using a Zero Trust framework, which provides an extra layer of protection for collaborative Machine Learning systems. This approach is paving the way for a bright future for this groundbreaking technology.
SE(3)-Equivariant Robot Learning and Control: A Tutorial Survey
2025-03-12
Joohwan Seo, Soochul Yoo, Junwoo Chang, Hyunseok An, Hyunwoo Ryu, Soomi Lee, Arvind Kruthiventy, Jongeun CHoi, Roberto Horowitz
Recent advances in deep learning and Transformers have driven major breakthroughs in robotics by employing techniques such as imitation learning, reinforcement learning, and LLM-based multimodal perception and decision-making. However, conventional deep learning and Transformer models often struggle to process data with inherent symmetries and invariances, typically relying on large datasets or extensive data augmentation. Equivariant neural networks overcome these limitations by explicitly integrating symmetry and invariance into their architectures, leading to improved efficiency and generalization. This tutorial survey reviews a wide range of equivariant deep learning and control methods for robotics, from classic to state-of-the-art, with a focus on SE(3)-equivariant models that leverage the natural 3D rotational and translational symmetries in visual robotic manipulation and control design. Using unified mathematical notation, we begin by reviewing key concepts from group theory, along with matrix Lie groups and Lie algebras. We then introduce foundational group-equivariant neural network design and show how the group-equivariance can be obtained through their structure. Next, we discuss the applications of SE(3)-equivariant neural networks in robotics in terms of imitation learning and reinforcement learning. The SE(3)-equivariant control design is also reviewed from the perspective of geometric control. Finally, we highlight the challenges and future directions of equivariant methods in developing more robust, sample-efficient, and multi-modal real-world robotic systems.
A Comprehensive Multi-Vocal Empirical Study of ML Cloud Service Misuses
2025-03-12
Hadil Ben Amor, Manel Abdellatif, Taher Ghaleb
Machine Learning (ML) models are widely used across various domains, including medical diagnostics and autonomous driving. To support this growth, cloud providers offer ML services to ease the integration of ML components in software systems. The evolving business requirements and the popularity of ML services have led practitioners of all skill levels to implement, and maintain ML service-based systems. However, they may not always adhere to optimal design and usage practices for ML cloud services, resulting in common misuse which could significantly degrade the quality of ML service-based systems and adversely affect their maintenance and evolution. Though much research has been conducted on ML service misuse, a consistent terminology and specification for these misuses remain absent. We therefore conduct in this paper a comprehensive, multi-vocal empirical study exploring the prevalence of ML cloud service misuses in practice. We propose a catalog of 20 ML cloud service misuses, most of which have not been studied in prior research. To achieve this, we conducted a) a systematic literature review of studies on ML misuses, b) a gray literature review of the official documentation provided by major cloud providers, c) an empirical analysis of a curated set of 377 ML service-based systems on GitHub, and d) a survey with 50 ML practitioners. Our results show that ML service misuses are common in both open-source projects and industry, often stemming from a lack of understanding of service capabilities, and insufficient documentation. This emphasizes the importance of ongoing education in best practices for ML services, which is the focus of this paper, while also highlighting the need for tools to automatically detect and refactor ML misuses.
A practical guide to machine learning interatomic potentials -- Status and future
2025-03-12
Ryan Jacobs, Dane Morgan, Siamak Attarian, Jun Meng, Chen Shen, Zhenghao Wu, Clare Yijia Xie, Julia H. Yang, Nongnuch Artrith, Ben Blaiszik, Gerbrand Ceder, Kamal Choudhary, Gabor Csanyi, Ekin Dogus Cubuk, Bowen Deng, Ralf Drautz, Xiang Fu, Jonathan Godwin, Vasant Honavar, Olexandr Isayev, Anders Johansson, Boris Kozinsky, Stefano Martiniani, Shyue Ping Ong, Igor Poltavsky, KJ Schmidt, So Takamoto, Aidan Thompson, Julia Westermayr, Brandon M. Wood
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3-10+ years.
Methods of Selective Inference for Linear Mixed Models: a Review and Empirical Comparison
2025-03-12
Matteo D'Alessandro, Magne Thoresen
Selective inference aims at providing valid inference after a data-driven selection of models or hypotheses. It is essential to avoid overconfident results and replicability issues. While significant advances have been made in this area for standard regression models, relatively little attention has been given to linear mixed models (LMMs), which are widely used for analyzing clustered or longitudinal data. This paper reviews the existing selective inference approaches developed for LMMs, focusing on selection of fixed effects, where the random effects structure is given. We present these methods in detail and, through comparative simulations, assess their practical performance and computational feasibility under varying data structures. In addition, we apply them to a real-world biological dataset to examine how method choice can impact inference in practice. Our findings highlight an existing trade-off between computational complexity and statistical power and emphasize the scarcity of methods that perform well as the number of variables increases. In such scenarios, basic sample splitting emerges as the most reliable approach.
The next generation of galaxy surveys will provide highly precise measurements of galaxy clustering, therefore requiring a corresponding accuracy. Current approaches, which rely on approximations and idealized assumptions, may fall short in capturing the level of detail required for high-precision observations. In order to increase the modeling accuracy, recently, unequal-time contributions to the galaxy power spectrum have been introduced in order to include the effects of radial correlations. We present a generalization of the formalism for the observed unequal-time power spectrum, that includes Doppler and local general relativistic corrections, plus local primordial non-Gaussianity. We find that unequal time corrections can potentially mimic an effective $f_{\mathrm{NL}}$ of order unity. We provide a first assessment of the significance of unequal-time corrections for future galaxy clustering experiments, estimating a Signal-to-Noise-Ratio of $\sim3$ for Stage IV-like surveys.
The Evolution of central and satellite galaxies in the local universe: Insights from galactic archeology
2025-03-12
V. M. Sampaio
This thesis investigates the evolution of galaxies in diverse environments, utilizing Sloan Digital Sky Survey (SDSS) data to explore the impact of environmental richness on central and satellite galaxies across stellar mass ranges, compared to isolated systems. The sample is limited to 0.03 < z < 0.1 and apparent magnitudes brighter than 17.78, ensuring spectroscopic completeness and reliable stellar population estimates. Galaxies are categorized by environment as field or cluster/group systems, with further separation into satellites and centrals. By analyzing the star formation rate (SFR)-stellar mass plane, this work identifies systematic differences in the blue cloud (BC), green valley (GV), and red sequence (RS) across environments. Morphological and stellar population analyses reveal that T-type, metallicity, and stellar age transitions highlight the role of environmental quenching. A newly introduced T-Type \emph{vs.} specific SFR diagram provides evidence that morphological transformation precedes full quenching. Correlating galaxy properties with time since infall through projected phase space confirms the delayed-then-rapid quenching model for low- and intermediate-mass galaxies, extending it to morphology. Time-scales for quenching and morphological transitions are also derived as a function of stellar mass.
Advancing Education through Tutoring Systems: A Systematic Literature Review
2025-03-12
Vincent Liu, Ehsan Latif, Xiaoming Zhai
This study systematically reviews the transformative role of Tutoring Systems, encompassing Intelligent Tutoring Systems (ITS) and Robot Tutoring Systems (RTS), in addressing global educational challenges through advanced technologies. As many students struggle with proficiency in core academic areas, Tutoring Systems emerge as promising solutions to bridge learning gaps by delivering personalized and adaptive instruction. ITS leverages artificial intelligence (AI) models, such as Bayesian Knowledge Tracing and Large Language Models, to provide precise cognitive support, while RTS enhances social and emotional engagement through human-like interactions. This systematic review, adhering to the PRISMA framework, analyzed 86 representative studies. We evaluated the pedagogical and technological advancements, engagement strategies, and ethical considerations surrounding these systems. Based on these parameters, Latent Class Analysis was conducted and identified three distinct categories: computer-based ITS, robot-based RTS, and multimodal systems integrating various interaction modes. The findings reveal significant advancements in AI techniques that enhance adaptability, engagement, and learning outcomes. However, challenges such as ethical concerns, scalability issues, and gaps in cognitive adaptability persist. The study highlights the complementary strengths of ITS and RTS, proposing integrated hybrid solutions to maximize educational benefits. Future research should focus on bridging gaps in scalability, addressing ethical considerations comprehensively, and advancing AI models to support diverse educational needs.
Review GIDE -- Restaurant Review Gastrointestinal Illness Detection and Extraction with Large Language Models
2025-03-12
Timothy Laurence, Joshua Harris, Leo Loman, Amy Douglas, Yung-Wai Chan, Luke Hounsome, Lesley Larkin, Michael Borowitz
Foodborne gastrointestinal (GI) illness is a common cause of ill health in the UK. However, many cases do not interact with the healthcare system, posing significant challenges for traditional surveillance methods. The growth of publicly available online restaurant reviews and advancements in large language models (LLMs) present potential opportunities to extend disease surveillance by identifying public reports of GI illness. In this study, we introduce a novel annotation schema, developed with experts in GI illness, applied to the Yelp Open Dataset of reviews. Our annotations extend beyond binary disease detection, to include detailed extraction of information on symptoms and foods. We evaluate the performance of open-weight LLMs across these three tasks: GI illness detection, symptom extraction, and food extraction. We compare this performance to RoBERTa-based classification models fine-tuned specifically for these tasks. Our results show that using prompt-based approaches, LLMs achieve micro-F1 scores of over 90% for all three of our tasks. Using prompting alone, we achieve micro-F1 scores that exceed those of smaller fine-tuned models. We further demonstrate the robustness of LLMs in GI illness detection across three bias-focused experiments. Our results suggest that publicly available review text and LLMs offer substantial potential for public health surveillance of GI illness by enabling highly effective extraction of key information. While LLMs appear to exhibit minimal bias in processing, the inherent limitations of restaurant review data highlight the need for cautious interpretation of results.
Stochastic Geometry for Modeling and Analysis of Sensing and Communications: A Survey
2025-03-12
Harris K. Armeniakos, Petros S. Bithas, Sotiris A. Tegos, Athanasios G. Kanatas, George K. Karagiannidis
One of the most promising technologies for next-generation wireless networks is integrated communication and sensing (ISAC). It is considered a key enabler for applications that require both enhanced communication and accurate sensing capabilities. Examples of such applications include smart environments, augmented and virtual reality, or the internet of things, where the capabilities of intelligent sensing and broadband communications are vital. Therefore, ISAC has attracted the research interest of both academia and industry, and many investigations have been carried out over the past decade. The articles in the literature include system models, performance evaluation, and optimization studies of several ISAC alternative designs. Stochastic geometry is the study and analysis of random spatial patterns, and as such, stochastic geometry tools have been considered for the performance evaluation of wireless networks with different types of nodes. In this paper, we aim to provide a comprehensive survey of current research progress in performance evaluation of ISAC systems using stochastic geometry tools. The survey covers terrestrial, aerial, and vehicular networks, where the random spatial location of the corresponding network elements and propagation scatterers and/or blockages is treated with various point processes. The paper starts with a short tutorial on ISAC technology, stochastic geometry tools, and metrics used in performance evaluation of communication and sensing. Then, the technical components of the system models utilized in the surveyed papers are discussed. Subsequently, we present the key results of the literature in all types of networks using three levels of integration: sensing-assisted communication, communication-assisted sensing, and joint sensing and communication. Finally, future research challenges and promising directions are discussed.
Genome evolution in and endangered freshwater mussel
2025-03-12
Rebekah L. Rogers, John P. Wares, Jeffrey T. Garner
Nearly neutral theory predicts that evolutionary processes will differ in small populations compared to large populations, a key point of concern for endangered species. The nearly-neutral threshold, the span of neutral variation, and the adaptive potential from new mutations all differ depending on $N_e$. To determine how genomes respond in small populations, we have created a reference genome for a US federally endangered IUCN Red List freshwater mussel, \emph{Elliptio spinosa}, and compare it to genetic variation for a common and successful relative, \emph{Elliptio crassidens}. We find higher rates of background duplication rates in \emph{E. spinosa} consistent with proposed theories of duplicate gene accumulation according to nearly-neutral processes. Along with these changes we observe fewer cases of adaptive gene family amplification in this endangered species. However, TE content is not consistent with nearly-neutral theory. We observe substantially less recent TE proliferation in the endangered species with over 500 Mb of newly copied TEs in \emph{Elliptio crassidens}. These results suggest a more complex interplay between TEs and duplicate genes than previously proposed for small populations. They further suggest that TEs and duplications require greater attention in surveys of genomic health for endangered species.
Have LLMs Made Active Learning Obsolete? Surveying the NLP Community
2025-03-12
Julia Romberg, Christopher Schröder, Julius Gonsior, Katrin Tomanek, Fredrik Olsson
Supervised learning relies on annotated data, which is expensive to obtain. A longstanding strategy to reduce annotation costs is active learning, an iterative process, in which a human annotates only data instances deemed informative by a model. Large language models (LLMs) have pushed the effectiveness of active learning, but have also improved methods such as few- or zero-shot learning, and text synthesis - thereby introducing potential alternatives. This raises the question: has active learning become obsolete? To answer this fully, we must look beyond literature to practical experiences. We conduct an online survey in the NLP community to collect previously intangible insights on the perceived relevance of data annotation, particularly focusing on active learning, including best practices, obstacles and expected future developments. Our findings show that annotated data remains a key factor, and active learning continues to be relevant. While the majority of active learning users find it effective, a comparison with a community survey from over a decade ago reveals persistent challenges: setup complexity, estimation of cost reduction, and tooling. We publish an anonymized version of the collected dataset
Anomaly Detection to identify Transients in LSST Time Series Data
2025-03-12
Miguel Crispim Romao, Djuna Croon, Daniel Godines
We introduce a novel approach to detecting microlensing events and other transients in light curves, utilising the isolation forest (iForest) algorithm for anomaly detection. Focusing on the Legacy Survey of Space and Time by the Vera C. Rubin Observatory, we show that an iForest trained on signal-less light curves can efficiently identify microlensing events by different types of dark objects and binaries, as well as variable stars. We further show that the iForest has real-time applicability through a drip-feed analysis, demonstrating its potential as a valuable tool for LSST alert brokers to efficiently prioritise and classify transient candidates for follow-up observations.
2025-03-12
Philip Taranto, Simon Milz, Mio Murao, Marco Túlio Quintino, Kavan Modi
An operational description of quantum phenomena concerns developing models that describe experimentally observed behaviour. $\textit{Higher-order quantum operations}\unicode{x2014}$quantum operations that transform quantum operations$\unicode{x2014}$are fundamental to modern quantum theory, extending beyond basic state preparations, evolutions, and measurements described by the Born rule. These operations naturally emerge in quantum circuit architectures, correlated open dynamics, and investigations of quantum causality, to name but a few fields of application. This Review Article provides both a pedagogical introduction to the framework of higher-order quantum operations and a comprehensive survey of current literature, illustrated through physical examples. We conclude by identifying open problems and future research directions in this rapidly evolving field.
The Complete Spitzer Survey of Stellar Structure in Galaxies (CS$^4$G)
2025-03-12
P. M. Sánchez-Alarcón, H. Salo, J. H. Knapen, S. Comerón, J. Román, A. E. Watkins, R. J. Buta, S. Laine, J. M. Falcón-Ramírez, M. Anetjärvi, E. Athanassoula, A. Bosma, D. A. Gadotti, J. L. Hinz, L. C. Ho, B. W. Holwerda, J. Janz, T. Kim, J. Koda, J. Laine, E. Laurikainen, B. F. Madore, K. Menéndez-Delmestre, R. F. Peletier, M. Querejeta, A. Ruokanen, K. Sheth, D. Zaritsky
The Spitzer Survey of Stellar Structure in Galaxies (S$^4$G), together with its Early Type Galaxy (ETG) extension, stand as the most extensive dataset of deep, uniform mid-infrared (mid-IR; 3.6 and 4.5$\,\mu$m) imaging for a sample of $2817$ nearby ($d<40 \,$Mpc) galaxies. However, the velocity criterion used to select the original sample results in an additional 422 galaxies without HI detection that ought to have been included in the S$^4$G on the basis of their optical recession velocities. In order to create a complete magnitude-, size- and volume-limited sample of nearby galaxies, we collect $3.6\,\mu$m and $i$-band images using archival data from different surveys and complement it with new observations for the missing galaxies. We denote the sample of these additional galaxies as Disc Galaxy (DG) extension. We present the Complete Spitzer Survey of Stellar Structure in Galaxies (CS$^4$G), encompassing a sample of $3239$ galaxies with consistent imaging, surface brightness profiles, photometric parameters, and revised morphological classification. Following the original strategy of the S$^4$G survey, we produce masks, surface brightness profiles, and curves of growth using masked $3.6\,\mu$m and $i$-band images. From these profiles, we derive the integrated quantities: total magnitude, stellar mass, concentration parameter, and galaxy size, converting to $3.6\,\mu$m. We re-measure these parameters also for the S$^4$G and ETG to create a homogenous sample. We present new morphological revised $T$-types, and we showcase mid-IR scaling relations for the photometric parameters. We complete the S$^4$G sample by incorporating 422 galaxies. The CS$^4$G includes at least 99.94\% of the complete sample of nearby galaxies, meeting the original selection criteria, and it will enable a wide set of investigations into galaxy structure and evolution.
Modeling the BMS transformation induced by a binary black hole merger
2025-03-12
Guido Da Re, Keefe Mitman, Leo C. Stein, Mark A. Scheel, Saul A. Teukolsky, Dongze Sun, Michael Boyle, Nils Deppe, Scott E. Field, Lawrence E. Kidder, Jordan Moxon, Kyle C. Nelli, William Throwe, Vijay Varma, Nils L. Vu
Understanding the characteristics of the remnant black hole formed in a binary black hole merger is crucial for conducting gravitational wave astronomy. Typically, models of remnant black holes provide information about their mass, spin, and kick velocity. However, other information related to the supertranslation symmetries of the BMS group, such as the memory effect, is also important for characterizing the final state of the system. In this work, we build a model of the BMS transformation that maps a binary black hole's inspiral frame to the remnant black hole's canonical rest frame. Training data for this model are created using high-precision numerical relativity simulations of quasi-circular systems with mass ratios $q \le 8$ and spins parallel to the orbital angular momentum with magnitudes $\chi_{1}, \chi_{2} \le 0.8$. We use Gaussian Process Regression to model the BMS transformations over the three-dimensional parameter space $\left(q, \chi_{1}^{z}, \chi_{2}^{z}\right)$. The physics captured by this model is strictly non-perturbative and cannot be obtained from post-Newtonian approximations alone, as it requires knowledge of the strong nonlinear effects that are sourced during the merger. Apart from providing the first model of the supertranslation induced by a binary black hole merger, we also find that the kick velocities predicted using Cauchy-characteristic evolution waveforms are, on average, $\sim5\%$ larger than the ones obtained from extrapolated waveforms. Our work has broad implications for improving gravitational wave models and studying the large-scale impact of memory, such as on the cosmological background. The fits produced in this work are available through the Python package $\texttt{surfinBH}$ under the name $\texttt{NRSur3dq8BMSRemnant}$.
Biological Multi-Layer and Single Cell Network-Based Multiomics Models - a Review
2025-03-12
Marcello Barylli, Joyaditya Saha, Tineke E. Buffart, Jan Koster, Kristiaan J. Lenos, Louis Vermeulen, Vivek M. Sheraton
Recent advances in single cell sequencing and multi-omics techniques have significantly improved our understanding of biological phenomena and our capacity to model them. Despite combined capture of data modalities showing similar progress, notably single cell transcriptomics and proteomics, simultaneous multi-omics level probing still remains challenging. As an alternative to combined capture of biological data, in this review, we explore current and upcoming methods for post-hoc network inference and integration with an emphasis on single cell transcriptomics and proteomics. By examining various approaches, from probabilistic models to graph-based algorithms, we outline the challenges and potential strategies for effectively combining biological data types while simultaneously highlighting the importance of model validation. With this review, we aim to inform readers of the breadth of tools currently available for the purpose-specific generation of heterogeneous multi-layer networks.
Recent advancements in reasoning with large language models (RLLMs), such as OpenAI-O1 and DeepSeek-R1, have demonstrated their impressive capabilities in complex domains like mathematics and coding. A central factor in their success lies in the application of long chain-of-thought (Long CoT) characteristics, which enhance reasoning abilities and enable the solution of intricate problems. However, despite these developments, a comprehensive survey on Long CoT is still lacking, limiting our understanding of its distinctions from traditional short chain-of-thought (Short CoT) and complicating ongoing debates on issues like "overthinking" and "test-time scaling." This survey seeks to fill this gap by offering a unified perspective on Long CoT. (1) We first distinguish Long CoT from Short CoT and introduce a novel taxonomy to categorize current reasoning paradigms. (2) Next, we explore the key characteristics of Long CoT: deep reasoning, extensive exploration, and feasible reflection, which enable models to handle more complex tasks and produce more efficient, coherent outcomes compared to the shallower Short CoT. (3) We then investigate key phenomena such as the emergence of Long CoT with these characteristics, including overthinking, and test-time scaling, offering insights into how these processes manifest in practice. (4) Finally, we identify significant research gaps and highlight promising future directions, including the integration of multi-modal reasoning, efficiency improvements, and enhanced knowledge frameworks. By providing a structured overview, this survey aims to inspire future research and further the development of logical reasoning in artificial intelligence.
Properties of Contracting Massive Protostellar Cores
2025-03-12
L. E. Pirogov, P. M. Zemlyanukha, E. M. Dombek
Studies of the structure and kinematics of cores associated with the regions of massive star and star cluster formation are necessary for constructing scenario for the evolution of these objects. We analyzed spectral maps of the massive cores of G012.418+00.506, G326.472+00.888, G328.567--00.535, G335.586--00.289 and G343.127--00.063 from the MALT90 survey in the HCO$^+$(1--0) and H$^{13}$CO$^+$(1--0) lines. The cores are at different stages of evolution and have signs of contraction. By fitting spectral maps calculated within the framework of a spherically symmetric model into the observed ones, the parameters of the radial profiles of density, turbulent velocity and contraction velocity were calculated. The power-law index of the density decay with distance from the center varies in the range of $\sim 1.5-2.8$. The lowest value is obtained for the core of G326.472+00.888 without internal sources. The contraction velocity in all cores depends weakly on the distance from the center, decreasing with an index of $\sim 0.1$, which differs from the free-fall mode. There are indications of rotation for the cores of G328.567--00.535 and G335.586--00.289. Analysis of $^{13}$CO(2--1) data from the SEDIGISM survey for the regions G012.418+00.506, G335.586--00.289, and G343.127--00.063 revealed motions from the surrounding gas toward the cores. The results obtained indicate that the massive cores under consideration interact with their environment and are apparently in a state of global collapse.t the massive cores under consideration interact with their environment and are apparently in a state of global collapse.
Large Language Models for Multi-Facility Location Mechanism Design
2025-03-12
Nguyen Thach, Fei Liu, Houyu Zhou, Hau Chan
Designing strategyproof mechanisms for multi-facility location that optimize social costs based on agent preferences had been challenging due to the extensive domain knowledge required and poor worst-case guarantees. Recently, deep learning models have been proposed as alternatives. However, these models require some domain knowledge and extensive hyperparameter tuning as well as lacking interpretability, which is crucial in practice when transparency of the learned mechanisms is mandatory. In this paper, we introduce a novel approach, named LLMMech, that addresses these limitations by incorporating large language models (LLMs) into an evolutionary framework for generating interpretable, hyperparameter-free, empirically strategyproof, and nearly optimal mechanisms. Our experimental results, evaluated on various problem settings where the social cost is arbitrarily weighted across agents and the agent preferences may not be uniformly distributed, demonstrate that the LLM-generated mechanisms generally outperform existing handcrafted baselines and deep learning models. Furthermore, the mechanisms exhibit impressive generalizability to out-of-distribution agent preferences and to larger instances with more agents.
Revisiting Karp, Vazirani, and Vazirani (STOC 1990): A Simple Yet Rigorous Fix to the $1 - 1/e$ Upper-Bound Analysis
2025-03-12
Pan Xu
In this paper, we present a comprehensive review of the analysis of the well-known $1 - 1/e$ upper bound on the competitiveness that any online algorithm can achieve, as established in the classical paper by Karp, Vazirani, and Vazirani (STOC 1990). We discuss in detail all the minor and major technical issues in their approach and present a \emph{simple yet rigorous} method to address them. Specifically, we show that the upper bound of $n(1 - 1/e) + o(n)$ on the performance of any online algorithm, as shown in the paper, can be replaced by $\lceil n \cdot (1 - 1/e) + 2 - 1/e \rceil$. Our approach is notable for its simplicity and is significantly less technically involved than existing ones.
Automating Code Review: A Systematic Literature Review
2025-03-12
Rosalia Tufano, Gabriele Bavota
Code Review consists in assessing the code written by teammates with the goal of increasing code quality. Empirical studies documented the benefits brought by such a practice that, however, has its cost to pay in terms of developers' time. For this reason, researchers have proposed techniques and tools to automate code review tasks such as the reviewers selection (i.e., identifying suitable reviewers for a given code change) or the actual review of a given change (i.e., recommending improvements to the contributor as a human reviewer would do). Given the substantial amount of papers recently published on the topic, it may be challenging for researchers and practitioners to get a complete overview of the state-of-the-art. We present a systematic literature review (SLR) featuring 119 papers concerning the automation of code review tasks. We provide: (i) a categorization of the code review tasks automated in the literature; (ii) an overview of the under-the-hood techniques used for the automation, including the datasets used for training data-driven techniques; (iii) publicly available techniques and datasets used for their evaluation, with a description of the evaluation metrics usually adopted for each task. The SLR is concluded by a discussion of the current limitations of the state-of-the-art, with insights for future research directions.
Prospects for ${\rm kSZ^2}$-${\rm 21cm^2}$ Cross-Correlations during Reionization
2025-03-12
Meng Zhou, Paul La Plante, Adam Lidz, Yi Mao, Yin-Zhe Ma
The 21\,cm line and the patchy kinetic Sunyaev-Zel'dovich (kSZ) effect are promising and complementary probes of the Epoch of Reionization (EoR). A challenge for cross-correlating these two signals is that foreground avoidance or removal algorithms applied to the 21\,cm data inevitably sacrifice Fourier modes with long wavelengths along the line-of-sight (i.e., low-$k_\parallel$ modes), yet \textit{only} these same modes contribute to the kSZ signal. Here we show that a suitable kSZ$^2$ $\times$ 21\,cm$^2$ cross-correlation statistic nevertheless remains non-vanishing, even after filtering out the corrupted low-$k_\parallel$ Fourier modes from the 21\,cm data. We simulate the kSZ$^2$ $\times$ 21\,cm$^2$ cross-correlation signal across reionization-era redshifts and find distinctive redshift evolution. This signal peaks early in the reionization history, when the volume-averaged fraction is around $0.1 \lesssim x_\mathrm{HII} \lesssim 0.2$, after which it changes sign and reaches a minimum near reionization's midpoint ($x_\mathrm{HII} \sim 0.5$), while the signal gradually vanishes as reionization completes. These trends appear generic across three simulated models which differ in their reionization histories. We forecast the detectability of the kSZ$^2$ $\times$ 21\,cm$^2$ cross-power spectrum for the HERA and SKA1-Low 21\,cm experiments in combination with current and next-generation CMB surveys including the Simons Observatory, CMB-S4, and CMB-HD. We find that a high-significance detection ($\mathrm{S/N} \gtrsim 5\sigma$) is possible with SKA1-Low and CMB-S4.
The Cahn-Hilliard-Navier-Stokes Framework for Multiphase Fluid Flows: Laminar, Turbulent, and Active
2025-03-12
Nadia Bihari Padhan, Rahul Pandit
The Cahn-Hilliard-Navier-Stokes (CHNS) partial differential equations (PDEs) provide a powerful framework for the study of the statistical mechanics and fluid dynamics of multiphase fluids. We provide an introduction to the equilibrium and nonequilibrium statistical mechanics of systems in which coexisting phases, distinguished from each other by scalar order parameters, are separated by an interface. We then introduce the coupled Cahn-Hilliard-Navier-Stokes (CHNS) PDEs for two immiscible fluids and generalisations for (a) coexisting phases with different viscosities, (b) CHNS with gravity, (c) the three-component fluids (CHNS3), and (d) the CHNS for active fluids. We discuss mathematical issues of the regularity of solutions of the CHNS PDEs. Finally we provide a survey of the rich variety of results that have been obtained by numerical studies of CHNS-type PDEs for diverse systems, including bubbles in turbulent flows, antibubbles, droplet and liquid-lens mergers, turbulence in the active-CHNS model, and its generalisation that can lead to a self-propelled droplet.
Bayesian nonparametric modeling of mixed-type bounded data
2025-03-12
Rufeng Liu, Claudia Wehrhahn, Andrés F. Barrientos, Alejandro Jara
We propose a Bayesian nonparametric model for mixed-type bounded data, where some variables are compositional and others are interval-bounded. Compositional variables are non-negative and sum to a given constant, such as the proportion of time an individual spends on different activities during the day or the fraction of different types of nutrients in a person's diet. Interval-bounded variables, on the other hand, are real numbers constrained by both a lower and an upper bound. Our approach relies on a novel class of random multivariate Bernstein polynomials, which induce a Dirichlet process mixture model of products of Dirichlet and beta densities. We study the theoretical properties of the model, including its topological support and posterior consistency. The model can be used for density and conditional density estimation, where both the response and predictors take values in the simplex space and/or hypercube. We illustrate the model's behavior through the analysis of simulated data and data from the 2005-2006 cycle of the U.S. National Health and Nutrition Examination Survey.
The main contributors of the IceCube diffuse neutrino flux remain unclear. Tidal disruption events (TDEs) have been proposed as potential emitters of the high-energy neutrinos detected by IceCube. Therefore, investigating the correlation between the TDE population and IceCube neutrinos could help us better understand whether the TDE population could be potential high-energy neutrino emitters. In this paper, we perform a systematic search for TDEs that are associated with neutrinos in a sample including 143 IceCube neutrino alert events and 61 TDEs classified by the Zwicky Transient Facility (ZTF) - Bright Transient Survey (BTS). Furthermore, considering that the TDEs/TDE candidates reported as potential IceCube neutrino emitters are all accompanied by infrared (IR) observations, we further select the TDEs with IR observations from these 61 TDEs as a subsample to examine the correlation with neutrinos. Based on the Wide-field Infrared Survey Explorer (WISE) mission database, seven TDEs are identified as having IR observations. Due to good spatial localization is crucial for association analysis, we employ two methods to handle alert events with large error radii in our sample. Then we employ three Monte Carlo simulation methods to investigate the correlation between TDE sample/subsample and IceCube neutrinos. Finally, after considering spatial and temporal criteria, seven TDEs with IR flares show the most significant correlation at a 2.43{\sigma} confidence level. If we tentatively further take the time delay factor into account in the weighting scheme, the correlation enhances to 2.54{\sigma} confidence level.
Quantum computing is an emerging paradigm with the potential to transform numerous application areas by addressing problems considered intractable in the classical domain. However, its integration into cyberspace introduces significant security and privacy challenges. The exponential rise in cyber attacks, further complicated by quantum capabilities, poses serious risks to financial systems and national security. The scope of quantum threats extends beyond traditional software, operating system, and network vulnerabilities, necessitating a shift in cybersecurity education. Traditional cybersecurity education, often reliant on didactic methods, lacks hands on, student centered learning experiences necessary to prepare students for these evolving challenges. There is an urgent need for curricula that address both classical and quantum security threats through experiential learning. In this work, we present the design and evaluation of EE 597: Introduction to Hardware Security, a graduate level course integrating hands-on quantum security learning with classical security concepts through simulations and cloud-based quantum hardware. Unlike conventional courses focused on quantum threats to cryptographic systems, EE 597 explores security challenges specific to quantum computing itself. We employ a mixed-methods evaluation using pre and post surveys to assess student learning outcomes and engagement. Results indicate significant improvements in students' understanding of quantum and hardware security, with strong positive feedback on course structure and remote instruction (mean scores: 3.33 to 3.83 on a 4 point scale).
Investigating User Perspectives on Differentially Private Text Privatization
2025-03-12
Stephen Meisenbacher, Alexandra Klymenko, Alexander Karpp, Florian Matthes
Recent literature has seen a considerable uptick in $\textit{Differentially Private Natural Language Processing}$ (DP NLP). This includes DP text privatization, where potentially sensitive input texts are transformed under DP to achieve privatized output texts that ideally mask sensitive information $\textit{and}$ maintain original semantics. Despite continued work to address the open challenges in DP text privatization, there remains a scarcity of work addressing user perceptions of this technology, a crucial aspect which serves as the final barrier to practical adoption. In this work, we conduct a survey study with 721 laypersons around the globe, investigating how the factors of $\textit{scenario}$, $\textit{data sensitivity}$, $\textit{mechanism type}$, and $\textit{reason for data collection}$ impact user preferences for text privatization. We learn that while all these factors play a role in influencing privacy decisions, users are highly sensitive to the utility and coherence of the private output texts. Our findings highlight the socio-technical factors that must be considered in the study of DP NLP, opening the door to further user-based investigations going forward.
CyberLLMInstruct: A New Dataset for Analysing Safety of Fine-Tuned LLMs Using Cyber Security Data
2025-03-12
Adel ElZemity, Budi Arief, Shujun Li
The integration of large language models (LLMs) into cyber security applications presents significant opportunities, such as enhancing threat analysis and malware detection, but can also introduce critical risks and safety concerns, including personal data leakage and automated generation of new malware. To address these challenges, we developed CyberLLMInstruct, a dataset of 54,928 instruction-response pairs spanning cyber security tasks such as malware analysis, phishing simulations, and zero-day vulnerabilities. The dataset was constructed through a multi-stage process. This involved sourcing data from multiple resources, filtering and structuring it into instruction-response pairs, and aligning it with real-world scenarios to enhance its applicability. Seven open-source LLMs were chosen to test the usefulness of CyberLLMInstruct: Phi 3 Mini 3.8B, Mistral 7B, Qwen 2.5 7B, Llama 3 8B, Llama 3.1 8B, Gemma 2 9B, and Llama 2 70B. In our primary example, we rigorously assess the safety of fine-tuned models using the OWASP top 10 framework, finding that fine-tuning reduces safety resilience across all tested LLMs and every adversarial attack (e.g., the security score of Llama 3.1 8B against prompt injection drops from 0.95 to 0.15). In our second example, we show that these same fine-tuned models can also achieve up to 92.50 percent accuracy on the CyberMetric benchmark. These findings highlight a trade-off between performance and safety, showing the importance of adversarial testing and further research into fine-tuning methodologies that can mitigate safety risks while still improving performance across diverse datasets and domains. All scripts required to reproduce the dataset, along with examples and relevant resources for replicating our results, will be made available upon the paper's acceptance.
Large language models (LLMs) have recently shown remarkable performance in language tasks and beyond. However, due to their limited inherent causal reasoning ability, LLMs still face challenges in handling tasks that require robust causal reasoning ability, such as health-care and economic analysis. As a result, a growing body of research has focused on enhancing the causal reasoning ability of LLMs. Despite the booming research, there lacks a survey to well review the challenges, progress and future directions in this area. To bridge this significant gap, we systematically review literature on how to strengthen LLMs' causal reasoning ability in this paper. We start from the introduction of background and motivations of this topic, followed by the summarisation of key challenges in this area. Thereafter, we propose a novel taxonomy to systematically categorise existing methods, together with detailed comparisons within and between classes of methods. Furthermore, we summarise existing benchmarks and evaluation metrics for assessing LLMs' causal reasoning ability. Finally, we outline future research directions for this emerging field, offering insights and inspiration to researchers and practitioners in the area.
Adaptive political surveys and GPT-4: Tackling the cold start problem with simulated user interactions
2025-03-12
Fynn Bachmann, Daan van der Weijden, Lucien Heitz, Cristina Sarasua, Abraham Bernstein
Adaptive questionnaires dynamically select the next question for a survey participant based on their previous answers. Due to digitalisation, they have become a viable alternative to traditional surveys in application areas such as political science. One limitation, however, is their dependency on data to train the model for question selection. Often, such training data (i.e., user interactions) are unavailable a priori. To address this problem, we (i) test whether Large Language Models (LLM) can accurately generate such interaction data and (ii) explore if these synthetic data can be used to pre-train the statistical model of an adaptive political survey. To evaluate this approach, we utilise existing data from the Swiss Voting Advice Application (VAA) Smartvote in two ways: First, we compare the distribution of LLM-generated synthetic data to the real distribution to assess its similarity. Second, we compare the performance of an adaptive questionnaire that is randomly initialised with one pre-trained on synthetic data to assess their suitability for training. We benchmark these results against an "oracle" questionnaire with perfect prior knowledge. We find that an off-the-shelf LLM (GPT-4) accurately generates answers to the Smartvote questionnaire from the perspective of different Swiss parties. Furthermore, we demonstrate that initialising the statistical model with synthetic data can (i) significantly reduce the error in predicting user responses and (ii) increase the candidate recommendation accuracy of the VAA. Our work emphasises the considerable potential of LLMs to create training data to improve the data collection process in adaptive questionnaires in LLM-affine areas such as political surveys.
We consider a broad class of nonlinear integro-differential equations with a kernel whose differentiability order is described by a general function $\phi$. This class includes not only the fractional $p$-Laplace equations, but also borderline cases when the fractional order approaches $1$. Under mild assumptions on $\phi$, we establish sharp Sobolev-Poincar\'e type inequalities for the associated Sobolev spaces, which are connected to a question raised by Brezis (Russian Math. Surveys 57:693--708, 2002). Using these inequalities, we prove H\"older regularity and Harnack inequalities for weak solutions to such nonlocal equations. All the estimates in our results remain stable as the associated nonlocal energy functional approaches its local counterpart.
Stellar Parameters for over Fifty Million stars from SMSS DR4 and Gaia DR3
2025-03-12
Yang Huang, Timothy C. Beers
We present an updated catalog of stellar parameters, including effective temperature, luminosity classification, and metallicity, for over fifty million stars from the SkyMapper Southern Survey (SMSS) DR4 and Gaia DR3. The accuracy of the derived parameters remains consistent with those achieved in SMSS DR2 using the same methods. Thanks to the advancements in SMSS DR4, photometric-metallicity estimates are now available for an unprecedented number of metal-poor stars. The catalog includes over 13 million metal-poor (MP; [Fe/H]<-1) stars, nearly three million very metal-poor (VMP; [Fe/H]<-2.0) stars, and approximately 120,000 extremely metal-poor (EMP; [Fe/H]<-3.0) stars -- representing an increase by a factor of 4-6 compared to SMSS DR2. This catalog, combined with other stellar parameters obtained through our efforts, will be made available at https://nadc.china-vo.org/data/sports/.
Detecting and Preventing Data Poisoning Attacks on AI Models
2025-03-12
Halima I. Kure, Pradipta Sarkar, Ahmed B. Ndanusa, Augustine O. Nwajana
This paper investigates the critical issue of data poisoning attacks on AI models, a growing concern in the ever-evolving landscape of artificial intelligence and cybersecurity. As advanced technology systems become increasingly prevalent across various sectors, the need for robust defence mechanisms against adversarial attacks becomes paramount. The study aims to develop and evaluate novel techniques for detecting and preventing data poisoning attacks, focusing on both theoretical frameworks and practical applications. Through a comprehensive literature review, experimental validation using the CIFAR-10 and Insurance Claims datasets, and the development of innovative algorithms, this paper seeks to enhance the resilience of AI models against malicious data manipulation. The study explores various methods, including anomaly detection, robust optimization strategies, and ensemble learning, to identify and mitigate the effects of poisoned data during model training. Experimental results indicate that data poisoning significantly degrades model performance, reducing classification accuracy by up to 27% in image recognition tasks (CIFAR-10) and 22% in fraud detection models (Insurance Claims dataset). The proposed defence mechanisms, including statistical anomaly detection and adversarial training, successfully mitigated poisoning effects, improving model robustness and restoring accuracy levels by an average of 15-20%. The findings further demonstrate that ensemble learning techniques provide an additional layer of resilience, reducing false positives and false negatives caused by adversarial data injections.
Unmask It! AI-Generated Product Review Detection in Dravidian Languages
2025-03-12
Somsubhra De, Advait Vats
The rise of Generative AI has led to a surge in AI-generated reviews, often posing a serious threat to the credibility of online platforms. Reviews serve as the primary source of information about products and services. Authentic reviews play a vital role in consumer decision-making. The presence of fabricated content misleads consumers, undermines trust and facilitates potential fraud in digital marketplaces. This study focuses on detecting AI-generated product reviews in Tamil and Malayalam, two low-resource languages where research in this domain is relatively under-explored. We worked on a range of approaches - from traditional machine learning methods to advanced transformer-based models such as Indic-BERT, IndicSBERT, MuRIL, XLM-RoBERTa and MalayalamBERT. Our findings highlight the effectiveness of leveraging the state-of-the-art transformers in accurately identifying AI-generated content, demonstrating the potential in enhancing the detection of fake reviews in low-resource language settings.
2025-03-12
Francis X. Diebold, Aaron Mora, Minchul Shin
We study the properties of macroeconomic survey forecast response averages as the number of survey respondents grows. Such averages are "portfolios" of forecasts. We characterize the speed and pattern of the gains from diversification and their eventual decrease with portfolio size (the number of survey respondents) in both (1) the key real-world data-based environment of the U.S. Survey of Professional Forecasters (SPF), and (2) the theoretical model-based environment of equicorrelated forecast errors. We proceed by proposing and comparing various direct and model-based "crowd size signature plots," which summarize the forecasting performance of k-average forecasts as a function of k, where k is the number of forecasts in the average. We then estimate the equicorrelation model for growth and inflation forecast errors by choosing model parameters to minimize the divergence between direct and model-based signature plots. The results indicate near-perfect equicorrelation model fit for both growth and inflation, which we explicate by showing analytically that, under conditions, the direct and fitted equicorrelation model-based signature plots are identical at a particular model parameter configuration, which we characterize. We find that the gains from diversification are greater for inflation forecasts than for growth forecasts, but that both gains nevertheless decrease quite quickly, so that fewer SPF respondents than currently used may be adequate.
In operating system development, concurrency poses significant challenges. It is difficult for humans to manually review concurrent behaviors or to write test cases covering all possible executions, often resulting in critical bugs. Preemption in schedulers serves as a typical example. This paper proposes a development method for concurrent software, such as schedulers. Our method incorporates model checking as an aid for tracing code, simplifying the analysis of concurrent behavior; we refer to this as model checking-assisted code review. While this approach aids in tracing behaviors, the accuracy of the results is limited because of the semantics gap between the modeling language and the programming language. Therefore, we also introduce runtime verification to address this limitation in model checking-assisted code review. We applied our approach to a real-world operating system, Awkernel, as a case study. This new operating system, currently under development for autonomous driving, is designed for preemptive task execution using asynchronous functions in Rust. After implementing our method, we identified several bugs that are difficult to detect through manual reviews or simple tests.
Theoretical challenges in understanding the nature of glass and the glass transition remain significant open questions in statistical and condensed matter physics. As a prototypical example of complex physical systems, glasses and the vitrification process have been central research topics, consistently attracting broad scientific interest. This focus has driven extensive studies on phenomena such as aging, non-exponential relaxation, dynamic anomalies, glass-forming ability, and the mechanical response of glasses under stress. Recent advances in computational and experimental techniques have enabled rigorous testing of theoretical models, shedding new light on glassy behavior. However, the intrinsic complexity of glass and the glass transition that lies in their physics, which spans multiple length and time scales, makes the system challenging to characterize. In this review, we emphasize the need to move beyond conventional approaches and propose a topological perspective as a promising alternative to address these challenges. Specifically, our findings reveal that the diversity in particle relaxation behavior is statistically linked to a global topological feature of the transient network structures formed by the particles in a given liquid. This direction offers opportunities to uncover novel phenomena that could fundamentally reshape our understanding of glassy materials.
2025-03-12
Eva Hackmann, Moritz Huckfeldt, Claus Lämmerzahl, Dennis Philipp, Benny Rievers
Mass redistribution on Earth due to dynamic processes such as ice melting and sea level rise leads to a changing gravitational field, observable by geodetic techniques. Monitoring this change over time allows us to learn more about our planet and its dynamic evolution. In this paper, we highlight the impact of General Relativity (GR) on geodesy: it provides corrections essential for the interpretation of high-precision measurements and enables a completely novel measurement approach using chronometry, i.e., clock-based observations. Focusing on the latter, we review the construction of the relativistic gravity potential and the corresponding geoid definition as an isochronometric surface to elucidate the comparison to the conventional Newtonian geoid. Furthermore, we comment on additional potentials due to the non-Newtonian degrees of freedom of the relativistic gravitational field, and assess the feasibility of clock-based measurements for Gravity Field Recovery (GFR) from space. Although clock observations in space demonstrate technical promise for GFR, achieving the necessary precision for practical applications remains challenging.
Revisiting products and powers of $(m,p)$ and $(m,\infty)$-isometries
2025-03-12
Michael Mackey
We review known results concerning powers and products of $(m,p)$-isometries with a view to providing elementary proofs based on properties of polynomials. We consider also the situation when $p=\infty$ where we find elements of graph theory and combinatorics arise naturally.
2025-03-12
Helge Ritter, Otthein Herzog, Kurt Rothermel, Anthony G. Cohn, Zhiqiang Wu
We attempt to take a comprehensive look at the challenges of representing the spatio-temporal structures and dynamic processes defining a city's overall characteristics. For the task of urban planning and urban operation, we take the stance that even if the necessary representations of these structures and processes can be achieved, the most important representation of the relevant mindsets of the citizens are, unfortunately, mostly neglected. After a review of major "traditional" urban models of structures behind urban scale, form, and dynamics, we turn to major recent modeling approaches triggered by recent advances in AI that enable multi-modal generative models. Some of these models can create representations of geometries, networks and images, and reason flexibly at a human-compatible semantic level. They provide huge amounts of knowledge extracted from Terabytes of text and image documents and cover the required rich representation spectrum including geographic knowledge by different knowledge sources, degrees of granularity and scales. We then discuss what these new opportunities mean for the modeling challenges posed by cities, in particular with regard to the role and impact of citizens and their interactions within the city infrastructure. We propose to integrate these possibilities with existing approaches, such as agent-based models, which opens up new modeling spaces including rich citizen models which are able to also represent social interactions. Finally, we put forward some thoughts about a vision of a "social AI in a city ecosystem" that adds relevant citizen models to state-of-the-art structural and process models. This extended city representation will enable urban planners to establish citizen-oriented planning of city infrastructures for human culture, city resilience and sustainability.
Exploring various unexpected new quantum states and their corresponding extraordinary physics in low dimensional quantum materials, and investing them into application fields, is a primary concern of condensed matter physics. Previously, we performed joint experiments and theoretical calculations to investigate a superatomic crystal of Au6Te12Se8 with low symmetry, which stacks through non-covalent inter-cube quasi-bonds. Au6Te12Se8 exhibits a triple-cube charge density wave and spatially polarized metallic states interweaving at 9 K. In addition, it undergoes a BKT phase transition at 2.8 K to form a quasi-two-dimensional superconducting state. The subsequent high-pressure experimental results indicate that the two quantum states above compete with superconductivity. Here, we experimentally revealed competition and coexistence among superconductivity, triple-cube charge density waves, and polarized metallic states during further temperature-decreasing process, as examined using ultra-low temperature scanning tunneling microscopy/spectroscopy, transport measurement, and Raman spectra. An extraordinary inversion symmetry broken emerged inside the triple-cube-period of the original CDW at 300 mK, leading to the polarized metallic states transforming into parallel polarization states. The evidence of spontaneous polarization coexisting with superconductivity in real space is extremely rare and has been revealed by STM. In addition, transport and Raman measurements indicate that there are multiple phase transition temperatures from 80K to that below the superconducting transition temperature of Au6Te12Se8, which may also contain more unknown exotic quantum states, providing a platform for further exploration of intriguing physical properties.
Refined algebraic domains with finite sets in the boundaries
2025-03-12
Naoki Kitazawa
Refined algebraic domains are regions in the plane surrounded by finitely many non-singular real algebraic curves which may intersect with normal crossing. We are interested in shapes of such regions with surrounding real algebraic curves. Poincar'e-Reeb Graphs of them are graphs the regions naturally collapse to respecting the projection to a straight line. Such graphs were first formulated by Sorea, for example, around 2020, and regions surrounded by mutually disjoint non-singular real algebraic curves were mainly considered. The author has generalized the studies to several general situations. We find classes of such objects defined inductively by adding curves. We respect characteristic finite sets in the curves. We consider regions surrounded by the curves and of a new type. We investigate geometric properties and combinatorial ones of them and discuss important examples. We also previously studied explicit classes defined inductively in this way and review them.
A Review on Proprietary Accelerators for Large Language Models
2025-03-12
Sihyeong Park, Jemin Lee, Byung-Soo Kim, Seokhun Jeon
With the advancement of Large Language Models (LLMs), the importance of accelerators that efficiently process LLM computations has been increasing. This paper discusses the necessity of LLM accelerators and provides a comprehensive analysis of the hardware and software characteristics of the main commercial LLM accelerators. Based on this analysis, we propose considerations for the development of next-generation LLM accelerators and suggest future research directions.
The integration of blockchain technology with data analytics is essential for extracting insights in the cryptocurrency space. Although academic literature on blockchain data analytics is limited, various industry solutions have emerged to address these needs. This paper provides a comprehensive literature review, drawing from both academic research and industry applications. We classify blockchain analytics tools into categories such as block explorers, on-chain data providers, research platforms, and crypto market data providers. Additionally, we discuss the challenges associated with blockchain data analytics, including data accessibility, scalability, accuracy, and interoperability. Our findings emphasize the importance of bridging academic research and industry innovations to advance blockchain data analytics.
AI-Driven Decision Support in Oncology: Evaluating Data Readiness for Skin Cancer Treatment
2025-03-12
Joscha Grüger, Tobias Geyer, Tobias Brix, Michael Storck, Sonja Leson, Laura Bley, Carsten Weishaupt, Ralph Bergmann, Stephan A. Braun
This research focuses on evaluating and enhancing data readiness for the development of an Artificial Intelligence (AI)-based Clinical Decision Support System (CDSS) in the context of skin cancer treatment. The study, conducted at the Skin Tumor Center of the University Hospital M\"unster, delves into the essential role of data quality, availability, and extractability in implementing effective AI applications in oncology. By employing a multifaceted methodology, including literature review, data readiness assessment, and expert workshops, the study addresses the challenges of integrating AI into clinical decision-making. The research identifies crucial data points for skin cancer treatment decisions, evaluates their presence and quality in various information systems, and highlights the difficulties in extracting information from unstructured data. The findings underline the significance of high-quality, accessible data for the success of AI-driven CDSS in medical settings, particularly in the complex field of oncology.
Technical Insights and Legal Considerations for Advancing Federated Learning in Bioinformatics
2025-03-12
Daniele Malpetti, Marco Scutari, Francesco Gualdi, Jessica van Setten, Sander van der Laan, Saskia Haitjema, Aaron Mark Lee, Isabelle Hering, Francesca Mangili
Federated learning leverages data across institutions to improve clinical discovery while complying with data-sharing restrictions and protecting patient privacy. As the evolution of biobanks in genetics and systems biology has proved, accessing more extensive and varied data pools leads to a faster and more robust exploration and translation of results. More widespread use of federated learning may have the same impact in bioinformatics, allowing access to many combinations of genotypic, phenotypic and environmental information that are undercovered or not included in existing biobanks. This paper reviews the methodological, infrastructural and legal issues that academic and clinical institutions must address before implementing it. Finally, we provide recommendations for the reliable use of federated learning and its effective translation into clinical practice.
A Survey on Trustworthy LLM Agents: Threats and Countermeasures
2025-03-12
Miao Yu, Fanci Meng, Xinyun Zhou, Shilong Wang, Junyuan Mao, Linsey Pang, Tianlong Chen, Kun Wang, Xinfeng Li, Yongfeng Zhang, Bo An, Qingsong Wen
With the rapid evolution of Large Language Models (LLMs), LLM-based agents and Multi-agent Systems (MAS) have significantly expanded the capabilities of LLM ecosystems. This evolution stems from empowering LLMs with additional modules such as memory, tools, environment, and even other agents. However, this advancement has also introduced more complex issues of trustworthiness, which previous research focused solely on LLMs could not cover. In this survey, we propose the TrustAgent framework, a comprehensive study on the trustworthiness of agents, characterized by modular taxonomy, multi-dimensional connotations, and technical implementation. By thoroughly investigating and summarizing newly emerged attacks, defenses, and evaluation methods for agents and MAS, we extend the concept of Trustworthy LLM to the emerging paradigm of Trustworthy Agent. In TrustAgent, we begin by deconstructing and introducing various components of the Agent and MAS. Then, we categorize their trustworthiness into intrinsic (brain, memory, and tool) and extrinsic (user, agent, and environment) aspects. Subsequently, we delineate the multifaceted meanings of trustworthiness and elaborate on the implementation techniques of existing research related to these internal and external modules. Finally, we present our insights and outlook on this domain, aiming to provide guidance for future endeavors.
Sometimes Painful but Certainly Promising: Feasibility and Trade-offs of Language Model Inference at the Edge
2025-03-12
Maximilian Abstreiter, Sasu Tarkoma, Roberto Morabito
The rapid rise of Language Models (LMs) has expanded the capabilities of natural language processing, powering applications from text generation to complex decision-making. While state-of-the-art LMs often boast hundreds of billions of parameters and are primarily deployed in data centers, recent trends show a growing focus on compact models-typically under 10 billion parameters-enabled by techniques such as quantization and other model compression techniques. This shift paves the way for LMs on edge devices, offering potential benefits such as enhanced privacy, reduced latency, and improved data sovereignty. However, the inherent complexity of even these smaller models, combined with the limited computing resources of edge hardware, raises critical questions about the practical trade-offs in executing LM inference outside the cloud. To address these challenges, we present a comprehensive evaluation of generative LM inference on representative CPU-based and GPU-accelerated edge devices. Our study measures key performance indicators-including memory usage, inference speed, and energy consumption-across various device configurations. Additionally, we examine throughput-energy trade-offs, cost considerations, and usability, alongside an assessment of qualitative model performance. While quantization helps mitigate memory overhead, it does not fully eliminate resource bottlenecks, especially for larger models. Our findings quantify the memory and energy constraints that must be considered for practical real-world deployments, offering concrete insights into the trade-offs between model size, inference performance, and efficiency. The exploration of LMs at the edge is still in its early stages. We hope this study provides a foundation for future research, guiding the refinement of models, the enhancement of inference efficiency, and the advancement of edge-centric AI systems.
ASKAP and VLASS search for a radio-continuum counterpart of ultra-high-energy neutrino event KM3-230213A
2025-03-12
M. D. Filipović, Z. J. Smeaton, A. C. Bradley, D. Dobie, B. S. Koribalski, R. Kothes, L. Rudnick, A. Ahmad, R. Z. E. Alsaberi, C. S. Anderson, L. A. Barnes, M. Breuhaus, E. J. Crawford, S. Dai, Y. A. Gordon, N. Gupta, A. M. Hopkins, D. Leahy, K. J. Luken, N. McClure-Griffiths, M. J. Michalowski, M. Sasaki, N. F. H. Tothill, G. M. Umana, T. Vernstrom, J. West
We present the results of an Australian Square Kilometre Array Pathfinder (ASKAP) 944 MHz and Very Large Array Sky Survey (VLASS) 3~GHz search for a radio-continuum counterpart of the recent ultra-high-energy (UHE) neutrino event, KM3-230213A. Using (ASKAP), we catalog 1052 radio sources within the 1.5$^\circ$ radius search area (68% certainty region) around the particle's calculated origin, 10 of which we classify as blazar candidates based on their radio spectra. The most prominent radio source in the search area is the nearby spiral galaxy UGCA 127 (nicknamed Phaedra, From Greek: $\phi\alpha i\delta\rho\alpha$, a Cretan princess of Greek Mythology, derived from Phaidros, Greek: ${\phi}{\alpha}{\iota}{\delta}{\rho}o{\varsigma}$, meaning 'bright'.). Its non-thermal radio spectrum classifies it as a non-blazar active galactic nucleus (AGN). We also present an extended radio source, WISEA J061715.89-075455.4 (nicknamed Hebe, From Greek: $H{\beta}{\eta}$, the Greek goddess of youth.), located only ~7' from the geometric center of the search area, with a very unusual highly polarized compact component. Finally, we present a strong radio source, EMU J062248-072246 (nicknamed Narcissus, From Greek $N{\alpha}{\rho}{\kappa}{\iota}{\sigma}{\sigma}o{\zeta}$ was a self-absorbed hunter from Thespiae in Boeotia.), which has a maximum self-absorption spectral slope of +2.5 at low frequencies, and exhibits ~25% flux density variability over the ~5-year VLASS 3~GHz survey.
Multi-Modal Foundation Models for Computational Pathology: A Survey
2025-03-12
Dong Li, Guihong Wan, Xintao Wu, Xinyu Wu, Xiaohui Chen, Yi He, Christine G. Lian, Peter K. Sorger, Yevgeniy R. Semenov, Chen Zhao
Foundation models have emerged as a powerful paradigm in computational pathology (CPath), enabling scalable and generalizable analysis of histopathological images. While early developments centered on uni-modal models trained solely on visual data, recent advances have highlighted the promise of multi-modal foundation models that integrate heterogeneous data sources such as textual reports, structured domain knowledge, and molecular profiles. In this survey, we provide a comprehensive and up-to-date review of multi-modal foundation models in CPath, with a particular focus on models built upon hematoxylin and eosin (H&E) stained whole slide images (WSIs) and tile-level representations. We categorize 32 state-of-the-art multi-modal foundation models into three major paradigms: vision-language, vision-knowledge graph, and vision-gene expression. We further divide vision-language models into non-LLM-based and LLM-based approaches. Additionally, we analyze 28 available multi-modal datasets tailored for pathology, grouped into image-text pairs, instruction datasets, and image-other modality pairs. Our survey also presents a taxonomy of downstream tasks, highlights training and evaluation strategies, and identifies key challenges and future directions. We aim for this survey to serve as a valuable resource for researchers and practitioners working at the intersection of pathology and AI.
TSConnect: An Enhanced MOOC Platform for Bridging Communication Gaps Between Instructors and Students in Light of the Curse of Knowledge
2025-03-12
Qianyu Liu, Xinran Li, Xiaocong Du, Quan Li
Knowledge dissemination in educational settings is profoundly influenced by the curse of knowledge, a cognitive bias that causes experts to underestimate the challenges faced by learners due to their own in-depth understanding of the subject. This bias can hinder effective knowledge transfer and pedagogical effectiveness, and may be exacerbated by inadequate instructor-student communication. To encourage more effective feedback and promote empathy, we introduce TSConnect, a bias-aware, adaptable interactive MOOC (Massive Open Online Course) learning system, informed by a need-finding survey involving 129 students and 6 instructors. TSConnect integrates instructors, students, and Artificial Intelligence (AI) into a cohesive platform, facilitating diverse and targeted communication channels while addressing previously overlooked information needs. A notable feature is its dynamic knowledge graph, which enhances learning support and fosters a more interconnected educational experience. We conducted a between-subjects user study with 30 students comparing TSConnect to a baseline system. Results indicate that TSConnect significantly encourages students to provide more feedback to instructors. Additionally, interviews with 4 instructors reveal insights into how they interpret and respond to this feedback, potentially leading to improvements in teaching strategies and the development of broader pedagogical skills.
DancingBoard: Streamlining the Creation of Motion Comics to Enhance Narratives
2025-03-12
Longfei Chen, Shengxin Li, Ziang Li, Quan Li
Motion comics, a digital animation format that enhances comic book narratives, have wide applications in storytelling, education, and advertising. However, their creation poses significant challenges for amateur creators, primarily due to the need for specialized skills and complex workflows. To address these issues, we conducted an exploratory survey (N=58) to understand the challenges associated with creating motion comics, and an expert interview (N=4) to identify a typical workflow for creation. We further analyzed $95$ online motion comics to gain insights into the design space of character and object actions. Based on our findings, we proposed DancingBoard, an integrated authoring tool designed to simplify the creation process. This tool features a user-friendly interface and a guided workflow, providing comprehensive support throughout each step of the creation process. A user study involving 23 creators showed that, compared to professional tools, DancingBoard is easily comprehensible and provides improved guidance and support, requiring less effort from users. Additionally, a separate study with $18$ audience members confirmed the tool's effectiveness in conveying the story to its viewers.
The SAMI Galaxy Survey: large-scale environment affects galaxy spin amplitudes and the formation of slow rotators
2025-03-12
Stefania Barsanti, Scott M. Croom, Matthew Colless, Joss Bland-Hawthorn, Sarah Brough, Julia J. Bryant, Nuria Lorente, Sree Oh, Giulia Santucci, Sarah Sweet, Jesse van de Sande, Charlotte Welker
We explore the impact of the large-scale 3D density field, as defined by deep, wide-field galaxy surveys, on stellar spin ($\lambda_{\rm R_e}$) and the distributions of fast and slow rotators. We use the GAMA spectroscopic redshift survey to reconstruct the cosmic web and obtain spatially-resolved stellar kinematics from the SAMI Galaxy Survey. Among various local and large-scale environment metrics, the distance to the closest filament ($D_{\rm fil}$) correlates most significantly with $\lambda_{\rm R_e}$, but it is secondary to the more dominant roles played by stellar age and mass. Fast rotators tend to have increasing $\lambda_{\rm R_e}$ going from nodes to filaments to voids, independently of mass. Slow rotators and mass-matched fast rotators are found to have significantly different distributions of large-scale environment metrics but consistent distributions of local environment metrics. About 95% of slow rotators have $D_{\rm fil}\leq2$Mpc, while covering broader ranges (similar to fast rotators) in distance to nodes and voids, local galaxy density, halo mass, and position with respect to the halo. At fixed mass, the fraction of slow rotators, $f_{\rm SR}$, increases for smaller $D_{\rm fil}$, especially for massive galaxies. While controlling for age or mass, only galaxies very close to filaments and nodes show a significant impact of local environment on $f_{\rm SR}$. Our results demonstrate that the cosmic web leaves an imprint on galactic spin amplitudes, and that pre-processing by mergers occurring within filaments is likely to be an important physical mechanism for the formation of slow rotators before they reach nodes.
FAST Discovery of A Gas-rich and Ultra-faint Dwarf Galaxy: KK153
2025-03-12
Jin-Long Xu, Ming Zhu, Nai-Ping Yu, Chuan-Peng Zhang, Xiao-Lan Liu, Mei Ai, Peng Jiang
Based on a high-sensitivity HI survey using the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we identified an isolated HI cloud with a system velocity of ~127.0 km/s, which is associated with an optical galaxy KK153 in space. The HI gas of KK153 shows a typical disk-galaxy structure. Using the Baryonic Tully-Fisher relation, we obtained that the distance to KK153 is 2.0_{-0.8}^{+1.7} Mpc. Adopting such distance, we derived a stellar mass of 4.1_{-2.6}^{+10.0}*10^{5} Msun and a neutral gas fraction of 0.63, implying that KK153 is a gas-rich ultra-faint dwarf (UFD) galaxy in the Local Group or its outskirts. KK153 shows a cool (~200 K) and warm (~7400 K) two-phase neutral medium. The g-r color distribution of KK153 suggests that new stars are mostly forming in its inner disk. The dynamical mass of KK153 is 6.9_{-3.0}^{+5.5}*10^{7} Msun, which is about 60 times larger than its baryonic matter. Detection of such a low-mass and gas-rich halo poses a challenge to the theory of cosmic reionization.
From Task-Specific Models to Unified Systems: A Review of Model Merging Approaches
2025-03-12
Wei Ruan, Tianze Yang, Yifan Zhou, Tianming Liu, Jin Lu
Model merging has achieved significant success, with numerous innovative methods proposed to enhance capabilities by combining multiple models. However, challenges persist due to the lack of a unified framework for classification and systematic comparative analysis, leading to inconsistencies in terminologies and categorizations. Meanwhile, as an increasing number of fine-tuned models are publicly available, their original training data often remain inaccessible due to privacy concerns or intellectual property restrictions. This makes traditional multi-task learning based on shared training data impractical. In scenarios where direct access to training data is infeasible, merging model parameters to create a unified model with broad generalization across multiple domains becomes crucial, further underscoring the importance of model merging techniques. Despite the rapid progress in this field, a comprehensive taxonomy and survey summarizing recent advances and predicting future directions are still lacking. This paper addresses these gaps by establishing a new taxonomy of model merging methods, systematically comparing different approaches, and providing an overview of key developments. By offering a structured perspective on this evolving area, we aim to help newcomers quickly grasp the field's landscape and inspire further innovations.
Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions
2025-03-12
Mourad Gridach, Jay Nanavati, Khaldoun Zine El Abidine, Lenon Mendes, Christina Mack
The integration of Agentic AI into scientific discovery marks a new frontier in research automation. These AI systems, capable of reasoning, planning, and autonomous decision-making, are transforming how scientists perform literature review, generate hypotheses, conduct experiments, and analyze results. This survey provides a comprehensive overview of Agentic AI for scientific discovery, categorizing existing systems and tools, and highlighting recent progress across fields such as chemistry, biology, and materials science. We discuss key evaluation metrics, implementation frameworks, and commonly used datasets to offer a detailed understanding of the current state of the field. Finally, we address critical challenges, such as literature review automation, system reliability, and ethical concerns, while outlining future research directions that emphasize human-AI collaboration and enhanced system calibration.
How causal perspectives can inform problems in computational neuroscience
2025-03-12
Eric W. Bridgeford, Brian S. Caffo, Maya B. Mathur, Russell A. Poldrack
Over the past two decades, considerable strides have been made in advancing neuroscience techniques, yet the translation of these advancements into clinically relevant insights for human mental health remains a challenge. This review addresses a fundamental issue in neuroscience - attributing causality - and advocates for the development of robust causal frameworks. We systematically introduce the necessary definitions and concepts, emphasizing the implicit role of causal frameworks in neuroscience investigations. We illustrate how persistent challenges in neuroscience, such as batch effects and selection biases, can be conceptualized and approached using causal frameworks. Through theoretical development and real-world examples, we show how these causal perspectives highlight numerous shortcomings of existing data collection strategies and analytical approaches. We demonstrate how causal frameworks can inform both experimental design and analysis, particularly for observational studies where traditional randomization is infeasible. Using neuroimaging as a detailed case study, we explore the advantages, shortcomings, and implications for generalizability that these perspectives afford to existing and novel research paradigms. Together, we believe that this perspective offers a framework for conceptualizing, framing, and inspiring innovative approaches to problems in neuroscience.
In Search of the Potentially Hazardous Asteroids in the Taurid Resonant Swarm
2025-03-11
Jasmine Li, Quanzhi Ye, Denis Vida, David L. Clark, Eric C. Bellm, Richard Dekany, Matthew J. Graham, Frank J. Masci, Josiah Purdum, Benjamin Racine, Avery Wold
The Taurid Complex is a large interplanetary system that contains comet 2P/Encke, several meteoroid streams, and possibly a number of near-Earth asteroids. The size and nature of the system has led to the speculation that it was formed through a large-scale cometary breakup. Numerical investigations have suggested that planetary dynamics can create a resonant region with a large number of objects concentrated in a small segment of the orbit, known as the Taurid swarm, which approaches the Earth in certain years and provides favorable conditions to study the Taurid Complex. Recent meteor observations confirmed the existence of the swarm for mm- to m-sized objects. Here we present a dedicated telescopic search for potentially hazardous asteroids and other macroscopic objects in the Taurid swarm using the Zwicky Transient Facility survey. We determine from our non-detection that there are no more than 9--14 $H\leq24$ (equivalent to a diameter of $D\gtrsim100$~m) objects in the swarm, suggesting that the Encke--Taurid progenitor was $\sim10$~km in size. A progenitor of such a size is compatible with the prediction of state-of-the-art Solar System dynamical models, which expects $\sim0.1$ $D>10$~km objects on Encke-like orbits at any given time.
Nonparametric reconstructions of dynamical dark energy via flexknots
2025-03-11
A. N. Ormondroyd, W. J. Handley, M. P. Hobson, A. N. Lasenby
Recent cosmological surveys have provided unprecedented datasets that can be used to reconstruct the history of the dark energy equation of state. In this work, a free-form "flexknot'' parameterisation is employed to represent $w(a)$ as a linear spline between free-moving nodes, the number of which may vary. By combining DESI Baryon Acoustic Oscillation measurements with Pantheon+ or DES5Y supernovae, the functional posteriors of $w(a)$ reveal an unexpected W-shaped structure. While the Bayesian evidence may still favour $\Lambda$CDM, the robustness of these results suggests the structure is indeed present in the data. The tension $R$-statistic and suspiciousness have been marginalised over models, and demonstrate that while the reconstructions from DESI and Pantheon+ agree, DESI and DES5Y do not. We conclude that, while there is no smoking gun for dynamical dark energy, the structure unearthed in this work is generally too complex to be captured by the restrictive $w$CDM or CPL parameterisations.
Leveraging national forest inventory data to estimate forest carbon density status and trends for small areas
2025-03-11
Elliot S. Shannon, Andrew O. Finley, Paul B. May, Grant M. Domke, Hans-Erik Andersen, George C. Gaines III, Arne Nothdurft, Sudipto Banerjee
National forest inventory (NFI) data are often costly to collect, which inhibits efforts to estimate parameters of interest for small spatial, temporal, or biophysical domains. Traditionally, design-based estimators are used to estimate status of forest parameters of interest, but are unreliable for small areas where data are sparse. Additionally, design-based estimates constructed directly from the survey data are often unavailable when sample sizes are especially small. Traditional model-based small area estimation approaches, such as the Fay-Herriot (FH) model, rely on these direct estimates for inference; hence, missing direct estimates preclude the use of such approaches. Here, we detail a Bayesian spatio-temporal small area estimation model that efficiently leverages sparse NFI data to estimate status and trends for forest parameters. The proposed model bypasses the use of direct estimates and instead uses plot-level NFI measurements along with auxiliary data including remotely sensed tree canopy cover. We produce forest carbon estimates from the United States NFI over 14 years across the contiguous US (CONUS) and conduct a simulation study to assess our proposed model's accuracy, precision, and bias, compared to that of a design-based estimator. The proposed model provides improved precision and accuracy over traditional estimation methods, and provides useful insights into county-level forest carbon dynamics across the CONUS.
Isotropic embeddings of coadjoint orbits and magnetic geodesic flows
2025-03-11
Dmitri Bykov, Andrew Kuzovchikov
We consider isotropic and Lagrangian embeddings of coadjoint orbits of compact Lie groups into products of coadjoint orbits. After reviewing the known facts in the case of $\mathrm{SU}(n)$ we initiate a similar study for $\mathrm{SO}$ and $\mathrm{Sp}$ cases. In the second part we apply this to the study of dynamical systems with $\mathrm{SU}(n)$ symmetry, proving equivalence between systems of two types: those describing magnetic geodesic flow on flag manifolds and classical `spin chains' of a special type.
2025-03-11
Aleksandr Berezutskii, Atithi Acharya, Roman Ellerbrock, Johnnie Gray, Reza Haghshenas, Zichang He, Abid Khan, Viacheslav Kuzmin, Minzhao Liu, Dmitry Lyakh, Danylo Lykov, Salvatore Mandrà, Christopher Mansell, Alexey Melnikov, Artem Melnikov, Vladimir Mironov, Dmitry Morozov, Florian Neukart, Alberto Nocera, Michael A. Perlin, Michael Perelshtein, Ruslan Shaydulin, Benjamin Villalonga, Markus Pflitsch, Marco Pistoia, Valerii Vinokur, Yuri Alexeev
In the rapidly evolving field of quantum computing, tensor networks serve as an important tool due to their multifaceted utility. In this paper, we review the diverse applications of tensor networks and show that they are an important instrument for quantum computing. Specifically, we summarize the application of tensor networks in various domains of quantum computing, including simulation of quantum computation, quantum circuit synthesis, quantum error correction, and quantum machine learning. Finally, we provide an outlook on the opportunities and the challenges of the tensor-network techniques.
Cross-Embodiment Robotic Manipulation Synthesis via Guided Demonstrations through CycleVAE and Human Behavior Transformer
2025-03-11
Apan Dastider, Hao Fang, Mingjie Lin
Cross-embodiment robotic manipulation synthesis for complicated tasks is challenging, partially due to the scarcity of paired cross-embodiment datasets and the impediment of designing intricate controllers. Inspired by robotic learning via guided human expert demonstration, we here propose a novel cross-embodiment robotic manipulation algorithm via CycleVAE and human behavior transformer. First, we utilize unsupervised CycleVAE together with a bidirectional subspace alignment algorithm to align latent motion sequences between cross-embodiments. Second, we propose a casual human behavior transformer design to learn the intrinsic motion dynamics of human expert demonstrations. During the test case, we leverage the proposed transformer for the human expert demonstration generation, which will be aligned using CycleVAE for the final human-robotic manipulation synthesis. We validated our proposed algorithm through extensive experiments using a dexterous robotic manipulator with the robotic hand. Our results successfully generate smooth trajectories across intricate tasks, outperforming prior learning-based robotic motion planning algorithms. These results have implications for performing unsupervised cross-embodiment alignment and future autonomous robotics design. Complete video demonstrations of our experiments can be found in https://sites.google.com/view/humanrobots/home.
CSST Strong Lensing Preparation: Fast Modeling of Galaxy-Galaxy Strong Lenses in the Big Data Era
2025-03-11
Xiaoyue Cao, Ran Li, Nan Li, Yun Chen, Rui Li, Huanyuan Shan, Tian Li
Galaxy-galaxy strong lensing provides a powerful probe of galaxy formation, evolution, and the properties of dark matter and dark energy. However, conventional lens-modeling approaches are computationally expensive and require fine-tuning to avoid local optima, rendering them impractical for the hundreds of thousands of lenses expected from surveys such as Euclid, CSST, and Roman Space Telescopes. To overcome these challenges, we introduce TinyLensGPU, a GPU-accelerated lens-modeling tool that employs XLA-based acceleration with JAX and a neural-network-enhanced nested sampling algorithm, nautilus-sampler. Tests on 1,000 simulated galaxy-galaxy lenses demonstrate that on an RTX 4060 Ti GPU, TinyLensGPU achieves likelihood evaluations approximately 2,000 times faster than traditional methods. Moreover, the nautilus-sampler reduces the number of likelihood evaluations by a factor of 3, decreasing the overall modeling time per lens from several days to roughly 3 minutes. Application to 63 SLACS lenses observed by the Hubble Space Telescope recovers Einstein radii consistent with the literature values (within $\lesssim 5\%$ deviation), which is within known systematic uncertainties. Catastrophic failures, where the sampler becomes trapped in local optima, occur in approximately 5\% of the simulated cases and 10\% of the SLACS sample. We argue that such issues are inherent to automated lens modeling but can be mitigated by incorporating prior knowledge from machine learning techniques. This work thus marks a promising step toward the efficient analysis of strong lenses in the era of big data. The code and data are available online: https://github.com/caoxiaoyue/TinyLensGpu.
The Case for Edge-On Binaries: An Avenue Toward Comparative Exoplanet Demographics
2025-03-11
Joseph E. Hand, Malena Rice, Konstantin Gerbig
Most Sun-like and higher-mass stars reside in systems that include one or more gravitationally bound stellar companions. These systems offer an important probe of planet formation in the most common stellar systems, while also providing key insights into how gravitational perturbations and irradiation differences from a companion star alter the outcomes of planet formation. Recent dynamical clues have begun to emerge that reveal systematic, non-random structure in the configurations of many planet-hosting binary systems: in close- to moderate-separation ($s < 800$ au) binary star systems, the orbits of exoplanets around individual stellar components are preferentially aligned with the orbital plane of their host stellar binary. In this work, we flip this narrative and search for nearby, edge-on binary star systems that, due to this preferential alignment, are top candidates for radial velocity and transiting exoplanet searches. We present a sample of 591 moderate-separation, relatively bright ($G < 14$) Gaia-resolved binary star systems in likely near-edge-on configurations. Using a simulated population of exoplanets drawn from transit survey occurrence rate constraints, we provide an overview of the expected planet yields from a targeted search in these systems. We describe the opportunities for comparative exoplanet demographics in the case that both stars can be inferred to host edge-on planetary systems - a configuration toward which the presented sample may be biased, given recent observations of orbit-orbit alignment in exoplanet-hosting binary systems.
Chatbots for Data Collection in Surveys: A Comparison of Four Theory-Based Interview Probes
2025-03-11
Rune M. Jacobsen, Samuel Rhys Cox, Carla F. Griggio, Niels van Berkel
Surveys are a widespread method for collecting data at scale, but their rigid structure often limits the depth of qualitative insights obtained. While interviews naturally yield richer responses, they are challenging to conduct across diverse locations and large participant pools. To partially bridge this gap, we investigate the potential of using LLM-based chatbots to support qualitative data collection through interview probes embedded in surveys. We assess four theory-based interview probes: descriptive, idiographic, clarifying, and explanatory. Through a split-plot study design (N=64), we compare the probes' impact on response quality and user experience across three key stages of HCI research: exploration, requirements gathering, and evaluation. Our results show that probes facilitate the collection of high-quality survey data, with specific probes proving effective at different research stages. We contribute practical and methodological implications for using chatbots as research tools to enrich qualitative data collection.
Large Language Models (LLMs) are increasingly utilized in scientific research assessment, particularly in automated paper review. However, existing LLM-based review systems face significant challenges, including limited domain expertise, hallucinated reasoning, and a lack of structured evaluation. To address these limitations, we introduce DeepReview, a multi-stage framework designed to emulate expert reviewers by incorporating structured analysis, literature retrieval, and evidence-based argumentation. Using DeepReview-13K, a curated dataset with structured annotations, we train DeepReviewer-14B, which outperforms CycleReviewer-70B with fewer tokens. In its best mode, DeepReviewer-14B achieves win rates of 88.21\% and 80.20\% against GPT-o1 and DeepSeek-R1 in evaluations. Our work sets a new benchmark for LLM-based paper review, with all resources publicly available. The code, model, dataset and demo have be released in http://ai-researcher.net.
Privacy Law Enforcement Under Centralized Governance: A Qualitative Analysis of Four Years' Special Privacy Rectification Campaigns
2025-03-11
Tao Jing, Yao Li, Jingzhou Ye, Jie Wang, Xueqiang Wang
In recent years, major privacy laws like the GDPR have brought about positive changes. However, challenges remain in enforcing the laws, particularly due to under-resourced regulators facing a large number of potential privacy-violating software applications (apps) and the high costs of investigating them. Since 2019, China has launched a series of privacy enforcement campaigns known as Special Privacy Rectification Campaigns (SPRCs) to address widespread privacy violations in its mobile application (app) ecosystem. Unlike the enforcement of the GDPR, SPRCs are characterized by large-scale privacy reviews and strict sanctions, under the strong control of central authorities. In SPRCs, central government authorities issue administrative orders to mobilize various resources for market-wide privacy reviews of mobile apps. They enforce strict sanctions by requiring privacy-violating apps to rectify issues within a short timeframe or face removal from app stores. While there are a few reports on SPRCs, the effectiveness and potential problems of this campaign-style privacy enforcement approach remain unclear to the community. In this study, we conducted 18 semi-structured interviews with app-related engineers involved in SPRCs to better understand the campaign-style privacy enforcement. Based on the interviews, we reported our findings on a variety of aspects of SPRCs, such as the processes that app engineers regularly follow to achieve privacy compliance in SPRCs, the challenges they encounter, the solutions they adopt to address these challenges, and the impacts of SPRCs, etc. We found that app engineers face a series of challenges in achieving privacy compliance in their apps...
Exploring Socio-Cultural Challenges and Opportunities in Designing Mental Health Chatbots for Adolescents in India
2025-03-11
Neil K. R. Sehgal, Hita Kambhamettu, Sai Preethi Matam, Lyle Ungar, Sharath Chandra Guntuku
Mental health challenges among Indian adolescents are shaped by unique cultural and systemic barriers, including high social stigma and limited professional support. Through a mixed-methods study involving a survey of 278 adolescents and follow-up interviews with 12 participants, we explore how adolescents perceive mental health challenges and interact with digital tools. Quantitative results highlight low self-stigma but significant social stigma, a preference for text over voice interactions, and low utilization of mental health apps but high smartphone access. Our qualitative findings reveal that while adolescents value privacy, emotional support, and localized content in mental health tools, existing chatbots lack personalization and cultural relevance. These findings inform recommendations for culturally sensitive chatbot design that prioritizes anonymity, tailored support, and localized resources to better meet the needs of adolescents in India. This work advances culturally sensitive chatbot design by centering underrepresented populations, addressing critical gaps in accessibility and support for adolescents in India.
This is a contribution to the memorial edition devoted to Professor Vladislav Gavrilovich Bagrov, who was my official adviser from the beginning of undergraduate period to the end of Ph.D. The text includes a mentioning of two my publications in Izvestia VUZov Fisica (Russian Physics Journal), where Vladislav Gavrilovich served as an Editor. The rest of this paper is based on the recent lectures about decoupling in quantum gravity, given in ICTP-SAIFR in Sao Paulo and at the school ``Estate Quantistica'' in Scalea, Italy. After a brief and mainly qualitative review of the decoupling theorem in semiclassical gravity and the scalar fourth-derivative model of Antoniadis and Mottola, we explain what is the expected result for the physical beta functions in fourth-derivative quantum gravity and what should remain from these beta functions in the IR.
Desirable Unfamiliarity: Insights from Eye Movements on Engagement and Readability of Dictation Interfaces
2025-03-11
Zhaohui Liang, Yonglin Chen, Naser Al Madi, Can Liu
Dictation interfaces support efficient text input, but the transcribed text can be hard to read. To understand how users read and review dictated text, we conducted a controlled eye-tracking experiment with 20 participants to compare five dictation interfaces: PLAIN (real-time transcription), AOC (periodic corrections), RAKE (keyword highlights), GP-TSM (grammar-preserving highlights), and SUMMARY (LLM-generated abstraction summary). The study analyzed participants' gaze patterns during their speech composition and reviewing processes. The findings show that during composition, participants spent only 7--11% of their time actively reading, and they favored real-time feedback and avoided distracting interface changes. During reviewing, although SUMMARY introduced unfamiliar words (requiring longer and more frequent fixation), they were easier to read (requiring fewer regressions). Participants preferred SUMMARY for the polished text that preserved fidelity to original meanings. RAKE guided the reading of self-produced text better than GP-TSM. These findings provide new ways to rethink the design of dictation interfaces.
Astrophysical properties of star clusters projected toward tidally perturbed SMC regions
2025-03-11
Denis M. F. Illesca, Andrés E. Piatti, Matías Chiarpotti, Roberto Butrón
We report on the astrophysical properties of a sample of star clusters in the Small Magellanic Cloud (SMC). They have been selected with the aim of looking for the connection between their ages, heliocentric distances and metallicities with the existence of tidally perturbed/induced outermost SMC regions. We derived the star cluster fundamental parameters from relatively deep Survey of the Magellanic Stellar History (SMASH) DR2 color magnitude diagrams, cleaned from field star contamination, and compared to thousand synthetic CMDs covering a wide range of heliocentric distances, ages and metal content. Heliocentric distances for 15 star clusters are derived for the first time, which represents an increase of 50 per cent of SMC clusters with estimated heliocentric distances. The analysis of the age-metallicity relationships (AMRs) of cluster located in outermost regions distributed around the SMC and in the SMC Main Body reveals that they have followed the overall galaxy chemical enrichment history. However, since half of the studied clusters are placed in front of or behind the SMC Main Body, we concluded that they formed in the SMC and have traveled outward because of the tidal effects from the interaction with the Large Magellanic Cloud (LMC). Furthermore, metal rich clusters formed recently in some of these outermost regions from gas that was also dragged by tidal effects from the inner SMC. This outcome leads to consider the SMC as a galaxy scarred by the LMC tidal interaction with distance-perturbed and newly induced outermost stellar substructures.
Academic paper review is a critical yet time-consuming task within the research community. With the increasing volume of academic publications, automating the review process has become a significant challenge. The primary issue lies in generating comprehensive, accurate, and reasoning-consistent review comments that align with human reviewers' judgments. In this paper, we address this challenge by proposing ReviewAgents, a framework that leverages large language models (LLMs) to generate academic paper reviews. We first introduce a novel dataset, Review-CoT, consisting of 142k review comments, designed for training LLM agents. This dataset emulates the structured reasoning process of human reviewers-summarizing the paper, referencing relevant works, identifying strengths and weaknesses, and generating a review conclusion. Building upon this, we train LLM reviewer agents capable of structured reasoning using a relevant-paper-aware training method. Furthermore, we construct ReviewAgents, a multi-role, multi-LLM agent review framework, to enhance the review comment generation process. Additionally, we propose ReviewBench, a benchmark for evaluating the review comments generated by LLMs. Our experimental results on ReviewBench demonstrate that while existing LLMs exhibit a certain degree of potential for automating the review process, there remains a gap when compared to human-generated reviews. Moreover, our ReviewAgents framework further narrows this gap, outperforming advanced LLMs in generating review comments.
Survey on Beyond Diagonal RIS Enabled 6G Wireless Networks: Fundamentals, Recent Advances, and Challenges
2025-03-11
Wali Ullah Khan, Manzoor Ahmed, Chandan Kumar Sheemar, Marco Di Renzo, Eva Lagunas, Asad Mahmood, Syed Tariq Shah, Octavia A. Dobre, Jorge Querol, Symeon Chatzinotas
Beyond Diagonal Reconfigurable Intelligent Surfaces (BD-RIS) represent a groundbreaking innovation in sixth-generation (6G) wireless networks, enabling unprecedented control over wireless propagation environments compared to conventional diagonal RIS (D-RIS). This survey provides a comprehensive analysis of BD-RIS, detailing its architectures, operational principles, and mathematical modeling while highlighting its performance benefits. BD-RIS classifications, including single-connected, fully-connected, and group-connected architectures, and their reflective, transmissive, hybrid, and multi-sector operating modes are examined. Recent advances in BD-RIS-enabled 6G networks are reviewed, focusing on critical areas such as channel estimation, sum-rate and spectral efficiency optimization, energy efficiency enhancement, and security. The survey identifies fundamental challenges in BD-RIS research, including hardware design limitations, adaptive channel estimation, and the impact of non-ideal hardware effects. Future research directions for BD-RIS are proposed, emphasizing the integration of artificial intelligence and machine learning (AI/ML), joint optimization of communication and sensing, and enhanced physical layer security (PLS). This study concludes by underscoring BD-RIS's transformative potential to redefine 6G wireless networks, offering valuable insights and lessons for future research and development.
The detection of strongly-lensed submillimetre galaxies
2025-03-11
Chris Sedgwick, Stephen Serjeant, Charles Weiner
We present predictions of the number and properties of strongly-lensed submillimetre galaxies, based on an adaption of the physically-motivated LensPop model covering galaxy-galaxy strong lensing by elliptical galaxies, which successfully predicted optical and near-infrared lenses. For submillimetre-luminous lensed galaxies, the most efficient observational selection identifies sources with high fluxes (S500um > 80 mJy), where lensed sources outnumber bright unlensed sources; several hundred candidates from Herschel surveys have been identified, and confirmed by follow-up observations. We have tested our model against these observations. The model predicts an all-sky number density of 0.09+/-0.05 deg-2 (in absolute numbers, 3,600+/-1,800) of bright lensed galaxies detectable by this method. Observations show considerable variation in sky density between fields, 0.08 - 0.31 deg-2. Predictions of redshift and magnification distributions are comparable to observations, although the model appears to under-predict lenses at the highest magnifications ( > 20). We predict that the apparent AB magnitudes at visible wavelengths of the foreground lenses will be as faint as 28, whereas observations typically reach ~ 23, implying that some apparently unlensed bright submillimetre galaxies may have lensing galaxies below this detection limit. For fainter lensed galaxies, the model predicts over 130,000 systems with flux S500um > 10 mJy across the sky, of which ~ 3,400 remain be be discovered in the Herschel catalogues. We also predict that Euclid should be able to detect some 25,000 lensed submillimetre galaxies that are VIS-band 'dropouts' - detectable in the near-infrared but not at optical wavelengths.
3D Medical Imaging Segmentation on Non-Contrast CT
2025-03-11
Canxuan Gang, Yuhan Peng
This technical report analyzes non-contrast CT image segmentation in computer vision. It revisits a proposed method, examines the background of non-contrast CT imaging, and highlights the significance of segmentation. The study reviews representative methods, including convolutional-based and CNN-Transformer hybrid approaches, discussing their contributions, advantages, and limitations. The nnUNet stands out as the state-of-the-art method across various segmentation tasks. The report explores the relationship between the proposed method and existing approaches, emphasizing the role of global context modeling in semantic labeling and mask generation. Future directions include addressing the long-tail problem, utilizing pre-trained models for medical imaging, and exploring self-supervised or contrastive pre-training techniques. This report offers insights into non-contrast CT image segmentation and potential advancements in the field.
There exist applications of reinforcement learning like medicine where policies need to be ''interpretable'' by humans. User studies have shown that some policy classes might be more interpretable than others. However, it is costly to conduct human studies of policy interpretability. Furthermore, there is no clear definition of policy interpretabiliy, i.e., no clear metrics for interpretability and thus claims depend on the chosen definition. We tackle the problem of empirically evaluating policies interpretability without humans. Despite this lack of clear definition, researchers agree on the notions of ''simulatability'': policy interpretability should relate to how humans understand policy actions given states. To advance research in interpretable reinforcement learning, we contribute a new methodology to evaluate policy interpretability. This new methodology relies on proxies for simulatability that we use to conduct a large-scale empirical evaluation of policy interpretability. We use imitation learning to compute baseline policies by distilling expert neural networks into small programs. We then show that using our methodology to evaluate the baselines interpretability leads to similar conclusions as user studies. We show that increasing interpretability does not necessarily reduce performances and can sometimes increase them. We also show that there is no policy class that better trades off interpretability and performance across tasks making it necessary for researcher to have methodologies for comparing policies interpretability.
A systematic literature review of unsupervised learning algorithms for anomalous traffic detection based on flows
2025-03-11
Alberto Miguel-Diez, Adrián Campazas-Vega, Claudia Álvarez-Aparicio, Gonzalo Esteban-Costales, Ángel Manuel Guerrero-Higueras
The constant increase of devices connected to the Internet, and therefore of cyber-attacks, makes it necessary to analyze network traffic in order to recognize malicious activity. Traditional packet-based analysis methods are insufficient because in large networks the amount of traffic is so high that it is unfeasible to review all communications. For this reason, flows is a suitable approach for this situation, which in future 5G networks will have to be used, as the number of packets will increase dramatically. If this is also combined with unsupervised learning models, it can detect new threats for which it has not been trained. This paper presents a systematic review of the literature on unsupervised learning algorithms for detecting anomalies in network flows, following the PRISMA guideline. A total of 63 scientific articles have been reviewed, analyzing 13 of them in depth. The results obtained show that autoencoder is the most used option, followed by SVM, ALAD, or SOM. On the other hand, all the datasets used for anomaly detection have been collected, including some specialised in IoT or with real data collected from honeypots.
A census of OB stars within 1 kpc and the star formation and core collapse supernova rates of the Milky Way
2025-03-11
Alexis L. Quintana, Nicholas J. Wright, Juan Martínez García
OB stars are crucial for our understanding of Galactic structure, star formation, stellar feedback and multiplicity. In this paper we have compiled a census of all OB stars within 1 kpc of the Sun. We performed evolutionary and atmospheric model fits to observed spectral energy distributions (SEDs) compiled from astro-photometric survey data. We have characterized and mapped 24,706 O- and B-type stars ($T_{\rm eff} > 10,000$ K) within 1 kpc of the Sun, whose overdensities correspond to well-studied OB associations and massive star-forming regions such as Sco-Cen, Orion OB1, Vela OB2, Cepheus and Circinus. We have assessed the quality of our catalogue by comparing it with spectroscopic samples and similar catalogues of OB(A) stars, as well as catalogues of OB associations, star-forming regions and young open clusters. Finally, we have also exploited our list of OB stars to estimate their scale height (76 $\pm$ 1 pc), a local star formation rate of $2896^{+417}_{-1}$ M$_{\odot}$ Myr$^{-1}$ and a local core-collapse supernova rate of $\sim$15--30 per Myr. We extrapolate these rates to the entire Milky Way to derive a Galactic SFR of $0.67^{+0.09}_{-0.01}$ M$_{\odot}$ yr$^{-1}$ and a core-collapse supernova rate of 0.4--0.5 per century. These are slightly lower than previous estimates, which we attribute to improvements in our census of OB stars and changes to evolutionary models. We calculate a near-Earth core collapse supernova rate of $\sim$2.5 per Gyr that supports the view that nearby supernova explosions could have caused one or more of the recorded mass extinction events on Earth.
The remarkable success of foundation models has been driven by scaling laws, demonstrating that model performance improves predictably with increased training data and model size. However, this scaling trajectory faces two critical challenges: the depletion of high-quality public data, and the prohibitive computational power required for larger models, which have been monopolized by tech giants. These two bottlenecks pose significant obstacles to the further development of AI. In this position paper, we argue that leveraging massive distributed edge devices can break through these barriers. We reveal the vast untapped potential of data and computational resources on massive edge devices, and review recent technical advancements in distributed/federated learning that make this new paradigm viable. Our analysis suggests that by collaborating on edge devices, everyone can participate in training large language models with small edge devices. This paradigm shift towards distributed training on edge has the potential to democratize AI development and foster a more inclusive AI community.
We present EgoBlind, the first egocentric VideoQA dataset collected from blind individuals to evaluate the assistive capabilities of contemporary multimodal large language models (MLLMs). EgoBlind comprises 1,210 videos that record the daily lives of real blind users from a first-person perspective. It also features 4,927 questions directly posed or generated and verified by blind individuals to reflect their needs for visual assistance under various scenarios. We provide each question with an average of 3 reference answers to alleviate subjective evaluation. Using EgoBlind, we comprehensively evaluate 15 leading MLLMs and find that all models struggle, with the best performers achieving accuracy around 56\%, far behind human performance of 87.4\%. To guide future advancements, we identify and summarize major limitations of existing MLLMs in egocentric visual assistance for the blind and provide heuristic suggestions for improvement. With these efforts, we hope EgoBlind can serve as a valuable foundation for developing more effective AI assistants to enhance the independence of the blind individuals' lives.
Realising the potential of large spectroscopic surveys with machine-learning
2025-03-11
G. Guiglion
Machine-learning is playing an increasing role in helping the astronomical community to face data analysis challenges, in particular in the field of Galactic Archaeology and large scale spectroscopic surveys. We present recent developments in the field of convolutional neural-networks (CNNs) for stellar abundances in the context of the Galactic spectroscopic surveys Gaia-ESO, and Gaia-RVS. Especially, by combining the full Gaia data product, we manage to characterize for the first time the [alpha/M] vs. [M/H] bimodality in the Galactic disc with Gaia-RVS spectra at low-S/N. This work is highly relevant for the next generation of large scale surveys such as MSE, 4MOST, and WST.
Rayleigh-B{é}nard Convection with Phase Change Close to the Critical Point
2025-03-11
Valentin Mouet, Guillaume Michel, François Pétrélis, Stephan Fauve
Rayleigh-B{\'e}nard convection is investigated with sulfur hexafluoride (SF$_6$) in the vicinity of its critical point. In the supercritical domain, direct measurements of the heat flux $Q$ as a function of the temperature difference $\Delta T$ are consistent with the usual scaling laws of single-phase turbulent convection. Along the liquid-vapor coexistence curve, heat fluxes are dramatically enhanced by condensation and boiling. Optical measurements are performed to document the size and velocity of the bubbles. We report $Q(\Delta T, \epsilon)$ in both domains, with $\epsilon$ the dimensionless distance to the critical point. Critical scaling laws are observed that can guide the development of theoretical models. In addition, this documents a system of diverging heat transfer coefficient, i.e., in which a significant heat flux can be achieved with an arbitrarily small temperature difference as $\epsilon\to 0$.
Dynamic Scene Reconstruction: Recent Advance in Real-time Rendering and Streaming
2025-03-11
Jiaxuan Zhu, Hao Tang
Representing and rendering dynamic scenes from 2D images is a fundamental yet challenging problem in computer vision and graphics. This survey provides a comprehensive review of the evolution and advancements in dynamic scene representation and rendering, with a particular emphasis on recent progress in Neural Radiance Fields based and 3D Gaussian Splatting based reconstruction methods. We systematically summarize existing approaches, categorize them according to their core principles, compile relevant datasets, compare the performance of various methods on these benchmarks, and explore the challenges and future research directions in this rapidly evolving field. In total, we review over 170 relevant papers, offering a broad perspective on the state of the art in this domain.
The identification of star clusters holds significant importance in studying galaxy formation and evolution history. However, the task of swiftly and accurately identifying star clusters from vast amounts of photometric images presents an immense challenge. To address these difficulties, we employ deep learning models for image classification to identify young disk star clusters in M31 from the Pan-Andromeda Archaeological Survey (PAndAS) images. For training, validation, and testing, we utilize the Panchromatic Hubble Andromeda Treasury (PHAT) survey catalogs. We evaluate the performance of various deep learning models, using different classification thresholds and limiting magnitudes. Our findings indicate that the ResNet-50 model exhibits the highest overall accuracy. Moreover, using brighter limiting magnitudes and increasing the classification thresholds can effectively enhance the accuracy and precision of cluster identification. Through our experiments, we found that the model achieves optimal performance when the limiting magnitude is set to brighter than 21 mag. Based on this, we constructed a training dataset with magnitudes less than 21 mag and trained a second ResNet-50 model. This model achieved a purity of 89.30%, a recall of 73.55%, and an F1 score of 80.66% when the classification threshold was set to 0.669. Applying the second model to all sources in the PAndAS fields within a projected radius of 30 kpc from the center of M31, we identified 2,228 new unique star cluster candidates. We conducted visual inspections to validate the results produced by our automated methods, and we ultimately obtained 1,057 star cluster candidates, of which 745 are newly identified.
On "confirmatory" methodological research in statistics and related fields
2025-03-11
F. J. D. Lange, Juliane Wilcke, Sabine Hoffmann, Moritz Herrmann, Anne-Laure Boulesteix
Empirical substantive research, such as in the life or social sciences, is commonly categorized into the two modes exploratory and confirmatory, both of which are essential to scientific progress. The former is also referred to as hypothesis-generating or data-contingent research, the latter is also called hypothesis-testing research. In the context of empirical methodological research in statistics, however, the exploratory-confirmatory distinction has received very little attention so far. Our paper aims to fill this gap. First, we revisit the concept of empirical methodological research through the lens of the exploratory-confirmatory distinction. Secondly, we examine current practice with respect to this distinction through a literature survey including 115 articles from the field of biostatistics. Thirdly, we provide practical recommendations towards more appropriate design, interpretation, and reporting of empirical methodological research in light of this distinction. In particular, we argue that both modes of research are crucial to methodological progress, but that most published studies - even if sometimes disguised as confirmatory - are essentially of exploratory nature. We emphasize that it may be adequate to consider empirical methodological research as a continuum between "pure" exploration and "strict" confirmation, recommend to transparently report the mode of conducted research within the spectrum between exploratory and confirmatory, and stress the importance of study protocols written before conducting the study, especially in confirmatory methodological research.
Wireless signal recognition (WSR) is a crucial technique for intelligent communications and spectrum sharing in the next six-generation (6G) wireless communication networks. It can be utilized to enhance network performance and efficiency, improve quality of service (QoS), and improve network security and reliability. Additionally, WSR can be applied for military applications such as signal interception, signal race, and signal abduction. In the past decades, great efforts have been made for the research of WSR. Earlier works mainly focus on model-based methods, including likelihood-based (LB) and feature-based (FB) methods, which have taken the leading position for many years. With the emergence of artificial intelligence (AI), intelligent methods including machine learning-based (ML-based) and deep learning-based (DL-based) methods have been developed to extract the features of the received signals and perform the classification. In this work, we provide a comprehensive review of WSR from the view of applications, main tasks, recent advances, datasets and evaluation metrics, challenges, and future directions. Specifically, intelligent WSR methods are introduced from the perspective of model, data, learning and implementation. Moreover, we analyze the challenges for WSR from the view of complex, dynamic, and open 6G wireless environments and discuss the future directions for WSR. This survey is expected to provide a comprehensive overview of the state-of-the-art WSR techniques and inspire new research directions for WSR in 6G networks.
LATMOS: Latent Automaton Task Model from Observation Sequences
2025-03-11
Weixiao Zhan, Qiyue Dong, Eduardo Sebastián, Nikolay Atanasov
Robot task planning from high-level instructions is an important step towards deploying fully autonomous robot systems in the service sector. Three key aspects of robot task planning present challenges yet to be resolved simultaneously, namely, (i) factorization of complex tasks specifications into simpler executable subtasks, (ii) understanding of the current task state from raw observations, and (iii) planning and verification of task executions. To address these challenges, we propose LATMOS, an automata-inspired task model that, given observations from correct task executions, is able to factorize the task, while supporting verification and planning operations. LATMOS combines an observation encoder to extract the features from potentially high-dimensional observations with automata theory to learn a sequential model that encapsulates an automaton with symbols in the latent feature space. We conduct extensive evaluations in three task model learning setups: (i) abstract tasks described by logical formulas, (ii) real-world human tasks described by videos and natural language prompts and (iii) a robot task described by image and state observations. The results demonstrate the improved plan generation and verification capabilities of LATMOS across observation modalities and tasks.
Lexical Semantic Change (LSC) offers insights into cultural and social dynamics. Yet, the validity of methods for measuring kinds of LSC has yet to be established due to the absence of historical benchmark datasets. To address this gap, we develop a novel three-stage evaluation framework that involves: 1) creating a scalable, domain-general methodology for generating synthetic datasets that simulate theory-driven LSC across time, leveraging In-Context Learning and a lexical database; 2) using these datasets to evaluate the effectiveness of various methods; and 3) assessing their suitability for specific dimensions and domains. We apply this framework to simulate changes across key dimensions of LSC (SIB: Sentiment, Intensity, and Breadth) using examples from psychology, and evaluate the sensitivity of selected methods to detect these artificially induced changes. Our findings support the utility of the synthetic data approach, validate the efficacy of tailored methods for detecting synthetic changes in SIB, and reveal that a state-of-the-art LSC model faces challenges in detecting affective dimensions of LSC. This framework provides a valuable tool for dimension- and domain-specific bench-marking and evaluation of LSC methods, with particular benefits for the social sciences.
The AGEL Survey Data Release 2: A Gravitational Lens Sample for Galaxy Evolution and Cosmology
2025-03-11
Tania M. Barone, Keerthi Vasan G. C., Kim-Vy Tran, Glenn G. Kacprzak, Karl Glazebrook, Tucker Jones, Duncan J. Bowden, Faith Dalessandro, Nandini Sahu, Hannah Skobe, Rebecca J. Allen, A. Makai Baker, Daniel J. Ballard, Yuguang Chen, Thomas E. Collett, Giovanni Ferrami, Jimena Gonzalez, William Gottemoller, Anishya Harshan, Xiaosheng Huang, Leena Iwamoto, Colin Jacobs, Tesla E. Jeltema, Kaustubh Rajesh Gupta, Geraint F. Lewis, Sebastian Lopez, Themiya Nanayakkara, Nikole M. Nielsen, Jackson O'Donnell, Huimin Qu, Sunny Rhoades, Anowar Shajib, Sarah M. Sweet, Nicolas Tejos
The ASTRO 3D Galaxy Evolution with Lenses (AGEL) Survey is an ongoing effort to spectroscopically confirm a diverse sample of gravitational lenses with high spatial resolution imaging, to facilitate a broad range of science outcomes. The AGEL systems span single galaxy-scale deflectors to groups and clusters, and include rare targets such as galaxy-scale lenses with multiple sources, lensed quiescent galaxies, and Einstein rings. We build on the 77 systems presented in Tran et al. 2022 (AGEL data release 1) to present a total 138 lenses, and high resolution F140W and F200LP Hubble Space Telescope images for 71 lenses from a completed HST SNAP program. Lens candidates were originally identified by convolutional neural networks in the DES and DECaLS imaging fields, and of the targets with follow-up spectroscopy we find a high (96%) success rate. Compared with other spectroscopic lens samples, AGEL lenses tend to have both higher redshift deflectors and sources. We briefly discuss the common causes of false-positive candidates, and strategies for mitigating false-positives in next generation lens searches. Lastly, we present 6 galaxy-scale double-source plane lenses useful for cosmological analyses. With next-generation telescopes and surveys such as Euclid, Vera Rubin's Legacy Survey of Space and Time, Keck Observatory's KAPA program, and 4MOST's 4SLSLS surveys on the horizon, the AGEL survey represents a pathfinder for refining automated candidate search methods and identifying and triaging candidates for followup based on scientific potential.
A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techniques, Datasets, and Future Research Prospects
2025-03-11
Fei Wang, Tingting Zhang, Bintong Zhao, Libao Xing, Tiantian Wang, Han Ding, Tony Xiao Han
Wi-Fi sensing has emerged as a transformative technology that leverages ubiquitous wireless signals to enable a variety of applications ranging from activity and gesture recognition to indoor localization and health monitoring. However, the inherent dependency of Wi-Fi signals on environmental conditions introduces significant generalization challenges,variations in surroundings, human positions, and orientations often lead to inconsistent signal features, impeding robust action recognition. In this survey, we review over 200 studies on Wi-Fi sensing generalization, categorizing them along the entire sensing pipeline: device deployment, signal processing, feature learning, and model deployment. We systematically analyze state-of-the-art techniques, which are employed to mitigate the adverse effects of environmental variability. Moreover, we provide a comprehensive overview of open-source datasets such as Widar3.0, XRF55, and XRFv2, highlighting their unique characteristics and applicability for multimodal fusion and cross-modal tasks. Finally, we discuss emerging research directions, such as multimodal approaches and the integration of large language models,to inspire future advancements in this rapidly evolving field. Our survey aims to serve as a valuable resource for researchers, offering insights into current methodologies, available datasets, and promising avenues for further investigation.
A Survey of Challenges and Sensing Technologies in Autonomous Retail Systems
2025-03-11
Shimmy Rukundo, David Wang, Front Wongnonthawitthaya, Youssouf Sidibé, Minsik Kim, Emily Su, Jiale Zhang
Autonomous stores leverage advanced sensing technologies to enable cashier-less shopping, real-time inventory tracking, and seamless customer interactions. However, these systems face significant challenges, including occlusion in vision-based tracking, scalability of sensor deployment, theft prevention, and real-time data processing. To address these issues, researchers have explored multi-modal sensing approaches, integrating computer vision, RFID, weight sensing, vibration-based detection, and LiDAR to enhance accuracy and efficiency. This survey provides a comprehensive review of sensing technologies used in autonomous retail environments, highlighting their strengths, limitations, and integration strategies. We categorize existing solutions across inventory tracking, environmental monitoring, people-tracking, and theft detection, discussing key challenges and emerging trends. Finally, we outline future directions for scalable, cost-efficient, and privacy-conscious autonomous store systems.
Inference of Hubble constant using standard sirens and reconstructed matter density field
2025-03-11
Supranta S. Boruah, Ghazal Geshnizjani, Guilhem Lavaux
We summarise a new approach for measuring the Hubble constant using standard sirens and the reconstructed matter density field obtained from observed galaxy surveys. Specifically, we describe and test this method using the Bayesian forward-modelled software BORG. This software evolves the initial density field to the present, sampling plausible density fields from the posterior, accounting for peculiar velocities, and automatically incorporating higher-order correlation functions. The advantage of adding additional information from correlations is expected to make this method more effective in low-signal-to-noise regimes, such as those with modest galaxy number density or incomplete surveys. Our results show that developing a cross-correlation framework between gravitational waves and galaxy surveys, based on the forward-modelled reconstructed density field, to measure H0 is promising. This review is based on the research conducted as part of Boruah's Ph.D. thesis.
The growing interest in hypergraph neural networks (HGNNs) is driven by their capacity to capture the complex relationships and patterns within hypergraph structured data across various domains, including computer vision, complex networks, and natural language processing. This paper comprehensively reviews recent advances in HGNNs and presents a taxonomy of mainstream models based on their architectures: hypergraph convolutional networks (HGCNs), hypergraph attention networks (HGATs), hypergraph autoencoders (HGAEs), hypergraph recurrent networks (HGRNs), and deep hypergraph generative models (DHGGMs). For each category, we delve into its practical applications, mathematical mechanisms, literature contributions, and open problems. Finally, we discuss some common challenges and promising research directions.This paper aspires to be a helpful resource that provides guidance for future research and applications of HGNNs.
We present spatially-resolved spectroscopic observations of 10 isolated Galactic HII regions using data from the LAMOST Medium-Resolution Spectroscopic Survey of Nebulae (LAMOST MRS-N). The high spatial resolution of the data allows us to investigate the 1D radial profiles of emission line fluxes (Ha, [S II] and [N II]), flux ratios ([N II]/Ha, [S II]/Ha and [S II]/[N II]), and radial velocities of these three emission lines. Among these regions, two are ionization-bounded, while the remaining eight are matter-bounded. The matter-bounded HII regions exhibit shallower slopes in their radial flux profiles compared to the ionization-bounded ones. In most cases, the [N II]/Ha and [S II]/Ha ratios increase with distance from the center of the HII regions, consistent with model predictions that low-ionization emissions dominate the outer zones of these regions. The two ionization-bounded HII regions have kinematic ages (t) of 0.2 and 0.3 Myr, while the matter-bounded regions span ages from 1 to 12 Myr. For the matter-bounded HII regions, the optical emission flux decreases continuously beyond the photodissociation region (PDR), extending to approximately 1-4 times the radius of the PDR (r_PDR). The escape fraction f_esc of ionizing photons, derived from 1D Ha radial flux profiles, is ~ 0% for ionization-bounded HII regions, while it ranges from 50% to 90% for the matter-bounded HII regions. The correlation between f_esc and t suggests that evolved HII regions (with t > 1 Myr) contribute more significantly to ionizing the surrounding diffuse ionized gas compared to younger, newly formed HII regions.
The kagome lattice is a promising platform for exploring exotic quantum phases. However, achieving a single-atomic-layer kagome lattice in elemental materials has remained a significant challenge. Here we introduce line-graph epitaxy, a novel approach that enables the synthesis of single-atomic-layer goldene with a kagome lattice. Through scanning tunneling microscopy (STM) and spectroscopy (STS), alongside density functional theory (DFT) calculations, we provide direct evidence of kagome goldene, which exhibits a flat band with a van Hove singularity approximately 1.1 eV below the Fermi level, indicative of strong electron correlation effects. Notably, the flat band breaks down at the zigzag edges of the goldene nanoflakes, underlining the role of edge effects. Additionally, our calculations reveal that weak interlayer interactions between goldene and the Au$_2$Ge substrate give rise to dual Dirac nodal lines due to a proximity effect. These findings offer a strategy for constructing elemental kagome lattices and provide a generalizable approach for fabricating and controlling line-graph materials. This work paves the way for exploring quantum phases driven by strong correlations and designing materials for quantum technologies.
Optimizing Charge-coupled Device Readout Enabled by the Floating-Gate Amplifier
2025-03-11
Kenneth W. Lin, Abby Bault, Armin Karcher, Julien Guy, Stephen E. Holland, William F. Kolbe, Peter E. Nugent
Multiple-Amplifier Sensing (MAS) charge-coupled devices (CCDs) have recently been shown to be promising silicon detectors that meet noise sensitivity requirements for next generation Stage-5 spectroscopic surveys and potentially, future space-based imaging of extremely faint objects such as the Habitable Worlds Observatory. Building upon the capability of the Skipper CCD to achieve deeply sub-electron noise floors, MAS CCDs utilize multiple floating-gate amplifiers along the serial register to increase the readout speed by a factor of the number of output nodes compared to a Skipper CCD. We introduce and experimentally demonstrate on a 16-channel prototype device new readout techniques that exploit the MAS CCD's floating-gate amplifiers to optimize the correlated double sampling (CDS) by resetting once per line instead of once per pixel. With this new mode, we find an optimal filter to subtract the noise from the signal during read out. We also take advantage of the MAS CCD's structure to tune the read time by independently changing integration times for the signal and reference level. Together with optimal weighted averaging of the 16 outputs, these approaches enable us to reach a sub-electron noise of 0.9 e$^-$ rms pix$^{-1}$ at 19 $\mu$s pix$^{-1}$ for a single charge measurement per pixel - simultaneously giving a 30% faster readout time and 10% lower read noise compared to performance previously evaluated without these techniques.
Acoustic Neural 3D Reconstruction Under Pose Drift
2025-03-11
Tianxiang Lin, Mohamad Qadri, Kevin Zhang, Adithya Pediredla, Christopher A. Metzler, Michael Kaess
We consider the problem of optimizing neural implicit surfaces for 3D reconstruction using acoustic images collected with drifting sensor poses. The accuracy of current state-of-the-art 3D acoustic modeling algorithms is highly dependent on accurate pose estimation; small errors in sensor pose can lead to severe reconstruction artifacts. In this paper, we propose an algorithm that jointly optimizes the neural scene representation and sonar poses. Our algorithm does so by parameterizing the 6DoF poses as learnable parameters and backpropagating gradients through the neural renderer and implicit representation. We validated our algorithm on both real and simulated datasets. It produces high-fidelity 3D reconstructions even under significant pose drift.
On the computation of endomorphism rings of abelian surfaces over finite fields
2025-03-11
Samuele Anni, Gaetan Bisson, Annamaria Iezzi, Elisa Lorenzo García, Benjamin Wesolowski
We study endomorphism rings of principally polarized abelian surfaces over finite fields from a computational viewpoint with a focus on exhaustiveness. In particular, we address the cases of non-ordinary and non-simple varieties. For each possible surface type, we survey known results and, whenever possible, provide improvements and missing results.
Towards High Precision Mass Measurements of Two Sub-Neptunes in the K2-266 Planetary System Through Transit Timing
2025-03-11
Ing-Guey Jiang, Li-Chin Yeh, Billy Edwards, Ming Yang, Keivan G. Stassun, Napaporn A-thano
Sub-Neptunes have been found to be one of the most common types of exoplanets, yet their physical parameters and properties are poorly determined and in need of further investigation. In order to improve the mass measurement and parameter determination of two sub-Neptunes, K2-266 d and K2-266 e, we present new transit observations obtained with CHaracterising ExOPlanets Satellite (CHEOPS) and Transiting Exoplanet Survey Satellite (TESS), increasing the baseline of transit data from a few epochs to 165 epochs for K2-266 d, and to 121 epochs for K2-266 e. Through a two-stage fitting process, it is found that the masses of K2-266 d and K2-266 e are 6.01$\pm$0.43 $M_\oplus$ and 7.70$\pm$0.58 $M_\oplus$, respectively. With these updated values and one order of magnitude better precision, we confirm the planets to belong to the population of planets that has been determined to be volatile-rich. Finally, we present the results of dynamical simulations, showing that the system is stable, the orbits are not chaotic, and that these two planets are close to but not in 4:3 mean motion resonance.
Small-$x$ evolution of gluon fields from incoherent J/$ψ$ photoproduction in ultraperipheral PbPb collisions
2025-03-11
CMS Collaboration
Incoherent J/$\psi$ photoproduction in heavy ion ultraperipheral collisions (UPCs), in which the photon interacts with localized, fluctuating gluonic hotspots rather than the entire nucleus, provides a unique probe of those fluctuations. This study presents the first measurement of the dependence of this photoproduction yield on the photon-nucleon center-of-mass energy ($W_\gamma^\mathrm{N}$), using PbPb UPCs at a nucleon-nucleon center-of-mass energy of 5.02 TeV. The data corresponds to an integrated luminosity of 1.52 nb$^{-1}$, recorded by the CMS experiment. The measurement covers a wide $W_\gamma^\mathrm{N}$ range of $\approx$40-400 GeV, probing gluons carrying a fraction of nucleon momentum $x$ in the range between 5.9 $\times$ 10$^{-3}$ and 6.5 $\times$ 10$^{-5}$. The measured incoherent J/$\psi$ photoproduction cross section is suppressed relative to theoretical predictions without nuclear effects. However, the ratio of incoherent to coherent photoproduction remains constant across the probed $W_\gamma^\mathrm{N}$ and $x$ range. Together, these results pose significant challenges to current theoretical models which include gluon saturation or nuclear shadowing effects.
Taylor-Couette flow with split endcaps: preparatory hydrodynamic study for upcoming DRESDYN-MRI experiment
2025-03-11
A. Mishra, P. Personnettaz, G. Mamatsashvili, V. Galindo, F. Stefani
Magnetorotational instability (MRI) is of great importance in astrophysical disks, driving angular momentum transport and accretion of matter onto a central object. A Taylor-Couette (TC) flow between two coaxial cylinders subject to an axial magnetic field is a preferred setup for MRI-experiments. A main challenge in those experiments has been to minimize the effects of axial boundaries, or endcaps, which substantially alter the flow structure compared to the axially unbounded idealized case. Understanding the influence of endcaps on the flow stability is crucial for the unambiguous experimental identification of MRI. In this paper, we examine the hydrodynamic evolution of a TC flow in the presence of split endcap rims up to Reynolds number $Re =$ $2\times 10^5$. At this $Re$, the flow deviates from the ideal TC flow profile, resulting in about $15\%$ deviation in angular velocity at the mid-height of the cylinders. Aside from turbulent fluctuations caused by shearing instability at the endcaps, the bulk flow remains axially independent and exhibits Rayleigh stability. We characterize the scaling of the Ekman and Stewartson boundary layer thickness with respect to $Re$. We also study the effect of changing the rotation ratio of the cylinders $\mu$ on the flow at large $Re$ and show that TC experiments can be conducted for larger $\mu \sim 0.5$ to safely ensure the hydrodynamic stability of the flow in the upcoming DRESDYN-MRI experiment. In all configurations considered, the modification of the flow profile by the endcaps further decreases the required critical threshold for the onset of MRI that can facilitate its detection in future experiments.
Low-Energy Backgrounds in Solid-State Phonon and Charge Detectors
2025-03-11
Daniel Baxter, Rouven Essig, Yonit Hochberg, Margarita Kaznacheeva, Belina von Krosigk, Florian Reindl, Roger K. Romani, Felix Wagner
Solid-state phonon and charge detectors probe the scattering of weakly interacting particles, such as dark matter and neutrinos, through their low recoil thresholds. Recent advancements have pushed sensitivity to eV-scale energy depositions, uncovering previously-unseen low-energy excess backgrounds. While some arise from known processes such as thermal radiation, luminescence, and stress, others remain unexplained. This review examines these backgrounds, their possible origins, and parallels to low-energy effects in solids. Their understanding is essential for interpreting particle interactions at and below the eV-scale.
Survey-Wide Asteroid Discovery with a High-Performance Computing Enabled Non-Linear Digital Tracking Framework
2025-03-11
Nathan Golovich, Trevor Steil, Alex Geringer-Sameth, Keita Iwabuchi, Ryan Dozier, Roger Pearce
Modern astronomical surveys detect asteroids by linking together their appearances across multiple images taken over time. This approach faces limitations in detecting faint asteroids and handling the computational complexity of trajectory linking. We present a novel method that adapts ``digital tracking" - traditionally used for short-term linear asteroid motion across images - to work with large-scale synoptic surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time (Rubin/LSST). Our approach combines hundreds of sparse observations of individual asteroids across their non-linear orbital paths to enhance detection sensitivity by several magnitudes. To address the computational challenges of processing massive data sets and dense orbital phase spaces, we developed a specialized high-performance computing architecture. We demonstrate the effectiveness of our method through experiments that take advantage of the extensive computational resources at Lawrence Livermore National Laboratory. This work enables the detection of significantly fainter asteroids in existing and future survey data, potentially increasing the observable asteroid population by orders of magnitude across different orbital families, from near-Earth objects (NEOs) to Kuiper belt objects (KBOs).
These notes review a description of quantum mechanics in terms of the topology of spaces, basing on the axioms of Topological Quantum Field Theory and path integral formalism. In this description quantum states and operators are encoded by the topology of spaces that are used as modules to build the quantum mechanical model, while expectation values and probabilities are given by topological invariants of spaces, knots and links. The notes focus on the specific way the topology encodes quantum mechanical features, or, equivalently, on how these features can be controlled through the topology. A topological classification of entanglement is discussed, as well as properties of entanglement entropy and basic quantum protocols. The primary aim is to build a less conventional diagrammatic intuition about quantum mechanics, expanding the paradigm of ``Quantum Picturalism".
2025-03-11
Darwish Ahmad Herati, Maria Clara Aderne, Fabio Kon
This manuscript focuses on the environmental, social, and individual sustainability dimensions within the modern software development lifecycle, aiming to establish a holistic approach termed Sustainable DevOps (SusDevOps). Moving beyond the already well-researched economic and technical aspects, our approach to SusDevOps emphasizes the importance of minimizing environmental impacts, fostering social inclusion, and supporting individual well-being in software engineering practices. We highlight some key challenges in incorporating these dimensions, such as reducing ecological footprints, promoting workforce inclusion, and addressing the individual well-being of developers. We plan to adopt a structured approach incorporating systematic literature reviews, surveys, and interviews to deepen our understanding, identify gaps, and evolve actionable, sustainable practices within the DevOps community. Collectively, these initiatives can contribute to a more sustainable software engineering ecosystem.
Relighting the fire in Hickson Compact Group (HCG) 15: magnetised fossil plasma revealed by the SKA Pathfinders & Precursors
2025-03-11
C. J. Riseley, T. Vernstrom, L. Lovisari, E. O'Sullivan, F. Gastaldello, M. Brienza, Prasanta K. Nayak, A. Bonafede, E. Carretti, S. W. Duchesne, S. Giacintucci, A. M. Hopkins, B. S. Koribalski, F. Loi, C. Pfrommer, W. Raja, K. Ross, K. Rubinur, M. Ruszkowski, T. W. Shimwell, M. S. de Villiers, J. West, H. R. M. Zovaro, T. Akahori, C. S. Anderson, D. J. Bomans, A. Drabent, L. Rudnick, R. Santra
In the context of the life cycle and evolution of active galactic nuclei (AGN), the environment plays an important role. In particular, the over-dense environments of galaxy groups, where dynamical interactions and bulk motions have significant impact, offer an excellent but under-explored window into the life cycles of AGN and the processes that shape the evolution of relativistic plasma. Pilot Survey observations with the Australian Square Kilometre Array Pathfinder (ASKAP) Evolutionary Map of the Universe (EMU) survey recovered diffuse emission associated with the nearby (z = 0.0228) galaxy group HCG15, which was revealed to be strongly linearly polarised. We study the properties of this emission in unprecedented detail to settle open questions about its nature and its relation to the group-member galaxies. We perform a multi-frequency spectropolarimetric study of HCG15 incorporating our ASKAP EMU observations as well as new data from MeerKAT, LOFAR, the GMRT, and the Karl G. Jansky Very Large Array (VLA), plus X-ray data from XMM-Newton and optical spectra from the Himalayan Chandra Telescope (HCT). Our study confirms that the diffuse structure represents remnant emission from historic AGN activity, likely associated with HCG15-D, some 80-86 Myr ago (based on ageing analysis). We detect significant highly linearly-polarised emission from a diffuse 'ridge'-like structure with a highly ordered magnetic field. Our analysis suggests that this emission is generated by draping of magnetic field lines in the intra-group medium (IGrM), although further exploration with simulations would aid our understanding. We confirm that HCG15-C is a group-member galaxy. Finally, we report the detection of thermal emission associated with a background cluster at redshift z ~ 0.87 projected onto the IGrM of HCG15, which matches the position and redshift of the recent SZ detection of ACT-CL J0207.8+0209.
A Critical Analysis of the Usage of Dimensionality Reduction in Four Domains
2025-03-11
Dylan Cashman, Mark Keller, Hyeon Jeon, Bum Chul Kwon, Qianwen Wang
Dimensionality reduction is used as an important tool for unraveling the complexities of high-dimensional datasets in many fields of science, such as cell biology, chemical informatics, and physics. Visualizations of the dimensionally reduced data enable scientists to delve into the intrinsic structures of their datasets and align them with established hypotheses. Visualization researchers have thus proposed many dimensionality reduction methods and interactive systems designed to uncover latent structures. At the same time, different scientific domains have formulated guidelines or common workflows for using dimensionality reduction techniques and visualizations for their respective fields. In this work, we present a critical analysis of the usage of dimensionality reduction in scientific domains outside of computer science. First, we conduct a bibliometric analysis of 21,249 academic publications that use dimensionality reduction to observe differences in the frequency of techniques across fields. Next, we conduct a survey of a 71-paper sample from four fields: biology, chemistry, physics, and business. Through this survey, we uncover common workflows, processes, and usage patterns, including the mixed use of confirmatory data analysis to validate a dataset and projection method and exploratory data analysis to then generate more hypotheses. We also find that misinterpretations and inappropriate usage is common, particularly in the visual interpretation of the resulting dimensionally reduced view. Lastly, we compare our observations with recent works in the visualization community in order to match work within our community to potential areas of impact outside our community.
Beyond Diagonal RIS-Aided Wireless Communications Systems: State-of-the-Art and Future Research Directions
2025-03-11
Omar Maraqa, Majid H. Khoshafa, Olutayo O. Oyerinde, Telex M. N. Ngatched
Integrating BD-RIS into wireless communications systems has attracted significant interest due to its transformative potential in enhancing system performance. This survey provides a comprehensive analysis of BD-RIS technology, examining its modeling, structural characteristics, and network integration while highlighting its advantages over traditional diagonal RIS. Specifically, we review various BD-RIS modeling approaches, including multiport network theory, graph theory, and matrix theory, and emphasize their application in diverse wireless scenarios. The survey also covers BD-RIS's structural diversity, including different scattering matrix types, transmission modes, intercell architectures, and circuit topologies, showing their flexibility in improving network performance. We delve into the potential applications of BD-RIS, such as enhancing wireless coverage, improving PLS, enabling multi-cell interference cancellation, improving precise sensing and localization, and optimizing channel manipulation. Further, we explore BD-RIS architectural development, providing insights into new configurations focusing on channel estimation, optimization, performance analysis, and circuit complexity perspectives. Additionally, we investigate the integration of BD-RIS with emerging wireless technologies, such as millimeter-wave and terahertz communications, integrated sensing and communications, mobile edge computing, and other cutting-edge technologies. These integrations are pivotal in advancing the capabilities and efficiency of future wireless networks. Finally, the survey identifies key challenges, including channel state information estimation, interference modeling, and phase-shift designs, and outlines future research directions. The survey aims to provide valuable insights into BD-RIS's potential in shaping the future of wireless communications systems.
The COSMOS-Web Lens Survey (COWLS) III: forecasts versus data
2025-03-11
Natalie B. Hogg, James W. Nightingale, Quihan He, Jacqueline McCleary, Guillaume Mahler, Aristeidis Amvrosiadis, Ghassem Gozaliasl, Edward Berman, Richard J. Massey, Diana Scognamiglio, Maximilien Franco, Daizhong Liu, Marko Shuntov, Louise Paquereau, Olivier Ilbert, Natalie Allen, Sune Toft, Hollis B. Akins, Caitlin M. Casey, Jeyhan S. Kartaltepe, Anton M. Koekemoer, Henry Joy McCracken, Jason D. Rhodes, Brant E. Robertson, Nicole E. Drakos, Andreas L. Faisst, Hossein Hatamnia, Sophie L. Newman
We compare forecasts for the abundance and properties of strong gravitational lenses in the COSMOS-Web survey, a $0.54$ deg$^2$ survey of the COSMOS field using the NIRCam and MIRI instruments aboard JWST, with the first catalogue of strong lens candidates identified in the observed NIRCam data, COWLS. We modify the lenspop package to produce a forecast for strong lensing in COSMOS-Web. We add a new mock galaxy catalogue to use as the source population, as well as the COSMOS-Web survey specifications, including the transmission data for the four NIRCam filters used. We forecast 107 strong lenses can be detected in COSMOS-Web across all bands, assuming complete subtraction of the lens galaxy light. The majority of the lenses are forecast to have small Einstein radii ($\theta_{\rm E} < 1$ arcsecond) and lie at redshifts between $0 < z <2$, whilst the source redshift distribution peaks at $z\sim 3$ and has a long tail extending up to $z \sim 11$, unambiguously showing that strong lensing in JWST can probe the entirety of the epoch of reionisation. We compare our forecast with the distributions of Einstein radii, lens photometric redshifts, and lens and source magnitudes in the observed lenses, finding that whilst the forecast and observed Einstein radii distributions match, the redshifts and magnitudes do not. The observed lens redshift distribution peaks at a slightly lower redshift than the forecast one, whilst the lens magnitudes are systematically brighter in the observed data than in the forecast. Assuming the validity of the lenspop forecast, this finding motivates further inspection of the COWLS data to search for the as-yet undiscovered population of faint, high-redshift lenses.
The COSMOS-Web Lens Survey (COWLS) II: depth, resolution, and NIR coverage from JWST reveal 17 spectacular lenses
2025-03-11
Guillaume Mahler, James W. Nightingale, Natalie B. Hogg, Ghassem Gozaliasl, Jacqueline McCleary, Qiuhan He, Edward Berman, Maximilien Franco, Daizhong Liu, Richard J. Massey, Wilfried Mercier, Diana Scognamiglio, Marko Shuntov, Maximilian von Wietersheim-Kramsta, Louise Paquereau, Olivier Ilbert, Natalie Allen, Sune Toft, Hollis B. Akins, Caitlin M. Casey, Jeyhan S. Kartaltepe, Anton M. Koekemoer, Henry Joy McCracken, Jason D. Rhodes, Brant E. Robertson, Jorge A. Zavala, Nicole E. Drakos, Andreas L. Faisst, Georgios E. Magdis, Shuowen Jin
The COSMOS-Web Lens Survey (COWLS) presents the first systematic search for strong gravitational lenses in the COSMOS-Web field using data from the \textit{James Webb} Space Telescope (\textit{JWST}). Using high-resolution NIRCam imaging, we visually inspected over 42\,660 galaxies and identified over 400 lensing candidates. From this sample and based on \textit{JWST}/NIRCam imaging only, we report here the 17 most obvious and spectacular strong lensing systems. These lenses, characterised by large Einstein rings and arcs and their distinct lens and source colours, were found through only the visual inspection of the lens-light-subtracted image data and were immediately visible due to their spectacular appearance. We showcase how spectacular strong lenses are at the extremes of lens parameter space. Their exceptionally high signal-to-noise, multi-wavelength imaging enables unprecedented lensing analysis, including `\textit{HST}-dark' source galaxies that are also invisible in the deeper bluer \textit{JWST} wavebands, enabling clean deblending between the lens and the source. Sources may exhibit dramatic morphological changes across wavelengths, and dust absorption within lenses may be detectable by eye. No other instrument, including the \textit{Hubble} Space Telescope, can discover or image such lenses with comparable detail. We estimate that \textit{JWST} uncovers a new spectacular lens approximately every 10 to 12 NIRCam pointings, suggesting that over 40 such lenses remain undetected within its first three years of observations. All COWLS data is publicly available on GitHub.
The COSMOS-Web Lens Survey (COWLS) I: Discovery of >100 high redshift strong lenses in contiguous JWST imaging
2025-03-11
James Nightingale, Guillaume Mahler, Jacqueline McCleary, Qiuhan He, Natalie B. Hogg, Aristeidis Amvrosiadis, Ghassem Gozaliasl, Wilfried Mercier, Diana Scognamiglio, Edward Berman, Gavin Leroy, Daizhong Liu, Richard J. Massey, Marko Shuntov, Maximilian von Wietersheim-Kramsta, Maximilien Franco, Louise Paquereau, Olivier Ilbert, Natalie Allen, Sune Toft, Hollis B. Akins, Caitlin M. Casey, Jeyhan S. Kartaltepe, Anton M. Koekemoer, Henry Joy McCracken, Jason D. Rhodes, Brant E. Robertson, Nicole E. Drakos, Andreas L. Faisst, Shuowen Jin
We present the COSMOS-Web Lens Survey (COWLS), a sample of over 100 strong lens candidates from the $0.54$\,deg$^2$ COSMOS-Web survey, discovered using exquisite James Webb Space Telescope (JWST) imaging across four wavebands. Following two rounds of visual inspection, over 100 candidates were ranked as `high confidence' or `likely' by at least $50\%$ of inspectors. The COWLS sample has several notable properties: (i) magnified source galaxies spanning redshifts $z \sim 0.1$ to $z \sim 9$, which therefore extend into the epoch of reionisation; (ii) the highest-redshift lens galaxies known, pushing galaxy density profile evolution studies beyond $z \sim 2$; (iii) all lenses are distributed within a contiguous $0.54$\,deg$^2$ region, allowing for joint strong and weak lensing analyses; and (iv) a subset exhibits lensed source emission ray-traced near the lens galaxy centers, enabling studies of supermassive black holes and dust absorption. A key innovation of our approach is the use of lens modelling to aid in identifying lenses that may otherwise be missed. This paper is accompanied by the first COWLS public release, providing JWST NIRCam imaging in four bands, lens models, pixelized source reconstructions and lens redshift estimates : https://github.com/Jammy2211/COWLS_COSMOS_Web_Lens_Survey
Southern binaries with the Zorro Speckle Camera @ Gemini-South
2025-03-11
Rene A. Mendez, Andrei Tokovinin, Edgardor Costa, Max Dirk
We present measurements in the context of a survey of southern hemisphere binary and multiple stellar systems observed with the Zorro Speckle dual diffraction-limited optical imaging camera on the 8.1m Gemini-South telescope carried out between 2019 and 2023. The overall motivation of our survey, as well as some initial results of these observations, are outlined to demonstrate the capabilities - and limitations - of Zorro. We report on the astrometric characterization of the instrument in terms of the precision and accuracy of our measurements and provide details of our custom-made data reduction pipeline. For targets with separations smaller than 0.4 arcsec, an overall precision of 1 mas in the radial and tangential directions is obtained, while the uncertainty in position angle is 0.2 deg. Relative astrometry and contrast brightness in the two Zorro filters at 562 and 832 nm are reported for 70 pairs on 64 distinct systems (six are triples). Eleven new binaries are found, mostly of small separations (down to 15 mas), and large brightness contrast (up to Delta m=6 in the red channel). Our results indicate that the Zorro instrument, when properly calibrated, delivers excellent quality data for visual binary studies of tight and/or faint companions.
Beyond the heat: The mental health toll of temperature and humidity in India
2025-03-11
Manuela Fritz
Evidence on the heat-mental health nexus remains mixed. I show that this can be partly explained by previous studies focusing solely on temperature while neglecting temperature-humidity interactions. Using a measure that considers both indicators (wet bulb temperature), I assess the causal link between extreme heat and mental health, and its heterogeneity across socioeconomic indicators. I combine self-reported depression and anxiety levels from three Indian WHO-SAGE survey waves with climate data, leveraging quasi-random variation in heat exposure due to survey timing and location. The results reveal that extreme heat increases the risk of depression but not of anxiety. Importantly, these effects are consistently smaller when humidity is not considered. Finally, the study provides evidence that the District Mental Health Program plays a protective role in mitigating adverse mental health effects. The findings suggest that the costs induced by climate change need to account for the economic consequences of deteriorated mental health.
Automatic welding detection by an intelligent tool pipe inspection
2025-03-11
C J Arizmendi, W L Garcia, M A Quintero
This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called smart pig in Oil and Gas pipelines . The model uses a signal noise reduction phase by means of preprocessing algorithms and attributeselection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent
In silico clinical trials in drug development: a systematic review
2025-03-11
Bohua Chen, Lucia Chantal Schneider, Christian Roever, Emmanuelle Comets, Markus Christian Elze, Andrew Hooker, Joanna IntHout, Anne-Sophie Jannot, Daria Julkowska, Yanis Mimouni, Marina Savelieva, Nigel Stallard, Moreno Ursino, Marc Vandemeulebroecke, Sebastian Weber, Martin Posch, Sarah Zohar, Tim Friede
In the context of clinical research, computational models have received increasing attention over the past decades. In this systematic review, we aimed to provide an overview of the role of so-called in silico clinical trials (ISCTs) in medical applications. Exemplary for the broad field of clinical medicine, we focused on in silico (IS) methods applied in drug development, sometimes also referred to as model informed drug development (MIDD). We searched PubMed and ClinicalTrials.gov for published articles and registered clinical trials related to ISCTs. We identified 202 articles and 48 trials, and of these, 76 articles and 19 trials were directly linked to drug development. We extracted information from all 202 articles and 48 clinical trials and conducted a more detailed review of the methods used in the 76 articles that are connected to drug development. Regarding application, most articles and trials focused on cancer and imaging related research while rare and pediatric diseases were only addressed in 18 and 4 studies, respectively. While some models were informed combining mechanistic knowledge with clinical or preclinical (in-vivo or in-vitro) data, the majority of models were fully data-driven, illustrating that clinical data is a crucial part in the process of generating synthetic data in ISCTs. Regarding reproducibility, a more detailed analysis revealed that only 24% (18 out of 76) of the articles provided an open-source implementation of the applied models, and in only 20% of the articles the generated synthetic data were publicly available. Despite the widely raised interest, we also found that it is still uncommon for ISCTs to be part of a registered clinical trial and their application is restricted to specific diseases leaving potential benefits of ISCTs not fully exploited.
Membership Inference Attacks (MIAs) aim to estimate whether a specific data point was used in the training of a given model. Previous attacks often utilize multiple reference models to approximate the conditional score distribution, leading to significant computational overhead. While recent work leverages quantile regression to estimate conditional thresholds, it fails to capture epistemic uncertainty, resulting in bias in low-density regions. In this work, we propose a novel approach - Bayesian Membership Inference Attack (BMIA), which performs conditional attack through Bayesian inference. In particular, we transform a trained reference model into Bayesian neural networks by Laplace approximation, enabling the direct estimation of the conditional score distribution by probabilistic model parameters. Our method addresses both epistemic and aleatoric uncertainty with only a reference model, enabling efficient and powerful MIA. Extensive experiments on five datasets demonstrate the effectiveness and efficiency of BMIA.
A Review on Geometry and Surface Inspection in 3D Concrete Printing
2025-03-10
K. Mawas, M. Maboudi, M. Gerke
Given the substantial growth in the use of additive manufacturing in construction (AMC), it is necessary to ensure the quality of printed specimens which can be much more complex than conventionally manufactured parts. This study explores the various aspects of geometry and surface quality control for 3D concrete printing (3DCP), with a particular emphasis on deposition-based methods, namely extrusion and shotcrete 3D printing (SC3DP). A comprehensive overview of existing quality control (QC) methods and strategies is provided and preceded by an in-depth discussion. Four categories of data capture technologies are investigated and their advantages and limitations in the context of AMC are discussed. Additionally, the effects of environmental conditions and objects' properties on data capture are also analyzed. The study extends to automated data capture planning methods for different sensors. Furthermore, various quality control strategies are explored across different stages of the fabrication cycle of the printed object including: (i) During printing, (ii) Layer-wise, (iii) Preassembly, and (iv) Assembly. In addition to reviewing the methods already applied in AMC, we also address various research gaps and future trends and highlight potential methodologies from adjacent domains that could be transferred to AMC.
On the completeness and reliability of visual source extraction : an examination of eight thousand data cubes by eye
2025-03-10
Rhys Taylor
[Edited for arXiv] Source extraction in HI radio surveys is still often performed using visual inspection, but the efficacy of such procedures lacks rigorous quantitative assessment due to their laborious nature. Algorithmic methods are often preferred due to their repeatable results and speed. I here quantitatively assess visual source extraction using a large sample of artificial sources and a comparatively rapid source extraction tool, and compare the results with those from automatic techniques. I injected 4,232 sources into a total of 8,500 emission-free data cubes, with at most one source per cube. Sources covered a wide range of signal-to-noise and velocity widths. I blindly searched all cubes, measuring the completeness and reliability for pairs of signal-to-noise and line width values. Smaller control tests were performed to account for the possible biases in the search, which gave results in good agreement with the main experiment. I also searched cubes injected with artificial sources using algorithmic extractors, and compare these results with a set of catalogues independently reported from real observational data. I find that the results of visual extraction follow a tight relation between integrated signal-to-noise and completeness. Visual extraction compares favourably in efficacy with the algorithmic methods, tending to recover a higher fraction of fainter sources. Visual source extraction can be a surprisingly rapid procedure which gives higher completeness levels than automatic techniques, giving predictable, quantifiable results which are not strongly subject to the whims of the observer. For recovering the faintest features, algorithmic extractors can be competitive with visual inspection but cannot yet out-perform it, though their advantage in speed can be a significant compensating factor.
Creating and Evaluating Privacy and Security Micro-Lessons for Elementary School Children
2025-03-10
Lan Gao, Elana B Blinder, Abigail Barnes, Kevin Song, Tamara Clegg, Jessica Vitak, Marshini Chetty
The growing use of technology in K--8 classrooms highlights a parallel need for formal learning opportunities aimed at helping children use technology safely and protect their personal information. Even the youngest students are now using tablets, laptops, and apps to support their learning; however, there are limited curricular materials available for elementary and middle school children on digital privacy and security topics. To bridge this gap, we developed a series of micro-lessons to help K--8 children learn about digital privacy and security at school. We first conducted a formative study by interviewing elementary school teachers to identify the design needs for digital privacy and security lessons. We then developed micro-lessons -- multiple 15-20 minute activities designed to be easily inserted into the existing curriculum -- using a co-design approach with multiple rounds of developing and revising the micro-lessons in collaboration with teachers. Throughout the process, we conducted evaluation sessions where teachers implemented or reviewed the micro-lessons. Our study identifies strengths, challenges, and teachers' tailoring strategies when incorporating micro-lessons for K--8 digital privacy and security topics, providing design implications for facilitating learning about these topics in school classrooms.
"Sighted People Have Their Pick Of The Litter": Unpacking The Need For Digital Mental Health (DMH) Tracking Services With And For The Blind Community
2025-03-10
Omar Khan, JooYoung Seo
The proliferation of digital mental health (DMH) tracking services promises personalized support, yet accessibility barriers limit equal access. This study investigates blind community experiences with DMH tracking services across the United States as a step toward inclusive health technology design. Working with blind advocacy organizations, we distributed a cross-sectional observational survey (n = 93) and analyzed open-ended responses using Norman and Skinner's eHealth Literacy framework. Our findings reveal significant challenges in navigation, content interpretation, and overall user experience, which impede the blind community's effective engagement with DMH tools. Results highlight the need for adaptive interfaces, accessible tracking strategies, and voice-guided interactions. These insights inform design recommendations for developers and policymakers, promoting more inclusive mental health technologies. By prioritizing accessibility, we make forward progress in ensuring that DMH tracking services fulfill their potential to support mental well-being across diverse user groups, fostering digital equality in mental health care.
Large value estimates in number theory, harmonic analysis, and computer science
2025-03-10
Larry Guth
We survey large value problems, including the large value problem for Dirichlet polynomials, the restriction problem, and problems from computer science. We describe known techniques and open problems, drawing on perspectives from all three fields.
Role of the isovector spin-orbit potential in mitigating the CREX-PREX dilemma
2025-03-10
Athul Kunjipurayil, Marc Salinas, J. Piekarewicz
Pioneering electroweak measurements of the neutron skin thickness in lead-208 and calcium-48 are challenging our understanding of nuclear dynamics. Many theoretical models suggest that the slope of the symmetry energy controls the development of a neutron skin in neutron-rich nuclei. This led to the expectation that if lead-208 exhibits a large neutron skin, calcium-48 should as well. Given that the PREX collaboration reported a relatively thick neutron skin in lead, we anticipated that calcium would also have a significant neutron skin. Instead, the CREX collaboration reported a thin neutron skin in calcium. Although many suggestions have been proposed, the ``CREX-PREX dilemma" remains unsolved. Recently, an intriguing scenario has emerged, suggesting that an enhanced isovector spin-orbit interaction could simultaneously account for both results. Following this approach, we performed relativistic mean-field calculations with an increased isovector spin-orbit potential. Our findings indicate that while this modification significantly affects the structure of calcium-48, it has only a marginal impact on lead-208, thereby bringing the results into better agreement with experiment. However, the strong enhancement required to mitigate the CREX-PREX dilemma destroys the agreement with a successful spin-orbit phenomenology, primarily by modifying the well-known ordering of spin-orbit partners.
Tools for Supergravity in the spin coframe formalism
2025-03-10
Filippo Fila-Robattino
This paper contains a review of the theoretical foundations of Clifford algebras, spinors and spinor bundles in the so-called co-frame formalism. A compact index-free notation is introduced, along with a series of identities useful for computations in supergravity theories.
In this work, we introduce a novel variant of the multivariate quadratic problem, which is at the core of one of the most promising post-quantum alternatives: multivariate cryptography. In this variant, the solution of a given multivariate quadratic system must also be regular, i.e. if it is split into multiple blocks of consecutive entries with the same fixed length, then each block has only one nonzero entry. We prove the NP-completeness of this variant and show similarities and differences with other computational problems used in cryptography. Then we analyze its hardness by reviewing the most common solvers for polynomial systems over finite fields, derive asymptotic formulas for the corresponding complexities and compare the different approaches.
AI Biases as Asymmetries: A Review to Guide Practice
2025-03-10
Gabriella Waters, Phillip Honenberger
The understanding of bias in AI is currently undergoing a revolution. Initially understood as errors or flaws, biases are increasingly recognized as integral to AI systems and sometimes preferable to less biased alternatives. In this paper, we review the reasons for this changed understanding and provide new guidance on two questions: First, how should we think about and measure biases in AI systems, consistent with the new understanding? Second, what kinds of bias in an AI system should we accept or even amplify, and what kinds should we minimize or eliminate, and why? The key to answering both questions, we argue, is to understand biases as "violations of a symmetry standard" (following Kelly). We distinguish three main types of asymmetry in AI systems-error biases, inequality biases, and process biases-and highlight places in the pipeline of AI development and application where bias of each type is likely to be good, bad, or inevitable.
VizTrust: A Visual Analytics Tool for Capturing User Trust Dynamics in Human-AI Communication
2025-03-10
Xin Wang, Stephanie Tulk Jesso, Sadamori Kojaku, David M Neyens, Min Sun Kim
Trust plays a fundamental role in shaping the willingness of users to engage and collaborate with artificial intelligence (AI) systems. Yet, measuring user trust remains challenging due to its complex and dynamic nature. While traditional survey methods provide trust levels for long conversations, they fail to capture its dynamic evolution during ongoing interactions. Here, we present VizTrust, which addresses this challenge by introducing a real-time visual analytics tool that leverages a multi-agent collaboration system to capture and analyze user trust dynamics in human-agent communication. Built on established human-computer trust scales-competence, integrity, benevolence, and predictability-, VizTrust enables stakeholders to observe trust formation as it happens, identify patterns in trust development, and pinpoint specific interaction elements that influence trust. Our tool offers actionable insights into human-agent trust formation and evolution in real time through a dashboard, supporting the design of adaptive conversational agents that responds effectively to user trust signals.
With the advancement of multi-robot technology, cooperative exploration tasks have garnered increasing attention. This paper presents a comprehensive review of multi-robot cooperative exploration systems. First, we review the evolution of robotic exploration and introduce a modular research framework tailored for multi-robot cooperative exploration. Based on this framework, we systematically categorize and summarize key system components. As a foundational module for multi-robot exploration, the localization and mapping module is primarily introduced by focusing on global and relative pose estimation, as well as multi-robot map merging techniques. The cooperative motion module is further divided into learning-based approaches and multi-stage planning, with the latter encompassing target generation, task allocation, and motion planning strategies. Given the communication constraints of real-world environments, we also analyze the communication module, emphasizing how robots exchange information within local communication ranges and under limited transmission capabilities. Finally, we discuss the challenges and future research directions for multi-robot cooperative exploration in light of real-world trends. This review aims to serve as a valuable reference for researchers and practitioners in the field.
Emulating the Non-Linear Matter Power-Spectrum in Mixed Axion Dark Matter Models
2025-03-10
Dennis Fremstad, Hans A. Winther
In order to constrain ultra light dark matter models with current and near future weak lensing surveys we need the predictions for the non-linear dark matter power-spectrum. This is commonly extracted from numerical simulations or from using semi-analytical methods. For ultra light dark matter models such numerical simulations are often very expensive due to the need of having a very low force-resolution often limiting them to very small simulation boxes which do not contain very large scales. In this work we take a different approach by relying on fast, approximate $N$-body simulations. In these simulations, axion physics are only included in the initial conditions, allowing us to run a large number of simulations with varying axion and cosmological parameters. From our simulation suite we use machine learning tools to create an emulator for the ratio of the dark matter power-spectrum in mixed axion models - models where dark matter is a combination of CDM and axion - to that of $\Lambda$CDM. The resulting emulator only needs to be combined with existing emulators for $\Lambda$CDM to be able to be used in parameter constraints. We compare the emulator to semi-analytical methods, but a more thorough test to full simulations to verify the true accuracy of this approach is not possible at the present time and is left for future work.
The classification of electrocardiogram (ECG) signals is crucial for early detection of arrhythmias and other cardiac conditions. However, despite advances in machine learning, many studies fail to follow standardization protocols, leading to inconsistencies in performance evaluation and real-world applicability. Additionally, hardware constraints essential for practical deployment, such as in pacemakers, Holter monitors, and wearable ECG patches, are often overlooked. Since real-world impact depends on feasibility in resource-constrained devices, ensuring efficient deployment is critical for continuous monitoring. This review systematically analyzes ECG classification studies published between 2017 and 2024, focusing on those adhering to the E3C (Embedded, Clinical, and Comparative Criteria), which include inter-patient paradigm implementation, compliance with Association for the Advancement of Medical Instrumentation (AAMI) recommendations, and model feasibility for embedded systems. While many studies report high accuracy, few properly consider patient-independent partitioning and hardware limitations. We identify state-of-the-art methods meeting E3C criteria and conduct a comparative analysis of accuracy, inference time, energy consumption, and memory usage. Finally, we propose standardized reporting practices to ensure fair comparisons and practical applicability of ECG classification models. By addressing these gaps, this study aims to guide future research toward more robust and clinically viable ECG classification systems.
Non-terrestrial networks (NTNs) are emerging as a core component of future 6G communication systems, providing global connectivity and supporting data-intensive applications. In this paper, we propose a distributed hierarchical federated learning (HFL) framework within the NTN architecture, leveraging a high altitude platform station (HAPS) constellation as intermediate distributed FL servers. Our framework integrates both low-Earth orbit (LEO) satellites and ground clients in the FL training process while utilizing geostationary orbit (GEO) and medium-Earth orbit (MEO) satellites as relays to exchange FL global models across other HAPS constellations worldwide, enabling seamless, global-scale learning. The proposed framework offers several key benefits: (i) enhanced privacy through the decentralization of the FL mechanism by leveraging the HAPS constellation, (ii) improved model accuracy and reduced training loss while balancing latency, (iii) increased scalability of FL systems through ubiquitous connectivity by utilizing MEO and GEO satellites, and (iv) the ability to use FL data, such as resource utilization metrics, to further optimize the NTN architecture from a network management perspective. A numerical study demonstrates the proposed framework's effectiveness, with improved model accuracy, reduced training loss, and efficient latency management. The article also includes a brief review of FL in NTNs and highlights key challenges and future research directions.
Optomechanical non-Gaussian quantum steering and remote preparation of large-size motional Schördinger cat states
2025-03-10
Miaomiao Wei, Huatang Tan
In this paper, we present a scheme for remotely generating large-size motional Schr\"{o}dinger cat states in cavity optomechanical (OM) systems with non-Gaussian quantum steering of continuous variables. We consider that the output field from the OM cavity undergoes three typical kinds of multiphoton operations: multiphoton subtraction, multiphoton addition, or multiphoton catalysis, followed by homodyne detection. We first demonstrate that these multiphoton operations can lead to non-Gaussian OM quantum steerable correlations, which are unveiled by the subsequent homodyne detection with a Fisher-information-based steering criterion. It is found that the non-Gaussian steering is obviously enhanced with an increasing number $n$ of photons in the multiphoton operations, which, as we show, fails to be revealed with the well-known Reid's steering criterion. It therefore suggests that the Fisher-information-based criterion is more effective for witnessing non-Gaussian quantum steering. We next show that the strong OM steering enables the remote preparation of large-size Schr\"{o}dinger odd or even cat states of the mechanical oscillator by the homodyne detection. Accordingly, the amplitudes of the cat states also increase significantly with the photon number $n$, particularly in the cases of multiphoton subtraction and addition. Our results reveal the properties of non-Gaussian steering generated by multiphoton operations, and the large cat states of macroscopic mechanical resonators hold promise for fundamental tests in quantum mechanics and practical applications in quantum science.
Multi-Modal 3D Mesh Reconstruction from Images and Text
2025-03-10
Melvin Reka, Tessa Pulli, Markus Vincze
6D object pose estimation for unseen objects is essential in robotics but traditionally relies on trained models that require large datasets, high computational costs, and struggle to generalize. Zero-shot approaches eliminate the need for training but depend on pre-existing 3D object models, which are often impractical to obtain. To address this, we propose a language-guided few-shot 3D reconstruction method, reconstructing a 3D mesh from few input images. In the proposed pipeline, receives a set of input images and a language query. A combination of GroundingDINO and Segment Anything Model outputs segmented masks from which a sparse point cloud is reconstructed with VGGSfM. Subsequently, the mesh is reconstructed with the Gaussian Splatting method SuGAR. In a final cleaning step, artifacts are removed, resulting in the final 3D mesh of the queried object. We evaluate the method in terms of accuracy and quality of the geometry and texture. Furthermore, we study the impact of imaging conditions such as viewing angle, number of input images, and image overlap on 3D object reconstruction quality, efficiency, and computational scalability.
Lawful and Accountable Personal Data Processing with GDPR-based Access and Usage Control in Distributed Systems
2025-03-10
L. Thomas van Binsbergen, Marten C. Steketee, Milen G. Kebede, Heleen L. Janssen, Tom M. van Engers
Compliance with the GDPR privacy regulation places a significant burden on organisations regarding the handling of personal data. The perceived efforts and risks of complying with the GDPR further increase when data processing activities span across organisational boundaries, as is the case in both small-scale data sharing settings and in large-scale international data spaces. This paper addresses these concerns by proposing a case-generic method for automated normative reasoning that establishes legal arguments for the lawfulness of data processing activities. The arguments are established on the basis of case-specific legal qualifications made by privacy experts, bringing the human in the loop. The obtained expert system promotes transparency and accountability, remains adaptable to extended or altered interpretations of the GDPR, and integrates into novel or existing distributed data processing systems. This result is achieved by defining a formal ontology and semantics for automated normative reasoning based on an analysis of the purpose-limitation principle of the GDPR. The ontology and semantics are implemented in eFLINT, a domain-specific language for specifying and reasoning with norms. The XACML architecture standard, applicable to both access and usage control, is extended, demonstrating how GDPR-based normative reasoning can integrate into (existing, distributed) systems for data processing. The resulting system is designed and critically assessed in reference to requirements extracted from the GPDR.
Analysing the flux stability of stellar calibrator candidates with TESS
2025-03-10
Elena Tonucci, Tim van Kempen, Jean-Philippe Beaulieu, Lilou Bernard
The ESA space mission Ariel requires bright sources that are stable at the level of 100ppm over 6 hours in order to accurately measure exoplanet atmospheres through transmission spectroscopy. To ensure this, in-flight instrument calibration can be performed by observing stellar calibrators. In this study, a stellar calibrator candidate list distributed over the sky is created and a flux variability analysis is performed to identify the best stellar calibrators for transit spectroscopy of exoplanet atmospheres with Ariel. A starting candidate sample of 1937 solar-type stars is created using the all-sky surveys Two Micron All Sky Survey and Gaia. Using stellar light curves from the Transit Exoplanet Survey Satellite (TESS), the flux variability of each star is characterised by computing its Lomb-Scargle periodogram and reduced chi-squared. This enables the elimination of stars with detectable variability from the sample. Approximately 22.2% of stars from the starting sample pass the selection as potential calibrators. These do not all necessarily meet Ariel's stability requirement, although some will. No correlation between flux stability and stellar properties is found, as long as the correct value ranges for the parameters are chosen, like a surface temperature between 5000 and 6300K. The only exception is stellar magnitude: Noise in TESS data increases as stars get dimmer, so, a high percentage of faint stars passes the selection since their variability is more likely hidden within the inherent TESS noise. Contrarily, stars brighter than 5mag cannot be used as calibrators. A list of 430 promising bright calibration targets distributed over the sky has been selected. These can potentially be used as stellar calibrators for the Ariel mission. Targets from this list will have to be further studied to determine which ones possess a flux stability better than 100ppm over 6 hours.
The integration of generative artificial intelligence (GenAI) into transportation planning has the potential to revolutionize tasks such as demand forecasting, infrastructure design, policy evaluation, and traffic simulation. However, there is a critical need for a systematic framework to guide the adoption of GenAI in this interdisciplinary domain. In this survey, we, a multidisciplinary team of researchers spanning computer science and transportation engineering, present the first comprehensive framework for leveraging GenAI in transportation planning. Specifically, we introduce a new taxonomy that categorizes existing applications and methodologies into two perspectives: transportation planning tasks and computational techniques. From the transportation planning perspective, we examine the role of GenAI in automating descriptive, predictive, generative, simulation, and explainable tasks to enhance mobility systems. From the computational perspective, we detail advancements in data preparation, domain-specific fine-tuning, and inference strategies, such as retrieval-augmented generation and zero-shot learning tailored to transportation applications. Additionally, we address critical challenges, including data scarcity, explainability, bias mitigation, and the development of domain-specific evaluation frameworks that align with transportation goals like sustainability, equity, and system efficiency. This survey aims to bridge the gap between traditional transportation planning methodologies and modern AI techniques, fostering collaboration and innovation. By addressing these challenges and opportunities, we seek to inspire future research that ensures ethical, equitable, and impactful use of generative AI in transportation planning.
Measuring the degree of clustering and diffusion of trans-Neptunian objects
2025-03-10
Gabriele Pichierri, Konstantin Batygin
The outer solar system is populated by a broad aggregate of minor bodies, which occupy orbits whose dynamical character ranges from long-term stable to rapidly diffusive. We investigate the chaotic properties of known distant trans-Neptunian objects (TNOs) by numerically integrating TNO clones and statistically analyzing their orbital diffusion. Comparing the measured diffusion with an analytical criterion yields a dynamically motivated separation into classes of stable, metastable and unstable objects. We then measure the level of clustering of the longitudes of perihelia and of the orbital poles, as functions of orbital distance and of their stability properties. Distant (meta)stable objects appear increasingly clustered in perihelion around $\varpi \sim 50^\circ$ for increasing semi-major axis, while the orbits of unstable objects are well described by two, roughly equally-populated groups of "clustered" and "anti-clustered" objects, with means around $\sim 25^\circ$ and $\sim 205^\circ$ respectively. We further find that, compared to the solar system's total angular momentum vector, the mean orbital poles of distant TNOs are significantly more misaligned for (meta)stable objects, while they remain roughly aligned for unstable objects. TNOs with intermediate orbital periods also appear to be misaligned with respect to the forced plane predicted by secular theory with the known planets. This gradation based on stability, if validated further by the upcoming VRO survey, necessitates a dynamical explanation.
MRCEval: A Comprehensive, Challenging and Accessible Machine Reading Comprehension Benchmark
2025-03-10
Shengkun Ma, Hao Peng, Lei Hou, Juanzi Li
Machine Reading Comprehension (MRC) is an essential task in evaluating natural language understanding. Existing MRC datasets primarily assess specific aspects of reading comprehension (RC), lacking a comprehensive MRC benchmark. To fill this gap, we first introduce a novel taxonomy that categorizes the key capabilities required for RC. Based on this taxonomy, we construct MRCEval, an MRC benchmark that leverages advanced Large Language Models (LLMs) as both sample generators and selection judges. MRCEval is a comprehensive, challenging and accessible benchmark designed to assess the RC capabilities of LLMs thoroughly, covering 13 distinct RC skills with a total of 2.1K high-quality multi-choice questions. We perform an extensive evaluation of 28 widely used open-source and proprietary models, highlighting that MRC continues to present significant challenges even in the era of LLMs.
Strat{é}gies de contr{ô}le pour les {é}oliennes flottantes : {é}tat de l'art et perspectives
2025-03-10
Flavie Didier, Salah Laghrouche, Daniel Depernet
The floating wind turbines sector has great energy potential. However, minimizing the movement of the structure under the combined effect of wind and waves while ensuring maximum power extraction over a wide operating range is one of the main challenges for the control of these turbines. This paper presents a review of control methods for floating wind turbines from the recent literature. The limitations of these controllers are discussed, before introducing a presentation of several promising data-based methods. In particular, this paper focuses on artificial intelligence techniques associated with data-based control methods. Finally, the CREATIF project dealing with real-time simulation of floating wind turbines and their intelligent controls is presented.
A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications
2025-03-10
Siyuan Mu, Sen Lin
Artificial intelligence (AI) has achieved astonishing successes in many domains, especially with the recent breakthroughs in the development of foundational large models. These large models, leveraging their extensive training data, provide versatile solutions for a wide range of downstream tasks. However, as modern datasets become increasingly diverse and complex, the development of large AI models faces two major challenges: (1) the enormous consumption of computational resources and deployment difficulties, and (2) the difficulty in fitting heterogeneous and complex data, which limits the usability of the models. Mixture of Experts (MoE) models has recently attracted much attention in addressing these challenges, by dynamically selecting and activating the most relevant sub-models to process input data. It has been shown that MoEs can significantly improve model performance and efficiency with fewer resources, particularly excelling in handling large-scale, multimodal data. Given the tremendous potential MoE has demonstrated across various domains, it is urgent to provide a comprehensive summary of recent advancements of MoEs in many important fields. Existing surveys on MoE have their limitations, e.g., being outdated or lacking discussion on certain key areas, and we aim to address these gaps. In this paper, we first introduce the basic design of MoE, including gating functions, expert networks, routing mechanisms, training strategies, and system design. We then explore the algorithm design of MoE in important machine learning paradigms such as continual learning, meta-learning, multi-task learning, and reinforcement learning. Additionally, we summarize theoretical studies aimed at understanding MoE and review its applications in computer vision and natural language processing. Finally, we discuss promising future research directions.
ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions -XXI. A Large-sample Observational Study of Ethanol and Dimethyl Ether in Hot Cores
2025-03-10
Zhiping Kou, Xiaohu Li, Sheng-Li Qin, Tie Liu, E. Mannfors, Xindi Tang, Prasanta Gorai, Guido Garay, Swagat R. Das, Pablo García, Leonardo Bronfman, M. Juvela, Li Chen, Xunchuan Liu, Patricio Sanhueza, Yaping Peng, Long-Fei Chen, Jiahang Zou, Dongting Yang, L. Viktor Tóth, Lokesh Dewangan, Hong-Li Liu, James O. Chibueze, Ziyang Li
Hot cores, as a stage of massive star formation, exhibit abundant line emissions of COMs. We present a deep line survey of two isomers of C$_2$H$_6$O: ethanol (C$_2$H$_5$OH; EA), and dimethyl ether (CH$_3$OCH$_3$; DE) as well as their possible precursor CH$_3$OH towards 60 hot cores by using the ALMA 3 mm line observations. EA is detected in 40 hot cores and DE is detected in 59 hot cores. Of these, EA and DE are simultaneously detected in 39 hot cores. We calculate rotation temperatures and column densities of EA and DE by using the XCLASS software. The average rotation temperature of EA is higher than that of DE, whereas the average column density of EA is lower than that of DE. Combined with previous studies of hot cores and hot corinos, we find strong column density correlations among EA and DE ($\rho$ = 0.92), EA and CH$_3$OH ($\rho$ = 0.82), as well as DE and CH$_3$OH ($\rho$ = 0.80). The column density ratios of EA/DE versus the column densities of CH$_3$OH remain nearly constant with values within ~ 1 order of magnitude. These strong correlations and the stable ratios, suggest that EA, DE, and CH$_3$OH could be chemically linked, with CH$_3$OH potentially serving as a precursor for EA and DE. Compared with chemical models, the three different warm-up timescale models result in the systematic overproduction of EA and the systematic underproduction of DE. Therefore, our large sample observations can provide crucial constraints on chemical models.
A Comprehensive Survey on Magnetic Resonance Image Reconstruction
2025-03-10
Xiaoyan Kui, Zijie Fan, Zexin Ji, Qinsong Li, Chengtao Liu, Weixin Si, Beiji Zou
Magnetic resonance imaging (MRI) reconstruction is a fundamental task aimed at recovering high-quality images from undersampled or low-quality MRI data. This process enhances diagnostic accuracy and optimizes clinical applications. In recent years, deep learning-based MRI reconstruction has made significant progress. Advancements include single-modality feature extraction using different network architectures, the integration of multimodal information, and the adoption of unsupervised or semi-supervised learning strategies. However, despite extensive research, MRI reconstruction remains a challenging problem that has yet to be fully resolved. This survey provides a systematic review of MRI reconstruction methods, covering key aspects such as data acquisition and preprocessing, publicly available datasets, single and multi-modal reconstruction models, training strategies, and evaluation metrics based on image reconstruction and downstream tasks. Additionally, we analyze the major challenges in this field and explore potential future directions.
JWST ASPIRE: How Did Galaxies Complete Reionization? Evidence for Excess IGM Transmission around ${\rm [O\,{\scriptstyle III}]}$ Emitters during Reionization
2025-03-10
Koki Kakiichi, Xiangyu Jin, Feige Wang, Romain A. Meyer, Enrico Garaldi, Sarah E. I. Bosman, Frederick B. Davies, Xiaohui Fan, Maxime Trebitsch, Jinyi Yang, Eduardo Bañados, Jaclyn B. Champagne, Anna-Christina Eilers, Joseph F. Hennawi, Fengwu Sun, Yunjing Wu, Siwei Zou, Rahul Kannan, Aaron Smith, George D. Becker, Valentina D'Odorico, Thomas Connor, Weizhe Liu, Klaudia Protušová, Fabian Walter, Huanian Zhang
The spatial correlation between galaxies and the Ly$\alpha$ forest provides insights into how galaxies reionized the Universe. Here, we present initial results on the spatial cross-correlation between [OIII] emitters and Ly$\alpha$ forest at 5.46.5 QSO fields. Using data from five QSO fields, we find $2\sigma$ evidence for excess Ly$\alpha$ forest transmission at ~20-40 cMpc around [OIII] emitters at z=5.86, indicating that [OIII] emitters reside within a highly ionized IGM. At smaller scales, the Ly$\alpha$ forest is preferentially absorbed, suggesting gas overdensities around [OIII] emitters. Comparing with models including THESAN simulations, we interpret the observed cross-correlation as evidence for significant large-scale fluctuations of the IGM and the late end of reionization at z<6, characterized by ionized bubbles over 50 cMpc around [OIII] emitters. The required UV background necessitates an unseen population of faint galaxies around the [OIII] emitters. Furthermore, we find that the number of observed [OIII] emitters near individual transmission spikes is insufficient to sustain reionization in their surroundings, even assuming all [OIII] emitters harbour AGN with 100 % LyC escape fractions. Despite broad agreement, a careful analysis of ASPIRE and THESAN, using the observed host halo mass from the clustering of [OIII] emitters, suggests that the simulations underpredict the observed excess IGM transmission around [OIII] emitters, challenging our model of reionization. Potential solutions include larger ionized bubbles at z<6, more enhanced large-scale UV background or temperature fluctuations of the IGM, and possibly a patchy early onset of reionization at z>10. Current observational errors are dominated by cosmic variance, meaning future analyses of more QSO fields from JWST will improve the results.
Generative method for aerodynamic optimization based on classifier-free guided denoising diffusion probabilistic model
2025-03-10
Shisong Deng, Qiang Zhang, Zhengyang Cai
Inverse design approach, which directly generates optimal aerodynamic shape with neural network models to meet designated performance targets, has drawn enormous attention. However, the current state-of-the-art inverse design approach for airfoils, which is based on generative adversarial network, demonstrates insufficient precision in its generating and training processes and struggles to reveal the coupling relationship among specified performance indicators. To address these issues, the airfoil inverse design framework based on the classifier-free guided denoising diffusion probabilistic model (CDDPM) is proposed innovatively in this paper. First, the CDDPM can effectively capture the correlations among specific performance indicators and, by adjusting the classifier-free guide coefficient, generate corresponding upper and lower surface pressure coefficient distributions based on designated pressure features. These distributions are then accurately translated into airfoil geometries through a mapping model. Experimental results using classical transonic airfoils as examples show that the inverse design based on CDDPM can generate a variety of pressure coefficient distributions, which enriches the diversity of design results. Compared with current state-of-the-art Wasserstein generative adversarial network methods, CDDPM achieves a 33.6% precision improvement in airfoil generating tasks. Moreover, a practical method to readjust each performance indicator value is proposed based on global optimization algorithm in conjunction with active learning strategy, aiming to provide rational value combination of performance indicators for the inverse design framework. This work is not only suitable for the airfoils design, but also has the capability to apply to optimization process of general product parts targeting selected performance indicators.
ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation
2025-03-10
Kaiyuan Liu, Youcheng Pan, Jing Li, Daojing He, Yang Xiang, Yexing Du, Tianrun Gao
Recently, LLM agents have made rapid progress in improving their programming capabilities. However, existing benchmarks lack the ability to automatically evaluate from users' perspective, and also lack the explainability of the results of LLM agents' code generation capabilities. Thus, we introduce ProjectEval, a new benchmark for LLM agents project-level code generation's automated evaluation by simulating user interaction. ProjectEval is constructed by LLM with human reviewing. It has three different level inputs of natural languages or code skeletons. ProjectEval can evaluate the generated projects by user interaction simulation for execution, and by code similarity through existing objective indicators. Through ProjectEval, we find that systematic engineering project code, overall understanding of the project and comprehensive analysis capability are the keys for LLM agents to achieve practical projects. Our findings and benchmark provide valuable insights for developing more effective programming agents that can be deployed in future real-world production.
This paper investigates the safety analysis and verification of nonlinear systems subject to high-relative-degree constraints and unknown disturbance. The closed-form solution of the high-order control barrier functions (HOCBF) optimization problem with and without a nominal controller is first provided, making it unnecessary to solve the quadratic program problem online and facilitating the analysis. Further, we introduce the concept of tunable input-to-state safety(ISSf), and a new tunable function in conjunction with HOCBF is provided. When combined with the existing ISSf theorem, produces controllers for constrained nonlinear systems with external disturbances. The theoretical results are proven and supported by numerical simulations.
Exploring Multimodal Perception in Large Language Models Through Perceptual Strength Ratings
2025-03-10
Jonghyun Lee, Dojun Park, Jiwoo Lee, Hoekeon Choi, Sung-Eun Lee
This study investigated the multimodal perception of large language models (LLMs), focusing on their ability to capture human-like perceptual strength ratings across sensory modalities. Utilizing perceptual strength ratings as a benchmark, the research compared GPT-3.5, GPT-4, GPT-4o, and GPT-4o-mini, highlighting the influence of multimodal inputs on grounding and linguistic reasoning. While GPT-4 and GPT-4o demonstrated strong alignment with human evaluations and significant advancements over smaller models, qualitative analyses revealed distinct differences in processing patterns, such as multisensory overrating and reliance on loose semantic associations. Despite integrating multimodal capabilities, GPT-4o did not exhibit superior grounding compared to GPT-4, raising questions about their role in improving human-like grounding. These findings underscore how LLMs' reliance on linguistic patterns can both approximate and diverge from human embodied cognition, revealing limitations in replicating sensory experiences.
MaNGA DynPop -- VII. A Unified Bulge-Disk-Halo Model for Explaining Diversity in Circular Velocity Curves of 6000 Spiral and Early-Type Galaxies
2025-03-10
Kai Zhu, Michele Cappellari, Shude Mao, Shengdong Lu, Ran Li, Yong Shi, David A. Simon, Youquan Fu, Xiaohan Wang
We derive circular velocity curves (CVCs) from stellar dynamical models for $\sim6{,}000$ nearby galaxies in the final data release of the SDSS-IV MaNGA survey with integral-field spectroscopy, exploring connections between the inner gravitational potential (traced by CVC amplitude/shape) and galaxy properties. The maximum circular velocity ($V_{\rm circ}^{\rm max}$) and circular velocity at the half-light radius ($V_{\rm circ}(R_{\rm e}^{\rm maj})$) both scale linearly with the stellar second velocity moment $\sigma_{\rm e}^2\equiv\langle V^2+\sigma^2\rangle$ within the half-light isophote, following $V_{\rm circ}^{\rm max} \approx 1.75\sigma_{\rm e}$ (8$\%$ error) and $V_{\rm circ}(R_{\rm e}^{\rm maj}) \approx 1.65\sigma_{\rm e}$ (9$\%$ error). CVC shapes (rising, flat, declining) correlate strongly with structural and stellar population properties: declining curves dominate in massive, early-type, bulge-dominated galaxies with old, metal-rich stars and early quenching, while rising CVCs prevail in disk-dominated systems with younger stellar populations and ongoing star formation. Using a unified bulge-disk-halo model, we predict CVC shapes with minimal bias, identifying three governing parameters: bulge-to-total mass ratio ($B/T$), dark matter fraction within $R_{\rm e}$, and bulge Sersic index. The distribution of CVC shapes across the mass-size plane reflects evolutionary pathways driven by (i) in situ star formation (spurring bulge growth) and (ii) dry mergers. This establishes CVC morphology as a diagnostic for galaxy evolution, linking dynamical signatures to structural and stellar population histories.
2025-03-10
Kyungho Kim, Sunwoo Kim, Geon Lee, Jinhong Jung, Kijung Shin
Traditional recommender systems primarily rely on a single type of user-item interaction, such as item purchases or ratings, to predict user preferences. However, in real-world scenarios, users engage in a variety of behaviors, such as clicking on items or adding them to carts, offering richer insights into their interests. Multi-behavior recommender systems leverage these diverse interactions to enhance recommendation quality, and research on this topic has grown rapidly in recent years. This survey provides a timely review of multi-behavior recommender systems, focusing on three key steps: (1) Data Modeling: representing multi-behaviors at the input level, (2) Encoding: transforming these inputs into vector representations (i.e., embeddings), and (3) Training: optimizing machine-learning models. We systematically categorize existing multi-behavior recommender systems based on the commonalities and differences in their approaches across the above steps. Additionally, we discuss promising future directions for advancing multi-behavior recommender systems.
Exceptional Topology of Non-Hermitian Brillouin Klein Bottles
2025-03-10
W. B. Rui, Z. D. Wang
Exceptional points (EPs) are prominent non-Hermitian band degeneracies that give rise to a variety of intriguing and unconventional phenomena. Similar to Weyl and Dirac points, EPs carry topological charges and comply with the celebrated fermion doubling theorems in lattices. Beyond these characteristics, EPs exhibit more exotic topological properties, particularly non-Abelian braiding topologies not seen in conventional degeneracies. Here, we investigate these foundational concepts of EPs in two-dimensional non-Hermitian lattices where the fundamental domain of the Brillouin zone is a Klein bottle, rather than a torus assumed in previous studies. We find that EPs do not necessarily appear in pairs with opposite topological charges in the Brillouin Klein bottle, thus violating the fermion doubling theorem. The violation occurs because, without crossing the boundary, the sum of the topological charges of EPs is in fact an even number rather than zero. Moreover, we uncover unique braiding topologies of EPs that cannot be captured by existing theories. Specifically, the composite braidings around all EPs equals the braiding along the boundary of the Brillouin Klein bottle. This novel braiding topology further confirms the failure of the fermion doubling theorem, and allows us to explore the non-Abelian braidings of EPs beyond the scope of topological charges. Our work highlights the fundamental role of Brillouin-zone topology in non-Hermitian systems.
This paper introduces new conditions for target output controllability and provides existence conditions for placing a specific number of poles with a target output controller. Additionally, an algorithm is presented for the design of a target output controller. Controllability of the system under consideration is not required for designing target output controllers in this context. The findings in this paper extend the principles of full state feedback control. Moreover, we present conditions for static output feedback control under specific constraints. Several numerical examples are provided to illustrate the results.
Policy Regularization on Globally Accessible States in Cross-Dynamics Reinforcement Learning
2025-03-10
Zhenghai Xue, Lang Feng, Jiacheng Xu, Kang Kang, Xiang Wen, Bo An, Shuicheng Yan
To learn from data collected in diverse dynamics, Imitation from Observation (IfO) methods leverage expert state trajectories based on the premise that recovering expert state distributions in other dynamics facilitates policy learning in the current one. However, Imitation Learning inherently imposes a performance upper bound of learned policies. Additionally, as the environment dynamics change, certain expert states may become inaccessible, rendering their distributions less valuable for imitation. To address this, we propose a novel framework that integrates reward maximization with IfO, employing F-distance regularized policy optimization. This framework enforces constraints on globally accessible states--those with nonzero visitation frequency across all considered dynamics--mitigating the challenge posed by inaccessible states. By instantiating F-distance in different ways, we derive two theoretical analysis and develop a practical algorithm called Accessible State Oriented Policy Regularization (ASOR). ASOR serves as a general add-on module that can be incorporated into various RL approaches, including offline RL and off-policy RL. Extensive experiments across multiple benchmarks demonstrate ASOR's effectiveness in enhancing state-of-the-art cross-domain policy transfer algorithms, significantly improving their performance.
In this paper we survey known results of characterizations of reflexive Banach spaces and properties stronger than reflexivity. Characterizations of reflexivity are mainly based on precise descriptions of weakly compact sets, affine sets in a Banach space $X$ or in $X^*$, properties of a Schauder basis of $X$ or $X^*$ and a bi-orthogonal system in $X$. Characterizations of stronger properties are based on the von Neumann-Jordan constant, finite-tree property and (finite-dimensional) representability.
Improving Visual Place Recognition with Sequence-Matching Receptiveness Prediction
2025-03-10
Somayeh Hussaini, Tobias Fischer, Michael Milford
In visual place recognition (VPR), filtering and sequence-based matching approaches can improve performance by integrating temporal information across image sequences, especially in challenging conditions. While these methods are commonly applied, their effects on system behavior can be unpredictable and can actually make performance worse in certain situations. In this work, we present a new supervised learning approach that learns to predict the per-frame sequence matching receptiveness (SMR) of VPR techniques, enabling the system to selectively decide when to trust the output of a sequence matching system. The approach is agnostic to the underlying VPR technique. Our approach predicts SMR-and hence significantly improves VPR performance-across a large range of state-of-the-art and classical VPR techniques (namely CosPlace, MixVPR, EigenPlaces, SALAD, AP-GeM, NetVLAD and SAD), and across three benchmark VPR datasets (Nordland, Oxford RobotCar, and SFU-Mountain). We also provide insights into a complementary approach that uses the predictor to replace discarded matches, as well as ablation studies, including an analysis of the interactions between our SMR predictor and the selected sequence length. We will release our code upon acceptance.
Robust local empirical Bayes correction for Bayesian modeling
2025-03-10
Yoshiko Hayashi
This paper investigates a robust empirical Bayes correction for Bayesian modeling. We show the application of the model on income distribution. Income shock includes temporal and permanent shocks. We aim to eliminate temporal shock and permanent shock using two-step local empirical correction method. Our results show that only 6.7% of the observed income shocks were permanent shock, and the posterior (permanent) mean weekly income was reduced from the observed income 415 pounds to 202 pounds for the United Kingdom using the Living Costs and Food Survey in 2021-2022. Keywords: Empirical Bayes correction; Outliers; Bayesian modeling
2025-03-10
Robert Besuner, Arjun Dey, Alex Drlica-Wagner, Haruki Ebina, Guillermo Fernandez Moroni, Simone Ferraro, Jaime Forero-Romero, Klaus Honscheid, Pat Jelinsky, Dustin Lang, Michael Levi, Paul Martini, Adam Myers, Nathalie Palanque-Delabrouille, Swayamtrupta Panda, Claire Poppett, Noah Sailer, David Schlegel, Arman Shafieloo, Joseph Silber, Martin White, Timothy Abbott, Lori Allen, Santiago Avila, Stephen Bailey, Abby Bault, Mohamed Bouri, Konstantina Boutsia, Eienne Burtin, Fernando Chierchie, William Coulton, Kyle Dawson, Biprateep Dey, Patrick Dunlop, Daniel Eisenstein, Castorina Emanuele, Stephanie Escoffier, Juan Estrada, Parker Fagrelius, Kevin Fanning, Timothy Fanning, Andreu Font-Ribera, Joshua Frieman, Malak Galal, Vera Gluscevic, Satya Gontcho A Gontcho, Daniel Green, Gaston Gutierrez, Julien Guy, Kevan Hashemi, Steve Heathcote, Steve Holland, Jiamin Hou, Dragan Huterer, Blas Irigoyen Gimenez, Mikhail Ivanov, Richard Joyce, Eric Jullo, Stephanie Juneau, Claire Juramy, Armin Karcher, Stephen Kent, David Kirkby, Jean-Paul Kneib, Elisabeth Krause, Alex Krolewski, Ofer Lahav, Agustin Lapi, Alexie Leauthaud, Matthew Lewandowski, Kenneth Lin, Marilena Loverde, Sean MacBride, Christophe Magneville, Jennifer Marshall, Patrick McDonald, Timothy Miller, John Moustakas, Moritz Münchmeyer, Joan Najita, Jeff Newman, Will Percival, Oliver Philcox, Priscila Pires, Anand Raichoor, Brandon Roach, Constance Rockosi, Maxime Rombach, Ashley Ross, Eusebio Sanchez, Luke Schmidt, Michael Schubnell, Rebekah Sebok, Uros Seljak, Eva Silverstein, Zachay Slepian, Robert Stupak, Gregory Tarlé, Li Ting, Luke Tyas, Mariana Vargas-Magaña, Alistair Walker, Nicholas Wenner, Christophe Yèche, Yuanyuan Zhang, Rongpu Zhou
Observational cosmology has provided an extraordinary perspective on our universe and our place within it. However, as our understanding of the universe has increased, some glaring holes in our knowledge have become apparent: What physics is responsible for the super-luminal expansion of the universe at early times? What drives the accelerating expansion of the universe at late times? What is the nature of the mysterious dark matter that makes up 83\% of the matter in the universe? These fundamental physical questions are intimately linked to fundamental astronomical questions about how galaxies and stars have formed and evolved within our universe. Cosmic surveys are the primary means by which we study the origin, structure, composition, and evolution of our universe. In particular, many of these questions require the spectroscopy of large numbers of astronomical sources. Spectroscopy provides key data on the physics of primordial inflation and late-time cosmic acceleration, the astrophysics of galaxy evolution, and the nature and effects of dark matter in galaxies. For example, observable features in the three-dimensional structure of the universe are key predictions of cosmological models, and their time evolution provides unique constraints on the nature of dark energy, inflation and the gravitational effects of dark matter. The next major advance in our understanding of the universe requires spectroscopic measurements of hundreds of millions of astronomical objects.
Some families of graphs, hypergraphs and digraphs defined by systems of equations
2025-03-10
Felix Lazebnik, Ye Wang
The families of graphs defined by a certain type of system of equations over commutative rings have been studied and used since 1990s. This survey presents these families and their applications related to graphs, digraphs, and hypergraphs. Some open problems and conjectures are mentioned.
Demystifying the Accuracy-Interpretability Trade-Off: A Case Study of Inferring Ratings from Reviews
2025-03-10
Pranjal Atrey, Michael P. Brundage, Min Wu, Sanghamitra Dutta
Interpretable machine learning models offer understandable reasoning behind their decision-making process, though they may not always match the performance of their black-box counterparts. This trade-off between interpretability and model performance has sparked discussions around the deployment of AI, particularly in critical applications where knowing the rationale of decision-making is essential for trust and accountability. In this study, we conduct a comparative analysis of several black-box and interpretable models, focusing on a specific NLP use case that has received limited attention: inferring ratings from reviews. Through this use case, we explore the intricate relationship between the performance and interpretability of different models. We introduce a quantitative score called Composite Interpretability (CI) to help visualize the trade-off between interpretability and performance, particularly in the case of composite models. Our results indicate that, in general, the learning performance improves as interpretability decreases, but this relationship is not strictly monotonic, and there are instances where interpretable models are more advantageous.
Visual and Text Prompt Segmentation: A Novel Multi-Model Framework for Remote Sensing
2025-03-10
Xing Zi, Kairui Jin, Xian Tao, Jun Li, Ali Braytee, Rajiv Ratn Shah, Mukesh Prasad
Pixel-level segmentation is essential in remote sensing, where foundational vision models like CLIP and Segment Anything Model(SAM) have demonstrated significant capabilities in zero-shot segmentation tasks. Despite their advances, challenges specific to remote sensing remain substantial. Firstly, The SAM without clear prompt constraints, often generates redundant masks, and making post-processing more complex. Secondly, the CLIP model, mainly designed for global feature alignment in foundational models, often overlooks local objects crucial to remote sensing. This oversight leads to inaccurate recognition or misplaced focus in multi-target remote sensing imagery. Thirdly, both models have not been pre-trained on multi-scale aerial views, increasing the likelihood of detection failures. To tackle these challenges, we introduce the innovative VTPSeg pipeline, utilizing the strengths of Grounding DINO, CLIP, and SAM for enhanced open-vocabulary image segmentation. The Grounding DINO+(GD+) module generates initial candidate bounding boxes, while the CLIP Filter++(CLIP++) module uses a combination of visual and textual prompts to refine and filter out irrelevant object bounding boxes, ensuring that only pertinent objects are considered. Subsequently, these refined bounding boxes serve as specific prompts for the FastSAM model, which executes precise segmentation. Our VTPSeg is validated by experimental and ablation study results on five popular remote sensing image segmentation datasets.
Evaluating Path Planning Strategies for Efficient Nitrate Sampling in Crop Rows
2025-03-10
Ruiji Liu, Abigail Breitfeld, Srinivasan Vijayarangan, George Kantor, Francisco Yandun
This paper presents a pipeline that combines high-resolution orthomosaic maps generated from UAS imagery with GPS-based global navigation to guide a skid-steered ground robot. We evaluated three path planning strategies: A* Graph search, Deep Q-learning (DQN) model, and Heuristic search, benchmarking them on planning time and success rate in realistic simulation environments. Experimental results reveal that the Heuristic search achieves the fastest planning times (0.28 ms) and a 100% success rate, while the A* approach delivers near-optimal performance, and the DQN model, despite its adaptability, incurs longer planning delays and occasional suboptimal routing. These results highlight the advantages of deterministic rule-based methods in geometrically constrained crop-row environments and lay the groundwork for future hybrid strategies in precision agriculture.
A primer on optimal transport for causal inference with observational data
2025-03-10
Florian F Gunsilius
The theory of optimal transportation has developed into a powerful and elegant framework for comparing probability distributions, with wide-ranging applications in all areas of science. The fundamental idea of analyzing probabilities by comparing their underlying state space naturally aligns with the core idea of causal inference, where understanding and quantifying counterfactual states is paramount. Despite this intuitive connection, explicit research at the intersection of optimal transport and causal inference is only beginning to develop. Yet, many foundational models in causal inference have implicitly relied on optimal transport principles for decades, without recognizing the underlying connection. Therefore, the goal of this review is to offer an introduction to the surprisingly deep existing connections between optimal transport and the identification of causal effects with observational data -- where optimal transport is not just a set of potential tools, but actually builds the foundation of model assumptions. As a result, this review is intended to unify the language and notation between different areas of statistics, mathematics, and econometrics, by pointing out these existing connections, and to explore novel problems and directions for future work in both areas derived from this realization.
Using Large Language Models to Develop Requirements Elicitation Skills
2025-03-10
Nelson Lojo, Rafael González, Rohan Philip, José Antonio Parejo, Amador Durán Toro, Armando Fox, Pablo Fernández
Requirements Elicitation (RE) is a crucial software engineering skill that involves interviewing a client and then devising a software design based on the interview results. Teaching this inherently experiential skill effectively has high cost, such as acquiring an industry partner to interview, or training course staff or other students to play the role of a client. As a result, a typical instructional approach is to provide students with transcripts of real or fictitious interviews to analyze, which exercises the skill of extracting technical requirements but fails to develop the equally important interview skill itself. As an alternative, we propose conditioning a large language model to play the role of the client during a chat-based interview. We perform a between-subjects study (n=120) in which students construct a high-level application design from either an interactive LLM-backed interview session or an existing interview transcript describing the same business processes. We evaluate our approach using both a qualitative survey and quantitative observations about participants' work. We find that both approaches provide sufficient information for participants to construct technically sound solutions and require comparable time on task, but the LLM-based approach is preferred by most participants. Importantly, we observe that LLM-backed interview is seen as both more realistic and more engaging, despite the LLM occasionally providing imprecise or contradictory information. These results, combined with the wide accessibility of LLMs, suggest a new way to practice critical RE skills in a scalable and realistic manner without the overhead of arranging live interviews.
Understanding the risk and protective factors associated with Parkinsons disease (PD) is crucial for improving outcomes for patients, individuals at risk, healthcare providers, and healthcare systems. Studying these factors not only enhances our knowledge of the disease but also aids in developing effective prevention, management, and treatment strategies. This paper reviews the key risk and protective factors associated with PD, with a particular focus on the biological mechanisms underlying these factors. Risk factors include genetic mutations, racial predispositions, and environmental exposures, all of which contribute to an increased likelihood of developing PD or accelerating disease progression. Conversely, protective factors such as regular physical exercise, adherence to a Mediterranean diet, and higher urate levels have demonstrated potential to reduce inflammation and support mitochondrial function, thereby mitigating disease risk. However, identifying and validating these factors presents significant challenges. To overcome challenges, we propose several solutions and recommendations. Future research should prioritize the development of standardized biomarkers for early diagnosis, investigate gene-environment interactions in greater depth, and refine animal models to better mimic human PD pathology. Additionally, we offer actionable recommendations for PD prevention and management, tailored to healthy individuals, patients diagnosed with PD, and healthcare systems. These strategies aim to improve clinical outcomes, enhance quality of life, and optimize healthcare delivery for PD.
MaNGA AGN dwarf galaxies (MAD) -- II. AGN outflows in dwarf galaxies
2025-03-10
V. Rodríguez Morales, M. Mezcua, H. Domínguez Sánchez, A. Audibert, F. Müller-Sánchez, M. Siudek, A. Eróstegui
Active Galactic Nuclei (AGN) feedback is one of the most important mechanisms in galaxy evolution. It is usually found in massive galaxies and regulates star formation. Although dwarf galaxies are assumed to be regulated by supernova feedback, recent studies show evidence for the presence of AGN outflows and feedback in dwarf galaxies. We investigate the presence of AGN outflows in a sample of 2292 dwarf galaxies with AGN signatures drawn from the MaNGA survey. Thanks to the integral field unit data from MaNGA we are able to spatially resolve these outflows and study their kinematics and energetics. Using the GELATO Python code, we fit the AGN-stacked spectrum of each galaxy, which is the stack of all the spaxels classified as AGN by emission line diagnostic diagrams, and in particular the [OIII]$\lambda$5007\AA\ emission line. If the galaxies show a broad [OIII] emission line component in the stacked spectrum, we run GELATO through all the spaxels that are classified as AGN in the emission line diagnostic diagrams. We find 11 new dwarf galaxies that present outflow signatures based on the presence of a broad [OIII] emission line component. Their velocity W$_{80}$ (width containing 80$\%$ of the flux of the [OIII]$\lambda$5007\AA\ emission line) ranges from 205 to 566 km s$^{-1}$ and the kinetic energy rate ranges from $\sim10^{35}$ to $\sim10^{39}$ erg s$^{-1}$. Stellar processes are unlikely to explain these outflow kinetic energy rates in the case of seven dwarf galaxies. We find a correlation between the W$_{80}$ velocity and the [OIII] luminosity and between the kinetic energy rate of the outflow and the bolometric luminosity spanning from massive to dwarf galaxies. This suggests a similar behavior between the AGN outflows in the dwarf galaxy population with those in massive galaxies.
JWST PRIMER: A deep JWST study of all ALMA-detected galaxies in PRIMER COSMOS -- dust-obscured star-formation history back to z $\simeq$ 7
2025-03-10
Feng-Yuan Liu, James S. Dunlop, Ross J. McLure, Derek J. McLeod, Laia Barrufet, Adam C. Carnall, Ryan Begley, Pablo G. Pérez-González, Callum T. Donnan, Richard S. Ellis, Norman A. Grogin, Dan Magee, Garth D. Illingworth, Fergus Cullen, Struan D. Stevenson, Anton M. Koekemoer, Adriano Fontana, Rebecca A. A. Bowler
We use the deep NIRCam and MIRI imaging from the JWST PRIMER survey to study the properties of (sub)mm sources detected by ALMA in the centre of the COSMOS field, with the aim of better constraining the history of dust-enshrouded star formation. The wealth of ALMA data in this field enabled us to isolate a robust sample of 128 (sub)mm sources within the 175 sq. arcmin of the PRIMER COSMOS survey footprint, spanning two decades in (sub)mm flux density. The JWST imaging is deep/red enough to reveal secure galaxy counterparts for all of these sources. Moreover, 52% of the galaxies have spectroscopic redshifts, enabling us to refine the photo-zs for the remaining galaxies. Armed with this robust redshift information, we calculate the star-formation rates (SFR) and stellar masses of all 128 ALMA-detected galaxies, and place them in the context of other galaxies in the field. We find that the vast majority of star formation is dust-enshrouded in the ALMA-detected galaxies, with SFR ranging from ~1000 down to ~20 solar masses per year. We also find that virtually all (126/128) have high stellar masses, at all redshifts, with log(M/Msun) > 10. The unusually high quality of our sample enables us to make a robust estimate of the contribution of the ALMA-detected galaxies to cosmic star-formation rate density from z = 2 out to z = 7. Finally, to correct for the fact that the deep ALMA pointings cover < 20% of the PRIMER COSMOS area, we use our knowledge of all other massive galaxies in the field to produce a completeness-corrected estimate of dust-enshrouded star-formation rate density over cosmic time. This confirms that UV-visible star formation dominates at z > 4, but also indicates that dust-enshrouded star formation likely still made a significant contribution at higher redshifts: extrapolation of our results suggest a ~20% contribution at z = 8, and potentially still ~5% at z = 10.
On the Origin of Abundance Variations in the Milky Way's High-$α$ Plateau
2025-03-10
Tawny Sit, David H. Weinberg, Emily J. Griffith
Using multi-element abundances from the SDSS APOGEE survey, we investigate the origin of abundance variations in Milky Way (MW) disk stars on the "high-$\alpha$ plateau," with $-0.8\leq\rm{[Fe/H]}\leq-0.4$ and $0.25\leq\rm{[Mg/Fe]}\leq0.35$. The elevated [$\alpha$/Fe] ratios of these stars imply low enrichment contributions from Type Ia supernovae (SNIa), but it is unclear whether their abundance patterns reflect pure core-collapse supernova (CCSN) enrichment. We find that plateau stars with higher [Fe/Mg] ratios also have higher [X/Mg] ratios for other iron-peak elements, suggesting that the [Fe/Mg] variations in the plateau population do reflect variations in the SNIa/CCSN ratio. To quantify this finding, we fit the observed abundance patterns with a two-process model, calibrated on the full MW disk, which represents each star's abundances as the sum of a prompt CCSN process with amplitude $A_{\text{cc}}$ and a delayed SNIa process with amplitude $A_{\text{Ia}}$. This model is generally successful at explaining the observed trends of [X/Mg] with $A_{\text{Ia}}/A_{\text{cc}}$, which are steeper for elements with a large SNIa contribution (e.g., Cr, Ni, Mn) and flatter for elements with low SNIa contribution (e.g., O, Si, Ca). Our analysis does not determine the value of [Mg/Fe] corresponding to pure CCSN enrichment, but it should be at least as high as the upper edge of the plateau at $\rm{[Mg/Fe]}\approx0.35$, and could be significantly higher. Compared to the two-process predictions, the observed trends of [X/Mg] with $A_{\text{Ia}}/A_{\text{cc}}$ are steeper for (C+N) but shallower for Ce, providing intriguing but contradictory clues about AGB enrichment in the early disk.
HOLISMOKES XVI: Lens search in HSC-PDR3 with a neural network committee and post-processing for false-positive removal
2025-03-10
S. Schuldt, R. Cañameras, Y. Shu, I. T. Andika, S. Bag, C. Grillo, A. Melo, S. H. Suyu, S. Taubenberger
We have carried out a systematic search for galaxy-scale lenses exploiting multi-band imaging data from the third public data release of the Hyper Suprime-Cam (HSC) survey with the focus on false-positive removal, after applying deep learning classifiers to all 110 million sources with i-Kron radius above 0.8". To improve the performance, we tested the combination of multiple networks from our previous lens search projects and found the best performance by averaging the scores from five of our networks. Although this ensemble network leads already to a false-positive rate (FPR) of 0.01% at a true-positive rate (TPR) of 75% on known real lenses, we have elaborated techniques to further clean the network candidate list before visual inspection. In detail, we tested the rejection using SExtractor and the modeling network from HOLISMOKES IX, which resulted together in a candidate rejection of 29% without lowering the TPR. We carried out a comprehensive multi-stage visual inspection involving eight individuals and identified 95 grade A (average grade G >2.5) and 503 grade B (2.5 >G >1.5) lens candidates, including 92 discoveries reported for the first time. This inspection also incorporated a novel environmental characterization using histograms of photometric redshifts. We publicly release the average grades, mass model predictions, and environment characterization of all visually inspected candidates, while including references for previously discovered systems, which makes this catalog one of the largest compilation of known lenses. The results demonstrate that (1) the combination of multiple networks enhances the selection performance and (2) both automated masking tools as well as modeling networks, which can be easily applied to hundreds of thousands of network candidates, help reduce the number of false positives that is the main limitation in lens search to date.
The $H_2$ angular momentum -- mass relation of local disc galaxies
2025-03-10
Nikki N. Geesink, Pavel E. Mancera Piña, Claudia del P. Lagos, Mariska Kriek
We present an analysis of the molecular specific angular momentum-mass ($j_{H_2}-M_{H_2}$) relation using a sample of 51 nearby disc galaxies from the PHANGS-ALMA survey with deep, high-resolution molecular gas rotation curves and surface density profiles. For the very first time, using a statistical sample, we report the discovery of a well-defined $j_{H_2}-M_{H_2}$ relation. We quantify the scaling law by fitting a power-law with a Bayesian framework, finding $j_{H_2} \propto M_{H_2}^{0.53}$. This slope closely resembles the well-known stellar $j_{\ast}$-$M_{\ast}$ (Fall) relation, highlighting the dynamical connection between molecular gas and stars. We show that the $j_{H_2}-M_{H_2}$ relation cannot be fully explained by analytic models of disc stability but instead is well recovered with more complex physics as implemented in the Shark semi-analytical model. These findings demonstrate the power of our novel $j_{H_2}-M_{H_2}$ relation in testing galaxy evolution theories and setting new constraints for models and simulations which aim to reproduce a realistic interstellar medium. Additionally, our findings provide a critical benchmark for upcoming high-redshift studies of molecular gas kinematics, offering a local baseline to study the evolution of cold gas dynamics across cosmic time.
More than a decade ago, the IceCube Neutrino Observatory discovered a diffuse flux of 10 TeV - 10 PeV neutrinos from our Universe. This flux of unknown origin most likely emanates from an extragalactic population of neutrino sources, which are individually too faint to appear as bright emitters. We review constraints on extragalactic neutrino source populations based on the non-detection of the brightest neutrino source. Extending previous work, we discuss limitations of source populations based on general neutrino luminosity functions. Our method provides more conservative but also statistically more robust predictions for the expected number of observable sources. We also show that the combined search of the brightest neutrino sources via weighted stacking searches or the analysis of non-Poissonian fluctuations in event-count histograms can improve the discovery potential by a factor of 2-3 relative to the brightest source.
A panchromatic view of N2CLS GOODS-N: the evolution of the dust cosmic density since z~7
2025-03-10
S. Berta, G. Lagache, A. Beelen, R. Adam, P. Ade, H. Ajeddig, S. Amarantidis, P. André, H. Aussel, A. Benoît, M. Bethermin, L. -J. Bing, A. Bongiovanni, J. Bounmy, O. Bourrion, M. Calvo, A. Catalano, D. Chérouvrier, L. Ciesla, M. De Petris, F. -X. Désert, S. Doyle, E. F. C. Driessen, G. Ejlali, D. Elbaz, A. Ferragamo, A. Gomez, J. Goupy, C. Hanser, S. Katsioli, F. Kéruzoré, C. Kramer, B. Ladjelate, S. Leclercq, J. -F. Lestrade, J. F. Macías-Pérez, S. C. Madden, A. Maury, F. Mayet, H. Messias, A. Monfardini, A. Moyer-Anin, M. Muñoz-Echeverría, I. Myserlis, R. Neri, A. Paliwal, L. Perotto, G. Pisano, N. Ponthieu, V. Revéret, A. J. Rigby, A. Ritacco, H. Roussel, F. Ruppin, M. Sánchez-Portal, S. Savorgnano, K. Schuster, A. Sievers, C. Tucker, M. -Y. Xiao, R. Zylka
(abridged) To understand early star formation, it is essential to determine the dust mass budget of high-redshift galaxies. Sub-millimeter rest-frame emission, dominated by cold dust, is an unbiased tracer of dust mass. The NIKA2 camera conducted a deep blank field survey at 1.2 and 2.0 mm in the GOODS-N field as part of the NIKA2 Cosmological Legacy Survey (N2CLS), detecting 65 sources with SNR>=4.2. Thanks to a dedicated interferometric program with NOEMA and other high-angular resolution data, we identify the multi-wavelength counterparts of these sources and resolve them into 71 individual galaxies. We build detailed SEDs and assign a redshift to 68 of them, over the range 0.6
Junior Software Developers' Perspectives on Adopting LLMs for Software Engineering: a Systematic Literature Review
2025-03-10
Samuel Ferino, Rashina Hoda, John Grundy, Christoph Treude
Many studies exploring the adoption of Large Language Model-based tools for software development by junior developers have emerged in recent years. These studies have sought to understand developers' perspectives about using those tools, a fundamental pillar for successfully adopting LLM-based tools in Software Engineering. The aim of this paper is to provide an overview of junior software developers' perspectives and use of LLM-based tools for software engineering (LLM4SE). We conducted a systematic literature review (SLR) following guidelines by Kitchenham et al. on 56 primary studies, applying the definition for junior software developers as software developers with equal or less than five years of experience, including Computer Science/Software Engineering students. We found that the majority of the studies focused on comprehending the different aspects of integrating AI tools in SE. Only 8.9\% of the studies provide a clear definition for junior software developers, and there is no uniformity. Searching for relevant information is the most common task using LLM tools. ChatGPT was the most common LLM tool present in the studies (and experiments). A majority of the studies (83.9\%) report both positive and negative perceptions about the impact of adopting LLM tools. We also found and categorised advantages, challenges, and recommendations regarding LLM adoption. Our results indicate that developers are using LLMs not just for code generation, but also to improve their development skills. Critically, they are not just experiencing the benefits of adopting LLM tools, but they are also aware of at least a few LLM limitations, such as the generation of wrong suggestions, potential data leaking, and AI hallucination. Our findings offer implications for software engineering researchers, educators, and developers.
Queueing, Predictions, and LLMs: Challenges and Open Problems
2025-03-10
Michael Mitzenmacher, Rana Shahout
Queueing systems present many opportunities for applying machine-learning predictions, such as estimated service times, to improve system performance. This integration raises numerous open questions about how predictions can be effectively leveraged to improve scheduling decisions. Recent studies explore queues with predicted service times, typically aiming to minimize job time in the system. We review these works, highlight the effectiveness of predictions, and present open questions on queue performance. We then move to consider an important practical example of using predictions in scheduling, namely Large Language Model (LLM) systems, which presents novel scheduling challenges and highlights the potential for predictions to improve performance. In particular, we consider LLMs performing inference. Inference requests (jobs) in LLM systems are inherently complex; they have variable inference times, dynamic memory footprints that are constrained by key-value (KV) store memory limitations, and multiple possible preemption approaches that affect performance differently. We provide background on the important aspects of scheduling in LLM systems, and introduce new models and open problems that arise from them. We argue that there are significant opportunities for applying insights and analysis from queueing theory to scheduling in LLM systems.
AI-Enabled Knowledge Sharing for Enhanced Collaboration and Decision-Making in Non-Profit Healthcare Organizations: A Scoping Review Protocol
2025-03-10
Maurice Ongala, Ruth Kiraka, Jyoti Choundrie, Javan Okello
This protocol outlines a scoping review designed to systematically map the existing body of evidence on AI-enabled knowledge sharing in resource-limited non-profit healthcare organizations. The review aims to investigate how such technologies enhance collaboration and decision-making, particularly in the context of reduced external support following the cessation of USAID operations. Guided by three theoretical frameworks namely, the Resource-Based View, Dynamic Capabilities Theory, and Absorptive Capacity Theory, this study will explore the dual role of AI as a strategic resource and an enabler of organizational learning and agility. The protocol details a rigorous methodological approach based on PRISMA-ScR guidelines, encompassing a systematic search strategy across multiple databases, inclusion and exclusion criteria, and a structured data extraction process. By integrating theoretical insights with empirical evidence, this scoping review seeks to identify critical gaps in the literature and inform the design of effective, resource-optimized AI solutions in non-profit healthcare settings.
Predicted Neutrino Signal Features of Core-Collapse Supernovae
2025-03-10
Lyla Choi, Adam Burrows, David Vartanyan
In this paper, we examine the neutrino signals from 24 initially non-rotating, three-dimensional core-collapse supernova (CCSN) simulations carried to late times. We find that not only does the neutrino luminosity signal encode information about each stage of the CCSN process, but that the monotonic dependence of the luminosity peak height with compactness enables one to infer the progenitor core structure from the neutrino signal. We highlight a systematic relationship between the luminosity peak height with its timing. Additionally, we emphasize that the total energy radiated in neutrinos is monotonic with progenitor compactness, and that the mean neutrino energy contains a unique spiral SASI signature for nonexploding, BH-forming models. We also find that neutrino emissions are not isotropic and that the anisotropy increases roughly with progenitor compactness. To assess the detectability of these neutrino signal features, we provide examples of the event rates for our models for the JUNO, DUNE, SK, and IceCube detectors using the SNEWPY software, and find that many of the trends in the luminosity signal can be detectable across several detectors and oscillation models. Finally, we discuss correlations between the radiated neutrino energy and the evolution of the gravitational-wave f-mode.
2025-03-10
Justus-Jonas Erker, Nils Reimers, Iryna Gurevych
Decomposition-based multi-hop retrieval methods rely on many autoregressive steps to break down complex queries, which breaks end-to-end differentiability and is computationally expensive. Decomposition-free methods tackle this, but current decomposition-free approaches struggle with longer multi-hop problems and generalization to out-of-distribution data. To address these challenges, we introduce GRITHopper-7B, a novel multi-hop dense retrieval model that achieves state-of-the-art performance on both in-distribution and out-of-distribution benchmarks. GRITHopper combines generative and representational instruction tuning by integrating causal language modeling with dense retrieval training. Through controlled studies, we find that incorporating additional context after the retrieval process, referred to as post-retrieval language modeling, enhances dense retrieval performance. By including elements such as final answers during training, the model learns to better contextualize and retrieve relevant information. GRITHopper-7B offers a robust, scalable, and generalizable solution for multi-hop dense retrieval, and we release it to the community for future research and applications requiring multi-hop reasoning and retrieval capabilities.
Artificial Intelligence in Deliberation: The AI Penalty and the Emergence of a New Deliberative Divide
2025-03-10
Andreas Jungherr, Adrian Rauchfleisch
Digital deliberation has expanded democratic participation, yet challenges remain. This includes processing information at scale, moderating discussions, fact-checking, or attracting people to participate. Recent advances in artificial intelligence (AI) offer potential solutions, but public perceptions of AI's role in deliberation remain underexplored. Beyond efficiency, democratic deliberation is about voice and recognition. If AI is integrated into deliberation, public trust, acceptance, and willingness to participate may be affected. We conducted a preregistered survey experiment with a representative sample in Germany (n=1850) to examine how information about AI-enabled deliberation influences willingness to participate and perceptions of deliberative quality. Respondents were randomly assigned to treatments that provided them information about deliberative tasks facilitated by either AI or humans. Our findings reveal a significant AI-penalty. Participants were less willing to engage in AI-facilitated deliberation and rated its quality lower than human-led formats. These effects were moderated by individual predispositions. Perceptions of AI's societal benefits and anthropomorphization of AI showed positive interaction effects on people's interest to participate in AI-enabled deliberative formats and positive quality assessments, while AI risk assessments showed negative interactions with information about AI-enabled deliberation. These results suggest AI-enabled deliberation faces substantial public skepticism, potentially even introducing a new deliberative divide. Unlike traditional participation gaps based on education or demographics, this divide is shaped by attitudes toward AI. As democratic engagement increasingly moves online, ensuring AI's role in deliberation does not discourage participation or deepen inequalities will be a key challenge for future research and policy.
Text in dashboards plays multiple critical roles, including providing context, offering insights, guiding interactions, and summarizing key information. Despite its importance, most dashboarding tools focus on visualizations and offer limited support for text authoring. To address this gap, we developed Plume, a system to help authors craft effective dashboard text. Through a formative review of exemplar dashboards, we created a typology of text parameters and articulated the relationship between visual placement and semantic connections, which informed Plume's design. Plume employs large language models (LLMs) to generate contextually appropriate content and provides guidelines for writing clear, readable text. A preliminary evaluation with 12 dashboard authors explored how assisted text authoring integrates into workflows, revealing strengths and limitations of LLM-generated text and the value of our human-in-the-loop approach. Our findings suggest opportunities to improve dashboard authoring tools by better supporting the diverse roles that text plays in conveying insights.
Sometimes the Model doth Preach: Quantifying Religious Bias in Open LLMs through Demographic Analysis in Asian Nations
2025-03-10
Hari Shankar, Vedanta S P, Tejas Cavale, Ponnurangam Kumaraguru, Abhijnan Chakraborty
Large Language Models (LLMs) are capable of generating opinions and propagating bias unknowingly, originating from unrepresentative and non-diverse data collection. Prior research has analysed these opinions with respect to the West, particularly the United States. However, insights thus produced may not be generalized in non-Western populations. With the widespread usage of LLM systems by users across several different walks of life, the cultural sensitivity of each generated output is of crucial interest. Our work proposes a novel method that quantitatively analyzes the opinions generated by LLMs, improving on previous work with regards to extracting the social demographics of the models. Our method measures the distance from an LLM's response to survey respondents, through Hamming Distance, to infer the demographic characteristics reflected in the model's outputs. We evaluate modern, open LLMs such as Llama and Mistral on surveys conducted in various global south countries, with a focus on India and other Asian nations, specifically assessing the model's performance on surveys related to religious tolerance and identity. Our analysis reveals that most open LLMs match a single homogeneous profile, varying across different countries/territories, which in turn raises questions about the risks of LLMs promoting a hegemonic worldview, and undermining perspectives of different minorities. Our framework may also be useful for future research investigating the complex intersection between training data, model architecture, and the resulting biases reflected in LLM outputs, particularly concerning sensitive topics like religious tolerance and identity.
From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges
2025-03-10
Qiongxiu Li, Wenrui Yu, Yufei Xia, Jun Pang
Federated Learning (FL) enables collaborative learning without directly sharing individual's raw data. FL can be implemented in either a centralized (server-based) or decentralized (peer-to-peer) manner. In this survey, we present a novel perspective: the fundamental difference between centralized FL (CFL) and decentralized FL (DFL) is not merely the network topology, but the underlying training protocol: separate aggregation vs. joint optimization. We argue that this distinction in protocol leads to significant differences in model utility, privacy preservation, and robustness to attacks. We systematically review and categorize existing works in both CFL and DFL according to the type of protocol they employ. This taxonomy provides deeper insights into prior research and clarifies how various approaches relate or differ. Through our analysis, we identify key gaps in the literature. In particular, we observe a surprising lack of exploration of DFL approaches based on distributed optimization methods, despite their potential advantages. We highlight this under-explored direction and call for more research on leveraging distributed optimization for federated learning. Overall, this work offers a comprehensive overview from centralized to decentralized FL, sheds new light on the core distinctions between approaches, and outlines open challenges and future directions for the field.
Trustworthy Machine Learning via Memorization and the Granular Long-Tail: A Survey on Interactions, Tradeoffs, and Beyond
2025-03-10
Qiongxiu Li, Xiaoyu Luo, Yiyi Chen, Johannes Bjerva
The role of memorization in machine learning (ML) has garnered significant attention, particularly as modern models are empirically observed to memorize fragments of training data. Previous theoretical analyses, such as Feldman's seminal work, attribute memorization to the prevalence of long-tail distributions in training data, proving it unavoidable for samples that lie in the tail of the distribution. However, the intersection of memorization and trustworthy ML research reveals critical gaps. While prior research in memorization in trustworthy ML has solely focused on class imbalance, recent work starts to differentiate class-level rarity from atypical samples, which are valid and rare intra-class instances. However, a critical research gap remains: current frameworks conflate atypical samples with noisy and erroneous data, neglecting their divergent impacts on fairness, robustness, and privacy. In this work, we conduct a thorough survey of existing research and their findings on trustworthy ML and the role of memorization. More and beyond, we identify and highlight uncharted gaps and propose new revenues in this research direction. Since existing theoretical and empirical analyses lack the nuances to disentangle memorization's duality as both a necessity and a liability, we formalize three-level long-tail granularity - class imbalance, atypicality, and noise - to reveal how current frameworks misapply these levels, perpetuating flawed solutions. By systematizing this granularity, we draw a roadmap for future research. Trustworthy ML must reconcile the nuanced trade-offs between memorizing atypicality for fairness assurance and suppressing noise for robustness and privacy guarantee. Redefining memorization via this granularity reshapes the theoretical foundation for trustworthy ML, and further affords an empirical prerequisite for models that align performance with societal trust.
Multimodal AI-driven Biomarker for Early Detection of Cancer Cachexia
2025-03-09
Sabeen Ahmed, Nathan Parker, Margaret Park, Evan W. Davis, Jennifer B. Permuth, Matthew B. Schabath, Yasin Yilmaz, Ghulam Rasool
Cancer cachexia is a multifactorial syndrome characterized by progressive muscle wasting, metabolic dysfunction, and systemic inflammation, leading to reduced quality of life and increased mortality. Despite extensive research, no single definitive biomarker exists, as cachexia-related indicators such as serum biomarkers, skeletal muscle measurements, and metabolic abnormalities often overlap with other conditions. Existing composite indices, including the Cancer Cachexia Index (CXI), Modified CXI (mCXI), and Cachexia Score (CASCO), integrate multiple biomarkers but lack standardized thresholds, limiting their clinical utility. This study proposes a multimodal AI-based biomarker for early cancer cachexia detection, leveraging open-source large language models (LLMs) and foundation models trained on medical data. The approach integrates heterogeneous patient data, including demographics, disease status, lab reports, radiological imaging (CT scans), and clinical notes, using a machine learning framework that can handle missing data. Unlike previous AI-based models trained on curated datasets, this method utilizes routinely collected clinical data, enhancing real-world applicability. Additionally, the model incorporates confidence estimation, allowing the identification of cases requiring expert review for precise clinical interpretation. Preliminary findings demonstrate that integrating multiple data modalities improves cachexia prediction accuracy at the time of cancer diagnosis. The AI-based biomarker dynamically adapts to patient-specific factors such as age, race, ethnicity, weight, cancer type, and stage, avoiding the limitations of fixed-threshold biomarkers. This multimodal AI biomarker provides a scalable and clinically viable solution for early cancer cachexia detection, facilitating personalized interventions and potentially improving treatment outcomes and patient survival.
Infinite Leagues Under the Sea: Photorealistic 3D Underwater Terrain Generation by Latent Fractal Diffusion Models
2025-03-09
Tianyi Zhang, Weiming Zhi, Joshua Mangelson, Matthew Johnson-Roberson
This paper tackles the problem of generating representations of underwater 3D terrain. Off-the-shelf generative models, trained on Internet-scale data but not on specialized underwater images, exhibit downgraded realism, as images of the seafloor are relatively uncommon. To this end, we introduce DreamSea, a generative model to generate hyper-realistic underwater scenes. DreamSea is trained on real-world image databases collected from underwater robot surveys. Images from these surveys contain massive real seafloor observations and covering large areas, but are prone to noise and artifacts from the real world. We extract 3D geometry and semantics from the data with visual foundation models, and train a diffusion model that generates realistic seafloor images in RGBD channels, conditioned on novel fractal distribution-based latent embeddings. We then fuse the generated images into a 3D map, building a 3DGS model supervised by 2D diffusion priors which allows photorealistic novel view rendering. DreamSea is rigorously evaluated, demonstrating the ability to robustly generate large-scale underwater scenes that are consistent, diverse, and photorealistic. Our work drives impact in multiple domains, spanning filming, gaming, and robot simulation.
We present SemHiTok, a unified image Tokenizer via Semantic-Guided Hierarchical codebook that provides consistent discrete feature representations for multimodal understanding and generation tasks. Recently, unified multimodal large models (MLLMs) for understanding and generation have sparked exploration within research community. Previous works attempt to train a unified image tokenizer by combining loss functions for semantic feature reconstruction and pixel reconstruction. However, due to the differing levels of features prioritized by multimodal understanding and generation tasks, joint training methods face significant challenges in achieving a good trade-off. SemHiTok addresses this challenge through Semantic-Guided Hierarchical codebook which builds texture sub-codebooks on pre-trained semantic codebook. This design decouples the training of semantic reconstruction and pixel reconstruction and equips the tokenizer with low-level texture feature extraction capability without degradation of high-level semantic feature extraction ability. Our experiments demonstrate that SemHiTok achieves state-of-the-art rFID score at 256X256resolution compared to other unified tokenizers, and exhibits competitive performance on multimodal understanding and generation tasks.
Search for new physics in jet scaling patterns of multilepton events at $\sqrt{s}$ = 13 TeV
2025-03-09
CMS Collaboration
A first search for beyond the standard model physics in jet scaling patterns of multilepton events is presented, using a data sample corresponding to an integrated luminosity of 138 fb$^{-1}$ of 13 TeV proton-proton collisions recorded by the CMS detector at the LHC. The search uses observed jet multiplicity distributions in one-, two-, and four-lepton events to explore possible enhancements in jet production rate in three-lepton events with and without bottom quarks. The data are found to be consistent with the standard model expectation. The results are interpreted in terms of supersymmetric production of electroweak chargino-neutralino superpartners with cascade decays terminating in prompt hadronic $R$-parity violating interactions.
Warm Ionized Gas Outflows in Active Galactic Nuclei: What Causes it?
2025-03-09
Payel Nandi, C. S. Stalin, D. J. Saikia
The driving force behind outflows, often invoked to understand the correlation between the supermassive black holes powering active galactic nuclei (AGN) and their host galaxy properties, remains uncertain. We provide new insights into the mechanisms that trigger warm ionized outflows in AGN, based on findings from the MaNGA survey. Our sample comprises 538 AGN with strong [OIII]$\lambda$5007 emission lines, of which 197 are detected in radio and 341 are radio-undetected. We analyzed [OIII]$\lambda$5007 line in summed spectra, extracted over their central 500$\times$500 pc$^2$ region. The calculated Balmer 4000 $\AA$ break, D$_n$4000 is larger than 1.45 for $\sim$95$\%$ of the sources indicating that the specific star-formation rate in their central regions is smaller than 10$^{-11.5}$ yr$^{-1}$, pointing to evidence of negative AGN feedback suppressing star-formation. Considering the whole sample, radio-detected sources show an increased outflow detection rate (56$\pm$7\%) compared to radio-undetected sources (25$\pm$3\%). They also show higher velocity, mass outflow rate, outflow power and outflow momentum rate. We noticed a strong correlation between outflow characteristics and bolometric luminosity in both samples, except that the correlation is steeper for the radio-detected sample. Our findings suggest (a) warm ionized outflows are prevalent in all types of AGN, (b) radiation from AGN is the primary driver of these outflows, (c) radio jets are likely to play a secondary role in enhancing the gas kinematics over and above that caused by radiation, and (d) very low star-formation in the central regions of the galaxies, possibly due to negative feedback of AGN activity.
Dark Energy Survey: implications for cosmological expansion models from the final DES Baryon Acoustic Oscillation and Supernova data
2025-03-09
DES Collaboration, T. M. C. Abbott, M. Acevedo, M. Adamow, M. Aguena, A. Alarcon, S. Allam, O. Alves, F. Andrade-Oliveira, J. Annis, P. Armstrong, S. Avila, D. Bacon, K. Bechtol, J. Blazek, S. Bocquet, D. Brooks, D. Brout, D. L. Burke, H. Camacho, R. Camilleri, G. Campailla, A. Carnero Rosell, A. Carr, J. Carretero, F. J. Castander, R. Cawthon, K. C. Chan, C. Chang, R. Chen, C. Conselice, M. Costanzi, M. Crocce, L. N. da Costa, M. E. S. Pereira, T. M. Davis, J. De Vicente, N. Deiosso, S. Desai, H. T. Diehl, S. Dodelson, C. Doux, A. Drlica-Wagner, J. Elvin-Poole, S. Everett, I. Ferrero, A. Ferté, B. Flaugher, J. Frieman, L. Galbany, J. García-Bellido, M. Gatti, E. Gaztanaga, G. Giannini, D. Gruen, R. A. Gruendl, G. Gutierrez, W. G. Hartley, K. Herner, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. Huterer, D. J. James, N. Jeffrey, T. Jeltema, R. Kessler, O. Lahav, J. Lee, S. Lee, C. Lidman, H. Lin, M. Lin, J. L. Marshall, J. Mena-Fernández, R. Miquel, J. Muir, A. Möller, R. C. Nichol, A. Palmese, M. Paterno, W. J. Percival, A. Pieres, A. A. Plazas Malagón, B. Popovic, A. Porredon, J. Prat, H. Qu, M. Raveri, M. Rodriguez-Monroy, A. K. Romer, E. S. Rykoff, M. Sako, S. Samuroff, E. Sanchez, D. Sanchez Cid, D. Scolnic, I. Sevilla-Noarbe, P. Shah, E. Sheldon, M. Smith, E. Suchyta, M. Sullivan, M. E. C. Swanson, B. O. Sánchez, G. Tarle, G. Taylor, D. Thomas, C. To, L. Toribio San Cipriano, M. Toy, M. A. Troxel, D. L. Tucker, V. Vikram, M. Vincenzi, A. R. Walker, N. Weaverdyck, J. Weller, P. Wiseman, M. Yamamoto, B. Yanny
The Dark Energy Survey (DES) recently released the final results of its two principal probes of the expansion history: Type Ia Supernovae (SNe) and Baryonic Acoustic Oscillations (BAO). In this paper, we explore the cosmological implications of these data in combination with external Cosmic Microwave Background (CMB), Big Bang Nucleosynthesis (BBN), and age-of-the-Universe information. The BAO measurement, which is $\sim2\sigma$ away from Planck's $\Lambda$CDM predictions, pushes for low values of $\Omega_{\rm m}$ compared to Planck, in contrast to SN which prefers a higher value than Planck. We identify several tensions among datasets in the $\Lambda$CDM model that cannot be resolved by including either curvature ($k\Lambda$CDM) or a constant dark energy equation of state ($w$CDM). By combining BAO+SN+CMB despite these mild tensions, we obtain $\Omega_k=-5.5^{+4.6}_{-4.2}\times10^{-3}$ in $k\Lambda$CDM, and $w=-0.948^{+0.028}_{-0.027}$ in $w$CDM. If we open the parameter space to $w_0$$w_a$CDM\$ (where the equation of state of dark energy varies as $w(a)=w_0+(1-a)w_a$), all the datasets are mutually more compatible, and we find concordance in the $[w_0>-1,w_a<0]$ quadrant. For DES BAO and SN in combination with Planck-CMB, we find a $3.2\sigma$ deviation from $\Lambda$CDM, with $w_0=-0.673^{+0.098}_{-0.097}$, $w_a = -1.37^{+0.51}_{-0.50}$, a Hubble constant of $H_0=67.81^{+0.96}_{-0.86}$km s$^{-1}$Mpc$^{-1}$, and an abundance of matter of $\Omega_{\rm m}=0.3109^{+0.0086}_{-0.0099}$. For the combination of all the background cosmological probes considered (including CMB $\theta_\star$), we still find a deviation of $2.8\sigma$ from $\Lambda$CDMin the $w_0-w_a$ plane. Assuming a minimal neutrino mass, this work provides further evidence for non-$\Lambda$CDM physics or systematics, which is consistent with recent claims in support of evolving dark energy.
A Survey on the Landscape of Self-adaptive Cloud Design and Operations Patterns: Goals, Strategies, Tooling, Evaluation and Dataset Perspectives
2025-03-09
Apostolos Angelis, George Kousiouris
Cloud-native applications have significantly advanced the development and scalability of online services through the use of microservices and modular architectures. However, achieving adaptability, resilience, and efficient performance management within cloud environments remains a key challenge. This survey provides an overview of self-adaptive cloud design and operations patterns published over the last seven years, focusing on a taxonomy of their objectives, scope of control, decision-making mechanisms approach, automation level and validation methodologies. Overall, 96 papers have been taken under consideration, indicating a significant increase in the years since 2023 in the produced output. The analysis highlights the prevalence of feedback loop structures, with both reactive and proactive implementations, and underscores the increasing role of machine learning techniques in predictive management, especially when it comes to resource provisioning and management of the executed applications. On the other hand, adaptive application architectures through direct application-level pattern-based management seem significantly underrepresented in the current field of research, thus serving as an uninvestigated area for future research. Furthermore, the current work highlights practical aspects such as validation datasets per category (application, resource, network, etc.), tools, technologies and frameworks usage during the experimentation, in order to guide researchers in the validation process for comparative and robust experimentation.
Some results on semiclassical spectral analysis of magnetic Schrödinger operators
2025-03-09
Yuri A. Kordyukov
In our recent papers, we studied semiclassical spectral problems for the Bochner-Schr\"odinger operator on a manifold of bounded geometry. We survey some results of these papers in the setting of the magnetic Schr\"odinger operator in the Euclidean space and describe some ideas of the proofs.
2025-03-09
Hasan Abed Al Kader Hammoud, Bernard Ghanem
We propose DiffCLIP, a novel vision-language model that extends the differential attention mechanism to CLIP architectures. Differential attention was originally developed for large language models to amplify relevant context while canceling out noisy information. In this work, we integrate this mechanism into CLIP's dual encoder (image and text) framework. With minimal additional parameters, DiffCLIP achieves superior performance on image-text understanding tasks. Across zero-shot classification, retrieval, and robustness benchmarks, DiffCLIP consistently outperforms baseline CLIP models. Notably, these gains come with negligible computational overhead, demonstrating that differential attention can significantly enhance multi-modal representations without sacrificing efficiency. Code can be found at https://github.com/hammoudhasan/DiffCLIP.
Improving Access to Trade and Investment Information in Thailand through Intelligent Document Retrieval
2025-03-09
Sirinda Palahan
Overseas investment and trade can be daunting for beginners due to the vast amount of complex information. This paper presents a chatbot system that integrates natural language processing and information retrieval techniques to simplify the document retrieval process. The proposed system identifies the most relevant content, enabling users to navigate the intricate landscape of foreign trade and investment more efficiently. Our methodology combines the BM25 model and a deep learning model to rank and retrieve documents, aiming to reduce noise in the document content and enhance the accuracy of the results. Experiments with Thai natural language queries have demonstrated the effectiveness of our system in retrieving pertinent documents. A user satisfaction survey further validated the system's effectiveness. Most respondents found the system helpful and agreed with the suggested documents, indicating its potential as a valuable tool for Thai entrepreneurs navigating foreign trade and investment.
Sign Language Translation using Frame and Event Stream: Benchmark Dataset and Algorithms
2025-03-09
Xiao Wang, Yuehang Li, Fuling Wang, Bo Jiang, Yaowei Wang, Yonghong Tian, Jin Tang, Bin Luo
Accurate sign language understanding serves as a crucial communication channel for individuals with disabilities. Current sign language translation algorithms predominantly rely on RGB frames, which may be limited by fixed frame rates, variable lighting conditions, and motion blur caused by rapid hand movements. Inspired by the recent successful application of event cameras in other fields, we propose to leverage event streams to assist RGB cameras in capturing gesture data, addressing the various challenges mentioned above. Specifically, we first collect a large-scale RGB-Event sign language translation dataset using the DVS346 camera, termed VECSL, which contains 15,676 RGB-Event samples, 15,191 glosses, and covers 2,568 Chinese characters. These samples were gathered across a diverse range of indoor and outdoor environments, capturing multiple viewing angles, varying light intensities, and different camera motions. Due to the absence of benchmark algorithms for comparison in this new task, we retrained and evaluated multiple state-of-the-art SLT algorithms, and believe that this benchmark can effectively support subsequent related research. Additionally, we propose a novel RGB-Event sign language translation framework (i.e., M$^2$-SLT) that incorporates fine-grained micro-sign and coarse-grained macro-sign retrieval, achieving state-of-the-art results on the proposed dataset. Both the source code and dataset will be released on https://github.com/Event-AHU/OpenESL.
SKG-LLM: Developing a Mathematical Model for Stroke Knowledge Graph Construction Using Large Language Models
2025-03-09
Ali Sarabadani, Kheirolah Rahsepar Fard, Hamid Dalvand
The purpose of this study is to introduce SKG-LLM. A knowledge graph (KG) is constructed from stroke-related articles using mathematical and large language models (LLMs). SKG-LLM extracts and organizes complex relationships from the biomedical literature, using it to increase the accuracy and depth of KG in stroke research. In the proposed method, GPT-4 was used for data pre-processing, and the extraction of embeddings was also done by GPT-4 in the whole KG construction process. The performance of the proposed model was tested with two evaluation criteria: Precision and Recall. For further validation of the proposed model, GPT-4 was used. Compared with Wikidata and WN18RR, the proposed KG-LLM approach performs better, especially in precision and recall. By including GPT-4 in the preprocessing process, the SKG-LLM model achieved a precision score of 0.906 and a recall score of 0.923. Expert reviews further improved the results and increased precision to 0.923 and recall to 0.918. The knowledge graph constructed by SKG-LLM contains 2692 nodes and 5012 edges, which are 13 distinct types of nodes and 24 types of edges.
SEED: Towards More Accurate Semantic Evaluation for Visual Brain Decoding
2025-03-09
Juhyeon Park, Peter Yongho Kim, Jiook Cha, Shinjae Yoo, Taesup Moon
We present SEED (\textbf{Se}mantic \textbf{E}valuation for Visual Brain \textbf{D}ecoding), a novel metric for evaluating the semantic decoding performance of visual brain decoding models. It integrates three complementary metrics, each capturing a different aspect of semantic similarity between images. Using carefully crowd-sourced human judgment data, we demonstrate that SEED achieves the highest alignment with human evaluations, outperforming other widely used metrics. Through the evaluation of existing visual brain decoding models, we further reveal that crucial information is often lost in translation, even in state-of-the-art models that achieve near-perfect scores on existing metrics. To facilitate further research, we open-source the human judgment data, encouraging the development of more advanced evaluation methods for brain decoding models. Additionally, we propose a novel loss function designed to enhance semantic decoding performance by leveraging the order of pairwise cosine similarity in CLIP image embeddings. This loss function is compatible with various existing methods and has been shown to consistently improve their semantic decoding performances when used for training, with respect to both existing metrics and SEED.
The detection of irregularly spaced pulses of non-negligible width is a fascinating yet under-explored topic in signal processing. It sits adjacent to other core topics such as radar and symbol detection yet has its own distinctive challenges. Even modern techniques such as compressed sensing perform worse than may be expected on pulse processing problems. Real-world applications include nuclear spectroscopy, flow cytometry, seismic signal processing and neural spike sorting, and these in turn have applications to environmental radiation monitoring, surveying, diagnostic medicine, industrial imaging, biomedical imaging, top-down proteomics, and security screening, to name just a few. This overview paper endeavours to position the pulse processing problem in the context of signal processing. It also describes some current challenges in the field.
Dwarf Active Galactic Nuclei from Variability for the Origins of Seeds (DAVOS): Properties of Variability-Selected AGNs in the Dark Energy Survey Deep Fields
2025-03-09
Yichen Liu, Colin J. Burke, Diego Miura, Xin Liu, Qian Yang, Priyamvada Natarajan, Charlotte A. Ward
We study the black hole mass $-$ host galaxy stellar mass relation, $M_{\rm{BH}}-M_{\ast}$, for a sample of 706 $z \lesssim 1.5$ and $i \lesssim 24$ optically-variable active galactic nuclei (AGNs) in three Dark Energy Survey (DES) deep fields: C3, X3, E2, which partially cover Chandra Deep Field-South, XMM Large Scale Structure survey, and European Large Area ISO Survey, respectively. The parent sample was identified by optical variability from the DES supernova survey program imaging. Using publicly available spectra and photometric catalogs, we consolidate their spectroscopic redshifts, estimate their black hole masses using broad line widths and luminosities, and obtain improved stellar masses using spectral energy distribution fitting from X-ray to mid-infrared wavelengths. Our results confirm previous work from Hyper-Suprime Camera imaging that variability searches with deep, high-precision photometry can reliably identify AGNs in low-mass galaxies up to $z\sim1$. However, we find that the hosted black holes are overmassive than predicted by the local AGN relation, fixing host galaxy stellar mass. Instead, $z\sim 0.1-1.5$ variability-selected AGNs lie in between the $M_{\rm{BH}}-M_{\ast}$ relation for local inactive early-type galaxies and local active galaxies. This result agrees with most previous studies of $M_{\rm{BH}}-M_{\ast}$ relation for AGNs at similar redshifts, regardless of selection technique. We demonstrate that studies of variability selected AGN provide critical insights into the low-mass end of the $M_{\rm{BH}}-M_{\ast}$ relation, shedding light on the occupation fraction of that provides constraints on early BH seeding mechanisms and self-regulated feedback processes during their growth and co-evolution with their hosts.
The AI Pentad, the CHARME$^{2}$D Model, and an Assessment of Current-State AI Regulation
2025-03-08
Di Kevin Gao, Sudip Mittal, Jiming Wu, Hongwei Du, Jingdao Chen, Shahram Rahimi
Artificial Intelligence (AI) has made remarkable progress in the past few years with AI-enabled applications beginning to permeate every aspect of our society. Despite the widespread consensus on the need to regulate AI, there remains a lack of a unified approach to framing, developing, and assessing AI regulations. Many of the existing methods take a value-based approach, for example, accountability, fairness, free from bias, transparency, and trust. However, these methods often face challenges at the outset due to disagreements in academia over the subjective nature of these definitions. This paper aims to establish a unifying model for AI regulation from the perspective of core AI components. We first introduce the AI Pentad, which comprises the five essential components of AI: humans and organizations, algorithms, data, computing, and energy. We then review AI regulatory enablers, including AI registration and disclosure, AI monitoring, and AI enforcement mechanisms. Subsequently, we present the CHARME$^{2}$D Model to explore further the relationship between the AI Pentad and AI regulatory enablers. Finally, we apply the CHARME$^{2}$D model to assess AI regulatory efforts in the European Union (EU), China, the United Arab Emirates (UAE), the United Kingdom (UK), and the United States (US), highlighting their strengths, weaknesses, and gaps. This comparative evaluation offers insights for future legislative work in the AI domain.
We derive the representation of the external constraints imposed on the system within the generating functional. These constraints are applied to both the degrees of freedom and their time derivatives, and the generating functional is expressed as a functional integral over the degrees of freedom. To achieve this, we employ the path integral method for handling second-class constraints, which we have briefly reviewed. We use the obtained result to derive the generating functional for matter vector fields constrained to the spin-one state. However, achieving the desired result for a system with a singular action requires certain sufficient conditions that we will obtain.
Unveiling Inefficiencies in LLM-Generated Code: Toward a Comprehensive Taxonomy
2025-03-08
Altaf Allah Abbassi, Leuson Da Silva, Amin Nikanjam, Foutse Khomh
Large Language Models (LLMs) are widely adopted for automated code generation with promising results. Although prior research has assessed LLM-generated code and identified various quality issues -- such as redundancy, poor maintainability, and sub-optimal performance a systematic understanding and categorization of these inefficiencies remain unexplored. Without such knowledge, practitioners struggle to optimize LLM-generated code for real-world applications, limiting its adoption. This study can also guide improving code LLMs, enhancing the quality and efficiency of code generation. Therefore, in this study, we empirically investigate inefficiencies in LLM-generated code by state-of-the-art models, i.e., CodeLlama, DeepSeek-Coder, and CodeGemma. To do so, we analyze 492 generated code snippets in the HumanEval++ dataset. We then construct a taxonomy of inefficiencies in LLM-generated code that includes 5 categories General Logic, Performance, Readability, Maintainability, and Errors) and 19 subcategories of inefficiencies. We then validate the proposed taxonomy through an online survey with 58 LLM practitioners and researchers. Our study indicates that logic and performance-related inefficiencies are the most popular, relevant, and frequently co-occur and impact overall code quality inefficiency. Our taxonomy provides a structured basis for evaluating the quality LLM-generated code and guiding future research to improve code generation efficiency.
Adaptive multi-wave sampling for efficient chart validation
2025-03-08
Georg Hahn, Sebastian Schneeweiss, Shirley Wang
Computable phenotypes are used to characterize patients and identify outcomes in studies conducted using healthcare claims and electronic health record data. Chart review studies establish reference labels against which computable phenotypes are compared to understand their measurement characteristics, the quantity of interest, for instance the positive predictive value. We describe a method to adaptively evaluate a quantity of interest over sequential samples of charts, with the goal to minimize the number of charts reviewed. With the help of a simultaneous confidence band, we stop the reviewing once the confidence band meets a pre-specified stopping threshold. The contribution of this article is threefold. First, we tested the use of an adaptive approach called Neyman's sampling of charts versus random or stratified random sampling. Second, we propose frequentist confidence bands and Bayesian credible intervals to sequentially evaluate the quantity of interest. Third, we propose a tool to predict the stopping time (defined as the number of charts reviewed) at which the chart review would be complete. We observe that Bayesian credible intervals proved to be tighter than its frequentist confidence band counterparts. Moreover, we observe that simple random sampling is often performing similarly to Neyman's sampling.
Hate in the Time of Algorithms: Evidence on Online Behavior from a Large-Scale Experiment
2025-03-08
Aarushi Kalra
Social media algorithms are thought to amplify variation in user beliefs, thus contributing to radicalization. However, quantitative evidence on how algorithms and user preferences jointly shape harmful online engagement is limited. I conduct an individually randomized experiment with 8 million users of an Indian TikTok-like platform, replacing algorithmic ranking with random content delivery. Exposure to "toxic" posts decreases by 27%, mainly due to reduced platform usage by users with higher interest in such content. Strikingly, these users increase engagement with toxic posts they find. Survey evidence indicates shifts to other platforms. Model-based counterfactuals highlight the limitations of blanket algorithmic regulation.
Arcs, Caps and Generalisations in a Finite Projective Space
2025-03-08
J. W. P. Hirschfeld, J. A. Thas
Arcs and caps are fundamental structures in finite projective spaces. They can be generalised. Here, a survey is given of some important results on these objects, in particular on generalised ovals and generalised ovoids. The paper also contains recent results and several open problems.
Achieving precise and generalizable grasping across diverse objects and environments is essential for intelligent and collaborative robotic systems. However, existing approaches often struggle with ambiguous affordance reasoning and limited adaptability to unseen objects, leading to suboptimal grasp execution. In this work, we propose GAT-Grasp, a gesture-driven grasping framework that directly utilizes human hand gestures to guide the generation of task-specific grasp poses with appropriate positioning and orientation. Specifically, we introduce a retrieval-based affordance transfer paradigm, leveraging the implicit correlation between hand gestures and object affordances to extract grasping knowledge from large-scale human-object interaction videos. By eliminating the reliance on pre-given object priors, GAT-Grasp enables zero-shot generalization to novel objects and cluttered environments. Real-world evaluations confirm its robustness across diverse and unseen scenarios, demonstrating reliable grasp execution in complex task settings.
The integration of Artificial Intelligence (AI) into Integrated Development Environments (IDEs) is reshaping software development, fundamentally altering how developers interact with their tools. This shift marks the emergence of Human-AI Experience in Integrated Development Environment (in-IDE HAX), a field that explores the evolving dynamics of Human-Computer Interaction in AI-assisted coding environments. Despite rapid adoption, research on in-IDE HAX remains fragmented which highlights the need for a unified overview of current practices, challenges, and opportunities. To provide a structured overview of existing research, we conduct a systematic literature review of 89 studies, summarizing current findings and outlining areas for further investigation. Our findings reveal that AI-assisted coding enhances developer productivity but also introduces challenges, such as verification overhead, automation bias, and over-reliance, particularly among novice developers. Furthermore, concerns about code correctness, security, and maintainability highlight the urgent need for explainability, verification mechanisms, and adaptive user control. Although recent advances have driven the field forward, significant research gaps remain, including a lack of longitudinal studies, personalization strategies, and AI governance frameworks. This review provides a foundation for advancing in-IDE HAX research and offers guidance for responsibly integrating AI into software development.
A Never-Ending Story: Revisiting Requirements Major Misunderstandings
2025-03-08
Julio Cesar Leite
A magic medallion is central in Michael Ender novel, and it is depicted as two snakes biting each other, in a loop. Folk tale says that the design of the medallion changed for the Wolfgang Petersen movie, depicting an even deeper image of infinity. The medallion turned out to be an icon for the story fans. This paper will unleash a broad view of the realm of requirements and requirements engineering, comparing it to Percival quest for the Holy Grail. Using literate and pop metaphors the paper posits that requirements engineering is an education process, which must be performed with transparency. Historical misunderstandings of requirements are reviewed, pitfalls to avoid are signaled and new trails to be built are proposed.
Skyrmions in Nanotechnology: Fundamental Properties, Experimental Advances, and Emerging Applications
2025-03-08
Davi Rodrigues, Alejandro Riveros, Andrea Meo, Emily Darwin, Vito Puliafito, Anna Giordano, Mario Carpentieri, Riccardo Tomasello, Giovanni Finocchio
Skyrmions, topologically protected textures, have been observed in different fields of nanotechnology and have emerged as promising candidates for different applications due to their topological stability, low-power operation, and dynamic response to external stimuli. First introduced in particle physics, skyrmions have since been observed in different condensed matter fields, including magnetism, ferroelectricity, photonics, and acoustics. Their unique topological properties enable robust manipulation and detection, paving the way for innovative applications in room temperature sensing, storage, and computing. Recent advances in materials engineering and device integration have demonstrated several strategies for an efficient manipulation of skyrmions, addressing key challenges in their practical implementation. In this review, we summarize the state-of-the-art research on skyrmions across different platforms, highlighting their fundamental properties and characteristics, recent experimental breakthroughs, and technological potential. We present future perspectives and remaining challenges, emphasizing the interdisciplinary impact of skyrmions on nanotechnology.
3D Morphable Models (3DMMs) have played a pivotal role as a fundamental representation or initialization for 3D avatar animation and reconstruction. However, extending 3DMMs to hair remains challenging due to the difficulty of enforcing vertex-level consistent semantic meaning across hair shapes. This paper introduces a novel method, Semantic-consistent Ray Modeling of Hair (SRM-Hair), for making 3D hair morphable and controlled by coefficients. The key contribution lies in semantic-consistent ray modeling, which extracts ordered hair surface vertices and exhibits notable properties such as additivity for hairstyle fusion, adaptability, flipping, and thickness modification. We collect a dataset of over 250 high-fidelity real hair scans paired with 3D face data to serve as a prior for the 3D morphable hair. Based on this, SRM-Hair can reconstruct a hair mesh combined with a 3D head from a single image. Note that SRM-Hair produces an independent hair mesh, facilitating applications in virtual avatar creation, realistic animation, and high-fidelity hair rendering. Both quantitative and qualitative experiments demonstrate that SRM-Hair achieves state-of-the-art performance in 3D mesh reconstruction. Our project is available at https://github.com/wang-zidu/SRM-Hair
Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifiers
2025-03-08
Huan Tian, Guangsheng Zhang, Bo Liu, Tianqing Zhu, Ming Ding, Wanlei Zhou
While in-processing fairness approaches show promise in mitigating biased predictions, their potential impact on privacy leakage remains under-explored. We aim to address this gap by assessing the privacy risks of fairness-enhanced binary classifiers via membership inference attacks (MIAs) and attribute inference attacks (AIAs). Surprisingly, our results reveal that enhancing fairness does not necessarily lead to privacy compromises. For example, these fairness interventions exhibit increased resilience against MIAs and AIAs. This is because fairness interventions tend to remove sensitive information among extracted features and reduce confidence scores for the majority of training data for fairer predictions. However, during the evaluations, we uncover a potential threat mechanism that exploits prediction discrepancies between fair and biased models, leading to advanced attack results for both MIAs and AIAs. This mechanism reveals potent vulnerabilities of fair models and poses significant privacy risks of current fairness methods. Extensive experiments across multiple datasets, attack methods, and representative fairness approaches confirm our findings and demonstrate the efficacy of the uncovered mechanism. Our study exposes the under-explored privacy threats in fairness studies, advocating for thorough evaluations of potential security vulnerabilities before model deployments.
This paper introduces the System 0/1/2/3 framework as an extension of dual-process theory, employing a quad-process model of cognition. Expanding upon System 1 (fast, intuitive thinking) and System 2 (slow, deliberative thinking), we incorporate System 0, which represents pre-cognitive embodied processes, and System 3, which encompasses collective intelligence and symbol emergence. We contextualize this model within Bergson's philosophy by adopting multi-scale time theory to unify the diverse temporal dynamics of cognition. System 0 emphasizes morphological computation and passive dynamics, illustrating how physical embodiment enables adaptive behavior without explicit neural processing. Systems 1 and 2 are explained from a constructive perspective, incorporating neurodynamical and AI viewpoints. In System 3, we introduce collective predictive coding to explain how societal-level adaptation and symbol emergence operate over extended timescales. This comprehensive framework ranges from rapid embodied reactions to slow-evolving collective intelligence, offering a unified perspective on cognition across multiple timescales, levels of abstraction, and forms of human intelligence. The System 0/1/2/3 model provides a novel theoretical foundation for understanding the interplay between adaptive and cognitive processes, thereby opening new avenues for research in cognitive science, AI, robotics, and collective intelligence.
Constraints on Evolutions of Fundamental Constants from Clustering of Fast Radio Burst Dispersion Measure
2025-03-08
Shi-Yuan Wang, Jun-Qing Xia
Constrained measurements of fundamental physical constants using astronomical observational data represent a powerful method for investigating potential new physics. In particular, the dispersion measure (DM) of fast radio bursts (FRBs), which probes the electron density along their propagation paths, may be influenced by the space-time variation of the fine-structure constant \(\alpha\). In this study, we analyze the cross-correlation signal between foreground galaxies and the DM of background FRBs to constrain the evolution of \(\alpha\). Assuming large-scale structure (LSS) galaxy surveys with the capabilities of the China Space Station Telescope (CSST) at \(z=0.15\) and { a mock FRB survey with \(N_{\text{FRB}}=10^5\) at \(z=0.4\), we test how well \(\alpha\) variation can be constrained}, with a standard deviation of \(\sigma(\Delta \alpha / \alpha) = 0.0007\) at \(z=0.15\). Furthermore, taking into account the nonminimal coupling between the scalar field and the electromagnetic field, the variation in \(\alpha\) can lead to the non-conservation of photon number along geodesics. This would result in a violation of the CDDR and affect the evolution of the Cosmic Microwave Background (CMB) temperature. In this work, we { obtain constraints results} on the CDDR parameter \(\eta\) and the parameter \(\beta\) governing CMB temperature evolution at \(z=0.15\), yielding \(\sigma(\eta) = 0.0004\) and \(\sigma(\beta) = 0.0006\), respectively. Finally, we relate the variation in \(\alpha\) to the time evolution of the proton-to-electron mass ratio, { reporting a standard deviation} of \(\sigma(\Delta \mu/\mu) = 0.002\) at $z=0.15$. Future FRB surveys hold significant potential for advancing our understanding of the evolution of fundamental physical constants.
Advancing Problem-Based Learning with Clinical Reasoning for Improved Differential Diagnosis in Medical Education
2025-03-08
Yuansong Xu, Yuheng Shao, Jiahe Dong, Shaohan Shi, Chang Jiang, Quan Li
Medical education increasingly emphasizes students' ability to apply knowledge in real-world clinical settings, focusing on evidence-based clinical reasoning and differential diagnoses. Problem-based learning (PBL) addresses traditional teaching limitations by embedding learning into meaningful contexts and promoting active participation. However, current PBL practices are often confined to medical instructional settings, limiting students' ability to self-direct and refine their approaches based on targeted improvements. Additionally, the unstructured nature of information organization during analysis poses challenges for record-keeping and subsequent review. Existing research enhances PBL realism and immersion but overlooks the construction of logic chains and evidence-based reasoning. To address these gaps, we designed e-MedLearn, a learner-centered PBL system that supports more efficient application and practice of evidence-based clinical reasoning. Through controlled study (N=19) and testing interviews (N=13), we gathered data to assess the system's impact. The findings demonstrate that e-MedLearn improves PBL experiences and provides valuable insights for advancing clinical reasoning-based learning.
A Survey on Post-training of Large Language Models
2025-03-08
Guiyao Tie, Zeli Zhao, Dingjie Song, Fuyang Wei, Rong Zhou, Yurou Dai, Wen Yin, Zhejian Yang, Jiangyue Yan, Yao Su, Zhenhan Dai, Yifeng Xie, Yihan Cao, Lichao Sun, Pan Zhou, Lifang He, Hechang Chen, Yu Zhang, Qingsong Wen, Tianming Liu, Neil Zhenqiang Gong, Jiliang Tang, Caiming Xiong, Heng Ji, Philip S. Yu, Jianfeng Gao
The emergence of Large Language Models (LLMs) has fundamentally transformed natural language processing, making them indispensable across domains ranging from conversational systems to scientific exploration. However, their pre-trained architectures often reveal limitations in specialized contexts, including restricted reasoning capacities, ethical uncertainties, and suboptimal domain-specific performance. These challenges necessitate advanced post-training language models (PoLMs) to address these shortcomings, such as OpenAI-o1/o3 and DeepSeek-R1 (collectively known as Large Reasoning Models, or LRMs). This paper presents the first comprehensive survey of PoLMs, systematically tracing their evolution across five core paradigms: Fine-tuning, which enhances task-specific accuracy; Alignment, which ensures alignment with human preferences; Reasoning, which advances multi-step inference despite challenges in reward design; Efficiency, which optimizes resource utilization amidst increasing complexity; and Integration and Adaptation, which extend capabilities across diverse modalities while addressing coherence issues. Charting progress from ChatGPT's foundational alignment strategies to DeepSeek-R1's innovative reasoning advancements, we illustrate how PoLMs leverage datasets to mitigate biases, deepen reasoning capabilities, and enhance domain adaptability. Our contributions include a pioneering synthesis of PoLM evolution, a structured taxonomy categorizing techniques and datasets, and a strategic agenda emphasizing the role of LRMs in improving reasoning proficiency and domain flexibility. As the first survey of its scope, this work consolidates recent PoLM advancements and establishes a rigorous intellectual framework for future research, fostering the development of LLMs that excel in precision, ethical robustness, and versatility across scientific and societal applications.
Visual Embedding of Screen Sequences for User-Flow Search in Example-driven Communication
2025-03-08
Daeheon Jeong, Hyehyun Chu
Effective communication of UX considerations to stakeholders (e.g., designers and developers) is a critical challenge for UX practitioners. To explore this problem, we interviewed four UX practitioners about their communication challenges and strategies. Our study identifies that providing an example user flow-a screen sequence representing a semantic task-as evidence reinforces communication, yet finding relevant examples remains challenging. To address this, we propose a method to systematically retrieve user flows using semantic embedding. Specifically, we design a model that learns to associate screens' visual features with user flow descriptions through contrastive learning. A survey confirms that our approach retrieves user flows better aligned with human perceptions of relevance. We analyze the results and discuss implications for the computational representation of user flows.
A Novel Trustworthy Video Summarization Algorithm Through a Mixture of LoRA Experts
2025-03-08
Wenzhuo Du, Gerun Wang, Guancheng Chen, Hang Zhao, Xin Li, Jian Gao
With the exponential growth of user-generated content on video-sharing platforms, the challenge of facilitating efficient searching and browsing of videos has garnered significant attention. To enhance users' ability to swiftly locate and review pertinent videos, the creation of concise and informative video summaries has become increasingly important. Video-llama is an effective tool for generating video summarization, but it cannot effectively unify and optimize the modeling of temporal and spatial features and requires a lot of computational resources and time. Therefore, we propose MiLoRA-ViSum to more efficiently capture complex temporal dynamics and spatial relationships inherent in video data and to control the number of parameters for training. By extending traditional Low-Rank Adaptation (LoRA) into a sophisticated mixture-of-experts paradigm, MiLoRA-ViSum incorporates a dual temporal-spatial adaptation mechanism tailored specifically for video summarization tasks. This approach dynamically integrates specialized LoRA experts, each fine-tuned to address distinct temporal or spatial dimensions. Extensive evaluations of the VideoXum and ActivityNet datasets demonstrate that MiLoRA-ViSum achieves the best summarization performance compared to state-of-the-art models, while maintaining significantly lower computational costs. The proposed mixture-of-experts strategy, combined with the dual adaptation mechanism, highlights the model's potential to enhance video summarization capabilities, particularly in large-scale applications requiring both efficiency and precision.
Fine-Grained Bias Detection in LLM: Enhancing detection mechanisms for nuanced biases
2025-03-08
Suvendu Mohanty
Recent advancements in Artificial Intelligence, particularly in Large Language Models (LLMs), have transformed natural language processing by improving generative capabilities. However, detecting biases embedded within these models remains a challenge. Subtle biases can propagate misinformation, influence decision-making, and reinforce stereotypes, raising ethical concerns. This study presents a detection framework to identify nuanced biases in LLMs. The approach integrates contextual analysis, interpretability via attention mechanisms, and counterfactual data augmentation to capture hidden biases across linguistic contexts. The methodology employs contrastive prompts and synthetic datasets to analyze model behaviour across cultural, ideological, and demographic scenarios. Quantitative analysis using benchmark datasets and qualitative assessments through expert reviews validate the effectiveness of the framework. Results show improvements in detecting subtle biases compared to conventional methods, which often fail to highlight disparities in model responses to race, gender, and socio-political contexts. The framework also identifies biases arising from imbalances in training data and model architectures. Continuous user feedback ensures adaptability and refinement. This research underscores the importance of proactive bias mitigation strategies and calls for collaboration between policymakers, AI developers, and regulators. The proposed detection mechanisms enhance model transparency and support responsible LLM deployment in sensitive applications such as education, legal systems, and healthcare. Future work will focus on real-time bias monitoring and cross-linguistic generalization to improve fairness and inclusivity in AI-driven communication tools.
The Role of Affective States in Computational Psychiatry
2025-03-08
David Benrimoh, Ryan Smith, Andreea O. Diaconescu, Timothy Friesen, Sara Jalali, Nace Mikus, Laura Gschwandtner, Jay Gandhi, Guillermo Horga, Albert Powers
Studying psychiatric illness has often been limited by difficulties in connecting symptoms and behavior to neurobiology. Computational psychiatry approaches promise to bridge this gap by providing formal accounts of the latent information processing changes that underlie the development and maintenance of psychiatric phenomena. Models based on these theories generate individual-level parameter estimates which can then be tested for relationships to neurobiology. In this review, we explore computational modelling approaches to one key aspect of health and illness: affect. We discuss strengths and limitations of key approaches to modelling affect, with a focus on reinforcement learning, active inference, the hierarchical gaussian filter, and drift-diffusion models. We find that, in this literature, affect is an important source of modulation in decision making, and has a bidirectional influence on how individuals infer both internal and external states. Highlighting the potential role of affect in information processing changes underlying symptom development, we extend an existing model of psychosis, where affective changes are influenced by increasing cortical noise and consequent increases in either perceived environmental instability or expected noise in sensory input, becoming part of a self-reinforcing process generating negatively valenced, over-weighted priors underlying positive symptom development. We then provide testable predictions from this model at computational, neurobiological, and phenomenological levels of description.
The rapid advancement of artificial intelligence (AI) technologies has led to an increasing deployment of AI models on edge and terminal devices, driven by the proliferation of the Internet of Things (IoT) and the need for real-time data processing. This survey comprehensively explores the current state, technical challenges, and future trends of on-device AI models. We define on-device AI models as those designed to perform local data processing and inference, emphasizing their characteristics such as real-time performance, resource constraints, and enhanced data privacy. The survey is structured around key themes, including the fundamental concepts of AI models, application scenarios across various domains, and the technical challenges faced in edge environments. We also discuss optimization and implementation strategies, such as data preprocessing, model compression, and hardware acceleration, which are essential for effective deployment. Furthermore, we examine the impact of emerging technologies, including edge computing and foundation models, on the evolution of on-device AI models. By providing a structured overview of the challenges, solutions, and future directions, this survey aims to facilitate further research and application of on-device AI, ultimately contributing to the advancement of intelligent systems in everyday life.
Quantifying the Impact of LSST $u$-band Survey Strategy on Photometric Redshift Estimation and the Detection of Lyman-break Galaxies
2025-03-08
John Franklin Crenshaw, Boris Leistedt, Melissa Lynn Graham, Constantin Payerne, Andrew J. Connolly, Eric Gawiser, Tanveer Karim, Alex I. Malz, Jeffrey A. Newman, Marina Ricci, The LSST Dark Energy Science Collaboration
The Vera C. Rubin Observatory will conduct the Legacy Survey of Space and Time (LSST), promising to discover billions of galaxies out to redshift 7, using six photometric bands ($ugrizy$) spanning the near-ultraviolet to the near-infrared. The exact number of and quality of information about these galaxies will depend on survey depth in these six bands, which in turn depends on the LSST survey strategy: i.e., how often and how long to expose in each band. $u$-band depth is especially important for photometric redshift (photo-z) estimation and for detection of high-redshift Lyman-break galaxies (LBGs). In this paper we use a simulated galaxy catalog and an analytic model for the LBG population to study how recent updates and proposed changes to Rubin's $u$-band throughput and LSST survey strategy impact photo-z accuracy and LBG detection. We find that proposed variations in $u$-band strategy have a small impact on photo-z accuracy for $z < 1.5$ galaxies, but the outlier fraction, scatter, and bias for higher redshift galaxies varies by up to 50%, depending on the survey strategy considered. The number of $u$-band dropout LBGs at $z \sim 3$ is also highly sensitive to the $u$-band depth, varying by up to 500%, while the number of $griz$-band dropouts is only modestly affected. Under the new $u$-band strategy recommended by the Rubin Survey Cadence Optimization Committee, we predict $u$-band dropout number densities of $110$ deg$^{-2}$ (3200 deg$^{-2}$) in year 1 (10) of LSST. We discuss the implications of these results for LSST cosmology.
Self-Correction based on feedback improves the output quality of Large Language Models (LLMs). Moreover, as Self-Correction functions like the slow and conscious System-2 thinking from cognitive psychology's perspective, it can potentially reduce LLMs' social biases. LLMs are sensitive to contextual ambiguities and inconsistencies; therefore, explicitly communicating their intentions during interactions when applying Self-Correction for debiasing is crucial. In this study, we demonstrate that clarifying intentions is essential for effectively reducing biases in LLMs through Self-Correction. We divide the components needed for Self-Correction into three parts: instruction, response, and feedback, and clarify intentions at each component. We incorporate an explicit debiasing prompt to convey the intention of bias mitigation from the instruction for response generation. In the response, we use Chain-of-Thought (CoT) to clarify the reasoning process. In the feedback, we define evaluation aspects necessary for debiasing and propose clear feedback through multi-aspect critiques and scoring. Through experiments, we demonstrate that self-correcting CoT responses obtained from a debiasing prompt based on multi-aspect feedback can reduce biased responses more robustly and consistently than the baselines. We also find the variation in debiasing efficacy when using models with different bias levels or separating models for response and feedback generation.
Unveiling the Infrared Excess of SIPS J2045-6332: Evidence for a Young Stellar Object with Potential Low-Mass Companion
2025-03-08
Michiharu Hyogo, Thomas P. Bickle, Joseph R. Biggs, Adam J. Burgasser, Dan Caselden, Sarah Casewell, Sergio B. Dieterich, Hugo A. Durantini Luca, Jacqueline Faherty, Daniella Bardalez Gagliuffi, Jonathan Gagne, J. Davy Kirkpatrick, Marc J. Kuchner, Carey M. Lisse, Federico Marocco, Aaron M. Meisner, Austin Rothermich, Adam C. Schneider, Steven M. Silverberg, Disk Detective Collaboration, The Backyard Worlds, :, Planet 9 Collaboration
The Disk Detective project, a citizen science initiative, aims to identify circumstellar discs around stars by detecting objects with infrared (IR) excess using data from the Wide-field Infrared Survey Explorer (WISE). In this study, we investigate SIPS J2045-6332, a potential brown dwarf with significant IR excess in WISE and 2MASS bands, initially identified by project volunteers. Despite early indicators of a circumstellar disc, discrepancies between observed brightness and expected Spectral Energy Distribution (SED) models suggested unusual properties. To explore potential explanations, we created SED templates for spectral types M9 to L4 and compared them with SIPS J2045-6332's photometric data, revealing an excess brightness that points to either an unresolved low-mass companion or a young, inflated primary star. Further analysis of infrared spectral features and surface gravity indicators supports a youthful classification, estimating the object's age at 26-200 million years. Observations also suggest the presence of a mid L-type companion at a projected distance of 6.7 AU. This study highlights SIPS J2045-6332 as an intriguing system with unique IR characteristics and recommends follow-up observations with high-resolution telescopes to confirm the companion hypothesis and further characterize the system.
MeerKAT view of Hickson Compact Groups: II. HI deficiency in the core and surrounding regions
2025-03-08
A. Sorgho, L. Verdes-Montenegro, R. Ianjamasimanana, K. M. Hess, M. G. Jones, M. Korsaga, Jing Wang, Xuchen Lin, J. M. Solanes, M. E. Cluver, J. M. Cannon, A. Bosma, E. Athanassoula, A. del Olmo, J. Perea, J. Moldón, T. Wiegert, S. Sanchez-Expósito, J. Garrido, R. García-Benito, G. I. G. Józsa, S. Borthakur, T. Jarrett, B. Namumba, E. Pérez, Javier Román, O. M. Smirnov, M. Yun
Hickson compact groups (HCGs) offer an ideal environment for investigating galaxy transformation as a result of interactions. It has been established that the evolutionary sequence of HCGs is marked by an intermediate stage characterised by a substantial amount of HI in their intragroup medium (IGrM) in the form of tidal tails and bridges (Phase 2), rapidly followed by a final stage where no IGrM gas is found and where their member galaxies are highly HI-deficient (Phase 3). Despite numerous single-dish and interferometric HI studies on the HCGs, a clear HI picture of the groups within their large-scale environment still remains to be uncovered. Taking advantage of the MeerKAT's high column density sensitivity and large field-of-view, we aim to investigate the rapid transformation of HCGs from the intermediate to late phases, and establish a picture of their gas content variations in the context of their large-scale environments. We performed MeerKAT observations of six HCGs selected to represent the intermediate and late phases of the proposed evolutionary sequence. Combining the HI observations with data from recent wide-field optical surveys, we evaluated the HI deficiencies of galaxies in a ~30' radius of the HCGs. We find that galaxies surrounding both phases exhibit similar distributions in their gas content. Similarly, galaxies making up the cores of Phase 2 HCGs are comparable to their neighbours in terms of HI deficiencies. However, Phase 3 groups are over an order of magnitude more deficient than their surroundings, supporting previous findings that late-phase HCG galaxies are more evolved than their large-scale environments.
Context: A deeper understanding of human factors in software engineering (SE) is essential for improving team collaboration, decision-making, and productivity. Communication channels like code reviews and chats provide insights into developers' psychological and emotional states. While large language models excel at text analysis, they often lack transparency and precision. Psycholinguistic tools like Linguistic Inquiry and Word Count (LIWC) offer clearer, interpretable insights into cognitive and emotional processes exhibited in text. Despite its wide use in SE research, no comprehensive review of LIWC's use has been conducted. Objective: We examine the importance of psycholinguistic tools, particularly LIWC, and provide a thorough analysis of its current and potential future applications in SE research. Methods: We conducted a systematic review of six prominent databases, identifying 43 SE-related papers using LIWC. Our analysis focuses on five research questions. Results: Our findings reveal a wide range of applications, including analyzing team communication to detect developer emotions and personality, developing ML models to predict deleted Stack Overflow posts, and more recently comparing AI-generated and human-written text. LIWC has been primarily used with data from project management platforms (e.g., GitHub) and Q&A forums (e.g., Stack Overflow). Key BSE concepts include Communication, Organizational Climate, and Positive Psychology. 26 of 43 papers did not formally evaluate LIWC. Concerns were raised about some limitations, including difficulty handling SE-specific vocabulary. Conclusion: We highlight the potential of psycholinguistic tools and their limitations, and present new use cases for advancing the research of human factors in SE (e.g., bias in human-LLM conversations).
Research on Superalignment Should Advance Now with Parallel Optimization of Competence and Conformity
2025-03-08
HyunJin Kim, Xiaoyuan Yi, Jing Yao, Muhua Huang, JinYeong Bak, James Evans, Xing Xie
The recent leap in AI capabilities, driven by big generative models, has sparked the possibility of achieving Artificial General Intelligence (AGI) and further triggered discussions on Artificial Superintelligence (ASI), a system surpassing all humans across all domains. This gives rise to the critical research question of: If we realize ASI, how do we align it with human values, ensuring it benefits rather than harms human society, a.k.a., the Superalignment problem. Despite ASI being regarded by many as solely a hypothetical concept, in this paper, we argue that superalignment is achievable and research on it should advance immediately, through simultaneous and alternating optimization of task competence and value conformity. We posit that superalignment is not merely a safeguard for ASI but also necessary for its realization. To support this position, we first provide a formal definition of superalignment rooted in the gap between capability and capacity and elaborate on our argument. Then we review existing paradigms, explore their interconnections and limitations, and illustrate a potential path to superalignment centered on two fundamental principles. We hope this work sheds light on a practical approach for developing the value-aligned next-generation AI, garnering greater benefits and reducing potential harms for humanity.
Electric-field sensing with driven-dissipative time crystals in room-temperature Rydberg vapor
2025-03-07
Darmindra Arumugam
Mode competition in nonequilibrium Rydberg gases enables the exploration of emergent many-body phases. This work leverages this emergent phase for electric field detection at room temperature. Sensitive frequency-resolved electric field measurements at very low-frequencies (VLF) are of central importance in a wide range of applications where deep-penetration is required in communications, navigation and imaging or surveying. The long wavelengths on order of 10-100 km (3-30 kHz) limit the efficiency, sensitivity, and bandwidth of compact classical detectors that are constrained by the Chu limit. Rydberg-atom electrometers are an attractive approach for microwave electric-field sensors but have reduced sensitivity at lower-frequencies. Very recent efforts to advance the standard Rydberg-atoms approach is based on DC electric-field (E-field) Stark shifting and have resulted in sensitivities between 67.9-2.2 uVcm-1Hz-1/2 (0.1-10 kHz) by fine optimization of the DC E-field. A major challenge in these approaches is the need for embedded electrodes or plates due to DC E-field Stark screening effect, which can perturb coupling of VLF signals when injected from external sources. In this article, it is demonstrated that state-of-art sensitivity (~1.6-2.3 uVcm-1Hz-1/2) can instead be achieved using limit-cycle oscillations in driven-dissipative Rydberg atoms by using a magnetic field (B-field) to develop mode-competition between nearby Rydberg states. The mode-competition between nearby Rydberg-states develop an effective transition centered at the oscillation frequency capable of supporting external VLF E-field coupling in the ~10-15kHz regime without the requirement for fine optimization of the B-field magnitude.
ALMAGAL III. Compact source catalog: Fragmentation statistics and physical evolution of the core population
2025-03-07
A. Coletta, S. Molinari, E. Schisano, A. Traficante, D. Elia, M. Benedettini, C. Mininni, J. D. Soler, Á. Sánchez-Monge, P. Schilke, C. Battersby, G. A. Fuller, H. Beuther, Q. Zhang, M. T. Beltrán, B. Jones, R. S. Klessen, S. Walch, F. Fontani, A. Avison, C. L. Brogan, S. D. Clarke, P. Hatchfield, P. Hennebelle, P. T. Ho, T. R. Hunter, K. G. Johnston, P. D. Klaassen, P. M. Koch, R. Kuiper, D. C. Lis, T. Liu, S. L. Lumsden, Y. Maruccia, T. Möller, L. Moscadelli, A. Nucara, A. J. Rigby, K. L. J. Rygl, P. Sanhueza, F. van der Tak, M. R. A. Wells, F. Wyrowski, F. De Angelis, S. Liu, A. Ahmadi, L. Bronfman, S. -Y. Liu, Y. -N. Su, Y. Tang, L. Testi, H. Zinnecker
The mechanisms behind the fragmentation of high-mass dense clumps into compact star-forming cores are fundamental topics in current astrophysical research. The ALMAGAL survey provides the opportunity to study this process at an unprecedented level of detail and statistical significance, featuring high-angular resolution $1.38$ mm ALMA observations of $1013$ massive dense clumps at various Galactic locations. These clumps cover a wide range of distances, masses, surface densities, and evolutionary stages. Here, we present the catalog of compact sources obtained with the CuTEx algorithm from continuum images of the full ALMAGAL clump sample combining ACA-$7$m and $12$m ALMA arrays, reaching a uniform high median spatial resolution of $\sim1400$ au. We discuss the fragmentation properties and the estimated physical parameters of the core population. The ALMAGAL compact source catalog includes $6348$ cores detected in $844$ clumps ($83\%$ of the total), with a number of cores per clump between $1$ and $49$ (median of $5$). The estimated core diameters are mostly within $\sim800-3000$ au (median of $1700$ au). We obtained core masses from $0.002$ to $345\,\mathrm{M_{\odot}}$. We evaluated the variation in the core mass function (CMF) with evolution as traced by the clump $L/M$, finding a clear, robust shift and change in slope among CMFs within subsamples at different stages. This finding suggests that the CMF shape is not constant throughout the star formation process, but rather it builds (and flattens) with evolution, with higher core masses reached at later stages. We found that all cores within a clump grow in mass on average with evolution, and the number of cores increases with the core masses. Our results favor a clump-fed scenario for high-mass star formation, in which cores form as low-mass seeds, and then gain mass while further fragmentation occurs in the clump.
Information technology has profoundly altered the way humans interact with information. The vast amount of content created, shared, and disseminated online has made it increasingly difficult to access relevant information. Over the past two decades, search and recommendation systems (collectively referred to as information retrieval systems) have evolved significantly to address these challenges. Recent advances in large language models (LLMs) have demonstrated capabilities that surpass human performance in various language-related tasks and exhibit general understanding, reasoning, and decision-making abilities. This paper explores the transformative potential of large language model agents in enhancing search and recommendation systems. We discuss the motivations and roles of LLM agents, and establish a classification framework to elaborate on the existing research. We highlight the immense potential of LLM agents in addressing current challenges in search and recommendation, providing insights into future research directions. This paper is the first to systematically review and classify the research on LLM agents in these domains, offering a novel perspective on leveraging this advanced AI technology for information retrieval. To help understand the existing works, we list the existing papers on agent-based simulation with large language models at this link: https://github.com/tsinghua-fib-lab/LLM-Agent-for-Recommendation-and-Search.
Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms: Challenges and a Roadmap
2025-03-07
Jianye Xu, Bassam Alrifaee, Johannes Betz, Armin Mokhtarian, Archak Mittal, Mengchi Cai, Rahul Mangharam, Omar M. Shehata, Catherine M. Elias, Jan-Nico Zaech, Patrick Scheffe, Felix Jahncke, Sangeet Sankaramangalam Ulhas, Kaj Munhoz Arfvidsson
This article proposes a roadmap to address the current challenges in small-scale testbeds for Connected and Automated Vehicles (CAVs) and robot swarms. The roadmap is a joint effort of participants in the workshop "1st Workshop on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms," held on June 2 at the IEEE Intelligent Vehicles Symposium (IV) 2024 in Jeju, South Korea. The roadmap contains three parts: 1) enhancing accessibility and diversity, especially for underrepresented communities, 2) sharing best practices for the development and maintenance of testbeds, and 3) connecting testbeds through an abstraction layer to support collaboration. The workshop features eight invited speakers, four contributed papers [1]-[4], and a presentation of a survey paper on testbeds [5]. The survey paper provides an online comparative table of more than 25 testbeds, available at https://bassamlab.github.io/testbeds-survey. The workshop's own website is available at https://cpm-remote.lrt.unibw-muenchen.de/iv24-workshop.
dARt Vinci: Egocentric Data Collection for Surgical Robot Learning at Scale
2025-03-07
Yihao Liu, Yu-Chun Ku, Jiaming Zhang, Hao Ding, Peter Kazanzides, Mehran Armand
Data scarcity has long been an issue in the robot learning community. Particularly, in safety-critical domains like surgical applications, obtaining high-quality data can be especially difficult. It poses challenges to researchers seeking to exploit recent advancements in reinforcement learning and imitation learning, which have greatly improved generalizability and enabled robots to conduct tasks autonomously. We introduce dARt Vinci, a scalable data collection platform for robot learning in surgical settings. The system uses Augmented Reality (AR) hand tracking and a high-fidelity physics engine to capture subtle maneuvers in primitive surgical tasks: By eliminating the need for a physical robot setup and providing flexibility in terms of time, space, and hardware resources-such as multiview sensors and actuators-specialized simulation is a viable alternative. At the same time, AR allows the robot data collection to be more egocentric, supported by its body tracking and content overlaying capabilities. Our user study confirms the proposed system's efficiency and usability, where we use widely-used primitive tasks for training teleoperation with da Vinci surgical robots. Data throughput improves across all tasks compared to real robot settings by 41% on average. The total experiment time is reduced by an average of 10%. The temporal demand in the task load survey is improved. These gains are statistically significant. Additionally, the collected data is over 400 times smaller in size, requiring far less storage while achieving double the frequency.
2025-03-07
Richard C. Brower, George T. Flemming, Nobuyuki Matsumoto, Rohan Misra
We review the recent construction~\cite{brower2024isingmodelmathbbs2} of the 2d Ising model on a triangulated sphere $\mathbb{S}^2$. Surprisingly, this led to a precise map of the lattice couplings to the target geometry in order to reach the conform field theory (CFT) in the continuum limit. For the integrable 2d Ising CFT, the map was found analytically~\cite{Brower_2023}. Here we conjecture how this might be generalized. The discrete geometry is implemented by the piecewise flat triangulation introduced by Regge in 1960 for the Einstein Hilbert action~\cite{Regge1961GeneralRW}. Then following our Ising example, we posit the existence of a smooth map of lattice couplings in affine parameters consistent with quantum correlators. A sequence of theoretical investigations and numerical simulations are recommended to test this conjecture. They begin with non-integrable CFT's -- the 2d $\phi^4$ theory on $\mathbb{S}^2$; the 3d Ising model on $\mathbb{S}^3$ and $\mathbb{R} \times \mathbb{S}^2$; QED3 on $\mathbb{R} \times \mathbb{S}^{2} $ as an intermediate step to 4d non-Abelian lattice gauge theory on $\mathbb{R} \times \mathbb{S}^3$.
Large Language Models (LLMs) have revolutionized natural language processing, yet their internal mechanisms remain largely opaque. Recently, mechanistic interpretability has attracted significant attention from the research community as a means to understand the inner workings of LLMs. Among various mechanistic interpretability approaches, Sparse Autoencoders (SAEs) have emerged as a particularly promising method due to their ability to disentangle the complex, superimposed features within LLMs into more interpretable components. This paper presents a comprehensive examination of SAEs as a promising approach to interpreting and understanding LLMs. We provide a systematic overview of SAE principles, architectures, and applications specifically tailored for LLM analysis, covering theoretical foundations, implementation strategies, and recent developments in sparsity mechanisms. We also explore how SAEs can be leveraged to explain the internal workings of LLMs, steer model behaviors in desired directions, and develop more transparent training methodologies for future models. Despite the challenges that remain around SAE implementation and scaling, they continue to provide valuable tools for understanding the internal mechanisms of large language models.
The shape of FIREbox galaxies and a potential tension with low-mass disks
2025-03-07
Courtney Klein, James S. Bullock, Luke Xia, Jorge Moreno, Robert Feldmann, Francisco J. Mercado, Claude-André Faucher-Giguère, Jonathan Stern, N. Nicole Sanchez, Abdelaziz Hussein
We study the intrinsic and observable shapes of approximately 700 star-forming galaxies with stellar masses $10^8 - 10^{11}$ M$_\odot$ from the FIREbox simulation at $z=0$. We calculate intrinsic axis ratios using inertia tensors weighted by: All Stars, Young Stars, and Luminosity-weighted Stars. Young Stars, in particular, are arranged in systematically different 3D configurations as a function of galaxy stellar mass, with spheroidal, elongated, and disky shapes dominant at stellar masses of $10^{8.5}$ M$_\odot$, $10^{9.5}$ M$_\odot$, and $10^{10.5}$ M$_\odot$, respectively. We construct mock images for each galaxy and show that projected short-to-long axis ratios, $q$, inferred from 2D S\'ersic fits are most closely related to Luminosity-weighted tensor shapes and least resemble the All Stars shapes. This suggests observed 2D shape distributions should not be compared to predictions based on 3D stellar mass shapes. We construct a sample of mock images projected in random orientations and compare them to observed axis ratio distributions from the GAMA survey. For galaxies with stellar masses $10^{10} - 10^{11}$ M$_\odot$, we reproduce axis ratios comparable to the thinnest observed in real galaxies ($q \sim 0.1$), suggesting this model is capable of making thin disk galaxies at Milky Way scale. However, at masses below $10^{10}$ M$_\odot$, we produce an insufficient population of galaxies with observed $q<0.4$ and none with $q<0.2$, suggesting that FIREbox does not produce enough low-mass disk galaxies. Future observational and theoretical programs aimed at understanding low-mass disk fractions will provide crucial tests of galaxy formation models.
From Theory to Application: A Practical Introduction to Neural Operators in Scientific Computing
2025-03-07
Prashant K. Jha
This focused review explores a range of neural operator architectures for approximating solutions to parametric partial differential equations (PDEs), emphasizing high-level concepts and practical implementation strategies. The study covers foundational models such as Deep Operator Networks (DeepONet), Principal Component Analysis-based Neural Networks (PCANet), and Fourier Neural Operators (FNO), providing comparative insights into their core methodologies and performance. These architectures are demonstrated on two classical linear parametric PDEs: the Poisson equation and linear elastic deformation. Beyond forward problem-solving, the review delves into applying neural operators as surrogates in Bayesian inference problems, showcasing their effectiveness in accelerating posterior inference while maintaining accuracy. The paper concludes by discussing current challenges, particularly in controlling prediction accuracy and generalization. It outlines emerging strategies to address these issues, such as residual-based error correction and multi-level training. This review can be seen as a comprehensive guide to implementing neural operators and integrating them into scientific computing workflows.
InDRiVE: Intrinsic Disagreement based Reinforcement for Vehicle Exploration through Curiosity Driven Generalized World Model
2025-03-07
Feeza Khan Khanzada, Jaerock Kwon
Model-based Reinforcement Learning (MBRL) has emerged as a promising paradigm for autonomous driving, where data efficiency and robustness are critical. Yet, existing solutions often rely on carefully crafted, task specific extrinsic rewards, limiting generalization to new tasks or environments. In this paper, we propose InDRiVE (Intrinsic Disagreement based Reinforcement for Vehicle Exploration), a method that leverages purely intrinsic, disagreement based rewards within a Dreamer based MBRL framework. By training an ensemble of world models, the agent actively explores high uncertainty regions of environments without any task specific feedback. This approach yields a task agnostic latent representation, allowing for rapid zero shot or few shot fine tuning on downstream driving tasks such as lane following and collision avoidance. Experimental results in both seen and unseen environments demonstrate that InDRiVE achieves higher success rates and fewer infractions compared to DreamerV2 and DreamerV3 baselines despite using significantly fewer training steps. Our findings highlight the effectiveness of purely intrinsic exploration for learning robust vehicle control behaviors, paving the way for more scalable and adaptable autonomous driving systems.
A-SEE2.0: Active-Sensing End-Effector for Robotic Ultrasound Systems with Dense Contact Surface Perception Enabled Probe Orientation Adjustment
2025-03-07
Yernar Zhetpissov, Xihan Ma, Kehan Yang, Haichong K. Zhang
Conventional freehand ultrasound (US) imaging is highly dependent on the skill of the operator, often leading to inconsistent results and increased physical demand on sonographers. Robotic Ultrasound Systems (RUSS) aim to address these limitations by providing standardized and automated imaging solutions, especially in environments with limited access to skilled operators. This paper presents the development of a novel RUSS system that employs dual RGB-D depth cameras to maintain the US probe normal to the skin surface, a critical factor for optimal image quality. Our RUSS integrates RGB-D camera data with robotic control algorithms to maintain orthogonal probe alignment on uneven surfaces without preoperative data. Validation tests using a phantom model demonstrate that the system achieves robust normal positioning accuracy while delivering ultrasound images comparable to those obtained through manual scanning. A-SEE2.0 demonstrates 2.47 ${\pm}$ 1.25 degrees error for flat surface normal-positioning and 12.19 ${\pm}$ 5.81 degrees normal estimation error on mannequin surface. This work highlights the potential of A-SEE2.0 to be used in clinical practice by testing its performance during in-vivo forearm ultrasound examinations.
2025-03-07
Michael R. Douglas, Cristofero Fraser-Taliente
We review the problem of finding paths in Cayley graphs of groups and group actions, using the Rubik's cube as an example, and we list several more examples of significant mathematical interest. We then show how to formulate these problems in the framework of diffusion models. The exploration of the graph is carried out by the forward process, while finding the target nodes is done by the inverse backward process. This systematizes the discussion and suggests many generalizations. To improve exploration, we propose a ``reversed score'' ansatz which substantially improves over previous comparable algorithms.
ALMAGAL I. The ALMA evolutionary study of high-mass protocluster formation in the Galaxy. Presentation of the survey and early results
2025-03-07
S. Molinari, P. Schilke, C. Battersby, P. T. P. Ho, A. Sanchez-Monge, A. Traficante, B. Jones, M. T. Beltran, H. Beuther, G. A. Fuller, Q. Zhang, R. S. Klessen, S. Walch, Y. -W. Tang, M. Benedettini, D. Elia, A. Coletta, C. Mininni, E. Schisano, A. Avison, C. Y. Law, A. Nucara, J. D. Soler, G. Stroud, J. Wallace, M. R. A. Wells, A. Ahmadi, C. L. Brogan, T. R. Hunter, S. -Y. Liu, S. Pezzuto, Y. -N. Su, B. Zimmermann, T. Zhang, F. Wyrowski, F. De Angelis, S. Liu, S. D. Clarke, F. Fontani, P. D. Klaassen, P. Koch, K. G. Johnston, U. Lebreuilly, T. Liu, S. L. Lumsden, T. Moeller, L. Moscadelli, R. Kuiper, D. Lis, N. Peretto, S. Pfalzner, A. J. Rigby, P. Sanhueza, K. L. J. Rygl, F. van der Tak, H. Zinnecker, F. Amaral, J. Bally, L. Bronfman, R. Cesaroni, K. Goh, M. G. Hoare, P. Hatchfield, P. Hennebelle, T. Henning, K. T. Kim, W. -J. Kim, L. Maud, M. Merello, F. Nakamura, R. Plume, S. -L. Qin, B. Svoboda, L. Testi, V. S. Veena, D. Walker
Fundamental questions about the physics responsible for fragmenting molecular parsec-scale clumps into cores of ~1000 au are still open, that only a statistically significant investigation with ALMA is able to address: what are the dominant agents that determine the core demographics, mass, and spatial distribution as a function of the physical properties of the hosting clumps, their evolutionary stage and the different Galactic environments in which they reside? To what extent extent is fragmentation driven by clumps dynamics or mass transport in filaments? With ALMAGAL we observed the 1.38 mm continuum and lines toward more than 1000 dense clumps in our Galaxy, with M>500M_sun, surface density > 0.1 g/cm2 and d<7.5 kpc. The ACA and two 12-m array setups were used to deliver a minimum resolution of ~1000 au over the entire sample distance range. The sample covers all evolutionary stages from infrared dark clouds (IRDCs) to HII regions from the tip of the Galactic bar to the outskirts of the Galaxy. The spectral setup includes several molecular lines to trace the multiscale physics and dynamics of gas, notably CH3CN, H2CO, SiO, CH3OH, DCN, HC3N, SO etc. We present an initial overview of the observations and the early science product and results, with a first characterization of the morphological properties of the continuum emission. We use "perimeter-versus-area" and convex hull-versus-area metrics to classify the different morphologies. More extended and morphologically complex shapes are found toward clumps that are relatively more evolved and have higher surface densities.
In recent years there has been impressive progress in quark flavour physics, with current efforts tackling complicated quantities such as for example inclusive decays, decays to QCD-unstable final states and radiative decays. At the same time current lattice flavour physics results are receiving a lot of attention from outside the lattice community. This requires careful scrutiny, even for well explored quantities. In this plenary contribution I review the status of flavour physics observables and, in the context of heavy flavours, suggest possible benchmark quantities that could allow to better compare intermediate results. These can in turn be used to investigate the origin of existing (and possible future) tensions.
PoSSUM: A Protocol for Surveying Social-media Users with Multimodal LLMs
2025-03-07
Roberto Cerina
This paper introduces PoSSUM, an open-source protocol for unobtrusive polling of social-media users via multimodal Large Language Models (LLMs). PoSSUM leverages users' real-time posts, images, and other digital traces to create silicon samples that capture information not present in the LLM's training data. To obtain representative estimates, PoSSUM employs Multilevel Regression and Post-Stratification (MrP) with structured priors to counteract the observable selection biases of social-media platforms. The protocol is validated during the 2024 U.S. Presidential Election, for which five PoSSUM polls were conducted and published on GitHub and X. In the final poll, fielded October 17-26 with a synthetic sample of 1,054 X users, PoSSUM accurately predicted the outcomes in 50 of 51 states and assigned the Republican candidate a win probability of 0.65. Notably, it also exhibited lower state-level bias than most established pollsters. These results demonstrate PoSSUM's potential as a fully automated, unobtrusive alternative to traditional survey methods.
Warped products and synthetic lower curvature bounds: an overview
2025-03-07
Christian Ketterer
This is a survey about the contruction of warped products between (semi-)Riemannian manifolds and metric (measure) spaces. The resulting spaces will be semi-Riemannian manifolds, metric (measure) spaces or Lorentzian metric and metric measure spaces. We present details of the contruction in each case and we will highlight important properties like fiber independence and the energy equation. Warped products behave nicely in relation with curvature lower bounds. Here we will focus on sectional and Ricci curvature lower bounds and their Lorentzian counterparts. Throughout the article we provide many examples and formulate questions and conjectures.
Statistical Guarantees of Correctness Coverage for Medical Multiple-Choice Question Answering
2025-03-07
Yusong Ke
Large language models (LLMs) are increasingly deployed in real-world question-answering (QA) applications. However, LLMs have been proven to generate hallucinations and nonfactual information, undermining their trustworthiness in high-stakes medical tasks. Conformal prediction (CP) is well-known to be model-agnostic and distribution-free, which creates statistically rigorous prediction sets in classification tasks. In this work, we for the first time adapt the CP framework to medical multiple-choice question-answering (MCQA) tasks, by correlating the nonconformity score with the frequency score of correct options grounded in self-consistency theory, assuming no access to internal model information. Considering that the adapted CP framework can only control the (mis)coverage rate, we employ a risk control framework, which can manage task-specific metrics by devising a monotonically decreasing loss function. We evaluate our framework on 3 popular medical MCQA datasets utilizing 4 ``off-the-shelf'' LLMs. Empirical results demonstrate that we achieve user-specified average (or marginal) error rates on the test set. Furthermore, we observe that the average prediction set size (APSS) on the test set decreases as the risk level increases, which concludes a promising evaluation metric for the uncertainty of LLMs.
Infrared Fluxes and Light Curves of Near-Earth Objects: The full Spitzer Sample
2025-03-07
Joseph L. Hora, Alicia J. Allen, David E. Trilling, Howard A. Smith, Andrew McNeill
The IRAC camera on the Spitzer Space Telescope observed 2175 Near Earth Objects (NEOs) during its Warm Mission phase, primarily in three large surveys, and also in a small number of a dedicated projects. In this paper we present the final reprocessing of the NEO data and determine fluxes at 3.6 microns (where available) and 4.5 microns. The observing windows range from minutes to nearly ten hours, which means that for 39 NEOs we observe a complete lightcurve, and for these objects we present period and amplitude estimates and derive minimum cohesive strengths for the objects with well-determined periods. For an additional 128 objects we detect a significant fraction of a complete lightcurve, and present estimated lower limits to their rotation periods. This paper presents the final and definitive Spitzer/IRAC NEO flux catalog.
Digital inequality remains a significant barrier for many older adults, limiting their ability to navigate online spaces securely and confidently while increasing their susceptibility to cyber threats. In response, we propose a novel shedding-type card game for older adults to conceptually learn and reinforce cyber hygiene practices in educational settings. We asked digital educators to participate as players alongside older adults (n = 16), departing from their usual role as teachers, they collaborated and shared a unique learning experience. The cybersafety game addresses 4 key topics: handling scams, password management, responding to cyber attacks, and staying private. We adopted a mixed-method approach of think-aloud playtesting, semi-structured interviews, and surveys to evaluate the game's reception and impact. Participants reported highly favorable gameplay experiences and found the cybersafety advice useful. Player feedback informed game modifications, detailed in this paper, to further enhance the game's usability and educational value.
We determined phase curves for 301,272 asteroids in the orange filter and 280,953 in the cyan filter from the latest ATLAS Solar System Catalog V2 (SSCAT-2). Among them, 3,345 and 492 asteroids in the orange and cyan filters, respectively, have uncertainties below 15%. Our simple model, which considers only the apparition effect, showed good consistency with more sophisticated methods requiring much less computational time. Database cross-matching allowed us to analyze G1 and G2 distributions according to taxonomy. We conducted two-dimensional Kolmogorov-Smirnov tests to investigate two distinct aspects: similarities in paired G1, G2 distributions across different taxa and wavelength dependency within the same taxa. When comparing different taxa, we couldn't reject the null hypothesis for 11% of the orange sample and 31% of the cyan sample, indicating more disparities in the orange filter. For wavelength dependency, paired distributions of G1, G2 (o) vs. G1, G2 (c) showed statistically significant differences across all complexes, except for the A class. Our analysis suggests that while phase coloring behaviors are observed without a clear preference for reddening or bluening at phase angles below 5 degrees, reddening predominates in the 10 - 30 degrees range. We also observed smaller uncertainties in G2 than in G1. Simulations showed that G2 is less sensitive to lack of data at small phase angles. This is related to the definition of the H,G1,G2 function, where G1 contributes more to the opposition effect and G2 the linear part of the phase curve. Our catalog-independent algorithms are adaptable to new data sets, including future LSST data.
2025-03-07
Maria Bergemann, Katharina Lodders, Herbert Palme
This chapter provides a brief introduction to the chemical composition of the Sun. The focus of the chapter is on results obtained from the physical analysis of the solar photosphere. Data obtained from meteorites, solar wind and corona measurements, as well as helioseismology, and solar neutrinos are briefly reviewed. The elemental and isotopic composition of the solar system is derived by combining the solar and meteoritic data. The cosmochemical and astronomical abundance scales are described. The results of the determinations of the protosolar chemical composition, as well as the initial and present-day mass fractions of hydrogen, helium, and metals (X, Y, Z) for the solar system are presented in extensive tables. All tables are also available in machine-readable form via Zenodo https://doi.org/10.5281/zenodo.14988840
Fluid antenna system (FAS) is an emerging technology that uses the new form of shape- and position-reconfigurable antennas to empower the physical layer for wireless communications. Prior studies on FAS were however limited to narrowband channels. Motivated by this, this paper addresses the integration of FAS in the fifth generation (5G) orthogonal frequency division multiplexing (OFDM) framework to address the challenges posed by wideband communications. We propose the framework of the wideband FAS OFDM system that includes a novel port selection matrix. Then we derive the achievable rate expression and design the adaptive modulation and coding (AMC) scheme based on the rate. Extensive link-level simulation results demonstrate striking improvements of FAS in the wideband channels, underscoring the potential of FAS in future wireless communications.
VLMs Play StarCraft II: A Benchmark and Multimodal Decision Method
2025-03-07
Weiyu Ma, Yuqian Fu, Zecheng Zhang, Guohao Li
We introduce VLM-Attention, a multimodal StarCraft II environment that aligns artificial agent perception with the human gameplay experience. Traditional frameworks such as SMAC rely on abstract state representations that diverge significantly from human perception, limiting the ecological validity of agent behavior. Our environment addresses this limitation by incorporating RGB visual inputs and natural language observations that more closely simulate human cognitive processes during gameplay. The VLM-Attention framework consists of three integrated components: (1) a vision-language model enhanced with specialized self-attention mechanisms for strategic unit targeting and battlefield assessment, (2) a retrieval-augmented generation system that leverages domain-specific StarCraft II knowledge to inform tactical decisions, and (3) a dynamic role-based task distribution system that enables coordinated multi-agent behavior. Our experimental evaluation across 21 custom scenarios demonstrates that VLM-based agents powered by foundation models (specifically Qwen-VL and GPT-4o) can execute complex tactical maneuvers without explicit training, achieving comparable performance to traditional MARL methods that require substantial training iterations. This work establishes a foundation for developing human-aligned StarCraft II agents and advances the broader research agenda of multimodal game AI. Our implementation is available at https://github.com/camel-ai/VLM-Play-StarCraft2.
A Survey on Web Testing: On the Rise of AI and Applications in Industry
2025-03-07
Iva Kertusha, Gebremariem Assress, Onur Duman, Andrea Arcuri
Web application testing is an essential practice to ensure the reliability, security, and performance of web systems in an increasingly digital world. This paper presents a systematic literature survey focusing on web testing methodologies, tools, and trends from 2014 to 2024. By analyzing \totalPapersIncluded research papers, the survey identifies key trends, demographics, contributions, tools, challenges, and innovations in this domain. In addition, the survey analyzes the experimental setups adopted by the studies, including the number of participants involved and the outcomes of the experiments. Our results show that web testing research has been highly active, with ICST as the leading venue. Most studies focus on novel techniques, emphasizing automation in black-box testing. Selenium is the most widely used tool, while industrial adoption and human studies remain comparatively limited. The findings provide a detailed overview of trends, advancements, and challenges in web testing research, the evolution of automated testing methods, the role of artificial intelligence in test case generation, and gaps in current research. Special attention was given to the level of collaboration and engagement with the industry. A positive trend in using industrial systems is observed, though many tools lack open-source availability.
Riemannian Metric Learning: Closer to You than You Imagine
2025-03-07
Samuel Gruffaz, Josua Sassen
Riemannian metric learning is an emerging field in machine learning, unlocking new ways to encode complex data structures beyond traditional distance metric learning. While classical approaches rely on global distances in Euclidean space, they often fall short in capturing intrinsic data geometry. Enter Riemannian metric learning: a powerful generalization that leverages differential geometry to model the data according to their underlying Riemannian manifold. This approach has demonstrated remarkable success across diverse domains, from causal inference and optimal transport to generative modeling and representation learning. In this review, we bridge the gap between classical metric learning and Riemannian geometry, providing a structured and accessible overview of key methods, applications, and recent advances. We argue that Riemannian metric learning is not merely a technical refinement but a fundamental shift in how we think about data representations. Thus, this review should serve as a valuable resource for researchers and practitioners interested in exploring Riemannian metric learning and convince them that it is closer to them than they might imagine-both in theory and in practice.
Derived deformation functors, Koszul duality, and Maurer-Cartan spaces
2025-03-07
J. P. Pridham
We summarise the chain of comparisons showing Hinich's derived Maurer-Cartan functor gives an equivalence between differential graded Lie algebras and derived Schlessinger functors on Artinian differential graded-commutative algebras. We include some motivating deformation problems and analogues for more general Koszul dual pairs of operads.
The MeerKAT Fornax Survey V. H i kinematics and Fornax cluster membership of the dwarf galaxy ESO 358-60
2025-03-07
P. Kamphuis, P. Serra, D. Kleiner, R. -J. Dettmar, G. I. G. Józsa
The MeerKAT Fornax Survey (MFS) is a large survey project mapping the HI in the Fornax cluster. Most of the cluster members detected in HI show significant signs of interaction with the intra-cluster medium or other galaxies. The galaxy ESO 358-60 however stands out as its large HI disk appears regular and undisturbed. A possible explanation for this undisturbed disk is that the galaxy is not in Fornax. We analyze the HI distribution within and around ESO 358-60 based on the MFS observations. We visually inspect the low resolution data in order to study the HI disk from the center to its outskirts and look for low column density gas that could reveal recent interactions. We then construct a detailed parameterization of the HI disk by fitting a tilted ring model to the high resolution data cube. We use the fitted rotational velocity to place the galaxy on the baryonic Tully-Fisher relationship. By equating the galaxy's HI and 3.6 $\mu$m fluxes to the thus retrieved baryonic mass, we obtain a redshift independent distance. Our modeling confirms the regularity of the HI disk in ESO 358-60 but also shows that the galaxy contains a significant line of sight warp and contains radial motions, of the order of 10 km s$^{-1}$, that cover the extent of the optical disk. From the modeling we obtain a velocity V$_{\rm flat} = $48.1 $\pm$ 1.4 km s$^{-1}$ for the best fit rotation curve. This leads to a distance from the baryonic Tully-Fisher relation of 9.4 $\pm$ 2.5 Mpc,$\sim$ 10 Mpc less than the distance to the Fornax cluster. This distance not only fits better with V$_{\rm flat}$ but also with the overall HI distribution of low mass galaxies and the fact that the galaxy appears undisturbed and reasonably symmetric. At 9.4 Mpc ESO 358-60 cannot be a member of the Fornax cluster but is a foreground field galaxy.
Spectrum analysis with parametrically modulated transmon qubits
2025-03-07
Nir Gavrielov, Santiago Oviedo-Casado, Alex Retzker
Exploring the noise spectrum impacting a qubit and extending its coherence duration are fundamental components of quantum technologies. In this study, we introduce parametric spectroscopy, a method that merges parametric modulation of a qubit's energy gap with dynamical decoupling sequences. The parametric modulation provides high sensitivity to extensive regions of the noise spectrum, while dynamical decoupling reduces the effect of driving noise. Our theoretical study shows that parametric spectroscopy enables access to the difficult high-frequency domain of the flux spectrum in transmons.
2025-03-07
Johanna P. Müller, Robert Wright, Thomas G. Day, Lorenzo Venturini, Samuel F. Budd, Hadrien Reynaud, Joseph V. Hajnal, Reza Razavi, Bernhard Kainz
Accurate analysis of prenatal ultrasound (US) is essential for early detection of developmental anomalies. However, operator dependency and technical limitations (e.g. intrinsic artefacts and effects, setting errors) can complicate image interpretation and the assessment of diagnostic uncertainty. We present L-FUSION (Laplacian Fetal US Segmentation with Integrated FoundatiON models), a framework that integrates uncertainty quantification through unsupervised, normative learning and large-scale foundation models for robust segmentation of fetal structures in normal and pathological scans. We propose to utilise the aleatoric logit distributions of Stochastic Segmentation Networks and Laplace approximations with fast Hessian estimations to estimate epistemic uncertainty only from the segmentation head. This enables us to achieve reliable abnormality quantification for instant diagnostic feedback. Combined with an integrated Dropout component, L-FUSION enables reliable differentiation of lesions from normal fetal anatomy with enhanced uncertainty maps and segmentation counterfactuals in US imaging. It improves epistemic and aleatoric uncertainty interpretation and removes the need for manual disease-labelling. Evaluations across multiple datasets show that L-FUSION achieves superior segmentation accuracy and consistent uncertainty quantification, supporting on-site decision-making and offering a scalable solution for advancing fetal ultrasound analysis in clinical settings.
Mining Q&A Platforms for Empirical Evidence on Quantum Software Programming
2025-03-07
Arif Ali Khan, Boshuai Ye, Muhammad Azeem Akbar, Javed Ali Khan, Davoud Mougouei, Xinyuan Ma
The rise of quantum computing has driven the need for quantum software engineering, yet its programming landscape remains largely unexplored in empirical research. As quantum technologies advance toward industrial adoption, understanding programming aspects is crucial to addressing software development challenges. This study analyzes 6,935 quantum software programming discussion posts from Stack Exchange platforms (Quantum Computing, Stack Overflow, Software Engineering, and Code Review). Using topic modeling and qualitative analysis, we identified key discussion topics, trends (popular and difficult), tools/frameworks, and practitioner challenges. Twenty topics were identified, including popular ones such as physical theories and mathematical foundations, as well as security and encryption algorithms, while the most difficult were object-oriented programming and parameter control in quantum algorithms. Additionally, we identified nine frameworks that support quantum programming, with Qiskit emerging as the most widely adopted. Our findings also reveal core challenges in quantum software programming, thematically mapped into four areas: theories and mathematical concepts, algorithms and applications, experimental practices and software development, and education and community engagement. This study provides empirical insights that can inform future research, tool development, and educational efforts, supporting the evolution of the quantum software ecosystem.
Unveiling Biases in AI: ChatGPT's Political Economy Perspectives and Human Comparisons
2025-03-07
Leonardo Becchetti, Nazaria Solferino
We explore the political and ideological positioning of ChatGPT, a leading large language model (LLM), by comparing its responses to political economy questions from the European Social Survey (ESS). The questions concern environmental sustainability, civil rights, income inequality, and government size. ChatGPT's self-assessed placement on a left-right political spectrum is compared to the ideological stances of individuals providing similar answers in the ESS dataset. Results highlight a significant left-oriented bias in ChatGPT's answers, particularly on environmental and civil rights topics, diverging from its same self-declared center-left stance. These findings underscore the need for transparency in AI systems to prevent potential ideological influences on users. We conclude by discussing the implications for AI governance, debiasing strategies, and educational use.
Operationalizing Cybersecurity Knowledge: Design, Implementation & Evaluation of a Knowledge Management System for CACAO Playbooks
2025-03-07
Orestis Tsirakis, Konstantinos Fysarakis, Vasileios Mavroeidis, Ioannis Papaefstathiou
Modern cybersecurity threats are growing in complexity, targeting increasingly intricate & interconnected systems. To effectively defend against these evolving threats, security teams utilize automation & orchestration to enhance response efficiency and consistency. In that sense, cybersecurity playbooks are key enablers, providing a structured, reusable, and continuously improving approach to incident response, enabling organizations to codify requirements, domain expertise, and best practices and automate decision-making processes to the extent possible. The emerging Collaborative Automated Course of Action Operations (CACAO) standard defines a common machine-processable schema for cybersecurity playbooks, facilitating interoperability for their exchange and ensuring the ability to orchestrate and automate cybersecurity operations. However, despite its potential and the fact that it is a relatively new standardization work, there is a lack of tools to support its adoption and, in particular, the management & lifecycle development of CACAO playbooks, limiting their practical deployment. Motivated by the above, this work presents the design, development, and evaluation of a Knowledge Management System (KMS) for managing CACAO cybersecurity playbooks throughout their lifecycle, providing essential tools to streamline playbook management. Using open technologies & standards, the proposed approach fosters standards-based interoperability & enhances the usability of state-of-the-art cybersecurity orchestration & automation primitives. To encourage adoption, the resulting implementation is released as open-source, which, to the extent of our knowledge, comprises the first publicly available & documented work in this domain, supporting the broader uptake of CACAO playbooks & promoting the widespread use of interoperable automation and orchestration mechanisms in cybersecurity operations.
The JCMT BISTRO Survey: Unveiling the Magnetic Fields around Galactic Center
2025-03-07
Meng-Zhe Yang, Shih-Ping Lai, Janik Karoly, Kate Pattle, Xing Lu, David Eden, Sheng-Jun Lin, Frédérick Poidevin, Ekta Sharma, Jihye Hwang, Lapo Fanciullo, Mehrnoosh Tahani, Patrick M. Koch, Shu-ichiro Inutsuka, Valentin J. M. Le Gouellec, Hao-Yuan Duan, Jia-Wei Wang, Gary Fuller, Ray S. Furuya, Qilao Gu, Tetsuo Hasegawa, Guangxing Li, Junhao Liu, M. S. Akshaya, Bijas Najimudeen, Le Ngoc Tram, Derek Ward-Thompson, Doris Arzoumanian, James Di Francesco, Yasuo Doi, Thiem Hoang, Ji-hyun Kang, Jungmi Kwon, Woojin Kwon, Chang Won Lee, Tie Liu, Takashi Onaka, Sarah Sadavoy, Motohide Tamura, Pierre Bastien, David Berry, Simon Coudé, Keping Qiu
We acquired 450 {\mu}m and 850 {\mu}m dust continuum polarization observations toward the inner region of the Central Molecular Zone (CMZ) as part of the B-Fields In Star-Forming Region Observations (BISTRO) survey using the POL-2 polarimeter on the James Clerk Maxwell Telescope. These observations encompassed three dense structures: the 20 km s{^{-1}} cloud (20MC), 50 km s{^{-1}} cloud (50MC), and circumnuclear disk (CND). Our aim is to investigate the magnetic field morphology and strength in the inner region of the CMZ using polarized dust continuum and the Davis-Chandrasekhar-Fermi method. The magnetic field morphology is highly ordered in all three dense regions. The plane-of-sky magnetic field strengths are {\sim}1 mG for the 20MC and the 50MC, and {\sim}2 mG for the CND. We compare the energy contributions of turbulence, gravity, and thermal motion with that of the magnetic field using the plasma {\beta}, mass-to-flux ratio, and Alfv\'en Mach number. The outcomes reveal the magnetic field stands out as the predominant factor within the inner region of the CMZ. The dominance of the magnetic field may explain the low star-forming rate in the CMZ. We further investigate the dust grain alignment efficiency by exploring the relationship between polarization fraction and total intensity. The results suggest that dust grains are well aligned with the magnetic fields.
Finance decision-making often relies on in-depth data analysis across various data sources, including financial tables, news articles, stock prices, etc. In this work, we introduce FinTMMBench, the first comprehensive benchmark for evaluating temporal-aware multi-modal Retrieval-Augmented Generation (RAG) systems in finance. Built from heterologous data of NASDAQ 100 companies, FinTMMBench offers three significant advantages. 1) Multi-modal Corpus: It encompasses a hybrid of financial tables, news articles, daily stock prices, and visual technical charts as the corpus. 2) Temporal-aware Questions: Each question requires the retrieval and interpretation of its relevant data over a specific time period, including daily, weekly, monthly, quarterly, and annual periods. 3) Diverse Financial Analysis Tasks: The questions involve 10 different tasks, including information extraction, trend analysis, sentiment analysis and event detection, etc. We further propose a novel TMMHybridRAG method, which first leverages LLMs to convert data from other modalities (e.g., tabular, visual and time-series data) into textual format and then incorporates temporal information in each node when constructing graphs and dense indexes. Its effectiveness has been validated in extensive experiments, but notable gaps remain, highlighting the challenges presented by our FinTMMBench.
phepy: Visual Benchmarks and Improvements for Out-of-Distribution Detectors
2025-03-07
Juniper Tyree, Andreas Rupp, Petri S. Clusius, Michael H. Boy
Applying machine learning to increasingly high-dimensional problems with sparse or biased training data increases the risk that a model is used on inputs outside its training domain. For such out-of-distribution (OOD) inputs, the model can no longer make valid predictions, and its error is potentially unbounded. Testing OOD detection methods on real-world datasets is complicated by the ambiguity around which inputs are in-distribution (ID) or OOD. We design a benchmark for OOD detection, which includes three novel and easily-visualisable toy examples. These simple examples provide direct and intuitive insight into whether the detector is able to detect (1) linear and (2) non-linear concepts and (3) identify thin ID subspaces (needles) within high-dimensional spaces (haystacks). We use our benchmark to evaluate the performance of various methods from the literature. Since tactile examples of OOD inputs may benefit OOD detection, we also review several simple methods to synthesise OOD inputs for supervised training. We introduce two improvements, $t$-poking and OOD sample weighting, to make supervised detectors more precise at the ID-OOD boundary. This is especially important when conflicts between real ID and synthetic OOD sample blur the decision boundary. Finally, we provide recommendations for constructing and applying out-of-distribution detectors in machine learning.
We carry out an imaging survey of six globular clusters (GCs) with a limit magnitude to 22 mag at the 5 sigma level, down to the main sequence stars of the respective cluster, as one of the pilot observing program of the Wide Field Survey Telescope (WFST). This paper present the early results of this survey, where we investigate the tidal characters at the periphery of the clusters NGC 4147, NGC 5024, NGC 5053, NGC 5272, NGC 5904 and NGC 6341. We present the estimated number density of cluster candidates and their spatial distribution. We confirm the presence of tidal arms in NGC 4147 and NGC 5904 and identify several intriguing potential tidal structures in NGC 4147, NGC 5024, NGC 5272, corroborated the elliptical morphology of the periphery of NGC 6341. Our findings underscore the WFST's capability for probing faint structural features in GCs, paving the way for future in-depth studies, especially for the search of the large scale tidal streams associated with the clusters with the future wide field survey.
Variational-hemivariational inequalities are an important mathematical framework for nonsmooth problems. The framework can be used to study application problems from physical sciences and engineering that involve non-smooth and even set-valued relations, monotone or non-monotone, among physical quantities. Since no analytic solution formulas are expected for variational-hemivariational inequalities from applications, numerical methods are needed to solve the problems. This paper focuses on numerical analysis of variational-hemivariational inequalities, reporting new results as well as surveying some recent published results in the area. A general convergence result is presented for Galerkin solutions of the inequalities under minimal solution regularity conditions available from the well-posedness theory, and C\'{e}a's inequalities are derived for error estimation of numerical solutions. The finite element method and the virtual element method are taken as examples of numerical methods, optimal order error estimates for the linear element solutions are derived when the methods are applied to solve three representative contact problems under certain solution regularity assumptions. Numerical results are presented to show the performance of both the finite element method and the virtual element method, including numerical convergence orders of the numerical solutions that match the theoretical predictions.
Medical image segmentation is fundamental for computer-aided diagnostics, providing accurate delineation of anatomical structures and pathological regions. While common metrics such as Accuracy, DSC, IoU, and HD primarily quantify spatial agreement between predictions and ground-truth labels, they do not assess the calibration quality of segmentation models, which is crucial for clinical reliability. To address this limitation, we propose pixel-wise Expected Calibration Error (pECE), a novel metric that explicitly measures miscalibration at the pixel level, thereby ensuring both spatial precision and confidence reliability. We further introduce a morphological adaptation strategy that applies morphological operations to ground-truth masks before computing calibration losses, particularly benefiting margin-based losses such as Margin SVLS and NACL. Additionally, we present the Signed Distance Calibration Loss (SDC), which aligns boundary geometry with calibration objectives by penalizing discrepancies between predicted and ground-truth signed distance functions (SDFs). Extensive experiments demonstrate that our method not only enhances segmentation performance but also improves calibration quality, yielding more trustworthy confidence estimates. Code is available at: https://github.com/EagleAdelaide/SDC-Loss.
In recent years, the application of behavioral testing in Natural Language Processing (NLP) model evaluation has experienced a remarkable and substantial growth. However, the existing methods continue to be restricted by the requirements for manual labor and the limited scope of capability assessment. To address these limitations, we introduce AutoTestForge, an automated and multidimensional testing framework for NLP models in this paper. Within AutoTestForge, through the utilization of Large Language Models (LLMs) to automatically generate test templates and instantiate them, manual involvement is significantly reduced. Additionally, a mechanism for the validation of test case labels based on differential testing is implemented which makes use of a multi-model voting system to guarantee the quality of test cases. The framework also extends the test suite across three dimensions, taxonomy, fairness, and robustness, offering a comprehensive evaluation of the capabilities of NLP models. This expansion enables a more in-depth and thorough assessment of the models, providing valuable insights into their strengths and weaknesses. A comprehensive evaluation across sentiment analysis (SA) and semantic textual similarity (STS) tasks demonstrates that AutoTestForge consistently outperforms existing datasets and testing tools, achieving higher error detection rates (an average of $30.89\%$ for SA and $34.58\%$ for STS). Moreover, different generation strategies exhibit stable effectiveness, with error detection rates ranging from $29.03\% - 36.82\%$.
An Oxidation Gradient Straddling the Small Planet Radius Valley
2025-03-07
Collin Cherubim, Robin Wordsworth, Dan Bower, Paolo Sossi, Danica Adams, Renyu Hu
We present a population-level view of volatile gas species (H$_2$, He, H$_2$O, O$_2$, CO, CO$_2$, CH$_4$) distribution during the sub-Neptune to rocky planet transition, revealing in detail the dynamic nature of small planet atmospheric compositions. Our novel model couples the atmospheric escape model $\texttt{IsoFATE}$ with the magma ocean-atmosphere equilibrium chemistry model $\texttt{Atmodeller}$ to simulate interior-atmosphere evolution over time for sub-Neptunes around G, K and M stars. Chiefly, our simulations reveal that atmospheric mass fractionation driven by escape and interior-atmosphere exchange conspire to create a distinct oxidation gradient straddling the small-planet radius valley. We discover a key mechanism in shaping the oxidation landscape is the dissolution of water into the molten mantle, which shields oxygen from early escape, buffers the escape rate, and leads to oxidized secondary atmospheres following mantle outgassing. Our simulations reproduce a prominent population of He-rich worlds along the upper edge of the radius valley, revealing that they are stable on shorter timescales than previously predicted. Our simulations also robustly predict a broad population of O$_2$-dominated atmospheres on close-in planets around low mass stars, posing a potential source of false positive biosignature detection and marking a high-priority opportunity for the first-ever atmospheric O$_2$ detection. We motivate future atmospheric characterization surveys by providing a target list of planet candidates predicted to have O$_2$-, He-, and deuterium-rich atmospheres.
2025-03-07
Siavash Monfared, Aleksandra Ardaševa, Amin Doostmohammadi
The development of complex multicellular organisms from a single parent cell is a highly orchestrated process that cells conduct collectively without central guidance, creating intricate dynamic patterns essential for development and regeneration. Despite significant advances in imaging spatiotemporal dynamics of cell collectives and mechanical characterization techniques, the role of physical forces in biological functions remains poorly understood. Physics-based models are crucial in complementing experiments, providing high-resolution spatiotemporal fields in three dimensions. This review focuses on dense, soft multicellular systems, such as tissues, where mechanical deformation of one cell necessitates the re-organization of neighboring cells. The multi-phase-field model offers a rich physics-based framework to advance our understanding of biological systems and provides a robust playground for non-equilibrium physics of active matter. We discuss the foundational aspects of the multi-phase-field model and their applications in understanding physics of active matter. We also explore the integration of biological physics with experimental data, covering cell migration, heterogeneous cell populations, and confined systems. Finally, we highlight current trends, the importance of multi-phase-field models in biological and physics research, and future challenges.
Differential forms and invariants of complex manifolds
2025-03-07
Jonas Stelzig
A survey of some results and open questions related to the following algebraic invariants of compact complex manifolds, that can be obtained from differential forms: cohomology groups, Chern classes, rational homotopy groups, and higher operations.
The Gaseous Blowout of the 30 Doradus Starburst Region in the LMC
2025-03-07
Suraj Poudel, April Horton, Jo Vazquez, Kathleen A. Barger, Frances H. Cashman, Andrew J. Fox, Nicolas Lehner, Scott Lucchini, Dhanesh Krishnarao, N. M. McClure-Griffiths, Elena D'Onghia, Jason Tumlinson, Ananya Goon Tuli, Lauren Sdun, Stone Gebhart, Katherine Anthony, Bryce Cole, Jacco Th. van Loon, Julia Roman-Duval, Yik Ki Ma, Callum Lynn, Min-Young Lee, Denis Leahy
Widespread galactic winds emanate from the Large Magellanic Cloud (LMC), with the 30 Doradus starburst region generating the fastest and most concentrated gas flows. We report on the gas distribution, kinematics, and ionization conditions of the near-side outflow along 8 down-the-barrel sightlines using UV absorption-line observations from the HST's ULLYSES program for this region along with H I 21-cm observations from the GASS and GASKAP surveys. We find that within 1.7 degrees from the center of 30 Doradus, the wind reaches maximum speeds of $100-150\,\text{km}\,\text{s}^{-1}$ from the LMC's disk. The total integrated column densities of low-ions (O I, Si II, and Fe II) in the blueshifted wind, up to $v_{\rm LSR}=150\,\text{km}\,\text{s}^{-1}$, are highest near the center and decline radially outward. We estimate an outflow mass of $M_{\rm outflow,\,Si II}\approx(5.7-8.6)\,\times 10^{5} M_{\odot}$, outflow rate of $\dot{M}_{\rm outflow}\gtrsim0.02 M_{\odot}\,\text{yr}^{-1}$, and mass loading factor of $\eta\gtrsim0.10$ within 0.52 degrees from the center of 30 Doradus. The observed ion ratios$-$together with photoionization modeling$-$reveal that this wind is roughly $40-97\%$ photoionized. The metallicities and dust depletion patterns of the high-velocity absorbers at $v_{\rm LSR}\approx+120\,\text{km}\,\text{s}^{-1}$ can be explained by either a foreground Milky Way (MW) halo cloud or an outflow from the LMC. For the high-ions, Si IV and C IV are broader and kinematically offset from the low-ions, suggesting turbulent mixing layers (TMLs) existing in the wind. Finally, our hydrodynamical simulations of the Magellanic Clouds (MCs) and MW system suggest that the Magellanic Corona can protect the LMC winds from the ram-pressure forces exerted by the MW's halo.
Explaining the Unexplainable: A Systematic Review of Explainable AI in Finance
2025-03-07
Md Talha Mohsin, Nabid Bin Nasim
Practitioners and researchers trying to strike a balance between accuracy and transparency center Explainable Artificial Intelligence (XAI) at the junction of finance. This paper offers a thorough overview of the changing scene of XAI applications in finance together with domain-specific implementations, methodological developments, and trend mapping of research. Using bibliometric and content analysis, we find topic clusters, significant research, and most often used explainability strategies used in financial industries. Our results show a substantial dependence on post-hoc interpretability techniques; attention mechanisms, feature importance analysis and SHAP are the most often used techniques among them. This review stresses the need of multidisciplinary approaches combining financial knowledge with improved explainability paradigms and exposes important shortcomings in present XAI systems.
Neutrino Oscillations in Core-Collapse Supernovae and Neutron Star Mergers
2025-03-07
Lucas Johns, Sherwood Richers, Meng-Ru Wu
Accurate neutrino transport is crucial for reliably modeling explosive astrophysical events like core-collapse supernovae (CCSNe) and neutron star mergers (NSMs). However, in these extremely neutrino-dense systems, flavor oscillations exhibit challenging nonlinear effects rooted in neutrino-neutrino forward scattering. Evidence is quickly accumulating that these collective phenomena can substantially affect explosion dynamics, neutrino signals, nucleosynthesis, and kilonova light curves. We review the progress made so far on the difficult and conceptually deep question of how to correctly include this physics in simulations of CCSNe and NSMs. Our aim is to take a broad view of where the problem stands, and so provide a critical assessment of where it is headed.
A Survey on Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, and Beyond
2025-03-07
Mihaela Cătălina Stoian, Eleonora Giunchiglia, Thomas Lukasiewicz
Generative modelling has become the standard approach for synthesising tabular data. However, different use cases demand synthetic data to comply with different requirements to be useful in practice. In this survey, we review deep generative modelling approaches for tabular data from the perspective of four types of requirements: utility of the synthetic data, alignment of the synthetic data with domain-specific knowledge, statistical fidelity of the synthetic data distribution compared to the real data distribution, and privacy-preserving capabilities. We group the approaches along two levels of granularity: (i) based on the primary type of requirements they address and (ii) according to the underlying model they utilise. Additionally, we summarise the appropriate evaluation methods for each requirement and the specific characteristics of each model type. Finally, we discuss future directions for the field, along with opportunities to improve the current evaluation methods. Overall, this survey can be seen as a user guide to tabular data generation: helping readers navigate available models and evaluation methods to find those best suited to their needs.
Evaluating Multilevel Regression and Poststratification with Spatial Priors with a Big Data Behavioural Survey
2025-03-07
Aja Sutton, Zack W. Almquist, Jon Wakefield
Multilevel regression and poststratification (MRP) is a computationally efficient indirect estimation method that can quickly produce improved population-adjusted estimates with limited data. Recent computational advancements allow efficient, relatively simple, and quick approximate Bayesian estimation for MRP. As population health outcomes of interest including vaccination uptake are known to have spatial structure, precision may be gained by including space in the model. We test a recently proposed spatial MRP method that includes a BYM2 spatial term that smooths across demographics and geographic areas using a large, unrepresentative survey. We produce California county-level estimates of first-dose COVID-19 vaccination up to June 2021 using classic and spatial MRP models, and poststratify using data from the American Community Survey (US Census Bureau). We assess validity using reported first-dose vaccination counts from the Centers for Disease Control (CDC). Neither classic nor spatial MRP models performed well, highlighting: 1. spatial MRP may be most appropriate for richer data contexts, 2. some demographics in the survey data are over-sampled and -aggregated, producing model over-smoothing, and 3. a need for survey producers to share user-representative metrics to better benchmark estimates.
Early Light Curve Excess in Type IIb Supernovae Observed by the ATLAS Survey: Qualitative Constraints on Progenitor Systems
2025-03-07
Bastian Ayala, Joseph P. Anderson, G. Pignata, Francisco Foerster, S. J. Smartt, A. Rest, Martin Solar, Nicolas Erasmus, Raya Dastidar, Mauricio Ramirez, Jonathan Pineda-Garcia
Type IIb supernovae (SNe IIb) often exhibit an early light curve excess (EE) preceding the main peak powered by radioactive nickel decay. The physical origin of this early emission remains an open question. Among the proposed scenarios, shock cooling emission-resulting from the interaction between the shockwave and extended envelopes-is considered the most plausible mechanism. The frequency of these events remains unconstrained. This study aims to quantify the frequency of EE in SNe IIb and investigate its physical origin by analyzing optical light curves from the Asteroid Terrestrial-impact Last Alert System (ATLAS) survey. We selected 74 SNe IIb from 153 spectroscopically classified events in the Transient Name Server (TNS) database, observed by ATLAS, with peak fluxes exceeding 150 {\mu}Jy and explosion epoch uncertainties lower than six days. Using light curve model fitting and outlier analysis, we identified SNe IIb exhibiting EE and analyzed their photometric properties. We found 21 SNe IIb with EE, corresponding to a frequency of approximately 28-40%, with the higher value obtained under the most stringent data cuts. The EE's duration and color evolution are consistent with shock cooling in extended hydrogen-rich envelopes. We also found that EE SNe IIb have longer rise times and faster post-peak decline rates than non-EE SNe IIb, while both groups share similar peak absolute magnitudes. Our findings suggest that EE and non-EE SNe IIb likely share similar initial progenitor masses but differ in ejecta mass properties, potentially due to varying degrees of binary interaction. This study provides constraints on the evolutionary pathways of SNe IIb progenitors as compact stars with and without extended hydrogen envelopes.
Riccati-type pseudo-potential approach to quasi-integrability of deformed soliton theories
2025-03-07
Harold Blas
This review paper explores the Riccati-type pseudo-potential formulation applied to the quasi-integrable sine-Gordon, KdV, and NLS models. The proposed framework provides a unified methodology for analyzing quasi-integrability properties across various integrable systems, including deformations of the sine-Gordon, Bullough-Dodd, Toda, KdV, pKdV, NLS and SUSY sine-Gordon models. Key findings include the emergence of infinite towers of anomalous conservation laws within the Riccati-type approach and the identification of exact non-local conservation laws in the linear formulations of deformed models. As modified integrable models play a crucial role in diverse fields of non-linear physics-such as Bose-Einstein condensation, superconductivity, gravity models, optics, and soliton turbulence-these results may have far-reaching applications.
QG-SMS: Enhancing Test Item Analysis via Student Modeling and Simulation
2025-03-07
Bang Nguyen, Tingting Du, Mengxia Yu, Lawrence Angrave, Meng Jiang
While the Question Generation (QG) task has been increasingly adopted in educational assessments, its evaluation remains limited by approaches that lack a clear connection to the educational values of test items. In this work, we introduce test item analysis, a method frequently used by educators to assess test question quality, into QG evaluation. Specifically, we construct pairs of candidate questions that differ in quality across dimensions such as topic coverage, item difficulty, item discrimination, and distractor efficiency. We then examine whether existing QG evaluation approaches can effectively distinguish these differences. Our findings reveal significant shortcomings in these approaches with respect to accurately assessing test item quality in relation to student performance. To address this gap, we propose a novel QG evaluation framework, QG-SMS, which leverages Large Language Model for Student Modeling and Simulation to perform test item analysis. As demonstrated in our extensive experiments and human evaluation study, the additional perspectives introduced by the simulated student profiles lead to a more effective and robust assessment of test items.
Decoding the Galactic Twirl: The Downfall of Milky Way-mass Galaxies Rotation Curves in the FIRE Simulations
2025-03-07
Xiaowei Ou, Lina Necib, Andrew Wetzel, Anna Frebel, Jeremy Bailin, Micah Oeur
Recent measurements of the Milky Way rotation curve found a sharp decline at around $15$-$20$ kpc from the center of the Galaxy, suggesting that the Galactic dark matter halo is much less massive than predicted by other dynamical tracers. To address this tension, we study the validity of the assumptions made in calculating the Milky Way's rotation curve. To do so, we apply Jeans' equation, the current standard approach of measuring rotation curves, to three cosmological zoom-in simulations of Milky Way-like galaxies from the FIRE-2 Latte suite. Using synthetic Gaia surveys, we replicate the sample selection process and calculation employed in measuring the Milky Way rotation curve. We examine four failure modes of this calculation and find that the measured curves deviate from the true curve by $5$-$20\%$ rather than below $5\%$, as estimated by previous works. Interestingly, there is a large galaxy-to-galaxy variance, and different systematics dominate different galaxies. We rederive the Milky Way's dark matter density profile with the rotation curve while incorporating systematics from the simulations. The posterior distribution of the density profiles is consistent with a fiducial NFW profile when assuming a gNFW profile for dark matter. We find that the virial mass, $7.32^{+1.98}_{-1.53}\times10^{11}~M_{\odot}$, consistent with other probes of the Milky Way's mass. However, we recommend that the field moves away from relying solely on the rotation curve when studying the dark matter profile, and adopts methods that incorporate additional probes and/or do not heavily depend on assumptions described in this study.
Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Enhancement Protocol
2025-03-07
Roham Koohestani, Philippe de Bekker, Maliheh Izadi
Benchmarks are essential for consistent evaluation and reproducibility. The integration of Artificial Intelligence into Software Engineering (AI4SE) has given rise to numerous benchmarks for tasks such as code generation and bug fixing. However, this surge presents challenges: (1) scattered benchmark knowledge across tasks, (2) difficulty in selecting relevant benchmarks, (3) the absence of a uniform standard for benchmark development, and (4) limitations of existing benchmarks. In this paper, we review 173 studies and identify 204 AI4SE benchmarks. We classify these benchmarks, analyze their limitations, and expose gaps in practices. Based on our review, we created BenchScout, a semantic search tool to find relevant benchmarks, using automated clustering of the contexts from associated studies. We conducted a user study with 22 participants to evaluate BenchScout's usability, effectiveness, and intuitiveness which resulted in average scores of 4.5, 4.0, and 4.1 out of 5. To advance benchmarking standards, we propose BenchFrame, a unified method to enhance benchmark quality. As a case study, we applied BenchFrame to the HumanEval benchmark and addressed its main limitations. This led to HumanEvalNext, featuring (1) corrected errors, (2) improved language conversion, (3) expanded test coverage, and (4) increased difficulty. We then evaluated ten state-of-the-art code language models on HumanEval, HumanEvalPlus, and HumanEvalNext. On HumanEvalNext, models showed a pass@1 score reduction of 31.22% and 19.94% compared to HumanEval and HumanEvalPlus, respectively.
Raman Forbidden Layer-Breathing Modes in Layered Semiconductor Materials Activated by Phonon and Optical Cavity Effects
2025-03-07
Miao-Ling Lin, Jiang-Bin Wu, Xue-Lu Liu, Tao Liu, Rui Mei, Heng Wu, Shan Guan, Jia-Liang Xie, Jun-Wei Luo, Lin-Wang Wang, Andrea C. Ferrari, Ping-Heng Tan
We report Raman forbidden layer-breathing modes (LBMs) in layered semiconductor materials (LSMs). The intensity distribution of all observed LBMs depends on layer number, incident light wavelength and refractive index mismatch between LSM and underlying substrate. These results are understood by a Raman scattering theory via the proposed spatial interference model, where the naturally occurring optical and phonon cavities in LSMs enable spatially coherent photon-phonon coupling mediated by the corresponding one-dimensional periodic electronic states. Our work reveals the spatial coherence of photon and phonon fields on the phonon excitation via photon/phonon cavity engineering.
Enhancing Thin-Film Wafer Inspection With A Multi-Sensor Array And Robot Constraint Maintenance
2025-03-07
Néstor Eduardo Sánchez-Arriaga, Ethan Canzini, Nathan John Espley-Plumb, Michael Farnsworth, Simon Pope, Adrian Leyland, Ashutosh Tiwari
Thin-film inspection on large-area substrates in coating manufacture remains a critical parameter to ensure product quality; however, extending the inspection process precisely over a large area presents major challenges, due to the limitations of the available inspection equipment. An additional manipulation problem arises when automating the inspection process, as the silicon wafer requires movement constraints to ensure accurate measurements and to prevent damage. Furthermore, there are other increasingly important large-area industrial applications, such as Roll-to-Roll (R2R) manufacturing where coating thickness inspection introduces additional challenges. This paper presents an autonomous inspection system using a robotic manipulator with a novel learned constraint manifold to control a wafer to its calibration point, and a novel multi-sensor array with high potential for scalability into large substrate areas. We demonstrate that the manipulator can perform required motions whilst adhering to movement constraints. We further demonstrate that the sensor array can perform thickness measurements statically with an error of $<2\%$ compared to a commercial reflectometer, and through the use of a manipulator can dynamically detect angle variations $>0.5^\circ$ from the calibration point whilst monitoring the RMSE and $R^2$ over 1406 data points. These features are potentially useful for detecting displacement variations in R2R manufacturing processes.
Extracting and Emulsifying Cultural Explanation to Improve Multilingual Capability of LLMs
2025-03-07
Hamin Koo, Jaehyung Kim
Large Language Models (LLMs) have achieved remarkable success, but their English-centric training data limits performance in non-English languages, highlighting the need for enhancements in their multilingual capabilities. While some work on multilingual prompting methods handles non-English queries by utilizing English translations or restructuring them to more closely align with LLM reasoning patterns, these works often overlook the importance of cultural context, limiting their effectiveness. To address this limitation, we propose EMCEI, a simple yet effective approach that improves LLMs' multilingual capabilities by incorporating cultural context for more accurate and appropriate responses. Specifically, EMCEI follows a two-step process that first extracts relevant cultural context from the LLM's parametric knowledge via prompting. Then, EMCEI employs an LLM-as-Judge mechanism to select the most appropriate response by balancing cultural relevance and reasoning ability. Experiments on diverse multilingual benchmarks show that EMCEI outperforms existing baselines, demonstrating its effectiveness in handling multilingual queries with LLMs.
An active repeating fast radio burst in a magnetized eruption environment
2025-03-06
Y. Li, S. B. Zhang, Y. P. Yang, C. W. Tsai, X. Yang, C. J. Law, R. Anna-Thomas, X. L. Chen, K. J. Lee, Z. F. Tang, D. Xiao, H. Xu, X. L. Yang, G. Chen, Y. Feng, D. Z. Li, R. Mckinven, J. R. Niu, K. Shin, B. J. Wang, C. F. Zhang, Y. K. Zhang, D. J. Zhou, Y. H. Zhu, Z. G. Dai, C. M. Chang, J. J. Geng, J. L. Han, L. Hu, D. Li, R. Luo, C. H. Niu, D. D. Shi, T. R. Sun, X. F. Wu, W. W. Zhu, P. Jiang, B. Zhang
Fast radio bursts (FRBs) are millisecond-duration radio bursts with unidentified extra-galactic origin. Some FRBs exhibit mild magneto-ionic environmental variations, possibly attributed to plasma turbulence or geometric configuration variation in a binary system. Here we report an abrupt magneto-ionic environment variation of FRB 20220529, a repeating FRB from a disk galaxy at redshift 0.1839. Initially, its Faraday rotation measure (RM) was $21 \pm 96~{\rm rad~m^{-2}}$ over 17 months. In December 2023, it jumped to $1976.9~{\rm rad~m^{-2}}$, exceeding twenty times of the standard deviation of the previous RM variation, and returned to the typical values within two weeks. Such a drastic RM variation suggests a dense magnetized clump moving across the line of sight, possibly due to coronal mass ejection associated with a stellar flare. It indicates that the FRB likely has a companion star that produced the stellar flare.
MIGHTEE: exploring the relationship between spectral index, redshift and radio luminosity
2025-03-06
Siddhant Pinjarkar, Martin J. Hardcastle, Dharam V. Lal, Daniel J. B. Smith, José Afonso, Davi Barbosa, Catherine L. Hale, Matt J. Jarvis, Sthabile Kolwa, Eric Murphy, Mattia Vaccari, Imogen H. Whittam
It has been known for many years that there is an apparent trend for the spectral index ({\alpha}) of radio sources to steepen with redshift z, which has led to attempts to select high-redshift objects by searching for radio sources with steep spectra. In this study we use data from the MeerKAT, LOFAR, GMRT, and uGMRT telescopes, particularly using the MIGHTEE and superMIGHTEE surveys, to select compact sources over a wide range of redshifts and luminosities. We investigate the relationship between spectral index, luminosity and redshift and compare our results to those of previous studies. Although there is a correlation between {\alpha} and z in our sample for some combinations of frequency where good data are available, there is a clear offset between the {\alpha}-z relations in our sample and those derived previously from samples of more luminous objects; in other words, the {\alpha}-z relation is different for low and high luminosity sources. The relationships between {\alpha} and luminosity are also weak in our sample but in general the most luminous sources are steeper-spectrum and this trend is extended by samples from previous studies. In detail, we argue that both a {\alpha}-luminosity relation and an {\alpha}-z relation can be found in the data, but it is the former that drives the apparent {\alpha}-z relation observed in earlier work, which only appears because of the strong redshift-luminosity relation in bright, flux density-limited samples. Steep-spectrum selection should be applied with caution in searching for high-z sources in future deep surveys.
Assessing Student Adoption of Generative Artificial Intelligence across Engineering Education from 2023 to 2024
2025-03-06
Jesan Ahammed Ovi, Gabe Fierro, C. Estelle Smith
Generative Artificial Intelligence (GenAI) tools and models have the potential to re-shape educational needs, norms, practices, and policies in all sectors of engineering education. Empirical data, rather than anecdata and assumptions, on how engineering students have adopted GenAI is essential to developing a foundational understanding of students' GenAI-related behaviors and needs during academic training. This data will also help formulate effective responses to GenAI by both academic institutions and industrial employers. We collected two representative survey samples at the Colorado School of Mines, a small engineering-focused R-1 university in the USA, in May 2023 ($n_1=601$) and September 2024 ($n_2=862$) to address research questions related to (RQ1) how GenAI has been adopted by engineering students, including motivational and demographic factors contributing to GenAI use, (RQ2) students' ethical concerns about GenAI, and (RQ3) students' perceived benefits v.s. harms for themselves, science, and society. Analysis revealed a statistically significant rise in GenAI adoption rates from 2023 to 2024. Students predominantly leverage GenAI tools to deepen understanding, enhance work quality, and stay informed about emerging technologies. Although most students assess their own usage of GenAI as ethical and beneficial, they nonetheless expressed significant concerns regarding GenAI and its impacts on society. We collected student estimates of ``P(doom)'' and discovered a bimodal distribution. Thus, we show that the student body at Mines is polarized with respect to future impacts of GenAI on the engineering workforce and society, despite being increasingly willing to explore GenAI over time. We discuss implications of these findings for future research and for integrating GenAI in engineering education.
The Influence of Prior Discourse on Conversational Agent-Driven Decision-Making
2025-03-06
Stephen Pilli, Vivek Nallur
Persuasion through conversation has been the focus of much research. Nudging is a popular strategy to influence decision-making in physical and digital settings. However, conversational agents employing "nudging" have not received significant attention. We explore the manifestation of cognitive biases-the underlying psychological mechanisms of nudging-and investigate how the complexity of prior dialogue tasks impacts decision-making facilitated by conversational agents. Our research used a between-group experimental design, involving 756 participants randomly assigned to either a simple or complex task before encountering a decision-making scenario. Three scenarios were adapted from Samuelson's classic experiments on status-quo bias, the underlying mechanism of default nudges. Our results aligned with previous studies in two out of three simple-task scenarios. Increasing task complexity consistently shifted effect-sizes toward our hypothesis, though bias was significant in only one case. These findings inform conversational nudging strategies and highlight inherent biases relevant to behavioural economics.
Finding White Dwarfs' Hidden Companions using an Unsupervised Machine Learning Technique
2025-03-06
Xabier Pérez-Couto, Minia Manteiga, Eva Villaver
White dwarfs (WD) with main-sequence (MS) companions are crucial probes of stellar evolution. However, due to the significant difference in their luminosities, the WD is often outshined by the MS star. The aim of this work is to find hidden companions in Gaia's sample of single WD candidates. Our methodology involves applying an unsupervised machine learning algorithm for dimensionality reduction and clustering, known as Self-Organizing Map (SOM), to Gaia BP/RP (XP) spectra. This strategy allows us to naturally separate WDMS binaries from single WDs from the detection of subtle red flux excesses in the XP spectra that are indicative of low-mass MS companions. We validate our approach using confirmed WDMS binary pairs from the SDSS and LAMOST surveys, achieving a precision of $\sim 90\%$. Applying our SOM to 90,667 sources, we identify 993 WDMS candidates, 801 of which have not been previously reported in the literature. If confirmed, our sample will increase the known WDMS binaries by $20\%$, making it a valuable source for stellar evolution studies. Additionally, we use the Virtual Observatory Spectral Energy Distribution Analyzer (VOSA) tool to further refine and parameterize a "golden sample" of 136 WDMS candidates through multi-wavelength photometry and a two-body Spectral Energy Distribution fitting. These high-confidence WDMS binaries are composed by low-mass WDs ($\sim 0.41 M_{\odot}$), with cool MS companions ($\sim 2800$ K). Finally, 13 systems exhibit periodic variability consistent with eclipsing binaries, making them prime targets for further follow-up observations.
Understanding and replicating the real world is a critical challenge in Artificial General Intelligence (AGI) research. To achieve this, many existing approaches, such as world models, aim to capture the fundamental principles governing the physical world, enabling more accurate simulations and meaningful interactions. However, current methods often treat different modalities, including 2D (images), videos, 3D, and 4D representations, as independent domains, overlooking their interdependencies. Additionally, these methods typically focus on isolated dimensions of reality without systematically integrating their connections. In this survey, we present a unified survey for multimodal generative models that investigate the progression of data dimensionality in real-world simulation. Specifically, this survey starts from 2D generation (appearance), then moves to video (appearance+dynamics) and 3D generation (appearance+geometry), and finally culminates in 4D generation that integrate all dimensions. To the best of our knowledge, this is the first attempt to systematically unify the study of 2D, video, 3D and 4D generation within a single framework. To guide future research, we provide a comprehensive review of datasets, evaluation metrics and future directions, and fostering insights for newcomers. This survey serves as a bridge to advance the study of multimodal generative models and real-world simulation within a unified framework.
SurveyForge: On the Outline Heuristics, Memory-Driven Generation, and Multi-dimensional Evaluation for Automated Survey Writing
2025-03-06
Xiangchao Yan, Shiyang Feng, Jiakang Yuan, Renqiu Xia, Bin Wang, Bo Zhang, Lei Bai
Survey paper plays a crucial role in scientific research, especially given the rapid growth of research publications. Recently, researchers have begun using LLMs to automate survey generation for better efficiency. However, the quality gap between LLM-generated surveys and those written by human remains significant, particularly in terms of outline quality and citation accuracy. To close these gaps, we introduce SurveyForge, which first generates the outline by analyzing the logical structure of human-written outlines and referring to the retrieved domain-related articles. Subsequently, leveraging high-quality papers retrieved from memory by our scholar navigation agent, SurveyForge can automatically generate and refine the content of the generated article. Moreover, to achieve a comprehensive evaluation, we construct SurveyBench, which includes 100 human-written survey papers for win-rate comparison and assesses AI-generated survey papers across three dimensions: reference, outline, and content quality. Experiments demonstrate that SurveyForge can outperform previous works such as AutoSurvey.
User reviews on e-commerce platforms exhibit dynamic sentiment patterns driven by temporal and contextual factors. Traditional sentiment analysis methods focus on static reviews, failing to capture the evolving temporal relationship between user sentiment rating and textual content. Sentiment analysis on streaming reviews addresses this limitation by modeling and predicting the temporal evolution of user sentiments. However, it suffers from data sparsity, manifesting in temporal, spatial, and combined forms. In this paper, we introduce SynGraph, a novel framework designed to address data sparsity in sentiment analysis on streaming reviews. SynGraph alleviates data sparsity by categorizing users into mid-tail, long-tail, and extreme scenarios and incorporating LLM-augmented enhancements within a dynamic graph-based structure. Experiments on real-world datasets demonstrate its effectiveness in addressing sparsity and improving sentiment modeling in streaming reviews.
Whole-Body Model-Predictive Control of Legged Robots with MuJoCo
2025-03-06
John Z. Zhang, Taylor A. Howell, Zeji Yi, Chaoyi Pan, Guanya Shi, Guannan Qu, Tom Erez, Yuval Tassa, Zachary Manchester
We demonstrate the surprising real-world effectiveness of a very simple approach to whole-body model-predictive control (MPC) of quadruped and humanoid robots: the iterative LQR (iLQR) algorithm with MuJoCo dynamics and finite-difference approximated derivatives. Building upon the previous success of model-based behavior synthesis and control of locomotion and manipulation tasks with MuJoCo in simulation, we show that these policies can easily generalize to the real world with few sim-to-real considerations. Our baseline method achieves real-time whole-body MPC on a variety of hardware experiments, including dynamic quadruped locomotion, quadruped walking on two legs, and full-sized humanoid bipedal locomotion. We hope this easy-to-reproduce hardware baseline lowers the barrier to entry for real-world whole-body MPC research and contributes to accelerating research velocity in the community. Our code and experiment videos will be available online at:https://johnzhang3.github.io/mujoco_ilqr
Obstacle-Induced Gurzhi Effect and Hydrodynamic Electron Flow in Two-Dimensional Systems
2025-03-06
A. D. Levin, G. M. Gusev, V. A. Chitta, Z. D. Kvon, A. S. Jaroshevich, D. E. Utkin, D. V. Dmitriev, A. K. Bakarov
The viscous flow of electrons in a narrow channel requires both strong electron-electron interactions and no-slip boundary conditions. However, introducing obstacles within the liquid can significantly increase flow resistance and, as a result, amplify the effects of viscosity. Even in samples with smooth walls, the presence of an obstacle can strongly alter electron behavior, leading to pronounced hydrodynamic effects. We investigated transport in mesoscopic samples containing a disordered array of obstacles. In contrast to samples without obstacles, which do not show a decrease in resistivity with rising temperature, samples with obstacles exhibit a significant resistivity reduction as temperature increases (the Gurzhi effect). By measuring the negative magnetoresistance, we extracted shear viscosity and other parameters through comparison with theoretical predictions. Consequently, narrow-channel samples with a disordered obstacle array provide a valuable platform for studying hydrodynamic electron flow independently of boundary conditions.
Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model
2025-03-06
Wenke Huang, Jian Liang, Xianda Guo, Yiyang Fang, Guancheng Wan, Xuankun Rong, Chi Wen, Zekun Shi, Qingyun Li, Didi Zhu, Yanbiao Ma, Ke Liang, Bin Yang, He Li, Jiawei Shao, Mang Ye, Bo Du
Multi-modal Large Language Models (MLLMs) integrate visual and linguistic reasoning to address complex tasks such as image captioning and visual question answering. While MLLMs demonstrate remarkable versatility, MLLMs appears limited performance on special applications. But tuning MLLMs for downstream tasks encounters two key challenges: Task-Expert Specialization, where distribution shifts between pre-training and target datasets constrain target performance, and Open-World Stabilization, where catastrophic forgetting erases the model general knowledge. In this work, we systematically review recent advancements in MLLM tuning methodologies, classifying them into three paradigms: (I) Selective Tuning, (II) Additive Tuning, and (III) Reparameterization Tuning. Furthermore, we benchmark these tuning strategies across popular MLLM architectures and diverse downstream tasks to establish standardized evaluation analysis and systematic tuning principles. Finally, we highlight several open challenges in this domain and propose future research directions. To facilitate ongoing progress in this rapidly evolving field, we provide a public repository that continuously tracks developments: https://github.com/WenkeHuang/Awesome-MLLM-Tuning.
Spatial regularisation for improved accuracy and interpretability in keypoint-based registration
2025-03-06
Benjamin Billot, Ramya Muthukrishnan, Esra Abaci-Turk, P. Ellen Grant, Nicholas Ayache, Hervé Delingette, Polina Golland
Unsupervised registration strategies bypass requirements in ground truth transforms or segmentations by optimising similarity metrics between fixed and moved volumes. Among these methods, a recent subclass of approaches based on unsupervised keypoint detection stand out as very promising for interpretability. Specifically, these methods train a network to predict feature maps for fixed and moving images, from which explainable centres of mass are computed to obtain point clouds, that are then aligned in closed-form. However, the features returned by the network often yield spatially diffuse patterns that are hard to interpret, thus undermining the purpose of keypoint-based registration. Here, we propose a three-fold loss to regularise the spatial distribution of the features. First, we use the KL divergence to model features as point spread functions that we interpret as probabilistic keypoints. Then, we sharpen the spatial distributions of these features to increase the precision of the detected landmarks. Finally, we introduce a new repulsive loss across keypoints to encourage spatial diversity. Overall, our loss considerably improves the interpretability of the features, which now correspond to precise and anatomically meaningful landmarks. We demonstrate our three-fold loss in foetal rigid motion tracking and brain MRI affine registration tasks, where it not only outperforms state-of-the-art unsupervised strategies, but also bridges the gap with state-of-the-art supervised methods. Our code is available at https://github.com/BenBillot/spatial_regularisation.
Large Language Models (LLMs) are revolutionizing bioinformatics, enabling advanced analysis of DNA, RNA, proteins, and single-cell data. This survey provides a systematic review of recent advancements, focusing on genomic sequence modeling, RNA structure prediction, protein function inference, and single-cell transcriptomics. Meanwhile, we also discuss several key challenges, including data scarcity, computational complexity, and cross-omics integration, and explore future directions such as multimodal learning, hybrid AI models, and clinical applications. By offering a comprehensive perspective, this paper underscores the transformative potential of LLMs in driving innovations in bioinformatics and precision medicine.
We give a survey of cardinal charcteristics of the higher Cicho\'n diagram defined on the higher Baire space ${}^\kappa\kappa$ for $\kappa$ regular with $2^{<\kappa}=\kappa$. Specifically, we will compare consistency proofs from the classical Cicho\'n diagram with various well-known forcing notions to similar constructions generalised to the higher Cicho\'n diagram. We are especially interested in separation in a horizontal direction, that is, the consistency of $\mathrm{add}(\mathcal M_\kappa)<\mathrm{non}(\mathcal M_\kappa)$ and of $\mathrm{cov}(\mathcal M_\kappa)<\mathrm{cof}(\mathcal M_\kappa)$. We will have a look at (higher analogues of) Cohen, Hechler, localisation, eventually different, Sacks, random, Laver, Mathias and Miller forcing, and their effect on the cardinal characteristics of the higher Cicho\'n diagram.
A Short Survey of the Well-posedness of the Two-dimensional Burgers' Equation
2025-03-06
Xiang Zhang, Shuhan Xie, Yule Sun
In this paper, we establish the existence and uniqueness of solutions to the two-dimensional Burgers equation using the framework of infinite-dimensional dynamical systems. The two-dimensional Burgers equation, which models the interplay between nonlinear advection and viscous dissipation, is given by: $$ u_{t} + u \cdot \nabla u = \nu \Delta u + f, $$ where $ u = (u_1, u_2) $ is the velocity field, $ \nu > 0 $ is the viscosity coefficient, and $ f $ represents an external force. We primarily employed Galerkin method to transform the partial differential equation into an ordinary differential equation. In addition, by employing Sobolev spaces, energy estimates, and compactness arguments, we rigorously prove the existence of global solutions and their uniqueness under appropriate initial and boundary conditions.
Background. Career abandonment, the process in which professionals leave the activity, assuming positions in another area, among software developers involves frustration with the lost investment and emotional and financial costs, even though being beneficial for the human being, depending on personal context. Previous studies have identified work-related motivators for career abandonment, such as the threat of obsolescence, unstable requirements, and low code quality, though these factors have primarily been examined in former developers. The relationship between these motivators and the intention to abandon among currently active developers remains unexplored. Goal. This article investigates the relationship between key work-related motivators and currently active software developers intention to abandon their careers. Method. We employed a quantitative approach, surveying 221 software developers to validate a theoretical model for career abandonment intention, based on an adaptation of the Investment Model, which incorporates satisfaction with technical aspects of the profession as well as the intention to abandon. Findings. Exploratory and confirmatory factor analyses, through structural equation modeling (SEM), provided robust support for the adapted Investment Model in explaining software developers intention to abandon their careers. Moreover, career commitment significantly impacts the intention to leave the profession, being positively influenced by satisfaction with technical work-related factors and negatively influenced by career alternatives and career investment. Conclusion. The paper offers valuable insights for organizational leaders and research, potentially guiding retention strategies to better support developers, and the adoption of theoretical models to explain career abandonment.
Named Entity Recognition (NER) and Part-of-Speech (POS) tagging are critical tasks for Natural Language Processing (NLP), yet their availability for low-resource languages (LRLs) like Bodo remains limited. This article presents a comparative empirical study investigating the effectiveness of Google's Gemini 2.0 Flash Thinking Experiment model for zero-shot cross-lingual transfer of POS and NER tagging to Bodo. We explore two distinct methodologies: (1) direct translation of English sentences to Bodo followed by tag transfer, and (2) prompt-based tag transfer on parallel English-Bodo sentence pairs. Both methods leverage the machine translation and cross-lingual understanding capabilities of Gemini 2.0 Flash Thinking Experiment to project English POS and NER annotations onto Bodo text in CONLL-2003 format. Our findings reveal the capabilities and limitations of each approach, demonstrating that while both methods show promise for bootstrapping Bodo NLP, prompt-based transfer exhibits superior performance, particularly for NER. We provide a detailed analysis of the results, highlighting the impact of translation quality, grammatical divergences, and the inherent challenges of zero-shot cross-lingual transfer. The article concludes by discussing future research directions, emphasizing the need for hybrid approaches, few-shot fine-tuning, and the development of dedicated Bodo NLP resources to achieve high-accuracy POS and NER tagging for this low-resource language.
Orbital and absolute magnitude distribution of Hilda population
2025-03-06
David Vokrouhlický, David Nesvorný, Miroslav Brož, William F. Bottke, Rogerio Deienno, Carson D. Fuls, Frank C. Shelly
The Hilda population of asteroids is located in a large orbital zone of long-term stability associated with the Jupiter J3/2 mean motion resonance. They are a sister population of the Jupiter Trojans, since both of them are likely made up of objects captured from the primordial Kuiper belt early in the solar system history. Comparisons between the orbital and physical properties of the Hilda and Trojan populations thus represent a test of outer planet formation models. Here we use a decade of observations from the Catalina Sky Survey (G96 site) to determine the bias-corrected orbital and magnitude distributions of Hildas. We also identify collisional families and the background population by computing a new catalog of synthetic proper elements for Hildas. We model the cumulative magnitude distribution of the background population using a local power-law representation with slope $\gamma(H)$, where $H$ is the absolute magnitude. For the largest Hildas, we find $\gamma\simeq 0.5$ with large uncertainty due to the limited population. Beyond $H\simeq 11$, we find that $\gamma$ transitions to a mean value ${\bar \gamma}=0.32\pm 0.04$ with a slight dependence on $H$ (significantly smaller than Jupiter Trojans with ${\bar \gamma}=0.43\pm 0.02$). We find that members of identified collisional families represent more than $60$\% of the total population (both bias counts). The bias-corrected populations contain about the same number of Hildas within the families and the background for $H\leq 16$, but this number may increase to $60$\% families when their location in the orbital space is further improved in the future.
Background: Multiple medical and non-medical stressors, along with the complicity of their exposure pathways, have posted significant challenges to the epidemiological interpretation of the non-communicable diseases, including cardiovascular disease (CVD). Objective: To develop a protocol for deconstructing the complex exposure pathways linking various stressors to adverse outcomes and to elucidate the sequential determinants contributing to CVD risk in depth. Methods: In this study, we developed a Path-Lasso approach, rooted in Adaptive Lasso regression, to construct the network and paths to interpret the determinants of CVD in an in-depth way by using data from the National Health and Nutrition Examination Survey (NHANES). Univariate logistic regression was initially employed to screen out all potential factors of influencing CVD. Then a programmed approach, using Path-Lasso technique, stratified covariates and established a causal network to predict CVD risk. Results: Age, smoking and waist circumference were identified as the most significant predictors of CVD risk. Other factors, such as race, marital status, physical activity, cadmium exposure and diabetes acted as the intermediary or proximal variables. All these stressors (or nodes) formed the network with paths (or edges to link the CVD), in which the latent layer variables that causally associate to the outcome are linearly formed by the stressors in each layer. Discussion: The Path-Lasso approach revealed the epidemiological pathways, linking covariates to CVD risk, which is instrumental in elucidating the inter-covariate transitions of their predication to the outcome, and providing the hierarchal network for foundation of the assessment of CVD risk and the beyond.
Computational Law: Datasets, Benchmarks, and Ontologies
2025-03-06
Dilek Küçük, Fazli Can
Recent developments in computer science and artificial intelligence have also contributed to the legal domain, as revealed by the number and range of related publications and applications. Machine and deep learning models require considerable amount of domain-specific data for training and comparison purposes, in order to attain high-performance in the legal domain. Additionally, semantic resources such as ontologies are valuable for building large-scale computational legal systems, in addition to ensuring interoperability of such systems. Considering these aspects, we present an up-to-date review of the literature on datasets, benchmarks, and ontologies proposed for computational law. We believe that this comprehensive and recent review will help researchers and practitioners when developing and testing approaches and systems for computational law.
This study leverages spatial machine learning (SML) to enhance the accuracy of Proxy Means Testing (PMT) for poverty targeting in Indonesia. Conventional PMT methodologies are prone to exclusion and inclusion errors due to their inability to account for spatial dependencies and regional heterogeneity. By integrating spatial contiguity matrices, SML models mitigate these limitations, facilitating a more precise identification and comparison of geographical poverty clusters. Utilizing household survey data from the Social Welfare Integrated Data Survey (DTKS) for the periods 2016 to 2020 and 2016 to 2021, this study examines spatial patterns in income distribution and delineates poverty clusters at both provincial and district levels. Empirical findings indicate that the proposed SML approach reduces exclusion errors from 28% to 20% compared to standard machine learning models, underscoring the critical role of spatial analysis in refining machine learning-based poverty targeting. These results highlight the potential of SML to inform the design of more equitable and effective social protection policies, particularly in geographically diverse contexts. Future research can explore the applicability of spatiotemporal models and assess the generalizability of SML approaches across varying socio-economic settings.
Qualitative In-Depth Analysis of GDPR Data Subject Access Requests and Responses from Major Online Services
2025-03-06
Daniela Pöhn, Nils Gruschka
The European General Data Protection Regulation (GDPR) grants European users the right to access their data processed and stored by organizations. Although the GDPR contains requirements for data processing organizations (e.g., understandable data provided within a month), it leaves much flexibility. In-depth research on how online services handle data subject access request is sparse. Specifically, it is unclear whether online services comply with the individual GDPR requirements, if the privacy policies and the data subject access responses are coherent, and how the responses change over time. To answer these questions, we perform a qualitative structured review of the processes and data exports of significant online services to (1) analyze the data received in 2023 in detail, (2) compare the data exports with the privacy policies, and (3) compare the data exports from November 2018 and November 2023. The study concludes that the quality of data subject access responses varies among the analyzed services, and none fulfills all requirements completely.
Quantumness and entropic uncertainty for a pair of static Unruh-DeWitt detectors
2025-03-06
Yu-Kun Zhang, Tariq Aziz, Li-Juan Li, Xue-Ke Song, Liu Ye, Dong Wang
In this study, we investigate a pair of detectors operating in Minkowski space-time and analyze the characteristics of various quantum resources within this framework. Specifically, we focus on examining the properties of Bell nonlocality, quantum coherence, the nonlocal advantage of quantum coherence (NAQC), and measured uncertainty in relation to the energy ratio and the distance between the detectors. Additionally, we examine how the initial states influence these quantum properties. Notably, our findings reveal that both a larger energy ratio and a greater separation between the detectors degrade the system's quantumness. Moreover, we explore the evolution of entropic uncertainty and demonstrate its inverse correlation with both Bell nonlocality and coherence, highlighting the intricate interplay between these quantum resources. These insights provide a deeper understanding of quantumness in a relativistic framework and may contribute to the ongoing discussion on the black hole information paradox.
In this paper we present an overview of various kinds of interference, that arise in the Orthogonal Frequency-Domain Multiplexing (OFDM)-based digital communications systems at the physical layer. Inter-symbol, inter-block, inter-carrier interference types are described in detail, valid for any OFDM transmission, along with Inter-cell interference specific to cellular networks. A survey of various communication disruptions techniques is presented - primarily focusing on intentional interference - jamming. Furthermore, we present a survey of modulation techniques that expand on the OFDM and may be considered viable candidates for modulation in the future 6G cellular networks.
Thermoelectric fluctuations of interfering Majorana bound states
2025-03-06
Sergey Smirnov
Nonequilibrium states produced by electric and thermal voltages ($V$, $V_T$) provide a straightforward insight into underlying degrees of freedom of composite nanostructures and are of particular interest to probe Majorana bound states. Here we explore fluctuations of thermoelectric currents at finite frequencies $\omega$ in a quantum dot coupled to two interfering Majorana bound states. At small $V$ we find that in the emission spectra the differential thermoelectric quantum noise $\partial S^>/\partial V_T$ shows an antiresonance whereas in the absorption spectra Majorana interference induces an antiresonance-resonance pair. At large $V$ this pair is preserved whereas the emission antiresonance turns into an antiresonance-resonance pair identical to the absorption one making $\partial S^>/\partial V_T$ antisymmetric in the frequency $\omega$. This antisymmetry distinguishes Majorana behavior from the one induced by Andreev bound states and does not break at higher temperatures making it attractive for experiments on Majorana interference via thermoelectric fluctuation response.
Advancing sustainable energy solutions with microfluidic porous media
2025-03-06
Wenhai Lei, Yuankai Yang, Shuo Yang, Ge Zhang, Jenna Poonoosamy, Anne Juel, Yves Meheust, Shervin Bagheria, Moran Wang
The transition to a sustainable, low-carbon energy future requires transformative advancements in energy and environmental technologies. Carbon capture and sequestration, underground hydrogen storage, and nuclear waste geological disposal will be central aspects of a sustainable energy future, both for mitigating CO2 emissions and providing green energy. A comprehensive understanding of multiphase flow through porous media, along with reactive transport and microbial activities, is essential for assessing the feasibility and managing the risks of these technologies. Microfluidic porous media platforms have emerged as powerful tools for the direct visualization of multiphase reactive flow in porous media and eventually optimizing these multiple physicochemical and biological processes. This review highlights critical scientific challenges associated with these sustainable energy solutions and summarizes the state-of-the-art microfluidic techniques for studying the interplay between multiphase flow, reactive transport, and biological effects in porous media. We provide a comprehensive overview of how these microfluidic approaches enhance the understanding of fundamental pore-scale dynamics and bridge the gap between pore-scale events and large-scale processes. This review is expected to promote both experimental and theoretical understanding of multiphase reactive flow in porous media, thereby informing material design, process optimization, and predictive modeling for scalable implementation. By fostering interdisciplinary collaboration across microfluidics, fluid mechanics, geophysics, materials science, and subsurface engineering, we hope to accelerate innovation and advance sustainable energy solutions.
Square lattice model with staggered magnetic fluxes: zero Chern number topological states and topological flat bands
2025-03-06
Li-Xiang Chen, Dong-Hao Guan, Lu Qi, Xiuyun Zhang, Ying Han, Ai-Lei He
Staggered magnetic fluxes (SMF) play a crucial role in achieving Chern insulators (CIs), by which a series of CI models have been established on various lattices. In addition, SMF induced higher-order topological insulator (HOTI) in a lattice model has been reported. In this work, we propose a square lattice model with SMF. We find intracellular SMF can induce zero-Chern-number topological insulator (ZCNTI) at quarter filling which hosts topologically protected edge states characterized by the quantized polarization, in analogy to the topological state in two dimensional Su-Schrieffer-Hegger model. When lattice dimerization and intracellular SMF are introduced, there exists HOTI state at half filling. Furthermore, this model hosts topological flat band (TFB) by considering the next-nearest-neighbor hoppings. Several fractional Chern insulator states are investigated when hard-core bosons are filled into this TFB model.
A Comparative Study of Diabetes Prediction Based on Lifestyle Factors Using Machine Learning
2025-03-06
Bruce Nguyen, Yan Zhang
Diabetes is a prevalent chronic disease with significant health and economic burdens worldwide. Early prediction and diagnosis can aid in effective management and prevention of complications. This study explores the use of machine learning models to predict diabetes based on lifestyle factors using data from the Behavioral Risk Factor Surveillance System (BRFSS) 2015 survey. The dataset consists of 21 lifestyle and health-related features, capturing aspects such as physical activity, diet, mental health, and socioeconomic status. Three classification models, Decision Tree, K-Nearest Neighbors (KNN), and Logistic Regression, are implemented and evaluated to determine their predictive performance. The models are trained and tested using a balanced dataset, and their performances are assessed based on accuracy, precision, recall, and F1-score. The results indicate that the Decision Tree, KNN, and Logistic Regression achieve an accuracy of 0.74, 0.72, and 0.75, respectively, with varying strengths in precision and recall. The findings highlight the potential of machine learning in diabetes prediction and suggest future improvements through feature selection and ensemble learning techniques.
Large Language models (LLMs) have emerged as powerful tools for addressing challenges across diverse domains. Notably, recent studies have demonstrated that large language models significantly enhance the efficiency of biomolecular analysis and synthesis, attracting widespread attention from academics and medicine. In this paper, we systematically investigate the application of prompt-based methods with LLMs to biological sequences, including DNA, RNA, proteins, and drug discovery tasks. Specifically, we focus on how prompt engineering enables LLMs to tackle domain-specific problems, such as promoter sequence prediction, protein structure modeling, and drug-target binding affinity prediction, often with limited labeled data. Furthermore, our discussion highlights the transformative potential of prompting in bioinformatics while addressing key challenges such as data scarcity, multimodal fusion, and computational resource limitations. Our aim is for this paper to function both as a foundational primer for newcomers and a catalyst for continued innovation within this dynamic field of study.
The JARVIS Infrastructure is All You Need for Materials Design
2025-03-06
Kamal Choudhary
Joint Automated Repository for Various Integrated Simulations (JARVIS) is a comprehensive infrastructure offering databases, tools, tutorials, and benchmarks for multiscale, multimodal, forward, and inverse materials design. Emphasizing open access principles and reproducibility, it integrates theoretical and experimental methodologies such as density functional theory, quantum Monte Carlo, tight-binding, classical force fields, and machine-learning approaches-including fingerprinting, graph neural networks, and transformer models. Its experimental data collection spans cryogenics, microscopy, and diffraction, covering materials like metals, semiconductors, insulators, superconductors, carbon capture systems, high-strength compounds, and low-dimensional materials, heterostructures and defects. JARVIS disseminates resources via open datasets, web applications, executable scripts, and peer-reviewed publications, ensuring broad accessibility and reproducibility. Widely adopted worldwide, it has facilitated millions of data and tool downloads. By unifying diverse methods and data under one platform, JARVIS drives both fundamental discoveries and real-world innovations, advancing conventional and data-driven materials design.
Artificial Intelligence in Pronunciation Teaching: Use and Beliefs of Foreign Language Teachers
2025-03-06
Georgios P. Georgiou
Pronunciation instruction in foreign language classrooms has often been an overlooked area of focus. With the widespread adoption of Artificial Intelligence (AI) and its potential benefits, investigating how AI is utilized in pronunciation teaching and understanding the beliefs of teachers about this tool is essential for improving learning outcomes. This study aims to examine how AI use for pronunciation instruction varies across different demographic and professional factors among teachers, and how these factors, including AI use, influence the beliefs of teachers about AI. The study involved 117 English as a Foreign Language (EFL) in-service teachers working in Cyprus, who completed an online survey designed to assess their beliefs about the effectiveness of AI, its drawbacks, and their willingness to integrate AI into their teaching practices. The results revealed that teachers were significantly more likely to agree on the perceived effectiveness of AI and their willingness to adopt it, compared to their concerns about its use. Furthermore, teachers working in higher education and adult education, as well as those who had received more extensive training, reported using AI more frequently in their teaching. Teachers who utilized AI more often expressed stronger agreement with its effectiveness, while those who had received more training were less likely to express concerns about its integration. Given the limited training that many teachers currently receive, these findings demonstrate the need for tailored training sessions that address the specific needs and concerns of educators, ultimately fostering the adoption of AI in pronunciation instruction.
Quadratic Surveys (QSs) elicit more accurate preferences than traditional methods like Likert-scale surveys. However, the cognitive load associated with QSs has hindered their adoption in digital surveys for collective decision-making. We introduce a two-phase "organize-then-vote'' QS to reduce cognitive load. As interface design significantly impacts survey results and accuracy, our design scaffolds survey takers' decision-making while managing the cognitive load imposed by QS. In a 2x2 between-subject in-lab study on public resource allotment, we compared our interface with a traditional text interface across a QS with 6 (short) and 24 (long) options. Two-phase interface participants spent more time per option and exhibited shorter voting edit distances. We qualitatively observed shifts in cognitive effort from mechanical operations to constructing more comprehensive preferences. We conclude that this interface promoted deeper engagement, potentially reducing satisficing behaviors caused by cognitive overload in longer QSs. This research clarifies how human-centered design improves preference elicitation tools for collective decision-making.
InterChat: Enhancing Generative Visual Analytics using Multimodal Interactions
2025-03-06
Juntong Chen, Jiang Wu, Jiajing Guo, Vikram Mohanty, Xueming Li, Jorge Piazentin Ono, Wenbin He, Liu Ren, Dongyu Liu
The rise of Large Language Models (LLMs) and generative visual analytics systems has transformed data-driven insights, yet significant challenges persist in accurately interpreting users' analytical and interaction intents. While language inputs offer flexibility, they often lack precision, making the expression of complex intents inefficient, error-prone, and time-intensive. To address these limitations, we investigate the design space of multimodal interactions for generative visual analytics through a literature review and pilot brainstorming sessions. Building on these insights, we introduce a highly extensible workflow that integrates multiple LLM agents for intent inference and visualization generation. We develop InterChat, a generative visual analytics system that combines direct manipulation of visual elements with natural language inputs. This integration enables precise intent communication and supports progressive, visually driven exploratory data analyses. By employing effective prompt engineering, and contextual interaction linking, alongside intuitive visualization and interaction designs, InterChat bridges the gap between user interactions and LLM-driven visualizations, enhancing both interpretability and usability. Extensive evaluations, including two usage scenarios, a user study, and expert feedback, demonstrate the effectiveness of InterChat. Results show significant improvements in the accuracy and efficiency of handling complex visual analytics tasks, highlighting the potential of multimodal interactions to redefine user engagement and analytical depth in generative visual analytics.
Disparities in LLM Reasoning Accuracy and Explanations: A Case Study on African American English
2025-03-06
Runtao Zhou, Guangya Wan, Saadia Gabriel, Sheng Li, Alexander J Gates, Maarten Sap, Thomas Hartvigsen
Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning tasks, leading to their widespread deployment. However, recent studies have highlighted concerning biases in these models, particularly in their handling of dialectal variations like African American English (AAE). In this work, we systematically investigate dialectal disparities in LLM reasoning tasks. We develop an experimental framework comparing LLM performance given Standard American English (SAE) and AAE prompts, combining LLM-based dialect conversion with established linguistic analyses. We find that LLMs consistently produce less accurate responses and simpler reasoning chains and explanations for AAE inputs compared to equivalent SAE questions, with disparities most pronounced in social science and humanities domains. These findings highlight systematic differences in how LLMs process and reason about different language varieties, raising important questions about the development and deployment of these systems in our multilingual and multidialectal world. Our code repository is publicly available at https://github.com/Runtaozhou/dialect_bias_eval.
Chart-HQA: A Benchmark for Hypothetical Question Answering in Charts
2025-03-06
Xiangnan Chen, Yuancheng Fang, Qian Xiao, Juncheng Li, Jun Lin, Siliang Tang, Yi Yang, Yueting Zhuang
Multimodal Large Language Models (MLLMs) have garnered significant attention for their strong visual-semantic understanding. Most existing chart benchmarks evaluate MLLMs' ability to parse information from charts to answer questions. However, they overlook the inherent output biases of MLLMs, where models rely on their parametric memory to answer questions rather than genuinely understanding the chart content. To address this limitation, we introduce a novel Chart Hypothetical Question Answering (HQA) task, which imposes assumptions on the same question to compel models to engage in counterfactual reasoning based on the chart content. Furthermore, we introduce HAI, a human-AI interactive data synthesis approach that leverages the efficient text-editing capabilities of LLMs alongside human expert knowledge to generate diverse and high-quality HQA data at a low cost. Using HAI, we construct Chart-HQA, a challenging benchmark synthesized from publicly available data sources. Evaluation results on 18 MLLMs of varying model sizes reveal that current models face significant generalization challenges and exhibit imbalanced reasoning performance on the HQA task.
Beyond Memorization: Evaluating the True Type Inference Capabilities of LLMs for Java Code Snippets
2025-03-06
Yiwen Dong, Zhenyang Xu, Yongqiang Tian, Chengnian Sun
Type inference is a crucial task for reusing online code snippets, often found on platforms like StackOverflow, which frequently lack essential type information such as fully qualified names (FQNs) and required libraries. Recent studies have leveraged Large Language Models (LLMs) for type inference on code snippets, showing promising results. However, these results are potentially affected by data leakage, as the benchmark suite (StatType-SO) has been public on GitHub since 2017 (full suite in 2023). Thus, it is uncertain whether LLMs' strong performance reflects genuine code semantics understanding or a mere retrieval of ground truth from training data. To comprehensively assess LLMs' type inference capabilities on Java code snippets, we conducted a three-pronged evaluation. First, utilizing Thalia, a program synthesis technique, we created ThaliaType--a new, unseen dataset for type inference evaluation. On unseen snippets, LLM performance dropped significantly, with up to a 59% decrease in precision and 72% in recall. Second, we developed semantic-preserving transformations that significantly degraded LLMs' type inference performance, revealing weaknesses in understanding code semantics. Third, we used delta debugging to identify the minimal syntax elements sufficient for LLM inference. While type inference primarily involves inferring FQNs for types in the code snippet, LLMs correctly infer FQNs even when the types were absent from the snippets, suggesting a reliance on knowledge from training instead of thoroughly analyzing the snippets. Our findings indicate that LLMs' strong past performance likely stemmed from data leakage, rather than a genuine understanding of the semantics of code snippets. Our findings highlight the crucial need for carefully designed benchmarks using unseen code snippets to assess the true capabilities of LLMs for type inference tasks.
Deep Learning Aided Software Vulnerability Detection: A Survey
2025-03-06
Md Nizam Uddin, Yihe Zhang, Xiali Hei
The pervasive nature of software vulnerabilities has emerged as a primary factor for the surge in cyberattacks. Traditional vulnerability detection methods, including rule-based, signature-based, manual review, static, and dynamic analysis, often exhibit limitations when encountering increasingly complex systems and a fast-evolving attack landscape. Deep learning (DL) methods excel at automatically learning and identifying complex patterns in code, enabling more effective detection of emerging vulnerabilities. This survey analyzes 34 relevant studies from high-impact journals and conferences between 2017 and 2024. This survey introduces the conceptual framework Vulnerability Detection Lifecycle for the first time to systematically analyze and compare various DL-based vulnerability detection methods and unify them into the same analysis perspective. The framework includes six phases: (1) Dataset Construction, (2) Vulnerability Granularity Definition, (3) Code Representation, (4) Model Design, (5) Model Performance Evaluation, and (6) Real-world Project Implementation. For each phase of the framework, we identify and explore key issues through in-depth analysis of existing research while also highlighting challenges that remain inadequately addressed. This survey provides guidelines for future software vulnerability detection, facilitating further implementation of deep learning techniques applications in this field.
AI-based Programming Assistants for Privacy-related Code Generation: The Developers' Experience
2025-03-06
Kashumi Madampe, John Grundy, Nalin Arachchilage
With the popularising of generative AI, the existence of AI-based programming assistants for developers is no surprise. Developers increasingly use them for their work, including generating code to fulfil the data protection requirements (privacy) of the apps they build. We wanted to know if the reality is the same as expectations of AI-based programming assistants when trying to fulfil software privacy requirements, and the challenges developers face when using AI-based programming assistants and how these can be improved. To this end, we conducted a survey with 51 developers worldwide. We found that AI-based programming assistants need to be improved in order for developers to better trust them with generating code that ensures privacy. In this paper, we provide some practical recommendations for developers to consider following when using AI-based programming assistants for privacy-related code development, and some key further research directions.
A Unified Framework with Novel Metrics for Evaluating the Effectiveness of XAI Techniques in LLMs
2025-03-06
Melkamu Abay Mersha, Mesay Gemeda Yigezu, Hassan shakil, Ali Al shami, Sanghyun Byun, Jugal Kalita
The increasing complexity of LLMs presents significant challenges to their transparency and interpretability, necessitating the use of eXplainable AI (XAI) techniques to enhance trustworthiness and usability. This study introduces a comprehensive evaluation framework with four novel metrics for assessing the effectiveness of five XAI techniques across five LLMs and two downstream tasks. We apply this framework to evaluate several XAI techniques LIME, SHAP, Integrated Gradients, Layer-wise Relevance Propagation (LRP), and Attention Mechanism Visualization (AMV) using the IMDB Movie Reviews and Tweet Sentiment Extraction datasets. The evaluation focuses on four key metrics: Human-reasoning Agreement (HA), Robustness, Consistency, and Contrastivity. Our results show that LIME consistently achieves high scores across multiple LLMs and evaluation metrics, while AMV demonstrates superior Robustness and near-perfect Consistency. LRP excels in Contrastivity, particularly with more complex models. Our findings provide valuable insights into the strengths and limitations of different XAI methods, offering guidance for developing and selecting appropriate XAI techniques for LLMs.
Bridging the AI Adoption Gap: Designing an Interactive Pedagogical Agent for Higher Education Instructors
2025-03-06
Si Chen, Reid Metoyer, Khiem Le, Adam Acunin, Izzy Molnar, Alex Ambrose, James Lang, Nitesh Chawla, Ronald Metoyer
Instructors play a pivotal role in integrating AI into education, yet their adoption of AI-powered tools remains inconsistent. Despite this, limited research explores how to design AI tools that support broader instructor adoption. This study applies a human-centered design approach, incorporating qualitative methods, to investigate the design of interactive pedagogical agents that provide instructional suggestions in response to instructors' questions. We conducted a formative study involving interviews with five pedagogy experts to examine existing strategies for supporting instructors' pedagogical needs. Building on these insights, we facilitated a participatory design session with ten pedagogy experts, where participants reviewed a storyboard depicting a chatbot designed for instructors with varying levels of AI literacy and differing attitudes toward AI. Experts also evaluated the quality of LLM-generated suggestions based on common teaching challenges. Our findings highlight the need for chatbot interactions that foster trust, especially for AI-conservative instructors. Experts emphasized the importance of social transparency (for example, showing how peers use the tool) and allowing instructors to flexibly control how much or how little they engage with the system. We also propose design recommendations to enhance the quality of AI-generated teaching suggestions, such as adapting them to reflect instructors' prior teaching experience. This work underscores the urgent need to support AI-conservative instructors, as AI literacy and attitudes are closely intertwined. Without thoughtful design, there is a risk of widening pedagogical divides and reducing students' learning opportunities.
Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A review
2025-03-06
Arash J. Khabbazi, Elias N. Pergantis, Levi D. Reyes Premer, Panagiotis Papageorgiou, Alex H. Lee, James E. Braun, Gregor P. Henze, Kevin J. Kircher
A large body of simulation research suggests that model predictive control (MPC) and reinforcement learning (RL) for heating, ventilation, and air-conditioning (HVAC) in residential and commercial buildings could reduce energy costs, pollutant emissions, and strain on power grids. Despite this potential, neither MPC nor RL has seen widespread industry adoption. Field demonstrations could accelerate MPC and RL adoption by providing real-world data that support the business case for deployment. This paper reviews 24 field demonstrations of MPC and RL in residential buildings and 80 in commercial buildings. After presenting demographic information -- such as experiment scopes, locations, and durations -- this paper analyzes experiment protocols and their influence on performance estimates. We find that 71% of the reviewed field demonstrations use experiment protocols that may lead to unreliable performance estimates. Over the remaining 29% that we view as reliable, the weighted-average cost savings, weighted by experiment duration, are 16% in residential buildings and 13% in commercial buildings. While these savings are potentially attractive, making the business case for MPC and RL also requires characterizing the costs of deployment, operation, and maintenance. Only 13 of the 104 reviewed papers report these costs or discuss related challenges. Based on these observations, we recommend directions for future field research, including: Improving experiment protocols; reporting deployment, operation, and maintenance costs; designing algorithms and instrumentation to reduce these costs; controlling HVAC equipment alongside other distributed energy resources; and pursuing emerging objectives such as peak shaving, arbitraging wholesale energy prices, and providing power grid reliability services.
Large language models (LLMs) like OpenAI ChatGPT, Google Gemini, and GitHub Copilot are rapidly gaining traction in the software industry, but their full impact on software engineering remains insufficiently explored. Despite their growing adoption, there is a notable lack of formal, qualitative assessments of how LLMs are applied in real-world software development contexts. To fill this gap, we conducted semi-structured interviews with sixteen early-adopter professional developers to explore their use of LLMs throughout various stages of the software development life cycle. Our investigation examines four dimensions: people - how LLMs affect individual developers and teams; process - how LLMs alter software engineering workflows; product - LLM impact on software quality and innovation; and society - the broader socioeconomic and ethical implications of LLM adoption. Thematic analysis of our data reveals that while LLMs have not fundamentally revolutionized the development process, they have substantially enhanced routine coding tasks, including code generation, refactoring, and debugging. Developers reported the most effective outcomes when providing LLMs with clear, well-defined problem statements, indicating that LLMs excel with decomposed problems and specific requirements. Furthermore, these early-adopters identified that LLMs offer significant value for personal and professional development, aiding in learning new languages and concepts. Early-adopters, highly skilled in software engineering and how LLMs work, identified early and persisting challenges for software engineering, such as inaccuracies in generated content and the need for careful manual review before integrating LLM outputs into production environments. Our study provides a nuanced understanding of how LLMs are shaping the landscape of software development, with their benefits, limitations, and ongoing implications.
Modeling Asteroseismic Yields for the Roman Galactic Bulge Time-Domain Survey
2025-03-06
Trevor J. Weiss, Noah J. Downing, Marc H. Pinsonneault, Joel C. Zinn, Dennis Stello, Timothy R. Bedding, Kaili Cao, Marc Hon, Claudia Reyes, B. Scott Gaudi, Robert F. Wilson, Daniel Huber, Sanjib Sharma
The Galactic Bulge Time Domain Survey (GBTDS) of the Roman Space Telescope will take high cadence data of the Galactic bulge. We investigate the asteroseismic potential of this survey for red giants. We simulate the detectability of global asteroseismic frequencies, $\nu_{\mathrm{max}}$ and $\Delta\nu$, by modify ing Kepler data to match nominal GBTDS observing strategies, considering different noise models, observing cadences, and detection algorithms. Our baseline case, using conservative assumptions, consistently leads to asteroseismic $\nu_{\mathrm{max}}$ detection probabilities above 80% for red clump and red giant branch stars brighter than 16th magnitude in Roman's F146 filter. We then inject these detection probabilities into a Galaxia model of the bulge to estimate asteroseismic yields. For our nominal case, we detect 290,000 stars in total, with 185,000 detections in the bulge. Different assumptions give bulge yields from 135,000 to 349,000 stars. For stars with measured $\nu_{\mathrm{max}}$, we find that we can recover $\Delta\nu$ in 21% to 42% of red clump stars, and 69% to 92% of RGB stars. Implications for survey strategy and asteroseismic population studies are discussed more.
Image-based sexual abuse (IBSA) refers to the nonconsensual creating, taking, or sharing of intimate images, including threats to share intimate images. Despite the significant harms of IBSA, there is limited data on its prevalence and how it affects different identity or demographic groups. This study examines prevalence of, impacts from, and responses to IBSA via a survey with over 16,000 adults in 10 countries. More than 1 in 5 (22.6%) respondents reported an experience of IBSA. Victimization rates were higher among LGBTQ+ and younger respondents. Although victimized at similar rates, women reported greater harms and negative impacts from IBSA than men. Nearly a third (30.9%) of victim-survivors did not report or disclose their experience to anyone. We provide large-scale, granular, baseline data on prevalence in a diverse set of countries to aid in the development of effective interventions that address the experiences and intersectional identities of victim-survivors.
Leveraging Large Language Models For Scalable Vector Graphics Processing: A Review
2025-03-06
Boris Malashenko, Ivan Jarsky, Valeria Efimova
In recent years, rapid advances in computer vision have significantly improved the processing and generation of raster images. However, vector graphics, which is essential in digital design, due to its scalability and ease of editing, have been relatively understudied. Traditional vectorization techniques, which are often used in vector generation, suffer from long processing times and excessive output complexity, limiting their usability in practical applications. The advent of large language models (LLMs) has opened new possibilities for the generation, editing, and analysis of vector graphics, particularly in the SVG format, which is inherently text-based and well-suited for integration with LLMs. This paper provides a systematic review of existing LLM-based approaches for SVG processing, categorizing them into three main tasks: generation, editing, and understanding. We observe notable models such as IconShop, StrokeNUWA, and StarVector, highlighting their strengths and limitations. Furthermore, we analyze benchmark datasets designed for assessing SVG-related tasks, including SVGEditBench, VGBench, and SGP-Bench, and conduct a series of experiments to evaluate various LLMs in these domains. Our results demonstrate that for vector graphics reasoning-enhanced models outperform standard LLMs, particularly in generation and understanding tasks. Furthermore, our findings underscore the need to develop more diverse and richly annotated datasets to further improve LLM capabilities in vector graphics tasks.
Joint Delay-Doppler Estimation using OFDMA Payloads for Integrated Sensing and Communications
2025-03-06
Marc Miranda, Sebastian Semper, Christian Schneider, Reiner Thomä, Giovanni Del Galdo
The use of future communication systems for sensing offers the potential for a number of new applications. In this paper, we show that leveraging user data payloads in multi-node Orthogonal Frequency Division Multiple Access (OFDMA) networks for estimating target delay and Doppler-shift parameters can yield a significant advantage in SNR and addressable bandwidth. However, gaps in the frequency-time resources, reference signal boosting and amplitude modulation schemes introduce challenges for estimation at the sensing receiver. In this work, we propose a joint delay and Doppler-shift model-based estimator designed to address these challenges. Furthermore, we demonstrate that incorporating knowledge of the device model into the estimation procedure helps mitigate the effects of the non-ideal radar ambiguity function caused by amplitude-modulated user payloads and sparse reference signals. Simulation results demonstrate that the estimator achieves the theoretical lower bound on estimation variance.
Unified model of the Hall effect from insulator to overdoped compounds in cuprate superconductors
2025-03-06
Júlia C. Anjos, Hércules S. Santana, E. V. L. de Mello
Measurements of the Hall coefficient in La$_{2-x}$Sr$_x$CuO$_4$, ranging from the undoped ($x = p = 0$) Mott insulator to overdoped compounds, exhibit a temperature dependence that offers insights into their electronic structure. We interpret these results using a model based on the theory of phase-separation (PS) dynamics, which begins at half-filled ($n = 1$) and at a temperature $T_{\rm PS}(p)$, near the pseudogap temperature $T^*(p)$. The $n = 1$ holes have low mobility and provide the modulations of the charge density waves (CDW). As doping increases from $p = 0$, these modulations guide the additional p holes to occupy alternating CDW domains. This charge inhomogeneity may facilitate the formation of localized superconducting amplitudes below the critical onset temperature $T_{\rm c}^{\rm max}(p)$. Using thermal activation expressions, along with quantum tunnelling between the charge domains, we successfully reproduce all Hall coefficient measurements $R_{\rm H}(p,T)$ and highlight the relevant energies of cuprates. The calculations confirm three significant electronic features: the phase-separating role of the pseudogap temperature, the superconducting state achieved through phase coherence, and the two types of charge carriers whose energies and mobilities become comparable at $p \approx 0.19$, where $T^*(p) \approx T_{\rm c}^{\rm max}(p)$. This results in a crossover from $n = p$ to $n = 1 + p$. These findings, along with the $R_{\rm H}(p,T)$ calculations from insulating to overdoped compounds, underscore the critical role of the electronic phase separation in the properties of cuprates.
MarsLGPR: Mars Rover Localization with Ground Penetrating Radar
2025-03-06
Anja Sheppard, Katherine A. Skinner
In this work, we propose the use of Ground Penetrating Radar (GPR) for rover localization on Mars. Precise pose estimation is an important task for mobile robots exploring planetary surfaces, as they operate in GPS-denied environments. Although visual odometry provides accurate localization, it is computationally expensive and can fail in dim or high-contrast lighting. Wheel encoders can also provide odometry estimation, but are prone to slipping on the sandy terrain encountered on Mars. Although traditionally a scientific surveying sensor, GPR has been used on Earth for terrain classification and localization through subsurface feature matching. The Perseverance rover and the upcoming ExoMars rover have GPR sensors already equipped to aid in the search of water and mineral resources. We propose to leverage GPR to aid in Mars rover localization. Specifically, we develop a novel GPR-based deep learning model that predicts 1D relative pose translation. We fuse our GPR pose prediction method with inertial and wheel encoder data in a filtering framework to output rover localization. We perform experiments in a Mars analog environment and demonstrate that our GPR-based displacement predictions both outperform wheel encoders and improve multi-modal filtering estimates in high-slip environments. Lastly, we present the first dataset aimed at GPR-based localization in Mars analog environments, which will be made publicly available upon publication.
Revealing Quantum Geometry in Nonlinear Quantum Materials
2025-03-06
Yiyang Jiang, Tobias Holder, Binghai Yan
Berry curvature-related topological phenomena have been a central topic in condensed matter physics. Yet, until recently other quantum geometric quantities such as the metric and connection received only little attention due to the relatively few effects which have been documented for them. This review gives a modern perspective how quantum geometric quantities naturally enter the nonlinear responses of quantum materials and demonstrate their deep connection with excitation energy, lifetimes, symmetry, and corresponding physical processes. The multitude of nonlinear responses can be subdivided into nonlinear optical effects, subgap responses, and nonlinear transport phenomena. Such a distinction by energy scales facilitates an intuitive understanding of the underlying electronic transitions, giving rise to a unified picture of the electron motion beyond linear order. The well-known injection and shift currents constitute the main resonances in the optical regime. Exploiting their respective lifetime and symmetry dependencies, this review elucidates how these resonances can be distinguished by a corresponding quantum geometric quantity that shares the same symmetry. This is followed by a brief exposition of the role of quasiparticle lifetimes for nonlinear subgap responses, which presents a window into the microscopic short-term dynamics as well as the ground state correlation and localization. We conclude with an account of the anomalous motion due to the Berry curvature dipole and quantum metric dipole in nonlinear transport, clarifying the correspondence between physical observables and the underlying mechanisms. This review highlights the close relationship between quantum geometry and nonlinear response, showing the way towards promising probes of quantum geometry and enabling novel avenues to characterize complex materials.
Discovery of a pair of very metal-poor stars enriched in neutron-capture elements: The proto-disk r-II star BPS CS 29529-0089 and the Gaia-Sausage-Enceladus r-I star TYC 9219-2422-1
2025-03-06
A. R. da Silva, R. Smiljanic
R-process enhanced metal-poor stars (\EuFe$\geq+0.3$ and \FeH$\leq-1.0$) are rare objects whose study can provide clues to the astrophysical sites of the rapid neutron capture process. In this study, we investigate the detailed chemical abundance patterns of two of these anomalous stars, originally identified among stars observed by the GALAH survey. Our aim is to obtain the detailed chemical abundance pattern of these stars with spectroscopy at higher resolution and signal-to-noise ratio. We use a calibration of the infrared flux method to determine accurate effective temperatures, and \Gaia~ parallaxes together with broadband photometry and theoretical bolometric corrections to determine surface gravity. Metallicities and chemical abundances are determined with model atmospheres and spectrum synthesis. We also integrate stellar orbits for a complete chemodynamic analysis. e determine abundances for up to 47 chemical species (44 elements), of which 27 are neutron-capture elements. Corrections because of deviations from the local thermodynamical equilibrium are applied to the metallicities and 12 elements. We find that one of the stars, BPS CS 29529-0089, is a proto-disk star of the Milky Way of r-II type, with \EuFe=+1.79~dex. The second star, TYC 9219-2422-1, is part of the halo and associated with the Gaia-Sausage-Enceladus merger event. It is of r-I type with [Eu/Fe] = +0.54. Abundances of Th are also provided for both stars. BPS CS 29529-0089 is the most extreme example of r-process enhanced star known with disk-like kinematics and that is not carbon enhanced. TYC 9219-2422-1 is found to be an archetypal Gaia-Sausage-Enceladus star. Their abundances of C, Mg, Ni, Sc, Mn, and Al seem consistent with expectations for stars enriched by a single population III core collapse supernova, despite their relatively high metallicities ([Fe/H] $\sim$ $-$2.4).
The HST-Hyperion Survey: Companion Fraction and Overdensity in a z ~ 2.5 Proto-supercluster
2025-03-06
F. Giddings, B. C. Lemaux, B. Forrest, L. Shen, D. Sikorski, R. Gal, O. Cucciati, E. Golden-Marx, W. Hu. K. Ronayne, E. Shah, R. O. Amorín, S. Bardelli, D. C. Baxter, L. P. Cassarà, G. De Lucia, F. Fontanot, G. Gururajan, N. Hathi, M. Hirschmann, D. Hung, L. Lubin, D. B. Sanders, D. Vergani, L. Xie, E. Zucca
We present a study of the galaxy merger and interaction activity within the Hyperion Proto-supercluster at z~2.5 in an effort to assess the occurrence of galaxy mergers and interactions in contrast to the coeval field and their impact on the build up of stellar mass in high density environments at higher-z. For this work, we utilize data from the Charting Cluster Construction with VUDS and ORELSE Survey (C3VO) along with extensive spectroscopic and photometric datasets available for the COSMOS field, including the HST-Hyperion Survey. To evaluate potential merger and interaction activity, we measure the fraction of galaxies with close kinematic companions ($f_{ckc}$) both within Hyperion and the coeval field by means of a Monte Carlo (MC) methodology developed in this work that probabilistically employs our entire combined spectroscopic and photometric dataset. We validate our $f_{ckc}$ MC methodology on a simulated lightcone built from the GAlaxy Evolution and Assembly semi-analytic model, and we determine correction factors that account for the underlying spectroscopic sampling rate of our dataset. We find that galaxies in Hyperion have close kinematic companions $\gtrsim 2\times$ more than galaxies in the field and measure a corrected $f_{ckc}=49_{-7.8}^{+7.4}$% for Hyperion and a corrected $f_{ckc}=23_{-1.3}^{+1.2}$% for the surrounding field; a $>3\sigma$ difference. This increase in $f_{ckc}$ indicates an enhancement in the merger and interaction activity within Hyperion and matches the trend seen in other structures. The rate of merger and interactions within the field implied from our field $f_{ckc}$ measurement is well aligned with values measured from other observations in similar redshift ranges. The enhanced $f_{ckc}$ measured within Hyperion suggests that merger and interaction activity play an important role in the mass growth of galaxies in denser environments at higher z.
Measuring photo-ionization rate and mean free path of HeII ionizing photons at $2.5 \leq z \leq 3.6$: Evidence for late and rapid HeII reionization Part-II
2025-03-06
Prakash Gaikwad, Frederick B. Davies, Martin G. Haehnelt
We present measurements of the spatially averaged HeII photo-ionization rate ($\langle \Gamma_{\rm HeII} \rangle$), mean free path of HeII ionizing photons ($\lambda_{\rm mfp, HeII}$), and HeII fraction ($f_{\rm HeII}$) across seven redshift bins within the redshift range $2
We discuss a novel mechanism for generating dark matter from a fast-rolling scalar field, relevant for both inflation and rotating axion models, and apply it specifically to the (QCD) axion. Dark matter comes from scalar field fluctuations generated by the product of the curvature perturbation and the fast-rolling background field. These fluctuations can explain the totality of dark matter in a vast axion parameter space, particularly for the QCD axion, which will be targeted by upcoming experiments. We review the constraints on this mechanism and potential gravitational-wave signatures.
The HST-Hyperion Survey: Grism Observations of a $z\sim2.5$ Proto-Supercluster
2025-03-06
Ben Forrest, Lu Shen, Brian C. Lemaux, Ekta Shah, Olga Cucciati, Roy R. Gal, Finn Giddings, Emmet Golden-Marx, Weida Hu, Kaila Ronayne, Derek Sikorski, Priti Staab, Ricardo O. Amorín, Sandro Bardelli, Bianca Garilli, Nimish Hathi, Denise Hung, Lori Lubin, Debora Pelliccia, Russell E. Ryan, Gianni Zamorani, Elena Zucca
We present first results and catalogs from the HST-Hyperion survey. This survey has collected 50 orbits of WFC3/F160W imaging and WFC3/G141 grism spectroscopy in the most overdense regions of the Hyperion proto-supercluster at $z\sim2.45$, which are analyzed in conjunction with the adjacent 56 orbits of WFC3/F140W imaging and WFC3/G141 grism spectroscopy from the 3D-HST survey. Sources were identified and spectra extracted using GRIZLI, which subsequently fit the combined grism data with object-matched photometric data from the COSMOS2020 catalog to obtain a redshift and best-fit spectral model. Each source was then visually inspected by multiple team members and quality flags were assigned. A total of 12814 objects with $m_{HST} \leq 25.0$ were inspected, of which 5629 (44%) have reliable redshifts from the grism data, which are sensitive to emission lines at a level of $\sim8.8 \times10^{-18}$ erg s$^{-1}$ cm$^{-2}$ ($1\sigma$). Comparison to high-quality ground-based spectroscopic redshifts yields a scatter of $\sigma_{\rm NMAD} = 0.0016$. The resulting catalogs contain 125 confirmed members of the Hyperion structure within $2.40
The Three-mm Ultimate Mopra Milky Way Survey. III. Data Release 6, An Atlas of Physical Conditions, Global Mass Conversion Laws, and 3D Physical Architecture of the Molecular ISM in the Fourth Quadrant
2025-03-06
Peter J. Barnes, Dylan G. H. Barnes, Audra K. Hernandez, Sebastian Lopez, Erik Muller
We present Data Release 6 of ThrUMMS, consisting of complete data cubes and various moments of line emission ($^{12}$CO, $^{13}$CO, C$^{18}$O) from molecular clouds, across 60$^{\circ}$$\times$2$^{\circ}$ of the Fourth Quadrant (4Q) of the Milky Way at a resolution of 72$''$ in (l,b) and 0.09 kms$^{-1}$ in V$_{LSR}$. From LTE radiative transfer analysis of the data cubes, we compute cubes and moments of the lines' opacity, excitation temperature, and column density $N_{12CO}$. Combining $I_{12CO}$ and $N_{12CO}$ data, we derive a global mass conversion law $N$=$N_{0}I^{p}$, where $N_{0}$$\approx$10$^{18}$mol/m$^{2}$ and $p$=2 at this resolution. We argue that the standard linear $N$=$XI$ is only approximately valid: $p$$\sim$1.5-1.0 at coarser resolutions or in atypical locations, such as Galactic Center clouds. Also, the velocity dispersion distributions are very different between $I_{12CO}$ and $N_{12CO}$, the former preferentially tracing more diffuse molecular gas. We re-evaluated Galactic rotation parameters for the 4Q, defining a new ``BGT'' model, and deprojected the (l,V) data onto (l,d) and (x,y) grids using standard kinematic procedures. To automate distance disambiguation inside the solar circle, we developed a simple $\zeta^{+}$ discriminator function and applied it to our deprojections. We discovered two previously unrecognised features of the molecular cloud population: widespread ripples in the midplane of wavelength 4kpc and amplitude 50pc, potentially generated by the last perigalactic passage of the Sgr dwarf; and three distant, massive molecular structures, the Far Ara clouds, two of which exhibit an exceptional velocity gradient, possibly arising in a massive spiral feather or a gas-rich dwarf galaxy $\sim$20--300kpc beyond the disk.
Privacy in Responsible AI: Approaches to Facial Recognition from Cloud Providers
2025-03-06
Anna Elivanova
As the use of facial recognition technology is expanding in different domains, ensuring its responsible use is gaining more importance. This paper conducts a comprehensive literature review of existing studies on facial recognition technology from the perspective of privacy, which is one of the key Responsible AI principles. Cloud providers, such as Microsoft, AWS, and Google, are at the forefront of delivering facial-related technology services, but their approaches to responsible use of these technologies vary significantly. This paper compares how these cloud giants implement the privacy principle into their facial recognition and detection services. By analysing their approaches, it identifies both common practices and notable differences. The results of this research will be valuable for developers and businesses by providing them insights into best practices of three major companies for integration responsible AI, particularly privacy, into their cloud-based facial recognition technologies.
Evidence for Primordial Alignment II: Insights from Stellar Obliquity Measurements for Hot Jupiters in Compact Multiplanet Systems
2025-03-05
Brandon Thomas Radzom, Jiayin Dong, Malena Rice, Xian-Yu Wang, Kyle Hixenbaugh, George Zhou, Chelsea X. Huang, Songhu Wang
A significant fraction of hot Jupiters have orbital axes misaligned with their host stars' spin axes. The large stellar obliquities of these giants have long been considered potential signatures of high-eccentricity migration, which is expected to clear out any nearby planetary companions. This scenario requires that only isolated hot Jupiters be spin-orbit misaligned while those with nearby companions, which must have more quiescent histories, maintain low-obliquity orbits, assuming they formed aligned within their primordial protoplanetary disks. Investigations of this stellar obliquity-companionship connection, however, have been severely limited by the lack of hot Jupiters found in compact multi-planet systems. Here we present the sky-projected stellar obliquity ($\lambda$) of a hot Jupiter with a nearby inner companion recently discovered by NASA's Transiting Exoplanet Survey Satellite: TOI-5143 c. Specifically, we utilize the Doppler shadow caused by the planet's transit, enabled by the Rossiter-McLaughlin (RM) effect, to find that the planet is aligned with $\lambda=2.1 ^{+2.8}_{-2.7} \circ$. Of the exoplanets with RM measurements, TOI-5143 c becomes just the third hot Jupiter with a nearby companion, and is part of the 19th compact multi-planet single-star system, with an RM measurement. The spin-orbit alignment of these 19 systems provides strong support for primordial alignment, and thus implies that large obliquities are gained primarily due to post-disk dynamical interactions such as those inherent to high-eccentricity migration. As such, the observed spin-orbit alignment of hot Jupiters with nearby companions affirms that some fraction of these giants instead have quiescent origins.
MaNGA AGN dwarf galaxies (MAD) -- III. The role of mergers and environment in AGN activity in dwarf galaxies
2025-03-05
A. Eróstegui, M. Mezcua, M. Siudek, H. Domínguez Sánchez, V. Rodríguez Morales
Investigating whether and how galaxy mergers affect black hole growth can be determinant for black hole-galaxy evolution models and, in particular, for understanding how early Universe seed black holes grew to become supermassive. However, while mergers have been observed to enhance the active galactic nucleus (AGN) activity, and thus black hole growth in massive galaxies, it is yet not known how this relation and the role of the environment translates to dwarf galaxies (the most likely hosts of the early seed black holes), since there are scarce and mixed results in the literature. We want to assess the impact of galaxy mergers and the environment on AGN triggering in dwarf galaxies. We use a sample of 3280 dwarf galaxies with integral-field spectroscopic data from the MaNGA survey to study the AGN fraction throughout the merger process and how it is affected by the environment (characterized by galaxy isolation, being in a void, and group richness). We also compare the fraction of interacting galaxies in AGN and non-AGN dwarf galaxies. We find that dwarf galaxy mergers can ignite AGNs at separations below 20 kpc. The AGN fraction increases notoriously after the first pass and remains enhanced until the final stage. Despite this, mergers are not the dominant AGN triggering mechanism. We also find that the environment has a non-negligible impact on AGN activity in dwarf galaxies, as the AGN fraction increases when moving to lower density environments. These findings provide the most statistically robust constraints to date on the effects of dwarf galaxy mergers and environment on AGN activity and black hole growth.
Peaky Finders: Characterizing Double-Peaked Type IIb Supernovae in Large-Scale Live-Stream Photometric Surveys
2025-03-05
Adrian Crawford, Tyler A. Pritchard, Maryam Modjaz, Craig Pellegrino, Sahana Kumar, Raphael Baer-Way
We present the first photometric population study of double-peaked Type IIb supernovae (SNe IIb). SNe IIb are produced from the core-collapse of massive stars whose outermost Hydrogen layer has been partially stripped prior to explosion. These double-peaked light curves, consisting of a shock-cooling emission peak (SCE) followed by the main nickel-powered peak, contain more crucial information about the progenitor system than the typical single-peaked light curves. We compiled and analyzed a sample of 14 spectroscopically confirmed SNe IIb -- including previously unpublished and re-classified -- with publicly available photometric observations, discovered between 2018--2022, from the ZTF and ATLAS surveys. We developed and fit a piecewise linear model, referred to as the ``lightning bolt model,'' to describe the early-time behavior of these objects to measure population statistics. Notably, we find the SCE peak lasts, on average, fewer than five days above half-maximum light with a mean rise time of $2.07\pm1.0$ and $1.1\pm0.8$ mags/day in the g- and r-band respectively. These SCE rise rates are over 10x faster than -- and last only a third the duration of -- the rise to the nickel-powered peak. These rise times are comparable to those of fast blue optical transient (FBOT) events and we discuss the implications in the text. Finally, we present a proof-of-concept alert filter, using the ANTARES broker, to demonstrate how to translate these population statistics into simple and effective filters to find potential double-peaked SNe IIb in large-scale survey alert streams, like the imminent Vera C. Rubin Observatory Legacy Survey of Space and Time (Rubin LSST).
High-Energy Neutrinos by Hydrogen-rich Supernovae interacting with low-massive Circumstellar Medium: The Case of SN 2023ixf
2025-03-05
S. P. Cosentino, M. L. Pumo, S. Cherubini
In hydrogen-rich (H-rich) Supernova (SN) events, the collision between the H-rich ejecta and the Circum-Stellar Medium (CSM) can accelerate particles and produce high-energy neutrinos (HE-$\nu$, TeV-PeV) through proton-proton inelastic scattering. Despite understanding the production mechanism of these neutrinos, the lack of direct observations raises questions about particle acceleration efficiency and the involved astrophysical conditions. This study focuses on neutrino emission from H-rich SNe with low-mass CSM, such as SN 2023ixf. We developed a semi-analytical model to characterize the progenitor and CSM at the explosion time, allowing us to infer the expected neutrino flux at Earth during the SN's interaction phase. Our model shows that neutrino emission depends not only on shock velocity and CSM mass but also on the spatial matter distribution of the CSM. By analysing the bolometric light curve of SN 2023ixf beyond 100 days post-explosion, we find that its ejecta, consisting of $9\,\text{M}_{\rm \odot}$ (including $0.07\,\text{M}_{\rm \odot}$ of radioactive $^{56}$Ni) and having a kinetic energy of $1.8\,\text{foe}$, collides with a low-mass CSM of $0.06\,\text{M}_{\rm \odot}$ distributed according to a power-law density profile with an exponent of $s=2.9$. Through these parameters, we estimate that up to $4\pm1\times 10^{-2}$ muon (anti-)neutrino events could be detected by IceCube within 50 days post-explosion. Although the predicted flux ($\lesssim 3\times 10^{-9}\,\text{GeV} \, \text{cm}^{-2} \, \text{s}^{-1}$) is below current IceCube sensitivity, future telescopes like IceCube-Gen2 and KM3NeT could detect HE-$\nu$ from similar SN events.
Optimal joint reconstruction from CMB observations: application to cosmic birefringence, patchy reionization and CMB lensing
2025-03-05
Omar Darwish
Line-of-sight distortions of the cosmic microwave background (CMB), including gravitational lensing, cosmic birefringence, and patchy screening, encode crucial cosmological information. While quadratic estimators (QE) have been excellent tools for extracting these signals, they become suboptimal for current- and next-generation CMB surveys, failing to maximize signal-to-noise and suffering from bias contamination that standard bias-hardening techniques cannot mitigate. We present a joint maximum a posteriori framework that simultaneously reconstructs multiple distortion fields while explicitly accounting for their mutual contamination. For cosmic birefringence searches, our method achieves up to 2.5 improvement in reconstruction noise compared to the QE, while significantly reducing CMB lensing-induced biases up to a factor of 5. These gains in lensing biases manifest not only in deep polarization surveys like CMB-S4 and SPT-3G, but also in higher-noise experiments like Simons Observatory, where our method reduces lensing-induced biases by a factor of two thanks to the power of delensing. Our code provides a first step to robust analyses of CMB secondary anisotropies, their cross-correlations with large-scale structure, and ultimately enabling more sensitive searches for primordial $B$-modes.
The $M_{*}-M_{\rm BH}$ Relation Evolution from z $\sim$ 6 to the Present Epoch
2025-03-05
Yang Sun, George H. Rieke, Jianwei Lyu, Meredith A. Stone, Zhiyuan Ji, Pierluigi Rinaldi, Christopher N. A. Willmer, Yongda Zhu
The ratio between the stellar mass of a galaxy, $M_{*}$, and that of its central supermassive black hole (SMBH), $M_\bullet$, the ``Magorrian'' relationship, traces their coevolution. JWST observations have suggested significant evolution in $M_\bullet/M_{*}$ relative to local scaling relationships both in low-mass galaxies and in quasars at z $\ge$ 4. We test this possibility by (1) determining the preferred $M_\bullet/M_{*}$ scaling relation among those proposed locally; and (2) providing uniform host galaxy stellar mass estimates. These steps reduce the prominence of the reported evolution. We then apply Monte Carlo simulations to account for observational biases. We still find a significant increase over the local scaling relation in $M_\bullet/M_{*}$ for z $\ge$ 4 SMBHs in very low-mass galaxies ($\log(M_*/M_{\odot})<10$). However, similarly high values of $M_\bullet/M_{*}$ are also found in low mass galaxies at $z \sim$ 0.5 to 3 that may be common at cosmic noon. Nonetheless, galaxies with similar behavior are rare locally and not accounted for in the local scaling relations. In contrast, z $\sim$ 6 quasars can have $M_\bullet/M_{*}$ well above the local relation value, but they can be explained as extreme cases still within the scaling relation for their higher mass host galaxies. Black holes in some of them and in the low-mass systems may be undergoing very high accretion episodes that result in high $M_\bullet/M_{*}$ but that will be followed by quiescent periods when growth of the host drives the systems toward more typical $M_\bullet/M_{*}$ values.
We give a survey of Darboux type theorems in multisymplectic geometry. These theorems establish when a closed differential form of a certain type admits a constant-coefficient expression in some local coordinate system. Beyond the classical cases of symplectic and volume forms, 0-deformability (i.e. constancy of linear type) is typically not automatic and has to be imposed, leading to distinct theorems 'per linear type'.
Facilitating Asynchronous Idea Generation and Selection with Chatbots
2025-03-05
Joongi Shin, Ankit Khatri, Michael A. Hedderich, Andrés Lucero, Antti Oulasvirta
People can generate high-quality ideas by building on each other's ideas. By enabling individuals to contribute their ideas at their own comfortable time and method (i.e., asynchronous ideation), they can deeply engage in ideation and improve idea quality. However, running asynchronous ideation faces a practical constraint. Whereas trained human facilitators are needed to guide effective idea exchange, they cannot be continuously available to engage with individuals joining at varying hours. In this paper, we ask how chatbots can be designed to facilitate asynchronous ideation. For this, we adopted the guidelines found in the literature about human facilitators and designed two chatbots: one provides a structured ideation process, and another adapts the ideation process to individuals' ideation performance. We invited 48 participants to generate and select ideas by interacting with one of our chatbots and invited an expert facilitator to review our chatbots. We found that both chatbots can guide users to build on each other's ideas and converge them into a few satisfying ideas. However, we also found the chatbots' limitations in social interaction with collaborators, which only human facilitators can provide. Accordingly, we conclude that chatbots can be promising facilitators of asynchronous ideation, but hybrid facilitation with human facilitators would be needed to address the social aspects of collaborative ideation.
The aim of this survey is to present applications of covering techniques in the theory of Krull-Gabriel dimension. We start with recalling fundamental facts of the classical covering theory of quivers and locally bounded categories. Then we present some recent results on covering theory of functor categories. These are interesting themselves, but also allow to relate Krull-Gabriel dimensions of locally bounded categories $R$ and $A$ when $R\rightarrow A$ is a Galois covering functor. Finally, we concentrate on applications of our methods in describing Krull-Gabriel dimensions of various classes of algebras and locally bounded categories.
Chemical abundance inventory in phosphorus-rich stars
2025-03-05
Maren Brauner, Thomas Masseron, Marco Pignatari, D. Aníbal García-Hernández
We provide an overview of the latest advances in the study of phosphorus-rich stars, covering their detailed chemical abundance analyses and innovative mining approaches. Following the discovery of 16 low-mass and low-metallicity stars rich in P, we expanded this sample by demonstrating that a recently identified group of Si-rich giants is also P-rich. A detailed abundance analysis was conducted on the nearinfrared spectra from APOGEE-2 DR17, encompassing 13 elements. Subsequently, a similar analysis was performed on the optical UVES spectra of four P-rich stars, resulting in the abundance determination of 48 light and heavy elements. This comprehensive analysis further refined the chemical fingerprint of these peculiar stars, which was employed to evaluate the plausibility of various nucleosynthetic formation scenarios. In order to obtain a statistically more reliable chemical fingerprint in the future, we explored the use of unsupervised machine learning algorithms to identify additional P-rich stars in extensive spectroscopic surveys, such as APOGEE-2. The primary objective of this research is to identify the progenitor of these stars and determine whether current nucleosynthetic models require revision or if a completely new source of P in the Galaxy is responsible for the existence of the P-rich stars.
Data Sharing, Privacy and Security Considerations in the Energy Sector: A Review from Technical Landscape to Regulatory Specifications
2025-03-05
Shiliang Zhang, Sabita Maharjan, Lee Andrew Bygrave, Shui Yu
Decarbonization, decentralization and digitalization are the three key elements driving the twin energy transition. The energy system is evolving to a more data driven ecosystem, leading to the need of communication and storage of large amount of data of different resolution from the prosumers and other stakeholders in the energy ecosystem. While the energy system is certainly advancing, this paradigm shift is bringing in new privacy and security issues related to collection, processing and storage of data - not only from the technical dimension, but also from the regulatory perspective. Understanding data privacy and security in the evolving energy system, regarding regulatory compliance, is an immature field of research. Contextualized knowledge of how related issues are regulated is still in its infancy, and the practical and technical basis for the regulatory framework for data privacy and security is not clear. To fill this gap, this paper conducts a comprehensive review of the data-related issues for the energy system by integrating both technical and regulatory dimensions. We start by reviewing open-access data, data communication and data-processing techniques for the energy system, and use it as the basis to connect the analysis of data-related issues from the integrated perspective. We classify the issues into three categories: (i) data-sharing among energy end users and stakeholders (ii) privacy of end users, and (iii) cyber security, and then explore these issues from a regulatory perspective. We analyze the evolution of related regulations, and introduce the relevant regulatory initiatives for the categorized issues in terms of regulatory definitions, concepts, principles, rights and obligations in the context of energy systems. Finally, we provide reflections on the gaps that still exist, and guidelines for regulatory frameworks for a truly participatory energy system.
Uncovering Extraplanar Gas in UGCA 250 with the Ultra-deep MHONGOOSE Survey
2025-03-05
Sushma Kurapati, D. J. Pisano, W. J. G. de Blok, Peter Kamphuis, Nikki Zabel, Mikhail de Villiers, Julia Healy, Filippo M. Maccagni, Dane Kleiner, Elizabeth A. K. Adams, Philippe Amram, E. Athanassoula, Frank Bigiel, Albert Bosma, Elias Brinks, Laurent Chemin, Francoise Combes, Ralf-Jürgen Dettmar, Gyula Józsa, Baerbel Koribalski, Antonino Marasco, Gerhardt Meurer, Moses Mogotsi, Abhisek Mohapatra, Sambatriniaina H. A. Rajohnson, Eva Schinnerer, Amidou Sorgho, Kristine Spekkens, Lourdes Verdes-Montenegro, Simone Veronese, Fabian Walter
We use the neutral atomic hydrogen (HI) observations of the edge-on galaxy UGCA 250, taken as part of the MeerKAT HI Observations of Nearby Galactic Objects - Observing Southern Emitters (MHONGOOSE) survey to investigate the amount, morphology, and kinematics of extraplanar gas. The combination of high column density sensitivity and high spatial resolution of the survey over a large field of view is ideal for studying the underlying physics governing the extraplanar gas. These data reveal 9 additional detections within the field of view along with UGCA 250, with 8 of them being within $\sim$ 200 km s$^{-1}$ of the galaxy's systemic velocity. The galaxy seems to have a tail-like feature extending away from it in the southern direction up to $\sim$ 41 kpc (in projection). We also detect a cloud at anomalous velocities, but we did not find any optical counterpart. We construct a detailed tilted ring model for this edge-on galaxy to gain a deeper understanding of the vertical structure of its neutral hydrogen. The model that best matches the data features a thick disc with a scale height of $\sim$ 3$\pm$1 kpc and an HI mass of about 15$\%$ of the total HI mass. This extraplanar gas is detected for the first time in UGCA 250. Our analysis favours a mixed origin for the extraplanar gas in UGCA 250, likely arising from a combination of internal stellar feedback and external tidal interactions.
Generative Artificial Intelligence in Robotic Manipulation: A Survey
2025-03-05
Kun Zhang, Peng Yun, Jun Cen, Junhao Cai, Didi Zhu, Hangjie Yuan, Chao Zhao, Tao Feng, Michael Yu Wang, Qifeng Chen, Jia Pan, Bo Yang, Hua Chen
This survey provides a comprehensive review on recent advancements of generative learning models in robotic manipulation, addressing key challenges in the field. Robotic manipulation faces critical bottlenecks, including significant challenges in insufficient data and inefficient data acquisition, long-horizon and complex task planning, and the multi-modality reasoning ability for robust policy learning performance across diverse environments. To tackle these challenges, this survey introduces several generative model paradigms, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), diffusion models, probabilistic flow models, and autoregressive models, highlighting their strengths and limitations. The applications of these models are categorized into three hierarchical layers: the Foundation Layer, focusing on data generation and reward generation; the Intermediate Layer, covering language, code, visual, and state generation; and the Policy Layer, emphasizing grasp generation and trajectory generation. Each layer is explored in detail, along with notable works that have advanced the state of the art. Finally, the survey outlines future research directions and challenges, emphasizing the need for improved efficiency in data utilization, better handling of long-horizon tasks, and enhanced generalization across diverse robotic scenarios. All the related resources, including research papers, open-source data, and projects, are collected for the community in https://github.com/GAI4Manipulation/AwesomeGAIManipulation
Self-Consistent JWST Census of Star Formation and AGN activity at z=5.5-13.5
2025-03-05
Jordan C. J. D'Silva, Simon P. Driver, Claudia D. P. Lagos, Aaron S. G. Robotham, Nathan J. Adams, Christopher J. Conselice, Brenda Frye, Nimish P. Hathi, Thomas Harvey, Rafael Ortiz III, Massimo Ricotti, Clayton Robertson, Ross M. Silver, Stephen M. Wilkins, Christopher N. A. Willmer, Rogier A. Windhorst, Seth H. Cohen, Rolf A. Jansen, Jake Summers, Anton M. Koekemoer, Dan Coe, Norman A. Grogin, Madeline A. Marshall, Mario Nonino, Nor Pirzkal, Russell E. Ryan, Jr., Haojing Yan
The cosmic star formation history (CSFH) and cosmic active galactic nuclei (AGN) luminosity history (CAGNH) are self consistently presented at $z = 5.5-13.5$. This is achieved by analyzing galaxies detected by the James Webb Space Telescope from $\approx 400 \, \mathrm{arcmin^{2}}$ fields from the PEARLS, CEERS, NGDEEP, JADES and PRIMER surveys. In particular, the combination of spectral energy distribution fitting codes, EAZY and ProSpect, are employed to estimate the photometric redshifts and astrophysical quantities of 3947 distant galaxies, from which we compute the stellar mass, star formation rate and AGN luminosity distribution functions in four redshift bins. Integrating the distribution functions, we find that the CAGNH tentatively rises by $\approx 2.2$ dex over $z = 5.5-13.5$ compared to $\approx 1.8$ dex for the CSFH, indicating that the growth of supermassive black holes (SMBHs) tends to outpace the assembly of stellar mass. We connect our results of the CSFH and CAGNH at $z=5.5-13.5$ to that from $z= 0-5$ to determine the summary of $\gtrsim 13$ Gyr of star formation and AGN activity, from the very onset of galaxy formation to the present day.
SoK: Microservice Architectures from a Dependability Perspective
2025-03-05
Dāvis Kažemaks, Jérémie Decouchant
The microservice software architecture leverages the idea of splitting large monolithic applications into multiple smaller services that interact using lightweight communication schemes. While the microservice architecture has proven its ability to support modern business applications, it also introduces new possible weak points in a system. Some scientific literature surveys have already addressed fault tolerance or security concerns but most of them lack analysis on the fault and vulnerability coverage that is introduced by microservice architectures. We explore the known faults and vulnerabilities that microservice architecture might suffer from, and the recent scientific literature that addresses them. We emphasize runtime detection and recovery mechanisms instead of offline prevention and mitigation mechanisms to limit the scope of this document.
The Gaia Astrometric Catalogue and Secular Aberration Drift in Proper Motions
2025-03-05
Anthony G. A. Brown, Ulrich Bastian, Sergei Klioner
A recent paper demonstrated the existence of a secular aberration drift term in stellar proper motions that arises when transforming an astrometric catalogue defined for an observer at rest with respect to the solar system barycentre to some other reference frame in which, for example, the observer is at rest with respect to the Galactic centre. Such a transformation requires an accurate and precise estimate of the velocity of the solar system barycentre. It was argued that the Gaia catalogue construction should account for this effect and also for the aberrational effect due to acceleration of the solar system barycentre. We argue that these two effects should not be accounted for in the construction of the Gaia astrometric catalogue. We briefly review the Gaia catalogue reference frame, the concepts of stellar aberration and secular aberration drift, and their observable consequences. The Gaia catalogue is (and should be) constructed in the Barycentric Celestial Reference System: the reference system with the origin at the solar system barycentre as defined by the underlying solar system ephemerides. We explain that the Gaia catalogue is consistent with the International Celestial Reference System despite the presence of proper motion terms due to the acceleration of the solar system barycentre. We also explain why transformation of the astrometry to a frame in which the observer is at rest with respect to the Galactic centre or distant universe is not needed for the interpretation of stellar kinematics, and that there are practical concerns with such a transformation. The estimation of the velocity and acceleration of the solar system barycentre, although important as a matter of scientific investigation, are not needed for the construction of the Gaia astrometric catalogue.
MIAdapt: Source-free Few-shot Domain Adaptive Object Detection for Microscopic Images
2025-03-05
Nimra Dilawar, Sara Nadeem, Javed Iqbal, Waqas Sultani, Mohsen Ali
Existing generic unsupervised domain adaptation approaches require access to both a large labeled source dataset and a sufficient unlabeled target dataset during adaptation. However, collecting a large dataset, even if unlabeled, is a challenging and expensive endeavor, especially in medical imaging. In addition, constraints such as privacy issues can result in cases where source data is unavailable. Taking in consideration these challenges, we propose MIAdapt, an adaptive approach for Microscopic Imagery Adaptation as a solution for Source-free Few-shot Domain Adaptive Object detection (SF-FSDA). We also define two competitive baselines (1) Faster-FreeShot and (2) MT-FreeShot. Extensive experiments on the challenging M5-Malaria and Raabin-WBC datasets validate the effectiveness of MIAdapt. Without using any image from the source domain MIAdapt surpasses state-of-the-art source-free UDA (SF-UDA) methods by +21.3% mAP and few-shot domain adaptation (FSDA) approaches by +4.7% mAP on Raabin-WBC. Our code and models will be publicly available.
The story of the anomalous magnetic moment of the muon, $a_{\mu}$, has been a long and absorbing read, with several unexpected plot twists. The theme: is new physics visible in $a_{\mu}$ or not? is clearly an important one. As we head towards what may be the final chapters, the pace of the narrative has quickened and lattice QCD is providing a critical new perspective on some of the key characters. I will review the story so far, focussing on recent episodes but (spoiler alert) depending on your point of view, it may not be heading to a happy ending.
Leveraging Large Language Models to Develop Heuristics for Emerging Optimization Problems
2025-03-05
Thomas Bömer, Nico Koltermann, Max Disselnmeyer, Laura Dörr, Anne Meyer
Combinatorial optimization problems often rely on heuristic algorithms to generate efficient solutions. However, the manual design of heuristics is resource-intensive and constrained by the designer's expertise. Recent advances in artificial intelligence, particularly large language models (LLMs), have demonstrated the potential to automate heuristic generation through evolutionary frameworks. Recent works focus only on well-known combinatorial optimization problems like the traveling salesman problem and online bin packing problem when designing constructive heuristics. This study investigates whether LLMs can effectively generate heuristics for niche, not yet broadly researched optimization problems, using the unit-load pre-marshalling problem as an example case. We propose the Contextual Evolution of Heuristics (CEoH) framework, an extension of the Evolution of Heuristics (EoH) framework, which incorporates problem-specific descriptions to enhance in-context learning during heuristic generation. Through computational experiments, we evaluate CEoH and EoH and compare the results. Results indicate that CEoH enables smaller LLMs to generate high-quality heuristics more consistently and even outperform larger models. Larger models demonstrate robust performance with or without contextualized prompts. The generated heuristics exhibit scalability to diverse instance configurations.
Navigating Intelligence: A Survey of Google OR-Tools and Machine Learning for Global Path Planning in Autonomous Vehicles
2025-03-05
Alexandre Benoit, Pedram Asef
We offer a new in-depth investigation of global path planning (GPP) for unmanned ground vehicles, an autonomous mining sampling robot named ROMIE. GPP is essential for ROMIE's optimal performance, which is translated into solving the traveling salesman problem, a complex graph theory challenge that is crucial for determining the most effective route to cover all sampling locations in a mining field. This problem is central to enhancing ROMIE's operational efficiency and competitiveness against human labor by optimizing cost and time. The primary aim of this research is to advance GPP by developing, evaluating, and improving a cost-efficient software and web application. We delve into an extensive comparison and analysis of Google operations research (OR)-Tools optimization algorithms. Our study is driven by the goal of applying and testing the limits of OR-Tools capabilities by integrating Reinforcement Learning techniques for the first time. This enables us to compare these methods with OR-Tools, assessing their computational effectiveness and real-world application efficiency. Our analysis seeks to provide insights into the effectiveness and practical application of each technique. Our findings indicate that Q-Learning stands out as the optimal strategy, demonstrating superior efficiency by deviating only 1.2% on average from the optimal solutions across our datasets.
Luminosity and stellar mass functions of faint photometric satellites around spectroscopic central galaxies from DESI Year-1 Bright Galaxy Survey
2025-03-05
Wenting Wang, Xiaohu Yang, Yipeng Jing, Ashley J. Ross, Malgorzata Siudek, John Moustakas, Samuel G. Moore, Shaun Cole, Carlos Frenk, Jiaxi Yu, Sergey E. Koposov, Jiaxin Han, Zhenlin Tan, Kun Xu, Yizhou Gu, Yirong Wang, Oleg Y. Gnedin, Jessica Nicole Aguilar, Steven Ahlen, Davide Bianchi, David Brooks, Todd Claybaugh, Axel de la Macorra, Arjun Dey, Peter Doel, Jaime E. Forero-Romero, Enrique Gaztanaga, Satya Gontcho A Gontcho, Gaston Gutierrez, Klaus Honscheid, Mustapha Ishak, Theodore Kisner, Martin Landriau, Laurent Le Guillou, Marc Manera, Aaron Meisner, Ramon Miquel, Seshadri Nadathur, Claire Poppett, Francisco Prada, Ignasi Pérez-Ràfols, Graziano Rossi, Eusebio Sanchez, David Schlegel, Hee-Jong Seo, Joseph Harry Silber, David Sprayberry, Gregory Tarle, Benjamin Alan Weaver, Hu Zou
We measure the luminosity functions (LFs) and stellar mass functions (SMFs) of photometric satellite galaxies around spectroscopically identified isolated central galaxies (ICGs). The photometric satellites are from the DESI Legacy Imaging Surveys (DR9), while the spectroscopic ICGs are selected from the DESI Year-1 BGS sample. We can measure satellite LFs down to $r$-band absolute magnitudes of $M_{r,\mathrm{sat}}\sim-7$, around ICGs as small as $7.1<\log_{10}M_{\ast,\mathrm{ICG}}/\mathrm{M_\odot}<7.8$, with the stellar mass of ICGs measured by the DESI Fastspecfit pipeline. The satellite SMF can be measured down to $\log_{10}M_{\ast,\mathrm{sat}}/\mathrm{M_\odot}\sim 5.5$. Interestingly, we discover that the faint/low-mass end slopes of satellite LFs/SMFs become steeper with the decrease in the stellar masses of host ICGs, with smaller and nearby host ICGs capable of being used to probe their fainter satellites.. The steepest slopes are $-2.298\pm0.656$ and $-$2.888$\pm$0.916 for satellite LF and SMF, respectively. Detailed comparisons are performed between the satellite LFs around ICGs selected from DESI BGS or from the SDSS NYU-VAGC spectroscopic Main galaxies over $7.1<\log_{10}M_{\ast,\mathrm{ICG}}/\mathrm{M_\odot}<11.7$, showing reasonable agreements, but we show that the differences between DESI and SDSS stellar masses for ICGs play a role to affect the results. We also compare measurements based on DESI Fastspecfit and Cigale stellar masses used to bin ICGs, with the latter including the modeling of AGN based on WISE photometry, and we find good agreements in the measured satellite LFs by using either of the DESI stellar mass catalogs.
Finding enjoyable fiction books can be challenging, partly because stories are multi-faceted and one's own literary taste might be difficult to ascertain. Here, we introduce the ISAAC method (Introspection-Support, AI-Annotation, and Curation), a pipeline which supports fiction readers in gaining awareness of their literary preferences and finding enjoyable books. ISAAC consists of four steps: a user supplies book ratings, an AI agent researches and annotates the provided books, patterns in book enjoyment are reviewed by the user, and the AI agent recommends new books. In this proof-of-concept self-study, the authors test whether ISAAC can highlight idiosyncratic patterns in their book enjoyment, spark a deeper reflection about their literary tastes, and make accurate, personalized recommendations of enjoyable books and underexplored literary niches. Results highlight substantial advantages of ISAAC over existing methods such as an integration of automation and intuition, accurate and customizable annotations, and explainable book recommendations. Observed disadvantages are that ISAAC's outputs can elicit false self-narratives (if statistical patterns are taken at face value), that books cannot be annotated if their online documentation is lacking, and that people who are new to reading have to rely on assumed book ratings or movie ratings to power the ISAAC pipeline. We discuss additional opportunities of ISAAC-style book annotations for the study of literary trends, and the scientific classification of books and readers.
Automated CT report generation plays a crucial role in improving diagnostic accuracy and clinical workflow efficiency. However, existing methods lack interpretability and impede patient-clinician understanding, while their static nature restricts radiologists from dynamically adjusting assessments during image review. Inspired by interactive segmentation techniques, we propose a novel interactive framework for 3D lesion morphology reporting that seamlessly generates segmentation masks with comprehensive attribute descriptions, enabling clinicians to generate detailed lesion profiles for enhanced diagnostic assessment. To our best knowledge, we are the first to integrate the interactive segmentation and structured reports in 3D CT medical images. Experimental results across 15 lesion types demonstrate the effectiveness of our approach in providing a more comprehensive and reliable reporting system for lesion segmentation and capturing. The source code will be made publicly available following paper acceptance.
As the potential for autonomous vehicles to be integrated on a large scale into modern traffic systems continues to grow, ensuring safe navigation in dynamic environments is crucial for smooth integration. To guarantee safety and prevent collisions, autonomous vehicles must be capable of accurately predicting the trajectories of surrounding traffic agents. Over the past decade, significant efforts from both academia and industry have been dedicated to designing solutions for precise trajectory forecasting. These efforts have produced a diverse range of approaches, raising questions about the differences between these methods and whether trajectory prediction challenges have been fully addressed. This paper reviews a substantial portion of recent trajectory prediction methods and devises a taxonomy to classify existing solutions. A general overview of the prediction pipeline is also provided, covering input and output modalities, modeling features, and prediction paradigms discussed in the literature. In addition, the paper discusses active research areas within trajectory prediction, addresses the posed research questions, and highlights the remaining research gaps and challenges.
Good practices for evaluation of synthesized speech
2025-03-05
Erica Cooper, Sébastien Le Maguer, Esther Klabbers, Junichi Yamagishi
This document is provided as a guideline for reviewers of papers about speech synthesis. We outline some best practices and common pitfalls for papers about speech synthesis, with a particular focus on evaluation. We also recommend that reviewers check the guidelines for authors written in the paper kit and consider those as reviewing criteria as well. This is intended to be a living document, and it will be updated as we receive comments and feedback from readers. We note that this document is meant to provide guidance only, and that reviewers should ultimately use their own discretion when evaluating papers.
CO Emission and Absorption-line Survey of the M87 Nucleus Using Archival ALMA Imaging
2025-03-05
Benjamin D. Boizelle, Xueyi Li, Nicholas LeVar, Sam Norcross, Benjamin J. Derieg, Jared R. Davidson, Kavin Siaw, Jonelle L. Walsh
We present an M87 molecular line search from archival Atacama Large Millimeter/sub-millimeter Array (ALMA) imaging, covering the circumnuclear disk (CND) as well as ionized gas filaments and dusty cloud regions. We find no evidence for CO emission in the central ~kpc and place an upper limit of $M_{H_2} < 2.3\times 10^5$ $M_\odot$ in the atomic gas CND, a factor of 20$\times$ lower than previous surveys. During this search, we discovered extragalactic CO absorption lines in the J = 1-0, 2-1, and 3-2 transitions against the bright (Jy-scale) active nucleus. These CO lines are narrow (~5 km s$^{-1}$) and blueshifted with respect to the galaxy's systemic velocity by -75 to -84 km s$^{-1}$. This CO absorber appears to be kinematically distinct from outflowing atomic gas seen in absorption. Assuming a diffuse molecular phase, low integrated opacities ranging from $\tau_{CO} \sim 0.02-0.06$ km s$^{-1}$ and column density $N_{CO} = 1.16\times 10^{15}$ cm$^{-2}$ translate to $N_{H_2} \sim (1-2) \times 10^{20}$ cm$^{-2}$. CO excitation temperatures spanning $T_{ex} \sim 8$ K to ~30 K do not follow local thermodynamic equilibrium (LTE) expectations, and non-LTE radex radiative transfer modeling suggests the CO absorber has a number density $n_{H_2} \sim 5000$ cm$^{-3}$. Taken together, the observed CO absorption lines are most consistent with a thin, pressure-confined filament seen slightly off-center from the M87 nucleus. We also use these ALMA data to explore the impact of residual telluric lines and atmospheric variability on the identification and classification of narrow extragalactic lines. Additionally, we demonstrate how bandpass calibration limitations may introduce broad but very low S/N absorption and emission signatures near such a bright continuum source.
Prediction of Halo Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and a Transformer Model
2025-03-05
Hongyang Zhang, Ju Jing, Jason T. L. Wang, Haimin Wang, Yasser Abduallah, Yan Xu, Khalid A. Alobaid, Hameedullah Farooki, Vasyl Yurchyshyn
We present a transformer model, named DeepHalo, to predict the occurrence of halo coronal mass ejections (CMEs). Our model takes as input an active region (AR) and a profile, where the profile contains a time series of data samples in the AR that are collected 24 hours before the beginning of a day, and predicts whether the AR would produce a halo CME during that day. Each data sample contains physical parameters, or features, derived from photospheric vector magnetic field data taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). We survey and match CME events in the Space Weather Database Of Notification, Knowledge, Information (DONKI) and Large Angle and Spectrometric Coronagraph (LASCO) CME Catalog, and compile a list of CMEs including halo CMEs and non-halo CMEs associated with ARs in the period between November 2010 and August 2023. We use the information gathered above to build the labels (positive versus negative) of the data samples and profiles at hand, where the labels are needed for machine learning. Experimental results show that DeepHalo with a true skill statistics (TSS) score of 0.907 outperforms a closely related long short-term memory network with a TSS score of 0.821. To our knowledge, this is the first time that the transformer model has been used for halo CME prediction.
On covariant and canonical Hamiltonian formalisms: Weakly Isolated Horizons
2025-03-05
Alejandro Corichi, Juan D. Reyes, Tatjana Vukasinac
The Hamiltonian description of classical gauge theories is a well studied subject. The two best known approaches, namely the covariant and canonical Hamiltonian formalisms have received a lot of attention in the literature. However, in our opinion, a full understanding of the relation between them is not available, specially for gauge theories that are defined over regions with boundaries. Here we consider this issue, for spacetimes with isolated horizons as inner boundaries (representing black holes in equilibrium), and assess whether their corresponding descriptions can be seen as equivalent. First, we shall review and reanalyze both formalisms from anew, focusing on some subtleties not considered before. Next we show that, if we compare them at face value, there are subtle differences between both formalisms, for instance in the derivation of horizon energy, a physical observable of the theory, as well as in the number of boundary degrees of freedom. At first sight, it seems that the canonical formalism introduces some additional ambiguities that make more challenging the comparison between both formalisms. We analyze different possible interpretations in both approaches and show how one can obtain complete correspondence between results within covariant and canonical formalisms. We also show that the correspondence can be achieved in the original phase spaces, with no boundary degrees of freedom, or by appropriately extending them by adding horizon degrees of freedom. Along the way, we shed light on the role of boundary terms in the symplectic structure in relation to boundary degrees of freedom.
Machine Learning for Estimation and Control of Quantum Systems
2025-03-05
Hailan Ma, Bo Qi, Ian R. Petersen, Re-Bing Wu, Herschel Rabitz, Daoyi Dong
The development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in computation, simulation, and sensing. This poses severe challenges in efficient control, calibration, and validation of quantum states and their dynamics. Machine learning methods have emerged as powerful tools owing to their remarkable capability to learn from data, and thus have been extensively utilized for different quantum tasks. This paper reviews several significant topics related to machine learning-aided quantum estimation and control. In particular, we discuss neural networks-based learning for quantum state estimation, gradient-based learning for optimal control of quantum systems, evolutionary computation for learning control of quantum systems, machine learning for quantum robust control, and reinforcement learning for quantum control. This review provides a brief background of key concepts recurring across many of these approaches with special emphasis on neural networks, evolutionary computation, and reinforcement learning.
Debugging ML software (i.e., the detection, localization and fixing of faults) poses unique challenges compared to traditional software largely due to the probabilistic nature and heterogeneity of its development process. Various methods have been proposed for testing, diagnosing, and repairing ML systems. However, the big picture informing important research directions that really address the dire needs of developers is yet to unfold, leaving several key questions unaddressed: (1) What faults have been targeted in the ML debugging research that fulfill developers needs in practice? (2) How are these faults addressed? (3) What are the challenges in addressing the yet untargeted faults? In this paper, we conduct a systematic study of debugging techniques for machine learning systems. We first collect technical papers focusing on debugging components in machine learning software. We then map these papers to a taxonomy of faults to assess the current state of fault resolution identified in existing literature. Subsequently, we analyze which techniques are used to address specific faults based on the collected papers. This results in a comprehensive taxonomy that aligns faults with their corresponding debugging methods. Finally, we examine previously released transcripts of interviewing developers to identify the challenges in resolving unfixed faults. Our analysis reveals that only 48 percent of the identified ML debugging challenges have been explicitly addressed by researchers, while 46.9 percent remain unresolved or unmentioned. In real world applications, we found that 52.6 percent of issues reported on GitHub and 70.3% of problems discussed in interviews are still unaddressed by research in ML debugging. The study identifies 13 primary challenges in ML debugging, highlighting a significant gap between the identification of ML debugging issues and their resolution in practice.
Modeling environmental ecosystems is essential for effective resource management, sustainable development, and understanding complex ecological processes. However, traditional methods frequently struggle with the inherent complexity, interconnectedness, and limited data of such systems. Foundation models, with their large-scale pre-training and universal representations, offer transformative opportunities by integrating diverse data sources, capturing spatiotemporal dependencies, and adapting to a broad range of tasks. This survey presents a comprehensive overview of foundation model applications in environmental science, highlighting advancements in forward prediction, data generation, data assimilation, downscaling, model ensembling, and decision-making across domains. We also detail the development process of these models, covering data collection, architecture design, training, tuning, and evaluation. By showcasing these emerging methods, we aim to foster interdisciplinary collaboration and advance the integration of cutting-edge machine learning for sustainable solutions in environmental science.
Knowledge Augmentation in Federation: Rethinking What Collaborative Learning Can Bring Back to Decentralized Data
2025-03-05
Wentai Wu, Ligang He, Saiqin Long, Ahmed M. Abdelmoniem, Yingliang Wu, Rui Mao
Data, as an observable form of knowledge, has become one of the most important factors of production for the development of Artificial Intelligence (AI). Meanwhile, increasing legislation and regulations on private and proprietary information results in scattered data sources also known as the "data islands". Although some collaborative learning paradigms such as Federated Learning (FL) can enable privacy-preserving training over decentralized data, they have inherent deficiencies in fairness, costs and reproducibility because of being learning-centric, which greatly limits the way how participants cooperate with each other. In light of this, we present a knowledge-centric paradigm termed Knowledge Augmentation in Federation (KAF), with focus on how to enhance local knowledge through collaborative effort. We provide the suggested system architecture, formulate the prototypical optimization objective, and review emerging studies that employ methodologies suitable for KAF. On our roadmap, with a three-way categorization we describe the methods for knowledge expansion, knowledge filtering, and label and feature space correction in the federation. Further, we highlight several challenges and open questions that deserve more attention from the community. With our investigation, we intend to offer new insights for what collaborative learning can bring back to decentralized data.
Spatially-Structured Models of Viral Dynamics: A Scoping Review
2025-03-05
Thomas Williams, James M. McCaw, James M. Osborne
There is growing recognition in both the experimental and modelling literature of the importance of spatial structure to the dynamics of viral infections in tissues. Aided by the evolution of computing power and motivated by recent biological insights, there has been an explosion of new, spatially-explicit models for within-host viral dynamics in recent years. This development has only been accelerated in the wake of the COVID-19 pandemic. Spatially-structured models offer improved biological realism and can account for dynamics which cannot be well-described by conventional, mean-field approaches. However, despite their growing popularity, spatially-structured models of viral dynamics are underused in biological applications. One major obstacle to the wider application of such models is the huge variety in approaches taken, with little consensus as to which features should be included and how they should be implemented for a given biological context. Previous reviews of the field have focused on specific modelling frameworks or on models for particular viral species. Here, we instead apply a scoping review approach to the literature of spatially-structured viral dynamics models as a whole to provide an exhaustive update of the state of the field. Our analysis is structured along two axes, methodology and viral species, in order to examine the breadth of techniques used and the requirements of different biological applications. We then discuss the contributions of mathematical and computational modelling to our understanding of key spatially-structured aspects of viral dynamics, and suggest key themes for future model development to improve robustness and biological utility.
"Watch My Health, Not My Data": Understanding Perceptions, Barriers, Emotional Impact, & Coping Strategies Pertaining to IoT Privacy and Security in Health Monitoring for Older Adults
2025-03-05
Suleiman Saka, Sanchari Das
The proliferation of "Internet of Things (IoT)" provides older adults with critical support for "health monitoring" and independent living, yet significant concerns about security and privacy persist. In this paper, we report on these issues through a two-phase user study, including a survey (N = 22) and semi-structured interviews (n = 9) with adults aged 65+. We found that while 81.82% of our participants are aware of security features like "two-factor authentication (2FA)" and encryption, 63.64% express serious concerns about unauthorized access to sensitive health data. Only 13.64% feel confident in existing protections, citing confusion over "data sharing policies" and frustration with "complex security settings" which lead to distrust and anxiety. To cope, our participants adopt various strategies, such as relying on family or professional support and limiting feature usage leading to disengagement. Thus, we recommend "adaptive security mechanisms," simplified interfaces, and real-time transparency notifications to foster trust and ensure "privacy and security by design" in IoT health systems for older adults.
Cosmology with second and third-order shear statistics for the Dark Energy Survey: Methods and simulated analysis
2025-03-05
R. C. H. Gomes, S. Sugiyama, B. Jain, M. Jarvis, D. Anbajagane, M. Gatti, D. Gebauer, Z. Gong, A. Halder, G. A. Marques, S. Pandey, J. L. Marshall, S. Allam, O. Alves, F. Andrade-Oliveira, D. Bacon, J. Blazek, S. Bocquet, D. Brooks, A. Carnero Rosell, J. Carretero, L. N. da Costa, P. Doel, C. Doux, S. Everett, B. Flaugher, J. Frieman, J. García-Bellido, E. Gaztanaga, D. Gruen, R. A. Gruendl, G. Gutierrez, K. Herner, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. Huterer, D. J. James, N. Jeffrey, J. Mena-Fernández, R. Miquel, J. Muir, R. L. C. Ogando, M. E. S. Pereira, A. Pieres, A. A. Plazas Malagón, S. Samuroff, E. Sanchez, D. Sanchez Cid, B. Santiago, I. Sevilla-Noarbe, M. Smith, E. Suchyta, M. E. C. Swanson, G. Tarle, C. To, V. Vikram, N. Weaverdyck, J. Weller
We present a new pipeline designed for the robust inference of cosmological parameters using both second- and third-order shear statistics. We build a theoretical model for rapid evaluation of three-point correlations using our fastnc code and integrate it into the CosmoSIS framework. We measure the two-point functions $\xi_{\pm}$ and the full configuration-dependent three-point shear correlation functions across all auto- and cross-redshift bins. We compress the three-point functions into the mass aperture statistic $\langle M_{\rm ap}^3\rangle$ for a set of 796 simulated shear maps designed to model the Dark Energy Survey (DES) Year 3 data. We estimate from it the full covariance matrix and model the effects of intrinsic alignments, shear calibration biases and photometric redshift uncertainties. We apply scale cuts to minimize the contamination from the baryonic signal as modeled through hydrodynamical simulations. We find a significant improvement of $83\%$ on the Figure of Merit in the $\Omega_{\rm m}$-$S_8$ plane when we add the $\langle M_{\rm ap}^3\rangle$ data to the $\xi_{\pm}$ information. We present our findings for all relevant cosmological and systematic uncertainty parameters and discuss the complementarity of third-order and second-order statistics.
The Dirac equation in General Relativity and the 3+1 formalism
2025-03-05
Miguel Alcubierre
I present a review of the Dirac equation in general relativity. Although the generalization of the Dirac equation to a curved spacetime is well known, it is not usually part of the standard toolkit of techniques known to people working on classical general relativity. Recently, there has been some renewed interest in studying solutions of the Einstein--Dirac system of equations, particularly in the context of the so-called ``Dirac stars''. Motivated by this, here I present a review of the Dirac equation in general relativity, starting from Minkowski spacetime, and then considering the Lorentz group and the tetrad formalism in order to generalize this equation to the case of a curved spacetime. I also derive the form of the Dirac equation and its associated stress--energy tensor for the case of the 3+1 formalism of general relativity, which can be useful for the study of the evolution of the Dirac field in a dynamical spacetime.
2025-03-05
Mario G. Santos, Stefano Camera, Zhaoting Chen, Steven Cunnington, Jose Fonseca
We discuss the possible synergies for cosmology between SKAO and ESO facilities, focusing on the combinations SKA-Mid with the Multi-Object Spectrograph Telescope (4MOST) instrument built for ESO's Visible and Infrared Survey Telescope for Astronomy (VISTA), and SKA-Low with ESO's Extremely Large Telescope (ELT) multi-object spectrograph MOSAIC. Combining multiple tracers allows for tackling systematics and lifting parameter degeneracies. It will play a crucial role in the pursuit of precision cosmology.
SKA-ESO Synergies for the Epoch of Reionization and Cosmic Dawn
2025-03-05
Andrei Mesinger, Benedetta Ciardi, James E. Davies, Samuel Gagnon-Hartman, Valentina D'Odorico
Mapping out the first billion years using the 21-cm line with the Square Kilometer Array (SKA) will revolutionize our understanding of the cosmic dawn, reionization and the galaxies that drove these milestones. However, synergies with other telescopes in the form of cross correlations will be fundamental in making and confirming initial, low signal-to-noise claims of a detection. Participants of the 2023 European Southern Observatory (ESO) - SKA Observatory workshop discussed such synergies for Epoch of Reionization (EoR) and Cosmic Dawn (CD) science. Here we highlight some of the most promising candidates for cross-correlating SKA EoR/CD observations with ESO instruments such as the Multi-Object Optical and Near-infrared Spectrograph (MOONS), the MOSAIC multi-object spectrograph, and the ArmazoNes high Dispersion Echelle Spectrograph (ANDES).
Proton Flows, Proton Gradients and Subcellular Architecture in Biological Energy Conversion
2025-03-05
Luca Quaroni
Hydrogen ions, or protons, provide the medium by which energy is stored and converted in biological systems. Such pre-eminence relies on the interplay between interfacial and bulk chemical transformations, according to mechanisms that are shared by organisms in all phyla of life. The present work provides an introduction to the fundamental aspects of biological energy management by focusing on the relationship between vectorial proton flows and the geometry of energy producing organelles in eukaryotes. The leading models of proton-mediated energy conversion, the delocalised proton (or chemiosmotic) model and the localised proton model, are presented in a complementary perspective. While the delocalised model provides a description that relies on equilibrium thermodynamics, the localised model addresses dynamic processes that are better described using out-of-equilibrium thermodynamics. The work reviews the salient aspects of such mechanisms, traces the development of our present understanding, and highlights areas that are open to future developments.
HST Transmission Spectra of the Hot-Neptune HD 219666 b: Detection of Water and the Challenge of Constraining Both Water and Methane with HST
2025-03-05
Matthew M. Murphy, Thomas G. Beatty, Luis Welbanks, Guangwei Fu
Although Neptunian-sized (2 - 5 R$_{Earth}$) planets appear to be extremely common in the Galaxy, many mysteries remain about their overall nature. To date, only eleven Neptunian-sized planets have had their atmospheres spectroscopically characterized, and these observations hint at interesting diversity within this class of planets. Much of our understanding of these worlds and others derive from transmission spectroscopy with the Hubble Space Telescope's Wide Field Camera 3 (HST/WFC3). One key outcome of HST/WFC3 observations has been the consistent detection of water but no methane in Neptunian atmospheres, though recent JWST observations are potentially starting to overturn this "missing methane" paradigm. In this work, we present the transmission spectrum of the hot Neptune HD 219666 b from 1.1 - 1.6 $\mu$m from two transit observations using HST/WFC3 G141. Our fiducial atmospheric retrieval detects water at ~3-$\sigma$ in HD 219666 b's atmosphere and prefers no contribution from methane, similar to these previous observations of other planets. Motivated by recent detections of methane in Neptunian atmospheres by JWST, we explore additional models and find that a methane-only scenario could adequately fit the data, though it is not preferred and likely unphysical. We discuss the impact of this methane detection challenge on our understanding of planetary atmospheres based on HST/WFC3 observations alone, and where JWST observations offer a solution.
Are Cognitive Biases as Important as they Seem for Data Visualization?
2025-03-05
Ali Baigelenov, Prakash Shukla, Zixu Zhang, Paul Parsons
Research on cognitive biases and heuristics has become increasingly popular in the visualization literature in recent years. Researchers have studied the effects of biases on visualization interpretation and subsequent decision-making. While this work is important, we contend that the view on biases has presented human cognitive abilities in an unbalanced manner, placing too much emphasis on the flaws and limitations of human decision-making, and potentially suggesting that it should not be trusted. Several decision researchers have argued that the flip side of biases -- i.e., mental shortcuts or heuristics -- demonstrate human ingenuity and serve as core markers of adaptive expertise. In this paper, we review the perspectives and sentiments of the visualization community on biases and describe literature arguing for more balanced views of biases and heuristics. We hope this paper will encourage visualization researchers to consider a fuller picture of human cognitive limitations and strategies for making decisions in complex environments.
SN 2024abfo: a partially stripped SN II from a white supergiant
2025-03-05
A. Reguitti, A. Pastorello, S. J. Smartt, G. Valerin, G. Pignata, S. Campana, T. -W. Chen, A. Sankar. K., S. Moran, P. A. Mazzali, J. Duarte, I. Salmaso, J. P. Anderson, C. Ashall, S. Benetti, M. Gromadzki, C. P. Gutierrez, C. Humina, C. Inserra, E. Kankare, T. Kravtsov, T. E. Muller-Bravo, P. J. Pessi, D. R. Young, K. Chambers, T. de Boer, H. Gao, M. Huber, C. -C. Lin, T. Lowe, E. Magnier, P. Minguez, I. A. Smith, K. W. Smith, S. Srivastav, R. Wainscoat, M. Benedet
We present photometric and spectroscopic data of the type IIb supernova (SN) 2024abfo in NGC 1493 (at 11 Mpc). The ATLAS survey discovered the object just a few hours after the explosion, and observed a fast rise on the first day. Signs of the sharp shock break-out peak and the subsequent cooling phase are observed in the ultraviolet and the bluest optical bands in the first couple of days, while no peak is visible in the reddest filters. Subsequently, in analogy with normal SNe IIb, the light curve of SN 2024abfo rises again in all bands to the broad peak, with the maximum light reached around one month after the explosion. Its absolute magnitude at peak is $M_r=-16.5\pm0.1$ mag, making it a faint SN IIb. The early spectra are dominated by Balmer lines with broad P-Cygni profiles indicating ejecta velocity of 22,500 \kms. One month after the explosion, the spectra display a transition towards being He-dominated, though the H lines do not completely disappear, supporting the classification of SN 2024abfo as a relatively H-rich SN IIb. We identify the progenitor of SN 2024abfo in archival images of the Hubble Space Telescope, the Dark Energy Survey, and the XMM-Newton space telescope, in multiple optical filters. From its spectral energy distribution, the progenitor is consistent with being a white supergiant, Deneb-like star, with a photospheric temperature of 8110 K, a radius of 176 \Rsun, a luminosity of $\log(L/L_{\odot})=5.08$, having an initial mass of 15-16 \Msun. This detection supports an emerging trend of SN IIb progenitors being more luminous and hotter than SN II ones, and being primaries of massive binaries. Within the SN IIb class, fainter events such as SN 2024abfo tend to have cooler and more expanded progenitors than luminous SNe IIb.
Antimatter Gravity and the Results of the ALPHA-g Experiment
2025-03-05
Massimo Villata
By combining general relativity and CPT symmetry, the theory of CPT gravity predicts gravitational repulsion between matter and CPT-transformed matter, i.e. antimatter inhabiting an inverted space-time. Such repulsive gravity turned out to be an excellent candidate for explaining the accelerated expansion of the Universe, without the need for dark energy. The recent results of the ALPHA-g experiment, which show gravitational attraction between antihydrogen atoms and the Earth, seem to undermine this success in the cosmological field. Analyzing the above theory, we find two solutions that can be consistent with the experimental results, while preserving the large-scale gravitational repulsion. The first highlights how repulsive gravity can be the result of the interaction with an inverted space-time, but occupied by matter and not antimatter, and therefore the antimatter present in our space-time has no reason to exhibit gravitational repulsion. The second retains the original CPT transformation, resulting in repulsive gravity between matter and antimatter, but with the caveat that antimatter immersed in our space-time cannot exhibit the PT transformation which is the cause of the repulsion. Finally, it is shown that, in a Newtonian approximation of the geodesic equation, time reversal is not a necessary operation for repulsive gravity, therefore opening the possibility of an expanding cosmos with a single time direction.
CAPOS: The bulge Cluster APOgee Survey V. Elemental abundances of the bulge globular cluster HP 1
2025-03-05
Lady Henao, Sandro Villanova, Doug Geisler, José G. Fernández-Trincado
We have performed a detailed abundance analysis of 10 red giant members of the heavily obscured bulge globular cluster HP~1 using high-resolution, high S/N near-infrared spectra collected with the Apache Point Observatory Galactic Evolution Experiment II survey (APOGEE-2), obtained as part of the bulge Cluster APOgee Survey (CAPOS). We investigate chemical abundances for a variety of species including the light (C,N), odd-Z (Al), $\alpha$ (O,Mg,Si,S,Ca and Ti), Fe-peak (Ni,Fe), and neutron-capture (Ce) elements. The derived mean cluster metallicity is [Fe/H]$=-1.15\pm0.03$, with no evidence for an intrinsic metallicity spread. HP~1 exhibits a typical $\alpha$-enrichment that follows the trend for similar metallicity Galactic GCs, such as NGC~288 and NGC~5904, although our [Si/Fe] abundances are relatively high. We find a significant nitrogen spread ($\sim 1$ dex), and a large fraction of nitrogen-enhanced ([N/Fe]$>+0.7$) stars that populate the cluster. We also detect intrinsic star-to-star spreads in [C/Fe], [O/Fe], [Al/Fe], and [Ca/Fe], which are (anti)correlated with several chemical species, indicating the prevalence of the multiple-population phenomenon in HP 1. We have uncovered for the first time a possible correlation between Ca and Al, although the sample is small. The mean $\langle$[Mg/Fe]$\rangle = +0.29$ and $\langle$[Al/Fe]$\rangle = +0.46$ place HP~1 into the region dominated by \textit{in-situ} GCs, supporting the \textit{in-situ} nature of this cluster.
Medium-band Astrophysics with the Grism of NIRCam In Frontier fields (MAGNIF): Spectroscopic Census of H$α$ Luminosity Functions and Cosmic Star Formation at $z\sim 4.5$ and 6.3
2025-03-05
Shuqi Fu, Fengwu Sun, Linhua Jiang, Xiaojing Lin, Jose M. Diego, Lukas J. Furtak, Mathilde Jauzac, Anton M. Koekemoer, Mingyu Li, Masamune Oguri, Nency R. Patel, Christopher N. A. Willmer, Rogier A. Windhorst, Adi Zitrin, Franz E. Bauer, Chian-Chou Chen, Wenlei Chen, Cheng Cheng, Christopher J. Conselice, Daniel J. Eisenstein, Eiichi Egami, Daniel Espada, Xiaohui Fan, Seiji Fujimoto, Tiger Yu-Yang Hsiao, Xiangyu Jin, Kotaro Kohno, David J. Lagattuta, Zihao Li, Weizhe Liu, Jordi Miralda-Escudé, Yuanhang Ning, Sandro Tacchella, Wei Leong Tee, Hideki Umehata, Feige Wang, Haojing Yan, Yongda Zhu
We measure H$\alpha$ luminosity functions (LFs) at redshifts $z \sim 4.5$ and 6.3 using the JWST MAGNIF (Medium-band Astrophysics with the Grism of NIRCam In Frontier fields) survey. MAGNIF obtained NIRCam grism spectra with the F360M and F480M filters in four Frontier Fields. We identify 248 H$\alpha$ emitters based on the grism spectra and photometric redshifts from combined HST and JWST imaging data. The numbers of the H$\alpha$ emitters show a large field-to-field variation, highlighting the necessity of multiple fields to mitigate cosmic variance. We calculate both observed and dust-corrected H$\alpha$ LFs in the two redshift bins. Thanks to the gravitational lensing, the measured H$\alpha$ LFs span three orders of magnitude in luminosity, and the faint-end luminosity reaches $L_{\mathrm{H}\alpha} \sim 10^{40.3} \mathrm{erg} \mathrm{s}^{-1}$ at $z \sim 4.5$ and $10^{41.5} \mathrm{erg} \mathrm{s}^{-1}$ at $z \sim 6.3$, corresponding to star-formation rates (SFRs) of $\sim$ 0.1 and 1.7 $\mathrm{M}_\odot \mathrm{yr}^{-1}$. We conclude no or weak redshift evolution of the faint-end slope of H$\alpha$ LF across $z\simeq0.4-6.3$, and the comparison with the faint-end slopes of UV LF indicates stochastic star formation history among low-mass H$\alpha$ emitters. The derived cosmic SFR densities are $0.058^{+0.008}_{-0.006}\ \ M_\odot\ \mathrm{yr}^{-1}\ \mathrm{Mpc}^{-3}$ at $z \sim 4.5$ and $0.025^{+0.009}_{-0.007}\ \ M_\odot\ \mathrm{yr}^{-1}\ \mathrm{Mpc}^{-3}$ at $z \sim 6.3$. These are approximately 2.2 times higher than previous estimates based on dust-corrected UV LFs, but consistent with recent measurements from infrared surveys. We discuss uncertainties in the H$\alpha$ LF measurements, including those propagate from the lens models, cosmic variance, and AGN contribution.
Apparent convergence in functional glueball calculations
2025-03-05
Markus Q. Huber, Christian S. Fischer, Hèlios Sanchis-Alepuz
We scrutinize the determination of glueball masses in pure Yang-Mills theory from functional equations, i.e. Dyson-Schwinger and Bethe-Salpeter equations. We survey the state-of-the-art input (dressed propagators and vertices) with an emphasis on the stability of the results under extensions of the employed truncations and explore the importance of different aspects of the bound state equations, focusing on the three lightest glueballs with $J^{PC} = 0^{++}$ , $0^{-+}$ and $2^{++}$ . As an important systematic extension compared to previous calculations we include two-loop diagrams in the Bethe-Salpeter kernels. In terms of the glueball spectrum we find only marginal mass shifts compared to previous results, indicating apparent convergence of the system. As a by-product, we also explore gauge invariance within a class of Landau-type gauges.
HI within and around observed and simulated galaxy discs -- Comparing MeerKAT observations with mock data from TNG50 and FIRE-2
2025-03-05
A. Marasco, W. J. G. de Blok, F. M. Maccagni, F. Fraternali, K. A. Oman, T. Oosterloo, F. Combes, S. S. McGaugh, P. Kamphuis, K. Spekkens, D. Kleiner, S. Veronese, P. Amram, L. Chemin, E. Brinks
Atomic hydrogen (HI) is an ideal tracer of gas flows in and around galaxies, and it is uniquely observable in the nearby Universe. Here we make use of wide-field (~1 square degree), spatially resolved (down to 22"), high-sensitivity (~$10^{18}$ cm$^{-2}$) HI observations of 5 nearby galaxies with stellar mass of $5\times10^{10}$ M$_\odot$, taken with the MeerKAT radio telescope. Four of these were observed as part of the MHONGOOSE survey. We characterise their main HI properties and compare these with synthetic HI data from a sample of 25 similarly massive star-forming galaxies from the TNG50 (20) and FIRE-2 (5) suites of cosmological hydrodynamical simulations. Globally, the simulated systems have HI and molecular hydrogen (H$_2$) masses in good agreement with the observations, but only when the H$_2$ recipe of Blitz & Rosolowsky (2006) is employed. The other recipes that we tested overestimate the H$_2$-to-HI mass fraction by up to an order of magnitude. On a local scale, we find two main discrepancies between observed and simulated data. First, the simulated galaxies show a more irregular HI morphology than the observed ones due to the presence of HI with column density $<10^{20}$ cm$^{-2}$ up to ~100 kpc from the galaxy centre, in spite of the fact that they inhabit more isolated environments than the observed targets. Second, the simulated galaxies and in particular those from the FIRE-2 suite, feature more complex and overall broader HI line profiles than the observed ones. We interpret this as being due to the combined effect of stellar feedback and gas accretion, which lead to a large-scale gas circulation that is more vigorous than in the observed galaxies. Our results indicate that, with respect to the simulations, gentler processes of gas inflows and outflows are at work in the nearby Universe, leading to more regular and less turbulent HI discs.
Graph neural networks (GNNs) have demonstrated success in modeling relational data primarily under the assumption of homophily. However, many real-world graphs exhibit heterophily, where linked nodes belong to different categories or possess diverse attributes. Additionally, nodes in many domains are associated with textual descriptions, forming heterophilic text-attributed graphs (TAGs). Despite their significance, the study of heterophilic TAGs remains underexplored due to the lack of comprehensive benchmarks. To address this gap, we introduce the Heterophilic Text-attributed Graph Benchmark (HeTGB), a novel benchmark comprising five real-world heterophilic graph datasets from diverse domains, with nodes enriched by extensive textual descriptions. HeTGB enables systematic evaluation of GNNs, pre-trained language models (PLMs) and co-training methods on the node classification task. Through extensive benchmarking experiments, we showcase the utility of text attributes in heterophilic graphs, analyze the challenges posed by heterophilic TAGs and the limitations of existing models, and provide insights into the interplay between graph structures and textual attributes. We have publicly released HeTGB with baseline implementations to facilitate further research in this field.
The SRG/eROSITA all-sky survey: View of the Fornax galaxy cluster
2025-03-04
T. H. Reiprich, A. Veronica, F. Pacaud, P. Stöcker, V. Nazaretyan, A. Srivastava, A. Pandya, J. Dietl, J. S. Sanders, M. C. H. Yeung, A. Chaturvedi, M. Hilker, B. Seidel, K. Dolag, J. Comparat, V. Ghirardini, M. Kluge, A. Liu, N. Malavasi, X. Zhang, E. Hernández-Martínez
The Fornax cluster is one of the most nearby X-ray bright galaxy clusters. Previous observations of the intracluster medium were limited to less than R500. We aim to significantly extend the X-ray coverage. We use data from five SRG/eROSITA all-sky surveys and perform a detailed one- and two-dimensional X-ray surface brightness analysis, allowing us to trace hot gas emission from kpc to Mpc scales with a single instrument. We compare the results to those from a recent numerical simulation of the local Universe (SLOW) and correlate the X-ray emission distribution with that of other tracers, including cluster member galaxies, ultra compact dwarf galaxies, intracluster globular clusters, and HI-tail galaxies. We detect X-ray emission out to well beyond the virial radius, R100=2.2 deg. We do not find obvious evidence for the bow shock several hundred kpc south of the cluster center predicted by previous numerical simulations of the Fornax cluster. Instead, we discover finger-like structures beyond R500 to the west and south-east that stretch out far beyond the virial radius. They might be due to gas being pushed outward by the previous merger with NGC 1404 or due to warm-hot gas infall along large-scale filaments. Intriguingly, we find the distributions of the other tracers -- galaxies and globular clusters -- to be correlated with the X-ray excess regions, favoring the latter infall scenario. Interestingly, we also discover an apparent Bridge of low surface brightness emission beyond the virial radius connecting to the Fornax A galaxy group, which is also traced by the member galaxy and globular cluster distribution. This X-ray Bridge furthermore approximately coincides with a region of enhanced Faraday depth detected previously. The gas distribution in the SLOW simulation shows similar features as those we have discovered with SRG/eROSITA.
A Multimodal Symphony: Integrating Taste and Sound through Generative AI
2025-03-04
Matteo Spanio, Massimiliano Zampini, Antonio Rodà, Franco Pierucci
In recent decades, neuroscientific and psychological research has traced direct relationships between taste and auditory perceptions. This article explores multimodal generative models capable of converting taste information into music, building on this foundational research. We provide a brief review of the state of the art in this field, highlighting key findings and methodologies. We present an experiment in which a fine-tuned version of a generative music model (MusicGEN) is used to generate music based on detailed taste descriptions provided for each musical piece. The results are promising: according the participants' ($n=111$) evaluation, the fine-tuned model produces music that more coherently reflects the input taste descriptions compared to the non-fine-tuned model. This study represents a significant step towards understanding and developing embodied interactions between AI, sound, and taste, opening new possibilities in the field of generative AI. We release our dataset, code and pre-trained model at: https://osf.io/xs5jy/.
Open Source at a Crossroads: The Future of Licensing Driven by Monetization
2025-03-04
Raula Gaikovina Kula, Christoph Treude
The widespread adoption of open source libraries and frameworks can be attributed to their licensing. Open Source Software Licenses (OSS licenses) ensure that software can be sold or distributed as part of aggregate programs from various sources without requiring a royalty or fee. The quality of such code rivals that of commercial software, with open source libraries forming large parts of the supply chain for critical commercial systems in industry. Despite this, most open source projects rely on volunteer contributions, and unpaid library maintainers face significant pressure to sustain their projects. One potential solution for these projects is to change their licensing to ensure that maintainers are compensated accordingly for their work. In this paper, we explore the potential of licensing to help alleviate funding issues, with a review of three different cases where OSS licenses were modified to allow for monetization. In addition, we explore licensing concerns related to the emergence of the use of artificial intelligence (AI) in software development. We argue that open source is at a crossroads, with a growing need to redefine its licensing models and support communities and critical software. We identify specific research opportunities and conclude with a research agenda comprising a series of research questions to guide future studies in this area.
Accounting for Missing Data in Public Health Research Using a Synthesis of Statistical and Mathematical Models
2025-03-04
Paul N Zivich, Bonnie E Shook-Sa, Stephen R Cole, Eric T Lofgren, Jessie K Edwards
Introduction: Missing data is a challenge to medical research. Accounting for missing data by imputing or weighting conditional on covariates relies on the variable with missingness being observed at least some of the time for all unique covariate values. This requirement is referred to as positivity, and violations can result in bias. Here, we review a novel approach to addressing positivity violations in the context of systolic blood pressure. Methods: To illustrate the proposed approach, we estimate the mean systolic blood pressure among children and adolescents aged 2-17 years old in the United States using data from 2017-2018 National Health and Nutrition Examination Survey (NHANES). As blood pressure was never measured for those aged 2-7, there exists a positivity violation by design. Using a recently proposed synthesis of statistical and mathematical models, we integrate external information with NHANES to address our motivating question. Results: With the synthesis model, the estimated mean systolic blood pressure was 100.5 (95\% confidence interval: 99.9, 101.0), which is notably lower than either a complete-case analysis or extrapolation from a statistical model. The synthesis results were supported by a diagnostic comparing the performance of the mathematical model in the positive region. Conclusion: Positivity violations pose a threat to quantitative medical research, and standard approaches to addressing nonpositivity rely on restrictive untestable assumptions. Using a synthesis model, like the one detailed here, offers a viable alternative through integration of external information.
Due to the implement of guardrails by developers, Large language models (LLMs) have demonstrated exceptional performance in explicit bias tests. However, bias in LLMs may occur not only explicitly, but also implicitly, much like humans who consciously strive for impartiality yet still harbor implicit bias. The unconscious and automatic nature of implicit bias makes it particularly challenging to study. This paper provides a comprehensive review of the existing literature on implicit bias in LLMs. We begin by introducing key concepts, theories and methods related to implicit bias in psychology, extending them from humans to LLMs. Drawing on the Implicit Association Test (IAT) and other psychological frameworks, we categorize detection methods into three primary approaches: word association, task-oriented text generation and decision-making. We divide our taxonomy of evaluation metrics for implicit bias into two categories: single-value-based metrics and comparison-value-based metrics. We classify datasets into two types: sentences with masked tokens and complete sentences, incorporating datasets from various domains to reflect the broad application of LLMs. Although research on mitigating implicit bias in LLMs is still limited, we summarize existing efforts and offer insights on future challenges. We aim for this work to serve as a clear guide for researchers and inspire innovative ideas to advance exploration in this task.
An Extremely Deep Rubin Survey to Explore the Extended Kuiper Belt and Identify Objects Observable by New Horizons
2025-03-04
JJ Kavelaars, Marc W. Buie, Wesley C. Fraser, Lowell Peltier, Susan D. Benecchi, Simon B. Porter, Anne J. Verbiscer, David W. Gerdes, Kevin J. Napier, Joseph Murtagh, Takashi Ito, Kelsi N. Singer, S. Alan Stern, Tsuyoshi Terai, Fumi Yoshida, Michele T. Bannister, Pedro H. Bernardinelli, Gary M. Bernstein, Colin Orion Chandler, Brett Gladman, Lynne Jones, Jean-Marc Petit, Megan E. Schwamb, Pontus C. Brandt, Joel W. Parker
A proposed Vera C. Rubin Observatory Deep Drilling micro-survey of the Kuiper Belt will investigate key properties of the distant solar system. Utilizing 30 hours of Rubin time across six 5-hour visits over one year starting in summer 2026, the survey aims to discover and determine orbits for up to 730 Kuiper Belt Objects (KBOs) to an $r$-magnitude of 27.5. These discoveries will enable precise characterization of the KBO size distribution, which is critical for understanding planetesimal formation. By aligning the survey field with NASA's {\it New Horizons} spacecraft trajectory, the micro-survey will facilitate discoveries for the mission operating in the Kuiper Belt. Modeling based on the Outer Solar System Origin Survey (OSSOS) predicts at least 12 distant KBOs observable with the {\it New Horizons} LOng Range Reconnaissance Imager (LORRI) and approximately three objects within 1~au of the spacecraft, allowing higher-resolution observations than Earth-based facilities. LORRI's high solar phase angle monitoring will reveal these objects' surface properties and shapes, potentially identifying contact binaries and orbit-class surface correlations. The survey could identify a KBO suitable for a future spacecraft flyby. The survey's size, depth, and cadence design will deliver transformative measurements of the Kuiper Belt's size distribution and rotational properties across distance, size, and orbital class. Additionally, the high stellar density in the survey field also offers synergies with transiting exoplanet studies.
Micro/nanoscale spacers for enhanced thermophotovoltaic and thermionic energy conversion: a comprehensive review
2025-03-04
Nicolas A Loubet, Katie Bezdjian, Esther Lopez, Alejandro Datas
Thermionics and thermophotovoltaics are solid-state technologies that convert high-temperature heat into electricity by utilizing fundamental particles, electrons in thermionics and photons in thermophotovoltaics, as energy carriers. Both systems have the potential to achieve high efficiency and power density, contingent on the optimization of radiative/electronic energy fluxes. A critical factor in enhancing energy flux in these devices is the introduction of microscale (thermionics) or nanoscale (thermophotovoltaics) gaps between the hot thermal emitter and the cooler receiver. In thermionic converters, microscale gaps mitigate space charge effects that create energy barriers to electron flow. For thermophotovoltaic systems, nanoscale gaps facilitate photon tunneling, significantly boosting photon flux towards the thermophotovoltaic cell. Forming these small-scale gaps often necessitates intermediate materials or spacers between the emitter and receiver. Over the past few decades, various spacer designs have been proposed and studied, demonstrating their effectiveness in enhancing energy transfer and conversion. However, challenges remain regarding their reliability and scalability. This article provides a comprehensive overview of spacer technologies for thermionics and thermophotovoltaics and summarizes recent advancements, current capabilities, and persistent challenges.
Internal threat detection aims to address security threats within organizations or enterprises by identifying potential or already occurring malicious threats within vast amounts of logs. Although organizations or enterprises have dedicated personnel responsible for reviewing these logs, it is impossible to manually examine all logs entirely. In response to the vast number of logs, we propose a system called RedChronos, which is a Large Language Model-Based Log Analysis System. This system incorporates innovative improvements over previous research by employing Query-Aware Weighted Voting and a Semantic Expansion-based Genetic Algorithm with LLM-driven Mutations. On the public datasets CERT 4.2 and 5.2, RedChronos outperforms or matches existing approaches in terms of accuracy, precision, and detection rate. Moreover, RedChronos reduces the need for manual intervention in security log reviews by 90\% in the Xiaohongshu SOC. Therefore, our RedChronos system demonstrates exceptional performance in handling Internal Threat Detection (IDT) tasks, providing innovative solutions for these challenges. We believe that future research can continue to enhance the system's performance in IDT tasks while also reducing the response time to internal risk events.
Scholars, awards committees, and laypeople frequently discuss the merit of written works. Literary professionals and journalists differ in how much perspectivism they concede in their book reviews. Here, we quantify how strongly book reviews are determined by the actual book contents vs. idiosyncratic reader tendencies. In our analysis of 624,320 numerical and textual book reviews, we find that the contents of professionally published books are not predictive of a random reader's reading enjoyment. Online reviews of popular fiction and non-fiction books carry up to ten times more information about the reviewer than about the book. For books of a preferred genre, readers might be less likely to give low ratings, but still struggle to converge in their relative assessments. We find that book evaluations generalize more across experienced review writers than casual readers. When discussing specific issues with a book, one review text had poor predictability of issues brought up in another review of the same book. We conclude that extreme perspectivism is a justifiable position when researching literary quality, bestowing literary awards, and designing recommendation systems.
A Deep, High-Angular Resolution 3D Dust Map of the Southern Galactic Plane
2025-03-04
Catherine Zucker, Andrew K. Saydjari, Joshua S. Speagle, Edward F. Schlafly, Gregory M. Green, Robert Benjamin, Joshua Peek, Gordian Edenhofer, Alyssa Goodman, Michael A. Kuhn, Douglas P. Finkbeiner
We present a deep, high-angular resolution 3D dust map of the southern Galactic plane over $239^\circ < \ell < 6^\circ$ and $|b| < 10^\circ$ built on photometry from the DECaPS2 survey, in combination with photometry from VVV, 2MASS, and unWISE and parallaxes from Gaia DR3 where available. To construct the map, we first infer the distance, extinction, and stellar types of over 700 million stars using the brutus stellar inference framework with a set of theoretical MIST stellar models. Our resultant 3D dust map has an angular resolution of $1'$, roughly an order of magnitude finer than existing 3D dust maps and comparable to the angular resolution of the Herschel 2D dust emission maps. We detect complexes at the range of distances associated with the Sagittarius-Carina and Scutum-Centaurus arms in the fourth quadrant, as well as more distant structures out to a maximum reliable distance of $d \approx$ 10 kpc from the Sun. The map is sensitive up to a maximum extinction of roughly $A_V \approx 12$ mag. We publicly release both the stellar catalog and the 3D dust map, the latter of which can easily be queried via the Python package dustmaps. When combined with the existing Bayestar19 3D dust map of the northern sky, the DECaPS 3D dust map fills in the missing piece of the Galactic plane, enabling extinction corrections over the entire disk $|b| < 10^\circ$. Our map serves as a pathfinder for the future of 3D dust mapping in the era of LSST and Roman, targeting regimes accessible with deep optical and near-infrared photometry but often inaccessible with Gaia.
Reflection on Data Storytelling Tools in the Generative AI Era from the Human-AI Collaboration Perspective
2025-03-04
Haotian Li, Yun Wang, Huamin Qu
Human-AI collaborative tools attract attentions from the data storytelling community to lower the barrier of expertise and streamline the workflow. The recent advance in large-scale generative AI techniques, e.g., large language models (LLMs) and text-to-image models, has the potential to enhance data storytelling with their power in visual and narration generation. After two years since these techniques were publicly available, it is important to reflect our progress of applying them and have an outlook for future opportunities. To achieve the goal, we compare the collaboration patterns of the latest tools with those of earlier ones using a dedicated framework for understanding human-AI collaboration in data storytelling. Through comparison, we identify persistent collaboration patterns, e.g., human-creator + AI-assistant, and emerging ones, e.g., AI-creator + human-reviewer. The benefits of these AI techniques and other implications to human-AI collaboration are also revealed. We further propose future directions to hopefully ignite innovations.
Assessing Galaxy Rotation Kinematics: Insights from Convolutional Neural Networks on Velocity Variations
2025-03-04
Amirmohammad Chegeni, Fatemeh Fazel Hesar, Mojtaba Raouf, Bernard Foing, Fons J. Verbeek
Distinguishing galaxies as either fast or slow rotators plays a vital role in understanding the processes behind galaxy formation and evolution. Standard techniques, which are based on the $\lambda_R$-spin parameter obtained from stellar kinematics, frequently face difficulties to classify fast and slow rotators accurately. These challenges arise particularly in cases where galaxies have complex interaction histories or exhibit significant morphological diversity. In this paper, we evaluate the performance of a Convolutional Neural Network (CNN) on classifying galaxy rotation kinematics based on stellar kinematic maps from the SAMI survey. Our results show that the optimal CNN architecture achieves an accuracy and precision of approximately 91% and 95% on the test dataset, respectively. Subsequently, we apply our trained model to classify previously unknown rotator galaxies for which traditional statistical tools have been unable to determine whether they exhibit fast or slow rotation, such as certain irregular galaxies or those in dense clusters. We also used Integrated Gradients (IG) to reveal the crucial kinematic features that influenced the CNN's classifications. This research highlights the power of CNNs to improve our comprehension of galaxy dynamics and emphasizes their potential to contribute to upcoming large-scale Integral Field Spectrograph (IFS) surveys.
Resonance-Driven Mechanisms of Ion Transport and Selectivity
2025-03-04
Ronald L. Westra
Ion channels selectively transport ions, yet the underlying mechanisms remain elusive. We propose a physical model based on the Driven Damped Harmonic Oscillator (DDHO), where self-organizing turbulent structures in the ionic flow generate oscillating pressure waves and toroidal vortices. These structures drive aqua-ions into resonance, facilitating the shedding of hydration shells and enabling ion permeation as free ions. To capture the spatiotemporal complexity of this process, we develop a macroscopic continuum model integrating the Navier--Stokes equations, Gauss's law, and convection-diffusion dynamics. Numerical simulations reveal strong oscillations that drive dehydration and ionic jet formation, supporting the DDHO mechanism. Model predictions closely match patch-clamp experimental data. The DDHO framework predicts a frequency-dependent resonance response, effectively acting as a selective filter. Applied to experimental data, the model reveals distinct separation between ion species and hydration states, quantified by a high Mahalanobis distance and oscillator quality factor. Furthermore, the model provides insight into the effects of single nucleotide polymorphisms (SNPs) on ion selectivity. Mutations that alter channel geometry shift resonance peaks, disrupting selective transport and potentially leading to genetic disorders.
In the era of large models, content generation is gradually shifting to Personalized Generation (PGen), tailoring content to individual preferences and needs. This paper presents the first comprehensive survey on PGen, investigating existing research in this rapidly growing field. We conceptualize PGen from a unified perspective, systematically formalizing its key components, core objectives, and abstract workflows. Based on this unified perspective, we propose a multi-level taxonomy, offering an in-depth review of technical advancements, commonly used datasets, and evaluation metrics across multiple modalities, personalized contexts, and tasks. Moreover, we envision the potential applications of PGen and highlight open challenges and promising directions for future exploration. By bridging PGen research across multiple modalities, this survey serves as a valuable resource for fostering knowledge sharing and interdisciplinary collaboration, ultimately contributing to a more personalized digital landscape.
Do you monitor CI Practices? I don't know. You tell me: A case study
2025-03-04
Jadson Santos, Daniel Alencar da Costa, Uirá Kulesza
Background: In this paper we seek for understand the benefits and challenges of monitoring CI practices in day-to-day software development. Aims: We aim to evaluate the impact of monitoring seven CI practices in a real-world scenario on three public organizations in Brazil. Method: We first developed a CI practices monitoring suite tool and conducted a multiple-case study applying a mixed-methods strategy, combining surveys, interviews, log data, and mining data from CI services. Results: We verified organization' interest in monitoring CI practices. Monitoring provided an overview of the organization's CI status, not covered by other tools, motivated constant improvement in these practices, a perception of software quality, improve the communication and and it is easy to adopt. Conclusions: We recommend that companies adopt monitoring of CI practices and that CI services integrate this monitoring into their dashboards.
Gravitational wave probes of particle dark matter: a review
2025-03-04
Andrew L. Miller
Various theories of dark matter predict distinctive astrophysical signatures in gravitational-wave sources that could be observed by ground- and space-based laser interferometers. Different candidates-including axions, dark photons, macroscopic dark matter, WIMPs, and dark-matter spikes-may appear in interferometer data via their coupling to gravity or the Standard Model, altering the measured gravitational-wave strain in distinct ways. Despite their differences, these candidates share two key features: (1) they can be probed through their effects on gravitational waves from inspiraling compact objects, isolated black holes, and neutron stars, or via direct interactions with detectors, and (2) their signatures likely persist far longer than the seconds-long mergers detected today, necessitating new data analysis methods beyond matched filtering. This review outlines these dark matter candidates, their observational signatures, and approaches for their detection.
Multimodal Large Language Models (MLLMs) have advanced in integrating diverse modalities but frequently suffer from hallucination. A promising solution to mitigate this issue is to generate text with citations, providing a transparent chain for verification. However, existing work primarily focuses on generating citations for text-only content, overlooking the challenges and opportunities of multimodal contexts. To address this gap, we introduce MCiteBench, the first benchmark designed to evaluate and analyze the multimodal citation text generation ability of MLLMs. Our benchmark comprises data derived from academic papers and review-rebuttal interactions, featuring diverse information sources and multimodal content. We comprehensively evaluate models from multiple dimensions, including citation quality, source reliability, and answer accuracy. Through extensive experiments, we observe that MLLMs struggle with multimodal citation text generation. We also conduct deep analyses of models' performance, revealing that the bottleneck lies in attributing the correct sources rather than understanding the multimodal content.
The importance of Luis Caffarelli's work in the study of fluids
2025-03-04
Maria J. Esteban
In this paper we describe the work of Luis Caffarelli in the area of fluid mechanics and related topics. Not only has his work on fluid mechanics been very influential, but many of his contributions that do not directly relate to fluid mechanics, such as his important results on the fractional Laplacian or the regularity of solutions to linear parabolic equations with oscillating coefficients, have been used in the study of fluids in many important ways. Thus, any review of his work has to include his contributions to the general (partial) regularity theory of solutions of Navier-Stokes equations and other studies related to fluid motion.
Federated nnU-Net for Privacy-Preserving Medical Image Segmentation
2025-03-04
Grzegorz Skorupko, Fotios Avgoustidis, Carlos Martín-Isla, Lidia Garrucho, Dimitri A. Kessler, Esmeralda Ruiz Pujadas, Oliver Díaz, Maciej Bobowicz, Katarzyna Gwoździewicz, Xavier Bargalló, Paulius Jaruševičius, Kaisar Kushibar, Karim Lekadir
The nnU-Net framework has played a crucial role in medical image segmentation and has become the gold standard in multitudes of applications targeting different diseases, organs, and modalities. However, so far it has been used primarily in a centralized approach where the data collected from hospitals are stored in one center and used to train the nnU-Net. This centralized approach has various limitations, such as leakage of sensitive patient information and violation of patient privacy. Federated learning is one of the approaches to train a segmentation model in a decentralized manner that helps preserve patient privacy. In this paper, we propose FednnU-Net, a federated learning extension of nnU-Net. We introduce two novel federated learning methods to the nnU-Net framework - Federated Fingerprint Extraction (FFE) and Asymmetric Federated Averaging (AsymFedAvg) - and experimentally show their consistent performance for breast, cardiac and fetal segmentation using 6 datasets representing samples from 18 institutions. Additionally, to further promote research and deployment of decentralized training in privacy constrained institutions, we make our plug-n-play framework public. The source-code is available at https://github.com/faildeny/FednnUNet .
Identification of high-redshift X-ray active galactic nuclei in the 4XMM-DR11 serendipitous catalogue using DES data: Comparative analysis with optically-selected QSOs
2025-03-04
E. Pouliasis, A. Ruiz, I. Georgantopoulos, A. Akylas, N. A. Webb, F. J. Carrera, S. Mateos, A. Nebot, M. G. Watson, F. X. Pineau, C. Motch
X-rays provide a robust method in identifying AGN. However, in the high-redshift Universe, their space density is relatively low, and, in combination with the small areas covered by X-ray surveys, the selected AGN are poorly sampled. Deep optical/infrared data are essential for locating counterparts and determining redshifts. In this work, we leverage the XMM-Newton 4XMM-DR11 serendipitous catalogue alongside the extensive optical Dark Energy Survey and the near-infrared VISTA Hemisphere Survey (VHS) to select one of the largest high-z X-ray AGN samples to date. Our analysis focuses on the overlapping area of these surveys, covering about 185 $deg^2$. In addition, we aspire to compare the properties of the X-ray AGN with the optically-selected QSOs. For sources without spectroscopic data (80%), we estimated the photometric redshifts using both SED fitting and machine-learning algorithms. Among the 65,000 X-ray sources in the 4XMM-DES-VHS area, we ended up with 833 z>3.5 AGN (11% having spec-z information) with high reliability and a fraction of outliers {\eta}<10%. The sample completeness is 90%, driven by the depth of DES data. Only 10% of the X-ray selected AGN are also optical QSOs and vice versa. Our findings indicate an observed absorbed fraction ($\log N_H~[cm^{-2}] \geq 23$) of 20-40% for the X-ray AGN, significantly higher than that of optical QSOs. X-ray AGN exhibit fainter observed optical magnitudes and brighter mid-IR magnitudes than optical QSOs. Their median rest-frame SED shapes differ notably with optical QSOs being dominated by AGN emission in the UV-optical wavelengths. While the median SEDs of X-ray AGN suggest extinction in the UV-optical range, individual sources exhibit a wide range of spectral shapes, indicating significant diversity within the population. This analysis supports that X-ray and optically-selected AGN represent distinct and complementary populations.
"Magic" is referred to here and there in the robotics literature, from "magical moments" afforded by a mobile bubble machine, to "spells" intended to entertain and motivate children--but what exactly could this concept mean for designers? Here, we present (1) some theoretical discussion on how magic could inform interaction designs based on reviewing the literature, followed by (2) a practical description of using such ideas to develop a simplified prototype, which received an award in an international robot magic competition. Although this topic can be considered unusual and some negative connotations exist (e.g., unrealistic thinking can be referred to as magical), our results seem to suggest that magic, in the experiential, supernatural, and illusory senses of the term, could be useful to consider in various robot design contexts, also for artifacts like home assistants and autonomous vehicles--thus, inviting further discussion and exploration.
A Systematic Literature Review on Safety of the Intended Functionality for Automated Driving Systems
2025-03-04
Milin Patel, Rolf Jung, Marzana Khatun
In the automobile industry, ensuring the safety of automated vehicles equipped with the Automated Driving System (ADS) is becoming a significant focus due to the increasing development and deployment of automated driving. Automated driving depends on sensing both the external and internal environments of a vehicle, utilizing perception sensors and algorithms, and Electrical/Electronic (E/E) systems for situational awareness and response. ISO 21448 is the standard for Safety of the Intended Functionality (SOTIF) that aims to ensure that the ADS operate safely within their intended functionality. SOTIF focuses on preventing or mitigating potential hazards that may arise from the limitations or failures of the ADS, including hazards due to insufficiencies of specification, or performance insufficiencies, as well as foreseeable misuse of the intended functionality. However, the challenge lies in ensuring the safety of vehicles despite the limited availability of extensive and systematic literature on SOTIF. To address this challenge, a Systematic Literature Review (SLR) on SOTIF for the ADS is performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The objective is to methodically gather and analyze the existing literature on SOTIF. The major contributions of this paper are: (i) presenting a summary of the literature by synthesizing and organizing the collective findings, methodologies, and insights into distinct thematic groups, and (ii) summarizing and categorizing the acknowledged limitations based on data extracted from an SLR of 51 research papers published between 2018 and 2023. Furthermore, research gaps are determined, and future research directions are proposed.
PennyLang: Pioneering LLM-Based Quantum Code Generation with a Novel PennyLane-Centric Dataset
2025-03-04
Haider Asif, Abdul Basit, Nouhaila Innan, Muhammad Kashif, Alberto Marchisio, Muhammad Shafique
Large Language Models (LLMs) offer remarkable capabilities in code generation, natural language processing, and domain-specific reasoning. Their potential in aiding quantum software development remains underexplored, particularly for the PennyLane framework-a leading platform for hybrid quantum-classical computing. To address this gap, we introduce a novel, high-quality dataset comprising 3,347 PennyLane-specific code samples of quantum circuits and their contextual descriptions, specifically curated to train/fine-tune LLM-based quantum code assistance. Our key contributions are threefold: (1) the automatic creation and open-source release of a comprehensive PennyLane dataset leveraging quantum computing textbooks, official documentation, and open-source repositories; (2) the development of a systematic methodology for data refinement, annotation, and formatting to optimize LLM training efficiency; and (3) a thorough evaluation, based on a Retrieval-Augmented Generation (RAG) framework, demonstrating the effectiveness of our dataset in streamlining PennyLane code generation and improving quantum development workflows. Compared to existing efforts that predominantly focus on Qiskit, our dataset significantly broadens the spectrum of quantum frameworks covered in AI-driven code assistance. By bridging this gap and providing reproducible dataset-creation methodologies, we aim to advance the field of AI-assisted quantum programming, making quantum computing more accessible to both newcomers and experienced developers.
Bottom-up Background Simulations of the 2016 COSI Balloon Flight
2025-03-04
Savitri Gallego, Uwe Oberlack, Jan Lommler, Christopher M. Karwin, Andreas Zoglauer, Pierre Jean, Peter von Ballmoos, Carolyn Kierans, Clio Sleator, John A. Tomsick, Steven E. Boggs
The Compton Spectrometer and Imager (COSI) is a Compton telescope designed to survey the 0.2-5 MeV sky, consisting of a compact array of cross-strip germanium detectors. As part of its development, in 2016 COSI had a successful 46 day flight on board NASA's Super Pressure Balloon platform. This was a precursor to the COSI Small Explorer (COSI-SMEX) satellite mission that will launch in 2027 into a equatorial low Earth (530 km) orbit. The observation of MeV gamma-rays is dominated by background radiation, especially due to the activation of the detector materials induced by cosmic-ray interactions. Thus, background simulation and identification are crucial for the data analysis. Because the COSI-SMEX detectors will be similar to the ones used for the balloon flight, the balloon measurements provide an important tool for testing and cross-checking our background simulations for the upcoming space mission. In this work we perform Monte Carlo simulations of the background emission from the 2016 COSI balloon flight. Including a phenomenological shape correction, we obtain an agreement with the data at the 10-20% level for energies between 0.1-1.6 MeV, and we successfully reproduce most of the activation lines induced by cosmic ray interactions.
The kinematic contribution to the cosmic number count dipole
2025-03-04
J. D. Wagenveld, S. von Hausegger, H-R. Klöckner, D. J. Schwarz
Measurements of the number count dipole with large surveys have shown amplitudes in tension with kinematic predictions based on the observed Doppler dipole of the cosmic microwave background (CMB). These observations seem to be in direct conflict with a homogeneous and isotropic Universe as asserted by the cosmological principle, demanding further investigation into the origin of the tension. Here, we investigate whether the observed number count dipoles are consistent with being fully kinematic or if there is any residual anisotropy contributing to the total observed dipole. To disentangle these contributions, we aim to leverage the fact that the kinematic matter dipole expected in a given galaxy catalogue scales with observed properties of the sample, and different catalogues used in the literature therefore have different kinematic dipole expectations. We here perform joint dipole fits using the NRAO VLA Sky Survey (NVSS), the Rapid ASKAP Continuum Survey (RACS), and the AGN catalogue derived from the Wide-field Infrared Survey Explorer (CatWISE). Assuming a common kinematic and non-kinematic dipole component between all catalogues, we find that a large residual, non-kinematic dipole anisotropy is detected, though a common direction between the two components is disfavoured by model selection. Freeing up both amplitude and direction for this residual dipole while fixing the kinematic dipole to the CMB dipole expectation, we recover a significant residual dipole with $\mathcal{D}_{resid} = (0.81\pm0.14)\times10^{-2}$, that is offset from the CMB dipole direction by $39\pm8$ degrees. While the present work provides a valuable first test of this concept, its scrutinising power is limited by the currently employed catalogues. Larger catalogues, especially in radio, will be needed to further lift the degeneracy between the kinematic and residual dipole components.
Inferring Galactic Parameters from Chemical Abundances with Simulation-Based Inference
2025-03-04
Tobias Buck, Berkay Günes, Giuseppe Viterbo, William H. Oliver, Sven Buder
Galactic chemical abundances provide crucial insights into fundamental galactic parameters, such as the high-mass slope of the initial mass function (IMF) and the normalization of Type Ia supernova (SN Ia) rates. Constraining these parameters is essential for advancing our understanding of stellar feedback, metal enrichment, and galaxy formation processes. However, traditional Bayesian inference techniques, such as Hamiltonian Monte Carlo (HMC), are computationally prohibitive when applied to large datasets of modern stellar surveys. We leverage simulation-based-inference (SBI) as a scalable, robust, and efficient method for constraining galactic parameters from stellar chemical abundances and demonstrate its the advantages over HMC in terms of speed, scalability, and robustness against model misspecifications. We combine a Galactic Chemical Evolution (GCE) model, CHEMPY, with a neural network emulator and a Neural Posterior Estimator (NPE) to train our SBI pipeline. Mock datasets are generated using CHEMPY, including scenarios with mismatched nucleosynthetic yields, with additional tests conducted on data from a simulated Milky Way-like galaxy. SBI results are benchmarked against HMC-based inference, focusing on computational performance, accuracy, and resilience to systematic discrepancies. SBI achieves a $\sim75,600\times$ speed-up compared to HMC, reducing inference runtime from $\gtrsim42$ hours to mere seconds for thousands of stars. Inference on $1,000$ stars yields precise estimates for the IMF slope ($\alpha_{\rm IMF} = -2.298 \pm 0.002$) and SN Ia normalization ($\log_{10}(N_{\rm Ia}) = -2.885 \pm 0.003$), deviating less than 0.05% from the ground truth. SBI also demonstrates similar robustness to model misspecification than HMC, recovering accurate parameters even with alternate yield tables or data from a cosmological simulation. (shortened...)
Privacy Preservation Techniques (PPTs) in IoT Systems: A Scoping Review and Future Directions
2025-03-04
Emmanuel Alalade, Ashraf Matrawy
Privacy preservation in Internet of Things (IoT) systems requires the use of privacy-enhancing technologies (PETs) built from innovative technologies such as cryptography and artificial intelligence (AI) to create techniques called privacy preservation techniques (PPTs). These PPTs achieve various privacy goals and address different privacy concerns by mitigating potential privacy threats within IoT systems. This study carried out a scoping review of different types of PPTs used in previous research works on IoT systems between 2010 and early 2023 to further explore the advantages of privacy preservation in these systems. This scoping review looks at privacy goals, possible technologies used for building PET, the integration of PPTs into the computing layer of the IoT architecture, different IoT applications in which PPTs are deployed, and the different privacy types addressed by these techniques within IoT systems. Key findings, such as the prominent privacy goal and privacy type in IoT, are discussed in this survey, along with identified research gaps that could inform future endeavors in privacy research and benefit the privacy research community and other stakeholders in IoT systems.
Joint Tensor and Inter-View Low-Rank Recovery for Incomplete Multiview Clustering
2025-03-04
Jianyu Wang, Zhengqiao Zhao, Nicolas Dobigeon, Jingdong Chen
Incomplete multiview clustering (IMVC) has gained significant attention for its effectiveness in handling missing sample challenges across various views in real-world multiview clustering applications. Most IMVC approaches tackle this problem by either learning consensus representations from available views or reconstructing missing samples using the underlying manifold structure. However, the reconstruction of learned similarity graph tensor in prior studies only exploits the low-tubal-rank information, neglecting the exploration of inter-view correlations. This paper propose a novel joint tensor and inter-view low-rank Recovery (JTIV-LRR), framing IMVC as a joint optimization problem that integrates incomplete similarity graph learning and tensor representation recovery. By leveraging both intra-view and inter-view low rank information, the method achieves robust estimation of the complete similarity graph tensor through sparse noise removal and low-tubal-rank constraints along different modes. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed approach, achieving significant improvements in clustering accuracy and robustness compared to state-of-the-art methods.
Artificial Intelligence in Reactor Physics: Current Status and Future Prospects
2025-03-04
Ruizhi Zhang, Shengfeng Zhu, Kan Wang, Ding She, Jean-Philippe Argaud, Bertrand Bouriquet, Qing Li, Helin Gong
Reactor physics is the study of neutron properties, focusing on using models to examine the interactions between neutrons and materials in nuclear reactors. Artificial intelligence (AI) has made significant contributions to reactor physics, e.g., in operational simulations, safety design, real-time monitoring, core management and maintenance. This paper presents a comprehensive review of AI approaches in reactor physics, especially considering the category of Machine Learning (ML), with the aim of describing the application scenarios, frontier topics, unsolved challenges and future research directions. From equation solving and state parameter prediction to nuclear industry applications, this paper provides a step-by-step overview of ML methods applied to steady-state, transient and combustion problems. Most literature works achieve industry-demanded models by enhancing the efficiency of deterministic methods or correcting uncertainty methods, which leads to successful applications. However, research on ML methods in reactor physics is somewhat fragmented, and the ability to generalize models needs to be strengthened. Progress is still possible, especially in addressing theoretical challenges and enhancing industrial applications such as building surrogate models and digital twins.
We review the superfield formulation of $\mathcal{N}=2$ higher-spin supergravity theory in harmonic superspace. The analysis of both the hypermultiplet higher-spin supersymmetries and conformal supersymmetries is performed. The analytic superspace gauging of these symmetries gives rise to a set of unconstrained analytical prepotentials describing $\mathcal{N}=2$ higher-spin off-shell supermultiplets. This procedure naturally yields cubic interaction vertices of $\mathcal{N}=2$ higher spins with the hypermultiplet. Based on these results, the consistent interaction of an infinite tower of $\mathcal{N}=2$ superconformal higher spins with hypermultiplet is presented. Proceeding from this model, a method to construct a consistent interacting theory of $\mathcal{N}=2$ higher-spin supergravity by making use of the conformal compensators is proposed.
NLI4DB: A Systematic Review of Natural Language Interfaces for Databases
2025-03-04
Mengyi Liu, Jianqiu Xu
As the demand for querying databases in all areas of life continues to grow, researchers have devoted significant attention to the natural language interface for databases (NLIDB). This paper presents a comprehensive survey of recently proposed NLIDBs. We begin with a brief introduction to natural language processing techniques, executable database languages and the intermediate representation between natural language and executable language, and then provide an overview of the translation process from natural language to executable database language. The translation process is divided into three stages: (i) natural language preprocessing, (ii) natural language understanding, and (iii) natural language translation. Traditional and data-driven methods are utilized in the preprocessing stage. Traditional approaches rely on predefined rules and grammars, and involve techniques such as regular expressions, dependency parsing and named entity recognition. Data-driven approaches depend on large-scale data and machine learning models, using techniques including word embedding and pattern linking. Natural language understanding methods are classified into three categories: (i) rule-based, (ii) machine learning-based, and (iii) hybrid. We then describe a general construction process for executable languages over relational and spatio-temporal databases. Subsequently, common benchmarks and evaluation metrics for transforming natural language into executable language are presented, and methods for generating new benchmarks are explored. Finally, we summarize the classification, development, and enhancement of NLIDB systems, and discuss deep language understanding and database interaction techniques related to NLIDB, including (i) using LLM for Text2SQL tasks, (ii) generating natural language interpretations from SQL, and (iii) transforming speech queries into SQL.
This article proposes a simple, graph-independent perspective on partitioning the node set of a graph and provides multi-agent systems (MASs) with objectives beyond cooperation and bipartition. Specifically, we first introduce the notion of $k$-partition transformation to achieve any desired partition of the nodes. Then, we use this notion to formulate the multi-partite output regulation problem (MORP) of heterogeneous linear MASs, which comprises the existing cooperative output regulation problem (CORP) and bipartite output regulation problem (BORP) as subcases. The goal of the MORP is to design a distributed control law such that each follower that belongs to the same set in the partition asymptotically tracks a predefined multiple of a reference while ensuring the internal stability of the closed-loop system. It is shown that the necessary and sufficient conditions for the solvability of the MORP with a feedforward-based distributed control law follow from the CORP and lead to the first design strategy for the control parameters. However, it has a drawback in terms of scalability due to a partition-dependent condition. We prove that this condition is implied by its partition-independent version under a mild structural condition. This implication yields the second design strategy that is much more scalable than the first one. Finally, numerical examples are provided to illustrate the generality of the MORP and compare both design strategies regarding scalability.
Machine unlearning aims to remove the influence of problematic training data after a model has been trained. The primary challenge in machine unlearning is ensuring that the process effectively removes specified data without compromising the model's overall performance on the remaining dataset. Many existing machine unlearning methods address this challenge by carefully balancing gradient ascent on the unlearn data with the gradient descent on a retain set representing the training data. Here, we propose OrthoGrad, a novel approach that mitigates interference between the unlearn set and the retain set rather than competing ascent and descent processes. Our method projects the gradient of the unlearn set onto the subspace orthogonal to all gradients in the retain batch, effectively avoiding any gradient interference. We demonstrate the effectiveness of OrthoGrad on multiple machine unlearning benchmarks, including automatic speech recognition, outperforming competing methods.
Exploring the world of rhamnolipids: A critical review of their production, interfacial properties, and potential application
2025-03-04
Eduardo Guzman, Francisco Ortega, Ramon G. Rubio
Rhamnolipids are very promising sugar-based biosurfactants, generally produced by bacteria, with a wide range of properties that can be exploited at an industrial and technological level, e.g. in cosmetics, food science, or oil recovery, to provide benefits for human health and the environment. This has led to intensive research into optimizing their production to increase yields and minimize costs, which is challenging because biotechnological methods for rhamnolipid production result in complex product mixtures and require the introduction of complex separation strategies to ensure the purity of the rhamnolipid obtained. This is an important issue for the introduction of rhamnolipids to the market due to the differences that exist between the properties of the different congeners. This review attempts to provide an overview of the interfacial properties, potential applications, and recent advances in understanding the molecular mechanisms that govern the adsorption to interfaces and assembly in solution of rhamnolipids. In addition, the review also discusses some general aspects related to the production and purification methods of rhamnolipids, highlighting the need for further research to fully exploit their potential. It is hoped that this review will contribute to the growing body of knowledge about rhamnolipids and stimulate further research in this field.
Towards Explainable Doctor Recommendation with Large Language Models
2025-03-04
Ziyang Zeng, Dongyuan Li, Yuqing Yang
The advent of internet medicine provides patients with unprecedented convenience in searching and communicating with doctors relevant to their diseases and desired treatments online. However, the current doctor recommendation systems fail to fully ensure the professionalism and interpretability of the recommended results. In this work, we formulate doctor recommendation as a ranking task and develop a large language model (LLM)-based pointwise ranking framework. Our framework ranks doctors according to their relevance regarding specific diseases-treatment pairs in a zero-shot setting. The advantage of our framework lies in its ability to generate precise and explainable doctor ranking results. Additionally, we construct DrRank, a new expertise-driven doctor ranking dataset comprising over 38 disease-treatment pairs. Experiment results on the DrRank dataset demonstrate that our framework significantly outperforms the strongest cross-encoder baseline, achieving a notable gain of +5.45 in the NDCG@10 score while maintaining affordable latency consumption. Furthermore, we comprehensively present the fairness analysis results of our framework from three perspectives of different diseases, patient gender, and geographical regions. Meanwhile, the interpretability of our framework is rigorously verified by three human experts, providing further evidence of the reliability of our proposed framework for doctor recommendation.
AI Automatons: AI Systems Intended to Imitate Humans
2025-03-04
Alexandra Olteanu, Solon Barocas, Su Lin Blodgett, Lisa Egede, Alicia DeVrio, Myra Cheng
There is a growing proliferation of AI systems designed to mimic people's behavior, work, abilities, likenesses, or humanness -- systems we dub AI automatons. Individuals, groups, or generic humans are being simulated to produce creative work in their styles, to respond to surveys in their places, to probe how they would use a new system before deployment, to provide users with assistance and companionship, and to anticipate their possible future behavior and interactions with others, just to name a few applications. The research, design, deployment, and availability of such AI systems have, however, also prompted growing concerns about a wide range of possible legal, ethical, and other social impacts. To both 1) facilitate productive discussions about whether, when, and how to design and deploy such systems, and 2) chart the current landscape of existing and prospective AI automatons, we need to tease apart determinant design axes and considerations that can aid our understanding of whether and how various design choices along these axes could mitigate -- or instead exacerbate -- potential adverse impacts that the development and use of AI automatons could give rise to. In this paper, through a synthesis of related literature and extensive examples of existing AI systems intended to mimic humans, we develop a conceptual framework to help foreground key axes of design variations and provide analytical scaffolding to foster greater recognition of the design choices available to developers, as well as the possible ethical implications these choices might have.
From Code to Courtroom: LLMs as the New Software Judges
2025-03-04
Junda He, Jieke Shi, Terry Yue Zhuo, Christoph Treude, Jiamou Sun, Zhenchang Xing, Xiaoning Du, David Lo
Recently, Large Language Models (LLMs) have been increasingly used to automate SE tasks such as code generation and summarization. However, evaluating the quality of LLM-generated software artifacts remains challenging. Human evaluation, while effective, is very costly and time-consuming. Traditional automated metrics like BLEU rely on high-quality references and struggle to capture nuanced aspects of software quality, such as readability and usefulness. In response, the LLM-as-a-Judge paradigm, which employs LLMs for automated evaluation, has emerged. Given that LLMs are typically trained to align with human judgment and possess strong coding abilities and reasoning skills, they hold promise as cost-effective and scalable surrogates for human evaluators. Nevertheless, LLM-as-a-Judge research in the SE community is still in its early stages, with many breakthroughs needed. This forward-looking SE 2030 paper aims to steer the research community toward advancing LLM-as-a-Judge for evaluating LLMgenerated software artifacts, while also sharing potential research paths to achieve this goal. We provide a literature review of existing SE studies on LLM-as-a-Judge and envision these frameworks as reliable, robust, and scalable human surrogates capable of evaluating software artifacts with consistent, multi-faceted assessments by 2030 and beyond. To validate this vision, we analyze the limitations of current studies, identify key research gaps, and outline a detailed roadmap to guide future developments of LLM-as-a-Judge in software engineering. While not intended to be a definitive guide, our work aims to foster further research and adoption of LLM-as-a-Judge frameworks within the SE community, ultimately improving the effectiveness and scalability of software artifact evaluation methods.
DESI Spectroscopy of HETDEX Emission-line Candidates I: Line Discrimination Validation
2025-03-04
Martin Landriau, Erin Mentuch Cooper, Dustin Davis, Karl Gebhardt, Robin Ciardullo, Éric Armengaud, Arjun Dey, Anand Raichoor, David J. Schlegel, Michael Wilson, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, A. de la Macorra, S. Ferraro, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, C. Hahn, K. Honscheid, C. Howlett, M. Ishak, S. Juneau, R. Kehoe, T. Kisner, Anthony Kremin, L. Le Guillou, Michael E. Levi, M. Manera, A. Meisner, R. Miquel, J. Moustakas, S. Nadathur, I. Pérez-Ràfols, C. Poppett, F. Prada, G. Rossi, E. Sanchez, M. Schubnell, D. Sprayberry, G. Tarlé, B. A. Weaver, R. Zhou, H. Zou, Daniel J. Farrow, Gary J. Hill, Donghui Jeong, Chenxu Liu, Shun Saito, Donald P. Schneider
The Hobby-Eberly Dark Energy Experiment (HETDEX) is an untargeted spectroscopic galaxy survey that uses Ly$\alpha$ emitting galaxies (LAEs) as tracers of 1.9 < z < 3.5 large scale structure. Most detections consist of a single emission line, whose identity is inferred via a Bayesian analysis of ancillary data. To determine the accuracy of these line identifications, HETDEX detections were observed with the Dark Energy Spectroscopic Instrument (DESI). In two DESI pointings, high confidence spectroscopic redshifts are obtained for 1157 sources, including 982 LAEs. The DESI spectra are used to evaluate the accuracy of the HETDEX object classifications, and tune the methodology to achieve the HETDEX science requirement of $\lesssim 2\%$ contamination of the LAE sample by low-redshift emission-line galaxies, while still assigning $96\%$ of the true Ly$\alpha$ emission sample with the correct spectroscopic redshift. We compare emission line measurements between the two experiments assuming a simple Gaussian line fitting model. Fitted values for the central wavelength of the emission line, the measured line flux and line widths are consistent between the surveys within uncertainties. Derived spectroscopic redshifts, from the two classification pipelines, when both agree as an LAE classification, are consistent to within $\langle \Delta z / (1 + z) \rangle = 6.9\times 10^{-5}$ with an rms scatter of $3.3\times 10^{-4}$.
The large-scale structure supplies the formation of gas-star misaligned galaxies
2025-03-04
Min Bao, Yanmei Chen, Qiusheng Gu, Huiyuan Wang, Yong Shi, Peng Wang
Using the integral field unit data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, we build a sample of gas-star misaligned galaxies. The large-scale environment of misaligned galaxies is dominated by filaments and clusters, while is less dense relative to the gas-star aligned control galaxies. The direction of the large-scale structure (LSS) is defined by its minor axis ($\vec{e_{3}}$), which indicates the slowest collapsing direction. For the aligned controls, the gas and stellar spins are preferentially perpendicular to $\vec{e_{3}}$, since these galaxies reside in high-mass host haloes. For the misaligned galaxies, the gas spins also tend to be perpendicular to $\vec{e_{3}}$, suggesting that misaligned gas is recently accreted from the LSS. Meanwhile, there is no correlation between their stellar spins and $\vec{e_{3}}$. There are two possible explanations for this observational phenomenon: (1) the large-scale environments of misaligned galaxies evolve as they grow, with stellar angular momenta acquiring in different environments having different orientations; (2) the correlation between stellar spins and the LSS is smeared out since a relatively higher portion of misaligned galaxies in sheet environments are statistically analysed together with those in filament environments.
RPF-Search: Field-based Search for Robot Person Following in Unknown Dynamic Environments
2025-03-04
Hanjing Ye, Kuanqi Cai, Yu Zhan, Bingyi Xia, Arash Ajoudani, Hong Zhang
Autonomous robot person-following (RPF) systems are crucial for personal assistance and security but suffer from target loss due to occlusions in dynamic, unknown environments. Current methods rely on pre-built maps and assume static environments, limiting their effectiveness in real-world settings. There is a critical gap in re-finding targets under topographic (e.g., walls, corners) and dynamic (e.g., moving pedestrians) occlusions. In this paper, we propose a novel heuristic-guided search framework that dynamically builds environmental maps while following the target and resolves various occlusions by prioritizing high-probability areas for locating the target. For topographic occlusions, a belief-guided search field is constructed and used to evaluate the likelihood of the target's presence, while for dynamic occlusions, a fluid-field approach allows the robot to adaptively follow or overtake moving occluders. Past motion cues and environmental observations refine the search decision over time. Our results demonstrate that the proposed method outperforms existing approaches in terms of search efficiency and success rates, both in simulations and real-world tests. Our target search method enhances the adaptability and reliability of RPF systems in unknown and dynamic environments to support their use in real-world applications. Our code, video, experimental results and appendix are available at https://medlartea.github.io/rpf-search/.
SDSS-IV MaNGA: Spatial Evolution of Gas-Phase Metallicity Changes Induced by Galaxy Interactions
2025-03-04
Hsi-An Pan, Lihwai Lin, Sebastian F. Sanchez, Jorge K. Barrera-Ballesteros, Bau-Ching Hsieh
Gas-phase metallicity in interacting and merging galaxies offers key insights into their star formation processes and evolutionary histories. This study investigates the spatial evolution of gas-phase metallicity (i.e, oxygen abundance, 12 $+$ log(O/H)) in these galaxies using integral field unit (IFU) data from the SDSS-IV MaNGA survey, focusing on changes in metallicity gradients across different stages of interactions -- from early encounters to final coalescence. By comparing interacting and merging galaxies with isolated counterparts, we identify characteristic trends in how interactions influence metallicity gradients over time. Our analysis reveals that metallicity gradients typically flatten shortly after the first pericenter passage, likely due to radial gas mixing, with later stages showing either metallicity enrichment or dilution depending on the intensity of the interaction and star formation activity. These changes can result in gradients that are either flatter or steeper than the initial profiles. Notably, we observe steeper metallicity gradients in interacting galaxies at certain merger stages, which is inconsistent with predictions from some galaxy simulations. This discrepancy emphasizes the complexity of galaxy interactions. Overall, our findings provide valuable insights into how galaxy interactions reshape metallicity distribution, enhancing our understanding of the processes driving galaxy evolution during mergers.
Supernova remnant candidates identified using MWA Galactic Plane Monitoring over 285deg < l < 70deg and |b| < 16deg
2025-03-04
S. Mantovanini, N. Hurley-Walker, G. E. Anderson
Observations of Galactic supernova remnants (SNRs) are crucial to understanding supernova explosion mechanisms and their impact on our Galaxy's evolution. SNRs are usually identified by searching for extended, circular structures in all-sky surveys. However, the resolution and sensitivity of any given survey results in selection biases related to the brightness and angular scale of a subset of the total SNR population. As a result, we have only identified 1/3 of the expected number of SNRs in our Galaxy. We used data collected by the Murchison Widefield Array (MWA) to perform a visual search for SNR candidates over 285deg < l < 70deg and |b| < 16deg. We then used the Widefield Infrared Survey Explorer to eliminate likely HII regions from our SNR candidate sample. By exploiting the resolution and sensitivity of MWA data, we have successfully detected 10 new candidates using our proposed method. In addition, our method has also enabled us to detect and verify 10 previously known but unconfirmed candidates. The 20 SNR candidates described in the paper will increase the known SNR population in the Galaxy by 7{\%}.
Leveraging Large Language Models for Enhanced Digital Twin Modeling: Trends, Methods, and Challenges
2025-03-04
Linyao Yang, Shi Luo, Xi Cheng, Lei Yu
Digital twin technology is a transformative innovation driving the digital transformation and intelligent optimization of manufacturing systems. By integrating real-time data with computational models, digital twins enable continuous monitoring, simulation, prediction, and optimization, effectively bridging the gap between the physical and digital worlds. Recent advancements in communication, computing, and control technologies have accelerated the development and adoption of digital twins across various industries. However, significant challenges remain, including limited data for accurate system modeling, inefficiencies in system analysis, and a lack of explainability in the interactions between physical and digital systems. The rise of large language models (LLMs) offers new avenues to address these challenges. LLMs have shown exceptional capabilities across diverse domains, exhibiting strong generalization and emergent abilities that hold great potential for enhancing digital twins. This paper provides a comprehensive review of recent developments in LLMs and their applications to digital twin modeling. We propose a unified description-prediction-prescription framework to integrate digital twin modeling technologies and introduce a structured taxonomy to categorize LLM functionalities in these contexts. For each stage of application, we summarize the methodologies, identify key challenges, and explore potential future directions. To demonstrate the effectiveness of LLM-enhanced digital twins, we present an LLM-enhanced enterprise digital twin system, which enables automatic modeling and optimization of an enterprise. Finally, we discuss future opportunities and challenges in advancing LLM-enhanced digital twins, offering valuable insights for researchers and practitioners in related fields.
MedHEval: Benchmarking Hallucinations and Mitigation Strategies in Medical Large Vision-Language Models
2025-03-04
Aofei Chang, Le Huang, Parminder Bhatia, Taha Kass-Hout, Fenglong Ma, Cao Xiao
Large Vision Language Models (LVLMs) are becoming increasingly important in the medical domain, yet Medical LVLMs (Med-LVLMs) frequently generate hallucinations due to limited expertise and the complexity of medical applications. Existing benchmarks fail to effectively evaluate hallucinations based on their underlying causes and lack assessments of mitigation strategies. To address this gap, we introduce MedHEval, a novel benchmark that systematically evaluates hallucinations and mitigation strategies in Med-LVLMs by categorizing them into three underlying causes: visual misinterpretation, knowledge deficiency, and context misalignment. We construct a diverse set of close- and open-ended medical VQA datasets with comprehensive evaluation metrics to assess these hallucination types. We conduct extensive experiments across 11 popular (Med)-LVLMs and evaluate 7 state-of-the-art hallucination mitigation techniques. Results reveal that Med-LVLMs struggle with hallucinations arising from different causes while existing mitigation methods show limited effectiveness, especially for knowledge- and context-based errors. These findings underscore the need for improved alignment training and specialized mitigation strategies to enhance Med-LVLMs' reliability. MedHEval establishes a standardized framework for evaluating and mitigating medical hallucinations, guiding the development of more trustworthy Med-LVLMs.
Nonconvex optimization and convergence of stochastic gradient descent, and solution of asynchronous game
2025-03-04
Kevin Buck, Jessica Babyak, Paolo Piersanti, Kevin Zumbrun, Christiane Gallos, Dorothea Gallos
We review convergence and behavior of stochastic gradient descent for convex and nonconvex optimization, establishing various conditions for convergence to zero of the variance of the gradient of the objective function, and presenting a number of simple examples demonstrating the approximate evolution of the probability density under iteration, including applications to both classical two-player and asynchronous multiplayer games
2025-03-04
Elisa Baldazzi, Pietro Biroli, Marina Della Giusta, Florent Dubois
Reliance on stereotypes is a persistent feature of human decision-making and has been extensively documented in educational settings, where it can shape students' confidence, performance, and long-term human capital accumulation. While effective techniques exist to mitigate these negative effects, a crucial first step is to establish whether teachers can recognize stereotypes in their professional environment. We introduce the Stereotype Identification Test (SIT), a novel survey tool that asks teachers to evaluate and comment on the presence of stereotypes in images randomly drawn from school textbooks. Their responses are systematically linked to established measures of implicit bias (Implicit Association Test, IAT) and explicit bias (survey scales on teaching stereotypes and social values). Our findings demonstrate that the SIT is a valid and reliable measure of stereotype recognition. Teachers' ability to recognize stereotypes is linked to trainable traits such as implicit bias awareness and inclusive teaching practices. Moreover, providing personalized feedback on implicit bias improves SIT scores by 0.25 standard deviations, reinforcing the idea that stereotype recognition is malleable and can be enhanced through targeted interventions.
Malware Classification from Memory Dumps Using Machine Learning, Transformers, and Large Language Models
2025-03-04
Areej Dweib, Montaser Tanina, Shehab Alawi, Mohammad Dyab, Huthaifa I. Ashqar
This study investigates the performance of various classification models for a malware classification task using different feature sets and data configurations. Six models-Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees, Random Forest (RF), and Extreme Gradient Boosting (XGB)-were evaluated alongside two deep learning models, Recurrent Neural Networks (RNN) and Transformers, as well as the Gemini zero-shot and few-shot learning methods. Four feature sets were tested including All Features, Literature Review Features, the Top 45 Features from RF, and Down-Sampled with Top 45 Features. XGB achieved the highest accuracy of 87.42% using the Top 45 Features, outperforming all other models. RF followed closely with 87.23% accuracy on the same feature set. In contrast, deep learning models underperformed, with RNN achieving 66.71% accuracy and Transformers reaching 71.59%. Down-sampling reduced performance across all models, with XGB dropping to 81.31%. Gemini zero-shot and few-shot learning approaches showed the lowest performance, with accuracies of 40.65% and 48.65%, respectively. The results highlight the importance of feature selection in improving model performance while reducing computational complexity. Traditional models like XGB and RF demonstrated superior performance, while deep learning and few-shot methods struggled to match their accuracy. This study underscores the effectiveness of traditional machine learning models for structured datasets and provides a foundation for future research into hybrid approaches and larger datasets.
Observation of the charged-particle multiplicity dependence of $σ_{ψ(2S)}/σ_{\text{J}/ψ}$ in pPb collisions at 8.16 TeV
2025-03-04
CMS Collaboration
Bound states of charm and anticharm quarks, known as charmonia, have a rich spectroscopic structure that can be used to probe the dynamics of hadron production in high-energy hadron collisions. Here, the cross section ratio of excited ($\psi$(2S)) and ground state (J/$\psi$) vector mesons is measured as a function of the charged-particle multiplicity in proton-lead (pPb) collisions at a center-of-mass (CM) energy per nucleon pair of 8.16 TeV. The data corresponding to an integrated luminosity of 175 nb$^{-1}$ were collected using the CMS detector. The ratio is measured separately for prompt and nonprompt charmonia in the transverse momentum range 6.5 $\lt$ $p_\text{T}$ $\lt$ 30 GeV and in four rapidity ranges spanning $-$2.865 $\lt$ $y_\text{CM}$ $\lt$ 1.935. For the first time, a statistically significant multiplicity dependence of the prompt cross section ratio is observed in proton-nucleus collisions. There is no clear rapidity dependence in the ratio. The prompt measurements are compared with a theoretical model which includes interactions with nearby particles during the evolution of the system. These results provide additional constraints on hadronization models of heavy quarks in nuclear collisions.
Adopt a PET! An Exploration of PETs, Policy, and Practicalities for Industry in Canada
2025-03-04
Masoumeh Shafieinejad, Xi He, Bailey Kacsmar
Privacy enhancing technologies (PETs) are technical solutions for privacy issues that exist in our digital society. Despite increased privacy challenges and a corresponding increase in new regulations being proposed by governments across the globe, a low adoption rate of PETs persists. In this work, we investigate the relationship that new privacy regulations have on industry's decision-making processes as well as the extent to which privacy regulations inspire the adoption of PETs. We conducted a qualitative survey study with 22 industry participants from across Canada to investigate how businesses in Canada make decisions to adopt novel technologies and how new privacy regulations impact their business processes. Through this study, we identify the breadth of approaches employed by organizations considering PETs and the challenges they face in their efforts to ensure compliance with all pertinent laws and regulations. We further identify a gap between how companies think of privacy technologies and how researchers think of privacy technologies that can contribute to low adoption of the increasingly sophisticated privacy technologies produced by researchers, such as applications of differential privacy, multiparty computation, and trusted execution environments. Informed by the results of our analysis, we make recommendations for industry, researchers, and policymakers on how to support what each of them seeks from the other when attempting to improve digital privacy protections. By advancing our understanding of what challenges industry faces in ensuring compliance with novel and existing privacy regulations, we increase the effectiveness of future privacy research that aims to help overcome these issues.
This paper investigates an integrated sensing and communication system where the base station serves multiple downlink users, while employing a passive reconfigurable intelligent surface to detect small, noncooperative airborne targets. We propose a method to design the two-way beampattern of the RIS-assisted monostatic radar, which allows controlling the sidelobe levels in the presence of eavesdroppers, jammers, and other scattering objects and avoiding any radar interference to the users. To obtain more favorable system tradeoffs, we exploit the correlation of the target echoes over consecutive scans by resorting to a multi-frame radar detector, which includes a detector, a plot-extractor, and a track-before-detect processor. A numerical analysis is provided to verify the effectiveness of the proposed solutions and to assess the achievable tradeoffs. Our results show that, by increasing the number of scans processed by the radar detector (and therefore its implementation complexity), we can reduce the amount of power dedicated to the radar function while maintaining the same sensing performance (measured in terms of probability of target detection and root mean square error in the estimation of target position); this excess power can be reused to increase the user sum-rate.
Objestures: Bimanual Interactions with Everyday Objects and Mid-Air Gestures in Mixed Reality
2025-03-04
Zhuoyue Lyu, Per Ola Kristensson
Everyday objects have been explored as input devices, but their intended functionality is compromised when these objects are absent or unsuitable. Mid-air gestures are convenient, but lack haptic feedback. Combining both can be beneficial, yet existing work lacks systematic exploration. We address this by proposing a bimanual interaction design space for everyday objects and mid-air gestures, with a functional prototype using only hand tracking in mixed reality headsets. Study~1 with 12 participants on common 3D manipulations (Rotation and Scaling) showed that our approach was significantly more accurate, required less arm movement, and had no significant differences in task completion time or user experience compared to free-hand manipulations. Study~2 with the same group on real-life applications (Sound, Draw, and Shadow) found our approach intuitive, engaging, expressive, with interest in everyday use. We identified 30 potential applications across various fields, including everyday tasks, creative arts, education, healthcare, engineering, and gaming, and discussed the limitations and implications of our work.
InfiniSST: Simultaneous Translation of Unbounded Speech with Large Language Model
2025-03-04
Siqi Ouyang, Xi Xu, Lei Li
Simultaneous translation of unbounded streaming speech remains a challenging problem due to the need for effectively processing the history speech context and past translations so that quality and latency, including computation overhead, can be balanced. Most prior works assume pre-segmented speech, limiting their real-world applicability. In this paper, we propose InfiniSST, a novel approach that formulates SST as a multi-turn dialogue task, enabling seamless translation of unbounded speech. We construct translation trajectories and robust segments from MuST-C with multi-latency augmentation during training and develop a key-value (KV) cache management strategy to facilitate efficient inference. Experiments on MuST-C En-Es, En-De, and En-Zh demonstrate that InfiniSST reduces computation-aware latency by 0.5 to 1 second while maintaining the same translation quality compared to baselines. Ablation studies further validate the contributions of our data construction and cache management strategy. We release the code at https://github.com/LeiLiLab/InfiniSST
With the rise of deep learning, there is a fundamental change in visual SLAM algorithms toward developing different modules trained as end-to-end pipelines. However, regardless of the implementation domain, visual SLAM's performance is subject to diverse environmental challenges, such as dynamic elements in outdoor environments, harsh imaging conditions in underwater environments, or blurriness in high-speed setups. These environmental challenges need to be identified to study the real-world viability of SLAM implementations. Motivated by the aforementioned challenges, this paper surveys the current state of visual SLAM algorithms according to the two main frameworks: geometry-based and learning-based SLAM. First, we introduce a general formulation of the SLAM pipeline that includes most of the implementations in the literature. Second, those implementations are classified and surveyed for geometry and learning-based SLAM. After that, environment-specific challenges are formulated to enable experimental evaluation of the resilience of different visual SLAM classes to varying imaging conditions. We address two significant issues in surveying visual SLAM, providing (1) a consistent classification of visual SLAM pipelines and (2) a robust evaluation of their performance under different deployment conditions. Finally, we give our take on future opportunities for visual SLAM implementations.
Spectroscopic AGN survey at $z$ $\sim$ 2 with NTT/SOFI for GRAVITY+ observations
2025-03-04
D. J. D. Santos, T. Shimizu, R. Davies, Y. Cao, J. Dexter, P. T. de Zeeuw, F. Eisenhauer, N. M. Förster-Schreiber, H. Feuchtgruber, R. Genzel, S. Gillessen, L. Kuhn, D. Lutz, T. Ott, S. Rabien, J. Shangguan, E. Sturm, L. J. Tacconi
With the advent of GRAVITY+, the upgrade to the beam combiner GRAVITY at the Very Large Telescope Interferometer (VLTI), fainter and higher redshift active galactic nuclei (AGNs) are becoming observable, opening an unprecedented opportunity to further our understanding of the cosmic coevolution of supermassive black holes and their host galaxies. To identify an initial sample of high-redshift type~1 AGNs that can be observed with GRAVITY+, we have obtained spectroscopic data with NTT/SOFI of the most promising candidates. Our goal is to measure their broad line region (BLR) fluxes and assess their physical geometries by analysing the spectral profiles of their Balmer lines. We present 29 $z$ $\sim$ 2 targets with strong H$\alpha$ emission in the $K$-band. Their line profiles are strongly non-Gaussian, with a narrow core and broad wings. This can be explained as a combination of rotation and turbulence contributing to the total profile or two physically distinct inner and outer regions. We find small H$\alpha$ virial factors, which we attribute to the low full-width-half-maximum (FWHM)/$\sigma$ ratios of their non-Gaussian profiles, noting that this can lead to discrepancies in black hole masses derived from scaling relations. We also find two targets that show tentative evidence of BLRs dominated by radial motions. Lastly, we estimate the expected differential phase signals that will be seen with GRAVITY+, which will provide guidance for the observing strategy that will be adopted.
Applications of Entropy in Data Analysis and Machine Learning: A Review
2025-03-04
Salomé A. Sepúveda Fontaine, José M. Amigó
Since its origin in the thermodynamics of the 19th century, the concept of entropy has also permeated other fields of physics and mathematics, such as Classical and Quantum Statistical Mechanics, Information Theory, Probability Theory, Ergodic Theory and the Theory of Dynamical Systems. Specifically, we are referring to the classical entropies: the Boltzmann-Gibbs, von Neumann, Shannon, Kolmogorov-Sinai and topological entropies. In addition to their common name, which is historically justified (as we briefly describe in this review), other commonality of the classical entropies is the important role that they have played and are still playing in the theory and applications of their respective fields and beyond. Therefore, it is not surprising that, in the course of time, many other instances of the overarching concept of entropy have been proposed, most of them tailored to specific purposes. Following the current usage, we will refer to all of them, whether classical or new, simply as entropies. Precisely, the subject of this review is their applications in data analysis and machine learning. The reason for these particular applications is that entropies are very well suited to characterize probability mass distributions, typically generated by finite-state processes or symbolized signals. Therefore, we will focus on entropies defined as positive functionals on probability mass distributions and provide an axiomatic characterization that goes back to Shannon and Khinchin. Given the plethora of entropies in the literature, we have selected a representative group, including the classical ones. The applications summarized in this review finely illustrate the power and versatility of entropy in data analysis and machine learning.
Monocular Person Localization under Camera Ego-motion
2025-03-04
Yu Zhan, Hanjing Ye, Hong Zhang
Localizing a person from a moving monocular camera is critical for Human-Robot Interaction (HRI). To estimate the 3D human position from a 2D image, existing methods either depend on the geometric assumption of a fixed camera or use a position regression model trained on datasets containing little camera ego-motion. These methods are vulnerable to fierce camera ego-motion, resulting in inaccurate person localization. We consider person localization as a part of a pose estimation problem. By representing a human with a four-point model, our method jointly estimates the 2D camera attitude and the person's 3D location through optimization. Evaluations on both public datasets and real robot experiments demonstrate our method outperforms baselines in person localization accuracy. Our method is further implemented into a person-following system and deployed on an agile quadruped robot.
Characterization of the Deep, Extended Kuiper Belt in the Galactic Disk
2025-03-04
Susan D. Benecchi, Simon B. Porter, Anne J. Verbiscer, David W. Gerdes, Wesley C. Fraser, Lowell Peltier, JJ Kavelaars, Marc W. Buie, S. Alan Stern, Tsuyoshi Terai, Takashi Ito, Fumi Yoshida, Darin Ragozzine, Bryan Holler
We propose a Roman Space Telescope survey to investigate fundamental properties of the distant solar system in the region of the Kuiper Belt where object characteristics and the size distribution are inaccessible from any other telescope. Our pointing is coincident with the search space accessible to NASA's New Horizons spacecraft meaning, that a discovered object sufficiently near the orbit of New Horizons would potentially be investigated by a close flyby. In addition, numerous objects expected to be discovered by this search can be observed in the distance by New Horizons allowing their surface properties and satellite systems to both be probed. As designed, this survey will discover and determine orbits for as many as 900 Kuiper Belt objects (KBOs), providing a unique opportunity for ground-breaking Kuiper Belt science. It will simultaneously: (1) Probe and characterize the deep Kuiper Belt by identifying objects as small as a few km and taking our understanding of the size distribution to a new level. This has implications for understanding the the standard model (the Streaming Instability) of KBO formation and elucidating crater formation physics on these icy bodies. (2) Open KBO rotation studies, in particular of those objects with long rotation periods,(3) Discover and characterize KBO binaries at large distances, important because their duplicity offers information about object densities at these distant locations from the Sun. (4) Shed light on the cratering history of KBOs and improving the dating of the surfaces of Arrokoth, Pluto and Charon in addition to helping to place the 32 distant KBOs New Horizons has observed in context. This project also has synergies with transiting exoplanet studies due to the stellar density of our search fields. Coupled with our timing requirements it is sensitive to discovery of hot Jupiters and hot Neptunes.
Call for Rigor in Reporting Quality of Instruction Tuning Data
2025-03-04
Hyeonseok Moon, Jaehyung Seo, Heuiseok Lim
Instruction tuning is crucial for adapting large language models (LLMs) to align with user intentions. Numerous studies emphasize the significance of the quality of instruction tuning (IT) data, revealing a strong correlation between IT data quality and the alignment performance of LLMs. In these studies, the quality of IT data is typically assessed by evaluating the performance of LLMs trained with that data. However, we identified a prevalent issue in such practice: hyperparameters for training models are often selected arbitrarily without adequate justification. We observed significant variations in hyperparameters applied across different studies, even when training the same model with the same data. In this study, we demonstrate the potential problems arising from this practice and emphasize the need for careful consideration in verifying data quality. Through our experiments on the quality of LIMA data and a selected set of 1,000 Alpaca data points, we demonstrate that arbitrary hyperparameter decisions can make any arbitrary conclusion.
Towards Effective and Efficient Context-aware Nucleus Detection in Histopathology Whole Slide Images
2025-03-04
Zhongyi Shui, Ruizhe Guo, Honglin Li, Yuxuan Sun, Yunlong Zhang, Chenglu Zhu, Jiatong Cai, Pingyi Chen, Yanzhou Su, Lin Yang
Nucleus detection in histopathology whole slide images (WSIs) is crucial for a broad spectrum of clinical applications. Current approaches for nucleus detection in gigapixel WSIs utilize a sliding window methodology, which overlooks boarder contextual information (eg, tissue structure) and easily leads to inaccurate predictions. To address this problem, recent studies additionally crops a large Filed-of-View (FoV) region around each sliding window to extract contextual features. However, such methods substantially increases the inference latency. In this paper, we propose an effective and efficient context-aware nucleus detection algorithm. Specifically, instead of leveraging large FoV regions, we aggregate contextual clues from off-the-shelf features of historically visited sliding windows. This design greatly reduces computational overhead. Moreover, compared to large FoV regions at a low magnification, the sliding window patches have higher magnification and provide finer-grained tissue details, thereby enhancing the detection accuracy. To further improve the efficiency, we propose a grid pooling technique to compress dense feature maps of each patch into a few contextual tokens. Finally, we craft OCELOT-seg, the first benchmark dedicated to context-aware nucleus instance segmentation. Code, dataset, and model checkpoints will be available at https://github.com/windygoo/PathContext.
Structural Transitions and Melting of Two-Dimensional Ion Crystals in RF Traps
2025-03-03
Boris V. Pashinsky, Alexander Kato, Boris B. Blinov
We investigate the structural properties and melting behavior of two-dimensional ion crystals in an RF trap, focusing on the effects of ion temperature and trap potential symmetry. We identify distinct crystal structures that form under varying trapping conditions and temperatures through experimental observations and theoretical analyses. As the temperature increases or the trap potential becomes more symmetric, we observe a transition from a lattice arrangement to elongated ring-like formations aligned along the trap axes. Our experimental and theoretical efforts enhance our understanding of phase transitions in low-dimensional, confined systems, offering insights into the controlled formation of quantum crystals for applications in quantum simulations and many-body physics.
Correcting Mode Proportion Bias in Generalized Bayesian Inference via a Weighted Kernel Stein Discrepancy
2025-03-03
Elham Afzali, Saman Muthukumarana, Liqun Wang
Generalized Bayesian Inference (GBI) provides a flexible framework for updating prior distributions using various loss functions instead of the traditional likelihoods, thereby enhancing the model robustness to model misspecification. However, GBI often suffers the problem associated with intractable likelihoods. Kernelized Stein Discrepancy (KSD), as utilized in a recent study, addresses this challenge by relying only on the gradient of the log-likelihood. Despite this innovation, KSD-Bayes suffers from critical pathologies, including insensitivity to well-separated modes in multimodal posteriors. To address this limitation, we propose a weighted KSD method that retains computational efficiency while effectively capturing multimodal structures. Our method improves the GBI framework for handling intractable multimodal posteriors while maintaining key theoretical properties such as posterior consistency and asymptotic normality. Experimental results demonstrate that our method substantially improves mode sensitivity compared to standard KSD-Bayes, while retaining robust performance in unimodal settings and in the presence of outliers.
Foundation models, first introduced in 2021, are large-scale pre-trained models (e.g., large language models (LLMs) and vision-language models (VLMs)) that learn from extensive unlabeled datasets through unsupervised methods, enabling them to excel in diverse downstream tasks. These models, like GPT, can be adapted to various applications such as question answering and visual understanding, outperforming task-specific AI models and earning their name due to broad applicability across fields. The development of biomedical foundation models marks a significant milestone in leveraging artificial intelligence (AI) to understand complex biological phenomena and advance medical research and practice. This survey explores the potential of foundation models across diverse domains within biomedical fields, including computational biology, drug discovery and development, clinical informatics, medical imaging, and public health. The purpose of this survey is to inspire ongoing research in the application of foundation models to health science.
Re-recognized universality of Kozai oscillation on three-body dynamics
2025-03-03
Masanori Iye, Takashi Ito
In 1962, Yoshihide Kozai reported his findings on the secular dynamics of asteroids moving in orbits with high inclination and eccentricity. In contrast to the classic understanding of the stability of planetary motion in the solar system, Kozai showed that asteroids can significantly change their orbital shape over a long timescale in an oscillatory manner between nearly circular orbits and highly elliptic orbits. An anti-correlated variation between orbital inclination and eccentricity characterizes this oscillation. The importance of Kozai's work in understanding the dynamical evolution of various systems was recognized decades later, including the fields of irregular satellites of planets, Oort Cloud, extrasolar planets, binary star systems, type Ia supernovae, planet climate, merging black hole systems, and so on.
Which Code Statements Implement Privacy Behaviors in Android Applications?
2025-03-03
Chia-Yi Su, Aakash Bansal, Vijayanta Jain, Sepideh Ghanavati, Sai Teja Peddinti, Collin McMillan
A "privacy behavior" in software is an action where the software uses personal information for a service or a feature, such as a website using location to provide content relevant to a user. Programmers are required by regulations or application stores to provide privacy notices and labels describing these privacy behaviors. Although many tools and research prototypes have been developed to help programmers generate these notices by analyzing the source code, these approaches are often fairly coarse-grained (i.e., at the level of whole methods or files, rather than at the statement level). But this is not necessarily how privacy behaviors exist in code. Privacy behaviors are embedded in specific statements in code. Current literature does not examine what statements programmers see as most important, how consistent these views are, or how to detect them. In this paper, we conduct an empirical study to examine which statements programmers view as most-related to privacy behaviors. We find that expression statements that make function calls are most associated with privacy behaviors, while the type of privacy label has little effect on the attributes of the selected statements. We then propose an approach to automatically detect these privacy-relevant statements by fine-tuning three large language models with the data from the study. We observe that the agreement between our approach and participants is comparable to or higher than an agreement between two participants. Our study and detection approach can help programmers understand which statements in code affect privacy in mobile applications.
Interactive Debugging and Steering of Multi-Agent AI Systems
2025-03-03
Will Epperson, Gagan Bansal, Victor Dibia, Adam Fourney, Jack Gerrits, Erkang Zhu, Saleema Amershi
Fully autonomous teams of LLM-powered AI agents are emerging that collaborate to perform complex tasks for users. What challenges do developers face when trying to build and debug these AI agent teams? In formative interviews with five AI agent developers, we identify core challenges: difficulty reviewing long agent conversations to localize errors, lack of support in current tools for interactive debugging, and the need for tool support to iterate on agent configuration. Based on these needs, we developed an interactive multi-agent debugging tool, AGDebugger, with a UI for browsing and sending messages, the ability to edit and reset prior agent messages, and an overview visualization for navigating complex message histories. In a two-part user study with 14 participants, we identify common user strategies for steering agents and highlight the importance of interactive message resets for debugging. Our studies deepen understanding of interfaces for debugging increasingly important agentic workflows.
The increasing reliance on AI-based security tools in Security Operations Centers (SOCs) has transformed threat detection and response, yet analysts frequently struggle with alert overload, false positives, and lack of contextual relevance. The inability to effectively analyze AI-generated security alerts lead to inefficiencies in incident response and reduces trust in automated decision-making. In this paper, we show results and analysis of our investigation of how SOC analysts navigate AI-based alerts, their challenges with current security tools, and how explainability (XAI) integrated into their security workflows has the potential to become an effective decision support. In this vein, we conducted an industry survey. Using the survey responses, we analyze how security analysts' process, retrieve, and prioritize alerts. Our findings indicate that most analysts have not yet adopted XAI-integrated tools, but they express high interest in attack attribution, confidence scores, and feature contribution explanations to improve interpretability, and triage efficiency. Based on our findings, we also propose practical design recommendations for XAI-enhanced security alert systems, enabling AI-based cybersecurity solutions to be more transparent, interpretable, and actionable.
USeR: A Web-based User Story eReviewer for Assisted Quality Optimizations
2025-03-03
Daniel Hallmann, Kerstin Jacob, Gerald Lüttgen, Ute Schmid, Rüdiger von der Weth
User stories are widely applied for conveying requirements within agile software development teams. Multiple user story quality guidelines exist, but authors like Product Owners in industry projects frequently fail to write high-quality user stories. This situation is exacerbated by the lack of tools for assessing user story quality. In this paper, we propose User Story eReviewer (USeR) a web-based tool that allows authors to determine and optimize user story quality. For developing USeR, we collected 77 potential quality metrics through literature review, practitioner sessions, and research group meetings and refined these to 34 applicable metrics through expert sessions. Finally, we derived algorithms for eight prioritized metrics using a literature review and research group meetings and implemented them with plain code and machine learning techniques. USeR offers a RESTful API and user interface for instant, consistent, and explainable user feedback supporting fast and easy quality optimizations. It has been empirically evaluated with an expert study using 100 user stories and four experts from two real-world agile software projects in the automotive and health sectors.
Probing the Gaia atmospheric parameters of stars in globular clusters
2025-03-03
Sergen Özdemir, John Eduard Martínez-Fernández, Rodolfo Smiljanic
Numerous stellar surveys have been or will provide photometric, astrometric, and spectroscopic data for a large number of stars in the Milky Way and neighbouring galaxies. Modern data processing tools and analysis methods are needed to deal with these data sets and obtain accurate and precise results. In this context, we are developing a new spectroscopic analysis pipeline based on the differential analysis method. The CHEmical Survey analysis System (CHESS) aims to automate the steps needed to obtain high-quality stellar parameters and abundances from large samples of spectra. To automatically identify the spectra of similar stars that are suitable for a differential analysis, CHESS first performs what we call a similarity analysis by directly using the observed spectra. This step of analysis uses unsupervised machine learning algorithms (such as dimensionality reduction methods). To validate the findings, we used atmospheric parameters from several catalogues (in particular those available in Gaia DR3). Alternatively, such a similarity analysis also serves as a consistency check of the atmospheric parameters in these catalogues. Here, we present our method for finding similar stars in globular clusters and the first preliminary results of their atmospheric parameters.
Interval Regression: A Comparative Study with Proposed Models
2025-03-03
Tung L Nguyen, Toby Dylan Hocking
Regression models are essential for a wide range of real-world applications. However, in practice, target values are not always precisely known; instead, they may be represented as intervals of acceptable values. This challenge has led to the development of Interval Regression models. In this study, we provide a comprehensive review of existing Interval Regression models and introduce alternative models for comparative analysis. Experiments are conducted on both real-world and synthetic datasets to offer a broad perspective on model performance. The results demonstrate that no single model is universally optimal, highlighting the importance of selecting the most suitable model for each specific scenario.
PAC in DESI. I. Galaxy Stellar Mass Function into the $10^{6}{\rm M}_{\odot}$ Frontier
2025-03-03
Kun Xu, Y. P. Jing, Shaun Cole, Carlos S. Frenk, Sownak Bose, Willem Elbers, Wenting Wang, Yirong Wang, Samuel Moore, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, A. de la Macorra, Arjun Dey, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, M. Ishak, T. Kisner, S. E. Koposov, M. Landriau, L. Le Guillou, R. Miquel, J. Moustakas, C. Poppett, F. Prada, I. Pérez-Ràfols, G. Rossi, E. Sanchez, D. Sprayberry, G. Tarlé, B. A. Weaver, H. Zou
The Photometric Objects Around Cosmic Webs (PAC) method integrates cosmological photometric and spectroscopic surveys, offering valuable insights into galaxy formation. PAC measures the excess surface density of photometric objects, $\bar{n}_2w_{\rm{p}}$, with specific physical properties around spectroscopic tracers. In this study, we improve the PAC method to make it more rigorous and eliminate the need for redshift bins. We apply the enhanced PAC method to the DESI Y1 BGS Bright spectroscopic sample and the deep DECaLS photometric sample, obtaining $\bar{n}_2w_{\rm{p}}$ measurements across the complete stellar mass range, from $10^{5.3}{\rm M}_{\odot}$ to $10^{11.5}{\rm M}_{\odot}$ for blue galaxies, and from $10^{6.3}{\rm M}_{\odot}$ to $10^{11.9}{\rm M}_{\odot}$ for red galaxies. We combine $\bar{n}_2w_{\rm{p}}$ with $w_{\rm{p}}$ measurements from the BGS sample, which is not necessarily complete in stellar mass. Assuming that galaxy bias is primarily determined by stellar mass and colour, we derive the galaxy stellar mass functions (GSMFs) down to $10^{5.3}{\rm M}_{\odot}$ for blue galaxies and $10^{6.3}{\rm M}_{\odot}$ for red galaxies, while also setting lower limits for smaller masses. The blue and red GSMFs are well described by single and double Schechter functions, respectively, with low-mass end slopes of $\alpha_{\rm{blue}}=-1.54^{+0.02}_{-0.02}$ and $\alpha_{\rm{red}}=-2.50^{+0.08}_{-0.08}$, resulting in the dominance of red galaxies below $10^{7.6}{\rm M}_{\odot}$. Stage-IV cosmological photometric surveys, capable of reaching 2-3 magnitudes deeper than DECaLS, present an opportunity to explore the entire galaxy population in the local universe with PAC. This advancement allows us to address critical questions regarding the nature of dark matter, the physics of reionization, and the formation of dwarf galaxies.
Sasaki-Einstein Geometry, GK Geometry and the AdS/CFT correspondence
2025-03-03
Jerome P. Gauntlett, Dario Martelli, James Sparks
We review various aspects of Sasaki-Einstein and GK geometry, emphasising their similarities, interconnections and significance for the AdS/CFT correspondence. In particular, we highlight the key role that physical considerations have played in formulating geometric extremization principles, which have been instrumental in both understanding the geometry and identifying the corresponding dual field theories.
Lost in the FoG: Pitfalls of Models for Large-Scale Hydrogen Distributions
2025-03-03
Calvin Osinga, Benedikt Diemer, Francisco Villaescusa-Navarro
Large-scale HI surveys and their cross-correlations with galaxy distributions have immense potential as cosmological probes. Interpreting these measurements requires theoretical models that must incorporate redshift-space distortions (RSDs), such as the Kaiser and fingers-of-God (FoG) effect, and differences in the tracer and matter distributions via the tracer bias. These effects are commonly approximated with assumptions that should be tested on simulated distributions. In this work, we use the hydrodynamical simulation suite IllustrisTNG to assess the performance of models of $z \leq 1$ HI auto and HI-galaxy cross-power spectra, finding that the models employed by recent observations introduce errors comparable to or exceeding their measurement uncertainties. In particular, neglecting FoG causes $\gtrsim 10\%$ deviations between the modeled and simulated power spectra at $k \gtrsim 0.1$ $h$ / Mpc, larger than assuming a constant bias which reaches the same error threshold at slightly smaller scales. However, even without these assumptions, models can still err by $\sim 10\%$ on relevant scales. These remaining errors arise from multiple RSD damping sources on HI clustering, which are not sufficiently described with a single FoG term. Overall, our results highlight the need for an improved understanding of RSDs to harness the capabilities of future measurements of HI distributions.
A projected complex Langevin sampling method for bosons in the canonical and microcanonical ensembles
2025-03-03
Ethan C. McGarrigle, Hector D. Ceniceros, Glenn H. Fredrickson
We introduce a projected complex Langevin (CL) numerical sampling method -- a fictitious Langevin dynamics scheme that uses numerical projection to sample a constrained stationary distribution with highly oscillatory character. Despite the complex-valued degrees of freedom and associated sign-problem, the projected CL method succeeds as a natural extension of real-valued projected Langevin processes. In the new proposed method, complex-valued Lagrange multipliers are determined to enforce constraints to machine precision at each iteration. To illustrate the efficacy of this approach, we adapt the projected CL method to sample coherent state quantum field theories describing interacting Bose gases, which are realized in modern cold-atom experiments. We apply projected CL to two scenarios with holomorphic constraints, the canonical and microcanonical ensembles, and show that projected CL reproduces the correct thermodynamic observables. We further observe improved numerical stability and accuracy at larger timesteps when compared to the previous state-of-the-art method for performing constrained CL sampling.
Using Collective Dialogues and AI to Find Common Ground Between Israeli and Palestinian Peacebuilders
2025-03-03
Andrew Konya, Luke Thorburn, Wasim Almasri, Oded Adomi Leshem, Ariel D. Procaccia, Lisa Schirch, Michiel A. Bakker
A growing body of work has shown that AI-assisted methods -- leveraging large language models (LLMs), social choice methods, and collective dialogues -- can help reduce polarization and foster common ground in controlled lab settings. But what can these approaches contribute in real-world contexts? We present a case study applying these techniques to find common ground between Israeli and Palestinian peacebuilders in the period following October 7th, 2023. From April to July 2024 an iterative deliberative process combining LLMs, bridging-based ranking, and collective dialogues was conducted in partnership with the Alliance for Middle East Peace. More than 100 civil society peacebuilders participated including Israeli Jews, Palestinian citizens of Israel, and Palestinians from the West Bank and Gaza. The process culminated in a set of collective statements, including joint demands to world leaders, with at least 84% agreement from participants on each side. In this paper we review the mechanics and implementation of the process, discuss results and learnings, and highlight open problems that warrant future work.
An 18-25 GHz spectroscopic survey of dense cores in the Chamaeleon I molecular cloud
2025-03-03
Dariusz C. Lis, William D. Langer, Jorge L. Pineda, Kahaan Gandhi, Karen Willacy, Paul F. Goldsmith, Susanna Widicus Weaver, Liton Majumdar, Youngmin Seo, Shinji Horiuchi, Cheikh Bop, François Lique
We extend the survey for organics in the southern hemisphere by observing two cores in the Chamaeleon complex using NASA's Deep Space Network 70-m antenna in Canberra, Australia, over the frequency range of 18 to 25 GHz. We surveyed the class 0 protostar Cha-MMS1 and the prestellar core Cha-C2, which represent two stages in the evolution of dense cores. We detect several molecules including HC$_3$N, HC$_5$N, C$_4$H, CCS, C$_3$S, NH$_3$, and c-C$_3$H$_2$. A longer cyanopolyyne, HC$_7$N, is detected with high confidence via spectral stacking analysis. While molecular column densities in the two Chamaeleon cores are typically an order of magnitude lower compared to the cynaopolyyne peak in TMC-1, the molecular abundance ratios are in general agreement with the TMC-1 values. The two exceptions are c-C$_3$H$_2$, which is enhanced by a factor of \about 25 with respect to cyanopolyynes in the Chamaeleon cores, and ammonia, which is enhanced by a factor of ~ 125. The deuterated species c-C$_3$HD is detected in both cores, with a high D/H ratio of ~0.23 in c-C$_3$H$_2$. A rare isotopologue of ammonia, $^{15}$NH$_3$, is also detected in Cha-MMS1 suggesting a high $^{14}$N/$^{15}$N ratio of ~ 690 in ammonia. However, this ratio may be artificially enhanced due to the high optical depth of the $^{14}$NH$_3$ (1,1) line, which increases the effective source size. We use the detections of ammonia, cyanopolyynes, and far-infrared dust continuum to characterize the density and temperature in the Chamaeleon cores and calculate the molecular column densities and their relative ratios. The ring molecule benzonitrile is not detected in either Chamaeleon core. The $3 \sigma$ upper limits for its column density are a factor of 2 higher than the value derived for TMC-1 and the upper limits for its relative abundance with respect to HC$_5$N are a factor of 3 higher than the TMC-1 value.
Building Safe GenAI Applications: An End-to-End Overview of Red Teaming for Large Language Models
2025-03-03
Alberto Purpura, Sahil Wadhwa, Jesse Zymet, Akshay Gupta, Andy Luo, Melissa Kazemi Rad, Swapnil Shinde, Mohammad Shahed Sorower
The rapid growth of Large Language Models (LLMs) presents significant privacy, security, and ethical concerns. While much research has proposed methods for defending LLM systems against misuse by malicious actors, researchers have recently complemented these efforts with an offensive approach that involves red teaming, i.e., proactively attacking LLMs with the purpose of identifying their vulnerabilities. This paper provides a concise and practical overview of the LLM red teaming literature, structured so as to describe a multi-component system end-to-end. To motivate red teaming we survey the initial safety needs of some high-profile LLMs, and then dive into the different components of a red teaming system as well as software packages for implementing them. We cover various attack methods, strategies for attack-success evaluation, metrics for assessing experiment outcomes, as well as a host of other considerations. Our survey will be useful for any reader who wants to rapidly obtain a grasp of the major red teaming concepts for their own use in practical applications.
FLAME: A Federated Learning Benchmark for Robotic Manipulation
2025-03-03
Santiago Bou Betran, Alberta Longhini, Miguel Vasco, Yuchong Zhang, Danica Kragic
Recent progress in robotic manipulation has been fueled by large-scale datasets collected across diverse environments. Training robotic manipulation policies on these datasets is traditionally performed in a centralized manner, raising concerns regarding scalability, adaptability, and data privacy. While federated learning enables decentralized, privacy-preserving training, its application to robotic manipulation remains largely unexplored. We introduce FLAME (Federated Learning Across Manipulation Environments), the first benchmark designed for federated learning in robotic manipulation. FLAME consists of: (i) a set of large-scale datasets of over 160,000 expert demonstrations of multiple manipulation tasks, collected across a wide range of simulated environments; (ii) a training and evaluation framework for robotic policy learning in a federated setting. We evaluate standard federated learning algorithms in FLAME, showing their potential for distributed policy learning and highlighting key challenges. Our benchmark establishes a foundation for scalable, adaptive, and privacy-aware robotic learning.
Human readers can efficiently comprehend scrambled words, a phenomenon known as Typoglycemia, primarily by relying on word form; if word form alone is insufficient, they further utilize contextual cues for interpretation. While advanced large language models (LLMs) exhibit similar abilities, the underlying mechanisms remain unclear. To investigate this, we conduct controlled experiments to analyze the roles of word form and contextual information in semantic reconstruction and examine LLM attention patterns. Specifically, we first propose SemRecScore, a reliable metric to quantify the degree of semantic reconstruction, and validate its effectiveness. Using this metric, we study how word form and contextual information influence LLMs' semantic reconstruction ability, identifying word form as the core factor in this process. Furthermore, we analyze how LLMs utilize word form and find that they rely on specialized attention heads to extract and process word form information, with this mechanism remaining stable across varying levels of word scrambling. This distinction between LLMs' fixed attention patterns primarily focused on word form and human readers' adaptive strategy in balancing word form and contextual information provides insights into enhancing LLM performance by incorporating human-like, context-aware mechanisms.
MAPS: Motivation-Aware Personalized Search via LLM-Driven Consultation Alignment
2025-03-03
Weicong Qin, Yi Xu, Weijie Yu, Chenglei Shen, Ming He, Jianping Fan, Xiao Zhang, Jun Xu
Personalized product search aims to retrieve and rank items that match users' preferences and search intent. Despite their effectiveness, existing approaches typically assume that users' query fully captures their real motivation. However, our analysis of a real-world e-commerce platform reveals that users often engage in relevant consultations before searching, indicating they refine intents through consultations based on motivation and need. The implied motivation in consultations is a key enhancing factor for personalized search. This unexplored area comes with new challenges including aligning contextual motivations with concise queries, bridging the category-text gap, and filtering noise within sequence history. To address these, we propose a Motivation-Aware Personalized Search (MAPS) method. It embeds queries and consultations into a unified semantic space via LLMs, utilizes a Mixture of Attention Experts (MoAE) to prioritize critical semantics, and introduces dual alignment: (1) contrastive learning aligns consultations, reviews, and product features; (2) bidirectional attention integrates motivation-aware embeddings with user preferences. Extensive experiments on real and synthetic data show MAPS outperforms existing methods in both retrieval and ranking tasks.
Density functional theory has become the world's favorite electronic structure method, and is routinely applied to both materials and molecules. Here, we review recent attempts to use modern machine-learning to improve density functional approximations. Many different researchers have tried many different approaches, but some common themes and lessons have emerged. We discuss these trends and where they might bring us in the future.
Perceptual Motor Learning with Active Inference Framework for Robust Lateral Control
2025-03-03
Elahe Delavari, John Moore, Junho Hong, Jaerock Kwon
This paper presents a novel Perceptual Motor Learning (PML) framework integrated with Active Inference (AIF) to enhance lateral control in Highly Automated Vehicles (HAVs). PML, inspired by human motor learning, emphasizes the seamless integration of perception and action, enabling efficient decision-making in dynamic environments. Traditional autonomous driving approaches--including modular pipelines, imitation learning, and reinforcement learning--struggle with adaptability, generalization, and computational efficiency. In contrast, PML with AIF leverages a generative model to minimize prediction error ("surprise") and actively shape vehicle control based on learned perceptual-motor representations. Our approach unifies deep learning with active inference principles, allowing HAVs to perform lane-keeping maneuvers with minimal data and without extensive retraining across different environments. Extensive experiments in the CARLA simulator demonstrate that PML with AIF enhances adaptability without increasing computational overhead while achieving performance comparable to conventional methods. These findings highlight the potential of PML-driven active inference as a robust alternative for real-world autonomous driving applications.
Scanning HTML at Tens of Gigabytes per Second on ARM Processors
2025-03-03
Daniel Lemire
Modern processors have instructions to process 16 bytes or more at once. These instructions are called SIMD, for single instruction, multiple data. Recent advances have leveraged SIMD instructions to accelerate parsing of common Internet formats such as JSON and base64. During HTML parsing, they quickly identify specific characters with a strategy called vectorized classification. We review their techniques and compare them with a faster alternative. We measure a 20-fold performance improvement in HTML scanning compared to traditional methods on recent ARM processors. Our findings highlight the potential of SIMD-based algorithms for optimizing Web browser performance.
We review the development of understanding for the problem of ultraviolet divergences in supergravity. This history proceeds from initial constructions of counterterms invariant under the relevant degrees of local supersymmetry, through a deeper understanding of non-renormalisation theorems for the relevant degrees of ``off-shell'' linearly realisable supersymmetry, and on to current understanding of limitations on counterterm eligibility based on the duality symmetries of supergravity theories, as well as of the related structures emerging in superstring theory.
Is Triboelectricity Confusing, Confused or Complex?
2025-03-03
Karl P. Olson, Laurence D. Marks
In this report, we look at the fundamental physics of triboelectricity, charge transfer due to contact and sliding. While much of the report focuses upon recent advances such as the incorporation of flexoelectric contributions, we also include older work, some from centuries ago, which only now can be understood in a general sense. Basic concepts and theories ranging from elements of tribology and contact mechanics through semiconductor built-in potentials, electromechanical terms, mechanochemistry and trap states are briefly described, linking to established surface science and interface physics. We then overview the main models that have been proposed, showing that they all fall within conventional electrostatics combined with other established science. We conclude with some suggestions for the future. Based upon this overview, our conclusion is that triboelectricity is a slightly complex combination of standard electrostatic phenomena that can be understood using the generalized Ampere's law connecting the electric displacement field with both Coulomb and polarization contributions, and the free carrier density, that is Grad.D=rho. Triboelectricity may be confusing, it is not really confused if care is taken, but it is complex.
The Role of Deep Learning in Financial Asset Management: A Systematic Review
2025-03-03
Pedro Reis, Ana Paula Serra, João Gama
This review systematically examines deep learning applications in financial asset management. Unlike prior reviews, this study focuses on identifying emerging trends, such as the integration of explainable artificial intelligence (XAI) and deep reinforcement learning (DRL), and their transformative potential. It highlights new developments, including hybrid models (e.g., transformer-based architectures) and the growing use of alternative data sources such as ESG indicators and sentiment analysis. These advancements challenge traditional financial paradigms and set the stage for a deeper understanding of the evolving landscape. We use the Scopus database to select the most relevant articles published from 2018 to 2023. The inclusion criteria encompassed articles that explicitly apply deep learning models within financial asset management. We excluded studies focused on physical assets. This review also outlines our methodology for evaluating the relevance and impact of the included studies, including data sources and analytical methods. Our search identified 934 articles, with 612 meeting the inclusion criteria based on their focus and methodology. The synthesis of results from these articles provides insights into the effectiveness of deep learning models in improving portfolio performance and price forecasting accuracy. The review highlights the broad applicability and potential enhancements deep learning offers to financial asset management. Despite some limitations due to the scope of model application and variation in methodological rigour, the overall evidence supports deep learning as a valuable tool in this field. Our systematic review underscores the progressive integration of deep learning in financial asset management, suggesting a trajectory towards more sophisticated and impactful applications.
SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models
2025-03-03
Cansu Sancaktar, Christian Gumbsch, Andrii Zadaianchuk, Pavel Kolev, Georg Martius
Exploration is a cornerstone of reinforcement learning (RL). Intrinsic motivation attempts to decouple exploration from external, task-based rewards. However, established approaches to intrinsic motivation that follow general principles such as information gain, often only uncover low-level interactions. In contrast, children's play suggests that they engage in meaningful high-level behavior by imitating or interacting with their caregivers. Recent work has focused on using foundation models to inject these semantic biases into exploration. However, these methods often rely on unrealistic assumptions, such as language-embedded environments or access to high-level actions. We propose SEmaNtically Sensible ExploratIon (SENSEI), a framework to equip model-based RL agents with an intrinsic motivation for semantically meaningful behavior. SENSEI distills a reward signal of interestingness from Vision Language Model (VLM) annotations, enabling an agent to predict these rewards through a world model. Using model-based RL, SENSEI trains an exploration policy that jointly maximizes semantic rewards and uncertainty. We show that in both robotic and video game-like simulations SENSEI discovers a variety of meaningful behaviors from image observations and low-level actions. SENSEI provides a general tool for learning from foundation model feedback, a crucial research direction, as VLMs become more powerful.
CANUCS/Technicolor: JWST Medium Band Photometry Finds Half of the Star Formation at $z>7.5$ is Obscured
2025-03-03
Nicholas Martis, Sunna Withers, Giordano Felicioni, Adam Muzzin, Maruša Bradač, Roberto Abraham, Yoshihisa Asada, Guillaume Desprez, Kartheik Iyer, Gael Noirot, Ghassan T. E. Sarrouh, Marcin Sawicki, Victoria Strait, Chris Willot, Naadiyah Jaggah, Jon Judež, Anishya Harshan, Danilo Marchesini, Vladan Markov, Rosa M. Mérida, Gregor Rihtaršič, Roberta Tripodi
We present a sample of 146 high-redshift ($z>7.5$) galaxies from the CANUCS/Technicolor surveys, showcasing photometry in every wide- and medium-band NIRCam filter in addition to ancillary HST data sampling $0.4-5 \mu m$ (22 JWST bands out of 29 bands total). Additionally, 48 ($33\%$) galaxies in our sample meet criteria to be classified as extreme emission line galaxies, 15 ($10\%$) of which are completely missed by typical dropout selections due to faint UV emission. By fitting the SEDs covering the rest-frame UV to optical at $z > 7.5$, we investigate the dust obscuration properties, giving an unbiased view of dust buildup in high-redshift galaxies free from spectroscopic follow-up selection effects. Dust attenuation correlates with stellar mass, but more strongly with star formation rate. We find typical galaxies at $z>7.5$ have $\sim 25 \%$ of their star formation obscured. However, since galaxies with higher star formation rates suffer more attenuation, $\sim 50 \%$ of the total star formation rate density at $7.5 15$, implying that epoch as when dust first forms in bright galaxies.
Guidelines for writing and reviewing software in computer algebra
2025-03-03
Jeroen Hanselman
The advent of computers has allowed mathematicians to do increasingly more difficult computations that used to be practically impossible. Peer reviewers will seldom look at any code attached to a math paper, however. In this article, we advocate for a software peer-reviewing process and will discuss some best practices for reviewers on how to peer review mathematical papers with code and providing guidelines for authors to improve the code that accompanies their mathematical paper.
Flat-band materials have garnered extensive attention due to their captivating properties associated with strong correlation effects. While flat bands have been discovered in several types of 2D materials, their existence in 1D systems remains elusive. Here, we propose a 1D frustrated lattice, specifically the 1D zigzag lattice, as a platform for hosting flat bands. This lattice can be experimentally realized by growing CuTe chains on Cu(111). The presence of flat bands was confirmed by tight-binding model analysis, first-principles calculations, and angle-resolved photoemission spectroscopy measurements. In addition, we discovered a temperature-driven phase transition at approximately 250 K. Detailed analyses demonstrate that the system has a Tomonaga-Luttinger liquid behavior, accompanied by spin-charge separation effects. Our work unveils new prospects for investigating strongly correlated electron behaviors and topological properties in the 1D limit.
Gaia: Ten Years of Surveying the Milky Way and Beyond
2025-03-03
Anthony G. A. Brown
On January 15 2025, the Gaia mission completed the collection of the astrometric, photometric, and spectroscopic data for about 2.5 billion celestial sources, from the solar system to the Milky Way to the distant universe. Work is ongoing to produce Gaia DR4 based on the first 5.5 years of data, with the release expected in 2026. The full 10.5 year survey will be turned into Giaa DR5 which will open up scientific possibilities beyond Gaia DR4}. In this contribution I give a brief overview of the Gaia mission, summarize results from the GaiaUnlimited project, provide a glimpse of what is to come in Gaia DR4, and summarize the new science opportunities that Gaia DR5 will bring. I close with a look ahead at the successor to Gaia, the GaiaNIR mission, which will survey the Milky Way in the infrared, thus probing the Galactic ecosystem in the regions hidden to the Gaia mission.
Trigonometric derivatives are fundamental in both mathematics and physics, yet their proper application, particularly the distinction between radians and degrees, poses a significant challenge for college students globally. This study identifies a widespread "blind spot" in understanding trigonometric derivatives and their implications for physical systems, highlighting a critical gap in physics education. A multinational survey of 769 college students, primarily undergraduate and graduate STEM majors, from Israel, the United States, China, and India assessed their ability to differentiate between radians and degrees in mathematical and physical contexts, focusing on harmonic motion. Results reveal that only 26.3\% of students correctly identified that the well-known expressions for trigonometric derivatives hold exclusively in radians, while 70.7\% incorrectly assumed both radians and degrees are valid. Notably, students demonstrated improved recognition of radians in physical contexts (59.0\% correct responses) compared to mathematical ones, suggesting that students rely on familiar physical equations as cognitive reference points when applying mathematical concepts. These misunderstandings appear worldwide, suggesting a universal challenge. The findings highlight the need for curriculum reforms to better connect mathematical formalism with physical application.
Evaluation and Facilitation of Online Discussions in the LLM Era: A Survey
2025-03-03
Katerina Korre, Dimitris Tsirmpas, Nikos Gkoumas, Emma Cabalé, Dionysis Kontarinis, Danai Myrtzani, Theodoros Evgeniou, Ion Androutsopoulos, John Pavlopoulos
We present a survey of methods for assessing and enhancing the quality of online discussions, focusing on the potential of Large Language Models (LLMs). While online discourses aim, at least in theory, to foster mutual understanding, they often devolve into harmful exchanges, such as hate speech, threatening social cohesion and democratic values. Recent advancements in LLMs enable facilitation agents that not only moderate content, but also actively improve the quality of interactions. Our survey synthesizes ideas from Natural Language Processing (NLP) and Social Sciences to provide (a) a new taxonomy on discussion quality evaluation, (b) an overview of intervention and facilitation strategies, along with a new taxonomy on conversation facilitation datasets, (c) an LLM-oriented roadmap of good practices and future research directions, from technological and societal perspectives.
Compare different SG-Schemes based on large least square problems
2025-03-03
Ramkrishna Acharya
This study reviews popular stochastic gradient-based schemes based on large least-square problems. These schemes, often called optimizers in machine learning, play a crucial role in finding better model parameters. Hence, this study focuses on viewing such optimizers with different hyper-parameters and analyzing them based on least square problems. Codes that produced results in this work are available on https://github.com/q-viper/gradients-based-methods-on-large-least-square.
Lossy Neural Compression for Geospatial Analytics: A Review
2025-03-03
Carlos Gomes, Isabelle Wittmann, Damien Robert, Johannes Jakubik, Tim Reichelt, Michele Martone, Stefano Maurogiovanni, Rikard Vinge, Jonas Hurst, Erik Scheurer, Rocco Sedona, Thomas Brunschwiler, Stefan Kesselheim, Matej Batic, Philip Stier, Jan Dirk Wegner, Gabriele Cavallaro, Edzer Pebesma, Michael Marszalek, Miguel A Belenguer-Plomer, Kennedy Adriko, Paolo Fraccaro, Romeo Kienzler, Rania Briq, Sabrina Benassou, Michele Lazzarini, Conrad M Albrecht
Over the past decades, there has been an explosion in the amount of available Earth Observation (EO) data. The unprecedented coverage of the Earth's surface and atmosphere by satellite imagery has resulted in large volumes of data that must be transmitted to ground stations, stored in data centers, and distributed to end users. Modern Earth System Models (ESMs) face similar challenges, operating at high spatial and temporal resolutions, producing petabytes of data per simulated day. Data compression has gained relevance over the past decade, with neural compression (NC) emerging from deep learning and information theory, making EO data and ESM outputs ideal candidates due to their abundance of unlabeled data. In this review, we outline recent developments in NC applied to geospatial data. We introduce the fundamental concepts of NC including seminal works in its traditional applications to image and video compression domains with focus on lossy compression. We discuss the unique characteristics of EO and ESM data, contrasting them with "natural images", and explain the additional challenges and opportunities they present. Moreover, we review current applications of NC across various EO modalities and explore the limited efforts in ESM compression to date. The advent of self-supervised learning (SSL) and foundation models (FM) has advanced methods to efficiently distill representations from vast unlabeled data. We connect these developments to NC for EO, highlighting the similarities between the two fields and elaborate on the potential of transferring compressed feature representations for machine--to--machine communication. Based on insights drawn from this review, we devise future directions relevant to applications in EO and ESM.
Shape and spin state model of contact binary (388188) 2006 DP14 using combined radar and optical observations
2025-03-03
Richard E. Cannon, Agata Rozek, Marina Brozović, Petr Pravec, Colin Snodgrass, Michael W. Busch, James E. Robinson, Abbie Donaldson, Tanja Holc, Lance A. M. Benner, Shantanu Naidu, Peter Kušnirák, Daniel Gardener, Hana Kučáková, Elaha Khalouei, Joseph Pollock, Mariangela Bonavita, Petr Fatka, Kamil Hornoch, Sedighe Sajadian, Lara Alegre, Flavia Amadio, Michael I. Andersen, Valerio Bozza, Martin J. Burgdorf, Gabriele Columba, Martin Dominik, R. Figuera Jaimes, Tobias C. Hinse, Markus Hundertmark, Uffe G. Jørgensen, Penelope Longa-Peña, Nuno Peixinho, Markus Rabus, Sohrab Rahvar, Paolo Rota, Jesper Skottfelt, John Southworth, Jeremy Tregloan-Reed
Contact binaries are found throughout the solar system. The recent discovery of Selam, the satellite of MBA (152830) Dinkinesh, by the NASA LUCY mission has made it clear that the term `contact binary' covers a variety of different types of bi-modal mass distributions and formation mechanisms. Only by modelling more contact binaries can this population be properly understood. We determined a spin state and shape model for the Apollo group contact binary asteroid (388188) 2006 DP14 using ground-based optical and radar observations collected between 2014 and 2023. Radar delay-Doppler images and continuous wave spectra were collected over two days in February 2014, while 16 lightcurves in the Cousins R and SDSS-r filters were collected in 2014, 2022 and 2023. We modelled the spin state using convex inversion before using the SHAPE modelling software to include the radar observations in modelling concavities and the distinctive neck structure connecting the two lobes. We find a spin state with a period of $(5.7860\pm0.0001)$ hours and pole solution of $\lambda = (180\pm121)^\circ$ and $\beta = (-80\pm7)^\circ$ with morphology indicating a 520 m long bi-lobed shape. The model's asymmetrical bi-modal mass distribution resembles other small NEA contact binaries such as (85990) 1999 JV6 or (8567) 1996 HW1, which also feature a smaller `head' attached to a larger `body'. The final model features a crater on the larger lobe, similar to several other modelled contact binaries. The model's resolution is 25 m, comparable to that of the radar images used.
The shooting methods to solve 3D nonlinear strings assemblies
2025-03-03
Florian Surmont
This article presents an alternative approach to finite elements for modeling and analyzing 3D static mooring lines using string theory and the shooting method (SM) to solve two-point boundary value problems (TPBVPs) for 3D nonlinear static string equations with various boundary condition (BC) types relevant to offshore slender system assemblies.The two-point boundary value problem for nonlinear extensible elastic strings was formulated by incorporating arbitrary 3D external distributed loads that are not restricted to gravity alone. The TPBVP was reformulated based on the formalism of the shooting method. A multi-body/multi-shooting approach is proposed to handle multi-material segments and line assemblies. A formulation of the boundary conditions that allows the modeling of Dirichlet, Robin, and mixed boundary conditions representing the displacement, force, and combined force/displacement constraints is presented.Four validation cases are presented, comparing the results to analytical solutions: (1) a single catenary segment with ball-prismatic joint boundary conditions under several imposed forces, (2) a review of all possible boundary conditions for strings, including spring-based BCs, (3) the Velaria problem with nonlinear radial distributed load, and (4) a three-segment hanging configuration with different material properties connected by a buoy.The results demonstrate an accuracy under 10-9 in terms of absolute errors for both positions and tensions along the entire length of the mooring lines. The proposed method also provides error control through adaptive step integration. It demonstrates high accuracy in modeling complex 3D kinematics and configurations for mooring lines, while limiting the iterative problem size.The proposed method provides an efficient alternative to discretization-based techniques for analyzing static configurations of string kinematics slender systems with various end constraints, such as mooring lines and hawsers assemblies in offshore engineering, while maintaining simplicity in approach and implementation.
The concept of synthetic dimensions has emerged as a powerful framework in photonics and atomic physics, enabling the exploration of high-dimensional physics beyond conventional spatial constraints. Originally developed for quantum simulations in high dimensions, synthetic dimensions have since demonstrated advantages in designing novel Hamiltonians and manipulating quantum or optical states for exploring topological physics, and for applications in computing and information processing. Here we provide a comprehensive overview of progress in synthetic dimensions across photonic, atomic, and other physical platforms over the past decade. We showcase different approaches used to construct synthetic dimensions and highlight key physical phenomena enabled by the advantage of such a framework. By offering a unified perspective on developments in this field, we aim to provide insights into how synthetic dimensions can bridge fundamental physics and applied technologies, fostering interdisciplinary engagement in quantum simulation, atomic and photonic engineering, and information processing.
Research is a fundamental process driving the advancement of human civilization, yet it demands substantial time and effort from researchers. In recent years, the rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. To monitor relevant advancements, this paper presents a systematic review of the progress in this domain. Specifically, we organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. Hypothesis formulation involves knowledge synthesis and hypothesis generation. Hypothesis validation includes the verification of scientific claims, theorem proving, and experiment validation. Manuscript publication encompasses manuscript writing and the peer review process. Furthermore, we identify and discuss the current challenges faced in these areas, as well as potential future directions for research. Finally, we also offer a comprehensive overview of existing benchmarks and tools across various domains that support the integration of AI into the research process. We hope this paper serves as an introduction for beginners and fosters future research. Resources have been made publicly available at https://github.com/zkzhou126/AI-for-Research.
Digital Dybbuks and Virtual Golems: AI, Memory, and the Ethics of Holocaust Testimony
2025-03-03
Atay Kozlovski, Mykola Makhortykh
Advances in generative artificial intelligence (AI) have driven a growing effort to create digital duplicates. These semi-autonomous recreations of living and dead people can be used for many purposes. Some of these purposes include tutoring, coping with grief, and attending business meetings. However, the normative implications of digital duplicates remain obscure, particularly considering the possibility of them being applied to genocide memory and education. To address this gap, we examine normative possibilities and risks associated with the use of more advanced forms of generative AI-enhanced duplicates for transmitting Holocaust survivor testimonies. We first review the historical and contemporary uses of survivor testimonies. Then, we scrutinize the possible benefits of using digital duplicates in this context and apply the Minimally Viable Permissibility Principle (MVPP). The MVPP is an analytical framework for evaluating the risks of digital duplicates. It includes five core components: the need for authentic presence, consent, positive value, transparency, and harm-risk mitigation. Using MVPP, we identify potential harms digital duplicates might pose to different actors, including survivors, users, and developers. We also propose technical and socio-technical mitigation strategies to address these harms.
Analogs of the lower and upper central series in skew braces
2025-03-03
Cindy Tsang
Skew brace is a ring-like and group-like algebraic structure that was introduced in the study of set-theoretic solutions to the Yang-Baxter equation. In this paper, we shall give a survey on the left series, right series, socle series, and annihilator series of skew braces. They are analogs of the lower and upper central series of groups. Other than the well-known facts regarding these series, we shall also prove several new results about the relationship among their terms.
ASKAP observations of the radio shell in the composite supernova remnant G310.6-1.6
2025-03-03
Wenhui Jing, Jennifer L. West, Xiaohui Sun, Wasim Raja, Xianghua Li, Lingxiao Dang, Ping Zhou, Miroslav D. Filipovic, Andrew M. Hopkins, Roland Kothes, Sanja Lazarevic, Denis Leahy, Emil Lenc, Yik Ki Ma, Cameron L. Van Eck
We report the observations of the radio shell of the supernova remnant (SNR) G310.6-1.6 at 943 MHz from the Evolutionary Map of the Universe (EMU) and the Polarization Sky Survey of the Universe's Magnetism (POSSUM) surveys by using the Australian Square Kilometre Array Pathfinder (ASKAP). We detect polarized emission from the central pulsar wind nebula (PWN) with rotation measures varying from -696 rad m$^{-2}$ to -601 rad m$^{-2}$. We measure the integrated flux density of the shell to be 36.4 +/- 2.2 mJy at 943 MHz and derive a spectral index of -0.4 +/- 0.1 for the PWN and -0.7 +/- 0.3 for the SNR shell. From the combined radio and X-ray observations, the object can be identified as a supernova explosion of about 2500 yr ago with energy of about 1.3 x 10$^{50}$ erg, suggesting an ejected mass of about 10 M_sun. The circular radio shell outside the circular hard X-ray shell is unique among Galactic SNRs. We discuss several possible scenarios, including blast wave, reverse shock, and pulsar-fed emission, but find that none of them can fully explain the observed characteristics of the shell. This poses a challenge for understanding the evolution of SNRs. The results of this paper demonstrate the potential of the ASKAP EMU and POSSUM surveys in discovering more objects of small angular size and low surface brightness.
Understanding the formation of Saturn's regular moons in the context of giant planet moons formation scenarios
2025-03-03
Michel Blanc, Aurélien Crida, Yuhito Shibaike, Sebastien Charnoz, Maryame El Moutamid, Paul Estrada, Olivier Mousis, Julien Salmon, Antoine Schneeberger, Pierre Vernazza
This article explores the different formation scenarios of the Kronian moons system in the context of a highly dissipative Saturn, with the objective of identifying the most likely of these scenarios. First, we review the diversity of objects - moons and rings - orbiting solar system giant planets, and the diversity of their architectures, which formation scenarios must reproduce. We then identify in this broader context the specific features of the Saturn system, such as the particularly large spectrum of its moon masses, the uniqueness of Titan and the presence of both dense and tenuous rings, before discussing the applicability of the different giant planet moon formation scenarios to the Saturn case. We discuss each of the most relevant scenarios and their respective merits. Finally, we tentatively propose a "favorite" scenario and we identify the key observations to be made by future space missions and/or Earth-based telescopic observations to validate this scenario or possibly alternative ones.
Composed Multi-modal Retrieval: A Survey of Approaches and Applications
2025-03-03
Kun Zhang, Jingyu Li, Zhe Li, Jingjing Zhang
With the rapid growth of multi-modal data from social media, short video platforms, and e-commerce, content-based retrieval has become essential for efficiently searching and utilizing heterogeneous information. Over time, retrieval techniques have evolved from Unimodal Retrieval (UR) to Cross-modal Retrieval (CR) and, more recently, to Composed Multi-modal Retrieval (CMR). CMR enables users to retrieve images or videos by integrating a reference visual input with textual modifications, enhancing search flexibility and precision. This paper provides a comprehensive review of CMR, covering its fundamental challenges, technical advancements, and categorization into supervised, zero-shot, and semi-supervised learning paradigms. We discuss key research directions, including data augmentation, model architecture, and loss optimization in supervised CMR, as well as transformation frameworks and external knowledge integration in zero-shot CMR. Additionally, we highlight the application potential of CMR in composed image retrieval, video retrieval, and person retrieval, which have significant implications for e-commerce, online search, and public security. Given its ability to refine and personalize search experiences, CMR is poised to become a pivotal technology in next-generation retrieval systems. A curated list of related works and resources is available at: https://github.com/kkzhang95/Awesome-Composed-Multi-modal-Retrieval
Victim-Centred Abuse Investigations and Defenses for Social Media Platforms
2025-03-03
Zaid Hakami, Ashfaq Ali Shafin, Peter J. Clarke, Niki Pissinou, Bogdan Carbunar
Online abuse, a persistent aspect of social platform interactions, impacts user well-being and exposes flaws in platform designs that include insufficient detection efforts and inadequate victim protection measures. Ensuring safety in platform interactions requires the integration of victim perspectives in the design of abuse detection and response systems. In this paper, we conduct surveys (n = 230) and semi-structured interviews (n = 15) with students at a minority-serving institution in the US, to explore their experiences with abuse on a variety of social platforms, their defense strategies, and their recommendations for social platforms to improve abuse responses. We build on study findings to propose design requirements for abuse defense systems and discuss the role of privacy, anonymity, and abuse attribution requirements in their implementation. We introduce ARI, a blueprint for a unified, transparent, and personalized abuse response system for social platforms that sustainably detects abuse by leveraging the expertise of platform users, incentivized with proceeds obtained from abusers.
2025-03-03
Anders Jerkstrand, Dan Milisavljevic, Bernhard Müller
Core-collapse supernovae (CCSNe) are the explosive end-points of stellar evolution for $M_{ZAMS} \gtrsim 8$ $M_\odot$ stars. The cores of these stars collapse to neutron stars, a process in which high neutrino luminosity drives off the overlying stellar layers, which get ejected with thousands of kilometers per second. These supernovae enrich their host galaxies with elements made both during the star's life and in the explosion, providing the main cosmic source of elements such as oxygen, neon and silicon. Their high luminosities ($\sim$ $10^{42}$ erg s$^{-1}$ at peak) make SNe beacons to large distances, and their light curves and spectra provide rich information on single and binary stellar evolution, nucleosynthesis, and a diverse set of high-energy physical processes. As the SN ejecta sweep up circumstellar and interstellar matter, it eventually enters a supernova remnant phase, exemplified by nearby, spatially resolved remnants such as Cas A and the Crab Nebula. In this phase, shocks and pulsar winds continue to light up the interior of the exploded stars, giving detailed information about their 3D structure. We review the central concepts of CCSNe, from the late stages of evolution of massive stars, through collapse, explosion, and electromagnetic display, to the final remnant phase. We briefly discuss still open questions, and current and future research avenues.
In electrical equipment, even minor contact issues can lead to arc faults. Traditional methods often struggle to balance the accuracy and rapid response required for effective arc fault detection. To address this challenge, we introduce DCAMamba, a novel framework for arc fault detection. Specifically, DCAMamba is built upon a state-space model (SSM) and utilizes a hardware-aware parallel algorithm, designed in a cyclic mode using the Mamba architecture. To meet the dual demands of high accuracy and fast response in arc fault detection, we have refined the original Mamba model and incorporated a Feature Amplification Strategy (FAS), a simple yet effective method that enhances the model's ability to interpret arc fault data. Experimental results show that DCAMamba, with FAS, achieves a 12$\%$ improvement in accuracy over the original Mamba, while maintaining an inference time of only 1.87 milliseconds. These results highlight the significant potential of DCAMamba as a future backbone for signal processing. Our code will be made open-source after peer review.
Existing vision-language models (VLMs) such as CLIP have showcased an impressive capability to generalize well across various downstream tasks. These models leverage the synergy between visual and textual information, enabling them to understand and reason about the content present in images and text in a unified manner. This article provides a brief overview of CLIP based on few-shot prompt learning, including experimental data and technical characteristics of some methods. The purpose of this review is to provide a reference for researchers who have just started their research in generalizable prompting of CLIP through few-shot training for classification across 15 datasets and also to facilitate the integration of this field by researchers in other downstream tasks.
Intelligent surgical robots have the potential to revolutionize clinical practice by enabling more precise and automated surgical procedures. However, the automation of such robot for surgical tasks remains under-explored compared to recent advancements in solving household manipulation tasks. These successes have been largely driven by (1) advanced models, such as transformers and diffusion models, and (2) large-scale data utilization. Aiming to extend these successes to the domain of surgical robotics, we propose a diffusion-based policy learning framework, called Diffusion Stabilizer Policy (DSP), which enables training with imperfect or even failed trajectories. Our approach consists of two stages: first, we train the diffusion stabilizer policy using only clean data. Then, the policy is continuously updated using a mixture of clean and perturbed data, with filtering based on the prediction error on actions. Comprehensive experiments conducted in various surgical environments demonstrate the superior performance of our method in perturbation-free settings and its robustness when handling perturbed demonstrations.
A Survey On Large Language Models For Code Generation
2025-03-03
Nam Huynh, Beiyu Lin
Large Language Models (LLMs) have demonstrated their remarkable capabilities in numerous fields. This survey focuses on how LLMs empower users, regardless of their technical background, to use human languages to automatically generate executable code. We begin with understanding LLMs' limitations and challenges in automated code generation. Subsequently, we review various fine-tuning techniques designed to enhance both the performance and adaptability of LLMs in code generation tasks. We then review the existing metrics and benchmarks for evaluations to assess model performance based on fine-tuning techniques. Finally, we explore the applications of LLMs (e.g. CodeLlama, GitHub Copilot, ToolGen) in code generation tasks to illustrate their roles and functionalities. This survey provides a comprehensive overview of LLMs for code generation, helps researchers in diverse fields better understand the current state-of-the-art technologies, and offers the potential of effectively leveraging LLMs for code generation tasks.
Metal defect detection is critical in industrial quality assurance, yet existing methods struggle with grayscale variations and complex defect states, limiting its robustness. To address these challenges, this paper proposes a Self-Adaptive Gamma Context-Aware SSM-based model(GCM-DET). This advanced detection framework integrating a Dynamic Gamma Correction (GC) module to enhance grayscale representation and optimize feature extraction for precise defect reconstruction. A State-Space Search Management (SSM) architecture captures robust multi-scale features, effectively handling defects of varying shapes and scales. Focal Loss is employed to mitigate class imbalance and refine detection accuracy. Additionally, the CD5-DET dataset is introduced, specifically designed for port container maintenance, featuring significant grayscale variations and intricate defect patterns. Experimental results demonstrate that the proposed model achieves substantial improvements, with mAP@0.5 gains of 27.6\%, 6.6\%, and 2.6\% on the CD5-DET, NEU-DET, and GC10-DET datasets.
Machine Learning for Airborne Electromagnetic Data Inversion: a Bootstrapped Approach
2025-03-03
Ophir Greif, Bas Peters, Michael S. McMillan, Paulina Wozniakowska, Eldad Haber
Aircraft-based surveying to collect airborne electromagnetic data is a key method to image large swaths of the Earth's surface in pursuit of better knowledge of aquifer systems. Despite many years of advancements, 3D inversion still poses challenges in terms of computational requirements, regularization selection, hyperparameter tuning and real-time inversion. We present a new approach for the inversion of airborne electromagnetic data that leverages machine learning to overcome the computational burden of traditional 3D inversion methods, which implicitly includes learned regularization and is applicable in real-time. The method combines 1D inversion results with geostatistical modeling to create tailored training datasets, enabling the development of a specialized neural network that predicts 2D conductivity models from airborne electromagnetic data. This approach requires 3D forward modeling and 1D inversion up front, but no forward modeling during inference. The workflow is applied to the Kaweah Subbasin in California, where it successfully reconstructs conductivity models consistent with real-world data and geological drill hole information. The results highlight the method's capability to deliver fast and accurate subsurface imaging, offering a valuable tool for groundwater exploration and other near-surface applications.
HREB-CRF: Hierarchical Reduced-bias EMA for Chinese Named Entity Recognition
2025-03-03
Sijin Sun, Ming Deng, Xinrui Yu, Liangbin Zhao
Incorrect boundary division, complex semantic representation, and differences in pronunciation and meaning often lead to errors in Chinese Named Entity Recognition(CNER). To address these issues, this paper proposes HREB-CRF framework: Hierarchical Reduced-bias EMA with CRF. The proposed method amplifies word boundaries and pools long text gradients through exponentially fixed-bias weighted average of local and global hierarchical attention. Experimental results on the MSRA, Resume, and Weibo datasets show excellent in F1, outperforming the baseline model by 1.1\%, 1.6\%, and 9.8\%. The significant improvement in F1 shows evidences of strong effectiveness and robustness of approach in CNER tasks.
PostHoc FREE Calibrating on Kolmogorov Arnold Networks
2025-03-03
Wenhao Liang, Wei Emma Zhang, Lin Yue, Miao Xu, Olaf Maennel, Weitong Chen
Kolmogorov Arnold Networks (KANs) are neural architectures inspired by the Kolmogorov Arnold representation theorem that leverage B Spline parameterizations for flexible, locally adaptive function approximation. Although KANs can capture complex nonlinearities beyond those modeled by standard MultiLayer Perceptrons (MLPs), they frequently exhibit miscalibrated confidence estimates manifesting as overconfidence in dense data regions and underconfidence in sparse areas. In this work, we systematically examine the impact of four critical hyperparameters including Layer Width, Grid Order, Shortcut Function, and Grid Range on the calibration of KANs. Furthermore, we introduce a novel TemperatureScaled Loss (TSL) that integrates a temperature parameter directly into the training objective, dynamically adjusting the predictive distribution during learning. Both theoretical analysis and extensive empirical evaluations on standard benchmarks demonstrate that TSL significantly reduces calibration errors, thereby improving the reliability of probabilistic predictions. Overall, our study provides actionable insights into the design of spline based neural networks and establishes TSL as a robust loss solution for enhancing calibration.
Entropy created when colliding particles fall into a black hole
2025-03-03
De-Chang Dai
If two particles collide in the vicinity of a black hole horizon, their center of mass energy is practically unlimited, so another black hole with a large mass and thus entropy can be created. The resulting black hole can then merge with the original one. If the black hole is created very close to the horizon, its energy will be highly redshifted for an asymptotic observer. However, its entropy is not redshifted. We demonstrated that the newly created entropy can be higher than the Bekenstein-Hawking entropy of the final black hole, though we neglect that a certain amount of energy can escape to infinity, carrying away part of the entropy produced in the process. This is a counter-example to the statement that the black hole thermal entropy counts all the states inside the black hole. Unlike similar examples, this colliding process does not involve exotic matter, alternative theories of gravity, nor artificial ad hoc gluing of two different spacetimes.
Large Language Models for Healthcare Text Classification: A Systematic Review
2025-03-03
Hajar Sakai, Sarah S. Lam
Large Language Models (LLMs) have fundamentally transformed approaches to Natural Language Processing (NLP) tasks across diverse domains. In healthcare, accurate and cost-efficient text classification is crucial, whether for clinical notes analysis, diagnosis coding, or any other task, and LLMs present promising potential. Text classification has always faced multiple challenges, including manual annotation for training, handling imbalanced data, and developing scalable approaches. With healthcare, additional challenges are added, particularly the critical need to preserve patients' data privacy and the complexity of the medical terminology. Numerous studies have been conducted to leverage LLMs for automated healthcare text classification and contrast the results with existing machine learning-based methods where embedding, annotation, and training are traditionally required. Existing systematic reviews about LLMs either do not specialize in text classification or do not focus on the healthcare domain. This research synthesizes and critically evaluates the current evidence found in the literature regarding the use of LLMs for text classification in a healthcare setting. Major databases (e.g., Google Scholar, Scopus, PubMed, Science Direct) and other resources were queried, which focused on the papers published between 2018 and 2024 within the framework of PRISMA guidelines, which resulted in 65 eligible research articles. These were categorized by text classification type (e.g., binary classification, multi-label classification), application (e.g., clinical decision support, public health and opinion analysis), methodology, type of healthcare text, and metrics used for evaluation and validation. This review reveals the existing gaps in the literature and suggests future research lines that can be investigated and explored.
The rise of foundation models has revolutionized natural language processing and computer vision, yet their best practices to time series forecasting remains underexplored. Existing time series foundation models often adopt methodologies from these fields without addressing the unique characteristics of time series data. In this paper, we identify two key challenges in cross-domain time series forecasting: the complexity of temporal patterns and semantic misalignment. To tackle these issues, we propose the ``Unify and Anchor" transfer paradigm, which disentangles frequency components for a unified perspective and incorporates external context as domain anchors for guided adaptation. Based on this framework, we introduce ContexTST, a Transformer-based model that employs a time series coordinator for structured representation and the Transformer blocks with a context-informed mixture-of-experts mechanism for effective cross-domain generalization. Extensive experiments demonstrate that ContexTST advances state-of-the-art forecasting performance while achieving strong zero-shot transferability across diverse domains.
Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction
2025-03-03
Nazmul Islam, Mohammad Zulkernine
Machine learning (ML) has revolutionized Internet of Vehicles (IoV) applications by enhancing intelligent transportation, autonomous driving capabilities, and various connected services within a large, heterogeneous network. However, the increased connectivity and massive data exchange for ML applications introduce significant privacy challenges. Privacy-preserving machine learning (PPML) offers potential solutions to address these challenges by preserving privacy at various stages of the ML pipeline. Despite the rapid development of ML-based IoV applications and the growing data privacy concerns, there are limited comprehensive studies on the adoption of PPML within this domain. Therefore, this study provides a comprehensive review of the fundamentals, recent advancements, and the challenges of integrating PPML into IoV applications. To conduct an extensive study, we first review existing surveys of various PPML techniques and their integration into IoV across different scopes. We then discuss the fundamentals of IoV and propose a four-layer IoV architecture. Additionally, we categorize IoV applications into three key domains and analyze the privacy challenges in leveraging ML for these application domains. Next, we provide an overview of various PPML techniques, highlighting their applicability and performance to address the privacy challenges. Building on these fundamentals, we thoroughly review recent advancements in integrating various PPML techniques within IoV applications, discussing their frameworks, key features, and performance evaluation in terms of privacy, utility, and efficiency. Finally, we identify current challenges and propose future research directions to enhance privacy and reliability in IoV applications.
Machine Learning Applications to Diffuse Reflectance Spectroscopy in Optical Diagnosis; A Systematic Review
2025-03-03
Nicola Rossberg, Celina L. Li, Simone Innocente, Stefan Andersson-Engels, Katarzyna Komolibus, Barry O'Sullivan, Andrea Visentin
Diffuse Reflectance Spectroscopy has demonstrated a strong aptitude for identifying and differentiating biological tissues. However, the broadband and smooth nature of these signals require algorithmic processing, as they are often difficult for the human eye to distinguish. The implementation of machine learning models for this task has demonstrated high levels of diagnostic accuracies and led to a wide range of proposed methodologies for applications in various illnesses and conditions. In this systematic review, we summarise the state of the art of these applications, highlight current gaps in research and identify future directions. This review was conducted in accordance with the PRISMA guidelines. 77 studies were retrieved and in-depth analysis was conducted. It is concluded that diffuse reflectance spectroscopy and machine learning have strong potential for tissue differentiation in clinical applications, but more rigorous sample stratification in tandem with in-vivo validation and explainable algorithm development is required going forward.
Asymmetric Cross-Correlation in Multivariate Spatial Stochastic Processes: A Primer
2025-03-03
Xiaoqing Chen
Multivariate spatial phenomena are ubiquitous, spanning domains such as climate, pandemics, air quality, and social economy. Cross-correlation between different quantities of interest at different locations is asymmetric in general. This paper provides the visualization, structure, and properties of asymmetric cross-correlation as well as symmetric auto-correlation. It reviews mainstream multivariate spatial models and analyzes their capability to accommodate asymmetric cross-correlation. It also illustrates the difference in model accuracy with and without asymmetric accommodation using a 1D simulated example.
A Survey on Semantic Communications in Internet of Vehicles
2025-03-03
Sha Ye, Qiong Wu, Pingyi Fan, Qiang Fan
Internet of Vehicles (IoV), as the core of intelligent transportation system, enables comprehensive interconnection between vehicles and their surroundings through multiple communication modes, which is significant for autonomous driving and intelligent traffic management. However, with the emergence of new applications, traditional communication technologies face the problems of scarce spectrum resources and high latency. Semantic communication, which focuses on extracting, transmitting, and recovering some useful semantic information from messages, can reduce redundant data transmission, improve spectrum utilization, and provide innovative solutions to communication challenges in the IoV. This paper systematically reviews state of art of semantic communications in the IoV, elaborates the technical background of IoV and semantic communications, and deeply discusses key technologies of semantic communications in IoV, including semantic information extraction, semantic communication architecture, resource allocation and management, and so on. Through specific case studies, it demonstrates that semantic communications can be effectively employed in the scenarios of traffic environment perception and understanding, intelligent driving decision support, IoV service optimization, and intelligent traffic management. Additionally, it analyzes the current challenges and future research directions. This survey reveals that semantic communications has broad application prospects in IoV, but it is necessary to solve the real existing problems by combining advanced technologies to promote its wide application in IoV and contributing to the development of intelligent transportation system.
The environments of radio galaxies and quasars in LoTSS data release 2
2025-03-02
Tong Pan, Yuming Fu, H. J. A. Rottgering, R. J. van Weeren, A. B. Drake, B. H. Yue, J. W. Petley
Aims. The orientation-based unification scheme of radio-loud active galactic nuclei (AGNs) asserts that radio galaxies and quasars are essentially the same type of object, but viewed from different angles. To test this unification model, we compared the environments of radio galaxies and quasars, which would reveal similar properties when an accurate model is utilized. Methods. Using the second data release of the LOFAR Two-metre Sky Survey (LoTSS DR2), we constructed a sample of 26,577 radio galaxies and 2028 quasars at 0.08 < z < 0.4. For radio galaxies with optical spectra, we further classified them as 3631 low-excitation radio galaxies (LERGs) and 1143 high-excitation radio galaxies (HERGs). We crossmatched these samples with two galaxy cluster catalogs from the Sloan Digital Sky Survey (SDSS). Results. We find that $17.1 \pm 0.2%$ of the radio galaxies and $4.1 \pm 0.4%$ of the quasars are associated with galaxy clusters. Luminous quasars are very rare in clusters, while $18.7 \pm 0.7%$ LERGs and $15.2 \pm 1.1%$ HERGs reside in clusters. We also note that in radio galaxies, both HERGs and LERGs tend to reside in the centers of clusters, while quasars do not show a strong preference for their positions in clusters. Conclusions. This study shows that local quasars and radio galaxies exist in different environments, challenging the orientation-based unification model. This means that factors other than orientation may play an important role in distinguishing radio galaxies from quasars. The future WEAVE-LOFAR survey will offer high-quality spectroscopic data for a large number of radio sources and allow for a more comprehensive exploration of the environments of radio galaxies and quasars.
Brownian-motion approach to statistical mechanics: Langevin equations, fluctuations, and timescales
2025-03-02
Sushanta Dattagupta, Aritra Ghosh
We briefly review the problem of Brownian motion and describe some intriguing facets. The problem is first treated in its original form as enunciated by Einstein, Langevin, and others. Then, utilizing the problem of Brownian motion as a paradigm and upon using the Langevin equation(s), we present a brief exposition of the modern areas of stochastic thermodynamics and fluctuation theorems in a manner accessible to a non-expert. This is followed by an analysis of non-Markovian Brownian dynamics via generalized Langevin equation(s) in which we particularly shed light onto its derivation, the emergence of the fluctuation-dissipation relation, and the recently-discovered effective-mass framework.
Combinatorial and Computational Insights about Patient-to-room Assignment under Consideration of Roommate Compatibility
2025-03-02
Tabea Brandt, Christina Büsing, Felix Engelhardt
During a hospital stay, a roommate can significantly influence a patient's overall experience both positivly and negatively. Therefore, hospital staff tries to assign patients together to a room that are likely to be compatible. However, there are more conditions and objectives to be respected by the patient-to-room assignment (PRA), e.g., ensuring gender separated rooms and avoiding transfers. In this paper, we review the literature for reasons why roommate compatibility is important as well as for criteria that can help to increase the probability that two patients are suitable roommates. We further present combinatorial insights about computing patient-to-room assignments with optimal overall roommate compatibility. We then compare different IP-formulations for PRA as well as the influence of different scoring functions for patient compatibility on the runtime of PRA integer programming (IP) optimisation. Using these results and real-world data, we conclude this paper by developing and evaluating a fast IP-based solution approach for the dynamic PRA.
Machine Learning for Health symposium 2024 -- Findings track
2025-03-02
Stefan Hegselmann, Helen Zhou, Elizabeth Healey, Trenton Chang, Caleb Ellington, Vishwali Mhasawade, Sana Tonekaboni, Peniel Argaw, Haoran Zhang
A collection of the accepted Findings papers that were presented at the 4th Machine Learning for Health symposium (ML4H 2024), which was held on December 15-16, 2024, in Vancouver, BC, Canada. ML4H 2024 invited high-quality submissions describing innovative research in a variety of health-related disciplines including healthcare, biomedicine, and public health. Works could be submitted to either the archival Proceedings track, or the non-archival Findings track. The Proceedings track targeted mature, cohesive works with technical sophistication and high-impact relevance to health. The Findings track promoted works that would spark new insights, collaborations, and discussions at ML4H. Both tracks were given the opportunity to share their work through the in-person poster session. All the manuscripts submitted to ML4H Symposium underwent a double-blind peer-review process.
Extended Haldane model -- a modern gateway to topological insulators
2025-03-02
Tanay Nag, Saptarshi Mandal
The seminal Haldane model brings up a paradigm beyond the quantum Hall effect to look for a plethora of topological phases in the honeycomb and other lattices. Here we dwell into this model considering a full parameter space in the presence of spin-orbit interaction as well as Zeeman field such that the flavour of Kane-Mele model is invoked. Adopting this extended Haldane model as an example, we elucidate, in a transparent manner, a number of topological features in a pedagogical manner. First, we describe various first order topological insulator phases and their characterizations while explaining various anomalous quantum Hall effects and quantum spin Hall effects in the extended Haldane model. Second, we demonstrate the concepts of higher order topological insulator phases along with the topological invariants in the anisotropic limit of the extended Haldane model. At the end, we discuss various open issues involving \textcolor{black}{emergent or extended} symmetries that might lead to a broader understanding of various topological phases and the associated criteria behind their emergence.
Insights into new mechanosensitive behaviors of G protein-coupled receptors
2025-03-02
Aakanskha J Shetty, Alexei Sirbu, Paolo Annibale
G protein-coupled receptors (GPCRs) represent a diverse and vital family of membrane proteins that mediate intracellular signaling in response to extracellular stimuli, playing critical roles in physiology and disease. Traditionally recognized as chemical signal transducers, GPCRs have recently been implicated in mechanotransduction, the process of converting mechanical stimuli into cellular responses. This review explores the emerging role of GPCRs in sensing and responding to mechanical forces, with a particular focus on the cardiovascular system. Cardiovascular homeostasis is heavily influenced by mechanical forces such as shear stress, cyclic stretch, and pressure, which are central to both normal physiology and the pathogenesis of diseases like hypertension and atherosclerosis. GPCRs, including the angiotensin II type 1 receptor (AT1R) and the $\beta$2-adrenergic receptor ($\beta$2-AR), have demonstrated the ability to integrate mechanical and chemical signals, potentially through conformational changes and/or modulation of lipid interactions, leading to biased signaling. Recent studies highlight the dual activation mechanisms of GPCRs, with $\beta$2-AR now serving as a key example of how mechanical and ligand-dependent pathways contribute to cardiovascular regulation. This review synthesizes current knowledge of GPCR mechanosensitivity, emphasizing its implications for cardiovascular health and disease, and explores advancements in methodologies poised to further unravel the mechanistic intricacies of these receptors.
A Survey on Ordinal Regression: Applications, Advances and Prospects
2025-03-02
Jinhong Wang, Jintai Chen, Jian Liu, Dongqi Tang, Danny Z. Chen, Jian Wu
Ordinal regression refers to classifying object instances into ordinal categories. Ordinal regression is crucial for applications in various areas like facial age estimation, image aesthetics assessment, and even cancer staging, due to its capability to utilize ordered information effectively. More importantly, it also enhances model interpretation by considering category order, aiding the understanding of data trends and causal relationships. Despite significant recent progress, challenges remain, and further investigation of ordinal regression techniques and applications is essential to guide future research. In this survey, we present a comprehensive examination of advances and applications of ordinal regression. By introducing a systematic taxonomy, we meticulously classify the pertinent techniques and applications into three well-defined categories based on different strategies and objectives: Continuous Space Discretization, Distribution Ordering Learning, and Ambiguous Instance Delving. This categorization enables a structured exploration of diverse insights in ordinal regression problems, providing a framework for a more comprehensive understanding and evaluation of this field and its related applications. To our best knowledge, this is the first systematic survey of ordinal regression, which lays a foundation for future research in this fundamental and generic domain.
2025-03-02
Sitong Li, Stefano Padilla, Pierre Le Bras, Junyu Dong, Mike Chantler
We present a comprehensive, in-depth review of ideation assisted by large language models (LLMs), highlighting emerging trends and identifying unaddressed research gaps. In total, we examined 61 studies investigating the application of LLMs in both group and individual ideation processes. From these studies, we derived the Hourglass Ideation Framework for LLM-assisted ideation, comprising three phases and seven key ideation stages, which served as the basis for our systematic survey. Our analysis reveals that LLMs are most frequently used for idea generation and refinement, but their use in scope specification, foundational material structuring and multi-idea evaluation and selection remains limited. We provide our findings in extensive tabular and online formats. These catalogues detail research on LLM-assisted, purely LLM-based, and human-only activities across the seven ideation stages for each of the 61 studies. These also detail creative domains, publication outlets, interaction designs, user study designs, and assessment methods. Our analysis of system interaction design reveals a predominant focus on supporting individual ideation activities and text-based interaction, with a growing trend of incorporating multimedia elements. However, in group ideation, tools and interaction modalities targeting both synchronous and asynchronous collaboration are much scarcer. We synthesize the primary findings of our review and outline promising directions for future research in LLM-assisted ideation. We hope this review will help researchers quickly gain an overview of this rapidly expanding area, efficiently locate relevant work, and identify underexplored areas for further investigation. In addition, we believe the framework we present here will form the basis for the development of future problem and solution space taxonomies, and methodologies for LLM-assisted ideation development and use.
Applying computational protein design to therapeutic antibody discovery -- current state and perspectives
2025-03-02
Weronika Bielska, Igor Jaszczyszyn, Pawel Dudzic, Bartosz Janusz, Dawid Chomicz, Sonia Wrobel, Victor Greiff, Ryan Feehan, Jared Adolf-Bryfogle, Konrad Krawczyk
Machine learning applications in protein sciences have ushered in a new era for designing molecules in silico. Antibodies, which currently form the largest group of biologics in clinical use, stand to benefit greatly from this shift. Despite the proliferation of these protein design tools, their direct application to antibodies is often limited by the unique structural biology of these molecules. Here, we review the current computational methods for antibody design, highlighting their role in advancing computational drug discovery.
Investigating the formation of small Solar System objects using stellar occultations by satellites: present, future and its use to update satellite orbits
2025-03-02
Felipe Braga-Ribas, Frederic Vachier, Josselin Desmars, Giuliano Margoti, Bruno Sicardy
The history of the outer solar system is intrinsically related to the Giant Planets migration. A massive disk of material within a radius of 30~au was scattered during the planetary migration, creating different dynamic populations in the Transneptunian region. They were formed in a collisional environment when massive collisions allowed them to grow and form much smaller moons than the primary body. The dynamical group, known as the Cold Classicals, was formed in a sparse disk from 42 to about 47~au and did not suffer much from planet migration. Observations show that many of Cold Classical are binary, consistent with the streaming instability process. The stellar occultation technique, with a spatial resolution of a few kilometres, can be used to search for binaries where other techniques are unable to do so, and to characterise the known satellites of Trans-Neptunian Objects (TNO), constraining their formation scenarios. We review here the first stellar occultations by TNO's satellites (besides Charon), discuss the methods used to detect these events. We also fit new orbital elements and system mass for Vanth (Orcus/1) and Weywot (Quaoar/1), finding reasonable solutions for pure Keplerian orbits. Finally, we discuss the prospects regarding the stellar occultations by TNO binaries and their implications for the study of the history of the Solar System.
Systematic Literature Review on Clinical Trial Eligibility Matching
2025-03-02
Muhammad Talha Sharif, Abdul Rehman
Clinical trial eligibility matching is a critical yet often labor-intensive and error-prone step in medical research, as it ensures that participants meet precise criteria for safe and reliable study outcomes. Recent advances in Natural Language Processing (NLP) have shown promise in automating and improving this process by rapidly analyzing large volumes of unstructured clinical text and structured electronic health record (EHR) data. In this paper, we present a systematic overview of current NLP methodologies applied to clinical trial eligibility screening, focusing on data sources, annotation practices, machine learning approaches, and real-world implementation challenges. A comprehensive literature search (spanning Google Scholar, Mendeley, and PubMed from 2015 to 2024) yielded high-quality studies, each demonstrating the potential of techniques such as rule-based systems, named entity recognition, contextual embeddings, and ontology-based normalization to enhance patient matching accuracy. While results indicate substantial improvements in screening efficiency and precision, limitations persist regarding data completeness, annotation consistency, and model scalability across diverse clinical domains. The review highlights how explainable AI and standardized ontologies can bolster clinician trust and broaden adoption. Looking ahead, further research into advanced semantic and temporal representations, expanded data integration, and rigorous prospective evaluations is necessary to fully realize the transformative potential of NLP in clinical trial recruitment.
A Transfer Framework for Enhancing Temporal Graph Learning in Data-Scarce Settings
2025-03-02
Shubham Gupta, Srikanta Bedathur
Dynamic interactions between entities are prevalent in domains like social platforms, financial systems, healthcare, and e-commerce. These interactions can be effectively represented as time-evolving graphs, where predicting future connections is a key task in applications such as recommendation systems. Temporal Graph Neural Networks (TGNNs) have achieved strong results for such predictive tasks but typically require extensive training data, which is often limited in real-world scenarios. One approach to mitigating data scarcity is leveraging pre-trained models from related datasets. However, direct knowledge transfer between TGNNs is challenging due to their reliance on node-specific memory structures, making them inherently difficult to adapt across datasets. To address this, we introduce a novel transfer approach that disentangles node representations from their associated features through a structured bipartite encoding mechanism. This decoupling enables more effective transfer of memory components and other learned inductive patterns from one dataset to another. Empirical evaluations on real-world benchmarks demonstrate that our method significantly enhances TGNN performance in low-data regimes, outperforming non-transfer baselines by up to 56\% and surpassing existing transfer strategies by 36\%
The J-PAS survey: The effect of photometric redshift errors on cosmic voids
2025-03-02
J. A. Mansour, L. J. Liivamägi, A. Tamm, J. Laur, R. Abramo, E. Tempel, R. Kipper, A. Hernán-Caballero, V. Marra, J. Alcaniz, N. Benitez, S. Bonoli, S. Carneiro, J. Cenarro, D. Cristóbal-Hornillos, R. Dupke, A. Ederoclite, C. Hernández-Monteagudo, C. López-Sanjuan, A. Marín-Franch, C. M. de Oliveira, M. Moles, L. Sodré Jr, K. Taylor, J. Varela, H. Vázquez Ramió
We investigated the impact of photometric redshift errors in the ongoing Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) on void identification and properties using a watershed-based method, aiming to assess the recovery of individual voids and the overall void environment. We created galaxy mock catalogues for redshift z = 0.1 using the IllustrisTNG300-1 simulation, defining two datasets: an $ideal$ sample ($m_r < 21$ mag) and a $perturbed$ sample with the Z-coordinate errors mimicking J-PAS's line-of-sight errors, derived from the precursor miniJPAS survey data. We identified voids using ZOBOV, a watershed algorithm. We found 1065 voids in the $ideal$ sample and 2558 voids in the $perturbed$ sample. The $perturbed$ sample voids have, on average, smaller sizes and denser interiors. We filtered out voids based on density and radius in order to eliminate overdense and small spurious instances. The stacked density profile of filtered voids in the $perturbed$ sample remains close to the average density even at the boundary peak, indicating a strong blurring of structures by the redshift errors. The number of $ideal$ sample voids for which at least $50\%$ of the volume is recovered by a void in the $perturbed$ sample is 53 (29 for the filtered sample). The volume occupied by these voids is less than $10\%$ of the simulation volume. Merging voids in the $perturbed$ sample marginally improves the recovery. The overall volumes defined as voids in the two samples have an overlap of $80\%$, making up $61\%$ of the simulation box volume. While some statistical properties of voids might be recovered sufficiently well, the watershed algorithms may not be optimal for recovering the large-scale structure voids if applied straight to photometric redshift survey data.
VTuber's Atelier: The Design Space, Challenges, and Opportunities for VTubing
2025-03-02
Daye Kim, Sebin Lee, Yoonseo Jun, Yujin Shin, Jungjin Lee
VTubing, the practice of live streaming using virtual avatars, has gained worldwide popularity among streamers seeking to maintain anonymity. While previous research has primarily focused on the social and cultural aspects of VTubing, there is a noticeable lack of studies examining the practical challenges VTubers face in creating and operating their avatars. To address this gap, we surveyed VTubers' equipment and expanded the live-streaming design space by introducing six new dimensions related to avatar creation and control. Additionally, we conducted interviews with 16 professional VTubers to comprehensively explore their practices, strategies, and challenges throughout the VTubing process. Our findings reveal that VTubers face significant burdens compared to real-person streamers due to fragmented tools and the multi-tasking nature of VTubing, leading to unique workarounds. Finally, we summarize these challenges and propose design opportunities to improve the effectiveness and efficiency of VTubing.
Quantum computing has shown great potential to revolutionize traditional computing and can provide an exponential speedup for a wide range of possible applications, attracting various stakeholders. However, understanding fundamental quantum computing concepts remains a significant challenge for novices because of their abstract and counterintuitive nature. Thus, we propose an analogy-based characterization framework to construct the mental mapping between quantum computing concepts and daily objects, informed by in-depth expert interviews and a literature review, covering key quantum concepts and characteristics like number of qubits, output state duality, quantum concept type, and probability quantification. Then, we developed an AR-based prototype system, Intuit, using situated analytics to explain quantum concepts through daily objects and phenomena (e.g., rotating coins, paper cutters). We thoroughly evaluated our approach through in-depth user and expert interviews. The Results demonstrate the effectiveness and usability of Intuit in helping learners understand abstract concepts in an intuitive and engaging manner.
Methods for exact solutions of nonlinear ordinary differential equations\
2025-03-02
Robert Conte, Micheline Musette, Tuen Wai Ng, Chengfa Wu
In order to find closed form solutions of nonintegrable nonlinear ordinary differential equations, numerous tricks have been proposed. The goal of this short review is to recall classical, 19th-century results, completed in 2006 by Eremenko, which can be turned into algorithms, thus avoiding \textit{ad hoc} assumptions, able to provide \textit{all} (as opposed to some) solutions in a precise class. To illustrate these methods, we present some new such exact solutions, physically relevent.
Validity of the total quasi-steady-state approximation in stochastic biochemical reaction networks
2025-03-02
Yun Min Song, Kangmin Lee, Jae Kyoung Kim
Stochastic models for biochemical reaction networks are widely used to explore their complex dynamics but face significant challenges, including difficulties in determining rate constants and high computational costs. To address these issues, model reduction approaches based on deterministic quasi-steady-state approximations (QSSA) have been employed, resulting in propensity functions in the form of deterministic non-elementary reaction functions, such as the Michaelis-Menten equation. In particular, the total QSSA (tQSSA), known for its accuracy in deterministic frameworks, has been perceived as universally valid for stochastic model reduction. However, recent studies have challenged this perception. In this review, we demonstrate that applying tQSSA in stochastic model reduction can distort dynamics, even in cases where the deterministic tQSSA is rigorously valid. This highlights the need for caution when using deterministic QSSA in stochastic model reduction to avoid erroneous conclusions from model simulations.
Exploring Broad-Spectrum Antimicrobial Nanotopographies: Implications for Bactericidal, Antifungal, and Virucidal Surface Design
2025-03-02
Vladimir A. Baulin, Denver P. Linklater, Saulius Juodkazis, Elena P. Ivanova
Inspired by the natural defence strategies of insect wings and plant leaves, nanostructured surfaces have emerged as a promising tool in various fields, including engineering, biomedical sciences, and materials science to combat bacterial contamination and disrupt biofilm formation. However, the development of effective antimicrobial surfaces against fungal and viral pathogens presents distinct challenges, necessitating tailored approaches. Here, we aimed to review the recent advancements of the use of nanostructured surfaces to combat microbial contamination, particularly focusing on their mechanobactericidal and antifungal properties, as well as their potential in mitigating viral transmission. We comparatively analysed the diverse geometries and nano-architectures of these surfaces and discussed their applications in various biomedical contexts, such as dental and orthopedic implants, drug delivery systems, and tissue engineering. Our review highlights the importance of preventing microbial attachment and biofilm formation, especially in the context of rising antimicrobial resistance and the economic impact of biofilms. We also discussed the latest progress in material science, particularly nanostructured surface engineering, as promising strategies for reducing viral transmission through surfaces. Overall, our findings underscore the significance of innovative strategies to mitigate microbial attachment and surface-mediated transmission, while also emphasizing the need for further interdisciplinary research in this area to optimize antimicrobial efficacy and address emerging challenges.
Towards Refining Developer Questions using LLM-Based Named Entity Recognition for Developer Chatroom Conversations
2025-03-02
Pouya Fathollahzadeh, Mariam El Mezouar, Hao Li, Ying Zou, Ahmed E. Hassan
In software engineering chatrooms, communication is often hindered by imprecise questions that cannot be answered. Recognizing key entities can be essential for improving question clarity and facilitating better exchange. However, existing research using natural language processing techniques often overlooks these software-specific nuances. In this paper, we introduce Software-specific Named Entity Recognition, Intent Detection, and Resolution Classification (SENIR), a labeling approach that leverages a Large Language Model to annotate entities, intents, and resolution status in developer chatroom conversations. To offer quantitative guidance for improving question clarity and resolvability, we build a resolution prediction model that leverages SENIR's entity and intent labels along with additional predictive features. We evaluate SENIR on the DISCO dataset using a subset of annotated chatroom dialogues. SENIR achieves an 86% F-score for entity recognition, a 71% F-score for intent detection, and an 89% F-score for resolution status classification. Furthermore, our resolution prediction model, tested with various sampling strategies (random undersampling and oversampling with SMOTE) and evaluation methods (5-fold cross-validation, 10-fold cross-validation, and bootstrapping), demonstrates AUC values ranging from 0.7 to 0.8. Key factors influencing resolution include positive sentiment and entities such as Programming Language and User Variable across multiple intents, while diagnostic entities are more relevant in error-related questions. Moreover, resolution rates vary significantly by intent: questions about API Usage and API Change achieve higher resolution rates, whereas Discrepancy and Review have lower resolution rates. A Chi-Square analysis confirms the statistical significance of these differences.
Generative Artificial Intelligence for Academic Research: Evidence from Guidance Issued for Researchers by Higher Education Institutions in the United States
The recent development and use of generative AI (GenAI) has signaled a significant shift in research activities such as brainstorming, proposal writing, dissemination, and even reviewing. This has raised questions about how to balance the seemingly productive uses of GenAI with ethical concerns such as authorship and copyright issues, use of biased training data, lack of transparency, and impact on user privacy. To address these concerns, many Higher Education Institutions (HEIs) have released institutional guidance for researchers. To better understand the guidance that is being provided we report findings from a thematic analysis of guidelines from thirty HEIs in the United States that are classified as R1 or 'very high research activity.' We found that guidance provided to researchers: (1) asks them to refer to external sources of information such as funding agencies and publishers to keep updated and use institutional resources for training and education; (2) asks them to understand and learn about specific GenAI attributes that shape research such as predictive modeling, knowledge cutoff date, data provenance, and model limitations, and educate themselves about ethical concerns such as authorship, attribution, privacy, and intellectual property issues; and (3) includes instructions on how to acknowledge sources and disclose the use of GenAI, how to communicate effectively about their GenAI use, and alerts researchers to long term implications such as over reliance on GenAI, legal consequences, and risks to their institutions from GenAI use. Overall, guidance places the onus of compliance on individual researchers making them accountable for any lapses, thereby increasing their responsibility.
The Simons Observatory: Science Goals and Forecasts for the Enhanced Large Aperture Telescope
2025-03-01
M. Abitbol, I. Abril-Cabezas, S. Adachi, P. Ade, A. E. Adler, P. Agrawal, J. Aguirre, Z. Ahmed, S. Aiola, T. Alford, A. Ali, D. Alonso, M. A. Alvarez, R. An, K. Arnold, P. Ashton, Z. Atkins, J. Austermann, S. Azzoni, C. Baccigalupi, A. Baleato Lizancos, D. Barron, P. Barry, J. Bartlett, N. Battaglia, R. Battye, E. Baxter, A. Bazarko, J. A. Beall, R. Bean, D. Beck, S. Beckman, J. Begin, A. Beheshti, B. Beringue, T. Bhandarkar, S. Bhimani, F. Bianchini, E. Biermann, S. Biquard, B. Bixler, S. Boada, D. Boettger, B. Bolliet, J. R. Bond, J. Borrill, J. Borrow, C. Braithwaite, T. L. R. Brien, M. L. Brown, S. M. Bruno, S. Bryan, R. Bustos, H. Cai, E. Calabrese, V. Calafut, F. M. Carl, A. Carones, J. Carron, A. Challinor, P. Chanial, N. Chen, K. Cheung, B. Chiang, Y. Chinone, J. Chluba, H. S. Cho, S. K. Choi, M. Chu, J. Clancy, S. E. Clark, P. Clarke, D. L. Clements, J. Connors, C. Contaldi, G. Coppi, L. Corbett, N. F. Cothard, W. Coulton, K. D. Crowley, K. T. Crowley, A. Cukierman, J. M. D'Ewart, K. Dachlythra, R. Datta, S. Day-Weiss, T. de Haan, M. Devlin, L. Di Mascolo, S. Dicker, B. Dober, C. Doux, P. Dow, S. Doyle, C. J. Duell, S. M. Duff, A. J. Duivenvoorden, J. Dunkley, D. Dutcher, R. Dünner, M. Edenton, H. El Bouhargani, J. Errard, G. Fabbian, V. Fanfani, G. S. Farren, J. Fergusson, S. Ferraro, R. Flauger, A. Foster, K. Freese, J. C. Frisch, A. Frolov, G. Fuller, N. Galitzki, P. A. Gallardo, J. T. Galvez Ghersi, K. Ganga, J. Gao, X. Garrido, E. Gawiser, M. Gerbino, S. Giardiello, A. Gill, V. Gilles, U. Giri, E. Gleave, V. Gluscevic, N. Goeckner-Wald, J. E. Golec, S. Gordon, M. Gralla, S. Gratton, D. Green, J. C. Groh, C. Groppi, Y. Guan, N. Gupta, J. E. Guðmundsson, S. Hagstotz, P. Hargrave, S. Haridas, K. Harrington, I. Harrison, M. Hasegawa, M. Hasselfield, V. Haynes, M. Hazumi, A. He, E. Healy, S. W. Henderson, B. S. Hensley, E. Hertig, C. Hervías-Caimapo, M. Higuchi, C. A. Hill, J. C. Hill, G. Hilton, M. Hilton, A. D. Hincks, G. Hinshaw, R. Hložek, A. Y. Q. Ho, S. Ho, S. P. Ho, T. D. Hoang, J. Hoh, E. Hornecker, A. L. Hornsby, D. Hoshino, S. C. Hotinli, Z. Huang, Z. B. Huber, J. Hubmayr, K. Huffenberger, J. P. Hughes, A. Idicherian Lonappan, M. Ikape, K. Irwin, J. Iuliano, A. H. Jaffe, B. Jain, H. T. Jense, O. Jeong, A. Johnson, B. R. Johnson, M. Johnson, M. Jones, B. Jost, D. Kaneko, E. D. Karpel, Y. Kasai, N. Katayama, B. Keating, B. Keller, R. Keskitalo, J. Kim, T. Kisner, K. Kiuchi, J. Klein, K. Knowles, A. M. Kofman, B. J. Koopman, A. Kosowsky, R. Kou, N. Krachmalnicoff, D. Kramer, A. Krishak, A. Krolewski, A. Kusaka, A. Kusiak, P. La Plante, A. La Posta, A. Laguë, J. Lashner, M. Lattanzi, A. Lee, E. Lee, J. Leech, C. Lessler, J. S. Leung, A. Lewis, Y. Li, Z. Li, M. Limon, L. Lin, M. Link, J. Liu, Y. Liu, J. Lonergan, T. Louis, T. Lucas, M. Ludlam, M. Lungu, M. Lyons, N. MacCrann, A. MacInnis, M. Madhavacheril, D. Mak, F. Maldonado, A. Manduca, A. Mangu, H. Mani, A. S. Maniyar, G. A. Marques, J. Mates, F. Matsuda, T. Matsumura, P. Mauskopf, A. May, N. McCallum, H. McCarrick, F. McCarthy, M. McCulloch, J. McMahon, P. D. Meerburg, Y. Mehta, J. Melin, J. Mertens, J. Meyers, A. Middleton, A. Miller, M. Mirmelstein, K. Moodley, J. Moore, M. Morshed, T. Morton, E. Moser, T. Mroczkowski, M. Murata, M. Münchmeyer, S. Naess, H. Nakata, T. Namikawa, M. Nashimoto, F. Nati, P. Natoli, M. Negrello, S. K. Nerval, L. Newburgh, D. V. Nguyen, A. Nicola, M. D. Niemack, H. Nishino, Y. Nishinomiya, A. Orlando, G. Orlando, J. Orlowski-Scherer, L. Pagano, L. A. Page, S. Pandey, A. Papageorgiou, I. Paraskevakos, B. Partridge, R. Patki, M. Peel, K. Perez Sarmiento, F. Perrotta, P. Phakathi, L. Piccirillo, E. Pierpaoli, T. Pinsonneault-Marotte, G. Pisano, D. Poletti, R. Puddu, G. Puglisi, F. J. Qu, M. J. Randall, C. Ranucci, C. Raum, R. Reeves, C. L. Reichardt, M. Remazeilles, Y. Rephaeli, D. Riechers, J. Robe, M. F. Robertson, N. Robertson, K. Rogers, F. Rojas, A. Romero, E. Rosenberg, A. Rotti, S. Rowe, A. Roy, S. Sadeh, N. Sailer, K. Sakaguri, T. Sakuma, Y. Sakurai, M. Salatino, G. H. Sanders, D. Sasaki, M. Sathyanarayana Rao, T. P. Satterthwaite, L. Saunders, L. Scalcinati, E. Schaan, B. Schmitt, M. Schmittfull, N. Sehgal, J. Seibert, Y. Seino, U. Seljak, S. Shaikh, E. Shaw, P. Shellard, B. Sherwin, M. Shimon, J. E. Shroyer, C. Sierra, J. Sievers, C. Sifón, P. Sikhosana, M. Silva-Feaver, S. M. Simon, A. Sinclair, K. Smith, W. Sohn, X. Song, R. F. Sonka, D. Spergel, J. Spisak, S. T. Staggs, G. Stein, J. R. Stevens, R. Stompor, E. Storer, R. Sudiwala, J. Sugiyama, K. M. Surrao, A. Suzuki, J. Suzuki, O. Tajima, S. Takakura, A. Takeuchi, I. Tansieri, A. C. Taylor, G. Teply, T. Terasaki, A. Thomas, D. B. Thomas, R. Thornton, H. Trac, T. Tsan, E. Tsang King Sang, C. Tucker, J. Ullom, L. Vacher, S. Vagnozzi, L. Vale, A. van Engelen, J. Van Lanen, J. van Marrewijk, D. D. Van Winkle, C. Vargas, E. M. Vavagiakis, I. Veenendaal, C. Vergès, M. Vissers, M. Viña, K. Wagoner, S. Walker, L. Walters, Y. Wang, B. Westbrook, J. Williams, P. Williams, H. Winch, E. J. Wollack, K. Wolz, J. Wong, Z. Xu, K. Yamada, E. Young, B. Yu, C. Yu, M. Zannoni, K. Zheng, N. Zhu, A. Zonca, I. Zubeldia
We describe updated scientific goals for the wide-field, millimeter-wave survey that will be produced by the Simons Observatory (SO). Significant upgrades to the 6-meter SO Large Aperture Telescope (LAT) are expected to be complete by 2028, and will include a doubled mapping speed with 30,000 new detectors and an automated data reduction pipeline. In addition, a new photovoltaic array will supply most of the observatory's power. The LAT survey will cover about 60% of the sky at a regular observing cadence, with five times the angular resolution and ten times the map depth of Planck. The science goals are to: (1) determine the physical conditions in the early universe and constrain the existence of new light particles; (2) measure the integrated distribution of mass, electron pressure, and electron momentum in the late-time universe, and, in combination with optical surveys, determine the neutrino mass and the effects of dark energy via tomographic measurements of the growth of structure at $z < 3$; (3) measure the distribution of electron density and pressure around galaxy groups and clusters, and calibrate the effects of energy input from galaxy formation on the surrounding environment; (4) produce a sample of more than 30,000 galaxy clusters, and more than 100,000 extragalactic millimeter sources, including regularly sampled AGN light-curves, to study these sources and their emission physics; (5) measure the polarized emission from magnetically aligned dust grains in our Galaxy, to study the properties of dust and the role of magnetic fields in star formation; (6) constrain asteroid regoliths, search for Trans-Neptunian Objects, and either detect or eliminate large portions of the phase space in the search for Planet 9; and (7) provide a powerful new window into the transient universe on time scales of minutes to years, concurrent with observations from Rubin of overlapping sky.
Perceptual Visual Quality Assessment: Principles, Methods, and Future Directions
2025-03-01
Wei Zhou, Hadi Amirpour, Christian Timmerer, Guangtao Zhai, Patrick Le Callet, Alan C. Bovik
As multimedia services such as video streaming, video conferencing, virtual reality (VR), and online gaming continue to expand, ensuring high perceptual visual quality becomes a priority to maintain user satisfaction and competitiveness. However, multimedia content undergoes various distortions during acquisition, compression, transmission, and storage, resulting in the degradation of experienced quality. Thus, perceptual visual quality assessment (PVQA), which focuses on evaluating the quality of multimedia content based on human perception, is essential for optimizing user experiences in advanced communication systems. Several challenges are involved in the PVQA process, including diverse characteristics of multimedia content such as image, video, VR, point cloud, mesh, multimodality, etc., and complex distortion scenarios as well as viewing conditions. In this paper, we first present an overview of PVQA principles and methods. This includes both subjective methods, where users directly rate their experiences, and objective methods, where algorithms predict human perception based on measurable factors such as bitrate, frame rate, and compression levels. Based on the basics of PVQA, quality predictors for different multimedia data are then introduced. In addition to traditional images and videos, immersive multimedia and generative artificial intelligence (GenAI) content are also discussed. Finally, the paper concludes with a discussion on the future directions of PVQA research.
Urban Safety Perception Through the Lens of Large Multimodal Models: A Persona-based Approach
2025-03-01
Ciro Beneduce, Bruno Lepri, Massimiliano Luca
Understanding how urban environments are perceived in terms of safety is crucial for urban planning and policymaking. Traditional methods like surveys are limited by high cost, required time, and scalability issues. To overcome these challenges, this study introduces Large Multimodal Models (LMMs), specifically Llava 1.6 7B, as a novel approach to assess safety perceptions of urban spaces using street-view images. In addition, the research investigated how this task is affected by different socio-demographic perspectives, simulated by the model through Persona-based prompts. Without additional fine-tuning, the model achieved an average F1-score of 59.21% in classifying urban scenarios as safe or unsafe, identifying three key drivers of perceived unsafety: isolation, physical decay, and urban infrastructural challenges. Moreover, incorporating Persona-based prompts revealed significant variations in safety perceptions across the socio-demographic groups of age, gender, and nationality. Elder and female Personas consistently perceive higher levels of unsafety than younger or male Personas. Similarly, nationality-specific differences were evident in the proportion of unsafe classifications ranging from 19.71% in Singapore to 40.15% in Botswana. Notably, the model's default configuration aligned most closely with a middle-aged, male Persona. These findings highlight the potential of LMMs as a scalable and cost-effective alternative to traditional methods for urban safety perceptions. While the sensitivity of these models to socio-demographic factors underscores the need for thoughtful deployment, their ability to provide nuanced perspectives makes them a promising tool for AI-driven urban planning.
Brain Foundation Models: A Survey on Advancements in Neural Signal Processing and Brain Discovery
2025-03-01
Xinliang Zhou, Chenyu Liu, Zhisheng Chen, Kun Wang, Yi Ding, Ziyu Jia, Qingsong Wen
Brain foundation models (BFMs) have emerged as a transformative paradigm in computational neuroscience, offering a revolutionary framework for processing diverse neural signals across different brain-related tasks. These models leverage large-scale pre-training techniques, allowing them to generalize effectively across multiple scenarios, tasks, and modalities, thus overcoming the traditional limitations faced by conventional artificial intelligence (AI) approaches in understanding complex brain data. By tapping into the power of pretrained models, BFMs provide a means to process neural data in a more unified manner, enabling advanced analysis and discovery in the field of neuroscience. In this survey, we define BFMs for the first time, providing a clear and concise framework for constructing and utilizing these models in various applications. We also examine the key principles and methodologies for developing these models, shedding light on how they transform the landscape of neural signal processing. This survey presents a comprehensive review of the latest advancements in BFMs, covering the most recent methodological innovations, novel views of application areas, and challenges in the field. Notably, we highlight the future directions and key challenges that need to be addressed to fully realize the potential of BFMs. These challenges include improving the quality of brain data, optimizing model architecture for better generalization, increasing training efficiency, and enhancing the interpretability and robustness of BFMs in real-world applications.
In this paper, we explore the fundamental role of the Monge-Amp\`ere equation in deep learning, particularly in the context of Boltzmann machines and energy-based models. We first review the structure of Boltzmann learning and its relation to free energy minimization. We then establish a connection between optimal transport theory and deep learning, demonstrating how the Monge-Amp\`ere equation governs probability transformations in generative models. Additionally, we provide insights from quantum geometry, showing that the space of covariance matrices arising in the learning process coincides with the Connes-Araki-Haagerup (CAH) cone in von Neumann algebra theory. Furthermore, we introduce an alternative approach based on renormalization group (RG) flow, which, while distinct from the optimal transport perspective, reveals another manifestation of the Monge-Amp\`ere domain in learning dynamics. This dual perspective offers a deeper mathematical understanding of hierarchical feature learning, bridging concepts from statistical mechanics, quantum geometry, and deep learning theory.
Surveying the Whirlpool at Arcseconds with NOEMA (SWAN) II: Survey design and observations
2025-03-01
K. Sophia Stuber, Jerome Pety, Antonio Usero, Eva Schinnerer, Frank Bigiel, J. María Jiménez-Donaire, Jakob den Brok, K. Adam Leroy, Ina Galić, Annie Hughes, Mallory Thorp, T. Ashley. Barnes, Ivana Bešlić, Cosima Eibensteiner, R. Damian Gleis, S. Ralf Klessen, Daizhong Liu, Hsi-An Pan, Toshiki Saito, K. Sumit Sarbadhicary, G. Thomas Williams
We present Surveying the Whirlpool at Arcseconds with NOEMA (SWAN), a high-resolution, high-sensitivity survey to map molecular lines in the 3mm band in M51 (the Whirlpool galaxy). SWAN has obtained the largest high-sensitivity map (5x7 kpc2) of N2H+ emission at cloud-scale resolution (3" ~125 pc) in an external galaxy to date. We describe the observations and data reduction of ~214 hours of interferometric data from NOEMA, ~55 hours of tailored new observations with the IRAM-30m telescope and the combination of NOEMA, new and ~14 hours of archival 30m observations. We detect widespread emission from 9 molecular transition lines. The J=1-0 transitions of CO isotopologues 13CO and C18O are detected at high significance across the full observed field-of-view (FoV). HCN, HNC, HCO+, and N2H+(1-0) are detected in the center, molecular ring and spiral arms of the galaxy, while the shock tracer HNCO(4-3), (5-4) and PDR tracer C2H(1-0) are detected in the central ~1 kpc and molecular ring only. For most of the lines that we detect, average line ratios with respect to CO are increased by up to a factor of ~3 in the central 1 kpc, where an AGN and its low-inclination outflow are present, compared to the disk. Across the full SWAN FoV, 13CO, C18O, HCN, HNC, HCO+ and N2H+ are 8\pm2, 29\pm6, 17\pm3,37\pm5, 26\pm5 and 63\pm38 times fainter than 12CO, respectively, in pixels where each line is significantly detected. Although we observe variations in line ratios between larger-scale environments like the center and disk of M51, the scatter within each environment also indicates the influence of smaller-scale processes. The ability to measure these effects is only possible thanks to the high resolution and high sensitivity of the SWAN dataset across multiple environments. This provides the sharpest view of these molecular transitions over the largest physical area ever captured in an external galaxy.
Roadmap on Nonlocality in Photonic Materials and Metamaterials
2025-03-01
Francesco Monticone, N. Asger Mortensen, Antonio I. Fernández-Domínguez, Yu Luo, Christos Tserkezis, Jacob B. Khurgin, Tigran V. Shahbazyan, André J. Chaves, Nuno M. R. Peres, Gino Wegner, Kurt Busch, Huatian Hu, Fabio Della Sala, Pu Zhang, Cristian Ciracì, Javier Aizpurua, Antton Babaze, Andrei G. Borisov, Xue-Wen Chen, Thomas Christensen, Wei Yan, Yi Yang, Ulrich Hohenester, Lorenz Huber, Martijn Wubs, Simone De Liberato, P. A. D. Gonçalves, F. Javier García De Abajo, Ortwin Hess, Illya Tarasenko, Joel D. Cox, Line Jelver, Eduardo J. C. Dias, Miguel Sánchez Sánchez, Dionisios Margetis, Guillermo Gómez-Santos, Tobias Stauber, Sergei Tretyakov, Constantin Simovski, Samaneh Pakniyat, J. Sebastián Gómez-Díaz, Igor V. Bondarev, Svend-Age Biehs, Alexandra Boltasseva, Vladimir M. Shalaev, Alexey V. Krasavin, Anatoly V. Zayats, Andrea Alù, Jung-Hwan Song, Mark L. Brongersma, Uriel Levy, Olivia Y. Long, Cheng Guo, Shanhui Fan, Sergey I. Bozhevolnyi, Adam Overvig, Filipa R. Prudêncio, Mário G. Silveirinha, S. Ali Hassani Gangaraj, Christos Argyropoulos, Paloma A. Huidobro, Emanuele Galiffi, Fan Yang, John B. Pendry, David A. B. Miller
Photonic technologies continue to drive the quest for new optical materials with unprecedented responses. A major frontier in this field is the exploration of nonlocal (spatially dispersive) materials, going beyond the local, wavevector-independent assumption traditionally made in optical material modeling. On one end, the growing interest in plasmonic, polaritonic and quantum materials has revealed naturally occurring nonlocalities, emphasizing the need for more accurate models to predict and design their optical responses. This has major implications also for topological, nonreciprocal, and time-varying systems based on these material platforms. Beyond natural materials, artificially structured materials--metamaterials and metasurfaces--can provide even stronger and engineered nonlocal effects, emerging from long-range interactions or multipolar effects. This is a rapidly expanding area in the field of photonic metamaterials, with open frontiers yet to be explored. In the case of metasurfaces, in particular, nonlocality engineering has become a powerful tool for designing strongly wavevector-dependent responses, enabling enhanced wavefront control, spatial compression, multifunctional devices, and wave-based computing. Furthermore, nonlocality and related concepts play a critical role in defining the ultimate limits of what is possible in optics, photonics, and wave physics. This Roadmap aims to survey the most exciting developments in nonlocal photonic materials, highlight new opportunities and open challenges, and chart new pathways that will drive this emerging field forward--toward new scientific discoveries and technological advancements.
We briefly review the status of threshold resummation for two massive $Z$-bosons in the Standard Model. We discuss some recent results for $Z$-boson pair production at next-to-next-to-leading order + next-to-next-to-leading logarithmic accuracy.
A Bayesian Interpretation of the Internal Model Principle
2025-03-01
Manuel Baltieri, Martin Biehl, Matteo Capucci, Nathaniel Virgo
The internal model principle, originally proposed in the theory of control of linear systems, nowadays represents a more general class of results in control theory and cybernetics. The central claim of these results is that, under suitable assumptions, if a system (a controller) can regulate against a class of external inputs (from the environment), it is because the system contains a model of the system causing these inputs, which can be used to generate signals counteracting them. Similar claims on the role of internal models appear also in cognitive science, especially in modern Bayesian treatments of cognitive agents, often suggesting that a system (a human subject, or some other agent) models its environment to adapt against disturbances and perform goal-directed behaviour. It is however unclear whether the Bayesian internal models discussed in cognitive science bear any formal relation to the internal models invoked in standard treatments of control theory. Here, we first review the internal model principle and present a precise formulation of it using concepts inspired by categorical systems theory. This leads to a formal definition of `model' generalising its use in the internal model principle. Although this notion of model is not a priori related to the notion of Bayesian reasoning, we show that it can be seen as a special case of possibilistic Bayesian filtering. This result is based on a recent line of work formalising, using Markov categories, a notion of `interpretation', describing when a system can be interpreted as performing Bayesian filtering on an outside world in a consistent way.
The one-pion exchange interaction between the kaon and the vector antikaon is investigated by solving the Schrodinger equation in the S-wave approximation. In addition to the $f_1(1285)$ particle, another bound state of $K \bar{K}^*$ is found, which is approximately 9 MeV below the threshold of $K \bar{K}^*$ and labeled $f_1(1378)$ for convenience in this manuscript. Under the outgoing wave condition, two resonance states of $K \bar{K}^*$ are produced by solving the Schrodinger equation with different coupling constants in the one-pion-exchange potential fixed with the binding energies of $K \bar{K}^*$ bound states $f_1(1285)$ and $f_1(1378)$ respectively. Both of the resonance states are located in the vicinity of 1400 MeV, and thus it is reasonable to assume that these two resonance states correspond to the $f_1(1420)$ particle in the review of the Particle Data Group although they arise from different couplings of the kaon and vector antikaon, respectively.
Zeeman split Kramers doublets in spin-supersolid candidate Na$_{2}$BaCo(PO$_{4}$)$_{2}$
2025-03-01
T. I. Popescu, N. Gora, F. Demmel, Z. Xu, R. Zhong, T. J. Williams, R. J. Cava, G. Xu, C. Stock
Na$_{2}$BaCo(PO$_{4}$)$_{2}$ is a triangular antiferromagnet that displays highly efficient adiabatic demagnetization cooling (J. Xiang $\textit{et al.}$ Nature ${\bf{625}}$, 270 (2024)) near a quantum critical point at $\mu_{0}H_{c}\sim 1.6$ T, separating a low-field magnetically disordered from a high-field fully polarized ferromagnetic phase. We apply high resolution backscattering neutron spectroscopy in an applied field to study the magnetic excitations near $\mu_{0}H_{c}$. At large fields we observe ferromagnetic fluctuations that gradually transition to being overdamped in energy below $\mu_{0}H_{c}$ where the magnetism is spatially disordered. We parameterize the excitations in the high field polarized phase in terms of coupled Zeeman split Kramers doublets originating from the presence of spin-orbit coupling. On reducing the field, the splitting between the Kramers doublets is reduced and if done adiabatically, provides a mechanism for reducing temperature. On lowering the applied field through the $\mu_{0}H_{c}$ the excitations characterize a textured phase that we suggest is inefficient for cooling. Low temperature disordered frustrated magnets built on Kramers doublets with nearby quantum critical points provide a route for efficient magnetocalorics.
The effect of remote work on urban transportation emissions: evidence from 141 cities
2025-03-01
Sophia Shen, Xinyi Wang, Nicholas Caros, Jinhua Zhao
The overall impact of working from home (WFH) on transportation emissions remains a complex issue, with significant implications for policymaking. This study matches socioeconomic information from American Community Survey (ACS) to the global carbon emissions dataset for selected Metropolitan Statistical Areas (MSAs) in the US. We analyze the impact of WFH on transportation emissions before and during the COVID-19 pandemic. Employing cross-sectional multiple regression models and Blinder-Oaxaca decomposition, we examine how WFH, commuting mode, and car ownership influence transportation emissions across 141 MSAs in the United States. We find that the prevalence of WFH in 2021 is associated with lower transportation emissions, whereas WFH in 2019 did not significantly impact transportation emissions. After controlling for public transportation usage and car ownership, we find that a 1% increase in WFH corresponds to a 0.17 kilogram or 1.8% reduction of daily average transportation emissions per capita. The Blinder-Oaxaca decomposition shows that WFH is the main driver in reducing transportation emissions per capita during the pandemic. Our results show that the reductive influence of public transportation on transportation emissions has declined, while the impact of car ownership on increasing transportation emissions has risen. Collectively, these results indicate a multifaceted impact of WFH on transportation emissions. This study underscores the need for a nuanced, data-driven approach in crafting WFH policies to mitigate transportation emissions effectively.
The intriguing outcomes of double degenerate mergers
2025-03-01
Lilia Ferrario
This review talk explores the diverse outcomes of white dwarf mergers, emphasising that not all double degenerate mergers result in supernovae. Possible outcomes include the formation of a more massive WD, partial explosions that leave behind unusual remnants, and merger-induced collapse leading to the formation of rapidly spinning neutron stars or magnetars. Additionally, merging WDs have been proposed as potential progenitors for long gamma-ray bursts and Fast Radio Bursts.
A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challenges
2025-03-01
Nandish Chattopadhyay, Abdul Basit, Bassem Ouni, Muhammad Shafique
Adversarial attacks have emerged as a major challenge to the trustworthy deployment of machine learning models, particularly in computer vision applications. These attacks have a varied level of potency and can be implemented in both white box and black box approaches. Practical attacks include methods to manipulate the physical world and enforce adversarial behaviour by the corresponding target neural network models. Multiple different approaches to mitigate different kinds of such attacks are available in the literature, each with their own advantages and limitations. In this survey, we present a comprehensive systematization of knowledge on adversarial defenses, focusing on two key computer vision tasks: image classification and object detection. We review the state-of-the-art adversarial defense techniques and categorize them for easier comparison. In addition, we provide a schematic representation of these categories within the context of the overall machine learning pipeline, facilitating clearer understanding and benchmarking of defenses. Furthermore, we map these defenses to the types of adversarial attacks and datasets where they are most effective, offering practical insights for researchers and practitioners. This study is necessary for understanding the scope of how the available defenses are able to address the adversarial threats, and their shortcomings as well, which is necessary for driving the research in this area in the most appropriate direction, with the aim of building trustworthy AI systems for regular practical use-cases.
Review on Determining the Number of Communities in Network Data
2025-03-01
Zhengyuan Du, Jason Cui
This paper reviews statistical methods for hypothesis testing and clustering in network models. We analyze the method by Bickel et al. (2016) for deriving the asymptotic null distribution of the largest eigenvalue, noting its slow convergence and the need for bootstrap corrections. The SCORE method by Jin et al. (2015) and the NCV method by Chen et al. (2018) are evaluated for their efficacy in clustering within Degree-Corrected Block Models, with NCV facing challenges due to its time-intensive nature. We suggest exploring eigenvector entry distributions as a potential efficiency improvement.
In optoelectronics, achieving electrical reconfigurability is crucial as it enables the encoding, decoding, manipulating, and processing of information carried by light. In recent years, two-dimensional van der Waals (2-D vdW) materials have emerged as promising platforms for realizing reconfigurable optoelectronic devices. Compared to materials with bulk crystalline lattice, 2-D vdW materials offer superior electrical reconfigurability due to high surface-to-volume ratio, quantum confinement, reduced dielectric screening effect, and strong dipole resonances. Additionally, their unique band structures and associated topology and quantum geometry provide novel tuning capabilities. This review article seeks to establish a connection between the fundamental physics underlying reconfigurable optoelectronics in 2-D materials and their burgeoning applications in intelligent optoelectronics. We first survey various electrically reconfigurable properties of 2-D vdW materials and the underlying tuning mechanisms. Then we highlight the emerging applications of such devices, including dynamic intensity, phase and polarization control, and intelligent sensing. Finally, we discuss the opportunities for future advancements in this field.
Current-driven collective control of helical spin texture in van der Waals antiferromagnet
2025-03-01
Kai-Xuan Zhang, Suik Cheon, Hyuncheol Kim, Pyeongjae Park, Yeochan An, Suhan Son, Jingyuan Cui, Jihoon Keum, Joonyoung Choi, Younjung Jo, Hwiin Ju, Jong-Seok Lee, Youjin Lee, Maxim Avdeev, Armin Kleibert, Hyun-Woo Lee, Je-Geun Park
Electrical control of quantum magnetic states is essential in spintronic science. Initial studies on the ferromagnetic state control were extended to collinear antiferromagnets and, more recently, noncollinear antiferromagnets. However, electrical control mechanisms of such exotic magnetic states remain poorly understood. Here, we report the first experimental and theoretical example of the current control of helical antiferromagnets, arising from the competition between collinear antiferromagnetic exchange and interlayer Dzyaloshinskii-Moriya interaction in new van-der-Waals (vdW) material Ni1/3NbS2. Due to the intrinsic broken inversion symmetry, an in-plane current generates spin-orbit torque that, in turn, interacts directly with the helical antiferromagnetic order. Our theoretical analyses indicate that a weak ferromagnetic order coexists due to the Dzyaloshinskii-Moriya interaction, mediating the spin-orbit torque to collectively rotate the helical antiferromagnetic order. Our Ni1/3NbS2 nanodevice experiments produce current-dependent resistance change consistent with the theoretical prediction. This work widens our understanding of the electrical control of helical antiferromagnets and promotes vdW quantum magnets as interesting material platforms for electrical control.
Advances in Anti-Deception Jamming Strategies for Radar Systems: A Survey
2025-03-01
Helena Calatrava, Shuo Tang, Pau Closas
Deception jamming has long been a significant threat to radar systems, interfering with search, acquisition, and tracking by introducing false information that diverts attention from the targets of interest. As deception strategies become more sophisticated, the vulnerability of radar systems to these attacks continues to escalate. This paper offers a comprehensive review of the evolution of anti-deception jamming techniques, starting with legacy solutions and progressing to the latest advancements. Current research is categorized into three key areas: prevention strategies, which hinder the ability of jammers to alter radar processing; detection strategies, which alert the system to deception and may classify the type of attack; and mitigation strategies, which aim to reduce or suppress the impact of jamming. Additionally, key avenues for further research are highlighted, with a particular emphasis on distributed, cognitive, and AI-enabled radar systems. We envision this paper as a gateway to the existing literature on anti-deception jamming, a critical area for safeguarding radar systems against evolving threats.
The introduction of Large Language Models (LLMs) has significantly impacted various fields, including education, for example, by enabling the creation of personalized learning materials. However, their use in Systematic Reviews (SRs) reveals limitations such as restricted access to specialized vocabularies, lack of domain-specific reasoning, and a tendency to generate inaccurate information. Existing SR tools often rely on traditional NLP methods and fail to address these issues adequately. To overcome these challenges, we developed the ``NeuroLit Navigator,'' a system that combines domain-specific LLMs with structured knowledge sources like Medical Subject Headings (MeSH) and the Unified Medical Language System (UMLS). This integration enhances query formulation, expands search vocabularies, and deepens search scopes, enabling more precise searches. Deployed in multiple universities and tested by over a dozen librarians, the NeuroLit Navigator has reduced the time required for initial literature searches by 90\%. Despite this efficiency, the initial set of articles retrieved can vary in relevance and quality. Nonetheless, the system has greatly improved the reproducibility of search results, demonstrating its potential to support librarians in the SR process.
LLM Post-Training: A Deep Dive into Reasoning Large Language Models
2025-02-28
Komal Kumar, Tajamul Ashraf, Omkar Thawakar, Rao Muhammad Anwer, Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, Phillip H. S. Torr, Salman Khan, Fahad Shahbaz Khan
Large Language Models (LLMs) have transformed the natural language processing landscape and brought to life diverse applications. Pretraining on vast web-scale data has laid the foundation for these models, yet the research community is now increasingly shifting focus toward post-training techniques to achieve further breakthroughs. While pretraining provides a broad linguistic foundation, post-training methods enable LLMs to refine their knowledge, improve reasoning, enhance factual accuracy, and align more effectively with user intents and ethical considerations. Fine-tuning, reinforcement learning, and test-time scaling have emerged as critical strategies for optimizing LLMs performance, ensuring robustness, and improving adaptability across various real-world tasks. This survey provides a systematic exploration of post-training methodologies, analyzing their role in refining LLMs beyond pretraining, addressing key challenges such as catastrophic forgetting, reward hacking, and inference-time trade-offs. We highlight emerging directions in model alignment, scalable adaptation, and inference-time reasoning, and outline future research directions. We also provide a public repository to continually track developments in this fast-evolving field: https://github.com/mbzuai-oryx/Awesome-LLM-Post-training.
Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis
2025-02-28
Li Yang, Mirna El Rajab, Abdallah Shami, Sami Muhaidat
Zero-Touch Networks (ZTNs) represent a state-of-the-art paradigm shift towards fully automated and intelligent network management, enabling the automation and intelligence required to manage the complexity, scale, and dynamic nature of next-generation (6G) networks. ZTNs leverage Artificial Intelligence (AI) and Machine Learning (ML) to enhance operational efficiency, support intelligent decision-making, and ensure effective resource allocation. However, the implementation of ZTNs is subject to security challenges that need to be resolved to achieve their full potential. In particular, two critical challenges arise: the need for human expertise in developing AI/ML-based security mechanisms, and the threat of adversarial attacks targeting AI/ML models. In this survey paper, we provide a comprehensive review of current security issues in ZTNs, emphasizing the need for advanced AI/ML-based security mechanisms that require minimal human intervention and protect AI/ML models themselves. Furthermore, we explore the potential of Automated ML (AutoML) technologies in developing robust security solutions for ZTNs. Through case studies, we illustrate practical approaches to securing ZTNs against both conventional and AI/ML-specific threats, including the development of autonomous intrusion detection systems and strategies to combat Adversarial ML (AML) attacks. The paper concludes with a discussion of the future research directions for the development of ZTN security approaches.
JWST/MIRI Study of the Enigmatic Mid-Infrared Rings in the Planetary Nebula NGC 1514
2025-02-28
Michael E. Ressler, Alba Aller, David Jones, Ryan M. Lau, Luis F. Miranda, Karen Willacy
While NGC 1514 is an elliptical, but complex, planetary nebula at optical wavelengths, it was discovered to have a pair of infrared-bright, axisymmetric rings contained within its faint outer shell during the course of the WISE all-sky survey. We have obtained JWST mid-infrared imaging and spectroscopy of the nebula through the use of simultaneous observations with the MIRI Imager and Medium Resolution Spectrometer, selecting the F770W, F1280W, and F2550W filters to match each of the MRS's three grating positions. These observations show that the rings are clearly resolved and relatively distinct structures, with both filamentary and clumpy detail throughout. There is also cloud-like material that has a turbulent appearance in the interior of the rings, particularly at the longest wavelengths, and faint ejecta-like structures just outside the ring boundaries. Despite their brightness, the emission from the rings within the three imager passbands is shown to be dominated by thermal emission from very small grains, not line emission from atomic hydrogen or forbidden atomic lines, shocked molecular hydrogen, or PAHs. The doppler velocities derived from the two brightest emission lines in the rings, however, suggest that the material from which the rings were formed was ejected during an early period of very heavy mass loss from the PN progenitor, then shaped by asymmetrical fast winds from the central binary pair.
Supporting the development of Machine Learning for fundamental science in a federated Cloud with the AI_INFN platform
2025-02-28
Lucio Anderlini, Matteo Barbetti, Giulio Bianchini, Diego Ciangottini, Stefano Dal Pra, Diego Michelotto, Carmelo Pellegrino, Rosa Petrini, Alessandro Pascolini, Daniele Spiga
Machine Learning (ML) is driving a revolution in the way scientists design, develop, and deploy data-intensive software. However, the adoption of ML presents new challenges for the computing infrastructure, particularly in terms of provisioning and orchestrating access to hardware accelerators for development, testing, and production. The INFN-funded project AI_INFN ("Artificial Intelligence at INFN") aims at fostering the adoption of ML techniques within INFN use cases by providing support on multiple aspects, including the provision of AI-tailored computing resources. It leverages cloud-native solutions in the context of INFN Cloud, to share hardware accelerators as effectively as possible, ensuring the diversity of the Institute's research activities is not compromised. In this contribution, we provide an update on the commissioning of a Kubernetes platform designed to ease the development of GPU-powered data analysis workflows and their scalability on heterogeneous, distributed computing resources, possibly federated as Virtual Kubelets with the interLink provider.
Token-level Ensembling of Models with Different Vocabularies
2025-02-28
Rachel Wicks, Kartik Ravisankar, Xinchen Yang, Philipp Koehn, Matt Post
Model ensembling is a technique to combine the predicted distributions of two or more models, often leading to improved robustness and performance. For ensembling in text generation, the next token's probability distribution is derived from a weighted sum of the distributions of each individual model. This requires the underlying models to share the same subword vocabulary, limiting the applicability of ensembling, since many open-sourced models have distinct vocabularies. In research settings, experimentation or upgrades to vocabularies may introduce multiple vocabulary sizes. This paper proposes an inference-time only algorithm that allows for ensembling models with different vocabularies, without the need to learn additional parameters or alter the underlying models. Instead, the algorithm ensures that tokens generated by the ensembled models \textit{agree} in their surface form. We apply this technique to combinations of traditional encoder-decoder models and decoder-only LLMs and evaluate on machine translation. In addition to expanding to model pairs that were previously incapable of token-level ensembling, our algorithm frequently improves translation performance over either model individually.
On projection from $A^p_ω$ to the Hardy spaces $H^p$
2025-02-28
Armen Jerbashian, Joel E. Restrepo
This paper describes the known results on the projection from the most general holomorphic spaces $A^p_\omega$, which depend on a functional parameter $\omega$ and are over the unit disc, upper half-plane and the finite complex plane, to the classical Hardy spaces $H^p.$ The paper can be considered as a survey in the mentioned topic. A new result on the projection of the half-plane $A^p_\omega$ to the half-plane Hardy space $H^p$ is obtained.
We report the measurement, using angle-resolved photoemission spectroscopy, of the metallic electronic structure of the hole-doped thermoelectric oxide CuRh$_{0.9}$Mg$_{0.1}$O$_2$. The material is found to have a ``pudding mold'' type band structure, with a nearly flat band edge located near the Fermi level, which is thought to be the origin of the thermoelectric behavior of this material. The experimental data match the density functional theory of the undoped parent compound, simply corrected by a rigid shift of the bands. Transport calculations based on the observed band structure yield a Seebeck coefficient of $\sim 200 \,\mu$V/K for the undoped parent material, consistent with experimental measurements. Our results show that CuRhO$_2$ is a textbook example of how pure band-structural effects can result in a large thermoelectric figure of merit, demonstrating that flat band edges in oxides are a realistic route for the efficient conversion of thermal energy.
In today's digital landscape, Deep Recommender Systems (DRS) play a crucial role in navigating and customizing online content for individual preferences. However, conventional methods, which mainly depend on single recommendation task, scenario, data modality and user behavior, are increasingly seen as insufficient due to their inability to accurately reflect users' complex and changing preferences. This gap underscores the need for joint modeling approaches, which are central to overcoming these limitations by integrating diverse tasks, scenarios, modalities, and behaviors in the recommendation process, thus promising significant enhancements in recommendation precision, efficiency, and customization. In this paper, we comprehensively survey the joint modeling methods in recommendations. We begin by defining the scope of joint modeling through four distinct dimensions: multi-task, multi-scenario, multi-modal, and multi-behavior modeling. Subsequently, we examine these methods in depth, identifying and summarizing their underlying paradigms based on the latest advancements and potential research trajectories. Ultimately, we highlight several promising avenues for future exploration in joint modeling for recommendations and provide a concise conclusion to our findings.
When Is a Bulge Not a Bulge? Revealing the Satellite Nature of NGC 5474's Bulge
2025-02-28
Ray Garner III, J. Christopher Mihos, F. Fabián Rosales-Ortega
A satellite galaxy of the nearby spiral M101, NGC 5474 has a prominent bulge offset from the kinematic center of the underlying star-forming disk that has gained attention in recent years. Recent studies have proposed that this putative offset bulge is not a classical bulge within the plane of the disk but instead a dwarf companion galaxy along the line-of-sight. Using integral field spectroscopy data taken as part of the PPak IFS Nearby Galaxies Survey (PINGS), we perform the first analysis of the stellar and gas kinematics of this putative bulge and portions of the disk. We find a radial velocity offset of ~24 km/s between the emission lines produced by the disk HII regions and the absorption lines produced by the putative bulge stellar component. We interpret this velocity offset as evidence that the putative bulge and disk are two separate objects, the former orbiting around the latter, supporting simulations and observations of this peculiar system. We attempt to place this external companion into the context of the M101 Group and the M101-NGC 5474 interaction.
2025-02-28
Jiafeng Xiong, Ahmad Zareie, Rizos Sakellariou
Temporal networks have gained significant prominence in the past decade for modelling dynamic interactions within complex systems. A key challenge in this domain is Temporal Link Prediction (TLP), which aims to forecast future connections by analysing historical network structures across various applications including social network analysis. While existing surveys have addressed specific aspects of TLP, they typically lack a comprehensive framework that distinguishes between representation and inference methods. This survey bridges this gap by introducing a novel taxonomy that explicitly examines representation and inference from existing methods, providing a novel classification of approaches for TLP. We analyse how different representation techniques capture temporal and structural dynamics, examining their compatibility with various inference methods for both transductive and inductive prediction tasks. Our taxonomy not only clarifies the methodological landscape but also reveals promising unexplored combinations of existing techniques. This taxonomy provides a systematic foundation for emerging challenges in TLP, including model explainability and scalable architectures for complex temporal networks.
Effect of inflow conditions on tip vortex breakdown in a high Reynolds number wind turbine wake
2025-02-28
Mano Grunwald, Claudia E. Brunner
Understanding the re-energization of wind turbine wakes is crucial for the design and control of wind farms. Close to the rotor, this process is determined by the dynamics of the tip vortices. Here, we experimentally investigate the downstream evolution of the tip vortices for different inflow conditions. The experiments were performed in the Variable Density Turbulence Tunnel at the Max Planck Institute for Dynamics and Self-Organization, which uses pressurized $\mathrm{SF}_6$ as the working fluid to achieve a turbine diameter-based Reynolds number of $\mathrm{Re}_D=2.9\times10^6$. An active turbulence grid was used to generate atmospheric inflow conditions with varying levels of mean shear and turbulence intensity. Hot wire measurements of the streamwise velocity component were conducted in the inflow and the wake of a model wind turbine MoWiTO 0.6 for various tip speed ratios and are used to investigate the scaling of tip vortex breakdown in the near wake. While the scaling is only weakly affected by variations in mean velocity shear, both turbulence intensity and tip speed ratio have a strong effect on vortex breakdown.
2025-02-28
Naomi Andrew, Sebastian Hensel, Sam Hughes, Richard D. Wade
We survey a number of constructions and open problems related to the handlebody group, with a focus on recent trends in geometric group theory, (co)homological properties, and its relationship to outer automorphism groups of free groups. We also briefly describe how the \emph{cheap $\alpha$-rebuilding property} of Abert, Bergeron, Fraczyk, and Gaboriau can be applied using the disc complex to deduce results about the homology growth of the handlebody group.
A Review on Generative AI For Text-To-Image and Image-To-Image Generation and Implications To Scientific Images
2025-02-28
Zineb Sordo, Eric Chagnon, Daniela Ushizima
This review surveys the state-of-the-art in text-to-image and image-to-image generation within the scope of generative AI. We provide a comparative analysis of three prominent architectures: Variational Autoencoders, Generative Adversarial Networks and Diffusion Models. For each, we elucidate core concepts, architectural innovations, and practical strengths and limitations, particularly for scientific image understanding. Finally, we discuss critical open challenges and potential future research directions in this rapidly evolving field.
This review article discusses some common enhanced sampling methods in relation to the process of self-assembly of biomolecules. An introduction to self-assembly and its challenges is covered followed by a brief overview of the methods and analysis for replica-exchange molecular dynamics, umbrella sampling, metadynamics, and machine learning based techniques. Applications of select methods towards peptides, proteins, polymers, nucleic acids, and supramolecules are discussed. Finally, a short discussion of the future directions of some of these methods is provided.
Multimodal Dreaming: A Global Workspace Approach to World Model-Based Reinforcement Learning
2025-02-28
Léopold Maytié, Roland Bertin Johannet, Rufin VanRullen
Humans leverage rich internal models of the world to reason about the future, imagine counterfactuals, and adapt flexibly to new situations. In Reinforcement Learning (RL), world models aim to capture how the environment evolves in response to the agent's actions, facilitating planning and generalization. However, typical world models directly operate on the environment variables (e.g. pixels, physical attributes), which can make their training slow and cumbersome; instead, it may be advantageous to rely on high-level latent dimensions that capture relevant multimodal variables. Global Workspace (GW) Theory offers a cognitive framework for multimodal integration and information broadcasting in the brain, and recent studies have begun to introduce efficient deep learning implementations of GW. Here, we evaluate the capabilities of an RL system combining GW with a world model. We compare our GW-Dreamer with various versions of the standard PPO and the original Dreamer algorithms. We show that performing the dreaming process (i.e., mental simulation) inside the GW latent space allows for training with fewer environment steps. As an additional emergent property, the resulting model (but not its comparison baselines) displays strong robustness to the absence of one of its observation modalities (images or simulation attributes). We conclude that the combination of GW with World Models holds great potential for improving decision-making in RL agents.
CuPID: Leveraging Masked Single-Lead ECG Modelling for Enhancing the Representations
2025-02-28
Adtian Atienza, Gouthamaan Manimaran, Jakob E. Bardram, Sadasivan Puthusserypady
Wearable sensing devices, such as Electrocardiogram (ECG) heart-rate monitors, will play a crucial role in the future of digital health. This continuous monitoring leads to massive unlabeled data, incentivizing the development of unsupervised learning frameworks. While Masked Data Modelling (MDM) techniques have enjoyed wide use, their direct application to single-lead ECG data is suboptimal due to the decoder's difficulty handling irregular heartbeat intervals when no contextual information is provided. In this paper, we present Cueing the Predictor Increments the Detailing (CuPID), a novel MDM method tailored to single-lead ECGs. CuPID enhances existing MDM techniques by cueing spectrogram-derived context to the decoder, thus incentivizing the encoder to produce more detailed representations. This has a significant impact on the encoder's performance across a wide range of different configurations, leading CuPID to outperform state-of-the-art methods in a variety of downstream tasks.
Detection of the 2175Å UV Bump at z>7: Evidence for Rapid Dust Evolution in a Merging Reionisation-Era Galaxy
2025-02-28
Katherine Ormerod, Joris Witstok, Renske Smit, Anna de Graaff, Jakob M. Helton, Michael V. Maseda, Irene Shivaei, Andrew J. Bunker, Stefano Carniani, Francesco D'Eugenio, Rachana Bhatawdekar, Jacopo Chevallard, Marijn Franx, Nimisha Kumari, Roberto Maiolino, Pierluigi Rinaldi, Brant Robertson, Sandro Tacchella
Dust is a fundamental component of the interstellar medium (ISM) within galaxies, as dust grains are highly efficient absorbers of UV and optical photons. Accurately quantifying this obscuration is crucial for interpreting galaxy spectral energy distributions (SEDs). The extinction curves in the Milky Way (MW) and Large Magellanic Cloud (LMC) exhibit a strong feature known as the 2175A UV bump, most often attributed to small carbonaceous dust grains. This feature was recently detected in faint galaxies out to z~7 suggesting rapid formation channels. Here we report the detection of a strong UV bump in a luminous Lyman-break galaxy at z = 7.11235, GNWY-7379420231, through observations taken as part of the NIRSpec Wide GTO survey. We fit a dust attenuation curve that is consistent with the MW extinction curve within 1{\sigma}, in a galaxy just ~700 Myr after the Big Bang. From the integrated spectrum, we infer a young mass-weighted age (t* ~ 22-59 Myr) for this galaxy, however spatially resolved SED fitting unveils the presence of an older stellar population (t* ~ 252 Myr). Furthermore, morphological analysis provides evidence for a potential merger. The underlying older stellar population suggests the merging system could be pre-enriched, with the dust illuminated by a merger-induced starburst. Moreover, turbulence driven by stellar feedback in this bursty region may be driving PAH formation through top-down shattering. The presence of a UV bump in GNWY-7379420231 solidifies growing evidence for the rapid evolution of dust properties within the first billion years of cosmic time.
Re-evaluating Theory of Mind evaluation in large language models
2025-02-28
Jennifer Hu, Felix Sosa, Tomer Ullman
The question of whether large language models (LLMs) possess Theory of Mind (ToM) -- often defined as the ability to reason about others' mental states -- has sparked significant scientific and public interest. However, the evidence as to whether LLMs possess ToM is mixed, and the recent growth in evaluations has not resulted in a convergence. Here, we take inspiration from cognitive science to re-evaluate the state of ToM evaluation in LLMs. We argue that a major reason for the disagreement on whether LLMs have ToM is a lack of clarity on whether models should be expected to match human behaviors, or the computations underlying those behaviors. We also highlight ways in which current evaluations may be deviating from "pure" measurements of ToM abilities, which also contributes to the confusion. We conclude by discussing several directions for future research, including the relationship between ToM and pragmatic communication, which could advance our understanding of artificial systems as well as human cognition.
Some computational aspects of using Huygens-Fresnel-Kirchoff diffraction theory
2025-02-28
Ilya A. Kudryavtsev
Diffraction is a phenomenon, discussed for centuries from various points of view. The very simple principle, proposed by Huygens [1] and then modified by Fresnel[2], Stokes [3] and Kirchoff [4], allows us to make calculations, substituting an incident wave by the multitude of waves, radiated by the number of secondary sources with regard for interference. Besides mentioned scientists, many others have contributed to this theory. A reasonable historical review can reveal a challenging history of the research of this phenomenon with many names and this story is not still ended. A good historical re-view can be seen in [5]. Classical cases of the diffraction by a hole and a disk are included in many text-books for scholars. Nevertheless, there is still some place for discussions. This paper is an attempt to discuss some computational issues, based on the original Huygens-Fresnel-Kirchoff method and apply it to 3D objects.
HoloMine: A Synthetic Dataset for Buried Landmines Recognition using Microwave Holographic Imaging
2025-02-28
Emanuele Vivoli, Lorenzo Capineri, Marco Bertini
The detection and removal of landmines is a complex and risky task that requires advanced remote sensing techniques to reduce the risk for the professionals involved in this task. In this paper, we propose a novel synthetic dataset for buried landmine detection to provide researchers with a valuable resource to observe, measure, locate, and address issues in landmine detection. The dataset consists of 41,800 microwave holographic images (2D) and their holographic inverted scans (3D) of different types of buried objects, including landmines, clutter, and pottery objects, and is collected by means of a microwave holography sensor. We evaluate the performance of several state-of-the-art deep learning models trained on our synthetic dataset for various classification tasks. While the results do not yield yet high performances, showing the difficulty of the proposed task, we believe that our dataset has significant potential to drive progress in the field of landmine detection thanks to the accuracy and resolution obtainable using holographic radars. To the best of our knowledge, our dataset is the first of its kind and will help drive further research on computer vision methods to automatize mine detection, with the overall goal of reducing the risks and the costs of the demining process.
On Long-Term Problems in Multiplicative Ideal Theory and Factorization Theory
2025-02-28
Alfred Geroldinger, Hwankoo Kim, K. Alan Loper
In this survey article we discuss key open problems which could serve as a guidance for further research directions of multiplicative ideal theory and factorization theory.
2025-02-28
Martin Cooney, Sivadinesh Ponrajan, Fernando Alonso-Fernandez
Technologies such as robotics, Artificial Intelligence (AI), and Computer Vision (CV) can be applied to crime scene analysis (CSA) to help protect lives, facilitate justice, and deter crime, but an overview of the tasks that can be automated has been lacking. Here we follow a speculate prototyping approach: First, the STAIR tool is used to rapidly review the literature and identify tasks that seem to have not received much attention, like accessing crime sites through a window, mapping/gathering evidence, and analyzing blood smears. Secondly, we present a prototype of a small drone that implements these three tasks with 75%, 85%, and 80% performance, to perform a minimal analysis of an indoor crime scene. Lessons learned are reported, toward guiding next work in the area.
Clinical knowledge is the collection of information learned from studies on the causes, prognosis, diagnosis, and treatment of diseases. This type of knowledge can improve curing performances, and promote physical health. With the emergence of large language models (LLMs), medical artificial intelligence (medical AI), which aims to apply academic medical AI systems to real-world medical scenarios, has entered a new age of development, resulting in excellent works such as DoctorGPT and Pangu-Drug from academic and industrial researches. However, the field lacks a comprehensive compendium and comparison of building medical AI systems from academia and industry. Therefore, this survey focuses on the building paradigms of medical AI systems including the use of clinical databases, datasets, training pipelines, integrating medical knowledge graphs, system applications, and evaluation systems. We hope that this survey can help relevant practical researchers understand the current performance of academic models in various fields of healthcare, as well as the potential problems and future directions for implementing these scientific achievements.
Target Selection for the Redshift-Limited WAVES-Wide with Machine Learning
2025-02-28
Gursharanjit Kaur, Maciej Bilicki, Wojciech Hellwing, the WAVES team
The forthcoming Wide Area Vista Extragalactic Survey (WAVES) on the 4-metre Multi-Object Spectroscopic Telescope (4MOST) has a key science goal of probing the halo mass function to lower limits than possible with previous surveys. For that purpose, in its Wide component, galaxies targetted by WAVES will be flux-limited to $Z<21.1$ mag and will cover the redshift range of $z<0.2$, at a spectroscopic success rate of $\sim95\%$. Meeting this completeness requirement, when the redshift is unknown a priori, is a challenge. We solve this problem with supervised machine learning to predict the probability of a galaxy falling within the WAVES-Wide redshift limit, rather than estimate each object's redshift. This is done by training an XGBoost tree-based classifier to decide if a galaxy should be a target or not. Our photometric data come from 9-band VST+VISTA observations, including KiDS+VIKING surveys. The redshift labels for calibration are derived from an extensive spectroscopic sample overlapping with KiDS and ancillary fields. Our current results indicate that with our approach, we should be able to achieve the completeness of $\sim95\%$, which is the WAVES success criterion.
Enhancing software-hardware co-design for HEP by low-overhead profiling of single- and multi-threaded programs on diverse architectures with Adaptyst
2025-02-28
Maksymilian Graczyk, Stefan Roiser
Given the recent technological trends and novel computing paradigms spanning both software and hardware, physicists and software developers can no longer just rely on computers becoming faster to meet the ever-increasing computing demands of their research. Adapting systems to the new environment may be difficult though, especially in case of large and complex applications. Therefore, we introduce Adaptyst (formerly AdaptivePerf): an open-source and architecture-agnostic tool aiming for making these computational and procurement challenges easier to address. At the moment, Adaptyst profiles on- and off-CPU activity of codes, traces all threads and processes spawned by them, and analyses low-level software-hardware interactions to the extent supported by hardware. The tool addresses the main shortcomings of Linux "perf" and has been successfully tested on x86-64, arm64, and RISC-V instruction set architectures. Adaptyst is planned to be evolved towards a software-hardware co-design framework which scales from embedded to high-performance computing in both legacy and new applications and takes into account a bigger picture than merely choosing between CPUs and GPUs. Our paper describes the current development of the project and its roadmap.
Mean Field Approaches to Lattice Gauge Theories: A Review
2025-02-28
Pierpaolo Fontana, Andrea Trombettoni
Due to their broad applicability, gauge theories (GTs) play a crucial role in various areas of physics, from high-energy physics to condensed matter. Their formulations on lattices, lattice gauge theories (LGTs), can be studied, among many other methods, with tools coming from statistical mechanics lattice models, such as mean field methods, which are often used to provide approximate results. Applying these methods to LGTs requires particular attention due to the intrinsic local nature of gauge symmetry, how it is reflected in the variables used to formulate the theory, and the breaking of gauge invariance when approximations are introduced. This issue has been addressed over the decades in the literature, yielding different conclusions depending on the formulation of the theory under consideration. In this article, we focus on the mean field theoretical approach to the analysis of GTs and LGTs, connecting both older and more recent results that, to the best of our knowledge, have not been compared in a pedagogical manner. After a brief overview of mean field theory in statistical mechanics and many-body systems, we examine its application to pure LGTs with a generic compact gauge group. Finally, we review the existing literature on the subject, discussing the results obtained so far and their dependence on the formulation of the theory.
Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptions
2025-02-28
Matthias Orlikowski, Jiaxin Pei, Paul Röttger, Philipp Cimiano, David Jurgens, Dirk Hovy
People naturally vary in their annotations for subjective questions and some of this variation is thought to be due to the person's sociodemographic characteristics. LLMs have also been used to label data, but recent work has shown that models perform poorly when prompted with sociodemographic attributes, suggesting limited inherent sociodemographic knowledge. Here, we ask whether LLMs can be trained to be accurate sociodemographic models of annotator variation. Using a curated dataset of five tasks with standardized sociodemographics, we show that models do improve in sociodemographic prompting when trained but that this performance gain is largely due to models learning annotator-specific behaviour rather than sociodemographic patterns. Across all tasks, our results suggest that models learn little meaningful connection between sociodemographics and annotation, raising doubts about the current use of LLMs for simulating sociodemographic variation and behaviour.
We present the discovery and timing results of 15 pulsars discovered in a high Galactic latitude survey conducted with the Five-hundred-meter Aperture Spherical Telescope (FAST). The survey targeted a region as close as possible to the Galactic Center, encompassing an area near the Galactic Bulge. The newly discovered pulsars consist of eleven normal pulsars and four millisecond pulsars (MSPs). Among the MSPs, three are identified in binary systems with orbital periods of ~3.1, 4.6 and 12.5 days, respectively. We have successfully obtained coherent timing solutions for three of the normal pulsars (PSRs J1745-0059, J1746-0156 and J1800-0059). Furthermore, within our data set we found that four pulsars (three new and one known) show mode-changing and/or subpulse drifting phenomena. Comparing our discoveries with simulations of the Galactic disk and Bulge MSP populations indicates that these new pulsars are most likely located in the disk. Nonetheless, our discoveries demonstrate that deep surveys at high Galactic latitudes have significant potential to enhance our understanding of the MSP population in the direction of the Bulge.
In this work celebrating the centenary of quantum mechanics, we rewiev the principles of de Broglie Bohm theory, also known as pilot-wave theory and Bohmian mechanics. We assess the most common reading of it (the Nomological interpretation based on the notion of primitive ontology in tridimensional space) and defend instead a more causal and pluralistic approach, drawing on classical analogies with optics and hydrodynamics. Within this framework, we review some of the approaches exploiting mechanical analogies to overcome the limitations of current Bohmian theory and perhaps quantum mechanics itself.
VLEER: Vision and Language Embeddings for Explainable Whole Slide Image Representation
2025-02-28
Anh Tien Nguyen, Keunho Byeon, Kyungeun Kim, Jin Tae Kwak
Recent advances in vision-language models (VLMs) have shown remarkable potential in bridging visual and textual modalities. In computational pathology, domain-specific VLMs, which are pre-trained on extensive histopathology image-text datasets, have succeeded in various downstream tasks. However, existing research has primarily focused on the pre-training process and direct applications of VLMs on the patch level, leaving their great potential for whole slide image (WSI) applications unexplored. In this study, we hypothesize that pre-trained VLMs inherently capture informative and interpretable WSI representations through quantitative feature extraction. To validate this hypothesis, we introduce Vision and Language Embeddings for Explainable WSI Representation (VLEER), a novel method designed to leverage VLMs for WSI representation. We systematically evaluate VLEER on three pathological WSI datasets, proving its better performance in WSI analysis compared to conventional vision features. More importantly, VLEER offers the unique advantage of interpretability, enabling direct human-readable insights into the results by leveraging the textual modality for detailed pathology annotations, providing clear reasoning for WSI-level pathology downstream tasks.
Recent Advances in Numerical Solutions for Hamilton-Jacobi PDEs
2025-02-28
Tingwei Meng, Siting Liu, Samy Wu Fung, Stanley Osher
Hamilton-Jacobi partial differential equations (HJ PDEs) play a central role in many applications such as economics, physics, and engineering. These equations describe the evolution of a value function which encodes valuable information about the system, such as action, cost, or level sets of a dynamic process. Their importance lies in their ability to model diverse phenomena, ranging from the propagation of fronts in computational physics to optimal decision-making in control systems. This paper provides a review of some recent advances in numerical methods to address challenges such as high-dimensionality, nonlinearity, and computational efficiency. By examining these developments, this paper sheds light on important techniques and emerging directions in the numerical solution of HJ PDEs.
2025-02-28
Ahmad Faraz Khan, Azal Ahmad Khan, Anas Mohamed, Haider Ali, Suchithra Moolinti, Sabaat Haroon, Usman Tahir, Mattia Fazzini, Ali R. Butt, Ali Anwar
Automating cloud configuration and deployment remains a critical challenge due to evolving infrastructures, heterogeneous hardware, and fluctuating workloads. Existing solutions lack adaptability and require extensive manual tuning, leading to inefficiencies and misconfigurations. We introduce LADs, the first LLM-driven framework designed to tackle these challenges by ensuring robustness, adaptability, and efficiency in automated cloud management. Instead of merely applying existing techniques, LADs provides a principled approach to configuration optimization through in-depth analysis of what optimization works under which conditions. By leveraging Retrieval-Augmented Generation, Few-Shot Learning, Chain-of-Thought, and Feedback-Based Prompt Chaining, LADs generates accurate configurations and learns from deployment failures to iteratively refine system settings. Our findings reveal key insights into the trade-offs between performance, cost, and scalability, helping practitioners determine the right strategies for different deployment scenarios. For instance, we demonstrate how prompt chaining-based adaptive feedback loops enhance fault tolerance in multi-tenant environments and how structured log analysis with example shots improves configuration accuracy. Through extensive evaluations, LADs reduces manual effort, optimizes resource utilization, and improves system reliability. By open-sourcing LADs, we aim to drive further innovation in AI-powered DevOps automation.
Measuring Determinism in Large Language Models for Software Code Review
2025-02-28
Eugene Klishevich, Yegor Denisov-Blanch, Simon Obstbaum, Igor Ciobanu, Michal Kosinski
Large Language Models (LLMs) promise to streamline software code reviews, but their ability to produce consistent assessments remains an open question. In this study, we tested four leading LLMs -- GPT-4o mini, GPT-4o, Claude 3.5 Sonnet, and LLaMA 3.2 90B Vision -- on 70 Java commits from both private and public repositories. By setting each model's temperature to zero, clearing context, and repeating the exact same prompts five times, we measured how consistently each model generated code-review assessments. Our results reveal that even with temperature minimized, LLM responses varied to different degrees. These findings highlight a consideration about the inherently limited consistency (test-retest reliability) of LLMs -- even when the temperature is set to zero -- and the need for caution when using LLM-generated code reviews to make real-world decisions.
2025-02-28
Xinyue Wang, Liming Li, Michael Roman, Xi Zhang, Xun Jiang, Patrick M. Fry, Cheng Li, Gwenael Milcareck, Agustin Sanchez-Lavega, Santiago Perez-Hoyos, Ricardo Hueso, Tristan Guillot, Conor A. Nixon, Ulyana A. Dyudina, Robert A. West, Matthew E. Kenyon
With its extreme axial tilt, radiant energy budget and internal heat of Uranus remain among the most intriguing mysteries of our Solar System. Here, we present the global radiant energy budget spanning a complete orbital period, revealing significant seasonal variations driven primarily by the highly variable solar flux. Despite these fluctuations, emitted thermal power consistently exceeds absorbed solar power, indicating a net energy loss and ongoing global cooling. Based on the seasonal variations of radiant energy budget, we determine a statistically significant internal heat flux. This finding resolves a long-standing debate over whether Uranus possesses internal heat. We also examine the energy budget of the weather layer by combining the internal heat with the radiant energies, revealing significant energy imbalances at both global and hemispheric scales. These global and hemispheric imbalances should be considered in theoretical and numerical models. The Uranus flagship mission, as recommended by the recent survey, will provide crucial observations to address more unresolved questions and advance our understanding of this enigmatic ice giant.
From Safety Standards to Safe Operation with Mobile Robotic Systems Deployment
2025-02-28
Bruno Belzile, Tatiana Wanang-Siyapdjie, Sina Karimi, Rafael Gomes Braga, Ivanka Iordanova, David St-Onge
Mobile robotic systems are increasingly used in various work environments to support productivity. However, deploying robots in workplaces crowded by human workers and interacting with them results in safety challenges and concerns, namely robot-worker collisions and worker distractions in hazardous environments. Moreover, the literature on risk assessment as well as the standard specific to mobile platforms is rather limited. In this context, this paper first conducts a review of the relevant standards and methodologies and then proposes a risk assessment for the safe deployment of mobile robots on construction sites. The approach extends relevant existing safety standards to encompass uncovered scenarios. Safety recommendations are made based on the framework, after its validation by field experts.
Fine-tuning BERT with Bidirectional LSTM for Fine-grained Movie Reviews Sentiment Analysis
2025-02-28
Gibson Nkhata, Susan Gauch, Usman Anjum, Justin Zhan
Sentiment Analysis (SA) is instrumental in understanding peoples viewpoints facilitating social media monitoring recognizing products and brands and gauging customer satisfaction. Consequently SA has evolved into an active research domain within Natural Language Processing (NLP). Many approaches outlined in the literature devise intricate frameworks aimed at achieving high accuracy, focusing exclusively on either binary sentiment classification or fine-grained sentiment classification. In this paper our objective is to fine-tune the pre-trained BERT model with Bidirectional LSTM (BiLSTM) to enhance both binary and fine-grained SA specifically for movie reviews. Our approach involves conducting sentiment classification for each review followed by computing the overall sentiment polarity across all reviews. We present our findings on binary classification as well as fine-grained classification utilizing benchmark datasets. Additionally we implement and assess two accuracy improvement techniques Synthetic Minority Oversampling Technique (SMOTE) and NLP Augmenter (NLPAUG) to bolster the models generalization in fine-grained sentiment classification. Finally a heuristic algorithm is employed to calculate the overall polarity of predicted reviews from the BERT+BiLSTM output vector. Our approach performs comparably with state-of-the-art (SOTA) techniques in both classifications. For instance in binary classification we achieve 97.67% accuracy surpassing the leading SOTA model NB-weighted-BON+dv-cosine by 0.27% on the renowned IMDb dataset. Conversely for five-class classification on SST-5 while the top SOTA model RoBERTa+large+Self-explaining attains 55.5% accuracy our model achieves 59.48% accuracy surpassing the BERT-large baseline by 3.6%.
Searching for objects in cluttered environments requires selecting efficient viewpoints and manipulation actions to remove occlusions and reduce uncertainty in object locations, shapes, and categories. In this work, we address the problem of manipulation-enhanced semantic mapping, where a robot has to efficiently identify all objects in a cluttered shelf. Although Partially Observable Markov Decision Processes~(POMDPs) are standard for decision-making under uncertainty, representing unstructured interactive worlds remains challenging in this formalism. To tackle this, we define a POMDP whose belief is summarized by a metric-semantic grid map and propose a novel framework that uses neural networks to perform map-space belief updates to reason efficiently and simultaneously about object geometries, locations, categories, occlusions, and manipulation physics. Further, to enable accurate information gain analysis, the learned belief updates should maintain calibrated estimates of uncertainty. Therefore, we propose Calibrated Neural-Accelerated Belief Updates (CNABUs) to learn a belief propagation model that generalizes to novel scenarios and provides confidence-calibrated predictions for unknown areas. Our experiments show that our novel POMDP planner improves map completeness and accuracy over existing methods in challenging simulations and successfully transfers to real-world cluttered shelves in zero-shot fashion.
A Framework to Analyze Multiscale Sampling MCMC Methods
2025-02-28
Lucas Seiffert, Felipe Pereira
We consider the theoretical analysis of Multiscale Sampling Methods, which are a new class of gradient-free Markov chain Monte Carlo (MCMC) methods for high dimensional inverse differential equation problems. A detailed presentation of those methods is given, including a review of each MCMC technique that they employ. Then, we propose a two-part framework to study and compare those methods. The first part identifies the new corresponding state space for the chain of random fields, and the second assesses convergence conditions on the instrumental and target distributions. Three Multiscale Sampling Methods are then analyzed using this new framework.
Quantifying First-Order Markov Violations in Noisy Reinforcement Learning: A Causal Discovery Approach
2025-02-28
Naveen Mysore
Reinforcement learning (RL) methods frequently assume that each new observation completely reflects the environment's state, thereby guaranteeing Markovian (one-step) transitions. In practice, partial observability or sensor/actuator noise often invalidates this assumption. This paper proposes a systematic methodology for detecting such violations, combining a partial correlation-based causal discovery process (PCMCI) with a novel Markov Violation score (MVS). The MVS measures multi-step dependencies that emerge when noise or incomplete state information disrupts the Markov property. Classic control tasks (CartPole, Pendulum, Acrobot) serve as examples to illustrate how targeted noise and dimension omissions affect both RL performance and measured Markov consistency. Surprisingly, even substantial observation noise sometimes fails to induce strong multi-lag dependencies in certain domains (e.g., Acrobot). In contrast, dimension-dropping investigations show that excluding some state variables (e.g., angular velocities in CartPole and Pendulum) significantly reduces returns and increases MVS, while removing other dimensions has minimal impact. These findings emphasize the importance of locating and safeguarding the most causally essential dimensions in order to preserve effective single-step learning. By integrating partial correlation tests with RL performance outcomes, the proposed approach precisely identifies when and where the Markov assumption is violated. This framework offers a principled mechanism for developing robust policies, informing representation learning, and addressing partial observability in real-world RL scenarios. All code and experimental logs are accessible for reproducibility (https://github.com/ucsb/markovianess).
PRISM: High-Resolution & Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusion
2025-02-28
Amar Kumar, Anita Kriz, Mohammad Havaei, Tal Arbel
Developing reliable and generalizable deep learning systems for medical imaging faces significant obstacles due to spurious correlations, data imbalances, and limited text annotations in datasets. Addressing these challenges requires architectures robust to the unique complexities posed by medical imaging data. The rapid advancements in vision-language foundation models within the natural image domain prompt the question of how they can be adapted for medical imaging tasks. In this work, we present PRISM, a framework that leverages foundation models to generate high-resolution, language-guided medical image counterfactuals using Stable Diffusion. Our approach demonstrates unprecedented precision in selectively modifying spurious correlations (the medical devices) and disease features, enabling the removal and addition of specific attributes while preserving other image characteristics. Through extensive evaluation, we show how PRISM advances counterfactual generation and enables the development of more robust downstream classifiers for clinically deployable solutions. To facilitate broader adoption and research, we make our code publicly available at https://github.com/Amarkr1/PRISM.
Predicting Human-Chatbot Relationships: A Mixed-Method Study on the Key Psychological Factors
2025-02-28
Paula Ebner, Jessica Szczuka
Romantic relationships with social chatbots are becoming increasingly prevalent, raising important questions about their societal and psychological implications. Despite this growing trend, little is known about the individuals entering these synthetic relationships. This three-part study seeks to enhance understanding of the factors encompassing human-chatbot relationships by quantitatively examining the commonly discussed characteristics romantic and sexual fantasy, loneliness, attachment style, anthropomorphism, and sexual sensation seeking (Study 1A), comparing the impact of romantic and sexual fantasizing for human-chatbot versus human-human relationships (Study 1B), and providing qualitative insights into how individuals conceptualize romantic and sexual fantasies in their interactions with chatbots (Study 2). Individuals with romantic chatbot connections were interviewed (N=15) or surveyed (N=92), while participants in the comparison groups, long-distance (N=90) and cohabiting relationships (N=82), completed a questionnaire. Romantic fantasizing emerged as the strongest predictor of human-chatbot relationships, alongside anthropomorphism and anxious-avoidant attachment. Notably, romantic fantasy also predicted partner closeness across all relationship types, revealing shared psychological dynamics between human-chatbot and human-human bonds. Interviews further reinforced this, with all participants engaging in fantasy exploration while desiring their chatbot to feel as human as possible. This paper provides a novel and multifaceted examination of the psychological dynamics within human-chatbot relationships, highlighting the central yet understudied role of fantasy.
Condenser Pressure Influence on Ideal Steam Rankine Power Vapor Cycle using the Python Extension Package Cantera for Thermodynamics
2025-02-28
Osama A. Marzouk
This study investigates the Rankine vapor power thermodynamic cycle using steam/water as the working fluid, which is common in commercial power plants for power generation as the source of the rotary shaft power needed to drive electric generators. The four-process cycle version, which comprises a water pump section, a boiler/superheater section, a steam turbine section, and a condenser section, was considered. The performance of this thermodynamic power cycle depends on several design parameters. This study varied a single independent variable, the absolute pressure of the condenser, by a factor of 256, from 0.78125 to 200 kPa. The peak pressure and peak temperature in the cycle were fixed at 50 bar (5,000 kPa) and 600{\deg}C, respectively, corresponding to a base case with a base value for the condenser's absolute pressure of 12.5 kPa (0.125 bar). The analysis was performed using the thermodynamics software package Cantera as an extension of the Python programming language. The results suggest that over the range of condenser pressures examined, a logarithmic function can be deployed to describe the dependence of input heat, the net output work, and cycle efficiency on the absolute pressure of the condenser. Each of these three performance metrics decreases as the absolute pressure of the condenser increases. However, a power function is a better choice to describe how the steam dryness (steam quality) at the end of the turbine section increases as the absolute pressure of the condenser rises.
A Survey of Uncertainty Estimation Methods on Large Language Models
2025-02-28
Zhiqiu Xia, Jinxuan Xu, Yuqian Zhang, Hang Liu
Large language models (LLMs) have demonstrated remarkable capabilities across various tasks. However, these models could offer biased, hallucinated, or non-factual responses camouflaged by their fluency and realistic appearance. Uncertainty estimation is the key method to address this challenge. While research efforts in uncertainty estimation are ramping up, there is a lack of comprehensive and dedicated surveys on LLM uncertainty estimation. This survey presents four major avenues of LLM uncertainty estimation. Furthermore, we perform extensive experimental evaluations across multiple methods and datasets. At last, we provide critical and promising future directions for LLM uncertainty estimation.
The study of collisionless shocks and their role in cosmic ray acceleration has gained importance through observations and simulations, driving interest in reproducing these conditions in laboratory experiments using high-power lasers. In this work, we examine the role of three-dimensional (3D) effects in ion acceleration in quasi-perpendicular shocks under laboratory-relevant conditions. Using hybrid particle-in-cell simulations (kinetic ions and fluid electrons), we explore how the Alfv\'enic and sonic Mach numbers, along with plasma beta, influence ion energization, unlocked only in 3D, and establish scaling criteria for when conducting 3D simulations is necessary. Our results show that efficient ion acceleration requires Alfv\'enic Mach numbers $\geq 25$ and sonic Mach numbers $\geq 13$, with plasma-$\beta \leq 5$. We theoretically found that, while 2D simulations suffice for current laboratory-accessible shock conditions, 3D effects become crucial for shock velocities exceeding 1000 km/s and experiments sustaining the shock for at least 10 ns. We surveyed previous laboratory experiments on collisionless shocks and found that 3D effects are unimportant under those conditions, implying that 1D and 2D simulations should be enough to model the accelerated ion spectra. However, we do find that the same experiments are realistically close to accessing the regime relevant to 3D effects, an exciting prospect for future laboratory efforts. We propose modifications to past experimental configurations to optimize and control 3D effects on ion acceleration. These proposed experiments could be used to benchmark plasma astrophysics kinetic codes and/or employed as controllable sources of energetic particles.
Neural Posterior Estimation for Cataloging Astronomical Images with Spatially Varying Backgrounds and Point Spread Functions
2025-02-28
Aakash Patel, Tianqing Zhang, Camille Avestruz, Jeffrey Regier, the LSST Dark Energy Science Collaboration
Neural posterior estimation (NPE), a type of amortized variational inference, is a computationally efficient means of constructing probabilistic catalogs of light sources from astronomical images. To date, NPE has not been used to perform inference in models with spatially varying covariates. However, ground-based astronomical images have spatially varying sky backgrounds and point spread functions (PSFs), and accounting for this variation is essential for constructing accurate catalogs of imaged light sources. In this work, we introduce a method of performing NPE with spatially varying backgrounds and PSFs. In this method, we generate synthetic catalogs and semi-synthetic images for these catalogs using randomly sampled PSF and background estimates from existing surveys. Using this data, we train a neural network, which takes an astronomical image and representations of its background and PSF as input, to output a probabilistic catalog. Our experiments with Sloan Digital Sky Survey data demonstrate the effectiveness of NPE in the presence of spatially varying backgrounds and PSFs for light source detection, star/galaxy separation, and flux measurement.
Recent observations by the James Webb Space Telescope have revealed massive galaxies at very high redshift ($z\simeq 7-15$). The question of whether the existence of such galaxies is expected in the corresponding JWST surveys has received a lot of attention, though the answer straddles areas of cosmology and complex astrophysical details of high-redshift galaxy formation. The growth rate of density fluctuations determines the amplitude of overdensities that collapse to form galaxies. Late-time modifications of growth, combined with measurements at both $z\sim 1$ from large-scale structure and $z\sim 1000$ from the cosmic microwave background, affect the predictions for the abundance of first galaxies in the universe. In this paper, we point out that the late-time growth rate of structure affects the statistical significance of high-redshift, high-mass objects very weakly. Consequently, if the existence and abundance of these objects are confirmed to be unexpected, the variations in the late-time growth history are unlikely to explain these anomalies.
Learner and Instructor Needs in AI-Supported Programming Learning Tools: Design Implications for Features and Adaptive Control
2025-02-28
Zihan Wu, Yicheng Tang, Barbara Ericson
AI-supported tools can help learners overcome challenges in programming education by providing adaptive assistance. However, existing research often focuses on individual tools rather than deriving broader design recommendations. A key challenge in designing these systems is balancing learner control with system-driven guidance. To explore user preferences for AI-supported programming learning tools, we conducted a participatory design study with 15 undergraduate novice programmers and 10 instructors to gather insights on their desired help features and control preferences, as well as a follow-up survey with 172 introductory programming students. Our qualitative findings show that learners prefer help that is encouraging, incorporates visual aids, and includes peer-related insights, whereas instructors prioritize scaffolding that reflects learners' progress and reinforces best practices. Both groups favor shared control, though learners generally prefer more autonomy, while instructors lean toward greater system guidance to prevent cognitive overload. Additionally, our interviews revealed individual differences in control preferences. Based on our findings, we propose design guidelines for AI-supported programming tools, particularly regarding user-centered help features and adaptive control mechanisms. Our work contributes to the human-centered design of AI-supported learning environments by informing the development of systems that effectively balance autonomy and guidance, enhancing AI-supported educational tools for programming and beyond.
Influence Phase of a dS Observer II: Electromagnetism
2025-02-28
R. Loganayagam, Omkar Shetye
We extend our proposal for static patch holography made in part I (arXiv:2309.07290) to the case of electromagnetism. Using the on-shell action on de Sitter Schwinger-Keldysh (dS-SK) geometry, we derive the influence phase of an observer in dS$_{d+1}$ interacting with bulk electromagnetic fields. This influence phase, computed with appropriate boundary conditions and counterterms, encodes the physics of electromagnetic radiation reaction and corresponding dS Hawking radiation. The self-force is rendered finite through holographic renormalisation. In the short-time limit, we reproduce electromagnetic flat space radiation reaction ala Abraham-Lorentz-Dirac, along with cosmological corrections. We give a fully covariantised expression for this radiation reaction in several even spacetime dimensions. In this process, we also extend many existing results to cosmological spacetimes: multipole expansions including smearing effects, non-relativistic expansion for electromagnetism, and classical renormalisation in odd spacetime dimensions. Further, we review and extend many properties of vector spherical harmonics(VSHs) in arbitrary dimensions, explain the relation between spherical and cartesian VSH, and derive an addition theorem for VSHs.
This survey explores the geometry of three-dimensional Anosov flows from the perspective of contact and symplectic geometry, following the work of Mitsumatsu, Eliashberg-Thurston, Hozoori, and the author. We also present a few original results and discuss various open questions and conjectures.
Going deeper into the dark with COSMOS-Web: JWST unveils the total contribution of Radio-Selected NIRfaint galaxies to the cosmic Star Formation Rate Density
2025-02-28
Fabrizio Gentile, Margherita Talia, Andrea Enia, Francesca Pozzi, Alberto Traina, Giovanni Zamorani, Irham T. Andika, Meriem Behiri, Laia Barrufet, Caitlin M. Casey, Andrea Cimatti, Nicole E. Drakos, Andreas L. Faisst, Maximilien Franco, Steven Gillman, Marika Giulietti, Rashmi Gottumukkala, Christopher C. Hayward, Olivier Ilbert, Shuowen Jin, Andrea Lapi, Jed McKinney, Marko Shuntov, Mattia Vaccari, Cristian Vignali, Hollis B. Akins, Natalie Allen, Santosh Harish, Henry Joy McCracken, Jeyhan S. Kartaltepe, Anton M. Koekemoer, Daizhong Liu, Louise Paquereau, Jason Rhodes, Micheal R. Rich, Brant E. Robertson, Sune Toft
We present the first follow-up with JWST of radio-selected NIRfaint galaxies as part of the COSMOS-Web survey. By selecting galaxies detected at radio frequencies ($S_{\rm 3 GHz}>11.5$ $\mu$Jy; i.e. S/N$>5$) and with faint counterparts at NIR wavelengths (F150W$>26.1$ mag), we collect a sample of 127 likely dusty star-forming galaxies (DSFGs). We estimate their physical properties through SED fitting, compute the first radio luminosity function for these types of sources, and their contribution to the total cosmic star formation rate density. Our analysis confirms that these sources represent a population of highly dust-obscured ($\langle A_{\rm v} \rangle \sim3.5$ mag), massive ($\langle M_\star \rangle \sim10^{10.8}$ M$_\odot$) and star-forming galaxies ($\langle {\rm SFR} \rangle\sim300$ M$_\odot$ yr$^{-1}$) located at $\langle z \rangle\sim3.6$, representing the high-redshift tail of the full distribution of radio sources. Our results also indicate that these galaxies could dominate the bright end of the radio luminosity function and reach a total contribution to the cosmic star formation rate density equal to that estimated only considering NIR-bright sources at $z\sim4.5$. Finally, our analysis further confirms that the radio selection can be employed to collect statistically significant samples of DSFGs, representing a complementary alternative to the other selections based on JWST colors or detection at FIR/(sub)mm wavelengths.
A Local Dwarf Galaxy Search Using Machine Learning
2025-02-28
Huanian Zhang, Guangping Ye, Rongyu Wu, Dennis Zaritsky
We present a machine learning search for local, low-mass galaxies ($z < 0.02$ and $10^6 M_\odot < M_* < 10^9 M_\odot$) using the combined photometric data from the DESI Imaging Legacy Surveys and the WISE survey. We introduce the spectrally confirmed training sample, discuss evaluation metrics, investigate the features, compare different machine learning algorithms, and find that a 7-class neural network classification model is highly effective in separating the signal (local, low-mass galaxies) from various contaminants, reaching a precision of $95\%$ and a recall of $76\%$. The principal contaminants are nearby sub-$L^*$ galaxies at $0.02 < z < 0.05$ and nearby massive galaxies at $0.05 < z < 0.2$. We find that the features encoding surface brightness information are essential to achieving a correct classification. Our final catalog, which we make available, consists of 112,859 local, low-mass galaxy candidates, where 36,408 have high probability ($p_{\rm signal} > 0.95$), covering the entire Legacy Surveys DR9 footprint. Using DESI-EDR public spectra and data from the SAGA and ELVES surveys, we find that our model has a precision of $\sim 100\%$, $96\%$, and $97\%$, respectively, and a recall of $\sim 51\%$, $68\%$ and $53\%$, respectively. The results of those independent spectral verification demonstrate the effectiveness and efficiency of our machine learning classification model.
FuzzifiED -- Julia package for numerics on the fuzzy sphere
2025-02-28
Zheng Zhou
The Julia package FuzzifiED aims at simplifying the numerical calculations on the fuzzy sphere. It supports exact diagonalisation (ED) and density matrix renormalisation group (DMRG) calculations. FuzzifiED can also apply to generic fermionic and bosonic models. This documentation provides a review of the fuzzy sphere regularisation and an instruction for using FuzzifiED for numerical calculations.
BixBench: a Comprehensive Benchmark for LLM-based Agents in Computational Biology
2025-02-28
Ludovico Mitchener, Jon M Laurent, Benjamin Tenmann, Siddharth Narayanan, Geemi P Wellawatte, Andrew White, Lorenzo Sani, Samuel G Rodriques
Large Language Models (LLMs) and LLM-based agents show great promise in accelerating scientific research. Existing benchmarks for measuring this potential and guiding future development continue to evolve from pure recall and rote knowledge tasks, towards more practical work such as literature review and experimental planning. Bioinformatics is a domain where fully autonomous AI-driven discovery may be near, but no extensive benchmarks for measuring progress have been introduced to date. We therefore present the Bioinformatics Benchmark (BixBench), a dataset comprising over 50 real-world scenarios of practical biological data analysis with nearly 300 associated open-answer questions designed to measure the ability of LLM-based agents to explore biological datasets, perform long, multi-step analytical trajectories, and interpret the nuanced results of those analyses. We evaluate the performance of two frontier LLMs (GPT-4o and Claude 3.5 Sonnet) using a custom agent framework we open source. We find that even the latest frontier models only achieve 17% accuracy in the open-answer regime, and no better than random in a multiple-choice setting. By exposing the current limitations of frontier models, we hope BixBench can spur the development of agents capable of conducting rigorous bioinformatic analysis and accelerate scientific discovery.
Gauge-dependence of Scalar Induced Gravitational Waves
2025-02-28
Anjali Abirami Kugarajh
In this review we look into the gauge-dependence of scalar-induced gravitational waves (SIGWs) that are second-order tensors produced by first-order scalar-modes. The method includes deriving the background, first- and second-order Einstein field equations without imposing a gauge. We address the gauge-invariant approach and study the source-term of SIGWs in three different gauges, synchronous, Poisson and uniform curvature gauge. We find that numerically computed kernels in all three gauges behave closely with minimal discrepancy. As expected, when going in sub-horizon modes, $x\gg1$, the discrepancy decreases and the behavior matches, pointing to a gauge-invariant observable.
A physically motivated galaxy size definition across different state-of-the-art hydrodynamical simulations
2025-02-27
Elena Arjona-Galvez, Salvador Cardona-Barrero, Robert J. J. Grand, Arianna Di Cintio, Claudio Dalla Vecchia, Jose A. Benavides, Andrea V. Maccio, Noam Libeskind, Alexander Knebe
Galaxy sizes are a key parameter to distinguishing between different galaxy types and morphologies, reflecting their formation and assembly histories. Several methods define galaxy boundaries, often relying on light concentration or isophotal densities. However, these approaches were often constrained by observational limitations and did not necessarily provide a clear physical boundary for galaxy outskirts. With modern deep imaging surveys, a new physically motivated definition has emerged using the radial position of the star formation threshold as the galaxy size, approximated by the stellar mass density contour at 1 Msun pc^-2 (R_1). We test this definition using three state-of-the-art hydrodynamical simulation suites, analyzing stellar surface density profiles across a wide range of stellar masses and redshifts. We measure the galaxy sizes according to this new definition and compare them with the most traditional size metric, the stellar half-mass radius. Our analysis demonstrates that the R_1-M_star relation exhibits consistent behaviour across both low and high-stellar mass galaxies, with remarkably low scatter. This relation is independent of redshift and holds across the three different cosmological hydrodynamical simulation suites, highlighting its robustness to variations in galaxy formation models. Furthermore, we explore the connection between a galaxy's total mass within R1 and its stellar mass, finding very little scatter in this relation. This suggests that R1 could serve as a reliable observational tracer for the galaxy's dynamical mass. The size-stellar mass relation proposed provides a reliable and physically motivated method for defining the outskirts of galaxies. This method remains consistent not only at z=0 but also throughout the evolutionary history of galaxies, offering a robust and meaningful framework for galaxy evolution studies.
Naturalistic Computational Cognitive Science: Towards generalizable models and theories that capture the full range of natural behavior
2025-02-27
Wilka Carvalho, Andrew Lampinen
Artificial Intelligence increasingly pursues large, complex models that perform many tasks within increasingly realistic domains. How, if at all, should these developments in AI influence cognitive science? We argue that progress in AI offers timely opportunities for cognitive science to embrace experiments with increasingly naturalistic stimuli, tasks, and behaviors; and computational models that can accommodate these changes. We first review a growing body of research spanning neuroscience, cognitive science, and AI that suggests that incorporating a broader range of naturalistic experimental paradigms (and models that accommodate them) may be necessary to resolve some aspects of natural intelligence and ensure that our theories generalize. We then suggest that integrating recent progress in AI and cognitive science will enable us to engage with more naturalistic phenomena without giving up experimental control or the pursuit of theoretically grounded understanding. We offer practical guidance on how methodological practices can contribute to cumulative progress in naturalistic computational cognitive science, and illustrate a path towards building computational models that solve the real problems of natural cognition - together with a reductive understanding of the processes and principles by which they do so.
Cell-free (CF) integrated sensing and communication (ISAC) combines CF architecture with ISAC. CF employs distributed access points, eliminates cell boundaries, and enhances coverage, spectral efficiency, and reliability. ISAC unifies radar sensing and communication, enabling simultaneous data transmission and environmental sensing within shared spectral and hardware resources. CF-ISAC leverages these strengths to improve spectral and energy efficiency while enhancing sensing in wireless networks. As a promising candidate for next-generation wireless systems, CF-ISAC supports robust multi-user communication, distributed multi-static sensing, and seamless resource optimization. However, a comprehensive survey on CF-ISAC has been lacking. This paper fills that gap by first revisiting CF and ISAC principles, covering cooperative transmission, radar cross-section, target parameter estimation, ISAC integration levels, sensing metrics, and applications. It then explores CF-ISAC systems, emphasizing their unique features and the benefits of multi-static sensing. State-of-the-art developments are categorized into performance analysis, resource allocation, security, and user/target-centric designs, offering a thorough literature review and case studies. Finally, the paper identifies key challenges such as synchronization, multi-target detection, interference management, and fronthaul capacity and latency. Emerging trends, including next-generation antenna technologies, network-assisted systems, near-field CF-ISAC, integration with other technologies, and machine learning approaches, are highlighted to outline the future trajectory of CF-ISAC research.
Reservoir Computing and Photoelectrochemical Sensors: A Marriage of Convenience
2025-02-27
Gisya Abdi, Lulu Alluhaibi, Ewelina Kowalewska, Tomasz Mazur, Krzysztof Mech, Agnieszka Podborska, Andrzej Sławek, Hirofumi Tanaka, Konrad Szaciłowski
Sensing technology is an important aspect of information processing. Current development in artificial intelligence systems (especially those aimed at medical and environmental applications) requires a lot of data on the chemical composition of biological fluids or environmental samples. These complex matrices require advanced sensing devices, and photoelectrochemical ones seem to have potential to overcome at least some of the obstacles. Furthermore, the development of artificial intelligence (AI) technology for autonomous robotics requires technology mimicking human senses, also those operating at the molecular level, such as gustation and olfaction. Again, photoelectrochemical sensing can provide some suitable solutions. In this review, we introduce the idea of integration of photoelectrochemical sensors with some unconventional computing paradigm - reservoir computing. This approach should not only boost the performance of the sensors itself, but also open new pathways through science. Integration of sensing devices with computing systems will also contribute to a better understanding (or at least mimicking) of the human senses and neuromorphic sensory information processing. Although reservoir systems can be considered magic "black boxes" and their operation is at the same time simple and hard to comprehend, this combination is expected to open a new era of effective information harvesting and processing systems.
Boltzmann framework for polyatomic gases: review on well-posedness, higher integrability and physical relevance
2025-02-27
Ricardo Alonso, Milana Colic
This paper reviews results on the scalar Boltzmann equation for a single-component polyatomic gas with continuous internal energy. For the space homogeneous problem, $L^1$-theory is established, for solutions with initial strictly positive mass and bounded energy, which enables to solve the Cauchy problem for initial data with $L^1_{2^+}$-moments using the comparison principle for ODEs. Then, deriving entropy-based estimates, $L^p$-integrability properties of the solution are explored, $p\in (1,\infty]$. All these analytical results hold under a specific assumption on the collision kernel corresponding to cut-off and hard-potentials type. A mean to verify physical applicability of the model is to evaluate the corresponding Boltzmann collision operator and to derive models for transport coefficients in terms of the collision kernel parameters. Comparison with experimental data for polytropic gases determines values of these parameters showing the physical relevance of the collision kernel.
Gender Dynamics in Software Engineering: Insights from Research on Concurrency Bug Reproduction
2025-02-27
Tarannum Shaila Zaman, Macharla Hemanth Kishan, Lutfun Nahar Lota
Reproducing concurrency bugs is a complex task due to their unpredictable behavior. Researchers, regardless of gender, are contributing to automating this complex task to aid software developers. While some studies have investigated gender roles in the broader software industry, limited research exists on gender representation specifically among researchers working in concurrent bug reproduction. To address this gap, in this paper, we present a literature review to assess the gender ratio in this field. We also explore potential variations in technique selection and bug-type focus across genders. Our findings indicate that female researchers are underrepresented compared to their male counterparts in this area, with a current male-to-female author ratio of 29:6. Through this study, we emphasize the importance of fostering gender equity in software engineering research, ensuring a diversity of perspectives in the development of automated bug reproduction tools.
A review of Bayesian sensor-based estimation and uncertainty quantification of aerodynamic flows
2025-02-27
Jeff D. Eldredge, Hanieh Mousavi
Many applications in aerodynamics depend on the use of sensors to estimate the evolving state of the flow. In particular, a wide variety of traditional and learning-based strategies for closed-loop control rely on some knowledge of the aerodynamic state in order to decide on actions. This estimation task is inherently accompanied by uncertainty due to the noisy measurements of sensors or the non-uniqueness of the underlying mapping, and knowledge of this uncertainty can be as important for decision-making as that of the state itself. The tracking of uncertainty is challenged by the often-nonlinear relationship between the sensor measurements and the flow state. For example, a collection of passing vortices leaves a footprint in wall pressure sensors that depends nonlinearly on the strengths and positions of the vortices. In this paper, we will review the recent body of work on flow estimation. We will discuss the basic tools of probability, including sampling and estimation, in the powerful setting of Bayesian inference and demonstrate these tools in static flow estimation examples. We will then proceed to unsteady examples and illustrate the application of sequential estimation, and particularly, the ensemble Kalman filter. Finally, we will discuss uncertainty quantification in neural network approximations of the mappings between sensor measurements and flow states. Recent aerodynamic applications of neural networks have shown that the flow state can be encoded into a very low-dimensional latent space, and we will discuss the implications of this encoding on uncertainty.
2025-02-27
Nicola Biagi, Saverio Francesconi, Alessandro Zavatta, Marco Bellini
The ability to manipulate light at the level of single photons, its elementary excitation quanta, has recently made it possible to produce a rich variety of tailor-made quantum states and arbitrary quantum operations, of high interest for fundamental science and applications. Here we present a concise review of the progress made over the last few decades in the engineering of quantum light states. Although far from exhaustive, this review aims at providing a sufficiently wide and updated introduction that may serve as the entry point to such a fascinating and rapidly evolving field.
Environment-friendly technologies with lead-free piezoelectric materials: A review of recent developments, applications, and modelling approaches
2025-02-27
Akshayveer Akshayveer, Federico C Buroni, Roderick Melnik, Luis Rodriguez-Tembleque, Andres Saez
Piezoelectric materials are widely used in several industries, including power sources, energy harvesting, biomedical, electronics, haptic, photostrictive, and sensor/actuator technologies. Conventional piezoelectric materials, such lead zirconate titanate (PZT), pose significant environmental and health risks due to lead content. Recent years have seen a growing need for eco-friendly alternatives to lead-based piezoelectric technologies. The drawback of lead-free piezoelectric materials is a reduced responsiveness. To enhance the performance of lead-free piezoelectric materials, substantial research is being undertaken using both experimental and numerical methods. The experimental studies provide a deep understanding of the process and are crucial for developing lead-free piezoelectric materials. Relying only on experimental research is not feasible due to high expenditures. To understand the piezoelectric properties of lead-free materials and enhance their efficiency, it is crucial to develop varied numerical models and computational methods. This paper is a comprehensive summary of lead-free piezoelectric technology breakthroughs. The study emphasizes numerical models that demonstrate enhanced piezoelectric behaviour and their use in eco-friendly technologies like energy harvesting, haptics, nano-electromechanical, photostrictive, and biomedical sensors and actuators using lead-free materials. The paper discusses the synthesis, properties, and applications of eco-friendly materials, highlighting their potential to revolutionize piezoelectric devices and promote sustainable development and conservation.
Off-diagonal upper heat kernel bounds on graphs with unbounded geometry
2025-02-27
Christian Rose
Results regarding off-diagonal Gaussian upper heat kernel bounds on discrete weighted graphs with possibly unbounded geometry are summarized and related. After reviewing uniform upper heat kernel bounds obtained by Carlen, Kusuoka, and Stroock, the universal Gaussian term on graphs found by Davies is addressed and related to corresponding results in terms of intrinsic metrics. Then we present a version of Grigor'yan's two-point method with Gaussian term involving an intrinsic metric. A discussion of upper heat kernel bounds for graph Laplacians with possibly unbounded but integrable weights on bounded combinatorial graphs preceeds the presentation of compatible bounds for anti-trees, an example of combinatorial graph with unbounded Laplacian. Characterizations of localized heat kernel bounds in terms of intrinsic metrics and universal Gaussian are reconsidered. Finally, the problem of optimality of the Gaussian term is discussed by relating Davies' optimal metric with the supremum over all intrinsic metrics.
Spatio-temporal tensor-network approaches to out-of-equilibrium dynamics bridging open and closed systems
2025-02-27
Sergio Cerezo-Roquebrún, Aleix Bou-Comas, Jan T. Schneider, Esperanza López, Luca Tagliacozzo, Stefano Carignano
The study of many-body quantum systems out of equilibrium remains a significant challenge with complexity barriers arising in both state and operator-based representations. In this work, we review recent approaches based on finding better contraction strategies for the full spatio-temporal tensor networks that encode the path integral of the dynamics, as well as the conceptual integration of influence functionals, process tensors, and transfer matrices within the tensor network formalism. We discuss recent algorithmic developments, highlight the complexity of influence functionals in various dynamical regimes and present consistent results of different communities, showing how ergodic dynamics render these functionals exponentially difficult to compress. Finally, we provide an outlook on strategies to encode complementary influence functional overlaps, paving the way for accurate descriptions of open and closed quantum systems with tensor networks.
Exploring experimental limit of deep quantum signal processing using a trapped-ion simulator
2025-02-27
J. -T. Bu, Lei Zhang, Zhan Yu, Jing-Bo Wang, W. -Q. Ding, W. -F. Yuan, B. Wang, H. -J. Du, W. -J. Chen, L. Chen, J. -W. Zhang, J. -C. Li, F. Zhou, Xin Wang, M. Feng
Quantum signal processing (QSP), which enables systematic polynomial transformations on quantum data through sequences of qubit rotations, has emerged as a fundamental building block for quantum algorithms and data re-uploading quantum neural networks. While recent experiments have demonstrated the feasibility of shallow QSP circuits, the inherent limitations in scaling QSP to achieve complex transformations on quantum hardware remain an open and critical question. Here we report the first experimental realization of deep QSP circuits in a trapped-ion quantum simulator. By manipulating the qubit encoded in a trapped $^{43}\textrm{Ca}^{+}$ ion, we demonstrate high-precision simulation of some prominent functions used in quantum algorithms and machine learning, with circuit depths ranging from 15 to 360 layers and implementation time significantly longer than coherence time of the qubit. Our results reveal a crucial trade-off between the precision of function simulation and the concomitant accumulation of hardware noise, highlighting the importance of striking a balance between circuit depth and accuracy in practical QSP implementation. This work addresses a key gap in understanding the scalability and limitations of QSP-based algorithms on quantum hardware, providing valuable insights for developing quantum algorithms as well as practically realizing quantum singular value transformation and data re-uploading quantum machine learning models.
Optics of Plasmon-Exciton Nanostructures: Theoretical Models and Physical Phenomena in Metal/J-aggregate Systems
2025-02-27
V. S. Lebedev, A. D. Kondorskiy
We review the studies of a wide range of optical phenomena resulting from near-field coupling between excitons and localized surface plasmon-polaritons in hybrid nanostructures. Modern physical approaches and theoretical models reported here for the description of light absorption, scattering, and extinction spectra are appropriate for interpreting physical effects in nanosystems containing metals and various excitonic materials, such as molecular aggregates of organic dyes or inorganic quantum-confined semiconductor structures. Using the example of hybrid nanosystems composed of a metal core and an outer shell of dye J-aggregate, we perform a theoretical analysis of the optical spectra behavior in the regimes of weak, strong, and ultrastrong plasmon--exciton coupling. We consider resonance and antiresonance phenomena induced by the coupling of an exciton with dipole and multipole plasmons, including a pronounced dip in light absorption, as well as the spectral band replication effect of plexcitonic nanoparticles and their dimers. We discuss the significant roles of the size-dependent permittivity of the metal core, the effects of anisotropy and chirality of the excitonic J-aggregate shell, and the influence of an intermediate passive layer on the formation of the optical spectra of bilayer, trilayer, and multilayer nanoparticles. The review outlines the experimental and theoretical results for hybrid nanosystems of various geometrical shapes, sizes, and compositions, broadens our understanding of the physical phenomena caused by the plasmon--exciton coupling, and represents the current state of research in the optics of metalorganic nanostructures.
An Extensive Evaluation of PDDL Capabilities in off-the-shelf LLMs
2025-02-27
Kaustubh Vyas, Damien Graux, Sébastien Montella, Pavlos Vougiouklis, Ruofei Lai, Keshuang Li, Yang Ren, Jeff Z. Pan
In recent advancements, large language models (LLMs) have exhibited proficiency in code generation and chain-of-thought reasoning, laying the groundwork for tackling automatic formal planning tasks. This study evaluates the potential of LLMs to understand and generate Planning Domain Definition Language (PDDL), an essential representation in artificial intelligence planning. We conduct an extensive analysis across 20 distinct models spanning 7 major LLM families, both commercial and open-source. Our comprehensive evaluation sheds light on the zero-shot LLM capabilities of parsing, generating, and reasoning with PDDL. Our findings indicate that while some models demonstrate notable effectiveness in handling PDDL, others pose limitations in more complex scenarios requiring nuanced planning knowledge. These results highlight the promise and current limitations of LLMs in formal planning tasks, offering insights into their application and guiding future efforts in AI-driven planning paradigms.
Cutting-edge 3D reconstruction solutions for underwater coral reef images: A review and comparison
2025-02-27
Jiageng Zhong, Ming Li, Armin Gruen, Konrad Schindler, Xuan Liao, Qinghua Guo
Corals serve as the foundational habitat-building organisms within reef ecosystems, constructing extensive structures that extend over vast distances. However, their inherent fragility and vulnerability to various threats render them susceptible to significant damage and destruction. The application of advanced 3D reconstruction technologies for high-quality modeling is crucial for preserving them. These technologies help scientists to accurately document and monitor the state of coral reefs, including their structure, species distribution and changes over time. Photogrammetry-based approaches stand out among existing solutions, especially with recent advancements in underwater videography, photogrammetric computer vision, and machine learning. Despite continuous progress in image-based 3D reconstruction techniques, there remains a lack of systematic reviews and comprehensive evaluations of cutting-edge solutions specifically applied to underwater coral reef images. The emerging advanced methods may have difficulty coping with underwater imaging environments, complex coral structures, and computational resource constraints. They need to be reviewed and evaluated to bridge the gap between many cutting-edge technical studies and practical applications. This paper focuses on the two critical stages of these approaches: camera pose estimation and dense surface reconstruction. We systematically review and summarize classical and emerging methods, conducting comprehensive evaluations through real-world and simulated datasets. Based on our findings, we offer reference recommendations and discuss the development potential and challenges of existing approaches in depth. This work equips scientists and managers with a technical foundation and practical guidance for processing underwater coral reef images for 3D reconstruction....
The FCNC semileptonic decays of B mesons provide an excellent platform for indirectly probing New Physics (NP) beyond the Standard Model (SM). Although precise tests of lepton flavor universality (LFU), such as the ratios $R_K$ and $R_{K^*}$, are consistent with the SM predictions, other observables in $b \to s \ell \bar{\ell}$ transitions show significant deviations. Specifically, discrepancies are observed in the individual branching fractions $\mathcal{B}(B^+ \to K^+ \mu^+ \mu^-)$ and $\mathcal{B}(B^+ \to K^+ e^+ e^-)$, as well as in $\mathcal{B}(B^0 \to K^{0*} \mu^+ \mu^-)$ and $\mathcal{B}(B^0 \to K^{0*} e^+ e^-)$. Furthermore, the angular observable $P'_5$ in $B \to K^{*} \mu^+ \mu^-$ shows a deviation of $3.3\sigma$ from the SM expectation, while a $3.6\sigma$ discrepancy has been reported in $\mathcal{B}(B_s \to \phi \mu^+ \mu^-)$. For the first time, LHCb has measured $R_\phi$ through the $\mathcal{B}(B_s \to \phi \mu^+ \mu^-)$ and $\mathcal{B}(B_s \to \phi e^+ e^-)$. Although $R_\phi$ aligns with the SM prediction, the individual branching fractions remain inconsistent with SM results. These anomalies point to the potential presence of NP in both the muon and electron sectors. Although current experimental constraints on $b\, \to\, s\, e\, \bar{e}$ decays are less stringent than their muonic counterparts, it is possible that NP could also manifest in interactions involving first-generation leptons. Furthermore, recent results from Belle-II reveal a $2.2\sigma$ deviation in $\mathcal{B}(B^+ \to K^+ \nu \bar{\nu})$ from the SM prediction, providing further avenues to explore NP in $b\, \to\, s\, \ell\, \bar{\ell}$ transitions. Motivated by these observations, we perform a global analysis of dimension-6 SMEFT operators, considering the possibility of NP contributions not only in $b\, \to\, s\, \mu\, \bar{\mu}$ transitions but also in $b\, \to\, s\, e\, \bar{e}$ transitions.
Telephone Surveys Meet Conversational AI: Evaluating a LLM-Based Telephone Survey System at Scale
2025-02-27
Max M. Lang, Sol Eskenazi
Telephone surveys remain a valuable tool for gathering insights but typically require substantial resources in training and coordinating human interviewers. This work presents an AI-driven telephone survey system integrating text-to-speech (TTS), a large language model (LLM), and speech-to-text (STT) that mimics the versatility of human-led interviews on scale. We tested the system across two populations, a pilot study in the United States (n = 75) and a large-scale deployment in Peru (n = 2,739), inviting participants via web-based links and contacting them via direct phone calls. The AI agent successfully administered open-ended and closed-ended questions, handled basic clarifications, and dynamically navigated branching logic, allowing fast large-scale survey deployment without interviewer recruitment or training. Our findings demonstrate that while the AI system's probing for qualitative depth was more limited than human interviewers, overall data quality approached human-led standards for structured items. This study represents one of the first successful large-scale deployments of an LLM-based telephone interviewer in a real-world survey context. The AI-powered telephone survey system has the potential for expanding scalable, consistent data collecting across market research, social science, and public opinion studies, thus improving operational efficiency while maintaining appropriate data quality for research.
Regional climate projections using a deep-learning-based model-ranking and downscaling framework: Application to European climate zones
2025-02-27
Parthiban Loganathan, Elias Zea, Ricardo Vinuesa, Evelyn Otero
Accurate regional climate forecast calls for high-resolution downscaling of Global Climate Models (GCMs). This work presents a deep-learning-based multi-model evaluation and downscaling framework ranking 32 Coupled Model Intercomparison Project Phase 6 (CMIP6) models using a Deep Learning-TOPSIS (DL-TOPSIS) mechanism and so refines outputs using advanced deep-learning models. Using nine performance criteria, five K\"oppen-Geiger climate zones -- Tropical, Arid, Temperate, Continental, and Polar -- are investigated over four seasons. While TaiESM1 and CMCC-CM2-SR5 show notable biases, ranking results show that NorESM2-LM, GISS-E2-1-G, and HadGEM3-GC31-LL outperform other models. Four models contribute to downscaling the top-ranked GCMs to 0.1$^{\circ}$ resolution: Vision Transformer (ViT), Geospatial Spatiotemporal Transformer with Attention and Imbalance-Aware Network (GeoSTANet), CNN-LSTM, and CNN-Long Short-Term Memory (ConvLSTM). Effectively capturing temperature extremes (TXx, TNn), GeoSTANet achieves the highest accuracy (Root Mean Square Error (RMSE) = 1.57$^{\circ}$C, Kling-Gupta Efficiency (KGE) = 0.89, Nash-Sutcliffe Efficiency (NSE) = 0.85, Correlation ($r$) = 0.92), so reducing RMSE by 20% over ConvLSTM. CNN-LSTM and ConvLSTM do well in Continental and Temperate zones; ViT finds fine-scale temperature fluctuations difficult. These results confirm that multi-criteria ranking improves GCM selection for regional climate studies and transformer-based downscaling exceeds conventional deep-learning methods. This framework offers a scalable method to enhance high-resolution climate projections, benefiting impact assessments and adaptation plans.
New Dataset and Methods for Fine-Grained Compositional Referring Expression Comprehension via Specialist-MLLM Collaboration
2025-02-27
Xuzheng Yang, Junzhuo Liu, Peng Wang, Guoqing Wang, Yang Yang, Heng Tao Shen
Referring Expression Comprehension (REC) is a foundational cross-modal task that evaluates the interplay of language understanding, image comprehension, and language-to-image grounding. To advance this field, we introduce a new REC dataset with two key features. First, it is designed with controllable difficulty levels, requiring fine-grained reasoning across object categories, attributes, and relationships. Second, it incorporates negative text and images generated through fine-grained editing, explicitly testing a model's ability to reject non-existent targets, an often-overlooked yet critical challenge in existing datasets. To address fine-grained compositional REC, we propose novel methods based on a Specialist-MLLM collaboration framework, leveraging the complementary strengths of them: Specialist Models handle simpler tasks efficiently, while MLLMs are better suited for complex reasoning. Based on this synergy, we introduce two collaborative strategies. The first, Slow-Fast Adaptation (SFA), employs a routing mechanism to adaptively delegate simple tasks to Specialist Models and complex tasks to MLLMs. Additionally, common error patterns in both models are mitigated through a target-refocus strategy. The second, Candidate Region Selection (CRS), generates multiple bounding box candidates based on Specialist Model and uses the advanced reasoning capabilities of MLLMs to identify the correct target. Extensive experiments on our dataset and other challenging compositional benchmarks validate the effectiveness of our approaches. The SFA strategy achieves a trade-off between localization accuracy and efficiency, and the CRS strategy greatly boosts the performance of both Specialist Models and MLLMs. We aim for this work to offer valuable insights into solving complex real-world tasks by strategically combining existing tools for maximum effectiveness, rather than reinventing them.
Generative augmentations for improved cardiac ultrasound segmentation using diffusion models
2025-02-27
Gilles Van De Vyver, Aksel Try Lenz, Erik Smistad, Sindre Hellum Olaisen, Bjørnar Grenne, Espen Holte, Håavard Dalen, Lasse Løvstakken
One of the main challenges in current research on segmentation in cardiac ultrasound is the lack of large and varied labeled datasets and the differences in annotation conventions between datasets. This makes it difficult to design robust segmentation models that generalize well to external datasets. This work utilizes diffusion models to create generative augmentations that can significantly improve diversity of the dataset and thus the generalisability of segmentation models without the need for more annotated data. The augmentations are applied in addition to regular augmentations. A visual test survey showed that experts cannot clearly distinguish between real and fully generated images. Using the proposed generative augmentations, segmentation robustness was increased when training on an internal dataset and testing on an external dataset with an improvement of over 20 millimeters in Hausdorff distance. Additionally, the limits of agreement for automatic ejection fraction estimation improved by up to 20% of absolute ejection fraction value on out of distribution cases. These improvements come exclusively from the increased variation of the training data using the generative augmentations, without modifying the underlying machine learning model. The augmentation tool is available as an open source Python library at https://github.com/GillesVanDeVyver/EchoGAINS.
InCoRe -- An Interactive Co-Regulation Model: Training Teacher Communication Skills in Demanding Classroom Situations
2025-02-27
Chirag Bhuvaneshwara, Lara Chehayeb, Alexander Haberl, Julius Siedentopf, Dimitra Tsovaltzi, Patrick Gebhard
Socioemotional and regulation processes in learning are important. We add to the understanding of previous work on co-regulation processes in the learning sciences, extending the caregiver-child paradigm and focusing on the teacher-student relation by presenting an interactive co-regulation model and the methodology for developing empirically grounded systems for training teachers. We focus on the combination of classroom management and affect models and detail the use of a psychological model to operationalise and automate the interaction with the virtual student. We delve into an annotation scheme developed to capture teacher subjective psychological experiences during training and how these affect their co-regulation behavior with students and contributes to understanding the role of teacher emotional experiences and their consequences of co-regulation processes for classroom management. This research is also a contribution to developing hybrid AI systems.
Temperature Profiles of Accretion Disks in Luminous Active Galactic Nuclei derived from Ultraviolet Spectroscopic Variability
2025-02-27
Suyeon Son, Minjin Kim, Luis C. Ho
The characteristic timescale ($\tau$) of continuum variability of the accretion disk in active galactic nuclei (AGNs) is known to be related to the thermal timescale, which is predicted to scale with AGN luminosity ($L$) and restframe wavelength ($\lambda_{\rm RF}$) as $t_{\rm th} \propto L^{0.5} \lambda_{\rm RF}^2$ in the standard disk model. Using multi-epoch spectroscopic data from the Sloan Digital Sky Survey Reverberation Mapping project, we construct ultraviolet ensemble structure functions of luminous AGNs as a function of their luminosity and wavelength. Assuming that AGNs exhibit a single universal structure function when $\Delta t$ is normalized by $\tau$, wherein $\tau \propto L^{a} \lambda_{\rm RF}^{b}$, we find $a=0.50\pm0.03$ and $b=1.42\pm0.09$. While the value of $a$ aligns with the prediction from the standard disk model, $b$ is significantly smaller than expected, suggesting that the radial temperature (color) profile of the accretion disk is significantly steeper (shallower) than the standard disk model. Notably, this discrepancy with theory has been observed in previous studies based on spectroscopic reverberation mapping and gravitational microlensing. Although no current model of accretion disks fully matches our results, our findings provide valuable constraints for testing future physical models.
Systems-of-Systems for Environmental Sustainability: A Systematic Mapping Study
2025-02-27
Ana Clara Araújo Gomes da Silva, Gilmar Teixeira Junior, Lívia Mancine C. de Campos, Renato F. Bulcão-Neto, Valdemar Vicente Graciano Neto
Environmental sustainability in Systems-of-Systems (SoS) is an emerging field that seeks to integrate technological solutions to promote the efficient management of natural resources. While systematic reviews address sustainability in the context of Smart Cities (a category of SoS), a systematic study synthesizing the existing knowledge on environmental sustainability applied to SoS in general does not exist. Although literature includes other types of sustainability, such as financial and social, this study focuses on environmental sustainability, analyzing how SoS contribute to sustainable practices such as carbon emission reduction, energy efficiency, and biodiversity conservation. We conducted a Systematic Mapping Study to identify the application domains of SoS in sustainability, the challenges faced, and research opportunities. We planned and executed a research protocol including an automated search over four scientific databases. Of 926 studies retrieved, we selected, analyzed, and reported the results of 39 relevant studies. Our findings reveal that most studies focus on Smart Cities and Smart Grids, while applications such as sustainable agriculture and wildfire prevention are less explored. We identified challenges such as system interoperability, scalability, and data governance. Finally, we propose future research directions for SoS and environmental sustainability.
Step-by-Step Guide to Conducting Meta-Analysis of Dichotomous Outcomes Using RevMan in Dental Research Step-by-Step Guide to Conducting Meta-Analysis of Dichotomous Outcomes Using RevMan in Dental Research
2025-02-27
Hoi-Jeong Lim, Su-Hyeon Park
Meta-analysis is a statistical method that combines the results of individual studies on the same topic. This method is becoming popular, due to providing the combined result that individual studies cannot provide and giving a more precise result. Despite meta-analysis having such significance, there are few Korean guides for the use of the Review Manager (RevMan) software. This study will provide a step-by-step guide, using orthodontic mini-screw as a dental example, to help researcher carry out meta-analysis more easily and accurately.
We present $I$-band time-series photometric variability studies of three known nearby ($\sim$ 140 pc) and young ( $\sim$ 1 Myr) brown dwarfs (BD) in the Taurus star-forming region in the Perseus Molecular Cloud. From 10 nights of observations over a time span of 10 years, with a typical run of 3 to 6 hours each night, we estimated that the BDs show unstable short-scale periodicity from 1.5 to 4.8 hours. Using the long-term photometry from the Transiting Exoplanet Survey Satellite (TESS), we have conducted a time-resolved variability analysis of CFHT-BD-Tau 3 and CFHT-BD-Tau 4, revealing orbital periods of $\sim$ 0.96 days and $\sim$ 3 days respectively, consistent with earlier studies. We also found two superflares in TESS sector 43 data for CFHT-BD-Tau 4 and estimated the flare energies as $7.09\times10^{35}$ erg and $3.75\times10^{36}$ erg. A magnetic field of $\sim3.39 ~kG$ is required to generate such flare energies on this BD. We performed spot modelling analysis on CFHT-BD-Tau 3 and CFHT-BD-Tau 4 to address the variability detected in the data using the package BASSMAN. Spectral energy distribution and infrared colours of the sources suggest that they have a sufficient amount of circumstellar material around them.
Modern DDoS Threats and Countermeasures: Insights into Emerging Attacks and Detection Strategies
2025-02-27
Jincheng Wang, Le Yu, John C. S. Lui, Xiapu Luo
Distributed Denial of Service (DDoS) attacks persist as significant threats to online services and infrastructure, evolving rapidly in sophistication and eluding traditional detection mechanisms. This evolution demands a comprehensive examination of current trends in DDoS attacks and the efficacy of modern detection strategies. This paper offers an comprehensive survey of emerging DDoS attacks and detection strategies over the past decade. We delve into the diversification of attack targets, extending beyond conventional web services to include newer network protocols and systems, and the adoption of advanced adversarial tactics. Additionally, we review current detection techniques, highlighting essential features that modern systems must integrate to effectively neutralize these evolving threats. Given the technological demands of contemporary network systems, such as high-volume and in-line packet processing capabilities, we also explore how innovative hardware technologies like programmable switches can significantly enhance the development and deployment of robust DDoS detection systems. We conclude by identifying open problems and proposing future directions for DDoS research. In particular, our survey sheds light on the investigation of DDoS attack surfaces for emerging systems, protocols, and adversarial strategies. Moreover, we outlines critical open questions in the development of effective detection systems, e.g., the creation of defense mechanisms independent of control planes.
High-contrast spectroscopy with the new VLT/ERIS instrument: Molecular maps and radial velocity of the gas giant AF Lep b
2025-02-27
Jean Hayoz, Markus Johannes Bonse, Felix Dannert, Emily Omaya Garvin, Gabriele Cugno, Polychronis Patapis, Timothy D. Gebhard, William O. Balmer, Robert J. De Rosa, Alexander Agudo Berbel, Yixian Cao, Gilles Orban de Xivry, Tomas Stolker, Richard Davies, Olivier Absil, Hans Martin Schmid, Sascha Patrick Quanz, Guido Agapito, Andrea Baruffolo, Martin Black, Marco Bonaglia, Runa Briguglio, Luca Carbonaro, Giovanni Cresci, Yigit Dallilar, Matthias Deysenroth, Ivan Di Antonio, Amico Di Cianno, Gianluca Di Rico, David Doelman, Mauro Dolci, Frank Eisenhauer, Simone Esposito, Daniela Fantinel, Debora Ferruzzi, Helmut Feuchtgruber, Natascha Förster-Schreiber, Xiaofeng Gao, Reinhard Genzel, Stefan Gillessen, Adrian Glauser, Paolo Grani, Michael Hartl, David Henry, Heinrich Huber, Christoph Keller, Matthew Kenworthy, Kateryna Kravchenko, John Lightfoot, David Lunney, Dieter Lutz, Mike Macintosh, Filippo Manucci, Thomas Ott, David Pearson, Alfio Puglisi, Sebastian Rabien, Christian Rau, Armando Riccardi, Bernardo Salasnich, Taro Shimizu, Frans Snik, Eckhard Sturm, Linda Tacconi, William Taylor, Angelo Valentini, Christopher Waring, Erich Wiezorrek, Marco Xompero
The Enhanced Resolution Imager and Spectrograph (ERIS) is the new Adaptive-Optics (AO) assisted Infrared instrument at the Very Large Telescope (VLT). Its refurbished Integral Field Spectrograph (IFS) SPIFFIER leverages a new AO module, enabling high-contrast imaging applications and giving access to the orbital and atmospheric characterisation of super-Jovian exoplanets. We test the detection limits of ERIS and demonstrate its scientific potential by exploring the atmospheric composition of the young super-Jovian AF Lep b and improving its orbital solution by measuring its radial velocity relative to its host star. We present new spectroscopic observations of AF Lep b in $K$-band at $R\sim 11000$ obtained with ERIS/SPIFFIER at the VLT. We reduce the data using the standard pipeline together with a custom wavelength calibration routine, and remove the stellar PSF using principal component analysis along the spectral axis. We compute molecular maps by cross-correlating the residuals with molecular spectral templates and measure the radial velocity of the planet relative to the star. Furthermore, we compute contrast grids for molecular mapping by injecting fake planets. We detect a strong signal from H$_{2}$O and CO but not from CH$_{4}$ or CO$_{2}$. This result corroborates the hypothesis of chemical disequilibrium in the atmosphere of AF Lep b. Our measurement of the RV of the planet yields $\Delta v_{\mathrm{R,P\star}} = 7.8 \pm 1.7$ km s$^{-1}$. This enables us to disentangle the degeneracy of the orbital solution, namely the correct longitude of the ascending node is $\Omega=248^{+0.4}_{-0.7}$ deg and the argument of periapsis is $\omega=109^{+13}_{-21}$ deg. Our results demonstrate the competitiveness of the new ERIS/SPIFFIER instrument for the orbital and atmospheric characterisation of exoplanets at high contrast and small angular separation.
Observing the influence of second-harmonic tangential surface source in gold plasmonic nanostructures
2025-02-27
Sandy Mathew, Maëliss Ethis de Corny, Nicolas Chauvet, Laureen Moreaud, Erik Dujardin, Gilles Nogues, Guillaume Bachelier
The origin of the second harmonic generation (SHG) from plasmonic structures remains a subject of debate. Here, we investigate SHG from gold plasmonic nanostructures by scanning nanostructures at the single-particle level to acquire two-dimensional (2D) SHG maps which are qualitatively and quantitatively compared with numerically simulated SHG maps. This approach allows us to discriminate the role played by the surface and bulk contributions to SHG. Our findings reveal that the tangential surface source contribution, often neglected in the literature, plays a dominant role and is affected by surface roughness. This is evident when comparing the SHG maps obtained from identically sized gold nanoparticles that are fabricated using physical and chemical techniques. Additionally, the normal surface source is not found to significantly impact SHG in these configurations.
TOI-6508b: A massive transiting brown dwarf orbiting a low-mass star
2025-02-27
K. Barkaoui, D. Sebastian, S. Zúñiga-Fernández, A. H. M. J. Triaud, B. V. Rackham, A. J. Burgasser, T. W. Carmichael, M. Gillon, C. Theissen, E. Softich, B. Rojas-Ayala, G. Srdoc, A. Soubkiou, A. Fukui, M. Timmermans, M. Stalport, A. Burdanov, D. R. Ciardi, K. A. Collins, Y. T. Davis, F. Davoudi, J. de Wit, B. O. Demory, S. Deveny, G. Dransfield, E. Ducrot, L. Florian, T. Gan, Y. Gómez Maqueo Chew, M. J. Hooton, S. B. Howell, J. M. Jenkins, C. Littlefield, E. L. Martín, F. Murgas, P. Niraula, E. Palle, P. P. Pedersen, F. J. Pozuelos, D. Queloz, G. Ricker, R. P. Schwarz, S. Seager, A. Shporer, M. G. Scott, C. Stockdale, J. Winn
We report the discovery of a transiting brown dwarf orbiting a low-mass star, TOI-6508b. Today, only ~50 transiting brown dwarfs have been discovered. TOI-6508b was first detected with data from the Transiting Exoplanet Survey Satellite (TESS) in Sectors 10, 37, and 63. Ground-based follow-up photometric data were collected with the SPECULOOS-South and LCOGT-1.0m telescopes, and RV measurements were obtained with the Near InfraRed Planet Searcher (NIRPS) spectrograph. We find that TOI-6508b has a mass of Mp=72.5+7.6-5.1MJup and a radius of Rp=1.03+/-0.03RJup. Our modeling shows that the data are consistent with an eccentric orbit of 19day and an eccentricity of e=0.28+0.09-0.08. TOI-6508b has a mass ratio of M_BD/Ms=0.40, makes it the second highest mass ratio brown dwarf that transits a low-mass star. The host has a mass of Ms=0.174+/-0.004M_Sun, a radius of Rs=0.205+/-0.006R_Sun, an effective temperature of Teff=2930+/-70K, and a metallicity of [Fe/H]=-0.22+/-0.08. This makes TOI-6508b an interesting discovery that has come to light in a region still sparsely populated.
Playing Pokémon Red via Deep Reinforcement Learning
2025-02-27
Marco Pleines, Daniel Addis, David Rubinstein, Frank Zimmer, Mike Preuss, Peter Whidden
Pok\'emon Red, a classic Game Boy JRPG, presents significant challenges as a testbed for agents, including multi-tasking, long horizons of tens of thousands of steps, hard exploration, and a vast array of potential policies. We introduce a simplistic environment and a Deep Reinforcement Learning (DRL) training methodology, demonstrating a baseline agent that completes an initial segment of the game up to completing Cerulean City. Our experiments include various ablations that reveal vulnerabilities in reward shaping, where agents exploit specific reward signals. We also discuss limitations and argue that games like Pok\'emon hold strong potential for future research on Large Language Model agents, hierarchical training algorithms, and advanced exploration methods. Source Code: https://github.com/MarcoMeter/neroRL/tree/poke_red
Accessibility for Whom? Perceptions of Sidewalk Barriers Across Disability Groups and Implications for Designing Personalized Maps
2025-02-27
Chu Li, Rock Yuren Pang, Delphine Labbé, Yochai Eisenberg, Maryam Hosseini, Jon E. Froehlich
Despite diverse mobility needs worldwide, existing mapping tools fail to address the varied experiences of different mobility device users. This paper presents a large-scale online survey exploring how five mobility groups -- users of canes, walkers, mobility scooters, manual wheelchairs, and motorized wheelchairs -- perceive sidewalk barriers. Using 52 sidewalk barrier images, respondents evaluated their confidence in navigating each scenario. Our findings (N=190) reveal variations in barrier perceptions across groups, while also identifying shared concerns. To further demonstrate the value of this data, we showcase its use in two custom prototypes: a visual analytics tool and a personalized routing tool. Our survey findings and open dataset advance work in accessibility-focused maps, routing algorithms, and urban planning.
The diffuse extragalactic gamma-ray background radiation: star-forming galaxies are not the dominant component
2025-02-27
Junling Chen, Tomonori Totani
Star-forming galaxies (SFGs) are considered to be an important component of the diffuse extragalactic gamma-ray background (EGB) radiation observed in 0.1 -- 820 GeV, but their quantitative contribution has not yet been precisely determined. In this study, we aim to provide the currently most reliable estimate of the contribution of SFGs based on careful calibration with gamma-ray luminosities of nearby galaxies and physical quantities (star formation rate, stellar mass, and size) of galaxies observed by high-redshift galaxy surveys. Our calculations are based on the latest database of particle collision cross-sections and energy spectra of secondary particles, and take into account not only hadronic but also leptonic processes with various radiation fields in a galaxy. We find that SFGs are not the dominant component of the unresolved EGB measured by Fermi; the largest contribution is around 50% -- 60% in the 1 -- 10 GeV region, and the contribution falls rapidly in lower and higher energy ranges. This result appears to contradict a previous study, which claimed that SFGs are the dominant component of the unresolved EGB, and the origin of the discrepancy is examined. In calculations of cosmic-ray production, propagation, and interaction in a galaxy, we try models developed by two independent groups and find that they have little impact on EGB.
Milli-Kelvin atomic force microscopy (mK-AFM) presents an ongoing experimental challenge due to the intense vibrations in a cryogen-free dilution refrigerator and the low cooling power available at mK temperatures. A viable approach is to make the system exceptionally rigid and thermally insulating to decouple external vibrations and isolate heat dissipation from the piezo elements. Here, we present a low-cost and large scan-range mK-AFM that operates below 100 mK. All the essential parts of our mK-AFM, including the scanners, tip assembly, and microscope body, are custom-made of fused silica glass by taking advantage of its high specific modulus, extremely low thermal expansion coefficient, and excellent thermal insulation properties. We carefully balance the scan range (25 ${\mu}$m $\times$ 25 ${\mu}$m), heat dissipation, and stiffness of the system to reach optimal performance at mK temperatures.
NaijaNLP: A Survey of Nigerian Low-Resource Languages
2025-02-27
Isa Inuwa-Dutse
With over 500 languages in Nigeria, three languages -- Hausa, Yor\`ub\'a and Igbo -- spoken by over 175 million people, account for about 60% of the spoken languages. However, these languages are categorised as low-resource due to insufficient resources to support tasks in computational linguistics. Several research efforts and initiatives have been presented, however, a coherent understanding of the state of Natural Language Processing (NLP) - from grammatical formalisation to linguistic resources that support complex tasks such as language understanding and generation is lacking. This study presents the first comprehensive review of advancements in low-resource NLP (LR-NLP) research across the three major Nigerian languages (NaijaNLP). We quantitatively assess the available linguistic resources and identify key challenges. Although a growing body of literature addresses various NLP downstream tasks in Hausa, Igbo, and Yor\`ub\'a, only about 25.1% of the reviewed studies contribute new linguistic resources. This finding highlights a persistent reliance on repurposing existing data rather than generating novel, high-quality resources. Additionally, language-specific challenges, such as the accurate representation of diacritics, remain under-explored. To advance NaijaNLP and LR-NLP more broadly, we emphasise the need for intensified efforts in resource enrichment, comprehensive annotation, and the development of open collaborative initiatives.
The substantial growth of textual content in diverse domains and platforms has led to a considerable need for Automatic Text Summarization (ATS) techniques that aid in the process of text analysis. The effectiveness of text summarization models has been significantly enhanced in a variety of technical domains because of advancements in Natural Language Processing (NLP) and Deep Learning (DL). Despite this, the process of summarizing textual information continues to be significantly constrained by the intricate writing styles of a variety of texts, which involve a range of technical complexities. Text summarization techniques can be broadly categorized into two main types: abstractive summarization and extractive summarization. Extractive summarization involves directly extracting sentences, phrases, or segments of text from the content without making any changes. On the other hand, abstractive summarization is achieved by reconstructing the sentences, phrases, or segments from the original text using linguistic analysis. Through this study, a linguistically diverse categorizations of text summarization approaches have been addressed in a constructive manner. In this paper, the authors explored existing hybrid techniques that have employed both extractive and abstractive methodologies. In addition, the pros and cons of various approaches discussed in the literature are also investigated. Furthermore, the authors conducted a comparative analysis on different techniques and matrices to evaluate the generated summaries using language generation models. This survey endeavors to provide a comprehensive overview of ATS by presenting the progression of language processing regarding this task through a breakdown of diverse systems and architectures accompanied by technical and mathematical explanations of their operations.
Understanding the two-step nucleation of iron at Earth's inner core conditions: a comparative molecular dynamics study
2025-02-27
Chen Gao, Kai-Ming Ho, Renata M. Wentzcovitch, Yang Sun
The metastable phases can lead to multistep nucleation processes, influencing the liquid-to-solid transition in various systems. In this study, we investigate the homogeneous nucleation of iron's crystalline phases under Earth's inner core conditions, employing two previously developed interatomic potentials. We compare the thermodynamic and kinetic properties of iron relevant to the nucleation as predicted by these potentials. While the potentials differ in their predictions of melting temperature by a few hundred Kelvins, they show a consistent description of the relative Gibbs free energy between solid and liquid phases with respect to the undercooling. Both potentials also predict that the metastable bcc phase exhibits a significantly higher nucleation rate than the hcp phase over a wide range of undercooling. This substantially lowers the conditions required for the initial nucleation of Earth's inner core. The results validate the commonality of the two-step nucleation mechanism of iron under Earth's inner core conditions for two different potentials, providing a foundation for future studies about the influence of other elements on the nucleation of Earth's core.
Sequential dependencies present a fundamental bottleneck in deploying large-scale autoregressive models, particularly for real-time applications. While traditional optimization approaches like pruning and quantization often compromise model quality, recent advances in generation-refinement frameworks demonstrate that this trade-off can be significantly mitigated. This survey presents a comprehensive taxonomy of generation-refinement frameworks, analyzing methods across autoregressive sequence tasks. We categorize methods based on their generation strategies (from simple n-gram prediction to sophisticated draft models) and refinement mechanisms (including single-pass verification and iterative approaches). Through systematic analysis of both algorithmic innovations and system-level implementations, we examine deployment strategies across computing environments and explore applications spanning text, images, and speech generation. This systematic examination of both theoretical frameworks and practical implementations provides a foundation for future research in efficient autoregressive decoding.
Tripartite Perspective on the Copyright-Sharing Economy in China
2025-02-27
Jyh-An Lee
Internet and digital technologies have facilitated copyright sharing in an unprecedented way, creating significant tensions between the free flow of information and the exclusive nature of intellectual property. Copyright owners, users, and online platforms are the three major players in the copyright system. These stakeholders and their relations form the main structure of the copyright-sharing economy. Using China as an example, this paper provides a tripartite perspective on the copyright ecology based on three categories of sharing, namely unauthorized sharing, altruistic sharing, and freemium sharing. The line between copyright owners, users, and platforms has been blurred by rapidly changing technologies and market forces. By examining the strategies and practices of these parties, this paper illustrate the opportunities and challenges for China's copyright industry and digital economy. The paper concludes that under the shadow of the law, a sustainable copyright-sharing model must carefully align the interests of businesses and individual users.
Recent Advances on Generalizable Diffusion-generated Image Detection
2025-02-27
Qijie Xu, Defang Chen, Jiawei Chen, Siwei Lyu, Can Wang
The rise of diffusion models has significantly improved the fidelity and diversity of generated images. With numerous benefits, these advancements also introduce new risks. Diffusion models can be exploited to create high-quality Deepfake images, which poses challenges for image authenticity verification. In recent years, research on generalizable diffusion-generated image detection has grown rapidly. However, a comprehensive review of this topic is still lacking. To bridge this gap, we present a systematic survey of recent advances and classify them into two main categories: (1) data-driven detection and (2) feature-driven detection. Existing detection methods are further classified into six fine-grained categories based on their underlying principles. Finally, we identify several open challenges and envision some future directions, with the hope of inspiring more research work on this important topic. Reviewed works in this survey can be found at https://github.com/zju-pi/Awesome-Diffusion-generated-Image-Detection.
Old Experience Helps: Leveraging Survey Methodology to Improve AI Text Annotation Reliability in Social Sciences
2025-02-27
Linzhuo li
This paper introduces a framework for assessing the reliability of Large Language Model (LLM) text annotations in social science research by adapting established survey methodology principles. Drawing parallels between survey respondent behavior and LLM outputs, the study implements three key interventions: option randomization, position randomization, and reverse validation. While traditional accuracy metrics may mask model instabilities, particularly in edge cases, our framework provides a more comprehensive reliability assessment. Using the F1000 dataset in biomedical science and three sizes of Llama models (8B, 70B, and 405B parameters), the paper demonstrates that these survey-inspired interventions can effectively identify unreliable annotations that might otherwise go undetected through accuracy metrics alone. The results show that 5-25% of LLM annotations change under these interventions, with larger models exhibiting greater stability. Notably, for rare categories approximately 50% of "correct" annotations demonstrate low reliability when subjected to this framework. The paper introduce an information-theoretic reliability score (R-score) based on Kullback-Leibler divergence that quantifies annotation confidence and distinguishes between random guessing and meaningful annotations at the case level. This approach complements existing expert validation methods by providing a scalable way to assess internal annotation reliability and offers practical guidance for prompt design and downstream analysis.
Multilingual research dissemination: Current practices and implications for bibliometrics
2025-02-27
Faizhal Arif Santosa, Barbara S. Lancho Barrantes
English is widely used as a lingua franca in scholarly communication, yet preserving local languages is vital to reaching a broader audience. Disseminating research in multiple languages can help ensure equitable access, a responsibility shared by both publishers and authors. This study examines the practices of both groups to identify any notable differences. Several academic social networks, preprint servers, and repositories are analysed to evaluate the resources currently available and their existing policies. Additionally, journals that actively promote multilingual dissemination are reviewed to understand their implementation strategies and how these align with the standards set by the DOI Registration Agency (DOI RA). From the author's perspective, differing policies across platforms can heavily influence decisions, mainly because not all platforms provide relationship metadata. Publishers face similar challenges, underscoring the urgent need for standardisation. Moreover, the lack of consistency creates opportunities for unethical practices in academia, such as counting total of citations originating from the same article in different languages. This highlights the importance of a more comprehensive approach to evaluating research beyond citation and document counts. Collaboration among publishers, authors, and other stakeholders is essential to fostering greater understanding and preventing misconceptions in the academic landscape.
Taxonomy, Opportunities, and Challenges of Representation Engineering for Large Language Models
2025-02-27
Jan Wehner, Sahar Abdelnabi, Daniel Tan, David Krueger, Mario Fritz
Representation Engineering (RepE) is a novel paradigm for controlling the behavior of LLMs. Unlike traditional approaches that modify inputs or fine-tune the model, RepE directly manipulates the model's internal representations. As a result, it may offer more effective, interpretable, data-efficient, and flexible control over models' behavior. We present the first comprehensive survey of RepE for LLMs, reviewing the rapidly growing literature to address key questions: What RepE methods exist and how do they differ? For what concepts and problems has RepE been applied? What are the strengths and weaknesses of RepE compared to other methods? To answer these, we propose a unified framework describing RepE as a pipeline comprising representation identification, operationalization, and control. We posit that while RepE methods offer significant potential, challenges remain, including managing multiple concepts, ensuring reliability, and preserving models' performance. Towards improving RepE, we identify opportunities for experimental and methodological improvements and construct a guide for best practices.
Kinematical Modeling of the Resolved Stellar Outskirts of M32: Constraints on Tidal Stripping Scenarios
2025-02-27
Ivanna Escala, Douglas Grion Filho, Karoline Gilbert, Puragra Guhathakurta, Laura Cullinane, Mark Fardal, Erik Tollerud, Amanda Quirk, Zhuo Chen, Molly Hyver, Benjamin Williams
As the only compact elliptical close enough to resolve into individual stars, the satellite dwarf galaxy M32 provides a unique opportunity for exploring the origins of such rare galaxies. In this work, we combined archival and novel Keck/DEIMOS spectroscopy from a southern extension of the Spectroscopic and Photometric Landscape of Andromeda's Stellar Halo (SPLASH) survey with optical HST imaging from the Panchromatic Hubble Andromeda Southern Treasury (PHAST) survey. The resulting sample of 2525 giant stars is unprecedented both in size and spatial coverage (0.9-15.5 arcmin, or out to $\sim$23$r_{\rm eff}$ and $\sim$30$r_{\rm eff}$ along M32's major and minor axes) for probing the resolved stellar outskirts of M32. Given the structurally complex region near M32 on the sky, we modeled M32's line-of-sight kinematics simultaneously alongside M31's rotating stellar disk and potential outliers corresponding to M31's kinematically hot stellar halo and/or tidal substructure. Inside the radius corresponding to the observed twisting of isophotal contours in M32's surface brightness profile ($R_{\rm iso} \sim$ 5$r_{\rm eff}$ $\sim$ 150'' or 0.56 kpc), M32 exhibits a line-of-sight velocity distribution characteristic of ordered rotation, transitioning to a distribution with heavier outliers beyond this radius. Within $R_{\rm iso}$, the rotational direction is aligned with M32's major-axis rotation, but shifts to become roughly aligned with M32's minor axis beyond $R_{\rm iso}$. We interpret these kinematical signatures in the stellar outskirts of M32 as evidence of tidal distortion from interactions with M31 and discuss their implications for M32 formation pathways.
In physics, all dynamical equations that describe fundamental interactions are second order ordinary differential equations in the time derivatives. In the literature, this property is traced back to a result obtained by Ostrogradski in the mid 19th century, which is the technical basis of a 'no-go' theorem for higher order theories. In this work, we review the connection of symmetry properties with the order of dynamical equations, before reconsidering Ostrogradski's result. Then, we show how Ostrogradski's conclusion is reached by applying to higher order theories concepts and method that have been specifically developed for second order theories. We discuss a potential lack of consistency in this approach, to support the claim that Ostrogradski's result applies to a class of higher order theories that is nowhere representative of generic ones: we support this claim by giving an example of a higher-order Lagrangian that is asymptotically stable, but that would be unstable under Ostrogradski's criterion. We also conclude that, when considering higher order theories as fundamental, we may need to reconsider and extend the conceptual framework on which our standard treatment of second order theories is based.
A Tutorial on Multi-time Scale Optimization Models and Algorithms
2025-02-27
Asha Ramanujam, Can Li
Systems across different industries consist of interrelated processes and decisions in different time scales including long-time decisions and short-term decisions. To optimize such systems, the most effective approach is to formulate and solve multi-time scale optimization models that integrate various decision layers. In this tutorial, we provide an overview of multi-time scale optimization models and review the algorithms used to solve them. We also discuss the metric Value of the Multi-scale Model (VMM) introduced to quantify the benefits of using multi-time scale optimization models as opposed to sequentially solving optimization models from high-level to low-level. Finally, we present an illustrative example of a multi-time scale capacity expansion planning model and showcase how it can be solved using some of the algorithms (https://github.com/li-group/MultiScaleOpt-Tutorial.git). This tutorial serves as both an introductory guide for beginners with no prior experience and a high-level overview of current algorithms for solving multi-time scale optimization models, catering to experts in process systems engineering.
NANOGPT: A Query-Driven Large Language Model Retrieval-Augmented Generation System for Nanotechnology Research
2025-02-27
Achuth Chandrasekhar, Omid Barati Farimani, Olabode T. Ajenifujah, Janghoon Ock, Amir Barati Farimani
This paper presents the development and application of a Large Language Model Retrieval-Augmented Generation (LLM-RAG) system tailored for nanotechnology research. The system leverages the capabilities of a sophisticated language model to serve as an intelligent research assistant, enhancing the efficiency and comprehensiveness of literature reviews in the nanotechnology domain. Central to this LLM-RAG system is its advanced query backend retrieval mechanism, which integrates data from multiple reputable sources. The system retrieves relevant literature by utilizing Google Scholar's advanced search, and scraping open-access papers from Elsevier, Springer Nature, and ACS Publications. This multifaceted approach ensures a broad and diverse collection of up-to-date scholarly articles and papers. The proposed system demonstrates significant potential in aiding researchers by providing a streamlined, accurate, and exhaustive literature retrieval process, thereby accelerating research advancements in nanotechnology. The effectiveness of the LLM-RAG system is validated through rigorous testing, illustrating its capability to significantly reduce the time and effort required for comprehensive literature reviews, while maintaining high accuracy, query relevance and outperforming standard, publicly available LLMS.
Study of the Use of Property Probes in an Educational Setting
2025-02-27
Anton Risberg Alaküla, Niklas Fors, Emma Söderberg
Context: Developing compilers and static analysis tools ("language tools") is a difficult and time-consuming task. We have previously presented *property probes*, a technique to help the language tool developer build understanding of their tool. A probe presents a live view into the internals of the compiler, enabling the developer to see all the intermediate steps of a compilation or analysis rather than just the final output. This technique has been realized in a tool called CodeProber. Inquiry: CodeProber has been in active use in both research and education for over two years, but its practical use has not been well studied. CodeProber combines liveness, AST exploration and presenting program analysis results on top of source code. While there are other tools that specifically target language tool developers, we are not aware of any that has the same design as CodeProber, much less any such tool with an extensive user study. We therefore claim there is a lack of knowledge how property probes (and by extension CodeProber) are used in practice. Approach: We present the results from a mixed-method study of use of CodeProber in an educational setting, with the goal to discover if and how property probes help, and how they compare to more traditional techniques such as test cases and print debugging. In the study, we analyzed data from 11 in-person interviews with students using CodeProber as part of a course on program analysis. We also analyzed CodeProber event logs from 24 students in the same course, and 51 anonymized survey responses across two courses where CodeProber was used. Knowledge: Our findings show that the students find CodeProber to be useful, and they make continuous use of it during the course labs. We further find that the students in our study seem to partially or fully use CodeProber instead of other development tools and techniques, e.g. breakpoint/step-debugging, test cases and print debugging. Grounding: Our claims are supported by three different data sources: 11 in-person interviews, log analysis from 24 students, and surveys with 51 responses. Importance: We hope our findings inspire others to consider live exploration to help language tool developers build understanding of their tool.
Two Approaches for Programming Education in the Domain of Graphics: An Experiment
2025-02-27
Luca Chiodini, Juha Sorva, Arto Hellas, Otto Seppälä, Matthias Hauswirth
Context: Graphics is a popular domain for teaching introductory programming in a motivating way, even in text-based programming languages. Over the last few decades, a large number of libraries using different approaches have been developed for this purpose. Inquiry: Prior work in introductory programming that uses graphics as input and output has shown positive results in terms of engagement, but research is scarce on whether learners are able to use programming concepts learned through graphics for programming in other domains, transferring what they have learned. Approach: We conducted a randomized, controlled experiment with 145 students as participants divided into two groups. Both groups programmed using graphics in Python, but used different approaches: one group used a compositional graphics library named PyTamaro; the other used the Turtle graphics library from Python's standard library. Student engagement was assessed with surveys, and programming knowledge with a post-test on general programming concepts and programming tasks in the domain of graphics. Knowledge: We find few differences between the two groups on the post-test, despite the PyTamaro group having practiced on problems isomorphic to those in the post-test. The participants traced a compositional graphics program more accurately than a 'comparable' turtle graphics program. Both groups report high engagement and perceived learning; both perform well on simple program-writing tasks to create graphics. Grounding: Our findings are based on a controlled experiment with a count of 145 participants, which exceeds the sample size indicated by power analysis to detect a medium effect size. The complete instrument and teaching materials used in the study are available as appendixes. Importance: This study adds further evidence that graphics is an engaging domain for introductory programming; moreover, it shows that the compositional graphics approach adopted by PyTamaro yields engagement levels comparable to the venerable turtle approach. Compositional graphics code appears to be easier to trace than turtle graphics code. As for conceptual knowledge, our results indicate that practicing on programming tasks isomorphic to those of the test can still not be enough to achieve better transfer. This challenges programming educators and researchers to investigate further which graphics-based approaches work best and how to facilitate transfer.
Surveying the Giant HII Regions of the Milky Way with SOFIA: VII. Galactic Center Regions Sgr B1, Sgr B2, and Sgr C
2025-02-27
James M. De Buizer, Wanggi Lim, James T. Radomski, Nicole Karnath
This study examines the mid-infrared properties of Giant HII (GHII) regions in the Milky Way's Central Molecular Zone (CMZ) -- Sgr B1, Sgr B2, and Sgr C -- using SOFIA-FORCAST imaging at 25 and 37 microns. It compares these mid-infrared data with previous multi-wavelength observations to explore their present star formation activity and global properties. The study identifies 77 massive young stellar object (MYSO) candidates in and around the three regions. Sgr B2 appears to host the youngest MYSOs and have much higher extinction than the other regions, containing several radio sources not detected in the mid-infrared even at 37 microns. Meanwhile, cm radio continuum regions of Sgr B1 shows remarkable correspondence to its mid-infrared emission. Sgr C has fewer confirmed MYSOs, and seems to have a higher fraction of low-mass young stellar objects and contamination from more evolved interloper/foreground stars. Derived MYSO densities are consistent with GHII regions elsewhere in the Galactic plane, though the CMZ GHII regions appear to have less prolific present star formation overall. Unlike Sgr B2, the cm continuum emission in Sgr B1 and Sgr C GHII regions appears to be absent cold dust and molecular gas, suggesting environmental differences, possibly driven by turbulence and rapid dynamical changes near the Galactic Center. Furthermore, unlike typical GHII regions, Sgr B1 and Sgr C are significantly ionized by evolved interloper stars, which likely did not form within these regions. In these ways, Sgr B1 and Sgr C deviate from classical GHII region behavior, thus potentially representing a new category of GHII region or challenging their classification as GHII regions.
Enhanced spatiotemporal optical vortices and vortex chains from Hermite-Gauss modes with a tilted pulse front
2025-02-27
Miguel A. Porras, Spencer W. Jolly
Hermite-Gaussian (HG) beams are standard modes delivered by continuous or pulsed lasers systems, and pulse-front tilt is one of the most common, detrimental or beneficial, spatiotemporal couplings affecting ultrashort pulses. Combining them, we show that focusing a pulsed HG beam with a tilt generates an elliptical spatiotemporal optical vortex (STOV), or a chain of them. The elliptical STOVs differ from standard STOVs in an additional spatial chirp that is manifested as a wave front temporal rotation, and results in an enhanced transverse orbital angular momentum. The longitudinal field is significantly larger than that of normal STOVs, and may also take the form of an elliptical STOV. Our concept greatly simplifies previous arrangements for the generation of STOVs, whose additional features make them attractive for improving their applications in electron trapping and acceleration, or as driving fields for the generation of higher-order harmonics and other interactions with matter.
Leveraging Pre-Trained Visual Transformers for Multi-Band Photometric Light Curve Classification
2025-02-27
Daniel Moreno-Cartagena, Pavlos Protopapas, Guillermo Cabrera-Vives, Martina Cádiz-Leyton, Ignacio Becker, Cristóbal Donoso-Oliva
This study investigates the potential of a pre-trained visual transformer (VT) model, specifically the Swin Transformer V2 (SwinV2), to classify photometric light curves without the need for feature extraction or multi-band preprocessing. The goal is to assess whether this image-based approach can accurately differentiate astronomical phenomena and serve as a viable option for working with multi-band photometric light curves. We transformed each multi-band light curve into an image. These images serve as input to the SwinV2 model, which is pre-trained on ImageNet-21K. The datasets employed include the public Catalog of Variable Stars from the Massive Compact Halo Object (MACHO) survey, using both one and two bands, and the first round of the recent Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC), which includes six bands. The performance of the model was evaluated on six classes for the MACHO dataset and 20 distinct classes of variable stars and transient events for the ELAsTiCC dataset. The fine-tuned SwinV2 achieved better performance than models specifically designed for light curves, such as Astromer and the Astronomical Transformer for Time Series and Tabular Data (ATAT). When trained on the full MACHO dataset, it attained a macro F1-score of 80.2 and outperformed Astromer in single-band experiments. Incorporating a second band further improved performance, increasing the F1-score to 84.1. In the ELAsTiCC dataset, SwinV2 achieved a macro F1-score of 65.5, slightly surpassing ATAT by 1.3.
Explainable AI for Clinical Outcome Prediction: A Survey of Clinician Perceptions and Preferences
2025-02-27
Jun Hou, Lucy Lu Wang
Explainable AI (XAI) techniques are necessary to help clinicians make sense of AI predictions and integrate predictions into their decision-making workflow. In this work, we conduct a survey study to understand clinician preference among different XAI techniques when they are used to interpret model predictions over text-based EHR data. We implement four XAI techniques (LIME, Attention-based span highlights, exemplar patient retrieval, and free-text rationales generated by LLMs) on an outcome prediction model that uses ICU admission notes to predict a patient's likelihood of experiencing in-hospital mortality. Using these XAI implementations, we design and conduct a survey study of 32 practicing clinicians, collecting their feedback and preferences on the four techniques. We synthesize our findings into a set of recommendations describing when each of the XAI techniques may be more appropriate, their potential limitations, as well as recommendations for improvement.
Reconstructing orbits of galaxies in extreme regions (ROGER). IV. Unveiling galaxy evolution patterns in OmegaWINGS clusters
2025-02-27
Hernán Muriel, David Pérez-Millán, Martín de los Rios, Andrea Biviano, Valeria Coenda, Héctor J. Martínez, Andrés N. Ruiz, Benedetta Vulcani, Selene Levis
Clusters of galaxies have proven to be efficient systems in modifying various properties of galaxies, such as star formation or morphology. However, projection effects impose serious challenges in determining how, when, and to what extent galaxies are affected by the cluster environment. Using innovative techniques to classify galaxies based on their history within the cluster, we aim to determine how galaxies of different classes are affected by the cluster environment. We applied the ROGER code to select trajectories of galaxies in the phase space for 35 galaxy clusters from the OmegaWINGS survey. A new algorithm was applied to minimize contamination effects. We found that both morphological transformation and the quenching of star formation begin shortly after galaxies enter the cluster. Even though over the last $2-3$ Gyr, galaxies entering clusters have undergone significant transformations in both their star formation and morphology these transformation processes are not complete, that is, they are not completely quenched and are not early type yet. Backsplash galaxies and recent infallers show a higher fraction of jellyfish galaxies compared to older cluster members, suggesting that the timescale of this phenomenon is typically less than 3 Gyr.
Detecting galaxy-21-cm cross-correlation during reionization
2025-02-27
Samuel Gagnon-Hartman, James Davies, Andrei Mesinger
The cosmic 21-cm signal promises to revolutionize studies of the Epoch of Reionization (EoR). Radio interferometers are aiming for a preliminary, low signal-to-noise (S/N) detection of the 21-cm power spectrum. Cross-correlating 21-cm with galaxies will be especially useful in these efforts, providing both a sanity check for initial 21-cm detection claims and potentially increasing the S/N due to uncorrelated residual systematics. Here we self-consistently simulate large-scale (1 Gpc) galaxy and 21-cm fields, computing their cross-power spectra for various choices of instruments as well as survey properties. We use 1080h observations with SKA-low AA* and HERA-350 as our benchmark 21-cm observations. We create mock Lyman alpha narrow-band, slitless and slit spectroscopic surveys, using benchmarks from instruments such as Subaru HyperSupremeCam, Roman grism, VLT MOONS, ELT MOSAIC, and JWST NIRCam. We forecast the resulting S/N of the galaxy-21-cm cross power spectrum, varying the galaxy survey area, depth and level of 21-cm foreground contamination for each pair of instruments. We find that the highest S/N is achievable through slitless, wide-area spectroscopic surveys, with the proposed Roman HLS survey resulting in a 55$\sigma$ detection of the cross power with 21-cm as observed with SKA-low AA* for our fiducial model. Narrow-band dropout surveys are unlikely to result in a detectable cross-power, due to their poor redshift localization. Slit spectroscopy can provide a high S/N detection of the cross power for SKA-low AA* observations. Specifically, the planned MOONRISE survey with MOONS on the VLT can result in a 3$\sigma$ detection, while a survey of comparable observing time using MOSAIC on the ELT can result in a 4$\sigma$ detection. Our results can be used to guide survey strategies, facilitating the detection of the galaxy-21-cm cross power spectrum.
Recent progress in high-temperature superconducting undulators
2025-02-27
Zhuangwei Chen, Marco Calvi, John Durrell, Cristian Boffo, Dabin Wei, Kai Zhang, Zhentang Zhao
Considerable effort has been devoted to the development of superconducting undulators (SCUs) intended for particle accelerator-based light sources, including synchrotrons and free electron laser (FEL) facilities. Recently, a high-temperature superconducting (HTS) undulator prototype, consisting of staggered-array Re-Ba-Cu-O bulks, achieved an on-axis sinusoidal magnetic field profile with a peak amplitude B$_0$ of 2.1 T and a period length of 10 mm, resulting in a deflection parameter K = 1.96. Such a short period HTS undulator not only enables the generation of higher-energy photons, but also supports the construction of economically feasible and compact FELs with shorter linear accelerators (LINACs). This article provides a comprehensive review of recent advances in the staggered-array bulk HTS undulator as well as other types of HTS undulators. Furthermore, it offers insights into the development of engineering HTS undulator prototypes designed for deployment in synchrotron and free electron laser (FEL) facilities. We conclude by discussing opportunities for and the challenges facing the use of HTS undulators in practical applications.
In large language models (LLMs), code and reasoning reinforce each other: code offers an abstract, modular, and logic-driven structure that supports reasoning, while reasoning translates high-level goals into smaller, executable steps that drive more advanced code intelligence. In this study, we examine how code serves as a structured medium for enhancing reasoning: it provides verifiable execution paths, enforces logical decomposition, and enables runtime validation. We also explore how improvements in reasoning have transformed code intelligence from basic completion to advanced capabilities, enabling models to address complex software engineering tasks through planning and debugging. Finally, we identify key challenges and propose future research directions to strengthen this synergy, ultimately improving LLM's performance in both areas.
Combinatorial optimization (CO) problems arise in a wide range of fields from medicine to logistics and manufacturing. While exact solutions are often not necessary, many applications require finding high-quality solutions quickly. For this purpose, we propose a data-driven approach to improve existing non-learned approximation algorithms for CO. We parameterize the approximation algorithm and train a graph neural network (GNN) to predict parameter values that lead to the best possible solutions. Our pipeline is trained end-to-end in a self-supervised fashion using gradient estimation, treating the approximation algorithm as a black box. We propose a novel gradient estimation scheme for this purpose, which we call preference-based gradient estimation. Our approach combines the benefits of the neural network and the non-learned approximation algorithm: The GNN leverages the information from the dataset to allow the approximation algorithm to find better solutions, while the approximation algorithm guarantees that the solution is feasible. We validate our approach on two well-known combinatorial optimization problems, the travelling salesman problem and the minimum k-cut problem, and show that our method is competitive with state of the art learned CO solvers.
An emulation-based model for the projected correlation function
2025-02-26
Vetle A. Vikenes, Cheng-Zong Ruan, David F. Mota
Data from the ongoing \textit{Euclid} survey will map out billions of galaxies in the Universe, covering more than a third of the sky. This data will provide a wealth of information about the large-scale structure (LSS) of the Universe and will have a significant impact on cosmology in the coming years. In this paper, we introduce an emulator-based halo model approach to forward model the relationship between cosmological parameters and the projected galaxy-galaxy two-point correlation function (2PCF). Utilizing the large \textsc{AbacusSummit} simulation suite, we emulate the 2PCF by generating mock-galaxy catalogues within the Halo Occupation Distribution (HOD) framework. Our emulator is designed to predict the 2PCF over scales $0.1 \leq r / (h^{-1}\text{Mpc}) \leq 105$, from which we derive the projected correlation function, independent of redshift space distortions. We demonstrate that the emulator accurately predicts the projected correlation function over scales $0.5 \leq r_\perp/(h^{-1}\text{Mpc}) \leq 40$, given a set of cosmological and HOD parameters. This model is then employed in a parameter inference analysis, showcasing its ability to constrain cosmological parameters. Our findings indicate that while the projected correlation function places weak constraints on several cosmological parameters due to its intrinsic lack of information, additional clustering statistics are necessary to better probe the underlying cosmology. Despite the simplified covariance matrix used in the likelihood model, the posterior distributions of several cosmological parameters remain broad, underscoring the need for a more comprehensive approach.
FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real Users
2025-02-26
Anikait Singh, Sheryl Hsu, Kyle Hsu, Eric Mitchell, Stefano Ermon, Tatsunori Hashimoto, Archit Sharma, Chelsea Finn
Effective personalization of LLMs is critical for a broad range of user-interfacing applications such as virtual assistants and content curation. Inspired by the strong in-context learning capabilities of LLMs, we propose Few-Shot Preference Optimization (FSPO), which reframes reward modeling as a meta-learning problem. Under this framework, an LLM learns to quickly adapt to a user via a few labeled preferences from that user, constructing a personalized reward function for them. Additionally, since real-world preference data is scarce and challenging to collect at scale, we propose careful design choices to construct synthetic preference datasets for personalization, generating over 1M synthetic personalized preferences using publicly available LLMs. In particular, to successfully transfer from synthetic data to real users, we find it crucial for the data to exhibit both high diversity and coherent, self-consistent structure. We evaluate FSPO on personalized open-ended generation for up to 1,500 synthetic users across across three domains: movie reviews, pedagogical adaptation based on educational background, and general question answering, along with a controlled human study. Overall, FSPO achieves an 87% Alpaca Eval winrate on average in generating responses that are personalized to synthetic users and a 72% winrate with real human users in open-ended question answering.
Integrating Biological and Machine Intelligence: Attention Mechanisms in Brain-Computer Interfaces
2025-02-26
Jiyuan Wang, Weishan Ye, Jialin He, Li Zhang, Gan Huang, Zhuliang Yu, Zhen Liang
With the rapid advancement of deep learning, attention mechanisms have become indispensable in electroencephalography (EEG) signal analysis, significantly enhancing Brain-Computer Interface (BCI) applications. This paper presents a comprehensive review of traditional and Transformer-based attention mechanisms, their embedding strategies, and their applications in EEG-based BCI, with a particular emphasis on multimodal data fusion. By capturing EEG variations across time, frequency, and spatial channels, attention mechanisms improve feature extraction, representation learning, and model robustness. These methods can be broadly categorized into traditional attention mechanisms, which typically integrate with convolutional and recurrent networks, and Transformer-based multi-head self-attention, which excels in capturing long-range dependencies. Beyond single-modality analysis, attention mechanisms also enhance multimodal EEG applications, facilitating effective fusion between EEG and other physiological or sensory data. Finally, we discuss existing challenges and emerging trends in attention-based EEG modeling, highlighting future directions for advancing BCI technology. This review aims to provide valuable insights for researchers seeking to leverage attention mechanisms for improved EEG interpretation and application.
The Quantum Measurement Problem: A Review of Recent Trends
2025-02-26
Anderson A. Tomaz, Rafael S. Mattos, Mario Barbatti
Left on its own, a quantum state evolves deterministically under the Schr\"odinger Equation, forming superpositions. Upon measurement, however, a stochastic process governed by the Born rule collapses it to a single outcome. This dual evolution of quantum states$-$the core of the Measurement Problem$-$has puzzled physicists and philosophers for nearly a century. Yet, amid the cacophony of competing interpretations, the problem today is not as impenetrable as it once seemed. This paper reviews the current status of the Measurement Problem, distinguishing between what is well understood and what remains unresolved. We examine key theoretical approaches, including decoherence, many-worlds interpretation, objective collapse theories, hidden-variable theories, dualistic approaches, deterministic models, and epistemic interpretations. To make these discussions accessible to a broader audience, we also reference curated online resources that provide high-quality introductions to central concepts.
Limits and challenges of the detection of cluster-scale diffuse radio emission at high redshift: The Massive and Distant Clusters of WISE Survey (MaDCoWS) in LoTSS-DR2
2025-02-26
G. Di Gennaro, M. Brüggen, E. Moravec, L. Di Mascolo, R. J. van Weeren, G. Brunetti, R. Cassano, A. Botteon, E. Churazov, I. Khabibullin, N. Lyskova, F. de Gasperin, M. J. Hardcastle, H. J. A. Röttgering, T. Shimwell, R. Sunyaev, A. Stanford
Diffuse radio emission in galaxy clusters is a tracer of ultra-relativistic particles and $\mu$G-level magnetic fields, and is thought to be triggered by cluster merger events. In the distant Universe (i.e. $z>0.6$), such sources have been observed only in a handful of systems, and their study is important to understand the evolution of large-scale magnetic fields over the cosmic time. Previous studies of nine {\it Planck} clusters up to $z\sim0.9$ suggest a fast amplification of cluster-scale magnetic fields, at least up to half of the current Universe's age, and steep spectrum cluster scale emission, in line with particle re-acceleration due to turbulence. In this paper, we investigate the presence of diffuse radio emission in a larger sample of galaxy clusters reaching even higher redshifts (i.e. $z\gtrsim1$). We selected clusters from the Massive and Distant Clusters of {\it WISE} Survey (MaDCoWS) with richness $\lambda_{15}>40$ covering the area of the second data release of the LOFAR Two-Meter Sky Survey (LoTSS-DR2) at 144 MHz. These selected clusters are in the redshift range $0.78-1.53$ (with a median value of 1.05). We detect the possible presence of diffuse radio emission, with the largest linear sizes of $350-500$ kpc, in 5 out of the 56 clusters in our sample. If this diffuse radio emission is due to a radio halo, these radio sources lie on or above the scatter of the $P_\nu-M_{500}$ radio halo correlations (at 150 MHz and 1.4 GHz) found at $z<0.6$, depending on the mass assumed. We also find that these radio sources are at the limit of the detection by LoTSS, and therefore deeper observations will be important for future studies.
Age Group Sensitivity Analysis of Epidemic Models: Investigating the Impact of Contact Matrix Structure
2025-02-26
Zsolt Vizi, Evans Kiptoo Korir, Norbert Bogya, Csaba Rosztóczy, Géza Makay, Péter Boldog
Understanding the role of different age groups in disease transmission is crucial for designing effective intervention strategies. A key parameter in age-structured epidemic models is the contact matrix, which defines the interaction structure between age groups. However, accurately estimating contact matrices is challenging, as different age groups respond differently to surveys and are accessible through different channels. This variability introduces significant epistemic uncertainty in epidemic models. In this study, we introduce the Age Group Sensitivity Analysis (AGSA) method, a novel framework for assessing the impact of age-structured contact patterns on epidemic outcomes. Our approach integrates age-stratified epidemic models with Latin Hypercube Sampling (LHS) and the Partial Rank Correlation Coefficient (PRCC) method, enabling a systematic sensitivity analysis of age-specific interactions. Additionally, we propose a new sensitivity aggregation technique that quantifies the contribution of each age group to key epidemic parameters. By identifying the age groups to which the model is most sensitive, AGSA helps pinpoint those that introduce the greatest epistemic uncertainty. This allows for targeted data collection efforts, focusing surveys and empirical studies on the most influential age groups to improve model accuracy. As a result, AGSA can enhance epidemic forecasting and inform the design of more effective and efficient public health interventions.
A Model-Centric Review of Deep Learning for Protein Design
2025-02-26
Gregory W. Kyro, Tianyin Qiu, Victor S. Batista
Deep learning has transformed protein design, enabling accurate structure prediction, sequence optimization, and de novo protein generation. Advances in single-chain protein structure prediction via AlphaFold2, RoseTTAFold, ESMFold, and others have achieved near-experimental accuracy, inspiring successive work extended to biomolecular complexes via AlphaFold Multimer, RoseTTAFold All-Atom, AlphaFold 3, Chai-1, Boltz-1 and others. Generative models such as ProtGPT2, ProteinMPNN, and RFdiffusion have enabled sequence and backbone design beyond natural evolution-based limitations. More recently, joint sequence-structure co-design models, including ESM3, have integrated both modalities into a unified framework, resulting in improved designability. Despite these advances, challenges still exist pertaining to modeling sequence-structure-function relationships and ensuring robust generalization beyond the regions of protein space spanned by the training data. Future advances will likely focus on joint sequence-structure-function co-design frameworks that are able to model the fitness landscape more effectively than models that treat these modalities independently. Current capabilities, coupled with the dizzying rate of progress, suggest that the field will soon enable rapid, rational design of proteins with tailored structures and functions that transcend the limitations imposed by natural evolution. In this review, we discuss the current capabilities of deep learning methods for protein design, focusing on some of the most revolutionary and capable models with respect to their functionality and the applications that they enable, leading up to the current challenges of the field and the optimal path forward.
The JWST/PASSAGE Survey: Testing Reionization Histories with JWST's First Unbiased Survey for Lyman alpha Emitters at Redshifts 7.5-9.5
2025-02-26
Axel Runnholm, Matthew J. Hayes, Vihang Mehta, Matthew A. Malkan, Claudia Scarlata, Kalina V. Nedkova, Marc Rafelski, Benedetta Vulcani, Mason Huberty, E. Christian Herenz, Anne Hutter, Sean Bruton, Ayan Acharyya, Hakim Atek, Ivano Baronchelli, Andrew J. Battisti, Maruša Bradač, Andrew J. Bunker, Y. Sophia Dai, Clea Hannahs, Farhanul Hasan, Keunho J. Kim, Nicha Leethochawalit, Yu-Heng Lin, Michael J. Rutkowski, Alberto Saldana-Lopez, Zahra Sattari, Xin Wang
Lyman $\alpha$ (Ly$\alpha$) emission is one of few observable features of galaxies that can trace the neutral hydrogen content in the Universe during the Epoch of Reionization (EoR). To accomplish this we need an efficient way to survey for Ly$\alpha$ emitters (LAEs) at redshifts beyond 7, requiring unbiased emission-line observations that are both sufficiently deep and wide to cover enough volume to detect them. Here we present results from PASSAGE -- a pure-parallel JWST/NIRISS slitless spectroscopic survey to detect Ly$\alpha$ emitters deep into the EoR, without the bias of photometric preselection. We identify four LAEs at $7.5\leq z\leq9.5$ in four surveyed pointings, and estimate the luminosity function (LF). We find that the LF does show a marked decrease compared to post-reionization measurements, but the change is a factor of $\lesssim 10$, which is less than expected from theoretical calculations and simulations, as well as observational expectations from the pre-JWST literature. Modeling of the IGM and expected \lya\ profiles implies these galaxies reside in ionized bubbles of $\gtrapprox 2$ physical Mpc. We also report that in the four fields we detect {3,1,0,0} LAEs, which could indicate strong field-to-field variation in the LAE distribution, consistent with a patchy HI distribution at $z\sim8$. We compare the recovered LAE number counts with expectations from simulations and discuss the potential implications for reionization and its morphology.
We study the quantum Rabi model (QRM) in the deep strong coupling (DSC) regime. To capture the full dynamics of the QRM in the DSC regime, we implemented single-qubit rotations combined with integrated digital steps and qubit-bosonic blocks. This approach leads to a paradigm known as digital analog quantum simulations (DAQSs). In this work, we review the encoding of QRM in the DSC regime through emerging paradigms of digital and analog techniques. Using DAQSs encoding, an efficient simulation can be performed on state-of-the-art circuit quantum electrodynamics platforms. Finally, we provide detailed information on the dynamics of the QRM in varity of parameter regions. We demonstrate the effectiveness of the DAQS paradigms in achieving prolonged coherent measurements during time evolution, even in the case of perturbative DSC regime dynamics. This proposal lays the groundwork for simulating complex many-body dynamics that involve bosonic modes.
From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Review
2025-02-26
Behnaz Elhaminia, Abdullah Alsalemi, Esha Nasir, Mostafa Jahanifar, Ruqayya Awan, Lawrence S. Young, Nasir M. Rajpoot, Fayyaz Minhas, Shan E Ahmed Raza
Whole slide image (WSI) registration is an essential task for analysing the tumour microenvironment (TME) in histopathology. It involves the alignment of spatial information between WSIs of the same section or serial sections of a tissue sample. The tissue sections are usually stained with single or multiple biomarkers before imaging, and the goal is to identify neighbouring nuclei along the Z-axis for creating a 3D image or identifying subclasses of cells in the TME. This task is considerably more challenging compared to radiology image registration, such as magnetic resonance imaging or computed tomography, due to various factors. These include gigapixel size of images, variations in appearance between differently stained tissues, changes in structure and morphology between non-consecutive sections, and the presence of artefacts, tears, and deformations. Currently, there is a noticeable gap in the literature regarding a review of the current approaches and their limitations, as well as the challenges and opportunities they present. We aim to provide a comprehensive understanding of the available approaches and their application for various purposes. Furthermore, we investigate current deep learning methods used for WSI registration, emphasising their diverse methodologies. We examine the available datasets and explore tools and software employed in the field. Finally, we identify open challenges and potential future trends in this area of research.
As industrial products become abundant and sophisticated, visual industrial defect detection receives much attention, including two-dimensional and three-dimensional visual feature modeling. Traditional methods use statistical analysis, abnormal data synthesis modeling, and generation-based models to separate product defect features and complete defect detection. Recently, the emergence of foundation models has brought visual and textual semantic prior knowledge. Many methods are based on foundation models (FM) to improve the accuracy of detection, but at the same time, increase model complexity and slow down inference speed. Some FM-based methods have begun to explore lightweight modeling ways, which have gradually attracted attention and deserve to be systematically analyzed. In this paper, we conduct a systematic survey with comparisons and discussions of foundation model methods from different aspects and briefly review non-foundation model (NFM) methods recently published. Furthermore, we discuss the differences between FM and NFM methods from training objectives, model structure and scale, model performance, and potential directions for future exploration. Through comparison, we find FM methods are more suitable for few-shot and zero-shot learning, which are more in line with actual industrial application scenarios and worthy of in-depth research.
LongEval: A Comprehensive Analysis of Long-Text Generation Through a Plan-based Paradigm
2025-02-26
Siwei Wu, Yizhi Li, Xingwei Qu, Rishi Ravikumar, Yucheng Li, Tyler Loakman Shanghaoran Quan Xiaoyong Wei, Riza Batista-Navarro, Chenghua Lin
Large Language Models (LLMs) have achieved remarkable success in various natural language processing tasks, yet their ability to generate long-form content remains poorly understood and evaluated. Our analysis reveals that current LLMs struggle with length requirements and information density in long-text generation, with performance deteriorating as text length increases. To quantitively locate such a performance degradation and provide further insights on model development, we present LongEval, a benchmark that evaluates long-text generation through both direct and plan-based generation paradigms, inspired by cognitive and linguistic writing models. The comprehensive experiments in this work reveal interesting findings such as that while model size correlates with generation ability, the small-scale model (e.g., LongWriter), well-trained on long texts, has comparable performance. All code and datasets are released in https://github.com/Wusiwei0410/LongEval.
Forecasting intermittent time series with Gaussian Processes and Tweedie likelihood
2025-02-26
Stefano Damato, Dario Azzimonti, Giorgio Corani
We introduce the use of Gaussian Processes (GPs) for the probabilistic forecasting of intermittent time series. The model is trained in a Bayesian framework that accounts for the uncertainty about the latent function and marginalizes it out when making predictions. We couple the latent GP variable with two types of forecast distributions: the negative binomial (NegBinGP) and the Tweedie distribution (TweedieGP). While the negative binomial has already been used in forecasting intermittent time series, this is the first time in which a fully parameterized Tweedie density is used for intermittent time series. We properly evaluate the Tweedie density, which is both zero-inflated and heavy tailed, avoiding simplifying assumptions made in existing models. We test our models on thousands of intermittent count time series. Results show that our models provide consistently better probabilistic forecasts than the competitors. In particular, TweedieGP obtains the best estimates of the highest quantiles, thus showing that it is more flexible than NegBinGP.
X-ray and CO-Derived Column Densities in AGN: A Study of Obscuration Properties in CTAGN and Non-CTAGN
2025-02-26
M. L. H. Musa, Z. Z. Abidin, A. Annuar, D. A. A. Lee
Obscuration in active galactic nuclei (AGN) provides insights into the material surrounding the central engine. Compton-thick AGN (CTAGN), characterized by a column density of $N_{\mathrm{H}} \geq 1.5 \times 10^{24} \ \mathrm{cm}^{-2}$, are heavily obscured by dust and gas. While X-ray observations primarily determine this column density, the sub-mm obscuration properties of CTAGN remain less explored. We analyze archival ALMA CO(3-2) data for CTAGN and non-CTAGN from the 70-month $\textit{Swift}$/BAT catalog and other X-ray surveys. Integrated intensity maps (moment 0) reveal dense gas concentrated around the nucleus. Assuming a constant CO-to-$\mathrm{H_2}$ conversion factor, $X_{\mathrm{CO}} = 2.2 \times 10^{20} \ \mathrm{cm}^{-2} \ (\mathrm{K\ km\ s}^{-1})^{-1}$, we find that molecular hydrogen column densities ($N_{\mathrm{H_2}}$) are generally lower than X-ray-derived total hydrogen column densities ($N_{\mathrm{H}}$). However, $N_{\mathrm{H_2}}$ values in this work are slightly higher than in previous studies due to the adopted conversion factor. The discrepancy between $N_{\mathrm{H}}$ and $N_{\mathrm{H_2}}$ aligns with prior findings that X-ray-derived values tend to be higher, except for non-CTAGN, where $N_{\mathrm{H_2}}$ can exceed $N_{\mathrm{H}}$. Kendall and Spearman tests indicate a positive monotonic correlation, though not statistically significant, suggesting a complex interplay of factors. The optically thick nature of CO in dense regions may contribute to the observed differences. Our results highlight the need for an accurate CO-to-$\mathrm{H_2}$ conversion factor in deriving column densities, potentially offering an alternative method for identifying CTAGN. Future studies with larger datasets and refined methodologies are essential for a deeper understanding of sub-mm and X-ray properties in AGN.
Dealing with Inconsistency for Reasoning over Knowledge Graphs: A Survey
2025-02-26
Anastasios Nentidis, Charilaos Akasiadis, Angelos Charalambidis, Alexander Artikis
In Knowledge Graphs (KGs), where the schema of the data is usually defined by particular ontologies, reasoning is a necessity to perform a range of tasks, such as retrieval of information, question answering, and the derivation of new knowledge. However, information to populate KGs is often extracted (semi-) automatically from natural language resources, or by integrating datasets that follow different semantic schemas, resulting in KG inconsistency. This, however, hinders the process of reasoning. In this survey, we focus on how to perform reasoning on inconsistent KGs, by analyzing the state of the art towards three complementary directions: a) the detection of the parts of the KG that cause the inconsistency, b) the fixing of an inconsistent KG to render it consistent, and c) the inconsistency-tolerant reasoning. We discuss existing work from a range of relevant fields focusing on how, and in which cases they are related to the above directions. We also highlight persisting challenges and future directions.
The Ising model as a window on quantum gravity with matter
2025-02-26
Romuald A. Janik
We argue that the Ising model CFT can be used to obtain some clear insights into 3D (quantum) gravity with matter. We review arguments for the existence of its holographic description, and concentrate on the time dependence of perturbations of the theory at high temperature, which would correspond to throwing matter into a black hole in the dual picture. Apart from an expected QNM-like exponential damping, we observe a plateau, a burst and a subsequent re-emergence of the whole signal, the latter being apparently at odds with a black hole interpretation. We provide an explanation of this phenomenon in terms of the properties of bulk matter fields and the fact that the geometry/metric is not fundamental but a derived quantity in the Chern-Simons formulation of 3D gravity. This allows for evading the black hole information paradox in the present context.
Binary Neural Networks for Large Language Model: A Survey
2025-02-26
Liangdong Liu, Zhitong Zheng, Cong Wang, Tianhuang Su, Zhenyu Yang
Large language models (LLMs) have wide applications in the field of natural language processing(NLP), such as GPT-4 and Llama. However, with the exponential growth of model parameter sizes, LLMs bring significant resource overheads. Low-bit quantization, as a key technique, reduces memory usage and computational demands by decreasing the bit-width of model parameters, activations, and gradients. Previous quantization methods for LLMs have largely employed Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT). PTQ does not require any retraining of the original model, while QAT involves optimizing precision during training to achieve the best quantization parameters. The BitNet team proposed a radically different approach, where quantization is performed from the start of model training, utilizing low-precision binary weights during the training process. This approach has led to the emergence of many binary quantization techniques for large language models. This paper provides a comprehensive review of these binary quantization techniques. Specifically, we will introduce binary quantization techniques in deep neural networks and further explore their application to LLMs, reviewing their various contributions, implementations, and applications.
The Rise of Refractory Transition-Metal Nitride Films for Advanced Electronics and Plasmonics
2025-02-26
Jiachang Bi, Ruyi Zhang, Xiong Yao, Yanwei Cao
The advancement of semiconductor materials has played a crucial role in the development of electronic and optical devices. However, scaling down semiconductor devices to the nanoscale has imposed limitations on device properties due to quantum effects. Hence, the search for successor materials has become a central focus in the fields of materials science and physics. Transition-metal nitrides (TMNs) are extraordinary materials known for their outstanding stability, biocompatibility, and ability to integrate with semiconductors. Over the past few decades, TMNs have been extensively employed in various fields. However, the synthesis of single-crystal TMNs has long been challenging, hindering the advancement of their high-performance electronics and plasmonics. Fortunately, progress in film deposition techniques has enabled the successful epitaxial growth of high-quality TMN films. In comparison to reported reviews, there is a scarcity of reviews on epitaxial TMN films from the perspective of materials physics and condensed matter physics, particularly at the atomic level. Therefore, this review aims to provide a brief summary of recent progress in epitaxial growth at atomic precision, emergent physical properties (superconductivity, magnetism, ferroelectricity, and plasmon), and advanced electronic and plasmonic devices associated with epitaxial TMN films.
2025-02-26
Margaret Li, Sneha Kudugunta, Luke Zettlemoyer
Modern foundation models rely heavily on using scaling laws to guide crucial training decisions. Researchers often extrapolate the optimal architecture and hyper parameters settings from smaller training runs by describing the relationship between, loss, or task performance, and scale. All components of this process vary, from the specific equation being fit, to the training setup, to the optimization method. Each of these factors may affect the fitted law, and therefore, the conclusions of a given study. We discuss discrepancies in the conclusions that several prior works reach, on questions such as the optimal token to parameter ratio. We augment this discussion with our own analysis of the critical impact that changes in specific details may effect in a scaling study, and the resulting altered conclusions. Additionally, we survey over 50 papers that study scaling trends: while 45 of these papers quantify these trends using a power law, most under-report crucial details needed to reproduce their findings. To mitigate this, we we propose a checklist for authors to consider while contributing to scaling law research.
A Multifacet Hierarchical Sentiment-Topic Model with Application to Multi-Brand Online Review Analysis
2025-02-26
Qiao Liang, Xinwei Deng
Multi-brand analysis based on review comments and ratings is a commonly used strategy to compare different brands in marketing. It can help consumers make more informed decisions and help marketers understand their brand's position in the market. In this work, we propose a multifacet hierarchical sentiment-topic model (MH-STM) to detect brand-associated sentiment polarities towards multiple comparative aspects from online customer reviews. The proposed method is built on a unified generative framework that explains review words with a hierarchical brand-associated topic model and the overall polarity score with a regression model on the empirical topic distribution. Moreover, a novel hierarchical Polya urn (HPU) scheme is proposed to enhance the topic-word association among topic hierarchy, such that the general topics shared by all brands are separated effectively from the unique topics specific to individual brands. The performance of the proposed method is evaluated on both synthetic data and two real-world review corpora. Experimental studies demonstrate that the proposed method can be effective in detecting reasonable topic hierarchy and deriving accurate brand-associated rankings on multi-aspects.
Brain-inspired analogical mixture prototypes for few-shot class-incremental learning
2025-02-26
Wanyi Li, Wei Wei, Yongkang Luo, Peng Wang
Few-shot class-incremental learning (FSCIL) poses significant challenges for artificial neural networks due to the need to efficiently learn from limited data while retaining knowledge of previously learned tasks. Inspired by the brain's mechanisms for categorization and analogical learning, we propose a novel approach called Brain-inspired Analogical Mixture Prototypes (BAMP). BAMP has three components: mixed prototypical feature learning, statistical analogy, and soft voting. Starting from a pre-trained Vision Transformer (ViT), mixed prototypical feature learning represents each class using a mixture of prototypes and fine-tunes these representations during the base session. The statistical analogy calibrates the mean and covariance matrix of prototypes for new classes according to similarity to the base classes, and computes classification score with Mahalanobis distance. Soft voting combines both merits of statistical analogy and an off-shelf FSCIL method. Our experiments on benchmark datasets demonstrate that BAMP outperforms state-of-the-art on both traditional big start FSCIL setting and challenging small start FSCIL setting. The study suggests that brain-inspired analogical mixture prototypes can alleviate catastrophic forgetting and over-fitting problems in FSCIL.
Implications of σ-cut potential on Antikaon condensates in neutron stars
2025-02-26
Prashant Thakur, Yashmitha Kumaran, Lakshana Sudarsan, Krishna Kunnampully, B. K. Sharma, T. K. Jha
We investigate the properties of neutron stars with antikaon condensation in the framework of the Relativistic Mean-Field (RMF) model with a $\sigma$-cut potential. The well-known RMF models, TM1 and TM1e, are used to analyze the structure and composition of neutron stars. The antikaon condensation part of the equation of state (EoS) is constrained from the experimental data of K$^{-}$ atomic and kaon-nucleon scattering. The $\sigma$-cut potential, which is known to make the EoS stiffer at high densities, is modulated by a free parameter $f_{s}$. Our present analysis suggests that one can obtain neutron star configurations heavier than 2$M_{\odot}$ with antikaon condensates in most cases for $f_{s}$ = 0.6. The antikaon phase transition is a second-order for $f_{s}$ = 0.6 for both TM1 and TM1e parameter sets. The calculated global properties of neutron stars with antikaon condensates i.e., mass and radius seem to be in resonable agreement with other theoretical and observational data.
Sentiment Analysis (SA) or opinion mining is analysis of emotions and opinions from any kind of text. SA helps in tracking peoples viewpoints and it is an important factor when it comes to social media monitoring product and brand recognition customer satisfaction customer loyalty advertising and promotions success and product acceptance. That is why SA is one of the active research areas in Natural Language Processing (NLP). SA is applied on data sourced from various media platforms to mine sentiment knowledge from them. Various approaches have been deployed in the literature to solve the problem. Most techniques devise complex and sophisticated frameworks in order to attain optimal accuracy. This work aims to finetune Bidirectional Encoder Representations from Transformers (BERT) with Bidirectional Long Short-Term Memory (BiLSTM) for movie reviews sentiment analysis and still provide better accuracy than the State-of-The-Art (SOTA) methods. The paper also shows how sentiment analysis can be applied if someone wants to recommend a certain movie for example by computing overall polarity of its sentiments predicted by the model. That is our proposed method serves as an upper-bound baseline in prediction of a predominant reaction to a movie. To compute overall polarity a heuristic algorithm is applied to BERTBiLSTM output vector. Our model can be extended to three-class four-class or any fine-grained classification and apply overall polarity computation again. This is intended to be exploited in future work.
Adaptive and Accessible User Interfaces for Seniors Through Model-Driven Engineering
2025-02-26
Shavindra Wickramathilaka, John Grundy, Kashumi Madampe, Omar Haggag
The use of diverse apps among senior users is increasing. However, despite their diverse age-related accessibility needs and preferences, these users often encounter apps with significant accessibility barriers. Even in the best-case scenarios, they are provided with one-size-fits-all user interfaces that offer very limited personalisation support. To address this issue, we describe AdaptForge, a novel model-driven engineering (MDE)-based approach to support sophisticated adaptations of Flutter app user interfaces and behaviour based on the age-related accessibility needs of senior users. We explain how AdaptForge employs Domain-Specific Languages to capture seniors' context-of-use scenarios and how this information is used via adaptation rules to perform design-time modifications to a Flutter app's source code. Additionally, we report on evaluations conducted with real-world Flutter developers to demonstrate the promise and practical applicability of AdaptForge, as well as with senior end-users using our adapted Flutter app prototypes.
Battery Life Prediction (BLP), which relies on time series data produced by battery degradation tests, is crucial for battery utilization, optimization, and production. Despite impressive advancements, this research area faces three key challenges. Firstly, the limited size of existing datasets impedes insights into modern battery life data. Secondly, most datasets are restricted to small-capacity lithium-ion batteries tested under a narrow range of diversity in labs, raising concerns about the generalizability of findings. Thirdly, inconsistent and limited benchmarks across studies obscure the effectiveness of baselines and leave it unclear if models popular in other time series fields are effective for BLP. To address these challenges, we propose BatteryLife, a comprehensive dataset and benchmark for BLP. BatteryLife integrates 16 datasets, offering a 2.4 times sample size compared to the previous largest dataset, and provides the most diverse battery life resource with batteries from 8 formats, 80 chemical systems, 12 operating temperatures, and 646 charge/discharge protocols, including both laboratory and industrial tests. Notably, BatteryLife is the first to release battery life datasets of zinc-ion batteries, sodium-ion batteries, and industry-tested large-capacity lithium-ion batteries. With the comprehensive dataset, we revisit the effectiveness of baselines popular in this and other time series fields. Furthermore, we propose CyclePatch, a plug-in technique that can be employed in a series of neural networks. Extensive benchmarking of 18 methods reveals that models popular in other time series fields can be unsuitable for BLP, and CyclePatch consistently improves model performance establishing state-of-the-art benchmarks. Moreover, BatteryLife evaluates model performance across aging conditions and domains. BatteryLife is available at https://github.com/Ruifeng-Tan/BatteryLife.
Seeing the Forest for the Trees: A Large Scale, Continuously Updating Meta-Analysis of Frontier LLMs
2025-02-26
Jungsoo Park, Junmo Kang, Gabriel Stanovsky, Alan Ritter
The surge of LLM studies makes synthesizing their findings challenging. Meta-analysis can uncover important trends across studies, but its use is limited by the time-consuming nature of manual data extraction. Our study presents a semi-automated approach for meta-analysis that accelerates data extraction using LLMs. It automatically identifies relevant arXiv papers, extracts experimental results and related attributes, and organizes them into a structured dataset. We conduct a comprehensive meta-analysis of frontier LLMs using an automatically extracted dataset, reducing the effort of paper surveying and data extraction by more than 93\% compared to manual approaches. We validate our dataset by showing that it reproduces key findings from a recent manual meta-analysis about Chain-of-Thought (CoT), and also uncovers new insights that go beyond it, showing for example that in-context examples benefit multimodal tasks but offer limited gains in mathematical tasks compared to CoT. Our automatically updatable dataset enables continuous tracking of target models by extracting evaluation studies as new data becomes available. Through our scientific artifacts and empirical analysis, we provide novel insights into LLMs while facilitating ongoing meta-analyses of their behavior.
Delensing for Precision Cosmology: Optimizing Future CMB B-mode Surveys to Constrain r
2025-02-26
Wen-Zheng Chen, Yang Liu, Yi-Ming Wang, Hong Li
The detection of primordial B-modes, a key probe of cosmic inflation, is increasingly challenged by contamination from weak gravitational lensing B-modes induced by large-scale structure (LSS). We present a delensing pipeline designed to enhance the sensitivity to the inflationary parameter r, minimizing reliance on foreground mitigation during lensing reconstruction. Using simulations of Simons Observatory-like CMB observations and Euclid-like LSS surveys in the Northern hemisphere, we demonstrate that excluding low-l modes (l<200) effectively mitigates foreground biases, enabling robust lensing potential reconstruction using observed CMB polarization maps. We reconstruct the lensing potential with a minimum-variance (MV) quadratic estimator (QE) applied to CMB polarization data and combine this with external LSS tracers to improve delensing efficiency. Two complementary methods, the Gradient-order template and the Inverse-lensing approach, are used to generate lensing B-mode templates, which are cross-correlated with observed B-modes. This achieves a 40 percent reduction in the uncertainty of r with CMB-only reconstruction, improving to 60 percent when incorporating external LSS tracers. We validate our results using both the Hamimeche and Lewis likelihood and a Gaussian approximation, finding consistent constraints on r. Our work establishes a streamlined framework for ground-based CMB experiments, demonstrating that synergies with LSS surveys significantly enhance sensitivity to primordial gravitational waves.
The element abundances of galaxies provide crucial insights into their formation and evolution. Using high-resolution IFU data from the MaNGA survey, we analyze the central spectra (0-0.5 $R_{\rm e}$) of 1,185 quenched galaxies ($z = 0.012-0.15$) to study their element abundances and stellar populations. We employ the full-spectrum fitting code {\tt alf} to derive stellar ages and element abundances from synthetic spectra and empirical libraries. Our key findings are: (1) Central velocity dispersion ($\sigma_*$) is the most effective parameter correlating with (relative) element abundances, especially [Na/Fe], [Mg/Fe], [C/Fe], and [N/Fe], outperforming $M_\ast$ and $M_\ast/R_{\rm e}$. (2) When binned by $\sigma_*$, the relative abundances of Na, Mg, C, and N remain stable across different formation times ($T_{\rm form}$), suggesting these elements are primarily influenced by the burstiness of star formation (traced by $\sigma_*$) rather than prolonged evolutionary processes. (3) Fe and Ca show little variation with $\sigma_*$, indicating weaker sensitivity to $\sigma_*$-driven processes. However, $T_{\rm form}$ has a global influence on all elements, contributing to their overall chemical evolution, albeit secondary to $\sigma_*$ for most elements. These results support the primary role of $\sigma_*$ in shaping the abundance patterns, likely stemming from the connection between central massive black holes and possibly dark matter halos, which influences the burstiness of star formation histories.
The distribution of galaxy rotation in JWST Advanced Deep Extragalactic Survey
2025-02-26
Lior Shamir
JWST provides a view of the Universe never seen before, and specifically fine details of galaxies in deep space. JWST Advanced Deep Extragalactic Survey (JADES) is a deep field survey, providing unprecedentedly detailed view of galaxies in the early Universe. The field is also in relatively close proximity to the Galactic pole. Analysis of spiral galaxies by their direction of rotation in JADES shows that the number of galaxies in that field that rotate in the opposite direction relative to the Milky Way galaxy is ~50% higher than the number of galaxies that rotate in the same direction relative to the Milky Way. The analysis is done using a computer-aided quantitative method, but the difference is so extreme that it can be noticed and inspected even by the unaided human eye. These observations are in excellent agreement with deep fields taken at around the same footprint by HST and JWST. The reason for the difference may be related to the structure of the early Universe, but it can also be related to the physics of galaxy rotation and the internal structure of galaxies. In that case the observation can provide possible explanations to other puzzling anomalies such as the Ho tension and the observation of massive mature galaxies at very high redshifts.
Measuring risks inherent to our digital economies using Amazon purchase histories from US consumers
2025-02-26
Alex Berke, Kent Larson, Sandy Pentland, Dana Calacci
What do pickles and trampolines have in common? In this paper we show that while purchases for these products may seem innocuous, they risk revealing clues about customers' personal attributes - in this case, their race. As online retail and digital purchases become increasingly common, consumer data has become increasingly valuable, raising the risks of privacy violations and online discrimination. This work provides the first open analysis measuring these risks, using purchase histories crowdsourced from (N=4248) US Amazon.com customers and survey data on their personal attributes. With this limited sample and simple models, we demonstrate how easily consumers' personal attributes, such as health and lifestyle information, gender, age, and race, can be inferred from purchases. For example, our models achieve AUC values over 0.9 for predicting gender and over 0.8 for predicting diabetes status. To better understand the risks that highly resourced firms like Amazon, data brokers, and advertisers present to consumers, we measure how our models' predictive power scales with more data. Finally, we measure and highlight how different product categories contribute to inference risk in order to make our findings more interpretable and actionable for future researchers and privacy advocates.
Online Prototypes and Class-Wise Hypergradients for Online Continual Learning with Pre-Trained Models
2025-02-26
Nicolas Michel, Maorong Wang, Jiangpeng He, Toshihiko Yamasaki
Continual Learning (CL) addresses the problem of learning from a data sequence where the distribution changes over time. Recently, efficient solutions leveraging Pre-Trained Models (PTM) have been widely explored in the offline CL (offCL) scenario, where the data corresponding to each incremental task is known beforehand and can be seen multiple times. However, such solutions often rely on 1) prior knowledge regarding task changes and 2) hyper-parameter search, particularly regarding the learning rate. Both assumptions remain unavailable in online CL (onCL) scenarios, where incoming data distribution is unknown and the model can observe each datum only once. Therefore, existing offCL strategies fall largely behind performance-wise in onCL, with some proving difficult or impossible to adapt to the online scenario. In this paper, we tackle both problems by leveraging Online Prototypes (OP) and Class-Wise Hypergradients (CWH). OP leverages stable output representations of PTM by updating its value on the fly to act as replay samples without requiring task boundaries or storing past data. CWH learns class-dependent gradient coefficients during training to improve over sub-optimal learning rates. We show through experiments that both introduced strategies allow for a consistent gain in accuracy when integrated with existing approaches. We will make the code fully available upon acceptance.
Recent advances in Large Language Models have led to remarkable achievements across a variety of Natural Language Processing tasks, making prompt engineering increasingly central to guiding model outputs. While manual methods can be effective, they typically rely on intuition and do not automatically refine prompts over time. In contrast, automatic prompt optimization employing heuristic-based search algorithms can systematically explore and improve prompts with minimal human oversight. This survey proposes a comprehensive taxonomy of these methods, categorizing them by where optimization occurs, what is optimized, what criteria drive the optimization, which operators generate new prompts, and which iterative search algorithms are applied. We further highlight specialized datasets and tools that support and accelerate automated prompt refinement. We conclude by discussing key open challenges pointing toward future opportunities for more robust and versatile LLM applications.
Variable stars play a very important role in our understanding of the Milky Way and the universe. In recent years, many survey projects have generated a large amount of photometric data, necessitating classifiers that can quickly identify various types of variable stars. However, obtaining these classifiers often requires substantial manpower and computational resources. To conserve these resources, it would be best to have a classifier that can be used across surveys. We explore the possibility that a classifier created in one optical band can also work in other bands, likely from different survey facilities. We construct a random forest classifier based on photometric data in ASAS-SN V-band and OGLE I-band, and apply the classifier on ASAS-SN V-band and ZTF r-band light curves of variable star samples. We explore the classification differences of using the magnitude light-curves or the normalized flux light-curves, the periods derived from single band light-curves or the periods derived from the multi-band combined light-curves, and with or without color-related features. We find it feasible to develop a classifier capable of working in both V and r bands for certain types of variable stars, such as RRAB variables. For other types of variable stars, like Cepheids, the classifier is unable to make accurate identifications.
Random Forest-of-Thoughts: Uncertainty-aware Reasoning for Computational Social Science
2025-02-26
Xiaohua Wu, Xiaohui Tao, Wenjie Wu, Yuefeng Li, Lin Li
Social surveys in computational social science are well-designed by elaborate domain theories that can effectively reflect the interviewee's deep thoughts without concealing their true feelings. The candidate questionnaire options highly depend on the interviewee's previous answer, which results in the complexity of social survey analysis, the time, and the expertise required. The ability of large language models (LLMs) to perform complex reasoning is well-enhanced by prompting learning such as Chain-of-thought (CoT) but still confined to left-to-right decision-making processes or limited paths during inference. This means they can fall short in problems that require exploration and uncertainty searching. In response, a novel large language model prompting method, called Random Forest of Thoughts (RFoT), is proposed for generating uncertainty reasoning to fit the area of computational social science. The RFoT allows LLMs to perform deliberate decision-making by generating diverse thought space and randomly selecting the sub-thoughts to build the forest of thoughts. It can extend the exploration and prediction of overall performance, benefiting from the extensive research space of response. The method is applied to optimize computational social science analysis on two datasets covering a spectrum of social survey analysis problems. Our experiments show that RFoT significantly enhances language models' abilities on two novel social survey analysis problems requiring non-trivial reasoning.
The Detection Rate of Associated \mgii\ Absorption Lines in Quasars Depends on Their Radio Emission
2025-02-26
Chen ZF, Luis C. Ho
In active galactic nuclei, jet-driven feedback plays a significant role in influencing the properties of gas within their host galaxy and the circumgalactic medium. By combining observations from the Very Large Array Sky Survey, the Faint Images of the Radio Sky at Twenty-cm, the LOFAR Two Metre Sky Survey, and the Sloan Digital Sky Survey, we assembled a sample of 3,141 radio-loud quasars, among which 418 exhibit \mgii\ associated absorption lines in their Sloan spectra. We classify these quasars into evolutionary stages based on their radio spectral shapes. Our analysis reveals that evolved quasars exhibit a significantly higher incidence of \mgii\ associated absorption lines compared to younger sources, particularly among quasars with ``non-peaked'' radio spectra, which show an incidence of \mgii\ associated absorbers approximately 1.7 times greater than that of gigahertz-peaked spectrum sources. This observation can be explained effectively by jet-driven feedback. As quasars age, their jets expand and expel substantial amounts of gas from small scales to larger scales, ultimately reaching the circumgalactic medium. The gas expelled from the inner regions and distributed over larger scales results in a greater coverage fraction of absorbing gas. Consequently, evolved quasars exhibit a higher incidence of \mgii\ absorption lines.
Symmetries of Living Systems: Symmetry Fibrations and Synchronization in Biological Networks
2025-02-26
Hernan A. Makse, Paolo Boldi, Francesco Sorrentino, Ian Stewart
A symmetry is a `change without change'. As simple as it sounds, this concept is the fundamental cornerstone that unifies all branches of theoretical physics. Virtually all physical laws -- ranging from classical mechanics and electrodynamics to relativity, quantum mechanics, and the standard model -- can be expressed in terms of symmetry invariances. In this book, we explore whether the same principle can also explain the emergent laws of biological systems. We introduce a new geometry for biological networks and AI architectures, drawing inspiration from the mystic genius of Grothendieck's fibrations in category theory. We attempt to bridge the gap between physics and biology using symmetries but with a twist. The traditional symmetry groups of physics are global and too rigid to describe biology. Instead, the novel notion of symmetry fibration is local, flexible, and adaptable to evolutionary pressures, providing the right framework for understanding biological complexity. In other words, this more general symmetry invariance is necessary and sufficient to ensure that a given biological network configuration can support a synchronized function. In this book, we review the theoretical progress over the last decades from mathematics, physics, computer science, dynamical systems, and graph theory that has led to the discovery of symmetry fibrations in biological networks. These symmetries act as organizing principles for biological networks. They serve as effective tools for describing the structure of these networks, blending geometry and topology. Fibrations explain how structure dictates function across various biological domains, including the transcriptome, proteome, metabolome, and connectome. Additionally, they facilitate a reduction in the dimensionality of the network, simplifying it into its fundamental building blocks for biological computation.
Confined tumbling state as the origin of the excess of slowly rotating asteroids
2025-02-26
Wen-Han Zhou, Patrick Michel, Marco Delbo, Wenchao Wang, Bonny Y. Wang, Josef Ďurech, Josef Hanuš
The rotational distribution of asteroids as a function of their size is used {as a diagnostic of} their physical properties and evolution. Recent photometric surveys from the Gaia mission, allowing observation of asteroids with long spin periods (for example $\geq 24$h), found an excessive group of slow rotators and a gap separating them from faster rotators, which is unexplained by current theories. Here we developed an asteroid rotational evolution model capable of reproducing the observed distribution. {We suggest that this distribution is regulated by the competition between collisions and internal friction dampening of "tumblers" -asteroids with unstable rotation vectors, and that the slow rotator group is mainly populated by tumblers.} {We constrain the product of the rigidity and quality factor, which relates to the body's viscosity, to $\mu Q \sim 4 \times 10^9~$Pa. This number, two orders of magnitude smaller than the one assumed for monolithic boulders,} implies that {rubble pile} asteroids could have a porous structure or a thick regolith layer, and undergo stronger tidal effects.
3D Nephrographic Image Synthesis in CT Urography with the Diffusion Model and Swin Transformer
2025-02-26
Hongkun Yu, Syed Jamal Safdar Gardezi, E. Jason Abel, Daniel Shapiro, Meghan G. Lubner, Joshua Warner, Matthew Smith, Giuseppe Toia, Lu Mao, Pallavi Tiwari, Andrew L. Wentland
Purpose: This study aims to develop and validate a method for synthesizing 3D nephrographic phase images in CT urography (CTU) examinations using a diffusion model integrated with a Swin Transformer-based deep learning approach. Materials and Methods: This retrospective study was approved by the local Institutional Review Board. A dataset comprising 327 patients who underwent three-phase CTU (mean $\pm$ SD age, 63 $\pm$ 15 years; 174 males, 153 females) was curated for deep learning model development. The three phases for each patient were aligned with an affine registration algorithm. A custom deep learning model coined dsSNICT (diffusion model with a Swin transformer for synthetic nephrographic phase images in CT) was developed and implemented to synthesize the nephrographic images. Performance was assessed using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Absolute Error (MAE), and Fr\'{e}chet Video Distance (FVD). Qualitative evaluation by two fellowship-trained abdominal radiologists was performed. Results: The synthetic nephrographic images generated by our proposed approach achieved high PSNR (26.3 $\pm$ 4.4 dB), SSIM (0.84 $\pm$ 0.069), MAE (12.74 $\pm$ 5.22 HU), and FVD (1323). Two radiologists provided average scores of 3.5 for real images and 3.4 for synthetic images (P-value = 0.5) on a Likert scale of 1-5, indicating that our synthetic images closely resemble real images. Conclusion: The proposed approach effectively synthesizes high-quality 3D nephrographic phase images. This model can be used to reduce radiation dose in CTU by 33.3\% without compromising image quality, which thereby enhances the safety and diagnostic utility of CT urography.
Is Your Paper Being Reviewed by an LLM? A New Benchmark Dataset and Approach for Detecting AI Text in Peer Review
2025-02-26
Sungduk Yu, Man Luo, Avinash Madusu, Vasudev Lal, Phillip Howard
Peer review is a critical process for ensuring the integrity of published scientific research. Confidence in this process is predicated on the assumption that experts in the relevant domain give careful consideration to the merits of manuscripts which are submitted for publication. With the recent rapid advancements in large language models (LLMs), a new risk to the peer review process is that negligent reviewers will rely on LLMs to perform the often time consuming process of reviewing a paper. However, there is a lack of existing resources for benchmarking the detectability of AI text in the domain of peer review. To address this deficiency, we introduce a comprehensive dataset containing a total of 788,984 AI-written peer reviews paired with corresponding human reviews, covering 8 years of papers submitted to each of two leading AI research conferences (ICLR and NeurIPS). We use this new resource to evaluate the ability of 18 existing AI text detection algorithms to distinguish between peer reviews written by humans and different state-of-the-art LLMs. Motivated by the shortcomings of existing methods, we propose a new detection approach which surpasses existing methods in the identification of AI written peer reviews. Our work reveals the difficulty of identifying AI-generated text at the individual peer review level, highlighting the urgent need for new tools and methods to detect this unethical use of generative AI.
2025-02-26
Jonas Becker, Lars Benedikt Kaesberg, Andreas Stephan, Jan Philip Wahle, Terry Ruas, Bela Gipp
Multi-agent debate - multiple instances of large language models discussing problems in turn-based interaction - has shown promise for solving knowledge and reasoning tasks. However, these methods show limitations, particularly when scaling them to longer reasoning chains. In this study, we unveil a new issue of multi-agent debate: discussions drift away from the initial problem over multiple turns. We define this phenomenon as problem drift and quantify its presence across ten tasks (i.e., three generative, three knowledge, three reasoning, and one instruction-following task). To identify the reasons for this issue, we perform a human study with eight experts on discussions suffering from problem drift, who find the most common issues are a lack of progress (35% of cases), low-quality feedback (26% of cases), and a lack of clarity (25% of cases). To systematically address the issue of problem drift, we propose DRIFTJudge, a method based on LLM-as-a-judge, to detect problem drift at test-time. We further propose DRIFTPolicy, a method to mitigate 31% of problem drift cases. Our study can be seen as a first step to understanding a key limitation of multi-agent debate, highlighting pathways for improving their effectiveness in the future.
Real-Time Active Learning for optimised spectroscopic follow-up: Enhancing early SN Ia classification with the Fink broker
2025-02-26
A. Möller, E. E. O. Ishida, J. Peloton, O. Vidal Velázquez, J. Soon, B. Martin, M. Cluver, M. Leoni, E. Taylor
Current and future surveys rely on machine learning classification to obtain large and complete samples of transients. Many of these algorithms are restricted by training samples that contain a limited number of spectroscopically confirmed events. Here, we present the first real-time application of Active Learning to optimise spectroscopic follow-up with the goal of improving training sets of early type Ia supernovae (SNe Ia) classifiers. Using a photometric classifier for early SN Ia, we apply an Active Learning strategy for follow-up optimisation using the real-time FINK broker processing of the ZTF public stream. We perform follow-up observations at the ANU 2.3m telescope in Australia and obtain 92 spectroscopic classified events that are incorporated in our training set. We show that our follow-up strategy yields a training set that, with 25% less spectra, improves classification metrics when compared to publicly reported spectra. Our strategy selects in average fainter events and, not only supernovae types, but also microlensing events and flaring stars which are usually not incorporated on training sets. Our results confirm the effectiveness of active learning strategies to construct optimal training samples for astronomical classifiers. With the Rubin Observatory LSST soon online, we propose improvements to obtain earlier candidates and optimise follow-up. This work paves the way to the deployment of real-time AL follow-up strategies in the era of large surveys.
Recent advancements in large language models (LLMs) have spurred interest in expanding their application beyond text-based tasks. A large number of studies have explored integrating other modalities with LLMs, notably speech modality, which is naturally related to text. This paper surveys the integration of speech with LLMs, categorizing the methodologies into three primary approaches: text-based, latent-representation-based, and audio-token-based integration. We also demonstrate how these methods are applied across various speech-related applications and highlight the challenges in this field to offer inspiration for
Repurposing the scientific literature with vision-language models
2025-02-26
Anton Alyakin, Jaden Stryker, Daniel Alexander Alber, Karl L. Sangwon, Brandon Duderstadt, Akshay Save, David Kurland, Spencer Frome, Shrutika Singh, Jeff Zhang, Eunice Yang, Ki Yun Park, Cordelia Orillac, Aly A. Valliani, Sean Neifert, Albert Liu, Aneek Patel, Christopher Livia, Darryl Lau, Ilya Laufer, Peter A. Rozman, Eveline Teresa Hidalgo, Howard Riina, Rui Feng, Todd Hollon, Yindalon Aphinyanaphongs, John G. Golfinos, Laura Snyder, Eric Leuthardt, Douglas Kondziolka, Eric Karl Oermann
Research in AI for Science often focuses on using AI technologies to augment components of the scientific process, or in some cases, the entire scientific method; how about AI for scientific publications? Peer-reviewed journals are foundational repositories of specialized knowledge, written in discipline-specific language that differs from general Internet content used to train most large language models (LLMs) and vision-language models (VLMs). We hypothesized that by combining a family of scientific journals with generative AI models, we could invent novel tools for scientific communication, education, and clinical care. We converted 23,000 articles from Neurosurgery Publications into a multimodal database - NeuroPubs - of 134 million words and 78,000 image-caption pairs to develop six datasets for building AI models. We showed that the content of NeuroPubs uniquely represents neurosurgery-specific clinical contexts compared with broader datasets and PubMed. For publishing, we employed generalist VLMs to automatically generate graphical abstracts from articles. Editorial board members rated 70% of these as ready for publication without further edits. For education, we generated 89,587 test questions in the style of the ABNS written board exam, which trainee and faculty neurosurgeons found indistinguishable from genuine examples 54% of the time. We used these questions alongside a curriculum learning process to track knowledge acquisition while training our 34 billion-parameter VLM (CNS-Obsidian). In a blinded, randomized controlled trial, we demonstrated the non-inferiority of CNS-Obsidian to GPT-4o (p = 0.1154) as a diagnostic copilot for a neurosurgical service. Our findings lay a novel foundation for AI with Science and establish a framework to elevate scientific communication using state-of-the-art generative artificial intelligence while maintaining rigorous quality standards.
The Massive and Distant Clusters of $WISE$ Survey. XII. Exploring X-ray AGN in Dynamically Active Massive Galaxy Clusters at z~1
2025-02-26
Mustafa Muhibullah, Mark Brodwin, Michael McDonald, Anthony H. Gonzalez, Emily Moravec, Thomas Connor, S. A. Stanford, Florian Ruppin, Taweewat Somboonpanyakul, Peter R. M. Eisenhardt, Bandon Decker, Daniel Stern, Ariane Trudeau
We present an analysis of the cluster X-ray morphology and active galactic nucleus (AGN) activity in nine $z\sim1$ galaxy clusters from the Massive and Distant Clusters of $WISE$ Survey (MaDCoWS) observed with $Chandra$. Using photon asymmetry ($A_{\text{phot}}$) to quantify X-ray morphologies, we find evidence that the four most dynamically disturbed clusters are likely to be mergers. Employing a luminosity cut of $7.6\times10^{42}$ erg/s to identify AGN in the 0.7-7.0 keV, we show that the majority of these clusters host excess AGN compared to the local field. We use the cumulative number-count ($\log N-\log S$) model to predict AGN incidence in cluster isophotes under this luminosity cut. Our analysis finds evidence (at $> 2\sigma$) of a positive correlation between AGN surface densities and photon asymmetry, suggesting that a disturbed cluster environment plays a pivotal role in regulating AGN triggering. Studying AGN incidence in cluster X-ray isophotes equivalent in area to $1.0r_{500}$, we find that the AGN space density inversely scales with cluster mass as $\sim M^{-0.5^{+0.18}_{-0.18}}$ at the 3.18$\sigma$ level. Finally, when we separately explore the cluster mass dependence of excess AGN surface density in disturbed and relaxed clusters, we see tentative evidence that the two morphologically distinct sub-populations exhibit diverging trends, especially near the outskirts, likely due to cluster merger-driven AGN triggering/suppression.
This paper presents a game master AI for single-player role-playing games. The AI is designed to deliver interactive text-based narratives and experiences typically associated with multiplayer tabletop games like Dungeons & Dragons. We report on the design process and the series of experiments to improve the functionality and experience design, resulting in two functional versions of the system. While v1 of our system uses simplified prompt engineering, v2 leverages a multi-agent architecture and the ReAct framework to include reasoning and action. A comparative evaluation demonstrates that v2 as an agentic system maintains play while significantly improving modularity and game experience, including immersion and curiosity. Our findings contribute to the evolution of AI-driven interactive fiction, highlighting new avenues for enhancing solo role-playing experiences.
Andromeda XXXV: The Faintest Dwarf Satellite of the Andromeda Galaxy
2025-02-26
Jose Marco Arias, Eric F. Bell, Katya Gozman, In Sung Jang, Saxon Stockton, Oleg Y. Gnedin, Richard D'Souza, Antonela Monachesi, Jeremy Bailin, David Nidever, Roelof S. de Jong
We present the discovery of Andromeda XXXV, the faintest Andromeda satellite galaxy discovered to date, identified as an overdensity of stars in the Pan-Andromeda Archaeological Survey and confirmed via Hubble Space Telescope imaging. Located at a heliocentric distance of $927^{+76}_{-63}$ kpc and $158^{+57}_{-45}$ kpc from Andromeda, Andromeda XXXV is an extended ($r_h = 53\,^{+13}_{-11}$ pc), elliptical ($\epsilon = 0.4\, \pm 0.2$), metal-poor ($[\text{Fe}/\text{H}]\sim-1.9$) system, and the least luminous ($M_V=-5.2 \pm 0.3$) of Andromeda's dwarf satellites discovered so far. Andromeda XXXV's properties are consistent with the known population of dwarf galaxies around the Local Group, bearing close structural resemblance to the Canes Venatici II and Hydra II Milky Way (MW) dwarf satellite galaxies. Its stellar population, characterized by a red horizontal branch or a red clump feature, mirrors that of other Andromeda satellite galaxies in showing evidence for a spread in age and metallicity, with no signs of younger stellar generations. This age-metallicity spread is not observed in MW satellites of comparable stellar mass, and highlights the persistent differences between the satellite systems of Andromeda and the MW, extending even into the ultrafaint regime.
White dwarf stars are the most common final stage of stellar evolution. Since the serendipitous discovery of the first white dwarf by William Herschel and the first physical models by Subrahmanyan Chandrasekhar and Arthur Eddington, there have been a lot of advances in the field fueled by new observational data. With new astrometric measurements enabling us to identify hundreds of thousands of white dwarf candidates, and spectroscopic surveys revealing a plethora of chemical elements in white dwarf atmospheres pointing at spectral evolution and interaction with planetary bodies, there is a lot we can learn from the characterization of observed white dwarfs. Here we provide an observational overview of white dwarf stars, describing how they are identified and characterized, and the main properties of the observed population.
2025-02-26
E. Mamuzic, T. Ryu, S. H. Suyu, K. Szekerczes, S. Huber, L. Dai, M. Oguri
In the coming years, surveys such as the Rubin Observatory's Legacy Survey of Space and Time (LSST) are expected to increase the number of observed Tidal Disruption Events (TDEs) substantially. We employ Monte Carlo integration to calculate the unlensed and lensed TDE rate as a function of limiting magnitude in $u$, $g$, $r$, and $i$-bands. We investigate the impact of multiple luminosity models, black hole mass functions (BHMFs), and flare temperatures on the TDE rate. Notably, this includes a semi-analytical model, which enables the determination of the TDE temperature in terms of black hole (BH) mass. We predict the highest unlensed TDE rate to be in $g$-band. It ranges from $16$ to $5,440\;\mathrm{yr}^{-1}\;(20,000\;\mathrm{deg}^2)^{-1}$ for the Zwicky Transient Facility, being more consistent with the observed rate at the low end. For LSST, we expect a rate in $g$-band between $3,580$ and $82,060\;\mathrm{yr}^{-1}\;(20,000\;\mathrm{deg}^2)^{-1}$. A higher theoretical prediction is understandable, as we do not consider observational effects such as completeness. The unlensed and lensed TDE rates are insensitive to the redshift evolution of the BHMF, even for LSST limiting magnitudes. The best band for detecting lensed TDEs is also $g$-band. Its predicted rates range from $0.43$ to $15\;\mathrm{yr}^{-1}\;(20,000\;\mathrm{deg}^2)^{-1}$ for LSST. The scatter of predicted rates reduces when we consider the fraction of lensed TDEs; that is, a few in ten thousand TDEs will be lensed. Despite the large scatter in the rates of lensed TDEs, our comprehensive considerations of multiple models suggest that lensed TDEs will occur in the $10$-year LSST lifetime, providing an exciting prospect for detecting such events. We expect the median redshift of a lensed TDE to be between $1.5$ and $2$. In this paper, we additionally report on lensed TDE properties, such as the BH mass and time delays.
Low-Luminosity Type IIP Supernovae from the Zwicky Transient Facility Census of the Local Universe. I: Luminosity Function, Volumetric Rate
2025-02-26
Kaustav K. Das, Mansi M. Kasliwal, Christoffer Fremling, Jesper Sollerman, Daniel A. Perley, Kishalay De, Anastasios Tzanidakis, Tawny Sit, Scott Adams, Shreya Anand, Tomas Ahumuda, Igor Andreoni, Sean Brennan, Thomas Brink, Rachel J. Bruch, Ping Chen, Matthew R. Chu, David O. Cook, Sofia Covarrubias, Aishwarya Dahiwale, Nicholas Earley, Anna Y. Q. Ho, Avishay Gal-Yam, Anjasha Gangopadhyay, Erica Hammerstein, K-Ryan Hinds, Viraj Karambelkar, Yihan Kong, S. R. Kulkarni, Theophile Jegou du Laz, Chang Liu, William Meynardie, Adam A. Miller, Guy Nir, Kishore C. Patra, Priscila J. Pessi, R. Michael Rich, Nabeel Rehemtulla, Sam Rose, Ben Rusholme, Steve Schulze, Yashvi Sharma, Avinash Singh, Roger Smith, Robert Stein, Milan Sharma Mandigo-Stoba, Nora L. Strotjohann, Yu-Jing Qin, Jacob Wise, Avery Wold, Lin Yan, Yi Yang, Yuhan Yao, Erez Zimmerman
We present the luminosity function and volumetric rate of a sample of Type IIP supernovae (SNe) from the Zwicky Transient Facility Census of the Local Universe survey (CLU). This is the largest sample of Type IIP SNe from a systematic volume-limited survey to-date. The final sample includes 330 Type IIP SNe and 36 low-luminosity Type II (LLIIP) SNe with $M_{\textrm{r,peak}}>-16$ mag, which triples the literature sample of LLIIP SNe. The fraction of LLIIP SNe is $19^{+3}_{-4}\%$ of the total CLU Type IIP SNe population ($8^{+1}_{-2}\%$ of all core-collapse SNe). This implies that while LLIIP SNe likely represent the fate of core-collapse SNe of $8-12$ \Msun\ progenitors, they alone cannot account for the fate of all massive stars in this mass range. To derive an absolute rate, we estimate the ZTF pipeline efficiency as a function of the apparent magnitude and the local surface brightness. We derive a volumetric rate of $(3.9_{-0.4}^{+0.4}) \times 10^{4}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ for Type IIP SNe and $(7.3_{-0.6}^{+0.6}) \times 10^{3}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ for LLIIP SNe. Now that the rate of LLIIP SNe is robustly derived, the unresolved discrepancy between core-collapse SN rates and star-formation rates cannot be explained by LLIIP SNe alone.
A Tentative Detection of Molecular Hydrogen (H$_2$) Emission Lines at Cosmic Dawn
2025-02-26
Madisen Johnson, Blakesley Burkhart, Francesco D'Eugenio, Jacques Le Bourlot, Shmuel Bialy, Sandro Tacchella, Roberto Maiolino, Evelyne Roueff, Franck Le Petit, Emeric Bron, Herve Abgrall, Erica Nelson, Shyam Menon, Matthew E. Orr
Using data from the James Webb Space Telescope (JWST) Advanced Deep Extragalactic Survey (JADES), we tentatively detect two molecular hydrogen (H$_2$) fluorescent emission features in high-redshift galaxies at 2.3 and 3.1$\sigma$. These features consist of many blended emission lines that result from the de-excitation cascade of H$_2$ molecules that have absorbed Lyman-Werner band photons. Our study targets galaxies at redshifts $z \geq 7$ galaxies of the early Universe, as they host some of the most extreme conditions in terms of star formation, molecular gas content, and the possible presence of outflows driven by starbursts and active galactic nuclei. To enhance the signal-to-noise ratio of H$_2$ emission features in the rest-frame wavelength range of 155-191 nm, we stack JWST/NIRSpec spectra from $z=7.0-13.4$. These stacked spectra also exhibit atomic emission features, such as CIV emission with a P-Cygni profile, as well as CIII], OIII], and HeII. The presence of these features and the slightly blue-shifted fluorescent H$_2$ lines suggest active multiphase atomic and molecular outflows may be common at these redshifts. Our results suggest that it is possible to search for FUV fluorescent H$_2$ lines in high redshift galaxies using JWST, which would enable the characterization of interstellar radiation fields, densities, and temperatures in extreme photodissociation environments.
I review some recent results on understanding the physics of metals in an exact non-perturbative way through the powerful field-theoretic concepts of emergent symmetries and 't Hooft anomalies. A 't Hooft anomaly is a discrete topological property that quantum field theories with global symmetries can have. I explain how many of the properties of metals can in fact be viewed as direct consequences of the anomaly. This allows a structural understanding of metals, including non-Fermi liquids, to be obtained even in the absence of any exact solution for the strongly coupled dynamics. I then outline the main limitations and outstanding questions.
Compression in 3D Gaussian Splatting: A Survey of Methods, Trends, and Future Directions
2025-02-26
Muhammad Salman Ali, Chaoning Zhang, Marco Cagnazzo, Giuseppe Valenzise, Enzo Tartaglione, Sung-Ho Bae
3D Gaussian Splatting (3DGS) has recently emerged as a pioneering approach in explicit scene rendering and computer graphics. Unlike traditional neural radiance field (NeRF) methods, which typically rely on implicit, coordinate-based models to map spatial coordinates to pixel values, 3DGS utilizes millions of learnable 3D Gaussians. Its differentiable rendering technique and inherent capability for explicit scene representation and manipulation positions 3DGS as a potential game-changer for the next generation of 3D reconstruction and representation technologies. This enables 3DGS to deliver real-time rendering speeds while offering unparalleled editability levels. However, despite its advantages, 3DGS suffers from substantial memory and storage requirements, posing challenges for deployment on resource-constrained devices. In this survey, we provide a comprehensive overview focusing on the scalability and compression of 3DGS. We begin with a detailed background overview of 3DGS, followed by a structured taxonomy of existing compression methods. Additionally, we analyze and compare current methods from the topological perspective, evaluating their strengths and limitations in terms of fidelity, compression ratios, and computational efficiency. Furthermore, we explore how advancements in efficient NeRF representations can inspire future developments in 3DGS optimization. Finally, we conclude with current research challenges and highlight key directions for future exploration.
Unveiling the Aromatic and Aliphatic Universe at Redshifts $z\sim$0.2--0.5 with JWST/NIRCam
2025-02-25
Jianwei Lyu, Xuejuan Yang, Aigen Li, Fengwu Sun, George H. Rieke, Stacey Alberts, Irene Shivaei
Utilizing deep NIRCam/WFSS data from JWST's FRESCO program, we spectroscopically survey the 3.3 $\mu m$ aromatic and 3.4 $\mu m$ aliphatic C--H stretching emission bands of polycyclic aromatic hydrocarbon (PAH) molecules in galaxies at redshifts $z$$\sim$0.2--0.5. Unlike pre-JWST studies, largely limited to infrared (IR)-bright galaxies ($L_{\rm IR}\gtrsim10^{11}~L_\odot$) at $z\lesssim0.1$, we probe 200 galaxies down to $L_{\rm IR}$$\sim$$10^{8.5}$--$10^{10}~L_\odot$ well beyond the local Universe. The 3.3 $\mu m$ emission is detected at $\geq$3-$\sigma$ in 88 out of 187 galaxies, correlating tightly with galaxy IR luminosity and star formation rate (SFR) and confirming the 3.3 $\mu m$ PAH as a viable SFR tracer. Despite a large scatter, the 3.3 $\mu m$-to-IR luminosity ratio ($L_{3.3}/L_{\rm IR}$) exhibits a strong metallicity dependence with a drop of $L_{3.3}/L_{\rm IR}$ by a factor of $\gtrsim10$ at 12+log(O/H)$\sim$8.4--8.5 towards lower metallicities. The 3.4 $\mu m$ emission is detected in 37 out of 159 galaxies, with the 3.4 $\mu m$-to-3.3 $\mu m$ luminosity ratio ($L_{3.4}/L_{3.3}$) spanning from $\sim$0.05 to $\sim$0.58 (median $\sim$0.19), corresponding to PAH aliphatic fractions of $\sim$0.78%--8.3% (median $\sim$2.9%) in terms of fractional carbon atoms in aliphatic units. While $L_{3.4}/L_{3.3}$ does not depend significantly on redshift, stellar mass, metallicity, or galaxy morphology, it does decrease with various SFR tracers, suggesting that ultraviolet photons in active star-forming regions may strip aliphatic sidegroups from PAH molecules. Our study showcases the unique power of JWST's NIRCam/WFSS to systematically map PAH aromatic and aliphatic content in statistically significant, less-biased galaxy samples, providing critical insights into PAH chemistry and its connection to galaxy properties.
Nuclear Spin Engineering for Quantum Information Science
2025-02-25
Jonathan C. Marcks, Benjamin Pingault, Jiefei Zhang, Cyrus Zeledon, F. Joseph Heremans, David D. Awschalom
Semiconductors are the backbone of modern technology, garnering decades of investment in high quality materials and devices. Electron spin systems in semiconductors, including atomic defects and quantum dots, have been demonstrated in the last two decades to host quantum coherent spin qubits, often with coherent spin-photon interfaces and proximal nuclear spins. These systems are at the center of developing quantum technology. However, new material challenges arise when considering the isotopic composition of host and qubit systems. The isotopic composition governs the nature and concentration of nuclear spins, which naturally occur in leading host materials. These spins generate magnetic noise -- detrimental to qubit coherence -- but also show promise as local quantum memories and processors, necessitating careful engineering dependent on the targeted application. Reviewing recent experimental and theoretical progress towards understanding local nuclear spin environments in semiconductors, we show this aspect of material engineering as critical to quantum information technology.
Is OpenAlex Suitable for Research Quality Evaluation and Which Citation Indicator is Best?
2025-02-25
Mike Thelwall, Xiaorui Jiang
This article compares (1) citation analysis with OpenAlex and Scopus, testing their citation counts, document type/coverage and subject classifications and (2) three citation-based indicators: raw counts, (field and year) Normalised Citation Scores (NCS) and Normalised Log-transformed Citation Scores (NLCS). Methods (1&2): The indicators calculated from 28.6 million articles were compared through 8,704 correlations on two gold standards for 97,816 UK Research Excellence Framework (REF) 2021 articles. The primary gold standard is ChatGPT scores, and the secondary is the average REF2021 expert review score for the department submitting the article. Results: (1) OpenAlex provides better citation counts than Scopus and its inclusive document classification/scope does not seem to cause substantial field normalisation problems. The broadest OpenAlex classification scheme provides the best indicators. (2) Counterintuitively, raw citation counts are at least as good as nearly all field normalised indicators, and better for single years, and NCS is better than NLCS. (1&2) There are substantial field differences. Thus, (1) OpenAlex is suitable for citation analysis in most fields and (2) the major citation-based indicators seem to work counterintuitively compared to quality judgements. Field normalisation seems ineffective because more cited fields tend to produce higher quality work, affecting interdisciplinary research or within-field topic differences.
The decays of pions, the lightest particles composed of quarks, provide important insight into fundamental questions in particle physics including basic symmetries, the universality of fermionic weak interactions, and aspects of the strong interaction described by Quantum Chromodynamics (QCD). An introduction to the theoretical and experimental study of charged and neutral pion decays is presented.
Which Contributions Deserve Credit? Perceptions of Attribution in Human-AI Co-Creation
2025-02-25
Jessica He, Stephanie Houde, Justin D. Weisz
AI systems powered by large language models can act as capable assistants for writing and editing. In these tasks, the AI system acts as a co-creative partner, making novel contributions to an artifact-under-creation alongside its human partner(s). One question that arises in these scenarios is the extent to which AI should be credited for its contributions. We examined knowledge workers' views of attribution through a survey study (N=155) and found that they assigned different levels of credit across different contribution types, amounts, and initiative. Compared to a human partner, we observed a consistent pattern in which AI was assigned less credit for equivalent contributions. Participants felt that disclosing AI involvement was important and used a variety of criteria to make attribution judgments, including the quality of contributions, personal values, and technology considerations. Our results motivate and inform new approaches for crediting AI contributions to co-created work.
High-velocity outflows in [OIII] emitters at z=2.5-9 from JWST NIRSpec medium-resolution spectroscopy
2025-02-25
Ryan A. Cooper, Karina I. Caputi, Edoardo Iani, Pierluigi Rinaldi, Guillaume Desprez, Rafael Navarro-Carrera
We identify galaxies hosting ionised-gas high-velocity outflows from the complete sample of medium resolution (R1000) JWST/NIRSpec MSA spectroscopy taken as part of the JWST Advanced Deep Extragalactic Survey (JADES). From a total sample of 1087 [OIII]5007 emitters we identify 40 galaxies with a blue-side broadened [OIII]5007 line at z=2.5-9. Of these, 34 are strong outflow candidates whilst 6 sources have broadening potentially driven by rotating clumps. Our outflow candidate sample is mainly composed of star-forming galaxies, including ~65% starbursts, which span the stellar mass range log10(M*/Msun)=7.5-11.0. It also includes two candidate type-2 active galactic nuclei (AGN) and a 'little red dot' (LRD). We report a median outflow velocity of 531(+146)(-159) km/s and an overall incidence rate of 3.4%. These values are significantly higher and lower respectively than recent similar works, which we accredit to the limiting resolution of the R1000 spectroscopy and a stricter outflow selection criterion. We find no correlation between the outflow velocity and the galaxy stellar mass or star-formation rate. The median ratio between outflow velocity and escape velocity is 0.77(+0.36)(-0.32), indicating that most outflows cannot escape the galaxy gravitational potentials. We do find an anti-correlation between mass loading factor and stellar mass up to M* ~(10^10)Msun, with most lowest stellar-mass M*<(10^9)Msun galaxies reaching values well above unity, as is the case for local starburst galaxies.
Experimental Analysis of Efficiency of the Messaging Layer Security for Multiple Delivery Services
2025-02-25
David Soler, Carlos Dafonte, Manuel Fernández-Veiga, Ana Fernández Vilas, Francisco J. Nóvoa
Messaging Layer security (MLS) and its underlying Continuous Group Key Agreement (CGKA) protocol allows a group of users to share a cryptographic secret in a dynamic manner, such that the secret is modified in member insertions and deletions. One of the most relevant contributions of MLS is its efficiency, as its communication cost scales logarithmically with the number of members. However, this claim has only been analysed in theoretical models and thus it is unclear how efficient MLS is in real-world scenarios. Furthermore, practical decisions such as the chosen Delivery Service and paradigm can also influence the efficiency and evolution of an MLS group. In this work we analyse MLS from an empirical viewpoint: we provide real-world measurements for metrics such as commit generation and processing times and message sizes under different conditions. In order to obtain these results we have developed a highly configurable environment for empirical evaluations of MLS through the simulation of MLS clients. Among other findings, our results show that computation costs scale linearly in practical scenarios even in the best-case scenario.
2025-02-25
Wenlong Chen, Bolian Li, Ruqi Zhang, Yingzhen Li
This review paper is intended for the 2nd edition of the Handbook of Markov chain Monte Carlo. We provide an introduction to approximate inference techniques as Bayesian computation methods applied to deep learning models. We organize the chapter by presenting popular computational methods for Bayesian neural networks and deep generative models, explaining their unique challenges in posterior inference as well as the solutions.
Better Aligned with Survey Respondents or Training Data? Unveiling Political Leanings of LLMs on U.S. Supreme Court Cases
2025-02-25
Shanshan Xu, T. Y. S. S Santosh, Yanai Elazar, Quirin Vogel, Barbara Plank, Matthias Grabmair
The increased adoption of Large Language Models (LLMs) and their potential to shape public opinion have sparked interest in assessing these models' political leanings. Building on previous research that compared LLMs and human opinions and observed political bias in system responses, we take a step further to investigate the underlying causes of such biases by empirically examining how the values and biases embedded in training corpora shape model outputs. Specifically, we propose a method to quantitatively evaluate political leanings embedded in the large pretraining corpora. Subsequently we investigate to whom are the LLMs' political leanings more aligned with, their pretrainig corpora or the surveyed human opinions. As a case study, we focus on probing the political leanings of LLMs in 32 U.S. Supreme Court cases, addressing contentious topics such as abortion and voting rights. Our findings reveal that LLMs strongly reflect the political leanings in their training data, and no strong correlation is observed with their alignment to human opinions as expressed in surveys. These results underscore the importance of responsible curation of training data and the need for robust evaluation metrics to ensure LLMs' alignment with human-centered values.
Determined Blind Source Separation with Sinkhorn Divergence-based Optimal Allocation of the Source Power
2025-02-25
Jianyu Wang, Shanzheng Guan, Nicolas Dobigeon, Jingdong Chen
Blind source separation (BSS) refers to the process of recovering multiple source signals from observations recorded by an array of sensors. Common approaches to BSS, including independent vector analysis (IVA), and independent low-rank matrix analysis (ILRMA), typically rely on second-order models to capture the statistical independence of source signals for separation. However, these methods generally do not account for the implicit structural information across frequency bands, which may lead to model mismatches between the assumed source distributions and the distributions of the separated source signals estimated from the observed mixtures. To tackle these limitations, this paper shows that conventional approaches such as IVA and ILRMA can easily be leveraged by the Sinkhorn divergence, incorporating an optimal transport (OT) framework to adaptively correct source variance estimates. This allows for the recovery of the source distribution while modeling the inter-band signal dependence and reallocating source power across bands. As a result, enhanced versions of these algorithms are developed, integrating a Sinkhorn iterative scheme into their standard implementations. Extensive simulations demonstrate that the proposed methods consistently enhance BSS performance.
The goal of this paper is to estimate some parameters of simple social harmonic oscillations modelling U.S. Presidential Approval and Macropartisanship. The harmonic oscillations are simplest solutions of equations of the deterministic social dynamics (Zhdanov M., 2024), relating social bodies positions in the Bayesian space of assessments to noncoercive driving forces acting on these social bodies. In some limits, such as individuals assessing each other, the space may be reduced to just sets of scores, ratings, or indexes used to measure social variables like rates of presidential approval or consumer sentiments. Thereby, relations are established between forces of social attraction and elasticity, considered in the presented paper, and assessments of social bodies including individuals, by other social bodies. The results are applied to estimate parameters of social bodies and their motions from survey data on Presidential Approval, U.S. Senator Approval, and Macropartisanship and provide one more perspective on some well-known issues like what moves macropartisanship and why presidential approval fluctuates.
Monitoring snow avalanches from SAR data with deep learning
2025-02-25
Filippo Maria Bianchi, Jakob Grahn
Snow avalanches present significant risks to human life and infrastructure, particularly in mountainous regions, making effective monitoring crucial. Traditional monitoring methods, such as field observations, are limited by accessibility, weather conditions, and cost. Satellite-borne Synthetic Aperture Radar (SAR) data has become an important tool for large-scale avalanche detection, as it can capture data in all weather conditions and across remote areas. However, traditional processing methods struggle with the complexity and variability of avalanches. This chapter reviews the application of deep learning for detecting and segmenting snow avalanches from SAR data. Early efforts focused on the binary classification of SAR images, while recent advances have enabled pixel-level segmentation, providing greater accuracy and spatial resolution. A case study using Sentinel-1 SAR data demonstrates the effectiveness of deep learning models for avalanche segmentation, achieving superior results over traditional methods. We also present an extension of this work, testing recent state-of-the-art segmentation architectures on an expanded dataset of over 4,500 annotated SAR images. The best-performing model among those tested was applied for large-scale avalanche detection across the whole of Norway, revealing important spatial and temporal patterns over several winter seasons.
Magnetodielectric (MD) materials are important for their ability to spin-charge conversion, magnetic field control of electric polarization and vice versa. Among these, two-dimensional (2D) van der Waals (vdW) magnetic materials are of particular interest due to the presence of magnetic anisotropy (MA) originating from the interaction between the magnetic moments and the crystal field. Also, these materials indicate a high degree of stability in the long-range spin order and may be described using suitable spin Hamiltonians of the Heisenberg, XY, or Ising type. Recent reports have suggested effective interactions between magnetization and electric polarization in 2D magnets. However, MD coupling studies on layered magnetic materials are still few. This review covers the fundamentals of magnetodielectric coupling by explaining related key terms. It includes the necessary conditions for having this coupling and sheds light on the possible physical mechanisms behind this coupling starting from phenomenological descriptions. Apart from that, this review classifies 2D magnetic materials into several categories for reaching out each and every class of materials. Additionally, this review summarizes recent advancements of some pioneer 2D magnetodielectric materials. Last but not the least, the current review provides possible research directions for enhancing magnetodielectric coupling in those and mentions the possibilities for future developments.
Recent interest in human-AI interactions in agent-based modeling and simulation (ABMS) has grown rapidly due to the widespread utilization of large language models (LLMs). ABMS is an intelligent approach that simulates autonomous agents' behaviors within a defined environment to research emergent phenomena. Integrating LLMs into ABMS enables natural language interaction between humans and models. Meanwhile, it introduces new challenges that rely on human interaction to address. Human involvement can assist ABMS in adapting to flexible and complex research demands. However, systematic reviews of interactions that examine how humans and AI interact in ABMS are lacking. In this paper, we investigate existing works and propose a novel taxonomy to categorize the interactions derived from them. Specifically, human users refer to researchers who utilize ABMS tools to conduct their studies in our survey. We decompose interactions into five dimensions: the goals that users want to achieve (Why), the phases that users are involved (When), the components of the system (What), the roles of users (Who), and the means of interactions (How). Our analysis summarizes the findings that reveal existing interaction patterns. They provide researchers who develop interactions with comprehensive guidance on how humans and AI interact. We further discuss the unexplored interactions and suggest future research directions.
Atmospheric escape from exoplanets: recent observations and theoretical models
2025-02-25
Gopal Hazra
The review aims to give an overview of atmospheric escape processes from exoplanets. I briefly discuss the physics of various escape processes responsible for atmospheric escape across different types of exoplanets. Transmission spectroscopy is one of the major workhorses to observe the escaping atmosphere from exoplanets. I discuss recent observations that established the fact that atmospheric escape is very common in exoplanets, especially during the early phase of their evolution when stellar high-energy radiation (X-ray and extreme ultraviolet, hence XUV) is strong. There are many theoretical efforts/models to understand atmospheric escape processes. Stellar radiation is one of the major drivers of atmospheric escape, but other stellar environments (e.g., stellar flares, stellar winds, stellar coronal mass ejections, and stellar magnetic field) also have control over how the escape process will be affected for a given property of exoplanet, as the planetary properties (e.g., gravity, thermal energy, magnetic field) plays an important role for atmospheric escape. I discuss all governing factors for the atmospheric escape process and corresponding theoretical models in detail. I also discuss how atmospheric escape plays a crucial role in the overall atmospheric evolution of exoplanets and can lead us to understand some features in recently observed exoplanet demographics.
Generative AI-enabled Wireless Communications for Robust Low-Altitude Economy Networking
2025-02-25
Changyuan Zhao, Jiacheng Wang, Ruichen Zhang, Dusit Niyato, Geng Sun, Hongyang Du, Dong In Kim, Abbas Jamalipour
Low-Altitude Economy Networks (LAENets) have emerged as significant enablers of social activities, offering low-altitude services such as the transportation of packages, groceries, and medical supplies. Unlike traditional terrestrial networks, LAENets are characterized by control mechanisms and ever-changing operational factors, which make them more complex and susceptible to vulnerabilities. As applications of LAENet continue to expand, robustness of these systems becomes crucial. In this paper, we investigate a novel application of Generative Artificial Intelligence (GenAI) to improve the robustness of LAENets. We conduct a systematic analysis of robustness requirements for LAENets, complemented by a comprehensive review of robust Quality of Service (QoS) metrics from the wireless physical layer perspective. We then investigate existing GenAI-enabled approaches for robustness enhancement. This leads to our proposal of a novel diffusion-based optimization framework with a Mixture of Expert (MoE)-transformer actor network. In the robust beamforming case study, the proposed framework demonstrates its effectiveness by optimizing beamforming under uncertainties, achieving a more than 44% increase in the worst-case achievable secrecy rate. These findings highlight the significant potential of GenAI in strengthening LAENet robustness.
Interfacial superconductivity discovered in bilayer nickelate heterostructures opens new avenues for effective tunability and control. Here, we report the superconductor-insulator transition in La$_{2.85}$Pr$_{0.15}$Ni$_2$O$_7$/SrLaAlO$_4$ interfaces, with corresponding electronic structures revealed using synchrotron low-temperature X-ray absorption spectroscopy (XAS) and X-ray linear dichroism (XLD). In addition to the valence states of each element within the films determined by XAS, XLD at the O K edge pre-peak reveals hybridizations involving both 2p$_{z}$ and 2p$_{x}$/2p$_{y}$ orbitals, which are concomitantly suppressed upon oxygen loss. Intriguingly, such oxygen loss that eliminates superconductivity, induces a qualitative XLD change at the Ni L$_{2}$ edge, implying significant transformations among 3d$_{x^{2}}$ $_{-y^{2}}$ and 3d$_{z^{2}}$ energy bands near the Fermi level. Our findings establish an element- and orbital-resolved electronic framework for the origin of superconductivity.
To Deepfake or Not to Deepfake: Higher Education Stakeholders' Perceptions and Intentions towards Synthetic Media
2025-02-25
Jasper Roe, Mike Perkins, Klaire Somoray, Dan Miller, Leon Furze
Advances in deepfake technologies, which use generative artificial intelligence (GenAI) to mimic a person's likeness or voice, have led to growing interest in their use in educational contexts. However, little is known about how key stakeholders perceive and intend to use these tools. This study investigated higher education stakeholder perceptions and intentions regarding deepfakes through the lens of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Using a mixed-methods approach combining survey data (n=174) with qualitative interviews, we found that academic stakeholders demonstrated a relatively low intention to adopt these technologies (M=41.55, SD=34.14) and held complex views about their implementation. Quantitative analysis revealed adoption intentions were primarily driven by hedonic motivation, with a gender-specific interaction in price-value evaluations. Qualitative findings highlighted potential benefits of enhanced student engagement, improved accessibility, and reduced workload in content creation, but concerns regarding the exploitation of academic labour, institutional cost-cutting leading to automation, degradation of relationships in education, and broader societal impacts. Based on these findings, we propose a framework for implementing deepfake technologies in higher education that addresses institutional policies, professional development, and equitable resource allocation to thoughtfully integrate AI while maintaining academic integrity and professional autonomy.
Harnessing Multiple Large Language Models: A Survey on LLM Ensemble
2025-02-25
Zhijun Chen, Jingzheng Li, Pengpeng Chen, Zhuoran Li, Kai Sun, Yuankai Luo, Qianren Mao, Dingqi Yang, Hailong Sun, Philip S. Yu
LLM Ensemble -- which involves the comprehensive use of multiple large language models (LLMs), each aimed at handling user queries during downstream inference, to benefit from their individual strengths -- has gained substantial attention recently. The widespread availability of LLMs, coupled with their varying strengths and out-of-the-box usability, has profoundly advanced the field of LLM Ensemble. This paper presents the first systematic review of recent developments in LLM Ensemble. First, we introduce our taxonomy of LLM Ensemble and discuss several related research problems. Then, we provide a more in-depth classification of the methods under the broad categories of "ensemble-before-inference, ensemble-during-inference, ensemble-after-inference'', and review all relevant methods. Finally, we introduce related benchmarks and applications, summarize existing studies, and suggest several future research directions. A curated list of papers on LLM Ensemble is available at https://github.com/junchenzhi/Awesome-LLM-Ensemble.
Biological knowledge bases provide systemically functional pathways of cells or organisms in terms of molecular interaction. However, recognizing more targeted pathways, particularly when incorporating wet-lab experimental data, remains challenging and typically requires downstream biological analyses and expertise. In this paper, we frame this challenge as a solvable graph learning and explaining task and propose a novel pathway inference framework, ExPath, that explicitly integrates experimental data, specifically amino acid sequences (AA-seqs), to classify various graphs (bio-networks) in biological databases. The links (representing pathways) that contribute more to classification can be considered as targeted pathways. Technically, ExPath comprises three components: (1) a large protein language model (pLM) that encodes and embeds AA-seqs into graph, overcoming traditional obstacles in processing AA-seq data, such as BLAST; (2) PathMamba, a hybrid architecture combining graph neural networks (GNNs) with state-space sequence modeling (Mamba) to capture both local interactions and global pathway-level dependencies; and (3) PathExplainer, a subgraph learning module that identifies functionally critical nodes and edges through trainable pathway masks. We also propose ML-oriented biological evaluations and a new metric. The experiments involving 301 bio-networks evaluations demonstrate that pathways inferred by ExPath maintain biological meaningfulness. We will publicly release curated 301 bio-network data soon.
Despite the advancements made in Visual Large Language Models (VLLMs), like text Large Language Models (LLMs), they have limitations in addressing questions that require real-time information or are knowledge-intensive. Indiscriminately adopting Retrieval Augmented Generation (RAG) techniques is an effective yet expensive way to enable models to answer queries beyond their knowledge scopes. To mitigate the dependence on retrieval and simultaneously maintain, or even improve, the performance benefits provided by retrieval, we propose a method to detect the knowledge boundary of VLLMs, allowing for more efficient use of techniques like RAG. Specifically, we propose a method with two variants that fine-tunes a VLLM on an automatically constructed dataset for boundary identification. Experimental results on various types of Visual Question Answering datasets show that our method successfully depicts a VLLM's knowledge boundary based on which we are able to reduce indiscriminate retrieval while maintaining or improving the performance. In addition, we show that the knowledge boundary identified by our method for one VLLM can be used as a surrogate boundary for other VLLMs. Code will be released at https://github.com/Chord-Chen-30/VLLM-KnowledgeBoundary
Late Breaking Results: The Art of Beating the Odds with Predictor-Guided Random Design Space Exploration
2025-02-25
Felix Arnold, Maxence Bouvier, Ryan Amaudruz, Renzo Andri, Lukas Cavigelli
This work introduces an innovative method for improving combinational digital circuits through random exploration in MIG-based synthesis. High-quality circuits are crucial for performance, power, and cost, making this a critical area of active research. Our approach incorporates next-state prediction and iterative selection, significantly accelerating the synthesis process. This novel method achieves up to 14x synthesis speedup and up to 20.94% better MIG minimization on the EPFL Combinational Benchmark Suite compared to state-of-the-art techniques. We further explore various predictor models and show that increased prediction accuracy does not guarantee an equivalent increase in synthesis quality of results or speedup, observing that randomness remains a desirable factor.
FediverseSharing: A Novel Dataset on Cross-Platform Interaction Dynamics between Threads and Mastodon Users
2025-02-25
Ujun Jeong, Alimohammad Beigi, Anique Tahir, Susan Xu Tang, H. Russell Bernard, Huan Liu
Traditional social media platforms, once envisioned as digital town squares, face growing criticism over corporate control, content moderation, and privacy concerns. Events such as Twitter's acquisition(now X) and major policy changes have driven users toward alternative platforms like Mastodon and Threads. However, this diversification has led to user dispersion and fragmented discussions across isolated social media platforms. To address these issues, federation protocols like ActivityPub have been adopted, with Mastodon leading efforts to build decentralized yet interconnected networks. In March 2024, Threads joined this federation by introducing its Fediverse Sharing service, which enables interactions such as posts, replies, and likes between Threads and Mastodon users as if on a unified platform. Building on this development, we introduce FediverseSharing, the first dataset capturing interactions between 20,000+ Threads users and 20,000+ Mastodon users over a ten-month period. This dataset serves as a foundation for studying cross-platform interactions and the impact of federation as previously two separate platforms integrate.
Upper Mid-Band Spectrum for 6G: Vision, Opportunity and Challenges
2025-02-25
Ahmad Bazzi, Roberto Bomfin, Marco Mezzavilla, Sundeep Rangan, Theodore Rappaport, Marwa Chafii
Driven by the pursuit of gigabit-per-second data speeds for future 6G mobile networks, in addition to the support of sensing and artificial intelligence applications, the industry is expanding beyond crowded sub-6 GHz bands with innovative new spectrum allocations. In this paper, we chart a compelling vision for 6G within the frequency range 3 (FR3) spectrum, i.e. $7.125$-$24.25$ $\GHz$, by delving into its key enablers and addressing the multifaceted challenges that lie ahead for these new frequency bands. Here we highlight the physical properties of this \textcolor{black}{never-before} used spectrum by reviewing recent channel measurements for outdoor and indoor environments, including path loss, delay and angular spreads, and material penetration loss, all which offer insights that underpin future 5G/6G wireless communication designs. Building on the fundamental knowledge of the channel properties, we explore FR3 spectrum agility strategies that balance coverage and capacity (e.g. data rate) tradeoffs, while also examining coexistence with incumbent systems, such as satellites, radio astronomy, and earth exploration. Moreover, we discuss the potential of massive multiple-input multiple-output, compact and digital architectures, and evaluate the potential of multiband sensing for FR3 integrated sensing and communications. Finally, we outline 6G standardization features that are likely to emerge from 3GPP radio frame innovations and open radio access network developments.
Scaling LLM Pre-training with Vocabulary Curriculum
2025-02-25
Fangyuan Yu
Modern language models rely on static vocabularies, fixed before pretraining, in contrast to the adaptive vocabulary acquisition observed in human language learning. To bridge this gap, we introduce vocabulary curriculum learning, an approach that improves pretraining efficiency with log-linear scaling gains relative to vocabulary size. Our method alternates between entropy-guided vocabulary expansion and model optimization, enabling models to learn transferable representations across diverse tokenization granularities. This approach naturally gives rise to an optimal computation allocation pattern: longer tokens capture predictable content, while shorter tokens focus on more complex, harder-to-predict contexts. Experiments on small-scale GPT models demonstrate improved scaling efficiency, reinforcing the effectiveness of dynamic tokenization. We release our code to support further research and plan to extend our experiments to larger models and diverse domains.
Movable antenna (MA) has been recognized as a promising technology to enhance the performance of wireless communication and sensing by enabling antenna movement. Such a significant paradigm shift from conventional fixed antennas (FAs) to MAs offers tremendous new opportunities towards realizing more versatile, adaptive and efficient next-generation wireless networks such as 6G. In this paper, we provide a comprehensive tutorial on the fundamentals and advancements in the area of MA-empowered wireless networks. First, we overview the historical development and contemporary applications of MA technologies. Next, to characterize the continuous variation in wireless channels with respect to antenna position and/or orientation, we present new field-response channel models tailored for MAs, which are applicable to narrowband and wideband systems as well as far-field and near-field propagation conditions. Subsequently, we review the state-of-the-art architectures for implementing MAs and discuss their practical constraints. A general optimization framework is then formulated to fully exploit the spatial degrees of freedom (DoFs) in antenna movement for performance enhancement in wireless systems. In particular, we delve into two major design issues for MA systems. First, we address the intricate antenna movement optimization problem for various communication and/or sensing systems to maximize the performance gains achievable by MAs. Second, we deal with the challenging channel acquisition issue in MA systems for reconstructing the channel mapping between arbitrary antenna positions inside the transmitter and receiver regions. Moreover, we show existing prototypes developed for MA-aided communication/sensing and the experimental results based on them. Finally, the extension of MA design to other wireless systems and its synergy with other emerging wireless technologies are discussed.
ASurvey: Spatiotemporal Consistency in Video Generation
2025-02-25
Zhiyu Yin, Kehai Chen, Xuefeng Bai, Ruili Jiang, Juntao Li, Hongdong Li, Jin Liu, Yang Xiang, Jun Yu, Min Zhang
Video generation, by leveraging a dynamic visual generation method, pushes the boundaries of Artificial Intelligence Generated Content (AIGC). Video generation presents unique challenges beyond static image generation, requiring both high-quality individual frames and temporal coherence to maintain consistency across the spatiotemporal sequence. Recent works have aimed at addressing the spatiotemporal consistency issue in video generation, while few literature review has been organized from this perspective. This gap hinders a deeper understanding of the underlying mechanisms for high-quality video generation. In this survey, we systematically review the recent advances in video generation, covering five key aspects: foundation models, information representations, generation schemes, post-processing techniques, and evaluation metrics. We particularly focus on their contributions to maintaining spatiotemporal consistency. Finally, we discuss the future directions and challenges in this field, hoping to inspire further efforts to advance the development of video generation.
Photonic Lightsails: Fast and Stable Propulsion for Interstellar Travel
2025-02-25
Jadon Y. Lin, C. Martijn de Sterke, Ognjen Ilic, Boris T. Kuhlmey
Lightsails are a highly promising spacecraft concept that has attracted interest in recent years due to its potential to travel at near-relativistic speeds. Such speeds, which current conventional crafts cannot reach, offer tantalizing opportunities to probe nearby stellar systems within a human lifetime. Recent advancements in photonics and metamaterials have created novel solutions to challenges in propulsion and stability facing lightsail missions. This review introduces the physical principles underpinning lightsail spacecrafts and discusses how photonics coupled with inverse design substantially enhance lightsail performance compared to plain reflectors. These developments pave the way through a previously inaccessible frontier of space exploration.
Weakly Supervised Pixel-Level Annotation with Visual Interpretability
2025-02-25
Basma Nasir, Tehseen Zia, Muhammad Nawaz, Catarina Moreira
Medical image annotation is essential for diagnosing diseases, yet manual annotation is time-consuming, costly, and prone to variability among experts. To address these challenges, we propose an automated explainable annotation system that integrates ensemble learning, visual explainability, and uncertainty quantification. Our approach combines three pre-trained deep learning models - ResNet50, EfficientNet, and DenseNet - enhanced with XGrad-CAM for visual explanations and Monte Carlo Dropout for uncertainty quantification. This ensemble mimics the consensus of multiple radiologists by intersecting saliency maps from models that agree on the diagnosis while uncertain predictions are flagged for human review. We evaluated our system using the TBX11K medical imaging dataset and a Fire segmentation dataset, demonstrating its robustness across different domains. Experimental results show that our method outperforms baseline models, achieving 93.04% accuracy on TBX11K and 96.4% accuracy on the Fire dataset. Moreover, our model produces precise pixel-level annotations despite being trained with only image-level labels, achieving Intersection over Union IoU scores of 36.07% and 64.7%, respectively. By enhancing the accuracy and interpretability of image annotations, our approach offers a reliable and transparent solution for medical diagnostics and other image analysis tasks.
The transition from isolated systems to integrated solutions has driven the development of space-air-ground integrated networks (SAGIN) as well as the integration of communication and radar sensing functionalities. By leveraging the unique properties of the Terahertz (THz) band, THz joint communication and radar sensing (JCRS) supports high-speed communication and precise sensing, addressing the growing demands of SAGIN for connectivity and environmental awareness. However, most existing THz studies focus on terrestrial and static scenarios, with limited consideration for the dynamic and non-terrestrial environments of SAGIN. In this paper, the THz JCRS techniques for SAGIN are comprehensively investigated. Specifically, propagation characteristics and channel models of THz waves in non-terrestrial environments are analyzed. A link capacity comparison with millimeter-wave, THz, and free-space optical frequency bands is conducted to highlight the advantages of THz frequencies. Moreover, novel JCRS waveform design strategies are presented to achieve mutual merit of communication and radar sensing, while networking strategies are developed to overcome challenges in SAGIN such as high mobility. Furthermore, advancements in THz device technologies, including antennas and amplifiers, are reviewed to assess their roles in enabling practical JCRS implementations.
Demonstrating the utility of quantum algorithms is a long-standing challenge, where quantum machine learning becomes one of the most promising candidate that can be resorted to. In this study, we investigate a quantum neural compressive sensing algorithm for ghost imaging to showcase its utility. The algorithm utilizes the variational quantum circuits to reparameterize the inverse problem of ghost imaging and uses the inductive bias of the physical forward model to perform optimization. To validate the algorithm's effectiveness, we conduct optical ghost imaging experiments, capturing signals from objects at different physical sampling rates and detection signal-to-noise ratios. The experimental results show that our proposed algorithm surpasses conventional methods in both visual appearance and quantitative metrics, achieving state-of-the-art performance. Importantly, we observe that the quantum neural network, guided by prior knowledge of physics, effectively overcomes the challenge of barren plateau in the optimization process. The proposed algorithm demonstrates robustness against various quantum noise levels, making it suitable for near-term quantum devices. Our study leverages physical inductive bias guided variational quantum algorithm, underscoring the potential of quantum computation in tackling a broad range of optimization and inverse problems.
On-device edge learning for IoT data streams: a survey
2025-02-25
Afonso Lourenço, João Rodrigo, João Gama, Goreti Marreiros
This literature review explores continual learning methods for on-device training in the context of neural networks (NNs) and decision trees (DTs) for classification tasks on smart environments. We highlight key constraints, such as data architecture (batch vs. stream) and network capacity (cloud vs. edge), which impact TinyML algorithm design, due to the uncontrolled natural arrival of data streams. The survey details the challenges of deploying deep learners on resource-constrained edge devices, including catastrophic forgetting, data inefficiency, and the difficulty of handling IoT tabular data in open-world settings. While decision trees are more memory-efficient for on-device training, they are limited in expressiveness, requiring dynamic adaptations, like pruning and meta-learning, to handle complex patterns and concept drifts. We emphasize the importance of multi-criteria performance evaluation tailored to edge applications, which assess both output-based and internal representation metrics. The key challenge lies in integrating these building blocks into autonomous online systems, taking into account stability-plasticity trade-offs, forward-backward transfer, and model convergence.
Uncertainty Quantification for LLM-Based Survey Simulations
2025-02-25
Chengpiao Huang, Yuhang Wu, Kaizheng Wang
We investigate the reliable use of simulated survey responses from large language models (LLMs) through the lens of uncertainty quantification. Our approach converts synthetic data into confidence sets for population parameters of human responses, addressing the distribution shift between the simulated and real populations. A key innovation lies in determining the optimal number of simulated responses: too many produce overly narrow confidence sets with poor coverage, while too few yield excessively loose estimates. To resolve this, our method adaptively selects the simulation sample size, ensuring valid average-case coverage guarantees. It is broadly applicable to any LLM, irrespective of its fidelity, and any procedure for constructing confidence sets. Additionally, the selected sample size quantifies the degree of misalignment between the LLM and the target human population. We illustrate our method on real datasets and LLMs.
The use of autonomous underwater vehicles (AUVs) for surveying, mapping, and inspecting unexplored underwater areas plays a crucial role, where maneuverability and power efficiency are key factors for extending the use of these platforms, making six degrees of freedom (6-DOF) holonomic platforms essential tools. Although Proportional-Integral-Derivative (PID) and Model Predictive Control controllers are widely used in these applications, they often require accurate system knowledge, struggle with repeatability when facing payload or configuration changes, and can be time-consuming to fine-tune. While more advanced methods based on Deep Reinforcement Learning (DRL) have been proposed, they are typically limited to operating in fewer degrees of freedom. This paper proposes a novel DRL-based approach for controlling holonomic 6-DOF AUVs using the Truncated Quantile Critics (TQC) algorithm, which does not require manual tuning and directly feeds commands to the thrusters without prior knowledge of their configuration. Furthermore, it incorporates power consumption directly into the reward function. Simulation results show that the TQC High-Performance method achieves better performance to a fine-tuned PID controller when reaching a goal point, while the TQC Energy-Aware method demonstrates slightly lower performance but consumes 30% less power on average.
Phoeni6: a Systematic Approach for Evaluating the Energy Consumption of Neural Networks
2025-02-25
Antônio Oliveira-Filho, Wellington Silva-de-Souza, Carlos Alberto Valderrama Sakuyama, Samuel Xavier-de-Souza
This paper presents Phoeni6, a systematic approach for assessing the energy consumption of neural networks while upholding the principles of fair comparison and reproducibility. Phoeni6 offers a comprehensive solution for managing energy-related data and configurations, ensuring portability, transparency, and coordination during evaluations. The methodology automates energy evaluations through containerized tools, robust database management, and versatile data models. In the first case study, the energy consumption of AlexNet and MobileNet was compared using raw and resized images. Results showed that MobileNet is up to 6.25% more energy-efficient for raw images and 2.32% for resized datasets, while maintaining competitive accuracy levels. In the second study, the impact of image file formats on energy consumption was evaluated. BMP images reduced energy usage by up to 30% compared to PNG, highlighting the influence of file formats on energy efficiency. These findings emphasize the importance of Phoeni6 in optimizing energy consumption for diverse neural network applications and establishing sustainable artificial intelligence practices.
Automated software testing has the potential to enhance efficiency and reliability in software development, yet its adoption remains hindered by challenges in aligning test generation with software requirements. REquirements-Driven Automated Software Testing (REDAST) aims to bridge this gap by leveraging requirements as the foundation for automated test artifact generation. This systematic literature review (SLR) explores the landscape of REDAST by analyzing requirements input, transformation techniques, test outcomes, evaluation methods, and existing limitations. We conducted a comprehensive review of 156 papers selected from six major research databases. Our findings reveal the predominant types, formats, and notations used for requirements in REDAST, the automation techniques employed for generating test artifacts from requirements, and the abstraction levels of resulting test cases. Furthermore, we evaluate the effectiveness of various testing frameworks and identify key challenges such as scalability, automation gaps, and dependency on input quality. This study synthesizes the current state of REDAST research, highlights trends, and proposes future directions, serving as a reference for researchers and practitioners aiming to advance automated software testing.
Based on a nonsmooth coherence condition, we construct and prove the convergence of a forward-backward splitting method that alternates between steps on a fine and a coarse grid. Our focus is a total variation regularised inverse imaging problems, specifically, their dual problems, for which we develop in detail the relevant coarse-grid problems. We demonstrate the performance of our method on total variation denoising and magnetic resonance imaging.
Radiative Transfer Simulations of Ly$α$ Intensity Mapping During Cosmic Reionization Including Sources from Galaxies and the Intergalactic Medium
2025-02-25
Abigail E. Ambrose, Eli Visbal, Mihir Kulkarni, Matthew McQuinn
We present new simulations of Lyman-$\alpha$ (Ly$\alpha$) intensity maps that include Ly$\alpha$ radiative transfer in the intergalactic medium (IGM) and all significant sources of Ly$\alpha$ photons. The sources considered include Ly$\alpha$ directly from galaxies, cooling at the edges of ionized bubbles, recombinations within these bubbles, and reprocessing of galaxy continuum emission in the IGM. We also vary astrophysical parameters including the average neutral fraction of the IGM, the dust absorption of Ly$\alpha$ in galaxies, and the ionizing escape fraction. Previous work has suggested that Ly$\alpha$ intensity mapping can be used to constrain the neutral fraction of the IGM when accounting for radiative transfer in the IGM. When radiative transfer is ignored, direct Ly$\alpha$ emission from galaxies has the highest amplitude of power on all scales. When we include radiative transfer in our simulations, we find continuum emission reprocessed as Ly$\alpha$ is comparable to the Ly$\alpha$ emission directly from galaxies. For high neutral fraction in the IGM, emission from recombinations is comparable to galaxies on large scales. We find that the slope of the power spectrum is sensitive to the neutral fraction of the IGM when radiative transfer is included, suggesting that this may be useful for placing constraints on cosmic reionization. In addition, we find the power of galaxies is decreased across all scales due to dust absorption. We also find the escape fraction must be large for recombinations and bubble edges to contribute significantly to the power. We find the cross power is observable between SPHEREx and a hypothetical galaxy survey with a total signal-to-noise of 4 from $k = 0.035$ Mpc$^{-1}$ to $k = 1$ Mpc$^{-1}$.
Variability of Central Stars of Planetary Nebulae with the Zwicky Transient Facility. II. Long-Timescale Variables including Wide Binary and Late Thermal Pulse Candidates
2025-02-25
Soumyadeep Bhattacharjee, Nicole Reindl, Howard E. Bond, Klaus Werner, Gregory R. Zeimann, David Jones, Nicholas Chornay, Nina Mackensen, S. R. Kulkarni, Ilaria Caiazzo, Jan van Roestel, Antonio C. Rodriguez, Kareem El-Badry, Thomas A. Prince, Ben Rusholme, Russ R. Laher, Roger Smith
In this second paper on the variability survey of central stars of planetary nebulae (CSPNe) using ZTF, we focus on the 11 long-timescale variables with variability timescales ranging from months to years. We also present preliminary analyses based on spectroscopic and/or photometric follow-up observations for six of them. Among them is NGC 6833, which shows a 980 day periodic variability with strange characteristics: 'triangle-shaped' brightening in $r$, $i$, and WISE bands but almost coincidental shallow dips in the $g$-band. We speculate this to be a wide but eccentric binary with the same orbital period. Long-period near-sinusoidal variability was detected in two other systems, NGC 6905 and Kn 26, with periods of 700 days and 230 days, respectively, making them additional wide-binary candidates. The latter also shows a short period at 1.18 hours which can either be from a close inner binary or pulsational origin. We present CTSS 2 and PN K 3-5 which show brightening and significant reddening over the whole ZTF baseline. A stellar model fit to the optical spectrum of CTSS 2 reveals it to be one of the youngest post-AGB CSPN known. Both show high-density emission-line cores. These appear to be late thermal pulse candidates, currently evolving towards the AGB phase, though alternative explanations are possible. We then present recent HST/COS ultraviolet spectroscopy of the known wide-binary candidate LoTr 1 showing that the hot star is a spectroscopic twin of the extremely hot white dwarf in UCAC2 46706450. We think that the long photometric period of 11 years is the binary orbital period. Finally, we briefly discuss the ZTF light curves of the remaining variables, namely Tan 2, K 3-20, WHTZ 3, Kn J1857+3931, and IPHAS J1927+0814. With these examples, we present the effectiveness of the von Neumann statistics and Pearson Skew-based metric space in searching for long-timescale variables.
We propose a range-separated hybrid exchange-correlation functional to calculate solid-state material properties. The functional mixes Hartree-Fock exchange with the semilocal exchange of the meta-generalized gradient approximation (meta-GGA) and the fraction of Hartree-Fock exchange is determined from the dielectric function. First-principles calculations and comparison with other meta-GGA approximations show that the functional leads to reasonably good performance for the band gap and optical properties. We also show that the present functional also successfully resolves the well-known ``band gap problem'' of narrow gap Cu-based semiconductors, such as Cu3SbSe4 and Cu3AsSe4, where, in general, a considerably large band inversion energy leads to a ``false'' negative or metallic band gap for all other methods. Furthermore, reasonable accuracy for the occupied d-bands and transition energies is also obtained for bulk solids. Thus, overall, our results demonstrate the predictive power of range-separated meta-GGA hybrid functionals for quantum materials simulations.
In recent years, deep learning methods have been extensively developed for inverse imaging problems (IIPs), encompassing supervised, self-supervised, and generative approaches. Most of these methods require large amounts of labeled or unlabeled training data to learn effective models. However, in many practical applications, such as medical image reconstruction, extensive training datasets are often unavailable or limited. A significant milestone in addressing this challenge came in 2018 with the work of Ulyanov et al., which introduced the Deep Image Prior (DIP)--the first training-data-free neural network method for IIPs. Unlike conventional deep learning approaches, DIP requires only a convolutional neural network, the noisy measurements, and a forward operator. By leveraging the implicit regularization of deep networks initialized with random noise, DIP can learn and restore image structures without relying on external datasets. However, a well-known limitation of DIP is its susceptibility to overfitting, primarily due to the over-parameterization of the network. In this tutorial paper, we provide a comprehensive review of DIP, including a theoretical analysis of its training dynamics. We also categorize and discuss recent advancements in DIP-based methods aimed at mitigating overfitting, including techniques such as regularization, network re-parameterization, and early stopping. Furthermore, we discuss approaches that combine DIP with pre-trained neural networks, present empirical comparison results against data-centric methods, and highlight open research questions and future directions.
CAPOS: The bulge Cluster APOGEE Survey VII: First detailed chemical analysis of NGC 6316
2025-02-25
Heinz Frelijj, Danilo González-Díaz, Douglas Geisler, Sandro Villanova, Cesar Muñoz, Christian Moni Bidin
As part of the bulge Cluster APOgee Survey (CAPOS), high-resolution, high Signal-to-Noise Ratio Near-Infrared spectroscopy, we aim to conduct the most robust chemical study to date for NGC 6316, deriving abundances for a number of elements with a variety of nucleosynthetic origins, most of which have never been studied before in this cluster. We use the Brussels Automatic Code for Characterizing High accuracy Spectra (BACCHUS) with atmospheric parameters photometrically obtained in order to determine, for the first time, abundances for C, N, O, Mg, Al, Si, P, K, Ca, Ti, V, Cr, Mn, Fe, Ni and Ce for this cluster. We obtained a mean metallicity [Fe/H] = -0.87 +- 0.02, finding no indication of an intrinsic metallicity spread. Our metallicity agrees with the most recent values from other studies, revising earlier values that were ~0.5 dex metal-richer. With this new value, this cluster, long believed to be a member of the classical metal-rich group of bulge GCs around -0.5, now falls in the dominant bulge globular cluster peak around [Fe/H] = -1. The cluster presents a clear C-N anticorrelation. We also found a [{\alpha}/Fe] = 0.3 +- 0.02. Our abundances show similar behaviour to other in situ globular clusters with comparable metallicity. We obtained E(B-V) = 0.71 and (M-m)_0 = 15.32 +- 0.05 by isochrone fitting, in good agreement with the recent determinations from other works. We derive an overall metallicity [M/H] = -0.6 +- 0.05 by isochrone fitting, in agreement with our abundance determination. According to the mean [Mg/Fe] and [Al/Fe] abundances from first population stars, NGC 6316 is an in-situ globular cluster, in accordance with various dynamical classifications.
"Frog-eyes" in Astronomy: Monitoring Binary Radial Velocity Variations Through A Pair of Narrow-Band Filters
2025-02-25
Chuanjie Zheng, Yang Huang, Jifeng Liu, Hongrui Gu, Hong Wu, Youjun Lu, Yongkang Sun, Henggeng Han, Song Wang, Timothy C. Beers, Kai Xiao, Zhirui Li, Boweng Zhang, Yongna Mao, Zhengyang Li, Hangxin Ji
Spectroscopic observations are a crucial step in driving major discoveries in the era of time-domain surveys. However, the pace of current spectroscopic surveys is increasingly unable to meet the demands of rapidly advancing large-scale time-domain surveys. To address this issue, we propose the ``Frog-eyes" system, which employs a pair of narrow-band filters: one positioned near a strong absorption line to capture signals from Doppler shifts, and the other placed on the adjacent continuum to monitor intrinsic variations. The combination of observations from the two filters enables the extraction of radial velocity (RV) curves from a large sample of binary stars, and is particularly efficient for single-lined binaries (SB1), using photometric techniques. Comprehensive mock simulations on SB1 demonstrate that the binary orbital parameters can be precisely measured from the extracted RV curves for binary systems where the primary star has an effective temperature greater than 6000 K. With a typical ground-based photometric precision of approximately 0.3%, the uncertainties in the derived semi-amplitude K and eccentricity e are less than 10% and 0.1, respectively, for binary systems with K $\ge$ 30 km/s. These encouraging results are further validated by real observations of the hot subdwarf-white dwarf binary system HD 265435, using a non-specialized ``Frog-eyes" system installed on the Chinese 2.16m telescope. Once this system is properly installed on large-field-of-view survey telescopes, the rate of acquiring RV curves for binaries will approach their detection rate in leading time-domain photometric surveys.
Topolectrical circuits $-$ recent experimental advances and developments
2025-02-25
Haydar Sahin, Mansoor B. A. Jalil, Ching Hua Lee
Metamaterials serve as versatile platforms for demonstrating condensed matter physics and non-equilibrium phenomena, with electrical circuits emerging as a particularly compelling medium. This review highlights recent advances in the experimental circuit realizations of topological, non-Hermitian, non-linear, Floquet and other notable phenomena. Initially performed mostly with passive electrical components, topolectrical circuits have evolved to incorporate active elements such as operational amplifiers and analog multipliers that combine to form negative impedance converters, complex phase elements, high-frequency temporal modulators and self-feedback mechanisms. This review provides a summary of these contemporary studies and discusses the broader potential of electrical circuits in physics.
Transfer Learning for Transient Classification: From Simulations to Real Data and ZTF to LSST
2025-02-25
Rithwik Gupta, Daniel Muthukrishna
Machine learning has become essential for automated classification of astronomical transients, but current approaches face significant limitations: classifiers trained on simulations struggle with real data, models developed for one survey cannot be easily applied to another, and new surveys require prohibitively large amounts of labelled training data. These challenges are particularly pressing as we approach the era of the Vera Rubin Observatory's Legacy Survey of Space and Time (LSST), where existing classification models will need to be retrained using LSST observations. We demonstrate that transfer learning can overcome these challenges by repurposing existing models trained on either simulations or data from other surveys. Starting with a model trained on simulated Zwicky Transient Facility (ZTF) light curves, we show that transfer learning reduces the amount of labelled real ZTF transients needed by 75\% while maintaining equivalent performance to models trained from scratch. Similarly, when adapting ZTF models for LSST simulations, transfer learning achieves 95\% of the baseline performance while requiring only 30\% of the training data. These findings have significant implications for the early operations of LSST, suggesting that reliable automated classification will be possible soon after the survey begins, rather than waiting months or years to accumulate sufficient training data.
ELemental abundances of Planets and brown dwarfs Imaged around Stars (ELPIS): II. The Jupiter-like Inhomogeneous Atmosphere of the First Directly Imaged Planetary-Mass Companion 2MASS 1207 b
2025-02-25
Zhoujian Zhang, Paul Mollière, Jonathan J. Fortney, Mark S. Marley
2MASS 1207 b, the first directly imaged planetary-mass companion, has been instrumental in advancing our understanding of exoplanets and brown dwarfs over the past 20 years. We have performed extensive atmospheric retrieval analyses of 2MASS 1207 b's JWST/NIRSpec spectrum using petitRADTRANS and a new atmospheric inhomogeneity framework, which characterizes homogeneous atmospheres, patchy clouds, cloud-free hot spots, or the combination of patchy clouds and spots. Among 24 retrieval runs with various assumptions, the most statistically preferred model corresponds to the patchy cloud scheme, with Teff$=1174^{+4}_{-3}$ K, log(g)=$3.62^{+0.03}_{-0.02}$ dex, and R$=1.399^{+0.008}_{-0.010}$ R$_{\rm Jup}$, along with near-solar atmospheric compositions of [M/H]$=-0.05\pm0.03$ dex and C/O$=0.440\pm0.012$. This model suggests ~9% of 2MASS 1207 b's atmosphere is covered by thin iron clouds, producing L-dwarf-like spectra, while the remaining 91% consists of thick silicate and iron clouds, emitting blackbody-like spectra. These thin-cloud and thick-cloud regions resemble Jupiter's belts and zones, respectively; this scenario is consistently supported by other retrieval runs incorporating inhomogeneous atmospheres. We demonstrate that 2MASS 1207 b's weak CO absorption can be explained by the veiling effects of patchy thick clouds; the absence of 3.3um CH4 absorption is attributed to its hot thermal structure, which naturally leads to a CO-dominant, CH4-deficient atmosphere. The retrieved atmospheric models also match the observed variability amplitudes of 2MASS 1207 b. Our analysis reveals that the inferred atmospheric properties show significant scatter in less statistically preferred retrieval runs but converge to consistent values among the preferred ones. This underscores the importance of exploring diverse assumptions in retrievals to avoid biased interpretations of atmospheres and formation.
Applications of Statistical Field Theory in Deep Learning
2025-02-25
Zohar Ringel, Noa Rubin, Edo Mor, Moritz Helias, Inbar Seroussi
Deep learning algorithms have made incredible strides in the past decade yet due to the complexity of these algorithms, the science of deep learning remains in its early stages. Being an experimentally driven field, it is natural to seek a theory of deep learning within the physics paradigm. As deep learning is largely about learning functions and distributions over functions, statistical field theory, a rich and versatile toolbox for tackling complex distributions over functions (fields) is an obvious choice of formalism. Research efforts carried out in the past few years have demonstrated the ability of field theory to provide useful insights on generalization, implicit bias, and feature learning effects. Here we provide a pedagogical review of this emerging line of research.
Behavioural Predictors that Influence Digital Legacy Management Intentions among Individuals in South Africa
2025-02-25
Jordan Young, Ayanda Pekane, Popyeni Kautondokwa
An emerging phenomenon, digital legacy management explores the management of digital data individuals accumulate throughout their lifetime. With the integration of digital systems and data into people's daily lives, it becomes crucial to understand the intricacies of managing data to eventually form one's digital legacy. This can be understood by investigating the significance of behavioral predictors in shaping digital legacy management. The objective of this study is to explore how behavioral predictors influence the intentions of individuals in South Africa towards managing their digital legacy. This entailed: Investigating the impact of attitude, subjective norms, and perceived behavioral control on these intentions. Exploring the perceived usefulness of digital legacy management systems. Understanding the implications of response cost and task-technology fit on individuals' inclinations towards digital legacy planning. Data were collected (n = 203 valid responses) from South African residents using an online survey and analyzed using partial least squares structural equation analysis (PLS-SEM). Results indicate that attitudes, peer opinions, personal resources, and skills are significant positive influences on digital legacy management intention. Recognizing and understanding these behavioral predictors is key when developing region-specific and culturally sensitive digital legacy management tools, awareness campaigns, and policies. Furthermore, it could pave the way for more tailored strategies, ensuring effective transfer of post-mortem data, reducing potential conflicts, and providing clarity when dealing with post-mortem data.
Co-Translational mRNA Decay in Plants: Recent advances and future directions
2025-02-25
Jean-Marc Deragon, Rémy Merret
Tight regulation of messenger RNA (mRNA) stability is essential to ensure accurate gene expression in response to developmental and environmental cues. mRNA stability is controlled by mRNA decay pathways, which have traditionally been proposed to occur independently of translation. However, the recent discovery of a co-translational mRNA decay pathway (also known as CTRD) reveals that mRNA translation and decay can be coupled. While being translated, a mRNA can be targeted for degradation. This pathway was first described in yeast and rapidly identified in several plant species. This review explores recent advances in our understanding of CTRD in plants, emphasizing its regulation and its importance for development and stress response. The different metrics used to assess CTRD activity are also presented. Furthermore, this review outlines future directions to explore the importance of mRNA decay in maintaining mRNA homeostasis in plants.
As machine learning technologies advance rapidly across various domains, concerns over data privacy and model security have grown significantly. These challenges are particularly pronounced when models are trained and deployed on cloud platforms or third-party servers due to the computational resource limitations of users' end devices. In response, zero-knowledge proof (ZKP) technology has emerged as a promising solution, enabling effective validation of model performance and authenticity in both training and inference processes without disclosing sensitive data. Thus, ZKP ensures the verifiability and security of machine learning models, making it a valuable tool for privacy-preserving AI. Although some research has explored the verifiable machine learning solutions that exploit ZKP, a comprehensive survey and summary of these efforts remain absent. This survey paper aims to bridge this gap by reviewing and analyzing all the existing Zero-Knowledge Machine Learning (ZKML) research from June 2017 to December 2024. We begin by introducing the concept of ZKML and outlining its ZKP algorithmic setups under three key categories: verifiable training, verifiable inference, and verifiable testing. Next, we provide a comprehensive categorization of existing ZKML research within these categories and analyze the works in detail. Furthermore, we explore the implementation challenges faced in this field and discuss the improvement works to address these obstacles. Additionally, we highlight several commercial applications of ZKML technology. Finally, we propose promising directions for future advancements in this domain.
Mind the gap: addressing data gaps and assessing noise mismodeling in LISA
2025-02-24
Ollie Burke, Sylvain Marsat, Jonathan R. Gair, Michael L. Katz
Due to the sheer complexity of the Laser Interferometer Space Antenna (LISA) space mission, data gaps arising from instrumental irregularities and/or scheduled maintenance are unavoidable. Focusing on merger-dominated massive black hole binary signals, we test the appropriateness of the Whittle-likelihood on gapped data in a variety of cases. From first principles, we derive the likelihood valid for gapped data in both the time and frequency domains. Cheap-to-evaluate proxies to p-p plots are derived based on a Fisher-based formalism, and verified through Bayesian techniques. Our tools allow to predict the altered variance in the parameter estimates that arises from noise mismodeling, as well as the information loss represented by the broadening of the posteriors. The result of noise mismodeling with gaps is sensitive to the characteristics of the noise model, with strong low-frequency (red) noise and strong high-frequency (blue) noise giving statistically significant fluctuations in recovered parameters. We demonstrate that the introduction of a tapering window reduces statistical inconsistency errors, at the cost of less precise parameter estimates. We also show that the assumption of independence between inter-gap segments appears to be a fair approximation even if the data set is inherently coherent. However, if one instead assumes fictitious correlations in the data stream, when the data segments are actually independent, then the resultant parameter recoveries could be inconsistent with the true parameters. The theoretical and numerical practices that are presented in this work could readily be incorporated into global-fit pipelines operating on gapped data.
Introducing Visual Perception Token into Multimodal Large Language Model
2025-02-24
Runpeng Yu, Xinyin Ma, Xinchao Wang
To utilize visual information, Multimodal Large Language Model (MLLM) relies on the perception process of its vision encoder. The completeness and accuracy of visual perception significantly influence the precision of spatial reasoning, fine-grained understanding, and other tasks. However, MLLM still lacks the autonomous capability to control its own visual perception processes, for example, selectively reviewing specific regions of an image or focusing on information related to specific object categories. In this work, we propose the concept of Visual Perception Token, aiming to empower MLLM with a mechanism to control its visual perception processes. We design two types of Visual Perception Tokens, termed the Region Selection Token and the Vision Re-Encoding Token. MLLMs autonomously generate these tokens, just as they generate text, and use them to trigger additional visual perception actions. The Region Selection Token explicitly identifies specific regions in an image that require further perception, while the Vision Re-Encoding Token uses its hidden states as control signals to guide additional visual perception processes. Extensive experiments demonstrate the advantages of these tokens in handling spatial reasoning, improving fine-grained understanding, and other tasks. On average, the introduction of Visual Perception Tokens improves the performance of a 2B model by 23.6\%, increasing its score from 0.572 to 0.708, and even outperforms a 7B parameter model by 13.4\% (from 0.624). Please check out our repo https://github.com/yu-rp/VisualPerceptionToken
Achieving human-level intelligence requires refining the transition from the fast, intuitive System 1 to the slower, more deliberate System 2 reasoning. While System 1 excels in quick, heuristic decisions, System 2 relies on logical reasoning for more accurate judgments and reduced biases. Foundational Large Language Models (LLMs) excel at fast decision-making but lack the depth for complex reasoning, as they have not yet fully embraced the step-by-step analysis characteristic of true System 2 thinking. Recently, reasoning LLMs like OpenAI's o1/o3 and DeepSeek's R1 have demonstrated expert-level performance in fields such as mathematics and coding, closely mimicking the deliberate reasoning of System 2 and showcasing human-like cognitive abilities. This survey begins with a brief overview of the progress in foundational LLMs and the early development of System 2 technologies, exploring how their combination has paved the way for reasoning LLMs. Next, we discuss how to construct reasoning LLMs, analyzing their features, the core methods enabling advanced reasoning, and the evolution of various reasoning LLMs. Additionally, we provide an overview of reasoning benchmarks, offering an in-depth comparison of the performance of representative reasoning LLMs. Finally, we explore promising directions for advancing reasoning LLMs and maintain a real-time \href{https://github.com/zzli2022/Awesome-Slow-Reason-System}{GitHub Repository} to track the latest developments. We hope this survey will serve as a valuable resource to inspire innovation and drive progress in this rapidly evolving field.
Measuring anisotropies in the PTA band with cross-correlations
2025-02-24
Giulia Cusin, Cyril Pitrou, Martin Pijnenburg, Alberto Sesana
The astrophysical gravitational wave background in the nanohertz (nHz) band is expected to be primarily composed of the superposition of signals from binaries of supermassive black holes. The spatial discreteness of these sources introduces shot noise, which, in certain regimes, would overwhelm efforts to measure the anisotropy of the gravitational wave background. In this work, we explicitly demonstrate, starting from first principles, that cross-correlating a gravitational wave background map with a sufficiently dense galaxy survey can mitigate this issue. This approach could potentially reveal underlying properties of the gravitational wave background that would otherwise remain obscured. We quantify the shot noise level and show that cross-correlating the gravitational wave background with a galaxy catalog improves by more than two orders of magnitude the prospects for a first detection of the background anisotropy by a gravitational wave observatory operating in the nHz frequency range, provided it has sufficient sensitivity.
Advances in multiparameter quantum sensing and metrology
2025-02-24
Luca Pezzè, Augusto Smerzi
Recent years have witnessed a growing interest in understating the limitations imposed by quantum noise in precision measurements and devising techniques to reduce it. The attention is currently turning to the simultaneously estimation of several parameters of interest, driven by its promising potential across a wide range of sensing applications as well as fueled by experimental progress in various optical and atomic platforms. Here, we provide a comprehensive overview of key research directions in multiparameter quantum sensing and metrology, highlighting both opportunities and challenges. We introduce the basic framework, discuss ultimate sensitivity bounds, optimal measurement strategies, and the role of quantum incompatibility, showing important differences with respect to single-parameter estimation. Additionally, we discuss emerging experimental implementations in distributed quantum sensing, including cutting-edge optimization techniques. This review aims to bridge the gap between theory and experiments, paving the way for the next-generation of quantum sensors and their integration with other quantum technologies.
FIG: Forward-Inverse Generation for Low-Resource Domain-specific Event Detection
2025-02-24
Tanmay Parekh, Yuxuan Dong, Lucas Bandarkar, Artin Kim, I-Hung Hsu, Kai-Wei Chang, Nanyun Peng
Event Detection (ED) is the task of identifying typed event mentions of interest from natural language text, which benefits domain-specific reasoning in biomedical, legal, and epidemiological domains. However, procuring supervised data for thousands of events for various domains is a laborious and expensive task. To this end, existing works have explored synthetic data generation via forward (generating labels for unlabeled sentences) and inverse (generating sentences from generated labels) generations. However, forward generation often produces noisy labels, while inverse generation struggles with domain drift and incomplete event annotations. To address these challenges, we introduce FIG, a hybrid approach that leverages inverse generation for high-quality data synthesis while anchoring it to domain-specific cues extracted via forward generation on unlabeled target data. FIG further enhances its synthetic data by adding missing annotations through forward generation-based refinement. Experimentation on three ED datasets from diverse domains reveals that FIG outperforms the best baseline achieving average gains of 3.3% F1 and 5.4% F1 in the zero-shot and few-shot settings respectively. Analyzing the generated trigger hit rate and human evaluation substantiates FIG's superior domain alignment and data quality compared to existing baselines.
Continuous Integration Practices in Machine Learning Projects: The Practitioners` Perspective
2025-02-24
João Helis Bernardo, Daniel Alencar da Costa, Filipe Roseiro Cogo, Sérgio Queiróz de Medeiros, Uirá Kulesza
Continuous Integration (CI) is a cornerstone of modern software development. However, while widely adopted in traditional software projects, applying CI practices to Machine Learning (ML) projects presents distinctive characteristics. For example, our previous work revealed that ML projects often experience longer build durations and lower test coverage rates compared to their non-ML counterparts. Building on these quantitative findings, this study surveys 155 practitioners from 47 ML projects to investigate the underlying reasons for these distinctive characteristics through a qualitative perspective. Practitioners highlighted eight key differences, including test complexity, infrastructure requirements, and build duration and stability. Common challenges mentioned by practitioners include higher project complexity, model training demands, extensive data handling, increased computational resource needs, and dependency management, all contributing to extended build durations. Furthermore, ML systems' non-deterministic nature, data dependencies, and computational constraints were identified as significant barriers to effective testing. The key takeaway from this study is that while foundational CI principles remain valuable, ML projects require tailored approaches to address their unique challenges. To bridge this gap, we propose a set of ML-specific CI practices, including tracking model performance metrics and prioritizing test execution within CI pipelines. Additionally, our findings highlight the importance of fostering interdisciplinary collaboration to strengthen the testing culture in ML projects. By bridging quantitative findings with practitioners' insights, this study provides a deeper understanding of the interplay between CI practices and the unique demands of ML projects, laying the groundwork for more efficient and robust CI strategies in this domain.
Bridging Gaps in Natural Language Processing for Yorùbá: A Systematic Review of a Decade of Progress and Prospects
2025-02-24
Toheeb A. Jimoh, Tabea De Wille, Nikola S. Nikolov
Natural Language Processing (NLP) is becoming a dominant subset of artificial intelligence as the need to help machines understand human language looks indispensable. Several NLP applications are ubiquitous, partly due to the myriads of datasets being churned out daily through mediums like social networking sites. However, the growing development has not been evident in most African languages due to the persisting resource limitation, among other issues. Yor\`ub\'a language, a tonal and morphologically rich African language, suffers a similar fate, resulting in limited NLP usage. To encourage further research towards improving this situation, this systematic literature review aims to comprehensively analyse studies addressing NLP development for Yor\`ub\'a, identifying challenges, resources, techniques, and applications. A well-defined search string from a structured protocol was employed to search, select, and analyse 105 primary studies between 2014 and 2024 from reputable databases. The review highlights the scarcity of annotated corpora, limited availability of pre-trained language models, and linguistic challenges like tonal complexity and diacritic dependency as significant obstacles. It also revealed the prominent techniques, including rule-based methods, among others. The findings reveal a growing body of multilingual and monolingual resources, even though the field is constrained by socio-cultural factors such as code-switching and desertion of language for digital usage. This review synthesises existing research, providing a foundation for advancing NLP for Yor\`ub\'a and in African languages generally. It aims to guide future research by identifying gaps and opportunities, thereby contributing to the broader inclusion of Yor\`ub\'a and other under-resourced African languages in global NLP advancements.
How Scientists Use Large Language Models to Program
2025-02-24
Gabrielle O'Brien
Scientists across disciplines write code for critical activities like data collection and generation, statistical modeling, and visualization. As large language models that can generate code have become widely available, scientists may increasingly use these models during research software development. We investigate the characteristics of scientists who are early-adopters of code generating models and conduct interviews with scientists at a public, research-focused university. Through interviews and reviews of user interaction logs, we see that scientists often use code generating models as an information retrieval tool for navigating unfamiliar programming languages and libraries. We present findings about their verification strategies and discuss potential vulnerabilities that may emerge from code generation practices unknowingly influencing the parameters of scientific analyses.
2025-02-24
Giulia Piccirilli, Matteo Zennaro, Carlos García-García, David Alonso
Standard cosmological weak lensing analyses using cosmic shear are inevitably sensitive to small-scale, non-linear clustering from low-redshift structures. The need to adequately model the clustering of matter on this non-linear regime, accounting for both gravitational and baryonic effects, adds significant uncertainty to weak lensing studies, particularly in the context of near-future Stage-IV datasets. In this paper, inspired by previous work on so-called ``nulling'' techniques, we present a general method that selects the linear combinations of a given tomographic cosmic shear dataset that are least sensitive to small-scale non-linearities, by essentially suppressing the contribution from low-redshift structures. We apply this method to the latest public cosmic shear data from the Dark Energy Survey, DES-Y3, that corresponds to 3 years of observation, and show: a) that a large fraction of the signal is dominated by the single mode that is most affected by non-linear scales, and b) that removing this mode leads to a $\sim1\sigma$ upwards shift in the preferred value of $S_8\equiv\sigma_8\sqrt{\Omega_M/0.3}$, alleviating the tension with current CMB data. However, the removal of the most contaminated mode also results in a significant increase in the statistical uncertainties. Taking this into account, we find this shift to be compatible with a random fluctuation caused by removing this most-contaminated mode at the $\sim1.4\sigma$ level. We also show that this technique may be used by future Stage-IV surveys to mitigate the sensitivity of the final constraints to baryonic effects, trading precision for robustness.
A Pristine-UNIONS view on the Galaxy: Kinematics of the distant spur feature of the Sagittarius stream traced by Blue Horizontal Branch stars
2025-02-24
M. Bayer, E. Starkenburg, G. F. Thomas, N. Martin, A. Helmi, A. Byström, T. de Boer, E. Fernández Alvar, S. Gwyn, R. Ibata, P. Jablonka, G. Kordopatis, T. Matsuno, A. W. McConnachie, G. E. Medina, R. Sánchez-Janssen, F. Sestito
Providing a detailed picture of the Sagittarius (Sgr) stream offers important constraints on the build-up of the Galactic halo as well as its gravitational potential at large radii. While several attempts have been made to model the structure of the Sgr stream, no model has yet been able to match all the features observed for the stream. Moreover, for several of these features, observational characterisation of their properties is rather limited, particularly at large distances. The aim of this work is to investigate the kinematics of the Sgr stream outermost spur feature using blue horizontal branch (BHB) stars. Candidate BHB stars were selected by combining two approaches; one capitalising on Pan-STARRS1 3$\Pi$ griz and u photometry taken as part of UNIONS, the other using Pristine Survey CaHK and SDSS ugr photometry. Follow-up optical spectra are obtained using ESO/VLT/FORS2 to confirm their BHB nature and obtain line-of-sight (LOS) velocities. Of our 25 candidates, 20 stars can be confirmed as bona fide BHB stars. Their LOS velocities, together with the 3D positions of these stars qualitatively match well with Sgr model predictions and trace the outer apocentre of the trailing arm and its spur feature very nicely. The quantitative offsets that are found between our data and the different models can be used to provide information about the Galactic gravitational potential at large distances. We present a first, tentative, analysis in this direction, showing that the model of Vasiliev et al. (2021) would provide better agreement with our observations if the enclosed mass of the Milky Way within 100 kpc were lowered to $(5.3\!\pm\!0.4)\!\times\!10^{11}$ M$_\odot$ (versus $(5.6\!\pm\!0.4)\!\times\!10^{11}$ M$_\odot$). Our selection of BHB stars provides a new view on the outermost structure in 3D positions and LOS velocities of the Sgr debris.
Strategic mining attacks, such as selfish mining, exploit blockchain consensus protocols by deviating from honest behavior to maximize rewards. Markov Decision Process (MDP) analysis faces scalability challenges in modern digital economics, including blockchain. To address these limitations, reinforcement learning (RL) provides a scalable alternative, enabling adaptive strategy optimization in complex dynamic environments. In this survey, we examine RL's role in strategic mining analysis, comparing it to MDP-based approaches. We begin by reviewing foundational MDP models and their limitations, before exploring RL frameworks that can learn near-optimal strategies across various protocols. Building on this analysis, we compare RL techniques and their effectiveness in deriving security thresholds, such as the minimum attacker power required for profitable attacks. Expanding the discussion further, we classify consensus protocols and propose open challenges, such as multi-agent dynamics and real-world validation. This survey highlights the potential of reinforcement learning (RL) to address the challenges of selfish mining, including protocol design, threat detection, and security analysis, while offering a strategic roadmap for researchers in decentralized systems and AI-driven analytics.
A Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE) XVII. Statistical properties of individual HII regions in unperturbed systems
2025-02-24
A. Boselli, M. Fossati, Y. Roehlly, P. Amram, S. Boissier, M. Boquien, J. Braine, P. Cote, J. C. Cuillandre, L. Ferrarese, G. Gavazzi, S. Gwyn, G. Hensler, G. Trinchieri, A. Zavagno
The Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE) is a blind narrow-band Halpha+[NII] imaging survey of the Virgo cluster carried out with MegaCam at the CFHT telescope. The survey provides deep narrow-band images for 385 galaxies hosting star forming HII regions. We identify individual HII regions and measure their main physical properties such as Halpha luminosity, equivalent diameter, and electron density with the purpose of deriving standard relations as reference for future local and high-z studies of HII regions in star forming systems in different environments. For this purpose we use a complete sample of ~ 13.000 HII regions of luminosity L(Halpha)>= 10^37 erg s^-1 to derive the main statistical properties of HII regions in unperturbed systems, identified as those galaxies with a normal HI gas content (64 objects). These are the composite Halpha luminosity function, equivalent diameter and electron density distribution, and luminosity-size relation. We also derive the main scaling relations between several parameters representative of the HII regions properties (total number, luminosity of the first ranked regions, fraction of the diffuse component, best fit parameters of the Schechter luminosity function measured for individual galaxies) and those characterising the properties of the host galaxies (stellar mass, star formation rate and specific star formation rate, stellar mass and star formation rate surface density, metallicity, molecular-to-atomic gas ratio, total gas-to-dust mass ratio). We briefly discuss the results of this analysis and their implications in the study of the star formation process in galaxy discs.
Additional jamming transition in 2D bidisperse granular packings
2025-02-24
Juan C. Petit, Matthias Sperl
We present a jamming diagram for 2D bidisperse granular systems, capturing two distinct jamming transitions. The first occurs as large particles form a jammed structure, while the second, emerging at a critical small-particle concentration, $X_{\mathrm{S}}^{*} \approx 0.21$, and size ratio, $\delta^{*} \approx 0.25$, involves small particles jamming into the voids of the existing large-particle structure upon further compression. Below this threshold, small particles fill voids within the large-particle network, increasing packing density. Beyond this point, excess small particles disrupt efficient packing, resulting in looser structures. The alignment of these findings with 3D results suggests a general geometric mechanism governing the second jamming transition.
Observed circulation trends in boreal summer linked to two spatially distinct teleconnection patterns
2025-02-24
Tamara Happé, Chiem van Straaten, Raed Hamed, Fabio D'Andrea, Dim Coumou
Various regions in the Northern Hemispheric midlatitudes have seen pronounced trends in upper-atmosphere summer circulation and surface temperature extremes over recent decades. Several of these regional trends lie outside the range of historic CMIP6 model simulations, and they might constitute a joined dynamic response that is missed by climate models. Here, we examine if the regional trends in circulation are indeed part of a coherent circumglobal wave pattern. Using ERA5 reanalysis data and CMIP6 historical simulations, we find that the observed upper-atmospheric circulation trends consist of at least two separate regional signatures: a US-Atlantic and a Eurasian trend pattern. The circulation trend in these two regions can explain up to 30% of the observed regional temperature trends. The circulation trend in the CMIP6 multi-model-mean does not resemble the observed trend pattern and is much weaker overall. Some individual CMIP6 models do show a resemblance to the observed pattern in ERA5, although still weak with a maximum pattern correlation of 0.47. The pattern correlation is higher for the two individual regions, reaching a maximum of 0.78. We show that both regional wave patterns in ERA5 are associated with distinct sea surface temperature and outgoing longwave radiation anomalies in the three weeks leading up to the atmospheric configuration, resembling known teleconnection patterns. CMIP6 models appear to lack these tropical-extra-tropical teleconnections. Our findings highlight the limitations of CMIP6 models in reproducing teleconnections and their associated regional imprint, creating deep uncertainty for regional climate projections on decadal to multi-decadal timescales.
Neurological disorders represent significant global health challenges, driving the advancement of brain signal analysis methods. Scalp electroencephalography (EEG) and intracranial electroencephalography (iEEG) are widely used to diagnose and monitor neurological conditions. However, dataset heterogeneity and task variations pose challenges in developing robust deep learning solutions. This review systematically examines recent advances in deep learning approaches for EEG/iEEG-based neurological diagnostics, focusing on applications across 7 neurological conditions using 46 datasets. We explore trends in data utilization, model design, and task-specific adaptations, highlighting the importance of pre-trained multi-task models for scalable, generalizable solutions. To advance research, we propose a standardized benchmark for evaluating models across diverse datasets to enhance reproducibility. This survey emphasizes how recent innovations can transform neurological diagnostics and enable the development of intelligent, adaptable healthcare solutions.
Motion-Robust T2* Quantification from Gradient Echo MRI with Physics-Informed Deep Learning
2025-02-24
Hannah Eichhorn, Veronika Spieker, Kerstin Hammernik, Elisa Saks, Lina Felsner, Kilian Weiss, Christine Preibisch, Julia A. Schnabel
Purpose: T2* quantification from gradient echo magnetic resonance imaging is particularly affected by subject motion due to the high sensitivity to magnetic field inhomogeneities, which are influenced by motion and might cause signal loss. Thus, motion correction is crucial to obtain high-quality T2* maps. Methods: We extend our previously introduced learning-based physics-informed motion correction method, PHIMO, by utilizing acquisition knowledge to enhance the reconstruction performance for challenging motion patterns and increase PHIMO's robustness to varying strengths of magnetic field inhomogeneities across the brain. We perform comprehensive evaluations regarding motion detection accuracy and image quality for data with simulated and real motion. Results: Our extended version of PHIMO outperforms the learning-based baseline methods both qualitatively and quantitatively with respect to line detection and image quality. Moreover, PHIMO performs on-par with a conventional state-of-the-art motion correction method for T2* quantification from gradient echo MRI, which relies on redundant data acquisition. Conclusion: PHIMO's competitive motion correction performance, combined with a reduction in acquisition time by over 40% compared to the state-of-the-art method, make it a promising solution for motion-robust T2* quantification in research settings and clinical routine.
With the advent of surveys like $Euclid$ and $Vera$ $C.$ $Rubin$, astrophysicists will have access to both deep, high-resolution images, and multi-band images. However, these two conditions are not simultaneously available in any single dataset. It is therefore vital to devise image deconvolution algorithms that exploit the best of the two worlds and that can jointly analyze datasets spanning a range of resolutions and wavelengths. In this work, we introduce a novel multi-band deconvolution technique aimed at improving the resolution of ground-based astronomical images by leveraging higher-resolution space-based observations. The method capitalizes on the fortunate fact that the $Vera$ $C.$ $Rubin$ $r$-, $i$-, and $z$-bands lie within the $Euclid$ $VIS$ band. The algorithm jointly deconvolves all the data to turn the $r$-, $i$-, and $z$-band $Vera$ $C.$ $Rubin$ images to the resolution of $Euclid$ by enabling us to leverage the correlations between the different bands. We also investigate the performance of deep learning-based denoising with DRUNet to further improve the results. We illustrate the effectiveness of our method in terms of resolution and morphology recovery, flux preservation, and generalization to different noise levels. This approach extends beyond the specific $Euclid$-$Rubin$ combination, offering a versatile solution to improve the resolution of ground-based images in multiple photometric bands by jointly using any space-based images with overlapping filters.
Long context is an important topic in Natural Language Processing (NLP), running through the development of NLP architectures, and offers immense opportunities for Large Language Models (LLMs) giving LLMs the lifelong learning potential akin to humans. Unfortunately, the pursuit of a long context is accompanied by numerous obstacles. Nevertheless, long context remains a core competitive advantage for LLMs. In the past two years, the context length of LLMs has achieved a breakthrough extension to millions of tokens. Moreover, the research on long-context LLMs has expanded from length extrapolation to a comprehensive focus on architecture, infrastructure, training, and evaluation technologies. Inspired by the symphonic poem, Thus Spake Zarathustra, we draw an analogy between the journey of extending the context of LLM and the attempts of humans to transcend its mortality. In this survey, We will illustrate how LLM struggles between the tremendous need for a longer context and its equal need to accept the fact that it is ultimately finite. To achieve this, we give a global picture of the lifecycle of long-context LLMs from four perspectives: architecture, infrastructure, training, and evaluation, showcasing the full spectrum of long-context technologies. At the end of this survey, we will present 10 unanswered questions currently faced by long-context LLMs. We hope this survey can serve as a systematic introduction to the research on long-context LLMs.
Diffusion Models for Tabular Data: Challenges, Current Progress, and Future Directions
2025-02-24
Zhong Li, Qi Huang, Lincen Yang, Jiayang Shi, Zhao Yang, Niki van Stein, Thomas Bäck, Matthijs van Leeuwen
In recent years, generative models have achieved remarkable performance across diverse applications, including image generation, text synthesis, audio creation, video generation, and data augmentation. Diffusion models have emerged as superior alternatives to Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) by addressing their limitations, such as training instability, mode collapse, and poor representation of multimodal distributions. This success has spurred widespread research interest. In the domain of tabular data, diffusion models have begun to showcase similar advantages over GANs and VAEs, achieving significant performance breakthroughs and demonstrating their potential for addressing unique challenges in tabular data modeling. However, while domains like images and time series have numerous surveys summarizing advancements in diffusion models, there remains a notable gap in the literature for tabular data. Despite the increasing interest in diffusion models for tabular data, there has been little effort to systematically review and summarize these developments. This lack of a dedicated survey limits a clear understanding of the challenges, progress, and future directions in this critical area. This survey addresses this gap by providing a comprehensive review of diffusion models for tabular data. Covering works from June 2015, when diffusion models emerged, to December 2024, we analyze nearly all relevant studies, with updates maintained in a \href{https://github.com/Diffusion-Model-Leiden/awesome-diffusion-models-for-tabular-data}{GitHub repository}. Assuming readers possess foundational knowledge of statistics and diffusion models, we employ mathematical formulations to deliver a rigorous and detailed review, aiming to promote developments in this emerging and exciting area.
2025-02-24
Yinchuan Li, Xinyu Shao, Jianping Zhang, Haozhi Wang, Leo Maxime Brunswic, Kaiwen Zhou, Jiqian Dong, Kaiyang Guo, Xiu Li, Zhitang Chen, Jun Wang, Jianye Hao
In recent years, the exceptional performance of generative models in generative tasks has sparked significant interest in their integration into decision-making processes. Due to their ability to handle complex data distributions and their strong model capacity, generative models can be effectively incorporated into decision-making systems by generating trajectories that guide agents toward high-reward state-action regions or intermediate sub-goals. This paper presents a comprehensive review of the application of generative models in decision-making tasks. We classify seven fundamental types of generative models: energy-based models, generative adversarial networks, variational autoencoders, normalizing flows, diffusion models, generative flow networks, and autoregressive models. Regarding their applications, we categorize their functions into three main roles: controllers, modelers and optimizers, and discuss how each role contributes to decision-making. Furthermore, we examine the deployment of these models across five critical real-world decision-making scenarios. Finally, we summarize the strengths and limitations of current approaches and propose three key directions for advancing next-generation generative directive models: high-performance algorithms, large-scale generalized decision-making models, and self-evolving and adaptive models.
Sliding ferroelectric control of unconventional magnetism in stacked bilayers
2025-02-24
Yongqian Zhu, Mingqiang Gu, Yuntian Liu, Xiaobing Chen, Yuhui Li, Shixuan Du, Qihang Liu
The control of unconventional magnetism, which displays an antiferromagnetic configuration with ferromagnetism-like properties, has drawn intense attention for advancing antiferromagnetic spintronics. Here, through symmetry analysis, we propose a general stacking rule, characterized by a connection operator linking two stacked bilayers, for controlling unconventional magnetism via sliding ferroelectricity. Such rule enables the simultaneous switching of both electric polarization and nonrelativistic spin splitting or anomalous Hall effect in altermagnets, a class of collinear unconventional magnets. By comprehensively surveying the 80 layer groups, we identify all the stacking orders that allow for such two types of simultaneous switching. Combined with first-principles calculations, we demonstrate the sliding ferroelectric control of spin polarization and anomalous Hall effect in the altermagnetic AgF2 bilayer. Our work provides a symmetry strategy for achieving ferroelectric control of unconventional magnetism in bilayer systems and opens avenues for exploring new types of magnetoelectric coupling.
Where are the earliest stars relics in the simulated Milky Way analogues?
2025-02-24
Hang Yang, Liang Gao, Qi Guo, Haining Li, Shi Shao, Gang Zhao
Using 6 Milky Way analogues with two different numerical resolutions from the Auriga simulation, we investigate the total mass, spatial distribution and kinematics of the earliest stars relics in the Milky Way at $z=0$. These relics (second generation stars) formed over a wide redshift range, from about $z=22$ to $z=4$, with an average formation redshift of $z \sim 10.0$, and comprise about $2\times10^{-5}$ of the entire galactic stellar population. The disk and bulge components host only a small fraction of these relics, contributing less than $12$ percent in total. The stellar halo, in particular the outer stellar halo of which galactic radius $r>30$ kpc, hosts the largest fraction (about 46 percent on average), with an average of one relic star for per $4,000$ to $10,000$ stars, making it a promising region for observational searches. Additionally, around $18$ percent of the earliest stars relics are found in satellite galaxies, with smaller and older satellite galaxies tending to contain a higher proportion of these stars. Thus, low-mass and early-formed satellite galaxies are also ideal targets for finding such relics, although some satellite galaxies may lack them entirely. The spatial distribution and kinematics of these stars show good numerical convergence across different simulation resolutions. Our results provide valuable guidance for searches of the earliest stars relics and offer insights for interpreting findings from ongoing and future stellar archaeology surveys.
Conditional Diffusion-Flow models for generating 3D cosmic density fields: applications to f(R) cosmologies
2025-02-24
Julieth Katherine Riveros, Paola Saavedra, Hector J. Hortua, Jorge Enrique Garcia-Farieta, Ivan Olier
Next-generation galaxy surveys promise unprecedented precision in testing gravity at cosmological scales. However, realising this potential requires accurately modelling the non-linear cosmic web. We address this challenge by exploring conditional generative modelling to create 3D dark matter density fields via score-based (diffusion) and flow-based methods. Our results demonstrate the power of diffusion models to accurately reproduce the matter power spectra and bispectra, even for unseen configurations. They also offer a significant speed-up with slightly reduced accuracy, when flow-based reconstructing the probability distribution function, but they struggle with higher-order statistics. To improve conditional generation, we introduce a novel multi-output model to develop feature representations of the cosmological parameters. Our findings offer a powerful tool for exploring deviations from standard gravity, combining high precision with reduced computational cost, thus paving the way for more comprehensive and efficient cosmological analyses
Automatically Evaluating the Paper Reviewing Capability of Large Language Models
2025-02-24
Hyungyu Shin, Jingyu Tang, Yoonjoo Lee, Nayoung Kim, Hyunseung Lim, Ji Yong Cho, Hwajung Hong, Moontae Lee, Juho Kim
Peer review is essential for scientific progress, but it faces challenges such as reviewer shortages and growing workloads. Although Large Language Models (LLMs) show potential for providing assistance, research has reported significant limitations in the reviews they generate. While the insights are valuable, conducting the analysis is challenging due to the considerable time and effort required, especially given the rapid pace of LLM developments. To address the challenge, we developed an automatic evaluation pipeline to assess the LLMs' paper review capability by comparing them with expert-generated reviews. By constructing a dataset consisting of 676 OpenReview papers, we examined the agreement between LLMs and experts in their strength and weakness identifications. The results showed that LLMs lack balanced perspectives, significantly overlook novelty assessment when criticizing, and produce poor acceptance decisions. Our automated pipeline enables a scalable evaluation of LLMs' paper review capability over time.
Robust treatment of finite nuclear size reduces CKM unitarity deficit
2025-02-24
M Gorchtein, V Katyal, B Ohayon, B K Sahoo, CY Seng
We revisit the extraction of the $|V_{ud}|$ CKM matrix element from the superallowed transition decay rate of $^{26m}$Al$\rightarrow$$^{26}$Mg, focusing on finite nuclear size effects. We show that the dependence of the decay rate of $^{26m}$Al on its charge radius is 4 times greater than previously believed, so it must be determined with high precision. However, for a short-lived isotope of an odd $Z$ element such as $^{26m}$Al, radius extraction relies on challenging many-body atomic calculations. We performed the needed calculations, finding an excellent agreement with previous ones, which used a highly different methodology. This sets a new standard for the reliability of isotope shift factor calculations in many-electron systems. The $\mathcal{F}t$ value obtained from our analysis is lower by $2.2\,\sigma$ than the corresponding value in the previous critical survey, resulting in an increase in $|V_{ud}|^2$ by $0.9\,\sigma$. Adopting $|V_{ud}|$ from this decay alone reduces the CKM unitarity deficit by one standard deviation, irrespective of the choice of $|V_{us}|
This study examines the determinants of the spousal age gap (SAG) in India, utilizing data from the 61st and 68th rounds of the National Sample Survey (NSSO). We employ regression analysis, including instrumental variables, to address selection bias and account for unobservable factors. We hypothesize an inverted U-shaped relationship between educational assortative mating and SAG, where, keeping the husband's education constant at the graduation level, the SAG first widens and then narrows as the wife's education level increases from primary to postgraduate. This pattern is shaped by distinct socio-economic factors across rural and urban settings. In rural India, increasing prosperity, changes in family structure, and educational hypergamy contribute to a wider age gap, with the influence of bride squeeze further exacerbating this disparity. Conversely, in urban areas, while the growth of white-collar jobs initially contributed to a narrowing of the SAG in 2004-05, this trend did not persist by 2011-12. Specifically, the influence of income on SAG becomes nonlinear, showing declining trends beyond the 7th income quantile, reflecting limited marriage mobility opportunities for females and hinting at a possible threat to the institution of marriage among the urban upper class. To the best of our knowledge, this is the first study to provide empirical evidence on how specific social, economic, and cultural dynamics influence the spousal age gap in Indian society. This increasing and persistent spousal age gap has significant implications for the treatment of women, power dynamics, and violence within marriage.
Quantum effects in charge control of semiconductor surfaces as elucidated by ab initio calculations a review
2025-02-24
Stanisław Krukowski, Pawel Kempisty, Pawel Strak
Recent progress in the investigations of the charge role in semiconductor surfaces is reviewed. This is based on ab intio investigations. That include elucidation of Coulomb interaction of the separated subsystems, such as slab copies or far distant adsorbate and the slab, the bonding in the bulk and on the surface, standard and resonant. The quantum nature of the bonding leads to emergence of the external surface dipole, which was well recognized prior to these investigations. The role of the external dipole layer in the thermalization of the adsorbate is proposed and formulated. The internal dipole charge well known to exist at the surface were supplemented by new finding including the simulation of these dipole fields within slab model, pinning the Fermi level at the surface and the role of bulk and surface charge. of the bonding states. This could also occur at the activated complex point where the energy of these states could be increased into the vicinity of Fermi level or even higher so that this affects the energy barrier for diffusion. Therefore the explicit incorporation of quantum effects in the charge role in the semiconductor surfaces changes their properties considerably as described in this review.
A survey of datasets for computer vision in agriculture
2025-02-24
Nico Heider, Lorenz Gunreben, Sebastian Zürner, Martin Schieck
In agricultural research, there has been a recent surge in the amount of Computer Vision (CV) focused work. But unlike general CV research, large high-quality public datasets are sparsely available. This can be partially attributed to the high variability between different agricultural tasks, crops and environments as well as the complexity of data collection, but it is also influenced by the reticence to publish datasets by many authors. This, as well as the lack of a widely used agricultural data repository, are impactful factors that hinder research in applied CV for agriculture as well as the usage of agricultural data in general-purpose CV research. In this survey, we provide a large number of high-quality datasets of images taken on fields. Overall, we find 45 datasets, which are listed in this paper as well as in an online catalog on the project website: https://smartfarminglab.github.io/field_dataset_survey/.
A Systematic Survey of Automatic Prompt Optimization Techniques
2025-02-24
Kiran Ramnath, Kang Zhou, Sheng Guan, Soumya Smruti Mishra, Xuan Qi, Zhengyuan Shen, Shuai Wang, Sangmin Woo, Sullam Jeoung, Yawei Wang, Haozhu Wang, Han Ding, Yuzhe Lu, Zhichao Xu, Yun Zhou, Balasubramaniam Srinivasan, Qiaojing Yan, Yueyan Chen, Haibo Ding, Panpan Xu, Lin Lee Cheong
Since the advent of large language models (LLMs), prompt engineering has been a crucial step for eliciting desired responses for various Natural Language Processing (NLP) tasks. However, prompt engineering remains an impediment for end users due to rapid advances in models, tasks, and associated best practices. To mitigate this, Automatic Prompt Optimization (APO) techniques have recently emerged that use various automated techniques to help improve the performance of LLMs on various tasks. In this paper, we present a comprehensive survey summarizing the current progress and remaining challenges in this field. We provide a formal definition of APO, a 5-part unifying framework, and then proceed to rigorously categorize all relevant works based on their salient features therein. We hope to spur further research guided by our framework.
TESSELLATE: Piecing Together the Variable Sky With TESS
2025-02-24
Hugh Roxburgh, Ryan Ridden-Harper, Andrew Moore, Clarinda Montilla, Brayden Leicester, Zachary G. Lane, James Freeburn, Armin Rest, Michele T. Bannister, Andrew R. Ridden-Harper, Lancia Hubley, Qinan Wang, Rebekah Hounsell, Jeff Cooke, Dave A. Coulter, Michael M. Fausnaugh
We present TESSELLATE, a dedicated pipeline for performing an untargeted search documenting all variable phenomena captured by the TESS space telescope. Building on the TESSreduce difference imaging pipeline, TESSELLATE extracts calibrated and reduced photometric data for every full frame image in the TESS archive. Using this data, we systematically identify transient, variable and non-sidereal signals across timescales ranging from minutes to weeks. The high cadence and wide field of view of TESS enables us to conduct a comprehensive search of the entire sky to a depth of ~17 $m_i$. Based on the volumetric rates for known fast transients, we expect there to be numerous Fast Blue Optical Transients and Gamma Ray Burst afterglows present in the existing TESS dataset. Beyond transients, TESSELLATE can also identify new variable stars and exoplanet candidates, and recover known asteroids. We classify events using machine learning techniques and the work of citizen scientists via the Zooniverse Cosmic Cataclysms project. Finally, we introduce the TESSELLATE Sky Survey: a complete, open catalog of the variable sky observed by TESS.
Culture-TRIP: Culturally-Aware Text-to-Image Generation with Iterative Prompt Refinment
2025-02-24
Suchae Jeong, Inseong Choi, Youngsik Yun, Jihie Kim
Text-to-Image models, including Stable Diffusion, have significantly improved in generating images that are highly semantically aligned with the given prompts. However, existing models may fail to produce appropriate images for the cultural concepts or objects that are not well known or underrepresented in western cultures, such as `hangari' (Korean utensil). In this paper, we propose a novel approach, Culturally-Aware Text-to-Image Generation with Iterative Prompt Refinement (Culture-TRIP), which refines the prompt in order to improve the alignment of the image with such culture nouns in text-to-image models. Our approach (1) retrieves cultural contexts and visual details related to the culture nouns in the prompt and (2) iteratively refines and evaluates the prompt based on a set of cultural criteria and large language models. The refinement process utilizes the information retrieved from Wikipedia and the Web. Our user survey, conducted with 66 participants from eight different countries demonstrates that our proposed approach enhances the alignment between the images and the prompts. In particular, C-TRIP demonstrates improved alignment between the generated images and underrepresented culture nouns. Resource can be found at https://shane3606.github.io/Culture-TRIP.
ReFocus: Reinforcing Mid-Frequency and Key-Frequency Modeling for Multivariate Time Series Forecasting
2025-02-24
Guoqi Yu, Yaoming Li, Juncheng Wang, Xiaoyu Guo, Angelica I. Aviles-Rivero, Tong Yang, Shujun Wang
Recent advancements have progressively incorporated frequency-based techniques into deep learning models, leading to notable improvements in accuracy and efficiency for time series analysis tasks. However, the Mid-Frequency Spectrum Gap in the real-world time series, where the energy is concentrated at the low-frequency region while the middle-frequency band is negligible, hinders the ability of existing deep learning models to extract the crucial frequency information. Additionally, the shared Key-Frequency in multivariate time series, where different time series share indistinguishable frequency patterns, is rarely exploited by existing literature. This work introduces a novel module, Adaptive Mid-Frequency Energy Optimizer, based on convolution and residual learning, to emphasize the significance of mid-frequency bands. We also propose an Energy-based Key-Frequency Picking Block to capture shared Key-Frequency, which achieves superior inter-series modeling performance with fewer parameters. A novel Key-Frequency Enhanced Training strategy is employed to further enhance Key-Frequency modeling, where spectral information from other channels is randomly introduced into each channel. Our approach advanced multivariate time series forecasting on the challenging Traffic, ECL, and Solar benchmarks, reducing MSE by 4%, 6%, and 5% compared to the previous SOTA iTransformer. Code is available at this GitHub Repository: https://github.com/Levi-Ackman/ReFocus.
CORAL: Learning Consistent Representations across Multi-step Training with Lighter Speculative Drafter
2025-02-24
Yepeng Weng, Dianwen Mei, Huishi Qiu, Xujie Chen, Li Liu, Jiang Tian, Zhongchao Shi
Speculative decoding is a powerful technique that accelerates Large Language Model (LLM) inference by leveraging a lightweight speculative draft model. However, existing designs suffers in performance due to misalignment between training and inference. Recent methods have tried to solve this issue by adopting a multi-step training strategy, but the complex inputs of different training steps make it harder for the draft model to converge. To address this, we propose CORAL, a novel framework that improves both accuracy and efficiency in speculative drafting. CORAL introduces Cross-Step Representation Alignment, a method that enhances consistency across multiple training steps, significantly improving speculative drafting performance. Additionally, we identify the LM head as a major bottleneck in the inference speed of the draft model. We introduce a weight-grouping mechanism that selectively activates a subset of LM head parameters during inference, substantially reducing the latency of the draft model. We evaluate CORAL on three LLM families and three benchmark datasets, achieving speedup ratios of 2.50x-4.07x, outperforming state-of-the-art methods such as EAGLE-2 and HASS. Our results demonstrate that CORAL effectively mitigates training-inference misalignment and delivers significant speedup for modern LLMs with large vocabularies.
Large Language Models (LLMs) have recently demonstrated remarkable performance in tasks such as Retrieval-Augmented Generation (RAG) and autonomous AI agent workflows. Yet, when faced with large sets of unstructured documents requiring progressive exploration, analysis, and synthesis, such as conducting literature survey, existing approaches often fall short. We address this challenge -- termed Progressive Document Investigation -- by introducing Graphy, an end-to-end platform that automates data modeling, exploration and high-quality report generation in a user-friendly manner. Graphy comprises an offline Scrapper that transforms raw documents into a structured graph of Fact and Dimension nodes, and an online Surveyor that enables iterative exploration and LLM-driven report generation. We showcase a pre-scrapped graph of over 50,000 papers -- complete with their references -- demonstrating how Graphy facilitates the literature-survey scenario. The demonstration video can be found at https://youtu.be/uM4nzkAdGlM.
The increasing complexity and scale of modern telecommunications networks demand intelligent automation to enhance efficiency, adaptability, and resilience. Agentic AI has emerged as a key paradigm for intelligent communications and networking, enabling AI-driven agents to perceive, reason, decide, and act within dynamic networking environments. However, effective decision-making in telecom applications, such as network planning, management, and resource allocation, requires integrating retrieval mechanisms that support multi-hop reasoning, historical cross-referencing, and compliance with evolving 3GPP standards. This article presents a forward-looking perspective on generative information retrieval-inspired intelligent communications and networking, emphasizing the role of knowledge acquisition, processing, and retrieval in agentic AI for telecom systems. We first provide a comprehensive review of generative information retrieval strategies, including traditional retrieval, hybrid retrieval, semantic retrieval, knowledge-based retrieval, and agentic contextual retrieval. We then analyze their advantages, limitations, and suitability for various networking scenarios. Next, we present a survey about their applications in communications and networking. Additionally, we introduce an agentic contextual retrieval framework to enhance telecom-specific planning by integrating multi-source retrieval, structured reasoning, and self-reflective validation. Experimental results demonstrate that our framework significantly improves answer accuracy, explanation consistency, and retrieval efficiency compared to traditional and semantic retrieval methods. Finally, we outline future research directions.
Functional magnetic resonance imaging (fMRI) based image reconstruction plays a pivotal role in decoding human perception, with applications in neuroscience and brain-computer interfaces. While recent advancements in deep learning and large-scale datasets have driven progress, challenges such as data scarcity, cross-subject variability, and low semantic consistency persist. To address these issues, we introduce the concept of fMRI-to-Image Learning (fMRI2Image) and present the first systematic review in this field. This review highlights key challenges, categorizes methodologies such as fMRI signal encoding, feature mapping, and image generator. Finally, promising research directions are proposed to advance this emerging frontier, providing a reference for future studies.
Fair Foundation Models for Medical Image Analysis: Challenges and Perspectives
2025-02-24
Dilermando Queiroz, Anderson Carlos, André Anjos, Lilian Berton
Ensuring equitable Artificial Intelligence (AI) in healthcare demands systems that make unbiased decisions across all demographic groups, bridging technical innovation with ethical principles. Foundation Models (FMs), trained on vast datasets through self-supervised learning, enable efficient adaptation across medical imaging tasks while reducing dependency on labeled data. These models demonstrate potential for enhancing fairness, though significant challenges remain in achieving consistent performance across demographic groups. Our review indicates that effective bias mitigation in FMs requires systematic interventions throughout all stages of development. While previous approaches focused primarily on model-level bias mitigation, our analysis reveals that fairness in FMs requires integrated interventions throughout the development pipeline, from data documentation to deployment protocols. This comprehensive framework advances current knowledge by demonstrating how systematic bias mitigation, combined with policy engagement, can effectively address both technical and institutional barriers to equitable AI in healthcare. The development of equitable FMs represents a critical step toward democratizing advanced healthcare technologies, particularly for underserved populations and regions with limited medical infrastructure and computational resources.
A Review of Memory Wall for Neuromorphic Computing
2025-02-24
Dexter Le, Baran Arig, Murat Isik, I. Can Dikmen, Teoman Karadag
This paper reviews memory technologies used in Field-Programmable Gate Arrays (FPGAs) for neuromorphic computing, a brain-inspired approach transforming artificial intelligence with improved efficiency and performance. It focuses on the essential role of memory in FPGA-based neuromorphic systems, evaluating memory types such as Static Random-Access Memory (SRAM), Dynamic Random-Access Memory (DRAM), High-Bandwidth Memory (HBM), and emerging non-volatile memories like Resistive RAM (ReRAM) and Phase-Change Memory (PCM). These technologies are analyzed based on latency, bandwidth, power consumption, density, and scalability to assess their suitability for storing and processing neural network models and synaptic weights. The review provides a comparative analysis of their strengths and limitations, supported by case studies illustrating real-world implementations and performance outcomes. This review offers insights to guide researchers and practitioners in selecting and optimizing memory technologies, enhancing the performance and energy efficiency of FPGA-based neuromorphic platforms, and advancing applications in artificial intelligence.
Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances
2025-02-24
Yaozu Wu, Dongyuan Li, Yankai Chen, Renhe Jiang, Henry Peng Zou, Liancheng Fang, Zhen Wang, Philip S. Yu
Autonomous Driving Systems (ADSs) are revolutionizing transportation by reducing human intervention, improving operational efficiency, and enhancing safety. Large Language Models (LLMs), known for their exceptional planning and reasoning capabilities, have been integrated into ADSs to assist with driving decision-making. However, LLM-based single-agent ADSs face three major challenges: limited perception, insufficient collaboration, and high computational demands. To address these issues, recent advancements in LLM-based multi-agent ADSs have focused on improving inter-agent communication and cooperation. This paper provides a frontier survey of LLM-based multi-agent ADSs. We begin with a background introduction to related concepts, followed by a categorization of existing LLM-based approaches based on different agent interaction modes. We then discuss agent-human interactions in scenarios where LLM-based agents engage with humans. Finally, we summarize key applications, datasets, and challenges in this field to support future research (https://anonymous.4open.science/r/LLM-based_Multi-agent_ADS-3A5C/README.md).
Large language models (LLMs) present novel opportunities in public opinion research by predicting survey responses in advance during the early stages of survey design. Prior methods steer LLMs via descriptions of subpopulations as LLMs' input prompt, yet such prompt engineering approaches have struggled to faithfully predict the distribution of survey responses from human subjects. In this work, we propose directly fine-tuning LLMs to predict response distributions by leveraging unique structural characteristics of survey data. To enable fine-tuning, we curate SubPOP, a significantly scaled dataset of 3,362 questions and 70K subpopulation-response pairs from well-established public opinion surveys. We show that fine-tuning on SubPOP greatly improves the match between LLM predictions and human responses across various subpopulations, reducing the LLM-human gap by up to 46% compared to baselines, and achieves strong generalization to unseen surveys and subpopulations. Our findings highlight the potential of survey-based fine-tuning to improve opinion prediction for diverse, real-world subpopulations and therefore enable more efficient survey designs. Our code is available at https://github.com/JosephJeesungSuh/subpop.
Detectability of Solar Rotation Period Across Various Wavelengths
2025-02-24
Valeriy Vasilyev, Timo Reinhold, Alexander I. Shapiro, Theodosios Chatzistergos, Natalie Krivova, Sami K. Solanki
The light curves of old G-dwarfs obtained in the visible and near-infrared wavelength ranges are highly irregular. This significantly complicates the detectability of the rotation periods of stars similar to the Sun in large photometric surveys, such as Kepler and TESS. In this study, we show that light curves collected in the ultraviolet wavelength range are much more suitable for measuring rotation periods. Motivated by the observation that the Sun's rotational period is clearly discernible in the UV part of the spectrum, we study the wavelength dependence of the rotational period detectability. We employ the Spectral and Total Solar Irradiance Reconstructions model, SATIRE-S, to characterize the detectability of the solar rotation period across various wavelengths using the autocorrelation technique. We find that at wavelengths above 400 nm, the probability of detecting the rotation period of the Sun observed at a random phase of its activity cycle is approximately 20\%. The probability increases to 80\% at wavelengths shorter than 400 nm. These findings underscore the importance of ultraviolet stellar photometry.
Refactoring Detection in C++ Programs with RefactoringMiner++
2025-02-24
Benjamin Ritz, Aleksandar Karakaš, Denis Helic
Commits often involve refactorings -- behavior-preserving code modifications aiming at software design improvements. Refactoring operations pose a challenge to code reviewers, as distinguishing them from behavior-altering changes is often not a trivial task. Accordingly, research on automated refactoring detection tools has flourished over the past two decades, however, the majority of suggested tools is limited to Java projects. In this work, we present RefactoringMiner++, a refactoring detection tool based on the current state of the art: RefactoringMiner 3. While the latter focuses exclusively on Java, our tool is -- to the best of our knowledge -- the first publicly available refactoring detection tool for C++ projects. RefactoringMiner's thorough evaluation provides confidence in our tool's performance. In addition, we test RefactoringMiner++ on a small seeded dataset and demonstrate the tool's capability in a short demo involving both refactorings and behavior-altering changes. A screencast demonstrating our tool can be found at https://cloud.tugraz.at/index.php/s/oCzmjfFSaBxNZoe.
On the usability of generative AI: Human generative AI
2025-02-24
Anna Ravera, Cristina Gena
Generative AI systems are transforming content creation, but their usability remains a key challenge. This paper examines usability factors such as user experience, transparency, control, and cognitive load. Common challenges include unpredictability and difficulties in fine-tuning outputs. We review evaluation metrics like efficiency, learnability, and satisfaction, highlighting best practices from various domains. Improving interpretability, intuitive interfaces, and user feedback can enhance usability, making generative AI more accessible and effective.
Evacuation decision prediction is critical for efficient and effective wildfire response by helping emergency management anticipate traffic congestion and bottlenecks, allocate resources, and minimize negative impacts. Traditional statistical methods for evacuation decision prediction fail to capture the complex and diverse behavioral logic of different individuals. In this work, for the first time, we introduce FLARE, short for facilitating LLM for advanced reasoning on wildfire evacuation decision prediction, a Large Language Model (LLM)-based framework that integrates behavioral theories and models to streamline the Chain-of-Thought (CoT) reasoning and subsequently integrate with memory-based Reinforcement Learning (RL) module to provide accurate evacuation decision prediction and understanding. Our proposed method addresses the limitations of using existing LLMs for evacuation behavioral predictions, such as limited survey data, mismatching with behavioral theory, conflicting individual preferences, implicit and complex mental states, and intractable mental state-behavior mapping. Experiments on three post-wildfire survey datasets show an average of 20.47% performance improvement over traditional theory-informed behavioral models, with strong cross-event generalizability. Our complete code is publicly available at https://github.com/SusuXu-s-Lab/FLARE
Search for new Galactic Wolf-Rayet stars using Gaia DR3. I. Candidate selection and the follow-up of the bright sample
2025-02-24
Lionel Mulato, Jaroslav Merc, Stéphane Charbonnel, Olivier Garde, Pascal Le Dû, Thomas Petit
Gaia DR3, released in June 2022, included low-resolution XP spectra that have been used for the classification of various types of emission-line objects through machine-learning techniques. The Gaia Extended Stellar Parametrizer for Emission-Line Stars (ESP-ELS) algorithm identified 565 sources as potential Wolf-Rayet (WR) stars. Over half of them were already known as WR stars in the Milky Way and Magellanic Clouds. This study aimed to utilize Gaia DR3 data to identify new Galactic WR stars. We extracted all sources classified as WC or WN type stars by the ESP-ELS algorithm from the Gaia catalog. By applying judicious 2MASS color selection criteria, leveraging Gaia H$\alpha$ measurements, and filtering out objects already cataloged in various databases, we selected 37 bright candidates ($G \leq $ 16 mag) and 22 faint candidates ($G > $ 16 mag). Spectroscopic follow-up observations of these candidates were conducted using the 2SPOT facilities in Chile and France, as well as 1-m C2PU's Epsilon telescope at the Calern Observatory. This paper focuses on the brighter sample. Among the 37 targets, we confirmed 17 and 16 new Galactic WC and WN type WR stars, respectively. Three of them were recently reported as new WR stars in an independent study. The Gaia mission provides a valuable resource for uncovering WR stars missed in earlier surveys. While this work concentrated on a relatively small starting sample provided by the ESP-ELS algorithm, our findings highlight the potential for refining selection criteria to identify additional candidates not included in the outputs of the algorithm. Furthermore, the observation program underscores the utility of small telescopes in acquiring initial spectral data for sources with magnitudes up to $G \sim 16$ mag.
A Fokker-Planck-Based Loss Function that Bridges Dynamics with Density Estimation
2025-02-24
Zhixin Lu, Łukasz Kuśmierz, Stefan Mihalas
We have derived a novel loss function from the Fokker-Planck equation that links dynamical system models with their probability density functions, demonstrating its utility in model identification and density estimation. In the first application, we show that this loss function can enable the extraction of dynamical parameters from non-temporal datasets, including timestamp-free measurements from steady non-equilibrium systems such as noisy Lorenz systems and gene regulatory networks. In the second application, when coupled with a density estimator, this loss facilitates density estimation when the dynamic equations are known. For density estimation, we propose a density estimator that integrates a Gaussian Mixture Model with a normalizing flow model. It simultaneously estimates normalized density, energy, and score functions from both empirical data and dynamics. It is compatible with a variety of data-based training methodologies, including maximum likelihood and score matching. It features a latent space akin to a modern Hopfield network, where the inherent Hopfield energy effectively assigns low densities to sparsely populated data regions, addressing common challenges in neural density estimators. Additionally, this Hopfield-like energy enables direct and rapid data manipulation through the Concave-Convex Procedure (CCCP) rule, facilitating tasks such as denoising and clustering. Our work demonstrates a principled framework for leveraging the complex interdependencies between dynamics and density estimation, as illustrated through synthetic examples that clarify the underlying theoretical intuitions.
The DECADE cosmic shear project IV: cosmological constraints from 107 million galaxies across 5,400 deg$^2$ of the sky
2025-02-24
D. Anbajagane, C. Chang, A. Drlica-Wagner, C. Y. Tan, M. Adamow, R. A. Gruendl, L. F. Secco, Z. Zhang, M. R. Becker, P. S. Ferguson, N. Chicoine, K. Herron, A. Alarcon, R. Teixeira, D. Suson, A. N. Alsina, A. Amon, F. Andrade-Oliveira, J. Blazek, C. R. Bom, H. Camacho, J. A. Carballo-Bello, A. Carnero Rosell, R. Cawthon, W. Cerny, A. Choi, Y. Choi, S. Dodelson, C. Doux, K. Eckert, J. Elvin-Poole, J. Esteves, M. Gatti, G. Giannini, D. Gruen, W. G. Hartley, K. Herner, E. M. Huff, D. J. James, M. Jarvis, E. Krause, N. Kuropatkin, C. E. Martínez-Vázquez, P. Massana, S. Mau, J. McCullough, G. E. Medina, B. Mutlu-Pakdil, J. Myles, M. Navabi, N. E. D. Noël, A. B. Pace, A. Porredon, J. Prat, M. Raveri, A. H. Riley, E. S. Rykoff, J. D. Sakowska, S. Samuroff, D. Sanchez-Cid, D. J. Sand, L. Santana-Silva, I. Sevilla-Noarbe, T. Shin, M. Soares-Santos, G. S. Stringfellow, C. To, A. Tong, M. A. Troxel, A. K. Vivas, M. Yamamoto, B. Yanny, B. Yin, Y. Zhang, J. Zuntz
We present cosmological constraints from the Dark Energy Camera All Data Everywhere (DECADE) cosmic shear analysis. This work uses shape measurements for 107 million galaxies measured through Dark Energy Camera (DECam) imaging of $5,\!412$ deg$^2$ of sky that is outside the Dark Energy Survey (DES) footprint. We derive constraints on the cosmological parameters $S_8 = 0.791^{+0.027}_{-0.032}$ and $\Omega_{\rm m} =0.269^{+0.034}_{-0.050}$ for the $\Lambda$CDM model, which are consistent with those from other weak lensing surveys and from the cosmic microwave background. We combine our results with cosmic shear results from DES Y3 at the likelihood level, since the two datasets span independent areas on the sky. The combined measurements, which cover $\approx\! 10,\!000$ deg$^2$, prefer $S_8 = 0.791 \pm 0.023$ and $\Omega_{\rm m} = 0.277^{+0.034}_{-0.046}$ under the $\Lambda$CDM model. These results are the culmination of a series of rigorous studies that characterize and validate the DECADE dataset and the associated analysis methodologies (Anbajagane et. al 2025a,b,c). Overall, the DECADE project demonstrates that the cosmic shear analysis methods employed in Stage-III weak lensing surveys can provide robust cosmological constraints for fairly inhomogeneous datasets. This opens the possibility of using data that have been previously categorized as ``unusable'' for cosmic shear analyses, thereby increasing the statistical power of upcoming weak lensing surveys.
The DECADE cosmic shear project III: validation of analysis pipeline using spatially inhomogeneous data
2025-02-24
D. Anbajagane, C. Chang, N. Chicoine, L. F. Secco, C. Y. Tan, P. S. Ferguson, A. Drlica-Wagner, K. Herron, M. Adamow, R. A. Gruendl, M. R. Becker, R. Teixeira, Z. Zhang, A. Alarcon, D. Suson, A. N. Alsina, A. Amon, F. Andrade-Oliveira, J. Blazek, H. Camacho, J. A. Carballo-Bello, W. Cerny, Y. Choi, C. Doux, M. Gatti, D. Gruen, D. J. James, E. Krause, N. Kuropatkin, C. E. Martínez-Vázquez, P. Massana, S. Mau, J. McCullough, G. E. Medina, B. Mutlu-Pakdil, M. Navabi, N. E. D. Noël, A. B. Pace, A. Porredon, M. Raveri, A. H. Riley, J. D. Sakowska, S. Samuroff, D. Sanchez-Cid, D. J. Sand, L. Santana-Silva, M. Soares-Santos, G. S. Stringfellow, C. To, A. K. Vivas, M. Yamamoto, A. Zenteno, J. Zuntz
We present the pipeline for the cosmic shear analysis of the Dark Energy Camera All Data Everywhere (DECADE) weak lensing dataset: a catalog consisting of 107 million galaxies observed by the Dark Energy Camera (DECam) in the northern Galactic cap. The catalog derives from a large number of disparate observing programs and is therefore more inhomogeneous across the sky compared to existing lensing surveys. First, we use simulated data-vectors to show the sensitivity of our constraints to different analysis choices in our inference pipeline, including sensitivity to residual systematics. Next we use simulations to validate our covariance modeling for inhomogeneous datasets. Finally, we show that our choices in the end-to-end cosmic shear pipeline are robust against inhomogeneities in the survey, by extracting relative shifts in the cosmology constraints across different subsets of the footprint/catalog and showing they are all consistent within $1\sigma$ to $2\sigma$. This is done for forty-six subsets of the data and is carried out in a fully consistent manner: for each subset of the data, we re-derive the photometric redshift estimates, shear calibrations, survey transfer functions, the data vector, measurement covariance, and finally, the cosmological constraints. Our results show that existing analysis methods for weak lensing cosmology can be fairly resilient towards inhomogeneous datasets. This also motivates exploring a wider range of image data for pursuing such cosmological constraints.
The DECADE cosmic shear project II: photometric redshift calibration of the source galaxy sample
2025-02-24
D. Anbajagane, A. Alarcon, R. Teixeira, C. Chang, L. F. Secco, C. Y. Tan, A. Drlica-Wagner, M. Adamow, R. A. Gruendl, G. Giannini, M. R. Becker, P. S. Ferguson, N. Chicoine, Z. Zhang, K. Herron, D. Suson, A. N. Alsina, A. Amon, C. R. Bom, J. A. Carballo-Bello, W. Cerny, A. Choi, Y. Choi, C. Doux, K. Eckert, M. Gatti, D. Gruen, W. G. Hartley, K. Herner, E. M. Huff, D. J. James, N. Kuropatkin, C. E. Martínez-Vázquez, P. Massana, S. Mau, J. McCullough, G. E. Medina, B. Mutlu-Pakdil, J. Myles, M. Navabi, N. E. D. Noël, A. B. Pace, M. Raveri, A. H. Riley, J. D. Sakowska, D. Sanchez-Cid, D. J. Sand, L. Santana-Silva, I. Sevilla-Noarbe, M. Soares-Santos, G. S. Stringfellow, A. K. Vivas, M. Yamamoto
We present the photometric redshift characterization and calibration for the Dark Energy Camera All Data Everywhere (DECADE) weak lensing dataset: a catalog of 107 million galaxies observed by the Dark Energy Camera (DECam) in the northern Galactic cap. The redshifts are estimated from a combination of wide-field photometry, deep-field photometry with associated redshift estimates, and a transfer function between the wide field and deep field that is estimated using a source injection catalog. We construct four tomographic bins for the galaxy catalog, and estimate the redshift distribution, $n(z)$, within each one using the Self-organizing Map Photo-Z (SOMPZ) methodology. Our estimates include the contributions from sample variance, zeropoint calibration uncertainties, and redshift biases, as quantified for the deep-field dataset. The total uncertainties on the mean redshifts are $\sigma_{\langle z \rangle} \approx 0.01$. The SOMPZ estimates are then compared to those from the clustering redshift method, obtained by cross-correlating our source galaxies with galaxies in spectroscopic surveys, and are shown to be consistent with each other.
The DECADE cosmic shear project I: A new weak lensing shape catalog of 107 million galaxies
2025-02-24
D. Anbajagane, C. Chang, Z. Zhang, C. Y. Tan, M. Adamow, L. F. Secco, M. R. Becker, P. S. Ferguson, A. Drlica-Wagner, R. A. Gruendl, K. Herron, A. Tong, M. A. Troxel, D. Sanchez-Cid, I. Sevilla-Noarbe, N. Chicoine, R. Teixeira, A. Alarcon, D. Suson, A. N. Alsina, A. Amon, C. R. Bom, J. A. Carballo-Bello, W. Cerny, A. Choi, Y. Choi, C. Doux, K. Eckert, M. Gatti, D. Gruen, D. J. James, M. Jarvis, N. Kuropatkin, C. E. Martínez-Vázquez, P. Massana, S. Mau, J. McCullough, G. E. Medina, B. Mutlu-Pakdil, M. Navabi, N. E. D. Noël, A. B. Pace, J. Prat, M. Raveri, A. H. Riley, E. S. Rykoff, J. D. Sakowska, D. J. Sand, L. Santana-Silva, T. Shin, M. Soares-Santos, G. S. Stringfellow, A. K. Vivas, M. Yamamoto
We present the Dark Energy Camera All Data Everywhere (DECADE) weak lensing dataset: a catalog of 107 million galaxies observed by the Dark Energy Camera (DECam) in the northern Galactic cap. This catalog was assembled from public DECam data including survey and standard observing programs. These data were consistently processed with the Dark Energy Survey Data Management pipeline as part of the DECADE campaign and serve as the basis of the DECam Local Volume Exploration survey (DELVE) Early Data Release 3 (EDR3). We apply the Metacalibration measurement algorithm to generate and calibrate galaxy shapes. After cuts, the resulting cosmology-ready galaxy shape catalog covers a region of $5,\!412 \,\,{\rm deg}^2$ with an effective number density of $4.59\,\, {\rm arcmin}^{-2}$. The coadd images used to derive this data have a median limiting magnitude of $r = 23.6$, $i = 23.2$, and $z = 22.6$, estimated at ${\rm S/N} = 10$ in a 2 arcsecond aperture. We present a suite of detailed studies to characterize the catalog, measure any residual systematic biases, and verify that the catalog is suitable for cosmology analyses. In parallel, we build an image simulation pipeline to characterize the remaining multiplicative shear bias in this catalog, which we measure to be $m = (-2.454 \pm 0.124) \times10^{-2}$ for the full sample. Despite the significantly inhomogeneous nature of the data set, due to it being an amalgamation of various observing programs, we find the resulting catalog has sufficient quality to yield competitive cosmological constraints.
A Massive Black Hole 0.8 kpc from the Host Nucleus Revealed by the Offset Tidal Disruption Event AT2024tvd
2025-02-24
Yuhan Yao, Ryan Chornock, Charlotte Ward, Erica Hammerstein, Itai Sfaradi, Raffaella Margutti, Luke Zoltan Kelley, Wenbin Lu, Chang Liu, Jacob Wise, Jesper Sollerman, Kate D. Alexander, Eric C. Bellm, Andrew J. Drake, Christoffer Fremling, Marat Gilfanov, Matthew J. Graham, Steven L. Groom, K. R. Hinds, S. R. Kulkarni, Adam A. Miller, James C. A. Miller-Jones, Matt Nicholl, Daniel A. Perley, Josiah Purdum, Vikram Ravi, R. Michael Rich, Nabeel Rehemtulla, Reed Riddle, Roger Smith, Robert Stein, Rashid Sunyaev, Sjoert van Velzen, Avery Wold
Tidal disruption events (TDEs) that are spatially offset from the nucleus of their host galaxies offer a new probe of massive black hole (MBH) wanderers, binaries, triples, and recoiling MBHs. Here we present AT2024tvd, the first off-nuclear TDE identified through optical sky surveys. High-resolution imaging with the Hubble Space Telescope shows that AT2024tvd is $0.914\pm 0.010$ arcsec offset from the apparent center of its host galaxy, corresponding to a projected distance of $0.808\pm 0.009$ kpc at $z=0.045$. AT2024tvd exhibits typical properties of nuclear TDEs, including a persistent hot UV/optical component that peaks at $L_{ bb}\sim 6\times 10^{43}\,erg\,s^{-1}$, broad hydrogen lines in its optical spectra, and delayed brightening of luminous ($L_{ X,peak}\sim 3\times 10^{43}\,erg\,s^{-1}$), highly variable soft X-ray emission. The MBH mass of AT2024tvd is $10^{6\pm1}\,M_\odot$, at least 10 times lower than its host galaxy's central black hole mass ($\gtrsim 10^8\,M_\odot$). The MBH in AT2024tvd has two possible origins: a wandering MBH from the lower-mass galaxy in a minor merger during the dynamical friction phase or a recoiling MBH ejected by triple interactions. Combining AT2024tvd with two previously known off-nuclear TDEs discovered in X-rays (3XMM\,J2150 and EP240222a), which likely involve intermediate-mass black holes in satellite galaxies, we find that the parent galaxies of all three events are very massive ($\sim 10^{10.9}\,M_\odot$). This result aligns with expectations from cosmological simulations that the number of offset MBHs scales linearly with the host halo mass.
Discovered over fifty years ago, neutron stars exhibit a remarkable variety of behaviors depending on their age, magnetic field strength, rotational dynamics, emission mechanisms, and surrounding environments. This diversity in their observational manifestations has led astronomers to classify neutron stars into numerous categories, much like wandering through a zoo and admiring various species. This chapter focuses on isolated (i.e., non-accreting) neutron stars. We review extensive observational results of rotation-powered pulsars, magnetars, X-ray dim isolated neutron stars and central compact objects, highlighting the unique properties and recent discoveries of these neutron star classes. We briefly touch on theoretical models that have significantly advanced our understanding of these classes, emphasizing the valuable insights they provide into the underlying physics of neutron stars.
Stress energy momentum in terms of geodesic accelerations and variational tensors including torsion
2025-02-24
Adam Marsh
General relativity and its extensions including torsion identify stress energy momentum as being proportional to the Einstein tensor, thus ensuring both symmetry and conservation. Here we visualize stress energy and momentum by identifying the associated relative fractional accelerations of geodesics encoded in the Einstein tensor. This also provides an intuitive explanation for the vanishing divergence of the Einstein tensor. In order to obtain this same energy and momentum for other actions such as that of Dirac theory including torsion, we then review the various stress energy momentum tensors resulting from the variation of different quantities derived from parallel transport, and detail their interrelationships. This provides an opportunity to revisit some classic material from a geometric point of view, including Einstein-Cartan theory, the Sciama-Kibble formalism, and the Belinfante-Rosenfeld relation, whose derivation in the mostly pluses signature would seem to not be otherwise readily available.
AI-assisted learning companion robots are increasingly used in early education. Many parents express concerns about content appropriateness, while they also value how AI and robots could supplement their limited skill, time, and energy to support their children's learning. We designed a card-based kit, SET, to systematically capture scenarios that have different extents of parental involvement. We developed a prototype interface, PAiREd, with a learning companion robot to deliver LLM-generated educational content that can be reviewed and revised by parents. Parents can flexibly adjust their involvement in the activity by determining what they want the robot to help with. We conducted an in-home field study involving 20 families with children aged 3-5. Our work contributes to an empirical understanding of the level of support parents with different expectations may need from AI and robots and a prototype that demonstrates an innovative interaction paradigm for flexibly including parents in supporting their children.
Data-Driven Pseudo-spectral Full Waveform Inversion via Deep Neural Networks
2025-02-24
Christopher Zerafa, Pauline Galea, Cristiana Sebu
FWI seeks to achieve a high-resolution model of the subsurface through the application of multi-variate optimization to the seismic inverse problem. Although now a mature technology, FWI has limitations related to the choice of the appropriate solver for the forward problem in challenging environments requiring complex assumptions, and very wide angle and multi-azimuth data necessary for full reconstruction are often not available. Deep Learning techniques have emerged as excellent optimization frameworks. These exist between data and theory-guided methods. Data-driven methods do not impose a wave propagation model and are not exposed to modelling errors. On the contrary, deterministic models are governed by the laws of physics. Application of seismic FWI has recently started to be investigated within Deep Learning. This has focussed on the time-domain approach, while the pseudo-spectral domain has not been yet explored. However, classical FWI experienced major breakthroughs when pseudo-spectral approaches were employed. This work addresses the lacuna that exists in incorporating the pseudo-spectral approach within Deep Learning. This has been done by re-formulating the pseudo-spectral FWI problem as a Deep Learning algorithm for a data-driven pseudo-spectral approach. A novel DNN framework is proposed. This is formulated theoretically, qualitatively assessed on synthetic data, applied to a two-dimensional Marmousi dataset and evaluated against deterministic and time-based approaches. Inversion of data-driven pseudo-spectral DNN was found to outperform classical FWI for deeper and over-thrust areas. This is due to the global approximator nature of the technique and hence not bound by forward-modelling physical constraints from ray-tracing.
Representation Engineering for Large-Language Models: Survey and Research Challenges
2025-02-24
Lukasz Bartoszcze, Sarthak Munshi, Bryan Sukidi, Jennifer Yen, Zejia Yang, David Williams-King, Linh Le, Kosi Asuzu, Carsten Maple
Large-language models are capable of completing a variety of tasks, but remain unpredictable and intractable. Representation engineering seeks to resolve this problem through a new approach utilizing samples of contrasting inputs to detect and edit high-level representations of concepts such as honesty, harmfulness or power-seeking. We formalize the goals and methods of representation engineering to present a cohesive picture of work in this emerging discipline. We compare it with alternative approaches, such as mechanistic interpretability, prompt-engineering and fine-tuning. We outline risks such as performance decrease, compute time increases and steerability issues. We present a clear agenda for future research to build predictable, dynamic, safe and personalizable LLMs.
Hallucination Detection in LLMs Using Spectral Features of Attention Maps
2025-02-24
Jakub Binkowski, Denis Janiak, Albert Sawczyn, Bogdan Gabrys, Tomasz Kajdanowicz
Large Language Models (LLMs) have demonstrated remarkable performance across various tasks but remain prone to hallucinations. Detecting hallucinations is essential for safety-critical applications, and recent methods leverage attention map properties to this end, though their effectiveness remains limited. In this work, we investigate the spectral features of attention maps by interpreting them as adjacency matrices of graph structures. We propose the $\text{LapEigvals}$ method, which utilises the top-$k$ eigenvalues of the Laplacian matrix derived from the attention maps as an input to hallucination detection probes. Empirical evaluations demonstrate that our approach achieves state-of-the-art hallucination detection performance among attention-based methods. Extensive ablation studies further highlight the robustness and generalisation of $\text{LapEigvals}$, paving the way for future advancements in the hallucination detection domain.
Synergizing Deep Learning and Full-Waveform Inversion: Bridging Data-Driven and Theory-Guided Approaches for Enhanced Seismic Imaging
2025-02-24
Christopher Zerafa, Pauline Galea, Cristiana Sebu
This review explores the integration of deep learning (DL) with full-waveform inversion (FWI) for enhanced seismic imaging and subsurface characterization. It covers FWI and DL fundamentals, geophysical applications (velocity estimation, deconvolution, tomography), and challenges (model complexity, data quality). The review also outlines future research directions, including hybrid, generative, and physics-informed models for improved accuracy, efficiency, and reliability in subsurface property estimation. The synergy between DL and FWI has the potential to transform geophysics, providing new insights into Earth's subsurface.
An Extremely Hot Pulsating Pre-White Dwarf from OGLE
2025-02-24
P. Pietrukowicz, K. Werner, M. J. Mroz, M. Ratajczak
We show that the blue 18.3-minute variable object discovered in the Galactic disk by the OGLE-III survey and named OGLE-GD-WD-0001 is a pulsating pre-white dwarf of PG 1159 spectral type. With an effective temperature of about 160,000 K it is among the hottest known pulsators being located close to the blue edge of the GW Virginis instability strip. The long-term OGLE observations indicate that the object has a positive period change rate of about $5 \times 10^{-10}$ $s~s^{-1}$ and thus already contracts. There are no traces of a planetary nebula around this star.
Optimal Follow-Up of Gravitational-Wave Events with the UltraViolet EXplorer (UVEX)
2025-02-24
Leo P. Singer, Alexander W. Criswell, Sydney C. Leggio, R. Weizmann Kiendrebeogo, Michael W. Coughlin, Hannah P. Earnshaw, Suvi Gezari, Brian W. Grefenstette, Fiona A. Harrison, Mansi M. Kasliwal, Brett M. Morris, Erik Tollerud, S. Bradley Cenko
The UltraViolet EXplorer (UVEX) is a wide-field ultraviolet space telescope selected as a NASA Medium-Class Explorer (MIDEX) mission for launch in 2030. UVEX will undertake deep, cadenced surveys of the entire sky to probe low mass galaxies and explore the ultraviolet (UV) time-domain sky, and it will carry the first rapidly deployable UV spectroscopic capability for a broad range of science applications. One of UVEX's prime objectives is to follow up gravitational wave (GW) binary neutron star mergers as targets of opportunity (ToOs), rapidly scanning across their localization regions to search for their kilonova (KN) counterparts. Early-time multiband ultraviolet light curves of KNe are key to explaining the interplay between jet and ejecta in binary neutron star mergers. Owing to high Galactic extinction in the ultraviolet and the variation of GW distance estimates over the sky, the sensitivity to kilonovae can vary significantly across the GW localization and even across the footprint of a single image given UVEX's large field of view. Good ToO observing strategies to trade off between area and depth are neither simple nor obvious. We present an optimal strategy for GW follow-up with UVEX in which exposure time is adjusted dynamically for each field individually to maximize the overall probability of detection. We model the scheduling problem using the expressive and powerful mathematical framework of mixed integer linear programming (MILP), and employ a state-of-the-art MILP solver to automatically generate observing plan timelines that achieve high probabilities of kilonova detection. We have implemented this strategy in an open-source astronomical scheduling software package called the Multi-Mission Multi-Messenger Observation Planning Toolkit (M4OPT), on GitHub at https://github.com/m4opt/m4opt.
The Lottery LLM Hypothesis, Rethinking What Abilities Should LLM Compression Preserve?
2025-02-24
Zhenheng Tang, Xiang Liu, Qian Wang, Peijie Dong, Bingsheng He, Xiaowen Chu, Bo Li
Motivated by reducing the computational and storage costs of LLMs, model compression and KV cache compression have attracted much attention from researchers. However, current methods predominantly emphasize maintaining the performance of compressed LLMs, as measured by perplexity or simple accuracy on tasks of common sense knowledge QA and basic arithmetic reasoning. In this blog, we present a brief review of recent advancements in LLMs related to retrieval-augmented generation, multi-step reasoning, external tools, and computational expressivity, all of which substantially enhance LLM performance. Then, we propose a lottery LLM hypothesis suggesting that for a given LLM and task, there exists a smaller lottery LLM capable of producing the same performance as the original LLM with the assistance of multi-step reasoning and external tools. Based on the review of current progress in LLMs, we discuss and summarize the essential capabilities that the lottery LLM and KV cache compression must possess, which are currently overlooked in existing methods.
Infrared thermography (IRT) is a widely used temperature measurement technology, but it faces the problem of measurement errors under interference factors. This paper attempts to summarize the common interference factors and temperature compensation methods when applying IRT. According to the source of factors affecting the infrared temperature measurement accuracy, the interference factors are divided into three categories: factors from the external environment, factors from the measured object, and factors from the infrared thermal imager itself. At the same time, the existing compensation methods are classified into three categories: Mechanism Modeling based Compensation method (MMC), Data-Driven Compensation method (DDC), and Mechanism and Data jointly driven Compensation method (MDC). Furthermore, we discuss the problems existing in the temperature compensation methods and future research directions, aiming to provide some references for researchers in academia and industry when using IRT technology for temperature measurement.
UNB StepUP: A footStep database for gait analysis and recognition using Underfoot Pressure
2025-02-24
Robyn Larracy, Angkoon Phinyomark, Ala Salehi, Eve MacDonald, Saeed Kazemi, Shikder Shafiul Bashar, Aaron Tabor, Erik Scheme
Gait refers to the patterns of limb movement generated during walking, which are unique to each individual due to both physical and behavioural traits. Walking patterns have been widely studied in biometrics, biomechanics, sports, and rehabilitation. While traditional methods rely on video and motion capture, advances in underfoot pressure sensing technology now offer deeper insights into gait. However, underfoot pressures during walking remain underexplored due to the lack of large, publicly accessible datasets. To address this, the UNB StepUP database was created, featuring gait pressure data collected with high-resolution pressure sensing tiles (4 sensors/cm$^2$, 1.2m by 3.6m). Its first release, UNB StepUP-P150, includes over 200,000 footsteps from 150 individuals across various walking speeds (preferred, slow-to-stop, fast, and slow) and footwear types (barefoot, standard shoes, and two personal shoes). As the largest and most comprehensive dataset of its kind, it supports biometric gait recognition while presenting new research opportunities in biomechanics and deep learning. The UNB StepUP-P150 dataset sets a new benchmark for pressure-based gait analysis and recognition. Please note that the hypertext links to the dataset on FigShare remain dormant while the document is under review.
The MAGPI Survey: the kinematic morphology-density relation (or lack thereof) and the Hubble sequence at $z\sim0.3$
2025-02-23
Caroline Foster, Mark W. Donoghoe, Andrew Battisti, Francesco D'Eugenio, Katherine Harborne, Thomas Venville, Claudia Del P. Lagos, J. Trevor Mendel, Ryan Bagge, Stefania Barsanti, Sabine Bellstedt, Alina Boecker, Qianhui Chen, Caro Derkenne, Anna Ferre-Matteu, Eda Gjergo, Anshu Gupta, Eric G. M. Muller, Giulia Santucci, Hye-Jin Park, Rhea-Silvia Remus, Sabine Thater, Jesse van de Sande, Sam Vaughan, Sarah Brough, Scott M. Croom, Lucas M. Valenzuela, Emily Wisnioski, the MAGPI Team
This work presents visual morphological and dynamical classifications for 637 spatially resolved galaxies, most of which are at intermediate redshift ($z\sim0.3$), in the Middle-Ages Galaxy Properties with Integral field spectroscopy (MAGPI) Survey. For each galaxy, we obtain a minimum of 11 independent visual classifications by knowledgeable classifiers. We use an extension of the standard Dawid-Skene bayesian model introducing classifier-specific confidence parameters and galaxy-specific difficulty parameters to quantify classifier confidence and infer reliable statistical confidence estimates. Selecting sub-samples of 86 bright ($r<20$ mag) high-confidence ($>0.98$) morphological classifications at redshifts ($0.2 \le z \le0.4$), we confirm the full range of morphological types is represented in MAGPI as intended in the survey design. Similarly, with a sub-sample of 82 bright high-confidence stellar kinematic classifications, we find that the rotating and non-rotating galaxies seen at low redshift are already in place at intermediate redshifts. We \textit{do not} find evidence that the kinematic morphology-density relation seen at $z\sim0$ is established at $z\sim0.3$. We suggest that galaxies without obvious stellar rotation are dynamically pre-processed sometime before $z\sim0.3$ within lower mass groups before joining denser environments.
Evidence of a Disk-wind Origin for Fluorescent H$_2$ in Classical T Tauri Stars
2025-02-23
Matt Kalscheur, Kevin France, Brunella Nisini, P. Christian Schneider, Richard Alexander, Jochen Eisloffel, Justyn Campbell-White, Hsien Shang, Manuele Gangi, Zhen Guo, Seok-Jun Chang
We use FUV spectra of 36 T Tauri stars, predominately from $\textit{Hubble Space Telescope}$'s ULLYSES program, to examine the kinematic properties of fluorescent H$_2$ emission lines for evidence of disk outflows. Leveraging improvements to the $\textit{HST}$-COS wavelength solution, we co-add isolated lines within four fluorescent progressions ([$\textit{v'}$,$\textit{J'}$] = [1,4], [1,7], [0,2], and [3,16]) to improve signal-to-noise, and we fit each co-added line profile with one or two Gaussian components. Of the high S/N line profiles (S/N $\geq$ 12 at the peak of the profile), over half are best fit with a combination of a broad and narrow Gaussian component. For profiles of the [1,4] and [1,7] progressions, we find a systematic blue-shift of a few km s$^{-1}$ between the broad and narrow centroid velocities and stellar radial velocities. For the [0,2] progression, we find centroid velocities consistently blueshifted with respect to stellar radial velocities on the order of -5 km s$^{-1}$ for the single and narrow components, and -10 km s$^{-1}$ for the broad components. Overall, the blueshifts observed in our sample suggest that the molecular gas traces an outflow from a disk wind in some sources, and not solely disk gas in Keplerian rotation. The low-velocity systematic blue-shifts, and emitting radii as inferred from line FWHMs, observed in our sample are similar to those observed with optical [O I] surveys of T Tauri stars. We estimate H$_2$ mass-loss rates of 10$^{-9}$ to 10$^{-11}$ $M_{\odot}$ yr$^{-1}$, but incomplete knowledge of wind parameters limits comparisons to global models.
The visibility of the Ōtautahi-Oxford interstellar object population model in LSST
2025-02-23
Rosemary C. Dorsey, Matthew J. Hopkins, Michele T. Bannister, Samantha M. Lawler, Chris Lintott, Alex H. Parker, John C. Forbes
With a new probabilistic technique for sampling interstellar object (ISO) orbits with high efficiency, we assess the observability of ISOs under a realistic cadence for the upcoming Vera Rubin Observatory's Legacy Survey of Space and Time (LSST). Using the \={O}tautahi-Oxford population model, we show that there will be complex on-sky structure in the pattern of direction and velocity revealed by the detected ISO population, with the expected enhanced northern flux complicating efforts to derive population parameters from the LSST's predominately southern footprint. For luminosity functions with slopes of $2.5\leq q_s\leq 4.0$, the most discoverable ISOs have $H_r\simeq 14.6-20.7$; for previously estimated spatial densities, between 6 and 51 total ISOs are expected. The slope of the luminosity function of ISOs will be relatively quickly constrained. Discoveries are evenly split around their perihelion passage and are biased to lower velocities. After their discovery by LSST, it will be rare for ISOs to be visible for less than a month; most will have $m_r \leq 23$ for months, and the window for spectroscopic characterization could be as long as two years. These probabilistic assessments are robust against model or spatial density refinements that change the absolute numbers of ISO discoveries.
Investigating the Security & Privacy Risks from Unsanctioned Technology Use by Educators
2025-02-23
Easton Kelso, Ananta Soneji, Syed Zami-Ul-Haque Navid, Yan Soshitaishvili, Sazzadur Rahaman, Rakibul Hasan
Educational technologies are revolutionizing how educational institutions operate. Consequently, it makes them a lucrative target for breach and abuse as they often serve as centralized hubs for diverse types of sensitive data, from academic records to health information. Existing studies looked into how existing stakeholders perceive the security and privacy risks of educational technologies and how those risks are affecting institutional policies for acquiring new technologies. However, outside of institutional vetting and approval, there is a pervasive practice of using applications and devices acquired personally. It is unclear how these applications and devices affect the dynamics of the overall institutional ecosystem. This study aims to address this gap by understanding why instructors use unsanctioned applications, how instructors perceive the associated risks, and how it affects institutional security and privacy postures. We designed and conducted an online survey-based study targeting instructors and administrators from K-12 and higher education institutions.
The growing demand for data privacy in Machine Learning (ML) applications has seen Machine Unlearning (MU) emerge as a critical area of research. As the `right to be forgotten' becomes regulated globally, it is increasingly important to develop mechanisms that delete user data from AI systems while maintaining performance and scalability of these systems. Incremental Unlearning (IU) is a promising MU solution to address the challenges of efficiently removing specific data from ML models without the need for expensive and time-consuming full retraining. This paper presents the various techniques and approaches to IU. It explores the challenges faced in designing and implementing IU mechanisms. Datasets and metrics for evaluating the performance of unlearning techniques are discussed as well. Finally, potential solutions to the IU challenges alongside future research directions are offered. This survey provides valuable insights for researchers and practitioners seeking to understand the current landscape of IU and its potential for enhancing privacy-preserving intelligent systems.
Spin and flavor oscillations of neutrinos in gravitational fields
2025-02-23
Maxim Dvornikov
We summarize our recent achievements in the description of neutrino oscillations in various gravitational fields. After the short review of the previous studies of neutrinos in gravitational fields, we consider the neutrinos propagation and oscillations in two gravitational backgrounds. First, we discuss neutrino spin oscillations in their gravitational scattering off a supermassive black hole surrounded by a thick magnetized accretion disk. Second, we study neutrino flavor oscillations in stochastic gravitational waves. We also consider applications of the obtained results for oscillations of astrophysical neutrinos.
Advanced Driver Assistance Systems (ADAS) enhance highway safety by improving environmental perception and reducing human errors. However, misconceptions, trust issues, and knowledge gaps hinder widespread adoption. This study examines driver perceptions, knowledge sources, and usage patterns of ADAS in passenger vehicles. A nationwide survey collected data from a diverse sample of U.S. drivers. Machine learning models predicted ADAS adoption, with SHAP (SHapley Additive Explanations) identifying key influencing factors. Findings indicate that higher trust levels correlate with increased ADAS usage, while concerns about reliability remain a barrier. Specific features, such as Forward Collision Warning and Driver Monitoring Systems, significantly influence adoption likelihood. Demographic factors (age, gender) and driving habits (experience, frequency) also shape ADAS acceptance. Findings emphasize the influence of socioeconomic, demographic, and behavioral factors on ADAS adoption, offering guidance for automakers, policymakers, and safety advocates to improve awareness, trust, and usability.
Some astrobiological models suggest that molecular clouds may serve as habitats for extraterrestrial life. This study reviews recent theoretical work addressing the physical and biochemical prerequisites for life in such environments, with particular focus on three subjects: (1) bioenergetic pathways under extreme low-temperature conditions; (2) the emergence and preservation of biomolecular chirality; and (3) detection methodologies for potential biosignatures. In this paper, we formally introduce the molecular cloud biology concept, which integrates all physicochemical and metabolic processes hypothesized to sustain life within molecular clouds. As a potential branch of astrobiology, molecular cloud biology warrants interdisciplinary collaborative research to validate its foundational assumptions and explore its scientific implications.
The recent emergence of Multi-modal Large Language Models (MLLMs) has introduced a new dimension to the Text-rich Image Understanding (TIU) field, with models demonstrating impressive and inspiring performance. However, their rapid evolution and widespread adoption have made it increasingly challenging to keep up with the latest advancements. To address this, we present a systematic and comprehensive survey to facilitate further research on TIU MLLMs. Initially, we outline the timeline, architecture, and pipeline of nearly all TIU MLLMs. Then, we review the performance of selected models on mainstream benchmarks. Finally, we explore promising directions, challenges, and limitations within the field.
Exploring the empirical instability strip for $γ$ Dor-type stars in eclipsing binaries
2025-02-23
O. Cakirli, B. Hoyman, O. Ozdarcan, S. Bilir
This research paper has light curve modelling, spectroscopy, and detailed asteroseismic studies for five detached eclipsing binaries with a $\gamma$ Dor component that have been found so far by the sky surveys. The objective is to study the pulsational characteristics of the oscillating stars of the systems, as well as to estimate their absolute parameters and their relation to the pulsational frequencies and enrich the sample size of this kind of system, which has been relatively poor up to today. The physical properties of these systems are compared with other similar cases and the locations of their components are plotted in the Hertzsprung-Russell (HR) diagrams. In order to discover the pulsation frequencies, the photometric data are analysed using eclipsing binary modelling methods and their residuals are subjected to Fourier analysis. Finally, we evaluated that there is a possible relationship between the pulsation periods of the primaries and the orbital period ($P_{\rm orb}$), the force exerted per gram on the surface of the pulsating star ($f$/M$_{\rm puls}$), and the fractional radius of the pulsating star ($r_{\rm puls}$) of the systems, using a sample of 39 eclipsing binary systems with $\gamma$ Dor type primaries. We utilised them to construct a unique observational $\gamma$ Dor instability strip, delineated by a lower limit of 6,850 K at the red edge and an upper limit of 7,360 K at the blue edge on the zero-age main sequence. The majority of 39 pure $\gamma$ Dor stars are located within the region that is covered by this observational strip.
Machine learning algorithms are used in diverse domains, many of which face significant challenges due to data imbalance. Studies have explored various approaches to address the issue, like data preprocessing, cost-sensitive learning, and ensemble methods. Generative Adversarial Networks (GANs) showed immense potential as a data preprocessing technique that generates good quality synthetic data. This study employs a systematic mapping methodology to analyze 3041 papers on GAN-based sampling techniques for imbalanced data sourced from four digital libraries. A filtering process identified 100 key studies spanning domains such as healthcare, finance, and cybersecurity. Through comprehensive quantitative analysis, this research introduces three categorization mappings as application domains, GAN techniques, and GAN variants used to handle the imbalanced nature of the data. GAN-based over-sampling emerges as an effective preprocessing method. Advanced architectures and tailored frameworks helped GANs to improve further in the case of data imbalance. GAN variants like vanilla GAN, CTGAN, and CGAN show great adaptability in structured imbalanced data cases. Interest in GANs for imbalanced data has grown tremendously, touching a peak in recent years, with journals and conferences playing crucial roles in transmitting foundational theories and practical applications. While with these advances, none of the reviewed studies explicitly explore hybridized GAN frameworks with diffusion models or reinforcement learning techniques. This gap leads to a future research idea develop innovative approaches for effectively handling data imbalance.
Multilingual != Multicultural: Evaluating Gaps Between Multilingual Capabilities and Cultural Alignment in LLMs
2025-02-23
Jonathan Rystrøm, Hannah Rose Kirk, Scott Hale
Large Language Models (LLMs) are becoming increasingly capable across global languages. However, the ability to communicate across languages does not necessarily translate to appropriate cultural representations. A key concern is US-centric bias, where LLMs reflect US rather than local cultural values. We propose a novel methodology that compares LLM-generated response distributions against population-level opinion data from the World Value Survey across four languages (Danish, Dutch, English, and Portuguese). Using a rigorous linear mixed-effects regression framework, we compare two families of models: Google's Gemma models (2B--27B parameters) and successive iterations of OpenAI's turbo-series. Across the families of models, we find no consistent relationships between language capabilities and cultural alignment. While the Gemma models have a positive correlation between language capability and cultural alignment across languages, the OpenAI models do not. Importantly, we find that self-consistency is a stronger predictor of multicultural alignment than multilingual capabilities. Our results demonstrate that achieving meaningful cultural alignment requires dedicated effort beyond improving general language capabilities.
A Survey of Graph Transformers: Architectures, Theories and Applications
2025-02-23
Chaohao Yuan, Kangfei Zhao, Ercan Engin Kuruoglu, Liang Wang, Tingyang Xu, Wenbing Huang, Deli Zhao, Hong Cheng, Yu Rong
Graph Transformers (GTs) have demonstrated a strong capability in modeling graph structures by addressing the intrinsic limitations of graph neural networks (GNNs), such as over-smoothing and over-squashing. Recent studies have proposed diverse architectures, enhanced explainability, and practical applications for Graph Transformers. In light of these rapid developments, we conduct a comprehensive review of Graph Transformers, covering aspects such as their architectures, theoretical foundations, and applications within this survey. We categorize the architecture of Graph Transformers according to their strategies for processing structural information, including graph tokenization, positional encoding, structure-aware attention and model ensemble. Furthermore, from the theoretical perspective, we examine the expressivity of Graph Transformers in various discussed architectures and contrast them with other advanced graph learning algorithms to discover the connections. Furthermore, we provide a summary of the practical applications where Graph Transformers have been utilized, such as molecule, protein, language, vision traffic, brain and material data. At the end of this survey, we will discuss the current challenges and prospective directions in Graph Transformers for potential future research.
Democracy prioritizes deliberation and equality, yet electoral competition, institutional rules, and digital networks consistently elevate group identities over policy agendas. This paper posits tribalism as an inherent democratic feature, integrating social identity theory, institutionalism, and network analysis. Patterns across diverse contexts illustrate this racial voting persists in the United States, caste/religious blocs define India elections, ethnic coalitions shape Kenya outcomes, and racial divides mark Brazil contests. These examples, drawn from election data and surveys, show how group affiliations, institutional advantages, and digital amplification sustain identity politics. Challenging modernization and deliberative theories, this framework reveals tribalism structural role, advocating identity-conscious designs like vote pooling to balance representation and equity. It reframes democratic legitimacy, emphasizing group dynamics over neutrality, and contributes a comparative lens to political science understanding of democratic governance across varied settings.
A Contemporary Survey on Semantic Communications:Theory of Mind, Generative AI, and Deep Joint Source-Channel Coding
2025-02-23
Loc X. Nguyen, Avi Deb Raha, Pyae Sone Aung, Dusit Niyato, Zhu Han, Choong Seon Hong
Semantic Communication is becoming the next pillar in wireless communication technology due to its various capabilities. However, it still encounters various challenging obstacles that need to be solved before real-world deployment. The major challenge is the lack of standardization across different directions, leading to variations in interpretations and objectives. In the survey, we provide detailed explanations of three leading directions in semantic communications, namely Theory of Mind, Generative AI, Deep Joint Source-Channel Coding. These directions have been widely studied, developed, and verified by institutes worldwide, and their effectiveness has increased along with the advancement in technology. We first introduce the concepts and background of these directions. Firstly, we introduce the Theory of Mind, where the communication agents interact with each other, gaining understanding from observations and slowly forming a common language. Secondly, we present generative AI models, which can create new content and offer more freedom to interpret the data beyond the limitation of semantic meaning compression of raw data before transmitting it. The received signal is then decoded by another generative AI model to execute the oriented task. Thirdly, we review deep learning models to jointly optimize the source and channel coding modules. Then, we present a comprehensive survey of existing works in each direction, thereby offering readers an overview of past achievements and potential avenues for further contribution. Moreover, for each direction, we identify and discuss the existing challenges that must be addressed before these approaches can be effectively deployed in real-world scenarios.
Deep learning approaches to surgical video segmentation and object detection: A Scoping Review
2025-02-23
Devanish N. Kamtam, Joseph B. Shrager, Satya Deepya Malla, Nicole Lin, Juan J. Cardona, Jake J. Kim, Clarence Hu
Introduction: Computer vision (CV) has had a transformative impact in biomedical fields such as radiology, dermatology, and pathology. Its real-world adoption in surgical applications, however, remains limited. We review the current state-of-the-art performance of deep learning (DL)-based CV models for segmentation and object detection of anatomical structures in videos obtained during surgical procedures. Methods: We conducted a scoping review of studies on semantic segmentation and object detection of anatomical structures published between 2014 and 2024 from 3 major databases - PubMed, Embase, and IEEE Xplore. The primary objective was to evaluate the state-of-the-art performance of semantic segmentation in surgical videos. Secondary objectives included examining DL models, progress toward clinical applications, and the specific challenges with segmentation of organs/tissues in surgical videos. Results: We identified 58 relevant published studies. These focused predominantly on procedures from general surgery [20(34.4%)], colorectal surgery [9(15.5%)], and neurosurgery [8(13.8%)]. Cholecystectomy [14(24.1%)] and low anterior rectal resection [5(8.6%)] were the most common procedures addressed. Semantic segmentation [47(81%)] was the primary CV task. U-Net [14(24.1%)] and DeepLab [13(22.4%)] were the most widely used models. Larger organs such as the liver (Dice score: 0.88) had higher accuracy compared to smaller structures such as nerves (Dice score: 0.49). Models demonstrated real-time inference potential ranging from 5-298 frames-per-second (fps). Conclusion: This review highlights the significant progress made in DL-based semantic segmentation for surgical videos with real-time applicability, particularly for larger organs. Addressing challenges with smaller structures, data availability, and generalizability remains crucial for future advancements.
Towards Fully-Automated Materials Discovery via Large-Scale Synthesis Dataset and Expert-Level LLM-as-a-Judge
2025-02-23
Heegyu Kim, Taeyang Jeon, Seungtaek Choi, Jihoon Hong, Dongwon Jeon, Sungbum Cho, Ga-Yeon Baek, Kyung-Won Kwak, Dong-Hee Lee, Sun-Jin Choi, Jisu Bae, Chihoon Lee, Yunseo Kim, Jinsung Park, Hyunsouk Cho
Materials synthesis is vital for innovations such as energy storage, catalysis, electronics, and biomedical devices. Yet, the process relies heavily on empirical, trial-and-error methods guided by expert intuition. Our work aims to support the materials science community by providing a practical, data-driven resource. We have curated a comprehensive dataset of 17K expert-verified synthesis recipes from open-access literature, which forms the basis of our newly developed benchmark, AlchemyBench. AlchemyBench offers an end-to-end framework that supports research in large language models applied to synthesis prediction. It encompasses key tasks, including raw materials and equipment prediction, synthesis procedure generation, and characterization outcome forecasting. We propose an LLM-as-a-Judge framework that leverages large language models for automated evaluation, demonstrating strong statistical agreement with expert assessments. Overall, our contributions offer a supportive foundation for exploring the capabilities of LLMs in predicting and guiding materials synthesis, ultimately paving the way for more efficient experimental design and accelerated innovation in materials science.
Contrastive Learning of English Language and Crystal Graphs for Multimodal Representation of Materials Knowledge
2025-02-23
Yang Jeong Park, Mayank Kumaran, Chia-Wei Hsu, Elsa Olivetti, Ju Li
Artificial intelligence (AI) is increasingly used for the inverse design of materials, such as crystals and molecules. Existing AI research on molecules has integrated chemical structures of molecules with textual knowledge to adapt to complex instructions. However, this approach has been unattainable for crystals due to data scarcity from the biased distribution of investigated crystals and the lack of semantic supervision in peer-reviewed literature. In this work, we introduce a contrastive language-crystals model (CLaC) pre-trained on a newly synthesized dataset of 126k crystal structure-text pairs. To demonstrate the advantage of using synthetic data to overcome data scarcity, we constructed a comparable dataset extracted from academic papers. We evaluate CLaC's generalization ability through various zero-shot cross-modal tasks and downstream applications. In experiments, CLaC achieves state-of-the-art zero-shot generalization performance in understanding crystal structures, surpassing latest large language models.
Advances in Continuous Variable Measurement-Device-Independent Quantum Key Distribution
2025-02-23
Pu Wang, Yan Tian, Yongmin Li
Continuous variable quantum key distribution (CV-QKD), utilizes continuous variables encoding such as the quadra-ture components of the quantized electromagnetic field and coherent detection decoding, offering good compatibility with the existing telecommunications technology and components. Continuous variable measurement-device-independent QKD (CV-MDI-QKD) can eliminate all the security threats arising from the receiver effectively, the crucial security loophole of CV-QKD implementations. Recently, CV-MDI-QKD has attracted extensive attentions and witnessed rapid progress. Here, we review the achievements that have been made in the field of CV-MDI-QKD, including the basic principle, advancements in theoretical protocols and experimental demonstrations. Finally, we discuss the challenges faced in practical applications and future research directions.
Automated Flow Pattern Classification in Multi-phase Systems Using AI and Capacitance Sensing Techniques
2025-02-23
Nian Ran, Fayez M. Al-Alweet, Richard Allmendinger, Ahmad Almakhlafi
In multiphase flow systems, classifying flow patterns is crucial to optimize fluid dynamics and enhance system efficiency. Current industrial methods and scientific laboratories mainly depend on techniques such as flow visualization using regular cameras or the naked eye, as well as high-speed imaging at elevated flow rates. These methods are limited by their reliance on subjective interpretations and are particularly applicable in transparent pipes. Consequently, conventional techniques usually achieve context-dependent accuracy rates and often lack generalizability. This study introduces a novel platform that integrates a capacitance sensor and AI-driven classification methods, benchmarked against traditional techniques. Experimental results demonstrate that the proposed approach, utilizing a 1D SENet deep learning model, achieves over 85\% accuracy on experiment-based datasets and 71\% accuracy on pattern-based datasets. These results highlight significant improvements in robustness and reliability compared to existing methodologies. This work offers a transformative pathway for real-time flow monitoring and predictive modeling, addressing key challenges in industrial applications.
A Joint Learning Framework for Bridging Defect Prediction and Interpretation
2025-02-23
Guifang Xu, Zhiling Zhu, Xingcheng Guo, Wei Wang
Over the past fifty years, numerous software defect prediction (SDP) approaches have been proposed. However, the ability to explain why predictors make certain predictions remains limited. Explainable SDP has emerged as a promising solution by using explainable artificial intelligence (XAI) methods to clarify the decision-making processes of predictors. Despite this progress, there is still significant potential to enhance the reliability of existing approaches. To address this limitation, we treat defect prediction and the corresponding interpretation as two distinct but closely related tasks and propose a joint learning framework that allows for the simultaneous training of the predictor and its interpreter. The novelty of our approach lies in two main aspects: 1. We design feedback loops that convey the decision-making logic from the predictor to the interpreter. This ensures a high level of conciseness in decision logic and feature engineering for both the predictor and the interpreter, enabling the interpreter to achieve reliable local and global interpretability. 2. We incorporate the interpretation results as a penalty term in the loss function of the joint-learning framework. This not only improves the accuracy of the predictor but also imposes a stronger constraint on the reliability of the interpreter. We validated our proposed method against several existing explainable SDPs across multiple datasets. The results demonstrate its effectiveness in both interpretation and defect prediction. The source code for the proposed method is available at: https://github.com/BugPredictor/software-defect-prediction.git
Much research effort has been devoted to interfacial or two-dimensional (2D) superconductors, but the underlying pairing mechanisms and pairing symmetries are highly controversial in most cases. Here we propose an innovative approach to probe the pairing symmetry of 2D superconductors, based on a van der Waals heterostructure consisting of a prototypical 2D Ising superconductor coupled with a 2D hole gas through an insulating spacer. We first show that, by tuning the Coulomb attraction between the superconducting and hole layers, the gap of the corresponding indirect exciton insulators is tuned as well, resulting in contrasting manifestations of the distinct superconducting channels with spin-singlet (s-, extended s-, and d-wave) and spin-triplet (p- and f-wave) pairings. Strikingly, we find that the application of in-plane magnetic fields can suppress all other channels while selecting the spin-triplet p-wave channel to be the pure superconducting state, thus providing an ideal and practical platform for realizing highly desirable topological superconductivity. Such an approach can also be readily extended to other types of superconducting systems, offering unprecedented opportunities to probe the microscopic mechanisms of unconventional superconductivity.
This paper comprehensively reviews anomaly synthesis methodologies. Existing surveys focus on limited techniques, missing an overall field view and understanding method interconnections. In contrast, our study offers a unified review, covering about 40 representative methods across Hand-crafted, Distribution-hypothesis-based, Generative models (GM)-based, and Vision-language models (VLM)-based synthesis. We introduce the first industrial anomaly synthesis (IAS) taxonomy. Prior works lack formal classification or use simplistic taxonomies, hampering structured comparisons and trend identification. Our taxonomy provides a fine-grained framework reflecting methodological progress and practical implications, grounding future research. Furthermore, we explore cross-modality synthesis and large-scale VLM. Previous surveys overlooked multimodal data and VLM in anomaly synthesis, limiting insights into their advantages. Our survey analyzes their integration, benefits, challenges, and prospects, offering a roadmap to boost IAS with multimodal learning. More resources are available at https://github.com/M-3LAB/awesome-anomaly-synthesis.
Recent Advances in Large Langauge Model Benchmarks against Data Contamination: From Static to Dynamic Evaluation
2025-02-23
Simin Chen, Yiming Chen, Zexin Li, Yifan Jiang, Zhongwei Wan, Yixin He, Dezhi Ran, Tianle Gu, Haizhou Li, Tao Xie, Baishakhi Ray
Data contamination has received increasing attention in the era of large language models (LLMs) due to their reliance on vast Internet-derived training corpora. To mitigate the risk of potential data contamination, LLM benchmarking has undergone a transformation from static to dynamic benchmarking. In this work, we conduct an in-depth analysis of existing static to dynamic benchmarking methods aimed at reducing data contamination risks. We first examine methods that enhance static benchmarks and identify their inherent limitations. We then highlight a critical gap-the lack of standardized criteria for evaluating dynamic benchmarks. Based on this observation, we propose a series of optimal design principles for dynamic benchmarking and analyze the limitations of existing dynamic benchmarks. This survey provides a concise yet comprehensive overview of recent advancements in data contamination research, offering valuable insights and a clear guide for future research efforts. We maintain a GitHub repository to continuously collect both static and dynamic benchmarking methods for LLMs. The repository can be found at this link.
This review explores modified theories of gravity, particularly $f(R)$ gravity, as extensions to General Relativity (GR) that offer alternatives to dark energy for explaining cosmic acceleration. These models generalize the Einstein-Hilbert action to include functions of the Ricci scalar, providing new insights into cosmology and astrophysics. The detection of gravitational waves (GWs) has enabled rigorous tests of $f(R)$ gravity, as deviations in GW propagation, speed, and polarization can signal modifications to GR. Constraints on $f(R)$ models arise from LIGO-Virgo observations of binary mergers, the stochastic gravitational wave background (SGWB), and complementary tests in cosmology and weak-field regimes. Future GW detectors, such as LISA and the Einstein Telescope, will enhance sensitivity to smaller deviations from GR, necessitating advancements in theoretical modeling. Among competing theories -- including scalar-tensor, massive gravity, and Horndeski models -- $f(R)$ gravity remains a pivotal framework for understanding fundamental gravitational physics and cosmology. This review highlights key developments, challenges, and future directions in the field.
A Comparative Review of the Histogram-based Image Segmentation Methods
2025-02-23
ZhenZhou Wang
The histogram of an image is the accurate graphical representation of the numerical grayscale distribution and it is also an estimate of the probability distribution of image pixels. Therefore, histogram has been widely adopted to calculate the clustering means and partitioning thresholds for image segmentation. There have been many classical histogram-based image segmentation methods proposed and played important roles in both academics and industry. In this article, the histories and recent advances of the histogram-based image segmentation techniques are first reviewed and then they are divided into four categories: (1), the means-based method; (2), the Gaussian-mixture-model-based method; (3), the entropy-based method and (4) the feature-points-based method. The principles of the classical histogram-based image segmentation methods are described at first and then their performances are compared objectively. In addition, the histogram-based image segmentation methods are compared with the general-purpose deep learning methods in segmenting objects with uniform or simple backgrounds. The histogram-based image segmentation methods are more accurate than the universal deep-learning methods without special training in segmenting many types of images.
2025-02-22
Beatriz Canaverde, Telmo Pessoa Pires, Leonor Melo Ribeiro, André F. T. Martins
The recent application of LLMs to the legal field has spurred the creation of benchmarks across various jurisdictions and languages. However, no benchmark has yet been specifically designed for the Portuguese legal system. In this work, we present LegalBench.PT, the first comprehensive legal benchmark covering key areas of Portuguese law. To develop LegalBench.PT, we first collect long-form questions and answers from real law exams, and then use GPT-4o to convert them into multiple-choice, true/false, and matching formats. Once generated, the questions are filtered and processed to improve the quality of the dataset. To ensure accuracy and relevance, we validate our approach by having a legal professional review a sample of the generated questions. Although the questions are synthetically generated, we show that their basis in human-created exams and our rigorous filtering and processing methods applied result in a reliable benchmark for assessing LLMs' legal knowledge and reasoning abilities. Finally, we evaluate the performance of leading LLMs on LegalBench.PT and investigate potential biases in GPT-4o's responses. We also assess the performance of Portuguese lawyers on a sample of questions to establish a baseline for model comparison and validate the benchmark.
Machine Learning-Based Cloud Computing Compliance Process Automation
2025-02-22
Yuqing Wang, Xiao Yang
Cloud computing adoption across industries has revolutionized enterprise operations while introducing significant challenges in compliance management. Organizations must continuously meet evolving regulatory requirements such as GDPR and ISO 27001, yet traditional manual review processes have become increasingly inadequate for modern business scales. This paper presents a novel machine learning-based framework for automating cloud computing compliance processes, addressing critical challenges including resource-intensive manual reviews, extended compliance cycles, and delayed risk identification. Our proposed framework integrates multiple machine learning technologies, including BERT-based document processing (94.5% accuracy), One-Class SVM for anomaly detection (88.7% accuracy), and an improved CNN-LSTM architecture for sequential compliance data analysis (90.2% accuracy). Implementation results demonstrate significant improvements: reducing compliance process duration from 7 days to 1.5 days, improving accuracy from 78% to 93%, and decreasing manual effort by 73.3%. A real-world deployment at a major securities firm validated these results, processing 800,000 daily transactions with 94.2% accuracy in risk identification.
The Design Space of Recent AI-assisted Research Tools for Ideation, Sensemaking, and Scientific Creativity
2025-02-22
Runlong Ye, Matthew Varona, Oliver Huang, Patrick Yung Kang Lee, Michael Liut, Carolina Nobre
Generative AI (GenAI) tools are radically expanding the scope and capability of automation in knowledge work such as academic research. AI-assisted research tools show promise for augmenting human cognition and streamlining research processes, but could potentially increase automation bias and stifle critical thinking. We surveyed the past three years of publications from leading HCI venues. We closely examined 11 AI-assisted research tools, five employing traditional AI approaches and six integrating GenAI, to explore how these systems envision novel capabilities and design spaces. We consolidate four design recommendations that inform cognitive engagement when working with an AI research tool: Providing user agency and control; enabling divergent and convergent thinking; supporting adaptability and flexibility; and ensuring transparency and accuracy. We discuss how these ideas mark a shift in AI-assisted research tools from mimicking a researcher's established workflows to generative co-creation with the researcher and the opportunities this shift affords the research community.
Human Preferences in Large Language Model Latent Space: A Technical Analysis on the Reliability of Synthetic Data in Voting Outcome Prediction
2025-02-22
Sarah Ball, Simeon Allmendinger, Frauke Kreuter, Niklas Kühl
Generative AI (GenAI) is increasingly used in survey contexts to simulate human preferences. While many research endeavors evaluate the quality of synthetic GenAI data by comparing model-generated responses to gold-standard survey results, fundamental questions about the validity and reliability of using LLMs as substitutes for human respondents remain. Our study provides a technical analysis of how demographic attributes and prompt variations influence latent opinion mappings in large language models (LLMs) and evaluates their suitability for survey-based predictions. Using 14 different models, we find that LLM-generated data fails to replicate the variance observed in real-world human responses, particularly across demographic subgroups. In the political space, persona-to-party mappings exhibit limited differentiation, resulting in synthetic data that lacks the nuanced distribution of opinions found in survey data. Moreover, we show that prompt sensitivity can significantly alter outputs for some models, further undermining the stability and predictiveness of LLM-based simulations. As a key contribution, we adapt a probe-based methodology that reveals how LLMs encode political affiliations in their latent space, exposing the systematic distortions introduced by these models. Our findings highlight critical limitations in AI-generated survey data, urging caution in its use for public opinion research, social science experimentation, and computational behavioral modeling.
Revolutionizing Datacenter Networks via Reconfigurable Topologies
2025-02-22
Chen Avin, Stefan Schmid
With the popularity of cloud computing and data-intensive applications such as machine learning, datacenter networks have become a critical infrastructure for our digital society. Given the explosive growth of datacenter traffic and the slowdown of Moore's law, significant efforts have been made to improve datacenter network performance over the last decade. A particularly innovative solution is reconfigurable datacenter networks (RDCNs): datacenter networks whose topologies dynamically change over time, in either a demand-oblivious or a demand-aware manner. Such dynamic topologies are enabled by recent optical switching technologies and stand in stark contrast to state-of-the-art datacenter network topologies, which are fixed and oblivious to the actual traffic demand. In particular, reconfigurable demand-aware and 'self-adjusting' datacenter networks are motivated empirically by the significant spatial and temporal structures observed in datacenter communication traffic. This paper presents an overview of reconfigurable datacenter networks. In particular, we discuss the motivation for such reconfigurable architectures, review the technological enablers, and present a taxonomy that classifies the design space into two dimensions: static vs. dynamic and demand-oblivious vs. demand-aware. We further present a formal model and discuss related research challenges. Our article comes with complementary video interviews in which three leading experts, Manya Ghobadi, Amin Vahdat, and George Papen, share with us their perspectives on reconfigurable datacenter networks.
Physical parameters of 12201 ASAS-SN contact binaries determined by the Neural Network
2025-02-22
Kai Li, Li-Heng Wang
In the era of astronomical big data, more than one million contact binaries have been discovered. Traditional approaches of light curve analysis are inadequate for investigating such an extensive number of systems. This paper builds on prior research to present an advanced Neural Network model combined with the Markov Chain Monte Carlo algorithm and including spot parameters. This model was applied to 12785 contact binaries selected from All-Sky Automated Survey for Supernovae. By removing those with goodness of fit less than 0.8, we obtained the physical parameters of 12201 contact binaries. Among these binaries, 4332 are A-subtype systems, while 7869 are W-type systems, and 1594 systems have mass ratios larger than 0.72 (H-subtype system). A statistical study of the physical parameters was carried out, and we found that there are two peaks in the mass ratio distribution and that the probability of the presence of spot is about 50%. In addition, the differences in flux between the two light maxima are from $-$0.1 to 0.1. As the orbital period and the temperature of the primary component decrease, the difference between the two light maxima becomes more pronounced. Based on the relationships between transfer parameter and luminosity ratio, as well as between luminosity ratio and mass ratio, we found that A-, W-, and H-type contact binaries are distributed in distinct regions.
Statistical Inference in Reinforcement Learning: A Selective Survey
2025-02-22
Chengchun Shi
Reinforcement learning (RL) is concerned with how intelligence agents take actions in a given environment to maximize the cumulative reward they receive. In healthcare, applying RL algorithms could assist patients in improving their health status. In ride-sharing platforms, applying RL algorithms could increase drivers' income and customer satisfaction. Over the past decade, RL has been arguably one of the most vibrant research frontiers in machine learning. Nevertheless, statistics as a field, as opposed to computer science, has only recently begun to engage with RL both in depth and in breadth. This paper present a selective review of statistical inferential tools for RL, covering both hypothesis testing and confidence interval construction. Our goal is to highlight the value of statistical inference in RL for both the statistics and machine learning communities, and to promote the broader application of classical statistical inference tools in this vibrant area of research.
Electromagnetic Form Factors and the Proton Radius Puzzle: A Critical Review of Extraction Methods via Electron-Proton Scattering
2025-02-22
Gerome A. Paterez, Jade C. Jusoy, Edmar Pantohan, Eulogio Auxtero Jr
The study of protons, fundamental constituents of atomic nuclei, is crucial for understanding nuclear physics and fundamental forces. This review examines advancements in extracting electromagnetic form factors, essential for probing proton structure. Electron-proton scattering experiments, particularly at Jefferson Lab (JLab) and Mainz Microtron (MAMI), have played a key role [arXiv:2009.10510, arXiv:1912.03881]. Various methodologies, such as Rosenbluth separation and polarization transfer, are discussed, highlighting their evolution and challenges, including systematic uncertainties and theoretical ambiguities [arXiv:1706.00696]. Literature indicates the Rosenbluth method yields a proton radius of approximately 0.8 fm, consistent with early measurements but differing from recent polarization transfer results, which suggest a smaller radius [arXiv:1912.03881]. The polarization transfer technique has become a preferred approach, offering precise measurements of the electric-to-magnetic form factor ratio, especially at high momentum transfer [arXiv:1706.00696, arXiv:1912.03881]. Alternative methods, such as cross-section ratios, extend form factor studies but still face residual uncertainties [arXiv:2009.10510]. While significant progress has been made, discrepancies persist, particularly in proton radius extraction. Future research must address systematic uncertainties to refine our understanding of proton electromagnetic properties and advance particle physics [arXiv:1912.03881, arXiv:1706.00696].
TutorUp: What If Your Students Were Simulated? Training Tutors to Address Engagement Challenges in Online Learning
2025-02-22
Sitong Pan, Robin Schmucker, Bernardo Garcia Bulle Bueno, Salome Aguilar Llanes, Fernanda Albo Alarcón, Hangxiao Zhu, Adam Teo, Meng Xia
With the rise of online learning, many novice tutors lack experience engaging students remotely. We introduce TutorUp, a Large Language Model (LLM)-based system that enables novice tutors to practice engagement strategies with simulated students through scenario-based training. Based on a formative study involving two surveys (N1=86, N2=102) on student engagement challenges, we summarize scenarios that mimic real teaching situations. To enhance immersion and realism, we employ a prompting strategy that simulates dynamic online learning dialogues. TutorUp provides immediate and asynchronous feedback by referencing tutor-students online session dialogues and evidence-based teaching strategies from learning science literature. In a within-subject evaluation (N=16), participants rated TutorUp significantly higher than a baseline system without simulation capabilities regarding effectiveness and usability. Our findings suggest that TutorUp provides novice tutors with more effective training to learn and apply teaching strategies to address online student engagement challenges.
Keystroke dynamics is a behavioral biometric that captures an individual's typing patterns for authentication and security applications. This paper presents a comparative analysis of keystroke authentication models using Gaussian Mixture Models (GMM), Mahalanobis Distance-based Classification, and Gunetti Picardi's Distance Metrics. These models leverage keystroke timing features such as hold time (H), up-down time (UD), and down-down time (DD) extracted from datasets including Aalto, Buffalo and Nanglae-Bhattarakosol. Each model is trained and validated using structured methodologies, with performance evaluated through False Acceptance Rate (FAR), False Rejection Rate (FRR), and Equal Error Rate (EER). The results, visualized through Receiver Operating Characteristic (ROC) curves, highlight the relative strengths and weaknesses of each approach in distinguishing genuine users from impostors.
2025-02-22
Lin Ge, Hengrui Cai, Runzhe Wan, Yang Xu, Rui Song
To make effective decisions, it is important to have a thorough understanding of the causal relationships among actions, environments, and outcomes. This review aims to surface three crucial aspects of decision-making through a causal lens: 1) the discovery of causal relationships through causal structure learning, 2) understanding the impacts of these relationships through causal effect learning, and 3) applying the knowledge gained from the first two aspects to support decision making via causal policy learning. Moreover, we identify challenges that hinder the broader utilization of causal decision-making and discuss recent advances in overcoming these challenges. Finally, we provide future research directions to address these challenges and to further enhance the implementation of causal decision-making in practice, with real-world applications illustrated based on the proposed causal decision-making. We aim to offer a comprehensive methodology and practical implementation framework by consolidating various methods in this area into a Python-based collection. URL: https://causaldm.github.io/Causal-Decision-Making.
Epilepsy and its driving forces: understanding the forces behind epileptical pathogenisis
2025-02-22
Shreya Shah, Manan Shah, Bhavin Parekh
Epilepsy is a neurological disorder characterized by seizures and epileptic events intertwined with religious and personal beliefs since prehistoric times. This review paper explores the historical context and challenges in defining epilepsy. The formal definition was established twenty years ago, and the multifaceted causes of this neurological disorder. It aims to pave the way for personalised therapeutic strategies, research advancements, and informed public health planning to enhance the lives of those affected by this complex neurological condition. In addition, this review paper focuses on the mechanisms and etiologies of epileptogenesis, categorizing them by mechanisms and the underlying causes of the disorder. The review paper provides a brief overview of the current state of the art in the diagnosis, diagnosis, treatment, and treatment of epileptiform seizures.
Neutrino Interactions in the SIS and DIS Regions: Current Insights and Future Challenges
2025-02-22
M. Sajjad Athar
In this review, we discuss the current understanding of charged current (anti)neutrino scattering off nucleons and nuclear targets in the few-GeV energy range, a domain of paramount importance for accelerator and atmospheric neutrino experiments. We provide a concise yet comprehensive overview of the experimental and theoretical landscape of neutrino interaction processes across the kinematic region of the shallow and deep inelastic scattering regimes. Moreover, we underscore the pressing unresolved questions and formidable challenges that lie ahead, stressing the urgent need for more refined theoretical models and high-precision measurements to deepen our understanding of neutrino-nucleon and neutrino-nucleus interaction cross sections.
AnxietyFaceTrack: A Smartphone-Based Non-Intrusive Approach for Detecting Social Anxiety Using Facial Features
2025-02-22
Nilesh Kumar Sahu, Snehil Gupta, Haroon R Lone
Social Anxiety Disorder (SAD) is a widespread mental health condition, yet its lack of objective markers hinders timely detection and intervention. While previous research has focused on behavioral and non-verbal markers of SAD in structured activities (e.g., speeches or interviews), these settings fail to replicate real-world, unstructured social interactions fully. Identifying non-verbal markers in naturalistic, unstaged environments is essential for developing ubiquitous and non-intrusive monitoring solutions. To address this gap, we present AnxietyFaceTrack, a study leveraging facial video analysis to detect anxiety in unstaged social settings. A cohort of 91 participants engaged in a social setting with unfamiliar individuals and their facial videos were recorded using a low-cost smartphone camera. We examined facial features, including eye movements, head position, facial landmarks, and facial action units, and used self-reported survey data to establish ground truth for multiclass (anxious, neutral, non-anxious) and binary (e.g., anxious vs. neutral) classifications. Our results demonstrate that a Random Forest classifier trained on the top 20% of features achieved the highest accuracy of 91.0% for multiclass classification and an average accuracy of 92.33% across binary classifications. Notably, head position and facial landmarks yielded the best performance for individual facial regions, achieving 85.0% and 88.0% accuracy, respectively, in multiclass classification, and 89.66% and 91.0% accuracy, respectively, across binary classifications. This study introduces a non-intrusive, cost-effective solution that can be seamlessly integrated into everyday smartphones for continuous anxiety monitoring, offering a promising pathway for early detection and intervention.
In 1966, Hedetniemi conjectured that for any positive integer $n$ and graphs $G$ and $H$, if neither $G$ nor $H$ is $n$-colourable, then $G \times H$ is not $n$-colourable. This conjecture has received significant attention over the past half century, and was disproved by Shitov in 2019. Shitov's proof shows that Hedetniemi's conjecture fails for sufficiently large $n$. Shortly after Shitov's result, smaller counterexamples were found in a series of papers, and it is now known that Hedetniemi's conjecture fails for all $n \ge 4$, and holds for $n \le 3$. Hedetniemi's conjecture has inspired extensive research, and many related problems remain open. This paper surveys the results and problems associated with the conjecture, and explains the ideas used in finding counterexamples.
Low-energy spin dynamics is investigated by $^{35}$ Cl NMR measurements in a frustrated antiferromagnet Ca$_3$ReO$_5$Cl$_2$. The local spin susceptibility measured with the Knight shift behaves as a one-dimensional Heisenberg antiferromagnet and remains constant down to low temperatures, as expected in a gapless Tomonaga-Luttinger liquid. The nuclear spin-lattice relaxation rate $T_1^{-1}$ demonstrates a slowing down of atomic motions and a power-law evolution of spin correlation. The Luttinger parameter is enhanced in a site-selective manner depending on the form factor of dynamical spin susceptibility. The strong anisotropy of $T_1^{-1}$ reflects the strong spin-orbit coupling through Dzyaloshinskii-Moriya interaction. The ground state exhibits an incommensurate antiferromagnetic ordering with low-lying magnon excitations.
Model Extraction Attacks (MEAs) threaten modern machine learning systems by enabling adversaries to steal models, exposing intellectual property and training data. With the increasing deployment of machine learning models in distributed computing environments, including cloud, edge, and federated learning settings, each paradigm introduces distinct vulnerabilities and challenges. Without a unified perspective on MEAs across these distributed environments, organizations risk fragmented defenses, inadequate risk assessments, and substantial economic and privacy losses. This survey is motivated by the urgent need to understand how the unique characteristics of cloud, edge, and federated deployments shape attack vectors and defense requirements. We systematically examine the evolution of attack methodologies and defense mechanisms across these environments, demonstrating how environmental factors influence security strategies in critical sectors such as autonomous vehicles, healthcare, and financial services. By synthesizing recent advances in MEAs research and discussing the limitations of current evaluation practices, this survey provides essential insights for developing robust and adaptive defense strategies. Our comprehensive approach highlights the importance of integrating protective measures across the entire distributed computing landscape to ensure the secure deployment of machine learning models.
Statistical Contextual Explanation of Quantum Paradoxes
2025-02-22
Marian Kupczynski
We celebrate this year hundred years of quantum mechanics but there is still no consensus regarding its interpretation and limitations. In this article we advocate the statistical contextual interpretation which is free of paradoxes. State vectors and various operators are purely mathematical entities allowing making quantitative probabilistic predictions. State vector describes an ensemble of identically prepared physical systems and a specific operator represents a class of equivalent measurements of a physical observable. A collapse of wave function is not a mysterious and instantaneous physical process. A collapsed quantum state describes a new ensemble of physical systems prepared in a particular way. Probabilities are objective properties of random experiments in which empirical frequencies stabilize. Therefore, quantum probabilities do not provide a complete description of individual physical systems and their interactions. Whether these probabilities can be explained as emergent is an open question which cannot be settled by philosophical discussions and no-go theorems. It can be only answered by more detailed study of experimental data then it is usually done. Bell Tests allowed rejecting Bell local and Bell causal hidden variable models but we even don't know whether quantum probabilities provide a complete description of existing experimental data. Time series of experimental data may contain much more information than it is obtained using empirical frequencies and histograms. Therefore, predictable completeness of quantum mechanics has be tested and not taken for granted.
In recent years, the pivotal role of emotions in information dissemination has attracted extensive attention. Research indicates that emotionally charged content significantly outperforms neutral content in dissemination speed and coverage due to its ability to evoke stronger emotional responses. Valence, arousal, and dominance are key factors influencing information retention, comprehension, and sharing. However, systematic analysis of the mechanisms by which emotions affect dissemination and the application of multidimensional emotional design remain limited. This study reviews 166 relevant articles, examining the role of emotions in understanding and sharing information, and proposes a multidimensional design framework based on text, visuals, sound, and interaction design. Incorporating the ``Four-System" model of emotion activation, it develops an emotion regulation framework to optimize the application of emotions in dissemination. This research offers theoretical support and practical guidance for emotion-driven dissemination, laying a foundation for advancing this field.
A Survey on Mechanistic Interpretability for Multi-Modal Foundation Models
2025-02-22
Zihao Lin, Samyadeep Basu, Mohammad Beigi, Varun Manjunatha, Ryan A. Rossi, Zichao Wang, Yufan Zhou, Sriram Balasubramanian, Arman Zarei, Keivan Rezaei, Ying Shen, Barry Menglong Yao, Zhiyang Xu, Qin Liu, Yuxiang Zhang, Yan Sun, Shilong Liu, Li Shen, Hongxuan Li, Soheil Feizi, Lifu Huang
The rise of foundation models has transformed machine learning research, prompting efforts to uncover their inner workings and develop more efficient and reliable applications for better control. While significant progress has been made in interpreting Large Language Models (LLMs), multimodal foundation models (MMFMs) - such as contrastive vision-language models, generative vision-language models, and text-to-image models - pose unique interpretability challenges beyond unimodal frameworks. Despite initial studies, a substantial gap remains between the interpretability of LLMs and MMFMs. This survey explores two key aspects: (1) the adaptation of LLM interpretability methods to multimodal models and (2) understanding the mechanistic differences between unimodal language models and crossmodal systems. By systematically reviewing current MMFM analysis techniques, we propose a structured taxonomy of interpretability methods, compare insights across unimodal and multimodal architectures, and highlight critical research gaps.
Calibration of MAJIS (Moons And Jupiter Imaging Spectrometer): I. On-ground setup description and characterisation
2025-02-21
Mathieu Vincendon, Pierre Guiot, Benoît Lecomte, Mathieu Condamin, François Poulet, Antoine Arondel, Julien Barbay, John Carter, Simone De Angelis, Cydalise Dumesnil, Gianrico Filacchione, Paolo Haffoud, Jérémie Hansotte, Yves Langevin, Pierre-Louis Mayeur, Giuseppe Piccioni, Cédric Pilorget, Eric Quirico, Sébastien Rodriguez
The visible and infrared Moon And Jupiter Imaging Spectrometer (MAJIS), aboard the JUpiter ICy Moons Explorer (JUICE) spacecraft, will characterize the composition of the surfaces and atmospheres of the Jupiter system. Prior to the launch, a campaign was carried out to obtain the measurements needed to calibrate the instrument. The aim was not only to produce data for the calculation of the radiometric, spectral, and spatial transfer functions, but also to evaluate MAJIS performance, such as signal-to-noise ratio and amount of straylight, under near-flight conditions. Here, we first describe the setup implemented to obtain these measurements, based on five optical channels. We notably emphasize the concepts used to mitigate thermal infrared emissions generated at ambient temperatures, since the MAJIS spectral range extends up to 5.6 $\mu$m. Then, we characterize the performance of the setup by detailing the validation measurements obtained before the campaign. In particular, the radiometric, geometric, and spectral properties of the setup needed for the inversion of collected data and the calculation of the instrument's calibration functions are presented and discussed. Finally, we provide an overview of conducted measurements with MAJIS, and we discuss unforeseen events encountered during the on-ground calibration campaign.
Empowering LLMs with Logical Reasoning: A Comprehensive Survey
2025-02-21
Fengxiang Cheng, Haoxuan Li, Fenrong Liu, Robert van Rooij, Kun Zhang, Zhouchen Lin
Large language models (LLMs) have achieved remarkable successes on various natural language tasks. However, recent studies have found that there are still significant challenges to the logical reasoning abilities of LLMs. This paper summarizes and categorizes the main challenges into two aspects: (1) Logical question answering, LLMs often fail to generate the correct answer within complex logical problem which requires sophisticated deductive, inductive or abductive reasoning given a collection of premises and constrains. (2) Logical consistency, LLMs are prone to producing responses contradicting themselves across different questions. For example, a state-of-the-art Macaw question-answering LLM answers Yes to both questions Is a magpie a bird? and Does a bird have wings? but answers No to Does a magpie have wings?. To facilitate this research direction, we comprehensively investigate the most cutting-edge methods and propose detailed taxonomies of these methods. Specifically, to accurately answer complex logic questions, previous methods can be categorized based on reliance on external solvers, prompts, pretraining, and fine-tuning. To avoid logical contradictions, we discuss concepts and solutions of various logical consistencies, including implication, negation, transitivity, factuality consistency, and their composites. In addition, we review commonly used benchmark datasets and evaluation metrics, and discuss promising research directions, such as extensions to modal logic to account for uncertainty, and efficient algorithms satisfying multiple logical consistencies simultaneously.
The SARAO MeerKAT Galactic Plane Survey extended source catalogue
2025-02-21
C. Bordiu, S. Riggi, F. Bufano, F. Cavallaro, T. Cecconello, F. Camilo, G. Umana, W. D. Cotton, M. A. Thompson, M. Bietenholz, S. Goedhart, L. D. Anderson, C. S. Buemi, J. O. Chibueze, A. Ingallinera, P. Leto, S. Loru, M. Mutale, A. Rigby, C. Trigilio, G. M. Williams
We present a catalogue of extended radio sources from the SARAO MeerKAT Galactic Plane Survey (SMGPS). Compiled from 56 survey tiles and covering approximately 500 deg$^2$ across the first, third, and fourth Galactic quadrants, the catalogue includes 16534 extended and diffuse sources with areas larger than 5 synthesised beams. Of them, 3891 (24\% of the total) are confidently associated with known Galactic radio-emitting objects in the literature, such as HII regions, supernova remnants, planetary nebulae, luminous blue variables, and Wolf-Rayet stars. A significant fraction of the remaining sources, 5462 (33\%), are candidate extragalactic sources, while 7181 (43\%) remain unclassified. Isolated radio filaments are excluded from the catalogue. The diversity of extended sources underscores MeerKAT's contribution to the completeness of censuses of Galactic radio emitters, and its potential for new scientific discoveries. For the catalogued sources, we derived basic positional and morphological parameters, as well as flux density estimates, using standard aperture photometry. This paper describes the methods followed to generate the catalogue from the original SMGPS tiles, detailing the source extraction, characterisation, and crossmatching procedures. Additionally, we analyse the statistical properties of the catalogued populations
Fundamental relation in isolated galaxies, pairs, and triplets in the local universe
2025-02-21
M. Argudo-Fernández, S. Duarte Puertas, S. Verley
In this work, we establish fundamental relations, for the first time, in isolated systems in the local universe (with 0.005$\leq$z$\leq$0.080), which can give insight into the underlying physics of star-formation. We use a sample of isolated galaxies to explore whether star formation is regulated by smooth secular processes. In addition, galaxies in physically bound isolated pairs and isolated triplets may also interact with each other, where interaction itself may enhance/regulate star-formation and the distribution of gas and metals within galaxies. We made use of published emission line fluxes information from the Sloan Digital Sky Survey (SDSS) to identify SF galaxies in the SDSS-based catalogue of isolated galaxies (SIG), isolated pairs (SIP), and isolated triplets (SIT). We also use this data to derive their aperture-corrected SFR (considering two different methods) and oxygen abundance, 12 + log(O/H), using bright lines calibrations. Stellar masses for SIG, SIP, and SIT galaxies were estimated by fitting their spectral energy distribution on the five SDSS bands. We compared our results with a sample of SF galaxies in the SDSS. We found that, on average, at a fixed stellar mass, the SIG SF galaxies have lower SFR values than Main Sequence (MS) SF galaxies in the SDSS and central galaxies in the SIP and SIT. On average, SIG galaxies have higher 12 + log(O/H) values than galaxies in the SIP, SIT, and comparison sample. When distinguishing between central and satellite galaxies in the SIP and SIT, centrals and SIG galaxies present similar values ($\sim$8.55) while satellites have values close to M33 ($\sim$8.4). Based on our results, we propose a ground level `nurture' free SFR-M$_\star$ and gas metallicity-SFR-M$_\star$ relations for SF galaxies in the local universe.
Paradigms of AI Evaluation: Mapping Goals, Methodologies and Culture
2025-02-21
John Burden, Marko Tešić, Lorenzo Pacchiardi, José Hernández-Orallo
Research in AI evaluation has grown increasingly complex and multidisciplinary, attracting researchers with diverse backgrounds and objectives. As a result, divergent evaluation paradigms have emerged, often developing in isolation, adopting conflicting terminologies, and overlooking each other's contributions. This fragmentation has led to insular research trajectories and communication barriers both among different paradigms and with the general public, contributing to unmet expectations for deployed AI systems. To help bridge this insularity, in this paper we survey recent work in the AI evaluation landscape and identify six main paradigms. We characterise major recent contributions within each paradigm across key dimensions related to their goals, methodologies and research cultures. By clarifying the unique combination of questions and approaches associated with each paradigm, we aim to increase awareness of the breadth of current evaluation approaches and foster cross-pollination between different paradigms. We also identify potential gaps in the field to inspire future research directions.
A Comprehensive Survey of Linear, Integer, and Mixed-Integer Programming Approaches for Optimizing Resource Allocation in 5G and Beyond Networks
2025-02-21
Naveed Ejaz, Salimur Choudhury
The introduction of 5G networks has significantly advanced communication technology, offering faster speeds, lower latency, and greater capacity. This progress sets the stage for Beyond 5G (B5G) networks, which present new complexity and performance requirements challenges. Linear Programming (LP), Integer Linear Programming (ILP), and Mixed-Integer Linear Programming (MILP) models have been widely used to model the optimization of resource allocation problems in networks. This paper reviews 103 studies on resource allocation strategies in 5G and B5G, focusing specifically on optimization problems modelled as LP, ILP, and MILP. The selected studies are categorized based on network architectures, types of resource allocation problems, and specific objective functions and constraints. The review also discusses solution methods for NP-hard ILP and MILP problems by categorizing the solution methods into different categories. Additionally, emerging trends, such as integrating AI and machine learning with optimization models, are explored, suggesting promising future research directions in network optimization. The paper concludes that LP, ILP, and MILP models have been widely adopted across various network architectures, resource types, objective functions, and constraints and remain critical to optimizing next-generation networks.
2025-02-21
Milan Gritta, Huiyin Xue, Gerasimos Lampouras
Speculative decoding (SD) accelerates Large Language Model (LLM) generation by using an efficient draft model to propose the next few tokens, which are verified by the LLM in a single forward call, reducing latency while preserving its outputs. We focus on retrieval-based SD where the draft model retrieves the next tokens from a non-parametric datastore. Sparse retrieval (REST), which operates on the surface form of strings, is currently the dominant paradigm due to its simplicity and scalability. However, its effectiveness is limited due to the usage of short contexts and exact string matching. Instead, we introduce Dense Retrieval for Speculative Decoding (DReSD), a novel framework that uses approximate nearest neighbour search with contextualised token embeddings to retrieve the most semantically relevant token sequences for SD. Extensive experiments show that DReSD achieves (on average) 87% higher acceptance rates, 65% longer accepted tokens and 19% faster generation speeds compared to sparse retrieval (REST).
Clump formation in ram-pressure stripped galaxies: evidence from mass function
2025-02-21
Eric Giunchi, Claudia Scarlata, Ariel Werle, Bianca M. Poggianti, Alessia Moretti, Marco Gullieuszik, Bendetta Vulcani, Alessandro Ignesti, Antonino Marasco, Anita Zanella, Anna Wolter
The mass function (MF) of young ($\mathrm{age\lesssim 200}$ Myr) stellar clumps is an indicator of the mechanism driving the collapse of the interstellar medium (ISM) into giant molecular clouds. Typically, the clump MF in main-sequence galaxies is described by a power law ($dN/dM_*\propto M_*^{-\alpha}$) with slope $\alpha=2$, hinting a turbulence-driven collapse. To understand whether the local environment affects star formation, we have modelled the clump MF of six cluster galaxies from the GASP survey, undergoing strong ram-pressure stripping. This process, exerted by the hot and high-pressure intra-cluster medium (ICM), has produced long tails of stripped ISM where clumps form far away from the galactic disk and surrounded by the ICM itself. Clumps were selected from HST-UVIS/WFC3 images, covering from near-UV to red-optical bands and including H$\alpha$-line maps. The catalogue comprises 398 H$\alpha$ (188 in tails, 210 in disk outskirts, the so-called extraplanar region) and 1270 UV clumps (593 tail, 677 extraplanar). Using mock images, we quantified the mass completeness and bias of our sample. Accounting for these two effects, we adopted a Bayesian approach to fit the clump mass catalogue to a chosen function. The resulting MFs are steeper than expected. In the tails, the H$\alpha$ clumps have slope $\alpha=2.31\pm0.12$, while the UV slope is larger ($2.60\pm0.09$), in agreement with ageing effects. Similar results are found in the extraplanar region, with H$\alpha$ slope $\alpha=2.45^{+0.20}_{-0.16}$ and UV slope $\alpha=2.63^{+0.20}_{-0.18}$, even if they are consistent within uncertainties. We suggest that the steepening results from the higher-than-usual turbulent environment, arising from the interaction between ISM and ICM. As shown by recent works, this process can favour the fragmentation of the largest ISM clouds, inhibiting the formation of very massive clumps.
Examination of the multiple-scattering expansion in the four-nucleon system
2025-02-21
A. Deltuva
The elastic neutron-${}^3\mathrm{H}$ scattering at intermediate energies is studied using rigorous integral equations solved in the momentum-space partial-wave basis. The four-particle transition operators are expanded into multiple-scattering series in terms of subsystem transition operators. Various approximations resulting from truncation of the series in different ways are evaluated and their validity is investigated. They fail at lower energies but at higher energies provide a rough reproduction of exact results at small scattering angles. In the large-angle region all approximations fail heavily, indicating that the scattering amplitude results from a delicate interplay of many multiple-scattering terms. The partial-wave analysis reveals that the developed approximations are reliable in higher partial waves, and for practical calculations an efficient ``hybrid'' approach is proposed, combining exact amplitudes in lower partial waves with approximations in higher partial waves. The implications for often used approximation of the first order in two-body transition matrix and development of microscopic optical potentials are discussed.
In these proceedings, I will review lattice QCD calculations of baryon-baryon scattering, their methods, and their challenges. In the last few years, there has been a new generation of calculations with increased focus on controlling systematic uncertainties. Contrary to the findings of earlier exploratory calculations, it now appears probable that at heavy pion masses there is no nucleon-nucleon bound state.
Recent advances in the analysis of the dissipative Aw-Rascle system
2025-02-21
Ewelina Zatorska
The one-dimensional Aw-Rascle (AR) system has become a cornerstone of macroscopic models for single-lane vehicular traffic. A possible generalization of this model to a multi-dimensional setting is the so-called dissipative AR model, which is more suited to capturing crowd dynamics. This review summarizes recent studies that analyze the dissipative AR model, its hard congestion limit, the non-uniqueness of weak solutions, the existence and asymptotics of solutions within the duality framework, non-local interactions, and the existence of regular solutions.
Weak radiative hyperon decays represent a rich interplay between weak interactions and the internal structure of baryons, offering profound insights into Quantum Chromodynamics and weak interactions. Recent experimental observations, particularly from BESIII, have revealed deviations from theoretical predictions. These deviations could signal new physics or the need for refined theoretical models incorporating intermediate resonance effects. This review discusses recent theoretical advancements and key experimental findings, focusing on recent measurements from BESIII and their implications for strong interactions and baryon structure.
Foundational aspects of spinor structures and exotic spinors
2025-02-21
J. M. Hoff da Silva
Spinors are mathematical objects susceptible to the spacetime characteristics upon which they are defined. Not all spacetimes admit spinor structure; when it does, it may have more than one spinor structure, depending on topological properties. When more than one nonequivalent spinor structure is allowed in a given spacetime, the spinors resulting from the extra structures are called exotic. In this review, we revisit the topological conditions driving the discussion about the spacetime characteristics leading to the existence and (non)uniqueness of spinor structures in a relatively comprehensive manner, accounting for step-to-step demonstrations. In the sequel, we delve into the topologically corrected Dirac operator, explicitly obtaining it and exploring the physical consequences encoded in the exotic spinor dynamics. Finally, we overview early and recent achievements in the area, pointing out possible directions within this research program.
Testing strong-field QED to second-order in the highly correlated atomic system berylliumlike Pb78+ by electron-ion recombination spectroscopy
2025-02-21
S. Schippers, C. Brandau, S. Fuchs, M. Lestinsky, S. X. Wang, C. Y. Zhang, N. R. Badnell, A. Borovik Jr., M. Fogle, V. Hannen, Z. Harman, P. -M. Hillenbrand, E. B. Menz, Y. Zhang, Z. Andelkovic, F. Herfurth, R. Heß, A. Kalinin, C. Kozhuharov, C. Krantz, S. Litvinov, B. Lorentz, U. Spillmann, M. Steck, G. Vorobyev, D. Banaś, S. Fritzsche, E. Lindroth, X. Ma, A. Müller, R. Schuch, A. Surzhykov, M. Trassinelli, K. Ueberholz, C. Weinheimer, Th. Stöhlker
A low-energy storage ring with an ultracold electron cooler has been coupled with a heavy-ion accelerator facilitating high-resolution electron-ion collision spectroscopy of the heaviest few-electron ions. In the present work resonant electron-ion recombination of berylliumlike Pb$^{78+}$ ions was measured in the collision-energy range 9.3-16.5eV and a value of 244.937(30) eV is derived for the Pb$^{78+}$($2s^2\;^1S_0 - 2s\,2p\;^3P_1$) excitation energy. This result agrees with the most recent (less accurate) theoretical value of 244.942(52) eV [Malyshev et al., Physical Review A 110, 062824 (2024)], which has been calculated by applying strong-field QED rigorously up to the second order. The present investigation suggests that further technical improvements can potentially increase the experimental accuracy by an order of magnitude.
We report a new CO observation survey of LHAASO J0341$+$5258 using the Nobeyama Radio Observatory (NRO) 45-m telescope. LHAASO J0341$+$5258 is one of the unidentified ultra-high-energy (UHE; E $>$100 TeV) gamma-ray sources detected by LHAASO. Our CO observations were conducted in February and March 2024, with a total observation time of 36 hours, covering the LHAASO source ($\sim$0.3-0.5 degrees in radius) and its surrounding area (1$\times$1.5 degrees). Within the LHAASO source extent, we identified five compact ($<$ 2 pc) molecular clouds at nearby distances ($<$ 1-4 kpc). These clouds can serve as proton-proton collision targets, producing hadronic gamma rays via neutral pion decays. Based on the hydrogen densities (700-5000 cm$^{-3}$) estimated from our CO observations and archived HI data from the DRAO survey, we derived the total proton energy of $W_p$ (E $>$ 1 TeV) $\sim$ 10$^{45}$ erg to account for the gamma-ray flux. One of the molecular clouds appears to be likely associated with an asymptotic giant branch (AGB) star with an extended CO tail, which may indicate some particle acceleration activities. However, the estimated maximum particle energy below 100 TeV makes the AGB-like star unlikely to be a PeVatron site. We conclude that the UHE emission observed in LHAASO J0341$+$5258 could be due to hadronic interactions between the newly discovered molecular clouds and TeV-PeV protons originating from a distant SNR or due to leptonic emission from a pulsar wind nebula candidate, which is reported in our companion X-ray observation paper (DiKerby et al. 2025).
Pub-Guard-LLM: Detecting Fraudulent Biomedical Articles with Reliable Explanations
2025-02-21
Lihu Chen, Shuojie Fu, Gabriel Freedman, Cemre Zor, Guy Martin, James Kinross, Uddhav Vaghela, Ovidiu Serban, Francesca Toni
A significant and growing number of published scientific articles is found to involve fraudulent practices, posing a serious threat to the credibility and safety of research in fields such as medicine. We propose Pub-Guard-LLM, the first large language model-based system tailored to fraud detection of biomedical scientific articles. We provide three application modes for deploying Pub-Guard-LLM: vanilla reasoning, retrieval-augmented generation, and multi-agent debate. Each mode allows for textual explanations of predictions. To assess the performance of our system, we introduce an open-source benchmark, PubMed Retraction, comprising over 11K real-world biomedical articles, including metadata and retraction labels. We show that, across all modes, Pub-Guard-LLM consistently surpasses the performance of various baselines and provides more reliable explanations, namely explanations which are deemed more relevant and coherent than those generated by the baselines when evaluated by multiple assessment methods. By enhancing both detection performance and explainability in scientific fraud detection, Pub-Guard-LLM contributes to safeguarding research integrity with a novel, effective, open-source tool.
Textual-to-Visual Iterative Self-Verification for Slide Generation
2025-02-21
Yunqing Xu, Xinbei Ma, Jiyang Qiu, Hai Zhao
Generating presentation slides is a time-consuming task that urgently requires automation. Due to their limited flexibility and lack of automated refinement mechanisms, existing autonomous LLM-based agents face constraints in real-world applicability. We decompose the task of generating missing presentation slides into two key components: content generation and layout generation, aligning with the typical process of creating academic slides. First, we introduce a content generation approach that enhances coherence and relevance by incorporating context from surrounding slides and leveraging section retrieval strategies. For layout generation, we propose a textual-to-visual self-verification process using a LLM-based Reviewer + Refiner workflow, transforming complex textual layouts into intuitive visual formats. This modality transformation simplifies the task, enabling accurate and human-like review and refinement. Experiments show that our approach significantly outperforms baseline methods in terms of alignment, logical flow, visual appeal, and readability.
In this work we review the recent advances on superconducting phenomena in junctions formed by superconductors and unconventional magnets. Conventional magnets, such as ferromagnets and antiferromagnets, are characterized by broken time-reversal symmetry but only ferromagnets produce a finite net magnetization due to parallel spin alignment and spin-split bands in momentum. Very recently, a new type of magnets have been reported and here we refer to them as \emph{unconventional magnets} because they exhibit special properties of both ferromagnets and antiferromagnets: they exhibit zero net magnetization (like antiferromagnets) and a nonrelativistic spin splitting of energy bands (like ferromagnets), both leading to anisotropic spin-polarized Fermi surfaces. An interesting property of unconventional magnets is that their magnetic order can be even or odd with respect to momentum, where \emph{$d$-wave altermagnets} and \emph{$p$-wave magnets} are the most representative examples. In this regard, $d$-wave altermagnetism and $p$-wave magnets are seen as counterparts in magnetism of the unconventional $d$-wave and $p$-wave superconducting states, respectively. While the impact of conventional magnetism on superconductivity has been largely studied, the combination of unconventional magnets and superconductivity has only lately attracted considerably attention. This work provides a comprehensive review of the recent progress on the interplay between superconductivity and unconventional magnets. In particular, we focus on the fundamental emerging superconducting phenomena and also discuss the potential implications towards quantum applications.
Sampling in Cloud Benchmarking: A Critical Review and Methodological Guidelines
2025-02-21
Saman Akbari, Manfred Hauswirth
Cloud benchmarks suffer from performance fluctuations caused by resource contention, network latency, hardware heterogeneity, and other factors along with decisions taken in the benchmark design. In particular, the sampling strategy of benchmark designers can significantly influence benchmark results. Despite this well-known fact, no systematic approach has been devised so far to make sampling results comparable and guide benchmark designers in choosing their sampling strategy for use within benchmarks. To identify systematic problems, we critically review sampling in recent cloud computing research. Our analysis identifies concerning trends: (i) a high prevalence of non-probability sampling, (ii) over-reliance on a single benchmark, and (iii) restricted access to samples. To address these issues and increase transparency in sampling, we propose methodological guidelines for researchers and reviewers. We hope that our work contributes to improving the generalizability, reproducibility, and reliability of research results.
Vision-Language Models (VLMs) occasionally generate outputs that contradict input images, constraining their reliability in real-world applications. While visual prompting is reported to suppress hallucinations by augmenting prompts with relevant area inside an image, the effectiveness in terms of the area remains uncertain. This study analyzes success and failure cases of Attention-driven visual prompting in object hallucination, revealing that preserving background context is crucial for mitigating object hallucination.
Identifying Features that Shape Perceived Consciousness in Large Language Model-based AI: A Quantitative Study of Human Responses
2025-02-21
Bongsu Kang, Jundong Kim, Tae-Rim Yun, Hyojin Bae, Chang-Eop Kim
This study quantitively examines which features of AI-generated text lead humans to perceive subjective consciousness in large language model (LLM)-based AI systems. Drawing on 99 passages from conversations with Claude 3 Opus and focusing on eight features -- metacognitive self-reflection, logical reasoning, empathy, emotionality, knowledge, fluency, unexpectedness, and subjective expressiveness -- we conducted a survey with 123 participants. Using regression and clustering analyses, we investigated how these features influence participants' perceptions of AI consciousness. The results reveal that metacognitive self-reflection and the AI's expression of its own emotions significantly increased perceived consciousness, while a heavy emphasis on knowledge reduced it. Participants clustered into seven subgroups, each showing distinct feature-weighting patterns. Additionally, higher prior knowledge of LLMs and more frequent usage of LLM-based chatbots were associated with greater overall likelihood assessments of AI consciousness. This study underscores the multidimensional and individualized nature of perceived AI consciousness and provides a foundation for better understanding the psychosocial implications of human-AI interaction.
Quantum black holes: supersymmetry and exact results
2025-02-21
Davide Cassani, Sameer Murthy
The explanation of black hole entropy as statistical entropy is one of the big successes of string theory. In this article we review recent progress in this subject, focussing on understanding quantum effects on black hole entropy. Supersymmetry plays a key role in these developments and leads to prototype systems where we can discuss quantum effects to great precision. Our discussion has two strands, both of which involve the gravitational path integral that calculates the supersymmetric index. In the first strand we discuss supersymmetric black holes in the microcanonical ensemble, which are decoupled from the environment and can be treated as independent quantum systems. Using methods of supersymmetric localization one can arrive at the integer quantum degeneracies of such systems purely in terms of the gravitational variables. In the second strand we consider grand-canonical ensembles in gravity in which black holes arise as a finite-action excitation in asymptotically flat or Anti de Sitter space. In this context we discuss the saddle-points of the gravitational index, and how they reproduce the black hole action and entropy that agree with the index of the holographic superconformal field theory even beyond the leading order in the semiclassical approximation. Finally we discuss how the gravitational index informs us about the detailed structure of the non-perturbative sum over saddle points and the resulting phases of the theory. Throughout, we try to highlight the central role played by the methods as well as foundational concepts of supergravity in driving these developments.
In the past couple of years a rich connection has been found between the fields of descriptive set theory and distributed computing. Frequently, and less surprisingly, finitary algorithms can be adopted to the infinite setting, resulting in theorems about infinite, definable graphs. In this survey, we take a different perspective and illustrate how results and ideas from descriptive set theory provide new insights and techniques to the theory of distributed computing. We focus on the two classical topics from graph theory, vertex and edge colorings. After summarizing the up-to-date results from both areas, we discuss the adaptation of Marks' games method to the LOCAL model of distributed computing and the development of the multi-step Vizing's chain technique, which led to the construction of the first non-trivial distributed algorithms for Vizing colorings. We provide a list of related open problems to complement our discussion. Finally, we describe an efficient deterministic distributed algorithm for Brooks coloring on graphs of subexponential growth.
Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Review
2025-02-21
Ali Vefghi, Zahed Rahmati, Mohammad Akbari
Drug discovery remains a slow and expensive process that involves many steps, from detecting the target structure to obtaining approval from the Food and Drug Administration (FDA), and is often riddled with safety concerns. Accurate prediction of how drugs interact with their targets and the development of new drugs by using better methods and technologies have immense potential to speed up this process, ultimately leading to faster delivery of life-saving medications. Traditional methods used for drug-target interaction prediction show limitations, particularly in capturing complex relationships between drugs and their targets. As an outcome, deep learning models have been presented to overcome the challenges of interaction prediction through their precise and efficient end results. By outlining promising research avenues and models, each with a different solution but similar to the problem, this paper aims to give researchers a better idea of methods for even more accurate and efficient prediction of drug-target interaction, ultimately accelerating the development of more effective drugs. A total of 180 prediction methods for drug-target interactions were analyzed throughout the period spanning 2016 to 2025 using different frameworks based on machine learning, mainly deep learning and graph neural networks. Additionally, this paper discusses the novelty, architecture, and input representation of these models.
Exploring Embodied Multimodal Large Models: Development, Datasets, and Future Directions
2025-02-21
Shoubin Chen, Zehao Wu, Kai Zhang, Chunyu Li, Baiyang Zhang, Fei Ma, Fei Richard Yu, Qingquan Li
Embodied multimodal large models (EMLMs) have gained significant attention in recent years due to their potential to bridge the gap between perception, cognition, and action in complex, real-world environments. This comprehensive review explores the development of such models, including Large Language Models (LLMs), Large Vision Models (LVMs), and other models, while also examining other emerging architectures. We discuss the evolution of EMLMs, with a focus on embodied perception, navigation, interaction, and simulation. Furthermore, the review provides a detailed analysis of the datasets used for training and evaluating these models, highlighting the importance of diverse, high-quality data for effective learning. The paper also identifies key challenges faced by EMLMs, including issues of scalability, generalization, and real-time decision-making. Finally, we outline future directions, emphasizing the integration of multimodal sensing, reasoning, and action to advance the development of increasingly autonomous systems. By providing an in-depth analysis of state-of-the-art methods and identifying critical gaps, this paper aims to inspire future advancements in EMLMs and their applications across diverse domains.
Constructing a Tutte polynomial for graphs embedded in surfaces
2025-02-21
Iain Moffatt
There are several different extensions of the Tutte polynomial to graphs embedded in surfaces. To help frame the different options, here we consider the problem of extending the Tutte polynomial to cellularly embedded graphs starting from first principles. We offer three different routes to defining such a polynomial and show that they all lead to the same polynomial. This resulting polynomial is known in the literature under a few different names including the ribbon graph polynomial, and 2-variable Bollobas-Riordan polynomial. Our overall aim here is to use this discussion as a mechanism for providing a gentle introduction to the topic of Tutte polynomials for graphs embedded in surfaces.
As a key part of aquaculture management, fish feeding behavior recognition and intensity quantification has been a hot area of great concern to researchers, and it plays a crucial role in monitoring fish health, guiding baiting work and improving aquaculture efficiency. In order to better carry out the related work in the future, this paper firstly reviews the research advances of fish feeding behavior recognition and intensity quantification methods based on computer vision, acoustics and sensors in a single modality. Then the application of the current emerging multimodal fusion in fish feeding behavior recognition and intensity quantification methods is expounded. Finally, the advantages and disadvantages of various techniques are compared and analyzed, and the future research directions are envisioned.
Machine Learning in Stellar Astronomy: Progress up to 2024
2025-02-21
Guangping Li, Zujia Lu, Junzhi Wang, Zhao Wang
Machine learning (ML) has become a key tool in astronomy, driving advancements in the analysis and interpretation of complex datasets from observations. This article reviews the application of ML techniques in the identification and classification of stellar objects, alongside the inference of their key astrophysical properties. We highlight the role of both supervised and unsupervised ML algorithms, particularly deep learning models, in classifying stars and enhancing our understanding of essential stellar parameters, such as mass, age, and chemical composition. We discuss ML applications in the study of various stellar objects, including binaries, supernovae, dwarfs, young stellar objects, variables, metal-poor, and chemically peculiar stars. Additionally, we examine the role of ML in investigating star-related interstellar medium objects, such as protoplanetary disks, planetary nebulae, cold neutral medium, feedback bubbles, and molecular clouds.
Double-peaked narrow emission lines (DPNELs) might be evidence for the existence of kpc-scale dual AGNs. There are so far large samples of objects with DPNELs in narrow emission line galaxies. Here, a systematic search is made to build a sample of type 1 AGNs with double-peaked [O~{\sc~iii}] from Data Release 16 of the Sloan Digital Sky Survey (SDSS). Through visually inspecting and fitting [O~{\sc~iii}], fitting broad H$\alpha$ emission lines, performing F-test for [O~{\sc~iii}] profiles, and checking broad H$\beta$ and [O~{\sc~iii}] emission lines, we select 62 type 1 AGNs with reliable double-peaked [O~{\sc~iii}] from 11557 QSOs with z < 0.3. After visually checking the 62 SDSS multi-color images, we find only seven objects with signs of merging. Four possible models for the double-peaked [O~{\sc~iii}] observed in our sample are discussed: the superposition model, AGN outflow model, dual AGN model, and rotating disk model. However, the current results can not provide any one explanation conclusively, and additional observational data are needed to provide the details of narrow line regions. But at least 22 objects with different velocity offsets between double-peaked [O~{\sc~iii}] and narrow H$\alpha$ emission lines could be excluded as dual AGN candidates. The relative velocity offsets of the [O~{\sc~iii}] blue-shifted/red-shifted components are negative to their line flux ratios, which is consistent with dual AGN model. This work provides a new sample of 62 type 1 AGNs with double-peaked [O~{\sc~iii}] for further study.
Time Warp: The Gap Between Developers' Ideal vs Actual Workweeks in an AI-Driven Era
2025-02-21
Sukrit Kumar, Drishti Goel, Thomas Zimmermann, Brian Houck, B. Ashok, Chetan Bansal
Software developers balance a variety of different tasks in a workweek, yet the allocation of time often differs from what they consider ideal. Identifying and addressing these deviations is crucial for organizations aiming to enhance the productivity and well-being of the developers. In this paper, we present the findings from a survey of 484 software developers at Microsoft, which aims to identify the key differences between how developers would like to allocate their time during an ideal workweek versus their actual workweek. Our analysis reveals significant deviations between a developer's ideal workweek and their actual workweek, with a clear correlation: as the gap between these two workweeks widens, we observe a decline in both productivity and satisfaction. By examining these deviations in specific activities, we assess their direct impact on the developers' satisfaction and productivity. Additionally, given the growing adoption of AI tools in software engineering, both in the industry and academia, we identify specific tasks and areas that could be strong candidates for automation. In this paper, we make three key contributions: 1) We quantify the impact of workweek deviations on developer productivity and satisfaction 2) We identify individual tasks that disproportionately affect satisfaction and productivity 3) We provide actual data-driven insights to guide future AI automation efforts in software engineering, aligning them with the developers' requirements and ideal workflows for maximizing their productivity and satisfaction.
DITING: A Static Analyzer for Identifying Bad Partitioning Issues in TEE Applications
2025-02-21
Chengyan Ma, Ruidong Han, Ye Liu, Yuqing Niu, Di Lu, Chuang Tian, Jianfeng Ma, Debin Gao, David Lo
Trusted Execution Environment (TEE) enhances the security of mobile applications and cloud services by isolating sensitive code in the secure world from the non-secure normal world. However, TEE applications are still confronted with vulnerabilities stemming from bad partitioning. Bad partitioning can lead to critical security problems of TEE, such as leaking sensitive data to the normal world or being adversely affected by malicious inputs from the normal world. To address this, we propose an approach to detect partitioning issues in TEE applications. First, we conducted a survey of TEE vulnerabilities caused by bad partitioning and found that the parameters exchanged between the secure and normal worlds often contain insecure usage with bad partitioning implementation. Second, we developed a tool named DITING that can analyze data-flows of these parameters and identify their violations of security rules we defined to find bad partitioning issues. Different from existing research that only focuses on malicious input to TEE, we assess the partitioning issues more comprehensively through input/output and shared memory. Finally, we created the first benchmark targeting bad partitioning, consisting of 110 test cases. Experiments demonstrate the DITING achieves an F1 score of 0.90 in identifying bad partitioning issues.
An increasing number of companies have begun providing services that leverage cloud-based large language models (LLMs), such as ChatGPT. However, this development raises substantial privacy concerns, as users' prompts are transmitted to and processed by the model providers. Among the various privacy protection methods for LLMs, those implemented during the pre-training and fine-tuning phrases fail to mitigate the privacy risks associated with the remote use of cloud-based LLMs by users. On the other hand, methods applied during the inference phrase are primarily effective in scenarios where the LLM's inference does not rely on privacy-sensitive information. In this paper, we outline the process of remote user interaction with LLMs and, for the first time, propose a detailed definition of a general pseudonymization framework applicable to cloud-based LLMs. The experimental results demonstrate that the proposed framework strikes an optimal balance between privacy protection and utility. The code for our method is available to the public at https://github.com/Mebymeby/Pseudonymization-Framework.
The Evolving Landscape of LLM- and VLM-Integrated Reinforcement Learning
2025-02-21
Sheila Schoepp, Masoud Jafaripour, Yingyue Cao, Tianpei Yang, Fatemeh Abdollahi, Shadan Golestan, Zahin Sufiyan, Osmar R. Zaiane, Matthew E. Taylor
Reinforcement learning (RL) has shown impressive results in sequential decision-making tasks. Meanwhile, Large Language Models (LLMs) and Vision-Language Models (VLMs) have emerged, exhibiting impressive capabilities in multimodal understanding and reasoning. These advances have led to a surge of research integrating LLMs and VLMs into RL. In this survey, we review representative works in which LLMs and VLMs are used to overcome key challenges in RL, such as lack of prior knowledge, long-horizon planning, and reward design. We present a taxonomy that categorizes these LLM/VLM-assisted RL approaches into three roles: agent, planner, and reward. We conclude by exploring open problems, including grounding, bias mitigation, improved representations, and action advice. By consolidating existing research and identifying future directions, this survey establishes a framework for integrating LLMs and VLMs into RL, advancing approaches that unify natural language and visual understanding with sequential decision-making.
Methods and Trends in Detecting Generated Images: A Comprehensive Review
2025-02-21
Arpan Mahara, Naphtali Rishe
The proliferation of generative models, such as Generative Adversarial Networks (GANs), Diffusion Models, and Variational Autoencoders (VAEs), has enabled the synthesis of high-quality multimedia data. However, these advancements have also raised significant concerns regarding adversarial attacks, unethical usage, and societal harm. Recognizing these challenges, researchers have increasingly focused on developing methodologies to detect synthesized data effectively, aiming to mitigate potential risks. Prior reviews have primarily focused on deepfake detection and often lack coverage of recent advancements in synthetic image detection, particularly methods leveraging multimodal frameworks for improved forensic analysis. To address this gap, the present survey provides a comprehensive review of state-of-the-art methods for detecting and classifying synthetic images generated by advanced generative AI models. This review systematically examines core detection methodologies, identifies commonalities among approaches, and categorizes them into meaningful taxonomies. Furthermore, given the crucial role of large-scale datasets in this field, we present an overview of publicly available datasets that facilitate further research and benchmarking in synthetic data detection.
Hadron Production in Open-charm Meson Pair at $e^+e^-$ Collider
2025-02-21
Xiongfei Wang, Xiang Liu, Yuanning Gao
The standard model of particle physics is a well-established theoretical framework, yet there remain several unresolved issues that warrant further experimental and theoretical exploration. In the realm of quark physics, these include understanding the nature of quark confinement and elucidating the mechanism linking quarks and gluons to strongly interacting particles within the standard model theory, which may offer insights into underlying physics mechanisms. These inquiries can be addressed through the study of hadron production in open-charm meson pair final states at $e^+e^-$ annihilations utilizing the capabilities of BABAR, Belle, BESIII, and CLEO-c experiments, which have yielded valuable insights into non-standard hadrons over recent decades. This review examines the contributions of $e^+e^-$ colliders from BABAR, Belle, BESIII, and CLEO-c experiments to such studies and discusses future prospects for $e^+e^-$ collider experiments.
Multi-Wavelength characterization of VVVX open clusters
2025-02-21
C. O. Obasi, E. R. Garro, J. G. Fernández-Trincado, D. Minniti, M. Gómez, M. C. Parisi, M. Ortigoza-Urdaneta
Context. Large-scale surveys across wavelengths, aided by machine learning, have improved the detection of old open clusters in high-extinction Milky Way regions. Aims. We aim to confirm or reject automatically detected open clusters in poorly studied, contaminated regions, refining samples for Galactic disk formation studies. Methods. Using VVVX, 2MASS, and Gaia DR3, we analyzed nine cluster candidates: BH118, BH144, Schuster-MWSC 1756, Saurer 3, FSR 1521, Saurer 2, Haffner 10-FSR 1231, Juchert 12, and Pismis 3. We examined density maps, proper motion diagrams, and photometry to derive physical parameters. Results. We identified star clusters via overdensities in 2MASS, WISE, and DECaPS, confirmed with VVVX and Gaussian Kernel Density Estimation. Using Gaia proper motions, we refined memberships, building a high-likelihood cluster catalog. We derived extinction ($A_{Ks} = 0.07$-$0.50$), color excess ($E(J-Ks) = 0.16$-$0.60$), distances ($D = 2.06$-$8.64$ kpc), and Galactocentric distances ($R_G = 7.69$-$14.81$ kpc). Ages range from 20 Myr to 5 Gyr, metallicity from [Fe/H] = -0.5 to 0.5. Structural parameters include core radii ($r_c = 0.71'$-$5.21'$) and tidal radii ($r_t = 3.4'$-$12.0'$). Conclusions. We confirm the open cluster nature of nine targets, updating their physical parameters.
Bayesian Unit-level Modeling of Categorical Survey Data with a Longitudinal Design
2025-02-21
Daniel Vedensky, Paul A. Parker, Scott H. Holan
Categorical response data are ubiquitous in complex survey applications, yet few methods model the dependence across different outcome categories when the response is ordinal. Likewise, few methods exist for the common combination of a longitudinal design and categorical data. By modeling individual survey responses at the unit-level, it is possible to capture both ordering information in ordinal responses and any longitudinal correlation. However, accounting for a complex survey design becomes more challenging in the unit-level setting. We propose a Bayesian hierarchical, unit-level, model-based approach for categorical data that is able to capture ordering among response categories, can incorporate longitudinal dependence, and accounts for the survey design. To handle computational scalability, we develop efficient Gibbs samplers with appropriate data augmentation as well as variational Bayes algorithms. Using public-use microdata from the Household Pulse Survey, we provide an analysis of an ordinal response that asks about the frequency of anxiety symptoms at the beginning of the COVID-19 pandemic. We compare both design-based and model-based estimators and demonstrate superior performance for the proposed approaches.
Social Genome: Grounded Social Reasoning Abilities of Multimodal Models
2025-02-21
Leena Mathur, Marian Qian, Paul Pu Liang, Louis-Philippe Morency
Social reasoning abilities are crucial for AI systems to effectively interpret and respond to multimodal human communication and interaction within social contexts. We introduce Social Genome, the first benchmark for fine-grained, grounded social reasoning abilities of multimodal models. Social Genome contains 272 videos of interactions and 1,486 human-annotated reasoning traces related to inferences about these interactions. These traces contain 5,777 reasoning steps that reference evidence from visual cues, verbal cues, vocal cues, and external knowledge (contextual knowledge external to videos). Social Genome is also the first modeling challenge to study external knowledge in social reasoning. Social Genome computes metrics to holistically evaluate semantic and structural qualities of model-generated social reasoning traces. We demonstrate the utility of Social Genome through experiments with state-of-the-art models, identifying performance gaps and opportunities for future research to improve the grounded social reasoning abilities of multimodal models.
The holographic space-time model of inflation and its predictions for the CMB primordial spectra
2025-02-21
Sidan A, Tom Banks
In 1998 Carlip and Solodukhin independently conjectured the equality between fluctuations in the modular Hamiltonian of black holes and its expectation value. Banks and Zurek's 2021 work generalized this observation to any causal diamonds. We use this observation to calculate the precise normalization of primordial scalar and tensor power spectra in the Holographic Space-time (HST) model of inflation. In previous work Banks and Fischler outlined the general calculation of inflationary power spectra in this model, which we review here. Its conceptual basis is radically different from field theoretic inflation and it has no Transplanckian problems, but its predictions for power spectra will be shown to fit extant data equally well, and the tensor-to-scalar ratio agrees with experimental observations. A brief presentation can be found on the Strings 2025 YouTube channel https://www.youtube.com/watch?v=MDi0gzwh1zY&feature=youtu.be
Decision tree optimization is fundamental to interpretable machine learning. The most popular approach is to greedily search for the best feature at every decision point, which is fast but provably suboptimal. Recent approaches find the global optimum using branch and bound with dynamic programming, showing substantial improvements in accuracy and sparsity at great cost to scalability. An ideal solution would have the accuracy of an optimal method and the scalability of a greedy method. We introduce a family of algorithms called SPLIT (SParse Lookahead for Interpretable Trees) that moves us significantly forward in achieving this ideal balance. We demonstrate that not all sub-problems need to be solved to optimality to find high quality trees; greediness suffices near the leaves. Since each depth adds an exponential number of possible trees, this change makes our algorithms orders of magnitude faster than existing optimal methods, with negligible loss in performance. We extend this algorithm to allow scalable computation of sets of near-optimal trees (i.e., the Rashomon set).
Sticky information and price controls: Evidence from a natural experiment
2025-02-21
Doron Sayag, Avichai Snir, Daniel Levy
We test the predictions of the sticky information model using a survey dataset by comparing shoppers accuracy in recalling the prices of regulated and comparable unregulated products. Because regulated product prices are capped, they are sold more than comparable unregulated products, while their prices change less frequently and vary less across stores and between brands, than the prices of comparable unregulated products. Therefore, shoppers would be expected to recall the regulated product prices more accurately. However, we find that shoppers are better at recalling the prices of unregulated products, in line with the sticky information model which predicts that shoppers will be more attentive to prices that change more frequently.
Accountability in Code Review: The Role of Intrinsic Drivers and the Impact of LLMs
2025-02-21
Adam Alami, Victor Vadmand Jensen, Neil A. Ernst
Accountability is an innate part of social systems. It maintains stability and ensures positive pressure on individuals' decision-making. As actors in a social system, software developers are accountable to their team and organization for their decisions. However, the drivers of accountability and how it changes behavior in software development are less understood. In this study, we look at how the social aspects of code review affect software engineers' sense of accountability for code quality. Since software engineering (SE) is increasingly involving Large Language Models (LLM) assistance, we also evaluate the impact on accountability when introducing LLM-assisted code reviews. We carried out a two-phased sequential qualitative study (interviews -> focus groups). In Phase I (16 interviews), we sought to investigate the intrinsic drivers of software engineers influencing their sense of accountability for code quality, relying on self-reported claims. In Phase II, we tested these traits in a more natural setting by simulating traditional peer-led reviews with focus groups and then LLM-assisted review sessions. We found that there are four key intrinsic drivers of accountability for code quality: personal standards, professional integrity, pride in code quality, and maintaining one's reputation. In a traditional peer-led review, we observed a transition from individual to collective accountability when code reviews are initiated. We also found that the introduction of LLM-assisted reviews disrupts this accountability process, challenging the reciprocity of accountability taking place in peer-led evaluations, i.e., one cannot be accountable to an LLM. Our findings imply that the introduction of AI into SE must preserve social integrity and collective accountability mechanisms.
Human Motion Prediction, Reconstruction, and Generation
2025-02-21
Canxuan Gang, Yiran Wang
This report reviews recent advancements in human motion prediction, reconstruction, and generation. Human motion prediction focuses on forecasting future poses and movements from historical data, addressing challenges like nonlinear dynamics, occlusions, and motion style variations. Reconstruction aims to recover accurate 3D human body movements from visual inputs, often leveraging transformer-based architectures, diffusion models, and physical consistency losses to handle noise and complex poses. Motion generation synthesizes realistic and diverse motions from action labels, textual descriptions, or environmental constraints, with applications in robotics, gaming, and virtual avatars. Additionally, text-to-motion generation and human-object interaction modeling have gained attention, enabling fine-grained and context-aware motion synthesis for augmented reality and robotics. This review highlights key methodologies, datasets, challenges, and future research directions driving progress in these fields.
The TRGB-SBF Project. III. Refining the HST Surface Brightness Fluctuation Distance Scale Calibration with JWST
2025-02-21
Joseph B. Jensen, John P. Blakeslee, Michele Cantiello, Mikaela Cowles, Gagandeep S. Anand, R. Brent Tully, Ehsan Kourkchi, Gabriella Raimondo
The TRGB-SBF Project team is developing an independent distance ladder using a geometrical calibration of the tip of the red giant branch (TRGB) method in elliptical galaxies that can in turn be used to set the surface brightness fluctuation (SBF) distance scale independent of Cepheid variables and Type Ia supernovae. In this paper, we use JWST TRGB distances calibrated using the megamaser galaxy NGC 4258 to determine a new Cepheid-independent SBF zero point for HST. This new calibration, along with improved optical color measurements from PanSTARRS and DECam, gives an updated value of the Hubble-Lemaitre constant $H_0 = 73.8 \pm 0.7 \pm 2.3$ km/s/Mpc that is virtually identical to the previously published result based on SBF distances calibrated using Cepheids (Blakeslee et al. 2021, arxiv:2101.02221). Future improvements in the geometrical calibration of TRGB and additions to the HST SBF survey will further reduce the random and systematic uncertainties on $H_0$ measured using the extensive HST WFC3/IR SBF dataset. JWST observations of the Coma cluster will soon establish a foundation for the SBF calibration that will extend to much larger distances than is possible with HST.
This paper extends the Nested Error Regression Model with High-Dimensional Parameters (NERHDP) to address challenges in small area poverty estimation. Building on the NERHDP framework, we develop a robust and flexible approach to derive empirical best predictors (EBPs) of small area poverty indicators, while accommodating heterogeneity in regression coefficients and sampling variances across areas. To overcome computational limitations in the existing algorithm, we introduce an efficient method that significantly reduces computation time, enhancing scalability for large datasets. Additionally, we propose a novel method for generating area-specific poverty estimates for out-of-sample areas, improving the reliability of synthetic estimates. To quantify uncertainty, we introduce a parametric bootstrap method tailored to the extended model. Through design-based simulation studies, we demonstrate that the proposed method has better performance in terms of relative bias and relative root mean squared prediction error compared to existing approaches. Furthermore, the proposed method is applied to household survey data from the 2002 Albania Living Standards Measurement Survey to estimate poverty indicators for 374 municipalities using auxiliary information from the 2001 census.
Sub-Millimeter Variability in the Envelope & Warped Protostellar Disk of the Class 0 Protostar HOPS 358
2025-02-21
Patrick D. Sheehan, Doug Johnstone, Carlos Contreras Peña, Seonjae Lee, Gregory Herczeg, Jeong-Eun Lee, Steve Mairs, John J. Tobin, Hyeong-Sik Yun, The JCMT Transient Team
The JCMT Transient Survey recently discovered that the Class 0 protostar HOPS 358 decreased in 350 GHz continuum brightness by $\sim25$% over the course of four years before brightening again for the next four. The JCMT lightcurve can be fit by a long timescale dip lasting roughly eight years. A shorter timescale periodicity is also apparent with a period of 1.75 years and a small 3% amplitude. NEOWise monitoring reveals that the mid-IR wavelength brightness of HOPS 358 follows a similar long-term pattern in time. Here, we present a study of nine epochs of ALMA observations of HOPS 358 taken over the course of the decline and subsequent rise in brightness seen with the JCMT to test whether the variation seen on $\sim15"$ scales, covering both disk and envelope, is also observed on smaller, $<1"$ scales that primarily probe HOPS 358's protostellar disk. We detect both HOPS 358 and its southern companion, HOPS 358B, in our ALMA observations, and find that at least one of the two is varying. Assuming that HOPS 358 is the variable, the light curve has the same shape as that found by the JCMT. Additionally, our high resolution ALMA imaging of HOPS 358 reveals that the disk is warped, with a $16^{\circ}$ warp at a disk radius of 35 au, about halfway through the extent of the disk. The physical origin of the warp along with how it relates to the variability seen towards HOPS 358, however, remain unclear.
A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcare
2025-02-21
Manar Aljohani, Jun Hou, Sindhura Kommu, Xuan Wang
The application of large language models (LLMs) in healthcare has the potential to revolutionize clinical decision-making, medical research, and patient care. As LLMs are increasingly integrated into healthcare systems, several critical challenges must be addressed to ensure their reliable and ethical deployment. These challenges include truthfulness, where models generate misleading information; privacy, with risks of unintentional data retention; robustness, requiring defenses against adversarial attacks; fairness, addressing biases in clinical outcomes; explainability, ensuring transparent decision-making; and safety, mitigating risks of misinformation and medical errors. Recently, researchers have begun developing benchmarks and evaluation frameworks to systematically assess the trustworthiness of LLMs. However, the trustworthiness of LLMs in healthcare remains underexplored, lacking a systematic review that provides a comprehensive understanding and future insights into this area. This survey bridges this gap by providing a comprehensive overview of the recent research of existing methodologies and solutions aimed at mitigating the above risks in healthcare. By focusing on key trustworthiness dimensions including truthfulness, privacy and safety, robustness, fairness and bias, and explainability, we present a thorough analysis of how these issues impact the reliability and ethical use of LLMs in healthcare. This paper highlights ongoing efforts and offers insights into future research directions to ensure the safe and trustworthy deployment of LLMs in healthcare.
In recent years, advancements in generative artificial intelligence have led to the development of sophisticated tools capable of mimicking diverse artistic styles, opening new possibilities for digital creativity and artistic expression. This paper presents a critical assessment of the style replication capabilities of contemporary generative models, evaluating their strengths and limitations across multiple dimensions. We examine how effectively these models reproduce traditional artistic styles while maintaining structural integrity and compositional balance in the generated images. The analysis is based on a new large dataset of AI-generated works imitating artistic styles of the past, holding potential for a wide range of applications: the "AI-pastiche" dataset. The study is supported by extensive user surveys, collecting diverse opinions on the dataset and investigation both technical and aesthetic challenges, including the ability to generate outputs that are realistic and visually convincing, the versatility of models in handling a wide range of artistic styles, and the extent to which they adhere to the content and stylistic specifications outlined in prompts. This paper aims to provide a comprehensive overview of the current state of generative tools in style replication, offering insights into their technical and artistic limitations, potential advancements in model design and training methodologies, and emerging opportunities for enhancing digital artistry, human-AI collaboration, and the broader creative landscape.
Protein Large Language Models: A Comprehensive Survey
2025-02-21
Yijia Xiao, Wanjia Zhao, Junkai Zhang, Yiqiao Jin, Han Zhang, Zhicheng Ren, Renliang Sun, Haixin Wang, Guancheng Wan, Pan Lu, Xiao Luo, Yu Zhang, James Zou, Yizhou Sun, Wei Wang
Protein-specific large language models (Protein LLMs) are revolutionizing protein science by enabling more efficient protein structure prediction, function annotation, and design. While existing surveys focus on specific aspects or applications, this work provides the first comprehensive overview of Protein LLMs, covering their architectures, training datasets, evaluation metrics, and diverse applications. Through a systematic analysis of over 100 articles, we propose a structured taxonomy of state-of-the-art Protein LLMs, analyze how they leverage large-scale protein sequence data for improved accuracy, and explore their potential in advancing protein engineering and biomedical research. Additionally, we discuss key challenges and future directions, positioning Protein LLMs as essential tools for scientific discovery in protein science. Resources are maintained at https://github.com/Yijia-Xiao/Protein-LLM-Survey.
Improving Value-based Process Verifier via Structural Prior Injection
2025-02-21
Zetian Sun, Dongfang Li, Baotian Hu, Jun Yu, Min Zhang
In the Large Language Model(LLM) reasoning scenario, people often estimate state value via Monte Carlo sampling. Though Monte Carlo estimation is an elegant method with less inductive bias, noise and errors are inevitably introduced due to the limited sampling. To handle the problem, we inject the structural prior into the value representation and transfer the scalar value into the expectation of a pre-defined categorical distribution, representing the noise and errors from a distribution perspective. Specifically, by treating the result of Monte Carlo sampling as a single sample from the prior ground-truth Binomial distribution, we quantify the sampling error as the mismatch between posterior estimated distribution and ground-truth distribution, which is thus optimized via distribution selection optimization. We test the performance of value-based process verifiers on Best-of-N task and Beam search task. Compared with the scalar value representation, we show that reasonable structural prior injection induced by different objective functions or optimization methods can improve the performance of value-based process verifiers for about 1$\sim$2 points at little-to-no cost. We also show that under different structural prior, the verifiers' performances vary greatly despite having the same optimal solution, indicating the importance of reasonable structural prior injection.
Spatial and Temporal Periodic Density Patterns in Driven Bose-Einstein Condensates
2025-02-20
A. del Río-Lima, J. A. Seman, R. Jáuregui, F. J. Poveda-Cuevas
The study of collective excitations is a crucial tool for understanding many-body quantum systems. For instance, they play a central role in the exploration of superfluidity and other quantum macroscopic phenomena in Bose and Fermi systems. In this work we present a variational and a numerical study of a parametrically driven Bose-Einstein condensate confined in a cylindrical harmonic trap in which the aspect ratio can be varied from a prolate (cigar-shaped) to an oblate (pancake-shaped) system. The excitation can be applied by periodically modulating the harmonic frequencies of the trap or, alternatively, the interatomic interaction strength at a frequency that matches that of the system breathing mode. As a result, we observe the formation of dynamical density patterns that depend on the geometry of the trap: a fringe pattern in a prolate system and a ring pattern in an oblate one. By decomposing the total energy into its kinetic, potential, and interaction terms, we show that the onset of these patterns coincides with the redistribution of kinetic energy along the weakly trapped directions of the sample, indicating the three-dimensional nature of the studied phenomena. Finally, our analysis shows that the difference between the two excitation mechanisms lies on the system stability. Modulating the trap destabilizes the system quicker than modulating the interactions, leading to earlier formation of the patterns.
Long-Term Multidimensional Models of Core-Collapse Supernovae: Progress and Challenges
2025-02-20
H. -Thomas Janka
Self-consistent, multidimensional core-collapse supernova (SN) simulations, especially in 3D, have achieved tremendous progress over the past 10 years. They are now able to follow the entire evolution from core collapse through bounce, neutrino-triggered shock revival, shock breakout at the stellar surface to the electromagnetic SN outburst and the subsequent SN remnant phase. Thus they provide general support for the neutrino-driven explosion mechanism by reproducing observed SN energies, neutron-star (NS) kicks, and diagnostically relevant radioactive isotope yields; they allow to predict neutrino and gravitational-wave signals for many seconds of proto-NS cooling; they confirm correlations between explosion and progenitor or remnant properties already expected from previous spherically symmetric (1D) and 2D models; and they carve out various scenarios for stellar-mass black-hole (BH) formation. Despite these successes it is currently unclear which stars explode or form BHs, because different modeling approaches disagree and suggest the possible importance of the 3D nature of the progenitors and of magnetic fields. The role of neutrino flavor conversion in SN cores still needs to be better understood, the nuclear equation of state including potential phase transitions implies major uncertainties, the SN 1987A neutrino measurements raise new puzzles, and tracing a possible correlation of NS spins and kicks requires still more refined SN simulations.
A Survey of Model Architectures in Information Retrieval
2025-02-20
Zhichao Xu, Fengran Mo, Zhiqi Huang, Crystina Zhang, Puxuan Yu, Bei Wang, Jimmy Lin, Vivek Srikumar
This survey examines the evolution of model architectures in information retrieval (IR), focusing on two key aspects: backbone models for feature extraction and end-to-end system architectures for relevance estimation. The review intentionally separates architectural considerations from training methodologies to provide a focused analysis of structural innovations in IR systems.We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs). We conclude by discussing emerging challenges and future directions, including architectural optimizations for performance and scalability, handling of multimodal, multilingual data, and adaptation to novel application domains beyond traditional search paradigms.
eC-Tab2Text: Aspect-Based Text Generation from e-Commerce Product Tables
2025-02-20
Luis Antonio Gutiérrez Guanilo, Mir Tafseer Nayeem, Cristian López, Davood Rafiei
Large Language Models (LLMs) have demonstrated exceptional versatility across diverse domains, yet their application in e-commerce remains underexplored due to a lack of domain-specific datasets. To address this gap, we introduce eC-Tab2Text, a novel dataset designed to capture the intricacies of e-commerce, including detailed product attributes and user-specific queries. Leveraging eC-Tab2Text, we focus on text generation from product tables, enabling LLMs to produce high-quality, attribute-specific product reviews from structured tabular data. Fine-tuned models were rigorously evaluated using standard Table2Text metrics, alongside correctness, faithfulness, and fluency assessments. Our results demonstrate substantial improvements in generating contextually accurate reviews, highlighting the transformative potential of tailored datasets and fine-tuning methodologies in optimizing e-commerce workflows. This work highlights the potential of LLMs in e-commerce workflows and the essential role of domain-specific datasets in tailoring them to industry-specific challenges.
A Survey on Text-Driven 360-Degree Panorama Generation
2025-02-20
Hai Wang, Xiaoyu Xiang, Weihao Xia, Jing-Hao Xue
The advent of text-driven 360-degree panorama generation, enabling the synthesis of 360-degree panoramic images directly from textual descriptions, marks a transformative advancement in immersive visual content creation. This innovation significantly simplifies the traditionally complex process of producing such content. Recent progress in text-to-image diffusion models has accelerated the rapid development in this emerging field. This survey presents a comprehensive review of text-driven 360-degree panorama generation, offering an in-depth analysis of state-of-the-art algorithms and their expanding applications in 360-degree 3D scene generation. Furthermore, we critically examine current limitations and propose promising directions for future research. A curated project page with relevant resources and research papers is available at https://littlewhitesea.github.io/Text-Driven-Pano-Gen/.
Large Language Models (LLMs) have demonstrated exceptional comprehension capabilities and a vast knowledge base, suggesting that LLMs can serve as efficient tools for automated survey generation. However, recent research related to automated survey generation remains constrained by some critical limitations like finite context window, lack of in-depth content discussion, and absence of systematic evaluation frameworks. Inspired by human writing processes, we propose SurveyX, an efficient and organized system for automated survey generation that decomposes the survey composing process into two phases: the Preparation and Generation phases. By innovatively introducing online reference retrieval, a pre-processing method called AttributeTree, and a re-polishing process, SurveyX significantly enhances the efficacy of survey composition. Experimental evaluation results show that SurveyX outperforms existing automated survey generation systems in content quality (0.259 improvement) and citation quality (1.76 enhancement), approaching human expert performance across multiple evaluation dimensions. Examples of surveys generated by SurveyX are available on www.surveyx.cn
Tree-of-Debate: Multi-Persona Debate Trees Elicit Critical Thinking for Scientific Comparative Analysis
2025-02-20
Priyanka Kargupta, Ishika Agarwal, Tal August, Jiawei Han
With the exponential growth of research facilitated by modern technology and improved accessibility, scientific discoveries have become increasingly fragmented within and across fields. This makes it challenging to assess the significance, novelty, incremental findings, and equivalent ideas between related works, particularly those from different research communities. Large language models (LLMs) have recently demonstrated strong quantitative and qualitative reasoning abilities, and multi-agent LLM debates have shown promise in handling complex reasoning tasks by exploring diverse perspectives and reasoning paths. Inspired by this, we introduce Tree-of-Debate (ToD), a framework which converts scientific papers into LLM personas that debate their respective novelties. To emphasize structured, critical reasoning rather than focusing solely on outcomes, ToD dynamically constructs a debate tree, enabling fine-grained analysis of independent novelty arguments within scholarly articles. Through experiments on scientific literature across various domains, evaluated by expert researchers, we demonstrate that ToD generates informative arguments, effectively contrasts papers, and supports researchers in their literature review.
User Awareness and Perspectives Survey on Privacy, Security and Usability of Auditory Prostheses
2025-02-20
Sohini Saha, Leslie M. Collins, Sherri L. Smith, Boyla O. Mainsah
According to the World Health Organization, over 466 million people worldwide suffer from disabling hearing loss, with approximately 34 million of these being children. Hearing aids (HA) and cochlear implants (CI) have become indispensable tools for restoring hearing and enhancing the quality of life for individuals with hearing impairments. Clinical research and consumer studies indicate that users of HAs and CIs report significant improvements in their daily lives, including enhanced communication abilities and social engagement and reduced psychological stress. Modern auditory prosthetic devices are more advanced and interconnected with digital networks to add functionality, such as streaming audio directly from smartphones and other devices, remote adjustments by audiologists, integration with smart home systems, and access to artificial intelligence-driven sound enhancement features. With this interconnectivity, issues surrounding data privacy and security have become increasingly pertinent. There is limited research on the usability perceptions of current HA and CI models from the perspective of end-users. In addition, no studies have investigated consumer mental models during the purchasing process, particularly which factors they prioritize when selecting a device. In this study, we assessed participants' satisfaction levels with various features of their auditory prostheses. This work contributes to the field by addressing gaps in user perceptions of HA and CI usability, identifying key factors in consumer purchasing decisions, and highlighting the need for improved privacy and security awareness and education among users.
Bispectrum constraints on Primordial non-Gaussianities with the eBOSS DR16 quasars
2025-02-20
Marina S. Cagliari, Matilde Barberi-Squarotti, Kevin Pardede, Emanuele Castorina, Guido D'Amico
We present constraints on $f_{\rm NL}$, the parameter quantifying the amplitude of local Primordial Non-Gaussianities (PNG), from a combined analysis of the tree-level power spectrum and bispectrum of Data Release $16$ (DR16) of the extended Baryon Oscillation Spectroscopic Survey (eBOSS) quasar sample. In our analysis, we use the power spectrum measured with the optimal redshift weights that maximize the local PNG information together with the bispectrum estimated with the standard Feldman-Kaiser-Peacock weights. In the modeling, we incorporate the global and radial integral constraint corrections both in the power spectrum and in the bispectrum, for which we observe that only the radial integral constraint correction has a significant impact. Our constraints read $-6 < f_{\rm NL} < 20$ at $68\%$ confidence level and improve by $\sim 16\%$ over the previous power spectrum analysis of the same dataset. We observe the same improvement over the power spectrum analysis when the quasar response to PNG is lower. In this case, we find $-23 < f_{\rm NL} < 14$ at $68\%$ confidence level. Our findings are consistent with the Fisher matrix expectations.
Multi-agent coordination studies the underlying mechanism enabling the trending spread of diverse multi-agent systems (MAS) and has received increasing attention, driven by the expansion of emerging applications and rapid AI advances. This survey outlines the current state of coordination research across applications through a unified understanding that answers four fundamental coordination questions: (1) what is coordination; (2) why coordination; (3) who to coordinate with; and (4) how to coordinate. Our purpose is to explore existing ideas and expertise in coordination and their connections across diverse applications, while identifying and highlighting emerging and promising research directions. First, general coordination problems that are essential to varied applications are identified and analyzed. Second, a number of MAS applications are surveyed, ranging from widely studied domains, e.g., search and rescue, warehouse automation and logistics, and transportation systems, to emerging fields including humanoid and anthropomorphic robots, satellite systems, and large language models (LLMs). Finally, open challenges about the scalability, heterogeneity, and learning mechanisms of MAS are analyzed and discussed. In particular, we identify the hybridization of hierarchical and decentralized coordination, human-MAS coordination, and LLM-based MAS as promising future directions.
Evaluating Feynman Integrals through differential equations and series expansions
2025-02-20
Tommaso Armadillo
We review the method of the differential equations for the evaluation of multi-loop Feynman integrals. In particular, we focus on the series expansion approach for solving the system of differential equation and we discuss how to perform the analytical continuation of the result to entire (complex) phase-space. This approach allow us to consider arbitrary internal complex masses. This review is based on a lecture given by the author at the "Advanced School and Workshop on Multiloop Scattering Amplitudes" held in NISER, Bhubaneswar (India) in January 2024.
H$α$ Variability of AB Aur b with the Hubble Space Telescope: Probing the Nature of a Protoplanet Candidate with Accretion Light Echoes
2025-02-20
Brendan P. Bowler, Yifan Zhou, Lauren I. Biddle, Lillian Yushu Jiang, Jaehan Bae, Laird M. Close, Katherine B. Follette, Kyle Franson, Adam L. Kraus, Aniket Sanghi, Quang Tran, Kimberly Ward-Duong, Ya-Lin Wu, Zhaohuan Zhu
Giant planets generate accretion luminosity as they form. Much of this energy is radiated in strong H$\alpha$ line emission, which has motivated direct imaging surveys at optical wavelengths to search for accreting protoplanets. However, compact disk structures can mimic accreting planets by scattering emission from the host star. This can complicate the interpretation of H$\alpha$ point sources, especially if the host star itself is accreting. We describe an approach to distinguish accreting protoplanets from scattered-light disk features using "accretion light echoes." This method relies on variable H$\alpha$ emission from a stochastically accreting host star to search for a delayed brightness correlation with a candidate protoplanet. We apply this method to the candidate protoplanet AB Aur b with a dedicated Hubble Space Telescope Wide Field Camera 3 program designed to sequentially sample the host star and the candidate planet in H$\alpha$ while accounting for the light travel time delay and orbital geometry of the source within the protoplanetary disk. Across five epochs spanning 14 months, AB Aur b is over 20 times more variable than its host star; AB Aur's H$\alpha$ emission changes by 15% while AB Aur b varies by 330%. These brightness changes are not correlated, which rules out unobstructed scattered starlight from the host star as the only source of AB Aur b's H$\alpha$ emission and is consistent with tracing emission from an independently accreting protoplanet, inner disk shadowing effects, or a physically evolving compact disk structure. More broadly, accretion light echoes offer a novel tool to explore the nature of protoplanet candidates with well-timed observations of the host star prior to deep imaging in H$\alpha$.
Internal Incoherency Scores for Constraint-based Causal Discovery Algorithms
2025-02-20
Sofia Faltenbacher, Jonas Wahl, Rebecca Herman, Jakob Runge
Causal discovery aims to infer causal graphs from observational or experimental data. Methods such as the popular PC algorithm are based on conditional independence testing and utilize enabling assumptions, such as the faithfulness assumption, for their inferences. In practice, these assumptions, as well as the functional assumptions inherited from the chosen conditional independence test, are typically taken as a given and not further tested for their validity on the data. In this work, we propose internal coherency scores that allow testing for assumption violations and finite sample errors, whenever detectable without requiring ground truth or further statistical tests. We provide a complete classification of erroneous results, including a distinction between detectable and undetectable errors, and prove that the detectable erroneous results can be measured by our scores. We illustrate our coherency scores on the PC algorithm with simulated and real-world datasets, and envision that testing for internal coherency can become a standard tool in applying constraint-based methods, much like a suite of tests is used to validate the assumptions of classical regression analysis.
2025-02-20
Tarek Mahmoud, Zhuohan Xie, Dimitar Dimitrov, Nikolaos Nikolaidis, Purificação Silvano, Roman Yangarber, Shivam Sharma, Elisa Sartori, Nicolas Stefanovitch, Giovanni Da San Martino, Jakub Piskorski, Preslav Nakov
We introduce a novel multilingual hierarchical corpus annotated for entity framing and role portrayal in news articles. The dataset uses a unique taxonomy inspired by storytelling elements, comprising 22 fine-grained roles, or archetypes, nested within three main categories: protagonist, antagonist, and innocent. Each archetype is carefully defined, capturing nuanced portrayals of entities such as guardian, martyr, and underdog for protagonists; tyrant, deceiver, and bigot for antagonists; and victim, scapegoat, and exploited for innocents. The dataset includes 1,378 recent news articles in five languages (Bulgarian, English, Hindi, European Portuguese, and Russian) focusing on two critical domains of global significance: the Ukraine-Russia War and Climate Change. Over 5,800 entity mentions have been annotated with role labels. This dataset serves as a valuable resource for research into role portrayal and has broader implications for news analysis. We describe the characteristics of the dataset and the annotation process, and we report evaluation results on fine-tuned state-of-the-art multilingual transformers and hierarchical zero-shot learning using LLMs at the level of a document, a paragraph, and a sentence.
From Knowledge Generation to Knowledge Verification: Examining the BioMedical Generative Capabilities of ChatGPT
2025-02-20
Ahmed Abdeen Hamed, Byung Suk Lee
The generative capabilities of LLM models present opportunities in accelerating tasks and concerns with the authenticity of the knowledge it produces. To address the concerns, we present a computational approach that systematically evaluates the factual accuracy of biomedical knowledge that an LLM model has been prompted to generate. Our approach encompasses two processes: the generation of disease-centric associations and the verification of them using the semantic knowledge of the biomedical ontologies. Using ChatGPT as the select LLM model, we designed a set of prompt-engineering processes to generate linkages between diseases, drugs, symptoms, and genes to establish grounds for assessments. Experimental results demonstrate high accuracy in identifying disease terms (88%-97%), drug names (90%-91%), and genetic information (88%-98%). The symptom term identification accuracy was notably lower (49%-61%), as verified against the DOID, ChEBI, SYMPTOM, and GO ontologies accordingly. The verification of associations reveals literature coverage rates of (89%-91%) among disease-drug and disease-gene associations. The low identification accuracy for symptom terms also contributed to the verification of symptom-related associations (49%-62%).
2025-02-20
P. Marziani, K. Garnica Luna, A. Floris, A. del Olmo, A. Deconto-Machado, T. M. Buendia-Rios, C. A. Negrete, D. Dultzin
This review provides an observational perspective on the fundamental properties of super-Eddington accretion onto supermassive black holes in quasars. It begins by outlining the selection criteria, particularly focusing on optical and UV broad-line intensity ratios, used to identify a population of unobscured super-Eddington candidates. Several defining features place these candidates at the extreme end of the Population A in main sequence of quasars: among them are the highest observed singly-ionized iron emission, extreme outflow velocities in UV resonance lines, and unusually high metal abundances. These key properties reflect the coexistence of a virialized sub-system within the broad-line region alongside powerful outflows, with the observed gas enrichment likely driven by nuclear or circumnuclear star formation. The most compelling evidence for the occurrence of super-Eddington accretion onto supermassive black holes comes from recent observations of massive black holes at early cosmic epochs. These black holes require rapid growth rates that are only achievable through radiatively inefficient super-Eddington accretion. Furthermore, extreme Eddington ratios, close to or slightly exceeding unity, are consistent with the saturation of radiative output per unit mass predicted by accretion disk theory for super-Eddington accretion rates. The extreme properties of super-Eddington candidates suggest that these quasars could make them stable and well-defined cosmological distance indicators, leveraging the correlation between broad-line width and luminosity expected in virialized systems. Finally, several analogies with accretion processes around stellar-mass black holes, particularly in the high/soft state, are explored to provide additional insight into the mechanisms driving super-Eddington accretion.
Instrumented mouthguards in elite sports: Validity and head acceleration event (HAE) incidence in NCAA American Football
2025-02-20
Mario Rotundo, Nicholas Murray, David Allan, Gregory Tierney
Objectives: To determine the on-field validity of instrumented mouthguards (iMGs) in American football and to quantify head acceleration event (HAE) incidence in NCAA football players. Methods: Instrumented mouthguards were fitted to 35 male NCAA football players. Head kinematic data were collected during 64 player matches. On-field validity was determined through video review with positive predictive value (PPV) and sensitivity values calculated. HAE incidence was calculated as the number of HAEs per player match and stratified by Offense and Defense positions. Results: On-field validity of the Prevent Biometrics iMG in NCAA American Football indicates a sensitivity was 0.89 and PPV ranging from 0.76-0.98 based on false positive definitions. The incidence of PLA and PAA HAEs above a range of thresholds in Defense and Offense appear similar. The incidence of HAEs above 10 g was 11.2 and 11.3 HAEs per player match for Defense and Offense, respectively, while PAA incidence above 1.0 krad/s2 was 5.5 and 6.9 HAEs per player match for Defense and Offense, respectively. Incidence of HAEs above 30 g was 1.6 and 2.6 per player match and 0.9 and 1.4 for HAEs above 2.0 krad/s2 for Defense and Offense, respectively. Conclusion: The Prevent Biometrics iMG appears suitable for measuring HAEs in elite American football. The study provides a benchmark assessment of HAE incidence in elite American Football and lays a foundation for the development of position-specific interventions aimed at reducing HAE exposure.
Catching the wisps: Stellar mass-loss limits from low-frequency radio observations
2025-02-20
Sanne Bloot, Harish K. Vedantham, Robert D. Kavanagh, Joseph R. Callingham, Benjamin J. S. Pope
The winds of low-mass stars carry away angular momentum and impact the atmospheres of surrounding planets. Determining the properties of these winds is necessary to understand the mass-loss history of the star and the evolution of exoplanetary atmospheres. Due to their tenuous nature, the winds of low-mass main-sequence stars are difficult to detect. The few existing techniques for measuring these winds are indirect, with the most common inference method for winds of low-mass stars being astrospheric Lyman-$\alpha$ absorption combined with complex hydrodynamical modelling of the interaction between the stellar wind and the interstellar medium. Here, we employ a more direct method to place upper limits on the mass-loss rates of low-mass stars by combining observations of low-frequency coherent radio emission, the lack of free-free absorption, and a simple stellar wind model. We determine upper limits on the mass-loss rate for a sample of 19 M dwarf stars detected with the LOFAR telescope at 120--168 MHz, reaching a sensitivity within an order of magnitude of the solar mass-loss rate for cold stars with a surface magnetic field strength of $\sim$100 G. The sensitivity of our method does not depend on distance or spectral type, allowing us to find mass-loss rate constraints for stars up to spectral type M6 and out to a distance of 50 pc, later and farther than previous measurements. With upcoming low-frequency surveys with both LOFAR and the Square Kilometre Array, the number of stars with mass-loss rate upper limits determined with this method could reach $\sim$1000.
Thematic analysis of student perceptions of resources and demands experienced in introductory physics
2025-02-20
Avital Pelakh, Melanie L. Good, Eric Kuo, Michael Tumminia, Nabila Jamal-Orozco, Amy Adelman, Jordann Antoan, Brian Galla, Timothy J. Nokes-Malach
The current work aims to better understand student course experiences for those who reported negative perceptions in introductory physics. Researchers conducted semi-structured interviews with 24 students who reported negative perceptions of their class on a screening survey. Participants were asked to share general reflections on challenges and successes they experienced, as well as their reflections on specific aspects of the course (e.g., experiences with instructors and peers). Interview transcripts were then coded to identify the types of experiences students reported, whether they were experienced as positive or negative, as well as the themes and features associated with those experiences. Experiences with the classroom, course structure, instructors, and exams were most frequently reported as negative. Experiences with peers, help-seeking, course curriculum, and specific learning activities were the most positive, though only experiences with peers had more positive reports than negative. We then used a resources vs. demands framework [Soc Personal Psychol Compass 7, 637 (2013)] to interpret the common instructional, cognitive, and motivational themes and features reported across multiple contexts. We discuss the implications of the results for theory and practice.
Data-Constrained Synthesis of Training Data for De-Identification
2025-02-20
Thomas Vakili, Aron Henriksson, Hercules Dalianis
Many sensitive domains -- such as the clinical domain -- lack widely available datasets due to privacy risks. The increasing generative capabilities of large language models (LLMs) have made synthetic datasets a viable path forward. In this study, we domain-adapt LLMs to the clinical domain and generate synthetic clinical texts that are machine-annotated with tags for personally identifiable information using capable encoder-based NER models. The synthetic corpora are then used to train synthetic NER models. The results show that training NER models using synthetic corpora incurs only a small drop in predictive performance. The limits of this process are investigated in a systematic ablation study -- using both Swedish and Spanish data. Our analysis shows that smaller datasets can be sufficient for domain-adapting LLMs for data synthesis. Instead, the effectiveness of this process is almost entirely contingent on the performance of the machine-annotating NER models trained using the original data.
The long-term solar variability, as reconstructed from historical sources: Several case studies in the 17th -- 18th centuries
2025-02-20
Hisashi Hayakawa
On a centennial timescale, solar activity was quantified based on records of instrumental sunspot observations. This article briefly discusses several aspects of the recent archival investigations of historical sunspot records in the 17th to 18th centuries. This article also reviews the recent updates for the active day fraction and positions of the reported sunspot groups of the Maunder Minimum to show their significance within the observational history. These archival investigations serve as base datasets for reconstructing solar activity.
Augmenting Coaching with GenAI: Insights into Use, Effectiveness, and Future Potential
2025-02-20
Jennifer Haase
The integration of generative AI (GenAI) tools, particularly large language models (LLMs), is transforming professional coaching workflows. This study explores how coaches use GenAI, the perceived benefits and limitations of these tools, and broader attitudes toward AI-assisted coaching. A survey of 205 coaching professionals reveals widespread adoption of GenAI for research, content creation, and administrative support, while its role in relational and interpretative coaching remains limited. Findings indicate that AI literacy and perceived AI impact strongly predict GenAI adoption, with positive attitudes fostering greater use. Ethical considerations, particularly transparency and data privacy, are a key concern, with frequent AI users demonstrating greater ethical awareness. Regression analyses show that while perceived effectiveness drives GenAI adoption, concerns about AI replacing human coaches do not significantly influence usage. Coaches express interest in future AI capabilities that enhance personalization, real-time feedback, and administrative automation while maintaining human oversight. The study highlights that GenAI functions best as an augmentation tool rather than a replacement, emphasizing the need for AI literacy training, ethical guidelines, and human-centered AI integration. These findings contribute to the ongoing discourse on human-AI collaboration, advocating for responsible and effective AI adoption in professional coaching.
The rapid development of Vision Foundation Models (VFMs), particularly Vision Transformers (ViT) and Segment Anything Model (SAM), has sparked significant advances in the field of medical image analysis. These models have demonstrated exceptional capabilities in capturing long-range dependencies and achieving high generalization in segmentation tasks. However, adapting these large models to medical image analysis presents several challenges, including domain differences between medical and natural images, the need for efficient model adaptation strategies, and the limitations of small-scale medical datasets. This paper reviews the state-of-the-art research on the adaptation of VFMs to medical image segmentation, focusing on the challenges of domain adaptation, model compression, and federated learning. We discuss the latest developments in adapter-based improvements, knowledge distillation techniques, and multi-scale contextual feature modeling, and propose future directions to overcome these bottlenecks. Our analysis highlights the potential of VFMs, along with emerging methodologies such as federated learning and model compression, to revolutionize medical image analysis and enhance clinical applications. The goal of this work is to provide a comprehensive overview of current approaches and suggest key areas for future research that can drive the next wave of innovation in medical image segmentation.
Multiscale Byte Language Models -- A Hierarchical Architecture for Causal Million-Length Sequence Modeling
2025-02-20
Eric Egli, Matteo Manica, Jannis Born
Bytes form the basis of the digital world and thus are a promising building block for multimodal foundation models. Recently, Byte Language Models (BLMs) have emerged to overcome tokenization, yet the excessive length of bytestreams requires new architectural paradigms. Therefore, we present the Multiscale Byte Language Model (MBLM), a model-agnostic hierarchical decoder stack that allows training with context windows of $5$M bytes on single GPU in full model precision. We thoroughly examine MBLM's performance with Transformer and Mamba blocks on both unimodal and multimodal tasks. Our experiments demonstrate that hybrid architectures are efficient in handling extremely long byte sequences during training while achieving near-linear generational efficiency. To the best of our knowledge, we present the first evaluation of BLMs on visual Q\&A tasks and find that, despite serializing images and the absence of an encoder, a MBLM with pure next token prediction can match custom CNN-LSTM architectures with designated classification heads. We show that MBLMs exhibit strong adaptability in integrating diverse data representations, including pixel and image filestream bytes, underlining their potential toward omnimodal foundation models. Source code is publicly available at: https://github.com/ai4sd/multiscale-byte-lm
Understanding Financial Contagion: A Complexity Modeling Perspective
2025-02-20
Fabio Caccioli
This chapter reviews key contributions of complexity science to the study of systemic risk in financial systems. The focus is on network models of financial contagion, where I explore various mechanisms of shock propagation, such as counterparty default risk and overlapping portfolios. I highlight how the interconnectedness of financial institutions can amplify risk, and I discuss how standard risk management tools, which neglect these interactions, can increase systemic risk.
LLM-based User Profile Management for Recommender System
2025-02-20
Seunghwan Bang, Hwanjun Song
The rapid advancement of Large Language Models (LLMs) has opened new opportunities in recommender systems by enabling zero-shot recommendation without conventional training. Despite their potential, most existing works rely solely on users' purchase histories, leaving significant room for improvement by incorporating user-generated textual data, such as reviews and product descriptions. Addressing this gap, we propose PURE, a novel LLM-based recommendation framework that builds and maintains evolving user profiles by systematically extracting and summarizing key information from user reviews. PURE consists of three core components: a Review Extractor for identifying user preferences and key product features, a Profile Updater for refining and updating user profiles, and a Recommender for generating personalized recommendations using the most current profile. To evaluate PURE, we introduce a continuous sequential recommendation task that reflects real-world scenarios by adding reviews over time and updating predictions incrementally. Our experimental results on Amazon datasets demonstrate that PURE outperforms existing LLM-based methods, effectively leveraging long-term user information while managing token limitations.
OptiRefine: Densest subgraphs and maximum cuts with $k$ refinements
2025-02-20
Sijing Tu, Aleksa Stankovic, Stefan Neumann, Aristides Gionis
Data-analysis tasks often involve an iterative process, which requires refining previous solutions. For instance, when analyzing dynamic social networks, we may be interested in monitoring the evolution of a community that was identified at an earlier snapshot. This task requires finding a community in the current snapshot of data that is ``close'' to the earlier-discovered community of interest. However, classic optimization algorithms, which typically find solutions from scratch, potentially return communities that are very dissimilar to the initial one. To mitigate these issues, we introduce the \emph{OptiRefine framework}. The framework optimizes initial solutions by making a small number of \emph{refinements}, thereby ensuring that the new solution remains close to the initial solution and simultaneously achieving a near-optimal solution for the optimization problem. We apply the OptiRefine framework to two classic graph-optimization problems: \emph{densest subgraph} and \emph{maximum cut}. For the \emph{densest-subgraph problem}, we optimize a given subgraph's density by adding or removing $k$~nodes. We show that this novel problem is a generalization of $k$-densest subgraph, and provide constant-factor approximation algorithms for $k=\Omega(n)$~refinements. We also study a version of \emph{maximum cut} in which the goal is to improve a given cut. We provide connections to maximum cut with cardinality constraints and provide an optimal approximation algorithm in most parameter regimes under the Unique Games Conjecture for $k=\Omega(n)$~refinements. We evaluate our theoretical methods and scalable heuristics on synthetic and real-world data and show that they are highly effective in practice.
From the $\Lambda$ cold dark matter paradigm it is expected that galaxies merge and grow in their environments. These processes form various tidal features depending on the merger mass ratio, orbital parameters, and gas richness. We inspected 170 $g'$-band Abell cluster observations from the 2.1m Fraunhofer-Teleskop Wendelstein and identify 111 of such features from which we select nine streams and five tails. A fast and innovative technique was developed for determining their photometric properties. The model is a Gaussian, including higher-order moments to describe the light profile in slices perpendicular to the elongation direction. From these models, FWHM apertures are generated. The method was developed, tested, and applied on the selected features and corresponding $g$- and $r$-band data from the Legacy Survey DR10. Regarding the novel modeling approach, we can measure surface and total brightnesses with precisions of 4% and 7%, respectively. Mean stream width precision, which also translates to the mean $R_\mathrm{e}$ along the feature is on average within 3% uncertainty. The measured streams have on average a surface brightness of $\sim26.25$ $g'$ mag arcsec$^{-2}$ and are dimmer than the tails in our sample ($\sim25.14$ $g'$ mag arcsec$^{-2}$). We infer that the progenitors of our streams can come from dwarfs, early-type galaxies or disks, based on the streams structural parameters. Furthermore, brightnesses and colors of the streams and tails are consistent with those of galaxies that populate the red sequence in the Coma cluster within $2\sigma$.
A Mobile Robotic Approach to Autonomous Surface Scanning in Legal Medicine
2025-02-20
Sarah Grube, Sarah Latus, Martin Fischer, Vidas Raudonis, Axel Heinemann, Benjamin Ondruschka, Alexander Schlaefer
Purpose: Comprehensive legal medicine documentation includes both an internal but also an external examination of the corpse. Typically, this documentation is conducted manually during conventional autopsy. A systematic digital documentation would be desirable, especially for the external examination of wounds, which is becoming more relevant for legal medicine analysis. For this purpose, RGB surface scanning has been introduced. While a manual full surface scan using a handheld camera is timeconsuming and operator dependent, floor or ceiling mounted robotic systems require substantial space and a dedicated room. Hence, we consider whether a mobile robotic system can be used for external documentation. Methods: We develop a mobile robotic system that enables full-body RGB-D surface scanning. Our work includes a detailed configuration space analysis to identify the environmental parameters that need to be considered to successfully perform a surface scan. We validate our findings through an experimental study in the lab and demonstrate the system's application in a legal medicine environment. Results: Our configuration space analysis shows that a good trade-off between coverage and time is reached with three robot base positions, leading to a coverage of 94.96 %. Experiments validate the effectiveness of the system in accurately capturing body surface geometry with an average surface coverage of 96.90 +- 3.16 % and 92.45 +- 1.43 % for a body phantom and actual corpses, respectively. Conclusion: This work demonstrates the potential of a mobile robotic system to automate RGB-D surface scanning in legal medicine, complementing the use of post-mortem CT scans for inner documentation. Our results indicate that the proposed system can contribute to more efficient and autonomous legal medicine documentation, reducing the need for manual intervention.
Analysis of the Effect of Bars on Environmental Dependence of Disc Galaxies with MaNGA Survey Data
2025-02-20
Pius Privatus, Umananda Dev Goswami
Bars are fundamental structures in disc galaxies, although their role in galaxy evolution is still not fully known. This study investigates the effect of the presence of bars on the environmental dependence of disc galaxies' properties using the volume-limited sample from Mapping Nearby Galaxies at APO (MaNGA) survey. The disc galaxies with and without bars samples were obtained using the Galaxy Zoo 2 project then assigned into field and group sub-samples. These sub-samples were used to compare the stellar mass, star formation rate, $g-r$ colour, concentration index and gas phase metallicity, and their relationships between field and group environments. Then these are used to investigate if there is an existence of any difference between galaxies with and without bars. A one-to-one correspondence between field and group galaxies' properties were observed, and a strong dependence on the environment for properties of unbarred galaxies was observed when compared to barred. The stellar mass against star formation rate, $g-r$ colour against concentration index and stellar mass against gas phase metallicity of unbarred galaxies strongly depend on environment while for barred these relations weakly depend on environment. The study concludes that bars in disc galaxies decrease the dependence of analysed properties and its relations on the environment.
Towards a Perspectivist Turn in Argument Quality Assessment
2025-02-20
Julia Romberg, Maximilian Maurer, Henning Wachsmuth, Gabriella Lapesa
The assessment of argument quality depends on well-established logical, rhetorical, and dialectical properties that are unavoidably subjective: multiple valid assessments may exist, there is no unequivocal ground truth. This aligns with recent paths in machine learning, which embrace the co-existence of different perspectives. However, this potential remains largely unexplored in NLP research on argument quality. One crucial reason seems to be the yet unexplored availability of suitable datasets. We fill this gap by conducting a systematic review of argument quality datasets. We assign them to a multi-layered categorization targeting two aspects: (a) What has been annotated: we collect the quality dimensions covered in datasets and consolidate them in an overarching taxonomy, increasing dataset comparability and interoperability. (b) Who annotated: we survey what information is given about annotators, enabling perspectivist research and grounding our recommendations for future actions. To this end, we discuss datasets suitable for developing perspectivist models (i.e., those containing individual, non-aggregated annotations), and we showcase the importance of a controlled selection of annotators in a pilot study.
Enhancing Language Multi-Agent Learning with Multi-Agent Credit Re-Assignment for Interactive Environment Generalization
2025-02-20
Zhitao He, Zijun Liu, Peng Li, May Fung, Ming Yan, Ji Zhang, Fei Huang, Yang Liu
LLM-based agents have made significant advancements in interactive environments, such as mobile operations and web browsing, and other domains beyond computer using. Current multi-agent systems universally excel in performance, compared to single agents, but struggle with generalization across environments due to predefined roles and inadequate strategies for generalizing language agents. The challenge of achieving both strong performance and good generalization has hindered the progress of multi-agent systems for interactive environments. To address these issues, we propose CollabUIAgents, a multi-agent reinforcement learning framework with a novel multi-agent credit re-assignment (CR) strategy, assigning process rewards with LLMs rather than environment-specific rewards and learning with synthesized preference data, in order to foster generalizable, collaborative behaviors among the role-free agents' policies. Empirical results show that our framework improves both performance and cross-environment generalizability of multi-agent systems. Moreover, our 7B-parameter system achieves results on par with or exceed strong closed-source models, and the LLM that guides the CR. We also provide insights in using granular CR rewards effectively for environment generalization, and accommodating trained LLMs in multi-agent systems.
Statistical Scenario Modelling and Lookalike Distributions for Multi-Variate AI Risk
2025-02-20
Elija Perrier
Evaluating AI safety requires statistically rigorous methods and risk metrics for understanding how the use of AI affects aggregated risk. However, much AI safety literature focuses upon risks arising from AI models in isolation, lacking consideration of how modular use of AI affects risk distribution of workflow components or overall risk metrics. There is also a lack of statistical grounding enabling sensitisation of risk models in the presence of absence of AI to estimate causal contributions of AI. This is in part due to the dearth of AI impact data upon which to fit distributions. In this work, we address these gaps in two ways. First, we demonstrate how scenario modelling (grounded in established statistical techniques such as Markov chains, copulas and Monte Carlo simulation) can be used to model AI risk holistically. Second, we show how lookalike distributions from phenomena analogous to AI can be used to estimate AI impacts in the absence of directly observable data. We demonstrate the utility of our methods for benchmarking cumulative AI risk via risk analysis of a logistic scenario simulations.
TOI-512: Super-Earth transiting a K-type star discovered by TESS and ESPRESSO
2025-02-20
J. Rodrigues, S. C. Barros, N. C. Santos, J. Davoult, M. Attia, A. Castro-González, S. G. Sousa, O. D. S. Demangeon, M. J. Hobson, D. Bossini, C. Ziegler, J. P. Faria, V. Adibekyan, C. Lovis, B. Lavie, M. Damasso, A. M. Silva, A. Suárez Mascareño, F. Pepe, F. Bouchy, Y. Alibert, J. I. González Hernández, A. Sozzetti, C. Allende Prieto, S. Cristiani, E. Palle, V. D'Odorico, D. Ehrenreich, P. Figueira, K. G. Stassun, R. Génova Santos, G. Lo Curto, C. J. A. P. Martins, A. Mehner, G. Micela, P. Molaro, N. J. Nunes, E. Poretti, R. Rebolo, S. Udry, M. R. Zapatero Osorio
One of the goals of the ESPRESSO guaranteed time observations (GTOs) at the ESO 8.2m telescope is to follow up on candidate planets from transit surveys such as the TESS mission. High-precision radial velocities are required to characterize small exoplanets. Aims. We intend to confirm the existence of a transiting super-Earth around the bright (V=9.74) K0-type star TOI-512 (TIC 119292328) and provide a characterization. Combining photometric data from TESS and 37 high-resolution spectroscopic observations from ESPRESSO in a joint Markov chain Monte Carlo analysis, we determined the planetary parameters of TOI-512b and characterized its internal structure. We find that TOI-512b is a super-Earth, with a radius of $1.54 \pm 0.10$ R$_\oplus$ and mass of $3.57_{-0.55}^{+0.53}$~M$_\oplus$, on a $7.19_{-6.1\cdot 10^{-5}}^{+7\cdot 10^{-5}}$ day orbit. This corresponds to a bulk density of $5.62_{-1.28}^{+1.59}$ g cm$^{-3}$. Our interior structure analysis presents a small inner core representing $0.13^{+0.13}_{-0.11}$ of the solid mass fraction for the planet, surrounded by a mantle with a mass fraction of $0.69^{+0.20}_{-0.22}$, and an upper limit of the water layer of $0.16$. The gas mass below $10^{-8.93}$ indicates a very small amount of gas on the planet. We find no evidence of the second candidate found by the TESS pipeline, TOI-512.02, neither in TESS photometry, nor in the ESPRESSO radial velocities. The low stellar activity makes it an interesting transmission spectroscopy candidate for future-generation instruments.
Modern neutrino-nucleus cross section predictions need to incorporate sophisticated nuclear models to achieve greater predictive precision. However, the computational complexity of these advanced models often limits their practicality for experimental analyses. To address this challenge, we introduce a new Monte Carlo method utilizing Normalizing Flows to generate surrogate cross sections that closely approximate those of the original model while significantly reducing computational overhead. As a case study, we built a Monte Carlo event generator for the neutrino-nucleus cross section model developed by the Ghent group. This model employs a Hartree-Fock procedure to establish a quantum mechanical framework in which both the bound and scattering nucleon states are solutions to the mean-field nuclear potential. The surrogate cross sections generated by our method demonstrate excellent accuracy with a relative effective sample size of more than $98.4 \%$, providing a computationally efficient alternative to traditional Monte Carlo sampling methods for differential cross sections.
Electrically active defects in 3C, 4H and 6H silicon carbide polytypes: A review
2025-02-20
Ivana Capan
This paper aims to critically review electrically active defects studied by junction spectroscopy techniques (deep-level transient spectroscopy and minority carrier transient spectroscopy) in the three most commonly used silicon carbide (SiC) polytypes: 3C-SiC, 4H-SiC, and 6H-SiC.
A Survey on Data Contamination for Large Language Models
2025-02-20
Yuxing Cheng, Yi Chang, Yuan Wu
Recent advancements in Large Language Models (LLMs) have demonstrated significant progress in various areas, such as text generation and code synthesis. However, the reliability of performance evaluation has come under scrutiny due to data contamination-the unintended overlap between training and test datasets. This overlap has the potential to artificially inflate model performance, as LLMs are typically trained on extensive datasets scraped from publicly available sources. These datasets often inadvertently overlap with the benchmarks used for evaluation, leading to an overestimation of the models' true generalization capabilities. In this paper, we first examine the definition and impacts of data contamination. Secondly, we review methods for contamination-free evaluation, focusing on three strategies: data updating-based methods, data rewriting-based methods, and prevention-based methods. Specifically, we highlight dynamic benchmarks and LLM-driven evaluation methods. Finally, we categorize contamination detecting methods based on model information dependency: white-Box, gray-Box, and black-Box detection approaches. Our survey highlights the requirements for more rigorous evaluation protocols and proposes future directions for addressing data contamination challenges.
Recent advances in natural language processing have raised expectations for generative models to produce coherent text across diverse language varieties. In the particular case of the Portuguese language, the predominance of Brazilian Portuguese corpora online introduces linguistic biases in these models, limiting their applicability outside of Brazil. To address this gap and promote the creation of European Portuguese resources, we developed a cross-domain language variety identifier (LVI) to discriminate between European and Brazilian Portuguese. Motivated by the findings of our literature review, we compiled the PtBrVarId corpus, a cross-domain LVI dataset, and study the effectiveness of transformer-based LVI classifiers for cross-domain scenarios. Although this research focuses on two Portuguese varieties, our contribution can be extended to other varieties and languages. We open source the code, corpus, and models to foster further research in this task.
Multi-Objective Alignment (MOA) aims to align LLMs' responses with multiple human preference objectives, with Direct Preference Optimization (DPO) emerging as a prominent approach. However, we find that DPO-based MOA approaches suffer from widespread preference conflicts in the data, where different objectives favor different responses. This results in conflicting optimization directions, hindering the optimization on the Pareto Front. To address this, we propose to construct Pareto-optimal responses to resolve preference conflicts. To efficiently obtain and utilize such responses, we propose a self-improving DPO framework that enables LLMs to self-generate and select Pareto-optimal responses for self-supervised preference alignment. Extensive experiments on two datasets demonstrate the superior Pareto Front achieved by our framework compared to various baselines. Code is available at \url{https://github.com/zyttt-coder/SIPO}.
The 4-400 GHz Survey for the 32 Class II Disks in the Taurus Molecular Cloud
2025-02-20
Chia-Ying Chung, An-Li Tsai, Melvyn Wright, Wenrui Xu, Feng Long, Mark A. Gurwell, Hauyu Baobab Liu
We have compiled the $\sim$4-400 GHz broad spectra of 32 Class II protoplanetary disks in the Taurus-Auriga region, which represents the brightest one-third of sources detected in the submillimeter band in this region. The spectra at >20 GHz frequency can be described with a piecewise function: (1) a power law with a spectral index $\sim$2 at >200 GHz, (2) a power law with spectral index in the range 0.3-4.2 at 20-50 GHz, and (3) a transition region in between these two power laws which can be characterized by a sigmoid function. This suggests that the flux densities at >200 GHz and <50 GHz are dominated by distinct emission components. At >200 GHz, the emission is likely dominated by the optically thick dust thermal emission in the bulk of the disks. In some sources that were not detected at 6.8 GHz or 10 GHz, embedded high-density dust substructures may contribute to a significant fraction of the flux densities at 30-50 GHz, and the spectral indices are mostly consistent with 2.0. Although, at 30-50 GHz, free-free and/or synchrotron emission may be significant, and some sources in our sample have spectral indices < 2.0. Based on these results, we hypothesize that high-density dust substructures (e.g., vortices) are often found in resolved Class II protoplanetary disks, and are a precursor to the formation of kilometer-sized planetesimals and rocky planets. They may not present high contrast at >200 GHz frequencies owing to the high optical depth. To probe these dust substructures, high angular resolution observations at <100 GHz are necessary to distinguish them from free-free and synchrotron emission sources. Otherwise, in the analyses of the spatially unresolved spectra, one needs to simultaneously constrain the flux densities of free-free, synchrotron, and dust emission with the observations at $\sim$5-50 GHz.
Information in text is communicated in a way that supports a goal for its reader. Product reviews, for example, contain opinions, tips, product descriptions, and many other types of information that provide both direct insights, as well as unexpected signals for downstream applications. We devise a typology of 24 communicative goals in sentences from the product review domain, and employ a zero-shot multi-label classifier that facilitates large-scale analyses of review data. In our experiments, we find that the combination of classes in the typology forecasts helpfulness and sentiment of reviews, while supplying explanations for these decisions. In addition, our typology enables analysis of review intent, effectiveness and rhetorical structure. Characterizing the types of information in reviews unlocks many opportunities for more effective consumption of this genre.
A Survey on Feedback-based Multi-step Reasoning for Large Language Models on Mathematics
2025-02-20
Ting-Ruen Wei, Haowei Liu, Xuyang Wu, Yi Fang
Recent progress in large language models (LLM) found chain-of-thought prompting strategies to improve the reasoning ability of LLMs by encouraging problem solving through multiple steps. Therefore, subsequent research aimed to integrate the multi-step reasoning process into the LLM itself through process rewards as feedback and achieved improvements over prompting strategies. Due to the cost of step-level annotation, some turn to outcome rewards as feedback. Aside from these training-based approaches, training-free techniques leverage frozen LLMs or external tools for feedback at each step to enhance the reasoning process. With the abundance of work in mathematics due to its logical nature, we present a survey of strategies utilizing feedback at the step and outcome levels to enhance multi-step math reasoning for LLMs. As multi-step reasoning emerges a crucial component in scaling LLMs, we hope to establish its foundation for easier understanding and empower further research.
Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems
2025-02-20
Bingyu Yan, Xiaoming Zhang, Litian Zhang, Lian Zhang, Ziyi Zhou, Dezhuang Miao, Chaozhuo Li
Large Language Models (LLMs) have recently demonstrated remarkable capabilities in reasoning, planning, and decision-making. Building upon these strengths, researchers have begun incorporating LLMs into multi-agent systems (MAS), where agents collaborate or compete through natural language interactions to tackle tasks beyond the scope of single-agent setups. In this survey, we present a communication-centric perspective on LLM-based multi-agent systems, examining key system-level features such as architecture design and communication goals, as well as internal mechanisms like communication strategies, paradigms, objects and content. We illustrate how these communication elements interplay to enable collective intelligence and flexible collaboration. Furthermore, we discuss prominent challenges, including scalability, security, and multimodal integration, and propose directions for future work to advance research in this emerging domain. Ultimately, this survey serves as a catalyst for further innovation, fostering more robust, scalable, and intelligent multi-agent systems across diverse application domains.
Generalization Certificates for Adversarially Robust Bayesian Linear Regression
2025-02-20
Mahalakshmi Sabanayagam, Russell Tsuchida, Cheng Soon Ong, Debarghya Ghoshdastidar
Adversarial robustness of machine learning models is critical to ensuring reliable performance under data perturbations. Recent progress has been on point estimators, and this paper considers distributional predictors. First, using the link between exponential families and Bregman divergences, we formulate an adversarial Bregman divergence loss as an adversarial negative log-likelihood. Using the geometric properties of Bregman divergences, we compute the adversarial perturbation for such models in closed-form. Second, under such losses, we introduce \emph{adversarially robust posteriors}, by exploiting the optimization-centric view of generalized Bayesian inference. Third, we derive the \emph{first} rigorous generalization certificates in the context of an adversarial extension of Bayesian linear regression by leveraging the PAC-Bayesian framework. Finally, experiments on real and synthetic datasets demonstrate the superior robustness of the derived adversarially robust posterior over Bayes posterior, and also validate our theoretical guarantees.
Evaluating Sakana's AI Scientist for Autonomous Research: Wishful Thinking or an Emerging Reality Towards 'Artificial Research Intelligence' (ARI)?
2025-02-20
Joeran Beel, Min-Yen Kan, Moritz Baumgart
A major step toward Artificial General Intelligence (AGI) and Super Intelligence is AI's ability to autonomously conduct research - what we term Artificial Research Intelligence (ARI). If machines could generate hypotheses, conduct experiments, and write research papers without human intervention, it would transform science. Sakana recently introduced the 'AI Scientist', claiming to conduct research autonomously, i.e. they imply to have achieved what we term Artificial Research Intelligence (ARI). The AI Scientist gained much attention, but a thorough independent evaluation has yet to be conducted. Our evaluation of the AI Scientist reveals critical shortcomings. The system's literature reviews produced poor novelty assessments, often misclassifying established concepts (e.g., micro-batching for stochastic gradient descent) as novel. It also struggles with experiment execution: 42% of experiments failed due to coding errors, while others produced flawed or misleading results. Code modifications were minimal, averaging 8% more characters per iteration, suggesting limited adaptability. Generated manuscripts were poorly substantiated, with a median of five citations, most outdated (only five of 34 from 2020 or later). Structural errors were frequent, including missing figures, repeated sections, and placeholder text like 'Conclusions Here'. Some papers contained hallucinated numerical results. Despite these flaws, the AI Scientist represents a leap forward in research automation. It generates full research manuscripts with minimal human input, challenging expectations of AI-driven science. Many reviewers might struggle to distinguish its work from human researchers. While its quality resembles a rushed undergraduate paper, its speed and cost efficiency are unprecedented, producing a full paper for USD 6 to 15 with 3.5 hours of human involvement, far outpacing traditional researchers.
On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective
2025-02-20
Yue Huang, Chujie Gao, Siyuan Wu, Haoran Wang, Xiangqi Wang, Yujun Zhou, Yanbo Wang, Jiayi Ye, Jiawen Shi, Qihui Zhang, Yuan Li, Han Bao, Zhaoyi Liu, Tianrui Guan, Dongping Chen, Ruoxi Chen, Kehan Guo, Andy Zou, Bryan Hooi Kuen-Yew, Caiming Xiong, Elias Stengel-Eskin, Hongyang Zhang, Hongzhi Yin, Huan Zhang, Huaxiu Yao, Jaehong Yoon, Jieyu Zhang, Kai Shu, Kaijie Zhu, Ranjay Krishna, Swabha Swayamdipta, Taiwei Shi, Weijia Shi, Xiang Li, Yiwei Li, Yuexing Hao, Yuexing Hao, Zhihao Jia, Zhize Li, Xiuying Chen, Zhengzhong Tu, Xiyang Hu, Tianyi Zhou, Jieyu Zhao, Lichao Sun, Furong Huang, Or Cohen Sasson, Prasanna Sattigeri, Anka Reuel, Max Lamparth, Yue Zhao, Nouha Dziri, Yu Su, Huan Sun, Heng Ji, Chaowei Xiao, Mohit Bansal, Nitesh V. Chawla, Jian Pei, Jianfeng Gao, Michael Backes, Philip S. Yu, Neil Zhenqiang Gong, Pin-Yu Chen, Bo Li, Xiangliang Zhang
Generative Foundation Models (GenFMs) have emerged as transformative tools. However, their widespread adoption raises critical concerns regarding trustworthiness across dimensions. This paper presents a comprehensive framework to address these challenges through three key contributions. First, we systematically review global AI governance laws and policies from governments and regulatory bodies, as well as industry practices and standards. Based on this analysis, we propose a set of guiding principles for GenFMs, developed through extensive multidisciplinary collaboration that integrates technical, ethical, legal, and societal perspectives. Second, we introduce TrustGen, the first dynamic benchmarking platform designed to evaluate trustworthiness across multiple dimensions and model types, including text-to-image, large language, and vision-language models. TrustGen leverages modular components--metadata curation, test case generation, and contextual variation--to enable adaptive and iterative assessments, overcoming the limitations of static evaluation methods. Using TrustGen, we reveal significant progress in trustworthiness while identifying persistent challenges. Finally, we provide an in-depth discussion of the challenges and future directions for trustworthy GenFMs, which reveals the complex, evolving nature of trustworthiness, highlighting the nuanced trade-offs between utility and trustworthiness, and consideration for various downstream applications, identifying persistent challenges and providing a strategic roadmap for future research. This work establishes a holistic framework for advancing trustworthiness in GenAI, paving the way for safer and more responsible integration of GenFMs into critical applications. To facilitate advancement in the community, we release the toolkit for dynamic evaluation.
Aligning small language models (SLMs) with human values typically involves distilling preference knowledge from large language models (LLMs). However, existing distillation methods model preference knowledge in teacher LLMs by comparing pairwise responses, overlooking the extent of difference between responses. This limitation hinders student SLMs from capturing the nuanced preferences for multiple responses. In this paper, we propose a Preference-Aligned Distillation (PAD) framework, which models teacher's preference knowledge as a probability distribution over all potential preferences, thereby providing more nuanced supervisory signals. Our insight in developing PAD is rooted in the demonstration that language models can serve as reward functions, reflecting their intrinsic preferences. Based on this, PAD comprises three key steps: (1) sampling diverse responses using high-temperature; (2) computing rewards for both teacher and student to construct their intrinsic preference; and (3) training the student's intrinsic preference distribution to align with the teacher's. Experiments on four mainstream alignment benchmarks demonstrate that PAD consistently and significantly outperforms existing approaches, achieving over 20\% improvement on AlpacaEval 2 and Arena-Hard, indicating superior alignment with human preferences. Notably, on MT-Bench, using the \textsc{Gemma} model family, the student trained by PAD surpasses its teacher, further validating the effectiveness of our PAD.
In the paper, we introduce a paper reading assistant, PaperHelper, a potent tool designed to enhance the capabilities of researchers in efficiently browsing and understanding scientific literature. Utilizing the Retrieval-Augmented Generation (RAG) framework, PaperHelper effectively minimizes hallucinations commonly encountered in large language models (LLMs), optimizing the extraction of accurate, high-quality knowledge. The implementation of advanced technologies such as RAFT and RAG Fusion significantly boosts the performance, accuracy, and reliability of the LLMs-based literature review process. Additionally, PaperHelper features a user-friendly interface that facilitates the batch downloading of documents and uses the Mermaid format to illustrate structural relationships between documents. Experimental results demonstrate that PaperHelper, based on a fine-tuned GPT-4 API, achieves an F1 Score of 60.04, with a latency of only 5.8 seconds, outperforming the basic RAG model by 7\% in F1 Score.
Theoretical studies on the spin-charge dynamics in Kondo-lattice models
2025-02-20
Masahito Mochizuki, Rintaro Eto
The Kondo-lattice model describes a typical spin-charge coupled system in which localized spins and itinerant electrons are strongly coupled via exchange interactions and exhibits a variety of long-wavelength magnetic orders originating from the nesting of Fermi surfaces. Recently, several magnetic materials that realize this model have been discovered experimentally, and they have turned out to exhibit rich topological magnetic phases including skyrmion crystals and hedgehog lattices. Our recent theoretical studies based on the large-scale spin-dynamics simulations have revealed several interesting nonequilibrium phenomena and excitation dynamics in the Kondo-lattice model, e.g., dynamical magnetic topology switching and peculiar spin-wave modes of the skyrmion crystals and the hedgehog lattices under irradiation with electromagnetic waves such as microwaves and light. These achievements are expected to open a new research field to explore novel nonequilibrium topological phenomena and related material functions of the spin-charge coupled magnets.
Augmented Reality In-the-Wild: Usage Patterns and Experiences of Working with AR Laptops in Real-World Settings
2025-02-20
Yi Fei Cheng, Ari Carden, Hyunsung Cho, Catarina G. Fidalgo, Jonathan Wieland, David Lindlbauer
Augmented Reality (AR) is increasingly positioned as a tool for knowledge work, providing beneficial affordances such as a virtually limitless display space that integrates digital information with the user's physical surroundings. However, for AR to supplant traditional screen-based devices in knowledge work, it must support prolonged usage across diverse contexts. Until now, few studies have explored the effects, opportunities, and challenges of working in AR outside a controlled laboratory setting and for an extended duration. This gap in research limits our understanding of how users may adapt its affordances to their daily workflows and what barriers hinder its adoption. In this paper, we present findings from a longitudinal diary study examining how participants incorporated an AR laptop -- Sightful's Spacetop EA -- into their daily work routines. 14 participants used the device for 40-minute daily sessions over two weeks, collectively completing 103 hours of AR-based work. Through survey responses, workspace photographs, and post-study interviews, we analyzed usage patterns, workspace configurations, and evolving user perceptions. Our findings reveal key factors influencing participants' usage of AR, including task demands, environmental constraints, social dynamics, and ergonomic considerations. We highlight how participants leveraged and configured AR's virtual display space, along with emergent hybrid workflows that involved physical screens and tasks. Based on our results, we discuss both overlaps with current literature and new considerations and challenges for the future design of AR systems for pervasive and productive use.
We review the development of light-cone-ordered perturbation theory in coordinate space (C-LCOPT). Compared to light-cone-ordered perturbation theory in momentum space (LCOPT), the role of intermediate states in LCOPT is played in C-LCOPT by paths, which are ordered sequences of lines and vertices that connect pairs of external points. Each path denominator of C-LCOPT equals the difference between the separation of the minus coordinates of the beginning and ending points of the path and the sum of the light-cone minus momenta of all lines along the path computed from their plus and transverse coordinates. We observe that this formalism, originally applied to amplitudes, can be extended to cross sections, which are given in terms of closed paths reminiscent of Schwinger-Keldysh formalisms.
Ask Me Anything: Exploring children's attitudes toward an age-tailored AI-powered chatbot
2025-02-20
Saniya Vahedian Movahed, Fred Martin
Conversational agents, such as chatbots, have increasingly found their way into many dimensions of our lives, including entertainment and education. In this exploratory study we built a child-friendly chatbot, "Ask Me Anything" (AMA), and investigated children's attitudes and trust toward AI-driven conversational agents. To prompt targeted questioning from students and drive engagement, AMA is a specialized chatbot that answers only topic--specific questions in three areas--astronomy, sneakers and shoes, and dinosaurs. We tested AMA with 63 students in a K-8 public school in the Northeast USA. Students worked in small groups, interacted with our tool for three to ten minutes, and completed a post survey. We identified three key themes that emerged from student conversational interactions with AMA: expressing wonder, surprise, and curiosity; building trust and developing confidence; and building relationships and anthropomorphizing. Also, we observed a broad attitude of openness and comfort. Students trusted the chatbot responses in general, indicating a high level of trust in and reliance on AI as a source of information. They described AMA as "knowledgeable," "smart," and that they could "trust it." To confirm their perception of reliability, some students tested the chatbot with questions to which they knew the answers. This behavior illustrated a fundamental aspect of children's cognitive development: the process of actively evaluating the credibility of sources. Our work extends and contributes to the existing body of literature that explores children's interactions with conversational agents.
Infrared sensing is a core method for supporting unmanned systems, such as autonomous vehicles and drones. Recently, infrared sensors have been widely deployed on mobile and stationary platforms for detection and classification of objects from long distances and in wide field of views. Given its success in the vision image analysis domain, deep learning has also been applied for object recognition in infrared images. However, techniques that have proven successful in visible light perception face new challenges in the infrared domain. These challenges include extremely low signal-to-noise ratios in infrared images, very small and blurred objects of interest, and limited availability of labeled/unlabeled training data due to the specialized nature of infrared sensors. Numerous methods have been proposed in the literature for the detection and classification of small objects in infrared images achieving varied levels of success. There is a need for a survey paper that critically analyzes existing techniques in this domain, identifies unsolved challenges and provides future research directions. This paper fills the gap and offers a concise and insightful review of deep learning-based methods. It also identifies the challenges faced by existing infrared object segmentation methods and provides a structured review of existing infrared perception methods from the perspective of these challenges and highlights the motivations behind the various approaches. Finally, this review suggests promising future directions based on recent advancements within this domain.
Homological properties of the module of differentials
2025-02-20
Jürgen Herzog, Benjamin Briggs, Srikanth B. Iyengar
These notes were produced by J\"urgen Herzog to accompany his lectures in Recife, Brazil, in 1980, on the homological algebra of noetherian local rings. They are are concerned with two conjectures made by Wolmer Vasconcelos: if the conormal module of a local ring has finite projective dimension, or if the module of differentials, taken over an appropriate field, has finite projective dimension, then the ring must be complete intersection. The notes present an accessible and self-contained account of the strongest results known at the time in connection with these problems; this includes a number of ideas that have not appeared elsewhere. In the last section, Herzog turns his attention to the cotangent complex, and conjectures himself that if the cotangent complex of a local ring has bounded homology groups, then the ring must be complete intersection. Among other results, he proves that the conjecture holds for local rings of characteristic zero over which all modules have rational Poincar\'e series. Sadly J\"urgen Herzog passed away in April of 2024. The notes in this form have been prepared in his memory, newly typeset and lightly edited. A short appendix has been added to survey some of the results of the intervening decades.
Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models
2025-02-20
Yeonjun In, Wonjoong Kim, Kanghoon Yoon, Sungchul Kim, Mehrab Tanjim, Kibum Kim, Chanyoung Park
As the use of large language model (LLM) agents continues to grow, their safety vulnerabilities have become increasingly evident. Extensive benchmarks evaluate various aspects of LLM safety by defining the safety relying heavily on general standards, overlooking user-specific standards. However, safety standards for LLM may vary based on a user-specific profiles rather than being universally consistent across all users. This raises a critical research question: Do LLM agents act safely when considering user-specific safety standards? Despite its importance for safe LLM use, no benchmark datasets currently exist to evaluate the user-specific safety of LLMs. To address this gap, we introduce U-SAFEBENCH, the first benchmark designed to assess user-specific aspect of LLM safety. Our evaluation of 18 widely used LLMs reveals current LLMs fail to act safely when considering user-specific safety standards, marking a new discovery in this field. To address this vulnerability, we propose a simple remedy based on chain-of-thought, demonstrating its effectiveness in improving user-specific safety. Our benchmark and code are available at https://github.com/yeonjun-in/U-SafeBench.
ODS: A self-reporting system for radio telescopes to coexist with adaptive satellite constellations
2025-02-20
Bang D. Nhan, Christopher G. De Pree, Anthony Beasley, Mark Whitehead, Kevin Ryan, Daniel Faes, Thomas Chamberlin, Dawn Pattison, Victoria Catlett, Aaron Lawson, Daniel Bautista, Sheldon Wasik, Daniel Dueri, Matt Iverson, Jacob Donenfeld, Brian Schepis, David Goldstein
Low Earth orbit (LEO) satellite constellations bring broadband internet and cellular service to the most remote locations on the planet. Unfortunately, many of these locations also host some of the world's best optical and radio astronomy (RA) observatories. With the number of LEO satellites expected to increase by an order of magnitude in the upcoming decade, satellite downlink radio frequency interference (RFI) is a growing concern in protected radio-quiet areas like the United States National Radio Quiet Zone. When these satellites transmit in the spectrum near protected RA bands, undesired out-of-band emission can leak into these protected bands and impact scientific observations. In this paper, we present a self-reporting system - Operational Data Sharing (ODS) - which enables mutual awareness by publishing radio telescopes' operational information to a protected database that is available to satellite operators through a representational state transfer application programming interface (REST API). Satellite operators can use the ODS data to adapt their downlink tasking algorithms in real time to avoid overwhelming sensitive RA facilities, particularly, through the novel Telescope Boresight Avoidance (TBA) technique. Preliminary results from recent experiments between the NRAO and the SpaceX Starlink teams demonstrate the effectiveness of the ODS and TBA in reducing downlink RFI in the Karl G. Jansky Very Large Array's observations in the 1990-1995 MHz and 10.7-12.7 GHz bands. This automated ODS system is beginning to be implemented by other RA facilities and could be utilized by other satellite operators in the near future.
The Modified Airy Function Approximation Applied to the Double-Well Potential
2025-02-20
N. Wine, J. Achtymichuk, F. Marsiglio
The single harmonic oscillator and double-well potentials are important systems in quantum mechanics. The single harmonic oscillator is {\it the} paradigm in physics, and is taught in nearly all beginner undergraduate classes, while the double-well potential illustrates the two important principles of quantum tunnelling and linear superposition. While exact analytical solutions of the Schrodinger equation exist for both of these potentials, they are also employed to benchmark the use of approximate techniques which may be the only recourse for more complicated potentials. In this paper, we review the Wentzel-Kramers-Brillouin (WKB) approximation for both these potentials. While this approximation is known for its accurate energies, we will instead emphasize how poor the WKB wave functions are. The inaccuracy of the WKB wave functions will then motivate us to adopt the lesser-known Modified Airy Function (MAF) approximation, which alleviates the deficiencies of the WKB wave functions. We will review the MAF solution to the simple harmonic oscillator potential, and then apply the MAF to the double-well potential. We find accurate eigenvalues and, more importantly, very accurate wave functions. We conclude with the suggestion that an introduction to the MAF should be included in undergraduate courses to complement the WKB.
Fundamental Survey on Neuromorphic Based Audio Classification
2025-02-20
Amlan Basu, Pranav Chaudhari, Gaetano Di Caterina
Audio classification is paramount in a variety of applications including surveillance, healthcare monitoring, and environmental analysis. Traditional methods frequently depend on intricate signal processing algorithms and manually crafted features, which may fall short in fully capturing the complexities of audio patterns. Neuromorphic computing, inspired by the architecture and functioning of the human brain, presents a promising alternative for audio classification tasks. This survey provides an exhaustive examination of the current state-of-the-art in neuromorphic-based audio classification. It delves into the crucial components of neuromorphic systems, such as Spiking Neural Networks (SNNs), memristors, and neuromorphic hardware platforms, highlighting their advantages in audio classification. Furthermore, the survey explores various methodologies and strategies employed in neuromorphic audio classification, including event-based processing, spike-based learning, and bio-inspired feature extraction. It examines how these approaches address the limitations of traditional audio classification methods, particularly in terms of energy efficiency, real-time processing, and robustness to environmental noise. Additionally, the paper conducts a comparative analysis of different neuromorphic audio classification models and benchmarks, evaluating their performance metrics, computational efficiency, and scalability. By providing a comprehensive guide for researchers, engineers and practitioners, this survey aims to stimulate further innovation and advancements in the evolving field of neuromorphic audio classification.
An Increase in the Prevalence of Ionized Outflows in Galaxies with Coronal Line Emission: Feedback in Action?
2025-02-20
William Matzko, Shobita Satyapal, Michael Reefe, Jeffrey McKaig, Remington O. Sexton, Sara Doan
Coronal lines (CLs), which arise from collisionally excited forbidden transitions from highly ionized species, are a powerful diagnostic tool in uncovering active galactic nuclei (AGNs) and constraining their properties. However, recent optical surveys are finding that coronal lines are rarely detected in the majority of local AGNs, possibly as a result of the depletion of elements from the interstellar gas onto dust grains. Prominent CL emission may therefore only arise when dust is being destroyed in the highly ionized gas in AGNs. To explore the possibility that dust destruction may be caused by ionized gas outflows in galaxies with prominent CLs, we present the first large-scale systematic study of ionized outflows, as traced by the [O III] $\lambda$5007 emission, in galaxies displaying CL emission relative to a robust control sample of non-CL-emitting galaxies. We find: 1) galaxies that display CL emission have a significantly elevated outflow incidence rate compared to their matched controls, 2) the outflow luminosity is significantly higher in the CL-emitters, 3) the CL-emitters have systematically lower intrinsic extinction toward the ionized gas compared with the controls, 4) there are significant correlations between the CL luminosity and outflow velocity for the iron CLs, with similar relationships found between the CL FWHM and outflow luminosity. These observations are consistent with dust destruction in an outflowing wind from a dusty torus causing efficient CL emission.
Diffusion models excel at creating images and videos thanks to their multimodal generative capabilities. These same capabilities have made diffusion models increasingly popular in robotics research, where they are used for generating robot motion. However, the stochastic nature of diffusion models is fundamentally at odds with the precise dynamical equations describing the feasible motion of robots. Hence, generating dynamically admissible robot trajectories is a challenge for diffusion models. To alleviate this issue, we introduce DDAT: Diffusion policies for Dynamically Admissible Trajectories to generate provably admissible trajectories of black-box robotic systems using diffusion models. A sequence of states is a dynamically admissible trajectory if each state of the sequence belongs to the reachable set of its predecessor by the robot's equations of motion. To generate such trajectories, our diffusion policies project their predictions onto a dynamically admissible manifold during both training and inference to align the objective of the denoiser neural network with the dynamical admissibility constraint. The auto-regressive nature of these projections along with the black-box nature of robot dynamics render these projections immensely challenging. We thus enforce admissibility by iteratively sampling a polytopic under-approximation of the reachable set of a state onto which we project its predicted successor, before iterating this process with the projected successor. By producing accurate trajectories, this projection eliminates the need for diffusion models to continually replan, enabling one-shot long-horizon trajectory planning. We demonstrate that our framework generates higher quality dynamically admissible robot trajectories through extensive simulations on a quadcopter and various MuJoCo environments, along with real-world experiments on a Unitree GO1 and GO2.
Fostering Inclusion: A Virtual Reality Experience to Raise Awareness of Dyslexia-Related Barriers in University Settings
2025-02-20
José Manuel Alcalde-Llergo, Pilar Aparicio-Martínez, Andrea Zingoni, Sara Pinzi, Enrique Yeguas-Bolívar
This work introduces the design, implementation, and validation of a virtual reality (VR) experience aimed at promoting the inclusion of individuals with dyslexia in university settings. Unlike traditional awareness methods, this immersive approach offers a novel way to foster empathy by allowing participants to experience firsthand the challenges faced by students with dyslexia. Specifically, the experience raises awareness by exposing non-dyslexic individuals to the difficulties commonly encountered by dyslexic students. In the virtual environment, participants explore a virtual campus with multiple buildings, navigating between them while completing tasks and simultaneously encountering barriers that simulate some of the challenges faced by individuals with dyslexia. These barriers include reading signs with shifting letters, following directional arrows that may point incorrectly, and dealing with a lack of assistance. The campus is a comprehensive model featuring both indoor and outdoor spaces and supporting various modes of locomotion. To validate the experience, more than 30 non-dyslexic participants from the university environment, mainly professors and students, evaluated it through ad hoc satisfaction surveys. The results indicated heightened awareness of the barriers encountered by students with dyslexia, with participants deeming the experience a valuable tool for increasing visibility and fostering understanding of dyslexic students.
Reinforcement Learning for Ultrasound Image Analysis A Comprehensive Review of Advances and Applications
2025-02-20
Maha Ezzelarab, Midhila Madhusoodanan, Shrimanti Ghosh, Geetika Vadali, Jacob Jaremko, Abhilash Hareendranathan
Over the last decade, the use of machine learning (ML) approaches in medicinal applications has increased manifold. Most of these approaches are based on deep learning, which aims to learn representations from grid data (like medical images). However, reinforcement learning (RL) applications in medicine are relatively less explored. Medical applications often involve a sequence of subtasks that form a diagnostic pipeline, and RL is uniquely suited to optimize over such sequential decision-making tasks. Ultrasound (US) image analysis is a quintessential example of such a sequential decision-making task, where the raw signal captured by the US transducer undergoes a series of signal processing and image post-processing steps, generally leading to a diagnostic suggestion. The application of RL in US remains limited. Deep Reinforcement Learning (DRL), that combines deep learning and RL, holds great promise in optimizing these pipelines by enabling intelligent and sequential decision-making. This review paper surveys the applications of RL in US over the last decade. We provide a succinct overview of the theoretic framework of RL and its application in US image processing and review existing work in each aspect of the image analysis pipeline. A comprehensive search of Scopus filtered on relevance yielded 14 papers most relevant to this topic. These papers were further categorized based on their target applications image classification, image segmentation, image enhancement, video summarization, and auto navigation and path planning. We also examined the type of RL approach used in each publication. Finally, we discuss key areas in healthcare where DRL approaches in US could be used for sequential decision-making. We analyze the opportunities, challenges, and limitations, providing insights into the future potential of DRL in US image analysis.
Non-Abelian topological order (TO) enables topologically protected quantum computation with its anyonic quasiparticles. Recently, TO with $S_3$ gauge symmetry was identified as a sweet spot -- simple enough to emerge from finite-depth adaptive circuits yet powerful enough to support a universal topological gate-set. In these notes, we review how anyon braiding and measurement in $S_3$ TO are primitives for topological quantum computation and we explicitly demonstrate universality. These topological operations are made concrete in the $S_3$ quantum double lattice model, aided by the introduction of a generalized ribbon operator. This provides a roadmap for near-term quantum platforms.
Are Gas-rich Ultra-diffuse Galaxies and Field Dwarfs Distinct?
2025-02-20
Khadeejah Motiwala, Ananthan Karunakaran, Kristine Spekkens, Nikhil Arora, Arianna Di Cintio, Anna C. Wright, Dennis Zaritsky, Andrea V. Macciò
We explore the differences in gas-rich field Ultra-Diffuse Galaxies (UDGs) and classical dwarf galaxies using an extensive atomic gas (HI) follow-up survey of optically-selected UDG candidates from the Systematically Measuring Ultra-Diffuse Galaxies (SMUDGes) catalogue. We also compare the SMUDGes-HI observations with two state-of-the-art cosmological hydrodynamical simulations: Numerical Investigation of a Hundred Astrophysical Objects (NIHAO), where UDGs form through a series of bursty star formation episodes and Romulus25, in which UDGs occupy dark matter halos with high spins as a result of major mergers. Although the suggested formation scenarios for UDGs within these simulations are different, the present-day HI masses $M_{HI}$, stellar masses $M_*$, and star formation rates $SFR$ of simulated galaxies are qualitatively and quantitatively consistent with each other and with the observed SMUDGes-HI sample. We find that when controlling for $M_*$, there is a positive correlation between the gas richness $M_{HI}$/$M_*$ and the effective optical radius $R_{eff}$ , and that this trend is not different between the UDG and dwarf populations, within the measured scatter. Taken together, our results suggest that gas-rich, star-forming UDGs and dwarfs are not distinct galaxy populations, either observationally or in simulations.
SOFIA/HAWC+ Far-Infrared Polarimetric Large Area CMZ Exploration Survey. V. The Magnetic Field Strength and Morphology in the Sagittarius C Complex
2025-02-20
Roy J. Zhao, Mark R. Morris, David T. Chuss, Dylan M. Paré, Jordan A. Guerra, Natalie O. Butterfield, Edward J. Wollack, Kaitlyn Karpovich
We present an analysis of the magnetic field strength and morphology in the Sagittarius C complex (Sgr C; G359.43-0.09) in the Milky Way Galaxy's Central Molecular Zone (CMZ) using the 214 $\mu$m polarimetry data acquired with the High-Angular-Resolution Wideband Camera+ (HAWC+) instrument aboard the Stratospheric Observatory for Infrared Astronomy (SOFIA). We use several hundred magnetic field pseudovectors in the Sgr C region to trace the projected magnetic field orientation within cold molecular gas clouds, and as is the trend throughout the CMZ, they show a higher polarization fraction toward the periphery of the clouds. We conduct a modified Davis-Chandrasekhar-Fermi (DCF) analysis of individual clouds and find that the sky-plane magnetic field strength varies from highly turbulent regions having inferred strengths of $\sim30~\mu{\rm G}$ to regions of relatively uniform field orientation having strengths of $\sim 1~{\rm mG}$. The magnetic field orientations suggest that outflows from active star-forming regions, such as the extended green object (EGO) G359.43-0.10 and the protostellar source FIR-4 (G359.43+0.02), cause high turbulence in their vicinity. The magnetic field direction is found to be tangential to the surface of the Sgr C HII region, as well as two [CII] emission cavities around this region. Several other features in the vicinity of Sgr C, especially numerous non-thermal filaments (NTFs) and a diffuse source of X-ray emission towards the southwest of the \hii{} region, are discussed with regard to the observed magnetic field pseudovectors.
A Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE) XVIII. Reconstructing the star formation history of early-type galaxies through the combination of their UV and H$α$ emission
2025-02-20
S. Martocchia, A. Boselli, C. Maraston, D. Thomas, M. Boquien, Y. Roehlly, M. Fossati, L. -M. Seillé, P. Amram, S. Boissier, V. Buat, P. Côté, J-C. Cuillandre, L. Ferrarese, S. Gwyn, J. Hutchings, Junais, C. R. Morgan, J. Postma, T. E. Woods, J. Roediger, A. Subramaniam, M. Sun, H. -X. Zhang
We reconstruct the SFHs of 7 massive ($M_{\star}\gtrsim 10^{10} M_{\odot}$) early-type galaxies (ETGs) in the Virgo cluster by analysing their spatially resolved stellar population (SP), including their UV and H$\alpha$ emission. As part of the VESTIGE survey, we used H$\alpha$ images to select ETGs that show no signs of ongoing star formation. We combined VESTIGE with images from Astrosat/UVIT, GALEX and CFHT from the NGVS to analyse radial spectral energy distributions (SEDs) from the FUV to the NIR. The UV emission in these galaxies is likely due to old, low mass stars in post main sequence (PMS) phases, the UV upturn. We fit the radial SEDs with novel SP models that include an old, hot stellar component of PMS stars with various temperatures and energetics. This way, we explore the main stellar parameters responsible for UV upturn stars irregardless of their evolutionary path. Standard models are not able to reproduce the galaxies' central FUV emission (SMA/$R_{eff}\lesssim1$), while the new models well characterise it through PMS stars with temperatures T$\gtrsim$25000 K. All galaxies are old (mass-weighted ages $\gtrsim10$ Gyr) and the most massive M49 and M87 are supersolar within SMA/$R_{\rm eff}\lesssim0.2$. Overall, we found flat age gradients ($\nabla$Log(Age)$\sim -0.04 - 0$ dex) and shallow metallicity gradients ($\nabla$Log(Z)$<-0.2$ dex), except for M87 ($\nabla$Log($Z_{\rm M87}$)$\simeq-0.45$ dex). Our results show that these ETGs formed with timescales $\tau\lesssim1500$ Myr, having assembled between $\sim40-90\%$ of their stellar mass at $z\sim5$. This is consistent with recent JWST observations of quiescent massive galaxies at high$-z$, which are likely the ancestors of the largest ETGs in the nearby Universe. The derived flat/shallow gradients indicate that major mergers might have contributed to the formation and evolution of these galaxies.
A Survey of Internet Censorship and its Measurement: Methodology, Trends, and Challenges
2025-02-20
Steffen Wendzel, Simon Volpert, Sebastian Zillien, Julia Lenz, Philip Rünz, Luca Caviglione
Internet censorship limits the access of nodes residing within a specific network environment to the public Internet, and vice versa. During the last decade, techniques for conducting Internet censorship have been developed further. Consequently, methodology for measuring Internet censorship had been improved as well. In this paper, we firstly provide a survey of Internet censorship techniques. Secondly, we survey censorship measurement methodology, including a coverage of available datasets. In cases where it is beneficial, we bridge the terminology and taxonomy of Internet censorship with related domains, namely traffic obfuscation and information hiding. We cover both, technical and human aspects, as well as recent trends, and challenges.
Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design
2025-02-20
Masatoshi Uehara, Xingyu Su, Yulai Zhao, Xiner Li, Aviv Regev, Shuiwang Ji, Sergey Levine, Tommaso Biancalani
To fully leverage the capabilities of diffusion models, we are often interested in optimizing downstream reward functions during inference. While numerous algorithms for reward-guided generation have been recently proposed due to their significance, current approaches predominantly focus on single-shot generation, transitioning from fully noised to denoised states. We propose a novel framework for inference-time reward optimization with diffusion models inspired by evolutionary algorithms. Our approach employs an iterative refinement process consisting of two steps in each iteration: noising and reward-guided denoising. This sequential refinement allows for the gradual correction of errors introduced during reward optimization. Besides, we provide a theoretical guarantee for our framework. Finally, we demonstrate its superior empirical performance in protein and cell-type-specific regulatory DNA design. The code is available at \href{https://github.com/masa-ue/ProDifEvo-Refinement}{https://github.com/masa-ue/ProDifEvo-Refinement}.
Denoising, segmentation and volumetric rendering of optical coherence tomography angiography (OCTA) image using deep learning techniques: a review
2025-02-20
Kejie Chen, Xiaochun Yang, Jing Na, Wenbo Wang
Optical coherence tomography angiography (OCTA) is a non-invasive imaging technique widely used to study vascular structures and micro-circulation dynamics in the retina and choroid. OCTA has been widely used in clinics for diagnosing ocular disease and monitoring its progression, because OCTA is safer and faster than dye-based angiography while retaining the ability to characterize micro-scale structures. However, OCTA data contains many inherent noises from the devices and acquisition protocols and suffers from various types of artifacts, which impairs diagnostic accuracy and repeatability. Deep learning (DL) based imaging analysis models are able to automatically detect and remove artifacts and noises, and enhance the quality of image data. It is also a powerful tool for segmentation and identification of normal and pathological structures in the images. Thus, the value of OCTA imaging can be significantly enhanced by the DL-based approaches for interpreting and performing measurements and predictions on the OCTA data. In this study, we reviewed literature on the DL models for OCTA images in the latest five years. In particular, we focused on discussing the current problems in the OCTA data and the corresponding design principles of the DL models. We also reviewed the state-of-art DL models for 3D volumetric reconstruction of the vascular networks and pathological structures such as the edema and distorted optic disc. In addition, the publicly available dataset of OCTA images are summarized at the end of this review. Overall, this review can provide valuable insights for engineers to develop novel DL models by utilizing the characteristics of OCTA signals and images. The pros and cons of each DL methods and their applications discussed in this review can be helpful to assist technicians and clinicians to use proper DL models for fundamental research and disease screening.
Hier-SLAM++: Neuro-Symbolic Semantic SLAM with a Hierarchically Categorical Gaussian Splatting
2025-02-20
Boying Li, Vuong Chi Hao, Peter J. Stuckey, Ian Reid, Hamid Rezatofighi
We propose Hier-SLAM++, a comprehensive Neuro-Symbolic semantic 3D Gaussian Splatting SLAM method with both RGB-D and monocular input featuring an advanced hierarchical categorical representation, which enables accurate pose estimation as well as global 3D semantic mapping. The parameter usage in semantic SLAM systems increases significantly with the growing complexity of the environment, making scene understanding particularly challenging and costly. To address this problem, we introduce a novel and general hierarchical representation that encodes both semantic and geometric information in a compact form into 3D Gaussian Splatting, leveraging the capabilities of large language models (LLMs) as well as the 3D generative model. By utilizing the proposed hierarchical tree structure, semantic information is symbolically represented and learned in an end-to-end manner. We further introduce a novel semantic loss designed to optimize hierarchical semantic information through both inter-level and cross-level optimization. Additionally, we propose an improved SLAM system to support both RGB-D and monocular inputs using a feed-forward model. To the best of our knowledge, this is the first semantic monocular Gaussian Splatting SLAM system, significantly reducing sensor requirements for 3D semantic understanding and broadening the applicability of semantic Gaussian SLAM system. We conduct experiments on both synthetic and real-world datasets, demonstrating superior or on-par performance with state-of-the-art NeRF-based and Gaussian-based SLAM systems, while significantly reducing storage and training time requirements.
RAGVA: Engineering Retrieval Augmented Generation-based Virtual Assistants in Practice
2025-02-20
Rui Yang, Michael Fu, Chakkrit Tantithamthavorn, Chetan Arora, Lisa Vandenhurk, Joey Chua
Retrieval-augmented generation (RAG)-based applications are gaining prominence due to their ability to leverage large language models (LLMs). These systems excel at combining retrieval mechanisms with generative capabilities, resulting in more accurate, contextually relevant responses that enhance user experience. In particular, Transurban, a road operation company, is replacing its rule-based virtual assistant (VA) with a RAG-based VA (RAGVA) to offer more flexible customer interactions and support a wider range of scenarios. In this paper, drawing from the experience at Transurban, we present a comprehensive step-by-step guide for building a conversational application and how to engineer a RAGVA. These guides aim to serve as references for future researchers and practitioners. While the engineering processes for traditional software applications are well-established, the development and evaluation of RAG-based applications are still in their early stages, with numerous emerging challenges remaining uncharted. To address this gap, we conduct a focus group study with Transurban practitioners regarding developing and evaluating their RAGVA. We identified eight challenges encountered by the engineering team and proposed eight future directions that should be explored to advance the development of RAG-based applications. This study contributes to the foundational understanding of a RAG-based conversational application and the emerging AI software engineering challenges it presents.
Cytogenetic, Hematobiochemical, and Histopathological Assessment of Albino Rats (Rattus norvegicus) Fed on Gluten Extracts
2025-02-20
Tajudeen Yahaya, Esther Oladele, Ufuoma Shemishere, Daniel Anyebe, Haliru Abdullahi, Maryam Lawal, Rufai Ahmad
Background: Literature shows that most of the information on the toxicity of gluten is generated from survey and observational studies, resulting in inconsistent outcomes and a decrease in the acceptability of gluten-rich foods. To determine gluten's safety, an in-depth in vitro and in vivo toxicological examination is required. This enables scientists to come up with ameliorative strategies if it turns out to have side effects, and consumers' trust can be restored. Objectives: The objective of this study was to assess the toxicity of gluten extracts on albino rats (Rattus norvegicus). Materials and Methods: Twenty-four rats were randomly selected and divided into four groups, each comprising six rats. Group 1 (control) rats were fed on pellet feeds and groups 2, 3, and 4 were fed on daily dosages of 0.5, 1.0, and 1.5 g gluten extracts, respectively. The rats' body weights and reactions were observed for 90 days before blood samples were collected for hematobiochemical and micronucleus tests. Histopathological examinations of the liver and kidneys were also performed. Results: There was no difference (P > 0.05) in body weight, blood glucose level, or micronuclei between the control and treated rats. The lymphocytes, alkaline phosphatase, alanine transaminase, total protein, and calcium ions of the test rats were all significantly (P < 0.05) altered but remained within the normal ranges. Other hematobiochemical parameters, including packed cell volume, hemoglobin, white and red blood cells, aspartate transaminase, albumin, sodium ions, potassium ions, chloride ions, and urea, revealed no marked changes. The treated rats' livers and kidneys showed no histopathological changes. Conclusion: Gluten had no adverse effects. However, it altered hematobiochemical parameters, particularly the lymphocytes, alkaline phosphatase, alanine transaminase, total protein, and calcium ions.
Utilizing AI and Machine Learning for Predictive Analysis of Post-Treatment Cancer Recurrence
2025-02-20
Muhammad Umer Qayyum, Muhammad Fahad, Nasrullah Abbasi
In oncology, recurrence after treatment is one of the major challenges, related to patients' survival and quality of life. Conventionally, prediction of cancer relapse has always relied on clinical observation with statistical model support, which almost fails to explain the complex, multifactorial nature of tumor recurrence. This research explores how AI and ML models may increase the accuracy and reliability of recurrence prediction in cancer. Therefore, AI and ML create new opportunities not only for personalized medicine but also for proactive management of patients through analyzing large volumes of data on genetics, clinical manifestations, and treatment. The paper describes the various AI and ML techniques for pattern identification and outcome prediction in cancer patients using supervised and unsupervised learning. Clinical implications provide an opportunity to review how early interventions could happen and the design of treatment planning.
Asteroseismology with TESS: Emergence of Dipole Mode Suppression From Subgiants?
2025-02-19
Shurui Lin, Tanda Li, Shude Mao, Jim Fuller
Dipole mode suppression is an observed behavior of solar-like oscillations in evolved stars. This study aims to search for depressed dipole modes in giant stars using data from the Transiting Exoplanet Survey Satellite (TESS) and investigate when the suppression starts to emerge. We study a sample of 8,651 TESS-evolved stars and find 179 stars with significant dipole mode depression by comparing the oscillation amplitudes of radial and dipole modes. Notably, 11 of them are located near the base of the red-giant branch, indicating that mode suppression appears earlier than the point inferred in previous studies with the Kepler data. These findings provide new evidence for the dipole mode suppression in giant stars, particularly in subgiants.
Lost in Sequence: Do Large Language Models Understand Sequential Recommendation?
2025-02-19
Sein Kim, Hongseok Kang, Kibum Kim, Jiwan Kim, Donghyun Kim, Minchul Yang, Kwangjin Oh, Julian McAuley, Chanyoung Park
Large Language Models (LLMs) have recently emerged as promising tools for recommendation thanks to their advanced textual understanding ability and context-awareness. Despite the current practice of training and evaluating LLM-based recommendation (LLM4Rec) models under a sequential recommendation scenario, we found that whether these models understand the sequential information inherent in users' item interaction sequences has been largely overlooked. In this paper, we first demonstrate through a series of experiments that existing LLM4Rec models do not fully capture sequential information both during training and inference. Then, we propose a simple yet effective LLM-based sequential recommender, called LLM-SRec, a method that enhances the integration of sequential information into LLMs by distilling the user representations extracted from a pre-trained CF-SRec model into LLMs. Our extensive experiments show that LLM-SRec enhances LLMs' ability to understand users' item interaction sequences, ultimately leading to improved recommendation performance. Furthermore, unlike existing LLM4Rec models that require fine-tuning of LLMs, LLM-SRec achieves state-of-the-art performance by training only a few lightweight MLPs, highlighting its practicality in real-world applications. Our code is available at https://github.com/Sein-Kim/LLM-SRec.
Grid Labeling: Crowdsourcing Task-Specific Importance from Visualizations
2025-02-19
Minsuk Chang, Yao Wang, Huichen Will Wang, Andreas Bulling, Cindy Xiong Bearfield
Knowing where people look in visualizations is key to effective design. Yet, existing research primarily focuses on free-viewing-based saliency models, even though visual attention is inherently task-dependent. Collecting task-relevant importance data remains a resource-intensive challenge. To address this, we introduce Grid Labeling, a novel annotation method for collecting task-specific importance data to enhance saliency prediction models. Grid Labeling dynamically segments visualizations into Adaptive Grids, enabling efficient, low-effort annotation while adapting to visualization structure. We conducted a human-subject study comparing Grid Labeling with existing annotation methods, ImportAnnots and BubbleView, across multiple metrics. Results show that Grid Labeling produces the least noisy data and the highest inter-participant agreement with fewer participants while requiring less physical (e.g., clicks/mouse movements) and cognitive effort.
Quantifying the Detectability of Milky Way Satellites with Image Simulations: a Case Study with KiDS
2025-02-19
Shiyang Zhang, Hendrik Hildebrandt, Ziang Yan, Simon E. T. Smith, Massimiliano Gatto, Massimo Dall'Ora, Crescenzo Tortora, Shun-Sheng Li, Dominik Elsässer
Ultra-faint dwarf galaxies, which can be detected as resolved satellite systems of the Milky Way, are critical to understanding galaxy formation, evolution, and the nature of dark matter, as they are the oldest, smallest, most metal-poor, and most dark matter-dominated stellar systems known. Quantifying the sensitivity of surveys is essential for understanding their capability and limitations in searching for ultra-faint satellites. In this paper, we present the first study of the image-level observational selection function for Kilo-Degree Survey (KiDS) based on the Synthetic UniveRses For Surveys (surfs)-based KiDS-Legacy-Like Simulations. We generate mock satellites and simulate images that include resolved stellar populations of the mock satellites and the background galaxies, capturing realistic observational effects such as source blending, photometric uncertainties, and star-galaxy separation. The matched-filter method is applied to recover the injected satellites. We derive the observational selection function of the survey in terms of the luminosity, half-light radius, and heliocentric distance of the satellites. Compared to a catalogue-level simulation as used in previous studies, the image-level simulation provides a more realistic assessment of survey sensitivity, accounting for observational limitations that are neglected in catalogue-level simulations. The image-level simulation shows a detection loss for compact sources with a distance $d \gtrsim 100~\rm kpc$. We argue that this is because compact sources are more likely to be identified as single sources rather than being resolved during the source extraction process.
Enhancing LLM-Based Recommendations Through Personalized Reasoning
2025-02-19
Jiahao Liu, Xueshuo Yan, Dongsheng Li, Guangping Zhang, Hansu Gu, Peng Zhang, Tun Lu, Li Shang, Ning Gu
Current recommendation systems powered by large language models (LLMs) often underutilize their reasoning capabilities due to a lack of explicit logical structuring. To address this limitation, we introduce CoT-Rec, a framework that integrates Chain-of-Thought (CoT) reasoning into LLM-driven recommendations by incorporating two crucial processes: user preference analysis and item perception evaluation. CoT-Rec operates in two key phases: (1) personalized data extraction, where user preferences and item perceptions are identified, and (2) personalized data application, where this information is leveraged to refine recommendations. Our experimental analysis demonstrates that CoT-Rec improves recommendation accuracy by making better use of LLMs' reasoning potential. The implementation is publicly available at https://anonymous.4open.science/r/CoT-Rec.
Enhancing Cross-Domain Recommendations with Memory-Optimized LLM-Based User Agents
2025-02-19
Jiahao Liu, Shengkang Gu, Dongsheng Li, Guangping Zhang, Mingzhe Han, Hansu Gu, Peng Zhang, Tun Lu, Li Shang, Ning Gu
Large Language Model (LLM)-based user agents have emerged as a powerful tool for improving recommender systems by simulating user interactions. However, existing methods struggle with cross-domain scenarios due to inefficient memory structures, leading to irrelevant information retention and failure to account for social influence factors such as popularity. To address these limitations, we introduce AgentCF++, a novel framework featuring a dual-layer memory architecture and a two-step fusion mechanism to filter domain-specific preferences effectively. Additionally, we propose interest groups with shared memory, allowing the model to capture the impact of popularity trends on users with similar interests. Through extensive experiments on multiple cross-domain datasets, AgentCF++ demonstrates superior performance over baseline models, highlighting its effectiveness in refining user behavior simulation for recommender systems. Our code is available at https://anonymous.4open.science/r/AgentCF-plus.
Mitigating Popularity Bias in Collaborative Filtering through Fair Sampling
2025-02-19
Jiahao Liu, Dongsheng Li, Hansu Gu, Peng Zhang, Tun Lu, Li Shang, Ning Gu
Recommender systems often suffer from popularity bias, where frequently interacted items are overrepresented in recommendations. This bias stems from propensity factors influencing training data, leading to imbalanced exposure. In this paper, we introduce a Fair Sampling (FS) approach to address this issue by ensuring that both users and items are selected with equal probability as positive and negative instances. Unlike traditional inverse propensity score (IPS) methods, FS does not require propensity estimation, eliminating errors associated with inaccurate calculations. Our theoretical analysis demonstrates that FS effectively neutralizes the influence of propensity factors, achieving unbiased learning. Experimental results validate that FS outperforms state-of-the-art methods in both point-wise and pair-wise recommendation tasks, enhancing recommendation fairness without sacrificing accuracy. The implementation is available at https://anonymous.4open.science/r/Fair-Sampling.
Can Multimodal Large Language Models (MLLMs), with capabilities in perception, recognition, understanding, and reasoning, function as independent assistants in art evaluation dialogues? Current MLLM evaluation methods, which rely on subjective human scoring or costly interviews, lack comprehensive coverage of various scenarios. This paper proposes a process-oriented Human-Computer Interaction (HCI) space design to facilitate more accurate MLLM assessment and development. This approach aids teachers in efficient art evaluation while also recording interactions for MLLM capability assessment. We introduce ArtMentor, a comprehensive space that integrates a dataset and three systems to optimize MLLM evaluation. The dataset consists of 380 sessions conducted by five art teachers across nine critical dimensions. The modular system includes agents for entity recognition, review generation, and suggestion generation, enabling iterative upgrades. Machine learning and natural language processing techniques ensure the reliability of evaluations. The results confirm GPT-4o's effectiveness in assisting teachers in art evaluation dialogues. Our contributions are available at https://artmentor.github.io/.
This paper offers a structured understanding of mediated social touch (MST) using a human-oriented approach, through an extensive review of literature spanning tactile interfaces, emotional information, mapping mechanisms, and the dynamics of human-human and human-robot interactions. By investigating the existing and exploratory mapping strategies of the 37 selected MST cases, we established the emotional expression space of MSTs that accommodated a diverse spectrum of emotions by integrating the categorical and Valence-arousal models, showcasing how emotional cues can be translated into tactile signals. Based on the expressive capacity of MSTs, a practical design space was structured encompassing factors such as the body locations, device form, tactile modalities, and parameters. We also proposed various design strategies for MSTs including workflow, evaluation methods, and ethical and cultural considerations, as well as several future research directions. MSTs' potential is reflected not only in conveying emotional information but also in fostering empathy, comfort, and connection in both human-human and human-robot interactions. This paper aims to serve as a comprehensive reference for design researchers and practitioners, which helps expand the scope of emotional communication of MSTs, facilitating the exploration of diverse applications of affective haptics, and enhancing the naturalness and sociability of haptic interaction.
The T16 Project: Image Subtraction Light Curves from TESS Cycle 1 Full-Frame Images for Stars with T < 16
2025-02-19
Joel Hartman, Gáspár Bakos, Luke G. Bouma, Zoltan Csubry
We present 83,717,159 light curves for 56,401,549 stars with T < 16 mag observed in the Full-Frame Images (FFIs) of Cycle 1 of the NASA TESS mission. These light curves were extracted from subtracted images produced by the Cluster Difference Imaging Survey (CDIPS; Bouma et al. 2019). We make public the raw image subtraction light curves, together with light curves detrended against instrumental systematics. We compare the light curves to other publicly available light curves from the TESS FFIs, finding that for a substantial fraction of stars with T < 16, the T16 project provides the highest precision FFI light curves available. We demonstrate that the detrended T16 light curves are generally as good as, or better than, the light curves from other projects for the known TOIs. We also show that the un-detrended light curves can be used to study high amplitude variable stars. The light curves are being made available through the NASA Mikulski Archive for Space Telescopes (MAST). Light curve production is underway for additional TESS Cycles.
Unveiling a 36 Billion Solar Mass Black Hole at the Centre of the Cosmic Horseshoe Gravitational Lens
2025-02-19
Carlos R. Melo-Carneiro, Thomas E. Collett, Lindsay J. Oldham, Wolfgang J. R. Enzi
Supermassive black holes (SMBHs) are found at the centre of every massive galaxy, with their masses tightly connected to their host galaxies through a co-evolution over cosmic time. For massive ellipticals, the SMBH mass ($M_\text{BH}$) strongly correlates with the central stellar velocity dispersion ($\sigma_e$), via the $M_\text{BH}-\sigma_e$ relation. However, SMBH mass measurements have traditionally relied on central stellar dynamics in nearby galaxies ($z < 0.1$), limiting our ability to explore the SMBHs across cosmic time. In this work, we present a self-consistent analysis combining 2D stellar dynamics and lens modelling of the Cosmic Horseshoe gravitational lens system ($z = 0.44$), one of the most massive galaxies ever observed. Using integral-field spectroscopic data from MUSE and high-resolution imaging from HST, we model the radial arc and stellar kinematics, constraining the galaxy's central mass distribution and SMBH mass. Bayesian model comparison yields a $5\sigma$ detection of an ultramassive black hole (UMBH) with $\log_{10}(M_\text{BH}/M_{\odot}) = 10.56^{+0.07}_{-0.08} \pm (0.12)^\text{sys}$, consistent across various systematic tests. Our findings place the Cosmic Horseshoe $\sim$$1.5\sigma$ above the $M_\text{BH}-\sigma_e$ relation, supporting an emerging trend observed in BGCs and other massive galaxies. This suggests a steeper $M_\text{BH}-\sigma_e$ relationship at the highest masses, potentially driven by a different co-evolution of SMBHs and their host galaxies. Future surveys will uncover more radial arcs, enabling the detection of SMBHs over a broader redshift and mass range. These discoveries will further refine our understanding of the $M_\text{BH}-\sigma_e$ relation and its evolution across cosmic time.
2020 Physics Critique: Can a muon collider be operational within the next 30 years?
2025-02-19
Rebecca Taylor
A 2020 physics critique report for Year 4 MSci Physics with Particle Physics & Cosmology with the University of Birmingham, School of Physics & Astronomy. Muon colliders are a proposed next-generation particle accelerator which benefit from the muon's fundamentality and relatively high mass to perform simultaneous high precision, high energy experiments. This critique reviews their physics potential and technological feasibility, then proposes a roadmap for how a 3 TeV muon collider could be built, concluding that it is experimentally possible for a muon collider to be operational in 30 years.
VITAL: A New Dataset for Benchmarking Pluralistic Alignment in Healthcare
2025-02-19
Anudeex Shetty, Amin Beheshti, Mark Dras, Usman Naseem
Alignment techniques have become central to ensuring that Large Language Models (LLMs) generate outputs consistent with human values. However, existing alignment paradigms often model an averaged or monolithic preference, failing to account for the diversity of perspectives across cultures, demographics, and communities. This limitation is particularly critical in health-related scenarios, where plurality is essential due to the influence of culture, religion, personal values, and conflicting opinions. Despite progress in pluralistic alignment, no prior work has focused on health, likely due to the unavailability of publicly available datasets. To address this gap, we introduce VITAL, a new benchmark dataset comprising 13.1K value-laden situations and 5.4K multiple-choice questions focused on health, designed to assess and benchmark pluralistic alignment methodologies. Through extensive evaluation of eight LLMs of varying sizes, we demonstrate that existing pluralistic alignment techniques fall short in effectively accommodating diverse healthcare beliefs, underscoring the need for tailored AI alignment in specific domains. This work highlights the limitations of current approaches and lays the groundwork for developing health-specific alignment solutions.
"Make the Voodoo Box Go Bleep Bloop:" Exploring End Users' Understanding and Information Needs Regarding Microchips
2025-02-19
Julian Speith, Steffen Becker, Timo Speith, Markus Weber, Yixin Zou, Asia Biega, Christof Paar
Microchips are fundamental components of modern electronic devices, yet they remain opaque to the users who rely on them daily. This opacity, compounded by the complexity of global supply chains and the concealment of proprietary information, raises significant security, trust, and accountability issues. We investigate end users' understanding of microchips, exploring their perceptions of the societal implications and information needs regarding these essential technologies. Through an online survey with 250 participants, we found that while our participants were aware of some microchip applications, they lacked awareness of the broader security, societal, and economic implications. While our participants unanimously desired more information on microchips, their specific information needs were shaped by various factors such as the microchip's application environment and one's affinity for technology interaction. Our findings underscore the necessity for improving end users' awareness and understanding of microchips, and we provide possible directions to pursue this end.
Hunting pre-stellar cores with APEX: Corona Australis 151, the densest pre-stellar core or the youngest protostar?
2025-02-19
E. Redaelli, S. Spezzano, P. Caselli, J. Harju, D. Arzoumanian, O. Sipilä, A. Belloche, F. Wyrowski, J. E. Pineda
Context. Pre-stellar cores are the birthplaces of Sun-like stars and represent the initial conditions for the assembly of protoplanetary systems. Due to their short lifespans, they are rare. In recent efforts to increase the number of such sources identified in the Solar neighbourhood, we have selected a sample of 40 starless cores from the publicly available core catalogs of the Herschel Gould Belt survey. In this work, we focus on one of the sources that stands out for its high central density: Corona Australis 151. Aims. We use molecular lines that trace dense gas (n>=10^6 cm-3) to confirm the exceptionally high density of this object, to study its physical structure, and to understand its evolutionary stage. Methods. We detected the N2H+ 3-2 and 5-4 transitions, and the N2D+ 3-2, 4-3, and 6-5 lines with the APEX telescope. We use the Herschel continuum data to infer a spherically symmetric model of the core's density and temperature. This is used as input to perform non-local-thermodynamic-equilibrium radiative transfer to fit the observed five lines. Results. Our analysis confirms that this core is characterised by very high densities (a few x 10^7 cm-3 at the centre) and cold temperatures. We infer a high deuteration level of N2D+/N2H+=0.50, indicative of an advanced evolutionary stage. In the large bandwidth covered by the APEX data, we detect several other deuterated species, including CHD2OH, D2CO, and ND3. We also detect multiple sulphurated species that present broader lines with signs of high-velocity wings. Conclusions. The observation of high-velocity wings and the fact that the linewidths of N2H+ and N2D+ become larger with increasing frequency can be interpreted either as an indication of supersonic infall motions developing in the central parts of a very evolved pre-stellar core or as the signature of outflows from a very low luminosity object (VeLLO). *SHORTENED*
PeerQA: A Scientific Question Answering Dataset from Peer Reviews
2025-02-19
Tim Baumgärtner, Ted Briscoe, Iryna Gurevych
We present PeerQA, a real-world, scientific, document-level Question Answering (QA) dataset. PeerQA questions have been sourced from peer reviews, which contain questions that reviewers raised while thoroughly examining the scientific article. Answers have been annotated by the original authors of each paper. The dataset contains 579 QA pairs from 208 academic articles, with a majority from ML and NLP, as well as a subset of other scientific communities like Geoscience and Public Health. PeerQA supports three critical tasks for developing practical QA systems: Evidence retrieval, unanswerable question classification, and answer generation. We provide a detailed analysis of the collected dataset and conduct experiments establishing baseline systems for all three tasks. Our experiments and analyses reveal the need for decontextualization in document-level retrieval, where we find that even simple decontextualization approaches consistently improve retrieval performance across architectures. On answer generation, PeerQA serves as a challenging benchmark for long-context modeling, as the papers have an average size of 12k tokens. Our code and data is available at https://github.com/UKPLab/peerqa.
Reliability Across Parametric and External Knowledge: Understanding Knowledge Handling in LLMs
2025-02-19
Youna Kim, Minjoon Choi, Sungmin Cho, Hyuhng Joon Kim, Sang-goo Lee, Taeuk Kim
Large Language Models (LLMs) enhance their problem-solving capability by leveraging both parametric and external knowledge. Beyond leveraging external knowledge to improve response accuracy, they require key capabilities for reliable knowledge-handling: resolving conflicts between knowledge sources, avoiding distraction from uninformative external knowledge, and abstaining when sufficient knowledge is unavailable. Prior studies have examined these scenarios in isolation or with limited scope. To systematically evaluate these capabilities, we introduce a comprehensive framework for analyzing knowledge-handling based on two key dimensions: the presence of parametric knowledge and the informativeness of external knowledge. Through analysis, we identify biases in knowledge utilization and examine how the ability to handle one scenario impacts performance in others. Furthermore, we demonstrate that training on data constructed based on the knowledge-handling scenarios improves LLMs' reliability in integrating and utilizing knowledge.
Catalysts are essential for accelerating chemical reactions and enhancing selectivity, which is crucial for the sustainable production of energy, materials, and bioactive compounds. Catalyst discovery is fundamental yet challenging in computational chemistry and has garnered significant attention due to the promising performance of advanced Artificial Intelligence (AI) techniques. The development of Large Language Models (LLMs) notably accelerates progress in the discovery of both homogeneous and heterogeneous catalysts, where their chemical reactions differ significantly in material phases, temperature, dynamics, etc. However, there is currently no comprehensive survey that discusses the progress and latest developments in both areas, particularly with the application of LLM techniques. To address this gap, this paper presents a thorough and systematic survey of AI-empowered catalyst discovery, employing a unified and general categorization for homogeneous and heterogeneous catalysts. We examine the progress of AI-empowered catalyst discovery, highlighting their individual advantages and disadvantages, and discuss the challenges faced in this field. Furthermore, we suggest potential directions for future research from the perspective of computer science. Our goal is to assist researchers in computational chemistry, computer science, and related fields in easily tracking the latest advancements, providing a clear overview and roadmap of this area. We also organize and make accessible relevant resources, including article lists and datasets, in an open repository at https://github.com/LuckyGirl-XU/Awesome-Artificial-Intelligence-Empowered-Catalyst-Discovery.
AV$_3$Sb$_5$ kagome superconductors: a review with transport measurements
2025-02-19
Zhuokai Xu, Tian Le, Xiao Lin
Kagome systems have garnered considerable attention due to the unique features of the sublattice structure and band topology. The recently discovered kagome metals AV$_3$Sb$_5$ (where A = K, Rb, Cs) host a rich array of symmetry-breaking phases, including exotic charge density waves (CDW), electronic nematicity, pair density waves (PDW) and superconductivity. Despite extensive experimental and theoretical investigations into the diverse phases, several key issues remain contentious, such as a solid clarification of the time-reversal symmetry breaking (TRS-breaking) in the CDW order and its implications for the nature of superconducting (SC) pairing symmetry. This review aims to shed light on the transport properties of these intertwined phases, emphasizing the pivotal role that transport measurements play in uncovering the non-trivial quantum states of matter.
Toward Robust Non-Transferable Learning: A Survey and Benchmark
2025-02-19
Ziming Hong, Yongli Xiang, Tongliang Liu
Over the past decades, researchers have primarily focused on improving the generalization abilities of models, with limited attention given to regulating such generalization. However, the ability of models to generalize to unintended data (e.g., harmful or unauthorized data) can be exploited by malicious adversaries in unforeseen ways, potentially resulting in violations of model ethics. Non-transferable learning (NTL), a task aimed at reshaping the generalization abilities of deep learning models, was proposed to address these challenges. While numerous methods have been proposed in this field, a comprehensive review of existing progress and a thorough analysis of current limitations remain lacking. In this paper, we bridge this gap by presenting the first comprehensive survey on NTL and introducing NTLBench, the first benchmark to evaluate NTL performance and robustness within a unified framework. Specifically, we first introduce the task settings, general framework, and criteria of NTL, followed by a summary of NTL approaches. Furthermore, we emphasize the often-overlooked issue of robustness against various attacks that can destroy the non-transferable mechanism established by NTL. Experiments conducted via NTLBench verify the limitations of existing NTL methods in robustness. Finally, we discuss the practical applications of NTL, along with its future directions and associated challenges.
Large language models (LLMs) have demonstrated remarkable in-context reasoning capabilities across a wide range of tasks, particularly with unstructured inputs such as language or images. However, LLMs struggle to handle structured data, such as graphs, due to their lack of understanding of non-Euclidean structures. As a result, without additional fine-tuning, their performance significantly lags behind that of graph neural networks (GNNs) in graph learning tasks. In this paper, we show that learning on graph data can be conceptualized as a retrieval-augmented generation (RAG) process, where specific instances (e.g., nodes or edges) act as queries, and the graph itself serves as the retrieved context. Building on this insight, we propose a series of RAG frameworks to enhance the in-context learning capabilities of LLMs for graph learning tasks. Comprehensive evaluations demonstrate that our proposed RAG frameworks significantly improve LLM performance on graph-based tasks, particularly in scenarios where a pretrained LLM must be used without modification or accessed via an API.
Towards a Robust Quality Assurance Framework for Cloud Computing Environments
2025-02-19
Mohammed Alharbi, RJ Qureshi
Trends such as cloud computing raise issues regarding stable and uniform quality assurance and validation of software requirements. Current QA frameworks are poorly defined, often not automated, and lack the flexibility needed for on-demand, cloud based environments. These gaps lead to inconsistencies in service delivery, challenges in scaling organizational capacity, and internal and external inefficiencies that affect the reliability and effectiveness of cloud services. This paper presents a detailed framework for QA in cloud computing systems and advocates for standardized, automated, and adaptable systems to address these challenges. It aims to establish generic QA policies, incorporate intelligent techniques to enhance extendibility, and create adaptive solutions to manage the inherent attributes of cloud computing environments. The proposed framework is evaluated through survey questionnaires from industry practitioners, and descriptive statistics summarize the results. The study demonstrates the promise, effectiveness, and potential applicability of integrating a single QA framework to enhance the software functionality, dependability, and future adaptability in cloud computing systems
Reproducing NevIR: Negation in Neural Information Retrieval
2025-02-19
Coen van den Elsen, Francien Barkhof, Thijmen Nijdam, Simon Lupart, Mohammad Alliannejadi
Negation is a fundamental aspect of human communication, yet it remains a challenge for Language Models (LMs) in Information Retrieval (IR). Despite the heavy reliance of modern neural IR systems on LMs, little attention has been given to their handling of negation. In this study, we reproduce and extend the findings of NevIR, a benchmark study that revealed most IR models perform at or below the level of random ranking when dealing with negation. We replicate NevIR's original experiments and evaluate newly developed state-of-the-art IR models. Our findings show that a recently emerging category - listwise Large Language Model (LLM) rerankers - outperforms other models but still underperforms human performance. Additionally, we leverage ExcluIR, a benchmark dataset designed for exclusionary queries with extensive negation, to assess the generalizability of negation understanding. Our findings suggest that fine-tuning on one dataset does not reliably improve performance on the other, indicating notable differences in their data distributions. Furthermore, we observe that only cross-encoders and listwise LLM rerankers achieve reasonable performance across both negation tasks.
Hidden Darkness in LLM-Generated Designs: Exploring Dark Patterns in Ecommerce Web Components Generated by LLMs
2025-02-19
Ziwei Chen, Jiawen Shen, Luna, Kristen Vaccaro
Recent work has highlighted the risks of LLM-generated content for a wide range of harmful behaviors, including incorrect and harmful code. In this work, we extend this by studying whether LLM-generated web design contains dark patterns. This work evaluated designs of ecommerce web components generated by four popular LLMs: Claude, GPT, Gemini, and Llama. We tested 13 commonly used ecommerce components (e.g., search, product reviews) and used them as prompts to generate a total of 312 components across all models. Over one-third of generated components contain at least one dark pattern. The majority of dark pattern strategies involve hiding crucial information, limiting users' actions, and manipulating them into making decisions through a sense of urgency. Dark patterns are also more frequently produced in components that are related to company interests. These findings highlight the need for interventions to prevent dark patterns during front-end code generation with LLMs and emphasize the importance of expanding ethical design education to a broader audience.
Transferring Textual Preferences to Vision-Language Understanding through Model Merging
2025-02-19
Chen-An Li, Tzu-Han Lin, Yun-Nung Chen, Hung-yi Lee
Large vision-language models (LVLMs) perform outstandingly across various multimodal tasks. However, their ability to evaluate generated content remains limited, and training vision-language reward models (VLRMs) with preference data is computationally expensive. This paper explores a training-free alternative by merging text-based reward models (RMs) with LVLMs to create VLRMs. Our approach shows that integrating these models leads to improved performance over LVLMs' scoring and text-based RMs, offering an efficient method for incorporating textual preferences into LVLMs.
Sparse approximation is important in many applications because of concise form of an approximant and good accuracy guarantees. The theory of compressed sensing, which proved to be very useful in the image processing and data sciences, is based on the concept of sparsity. A fundamental issue of sparse approximation is the problem of construction of efficient algorithms, which provide good approximation. It turns out that greedy algorithms with respect to dictionaries are very good from this point of view. They are simple in implementation and there are well developed theoretical guarantees of their efficiency. This survey/tutorial paper contains brief description of different kinds of greedy algorithms and results on their convergence and rate of convergence. Also, Chapter IV gives some typical proofs of convergence and rate of convergence results for important greedy algorithms and Chapter V gives some open problems.
Detecting LLM Fact-conflicting Hallucinations Enhanced by Temporal-logic-based Reasoning
2025-02-19
Ningke Li, Yahui Song, Kailong Wang, Yuekang Li, Ling Shi, Yi Liu, Haoyu Wang
Large language models (LLMs) face the challenge of hallucinations -- outputs that seem coherent but are actually incorrect. A particularly damaging type is fact-conflicting hallucination (FCH), where generated content contradicts established facts. Addressing FCH presents three main challenges: 1) Automatically constructing and maintaining large-scale benchmark datasets is difficult and resource-intensive; 2) Generating complex and efficient test cases that the LLM has not been trained on -- especially those involving intricate temporal features -- is challenging, yet crucial for eliciting hallucinations; and 3) Validating the reasoning behind LLM outputs is inherently difficult, particularly with complex logical relationships, as it requires transparency in the model's decision-making process. This paper presents Drowzee, an innovative end-to-end metamorphic testing framework that utilizes temporal logic to identify fact-conflicting hallucinations (FCH) in large language models (LLMs). Drowzee builds a comprehensive factual knowledge base by crawling sources like Wikipedia and uses automated temporal-logic reasoning to convert this knowledge into a large, extensible set of test cases with ground truth answers. LLMs are tested using these cases through template-based prompts, which require them to generate both answers and reasoning steps. To validate the reasoning, we propose two semantic-aware oracles that compare the semantic structure of LLM outputs to the ground truths. Across nine LLMs in nine different knowledge domains, experimental results show that Drowzee effectively identifies rates of non-temporal-related hallucinations ranging from 24.7% to 59.8%, and rates of temporal-related hallucinations ranging from 16.7% to 39.2%.
AutoTEE: Automated Migration and Protection of Programs in Trusted Execution Environments
2025-02-19
Ruidong Han, Zhou Yang, Chengyan Ma, Ye Liu, Yuqing Niu, Siqi Ma, Debin Gao, David Lo
Trusted Execution Environments (TEEs) isolate a special space within a device's memory that is not accessible to the normal world (also known as Untrusted Environment), even when the device is compromised. Thus, developers can utilize TEEs to provide strong security guarantees for their programs, making sensitive operations like encrypted data storage, fingerprint verification, and remote attestation protected from malicious attacks. Despite the strong protections offered by TEEs, adapting existing programs to leverage such security guarantees is non-trivial, often requiring extensive domain knowledge and manual intervention, which makes TEEs less accessible to developers. This motivates us to design AutoTEE, the first Large Language Model (LLM)-enabled approach that can automatically identify, partition, transform, and port sensitive functions into TEEs with minimal developer intervention. By manually reviewing 68 repositories, we constructed a benchmark dataset consisting of 385 sensitive functions eligible for transformation, on which AutoTEE achieves a high F1 score of 0.91. AutoTEE effectively transforms these sensitive functions into their TEE-compatible counterparts, achieving success rates of 90\% and 83\% for Java and Python, respectively. We further provide a mechanism to automatically port the transformed code to different TEE platforms, including Intel SGX and AMD SEV, demonstrating that the transformed programs run successfully and correctly on these platforms.
With the advent of 6G networks, offering ultra-high bandwidth and ultra-low latency, coupled with the enhancement of terminal device resolutions, holographic communication is gradually becoming a reality. Holographic digital twin (HDT) is considered one of key applications of holographic communication, capable of creating virtual replicas for real-time mapping and prediction of physical entity states, and performing three-dimensional reproduction of spatial information. In this context, integrated sensing and communication (ISAC) is expected to be a crucial pathway for providing data sources to HDT. This paper proposes a four-layer architecture assisted by ISAC for HDT, integrating emerging paradigms and key technologies to achieve low-cost, high-precision environmental data collection for constructing HDT. Specifically, to enhance sensing resolution, we explore super-resolution techniques from the perspectives of parameter estimation and point cloud construction. Additionally, we focus on multi-point collaborative sensing for constructing HDT, and provide a comprehensive review of four key techniques: node selection, multi-band collaboration, cooperative beamforming, and data fusion. Finally, we highlight several interesting research directions to guide and inspire future work.
Token Adaptation via Side Graph Convolution for Temporally and Spatially Efficient Fine-tuning of 3D Point Cloud Transformers
2025-02-19
Takahiko Furuya
Parameter-efficient fine-tuning (PEFT) of pre-trained 3D point cloud Transformers has emerged as a promising technique for 3D point cloud analysis. While existing PEFT methods attempt to minimize the number of tunable parameters, they still suffer from high temporal and spatial computational costs during fine-tuning. This paper proposes a novel PEFT algorithm for 3D point cloud Transformers, called Side Token Adaptation on a neighborhood Graph (STAG), to achieve superior temporal and spatial efficiency. STAG employs a graph convolutional side network that operates in parallel with a frozen backbone Transformer to adapt tokens to downstream tasks. STAG's side network realizes high efficiency through three key components: connection with the backbone that enables reduced gradient computation, parameter sharing framework, and efficient graph convolution. Furthermore, we present Point Cloud Classification 13 (PCC13), a new benchmark comprising diverse publicly available 3D point cloud datasets, enabling comprehensive evaluation of PEFT methods. Extensive experiments using multiple pre-trained models and PCC13 demonstrates the effectiveness of STAG. Specifically, STAG maintains classification accuracy comparable to existing methods while reducing tunable parameters to only 0.43M and achieving significant reductions in both computational time and memory consumption for fine-tuning. Code and benchmark will be available at: https://github.com/takahikof/STAG
The Effect of Vera C. Rubin Observatory Cadence Selections on Kilonova Detectability
2025-02-19
Cristina Andrade, Raiyah Alserkal, Luis Salazar Manzano, Emma Martin, Igor Andreoni, Michael W. Coughlin, Nidhal Guessoum, Liliana Rivera Sandoval
The discovery of the optical/infra-red counterpart (AT2017gfo) to the binary neutron star gravitational-wave detection (GW170817), which was followed by a short gamma-ray burst (GRB170817), marked a groundbreaking moment in multi-messenger astronomy. To date, it remains the only confirmed joint detection of its kind. However, many experiments are actively searching for similar fast-fading electromagnetic counterparts, known as kilonovae. Fortunately, the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) provides excellent prospects for identifying kilonova candidates either from, or independent of, gravitational-wave and gamma-ray burst triggers. Cadence choices for LSST surveys are especially important for maximising the likelihood of kilonovae detections. In this work, we explore the possibility of optimizing Rubin Observatory's ability to detect kilonovae by implementing a fast transient metric shown to be successful with an existing wide field survey, e.g. the Zwicky Transient Facility (ZTF). We study existing LSST cadences, how detection rates are affected by filter selections, the return timescales for visits of the same area in the sky, and other relevant factors. Through our analysis, we have found that employing baseline cadences and utilizing triplet families like presto gap produced the highest likelihood of kilonova detection.
Comprehensive Review on the Control of Heat Pumps for Energy Flexibility in Distribution Networks
2025-02-19
Gustavo L. Aschidamini, Mina Pavlovic, Bradley A. Reinholz, Malcolm S. Metcalfe, Taco Niet, Mariana Resener
Decarbonization plans promote the transition to heat pumps (HPs), creating new opportunities for their energy flexibility in demand response programs, solar photovoltaic integration and optimization of distribution networks. This paper reviews scheduling-based and real-time optimization methods for controlling HPs with a focus on energy flexibility in distribution networks. Scheduling-based methods fall into two categories: rule-based controllers (RBCs), which rely on predefined control rules without explicitly seeking optimal solutions, and optimization models, which are designed to determine the optimal scheduling of operations. Real-time optimization is achieved through model predictive control (MPC), which relies on a predictive model to optimize decisions over a time horizon, and reinforcement learning (RL), which takes a model-free approach by learning optimal strategies through direct interaction with the environment. The paper also examines studies on the impact of HPs on distribution networks, particularly those leveraging energy flexibility strategies. Key takeaways suggest the need to validate control strategies for extreme cold-weather regions that require backup heaters, as well as develop approaches designed for demand charge schemes that integrate HPs with other controllable loads. From a grid impact assessment perspective, studies have focused primarily on RBCs for providing energy flexibility through HP operation, without addressing more advanced methods such as real-time optimization using MPC or RL-based algorithms. Incorporating these advanced control strategies could help identify key limitations, including the impact of varying user participation levels and the cost-benefit trade-offs associated with their implementation.
Constraints on the active galactic nucleus and starburst activity of local ultraluminous infrared galaxies from a broad range of torus models
2025-02-19
Charalambia Varnava, Andreas Efstathiou, Duncan Farrah, Dimitra Rigopoulou
In this paper we further explore the properties of the HERschel Ultra Luminous Infrared Galaxy Survey (HERUS) sample of 42 local ultraluminous infrared galaxies (ULIRGs) with our recently developed Bayesian spectral energy distribution (SED) fitting code SMART (Spectral energy distributions Markov chain Analysis with Radiative Transfer models). SMART fits SEDs exclusively with multicomponent radiative transfer models. Mid-infrared spectroscopy can be included in the fitting at a spectral resolution matched to that of the radiative transfer models. We fit the SEDs of the HERUS ULIRGs with four different models for the active galactic nucleus (AGN) torus, a starburst and a spheroidal galaxy model, to put constraints on the AGN fraction of the galaxies and their star formation rate (SFR). Two of the AGN torus models we explored are smooth and two are two-phase. We find that, in most cases, a smooth tapered AGN torus provides the best fit to the data. We also find that solutions with other torus models may predict AGN and total luminosities up to an order of magnitude or more lower, but very rarely higher than the best-fitting model. In contrast, we find that, with minor exceptions, the predicted SFR and stellar mass of the ULIRGs are generally robustly estimated irrespective of the assumed torus model. This is despite the fact that one of the AGN torus models we use assumes fluffy grains with high emissivity in the far-infrared and submillimetre, which could potentially reduce the contribution of a starburst at those wavelengths and reduce the SFR.
Point Cloud Geometry Scalable Coding Using a Resolution and Quality-conditioned Latents Probability Estimator
2025-02-19
Daniele Mari, André F. R. Guarda, Nuno M. M. Rodrigues, Simone Milani, Fernando Pereira
In the current age, users consume multimedia content in very heterogeneous scenarios in terms of network, hardware, and display capabilities. A naive solution to this problem is to encode multiple independent streams, each covering a different possible requirement for the clients, with an obvious negative impact in both storage and computational requirements. These drawbacks can be avoided by using codecs that enable scalability, i.e., the ability to generate a progressive bitstream, containing a base layer followed by multiple enhancement layers, that allow decoding the same bitstream serving multiple reconstructions and visualization specifications. While scalable coding is a well-known and addressed feature in conventional image and video codecs, this paper focuses on a new and very different problem, notably the development of scalable coding solutions for deep learning-based Point Cloud (PC) coding. The peculiarities of this 3D representation make it hard to implement flexible solutions that do not compromise the other functionalities of the codec. This paper proposes a joint quality and resolution scalability scheme, named Scalable Resolution and Quality Hyperprior (SRQH), that, contrary to previous solutions, can model the relationship between latents obtained with models trained for different RD tradeoffs and/or at different resolutions. Experimental results obtained by integrating SRQH in the emerging JPEG Pleno learning-based PC coding standard show that SRQH allows decoding the PC at different qualities and resolutions with a single bitstream while incurring only in a limited RD penalty and increment in complexity w.r.t. non-scalable JPEG PCC that would require one bitstream per coding configuration.
A review of theories and models utilized by empirical studies about mental health help-seeking and implications for future research
2025-02-19
Jiaying "Lizzy" Liu, Yan Zhang
Purpose: With the rise of mental health risks globally, it is urgent to provide effective mental health support. However, a holistic understanding of how people seek help for mental health problems remains limited, impeding the development of evidence-based intervention programs to facilitate help-seeking behavior. This study reviews current theories that guide empirical research on young adults' help-seeking behavior using technologies, identifies limitations in existing frameworks, and proposes directions for future research. Methods: We searched databases that are most likely to contain mental health help-seeking practices in relation to information technology, including PubMed, ACM Digital Library, Web of Science, PsycInfo, ScienceDirect, EBSCO, and Cochrane Library. Results: Of 2443 abstracts reviewed, 43 studies met the criteria and were included in the analysis. We identified 16 theories and models. They represent seven perspectives to view mental health help-seeking and reveal factors such as accessibility, stigma, and social support as key factors influencing help-seeking. Limitations: We summarized the theories and models and categorized them based on their primary perspectives. Cross-perspective connections could be explored in future reviews. Conclusions: A holistic approach to creating culturally sensitive multi-level interventions that consider individual, interpersonal, and community factors is needed to advance effective mental health help-seeking support strategies.
Assessing Simulation Knowledge and Proficiency Among Undergraduate Computing Students in Brazil: Insights and Results from a Survey Research
2025-02-19
Fernando Brito Rodrigues, Valdemar Vicente Graciano Neto
This paper reports results of an investigation about the level of knowledge among undergraduate computer science students in Brazil regarding the topic of simulation. Amid rapid technological evolution, simulation emerges as a crucial resource for training professionals capable of facing complex challenges. The research seeks to analyze the presence and effectiveness of simulation education, exploring students' perceptions, the tools used, the challenges faced, and the prospects for deeper study. This report highlights the importance of academic training in a dynamic technological environment, emphasizing the crucial role of simulation education in undergraduate computer science, while exploring the foundations of the methodologies and educational strategies associated with the topic. A survey research approach is adopted. 108 answers were received from 10 Brazilian states. 19 respondents from 15 different institutions said they had some contact with simulation during their studies. Results reveal that MATLAB/Simulink is the most popular formalism/tool used to teach simulation in Brazil.
A Racing Dataset and Baseline Model for Track Detection in Autonomous Racing
2025-02-19
Shreya Ghosh, Yi-Huan Chen, Ching-Hsiang Huang, Abu Shafin Mohammad Mahdee Jameel, Chien Chou Ho, Aly El Gamal, Samuel Labi
A significant challenge in racing-related research is the lack of publicly available datasets containing raw images with corresponding annotations for the downstream task. In this paper, we introduce RoRaTrack, a novel dataset that contains annotated multi-camera image data from racing scenarios for track detection. The data is collected on a Dallara AV-21 at a racing circuit in Indiana, in collaboration with the Indy Autonomous Challenge (IAC). RoRaTrack addresses common problems such as blurriness due to high speed, color inversion from the camera, and absence of lane markings on the track. Consequently, we propose RaceGAN, a baseline model based on a Generative Adversarial Network (GAN) that effectively addresses these challenges. The proposed model demonstrates superior performance compared to current state-of-the-art machine learning models in track detection. The dataset and code for this work are available at github.com/RaceGAN.
Towards a Learning Theory of Representation Alignment
2025-02-19
Francesco Insulla, Shuo Huang, Lorenzo Rosasco
It has recently been argued that AI models' representations are becoming aligned as their scale and performance increase. Empirical analyses have been designed to support this idea and conjecture the possible alignment of different representations toward a shared statistical model of reality. In this paper, we propose a learning-theoretic perspective to representation alignment. First, we review and connect different notions of alignment based on metric, probabilistic, and spectral ideas. Then, we focus on stitching, a particular approach to understanding the interplay between different representations in the context of a task. Our main contribution here is relating properties of stitching to the kernel alignment of the underlying representation. Our results can be seen as a first step toward casting representation alignment as a learning-theoretic problem.
Dynamical Confinement and Magnetic Traps for Charges and Spins
2025-02-19
Afshin Besharat, Alexander A. Penin
We use the effective field theory approach to systematically study the dynamics of classical and quantum systems in an oscillating magnetic field. We find that the fast field oscillations give rise to an effective interaction which is able to confine charged particles as well as neutral particles with a spin magnetic moment. The effect is reminiscent of the renown dynamical stabilization of charges by the oscillating electric field and provides a foundation for a new class of magnetic traps. The properties characteristic to the dynamical magnetic confinement are reviewed.
Three Years of High-Contrast Imaging of the PDS 70 b and c Exoplanets at Hα with MagAO-X: Evidence of Strong Protoplanet Hα Variability and Circumplanetary Dust
2025-02-19
Laird M. Close, Jared R. Males, Jialin Li, Sebastiaan Y. Haffert, Joseph D. Long, Alexander D. Hedglen, Alycia J. Weinberger, Kate Follette, Daniel Apai, Rene Doyon, Warren Foster, Victor Gasho, Kyle Van Gorkom, Olivier Guyon, Maggie Y. Kautz, Jay Kueny, Jennifer Lumbres, Avalon McLeod, Eden McEwen, Clarissa Pavao, Logan Pearce, Laura Perez, Lauren Schatz, J. Szulágyi, Kevin Wagner, Ya-Lin Wu
We present 3 years of high-contrast imaging of the PDS 70 b and c accreting protoplanets with the new extreme AO system MagAO-X as part of the MaxProtoPlanetS survey of H$\alpha$ protoplanets. In 2023 and 2024 our sharp (25-27 mas FWHM); well AO corrected (20-26% Strehl), deep (2-3.6hr) images detect compact (r~30 mas; r~3 au) circumplanetary disks (CPDs) surrounding both protoplanets. Starlight scattering off the dusty outer edges of these CPDs is the likely source of the bright compact continuum light detected within ~30 mas of both planets in our simultaneously obtained continuum 668 nm filter images. After subtraction of contaminating continuum and PSF residuals with pyKLIP ADI and SDI we obtained high-contrast ASDI H$\alpha$ images of both planets in 2022, 2023 and 2024. We find the H$\alpha$ line flux of planet b fell by (8.1$\pm$1.6)x10$^{-16}$ ergs/s/cm$^2$ a factor of 4.6 drop in flux from 2022 to 2023. In March 2024, planet b continued to be faint with just a slight 1.6x rise to an H$\alpha$ line flux of (3.64$\pm$0.87)x10$^{-16}$ ergs/s/cm$^2$. For c we measure a significant increase of (2.74$\pm$0.51)x10$^{-16}$ ergs/s/cm$^2$ from 2023 to 2024 which is a factor of 2.3x increase. So both protoplanets have recently experienced significant H$\alpha$ variability with ~1 yr sampling. In 2024, planet c is brighter than b: as c is brightening and b generally fading. We also tentatively detect one new point source "CC3" inside the inner disk (~49 mas; at PA~295 deg; 2024) with orbital motion roughly consistent with a ~5.6 au orbit.
Evidence for Variable Accretion onto PDS 70 c and Implications for Protoplanet Detections
2025-02-19
Yifan Zhou, Brendan P. Bowler, Aniket Sanghi, Gabriel-Dominique Marleau, Shinsuke Takasao, Yuhiko Aoyama, Yasuhiro Hasegawa, Thanawuth Thanathibodee, Taichi Uyama, Jun Hashimoto, Kevin Wagner, Nuria Calvet, Dorian Demars, Ya-Lin Wu, Lauren I. Biddle, Sebastiaan Haffert, Marta L. Bryan
Understanding the processes of planet formation and accretion in young systems is essential to unraveling the initial conditions of planetary systems. The PDS 70 system, which hosts two directly imaged protoplanets, provides a unique laboratory for studying these phenomena, particularly through H-alpha emission a commonly used accretion tracer. We present multi-epoch observations and examine the variability in accretion signatures within this system, focusing on PDS 70 b and c. Using Hubble Space Telescope narrowband H-alpha imaging from 2020 and 2024, we achieve high signal-to-noise ratio detections of these planets and reveal significant changes in H-alpha flux. For PDS 70 c, the H-alpha flux more than doubled between 2020 and 2024. The trend is consistent with the one identified in recently published MagAO-X data, further confirming that PDS 70 c has become significantly brighter in H between 2023 March and 2024 May. The observed variability suggests dynamic accretion processes, possibly modulated by circumplanetary disk properties or transient accretion bursts. High-amplitude variability in PDS 70 c motivates simultaneous monitoring of multiple accretion tracers to probe the mechanisms of mass growth of gas giant planets. We quantify the impact of variability on the detectability of protoplanets in imaging surveys and emphasize the need for continued and regular monitoring to accurately assess the occurrence and characteristics of young, forming planets.
Benchmarking Self-Supervised Methods for Accelerated MRI Reconstruction
2025-02-19
Andrew Wang, Mike Davies
Reconstructing MRI from highly undersampled measurements is crucial for accelerating medical imaging, but is challenging due to the ill-posedness of the inverse problem. While supervised deep learning approaches have shown remarkable success, they rely on fully-sampled ground truth data, which is often impractical or impossible to obtain. Recently, numerous self-supervised methods have emerged that do not require ground truth, however, the lack of systematic comparison and standard experimental setups have hindered research. We present the first comprehensive review of loss functions from all feedforward self-supervised methods and the first benchmark on accelerated MRI reconstruction without ground truth, showing that there is a wide range in performance across methods. In addition, we propose Multi-Operator Equivariant Imaging (MO-EI), a novel framework that builds on the imaging model considered in existing methods to outperform all state-of-the-art and approaches supervised performance. Finally, to facilitate reproducible benchmarking, we provide implementations of all methods in the DeepInverse library (https://deepinv.github.io) and easy-to-use demo code at https://andrewwango.github.io/deepinv-selfsup-fastmri.
Tensor decomposition has emerged as a powerful framework for feature extraction in multi-modal biomedical data. In this review, we present a comprehensive analysis of tensor decomposition methods such as Tucker, CANDECOMP/PARAFAC, spiked tensor decomposition, etc. and their diverse applications across biomedical domains such as imaging, multi-omics, and spatial transcriptomics. To systematically investigate the literature, we applied a topic modeling-based approach that identifies and groups distinct thematic sub-areas in biomedicine where tensor decomposition has been used, thereby revealing key trends and research directions. We evaluated challenges related to the scalability of latent spaces along with obtaining the optimal rank of the tensor, which often hinder the extraction of meaningful features from increasingly large and complex datasets. Additionally, we discuss recent advances in quantum algorithms for tensor decomposition, exploring how quantum computing can be leveraged to address these challenges. Our study includes a preliminary resource estimation analysis for quantum computing platforms and examines the feasibility of implementing quantum-enhanced tensor decomposition methods on near-term quantum devices. Collectively, this review not only synthesizes current applications and challenges of tensor decomposition in biomedical analyses but also outlines promising quantum computing strategies to enhance its impact on deriving actionable insights from complex biomedical data.
Recent advances in large language models (LLMs) have shown that they can answer questions requiring complex reasoning. However, their ability to identify and respond to text containing logical fallacies or deliberately misleading premises remains less studied. To address this gap, we introduce RuozhiBench, a bilingual dataset comprising 677 carefully curated questions that contain various forms of deceptive reasoning, meticulously crafted through extensive human effort and expert review. In a comprehensive evaluation of 17 LLMs from 5 Series over RuozhiBench using both open-ended and two-choice formats, we conduct extensive analyses on evaluation protocols and result patterns. Despite their high scores on conventional benchmarks, these models showed limited ability to detect and reason correctly about logical fallacies, with even the best-performing model, Claude-3-haiku, achieving only 62% accuracy compared to the human of more than 90%.
No Evidence of Asymmetrically Enhanced Star Formation in Infalling Galaxies in UNIONS
2025-02-18
Lauren M. Foster, Laura C. Parker, Stephen Gwyn, Ian D. Roberts, James E. Taylor, Michael J. Hudson, Alan W. McConnachie, Thomas de Boer
Ram pressure stripping is a well-known environmental quenching mechanism that removes gas from galaxies infalling into groups and clusters. In some extreme examples of ram pressure stripping, galaxies with extended gas tails show evidence of enhanced star formation prior to quenching. In this work we use a sample of 5277 local satellite galaxies in which a stripped tail of gas has not necessarily been observed, to quantify the strength of ram pressure-enhanced star formation and compare these results to a control sample of 8360 field galaxies. We use u-band imaging from the Ultraviolet-Near Infrared Northern Survey (UNIONS) as a star formation tracer and several metrics to quantify star formation asymmetry. We compare these results to environmental properties of the galaxy, such as their time since infall and host halo mass, to constrain the degree of ram pressure enhanced star formation as a function of environment. We find no significant differences between the satellite and the field samples. We further restrict our sample to galaxies which we most expect to be experiencing significant ram pressure but find no strong evidence of these galaxies having systematically enhanced star formation. Finally, we investigate the properties of the most asymmetric galaxies in our sample and again find no strong evidence of ram pressure-induced star formation enhancement. We conclude that any star formation enhancement must be small for infalling galaxies, suggesting that this effect is either uncommon or short-lived.
AI and the Transformation of Accountability and Discretion in Urban Governance
2025-02-18
Stephen Goldsmith, Juncheng Yang
The integration of Artificial Intelligence (AI) in urban governance presents significant opportunities to transform decision-making and enhance accountability. The paper highlights AI's potential to reposition human discretion and reshape specific types of accountability, elevating the decision-making capabilities of both frontline bureaucrats and managers while ensuring ethical standards and public trust are maintained. While AI can enhance bureaucratic flexibility and efficiency, its integration will also necessitate new governance frameworks to mitigate risks associated with uneven capacity distribution, ethical concerns, and public trust. Following the literature review and theoretical discussion, this study introduces a set of guiding principles for AI-assisted urban governance, emphasizing equitable AI deployment, adaptive administrative structures, robust data governance, transparent human-AI collaboration, and citizen engagement in oversight mechanisms. By critically evaluating AI's dual role in expanding discretion and reinforcing accountability, this paper advances a framework for responsible AI adoption, ensuring that urban governance remains adaptive, transparent, and aligned with public values.
Quantitative First-Pass Perfusion CMR: from technical principles to clinical practice
2025-02-18
Catarina N Carvalho, Andreia Gaspar, Carlos Real, Carlos Galán-Arriola, Elisa Moya-Sáez, Rosa-María Menchón-Lara, Javier Sanchez, Carlos Alberola-López, Rita G Nunes, Borja Ibáñez, Teresa M Correia
Myocardial perfusion cardiovascular magnetic resonance (pCMR) using first-pass contrast-enhanced imaging could play an important role in the detection of epicardial and microvascular coronary artery disease. Recently, the emergence of quantitative pCMR has provided a more reliable and observer-independent analysis compared to visual interpretation of dynamic images. This review aims to cover the basics of quantitative pCMR, from acquisition protocols, its use in preclinical and clinical studies, image reconstruction and motion handling, to automated quantitative pCMR pipelines. It also offers an overview of emerging tools in the field, including artificial intelligence-based methods.
Personalized Image Generation with Deep Generative Models: A Decade Survey
2025-02-18
Yuxiang Wei, Yiheng Zheng, Yabo Zhang, Ming Liu, Zhilong Ji, Lei Zhang, Wangmeng Zuo
Recent advancements in generative models have significantly facilitated the development of personalized content creation. Given a small set of images with user-specific concept, personalized image generation allows to create images that incorporate the specified concept and adhere to provided text descriptions. Due to its wide applications in content creation, significant effort has been devoted to this field in recent years. Nonetheless, the technologies used for personalization have evolved alongside the development of generative models, with their distinct and interrelated components. In this survey, we present a comprehensive review of generalized personalized image generation across various generative models, including traditional GANs, contemporary text-to-image diffusion models, and emerging multi-model autoregressive models. We first define a unified framework that standardizes the personalization process across different generative models, encompassing three key components, i.e., inversion spaces, inversion methods, and personalization schemes. This unified framework offers a structured approach to dissecting and comparing personalization techniques across different generative architectures. Building upon this unified framework, we further provide an in-depth analysis of personalization techniques within each generative model, highlighting their unique contributions and innovations. Through comparative analysis, this survey elucidates the current landscape of personalized image generation, identifying commonalities and distinguishing features among existing methods. Finally, we discuss the open challenges in the field and propose potential directions for future research. We keep tracing related works at https://github.com/csyxwei/Awesome-Personalized-Image-Generation.
Hateful memes have become a significant concern on the Internet, necessitating robust automated detection systems. While large multimodal models have shown strong generalization across various tasks, they exhibit poor generalization to hateful meme detection due to the dynamic nature of memes tied to emerging social trends and breaking news. Recent work further highlights the limitations of conventional supervised fine-tuning for large multimodal models in this context. To address these challenges, we propose Large Multimodal Model Retrieval-Guided Contrastive Learning (LMM-RGCL), a novel two-stage fine-tuning framework designed to improve both in-domain accuracy and cross-domain generalization. Experimental results on six widely used meme classification datasets demonstrate that LMM-RGCL achieves state-of-the-art performance, outperforming agent-based systems such as VPD-PALI-X-55B. Furthermore, our method effectively generalizes to out-of-domain memes under low-resource settings, surpassing models like GPT-4o.
QZO: A Catalog of 5 Million Quasars from the Zwicky Transient Facility
2025-02-18
S. J. Nakoneczny, M. J. Graham, D. Stern, G. Helou, S. G. Djorgovski, E. C. Bellm, T. X. Chen, R. Dekany, A. Drake, A. A. Mahabal, T. A. Prince, R. Riddle, B. Rusholme, N. Sravan
Machine learning methods are well established in the classification of quasars (QSOs). However, the advent of light curve observations adds a great amount of complexity to the problem. Our goal is to use the Zwicky Transient Facility (ZTF) to create a catalog of QSOs. We process the ZTF DR20 light curves with a transformer artificial neural network and combine the Pan-STARRS (PS), AllWISE, and Gaia surveys with extreme gradient boosting. Using ZTF g-band data with at least 100 observational epochs per light curve, we obtain 97% F1 score for QSOs. We find that with 3 day median cadence, a survey time span of at least 900 days is required to achieve 90% QSO F1 score. However, one can obtain the same score with a survey time span of 1800 days and the median cadence prolonged to 12 days. We find that ZTF classification is superior to the PS static bands, and on par with WISE and Gaia measurements. Additionally, we find that the light curves provide the most important features for QSO classification in the ZTF dataset. We robustly classify objects fainter than the $5\sigma$ SNR limit at $g=20.8$ by requiring $g < \mathrm{n_{obs}} / 80 + 20.375$. For this sample, we run inference with added WISE observations, and find 4,849,574 objects classified as QSOs. For 33% of QZO objects, with available WISE data, we publish redshifts with estimated error $\Delta z/(1 + z) = 0.14$.
Ensuring the safety and reliability of power grids is critical as global energy demands continue to rise. Traditional inspection methods, such as manual observations or helicopter surveys, are resource-intensive and lack scalability. This paper explores the use of 3D computer vision to automate power grid inspections, utilizing the TS40K dataset -- a high-density, annotated collection of 3D LiDAR point clouds. By concentrating on 3D semantic segmentation, our approach addresses challenges like class imbalance and noisy data to enhance the detection of critical grid components such as power lines and towers. The benchmark results indicate significant performance improvements, with IoU scores reaching 95.53% for the detection of power lines using transformer-based models. Our findings illustrate the potential for integrating ML into grid maintenance workflows, increasing efficiency and enabling proactive risk management strategies.
Role-Playing Agent (RPA) is an increasingly popular type of LLM Agent that simulates human-like behaviors in a variety of tasks. However, evaluating RPAs is challenging due to diverse task requirements and agent designs. This paper proposes an evidence-based, actionable, and generalizable evaluation design guideline for LLM-based RPA by systematically reviewing 1,676 papers published between Jan. 2021 and Dec. 2024. Our analysis identifies six agent attributes, seven task attributes, and seven evaluation metrics from existing literature. Based on these findings, we present an RPA evaluation design guideline to help researchers develop more systematic and consistent evaluation methods.
Imputation Strategies for Rightcensored Wages in Longitudinal Datasets
2025-02-18
Jörg Drechsler, Johannes Ludsteck
Censoring from above is a common problem with wage information as the reported wages are typically top-coded for confidentiality reasons. In administrative databases the information is often collected only up to a pre-specified threshold, for example, the contribution limit for the social security system. While directly accounting for the censoring is possible for some analyses, the most flexible solution is to impute the values above the censoring point. This strategy offers the advantage that future users of the data no longer need to implement possibly complicated censoring estimators. However, standard cross-sectional imputation routines relying on the classical Tobit model to impute right-censored data have a high risk of introducing bias from uncongeniality (Meng, 1994) as future analyses to be conducted on the imputed data are unknown to the imputer. Furthermore, as we show using a large-scale administrative database from the German Federal Employment agency, the classical Tobit model offers a poor fit to the data. In this paper, we present some strategies to address these problems. Specifically, we use leave-one-out means as suggested by Card et al. (2013) to avoid biases from uncongeniality and rely on quantile regression or left censoring to improve the model fit. We illustrate the benefits of these modeling adjustments using the German Structure of Earnings Survey, which is (almost) unaffected by censoring and can thus serve as a testbed to evaluate the imputation procedures.
A Survey of Text Classification Under Class Distribution Shift
2025-02-18
Adriana Valentina Costache, Silviu Florin Gheorghe, Eduard Gabriel Poesina, Paul Irofti, Radu Tudor Ionescu
The basic underlying assumption of machine learning (ML) models is that the training and test data are sampled from the same distribution. However, in daily practice, this assumption is often broken, i.e.~the distribution of the test data changes over time, which hinders the application of conventional ML models. One domain where the distribution shift naturally occurs is text classification, since people always find new topics to discuss. To this end, we survey research articles studying open-set text classification and related tasks. We divide the methods in this area based on the constraints that define the kind of distribution shift and the corresponding problem formulation, i.e.~learning with the Universum, zero-shot learning, and open-set learning. We next discuss the predominant mitigation approaches for each problem setup. Finally, we identify several future work directions, aiming to push the boundaries beyond the state of the art. Interestingly, we find that continual learning can solve many of the issues caused by the shifting class distribution. We maintain a list of relevant papers at https://github.com/Eduard6421/Open-Set-Survey.
OGLE-2014-BLG-1760: A Jupiter-Sun analogue residing in the Galactic Bulge
2025-02-18
Natalia E. Rektsini, Clement Ranc, Naoki Koshimoto, Jean-Philippe Beaulieu, David P. Bennett, Andrew A. Cole, Aparna Bhattacharya, Etienne Bachelet, Ian A. Bond, Andrzej Udalski, Joshua W. Blackman, Aikaterini Vandorou, Thomas J. Plunkett, Jean-Baptiste Marquette
We present the analysis of OGLE-2014-BLG-1760, a planetary system in the galactic bulge. We combine Keck Adaptive Optics follow-up observations in $K$-band with re-reduced light curve data to confirm the source and lens star identifications and stellar types. The re-reduced MOA dataset had an important impact on the light curve model. We find the Einstein ring crossing time of the event to be $\sim$ 2.5 days shorter than previous fits, which increases the planetary mass-ratio and decreases the source angular size by a factor of 0.25. Our OSIRIS images obtained 6 years after the peak of the event show a source-lens separation of 54.20 $\pm$ 0.23 mas, which leads to a relative proper motion of $\mu_{\rm rel}$ = 9.14 $\pm$ 0.05 mas/yr, larger than the previous light curve-only models. Our analysis shows that the event consists of a Jupiter-mass planet of $M_{\rm p}$ = 0.931 $\pm$ 0.117 $M_{\rm Jup}$ orbiting a K-dwarf star of $M_*$ = 0.803 $\pm$ 0.097 $M_{\odot}$ with a $K$-magnitude of $K_{\rm L}$ = 18.30 $\pm$ 0.05, located in the galactic bulge or bar. We also attempt to constrain the source properties using the source angular size $\theta_*$ and $K$-magnitude. Our results favor the scenario of the source being a younger star in the galactic disk, behind the galactic center, but future multicolor observations are needed to constrain the source and thus the lens properties.
Neuro-oscillatory models of cortical speech processing
2025-02-18
Olesia Dogonasheva, Denis Zakharov, Anne-Lise Giraud, Boris Gutkin
In this review, we examine computational models that explore the role of neural oscillations in speech perception, spanning from early auditory processing to higher cognitive stages. We focus on models that use rhythmic brain activities, such as gamma, theta, and delta oscillations, to encode phonemes, segment speech into syllables and words, and integrate linguistic elements to infer meaning. We analyze the mechanisms underlying these models, their biological plausibility, and their potential applications in processing and understanding speech in real time, a computational feature that is achieved by the human brain but not yet implemented in speech recognition models. Real-time processing enables dynamic adaptation to incoming speech, allowing systems to handle the rapid and continuous flow of auditory information required for effective communication, interactive applications, and accurate speech recognition in a variety of real-world settings. While significant progress has been made in modeling the neural basis of speech perception, challenges remain, particularly in accounting for the complexity of semantic processing and the integration of contextual influences. Moreover, the high computational demands of biologically realistic models pose practical difficulties for their implementation and analysis. Despite these limitations, these models provide valuable insights into the neural mechanisms of speech perception. We conclude by identifying current limitations, proposing future research directions, and suggesting how these models can be further developed to achieve a more comprehensive understanding of speech processing in the human brain.
Selection Function of Clusters in Dark Energy Survey Year 3 Data from Cross-Matching with South Pole Telescope Detections
2025-02-18
S. Grandis, M. Costanzi, J. J. Mohr, L. E. Bleem, H. -Y. Wu, M. Aguena, S. Allam, F. Andrade-Oliveira, S. Bocquet, D. Brooks, A. Carnero Rosell, J. Carretero, L. N. da Costa, M. E. S. Pereira, T. M. Davis, S. Desai, H. T. Diehl, P. Doel, S. Everett, B. Flaugher, J. Frieman, J. García-Bellido, E. Gaztanaga, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, J. Hlacacek-Larrondo, D. L. Hollowood, K. Honscheid, D. J. James, M. Klein, J. L. Marshall, J. Mena-Fernández, R. Miquel, A. Palmese, A. A. Plazas Malagón, C. L. Reichardt, A. K. Romer, S. Samuroff, D. Sanchez Cid, E. Sanchez, B. Santiago, A. Saro, I. Sevilla-Noarbe, M. Smith, M. Soares-Santos, M. W. Sommer, E. Suchyta, G. Tarle, C. To, D. L. Tucker, N. Weaverdyck, J. Weller, P. Wiseman
Galaxy clusters selected based on overdensities of galaxies in photometric surveys provide the largest cluster samples. Yet modeling the selection function of such samples is complicated by non-cluster members projected along the line of sight (projection effects) and the potential detection of unvirialized objects (contamination). We empirically constrain the magnitude of these effects by cross-matching galaxy clusters selected in the Dark Energy survey data with the \rdmpr$\,$ algorithm with significant detections in three South Pole Telescope surveys (SZ, pol-ECS, pol-500d). For matched clusters, we augment the \rdmpr$\,$catalog by the SPT detection significance. For unmatched objects we use the SPT detection threshold as an upper limit on the SZe signature. Using a Bayesian population model applied to the collected multi-wavelength data, we explore various physically motivated models to describe the relationship between observed richness and halo mass. Our analysis reveals the limitations of a simple lognormal scatter model in describing the data. We rule out significant contamination by unvirialized objects at the high-richness end of the sample. While dedicated simulations offer a well-fitting calibration of projection effects, our findings suggest the presence of redshift-dependent trends that these simulations may not have captured. Our findings highlight that modeling the selection function of optically detected clusters remains a complicated challenge, requiring a combination of simulation and data-driven approaches.
Graph neural networks (GNNs) are powerful deep learning models for graph-structured data, demonstrating remarkable success across diverse domains. Recently, the database (DB) community has increasingly recognized the potentiality of GNNs, prompting a surge of researches focusing on improving database systems through GNN-based approaches. However, despite notable advances, There is a lack of a comprehensive review and understanding of how GNNs could improve DB systems. Therefore, this survey aims to bridge this gap by providing a structured and in-depth overview of GNNs for DB systems. Specifically, we propose a new taxonomy that classifies existing methods into two key categories: (1) Relational Databases, which includes tasks like performance prediction, query optimization, and text-to-SQL, and (2) Graph Databases, addressing challenges like efficient graph query processing and graph similarity computation. We systematically review key methods in each category, highlighting their contributions and practical implications. Finally, we suggest promising avenues for integrating GNNs into Database systems.
Unmanned aerial vehicles (UAVs) are playing an increasingly pivotal role in modern communication networks,offering flexibility and enhanced coverage for a variety of applica-tions. However, UAV networks pose significant challenges due to their dynamic and distributed nature, particularly when dealing with tasks such as power allocation, channel assignment, caching,and task offloading. Traditional optimization techniques often struggle to handle the complexity and unpredictability of these environments, leading to suboptimal performance. This survey provides a comprehensive examination of how deep reinforcement learning (DRL) can be applied to solve these mathematical optimization problems in UAV communications and networking.Rather than simply introducing DRL methods, the focus is on demonstrating how these methods can be utilized to solve complex mathematical models of the underlying problems. We begin by reviewing the fundamental concepts of DRL, including value-based, policy-based, and actor-critic approaches. Then,we illustrate how DRL algorithms are applied to specific UAV network tasks by discussing from problem formulations to DRL implementation. By framing UAV communication challenges as optimization problems, this survey emphasizes the practical value of DRL in dynamic and uncertain environments. We also explore the strengths of DRL in handling large-scale network scenarios and the ability to continuously adapt to changes in the environment. In addition, future research directions are outlined, highlighting the potential for DRL to further enhance UAV communications and expand its applicability to more complex,multi-agent settings.
2025-02-18
Sean Cleary, Mareike Fischer, Katherine St. John
Tree balance plays an important role in various research areas in phylogenetics and computer science. Typically, it is measured with the help of a balance index or imbalance index. There are more than 25 such indices available, recently surveyed in a book by Fischer et al. They are used to rank rooted binary trees on a scale from the most balanced to the stringiest. We show that a wide range of subtree-sized based measures satisfying concavity and monotonicity conditions are minimized by the complete or greedy-from-the-bottom (GFB) tree and maximized by the caterpillar tree, yielding an infinitely large family of distinct new imbalance indices. Answering an open question from the literature, we show that one such established measure, the $\widehat{s}$-shape statistic, has the GFB tree as its unique minimizer. We also provide an alternative characterization of GFB trees, showing that they are equivalent to complete trees, which arise in different contexts. We give asymptotic bounds on the expected $\widehat{s}$-shape statistic under the uniform and Yule-Harding distributions of trees, and answer questions for the related $Q$-shape statistic as well.
MOLLM: Multi-Objective Large Language Model for Molecular Design -- Optimizing with Experts
2025-02-18
Nian Ran, Yue Wang, Richard Allmendinger
Molecular design plays a critical role in advancing fields such as drug discovery, materials science, and chemical engineering. This work introduces the Multi-Objective Large Language Model for Molecular Design (MOLLM), a novel framework that combines domain-specific knowledge with the adaptability of Large Language Models to optimize molecular properties across multiple objectives. Leveraging in-context learning and multi-objective optimization, MOLLM achieves superior efficiency, innovation, and performance, significantly surpassing state-of-the-art (SOTA) methods. Recognizing the substantial impact of initial populations on evolutionary algorithms, we categorize them into three types: best initial, worst initial, and random initial, to ensure the initial molecules are the same for each method across experiments. Our results demonstrate that MOLLM consistently outperforms SOTA models in all of our experiments. We also provide extensive ablation studies to evaluate the superiority of our components.
Does dark matter fall in the same way as standard model particles? A direct constraint of Euler's equation with cosmological data
2025-02-18
Nastassia Grimm, Camille Bonvin, Isaac Tutusaus
Since dark matter particles have never been directly detected, we do not know how they move, and in particular we do not know how they fall inside gravitational potential wells. Usually it is assumed that dark matter only interacts gravitationally with itself and with particles of the standard model, and therefore that its motion is governed by Euler's equation. In this paper, we test this assumption for the first time at cosmological scales, by combining measurements of galaxy velocities with measurements of gravitational potential wells, encoded in the Weyl potential. We find that current data are consistent with Euler's equation at redshifts $z\in [0.3,0.8]$, and we place constraints on the strength of a potential fifth force, which would alter the way dark matter particles fall. We find that a positive fifth force cannot exceed 7% of the gravitational interaction strength, while a negative fifth force is limited to 21%. The coming generation of surveys, including the Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory and the Dark Energy Spectroscopic Instrument (DESI) will drastically improve the constraints, allowing to constrain a departure from pure gravitational interaction at the level of 2%.
Reinforcement Learning for Dynamic Resource Allocation in Optical Networks: Hype or Hope?
2025-02-18
Michael Doherty, Robin Matzner, Rasoul Sadeghi, Polina Bayvel, Alejandra Beghelli
The application of reinforcement learning (RL) to dynamic resource allocation in optical networks has been the focus of intense research activity in recent years, with almost 100 peer-reviewed papers. We present a review of progress in the field, and identify significant gaps in benchmarking practices and reproducibility. To determine the strongest benchmark algorithms, we systematically evaluate several heuristics across diverse network topologies. We find that path count and sort criteria for path selection significantly affect the benchmark performance. We meticulously recreate the problems from five landmark papers and apply the improved benchmarks. Our comparisons demonstrate that simple heuristics consistently match or outperform the published RL solutions, often with an order of magnitude lower blocking probability. Furthermore, we present empirical lower bounds on network blocking using a novel defragmentation-based method, revealing that potential improvements over the benchmark heuristics are limited to 19--36\% increased traffic load for the same blocking performance in our examples. We make our simulation framework and results publicly available to promote reproducible research and standardized evaluation https://doi.org/10.5281/zenodo.12594495.
Unsupervised Anomaly Detection through Mass Repulsing Optimal Transport
2025-02-18
Eduardo Fernandes Montesuma, Adel El Habazi, Fred Ngole Mboula
Detecting anomalies in datasets is a longstanding problem in machine learning. In this context, anomalies are defined as a sample that significantly deviates from the remaining data. Meanwhile, optimal transport (OT) is a field of mathematics concerned with the transportation, between two probability measures, at least effort. In classical OT, the optimal transportation strategy of a measure to itself is the identity. In this paper, we tackle anomaly detection by forcing samples to displace its mass, while keeping the least effort objective. We call this new transportation problem Mass Repulsing Optimal Transport (MROT). Naturally, samples lying in low density regions of space will be forced to displace mass very far, incurring a higher transportation cost. We use these concepts to design a new anomaly score. Through a series of experiments in existing benchmarks, and fault detection problems, we show that our algorithm improves over existing methods.
How Much Do LLMs Hallucinate across Languages? On Multilingual Estimation of LLM Hallucination in the Wild
2025-02-18
Saad Obaid ul Islam, Anne Lauscher, Goran Glavaš
In the age of misinformation, hallucination -- the tendency of Large Language Models (LLMs) to generate non-factual or unfaithful responses -- represents the main risk for their global utility. Despite LLMs becoming increasingly multilingual, the vast majority of research on detecting and quantifying LLM hallucination are (a) English-centric and (b) focus on machine translation (MT) and summarization, tasks that are less common ``in the wild'' than open information seeking. In contrast, we aim to quantify the extent of LLM hallucination across languages in knowledge-intensive long-form question answering. To this end, we train a multilingual hallucination detection model and conduct a large-scale study across 30 languages and 6 open-source LLM families. We start from an English hallucination detection dataset and rely on MT to generate (noisy) training data in other languages. We also manually annotate gold data for five high-resource languages; we then demonstrate, for these languages, that the estimates of hallucination rates are similar between silver (LLM-generated) and gold test sets, validating the use of silver data for estimating hallucination rates for other languages. For the final rates estimation, we build a knowledge-intensive QA dataset for 30 languages with LLM-generated prompts and Wikipedia articles as references. We find that, while LLMs generate longer responses with more hallucinated tokens for higher-resource languages, there is no correlation between length-normalized hallucination rates of languages and their digital representation. Further, we find that smaller LLMs exhibit larger hallucination rates than larger models.
Semi-empirical Models of Galaxy Formation and Evolution
2025-02-18
Andrea Lapi, Lumen Boco, Francesco Shankar
We provide a review on semi-empirical models of galaxy formation and evolution. We present a brief census of the three main modeling approaches to galaxy evolution, namely hydrodynamical simulations, semi-analytic models, and semi-empirical models (SEMs). We focus on SEMs in their different flavors, i.e. interpretative, descriptive and hybrid, discussing the peculiarities and highlighting virtues and shortcomings for each of these variants. We dissect a simple and recent hybrid SEM from our team to highlight some technical aspects. We offer some outlook on the prospective developments of SEMs. Finally, we provide a short summary of this review.
Geometric Flavours of Quantum Field Theory on a Cauchy Hypersurface: Gaussian Analysis for the Hamiltonian Formalism and Applications to Cosmology
2025-02-18
David Martínez-Crespo
This thesis explores Quantum Field Theory (QFT) on curved spacetimes using a geometric Hamiltonian approach to the Schr\"odinger-like representation. In particular it studies the theory of the scalar field described through its configurations over a Cauchy hypersurface. It is focused on mathematical consistency based on analytic and geometric tools. The mathematical aspects of Gaussian integration theory in infinite-dimensional Topological Vector Spaces (TVS) are thoroughly reviewed. It also reviews the complex and holomorphic versions of important results and concepts of Gaussian integration. For example, the Wiener-It\^o decomposition theorem or the definition of Hida test functions. The physical framework builds upon three interconnected levels: classical General Relativity (GR), Classical Statistical Field Theory (CSFT), and QFT. The work begins by extending the Koopman-van Hove (KvH) formalism of classical statistical mechanics to CSFT. This description is based upon prequantization theory. It reveals features inherent to both CSFT and QFT, that help delineate the genuine quantum features of a theory. Upon the prequantum program, the QFT of the scalar field is built mixing Geometric Quantization with the choice of Wick and Weyl orderings. Various quantum representations are introduced: the holomorphic, Schr\"odinger, field-momentum, and antiholomorphic. The relation among them is studied using integral transforms, including novel infinite-dimensional Fourier transforms. From a geometrical analysis, it is argued that a covariant time derivative that modifies the evolution equations should be added to the Schr\"odinger equation. This connection is unique and required by the geometrodynamical description ofthe coupling of QFT and GR. Finally, studying the free model on cosmological spacetimes it obtains particle creation effects on a dynamical equation.
Reinforcement learning (RL) has shown promise in solving various combinatorial optimization problems. However, conventional RL faces challenges when dealing with real-world constraints, especially when action space feasibility is explicit and dependent on the corresponding state or trajectory. In this work, we focus on using RL in container shipping, often considered the cornerstone of global trade, by dealing with the critical challenge of master stowage planning. The main objective is to maximize cargo revenue and minimize operational costs while navigating demand uncertainty and various complex operational constraints, namely vessel capacity and stability, which must be dynamically updated along the vessel's voyage. To address this problem, we implement a deep reinforcement learning framework with feasibility projection to solve the master stowage planning problem (MPP) under demand uncertainty. The experimental results show that our architecture efficiently finds adaptive, feasible solutions for this multi-stage stochastic optimization problem, outperforming traditional mixed-integer programming and RL with feasibility regularization. Our AI-driven decision-support policy enables adaptive and feasible planning under uncertainty, optimizing operational efficiency and capacity utilization while contributing to sustainable and resilient global supply chains.
"I know myself better, but not really greatly": Using LLMs to Detect and Explain LLM-Generated Texts
2025-02-18
Jiazhou Ji, Jie Guo, Weidong Qiu, Zheng Huang, Yang Xu, Xinru Lu, Xiaoyu Jiang, Ruizhe Li, Shujun Li
Large language models (LLMs) have demonstrated impressive capabilities in generating human-like texts, but the potential misuse of such LLM-generated texts raises the need to distinguish between human-generated and LLM-generated content. This paper explores the detection and explanation capabilities of LLM-based detectors of LLM-generated texts, in the context of a binary classification task (human-generated texts vs LLM-generated texts) and a ternary classification task (human-generated texts, LLM-generated texts, and undecided). By evaluating on six close/open-source LLMs with different sizes, our findings reveal that while self-detection consistently outperforms cross-detection, i.e., LLMs can detect texts generated by themselves more accurately than those generated by other LLMs, the performance of self-detection is still far from ideal, indicating that further improvements are needed. We also show that extending the binary to the ternary classification task with a new class "Undecided" can enhance both detection accuracy and explanation quality, with improvements being statistically significant and consistent across all LLMs. We finally conducted comprehensive qualitative and quantitative analyses on the explanation errors, which are categorized into three types: reliance on inaccurate features (the most frequent error), hallucinations, and incorrect reasoning. These findings with our human-annotated dataset emphasize the need for further research into improving both self-detection and self-explanation, particularly to address overfitting issues that may hinder generalization.
Machine-generated Text (MGT) detection is crucial for regulating and attributing online texts. While the existing MGT detectors achieve strong performance, they remain vulnerable to simple perturbations and adversarial attacks. To build an effective defense against malicious perturbations, we view MGT detection from a threat modeling perspective, that is, analyzing the model's vulnerability from an adversary's point of view and exploring effective mitigations. To this end, we introduce an adversarial framework for training a robust MGT detector, named GREedy Adversary PromoTed DefendER (GREATER). The GREATER consists of two key components: an adversary GREATER-A and a detector GREATER-D. The GREATER-D learns to defend against the adversarial attack from GREATER-A and generalizes the defense to other attacks. GREATER-A identifies and perturbs the critical tokens in embedding space, along with greedy search and pruning to generate stealthy and disruptive adversarial examples. Besides, we update the GREATER-A and GREATER-D synchronously, encouraging the GREATER-D to generalize its defense to different attacks and varying attack intensities. Our experimental results across 9 text perturbation strategies and 5 adversarial attacks show that our GREATER-D reduces the Attack Success Rate (ASR) by 10.61% compared with SOTA defense methods while our GREATER-A is demonstrated to be more effective and efficient than SOTA attack approaches.
Form and function in biological filaments: A physicist's review
2025-02-18
Jan Cammann, Hannah Laeverenz-Schlogelhofer, Kirsty Y. Wan, Marco G. Mazza
Nature uses elongated shapes and filaments to build stable structures, generate motion, and allow complex geometric interactions. In this Review, we examine the role of biological filaments across different length scales. From the molecular scale, where cytoskeletal filaments provides a robust but dynamic cellular scaffolding, over the scale of cellular appendages like cilia and flagella, to the scale of filamentous microorganisms like cyanobacteria which are among the most successful genera on Earth, and even to the scale of elongated animals like worms and snakes, whose motility modes inspire robotic analogues. We highlight the general mechanisms that couple form and function. Physical principles, such as classical elasticity and the non-reciprocity of active matter can be used to trace unifying themes linking these systems spanning about six orders of magnitude in length.
Two-dimensional (2D) layered nanomaterials heterostructures, arising from the combination of 2D materials with other low-dimensional species, feature large surface area to volume ratio, which provides a high density of active sites for catalytic ap-plications and in particular for (photo)electrocatalysis (PEC). Meanwhile, their unique electronic band structure and high electrical conductivity enable efficient charge transfer (CT) between the active material and the substrate, which is essential for catalytic activity. In recent years, researchers have demonstrated the potential of a range of 2D material interfaces, such as graphene, graphitic carbon nitride (g-C3N4), metal chalcogenides (MCs), and MXenes, for (photo)electrocatalytic applica-tions. For instance, MCs such as MoS2 and WS2 have shown excellent catalytic activity for hydrogen evolution, while gra-phene and MXenes have been used for the reduction of carbon dioxide to higher value chemicals. However, despite their great potential, there are still major challenges that need to be addressed in order to fully realize the potential of 2D materials for PEC. For example, their stability under harsh reaction conditions, as well as their scalability for large-scale production are important factors to be considered. Generating heterojunctions (HJs) by combining 2D layered structures with other na-nomaterials is a promising method to improve the photoelectrocatalytic properties of the former. In this review, we inspect thoroughly the recent literature, to demonstrate the significant potential that arises from utilizing 2D layered heterostructures in PEC processes across a broad spectrum of applications, from energy conversion and storage to environmental remediation. With the ongoing research and development, it is likely that the potential of these materials will be fully expressed in the near future.
The rapid advancement of perovskite solar cells (PSCs) has led to an exponential growth in research publications, creating an urgent need for efficient knowledge management and reasoning systems in this domain. We present a comprehensive knowledge-enhanced system for PSCs that integrates three key components. First, we develop Perovskite-KG, a domain-specific knowledge graph constructed from 1,517 research papers, containing 23,789 entities and 22,272 relationships. Second, we create two complementary datasets: Perovskite-Chat, comprising 55,101 high-quality question-answer pairs generated through a novel multi-agent framework, and Perovskite-Reasoning, containing 2,217 carefully curated materials science problems. Third, we introduce two specialized large language models: Perovskite-Chat-LLM for domain-specific knowledge assistance and Perovskite-Reasoning-LLM for scientific reasoning tasks. Experimental results demonstrate that our system significantly outperforms existing models in both domain-specific knowledge retrieval and scientific reasoning tasks, providing researchers with effective tools for literature review, experimental design, and complex problem-solving in PSC research.
The Preference for Evolving Dark Energy from Cosmological Distance Measurements and Possible Signatures in the Growth Rate of Perturbations
2025-02-18
Ryan E. Keeley, Kevork N. Abazajian, Manoj Kaplinghat, Arman Shafieloo
In this study, we use a flexible parametrization of the equation of state of dark energy to explore its possible evolution with datasets from the Dark Energy Spectroscopic Instrument (DESI), Planck cosmic microwave background, and either the 5-year Dark Energy Survey (DES) or the Pantheon+ (PP) supernova (SN) compilation. This parametrization, called transitional dark energy, allows for rapid changes in the equation of state but also changes like that in the Chevallier-Polarski-Linder parametrization. We find a 3.8{\sigma} preference for evolving dark energy over {\Lambda}CDM with the DES dataset and a weaker 2.4{\sigma} preference when using the PP dataset. This corroborates the finding of the DESI Collaboration, who found that their baryon acoustic oscillation data preferred evolving dark energy when fit with the CPL parametrization of the equation of state. Our analysis reveals no significant outliers in the DESI data around the TDE best-fit, while the data is asymmetrically distributed around the {\Lambda}CDM best-fit model such that the measured distances are on average smaller. The DESI and SN data both prefer an expansion history that implies a higher dark energy density around z=0.5 than in the Planck-{\Lambda}CDM model, with the inferred equation of state being greater than -1 around z=0 and close to or below -1 at z>0.5. We show that when the expansion rate is greater than that in the Planck-{\Lambda}CDM model (around z=0.5), the growth rate calculated assuming General Relativity is suppressed relative to the Planck-{\Lambda}CDM model, and it rebounds as the expansion rate differences between the models become smaller closer to the present time. The resulting flattening of the $f\sigma_8(z)$ curve compared to the {\Lambda}CDM model could be an independent signature of the temporal evolution of dark energy.
Where to search for supermassive binary black holes
2025-02-18
Paola Marziani, Edi Bon, Natasa Bon, Mauro D'Onofrio
Supermassive binary black holes (SMBBHs) are the anticipated byproducts of galaxy mergers and play a pivotal role in shaping galaxy evolution, gravitational wave emissions, and accretion physics. Despite their theoretical prevalence, direct observational evidence for SMBBHs remains elusive, with only a handful of candidates identified to date. This paper explores optimal strategies and key environments for locating SMBBHs, focusing on observational signatures in the broad Balmer lines. We present a preliminary analysis on a flux-limited sample of sources belonging to an evolved spectral type along the quasar main sequence, and we discuss the spectroscopic clues indicative of binary activity and highlight the critical role of time-domain spectroscopic surveys in uncovering periodic variability linked to binary systems.
Two-Component Theory of Classical Gravitons with Arbitrary Sources
2025-02-18
Clésio E. Mota, Jorge G. Cardoso
A review of the spin curvatures that arise in the framework of the Infeld-van der Waerden {\epsilon}-formalism for general relativity is presented. The sourceful extension formulated in an earlier work of the theory of gravitons in vacuum is then reviewed in a concise way. At this stage the {\epsilon}-formalism system of gravitational wave equations obtained thereabout is replicated on the basis of the implementation of the same calculational techniques as the ones that had been utilized in the sourceless situation. An important symmetry property borne by the right-hand sides of those wave equations is subsequently brought forward.
Can wormholes mirror the quasi-normal mode spectrum of Schwarzschild black holes?
2025-02-18
Ciro De Simone, Vittorio De Falco, Salvatore Capozziello
Wormholes are exotic compact objects characterized by the absence of essential singularities and horizons, acting as slender bridges linking two distinct regions of spacetime. Despite their theoretical significance, they remain however undetected, possibly due to their ability to closely mimic the observational properties of black holes. This study explores whether a static and spherically symmetric wormhole within General Relativity can reproduce the quasi-normal mode spectrum of a Schwarzschild black hole under scalar, electromagnetic, and axial gravitational perturbations, both individually and in combination. To address this, we reformulate the wormhole metric components using a near-throat parametrization. Our analysis concentrates on the fundamental mode and first overtone, estimated via the Wentzel-Kramers-Brillouin method. By employing a customized minimization strategy, we demonstrate that within a specific region of the parameter space, a wormhole can successfully replicate a subset of the black hole quasi-normal mode spectrum.
Who Writes What: Unveiling the Impact of Author Roles on AI-generated Text Detection
2025-02-18
Jiatao Li, Xiaojun Wan
The rise of Large Language Models (LLMs) necessitates accurate AI-generated text detection. However, current approaches largely overlook the influence of author characteristics. We investigate how sociolinguistic attributes-gender, CEFR proficiency, academic field, and language environment-impact state-of-the-art AI text detectors. Using the ICNALE corpus of human-authored texts and parallel AI-generated texts from diverse LLMs, we conduct a rigorous evaluation employing multi-factor ANOVA and weighted least squares (WLS). Our results reveal significant biases: CEFR proficiency and language environment consistently affected detector accuracy, while gender and academic field showed detector-dependent effects. These findings highlight the crucial need for socially aware AI text detection to avoid unfairly penalizing specific demographic groups. We offer novel empirical evidence, a robust statistical framework, and actionable insights for developing more equitable and reliable detection systems in real-world, out-of-domain contexts. This work paves the way for future research on bias mitigation, inclusive evaluation benchmarks, and socially responsible LLM detectors.
Adapting large language models (LLMs) to new and diverse knowledge is essential for their lasting effectiveness in real-world applications. This survey provides an overview of state-of-the-art methods for expanding the knowledge of LLMs, focusing on integrating various knowledge types, including factual information, domain expertise, language proficiency, and user preferences. We explore techniques, such as continual learning, model editing, and retrieval-based explicit adaptation, while discussing challenges like knowledge consistency and scalability. Designed as a guide for researchers and practitioners, this survey sheds light on opportunities for advancing LLMs as adaptable and robust knowledge systems.
Enhancing Semi-supervised Learning with Noisy Zero-shot Pseudolabels
2025-02-18
Jichan Chung, Irene Y. Chen
Semi-supervised learning (SSL) leverages limited labeled data alongside abundant unlabeled data to address labeling costs in machine learning. While recent foundation models enable zero-shot inference, attempts to integrate these capabilities into SSL through pseudo-labeling have shown mixed results due to unreliable zero-shot predictions. We present ZMT (Zero-Shot Multi-Task Learning), a framework that jointly optimizes zero-shot pseudo-labels and unsupervised representation learning objectives from contemporary SSL approaches. Our method introduces a multi-task learning-based mechanism that incorporates pseudo-labels while ensuring robustness to varying pseudo-label quality. Experiments across 8 datasets in vision, language, and audio domains demonstrate that ZMT reduces error by up to 56% compared to traditional SSL methods, with particularly compelling results when pseudo-labels are noisy and unreliable. ZMT represents a significant step toward making semi-supervised learning more effective and accessible in resource-constrained environments.
Exact expressions for the renormalization constants in the MS-like schemes
2025-02-18
Konstantin Stepanyantz
We briefly review how it is possible to derive some exact expressions for the renormalization constants for the MS-like renormalization prescriptions using the arguments based on the renormalization group. These expressions are obtained for a version of the dimensional technique in which the dimensionful parameter $\Lambda$ differs from the renormalization scale $\mu$. They encode the equations relating the coefficients at higher $\varepsilon$-poles, powers of $\ln \Lambda/\mu$, and mixed terms of the structure $\varepsilon^{-q} \ln^p \Lambda/\mu$ to the coefficients of the renormalization group functions (i.e. of the $\beta$-function and the anomalous dimension). The general results are verified by some multiloop calculations.
Like humans, Large Language Models (LLMs) struggle to generate high-quality long-form text that adheres to strict requirements in a single pass. This challenge is unsurprising, as successful human writing, according to the Cognitive Writing Theory, is a complex cognitive process involving iterative planning, translating, reviewing, and monitoring. Motivated by these cognitive principles, we aim to equip LLMs with human-like cognitive writing capabilities through CogWriter, a novel training-free framework that transforms LLM constrained long-form text generation into a systematic cognitive writing paradigm. Our framework consists of two key modules: (1) a Planning Agent that performs hierarchical planning to decompose the task, and (2) multiple Generation Agents that execute these plans in parallel. The system maintains quality via continuous monitoring and reviewing mechanisms, which evaluate outputs against specified requirements and trigger necessary revisions. CogWriter demonstrates exceptional performance on LongGenBench, a benchmark for complex constrained long-form text generation. Even when using Qwen-2.5-14B as its backbone, CogWriter surpasses GPT-4o by 22% in complex instruction completion accuracy while reliably generating texts exceeding 10,000 words. We hope this cognitive science-inspired approach provides a paradigm for LLM writing advancements: \href{https://github.com/KaiyangWan/CogWriter}{CogWriter}.
Understanding the impacts of U-turn configurations on intersection safety and traffic operations is essential for developing effective strategies to enhance road safety and efficiency. Extensive research has been conducted to investigate the role of geometric designs, driver behavior, and advanced technologies in mitigating crash risks and improving traffic flow at U-turn facilities. By synthesizing this collective body of work through the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), this paper provides a valuable resource for transportation professionals, policymakers, and researchers seeking evidence-based solutions. This systematic review draws on studies from diverse traffic environments and regional contexts, focusing on innovative design interventions, such as restricted crossing U-turns (RCUTs) and median U-turn intersections (MUTs), as well as integrated strategies leveraging technological advancements. By presenting a comprehensive analysis of U-turn-related challenges and opportunities, this review contributes to advancing transportation safety research and guiding the development of adaptive strategies tailored to varied traffic conditions and evolving technologies.
VLBI Imaging of Parsec-scale Radio Structures in Nearby Low-luminosity AGN
2025-02-18
Xiaopeng Cheng, Tao An, Willem Baan, Raneri D. Baldi, David R. A. Williams-Baldwin, Bong Won Sohn, Robert Beswick, Ian M. Mchardy
We report the results of high-resolution 5 GHz Very Long Baseline Array and European VLBI Network observations of 36 nearby galaxies, an extension of the Legacy e-MERLIN Multi-band Imaging of Nearby Galaxies (LeMMINGs) survey. Our sample includes 21 low ionization nuclear emission regions (LINERs), 4 Seyferts, 3 absorption line galaxies (ALGs), and 8 HII galaxies. We achieved an unprecedented detection rate, successfully imaging 23 out of 36 sources with a detection threshold of $\sim$20 $\mu$Jy beam$^{-1}$. The radio sizes are typically of $\leq$ 5 pc. Core identification was achieved in 16 sources, while 7 others were identified as core candidates. Radio luminosities of the sample range from 10$\rm ^{34}$ to 10$\rm ^{38}$ erg s$^{-1}$. Our analysis reveals a predominance of compact core structures, with ten sources exhibiting a one-sided core jet morphology and NGC 2146 exhibiting a rare two-sided jet structure. The study advances our understanding of the compactness of radio sources at various scales, indicating a core-dominated nature in all but one galaxy NGC2655. We find moderate to strong correlations between radio luminosity, black hole mass, optical [O III] line luminosity, and hard X-ray luminosity, suggesting a common active galactic nucleus (AGN) core origin. These results provide new insights into the fundamental plane of black hole activity and support the role of the synchrotron process in Low-luminosity AGN (LLAGN) radio emission.
Mixing Algorithm for Extending the Tiers of the Unapparent Information Send through the Audio Streams
2025-02-18
Sachith Dassanayaka
Usage of the fast development of real-life digital applications in modern technology should guarantee novel and efficient way-outs of their protection. Encryption facilitates the data hiding. With the express development of technology, people tend to figure out a method that is capable of hiding a message and the survival of the message. Secrecy and efficiency can be obtained through steganographic involvement, a novel approach, along with multipurpose audio streams. Generally, steganography advantages are not used among industry and learners even though it is extensively discussed in the present information world. Information hiding in audio files is exclusively inspiring due to the compassion of the Human Auditory System (HAS). The proposed resolution supports Advance Encryption Standard (AES)256 key encryption and tolerates all existing audio file types as the container. This paper analyzes and proposes a way out according to the performance based on robustness, security, and hiding capacity. Furthermore, a survey of audio steganography applications, as well as a proposed resolution, is discussed in this paper.
Cartoon videos have proven to be effective in learning vocabulary to preschool children.However, we have little knowledge about integrating AI into cartoon videos to provide systematic, multimodal vocabulary learning support. This late-breaking work present \name{}, an AI-powered cartoon video system that enables real-time Q\&A, vocabulary review, and contextual learning. Preliminary findings contextualized how families interact with \name{} to support vocabulary learning. Parents appreciated the system for its personalized, engaging experiences, fostering collaboration, and encouraging self-reflection on parenting. This study offers valuable design implications for informing future video systems to support vocabulary learning.
Mentoring Software in Education and Its Impact on Teacher Development: An Integrative Literature Review
2025-02-18
Ramiro Pesina
Mentoring software is a pivotal innovation in addressing critical challenges in teacher development within educational institutions. This study explores the transformative potential of digital mentoring platforms, evaluating their impact on enhancing traditional mentoring practices through scalable, data-driven, and accessible frameworks. The research synthesizes findings from existing literature to assess the effectiveness of key features, including structured goal setting, progress monitoring, and advanced analytics, in improving teacher satisfaction, retention, and professional growth. Using an integrative literature review approach, this study identifies both the advantages and barriers to implementing mentoring software in education. Financial constraints, limited institutional support, and data privacy concerns remain significant challenges, necessitating strategic interventions. Drawing insights from successful applications in healthcare and corporate sectors, the review highlights adaptive strategies such as leveraging open-source tools, cross-sector collaborations, and integrating mentoring software with existing professional development frameworks. The research emphasizes the necessity of integrating digital mentoring tools with institutional objectives to create enduring support systems for teacher development. Mentoring software not only enhances traditional mentorship but also facilitates broader professional networks that contribute to collective knowledge sharing.
We propose an aspect-guided, multi-level perturbation framework to evaluate the robustness of Large Language Models (LLMs) in automated peer review. Our framework explores perturbations in three key components of the peer review process-papers, reviews, and rebuttals-across several quality aspects, including contribution, soundness, presentation, tone, and completeness. By applying targeted perturbations and examining their effects on both LLM-as-Reviewer and LLM-as-Meta-Reviewer, we investigate how aspect-based manipulations, such as omitting methodological details from papers or altering reviewer conclusions, can introduce significant biases in the review process. We identify several potential vulnerabilities: review conclusions that recommend a strong reject may significantly influence meta-reviews, negative or misleading reviews may be wrongly interpreted as thorough, and incomplete or hostile rebuttals can unexpectedly lead to higher acceptance rates. Statistical tests show that these biases persist under various Chain-of-Thought prompting strategies, highlighting the lack of robust critical evaluation in current LLMs. Our framework offers a practical methodology for diagnosing these vulnerabilities, thereby contributing to the development of more reliable and robust automated reviewing systems.
The burgeoning growth of video-to-music generation can be attributed to the ascendancy of multimodal generative models. However, there is a lack of literature that comprehensively combs through the work in this field. To fill this gap, this paper presents a comprehensive review of video-to-music generation using deep generative AI techniques, focusing on three key components: visual feature extraction, music generation frameworks, and conditioning mechanisms. We categorize existing approaches based on their designs for each component, clarifying the roles of different strategies. Preceding this, we provide a fine-grained classification of video and music modalities, illustrating how different categories influence the design of components within the generation pipelines. Furthermore, we summarize available multimodal datasets and evaluation metrics while highlighting ongoing challenges in the field.
Humans interpret and perceive the world by integrating sensory information from multiple modalities, such as vision and hearing. Spiking Neural Networks (SNNs), as brain-inspired computational models, exhibit unique advantages in emulating the brain's information processing mechanisms. However, existing SNN models primarily focus on unimodal processing and lack efficient cross-modal information fusion, thereby limiting their effectiveness in real-world multimodal scenarios. To address this challenge, we propose a semantic-alignment cross-modal residual learning (S-CMRL) framework, a Transformer-based multimodal SNN architecture designed for effective audio-visual integration. S-CMRL leverages a spatiotemporal spiking attention mechanism to extract complementary features across modalities, and incorporates a cross-modal residual learning strategy to enhance feature integration. Additionally, a semantic alignment optimization mechanism is introduced to align cross-modal features within a shared semantic space, improving their consistency and complementarity. Extensive experiments on three benchmark datasets CREMA-D, UrbanSound8K-AV, and MNISTDVS-NTIDIGITS demonstrate that S-CMRL significantly outperforms existing multimodal SNN methods, achieving the state-of-the-art performance. The code is publicly available at https://github.com/Brain-Cog-Lab/S-CMRL.
MSE-Adapter: A Lightweight Plugin Endowing LLMs with the Capability to Perform Multimodal Sentiment Analysis and Emotion Recognition
2025-02-18
Yang Yang, Xunde Dong, Yupeng Qiang
Current Multimodal Sentiment Analysis (MSA) and Emotion Recognition in Conversations (ERC) methods based on pre-trained language models exhibit two primary limitations: 1) Once trained for MSA and ERC tasks, these pre-trained language models lose their original generalized capabilities. 2) They demand considerable computational resources. As the size of pre-trained language models continues to grow, training larger multimodal sentiment analysis models using previous approaches could result in unnecessary computational cost. In response to this challenge, we propose \textbf{M}ultimodal \textbf{S}entiment Analysis and \textbf{E}motion Recognition \textbf{Adapter} (MSE-Adapter), a lightweight and adaptable plugin. This plugin enables a large language model (LLM) to carry out MSA or ERC tasks with minimal computational overhead (only introduces approximately 2.6M to 2.8M trainable parameters upon the 6/7B models), while preserving the intrinsic capabilities of the LLM. In the MSE-Adapter, the Text-Guide-Mixer (TGM) module is introduced to establish explicit connections between non-textual and textual modalities through the Hadamard product. This allows non-textual modalities to better align with textual modalities at the feature level, promoting the generation of higher-quality pseudo tokens. Extensive experiments were conducted on four public English and Chinese datasets using consumer-grade GPUs and open-source LLMs (Qwen-1.8B, ChatGLM3-6B-base, and LLaMA2-7B) as the backbone. The results demonstrate the effectiveness of the proposed plugin. The code will be released on GitHub after a blind review.
RTPD: Penetration Depth calculation using Hardware accelerated Ray-Tracing
2025-02-18
YoungWoo Kim, Sungmin Kwon, Duksu Kim
Penetration depth calculation quantifies the extent of overlap between two objects and is crucial in fields like simulations, the metaverse, and robotics. Recognizing its significance, efforts have been made to accelerate this computation using parallel computing resources, such as CPUs and GPUs. Unlike traditional GPU cores, modern GPUs incorporate specialized ray-tracing cores (RT-cores) primarily used for rendering applications. We introduce a novel algorithm for penetration depth calculation that leverages RT-cores. Our approach includes a ray-tracing based algorithm for penetration surface extraction and another for calculating Hausdorff distance, optimizing the use of RT-cores. We tested our method across various generations of RTX GPUs with different benchmark scenes. The results demonstrated that our algorithm outperformed a state-of-the-art penetration depth calculation method and conventional GPU implementations by up to 37.66 and 5.33 times, respectively. These findings demonstrate the efficiency of our RT core-based method and suggest broad applicability for RT-cores in diverse computational tasks.
From Principles to Applications: A Comprehensive Survey of Discrete Tokenizers in Generation, Comprehension, Recommendation, and Information Retrieval
2025-02-18
Jian Jia, Jingtong Gao, Ben Xue, Junhao Wang, Qingpeng Cai, Quan Chen, Xiangyu Zhao, Peng Jiang, Kun Gai
Discrete tokenizers have emerged as indispensable components in modern machine learning systems, particularly within the context of autoregressive modeling and large language models (LLMs). These tokenizers serve as the critical interface that transforms raw, unstructured data from diverse modalities into discrete tokens, enabling LLMs to operate effectively across a wide range of tasks. Despite their central role in generation, comprehension, and recommendation systems, a comprehensive survey dedicated to discrete tokenizers remains conspicuously absent in the literature. This paper addresses this gap by providing a systematic review of the design principles, applications, and challenges of discrete tokenizers. We begin by dissecting the sub-modules of tokenizers and systematically demonstrate their internal mechanisms to provide a comprehensive understanding of their functionality and design. Building on this foundation, we synthesize state-of-the-art methods, categorizing them into multimodal generation and comprehension tasks, and semantic tokens for personalized recommendations. Furthermore, we critically analyze the limitations of existing tokenizers and outline promising directions for future research. By presenting a unified framework for understanding discrete tokenizers, this survey aims to guide researchers and practitioners in addressing open challenges and advancing the field, ultimately contributing to the development of more robust and versatile AI systems.
A Survey on Large Language Models for Automated Planning
2025-02-18
Mohamed Aghzal, Erion Plaku, Gregory J. Stein, Ziyu Yao
The planning ability of Large Language Models (LLMs) has garnered increasing attention in recent years due to their remarkable capacity for multi-step reasoning and their ability to generalize across a wide range of domains. While some researchers emphasize the potential of LLMs to perform complex planning tasks, others highlight significant limitations in their performance, particularly when these models are tasked with handling the intricacies of long-horizon reasoning. In this survey, we critically investigate existing research on the use of LLMs in automated planning, examining both their successes and shortcomings in detail. We illustrate that although LLMs are not well-suited to serve as standalone planners because of these limitations, they nonetheless present an enormous opportunity to enhance planning applications when combined with other approaches. Thus, we advocate for a balanced methodology that leverages the inherent flexibility and generalized knowledge of LLMs alongside the rigor and cost-effectiveness of traditional planning methods.
2025-02-18
Riting Xia, Huibo Liu, Anchen Li, Xueyan Liu, Yan Zhang, Chunxu Zhang, Bo Yang
Graph learning is a prevalent field that operates on ubiquitous graph data. Effective graph learning methods can extract valuable information from graphs. However, these methods are non-robust and affected by missing attributes in graphs, resulting in sub-optimal outcomes. This has led to the emergence of incomplete graph learning, which aims to process and learn from incomplete graphs to achieve more accurate and representative results. In this paper, we conducted a comprehensive review of the literature on incomplete graph learning. Initially, we categorize incomplete graphs and provide precise definitions of relevant concepts, terminologies, and techniques, thereby establishing a solid understanding for readers. Subsequently, we classify incomplete graph learning methods according to the types of incompleteness: (1) attribute-incomplete graph learning methods, (2) attribute-missing graph learning methods, and (3) hybrid-absent graph learning methods. By systematically classifying and summarizing incomplete graph learning methods, we highlight the commonalities and differences among existing approaches, aiding readers in selecting methods and laying the groundwork for further advancements. In addition, we summarize the datasets, incomplete processing modes, evaluation metrics, and application domains used by the current methods. Lastly, we discuss the current challenges and propose future directions for incomplete graph learning, with the aim of stimulating further innovations in this crucial field. To our knowledge, this is the first review dedicated to incomplete graph learning, aiming to offer valuable insights for researchers in related fields.We developed an online resource to follow relevant research based on this review, available at https://github.com/cherry-a11y/Incomplete-graph-learning.git
The "Who'', "What'', and "How'' of Responsible AI Governance: A Systematic Review and Meta-Analysis of (Actor, Stage)-Specific Tools
2025-02-18
Blaine Kuehnert, Rachel M. Kim, Jodi Forlizzi, Hoda Heidari
The implementation of responsible AI in an organization is inherently complex due to the involvement of multiple stakeholders, each with their unique set of goals and responsibilities across the entire AI lifecycle. These responsibilities are often ambiguously defined and assigned, leading to confusion, miscommunication, and inefficiencies. Even when responsibilities are clearly defined and assigned to specific roles, the corresponding AI actors lack effective tools to support their execution. Toward closing these gaps, we present a systematic review and comprehensive meta-analysis of the current state of responsible AI tools, focusing on their alignment with specific stakeholder roles and their responsibilities in various AI lifecycle stages. We categorize over 220 tools according to AI actors and stages they address. Our findings reveal significant imbalances across the stakeholder roles and lifecycle stages addressed. The vast majority of available tools have been created to support AI designers and developers specifically during data-centric and statistical modeling stages while neglecting other roles such as institutional leadership, deployers, end-users, and impacted communities, and stages such as value proposition and deployment. The uneven distribution we describe here highlights critical gaps that currently exist in responsible AI governance research and practice. Our analysis reveals that despite the myriad of frameworks and tools for responsible AI, it remains unclear \emph{who} within an organization and \emph{when} in the AI lifecycle a tool applies. Furthermore, existing tools are rarely validated, leaving critical gaps in their usability and effectiveness. These gaps provide a starting point for researchers and practitioners to create more effective and holistic approaches to responsible AI development and governance.
On the Impacts of Halo Model Implementations in Sunyaev-Zeldovich Cross-Correlation Analyses
2025-02-18
Chad Popik, Nicholas Battaglia, Aleksandra Kusiak, Boris Bolliet, J. Colin Hill
Statistical studies of the circumgalactic medium (CGM) using Sunyaev-Zeldovich (SZ) observations offer a promising method of studying the gas properties of galaxies and the astrophysics that govern their evolution. Forward modeling profiles from theory and simulations allows them to be refined directly off of data, but there are currently significant differences between the thermal SZ (tSZ) observations of the CGM and the predicted tSZ signal. While these discrepancies could be inherent, they could also be the result of decisions in the forward modeling used to build statistical measures off of theory. In order to see effects of this, we compare an analysis utilizing halo occupancy distributions (HODs) implemented in halo models to simulate the galaxy distribution against a previous studies which weighted their results off of the CMASS galaxy sample, which contains nearly one million galaxies, mainly centrals of group sized halos, selected for relatively uniform stellar mass across redshifts between $0.4
The interaction between planets and their host stars is governed by the forces of gravity, radiation, and magnetic fields. For planets orbiting their stars at distances of approximately 10 stellar radii or less, these effects are significantly intensified. Such interactions can be investigated through a combination of photometric, spectroscopic, and spectropolarimetric studies spanning wavelengths from X-rays to radio frequencies. When a hot planet resides within the star's sub-Alfv\'enic radius, magnetic star-planet interactions (SPI) become possible, often observable as stellar activity enhancements influenced by the planet's orbital motion rather than stellar rotation alone. Such interactions offer a unique perspective on the atmospheric erosion and magnetospheric characteristics of close-in exoplanets.The behavior and impacts of these magnetic interactions are highly sensitive to the magnetic fields of both the planet and its host star. This interplay can influence the magnetic activity of both bodies and has implications for the planet's irradiation levels, orbital migration, and the star's rotational dynamics. By employing phase-resolved observational methods on an expanding sample of hot Jupiter (HJ) systems, researchers can now extend these studies to other compact star-planet systems, including smaller planets in the habitable zones of M dwarfs. Efforts to comprehend magnetic SPI have led to extensive advancements in theoretical research and computational modeling. These efforts include investigations into the space weather environments of close-in giant exoplanets. Utilizing hydrodynamical (HD) and magnetohydrodynamical (MHD) simulations, researchers aim to provide both qualitative and quantitative descriptions of SPI. In this chapter, we first review notable SPI detections before summarizing the current understanding of the underlying physical mechanisms driving SPI.
A Survey of Anomaly Detection in Cyber-Physical Systems
2025-02-18
Danial Abshari, Meera Sridhar
In our increasingly interconnected world, Cyber-Physical Systems (CPS) play a crucial role in industries like healthcare, transportation, and manufacturing by combining physical processes with computing power. These systems, however, face many challenges, especially regarding security and system faults. Anomalies in CPS may indicate unexpected problems, from sensor malfunctions to cyber-attacks, and must be detected to prevent failures that can cause harm or disrupt services. This paper provides an overview of the different ways researchers have approached anomaly detection in CPS. We categorize and compare methods like machine learning, deep learning, mathematical models, invariant, and hybrid techniques. Our goal is to help readers understand the strengths and weaknesses of these methods and how they can be used to create safer, more reliable CPS. By identifying the gaps in current solutions, we aim to encourage future research that will make CPS more secure and adaptive in our increasingly automated world.
Learning the Universe: $3\ h^{-1}{\rm Gpc}$ Tests of a Field Level $N$-body Simulation Emulator
2025-02-18
Matthew T. Scoggins, Matthew Ho, Francisco Villaescusa-Navarro, Drew Jamieson, Ludvig Doeser, Greg L. Bryan
We apply and test a field-level emulator for non-linear cosmic structure formation in a volume matching next-generation surveys. Inferring the cosmological parameters and initial conditions from which the particular galaxy distribution of our Universe was seeded can be achieved by comparing simulated data to observational data. Previous work has focused on building accelerated forward models that efficiently mimic these simulations. One of these accelerated forward models uses machine learning to apply a non-linear correction to the linear $z=0$ Zeldovich approximation (ZA) fields, closely matching the cosmological statistics in the $N$-body simulation. This emulator was trained and tested at $(h^{-1}{\rm Gpc})^3$ volumes, although cosmological inference requires significantly larger volumes. We test this emulator at $(3\ h^{-1}{\rm Gpc})^3$ by comparing emulator outputs to $N$-body simulations for eight unique cosmologies. We consider several summary statistics, applied to both the raw particle fields and the dark matter (DM) haloes. We find that the power spectrum, bispectrum and wavelet statistics of the raw particle fields agree with the $N$-body simulations within ${\sim} 5 \%$ at most scales. For the haloes, we find a similar agreement between the emulator and the $N$-body for power spectrum and bispectrum, though a comparison of the stacked profiles of haloes shows that the emulator has slight errors in the positions of particles in the highly non-linear interior of the halo. At these large $(3\ h^{-1}{\rm Gpc})^3$ volumes, the emulator can create $z=0$ particle fields in a thousandth of the time required for $N$-body simulations and will be a useful tool for large-scale cosmological inference. This is a Learning the Universe publication.
Learning the Universe: Learning to Optimize Cosmic Initial Conditions with Non-Differentiable Structure Formation Models
2025-02-18
Ludvig Doeser, Metin Ata, Jens Jasche
Making the most of next-generation galaxy clustering surveys requires overcoming challenges in complex, non-linear modelling to access the significant amount of information at smaller cosmological scales. Field-level inference has provided a unique opportunity beyond summary statistics to use all of the information of the galaxy distribution. However, addressing current challenges often necessitates numerical modelling that incorporates non-differentiable components, hindering the use of efficient gradient-based inference methods. In this paper, we introduce Learning the Universe by Learning to Optimize (LULO), a gradient-free framework for reconstructing the 3D cosmic initial conditions. Our approach advances deep learning to train an optimization algorithm capable of fitting state-of-the-art non-differentiable simulators to data at the field level. Importantly, the neural optimizer solely acts as a search engine in an iterative scheme, always maintaining full physics simulations in the loop, ensuring scalability and reliability. We demonstrate the method by accurately reconstructing initial conditions from $M_{200\mathrm{c}}$ halos identified in a dark matter-only $N$-body simulation with a spherical overdensity algorithm. The derived dark matter and halo overdensity fields exhibit $\geq80\%$ cross-correlation with the ground truth into the non-linear regime $k \sim 1h$ Mpc$^{-1}$. Additional cosmological tests reveal accurate recovery of the power spectra, bispectra, halo mass function, and velocities. With this work, we demonstrate a promising path forward to non-linear field-level inference surpassing the requirement of a differentiable physics model.
Study of magnification and angular resolution of a single water droplet placed on a glass surface
2025-02-18
Luka Chakhnashvili, Giorgi Bakhtadze
In this study, we investigate the magnification and angular resolution of a single water droplet positioned on a glass surface, functioning as an optical imaging system. Through theoretical analysis of the droplet's shape, magnification, and angular resolution, we derive predictions that are subsequently validated through experiments. Our study explores the impact of key parameters, including droplet size, the distance between the droplet and the object, and the contact angle, on the aforementioned optical characteristics. Our findings reveal that smaller droplets exhibit higher magnification at shorter object-to-droplet distances and demonstrate superior resolving capability (i.e., smaller angular resolution).
PHIBSS: Searching for Molecular Gas Outflows in Star-Forming Galaxies at $z =$ 0.5-2.6
2025-02-18
Capucine Barfety, Jean-Baptiste Jolly, Natascha M. Förster Schreiber, Linda J. Tacconi, Reinhard Genzel, Giulia Tozzi, Andreas Burkert, Jianhang Chen, Françoise Combes, Ric Davies, Frank Eisenhauer, Juan M. Espejo Salcedo, Rodrigo Herrera-Camus, Lilian L. Lee, Minju M. Lee, Daizhong Liu, Roberto Neri, Amit Nestor Shachar, Sedona H. Price, Alvio Renzini, Amiel Sternberg, Eckhard Sturm, Dieter Lutz, Thorsten Naab, Stavros Pastras, Claudia Pulsoni, Karl Schuster, Taro T. Shimizu, Hannah Übler, Stijn Wuyts
We present an analysis of millimeter CO observations to search and quantify signatures of molecular gas outflows. We exploit the large sample of $0.5 < z < 2.6$ galaxies observed as part of the PHIBSS1/2 surveys with the IRAM Plateau de Bure interferometer, focusing on the 154 typical massive star-forming galaxies with CO detections (mainly CO(3-2), but including also CO(2-1) and CO(6-5)) at signal-to-noise (SNR) > 1.5 and available properties (stellar mass, star formation rate, size) from ancillary data. None of the individual spectra exhibit a compelling signature of CO outflow emission even at high SNR > 7. To search for fainter outflow signatures, we carry out an analysis of stacked spectra, including the full sample, as well as subsets, split in terms of stellar mass, redshift, inclination, offset in star formation rate (SFR) from the main sequence, and AGN activity. None of the physically motivated subsamples show any outflow signature. We report a tentative detection in a subset statistically designed to maximize outflow signatures. We derive upper limits on molecular gas outflow rate and mass loading factors $\eta$ based on our results and find $\eta \leq$ 2.2-35.4, depending on the subsample. Much deeper CO data and observations of alternative tracers are needed to decisively constrain the importance of cold molecular gas component of outflows relative to other gas phases.
Astrometry, orbit determination, and thermal inertia of the Tianwen-2 target asteroid (469219) Kamo`oalewa
2025-02-18
Marco Fenucci, Bojan Novaković, Pengfei Zhang, Albino Carbognani, Marco Micheli, Laura Faggioli, Francesco Gianotto, Francisco Ocaña, Dora Föhring, Juan Luis Cano, Luca Conversi, Richard Moissl
Context. (469219) Kamo`oalewa is a small near-Earth asteroid, which is currently a quasi-satellite of the Earth. Lightcurve measurements also reveal a rotation period of only about 30 minutes. This asteroid has been selected as the target of the Tianwen-2 sample-return mission of the China National Space Administration. Aims. The first goal of this paper is to observe and improve the orbit determination of (469219) Kamo`oalewa, and better determine the Yarkovsky effect acting on it. The second goal is to estimate the thermal inertia of the asteroid, taking advantage of an improved Yarkovsky effect determination. Methods. Our observational campaign imaged the asteroid from the Loiano Astronomical Station and from the Calar Alto Observatory, in March 2024. We also accurately re-measured a precovery detection from the Sloan Digital Sky Survey from 2004. New astrometry was later used in a 7-dimensional orbit determination, aimed at estimating both the orbital elements and the Yarkovsky effect. Thermal inertia is later studied by using the ASTERIA, a new method that is suitable to estimate thermal inertia of small asteroids. Results. We detected a semi-major axis drift of $(-67.35 \pm 4.70) \times 10^{-4}$ au My$^{-1}$ due to the Yarkovsky effect, with a high signal-to-noise ratio of 14. The new orbit solution also significantly reduced the position uncertainty for the arrival of the Tianwen-2 spacecraft. By using different models for the physical parameters of Kamo`oalewa, the ASTERIA model estimated the thermal inertia at $\Gamma = 150^{+90}_{-45}$ J m$^{-2}$ K$^{-1}$ s$^{-1/2}$ or $\Gamma = 181^{+95}_{-60}$ J m$^{-2}$ K$^{-1}$ s$^{-1/2}$.
We search for parameters defined from photometric images to quantify the ex situ stellar mass fraction of galaxies. We created mock images using galaxies in the cosmological hydrodynamical simulations TNG100, EAGLE, and TNG50 at redshift $z=0$. We define a series of parameters describing their structures. In particular, the inner and outer halo of a galaxy are defined by sectors ranging from $45-135$ degrees from the disk major axis, and with radii ranging from $3.5-10$ kpc and $10-30$ kpc, respectively, to avoid the contamination of disk and bulge. The surface brightness and colour gradients are defined by the same sectors along the minor axis and with similar radii ranges. We used the Random Forest method to create a model that predicts $f_{\rm exsitu}$ from morphological parameters. The model predicts $f_{\rm exsitu}$ well with a scatter smaller than 0.1 compared to the ground truth in all mass ranges. The models trained from TNG100 and EAGLE work similarly well and are cross-validated; they also work well in making predictions for TNG50 galaxies. The analysis using Random Forest reveals that $\nabla \rho_{\rm outer}$, $\nabla (g-r)_{\rm outer}$, $f_{\rm outerhalo}$ and $f_{\rm innerhalo}$ are the most influential parameters in predicting $f_{\rm exsitu}$, underscoring their significance in uncovering the merging history of galaxies. We further analyse how the quality of images will affect the results by using SDSS-like and HSC-like mock images for galaxies at different distances. Our results can be used to infer the ex situ stellar mass fractions for a large sample of galaxies from photometric surveys.
NGC 3259: A Signal for an Untapped Population of Slowly Accreting Intermediate-Mass Black Holes
2025-02-18
Kirill A. Grishin, Igor V. Chilingarian, Francoise Combes, Franz E. Bauer, Victoria A. Toptun, Ivan Yu. Katkov, Daniel Fabricant
Low-mass active galactic nuclei (AGNs) can provide important constraints on the formation and evolution of supermassive black holes (SMBHs), a central challenge in modern cosmology. To date only small samples of intermediate-mass black holes (IMBHs, $M_{BH}<10^5M_{\odot}$) and 'lesser' supermassive black holes (LSMBHs, $M_{BH}<10^6M_{\odot}$) have been identified. Our present study of NGC 3259 at D=27 Mpc with the Binospec integral field unit spectrograph complemented with Keck Echelle Spectrograph and Imager observations demonstrates the need for and the power of the spectroscopic follow-up. NGC 3259 hosts a black hole with a mass of $M_{BH}=(1.7-4.1)\times10^5M_{\odot}$, inferred from multi-epoch spectroscopic data, that accretes at 1% of the Eddington limit as suggested by the analysis of archival XMM-Newton observations. It is the second nearest low-mass AGN after the archetypal galaxy NGC 4395. The spectroscopic data reveals a variable broad $H\alpha$ profile that is likely the result of asymmetrically distributed broad-line region (BLR) clouds or BLR outflow events. X-ray observations and the absence of an optical power-law continuum suggest partial obscuration of the accretion disk and hot corona by a dust torus. We estimate that the Sloan Digital Sky Survey could only detect similar objects to D=35 Mpc. A detailed photometric analysis of NGC 3259 using HST images provides a central spheroid stellar mass estimate 25 times lower than expected from the $M_{BH}-M^*_{sph}$ relation, making this galaxy a strong outlier. This discrepancy suggests divergent growth pathways for the central black hole and spheroid, potentially influenced by the presence of a bar in the galaxy. Finally, we demonstrate that the DESI and 4MOST surveys will detect low-accretion rate IMBHs and LSMBHs and the sensitivity of future X-ray instruments (such as AXIS and Athena) will secure their classification.
GitHub Copilot is transforming software development by automating tasks and boosting productivity through AI-driven code generation. In this paper, we con-duct a literature survey to synthesize insights on Copilot's impact on productivity and security. We review academic journal databases, industry reports, and official docu-mentation to highlight key findings and challenges. While Copilot accelerates coding and prototyping, concerns over security vulnerabilities and intellectual property risks persist. Drawing from the literature, we provide a perspective on best practices and future directions for responsible AI adoption in software engineering, offering action-able insights for developers and organizations to integrate Copilot effectively while maintaining high standards of quality and security.
Girth of the Cayley graph and Cayley hash functions
2025-02-18
Vladimir Shpilrain
Cayley hash functions are based on a simple idea of using a pair of semigroup elements, A and B, to hash the 0 and 1 bit, respectively, and then to hash an arbitrary bit string in the natural way, by using multiplication of elements in the semigroup. The main advantage of Cayley hash functions compared to, say, hash functions in the SHA family is that when an already hashed document is amended, one does not have to hash the whole amended document all over again, but rather hash just the amended part and then multiply the result by the hash of the original document. In this article, we survey some of the previously proposed Cayley hash functions and single out a very simple hash function whose security has not been compromised up to date.
A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models
2025-02-18
Longchao Da, Justin Turnau, Thirulogasankar Pranav Kutralingam, Alvaro Velasquez, Paulo Shakarian, Hua Wei
Deep Reinforcement Learning (RL) has been explored and verified to be effective in solving decision-making tasks in various domains, such as robotics, transportation, recommender systems, etc. It learns from the interaction with environments and updates the policy using the collected experience. However, due to the limited real-world data and unbearable consequences of taking detrimental actions, the learning of RL policy is mainly restricted within the simulators. This practice guarantees safety in learning but introduces an inevitable sim-to-real gap in terms of deployment, thus causing degraded performance and risks in execution. There are attempts to solve the sim-to-real problems from different domains with various techniques, especially in the era with emerging techniques such as large foundations or language models that have cast light on the sim-to-real. This survey paper, to the best of our knowledge, is the first taxonomy that formally frames the sim-to-real techniques from key elements of the Markov Decision Process (State, Action, Transition, and Reward). Based on the framework, we cover comprehensive literature from the classic to the most advanced methods including the sim-to-real techniques empowered by foundation models, and we also discuss the specialties that are worth attention in different domains of sim-to-real problems. Then we summarize the formal evaluation process of sim-to-real performance with accessible code or benchmarks. The challenges and opportunities are also presented to encourage future exploration of this direction. We are actively maintaining a to include the most up-to-date sim-to-real research outcomes to help the researchers in their work.
Direct Preference Optimization (DPO) demonstrates the advantage of aligning a large language model with human preference using only an offline dataset. However, DPO has the limitation that the KL penalty, which prevents excessive deviation from the reference model, is static throughout the training process. Several methods try to turn this static KL penalty into a dynamic one, but no approach can adaptively assign different KL penalties for each preference pair. In this paper, we propose $\varepsilon$-Direct Preference Optimization ($\varepsilon$-DPO), which allows adaptive control of the KL penalty strength $\beta$ for each preference pair. Specifically, $\varepsilon$-DPO adaptively controls $\beta$ for each preference pair based on the monotonicity of logits as a preference model under the perturbation of $\beta$ during training by simply reusing the logit of the current policy and the reference policy. Experimental results show that $\varepsilon$-DPO outperforms existing direct alignment algorithms and KL penalty relaxation methods on general chatbot benchmarks, highlighting the significance of adaptive KL penalty relaxation at the instance-level in DPO.
Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks
2025-02-18
Wenpeng Xing, Minghao Li, Mohan Li, Meng Han
Embodied AI systems, including robots and autonomous vehicles, are increasingly integrated into real-world applications, where they encounter a range of vulnerabilities stemming from both environmental and system-level factors. These vulnerabilities manifest through sensor spoofing, adversarial attacks, and failures in task and motion planning, posing significant challenges to robustness and safety. Despite the growing body of research, existing reviews rarely focus specifically on the unique safety and security challenges of embodied AI systems. Most prior work either addresses general AI vulnerabilities or focuses on isolated aspects, lacking a dedicated and unified framework tailored to embodied AI. This survey fills this critical gap by: (1) categorizing vulnerabilities specific to embodied AI into exogenous (e.g., physical attacks, cybersecurity threats) and endogenous (e.g., sensor failures, software flaws) origins; (2) systematically analyzing adversarial attack paradigms unique to embodied AI, with a focus on their impact on perception, decision-making, and embodied interaction; (3) investigating attack vectors targeting large vision-language models (LVLMs) and large language models (LLMs) within embodied systems, such as jailbreak attacks and instruction misinterpretation; (4) evaluating robustness challenges in algorithms for embodied perception, decision-making, and task planning; and (5) proposing targeted strategies to enhance the safety and reliability of embodied AI systems. By integrating these dimensions, we provide a comprehensive framework for understanding the interplay between vulnerabilities and safety in embodied AI.
Software Security in Software-Defined Networking: A Systematic Literature Review
2025-02-18
Moustapha Awwalou Diouf, Samuel Ouya, Jacques Klein, Tegawendé F. Bissyandé
Software-defined networking (SDN) has shifted network management by decoupling the data and control planes. This enables programmatic control via software applications using open APIs. SDN's programmability has fueled its popularity but may have opened issues extending the attack surface by introducing vulnerable software. Therefore, the research community needs to have a deep and broad understanding of the risks posed by SDN to propose mitigating measures. The literature, however, lacks a comprehensive review of the current state of research in this direction. This paper addresses this gap by providing a comprehensive overview of the state-of-the-art research in SDN security focusing on the software (i.e., the controller, APIs, applications) part. We systematically reviewed 58 relevant publications to analyze trends, identify key testing and analysis methodologies, and categorize studied vulnerabilities. We further explore areas where the research community can make significant contributions. This work offers the most extensive and in-depth analysis of SDN software security to date.
Spindown of massive main sequence stars in the Milky Way
2025-02-17
K. Nathaniel, N. Langer, S. Simón-Díaz, G. Holgado, A. de Burgos, B. Hastings
Context. We need to understand the spin evolution of massive stars to compute their internal rotationally induced mixing processes, isolate effects of close binary evolution, and predict the rotation rates of white dwarfs, neutron stars and black holes. Aims. We discuss the spindown of massive main sequence stars imposed by stellar winds. Methods. We use detailed grids of single star evolutionary models to predict the distribution of the surface rotational velocities of core-hydrogen burning Galactic massive stars as function of their mass and evolutionary state. We then compare the spin properties of our synthetic populations with appropriately selected sub-samples of Galactic main sequence OB-type stars extracted from the IACOB survey. Results. We find that below $\sim 40 M_\odot$, observations and models agree in finding that the surface rotational velocities of Galactic massive stars remain relatively constant during their main sequence evolution. The more massive stars in the IACOB sample appear to spin down less than predicted, while our updated angular momentum loss prescription predicts an enhanced spindown. Furthermore, the observations show a population of fast rotators, with $v \sin I \gtrsim 200$ km/s persisting for all ages, which is not reproduced by our synthetic single star populations. Conclusions. We conclude that the wind-induced spindown of massive main sequence stars is yet to be fully understood, and that close binary evolution might significantly contribute to the fraction of rapid rotators in massive stars.
Recent works have demonstrated that incorporating search during inference can significantly improve reasoning capabilities of language agents. Some approaches may make use of the ground truth or rely on model's own generated feedback. The search algorithm uses this feedback to then produce values that will update its criterion for exploring and exploiting various reasoning paths. In this study, we investigate how search and model's self-feedback can be leveraged for reasoning tasks. First, we explore differences in ground-truth feedback and self-feedback during search for math reasoning. Second, we observe limitations in applying search techniques to more complex tasks like tool-calling and design domain-specific approaches to address these gaps. Our experiments reveal challenges related to generalization when solely relying on self-feedback during search. For search to work effectively, either access to the ground-truth is needed or feedback mechanisms need to be carefully designed for the specific task.
JADES: Average Nitrogen Enhancement in High-Redshift Broad-Line Active Galactic Nuclei
2025-02-17
Yuki Isobe, Roberto Maiolino, Francesco D'Eugenio, Mirko Curti, Xihan Ji, Ignas Juodžbalis, Jan Scholtz, Anne Feltre, Stéphane Charlot, Hannah Übler, Andrew J. Bunker, Stefano Carniani, Emma Curtis-Lake, Zhiyuan Ji, Nimisha Kumari, Pierluigi Rinaldi, Brant Robertson, Chris Willott, Joris Witstok
The unexpectedly high nitrogen-to-oxygen (N/O) ratios observed in high-redshift (z) galaxies have challenged our understanding of early star formation. Notably, many of these nitrogen-rich galaxies show signatures of active galactic nuclei (AGNs), suggesting a possible connection between black hole formation and nitrogen enrichment. To explore this connection, we analyse stacked spectra of z=4-7 broad-line and narrow-line AGNs using deep NIRSpec data from the JADES survey. We identify a significant Niii] quintuplet and a high electron density ($\sim10^{4}\,\mathrm{cm^{-3}}$) only in the broad-line AGN stack, indicating nitrogen-rich ($\log(\mathrm{N/C})\simeq0.5$, $\log(\mathrm{N/O})>-0.6$) and dense gas similar to the high-z nitrogen-rich galaxies. Our findings suggest that dense nuclear star formation may trap nitrogen-rich gas in proto-globular clusters, in line with the high N/O observed in local globular clusters; associated runaway stellar collisions could produce intermediate-mass black hole seeds, as predicted by some models and simulations, whose accretion results into AGN signatures. These findings support scenarios connecting the early black hole seeding and growth to merging processes within and between proto-globular clusters in primeval galaxies.
Using Infrared Dust Echoes to Identify Bright Quasi-periodic Eruption Sources
2025-02-17
Dheeraj R. Pasham, Eric Coughlin, Sjoert van Velzen, Jason Hinkle
Quasi-periodic eruptions (QPEs) are recurring soft X-ray outbursts from galactic nuclei and represent an intriguing new class of transients. Currently, 10 QPE sources are reported in the literature, and a major challenge lies in identifying more because they are (apparently) intrinsically and exclusively X-ray bright. Here we highlight the unusual infrared (IR) echo of the tidal disruption event (TDE) -- and subsequent QPE source -- AT2019qiz, which rose continuously and approximately linearly with time over roughly 1000 days (between 2019 and 2024). We argue that this continuous long rise alongside the relatively high inferred IR temperature (800-1200 K) cannot be generated by the TDE itself, including the late-time/remnant TDE disk, but that the reprocessing of the light from the QPEs by a shell of dust can reproduce the observations. This model predicts 1) IR QPEs at the 0.1 percent level that are potentially detectable with the James Webb Space Telescope, and 2) that if the QPEs cease in AT2019qiz, the IR light curve should decline steadily and linearly over the same 1000-day timescale. We identify another TDE with similar IR behavior, AT2020ysg, which could thus harbor QPEs. Our findings and inferences constitute a novel method for identifying ``bright'' QPEs (with peak bolometric luminosities $\gtrsim$10$^{44}$ erg/sec), i.e., that the follow-up of optically selected TDEs with wide-field infrared surveys can indirectly reveal the presence of QPEs. This approach could be particularly effective with the upcoming Roman telescope, which could detect dozens of QPE candidates for high-cadence X-ray follow-up.
A survey about perceptions of mobility to inform an agent-based simulator of subjective modal choice
2025-02-17
Carole Adam, Benoit Gaudou
In order to adapt to the issues of climate change and public health, urban policies are trying to encourage soft mobility, but the share of the car remains significant. Beyond known constraints, we study here the impact of perception biases on individual choices. We designed a multi-criteria decision model, integrating the influence of habits and biases. We then conducted an online survey, which received 650 responses. We used these to calculate realistic mobility perception values, in order to initialise the environment and the population of a modal choice simulator, implemented in Netlogo. This allows us to visualize the adaptation of the modal distribution in reaction to the evolution of urban planning, depending on whether or not we activate biases and habits in individual reasoning. This is an extended and translated version of a demo paper published in French at JFSMA-JFMS 2024 "Un simulateur multi-agent de choix modal subjectif"
Neural scaling laws have revolutionized the design and optimization of large-scale AI models by revealing predictable relationships between model size, dataset volume, and computational resources. Early research established power-law relationships in model performance, leading to compute-optimal scaling strategies. However, recent studies highlighted their limitations across architectures, modalities, and deployment contexts. Sparse models, mixture-of-experts, retrieval-augmented learning, and multimodal models often deviate from traditional scaling patterns. Moreover, scaling behaviors vary across domains such as vision, reinforcement learning, and fine-tuning, underscoring the need for more nuanced approaches. In this survey, we synthesize insights from over 50 studies, examining the theoretical foundations, empirical findings, and practical implications of scaling laws. We also explore key challenges, including data efficiency, inference scaling, and architecture-specific constraints, advocating for adaptive scaling strategies tailored to real-world applications. We suggest that while scaling laws provide a useful guide, they do not always generalize across all architectures and training strategies.
A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond
2025-02-17
Shreya Shukla, Jose Torres, Abhijit Mishra, Jacek Gwizdka, Shounak Roychowdhury
Integration of Brain-Computer Interfaces (BCIs) and Generative Artificial Intelligence (GenAI) has opened new frontiers in brain signal decoding, enabling assistive communication, neural representation learning, and multimodal integration. BCIs, particularly those leveraging Electroencephalography (EEG), provide a non-invasive means of translating neural activity into meaningful outputs. Recent advances in deep learning, including Generative Adversarial Networks (GANs) and Transformer-based Large Language Models (LLMs), have significantly improved EEG-based generation of images, text, and speech. This paper provides a literature review of the state-of-the-art in EEG-based multimodal generation, focusing on (i) EEG-to-image generation through GANs, Variational Autoencoders (VAEs), and Diffusion Models, and (ii) EEG-to-text generation leveraging Transformer based language models and contrastive learning methods. Additionally, we discuss the emerging domain of EEG-to-speech synthesis, an evolving multimodal frontier. We highlight key datasets, use cases, challenges, and EEG feature encoding methods that underpin generative approaches. By providing a structured overview of EEG-based generative AI, this survey aims to equip researchers and practitioners with insights to advance neural decoding, enhance assistive technologies, and expand the frontiers of brain-computer interaction.
Review on thermoelectric properties of transition metal dichalcogenides
2025-02-17
I. Pallecchi, N. Manca, B. Patil, L. Pellegrino, D. Marre'
Transition metal dichalcogenides (TMDs) are considered an advantageous alternative to their celebrated two-dimensional (2D) van der Waals akin compound, graphene, for a number of applications, especially those requiring a gapped and possibly tunable band structure. Thermoelectricity is one of the application fields where TMDs could indeed outperform graphene, thanks to their lower thermal conductivity, large effective masses, valley degeneracy, varied and tunable transport properties, as well as sensitivity of their band structures and phonon spectra to confinement. Yet, despite promising theoretical predictions, thermoelectric properties of TMDs have not been extensively investigated so far and a clear assessment of TMDs as viable thermoelectric materials, based on experimental results, is still missing. In this paper, we review the experimental findings of literature on thermoelectric properties of TMDs, to sort out the countless combinations of chemical compositions, doping, off-stoichiometry and sample forms which could potentially result in optimized and possibly competitive thermoelectric properties. Based on the experimental data of literature, we simulate the performance of an all-TMD thermoelectric device for practical application as a micron sized cryocooler or power generator.
2025-02-17
Anirban Chakraborty, Tirthankar Roy Choudhury
Reionization represents an important phase in the history of our Universe when ultraviolet radiation from the first luminous sources, primarily stars and accreting black holes, ionized the neutral hydrogen atoms in the intergalactic medium (IGM). This process follows the ``Dark Ages'', a period with no luminous sources, and is initiated by the formation of the first sources, marking the ``Cosmic Dawn''. Reionization proceeds through multiple stages: initially, ionized bubbles form around galaxies, then expand and overlap across the IGM, culminating in a fully ionized state, with neutral hydrogen remaining only in dense regions. Understanding reionization involves a diverse range of physical concepts, from large-scale structure formation and star formation to radiation propagation through the IGM. Observationally, reionization can be explored using the cosmic microwave background (CMB), Lyman-$\alpha$ absorption, high-redshift galaxy surveys, and emerging 21~cm experiments, which together offer invaluable insights into this transformative epoch.
Names are deeply tied to human identity. They can serve as markers of individuality, cultural heritage, and personal history. However, using names as a core indicator of identity can lead to over-simplification of complex identities. When interacting with LLMs, user names are an important point of information for personalisation. Names can enter chatbot conversations through direct user input (requested by chatbots), as part of task contexts such as CV reviews, or as built-in memory features that store user information for personalisation. We study biases associated with names by measuring cultural presumptions in the responses generated by LLMs when presented with common suggestion-seeking queries, which might involve making assumptions about the user. Our analyses demonstrate strong assumptions about cultural identity associated with names present in LLM generations across multiple cultures. Our work has implications for designing more nuanced personalisation systems that avoid reinforcing stereotypes while maintaining meaningful customisation.
Single-cell proteomics (SCP) is transforming our understanding of biological complexity by shifting from bulk proteomics, where signals are averaged over thousands of cells, to the proteome analysis of individual cells. This granular perspective reveals distinct cell states, population heterogeneity, and the underpinnings of disease pathogenesis that bulk approaches may obscure. However, SCP demands exceptional sensitivity, precise cell handling, and robust data processing to overcome the inherent challenges of analyzing picogram-level protein samples without amplification. Recent innovations in sample preparation, separations, data acquisition strategies, and specialized mass spectrometry instrumentation have substantially improved proteome coverage and throughput. Approaches that integrate complementary -omics, streamline multi-step sample processing, and automate workflows through microfluidics and specialized platforms promise to further push SCP boundaries. Advances in computational methods, especially for data normalization and imputation, address the pervasive issue of missing values, enabling more reliable downstream biological interpretations. Despite these strides, higher throughput, reproducibility, and consensus best practices remain pressing needs in the field. This mini review summarizes the latest progress in SCP technology and software solutions, highlighting how closer integration of analytical, computational, and experimental strategies will facilitate deeper and broader coverage of single-cell proteomes.
Image Inversion: A Survey from GANs to Diffusion and Beyond
2025-02-17
Yinan Chen, Jiangning Zhang, Yali Bi, Xiaobin Hu, Teng Hu, Zhucun Xue, Ran Yi, Yong Liu, Ying Tai
Image inversion is a fundamental task in generative models, aiming to map images back to their latent representations to enable downstream applications such as editing, restoration, and style transfer. This paper provides a comprehensive review of the latest advancements in image inversion techniques, focusing on two main paradigms: Generative Adversarial Network (GAN) inversion and diffusion model inversion. We categorize these techniques based on their optimization methods. For GAN inversion, we systematically classify existing methods into encoder-based approaches, latent optimization approaches, and hybrid approaches, analyzing their theoretical foundations, technical innovations, and practical trade-offs. For diffusion model inversion, we explore training-free strategies, fine-tuning methods, and the design of additional trainable modules, highlighting their unique advantages and limitations. Additionally, we discuss several popular downstream applications and emerging applications beyond image tasks, identifying current challenges and future research directions. By synthesizing the latest developments, this paper aims to provide researchers and practitioners with a valuable reference resource, promoting further advancements in the field of image inversion. We keep track of the latest works at https://github.com/RyanChenYN/ImageInversion
The Ultraviolet Type Ia Supernova CubeSat (UVIa): Science Motivation & Mission Concept
2025-02-17
Keri Hoadley, Curtis McCully, Gillian Kyne, Fernando Cruz Aguirre, Moira Andrews, Christophe Basset, K. Azalee Bostroem, Peter J. Brown, Greyson Davis, Erika T. Hamden, Daniel Harbeck, John Hennessy, Michael Hoenk, Griffin Hosseinzadeh, D. Andrew Howell, April Jewell, Saurabh Jha, Jessica Li, Peter Milne, Leonidas Moustakas, Shouleh Nikzad, Craig Pellegrino, Abigail Polin, David J. Sand, Ken J. Shen, Lisa Storrie-Lombardi
The Ultraviolet (UV) Type Ia Supernova CubeSat (UVIa) is a CubeSat/SmallSat mission concept that stands to test critical space-borne UV technology for future missions like the Habitable Worlds Observatory (HWO) while elucidating long-standing questions about the explosion mechanisms of Type Ia supernovae (SNe Ia). UVIa will observe whether any SNe Ia emit excess UV light shortly after explosion to test progenitor/explosion models and provide follow-up over many days to characterize their UV and optical flux variations over time, assembling a comprehensive multi-band UV and optical low-redshift anchor sample for upcoming high-redshift SNe Ia surveys (e.g., Euclid, Vera Rubin Observatory, Nancy Roman Space Telescope). UVIa's mission profile requires it to perform rapid and frequent visits to newly discovered SNe Ia, simultaneously observing each SNe Ia in two UV bands (FUV: 1500-1800A and NUV: 1800-2400A) and one optical band (u-band: 3000-4200A). In this study, we describe the UVIa mission concept science motivation, mission design, and key technology development.
BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages
2025-02-17
Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin, Jan Philip Wahle, Terry Ruas, Meriem Beloucif, Christine de Kock, Nirmal Surange, Daniela Teodorescu, Ibrahim Said Ahmad, David Ifeoluwa Adelani, Alham Fikri Aji, Felermino D. M. A. Ali, Ilseyar Alimova, Vladimir Araujo, Nikolay Babakov, Naomi Baes, Ana-Maria Bucur, Andiswa Bukula, Guanqun Cao, Rodrigo Tufino Cardenas, Rendi Chevi, Chiamaka Ijeoma Chukwuneke, Alexandra Ciobotaru, Daryna Dementieva, Murja Sani Gadanya, Robert Geislinger, Bela Gipp, Oumaima Hourrane, Oana Ignat, Falalu Ibrahim Lawan, Rooweither Mabuya, Rahmad Mahendra, Vukosi Marivate, Andrew Piper, Alexander Panchenko, Charles Henrique Porto Ferreira, Vitaly Protasov, Samuel Rutunda, Manish Shrivastava, Aura Cristina Udrea, Lilian Diana Awuor Wanzare, Sophie Wu, Florian Valentin Wunderlich, Hanif Muhammad Zhafran, Tianhui Zhang, Yi Zhou, Saif M. Mohammad
People worldwide use language in subtle and complex ways to express emotions. While emotion recognition -- an umbrella term for several NLP tasks -- significantly impacts different applications in NLP and other fields, most work in the area is focused on high-resource languages. Therefore, this has led to major disparities in research and proposed solutions, especially for low-resource languages that suffer from the lack of high-quality datasets. In this paper, we present BRIGHTER -- a collection of multilabeled emotion-annotated datasets in 28 different languages. BRIGHTER covers predominantly low-resource languages from Africa, Asia, Eastern Europe, and Latin America, with instances from various domains annotated by fluent speakers. We describe the data collection and annotation processes and the challenges of building these datasets. Then, we report different experimental results for monolingual and crosslingual multi-label emotion identification, as well as intensity-level emotion recognition. We investigate results with and without using LLMs and analyse the large variability in performance across languages and text domains. We show that BRIGHTER datasets are a step towards bridging the gap in text-based emotion recognition and discuss their impact and utility.
Agents built on large language models (LLMs) have excelled in turn-by-turn human-AI collaboration but struggle with simultaneous tasks requiring real-time interaction. Latency issues and the challenge of inferring variable human strategies hinder their ability to make autonomous decisions without explicit instructions. Through experiments with current independent System 1 and System 2 methods, we validate the necessity of using Dual Process Theory (DPT) in real-time tasks. We propose DPT-Agent, a novel language agent framework that integrates System 1 and System 2 for efficient real-time simultaneous human-AI collaboration. DPT-Agent's System 1 uses a Finite-state Machine (FSM) and code-as-policy for fast, intuitive, and controllable decision-making. DPT-Agent's System 2 integrates Theory of Mind (ToM) and asynchronous reflection to infer human intentions and perform reasoning-based autonomous decisions. We demonstrate the effectiveness of DPT-Agent through further experiments with rule-based agents and human collaborators, showing significant improvements over mainstream LLM-based frameworks. To the best of our knowledge, DPT-Agent is the first language agent framework that achieves successful real-time simultaneous human-AI collaboration autonomously. Code of DPT-Agent can be found in https://github.com/sjtu-marl/DPT-Agent.
Searches for light Dark Matter with Spherical Proportional Counters
2025-02-17
Patrick Knights, Konstantinos Nikolopoulos
Elucidating the nature of dark matter is a key priority that would involve discovering new fundamental physics and is essential for understanding the structure and evolution of the universe. Despite the decades-long ever-more-sensitive searches, the particle content of dark matter remains elusive. Direct searches for dark matter candidates, to-date, focused mainly on candidates in the 10 GeV to 1 TeV, however, more recently lighter candidates with sub-GeV mass have been brought to the spotlight. This is an experimentally challenging mass region, which remains largely uncharted. The spherical proportional counter is a new type of gaseous detector which exhibits several features that make it ideally suited for the exploration of this mass range. In this article the invention and development of the spherical proportional counter are presented, its applications in the search for particle dark matter and beyond are reviewed, and possible future directions are discussed.
This study reviewed the use of Large Language Models (LLMs) in healthcare, focusing on their training corpora, customization techniques, and evaluation metrics. A systematic search of studies from 2021 to 2024 identified 61 articles. Four types of corpora were used: clinical resources, literature, open-source datasets, and web-crawled data. Common construction techniques included pre-training, prompt engineering, and retrieval-augmented generation, with 44 studies combining multiple methods. Evaluation metrics were categorized into process, usability, and outcome metrics, with outcome metrics divided into model-based and expert-assessed outcomes. The study identified critical gaps in corpus fairness, which contributed to biases from geographic, cultural, and socio-economic factors. The reliance on unverified or unstructured data highlighted the need for better integration of evidence-based clinical guidelines. Future research should focus on developing a tiered corpus architecture with vetted sources and dynamic weighting, while ensuring model transparency. Additionally, the lack of standardized evaluation frameworks for domain-specific models called for comprehensive validation of LLMs in real-world healthcare settings.
LLMs as a synthesis between symbolic and continuous approaches to language
2025-02-17
Gemma Boleda
Since the middle of the 20th century, a fierce battle is being fought between symbolic and continuous approaches to language and cognition. The success of deep learning models, and LLMs in particular, has been alternatively taken as showing that the continuous camp has won, or dismissed as an irrelevant engineering development. However, in this position paper I argue that deep learning models for language actually represent a synthesis between the two traditions. This is because 1) deep learning architectures allow for both continuous/distributed and symbolic/discrete-like representations and computations; 2) models trained on language make use this flexibility. In particular, I review recent research in mechanistic interpretability that showcases how a substantial part of morphosyntactic knowledge is encoded in a near-discrete fashion in LLMs. This line of research suggests that different behaviors arise in an emergent fashion, and models flexibly alternate between the two modes (and everything in between) as needed. This is possibly one of the main reasons for their wild success; and it is also what makes them particularly interesting for the study of language and cognition. Is it time for peace?
Diffusive spin transport of the spin-1/2 XXZ chain in the Ising regime at zero magnetic field and finite temperature
2025-02-17
Jose M. P. Carmelo, Pedro D. Sacramento
The studies of this paper on the spin-1/2 XXZ chain at finite temperatures T>0 have two complementary goals. The first is to identify the spin carriers of all its Sq>0 energy eigenstates and to show that their spin elementary currents fully control the spin-transport quantities. Here Sq is the q-spin of the continuous SUq(2) symmetry of the model for anisotropy >1. To achieve this goal, our studies rely on a suitable exact physical-spin representation.Both the spin stiffness and the zero-field spin-diffusion constant are expressed in terms of thermal expectation values of the square of the elementary currents carried by the spin carriers. Our second goal is to confirm that the zero-field and finite-temperature spin transport is normal diffusive for anisotropy >1. We use two complementary methods that rely on an inequality for the T>0 spin stiffness and the above thermal expectation values, respectively, to show that the contributions to ballistic spin transport vanish. Complementarily, for T>0 and anisotropy >1, the spin-diffusion constant is found to be finite and enhanced upon lowering T, reaching its largest yet finite values at low temperatures. Evidence suggests that it diverges in the anisotropy limit to 1 for T>0, consistent with T>0 anomalous superdiffusive spin transport at anisotropy 1 and zero field.
Mass loss along the red giant branch of the intermediate stellar populations in NGC6752 and NGC2808
2025-02-17
M. Tailo, A. P. Milone, A. F. Marino, F. D'Antona, M. V. Legnardi, T. Ziliotto, E. P. Lagioia, S. Jang, E. Bortolan, P. Ventura, C. Ventura, E. Dondoglio, F. Muratore, A. Mohandasan, M. Barbieri, S. Lionetto, G. Cordoni, F. Dell'Agli
The morphology of the Horizontal Branch (HB) in Globular Clusters (GC) is among the early evidences that they contain multiple populations of stars. Indeed, the location of each star along the HB depends both on its initial helium content (Y) and on the global average mass loss along the red giant branch ($\mu$). In most GCs, it is generally straightforward to analyse the first stellar population (standard Y), and the most extreme one (largest Y), while it is more tricky to look at the "intermediate" populations (mildly enhanced Y). In this work, we do this for the GCs NGC6752 and NGC2808; wherever possible the helium abundance for each stellar populations is constrained by using independent measurements present in the literature. We compare population synthesis models with photometric catalogues from the Hubble Space Telescope Treasury survey to derive the parameters of these HB stars. We find that the location of helium enriched stars on the HB is reproduced only by adopting a higher value of $\mu$ with respect to the first generation stars in all the analysed stellar populations. We also find that $\mu$ correlates with the helium enhancement of the populations. This holds for both clusters. This finding is naturally predicted by the model of ''pre-main sequence disc early loss'', previously suggested in the literature, and is consistent with the findings of multiple-populations formation models that foresee the formation of second generation stars in a cooling flow.
Ultrahigh energy cosmic rays and neutrino flux models
2025-02-17
Marco Stein Muzio
In this review we motivate ultrahigh energy neutrino searches and their connection to ultrahigh energy cosmic rays. We give an overview of neutrino production mechanisms and their potential sources. Several model-independent benchmarks of the ultrahigh energy neutrino flux are discussed. Finally, a brief discussion of approaches for model-dependent neutrino flux predictions are given, highlighting a few examples from the literature.
While the early literature on black holes focused on event horizons, subsequently it was realized that their teleological nature makes them unsuitable for many physical applications both in classical and quantum gravity. Therefore, over the past two decades, event horizons have been steadily replaced by quasi-local horizons which do not suffer from teleology. In numerical simulations event horizons can be located as an `after thought' only after the entire space-time has been constructed. By contrast, quasi-local horizons naturally emerge in the course of these simulations, providing powerful gauge-invariant tools to extract physics from the numerical outputs. They also lead to interesting results in mathematical GR, providing unforeseen insights. For example, for event horizons we only have a qualitative result that their area cannot decrease, while for quasi-local horizons the increase in the area during a dynamical phase is quantitatively related to local physical processes at the horizon. In binary black hole mergers, there are interesting correlations between observables associated with quasi-local horizons and those defined at future null infinity. Finally, the quantum Hawking process is naturally described as formation and evaporation of a quasi-local horizon. This review focuses on the dynamical aspects of quasi-local horizons in classical general relativity, emphasizing recent results and ongoing research.
Aspect-based sentiment analysis (ABSA) is a crucial task in information extraction and sentiment analysis, aiming to identify aspects with associated sentiment elements in text. However, existing ABSA datasets are predominantly English-centric, limiting the scope for multilingual evaluation and research. To bridge this gap, we present M-ABSA, a comprehensive dataset spanning 7 domains and 21 languages, making it the most extensive multilingual parallel dataset for ABSA to date. Our primary focus is on triplet extraction, which involves identifying aspect terms, aspect categories, and sentiment polarities. The dataset is constructed through an automatic translation process with human review to ensure quality. We perform extensive experiments using various baselines to assess performance and compatibility on M-ABSA. Our empirical findings highlight that the dataset enables diverse evaluation tasks, such as multilingual and multi-domain transfer learning, and large language model evaluation, underscoring its inclusivity and its potential to drive advancements in multilingual ABSA research.
From Selection to Generation: A Survey of LLM-based Active Learning
2025-02-17
Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley
Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent active learning frameworks, Large Language Models (LLMs) have been employed not only for selection but also for generating entirely new data instances and providing more cost-effective annotations. Motivated by the increasing importance of high-quality data and efficient model training in the era of LLMs, we present a comprehensive survey on LLM-based Active Learning. We introduce an intuitive taxonomy that categorizes these techniques and discuss the transformative roles LLMs can play in the active learning loop. We further examine the impact of AL on LLM learning paradigms and its applications across various domains. Finally, we identify open challenges and propose future research directions. This survey aims to serve as an up-to-date resource for researchers and practitioners seeking to gain an intuitive understanding of LLM-based AL techniques and deploy them to new applications.
Approximate radial symmetry for $p$-Laplace equations via the moving planes method
2025-02-17
Michele Gatti
We investigate quasi-symmetry for small perturbations of the Gidas-Ni-Nirenberg problem involving the $p$-Laplacian and for small perturbations the critical $p$-Laplace equation for $p>2$. To achieve these results, we provide a quantitative review of the work by Damascelli & Sciunzi (Calc. Var. Partial Differential Equations 25 (2006), no. 2, 139-159) concerning the weak Harnack comparison inequality and the local boundedness comparison inequality. Moreover, we prove a comparison principle for small domains.
EBOP MAVEN: A machine learning model for predicting eclipsing binary light curve fitting parameters
2025-02-17
Stephen Overall, John Southworth
Detached eclipsing binary stars (dEBs) are a key source of data on fundamental stellar parameters. While there is a vast source of candidate systems in the light curve databases of survey missions such as Kepler and TESS, published catalogues of well-characterised systems fall short of reflecting this abundance. We seek to improve the efficiency of efforts to process these data with the development of a machine learning model to inspect dEB light curves and predict the input parameters for subsequent formal analysis by the jktebop code.
The escalating volume of academic research, coupled with a shortage of qualified reviewers, necessitates innovative approaches to peer review. While large language model (LLMs) offer potential for automating this process, their current limitations include superficial critiques, hallucinations, and a lack of actionable insights. This research addresses these challenges by introducing a comprehensive evaluation framework for AI-generated reviews, that measures alignment with human evaluations, verifies factual accuracy, assesses analytical depth, and identifies actionable insights. We also propose a novel alignment mechanism that tailors LLM-generated reviews to the unique evaluation priorities of individual conferences and journals. To enhance the quality of these reviews, we introduce a self-refinement loop that iteratively optimizes the LLM's review prompts. Our framework establishes standardized metrics for evaluating AI-based review systems, thereby bolstering the reliability of AI-generated reviews in academic research.
In recent years, various quantum inequalities have been established on quantum symmetries in the framework of quantum Fourier analysis. We provide a detailed introduction to quantum inequalities including Hausdorff-Young inequality, Young's inequality, uncertainty principles, entropic convolution inequalities etc on subfactors, an important type of quantum symmetries. We cite several applications of the complete positivity of the comultiplication in category theory and subfactor theory, which indicate the fundamental differences between quantum inequalities and non-commutative inequalities. We also review the Perron-Frobenius theorem together with the algebraic structures of eigenvector spaces.
Generating natural language explanations for recommendations has become increasingly important in recommender systems. Traditional approaches typically treat user reviews as ground truth for explanations and focus on improving review prediction accuracy by designing various model architectures. However, due to limitations in data scale and model capability, these explanations often fail to meet key user-centric aspects such as factuality, personalization, and sentiment coherence, significantly reducing their overall helpfulness to users. In this paper, we propose a novel paradigm that refines initial explanations generated by existing explainable recommender models during the inference stage to enhance their quality in multiple aspects. Specifically, we introduce a multi-agent collaborative refinement framework based on large language models. To ensure alignment between the refinement process and user demands, we employ a plan-then-refine pattern to perform targeted modifications. To enable continuous improvements, we design a hierarchical reflection mechanism that provides feedback on the refinement process from both strategic and content perspectives. Extensive experiments on three datasets demonstrate the effectiveness of our framework.
Superconducting Diode Effects: Mechanisms, Materials and Applications
2025-02-17
Jiajun Ma, Ruiya Zhan, Xiao Lin
Superconducting diode effects (SDEs) generally emerge in superconducting systems where both time-reversal and inversion symmetries are broken, showing nonreciprocal current characteristics: nondissipative in one direction and ohmic in the opposite. Since the discovery of the SDEs by Ando et al. in the noncentrosymmetric superconductor [Nb/V/Ta]n in 2020, notable progress has been achieved on both the theoretical and experimental fronts. It has been proposed that intrinsic SDEs are closely linked to various exotic superconducting states, such as the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state, topological superconductivity, and chiral superconductivity. Recently, SDEs have emerged as important experimental tools for detecting symmetry breaking in exotic superconducting states. This advancement not only enhances our understanding of the fundamental nature of SDEs but also opens new possibilities for their applications in superconducting physics and related fields. This review focuses on the recent experimental progress in the observation of the SDEs and discusses their primary mechanisms from the perspective of material properties and symmetry breaking. Finally, we summarize the observed rectification efficiency of SDE devices and discuss future research directions in this rapidly developing field.
Phase-change materials for volatile threshold resistive switching and neuronal device applications
2025-02-17
Huandong Chen, Jayakanth Ravichandran
Volatile threshold resistive switching and neuronal oscillations in phase-change materials, specifically those undergoing metal-to-insulator and charge density wave transitions, offer unique attributes such as fast and low-field volatile switching, tunability, and non-linear behaviors. These characteristics are particularly promising for emulating neuronal behavior and thus hold great potential for realizing energy-efficient neuromorphic computing. In this review, we summarize recent advances in the development of neuronal oscillator devices based on three archetypal electronic phase-change materials: the correlated oxide VO2, the charge density wave transition metal dichalcogenide 1T-TaS2, and the emerging phase-change chalcogenide perovskite BaTiS3. We discuss progress from the perspective of materials development, including structural phase transitions, synthesis methods, electrical properties, and device implementation. Finally, we emphasize the major challenges that must be addressed for practical applications of these phase-change materials and provide our outlook on the future research directions in this rapidly evolving field.
Alignment tuning is crucial for ensuring large language models (LLMs) behave ethically and helpfully. Current alignment approaches require high-quality annotations and significant training resources. This paper proposes a low-cost, tuning-free method using in-context learning (ICL) to enhance LLM alignment. Through an analysis of high-quality ICL demos, we identified style as a key factor influencing LLM alignment capabilities and explicitly restyled ICL exemplars based on this stylistic framework. Additionally, we combined the restyled demos to achieve a balance between the two conflicting aspects of LLM alignment--factuality and safety. We packaged the restyled examples as prompts to trigger few-shot learning, improving LLM alignment. Compared to the best baseline approach, with an average score of 5.00 as the maximum, our method achieves a maximum 0.10 increase on the Alpaca task (from 4.50 to 4.60), a 0.22 enhancement on the Just-eval benchmark (from 4.34 to 4.56), and a maximum improvement of 0.32 (from 3.53 to 3.85) on the MT-Bench dataset. We release the code and data at https://github.com/AnonymousCode-ComputerScience/RIDE.
"I'm not for sale" -- Perceptions and limited awareness of privacy risks by digital natives about location data
2025-02-17
Antoine Boutet, Victor Morel
Although mobile devices benefit users in their daily lives in numerous ways, they also raise several privacy concerns. For instance, they can reveal sensitive information that can be inferred from location data. This location data is shared through service providers as well as mobile applications. Understanding how and with whom users share their location data -- as well as users' perception of the underlying privacy risks --, are important notions to grasp in order to design usable privacy-enhancing technologies. In this work, we perform a quantitative and qualitative analysis of smartphone users' awareness, perception and self-reported behavior towards location data-sharing through a survey of n=99 young adult participants (i.e., digital natives). We compare stated practices with actual behaviors to better understand their mental models, and survey participants' understanding of privacy risks before and after the inspection of location traces and the information that can be inferred therefrom. Our empirical results show that participants have risky privacy practices: about 54% of participants underestimate the number of mobile applications to which they have granted access to their data, and 33% forget or do not think of revoking access to their data. Also, by using a demonstrator to perform inferences from location data, we observe that slightly more than half of participants (57%) are surprised by the extent of potentially inferred information, and that 47% intend to reduce access to their data via permissions as a result of using the demonstrator. Last, a majority of participants have little knowledge of the tools to better protect themselves, but are nonetheless willing to follow suggestions to improve privacy (51%). Educating people, including digital natives, about privacy risks through transparency tools seems a promising approach.
Projects aiming to detect gravitational waves (GWs) in space in the millihertz range will utilize interferometers to measure the separations between free-falling test masses. The phasemeter measures the phase changes of the interference signals caused by the test masses' relative movements. The measurement sensitivity of the phasemeter is one of the key factors in the detection. In this work, we reviewed the core metrology of the phasemeter and evaluated the ultra-low noise performance of the phasemeter with analog signals. Frequency readout noise related to the bit width of the numerically controlled oscillator (NCO) inside the phasemeter is identified as one of the main noise sources of phase measurement theoretically and experimentally. After increasing the NCO bit widths, the single-channel phase noise of the phasemeter reached 2.0 {\mu}rad/Hz^{1/2} at 6 mHz, and the differential phase noise reached 0.4 {\mu}rad/Hz^{1/2} at 6 mHz. The phase noise performances remained consistent within the carrier frequency range of 4.9 MHz to 25.1 MHz.
Automatic target validation based on neuroscientific literature mining for tractography
2025-02-17
Xavier Vasques, Renaud Richardet, Sean L Hill, David Slater, Jean-Cedric Chappelier, Etienne Pralong, Jocelyne Bloch, Bogdan Draganski, Laura Cif
Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com
The integration of artificial intelligence into automated penetration testing (AutoPT) has highlighted the necessity of simulation modeling for the training of intelligent agents, due to its cost-efficiency and swift feedback capabilities. Despite the proliferation of AutoPT research, there is a recognized gap in the availability of a unified framework for simulation modeling methods. This paper presents a systematic review and synthesis of existing techniques, introducing MDCPM to categorize studies based on literature objectives, network simulation complexity, dependency of technical and tactical operations, and scenario feedback and variation. To bridge the gap in unified method for multi-dimensional and multi-level simulation modeling, dynamic environment modeling, and the scarcity of public datasets, we introduce AutoPT-Sim, a novel modeling framework that based on policy automation and encompasses the combination of all sub dimensions. AutoPT-Sim offers a comprehensive approach to modeling network environments, attackers, and defenders, transcending the constraints of static modeling and accommodating networks of diverse scales. We publicly release a generated standard network environment dataset and the code of Network Generator. By integrating publicly available datasets flexibly, support is offered for various simulation modeling levels focused on policy automation in MDCPM and the network generator help researchers output customized target network data by adjusting parameters or fine-tuning the network generator.
Towards Reasoning Ability of Small Language Models
2025-02-17
Gaurav Srivastava, Shuxiang Cao, Xuan Wang
Reasoning has long been viewed as an emergent property of large language models (LLMs), appearing at or above a certain scale ($\sim$100B parameters). However, recent studies challenge this assumption, showing that small language models (SLMs) can also achieve competitive reasoning performance. SLMs are increasingly favored for their efficiency and deployability. However, there is a lack of systematic study on the reasoning abilities of diverse SLMs, including those trained from scratch or derived from LLMs through quantization, pruning, and distillation. This raises a critical question: Can SLMs achieve reasoning abilities comparable to LLMs? In this work, we systematically survey, benchmark, and analyze 72 SLMs from six model families across 14 reasoning benchmarks. For reliable evaluation, we examine four evaluation methods and compare four LLM judges against human evaluations on 800 data points. We repeat all experiments three times to ensure a robust performance assessment. Additionally, we analyze the impact of different prompting strategies in small models. Beyond accuracy, we also evaluate model robustness under adversarial conditions and intermediate reasoning steps. Our findings challenge the assumption that scaling is the only way to achieve strong reasoning. Instead, we foresee a future where SLMs with strong reasoning capabilities can be developed through structured training or post-training compression. They can serve as efficient alternatives to LLMs for reasoning-intensive tasks.
A Survey of Automatic Prompt Engineering: An Optimization Perspective
2025-02-17
Wenwu Li, Xiangfeng Wang, Wenhao Li, Bo Jin
The rise of foundation models has shifted focus from resource-intensive fine-tuning to prompt engineering, a paradigm that steers model behavior through input design rather than weight updates. While manual prompt engineering faces limitations in scalability, adaptability, and cross-modal alignment, automated methods, spanning foundation model (FM) based optimization, evolutionary methods, gradient-based optimization, and reinforcement learning, offer promising solutions. Existing surveys, however, remain fragmented across modalities and methodologies. This paper presents the first comprehensive survey on automated prompt engineering through a unified optimization-theoretic lens. We formalize prompt optimization as a maximization problem over discrete, continuous, and hybrid prompt spaces, systematically organizing methods by their optimization variables (instructions, soft prompts, exemplars), task-specific objectives, and computational frameworks. By bridging theoretical formulation with practical implementations across text, vision, and multimodal domains, this survey establishes a foundational framework for both researchers and practitioners, while highlighting underexplored frontiers in constrained optimization and agent-oriented prompt design.
The JCMT BISTRO Survey: Magnetic Fields Align with Orbital Structure in the Galactic Center
2025-02-17
Janik Karoly, Derek Ward-Thompson, Kate Pattle, Steven N. Longmore, James Di Francesco, Anthony Whitworth, Doug Johnstone, Sarah Sadavoy, Patrick M. Koch, Meng-Zhe Yang, Ray Furuya, Xing Lu, Motohide Tamura, Victor Debattista, David Eden, Jihye Hwang, Frederick Poidevin, Bijas Najimudeen, Szu-Ting Chen, Eun Jung Chung, Simon Coude, Sheng-Jun Lin, Yasuo Doi, Takashi Onaka, Lapo Fanciullo, Tie Liu, Guangxing Li, Pierre Bastien, Tetsuo Hasegawa, Woojin Kwon, Shih-Ping Lai, Keping Qiu
We present the magnetic field in the dense material of the Central Molecular Zone (CMZ) of the Milky Way, traced in 850 $\mu$m polarized dust emission as part of the James Clerk Maxwell Telescope (JCMT) B-fields In STar-forming Region Observations (BISTRO) Survey. We observe a highly ordered magnetic field across the CMZ between Sgr B2 and Sgr C, which is strongly preferentially aligned with the orbital gas flows within the clouds of the CMZ. We find that the observed relative orientations are non-random at a $>$99% confidence level and are consistent with models in which the magnetic field vectors are aligned within 30$^{o}$ to the gas flows in 3D. The deviations from aligned magnetic fields are most prominent at positive Galactic longitudes, where the CMZ clouds are more massive, denser, and more actively forming stars. Our observed strongly preferentially parallel magnetic field morphology leads us to hypothesize that in the absence of star formation, the magnetic field in the CMZ is entrained in the orbital gas flows around Sgr A$^{*}$, while gravitational collapse and feedback in star-forming regions can locally reorder the field. This magnetic field behavior is similar to that observed in the CMZ of the nuclear starburst galaxy NGC 253. This suggests that despite its current low star formation rate, the CMZ of the Milky Way is analogous to those of more distant, actively star-forming, galaxies.
CSST Large Scale Structure Analysis Pipeline: III. Emission-line Redshift Measurement for Slitless Spectra
2025-02-17
Jipeng Sui, Hu Zou, Xiaohu Yang, Xianzhong Zheng, Run Wen, Yizhou Gu, Weiyu Ding, Lu Feng, Hong Guo, Wei-Jian Guo, Yunkun Han, Yipeng Jing, Cheng Li, Wenxiong Li, Shufei Liu, Zhixia Shen, Gaurav Singh, Jiali Wang, Peng Wei, Yunao Xiao, Suijian Xue, Hu Zhan, Pengjie Zhang, Gongbo Zhao
The China Space Station Telescope (CSST) is a forthcoming space-based optical telescope designed to co-orbit with the Chinese Space Station. With a planned slitless spectroscopic survey spanning a broad wavelength range of $255-1000$nm and an average spectral resolution exceeding 200, the CSST holds significant potential for cosmic large-scale structure analysis. In this study, we focus on redshift determinations from slitless spectra through emission line analysis within the CSST framework. Our tailored redshift measurement process involves identifying emission lines in one-dimensional slitless spectra, aligning observed wavelengths with their rest-frame counterparts from prominent galaxy emissions, and calculating wavelength shifts to determine redshifts accurately. To validate our redshift measurement algorithm, we leverage simulated spectra generated by the CSST emulator for slitless spectroscopy. The outcomes demonstrate a remarkable redshift completeness exceeding 95 per cent for emission line galaxies (ELGs), alongside a purity surpassing 85 per cent. The redshift uncertainty remains impressively below than $\sim 0.001$. Notably, when concentrating on galaxies with more than three matched emission lines, the completeness of ELGs and the purity of measurable galaxies can reach 98 per cent and 97 per cent, respectively. Furthermore, we explore the influence of parameters like magnitude, spectral signal-to-noise ratio, and redshift on redshift completeness and purity. The discussion also delves into redshift degeneracies stemming from emission-line matching confusion. Our developed redshift measurement process will be applied to extensive simulated datasets and forthcoming CSST slitless spectroscopic observations for further cosmological and extragalactic analyses.
Large Language Models (LLMs) excel in handling general knowledge tasks, yet they struggle with user-specific personalization, such as understanding individual emotions, writing styles, and preferences. Personalized Large Language Models (PLLMs) tackle these challenges by leveraging individual user data, such as user profiles, historical dialogues, content, and interactions, to deliver responses that are contextually relevant and tailored to each user's specific needs. This is a highly valuable research topic, as PLLMs can significantly enhance user satisfaction and have broad applications in conversational agents, recommendation systems, emotion recognition, medical assistants, and more. This survey reviews recent advancements in PLLMs from three technical perspectives: prompting for personalized context (input level), finetuning for personalized adapters (model level), and alignment for personalized preferences (objective level). To provide deeper insights, we also discuss current limitations and outline several promising directions for future research. Updated information about this survey can be found at the https://github.com/JiahongLiu21/Awesome-Personalized-Large-Language-Models.
The reasoning capabilities of advanced large language models (LLMs) like o1 have revolutionized artificial intelligence applications. Nevertheless, evaluating and optimizing complex reasoning processes remain significant challenges due to diverse policy distributions and the inherent limitations of human effort and accuracy. In this paper, we present AURORA, a novel automated framework for training universal process reward models (PRMs) using ensemble prompting and reverse verification. The framework employs a two-phase approach: First, it uses diverse prompting strategies and ensemble methods to perform automated annotation and evaluation of processes, ensuring robust assessments for reward learning. Second, it leverages practical reference answers for reverse verification, enhancing the model's ability to validate outputs and improving training accuracy. To assess the framework's performance, we extend beyond the existing ProcessBench benchmark by introducing UniversalBench, which evaluates reward predictions across full trajectories under diverse policy distribtion with long Chain-of-Thought (CoT) outputs. Experimental results demonstrate that AURORA enhances process evaluation accuracy, improves PRMs' accuracy for diverse policy distributions and long-CoT responses. The project will be open-sourced at https://auroraprm.github.io/. The Universal-PRM-7B is available at https://huggingface.co/infly/Universal-PRM-7B.
Generative Multi-Agent Collaboration in Embodied AI: A Systematic Review
2025-02-17
Di Wu, Xian Wei, Guang Chen, Hao Shen, Xiangfeng Wang, Wenhao Li, Bo Jin
Embodied multi-agent systems (EMAS) have attracted growing attention for their potential to address complex, real-world challenges in areas such as logistics and robotics. Recent advances in foundation models pave the way for generative agents capable of richer communication and adaptive problem-solving. This survey provides a systematic examination of how EMAS can benefit from these generative capabilities. We propose a taxonomy that categorizes EMAS by system architectures and embodiment modalities, emphasizing how collaboration spans both physical and virtual contexts. Central building blocks, perception, planning, communication, and feedback, are then analyzed to illustrate how generative techniques bolster system robustness and flexibility. Through concrete examples, we demonstrate the transformative effects of integrating foundation models into embodied, multi-agent frameworks. Finally, we discuss challenges and future directions, underlining the significant promise of EMAS to reshape the landscape of AI-driven collaboration.
Chinese Spelling Correction (CSC) is a critical task in natural language processing, aimed at detecting and correcting spelling errors in Chinese text. This survey provides a comprehensive overview of CSC, tracing its evolution from pre-trained language models to large language models, and critically analyzing their respective strengths and weaknesses in this domain. Moreover, we further present a detailed examination of existing benchmark datasets, highlighting their inherent challenges and limitations. Finally, we propose promising future research directions, particularly focusing on leveraging the potential of LLMs and their reasoning capabilities for improved CSC performance. To the best of our knowledge, this is the first comprehensive survey dedicated to the field of CSC. We believe this work will serve as a valuable resource for researchers, fostering a deeper understanding of the field and inspiring future advancements.
Connector-S: A Survey of Connectors in Multi-modal Large Language Models
2025-02-17
Xun Zhu, Zheng Zhang, Xi Chen, Yiming Shi, Miao Li, Ji Wu
With the rapid advancements in multi-modal large language models (MLLMs), connectors play a pivotal role in bridging diverse modalities and enhancing model performance. However, the design and evolution of connectors have not been comprehensively analyzed, leaving gaps in understanding how these components function and hindering the development of more powerful connectors. In this survey, we systematically review the current progress of connectors in MLLMs and present a structured taxonomy that categorizes connectors into atomic operations (mapping, compression, mixture of experts) and holistic designs (multi-layer, multi-encoder, multi-modal scenarios), highlighting their technical contributions and advancements. Furthermore, we discuss several promising research frontiers and challenges, including high-resolution input, dynamic compression, guide information selection, combination strategy, and interpretability. This survey is intended to serve as a foundational reference and a clear roadmap for researchers, providing valuable insights into the design and optimization of next-generation connectors to enhance the performance and adaptability of MLLMs.
Do we Really Need Visual Instructions? Towards Visual Instruction-Free Fine-tuning for Large Vision-Language Models
2025-02-17
Zikang Liu, Kun Zhou, Wayne Xin Zhao, Dawei Gao, Yaliang Li, Ji-Rong Wen
Visual instruction tuning has become the predominant technology in eliciting the multimodal task-solving capabilities of large vision-language models (LVLMs). Despite the success, as visual instructions require images as the input, it would leave the gap in inheriting the task-solving capabilities from the backbone LLMs, and make it costly to collect a large-scale dataset. To address it, we propose ViFT, a visual instruction-free fine-tuning framework for LVLMs. In ViFT, we only require the text-only instructions and image caption data during training, to separately learn the task-solving and visual perception abilities. During inference, we extract and combine the representations of the text and image inputs, for fusing the two abilities to fulfill multimodal tasks. Experimental results demonstrate that ViFT can achieve state-of-the-art performance on several visual reasoning and visual instruction following benchmarks, with rather less training data. Our code and data will be publicly released.
Large language models (LLMs) have shown remarkable capabilities in commonsense reasoning; however, some variations in questions can trigger incorrect responses. Do these models truly understand commonsense knowledge, or just memorize expression patterns? To investigate this question, we present the first extensive robustness evaluation of LLMs in commonsense reasoning. We introduce HellaSwag-Pro, a large-scale bilingual benchmark consisting of 11,200 cases, by designing and compiling seven types of question variants. To construct this benchmark, we propose a two-stage method to develop Chinese HellaSwag, a finely annotated dataset comprising 12,000 instances across 56 categories. We conduct extensive experiments on 41 representative LLMs, revealing that these LLMs are far from robust in commonsense reasoning. Furthermore, this robustness varies depending on the language in which the LLM is tested. This work establishes a high-quality evaluation benchmark, with extensive experiments offering valuable insights to the community in commonsense reasoning for LLMs.
A conceptual space represents concepts as nodes and semantic relatedness as edges. Word embeddings, combined with a similarity metric, provide an effective approach to constructing such a space. Typically, embeddings are derived from traditional distributed models or encoder-only pretrained models, whose objectives directly capture the meaning of the current token. In contrast, decoder-only models, including large language models (LLMs), predict the next token, making their embeddings less directly tied to the current token's semantics. Moreover, comparative studies on LLMs of different scales remain underexplored. In this paper, we construct a conceptual space using word embeddings from LLMs of varying scales and comparatively analyze their properties. We establish a network based on a linguistic typology-inspired connectivity hypothesis, examine global statistical properties, and compare LLMs of varying scales. Locally, we analyze conceptual pairs, WordNet relations, and a cross-lingual semantic network for qualitative words. Our results indicate that the constructed space exhibits small-world properties, characterized by a high clustering coefficient and short path lengths. Larger LLMs generate more intricate spaces, with longer paths reflecting richer relational structures and connections. Furthermore, the network serves as an efficient bridge for cross-lingual semantic mapping.
The paper explores the performance of LLMs in the context of multi-dimensional analytic writing assessments, i.e. their ability to provide both scores and comments based on multiple assessment criteria. Using a corpus of literature reviews written by L2 graduate students and assessed by human experts against 9 analytic criteria, we prompt several popular LLMs to perform the same task under various conditions. To evaluate the quality of feedback comments, we apply a novel feedback comment quality evaluation framework. This framework is interpretable, cost-efficient, scalable, and reproducible, compared to existing methods that rely on manual judgments. We find that LLMs can generate reasonably good and generally reliable multi-dimensional analytic assessments. We release our corpus for reproducibility.
The rapid rise of AI-generated content has made detecting disinformation increasingly challenging. In particular, multimodal disinformation, i.e., online posts-articles that contain images and texts with fabricated information are specially designed to deceive. While existing AI safety benchmarks primarily address bias and toxicity, multimodal disinformation detection remains largely underexplored. To address this challenge, we present the Vision-Language Disinformation Detection Benchmark VLDBench, the first comprehensive benchmark for detecting disinformation across both unimodal (text-only) and multimodal (text and image) content, comprising 31,000} news article-image pairs, spanning 13 distinct categories, for robust evaluation. VLDBench features a rigorous semi-automated data curation pipeline, with 22 domain experts dedicating 300 plus hours} to annotation, achieving a strong inter-annotator agreement (Cohen kappa = 0.78). We extensively evaluate state-of-the-art Large Language Models (LLMs) and Vision-Language Models (VLMs), demonstrating that integrating textual and visual cues in multimodal news posts improves disinformation detection accuracy by 5 - 35 % compared to unimodal models. Developed in alignment with AI governance frameworks such as the EU AI Act, NIST guidelines, and the MIT AI Risk Repository 2024, VLDBench is expected to become a benchmark for detecting disinformation in online multi-modal contents. Our code and data will be publicly available.
We review the exotic states, such as the $X$, $Y$, $Z$, $T$ and $P$ states, and present their possible assignments based on the QCD sum rules. We present many predictions which can be confronted to the experimental data in the future to diagnose the exotic states. Furthermore, we also mention other theoretical methods.
Detecting texts generated by Large Language Models (LLMs) could cause grave mistakes due to incorrect decisions, such as undermining student's academic dignity. LLM text detection thus needs to ensure the interpretability of the decision, which can help users judge how reliably correct its prediction is. When humans verify whether a text is human-written or LLM-generated, they intuitively investigate with which of them it shares more similar spans. However, existing interpretable detectors are not aligned with the human decision-making process and fail to offer evidence that users easily understand. To bridge this gap, we introduce ExaGPT, an interpretable detection approach grounded in the human decision-making process for verifying the origin of a text. ExaGPT identifies a text by checking whether it shares more similar spans with human-written vs. with LLM-generated texts from a datastore. This approach can provide similar span examples that contribute to the decision for each span in the text as evidence. Our human evaluation demonstrates that providing similar span examples contributes more effectively to judging the correctness of the decision than existing interpretable methods. Moreover, extensive experiments in four domains and three generators show that ExaGPT massively outperforms prior powerful detectors by up to +40.9 points of accuracy at a false positive rate of 1%.
Achieving Upper Bound Accuracy of Joint Training in Continual Learning
2025-02-17
Saleh Momeni, Bing Liu
Continual learning has been an active research area in machine learning, focusing on incrementally learning a sequence of tasks. A key challenge is catastrophic forgetting (CF), and most research efforts have been directed toward mitigating this issue. However, a significant gap remains between the accuracy achieved by state-of-the-art continual learning algorithms and the ideal or upper-bound accuracy achieved by training all tasks together jointly. This gap has hindered or even prevented the adoption of continual learning in applications, as accuracy is often of paramount importance. Recently, another challenge, termed inter-task class separation (ICS), was also identified, which spurred a theoretical study into principled approaches for solving continual learning. Further research has shown that by leveraging the theory and the power of large foundation models, it is now possible to achieve upper-bound accuracy, which has been empirically validated using both text and image classification datasets. Continual learning is now ready for real-life applications. This paper surveys the main research leading to this achievement, justifies the approach both intuitively and from neuroscience research, and discusses insights gained.
Bridging the Data Gap in AI Reliability Research and Establishing DR-AIR, a Comprehensive Data Repository for AI Reliability
2025-02-17
Simin Zheng, Jared M. Clark, Fatemeh Salboukh, Priscila Silva, Karen da Mata, Fenglian Pan, Jie Min, Jiayi Lian, Caleb B. King, Lance Fiondella, Jian Liu, Xinwei Deng, Yili Hong
Artificial intelligence (AI) technology and systems have been advancing rapidly. However, ensuring the reliability of these systems is crucial for fostering public confidence in their use. This necessitates the modeling and analysis of reliability data specific to AI systems. A major challenge in AI reliability research, particularly for those in academia, is the lack of readily available AI reliability data. To address this gap, this paper focuses on conducting a comprehensive review of available AI reliability data and establishing DR-AIR: a data repository for AI reliability. Specifically, we introduce key measurements and data types for assessing AI reliability, along with the methodologies used to collect these data. We also provide a detailed description of the currently available datasets with illustrative examples. Furthermore, we outline the setup of the DR-AIR repository and demonstrate its practical applications. This repository provides easy access to datasets specifically curated for AI reliability research. We believe these efforts will significantly benefit the AI research community by facilitating access to valuable reliability data and promoting collaboration across various academic domains within AI. We conclude our paper with a call to action, encouraging the research community to contribute and share AI reliability data to further advance this critical field of study.
Programmable photonic waveguide arrays: opportunities and challenges
2025-02-17
Yang Yang, Akram Youssry, Alberto Peruzzo
The field of programmable photonics has advanced significantly in recent decades, driven by the rising demand for complex applications, such as optical quantum computing and photonic neural networks. However, as the complexity of these applications increases, there is an increasing need for novel designs that enhance circuit transmission and enable further miniaturization. Photonic waveguide arrays (WAs) hold a unique position in integrated photonics, as they implement ``always-on'' Hamiltonians and have no direct analogs in free-space optics. They find applications in various fields, including light propagation studies, quantum walks, and topological photonics. Despite their versatility, the lack of reconfigurability has limited their utility and hindered further advancements for a long time. Recently, programmable waveguide arrays (PWAs) have emerged as a promising solution for overcoming the limitations of static WAs and PWA-based architectures have been proven to be universal. This perspective proposes a vision for photonic circuits based on PWAs as a new, interdisciplinary field. We review the history of the development of PWAs and outline their potential in areas such as simulation, communication, sensing, and classical and quantum information processing. This technology is expected to become increasingly feasible with advancements in programmable photonics, nanofabrication, and quantum control.
Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges
2025-02-17
Bolei Ma, Yuting Li, Wei Zhou, Ziwei Gong, Yang Janet Liu, Katja Jasinskaja, Annemarie Friedrich, Julia Hirschberg, Frauke Kreuter, Barbara Plank
Understanding pragmatics-the use of language in context-is crucial for developing NLP systems capable of interpreting nuanced language use. Despite recent advances in language technologies, including large language models, evaluating their ability to handle pragmatic phenomena such as implicatures and references remains challenging. To advance pragmatic abilities in models, it is essential to understand current evaluation trends and identify existing limitations. In this survey, we provide a comprehensive review of resources designed for evaluating pragmatic capabilities in NLP, categorizing datasets by the pragmatics phenomena they address. We analyze task designs, data collection methods, evaluation approaches, and their relevance to real-world applications. By examining these resources in the context of modern language models, we highlight emerging trends, challenges, and gaps in existing benchmarks. Our survey aims to clarify the landscape of pragmatic evaluation and guide the development of more comprehensive and targeted benchmarks, ultimately contributing to more nuanced and context-aware NLP models.
The impact of job stability on monetary poverty in Italy: causal small area estimation
2025-02-17
Katarzyna Reluga, Dehan Kong, Setareh Ranjbar, Nicola Salvati, Mark van der Laan
Job stability - encompassing secure contracts, adequate wages, social benefits, and career opportunities - is a critical determinant in reducing monetary poverty, as it provides households with reliable income and enhances economic well-being. This study leverages EU-SILC survey and census data to estimate the causal effect of job stability on monetary poverty across Italian provinces, quantifying its influence and analyzing regional disparities. We introduce a novel causal small area estimation (CSAE) framework that integrates global and local estimation strategies for heterogeneous treatment effect estimation, effectively addressing data sparsity at the provincial level. Furthermore, we develop a general bootstrap scheme to construct reliable confidence intervals, applicable regardless of the method used for estimating nuisance parameters. Extensive simulation studies demonstrate that our proposed estimators outperform classical causal inference methods in terms of stability while maintaining computational scalability for large datasets. Applying this methodology to real-world data, we uncover significant relationships between job stability and poverty across six Italian regions, offering critical insights into regional disparities and their implications for evidence-based policy design.
Soft Robotics for Search and Rescue: Advancements, Challenges, and Future Directions
2025-02-17
Abhishek Sebastian
Soft robotics has emerged as a transformative technology in Search and Rescue (SAR) operations, addressing challenges in navigating complex, hazardous environments that often limit traditional rigid robots. This paper critically examines advancements in soft robotic technologies tailored for SAR applications, focusing on their unique capabilities in adaptability, safety, and efficiency. By leveraging bio-inspired designs, flexible materials, and advanced locomotion mechanisms, such as crawling, rolling, and shape morphing, soft robots demonstrate exceptional potential in disaster scenarios. However, significant barriers persist, including material durability, power inefficiency, sensor integration, and control complexity. This comprehensive review highlights the current state of soft robotics in SAR, discusses simulation methodologies and hardware validations, and introduces performance metrics essential for their evaluation. By bridging the gap between theoretical advancements and practical deployment, this study underscores the potential of soft robotic systems to revolutionize SAR missions and advocates for continued interdisciplinary innovation to overcome existing limitations.
Positional Encoding in Transformer-Based Time Series Models: A Survey
2025-02-17
Habib Irani, Vangelis Metsis
Recent advancements in transformer-based models have greatly improved time series analysis, providing robust solutions for tasks such as forecasting, anomaly detection, and classification. A crucial element of these models is positional encoding, which allows transformers to capture the intrinsic sequential nature of time series data. This survey systematically examines existing techniques for positional encoding in transformer-based time series models. We investigate a variety of methods, including fixed, learnable, relative, and hybrid approaches, and evaluate their effectiveness in different time series classification tasks. Furthermore, we outline key challenges and suggest potential research directions to enhance positional encoding strategies. By delivering a comprehensive overview and quantitative benchmarking, this survey intends to assist researchers and practitioners in selecting and designing effective positional encoding methods for transformer-based time series models.
Modelling the impact of Multi Cancer Early Detection tests: a review of natural history of disease models
2025-02-17
O Mandrik, S Whyte, N Kunst, A Rayner, M Harden, S Dias, K Payne, S Palmer, MO Soares
Introduction: The potential for multi-cancer early detection (MCED) tests to detect cancer at earlier stages is currently being evaluated in screening clinical trials. Once trial evidence becomes available, modelling will be necessary to predict impacts on final outcomes (benefits and harms), account for heterogeneity in determining clinical and cost-effectiveness, and explore alternative screening programme specifications. The natural history of disease (NHD) component of a MCED model will use statistical, mathematical or calibration methods. Methods: Modelling approaches for MCED screening that include an NHD component were identified from the literature, reviewed and critically appraised. Purposively selected (non-MCED) cancer screening models were also reviewed. The appraisal focussed on the scope, data sources, evaluation approaches and the structure and parameterisation of the models. Results: Five different MCED NHD models were identified and reviewed, alongside four additional (non-MCED) models. The critical appraisal highlighted several features of this literature. In the absence of trial evidence, MCED effects are based on predictions derived from test accuracy. These predictions rely on simplifying assumptions with unknown impacts, such as the stage-shift assumption used to estimate mortality impacts from predicted stage-shifts. None of the MCED models fully characterised uncertainty in the NHD or examined uncertainty in the stage-shift assumption. Conclusion: MCED technologies are developing rapidly, and large and costly clinical studies are being designed and implemented across the globe. Currently there is no modelling approach that can integrate clinical study evidence and therefore, in support of policy, it is important that similar efforts are made in the development of MCED models that make best use of the available data on benefits and harms.
2025-02-17
Tarik P. Cysne, Luis M. Canonico, Marcio Costa, R. B. Muniz, Tatiana G. Rappoport
Orbitronics explores the control and manipulation of electronic orbital angular momentum in solid-state systems, opening new pathways for information processing and storage. One significant advantage of orbitronics over spintronics is that it does not rely on spin-orbit coupling, thereby broadening the range of non-magnetic materials that can be utilized for these applications. It also introduces new topological features related to electronic orbital angular momentum, and clarifies some long-standing challenges in understanding experiments that rely on the conventional concept of valley transport. This review highlights recent advances in orbitronics, particularly in relation to two-dimensional materials. We examine the fundamental principles underlying the generation, transport, and dynamics of orbital angular momentum to illustrate how the unique properties of two-dimensional materials can promote orbitronic phenomena. We also outline potential future research directions and address some outstanding questions in this field.
A comprehensive survey of the GEO-belt using simultaneous four-colour observations with STING
2025-02-17
Robert J. S. Airey, Paul Chote, James A. Blake, Benjamin F. Cooke, James McCormac, Phineas Allen, Alex MacManus, Don Pollacco, Billy Shrive, Richard West
Colour light curves of resident space objects (RSOs) encapsulate distinctive features that can offer insights into an object's structure and design, making them an invaluable tool for classification and characterisation. We present the results of the first large systematic colour survey of the GEO belt in which we obtain full-night multi-colour light curves for 112 active geostationary objects between April and May 2023. Colour light curve maps were created to compare and contrast the colours between different satellites and bus configurations. We find that satellites with BSS-702 and STAR-2 buses can be effectively distinguished from the colour measurements on these maps, but comparing the average colour of individual satellites within given solar equatorial phase angle ranges shows that it is difficult to distinguish between bus configurations based on colour alone. We also find tentative evidence to suggest that there is a relationship between colour and time spent on orbit for the Eurostar-3000 class satellites, which is unseen behaviour within other bus configuration classes. The satellites in our sample exhibit `redder' colours than the Sun, which is in agreement with previous findings. We found common light curve features such as symmetrical colour changes as well as unique regions of short timescale glinting which are `bluer' than other regimes within the colour light curves. If these features are indeed seasonal, this would be a powerful characterisation tool. We are able to detect and resolve features in the light curve of the LDPE-3A satellite related to manoeuvres being performed. Finally, we measured the solar panel offsets of 54 satellites in our sample and found variation in the type of colour response. The majority of which did not exhibit any colour change across the solar panel glints compared to them shifting towards 'redder' or 'bluer' colours.
Per-channel autoregressive linear prediction padding in tiled CNN processing of 2D spatial data
2025-02-17
Olli Niemitalo, Otto Rosenberg, Nathaniel Narra, Olli Koskela, Iivari Kunttu
We present linear prediction as a differentiable padding method. For each channel, a stochastic autoregressive linear model is fitted to the padding input by minimizing its noise terms in the least-squares sense. The padding is formed from the expected values of the autoregressive model given the known pixels. We trained the convolutional RVSR super-resolution model from scratch on satellite image data, using different padding methods. Linear prediction padding slightly reduced the mean square super-resolution error compared to zero and replication padding, with a moderate increase in time cost. Linear prediction padding better approximated satellite image data and RVSR feature map data. With zero padding, RVSR appeared to use more of its capacity to compensate for the high approximation error. Cropping the network output by a few pixels reduced the super-resolution error and the effect of the choice of padding method on the error, favoring output cropping with the faster replication and zero padding methods, for the studied workload.
Evaluating Step-by-step Reasoning Traces: A Survey
2025-02-17
Jinu Lee, Julia Hockenmaier
Step-by-step reasoning is widely used to enhance the reasoning ability of large language models (LLMs) in complex problems. Evaluating the quality of reasoning traces is crucial for understanding and improving LLM reasoning. However, the evaluation criteria remain highly unstandardized, leading to fragmented efforts in developing metrics and meta-evaluation benchmarks. To address this gap, this survey provides a comprehensive overview of step-by-step reasoning evaluation, proposing a taxonomy of evaluation criteria with four top-level categories (groundedness, validity, coherence, and utility). We then categorize metrics based on their implementations, survey which metrics are used for assessing each criterion, and explore whether evaluator models can transfer across different criteria. Finally, we identify key directions for future research.
Improved constraints on the Faraday rotation towards eight fast radio bursts using dense grids of polarized radio galaxies
2025-02-17
Ayush Pandhi, Bryan M. Gaensler, Ziggy Pleunis, Sebastian Hutschenreuter, Casey Law, Ryan Mckinven, Shane P. O'Sullivan, Emily B. Petroff, Tessa Vernstrom
We present 2-4 GHz observations of polarized radio galaxies towards eight fast radio bursts (FRBs), producing grids of Faraday rotation measure (RM) sources with sky densities of 9-28 polarized sources per square degree. Using a Bayesian interpolation framework, we constrain Galactic RM fluctuations below ~ 1 degree squared angular scales around the FRB positions. Despite the positions of all eight FRBs far from the Galactic plane, we constrain previously unresolved small-scale Galactic RM structures around six of the eight FRBs. In two of these fields, we find potential changes in the sign of the Galactic RM that are not captured by previous, sparsely sampled RM grid observations. Our Galactic RM estimate towards the FRBs differs between a few rad m^-2 up to ~ 40 rad m^-2 from the all-sky Galactic RM map of Hutschenreuter et al. (2022). Extrapolating our results to the known population of polarized FRB sources, we may be incorrectly interpreting the host galaxy RM for ~ 30% of the FRB source population with current RM grid observations. Measuring small-scale Galactic RM variations is crucial for identifying FRBs in low density and weakly magnetized environments, which in turn could serve as potent probes of cosmic magnetism. This framework of reconstructing continuous Galactic RM structure from RM grid observations can be readily applied to FRBs that fall in the sky coverage of upcoming large-sky radio polarization surveys of radio galaxies, such as the Very Large Array Sky Survey (VLASS) and the Polarization Sky Survey of the Universe's Magnetism (POSSUM).
Binarity at LOw Metallicity (BLOeM): Multiplicity of early B-type supergiants in the Small Magellanic Cloud
2025-02-17
N. Britavskiy, L. Mahy, D. J. Lennon, L. R. Patrick, H. Sana, J. I. Villaseñor, T. Shenar, J. Bodensteiner, M. Bernini-Peron, S. R. Berlanas, D. M. Bowman, P. A. Crowther, S. E. de Mink, C. J. Evans, Y. Götberg, G. Holgado, C. Johnston, Z. Keszthelyi, J. Klencki, N. Langer, I. Mandel, A. Menon, M. Moe, L. M. Oskinova, D. Pauli, M. Pawlak, V. Ramachandran, M. Renzo, A. A. C. Sander, F. R. N. Schneider, A. Schootemeijer, K. Sen, S. Simón-Díaz, J. Th. van Loon, J. S. Vink
The blue supergiant (BSG) domain contains a large variety of stars whose past and future evolutionary paths are still highly uncertain. Since binary interaction plays a crucial role in the fate of massive stars, investigating the multiplicity among BSGs helps shed light on the fate of such objects. We aim to estimate the binary fraction of a large sample of BSGs in the Small Magellanic Cloud within the Binarity at LOw Metallicity (BLOeM) survey. In total, we selected 262 targets with spectral types B0-B3 and luminosity classes I-II. This work is based on spectroscopic data collected by the GIRAFFE instrument, mounted on the Very Large Telescope, which gathered nine epochs over three months. Our spectroscopic analysis for each target includes the individual and peak-to-peak radial velocity measurements, an investigation of the line profile variability, and a periodogram analysis to search for possible short- and long-period binaries. By applying a 20 km s$^{-1}$ threshold on the peak-to-peak radial velocities above which we would consider the star to be binary, the resulting observed spectroscopic binary fraction for our BSG sample is 23 $\pm$ 3$\%$. In addition, we derived reliable orbital periods for 41 spectroscopic binaries and potential binary candidates, among which there are 17 eclipsing binaries, including 20 SB1 and SB2 systems with periods of less than 10 days. We reported a significant drop in the binary fraction of BSGs with spectral types later than B2 and effective temperatures less than 18 kK, which could indicate the end of the main sequence phase in this temperature regime. We found no metallicity dependence in the binary fraction of BSGs, compared to existing spectroscopic surveys of the Galaxy and Large Magellanic Cloud.
VAQUUM: Are Vague Quantifiers Grounded in Visual Data?
2025-02-17
Hugh Mee Wong, Rick Nouwen, Albert Gatt
Vague quantifiers such as "a few" and "many" are influenced by many contextual factors, including how many objects are present in a given context. In this work, we evaluate the extent to which vision-and-language models (VLMs) are compatible with humans when producing or judging the appropriateness of vague quantifiers in visual contexts. We release a novel dataset, VAQUUM, containing 20300 human ratings on quantified statements across a total of 1089 images. Using this dataset, we compare human judgments and VLM predictions using three different evaluation methods. Our findings show that VLMs, like humans, are influenced by object counts in vague quantifier use. However, we find significant inconsistencies across models in different evaluation settings, suggesting that judging and producing vague quantifiers rely on two different processes.
IMPACTX: Improving Model Performance by Appropriately predicting CorrecT eXplanations
2025-02-17
Andrea Apicella, Salvatore Giugliano, Francesco Isgrò, Roberto Prevete
The eXplainable Artificial Intelligence (XAI) research predominantly concentrates to provide explainations about AI model decisions, especially Deep Learning (DL) models. However, there is a growing interest in using XAI techniques to automatically improve the performance of the AI systems themselves. This paper proposes IMPACTX, a novel approach that leverages XAI as a fully automated attention mechanism, without requiring external knowledge or human feedback. Experimental results show that IMPACTX has improved performance respect to the standalone ML model by integrating an attention mechanism based an XAI method outputs during the model training. Furthermore, IMPACTX directly provides proper feature attribution maps for the model's decisions, without relying on external XAI methods during the inference process. Our proposal is evaluated using three widely recognized DL models (EfficientNet-B2, MobileNet, and LeNet-5) along with three standard image datasets: CIFAR-10, CIFAR-100, and STL-10. The results show that IMPACTX consistently improves the performance of all the inspected DL models across all evaluated datasets, and it directly provides appropriate explanations for its responses.
Large Language Model (LLM) agents have become increasingly prevalent across various real-world applications. They enhance decision-making by storing private user-agent interactions in the memory module for demonstrations, introducing new privacy risks for LLM agents. In this work, we systematically investigate the vulnerability of LLM agents to our proposed Memory EXTRaction Attack (MEXTRA) under a black-box setting. To extract private information from memory, we propose an effective attacking prompt design and an automated prompt generation method based on different levels of knowledge about the LLM agent. Experiments on two representative agents demonstrate the effectiveness of MEXTRA. Moreover, we explore key factors influencing memory leakage from both the agent's and the attacker's perspectives. Our findings highlight the urgent need for effective memory safeguards in LLM agent design and deployment.
Vulnerabilities in open-source operating systems (OSs) pose substantial security risks to software systems, making their detection crucial. While fuzzing has been an effective vulnerability detection technique in various domains, OS fuzzing (OSF) faces unique challenges due to OS complexity and multi-layered interaction, and has not been comprehensively reviewed. Therefore, this work systematically surveys the state-of-the-art OSF techniques, categorizes them based on the general fuzzing process, and investigates challenges specific to kernel, file system, driver, and hypervisor fuzzing. Finally, future research directions for OSF are discussed.
Microscopic contact line dynamics dictate the emergent behaviors of particle rafts
2025-02-16
Ranit Mukherjee, Zih-Yin Chen, Xiang Cheng, Sungyon Lee
Fluid-fluid interfaces laden with discrete particles behave curiously like continuous elastic sheets, leading to their applications in emulsion and foam stabilization. Although existing continuum models can qualitatively capture the elastic buckling of these particle-laden interfaces -- often referred to as particle rafts -- under compression, they fail to link their macroscopic collective properties to the microscopic behaviors of individual particles. Thus, phenomena such as particle expulsion from the compressed rafts remain unexplained. Here, by combining systematic experiments with first-principle modeling, we reveal how the macroscopic mechanical properties of particle rafts emerge from particle-scale interactions. We construct a phase diagram that delineates the conditions under which a particle raft collapses via collective folding versus single-particle expulsion. Guided by this theoretical framework, we demonstrate control over the raft's failure mode by tuning the physicochemical properties of individual particles. Our study highlights the previously overlooked dual nature of particle rafts and exemplifies how collective dynamics can arise from discrete components with simple interactions.
Knowing the truth is rarely enough -- we also seek out reasons why the fact is true. While much is known about how we explain contingent truths, we understand less about how we explain facts, such as those in mathematics, that are true as a matter of logical necessity. We present a framework, based in computational complexity, where explanations for deductive truths co-emerge with discoveries of simplifying steps during the search process. When such structures are missing, we revert, in turn, to error-based reasons, where a (corrected) mistake can serve as fictitious, but explanatory, contingency-cause: not making the mistake serves as a reason why the truth takes the form it does. We simulate human subjects, using GPT-4o, presented with SAT puzzles of varying complexity and reasonableness, validating our theory and showing how its predictions can be tested in future human studies.
LLMs and Childhood Safety: Identifying Risks and Proposing a Protection Framework for Safe Child-LLM Interaction
2025-02-16
Junfeng Jiao, Saleh Afroogh, Kevin Chen, Abhejay Murali, David Atkinson, Amit Dhurandhar
This study examines the growing use of Large Language Models (LLMs) in child-centered applications, highlighting safety and ethical concerns such as bias, harmful content, and cultural insensitivity. Despite their potential to enhance learning, there is a lack of standardized frameworks to mitigate these risks. Through a systematic literature review, we identify key parental and empirical concerns, including toxicity and ethical breaches in AI outputs. Moreover, to address these issues, this paper proposes a protection framework for safe Child-LLM interaction, incorporating metrics for content safety, behavioral ethics, and cultural sensitivity. The framework provides practical tools for evaluating LLM safety, offering guidance for developers, policymakers, and educators to ensure responsible AI deployment for children.
Teaching Well-Structured Code: A Literature Review of Instructional Approaches
2025-02-16
Sara Nurollahian, Hieke Keuning, Eliane Wiese
Teaching the software engineers of the future to write high-quality code with good style and structure is important. This systematic literature review identifies existing instructional approaches, their objectives, and the strategies used for measuring their effectiveness. Building on an existing mapping study of code quality in education, we identified 53 papers on code structure instruction. We classified these studies into three categories: (1) studies focused on developing or evaluating automated tools and their usage (e.g., code analyzers, tutors, and refactoring tools), (2) studies discussing other instructional materials, such as learning resources (e.g., refactoring lessons and activities), rubrics, and catalogs of violations, and (3) studies discussing how to integrate code structure into the curriculum through a holistic approach to course design to support code quality. While most approaches use analyzers that point students to problems in their code, incorporating these tools into classrooms is not straightforward. Combined with further research on code structure instruction in the classroom, we call for more studies on effectiveness. Over 40% of instructional studies had no evaluation. Many studies show promise for their interventions by demonstrating improvement in student performance (e.g., reduced violations in student code when using the intervention compared with code that was written without access to the intervention). These interventions warrant further investigation on learning, to see how students apply their knowledge after the instructional supports are removed.
LLMs have immense potential for generating plans, transforming an initial world state into a desired goal state. A large body of research has explored the use of LLMs for various planning tasks, from web navigation to travel planning and database querying. However, many of these systems are tailored to specific problems, making it challenging to compare them or determine the best approach for new tasks. There is also a lack of clear and consistent evaluation criteria. Our survey aims to offer a comprehensive overview of current LLM planners to fill this gap. It builds on foundational work by Kartam and Wilkins (1990) and examines six key performance criteria: completeness, executability, optimality, representation, generalization, and efficiency. For each, we provide a thorough analysis of representative works and highlight their strengths and weaknesses. Our paper also identifies crucial future directions, making it a valuable resource for both practitioners and newcomers interested in leveraging LLM planning to support agentic workflows.
Large Language Models (LLMs) are transforming healthcare through the development of LLM-based agents that can understand, reason about, and assist with medical tasks. This survey provides a comprehensive review of LLM-based agents in medicine, examining their architectures, applications, and challenges. We analyze the key components of medical agent systems, including system profiles, clinical planning mechanisms, medical reasoning frameworks, and external capacity enhancement. The survey covers major application scenarios such as clinical decision support, medical documentation, training simulations, and healthcare service optimization. We discuss evaluation frameworks and metrics used to assess these agents' performance in healthcare settings. While LLM-based agents show promise in enhancing healthcare delivery, several challenges remain, including hallucination management, multimodal integration, implementation barriers, and ethical considerations. The survey concludes by highlighting future research directions, including advances in medical reasoning inspired by recent developments in LLM architectures, integration with physical systems, and improvements in training simulations. This work provides researchers and practitioners with a structured overview of the current state and future prospects of LLM-based agents in medicine.
Housing and household characteristics are key determinants of social and economic well-being, yet our understanding of their interrelationships remains limited. This study addresses this knowledge gap by developing a deep contrastive learning (DCL) model to infer housing-household relationships using the American Community Survey (ACS) Public Use Microdata Sample (PUMS). More broadly, the proposed model is suitable for a class of problems where the goal is to learn joint relationships between two distinct entities without explicitly labeled ground truth data. Our proposed dual-encoder DCL approach leverages co-occurrence patterns in PUMS and introduces a bisect K-means clustering method to overcome the absence of ground truth labels. The dual-encoder DCL architecture is designed to handle the semantic differences between housing (building) and household (people) features while mitigating noise introduced by clustering. To validate the model, we generate a synthetic ground truth dataset and conduct comprehensive evaluations. The model further demonstrates its superior performance in capturing housing-household relationships in Delaware compared to state-of-the-art methods. A transferability test in North Carolina confirms its generalizability across diverse sociodemographic and geographic contexts. Finally, the post-hoc explainable AI analysis using SHAP values reveals that tenure status and mortgage information play a more significant role in housing-household matching than traditionally emphasized factors such as the number of persons and rooms.
Large Language Models Penetration in Scholarly Writing and Peer Review
2025-02-16
Li Zhou, Ruijie Zhang, Xunlian Dai, Daniel Hershcovich, Haizhou Li
While the widespread use of Large Language Models (LLMs) brings convenience, it also raises concerns about the credibility of academic research and scholarly processes. To better understand these dynamics, we evaluate the penetration of LLMs across academic workflows from multiple perspectives and dimensions, providing compelling evidence of their growing influence. We propose a framework with two components: \texttt{ScholarLens}, a curated dataset of human- and LLM-generated content across scholarly writing and peer review for multi-perspective evaluation, and \texttt{LLMetrica}, a tool for assessing LLM penetration using rule-based metrics and model-based detectors for multi-dimensional evaluation. Our experiments demonstrate the effectiveness of \texttt{LLMetrica}, revealing the increasing role of LLMs in scholarly processes. These findings emphasize the need for transparency, accountability, and ethical practices in LLM usage to maintain academic credibility.
On the Power of Randomization for Obviously Strategy-Proof Mechanisms
2025-02-16
Shiri Ron, Daniel Schoepflin
We investigate the problem of designing randomized obviously strategy-proof (OSP) mechanisms in several canonical auction settings. Obvious strategy-proofness, introduced by Li [American Economic Review, 2017], strengthens the well-known concept of dominant-strategy incentive compatibility (DSIC). Loosely speaking, it ensures that even agents who struggle with contingent reasoning can identify that their dominant strategy is optimal. Thus, one would hope to design OSP mechanisms with good approximation guarantees. Unfortunately, Ron [SODA,2024] has shown that deterministic OSP mechanisms fail to achieve an approximation better than $\min\{m,n\}$ where $m$ is the number of items and $n$ is the number of bidders, even for the simple settings of additive and unit-demand bidders. We circumvent these impossibilities by showing that randomized mechanisms that are obviously strategy-proof in the universal sense obtain a constant factor approximation for these classes. We show that this phenomenon occurs also for the setting of a multi-unit auction with single-minded bidders. Thus, our results provide a more positive outlook on the design of OSP mechanisms and exhibit a stark separation between the power of randomized and deterministic OSP mechanisms. To complement the picture, we provide impossibilities for randomized OSP mechanisms in each setting. While the deterministic VCG mechanism is well known to output an optimal allocation in dominant strategies, we show that even randomized OSP mechanisms cannot obtain more than $87.5\%$ of the optimal welfare. This further demonstrates that OSP mechanisms are significantly weaker than dominant-strategy mechanisms.
Machine Learning-Based Intrusion Detection and Prevention System for IIoT Smart Metering Networks: Challenges and Solutions
2025-02-16
Sahar Lazim, Qutaiba I. Ali
The Industrial Internet of Things (IIoT) has revolutionized industries by enabling automation, real-time data exchange, and smart decision-making. However, its increased connectivity introduces cybersecurity threats, particularly in smart metering networks, which play a crucial role in monitoring and optimizing energy consumption. This paper explores the challenges associated with securing IIoT-based smart metering networks and proposes a Machine Learning (ML)-based Intrusion Detection and Prevention System (IDPS) for safeguarding edge devices. The study reviews various intrusion detection approaches, highlighting the strengths and limitations of both signature-based and anomaly-based detection techniques. The findings suggest that integrating ML-driven IDPS in IIoT smart metering environments enhances security, efficiency, and resilience against evolving cyber threats.
A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions
2025-02-16
Hongbin Na, Yining Hua, Zimu Wang, Tao Shen, Beibei Yu, Lilin Wang, Wei Wang, John Torous, Ling Chen
Mental health remains a critical global challenge, with increasing demand for accessible, effective interventions. Large language models (LLMs) offer promising solutions in psychotherapy by enhancing the assessment, diagnosis, and treatment of mental health conditions through dynamic, context-aware interactions. This survey provides a comprehensive overview of the current landscape of LLM applications in psychotherapy, highlighting the roles of LLMs in symptom detection, severity estimation, cognitive assessment, and therapeutic interventions. We present a novel conceptual taxonomy to organize the psychotherapy process into three core components: assessment, diagnosis, and treatment, and examine the challenges and advancements in each area. The survey also addresses key research gaps, including linguistic biases, limited disorder coverage, and underrepresented therapeutic models. Finally, we discuss future directions to integrate LLMs into a holistic, end-to-end psychotherapy framework, addressing the evolving nature of mental health conditions and fostering more inclusive, personalized care.
In the highly interconnected digital landscape of today, safeguarding complex infrastructures against cyber threats has become increasingly challenging due to the exponential growth in the number and complexity of vulnerabilities. Resource constraints necessitate effective vulnerability prioritization strategies, focusing efforts on the most critical risks. This paper presents a systematic literature review of 82 studies, introducing a novel taxonomy that categorizes metrics into severity, exploitability, contextual factors, predictive indicators, and aggregation methods. Our analysis reveals significant gaps in existing approaches and challenges with multi-domain applicability. By emphasizing the need for dynamic, context-aware metrics and scalable solutions, we provide actionable insights to bridge the gap between research and real-world applications. This work contributes to the field by offering a comprehensive framework for evaluating vulnerability prioritization methodologies and setting a research agenda to advance the state of practice.
Factors influencing the perceived usability of mobile applications
2025-02-16
Pawel Weichbroth
The advent of mobile applications has brought new frontiers to usability studies. So far, the ongoing research has undertaken considerable efforts to model usability in such new challenging context. One of these endeavors is the PACMAD+3 model, which consists of a total of ten unique factors. However, to the best of our knowledge, little or no effort has been made to empirically evaluate these factors against perceived influence. With this in mind, the objective of this study is to explore this issue by evaluating the selected factors. To achieve this goal in a reliable and reproducible manner, we took advantage of previous attempts to conceptualize the mobile usability factors, but we contribute by operationalizing these theoretical constructs into observable and measurable phenomena. In this sense, the survey was designed and carried out on the sample of 838 users in order to evaluate the significance of the PACMAD+3 factors on the perceived usability of mobile applications. Our findings show that, on average, users rated efficiency as highly important, while the remaining seven, namely: cognitive load, errors, learnability, operability, effectiveness, memorability, and understandability, were rated as moderately important. The discussed results provide insight into the importance of usability attributes and quality criteria from both perspectives, ultimately facilitating and securing the design and development of mobile applications. Therefore, our research contributes to the field of human-computer interaction, with both theoretical and practical implications for mobile usability researchers, UX designers, and quality assurance engineers.
Active feature acquisition studies the challenge of making accurate predictions while limiting the cost of collecting complete data. By selectively acquiring only the most informative features for each instance, these strategies enable efficient decision-making in scenarios where data collection is expensive or time-consuming. This survey reviews recent progress in active feature acquisition, discussing common problem formulations, practical challenges, and key insights. We also highlight open issues and promising directions for future research.
Demystifying 5G Polar and LDPC Codes: A Comprehensive Review and Foundations
2025-02-16
Mody Sy
This paper serves as a comprehensive guide for practitioners and scholars aiming to understand the channel coding and decoding schemes integral to the 5G NR standard, with a particular focus on LDPC and polar codes. We start by explaining the design procedures that underlie these channel codes, offering fundamental information from perspectives of both encoding and decoding. In order to determine the present status of research in this area, we also provide a thorough literature review. Notably, we add comprehensive, standard-specific information to these foundational evaluations that is frequently difficult to extract from technical specification documents.
A Survey: Potential Dimensionality Reduction Methods
2025-02-16
Yuan-chin Ivan Chang
Dimensionality reduction is a fundamental technique in machine learning and data analysis, enabling efficient representation and visualization of high-dimensional data. This paper explores five key methods: Principal Component Analysis (PCA), Kernel PCA (KPCA), Sparse Kernel PCA, t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). PCA provides a linear approach to capturing variance, whereas KPCA and Sparse KPCA extend this concept to non-linear structures using kernel functions. Meanwhile, t-SNE and UMAP focus on preserving local relationships, making them effective for data visualization. Each method is examined in terms of its mathematical formulation, computational complexity, strengths, and limitations. The trade-offs between global structure preservation, computational efficiency, and interpretability are discussed to guide practitioners in selecting the appropriate technique based on their application needs.
Exact variance estimation for model-assisted survey estimators using U- and V-statistics
2025-02-16
Ameer Dharamshi, Peter Gao, Jon Wakefield
Model-assisted estimation combines sample survey data with auxiliary information to increase precision when estimating finite population quantities. Accurately estimating the variance of model-assisted estimators is challenging: the classical approach ignores uncertainty from estimating the working model for the functional relationship between survey and auxiliary variables. This approach may be asymptotically valid, but can underestimate variance in practical settings with limited sample sizes. In this work, we develop a connection between model-assisted estimation and the theory of U- and V-statistics. We demonstrate that when predictions from the working model for the variable of interest can be represented as a U- or V-statistic, the resulting model-assisted estimator also admits a U- or V-statistic representation. We exploit this connection to derive an improved estimator of the exact variance of such model-assisted estimators. The class of working models for which this strategy can be used is broad, ranging from linear models to modern ensemble methods. We apply our approach to the model-assisted estimator constructed with a linear regression working model, commonly referred to as the generalized regression estimator, show that it can be re-written as a U-statistic, and propose an estimator of its exact variance. We illustrate our proposal and compare it against the classical asymptotic variance estimator using household survey data from the American Community Survey.
Bias issues of neural networks garner significant attention along with its promising advancement. Among various bias issues, mitigating two predominant biases is crucial in advancing fair and trustworthy AI: (1) ensuring neural networks yields even performance across demographic groups, and (2) ensuring algorithmic decision-making does not rely on protected attributes. However, upon the investigation of \pc papers in the relevant literature, we find that there exists a persistent, extensive but under-explored confusion regarding these two types of biases. Furthermore, the confusion has already significantly hampered the clarity of the community and subsequent development of debiasing methodologies. Thus, in this work, we aim to restore clarity by providing two mathematical definitions for these two predominant biases and leveraging these definitions to unify a comprehensive list of papers. Next, we highlight the common phenomena and the possible reasons for the existing confusion. To alleviate the confusion, we provide extensive experiments on synthetic, census, and image datasets, to validate the distinct nature of these biases, distinguish their different real-world manifestations, and evaluate the effectiveness of a comprehensive list of bias assessment metrics in assessing the mitigation of these biases. Further, we compare these two types of biases from multiple dimensions including the underlying causes, debiasing methods, evaluation protocol, prevalent datasets, and future directions. Last, we provide several suggestions aiming to guide researchers engaged in bias-related work to avoid confusion and further enhance clarity in the community.
RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation
2025-02-16
Pengcheng Jiang, Lang Cao, Ruike Zhu, Minhao Jiang, Yunyi Zhang, Jimeng Sun, Jiawei Han
Retrieval-augmented language models often struggle with knowledge-intensive tasks due to inefficient retrieval, unstructured knowledge integration, and single-pass architectures. We present Retrieval-And-Structuring (RAS), a novel framework that dynamically constructs and reasons over query-specific knowledge graphs through iterative retrieval and structuring. RAS introduces four key technical innovations: (1) a themescoped retrieval mechanism that efficiently narrows the search space while maintaining retrieval quality, (2) an action planning module that determines knowledge needs and generates focused sub-queries, (3) a dynamic knowledge structuring approach that converts retrieved text into an evolving knowledge graph, and (4) a graph-augmented answering component that leverages the accumulated structured information. Our framework achieves state-of-the-art performance, surpassing leading baselines by 6.4% with open-source language models and 7.0% with proprietary models on seven knowledge-intensive generation datasets across all evaluation metrics. Detailed ablation studies verify the contribution of each technical component to the overall system performance.
Evaluating Large language models on Understanding Korean indirect Speech acts
2025-02-16
Youngeun Koo, Jiwoo Lee, Dojun Park, Seohyun Park, Sungeun Lee
To accurately understand the intention of an utterance is crucial in conversational communication. As conversational artificial intelligence models are rapidly being developed and applied in various fields, it is important to evaluate the LLMs' capabilities of understanding the intentions of user's utterance. This study evaluates whether current LLMs can understand the intention of an utterance by considering the given conversational context, particularly in cases where the actual intention differs from the surface-leveled, literal intention of the sentence, i.e. indirect speech acts. Our findings reveal that Claude3-Opus outperformed the other competing models, with 71.94% in MCQ and 65% in OEQ, showing a clear advantage. In general, proprietary models exhibited relatively higher performance compared to open-source models. Nevertheless, no LLMs reached the level of human performance. Most LLMs, except for Claude3-Opus, demonstrated significantly lower performance in understanding indirect speech acts compared to direct speech acts, where the intention is explicitly revealed through the utterance. This study not only performs an overall pragmatic evaluation of each LLM's language use through the analysis of OEQ response patterns, but also emphasizes the necessity for further research to improve LLMs' understanding of indirect speech acts for more natural communication with humans.
We conducted precise measurements of Active Galactic Nuclei (AGNs) clustering at $z\sim1$ and $z\sim2$ by measuring the two-point cross-correlation function (CCF) between galaxies and X-ray-selected AGNs, and the two-point auto-correlation function (ACF) of galaxies in the COSMOS field to interpret the CCF results. The galaxy sample was selected from the COSMOS2015 catalog, while the AGN sample was chosen from the {\sl Chandra} COSMOS-Legacy survey catalog. For the AGN samples at $z\sim1$ and $z\sim2$, we calculated AGN bias values of $b=1.16\ (1.16;1.31)$ and $b=2.95\ (2.30;3.55)$, respectively. These values correspond to typical host dark matter halo (DMH) masses of log$(M_{\rm typ}/M_{\odot})=11.82\ (11.82;12.12)$ and log$(M_{\rm typ}/M_{\odot})=12.80\ (12.38;13.06)$, respectively. Subsequently, we performed Halo Occupation Distribution (HOD) modeling of X-ray-selected AGNs using the CCF and ACF of galaxies. We have found a significant satellite AGN population at $z\sim 1$ all over the DMH mass ($M_{\rm DMH}$) range occupied by AGNs. While $z\sim 2$ AGNs in our sample are associated with higher mass DMHs and smaller satellite fractions. The HOD analysis suggests a marginal tendency of increasing satellite slope with redshift, but larger samples are needed to confirm this with sufficient statistical significance. We find that the best-fit values of satellite slope in both redshift bins are greater than 0, suggesting tendencies of increasing satellite AGN number with $M_{\rm DMH}$.
The Impact of Bars, Spirals and Bulge-Size on Gas-Phase Metallicity Gradients in MaNGA Galaxies
2025-02-15
M. E. Wisz, Karen L. Masters, Kathryne J. Daniel, David V. Stark, Francesco Belfiore
As galaxies evolve over time, the orbits of their constituent stars are expected to change in size and shape, moving stars away from their birth radius. Radial gas flows are also expected. Spiral arms and bars in galaxies are predicted to help drive this radial relocation, which may be possible to trace observationally via a flattening of metallicity gradients. We use data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, part of the fourth phase of the Sloan Digital Sky Surveys (SDSS-IV), to look for correlations of the steepness of gas-phase metallicity gradients with various galaxy morphological features (e.g. presence and pitch angle of spiral arms, presence of a large scale bar, bulge size). We select from MaNGA a sample of star forming galaxies for which gas phase metallicity trends can be measured, and use morphologies from Galaxy Zoo. We observe that at fixed galaxy mass (1) the presence of spiral structure correlates with steeper gas phase metallicity gradients; (2) spiral galaxies with larger bulges have both higher gas-phase metallicities and shallower gradients; (3) there is no observable difference with azimuthally averaged radial gradients between barred and unbarred spirals and (4) there is no observable difference in gradient between tight and loosely wound spirals, but looser wound spirals have lower average gas-phase metallicity values at fixed mass. We discuss the possible implications of these observational results.
LLM-driven Knowledge Distillation for Dynamic Text-Attributed Graphs
2025-02-15
Amit Roy, Ning Yan, Masood Mortazavi
Dynamic Text-Attributed Graphs (DyTAGs) have numerous real-world applications, e.g. social, collaboration, citation, communication, and review networks. In these networks, nodes and edges often contain text descriptions, and the graph structure can evolve over time. Future link prediction, edge classification, relation generation, and other downstream tasks on DyTAGs require powerful representations that encode structural, temporal, and textual information. Although graph neural networks (GNNs) excel at handling structured data, encoding temporal information within dynamic graphs remains a significant challenge. In this work, we propose LLM-driven Knowledge Distillation for Dynamic Text Attributed Graph (LKD4DyTAG) with temporal encoding to address these challenges. We use a simple, yet effective approach to encode temporal information in edges so that graph convolution can simultaneously capture both temporal and structural information in the hidden representations. To leverage LLM's text processing capabilities for learning richer representations on DyTAGs, we distill knowledge from LLM-driven edge representations (based on a neighborhood's text attributes) into saptio-temporal representations using a lightweight GNN model that encodes temporal and structural information. The objective of knowledge distillation enables the GNN to learn representations that more effectively encode the available structural, temporal, and textual information in DyTAG. We conducted extensive experimentation on six real-world DyTAG datasets to verify the effectiveness of our approach LKD4DyTAG for future link prediction and edge classification task. The results show that our approach significantly improves the performance of downstream tasks compared to the baseline models.
On the fluctuations of the number of atoms in the condensate
2025-02-15
Maciej B. Kruk, Piotr Kulik, Malthe F. Andersen, Piotr Deuar, Mariusz Gajda, Krzysztof Pawłowski, Emilia Witkowska, Jan J. Arlt, Kazimierz Rzążewski
Bose-Einstein condensation represents a remarkable phase transition, characterized by the formation of a single quantum subsystem. As a result, the statistical properties of the condensate are highly unique. In the case of a Bose gas, while the mean number of condensed atoms is independent of the choice of statistical ensemble, the microcanonical, canonical, or grand canonical variances differ significantly among these ensembles. In this paper, we review the progress made over the past 30 years in studying the statistical fluctuations of Bose-Einstein condensates. Focusing primarily on the ideal Bose gas, we emphasize the inequivalence of the Gibbs statistical ensembles and examine various approaches to this problem. These approaches include explicit analytic results for primarily one-dimensional systems, methods based on recurrence relations, asymptotic results for large numbers of particles, techniques derived from laser theory, and methods involving the construction of statistical ensembles via stochastic processes, such as the Metropolis algorithm. We also discuss the less thoroughly resolved problem of the statistical behavior of weakly interacting Bose gases. In particular, we elaborate on our stochastic approach, known as the hybrid sampling method. The experimental aspect of this field has gained renewed interest, especially following groundbreaking recent measurements of condensate fluctuations. These advancements were enabled by unprecedented control over the total number of atoms in each experimental realization. Additionally, we discuss the fluctuations in photonic condensates as an illustrative example of grand canonical fluctuations. Finally, we briefly consider the future directions for research in the field of condensate statistics.
Prospects for supersymmetry at high luminosity LHC
2025-02-15
Howard Baer, Vernon Barger, Jessica Bolich, Juhi Dutta, Dakotah Martinez, Shadman Salam, Dibyashree Sengupta, Kairui Zhang
Weak scale supersymmetry (SUSY) is highly motivated in that it provides a 't Hooft technically natural solution to the gauge hierarchy problem. However, recent strong limits from superparticle searches at LHC Run 2 may exacerbate a so-called Little Hierarchy problem (LHP) which is a matter of practical naturalness: why is m_{weak}<< m_{soft}? We review recent LHC and WIMP dark matter search bounds as well as their impact on a variety of proposed SUSY models: gravity-, gauge-, anomaly-, mirage- and gaugino-mediation along with some dark matter proposals such as well-tempered neutralinos. We address the naturalness question. We also address the emergence of the string landscape at the beginning of the 21st century and its impact on expectations for SUSY. Rather generally, the string landscape statistically prefers large soft SUSY breaking terms but subject to the anthropic requirement that the derived value of the weak scale for each pocket universe (PU) within the greater multiverse lies with the ABDS window of values. This {\it stringy natural} (SN) approach implies m_h~ 125 GeV more often than not with sparticles beyond or well-beyond present LHC search limits. We review detailed reach calculations of the high-lumi LHC (HL-LHC) for non-universal Higgs mass models which present perhaps the most plausible realization of SUSY from the string landscape. In contrast to conventional wisdom, from a stringy naturalness point of view, the search for SUSY at LHC has only just begun to explore the interesting regimes of parameter space. We comment on how non-universal Higgs models could be differentiated from other expressions of natural SUSY such as natural anomaly-mediation and natural mirage mediation at HL-LHC.
Multiple Approaches for Teaching Responsible Computing
2025-02-15
Stacy A. Doore, Michelle Trim, Joycelyn Streator, Richard L. Blumenthal, Atri Rudra, Robert B. Schnabel
Teaching applied ethics in computer science has shifted from a perspective of teaching about professional codes of conduct and an emphasis on risk management towards a broader understanding of the impacts of computing on humanity and the environment and the principles and practices of responsible computing. One of the primary shifts in the approach to teaching computing ethics comes from research in the social sciences and humanities. This position is grounded in the idea that all computing artifacts, projects, tools, and products are situated within a set of ideas, attitudes, goals, and cultural norms. This means that all computing endeavors have embedded within them a set of values. To teach responsible computing always requires us to first recognize that computing happens in a context that is shaped by cultural values, including our own professional culture and values. The purpose of this paper is to highlight current scholarship, principles, and practices in the teaching of responsible computing in undergraduate computer science settings. The paper is organized around four primary sections: 1) a high-level rationale for the adoption of different pedagogical approaches based on program context and course learning goals, 2) a brief survey of responsible computing pedagogical approaches; 3) illustrative examples of how topics within the CS 2023 Social, Ethical, and Professional (SEP) knowledge area can be implemented and assessed across the broad spectrum of undergraduate computing courses; and 4) links to examples of current best practices, tools, and resources for faculty to build responsible computing teaching into their specific instructional settings and CS2023 knowledge areas.
Hard X-ray/Soft gamma-ray Laue Lenses for High Energy Astrophysics
2025-02-15
Filippo Frontera
The study of the celestial phenomena in the hard X-ray/soft gamma-ray band(20 keV--1 MeV) is very intriguing but also very difficult to be performed with the needed sensitivity. In this review I will discuss the astrophysical importance of the soft gamma-ray astronomy, its difficulties to solve its issues with the current instrumentation, and a possible solution achievable using focusing Laue lens. Concerning these instruments, I will discuss their functioning principle, how to achieve a high reflection efficiency, their imaging properties, the current feasibility studies, the technological developments and observation prospects.
MITRE ATT&CK Applications in Cybersecurity and The Way Forward
2025-02-15
Yuning Jiang, Qiaoran Meng, Feiyang Shang, Nay Oo, Le Thi Hong Minh, Hoon Wei Lim, Biplab Sikdar
The MITRE ATT&CK framework is a widely adopted tool for enhancing cybersecurity, supporting threat intelligence, incident response, attack modeling, and vulnerability prioritization. This paper synthesizes research on its application across these domains by analyzing 417 peer-reviewed publications. We identify commonly used adversarial tactics, techniques, and procedures (TTPs) and examine the integration of natural language processing (NLP) and machine learning (ML) with ATT&CK to improve threat detection and response. Additionally, we explore the interoperability of ATT&CK with other frameworks, such as the Cyber Kill Chain, NIST guidelines, and STRIDE, highlighting its versatility. The paper further evaluates the framework from multiple perspectives, including its effectiveness, validation methods, and sector-specific challenges, particularly in industrial control systems (ICS) and healthcare. We conclude by discussing current limitations and proposing future research directions to enhance the applicability of ATT&CK in dynamic cybersecurity environments.
Metal Ions based Dynamic Nuclear Polarization: MI-DNP
2025-02-15
Daniel Jardon- Alvarez, Michal Leskes
Over the last two decades magic angle spinning dynamic nuclear polarization (MAS DNP) has revolutionized NMR for materials characterization, tackling its main limitation of intrinsically low sensitivity. Progress in theoretical understanding, instrumentation, and sample formulation expanded the range of materials applications and research questions that can benefit from MAS DNP. Currently the most common approach for hyperpolarization under MAS consists in impregnating the sample of interest with a solution containing nitroxide radicals, which upon microwave irradiation serve as exogenous polarizing agents. On the other hand, in metal ion based (MI)-DNP, inorganic materials are doped with paramagnetic metal centres, which then can be used as endogenous polarizing agents. In this work we give an overview of the electron paramagnetic resonance (EPR) concepts required to characterize the metal ions and discuss the expected changes in the NMR response due to the presence of paramagnetic species. We highlight which properties of the electron spins are beneficial for applications as polarizing agents in DNP and how to recognize them, both from the EPR and NMR data. A theoretical description of the main DNP mechanisms is given, employing a quantum mechanical formalism, and these concepts are used to explain the spin dynamics observed in the DNP experiment. In addition, we highlight the main differences between MI-DNP and the more common approaches in MAS DNP, which use organic radicals as exogenous polarizing source. Finally, we review some applications of metal ions as polarizing agents in general and then focus particularly on research questions in materials science that can benefit from MI-DNP.
Sensing With Communication Signals: From Information Theory to Signal Processing
2025-02-15
Fan Liu, Ya-Feng Liu, Yuanhao Cui, Christos Masouros, Jie Xu, Tony Xiao Han, Stefano Buzzi, Yonina C. Eldar, Shi Jin
The Integrated Sensing and Communications (ISAC) paradigm is anticipated to be a cornerstone of the upcoming 6G networks. In order to optimize the use of wireless resources, 6G ISAC systems need to harness the communication data payload signals, which are inherently random, for both sensing and communication (S&C) purposes. This tutorial paper provides a comprehensive technical overview of the fundamental theory and signal processing methodologies for ISAC transmission with random communication signals. We begin by introducing the deterministic-random tradeoff (DRT) between S&C from an information-theoretic perspective, emphasizing the need for specialized signal processing techniques tailored to random ISAC signals. Building on this foundation, we review the core signal models and processing pipelines for communication-centric ISAC systems, and analyze the average squared auto-correlation function (ACF) of random ISAC signals, which serves as a fundamental performance metric for multi-target ranging tasks. Drawing insights from these theoretical results, we outline the design principles for the three key components of communication-centric ISAC systems: modulation schemes, constellation design, and pulse shaping filters. The goal is to either enhance sensing performance without compromising communication efficiency or to establish a scalable tradeoff between the two. We then extend our analysis from a single-antenna ISAC system to its multi-antenna counterpart, discussing recent advancements in multi-input multi-output (MIMO) precoding techniques specifically designed for random ISAC signals. We conclude by highlighting several open challenges and future research directions in the field of sensing with communication signals.
Multimodal learning has gained attention for its capacity to integrate information from different modalities. However, it is often hindered by the multimodal imbalance problem, where certain modality dominates while others remain underutilized. Although recent studies have proposed various methods to alleviate this problem, they lack comprehensive and fair comparisons. In this paper, we systematically categorize various mainstream multimodal imbalance algorithms into four groups based on the strategies they employ to mitigate imbalance. To facilitate a comprehensive evaluation of these methods, we introduce BalanceBenchmark, a benchmark including multiple widely used multidimensional datasets and evaluation metrics from three perspectives: performance, imbalance degree, and complexity. To ensure fair comparisons, we have developed a modular and extensible toolkit that standardizes the experimental workflow across different methods. Based on the experiments using BalanceBenchmark, we have identified several key insights into the characteristics and advantages of different method groups in terms of performance, balance degree and computational complexity. We expect such analysis could inspire more efficient approaches to address the imbalance problem in the future, as well as foundation models. The code of the toolkit is available at https://github.com/GeWu-Lab/BalanceBenchmark.
First measurement of 87Rb(α, xn) cross sections at weak r-process energies in supernova ν-driven ejecta to investigate elemental abundances in low-metallicity stars
2025-02-15
C. Fougères, M. L. Avila, A. Psaltis, M. Anastasiou, S. Bae, L. Balliet, K. Bhatt, L. Dienis, H. Jayatissa, V. Karayonchev, P. Mohr, F. Montes, D. Neto, F. de Oliveira Santos, W. -J. Ong, K. E. Rehm, W. Reviol, D. Santiago-Gonzalez, N. Sensharma, R. S. Sidhu, I. A. Tolstukhin
Observed abundances of Z ~ 40 elements in metal-poor stars vary from star to star, indicating that the rapid and slow neutron capture processes may not contribute alone to the synthesis of elements beyond iron. The weak r-process was proposed to produce Z ~ 40 elements in a subset of old stars. Thought to occur in the {\nu}-driven ejecta of a core-collapse supernova, ({\alpha}, xn) reactions would drive the nuclear flow toward heavier masses at T = 2-5 GK. However, current comparisons between modelled and observed yields do not bring satisfactory insights into the stellar environment, mainly due to the uncertainties of the nuclear physics inputs where the dispersion in a given reaction rate often exceeds one order of magnitude. Involved rates are calculated with the statistical model where the choice of an {\alpha}-optical-model potential ({\alpha}OMP) leads to such a poor precision. The first experiment on 87Rb({\alpha}, xn) reactions at weak r-process energies is reported here. Total inclusive cross sections were assessed at Ec.m. = 8.1 - 13 MeV (3.7 - 7.6 GK) with the active target MUlti-Sampling Ionization Chamber (MUSIC). With a N = 50 seed nucleus, the measured values agree with statistical model estimates using the {\alpha}OMP Atomki-V2. A re-evaluated reaction rate was incorporated into new nucleosynthesis calculations, focusing on {\nu}-driven ejecta conditions known to be sensitive to this specific rate. These conditions were found to fail to reproduce the lighter-heavy element abundances in metal-poor stars.
NPA Hierarchy and Extremal Criterion in the Simplest Bell Scenario
2025-02-15
Satoshi Ishizaka
It is difficult to establish an analytical criterion to identify the boundaries of quantum correlations, even for the simplest Bell scenario. Here, we briefly reviewed the plausible analytical criterion, and we found a way to confirm the extremal conditions from another direction. For that purpose, we analyzed the Navascu\'es-Pironio-Ac\'{\i}n (NPA) hierarchy to study the algebraic structure and found that the problem could not be simplified using $1\!+\!AB$ level. However, considering the plausible criterion, the $1\!+\!AB$ and second levels for correlations were equal, and the extremal condition in the simplest Bell scenario was replaced by that in the $1\!+\!AB$ level. Thus, the correctness of the plausible criterion was verified, and the results demonstrated that the plausible criterion held, thereby explaining its simplicity. It seemed plausible, but now it becomes more certain.
Robust Estimation of Item Parameters via Divergence Measures in Item Response Theory
2025-02-15
Yuki Itaya, Kenichi Hayashi
Marginal maximum likelihood estimation (MMLE) in item response theory (IRT) is highly sensitive to aberrant responses, such as careless answering and random guessing, which can reduce estimation accuracy. To address this issue, this study introduces robust estimation methods for item parameters in IRT. Instead of empirically minimizing Kullback--Leibler divergence as in MMLE, the proposed approach minimizes the objective functions based on robust divergences, specifically density power divergence and {\gamma}-divergence. The resulting estimators are statistically consistent and asymptotically normal under appropriate regularity conditions. Furthermore, they offer a flexible trade-off between robustness and efficiency through hyperparameter tuning, forming a generalized estimation framework encompassing MMLE as a special case. To evaluate the effectiveness of the proposed methods, we conducted simulation experiments under various conditions, including scenarios with aberrant responses. The results demonstrated that the proposed methods surpassed existing ones in performance across various conditions. Moreover, numerical analysis of influence functions verified that increasing the hyperparameters effectively suppressed the impact of responses with low occurrence probabilities, which are potentially aberrant. These findings highlight that the proposed approach offers a robust alternative to MMLE, significantly enhancing measurement accuracy in testing and survey contexts prone to aberrant responses.
Deep Reinforcement Learning (RL) is remarkably effective in addressing sequential resource allocation problems in domains such as healthcare, public policy, and resource management. However, deep RL policies often lack transparency and adaptability, challenging their deployment alongside human decision-makers. In contrast, Language Agents, powered by large language models (LLMs), provide human-understandable reasoning but may struggle with effective decision making. To bridge this gap, we propose Rule-Bottleneck Reinforcement Learning (RBRL), a novel framework that jointly optimizes decision and explanations. At each step, RBRL generates candidate rules with an LLM, selects among them using an attention-based RL policy, and determines the environment action with an explanation via chain-of-thought reasoning. The RL rule selection is optimized using the environment rewards and an explainability metric judged by the LLM. Evaluations in real-world scenarios highlight RBRL's competitive performance with deep RL and efficiency gains over LLM fine-tuning. A survey further confirms the enhanced quality of its explanations.
Multivariate Time Series Forecasting (MTSF) plays a crucial role across diverse fields, ranging from economic, energy, to traffic. In recent years, deep learning has demonstrated outstanding performance in MTSF tasks. In MTSF, modeling the correlations among different channels is critical, as leveraging information from other related channels can significantly improve the prediction accuracy of a specific channel. This study systematically reviews the channel modeling strategies for time series and proposes a taxonomy organized into three hierarchical levels: the strategy perspective, the mechanism perspective, and the characteristic perspective. On this basis, we provide a structured analysis of these methods and conduct an in-depth examination of the advantages and limitations of different channel strategies. Finally, we summarize and discuss some future research directions to provide useful research guidance. Moreover, we maintain an up-to-date Github repository (https://github.com/decisionintelligence/CS4TS) which includes all the papers discussed in the survey.
Constraining Statistical Isotropy using 21cm Power Spectrum and Bispectrum
2025-02-15
Bhuwan Joshi, Rahul kothari
The Cosmological Principle states that the universe is statistically isotropic and homogeneous on large length scales, typically $\gtrsim 70$Mpc. A detection of significant deviation would help us falsify the simplest models of inflation. In this regard, there are potential indications of departures from this principle, e.g., observations from WMAP and Planck show signs of a preferred direction in the temperature fluctuations known as hemispherical asymmetry in CMB. Phenomenologically, this has been studied using a dipole modulation model. In addition to this, a number of possible mechanisms have been proposed in the literature to explain this anomaly. Some of these scenarios generate dipolar asymmetry or predict quadrupolar asymmetry in the primordial power spectrum of curvature perturbations. In this paper, we study both these asymmetries. To fulfill the objective, we employ 21cm intensity mapping technique post during post-reionization era, i.e., $z\lesssim 7$. We apply Fisher formalism to constrain dipolar and quadrupolar anisotropy parameters using both 21cm power and bispectra and give forecasts for three intensity mapping surveys: SKA-Mid, HIRAX and PUMA. Although 21cm intensity mapping is a very promising cosmological probe, the signals are severely affected by foregrounds. To mitigate the foreground effects, we use foreground avoidance approach. For the interferometer mode of operation, we also include the wedge effect. From our analysis, we find that PUMA, on account of its high redshift range is able to constrain both dipolar and quadrupolar parameters to better than $\sim 10^{-3}$ for redshifts $z \gtrsim 1$. This is one order of magnitude better constraints as compared to those provided by the latest CMB surveys. We also find that as compared to power spectrum, the constraining power of bispectrum is more sensitive towards foregrounds.
Large Language Models (LLMs) have demonstrated remarkable success in various tasks such as natural language understanding, text summarization, and machine translation. However, their general-purpose nature often limits their effectiveness in domain-specific applications that require specialized knowledge, such as healthcare, chemistry, or legal analysis. To address this, researchers have explored diverse methods to enhance LLMs by integrating domain-specific knowledge. In this survey, we provide a comprehensive overview of these methods, which we categorize into four key approaches: dynamic knowledge injection, static knowledge embedding, modular adapters, and prompt optimization. Each approach offers unique mechanisms to equip LLMs with domain expertise, balancing trade-offs between flexibility, scalability, and efficiency. We discuss how these methods enable LLMs to tackle specialized tasks, compare their advantages and disadvantages, evaluate domain-specific LLMs against general LLMs, and highlight the challenges and opportunities in this emerging field. For those interested in delving deeper into this area, we also summarize the commonly used datasets and benchmarks. To keep researchers updated on the latest studies, we maintain an open-source at: https://github.com/abilliyb/Knowledge_Injection_Survey_Papers, dedicated to documenting research in the field of specialized LLM.
The rapid emergence of highly adaptable and reusable artificial intelligence (AI) models is set to revolutionize the medical field, particularly in the diagnosis and management of Parkinson's disease (PD). Currently, there are no effective biomarkers for diagnosing PD, assessing its severity, or tracking its progression. Numerous AI algorithms are now being used for PD diagnosis and treatment, capable of performing various classification tasks based on multimodal and heterogeneous disease symptom data, such as gait, hand movements, and speech patterns of PD patients. They provide expressive feedback, including predicting the potential likelihood of PD, assessing the severity of individual or multiple symptoms, aiding in early detection, and evaluating rehabilitation and treatment effectiveness, thereby demonstrating advanced medical diagnostic capabilities. Therefore, this work provides a surveyed compilation of recent works regarding PD detection and assessment through biometric symptom recognition with a focus on machine learning and deep learning approaches, emphasizing their benefits, and exposing their weaknesses, and their impact in opening up newer research avenues. Additionally, it also presents categorized and characterized descriptions of the datasets, approaches, and architectures employed to tackle associated constraints. Furthermore, the paper explores the potential opportunities and challenges presented by data-driven AI technologies in the diagnosis of PD.
Hybrid Deepfake Image Detection: A Comprehensive Dataset-Driven Approach Integrating Convolutional and Attention Mechanisms with Frequency Domain Features
Effective deepfake detection tools are becoming increasingly essential over the last few years due to the growing usage of deepfakes in unethical practices. There exists a diverse range of deepfake generation techniques, which makes it challenging to develop an accurate universal detection mechanism. The 2025 Signal Processing Cup (DFWild-Cup competition) provided a diverse dataset of deepfake images, which are generated from multiple deepfake image generators, for training machine learning model(s) to emphasize the generalization of deepfake detection. To this end, we proposed an ensemble-based approach that employs three different neural network architectures: a ResNet-34-based architecture, a data-efficient image transformer (DeiT), and an XceptionNet with Wavelet Transform to capture both local and global features of deepfakes. We visualize the specific regions that these models focus for classification using Grad-CAM, and empirically demonstrate the effectiveness of these models in grouping real and fake images into cohesive clusters using t-SNE plots. Individually, the ResNet-34 architecture has achieved 88.9% accuracy, whereas the Xception network and the DeiT architecture have achieved 87.76% and 89.32% accuracy, respectively. With these networks, our weighted ensemble model achieves an excellent accuracy of 93.23% on the validation dataset of the SP Cup 2025 competition. Finally, the confusion matrix and an Area Under the ROC curve of 97.44% further confirm the stability of our proposed method.
Multi-Carrier Thermal Transport in Electronic and Energy Conversion Devices
2025-02-15
Te-Huan Liu, Tianyu Wang, Jun Zhou, Xin Qian, Ronggui Yang
Nonequilibrium multi-carrier thermal transport is essential for both scientific research and technological applications in electronic, spintronic, and energy conversion devices. This article reviews the fundamentals of phonon, electron, spin, and ion transport driven by temperature gradients in solid-state and soft condensed matters, and the microscopic interactions between energy/charge carriers that can be leveraged for manipulating electrical and thermal transport in energy conversion devices, such as electron-phonon coupling, spin-phonon interaction, and ion-solvent interactions, etc. In coupled electron-phonon transport, we discuss the basics of electron-phonon interactions and their effects on phonon dynamics, thermalization, and nonequilibrium thermal transport. For the phonon-spin interaction, nonequilibrium transport formulation is introduced first, followed by the physics of spin thermoelectric effect and strategies to manipulate them. Contributions to thermal conductivity from magnons as heat carriers are also reviewed. For coupled transport of heat and ions/molecules, we highlight the importance of local molecular configurations that determine the magnitude of the electrochemical gradient, which is the key to improving the efficiency of low-grade heat energy conversion.
Access to health resources is a critical determinant of public well-being and societal resilience, particularly during public health crises when demand for medical services and preventive care surges. However, disparities in accessibility persist across demographic and geographic groups, raising concerns about equity. Traditional survey methods often fall short due to limitations in coverage, cost, and timeliness. This study leverages crowdsourced data from Google Maps reviews, applying advanced natural language processing techniques, specifically ModernBERT, to extract insights on public perceptions of health resource accessibility in the United States during the COVID-19 pandemic. Additionally, we employ Partial Least Squares regression to examine the relationship between accessibility perceptions and key socioeconomic and demographic factors including political affiliation, racial composition, and educational attainment. Our findings reveal that public perceptions of health resource accessibility varied significantly across the U.S., with disparities peaking during the pandemic and slightly easing post-crisis. Political affiliation, racial demographics, and education levels emerged as key factors shaping these perceptions. These findings underscore the need for targeted interventions and policy measures to address inequities, fostering a more inclusive healthcare infrastructure that can better withstand future public health challenges.
Script&Shift: A Layered Interface Paradigm for Integrating Content Development and Rhetorical Strategy with LLM Writing Assistants
2025-02-15
Momin Siddiqui, Roy Pea, Hari Subramonyam
Good writing is a dynamic process of knowledge transformation, where writers refine and evolve ideas through planning, translating, and reviewing. Generative AI-powered writing tools can enhance this process but may also disrupt the natural flow of writing, such as when using LLMs for complex tasks like restructuring content across different sections or creating smooth transitions. We introduce Script&Shift, a layered interface paradigm designed to minimize these disruptions by aligning writing intents with LLM capabilities to support diverse content development and rhetorical strategies. By bridging envisioning, semantic, and articulatory distances, Script&Shift's interactions allow writers to leverage LLMs for various content development tasks (scripting) and experiment with diverse organization strategies while tailoring their writing for different audiences (shifting). This approach preserves creative control while encouraging divergent and iterative writing. Our evaluation shows that Script&Shift enables writers to creatively and efficiently incorporate LLMs while preserving a natural flow of composition.
Robustness tests for biomedical foundation models should tailor to specification
2025-02-14
R. Patrick Xian, Noah R. Baker, Tom David, Qiming Cui, A. Jay Holmgren, Stefan Bauer, Madhumita Sushil, Reza Abbasi-Asl
Existing regulatory frameworks for biomedical AI include robustness as a key component but lack detailed implementational guidance. The recent rise of biomedical foundation models creates new hurdles in testing and certification given their broad capabilities and susceptibility to complex distribution shifts. To balance test feasibility and effectiveness, we suggest a priority-based, task-oriented approach to tailor robustness evaluation objectives to a predefined specification. We urge concrete policies to adopt a granular categorization of robustness concepts in the specification. Our approach promotes the standardization of risk assessment and monitoring, which guides technical developments and mitigation efforts.
Detection of a peculiar noise type in the TESS "fast" light curves
2025-02-14
Sz. Kálmán, Sz. Csizmadia, A. Pál, Gy. M. Szabó
We present the detection of a peculiar high-frequency noise component in the 20 second cadence SAP (Simple Aperture Photometry) light curve of TESS (Transiting Exoplanets Survey Satellite). This effect (labeled as blue noise) may be attributed to the pointing instability (also known as satellite jiiter) of the satellite. We present a common technique used in the mitigation of the jitter, by decorrelating against the subpixel position of the photo-center of the point spread function of the star. We also show that a simple linear or polynomial technique may not yield satisfactory corrections, as the behavior or attitude of the noise properties may change considerably throughout the light curve.
Neutron capture measurements for s-process nucleosynthesis; A review about CERN n_TOF developments and contributions
2025-02-14
C. Domingo-Pardo, O. Aberle, V. Alcayne, G. Alpar, M. Al Halabi, S. Amaducci, V. Babiano, M. Bacak, J. Balibrea-Correa, J. Bartolomé, A. P. Bernardes, B. Bernardino Gameiro, E. Berthoumieux, R. Beyer, M. Birch, M. Boromiza, D. Bosnar, B. Brusasco, M. Caamaño, A. Cahuzac, F. Calviño, M. Calviani, D. Cano-Ott, A. Casanovas, D. M. Castelluccio, D. Catlett, F. Cerutti, G. Cescutti, E. Chiaveri, G. Claps, P. Colombetti, N. Colonna, P. Console Camprini, G. Cortés, M. A. Cortés-Giraldo, L. Cosentino, S. Cristallo, A. D'Ottavi, G. de la Fuente Rosales, S. F. Dellmann, M. Diakaki, M. Di Castro, A. Di Chicco, M. Dietz, E. Dupont, I. Durán, Z. Eleme, M. Eslami, S. Fargier, B. Fernández-Domínguez, P. Finocchiaro, W. Flanagan, V. Furman, A. Gandhi, F. García-Infantes, A. Gawlik-Ramiega, G. Gervino, S. Gilardoni, E. González-Romero, S. Goula, E. Griesmayer, C. Guerrero, F. Gunsing, C. Gustavino, J. Heyse, W. Hillman, D. G. Jenkins, E. Jericha, A. Junghans, Y. Kadi, K. Kaperoni, I. Kelly, M. Kokkoris, Y. Kopatch, M. Krtička, N. Kyritsis, C. Lederer-Woods, J. Lerendegui-Marco, A. Manna, T. Martínez, M. Martínez-Cañada, A. Masi, C. Massimi, P. Mastinu, M. Mastromarco, E. A. Maugeri, A. Mazzone, E. Mendoza, A. Mengoni, V. Michalopoulou, P. M. Milazzo, J. Moldenhauer, R. Mucciola, E. Musacchio González, A. Musumarra, A. Negret, E. Odusina, D. Papanikolaou, N. Patronis, J. A. Pavón-Rodríguez, M. G. Pellegriti, P. Pérez-Maroto, A. Pérez de Rada Fiol, G. Perfetto, J. Perkowski, C. Petrone, N. Pieretti, L. Piersanti, E. Pirovano, I. Porras, J. Praena, J. M. Quesada, R. Reifarth, D. Rochman, Y. Romanets, A. Rooney, G. Rovira, C. Rubbia, A. Sánchez-Caballero, R. N. Sahoo, D. Scarpa, P. Schillebeeckx, A. G. Smith, N. V. Sosnin, M. Spelta, M. E. Stamati, K. Stasiak, G. Tagliente, A. Tarifeño-Saldivia, D. Tarrío, P. Torres-Sánchez, S. Tosi, G. Tsiledakis, S. Valenta, P. Vaz, G. Vecchio, D. Vescovi, V. Vlachoudis, R. Vlastou, A. Wallner, C. Weiss, P. J. Woods, T. Wright, R. Wu, P. Žugec, The n\_TOF Collaboration
This article presents a review about the main CERN n\_TOF contributions to the field of neutron-capture experiments of interest for $s$-process nucleosynthesis studies over the last 25 years, with special focus on the measurement of radioactive isotopes. A few recent capture experiments on stable isotopes of astrophysical interest are also discussed. Results on $s$-process branching nuclei are appropriate to illustrate how advances in detection systems and upgrades in the facility have enabled increasingly challenging experiments and, as a consequence, have led to a better understanding and modeling of the $s$-process mechanism of nucleosynthesis. New endeavors combining radioactive-ion beams from ISOLDE for the production of radioisotopically pure samples for activation experiments at the new NEAR facility at n\_TOF are briefly discussed. On the basis of these new exciting results, also current limitations of state-of-the-art TOF and activation techniques will be depicted, thereby showing the pressing need for further upgrades and enhancements on both facilities and detection systems. A brief account of the potential technique based on inverse kinematics for direct neutron-capture measurements is also presented.
Reinforcement Learning in Strategy-Based and Atari Games: A Review of Google DeepMinds Innovations
2025-02-14
Abdelrhman Shaheen, Anas Badr, Ali Abohendy, Hatem Alsaadawy, Nadine Alsayad
Reinforcement Learning (RL) has been widely used in many applications, particularly in gaming, which serves as an excellent training ground for AI models. Google DeepMind has pioneered innovations in this field, employing reinforcement learning algorithms, including model-based, model-free, and deep Q-network approaches, to create advanced AI models such as AlphaGo, AlphaGo Zero, and MuZero. AlphaGo, the initial model, integrates supervised learning and reinforcement learning to master the game of Go, surpassing professional human players. AlphaGo Zero refines this approach by eliminating reliance on human gameplay data, instead utilizing self-play for enhanced learning efficiency. MuZero further extends these advancements by learning the underlying dynamics of game environments without explicit knowledge of the rules, achieving adaptability across various games, including complex Atari games. This paper reviews the significance of reinforcement learning applications in Atari and strategy-based games, analyzing these three models, their key innovations, training processes, challenges encountered, and improvements made. Additionally, we discuss advancements in the field of gaming, including MiniZero and multi-agent models, highlighting future directions and emerging AI models from Google DeepMind.
2025-02-14
Sophia Sklaviadis, Sweta Agrawal, Antonio Farinhas, Andre Martins, Mario Figueiredo
From a variational perspective, many statistical learning criteria involve seeking a distribution that balances empirical risk and regularization. In this paper, we broaden this perspective by introducing a new general class of variational methods based on Fenchel-Young (FY) losses, treated as divergences that generalize (and encompass) the familiar Kullback-Leibler divergence at the core of classical variational learning. Our proposed formulation -- FY variational learning -- includes as key ingredients new notions of FY free energy, FY evidence, FY evidence lower bound, and FY posterior. We derive alternating minimization and gradient backpropagation algorithms to compute (or lower bound) the FY evidence, which enables learning a wider class of models than previous variational formulations. This leads to generalized FY variants of classical algorithms, such as an FY expectation-maximization (FYEM) algorithm, and latent-variable models, such as an FY variational autoencoder (FYVAE). Our new methods are shown to be empirically competitive, often outperforming their classical counterparts, and most importantly, to have qualitatively novel features. For example, FYEM has an adaptively sparse E-step, while the FYVAE can support models with sparse observations and sparse posteriors.
A Roadmap to Address Burnout in the Cybersecurity Profession: Outcomes from a Multifaceted Workshop
2025-02-14
Ann Rangarajan, Calvin Nobles, Josiah Dykstra, Margaret Cunningham, Nikki Robinson, Tammie Hollis, Celeste Lyn Paul, Charles Gulotta
This paper addresses the critical issue of burnout among cybersecurity professionals, a growing concern that threatens the effectiveness of digital defense systems. As the industry faces a significant attrition crisis, with nearly 46% of cybersecurity leaders contemplating departure from their roles, it is imperative to explore the causes and consequences of burnout through a socio-technical lens. These challenges were discussed by experts from academia and industry in a multi-disciplinary workshop at the 26th International Conference on Human-Computer Interaction to address broad antecedents of burnout, manifestation and its consequences among cybersecurity professionals, as well as programs to mitigate impacts from burnout. Central to the analysis is an empirical study of former National Security Agency (NSA) tactical cyber operators. This paper presents key insights in the following areas based on discussions in the workshop: lessons for public and private sectors from the NSA study, a comparative review of addressing burnout in the healthcare profession. It also outlines a roadmap for future collaborative research, thereby informing interdisciplinary studies.
A Latent Causal Inference Framework for Ordinal Variables
2025-02-14
Martina Scauda, Jack Kuipers, Giusi Moffa
Ordinal variables, such as on the Likert scale, are common in applied research. Yet, existing methods for causal inference tend to target nominal or continuous data. When applied to ordinal data, this fails to account for the inherent ordering or imposes well-defined relative magnitudes. Hence, there is a need for specialised methods to compute interventional effects between ordinal variables while accounting for their ordinality. One potential framework is to presume a latent Gaussian Directed Acyclic Graph (DAG) model: that the ordinal variables originate from marginally discretizing a set of Gaussian variables whose latent covariance matrix is constrained to satisfy the conditional independencies inherent in a DAG. Conditioned on a given latent covariance matrix and discretisation thresholds, we derive a closed-form function for ordinal causal effects in terms of interventional distributions in the latent space. Our causal estimation combines naturally with algorithms to learn the latent DAG and its parameters, like the Ordinal Structural EM algorithm. Simulations demonstrate the applicability of the proposed approach in estimating ordinal causal effects both for known and unknown structures of the latent graph. As an illustration of a real-world use case, the method is applied to survey data of 408 patients from a study on the functional relationships between symptoms of obsessive-compulsive disorder and depression.
An overview of what current data can (and cannot yet) say about evolving dark energy
2025-02-14
William Giarè, Tariq Mahassen, Eleonora Di Valentino, Supriya Pan
Recent measurements of Baryon Acoustic Oscillations (BAO) and distance moduli from Type Ia supernovae suggest a preference for Dynamical Dark Energy (DDE) scenarios characterized by a time-varying equation of state (EoS). This focused review assesses its robustness across independent measurements and surveys. Using the Chevallier-Polarski-Linder (CPL) parameterization to describe the evolution of the DE EoS, we analyze over 35 dataset combinations, incorporating Planck Cosmic Microwave Background (CMB) anisotropies, three independent Type Ia supernova (SN) catalogs (PantheonPlus, Union3, DESY5), BAO measurements from DESI and SDSS, and expansion rate measurements $H(z)$ inferred from the relative ages of massive, passively evolving galaxies at early cosmic times known as Cosmic Chronometers (CC). This review has two main objectives: first, to evaluate the statistical significance of the DDE preference across different dataset combinations, which incorporate varying sources of information. Specifically, we consider cases where only low-redshift probes are used in different combinations, others where individual low-redshift probes are analyzed together with CMB data, and finally, scenarios where high- and low-redshift probes are included in all possible independent combinations. Second, we provide a reader-friendly synthesis of what the latest cosmological and astrophysical probes can (and cannot yet) reveal about DDE. Overall, our findings highlight that combinations that \textit{simultaneously} include PantheonPlus SN and SDSS BAO significantly weaken the preference for DDE. However, intriguing hints supporting DDE emerge in combinations that do not include DESI-BAO measurements: SDSS-BAO combined with SN from Union3 and DESY5 (with and without CMB) support the preference for DDE.
Understanding the relationships between the perceptions of burnout and instability in Software Engineering
2025-02-14
Danilo Monteiro Ribeiro
Changes are inherent in software development, often increasing developers' perception of instability. Understanding the relationship between human factors and Software Engineering processes is crucial to mitigating and preventing issues. One such factor is burnout, a recognized disease that impacts productivity, turnover, and, most importantly, developers' well-being. Investigating the link between instability and burnout can help organizations implement strategies to improve developers' work conditions and performance. This study aims to identify and describe the relationship between perceived instability and burnout among software developers. A cross-sectional survey was conducted with 411 respondents, using convenience sampling and self-selection. In addition to analyzing variable relationships, confirmatory factor analysis was applied. Key findings include: (1) A significant positive relationship between burnout (exhaustion and cynicism) and team, technological, and task instability; (2) A weak negative relationship between efficacy and technological/team instability, with no correlation to task instability; (3) Exhaustion was the most frequently reported burnout symptom, while task instability was the most perceived type of instability. These results are valuable for both industry and academia, providing insights to reduce burnout and instability among software engineers. Future research can further explore the impact of instability, offering new perspectives on monitoring and mitigating its effects in software development.
A Survey on Constructing Parseval Fusion Frames via Scaling Weights
2025-02-14
Ehsan Ameli, Ali Akbar Arefijamaal, Fahimeh Arabyani Neyshaburi
The construction of Parseval fusion frames is highly desirable in a wide range of signal processing applications. In this paper, we study the problem of modifying the weights of a fusion frame in order to generate a Parseval fusion frame. To this end, we extend the notion of the scalability to the fusion frame setting. We then proceed to characterize scalable fusion Riesz bases and $1$-excess fusion frames. Furthermore, we provide the necessary and sufficient conditions for the scalability of certain $k$-excess fusion frames, $k\geq 2$. Finally, we present several pertinent examples to confirm the obtained results.
The Power Spectrum of the Thermal Sunyaev-Zeldovich Effect
2025-02-14
George Efstathiou, Fiona McCarthy
The power spectrum of unresolved thermal Sunyaev-Zeldovich (tSZ) clusters is extremely sensitive to the amplitude of the matter fluctuations. This paper present an analysis of the tSZ power spectrum using temperature power spectra of the cosmic microwave background (CMB) rather than maps of the Compton y-parameter. Our analysis is robust and insensitive to the cosmic infrared background. Using data from Planck, and higher resolution CMB data from the Atacama Cosmology Telescope and the South Pole Telescope, we find strong evidence that the tSZ spectrum has a shallower slope and a much lower amplitude at multipoles l > 2000$compared to the predictions of the FLAMINGO hydrodynamic simulations of the LCDM cosmology. Recent results on CMB lensing, cross-correlations of CMB lensing with galaxy surveys and full shape analysis of galaxies and quasars from the Dark Energy Spectroscopic Instrument suggests that this discrepancy cannot be resolved by lowering the amplitude of the matter fluctuations. An alternative possibility is that the impact of baryonic feedback in the FLAMINGO simulations is underestimated.
Mapping bathymetry of inland water bodies on the North Slope of Alaska with Landsat using Random Forest
2025-02-14
Mark L. Carroll, Margaret R. Wooten, Claire E. Simpson, Caleb S. Spradlin, Melanie J. Frost, Mariana Blanco-Rojas, Zachary W. Williams, Jordan A. Caraballo-Vega, Christopher S. R. Neigh
The North Slope of Alaska is dominated by small waterbodies that provide critical ecosystem services for local population and wildlife. Detailed information on the depth of the waterbodies is scarce due to the challenges with collecting such information. In this work we have trained a machine learning (Random Forest Regressor) model to predict depth from multispectral Landsat data in waterbodies across the North Slope of Alaska. The greatest challenge is the scarcity of in situ data, which is expensive and difficult to obtain, to train the model. We overcame this challenge by using modeled depth predictions from a prior study as synthetic training data to provide a more diverse training data pool for the Random Forest. The final Random Forest model was more robust than models trained directly on the in situ data and when applied to 208 Landsat 8 scenes from 2016 to 2018 yielded a map with an overall $r^{2}$ value of 0.76 on validation. The final map has been made available through the Oak Ridge National Laboratory Distribute Active Archive Center (ORNL-DAAC). This map represents a first of its kind regional assessment of waterbody depth with per pixel estimates of depth for the entire North Slope of Alaska.
VideoDiff: Human-AI Video Co-Creation with Alternatives
2025-02-14
Mina Huh, Dingzeyu Li, Kim Pimmel, Hijung Valentina Shin, Amy Pavel, Mira Dontcheva
To make an engaging video, people sequence interesting moments and add visuals such as B-rolls or text. While video editing requires time and effort, AI has recently shown strong potential to make editing easier through suggestions and automation. A key strength of generative models is their ability to quickly generate multiple variations, but when provided with many alternatives, creators struggle to compare them to find the best fit. We propose VideoDiff, an AI video editing tool designed for editing with alternatives. With VideoDiff, creators can generate and review multiple AI recommendations for each editing process: creating a rough cut, inserting B-rolls, and adding text effects. VideoDiff simplifies comparisons by aligning videos and highlighting differences through timelines, transcripts, and video previews. Creators have the flexibility to regenerate and refine AI suggestions as they compare alternatives. Our study participants (N=12) could easily compare and customize alternatives, creating more satisfying results.
Assessing the Accuracy of Multisource Register-based Official Statistics for Multinomial Outcomes
2025-02-14
Nina Deliu, Piero Demetrio Falorsi, Stefano Falorsi, Diego Chianella, Giorgio Alleva
The emergence of new data sources and statistical methods is driving an update in the traditional official statistics paradigm. As an example, the Italian National Institute of Statistics (ISTAT) is undergoing a significant modernisation of the data production process, transitioning from a statistical paradigm based on single sources (census, sample surveys, or administrative data) to an integrated system of statistical registers. The latter results from an integration process of administrative and survey data based on different statistical methods, and, as such, prone to different sources of error. This work discusses and validates a global measure of error assessment for such multisource register-based statistics. Focusing on two important sources of uncertainty (sampling and modelling), we provide an analytical solution that well approximates the global error of mass-imputation procedures for multi-category type of outcomes, assuming a multinomial logistic model. Among other advantages, the proposed measure results in an interpretable, computationally feasible, and flexible approach, while allowing for unplanned on-the-fly statistics on totals to be supported by accuracy estimates. An application to education data from the Base Register of Individuals from ISTAT's integrated system of statistical registers is presented.
CMB lensing tomography with clustering estimation of lens redshift distributions
2025-02-14
Shun Arai, John A. Peacock, Hironao Miyatake, Atsushi J. Nishizawa
We develop a clustering-based redshift estimation approach for CMB lensing tomography, focusing on the kernel function of the lensing galaxies. Within a linear galaxy bias framework, we derive estimators for this kernel from two-point cross-correlations between lens mass and reference samples. The reconstructed kernel then enables a theoretical prediction for the angular cross-power spectrum \(C_{g\kappa}\) between CMB lensing convergence and lens galaxies. As a proof of concept, we measure \(C_{g\kappa}\) by correlating the \emph{Planck} PR4 convergence map with NVSS+SUMSS radio galaxies (\(0\lesssim z\lesssim 3\)). We estimate the radio-galaxy kernel by collectively cross-correlating their distribution with spectroscopic and photometric surveys (2MPZ, LOWZ-CMASS, eBOSS DR16 LRGs, and Gaia-unWISE QSOs). From the measured \(C_{g\kappa}\), we obtain \(\sigma_8 = 0.86^{+0.12}_{-0.09}\) when the density parameter is set to the {\it Planck} value of $\Omega_m = 0.315$; this is in good agreement with the \emph{Planck} normalisation of $\sigma_8 = 0.812$.
Combinatorial Reinforcement Learning with Preference Feedback
2025-02-14
Joongkyu Lee, Min-hwan Oh
In this paper, we consider combinatorial reinforcement learning with preference feedback, where a learning agent sequentially offers an action--an assortment of multiple items to--a user, whose preference feedback follows a multinomial logistic (MNL) model. This framework allows us to model real-world scenarios, particularly those involving long-term user engagement, such as in recommender systems and online advertising. However, this framework faces two main challenges: (1) the unknown value of each item, unlike traditional MNL bandits that only address single-step preference feedback, and (2) the difficulty of ensuring optimism while maintaining tractable assortment selection in the combinatorial action space with unknown values. In this paper, we assume a contextual MNL preference model, where the mean utilities are linear, and the value of each item is approximated by a general function. We propose an algorithm, MNL-VQL, that addresses these challenges, making it both computationally and statistically efficient. As a special case, for linear MDPs (with the MNL preference feedback), we establish the first regret lower bound in this framework and show that MNL-VQL achieves nearly minimax-optimal regret. To the best of our knowledge, this is the first work to provide statistical guarantees in combinatorial RL with preference feedback.
With the rise of sophisticated scam websites that exploit human psychological vulnerabilities, distinguishing between legitimate and scam websites has become increasingly challenging. This paper presents ScamFerret, an innovative agent system employing a large language model (LLM) to autonomously collect and analyze data from a given URL to determine whether it is a scam. Unlike traditional machine learning models that require large datasets and feature engineering, ScamFerret leverages LLMs' natural language understanding to accurately identify scam websites of various types and languages without requiring additional training or fine-tuning. Our evaluation demonstrated that ScamFerret achieves 0.972 accuracy in classifying four scam types in English and 0.993 accuracy in classifying online shopping websites across three different languages, particularly when using GPT-4. Furthermore, we confirmed that ScamFerret collects and analyzes external information such as web content, DNS records, and user reviews as necessary, providing a basis for identifying scam websites from multiple perspectives. These results suggest that LLMs have significant potential in enhancing cybersecurity measures against sophisticated scam websites.
Comparison of definitions of angular momentum at null infinity
2025-02-14
Szymon Bagiński, Jacek Jezierski
The supertranslation ambiguity of angular momentum at null infinity creates a technical difficulty in choosing the proper definition for this conserved quantity. As a result, many different approaches are found in the literature. This article aims to review the most relevant of these notions and compare their most significant properties. A new definition of angular momentum at null infinity is also proposed, which is supertranslation invariant under certain assumptions.
What are Asset Price Bubbles? A Survey on Definitions of Financial Bubbles
2025-02-14
Michael Heinrich Baumann, Anja Janischewski
Financial bubbles and crashes have repeatedly caused economic turmoil notably but not only during the 2008 financial crisis. However, both in the popular press as well as scientific publications, the meaning of bubble is sometimes unspecified. Due to the multitude of bubble definitions, we conduct a systematic review with the following questions: What definitions of asset price bubbles exist in the literature? Which definitions are used in which disciplines and how frequently? We develop a system of definition categories and categorize a total of 122 papers from eleven research areas. Our results show that although one definition is indeed prevalent in the literature, the overall definition landscape is not uniform. Next to the mostly used definition as deviation from a present value of expected future cash flows, we identify several other definitions, which rely on price properties or other specifications of a fundamental value. This research contributes by shedding light on the possible variations in which bubbles are defined and operationalized.
Coordinated control of multiple autonomous surface vehicles: challenges and advances - a systematic review
2025-02-14
Manuel Gantiva Osorioa, Carmelina Ierardia, Isabel Jurado Floresa, Mario Pereira Martína, Pablo Millán Gata
The increasing use and implementation of Autonomous Surface Vessels (ASVs) for various activities in maritime environments is expected to drive a rise in developments and research on their control. Particularly, the coordination of multiple ASVs presents novel challenges and opportunities, requiring interdisciplinary research efforts at the intersection of robotics, control theory, communication systems, and marine sciences. The wide variety of missions or objectives for which these vessels can be collectively used allows for the application and combination of different control techniques. This includes the exploration of machine learning to consider aspects previously deemed infeasible. This review provides a comprehensive exploration of coordinated ASV control while addressing critical gaps left by previous reviews. Unlike previous works, we adopt a systematic approach to ensure integrity and minimize bias in article selection. We delve into the complex world of sub-actuated ASVs with a focus on customized control strategies and the integration of machine learning techniques for increased autonomy. By synthesizing recent advances and identifying emerging trends, we offer insights that drive this field forward, providing both a comprehensive overview of state-of-the-art techniques and guidance for future research efforts.
Recommender systems are essential components of many online platforms, yet traditional approaches still struggle with understanding complex user preferences and providing explainable recommendations. The emergence of Large Language Model (LLM)-powered agents offers a promising approach by enabling natural language interactions and interpretable reasoning, potentially transforming research in recommender systems. This survey provides a systematic review of the emerging applications of LLM-powered agents in recommender systems. We identify and analyze three key paradigms in current research: (1) Recommender-oriented approaches, which leverage intelligent agents to enhance the fundamental recommendation mechanisms; (2) Interaction-oriented approaches, which facilitate dynamic user engagement through natural dialogue and interpretable suggestions; and (3) Simulation-oriented approaches, which employ multi-agent frameworks to model complex user-item interactions and system dynamics. Beyond paradigm categorization, we analyze the architectural foundations of LLM-powered recommendation agents, examining their essential components: profile construction, memory management, strategic planning, and action execution. Our investigation extends to a comprehensive analysis of benchmark datasets and evaluation frameworks in this domain. This systematic examination not only illuminates the current state of LLM-powered agent recommender systems but also charts critical challenges and promising research directions in this transformative field.
Improved Online Confidence Bounds for Multinomial Logistic Bandits
2025-02-14
Joongkyu Lee, Min-hwan Oh
In this paper, we propose an improved online confidence bound for multinomial logistic (MNL) models and apply this result to MNL bandits, achieving variance-dependent optimal regret. Recently, Lee & Oh (2024) established an online confidence bound for MNL models and achieved nearly minimax-optimal regret in MNL bandits. However, their results still depend on the norm-boundedness of the unknown parameter $B$ and the maximum size of possible outcomes $K$. To address this, we first derive an online confidence bound of $O\left(\sqrt{d \log t} + B \right)$, which is a significant improvement over the previous bound of $O (B \sqrt{d} \log t \log K )$ (Lee & Oh, 2024). This is mainly achieved by establishing tighter self-concordant properties of the MNL loss and introducing a novel intermediary term to bound the estimation error. Using this new online confidence bound, we propose a constant-time algorithm, OFU-MNL++, which achieves a variance-dependent regret bound of $O \Big( d \log T \sqrt{ \smash[b]{\sum_{t=1}^T} \sigma_t^2 } \Big) $ for sufficiently large $T$, where $\sigma_t^2$ denotes the variance of the rewards at round $t$, $d$ is the dimension of the contexts, and $T$ is the total number of rounds. Furthermore, we introduce a Maximum Likelihood Estimation (MLE)-based algorithm, OFU-MN$^2$L, which achieves an anytime poly(B)-free regret of $O \Big( d \log (BT) \sqrt{ \smash[b]{\sum_{t=1}^T} \sigma_t^2 } \Big) $.
Discovering Polynomial and Quadratic Structure in Nonlinear Ordinary Differential Equations
2025-02-14
Boris Kramer, Gleb Pogudin
Dynamical systems with quadratic or polynomial drift exhibit complex dynamics, yet compared to nonlinear systems in general form, are often easier to analyze, simulate, control, and learn. Results going back over a century have shown that the majority of nonpolynomial nonlinear systems can be recast in polynomial form, and their degree can be reduced further to quadratic. This process of polynomialization/quadratization reveals new variables (in most cases, additional variables have to be added to achieve this) in which the system dynamics adhere to that specific form, which leads us to discover new structures of a model. This chapter summarizes the state of the art for the discovery of polynomial and quadratic representations of finite-dimensional dynamical systems. We review known existence results, discuss the two prevalent algorithms for automating the discovery process, and give examples in form of a single-layer neural network and a phenomenological model of cell signaling.
Weighted abduction computes hypotheses that explain input observations. A reasoner of weighted abduction first generates possible hypotheses and then selects the hypothesis that is the most plausible. Since a reasoner employs parameters, called weights, that control its plausibility evaluation function, it can output the most plausible hypothesis according to a specific application using application-specific weights. This versatility makes it applicable from plant operation to cybersecurity or discourse analysis. However, the predetermined application-specific weights are not applicable to all cases of the application. Hence, the hypothesis selected by the reasoner does not necessarily seem the most plausible to the user. In order to resolve this problem, this article proposes two types of user-feedback dialogue protocols, in which the user points out, either positively, negatively or neutrally, properties of the hypotheses presented by the reasoner, and the reasoner regenerates hypotheses that satisfy the user's feedback. As it is required for user-feedback dialogue protocols, we then prove: (i) our protocols necessarily terminate under certain reasonable conditions; (ii) they achieve hypotheses that have the same properties in common as fixed target hypotheses do in common if the user determines the positivity, negativity or neutrality of each pointed-out property based on whether the target hypotheses have that property.
Fluorescence Phasor Analysis: Basic Principles and Biophysical Applications
2025-02-14
Alvaro A. Recoulat Angelini, Leonel Malacrida, F. Luis Gonzalez Flecha
Fluorescence is one of the most widely used techniques in biological sciences. Its exceptional sensitivity and versatility make it a tool of first choice for quantitative studies in biophysics. The concept of phasors, originally introduced by Charles Steinmetz in the late 19th century for analyzing alternating current circuits, has since found applications across diverse disciplines, including fluorescence spectroscopy. The main idea behind fluorescence phasors was posited by Gregorio Weber in 1981. By analyzing the complementary nature of pulse and phase fluorometry data, he shows that two magnitudes -denoted as G and S- derived from the frequency-domain fluorescence measurements correspond to the real and imaginary part of the Fourier transform of the fluorescence intensity in the time domain. This review provides a historical perspective on how the concept of phasors originates and how it integrates into fluorescence spectroscopy. We discuss their fundamental algebraic properties, which enable intuitive model-free analysis of fluorescence data despite the complexity of the underlying phenomena. Some applications in biophysics illustrate the power of this approach in studying diverse phenomena, including protein folding, protein interactions, phase transitions in lipid mixtures and formation of high-order structures in nucleic acids.
The information loss problem and Hawking radiation as tunneling
2025-02-14
Baocheng Zhang, Christian Corda, Qing-yu Cai
In this paper, we review some methods that tried to solve the information loss problem. In particular, we revisit the solution based on Hawking radiation as tunneling, and provide a detailed statistical interpretation on the black hole entropy in terms of the quantum tunneling probability of Hawking radiation from the black hole. In addition, we show that black hole evaporation is governed by a time-dependent Schrodinger equation that sends pure states into pure states rather than into mixed states (Hawking had originally established that the final result would be mixed states). This is further confirmation of the fact that black hole evaporation is unitary.
Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond
2025-02-14
Kehan Guo, Yili Shen, Gisela Abigail Gonzalez-Montiel, Yue Huang, Yujun Zhou, Mihir Surve, Zhichun Guo, Prayel Das, Nitesh V Chawla, Olaf Wiest, Xiangliang Zhang
The rapid advent of machine learning (ML) and artificial intelligence (AI) has catalyzed major transformations in chemistry, yet the application of these methods to spectroscopic and spectrometric data, referred to as Spectroscopy Machine Learning (SpectraML), remains relatively underexplored. Modern spectroscopic techniques (MS, NMR, IR, Raman, UV-Vis) generate an ever-growing volume of high-dimensional data, creating a pressing need for automated and intelligent analysis beyond traditional expert-based workflows. In this survey, we provide a unified review of SpectraML, systematically examining state-of-the-art approaches for both forward tasks (molecule-to-spectrum prediction) and inverse tasks (spectrum-to-molecule inference). We trace the historical evolution of ML in spectroscopy, from early pattern recognition to the latest foundation models capable of advanced reasoning, and offer a taxonomy of representative neural architectures, including graph-based and transformer-based methods. Addressing key challenges such as data quality, multimodal integration, and computational scalability, we highlight emerging directions such as synthetic data generation, large-scale pretraining, and few- or zero-shot learning. To foster reproducible research, we also release an open-source repository containing recent papers and their corresponding curated datasets (https://github.com/MINE-Lab-ND/SpectrumML_Survey_Papers). Our survey serves as a roadmap for researchers, guiding progress at the intersection of spectroscopy and AI.
Comprehensive Review of Neural Differential Equations for Time Series Analysis
2025-02-14
YongKyung Oh, Seungsu Kam, Jonghun Lee, Dong-Young Lim, Sungil Kim, Alex Bui
Time series modeling and analysis has become critical in various domains. Conventional methods such as RNNs and Transformers, while effective for discrete-time and regularly sampled data, face significant challenges in capturing the continuous dynamics and irregular sampling patterns inherent in real-world scenarios. Neural Differential Equations (NDEs) represent a paradigm shift by combining the flexibility of neural networks with the mathematical rigor of differential equations. This paper presents a comprehensive review of NDE-based methods for time series analysis, including neural ordinary differential equations, neural controlled differential equations, and neural stochastic differential equations. We provide a detailed discussion of their mathematical formulations, numerical methods, and applications, highlighting their ability to model continuous-time dynamics. Furthermore, we address key challenges and future research directions. This survey serves as a foundation for researchers and practitioners seeking to leverage NDEs for advanced time series analysis.
A Survey on Human-Centered Evaluation of Explainable AI Methods in Clinical Decision Support Systems
2025-02-14
Alessandro Gambetti, Qiwei Han, Hong Shen, Claudia Soares
Explainable AI (XAI) has become a crucial component of Clinical Decision Support Systems (CDSS) to enhance transparency, trust, and clinical adoption. However, while many XAI methods have been proposed, their effectiveness in real-world medical settings remains underexplored. This paper provides a survey of human-centered evaluations of Explainable AI methods in Clinical Decision Support Systems. By categorizing existing works based on XAI methodologies, evaluation frameworks, and clinical adoption challenges, we offer a structured understanding of the landscape. Our findings reveal key challenges in the integration of XAI into healthcare workflows and propose a structured framework to align the evaluation methods of XAI with the clinical needs of stakeholders.
Deploying Fair and Efficient Course Allocation Mechanisms
2025-02-14
George Bissias, Cyrus Cousins, Paula Navarrete Diaz, Yair Zick
Universities regularly face the challenging task of assigning classes to thousands of students while considering their preferences, along with course schedules and capacities. Ensuring the effectiveness and fairness of course allocation mechanisms is crucial to guaranteeing student satisfaction and optimizing resource utilization. We approach this problem from an economic perspective, using formal justice criteria to evaluate different algorithmic frameworks. To evaluate our frameworks, we conduct a large scale survey of university students at University of Massachusetts Amherst, collecting over 1,000 student preferences. This is, to our knowledge, the largest publicly available dataset of student preferences. We develop software for generating synthetic student preferences over courses, and implement four allocation algorithms: the serial dictatorship algorithm used by University of Massachusetts Amherst; Round Robin; an Integer Linear Program; and the Yankee Swap algorithm. We propose improvements to the Yankee Swap framework to handle scenarios with item multiplicities. Through experimentation with the Fall 2024 Computer Science course schedule at University of Massachusetts Amherst, we evaluate each algorithm's performance relative to standard justice criteria, providing insights into fair course allocation in large university settings.
Solar System Elemental Abundances from the Solar Photosphere and CI-Chondrites
2025-02-14
Katharina Lodders, Maria Bergemann, Herbert Palme
Solar photospheric abundances and CI-chondrite compositions are reviewed and updated to obtain representative solar system abundances of the elements and their isotopes. The new photospheric abundances obtained here lead to higher solar metallicity. Full 3D NLTE photospheric analyses are only available for 11 elements. A quality index for analyses is introduced. For several elements, uncertainties remain large. Protosolar mass fractions are H (X = 0.7060), He (Y = 0.2753), and for metals Li to U (Z = 0.0187). The protosolar (C+N)/H agrees within 13% with the ratio for the solar core from the Borexino experiment. Elemental abundances in CI-chondrites were screened by analytical methods, sample sizes, and evaluated using concentration frequency distributions. Aqueously mobile elements (e.g., alkalis, alkaline earths, etc.) often deviate from normal distributions indicating mobilization and/or sequestration into carbonates, phosphates, and sulfates. Revised CI-chondrite abundances of non-volatile elements are similar to earlier estimates. The moderately volatile elements F and Sb are higher than before, as are C, Br and I, whereas the CI-abundances of Hg and N are now significantly lower. The solar system nuclide distribution curves of s-process elements agree within 4% with s-process predictions of Galactic chemical evolution models. P-process nuclide distributions are assessed. No obvious correlation of CI-chondritic to solar elemental abundance ratios with condensation temperatures is observed, nor is there one for ratios of CI-chondrites/solar wind abundances.
It was first shown in (Catanese-LeBrun 1997) that certain high-dimensional smooth closed manifolds admit pairs of Einstein metrics with Ricci curvatures of opposite sign. After reviewing subsequent progress that has been made on this topic, we then prove various related results, with the ultimate goal of stimulating further research on associated questions.
Recent Advances in Malware Detection: Graph Learning and Explainability
2025-02-14
Hossein Shokouhinejad, Roozbeh Razavi-Far, Hesamodin Mohammadian, Mahdi Rabbani, Samuel Ansong, Griffin Higgins, Ali A Ghorbani
The rapid evolution of malware has necessitated the development of sophisticated detection methods that go beyond traditional signature-based approaches. Graph learning techniques have emerged as powerful tools for modeling and analyzing the complex relationships inherent in malware behavior, leveraging advancements in Graph Neural Networks (GNNs) and related methods. This survey provides a comprehensive exploration of recent advances in malware detection, focusing on the interplay between graph learning and explainability. It begins by reviewing malware analysis techniques and datasets, emphasizing their foundational role in understanding malware behavior and supporting detection strategies. The survey then discusses feature engineering, graph reduction, and graph embedding methods, highlighting their significance in transforming raw data into actionable insights, while ensuring scalability and efficiency. Furthermore, this survey focuses on explainability techniques and their applications in malware detection, ensuring transparency and trustworthiness. By integrating these components, this survey demonstrates how graph learning and explainability contribute to building robust, interpretable, and scalable malware detection systems. Future research directions are outlined to address existing challenges and unlock new opportunities in this critical area of cybersecurity.
We consider a class of zero-sum search games in which a Hider hides one or more target among a set of $n$ boxes. The boxes may require differing amount of time to search, and detection may be imperfect, so that there is a certain probability that a target may not be found when a box is searched, even when it is there. A Searcher must choose how to search the boxes sequentially, and wishes to minimize the expected time to find the target(s), whereas the Hider wishes to maximize this payoff. We review some known solutions to different cases of this game.
2025-02-14
Thibaud Gloaguen, Nikola Jovanović, Robin Staab, Martin Vechev
While watermarks for closed LLMs have matured and have been included in large-scale deployments, these methods are not applicable to open-source models, which allow users full control over the decoding process. This setting is understudied yet critical, given the rising performance of open-source models. In this work, we lay the foundation for systematic study of open-source LLM watermarking. For the first time, we explicitly formulate key requirements, including durability against common model modifications such as model merging, quantization, or finetuning, and propose a concrete evaluation setup. Given the prevalence of these modifications, durability is crucial for an open-source watermark to be effective. We survey and evaluate existing methods, showing that they are not durable. We also discuss potential ways to improve their durability and highlight remaining challenges. We hope our work enables future progress on this important problem.
2025-02-14
Thomas Bläsius, Valentin Buchhold, Dorothea Wagner, Tim Zeitz, Michael Zündorf
This work establishes the technical fundamentals of a well-tuned Customizable Contraction Hierarchies (CCH) implementation that is simple and elegant. We give a detailed overview of the state of the art of CCH, review recent advances on CCH and show how to combine them. Additionally, we propose further refinements that improve the performance of CCH. An extensive evaluation confirms that a CCH framework is not only comprehensive in supported features but also competitive in performance to both Contraction Hierarchies (CH) and Customizable Route Planning (CRP).
No evidence for dynamical dark energy from DESI and SN data: a symbolic regression analysis
2025-02-14
Agripino Sousa-Neto, Carlos Bengaly, Javier E. González, Jailson Alcaniz
Recent measurements of Baryon Acoustic Oscillations (BAO) from the Dark Energy Spectroscopic Survey (DESI), combined with data from the cosmic microwave background (CMB) and Type Ia supernovae (SNe), challenge the $\Lambda$-Cold Dark Matter ($\Lambda$CDM) paradigm. They indicate a potential evolution in the dark energy equation of state (EoS), $w(z)$, as suggested by analyses that employ parametric models. In this paper, we use a model-independent approach known as high performance symbolic regression (PySR) to reconstruct $w(z)$ directly from observational data, allowing us to bypass prior assumptions about the underlying cosmological model. Our findings confirm that the DESI data alone agree with the $\Lambda$CDM model ($w(z) = -1$) at the redshift range considered. Notably, this agreement improves slightly when we exclude the data point at $z = 0.51$. Additionally, we observe similar conclusions when combining the DESI data with existing compilations of SNe distance measurements, such as Pantheon+ and the Dark Energy Survey Supernova 5-Year, regardless of the absolute magnitude prior values used. Therefore, these results suggest that it is premature to claim any statistically significant evidence for a dynamical EoS or deviations from the $\Lambda$CDM model based on the current DESI data, either alone or in combination with supernova measurements.
The JWST EXCELS survey: Probing strong-line diagnostics and the chemical evolution of galaxies over cosmic time using Te-metallicities
2025-02-14
D. Scholte, F. Cullen, A. C. Carnall, K. Z. Arellano-Córdova, T. M. Stanton, L. Barrufet, C. T. Donnan, J. S. Dunlop, H. -H. Leung, D. J. McLeod, R. J. McLure, J. M. Moustakas, C. L. Pollock, A. E. Shapley, S. Stevenson, H. Zou
We present an analysis of the rest-frame optical spectra of 22 [OIII]$\lambda$4363 detected galaxies in the redshift range $1.65 < z < 7.92$ (with $\langle z \rangle$ = 4.05) from JWST/NIRSpec medium-resolution observations taken as part of the EXCELS survey. To supplement these high-redshift sources, we also consider a sample of 782 local [OIII]$\lambda$4363 detected galaxies from the DESI Early Data Release. Our analysis demonstrates that many strong-line calibrations are biased in the early Universe due to the systematic evolution in ionization conditions with redshift. However, the recently introduced $\widehat{R}$ calibration mostly removes the dependence on ionization state and can be considered a largely redshift-independent calibration. In a similar spirit, we introduce a new strong-line diagnostic, $\widehat{RNe}$, which can be used to robustly estimate metallicities when the [OIII]$\lambda$5007 is redshifted out of the wavelength range of JWST/NIRSpec at $z > 9.5$. We also show that strong-line diagnostics using the [NII]$\lambda$6584 emission line are likely to be biased at high-redshift due to a moderate enhancement in the average N/O abundance ratios (at fixed O/H) in these sources. Finally, we discuss the location of our new [OIII]$\lambda$4363 detected galaxies at $z \simeq 4$ on the mass-metallicity plane and investigate the redshift evolution of the fundamental metallicity relation (FMR). We find tentative evidence for an increasing deviation from the FMR at $z > 4$ which might indicate fundamental differences in the baryon cycle at these redshifts. However, more data are required as our high-redshift constraints are still based on a relatively small sample of galaxies and the significance of the deviation is strongly dependent on the assumed form of the fundamental metallicity relation.
Driving World Model (DWM), which focuses on predicting scene evolution during the driving process, has emerged as a promising paradigm in pursuing autonomous driving. These methods enable autonomous driving systems to better perceive, understand, and interact with dynamic driving environments. In this survey, we provide a comprehensive overview of the latest progress in DWM. We categorize existing approaches based on the modalities of the predicted scenes and summarize their specific contributions to autonomous driving. In addition, high-impact datasets and various metrics tailored to different tasks within the scope of DWM research are reviewed. Finally, we discuss the potential limitations of current research and propose future directions. This survey provides valuable insights into the development and application of DWM, fostering its broader adoption in autonomous driving. The relevant papers are collected at https://github.com/LMD0311/Awesome-World-Model.
Pulsational characteristics of mass accreting stars in close binary systems
2025-02-14
A. Miszuda
Eclipsing binaries with pulsating components are a distinct subclass of binaries, merging orbital and pulsational analyses. In recent years, that subclass led to the definition of a newly formed branch of tidal asteroseismology. While single-star pulsators are well understood, the effects of binarity and possible mass transfer on pulsational characteristics, particularly in mass-gaining stars, remain to be systematically explored. Here, I present preliminary results on the asteroseismic properties of a mass-accreting model for a 10 $M_{\odot}$ $\beta$ Cephei-type star
Embed Any NeRF: Graph Meta-Networks for Neural Tasks on Arbitrary NeRF Architectures
2025-02-13
Francesco Ballerini, Pierluigi Zama Ramirez, Samuele Salti, Luigi Di Stefano
Neural Radiance Fields (NeRFs) have emerged as a groundbreaking paradigm for representing 3D objects and scenes by encoding shape and appearance information into the weights of a neural network. Recent works have shown how such weights can be used as input to frameworks processing them to solve deep learning tasks. Yet, these frameworks can only process NeRFs with a specific, predefined architecture. In this paper, we present the first framework that can ingest NeRFs with multiple architectures and perform inference on architectures unseen at training time. We achieve this goal by training a Graph Meta-Network in a representation learning framework. Moreover, we show how a contrastive objective is conducive to obtaining an architecture-agnostic latent space. In experiments on both MLP-based and tri-planar NeRFs, our approach demonstrates robust performance in classification and retrieval tasks that either matches or exceeds that of existing frameworks constrained to single architectures, thus providing the first architecture-agnostic method to perform tasks on NeRFs by processing their weights.
The morphological classification of galaxies provides vital physical information about the orbital motions of stars in galaxies, and correlates in interesting ways with star formation history, and other physical properties. Galaxy morphological classification is a field with a history of more than 100 years of development, and many scientists have introduced new classification schemes, resulting in a sometimes confusing array of terminologies and overlapping classes. In this article I provide a brief historical review of galaxy classification, but focus mostly on providing a summary of how the morphological variety of galaxies seen in our expanding Universe are described. I review traditional visual classification, morphometric measurements, crowd-sourcing for large scale visual classifications (Galaxy Zoo), and of course the recent explosion of interest in making use of machine learning techniques for galaxy morphology classification. A look up table is provided for cross matching of various terminologies currently in use for galaxy morphology classification as well as brief definitions of the main morphological types.
An Automated Bandwidth Division for the LHCb Upgrade Trigger
2025-02-13
T. Evans, C. Fitzpatrick, J. Horswill
The upgraded Large Hadron Collider beauty (LHCb) experiment is the first detector based at a hadron collider using a fully software based trigger. The first `High Level Trigger' stage (HLT1) reduces the event rate from 30 MHz to approximately 1 MHz based on reconstruction criteria from the tracking system and consists of O(100) trigger selections implemented on GPUs. These selections are further refined following the full offline-quality reconstruction at the second stage (HLT2) prior to saving for analysis. An automated bandwidth division has been performed to equitably divide this 1 MHz output rate between the signals of interest to the LHCb physics program. This was achieved by optimising a set of trigger selections that maximise efficiency for signals of interest to LHCb while keeping the total HLT1 readout capped to a maximum. The bandwidth division tool has been used to determine the optimal selection for 35 selection algorithms over 80 characteristic physics channels.
Thresholding Nonprobability Units in Combined Data for Efficient Domain Estimation
2025-02-13
Terrance D. Savitsky, Matthew R. Williams, Julie Gerrshunskaya, Vladislav Beresovsky
Quasi-randomization approaches estimate latent participation probabilities for units from a nonprobability / convenience sample. Estimation of participation probabilities for convenience units allows their combination with units from the randomized survey sample to form a survey weighted domain estimate. One leverages convenience units for domain estimation under the expectation that estimation precision and bias will improve relative to solely using the survey sample; however, convenience sample units that are very different in their covariate support from the survey sample units may inflate estimation bias or variance. This paper develops a method to threshold or exclude convenience units to minimize the variance of the resulting survey weighted domain estimator. We compare our thresholding method with other thresholding constructions in a simulation study for two classes of datasets based on degree of overlap between survey and convenience samples on covariate support. We reveal that excluding convenience units that each express a low probability of appearing in \emph{both} reference and convenience samples reduces estimation error.
Many large scale structure surveys sort their observations into redshift bins and treat every tracer as being located at the mean redshift of its bin, a treatment which we refer to as the equal time approximation. Recently, a new method was developed which allows for the estimation and correction of errors introduced by this approximation, which we refer to as the unequal time correlator-level projection. For single tracer power spectra, corrections arise at second order and above in a series expansion, with first order terms surviving only in multi-tracer analyses. In this paper we develop a new method which we refer to as the unequal time field level projection. This formalism projects the fields individually onto the celestial sphere, displaced from individual reference times, before defining their correlators. This method introduces new, first order correction terms even in the case of single tracer power spectra. Specifically, new first order terms are introduced which apply to both cross-bin and single bin correlators. All of these new corrections originate with derivatives over combinations of a delta function, a cross-bin phase term, and the power spectrum itself and stem from the introduction of two unequal time Fourier transforms into the analysis. We analyse these corrections in the context of a linearly biased power spectrum divided between two redshift bins and find that they can lead to non-trivial corrections, particularly to cross-bin correlators. We also show that these terms can be replicated by appropriately extending the correlator-level analysis to include a second Fourier transform which allows for a full redshift bin integration.
Diffusion Models for Molecules: A Survey of Methods and Tasks
2025-02-13
Liang Wang, Chao Song, Zhiyuan Liu, Yu Rong, Qiang Liu, Shu Wu, Liang Wang
Generative tasks about molecules, including but not limited to molecule generation, are crucial for drug discovery and material design, and have consistently attracted significant attention. In recent years, diffusion models have emerged as an impressive class of deep generative models, sparking extensive research and leading to numerous studies on their application to molecular generative tasks. Despite the proliferation of related work, there remains a notable lack of up-to-date and systematic surveys in this area. Particularly, due to the diversity of diffusion model formulations, molecular data modalities, and generative task types, the research landscape is challenging to navigate, hindering understanding and limiting the area's growth. To address this, this paper conducts a comprehensive survey of diffusion model-based molecular generative methods. We systematically review the research from the perspectives of methodological formulations, data modalities, and task types, offering a novel taxonomy. This survey aims to facilitate understanding and further flourishing development in this area. The relevant papers are summarized at: https://github.com/AzureLeon1/awesome-molecular-diffusion-models.
A systematic literature review on the application of analytical approaches and mathematical programming in public bus transit network design and operations planning: Part I
2025-02-13
Reza Mahmoudi, Saeid Saidi, S. Chan Wirasinghe
The Public Bus Transit Network Design Problem and Operations Planning (PBTNDP&OP) remains a core research area within transportation, in particular, because of the emergence of new transit technologies and services. Analytical approaches and mathematical programming are the most commonly applied methodologies to study this problem. Many studies utilize either of these two methods, often viewed as competing due to the unique benefits each provides that the other does not. This two-part paper systematically reviews the application of analytical approaches and mathematical programming in PBTNDP&OP, analyzing publications from 1968 to 2021. It begins by comparing analytical methods and mathematical programming through various statistical analyses, including the number of published papers, the most active journals and authors, keyword frequencies, keyword co-occurrence maps, and co-authorship maps. Subsequent analysis of the identified papers includes examinations from multiple perspectives: the problems investigated, modeling methods used, decision variables considered, network structures, and key findings. This is followed by a critical review of selected papers. The paper concludes by discussing the advantages and disadvantages of each approach and suggests potential extensions for future research based on identified gaps in existing studies.
HI-MaNGA: Results from (21cm-HI) single-dish observations of MaNGA Survey Galaxies
2025-02-13
Karen Masters, David Stark
In a poster presentation for IAU Symposium 392: "Neutral hydrogen in and around galaxies in the SKA era", we gave an overview of the HI-MaNGA project which is working to obtain complementary information about the cold gas (neutral hydrogen traced by the radio 21cm line) content of Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) sample galaxies. MaNGA, part of the fourth incarnation of the Sloan Digital Sky Surveys (SDSS-IV), obtained spatially resolved spectral maps for 10,000 nearby galaxies selected to create a representative sample out of the SDSS Main Galaxy Sample. MaNGA data have provided a census of the stellar and ionized gas content of these galaxies, as well as kinematics of both stars and gas. Adding HI information via the HI-MaNGA program, which has observed or collected 21cm line data for 70% of the full MaNGA sample, has been crucial for a number of applications, but especially understanding the physical mechanisms that regulate gas accretion, and through that star formation and quenching of star formation. This conference proceedings article accompanies the release of the DR3 version of HI-MaNGA data.
Assessing Generative AI value in a public sector context: evidence from a field experiment
2025-02-13
Trevor Fitzpatrick, Seamus Kelly, Patrick Carey, David Walsh, Ruairi Nugent
The emergence of Generative AI (Gen AI) has motivated an interest in understanding how it could be used to enhance productivity across various tasks. We add to research results for the performance impact of Gen AI on complex knowledge-based tasks in a public sector setting. In a pre-registered experiment, after establishing a baseline level of performance, we find mixed evidence for two types of composite tasks related to document understanding and data analysis. For the Documents task, the treatment group using Gen AI had a 17% improvement in answer quality scores (as judged by human evaluators) and a 34% improvement in task completion time compared to a control group. For the Data task, we find the Gen AI treatment group experienced a 12% reduction in quality scores and no significant difference in mean completion time compared to the control group. These results suggest that the benefits of Gen AI may be task and potentially respondent dependent. We also discuss field notes and lessons learned, as well as supplementary insights from a post-trial survey and feedback workshop with participants.
Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements
2025-02-13
Alexander Jenkins, Andrea Cini, Joseph Barker, Alexander Sharp, Arunashis Sau, Varun Valentine, Srushti Valasang, Xinyang Li, Tom Wong, Timothy Betts, Danilo Mandic, Cesare Alippi, Fu Siong Ng
Catheter ablation of Atrial Fibrillation (AF) consists of a one-size-fits-all treatment with limited success in persistent AF. This may be due to our inability to map the dynamics of AF with the limited resolution and coverage provided by sequential contact mapping catheters, preventing effective patient phenotyping for personalised, targeted ablation. Here we introduce FibMap, a graph recurrent neural network model that reconstructs global AF dynamics from sparse measurements. Trained and validated on 51 non-contact whole atria recordings, FibMap reconstructs whole atria dynamics from 10% surface coverage, achieving a 210% lower mean absolute error and an order of magnitude higher performance in tracking phase singularities compared to baseline methods. Clinical utility of FibMap is demonstrated on real-world contact mapping recordings, achieving reconstruction fidelity comparable to non-contact mapping. FibMap's state-spaces and patient-specific parameters offer insights for electrophenotyping AF. Integrating FibMap into clinical practice could enable personalised AF care and improve outcomes.
While reasoning and multilingual capabilities in Language Models (LMs) have achieved remarkable progress in recent years, their integration into a unified paradigm, multilingual reasoning, is at a nascent stage. Multilingual reasoning requires language models to handle logical reasoning across languages while addressing misalignment, biases, and challenges in low-resource settings. This survey provides the first in-depth review of multilingual reasoning in LMs. In this survey, we provide a systematic overview of existing methods that leverage LMs for multilingual reasoning, specifically outlining the challenges, motivations, and foundational aspects of applying language models to reason across diverse languages. We provide an overview of the standard data resources used for training multilingual reasoning in LMs and the evaluation benchmarks employed to assess their multilingual capabilities. Next, we analyze various state-of-the-art methods and their performance on these benchmarks. Finally, we explore future research opportunities to improve multilingual reasoning in LMs, focusing on enhancing their ability to handle diverse languages and complex reasoning tasks.
Intrinsic galaxy alignments in the KiDS-1000 bright sample: dependence on colour, luminosity, morphology and galaxy scale
2025-02-13
Christos Georgiou, Nora Elisa Chisari, Maciej Bilicki, Francesco La Barbera, Nicola R. Napolitano, Nivya Roy, Crescenzo Tortora
The intrinsic alignment of galaxies is a major astrophysical contaminant to weak gravitational lensing measurements, and the study of its dependence on galaxy properties helps provide meaningful physical priors that aid cosmological analyses. This work studies for the first time the dependence of intrinsic alignments on galaxy structural parameters. We measure the intrinsic alignments of bright galaxies, selected on $r$-band magnitude $r<20$, in the Kilo-Degree Survey. Machine-learning-based photometric redshift estimates are available for this galaxy sample that help obtain a clean measurement of its intrinsic alignment signal. We supplement this sample with a catalogue of structural parameters from S\'ersic profile fits to the surface brightness profiles of the galaxies. We split the sample in galaxy intrinsic colour, luminosity and S\'ersic index, and we fit the non-linear linear alignment model to galaxy position - shape projected correlation function measurements at large scales. We observe a power-law luminosity dependence of the large-scale intrinsic alignment amplitude, $A_\mathrm{IA}$, for both the red and high S\'ersic index ($n_s>2.5$) samples, and find no significant difference between the two samples. We measure a $\sim1.5\sigma$ lower $A_\mathrm{IA}$ for red galaxies that also have a S\'ersic index $n_s<4$, compared to the expected amplitude predicted using sample's luminosity. We also probe the intrinsic alignment of red galaxies as a function of galaxy scale by varying the radial weight employed in the shape measurement. We find no significant difference on large scales but on small scales, alignments increase with galaxy scale. For intrinsically blue galaxies, we find $A_\mathrm{IA}=-0.67\pm1.00$, consistent with previous works. We also find alignments to be consistent with zero for the low S\'ersic index ($n_s<2.5$) sample.
ALMA-IMF XVII - Census and lifetime of high-mass prestellar cores in 14 massive protoclusters
2025-02-13
M. Valeille-Manet, S. Bontemps, T. Csengeri, T. Nony, F. Motte, A. M. Stutz, A. Gusdorf, A. Ginsburg, R. Galván-Madrid, P. Sanhueza, M. Bonfand, N. Brouillet, P. Dell'Ova, F. Louvet, N. Cunningham, M. Fernández-López, F. Herpin, F. Wyrowski, R. H. Álvarez-Gutiérrez, M. Armante, A. E. Guzmán, N. Kessler, A. Koley, J. Salinas, T. Yoo, L. Bronfman, N. Le Nestour
High-mass prestellar cores are extremely rare. The search for such objects has long been hindered by small sample sizes, leading to large uncertainties in their lifetimes and the conditions in which high-mass stars ($> 8\,M_{\odot}$) form. We leverage the large sample ($\sim 580$ cores) detected in the ALMA-IMF survey to identify both protostellar and prestellar cores and estimate their relative lifetimes. We use CO and SiO outflows to identify protostellar cores and introduce a new automated method based on aperture line emission and background subtraction to systematically detect outflows associated with each of the 141 most massive cores. Massive cores that do not drive an outflow in either tracer are classified as prestellar. Our method enables efficient outflow detection with performance comparable to more traditional techniques. We identify 30 likely prestellar cores with $M > 8\,M_{\odot}$, including 12 with $M > 16\,M_{\odot}$, the best candidates for high-mass star precursors. Most of these 12 cores reside in the crowded central regions of protoclusters, where high-mass stars are expected to form. Using prestellar-to-protostellar core ratios and a 300 kyr protostellar lifetime, we estimate prestellar lifetimes of 120 to 240 kyr for $8\,M_{\odot} < M < 16\,M_{\odot}$ and 50 to 100 kyr for $30\,M_{\odot} < M < 55\,M_{\odot}$. These timescales, which depend on different mass reservoir evolution scenarios, significantly exceed the 4 to 15 kyr free-fall time of the cores, suggesting that high-mass cores persist for 10 to 30 free-fall times. This indicates that collapse is slowed by turbulence, magnetic fields, or rotation at or below the observed scale.
A Survey of Reinforcement Learning for Optimization in Automation
2025-02-13
Ahmad Farooq, Kamran Iqbal
Reinforcement Learning (RL) has become a critical tool for optimization challenges within automation, leading to significant advancements in several areas. This review article examines the current landscape of RL within automation, with a particular focus on its roles in manufacturing, energy systems, and robotics. It discusses state-of-the-art methods, major challenges, and upcoming avenues of research within each sector, highlighting RL's capacity to solve intricate optimization challenges. The paper reviews the advantages and constraints of RL-driven optimization methods in automation. It points out prevalent challenges encountered in RL optimization, including issues related to sample efficiency and scalability; safety and robustness; interpretability and trustworthiness; transfer learning and meta-learning; and real-world deployment and integration. It further explores prospective strategies and future research pathways to navigate these challenges. Additionally, the survey includes a comprehensive list of relevant research papers, making it an indispensable guide for scholars and practitioners keen on exploring this domain.
Photometric Objects Around Cosmic Webs (PAC) Delineated in a Spectroscopic Survey. VIII. Revisiting the Lensing is Low Effect
2025-02-13
Xiaolin Luo, Kun Xu, Yipeng Jing, Hongyu Gao, Hekun Li, Donghai Zhao, Jiaxin Han, Chengliang Wei, Yu Luo
The issue of over-predicting the galaxy-galaxy lensing (GGL) signal using conventional galaxy-halo connection models has become well-known as the ``Lensing is Low'' problem, which has been extensively investigated using the Baryon Oscillation Spectroscopic Survey (BOSS) galaxy samples. This issue is also tightly related to the so-called $S_8$ tension. By applying our Photometric objects Around Cosmic webs (PAC) method to the BOSS survey and the DESI deep photometric survey, we obtained hundreds of cross-correlation measurements to establish an accurate galaxy-halo connection for BOSS galaxies through the halo abundance matching technique (Paper IV). With this galaxy-halo connection, we show in this work that the predicted GGL signals for BOSS galaxies both in the Planck and WMAP Universes actually agree very well with the GGL measurements. We find the best-fitting value $S_8 = 0.8294 \pm 0.0110$, $0.8073 \pm 0.0372$ and $0.8189 \pm 0.0440$ for the CMASS samples with the source galaxies from HSC, DES and KiDS image surveys, respectively. Our work indicates that accurate modeling of the lens population is so critical to interpret the GGL observation. For the scale of $r_p < 0.6\,h^{-1}\rm{Mpc}$, our GGL prediction for LOWZ samples are also in good agreement with the observations of HSC and DES. However, the GGL observation of KiDS is much lower on the small scale. Our results indicate that no significant baryon feedback is needed to suppress the small scale clustering unless the the GGL observation of KiDS on the small scale will be confirmed.
Performance of the Stellar Abundances and atmospheric Parameters Pipeline adapted for M dwarfs I. Atmospheric parameters from the spectroscopic module
2025-02-13
Terese Olander, Matthew R. Gent, Ulrike Heiter, Oleg Kochukhov, Maria Bergemann, Ekaterina Magg, Santi Cassisi, Mikhail Kovalev, Thierry Morel, Nicola J. Miller, Diogo Souto, Yutong Shan, Bárbara Rojas-Ayala, Elisa Delgado-Mena, Haiyang S. Wang
M dwarfs are important targets in the search for Earth-like exoplanets due to their small masses and low luminosities. Several ongoing and upcoming space missions are targeting M dwarfs for this reason, and the ESA PLATO mission is one of these. In order to fully characterise a planetary system the properties of the host star must be known. For M dwarfs we can derive effective temperature, surface gravity, metallicity, and abundances of various elements from spectroscopic observations in combination with photometric data. The Stellar Abundances and atmospheric Parameters Pipeline (SAPP) has been developed as a prototype for one of the stellar science softwares within the PLATO consortium, it is aimed at FGK stars. We have modified it to be able to analyse the M dwarf among the PLATO targets. The current version of the pipeline for M dwarfs mostly relies on spectroscopic observations. The data processing is based on the machine learning algorithm The Payne and fits a grid of model spectra to an observed spectrum to derive effective temperature and metallicity. We use spectra in the H-band, as the near-infrared region is beneficial for M dwarfs. A method based on synthetic spectra was developed for the continuum normalisation of the spectra, taking into account the pseudo-continuum formed by numerous lines of the water molecule. Photometry is used to constrain the surface gravity. We tested the modified SAPP on spectra of M dwarfs from the APOGEE survey. Our validation sample of 26 stars includes stars with interferometric observations and binaries. We found a good agreement between our values and reference values from a range of studies. The overall uncertainties in the derived effective temperature, surface gravity, and metallicity is 100 K, 0.1 dex, and 0.15 dex, respectively. We find that the modified SAPP performs well on M dwarfs and identify possible areas of future development.
Let's Talk Futures: A Literature Review of HCI's Future-Orientation
2025-02-13
Camilo Sanchez, Sui Wang, Kaisa Savolainen, Felix Anand Epp, Antti Salovaara
HCI is future-oriented by nature: it explores new human--technology interactions and applies the findings to promote and shape vital visions of society. Still, the visions of futures in HCI publications seem largely implicit, techno-deterministic, narrow, and lacking in roadmaps and attention to uncertainties. A literature review centered on this problem examined futuring and its forms in the ACM Digital Library's most frequently cited HCI publications. This analysis entailed developing the four-category framework SPIN, informed by futures studies literature. The results confirm that, while technology indeed drives futuring in HCI, a growing body of HCI research is coming to challenge techno-centric visions. Emerging foci of HCI futuring demonstrate active exploration of uncertainty, a focus on human experience, and contestation of dominant narratives. The paper concludes with insight illuminating factors behind techno-centrism's continued dominance of HCI discourse, as grounding for five opportunities for the field to expand its contribution to futures and anticipation research.
The combination of words ``discrete curvature'' is only an apparent contradiction. In this survey we describe curvature notions associated with polygons, polyhedral surfaces, and with abstract polyhedral manifolds. Several theorems about the discrete curvature are stated that repeat literally classical theorems of differential and Riemannian geometry: Theorema Egregium, Gauss--Bonnet theorem, and the Chern--Gauss--Bonnet theorem among the others. Some convergence results are also mentioned: under certain assumptions the discrete curvature tends to the smooth curvature as a smooth object is approximated by polyhedral ones.
Machine learning for modelling unstructured grid data in computational physics: a review
2025-02-13
Sibo Cheng, Marc Bocquet, Weiping Ding, Tobias Sebastian Finn, Rui Fu, Jinlong Fu, Yike Guo, Eleda Johnson, Siyi Li, Che Liu, Eric Newton Moro, Jie Pan, Matthew Piggott, Cesar Quilodran, Prakhar Sharma, Kun Wang, Dunhui Xiao, Xiao Xue, Yong Zeng, Mingrui Zhang, Hao Zhou, Kewei Zhu, Rossella Arcucci
Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. Key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. These methodologies have proven effective across diverse fields, including fluid dynamics and environmental simulations. This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics. It places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. To support benchmarking, this review also provides a summary of open-access datasets of unstructured grid data in computational physics. Finally, emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field.
One-shot manipulation of coherence in dynamic quantum resource theory
2025-02-13
Yu Luo
A fundamental problem in quantum information is to understand the operational significance of quantum resources. Quantum resource theories (QRTs) provide a powerful theoretical framework that aids in analyzing and comprehending the operational meaning of these resources. Early resource theories primarily focused on analyzing static quantum resources. Recently, there has been growing interest in the study of dynamic quantum resources. In this paper, we utilize superchannel theory to describe the dynamic resource theory of quantum coherence. In this dynamic resource theory, we treat classical channels as free channels and consider two classes of free superchannels that preserve channel incoherence (maximally incoherent superchannels (MISC) and dephasing-covariant incoherent superchannels (DISC)) as free resources. We regard the quantum Fourier transform as the golden unit of dynamic coherence resources. We first establish the one-shot theory of dynamic coherence cost and dynamic coherence distillation, which involves converting the quantum Fourier transform into an arbitrary quantum channel using MISC and DISC. Next, we introduce a class of free superchannels known as $\delta$-MISC, which asymptotically generate negligible dynamic coherence. Finally, we provide upper and lower bounds for the one-shot catalytic dynamic coherence cost of quantum channels under the action of these $\delta$-MISC superchannels.
Neural Spatiotemporal Point Processes: Trends and Challenges
2025-02-13
Sumantrak Mukherjee, Mouad Elhamdi, George Mohler, David A. Selby, Yao Xie, Sebastian Vollmer, Gerrit Grossmann
Spatiotemporal point processes (STPPs) are probabilistic models for events occurring in continuous space and time. Real-world event data often exhibit intricate dependencies and heterogeneous dynamics. By incorporating modern deep learning techniques, STPPs can model these complexities more effectively than traditional approaches. Consequently, the fusion of neural methods with STPPs has become an active and rapidly evolving research area. In this review, we categorize existing approaches, unify key design choices, and explain the challenges of working with this data modality. We further highlight emerging trends and diverse application domains. Finally, we identify open challenges and gaps in the literature.
This looks like what? Challenges and Future Research Directions for Part-Prototype Models
2025-02-13
Khawla Elhadri, Tomasz Michalski, Adam Wróbel, Jörg Schlötterer, Bartosz Zieliński, Christin Seifert
The growing interest in eXplainable Artificial Intelligence (XAI) has prompted research into models with built-in interpretability, the most prominent of which are part-prototype models. Part-Prototype Models (PPMs) make decisions by comparing an input image to a set of learned prototypes, providing human-understandable explanations in the form of ``this looks like that''. Despite their inherent interpretability, PPMS are not yet considered a valuable alternative to post-hoc models. In this survey, we investigate the reasons for this and provide directions for future research. We analyze papers from 2019 to 2024, and derive a taxonomy of the challenges that current PPMS face. Our analysis shows that the open challenges are quite diverse. The main concern is the quality and quantity of prototypes. Other concerns are the lack of generalization to a variety of tasks and contexts, and general methodological issues, including non-standardized evaluation. We provide ideas for future research in five broad directions: improving predictive performance, developing novel architectures grounded in theory, establishing frameworks for human-AI collaboration, aligning models with humans, and establishing metrics and benchmarks for evaluation. We hope that this survey will stimulate research and promote intrinsically interpretable models for application domains. Our list of surveyed papers is available at https://github.com/aix-group/ppm-survey.
Recent discussions and research in AI safety have increasingly emphasized the deep connection between AI safety and existential risk from advanced AI systems, suggesting that work on AI safety necessarily entails serious consideration of potential existential threats. However, this framing has three potential drawbacks: it may exclude researchers and practitioners who are committed to AI safety but approach the field from different angles; it could lead the public to mistakenly view AI safety as focused solely on existential scenarios rather than addressing a wide spectrum of safety challenges; and it risks creating resistance to safety measures among those who disagree with predictions of existential AI risks. Through a systematic literature review of primarily peer-reviewed research, we find a vast array of concrete safety work that addresses immediate and practical concerns with current AI systems. This includes crucial areas like adversarial robustness and interpretability, highlighting how AI safety research naturally extends existing technological and systems safety concerns and practices. Our findings suggest the need for an epistemically inclusive and pluralistic conception of AI safety that can accommodate the full range of safety considerations, motivations, and perspectives that currently shape the field.
The EBLM project -- XIV. TESS light curves for eclipsing binaries with very low mass companions
2025-02-13
Jay Fitzpatrick, Pierre F. L. Maxted, Alix V. Freckelton, Amuary H. M. J. Triaud, David V. Martin, Alison Duck
Accurate limb-darkening models are needed for accurate characterisation of eclipsing binary stars and transiting exoplanets from the analysis of their light curves. The limb-darkening observed in solar-type stars from the analysis of light curves for transiting hot-Jupiter exoplanets are systematically less steep than predicted by stellar model atmospheres that do not account for the stellar magnetic field. Hot-Jupiter host stars tend to be metal rich ([Fe/H] ~0.25) leading to a lack of low- and solar-metallicity targets in previous studies, so we have analysed the TESS light curves for a sample of 19 stars with transiting M-dwarf companions to extend the range of limb-darkening measurements to [Fe/H] values more typical for solar-type stars. We find that the systematic offset between the observed and predicted limb-darkening profiles observed in metal-rich hot-Jupiter systems is also observed for these solar-type stars at lower metallicity. These observations provide additional measurements to explore the impact of magnetic fields on the atmospheres of solar-type stars. We have also used the TESS light curves to make precise estimates of the radius and effective temperature of the M-dwarf companions in these 19 binary systems. We confirm the results from previous studies that find very low mass stars tend to be about 3 per cent larger than predicted by stellar models that use a mixing length prescription calibrated on the Sun.
Understanding and predicting the emergence of novel materials is a fundamental challenge in condensed matter physics, materials science and technology. With the rapid growth of materials databases in both size and reliability, the challenge shifts from data collection to efficient exploration of this vast and complex space. A key strategy lies in a smart use of descriptors at multiple scales, ranging from atomic arrangements to macroscopic properties, to represent materials in high-dimensional abstract spaces. Network theory provides a powerful framework to structure and analyze these relationships, capturing hidden patterns and guiding discovery. Machine Learning complements this approach by enabling predictive modeling, dimensionality reduction, and the identification of promising material candidates. By integrating network-based methods with Machine Learning techniques, researchers can construct, analyze, and efficiently navigate the material space, uncovering novel materials with tailored properties. This review explores the synergy between network theory and ML, highlighting their role in accelerating materials discovery through a systematic and interpretable approach.
Generative artificial intelligence (AI) models, such as diffusion models and OpenAI's ChatGPT, are transforming medicine by enhancing diagnostic accuracy and automating clinical workflows. The field has advanced rapidly, evolving from text-only large language models for tasks such as clinical documentation and decision support to multimodal AI systems capable of integrating diverse data modalities, including imaging, text, and structured data, within a single model. The diverse landscape of these technologies, along with rising interest, highlights the need for a comprehensive review of their applications and potential. This scoping review explores the evolution of multimodal AI, highlighting its methods, applications, datasets, and evaluation in clinical settings. Adhering to PRISMA-ScR guidelines, we systematically queried PubMed, IEEE Xplore, and Web of Science, prioritizing recent studies published up to the end of 2024. After rigorous screening, 144 papers were included, revealing key trends and challenges in this dynamic field. Our findings underscore a shift from unimodal to multimodal approaches, driving innovations in diagnostic support, medical report generation, drug discovery, and conversational AI. However, critical challenges remain, including the integration of heterogeneous data types, improving model interpretability, addressing ethical concerns, and validating AI systems in real-world clinical settings. This review summarizes the current state of the art, identifies critical gaps, and provides insights to guide the development of scalable, trustworthy, and clinically impactful multimodal AI solutions in healthcare.
Fundamental Theory of Current-Induced Motion of Magnetic Skyrmions
2025-02-13
Yuto Ohki, Masahito Mochizuki
Magnetic skyrmions are topological spin textures that appear in magnets with broken spatial inversion symmetry via competition between the (anti)ferromagnetic exchange interactions and the Dzyaloshinskii-Moriya interactions in a magnetic field. Their current-driven dynamics have been extensively studied aiming at spintronic applications. However, current-induced skyrmion motion exhibits diverse behaviors depending on various factors and conditions such as the type of skyrmion, driving mechanism, system geometry, direction of applied current, and type of the magnet. While this variety attracts enormous research interest of fundamental science and enriches their possibilities of technical applications, it is, at the same time, a source of difficulty and complexity that hinders their comprehensive understandings. In this article, we discuss fundamental and systematic theoretical descriptions of current-induced motion of skyrmions driven by the spin-transfer torque and the spin-orbit torque. Specifically, we theoretically describe the behaviors of current-driven skyrmions depending on the factors and conditions mentioned above by means of analyses using the Thiele equation. Furthermore, the results of the analytical theory are visually demonstrated and quantitatively confirmed by micromagnetic simulations. In particular, we discuss dependence of the direction and velocity of motion on the type of skyrmion (Bloch type and Neel type) and its helicity, the system geometry (thin plate and nanotrack), the direction of applied current (length and width direction of the nanotrack) and its spin-polarization orientation, and the type of magnet (ferromagnet and antiferromagnet). The comprehensive theory provided by this article is expected to contribute significantly to research on the manipulation and control of magnetic skyrmions by electric currents for future spintronics applications.
Galactic Isolated Stellar-Mass Black Holes with the Magnetospheric Spark Gap as Possible GeV-TeV Gamma-ray Unidentified Sources
2025-02-13
Koki Kin, Riku Kuze, Shigeo S. Kimura
Billions of isolated stellar-mass black holes (IBHs) are thought to wander through the interstellar medium (ISM) in the Galaxy, yet only one has been detected. IBHs embedded in ISM would accrete gas via Bondi-Hoyle-Littleton accretion, and with efficient magnetic flux accumulation, the magnetosphere would be formed in the vicinity of IBHs. We explore the detectability of such IBHs through high-energy gamma rays from spark gaps in their magnetospheres based on our recent numerical simulation. The gap gamma rays can be bright at the GeV-TeV energies when IBHs are in the dense ISM. About $10^3$ and $10$ IBHs might be contained in unidentified objects of the $\textit{Fermi}$ Large Area Telescope and the High Energy Stereoscopic System, respectively. A future Galactic plane survey by the Cherenkov Telescope Array Observatory would lead to $\sim10^2$ detections. We also evaluate the combined gamma-ray emission of IBHs in the Galaxy and find that the IBHs may contribute to the Galactic diffuse gamma rays. IBHs will emit optical and X-ray photons from their accretion disk as counterparts, potentially useful for identifying candidates.
We give a brief and partial overview of Bernard Teissier's work in complex equisingularity theory, and a perspective on its legacy; in particular, we focus on the development of the theory in the real and the non-Archimedean contexts. Our aim is not to go into technical details, but, hopefully, rather to give a flavour of the forms taken by these developments, while providing enough definitions and references to give the reader access to old and new reference articles in the field.
2025-02-13
Arianna Graciotti, Nicolas Lazzari, Valentina Presutti, Rocco Tripodi
Linking named entities occurring in text to their corresponding entity in a Knowledge Base (KB) is challenging, especially when dealing with historical texts. In this work, we introduce Musical Heritage named Entities Recognition, Classification and Linking (MHERCL), a novel benchmark consisting of manually annotated sentences extrapolated from historical periodicals of the music domain. MHERCL contains named entities under-represented or absent in the most famous KBs. We experiment with several State-of-the-Art models on the Entity Linking (EL) task and show that MHERCL is a challenging dataset for all of them. We propose a novel unsupervised EL model and a method to extend supervised entity linkers by using Knowledge Graphs (KGs) to tackle the main difficulties posed by historical documents. Our experiments reveal that relying on unsupervised techniques and improving models with logical constraints based on KGs and heuristics to predict NIL entities (entities not represented in the KB of reference) results in better EL performance on historical documents.
Globular Clusters GMRT Pulsar Search (GCGPS) I: Survey description, discovery and timing of the first pulsar in NGC 6093 (M80)
2025-02-13
Jyotirmoy Das, Jayanta Roy, Paulo C. C. Freire, Scott M Ransom, Bhaswati Bhattacharyya, Karel Adámek, Wes Armour, Sanjay Kudale, Mekhala V. Muley
This paper describes the new Globular Clusters GMRT Pulsar Search (GCGPS) survey. This survey aims to find MSPs in the globular clusters (GCs) of the Milky Way using uGMRT. The observations use the uGMRT's Band-4 (550$-$750 MHz) and Band-3 (300$-$500 MHz) receivers, which are well suited for steep-spectral-index radio sources like MSPs; the survey will eventually cover the GCs accessible to the uGMRT sky (i.e. $\delta\:>\:\sim\:-\:53^\circ$), and that is South of $\delta = -17^\circ$ (FAST sky limit) and have not been targeted with the sensitivity of this survey. The observations started in May 2023, having so far resulted in seven new discoveries. In this paper, we present the discovery and follow-up study of the first pulsar from this survey, J1617$-$2258A, a 4.32 ms binary MSP that is also the first to be discovered in the globular cluster NGC 6093. We localised this MSP with arc-sec precision from imaging and obtained the unique timing solution from more than one year of timing observations with the Band-4 (550$-$750 MHz) receivers of the uGMRT. This revealed an unusual binary MSP, with a $\sim$ 19-hour, highly eccentric (e $\sim$ 0.54) orbit having a low-mass companion. This orbital eccentricity allowed the measurement of the rate of advance of periastron for this system, which led to the derivation of its total mass, $1.67 \, \pm \, 0.06 \, \rm M_{\odot}$; this together with the system's mass function implies, for the pulsar and the companion, $M_\mathrm{p} < 1.60 \, \rm M_{\odot}$ and $M_\mathrm{c} > 0.072 \, \rm M_{\odot}$. The system is likely a perturbed MSP-Helium WD system seen at a low orbital inclination.
Trust Me, I Know the Way: Predictive Uncertainty in the Presence of Shortcut Learning
2025-02-13
Lisa Wimmer, Bernd Bischl, Ludwig Bothmann
The correct way to quantify predictive uncertainty in neural networks remains a topic of active discussion. In particular, it is unclear whether the state-of-the art entropy decomposition leads to a meaningful representation of model, or epistemic, uncertainty (EU) in the light of a debate that pits ignorance against disagreement perspectives. We aim to reconcile the conflicting viewpoints by arguing that both are valid but arise from different learning situations. Notably, we show that the presence of shortcuts is decisive for EU manifesting as disagreement.
High mobility environment leads to severe Doppler effects and poses serious challenges to the conventional physical layer based on the widely popular orthogonal frequency division multiplexing (OFDM). The recent emergence of orthogonal time frequency space (OTFS) modulation, along with its many related variants, presents a promising solution to overcome such channel Doppler effects. This paper aims to clearly establish the relationships among the various manifestations of OTFS. Among these related modulations, we identify their connections, common features, and distinctions. Building on existing works, this work provides a general overview of various OTFS-related detection schemes and performance comparisons. We first provide an overview of OFDM and filter bank multi-carrier (FBMC) by demonstrating OTFS as a precoded FBMC through the introduction of inverse symplectic finite Fourier transform (ISFFT). We explore the relationship between OTFS and related modulation schemes with similar characteristics. We provide an effective channel model for high-mobility channels and offer a unified detection representation. We provide numerical comparisons of power spectrum density (PSD) and bit error rate (BER) to underscore the benefit of these modulation schemes in high-mobility scenarios. We also evaluate various detection schemes, revealing insights into their efficacies. We discuss opportunities and challenges for OTFS in high mobility, setting the stage for future research and development in this field.
One-shot Federated Learning Methods: A Practical Guide
2025-02-13
Xiang Liu, Zhenheng Tang, Xia Li, Yijun Song, Sijie Ji, Zemin Liu, Bo Han, Linshan Jiang, Jialin Li
One-shot Federated Learning (OFL) is a distributed machine learning paradigm that constrains client-server communication to a single round, addressing privacy and communication overhead issues associated with multiple rounds of data exchange in traditional Federated Learning (FL). OFL demonstrates the practical potential for integration with future approaches that require collaborative training models, such as large language models (LLMs). However, current OFL methods face two major challenges: data heterogeneity and model heterogeneity, which result in subpar performance compared to conventional FL methods. Worse still, despite numerous studies addressing these limitations, a comprehensive summary is still lacking. To address these gaps, this paper presents a systematic analysis of the challenges faced by OFL and thoroughly reviews the current methods. We also offer an innovative categorization method and analyze the trade-offs of various techniques. Additionally, we discuss the most promising future directions and the technologies that should be integrated into the OFL field. This work aims to provide guidance and insights for future research.
With the emergence of advanced reasoning models like OpenAI o3 and DeepSeek-R1, large language models (LLMs) have demonstrated remarkable reasoning capabilities. However, their ability to perform rigorous logical reasoning remains an open question. This survey synthesizes recent advancements in logical reasoning within LLMs, a critical area of AI research. It outlines the scope of logical reasoning in LLMs, its theoretical foundations, and the benchmarks used to evaluate reasoning proficiency. We analyze existing capabilities across different reasoning paradigms - deductive, inductive, abductive, and analogical - and assess strategies to enhance reasoning performance, including data-centric tuning, reinforcement learning, decoding strategies, and neuro-symbolic approaches. The review concludes with future directions, emphasizing the need for further exploration to strengthen logical reasoning in AI systems.
Blockchain-based Ecommerce It's an Evolution NOT a Revolution-Experimental Evidence from Users' Perspective
2025-02-13
David Lee Kuo Chuen, Yang Li, Weibiao Xu, Willy Zhao
Proponents of blockchains believe that this technology will revolutionize e-commerce. To evaluate this belief, we invite several groups of students to transact on a decentralized peer-to-peer marketplace built on the platform provided by Origin Protocol Inc., and then we conduct a survey about their experience of usage. Based on our survey results, we find that 33% of respondents play tricks on others, which implies that this undesirable result may hinder the widespread adoption of blockchain technologies. We also attempt to propose a conceptual mechanism to mitigate fraudulent behaviors. In the event of disputation, a trusted authority is entitled to the right to downgrade the fraudulent side's credit record, which is stored by a permissioned blockchain accessed only by the authority. Such a punishment can effectively decrease agents' incentives to sell counterfeits and leave fake ratings. In sum, we must distinguish what we proposed blockchains will do and what blockchains can do before enabling this technology in e-commerce.
Solitons in Bose-Einstein Condensates with Attractive Self-Interaction on a Möbius Strip
2025-02-13
Huan-Bo Luo, Guilong Li, Fu-Quan Dou, Bin Liu, Boris A. Malomed, Josep Batle, Yongyao Li
We study the matter-wave solitons in Bose-Einstein condensate (BEC) trapped on a M\"{o}bius strip (MS), based on the respective Gross-Pitaevskii (GP) equation with the mean-field theory. In the linear regime, vortex states are characterized by quantum numbers, $n$ and $m$, corresponding to the transverse and circumferential directions, with the phase structure determined by the winding number (WN) $m$. Odd and even values of $n$ must associate, respectively, with integer and half-integer values of $m$, the latter ones requiring two cycles of motion around MS for returning to the initial phase. Using variational and numerical methods, we solve the GP equation with the attractive nonlinearity, producing a family of ground-state (GS) solitons for values of the norm below the critical one, above which the collapse sets in. Vortex solitons with $n=1,m=1$ and $% n=2,m=1/2$ are obtained in a numerical form. The vortex solitons with $% n=1,m=1$ are almost uniformly distributed in the azimuthal direction, while ones with $n=2,m=1/2$ form localized states. The Vakhitov-Kolokolov criterion and linear-stability analysis for the GS soliton solutions and vortices with $n=1,m=1$ demonstrates that they are completely stable, while the localized states with $n=2,m=1/2$ are completely unstable. Finally, the motion of solitons on the MS and the collision of two solitons are discussed.
Application of Tabular Transformer Architectures for Operating System Fingerprinting
2025-02-13
Rubén Pérez-Jove, Cristian R. Munteanu, Alejandro Pazos, Jose Vázquez-Naya
Operating System (OS) fingerprinting is essential for network management and cybersecurity, enabling accurate device identification based on network traffic analysis. Traditional rule-based tools such as Nmap and p0f face challenges in dynamic environments due to frequent OS updates and obfuscation techniques. While Machine Learning (ML) approaches have been explored, Deep Learning (DL) models, particularly Transformer architectures, remain unexploited in this domain. This study investigates the application of Tabular Transformer architectures-specifically TabTransformer and FT-Transformer-for OS fingerprinting, leveraging structured network data from three publicly available datasets. Our experiments demonstrate that FT-Transformer generally outperforms traditional ML models, previous approaches and TabTransformer across multiple classification levels (OS family, major, and minor versions). The results establish a strong foundation for DL-based OS fingerprinting, improving accuracy and adaptability in complex network environments. Furthermore, we ensure the reproducibility of our research by providing an open-source implementation.
$^{18}$F-FDG brain PET hypometabolism in post-SARS-CoV-2 infection: substrate for persistent/delayed disorders?
2025-02-13
Eric Guedj, Matthieu Million, Pierre Dudouet, Hervé Tissot-Dupont, Fabienne Bregeon, Serge Cammilleri, Didier Raoult
Purpose: Several brain complications of SARS-CoV-2 infection have been reported. It has been moreover speculated that this neurotropism could potentially cause a delayed outbreak of neuropsychiatric and neurodegenerative diseases of neuroinflammatory origin. A propagation mechanism has been proposed across the cribriform plate of the ethmoid bone, from the nose to the olfactory epithelium, and possibly afterward to other limbic structures, and deeper parts of the brain including the brainstem. Methods: Review of clinical examination, and whole-brain voxel-based analysis of $^{18}$F-FDG PET metabolism in comparison with healthy subjects (p voxel<0.001, p-cluster<0.05, uncorrected), of two patients with confirmed diagnosis of SARS-CoV-2 explored at the post-viral stage of the disease. Results: Hypometabolism of the olfactory/rectus gyrus was found on the two patients, especially one with 4-week prolonged anosmia. Additional hypometabolisms were found within amygdala, hippocampus, parahippocampus, cingulate cortex, pre-/post-central gyrus, thalamus/hypothalamus, cerebellum, pons, and medulla in the other patient who complained of delayed onset of a painful syndrome. Conclusion: These preliminary findings reinforce the hypotheses of SARS-CoV-2 neurotropism through the olfactory bulb and the possible extension of this impairment to other brain structures. $^{18}$F-FDG PET hypometabolism could constitute a cerebral quantitative biomarker of this involvement. Post-viral cohort studies are required to specify the exact relationship between such hypometabolisms and the possible persistent disorders, especially involving cognitive or emotion disturbances, residual respiratory symptoms, or painful complaints.
Game theory establishes a fundamental framework for analyzing strategic interactions among rational decision-makers. The rapid advancement of large language models (LLMs) has sparked extensive research exploring the intersection of these two fields. Specifically, game-theoretic methods are being applied to evaluate and enhance LLM capabilities, while LLMs themselves are reshaping classic game models. This paper presents a comprehensive survey of the intersection of these fields, exploring a bidirectional relationship from three perspectives: (1) Establishing standardized game-based benchmarks for evaluating LLM behavior; (2) Leveraging game-theoretic methods to improve LLM performance through algorithmic innovations; (3) Characterizing the societal impacts of LLMs through game modeling. Among these three aspects, we also highlight how the equilibrium analysis for traditional game models is impacted by LLMs' advanced language understanding, which in turn extends the study of game theory. Finally, we identify key challenges and future research directions, assessing their feasibility based on the current state of the field. By bridging theoretical rigor with emerging AI capabilities, this survey aims to foster interdisciplinary collaboration and drive progress in this evolving research area.
Advances in Microphone Array Processing and Multichannel Speech Enhancement
2025-02-13
Gongping Huang, Jesper R. Jensen, Jingdong Chen, Jacob Benesty, Mads G. Christensen, Akihiko Sugiyama, Gary Elko, Tomas Gaensler
This paper reviews pioneering works in microphone array processing and multichannel speech enhancement, highlighting historical achievements, technological evolution, commercialization aspects, and key challenges. It provides valuable insights into the progression and future direction of these areas. The paper examines foundational developments in microphone array design and optimization, showcasing innovations that improved sound acquisition and enhanced speech intelligibility in noisy and reverberant environments. It then introduces recent advancements and cutting-edge research in the field, particularly the integration of deep learning techniques such as all-neural beamformers. The paper also explores critical applications, discussing their evolution and current state-of-the-art technologies that significantly impact user experience. Finally, the paper outlines future research directions, identifying challenges and potential solutions that could drive further innovation in these fields. By providing a comprehensive overview and forward-looking perspective, this paper aims to inspire ongoing research and contribute to the sustained growth and development of microphone arrays and multichannel speech enhancement.
EventSTR: A Benchmark Dataset and Baselines for Event Stream based Scene Text Recognition
2025-02-13
Xiao Wang, Jingtao Jiang, Dong Li, Futian Wang, Lin Zhu, Yaowei Wang, Yongyong Tian, Jin Tang
Mainstream Scene Text Recognition (STR) algorithms are developed based on RGB cameras which are sensitive to challenging factors such as low illumination, motion blur, and cluttered backgrounds. In this paper, we propose to recognize the scene text using bio-inspired event cameras by collecting and annotating a large-scale benchmark dataset, termed EventSTR. It contains 9,928 high-definition (1280 * 720) event samples and involves both Chinese and English characters. We also benchmark multiple STR algorithms as the baselines for future works to compare. In addition, we propose a new event-based scene text recognition framework, termed SimC-ESTR. It first extracts the event features using a visual encoder and projects them into tokens using a Q-former module. More importantly, we propose to augment the vision tokens based on a memory mechanism before feeding into the large language models. A similarity-based error correction mechanism is embedded within the large language model to correct potential minor errors fundamentally based on contextual information. Extensive experiments on the newly proposed EventSTR dataset and two simulation STR datasets fully demonstrate the effectiveness of our proposed model. We believe that the dataset and algorithmic model can innovatively propose an event-based STR task and are expected to accelerate the application of event cameras in various industries. The source code and pre-trained models will be released on https://github.com/Event-AHU/EventSTR
Recent Progress in Studies of Cobalt-based Quasi-1-dimensional Quantum Magnets
2025-02-13
Lun Jin, Robert J. Cava
The interplay of crystal electric field, temperature, and spin-orbit coupling can yield a Kramers ion and thus an effective S = 1/2 ground state for Co2+ ions (3d7), which is often the case for low dimensional materials. This is because a highly anisotropic structural motif can force the spins to point either up or down, hence becoming a system where spins communicate via Ising interactions. Cobalt-based quasi-1-dimensional materials have been studied in this context since the latter half of the 20th century, but due to the development of modern characterization techniques and advances in sample preparation, the exotic physical phenomena that have generated the most interest have only emerged in the most recent three to four decades. This topical review mainly summarizes progress in cobalt-based quasi-1-dimensional quantum magnets, and comments on a few research directions of potential future interest.
A Decade of Metric Differential Privacy: Advancements and Applications
2025-02-13
Xinpeng Xie, Chenyang Yu, Yan Huang, Yang Cao, Chenxi Qiu
Metric Differential Privacy (mDP) builds upon the core principles of Differential Privacy (DP) by incorporating various distance metrics, which offer adaptable and context-sensitive privacy guarantees for a wide range of applications, such as location-based services, text analysis, and image processing. Since its inception in 2013, mDP has garnered substantial research attention, advancing theoretical foundations, algorithm design, and practical implementations. Despite this progress, existing surveys mainly focus on traditional DP and local DP, and they provide limited coverage of mDP. This paper provides a comprehensive survey of mDP research from 2013 to 2024, tracing its development from the foundations of DP. We categorize essential mechanisms, including Laplace, Exponential, and optimization-based approaches, and assess their strengths, limitations, and application domains. Additionally, we highlight key challenges and outline future research directions to encourage innovation and real-world adoption of mDP. This survey is designed to be a valuable resource for researchers and practitioners aiming to deepen their understanding and drive progress in mDP within the broader privacy ecosystem.
With the expansion of data availability, machine learning (ML) has achieved remarkable breakthroughs in both academia and industry. However, imbalanced data distributions are prevalent in various types of raw data and severely hinder the performance of ML by biasing the decision-making processes. To deepen the understanding of imbalanced data and facilitate the related research and applications, this survey systematically analyzing various real-world data formats and concludes existing researches for different data formats into four distinct categories: data re-balancing, feature representation, training strategy, and ensemble learning. This structured analysis help researchers comprehensively understand the pervasive nature of imbalance across diverse data format, thereby paving a clearer path toward achieving specific research goals. we provide an overview of relevant open-source libraries, spotlight current challenges, and offer novel insights aimed at fostering future advancements in this critical area of study.
The Multi-wavelength Extinction Law and its Variation in the Coalsack Molecular Cloud Based on the Gaia, APASS, SMSS, 2MASS, GLIMPSE, and WISE Surveys
2025-02-13
Juan Deng, Shu Wang, Biwei Jiang, He Zhao
Accurate interpretation of observations relies on the interstellar dust extinction law, which also serves as a powerful diagnostic for probing dust properties. In this study, we investigate the multi-wavelength extinction law of the quiescent, starless molecular cloud Coalsack and explore its potential variation across different interstellar environments: the surrounding region, the nearby high Galactic latitude region, the inner dense region, and the inner diffuse region. Using a sample of 368,524 dwarf stars selected from Gaia DR3 as tracers, we establish the effective temperature Teff-intrinsic color relations to derive the intrinsic color indices and optical-mid-infrared (MIR) color excess (CE) for 20 bands. Linear fits to the CE-CE diagrams provide color excess ratios (CERs), which are subsequently converted into relative extinction. The resulting extinction curves for different environments exhibit steep slopes in the near-infrared (NIR) and flat profiles in the MIR. In the optical-NIR range, the Coalsack extinction law is consistent with R_V = 3.1 while in the MIR it follows R_V= 5.5 similar to the results of active star-forming clouds. At an angular resolution of 1.3', our extinction map reveals fine cloud structures. No correlation is found between R_V and E(B-V) for E(B-V) > 0.3 mag, implying a uniform optical extinction law in the Coalsack cloud. The derived average R_V value is 3.24.
Quantum Software Engineering and Potential of Quantum Computing in Software Engineering Research: A Review
2025-02-13
Ashis Kumar Mandal, Md Nadim, Chanchal K. Roy, Banani Roy, Kevin A. Schneider
Research in software engineering is essential for improving development practices, leading to reliable and secure software. Leveraging the principles of quantum physics, quantum computing has emerged as a new computational paradigm that offers significant advantages over classical computing. As quantum computing progresses rapidly, its potential applications across various fields are becoming apparent. In software engineering, many tasks involve complex computations where quantum computers can greatly speed up the development process, leading to faster and more efficient solutions. With the growing use of quantum-based applications in different fields, quantum software engineering (QSE) has emerged as a discipline focused on designing, developing, and optimizing quantum software for diverse applications. This paper aims to review the role of quantum computing in software engineering research and the latest developments in QSE. To our knowledge, this is the first comprehensive review on this topic. We begin by introducing quantum computing, exploring its fundamental concepts, and discussing its potential applications in software engineering. We also examine various QSE techniques that expedite software development. Finally, we discuss the opportunities and challenges in quantum-driven software engineering and QSE. Our study reveals that quantum machine learning (QML) and quantum optimization have substantial potential to address classical software engineering tasks, though this area is still limited. Current QSE tools and techniques lack robustness and maturity, indicating a need for more focus. One of the main challenges is that quantum computing has yet to reach its full potential.
Recent advances in high-dimensional quantum frequency combs
2025-02-13
Kai-Chi Chang, Xiang Cheng, Murat Can Sarihan, Chee Wei Wong
High-dimensional entanglement in qudit states offers a promising pathway towards the realization of practical, large-scale quantum systems that are highly controllable. These systems can be leveraged for various applications, including advanced quantum information processing, secure communications, computation, and metrology. In this context, quantum frequency combs have a crucial role as they inherently support multiple modes in both temporal and frequency domains, while preserving a single spatial mode. The multiple temporal and frequency modes of quantum frequency combs facilitate the generation, characterization, and control of high-dimensional time-frequency entanglement in extensive quantum systems. In this review article, we provide an overview of recent technological advancements in high-dimensional energy-time entangled quantum frequency combs. We explore how these time-frequency qudits, achieved using scalable telecommunications-wavelength components, can empower the creation of large-scale quantum states. Advances in quantum frequency combs can unlock new capabilities and versatility for promising developments in quantum science and technology.
Notes on conformal integrals: Coulomb branch amplitudes, magic identities and bootstrap
2025-02-13
Song He, Xuhang Jiang, Jiahao Liu, Yao-Qi Zhang
We study multi-loop conformal integrals for four-point correlators of planar ${\cal N}=4$ super-Yang-Mills theory, and in particular those contributing to Coulomb branch amplitudes in the ten-dimensional lightlike limit, where linear combinations of such integrals are determined by the large R-charge octagons exactly known from integrability. Exploiting known results for integrands, we review those combinations of dual conformal invariant (DCI) integrals that must evaluate to determinants of ladders, generalizing the simplest cases of Basso-Dixon fishnet integrals; in this way, we summarize all-loop predictions for the integrands (which are extracted from $f$-graphs) contributing to components of Coulomb branch amplitudes, such as next-to-fishnet integrals. Moreover, this exercise produces new ``magic identities", {\it i.e.} certain combinations of DCI integrals equal zero, and we enumerate and simplify such identities up to six loops explicitly. On the other hand, most of these individual integrals have not been computed beyond three loops, and as a first step we consider a bootstrap program for DCI integrals based on their leading singularities and the space of pure functions. We bootstrap the $3$ non-trivial DCI integrals for four-loop Coulomb branch amplitudes (providing an independent verification of the four-loop magic identity), which all take remarkably simple form as weight-$8$ single-valued harmonic polylogarithms. We also compute all leading singularities and a large portion of the pure functions for the $34$ DCI integrals contributing to five-loop amplitudes, where not only some integrals evaluate to functions beyond harmonic polylogarithms but they also contain lower-weight pieces individually.
Time series analysis has witnessed the inspiring development from traditional autoregressive models, deep learning models, to recent Transformers and Large Language Models (LLMs). Efforts in leveraging vision models for time series analysis have also been made along the way but are less visible to the community due to the predominant research on sequence modeling in this domain. However, the discrepancy between continuous time series and the discrete token space of LLMs, and the challenges in explicitly modeling the correlations of variates in multivariate time series have shifted some research attentions to the equally successful Large Vision Models (LVMs) and Vision Language Models (VLMs). To fill the blank in the existing literature, this survey discusses the advantages of vision models over LLMs in time series analysis. It provides a comprehensive and in-depth overview of the existing methods, with dual views of detailed taxonomy that answer the key research questions including how to encode time series as images and how to model the imaged time series for various tasks. Additionally, we address the challenges in the pre- and post-processing steps involved in this framework and outline future directions to further advance time series analysis with vision models.
Generative AI & Changing Work: Systematic Review of Practitioner-led Work Transformations through the Lens of Job Crafting
2025-02-13
Matthew Law, Rama Adithya Varanasi
Widespread integration of Generative AI tools is transforming white-collar work, reshaping how workers define their roles, manage their tasks, and collaborate with peers. This has created a need to develop an overarching understanding of common worker-driven patterns around these transformations. To fill this gap, we conducted a systematic literature review of 23 studies from the ACM Digital Library that focused on workers' lived-experiences and practitioners with GenAI. Our findings reveal that while many professionals have delegated routine tasks to GenAI to focus on core responsibilities, they have also taken on new forms of AI managerial labor to monitor and refine GenAI outputs. Additionally, practitioners have restructured collaborations, sometimes bypassing traditional peer and subordinate interactions in favor of GenAI assistance. These shifts have fragmented cohesive tasks into piecework creating tensions around role boundaries and professional identity. Our analysis suggests that current frameworks, like job crafting, need to evolve to address the complexities of GenAI-driven transformations.
In recent years, remarkable advancements in deep- fake generation technology have led to unprecedented leaps in its realism and capabilities. Despite these advances, we observe a notable lack of structured and deep analysis deepfake technology. The principal aim of this survey is to contribute a thorough theoretical analysis of state-of-the-art face deepfake generation and detection methods. Furthermore, we provide a coherent and systematic evaluation of the implications of deepfakes on face biometric recognition approaches. In addition, we outline key applications of face deepfake technology, elucidating both positive and negative applications of the technology, provide a detailed discussion regarding the gaps in existing research, and propose key research directions for further investigation.
Inclusive Avatar Guidelines for People with Disabilities: Supporting Disability Representation in Social Virtual Reality
2025-02-13
Kexin Zhang, Edward Glenn Scott Spencer, Abijith Manikandan, Andric Li, Ang Li, Yaxing Yao, Yuhang Zhao
Avatar is a critical medium for identity representation in social virtual reality (VR). However, options for disability expression are highly limited on current avatar interfaces. Improperly designed disability features may even perpetuate misconceptions about people with disabilities (PWD). As more PWD use social VR, there is an emerging need for comprehensive design standards that guide developers and designers to create inclusive avatars. Our work aim to advance the avatar design practices by delivering a set of centralized, comprehensive, and validated design guidelines that are easy to adopt, disseminate, and update. Through a systematic literature review and interview with 60 participants with various disabilities, we derived 20 initial design guidelines that cover diverse disability expression methods through five aspects, including avatar appearance, body dynamics, assistive technology design, peripherals around avatars, and customization control. We further evaluated the guidelines via a heuristic evaluation study with 10 VR practitioners, validating the guideline coverage, applicability, and actionability. Our evaluation resulted in a final set of 17 design guidelines with recommendation levels.
Euclid: Finding strong gravitational lenses in the Early Release Observations using convolutional neural networks
2025-02-13
B. C. Nagam, J. A. Acevedo Barroso, J. Wilde, I. T. Andika, A. Manjón-García, R. Pearce-Casey, D. Stern, J. W. Nightingale, L. A. Moustakas, K. McCarthy, E. Moravec, L. Leuzzi, K. Rojas, S. Serjeant, T. E. Collett, P. Matavulj, M. Walmsley, B. Clément, C. Tortora, R. Gavazzi, R. B. Metcalf, C. M. O'Riordan, G. Verdoes Kleijn, L. V. E. Koopmans, E. A. Valentijn, V. Busillo, S. Schuldt, F. Courbin, G. Vernardos, M. Meneghetti, A. Díaz-Sánchez, J. M. Diego, L. R. Ecker, T. T. Thai, A. R. Cooray, H. M. Courtois, L. Delchambre, G. Despali, D. Sluse, L. Ulivi, A. Melo, P. Corcho-Caballero, B. Altieri, A. Amara, S. Andreon, N. Auricchio, H. Aussel, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, A. Caillat, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, J. Dinis, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, M. Fabricius, M. Farina, S. Farrens, S. Ferriol, M. Frailis, E. Franceschi, M. Fumana, K. George, W. Gillard, B. Gillis, C. Giocoli, P. Gómez-Alvarez, A. Grazian, F. Grupp, L. Guzzo, S. V. H. Haugan, J. Hoar, W. Holmes, I. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, B. Kubik, K. Kuijken, M. Kümmel, M. Kunz, H. Kurki-Suonio, R. Laureijs, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, E. Medinaceli, M. Melchior, Y. Mellier, E. Merlin, G. Meylan, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, M. Schirmer, P. Schneider, T. Schrabback, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, J. Skottfelt, L. Stanco, J. -L. Starck, J. Steinwagner, P. Tallada-Crespí, D. Tavagnacco, A. N. Taylor, H. I. Teplitz, I. Tereno, R. Toledo-Moreo, F. Torradeflot, A. Tsyganov, I. Tutusaus, L. Valenziano, T. Vassallo, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, E. Zucca, C. Burigana, A. Mora, M. Pöntinen, V. Scottez
The Early Release Observations (ERO) from Euclid have detected several new galaxy-galaxy strong gravitational lenses, with the all-sky survey expected to find 170,000 new systems, greatly enhancing studies of dark matter, dark energy, and constraints on the cosmological parameters. As a first step, visual inspection of all galaxies in one of the ERO fields (Perseus) was carried out to identify candidate strong lensing systems and compared to the predictions from Convolutional Neural Networks (CNNs). However, the entire ERO data set is too large for expert visual inspection. In this paper, we therefore extend the CNN analysis to the whole ERO data set, using different CNN architectures and methodologies. Using five CNN architectures, we identified 8,469 strong gravitational lens candidates from IE-band cutouts of 13 Euclid ERO fields, narrowing them to 97 through visual inspection, including 14 grade A and 31 grade B candidates. We present the spectroscopic confirmation of a strong gravitational lensing candidate, EUCLJ081705.61+702348.8. The foreground lensing galaxy, an early-type system at redshift z = 0.335, and the background source, a star-forming galaxy at redshift z = 1.475 with [O II] emission, are both identified. Lens modeling using the Euclid strong lens modeling pipeline reveals two distinct arcs in a lensing configuration, with an Einstein radius of 1.18 \pm 0.03 arcseconds, confirming the lensing nature of the system. These findings highlight the importance of a broad CNN search to efficiently reduce candidates, followed by visual inspection to eliminate false positives and achieve a high-purity sample of strong lenses in Euclid.
A tutorial for mesoscale computer simulations of lipid membranes: tether pulling, tubulation and fluctuation
2025-02-13
Maitane Muñoz-Basagoiti, Felix Frey, Billie Meadowcroft, Miguel Amaral, Adam Prada, Anđela Šarić
Lipid membranes and membrane deformations are a long-standing area of research in soft matter and biophysics. Computer simulations have complemented analytical and experimental approaches as one of the pillars in the field. However, setting up and using membrane simulations can come with barriers due to the multidisciplinary effort involved and the vast choice of existing simulations models. In this review, we introduce the non-expert reader to coarse-grained membrane simulations (CGMS) at the mesoscale. Firstly, we give a concise overview of the modelling approaches to study fluid membranes, together with guidance to more specialized references. Secondly, we provide a conceptual guide on how to develop CGMS. Lastly, we construct a hands-on tutorial on how to apply CGMS, by providing a pedagogical examination of tether pulling, tubulation and fluctuations with three different membrane models, and discussing them in terms of their scope and how resource-intensive they are. To ease the reader's venture into the field, we provide a repository with ready-to-run tutorials.
News recommender systems play a critical role in mitigating the information overload problem. In recent years, due to the successful applications of large language model technologies, researchers have utilized Discriminative Large Language Models (DLLMs) or Generative Large Language Models (GLLMs) to improve the performance of news recommender systems. Although several recent surveys review significant challenges for deep learning-based news recommender systems, such as fairness, privacy-preserving, and responsibility, there is a lack of a systematic survey on Large Language Model (LLM)-based news recommender systems. In order to review different core methodologies and explore potential issues systematically, we categorize DLLM-based and GLLM-based news recommender systems under the umbrella of LLM-based news recommender systems. In this survey, we first overview the development of deep learning-based news recommender systems. Then, we review LLM-based news recommender systems based on three aspects: news-oriented modeling, user-oriented modeling, and prediction-oriented modeling. Next, we examine the challenges from various perspectives, including datasets, benchmarking tools, and methodologies. Furthermore, we conduct extensive experiments to analyze how large language model technologies affect the performance of different news recommender systems. Finally, we comprehensively explore the future directions for LLM-based news recommendations in the era of LLMs.
Deep in the Fields of the Andromeda Halo: Discovery of the Pegasus VII dwarf galaxy in UNIONS
2025-02-13
Simon E. T. Smith, Alan W. McConnachie, Stephen Gwyn, Christian R. Hayes, Massimiliano Gatto, Ken Chambers, Jean-Charles Cuillandre, Michael J. Hudson, Eugene Magnier, Nicolas Martin, Julio Navarro
We present the newly discovered dwarf galaxy Pegasus VII (Peg VII), a member of the M31 sub-group which has been uncovered in the $ri$ photometric catalogs from the Ultraviolet Near-Infrared Optical Northern Survey and confirmed with follow-up imaging from both the Canada-France-Hawaii Telescope and the Gemini-North Telescope. This system has an absolute $V$-band magnitude of $-5.7 \pm 0.2$ mag and a physical half-light radius of $177^{+36}_{-34}$ pc, which is characteristic of dynamically-confirmed Milky Way satellite dwarf galaxies and about 5 times more extended than the most extended M31 globular clusters. Peg VII lies at a three-dimensional separation from M31 of $331^{+15}_{-4}$ kpc and a significant elongation ($\epsilon \sim 0.5$) towards the projected direction of M31 could be indicative of a past tidal interaction, but additional investigation into the orbit, star formation history, and whether any gas remains in the galaxy is needed to better understand the evolution of Peg VII.
Graph Convolutional Networks (GCNs) have emerged as a promising approach to machine learning on Electronic Health Records (EHRs). By constructing a graph representation of patient data and performing convolutions on neighborhoods of nodes, GCNs can capture complex relationships and extract meaningful insights to support medical decision making. This survey provides an overview of the current research in applying GCNs to EHR data. We identify the key medical domains and prediction tasks where these models are being utilized, common benchmark datasets, and architectural patterns to provide a comprehensive survey of this field. While this is a nascent area of research, GCNs demonstrate strong potential to leverage the complex information hidden in EHRs. Challenges and opportunities for future work are also discussed.
Implementation and Analysis of Regev's Quantum Factorization Algorithm
2025-02-13
Przemysław Pawlitko, Natalia Moćko, Marcin Niemiec, Piotr Chołda
Quantum computing represents a significant advancement in computational capabilities. Of particular concern is its impact on asymmetric cryptography through, notably, Shor's algorithm and the more recently developed Regev's algorithm for factoring composite numbers. We present our implementation of the latter. Our analysis encompasses both quantum simulation results and classical component examples, with particular emphasis on comparative cases between Regev's and Shor's algorithms. Our experimental results reveal that Regev's algorithm indeed outperforms Shor's algorithm for certain composite numbers in practice. However, we observed significant performance variations across different input values. Despite Regev's algorithm's theoretical asymptotic efficiency advantage, our implementation exhibited execution times longer than Shor's algorithm for small integer factorization in both quantum and classical components. These findings offer insights into the practical challenges and performance characteristics of implementing Regev's algorithm in realistic quantum computing scenarios.
Weakly supervised anomaly detection for resonant new physics in the dijet final state using proton-proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
2025-02-13
ATLAS Collaboration
An anomaly detection search for narrow-width resonances beyond the Standard Model that decay into a pair of jets is presented. The search is based on 139 fb$^{-1}$ of proton-proton collisions at $\sqrt{s}=13$ TeV recorded during 2015-2018 with the ATLAS detector at the Large Hadron Collider. The analysis is optimized without a particular signal model and aims to be sensitive to a broad range of new physics. It uses two different machine learning strategies to estimate the background in different signal regions. In each region, a weakly supervised classifier is trained to distinguish this background estimate from data. The analysis focuses on events with high transverse momentum jets reconstructed as large-radius jets. The mass and substructure of these jets are used as inputs to the classifiers. After a classifier-based selection, the distribution of the invariant mass of the two jets is used to search for potential local excesses. The model-independent results of both the anomaly detection methods show no signs of significant local excesses. In addition to model-independent results, a representative set of signal models is injected into the data, and the sensitivity of the methods to these scenarios is reported.
Non-Markovian Discrete Diffusion with Causal Language Models
2025-02-13
Yangtian Zhang, Sizhuang He, Daniel Levine, Lawrence Zhao, David Zhang, Syed A Rizvi, Emanuele Zappala, Rex Ying, David van Dijk
Discrete diffusion models have emerged as a flexible and controllable paradigm for structured sequence modeling, yet they still lag behind causal language models in expressiveness. To bridge the gap between two paradigms, we introduce CaDDi, a causal discrete diffusion model that unifies sequential and temporal modeling within a non-Markovian diffusion framework. Unlike conventional diffusion models that operate step by step with no access to prior states, CaDDi integrates the temporal trajectory, enabling more expressive and controllable generation. Our approach also treats causal language models as a special case, allowing seamless adoption of pretrained large language models (LLMs) for discrete diffusion without the need for architectural modifications. Empirically, we demonstrate that CaDDi outperforms state-of-the-art discrete diffusion models on both natural language and biological sequence tasks, narrowing the gap between diffusion-based methods and large-scale autoregressive transformers.
LLM-Generated Microservice Implementations from RESTful API Definitions
2025-02-13
Saurabh Chauhan, Zeeshan Rasheed, Abdul Malik Sami, Zheying Zhang, Jussi Rasku, Kai-Kristian Kemell, Pekka Abrahamsson
The growing need for scalable, maintainable, and fast-deploying systems has made microservice architecture widely popular in software development. This paper presents a system that uses Large Language Models (LLMs) to automate the API-first development of RESTful microservices. This system assists in creating OpenAPI specification, generating server code from it, and refining the code through a feedback loop that analyzes execution logs and error messages. By focusing on the API-first methodology, this system ensures that microservices are designed with well-defined interfaces, promoting consistency and reliability across the development life-cycle. The integration of log analysis enables the LLM to detect and address issues efficiently, reducing the number of iterations required to produce functional and robust services. This process automates the generation of microservices and also simplifies the debugging and refinement phases, allowing developers to focus on higher-level design and integration tasks. This system has the potential to benefit software developers, architects, and organizations to speed up software development cycles and reducing manual effort. To assess the potential of the system, we conducted surveys with six industry practitioners. After surveying practitioners, the system demonstrated notable advantages in enhancing development speed, automating repetitive tasks, and simplifying the prototyping process. While experienced developers appreciated its efficiency for specific tasks, some expressed concerns about its limitations in handling advanced customizations and larger scale projects. The code is publicly available at https://github.com/sirbh/code-gen
Experimental Search for the Chiral Magnetic Effect in Relativistic Heavy-Ion Collisions: A Perspective
2025-02-13
Yicheng Feng, Sergei A. Voloshin, Fuqiang Wang
The chiral magnetic effect (CME) refers to generation of the electric current along a magnetic field in a chirally imbalanced system of quarks. The latter is predicted by quantum chromodynamics to arise from quark interaction with non-trivial topological fluctuations of the vacuum gluonic field. The CME has been actively searched for in relativistic heavy-ion collisions, where such gluonic field fluctuations and a strong magnetic field are believed to be present. The CME-sensitive observables are unfortunately subject to a possibly large non-CME background, and firm conclusions on a CME observation have not yet been reached. In this perspective, we review the experimental status and progress in the CME search, from the initial measurements more than a decade ago, to the dedicated program of isobar collisions in 2018 and the release of the isobar blind analysis result in 2022, to intriguing hints of a possible CME signal in Au+Au collisions, and discuss future prospects of a potential CME discovery in the anticipated high-statistic Au+Au collision data at the Relativistic Heavy-Ion Collider by 2025. We hope such a perspective will help sharpening our focus on the fundamental physics of the CME and steer its experimental search.
The Pandora SmallSat: A Low-Cost, High Impact Mission to Study Exoplanets and Their Host Stars
2025-02-13
Thomas Barclay, Elisa V. Quintana, Knicole Colón, Benjamin J. Hord, Gregory Mosby, Joshua E. Schlieder, Robert T. Zellem, Jordan Karburn, Lance M. Simms, Peter F. Heatwole, Christina L. Hedges, Jessie L. Dotson, Thomas P. Greene, Trevor O. Foote, Nikole K. Lewis, Benjamin V. Rackham, Brett M. Morris, Emily A. Gilbert, Veselin B. Kostov, Jason F. Rowe, Lindsay S. Wiser
The Pandora SmallSat is a NASA flight project aimed at studying the atmospheres of exoplanets -- planets orbiting stars outside our Solar System. Pandora will provide the first dataset of simultaneous, multiband (visible and NIR), long-baseline observations of exoplanets and their host stars. Pandora is an ambitious project that will fly a 0.44 m telescope in a small form factor. To achieve the scientific goals, the mission requires a departure from the traditional cost-schedule paradigm of half-meter-class observatories. Pandora achieves this by leveraging existing capabilities that necessitate minimal engineering development, disruptive and agile management, trusted partnerships with vendors, and strong support from the lead institutions. The Pandora team has developed a suite of high-fidelity parameterized simulation and modeling tools to estimate the performance of both imaging channels. This has enabled a unique bottom-up approach to deriving trades and system requirements. Pandora is a partnership between NASA and Lawrence Livermore National Laboratory. The project completed its Critical Design Review in October 2023 and is slated for launch into Sun-synchronous, low-Earth orbit in Fall 2025.
2025-02-13
Oscar Garcia-Montero, Sören Schlichting
We discuss the application of the Color Glass Condensate (CGC), an effective field theory of Quantum Chromodynamics (QCD), to describe high-energy nuclear interactions. We first provide an introduction to the methods and language of the CGC, its role in understanding gluon saturation in heavy-ion collisions at the LHC and RHIC, and its relevance in various scattering processes such as Deep Inelastic Scattering (DIS). The application of the CGC effective field theory to describe hadron-hadron collisions is discussed in the scope of asymmetric \textit{dilute-dense} collisions, and Heavy-Ion Collisions in the \textit{dense-dense} limit. The review covers theoretical foundations, recent advancements, and phenomenological applications, focusing on using the CGC to determine the initial conditions of heavy-ion collisions.
Evaluating GPT's Capability in Identifying Stages of Cognitive Impairment from Electronic Health Data
2025-02-13
Yu Leng, Yingnan He, Colin Magdamo, Ana-Maria Vranceanu, Christine S. Ritchie, Shibani S. Mukerji, Lidia M. V. R. Moura, John R. Dickson, Deborah Blacker, Sudeshna Das
Identifying cognitive impairment within electronic health records (EHRs) is crucial not only for timely diagnoses but also for facilitating research. Information about cognitive impairment often exists within unstructured clinician notes in EHRs, but manual chart reviews are both time-consuming and error-prone. To address this issue, our study evaluates an automated approach using zero-shot GPT-4o to determine stage of cognitive impairment in two different tasks. First, we evaluated the ability of GPT-4o to determine the global Clinical Dementia Rating (CDR) on specialist notes from 769 patients who visited the memory clinic at Massachusetts General Hospital (MGH), and achieved a weighted kappa score of 0.83. Second, we assessed GPT-4o's ability to differentiate between normal cognition, mild cognitive impairment (MCI), and dementia on all notes in a 3-year window from 860 Medicare patients. GPT-4o attained a weighted kappa score of 0.91 in comparison to specialist chart reviews and 0.96 on cases that the clinical adjudicators rated with high confidence. Our findings demonstrate GPT-4o's potential as a scalable chart review tool for creating research datasets and assisting diagnosis in clinical settings in the future.
The Pristine survey: XXVIII. The extremely metal-poor stream C-19 stretches over more than 100 degrees
2025-02-13
Zhen Yuan, Tadafumi Matsuno, Tatyana Sitnova, Nicolas F. Martin, Rodrigo A. Ibata, Anke Ardern-Arentsen, Raymond Carlberg, Jonay I. Gonzalez Hernandez, Erika Holmbeck, Georges Kordopatis, Fangzhou Jiang, Khyati Malhan, Julio Navarro, Federico Sestito, Kim A. Venn, Akshara Viswanathan, Sara Vitali
The discovery of the most metal-poor stream, C-19, provides us with a fossil record of a stellar structure born very soon after the Big Bang. In this work, we search for new C-19 members over the whole sky by combining two complementary stream-searching algorithms, STREAMFINDER and StarGO,, and utilizing low-metallicity star samples from the Pristine survey as well as Gaia BP/RP spectro-photometric catalogues. We confirm twelve new members, spread over more than 100$^\circ$, using velocity and metallicity information from a set of spectroscopic follow-up programs that targeted a quasi-complete sample of our bright candidates ($G \lesssim 16.0$). From the updated set of stream members, we confirm that the stream is wide, with a stream width of $\sim200$ pc, and dynamically hot, with a derived velocity dispersion of $11.1^{+1.9}_{-1.6}$ km/s. The tension remains between these quantities and a purely baryonic scenario in which the relatively low-mass stream (even updated to a few $10^4M_{\odot}$) stems from a globular cluster progenitor, as suggested by its chemical abundances. Some heating mechanism, such as preheating of the cluster in its own dark matter halo or through interactions with halo sub-structures appears necessary to explain the tension. The impact of binaries on the measured dispersion also remains unknown. Detailed elemental abundances of more stream members as well as multi-epoch radial velocities from spectroscopic observations are therefore crucial to fully understand the nature and past history of the most metal-poor stream of the Milky Way.
Tracing high-z Galaxies in X-rays with JWST and Chandra
2025-02-13
A. Kaminsky, N. Cappelluti, G. Hasinger, A. Peca, C. M. Casey, N. E. Drakos, A. Faisst, G. Gozaliasl, O. Ilbert, J. S. Kartaltepe, A. Kashlinsky, A. M. Koekemoer, H. J. McCracken, J. Rhodes, B. E. Robertson, M. Shuntov, J. Sterling
We leverage JWST data from the COSMOS-Web Survey in order to provide updated measurements on the auto-power spectrum of the now resolved Cosmic Infrared Background (CIB) and its coherence with the unresolved soft Cosmic X-ray Background (CXB) observed by Chandra at z > 6. Maps of the CIB in the F277W and F444W NIRCam filters are constructed with sources fainter than AB mag = 25 and cross-correlated with the CXB in the [0.5-2] keV band. We find that on scales between 1 and 1000'' the CIB-CXB cross-power in both NIRCam filters is statistically significant with signal-to-noise ratios (S/N) of 4.80 and 6.20 respectively from redshifts 0 < z < 13. In our high-z (6 < z < 13) interval we find coherence in both filters with a S/N of 7.32 and 5.39 respectively. These results suggest that there are X-ray emitting galaxies resolved by JWST, including star-forming galaxies (SFGs) and active galactic nuclei (AGNs). We fit the large-scale biasing of the IR sources producing the CIB as a function of z with results consistent with prior measurements and place constraints on the CXB flux and biasing at low- and high-z. The CXB flux measurements presented in this study suggest that approximately 94% of the [0.5-2] keV CXB is resolved, and this value is consistent within 2$\sigma$ with the complete resolution of the [0.5-2] keV CXB.
In-Silico Investigation of 3D Quantitative Angiography for Internal Carotid Aneurysms Using Biplane Imaging and 3D Vascular Geometry Constraints
2025-02-13
Kyle A. Williams, Swetadri Vasan Setlur Nagesh, Daniel R. Bednarek, Stephen Rudin, Ciprian N. Ionita
Quantitative angiography (QA) in two dimensions has been instrumental in assessing neurovascular contrast flow patterns, aiding disease severity and treatment outcome evaluations. However, QA requires high spatio-temporal resolution, restricting its use to digital subtraction angiography (DSA), and is prone to errors in quantification of highly 3D flow patterns. This study examines whether 3D QA information can be recovered by reconstructing four-dimensional (4D) angiography using data from standard clinical imaging protocols. Patient-specific internal carotid aneurysm models were used to generate high-fidelity computational fluid dynamics (CFD) simulations of contrast flow. The resulting 4D angiograms were used to simulate biplane DSA under clinical imaging protocols. 4D angiography was reconstructed from two views using back-projection constrained by an a priori 3D geometry. Quantitative angiographic parametric imaging (API) metrics obtained from the CFD-based 4D angiography and reconstructed 4D angiography were compared using mean square error (MSE) and mean absolute percentage error (MAPE). The reconstructed 4D datasets effectively captured 3D flow dynamics, achieving an average MSE of 0.007 across models and flow conditions. API metrics such as PH and AUC closely matched the CFD ground truth, with temporal metrics showing some variability in regions with overlapping projections. These results demonstrate the potential to recover 3D QA information using simulated 4D angiography constrained by standard clinical imaging parameters. The method provides a robust framework for evaluating and improving QA in clinical neurovascular applications, offering new insights into the dynamics of aneurysmal contrast flow.
2025-02-13
Albina Klepach, Alexander Nikulin, Ilya Zisman, Denis Tarasov, Alexander Derevyagin, Andrei Polubarov, Nikita Lyubaykin, Vladislav Kurenkov
Leveraging vast amounts of internet video data for Embodied AI is currently bottle-necked by the lack of action annotations and the presence of action-correlated distractors. We propose a novel object-centric latent action learning approach, based on VideoSaur and LAPO, that employs self-supervised decomposition of scenes into object representations and annotates video data with proxy-action labels. This method effectively disentangles causal agent-object interactions from irrelevant background noise and reduces the performance degradation of latent action learning approaches caused by distractors. Our preliminary experiments with the Distracting Control Suite show that latent action pretraining based on object decompositions improve the quality of inferred latent actions by x2.7 and efficiency of downstream fine-tuning with a small set of labeled actions, increasing return by x2.6 on average.
Managing target of opportunity (ToO) observations at Observatorio Astrofísico de Javalambre (OAJ)
2025-02-13
Civera Tamara
The Observatorio Astrof\'isico de Javalambre (OAJ) is a Spanish astronomical ICTS (Unique Scientific and Technical Infrastructures) located in the Sierra de Javalambre in Teruel (Spain). It has been particularly conceived for carrying out large-sky multi-filter surveys. As an ICTS, the OAJ offers Open Time to the astronomical community, offering more than 25% through Legacy Surveys, Regular Programs (RP) and Director discretionary time (DDT). Regarding the RP, a new call for proposals is made public each semester accepting only proposals under the modality of Target of Opportunity (ToO). This contribution summarizes how ToOs are managed at OAJ presenting the different applications designed and implemented at the observatory to deal with them: the Proposal Preparation portal (to request observing time), the Phase2 Observing tool and the submitphase2 web service (to trigger the ToOs), the TAC Tracking portal (for telescope operators to support the observations) and the TACData portal (to publish and offer the images and their data products).
Improving Acoustic Side-Channel Attacks on Keyboards Using Transformers and Large Language Models
2025-02-13
Jin Hyun Park, Seyyed Ali Ayati, Yichen Cai
The increasing prevalence of microphones in everyday devices and the growing reliance on online services have amplified the risk of acoustic side-channel attacks (ASCAs) targeting keyboards. This study explores deep learning techniques, specifically vision transformers (VTs) and large language models (LLMs), to enhance the effectiveness and applicability of such attacks. We present substantial improvements over prior research, with the CoAtNet model achieving state-of-the-art performance. Our CoAtNet shows a 5.0% improvement for keystrokes recorded via smartphone (Phone) and 5.9% for those recorded via Zoom compared to previous benchmarks. We also evaluate transformer architectures and language models, with the best VT model matching CoAtNet's performance. A key advancement is the introduction of a noise mitigation method for real-world scenarios. By using LLMs for contextual understanding, we detect and correct erroneous keystrokes in noisy environments, enhancing ASCA performance. Additionally, fine-tuned lightweight language models with Low-Rank Adaptation (LoRA) deliver comparable performance to heavyweight models with 67X more parameters. This integration of VTs and LLMs improves the practical applicability of ASCA mitigation, marking the first use of these technologies to address ASCAs and error correction in real-world scenarios.
Knowledge Integration Strategies in Autonomous Vehicle Prediction and Planning: A Comprehensive Survey
2025-02-13
Kumar Manas, Adrian Paschke
This comprehensive survey examines the integration of knowledge-based approaches into autonomous driving systems, with a focus on trajectory prediction and planning. We systematically review methodologies for incorporating domain knowledge, traffic rules, and commonsense reasoning into these systems, spanning purely symbolic representations to hybrid neuro-symbolic architectures. In particular, we analyze recent advancements in formal logic and differential logic programming, reinforcement learning frameworks, and emerging techniques that leverage large foundation models and diffusion models for knowledge representation. Organized under a unified literature survey section, our discussion synthesizes the state-of-the-art into a high-level overview, supported by a detailed comparative table that maps key works to their respective methodological categories. This survey not only highlights current trends -- including the growing emphasis on interpretable AI, formal verification in safety-critical systems, and the increased use of generative models in prediction and planning -- but also outlines the challenges and opportunities for developing robust, knowledge-enhanced autonomous driving systems.
General relativity, as a diffeomorphism-invariant theory, allows the description of physical phenomena in a wide variety of coordinate systems. In the presence of boundaries, such as event horizons and null infinity, time coordinates must be carefully adapted to the global causal structure of spacetime to ensure a computationally efficient description. Horizon-penetrating time is used to describe the dynamics of infalling matter and radiation across the event horizon, while hyperboloidal time is used to study the propagation of radiation toward the idealized observer at null infinity. In this paper, we explore the historical and mathematical connection between horizon-penetrating and hyperboloidal time coordinates, arguing that both classes of coordinates are simply regular choices of time across null horizons. We review the height-function formalism in stationary spacetimes, providing examples that may be useful in computations, such as source-adapted foliations or Fefferman-Graham-Bondi coordinates near null infinity. We discuss bridges connecting the boundaries of spacetime through a time hypersurface across null horizons, including the event horizon, null infinity, and the cosmological horizon. This work is motivated by the broader effort to understand the role of time in general relativity and reviews a unified framework for handling null boundaries in analytical and numerical approaches. The insights developed here offer practical tools for numerical relativity, gravitational wave astronomy, and other explorations of the large-scale structure of spacetimes.
Mapping the Landscape of Generative AI in Network Monitoring and Management
2025-02-12
Giampaolo Bovenzi, Francesco Cerasuolo, Domenico Ciuonzo, Davide Di Monda, Idio Guarino, Antonio Montieri, Valerio Persico, Antonio Pescapè
Generative Artificial Intelligence (GenAI) models such as LLMs, GPTs, and Diffusion Models have recently gained widespread attention from both the research and the industrial communities. This survey explores their application in network monitoring and management, focusing on prominent use cases, as well as challenges and opportunities. We discuss how network traffic generation and classification, network intrusion detection, networked system log analysis, and network digital assistance can benefit from the use of GenAI models. Additionally, we provide an overview of the available GenAI models, datasets for large-scale training phases, and platforms for the development of such models. Finally, we discuss research directions that potentially mitigate the roadblocks to the adoption of GenAI for network monitoring and management. Our investigation aims to map the current landscape and pave the way for future research in leveraging GenAI for network monitoring and management.
Testing the growth of cosmic structures during the Dark Ages
2025-02-12
Elena Vanetti, Eleonora Vanzan, Nicola Bellomo, Alvise Raccanelli
Hydrogen 21-cm Line Intensity Mapping offers the unique opportunity to access the Dark Ages and trace the formation and evolution of the large scale structure of the Universe prior to star and galaxy formation. In this work we investigate the potential of future Earth- and Moon-based 21-cm surveys to constrain the growth of structures during the currently unexplored redshift range $30 < z < 200$. On the one hand we show how foregrounds limit the capabilities of Earth-based instruments in achieving precision below $10\%$ level. On the other hand, observations from the far side of the Moon, not affected by foregrounds generated by Earth's atmosphere, will reach percent or even sub-percent precision in terms of reconstructing the growth of cosmic structures. Such remarkable precision will improve by orders of magnitude parameter constraints on models that induce deviations from $\Lambda$CDM, not only during the Dark Ages, but also during recombination or that manifests mostly in the low-redshift Universe, like Early Dark Energy and nDGP models. Thus, because of their insensitivity to non-linearities or astrophysical processes, line intensity mapping surveys will provide a formidable consistency check to potential claims of discoveries of new physics that affect the growth of structures.
Quality-Aware Decoding: Unifying Quality Estimation and Decoding
2025-02-12
Sai Koneru, Matthias Huck, Miriam Exel, Jan Niehues
An emerging research direction in NMT involves the use of Quality Estimation (QE) models, which have demonstrated high correlations with human judgment and can enhance translations through Quality-Aware Decoding. Although several approaches have been proposed based on sampling multiple candidate translations, none have integrated these models directly into the decoding process. In this paper, we address this by proposing a novel token-level QE model capable of reliably scoring partial translations. We build a uni-directional QE model for this, as decoder models are inherently trained and efficient on partial sequences. We then present a decoding strategy that integrates the QE model for Quality-Aware decoding and demonstrate that the translation quality improves when compared to the N-best list re-ranking with state-of-the-art QE models (upto $1.39$ XCOMET-XXL $\uparrow$). Finally, we show that our approach provides significant benefits in document translation tasks, where the quality of N-best lists is typically suboptimal.
Human-Centric Foundation Models: Perception, Generation and Agentic Modeling
2025-02-12
Shixiang Tang, Yizhou Wang, Lu Chen, Yuan Wang, Sida Peng, Dan Xu, Wanli Ouyang
Human understanding and generation are critical for modeling digital humans and humanoid embodiments. Recently, Human-centric Foundation Models (HcFMs) inspired by the success of generalist models, such as large language and vision models, have emerged to unify diverse human-centric tasks into a single framework, surpassing traditional task-specific approaches. In this survey, we present a comprehensive overview of HcFMs by proposing a taxonomy that categorizes current approaches into four groups: (1) Human-centric Perception Foundation Models that capture fine-grained features for multi-modal 2D and 3D understanding. (2) Human-centric AIGC Foundation Models that generate high-fidelity, diverse human-related content. (3) Unified Perception and Generation Models that integrate these capabilities to enhance both human understanding and synthesis. (4) Human-centric Agentic Foundation Models that extend beyond perception and generation to learn human-like intelligence and interactive behaviors for humanoid embodied tasks. We review state-of-the-art techniques, discuss emerging challenges and future research directions. This survey aims to serve as a roadmap for researchers and practitioners working towards more robust, versatile, and intelligent digital human and embodiments modeling.
In recent decades, Earth-to-Air Heat Exchangers (EAHEs), also known as underground air ducts, have garnered significant attention for their ability to provide energy-efficient cooling and heating solutions while maintaining a minimal environmental footprint. These systems leverage the relatively stable underground temperature to regulate indoor climates, reducing reliance on conventional heating, ventilation, and air conditioning (HVAC) systems. This review systematically categorizes and synthesizes research on EAHEs into three primary areas: analytical, numerical, and exergoeconomic studies. Analytical approaches focus on developing theoretical models to predict thermal performance, while numerical simulations provide insights into system optimization and real-world applications. Exergoeconomic analyses, integrating thermodynamic efficiency with economic considerations, offer valuable perspectives on cost-effectiveness and long-term viability. By consolidating existing contributions across these domains, this study serves as a comprehensive reference for researchers, engineers, and policymakers seeking to enhance the design, implementation, and performance of EAHE systems. The findings emphasize the pivotal role of EAHEs in reducing energy consumption, lowering greenhouse gas emissions, and improving economic sustainability. Additionally, this review identifies key challenges, including soil thermal conductivity variations, moisture effects, and system integration with renewable energy sources, which require further investigation. By addressing these challenges, EAHEs can be further optimized to serve as a cornerstone in sustainable energy management, contributing to global efforts toward energy-efficient building solutions and climate change mitigation.
A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook
2025-02-12
Chengqian Ma, Zhengyi Shi, Zhiqiang Lu, Shenghao Xie, Fei Chao, Yao Sui
Image quality assessment (IQA) represents a pivotal challenge in image-focused technologies, significantly influencing the advancement trajectory of image processing and computer vision. Recently, IQA has witnessed a notable surge in innovative research efforts, driven by the emergence of novel architectural paradigms and sophisticated computational techniques. This survey delivers an extensive analysis of contemporary IQA methodologies, organized according to their application scenarios, serving as a beneficial reference for both beginners and experienced researchers. We analyze the advantages and limitations of current approaches and suggest potential future research pathways. The survey encompasses both general and specific IQA methodologies, including conventional statistical measures, machine learning techniques, and cutting-edge deep learning models such as convolutional neural networks (CNNs) and Transformer models. The analysis within this survey highlights the necessity for distortion-specific IQA methods tailored to various application scenarios, emphasizing the significance of practicality, interpretability, and ease of implementation in future developments.
It remains challenging to systematically survey nearby diffuse dwarf galaxies and address the formation mechanism of this population distinguishing from regular ones. We carry out a pilot search for these galaxies in the COSMOS field using the deep \textit{HST}/F814W imaging data. We report three diffuse dwarf galaxies satisfying the criteria: (1) redshift $z<0.2$, (2) effective radius $r_{\rm e}>1.0''$, and (3) central surface brightness $\mu_{\rm 0}>24$ mag arcsec$^{-2}$. Two of the three galaxies, COSMOS-UDG1 and COSMOS-UDG2, are recognized as ultra-diffuse galaxies (UDGs) with redshift $z=0.130$ and $0.049$, respectively. The third galaxy, COSMOS-dw1, is spectroscopically confirmed as a dwarf galaxy at $z=0.004$. We derive the physical properties through fitting their spectral energy distributions (SEDs) extracted from deep multiwavelength observations. COSMOS-dw1 has a stellar mass of $5.6_{-2.7}^{+2.5}\times10^{6}$ M$_{\odot}$, harboring neutral hydrogen gas of mass $4.90\pm0.90\times10^{6}$ M$_{\odot}$, hinting that this galaxy may be in the nascent stages of quenching. The estimated dynamical mass of $3.4\times10^{7}\,M_{\odot}$ further suggests that COSMOS-dw1 is predominantly of dark matter. COSMOS-UDG1 and COSMOS-UDG2 exhibit comparable stellar masses of $\sim 2\times10^{8}$ M$_{\odot}$. Notably, COSMOS-UDG1 is younger and more metal-rich than COSMOS-UDG2 and COSMOS-dw1. Conversely, COSMOS-UDG2 and COSMOS-dw1 have similar stellar metallicities, yet COSMOS-UDG2 is older than COSMOS-dw1. All three galaxies adhere to the stellar mass-metallicity relation (MZR) for dwarf galaxies in the local Universe, implying they belong to the dwarf galaxy population.
Analysis of the weak lensing mass-richness relation of redMaPPer clusters in the LSST DESC DC2 simulations
2025-02-12
Constantin Payerne, Zhuowen Zhang, Michel Aguena, Céline Combet, Thibault Guillemin, Marina Ricci, Nathan Amouroux, Camille Avestruz, Eduardo J. Barroso, Arya Farahi, Eve Kovacs, Calum Murray, Markus M. Rau, Eli S. Rykoff, Samuel J. Schmidt, the LSST Dark Energy Science Collaboration
Cluster scaling relations are key ingredients in cluster abundance-based cosmological studies. In optical cluster cosmology, weak gravitational lensing has proven to be a powerful tool to constrain the cluster mass-richness relation. This work is conducted as part of the Dark Energy Science Collaboration (DESC), which aims to analyze the Legacy Survey of Space and Time (LSST) of Vera C. Rubin Observatory, starting in 2026. Weak lensing-inferred cluster properties, such as mass, suffer from several sources of bias. In this paper, we aim to test the impact of modeling choices and observational systematics in cluster lensing on the inference of the mass-richness relation. We constrain the mass-richness relation of 3,600 clusters detected by the redMaPPer algorithm in the cosmoDC2 extra-galactic mock catalog (covering $440$ deg$^2$) of the LSST DESC DC2 simulation, using number count measurements and stacked weak lensing profiles in several intervals of richness ($20 \leq \lambda \leq 200$) and redshift ($0.2 \leq z \leq 1$). By modeling the mean of the scaling relation as $\langle \ln \lambda|M_{\rm 200c}, z\rangle = \ln\lambda_0 + \mu_z\log[(1+z)/(1+0.5)] + \mu_m[\log_{10}(M_{\rm 200c}) - 14.3]$, our baseline constraints are $\ln\lambda_0 = 3.37\pm 0.03$, $\mu_z = 0.08\pm 0.07$ and $\mu_m = 2.18 \pm 0.07$. We have found that, for a LSST-like source galaxy density, our constraints are robust to a change in concentration-mass relation and dark matter density profile modeling choices, when source redshifts and shapes are perfectly known. We have found that photometric redshift uncertainties can introduce bias at the $1\sigma$ level, which can be mitigated by an overall correcting factor, fitted jointly with scaling parameters. We find that including positive shear-richness covariance in the fit shifts the results by up to 0.5$\sigma$.
Augmented Journeys: Interactive Points of Interest for In-Car Augmented Reality
2025-02-12
Robin Connor Schramm, Ginevra Fedrizzi, Markus Sasalovici, Jann Philipp Freiwald, Ulrich Schwanecke
As passengers spend more time in vehicles, the demand for non-driving related tasks (NDRTs) increases. In-car Augmented Reality (AR) has the potential to enhance passenger experiences by enabling interaction with the environment through NDRTs using world-fixed Points of Interest (POIs). However, the effectiveness of existing interaction techniques and visualization methods for in-car AR remains unclear. Based on a survey (N=110) and a pre-study (N=10), we developed an interactive in-car AR system using a video see-through head-mounted display to engage with POIs via eye-gaze and pinch. Users could explore passed and upcoming POIs using three visualization techniques: List, Timeline, and Minimap. We evaluated the system's feasibility in a field study (N=21). Our findings indicate general acceptance of the system, with the List visualization being the preferred method for exploring POIs. Additionally, the study highlights limitations of current AR hardware, particularly the impact of vehicle movement on 3D interaction.
From Haystack to Needle: Label Space Reduction for Zero-shot Classification
2025-02-12
Nathan Vandemoortele, Bram Steenwinckel, Femke Ongenae, Sofie Van Hoecke
We present Label Space Reduction (LSR), a novel method for improving zero-shot classification performance of Large Language Models (LLMs). LSR iteratively refines the classification label space by systematically ranking and reducing candidate classes, enabling the model to concentrate on the most relevant options. By leveraging unlabeled data with the statistical learning capabilities of data-driven models, LSR dynamically optimizes the label space representation at test time. Our experiments across seven benchmarks demonstrate that LSR improves macro-F1 scores by an average of 7.0% (up to 14.2%) with Llama-3.1-70B and 3.3% (up to 11.1%) with Claude-3.5-Sonnet compared to standard zero-shot classification baselines. To reduce the computational overhead of LSR, which requires an additional LLM call at each iteration, we propose distilling the model into a probabilistic classifier, allowing for efficient inference.
2025-02-12
Carlos Garrido-Munoz, Antonio Rios-Vila, Jorge Calvo-Zaragoza
Handwritten Text Recognition (HTR) has become an essential field within pattern recognition and machine learning, with applications spanning historical document preservation to modern data entry and accessibility solutions. The complexity of HTR lies in the high variability of handwriting, which makes it challenging to develop robust recognition systems. This survey examines the evolution of HTR models, tracing their progression from early heuristic-based approaches to contemporary state-of-the-art neural models, which leverage deep learning techniques. The scope of the field has also expanded, with models initially capable of recognizing only word-level content progressing to recent end-to-end document-level approaches. Our paper categorizes existing work into two primary levels of recognition: (1) \emph{up to line-level}, encompassing word and line recognition, and (2) \emph{beyond line-level}, addressing paragraph- and document-level challenges. We provide a unified framework that examines research methodologies, recent advances in benchmarking, key datasets in the field, and a discussion of the results reported in the literature. Finally, we identify pressing research challenges and outline promising future directions, aiming to equip researchers and practitioners with a roadmap for advancing the field.
Large language models (LLMs) are helping millions of users write texts about diverse issues, and in doing so expose users to different ideas and perspectives. This creates concerns about issue bias, where an LLM tends to present just one perspective on a given issue, which in turn may influence how users think about this issue. So far, it has not been possible to measure which issue biases LLMs actually manifest in real user interactions, making it difficult to address the risks from biased LLMs. Therefore, we create IssueBench: a set of 2.49m realistic prompts for measuring issue bias in LLM writing assistance, which we construct based on 3.9k templates (e.g. "write a blog about") and 212 political issues (e.g. "AI regulation") from real user interactions. Using IssueBench, we show that issue biases are common and persistent in state-of-the-art LLMs. We also show that biases are remarkably similar across models, and that all models align more with US Democrat than Republican voter opinion on a subset of issues. IssueBench can easily be adapted to include other issues, templates, or tasks. By enabling robust and realistic measurement, we hope that IssueBench can bring a new quality of evidence to ongoing discussions about LLM biases and how to address them.
2025-02-12
Jakub Homola, Radim Vavřík, Ondřej Meca, Tomáš Brzobohatý, Lubomír Říha
FETI is a numerical method used to solve engineering problems. It builds on the ideas of domain decomposition, which makes it highly scalable and capable of efficiently utilizing whole supercomputers. One of the most time-consuming parts of the FETI solver is the application of the dual operator F in every iteration of the solver. It is traditionally performed on the CPU using an implicit approach of applying the individual sparse matrices that form F right-to-left. Another approach is to apply the dual operator explicitly, which primarily involves a simple dense matrix-vector multiplication and can be efficiently performed on the GPU. However, this requires additional preprocessing on the CPU where the dense matrix is assembled, which makes the explicit approach beneficial only after hundreds of iterations are performed. In this paper, we use the GPU to accelerate the assembly process as well. This significantly shortens the preprocessing time, thus decreasing the number of solver iterations needed to make the explicit approach beneficial. With a proper configuration, we only need a few tens of iterations to achieve speedup relative to the implicit CPU approach. Compared to the CPU-only explicit approach, we achieved up to 10x speedup for the preprocessing and 25x for the application.
Unveiling Global Discourse Structures: Theoretical Analysis and NLP Applications in Argument Mining
2025-02-12
Christopher van Le
Particularly in the structure of global discourse, coherence plays a pivotal role in human text comprehension and is a hallmark of high-quality text. This is especially true for persuasive texts, where coherent argument structures support claims effectively. This paper discusses and proposes methods for detecting, extracting and representing these global discourse structures in a proccess called Argument(ation) Mining. We begin by defining key terms and processes of discourse structure analysis, then continue to summarize existing research on the matter, and identify shortcomings in current argument component extraction and classification methods. Furthermore, we will outline an architecture for argument mining that focuses on making models more generalisable while overcoming challenges in the current field of research by utilizing novel NLP techniques. This paper reviews current knowledge, summarizes recent works, and outlines our NLP pipeline, aiming to contribute to the theoretical understanding of global discourse structures.
A Survey on Pre-Trained Diffusion Model Distillations
2025-02-12
Xuhui Fan, Zhangkai Wu, Hongyu Wu
Diffusion Models~(DMs) have emerged as the dominant approach in Generative Artificial Intelligence (GenAI), owing to their remarkable performance in tasks such as text-to-image synthesis. However, practical DMs, such as stable diffusion, are typically trained on massive datasets and thus usually require large storage. At the same time, many steps may be required, i.e., recursively evaluating the trained neural network, to generate a high-quality image, which results in significant computational costs during sample generation. As a result, distillation methods on pre-trained DM have become widely adopted practices to develop smaller, more efficient models capable of rapid, few-step generation in low-resource environment. When these distillation methods are developed from different perspectives, there is an urgent need for a systematic survey, particularly from a methodological perspective. In this survey, we review distillation methods through three aspects: output loss distillation, trajectory distillation and adversarial distillation. We also discuss current challenges and outline future research directions in the conclusion.
Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing
2025-02-12
Tommaso Baldi, Javier Campos, Olivia Weng, Caleb Geniesse, Nhan Tran, Ryan Kastner, Alessandro Biondi
In this paper, we propose a method to perform empirical analysis of the loss landscape of machine learning (ML) models. The method is applied to two ML models for scientific sensing, which necessitates quantization to be deployed and are subject to noise and perturbations due to experimental conditions. Our method allows assessing the robustness of ML models to such effects as a function of quantization precision and under different regularization techniques -- two crucial concerns that remained underexplored so far. By investigating the interplay between performance, efficiency, and robustness by means of loss landscape analysis, we both established a strong correlation between gently-shaped landscapes and robustness to input and weight perturbations and observed other intriguing and non-obvious phenomena. Our method allows a systematic exploration of such trade-offs a priori, i.e., without training and testing multiple models, leading to more efficient development workflows. This work also highlights the importance of incorporating robustness into the Pareto optimization of ML models, enabling more reliable and adaptive scientific sensing systems.
With the extensive application of Graph Neural Networks (GNNs) across various domains, their trustworthiness has emerged as a focal point of research. Some existing studies have shown that the integration of large language models (LLMs) can improve the semantic understanding and generation capabilities of GNNs, which in turn improves the trustworthiness of GNNs from various aspects. Our review introduces a taxonomy that offers researchers a clear framework for comprehending the principles and applications of different methods and helps clarify the connections and differences among various approaches. Then we systematically survey representative approaches along the four categories of our taxonomy. Through our taxonomy, researchers can understand the applicable scenarios, potential advantages, and limitations of each approach for the the trusted integration of GNNs with LLMs. Finally, we present some promising directions of work and future trends for the integration of LLMs and GNNs to improve model trustworthiness.
Graph Foundation Models for Recommendation: A Comprehensive Survey
2025-02-12
Bin Wu, Yihang Wang, Yuanhao Zeng, Jiawei Liu, Jiashu Zhao, Cheng Yang, Yawen Li, Long Xia, Dawei Yin, Chuan Shi
Recommender systems (RS) serve as a fundamental tool for navigating the vast expanse of online information, with deep learning advancements playing an increasingly important role in improving ranking accuracy. Among these, graph neural networks (GNNs) excel at extracting higher-order structural information, while large language models (LLMs) are designed to process and comprehend natural language, making both approaches highly effective and widely adopted. Recent research has focused on graph foundation models (GFMs), which integrate the strengths of GNNs and LLMs to model complex RS problems more efficiently by leveraging the graph-based structure of user-item relationships alongside textual understanding. In this survey, we provide a comprehensive overview of GFM-based RS technologies by introducing a clear taxonomy of current approaches, diving into methodological details, and highlighting key challenges and future directions. By synthesizing recent advancements, we aim to offer valuable insights into the evolving landscape of GFM-based recommender systems.
Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact
2025-02-12
Mohsin Bilal, Aadam, Manahil Raza, Youssef Altherwy, Anas Alsuhaibani, Abdulrahman Abduljabbar, Fahdah Almarshad, Paul Golding, Nasir Rajpoot
From self-supervised, vision-only models to contrastive visual-language frameworks, computational pathology has rapidly evolved in recent years. Generative AI "co-pilots" now demonstrate the ability to mine subtle, sub-visual tissue cues across the cellular-to-pathology spectrum, generate comprehensive reports, and respond to complex user queries. The scale of data has surged dramatically, growing from tens to millions of multi-gigapixel tissue images, while the number of trainable parameters in these models has risen to several billion. The critical question remains: how will this new wave of generative and multi-purpose AI transform clinical diagnostics? In this article, we explore the true potential of these innovations and their integration into clinical practice. We review the rapid progress of foundation models in pathology, clarify their applications and significance. More precisely, we examine the very definition of foundational models, identifying what makes them foundational, general, or multipurpose, and assess their impact on computational pathology. Additionally, we address the unique challenges associated with their development and evaluation. These models have demonstrated exceptional predictive and generative capabilities, but establishing global benchmarks is crucial to enhancing evaluation standards and fostering their widespread clinical adoption. In computational pathology, the broader impact of frontier AI ultimately depends on widespread adoption and societal acceptance. While direct public exposure is not strictly necessary, it remains a powerful tool for dispelling misconceptions, building trust, and securing regulatory support.
Moment estimates for polyatomic Boltzmann equation with frozen collisions
2025-02-12
Ricardo Alonso, Milana Čolić
In this paper, a polyatomic gas with continuous internal energy is considered, allowing for frozen collisions, in which the kinetic energy of the colliding particle pair is conserved, and the internal energy of each particle remains unchanged. A priori moment estimates are derived for solutions of the space-homogeneous Boltzmann equation with a collision kernel of the hard potentials type with cut-off. The model with frozen collisions is first analyzed, followed by a review of general collisions--referred to as pure polyatomic--which preserve the total kinetic and internal energy. By combining existing results for pure polyatomic collisions with the newly derived estimates for frozen collisions, moment estimates are established for the Boltzmann equation with a collision operator that convexly combines both types of collisions. In particular, the moment generation property is shown to be driven by the rate of the pure polyatomic operator, and the moment propagation property holds.
Automatic monitoring of tree plantations plays a crucial role in agriculture. Flawless monitoring of tree health helps farmers make informed decisions regarding their management by taking appropriate action. Use of drone images for automatic plantation monitoring can enhance the accuracy of the monitoring process, while still being affordable to small farmers in developing countries such as India. Small, low cost drones equipped with an RGB camera can capture high-resolution images of agricultural fields, allowing for detailed analysis of the well-being of the plantations. Existing methods of automated plantation monitoring are mostly based on satellite images, which are difficult to get for the farmers. We propose an automated system for plantation health monitoring using drone images, which are becoming easier to get for the farmers. We propose a dataset of images of trees with three categories: ``Good health", ``Stunted", and ``Dead". We annotate the dataset using CVAT annotation tool, for use in research purposes. We experiment with different well-known CNN models to observe their performance on the proposed dataset. The initial low accuracy levels show the complexity of the proposed dataset. Further, our study revealed that, depth-wise convolution operation embedded in a deep CNN model, can enhance the performance of the model on drone dataset. Further, we apply state-of-the-art object detection models to identify individual trees to better monitor them automatically.
Fare planning is one among several steps in public transport planning. Fares are relevant for the covering of costs of the public transport operator, but also affect the ridership and the passenger satisfaction. A fare structure is the assignment of prices to all paths in a network. In practice, often a given fare structure shall be changed to fulfill new requirements, meaning that a new fare strategy is desired. This motivates the usage of prices of the former fare structure or other desirable prices as reference prices. In this paper, we investigate the fare structure design problem that aims to determine fares such that the sum of absolute deviations between the new fares and the reference prices is minimized. Fare strategies that are considered here are flat tariffs, affine distance tariffs and zone tariffs. Additionally, we regard constraints that ensure that it is not beneficial to buy a ticket for a longer journey than actually traveled (no-elongation property) or to split a ticket into several sub-tickets to cover a journey (no-stopover property). Our literature review provides an overview of the research on fare planning. We analyze the fare structure design problem for flat, distance and zone tariffs, pointing out connections to median problems. Further, we study its complexity which ranges from linear-time solvability to NP-complete cases.
Recent advancements in Large Vision Language Models (LVLMs) have enabled the development of LVLM-based Graphical User Interface (GUI) agents under various paradigms. Training-based approaches, such as CogAgent and SeeClick, struggle with cross-dataset and cross-platform generalization due to their reliance on dataset-specific training. Generalist LVLMs, such as GPT-4V, employ Set-of-Marks (SoM) for action grounding, but obtaining SoM labels requires metadata like HTML source, which is not consistently available across platforms. Moreover, existing methods often specialize in singular GUI tasks rather than achieving comprehensive GUI understanding. To address these limitations, we introduce TRISHUL, a novel, training-free agentic framework that enhances generalist LVLMs for holistic GUI comprehension. Unlike prior works that focus on either action grounding (mapping instructions to GUI elements) or GUI referring (describing GUI elements given a location), TRISHUL seamlessly integrates both. At its core, TRISHUL employs Hierarchical Screen Parsing (HSP) and the Spatially Enhanced Element Description (SEED) module, which work synergistically to provide multi-granular, spatially, and semantically enriched representations of GUI elements. Our results demonstrate TRISHUL's superior performance in action grounding across the ScreenSpot, VisualWebBench, AITW, and Mind2Web datasets. Additionally, for GUI referring, TRISHUL surpasses the ToL agent on the ScreenPR benchmark, setting a new standard for robust and adaptable GUI comprehension.
How to set up your first machine learning project in astronomy
2025-02-12
Johannes Buchner, Sotiria Fotopoulou
Large, freely available, well-maintained data sets have made astronomy a popular playground for machine learning projects. Nevertheless, robust insights gained to both machine learning and physics could be improved by clarity in problem definition and establishing workflows that critically verify, characterize and calibrate machine learning models. We provide a collection of guidelines to setting up machine learning projects to make them likely useful for science, less frustrating and time-intensive for the scientist and their computers, and more likely to lead to robust insights. We draw examples and experience from astronomy, but the advice is potentially applicable to other areas in science.
We use HI data from the FAST Extended Atlas of Selected Targets Survey (FEASTS) to study the interplay between gas and star formation of galaxies in interacting systems. We build control and mock HI disks and parameterize HI disorder by a series of disorder parameters, describing the piling, clumpiness and expansion of HI. We find that interacting galaxies have higher HI disorder described by almost all disorder parameters. Systems with comparable stellar masses and small relative velocities tend to have stronger expansion and clumpiness of HI. At a given stellar mass, decreased HI and total neutral gas mass and suppressed star formation rate of secondary galaxies are correlated with most disorder parameters. For primary galaxies, HI and total neutral gas deficiency correlate with more HI piling at two ends of the system outside HI disks but not with the expansion or clumpiness of HI. We also find that the HI surface densities of both primary and secondary galaxies are lower within the HI disks and higher outside compared to the control galaxies. Our results suggest that while all the disorder parameters quantify the interaction strength almost equally well, they have different sensitivities in tracing star formation rate and gas mass enhancements. They also imply that while gas removal likely dominates the tidal effects on secondary galaxies, primary galaxies experience more complex situation that are possibly related to gas depletion and accretion happening at different interaction stages.
Dynamics of Hot QCD Matter 2024 - New facilities and instrumentation
2025-02-12
Amal Sarkar, Prabhakar Palni, Santosh K. Das, Jaideep Kalani, Ganesh J. Tambve, Saptarshi Datta, Saloni Atreya, Sachin Rana, Md Kaosor Ali Mondal, Poojan Angiras, Anusree Vijay, Ganapati Dash, Prafulla Kumar Behera, Deepak Samuel, Theertha C
This part of the conference proceeding provides a detailed overview of cutting-edge advancements in detector technologies, focusing on their optimization, characterization, and applications in particle physics experiments. Building on the insights and developments presented at the Hot QCD Matter 2022 conference, this section of the Hot QCD Matter 2024 proceedings highlights significant advancements in detector technologies. The development of Low Gain Avalanche Diodes (LGADs) into Ultra-Fast Silicon Detectors is explored, demonstrating their potential for superior timing resolution in future high-energy experiments. Simulation studies of Micropattern Gaseous Detectors (MPGDs), including MICROMEGAS and Gas Electron Multiplier (GEM) detectors, provide insights into their performance under high-radiation environments using tools like ANSYS and GARFIELD$^{++}$. A novel GEM foil geometry is proposed for improved gain and durability. Characterization of semiconductor detectors, such as Monolithic MALTA pixel detectors and CMS prototype silicon sensors, is also presented, highlighting their radiation tolerance, imaging capabilities, and structural integrity. These studies underscore the critical role of silicon sensors in ensuring detector reliability and performance. Additionally, the J-PARC muon g-2/EDM experiment is reviewed, showcasing its precision measurements to test Standard Model predictions and explore potential physics beyond. By addressing the interplay between detector development, simulation, and characterization, this proceeding showcases a collective effort toward advancing detector technologies and their pivotal role in pushing the boundaries of modern particle physics.
Latest Advancements Towards Catastrophic Forgetting under Data Scarcity: A Comprehensive Survey on Few-Shot Class Incremental Learning
2025-02-12
M. Anwar Ma'sum, Mahardhika Pratama, Igor Skrjanc
Data scarcity significantly complicates the continual learning problem, i.e., how a deep neural network learns in dynamic environments with very few samples. However, the latest progress of few-shot class incremental learning (FSCIL) methods and related studies show insightful knowledge on how to tackle the problem. This paper presents a comprehensive survey on FSCIL that highlights several important aspects i.e. comprehensive and formal objectives of FSCIL approaches, the importance of prototype rectifications, the new learning paradigms based on pre-trained model and language-guided mechanism, the deeper analysis of FSCIL performance metrics and evaluation, and the practical contexts of FSCIL in various areas. Our extensive discussion presents the open challenges, potential solutions, and future directions of FSCIL.
Intention is All You Need: Refining Your Code from Your Intention
2025-02-12
Qi Guo, Xiaofei Xie, Shangqing Liu, Ming Hu, Xiaohong Li, Lei Bu
Code refinement aims to enhance existing code by addressing issues, refactoring, and optimizing to improve quality and meet specific requirements. As software projects scale in size and complexity, the traditional iterative exchange between reviewers and developers becomes increasingly burdensome. While recent deep learning techniques have been explored to accelerate this process, their performance remains limited, primarily due to challenges in accurately understanding reviewers' intents. This paper proposes an intention-based code refinement technique that enhances the conventional comment-to-code process by explicitly extracting reviewer intentions from the comments. Our approach consists of two key phases: Intention Extraction and Intention Guided Revision Generation. Intention Extraction categorizes comments using predefined templates, while Intention Guided Revision Generation employs large language models (LLMs) to generate revised code based on these defined intentions. Three categories with eight subcategories are designed for comment transformation, which is followed by a hybrid approach that combines rule-based and LLM-based classifiers for accurate classification. Extensive experiments with five LLMs (GPT4o, GPT3.5, DeepSeekV2, DeepSeek7B, CodeQwen7B) under different prompting settings demonstrate that our approach achieves 79% accuracy in intention extraction and up to 66% in code refinement generation. Our results highlight the potential of our approach in enhancing data quality and improving the efficiency of code refinement.
Dynamical Models of the Milky Way in Action Space with LAMOST DR8 and GAIA EDR3
2025-02-12
Guang-Chen Sun, Qiao Wang, Shude Mao, Yichao Li, Richard J. Long, Ping-Jie Ding, Yougang Wang, Xin Zhang, Xuelei Chen
This work explores dynamical models of the Milky Way (MW) by analyzing a sample of 86,109 K giant stars selected through cross-matching the LAMOST DR8 and Gaia EDR3 surveys. Our earlier torus models in Wang et al. (2017) did not include Gaia data, making them incompatible with the new proper motion distributions of samples. Here, we refine the construction of action-based, self-consistent models to constrain the three-dimensional velocity distribution of K giants over a larger parameter space, drawing on a series of existing MW models. This approach produces several new MW models. Our best-fit model for the local kinematics near the Sun indicates a MW virial mass of 1.35 $\times 10^{12} M_\odot$, a local stellar density of 0.0696 $\rm M_\odot pc^{-3}$, and a local dark matter density of 0.0115 $\rm M_\odot pc^{-3}$. Our main conclusion supports a thicker and more extended thick disk, alongside a cooler thin disk, compared to the best-fitting model in Wang et al. (2017). Near the Sun, our model aligns well with observations, but is less satisfactory at distances far from the Galactic center, perhaps implying unidentified structures. Further high-precision observations will be critical for understanding the dynamics in these outer Galactic regions, and will require a more realistic model.
Vertical Federated Learning (VFL) is a privacy-preserving collaborative learning paradigm that enables multiple parties with distinct feature sets to jointly train machine learning models without sharing their raw data. Despite its potential to facilitate cross-organizational collaborations, the deployment of VFL systems in real-world applications remains limited. To investigate the gap between existing VFL research and practical deployment, this survey analyzes the real-world data distributions in potential VFL applications and identifies four key findings that highlight this gap. We propose a novel data-oriented taxonomy of VFL algorithms based on real VFL data distributions. Our comprehensive review of existing VFL algorithms reveals that some common practical VFL scenarios have few or no viable solutions. Based on these observations, we outline key research directions aimed at bridging the gap between current VFL research and real-world applications.
A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters
2025-02-12
Shasvat Desai, Debasmita Ghose, Deep Chakraborty
Visual contrastive learning aims to learn representations by contrasting similar (positive) and dissimilar (negative) pairs of data samples. The design of these pairs significantly impacts representation quality, training efficiency, and computational cost. A well-curated set of pairs leads to stronger representations and faster convergence. As contrastive pre-training sees wider adoption for solving downstream tasks, data curation becomes essential for optimizing its effectiveness. In this survey, we attempt to create a taxonomy of existing techniques for positive and negative pair curation in contrastive learning, and describe them in detail.
Control Barrier Function-Based Quadratic Programming for SafeOperation of Tethered UAVs
2025-02-12
Samuel O. Folorunsho, Maggi Ni, William R. Norris
Consider an unmanned aerial vehicle (UAV) physically connected to the ground station with a tether operating in a space, tasked with performing precise maneuvers while constrained by the physical limitation of its tether, which prevents it from flying beyond a maximum allowable length. Violating this tether constraint could lead to system failure or operational hazards, making it essential to enforce safety constraints dynamically while ensuring the drone can track desired trajectories accurately. This paper presents a Control Barrier Function Quadratic Programming Framework (CBF-QP) for ensuring the safe and efficient operation of tethered unmanned aerial vehicles (TUAVs). The framework leverages nominal backstepping control to achieve trajectory tracking, augmented with control barrier functions to ensure compliance with the tether constraint. In this proposed method, the tether constraint is directly embedded in the control design and therefore guarantees the TUAV remains within a predefined operational region defined by the maximum tether length while achieving precise trajectory tracking. The effectiveness of the proposed framework is validated through simulations involving set-point tracking, dynamic trajectory following, and disturbances such as incorrect user inputs. The results demonstrate that the TUAV respects the tether constraint ||x(t)||
The rapid proliferation of data science forced different groups of individuals with different backgrounds to adapt to statistical analysis. We hypothesize that conversational agents are better suited for statistical analysis than traditional graphical user interfaces (GUI). In this work, we propose a novel conversational agent, StatZ, for statistical analysis. We evaluate the efficacy of StatZ relative to established statistical software:SPSS, SAS, Stata, and JMP in terms of accuracy, task completion time, user experience, and user satisfaction. We combined the proposed analysis question from state-of-the-art language models with suggestions from statistical analysis experts and tested with 51 participants from diverse backgrounds. Our experimental design assessed each participant's ability to perform statistical analysis tasks using traditional statistical analysis tools with GUI and our conversational agent. Results indicate that the proposed conversational agents significantly outperform GUI statistical software in all assessed metrics, including quantitative (task completion time, accuracy, and user experience), and qualitative (user satisfaction) metrics. Our findings underscore the potential of using conversational agents to enhance statistical analysis processes, reducing cognitive load and learning curves and thereby proliferating data analysis capabilities, to individuals with limited knowledge of statistics.
2025-02-12
Tingyi Cai, Yunliang Jiang, Yixin Liu, Ming Li, Changqin Huang, Shirui Pan
Graph machine learning has witnessed rapid growth, driving advancements across diverse domains. However, the in-distribution assumption, where training and testing data share the same distribution, often breaks in real-world scenarios, leading to degraded model performance under distribution shifts. This challenge has catalyzed interest in graph out-of-distribution (GOOD) detection, which focuses on identifying graph data that deviates from the distribution seen during training, thereby enhancing model robustness. In this paper, we provide a rigorous definition of GOOD detection and systematically categorize existing methods into four types: enhancement-based, reconstruction-based, information propagation-based, and classification-based approaches. We analyze the principles and mechanisms of each approach and clarify the distinctions between GOOD detection and related fields, such as graph anomaly detection, outlier detection, and GOOD generalization. Beyond methodology, we discuss practical applications and theoretical foundations, highlighting the unique challenges posed by graph data. Finally, we discuss the primary challenges and propose future directions to advance this emerging field. The repository of this survey is available at https://github.com/ca1man-2022/Awesome-GOOD-Detection.
Chain-of-thought (CoT) prompting has achieved remarkable success in natural language processing (NLP). However, its vast potential remains largely unexplored for graphs. This raises an interesting question: How can we design CoT prompting for graphs to guide graph models to learn step by step? On one hand, unlike natural languages, graphs are non-linear and characterized by complex topological structures. On the other hand, many graphs lack textual data, making it difficult to formulate language-based CoT prompting. In this work, we propose the first CoT prompt learning framework for text-free graphs, GCoT. Specifically, we decompose the adaptation process for each downstream task into a series of inference steps, with each step consisting of prompt-based inference, ``thought'' generation, and thought-conditioned prompt learning. While the steps mimic CoT prompting in NLP, the exact mechanism differs significantly. Specifically, at each step, an input graph, along with a prompt, is first fed into a pre-trained graph encoder for prompt-based inference. We then aggregate the hidden layers of the encoder to construct a ``thought'', which captures the working state of each node in the current step. Conditioned on this thought, we learn a prompt specific to each node based on the current state. These prompts are fed into the next inference step, repeating the cycle. To evaluate and analyze the effectiveness of GCoT, we conduct comprehensive experiments on eight public datasets, which demonstrate the advantage of our approach.
2025-02-12
Filiberto Ares, Pasquale Calabrese, Sara Murciano
The Mpemba effect, where a hotter system can equilibrate faster than a cooler one, has long been a subject of fascination in classical physics. In the past few years, significant theoretical and experimental progress has been made in understanding its occurrence in both classical and quantum systems. In this review, we provide a concise overview of the Mpemba effect in quantum systems, with a focus on both open and isolated dynamics which give rise to distinct manifestations of this anomalous non-equilibrium phenomenon. We discuss key theoretical frameworks, highlight experimental observations, and explore the fundamental mechanisms that give rise to anomalous relaxation behaviors. Particular attention is given to the role of quantum fluctuations, integrability, and symmetry in shaping equilibration pathways. Finally, we outline open questions and future directions.
Can Machine Learning Support the Selection of Studies for Systematic Literature Review Updates?
2025-02-12
Marcelo Costalonga, Bianca Minetto Napoleão, Maria Teresa Baldassarre, Katia Romero Felizardo, Igor Steinmacher, Marcos Kalinowski
[Background] Systematic literature reviews (SLRs) are essential for synthesizing evidence in Software Engineering (SE), but keeping them up-to-date requires substantial effort. Study selection, one of the most labor-intensive steps, involves reviewing numerous studies and requires multiple reviewers to minimize bias and avoid loss of evidence. [Objective] This study aims to evaluate if Machine Learning (ML) text classification models can support reviewers in the study selection for SLR updates. [Method] We reproduce the study selection of an SLR update performed by three SE researchers. We trained two supervised ML models (Random Forest and Support Vector Machines) with different configurations using data from the original SLR. We calculated the study selection effectiveness of the ML models for the SLR update in terms of precision, recall, and F-measure. We also compared the performance of human-ML pairs with human-only pairs when selecting studies. [Results] The ML models achieved a modest F-score of 0.33, which is insufficient for reliable automation. However, we found that such models can reduce the study selection effort by 33.9% without loss of evidence (keeping a 100% recall). Our analysis also showed that the initial screening by pairs of human reviewers produces results that are much better aligned with the final SLR update result. [Conclusion] Based on our results, we conclude that although ML models can help reduce the effort involved in SLR updates, achieving rigorous and reliable outcomes still requires the expertise of experienced human reviewers for the initial screening phase.
A large number of studies rely on closed-style multiple-choice surveys to evaluate cultural alignment in Large Language Models (LLMs). In this work, we challenge this constrained evaluation paradigm and explore more realistic, unconstrained approaches. Using the World Values Survey (WVS) and Hofstede Cultural Dimensions as case studies, we demonstrate that LLMs exhibit stronger cultural alignment in less constrained settings, where responses are not forced. Additionally, we show that even minor changes, such as reordering survey choices, lead to inconsistent outputs, exposing the limitations of closed-style evaluations. Our findings advocate for more robust and flexible evaluation frameworks that focus on specific cultural proxies, encouraging more nuanced and accurate assessments of cultural alignment in LLMs.
Innovations in Nanotechnology: A Comprehensive Review of Applications Beyond Space Exploration
2025-02-12
Syed Muhammad Muslim Hussain, Batool Zehra Ladha, Muhammad Hasan Khan
Nanotechnology has emerged as a transformative force across multiple industries, enhancing materials, improving instrumentation precision, and developing intelligent systems. This review explores various nanotechnology applications, including advancements in materials science, healthcare, energy storage, environmental monitoring, and robotics. Nanomaterials, such as carbon nanotubes and graphene, offer significant improvements in fields like energy generation and medicine, while nanosensors revolutionize environmental and industrial monitoring. Micro and nano robots provide automation solutions across industries. By expanding beyond space exploration, this review highlights the far-reaching potential of nanotechnology to reshape industries through interdisciplinary collaboration and innovation.
Digital Twin for Porous Electrodes in Redox Flow Batteries
2025-02-12
Michael S. Emanuel, Chris H. Rycroft
Porous electrodes are a vital component of redox flow batteries, fuel cells and electrolyzers. We present a 3D digital twin for a porous electrode by direct numerical solution of the governing Navier-Stokes and Nernst-Planck equations for incompressible flow and electrochemical mass transport with Butler-Volmer reaction kinetics. We demonstrate our method by simulating a laboratory sized system at a sub-fiber scale of $1.25 {\mu}m$ using a single workstation. We establish convergence and the consistency of mesh refined lattices, and review an experimental validation of our model. We show the efficacy of two novel techniques to speed convergence to steady state, iterative upsampling and model refinement. We prove that the state of charge ($s$) at steady state can be formulated as the solution to a single PDE in $s$ with a Dirichlet boundary condition, and introduce a novel figure of merit $U_{\text{mt}}$, the utilization of an electrode geometry at the mass transport limit, both of which can be rapidly simulated with explicit methods from a given fluid velocity field. All of our code is highly performant, parallelizable, open source C++ that is published with this work and compatible with the largest modern scientific supercomputers available today.
An ultraviolet survey of M31 has been carried out during 2017-23 with the UVIT instrument onboard the AstroSat Observatory. Here we present far and near ultraviolet (FUV and NUV) observations from the M31 UVIT survey, which covers a sky area of $\simeq 3.5^\circ \times 1.3^\circ$ with spatial resolution of $\simeq1^{\prime \prime}$. The observations included six filter bands in the wavelength range of 120 nm to 280 nm. Including the six bands, $\simeq$115,000 sources with signal-to-noise S/N$\ge$3 ( $\simeq$95,000 sources with signal-to-noise S/N$\ge$5) were detected at FUV or NUV wavelengths, with the largest set of detections ($\simeq$54,000 sources) in the FUV 150 nm band (F148W filter). This is considerably more than for the first version of the M31 source catalog (published in 2020), in part due to additional observations of M31 by UVIT and in part due to improved data processing. The magnitude (m$_{AB}$) at which incompleteness sets in is $\simeq$23.0 in the F148W band, with the other bands somewhat less sensitive, with least sensitive band (in m$_{AB}$ units) N279N with incompleteness for sources fainter than $\simeq$20.3). The faintest sources detectable in F148W have m$_{AB}\simeq$25.4, with other bands having higher minimum detectable brightness, with N279N having minimum detectable limit of m$_{AB}\simeq$22.1. The product of this work is the new M31 UVIT compact source catalog, containing positions, fluxes , magnitudes and S/N for the sources.
SpeechCompass: Enhancing Mobile Captioning with Diarization and Directional Guidance via Multi-Microphone Localization
2025-02-12
Artem Dementyev, Dimitri Kavensky, Samuel J. Yang, Mathieu Parvaix, Chiong Lai, Alex Olwal
Speech-to-text capabilities on mobile devices have proven helpful for hearing and speech accessibility, language translation, note-taking, and meeting transcripts. However, our foundational large-scale survey (n=263) shows that the inability to distinguish and indicate speaker direction makes them challenging in group conversations. SpeechCompass addresses this limitation through real-time, multi-microphone speech localization, where the direction of speech allows visual separation and guidance (e.g., arrows) in the user interface. We introduce efficient real-time audio localization algorithms and custom sound perception hardware running on a low-power microcontroller and four integrated microphones, which we characterize in technical evaluations. Informed by a large-scale survey (n=494), we conducted an in-person study of group conversations with eight frequent users of mobile speech-to-text, who provided feedback on five visualization styles. The value of diarization and visualizing localization was consistent across participants, with everyone agreeing on the value and potential of directional guidance for group conversations.
Survey on Single-Image Reflection Removal using Deep Learning Techniques
2025-02-12
Kangning Yang, Huiming Sun, Jie Cai, Lan Fu, Jiaming Ding, Jinlong Li, Chiu Man Ho, Zibo Meng
The phenomenon of reflection is quite common in digital images, posing significant challenges for various applications such as computer vision, photography, and image processing. Traditional methods for reflection removal often struggle to achieve clean results while maintaining high fidelity and robustness, particularly in real-world scenarios. Over the past few decades, numerous deep learning-based approaches for reflection removal have emerged, yielding impressive results. In this survey, we conduct a comprehensive review of the current literature by focusing on key venues such as ICCV, ECCV, CVPR, NeurIPS, etc., as these conferences and journals have been central to advances in the field. Our review follows a structured paper selection process, and we critically assess both single-stage and two-stage deep learning methods for reflection removal. The contribution of this survey is three-fold: first, we provide a comprehensive summary of the most recent work on single-image reflection removal; second, we outline task hypotheses, current deep learning techniques, publicly available datasets, and relevant evaluation metrics; and third, we identify key challenges and opportunities in deep learning-based reflection removal, highlighting the potential of this rapidly evolving research area.
Tabular data is one of the most widely used data formats across various domains such as bioinformatics, healthcare, and marketing. As artificial intelligence moves towards a data-centric perspective, improving data quality is essential for enhancing model performance in tabular data-driven applications. This survey focuses on data-driven tabular data optimization, specifically exploring reinforcement learning (RL) and generative approaches for feature selection and feature generation as fundamental techniques for refining data spaces. Feature selection aims to identify and retain the most informative attributes, while feature generation constructs new features to better capture complex data patterns. We systematically review existing generative methods for tabular data engineering, analyzing their latest advancements, real-world applications, and respective strengths and limitations. This survey emphasizes how RL-based and generative techniques contribute to the automation and intelligence of feature engineering. Finally, we summarize the existing challenges and discuss future research directions, aiming to provide insights that drive continued innovation in this field.
Large Language Models (LLMs) struggle with hallucinations and outdated knowledge due to their reliance on static training data. Retrieval-Augmented Generation (RAG) mitigates these issues by integrating external dynamic information enhancing factual and updated grounding. Recent advances in multimodal learning have led to the development of Multimodal RAG, incorporating multiple modalities such as text, images, audio, and video to enhance the generated outputs. However, cross-modal alignment and reasoning introduce unique challenges to Multimodal RAG, distinguishing it from traditional unimodal RAG. This survey offers a structured and comprehensive analysis of Multimodal RAG systems, covering datasets, metrics, benchmarks, evaluation, methodologies, and innovations in retrieval, fusion, augmentation, and generation. We precisely review training strategies, robustness enhancements, and loss functions, while also exploring the diverse Multimodal RAG scenarios. Furthermore, we discuss open challenges and future research directions to support advancements in this evolving field. This survey lays the foundation for developing more capable and reliable AI systems that effectively leverage multimodal dynamic external knowledge bases. Resources are available at https://github.com/llm-lab-org/Multimodal-RAG-Survey.
70 Years of Hyperon Spectroscopy: A review of strange $Ξ$, $Ω$ baryons, and the spectrum of charmed and bottom baryons
2025-02-12
Volker Crede, John Yelton
The first hyperon was discovered about 70 years ago, but the nature of these particles, particularly with regard to multistrange hyperons, and many of their properties can still be considered to be literally strange. A dedicated and successful global spectroscopy program in the 1960s and 1970s using $K^-$ beams revealed many multistrange candidates, but the available evidence of their existence is statistically limited. For this reason, there is still much to learn about the systematics of the spectrum of excited hyperon states and what they have in common with their non-strange companions, or how they differ from the nucleon and $\Delta$ resonances. Recent years have also seen a great deal of progress in the field of charmed and bottom baryon spectroscopy. Unprecedented data from the Large Hadron Collider in particular indicate continued rapid progress in the field of bottom baryons. On the theoretical side, baryons with one heavy quark $Q$ and a light $qq$ system serve as an ideal laboratory for studying light $qq$ (diquark) correlations and the dynamics of the light quarks in the colour environment of a heavy quark. In this review, we discuss the status of doubly and triply strange $\Xi$ as well as $\Omega$ baryons, and the properties of all the known charmed and bottom states. The comparison of the two heavy sectors reveals many similarities as predicted by heavy-quark symmetries, together with differences in mass splittings easily understood by potential models. The multi-strange hyperons bridge the under-explored gap between the light- and the heavy-flavour baryons. How do the properties of a singly charmed $Q$-$qq$ system change with decreasing mass of the heavy quark in the transition to a doubly strange $q$-$QQ$ system with a heavier quark-quark system relative to one light quark?
A Systematic Review on the Evaluation of Large Language Models in Theory of Mind Tasks
2025-02-12
Karahan Sarıtaş, Kıvanç Tezören, Yavuz Durmazkeser
In recent years, evaluating the Theory of Mind (ToM) capabilities of large language models (LLMs) has received significant attention within the research community. As the field rapidly evolves, navigating the diverse approaches and methodologies has become increasingly complex. This systematic review synthesizes current efforts to assess LLMs' ability to perform ToM tasks, an essential aspect of human cognition involving the attribution of mental states to oneself and others. Despite notable advancements, the proficiency of LLMs in ToM remains a contentious issue. By categorizing benchmarks and tasks through a taxonomy rooted in cognitive science, this review critically examines evaluation techniques, prompting strategies, and the inherent limitations of LLMs in replicating human-like mental state reasoning. A recurring theme in the literature reveals that while LLMs demonstrate emerging competence in ToM tasks, significant gaps persist in their emulation of human cognitive abilities.
Modeling spatially-resolved galactic H$α$ emission for galaxy clustering
2025-02-12
Ivan Rapoport, Vincent Desjacques, Gabriele Parimbelli, Ehud Behar, Martin Crocce
Near-infrared spectroscopic surveys target high-redshift emission-line galaxies (ELGs) to probe cosmological scenarios. Understanding the clustering properties of ELGs is essential to derive optimal constraints. We present a simple radiative transfer model for spatially resolved galactic H$\alpha$ emission, which includes emission from the warm-hot diffuse interstellar medium. The atomic level populations are in steady-state and computed in the coronal approximation. The model is applied to multiple IllustrisTNG simulations in the redshift range $1\leq z \leq 2$ to produce the luminosity function (LF) and the halo occupation distribution (HOD). Collisional processes account for a significant fraction of $\approx 40\%$ of the total ${\rm H}\alpha$ luminosity ($L_{{\rm H}\alpha}$). Our LFs are in reasonable agreement with measurements from H$\alpha$ surveys if a uniform extinction of $0.3
A Guide to Molecular Properties from the Bethe-Salpeter Equation
2025-02-12
Christof Holzer, Yannick J. Franzke
The Bethe-Salpeter equation (BSE) combined with the Green's function GW method has successfully transformed into a robust computational tool to describe light-matter interactions and excitation spectra for molecules, solids, and materials from first principles. Thanks to its ability to accurately describe charge-transfer and Rydberg excitations, the GW-BSE already forms an established and cost-efficient alternative to time-dependent density functional theory. This raises the question whether the GW-BSE approach can become a more general framework for molecular properties beyond excitation energies. In this mini-review, we recapitulate recent endeavors along this point in terms of both theoretical and practical developments for quantum chemistry, physical chemistry, and related fields. In doing so, we provide guidelines for current applications to chemical challenges in collaboration with experimentalists as well as to future developments to extended the GW-BSE toolkit.
The EXO-UV program: lastest advances of experimental studies to investigate the biological impact of UV radiation on exoplanets
2025-02-12
Ximena C. Abrevaya, Petra Odert, Oscar J. Oppezzo, Martin Leizinger, Gerardo Juan M. Luna, Manish R. Patel, Arnold Hanslmeier
The EXO-UV program is an international, interdisciplinary collaboration between astrophysicists and biologists aimed at expanding the characterization of ultraviolet radiation (UVR) environments on exoplanets. This approach combines astrophysical studies with biological experiments to better understand the potential impacts of UVR on exoplanetary surfaces. UVR is particularly relevant because it reaches the surface of planets and can influence their habitability. The specific wavelengths within the UVR spectrum depend on the planet's atmospheric composition and the spectral energy distribution of its host star. Additionally, high UVR fluxes emitted during flares and superflares are of particular interest due to the limited information available regarding their biological impact. The EXO-UV program has successfully led to the first experimental study examining the biological effects of high UVR fluences, such as those produced by flares and superflares. Future experimental studies aim to investigate the biological effects of repetitive flares. In this paper, we review the latest results from our EXO-UV program.
Signed, Sealed,... Confused: Exploring the Understandability and Severity of Policy Documents
2025-02-12
Shikha Soneji, Sourav Panda, Sameer Neve, Jonathan Dodge
In general, Terms of Service (ToS) and other policy documents are verbose and full of legal jargon, which poses challenges for users to understand. To improve user accessibility and transparency, the "Terms of Service; Didn't Read" (ToS;DR) project condenses intricate legal terminology into summaries and overall grades for the website's policy documents. Nevertheless, uncertainties remain about whether users could truly grasp the implications of simplified presentations. We conducted an online survey to assess the perceived understandability and severity of randomly chosen cases from the ToS;DR taxonomy. Preliminary results indicate that, although most users report understanding the cases, they find a bias towards service providers in about two-thirds of the cases. The findings of our study emphasize the necessity of prioritizing user-centric policy formulation. This study has the potential to reveal the extent of information imbalance in digital services and promote more well-informed user consent.
"Active Neighbour": A Novel Monitoring Model for Cyber-Physical Systems
2025-02-12
Vasileios Apostolidis-Afentoulis
Over the past decade, advancements in technology have enabled Cyber-Physical Systems (CPS) to monitor sensor networks through various methodologies. However, these developments have concurrently introduced significant security challenges, necessitating robust protective measures. As a result, securing CPS has become a critical area of research. This paper reviews existing CPS monitoring models and introduces an innovative role-based monitoring model designed to meet contemporary security requirements. The proposed model is implemented within the COOJA simulator of the Contiki OS and evaluated under three distinct security configurations. Preliminary results demonstrate promising outcomes, although further comprehensive testing is ongoing.
A Taxonomy of Real Faults in Hybrid Quantum-Classical Architectures
2025-02-12
Avner Bensoussan, Gunel Jahangirova, Mohammad Reza Mousavi
With the popularity of Hybrid Quantum-Classical architectures, particularly noisy intermediate-scale quantum (NISQ) architectures, comes the need for quality assurance methods tailored to their specific faults. In this study, we propose a taxonomy of faults in Hybrid Quantum-Classical architectures accompanied by a dataset of real faults in the identified categories. To achieve this, we empirically analysed open-source repositories for fixed faults. We analysed over 5000 closed issues on GitHub and pre-selected 529 of them based on rigorously defined inclusion criteria. We selected 133 faults that we labelled around symptoms and the origin of the faults. We cross-validated the classification and labels assigned to every fault between two of the authors. As a result, we introduced a taxonomy of real faults in Hybrid Quantum-Classical architectures. Subsequently, we validated the taxonomy through interviews conducted with eleven developers. The taxonomy was dynamically updated throughout the cross-validation and interview processes. The final version was validated and discussed through surveys conducted with an independent group of domain experts to ensure its relevance and to gain further insights.
Investigating the effects of fresh gas on the Active Galactic Nuclei luminosity of early- and late-type galaxies
2025-02-12
Martyna W. Winiarska, Sandra I. Raimundo, Timothy A. Davis, Rogerio Riffel, Francesco Shankar, Phil Wiseman
The main fuelling processes for Active Galactic Nuclei (AGN) are currently unknown. Previous work showed that galaxies with a large kinematic misalignment between their stellar and gas reservoirs have a higher AGN fraction than galaxies without misalignment. Such misalignment is a strong indication of a past galaxy interaction or an external accretion event. In this work we use integral field spectroscopy data from the SAMI and MaNGA surveys to investigate the AGN luminosity as a function of kinematic misalignment angle. Our sample of AGN exhibit bolometric luminosities in the range 10^40 to 10^43 erg/s, indicative of low to moderate luminosity AGN. We find no correlation between AGN luminosity as a function of misalignment for AGN host galaxies from both surveys. We find some differences between the AGN luminosity of early- and late-type AGN host galaxies (ETGs, LTGs). AGN in LTG hosts have a wider luminosity range, with most LTG hosts showing aligned stellar to gas kinematics. AGN in ETG hosts have a luminosity range that does not depend on misalignment angle, suggesting AGN in ETG hosts are consistent with being fuelled by external accretion events, irrespective of their stellar to gas kinematic misalignment. While all the AGN in ETGs in our sample are consistent with being activated and fuelled by external gas, the range of observed AGN luminosities is likely caused by secondary factors such as the amount of fresh gas brought into the galaxy by the external interaction.
Distribution of Cataclysmic Variables in our Galaxy and Their Position in the HR Diagram in the Gaia Era
2025-02-12
R. Canbay
In this study, the distances of stellar systems classified as cataclysmic variables in the literature were determined by using the distance compiled from Bailer-Jones et al. (2021). The spatial distributions of cataclysmic variables in the heliocentric Galactic coordinate system are obtained and their positions in the Hertzsprung-Russell (HR) diagram constructed from Gaia colors are discussed.
Beyond the Lens: Quantifying the Impact of Scientific Documentaries through Amazon Reviews
2025-02-12
Jill Naiman, Aria Pessianzadeh, Hanyu Zhao, AJ Christensen, Alistair Nunn, Shriya Srikanth, Anushka Gami, Emma Maxwell, Louisa Zhang, Sri Nithya Yeragorla, Rezvaneh Rezapour
Engaging the public with science is critical for a well-informed population. A popular method of scientific communication is documentaries. Once released, it can be difficult to assess the impact of such works on a large scale, due to the overhead required for in-depth audience feedback studies. In what follows, we overview our complementary approach to qualitative studies through quantitative impact and sentiment analysis of Amazon reviews for several scientific documentaries. In addition to developing a novel impact category taxonomy for this analysis, we release a dataset containing 1296 human-annotated sentences from 1043 Amazon reviews for six movies created in whole or part by a team of visualization designers who focus on cinematic presentations of scientific data. Using this data, we train and evaluate several machine learning and large language models, discussing their effectiveness and possible generalizability for documentaries beyond those focused on for this work. Themes are also extracted from our annotated dataset which, along with our large language model analysis, demonstrate a measure of the ability of scientific documentaries to engage with the public.
The AURORA Survey: The Evolution of Multi-phase Electron Densities at High Redshift
2025-02-12
Michael W. Topping, Ryan L. Sanders, Alice E. Shapley, Anthony J. Pahl, Naveen A. Reddy, Daniel P. Stark, Danielle A. Berg, Leonardo Clarke, Fergus Cullen, James S. Dunlop, Richard S. Ellis, N. M. Förster Schreiber, Garth D. Illingworth, Tucker Jones, Desika Narayanan, Max Pettini, Daniel Schaerer
We present an analysis of deep $\textit{JWST}$/NIRSpec spectra of star-forming galaxies at $z\simeq1.4-10$, observed as part of the AURORA survey. We infer median low-ionization electron densities of $268_{-49}^{+45}~\rm cm^{-3}$, $350_{-76}^{+140}~\rm cm^{-3}$, and $480_{-310}^{+390}~\rm cm^{-3}$ at redshifts z$=2.3$, $z=3.2$, and $z=5.3$, respectively, revealing an evolutionary trend following $(1+z)^{1.5\pm0.6}$. We identify weak positive correlations between electron density and star formation rate (SFR) as well as SFR surface density, but no significant trends with stellar mass or specific SFR. Correlations with rest-optical emission line ratios show densities increasing with $\rm [NeIII]\lambda3869/[OII]\lambda3727$ and, potentially, $\rm [OIII]\lambda5007/[OII]\lambda3727$, although variations in dust attenuation complicate the latter. Additionally, electron density is more strongly correlated with distance from the local BPT sequence than can be explained by simple photoionization models. We further derive electron densities from the CIII] doublet probing higher-ionization gas, and find a median value of $1.4_{-0.5}^{+0.7}\times10^4~\rm cm^{-3}$, $\sim30$ times higher than densities inferred from [SII]. This comparison suggests a consistent HII region structure across cosmic time with dense, high-ionization interiors surrounded by less dense, low-ionization gas. We compare measurements of AURORA galaxies to predictions from the SPHINX galaxy formations, highlighting the interplay between residual molecular cloud pressure in young galaxies and feedback from stellar winds and supernovae as galaxies mature.
Early Galaxies from Rare Inflationary Processes and JWST Observations
2025-02-12
Soubhik Kumar, Neal Weiner
Rare Poisson processes (PP) during cosmic inflation can lead to signatures that are localized in position space and are not well captured by the standard two- or higher-point correlation functions of primordial density perturbations. As an example, PP can lead to localized overdense regions that are far denser than the ones produced through standard inflationary fluctuations. As a result, such overdense regions collapse earlier than expected based on the standard $\Lambda$CDM model and would host anomalously high-redshift galaxies. We describe some general aspects of such PP and consider a particular realization in the context of inflationary particle production. We then show that the masses and redshifts of the resulting galaxies can lie in a range discoverable by the James Webb Space Telescope (JWST) and future surveys, while being consistent with existing constraints on the matter power spectrum and UV luminosity functions at lower redshifts.
AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society
2025-02-12
Jinghua Piao, Yuwei Yan, Jun Zhang, Nian Li, Junbo Yan, Xiaochong Lan, Zhihong Lu, Zhiheng Zheng, Jing Yi Wang, Di Zhou, Chen Gao, Fengli Xu, Fang Zhang, Ke Rong, Jun Su, Yong Li
Understanding human behavior and society is a central focus in social sciences, with the rise of generative social science marking a significant paradigmatic shift. By leveraging bottom-up simulations, it replaces costly and logistically challenging traditional experiments with scalable, replicable, and systematic computational approaches for studying complex social dynamics. Recent advances in large language models (LLMs) have further transformed this research paradigm, enabling the creation of human-like generative social agents and realistic simulacra of society. In this paper, we propose AgentSociety, a large-scale social simulator that integrates LLM-driven agents, a realistic societal environment, and a powerful large-scale simulation engine. Based on the proposed simulator, we generate social lives for over 10k agents, simulating their 5 million interactions both among agents and between agents and their environment. Furthermore, we explore the potential of AgentSociety as a testbed for computational social experiments, focusing on four key social issues: polarization, the spread of inflammatory messages, the effects of universal basic income policies, and the impact of external shocks such as hurricanes. These four issues serve as valuable cases for assessing AgentSociety's support for typical research methods -- such as surveys, interviews, and interventions -- as well as for investigating the patterns, causes, and underlying mechanisms of social issues. The alignment between AgentSociety's outcomes and real-world experimental results not only demonstrates its ability to capture human behaviors and their underlying mechanisms, but also underscores its potential as an important platform for social scientists and policymakers.
Advancing machine fault diagnosis: A detailed examination of convolutional neural networks
2025-02-12
Govind Vashishtha, Sumika Chauhan, Mert Sehri, Justyna Hebda-Sobkowicz, Radoslaw Zimroz, Patrick Dumond, Rajesh Kumar
The growing complexity of machinery and the increasing demand for operational efficiency and safety have driven the development of advanced fault diagnosis techniques. Among these, convolutional neural networks (CNNs) have emerged as a powerful tool, offering robust and accurate fault detection and classification capabilities. This comprehensive review delves into the application of CNNs in machine fault diagnosis, covering its theoretical foundation, architectural variations, and practical implementations. The strengths and limitations of CNNs are analyzed in this domain, discussing their effectiveness in handling various fault types, data complexities, and operational environments. Furthermore, we explore the evolving landscape of CNN-based fault diagnosis, examining recent advancements in data augmentation, transfer learning, and hybrid architectures. Finally, we highlight future research directions and potential challenges to further enhance the application of CNNs for reliable and proactive machine fault diagnosis.
2025-02-12
Jiayi Yuan, Jiamu Zhang, Andrew Wen, Xia Hu
The emergent phenomena of large foundation models have revolutionized natural language processing. However, evaluating these models presents significant challenges due to their size, capabilities, and deployment across diverse applications. Existing literature often focuses on individual aspects, such as benchmark performance or specific tasks, but fails to provide a cohesive process that integrates the nuances of diverse use cases with broader ethical and operational considerations. This work focuses on three key aspects: (1) Formalizing the Evaluation Process by providing a structured framework tailored to specific use-case contexts, (2) Offering Actionable Tools and Frameworks such as checklists and templates to ensure thorough, reproducible, and practical evaluations, and (3) Surveying Recent Work with a targeted review of advancements in LLM evaluation, emphasizing real-world applications.
Modified Homotopic approach for diffractive production
2025-02-12
Carlos Contreras, José Garrido, Eugene Levin, Rodrigo Meneses
We review the recent developments of the use of the homotopy method for solving the non-linear evolution equation for the diffractive production in deep inelastic scattering. We introduce part of the non-linear corrections in the linear term. This simplified non-linear evolution equation is solved analytically taking into account the initial and boundary conditions for the process. It turns out that these corrections are rather small and can be estimated in the regular iterative procedure.
Detectability of dark matter subhalo impacts in Milky Way stellar streams
2025-02-11
Junyang Lu, Tongyan Lin, Mukul Sholapurkar, Ana Bonaca
Stellar streams are a promising way to probe the gravitational effects of low-mass dark matter (DM) subhalos. In recent years, there has been a remarkable explosion in the number of stellar streams detected in the Milky Way, and hundreds more may be discovered with future surveys such as LSST. Studies of DM subhalo impacts on streams have so far focused on a few of the thinnest and brightest streams, and it is not known how much information can be gained from the others. In this work, we develop a method to quickly estimate the minimum detectable DM subhalo mass of a given stream, where subhalo mass here refers to the total mass of a Plummer sphere. Our work is based on an analytic model for subhalo impacts on circular streams, which allows us to model streams with a wide range of properties including width, length, distance, and stellar density. We consider several observational scenarios, based on current and future surveys including Gaia, DESI, Via, and LSST. We find that at 95% confidence level, a stream like GD-1 has a minimum detectable subhalo mass of $\sim 6\times 10^6~\mathrm{M}_{\odot}$ in Gaia data and $\sim 8\times 10^5~\mathrm{M}_{\odot}$ with LSST 10 year sensitivity. Applying our results to confirmed Milky Way streams, we rank order them by their sensitivity to DM subhalos and identify promising ones for further study.
Analysis of the previously classified delta Scuti variable star MW Camelopardalis using data from the Transiting Exoplanet Survey Telescope sparked a deeper inquiry due to the unexpected patterns within the target's observed-calculated graph. From the shape of the O-C diagram we have designed these objects as Staircase delta Scuti. The pattern was found to be replicated in the O-C graphs of seven additional targets. The objects are TIC 17931346, TIC 44845403, TIC 123580083, TIC 173503902, TIC 302394816, TIC 194944219, and TIC 396465600. The Q value for the targets, their position in the delta Scuti Leavitt Law, and location in the instability strip would show these objects to be low mass, fundamental pulsators, near the red edge of the instability strip. We also discuss the impact this phenomenon could have on the analysis of all pulsating variable stars.
Cosmic Polarisation Rotation from CMB Data: a Review for GR110
2025-02-11
Alessandro Gruppuso, Sperello di Serego Alighieri
We provide an update on the work of di Serego Alighieri (2015), focusing on recent developments regarding constraints on Cosmic Polarization Rotation (CPR), also known as Cosmic Birefringence (CB), derived from Cosmic Microwave Background (CMB) polarization data.
In Search of the First Stars: An Ultra-Compact and Very Low Metallicity Lyman-$α$ Emitter Deep Within the Epoch of Reionization
2025-02-11
Chris J. Willott, Yoshihisa Asada, Kartheik G. Iyer, Jon Judez, Gregor Rihtarsic, Nicholas S. Martis, Ghassan T. E. Sarrouh, Guillaume Desprez, Anishya Harshan, Lamiya Mowla, Gael Noirot, Giordano Felicioni, Marusa Bradac, Gabe Brammer, Adam Muzzin, Marcin Sawicki, Jacqueline Antwi-Danso, Vladan Markov, Roberta Tripodi
We present {\it JWST} observations of a gravitationally-lensed, extremely metal-poor galaxy at redshift $z=8.203\pm 0.001$ from the CANUCS survey. Based on the low oxygen to Balmer line ratios we infer a gas-phase metallicity of $12+{\rm log(O/H)}=6.85$ (1.4\% solar), making CANUCS-A370-z8-LAE the most metal-poor galaxy known at $z>7$. With a high H$\beta$ equivalent width of $225\pm50$\,\AA\ and a half-light radius of only $r_{\rm hl} = 38 ^{+3}_{-19} $\,pc, the galaxy has a high star-formation-rate density of $50 - 100\,M_{\odot}$\,yr$^{-1}$\,kpc$^{-2}$. The galaxy shows high equivalent width Lyman-$\alpha$ emission with an inferred Lyman-$\alpha$ escape fraction of $0.21 \pm 0.05$. The high escape fraction of Lyman-$\alpha$ is likely due to the compact starbursting nature of the galaxy combined with its location in an overdensity traced by at least two other galaxies spectroscopically confirmed to lie within $\delta z = 0.01$ that have helped to reionize the environment. The low metallicity of CANUCS-A370-z8-LAE is best explained by a model where infalling metal-poor gas dilutes the interstellar medium, rather than being a young galaxy forming its first stellar populations.
Statistical selection of high-redshift, neutral-hydrogen-rich, lensed galaxies with the Square Kilometre Array
2025-02-11
Charissa Button, Roger Deane
Deep wide spectral line surveys with the Square Kilometre Array (SKA) will expand the cosmic frontiers of neutral atomic hydrogen (HI) in galaxies. However, at cosmologically significant redshifts ($z \gtrsim 0.5$), detections will typically be spatially unresolved and limited to the highest mass systems. Gravitational lensing could potentially alleviate these limitations, enabling lower mass systems to be studied at higher redshift and spatially resolved dynamical studies of some HI discs. Additionally, lensed HI systems would select foreground dark matter haloes using a different, more extended baryonic tracer compared to other lens surveys. This may result in a wider selected range of foreground dark matter halo properties, such as the concentration parameter. This paper uses the distortion of the observed HI mass function (HIMF) produced by strong gravitational lensing to find a flux density criterion for selecting lensed HI sources in future SKA-Mid spectral line surveys. This selection approach could yield lensed HI source densities in the range of $\sim 0.1$--$10$ galaxies per square degree out to a redshift of $z \simeq 3$ covered by SKA-MID Band 1. Although the sample sizes are modest, even with the proposed SKA-Mid surveys, the selection approach is straightforward and should have a 50% efficiency without any additional information, such as low-impact-factor or lower-redshift massive galaxies. The efficiency of selecting high-redshift, neutral-hydrogen-rich, lensed galaxies should then be greatly enhanced by using SKA-MID data in concert with the Vera C. Rubin Large Survey of Space and Time.
Geometric Interpretations of the $k$-Nearest Neighbour Distributions
2025-02-11
Kwanit Gangopadhyay, Arka Banerjee, Tom Abel
The $k$-Nearest Neighbour Cumulative Distribution Functions are measures of clustering for discrete datasets that are fast and efficient to compute. They are significantly more informative than the 2-point correlation function. Their connection to $N$-point correlation functions, void probability functions and Counts-in-Cells is known. However, the connections between the CDFs and other geometric and topological spatial summary statistics are yet to be fully explored in the literature. This understanding will be crucial to find optimally informative summary statistics to analyse data from stage 4 cosmological surveys. We explore quantitatively the geometric interpretations of the $k$NN CDF summary statistics. We establish an equivalence between the 1NN CDF at radius $r$ and the volume of spheres with the same radius around the data points. We show that higher $k$NN CDFs are equivalent to the volumes of intersections of $\ge k$ spheres around the data points. We present similar geometric interpretations for the $k$NN cross-correlation joint CDFs. We further show that the volume, or the CDFs, have information about the angles and arc lengths created at the intersections of spheres around the data points, which can be accessed through the derivatives of the CDF. We show this information is very similar to that captured by Germ Grain Minkowski Functionals. Using a Fisher analysis we compare the information content and constraining power of various data vectors constructed from the $k$NN CDFs and Minkowski Functionals. We find that the CDFs and their derivatives and the Minkowski Functionals have nearly identical information content. However, $k$NN CDFs are computationally orders of magnitude faster to evaluate. Finally, we find that there is information in the full shape of the CDFs, and therefore caution against using the values of the CDF only at sparsely sampled radii.
Mock Deep Testing: Toward Separate Development of Data and Models for Deep Learning
2025-02-11
Ruchira Manke, Mohammad Wardat, Foutse Khomh, Hridesh Rajan
While deep learning (DL) has permeated, and become an integral component of many critical software systems, today software engineering research hasn't explored how to separately test data and models that are integral for DL approaches to work effectively. The main challenge in independently testing these components arises from the tight dependency between data and models. This research explores this gap, introducing our methodology of mock deep testing for unit testing of DL applications. To enable unit testing, we introduce a design paradigm that decomposes the workflow into distinct, manageable components, minimizes sequential dependencies, and modularizes key stages of the DL. For unit testing these components, we propose modeling their dependencies using mocks. This modular approach facilitates independent development and testing of the components, ensuring comprehensive quality assurance throughout the development process. We have developed KUnit, a framework for enabling mock deep testing for the Keras library. We empirically evaluated KUnit to determine the effectiveness of mocks. Our assessment of 50 DL programs obtained from Stack Overflow and GitHub shows that mocks effectively identified 10 issues in the data preparation stage and 53 issues in the model design stage. We also conducted a user study with 36 participants using KUnit to perceive the effectiveness of our approach. Participants using KUnit successfully resolved 25 issues in the data preparation stage and 38 issues in the model design stage. Our findings highlight that mock objects provide a lightweight emulation of the dependencies for unit testing, facilitating early bug detection. Lastly, to evaluate the usability of KUnit, we conducted a post-study survey. The results reveal that KUnit is helpful to DL application developers, enabling them to independently test each component effectively in different stages.
SoK: A Classification for AI-driven Personalized Privacy Assistants
2025-02-11
Victor Morel, Leonardo Iwaya, Simone Fischer-Hübner
To help users make privacy-related decisions, personalized privacy assistants based on AI technology have been developed in recent years. These AI-driven Personalized Privacy Assistants (AI-driven PPAs) can reap significant benefits for users, who may otherwise struggle to make decisions regarding their personal data in environments saturated with privacy-related decision requests. However, no study systematically inquired about the features of these AI-driven PPAs, their underlying technologies, or the accuracy of their decisions. To fill this gap, we present a Systematization of Knowledge (SoK) to map the existing solutions found in the scientific literature. We screened 1697 unique research papers over the last decade (2013-2023), constructing a classification from 39 included papers. As a result, this SoK reviews several aspects of existing research on AI-driven PPAs in terms of types of publications, contributions, methodological quality, and other quantitative insights. Furthermore, we provide a comprehensive classification for AI-driven PPAs, delving into their architectural choices, system contexts, types of AI used, data sources, types of decisions, and control over decisions, among other facets. Based on our SoK, we further underline the research gaps and challenges and formulate recommendations for the design and development of AI-driven PPAs as well as avenues for future research.
The peer review process is crucial for ensuring the quality and reliability of scholarly work, yet assigning suitable reviewers remains a significant challenge. Traditional manual methods are labor-intensive and often ineffective, leading to nonconstructive or biased reviews. This paper introduces the exHarmony (eHarmony but for connecting experts to manuscripts) benchmark, designed to address these challenges by re-imagining the Reviewer Assignment Problem (RAP) as a retrieval task. Utilizing the extensive data from OpenAlex, we propose a novel approach that considers a host of signals from the authors, most similar experts, and the citation relations as potential indicators for a suitable reviewer for a manuscript. This approach allows us to develop a standard benchmark dataset for evaluating the reviewer assignment problem without needing explicit labels. We benchmark various methods, including traditional lexical matching, static neural embeddings, and contextualized neural embeddings, and introduce evaluation metrics that assess both relevance and diversity in the context of RAP. Our results indicate that while traditional methods perform reasonably well, contextualized embeddings trained on scholarly literature show the best performance. The findings underscore the importance of further research to enhance the diversity and effectiveness of reviewer assignments.
Most recommendation systems typically follow a product-based paradigm utilizing user-product interactions to identify the most engaging items for users. However, this product-based paradigm has notable drawbacks for Xianyu~\footnote{Xianyu is China's largest online C2C e-commerce platform where a large portion of the product are post by individual sellers}. Most of the product on Xianyu posted from individual sellers often have limited stock available for distribution, and once the product is sold, it's no longer available for distribution. This result in most items distributed product on Xianyu having relatively few interactions, affecting the effectiveness of traditional recommendation depending on accumulating user-item interactions. To address these issues, we introduce \textbf{IU4Rec}, an \textbf{I}nterest \textbf{U}nit-based two-stage \textbf{Rec}ommendation system framework. We first group products into clusters based on attributes such as category, image, and semantics. These IUs are then integrated into the Recommendation system, delivering both product and technological innovations. IU4Rec begins by grouping products into clusters based on attributes such as category, image, and semantics, forming Interest Units (IUs). Then we redesign the recommendation process into two stages. In the first stage, the focus is on recommend these Interest Units, capturing broad-level interests. In the second stage, it guides users to find the best option among similar products within the selected Interest Unit. User-IU interactions are incorporated into our ranking models, offering the advantage of more persistent IU behaviors compared to item-specific interactions. Experimental results on the production dataset and online A/B testing demonstrate the effectiveness and superiority of our proposed IU-centric recommendation approach.
Foliated Plateau problems, geometric rigidity and equidistribution of closed $k$-surfaces
2025-02-11
Sébastien Alvarez
In this note, we survey recent advances in the study of dynamical properties of the space of surfaces with constant curvature in three-dimensional manifolds of negative sectional curvature. We interpret this space as a two-dimensional analogue of the geodesic flow and explore the extent to which the thermodynamic properties of the latter can be generalized to the surface setting. Additionally, we apply this theory to derive geometric rigidity results, including the rigidity of the hyperbolic marked area spectrum.
Spatial and Chemical Complexity in the W75N Star-Forming Region
2025-02-11
Morgan M. Giese, Will E. Thompson, Dariusz C. Lis, Susanna L. Widicus Weaver
We present the analysis of NOEMA interferometric observations of the high-mass star-forming region W75N(B) with a focus on molecular composition and distribution of prebiotic molecules in the source's multiple cores. Over twenty molecules are identified across the region, with many being fit for column density, rotational temperature, spectral line full width half maximum, and v$_{lsr}$. This work includes the first known detection and initial analysis of complex organic molecules in the MM2 and MM3 regions. Furthermore, parameter maps were created from the six molecules that were well fit across multiple regions. The molecular emission was imaged and correlated across different molecules and the continuum to reveal structural features. From the spatial and spectral analysis of the MM1 region, these results concur with those from other studies showing that there is a difference in chemical composition between the MM1a and MM1b regions, with sulfur-bearing molecules tracing MM1a and organic molecules tracing MM1b. The molecular emission imaged toward the MM3 region reveals two peaks, possibly indicating the presence of multiple young stellar objects. These results provide detailed quantitative information about the physical parameters and distributions of molecules in this source. Additionally, these results are part of a follow-up of a single-dish survey of multiple star-forming regions and are discussed in this context.
We review some recent developments in 1-st order GLSM construction, or so-called Gross-Neveu formalism for sigma models. We recall the general idea behind this framework and describe a 1-st order GLSM data from which the general generalized Gross-Neveu model can be constructed, using $\mathbb{CP}^{n-1}$ sigma model as simplest example. Then, we formulate a general statement that answers the question of which generalized Gross-Neveu models are equivalent to sigma models on compact homogeneous Hermitian spaces of classical groups endowed with the normal metrics.
Beyond Prompting: Time2Lang -- Bridging Time-Series Foundation Models and Large Language Models for Health Sensing
2025-02-11
Arvind Pillai, Dimitris Spathis, Subigya Nepal, Amanda C Collins, Daniel M Mackin, Michael V Heinz, Tess Z Griffin, Nicholas C Jacobson, Andrew Campbell
Large language models (LLMs) show promise for health applications when combined with behavioral sensing data. Traditional approaches convert sensor data into text prompts, but this process is prone to errors, computationally expensive, and requires domain expertise. These challenges are particularly acute when processing extended time series data. While time series foundation models (TFMs) have recently emerged as powerful tools for learning representations from temporal data, bridging TFMs and LLMs remains challenging. Here, we present Time2Lang, a framework that directly maps TFM outputs to LLM representations without intermediate text conversion. Our approach first trains on synthetic data using periodicity prediction as a pretext task, followed by evaluation on mental health classification tasks. We validate Time2Lang on two longitudinal wearable and mobile sensing datasets: daily depression prediction using step count data (17,251 days from 256 participants) and flourishing classification based on conversation duration (46 participants over 10 weeks). Time2Lang maintains near constant inference times regardless of input length, unlike traditional prompting methods. The generated embeddings preserve essential time-series characteristics such as auto-correlation. Our results demonstrate that TFMs and LLMs can be effectively integrated while minimizing information loss and enabling performance transfer across these distinct modeling paradigms. To our knowledge, we are the first to integrate a TFM and an LLM for health, thus establishing a foundation for future research combining general-purpose large models for complex healthcare tasks.
Towards spatial computing: recent advances in multimodal natural interaction for XR headsets
2025-02-11
Zhimin Wang, Maohang Rao, Shanghua Ye, Weitao Song, Feng Lu
With the widespread adoption of Extended Reality (XR) headsets, spatial computing technologies are gaining increasing attention. Spatial computing enables interaction with virtual elements through natural input methods such as eye tracking, hand gestures, and voice commands, thus placing natural human-computer interaction at its core. While previous surveys have reviewed conventional XR interaction techniques, recent advancements in natural interaction, particularly driven by artificial intelligence (AI) and large language models (LLMs), have introduced new paradigms and technologies. In this paper, we review research on multimodal natural interaction for wearable XR, focusing on papers published between 2022 and 2024 in six top venues: ACM CHI, UIST, IMWUT (Ubicomp), IEEE VR, ISMAR, and TVCG. We classify and analyze these studies based on application scenarios, operation types, and interaction modalities. This analysis provides a structured framework for understanding how researchers are designing advanced natural interaction techniques in XR. Based on these findings, we discuss the challenges in natural interaction techniques and suggest potential directions for future research. This review provides valuable insights for researchers aiming to design natural and efficient interaction systems for XR, ultimately contributing to the advancement of spatial computing.
Decision-Making Under Complete Uncertainty: You Will Regret Not Being Greedy
2025-02-11
Kristijan Atanasov, Mehmet Ismail, Frederik Mallmann-Trenn
In this paper, we propose a probabilistic game-theoretic model to study the properties of the worst-case regret of the greedy strategy under complete (Knightian) uncertainty. In a game between a decision-maker (DM) and an adversarial agent (Nature), the DM observes a realization of product ratings for each product. Upon observation, the DM chooses a strategy, which is a function from the set of observations to the set of products. We study the theoretical properties, including the worst-case regret of the greedy strategy that chooses the product with the highest observed average rating. We prove that, with respect to the worst-case regret, the greedy strategy is optimal and that, in the limit, the regret of the greedy strategy converges to zero. We validate the model on data collected from Google reviews for restaurants, showing that the greedy strategy not only performs according to the theoretical findings but also outperforms the uniform strategy and the Thompson Sampling algorithm.
Automated Capability Discovery via Model Self-Exploration
2025-02-11
Cong Lu, Shengran Hu, Jeff Clune
Foundation models have become general-purpose assistants, exhibiting diverse capabilities across numerous domains through training on web-scale data. It remains challenging to precisely characterize even a fraction of the full spectrum of capabilities and potential risks in any new model. Existing evaluation approaches often require significant human effort, and it is taking increasing effort to design ever harder challenges for more capable models. We introduce Automated Capability Discovery (ACD), a framework that designates one foundation model as a scientist to systematically propose open-ended tasks probing the abilities of a subject model (potentially itself). By combining frontier models with ideas from the field of open-endedness, ACD automatically and systematically uncovers both surprising capabilities and failures in the subject model. We demonstrate ACD across a range of foundation models (including the GPT, Claude, and Llama series), showing that it automatically reveals thousands of capabilities that would be challenging for any single team to uncover. We further validate our method's automated scoring with extensive human surveys, observing high agreement between model-generated and human evaluations. By leveraging foundation models' ability to both create tasks and self-evaluate, ACD is a significant step toward scalable, automated evaluation of novel AI systems. All code and evaluation logs are open-sourced at https://github.com/conglu1997/ACD.
The Expanding 3-Kiloparsec Arms are neither Expanding nor Spiral Arms, but X1 Orbits driven by the Galactic Bar
2025-02-11
Jayender Kumar, Mark J. Reid, T. M. Dame, Simon P. Ellingsen, Lucas J. Hyland, Andreas Brunthale, Karl M. Menten, Xing-Wu Zheng, Alberto Sanna
Near the center of our Milky Way is a bar-like structure and the so-called Expanding 3-kpc arms. We currently have limited knowledge of this important region, since we are about 8.2 kpc from the center and cannot directly observe it at optical wavelengths, owing to strong extinction from interstellar dust. Here we present extremely precise VLBI measurements of water maser sources from the BeSSeL Survey, where extinction is not a problem, which accurately determine the 3-dimensional locations and motions of three massive young stars. Combined with previous measurements, these stars delineate a trail of orbits outlining the Milky Way's Galactic Bar. We present the first measurements capturing the dynamics of quasi-elliptical (X1) orbits around the Galactic Bar. Our findings provide evidence substantiating the existence of such orbits populated by massive young stars. Our measurements of the position and velocity of a number of massive young stars, previously identified with the Expanding 3-kpc arms, show that they are more likely located in the X1 orbits about the Galactic Bar. Also, some stars previously assigned to the Norma spiral arm appear to be in these orbits, which suggests that this spiral arm does not extend past the end of the bar.
Exoplanet Transit Candidate Identification in TESS Full-Frame Images via a Transformer-Based Algorithm
2025-02-11
Helem Salinas, Rafael Brahm, Greg Olmschenk, Richard K. Barry, Karim Pichara, Stela Ishitani Silva, Vladimir Araujo
The Transiting Exoplanet Survey Satellite (TESS) is surveying a large fraction of the sky, generating a vast database of photometric time series data that requires thorough analysis to identify exoplanetary transit signals. Automated learning approaches have been successfully applied to identify transit signals. However, most existing methods focus on the classification and validation of candidates, while few efforts have explored new techniques for the search of candidates. To search for new exoplanet transit candidates, we propose an approach to identify exoplanet transit signals without the need for phase folding or assuming periodicity in the transit signals, such as those observed in multi-transit light curves. To achieve this, we implement a new neural network inspired by Transformers to directly process Full Frame Image (FFI) light curves to detect exoplanet transits. Transformers, originally developed for natural language processing, have recently demonstrated significant success in capturing long-range dependencies compared to previous approaches focused on sequential data. This ability allows us to employ multi-head self-attention to identify exoplanet transit signals directly from the complete light curves, combined with background and centroid time series, without requiring prior transit parameters. The network is trained to learn characteristics of the transit signal, like the dip shape, which helps distinguish planetary transits from other variability sources. Our model successfully identified 214 new planetary system candidates, including 122 multi-transit light curves, 88 single-transit and 4 multi-planet systems from TESS sectors 1-26 with a radius > 0.27 $R_{\mathrm{Jupiter}}$, demonstrating its ability to detect transits regardless of their periodicity.
Corporate Greenwashing Detection in Text - a Survey
2025-02-11
Tom Calamai, Oana Balalau, Théo Le Guenedal, Fabian M. Suchanek
Greenwashing is an effort to mislead the public about the environmental impact of an entity, such as a state or company. We provide a comprehensive survey of the scientific literature addressing natural language processing methods to identify potentially misleading climate-related corporate communications, indicative of greenwashing. We break the detection of greenwashing into intermediate tasks, and review the state-of-the-art approaches for each of them. We discuss datasets, methods, and results, as well as limitations and open challenges. We also provide an overview of how far the field has come as a whole, and point out future research directions.
Evidence for similar collectivity of high transverse momentum particles in pPb and PbPb collisions
2025-02-11
CMS Collaboration
Charged hadron elliptic anisotropies ($v_2$) are presented over a wide transverse momentum ($p_\text{T}$) range for proton-lead (pPb) and lead-lead (PbPb) collisions at nucleon-nucleon center-of-mass energies of 8.16 and 5.02 TeV, respectively. The data were recorded by the CMS experiment and correspond to integrated luminosities of 186 nb$^{-1}$ and 0.607 nb$^{-1}$ for the pPb and PbPb systems, respectively. A four-particle cumulant analysis is performed using subevents separated in pseudorapidity to effectively suppress non-collective effects. At high $p_\text{T}$ ($p_\text{T}$ $\gt$ 8 GeV), significant positive $v_2$ values are observed that are similar between pPb and PbPb collisions at comparable charged particle multiplicities. This observation suggests a common origin for the multi-particle collectivity for high-$p_\text{T}$ particles in the two systems.
Modified theories of gravity at different curvature scales
2025-02-11
Susobhan Mandal, S. Shankaranarayanan
General Relativity (GR) remains the cornerstone of gravitational physics, providing remarkable success in describing a wide range of astrophysical and cosmological phenomena. However, several challenges underscore the urgent need to explore modified gravity theories. GR struggles to reconcile with quantum mechanics, fails to provide fundamental explanations for dark matter and dark energy, and faces limitations in describing extreme regimes such as black hole singularities and the very early universe. This review provides an organized perspective on modified gravity theories by classifying them based on the principles of GR they preserve or violate. Specifically, we consider three broad categories: (1) metric theories that uphold local Lorentz invariance (LLI) and gauge invariance, (2) theories that break gauge invariance, LLI, or parity, and (3) beyond-metric theories that violate the Einstein's equivalence principle (EEP). This classification highlights the underlying assumptions of GR that these theories challenge or extend, providing a framework for understanding their motivations and implications. The review also discusses the current and upcoming experimental and observational tests of GR, including those probing its foundational principles, such as LLI, gauge invariance, and EEP. For each class of modified theories, we examine their ability to address critical open questions in cosmology and black hole physics. These include their potential to explain the accelerated expansion of the current universe, the nature of dark matter, and deviations in black hole dynamics from GR predictions. This review aims to provide a structured understanding of modified gravity theories and their observational implications in the multimessenger era by focusing on the principles preserved or violated. [abridged]
The Human Capital Accumulation at Research Infrastructures: Reexamining Wage Returns to Training, Models, Interpretation, and Magnitude
2025-02-11
Erica Delugas, Francesco Giffoni, Emanuela Sirtori, Johannes Gutleber
We design a research strategy to measure wage returns of research training targeting early career researchers (ECRs) at research infrastructures (RIs). Grounded in established economic models of education and training, our strategy improves upon existing studies on the same topic in labour market relevance, scope, and economic modelling. We draw on a survey of ECRs at the European Organisation for Nuclear Research (CERN) and find that CERN research training increases ECRs wages by 7 per cent on average, ranging from 2 to 10 per cent. Wage gains materialise early in their careers, typically within the first decade of employment. Wage returns are driven by hard skills, such as software development, data analysis, and problem solving capacity, as well as sought after soft skills, such as communication, leadership, and networking. Our findings suggest that ECRs wage returns primarily reflect productivity improvements rather than the signalling effect of CERN affiliation. Our research applies to other RIs. We conclude by discussing methodological considerations, policy implications, and avenues for future research.
The perception system is a a critical role of an autonomous driving system for ensuring safety. The driving scene perception system fundamentally represents an object detection task that requires achieving a balance between accuracy and processing speed. Many contemporary methods focus on improving detection accuracy but often overlook the importance of real-time detection capabilities when computational resources are limited. Thus, it is vital to investigate efficient object detection strategies for driving scenes. This paper introduces Fast-COS, a novel single-stage object detection framework crafted specifically for driving scene applications. The research initiates with an analysis of the backbone, considering both macro and micro architectural designs, yielding the Reparameterized Attention Vision Transformer (RAViT). RAViT utilizes Reparameterized Multi-Scale Depth-Wise Convolution (RepMSDW) and Reparameterized Self-Attention (RepSA) to enhance computational efficiency and feature extraction. In extensive tests across GPU, edge, and mobile platforms, RAViT achieves 81.4% Top-1 accuracy on the ImageNet-1K dataset, demonstrating significant throughput improvements over comparable backbone models such as ResNet, FastViT, RepViT, and EfficientFormer. Additionally, integrating RepMSDW into a feature pyramid network forms RepFPN, enabling fast and multi-scale feature fusion. Fast-COS enhances object detection in driving scenes, attaining an AP50 score of 57.2% on the BDD100K dataset and 80.0% on the TJU-DHD Traffic dataset. It surpasses leading models in efficiency, delivering up to 75.9% faster GPU inference and 1.38 higher throughput on edge devices compared to FCOS, YOLOF, and RetinaNet. These findings establish Fast-COS as a highly scalable and reliable solution suitable for real-time applications, especially in resource-limited environments like autonomous driving systems
Optimality in importance sampling: a gentle survey
2025-02-11
Fernando Llorente, Luca Martino
The performance of the Monte Carlo sampling methods relies on the crucial choice of a proposal density. The notion of optimality is fundamental to design suitable adaptive procedures of the proposal density within Monte Carlo schemes. This work is an exhaustive review around the concept of optimality in importance sampling. Several frameworks are described and analyzed, such as the marginal likelihood approximation for model selection, the use of multiple proposal densities, a sequence of tempered posteriors, and noisy scenarios including the applications to approximate Bayesian computation (ABC) and reinforcement learning, to name a few. Some theoretical and empirical comparisons are also provided.
Simulation has been pivotal in recent robotics milestones and is poised to play a prominent role in the field's future. However, recent robotic advances often rely on expensive and high-maintenance platforms, limiting access to broader robotics audiences. This work introduces Wheeled Lab, a framework for the low-cost, open-source wheeled platforms that are already widely established in education and research. Through integration with Isaac Lab, Wheeled Lab introduces modern techniques in Sim2Real, such as domain randomization, sensor simulation, and end-to-end learning, to new user communities. To kickstart education and demonstrate the framework's capabilities, we develop three state-of-the-art policies for small-scale RC cars: controlled drifting, elevation traversal, and visual navigation, each trained in simulation and deployed in the real world. By bridging the gap between advanced Sim2Real methods and affordable, available robotics, Wheeled Lab aims to democratize access to cutting-edge tools, fostering innovation and education in a broader robotics context. The full stack, from hardware to software, is low cost and open-source.
The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials
2025-02-11
Junfan Xia, Yaolong Zhang, Bin Jiang
Recent years have witnessed the fast development of machine learning potentials (MLPs) and their widespread applications in chemistry, physics, and material science. By fitting discrete ab initio data faithfully to continuous and symmetry-preserving mathematical forms, MLPs have enabled accurate and efficient atomistic simulations in a large scale from first principles. In this review, we provide an overview of the evolution of MLPs in the past two decades and focus on the state-of-the-art MLPs proposed in the last a few years for molecules, reactions, and materials. We discuss some representative applications of MLPs and the trend of developing universal potentials across a variety of systems. Finally, we outline a list of open challenges and opportunities in the development and applications of MLPs.
2025-02-11
Joseph M. Lukens, Nicholas A. Peters, Bing Qi
Over the past several decades, the proliferation of global classical communication networks has transformed various facets of human society. Concurrently, quantum networking has emerged as a dynamic field of research, driven by its potential applications in distributed quantum computing, quantum sensor networks, and secure communications. This prompts a fundamental question: rather than constructing quantum networks from scratch, can we harness the widely available classical fiber-optic infrastructure to establish hybrid quantum-classical networks? This paper aims to provide a comprehensive review of ongoing research endeavors aimed at integrating quantum communication protocols, such as quantum key distribution, into existing lightwave networks. This approach offers the substantial advantage of reducing implementation costs by allowing classical and quantum communication protocols to share optical fibers, communication hardware, and other network control resources, arguably the most pragmatic solution in the near term. In the long run, classical communication will also reap the rewards of innovative quantum communication technologies, such as quantum memories and repeaters. Accordingly, our vision for the future of the Internet is that of heterogeneous communication networks thoughtfully designed for the seamless support of both classical and quantum communications.
Generation of Drug-Induced Cardiac Reactions towards Virtual Clinical Trials
2025-02-11
Qian Shao, Bang Du, Zepeng Li, Qiyuan Chen, Hongxia Xu, Jimeng Sun, Jian Wu, Jintai Chen
Clinical trials are pivotal in cardiac drug development, yet they often fail due to inadequate efficacy and unexpected safety issues, leading to significant financial losses. Using in-silico trials to replace a part of physical clinical trials, e.g., leveraging advanced generative models to generate drug-influenced electrocardiograms (ECGs), seems an effective method to reduce financial risk and potential harm to trial participants. While existing generative models have demonstrated progress in ECG generation, they fall short in modeling drug reactions due to limited fidelity and inability to capture individualized drug response patterns. In this paper, we propose a Drug-Aware Diffusion Model (DADM), which could simulate individualized drug reactions while ensuring fidelity. To ensure fidelity, we construct a set of ordinary differential equations to provide external physical knowledge (EPK) of the realistic ECG morphology. The EPK is used to adaptively constrain the morphology of the generated ECGs through a dynamic cross-attention (DCA) mechanism. Furthermore, we propose an extension of ControlNet to incorporate demographic and drug data, simulating individual drug reactions. We compare DADM with the other eight state-of-the-art ECG generative models on two real-world databases covering 8 types of drug regimens. The results demonstrate that DADM can more accurately simulate drug-induced changes in ECGs, improving the accuracy by at least 5.79% and recall by 8%.
Learning Inverse Laplacian Pyramid for Progressive Depth Completion
2025-02-11
Kun Wang, Zhiqiang Yan, Junkai Fan, Jun Li, Jian Yang
Depth completion endeavors to reconstruct a dense depth map from sparse depth measurements, leveraging the information provided by a corresponding color image. Existing approaches mostly hinge on single-scale propagation strategies that iteratively ameliorate initial coarse depth estimates through pixel-level message passing. Despite their commendable outcomes, these techniques are frequently hampered by computational inefficiencies and a limited grasp of scene context. To circumvent these challenges, we introduce LP-Net, an innovative framework that implements a multi-scale, progressive prediction paradigm based on Laplacian Pyramid decomposition. Diverging from propagation-based approaches, LP-Net initiates with a rudimentary, low-resolution depth prediction to encapsulate the global scene context, subsequently refining this through successive upsampling and the reinstatement of high-frequency details at incremental scales. We have developed two novel modules to bolster this strategy: 1) the Multi-path Feature Pyramid module, which segregates feature maps into discrete pathways, employing multi-scale transformations to amalgamate comprehensive spatial information, and 2) the Selective Depth Filtering module, which dynamically learns to apply both smoothness and sharpness filters to judiciously mitigate noise while accentuating intricate details. By integrating these advancements, LP-Net not only secures state-of-the-art (SOTA) performance across both outdoor and indoor benchmarks such as KITTI, NYUv2, and TOFDC, but also demonstrates superior computational efficiency. At the time of submission, LP-Net ranks 1st among all peer-reviewed methods on the official KITTI leaderboard.
Diverse Perspectives on AI: Examining People's Acceptability and Reasoning of Possible AI Use Cases
2025-02-11
Jimin Mun, Wei Bin Au Yeong, Wesley Hanwen Deng, Jana Schaich Borg, Maarten Sap
In recent years, there has been a growing recognition of the need to incorporate lay-people's input into the governance and acceptability assessment of AI usage. However, how and why people judge different AI use cases to be acceptable or unacceptable remains under-explored. In this work, we investigate the attitudes and reasons that influence people's judgments about AI's development via a survey administered to demographically diverse participants (N=197). We focus on ten distinct professional (e.g., Lawyer AI) and personal (e.g., Digital Medical Advice AI) AI use cases to understand how characteristics of the use cases and the participants' demographics affect acceptability. We explore the relationships between participants' judgments and their rationales such as reasoning approaches (cost-benefit reasoning vs. rule-based). Our empirical findings reveal number of factors that influence acceptance such as general negative acceptance and higher disagreement of professional usage over personal, significant influence of demographics factors such as gender, employment, and education as well as AI literacy level, and reasoning patterns such as rule-based reasoning being used more when use case is unacceptable. Based on these findings, we discuss the key implications for soliciting acceptability and reasoning of AI use cases to collaboratively build consensus. Finally, we shed light on how future FAccT researchers and practitioners can better incorporate diverse perspectives from lay people to better develop AI that aligns with public expectations and needs.
Negative Dependence as a toolbox for machine learning : review and new developments
2025-02-11
Hoang-Son Tran, Vladimir Petrovic, Remi Bardenet, Subhroshekhar Ghosh
Negative dependence is becoming a key driver in advancing learning capabilities beyond the limits of traditional independence. Recent developments have evidenced support towards negatively dependent systems as a learning paradigm in a broad range of fundamental machine learning challenges including optimization, sampling, dimensionality reduction and sparse signal recovery, often surpassing the performance of current methods based on statistical independence. The most popular negatively dependent model has been that of determinantal point processes (DPPs), which have their origins in quantum theory. However, other models, such as perturbed lattice models, strongly Rayleigh measures, zeros of random functions have gained salience in various learning applications. In this article, we review this burgeoning field of research, as it has developed over the past two decades or so. We also present new results on applications of DPPs to the parsimonious representation of neural networks. In the limited scope of the article, we mostly focus on aspects of this area to which the authors contributed over the recent years, including applications to Monte Carlo methods, coresets and stochastic gradient descent, stochastic networks, signal processing and connections to quantum computation. However, starting from basics of negative dependence for the uninitiated reader, extensive references are provided to a broad swath of related developments which could not be covered within our limited scope. While existing works and reviews generally focus on specific negatively dependent models (e.g. DPPs), a notable feature of this article is that it addresses negative dependence as a machine learning methodology as a whole. In this vein, it covers within its span an array of negatively dependent models and their applications well beyond DPPs, thereby putting forward a very general and rather unique perspective.
Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal Analysis
2025-02-11
Amir Hosein Fadaei, Mohammad-Reza A. Dehaqani
It's no secret that video has become the primary way we share information online. That's why there's been a surge in demand for algorithms that can analyze and understand video content. It's a trend going to continue as video continues to dominate the digital landscape. These algorithms will extract and classify related features from the video and will use them to describe the events and objects in the video. Deep neural networks have displayed encouraging outcomes in the realm of feature extraction and video description. This paper will explore the spatiotemporal features found in videos and recent advancements in deep neural networks in video understanding. We will review some of the main trends in video understanding models and their structural design, the main problems, and some offered solutions in this topic. We will also review and compare significant video understanding and action recognition datasets.
Testing Practices, Challenges, and Developer Perspectives in Open-Source IoT Platforms
2025-02-11
Daniel Rodriguez-Cardenas, Safwat Ali Khan, Prianka Mandal, Adwait Nadkarni, Kevin Moran, Denys Poshyvanyk
As the popularity of Internet of Things (IoT) platforms grows, users gain unprecedented control over their homes, health monitoring, and daily task automation. However, the testing of software for these platforms poses significant challenges due to their diverse composition, e.g., common smart home platforms are often composed of varied types of devices that use a diverse array of communication protocols, connections to mobile apps, cloud services, as well as integration among various platforms. This paper is the first to uncover both the practices and perceptions behind testing in IoT platforms, particularly open-source smart home platforms. Our study is composed of two key components. First, we mine and empirically analyze the code and integrations of two highly popular and well-maintained open-source IoT platforms, OpenHab and HomeAssitant. Our analysis involves the identification of functional and related test methods based on the focal method approach. We find that OpenHab has only 0.04 test ratio ($\approx 4K$ focal test methods from $\approx 76K$ functional methods) in Java files, while HomeAssitant exhibits higher test ratio of $0.42$, which reveals a significant dearth of testing. Second, to understand the developers' perspective on testing in IoT, and to explain our empirical observations, we survey 80 open-source developers actively engaged in IoT platform development. Our analysis of survey responses reveals a significant focus on automated (unit) testing, and a lack of manual testing, which supports our empirical observations, as well as testing challenges specific to IoT. Together, our empirical analysis and survey yield 10 key findings that uncover the current state of testing in IoT platforms, and reveal key perceptions and challenges. These findings provide valuable guidance to the research community in navigating the complexities of effectively testing IoT platforms.
Ensuring fairness in decentralized multi-agent systems presents significant challenges due to emergent biases, systemic inefficiencies, and conflicting agent incentives. This paper provides a comprehensive survey of fairness in multi-agent AI, introducing a novel framework where fairness is treated as a dynamic, emergent property of agent interactions. The framework integrates fairness constraints, bias mitigation strategies, and incentive mechanisms to align autonomous agent behaviors with societal values while balancing efficiency and robustness. Through empirical validation, we demonstrate that incorporating fairness constraints results in more equitable decision-making. This work bridges the gap between AI ethics and system design, offering a foundation for accountable, transparent, and socially responsible multi-agent AI systems.
Improving the coincidence time resolution (CTR) of time-of-flight positron emission tomography (TOF-PET) systems to achieve a higher signal-to-noise ratio (SNR) gain or even direct positron emission imaging (dPEI) is of paramount importance for many advanced new clinical applications of PET imaging. This places higher demands on the timing performance of all aspects of PET systems. One effective approach is to use microchannel plate photomultiplier tubes (MCP-PMTs) for prompt Cherenkov photon detection. In this study, we developed a dual-module Cherenkov PET imaging experimental platform, utilising our proprietary 8 * 8-anode Cherenkov radiator-integrated window MCP-PMTs in combination with custom-designed multi-channel electronics, and designed a specific calibration and correction method for the platform. Using this platform, a CTR of 103 ps FWHM was achieved. We overcame the limitations of single-anode detectors in previous experiments, significantly enhanced imaging efficiency and achieved module-level Cherenkov PET imaging for the first time. Imaging experiments involving radioactive sources and phantoms of various shapes and types were conducted, which preliminarily validated the feasibility and advancement of this imaging method. In addition, the effects of normalisation correction and the interaction probability between the gamma rays and the MCP on the images and experimental results were analysed and verified.
With increasing demands for fuel efficiency and operational adaptability in commercial aviation}, this paper provides a systematic review and classification of morphing wing technologies, analyzing their aerodynamic performance characteristics and atmospheric condition adaptability. We first develop a comprehensive classification framework for morphing wing designs based on their scale of morphing, actuation mechanisms, and intended purposes. Through analysis of historical developments and current implementations, we evaluate two significant case studies: the Mission Adaptive Compliant Wing (MACW) and Adaptive Aspect Ratio (AdAR) morphing wing, demonstrating performance improvements of up to 25% in drag reduction and 40% in control authority. Our investigation reveals critical trade-offs between full-span and partial morphing approaches, particularly regarding implementation complexity, certification requirements, and operational reliability. The study concludes with an assessment of technical barriers and opportunities, providing specific recommendations for advancing morphing wing technology in commercial aviation applications. Key findings indicate that while material science and control system advances enable practical implementation, certification pathways and maintenance considerations remain critical challenges for widespread adoption.
SemiHMER: Semi-supervised Handwritten Mathematical Expression Recognition using pseudo-labels
2025-02-11
Kehua Chen, Haoyang Shen
In recent years, deep learning with Convolutional Neural Networks (CNNs) has achieved remarkable results in the field of HMER (Handwritten Mathematical Expression Recognition). However, it remains challenging to improve performance with limited labeled training data. This paper presents, for the first time, a simple yet effective semi-supervised HMER framework by introducing dual-branch semi-supervised learning. Specifically, we simplify the conventional deep co-training from consistency regularization to cross-supervised learning, where the prediction of one branch is used as a pseudo-label to supervise the other branch directly end-to-end. Considering that the learning of the two branches tends to converge in the later stages of model optimization, we also incorporate a weak-to-strong strategy by applying different levels of augmentation to each branch, which behaves like expanding the training data and improving the quality of network training. Meanwhile, We propose a novel module, Global Dynamic Counting Module(GDCM), to enhance the performance of the HMER decoder, which alleviates recognition inaccuracies in long-distance formula recognition and the occurrence of repeated characters. We release our code at https://github.com/chenkehua/SemiHMER.
Quantifying Quantumness in (A)dS spacetimes with Unruh-DeWitt Detector
2025-02-11
Li-Juan Li, Xue-Ke Song, Liu Ye, Dong Wang
Probing quantumness in curved spacetime is regarded as one of fundamental and important topics in the framework of relativistic quantum information. In this work, we focus on the theoretical feasibility of probing quantum properties in de Sitter (dS) and Anti-de Sitter (AdS) spacetimes via detectors. By employing the Unruh-DeWitt detector coupled with a massless scalar field, which is treated as an open system, quantum uncertainty and quantum coherence in both dS and AdS spacetimes are investigated. Our analysis reveals that the acceleration in dS spacetime and the boundary conditions in AdS spacetime significantly impact the detector's evolution in the initial stage. Notably, both of the uncertainty and coherence will oscillate with the initial state being in a superposition state, however the high temperature is able to suppress their oscillation. Interestingly, it is found that the constant values of the final uncertainty and coherence are identical as those in dS and AdS spacetimes, which are determined by the ratio of energy gap to temperature. Hence, the current exploration offers insight into quantumness in dS and AdS spacetimes, and might be helpful to facilitate the curved-spacetime-based quantum information processing.
A Survey on Mamba Architecture for Vision Applications
2025-02-11
Fady Ibrahim, Guangjun Liu, Guanghui Wang
Transformers have become foundational for visual tasks such as object detection, semantic segmentation, and video understanding, but their quadratic complexity in attention mechanisms presents scalability challenges. To address these limitations, the Mamba architecture utilizes state-space models (SSMs) for linear scalability, efficient processing, and improved contextual awareness. This paper investigates Mamba architecture for visual domain applications and its recent advancements, including Vision Mamba (ViM) and VideoMamba, which introduce bidirectional scanning, selective scanning mechanisms, and spatiotemporal processing to enhance image and video understanding. Architectural innovations like position embeddings, cross-scan modules, and hierarchical designs further optimize the Mamba framework for global and local feature extraction. These advancements position Mamba as a promising architecture in computer vision research and applications.
Image compression under ultra-low bitrates remains challenging for both conventional learned image compression (LIC) and generative vector-quantized (VQ) modeling. Conventional LIC suffers from severe artifacts due to heavy quantization, while generative VQ modeling gives poor fidelity due to the mismatch between learned generative priors and specific inputs. In this work, we propose Hybrid-Diffusion Image Compression (HDCompression), a dual-stream framework that utilizes both generative VQ-modeling and diffusion models, as well as conventional LIC, to achieve both high fidelity and high perceptual quality. Different from previous hybrid methods that directly use pre-trained LIC models to generate low-quality fidelity-preserving information from heavily quantized latent, we use diffusion models to extract high-quality complimentary fidelity information from the ground-truth input, which can enhance the system performance in several aspects: improving indices map prediction, enhancing the fidelity-preserving output of the LIC stream, and refining conditioned image reconstruction with VQ-latent correction. In addition, our diffusion model is based on a dense representative vector (DRV), which is lightweight with very simple sampling schedulers. Extensive experiments demonstrate that our HDCompression outperforms the previous conventional LIC, generative VQ-modeling, and hybrid frameworks in both quantitative metrics and qualitative visualization, providing balanced robust compression performance at ultra-low bitrates.
Optimizing Likelihoods via Mutual Information: Bridging Simulation-Based Inference and Bayesian Optimal Experimental Design
2025-02-11
Vincent D. Zaballa, Elliot E. Hui
Simulation-based inference (SBI) is a method to perform inference on a variety of complex scientific models with challenging inference (inverse) problems. Bayesian Optimal Experimental Design (BOED) aims to efficiently use experimental resources to make better inferences. Various stochastic gradient-based BOED methods have been proposed as an alternative to Bayesian optimization and other experimental design heuristics to maximize information gain from an experiment. We demonstrate a link via mutual information bounds between SBI and stochastic gradient-based variational inference methods that permits BOED to be used in SBI applications as SBI-BOED. This link allows simultaneous optimization of experimental designs and optimization of amortized inference functions. We evaluate the pitfalls of naive design optimization using this method in a standard SBI task and demonstrate the utility of a well-chosen design distribution in BOED. We compare this approach on SBI-based models in real-world simulators in epidemiology and biology, showing notable improvements in inference.
2025-02-11
Amir Moeini, Jiuqi Wang, Jacob Beck, Ethan Blaser, Shimon Whiteson, Rohan Chandra, Shangtong Zhang
Reinforcement learning (RL) agents typically optimize their policies by performing expensive backward passes to update their network parameters. However, some agents can solve new tasks without updating any parameters by simply conditioning on additional context such as their action-observation histories. This paper surveys work on such behavior, known as in-context reinforcement learning.
The exponential growth of academic publications poses challenges for the research process, such as literature review and procedural planning. Large Language Models (LLMs) have emerged as powerful AI tools, especially when combined with additional tools and resources. Recent LLM-powered frameworks offer promising solutions for handling complex domain-specific tasks, yet their domain-specific implementation limits broader applicability. This highlights the need for LLM-integrated systems that can assist in cross-disciplinary tasks, such as streamlining the research process across science and engineering disciplines. To address this need, we introduce Artificial Research Innovator Assistant (ARIA), a four-agent, multi-LLM framework. By emulating a team of expert assistants, ARIA systematically replicates the human research workflow to autonomously search, retrieve, and filter hundreds of papers, subsequently synthesizing relevant literature into actionable research procedures. In a case study on dropwise condensation enhancement, ARIA demonstrates its capability to streamline research tasks within an hour, maintaining user oversight during execution and ultimately liberating researchers from time-intensive tasks.
2025-02-11
Raj Pabari, Udaya Ghai, Dominique Perrault-Joncas, Kari Torkkola, Orit Ronen, Dhruv Madeka, Dean Foster, Omer Gottesman
We introduce and analyze a variation of the Bertrand game in which the revenue is shared between two players. This game models situations in which one economic agent can provide goods/services to consumers either directly or through an independent seller/contractor in return for a share of the revenue. We analyze the equilibria of this game, and show how they can predict different business outcomes as a function of the players' costs and the transferred revenue shares. Importantly, we identify game parameters for which independent sellers can simultaneously increase the original player's payoff while increasing consumer surplus. We then extend the shared-revenue Bertrand game by considering the shared revenue proportion as an action and giving the independent seller an outside option to sell elsewhere. This work constitutes a first step towards a general theory for how partnership and sharing of resources between economic agents can lead to more efficient markets and improve the outcomes of both agents as well as consumers.
Small Area Estimation of Education Levels in Low- and Middle-Income Countries
2025-02-11
Yunhan Wu, Ameer Dharamshi, Jon Wakefield
Education is a key driver of social and economic mobility, yet disparities in attainment persist, particularly in low- and middle-income countries (LMICs). Existing indicators, such as mean years of schooling for adults aged 25 and older (MYS25) and expected years of schooling (EYS), offer a snapshot of an educational system, but lack either cohort-specific or temporal granularity. To address these limitations, we introduce the ultimate years of schooling (UYS)-a birth cohort-based metric targeting the final educational attainment of any individual cohort, including those with ongoing schooling trajectories. As with many attainment indicators, we propose to estimate UYS with cross-sectional household surveys. However, for younger cohorts, estimation fails, because these individuals are right-censored leading to severe downwards bias. To correct for this, we propose to re-frame educational attainment as a time-to-event process and deploy discrete-time survival models that explicitly account for censoring in the observations. At the national level, we estimate the parameters of the model using survey-weighted logistic regression, while for finer spatial resolutions, where sample sizes are smaller, we embed the discrete-time survival model within a Bayesian spatiotemporal framework to improve stability and precision. Applying our proposed methods to data from the 2022 Tanzania Demographic and Health Surveys, we estimate female educational trajectories corrected for censoring biases, and reveal substantial subnational disparities. By providing a dynamic, bias-corrected, and spatially disaggregated measure, our approach enhances education monitoring; it equips policymakers and researchers with a more precise tool for monitoring current progress towards education goals, and for designing future targeted policy interventions in LMICs.
Educating a Responsible AI Workforce: Piloting a Curricular Module on AI Policy in a Graduate Machine Learning Course
2025-02-11
James Weichert, Hoda Eldardiry
As artificial intelligence (AI) technologies begin to permeate diverse fields-from healthcare to education-consumers, researchers and policymakers are increasingly raising concerns about whether and how AI is regulated. It is therefore reasonable to anticipate that alignment with principles of 'ethical' or 'responsible' AI, as well as compliance with law and policy, will form an increasingly important part of AI development. Yet, for the most part, the conventional computer science curriculum is ill-equipped to prepare students for these challenges. To this end, we seek to explore how new educational content related to AI ethics and AI policy can be integrated into both ethics- and technical-focused courses. This paper describes a two-lecture 'AI policy module' that was piloted in a graduate-level introductory machine learning course in 2024. The module, which includes an in-class active learning game, is evaluated using data from student surveys before and after the lectures, and pedagogical motivations and considerations are discussed. We find that the module is successful in engaging otherwise technically-oriented students on the topic of AI policy, increasing student awareness of the social impacts of a variety of AI technologies and developing student interest in the field of AI regulation.
Impostor Phenomenon Among Software Engineers: Investigating Gender Differences and Well-Being
2025-02-11
Paloma Guenes, Rafael Tomaz, Bianca Trinkenreich, Maria Teresa Baldassarre, Margarte-Anne Storey, Marcos Kalinowski
Research shows that more than half of software professionals experience the Impostor Phenomenon (IP), with a notably higher prevalence among women compared to men. IP can lead to mental health consequences, such as depression and burnout, which can significantly impact personal well-being and software professionals' productivity. This study investigates how IP manifests among software professionals across intersections of gender with race/ethnicity, marital status, number of children, age, and professional experience. Additionally, it examines the well-being of software professionals experiencing IP, providing insights into the interplay between these factors. We analyzed data collected through a theory-driven survey (n = 624) that used validated psychometric instruments to measure IP and well-being in software engineering professionals. We explored the prevalence of IP in the intersections of interest. Additionally, we applied bootstrapping to characterize well-being within our field and statistically tested whether professionals of different genders suffering from IP have lower well-being. The results show that IP occurs more frequently in women and that the prevalence is particularly high among black women as well as among single and childless women. Furthermore, regardless of gender, software engineering professionals suffering from IP have significantly lower well-being. Our findings indicate that effective IP mitigation strategies are needed to improve the well-being of software professionals. Mitigating IP would have particularly positive effects on the well-being of women, who are more frequently affected by IP.
Observation of $Λ$ hyperon local polarization in pPb collisions at $\sqrt{s_\mathrm{NN}}$ = 8.16 TeV
2025-02-11
CMS Collaboration
The polarization of the $\Lambda$ and $\overline{\Lambda}$ hyperons along the beam direction has been measured in proton-lead (pPb) collisions at a center-of-mass energy per nucleon pair of 8.16 TeV. The data were obtained with the CMS detector at the LHC and correspond to an integrated luminosity of 186.0 $\pm$ 6.5 nb$^{-1}$. A significant azimuthal dependence of the hyperon polarization, characterized by the second-order Fourier sine coefficient $P_{z,s2}$, is observed. The $P_{z,s2}$ values decrease as a function of charged particle multiplicity, but increase with transverse momentum. A hydrodynamic model that describes the observed $P_{z,s2}$ values in nucleus-nucleus collisions by introducing vorticity effects does not reproduce either the sign or the magnitude of the pPb results. These observations pose a challenge to the current theoretical implementation of spin polarization in heavy ion collisions and offer new insights into the origin of spin polarization in hadronic collisions at LHC energies.
2025-02-11
Marco Barbieri, Ilaria Gianani, Aaron Z. Goldberg, Luis L. Sánchez-Soto
Quantum phase estimation is fundamental to advancing quantum science and technology. While much of the research has concentrated on estimating a single phase, the simultaneous estimation of multiple phases can yield significantly enhanced sensitivities when using specially tailored input quantum states. This work reviews recent theoretical and experimental advancements in the parallel estimation of multiple arbitrary phases. We highlight strategies for constructing optimal measurement protocols and discuss the experimental platforms best suited for implementing these techniques.
Right-handed neutrinos: seesaw models and signatures
2025-02-11
Stephen F King
We give a pedagogical introduction to right-handed neutrinos as a simple extension to the Standard Model (SM), focussing on seesaw models and their possible experimental signatures. We preface this with a review of the lepton sector of the SM, where charged lepton masses arise from Yukawa couplings and neutrino Majorana masses from the Weinberg operator, leading to a unitary lepton mixing matrix. We first introduce a single right-handed neutrino and the seesaw mechanism, yielding a heavy neutral lepton, then generalise the results to the canonical case of three right-handed neutrinos within a general parameterisation, leading to non-unitary lepton mixing and three heavy neutral leptons. We show how the sequential dominance of three right-handed neutrinos with diagonal masses naturally leads to an effective two right-handed neutrino model with lepton mixing angle predictions in the constrained cases, but unobservable heavy neutral leptons. On the other hand, with degenerate off-diagonal masses, the two right-handed neutrinos can form a single heavy and observable Dirac neutrino, within a two Higgs doublet or Majoron model. Finally we discuss extra singlet neutrinos which can lead to either a double seesaw or an inverse seesaw, depending on their Majorana masses, where the latter allows observable heavy neutral leptons, and the possibility of a minimal inverse seesaw model where mixing angles can be predicted.
The Magnetically Induced Radial Velocity Variation of Gliese 341 and an Upper Limit to the Mass of Its Transiting Earth-sized Planet
2025-02-11
Victoria DiTomasso, Mercedes Lopez-Morales, Sarah Peacock, Luca Malavolta, James Kirk, Kevin B. Stevenson, Guangwei Fu, Jacob Lustig-Yaeger
The Transiting Exoplanet Survey Satellite (TESS) mission identified a potential 0.88 REarth planet with a period of 7.577 days, orbiting the nearby M1V star GJ 341 (TOI 741.01). This system has already been observed by the James Webb Space Telescope (JWST) to search for presence of an atmosphere on this planet. Here, we present an in-depth analysis of the GJ 341 system using all available public data. We provide improved parameters for the host star, an updated value of the planet radius, and support the planetary nature of the object (now GJ 341 b). We use 57 HARPS radial velocities to model the magnetic cycle and activity of the host star, and constrain the mass of GJ 341 b to upper limits of 4.0 MEarth (3 sigma) and 2.9 MEarth (1 sigma). We also rule out the presence of additional companions with M sin i > 15.1 MEarth, and P < 1750 days, and the presence of contaminating background objects during the TESS and JWST observations. These results provide key information to aid the interpretation of the recent JWST atmospheric observations and other future observations of this planet.
Vision-Language Models for Edge Networks: A Comprehensive Survey
2025-02-11
Ahmed Sharshar, Latif U. Khan, Waseem Ullah, Mohsen Guizani
Vision Large Language Models (VLMs) combine visual understanding with natural language processing, enabling tasks like image captioning, visual question answering, and video analysis. While VLMs show impressive capabilities across domains such as autonomous vehicles, smart surveillance, and healthcare, their deployment on resource-constrained edge devices remains challenging due to processing power, memory, and energy limitations. This survey explores recent advancements in optimizing VLMs for edge environments, focusing on model compression techniques, including pruning, quantization, knowledge distillation, and specialized hardware solutions that enhance efficiency. We provide a detailed discussion of efficient training and fine-tuning methods, edge deployment challenges, and privacy considerations. Additionally, we discuss the diverse applications of lightweight VLMs across healthcare, environmental monitoring, and autonomous systems, illustrating their growing impact. By highlighting key design strategies, current challenges, and offering recommendations for future directions, this survey aims to inspire further research into the practical deployment of VLMs, ultimately making advanced AI accessible in resource-limited settings.
Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data
2025-02-11
Luoting Zhuang, Stephen H. Park, Steven J. Skates, Ashley E. Prosper, Denise R. Aberle, William Hsu
Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenvironmental, and phenotypic changes. This dynamic behavior drives uncontrolled cell growth, metastasis, immune evasion, and therapy resistance, posing challenges for effective monitoring and treatment. However, today's data-driven research in oncology has primarily focused on cross-sectional analysis using data from a single modality, limiting the ability to fully characterize and interpret the disease's dynamic heterogeneity. Advances in multiscale data collection and computational methods now enable the discovery of longitudinal multimodal biomarkers for precision oncology. Longitudinal data reveal patterns of disease progression and treatment response that are not evident from single-timepoint data, enabling timely abnormality detection and dynamic treatment adaptation. Multimodal data integration offers complementary information from diverse sources for more precise risk assessment and targeting of cancer therapy. In this review, we survey methods of longitudinal and multimodal modeling, highlighting their synergy in providing multifaceted insights for personalized care tailored to the unique characteristics of a patient's cancer. We summarize the current challenges and future directions of longitudinal multimodal analysis in advancing precision oncology.
2025-02-11
James Tuttle Keane, Barbara Cohen, Carolyn Crow, Benjamin Greenhagen, Bradley Jolliff, Yang Liu, Charles Shearer, Sonia Tikoo, Sarah Valencia
Endurance is a mission concept to explore and ultimately return samples from the Moon's largest and oldest impact basin, South Pole-Aitken (SPA). SPA holds the answers to many outstanding planetary science questions, including the earliest impact bombardment of the Solar System and the evolution of the Moon's interior. Endurance would address these questions by traversing 2,000 kilometers across the lunar farside, collecting samples, and delivering those samples to Artemis astronauts for return to Earth. Endurance was identified as the highest priority strategic mission for NASA's Lunar Discovery and Program in the recent Planetary Science and Astrobiology Decadal Survey. This report summarizes the results from the first public workshop about the concept. Major findings include: (1) Endurance is an exciting concept that would address long-standing, high-priority lunar and planetary science questions, and the community is ready for it. (2) Endurance's sample science objectives are achievable, although they would require coordinated analysis techniques and numerous diverse samples. (3) Geologic context is essential for addressing Endurance's science objectives. (4) While Endurance's objectives center on sample return, Endurance's long traverse would enable a variety of additional transformative science investigations. (5) Endurance is an ambitious mission that would be enabled and enhanced by investing in developing key technologies now. (6) Endurance should strive to include more diverse perspectives in its formulation, particularly from early-career scientists and engineers who will ultimately operate the rover and analyze the samples. Endurance is early in its formulation and the next major activity will be a Science Definition Team (SDT). It is expected that this report and the findings therein may be useful input to the Endurance SDT.
Image-to-image translation (I2I) transforms an image from a source domain to a target domain while preserving source content. Most computer vision applications are in the field of image-to-image translation, such as style transfer, image segmentation, and photo enhancement. The degree of preservation of the content of the source images in the translation process can be different according to the problem and the intended application. From this point of view, in this paper, we divide the different tasks in the field of image-to-image translation into three categories: Fully Content preserving, Partially Content preserving, and Non-Content preserving. We present different tasks, datasets, methods, results of methods for these three categories in this paper. We make a categorization for I2I methods based on the architecture of different models and study each category separately. In addition, we introduce well-known evaluation criteria in the I2I translation field. Specifically, nearly 70 different I2I models were analyzed, and more than 10 quantitative evaluation metrics and 30 distinct tasks and datasets relevant to the I2I translation problem were both introduced and assessed. Translating from simulation to real images could be well viewed as an application of fully content preserving or partially content preserving unsupervised image-to-image translation methods. So, we provide a benchmark for Sim-to-Real translation, which can be used to evaluate different methods. In general, we conclude that because of the different extent of the obligation to preserving content in various applications, it is better to consider this issue in choosing a suitable I2I model for a specific application.
ALMACAL XIII. Evolution of the CO luminosity function and the molecular gas mass density out to $z$ ~ 6
2025-02-10
Victoria Bollo, Céline Péroux, Martin Zwaan, Aleksandra Hamanowicz, Jianhang Chen, Simon Weng, Claudia del P. Lagos, Matías Bravo, R. J. Ivison, Andrew Biggs
Cold molecular gas, largely traced by CO emission, is the primary fuel for star formation, making it essential for understanding galaxy evolution. ALMA has made significant progress in the study of the cosmic evolution of cold molecular gas. Here, we exploit the ALMACAL survey to address issues relating to small sample sizes and cosmic variance, utilising calibration data from ALMA to compile a statistically significant and essentially unbiased sample of CO-selected galaxies. By employing a novel statistical approach to emission-line classification using semi-analytical models, we place strong constraints on the CO luminosity function and the cosmic evolution of molecular gas mass density ($\rho_{H_2}$) back to $z \sim 6$. The cosmic molecular gas mass density increases with redshift, peaking around $z \sim 1.5$, then slowly declines towards higher redshifts by $\sim 1$ dex. Our findings confirm the key role of molecular gas in fuelling star formation. The new $\rho_{H_2}$ estimates allow us to revisit the cosmic baryon cycle, showing that the ratio of molecular gas-to-stellar mass density is consistent with the so-called 'bathtub model' of baryons, which implies a continuous replenishment of gas. The cosmic gas depletion timescale, estimated on a global scale, is shown to be fairly constant at all redshifts. We emphasise the importance of surveys using multiple small fields rather than a single contiguous area to mitigate the effects of cosmic variance.
2025-02-10
Brandon Trabucco, Gunnar Sigurdsson, Robinson Piramuthu, Ruslan Salakhutdinov
The predominant approach for training web navigation agents gathers human demonstrations for a set of popular websites and hand-written tasks, but it is becoming clear that human data are an inefficient resource. We develop a pipeline to facilitate Internet-scale training for agents without laborious human annotations. In the first stage, an LLM generates tasks for 150k diverse websites. In the next stage, LLM agents complete tasks and produce trajectories. In the final stage, an LLM reviews the trajectories and judges their success. Language models are competitive with human annotators, detecting and filtering out harmful content with an accuracy of 97%, generating feasible tasks with an 89% rate, and judging successful trajectories with an 82.6% accuracy. Scaling the pipeline, agents based on Llama 3.1 70B solve 16.7% of tasks for 150k sites. Training on the data generated by our pipeline is competitive with training on human demonstrations. In data-limited settings derived from Mind2Web and WebLINX, we improve Step Accuracy by up to +89.5% and +122.1% respectively for agents trained on mixtures of data from our pipeline, and human data. When training agents with all available human data from these benchmarks, agents fail to generalize to diverse real sites, and adding our data improves their generalization by +149.0% for WebLINX and +156.3% for Mind2Web. Code will be available at: data-for-agents.github.io.
A Review and Collection of Metrics and Benchmarks for Quantum Computers: definitions, methodologies and software
2025-02-10
Deep Lall, Abhishek Agarwal, Weixi Zhang, Lachlan Lindoy, Tobias Lindström, Stephanie Webster, Simon Hall, Nicholas Chancellor, Petros Wallden, Raul Garcia-Patron, Elham Kashefi, Viv Kendon, Jonathan Pritchard, Alessandro Rossi, Animesh Datta, Theodoros Kapourniotis, Konstantinos Georgopoulos, Ivan Rungger
Quantum computers have the potential to provide an advantage over classical computers in a number of areas. Numerous metrics to benchmark the performance of quantum computers, ranging from their individual hardware components to entire applications, have been proposed over the years. Navigating the resulting extensive literature can be overwhelming. Objective comparisons are further hampered in practice as different variations of the same metric are used, and the data disclosed together with a reported metric value is often not sufficient to reproduce the measurements. This article addresses these challenges by providing a review of metrics and benchmarks for quantum computers and 1) a comprehensive collection of benchmarks allowing holistic comparisons of quantum computers, 2) a consistent format of the definitions across all metrics including a transparent description of the methodology and of the main assumptions and limitations, and 3) a reproducible approach by linking the metrics to open-source software used to evaluate them. We identify five areas where international standardization working groups could be established, namely: i) the identification and agreement on the categories of metrics that comprehensively benchmark device performance; ii) the identification and agreement on a set of well-established metrics that together comprehensively benchmark performance; iii) the identification of metrics specific to hardware platforms, including non-gate-based quantum computers; iv) inter-laboratory comparison studies to develop best practice guides for measurement methodology; and v) agreement on what data and software should be reported together with a metric value to ensure trust, transparency and reproducibility. We provide potential routes to advancing these areas. We expect this compendium to accelerate the progress of quantum computing hardware towards quantum advantage.
A Case Study in Gamification for a Cybersecurity Education Program: A Game for Cryptography
2025-02-10
Dylan Huitema, Albert Wong
Advances in technology, a growing pool of sensitive data, and heightened global tensions has increased the demand for skilled cybersecurity professionals. Despite the recent increase in attention given to cybersecurity education, traditional approaches have continue in failing to keep pace with the rapidly evolving cyber threat landscape. Challenges such as a shortage of qualified educators and resource-intensive practical training exacerbate these issues. Gamification offers an innovative approach to provide practical hands-on experiences, and equip educators with up-to-date and accessible teaching tools that are targeted to industry-specific concepts. The paper begins with a review of the literature on existing challenges in cybersecurity education and gamification methods already employed in the field, before presenting a real-world case study of a gamified cryptography teaching tool. The paper discusses the design, development process, and intended use cases for this tool. This research highlights and provides an example of how integrating gamification into curricula can address key educational gaps, ensuring a more robust and effective pipeline of cybersecurity talent for the future.
Expanding the Quantum-Limited Gravitational-Wave Detection Horizon
2025-02-10
Liu Tao, Mohak Bhattacharya, Peter Carney, Luis Martin Gutierrez, Luke Johnson, Shane Levin, Cynthia Liang, Xuesi Ma, Michael Padilla, Tyler Rosauer, Aiden Wilkin, Jonathan W. Richardson
We demonstrate the potential of new adaptive optical technology to expand the detection horizon of gravitational-wave observatories. Achieving greater quantum-noise-limited sensitivity to spacetime strain hinges on achieving higher circulating laser power, in excess of 1~MW, in conjunction with highly-squeezed quantum states of light. The new technology will enable significantly higher levels of laser power and squeezing in gravitational-wave detectors, by providing high-precision, low-noise correction of limiting sources of thermal distortions directly to the core interferometer optics. In simulated projections for LIGO~A+, assuming an input laser power of 125~W and an effective injected squeezing level of 9~dB entering the interferometer, an initial concept of this technology can reduce the noise floor of the detectors by up to 20\% from 200~Hz to 5~kHz, corresponding to an increment of 4~Mpc in the sky-averaged detection range for binary neutron star mergers. This work lays the foundation for one of the key technology improvements essential to fully utilize the scientific potential of the existing 4-km LIGO facilities, to observe black hole merger events past a redshift of~5, and opens a realistic pathway towards a next-generation 40-km gravitational-wave observatory in the United States, Cosmic~Explorer.
Network Intrusion Datasets: A Survey, Limitations, and Recommendations
2025-02-10
Patrik Goldschmidt, Daniela Chudá
Data-driven cyberthreat detection has become a crucial defense technique in modern cybersecurity. Network defense, supported by Network Intrusion Detection Systems (NIDSs), has also increasingly adopted data-driven approaches, leading to greater reliance on data. Despite its importance, data scarcity has long been recognized as a major obstacle in NIDS research. In response, the community has published many new datasets recently. However, many of them remain largely unknown and unanalyzed, leaving researchers uncertain about their suitability for specific use cases. In this paper, we aim to address this knowledge gap by performing a systematic literature review (SLR) of 89 public datasets for NIDS research. Each dataset is comparatively analyzed across 13 key properties, and its potential applications are outlined. Beyond the review, we also discuss domain-specific challenges and common data limitations to facilitate a critical view on data quality. To aid in data selection, we conduct a dataset popularity analysis in contemporary state-of-the-art NIDS research. Furthermore, the paper presents best practices for dataset selection, generation, and usage. By providing a comprehensive overview of the domain and its data, this work aims to guide future research toward improving data quality and the robustness of NIDS solutions.
We reconstruct the non-linear matter power spectrum $P(k)$ using a joint analysis of gravitational lensing of the cosmic microwave background (CMB) and lensing of galaxies. This reconstruction is motivated by the $S_8$ tension between early-universe CMB predictions and late-time observables. We use CMB lensing data from the Atacama Cosmology Telescope DR6 and cosmic shear data from the Dark Energy Survey (DES) Y3 release to perform a gravity-only (i.e. no baryonic feedback) fit to $P(k)$ in bins of wave-number, within $\rm{\Lambda CDM}$. We find that with DES cosmic shear data alone, $P(k)$ departs from the early-universe CMB prediction on all scales. The joint fit with CMB lensing is consistent on large scales $k<0.2 \;{\rm Mpc}^{-1}$ but shows a $\sim 2 \sigma$ deviation from scale-independence when extending to $k = 10 \;h/\mathrm{Mpc}$. We compare our agnostic $P(k)$ reconstruction to baryonic feedback models and non-standard dark matter models: reasonable variations of both scenarios can recover the shape and amplitude of the suppression. We discuss the advances needed to disentangle these physical effects with a full mapping of $P(k,z)$.
Supersolids--the enigmatic phase of quantum matter, with properties resembling both the superfluid and solid states--have been actively sought over the past 70 years. We provide a comprehensive review of the to-date development in experimental and theoretical studies of supersolid bosons, with a particular focus on their observation in ultracold atomic gases. Additionally, the use of optical lattice facilitates the realization of 'lattice-supersolids', which paves the way to study the effect of correlations in a controlled manner. A brief theoretical framework is presented to characterize this puzzling state with competing orders and to gain insight into its basic properties. Various types of supersolid phases and the different platforms used to achieve them are described. Finally, we discuss the future prospects of research and the potential to achieve supersolids with more exotic features.
Who Taught You That? Tracing Teachers in Model Distillation
2025-02-10
Somin Wadhwa, Chantal Shaib, Silvio Amir, Byron C. Wallace
Model distillation -- using outputs from a large teacher model to teach a small student model -- is a practical means of creating efficient models for a particular task. We ask: Can we identify a students' teacher based on its outputs? Such "footprints" left by teacher LLMs would be interesting artifacts. Beyond this, reliable teacher inference may have practical implications as actors seek to distill specific capabilities of massive proprietary LLMs into deployed smaller LMs, potentially violating terms of service. We consider practical task distillation targets including summarization, question answering, and instruction-following. We assume a finite set of candidate teacher models, which we treat as blackboxes. We design discriminative models that operate over lexical features. We find that $n$-gram similarity alone is unreliable for identifying teachers, but part-of-speech (PoS) templates preferred by student models mimic those of their teachers.
The recent development of powerful AI systems has highlighted the need for robust risk management frameworks in the AI industry. Although companies have begun to implement safety frameworks, current approaches often lack the systematic rigor found in other high-risk industries. This paper presents a comprehensive risk management framework for the development of frontier AI that bridges this gap by integrating established risk management principles with emerging AI-specific practices. The framework consists of four key components: (1) risk identification (through literature review, open-ended red-teaming, and risk modeling), (2) risk analysis and evaluation using quantitative metrics and clearly defined thresholds, (3) risk treatment through mitigation measures such as containment, deployment controls, and assurance processes, and (4) risk governance establishing clear organizational structures and accountability. Drawing from best practices in mature industries such as aviation or nuclear power, while accounting for AI's unique challenges, this framework provides AI developers with actionable guidelines for implementing robust risk management. The paper details how each component should be implemented throughout the life-cycle of the AI system - from planning through deployment - and emphasizes the importance and feasibility of conducting risk management work prior to the final training run to minimize the burden associated with it.
Differentially Private Empirical Cumulative Distribution Functions
2025-02-10
Antoine Barczewski, Amal Mawass, Jan Ramon
In order to both learn and protect sensitive training data, there has been a growing interest in privacy preserving machine learning methods. Differential privacy has emerged as an important measure of privacy. We are interested in the federated setting where a group of parties each have one or more training instances and want to learn collaboratively without revealing their data. In this paper, we propose strategies to compute differentially private empirical distribution functions. While revealing complete functions is more expensive from the point of view of privacy budget, it may also provide richer and more valuable information to the learner. We prove privacy guarantees and discuss the computational cost, both for a generic strategy fitting any security model and a special-purpose strategy based on secret sharing. We survey a number of applications and present experiments.
Accurate monitoring of eating behavior is crucial for managing obesity and eating disorders such as bulimia nervosa. At the same time, existing methods rely on multiple and/or specialized sensors, greatly harming adherence and ultimately, the quality and continuity of data. This paper introduces a novel approach for estimating the weight of a bite, from a commercial smartwatch. Our publicly-available dataset contains smartwatch inertial data from ten participants, with manually annotated start and end times of each bite along with their corresponding weights from a smart scale, under semi-controlled conditions. The proposed method combines extracted behavioral features such as the time required to load the utensil with food, with statistical features of inertial signals, that serve as input to a Support Vector Regression model to estimate bite weights. Under a leave-one-subject-out cross-validation scheme, our approach achieves a mean absolute error (MAE) of 3.99 grams per bite. To contextualize this performance, we introduce the improvement metric, that measures the relative MAE difference compared to a baseline model. Our method demonstrates a 17.41% improvement, while the adapted state-of-the art method shows a -28.89% performance against that same baseline. The results presented in this work establish the feasibility of extracting meaningful bite weight estimates from commercial smartwatch inertial sensors alone, laying the groundwork for future accessible, non-invasive dietary monitoring systems.
Our standard model of the Universe predicts the distribution of dark matter to $1\%$ at the scales needed for upcoming experiments, yet our predictions for how the luminous matter -which has interactions besides gravity- is distributed remain highly uncertain. Understanding how much gas and stars there are in the Universe and where they preferentially live is challenging, and the uncertainty affects how well we can understand the cosmological model itself. For example, it compromises our ability to tell apart different models for dark energy, the mysterious force driving the accelerated expansion of the Universe. In this Essay, I will touch upon many recent developments that suggest we will be able to overcome this limitation before data from new experiments become available. More excitingly, I will describe how our efforts to model luminous and dark matter jointly will create new possibilities for constraining the physics of supermassive black holes, galaxies, and gas over time.
Combining Large Language Models with Static Analyzers for Code Review Generation
2025-02-10
Imen Jaoua, Oussama Ben Sghaier, Houari Sahraoui
Code review is a crucial but often complex, subjective, and time-consuming activity in software development. Over the past decades, significant efforts have been made to automate this process. Early approaches focused on knowledge-based systems (KBS) that apply rule-based mechanisms to detect code issues, providing precise feedback but struggling with complex, context-dependent cases. More recent work has shifted toward fine-tuning pre-trained language models for code review, enabling broader issue coverage but often at the expense of precision. In this paper, we propose a hybrid approach that combines the strengths of KBS and learning-based systems (LBS) to generate high-quality, comprehensive code reviews. Our method integrates knowledge at three distinct stages of the language model pipeline: during data preparation (Data-Augmented Training, DAT), at inference (Retrieval-Augmented Generation, RAG), and after inference (Naive Concatenation of Outputs, NCO). We empirically evaluate our combination strategies against standalone KBS and LBS fine-tuned on a real-world dataset. Our results show that these hybrid strategies enhance the relevance, completeness, and overall quality of review comments, effectively bridging the gap between rule-based tools and deep learning models.
We present DDTO--deferred-decision trajectory optimization--a framework for trajectory generation with resilience to unmodeled uncertainties and contingencies. The key idea is to ensure that a collection of candidate targets is reachable for as long as possible while satisfying constraints, which provides time to quantify the uncertainties. We propose optimization-based constrained reachability formulations and construct equivalent cardinality minimization problems, which then inform the design of computationally tractable and efficient solution methods that leverage state-of-the-art convex solvers and sequential convex programming (SCP) algorithms. The goal of establishing the equivalence between constrained reachability and cardinality minimization is to provide theoretically-sound underpinnings for the proposed solution methods. We demonstrate the solution methods on real-world optimal control applications encountered in quadrotor motion planning.
A Review of Conceptualizations of Safety and Risk in Current Automated Driving Regulation
2025-02-10
Marcus Nolte, Leon Johann Brettin, Hans Steege, Nayel Salem, Marvin Loba, Robert Graubohm, Markus Maurer
"Safety" and "Risk" are key concepts for the design and development of automated vehicles. For the market introduction or large-scale field tests, both concepts are not only relevant for engineers developing the vehicles, but for all stakeholders (e.g., regulators, lawyers, or the general public) who have stakes in the technology. In the communication between stakeholder groups, common notions of these abstract concepts are key for efficient communication and setting mutual expectations. In the European market, automated vehicles require Europe-wide type approval or at least operating permits in the individual states. For this, a central means of communication between regulators and engineers are regulatory documents. Flawed terminology regarding the safety expectations for automated vehicles can unnecessarily complicate relations between regulators and manufacturers, and thus hinder the introduction of the technology. In this paper, we review relevant documents at the UN- and EU-level, for the UK, and Germany regarding their (implied) notions of safety and risk. We contrast the regulatory notions with established and more recently developing notions of safety and risk in the field of automated driving. Based on the analysis, we provide recommendations on how explicit definitions of safety and risk in regulatory documents can support rather than hinder the market introduction of automated vehicles.
The explosive growth of video data has driven the development of distributed video analytics in cloud-edge-terminal collaborative (CETC) systems, enabling efficient video processing, real-time inference, and privacy-preserving analysis. Among multiple advantages, CETC systems can distribute video processing tasks and enable adaptive analytics across cloud, edge, and terminal devices, leading to breakthroughs in video surveillance, autonomous driving, and smart cities. In this survey, we first analyze fundamental architectural components, including hierarchical, distributed, and hybrid frameworks, alongside edge computing platforms and resource management mechanisms. Building upon these foundations, edge-centric approaches emphasize on-device processing, edge-assisted offloading, and edge intelligence, while cloud-centric methods leverage powerful computational capabilities for complex video understanding and model training. Our investigation also covers hybrid video analytics incorporating adaptive task offloading and resource-aware scheduling techniques that optimize performance across the entire system. Beyond conventional approaches, recent advances in large language models and multimodal integration reveal both opportunities and challenges in platform scalability, data protection, and system reliability. Future directions also encompass explainable systems, efficient processing mechanisms, and advanced video analytics, offering valuable insights for researchers and practitioners in this dynamic field.
Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation
2025-02-10
Maria Eriksson, Erasmo Purificato, Arman Noroozian, Joao Vinagre, Guillaume Chaslot, Emilia Gomez, David Fernandez-Llorca
Quantitative Artificial Intelligence (AI) Benchmarks have emerged as fundamental tools for evaluating the performance, capability, and safety of AI models and systems. Currently, they shape the direction of AI development and are playing an increasingly prominent role in regulatory frameworks. As their influence grows, however, so too does concerns about how and with what effects they evaluate highly sensitive topics such as capabilities, including high-impact capabilities, safety and systemic risks. This paper presents an interdisciplinary meta-review of about 100 studies that discuss shortcomings in quantitative benchmarking practices, published in the last 10 years. It brings together many fine-grained issues in the design and application of benchmarks (such as biases in dataset creation, inadequate documentation, data contamination, and failures to distinguish signal from noise) with broader sociotechnical issues (such as an over-focus on evaluating text-based AI models according to one-time testing logic that fails to account for how AI models are increasingly multimodal and interact with humans and other technical systems). Our review also highlights a series of systemic flaws in current benchmarking practices, such as misaligned incentives, construct validity issues, unknown unknowns, and problems with the gaming of benchmark results. Furthermore, it underscores how benchmark practices are fundamentally shaped by cultural, commercial and competitive dynamics that often prioritise state-of-the-art performance at the expense of broader societal concerns. By providing an overview of risks associated with existing benchmarking procedures, we problematise disproportionate trust placed in benchmarks and contribute to ongoing efforts to improve the accountability and relevance of quantitative AI benchmarks within the complexities of real-world scenarios.
Computational neuroimaging involves analyzing brain images or signals to provide mechanistic insights and predictive tools for human cognition and behavior. While diffusion models have shown stability and high-quality generation in natural images, there is increasing interest in adapting them to analyze brain data for various neurological tasks such as data enhancement, disease diagnosis and brain decoding. This survey provides an overview of recent efforts to integrate diffusion models into computational neuroimaging. We begin by introducing the common neuroimaging data modalities, follow with the diffusion formulations and conditioning mechanisms. Then we discuss how the variations of the denoising starting point, condition input and generation target of diffusion models are developed and enhance specific neuroimaging tasks. For a comprehensive overview of the ongoing research, we provide a publicly available repository at https://github.com/JoeZhao527/dm4neuro.
How Does Users' App Knowledge Influence the Preferred Level of Detail and Format of Software Explanations?
2025-02-10
Martin Obaidi, Jannik Fischbach, Marc Herrmann, Hannah Deters, Jakob Droste, Jil Klünder, Kurt Schneider
Context and Motivation: Due to their increasing complexity, everyday software systems are becoming increasingly opaque for users. A frequently adopted method to address this difficulty is explainability, which aims to make systems more understandable and usable. Question/problem: However, explanations can also lead to unnecessary cognitive load. Therefore, adapting explanations to the actual needs of a user is a frequently faced challenge. Principal ideas/results: This study investigates factors influencing users' preferred the level of detail and the form of an explanation (e.g., short text or video tutorial) in software. We conducted an online survey with 58 participants to explore relationships between demographics, software usage, app-specific knowledge, as well as their preferred explanation form and level of detail. The results indicate that users prefer moderately detailed explanations in short text formats. Correlation analyses revealed no relationship between app-specific knowledge and the preferred level of detail of an explanation, but an influence of demographic aspects (like gender) on app-specific knowledge and its impact on application confidence were observed, pointing to a possible mediated relationship between knowledge and preferences for explanations. Contribution: Our results show that explanation preferences are weakly influenced by app-specific knowledge but shaped by demographic and psychological factors, supporting the development of adaptive explanation systems tailored to user expertise. These findings support requirements analysis processes by highlighting important factors that should be considered in user-centered methods such as personas.
Decentralizing Trust: Consortium Blockchains and Hyperledger Fabric Explained
2025-02-10
Angelo Vera-Rivera, Ekram Hossain
Trust models are essential components of networks of any nature, as they refer to confidence frameworks to evaluate and verify if their participants act reliably and fairly. They are necessary to any social, organizational, or computer network model to ensure truthful interactions, data integrity, and overall system resilience. Trust models can be centralized or distributed, each providing a good fair of benefits and challenges. Blockchain is a special case of distributed trust models that utilize advanced cryptographic techniques and decentralized consensus mechanisms to enforce confidence among participants within a network. In this piece, we provide an overview of blockchain networks from the trust model perspective, with a special focus on the Hyperledger Fabric framework, a widespread blockchain implementation with a consortium architecture. We explore Fabric in detail, including its trust model, components, overall architecture, and a general implementation blueprint for the platform. We intend to offer readers with technical backgrounds but not necessarily experts in the blockchain field a friendly review of these topics to spark their curiosity to continue expanding their knowledge on these increasingly popular technologies.
SIREN: Semantic, Initialization-Free Registration of Multi-Robot Gaussian Splatting Maps
2025-02-10
Ola Shorinwa, Jiankai Sun, Mac Schwager, Anirudha Majumdar
We present SIREN for registration of multi-robot Gaussian Splatting (GSplat) maps, with zero access to camera poses, images, and inter-map transforms for initialization or fusion of local submaps. To realize these capabilities, SIREN harnesses the versatility and robustness of semantics in three critical ways to derive a rigorous registration pipeline for multi-robot GSplat maps. First, SIREN utilizes semantics to identify feature-rich regions of the local maps where the registration problem is better posed, eliminating the need for any initialization which is generally required in prior work. Second, SIREN identifies candidate correspondences between Gaussians in the local maps using robust semantic features, constituting the foundation for robust geometric optimization, coarsely aligning 3D Gaussian primitives extracted from the local maps. Third, this key step enables subsequent photometric refinement of the transformation between the submaps, where SIREN leverages novel-view synthesis in GSplat maps along with a semantics-based image filter to compute a high-accuracy non-rigid transformation for the generation of a high-fidelity fused map. We demonstrate the superior performance of SIREN compared to competing baselines across a range of real-world datasets, and in particular, across the most widely-used robot hardware platforms, including a manipulator, drone, and quadruped. In our experiments, SIREN achieves about 90x smaller rotation errors, 300x smaller translation errors, and 44x smaller scale errors in the most challenging scenes, where competing methods struggle. We will release the code and provide a link to the project page after the review process.
Photometric metallicities of 0.8 million KiDS stars
2025-02-10
Bao-Kun Sun, Bing-Qiu Chen, Xiao-Wei Liu
Accurate determinations of metallicity for large, complete stellar samples are essential for advancing various studies of the Milky Way. In this paper, we present a data-driven algorithm that leverages photometric data from the KiDS and the VIKING surveys to estimate stellar absolute magnitude, effective temperature and metallicities. The algorithm is trained and validated using spectroscopic data from LAMOST, SEGUE, APOGEE, and GALAH, as well as a catalog of very metal-poor stars from the literature, and Gaia EDR3 data. This approach enables us to estimate metallicities, effective temperatures, and g-band absolute magnitudes for approximately 0.8 million stars in the KiDS dataset. The photometric metallicity estimates exhibit an uncertainty of around 0.28 dex when compared to spectroscopic studies, within the metallicity range of -2 dex to 0.5 dex. The photometric effective temperature estimates have an uncertainty of around 149 K, while the uncertainty in the absolute magnitude is approximately 0.36 mag. The metallicity estimates are reliable for values down to about -2 dex. This catalog represents a valuable resource for studying the structure and chemical properties of the Milky Way, offering an extensive dataset for future investigations into Galactic formation and evolution.
2025-02-10
C. M. O'Riordan, L. J. Oldham, A. Nersesian, T. Li, T. E. Collett, D. Sluse, B. Altieri, B. Clément, K. Vasan G. C., S. Rhoades, Y. Chen, T. Jones, C. Adami, R. Gavazzi, S. Vegetti, D. M. Powell, J. A. Acevedo Barroso, I. T. Andika, R. Bhatawdekar, A. R. Cooray, G. Despali, J. M. Diego, L. R. Ecker, A. Galan, P. Gómez-Alvarez, L. Leuzzi, M. Meneghetti, R. B. Metcalf, M. Schirmer, S. Serjeant, C. Tortora, M. Vaccari, G. Vernardos, M. Walmsley, A. Amara, S. Andreon, N. Auricchio, H. Aussel, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, A. Basset, P. Battaglia, R. Bender, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, A. Caillat, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, F. J. Castander, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, L. Corcione, F. Courbin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, J. Dinis, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, M. Farina, S. Farrens, F. Faustini, S. Ferriol, N. Fourmanoit, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, W. Gillard, B. Gillis, C. Giocoli, B. R. Granett, A. Grazian, F. Grupp, L. Guzzo, S. V. H. Haugan, J. Hoar, H. Hoekstra, W. Holmes, I. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, M. Kilbinger, R. Kohley, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, O. Lahav, R. Laureijs, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, E. Merlin, G. Meylan, M. Moresco, L. Moscardini, R. Nakajima, R. C. Nichol, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, H. -W. Rix, E. Romelli, M. Roncarelli, E. Rossetti, B. Rusholme, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, P. Schneider, T. Schrabback, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, P. Tallada-Crespí, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, E. Zucca, C. Burigana, P. Casenove, A. Mora, V. Scottez, M. Viel, M. Jauzac, H. Dannerbauer
We report the discovery of a complete Einstein ring around the elliptical galaxy NGC 6505, at $z=0.042$. This is the first strong gravitational lens discovered in Euclid and the first in an NGC object from any survey. The combination of the low redshift of the lens galaxy, the brightness of the source galaxy ($I_\mathrm{E}=18.1$ lensed, $I_\mathrm{E}=21.3$ unlensed), and the completeness of the ring make this an exceptionally rare strong lens, unidentified until its observation by Euclid. We present deep imaging data of the lens from the Euclid Visible Camera (VIS) and Near-Infrared Spectrometer and Photometer (NISP) instruments, as well as resolved spectroscopy from the Keck Cosmic Web Imager (KCWI). The Euclid imaging in particular presents one of the highest signal-to-noise ratio optical/near-infrared observations of a strong gravitational lens to date. From the KCWI data we measure a source redshift of $z=0.406$. Using data from the Dark Energy Spectroscopic Instrument (DESI) we measure a velocity dispersion for the lens galaxy of $\sigma_\star=303\pm15\,\mathrm{kms}^{-1}$. We model the lens galaxy light in detail, revealing angular structure that varies inside the Einstein ring. After subtracting this light model from the VIS observation, we model the strongly lensed images, finding an Einstein radius of 2.5 arcsec, corresponding to $2.1\,\mathrm{kpc}$ at the redshift of the lens. This is small compared to the effective radius of the galaxy, $R_\mathrm{eff}\sim 12.3\,\mathrm{arcsec}$. Combining the strong lensing measurements with analysis of the spectroscopic data we estimate a dark matter fraction inside the Einstein radius of $f_\mathrm{DM} = (11.1_{-3.5}^{+5.4})\%$ and a stellar initial mass-function (IMF) mismatch parameter of $\alpha_\mathrm{IMF} = 1.26_{-0.08}^{+0.05}$, indicating a heavier-than-Chabrier IMF in the centre of the galaxy.
Variable stars in the VVV globular clusters III. RR Lyrae stars in the inner Galactic globular clusters
2025-02-10
Javier Alonso-García, Leigh C. Smith, Jason L. Sanders, Dante Minniti, Márcio Catelan, Gonzalo Aravena Rojas, Julio A. Carballo-Bello, José G. Fernández-Trincado, Carlos E. Ferreira Lopes, Elisa R. Garro, Zhen Guo, Maren Hempel, Philip W. Lucas, Daniel Majaess, Roberto K. Saito, A. Katherina Vivas
High reddening near the Galactic plane hampers observations and proper characterization of the globular clusters (GCs) located toward the inner regions of the Milky Way. The VISTA Variables in the Via Lactea (VVV) survey observed the Galactic bulge and adjacent disk for several years, providing multi-epoch, near-infrared images for 41 Galactic GCs. Detecting RRLyrae variables belonging to these GCs will aid in their accurate parameterization. By fully leveraging the astrometric, photometric, and variability VVV catalogs, we searched for RRLyrae stars associated with GCs. Our selection criteria, based on proper motions, proximity to the cluster centers, and distances inferred from their period-luminosity-metallicity relations, enable us to accurately identify the RRLyrae population in these GCs and determine color excesses and distances in a homogeneous manner. Since the VVV catalogs cover from the innermost regions of the GCs to their outskirts, we can provide a comprehensive picture of the entire RRLyrae population in these GCs. We have discovered significant RRLyrae populations in two highly reddened Galactic GCs: UKS1 and VVV-CL160, previously unknown to host RRLyrae stars. Additionally, we have detected one RRLyrae candidate in each of Terzan4 and Terzan9, also new to RRLyrae detection. We further confirm and increase the number of RRLyrae stars detected in 22 other low-latitude Galactic GCs. The RRLyrae distances place most of these GCs within the Galactic bulge, aligning well with the few GCs in our sample with reliable Gaia or Hubble Space Telescope measurements. However, most of the VVV GCs lack accurate Gaia distances, and literature distances are generally significantly smaller than those derived in this work. As a byproduct of our analysis, we have obtained the proper motions for all the VVV GCs, independently confirming Gaia results, except for UKS1 and 2MASS-GC02.
An Overview and Recent Developments in the Analysis of Multistate Processes
2025-02-10
Malka Gorfine, Richard J. Cook, Per Kragh Andersen, Terry M. Therneau, Pierre Joly, Hein Putter, Maja Pohar Perme, Michal Abrahamowicz, On Behalf of Topic Group 8 "Survival Analysis" of the STRATOS Initiative
Multistate models offer a powerful framework for studying disease processes and can be used to formulate intensity-based and more descriptive marginal regression models. They also represent a natural foundation for the construction of joint models for disease processes and dynamic marker processes, as well as joint models incorporating random censoring and intermittent observation times. This article reviews the ways multistate models can be formed and fitted to life history data. Recent work on pseudo-values and the incorporation of random effects to model dependence on the process history and between-process heterogeneity are also discussed. The software available to facilitate such analyses is listed.
The rapid advancement of speech generation technologies in the era of large language models (LLMs) has established discrete speech tokens as a foundational paradigm for speech representation. These tokens, characterized by their discrete, compact, and concise nature, are not only advantageous for efficient transmission and storage, but also inherently compatible with the language modeling framework, enabling seamless integration of speech into text-dominated LLM architectures. Current research categorizes discrete speech tokens into two principal classes: acoustic tokens and semantic tokens, each of which has evolved into a rich research domain characterized by unique design philosophies and methodological approaches. This survey systematically synthesizes the existing taxonomy and recent innovations in discrete speech tokenization, conducts a critical examination of the strengths and limitations of each paradigm, and presents systematic experimental comparisons across token types. Furthermore, we identify persistent challenges in the field and propose potential research directions, aiming to offer actionable insights to inspire future advancements in the development and application of discrete speech tokens.
A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks
2025-02-10
Hieu Minh "Jord" Nguyen
Theory of Mind (ToM), the ability to attribute mental states to others and predict their behaviour, is fundamental to social intelligence. In this paper, we survey studies evaluating behavioural and representational ToM in Large Language Models (LLMs), identify important safety risks from advanced LLM ToM capabilities, and suggest several research directions for effective evaluation and mitigation of these risks.
Chien-Shiung Wu as the experimental pioneer in quantum entanglement
2025-02-10
Yu Shi
Advised by Prof. Chen Ning Yang, we review the early pioneering work by Chien-Shiung Wu on entangled photons created from the electron-positron annihilation. This paper formed a basis of the author's speech at International Symposium Commemorating 110th Birth Anniversary of Chien-Shiung Wu, and of a few articles on the subject in Chinese.
On the distance to the black hole X-ray binary Swift J1727.8$-$1613
2025-02-10
Benjamin J. Burridge, James C. A. Miller-Jones, Arash Bahramian, Steve R. Prabu, Reagan Streeter, Noel Castro Segura, Jesús M. Corral Santana, Christian Knigge, Evangelia Tremou, Francesco Carotenuto, Rob Fender, Payaswini Saikia
We review the existing distance estimates to the black hole X-ray binary Swift J1727.8$-$1613 with a discussion of the accuracies and caveats of the associated methodologies. As part of this, we present new line-of-sight HI absorption spectra captured using the MeerKAT radio telescope. We estimate a maximum radial velocity with respect to the local standard of rest of $24.8 \pm 2.8$ km s$^{-1}$, which is significantly lower than that found towards an extragalactic reference source. Given the location of Swift J1727.8$-$1613 at Galactic longitude and latitude $(l, b) \approx (8.6\deg, 10.3\deg)$, we explore the feasibility of the HI absorption method. From this we derive a near kinematic distance of $d_{\rm{near}} = 3.6 \pm 0.3\ (stat) \pm 2.3\ (sys)$ kpc as lower bound for the distance to Swift J1727.8$-$1613. We compare our results with those derived from different distance determination methods including the use of colour excess or reddening along the line of sight, which we constrain to $E(B-V) = 0.37 \pm 0.01\ (stat) \pm 0.025\ (sys)$ using near-UV spectra. By combining this with donor star magnitudes reported by Mata S\'anchez et al. (2024b), we suggest an increased distance of $5.5^{+1.4}_{-1.1}$ kpc, which would imply a natal kick velocity of $190 \pm 30$ km s$^{-1}$.
2025-02-10
Alberto Rodriguez-Ardila, Fernando Cerqueira-Campos
Forbidden coronal lines has traditionally called the attention due to the high-energy photons required for their production (IP $>$100~eV, where IP is the ionisation potential of the transitions that originate the line). As such, they are regarded as the most highly ionised component of Active Galactic Nuclei (AGN). For decades, it was thought that they were only formed in the inner portions of the narrow line region (NLR). Nowadays, due to the larger sensitivity of the detectors and the availability of integral field unit (IFU) spectrographs, that emission in addition to the nuclear component is found to be extended up to a few kiloparsecs away from the active centre. In this review, we highlight the most important aspects of the coronal emission and discuss the recent developments in the field. In particular, we emphasize the discovery that they can be used to determine the mass of the central supermassive black hole, to reconstruct the SED, as well as to trace the most energetic feedback component of the ionised gas in AGN.
Integrated Multiphysics Modeling of a Piezoelectric Micropump
2025-02-10
AmirHossein Ghaemi, Abbas Ebrahimi, Majid Hajipour
This paper presents an integrated multiphysics simulation approach of piezoelectric micropumps. Micropumps and micro blowers are essential devices in various cutting-edge industries like laboratory equipment, medical devices, and fuel cells. A piezoelectric micropump involves complex physics including microfluidics, flow-structure interaction, electricity, and piezoelectric material. Hence, a comprehensive analysis of the interactions between different physical phenomena, would be essential for the effective design and optimization of these micropumps. Prior studies on piezoelectric micropump were mainly focused on isolated physical aspects of these pumps, such as piezoelectric mechanics, fluid dynamics, electrical properties, and also fluid-structure Interactions. The present paper fills this gap by integrating these aspects into a holistic simulation and design approach, introducing a new methodology for micropump analysis. Advanced simulation and design tools like COMSOL and SolidWorks were employed in accordance. A brief review of piezoelectric materials, and an exploration of different types of micropumps and their operating principles is discussed. Also, a comparison of various piezoelectric materials, including their properties and applications is investigated. Further, the paper discusses the simulation process of the micropumps, using COMSOL software, and presents an in-depth analysis of the simulation results. This structured approach provides a comprehensive understanding of piezoelectric micropumps, from theoretical underpinnings to practical design considerations. ..
Recent Developments on the HI Gas of Low-Redshift Galaxies Seen by the 21cm Emission Lines
2025-02-10
Jing Wang, Xuchen Lin
As a major interstellar medium, the atomic neutral hydrogen (HI) plays an important role in the galaxy evolution. It provides the ingredient for star formation, and sensitively traces the internal processes and external perturbations influencing the galaxy. With the beginning of many new radio telescopes and surveys, HI may make a more significant contribution to the understanding of galaxies in the near future. This review discusses the major development of the $21\,\text{cm}$ emission-line HI observations and studies in the past few years, including its scaling relations with other galaxy properties, its kinematics and structures, its role in environmental studies, and its constraints on hydrodynamical simulations. The local-Universe HI scaling relations of stellar-mass--selected samples extend smoothly to $10^9\,\text{M}_\odot$ stellar mass, with a tentative evolution to the redshift of ${\sim}0.1$. The development of measurement techniques enables better estimations of HI non-circular motion, dispersion, and thickness, and new observations revealed extended or extra-planar HI structures, both helpfully constraining the gas accretion, stellar feedback, and star formation processes of galaxy evolution models. HI is very useful for tracing the on-going satellite evolution in dense environments, the studies of which would benefit from ongoing blind HI surveys. Though simulations still cannot fully reproduce HI gas properties, they help to understand the role of possible factors in regulating HI properties. We also discuss possible future progress with new observations at FAST.
When Data Manipulation Meets Attack Goals: An In-depth Survey of Attacks for VLMs
2025-02-10
Aobotao Dai, Xinyu Ma, Lei Chen, Songze Li, Lin Wang
Vision-Language Models (VLMs) have gained considerable prominence in recent years due to their remarkable capability to effectively integrate and process both textual and visual information. This integration has significantly enhanced performance across a diverse spectrum of applications, such as scene perception and robotics. However, the deployment of VLMs has also given rise to critical safety and security concerns, necessitating extensive research to assess the potential vulnerabilities these VLM systems may harbor. In this work, we present an in-depth survey of the attack strategies tailored for VLMs. We categorize these attacks based on their underlying objectives - namely jailbreak, camouflage, and exploitation - while also detailing the various methodologies employed for data manipulation of VLMs. Meanwhile, we outline corresponding defense mechanisms that have been proposed to mitigate these vulnerabilities. By discerning key connections and distinctions among the diverse types of attacks, we propose a compelling taxonomy for VLM attacks. Moreover, we summarize the evaluation metrics that comprehensively describe the characteristics and impact of different attacks on VLMs. Finally, we conclude with a discussion of promising future research directions that could further enhance the robustness and safety of VLMs, emphasizing the importance of ongoing exploration in this critical area of study. To facilitate community engagement, we maintain an up-to-date project page, accessible at: https://github.com/AobtDai/VLM_Attack_Paper_List.
Relativistic Gas Accretion onto Supermassive Black Hole Binaries from Inspiral through Merger
2025-02-10
Lorenzo Ennoggi, Manuela Campanelli, Yosef Zlochower, Scott C. Noble, Julian Krolik, Federico Cattorini, Jay V. Kalinani, Vassilios Mewes, Michail Chabanov, Liwei Ji, Maria Chiara de Simone
Accreting supermassive black hole binaries are powerful multimessenger sources emitting both gravitational and electromagnetic (EM) radiation. Understanding the accretion dynamics of these systems and predicting their distinctive EM signals is crucial to informing and guiding upcoming efforts aimed at detecting gravitational waves produced by these binaries. To this end, accurate numerical modeling is required to describe both the spacetime and the magnetized gas around the black holes. In this paper, we present two key advances in this field of research. First, we have developed a novel 3D general relativistic magnetohydrodynamics (GRMHD) framework that combines multiple numerical codes to simulate the inspiral and merger of supermassive black hole binaries starting from realistic initial data and running all the way through merger. Throughout the evolution, we adopt a simple but functional prescription to account for gas cooling through the emission of photons. Next, we have applied our new computational method to follow the time evolution of a circular, equal-mass, non-spinning black hole binary for ${\sim\!200}$ orbits starting from a separation of ${20\,r_g}$ and reaching the post-merger evolutionary stage of the system. We have identified how and when the minidisks dissolve as the binary compresses. We also show that even when the binary ``decouples'' from its surrounding disk, its luminosity decreases by only a factor of a few and abruptly increases by ${\sim\!50\%}$ at the time of merger, accompanied by an equally abrupt change in spectrum. Finally, the magnetic flux brought to the spin-parameter ${\sim\!0.68}$ merger remnant is able to drive a relativistic, Poynting-flux-dominated jet.
The EDIBLES survey. X. The 6196 Å diffuse interstellar band: Identification of side DIBs as indication for a small carrier molecule
2025-02-10
A. Ebenbichler, M. Ončák, N. Przybilla, H. R. Hrodmarsson, J. V. Smoker, R. Lallement, A. Farhang, C. Bhatt, J. Cami, M. Cordiner, P. Ehrenfreund, N. L. J. Cox, J. Th. van Loon, B. Foing
Context: Numerous studies of diffuse interstellar band (DIB) profiles have detected substructures, implying large molecules as their carriers. However, some of the narrowest DIBs generally do not show such substructure, suggesting the possibility of very small carriers. Aims: Based on the previously found tight correlation of the three narrow DIBs at 6196, 6440 and 6623 A and the present detection of weaker side DIBs to each of them in the extensive data set from the ESO Diffuse Interstellar Bands Large Exploration Survey, we investigate whether they may stem from small linear carrier molecules. This approach can lead to concrete DIB carrier suggestions, which can be tested in laboratory measurements in future studies. Methods: We suggest that the DIBs we study here represent individual rotational transitions of a small molecule. We determined the molecular constants from observation and compared them with data from a large set of quantum-chemical calculations to confine possible carrier candidates. Furthermore, we determined rotational temperatures by fitting line ratios using the fitted molecular models. Results: We determined molecular constants for three DIB systems and the corresponding transition types. The fitted rotational temperatures lie within the range of known interstellar diatomic molecules. We identified several DIB carrier candidates, almost all of them molecular ions. Some of them are metastable species, indicating the possibility of collision complexes as DIB carriers. Conclusions: If our hypothesis holds, this would be a major step toward the identification of a carrier molecule of the 6196 A DIB, the strongest among the narrow DIBs.
A survey of special curves, special subvarieties of $\mathcal{M}_g$, and related topics. A large portion of the text discusses various possible interpretation of the word 'special' in this context by giving also concrete examples. One highlight is the bi-algebraic viewpoint for atypical intersections appearing in Hodge theory as well as, more recently, in Teichm\"{u}ller theory.
The randomization method in stochastic optimal control
2025-02-10
Marco Fuhrman
In this paper we make a survey on the so called randomization method, a recent methodology to study stochastic optimization problems. It allows to represent the value function of an optimal control problem by a suitable backward stochastic differential equation (BSDE), by means of an auxiliary optimization problem having the same value as the starting one. This method works for a large class of control problems and provides a BSDE representation to many related PDEs of Hamilton-Jacobi-Bellman type, even in the fully non linear case. After a general informal introduction we explain the method giving full details in a basic case. Then we try to give a complete picture of the existing applications and we present some related open problems.
We presented the first photometric light curve solutions of four W Ursae Majoris (W UMa)-type contact binary systems. This investigation utilized photometric data from the Transiting Exoplanet Survey Satellite (TESS) and Gaia Data Release 3 (DR3). We used the PHysics Of Eclipsing BinariEs (PHOEBE) Python code and the Markov Chain Monte Carlo (MCMC) method for these light curve solutions. Only TIC 249064185 among the target systems needed a cold starspot to be included in the analysis. Based on the estimated mass ratios for these total eclipse systems, three of them are categorized as low mass ratio contact binary stars. The absolute parameters of the systems were estimated using the Gaia DR3 parallax method and the orbital period and semi-major axis ($P-a$) empirical relationship. We defined that TIC 318015356 and TIC 55522736 systems are A-subtypes, while TIC 249064185 and TIC 397984843 are W-subtypes, depending on each component's effective temperature and mass. We estimated the initial masses of the stars, the mass lost by the binary system, and the systems' ages. We displayed star positions in the mass-radius, mass-luminosity, and total mass-orbital angular momentum diagrams. In addition, our findings indicate a good agreement with the mass-temperature empirical parameter relationship for the primary stars.
Entity retrieval plays a crucial role in the utilization of Electronic Health Records (EHRs) and is applied across a wide range of clinical practices. However, a comprehensive evaluation of this task is lacking due to the absence of a public benchmark. In this paper, we propose the development and release of a novel benchmark for evaluating entity retrieval in EHRs, with a particular focus on the semantic gap issue. Using discharge summaries from the MIMIC-III dataset, we incorporate ICD codes and prescription labels associated with the notes as queries, and annotate relevance judgments using GPT-4. In total, we use 1,000 patient notes, generate 1,246 queries, and provide over 77,000 relevance annotations. To offer the first assessment of the semantic gap, we introduce a novel classification system for relevance matches. Leveraging GPT-4, we categorize each relevant pair into one of five categories: string, synonym, abbreviation, hyponym, and implication. Using the proposed benchmark, we evaluate several retrieval methods, including BM25, query expansion, and state-of-the-art dense retrievers. Our findings show that BM25 provides a strong baseline but struggles with semantic matches. Query expansion significantly improves performance, though it slightly reduces string match capabilities. Dense retrievers outperform traditional methods, particularly for semantic matches, and general-domain dense retrievers often surpass those trained specifically in the biomedical domain.
C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation
2025-02-10
Guoxin Chen, Minpeng Liao, Peiying Yu, Dingmin Wang, Zile Qiao, Chao Yang, Xin Zhao, Kai Fan
Retrieval-augmented generation (RAG) systems face a fundamental challenge in aligning independently developed retrievers and large language models (LLMs). Existing approaches typically involve modifying either component or introducing simple intermediate modules, resulting in practical limitations and sub-optimal performance. Inspired by human search behavior -- typically involving a back-and-forth process of proposing search queries and reviewing documents, we propose C-3PO, a proxy-centric framework that facilitates communication between retrievers and LLMs through a lightweight multi-agent system. Our framework implements three specialized agents that collaboratively optimize the entire RAG pipeline without altering the retriever and LLMs. These agents work together to assess the need for retrieval, generate effective queries, and select information suitable for the LLMs. To enable effective multi-agent coordination, we develop a tree-structured rollout approach for reward credit assignment in reinforcement learning. Extensive experiments in both in-domain and out-of-distribution scenarios demonstrate that C-3PO significantly enhances RAG performance while maintaining plug-and-play flexibility and superior generalization capabilities.
This study evaluates the performance and usability of Mixed Reality (MR), Virtual Reality (VR), and camera stream interfaces for remote error resolution tasks, such as correcting warehouse packaging errors. Specifically, we consider a scenario where a robotic arm halts after detecting an error, requiring a remote operator to intervene and resolve it via pick-and-place actions. Twenty-one participants performed simulated pick-and-place tasks using each interface. A linear mixed model (LMM) analysis of task resolution time, usability scores (SUS), and mental workload scores (NASA-TLX) showed that the MR interface outperformed both VR and camera interfaces. MR enabled significantly faster task completion, was rated higher in usability, and was perceived to be less cognitively demanding. Notably, the MR interface, which projected a virtual robot onto a physical table, provided superior spatial understanding and physical reference cues. Post-study surveys further confirmed participants' preference for MR over other interfaces.
By monitoring temporal contrast, event-based vision sensors can provide high temporal resolution and low latency while maintaining low power consumption and simplicity in circuit structure. These characteristics have garnered significant attention in both academia and industry. In recent years, the application of back-illuminated (BSI) technology, wafer stacking techniques, and industrial interfaces has brought new opportunities for enhancing the performance of event-based vision sensors. This is evident in the substantial advancements made in reducing noise, improving resolution, and increasing readout rates. Additionally, the integration of these technologies has enhanced the compatibility of event-based vision sensors with current and edge vision systems, providing greater possibilities for their practical applications. This paper will review the progression from neuromorphic engineering to state-of-the-art event-based vision sensor technologies, including their development trends, operating principles, and key features. Moreover, we will delve into the sensitivity of event-based vision sensors and the opportunities and challenges they face in the realm of infrared imaging, providing references for future research and applications.
Towards Bio-inspired Heuristically Accelerated Reinforcement Learning for Adaptive Underwater Multi-Agents Behaviour
2025-02-10
Antoine Vivien, Thomas Chaffre, Matthew Stephenson, Eva Artusi, Paulo Santos, Benoit Clement, Karl Sammut
This paper describes the problem of coordination of an autonomous Multi-Agent System which aims to solve the coverage planning problem in a complex environment. The considered applications are the detection and identification of objects of interest while covering an area. These tasks, which are highly relevant for space applications, are also of interest among various domains including the underwater context, which is the focus of this study. In this context, coverage planning is traditionally modelled as a Markov Decision Process where a coordinated MAS, a swarm of heterogeneous autonomous underwater vehicles, is required to survey an area and search for objects. This MDP is associated with several challenges: environment uncertainties, communication constraints, and an ensemble of hazards, including time-varying and unpredictable changes in the underwater environment. MARL algorithms can solve highly non-linear problems using deep neural networks and display great scalability against an increased number of agents. Nevertheless, most of the current results in the underwater domain are limited to simulation due to the high learning time of MARL algorithms. For this reason, a novel strategy is introduced to accelerate this convergence rate by incorporating biologically inspired heuristics to guide the policy during training. The PSO method, which is inspired by the behaviour of a group of animals, is selected as a heuristic. It allows the policy to explore the highest quality regions of the action and state spaces, from the beginning of the training, optimizing the exploration/exploitation trade-off. The resulting agent requires fewer interactions to reach optimal performance. The method is applied to the MSAC algorithm and evaluated for a 2D covering area mission in a continuous control environment.
The existence of latent cellular reservoirs is recognized as the major barrier to an HIV cure. Reactivating and eliminating "shock and kill" or permanently silencing "block and lock" the latent HIV reservoir, as well as gene editing, remain promising approaches, but so far have proven to be only partially successful. Moreover, using latency reversing agents or "block and lock" drugs pose additional considerations, including the ability to cause cellular toxicity, a potential lack of specificity for HIV, or low potency when each agent is used alone. RNA molecules, such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are becoming increasingly recognized as important regulators of gene expression. RNA-based approaches for combatting HIV latency represent a promising strategy since both miRNAs and lncRNAs are more cell-type and tissue specific than protein coding genes. Thus, a higher specificity of targeting the latent HIV reservoir with less overall cellular toxicity can likely be achieved. In this review, we summarize current knowledge about HIV gene expression regulation by miRNAs and lncRNAs encoded in the human genome, as well as regulatory molecules encoded in the HIV genome. We discuss both the transcriptional and post-transcriptional regulation of HIV gene expression to align with the current definition of latency, and describe RNA molecules that either promote HIV latency or have anti-latency properties. Finally, we provide perspectives on using each class of RNAs as potential targets for combatting HIV latency, and describe the complexity of the interactions between different RNA molecules, their protein targets, and HIV.
Optical lattice clocks (OLCs) enable us to measure time and frequency with a fractional uncertainty at $10^{-18}$ level, which is 2 orders of magnitude better than Cs clocks. In this article, after briefly reviewing OLCs and the history of testing the fundamental principles of general relativity, we report our experiments of measuring the gravitational redshift between RIKEN and The University of Tokyo, and at Tokyo Skytree using transportable OLCs. We also discuss a couple of future applications of OLCs, such as detecting gravitational waves in space and relativistic geodesy. The possibility of testing second-order parametrized post-Newtonian potential around the Earth is also mentioned.
Geolocation with Large LEO Constellations: Insights from Fisher Information
2025-02-10
Don-Roberts Emenonye, Harpreet S. Dhillon, R. Michael Buehrer
Interest in the use of the low earth orbit (LEO) in space - from $160 \text{ km}$ to $2000 \text{ km}$ - has skyrocketed; this is evident by the fact that National Aeronautics and Space Administration (NASA) has partnered with various commercial platforms like Axiom Space, Blue Origin, SpaceX, Sierra Space, Starlab Space, ThinkOrbital, and Vast Space to deploy satellites. %and platforms like Northrop Grumman and Boeing to transport cargo and crew. The most apparent advantage of satellites in LEO over satellites in Geostationary (GEO) and medium earth orbit (MEO) is their closeness to the earth; hence, signals from LEOs encounter lower propagation losses and reduced propagation delay, opening up the possibility of using these LEO satellites for localization. This article reviews the existing signal processing algorithms for localization using LEO satellites, introduces the basics of estimation theory, connects estimation theory to model identifiability with Fisher Information Matrix (FIM), and with the FIM, provides conditions that allow for $9$D localization of a terrestrial receiver using signals from multiple LEOs (unsynchronized in time and frequency) across multiple time slots and multiple receive antennas. We also compare the structure of the information available in LEO satellites with the structure of the information available in the Global Positioning System (GPS).
A nested MLMC framework for efficient simulations on FPGAs
2025-02-10
Irina-Beatrice Haas, Michael B. Giles
Multilevel Monte Carlo (MLMC) reduces the total computational cost of financial option pricing by combining SDE approximations with multiple resolutions. This paper explores a further avenue for reducing cost and improving power efficiency through the use of low precision calculations on configurable hardware devices such as Field-Programmable Gate Arrays (FPGAs). We propose a new framework that exploits approximate random variables and fixed-point operations with optimised precision to generate most SDE paths with a lower cost and reduce the overall cost of the MLMC framework. We first discuss several methods for the cheap generation of approximate random Normal increments. To set the bit-width of variables in the path generation we then propose a rounding error model and optimise the precision of all variables on each MLMC level. With these key improvements, our proposed framework offers higher computational savings than the existing mixed-precision MLMC frameworks.
Choroidal image analysis for OCT image sequences with applications in systemic health
2025-02-10
Jamie Burke
The choroid, a highly vascular layer behind the retina, is an extension of the central nervous system and has parallels with the renal cortex, with blood flow far exceeding that of the brain and kidney. Thus, there has been growing interest of choroidal blood flow reflecting physiological status of systemic disease. Optical coherence tomography (OCT) enables high-resolution imaging of the choroid, but conventional analysis methods remain manual or semi-automatic, limiting reproducibility, standardisation and clinical utility. In this thesis, I develop several new methods to analyse the choroid in OCT image sequences, with each successive method improving on its predecessors. I first develop two semi-automatic approaches for choroid region (Gaussian Process Edge Tracing, GPET) and vessel (Multi-scale Median Cut Quantisation, MMCQ) analysis, which improve on manual approaches but remain user-dependent. To address this, I introduce DeepGPET, a deep learning-based region segmentation method which improves on execution time, reproducibility, and end-user accessibility, but lacks choroid vessel analysis and automatic feature measurement. Improving on this, I developed Choroidalyzer, a deep learning-based pipeline to segment the choroidal space and vessels and generate fully automatic, clinically meaningful and reproducible choroidal features. I provide rigorous evaluation of these four approaches and consider their potential clinical value in three applications into systemic health: OCTANE, assessing choroidal changes in renal transplant recipients and donors; PREVENT, exploring choroidal associations with Alzheimer's risk factors at mid-life; D-RISCii, assessing choroidal variation and feasibility of OCT in critical care. In short, this thesis contributes many open-source tools for standardised choroidal measurement and highlights the choroid's potential as a biomarker in systemic health.
Towards MatCore: A Unified Metadata Standard for Materials Science
2025-02-10
Jane Greenberg, Pamela Boveda-Aguirre, John Allison, Pietro Asinari, Maria Chan, Anand Chandrasekaran, Elif Ertekin, Emmanouel Garoufallou, Giulia Galli, Paolo Giannozzi, Feliciano Giustino, Gerhard Goldbeck, Hendrik Heinz, Arthi Jayaraman, Vincenzo Lordi, Kristin A. Persson, Gian-Marco Rignanese, Aidan Thompson, Eric Toberer, Scott McClellan, Ellad B. Tadmor
The materials science community seeks to support the FAIR principles for computational simulation research. The MatCore Project was recently launched to address this need, with the goal of developing an overall metadata framework and accompanying guidelines. This paper reports on the MatCore goals and overall progress. Historical background context is provided, including a review of the principles underlying successful core metadata standards. The paper also presents selected MatCore examples and discusses future plans.
The SPIRou Legacy Survey: near-infrared and optical radial velocity analysis of Gl 480 and Gl 382 using SPIRou, HARPS and CARMENES spectrographs
2025-02-10
M. Ould-Elhkim, C. Moutou, J-F. Donati, É. Artigau, C. Cadieux, E. Martioli, T. Forveille, J. Gomes da Silva, R. Cloutier, A. Carmona, P. Fouqué, P. Charpentier, P. Larue, N. J. Cook, X. Delfosse, R. Doyon
Context: Advancements in the field of exoplanetary research have extended radial velocity (RV) observations from the optical to the near-infrared (nIR) domain. M dwarf stars, characterized by their lower masses and higher prevalence of rocky planets, have become a focal point of investigation. This study uses data from the near-infrared spectropolarimeter SPIRou and data available in the literature from the HARPS and CARMENES spectrographs operating in the optical to analyze RVs of two nearby M dwarfs, Gl 480 and Gl 382. Aims: This work aims to detect and characterize exoplanetary companions around Gl 480 and Gl 382 by mitigating stellar activity effects through advanced data analysis techniques. The study seeks to improve the reliability of RV signals by integrating multi-wavelength observations and stellar activity diagnostics. Methods: The study employs a comprehensive approach that combines the line-by-line (LBL) framework with the Wapiti (Weighted principAl comPonent analysIs reconsTructIon) method to correct for systematics in SPIRou data. Through an extensive analysis of available stellar activity indicators and by combining optical data from the HARPS and CARMENES instruments, we perform a joint analysis of RV measurements in both the nIR and optical domains. Results: Our analysis confirms the detection of a planet orbiting Gl 480 with a period of $9.5537 \pm 0.0005$ d and a minimum mass of $8.8 \pm 0.7$ M$_\oplus$. Additionally, we detect a tentative signal at 6.4 d, whose significance depends strongly on the choice of Gaussian Process priors constrained by stellar activity indicators and would require further observations for confirmation. In contrast, no planetary signals are detected for Gl 382, where RV variations are dominated by stellar activity.
The K-Modes algorithm, developed for clustering categorical data, is of high algorithmic simplicity but suffers from unreliable performances in clustering quality and clustering efficiency, both heavily influenced by the choice of initial cluster centers. In this paper, we investigate Bisecting K-Modes (BK-Modes), a successive bisecting process to find clusters, in examining how good the cluster centers out of the bisecting process will be when used as initial centers for the K-Modes. The BK-Modes works by splitting a dataset into multiple clusters iteratively with one cluster being chosen and bisected into two clusters in each iteration. We use the sum of distances of data to their cluster centers as the selection metric to choose a cluster to be bisected in each iteration. This iterative process stops when K clusters are produced. The centers of these K clusters are then used as the initial cluster centers for the K-Modes. Experimental studies of the BK-Modes were carried out and were compared against the K-Modes with multiple sets of initial cluster centers as well as the best of the existing methods we found so far in our survey. Experimental results indicated good performances of BK-Modes both in the clustering quality and efficiency for large datasets.
Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations
2025-02-10
Yong Cao, Haijiang Liu, Arnav Arora, Isabelle Augenstein, Paul Röttger, Daniel Hershcovich
Large-scale surveys are essential tools for informing social science research and policy, but running surveys is costly and time-intensive. If we could accurately simulate group-level survey results, this would therefore be very valuable to social science research. Prior work has explored the use of large language models (LLMs) for simulating human behaviors, mostly through prompting. In this paper, we are the first to specialize LLMs for the task of simulating survey response distributions. As a testbed, we use country-level results from two global cultural surveys. We devise a fine-tuning method based on first-token probabilities to minimize divergence between predicted and actual response distributions for a given question. Then, we show that this method substantially outperforms other methods and zero-shot classifiers, even on unseen questions, countries, and a completely unseen survey. While even our best models struggle with the task, especially on unseen questions, our results demonstrate the benefits of specialization for simulation, which may accelerate progress towards sufficiently accurate simulation in the future.
2025-02-10
Nojan Sheybani, Anees Ahmed, Michel Kinsy, Farinaz Koushanfar
Zero-Knowledge Proofs (ZKPs) are a cryptographic primitive that allows a prover to demonstrate knowledge of a secret value to a verifier without revealing anything about the secret itself. ZKPs have shown to be an extremely powerful tool, as evidenced in both industry and academic settings. In recent years, the utilization of user data in practical applications has necessitated the rapid development of privacy-preserving techniques, including ZKPs. This has led to the creation of several robust open-source ZKP frameworks. However, there remains a significant gap in understanding the capabilities and real-world applications of these frameworks. Furthermore, identifying the most suitable frameworks for the developers' specific applications and settings is a challenge, given the variety of options available. The primary goal of our work is to lower the barrier to entry for understanding and building applications with open-source ZKP frameworks. In this work, we survey and evaluate 25 general-purpose, prominent ZKP frameworks. Recognizing that ZKPs have various constructions and underlying arithmetic schemes, our survey aims to provide a comprehensive overview of the ZKP landscape. These systems are assessed based on their usability and performance in SHA-256 and matrix multiplication experiments. Acknowledging that setting up a functional development environment can be challenging for these frameworks, we offer a fully open-source collection of Docker containers. These containers include a working development environment and are accompanied by documented code from our experiments. We conclude our work with a thorough analysis of the practical applications of ZKPs, recommendations for ZKP settings in different application scenarios, and a discussion on the future development of ZKP frameworks.
Federated Continual Learning (FCL) has emerged as a robust solution for collaborative model training in dynamic environments, where data samples are continuously generated and distributed across multiple devices. This survey provides a comprehensive review of FCL, focusing on key challenges such as heterogeneity, model stability, communication overhead, and privacy preservation. We explore various forms of heterogeneity and their impact on model performance. Solutions to non-IID data, resource-constrained platforms, and personalized learning are reviewed in an effort to show the complexities of handling heterogeneous data distributions. Next, we review techniques for ensuring model stability and avoiding catastrophic forgetting, which are critical in non-stationary environments. Privacy-preserving techniques are another aspect of FCL that have been reviewed in this work. This survey has integrated insights from federated learning and continual learning to present strategies for improving the efficacy and scalability of FCL systems, making it applicable to a wide range of real-world scenarios.
Using Contextually Aligned Online Reviews to Measure LLMs' Performance Disparities Across Language Varieties
2025-02-10
Zixin Tang, Chieh-Yang Huang, Tsung-Chi Li, Ho Yin Sam Ng, Hen-Hsen Huang, Ting-Hao 'Kenneth' Huang
A language can have different varieties. These varieties can affect the performance of natural language processing (NLP) models, including large language models (LLMs), which are often trained on data from widely spoken varieties. This paper introduces a novel and cost-effective approach to benchmark model performance across language varieties. We argue that international online review platforms, such as Booking.com, can serve as effective data sources for constructing datasets that capture comments in different language varieties from similar real-world scenarios, like reviews for the same hotel with the same rating using the same language (e.g., Mandarin Chinese) but different language varieties (e.g., Taiwan Mandarin, Mainland Mandarin). To prove this concept, we constructed a contextually aligned dataset comprising reviews in Taiwan Mandarin and Mainland Mandarin and tested six LLMs in a sentiment analysis task. Our results show that LLMs consistently underperform in Taiwan Mandarin.
LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights
2025-02-10
Ze Sheng, Zhicheng Chen, Shuning Gu, Heqing Huang, Guofei Gu, Jeff Huang
Large Language Models (LLMs) are emerging as transformative tools for software vulnerability detection, addressing critical challenges in the security domain. Traditional methods, such as static and dynamic analysis, often falter due to inefficiencies, high false positive rates, and the growing complexity of modern software systems. By leveraging their ability to analyze code structures, identify patterns, and generate repair sugges- tions, LLMs, exemplified by models like GPT, BERT, and CodeBERT, present a novel and scalable approach to mitigating vulnerabilities. This paper provides a detailed survey of LLMs in vulnerability detection. It examines key aspects, including model architectures, application methods, target languages, fine-tuning strategies, datasets, and evaluation metrics. We also analyze the scope of current research problems, highlighting the strengths and weaknesses of existing approaches. Further, we address challenges such as cross-language vulnerability detection, multimodal data integration, and repository-level analysis. Based on these findings, we propose solutions for issues like dataset scalability, model interpretability, and applications in low-resource scenarios. Our contributions are threefold: (1) a systematic review of how LLMs are applied in vulnerability detection; (2) an analysis of shared patterns and differences across studies, with a unified framework for understanding the field; and (3) a summary of key challenges and future research directions. This work provides valuable insights for advancing LLM-based vulnerability detection. We also maintain and regularly update latest selected paper on https://github.com/OwenSanzas/LLM-For-Vulnerability-Detection
Scalable and Ethical Insider Threat Detection through Data Synthesis and Analysis by LLMs
2025-02-10
Haywood Gelman, John D. Hastings
Insider threats wield an outsized influence on organizations, disproportionate to their small numbers. This is due to the internal access insiders have to systems, information, and infrastructure. %One example of this influence is where anonymous respondents submit web-based job search site reviews, an insider threat risk to organizations. Signals for such risks may be found in anonymous submissions to public web-based job search site reviews. This research studies the potential for large language models (LLMs) to analyze and detect insider threat sentiment within job site reviews. Addressing ethical data collection concerns, this research utilizes synthetic data generation using LLMs alongside existing job review datasets. A comparative analysis of sentiment scores generated by LLMs is benchmarked against expert human scoring. Findings reveal that LLMs demonstrate alignment with human evaluations in most cases, thus effectively identifying nuanced indicators of threat sentiment. The performance is lower on human-generated data than synthetic data, suggesting areas for improvement in evaluating real-world data. Text diversity analysis found differences between human-generated and LLM-generated datasets, with synthetic data exhibiting somewhat lower diversity. Overall, the results demonstrate the applicability of LLMs to insider threat detection, and a scalable solution for insider sentiment testing by overcoming ethical and logistical barriers tied to data acquisition.
Finding Words Associated with DIF: Predicting Differential Item Functioning using LLMs and Explainable AI
2025-02-10
Hotaka Maeda, Yikai Lu
We fine-tuned and compared several encoder-based Transformer large language models (LLM) to predict differential item functioning (DIF) from the item text. We then applied explainable artificial intelligence (XAI) methods to these models to identify specific words associated with DIF. The data included 42,180 items designed for English language arts and mathematics summative state assessments among students in grades 3 to 11. Prediction $R^2$ ranged from .04 to .32 among eight focal and reference group pairs. Our findings suggest that many words associated with DIF reflect minor sub-domains included in the test blueprint by design, rather than construct-irrelevant item content that should be removed from assessments. This may explain why qualitative reviews of DIF items often yield confusing or inconclusive results. Our approach can be used to screen words associated with DIF during the item-writing process for immediate revision, or help review traditional DIF analysis results by highlighting key words in the text. Extensions of this research can enhance the fairness of assessment programs, especially those that lack resources to build high-quality items, and among smaller subpopulations where we do not have sufficient sample sizes for traditional DIF analyses.
This research developed a prototype data warehouse to integrate multi-source forestry data for long-term monitoring, management, and sustainability. The data warehouse is intended to accommodate all types of imagery from various platforms, LiDAR point clouds, survey records, and paper documents, with the capability to transform these datasets into machine learning (ML) and deep learning classification and segmentation models. In this study, we pioneered the integration of unmanned aerial vehicle (UAV) imagery and paper records, testing the merged data on the YOLOv11 model. Paper records improved ground truth, and preliminary results demonstrated notable performance improvements. This research aims to implement a data warehouse (DW) to manage data for a YOLO (You Only Look Once) model, which identifies objects in images. It does this by integrating advanced data processing pipelines. Data are also stored and easily accessible for future use, including comparing current and historical data to understand growth or declining patterns. In addition, the design is used to optimize resource usage. It also scales easily, not affecting other parts of the data warehouse when adding dimension tables or other fields to the fact table. DW performance and estimations for growing workloads are also explored in this paper.
Early Operative Difficulty Assessment in Laparoscopic Cholecystectomy via Snapshot-Centric Video Analysis
2025-02-10
Saurav Sharma, Maria Vannucci, Leonardo Pestana Legori, Mario Scaglia, Giovanni Guglielmo Laracca, Didier Mutter, Sergio Alfieri, Pietro Mascagni, Nicolas Padoy
Purpose: Laparoscopic cholecystectomy (LC) operative difficulty (LCOD) is highly variable and influences outcomes. Despite extensive LC studies in surgical workflow analysis, limited efforts explore LCOD using intraoperative video data. Early recog- nition of LCOD could allow prompt review by expert surgeons, enhance operating room (OR) planning, and improve surgical outcomes. Methods: We propose the clinical task of early LCOD assessment using limited video observations. We design SurgPrOD, a deep learning model to assess LCOD by analyzing features from global and local temporal resolutions (snapshots) of the observed LC video. Also, we propose a novel snapshot-centric attention (SCA) module, acting across snapshots, to enhance LCOD prediction. We introduce the CholeScore dataset, featuring video-level LCOD labels to validate our method. Results: We evaluate SurgPrOD on 3 LCOD assessment scales in the CholeScore dataset. On our new metric assessing early and stable correct predictions, SurgPrOD surpasses baselines by at least 0.22 points. SurgPrOD improves over baselines by at least 9 and 5 percentage points in F1 score and top1-accuracy, respectively, demonstrating its effectiveness in correct predictions. Conclusion: We propose a new task for early LCOD assessment and a novel model, SurgPrOD analyzing surgical video from global and local perspectives. Our results on the CholeScore dataset establishes a new benchmark to study LCOD using intraoperative video data.
Grounding Creativity in Physics: A Brief Survey of Physical Priors in AIGC
2025-02-10
Siwei Meng, Yawei Luo, Ping Liu
Recent advancements in AI-generated content have significantly improved the realism of 3D and 4D generation. However, most existing methods prioritize appearance consistency while neglecting underlying physical principles, leading to artifacts such as unrealistic deformations, unstable dynamics, and implausible objects interactions. Incorporating physics priors into generative models has become a crucial research direction to enhance structural integrity and motion realism. This survey provides a review of physics-aware generative methods, systematically analyzing how physical constraints are integrated into 3D and 4D generation. First, we examine recent works in incorporating physical priors into static and dynamic 3D generation, categorizing methods based on representation types, including vision-based, NeRF-based, and Gaussian Splatting-based approaches. Second, we explore emerging techniques in 4D generation, focusing on methods that model temporal dynamics with physical simulations. Finally, we conduct a comparative analysis of major methods, highlighting their strengths, limitations, and suitability for different materials and motion dynamics. By presenting an in-depth analysis of physics-grounded AIGC, this survey aims to bridge the gap between generative models and physical realism, providing insights that inspire future research in physically consistent content generation.
WilloWISPs: A New Dark Growth Channel for Black Holes Suggests a Full-Spectrum Hierarchical MACHO Mass Function for Dark Matter
2025-02-10
Zachary R. Smith, Neil F. Comins
Evidence of neutron stars with deconfined quark-matter cores suggest a new pathway for the evolution of black holes. New theories about the cores of neutron stars support the idea that quarkonium is likely to grow there as the neutron star ages. Surveys of stellar remnants have shown that there is no major mass gap between neutron stars and black holes. Black holes, specifically primordial ones (PBHs), have been suggested as an explanation for dark matter before. However, the way that very large black holes can form in the lifetime of the visible universe has only recently been explained with the solution to The Final Parsec Problem. If neutron stars can become exotic stars or black holes, then they may persist long enough to quiescently provide enough mass in dense matter regions to allow Intermediate-Mass Black Holes (IMBH) and Supermassive Black Holes (SMBH) to form quickly via coalescence. We find that a hierarchical clustering of Massive and Compact Halo Objects (MACHOs) with axion-dominated mini-halos can help to explain all of the missing dark matter. The model presented here suggests that this type of MACHO is likely equivalent to black holes above an unknown critical mass, which is less than ~5 $M_{\odot}$, and that they ought to form quark stars below this mass. If quark stars are a metastable transition between neutron stars and black holes, then black holes ought to be equivalent to boson stars with event horizons, after all the residual quark material has formed a Bose-Einstein condensate of mesons.
Task Offloading in Vehicular Edge Computing using Deep Reinforcement Learning: A Survey
2025-02-10
Ashab Uddin, Ahmed Hamdi Sakr, Ning Zhang
The increasing demand for Intelligent Transportation Systems (ITS) has introduced significant challenges in managing the complex, computation-intensive tasks generated by modern vehicles while offloading tasks to external computing infrastructures such as edge computing (EC), nearby vehicular , and UAVs has become influential solution to these challenges. However, traditional computational offloading strategies often struggle to adapt to the dynamic and heterogeneous nature of vehicular environments. In this study, we explored the potential of Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) frameworks to optimize computational offloading through adaptive, real-time decision-making, and we have thoroughly investigated the Markov Decision Process (MDP) approaches on the existing literature. The paper focuses on key aspects such as standardized learning models, optimized reward structures, and collaborative multi-agent systems, aiming to advance the understanding and application of DRL in vehicular networks. Our findings offer insights into enhancing the efficiency, scalability, and robustness of ITS, setting the stage for future innovations in this rapidly evolving field.
Hydrodynamic methods and sub-resolution models for cosmological simulations
2025-02-10
Milena Valentini, Klaus Dolag
Cosmological simulations are powerful tools in the context of structure formation. They allow us to explore the assembly and clustering of dark matter halos, to validate or reject possible scenarios of structure formation, and to investigate the physical properties of evolving galaxies across time. Cosmological hydrodynamical simulations are especially key to study how the complex interstellar medium of forming galaxies responds to the most energetic processes during galaxy evolution, such as stellar feedback ensuing supernova explosions and feedback from AGN. Given the huge dynamical range of physical scales spanned by the astrophysical processes involved in cosmic structure formation and evolution, cosmological simulations resort to sub-resolution models to capture processes occurring below their resolution limit. The impact of different sub-grid prescriptions accounting for the same process is striking, though often overlooked. Some among the main aforementioned processes include: hot gas cooling, star formation and stellar feedback, stellar evolution and chemical enrichment, black hole growth and feedback. Producing simulations of cosmic structure formation and galaxy evolution in large computational volumes is key to shed light on what drives the formation of the first structures in the Universe, and their subsequent evolution. Not only are predictions from simulations crucial to compare with data from ongoing observational instruments, but they can also guide future observational campaigns. Besides, since we have entered the era of high-performance computing, it is fundamental to have numerical codes which are very efficient from the computational point of view. In this chapter, we review the main hydrodynamic methods used in cosmological simulations and the most common techniques adopted to include the astrophysical processes which drive galaxy formation and evolution (abridged).
The satellite galaxies of the Milky Way and Andromeda
2025-02-10
Amandine Doliva-Dolinsky, Michelle L. M. Collins, Nicolas F. Martin
The satellite galaxies of the Local Group provide us with an important probe of galaxy formation, evolution, and cosmology. The two large spirals that dominate this group -- the Milky Way and Andromeda -- are each host to tens of satellites, ranging in stellar mass from $M_*=3\times10^9\,{\rm M_\odot}$ down to as little as $M_*\sim1000\,{\rm M_\odot}$. In this review, we (1) provide an overview of the known satellite population of the Milky Way and Andromeda, including how they are discovered and their observed properties; (2) discuss their importance in understanding the nature of dark matter, star formation in the early Universe, the assembly histories of their massive hosts, and the impact of reionisation on the lowest mass galaxies; and (3) highlight the coming revolution and challenges of this field as new observatories and facilities come online. In the coming decades, the study of Local Group satellites should allow us to place competitive constraints on both dark matter and galaxy evolution.
2025-02-10
Mansi M. Kasliwal, Nicholas Earley, Roger Smith, Tristan Guillot, Tony Travouillon, Jason Fucik, Lyu Abe, Timothee Greffe, Abdelkrim Agabi, Michael C. B. Ashley, Amaury H. M. J. Triaud, Samaporn Tinyanont, Sarah Antier, Philippe Bendjoya, Rohan Bhattarai, Rob Bertz, James Brugger, Artem Burdanov, Ilaria Caiazzo, Benoit Carry, Luca Casagrande, Jeff Cooke, Kishalay De, Richard Dekany, Vincent Deloupy, Damien Dornic, Lauren Fahey, Don Figer, Kenneth Freeman, Danielle Frostig, Maximilian Günther, David Hale, Joss Bland-Hawthorn, Giulia Illuminati, Jacob Jencson, Viraj Karambelkar, Renee Key, Ryan M. Lau, Maggie Li, Philip Lubin, Reston Nash, Don Neill, Rishi Pahuja, Elena Pian, Antonio de Ugarte Postigo, Mitsuko Roberts, Hector Rodriguez, Sam Rose, Ashley J. Ruiter, François-Xavier Schmider, Robert A. Simcoe, Robert Stein, Olga Suarez, Edward N. Taylor, Bob Weber, Linqing Wen, Julien de Wit, Ray Zarzaca, Jake Zimmer
We present Cryoscope -- a new 50 sq. deg field-of-view, 1.2 m aperture, K-dark survey telescope to be located at Dome C, Antarctica. Cryoscope has an innovative optical-thermal design wherein the entire telescope is cryogenically cooled. Cryoscope also explores new detector technology to cost-effectively tile the full focal plane. Leveraging the dark Antarctic sky and minimizing telescope thermal emission, Cryoscope achieves unprecedented deep, wide, fast and red observations, matching and exceeding volumetric survey speeds from the Ultraviolet Explorer, Vera Rubin Observatory, and Nancy Grace Roman Space Telescope. By providing coverage beyond wavelengths of 2 $\mu$m, we aim to create the most comprehensive dynamic movie of the most obscured reaches of the Universe. Cryoscope will be a dedicated discovery engine for electromagnetic emission from coalescing compact binaries, Earth-like exoplanets orbiting cold stars, and multiple facets of time-domain, stellar and solar system science. In this paper, we describe the scientific drivers and technical innovations for this new discovery engine operating in the K-dark passband, why we choose to deploy it in Antarctica, and the status of a fifth-scale prototype designed as a Pathfinder to retire technological risks prior to full-scale implementation.
A decade of sub-arcsecond imaging with the International LOFAR Telescope
2025-02-10
Leah K. Morabito, Neal Jackson, Jurjen de Jong, Emmy Escott, Christian Groeneveld, Vijay Mahatma, James Petley, Frits Sweijen, Roland Timmerman, Reinout J. van Weeren
The International LOFAR Telescope (ILT) is a pan-European radio interferometer with baselines up to 2,000 km. This provides sub-arcsecond resolution at frequencies of <200 MHz. Since starting science operations in 2012, the ILT has carried out observations for the state-of-the-art LOFAR Two-metre Sky Survey, which has 6 arcsec resolution at 144 MHz. Wide-area surveys at low frequencies, while scientifically productive, have to compromise on resolution. Sub-arcsecond imaging with the ILT has become more accessible over the last decade, thanks to efforts to build a publicly available pipeline using LOFAR-specific tools, which has resulted in a dramatic increase in the number of publications. The ILT's combination of resolution, field of view, and low observing frequency make it a unique instrument for a wide range of scientific applications, and it will remain unparalleled even in the era of the Square Kilometre Array Observatory. Here we provide an overview of the technical considerations and calibration methods sub-arcsecond imaging with the ILT. This is followed by a review of the unique capabilities unlocked by sub-arcsecond imaging with the ILT, using examples from the literature for demonstration. Finally we describe ongoing work including: surveying large areas of the sky at high resolution, going deeper in fields with excellent ancillary information, producing images of polarisation, and extending to lower frequencies (<100 MHz).
Evolving dark energy models: Current and forecast constraints
2025-02-10
Anowar J. Shajib, Joshua A. Frieman
Recent results from Type Ia supernovae (SNe Ia) and baryon acoustic oscillations (BAO), in combination with cosmic microwave background (CMB) measurements, have focused renewed attention on dark energy models with a time-varying equation-of-state parameter, $w(z)$. In this paper, we describe the simplest, physically motivated models of evolving dark energy that are consistent with the recent data, a broad subclass of the so-called thawing scalar field models. We provide a quasi-universal, quasi-one-parameter functional fit to the scalar-field $w_\phi(z)$ that captures the behavior of these models more informatively than the standard $w_0w_a$ phenomenological parametrization; their behavior is completely described by the current value of the equation-of-state parameter, $w_0=w(z=0)$. Combining current data from SNe Ia (DES-SN5YR), BAO (SDSS + DESI Year 1), the CMB (Planck and ACT), large-scale structure (DES Year-3 $3\times2$pt), and strong lensing (TDCOSMO + SLACS), we obtain $w_0=-0.908\pm0.035$, 2.6$\sigma$ discrepant from the $\Lambda$ cold dark matter ($\Lambda$CDM) model. The Bayesian evidence ratio substantially favors this $w_\phi$CDM model over $\Lambda$CDM. The data combination that yields the strongest discrepancy with $\Lambda$CDM is SNe Ia+BAO, for which $w_0=-0.840^{+0.048}_{-0.050}$, $3.2\sigma$ discrepant from $\Lambda$CDM and with a Bayesian evidence ratio strongly in favor. We find that the so-called $S_8$ tension between the CMB and large-scale structure is slightly reduced in these models, while the Hubble tension is slightly increased. We forecast constraints on these models from near-future surveys (DESI-extension and the Vera Rubin Observatory LSST), showing that the current best-fit $w_\phi$CDM model will be distinguishable from $\Lambda$CDM at over 9$\sigma$.
From Pixels to Components: Eigenvector Masking for Visual Representation Learning
2025-02-10
Alice Bizeul, Thomas Sutter, Alain Ryser, Bernhard Schölkopf, Julius von Kügelgen, Julia E. Vogt
Predicting masked from visible parts of an image is a powerful self-supervised approach for visual representation learning. However, the common practice of masking random patches of pixels exhibits certain failure modes, which can prevent learning meaningful high-level features, as required for downstream tasks. We propose an alternative masking strategy that operates on a suitable transformation of the data rather than on the raw pixels. Specifically, we perform principal component analysis and then randomly mask a subset of components, which accounts for a fixed ratio of the data variance. The learning task then amounts to reconstructing the masked components from the visible ones. Compared to local patches of pixels, the principal components of images carry more global information. We thus posit that predicting masked from visible components involves more high-level features, allowing our masking strategy to extract more useful representations. This is corroborated by our empirical findings which demonstrate improved image classification performance for component over pixel masking. Our method thus constitutes a simple and robust data-driven alternative to traditional masked image modeling approaches.
Foundation Models for Anomaly Detection: Vision and Challenges
2025-02-10
Jing Ren, Tao Tang, Hong Jia, Haytham Fayek, Xiaodong Li, Suyu Ma, Xiwei Xu, Feng Xia
As data continues to grow in volume and complexity across domains such as finance, manufacturing, and healthcare, effective anomaly detection is essential for identifying irregular patterns that may signal critical issues. Recently, foundation models (FMs) have emerged as a powerful tool for advancing anomaly detection. They have demonstrated unprecedented capabilities in enhancing anomaly identification, generating detailed data descriptions, and providing visual explanations. This survey presents the first comprehensive review of recent advancements in FM-based anomaly detection. We propose a novel taxonomy that classifies FMs into three categories based on their roles in anomaly detection tasks, i.e., as encoders, detectors, or interpreters. We provide a systematic analysis of state-of-the-art methods and discuss key challenges in leveraging FMs for improved anomaly detection. We also outline future research directions in this rapidly evolving field.
The integration of preference alignment with diffusion models (DMs) has emerged as a transformative approach to enhance image generation and editing capabilities. Although integrating diffusion models with preference alignment strategies poses significant challenges for novices at this intersection, comprehensive and systematic reviews of this subject are still notably lacking. To bridge this gap, this paper extensively surveys preference alignment with diffusion models in image generation and editing. First, we systematically review cutting-edge optimization techniques such as reinforcement learning with human feedback (RLHF), direct preference optimization (DPO), and others, highlighting their pivotal role in aligning preferences with DMs. Then, we thoroughly explore the applications of aligning preferences with DMs in autonomous driving, medical imaging, robotics, and more. Finally, we comprehensively discuss the challenges of preference alignment with DMs. To our knowledge, this is the first survey centered on preference alignment with DMs, providing insights to drive future innovation in this dynamic area.
Deep Learning in Automated Power Line Inspection: A Review
2025-02-10
Md. Ahasan Atick Faisal, Imene Mecheter, Yazan Qiblawey, Javier Hernandez Fernandez, Muhammad E. H. Chowdhury, Serkan Kiranyaz
In recent years, power line maintenance has seen a paradigm shift by moving towards computer vision-powered automated inspection. The utilization of an extensive collection of videos and images has become essential for maintaining the reliability, safety, and sustainability of electricity transmission. A significant focus on applying deep learning techniques for enhancing power line inspection processes has been observed in recent research. A comprehensive review of existing studies has been conducted in this paper, to aid researchers and industries in developing improved deep learning-based systems for analyzing power line data. The conventional steps of data analysis in power line inspections have been examined, and the body of current research has been systematically categorized into two main areas: the detection of components and the diagnosis of faults. A detailed summary of the diverse methods and techniques employed in these areas has been encapsulated, providing insights into their functionality and use cases. Special attention has been given to the exploration of deep learning-based methodologies for the analysis of power line inspection data, with an exposition of their fundamental principles and practical applications. Moreover, a vision for future research directions has been outlined, highlighting the need for advancements such as edge-cloud collaboration, and multi-modal analysis among others. Thus, this paper serves as a comprehensive resource for researchers delving into deep learning for power line analysis, illuminating the extent of current knowledge and the potential areas for future investigation.
Enhancing kidney transplantation through multi-agent kidney exchange programs: A comprehensive review and optimization models
2025-02-10
Shayan Sharifi
This paper presents a comprehensive review of the last two decades of research on Kidney Exchange Programs (KEPs), systematically categorizing and classifying key contributions to provide readers with a structured understanding of advancements in the field. The review highlights the evolution of KEP methodologies and lays the foundation for our contribution. We propose three mathematical models aimed at improving both the quantity and quality of kidney transplants. Model 1 maximizes the number of transplants by focusing on compatibility based on blood type and PRA, without additional constraints. Model 2 introduces a minimum Human Leukocyte Antigen (HLA) compatibility threshold to enhance transplant quality, though this leads to fewer matches. Model 3 extends the problem to a Multi-Agent Kidney Exchange Program (MKEP), pooling incompatible donor-recipient pairs across multiple agents, resulting in a higher number of successful transplants while ensuring fairness across agents. Sensitivity analyses demonstrate trade-offs between transplant quantity and quality, with Model 3 striking the optimal balance by leveraging multi-agent collaboration to improve both the number and quality of transplants. These findings underscore the potential benefits of more integrated kidney exchange systems.
Study of giant radio galaxies using spectroscopic observations from the Himalayan Chandra Telescope
2025-02-09
Sagar Sethi, Pratik Dabhade, K. G. Biju, C. S. Stalin, Marek Jamrozy
We present the results of spectroscopic observations of host galaxies of eleven candidate giant radio galaxies (GRGs), powered by active galactic nuclei (AGNs), conducted with the 2-m Himalayan Chandra Telescope (HCT). The primary aim of these observations, performed with the Hanle Faint Object Spectrograph Camera (HFOSC), was to secure accurate spectroscopic redshifts, enabling precise calculations of their projected linear sizes. Based on these measurements, we confirm all eleven sources as giants, with linear sizes ranging from 0.7 to 2.9 Mpc, including ten GRGs and one giant radio quasar (GRQ). One of the GRGs shows evidence of a potential AGN jet-driven ionized outflow, extending up to $\sim$12 kpc, which, if confirmed, would represent a rarely observed feature. Two of the confirmed GRGs exceed 2 Mpc in size, which are relatively rare examples of GRG. The redshifts of the host galaxies span 0.09323 $\leq$ z $\leq$ 0.41134. Using the obtained spectroscopic data, we characterised their AGN states based on the optical emission line properties. To complement these observations, archival radio and optical survey data were utilised to characterise their large-scale radio morphology and estimate projected linear sizes, arm-length ratios, flux densities, luminosities, and core dominance factors. These results provide new insights into the properties of GRSs and form a critical foundation for further detailed studies of their environments, AGN activity, and evolution using future high-sensitivity optical and radio datasets.
The classic theory of cold-trapped ice on the Moon and some alternatives are reviewed and compared with observational constraints. The emphasis is on fundamental theoretical concepts. The "standard model" of lunar ice involves moderate modifications of the classical ideas and is consistent with major observational constraints. Only a few less established observational claims are unaccounted for. The text assumes some familiarity with the topic of lunar polar volatiles.
Results and open questions on the boundary control of moving sets
2025-02-09
Alberto Bressan
These notes provide a survey of recent results and open problems on the boundary control of moving sets. Motivated by the control of an invasive biological species, we consider a class of optimization problems for a moving set $t\mapsto \Omega(t)$, where the goal is to minimize the area of the contaminated set $\Omega(t)$ over time, plus a cost related to the control effort. Here the control function is the inward normal speed, assigned along the boundary $\partial \Omega(t)$. We also consider problems with geographical constraints, where the invasive population is restricted within an island. Existence and structure of eradication strategies, which entirely remove the invasive population in minimum time, is also discussed.
Benchmarking Prompt Engineering Techniques for Secure Code Generation with GPT Models
2025-02-09
Marc Bruni, Fabio Gabrielli, Mohammad Ghafari, Martin Kropp
Prompt engineering reduces reasoning mistakes in Large Language Models (LLMs). However, its effectiveness in mitigating vulnerabilities in LLM-generated code remains underexplored. To address this gap, we implemented a benchmark to automatically assess the impact of various prompt engineering strategies on code security. Our benchmark leverages two peer-reviewed prompt datasets and employs static scanners to evaluate code security at scale. We tested multiple prompt engineering techniques on GPT-3.5-turbo, GPT-4o, and GPT-4o-mini. Our results show that for GPT-4o and GPT-4o-mini, a security-focused prompt prefix can reduce the occurrence of security vulnerabilities by up to 56%. Additionally, all tested models demonstrated the ability to detect and repair between 41.9% and 68.7% of vulnerabilities in previously generated code when using iterative prompting techniques. Finally, we introduce a "prompt agent" that demonstrates how the most effective techniques can be applied in real-world development workflows.
An assessment of observational coverage and gaps for robust Sun to heliosphere integrated science
2025-02-09
Yeimy J. Rivera, Samuel T. Badman
Understanding the generation and development of the continuous outflow from the Sun requires tracing the physical conditions from deep in the corona to the heliosphere. Detailed global observations of plasma state variables and the magnetic field are needed to provide critical constraints to the underlying physics driving models of the corona and solar wind. Key diagnostics of the solar wind require measurements at its formation site and during its outflow to continuously track it across rapidly changing regions of space. A unified view of the solar wind is only possible through coordinated remote and in situ observations that probe these different regions. Here, we discuss current observational coverage and gaps of different plasma properties and review recent coordinated studies. We highlight how these efforts may become more routine with the launch of upcoming and planned missions.
Media Bias Detector: Designing and Implementing a Tool for Real-Time Selection and Framing Bias Analysis in News Coverage
2025-02-09
Jenny S Wang, Samar Haider, Amir Tohidi, Anushkaa Gupta, Yuxuan Zhang, Chris Callison-Burch, David Rothschild, Duncan J Watts
Mainstream media, through their decisions on what to cover and how to frame the stories they cover, can mislead readers without using outright falsehoods. Therefore, it is crucial to have tools that expose these editorial choices underlying media bias. In this paper, we introduce the Media Bias Detector, a tool for researchers, journalists, and news consumers. By integrating large language models, we provide near real-time granular insights into the topics, tone, political lean, and facts of news articles aggregated to the publisher level. We assessed the tool's impact by interviewing 13 experts from journalism, communications, and political science, revealing key insights into usability and functionality, practical applications, and AI's role in powering media bias tools. We explored this in more depth with a follow-up survey of 150 news consumers. This work highlights opportunities for AI-driven tools that empower users to critically engage with media content, particularly in politically charged environments.
A Comprehensive Survey on Image Signal Processing Approaches for Low-Illumination Image Enhancement
2025-02-09
Muhammad Turab
The usage of digital content (photos and videos) in a variety of applications has increased due to the popularity of multimedia devices. These uses include advertising campaigns, educational resources, and social networking platforms. There is an increasing need for high-quality graphic information as people become more visually focused. However, captured images frequently have poor visibility and a high amount of noise due to the limitations of image-capturing devices and lighting conditions. Improving the visual quality of images taken in low illumination is the aim of low-illumination image enhancement. This problem is addressed by traditional image enhancement techniques, which alter noise, brightness, and contrast. Deep learning-based methods, however, have dominated recently made advances in this area. These methods have effectively reduced noise while preserving important information, showing promising results in the improvement of low-illumination images. An extensive summary of image signal processing methods for enhancing low-illumination images is provided in this paper. Three categories are classified in the review for approaches: hybrid techniques, deep learning-based methods, and traditional approaches. Conventional techniques include denoising, automated white balancing, and noise reduction. Convolutional neural networks (CNNs) are used in deep learningbased techniques to recognize and extract characteristics from low-light images. To get better results, hybrid approaches combine deep learning-based methodologies with more conventional methods. The review also discusses the advantages and limitations of each approach and provides insights into future research directions in this field.
Speech to Speech Translation with Translatotron: A State of the Art Review
2025-02-09
Jules R. Kala, Emmanuel Adetiba, Abdultaofeek Abayom, Oluwatobi E. Dare, Ayodele H. Ifijeh
A cascade-based speech-to-speech translation has been considered a benchmark for a very long time, but it is plagued by many issues, like the time taken to translate a speech from one language to another and compound errors. These issues are because a cascade-based method uses a combination of methods such as speech recognition, speech-to-text translation, and finally, text-to-speech translation. Translatotron, a sequence-to-sequence direct speech-to-speech translation model was designed by Google to address the issues of compound errors associated with cascade model. Today there are 3 versions of the Translatotron model: Translatotron 1, Translatotron 2, and Translatotron3. The first version was designed as a proof of concept to show that a direct speech-to-speech translation was possible, it was found to be less effective than the cascade model but was producing promising results. Translatotron2 was an improved version of Translatotron 1 with results similar to the cascade model. Translatotron 3 the latest version of the model is better than the cascade model at some points. In this paper, a complete review of speech-to-speech translation will be presented, with a particular focus on all the versions of Translatotron models. We will also show that Translatotron is the best model to bridge the language gap between African Languages and other well-formalized languages.
The application of machine learning in wireless communications has been extensively explored, with deep unfolding emerging as a powerful model-based technique. Deep unfolding enhances interpretability by transforming complex iterative algorithms into structured layers of deep neural networks (DNNs). This approach seamlessly integrates domain knowledge with deep learning (DL), leveraging the strengths of both methods to simplify complex signal processing tasks in communication systems. To provide a solid foundation, we first present a brief overview of DL and deep unfolding. We then explore the applications of deep unfolding in key areas, including signal detection, channel estimation, beamforming design, decoding for error-correcting codes, sensing and communication, power allocation, and security. Each section focuses on a specific task, highlighting its significance in emerging 6G technologies and reviewing recent advancements in deep unfolding-based solutions. Finally, we discuss the challenges associated with developing deep unfolding techniques and propose potential improvements to enhance their applicability across diverse wireless communication scenarios.
Neuromorphic HCI is a new theoretical approach to designing better UX inspired by the neurophysiology of the brain. Here, we apply the neuroscientific theory of Active Inference to HCI, postulating that users perform Bayesian inference on progress and goal distributions to predict their next action (Interactive Inference). We show how Bayesian surprise between goal and progress distributions follows a mean square error function of the signal-to-noise ratio (SNR) of the task. However, capacity to process Bayesian surprise follows the logarithm of SNR, and errors occur when average capacity is exceeded. Our model allows the quantitative analysis of performance and error in one framework with real-time estimation of mental load. We show through mathematical theorems how three basic laws of HCI, Hick's Law, Fitts' Law and the Power Law fit our model. We then test the validity of the general model by empirically measuring how well it predicts human performance in a car following task. Results suggest that driver processing capacity indeed is a logarithmic function of the SNR of the distance to a lead car. This positive result provides initial evidence that Interactive Interference can work as a new theoretical underpinning for HCI, deserving further exploration.
J-PLUS: Spectroscopic validation of H$α$ emission line maps in spatially resolved galaxies
2025-02-09
P. T. Rahna, M. Akhlaghi, C. López-Sanjuan, R. Logroño-García, D. J. Muniesa, H. Domínguez-Sánchez, J. A. Fernández-Ontiveros, David Sobral, A. Lumbreras-Calle, A. L. Chies-Santos, J. E. Rodríguez-Martín, S. Eskandarlou, A. Ederoclite, A. Alvarez-Candal, H. Vázquez Ramió, A. J. Cenarro, A. Marín-Franch, J. Alcaniz, R. E. Angulo, D. Cristóbal-Hornillos, R. A. Dupke, C. Hernández-Monteagudo, M. Moles, L. Sodré Jr., J. Varela
We present a dedicated automated pipeline to construct spatially resolved emission H$\alpha$+[NII] maps and to derive the spectral energy distributions (SEDs) in 12 optical filters (five broad and seven narrow/medium) of H$\alpha$ emission line regions in nearby galaxies (z $<$ 0.0165) observed by the Javalambre Photometric Local Universe Survey (J-PLUS). We used the $J0660$ filter of $140${\AA} width centered at $6600${\AA} to trace H$\alpha$ + [NII] emission and $r$ and $i$ broad bands were used to estimate the stellar continuum. We create pure emission line images after the continnum subtraction, where the H$\alpha$ emission line regions were detected. This method was also applied to Integral Field Unit (IFU) spectroscopic data from PHANGS-MUSE, CALIFA and MaNGA surveys by building synthetic narrow-bands based on J-PLUS filters. The studied sample includes the cross-matched catalog of these IFU surveys with J-PLUS third data release (DR3), amounting to $2$ PHANGS-MUSE, $78$ CALIFA, and $78$ MaNGA galaxies at $z < 0.0165$, respectively. We compared the H$\alpha$+[NII] radial profiles from J-PLUS and the IFU surveys, finding good agreement within the expected uncertainties. We also compared the SEDs from the emission line regions detected in J-PLUS images, reproducing the main spectral features present in the spectroscopic data. Finally, we compared the emission fluxes from the J-PLUS and IFU surveys accounting for scale differences, finding a difference of only 2% with a dispersion of 7% in the measurements. The J-PLUS data provides reliable spatially resolved H$\alpha$+[NII] emission maps for nearby galaxies. We provide the J-PLUS DR3 catalog for the $158$ galaxies with IFU data, including emission maps, SEDs of star-forming clumps, and radial profiles.
Little ado about everything II: an `emergent' dark energy from structure formation to rule cosmic tensions
2025-02-09
Andrea Lapi, Balakrishna S. Haridasu, Lumen Boco, Marcos M. Cueli, Carlo Baccigalupi, Luigi Danese
[abridged] The $\eta$CDM framework is a new cosmological model aimed to cure some drawbacks of the standard $\Lambda$CDM scenario, such as the origin of the accelerated expansion at late times, the cosmic tensions, and the violation of the cosmological principle due to the progressive development of inhomogeneous/anisotropic conditions in the Universe during structure formation. To this purpose, the model adopts a statistical perspective envisaging a stochastic evolution of large-scale patches in the Universe with typical sizes $10-50\, h^{-1}$ Mpc, which is meant to describe the complex gravitational processes leading to the formation of the cosmic web. The stochasticity among different patches is technically rendered via the diverse realizations of a multiplicative noise term (`a little ado') in the cosmological equations, and the overall background evolution of the Universe is then operationally defined as an average over the patch ensemble. In this paper we show that such an ensemble-averaged evolution in $\eta$CDM can be described in terms of a spatially flat cosmology and of an `emergent' dark energy with a time-dependent equation of state, able to originate the cosmic acceleration with the right timing and to solve the coincidence problem. Then we test the $\eta$CDM model against the most recent supernova type-I$a$, baryon acoustic oscillations and structure growth rate datasets, finding an excellent agreement. Remarkably, we demonstrate that $\eta$CDM is able to alleviate simultaneously both the $H_0$ and the $f\sigma_8$ tensions. Finally, we discuss that the Linders' diagnostic test could be helpful to better distinguish $\eta$CDM from the standard scenario in the near future via upcoming galaxy redshift surveys at intermediate redshifts such as those being conducted by the Euclid mission.
The SUPERCOLD-CGM survey: II. [\ion{C}{1}]$(1-0)$ emission and the physical conditions of cold gas in Enormous Ly$α$ nebulae at $z\,\sim\,2$
2025-02-09
Jianan Li, Bjorn H. C. Emonts, Zheng Cai, Jianrui Li, Ran Wang, Montserrat Villar-Martín, Fabrizio Arrigoni Battaia, Mingyu Li, Yunjing Wu, Ilsang Yoon, Matthew D. Lehnert, Kyle Massingill, Craig Sarazin, Jason X Prochaska, Mark Lacy, Brian Mason
We report ALMA and ACA observations of atomic carbon ([\ion{C}{1}]$(1-0)$) and dust continuum in 10 Enormous Ly$\alpha$ Nebulae hosting ultra-luminous Type-I QSOs at $z=2.2-2.5$, as part of the SUrvey of Protocluster ELANe Revealing CO/CI in the Ly$\alpha$ Detected CGM (SUPERCOLD-CGM). We detect [\ion{C}{1}]$(1-0)$ and dust in all ten QSOs and five companion galaxies. We find that the QSOs and companions have higher gas densities and more intense radiation fields than Luminous Infrared galaxies and high-$z$ main sequence galaxies, with the highest values found in the QSOs. By comparing molecular gas masses derived from [\ion{C}{1}]$(1-0)$, CO(4$-$3) and dust continuum, we find that the QSOs and companions display a similar low CO conversion factor of $\alpha_{\rm CO}$\,$\sim$\,0.8 $\rm M_{\sun}$${[\rm K\,km/s\,pc^2]}^{-1}$. After tapering our data to low resolution, the [\ion{C}{1}]$(1-0)$ flux increases for nine QSOs, hinting at the possibility of [\ion{C}{1}]$(1-0)$ in the circum-galactic medium (CGM) on a scale of 16$-$40 kpc. However, the [\ion{C}{1}]$(1-0)$ sensitivity is too low to confirm this for individual targets, except for a tentative (2.7$\sigma$) CGM detection in Q0050+0051{} with M$_{\rm H_2}$\,=\, ($1.0 - 2.8$)$\times 10^{10}$ $\rm M_{\sun}$. The 3$\sigma$ mass limits of molecular CGM for the remaining QSO fields are ($0.2-1.4$)\,$\times$\,10$^{10}$ $\rm M_{\sun}$. This translates into a baryon fraction of $<$0.4-3$\% $ in the molecular CGM relative to the total baryonic halo mass. Our sample also includes a radio-detected AGN, Q1416+2649{}, which shows [\ion{C}{1}]$(1-0)$ and CO(4$-$3) luminosities an order of magnitude fainter for its far-infrared luminosity than other QSOs in our sample, possibly due to a lower molecular gas mass.
Black Holes Thermodynamics and Generalised Non-Extensive Entropy
2025-02-09
Emilio Elizalde, Shin'ichi Nojiri, Sergei D. Odintsov
The first part of this work provides a review of recent research on generalised entropies and their origin, as well as its application to black hole thermodynamics. To start, it is shown that the Hawking temperature and the Bekenstein-Hawking entropy are, respectively, the only possible thermodynamical temperature and entropy of the Schwarzschild black hole. Moreover, it is investigated if the other known generalised entropies, which include R\'enyi's entropy, the Tsallis one, and the four- and five-parameter generalised entropies, could correctly yield the Hawking temperature and the ADM mass. The possibility that generalised entropies could describe hairy black hole thermodynamics is also considered, both for the Reissner-Nordstr\"{o}m black hole and for Einstein's gravity coupled with two scalar fields. Two possibilities are investigated, namely, the case when the ADM mass does not yield the Bekenstein-Hawking entropy, and the case in which the effective mass expressing the energy inside the horizon does not yield the Hawking temperature. For the model with two scalar fields, the radii of the photon sphere and of the black hole shadow are calculated, which gives constraints on the BH parameters. These constraints are seen to be consistent, provided the black hole is of Schwarzschild type. Subsequently, the origin of the generalised entropies is investigated, by using their microscopic particle descriptions in the frameworks of a microcanonical and of a canonical ensemble, respectively. To finish, the McLaughlin expansion for the generalised entropies is used to derive, in each case, the microscopic interpretation of the generalised entropies, via the canonical and the grand canonical ensembles.
Generative AI (GenAI) has brought opportunities and challenges for higher education as it integrates into teaching and learning environments. As instructors navigate this new landscape, understanding their engagement with and attitudes toward GenAI is crucial. We surveyed 178 instructors from a single U.S. university to examine their current practices, perceptions, trust, and distrust of GenAI in higher education in March 2024. While most surveyed instructors reported moderate to high familiarity with GenAI-related concepts, their actual use of GenAI tools for direct instructional tasks remained limited. Our quantitative results show that trust and distrust in GenAI are related yet distinct; high trust does not necessarily imply low distrust, and vice versa. We also found significant differences in surveyed instructors' familiarity with GenAI across different trust and distrust groups. Our qualitative results show nuanced manifestations of trust and distrust among surveyed instructors and various approaches to support calibrated trust in GenAI. We discuss practical implications focused on (dis)trust calibration among instructors.
Visual Text Mining with Progressive Taxonomy Construction for Environmental Studies
2025-02-09
Sam Yu-Te Lee, Cheng-Wei Hung, Mei-Hua Yuan, Kwan-Liu Ma
Environmental experts have developed the DPSIR (Driver, Pressure, State, Impact, Response) framework to systematically study and communicate key relationships between society and the environment. Using this framework requires experts to construct a DPSIR taxonomy from a corpus, annotate the documents, and identify DPSIR variables and relationships, which is laborious and inflexible. Automating it with conventional text mining faces technical challenges, primarily because the taxonomy often begins with abstract definitions, which experts progressively refine and contextualize as they annotate the corpus. In response, we develop GreenMine, a system that supports interactive text mining with prompt engineering. The system implements a prompting pipeline consisting of three simple and evaluable subtasks. In each subtask, the DPSIR taxonomy can be defined in natural language and iteratively refined as experts analyze the corpus. To support users evaluate the taxonomy, we introduce an uncertainty score based on response consistency. Then, we design a radial uncertainty chart that visualizes uncertainties and corpus topics, which supports interleaved evaluation and exploration. Using the system, experts can progressively construct the DPSIR taxonomy and annotate the corpus with LLMs. Using real-world interview transcripts, we present a case study to demonstrate the capability of the system in supporting interactive mining of DPSIR relationships, and an expert review in the form of collaborative discussion to understand the potential and limitations of the system. We discuss the lessons learned from developing the system and future opportunities for supporting interactive text mining in knowledge-intensive tasks for other application scenarios.
A Comprehensive Review of U-Net and Its Variants: Advances and Applications in Medical Image Segmentation
2025-02-09
Wang Jiangtao, Nur Intan Raihana Ruhaiyem, Fu Panpan
Medical images often exhibit low and blurred contrast between lesions and surrounding tissues, with considerable variation in lesion edges and shapes even within the same disease, leading to significant challenges in segmentation. Therefore, precise segmentation of lesions has become an essential prerequisite for patient condition assessment and formulation of treatment plans. Significant achievements have been made in research related to the U-Net model in recent years. It improves segmentation performance and is extensively applied in the semantic segmentation of medical images to offer technical support for consistent quantitative lesion analysis methods. First, this paper classifies medical image datasets on the basis of their imaging modalities and then examines U-Net and its various improvement models from the perspective of structural modifications. The research objectives, innovative designs, and limitations of each approach are discussed in detail. Second, we summarize the four central improvement mechanisms of the U-Net and U-Net variant algorithms: the jump-connection mechanism, residual-connection mechanism, 3D-UNet, and transformer mechanism. Finally, we examine the relationships among the four core enhancement mechanisms and commonly utilized medical datasets and propose potential avenues and strategies for future advancements. This paper provides a systematic summary and reference for researchers in related fields, and we look forward to designing more efficient and stable medical image segmentation network models based on the U-Net network.
Using agent-based models and EXplainable Artificial Intelligence (XAI) to simulate social behaviors and policy intervention scenarios: A case study of private well users in Ireland
2025-02-08
Rabia Asghar, Simon Mooney, Eoin O Neill, Paul Hynds
Around 50 percent of Irelands rural population relies on unregulated private wells vulnerable to agricultural runoff and untreated wastewater. High national rates of Shiga toxin-producing Escherichia coli (STEC) and other waterborne illnesses have been linked to well water exposure. Periodic well testing is essential for public health, yet the lack of government incentives places the financial burden on households. Understanding environmental, cognitive, and material factors influencing well-testing behavior is critical. This study employs Agent-Based Modeling (ABM) to simulate policy interventions based on national survey data. The ABM framework, designed for private well-testing behavior, integrates a Deep Q-network reinforcement learning model and Explainable AI (XAI) for decision-making insights. Key features were selected using Recursive Feature Elimination (RFE) with 10-fold cross-validation, while SHAP (Shapley Additive Explanations) provided further interpretability for policy recommendations. Fourteen policy scenarios were tested. The most effective, Free Well Testing plus Communication Campaign, increased participation to 435 out of 561 agents, from a baseline of approximately 5 percent, with rapid behavioral adaptation. Free Well Testing plus Regulation also performed well, with 433 out of 561 agents initiating well testing. Free testing alone raised participation to over 75 percent, with some agents testing multiple times annually. Scenarios with free well testing achieved faster learning efficiency, converging in 1000 episodes, while others took 2000 episodes, indicating slower adaptation. This research demonstrates the value of ABM and XAI in public health policy, providing a framework for evaluating behavioral interventions in environmental health.
From "I have nothing to hide" to "It looks like stalking": Measuring Americans' Level of Comfort with Individual Mobility Features Extracted from Location Data
2025-02-08
Naman Awasthi, Saad Mohammad Abrar, Daniel Smolyak, Vanessa Frias-Martinez
Location data collection has become widespread with smart phones becoming ubiquitous. Smart phone apps often collect precise location data from users by offering \textit{free} services and then monetize it for advertising and marketing purposes. While major tech companies only sell aggregate behaviors for marketing purposes; data aggregators and data brokers offer access to individual location data. Some data brokers and aggregators have certain rules in place to preserve privacy; and the FTC has also started to vigorously regulate consumer privacy for location data. In this paper, we present an in-depth exploration of U.S. privacy perceptions with respect to specific location features derivable from data made available by location data brokers and aggregators. These results can provide policy implications that could assist organizations like the FTC in defining clear access rules. Using a factorial vignette survey, we collected responses from 1,405 participants to evaluate their level of comfort with sharing different types of location features, including individual trajectory data and visits to points of interest, available for purchase from data brokers worldwide. Our results show that trajectory-related features are associated with higher privacy concerns, that some data broker based obfuscation practices increase levels of comfort, and that race, ethnicity and education have an effect on data sharing privacy perceptions. We also model the privacy perceptions of people as a predictive task with F1 score \textbf{0.6}.
The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions
2025-02-08
Ping Liu, Jiawei Du
Dataset distillation, which condenses large-scale datasets into compact synthetic representations, has emerged as a critical solution for training modern deep learning models efficiently. While prior surveys focus on developments before 2023, this work comprehensively reviews recent advances, emphasizing scalability to large-scale datasets such as ImageNet-1K and ImageNet-21K. We categorize progress into a few key methodologies: trajectory matching, gradient matching, distribution matching, scalable generative approaches, and decoupling optimization mechanisms. As a comprehensive examination of recent dataset distillation advances, this survey highlights breakthrough innovations: the SRe2L framework for efficient and effective condensation, soft label strategies that significantly enhance model accuracy, and lossless distillation techniques that maximize compression while maintaining performance. Beyond these methodological advancements, we address critical challenges, including robustness against adversarial and backdoor attacks, effective handling of non-IID data distributions. Additionally, we explore emerging applications in video and audio processing, multi-modal learning, medical imaging, and scientific computing, highlighting its domain versatility. By offering extensive performance comparisons and actionable research directions, this survey equips researchers and practitioners with practical insights to advance efficient and generalizable dataset distillation, paving the way for future innovations.
Search for $γ$H production and constraints on the Yukawa couplings of light quarks to the Higgs boson
2025-02-08
CMS Collaboration
A search for $\gamma$H production is performed with data from the CMS experiment at the LHC corresponding to an integrated luminosity of 138 fb$^{-1}$ at a proton-proton center-of-mass collision energy of 13 TeV. The analysis focuses on the topology of a boosted Higgs boson recoiling against a high-energy photon. The final states of H $\to$ $\mathrm{b\bar{b}}$ and H $\to$ 4$\ell$ are analyzed. This study examines effective HZ$\gamma$ and H$\gamma\gamma$ anomalous couplings within the context of an effective field theory. In this approach, the production cross section is constrained to be $\sigma_{\gamma\text{H}}$ $\lt$ 16.4 fb at 95% confidence level (CL). Simultaneous constraints on four anomalous couplings involving HZ$\gamma$ and H$\gamma\gamma$ are provided. Additionally, the production rate for H $\to$ 4$\ell$ is examined to assess potential enhancements in the Yukawa couplings between light quarks and the Higgs boson. Assuming the standard model values for the Yukawa couplings of the bottom and top quarks,the following simultaneous constraints are obtained: $\kappa_\text{u}$ = (0.0 $\pm$ 1.5) $\times$ 10$^{3}$, $\kappa_\text{d}$ = (0.0 $\pm$ 7.1) $\times$ 10$^{2}$, $\kappa_\text{s}$ = 0$^{+33}_{-34}$, and $\kappa_\text{c}$ = 0.0$^{+2.7}_{-3.0}$. This rules out the hypothesis that up- or down-type quarks in the first or second generation have the same Yukawa couplings as those in the third generation, with a CL greater than 95%.
Incongruence detection in eyewitness narratives is critical for understanding the reliability of testimonies, yet traditional approaches often fail to address the nuanced inconsistencies inherent in such accounts. In this paper, we introduce a novel task of incongruence detection in eyewitness testimonies. Given a pair of testimonies containing of multiple pairs of question and answer by two subjects, we identify contextually related incongruence between the two subjects. We also mark the span of incongruences in the utterances. To achieve this, we developed MIND(MultI-EyewitNess Deception) - a comprehensive dataset consisting of 2927 pairs of contextually related answers designed to capture both explicit and implicit contradictions. INstruction - TunEd iNcongruity Detection framework based on 6W and multi-hop reasoning approach, aka. INTEND. Drawing from investigative techniques, INTEND address the task as a close-style problem, contradicting on the who, what, when, where and why aspect of the content. Our findings shows that prompt tuning, especially when utilizing our framework, enhances the detection of incongruences by a margin of +5.63 percent. We compare our approach with multiple fine-tuning and prompt tuning techniques on MLMs and LLMs. Emperical results demonstrate convincing performance improvement in F1-score over fine-tuned and regular prompt-tuning techniques, highlighting the effectiveness of our approach.
ML DevOps Adoption in Practice: A Mixed-Method Study of Implementation Patterns and Organizational Benefits
2025-02-08
Dileepkumar S R, Juby Mathew
Machine Learning (ML) DevOps, also known as MLOps, has emerged as a critical framework for efficiently operationalizing ML models in various industries. This study investigates the adoption trends, implementation efforts, and benefits of ML DevOps through a combination of literature review and empirical analysis. By surveying 150 professionals across industries and conducting in-depth interviews with 20 practitioners, the study provides insights into the growing adoption of ML DevOps, particularly in sectors like finance and healthcare. The research identifies key challenges, such as fragmented tooling, data management complexities, and skill gaps, which hinder widespread adoption. However, the findings highlight significant benefits, including improved deployment frequency, reduced error rates, enhanced collaboration between data science and DevOps teams, and lower operational costs. Organizations leveraging ML DevOps report accelerated model deployment, increased scalability, and better compliance with industry regulations. The study also explores the technical and cultural efforts required for successful implementation, such as investments in automation tools, real-time monitoring, and upskilling initiatives. The results indicate that while challenges remain, ML DevOps presents a viable path to optimizing ML lifecycle management, ensuring model reliability, and enhancing business value. Future research should focus on standardizing ML DevOps practices, assessing the return on investment across industries, and developing frameworks for seamless integration with traditional DevOps methodologies
Data efficient Robotic Object Throwing with Model-Based Reinforcement Learning
2025-02-08
Niccolò Turcato, Giulio Giacomuzzo, Matteo Terreran, Davide Allegro, Ruggero Carli, Alberto Dalla Libera
Pick-and-place (PnP) operations, featuring object grasping and trajectory planning, are fundamental in industrial robotics applications. Despite many advancements in the field, PnP is limited by workspace constraints, reducing flexibility. Pick-and-throw (PnT) is a promising alternative where the robot throws objects to target locations, leveraging extrinsic resources like gravity to improve efficiency and expand the workspace. However, PnT execution is complex, requiring precise coordination of high-speed movements and object dynamics. Solutions to the PnT problem are categorized into analytical and learning-based approaches. Analytical methods focus on system modeling and trajectory generation but are time-consuming and offer limited generalization. Learning-based solutions, in particular Model-Free Reinforcement Learning (MFRL), offer automation and adaptability but require extensive interaction time. This paper introduces a Model-Based Reinforcement Learning (MBRL) framework, MC-PILOT, which combines data-driven modeling with policy optimization for efficient and accurate PnT tasks. MC-PILOT accounts for model uncertainties and release errors, demonstrating superior performance in simulations and real-world tests with a Franka Emika Panda manipulator. The proposed approach generalizes rapidly to new targets, offering advantages over analytical and Model-Free methods.
Vector data is prevalent across business and scientific applications, and its popularity is growing with the proliferation of learned embeddings. Vector data collections often reach billions of vectors with thousands of dimensions, thus, increasing the complexity of their analysis. Vector search is the backbone of many critical analytical tasks, and graph-based methods have become the best choice for analytical tasks that do not require guarantees on the quality of the answers. We briefly survey in-memory graph-based vector search, outline the chronology of the different methods and classify them according to five main design paradigms: seed selection, incremental insertion, neighborhood propagation, neighborhood diversification, and divide-and-conquer. We conduct an exhaustive experimental evaluation of twelve state-of-the-art methods on seven real data collections, with sizes up to 1 billion vectors. We share key insights about the strengths and limitations of these methods; e.g., the best approaches are typically based on incremental insertion and neighborhood diversification, and the choice of the base graph can hurt scalability. Finally, we discuss open research directions, such as the importance of devising more sophisticated data-adaptive seed selection and diversification strategies.
Large Multimodal Models for Low-Resource Languages: A Survey
2025-02-08
Marian Lupascu, Ana-Cristina Rogoz, Mihai Sorin Stupariu, Radu Tudor Ionescu
In this survey, we systematically analyze techniques used to adapt large multimodal models (LMMs) for low-resource (LR) languages, examining approaches ranging from visual enhancement and data creation to cross-modal transfer and fusion strategies. Through a comprehensive analysis of 106 studies across 75 LR languages, we identify key patterns in how researchers tackle the challenges of limited data and computational resources. We find that visual information often serves as a crucial bridge for improving model performance in LR settings, though significant challenges remain in areas such as hallucination mitigation and computational efficiency. We aim to provide researchers with a clear understanding of current approaches and remaining challenges in making LMMs more accessible to speakers of LR (understudied) languages. We complement our survey with an open-source repository available at: https://github.com/marianlupascu/LMM4LRL-Survey.
Does the financialization of agricultural commodities impact food security? An empirical investigation
2025-02-08
Manogna R. L., Nishil Kulkarni
The financialization of agricultural commodities and its impact on food security has become an increasing concern. This study empirically investigates the role of financialization in global food markets and its policy implications for a stable and secure food system. Using panel data regression models, moderating effects models, and panel regression with a threshold variable, we analyze wheat, maize, and soybean futures traded on the Chicago Board of Trade. We incorporate data on annual trading volume, open interest contracts, and their ratio. The sample consists of five developed countries (United States, Australia, Canada, France, Germany) and seven developing countries (China, Russia, India, Indonesia, Brazil, Vietnam, Thailand), covering the period 2000 to 2021. The Human Development Index (HDI) serves as a threshold variable to differentiate the impact across countries. Our findings indicate that the financialization of agricultural commodities has negatively affected global food security, with wheat and soybean showing a greater adverse impact than maize. The effects are more pronounced in developing countries. Additionally, we find that monetary policy has the potential to mitigate these negative effects. These results provide insights for policymakers to design strategies that ensure a secure and accessible global food supply.
Observational and Theoretical Constraints on First-Order Phase Transitions in Neutron Stars
2025-02-08
Jiarui Chen, Zuhua Ji
Understanding the equation of state (EOS) of neutron stars (NSs) is a fundamental challenge in astrophysics and nuclear physics. A first-order phase transition (FOPT) at high densities could lead to the formation of a quark core, significantly affecting NS properties. This review explores observational and theoretical constraints on such transitions using multi-messenger astrophysics. X-ray observations, including mass-radius measurements from NICER and spectral features like quasi-periodic oscillations (QPOs) and cyclotron resonance scattering features (CRSFs), provide indirect evidence of EOS modifications. Gravitational wave detections, particularly from binary NS mergers such as GW170817, constrain tidal deformability and post-merger oscillations, which may carry signatures of phase transitions. Pulsar timing offers additional constraints through measurements of mass, spin evolution, and glitches, with millisecond pulsars exceeding twice the solar mass posing challenges to purely hadronic EOSs. Theoretical models and numerical simulations predict that an FOPT could impact gravitational wave signals, twin-star configurations, and NS cooling. Future advancements, including next-generation gravitational wave detectors, high-precision X-ray telescopes, and improved theoretical modeling, will enhance our ability to probe phase transitions in NSs. A combination of these approaches will provide crucial insights into the existence and properties of deconfined quark matter in NS interiors.
The Equation of State of Neutron Stars: Theoretical Models, Observational Constraints, and Future Perspectives
2025-02-08
Zuhua Ji, Jiarui Chen
Understanding the equation of state (EOS) of neutron stars (NSs) is a fundamental challenge in astrophysics and nuclear physics. A first-order phase transition (FOPT) at high densities could lead to the formation of a quark core, significantly affecting NS properties. This review explores observational and theoretical constraints on such transitions using multi-messenger astrophysics. X-ray observations, including mass-radius measurements from NICER and spectral features like quasi-periodic oscillations (QPOs) and cyclotron resonance scattering features (CRSFs), provide indirect evidence of EOS modifications. Gravitational wave detections, particularly from binary NS mergers such as GW170817, constrain tidal deformability and post-merger oscillations, which may carry signatures of phase transitions. Pulsar timing offers additional constraints through measurements of mass, spin evolution, and glitches, with millisecond pulsars exceeding twice the solar mass posing challenges to purely hadronic EOSs. Theoretical models and numerical simulations predict that an FOPT could impact gravitational wave signals, twin-star configurations, and NS cooling. Future advancements, including next-generation gravitational wave detectors, high-precision X-ray telescopes, and improved theoretical modeling, will enhance our ability to probe phase transitions in NSs. A combination of these approaches will provide crucial insights into the existence and properties of deconfined quark matter in NS interiors.
A ZTF Search for Circumstellar Debris Transits in White Dwarfs: Six New Candidates, one with Gas Disk Emission, identified in a Novel Metric Space
2025-02-08
Soumyadeep Bhattacharjee, Zachary P. Vanderbosch, Mark A. Hollands, Pier-Emmanuel Tremblay, Siyi Xu, Joseph A. Guidry, J. J. Hermes, Ilaria Caiazzo, Antonio C. Rodriguez, Jan van Roestel, Benjamin R. Roulston, Reed Riddle, Ben Rusholme, Steven L. Groom, Roger Smith, Odette Toloza
White dwarfs (WDs) showing transits from orbiting planetary debris provide significant insights into the structure and dynamics of debris disks. This is a rare class of objects with only eight published systems. In this work, we perform a systematic search for such systems within 500 pc in the Gaia-eDR3 catalog of WDs using the light curves from the Zwicky Transient Facility (ZTF) and present six new candidates. Our selection process targets the top 1% most photometrically variable sources identified using a combined variability metric from ZTF and Gaia eDR3 photometry, boosted by a metric space we define using von Neumann statistics and Pearson-Skew as a novel discovery tool to identify these systems. This is followed by optical spectroscopic observations of visually selected variables to confirm metal pollution. Four of the six systems show long-timescale photometric variability spanning several months to years, resulting either from long-term evolution of transit activity or dust and debris clouds at wide orbits. Among them, WD J1013-0427 shows an indication of reddening during the long-duration dip. Interpreting this as dust extinction makes it the first system to indicate an abundance of small dust grains (radius $\lesssim$$0.3~{\rm \mu m}$) in the occulting material. The same object also shows metal emission lines that map an optically thick eccentric gas disk orbiting within the star's Roche limit. For each candidate, we infer the abundances of the photospheric metals and estimate accretion rates. We show that transiting debris systems tend to have higher inferred accretion rates compared to the general population of metal-polluted WDs. Growing the number of these systems will further illuminate such comparative properties in the near future. Separately, we also serendipitously discovered an AM CVn showing a very long-duration outburst $-$ only the fourth such system to be known.
Electrocardiogram (ECG) analysis is a fundamental tool for diagnosing cardiovascular conditions, yet anomaly detection in ECG signals remains challenging due to their inherent complexity and variability. We propose Multi-scale Masked Autoencoder for ECG anomaly detection (MMAE-ECG), a novel end-to-end framework that effectively captures both global and local dependencies in ECG data. Unlike state-of-the-art methods that rely on heartbeat segmentation or R-peak detection, MMAE-ECG eliminates the need for such pre-processing steps, enhancing its suitability for clinical deployment. MMAE-ECG partitions ECG signals into non-overlapping segments, with each segment assigned learnable positional embeddings. A novel multi-scale masking strategy and multi-scale attention mechanism, along with distinct positional embeddings, enable a lightweight Transformer encoder to effectively capture both local and global dependencies. The masked segments are then reconstructed using a single-layer Transformer block, with an aggregation strategy employed during inference to refine the outputs. Experimental results demonstrate that our method achieves performance comparable to state-of-the-art approaches while significantly reducing computational complexity-approximately 1/78 of the floating-point operations (FLOPs) required for inference. Ablation studies further validate the effectiveness of each component, highlighting the potential of multi-scale masked autoencoders for anomaly detection.
Superconducting Properties of the Titanium-Based Oxides Compounds: A Review
2025-02-08
Junqi He, Yi Zhou
In recent years, the superconductivity of novel layered materials, titanium-based pnictide oxides, was discovered. Due to the properties of possessing both cuprate and iron-based superconductors, these compounds have attracted the interest of researchers. Titanium pnictide oxides were reported to have CDW or SDW anomalies, theoretical calculations indicate that this DW behavior originates from the Ti2O layer. These compounds which have Ti2O layers provide a basis for studying the relationship between superconductivity and DW behavior. Superconductivity and DW behavior are two different electronic behaviors that typically compete with each other, but sometimes coexist. The relationship between them has always been a focus of condensed matter physics research. Through in-depth research on titanium-based superconductors, it may help us explain the unconventional superconducting transition phenomena present in iron-based superconductors. In this review, we introduce the latest research on titanium pnictide oxides and the electronic properties of these novel superconductors.
Quasars behind the Galactic plane (GPQs) are excellent tracers to probe the chemistry and kinematics of the interstellar/intergalactic medium (ISM/IGM) of the Milky Way along sight lines via absorption line spectroscopy. Moreover, the quasars located at low Galactic latitudes will fill the gap in the spatial distribution of known quasars near the Galactic plane, and can be used to construct an astrometric reference frame for accurate measurements of proper motions (PMs) of stars, and substructures of the Milky Way. We started a survey of background quasars in the low Galactic latitude region since the LAMOST phase II survey in 2017. Quasar candidates have been selected from the optical and infrared photometric data of Pan-STARRS1 and WISE surveys based on their variability and color properties. In this paper, we present a sample of 1982 spectroscopically confirmed GPQs with |b| <= 20 degree based on LAMOST Data Release 10 (DR10). Among them, 1338 are newly discovered. Most GPQs are located around 240
This paper describes the theory of Minkowski problems for geometric measures in convex geometric analysis. The theory goes back to Minkowski and Aleksandrov and has been developed extensively in recent years. The paper surveys classical and new Minkowski problems studied in convex geometry, PDEs, and harmonic analysis, and structured in a conceptual framework of the Brunn-Minkowski theory, its extensions, and related subjects.
Characterization of Residual Charge Images in LSST Camera e2v CCDs
2025-02-08
Daniel Polin, Adam Snyder, Craig Lage, J. Anthony Tyson
LSST Camera CCDs produced by the manufacturer e2v exhibit strong and novel residual charge images when exposed to bright sources. These manifest in images following bright exposures both in the same pixel areas as the bright source, and in the pixels trailing between the source and the serial register. Both of these pose systematic challenges to the Rubin Observatory Legacy Survey of Space and Time instrument signature removal. The latter trail region is especially impactful as it affects a much larger pixel area in a less well defined position. In our study of this effect at UC Davis, we imaged bright spots to characterize these residual charge effects. We find a strong dependence of the residual charge on the parallel clocking scheme, including the relative levels of the clocking voltages, and the timing of gate phase transition during the parallel transfer. Our study points to independent causes of residual charge in the bright spot region and trail region. We propose potential causes in both regions and suggest methodologies for minimizing residual charge. We consider the trade-offs to these methods including decreasing the camera's full well and dynamic range at the high end. Some of these results and suggestions have been reviewed by the camera commissioning team and may result in changes made to the clocking voltage scheme on the LSST Camera.
Probabilistic Foundations for Metacognition via Hybrid-AI
2025-02-08
Paulo Shakarian, Gerardo I. Simari, Nathaniel D. Bastian
Metacognition is the concept of reasoning about an agent's own internal processes, and it has recently received renewed attention with respect to artificial intelligence (AI) and, more specifically, machine learning systems. This paper reviews a hybrid-AI approach known as "error detecting and correcting rules" (EDCR) that allows for the learning of rules to correct perceptual (e.g., neural) models. Additionally, we introduce a probabilistic framework that adds rigor to prior empirical studies, and we use this framework to prove results on necessary and sufficient conditions for metacognitive improvement, as well as limits to the approach. A set of future
2025-02-08
Lei Wang, Xudong Li, Han Zhang, Jinyi Wang, Dingkang Jiang, Zhidong Xue, Yan Wang
At the intersection of the rapidly growing biological data landscape and advancements in Natural Language Processing (NLP), protein language models (PLMs) have emerged as a transformative force in modern research. These models have achieved remarkable progress, highlighting the need for timely and comprehensive overviews. However, much of the existing literature focuses narrowly on specific domains, often missing a broader analysis of PLMs. This study provides a systematic review of PLMs from a macro perspective, covering key historical milestones and current mainstream trends. We focus on the models themselves and their evaluation metrics, exploring aspects such as model architectures, positional encoding, scaling laws, and datasets. In the evaluation section, we discuss benchmarks and downstream applications. To further support ongoing research, we introduce relevant mainstream tools. Lastly, we critically examine the key challenges and limitations in this rapidly evolving field.
Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging
2025-02-08
Jinluan Yang, Dingnan Jin, Anke Tang, Li Shen, Didi Zhu, Zhengyu Chen, Daixin Wang, Qing Cui, Zhiqiang Zhang, Jun Zhou, Fei Wu, Kun Kuang
Achieving balanced alignment of large language models (LLMs) in terms of Helpfulness, Honesty, and Harmlessness (3H optimization) constitutes a cornerstone of responsible AI, with existing methods like data mixture strategies facing limitations including reliance on expert knowledge and conflicting optimization signals. While model merging offers a promising alternative by integrating specialized models, its potential for 3H optimization remains underexplored. This paper establishes the first comprehensive benchmark for model merging in 3H-aligned LLMs, systematically evaluating 15 methods (12 training-free merging and 3 data mixture techniques) across 10 datasets associated with 5 annotation dimensions, 2 LLM families, and 2 training paradigms. Our analysis reveals three pivotal insights: (i) previously overlooked collaborative/conflicting relationships among 3H dimensions, (ii) the consistent superiority of model merging over data mixture approaches in balancing alignment trade-offs, and (iii) the critical role of parameter-level conflict resolution through redundant component pruning and outlier mitigation. Building on these findings, we propose R-TSVM, a Reweighting-enhanced Task Singular Vector Merging method that incorporates outlier-aware parameter weighting and sparsity-adaptive rank selection strategies adapted to the heavy-tailed parameter distribution and sparsity for LLMs, further improving LLM alignment across multiple evaluations. Our models will be available at https://huggingface.co/Jinluan.
Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC). By integrating context retrieval into content generation, RAG provides reliable and up-to-date external knowledge, reduces hallucinations, and ensures relevant context across a wide range of tasks. However, despite RAG's success and potential, recent studies have shown that the RAG paradigm also introduces new risks, including robustness issues, privacy concerns, adversarial attacks, and accountability issues. Addressing these risks is critical for future applications of RAG systems, as they directly impact their trustworthiness. Although various methods have been developed to improve the trustworthiness of RAG methods, there is a lack of a unified perspective and framework for research in this topic. Thus, in this paper, we aim to address this gap by providing a comprehensive roadmap for developing trustworthy RAG systems. We place our discussion around five key perspectives: reliability, privacy, safety, fairness, explainability, and accountability. For each perspective, we present a general framework and taxonomy, offering a structured approach to understanding the current challenges, evaluating existing solutions, and identifying promising future research directions. To encourage broader adoption and innovation, we also highlight the downstream applications where trustworthy RAG systems have a significant impact.
A Survey on Explainable Deep Reinforcement Learning
2025-02-08
Zelei Cheng, Jiahao Yu, Xinyu Xing
Deep Reinforcement Learning (DRL) has achieved remarkable success in sequential decision-making tasks across diverse domains, yet its reliance on black-box neural architectures hinders interpretability, trust, and deployment in high-stakes applications. Explainable Deep Reinforcement Learning (XRL) addresses these challenges by enhancing transparency through feature-level, state-level, dataset-level, and model-level explanation techniques. This survey provides a comprehensive review of XRL methods, evaluates their qualitative and quantitative assessment frameworks, and explores their role in policy refinement, adversarial robustness, and security. Additionally, we examine the integration of reinforcement learning with Large Language Models (LLMs), particularly through Reinforcement Learning from Human Feedback (RLHF), which optimizes AI alignment with human preferences. We conclude by highlighting open research challenges and future directions to advance the development of interpretable, reliable, and accountable DRL systems.
Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation
2025-02-07
Steffen Eger, Yong Cao, Jennifer D'Souza, Andreas Geiger, Christian Greisinger, Stephanie Gross, Yufang Hou, Brigitte Krenn, Anne Lauscher, Yizhi Li, Chenghua Lin, Nafise Sadat Moosavi, Wei Zhao, Tristan Miller
With the advent of large multimodal language models, science is now at a threshold of an AI-based technological transformation. Recently, a plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently. This includes all aspects of the research cycle, especially (1) searching for relevant literature; (2) generating research ideas and conducting experimentation; generating (3) text-based and (4) multimodal content (e.g., scientific figures and diagrams); and (5) AI-based automatic peer review. In this survey, we provide an in-depth overview over these exciting recent developments, which promise to fundamentally alter the scientific research process for good. Our survey covers the five aspects outlined above, indicating relevant datasets, methods and results (including evaluation) as well as limitations and scope for future research. Ethical concerns regarding shortcomings of these tools and potential for misuse (fake science, plagiarism, harms to research integrity) take a particularly prominent place in our discussion. We hope that our survey will not only become a reference guide for newcomers to the field but also a catalyst for new AI-based initiatives in the area of "AI4Science".
Navigating Automated Hiring: Perceptions, Strategy Use, and Outcomes Among Young Job Seekers
2025-02-07
Lena Armstrong, Danaé Metaxa
As the use of automated employment decision tools (AEDTs) has rapidly increased in hiring contexts, especially for computing jobs, there is still limited work on applicants' perceptions of these emerging tools and their experiences navigating them. To investigate, we conducted a survey with 448 computer science students (young, current technology job-seekers) about perceptions of the procedural fairness of AEDTs, their willingness to be evaluated by different AEDTs, the strategies they use relating to automation in the hiring process, and their job seeking success. We find that young job seekers' procedural fairness perceptions of and willingness to be evaluated by AEDTs varied with the level of automation involved in the AEDT, the technical nature of the task being evaluated, and their own use of strategies, such as job referrals. Examining the relationship of their strategies with job outcomes, notably, we find that referrals and family household income have significant and positive impacts on hiring success, while more egalitarian strategies (using free online coding assessment practice or adding keywords to resumes) did not. Overall, our work speaks to young job seekers' distrust of automation in hiring contexts, as well as the continued role of social and socioeconomic privilege in job seeking, despite the use of AEDTs that promise to make hiring "unbiased."
Emission from multiple molecular isotopologues in a high-inclination protoplanetary disk
2025-02-07
Colette Salyk, Klaus M. Pontoppidan, Andrea Banzatti, Edwin Bergin, Nicole Arulanantham, Joan Najita, Geoffrey A. Blake, John Carr, Ke Zhang, Chengyan Xie
We present a MIRI-MRS spectrum of the high-inclination protoplanetary disk around the solar-mass (K0) star MY Lup, obtained as part of the JWST Disk Infrared Spectral Chemistry Survey (JDISCS). The spectrum shows an unusually weak water emission spectrum for a disk around a star of its spectral type, but strong emission from CO$_2$, HCN, and isotopologues of both molecules. This includes the first ever detection of C$^{18}$O$^{16}$O and H$^{13}$CN in an inner disk, as well as tentative detections of C$^{17}$O$^{16}$O and HC$^{15}$N. Slab modeling provides molecular temperatures, column densities and emitting areas of the detected molecules. The emitting molecular gas is cold compared to that of other observed protoplanetary disk spectra. We estimate the isotopologue ratios of CO$_2$ and HCN, albeit with significant uncertainty. We suggest that the unusual spectrum of MY Lup arises from a combination of inner disk clearing, which removes emission from warm water, and its nearly edge-on inclination, which enhances line-of-sight column densities, although unusual chemistry may also be required. MY Lup's spectrum highlights the potential to detect and measure trace isotopologues to study isotopic fractionation in protoplanetary disks; observations at higher spectral resolving power is needed to constrain the isotopologue ratios to greater precision.
New Security Challenges Towards In-Sensor Computing Systems
2025-02-07
Mashrafi Kajol, Qiaoyan Yu
Data collection and processing in advanced health monitoring systems are experiencing revolutionary change. In-Sensor Computing (ISC) systems emerge as a promising alternative to save energy on massive data transmission, analog-to-digital conversion, and ineffective processing. While the new paradigm shift of ISC systems gains increasing attention, the highly compacted systems could incur new challenges from a hardware security perspective. This work first conducts a literature review to highlight the research trend of this topic and then performs comprehensive analyses on the root of security challenges. This is the first work that compares the security challenges of traditional sensor-involved computing systems and emerging ISC systems. Furthermore, new attack scenarios are predicted for board-, chip-, and device-level ISC systems. Two proof-of-concept demos are provided to inspire new countermeasure designs against unique hardware security threats in ISC systems.
EnseSmells: Deep ensemble and programming language models for automated code smells detection
2025-02-07
Anh Ho, Anh M. T. Bui, Phuong T. Nguyen, Amleto Di Salle, Bach Le
A smell in software source code denotes an indication of suboptimal design and implementation decisions, potentially hindering the code understanding and, in turn, raising the likelihood of being prone to changes and faults. Identifying these code issues at an early stage in the software development process can mitigate these problems and enhance the overall quality of the software. Current research primarily focuses on the utilization of deep learning-based models to investigate the contextual information concealed within source code instructions to detect code smells, with limited attention given to the importance of structural and design-related features. This paper proposes a novel approach to code smell detection, constructing a deep learning architecture that places importance on the fusion of structural features and statistical semantics derived from pre-trained models for programming languages. We further provide a thorough analysis of how different source code embedding models affect the detection performance with respect to different code smell types. Using four widely-used code smells from well-designed datasets, our empirical study shows that incorporating design-related features significantly improves detection accuracy, outperforming state-of-the-art methods on the MLCQ dataset with with improvements ranging from 5.98% to 28.26%, depending on the type of code smell.
Multi-scale maps are essential representations of surveying and cartographic results, serving as fundamental components of geographic services. Current image generation networks can quickly produce map tiles from remote-sensing images. However, generative models designed for natural images often focus on texture features, neglecting the unique characteristics of remote-sensing features and the scale attributes of tile maps. This limitation in generative models impairs the accurate representation of geographic information, and the quality of tile map generation still needs improvement. Diffusion models have demonstrated remarkable success in various image generation tasks, highlighting their potential to address this challenge. This paper presents C2GM, a novel framework for generating multi-scale tile maps through conditional guided diffusion and multi-scale cascade generation. Specifically, we implement a conditional feature fusion encoder to extract object priors from remote sensing images and cascade reference double branch input, ensuring an accurate representation of complex features. Low-level generated tiles act as constraints for high-level map generation, enhancing visual continuity. Moreover, we incorporate map scale modality information using CLIP to simulate the relationship between map scale and cartographic generalization in tile maps. Extensive experimental evaluations demonstrate that C2GM consistently achieves the state-of-the-art (SOTA) performance on all metrics, facilitating the rapid and effective generation of multi-scale large-format maps for emergency response and remote mapping applications.
A Systematic Literature Review on Automated Exploit and Security Test Generation
2025-02-07
Quang-Cuong Bui, Emanuele Iannone, Maria Camporese, Torge Hinrichs, Catherine Tony, László Tóth, Fabio Palomba, Péter Hegedűs, Fabio Massacci, Riccardo Scandariato
The exploit or the Proof of Concept of the vulnerability plays an important role in developing superior vulnerability repair techniques, as it can be used as an oracle to verify the correctness of the patches generated by the tools. However, the vulnerability exploits are often unavailable and require time and expert knowledge to craft. Obtaining them from the exploit generation techniques is another potential solution. The goal of this survey is to aid the researchers and practitioners in understanding the existing techniques for exploit generation through the analysis of their characteristics and their usability in practice. We identify a list of exploit generation techniques from literature and group them into four categories: automated exploit generation, security testing, fuzzing, and other techniques. Most of the techniques focus on the memory-based vulnerabilities in C/C++ programs and web-based injection vulnerabilities in PHP and Java applications. We found only a few studies that publicly provided usable tools associated with their techniques.
Hilbert Bundles and Holographic Space-time: the Hydrodynamic Approach to Gravity
2025-02-07
T. Banks
Results of Jacobson, Carlip and Solodukhin, from the 1990s, suggest a hydrodynamic approach to quantum gravity in which a classical solution of Einstein's equations determines the density matrices of subsystems associated with causal diamonds in the "empty diamond" state of a corresponding quantum system. The subsystem operator algebras are finite dimensional and correspond to a UV cutoff $1 + 1$ dimensional field theory of fermions living on a "stretched horizon" near each diamond's holographic screen. The fields can be thought of as fluctuations of solutions of the screen's Dirac operator around that of the background geometry, expanded up to a maximal Dirac eigenvalue determined by the Carlip-Solodukhin relation between area and central charge. This cutoff renders the screen geometry "fuzzy". Quantum dynamics is defined in a Hilbert bundle over the space of time-like geodesics on the background geometry. A nesting of diamonds along a given geodesic defines a series of unitary embedding maps of diamond Hilbert spaces into each other, analogous to half sided modular flows in quantum field theory. These can be extended into a consistent set of unitary maps of each fiber Hilbert space into itself by a quantum version of the principle of relativity. According to the QPR, the largest diamond in the overlap between any two diamonds is identified with a tensor factor in each individual diamond Hilbert space, and must have the a density matrix with the same entanglement spectrum no matter which fiber dynamics is used to compute it. This brief review summarizes how these ideas play out in a variety of contexts in different dimensions.
Wide-field high-precision photometric observations such as \textit{Transiting Exoplanet Survey Satellite (TESS)} allowed the investigation of the stellar magnetic activity of cool stars. M-dwarf's starspots and stellar flares are the main indicators of magnetic activity. The present study focuses on modeling light curves (LCs) to analyze the distribution and characteristics of starspots e.g., location, temperature, and spot size. The \textit{TESS} light curves of two selected young M-dwarfs i.e. GJ~182 and 2MASS~J05160212+2214528 were reconstructed using the \textsc{BASSMAN} software, obtaining a three-spot model for GJ~182 and two-spot model for 2MASS~J05160212+2214528, describing their light curves. For GJ~182, the mean spot temperature was estimated to be approximately 3279~K, covering 5-8.5\% of the stellar surface while for 2MASS~J05160212+2214528 the average spot temperature was approximately 2631~K, with a mean spottedness of about 5.4\%. Using the 2-min cadence LC data, we identified and analyzed 48 flare events from GJ~182, while no flares were detected in 2MASS~J05160212+2214528. The estimated bolometric flare energy ranged from $10^{32} - 10^{35}$ erg, and 10$^{31}$ - 10$^{33}$ erg in the TESS bandpass. We derived the power-law index of -1.53 $\pm$ 0.12 and -1.86 $\pm$ 0.22 for flare frequency distribution in sectors 5 and 32 respectively in the flare energy 10$^{33}$ to 10$^{35}$ erg, consistent with previous studies for M-dwarfs. A positive linear correlation between flare energy and duration was found with a slope of $0.67 \pm 0.02$, suggesting a similar mechanism followed by stellar superflares and solar flares. By assuming the similarities with solar flares, we also estimated the lower limit of the magnetic field strength around 12 - 232~G to produce such superflare events.
The proliferation of digital interactions across diverse domains, such as healthcare, e-commerce, gaming, and finance, has resulted in the generation of vast volumes of event stream (ES) data. ES data comprises continuous sequences of timestamped events that encapsulate detailed contextual information relevant to each domain. While ES data holds significant potential for extracting actionable insights and enhancing decision-making, its effective utilization is hindered by challenges such as the scarcity of labeled data and the fragmented nature of existing research efforts. Self-Supervised Learning (SSL) has emerged as a promising paradigm to address these challenges by enabling the extraction of meaningful representations from unlabeled ES data. In this survey, we systematically review and synthesize SSL methodologies tailored for ES modeling across multiple domains, bridging the gaps between domain-specific approaches that have traditionally operated in isolation. We present a comprehensive taxonomy of SSL techniques, encompassing both predictive and contrastive paradigms, and analyze their applicability and effectiveness within different application contexts. Furthermore, we identify critical gaps in current research and propose a future research agenda aimed at developing scalable, domain-agnostic SSL frameworks for ES modeling. By unifying disparate research efforts and highlighting cross-domain synergies, this survey aims to accelerate innovation, improve reproducibility, and expand the applicability of SSL to diverse real-world ES challenges.
The non-monotonic temperature dependence and sign reversal of chirality-related anomalous Hall effect in highly conductive metals are studied. Through the analysis of scattering rate, we find that the non-monotonicity and sign reversal have two major origins: (1) competition between the contribution from short-range and long-range spin correlations and (2) non-monotonic spin correlation in the high field. The former mechanism gives rise to non-monotonic temperature dependence in a wide range of electron density and, in some cases, a sign reversal of Hall resistivity as the temperature decreases. On the other hand, the latter mechanism is responsible for the sign reversal of Hall conductivity in the high field, which sign reversal generally occurs in magnets with antiferromagnetic interactions. The results demonstrate how the Hall effect reflects local spin correlation and provide insights into the mechanism of non-monotonicity and sign reversal of the anomalous Hall effect by spin chirality.
The Role of Integrity Monitoring in Connected and Automated Vehicles: Current State-of-Practice and Future Directions
2025-02-07
Saswat Priyadarshi Nayak, Matthew Barth
Connected and Automated Vehicle (CAV) research has gained traction in the last decade due to significant advancements in perception, navigation, communication, and control functions. Accurate and reliable position information is needed to meet the requirements of CAV applications, especially when safety is concerned. With the advent of various perception sensors (e.g. camera, LiDAR, etc.), the vehicular positioning system has improved both in accuracy and robustness. Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) based cooperative positioning can improve the accuracy of the position estimates, but the integrity risks involved in multi-sensor fusion in a cooperative environment have not yet been fully explored. This paper reviews existing research in the field of positioning Integrity Monitoring (IM) and identifies various research gaps. Particular attention has been placed on identifying research that highlights cooperative IM methods. This analysis helps pave the way for the development of new IM frameworks for cooperative positioning solutions in the future.
The first in-depth photometric study of four Delta Scuti stars was performed. We used time series data from the Transiting Exoplanet Survey Satellite (TESS) that is available in different sectors. According to the extracted maxima from TESS space-based observations, we calculated an ephemeris for each star. We estimated the physical parameters of the target stars based on the Gaia Data Release 3 (DR3) parallax method. The results obtained for the surface gravity of the stars are consistent with the reports of the TESS Input Catalog and Gaia DR3. We estimated the pulsating constant based on the physical parameters and period of the stars. Therefore, we found that the stars 2MASS 15515693-7759002 and 2MASS 07513202+0526526 belong to the fundamental, while 2MASS 00044615+4936439 and 2MASS 10215638-3326137 relate to the first overtone. The Fourier analysis using the Period04 program was done for each star. As we showed in the Hertzsprung-Russell (H-R) diagram, the stars are located in the instability strip of the Delta Scuti stars region. Four target stars were found to be of the low-amplitude Delta Scuti star type.
How Do Developers Use Code Suggestions in Pull Request Reviews?
2025-02-07
Abir Bouraffa, Yen Dieu Pham, Walid Maalej
GitHub introduced the suggestion feature to enable reviewers to explicitly suggest code modifications in pull requests. These suggestions make the reviewers' feedback more actionable for the submitters and represent a valuable knowledge for newcomers. Still, little is known about how code review suggestions are used by developers, what impact they have on pull requests, and how they are influenced by social coding dynamics. To bridge this knowledge gap, we conducted an empirical study on pull requests from 46 engineered GitHub projects, in which developers used code review suggestions. We applied an open coding approach to uncover the types of suggestions and their usage frequency. We also mined pull request characteristics and assessed the impact of using suggestions on merge rate, resolution time, and code complexity. Furthermore, we conducted a survey with contributors of the studied projects to gain insights about the influence of social factors on the usage and acceptance of code review suggestions. We were able to uncover four suggestion types: code style suggestions, improvements, fixes, and documentation with improvements being the most frequent. We found that the use of suggestions positively affects the merge rate of pull requests but significantly increases resolution time without leading to a decrease in code complexity. Our survey results show that suggestions are more likely to be used by reviewers when the submitter is a newcomer. The results also show that developers mostly search suggestions when tracking rationale or looking for code examples. Our work provides insights on the usage of code suggestions and their potential as a knowledge sharing tool.
Estimating the duration of RT-PCR positivity for SARS-CoV-2 from doubly interval censored data with undetected infections
2025-02-07
Joshua Blake, Paul Birrell, A. Sarah Walker, Koen B. Pouwels, Thomas House, Brian D. M. Tom, Theodore Kypraios, Daniela De Angelis
Monitoring the incidence of new infections during a pandemic is critical for an effective public health response. General population prevalence surveys for SARS-CoV-2 can provide high-quality data to estimate incidence. However, estimation relies on understanding the distribution of the duration that infections remain detectable. This study addresses this need using data from the Coronavirus Infection Survey (CIS), a long-term, longitudinal, general population survey conducted in the UK. Analyzing these data presents unique challenges, such as doubly interval censoring, undetected infections, and false negatives. We propose a Bayesian nonparametric survival analysis approach, estimating a discrete-time distribution of durations and integrating prior information derived from a complementary study. Our methodology is validated through a simulation study, including its resilience to model misspecification, and then applied to the CIS dataset. This results in the first estimate of the full duration distribution in a general population, as well as methodology that could be transferred to new contexts.
Radio emission from a massive node of the cosmic web. A discovery powered by machine learning
2025-02-07
C. Stuardi, A. Botteon, M. Sereno, K. Umetsu, R. Gavazzi, A. Bonafede, C. Gheller
Aims. We aim to understand the nature of the diffuse radio emission surrounding the massive galaxy cluster PSZ2 G083.29-31.03, at z=0.412, already known to host a radio halo. Our investigation was triggered by Radio U-Net, a novel machine learning algorithm for detecting diffuse radio emission, which was previously applied to the LOFAR Two Meter Sky Survey (LoTSS). Methods. We re-processed LoTSS (120-168 MHz) data and analyzed archival XMM-Newton (0.7-1.2 keV) observations. We also analyzed optical and near-infrared data from the DESI Legacy Imaging Surveys and asses the mass distribution with weak-lensing analysis based on archival Subaru Suprime-Cam and CFHT MegaPrime/MegaCam observations. Results. We report the discovery of large-scale diffuse radio emission around PSZ2 G083.29-31.03, with a projected largest linear size of 5 Mpc at 144 MHz. The radio emission is aligned with the thermal X-ray emission and the distribution of galaxies, unveiling the presence of two low-mass systems, at similar redshifts on either side of the central cluster. The weak lensing analysis supports this scenario, demonstrating the presence of an extended and complex mass distribution. Conclusions. We propose to interpret the two faint radio sources as connected to the central cluster, thus illuminating the presence of two substructures merging into a massive node of the cosmic web. However, because of uncertainties in redshift and mass estimates, combined with the low resolution required to detect these sources, the classification of the two sources as independent radio halos associated with nearby low-mass clusters or even as a mixture of different types of diffuse radio emission cannot be definitively ruled out.
The XXL Survey LIV. X-ray Luminosity Function and Luminosity-Mass Relation of Optically Selected Galaxy Groups
2025-02-07
C. Wood, B. J. Maughan, J. P. Crossett, D. Eckert, M. Pierre, M. E. Ramos-Ceja, A. S. G. Robotham, C. Adami, L. Faccioli, E. Koulouridis, S. L. McGee, F. Pacaud, S. Phillipps
The overlap between the GAMA spectroscopic survey and the XXL X-ray survey was used to study the X-ray properties of optically-selected groups of galaxies. Forced X-ray aperture photometry was applied to an optically-selected sample of 235 groups (containing at least five member galaxies) to measure their X-ray luminosities in the regime of low signal to noise X-ray data. The sample encompasses X-ray luminosities over an order of magnitude fainter than typical X-ray selected samples, and avoids X-ray selection biases. This gives access to low mass groups where the effects of non-gravitational processes, such as AGN-feedback, should be most apparent and could inhibit their detection in an X-ray survey. We measured the X-ray luminosity function (XLF) of the sample, and found it to be consistent with the extrapolation of the XLF from X-ray selected samples at higher luminosities. The XLF was combined with a theoretical halo mass function to infer the form of the scaling relation between X-ray luminosity and mass (LM relation) for the GAMA groups. We found a slope of $1.87 \pm 0.12$, which is steeper than self similarity in this mass regime. When comparing with other measurements of the LM relation, we find evidence for a steepening of the slope in the low mass regime, likely due to the impact of non-gravitational processes. Our approach can be translated to eROSITA data using multi-wavelength surveys to constrain the X-ray properties of galaxy groups in the limits of high redshift and low mass.
Our contribution aims to celebrate the immeasurable contribution that Tom Kuo has provided to the understanding of the structure of atomic nuclei, and also of the infinite nuclear matter, in terms of the fundamental principles governing the realistic nuclear potential. The authors want to testify Tom Kuo's heritage and impact on their approach to the study of nuclear systems by reviewing some recent findings on the role of the two-body component of shell-model effective $\beta$-decay operators. The focus is spotted on the so-called Pauli-blocking effect, that plays a non-negligible role in nuclei characterized by a large number of valence nucleons.
The new Inner Tracking System (ITS2) is instrumental for tracking and vertex reconstruction in the ALICE experiment. The new tracker consists of seven cylindrical layers equipped with silicon Monolithic Active Pixel Sensors (MAPS) with a pixel size of 27 by 29 $\rm{\mu}$m. The sensors are thinned down to a thickness of 50 $\mu$m and 100 $\mu$m for the three innermost layers and for the four outer layers, respectively. The material budget of the innermost layers is as low as 0.36% $X_{0}$/ layer compared to 1.14% $X_{0}$/ layer of the previous ITS1. In combination with a radius of 23 mm for the innermost layer and a position resolution of about 5 $\mu$m, the low material budget greatly enhances the reconstruction capabilities of heavy-flavour and low-$p_{\rm T}$ particles compared to Run 2. ITS2 has been in operation since the beginning of Run 3 and has already recorded more than 42 pb$^{-1}$ proton-proton events at $\sqrt{s}$ = 13.6 TeV and more than 2 nb$^{-1}$ Pb--Pb events at $ \sqrt{s_{ \rm{NN} }} $ = 5.36 TeV, operating stably during these operations at interaction rate up to 4 MHz in pp and about 50 kHz in Pb--Pb collisions. This contribution will review the detector performance during LHC Run 3 and give an overview on the calibration methods and running experience
The primary focus of offline reinforcement learning (RL) is to manage the risk of hazardous exploitation of out-of-distribution actions. An effective approach to achieve this goal is through behavior regularization, which augments conventional RL objectives by incorporating constraints that enforce the policy to remain close to the behavior policy. Nevertheless, existing literature on behavior-regularized RL primarily focuses on explicit policy parameterizations, such as Gaussian policies. Consequently, it remains unclear how to extend this framework to more advanced policy parameterizations, such as diffusion models. In this paper, we introduce BDPO, a principled behavior-regularized RL framework tailored for diffusion-based policies, thereby combining the expressive power of diffusion policies and the robustness provided by regularization. The key ingredient of our method is to calculate the Kullback-Leibler (KL) regularization analytically as the accumulated discrepancies in reverse-time transition kernels along the diffusion trajectory. By integrating the regularization, we develop an efficient two-time-scale actor-critic RL algorithm that produces the optimal policy while respecting the behavior constraint. Comprehensive evaluations conducted on synthetic 2D tasks and continuous control tasks from the D4RL benchmark validate its effectiveness and superior performance.
Statistical Methods and Modal Decompositions for Gridded and Scattered Data: Meshless Statistics and Meshless Data Driven Modal Analysis
2025-02-07
Miguel A. Mendez, Manuel Ratz, Damien Rigutto
Statistical tools are crucial for studying and modeling turbulent flows, where chaotic velocity fluctuations span a wide range of spatial and temporal scales. Advances in image velocimetry, especially in tracking-based methods, now allow for high-speed, high-density particle image processing, enabling the collection of detailed 3D flow fields. This lecture provides a set of tutorials on processing such datasets to extract essential quantities like averages, second-order moments (turbulent stresses) and coherent patterns using modal decompositions such as the Proper Orthogonal Decomposition (POD). After a brief review of the fundamentals of statistical and modal analysis, the lecture delves into the challenges of processing scattered data from tracking velocimetry, comparing it to traditional gridded-data approaches. It also covers research topics, including physics-based Radial Basis Function (RBF) regression for meshless computation of turbulent statistics and the definition of an RBF inner product, which enables meshless computation of data-driven decompositions. These include traditional methods like Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD), as well as advanced variants such as Spectral POD (SPOD) and Multiscale POD (mPOD). We refer to this approach as the "Meshless Data-Driven Decomposition" framework. We refer to this framework as "Meshless Data Driven Decomposition". Six exercises in Python are provided. All codes are available at https://github.com/mendezVKI/PIV_LS_2024_Signal
Measuring SES-related traits relating to technology usage: Two validated surveys
2025-02-07
Chimdi Chikezie, Pannapat Chenpaiseng, Puja Agarwal, Sadia Afroz, Bhavika Madhwani, Rudrajit Choudhuri, Andrew Anderson, Prisha Velhal, Patricia Morreale, Christopher Bogart, Anita Sarma, Margaret Burnett
Software producers are now recognizing the importance of improving their products' suitability for diverse populations, but little attention has been given to measurements to shed light on products' suitability to individuals below the median socioeconomic status (SES) -- who, by definition, make up half the population. To enable software practitioners to attend to both lower- and higher-SES individuals, this paper provides two new surveys that together facilitate measuring how well a software product serves socioeconomically diverse populations. The first survey (SES-Subjective) is who-oriented: it measures who their potential or current users are in terms of their subjective SES (perceptions of their SES). The second survey (SES-Facets) is why-oriented: it collects individuals' values for an evidence-based set of facet values (individual traits) that (1) statistically differ by SES and (2) affect how an individual works and problem-solves with software products. Our empirical validations with deployments at University A and University B (464 and 522 responses, respectively) showed that both surveys are reliable. Further, our results statistically agree with both ground truth data on respondents' socioeconomic statuses and with predictions from foundational literature. Finally, we explain how the pair of surveys is uniquely actionable by software practitioners, such as in requirements gathering, debugging, quality assurance activities, maintenance activities, and fulfilling legal reporting requirements such as those being drafted by various governments for AI-powered software.
In large-scale distributed environments, avoiding concurrent access to the same resource by multiple processes becomes a core challenge, commonly termed distributed mutual exclusion (DME). Token-based mechanisms have long been recognized as an effective strategy, wherein a solitary token is handed around among processes as the key that allows access to the critical section. By doing so, they often reduce the messaging overhead compared to alternate methods. This work surveys the significance of mutual exclusion in distributed computing and examines token-based solutions across various network models (including tree-based, ring-based, fully interconnected graphs, mesh structures, and ad hoc networks). We also delve into essential performance measures such as communication costs and strategies for fault tolerance, then branch into specialized variants, such as k-mutual exclusion and self-stabilizing algorithms. Furthermore, a specialized approach that relies on finite projective planes is introduced to highlight how certain protocols can perform efficiently under both best- and worst-case conditions. Lastly, we explore future directions involving machine learning for token predictive routing and blockchain techniques to resist adversarial behavior. This aims to provide a thorough yet accessible overview of token-based DME approaches, together with insights on emerging research trends.
Doubly heavy tetraquarks have emerged as new probes to study the heavy hadron spectrum. With the experimental observation of the $J^P=1^+$ $T_{cc}^+$, they pose a unique opportunity to bring together efforts in experiment, phenomenology, and lattice QCD. In lattice calculations they are accessible as ground states, unlike hidden flavor tetraquarks, and this enables accurate determinations of the scattering parameters alongside the binding energies of these tetraquarks. Today, lattice calculations firmly predict $J^P=1^+$ $T_{bb}^{ud}$ and $T_{bb}^{us}$ as QCD bound states, while recent studies approaching the $J^P=1^+$ $T_{cc}^{ud}$ find it to be a virtual bound state at slightly non-physical input quark masses. Studies of the $J^P=1^+$ $T_{bc}^{ud}$ are ongoing and a new focus area. In light of these developments the evolution of this field until this point is reviewed. Emphasis is put on the methods in lattice spectroscopy that enable a robust evaluation of the lattice studies gathered. They are further reviewed towards their limitations and achievements. Current challenges and opportunities are discussed, including possibilities to approach the left-hand cut in the scattering analysis of the charm candidates and towards understanding the structure of those including two bottom quarks.
Diffusion models have recently emerged as powerful generative frameworks for producing high-quality images. A pivotal component of these models is the noise schedule, which governs the rate of noise injection during the diffusion process. Since the noise schedule substantially influences sampling quality and training quality, understanding its design and implications is crucial. In this discussion, various noise schedules are examined, and their distinguishing features and performance characteristics are highlighted.
Dark energy and lensing anomaly in Planck CMB data
2025-02-07
Ze-Yu Peng, Yun-Song Piao
In this paper, we investigate the impact of the lensing anomaly in Planck cosmic microwave background (CMB) data on the nature of dark energy (DE). We constrain the state equation ($w_0,w_a$) of DE with the lensing scaling parameter $A_L=1$ and varying $A_L$, using the Planck PR3 and two updated Planck PR4 likelihoods, CamSpec and HiLLiPoP respectively, combined with DESI baryon acoustic oscillation (BAO) and Pantheon+ supernova data. As expected, when $A_L$ is allowed to vary, the evolving DE is not preferred due to the degeneracy between $w_0,w_a$ and $A_L$. In particular, we also consider replacing DESI BAO data with pre-DESI BAO in our analysis, and observe that DESI BAO appears to exacerbate the lensing anomaly, which is caused by the smaller matter density $\Omega_m$ it prefers, however, this effect can be offset by the shifts in $w_0$ and $w_a$ preferring the evolving DE. Our work indicates that the lensing anomaly in Planck data is worth carefully reconsidering when one combined new cosmological survey data with CMB.
Overcoming data challenges to measure whole-person health in electronic health records
2025-02-07
Sarah C. Lotspeich, Sheetal Kedar, Rabeya Tahir, Aidan D. Keleghan, Amelia Miranda, Stephany N. Duda, Michael P. Bancks, Brian J. Wells, Ashish K. Khanna, Joseph Rigdon
The allostatic load index (ALI) is a composite measure of whole-person health. Data from electronic health records (EHR) present a huge opportunity to operationalize the ALI in the learning health system, except they are prone to missingness and errors. Validation of EHR data (e.g., through chart reviews) can provide better-quality data, but realistically, only a subset of patients' data can be validated, and most protocols do not recover missing data. Using a representative sample of 1000 patients from the EHR at an extensive learning health system (100 of whom could be validated), we propose methods to design, conduct, and analyze statistically efficient and robust studies of the ALI and healthcare utilization. With semiparametric maximum likelihood estimation, we robustly incorporate all available data into statistical models. Using targeted design strategies, we examine ways to select the most informative patients for validation. Incorporating clinical expertise, we devise a novel validation protocol to promote the quality and completeness of EHR data. Validating the EHR data uncovered relatively low error rates and recovered some missing data. Through simulation studies based on preliminary data, residual sampling was identified as the most informative strategy for completing our validation study. Statistical models of partially validated data indicated higher odds of engaging in the healthcare system were associated with worse whole-person health (i.e., higher ALI), adjusting for age. Targeted validation with an enriched protocol allowed us to ensure the quality and promote the completeness of the EHR. Findings from our validation study were incorporated into analyses as we operationalize the ALI as a whole-person health measure intended to predict healthcare utilization in the academic learning health system.
Point-Identifying Semiparametric Sample Selection Models with No Excluded Variable
2025-02-07
Dongwoo Kim, Young Jun Lee
Sample selection is pervasive in applied economic studies. This paper develops semiparametric selection models that achieve point identification without relying on exclusion restrictions, an assumption long believed necessary for identification in semiparametric selection models. Our identification conditions require at least one continuously distributed covariate and certain nonlinearity in the selection process. We propose a two-step plug-in estimator that is root-n-consistent, asymptotically normal, and computationally straightforward (readily available in statistical software), allowing for heteroskedasticity. Our approach provides a middle ground between Lee (2009)'s nonparametric bounds and Honor\'e and Hu (2020)'s linear selection bounds, while ensuring point identification. Simulation evidence confirms its excellent finite-sample performance. We apply our method to estimate the racial and gender wage disparity using data from the US Current Population Survey. Our estimates tend to lie outside the Honor\'e and Hu bounds.
Leveraging Order-Theoretic Tournament Graphs for Assessing Internal Consistency in Survey-Based Instruments Across Diverse Scenarios
2025-02-07
Muhammad Umair Danish, Umair Rehman, Katarina Grolinger
This paper introduces Monotone Delta, an order-theoretic measure designed to enhance the reliability assessment of survey-based instruments in human-machine interactions. Traditional reliability measures, such as Cronbach's Alpha and McDonald's Omega, often yield misleading estimates due to their sensitivity to redundancy, multidimensional constructs, and assumptions of normality and uncorrelated errors. These limitations can compromise decision-making in human-centric evaluations, where survey instruments inform adaptive interfaces, cognitive workload assessments, and human-AI trust models. Monotone Delta addresses these issues by quantifying internal consistency through the minimization of ordinal contradictions and alignment with a unidimensional latent order using weighted tournaments. Unlike traditional approaches, it operates without parametric or model-based assumptions. We conducted theoretical analyses and experimental evaluations on four challenging scenarios: tau-equivalence, redundancy, multidimensionality, and non-normal distributions, and proved that Monotone Delta provides more stable reliability assessments compared to existing methods. The Monotone Delta is a valuable alternative for evaluating questionnaire-based assessments in psychology, human factors, healthcare, and interactive system design, enabling organizations to optimize survey instruments, reduce costly redundancies, and enhance confidence in human-system interactions.
Gait Disorder Assessment Based on a Large-Scale Clinical Trial: WiFi vs. Video vs. Doctor's Visual Inspection
2025-02-07
Alireza Parsay, Mert Torun, Philip R. Delio, Yasamin Mostofi
Neurological gait disorders can affect a large population, with devastating consequences on the quality of life. In this paper, we are interested in fundamentally understanding the potential of emerging sensing modalities (e.g., WiFi) for neurological gait disorder assessment. Towards this goal, we conduct a one-year-long clinical trial in collaboration with the Neurology Associates of Santa Barbara, the results of which are detailed in this paper. More specifically, our medical campaign encompasses 114 real subjects and a wide spectrum of disorders (e.g., Parkinson's, Neuropathy, Post Stroke, Dementia, and Arthritis). We then develop the first WiFi-based gait disorder sensing system of its kind, distinguished by its scope of validation with a large and diverse patient cohort. To ensure generalizability, we mainly leverage publicly-accessible online videos of gait disorders for training, and develop a video-to-RF pipeline to convert them to synthetic RF data. We then extensively test the system in a neurology center (i.e., the Neurology Associates of Santa Barbara). In addition, we provide a 1-1 comparison with a vision-based system, by developing a vision-based gait disorder assessment system under the same exact conditions (e.g., same subjects, etc), a comparison crucial for developing smart health systems and the first of its kind to our knowledge. We finally contrast both systems with the accuracy of neurologists when basing evaluation solely on the visual inspection of the gait, by designing a large survey and distributing it to an extensive network of neurologists, thus offering the first such apples-to-apples comparison of these three sensing modalities. Overall, the findings can shape the integration of new sensing modalities into medical practice, and pave the way toward equitable healthcare.
Apple vs. Oranges: Evaluating the Apple Silicon M-Series SoCs for HPC Performance and Efficiency
2025-02-07
Paul Hübner, Andong Hu, Ivy Peng, Stefano Markidis
This paper investigates the architectural features and performance potential of the Apple Silicon M-Series SoCs (M1, M2, M3, and M4) for HPC. We provide a detailed review of the CPU and GPU designs, the unified memory architecture, and coprocessors such as Advanced Matrix Extensions (AMX). We design and develop benchmarks in the Metal Shading Language and Objective-C++ to assess computational and memory performance. We also measure power consumption and efficiency using Apple's powermetrics tool. Our results show that M-Series chips offer relatively high memory bandwidth and significant improvements in computational performance, particularly with the GPU outperforming the CPU from the M2 onward, peaking at 2.9 FP32 TFLOPS for the M4. Power consumption varies from a few watts to 10-20 watts, with more than 200 GFLOPS per Watt efficiency of GPU and accelerator reached by all four chips. Despite limitations in FP64 support on the GPU, the M-Series chips demonstrate strong potential for energy-efficient HPC applications. Our analysis examines whether the M-Series chips provide a competitive alternative to traditional HPC architectures or represent a distinct category altogether -- an apples-to-oranges comparison.
We present a review of high-performance automatic modulation recognition (AMR) models proposed in the literature to classify various Radio Frequency (RF) modulation schemes. We replicated these models and compared their performance in terms of accuracy across a range of signal-to-noise ratios. To ensure a fair comparison, we used the same dataset (RadioML-2016A), the same hardware, and a consistent definition of test accuracy as the evaluation metric, thereby providing a benchmark for future AMR studies. The hyperparameters were selected based on the authors' suggestions in the associated references to achieve results as close as possible to the originals. The replicated models are publicly accessible for further analysis of AMR models. We also present the test accuracies of the selected models versus their number of parameters, indicating their complexities. Building on this comparative analysis, we identify strategies to enhance these models' performance. Finally, we present potential opportunities for improvement, whether through novel architectures, data processing techniques, or training strategies, to further advance the capabilities of AMR models.
ASKAP EMU Radio Detection of the Reflection Nebula VdB-80 in the Monoceros Crossbones Filamentary Structure
2025-02-07
A. C. Bradley, Z. J. Smeaton, N. F. H. Tothill, M. D. Filipović, W. Becker, A. M. Hopkins, B. S. Koribalski, S. Lazarević, D. Leahy, G. Rowell, V. Velović, D. Urošević
We present a new radio detection from the Australian Square Kilometre Array Pathfinder (ASKAP) Evolutionary Map of the Universe (EMU) survey associated with the reflection nebula (RN) VdB-80. The radio detection is determined to be a previously unidentified HII region, now named Lagotis. The RN is located towards Monoceros, centred in the molecular cloud feature known as the `Crossbones'. The 944 MHz EMU image shows a roughly semicircular HII region with an integrated flux density of 30.2$\pm$0.3 mJy. The HII region is also seen at 1.4 GHz by NVSS, yielding an estimated spectral index of 0.65$\pm$0.51, consistent with thermal radio emission. Gaia DR3 and 2MASS data give a distance to the stars associated with the HII region of $\sim$960 pc. This implies a size of 0.76$\times$0.68($\pm$0.09) pc for the HII region. We derive an HII region electron density of the bright radio feature to be 26 cm$^{-3}$, requiring a Lyman-alpha photon flux of $10^{45.6}$ s$^{-1}$, which is consistent with the expected Lyman flux of HD 46060, the B2II type star which is the likely ionising star of the region. The derived distance to this region implies that the Crossbones feature is a superposition of two filamentary clouds, with Lagotis embedded in the far cloud.
Direct Evidence for AGN Feedback from Fast Molecular Outflows in Reionization-Era Quasars
2025-02-07
Justin S. Spilker, Jaclyn B. Champagne, Xiaohui Fan, Seiji Fujimoto, Paul P. van der Werf, Jinyi Yang, Minghao Yue
Galactic outflows driven by rapidly-accreting quasars at high redshift are widely expected to play a key role in the short- and long-term future evolution of their host galaxies. Using new and archival ALMA data, we observed the OH 119um doublet lines in order to search for cold molecular outflows in a sample of 11 unobscured, IR-luminous quasars at z>6. This represents the first survey for molecular winds in reionization-era quasars, and we detect unambiguous outflows in 8/11 (73%) of the quasars. The outflows we find are substantially faster, by ~300km/s on average, than outflows observed in a roughly co-eval sample of non-quasar IR-luminous galaxies, suggesting that the AGN drive the winds to higher velocities. On the other hand, the implied molecular outflow rates are relatively modest given the high luminosities, suggesting typical mass loading factors ~0.5 in the cold gas. The outflows are consistent with expectations for momentum-driven winds regardless of the driving source, but the kinetic energy in the outflows suggests that the AGN must be at least partially responsible for driving the winds. Accordingly, we find trends between the outflow properties and the Eddington ratio of the black hole accretion, though this may be linked to the underlying trend with AGN luminosity. We find that the kinetic power carried in the cold outflow phase is typically only ~0.1% of the total AGN luminosity. Our study provides evidence in favor of AGN feedback on the cold molecular gas in $z>6$ quasar host galaxies, demonstrating that cold outflows are very common and powerful in the most extreme reionization-era quasars.
Principles and Components of Federated Learning Architectures
2025-02-07
Sarwar Saif, MD Abdullah Al Nasim, Parag Biswas, Abdur Rashid, MD Mahim Anjum Haque, Md. Zihad Bin Jahangir
Federated learning, also known as FL, is a machine learning framework in which a significant amount of clients (such as mobile devices or whole enterprises) collaborate to collaboratively train a model while keeping decentralized training data, all overseen by a central server (such as a service provider). There are advantages in terms of privacy, security, regulations, and economy with this decentralized approach to model training. FL is not impervious to the flaws that plague conventional machine learning models, despite its seeming promise. This study offers a thorough analysis of the fundamental ideas and elements of federated learning architectures, emphasizing five important areas: communication architectures, machine learning models, data partitioning, privacy methods, and system heterogeneity. We additionally address the difficulties and potential paths for future study in the area. Furthermore, based on a comprehensive review of the literature, we present a collection of architectural patterns for federated learning systems. This analysis will help to understand the basic of Federated learning, the primary components of FL, and also about several architectural details.
Light curves and spectra for stellar collisions between main-sequence stars in galactic nuclei
2025-02-07
Taeho Ryu, Luc Dessart
High-velocity stellar collisions in galactic nuclei produce ejecta that generate potentially observable electromagnetic radiation, making them promising nuclear transients. However, the photometric and spectroscopic properties of these collisions, which would more frequently involve main-sequence stars, remain largely unexplored. Here, using 3D hydrodynamics and 1D radiation-transfer simulations, we investigate the properties and observables of the debris produced in high-velocity collisions between terminal-age main-sequence stars, covering a wide range of collision configurations. The ejecta produce bright UV flares with bolometric luminosities typically peaking at $\gtrsim10^{43}$ erg s$^{-1}$, declining steeply as $t^{-2}-t^{-4}$ to reach $\gtrsim10^{41}-10^{42}$ erg s$^{-1}$ at 0.5 d and leveling off on a plateau at $10^{39}-10^{41.5}$ erg s$^{-1}$ ($M_V$ between $-$10 to $-$15 mag) after a few days. Their spectra evolve considerably during the first few days, morphing from UV- to optical-dominated. The UV range shows numerous resonance transitions from metals like C, N, and O, whereas the optical primarily shows H I Balmer lines. These properties are qualitatively similar to those observed, as well as obtained in models of Type II supernovae. Observables from these events exhibit clear correlations with collision configurations, including impact parameter, relative velocity, and stellar masses. We provide fitting formulae to describe these correlations. Detecting these flares requires sub-day cadence surveys such as ULTRASAT, combined with spectroscopic observations to disentangle degeneracies and infer collision characteristics.
The Qz5 Survey (I): How the HI Mass Density of the Universe Evolves With Cosmic Time
2025-02-07
Grecco A. Oyarzún, Marc Rafelski, Lise Christensen, Fiona Ozyurt, Regina A. Jorgenson, Marcel Neeleman, Michele Fumagalli, J. Xavier Prochaska, G. Worseck, M. E. Wisz, George D. Becker, Sebastián López
We report that the neutral hydrogen (HI) mass density of the Universe ($\rho_{HI}$) increases with cosmic time since $z \sim 5$, peaks at $z \sim 3$, and then decreases toward $z \sim 0$. This is the first result of Qz5, our spectroscopic survey of 63 quasars at $z \gtrsim 5$ with VLT/X-SHOOTER and Keck/ESI aimed at characterizing intervening HI gas absorbers at $z \sim 5$. The main feature of Qz5 is the high resolution ($R \sim 7000 - 9000$) of the spectra, which allows us to (1) accurately detect high column density HI gas absorbers in an increasingly neutral intergalactic medium at $z \sim 5$ and (2) determine the reliability of previous $\rho_{HI}$ measurements derived with lower resolution spectroscopy. We find 5 intervening Damped Ly$\alpha$ absorbers (DLAs) at $z > 4.5$, which corresponds to the lowest DLA incidence rate ($0.034^{0.05}_{0.02}$) at $z \gtrsim 2$. We also measure the lowest $\rho_{HI}$ at $z \gtrsim 2$ from our sample of DLAs and subDLAs, corresponding to $\rho_{HI} = 0.56^{0.82}_{0.31} \times 10^8~$M$_{\odot}~$Mpc$^{-3}$ at $z \sim 5$. Taking into account our measurements at $z \sim 5$ and systematic biases in the DLA detection rate at lower spectral resolutions, we conclude that $\rho_{HI}$ doubles from $z \sim 5$ to $z \sim 3$. From these results emerges a qualitative agreement between how the cosmic densities of HI gas mass, molecular gas mass, and star-formation rate build up with cosmic time.
Cosmological Inference with Cosmic Voids and Neural Network Emulators
2025-02-07
Kai Lehman, Nico Schuster, Luisa Lucie-Smith, Nico Hamaus, Christopher T. Davies, Klaus Dolag
Cosmic Voids are a promising probe of cosmology for spectroscopic galaxy surveys due to their unique response to cosmological parameters. Their combination with other probes promises to break parameter degeneracies. Due to simplifying assumptions, analytical models for void statistics are only representative of a subset of the full void population. We present a set of neural-based emulators for void summary statistics of watershed voids, which retain more information about the full void population than simplified analytical models. We build emulators for the void size function and void density profiles traced by the halo number density using the Quijote suite of simulations for a broad range of the $\Lambda\mathrm{CDM}$ parameter space. The emulators replace the computation of these statistics from computationally expensive cosmological simulations. We demonstrate the cosmological constraining power of voids using our emulators, which offer orders-of-magnitude acceleration in parameter estimation, capture more cosmological information compared to analytic models, and produce more realistic posteriors compared to Fisher forecasts. We find that the parameters $\Omega_m$ and $\sigma_8$ in this Quijote setup can be recovered to $14.4\%$ and $8.4\%$ accuracy respectively using void density profiles; including the additional information in the void size function improves the accuracy on $\sigma_8$ to $6.8\%$. We demonstrate the robustness of our approach to two important variables in the underlying simulations, the resolution, and the inclusion of baryons. We find that our pipeline is robust to variations in resolution, and we show that the posteriors derived from the emulated void statistics are unaffected by the inclusion of baryons with the Magneticum hydrodynamic simulations. This opens up the possibility of a baryon-independent probe of the large-scale structure.
2025-02-07
Avery J. Tishue, Selim C. Hotinli, Peter Adshead, Ely D. Kovetz, Mathew S. Madhavacheril
We forecast neutrino mass constraints using Stage IV CMB and large-scale structure surveys, focusing on kSZ tomography as an independent probe of the growth of cosmic structure. We take into account several realistic factors, including the kSZ optical depth degeneracy. Our baseline setup consists of CMB S4 temperature and polarization (but not lensing) information, DESI BAO, the LSST galaxy power spectrum, and a Planck like $\tau$ prior, yielding $\sigma(\sum m_\nu) = 32\, \rm{meV}$. Adding kSZ tomography improves this by a few percent, while a kSZ optical depth prior can push this improvement to over $15\%$, giving $\sigma(\sum m_\nu) = 27\, \rm{meV}$. When CMB lensing is included in the baseline setup, kSZ does not further improve neutrino mass constraints. We find promising prospects for a scenario combining futuristic CMB and galaxy surveys.
Quantum photonic simulators have emerged as indispensable tools for modeling and optimizing quantum photonic circuits, bridging the gap between theoretical models and experimental implementations. This review explores the landscape of photonic quantum simulation, focusing on the transition from Gaussian to non-Gaussian models and the computational challenges associated with simulating large-scale photonic systems. Gaussian states and operations, which enable efficient simulations through covariance matrices and phase-space representations, serve as the foundation for photonic quantum computing. However, non-Gaussian states crucial for universal quantum computation introduce significant computational complexity, requiring advanced numerical techniques such as tensor networks and high-performance GPU acceleration. We evaluate the leading photonic quantum simulators, including Strawberry Fields, Piquasso, QuTiP SimulaQron, Perceval, and QuantumOPtics.jl analyzing their capabilities in handling continuous-variable (CV) and discrete-variable (DV) quantum systems. Special attention is given to hardware-accelerated methods, including GPU-based tensor network approaches, machine learning integration, and hybrid quantum-classical workflows. Furthermore, we investigate noise modeling techniques, such as photon loss and dark counts, and their impact on simulation accuracy. As photonic quantum computing moves toward practical implementations, advancements in high-performance computing (HPC) architectures, such as tensor processing units (TPUs) and system-on-a-chip (SoC) solutions, are accelerating the field. This review highlights emerging trends, challenges, and future directions for developing scalable and efficient photonic quantum simulators.
The Gaia optical observations are not the most suitable spectral domain for studying the low-mass, faintest and reddest part of the main sequence. Nevertheless, the large number of objects observed with an unprecedented precision, including trigonometric parallax, makes it an unrivaled dataset for studying the stellar-substellar boundary. In this paper, I review the contribution of the successive catalogues offered by the Gaia mission to study the low-mass, ultra-cool objetcs. I also present further characterisations and scientific exploitations of the Gaia sample.
Survey on AI-Generated Media Detection: From Non-MLLM to MLLM
2025-02-07
Yueying Zou, Peipei Li, Zekun Li, Huaibo Huang, Xing Cui, Xuannan Liu, Chenghanyu Zhang, Ran He
The proliferation of AI-generated media poses significant challenges to information authenticity and social trust, making reliable detection methods highly demanded. Methods for detecting AI-generated media have evolved rapidly, paralleling the advancement of Multimodal Large Language Models (MLLMs). Current detection approaches can be categorized into two main groups: Non-MLLM-based and MLLM-based methods. The former employs high-precision, domain-specific detectors powered by deep learning techniques, while the latter utilizes general-purpose detectors based on MLLMs that integrate authenticity verification, explainability, and localization capabilities. Despite significant progress in this field, there remains a gap in literature regarding a comprehensive survey that examines the transition from domain-specific to general-purpose detection methods. This paper addresses this gap by providing a systematic review of both approaches, analyzing them from single-modal and multi-modal perspectives. We present a detailed comparative analysis of these categories, examining their methodological similarities and differences. Through this analysis, we explore potential hybrid approaches and identify key challenges in forgery detection, providing direction for future research. Additionally, as MLLMs become increasingly prevalent in detection tasks, ethical and security considerations have emerged as critical global concerns. We examine the regulatory landscape surrounding Generative AI (GenAI) across various jurisdictions, offering valuable insights for researchers and practitioners in this field.
Stochastic interpretation of quantum mechanics assuming that vacuum fields are real
2025-02-07
Emilio Santos
I review the realistic interpretation of several typically quantum phenomena using a heuristic approach that rests on the assumption that the electromagnetic quantum vacuum is a stochastic field. I include the particle behaviour of light, the photoelectric effect, the hdrogen atom, the Casimir effect, and entanglement in the optical tests of Bell inequalities. The stochastic approach might be formally connected with the standard Hilbert space formalism via the Wigner representation of the field.
This paper presents an AI-generated review of Vision-Language-Action (VLA) models, summarizing key methodologies, findings, and future directions. The content is produced using large language models (LLMs) and is intended only for demonstration purposes. This work does not represent original research, but highlights how AI can help automate literature reviews. As AI-generated content becomes more prevalent, ensuring accuracy, reliability, and proper synthesis remains a challenge. Future research will focus on developing a structured framework for AI-assisted literature reviews, exploring techniques to enhance citation accuracy, source credibility, and contextual understanding. By examining the potential and limitations of LLM in academic writing, this study aims to contribute to the broader discussion of integrating AI into research workflows. This work serves as a preliminary step toward establishing systematic approaches for leveraging AI in literature review generation, making academic knowledge synthesis more efficient and scalable.
Site-engineered ferromagnetism in Ca and Cr co-substituted Bismuth Ferrite Nanoparticles
2025-02-06
Mehedi Hasan Prince, Abrar Daiyan, Troyee Mitra Aishi, Anika Rahman Riya, Md. Fakhrul Islam, Md. Abdullah Zubair, Takian Fakhrul
Multiferroic perovskites that exhibit room temperature magnetization and polarization have immense potential in the next generation of magneto-electric and spintronic memory devices. In this work, the magnetic and ferroelectric properties of Bismuth Ferrite, BiFeO3 (BFO) nanoparticles (NPs) were enhanced through simultaneous A and B site Ca and Cr co-substitution. Novel compositions of Bi0.97Ca0.03CrxFe1-xO3 (x=0, 0.01, 0.03, 0.05) were synthesized using the sol-gel route and annealed at 550 degrees Celcius. Rietveld Refinement of XRD patterns confirmed high phase purity, while SEM analysis revealed a decreasing trend in average particle size with increasing dopant concentration. Hysteresis loops showed enhanced magnetic properties as particle size approached the spin cycloid wavelength (around 62 nm), disrupting the intrinsic antiferromagnetic ordering of BFO. Moreover, the presence of exchange bias in the NPs was linked to the formation of core-shell structure. Temperature dependent magnetization studies showed an increase in N\'eel temperature upon Ca substitution. XPS analysis confirmed that Bi0.97Ca0.03FeO3 samples exhibited the highest oxygen vacancy concentration, while Fe3+ remained the dominant oxidation state across all compositions. Ferroelectric polarization loop measurements showed enhanced remanent polarization in doped samples, with leakage linked to oxygen vacancies and extrinsic microstructural effects.
The conference \emph{Black Holes Inside and Out} marked the 50th anniversary of Hawking's seminal paper on black hole radiance. It was clear already from Hawking's analysis that a proper quantum gravity theory would be essential for a more complete understanding of the evaporation process. This task was undertaken in Loop Quantum Gravity (LQG) two decades ago and by now the literature on the subject is quite rich. The goal of this contribution is to summarize a mainstream perspective that has emerged. The intended audience is the broader gravitational physics community, rather than quantum gravity experts. Therefore, the emphasis is on conceptual issues, especially on the key features that distinguish the LQG approach, and on concrete results that underlie the paradigm that has emerged. This is \emph{not} meant to be an exhaustive review. Rather, it is a broad-brush stroke portrait of the present status. Further details can be found in the references listed.
Multi-fidelity emulator for large-scale 21 cm lightcone images: a few-shot transfer learning approach with generative adversarial network
2025-02-06
Kangning Diao, Yi Mao
Emulators using machine learning techniques have emerged to efficiently generate mock data matching the large survey volume for upcoming experiments, as an alternative approach to large-scale numerical simulations. However, high-fidelity emulators have become computationally expensive as the simulation volume grows to hundreds of megaparsecs. Here, we present a {\it multi-fidelity} emulation of large-scale 21~cm lightcone images from the epoch of reionization, which is realized by applying the {\it few-shot transfer learning} to training generative adversarial networks (GAN) from small-scale to large-scale simulations. Specifically, a GAN emulator is first trained with a huge number of small-scale simulations, and then transfer-learned with only a limited number of large-scale simulations, to emulate large-scale 21~cm lightcone images. We test the precision of our transfer-learned GAN emulator in terms of representative statistics including global 21~cm brightness temperature history, 2D power spectrum, and scattering transform coefficients. We demonstrate that the lightcone images generated by the transfer-learned GAN emulator can reach the percentage level precision in most cases on small scales, and the error on large scales only increases mildly to the level of a few tens of per cent. Nevertheless, our multi-fidelity emulation technique saves a significant portion of computational resources that are mostly consumed for generating training samples for GAN. On estimate, the computational resource by training GAN completely with large-scale simulations would be one to two orders of magnitude larger than using our multi-fidelity technique. This implies that our technique allows for emulating high-fidelity, traditionally computationally prohibitive, images in an economic manner.
The DESI 2024 hint for dynamical dark energy is biased by low-redshift supernovae
2025-02-06
Lu Huang, Rong-Gen Cai, Shao-Jiang Wang
Recently, a $\sim3.9\sigma$ preference for dynamical dark energy from the Dark Energy Spectroscopic Instrument (DESI) collaboration inspired hot debates on new physics or systematics. In this letter, we reveal this significant preference is dominated by an external low-redshift supernova (low-$z$ SN) sample that combines with the Dark Energy Survey SN program (DES-SN) in their Year 5 data release (DESY5). Further implementing the $a_B$ (the intercept of the SN magnitude-redshift relation) diagnosis between low-$z$ and DES-SN samples, we find large dispersions in the low-$z$ SN sample with a $\sim0.043$ magnitude discrepancy in $-5a_B$ from the high-$z$ DES-SN sample, suggesting potential systematics in DESY5. Correcting for this low-$z$ systematics or directly ignoring the low-$z$ sample can largely reduce the preference for dynamical DE to be $<2\sigma$. Therefore, the DESI preference for dynamical DE might be a mirage of low-$z$ SN systematics with a mismatch intercept. Our additional test demonstrates the currently available data cannot provide decisive evidence for dynamical DE.
Enhanced Feature-based Image Stitching for Endoscopic Videos in Pediatric Eosinophilic Esophagitis
2025-02-06
Juming Xiong, Muyang Li, Ruining Deng, Tianyuan Yao, Regina N Tyree, Girish Hiremath, Yuankai Huo
Video endoscopy represents a major advance in the investigation of gastrointestinal diseases. Reviewing endoscopy videos often involves frequent adjustments and reorientations to piece together a complete view, which can be both time-consuming and prone to errors. Image stitching techniques address this issue by providing a continuous and complete visualization of the examined area. However, endoscopic images, particularly those of the esophagus, present unique challenges. The smooth surface, lack of distinct feature points, and non-horizontal orientation complicate the stitching process, rendering traditional feature-based methods often ineffective for these types of images. In this paper, we propose a novel preprocessing pipeline designed to enhance endoscopic image stitching through advanced computational techniques. Our approach converts endoscopic video data into continuous 2D images by following four key steps: (1) keyframe selection, (2) image rotation adjustment to correct distortions, (3) surface unwrapping using polar coordinate transformation to generate a flat image, and (4) feature point matching enhanced by Adaptive Histogram Equalization for improved feature detection. We evaluate stitching quality through the assessment of valid feature point match pairs. Experiments conducted on 20 pediatric endoscopy videos demonstrate that our method significantly improves image alignment and stitching quality compared to traditional techniques, laying a robust foundation for more effective panoramic image creation.
Ensuring Reliability via Hyperparameter Selection: Review and Advances
2025-02-06
Amirmohammad Farzaneh, Osvaldo Simeone
Hyperparameter selection is a critical step in the deployment of artificial intelligence (AI) models, particularly in the current era of foundational, pre-trained, models. By framing hyperparameter selection as a multiple hypothesis testing problem, recent research has shown that it is possible to provide statistical guarantees on population risk measures attained by the selected hyperparameter. This paper reviews the Learn-Then-Test (LTT) framework, which formalizes this approach, and explores several extensions tailored to engineering-relevant scenarios. These extensions encompass different risk measures and statistical guarantees, multi-objective optimization, the incorporation of prior knowledge and dependency structures into the hyperparameter selection process, as well as adaptivity. The paper also includes illustrative applications for communication systems.
The Merging Galaxy Cluster Environment Affects the Morphology of Radio-AGN
2025-02-06
Mary Rickel, Emily Moravec, Yjan A. Gordon, Martin J. Hardcastle, Jonathon C. S. Pierce, Lawrence E. Bilton, Ian D. Roberts
It has previously been found that the galaxy cluster environment can affect the fueling and evolution of Active Galactic Nuclei (AGN). This work examines the effect of the merging cluster environment on the properties of radio-AGN by comparing the radio morphology of cluster members in a sample of four merging and eight relaxed galaxy clusters at low redshift (z<0.2). Using 144-MHz data from the LOFAR Two-metre Sky Survey (LoTSS) and Zooniverse, we classify the radio morphology of the radio-detected cluster members using the following morphology classes: compact, compact extended, extended, jetted, and disturbed. We find that the merging cluster environment has a statistically significant, higher population proportion of disturbed (bent and head tail) sources, indicating that the merging environment can affect the morphology of cluster radio-AGN. We also investigate the number of AGN that are detected in the radio data only, and the number that are detected in both the radio and optical data in mergers and non-mergers. We find that the merging cluster environment has a higher population proportion of AGN that are identified only as radio-AGN compared to AGN that are identified as both radio and optical AGN. Overall, we find that the merging environment affects certain radio-AGN (disturbed and only radio identified AGN), but not all.
PixFoundation: Are We Heading in the Right Direction with Pixel-level Vision Foundation Models?
2025-02-06
Mennatullah Siam
Multiple works have emerged to push the boundaries on multi-modal large language models (MLLMs) towards pixel-level understanding. Such approaches have shown strong performance on benchmarks for referring expression segmentation and grounded conversation generation. The current trend in pixel-level MLLMs is to train with pixel-level grounding supervision on large-scale labelled data. However, we show that such MLLMs when evaluated on recent challenging vision centric benchmarks, exhibit a weak ability in visual question answering. Surprisingly, some of these methods even downgrade the grounding ability of MLLMs that were never trained with such supervision. In this work, we propose two novel challenging benchmarks and show that MLLMs without pixel-level grounding supervision can outperform the state of the art in such tasks when evaluating both the pixel-level grounding and visual question answering. We propose simple baselines to extract the grounding information that can be plugged into any MLLM, which we call as PixFoundation. More importantly, we study the research question of ``When does grounding emerge in MLLMs that are not trained with pixel-level grounding supervision?'' We show that grounding can coincide with object parts or location/appearance information. Code repository is at https://github.com/MSiam/PixFoundation/.
The Effects of Kinematic MHD on the Atmospheric Circulation of Eccentric Hot Jupiters
2025-02-06
Hayley Beltz, Willow Houck, Laura C. Mayorga, Thaddeus D. Komacek, Joseph R. Livesey, Juliette Becker
Hot Jupiters are typically considered to be tidally locked due to their short orbital periods. The extreme irradiation can result in atmospheric species becoming thermally ionized on the dayside, which then interact with the planet's magnetic field by resisting flow across magnetic field lines, shaping the atmospheric structure. However, an eccentric orbit results in temporally dependent irradiation and a non-permanent dayside, as the planet-star distance can change drastically during its orbit. In this paper, we present 3D atmospheric models of TOI-150b, an eccentric (e=0.26), Jupiter-mass 1.75 M_Jup planet whose equilibrium temperature varies from 1300K to 1700K. We conduct simulations for magnetic field strengths ranging from 0-30 Gauss using the kinematic magnetohydrodynamics (MHD) approach. When compared to simulations of the planet assuming a circular orbit, we find that the eccentric orbit results in a strengthened and narrowed equatorial jet, westward winds at mid-latitudes, and a phase-dependent thermal inversion. The strength and magnitude of these effects scale with the chosen global magnetic field strength. We also generate high-resolution (R=100,000) emission spectra to study net Doppler shifts and find inter-orbit spectroscopic variability at moderate magnetic field strengths, as well as decreased Doppler broadening as magnetic field strengths increase. This work represents the first time that the kinematic MHD approach has been applied to an eccentric hot Jupiter and highlights the importance of a locally calculated, temperature dependent magnetic drag prescription for predicting atmospheric structure and resulting spectra.
Synthetic Datasets for Machine Learning on Spatio-Temporal Graphs using PDEs
2025-02-06
Jost Arndt, Utku Isil, Michael Detzel, Wojciech Samek, Jackie Ma
Many physical processes can be expressed through partial differential equations (PDEs). Real-world measurements of such processes are often collected at irregularly distributed points in space, which can be effectively represented as graphs; however, there are currently only a few existing datasets. Our work aims to make advancements in the field of PDE-modeling accessible to the temporal graph machine learning community, while addressing the data scarcity problem, by creating and utilizing datasets based on PDEs. In this work, we create and use synthetic datasets based on PDEs to support spatio-temporal graph modeling in machine learning for different applications. More precisely, we showcase three equations to model different types of disasters and hazards in the fields of epidemiology, atmospheric particles, and tsunami waves. Further, we show how such created datasets can be used by benchmarking several machine learning models on the epidemiological dataset. Additionally, we show how pre-training on this dataset can improve model performance on real-world epidemiological data. The presented methods enable others to create datasets and benchmarks customized to individual requirements. The source code for our methodology and the three created datasets can be found on https://github.com/github-usr-ano/Temporal_Graph_Data_PDEs.
3D instance segmentation aims to predict a set of object instances in a scene and represent them as binary foreground masks with corresponding semantic labels. Currently, transformer-based methods are gaining increasing attention due to their elegant pipelines, reduced manual selection of geometric properties, and superior performance. However, transformer-based methods fail to simultaneously maintain strong position and content information during query initialization. Additionally, due to supervision at each decoder layer, there exists a phenomenon of object disappearance with the deepening of layers. To overcome these hurdles, we introduce Beyond the Final Layer: Hierarchical Query Fusion Transformer with Agent-Interpolation Initialization for 3D Instance Segmentation (BFL). Specifically, an Agent-Interpolation Initialization Module is designed to generate resilient queries capable of achieving a balance between foreground coverage and content learning. Additionally, a Hierarchical Query Fusion Decoder is designed to retain low overlap queries, mitigating the decrease in recall with the deepening of layers. Extensive experiments on ScanNetV2, ScanNet200, ScanNet++ and S3DIS datasets demonstrate the superior performance of BFL.
Generative Adversarial Networks Bridging Art and Machine Intelligence
2025-02-06
Junhao Song, Yichao Zhang, Ziqian Bi, Tianyang Wang, Keyu Chen, Ming Li, Qian Niu, Junyu Liu, Benji Peng, Sen Zhang, Ming Liu, Jiawei Xu, Xuanhe Pan, Jinlang Wang, Pohsun Feng, Yizhu Wen, Lawrence K. Q. Yan, Hong-Ming Tseng, Xinyuan Song, Jintao Ren, Silin Chen, Yunze Wang, Weiche Hsieh, Bowen Jing, Junjie Yang, Jun Zhou, Zheyu Yao, Chia Xin Liang
Generative Adversarial Networks (GAN) have greatly influenced the development of computer vision and artificial intelligence in the past decade and also connected art and machine intelligence together. This book begins with a detailed introduction to the fundamental principles and historical development of GANs, contrasting them with traditional generative models and elucidating the core adversarial mechanisms through illustrative Python examples. The text systematically addresses the mathematical and theoretical underpinnings including probability theory, statistics, and game theory providing a solid framework for understanding the objectives, loss functions, and optimisation challenges inherent to GAN training. Subsequent chapters review classic variants such as Conditional GANs, DCGANs, InfoGAN, and LAPGAN before progressing to advanced training methodologies like Wasserstein GANs, GANs with gradient penalty, least squares GANs, and spectral normalisation techniques. The book further examines architectural enhancements and task-specific adaptations in generators and discriminators, showcasing practical implementations in high resolution image generation, artistic style transfer, video synthesis, text to image generation and other multimedia applications. The concluding sections offer insights into emerging research trends, including self-attention mechanisms, transformer-based generative models, and a comparative analysis with diffusion models, thus charting promising directions for future developments in both academic and applied settings.
Asymmetry at Low Surface Brightness as an Indicator of Environmental Processes in the Fornax Cluster
2025-02-06
X. Xu, R. F. Peletier, P. Awad, M. A. Raj, R. Smith
Dwarf galaxies play an important role in studying the effects of the environment on galaxy formation and evolution. In this study, we aim to explore the relationship between the morphology, in particular the asymmetries of galaxies, and their distances to the cluster centre. For galaxies in the Fornax Deep Survey, we quantified the morphologies of dwarf galaxies using Asymmetry (A) and Smoothness (S). Unlike previous work, we use isophotal CAS-parameters, which are sensitive to the outer parts of galaxies. We constructed the A-r and S-r diagrams to investigate the relationship between morphology and distance. Additionally, we examined the effects of asymmetry on magnitude and colour. Furthermore, to better understand the assembly history of the galaxy cluster, we performed a phase-space analysis for Fornax dwarf galaxies. We find that dwarf galaxies in the outer regions of the Fornax cluster have higher values of asymmetry compared to other dwarfs in the cluster, indicating a greater degree of morphological disturbances within dwarf galaxies in these regions. We also find that galaxies in the very inner regions are more asymmetric than those further out. The A-magnitude relation reveals a trend where asymmetry increases as galaxies become fainter, and the A-colour relation shows that galaxies with bluer colours tend to exhibit higher asymmetry. We do not find any correlations with smoothness, except that smoothness strongly decreases with stellar mass. We propose that the higher asymmetry of dwarfs in the outer regions is most likely caused by ram pressure stripping. In the very inner parts, the asymmetries most likely are caused by tidal effects. In addition, our phase-space diagram suggests that galaxies near pericentre in the Fornax cluster exhibit significantly higher asymmetry, indicating that morphological disturbances happened during their first pericentric passage.
Three-body structures of low-lying nuclear states of $^8$Li
2025-02-06
E. Garrido, A. S. Jensen
The four nucleons in $^8$Li outside the $\alpha$-particle ($\alpha=^4$He) can be divided into pairs of one neutron ($n$) and 3 nucleons in the triton ($t=^3$H), or 2 in the deuteron ($d=^2$H) and two neutrons in a dineutron ($^2n$). The corresponding three-body structures, $\alpha$+$t$+$n$ or $\alpha$+$d$+$^2n$, are suggested to describe the bulk part of the low-energy ($<10$~MeV) states of $^8$Li. Several breakup thresholds influence the structures and possible decays. We calculate the three-body structures of the various $J^{\pi}$ states, where different clustering appear, e.g. $^7$Li*+$n$, $^6$Li*$+^2n$, $^6$He*$+d$. The experimental $^8$Li spectrum can be reproduced with fine tuning by a three-body potential parameter. Three unobserved $0^+$ and an excited 2$^+$ states are found. All states appear as bound states or resonances. The lowest or highest energies have cluster structures, $\alpha$+$t$+$n$ or $\alpha$+$d$+$^2n$, respectively. We give calculated energy and width (if possible), geometry, and partial wave decomposition for all states.
High Spatiotemporal Resolution Structured Illumination Microscopy: Principle, Instrumentation, and Applications
2025-02-06
Han Wang, Wenshu Wang, Xinzhu Xu, Meiqi Li, Peng Xi
Among super-resolution microscopy techniques, structured illumination microscopy (SIM) shows great advances of low phototoxicity, high speed, and excellent performance in long-term dynamic observation, making it especially suitable for live cell imaging. This review delves into the principles, instrumentation, and applications of SIM, highlighting its capabilities in achieving high spatiotemporal resolution. Two types of structured illumination mechanics are employed: (1) stripe-based SIM, where the illumination stripes are formed through interference or projection, with extended resolution achieved through Fourier-domain extension; (2) point-scanning based SIM, where illumination pattern are generated through projection of the focal point or focal array, with extended resolution achieved through photon reassignment. We discuss the evolution of SIM from mechanical to high-speed photoelectric devices, such as spatial light modulators, digital micromirror devices, galvanometers, etc., which significantly enhance imaging speed, resolution, and modulation flexibility. The review also explores SIM's applications in biological research, particularly in live-cell imaging and cellular interaction studies, providing insights into disease mechanisms and cellular functions. We conclude by outlining the future directions of SIM in life sciences. With the advancement of imaging techniques and reconstruction algorithms, SIM is poised to bring revolutionary impacts to frontier research fields, offering new avenues for exploring the intricacies of cellular biology.
Fundamental Oscillations of Massive Boson Stars -- II
2025-02-06
Swarnim Shirke, Bikram Keshari Pradhan, Debarati Chatterjee, Laura Sagunski, Jürgen Schaffner-Bielich
Boson Stars (BSs) are macroscopic self-gravitating configurations made of complex scalar fields. These exotic compact objects (ECO) would manifest as dark Boson stars and can contribute to a certain fraction of the dark matter (DM) in the universe. In this work, we study the fundamental non-radial oscillations ($f$-modes) of massive BSs and the associated gravitational wave (GW) emission. We consider massive scalar BSs having the potential of the form $V(\phi) = \frac{1}{2}m^2|\phi|^2 + \frac{1}{4}\lambda |\phi|^4$, restricting to the strong-coupling regime ($\lambda \gg m^2/M_{Pl}^2$) where solutions resembling fermionic stars are known to exist. We first review the available parameter space for scalar DM that can form massive BSs and enlist various constraints. We fit and provide simple analytical relations connecting various macroscopic observables for BSs, which can be directly incorporated into future studies of massive BSs throughout the strong coupling regime without requiring any numerical computation. We then solve for the non-radial $l=2$ fundamental quasinormal modes ($f$-modes) for massive BSs. Scaling relations for $f$-mode equations have been reported in another work. Using these, we perform a complete study of these oscillations spanning the entire available parameter space of massive BSs and provide analytical fits for the $f$-mode characteristics. We further study the universal relations for BSs and reveal the parameter space that is sensitive to the current and future planned GW detectors. We find that different parts of the parameters space can, in principle, be probed by the LISA, LIGO, and NEMO detectors. Finally, we briefly discuss the detectability of $f$-modes from BSs that have not been explored before.
Quantification of Biodiversity from Historical Survey Text with LLM-based Best-Worst Scaling
2025-02-06
Thomas Haider, Tobias Perschl, Malte Rehbein
In this study, we evaluate methods to determine the frequency of species via quantity estimation from historical survey text. To that end, we formulate classification tasks and finally show that this problem can be adequately framed as a regression task using Best-Worst Scaling (BWS) with Large Language Models (LLMs). We test Ministral-8B, DeepSeek-V3, and GPT-4, finding that the latter two have reasonable agreement with humans and each other. We conclude that this approach is more cost-effective and similarly robust compared to a fine-grained multi-class approach, allowing automated quantity estimation across species.
From Planetesimals to Dwarf Planets by Pebble Accretion
2025-02-06
Chris W. Ormel, Yukun Huang
The size distribution of trans-Neptunian objects (TNOs) in the Kuiper Belt provides crucial insights into the formation and evolution of the outer Solar System. Recent observational surveys, including OSSOS++, have revealed that dynamically cold and hot TNO populations exhibit similar size distributions for dimmer objects ($H_r > 5$), which are consistent with planetesimal formation by streaming instability (SI). However, the hot population contains a significantly larger number of massive bodies, including several dwarf planets. In this study, we investigate the role of pebble accretion in shaping the size distribution of hot TNOs, after their formation in the primordial disk (PB) between 20 and 30 au and before these bodies were dynamically implanted into their current orbits by a migrating Neptune. We find that pebble accretion grows the most massive bodies only, consistent with the flattening of the distribution brightwards of $H_r=5$. All results point to a correlation (degeneracy) between the pebble aerodynamic size and the intensity of the gas motions. Nevertheless, accretion from an inward-drifting stream of pebbles is unlikely, as it puts extreme demands on the mass budget of pebbles. In particular, the masses of the cold classicals are too low to trigger pebble accretion. Accretion in an environment where pebbles are entrained, as believed to be the case in ALMA rings, is preferable. Combining the constraints obtained from this study with ALMA imagery morphology fitting reveals a typical pebble aerodynamic size of $\tau_s \sim 10^{-2}$, a turbulent diffusivity parameter $\alpha_D\sim10^{-3}$, and a total accreted pebble mass of ${\sim}10\,m_\oplus$ in the primordial belt. Those TNOs formed through significant pebble accretion with masses exceeding ${\sim}10^{-4}\,m_\oplus$ are likely to satisfy the International Astronomical Union's "round shape" criterion for dwarf planets.
Fundamental mechanisms of high-electric-field vacuum arc initiation
2025-02-06
Walter Wuensch, Sergio Calatroni, Flyura Djurabekova, Andreas Kyritsakis, Yinon Ashkenazy
This review brings together a body of experimental, theoretical, and simulation work addressing the behaviour of high-field devices and the fundamental process of vacuum arcing, also referred to as vacuum breakdown. Detailed experimental observations and results relating to a wide range of aspects of high-field devices including conditioning, field and temperature dependence of breakdown rate, and the field-holding capability as a function of device geometry and of different materials are presented. The different observations are then addressed theoretically, and with simulation, capturing the sequence of processes that lead to vacuum breakdown and explaining the major observed experimental dependencies. The core of the work described in this review was carried out by a broad multi-disciplinary collaboration in an over a decade-long program to develop high-gradient, 100 MV/m-range, accelerating structures for the CLIC project, a possible future linear-collider high-energy physics facility. Connections are made to the broader linear collider, high-field, and breakdown communities.
Generative AI and Creative Work: Narratives, Values, and Impacts
2025-02-06
Baptiste Caramiaux, Kate Crawford, Q. Vera Liao, Gonzalo Ramos, Jenny Williams
Generative AI has gained a significant foothold in the creative and artistic sectors. In this context, the concept of creative work is influenced by discourses originating from technological stakeholders and mainstream media. The framing of narratives surrounding creativity and artistic production not only reflects a particular vision of culture but also actively contributes to shaping it. In this article, we review online media outlets and analyze the dominant narratives around AI's impact on creative work that they convey. We found that the discourse promotes creativity freed from its material realisation through human labor. The separation of the idea from its material conditions is achieved by automation, which is the driving force behind productive efficiency assessed as the reduction of time taken to produce. And the withdrawal of the skills typically required in the execution of the creative process is seen as a means for democratising creativity. This discourse tends to correspond to the dominant techno-positivist vision and to assert power over the creative economy and culture.
The measurement of spin-precession and orbital eccentricity in gravitational-wave (GW) signals is a key priority in GW astronomy, as these effects not only provide insights into the astrophysical formation and evolution of compact binaries but also, if neglected, could introduce significant biases in parameter estimation, searches, and tests of General Relativity. Despite the growing potential of upcoming LIGO-Virgo-KAGRA observing runs and future detectors to measure eccentric-precessing signals, accurately and efficiently modeling them remains a challenge. In this work, we present pyEFPE, a frequency-domain post-Newtonian (PN) waveform model for the inspiral of precessing-eccentric compact binaries. pyEFPE improves upon previous models by introducing analytical expressions for the Fourier mode amplitudes, enhancing the numerical stability of the multiple scale analysis framework, and adding recently derived PN corrections, critical to accurately describe signals in GW detectors. Additionally, we simplify the numerical implementation and introduce a scheme to interpolate the amplitudes, achieving a speedup of up to ~O(20) in the waveform computations, making the model practical for data analysis applications. We thoroughly validate pyEFPE by comparing it to other waveform models in the quasi-circular and eccentric-spin-aligned limits, finding good agreement. Additionally, we demonstrate pyEFPE's capability to analyze simulated GW events, accurately recovering the parameters of signals described by both pyEFPE and IMRPhenomXP. While pyEFPE still lacks important physical effects, such as higher-order PN corrections, higher-order modes, mode asymmetries, tidal interactions or the merger-ringdown phase, it represents a significant step towards more complete waveform models, offering a flexible and efficient framework that can be extended in future work to incorporate these effects.
Applications of dimension interpolation to orthogonal projections
2025-02-06
Jonathan M. Fraser
Dimension interpolation is a novel programme of research which attempts to unify the study of fractal dimension by considering various spectra which live in between well-studied notions of dimension such as Hausdorff, box, Assouad and Fourier dimension. These spectra often reveal novel features not witnessed by the individual notions and this information has applications in many directions. In this survey article, we discuss dimension interpolation broadly and then focus on applications to the dimension theory of orthogonal projections. We focus on three distinct applications coming from three different dimension spectra, namely, the Fourier spectrum, the intermediate dimensions, and the Assouad spectrum. The celebrated Marstrand--Mattila projection theorem gives the Hausdorff dimension of the orthogonal projection of a Borel set in Euclidean space for almost all orthogonal projections. This result has inspired much further research on the dimension theory of projections including the consideration of dimensions other than the Hausdorff dimension, and the study of the exceptional set in the Marstrand--Mattila theorem.
Exploring the Reform and Development Pathways of AIIB's Climate Accountability Mechanism in the Context of Global Climate Governance
2025-02-06
Yedong Zhang
After the Asian Infrastructure Investment Bank (AIIB) revised its Environmental and Social Framework, it has committed to certain climate-related objectives, yet an independent climate accountability mechanism has not been established. The absence of clear evaluation principles and procedural rules presents challenges in effectively addressing environmental investment disputes. This article reviews both domestic and international literature related to AIIB's climate accountability mechanism, identifying that the current Environmental and Social Framework's principled content and reliance on traditional approaches have resulted in issues of enforceability and the absence of an independent accountability institution. To enhance the effectiveness of AIIB's climate accountability mechanism, a reassessment of its development path is necessary. Future developments should transition from an additive path to a substitutive path, focusing on the application of international environmental and social standards, increasing stakeholder recognition and participation, promoting comprehensive reforms within AIIB, and establishing a coordinated independent accountability system. These measures aim to support the robust development of AIIB's climate accountability mechanism.
Position: Untrained Machine Learning for Anomaly Detection
2025-02-06
Juan Du, Dongheng Chen, Hao Yan
Anomaly detection based on 3D point cloud data is an important research problem and receives more and more attention recently. Untrained anomaly detection based on only one sample is an emerging research problem motivated by real manufacturing industries such as personalized manufacturing that only one sample can be collected without any additional labels. How to accurately identify anomalies based on one 3D point cloud sample is a critical challenge in both industrial applications and the field of machine learning. This paper aims to provide a formal definition of untrained anomaly detection problem based on 3D point cloud data, discuss the differences between untrained anomaly detection and current unsupervised anomaly detection methods. Unlike unsupervised learning, untrained methods do not rely on any data, including unlabeled data. Instead, they leverage prior knowledge about the manufacturing surfaces and anomalies. Examples are used to illustrate these prior knowledge and untrained machine learning model. Afterwards, literature review on untrained anomaly detection based on 3D point cloud data is also provided, and the potential of untrained deep neural networks for anomaly detection is also discussed as outlooks.
Numerical Continuation and Bifurcation in Nonlinear PDEs: Stability, invasion and wavetrains in the Swift-Hohenberg equation
2025-02-06
David Lloyd, Ryan Goh, Jens D. M. Rademacher
We discuss some aspects of numerical continuation and bifurcation for partial differential equations, specifically pattern formation and coherent structures. For the sake of clarity we focus on wavetrains, stability and associated invasion processes in the paradigmatic cubic Swift-Hohenberg equation (SHE). We do not aim at a review of numerical continuation for PDE or pattern formation in SHE in any generality, rather our goal is to provide an entry point for interested students and colleagues to the application of continuation methods. We provide access to numerical implementations and hope that our presentation provides an introductory guideline that can also be used for teaching.
2025-02-06
R. Gonzalo Parra, Elizabeth A. Komives, Peter G. Wolynes, Diego U. Ferreiro
Molecules provide the ultimate language in terms of which physiology and pathology must be understood. Myriads of proteins participate in elaborate networks of interactions and perform chemical activities coordinating the life of cells. To perform these often amazing tasks, proteins must move and we must think of them as dynamic ensembles of three dimensional structures formed first by folding the polypeptide chains so as to minimize the conflicts between the interactions of their constituent amino acids. It is apparent however that, even when completely folded, not all conflicting interactions have been resolved so the structure remains "locally frustrated". Over the last decades it has become clearer that this local frustration is not just a random accident but plays an essential part of the inner workings of protein molecules. We will review here the physical origins of the frustration concept and review evidence that local frustration is important for protein physiology, protein-protein recognition, catalysis and allostery. Also, we highlight examples showing how alterations in the local frustration patterns can be linked to distinct pathologies. Finally we explore the extensions of the impact of frustration in higher order levels of organization of systems including gene regulatory networks and the neural networks of the brain.
Estimating the evolution and the content fractions of baryonic gas for Luminous Infrared Galaxies
2025-02-06
M. N. Al Najm, Ahmed H. Abdullah, Y. E. Rashed
Luminous Infrared Galaxies (LIRGs) play a crucial role in understanding of galaxy evolution. The present study examined 82 LIRGs, using data taken from the Sloan Digital Sky Survey (SDSS), NASA/IPAC Extragalactic Database (NED), and HyperLEDA to explore their gas fractions and optical properties. The analysis of data highlights the relationship between molecular-to-atomic mass of hydrogen gas ratio MH2/MH1 and morphological types, gas mass fractions, and galaxy characteristics such as color and luminosity. The results showed that the regressions between Mdust - M*,V and Mdust - SFR are not quite flat (when correlation coefficient > 0.5), which indicates a decrease in the dust-to-stellar content ratio as the gas is consumed and transformed into stars, and also a relatively flat trend for M dust - M*,V and fdust,bar - M*,V. Moreover, as the star mass declines, the total gas mass fraction (fgas) increases quickly, with a high negative correlation coefficient of - 0.7 and a regression of - 0.85. Therefore, it can be inferred that galaxies with a high gas fraction (fgas) are either accreting gas at a rate sufficient to meet their energy requirements for star formation or converting gas into stars less effectively. According to the findings, the gas exhaustion time in these galaxies quickly reduces as the stellar mass increases, with a significant negative correlation coefficient of - 0.7 and a regression that is a nearly linear regression of - 0.9. On the other hand, when the baryonic gas mass fraction grows, which makes up the majority of the baryonic gas, grows, the gas depletion time increases quickly.
Unveiling the emission properties of three long-period pulsars using FAST
2025-02-06
H. M. Tedila, D. Li, P. Wang, R. Yuen, J. P. Yuan, N. Wang, Z. G. Wen, S. J. Dang, A. G. Tegegne, R. Rejep, C. C. Miao, FAST Collaboration
We detail the emission behaviors of three long-period pulsars detected using the Five-hundred-meter Aperture Spherical radio Telescope (FAST) during the CRAFTS survey. Their rotational periods range from 1.83 s to 4.75 s, and the null fractions measure between 28% and 53%. PSR J1945+1211 and PSR J2323+1214 exhibited quasi-periodic nulls, with duration of around 57 seconds. The longest null was observed in PSR J1945+1211, lasting 76 seconds. PSR J2323+1214 displayed varying null fractions between its leading and trailing components. For the first time in PSR J2323+1214, we detected five dwarf pulses, which are much weaker and narrower pulses than typical burst pulses. In addition, we investigate the microstructure of PSR J1900-0134 for the first time, revealing intricate pulses of up to 2.05 milliseconds and noting its complex emission characteristics. Bright pulses occur in all of these sources at different rates. These observations suggest complex magnetospheric processes, potentially related to magnetic reconnections, and provide insights into the origins of bright and microstructure pulses, as well as their distinctions from ordinary pulses.
A comprehensive survey of contemporary Arabic sentiment analysis: Methods, Challenges, and Future Directions
2025-02-06
Zhiqiang Shi, Ruchit Agrawal
Sentiment Analysis, a popular subtask of Natural Language Processing, employs computational methods to extract sentiment, opinions, and other subjective aspects from linguistic data. Given its crucial role in understanding human sentiment, research in sentiment analysis has witnessed significant growth in the recent years. However, the majority of approaches are aimed at the English language, and research towards Arabic sentiment analysis remains relatively unexplored. This paper presents a comprehensive and contemporary survey of Arabic Sentiment Analysis, identifies the challenges and limitations of existing literature in this field and presents avenues for future research. We present a systematic review of Arabic sentiment analysis methods, focusing specifically on research utilizing deep learning. We then situate Arabic Sentiment Analysis within the broader context, highlighting research gaps in Arabic sentiment analysis as compared to general sentiment analysis. Finally, we outline the main challenges and promising future directions for research in Arabic sentiment analysis.
Large Language Models for Multi-Robot Systems: A Survey
2025-02-06
Peihan Li, Zijian An, Shams Abrar, Lifeng Zhou
The rapid advancement of Large Language Models (LLMs) has opened new possibilities in Multi-Robot Systems (MRS), enabling enhanced communication, task planning, and human-robot interaction. Unlike traditional single-robot and multi-agent systems, MRS poses unique challenges, including coordination, scalability, and real-world adaptability. This survey provides the first comprehensive exploration of LLM integration into MRS. It systematically categorizes their applications across high-level task allocation, mid-level motion planning, low-level action generation, and human intervention. We highlight key applications in diverse domains, such as household robotics, construction, formation control, target tracking, and robot games, showcasing the versatility and transformative potential of LLMs in MRS. Furthermore, we examine the challenges that limit adapting LLMs in MRS, including mathematical reasoning limitations, hallucination, latency issues, and the need for robust benchmarking systems. Finally, we outline opportunities for future research, emphasizing advancements in fine-tuning, reasoning techniques, and task-specific models. This survey aims to guide researchers in the intelligence and real-world deployment of MRS powered by LLMs. Based on the fast-evolving nature of research in the field, we keep updating the papers in the open-source Github repository.
Privacy Risks in Health Big Data: A Systematic Literature Review
2025-02-06
Zhang Si Yuan, Manmeet Mahinderjit Singh
The digitization of health records has greatly improved the efficiency of the healthcare system and promoted the formulation of related research and policies. However, the widespread application of advanced technologies such as electronic health records, genomic data, and wearable devices in the field of health big data has also intensified the collection of personal sensitive data, bringing serious privacy and security issues. Based on a systematic literature review (SLR), this paper comprehensively outlines the key research in the field of health big data security. By analyzing existing research, this paper explores how cutting-edge technologies such as homomorphic encryption, blockchain, federated learning, and artificial immune systems can enhance data security while protecting personal privacy. This paper also points out the current challenges and proposes a future research framework in this key area.
Probabilistic computing has emerged as a viable approach to treat optimization problems. To achieve superior computing performance, the key aspect during computation is massive sampling and tuning on the probability states of each probabilistic bit (p-bit), demanding its high stability under extensive operations. Here, we demonstrate a p-bit constructed by manganite nanowire that shows exceptionally high stability. The p-bit contains an electronic domain that fluctuates between metallic (low resistance) and insulating (high resistance) states near its transition temperature. The probability for the two states can be directly controlled by nano-ampere electrical current. Under extensive operations, the standard error of its probability values is less than 1.3%. Simulations show that our operationally stable p-bit plays the key role to achieve correct inference in Bayesian network by strongly suppressing the relative error, displaying the potential for superior computing performance. Our p-bit also serves as high quality random number generator without extra data-processing, beneficial for cryptographic applications.
Measurements of $\varUpsilon$ States Production in $\textit{p+p}$ Collisions at $\sqrt{s} = 500\:\mathrm{GeV}$ with STAR: Cross Sections, Ratios, and Multiplicity Dependence
2025-02-06
The STAR Collaboration
We report measurements of $\varUpsilon(1S)$, $\varUpsilon(2S)$ and $\varUpsilon(3S)$ production in $\textit{p+p}$ collisions at $\sqrt{s}=500\:\mathrm{GeV}$ by the STAR experiment in year 2011, corresponding to an integrated luminosity $\mathcal{L}_{int}=13\:\mathrm{pb^{-1}}$. The results provide precise cross sections, transverse momentum ($p_{T}$) and rapidity ($y$) spectra, as well as cross section ratios for $p_{\mathrm{T}}<10\:\mathrm{GeV/c}$ and $|y|<1$. The dependence of the $\varUpsilon$ yield on charged particle multiplicity has also been measured, offering new insights into the mechanisms of quarkonium production. The data are compared to various theoretical models: the Color Evaporation Model (CEM) accurately describes the $\varUpsilon(1S)$ production, while the Color Glass Condensate + Non-relativistic Quantum Chromodynamics (CGC+NRQCD) model overestimates the data, particularly at low $p_{T}$. Conversely, the Color Singlet Model (CSM) underestimates the rapidity dependence. These discrepancies highlight the need for further development in understanding the production dynamics of heavy quarkonia in high-energy hadronic collisions. The trend in the multiplicity dependence is consistent with CGC/Saturation and String Percolation models or $\varUpsilon$ production happening in multiple parton interactions modeled by PYTHIA8.
Identifying Compton-thick AGNs in the COSMOS. I. Among X-ray AGNs with Low Photon Counts
2025-02-06
Xiaotong Guo, Qiusheng Gu, Guanwen Fang, Yongyun Chen, Nan Ding, Xiaoling Yu, Hongtao Wang
Compton-thick active galactic nuclei (CT-AGNs), characterized by a significant absorption with column densities of $\mathrm{N_H}\geqslant 1.5\times 10^{24} \ \mathrm{cm}^{-2}$, emit feeble X-ray radiation and are even undetectable by X-ray instruments, making them difficult to identify. X-ray radiation from AGNs is the predominant source of the cosmic X-ray background (CXB). Based on AGN synthesis models for the CXB, the fraction of CT-AGNs should constitute a substantial portion of AGN population, approximately 30\% or more. The fraction of CT-AGNs discovered in the Cosmological Evolution Survey (COSMOS) is significantly lower than this value. This means that many CT-AGNs may be hidden in AGNs that exhibit low photon counts or that have not been detected by X-ray instruments. This work focuses on identifying CT-AGNs hidden in AGNs with low photon counts. Firstly, we selected 440 AGNs with abundant multiwavelength data as our sample. Secondly, we analyzed multiwavelength data, extracting crucial physical parameters required for the CT-AGN diagnosis. Finally, we used multiwavelength approaches to identify CT-AGNs. We have successfully identified 18 CT-AGNs in our sample. Among the CT-AGNs, four AGNs show discrepant results across different diagnostic methods. We discuss the potential reasons behind these diagnostic discrepancies. We explore the impact of estimating [O~III]$\lambda~5007$ luminosities based on [O~II]$\lambda~3727$ luminosities for the CT-AGN diagnosis. We have also found that the properties of host galaxies for CT-AGNs and non-CT-AGNs do not show significant discrepancies.
Radio emission from little red dots may reveal their true nature
2025-02-06
Muhammad A. Latif, Ammara Aftab, Daniel J. Whalen, Mar Mezcua
The unprecedented sensitivity of the \textit{James Webb Space Telescope} (\textit{JWST}) has revolutionized our understanding of the early universe. Among the most intriguing \textit{JWST} discoveries are red, very compact objects showing broad line emission features nicknamed as little red dots (LRDs). The discovery of LRDs has triggered great interest about their origin as either extremely starbursting galaxies or highly-obscured active galactic nuclei (AGN). Their exact nature still remains unknown. The goal of this work is to estimate the radio emission from LRDs and predict which radio surveys would detect them. To achieve these objectives, we employ the fundamental plane of black hole (BH) accretion to estimate radio emission from AGN and the stellar radio fluxes from their host galaxies. We assume a range of BH mass, X-ray luminosity ($\rm L_{X}$) and star formation rate (SFR) to bracket the likely properties of LRDs. Our findings suggest that BH radio fluxes from LRDs are 10-100 times higher than the stellar fluxes from their host galaxies, depending on BH mass, $\rm L_X$ and SFR. The detection of a $\sim$ 500 nJy signal above 2 GHz at $z \geq$ 5 or a $\sim$ 2000 nJy flux at $z =$ 3-4 would be a smoking gun for the presence of AGN provided that SFRs in the host galaxies are $\rm < 30~ M_{\odot} ~yr^{-1}$. We find that LRDs are most likely radio quiet AGN otherwise would have been already detected in the current radio surveys. Our findings suggest that LRDs can be detected with the upcoming radio observatories such as ngVLA and SKA with integration times of 10-100 hrs, respectively.
On the relationship between the cosmic web and the alignment of galaxies and AGN jets
2025-02-06
Seoyoung Lyla Jung, I. H. Whittam, M. J. Jarvis, C. L. Hale, M. N. Tudorache, T. Yasin
The impact of active galactic nuclei (AGN) on the evolution of galaxies explains the steep decrease in the number density of the most massive galaxies in the Universe. However, the fueling of the AGN and the efficiency of this feedback largely depend on their environment. We use data from the Low Frequency Array (LOFAR) Two-metre Sky Survey Data Release 2 (LoTSS DR2), the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys, and the Sloan Digital Sky Survey (SDSS) DR12 to make the first study of the orientations of radio jets and their optical counterpart in relation to the cosmic web environment. We find that close to filaments ($\lesssim 11 \,\rm Mpc$), galaxies tend to have their optical major axes aligned with the nearest filaments. On the other hand, radio jets, which are generally aligned perpendicularly to the optical major axis of the host galaxy, show more randomised orientations with respect to host galaxies within $\lesssim 8 \,\rm Mpc$ of filaments. These results support the scenario that massive galaxies in cosmic filaments grow by numerous mergers directed along the orientation of the filaments while experiencing chaotic accretion of gas onto the central black hole. The AGN-driven jets consequently have a strong impact preferentially along the minor axes of dark matter halos within filaments. We discuss the implications of these results for large-scale radio jet alignments, intrinsic alignments between galaxies, and the azimuthal anisotropy of the distribution of circumgalactic medium and anisotropic quenching.
The nine-grid layout is commonly used for multi-image posts, arranging nine images in a tic-tac-toe board. This layout effectively presents content within limited space. Moreover, due to the numerous possible arrangements within the nine-image grid, the optimal arrangement that yields the highest level of attractiveness remains unknown. Our study investigates how the arrangement of images within a nine-grid layout affects the overall popularity of the image set, aiming to explore alignment schemes more aligned with user preferences. Based on survey results regarding user preferences in image arrangement, we have identified two ordering sequences that are widely recognized: sequential order and center prioritization, considering both image visual content and aesthetic quality as alignment metrics, resulting in four layout schemes. Finally, we recruited participants to annotate various layout schemes of the same set of images. Our experience-centered evaluation indicates that layout schemes based on aesthetic quality outperformed others. This research yields empirical evidence supporting the optimization of the nine-grid layout for multi-image posts, thereby furnishing content creators with valuable insights to enhance both attractiveness and user experience.
Aggregate and conquer: detecting and steering LLM concepts by combining nonlinear predictors over multiple layers
2025-02-06
Daniel Beaglehole, Adityanarayanan Radhakrishnan, Enric Boix-Adserà, Mikhail Belkin
A trained Large Language Model (LLM) contains much of human knowledge. Yet, it is difficult to gauge the extent or accuracy of that knowledge, as LLMs do not always ``know what they know'' and may even be actively misleading. In this work, we give a general method for detecting semantic concepts in the internal activations of LLMs. Furthermore, we show that our methodology can be easily adapted to steer LLMs toward desirable outputs. Our innovations are the following: (1) we use a nonlinear feature learning method to identify important linear directions for predicting concepts from each layer; (2) we aggregate features across layers to build powerful concept detectors and steering mechanisms. We showcase the power of our approach by attaining state-of-the-art results for detecting hallucinations, harmfulness, toxicity, and untruthful content on seven benchmarks. We highlight the generality of our approach by steering LLMs towards new concepts that, to the best of our knowledge, have not been previously considered in the literature, including: semantic disambiguation, human languages, programming languages, hallucinated responses, science subjects, poetic/Shakespearean English, and even multiple concepts simultaneously. Moreover, our method can steer concepts with numerical attributes such as product reviews. We provide our code (including a simple API for our methods) at https://github.com/dmbeaglehole/neural_controllers .
Dark Brain Energy: Toward an Integrative Model of Spontaneous Slow Oscillations
2025-02-06
ZhuQing Gong, XiNian Zuo
Neural oscillations facilitate the functioning of the human brain in spatial and temporal dimensions at various frequencies. These oscillations feature a universal frequency architecture that is governed by brain anatomy, ensuring frequency specificity remains invariant across different measurement techniques. Initial magnetic resonance imaging (MRI) methodology constrained functional MRI (fMRI) investigations to a singular frequency range, thereby neglecting the frequency characteristics inherent in blood oxygen level-dependent oscillations. With advancements in MRI technology, it has become feasible to decode intricate brain activities via multi-band frequency analysis (MBFA). During the past decade, the utilization of MBFA in fMRI studies has surged, unveiling frequency-dependent characteristics of spontaneous slow oscillations (SSOs) believed to base dark energy in the brain. There remains a dearth of conclusive insights and hypotheses pertaining to the properties and functionalities of SSOs in distinct bands. We surveyed the SSO MBFA studies during the past 15 years to delineate the attributes of SSOs and enlighten their correlated functions. We further proposed a model to elucidate the hierarchical organization of multi-band SSOs by integrating their function, aimed at bridging theoretical gaps and guiding future MBFA research endeavors.
Discovery and Timing of 49 Pulsars from the Arecibo 327-MHz Drift Survey
2025-02-06
Timothy E. E. Olszanski, Evan F. Lewis, Julia S. Deneva, Maura A. McLaughlin, Kevin Stovall, Paulo C. C. Freire, Benetge B. P. Perera, Manjari Bagchi, Jose G. Martinez
We present 18 pulsar discoveries from the AO327 pulsar survey, along with their timing solutions and those for an additional 31 AO327-discovered pulsars. Timing solutions were constructed using observations from a follow-up timing campaign taken between the periods of 2013 -- 2019 using the Arecibo Observatory's 327-MHz receiver. Aside from PSR J0916+0658, an isolated pulsar that shows evidence for partial recycling, the remaining discoveries are non-recycled pulsars. We present a brief census of emission features for all pulsars with the following standouts. PSR~J1942+0142 is found to exhibit the very rare phenomenon of subpulse bi-drifting and PSR~J0225+1727 has an interpulse. We also report distance estimates using the NE2001 and YMW16 Galactic electron density models, and identify at least 10 sources where either one or both models underestimate the maximum Galactic line of sight dispersion measure.
2025-02-06
Sandra C. Sandoval, Christabel Acquaye, Kwesi Cobbina, Mohammad Nayeem Teli, Hal Daumé III
We examine the representation of African American English (AAE) in large language models (LLMs), exploring (a) the perceptions Black Americans have of how effective these technologies are at producing authentic AAE, and (b) in what contexts Black Americans find this desirable. Through both a survey of Black Americans ($n=$ 104) and annotation of LLM-produced AAE by Black Americans ($n=$ 228), we find that Black Americans favor choice and autonomy in determining when AAE is appropriate in LLM output. They tend to prefer that LLMs default to communicating in Mainstream U.S. English in formal settings, with greater interest in AAE production in less formal settings. When LLMs were appropriately prompted and provided in context examples, our participants found their outputs to have a level of AAE authenticity on par with transcripts of Black American speech. Select code and data for our project can be found here: https://github.com/smelliecat/AAEMime.git
A search for the anomalous events detected by ANITA using the Pierre Auger Observatory
2025-02-06
The Pierre Auger Collaboration, A. Abdul Halim, P. Abreu, M. Aglietta, I. Allekotte, K. Almeida Cheminant, A. Almela, R. Aloisio, J. Alvarez-Muñiz, J. Ammerman Yebra, G. A. Anastasi, L. Anchordoqui, B. Andrada, S. Andringa, L. Apollonio, C. Aramo, P. R. Araújo Ferreira, E. Arnone, J. C. Arteaga Velázquez, P. Assis, G. Avila, E. Avocone, A. Bakalova, F. Barbato, A. Bartz Mocellin, J. A. Bellido, C. Berat, M. E. Bertaina, G. Bhatta, M. Bianciotto, P. L. Biermann, V. Binet, K. Bismark, T. Bister, J. Biteau, J. Blazek, C. Bleve, J. Blümer, M. Boháčová, D. Boncioli, C. Bonifazi, L. Bonneau Arbeletche, N. Borodai, J. Brack, P. G. Brichetto Orchera, F. L. Briechle, A. Bueno, S. Buitink, M. Buscemi, M. Büsken, A. Bwembya, K. S. Caballero-Mora, S. Cabana-Freire, L. Caccianiga, F. Campuzano, I. A. Caracas, R. Caruso, A. Castellina, F. Catalani, G. Cataldi, L. Cazon, M. Cerda, A. Cermenati, J. A. Chinellato, J. Chudoba, L. Chytka, R. W. Clay, A. C. Cobos Cerutti, R. Colalillo, M. R. Coluccia, R. Conceição, A. Condorelli, G. Consolati, M. Conte, F. Convenga, D. Correia dos Santos, P. J. Costa, C. E. Covault, M. Cristinziani, C. S. Cruz Sanchez, S. Dasso, K. Daumiller, B. R. Dawson, R. M. de Almeida, J. de Jesús, S. J. de Jong, J. R. T. de Mello Neto, I. De Mitri, J. de Oliveira, D. de Oliveira Franco, F. de Palma, V. de Souza, B. P. de Souza de Errico, E. De Vito, A. Del Popolo, O. Deligny, N. Denner, L. Deval, A. di Matteo, M. Dobre, C. Dobrigkeit, J. C. D'Olivo, L. M. Domingues Mendes, Q. Dorosti, J. C. dos Anjos, R. C. dos Anjos, J. Ebr, F. Ellwanger, M. Emam, R. Engel, I. Epicoco, M. Erdmann, A. Etchegoyen, C. Evoli, H. Falcke, G. Farrar, A. C. Fauth, N. Fazzini, F. Feldbusch, F. Fenu, A. Fernandes, B. Fick, J. M. Figueira, A. Filipčič, T. Fitoussi, B. Flaggs, T. Fodran, T. Fujii, A. Fuster, C. Galea, C. Galelli, B. García, C. Gaudu, H. Gemmeke, F. Gesualdi, A. Gherghel-Lascu, P. L. Ghia, U. Giaccari, J. Glombitza, F. Gobbi, F. Gollan, G. Golup, M. Gómez Berisso, P. F. Gómez Vitale, J. P. Gongora, J. M. González, N. González, D. Góra, A. Gorgi, M. Gottowik, T. D. Grubb, F. Guarino, G. P. Guedes, E. Guido, L. Gülzow, S. Hahn, P. Hamal, M. R. Hampel, P. Hansen, D. Harari, V. M. Harvey, A. Haungs, T. Hebbeker, C. Hojvat, J. R. Hörandel, P. Horvath, M. Hrabovský, T. Huege, A. Insolia, P. G. Isar, P. Janecek, V. Jilek, J. A. Johnsen, J. Jurysek, K. -H. Kampert, B. Keilhauer, A. Khakurdikar, V. V. Kizakke Covilakam, H. O. Klages, M. Kleifges, F. Knapp, J. Köhler, N. Kunka, B. L. Lago, N. Langner, M. A. Leigui de Oliveira, Y. Lema-Capeans, A. Letessier-Selvon, I. Lhenry-Yvon, L. Lopes, L. Lu, Q. Luce, J. P. Lundquist, A. Machado Payeras, M. Majercakova, D. Mandat, B. C. Manning, P. Mantsch, F. M. Mariani, A. G. Mariazzi, I. C. Mariş, G. Marsella, D. Martello, S. Martinelli, O. Martínez Bravo, M. A. Martins, M. Mastrodicasa, H. -J. Mathes, J. Matthews, G. Matthiae, E. Mayotte, S. Mayotte, P. O. Mazur, G. Medina-Tanco, J. Meinert, D. Melo, A. Menshikov, C. Merx, S. Michal, M. I. Micheletti, L. Miramonti, S. Mollerach, F. Montanet, L. Morejon, C. Morello, K. Mulrey, R. Mussa, W. M. Namasaka, S. Negi, L. Nellen, K. Nguyen, G. Nicora, M. Niechciol, D. Nitz, D. Nosek, V. Novotny, L. Nožka, A. Nucita, L. A. Núñez, C. Oliveira, M. Palatka, J. Pallotta, S. Panja, G. Parente, T. Paulsen, J. Pawlowsky, M. Pech, J. Pękala, R. Pelayo, L. A. S. Pereira, E. E. Pereira Martins, J. Perez Armand, C. Pérez Bertolli, L. Perrone, S. Petrera, C. Petrucci, T. Pierog, M. Pimenta, M. Platino, B. Pont, M. Pothast, M. Pourmohammad Shahvar, P. Privitera, M. Prouza, S. Querchfeld, J. Rautenberg, D. Ravignani, J. V. Reginatto Akim, M. Reininghaus, J. Ridky, F. Riehn, M. Risse, V. Rizi, W. Rodrigues de Carvalho, E. Rodriguez, J. Rodriguez Rojo, M. J. Roncoroni, S. Rossoni, M. Roth, E. Roulet, A. C. Rovero, P. Ruehl, A. Saftoiu, M. Saharan, F. Salamida, H. Salazar, G. Salina, J. D. Sanabria Gomez, F. Sánchez, E. M. Santos, E. Santos, F. Sarazin, R. Sarmento, R. Sato, P. Savina, C. M. Schäfer, V. Scherini, H. Schieler, M. Schimassek, M. Schimp, D. Schmidt, O. Scholten, H. Schoorlemmer, P. Schovánek, F. G. Schröder, J. Schulte, T. Schulz, S. J. Sciutto, M. Scornavacche, A. Sedoski, A. Segreto, S. Sehgal, S. U. Shivashankara, G. Sigl, G. Silli, O. Sima, K. Simkova, F. Simon, R. Smau, R. Šmída, P. Sommers, J. F. Soriano, R. Squartini, M. Stadelmaier, S. Stanič, J. Stasielak, P. Stassi, S. Strähnz, M. Straub, T. Suomijärvi, A. D. Supanitsky, Z. Svozilikova, Z. Szadkowski, F. Tairli, A. Tapia, C. Taricco, C. Timmermans, O. Tkachenko, P. Tobiska, C. J. Todero Peixoto, B. Tomé, Z. Torrès, A. Travaini, P. Travnicek, C. Trimarelli, M. Tueros, M. Unger, L. Vaclavek, M. Vacula, J. F. Valdés Galicia, L. Valore, E. Varela, A. Vásquez-Ramírez, D. Veberič, C. Ventura, I. D. Vergara Quispe, V. Verzi, J. Vicha, J. Vink, S. Vorobiov, C. Watanabe, A. A. Watson, A. Weindl, L. Wiencke, H. Wilczyński, D. Wittkowski, B. Wundheiler, B. Yue, A. Yushkov, O. Zapparrata, E. Zas, D. Zavrtanik, M. Zavrtanik, R. Prechelt, A. Romero-Wolf, S. Wissel, A. Zeolla
A dedicated search for upward-going air showers at zenith angles exceeding $110^\circ$ and energies $E>0.1$ EeV has been performed using the Fluorescence Detector of the Pierre Auger Observatory. The search is motivated by two "anomalous" radio pulses observed by the ANITA flights I and III which appear inconsistent with the Standard Model of particle physics. Using simulations of both regular cosmic ray showers and upward-going events, a selection procedure has been defined to separate potential upward-going candidate events and the corresponding exposure has been calculated in the energy range [0.1-33] EeV. One event has been found in the search period between 1 Jan 2004 and 31 Dec 2018, consistent with an expected background of $0.27 \pm 0.12$ events from mis-reconstructed cosmic ray showers. This translates to an upper bound on the integral flux of $(7.2 \pm 0.2) \times 10^{-21}$ cm$^{-2}$ sr$^{-1}$ y$^{-1}$ and $(3.6 \pm 0.2) \times 10^{-20}$ cm$^{-2}$ sr$^{-1}$ y$^{-1}$ for an $E^{-1}$ and $E^{-2}$ spectrum, respectively. An upward-going flux of showers normalized to the ANITA observations is shown to predict over 34 events for an $E^{-3}$ spectrum and over 8.1 events for a conservative $E^{-5}$ spectrum, in strong disagreement with the interpretation of the anomalous events as upward-going showers.
Chiral effective model of cold and dense two-color QCD: The linear sigma model approach
2025-02-06
Daiki Suenaga
This review is devoted to summarizing recent developments of the linear sigma model (LSM) in cold and dense two-color QCD (QC$_2$D), in which lattice simulations are straightforwardly applicable thanks to the disappearance of the sign problem. In QC$_2$D, both theoretical and numerical studies derive the presence of the so-called baryon superfluid phase at sufficiently large chemical potential ($\mu_q$), where diquark condensates govern the ground state. The hadron mass spectrum simulated in this phase shows that the mass of an iso-singlet ($I=0$) and $0^-$ state is remarkably reduced, but such a mode cannot be described by the chiral perturbation theory. Motivated by this fact, I invent the LSM constructed upon the linear representation of chiral symmetry, or more precisely the Pauli-G\"ursey symmetry. Then, it is shown that my LSM successfully reproduces the low-lying hadron mass spectrum in a broad range of $\mu_q$ simulated on the lattice. As applications of the LSM, topological susceptibility and sound velocity in cold and dense QC$_2$D are evaluated to compare with lattice results. Besides, generalized Gell-Mann-Oakes-Renner relation and hardon mass spectrum in the presence of a diquark source are analyzed. I also introduce an extended version of the LSM incorporating spin-$1$ hadrons.
No Images, No Problem: Retaining Knowledge in Continual VQA with Questions-Only Memory
2025-02-06
Imad Eddine Marouf, Enzo Tartaglione, Stephane Lathuiliere, Joost van de Weijer
Continual Learning in Visual Question Answering (VQACL) requires models to learn new visual-linguistic tasks (plasticity) while retaining knowledge from previous tasks (stability). The multimodal nature of VQACL presents unique challenges, requiring models to balance stability across visual and textual domains while maintaining plasticity to adapt to novel objects and reasoning tasks. Existing methods, predominantly designed for unimodal tasks, often struggle to balance these demands effectively. In this work, we introduce QUestion-only replay with Attention Distillation (QUAD), a novel approach for VQACL that leverages only past task questions for regularisation, eliminating the need to store visual data and addressing both memory and privacy concerns. QUAD achieves stability by introducing a question-only replay mechanism that selectively uses questions from previous tasks to prevent overfitting to the current task's answer space, thereby mitigating the out-of-answer-set problem. Complementing this, we propose attention consistency distillation, which uniquely enforces both intra-modal and inter-modal attention consistency across tasks, preserving essential visual-linguistic associations. Extensive experiments on VQAv2 and NExT-QA demonstrate that QUAD significantly outperforms state-of-the-art methods, achieving robust performance in continual VQA.
The Blue supergiant problem and the main-sequence width
2025-02-06
Jorick S. Vink, Rene D. Oudmaijer
Using Gaia DR3 we derive new distances and luminosities for a sample of Galactic B supergiants which were thought to be post main-sequence (MS) objects from their HR diagram location beyond the terminal-age MS (TAMS). When applying the newer Gaia distances in addition to enhanced amounts of core-boundary mixing, aka convective overshooting, we show that these Galactic B supergiants are likely enclosed within the MS band, indicating an evolutionary stage of steady core hydrogen burning. We discuss the importance of considering enhanced overshooting and how vectors in the mass-luminosity plane (ML-plane) can be used to disentangle the effects of wind mass loss from interior mixing. We finish with the key message that any proposed solution to the BSG problem should consider not only an explanation for the sheer number of B supergiants inside the Hertzsprung gap, but should at the same time also account for the steep drop in rotation rates identified at spectral type B1 -- corresponding to an effective temperature of $\sim$21 kK, and for which two distinct families of solutions have been proposed.
From Bedside to Desktop: A Data Protocol for Normative Intracranial EEG and Abnormality Mapping
2025-02-06
Heather Woodhouse, Sarah J. Gascoigne, Gerard Hall, Callum Simpson, Nathan Evans, Gabrielle M. Schroeder, Peter N. Taylor, Yujiang Wang
Normative mapping is a framework used to map population-level features of health-related variables. It is widely used in neuroscience research, but the literature lacks established protocols in modalities that do not support healthy control measurements, such as intracranial EEG (icEEG). An icEEG normative map would allow researchers to learn about population-level brain activity and enable comparison of individual data against these norms to identify abnormalities. Currently, no standardised guide exists for transforming clinical data into a normative, regional icEEG map. Papers often cite different software and numerous articles to summarise the lengthy method, making it laborious for other researchers to understand or apply the process. Our protocol seeks to remedy this gap by providing a dataflow guide and key decision points that summarise existing methods. This protocol is used heavily in published works from our own lab (twelve peer-reviewed journal publications). Briefly, we take as input, icEEG recordings and neuroimaging data from people with epilepsy who are undergoing evaluation for resective surgery. As final outputs, we obtain a normative icEEG map, comprising signal properties localised to brain regions. Optionally, we can also process new subjects through the same pipeline and obtain their z-scores (or centiles) in each brain region, for abnormality detection and localisation. To date, a single, cohesive, dataflow pipeline for generating normative icEEG maps, along with abnormality mapping, has not been created. We envisage that this dataflow guide will not only increase understanding and application of normative mapping methods, but will also improve the consistency and quality of studies in the field.
KiDS-1000: Detection of deviations from a purely cold dark-matter power spectrum with tomographic weak gravitational lensing
2025-02-06
Patrick Simon, Lucas Porth, Pierre Burger, Konrad Kuijken
Model uncertainties in the nonlinear structure growth limit current probes of cosmological parameters. To shed more light on the physics of nonlinear scales, we reconstruct the finely binned three-dimensional power-spectrum from lensing data of the Kilo-Degree Survey (KiDS), relying solely on the background cosmology, source redshift distributions, and the intrinsic alignment (IA) amplitude of sources (and their uncertainties). The adopted Tikhonov regularisation stabilises the deprojection, enabling a Bayesian reconstruction in separate $z$-bins. Following a detailed description of the algorithm and performance tests with mock data, we present our results for the power spectrum as relative deviations from a $\Lambda\rm CDM$ reference spectrum that includes only structure growth by cold dark matter. Averaged over the full range $z\lesssim1$, a \emph{Planck}-consistent reference then requires a significant suppression on nonlinear scales, $k=0.05$--$10\,h\,\rm Mpc^{-1}$, of up to $20\%$--$30\%$ to match KiDS-1000 ($68\%$ credible interval, CI). Conversely, a reference with a lower $S_8\approx0.73$ avoids suppression and matches the KiDS-1000 spectrum within a $20\%$ tolerance. When resolved into three $z$-bins, however, and regardless of the reference, we detect structure growth only between $z\approx0.4$--$0.13$, but not between $z\approx0.7$--$0.4$. This could indicate spurious systematic errors in KiDS-1000, inaccuracies in the intrinsic alignment (IA) model, or potentially a non-standard cosmological model with delayed structure growth. In the near future, analysing data from stage-IV surveys with our algorithm promises a substantially more precise reconstruction of the power spectrum.
Debris disks around M dwarfs: The Herschel DEBRIS survey
2025-02-06
Jean-Francois Lestrade, Brenda C. Matthews, Grant M. Kennedy, Bruce Sibthorpe, Mark C. Wyatt, Mark Booth, Jean S. Greaves, Gaspard Duchene, Amaya Moro-Martin, Cassien Jobic
The Herschel open-time key program Disc Emission via a Bias-free Reconnaissance in the Infrared and Sub-millimeter (DEBRIS) is an unbiased survey of the nearest ~100 stars for each stellar type A-M observed with a uniform photometric sensitivity to search for cold debris disks around them. The analysis of the Photoconductor Array Camera and Spectrometer (PACS) photometric observations of the 94 DEBRIS M dwarfs of this program is presented in this paper, following upon two companion papers on the DEBRIS A-star and FGK-star subsamples. In the M-dwarf subsample, two debris disks have been detected, around the M3V dwarf GJ581 and the M4V dwarf FomalhautC (LP876-10). This result gives a disk detection rate of 2.1^{+2.7}_{-0.7}% at the 68% confidence level, significantly less than measured for earlier stellar types in the DEBRIS program. However, we show that the survey of the DEBRIS M-dwarf subsample is about ten times shallower than the surveys of the DEBRIS FGK subsamples when studied in the physical parameter space of the disk's fractional dust luminosity versus blackbody radius. Furthermore, had the DEBRIS K-star subsample been observed at the same shallower depth in this parameter space, its measured disk detection rate would have been statistically consistent with the one found for the M-dwarf subsample. Hence, the incidence of debris disks does not appear to drop from the K subsample to the M subsample of the DEBRIS program, when considering disks in the same region of physical parameter space. An alternative explanation is that the only two bright disks discovered in the M-dwarf subsample would not, in fact, be statistically representative of the whole population.
The integration of slow-thinking mechanisms into large language models (LLMs) offers a promising way toward achieving Level 2 AGI Reasoners, as exemplified by systems like OpenAI's o1. However, several significant challenges remain, including inefficient overthinking and an overreliance on auxiliary reward models. We point out that these limitations stem from LLMs' inability to internalize the search process, a key component of effective reasoning. A critical step toward addressing this issue is enabling LLMs to autonomously determine when and where to backtrack, a fundamental operation in traditional search algorithms. To this end, we propose a self-backtracking mechanism that equips LLMs with the ability to backtrack during both training and inference. This mechanism not only enhances reasoning ability but also efficiency by transforming slow-thinking processes into fast-thinking through self-improvement. Empirical evaluations demonstrate that our proposal significantly enhances the reasoning capabilities of LLMs, achieving a performance gain of over 40 percent compared to the optimal-path supervised fine-tuning method. We believe this study introduces a novel and promising pathway for developing more advanced and robust Reasoners.
A Survey on Backdoor Threats in Large Language Models (LLMs): Attacks, Defenses, and Evaluations
2025-02-06
Yihe Zhou, Tao Ni, Wei-Bin Lee, Qingchuan Zhao
Large Language Models (LLMs) have achieved significantly advanced capabilities in understanding and generating human language text, which have gained increasing popularity over recent years. Apart from their state-of-the-art natural language processing (NLP) performance, considering their widespread usage in many industries, including medicine, finance, education, etc., security concerns over their usage grow simultaneously. In recent years, the evolution of backdoor attacks has progressed with the advancement of defense mechanisms against them and more well-developed features in the LLMs. In this paper, we adapt the general taxonomy for classifying machine learning attacks on one of the subdivisions - training-time white-box backdoor attacks. Besides systematically classifying attack methods, we also consider the corresponding defense methods against backdoor attacks. By providing an extensive summary of existing works, we hope this survey can serve as a guideline for inspiring future research that further extends the attack scenarios and creates a stronger defense against them for more robust LLMs.
On the origin of mid-infrared colors in $γ$-ray blazars
2025-02-05
Raniere de Menezes, Raffaele D'Abrusco, Francesco Massaro
Context. The combination between non-thermal and thermal emission in $\gamma$-ray blazars pushes them to a specific region of the mid-infrared three-dimensional color diagram, the so-called blazar locus, built based on observations performed with the Wide-field Infrared Survey Explorer. The selection of blazar candidates based on these mid-infrared colors has been extensively used in the past decade in the hunt for the counterparts of unassociated $\gamma$-ray sources observed with the Fermi Large Area Telescope and in the search for new blazars in optical spectroscopic campaigns. Aims. In this work, we provide a theoretical description of the origin of the blazar locus and show how we can reasonably reproduce it with a model consisting of only three spectral components: a log-parabola accounting for the non-thermal emission, and an elliptical host and dust torus accounting for the thermal emission. Methods. We simulate spectral energy distributions for blazars, starting with a pure log-parabola model and then increasing its complexity by adding a template elliptical galaxy and dust torus. From these simulations, we compute the mid-infrared magnitudes and corresponding colors to create our own version of the blazar locus. Results. Our modeling allows for the selection of spectral parameters that better characterize the mid-infrared emission of $\gamma$-ray blazars, such as the log-parabola curvature ($\beta < 0.04$ for 50\% of our sample) and an average spectral peak around $E_p \approx 1.5 \times 10^{-13}$ erg. We also find that the log-parabola is the main spectral component behind the observed mid-infrared blazar colors, although additional components such as a host galaxy and a dust torus are crucial to obtain a precise reconstruction of the blazar locus.
In cosmic web analysis, complementary to traditional cosmological probes, the extrema (e.g. peaks and voids) two-point correlation functions (2PCFs) are of particular interest for the study of both astrophysical phenomena and cosmological structure formation. However most previous studies constructed those statistics via N-body simulations without a robust theoretical derivation from first principles. A strong motivation exists for analytically describing the 2PCFs of these local extrema, taking into account the nonlinear gravitational evolution in the late Universe. In this paper, we derive analytical formulae for the power spectra and 2PCFs of 2D critical points, including peaks (maxima), voids (minima) and saddle points, in mildly non-Gaussian weak gravitational lensing fields. We apply a perturbative bias expansion to model the clustering of 2D critical points. We successfully derive the power spectrum of weak lensing critical points up to the next-to-next-to-leading order (NNLO) in gravitational perturbation theory, where trispectrum configurations of the weak lensing field have to be included. We numerically evaluate those power spectra up to the next-to-leading order (NLO), which correspond to the inclusion of bispectrum configurations, and transform them to the corresponding 2PCFs. An exact Monte Carlo (MC) integration is performed assuming a Gaussian distributed density field to validate our theoretical predictions. Overall, we find similar properties in 2D compared to the clustering of 3D critical points previously measured from N-body simulations. Contrary to standard lensing power spectra analysis, we find distinct BAO features in the lensing peak 2PCFs due to the gradient and curvature constraints, and we quantify that non-Gaussianity makes for ~10% of the signal at quasi-linear scales which could be important for current stage-IV surveys.
DESI Strong Lens Foundry I: HST Observations and Modeling with GIGA-Lens
2025-02-05
X. Huang, S. Baltasar, N. Ratier-Werbin, C. Storfer, W. Sheu, S. Agarwal, M. Tamargo-Arizmendi, D. J. Schlegel, J. Aguilar, S. Ahlen, G. Aldering, S. Banka, S. BenZvi, D. Bianchi, A. Bolton, D. Brooks, A. Cikota, T. Claybaugh, A. de la Macorra, A. Dey, P. Doel, J. Edelstein, A. Filipp, J. E. Forero-Romero, E. Gaztanaga, S. Gontcho A Gontcho, A. Gu, G. Gutierrez, K. Honscheid, E. Jullo, S. Juneau, R. Kehoe, D. Kirkby, T. Kisner, A. Kremin, K. J. Kwon, A. Lambert, M. Landriau, D. Lang, L. Le Guillou, J. Liu, A. Meisner, R. Miquel, J. Moustakas, A. D. Myers, S. Perlmutter, I. Perez-Rafols, F. Prada, G. Rossi, D. Rubin, E. Sanchez, M. Schubnell, Y. Shu, E. Silver, D. Sprayberry, N. Suzuki, G. Tarle, B. A. Weaver, H. Zou
We present the Dark Energy Spectroscopic Instrument (DESI) Strong Lens Foundry. We discovered $\sim 3500$ new strong gravitational lens candidates in the DESI Legacy Imaging Surveys using residual neural networks (ResNet). We observed a subset (51) of our candidates using the Hubble Space Telescope (HST). All of them were confirmed to be strong lenses. We also briefly describe spectroscopic follow-up observations by DESI and Keck NIRES programs. From this very rich dataset, a number of studies will be carried out, including evaluating the quality of the ResNet search candidates and lens modeling. In this paper, we present our initial effort in these directions. In particular, as a demonstration, we present the lens model for DESI-165.4754-06.0423, with imaging data from HST, and lens and source redshifts from DESI and Keck NIRES, respectively. In this effort, we have applied a \emph{fully} forward-modeling Bayesian approach (GIGA-Lens), using \emph{multiple} GPUs, for the first time in both regards, to a strong lens with HST data, or any high resolution imaging.
One of the key challenges faced by autistic children is understanding social affordances in complex environments, which further impacts their ability to respond appropriately to social signals. In traffic scenarios, this impairment can even lead to safety concerns. In this paper, we introduce an LLM-simulated immersive projection environment designed to improve this ability in autistic children while ensuring their safety. We first propose 17 design considerations across four major categories, derived from a comprehensive review of previous research. Next, we developed a system called AIroad, which leverages LLMs to simulate drivers with varying social intents, expressed through explicit multimodal social signals. AIroad helps autistic children bridge the gap in recognizing the intentions behind behaviors and learning appropriate responses through various stimuli. A user study involving 14 participants demonstrated that this technology effectively engages autistic children and leads to significant improvements in their comprehension of social affordances in traffic scenarios. Additionally, parents reported high perceived usability of the system. These findings highlight the potential of combining LLM technology with immersive environments for the functional rehabilitation of autistic children in the future.
Harnessing Large Language Models for Curated Code Reviews
2025-02-05
Oussama Ben Sghaier, Martin Weyssow, Houari Sahraoui
In code review, generating structured and relevant comments is crucial for identifying code issues and facilitating accurate code changes that ensure an efficient code review process. Well-crafted comments not only streamline the code review itself but are also essential for subsequent tasks like code refinement, where the code is modified to satisfy the input review comment. Although various AI-based approaches aimed to automate comment generation, their effectiveness remains limited by the quality of the training data. Existing code review datasets are often noisy and unrefined, posing limitations to the learning potential of AI models and hindering the automation process. To address these challenges, we propose a curation pipeline designed to enhance the quality of the largest publicly available code review dataset. We begin by establishing an evaluation framework, incorporating specific criteria and categories to empirically study the initial quality of the dataset. Using a large language model (LLM)-driven approach, we then apply our curation pipeline to refine the dataset. A comparative analysis of the newly curated dataset, based on the same evaluation framework, demonstrates substantial improvements in the clarity and conciseness of the comments. Additionally, we assess the impact of the curated dataset on automating downstream tasks, specifically comment generation and code refinement. Our findings show that the curated dataset leads to enhanced model performance in generating more accurate comments. Curated comments are also more useful as they lead to more accurate code refinement.
Prediction of the Most Fire-Sensitive Point in Building Structures with Differentiable Agents for Thermal Simulators
2025-02-05
Yuan Xinjie, Khalid M. Mosalam
Fire safety is a critical area of research in civil and mechanical engineering, particularly in ensuring the structural stability of buildings during fire events. The Most Fire-Sensitive Point (MFSP) in a structure is the location where a fire would cause the greatest impact on structural stability. Accurate prediction of the MFSP is vital for streamlining structural assessments and optimizing the design process. This paper presents a novel framework for MFSP prediction using a neural network-based approach that integrates fire dynamics and finite element analysis through a differentiable agent model. The framework focuses on predicting the Maximum Interstory Drift Ratio (MIDR), a key indicator of structural performance under fire conditions. By leveraging the differentiable agent model, we efficiently generate labeled data for MFSP and directly train a predictor for this critical metric. To achieve this, we generated extensive simulation data encompassing structural and fire scenarios and employed graph neural networks to represent the building structures. Transfer learning was applied to optimize the training process, and an edge update mechanism was introduced to dynamically adjust edge attributes, reflecting property changes under fire conditions. The proposed model was rigorously evaluated on simulation data, demonstrating strong performance in accurately predicting both MIDR and MFSP, thus advancing fire safety analysis for building structures.
Screening is a time-consuming and labour-intensive yet required task for medical systematic reviews, as tens of thousands of studies often need to be screened. Prioritising relevant studies to be screened allows downstream systematic review creation tasks to start earlier and save time. In previous work, we developed a dense retrieval method to prioritise relevant studies with reviewer feedback during the title and abstract screening stage. Our method outperforms previous active learning methods in both effectiveness and efficiency. In this demo, we extend this prior work by creating (1) a web-based screening tool that enables end-users to screen studies exploiting state-of-the-art methods and (2) a Python library that integrates models and feedback mechanisms and allows researchers to develop and demonstrate new active learning methods. We describe the tool's design and showcase how it can aid screening. The tool is available at https://densereviewer.ielab.io. The source code is also open sourced at https://github.com/ielab/densereviewer.
Interacting dark energy constraints from the full-shape analyses of BOSS DR12 and DES Year 3 measurements
2025-02-05
M. Tsedrik, S. Lee, K. Markovic, P. Carrilho, A. Pourtsidou, C. Moretti, B. Bose, E. Huff, A. Robertson, P. L. Taylor, J. Zuntz
Dark Scattering (DS) is an interacting dark energy model characterised by pure momentum exchange between dark energy and dark matter. It is phenomenologically interesting because it is unconstrained by CMB data and can alleviate the $S_8$ tension. We derive constraints on cosmological and DS parameters using three two-point correlation functions (3$\times$2pt) from the Dark Energy Survey third year data release (DES Y3). We then add information from the multipoles of the galaxy power spectrum combined with Baryonic Acoustic Oscillation (BAO) measurements using the twelfth data release of the Baryon Oscillation Spectroscopic Survey (BOSS DR12) and external BAO measurements. We compare results from the direct combination of the probes with the joint posterior distribution calculated with a normalising flow approach. Additionally, we run a CMB analysis with the Planck Public Release 4 (PR4) for comparison of the cosmological constraints. Overall, we find that the combination of probes allows minimising the projection effects and improves constraints without the need to include CMB information. It brings the marginalised posterior maxima closer to the corresponding best-fit values and weakens the sensitivity to the priors of the spectroscopic modelling nuisance parameters. These findings are highly relevant in light of forthcoming data of surveys like DESI, Euclid, and Rubin.
We provide an expository introduction to Euclidean Scissors Congruence, the study of polytopes in Euclidean space up to `cut and paste' relations. We first re-frame questions in scissors congruence as those in group homology. We then use this perspective to review the proof of the Dehn-Sydler-Jessen Theorem as found in the works of Dupont and Sah.
Demystifying Long Chain-of-Thought Reasoning in LLMs
2025-02-05
Edward Yeo, Yuxuan Tong, Morry Niu, Graham Neubig, Xiang Yue
Scaling inference compute enhances reasoning in large language models (LLMs), with long chains-of-thought (CoTs) enabling strategies like backtracking and error correction. Reinforcement learning (RL) has emerged as a crucial method for developing these capabilities, yet the conditions under which long CoTs emerge remain unclear, and RL training requires careful design choices. In this study, we systematically investigate the mechanics of long CoT reasoning, identifying the key factors that enable models to generate long CoT trajectories. Through extensive supervised fine-tuning (SFT) and RL experiments, we present four main findings: (1) While SFT is not strictly necessary, it simplifies training and improves efficiency; (2) Reasoning capabilities tend to emerge with increased training compute, but their development is not guaranteed, making reward shaping crucial for stabilizing CoT length growth; (3) Scaling verifiable reward signals is critical for RL. We find that leveraging noisy, web-extracted solutions with filtering mechanisms shows strong potential, particularly for out-of-distribution (OOD) tasks such as STEM reasoning; and (4) Core abilities like error correction are inherently present in base models, but incentivizing these skills effectively for complex tasks via RL demands significant compute, and measuring their emergence requires a nuanced approach. These insights provide practical guidance for optimizing training strategies to enhance long CoT reasoning in LLMs. Our code is available at: https://github.com/eddycmu/demystify-long-cot.
Constraints on Ultra-light Axion Dark Matter through Galaxy Cluster Number Counts
2025-02-05
S. Zelmer, E. Artis, E. Bulbul, S. Grandis, V. Ghirardini, A. von der Linden, Y. E. Bahar, F. Balzer, M. Brüggen, I. Chiu, N. Clerc, J. Comparat, F. Kleinebreil, M. Kluge, S. Krippendorf, A. Liu, N. Malavasi, A. Merloni, H. Miyatake, S. Miyazaki, K. Nandra, N. Okabe, M. E. Ramos-Ceja, J. S. Sanders, T. Schrabback, R. Seppi, J. Weller, X. Zhang
Ultra-light axions are hypothetical scalar particles that influence the evolution of large-scale structures of the Universe. Depending on their mass, they can potentially be part of the dark matter component of the Universe, as candidates commonly referred to as fuzzy dark matter. While strong constraints have been established for pure fuzzy dark matter models, the more general scenario where ultra-light axions constitute only a fraction of the dark matter has been limited to a few observational probes. In this work, we use the galaxy cluster number counts obtained from the first All-Sky Survey (eRASS1) of the SRG/eROSITA mission together with gravitational weak lensing data from the Dark Energy Survey, the Kilo-Degree Survey, and the Hyper Suprime-Cam, to constrain the fraction of ultra-light axions in the mass range $10^{-32}$ eV to $10^{-24}$ eV. We put upper bounds on the ultra-light axion relic density in independent logarithmic axion mass bins by performing a full cosmological parameter inference. We find an exclusion region in the intermediate ultra-light axion mass regime with the tightest bounds reported so far in the mass bins around $m_\mathrm{a}=10^{-27}$ eV with $\Omega_\mathrm{a} < 0.0036$ and $m_\mathrm{a}=10^{-26}$ eV with $\Omega_\mathrm{a} < 0.0084$, both at 95% confidence level. When combining with CMB probes, these bounds are tightened to $\Omega_\mathrm{a} < 0.0030$ in the $m_\mathrm{a}=10^{27}$ eV mass bin and $\Omega_\mathrm{a} < 0.0058$ in the $m_\mathrm{a}=10^{-26}$ eV mass bin, both at 95% confidence level. This is the first time that constraints on ultra-light axions have been obtained using the growth of structure measured by galaxy cluster number counts. These results pave the way for large surveys, which can be utilized to obtain tight constraints on the mass and relic density of ultra-light axions with better theoretical modeling of the abundance of halos.
DiversityOne: A Multi-Country Smartphone Sensor Dataset for Everyday Life Behavior Modeling
2025-02-05
Matteo Busso, Andrea Bontempelli, Leonardo Javier Malcotti, Lakmal Meegahapola, Peter Kun, Shyam Diwakar, Chaitanya Nutakki, Marcelo Dario Rodas Britez, Hao Xu, Donglei Song, Salvador Ruiz Correa, Andrea-Rebeca Mendoza-Lara, George Gaskell, Sally Stares, Miriam Bidoglia, Amarsanaa Ganbold, Altangerel Chagnaa, Luca Cernuzzi, Alethia Hume, Ronald Chenu-Abente, Roy Alia Asiku, Ivan Kayongo, Daniel Gatica-Perez, Amalia de Götzen, Ivano Bison, Fausto Giunchiglia
Understanding everyday life behavior of young adults through personal devices, e.g., smartphones and smartwatches, is key for various applications, from enhancing the user experience in mobile apps to enabling appropriate interventions in digital health apps. Towards this goal, previous studies have relied on datasets combining passive sensor data with human-provided annotations or self-reports. However, many existing datasets are limited in scope, often focusing on specific countries primarily in the Global North, involving a small number of participants, or using a limited range of pre-processed sensors. These limitations restrict the ability to capture cross-country variations of human behavior, including the possibility of studying model generalization, and robustness. To address this gap, we introduce DiversityOne, a dataset which spans eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, and the United Kingdom) and includes data from 782 college students over four weeks. DiversityOne contains data from 26 smartphone sensor modalities and 350K+ self-reports. As of today, it is one of the largest and most diverse publicly available datasets, while featuring extensive demographic and psychosocial survey data. DiversityOne opens the possibility of studying important research problems in ubiquitous computing, particularly in domain adaptation and generalization across countries, all research areas so far largely underexplored because of the lack of adequate datasets.
Intent Representation Learning with Large Language Model for Recommendation
2025-02-05
Yu Wang, Lei Sang, Yi Zhang, Yiwen Zhang
Intent-based recommender systems have garnered significant attention for uncovering latent fine-grained preferences. Intents, as underlying factors of interactions, are crucial for improving recommendation interpretability. Most methods define intents as learnable parameters updated alongside interactions. However, existing frameworks often overlook textual information (e.g., user reviews, item descriptions), which is crucial for alleviating the sparsity of interaction intents. Exploring these multimodal intents, especially the inherent differences in representation spaces, poses two key challenges: i) How to align multimodal intents and effectively mitigate noise issues; ii) How to extract and match latent key intents across modalities. To tackle these challenges, we propose a model-agnostic framework, Intent Representation Learning with Large Language Model (IRLLRec), which leverages large language models (LLMs) to construct multimodal intents and enhance recommendations. Specifically, IRLLRec employs a dual-tower architecture to learn multimodal intent representations. Next, we propose pairwise and translation alignment to eliminate inter-modal differences and enhance robustness against noisy input features. Finally, to better match textual and interaction-based intents, we employ momentum distillation to perform teacher-student learning on fused intent representations. Empirical evaluations on three datasets show that our IRLLRec framework outperforms baselines. The implementation is available at https://github.com/wangyu0627/IRLLRec.
STEM: Spatial-Temporal Mapping Tool For Spiking Neural Networks
2025-02-05
Sherif Eissa, Sander Stuijk, Floran De Putter, Andrea Nardi-Dei, Federico Corradi, Henk Corporaal
Spiking Neural Networks (SNNs) are promising bio-inspired third-generation neural networks. Recent research has trained deep SNN models with accuracy on par with Artificial Neural Networks (ANNs). Although the event-driven and sparse nature of SNNs show potential for more energy efficient computation than ANNs, SNN neurons have internal states which evolve over time. Keeping track of SNN states can significantly increase data movement and storage requirements, potentially losing its advantages with respect to ANNs. This paper investigates the energy effects of having neuron states, and how it is influenced by the chosen mapping to realistic hardware architectures with advanced memory hierarchies. Therefore, we develop STEMS, a mapping design space exploration tool for SNNs. STEMS models SNN's stateful behavior and explores intra-layer and inter-layer mapping optimizations to minimize data movement, considering both spatial and temporal SNN dimensions. Using STEMS, we show up to 12x reduction in off-chip data movement and 5x reduction in energy (on top of intra-layer optimizations), on two event-based vision SNN benchmarks. Finally, neuron states may not be needed for all SNN layers. By optimizing neuron states for one of our benchmarks, we show 20x reduction in neuron states and 1.4x better performance without accuracy loss.
Fault-Tolerant Control for System Availability and Continuous Operation in Heavy-Duty Wheeled Mobile Robots
2025-02-05
Mehdi Heydari Shahna, Pauli Mustalahti, Jouni Mattila
When the control system in a heavy-duty wheeled mobile robot (HD-WMR) malfunctions, deviations from ideal motion occur, significantly heightening the risks of off-road instability and costly damage. To meet the demands for safety, reliability, and controllability in HD-WMRs, the control system must tolerate faults to a certain extent, ensuring continuous operation. To this end, this paper introduces a model-free hierarchical control with fault accommodation (MFHCA) framework designed to address sensor and actuator faults in hydraulically powered HD-WMRs with independently controlled wheels. To begin, a novel mathematical representation of the motion dynamics of HD-WMRs, incorporating both sensor and actuator fault modes, is investigated. Subsequently, the MFHCA framework is proposed to manage all wheels under various fault modes, ensuring that each wheel tracks the reference driving velocities and steering angles, which are inverse kinematically mapped from the angular and linear velocities commanded in the HD-WMR's base frame. To do so, this framework generates appropriate power efforts in independently valve-regulated wheels to accommodate the adaptively isolated faults, thereby ensuring exponential stability. The experimental analysis of a 6,500-kg hydraulic-powered HD-WMR under various fault modes and rough terrains demonstrates the validity of the MFHCA framework.
Model Reference-Based Control with Guaranteed Predefined Performance for Uncertain Strict-Feedback Systems
2025-02-05
Mehdi Heydari Shahna, Jukka-Pekka Humaloja, Jouni Mattila
To address the complexities posed by time- and state-varying uncertainties and the computation of analytic derivatives in strict-feedback form (SFF) systems, this study introduces a novel model reference-based control (MRBC) framework which applies locally to each subsystem (SS), to ensure output tracking performance within the specified transient and steady-state response criteria. This framework includes 1) novel homogeneous adaptive estimators (HAEs) designed to match the uncertain nonlinear SFF system to a reference model, enabling easier analysis and control design at the $SS$ level, and 2) model-based homogeneous adaptive controllers enhanced by logarithmic barrier Lyapunov functions (HAC-BLFs), intended to control the reference model provided by HAEs in each SS, while ensuring the prescribed tracking responses under control amplitude saturation. The inherently robust MRBC achieves uniformly exponential stability using a generic stability connector term, which addresses dynamic interactions between the adjacent SSs. The parameter sensitivities of HAEs and HAC-BLFs in the MRBC framework are analyzed, focusing on the system's robustness and responsiveness. The proposed MRBC framework is experimentally validated through several scenarios involving an electromechanical linear actuator system with an uncertain SFF, subjected loading disturbance forces challenging 0-95% of its capacity.
PhotoD with LSST: Stellar Photometric Distances Out to the Edge of the Galaxy
2025-02-05
Lovro Palaversa, Željko Ivezić, Neven Caplar, Karlo Mrakovčić, Bob Abel, Oleksandra Razim, Filip Matković, Connor Yablonski, Toni Šarić, Tomislav Jurkić, Sandro Campos, Melissa DeLucchi, Derek Jones, Konstantin Malanchev, Alex I. Malz, Sean McGuire, Mario Jurić
As demonstrated with the Sloan Digital Sky Survey (SDSS), Pan-STARRS, and most recently with Gaia data, broadband near-UV to near-IR stellar photometry can be used to estimate distance, metallicity, and interstellar dust extinction along the line of sight for stars in the Galaxy. Anticipating photometric catalogs with tens of billions of stars from Rubin's Legacy Survey of Space and Time (LSST), we present a Bayesian model and pipeline that build on previous work and can handle LSST-sized datasets. Likelihood computations utilize MIST/Dartmouth isochrones and priors are derived from TRILEGAL-based simulated LSST catalogs from P. Dal Tio et al. The computation speed is about 10 ms per star on a single core for both optimized grid search and Markov Chain Monte Carlo methods; we show in a companion paper by K. Mrakov\v{c}i\'c et al. how to utilize neural networks to accelerate this performance by up to an order of magnitude. We validate our pipeline, named PhotoD (in analogy with photo-z, photometric redshifts of galaxies) using both simulated catalogs and SDSS, DECam, and Gaia photometry. We intend to make LSST-based value-added PhotoD catalogs publicly available via the Rubin Science Platform with every LSST data release.
Cavity engineering of solid-state materials without external driving
2025-02-05
I-Te Lu, Dongbin Shin, Mark Kamper Svendsen, Simone Latini, Hannes Hübener, Michael Ruggenthaler, Angel Rubio
Confining electromagnetic fields inside an optical cavity can enhance the light-matter coupling between quantum materials embedded inside the cavity and the confined photon fields. When the interaction between the matter and the photon fields is strong enough, even the quantum vacuum field fluctuations of the photons confined in the cavity can alter the properties of the cavity-embedded solid-state materials at equilibrium and room temperature. This approach to engineering materials with light avoids fundamental issues of laser-induced transient matter states. To clearly differentiate this field from phenomena in driven systems, we call this emerging field cavity materials engineering. In this review, we first present theoretical frameworks, especially, ab initio methods, for describing light-matter interactions in solid-state materials embedded inside a realistic optical cavity. Next, we overview a few experimental breakthroughs in this domain, detailing how the ground state properties of materials can be altered within such confined photonic environments. Moreover, we discuss state-of-the-art theoretical proposals for tailoring material properties within cavities. Finally, we outline the key challenges and promising avenues for future research in this exciting field.
Secure resource management (SRM) within a cloud computing environment is a critical yet infrequently studied research topic. This paper provides a comprehensive survey and comparative performance evaluation of potential cyber threat countermeasure strategies that address security challenges during cloud workload execution and resource management. Cybersecurity is explored specifically in the context of cloud resource management, with an emphasis on identifying the associated challenges. The cyber threat countermeasure methods are categorized into three classes: defensive strategies, mitigating strategies, and hybrid strategies. The existing countermeasure strategies belonging to each class are thoroughly discussed and compared. In addition to conceptual and theoretical analysis, the leading countermeasure strategies within these categories are implemented on a common platform and examined using two real-world virtual machine (VM) data traces. Based on this comprehensive study and performance evaluation, the paper discusses the trade-offs among these countermeasure strategies and their utility, providing imperative concluding remarks on the holistic study of cloud cyber threat countermeasures and secure resource management. Furthermore, the study suggests future methodologies that could effectively address the emerging challenges of secure cloud resource management.
DES to HSC: Detecting low surface brightness galaxies in the Abell 194 cluster using transfer learning
2025-02-05
H. Thuruthipilly, Junais, J. Koda, A. Pollo, M. Yagi, H. Yamanoi, Y. Komiyama, M. Romano, K. Małek, D. Donevski
Low surface brightness galaxies (LSBGs) are important for understanding galaxy evolution and cosmological models. The upcoming large-scale surveys are expected to uncover a large number of LSBGs, requiring accurate automated or machine learning-based methods for their detection. We study the scope of transfer learning for the identification of LSBGs. We use transformer models divided into two categories: LSBG Detection Transformer (LSBG DETR) and LSBG Vision Transformer (LSBG ViT), trained on Dark Energy Survey (DES) data, to identify LSBGs from dedicated Hyper Suprime-Cam (HSC) observations of the Abell 194 cluster, which are two magnitudes deeper than DES. The data from DES and HSC were standardized based on pixel-level surface brightness. We used two transformer ensembles to detect LSBGs. This was followed by a single-component S\'ersic model fit and a final visual inspection to filter out potential false positives and improve sample purity. We present a sample of 171 low surface brightness galaxies (LSBGs) in the Abell 194 cluster using HSC data, including 87 new discoveries. Of these, 159 were identified using transformer models, and 12 additional LSBGs were found through visual inspection. The transformer model achieved a true positive rate (TPR) of 93% in HSC data without any fine-tuning. Among the LSBGs, 28 were classified as ultra-diffuse galaxies (UDGs). The number of UDGs and the radial UDG number density suggest a linear relationship between UDG numbers and cluster mass on a log scale. UDGs share similar S\'ersic parameters with dwarf galaxies and occupy the extended end of the $R_{\mathrm{eff}}-M_g$ plane, suggesting they might be an extended subpopulation of dwarf galaxies. We have demonstrated that transformer models trained on shallower surveys can be successfully applied to deeper surveys with appropriate data normalization.
Multi-objective methods in Federated Learning: A survey and taxonomy
2025-02-05
Maria Hartmann, Grégoire Danoy, Pascal Bouvry
The Federated Learning paradigm facilitates effective distributed machine learning in settings where training data is decentralized across multiple clients. As the popularity of the strategy grows, increasingly complex real-world problems emerge, many of which require balancing conflicting demands such as fairness, utility, and resource consumption. Recent works have begun to recognise the use of a multi-objective perspective in answer to this challenge. However, this novel approach of combining federated methods with multi-objective optimisation has never been discussed in the broader context of both fields. In this work, we offer a first clear and systematic overview of the different ways the two fields can be integrated. We propose a first taxonomy on the use of multi-objective methods in connection with Federated Learning, providing a targeted survey of the state-of-the-art and proposing unambiguous labels to categorise contributions. Given the developing nature of this field, our taxonomy is designed to provide a solid basis for further research, capturing existing works while anticipating future additions. Finally, we outline open challenges and possible directions for further research.
Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation
2025-02-05
Nina Freise, Marius Heitlinger, Ruben Nuredini, Gerrit Meixner
Artificial Intelligence (AI) advancement is heavily dependent on access to large-scale, high-quality training data. However, in specialized domains such as healthcare, data acquisition faces significant constraints due to privacy regulations, ethical considerations, and limited availability. While synthetic data generation offers a promising solution, conventional approaches typically require substantial real data for training generative models. The emergence of large-scale prompt-based models presents new opportunities for synthetic data generation without direct access to protected data. However, crafting effective prompts for domain-specific data generation remains challenging, and manual prompt engineering proves insufficient for achieving output with sufficient precision and authenticity. We review recent developments in automatic prompt optimization, following PRISMA guidelines. We analyze six peer-reviewed studies published between 2020 and 2024 that focus on automatic data-free prompt optimization methods. Our analysis reveals three approaches: feedback-driven, error-based, and control-theoretic. Although all approaches demonstrate promising capabilities in prompt refinement and adaptation, our findings suggest the need for an integrated framework that combines complementary optimization techniques to enhance synthetic data generation while minimizing manual intervention. We propose future research directions toward developing robust, iterative prompt optimization frameworks capable of improving the quality of synthetic data. This advancement can be particularly crucial for sensitive fields and in specialized domains where data access is restricted, potentially transforming how we approach synthetic data generation for AI development.
A comprehensive study of the gas-phase formation network of HC$_5$N: theory, experiments, observations and models
2025-02-05
Lisa Giani, Eleonora Bianchi, Martin Fournier, Sidaty Cheikh Sid Ely, Cecilia Ceccarelli, Marzio Rosi, Jean-Claude Guillemin, Ian R. Sims, Nadia Balucani
Cyanopolyynes are among the largest and most commonly observed interstellar Complex Organic Molecules in star-forming regions. They are believed to form primarily in the gas-phase, but their formation routes are not well understood. We present a comprehensive study of the gas-phase formation network of cyanobutadiyne, HC$_5$N, based on new theoretical calculations, kinetics experiments, astronomical observations, and astrochemical modeling. We performed new quantum mechanics calculations for six neutral-neutral reactions in order to derive reliable rate coefficients and product branching fractions. We also present new CRESU data on the rate coefficients of three of these reactions (C$_3$N + C$_2$H$_2$, C$_2$H + HC$_3$N, CN + C$_4$H$_2$) obtained at temperatures as low as 24 K. In practice, six out of nine reactions currently used in astrochemical models have been updated in our reviewed network. We also report the tentative detection of the $^{13}$C isotopologues of HC$_5$N in the L1544 prestellar core. We derived a lower limit of $^{12}$C/$^{13}$C > 75 for the HC$_5$N isotopologues, which does not allow to bring new constraints to the HC$_5$N chemistry. Finally, we verified the impact of the revised reactions by running the GRETOBAPE astrochemical model. We found good agreement between the HC$_5$N predicted and observed abundances in cold ($\sim$10 K) objects, demonstrating that HC$_5$N is mainly formed by neutral-neutral reactions in these environments. In warm molecular shocks, instead, the predicted abundances are a factor of ten lower with respect to observed ones. In this environment possessing an higher gas ionization fraction, we speculate that the contribution of ion-neutral reactions could be significant.
More is better: Strong constraints on the stellar properties of LEGA-C z ~ 1 galaxies with Prospector
2025-02-05
Angelos Nersesian, Arjen van der Wel, Anna R. Gallazzi, Yasha Kaushal, Rachel Bezanson, Stefano Zibetti, Eric F. Bell, Francesco D'Eugenio, Joel Leja, Marco Martorano, Po-Feng Wu
We present the stellar properties of 2908 galaxies at 0.6 < z < 1.0 from the LEGA-C survey. We emphasize the importance of high signal-to-noise, high spectral resolution spectroscopy in the inference of stellar population properties of galaxies. We estimate the galaxy properties with the SED fitting code Prospector, by fitting spectroscopy and broadband photometry together, drawn from the LEGA-C DR3 and UltraVISTA catalogs respectively. We report a positive correlation between light-weighted ages and stellar velocity dispersion ($\sigma_\star$). The trend with $\sigma_\star$ is weaker for the mass-weighted ages and stellar metallicity ($Z_\star$). On average, quiescent galaxies are characterized by high $Z_\star$, they are \sim 1.1 Gyr older, less dusty, with steeper dust attenuation slopes compared to star-forming galaxies. Conversely, star-forming galaxies are characterized by significantly higher dust optical depths and shallower (grayer) attenuation slopes. Low mass (high mass) star-forming galaxies have lower (higher) $Z_\star$, while their stellar populations are on average younger (older). A key pragmatic result of our study is that a linear-space metallicity prior is preferable to a logarithmic-space one when using photometry alone, as the latter biases the posteriors downward. Spectroscopy greatly improves stellar population measurements and is required to provide meaningful constraints on age, metallicity, and other properties. Pairing spectroscopy with photometry helps resolving the dust-age-metallicity degeneracy, yielding more accurate mass- and light-weighted ages, with ages inferred from photometry alone suffering such large uncertainties. Stellar metallicities are constrained by our spectroscopy, but precise measurements remain challenging (and impossible with photometry alone), particularly in the absence of Mg and Fe lines redward of 5000 $\AA$ in the observed spectrum.
An analysis of optimization problems involving ReLU neural networks
2025-02-05
Christoph Plate, Mirko Hahn, Alexander Klimek, Caroline Ganzer, Kai Sundmacher, Sebastian Sager
Solving mixed-integer optimization problems with embedded neural networks with ReLU activation functions is challenging. Big-M coefficients that arise in relaxing binary decisions related to these functions grow exponentially with the number of layers. We survey and propose different approaches to analyze and improve the run time behavior of mixed-integer programming solvers in this context. Among them are clipped variants and regularization techniques applied during training as well as optimization-based bound tightening and a novel scaling for given ReLU networks. We numerically compare these approaches for three benchmark problems from the literature. We use the number of linear regions, the percentage of stable neurons, and overall computational effort as indicators. As a major takeaway we observe and quantify a trade-off between the often desired redundancy of neural network models versus the computational costs for solving related optimization problems.
Robust spin-orbit coupling in semi-metallic SrIrO3 under hydrostatic pressure
2025-02-05
Dirk Fuchs, Arun Kumar Jaiswal, Fabrice Wilhelm, Di Wang, Andre Rogalev, Matthieu Le Tacon
The semi-metallic behavior of the perovskite iridate SrIrO3 shifts the end-member of the strongly spin-orbit (SO) coupled Ruddlesden-Popper series Srn+1IrnO3n+1 away from the Mott insulating regime and the half-filled pseudospin Jeff=1/2 ground state well-established in the layered iridates (n = 1 and 2). To investigate the robustness of the SO coupled ground state of SrIrO3, X-ray absorption spectroscopy was carried out at the Ir L2,3 edges under hydrostatic pressure up to 50 GPa at room temperature. The effective SO coupling was deduced from the branching ratio of the Ir L2 and L3 white lines. With increasing pressure, the branching ratio decreases, and the Ir L2,3 peak positions shift to higher energies. The number of 5d holes remains constant indicating that the spectral weight redistribution and peak shifts arise from orbital mixing between t2g and eg related states. The expectation value of the angular part of the SO operator decreases by about 15% at 50 GPa. This reduction, which is very similar to that observed in the layered iridates, is well explained by an increase of the octahedral crystal field due to the shortening of the Ir-O bond-length under compression. Consistent with theoretical predictions, the orbital mixing and decrease as the crystal field increases. However, the effective SO coupling remains robust against pressure and does not indicate a covalency-driven breakdown within the investigated pressure range.
TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics
2025-02-05
Lu Yi, Jie Peng, Yanping Zheng, Fengran Mo, Zhewei Wei, Yuhang Ye, Yue Zixuan, Zengfeng Huang
Future link prediction is a fundamental challenge in various real-world dynamic systems. To address this, numerous temporal graph neural networks (temporal GNNs) and benchmark datasets have been developed. However, these datasets often feature excessive repeated edges and lack complex sequential dynamics, a key characteristic inherent in many real-world applications such as recommender systems and ``Who-To-Follow'' on social networks. This oversight has led existing methods to inadvertently downplay the importance of learning sequential dynamics, focusing primarily on predicting repeated edges. In this study, we demonstrate that existing methods, such as GraphMixer and DyGFormer, are inherently incapable of learning simple sequential dynamics, such as ``a user who has followed OpenAI and Anthropic is more likely to follow AI at Meta next.'' Motivated by this issue, we introduce the Temporal Graph Benchmark with Sequential Dynamics (TGB-Seq), a new benchmark carefully curated to minimize repeated edges, challenging models to learn sequential dynamics and generalize to unseen edges. TGB-Seq comprises large real-world datasets spanning diverse domains, including e-commerce interactions, movie ratings, business reviews, social networks, citation networks and web link networks. Benchmarking experiments reveal that current methods usually suffer significant performance degradation and incur substantial training costs on TGB-Seq, posing new challenges and opportunities for future research. TGB-Seq datasets, leaderboards, and example codes are available at https://tgb-seq.github.io/.
Human-Robot Interaction (pHRI) is critical for implementing Industry 5.0 which focuses on human-centric approaches. However, few studies explore the practical alignment of pHRI to industrial grade performance. This paper introduces a versatile control framework designed to bridge this gap by incorporating the torque-based control modes: compliance control, null-space compliance, dual compliance, all in static and dynamic scenarios. Thanks to our second-order Quadratic Programming (QP) formulation, strict kinematic and collision constraints are integrated into the system as safety features, and a weighted hierarchy guarantees singularity-robust task tracking performance. The framework is implemented on a Kinova Gen3 collaborative robot (cobot) equipped with a Bota force/torque sensor. A DualShock 4 game controller is attached at the robot's end-effector to demonstrate the framework's capabilities. This setup enables seamless dynamic switching between the modes, and real-time adjustment of parameters, such as transitioning between position and torque control or selecting a more robust custom-developed low-level torque controller over the default one.Built on the open-source robotic control software mc_rtc, to ensure reproducibility for both research and industrial deployment, this framework demonstrates industrial-grade performance and repeatability, showcasing its potential as a robust pHRI control system for industrial environments.
TopoCL: Topological Contrastive Learning for Time Series
2025-02-05
Namwoo Kim, Hyungryul Baik, Yoonjin Yoon
Universal time series representation learning is challenging but valuable in real-world applications such as classification, anomaly detection, and forecasting. Recently, contrastive learning (CL) has been actively explored to tackle time series representation. However, a key challenge is that the data augmentation process in CL can distort seasonal patterns or temporal dependencies, inevitably leading to a loss of semantic information. To address this challenge, we propose Topological Contrastive Learning for time series (TopoCL). TopoCL mitigates such information loss by incorporating persistent homology, which captures the topological characteristics of data that remain invariant under transformations. In this paper, we treat the temporal and topological properties of time series data as distinct modalities. Specifically, we compute persistent homology to construct topological features of time series data, representing them in persistence diagrams. We then design a neural network to encode these persistent diagrams. Our approach jointly optimizes CL within the time modality and time-topology correspondence, promoting a comprehensive understanding of both temporal semantics and topological properties of time series. We conduct extensive experiments on four downstream tasks-classification, anomaly detection, forecasting, and transfer learning. The results demonstrate that TopoCL achieves state-of-the-art performance.
MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations
2025-02-05
Namwoo Kim, Takahiro Yabe, Chanyoung Park, Yoonjin Yoon
Recently, learning effective representations of urban regions has gained significant attention as a key approach to understanding urban dynamics and advancing smarter cities. Existing approaches have demonstrated the potential of leveraging mobility data to generate latent representations, providing valuable insights into the intrinsic characteristics of urban areas. However, incorporating the temporal dynamics and detailed semantics inherent in human mobility patterns remains underexplored. To address this gap, we propose a novel urban region representation learning model, Mobility Time Series Contrastive Learning for Urban Region Representations (MobiCLR), designed to capture semantically meaningful embeddings from inflow and outflow mobility patterns. MobiCLR uses contrastive learning to enhance the discriminative power of its representations, applying an instance-wise contrastive loss to capture distinct flow-specific characteristics. Additionally, we develop a regularizer to align output features with these flow-specific representations, enabling a more comprehensive understanding of mobility dynamics. To validate our model, we conduct extensive experiments in Chicago, New York, and Washington, D.C. to predict income, educational attainment, and social vulnerability. The results demonstrate that our model outperforms state-of-the-art models.
Materials design is an important component of modern science and technology, yet traditional approaches rely heavily on trial-and-error and can be inefficient. Computational techniques, enhanced by modern artificial intelligence (AI), have greatly accelerated the design of new materials. Among these approaches, inverse design has shown great promise in designing materials that meet specific property requirements. In this mini-review, we summarize key computational advancements for materials design over the past few decades. We follow the evolution of relevant materials design techniques, from high-throughput forward machine learning (ML) methods and evolutionary algorithms, to advanced AI strategies like reinforcement learning (RL) and deep generative models. We highlight the paradigm shift from conventional screening approaches to inverse generation driven by deep generative models. Finally, we discuss current challenges and future perspectives of materials inverse design. This review may serve as a brief guide to the approaches, progress, and outlook of designing future functional materials with technological relevance.
Lowering the Barrier of Machine Learning: Achieving Zero Manual Labeling in Review Classification Using LLMs
2025-02-05
Yejian Zhang, Shingo Takada
With the internet's evolution, consumers increasingly rely on online reviews for service or product choices, necessitating that businesses analyze extensive customer feedback to enhance their offerings. While machine learning-based sentiment classification shows promise in this realm, its technical complexity often bars small businesses and individuals from leveraging such advancements, which may end up making the competitive gap between small and large businesses even bigger in terms of improving customer satisfaction. This paper introduces an approach that integrates large language models (LLMs), specifically Generative Pre-trained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT)-based models, making it accessible to a wider audience. Our experiments across various datasets confirm that our approach retains high classification accuracy without the need for manual labeling, expert knowledge in tuning and data annotation, or substantial computational power. By significantly lowering the barriers to applying sentiment classification techniques, our methodology enhances competitiveness and paves the way for making machine learning technology accessible to a broader audience.
SensorChat: Answering Qualitative and Quantitative Questions during Long-Term Multimodal Sensor Interactions
2025-02-05
Xiaofan Yu, Lanxiang Hu, Benjamin Reichman, Dylan Chu, Rushil Chandrupatla, Xiyuan Zhang, Larry Heck, Tajana Rosing
Natural language interaction with sensing systems is crucial for enabling all users to comprehend sensor data and its impact on their everyday lives. However, existing systems, which typically operate in a Question Answering (QA) manner, are significantly limited in terms of the duration and complexity of sensor data they can handle. In this work, we introduce SensorChat, the first end-to-end QA system designed for long-term sensor monitoring with multimodal and high-dimensional data including time series. SensorChat effectively answers both qualitative (requiring high-level reasoning) and quantitative (requiring accurate responses derived from sensor data) questions in real-world scenarios. To achieve this, SensorChat uses an innovative three-stage pipeline that includes question decomposition, sensor data query, and answer assembly. The first and third stages leverage Large Language Models (LLMs) for intuitive human interactions and to guide the sensor data query process. Unlike existing multimodal LLMs, SensorChat incorporates an explicit query stage to precisely extract factual information from long-duration sensor data. We implement SensorChat and demonstrate its capability for real-time interactions on a cloud server while also being able to run entirely on edge platforms after quantization. Comprehensive QA evaluations show that SensorChat achieves up to 26% higher answer accuracy than state-of-the-art systems on quantitative questions. Additionally, a user study with eight volunteers highlights SensorChat's effectiveness in handling qualitative and open-ended questions.
Differentially-Private Multi-Tier Federated Learning: A Formal Analysis and Evaluation
2025-02-05
Evan Chen, Frank Po-Chen Lin, Dong-Jun Han, Christopher G. Brinton
While federated learning (FL) eliminates the transmission of raw data over a network, it is still vulnerable to privacy breaches from the communicated model parameters. Differential privacy (DP) is often employed to address such issues. However, the impact of DP on FL in multi-tier networks -- where hierarchical aggregations couple noise injection decisions at different tiers, and trust models are heterogeneous across subnetworks -- is not well understood. To fill this gap, we develop \underline{M}ulti-Tier \underline{F}ederated Learning with \underline{M}ulti-Tier \underline{D}ifferential \underline{P}rivacy ({\tt M$^2$FDP}), a DP-enhanced FL methodology for jointly optimizing privacy and performance over such networks. One of the key principles of {\tt M$^2$FDP} is to adapt DP noise injection across the established edge/fog computing hierarchy (e.g., edge devices, intermediate nodes, and other tiers up to cloud servers) according to the trust models in different subnetworks. We conduct a comprehensive analysis of the convergence behavior of {\tt M$^2$FDP} under non-convex problem settings, revealing conditions on parameter tuning under which the training process converges sublinearly to a finite stationarity gap that depends on the network hierarchy, trust model, and target privacy level. We show how these relationships can be employed to develop an adaptive control algorithm for {\tt M$^2$FDP} that tunes properties of local model training to minimize energy, latency, and the stationarity gap while meeting desired convergence and privacy criterion. Subsequent numerical evaluations demonstrate that {\tt M$^2$FDP} obtains substantial improvements in these metrics over baselines for different privacy budgets and system configurations.
A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges
2025-02-05
Lei Ding, Danfeng Hong, Maofan Zhao, Hongruixuan Chen, Chenyu Li, Jie Deng, Naoto Yokoya, Lorenzo Bruzzone, Jocelyn Chanussot
In the last decade, the rapid development of deep learning (DL) has made it possible to perform automatic, accurate, and robust Change Detection (CD) on large volumes of Remote Sensing Images (RSIs). However, despite advances in CD methods, their practical application in real-world contexts remains limited due to the diverse input data and the applicational context. For example, the collected RSIs can be time-series observations, and more informative results are required to indicate the time of change or the specific change category. Moreover, training a Deep Neural Network (DNN) requires a massive amount of training samples, whereas in many cases these samples are difficult to collect. To address these challenges, various specific CD methods have been developed considering different application scenarios and training resources. Additionally, recent advancements in image generation, self-supervision, and visual foundation models (VFMs) have opened up new approaches to address the 'data-hungry' issue of DL-based CD. The development of these methods in broader application scenarios requires further investigation and discussion. Therefore, this article summarizes the literature methods for different CD tasks and the available strategies and techniques to train and deploy DL-based CD methods in sample-limited scenarios. We expect that this survey can provide new insights and inspiration for researchers in this field to develop more effective CD methods that can be applied in a wider range of contexts.
2025-02-05
Siyang Li, Hongbin Wang, Xiaoqing Chen, Dongrui Wu
Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. This review highlights the core decoding algorithms that enable multimodal BCIs, including a dissection of the elements, a unified view of diversified approaches, and a comprehensive analysis of the present state of the field. We emphasize algorithmic advancements in cross-modality mapping, sequential modeling, besides classic multi-modality fusion, illustrating how these novel AI approaches enhance decoding of brain data. The current literature of BCI applications on visual, speech, and affective decoding are comprehensively explored. Looking forward, we draw attention on the impact of emerging architectures like multimodal Transformers, and discuss challenges such as brain data heterogeneity and common errors. This review also serves as a bridge in this interdisciplinary field for experts with neuroscience background and experts that study AI, aiming to provide a comprehensive understanding for AI-powered multimodal BCIs.
A Decade of Action Quality Assessment: Largest Systematic Survey of Trends, Challenges, and Future Directions
2025-02-05
Hao Yin, Paritosh Parmar, Daoliang Xu, Yang Zhang, Tianyou Zheng, Weiwei Fu
Action Quality Assessment (AQA) -- the ability to quantify the quality of human motion, actions, or skill levels and provide feedback -- has far-reaching implications in areas such as low-cost physiotherapy, sports training, and workforce development. As such, it has become a critical field in computer vision & video understanding over the past decade. Significant progress has been made in AQA methodologies, datasets, & applications, yet a pressing need remains for a comprehensive synthesis of this rapidly evolving field. In this paper, we present a thorough survey of the AQA landscape, systematically reviewing over 200 research papers using the preferred reporting items for systematic reviews & meta-analyses (PRISMA) framework. We begin by covering foundational concepts & definitions, then move to general frameworks & performance metrics, & finally discuss the latest advances in methodologies & datasets. This survey provides a detailed analysis of research trends, performance comparisons, challenges, & future directions. Through this work, we aim to offer a valuable resource for both newcomers & experienced researchers, promoting further exploration & progress in AQA. Data are available at https://haoyin116.github.io/Survey_of_AQA/
This paper reviews object detection methods for finding small objects from remote sensing imagery and provides an empirical evaluation of four state-of-the-art methods to gain insights into method performance and technical challenges. In particular, we use car detection from urban satellite images and bee box detection from satellite images of agricultural lands as application scenarios. Drawing from the existing surveys and literature, we identify several top-performing methods for the empirical study. Public, high-resolution satellite image datasets are used in our experiments.
Advancing Reasoning in Large Language Models: Promising Methods and Approaches
2025-02-05
Avinash Patil
Large Language Models (LLMs) have succeeded remarkably in various natural language processing (NLP) tasks, yet their reasoning capabilities remain a fundamental challenge. While LLMs exhibit impressive fluency and factual recall, their ability to perform complex reasoning-spanning logical deduction, mathematical problem-solving, commonsense inference, and multi-step reasoning-often falls short of human expectations. This survey provides a comprehensive review of emerging techniques enhancing reasoning in LLMs. We categorize existing methods into key approaches, including prompting strategies (e.g., Chain-of-Thought reasoning, Self-Consistency, and Tree-of-Thought reasoning), architectural innovations (e.g., retrieval-augmented models, modular reasoning networks, and neuro-symbolic integration), and learning paradigms (e.g., fine-tuning with reasoning-specific datasets, reinforcement learning, and self-supervised reasoning objectives). Additionally, we explore evaluation frameworks used to assess reasoning in LLMs and highlight open challenges, such as hallucinations, robustness, and reasoning generalization across diverse tasks. By synthesizing recent advancements, this survey aims to provide insights into promising directions for future research and practical applications of reasoning-augmented LLMs.
Despite the generative model's groundbreaking success, the need to study its implications for privacy and utility becomes more urgent. Although many studies have demonstrated the privacy threats brought by GANs, no existing survey has systematically categorized the privacy and utility perspectives of GANs and VAEs. In this article, we comprehensively study privacy-preserving generative models, articulating the novel taxonomies for both privacy and utility metrics by analyzing 100 research publications. Finally, we discuss the current challenges and future research directions that help new researchers gain insight into the underlying concepts.
Bridging high resolution sub-cellular imaging with physiologically relevant engineered tissues
2025-02-05
Yasaman Kargar Gaz Kooh, Nathaniel Huebsch
While high-resolution microscopic techniques are crucial for studying cellular structures in cell biology, obtaining such images from thick 3D engineered tissues remains challenging. In this review, we explore advancements in fluorescence microscopy, alongside the use of various fluorescent probes and material processing techniques to address these challenges. We navigate through the diverse array of imaging options available in tissue engineering field, from wide field to super-resolution microscopy, so researchers can make more informed decisions based on the specific tissue and cellular structures of interest. Finally, we provide some recent examples of how traditional limitations on obtaining high-resolution images on sub-cellular architecture within 3D tissues have been overcome by combining imaging advancements with innovative tissue engineering approaches.
Artificial Intelligence Approaches for Anti-Addiction Drug Discovery
2025-02-05
Dong Chen, Jian Jiang, Zhe Su, Guo-Wei Wei
Drug addiction is a complex and pervasive global challenge that continues to pose significant public health concerns. Traditional approaches to anti-addiction drug discovery have struggled to deliver effective therapeutics, facing high attrition rates, long development timelines, and inefficiencies in processing large-scale data. Artificial intelligence (AI) has emerged as a transformative solution to address these issues. Using advanced algorithms, AI is revolutionizing drug discovery by enhancing the speed and precision of key processes. This review explores the transformative role of AI in the pipeline for anti-addiction drug discovery, including data collection, target identification, and compound optimization. By highlighting the potential of AI to overcome traditional barriers, this review systematically examines how AI addresses critical gaps in anti-addiction research, emphasizing its potential to revolutionize drug discovery and development, overcome challenges, and advance more effective therapeutic strategies.
2025-02-05
A. M. Heras, C. Eiroa, C. del Burgo, J. P. Marshall, B. Montesinos
We probe the effect of the ISM on debris disc occurrence rates and on the morphologies of the discs. We used results from the Herschel Space Observatory DUNES and DEBRIS surveys of 295 nearby FGK dwarf stars imaged at 100 $\mu$m and 160 $\mu$m. Most of the 48 debris discs in this sample have small optical depths, making them more likely to be affected by the ISM compared to optically thick discs. Since the stars in our sample are located within the Local Interstellar Cloud, we can infer that their debris discs encounter similar conditions. This allows us to use the stellar space velocity as a single indicator of the forces that can act on the debris disc dust grains when they interact with the ISM. The observed debris disc occurrence rates seem to depend on the stellar space velocities, as expected under the hypothesis that stars with higher space velocities have a higher probability of losing their circumstellar dust. The percentage of sources with debris discs in our sample reaches a maximum of $\approx$25% for stars with low space velocity component values, $|U_{\mathrm{rel}}|$, relative to the local ISM, and decreases for larger $|U_{\mathrm{rel}}|$ values down to the 10% level. A decrease in the average disc fractional luminosity as a function of $|U_{\mathrm{rel}}|$ is also observed. These dependences do not disappear after accounting for the reported higher dispersion of $U$ values with age. In extended discs, the impact of the ISM could also explain the links observed between the stellar space velocities and the debris disc projected ellipticities, position angles, and radii. Although these indications may not be fully conclusive on their own, they collectively reinforce the hypothesis that the ISM influences the occurrence rates and morphologies of debris discs.
Retrospectives are vital for software development teams to continuously enhance their processes and teamwork. Despite the increasing availability of objective data generated throughout the project and software development processes, many teams do not fully utilize this information in retrospective meetings. Instead, they often rely on subjective data, anecdotal insights and their memory. While some literature underscores the value of data-driven retrospectives, little attention has been given to the role data can play and the challenges of effectively incorporating objective project data into these meetings. To address this gap, we conducted a survey with 19 practitioners on retrospective meeting practices and how their teams gather and use subjective and objective data in their retrospectives. Our findings confirm that although teams routinely collect project data, they seldom employ it systematically during retrospectives. Furthermore, this study provides insights into retrospective practices by exploring barriers to project data utilization, including psychological safety concerns and the disconnect between data collection and meaningful integration of data into retrospective meetings. We close by considering preliminary insights that may help to mitigate these concerns and how future research might build on our paper findings to support the integration of project data into retrospective meetings, fostering a balance between human-centric reflections and data-driven insights.
The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning-Based Stellar parameters for 21 million stars from the First Data Release
2025-02-05
Hongrui Gu, Zhou Fan, Gang Zhao, Yang Huang, Timothy C. Beers, Wei Wang, Jie Zheng, Jingkun Zhao, Chun Li, Yuqin Chen, Haibo Yuan, Haining Li, Kefeng Tan, Yihan Song, Ali Luo, Nan Song, Yujuan Liu
Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar parameters. In this study, we present a machine-learning approach to derive estimates of stellar parameters, including [Fe/H], logg, and Teff, based on a combination of medium-band and broad-band photometric observations. Our analysis employs data primarily sourced from the SAGE Survey , which aims to observe much of the Northern Hemisphere. We combine the $uv$-band data from SAGES DR1 with photometric and astrometric data from Gaia EDR3, and apply the random forest method to estimate stellar parameters for approximately 21 million stars. We are able to obtain precisions of 0.09 dex for [Fe/H], 0.12 dex for logg, and 70 K for Teff. Furthermore, by incorporating 2MASS and WISE infrared photometric and GALEX ultraviolet data, we are able to achieve even higher precision estimates for over 2.2 million stars. These results are applicable to both giant and dwarf stars. Building upon this mapping, we construct a foundational dataset for research on metal-poor stars, the structure of the Milky Way, and beyond. With the forthcoming release of additional bands from SAGE Survey such DDO51 and H-alpha, this versatile machine learning approach is poised to play an important role in upcoming surveys featuring expanded filter sets
2025-02-05
M. Huertas-Company, M. Shuntov, Y. Dong, M. Walmsley, O. Ilbert, H. J. McCracken, H. B. Akins, N. Allen, C. M. Casey, L. Costantin, E. Daddi, A. Dekel, M. Franco, I. L. Garland, T. Géron, G. Gozaliasl, M. Hirschmann, J. S. Kartaltepe, A. M. Koekemoer, C. Lintott, D. Liu, R. Lucas, K. Masters, F. Pacucci, L. Paquereau, P. G. P'erez-Gonz'alez, J. D. Rhodes, B. E. Robertson, B. Simmons, R. Smethurst, S. Toft, L. Yang
Leveraging the wide area coverage of the COSMOS-Web survey, we quantify the abundance of different morphological types from $z\sim 7$ with unprecedented statistics and establish robust constraints on the epoch of emergence of the Hubble sequence. We measure the global (spheroids, disk-dominated, bulge-dominated, peculiar) and resolved (stellar bars) morphologies for about 400,000 galaxies down to F150W=27 using deep learning, representing a two-orders-of-magnitude increase over previous studies. We then provide reference Stellar Mass Functions (SMFs) of different morphologies between $z\sim 0.2$ and $z\sim 7$ and best-fit parameters to inform models of galaxy formation. All catalogs and data are made publicly available. (a)At redshift z > 4.5, the massive galaxy population ($\log M_*/M_\odot>10$) is dominated by disturbed morphologies (~70%) -- even in the optical rest frame -- and very compact objects (~30%) with effective radii smaller than ~500pc. This confirms that a significant fraction of the star formation at cosmic dawn occurs in very dense regions, although the stellar mass for these systems could be overestimated.(b)Galaxies with Hubble-type morphologies -- including bulge and disk-dominated galaxies -- arose rapidly around $z\sim 4$ and dominate the morphological diversity of massive galaxies as early as $z\sim 3$. (c)Using stellar bars as a proxy, we speculate that stellar disks in massive galaxies might have been common (>50%) among the star-forming population since cosmic noon ($z\sim2$-2.5) and formed as early as $z\sim 7$ (d)Massive quenched galaxies are predominantly bulge-dominated from z~4 onward, suggesting that morphological transformations briefly precede or are simultaneous to quenching mechanisms at the high-mass end. (e) Low-mass ($\log M_*/M_\odot<10$) quenched galaxies are typically disk-dominated, pointing to different quenching routes in the two ends of the stellar mass spectrum from cosmic dawn.
Gravitational lensing galaxies are commonly modeled with elliptical density profiles, to which angular complexity is sometimes added through a multipole expansion - encoding deformations of the elliptical iso-density contours. The formalism that is widely used in current studies and software packages, however, employs perturbations that are defined with respect to a circle. In this work, we show that this popular formulation (the "circular multipoles") leads to perturbation patterns that depend on the axis ratio and do not agree with physical expectations (from studies of galaxy isophotal shapes) when applied to profiles that are not near-circular. We propose a more appropriate formulation, the "elliptical multipoles", representing deviations from ellipticity suited for any axis ratio. We solve for the lensing potentials associated with the $m=1$ circular multipole (previously undetermined in the isothermal case), as well as the elliptical multipoles of any order $m$, assuming a near-isothermal reference profile. We implement these solutions into the lens modeling package $\mathtt{lenstronomy}$, and assess the importance of the multipole formulation by comparing flux-ratio perturbations in mock lensed systems with quadruply imaged quasars: we show that elliptical multipoles typically produce smaller flux-ratio perturbations than their circular counterparts.
Towards characterizing dark matter subhalo perturbations in stellar streams with graph neural networks
2025-02-05
Peter Xiangyuan Ma, Keir K. Rogers, Ting S. Li, Renée Hložek, Jeremy Webb, Ruth Huang, Julian Meunier
The phase space of stellar streams is proposed to detect dark substructure in the Milky Way through the perturbations created by passing subhalos - and thus is a powerful test of the cold dark matter paradigm and its alternatives. Using graph convolutional neural network (GCNN) data compression and simulation-based inference (SBI) on a simulated GD-1-like stream, we improve the constraint on the mass of a [$10^8$, $10^7$, $10^6$] $M_\odot$ perturbing subhalo by factors of [11, 7, 3] with respect to the current state-of-the-art density power spectrum analysis. We find that the GCNN produces posteriors that are more accurate (better calibrated) than the power spectrum. We simulate the positions and velocities of stars in a GD-1-like stream and perturb the stream with subhalos of varying mass and velocity. Leveraging the feature encoding of the GCNN to compress the input phase space data, we then use SBI to estimate the joint posterior of the subhalo mass and velocity. We investigate how our results scale with the size of the GCNN, the coordinate system of the input and the effect of incomplete observations. Our results suggest that a survey with $10 \times$ fewer stars (300 stars) with complete 6-D phase space data performs about as well as a deeper survey (3000 stars) with only 3-D data (photometry, spectroscopy). The stronger constraining power and more accurate posterior estimation motivate further development of GCNNs in combining future photometric, spectroscopic and astrometric stream observations.
Population Synthesis of Gravitational Wave Sources
2025-02-05
Katelyn Breivik
The simulation of gravitational wave source populations and their progenitors is an endeavor more than eighty years in the making. This is in part due to a wide variety of theoretical uncertainties that must be taken into account when describing how stellar populations evolve over cosmic time to produce double stellar remnant binaries. Population synthesis software has been developed as a means to investigate these uncertainties under a wide variety of physical assumptions and stellar population formation environments. In this chapter we discuss the development history of population synthesis software with a special focus on work aimed at understanding the formation of gravitational wave populations. We detail the assortment of population synthesis tools in use today that simulate GW populations which are born and evolve in different astrophysical environments. We further discuss the GW population rates and features associated with each environment that have been predicted for both ground and space-based GW detectors. We finish with considerations of future work that combines possible constraints from electromagnetic surveys that may provide key findings that break current degeneracies in population synthesis predictions of GW source populations.
2025-02-05
Bren E. Backhaus, Nikko J. Cleri, Jonathan R. Trump, Allison Kirkpatrick, Raymond C. Simons, Pablo Arrabal Haro, Micaela B. Bagley, Madisyn Brooks, Antonello Calabrò, Kelcey Davis, Mark Dickinson, Steven L. Finkelstein, Michaela Hirschmann, Jeyhan S. Kartaltepe, Anton M. Koekemoer, Mario Llerena, Fabio Pacucci, Nor Pirzkal, Casey Papovich, Stephen M. Wilkins
We use JWST Near-Infrared Spectrograph (NIRSpec) observations from the the Cosmic Evolution Early Release survey (CEERS), GLASS-JWST ERS (GLASS), and JWST Advanced Deep Extragalactic Survey (JADES) to measure rest-frame optical emission-line ratios of 90 galaxies at z>4. The stacked spectra of galaxies with and without a broad-line feature reveal a difference in the [OIII]$\lambda$ 4363 and H$\gamma$ ratios. This motivated our investigation of the [OIII]/H$\gamma$ vs [NeIII]/[OII] diagram. We define two AGN/SF classification lines based on 1869 SDSS galaxies at z$\sim$0. After applying a redshift correction to the AGN/SF lines we find 76.8% of BLAGN continue to land in the AGN region of the diagnostic largely due to the [NeIII]/[OII] ratio. However, 40.2% of non-BLAGN land in the AGN region as well, this could be due to star forming galaxies having harder ionization of there are narrow line AGN which are not accounted for. This indicates the potential of the [NeIII]/[OII] ratio to continue classifying galaxies to z$\sim$6. We further inspect galaxies without broad emission lines in each region of [OIII]/H\gamma vs [NeIII]/[OII] diagram and found that they have slightly stronger CIII]$\lambda$1908 fluxes and equivalent width when landing in the BLAGN region. However, the cause of this higher ionization is unclear. Additionally, we find that BLAGN are characterized by a higher ionization (at constant electron temperature) compared to non-broad line galaxies.
This study explores the integration of Blackout Diffusion into the DIFUSCO framework for combinatorial optimization, specifically targeting the Traveling Salesman Problem (TSP). Inspired by the success of discrete-time diffusion models (D3PM) in maintaining structural integrity, we extend the paradigm to a continuous-time framework, leveraging the unique properties of Blackout Diffusion. Continuous-time modeling introduces smoother transitions and refined control, hypothesizing enhanced solution quality over traditional discrete methods. We propose three key improvements to enhance the diffusion process. First, we transition from a discrete-time-based model to a continuous-time framework, providing a more refined and flexible formulation. Second, we refine the observation time scheduling to ensure a smooth and linear transformation throughout the diffusion process, allowing for a more natural progression of states. Finally, building upon the second improvement, we further enhance the reverse process by introducing finer time slices in regions that are particularly challenging for the model, thereby improving accuracy and stability in the reconstruction phase. Although the experimental results did not exceed the baseline performance, they demonstrate the effectiveness of these methods in balancing simplicity and complexity, offering new insights into diffusion-based combinatorial optimization. This work represents the first application of Blackout Diffusion to combinatorial optimization, providing a foundation for further advancements in this domain. * The code is available for review at https://github.com/Giventicket/BlackoutDIFUSCO.
New perspective on the multiple population phenomenon in Galactic globular clusters from a wide-field photometric survey
2025-02-04
S. Jang, A. P. Milone, A. F. Marino, M. Tailo, E. Dondoglio, M. V. Legnardi, G. Cordoni, T. Ziliotto, E. P. Lagioia, M. Carlos, A. Mohandasan, E. Bortolan, Y. -W. Lee
Wide-field photometry of Galactic globular clusters (GCs) has been investigated to overcome limitations from the small field of view of the Hubble Space Telescope in the study of multiple populations. In particular, 'chromosome maps' (ChMs) built with ground-based photometry were constructed to identify the first and second generation stars (1G and 2G) over the wide-field of view. The ChMs allow us to derive the fraction of distinct populations in an analyzed field of view. We present here the radial distribution of the 2G fraction in 29 GCs. The distributions show that all the GCs either have a flat distribution or more centrally concentrated 2G stars. Notably, we find that the fraction of 1G stars outside the half-light radius is clearly bifurcated across all mass range. It implies that a group of GCs with lower 1G fractions (hereafter Group II) have efficiently lost their 1G stars in the outermost cluster regions. In fact, in connection with the trends of the radial distribution, most GCs of Group II have spatially mixed populations, while only less massive GCs in Group I (a group with higher 1G fraction) show that feature. Lastly, we investigate links between these two groups and host cluster parameters. We find that most GCs of Group II are distributed along a broader range of galactocentric distances with smaller perigalactic distances < 3.5 kpc. Besides, by using the Gaia data, it is observed that Group II GCs have higher energy on the integrals of motion diagrams than Group I GCs.
DIISC -- VI (COS-DIISC): UV Metal Absorption Relative to the H I disk of Galaxies
2025-02-04
Brad Koplitz, Sanchayeeta Borthakur, Timothy Heckman, Mansi Padave, Tyler McCabe, Jason Tumlinson, Andrew J. Fox, Guinevere Kauffmann
As part of the Deciphering the Interplay between the Interstellar medium, Stars, and the Circumgalactic medium (DIISC) survey, we present the UV metal absorption features in the Circumgalactic Medium (CGM) near the H I gas disk ($<$4.5$R_\mathrm{HI}$) of 31 nearby galaxies through quasar absorption line spectroscopy. Of the ions under study, Si III $\lambda1206$ was most frequently detected (18 of 31 sight lines), while C II $\lambda1334$ and Si II $\lambda1260$ were detected in 17 and 15 of 31 sight lines, respectively. Many components were consistent with photoionization equilibrium models, most of the cold and cool gas phase clouds were found to have lengths smaller than 2 kpc. Sight lines with smaller impact parameters ($\rho$) normalized by the galaxy's virial radius ($R_\mathrm{vir}$) and H I radius ($R_\mathrm{HI}$) tend to have more components and larger rest-frame equivalent widths ($W_r$) than those that probe the CGM at larger radii. In particular, we find that the location of metals are better traced by $\rho$ / $R_\mathrm{HI}$ rather than the traditional $\rho$ / $R_\mathrm{vir}$. Larger covering fractions are found closer to galaxies, with a radial decline that depends on the $W_r$ limit used. Our results provide new insights into the spatial distribution of metals around the H I disks of low-redshift galaxies.
2025-02-04
Samuel P. D. Birch, Alexander G. Hayes, J. Taylor Perron
In this chapter we begin with a review of Titan's fluvial and lacustrine landscapes as observed with Cassini remote sensing data, and what the many discoveries have revealed about Titan's surface materials and climate. Yet Cassini remote sensing data are coarse, topographic data are largely lacking, and the absence of in situ field measurements means we have little understanding of what the surface is composed of. At present, our knowledge of Titan's hydrology is comparable to that of Mars in the 1970's during the Viking era. Fortunately, the coming decades promise many new and exciting discoveries that can be achieved through Earth-based experiments, numerical modeling, and a continued commitment to the exploration of Titan by future missions, including both Dragonfly and orbiting assets. We therefore close the chapter with a discussion about what can be done with the current Cassini data and how new data, from both Dragonfly and a potential future orbiter, would allow us to leverage Titan to help solve some of the largest problems both here on Earth and on hydrologic planets and exoplanets more generally.
The recent BESIII announcement of a pseudoscalar glueball candidate makes an update on glueballs from lattice QCD timely. A brief review of how glueballs are studied in lattice QCD is given, and the reasons that glueballs are difficult to study both in lattice QCD with dynamical quarks and in experiments are outlined. Recent glueball studies in lattice QCD are then presented, and an exploratory investigation of the scalar glueball using glueball, meson, and meson-meson operators is summarized, suggesting that no scalar state below 2 GeV or so can be considered to be predominantly a glueball state.
The next generation of gravitational wave detectors and electromagnetic telescopes are beckoning the onset of the multi-messenger era and the exciting science that lies ahead. Multi-messenger strong gravitational lensing will help probe some of the most important questions of the Universe in an unprecedented manner. In particular, understanding the nature of gravitational wave sources, the underlying physical processes and mechanisms that produce emissions well before or right until the time of the merger, their associations to the seemingly distinct populations of gamma ray bursts, fast radio bursts and kilonovae. Not to mention, multi-messenger lensing will offer unique probes of test of gravity models and constraints on cosmological parameters complementary to other probes. Enabling multi-messenger science calls for concerted follow-up efforts and development of new and shared resources required in the community.
A short review on the compositeness of the $X(3872)$
2025-02-04
Angelo Esposito, Alfredo Glioti, Davide Germani, Antonio D. Polosa
The $X(3872)$ could be a shallow $D\bar D^*$ bound state, a compact four-quark state, or a partially composite particle, i.e. a superposition of the two. We will review how these hypotheses could be tested experimentally, examining especially the cases in which the $X$ is a pure bound state or a pure compact tetraquark. The comparison with data is done by comparing the results on the $X\to D\bar D\pi$ decay with the analysis of its lineshape. Looking at decay, the pure bound state hypothesis corresponds to a well-defined region in parameter space defined by the width of the $D^*$ versus the binding energy of the $X$. As far as the lineshape is concerned, the currently available experimental analysis is limited to showing compatibility with the purely compact hypothesis. We propose how to modify the analysis to test the molecular or the partially composite hypotheses. We also review the analysis on the radiative decays of the $X$, showing that it leaves little room for its purely molecular nature, even if pion corrections are included.
Diffusion models have gained popularity in graph generation tasks; however, the extent of their expressivity concerning the graph distributions they can learn is not fully understood. Unlike models in other domains, popular backbones for graph diffusion models, such as Graph Transformers, do not possess universal expressivity to accurately model the distribution scores of complex graph data. Our work addresses this limitation by focusing on the frequency of specific substructures as a key characteristic of target graph distributions. When evaluating existing models using this metric, we find that they fail to maintain the distribution of substructure counts observed in the training set when generating new graphs. To address this issue, we establish a theoretical connection between the expressivity of Graph Neural Networks (GNNs) and the overall performance of graph diffusion models, demonstrating that more expressive GNN backbones can better capture complex distribution patterns. By integrating advanced GNNs into the backbone architecture, we achieve significant improvements in substructure generation.
Using Random Noise Equivariantly to Boost Graph Neural Networks Universally
2025-02-04
Xiyuan Wang, Muhan Zhang
Recent advances in Graph Neural Networks (GNNs) have explored the potential of random noise as an input feature to enhance expressivity across diverse tasks. However, naively incorporating noise can degrade performance, while architectures tailored to exploit noise for specific tasks excel yet lack broad applicability. This paper tackles these issues by laying down a theoretical framework that elucidates the increased sample complexity when introducing random noise into GNNs without careful design. We further propose Equivariant Noise GNN (ENGNN), a novel architecture that harnesses the symmetrical properties of noise to mitigate sample complexity and bolster generalization. Our experiments demonstrate that using noise equivariantly significantly enhances performance on node-level, link-level, subgraph, and graph-level tasks and achieves comparable performance to models designed for specific tasks, thereby offering a general method to boost expressivity across various graph tasks.
An approach to improve neural network interpretability is via clusterability, i.e., splitting a model into disjoint clusters that can be studied independently. We define a measure for clusterability and show that pre-trained models form highly enmeshed clusters via spectral graph clustering. We thus train models to be more modular using a "clusterability loss" function that encourages the formation of non-interacting clusters. Using automated interpretability techniques, we show that our method can help train models that are more modular and learn different, disjoint, and smaller circuits. We investigate CNNs trained on MNIST and CIFAR, small transformers trained on modular addition, and language models. Our approach provides a promising direction for training neural networks that learn simpler functions and are easier to interpret.
Computing with Smart Rings: A Systematic Literature Review
2025-02-04
Zeyu Wang, Ruotong Yu, Xiangyang Wang, Jiexin Ding, Jiankai Tang, Jun Fang, Zhe He, Zhuojun Li, Tobias Röddiger, Weiye Xu, Xiyuxing Zhang, huan-ang Gao, Nan Gao, Chun Yu, Yuanchun Shi, Yuntao Wang
A smart ring is a wearable electronic device in the form of a ring that incorporates diverse sensors and computing technologies to perform a variety of functions. Designed for use with fingers, smart rings are capable of sensing more subtle and abundant hand movements, thus making them a good platform for interaction. Meanwhile, fingers are abundant with blood vessels and nerve endings and accustomed to wearing rings, providing an ideal site for continuous health monitoring through smart rings, which combine comfort with the ability to capture vital biometric data, making them suitable for all-day wear. We collected in total of 206 smart ring-related publications and conducted a systematic literature review. We provide a taxonomy regarding the sensing and feedback modalities, applications, and phenomena. We review and categorize these literatures into four main areas: (1) interaction - input, (2) interaction - output, (3) passive sensing - in body feature, (4) passive sensing - out body activity. This comprehensive review highlights the current advancements within the field of smart ring and identifies potential areas for future research.
System Integrity Protection Schemes in the Nordics -- a comparative analysis
2025-02-04
Gabriel Malmer, Arvid Rolander, Emil Hillberg, Olof Samuelsson, Susanne Ackeby, Lars Nordström
To increase the utilisation rate of the power system and accelerate electrification while providing a high degree of security and reliability, System Integrity Protection Schemes (SIPS) are of great importance. SIPS functions are automatic remedial actions, detecting abnormal conditions or contingencies in the system and taking control action to mitigate these conditions. Design, implementation, maintenance and coordination of SIPS are all important aspects for desired operation. However, different actors have chosen different approaches to using SIPS for capacity enhancement, and there are discrepancies in how capacity is valued in relation to for example complexity, reliability and risk. Additionally, definitions often vary between countries. This paper reports on a joint survey and interview study on SIPS with stakeholders and experts in the Nordic countries - including TSOs, DSOs and industry. Combined with a literature review, a comparison and analysis of how SIPS are used in the Nordics is performed, particularly in relation to ENTSO-E capacity allocation.
Constraints on minimally and conformally coupled ultralight dark matter with the EPTA
2025-02-04
Clemente Smarra
Millisecond pulsars are extremely stable natural timekeepers. Pulsar Timing Array experiments, tracking subtle changes in the pulsars' rotation periods, can shed light on the presence of ultralight particles in our Galaxy. In this conference paper, we start by reviewing the most conservative scenario, in which ultralight particles interact only gravitationally. In this setting, we show that Pulsar Timing Arrays are able to constrain the presence of ultralight fields up to a few tenths of the observed dark matter abundance. Then, we consider conformally coupled ultralight candidates, demonstrating that the constraints on the universal scalar coupling of the field to Standard Model particles improve on existing bounds by several orders of magnitude, in the relevant mass range analyzed by Pulsar Timing Arrays. The discussion presented here is based on [1,2].
FPGA Innovation Research in the Netherlands: Present Landscape and Future Outlook
2025-02-04
Nikolaos Alachiotis, Sjoerd van den Belt, Steven van der Vlugt, Reinier van der Walle, Mohsen Safari, Bruno Endres Forlin, Tiziano De Matteis, Zaid Al-Ars, Roel Jordans, António J. Sousa de Almeida, Federico Corradi, Christiaan Baaij, Ana-Lucia Varbanescu
FPGAs have transformed digital design by enabling versatile and customizable solutions that balance performance and power efficiency, yielding them essential for today's diverse computing challenges. Research in the Netherlands, both in academia and industry, plays a major role in developing new innovative FPGA solutions. This survey presents the current landscape of FPGA innovation research in the Netherlands by delving into ongoing projects, advancements, and breakthroughs in the field. Focusing on recent research outcome (within the past 5 years), we have identified five key research areas: a) FPGA architecture, b) FPGA robustness, c) data center infrastructure and high-performance computing, d) programming models and tools, and e) applications. This survey provides in-depth insights beyond a mere snapshot of the current innovation research landscape by highlighting future research directions within each key area; these insights can serve as a foundational resource to inform potential national-level investments in FPGA technology.
Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) are a powerful tool for proving computation correctness, attracting significant interest from researchers, developers, and users. However, the complexity of zk-SNARKs has created gaps between these groups, hindering progress. Researchers focus on constructing efficient proving systems with stronger security and new properties, while developers and users prioritize toolchains, usability, and compatibility. In this work, we provide a comprehensive study of zk-SNARK, from theory to practice, pinpointing gaps and limitations. We first present a master recipe that unifies the main steps in converting a program into a zk-SNARK. We then classify existing zk-SNARKs according to their key techniques. Our classification addresses the main difference in practically valuable properties between existing zk-SNARK schemes. We survey over 40 zk-SNARKs since 2013 and provide a reference table listing their categories and properties. Following the steps in master recipe, we then survey 11 general-purpose popular used libraries. We elaborate on these libraries' usability, compatibility, efficiency and limitations. Since installing and executing these zk-SNARK systems is challenging, we also provide a completely virtual environment in which to run the compiler for each of them. We identify that the proving system is the primary focus in cryptography academia. In contrast, the constraint system presents a bottleneck in industry. To bridge this gap, we offer recommendations and advocate for the opensource community to enhance documentation, standardization and compatibility.
Creating data reports is time-consuming, as it requires iterative exploration and understanding of data, followed by summarizing the insights. While large language models (LLMs) are powerful tools for data processing and text generation, they often struggle to produce complete data reports that fully meet user expectations. One significant challenge is effectively communicating the entire analysis logic to LLMs. Moreover, determining a comprehensive analysis logic can be mentally taxing for users. To address these challenges, we propose ReSpark, an LLM-based method that leverages existing data reports as references for creating new ones. Given a data table, ReSpark searches for similar-topic reports, parses them into interdependent segments corresponding to analytical objectives, and executes them with new data. It identifies inconsistencies and customizes the objectives, data transformations, and textual descriptions. ReSpark allows users to review real-time outputs, insert new objectives, and modify report content. Its effectiveness was evaluated through comparative and user studies.
Gaussian processes for dynamics learning in model predictive control
2025-02-04
Anna Scampicchio, Elena Arcari, Amon Lahr, Melanie N. Zeilinger
Due to its state-of-the-art estimation performance complemented by rigorous and non-conservative uncertainty bounds, Gaussian process regression is a popular tool for enhancing dynamical system models and coping with their inaccuracies. This has enabled a plethora of successful implementations of Gaussian process-based model predictive control in a variety of applications over the last years. However, despite its evident practical effectiveness, there are still many open questions when attempting to analyze the associated optimal control problem theoretically and to exploit the full potential of Gaussian process regression in view of safe learning-based control. The contribution of this review is twofold. The first is to survey the available literature on the topic, highlighting the major theoretical challenges such as (i) addressing scalability issues of Gaussian process regression; (ii) taking into account the necessary approximations to obtain a tractable MPC formulation; (iii) including online model updates to refine the dynamics description, exploiting data collected during operation. The second is to provide an extensive discussion of future research directions, collecting results on uncertainty quantification that are related to (but yet unexploited in) optimal control, among others. Ultimately, this paper provides a toolkit to study and advance Gaussian process-based model predictive control.
Demographic bias in face recognition (FR) has emerged as a critical area of research, given its impact on fairness, equity, and reliability across diverse applications. As FR technologies are increasingly deployed globally, disparities in performance across demographic groups -- such as race, ethnicity, and gender -- have garnered significant attention. These biases not only compromise the credibility of FR systems but also raise ethical concerns, especially when these technologies are employed in sensitive domains. This review consolidates extensive research efforts providing a comprehensive overview of the multifaceted aspects of demographic bias in FR. We systematically examine the primary causes, datasets, assessment metrics, and mitigation approaches associated with demographic disparities in FR. By categorizing key contributions in these areas, this work provides a structured approach to understanding and addressing the complexity of this issue. Finally, we highlight current advancements and identify emerging challenges that need further investigation. This article aims to provide researchers with a unified perspective on the state-of-the-art while emphasizing the critical need for equitable and trustworthy FR systems.
Flow rate from a vertical silo with a tilted orifice
2025-02-04
Ryan Kozlowski, Luis A. Pugnaloni
The flow of dry granular materials from silos is of great practical interest in industry and of theoretical import for understanding multiphase dynamics. Recent studies have demonstrated that one way to control the rate of flow from a silo is to tilt it. However, this may not be practical in many industrial applications. Here, we demonstrate in experiments of quasi-2D silo discharge of monodisperse grains that the flow rate can be modulated by rotating the orifice - through elevating and shifting one side of the base - instead of tilting the entire silo. We use high-speed image analysis to track the average motion of grains in the silo. We first show that the flow rate decreases with orifice angle, but that this decrease is not as strong as when a silo is tilted or when a lateral orifice is used. However, with the addition of a grain-sized ridge on each side of the orifice, the flow rate collapses with prior tilted-silo results. We then characterize the flow velocity of grains exiting the orifice and highlight key features of the stagnant zones and slip zones on each orifice side. Finally, we model our results based on these measurements, demonstrating the importance of horizontal creep along slip zones next to the orifice and the narrowest opening cross-section through which the material flows. These findings reveal a simple method for controlling both flow rate and direction, and highlight the importance of both dynamics within and geometry of the stagnant zones near the orifice.
Survey of Quantization Techniques for On-Device Vision-based Crack Detection
2025-02-04
Yuxuan Zhang, Luciano Sebastian Martinez-Rau, Quynh Nguyen Phuong Vu, Bengt Oelmann, Sebastian Bader
Structural Health Monitoring (SHM) ensures the safety and longevity of infrastructure by enabling timely damage detection. Vision-based crack detection, combined with UAVs, addresses the limitations of traditional sensor-based SHM methods but requires the deployment of efficient deep learning models on resource-constrained devices. This study evaluates two lightweight convolutional neural network models, MobileNetV1x0.25 and MobileNetV2x0.5, across TensorFlow, PyTorch, and Open Neural Network Exchange platforms using three quantization techniques: dynamic quantization, post-training quantization (PTQ), and quantization-aware training (QAT). Results show that QAT consistently achieves near-floating-point accuracy, such as an F1-score of 0.8376 for MBNV2x0.5 with Torch-QAT, while maintaining efficient resource usage. PTQ significantly reduces memory and energy consumption but suffers from accuracy loss, particularly in TensorFlow. Dynamic quantization preserves accuracy but faces deployment challenges on PyTorch. By leveraging QAT, this work enables real-time, low-power crack detection on UAVs, enhancing safety, scalability, and cost-efficiency in SHM applications, while providing insights into balancing accuracy and efficiency across different platforms for autonomous inspections.
In tomographic weak lensing surveys, the presence of nulling properties reveals symmetries inherent in the data, which rely solely on the geometrical properties of the Universe. Ensuring its validity thus provides us with constraints on the cosmological parameters that describe the background evolution, particularly the redshift dependence of the cosmological angular distance. This forms the basis of the tomographic cosmic shear nulling test that we introduce here. We outline how such a test can be executed, what it can constrain, and its specific efficiency in constraining cosmological parameters. Additionally, we explore its sensitivity to astrophysical effects such as magnification bias and reduced shear corrections, as well as observational systematics like errors in the mean redshift of the source bins. Our findings indicate that in a scenario akin to that of Euclid, this nulling test can bring significant and complementary constraints on basic cosmological parameters such as $\{\Omega_m,{\rm w}_0\}$, provided that the mean redshift of the bins are known at a level of $10^{-3}$. We conclude that the combination of nulling as a cosmological probe or a consistence test with usual weak lensing and galaxy clustering probes could allow to better keep systematics under control and bring more precise results in the future.
The SRG/eROSITA all-sky survey: The morphologies of clusters of galaxies I: A catalogue of morphological parameters
2025-02-04
J. S. Sanders, Y. E. Bahar, E. Bulbul, V. Ghirardini, A. Liu, N. Clerc, M. E. Ramos-Ceja, T. H. Reiprich, F. Balzer, J. Comparat, M. Kluge, F. Pacaud, X. Zhang
The first SRG/eROSITA all-sky X-ray survey, eRASS1, resulted in a catalogue of over twelve thousand optically-confirmed galaxy groups and clusters in the western Galactic hemisphere. Using the eROSITA images of these objects, we measure and study their morphological properties, including their concentration, central density and slope, ellipticity, power ratios, photon asymmetry, centroid shift and Gini coefficient. We also introduce new forward-modelled parameters which take account of the instrument point spread function (PSF), which are slosh, which measures how asymmetric the surface brightness distribution is, and multipole magnitudes, which are analogues to power ratios. Using simulations, we find some non forward-modelled parameters are strongly biased due to PSF and data quality. For the same clusters, we find similar values of concentration and central density compared to results by ourselves using Chandra and previous results from XMM-Newton. The population as a whole has log concentrations which are typically around 0.3 dex larger than South Pole Telescope or Planck-selected samples and the deeper eFEDS sample. The exposure time, detection likelihood threshold, extension likelihood threshold and number of counts affect the concentration distribution, but generally not enough to reduce the concentration to match the other samples. The concentration of clusters in the survey strongly affects whether they are detected as a function of redshift and luminosity. We introduce a combined disturbance score based on a Gaussian mixture model fit to several of the parameters. For brighter clusters, around 1/4 of objects are classified as disturbed using this score, which may be due to our sensitivity to concentrated objects.
Backcasting Policies in Transport Systems as an Optimal Control Problem : An Example with Electric Vehicle Purchase Incentives
2025-02-04
Vinith Lakshmanan, Xavier Guichet, Antonio Sciarretta
This study represents a first attempt to build a backcasting methodology to identify the optimal policy roadmaps in transport systems. Specifically, it considers a passenger car fleet subsystem, modelling its evolution and greenhouse gas emissions. The policy decision under consideration is the monetary incentive to the purchase of electric vehicles. This process is cast as an optimal control problem with the objective to minimize the total budget of the state and reach a desired CO$_2$ target. A case study applied to Metropolitan France is presented to illustrate the approach. Additionally, alternative policy scenarios are also analyzed.
When Dimensionality Hurts: The Role of LLM Embedding Compression for Noisy Regression Tasks
2025-02-04
Felix Drinkall, Janet B. Pierrehumbert, Stefan Zohren
Large language models (LLMs) have shown remarkable success in language modelling due to scaling laws found in model size and the hidden dimension of the model's text representation. Yet, we demonstrate that compressed representations of text can yield better performance in LLM-based regression tasks. In this paper, we compare the relative performance of embedding compression in three different signal-to-noise contexts: financial return prediction, writing quality assessment and review scoring. Our results show that compressing embeddings, in a minimally supervised manner using an autoencoder's hidden representation, can mitigate overfitting and improve performance on noisy tasks, such as financial return prediction; but that compression reduces performance on tasks that have high causal dependencies between the input and target data. Our results suggest that the success of interpretable compressed representations such as sentiment may be due to a regularising effect.
The TechDebt Game -- Enabling Discussions about Technical Debt
2025-02-04
Marion Wiese, Angelina Heinrichs, Nino Rusieshvili, Rodrigo Rebouças de Almeida, Klara Borowa
Context. Technical Debt (TD), defined as software constructs that are beneficial in the short term but may hinder future change, is a frequently used term in software development practice. Nevertheless, practitioners do not always fully understand its definition and, in particular, conceptual model. Previous research highlights that communication about TD is challenging, especially with non-technical stakeholders. Discussions on this topic often cause conflicts due to misunderstandings related to other stakeholders' perspectives. Goal. We designed a board game to emulate TD concepts to make them tangible to all stakeholders, including non-technical ones. The game aims to encourage discussions about TD in an emulated and safe environment, thereby avoiding real-life conflicts. Method. To evaluate the game's effectiveness, we surveyed 46 practitioners from diverse domains, positions, and experience levels who played the game in 13 sessions following extensive testing during its development. In addition to the players' general feedback, we examined situations where players recognized new insights about TD or connected game scenarios to real-life experiences. Results. Overall, the feedback on the game and its enjoyment factor were highly positive. While developers and software architects often connected game situations to their real-world experiences, non-technical stakeholders, such as scrum masters, product owners, and less experienced developers, encountered multiple new insights on TD. Numerous players have shifted their attitudes toward TD and have outlined a plan to modify their behavior regarding TD management. Conclusions. Although the game may not lead to long-term behavior change among stakeholders, participants' feedback provides evidence that it might serve as a valuable starting point for team discussions on technical debt management.
Amari's Information Geometry is a dually affine formalism for parametric probability models. The literature proposes various nonparametric functional versions. Our approach uses classical Weyl's axioms so that the affine velocity of a one-parameter statistical model equals the classical Fisher's score. In the present note, we first offer a concise review of the notion of a statistical bundle as a set of couples of probability densities and Fisher's scores. Then, we show how the nonparametric dually affine setup deals with the basic Bayes and Kullback-Leibler divergence computations.
The ordered Ramsey number of a graph $G^<$ with a linearly ordered vertex set is the smallest positive integer $N$ such that any two-coloring of the edges of the ordered complete graph on $N$ vertices contains a monochromatic copy of $G^<$ in the given ordering. The study of the quantitative behavior of ordered Ramsey numbers is a relatively new theme in Ramsey theory full of interesting and difficult problems. In this survey paper, we summarize recent developments in the theory of ordered Ramsey numbers. We point out connections to other areas of combinatorics and some well-known conjectures. We also list several new and challenging open problems and highlight the often strikingly different behavior from the unordered case.
Solving quantum molecular systems presents a significant challenge for classical computation. The advent of early fault-tolerant quantum computing (EFTQC) devices offers a promising avenue to address these challenges, leveraging advanced quantum algorithms with reduced hardware requirements. This review surveys the latest developments in EFTQC and fully fault-tolerant quantum computing (FFTQC) algorithms for quantum molecular systems, covering encoding schemes, advanced Hamiltonian simulation techniques, and ground-state energy estimation methods. We highlight recent progress in overcoming practical barriers, such as reducing circuit depth and minimizing the use of ancillary qubits. Special attention is given to the potential quantum advantages achievable through these algorithms, as well as the limitations imposed by dequantization and classical simulation techniques. The review concludes with a discussion of future directions, emphasizing the need for optimized algorithms and experimental validation to bridge the gap between theoretical developments and practical implementation in EFTQC and FFTQC for quantum molecular systems.
Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification
2025-02-04
Rudolf Reiter, Jasper Hoffmann, Dirk Reinhardt, Florian Messerer, Katrin Baumgärtner, Shamburaj Sawant, Joschka Boedecker, Moritz Diehl, Sebastien Gros
The fields of MPC and RL consider two successful control techniques for Markov decision processes. Both approaches are derived from similar fundamental principles, and both are widely used in practical applications, including robotics, process control, energy systems, and autonomous driving. Despite their similarities, MPC and RL follow distinct paradigms that emerged from diverse communities and different requirements. Various technical discrepancies, particularly the role of an environment model as part of the algorithm, lead to methodologies with nearly complementary advantages. Due to their orthogonal benefits, research interest in combination methods has recently increased significantly, leading to a large and growing set of complex ideas leveraging MPC and RL. This work illuminates the differences, similarities, and fundamentals that allow for different combination algorithms and categorizes existing work accordingly. Particularly, we focus on the versatile actor-critic RL approach as a basis for our categorization and examine how the online optimization approach of MPC can be used to improve the overall closed-loop performance of a policy.
Discovery of an RRAT-like pulsar via its single pulses in an MWA imaging survey
2025-02-04
Samuel J. McSweeney, Jared Moseley, Natasha Hurley-Walker, Garvit Grover, Csanád Horváth, Timothy J. Galvin, Bradley W. Meyers, Chia Min Tan
We report the discovery of PSR J0031$-$5726 in the GaLactic and Extragalactic All-sky MWA eXtended imaging survey at a Galactic latitude of $b \approx -60^\circ$. The pulsar exhibits both sporadic, extremely bright pulses reminiscent of rotating radio transients (RRATs) as well as a persistent, dimmer pulses. The bright pulses tend to arrive at later rotation phases than their dimmer counterparts, and have dramatically varying polarization angle curves, such that the integrated profile appears almost completely depolarized down to the system noise level. The rotation measure of individual pulses was found to sometimes vary by up to ${\sim}0.8\,$rad/m$^2$, but was otherwise generally consistent with its average (ionosphere-corrected) value of $10.0 \pm 0.1\,$rad/m$^2$. We surmise that J0031$-$5726 may represent a class of pulsar that is intermediate between normal pulsars and RRATs.
Rethinking stance detection: A theoretically-informed research agenda for user-level inference using language models
2025-02-04
Prasanta Bhattacharya, Hong Zhang, Yiming Cao, Wei Gao, Brandon Siyuan Loh, Joseph J. P. Simons, Liang Ze Wong
Stance detection has emerged as a popular task in natural language processing research, enabled largely by the abundance of target-specific social media data. While there has been considerable research on the development of stance detection models, datasets, and application, we highlight important gaps pertaining to (i) a lack of theoretical conceptualization of stance, and (ii) the treatment of stance at an individual- or user-level, as opposed to message-level. In this paper, we first review the interdisciplinary origins of stance as an individual-level construct to highlight relevant attributes (e.g., psychological features) that might be useful to incorporate in stance detection models. Further, we argue that recent pre-trained and large language models (LLMs) might offer a way to flexibly infer such user-level attributes and/or incorporate them in modelling stance. To better illustrate this, we briefly review and synthesize the emerging corpus of studies on using LLMs for inferring stance, and specifically on incorporating user attributes in such tasks. We conclude by proposing a four-point agenda for pursuing stance detection research that is theoretically informed, inclusive, and practically impactful.
Sound Judgment: Properties of Consequential Sounds Affecting Human-Perception of Robots
2025-02-04
Aimee Allen, Tom Drummond, Dana Kulić
Positive human-perception of robots is critical to achieving sustained use of robots in shared environments. One key factor affecting human-perception of robots are their sounds, especially the consequential sounds which robots (as machines) must produce as they operate. This paper explores qualitative responses from 182 participants to gain insight into human-perception of robot consequential sounds. Participants viewed videos of different robots performing their typical movements, and responded to an online survey regarding their perceptions of robots and the sounds they produce. Topic analysis was used to identify common properties of robot consequential sounds that participants expressed liking, disliking, wanting or wanting to avoid being produced by robots. Alongside expected reports of disliking high pitched and loud sounds, many participants preferred informative and audible sounds (over no sound) to provide predictability of purpose and trajectory of the robot. Rhythmic sounds were preferred over acute or continuous sounds, and many participants wanted more natural sounds (such as wind or cat purrs) in-place of machine-like noise. The results presented in this paper support future research on methods to improve consequential sounds produced by robots by highlighting features of sounds that cause negative perceptions, and providing insights into sound profile changes for improvement of human-perception of robots, thus enhancing human robot interaction.
Resolved Stellar Mass Estimation of Nearby Late-type Galaxies for the SPHEREx Era: Dependence on Stellar Population Synthesis Models
2025-02-04
Jeong Hwan Lee, Minjin Kim, Taehyun Kim, Hyunjin Shim, Luis C. Ho, Ho Seong Hwang, Hyunmi Song, Dohyeong Kim, Yujin Yang, Woong-Seob Jeong
The upcoming all-sky infrared spectrophotometric SPHEREx mission is set to provide spatially resolved stellar mass maps of nearby galaxies, offering more detailed insights than integrated light observations. In this study, we develop a strategy for estimating stellar mass using SPHEREx by examining the dependence on different stellar population synthesis (SPS) models and proposing new scaling relations based on simulated SPHEREx data. We estimate the resolved stellar masses of 19 nearby late-type galaxies from the PHANGS-MUSE survey, treating these as fiducial masses. By testing four SPS models covering infrared wavelengths, i.e., E-MILES, Bruzual \& Charlot 2003 (BC03), Charlot \& Bruzual 2019 (CB19), and FSPS, we find systematic differences in mass-to-light ratios at $3.6~{\rm \mu m}$ ($M_{\ast}/L_{\rm 3.6\mu m}$) among the SPS models. In particular, BC03 and CB19 yield mass-to-light ratios on average $\sim0.2-0.3~{\rm dex}$ lower than those from E-MILES and FSPS. These mass-to-light ratios strongly correlate with stellar age, indicating a significant impact of young stellar populations on stellar mass measurements. Our analysis, incorporating fiducial masses and simulated SPHEREx data, identifies the $1.6~{\rm \mu m}$ band as the optimal wavelength for stellar mass estimation, with the lowest scatter ($0.15-0.20~{\rm dex}$) of the stellar mass. This scatter can be further reduced to $0.10-0.12~{\rm dex}$ across all SPS models by incorporating optical and SPHEREx colors. These results can provide guidance for measuring the stellar masses of the numerous nearby galaxies that SPHEREx will survey.
Pressure-induced structural and superconducting transitions in black arsenic
2025-02-04
Y. Y. Wu, L. Mu, Y. L. Zhang, D. Z. Dai, K. Meng, S. Y. Huang, X. Zhang, S. C. Huang, J. Chen, H. G. Yan, S. Y. Li
We report high-pressure Raman spectra and resistance measurements of black arsenic (b-As) up to 58 GPa, along with phonon density of states (DOS) and enthalpy calculations for four reported arsenic phases up to 50 GPa. It is found that metastable b-As transforms into gray arsenic (g-As) phase at a critical pressure of 1.51 GPa, followed by subsequent transitions to simple cubic arsenic (c-As) and incommensurate host-guest arsenic (hg-As) phases at 25.9 and 44.8 GPa, respectively. Superconductivity emerges above 25 GPa in the c-As phase, with the superconducting transition temperature ($T$$\rm_c$) remaining nearly a constant of 3 K. Upon further compression, $T$$\rm_c$ steeply increases to a higher value around 4.5 K in the incommensurate hg-As phase above 43 GPa. We use our results to update the structural and superconducting phase diagrams under pressure for the novel semiconductor, black arsenic.
A Kolmogorov High Order Deep Neural Network for High Frequency Partial Differential Equations in High Dimensions
2025-02-04
Yaqin Zhang, Ke Li, Zhipeng Chang, Xuejiao Liu, Yunqing Huang, Xueshuang Xiang
This paper proposes a Kolmogorov high order deep neural network (K-HOrderDNN) for solving high-dimensional partial differential equations (PDEs), which improves the high order deep neural networks (HOrderDNNs). HOrderDNNs have been demonstrated to outperform conventional DNNs for high frequency problems by introducing a nonlinear transformation layer consisting of $(p+1)^d$ basis functions. However, the number of basis functions grows exponentially with the dimension $d$, which results in the curse of dimensionality (CoD). Inspired by the Kolmogorov superposition theorem (KST), which expresses a multivariate function as superpositions of univariate functions and addition, K-HOrderDNN utilizes a HOrderDNN to efficiently approximate univariate inner functions instead of directly approximating the multivariate function, reducing the number of introduced basis functions to $d(p+1)$. We theoretically demonstrate that CoD is mitigated when target functions belong to a dense subset of continuous multivariate functions. Extensive numerical experiments show that: for high-dimensional problems ($d$=10, 20, 50) where HOrderDNNs($p>1$) are intractable, K-HOrderDNNs($p>1$) exhibit remarkable performance. Specifically, when $d=10$, K-HOrderDNN($p=7$) achieves an error of 4.40E-03, two orders of magnitude lower than that of HOrderDNN($p=1$) (see Table 10); for high frequency problems, K-HOrderDNNs($p>1$) can achieve higher accuracy with fewer parameters and faster convergence rates compared to HOrderDNNs (see Table 8).
Robust Cislunar Low-Thrust Trajectory Optimization under Uncertainties via Sequential Covariance Steering
2025-02-04
Naoya Kumagai, Kenshiro Oguri
Spacecraft operations are influenced by uncertainties such as dynamics modeling, navigation, and maneuver execution errors. Although mission design has traditionally incorporated heuristic safety margins to mitigate the effect of uncertainties, particularly before/after crucial events, it is yet unclear whether this practice will scale in the cislunar region, which features locally chaotic nonlinear dynamics and involves frequent lunar flybys. This paper applies chance-constrained covariance steering and sequential convex programming to simultaneously design an optimal trajectory and trajectory correction policy that can probabilistically guarantee safety constraints under the assumed physical/navigational error models. The results show that the proposed method can effectively control the state uncertainty in a highly nonlinear environment and provide a trajectory with better local stability properties than a trajectory designed without considering uncertainties. The framework allows faster computation and lossless covariance propagation compared to existing methods, enabling a rapid and accurate comparison of $\Delta V_{99}$ costs for different uncertainty parameters. We demonstrate the algorithm on several transfers in the Earth-Moon Circular Restricted Three Body Problem.
A Comprehensive Survey on Feature Extraction Techniques Using I/Q Imbalance in RFFI
2025-02-04
Muhammad Aqib Khan, Muhammad Usman Siddiqui
The proliferation of Internet of Things (IoT) devices has increased the need for secure authentication. While traditional encryption-based solutions can be robust, they often impose high computational and energy overhead on resource-limited IoT nodes. As an alternative, radio frequency fingerprint identification (RFFI) leverages hardware-induced imperfections-such as Inphase/Quadrature (I/Q) imbalance-in Radio Frequency (RF) front-end components as unique identifiers that are inherently difficult to clone or spoof. Despite recent advances, significant challenges remain in standardizing feature extraction methods, maintaining high accuracy across diverse environments, and efficiently handling large-scale IoT deployments. This paper addresses these gaps by providing a comprehensive review of feature extraction techniques that utilize I/Q imbalance for RFFI. We also discuss other hardware-based RF fingerprinting sources, including power amplifier nonlinearity and oscillator imperfections, and examine modern machine learning (ML) and deep learning (DL) approaches that enhance device identification performance.
3D Foundation AI Model for Generalizable Disease Detection in Head Computed Tomography
2025-02-04
Weicheng Zhu, Haoxu Huang, Huanze Tang, Rushabh Musthyala, Boyang Yu, Long Chen, Emilio Vega, Thomas O'Donnell, Seena Dehkharghani, Jennifer A. Frontera, Arjun V. Masurkar, Kara Melmed, Narges Razavian
Head computed tomography (CT) imaging is a widely-used imaging modality with multitudes of medical indications, particularly in assessing pathology of the brain, skull, and cerebrovascular system. It is commonly the first-line imaging in neurologic emergencies given its rapidity of image acquisition, safety, cost, and ubiquity. Deep learning models may facilitate detection of a wide range of diseases. However, the scarcity of high-quality labels and annotations, particularly among less common conditions, significantly hinders the development of powerful models. To address this challenge, we introduce FM-CT: a Foundation Model for Head CT for generalizable disease detection, trained using self-supervised learning. Our approach pre-trains a deep learning model on a large, diverse dataset of 361,663 non-contrast 3D head CT scans without the need for manual annotations, enabling the model to learn robust, generalizable features. To investigate the potential of self-supervised learning in head CT, we employed both discrimination with self-distillation and masked image modeling, and we construct our model in 3D rather than at the slice level (2D) to exploit the structure of head CT scans more comprehensively and efficiently. The model's downstream classification performance is evaluated using internal and three external datasets, encompassing both in-distribution (ID) and out-of-distribution (OOD) data. Our results demonstrate that the self-supervised foundation model significantly improves performance on downstream diagnostic tasks compared to models trained from scratch and previous 3D CT foundation models on scarce annotated datasets. This work highlights the effectiveness of self-supervised learning in medical imaging and sets a new benchmark for head CT image analysis in 3D, enabling broader use of artificial intelligence for head CT-based diagnosis.
Cross-Modality Embedding of Force and Language for Natural Human-Robot Communication
2025-02-04
Ravi Tejwani, Karl Velazquez, John Payne, Paolo Bonato, Harry Asada
A method for cross-modality embedding of force profile and words is presented for synergistic coordination of verbal and haptic communication. When two people carry a large, heavy object together, they coordinate through verbal communication about the intended movements and physical forces applied to the object. This natural integration of verbal and physical cues enables effective coordination. Similarly, human-robot interaction could achieve this level of coordination by integrating verbal and haptic communication modalities. This paper presents a framework for embedding words and force profiles in a unified manner, so that the two communication modalities can be integrated and coordinated in a way that is effective and synergistic. Here, it will be shown that, although language and physical force profiles are deemed completely different, the two can be embedded in a unified latent space and proximity between the two can be quantified. In this latent space, a force profile and words can a) supplement each other, b) integrate the individual effects, and c) substitute in an exchangeable manner. First, the need for cross-modality embedding is addressed, and the basic architecture and key building block technologies are presented. Methods for data collection and implementation challenges will be addressed, followed by experimental results and discussions.
Survey on bilinear spherical averages and associated maximal operators
2025-02-04
Tainara Borges
In this survey, we collect recent progress in the understanding of $L^{p}$ bounds for bilinear spherical averages and some associated maximal functions like the bilinear spherical maximal function and its lacunary counterpart. We describe necessary conditions satisfied by triples in the $L^{p}$ improving region of a bilinear spherical averaging operator and the localized bilinear spherical maximal function, as well as describe the best-known boundedness regions to date. We state some open questions along the way to motivate future research on this topic, and we exploit some possible generalizations.
Too Noisy To Learn: Enhancing Data Quality for Code Review Comment Generation
2025-02-04
Chunhua Liu, Hong Yi Lin, Patanamon Thongtanunam
Code review is an important practice in software development, yet it is time-consuming and requires substantial effort. While open-source datasets have been used to train neural models for automating code review tasks, including review comment generation, these datasets contain a significant amount of noisy comments (e.g., vague or non-actionable feedback) that persist despite cleaning methods using heuristics and machine learning approaches. Such remaining noise may lead models to generate low-quality review comments, yet removing them requires a complex semantic understanding of both code changes and natural language comments. In this paper, we investigate the impact of such noise on review comment generation and propose a novel approach using large language models (LLMs) to further clean these datasets. Based on an empirical study on a large-scale code review dataset, our LLM-based approach achieves 66-85% precision in detecting valid comments. Using the predicted valid comments to fine-tune the state-of-the-art code review models (cleaned models) can generate review comments that are 13.0% - 12.4% more similar to valid human-written comments than the original models. We also find that the cleaned models can generate more informative and relevant comments than the original models. Our findings underscore the critical impact of dataset quality on the performance of review comment generation. We advocate for further research into cleaning training data to enhance the practical utility and quality of automated code review.
Tomographic halo model of the unWISE-Blue galaxies using cross-correlations with BOSS CMASS galaxies
2025-02-04
Alex Krolewski, Jensen Lawrence, Will J. Percival
The halo model offers a framework for investigating galaxy clustering, and for understanding the growth of galaxies and the distribution of galaxies of different types. Here, we use the halo model to study the small-scale clustering and halo occupation distribution (HOD) of the unWISE-Blue galaxy sample, an infrared-selected sample of $\sim$100 million galaxies across the entire extragalactic sky at $z\sim 0.5$ $-$ similar redshifts to the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS sample. Although the photometric unWISE galaxies cannot be easily split in redshift, we use their cross-correlation with the BOSS CMASS sample to tomographically probe the HOD of the unWISE galaxies at $0.45 < z < 0.75$. To do so, we develop a new method for applying the halo model to cross-correlations between a photometric sample and a spectroscopic sample in narrow redshift bins, incorporating halo exclusion, post-Limber corrections, and redshift-space distortions. We reveal strong evolution in the CMASS HOD, and modest evolution in the unWISE-Blue HOD. For unWISE-Blue, we find that the average bias and mean halo mass drop from $b = 1.6$ and $\log_{10}(M_{\mathrm{h}}/M_{\odot}) \sim 13.4$ at $z \sim 0.5$ to $b = 1.4$ and $\log_{10}(M_{\mathrm{h}}/M_{\odot}) \sim 13.1$ at $z \sim 0.7$, and that the satellite fraction drops modestly from $\sim$20% to $\sim$10% in the same range. These results are useful for creating mock samples of the unWISE-Blue galaxies. Furthermore, the techniques developed to obtain these results are applicable to other tomographic cross-correlations between photometric samples and narrowly-binned spectroscopic samples, such as clustering redshifts.
2025-02-04
Cristobal Donoso-Oliva, Ignacio Becker, Pavlos Protopapas, Guillermo Cabrera-Vives, Martina Cádiz-Leyton, Daniel Moreno-Cartagena
Foundational models have emerged as a powerful paradigm in deep learning field, leveraging their capacity to learn robust representations from large-scale datasets and effectively to diverse downstream applications such as classification. In this paper, we present Astromer 2 a foundational model specifically designed for extracting light curve embeddings. We introduce Astromer 2 as an enhanced iteration of our self-supervised model for light curve analysis. This paper highlights the advantages of its pre-trained embeddings, compares its performance with that of its predecessor, Astromer 1, and provides a detailed empirical analysis of its capabilities, offering deeper insights into the model's representations. Astromer 2 is pretrained on 1.5 million single-band light curves from the MACHO survey using a self-supervised learning task that predicts randomly masked observations within sequences. Fine-tuning on a smaller labeled dataset allows us to assess its performance in classification tasks. The quality of the embeddings is measured by the F1 score of an MLP classifier trained on Astromer-generated embeddings. Our results demonstrate that Astromer 2 significantly outperforms Astromer 1 across all evaluated scenarios, including limited datasets of 20, 100, and 500 samples per class. The use of weighted per-sample embeddings, which integrate intermediate representations from Astromer's attention blocks, is particularly impactful. Notably, Astromer 2 achieves a 15% improvement in F1 score on the ATLAS dataset compared to prior models, showcasing robust generalization to new datasets. This enhanced performance, especially with minimal labeled data, underscores the potential of Astromer 2 for more efficient and scalable light curve analysis.
NDKF: A Neural-Enhanced Distributed Kalman Filter for Nonlinear Multi-Sensor Estimation
2025-02-04
Siavash Farzan
We propose a Neural-Enhanced Distributed Kalman Filter (NDKF) for multi-sensor state estimation in nonlinear systems. Unlike traditional Kalman filters that rely on explicit, linear models and centralized data fusion, the NDKF leverages neural networks to learn both the system dynamics and measurement functions directly from data. Each sensor node performs local prediction and update steps using these learned models and exchanges only compact summary information with its neighbors via a consensus-based fusion process, which reduces communication overhead and eliminates a single point of failure. Our theoretical convergence analysis establishes sufficient conditions under which the local linearizations of the neural models guarantee overall filter stability and provides a solid foundation for the proposed approach. Simulation studies on a 2D system with four partially observing nodes demonstrate that the NDKF significantly outperforms a distributed Extended Kalman Filter. These outcomes, as yielded by the proposed NDKF method, highlight its potential to improve the scalability, robustness, and accuracy of distributed state estimation in complex nonlinear environments.
Machine-learning approaches to accelerating lattice simulations
2025-02-04
Scott Lawrence
The last decade has seen an explosive growth of interest in exploiting developments in machine learning to accelerate lattice QCD calculations. On the sampling side, generative models are a promising approach to mitigating critical slowing down and topological freezing. Meanwhile, signal-to-noise problems have been shown to be improvable by the use of optimized improved observables. Both techniques can be made free of bias, resulting in trustworthy but reduced statistical errors. This talk reviews recent developments in this field.
A Training-Free Length Extrapolation Approach for LLMs: Greedy Attention Logit Interpolation (GALI)
2025-02-04
Yan Li, Tianyi Zhang, Zechuan Li, Soyeon Caren Han
Transformer-based Large Language Models (LLMs) struggle to process inputs exceeding their training context window, with performance degrading due to positional out-of-distribution (O.O.D.) that disrupt attention computations. Existing solutions, fine-tuning and training-free methods, are limited by computational inefficiency, attention logit outliers or loss of local positional information. To address this, we propose Greedy Attention Logit Interpolation (GALI), a training-free length extrapolation method that maximizes the utilization of pretrained positional intervals while avoiding attention logit outliers through attention logit interpolation. The result demonstrates that GALI consistently outperforms state-of-the-art training-free methods. Our findings reveal that LLMs interpret positional intervals unevenly within their training context window, suggesting that extrapolating within a smaller positional interval range yields superior results-even for short-context tasks. GALI represents a significant step toward resolving the positional O.O.D. challenge, enabling more reliable long-text understanding in LLMs. Our implementation of GALI, along with the experiments from our paper, is open-sourced at https://github.com/AcademyCityL/GALI.
SiLVR: Scalable Lidar-Visual Radiance Field Reconstruction with Uncertainty Quantification
2025-02-04
Yifu Tao, Maurice Fallon
We present a neural radiance field (NeRF) based large-scale reconstruction system that fuses lidar and vision data to generate high-quality reconstructions that are geometrically accurate and capture photorealistic texture. Our system adopts the state-of-the-art NeRF representation to additionally incorporate lidar. Adding lidar data adds strong geometric constraints on the depth and surface normals, which is particularly useful when modelling uniform texture surfaces which contain ambiguous visual reconstruction cues. Furthermore, we estimate the epistemic uncertainty of the reconstruction as the spatial variance of each point location in the radiance field given the sensor observations from camera and lidar. This enables the identification of areas that are reliably reconstructed by each sensor modality, allowing the map to be filtered according to the estimated uncertainty. Our system can also exploit the trajectory produced by a real-time pose-graph lidar SLAM system during online mapping to bootstrap a (post-processed) Structure-from-Motion (SfM) reconstruction procedure reducing SfM training time by up to 70%. It also helps to properly constrain the overall metric scale which is essential for the lidar depth loss. The globally-consistent trajectory can then be divided into submaps using Spectral Clustering to group sets of co-visible images together. This submapping approach is more suitable for visual reconstruction than distance-based partitioning. Each submap is filtered according to point-wise uncertainty estimates and merged to obtain the final large-scale 3D reconstruction. We demonstrate the reconstruction system using a multi-camera, lidar sensor suite in experiments involving both robot-mounted and handheld scanning. Our test datasets cover a total area of more than 20,000 square metres, including multiple university buildings and an aerial survey of a multi-storey.
The quenching of star formation in dwarf galaxies: new perspectives from deep-wide surveys
2025-02-04
S. Kaviraj, I. Lazar, A. E. Watkins, C. Laigle, G. Martin, R. A. Jackson
Dwarf galaxies dominate the galaxy number density, making them critical to our understanding of galaxy evolution. However, typical dwarfs are too faint to be visible outside the very local Universe in past surveys like the SDSS, which offer large footprints but are shallow. Dwarfs in such surveys have relatively high star formation rates, which boost their luminosity, making them detectable in shallow surveys, but also biased and potentially unrepresentative of dwarfs as a whole. Here, we use deep data to perform an unbiased statistical study of ~7,000 nearby (z<0.25) dwarfs (10^8 MSun < M < 10^9.5 MSun) in the COSMOS field which, at these redshifts, is a relatively low-density field. At z~0.05, ~40 per cent of dwarfs in low-density environments are red/quenched, falling to ~30 per cent by z~0.25. Red dwarfs reside closer to nodes, filaments and massive galaxies. Proximity to a massive galaxy appears to be more important in determining whether a dwarf is red, rather than simply its distance from nodes and filaments or the mean density of its local environment. Interestingly, around half of the red dwarfs reside outside the virial radii of massive galaxies and around a third of those also inhabit regions in the lower 50 per cent in density percentile (i.e. regions of very low ambient density). Around half of the red dwarf population is, therefore, quenched by mechanisms unrelated to environment, which are likely to be internal processes such as stellar and AGN feedback.
Ball Lightning as a profound manifestation of the Dark Matter physics
2025-02-04
Ariel Zhitnitsky
Ball lighting (BL) has been observed for centuries. There are large number of books, review articles, and original scientific papers devoted to different aspects of BL phenomenon. Yet, the basic features of this phenomenon have never been explained by known physics. The main problem is the source which could power the dynamics of the BL. We advocate an idea that the dark matter in form of the axion quark nuggets (AQN) made of standard model quarks and gluons (similar to the old idea of the Witten's strangelets) could internally generate the required power. The corresponding macroscopically large object in form of the AQN behaves as {\it chameleon}: it does not interact with the surrounding material in dilute environment and serves as perfect cold DM candidate. However, AQN becomes strongly interacting object in sufficiently dense environment. The AQN model was invented long ago without any relation to the BL physics. It was invented with a single motivation to explain the observed similarity $\Omega_{\rm DM}\sim \Omega_{\rm visible}$ between visible and DM components. This relation represents a very generic feature of this framework, not sensitive to any parameters of the construction. However, with the same set of parameters being fixed long ago this model is capable to address the key elements of the BL phenomenology, including the source of the energy powering the BL events. In particular, we argue that the visible size of BL, its typical life time, the frequency of appearance , etc are all consistent with suggested proposal when BL represents a profound manifestation of the DM physics represented by the AQN objects. We also formulate a unique possible test which can refute or unambiguously substantiate this unorthodox proposal on nature of BL.
Binarity at LOw Metallicity (BLOeM): The multiplicity properties and evolution of BAF-type supergiants
2025-02-04
L. R. Patrick, D. J. Lennon, F. Najarro, T. Shenar, J. Bodensteiner, H. Sana, P. A. Crowther, N. Britavskiy, N. Langer, A. Schootemeijer, C. J. Evans, L. Mahy, Y. Götberg, S. E. de Mink, F. R. N. Schneider, A. J. G. O'Grady, J. I. Villaseñor, M. Bernini-Peron, D. M. Bowman, A. de Koter, K. Deshmukh, A. Gilkis, G. González-Torà, V. M. Kalari, Z. Keszthelyi, I. Mandel, A. Menon, M. Moe, L. M. Oskinova, D. Pauli, M. Renzo, A. A. C. Sander, K. Sen, M. Stoop, J. Th. van Loon, S. Toonen, F. Tramper, J. S. Vink, C. Wang
Given the uncertain evolutionary status of blue supergiant stars, their multiplicity properties hold vital clues to better understand their origin and evolution. As part of The Binarity at LOw Metallicity (BLOeM) campaign in the Small Magellanic Cloud we present a multi-epoch spectroscopic survey of 128 supergiant stars of spectral type B5--F5, which roughly correspond to initial masses in the range 6 to 30 solar masses. The observed binary fraction for the B5-9 supergiants is 25+/-6 % (10+/-4 %) and 5+/-2 % (0 %) for the A-F stars, using a radial velocity (RV) variability threshold of 5 kms (10 kms) as a criterion for binarity. Accounting for observational biases we find an intrinsic multiplicity fraction of less than 18% for the B5-9 stars and 8$^{+9}_{-7}$% for the AF stars, for the orbital periods up to 10$^{3.5}$day and mass-ratios (q) in the range 0.1 < q < 1. The large stellar radii of these supergiant stars prevent short orbital periods but we demonstrate that this effect alone cannot explain our results. We assess the spectra and RV time series of the detected binary systems and find that only a small fraction display convincing solutions. We conclude that the multiplicity fractions are compromised by intrinsic stellar variability such that the true multiplicity fraction may be significantly smaller. Our main conclusions from comparing the multiplicity properties of the B5-9 and AF supergiants to that of their less evolved counterparts is that such stars cannot be explained by a direct evolution from the main sequence. Furthermore, by comparing their multiplicity properties to red supergiant stars we conclude that the AF supergiant stars are neither progenitors nor descendants of red supergiants.
Bounds on Velocity-Dependent Dark Matter-Baryon Scattering from Large-Scale Structure
2025-02-04
Adam He, Mikhail M. Ivanov, Rui An, Trey Driskell, Vera Gluscevic
We explore interacting dark matter (DM) models that allow DM and baryons to scatter off of each other with a cross section that scales with relative particle velocity. Using the effective field theory of large-scale structure, we perform the first analysis of BOSS full-shape galaxy clustering data for velocity-dependent DM-baryon interactions. We determine that while the addition of BOSS full-shape data visibly modifies the shape of the posterior distribution, it does not significantly alter the 95% confidence level intervals for the interaction cross section obtained from an analysis of the cosmic microwave (CMB) anisotropy from Planck measurements alone. Moreover, in agreement with previous findings, we note that the DM-baryon interacting model presents a good fit to both large-scale structure (LSS) data and CMB data and alleviates the $S_8$ tension between the two data sets. After combining LSS and CMB data with weak lensing data from the Dark Energy Survey, we find a $\gtrsim2\sigma$ preference for non-zero interactions between DM and baryons in a velocity-independent model. We also explore a scenario where only a fraction of DM undergoes scattering with baryons; we find a similar $\gtrsim2\sigma$ preference for the presence of interactions. Our results suggest that a suppression of the linear matter power spectrum at small scales may be needed to resolve certain discrepancies between LSS and CMB data that are found in the cold DM (CDM) scenario.
Ultra High-Redshift or Closer-by, Dust-Obscured Galaxies? Deciphering the Nature of Faint, Previously Missed F200W-Dropouts in CEERS
2025-02-04
G. Gandolfi, G. Rodighiero, L. Bisigello, A. Grazian, S. L. Finkelstein, M. Dickinson, M. Castellano, E. Merlin, A. Calabrò, C. Papovich, A. Bianchetti, E. Bañados, P. Benotto, F. Buitrago, E. Daddi, G. Girardi, M. Giulietti, M. Hirschmann, B. W. Holwerda, P. Arrabal Haro, A. Lapi, R. A. Lucas, Y. Lyu, M. Massardi, F. Pacucci, P. G. Pérez-González, T. Ronconi, M. Tarrasse, S. Wilkins, B. Vulcani, L. Y. A. Yung, J. A. Zavala, B. Backhaus, M. Bagley, V. Buat, D. Burgarella, J. Kartaltepe, Y. Khusanova, A. Kirkpatrick, D. Kocevski, A. M. Koekemoer, E. Lambrides, N. Pirzkal, G. Yang
The James Webb Space Telescope (JWST) is revolutionizing our understanding of the Universe by unveiling faint, near-infrared dropouts previously beyond our reach, ranging from exceptionally dusty sources to galaxies up to redshift $z \sim 14$. In this paper, we identify F200W-dropout objects in the Cosmic Evolution Early Release Science (CEERS) survey which are absent from existing catalogs. Our selection method can effectively identify obscured low-mass ($\log \text{M}_* \leq 9$) objects at $z \leq 6$, massive dust-rich sources up to $z \sim 12$, and ultra-high-redshift ($z > 15$) candidates. Primarily relying on NIRCam photometry from the latest CEERS data release and supplementing with Mid-Infrared/(sub-)mm data when available, our analysis pipeline combines multiple SED-fitting codes, star formation histories, and CosMix - a novel tool for astronomical stacking. Our work highlights three $215$, with best-fit masses compatible with $\Lambda$CDM and a standard baryons-to-star conversion efficiency. Their bi-modal redshift probability distributions suggest they could also be $z<1.5$ dwarf galaxies with extreme dust extinction. We also identify a strong line emitter galaxy at $z \sim 5$ mimicking the near-infrared emission of a $z \sim 13$ galaxy. Our sample holds promising candidates for future follow-ups. Confirming ultra high-redshift galaxies or lower-z dusty dwarfs will offer valuable insights into early galaxy formation, evolution with their central black holes and the nature of dark matter, and/or cosmic dust production mechanisms in low-mass galaxies, and will help us to understand degeneracies and contamination in high-z object searches.
An Alcock-Paczynski Test on Reionization Bubbles for Cosmology
2025-02-04
Emilie Thélie, Franco Del Balso, Julian B. Muñoz, Adrian Liu
In this paper, we propose an Alcock-Paczy\'nski (AP) test to constrain cosmology using HII bubbles during the Epoch of Reionization. Similarly to cosmic voids, a stack of HII bubbles is spherically symmetric because ionizing fronts propagate isotropically on average (even if individual bubbles may not be spherical), making them standard spheres to be used in an AP test. Upcoming 21-cm observations, from the Square Kilometer Array (SKA) for instance, will contain tomographic information about HII regions during reionization. However, extracting the bubbles from this signal is made difficult because of instrumental noise and foreground systematics. Here, we use a neural network to reconstruct neutral-fraction boxes from the noisy 21-cm signal, from which we extract bubbles using a watershed algorithm. We then run the purely geometrical AP test on these stacks, showing that a SKA-like experiment will be able to constrain the product of the angular-diameter distance $D_{\text{A}}$ and Hubble parameter $H$ at reionization redshifts with $\sim 2\%$ precision, robustly to astrophysical and cosmological uncertainties within the models tested here. This AP test, whether performed on 21-cm observations or other large surveys of ionized bubbles, will allow us to fill the knowledge gap about the expansion rate of our Universe at reionization redshifts.
Binarity at Low Metallicity (BLOeM) -- Multiplicity properties of Oe and Be stars
2025-02-04
J. Bodensteiner, T. Shenar, H. Sana, N. Britavskiy, P. A. Crowther, N. Langer, D. J. Lennon, L. Mahy, L. R. Patrick, J. I. Villaseñor, M. Abdul-Masih, D. M. Bowman, A. de Koter, S. E. de Mink, K. Deshmukh, M. Fabry, A. Gilkis, Y. Götberg, G. Holgado, R. G. Izzard, S. Janssens, V. M. Kalari, Z. Keszthelyi, J. Kubát, I. Mandel, G. Maravelias, L. M. Oskinova, D. Pauli, V. Ramachandran, D. F. Rocha, M. Renzo, A. A. C. Sander, F. R. N. Schneider, A. Schootemeijer, K. Sen, M. Stoop, S. Toonen, J. Th. van Loon, R. Valli, A. Vigna-Gómez, J. S. Vink, C. Wang, X. -T. Xu
Rapidly rotating classical OBe stars have been proposed as the products of binary interactions, and the fraction of Be stars with compact companions implies that at least some are. However, to constrain the interaction physics spinning up the OBe stars, a large sample of homogeneously analysed OBe stars with well-determined binary characteristics and orbital parameters are required. We investigate the multiplicity properties of a sample of 18 Oe, 62 Be, and two Of?p stars observed within the BLOeM survey in the Small Magellanic Cloud. We analyse the first nine epochs of spectroscopic observations obtained over approximately three months in 2023. Radial velocities (RVs) of all stars are measured. Applying commonly-used binarity criteria we classify objects as binaries, binary candidates, and apparently single (RV stable) objects. We further inspect the spectra for double-lined spectroscopic binaries and cross-match with catalogues of X-ray sources and photometric binaries. We classify 14 OBe stars as binaries, and an additional 11 as binary candidates. The two Of?p stars are apparently single. Two more objects are most likely currently interacting binaries. Without those, the observed binary fraction for the OBe sample (78 stars) is f_OBe_obs=0.18+/-0.04 (f_obs_cand=0.32+/-0.05 including candidates). This fraction is less than half of that measured for OB stars in BLOeM. Combined with the lower fraction of SB2s, this suggests that OBe stars have indeed fundamentally different binary properties than OB stars. We find no evidence for OBe binaries with massive compact companions, in contrast to expectations from binary population synthesis. Our results support the binary scenario as an important formation channel for OBe stars, as post-interaction binaries may have been disrupted or the stripped companions of OBe stars are harder to detect.
2D light distributions of dwarf galaxies -- key tests of the implementation of physical processes in simulations
2025-02-04
Aaron Watkins, Garreth Martin, Sugata Kaviraj, Chris Collins, Yohan Dubois, Katarina Kraljic, Christophe Pichon, Sukyoung K. Yi
Cosmological simulations provide much of the theoretical framework within which we interpret extragalactic observations. However, even if a given simulation reproduces the integrated properties of galaxies well, it may not reproduce the detailed structures of individual galaxies. Comparisons between the 2D light distributions of simulated and observed galaxies -- particularly in the dwarf regime, where key processes like tidal perturbations and baryonic feedback most strongly influence galaxy structure -- thus provide an additional valuable test of the simulation's efficacy. We compare scaling relations derived from mock observations of simulated galaxies, drawn from the two largest halos in the high-resolution NewHorizon cosmological simulation, with galaxies in the Fornax cluster. While Fornax is significantly more massive than either group, it is the lowest-mass cluster in the local Universe, and contains a well-studied population of spatially resolved dwarfs, hence serves as a useful benchmark. Per unit stellar mass, NewHorizon dwarfs are systematically larger in half-light radius, much fainter in surface brightness, and bluer in colour than their Fornax counterparts, albeit with similar light profile shapes. We discuss potential reasons for these discrepancies, including environmental effects, baryonic feedback, resolution, or couplings of these factors. As observations of dwarfs outside of the local Universe become more plentiful through on-going or up-coming surveys such as Euclid and LSST, 2D comparisons such as these, where properties are measured in the same way across both simulations and observations, can place strong constraints on processes that alter the spatial distribution of baryons in galaxies.
Graph Structure Learning for Tumor Microenvironment with Cell Type Annotation from non-spatial scRNA-seq data
2025-02-04
Yu-An Huang, Yue-Chao Li, Hai-Ru You, Jie Pan, Xiyue Cao, Xinyuan Li, Zhi-An Huang, Zhu-Hong You
The exploration of cellular heterogeneity within the tumor microenvironment (TME) via single-cell RNA sequencing (scRNA-seq) is essential for understanding cancer progression and response to therapy. Current scRNA-seq approaches, however, lack spatial context and rely on incomplete datasets of ligand-receptor interactions (LRIs), limiting accurate cell type annotation and cell-cell communication (CCC) inference. This study addresses these challenges using a novel graph neural network (GNN) model that enhances cell type prediction and cell interaction analysis. Our study utilized a dataset consisting of 49,020 cells from 19 patients across three cancer types: Leukemia, Breast Invasive Carcinoma, and Colorectal Cancer. The proposed scGSL model demonstrated robust performance, achieving an average accuracy of 84.83%, precision of 86.23%, recall of 81.51%, and an F1 score of 80.92% across all datasets. These metrics represent a significant enhancement over existing methods, which typically exhibit lower performance metrics. Additionally, by reviewing existing literature on gene interactions within the TME, the scGSL model proves to robustly identify biologically meaningful gene interactions in an unsupervised manner, validated by significant expression differences in key gene pairs across various cancers. The source code and data used in this paper can be found in https://github.com/LiYuechao1998/scGSL.
Sample Complexity of Bias Detection with Subsampled Point-to-Subspace Distances
2025-02-04
German Martinez Matilla, Jakub Marecek
Sample complexity of bias estimation is a lower bound on the runtime of any bias detection method. Many regulatory frameworks require the bias to be tested for all subgroups, whose number grows exponentially with the number of protected attributes. Unless one wishes to run a bias detection with a doubly-exponential run-time, one should like to have polynomial complexity of bias detection for a single subgroup. At the same time, the reference data may be based on surveys, and thus come with non-trivial uncertainty. Here, we reformulate bias detection as a point-to-subspace problem on the space of measures and show that, for supremum norm, it can be subsampled efficiently. In particular, our probabilistically approximately correct (PAC) results are corroborated by tests on well-known instances.
Backcasting the Optimal Decisions in Transport Systems: An Example with Electric Vehicle Purchase Incentives
2025-02-04
Vinith Lakshmanan, Xavier Guichet, Antonio Sciarretta
This study represents a first attempt to build a backcasting methodology to identify the optimal policy roadmaps in transport systems. In this methodology, desired objectives are set by decision makers at a given time horizon, and then the optimal combinations of policies to achieve these objectives are computed as a function of time (i.e., ``backcasted''). This approach is illustrated on the transportation sector by considering a specific subsystem with a single policy decision. The subsystem describes the evolution of the passenger car fleet within a given region and its impact on greenhouse gas emissions. The optimized policy is a monetary incentive for the purchase of electric vehicles while minimizing the total budget of the state and achieving a desired CO$_2$ target. A case study applied to Metropolitan France is presented to illustrate the approach. Additionally, alternative policy scenarios are also analyzed to provide further insights.
Deep Learning-Based Facial Expression Recognition for the Elderly: A Systematic Review
2025-02-04
F. Xavier Gaya-Morey, Jose M. Buades-Rubio, Philippe Palanque, Raquel Lacuesta, Cristina Manresa-Yee
The rapid aging of the global population has highlighted the need for technologies to support elderly, particularly in healthcare and emotional well-being. Facial expression recognition (FER) systems offer a non-invasive means of monitoring emotional states, with applications in assisted living, mental health support, and personalized care. This study presents a systematic review of deep learning-based FER systems, focusing on their applications for the elderly population. Following a rigorous methodology, we analyzed 31 studies published over the last decade, addressing challenges such as the scarcity of elderly-specific datasets, class imbalances, and the impact of age-related facial expression differences. Our findings show that convolutional neural networks remain dominant in FER, and especially lightweight versions for resource-constrained environments. However, existing datasets often lack diversity in age representation, and real-world deployment remains limited. Additionally, privacy concerns and the need for explainable artificial intelligence emerged as key barriers to adoption. This review underscores the importance of developing age-inclusive datasets, integrating multimodal solutions, and adopting XAI techniques to enhance system usability, reliability, and trustworthiness. We conclude by offering recommendations for future research to bridge the gap between academic progress and real-world implementation in elderly care.
Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription
2025-02-04
Mahdi Alkaeed, Sofiat Abioye, Adnan Qayyum, Yosra Magdi Mekki, Ilhem Berrou, Mohamad Abdallah, Ala Al-Fuqaha, Muhammad Bilal, Junaid Qadir
In response to the success of proprietary Large Language Models (LLMs) such as OpenAI's GPT-4, there is a growing interest in developing open, non-proprietary LLMs and AI foundation models (AIFMs) for transparent use in academic, scientific, and non-commercial applications. Despite their inability to match the refined functionalities of their proprietary counterparts, open models hold immense potential to revolutionize healthcare applications. In this paper, we examine the prospects of open-source LLMs and AIFMs for developing healthcare applications and make two key contributions. Firstly, we present a comprehensive survey of the current state-of-the-art open-source healthcare LLMs and AIFMs and introduce a taxonomy of these open AIFMs, categorizing their utility across various healthcare tasks. Secondly, to evaluate the general-purpose applications of open LLMs in healthcare, we present a case study on personalized prescriptions. This task is particularly significant due to its critical role in delivering tailored, patient-specific medications that can greatly improve treatment outcomes. In addition, we compare the performance of open-source models with proprietary models in settings with and without Retrieval-Augmented Generation (RAG). Our findings suggest that, although less refined, open LLMs can achieve performance comparable to proprietary models when paired with grounding techniques such as RAG. Furthermore, to highlight the clinical significance of LLMs-empowered personalized prescriptions, we perform subjective assessment through an expert clinician. We also elaborate on ethical considerations and potential risks associated with the misuse of powerful LLMs and AIFMs, highlighting the need for a cautious and responsible implementation in healthcare.
Countering Election Sway: Strategic Algorithms in Friedkin-Johnsen Dynamics
2025-02-03
Dragos Ristache, Fabian Spaeh, Charalampos E. Tsourakakis
Social influence profoundly impacts individual choices and collective behaviors in politics. In this work, driven by the goal of protecting elections from improper influence, we consider the following scenario: an individual, who has vested interests in political party $Y$, is aware through reliable surveys that parties $X$ and $Y$ are likely to get 50.1\% and 49.9\% of the vote, respectively. Could this individual employ strategies to alter public opinions and consequently invert these polling numbers in favor of party $Y$? We address this question by employing: (i) the Friedkin-Johnsen (FJ) opinion dynamics model, which is mathematically sophisticated and effectively captures the way individual biases and social interactions shape opinions, making it crucial for examining social influence, and (ii) interventions similar to those in Asch's experiments, which involve selecting a group of stooges within the network to spread a specific opinion. We mathematically formalize the aforementioned motivation as an optimization framework and establish that it is NP-hard and inapproximable within any constant factor. We introduce three efficient polynomial-time algorithms. The first two utilize a continuous approach: one employs gradient descent with Huber's estimator to approximate the median, and the other uses a sigmoid threshold influence function. The third utilizes a combinatorial greedy algorithm for targeted interventions. Through comparative analysis against various natural baselines and using real-world data, our results demonstrate that in numerous cases a small fraction of nodes chosen as stooges can significantly sway election outcomes under the Friedkin-Johnsen model.
Variational path sampling of rare dynamical events
2025-02-03
Aditya N. Singh, Avishek Das, David T. Limmer
This article reviews the concepts and methods of variational path sampling. These methods allow computational studies of rare events in systems driven arbitrarily far from equilibrium. Based upon a statistical mechanics of trajectory space and leveraging the theory of large deviations, they provide a perspective with which dynamical phenomena can be studied with the same types of ensemble reweighting ideas that have been used for static equilibrium properties. Applications to chemical, material, and biophysical systems are highlighted.
Scalable 3D Gaussian Splatting-Based RF Signal Spatial Propagation Modeling
2025-02-03
Kang Yang, Gaofeng Dong, Sijie Ji, Wan Du, Mani Srivastava
Effective network planning and sensing in wireless networks require resource-intensive site surveys for data collection. An alternative is Radio-Frequency (RF) signal spatial propagation modeling, which computes received signals given transceiver positions in a scene (e.g.s a conference room). We identify a fundamental trade-off between scalability and fidelity in the state-of-the-art method. To address this issue, we explore leveraging 3D Gaussian Splatting (3DGS), an advanced technique for the image synthesis of 3D scenes in real-time from arbitrary camera poses. By integrating domain-specific insights, we design three components for adapting 3DGS to the RF domain, including Gaussian-based RF scene representation, gradient-guided RF attribute learning, and RF-customized CUDA for ray tracing. Building on them, we develop RFSPM, an end-to-end framework for scalable RF signal Spatial Propagation Modeling. We evaluate RFSPM in four field studies and two applications across RFID, BLE, LoRa, and 5G, covering diverse frequencies, antennas, signals, and scenes. The results show that RFSPM matches the fidelity of the state-of-the-art method while reducing data requirements, training GPU-hours, and inference latency by up to 9.8\,$\times$, 18.6\,$\times$, and 84.4\,$\times$, respectively.
Policy Design for Two-sided Platforms with Participation Dynamics
2025-02-03
Haruka Kiyohara, Fan Yao, Sarah Dean
In two-sided platforms (e.g., video streaming or e-commerce), viewers and providers engage in interactive dynamics, where an increased provider population results in higher viewer utility and the increase of viewer population results in higher provider utility. Despite the importance of such "population effects" on long-term platform health, recommendation policies do not generally take the participation dynamics into account. This paper thus studies the dynamics and policy design on two-sided platforms under the population effects for the first time. Our control- and game-theoretic findings warn against the use of myopic-greedy policy and shed light on the importance of provider-side considerations (i.e., effectively distributing exposure among provider groups) to improve social welfare via population growth. We also present a simple algorithm to optimize long-term objectives by considering the population effects, and demonstrate its effectiveness in synthetic and real-data experiments.
Modes with zero longitudinal light-front momentum (zero modes) do have roles to play in the analysis of light-front field theories. These range from improvements in convergence for numerical calculations to implications for the light-front vacuum and beyond to fundamental issues in the connection with equal-time quantization. In particular, the discrepancy in values of the critical coupling for $\phi^4_{1+1}$ theory, between equal-time and light-front quantizations, would appear to be resolvable with the proper treatment of zero modes and near-zero modes. We provide a survey of these issues and point to open questions.
This paper investigates unmanned aerial vehicle (UAV) localization using time difference of arrival (TDOA) measurements under mixed line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. A 3D TDOA Cram\'er-Rao lower bound (CRLB) model is developed accounting for varying altitudes and signal bandwidths. The model is compared to five real-world UAV flight experiments conducted at different altitudes (40 m, 70 m, 100 m) and bandwidths (1.25 MHz, 2.5 MHz, 5 MHz) using Keysight N6841A radio frequency (RF) sensors of the NSF AERPAW platform. Results show that altitude, bandwidth, and NLOS obstructions significantly impact localization accuracy. Higher bandwidths enhance signal time resolution, while increased altitudes mitigate multipath and NLOS biases, both contributing to improved performance. However, hovering close to RF sensors degrades accuracy due to antenna pattern misalignment and geometric dilution of precision. These findings emphasize the inadequacy of traditional LOS-based models in NLOS environments and highlight the importance of adaptive approaches for accurate localization in challenging scenarios.
Speedups in nonequilibrium thermal relaxation: Mpemba and related effects
2025-02-03
Gianluca Teza, John Bechhoefer, Antonio Lasanta, Oren Raz, Marija Vucelja
Most of our intuition about the behavior of physical systems is shaped by observations at or near thermal equilibrium. However, even a thermal quench can lead to states far from thermal equilibrium, where counterintuitive, anomalous effects can occur. A prime example of anomalous thermal relaxation is the Mpemba effect, in which a system prepared at a hot temperature cools down to the temperature of the cold environment faster than an identical system prepared at a warm temperature. Although reported for water more than 2000 years ago by Aristotle, the recent observations of analogous relaxation speedups in a variety of systems have motivated the search for general explanations. We review anomalous relaxation effects, which all share a nonmonotonic dependence of relaxation time versus initial ``distance" from the final state or from the phase transition. The final state can be an equilibrium or a nonequilibrium steady state. We first review the water experiments and classify the anomalous relaxation phenomena related to the Mpemba effect. We then provide a modern definition of the Mpemba effect, focusing on the theoretical frameworks of stochastic thermodynamics, kinetic theory, Markovian dynamics, and phase transitions. We discuss the recent experimental and numerical developments that followed these theoretical advances. These developments paved the way for the prediction and observation of novel phenomena, such as the inverse Mpemba effect. The review is self-contained and introduces anomalous relaxation phenomena in single- and many-body systems, both classical and quantum. We also discuss the broader relevance of the Mpemba effect, including its relation with phase transitions and its experimental implications. We end with perspectives that connect anomalous speedups to ideas for designing optimal heating/cooling protocols, heat engines, and efficient samplers.
Kirby diagrams, trisections and gems of PL 4-manifolds: relationships, results and open problems
2025-02-03
Maria Rita Casali, Paola Cristofori
We review the main achievements regarding the interactions between gem theory (which makes use of edge-colored graphs to represent PL-manifolds of arbitrary dimension) and both the classical representation of PL 4-manifolds via Kirby diagrams and the more recent one via trisections. Original results also appear (in particular, about gems representing closed 4-manifolds which need 3-handles in their handle decomposition, as well as about trisection diagrams), together with open problems and further possible applications to the study of compact PL 4-manifolds.
$Lux$: A generative, multi-output, latent-variable model for astronomical data with noisy labels
2025-02-03
Danny Horta, Adrian M. Price-Whelan, David W. Hogg, Melissa K. Ness, Andrew R. Casey
The large volume of spectroscopic data available now and from near-future surveys will enable high-dimensional measurements of stellar parameters and properties. Current methods for determining stellar labels from spectra use physics-driven models, which are computationally expensive and have limitations in their accuracy due to simplifications. While machine learning methods provide efficient paths toward emulating physics-based pipelines, they often do not properly account for uncertainties and have complex model structure, both of which can lead to biases and inaccurate label inference. Here we present $Lux$: a data-driven framework for modeling stellar spectra and labels that addresses prior limitations. $Lux$ is a generative, multi-output, latent variable model framework built on JAX for computational efficiency and flexibility. As a generative model, $Lux$ properly accounts for uncertainties and missing data in the input stellar labels and spectral data and can either be used in probabilistic or discriminative settings. Here, we present several examples of how $Lux$ can successfully emulate methods for precise stellar label determinations for stars ranging in stellar type and signal-to-noise from the $APOGEE$ surveys. We also show how a simple $Lux$ model is successful at performing label transfer between the $APOGEE$ and $GALAH$ surveys. $Lux$ is a powerful new framework for the analysis of large-scale spectroscopic survey data. Its ability to handle uncertainties while maintaining high precision makes it particularly valuable for stellar survey label inference and cross-survey analysis, and the flexible model structure allows for easy extension to other data types.
The $Hubble$ Missing Globular Cluster Survey. I. Survey overview and the first precise age estimate for ESO452-11 and 2MASS-GC01
2025-02-03
D. Massari, M. Bellazzini, M. Libralato, A. Bellini, E. Dalessandro, E. Ceccarelli, F. Aguado-Agelet, S. Cassisi, C. Gallart, M. Monelli, A. Mucciarelli, E. Pancino, M. Salaris, S. Saracino, E. Dodd, F. R. Ferraro, E. R. Garro, B. Lanzoni, R. Pascale, L. Rosignoli
We present the $Hubble$ Missing Globular Cluster Survey (MGCS), a $Hubble$ $Space$ $Telescope$ treasury programme dedicated to the observation of all the kinematically confirmed Milky Way globular clusters that missed previous $Hubble$ imaging. After introducing the aims of the programme and describing its target clusters, we showcase the first results of the survey. These are related to two clusters, one located at the edge of the Milky Way Bulge and observed in optical bands, namely ESO452-11, and one located in the Galactic Disc observed in the near-IR, namely 2MASS-GC01. For both clusters, the deep colour-magnitude diagrams obtained from the MGCS observations reach several magnitudes below their main-sequence turn-off, and thus enable the first precise estimate of their age. By using the methods developed within the CARMA project, we find ESO452-11 to be an old, metal-intermediate globular cluster, with ${\rm [M/H]}\simeq-0.80^{+0.08}_{-0.11}$ and an age of ${\rm t}=13.59^{+0.48}_{-0.69}$ Gyr. Its location on the age-metallicity relation makes it consistent with an in-situ origin, in agreement with its dynamical properties. On the other hand, the results for 2MASS-GC01 highlight it as a young, metal-intermediate cluster, with an age of ${\rm t}=7.22^{+0.93}_{-1.11}$ Gyr at ${\rm [M/H]}=-0.73^{+0.06}_{-0.06}$. This is the first ever age estimate for this extremely extincted cluster, and indicates it either as the youngest globular known to date, or as a massive and compact open cluster, which is consistent with its almost circular, disc-like orbit.
Tuning the cosmic instrument: robust cosmology through combined probes
2025-02-03
Alexander Reeves, Andrina Nicola, Alexandre Refregier
As wide-field surveys yield increasingly precise data, multiprobe analyses offer significant advantages. In this work, we use our previously developed framework for jointly analyzing cosmic microwave background (CMB) and large-scale structure data. We analyze combinations of three CMB (Planck PR3, Planck PR4, and ACT+WMAP) datasets, DESI Y1 Baryon Acoustic Oscillation (BAO) data, and a $9\times 2$pt low-$z$ dataset comprising KiDS-1000, BOSS DR12, and Planck CMB lensing/Integrated Sachs Wolfe (including all cross-correlations). We first assess internal consistency, finding a mild ($<2\sigma$) tension between CMB and low-$z$ datasets in the full parameter space and hints of systematics in Planck PR3 and KiDS-1000. We then derive constraints in $\Lambda\mathrm{CDM}$ and, motivated by recent DESI results, dynamical dark energy ($w_0w_a\mathrm{CDM}$) and free neutrino mass extensions. In $\Lambda \mathrm{CDM}$, we derive a novel $9\times2$pt constraint of $S8=0.777^{+0.17}_{-0.17}$ and find strong consistency among CMB datasets. In $w_0w_a\mathrm{CDM}$, adding low-$z$ to CMB+BAO tightens $(w_0,w_a)$ constraints by 50\% (in figure-of-merit terms) in our baseline combination of Planck PR4 + low-$z$ + BAO. The posterior accommodates a cosmological constant ($w_0 = -1, w_a = 0$) within $1\sigma$, in contrast to the $\sim2\sigma$ preference for evolving dark energy from CMB+BAO alone. For neutrino masses, our baseline dataset yields a systematics-robust constraint of $M_\nu<0.12\mathrm{eV}$ in $\nu\Lambda\mathrm{CDM}$. Allowing dynamical dark energy and free neutrino mass ($\nu w_0w_a\mathrm{CDM}$) broadens and shifts the neutrino mass posterior higher, yielding a $1.8\sigma$ constraint ($M_\nu=0.16^{+0.09}_{-0.09}\mathrm{eV}$) in our baseline. Our analysis demonstrates the power of multiprobe analyses for assessing tensions, identifying systematics and providing robust constraints.
LLM-TA: An LLM-Enhanced Thematic Analysis Pipeline for Transcripts from Parents of Children with Congenital Heart Disease
2025-02-03
Muhammad Zain Raza, Jiawei Xu, Terence Lim, Lily Boddy, Carlos M. Mery, Andrew Well, Ying Ding
Thematic Analysis (TA) is a fundamental method in healthcare research for analyzing transcript data, but it is resource-intensive and difficult to scale for large, complex datasets. This study investigates the potential of large language models (LLMs) to augment the inductive TA process in high-stakes healthcare settings. Focusing on interview transcripts from parents of children with Anomalous Aortic Origin of a Coronary Artery (AAOCA), a rare congenital heart disease, we propose an LLM-Enhanced Thematic Analysis (LLM-TA) pipeline. Our pipeline integrates an affordable state-of-the-art LLM (GPT-4o mini), LangChain, and prompt engineering with chunking techniques to analyze nine detailed transcripts following the inductive TA framework. We evaluate the LLM-generated themes against human-generated results using thematic similarity metrics, LLM-assisted assessments, and expert reviews. Results demonstrate that our pipeline outperforms existing LLM-assisted TA methods significantly. While the pipeline alone has not yet reached human-level quality in inductive TA, it shows great potential to improve scalability, efficiency, and accuracy while reducing analyst workload when working collaboratively with domain experts. We provide practical recommendations for incorporating LLMs into high-stakes TA workflows and emphasize the importance of close collaboration with domain experts to address challenges related to real-world applicability and dataset complexity. https://github.com/jiaweixu98/LLM-TA
Multi-modal Large Language Models (MLLMs) excel in vision-language tasks but remain vulnerable to visual adversarial perturbations that can induce hallucinations, manipulate responses, or bypass safety mechanisms. Existing methods seek to mitigate these risks by applying constrained adversarial fine-tuning to CLIP vision encoders on ImageNet-scale data, ensuring their generalization ability is preserved. However, this limited adversarial training restricts robustness and broader generalization. In this work, we explore an alternative approach of leveraging existing vision classification models that have been adversarially pre-trained on large-scale data. Our analysis reveals two principal contributions: (1) the extensive scale and diversity of adversarial pre-training enables these models to demonstrate superior robustness against diverse adversarial threats, ranging from imperceptible perturbations to advanced jailbreaking attempts, without requiring additional adversarial training, and (2) end-to-end MLLM integration with these robust models facilitates enhanced adaptation of language components to robust visual features, outperforming existing plug-and-play methodologies on complex reasoning tasks. Through systematic evaluation across visual question-answering, image captioning, and jail-break attacks, we demonstrate that MLLMs trained with these robust models achieve superior adversarial robustness while maintaining favorable clean performance. Our framework achieves 2x and 1.5x average robustness gains in captioning and VQA tasks, respectively, and delivers over 10% improvement against jailbreak attacks. Code and pretrained models will be available at https://github.com/HashmatShadab/Robust-LLaVA.
V498 Hya, a new candidate for a period bouncer Cataclysmic Variable
2025-02-03
Gagik Tovmassian, Keith Inight, Anna Francesca Pala, Boris T. Gansicke, Vedant Chandra, Matthew Green, Odette Toloza, Matthias R. Schreiber
V498 Hya (SDSS J084555.07+033929.2) was identified as a short-period cataclysmic variable (CV) by the Catalina Real-Time Transient Survey (CRTS) in 2008. The superhump period was measured during the detected single superoutburst of V498 Hya. The quiescent spectrum subsequently taken by the \SDSSV\ Milky Way Mapper survey suggested that the CV donor may be a brown dwarf. We present time-resolved follow-up spectroscopy of V498 Hya in quiescence, obtained with the GTC OSIRIS spectrograph, from which we derived the 86.053 min spectroscopic period, systemic radial velocity, and the gravitational redshift of the Mg II line. We also modeled the spectral energy distribution to constrain the system parameters, including the > 0.82 Ms mass of the white dwarf and the best-fit value 0.043 +/- 0.004 Ms of the donor star mass. This combination of parameters implies that V498 Hya has evolved past the period minimum and is a relatively rare ``period bouncer''.
Search-Based Adversarial Estimates for Improving Sample Efficiency in Off-Policy Reinforcement Learning
2025-02-03
Federico Malato, Ville Hautamaki
Sample inefficiency is a long-lasting challenge in deep reinforcement learning (DRL). Despite dramatic improvements have been made, the problem is far from being solved and is especially challenging in environments with sparse or delayed rewards. In our work, we propose to use Adversarial Estimates as a new, simple and efficient approach to mitigate this problem for a class of feedback-based DRL algorithms. Our approach leverages latent similarity search from a small set of human-collected trajectories to boost learning, using only five minutes of human-recorded experience. The results of our study show algorithms trained with Adversarial Estimates converge faster than their original version. Moreover, we discuss how our approach could enable learning in feedback-based algorithms in extreme scenarios with very sparse rewards.
Observation Noise and Initialization in Wide Neural Networks
2025-02-03
Sergio Calvo-Ordoñez, Jonathan Plenk, Richard Bergna, Alvaro Cartea, Jose Miguel Hernandez-Lobato, Konstantina Palla, Kamil Ciosek
Performing gradient descent in a wide neural network is equivalent to computing the posterior mean of a Gaussian Process with the Neural Tangent Kernel (NTK-GP), for a specific choice of prior mean and with zero observation noise. However, existing formulations of this result have two limitations: i) the resultant NTK-GP assumes no noise in the observed target variables, which can result in suboptimal predictions with noisy data; ii) it is unclear how to extend the equivalence to an arbitrary prior mean, a crucial aspect of formulating a well-specified model. To address the first limitation, we introduce a regularizer into the neural network's training objective, formally showing its correspondence to incorporating observation noise into the NTK-GP model. To address the second, we introduce a \textit{shifted network} that enables arbitrary prior mean functions. This approach allows us to perform gradient descent on a single neural network, without expensive ensembling or kernel matrix inversion. Our theoretical insights are validated empirically, with experiments exploring different values of observation noise and network architectures.
Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective
2025-02-03
Xiaorui Ma, Haoran Xie, S. Joe Qin
The integration of vision-language modalities has been a significant focus in multimodal learning, traditionally relying on Vision-Language Pretrained Models. However, with the advent of Large Language Models (LLMs), there has been a notable shift towards incorporating LLMs with vision modalities. Following this, the training paradigms for incorporating vision modalities into LLMs have evolved. Initially, the approach was to integrate the modalities through pretraining the modality integrator, named Single-stage Tuning. It has since branched out into methods focusing on performance enhancement, denoted as Two-stage Tuning, and those prioritizing parameter efficiency, referred to as Direct Adaptation. However, existing surveys primarily address the latest Vision Large Language Models (VLLMs) with Two-stage Tuning, leaving a gap in understanding the evolution of training paradigms and their unique parameter-efficient considerations. This paper categorizes and reviews 34 VLLMs from top conferences, journals, and highly cited Arxiv papers, focusing on parameter efficiency during adaptation from the training paradigm perspective. We first introduce the architecture of LLMs and parameter-efficient learning methods, followed by a discussion on vision encoders and a comprehensive taxonomy of modality integrators. We then review three training paradigms and their efficiency considerations, summarizing benchmarks in the VLLM field. To gain deeper insights into their effectiveness in parameter efficiency, we compare and discuss the experimental results of representative models, among which the experiment of the Direct Adaptation paradigm is replicated. Providing insights into recent developments and practical uses, this survey is a vital guide for researchers and practitioners navigating the efficient integration of vision modalities into LLMs.
Prioritizing App Reviews for Developer Responses on Google Play
2025-02-03
Mohsen Jafari, Forough Majidi, Abbas Heydarnoori
The number of applications in Google Play has increased dramatically in recent years. On Google Play, users can write detailed reviews and rate apps, with these ratings significantly influencing app success and download numbers. Reviews often include notable information like feature requests, which are valuable for software maintenance. Users can update their reviews and ratings anytime. Studies indicate that apps with ratings below three stars are typically avoided by potential users. Since 2013, Google Play has allowed developers to respond to user reviews, helping resolve issues and potentially boosting overall ratings and download rates. However, responding to reviews is time-consuming, and only 13% to 18% of developers engage in this practice. To address this challenge, we propose a method to prioritize reviews based on response priority. We collected and preprocessed review data, extracted both textual and semantic features, and assessed their impact on the importance of responses. We labelled reviews as requiring a response or not and trained four different machine learning models to prioritize them. We evaluated the models performance using metrics such as F1-Score, Accuracy, Precision, and Recall. Our findings indicate that the XGBoost model is the most effective for prioritizing reviews needing a response.
Large language models have been widely applied, but can inadvertently encode sensitive or harmful information, raising significant safety concerns. Machine unlearning has emerged to alleviate this concern; however, existing training-time unlearning approaches, relying on coarse-grained loss combinations, have limitations in precisely separating knowledge and balancing removal effectiveness with model utility. In contrast, we propose Fine-grained Activation manipuLation by Contrastive Orthogonal uNalignment (FALCON), a novel representation-guided unlearning approach that leverages information-theoretic guidance for efficient parameter selection, employs contrastive mechanisms to enhance representation separation, and projects conflict gradients onto orthogonal subspaces to resolve conflicts between forgetting and retention objectives. Extensive experiments demonstrate that FALCON achieves superior unlearning effectiveness while maintaining model utility, exhibiting robust resistance against knowledge recovery attempts.
Position: Towards a Responsible LLM-empowered Multi-Agent Systems
2025-02-03
Jinwei Hu, Yi Dong, Shuang Ao, Zhuoyun Li, Boxuan Wang, Lokesh Singh, Guangliang Cheng, Sarvapali D. Ramchurn, Xiaowei Huang
The rise of Agent AI and Large Language Model-powered Multi-Agent Systems (LLM-MAS) has underscored the need for responsible and dependable system operation. Tools like LangChain and Retrieval-Augmented Generation have expanded LLM capabilities, enabling deeper integration into MAS through enhanced knowledge retrieval and reasoning. However, these advancements introduce critical challenges: LLM agents exhibit inherent unpredictability, and uncertainties in their outputs can compound across interactions, threatening system stability. To address these risks, a human-centered design approach with active dynamic moderation is essential. Such an approach enhances traditional passive oversight by facilitating coherent inter-agent communication and effective system governance, allowing MAS to achieve desired outcomes more efficiently.
Towards Safer Chatbots: A Framework for Policy Compliance Evaluation of Custom GPTs
2025-02-03
David Rodriguez, William Seymour, Jose M. Del Alamo, Jose Such
Large Language Models (LLMs) have gained unprecedented prominence, achieving widespread adoption across diverse domains and integrating deeply into society. The capability to fine-tune general-purpose LLMs, such as Generative Pre-trained Transformers (GPT), for specific tasks has facilitated the emergence of numerous Custom GPTs. These tailored models are increasingly made available through dedicated marketplaces, such as OpenAI's GPT Store. However, their black-box nature introduces significant safety and compliance risks. In this work, we present a scalable framework for the automated evaluation of Custom GPTs against OpenAI's usage policies, which define the permissible behaviors of these systems. Our framework integrates three core components: (1) automated discovery and data collection of models from the GPT store, (2) a red-teaming prompt generator tailored to specific policy categories and the characteristics of each target GPT, and (3) an LLM-as-a-judge technique to analyze each prompt-response pair for potential policy violations. We validate our framework with a manually annotated ground truth, and evaluate it through a large-scale study with 782 Custom GPTs across three categories: Romantic, Cybersecurity, and Academic GPTs. Our manual annotation process achieved an F1 score of 0.975 in identifying policy violations, confirming the reliability of the framework's assessments. The results reveal that 58.7% of the analyzed models exhibit indications of non-compliance, exposing weaknesses in the GPT store's review and approval processes. Furthermore, our findings indicate that a model's popularity does not correlate with compliance, and non-compliance issues largely stem from behaviors inherited from base models rather than user-driven customizations. We believe this approach is extendable to other chatbot platforms and policy domains, improving LLM-based systems safety.
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity
2025-02-03
Alessandro Pierro, Steven Abreu, Jonathan Timcheck, Philipp Stratmann, Andreas Wild, Sumit Bam Shrestha
Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge, but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption. Unstructured sparsity offers a compelling solution, enabling substantial reductions in compute and memory requirements--when accelerated by compatible hardware platforms. In this paper, we conduct a scaling study to investigate the Pareto front of performance and efficiency across inference compute budgets. We find that highly sparse linear RNNs consistently achieve better efficiency-performance trade-offs than dense baselines, with 2x less compute and 36% less memory at iso-accuracy. Our models achieve state-of-the-art results on a real-time streaming task for audio denoising. By quantizing our sparse models to fixed-point arithmetic and deploying them on the Intel Loihi 2 neuromorphic chip for real-time processing, we translate model compression into tangible gains of 42x lower latency and 149x lower energy consumption compared to a dense model on an edge GPU. Our findings showcase the transformative potential of unstructured sparsity, paving the way for highly efficient recurrent neural networks in real-world, resource-constrained environments.
The Homework Wars: Exploring Emotions, Behaviours, and Conflicts in Parent-Child Homework Interactions
2025-02-03
Nan Gao, Yibin Liu, Xin Tang, Yanyan Liu, Chun Yu, Yun Huang, Yuntao Wang, Flora D. Salim, Xuhai Orson Xu, Jun Wei, Yuanchun Shi
Parental involvement in homework is a crucial aspect of family education, but it often leads to emotional strain and conflicts that can severely impact family well-being. This paper presents findings from a 4-week in situ study involving 78 families in China, where we collected and analyzed 602 valid audio recordings (totalling 475 hours) and daily surveys. Leveraging large language models (LLMs) to analyze parent-child conversations, we gained a nuanced understanding of emotional and behavioural dynamics that overcomes the limitations of traditional one-time surveys and interviews. Our findings reveal significant emotional shifts in parents before and after homework involvement and summarise a range of positive, neutral and negative parental behaviours. We also catalogue seven common conflicts, with Knowledge Conflict being the most frequent. Notably, we found that even well-intentioned parental behaviours, such as Unlabelled Praise, were significantly positively correlated with specific conflict types. This work advances ubiquitous computing's research to sense and understand complex family dynamics, while offering evidence-based insights for designing future ambient intelligent systems to support healthy family education environments.
In search for the Local Universe dynamical homogeneity scale with CF4++ peculiar velocities
2025-02-03
Helene M. Courtois, Jeremy Mould, Amber M. Hollinger, Alexandra Dupuy, Chuan-Peng Zhang
This article explores a groundbreaking update to the cosmography of the local Universe within z = 0.1, incorporating galaxy peculiar velocity datasets from the first data releases of WALLABY, FAST and DESI surveys. The galaxies with peculiar velocities currently selected in each survey is 655, 4796 and 4191 respectively. The new CF4++ compendium enables a more comprehensive study of the nearby Universe bulk flow dynamics. This analysis reveals that the dynamical scale of homogeneity is not yet reached in the interval [200-300] Mpc/h from the observer. This new data also refines the structure of local superclusters, revealing more spherical shapes and more clearly defined boundaries for key regions such as Great Attractor (Laniakea) and Coma. Very few measurements make a big difference in revealing the hidden Vela supercluster.
Rational Gaussian wavelets and corresponding model driven neural networks
2025-02-03
Attila Miklós Ámon, Kristian Fenech, Péter Kovács, Tamás Dózsa
In this paper we consider the continuous wavelet transform using Gaussian wavelets multiplied by an appropriate rational term. The zeros and poles of this rational modifier act as free parameters and their choice highly influences the shape of the mother wavelet. This allows the proposed construction to approximate signals with complex morphology using only a few wavelet coefficients. We show that the proposed rational Gaussian wavelets are admissible and provide numerical approximations of the wavelet coefficients using variable projection operators. In addition, we show how the proposed variable projection based rational Gaussian wavelet transform can be used in neural networks to obtain a highly interpretable feature learning layer. We demonstrate the effectiveness of the proposed scheme through a biomedical application, namely, the detection of ventricular ectopic beats (VEBs) in real ECG measurements.
Soft is Safe: Human-Robot Interaction for Soft Robots
2025-02-03
Rajashekhar V S, Gowdham Prabhakar
With the presence of robots increasing in the society, the need for interacting with robots is becoming necessary. The field of Human-Robot Interaction (HRI) has emerged important since more repetitive and tiresome jobs are being done by robots. In the recent times, the field of soft robotics has seen a boom in the field of research and commercialization. The Industry 5.0 focuses on human robot collaboration which also spurs the field of soft robotics. However the HRI for soft robotics is still in the nascent stage. In this work we review and then discuss how HRI is done for soft robots. We first discuss the control, design, materials and manufacturing of soft robots. This will provide an understanding of what is being interacted with. Then we discuss about the various input and output modalities that are used in HRI. The applications where the HRI for soft robots are found in the literature are discussed in detail. Then the limitations of HRI for soft robots and various research opportunities that exist in this field are discussed in detail. It is concluded that there is a huge scope for development for HRI for soft robots.
KOBE 1: The first planetary system from the KOBE survey. Two planets likely residing in the sub-Neptune mass regime around a late K-dwarf
2025-02-03
O. Balsalobre-Ruza, J. Lillo-Box, A. M. Silva, S. Grouffal, J. Aceituno, A. Castro-González, C. Cifuentes, M. R. Standing, J. P. Faria, P. Figueira, A. Santerne, E. Marfil, A. Abreu, A. Aguichine, L. González-Ramírez, J. C. Morales, N. Santos, N. Huélamo, E. Delgado Mena, D. Barrado, V. Adibekyan, S. C. C. Barros, Á. Berihuete, M. Morales-Calderón, E. Nagel, E. Solano, S. G. Sousa, J. F. Agüí Fernández, M. Azzaro, G. Bergond, S. Cikota, A. Fernández-Martín, J. Flores, S. Góngora, A. Guijarro, I. Hermelo, V. Pinter, J. I. Vico-Linares
K-dwarf stars are promising targets in the exploration of potentially habitable planets. Their properties, falling between G and M dwarfs, provide an optimal trade-off between the prospect of habitability and ease of detection. The KOBE experiment is a blind-search survey exploiting this niche, monitoring the radial velocity of 50 late-type K-dwarf stars. It employs the CARMENES spectrograph, with an observational strategy designed to detect planets in the habitable zone of their system. In this work, we exploit the KOBE data set to characterize planetary signals in the K7V star HIP 5957 (KOBE-1) and to constrain the planetary population within its habitable zone. We used 82 CARMENES spectra over a time span of three years. We employed a GLS periodogram to search for significant periodic signals that would be compatible with Keplerian motion on KOBE-1. We carried out a model comparison within a Bayesian framework to ensure the significance of the planetary model over alternative configurations of lower complexity. We also inspected two available TESS sectors in search of planetary signals. We identified two signals: at 8.5d and 29.7d. We confirmed their planetary nature through ruling out other non-planetary configurations. Their minimum masses are 8.80+/-0.76ME and 12.4+/-1.1ME, corresponding to absolute masses within the planetary regime at a high certainty (>99.7%). By analyzing the sensitivity of the CARMENES time series to additional signals, we discarded planets above 8.5ME within the habitable zone. We identified a single transit-like feature in TESS, whose origin is still uncertain, but still compatible within 1sigma with a transit from planet c. We have explored future prospects for characterizing this system, concluding that nulling interferometry with the LIFE mission could be capable of directly imaging both planets and characterizing their atmospheres in future studies.
Efficient rule induction by ignoring pointless rules
2025-02-03
Andrew Cropper, David M. Cerna
The goal of inductive logic programming (ILP) is to find a set of logical rules that generalises training examples and background knowledge. We introduce an ILP approach that identifies pointless rules. A rule is pointless if it contains a redundant literal or cannot discriminate against negative examples. We show that ignoring pointless rules allows an ILP system to soundly prune the hypothesis space. Our experiments on multiple domains, including visual reasoning and game playing, show that our approach can reduce learning times by 99% whilst maintaining predictive accuracies.
Societal Attitudes Toward Service Robots: Adore, Abhor, Ignore, or Unsure?
2025-02-03
V. Yoganathan, V. -S. Osburg, A. Fronzetti Colladon, V. Charles, W. Toporowski
Societal or population-level attitudes are aggregated patterns of different individual attitudes, representing collective general predispositions. As service robots become ubiquitous, understanding attitudes towards them at the population (vs. individual) level enables firms to expand robot services to a broad (vs. niche) market. Targeting population-level attitudes would benefit service firms because: (1) they are more persistent, thus, stronger predictors of behavioral patterns and (2) this approach is less reliant on personal data, whereas individualized services are vulnerable to AI-related privacy risks. As for service theory, ignoring broad unobserved differences in attitudes produces biased conclusions, and our systematic review of previous research highlights a poor understanding of potential heterogeneity in attitudes toward service robots. We present five diverse studies (S1-S5), utilizing multinational and "real world" data (Ntotal = 89,541; years: 2012-2024). Results reveal a stable structure comprising four distinct attitude profiles (S1-S5): positive ("adore"), negative ("abhor"), indifferent ("ignore"), and ambivalent ("unsure"). The psychological need for interacting with service staff, and for autonomy and relatedness in technology use, function as attitude profile antecedents (S2). Importantly, the attitude profiles predict differences in post-interaction discomfort and anxiety (S3), satisfaction ratings and service evaluations (S4), and perceived sociability and uncanniness based on a robot's humanlikeness (S5).
Theoretical Analysis of KL-regularized RLHF with Multiple Reference Models
2025-02-03
Gholamali Aminian, Amir R. Asadi, Idan Shenfeld, Youssef Mroueh
Recent methods for aligning large language models (LLMs) with human feedback predominantly rely on a single reference model, which limits diversity, model overfitting, and underutilizes the wide range of available pre-trained models. Incorporating multiple reference models has the potential to address these limitations by broadening perspectives, reducing bias, and leveraging the strengths of diverse open-source LLMs. However, integrating multiple reference models into reinforcement learning with human feedback (RLHF) frameworks poses significant theoretical challenges, particularly in reverse KL-regularization, where achieving exact solutions has remained an open problem. This paper presents the first \emph{exact solution} to the multiple reference model problem in reverse KL-regularized RLHF. We introduce a comprehensive theoretical framework that includes rigorous statistical analysis and provides sample complexity guarantees. Additionally, we extend our analysis to forward KL-regularized RLHF, offering new insights into sample complexity requirements in multiple reference scenarios. Our contributions lay the foundation for more advanced and adaptable LLM alignment techniques, enabling the effective use of multiple reference models. This work paves the way for developing alignment frameworks that are both theoretically sound and better suited to the challenges of modern AI ecosystems.
Cryogenics of a superconducting LINAC : SPIRAL2 from commissioning to operation
2025-02-03
Adnan Ghribi, Muhammad Aburas, Pierre-Emmanuel Bernaudin, Patrick Bonnay, François Bonne, Frédéric Bouly, Marco Di Giacomo, Charly Lassalle, David Longuevergne, François Millet, Jean-Pierre Thermeau, Arnaud Trudel, Quentin Tura, Adrien Vassal
The SPIRAL2 superconducting linear accelerator (LINAC), which has been operational since 2019, employs superconducting, independently phased RF resonators to deliver a wide range of particle beams. Designed for flexibility in particle types, intensities, and energies, it utilizes superconducting quarter-wave resonators (QWRs), whose performances are critically dependent on the reliability of the cryogenic operation. This paper reviews the evolution from commissioning to the routine operation of the SPIRAL2 cryogenic system, initially commissioned in 2017. It highlights the key challenges encountered, including thermo-acoustic oscillations, thermal management, and abnormal behavior of cavities. Furthermore, it explores the integration of thermodynamic modeling and machine learning techniques to enhance system control and diagnose issues. This work serves as a comprehensive resource for advancing the cryogenic operation and performance of superconducting LINACs.
Improving knowledge of the acoustic factors involved in railway noise annoyance: first results of a pilot field survey of sixty-two local residents
2025-02-03
Matthieu Sineau, Manuel Hellot, Robin Mafféïs, Fanny Mietlicki, Laure Motio, Anne-Sophie Evrard, Jean-Philippe Regairaz
Railway transportation contributes to the objectives of decarbonization but also generates negative externalities, including noise. Energy noise indicators used to characterize population exposure do not adequately reflect the repetitive nature of railway noise peaks. The GENIFER pilot study aims to test a protocol designed to characterize railway noise events according to the instantaneous perceived annoyance when the train is passing, in order to improve understanding of the influence of acoustic factors on annoyance. The first phase of the survey was carried out in 2023 among 62 residents of a pilot site. An electronic device was used to collect around 5,000 ratings, ranging from 1 to 10, assessing the instantaneous annoyance induced by railway noise at passing trains. The site instrumentation included sixteen sound level meters and two video recording systems, enabling annoyance ratings to be associated with the acoustic characteristics of railway noise events. A questionnaire aimed at identifying co-determinants of long-term annoyance was also administered to participants. Feedback on the field implementation of this survey and initial results concerning acoustic measurements, instantaneous annoyance ratings and questionnaire responses will be presented.
EdgeMark: An Automation and Benchmarking System for Embedded Artificial Intelligence Tools
2025-02-03
Mohammad Amin Hasanpour, Mikkel Kirkegaard, Xenofon Fafoutis
The integration of artificial intelligence (AI) into embedded devices, a paradigm known as embedded artificial intelligence (eAI) or tiny machine learning (TinyML), is transforming industries by enabling intelligent data processing at the edge. However, the many tools available in this domain leave researchers and developers wondering which one is best suited to their needs. This paper provides a review of existing eAI tools, highlighting their features, trade-offs, and limitations. Additionally, we introduce EdgeMark, an open-source automation system designed to streamline the workflow for deploying and benchmarking machine learning (ML) models on embedded platforms. EdgeMark simplifies model generation, optimization, conversion, and deployment while promoting modularity, reproducibility, and scalability. Experimental benchmarking results showcase the performance of widely used eAI tools, including TensorFlow Lite Micro (TFLM), Edge Impulse, Ekkono, and Renesas eAI Translator, across a wide range of models, revealing insights into their relative strengths and weaknesses. The findings provide guidance for researchers and developers in selecting the most suitable tools for specific application requirements, while EdgeMark lowers the barriers to adoption of eAI technologies.
A Pristine View of Galactic Globular Clusters and their Peripheries: Omega Centauri
2025-02-03
P. B. Kuzma, M. N. Ishigaki
The central regions of the globular cluster Omega Centauri ($\omega$ Cen) have been extensively studied, but its outer regions and tidal structure have been less so. Gaia's astrometry uncovered substantial tidal substructure associated with $\omega$ Cen, yet the lack of chemical tagging makes these associations tenuous. In this paper, we utilise the Gaia-Synthetic CaHK-band photometry, metallicities from the Pristine survey and Gaia's astrometry to explore up to a clustercentric radius of 5 degrees from $\omega$ Cen. We identify $\omega$ Cen-like stars based on proper motion, colour-magnitude and colour-colour space, exploring the morphology, and stellar populations of the outer regions. Our probabilistic approach recovers the tidal tails of $\omega$ Cen, and we investigate the metallicity distribution of $\omega$ Cen ranging from a radius of 15 arcmin to the tidal radius, and beyond into the tidal tails. We present (1) two components between 15 arcmin and tidal radius at -1.83 and -1.45 dex which are also the dominant populations within 15 arcmin, and (2) the first evidence that the same two populations in the outer regions of the cluster are present outside the tidal radius and into the tidal tails. These populations are mixed about the stream, and are typically amongst the faintest stars in our sample; all indicating that the tidal tails are made of tidally stripped $\omega$ Cen stars.
Building generalist models has recently demonstrated remarkable capabilities in diverse scientific domains. Within the realm of molecular learning, several studies have explored unifying diverse tasks across diverse domains. However, negative conflicts and interference between molecules and knowledge from different domain may have a worse impact in threefold. First, conflicting molecular representations can lead to optimization difficulties for the models. Second, mixing and scaling up training data across diverse tasks is inherently challenging. Third, the computational cost of refined pretraining is prohibitively high. To address these limitations, this paper presents Omni-Mol, a scalable and unified LLM-based framework for direct instruction tuning. Omni-Mol builds on three key components to tackles conflicts: (1) a unified encoding mechanism for any task input; (2) an active-learning-driven data selection strategy that significantly reduces dataset size; (3) a novel design of the adaptive gradient stabilization module and anchor-and-reconcile MoE framework that ensures stable convergence. Experimentally, Omni-Mol achieves state-of-the-art performance across 15 molecular tasks, demonstrates the presence of scaling laws in the molecular domain, and is supported by extensive ablation studies and analyses validating the effectiveness of its design. The code and weights of the powerful AI-driven chemistry generalist are open-sourced at: https://anonymous.4open.science/r/Omni-Mol-8EDB.
Propagation Delays of Ultra-High-Energy Cosmic Ray from Active Galactic Nuclei
2025-02-03
Rostom Mbarek, Damiano Caprioli
We investigate the time delay incurred during ultra-high energy cosmic ray (UHECR) propagation over cosmological distances and its potential impact on the correlation between UHECR directions of arrival and sources such as Active Galactic Nuclei (AGNs), the UHECR chemical composition, and extragalactic magnetic field constraints. We propagate particles in different magnetic field configurations, spanning over an extended range of particle Larmor radii and magnetic field coherence lengths, also including attenuation losses. We find that UHECR delays could easily be comparable to (and longer than) AGN duty cycles, effectively erasing the correlation between known AGNs and UHECR anisotropies. We finally consider how strong constraints on the chemical composition of the heaviest UHECRs could enable a better characterization of extragalactic magnetic fields.
RiskHarvester: A Risk-based Tool to Prioritize Secret Removal Efforts in Software Artifacts
2025-02-03
Setu Kumar Basak, Tanmay Pardeshi, Bradley Reaves, Laurie Williams
Since 2020, GitGuardian has been detecting checked-in hard-coded secrets in GitHub repositories. During 2020-2023, GitGuardian has observed an upward annual trend and a four-fold increase in hard-coded secrets, with 12.8 million exposed in 2023. However, removing all the secrets from software artifacts is not feasible due to time constraints and technical challenges. Additionally, the security risks of the secrets are not equal, protecting assets ranging from obsolete databases to sensitive medical data. Thus, secret removal should be prioritized by security risk reduction, which existing secret detection tools do not support. The goal of this research is to aid software practitioners in prioritizing secrets removal efforts through our security risk-based tool. We present RiskHarvester, a risk-based tool to compute a security risk score based on the value of the asset and ease of attack on a database. We calculated the value of asset by identifying the sensitive data categories present in a database from the database keywords in the source code. We utilized data flow analysis, SQL, and ORM parsing to identify the database keywords. To calculate the ease of attack, we utilized passive network analysis to retrieve the database host information. To evaluate RiskHarvester, we curated RiskBench, a benchmark of 1,791 database secret-asset pairs with sensitive data categories and host information manually retrieved from 188 GitHub repositories. RiskHarvester demonstrates precision of (95%) and recall (90%) in detecting database keywords for the value of asset and precision of (96%) and recall (94%) in detecting valid hosts for ease of attack. Finally, we conducted a survey (52 respondents) to understand whether developers prioritize secret removal based on security risk score. We found that 86% of the developers prioritized the secrets for removal with descending security risk scores.
Synthetic Aperture Radar (SAR) and optical image registration is essential for remote sensing data fusion, with applications in military reconnaissance, environmental monitoring, and disaster management. However, challenges arise from differences in imaging mechanisms, geometric distortions, and radiometric properties between SAR and optical images. As image resolution increases, fine SAR textures become more significant, leading to alignment issues and 3D spatial discrepancies. Two major gaps exist: the lack of a publicly available multi-resolution, multi-scene registration dataset and the absence of systematic analysis of current methods. To address this, the MultiResSAR dataset was created, containing over 10k pairs of multi-source, multi-resolution, and multi-scene SAR and optical images. Sixteen state-of-the-art algorithms were tested. Results show no algorithm achieves 100% success, and performance decreases as resolution increases, with most failing on sub-meter data. XoFTR performs best among deep learning methods (40.58%), while RIFT performs best among traditional methods (66.51%). Future research should focus on noise suppression, 3D geometric fusion, cross-view transformation modeling, and deep learning optimization for robust registration of high-resolution SAR and optical images. The dataset is available at https://github.com/betterlll/Multi-Resolution-SAR-dataset-.
2025-02-03
Imogen Forbes, Farzad Ghafari, Edward C. R. Deacon, Sukhjit P. Singh, Emilien Lavie, Patrick Yard, Reece D. Shaw, Anthony Laing, Nora Tischler
Entangled states of photons form the backbone of many quantum technologies. Due to the lack of effective photon-photon interactions, the generation of these states is typically probabilistic. In the prevailing but fundamentally limited generation technique, known as postselection, the target photons are measured destructively in the generation process. By contrast, in the alternative approach -- heralded state generation -- the successful creation of a desired state is verified by the detection of ancillary photons. Heralded state generation is superior to postselection in several critical ways: It enables free usage of the prepared states, allows for the success probability to be arbitrarily increased via multiplexing, and provides a scalable route to quantum information processing using photons. Here, we review theoretical proposals and experimental realizations of heralded entangled photonic state generation, as well as the impact of realistic experimental errors. We then discuss the wide-ranging applications of these states for quantum technologies, including resource states in linear optical quantum computing, entanglement swapping for repeater networks, fundamental physics, and quantum metrology.
Efficient analysis of magnetic field line behavior in toroidal plasmas
2025-02-03
Allen H Boozer
The confinement of plasmas in tokamaks and stellarators depends on magnetic field lines lying in nested toroidal surfaces. The transition near the plasma edge away from the lines lying in magnetic surfaces defines properties of divertors. The transition in time defines properties of disruptions. Divertor design and disruption analysis require a detailed understanding of these transitions. The use of a Gaussian window function allows far more information to be extracted about these transitions using far shorter field line integrations than can be obtained using traditional methods based on Poincar\'e plots. The physics of divertors and disruptions is reviewed to clarify why the type of information that can be gained from more efficient methods of analysis is of central importance to the fusion program based on magnetic confinement.
Pushing the Boundaries of State Space Models for Image and Video Generation
2025-02-03
Yicong Hong, Long Mai, Yuan Yao, Feng Liu
While Transformers have become the dominant architecture for visual generation, linear attention models, such as the state-space models (SSM), are increasingly recognized for their efficiency in processing long visual sequences. However, the essential efficiency of these models comes from formulating a limited recurrent state, enforcing causality among tokens that are prone to inconsistent modeling of N-dimensional visual data, leaving questions on their capacity to generate long non-causal sequences. In this paper, we explore the boundary of SSM on image and video generation by building the largest-scale diffusion SSM-Transformer hybrid model to date (5B parameters) based on the sub-quadratic bi-directional Hydra and self-attention, and generate up to 2K images and 360p 8 seconds (16 FPS) videos. Our results demonstrate that the model can produce faithful results aligned with complex text prompts and temporal consistent videos with high dynamics, suggesting the great potential of using SSMs for visual generation tasks.
The Beatbots: A Musician-Informed Multi-Robot Percussion Quartet
2025-02-03
Isabella Pu, Jeff Snyder, Naomi Ehrich Leonard
Artistic creation is often seen as a uniquely human endeavor, yet robots bring distinct advantages to music-making, such as precise tempo control, unpredictable rhythmic complexities, and the ability to coordinate intricate human and robot performances. While many robotic music systems aim to mimic human musicianship, our work emphasizes the unique strengths of robots, resulting in a novel multi-robot performance instrument called the Beatbots, capable of producing music that is challenging for humans to replicate using current methods. The Beatbots were designed using an ``informed prototyping'' process, incorporating feedback from three musicians throughout development. We evaluated the Beatbots through a live public performance, surveying participants (N=28) to understand how they perceived and interacted with the robotic performance. Results show that participants valued the playfulness of the experience, the aesthetics of the robot system, and the unconventional robot-generated music. Expert musicians and non-expert roboticists demonstrated especially positive mindset shifts during the performance, although participants across all demographics had favorable responses. We propose design principles to guide the development of future robotic music systems and identify key robotic music affordances that our musician consultants considered particularly important for robotic music performance.
Standardized Measurement Approach (SMA) vs Advanced Measurement Approaches (AMA): A Critical Review of Approaches in Operational Risk
2025-02-03
Omar Briceno Cruzado
The Basel Committee on Banking Supervision proposed replacing all approaches for operational risk capital, including the Advanced Measurement Approach (AMA), with a simplified formula called the Standardized Measurement Approach (SMA). This paper examines and criticizes the weaknesses and failures of SMA, such as instability, insensitivity to risk, superadditivity, and the implicit relationship between the SMA capital model and systemic risk in the banking sector. Furthermore, it discusses the issues of the proposed Operational Risk Capital (OpCar) model by the Basel Committee, a precursor to SMA. The paper concludes by advocating for the maintenance of the AMA internal model framework and suggests a series of standardization recommendations to unify internal operational risk modeling. The findings and viewpoints presented in this paper have been discussed and supported by numerous operational risk professionals and academics from various regions of the world.
Earth Detecting Earth: At what distance could Earth's constellation of technosignatures be detected with present-day technology?
2025-02-03
Sofia Z. Sheikh, Macy J. Huston, Pinchen Fan, Jason T. Wright, Thomas Beatty, Connor Martini, Ravi Kopparapu, Adam Frank
The field of the Search for Extraterrestrial Intelligence (SETI) searches for ``technosignatures'' that could provide the first detection of life beyond Earth through the technology that an extraterrestrial intelligence (ETI) may have created. Any given SETI survey, if no technosignatures are detected, should set upper limits based on the kinds of technosignatures it should have been able to detect; the sensitivity of many SETI searches requires that their target sources (e.g., Dyson spheres or Kardashev II/III level radio transmitters) emit with power far exceeding the kinds of technology humans have developed. In this paper, we instead turn our gaze Earthward, minimizing the axis of extrapolation by only considering transmission and detection methods commensurate with an Earth-2024 level. We evaluate the maximum distance of detectability for various present-day Earth technosignatures -- radio transmissions, atmospheric technosignatures, optical and infrared signatures, and objects in space or on planetary surfaces -- using only present-day Earth instruments, providing one of the first fully cross-wavelength comparisons of the growing toolbox of SETI techniques. In this framework, we find that Earth's space-detectable signatures span 13 orders of magnitude in detectability, with intermittent, celestially-targeted radio transmission (i.e., planetary radar) beating out its nearest non-radio competitor by a factor of $10^3$ in detection distance. This work highlights the growing range of ways that exoplanet technosignatures may be expressed, the growing complexity and visibility of the human impact upon our planet, and the continued importance of the radio frequencies in SETI.
FetDTIAlign: A Deep Learning Framework for Affine and Deformable Registration of Fetal Brain dMRI
2025-02-03
Bo Li, Qi Zeng, Simon K. Warfield, Davood Karimi
Diffusion MRI (dMRI) provides unique insights into fetal brain microstructure in utero. Longitudinal and cross-sectional fetal dMRI studies can reveal crucial neurodevelopmental changes but require precise spatial alignment across scans and subjects. This is challenging due to low data quality, rapid brain development, and limited anatomical landmarks. Existing registration methods, designed for high-quality adult data, struggle with these complexities. To address this, we introduce FetDTIAlign, a deep learning approach for fetal brain dMRI registration, enabling accurate affine and deformable alignment. FetDTIAlign features a dual-encoder architecture and iterative feature-based inference, reducing the impact of noise and low resolution. It optimizes network configurations and domain-specific features at each registration stage, enhancing both robustness and accuracy. We validated FetDTIAlign on data from 23 to 36 weeks gestation, covering 60 white matter tracts. It consistently outperformed two classical optimization-based methods and a deep learning pipeline, achieving superior anatomical correspondence. Further validation on external data from the Developing Human Connectome Project confirmed its generalizability across acquisition protocols. Our results demonstrate the feasibility of deep learning for fetal brain dMRI registration, providing a more accurate and reliable alternative to classical techniques. By enabling precise cross-subject and tract-specific analyses, FetDTIAlign supports new discoveries in early brain development.
Mapping the Spiral of Silence: Surveying Unspoken Opinions in Online Communities
2025-02-02
Dora Zhao, Diyi Yang, Michael S. Bernstein
We often treat social media as a lens onto society. How might that lens be distorting the actual popularity of political and social viewpoints? In this paper, we examine the difference between the viewpoints publicly posted in a community and the privately surveyed viewpoints of community members, contributing a measurement of a theory called the "spiral of silence." This theory observes that people are less likely to voice their opinion when they believe they are in the minority--leading to a spiral where minority opinions are less likely to be shared, so they appear even further in the minority, and become even less likely to be shared. We surveyed active members of politically oriented Reddit communities to gauge their willingness to post on contentious topics, yielding 627 responses from 108 participants about 11 topics and 33 subreddits. We find that 72.6% of participants who perceive themselves in the minority remain silent, and are only half as likely to post their viewpoint compared to those who believe their opinion is in the majority. Communities perceived as being more inclusive reduce the magnitude of this effect. These results emphasize how far out of step the opinions we see online may be with the population they purport to represent.
The Simons Observatory: Validation of reconstructed power spectra from simulated filtered maps for the Small Aperture Telescope survey
2025-02-02
Carlos Hervías-Caimapo, Kevin Wolz, Adrien La Posta, Susanna Azzoni, David Alonso, Kam Arnold, Carlo Baccigalupi, Simon Biquard, Michael L. Brown, Erminia Calabrese, Yuji Chinone, Samuel Day-Weiss, Jo Dunkley, Rolando Dünner, Josquin Errard, Giulio Fabbian, Ken Ganga, Serena Giardiello, Emilie Hertig, Kevin M. Huffenberger, Bradley R. Johnson, Baptiste Jost, Reijo Keskitalo, Theodore S. Kisner, Thibaut Louis, Magdy Morshed, Lyman A. Page, Christian L. Reichardt, Erik Rosenberg, Max Silva-Feaver, Wuhyun Sohn, Yoshinori Sueno, Dan B. Thomas, Ema Tsang King Sang, Amalia Villarrubia-Aguilar, Kyohei Yamada
We present a transfer function-based method to estimate angular power spectra from filtered maps for cosmic microwave background (CMB) surveys. This is especially relevant for experiments targeting the faint primordial gravitational wave signatures in CMB polarisation at large scales, such as the Simons Observatory (SO) small aperture telescopes. While timestreams can be filtered to mitigate the contamination from low-frequency noise, usual methods that calculate the mode coupling at individual multipoles can be challenging for experiments covering large sky areas or reaching few-arcminute resolution. The method we present here, although approximate, is more practical and faster for larger data volumes. We validate it through the use of simulated observations approximating the first year of SO data, going from half-wave plate-modulated timestreams to maps, and using simulations to estimate the mixing of polarisation modes induced by an example of time-domain filtering. We show its performance through an example null test and with an end-to-end pipeline that performs inference on cosmological parameters, including the tensor-to-scalar ratio $r$. The performance demonstration uses simulated observations at multiple frequency bands. We find that the method can recover unbiased parameters for our simulated noise levels.
The survey of planetary nebulae in Andromeda (M31) VII. Predictions of a major merger simulation model compared with chemodynamical data of the disc and inner halo substructures
2025-02-02
C. Tsakonas, M. Arnaboldi, S. Bhattacharya, F. Hammer, Y. Yang, O. Gerhard, R. F. G. Wyse, D. Hatzidimitriou
The nearest spiral galaxy, M31, exhibits a kinematically hot stellar disc, a global star formation episode ~2-4 Gyr ago, and conspicuous substructures in its stellar halo, suggestive of a recent accretion event. Recent chemodynamical measurements in the M31 disc and inner halo can be used as additional constraints for N-body hydrodynamical simulations that successfully reproduce the disc age-velocity dispersion relation and star formation history, together with the morphology of the inner halo substructures. We combine an available N-body hydrodynamical simulation of a major merger (mass ratio 1:4) with a well-motivated chemical model to predict abundance distributions and gradients in the merger remnant at z=0. We computed the projected phase space and the [M/H] distributions for the substructures in the M31 inner halo, i.e. the GS, the NE-, W- Shelves. We compare these chemodynamical properties of the simulated M31 remnant with recent measurements for the M31 stars in the inner halo. This major merger model predicts (i) distinct multiple components within each of the substructure; (ii) a high mean metallicity and large spread in the GS, NE- and W- Shelves, explaining various photometric and spectroscopic metallicity measurements; (iii) simulated phase space diagrams that qualitatively reproduce various features identified in the projected phase space of the substructures in published data from the DESI; (iv) a large distance spread in the GS, as suggested by previous tip of the RGB measurements, and (v) phase space ridges caused by several wraps of the secondary, as well as up-scattered main M31 disc stars, that also have plausible counterparts in the observed phase spaces. These results provide further independent arguments for a major satellite merger in M31 ~3 Gyr ago and a coherent explanation for many of the observational results that make M31 look so different from the MW.
Toward Living Narrative Reviews: An Empirical Study of the Processes and Challenges in Updating Survey Articles in Computing Research
2025-02-02
Raymond Fok, Alexa Siu, Daniel S. Weld
Surveying prior literature to establish a foundation for new knowledge is essential for scholarly progress. However, survey articles are resource-intensive and challenging to create, and can quickly become outdated as new research is published, risking information staleness and inaccuracy. Keeping survey articles current with the latest evidence is therefore desirable, though there is a limited understanding of why, when, and how these surveys should be updated. Toward this end, through a series of in-depth retrospective interviews with 11 researchers, we present an empirical examination of the work practices in authoring and updating survey articles in computing research. We find that while computing researchers acknowledge the value in maintaining an updated survey, continuous updating remains unmanageable and misaligned with academic incentives. Our findings suggest key leverage points within current workflows that present opportunities for enabling technologies to facilitate more efficient and effective updates.
Paper Copilot: The Artificial Intelligence and Machine Learning Community Should Adopt a More Transparent and Regulated Peer Review Process
2025-02-02
Jing Yang
The rapid growth of submissions to top-tier Artificial Intelligence (AI) and Machine Learning (ML) conferences has prompted many venues to transition from closed to open review platforms. Some have fully embraced open peer reviews, allowing public visibility throughout the process, while others adopt hybrid approaches, such as releasing reviews only after final decisions or keeping reviews private despite using open peer review systems. In this work, we analyze the strengths and limitations of these models, highlighting the growing community interest in transparent peer review. To support this discussion, we examine insights from Paper Copilot, a website launched two years ago to aggregate and analyze AI / ML conference data while engaging a global audience. The site has attracted over 200,000 early-career researchers, particularly those aged 18-34 from 177 countries, many of whom are actively engaged in the peer review process. Drawing on our findings, this position paper advocates for a more transparent, open, and well-regulated peer review aiming to foster greater community involvement and propel advancements in the field.
Modified Adaptive Tree-Structured Parzen Estimator for Hyperparameter Optimization
2025-02-02
Szymon Sieradzki, Jacek Mańdziuk
In this paper, we review hyperparameter optimization methods for machine learning models, with a particular focus on the Adaptive Tree-Structured Parzen Estimator (ATPE) algorithm. We propose several modifications to ATPE and assess their efficacy on a diverse set of standard benchmark functions. Experimental results demonstrate that the proposed modifications significantly improve the effectiveness of ATPE hyperparameter optimization on selected benchmarks, a finding that holds practical relevance for their application in real-world machine learning / optimization tasks.
Predicting potentially unfair clauses in Chilean terms of services with natural language processing
2025-02-02
Christoffer Loeffler, Andrea Martínez Freile, Tomás Rey Pizarro
This study addresses the growing concern of information asymmetry in consumer contracts, exacerbated by the proliferation of online services with complex Terms of Service that are rarely even read. Even though research on automatic analysis methods is conducted, the problem is aggravated by the general focus on English-language Machine Learning approaches and on major jurisdictions, such as the European Union. We introduce a new methodology and a substantial dataset addressing this gap. We propose a novel annotation scheme with four categories and a total of 20 classes, and apply it on 50 online Terms of Service used in Chile. Our evaluation of transformer-based models highlights how factors like language- and/or domain-specific pre-training, few-shot sample size, and model architecture affect the detection and classification of potentially abusive clauses. Results show a large variability in performance for the different tasks and models, with the highest macro-F1 scores for the detection task ranging from 79% to 89% and micro-F1 scores up to 96%, while macro-F1 scores for the classification task range from 60% to 70% and micro-F1 scores from 64% to 80%. Notably, this is the first Spanish-language multi-label classification dataset for legal clauses, applying Chilean law and offering a comprehensive evaluation of Spanish-language models in the legal domain. Our work lays the ground for future research in method development for rarely considered legal analysis and potentially leads to practical applications to support consumers in Chile and Latin America as a whole.
Explainability in Practice: A Survey of Explainable NLP Across Various Domains
2025-02-02
Hadi Mohammadi, Ayoub Bagheri, Anastasia Giachanou, Daniel L. Oberski
Natural Language Processing (NLP) has become a cornerstone in many critical sectors, including healthcare, finance, and customer relationship management. This is especially true with the development and use of advanced models such as GPT-based architectures and BERT, which are widely used in decision-making processes. However, the black-box nature of these advanced NLP models has created an urgent need for transparency and explainability. This review explores explainable NLP (XNLP) with a focus on its practical deployment and real-world applications, examining its implementation and the challenges faced in domain-specific contexts. The paper underscores the importance of explainability in NLP and provides a comprehensive perspective on how XNLP can be designed to meet the unique demands of various sectors, from healthcare's need for clear insights to finance's emphasis on fraud detection and risk assessment. Additionally, this review aims to bridge the knowledge gap in XNLP literature by offering a domain-specific exploration and discussing underrepresented areas such as real-world applicability, metric evaluation, and the role of human interaction in model assessment. The paper concludes by suggesting future research directions that could enhance the understanding and broader application of XNLP.
The rise of single-cell sequencing technologies has revolutionized the exploration of drug resistance, revealing the crucial role of cellular heterogeneity in advancing precision medicine. By building computational models from existing single-cell drug response data, we can rapidly annotate cellular responses to drugs in subsequent trials. To this end, we developed scGSDR, a model that integrates two computational pipelines grounded in the knowledge of cellular states and gene signaling pathways, both essential for understanding biological gene semantics. scGSDR enhances predictive performance by incorporating gene semantics and employs an interpretability module to identify key pathways contributing to drug resistance phenotypes. Our extensive validation, which included 16 experiments covering 11 drugs, demonstrates scGSDR's superior predictive accuracy, when trained with either bulk-seq or scRNA-seq data, achieving high AUROC, AUPR, and F1 Scores. The model's application has extended from single-drug predictions to scenarios involving drug combinations. Leveraging pathways of known drug target genes, we found that scGSDR's cell-pathway attention scores are biologically interpretable, which helped us identify other potential drug-related genes. Literature review of top-ranking genes in our predictions such as BCL2, CCND1, the AKT family, and PIK3CA for PLX4720; and ICAM1, VCAM1, NFKB1, NFKBIA, and RAC1 for Paclitaxel confirmed their relevance. In conclusion, scGSDR, by incorporating gene semantics, enhances predictive modeling of cellular responses to diverse drugs, proving invaluable for scenarios involving both single drug and combination therapies and effectively identifying key resistance-related pathways, thus advancing precision medicine and targeted therapy development.
An overview of regularity results for the Laplacian and $p$-Laplacian in metric spaces
2025-02-02
Ivan Yuri Violo
We review some regularity results for the Laplacian and $p$-Laplacian in metric measure spaces. The focus is mainly on interior H\"older, Lipschitz and second-regularity estimates and on spaces supporting a Poincar\'e inequality or having Ricci curvature bounded below.
Detection of Ubiquitous Circumbinary Matter in Hot Subdwarfs Formed from Common-Envelope Ejections
2025-02-02
Jiangdan Li, Christian Wolf, Jiao Li, Yangping Luo, Jingkun Zhao, Bingqiu Chen, Lin Zhang, Shi Jia, Xuefei Chen, Zhanwen Han
The formation of compact binary systems is largely driven by their evolution through a common envelope (CE) phase, crucial for understanding phenomena such as type Ia supernovae and black hole mergers. Despite their importance, direct observational evidence for CE material has been elusive due to the transient nature of these envelopes. Numerical simulations suggest that some envelope material may persist post-ejection. In this study, we investigate circumstellar material (CSM) surrounding hot subdwarf (sdB) stars, focusing on material ejected during the CE phase of binary evolution. We analyze Ca II K absorption lines in 727 sdB candidates from the LAMOST-LRS survey, selecting 145 stars with strong absorption features, indicating the presence of CSM. We compare the velocities of the Ca II K lines with the systemic velocities of sdB binaries, confirming that the material originates from ejected common-envelope material. The results show that the CSM persists long after the CE event, suggesting the formation of a stable, long-lived circumstellar environment around sdB stars. This study enhances our understanding of the role of CSM in post-CE evolution and provides new insights into the physical processes shaping the evolution of sdB binaries.
Real-Time Bidding (RTB) enables advertisers to place competitive bids on impression opportunities instantaneously, striving for cost-effectiveness in a highly competitive landscape. Although RTB has widely benefited from the utilization of technologies such as deep learning and reinforcement learning, the reliability of related methods often encounters challenges due to the discrepancies between online and offline environments and the rapid fluctuations of online bidding. To handle these challenges, RTBAgent is proposed as the first RTB agent system based on large language models (LLMs), which synchronizes real competitive advertising bidding environments and obtains bidding prices through an integrated decision-making process. Specifically, obtaining reasoning ability through LLMs, RTBAgent is further tailored to be more professional for RTB via involved auxiliary modules, i.e., click-through rate estimation model, expert strategy knowledge, and daily reflection. In addition, we propose a two-step decision-making process and multi-memory retrieval mechanism, which enables RTBAgent to review historical decisions and transaction records and subsequently make decisions more adaptive to market changes in real-time bidding. Empirical testing with real advertising datasets demonstrates that RTBAgent significantly enhances profitability. The RTBAgent code will be publicly accessible at: https://github.com/CaiLeng/RTBAgent.
Green peas in the southern sky: Broad-band color selection and spectroscopic follow up
2025-02-02
Yejin Jeong, Hyunjin Shim, Eunchong Kim, Jeong Hwan Lee
We present a systematic search for 1696 Green Pea (GP) galaxy candidates in the southern hemisphere selected from the Dark Energy Survey Data Release 2 (DES DR2) and provide preliminary results from spectroscopic follow-up observations of 26 targets chosen among them. Our selection criteria include the colors in $gri$-bands and compact morphology in the color composite images. The multi-wavelength spectral energy distribution fitting shows that the selected GP candidates exhibit star formation rates up to several tens $\mathrm{M}_\odot\,\mathrm{yr}^{-1}$. With the mean stellar mass of $\mathrm{log}\,M_*/\mathrm{M}_\odot=8.6$, GP candidates are located at roughly 1 dex above the main sequence of star-forming galaxies at $z\sim0.3$. Spectroscopic follow-up observations of the GP candidates with Gemini/GMOS are underway. All 26 targets are spectroscopically confirmed to be at $z=0.3-0.41$ and have [OIII] equivalent width larger than 85$\r{A}$, classified to be starbursts with low to moderate dust attenuation. These confirmed GPs show a lower metallicity offset from the mass-metallicity relation of local star-forming galaxies, indicating that GPs are less chemically evolved systems at their early stage of evolution.
ZTF J172132.75+445851.0: A Possible New Cataclysmic Variable of the VY Sculptoris Type
2025-02-02
Klaus Bernhard, Christopher Lloyd, Peter Frank, Wolfgang Moschner
We report the discovery of ZTF J172132.75+445851.0 as a new possible VY Sculptoris variable, with an orbital period of 0.109765426(44) days. Survey observations from 2005 to the present reveal significant activity in the system, with brightness variations ranging between 18.1 and 21.3 (zr) magnitudes, including deep fades exceeding an amplitude of 3 magnitudes.
Machine learning based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide field: catalogs of spectroscopic and photometric redshifts
2025-02-02
Taewan Kim, Jubee Sohn, Ho Seong Hwang, Simon C. -C. Ho, Denis Burgarella, Tomotsugu Goto, Tetsuya Hashimoto, Woong-Seob Jeong, Seong Jin Kim, Matthew A. Malkan, Takamitsu Miyaji, Nagisa Oi, Hyunjin Shim, Hyunmi Song, Narae Hwang, Byeong-Gon Park
We perform an MMT/Hectospec redshift survey of the North Ecliptic Pole Wide (NEPW) field covering 5.4 square degrees, and use it to estimate the photometric redshifts for the sources without spectroscopic redshifts. By combining 2572 newly measured redshifts from our survey with existing data from the literature, we create a large sample of 4421 galaxies with spectroscopic redshifts in the NEPW field. Using this sample, we estimate photometric redshifts of 77755 sources in the band-merged catalog of the NEPW field with a random forest model. The estimated photometric redshifts are generally consistent with the spectroscopic redshifts, with a dispersion of 0.028, an outlier fraction of 7.3%, and a bias of -0.01. We find that the standard deviation of the prediction from each decision tree in the random forest model can be used to infer the fraction of catastrophic outliers and the measurement uncertainties. We test various combinations of input observables, including colors and magnitude uncertainties, and find that the details of these various combinations do not change the prediction accuracy much. As a result, we provide a catalog of 77755 sources in the NEPW field, which includes both spectroscopic and photometric redshifts up to z~2. This dataset has significant legacy value for studies in the NEPW region, especially with upcoming space missions such as JWST, Euclid, and SPHEREx.
A Survey of Quantized Graph Representation Learning: Connecting Graph Structures with Large Language Models
2025-02-02
Qika Lin, Zhen Peng, Kaize Shi, Kai He, Yiming Xu, Erik Cambria, Mengling Feng
Recent years have witnessed rapid advances in graph representation learning, with the continuous embedding approach emerging as the dominant paradigm. However, such methods encounter issues regarding parameter efficiency, interpretability, and robustness. Thus, Quantized Graph Representation (QGR) learning has recently gained increasing interest, which represents the graph structure with discrete codes instead of conventional continuous embeddings. Given its analogous representation form to natural language, QGR also possesses the capability to seamlessly integrate graph structures with large language models (LLMs). As this emerging paradigm is still in its infancy yet holds significant promise, we undertake this thorough survey to promote its rapid future prosperity. We first present the background of the general quantization methods and their merits. Moreover, we provide an in-depth demonstration of current QGR studies from the perspectives of quantized strategies, training objectives, distinctive designs, knowledge graph quantization, and applications. We further explore the strategies for code dependence learning and integration with LLMs. At last, we give discussions and conclude future directions, aiming to provide a comprehensive picture of QGR and inspire future research.
2025-02-02
Siraaj Akhtar, Saad Khan, Simon Parkinson
Event log analysis is an important task that security professionals undertake. Event logs record key information on activities that occur on computing devices, and due to the substantial number of events generated, they consume a large amount of time and resources to analyse. This demanding and repetitive task is also prone to errors. To address these concerns, researchers have developed automated techniques to improve the event log analysis process. Large Language Models (LLMs) have recently demonstrated the ability to successfully perform a wide range of tasks that individuals would usually partake in, to high standards, and at a pace and degree of complexity that outperform humans. Due to this, researchers are rapidly investigating the use of LLMs for event log analysis. This includes fine-tuning, Retrieval-Augmented Generation (RAG) and in-context learning, which affect performance. These works demonstrate good progress, yet there is a need to understand the developing body of knowledge, identify commonalities between works, and identify key challenges and potential solutions to further developments in this domain. This paper aims to survey LLM-based event log analysis techniques, providing readers with an in-depth overview of the domain, gaps identified in previous research, and concluding with potential avenues to explore in future.
Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks
2025-02-02
Kamal Acharya, Mehul Lad, Liang Sun, Houbing Song
Travel demand prediction is crucial for optimizing transportation planning, resource allocation, and infrastructure development, ensuring efficient mobility and economic sustainability. This study introduces a Neurosymbolic Artificial Intelligence (Neurosymbolic AI) framework that integrates decision tree (DT)-based symbolic rules with neural networks (NNs) to predict travel demand, leveraging the interpretability of symbolic reasoning and the predictive power of neural learning. The framework utilizes data from diverse sources, including geospatial, economic, and mobility datasets, to build a comprehensive feature set. DTs are employed to extract interpretable if-then rules that capture key patterns, which are then incorporated as additional features into a NN to enhance its predictive capabilities. Experimental results show that the combined dataset, enriched with symbolic rules, consistently outperforms standalone datasets across multiple evaluation metrics, including Mean Absolute Error (MAE), \(R^2\), and Common Part of Commuters (CPC). Rules selected at finer variance thresholds (e.g., 0.0001) demonstrate superior effectiveness in capturing nuanced relationships, reducing prediction errors, and aligning with observed commuter patterns. By merging symbolic and neural learning paradigms, this Neurosymbolic approach achieves both interpretability and accuracy.
Retracted Citations and Self-citations in Retracted Publications: A Comparative Study of Plagiarism and Fake Peer Review
2025-02-02
Kiran Sharmaa, Parul Khurana
Retracted citations remain a significant concern in academia as they perpetuate misinformation and compromise the integrity of scientific literature despite their invalidation. To analyze the impact of retracted citations, we focused on two retraction categories: plagiarism and fake peer review. The data set was sourced from Scopus and the reasons for the retraction were mapped using the Retraction Watch database. The retraction trend shows a steady average growth in plagiarism cases of 1.2 times, while the fake peer review exhibits a fluctuating pattern with an average growth of 5.5 times. Although fewer papers are retracted in the plagiarism category compared to fake peer reviews, plagiarism-related papers receive 2.5 times more citations. Furthermore, the total number of retracted citations for plagiarized papers is 1.8 times higher than that for fake peer review papers. Within the plagiarism category, 46% of the retracted citations are due to plagiarism, while 53.6% of the retracted citations in the fake peer review category are attributed to the fake peer review. The results also suggest that fake peer review cases are identified and retracted more rapidly than plagiarism cases. Finally, self-citations constitute a small percentage of citations to retracted papers but are notably higher among citations that are later retracted in both the categories.
SIGNALS on the mixing of oxygen and nitrogen in the spiral galaxy NGC 6946
2025-02-02
Fabio Bresolin, David Fernández-Arenas, Laurie Rousseau-Nepton, Ray Garner III, Almudena Zurita, Carmelle Robert, Laurent Drissen, René Pierre Martin, Philippe Amram, Salvador Duarte Puertas, Gabriel Savard, Sébastien Vicens, Mykola Posternak
As part of the SIGNALS survey, which comprises a sample of approximately 40 nearby galaxies observed with the Fourier transform spectrometer SITELLE, we present a study of metal mixing in the spiral galaxy NGC 6946. Taking advantage of the blue sensitivity of our setup, we measure the oxygen and nitrogen abundances of 638 H II regions, and focus our analysis on the abundance fluctuations about the radial gradients. We detect an azimuthal variation of about 0.1 dex in these abundances across the NE spiral arm, with the leading edge being more metal-poor than the trailing edge. This result aligns with galaxy simulations, where radial gas flows along the spiral arms lead to dilution on the leading edge and enrichment on the trailing edge, due to the presence of radial metallicity gradients. Our 2D analysis reveals that oxygen and nitrogen exhibit comparable spatial correlation scales, despite the different injection energies and distinct nucleosynthetic origins -- core-collapse supernovae in the case of oxygen and primarily AGB stars for nitrogen. The observed similarity suggests that stellar processes drive these two elements into the ISM over equivalent spatial scales.
A Two-Stage Trip Inference Model of Purposes and Socio-Economic Attributes of Regular Public Transit Users
2025-02-02
Yitong Chen, Wentao Dong, Chengcheng Yu, Quan Yuan, Chao Yang
Data-driven research is becoming a new paradigm in transportation, but the natural lack of individual socio-economic attributes in transportation data makes research such as activity purpose inference and mobility pattern identification lack convincingness and verifiability. In this paper, a two-stage trip purpose and socio-economic attributes inference model is proposed based on travel resident survey and smart card data. In the first stage, the trip purpose of each trip is inferred by a combination of rule-based and XGBoost models. In the second stage, based on the trip purpose, a machine-learning model is built to inference the socio-economic attributes of individuals. A teacher-student model based on self-training is then applied on the models above to transfer them to smart card data. The impact of independent variables of socio-economic attributes inference model is also investigated. The results show that models for inferring trip purposes and socio-economic attributes have overall accuracies of 92.7% and 76.3%, respectively. Travel time, arrival time, departure time and purpose of the first two trips are most important factors on age and job status, while the land price of jobs-housing are significant to the inference of individual incomes.
A Unified Bayesian Nonparametric Framework for Ordinal, Survival, and Density Regression Using the Complementary Log-Log Link
2025-02-02
Entejar Alam, Antonio R. Linero
In this work, we develop applications of the complementary log-log (cloglog) link to problems in Bayesian nonparametrics. Although less commonly used than the probit or logit links, we find that the cloglog link is computationally and theoretically well-suited to several commonly used Bayesian nonparametric methods. Our starting point is a Bayesian nonparametric model for ordinal regression. We first review how the cloglog link uniquely sits at the intersection of the cumulative link and continuation ratio approaches to ordinal regression. Then, we develop a convenient computational method for fitting these ordinal models using Bayesian additive regression trees. Next, we use our ordinal regression model to build a Bayesian nonparametric stick-breaking process and show that, under a proportional hazards assumption, our stick-breaking process can be used to construct a weight-dependent Dirichlet process mixture model. Again, Bayesian additive regression trees lead to convenient computations. We then extend these models to allow for Bayesian nonparametric survival analysis in both discrete and continuous time. Our models have desirable theoretical properties, and we illustrate this analyzing the posterior contraction rate of our ordinal models. Finally, we demonstrate the practical utility of our cloglog models through a series of illustrative examples.
Motivated by a popular code golf challenge, we review some key ideas from information theory and discuss how to efficiently compress a streaming file with an acceptable error rate.
Functional role of synchronization: A mean-field control perspective
2025-02-01
Prashant Mehta, Sean Meyn
The broad goal of the research surveyed in this article is to develop methods for understanding the aggregate behavior of interconnected dynamical systems, as found in mathematical physics, neuroscience, economics, power systems and neural networks. Questions concern prediction of emergent (often unanticipated) phenomena, methods to formulate distributed control schemes to influence this behavior, and these topics prompt many other questions in the domain of learning. The area of mean field games, pioneered by Peter Caines, are well suited to addressing these topics. The approach is surveyed in the present paper within the context of controlled coupled oscillators.
Benchmark on Peer Review Toxic Detection: A Challenging Task with a New Dataset
2025-02-01
Man Luo, Bradley Peterson, Rafael Gan, Hari Ramalingame, Navya Gangrade, Ariadne Dimarogona, Imon Banerjee, Phillip Howard
Peer review is crucial for advancing and improving science through constructive criticism. However, toxic feedback can discourage authors and hinder scientific progress. This work explores an important but underexplored area: detecting toxicity in peer reviews. We first define toxicity in peer reviews across four distinct categories and curate a dataset of peer reviews from the OpenReview platform, annotated by human experts according to these definitions. Leveraging this dataset, we benchmark a variety of models, including a dedicated toxicity detection model, a sentiment analysis model, several open-source large language models (LLMs), and two closed-source LLMs. Our experiments explore the impact of different prompt granularities, from coarse to fine-grained instructions, on model performance. Notably, state-of-the-art LLMs like GPT-4 exhibit low alignment with human judgments under simple prompts but achieve improved alignment with detailed instructions. Moreover, the model's confidence score is a good indicator of better alignment with human judgments. For example, GPT-4 achieves a Cohen's Kappa score of 0.56 with human judgments, which increases to 0.63 when using only predictions with a confidence score higher than 95%. Overall, our dataset and benchmarks underscore the need for continued research to enhance toxicity detection capabilities of LLMs. By addressing this issue, our work aims to contribute to a healthy and responsible environment for constructive academic discourse and scientific collaboration.
Unions with UNIONS: Using galaxy-galaxy lensing to probe galaxy mergers
2025-02-01
Isaac Cheng, Jack Elvin-Poole, Michael J. Hudson, Ruxin Barré, Sara L. Ellison, Robert W. Bickley, Thomas J. L. de Boer, Sébastien Fabbro, Leonardo Ferreira, Sacha Guerrini, Hendrik Hildebrandt, Martin Kilbinger, Alan W. McConnachie, Ludovic van Waerbeke, Anna Wittje
(ABRIDGED) We use galaxy-galaxy lensing to investigate how the dark matter (DM) haloes and stellar content of galaxies with $0.012 \leq z \leq 0.32$ and $10 \leq \log_{10}(M_\star/\mathrm{M}_\odot) \leq 12$ change as a result of the merger process. To this end, we construct two samples of galaxies obtained from the Ultraviolet Near Infrared Optical Northern Survey (UNIONS), comprising 1 623 post-mergers and $\sim$30 000 non-merging controls, that live in low-density environments to use as our lenses. These samples are weighted to share the same distributions of stellar mass, redshift, and geometric mean distance to a galaxy's three nearest neighbours to ensure differences in the lensing signal are due to the merger process itself. We do not detect a significant difference in the excess surface density profile of post-mergers and non-merging controls with current data. Fitting haloes composed of a point-like stellar mass component and an extended DM structure described by a Navarro-Frenk-White profile to the lensing measurements yields, for both samples, halo masses of $M_\text{halo}\sim 4\times10^{12}\,\mathrm{M}_\odot$ and a moderately negative correlation between $M_\text{halo}$ and concentration $c$. . . . Extreme merger-induced starbursts, in which more than 60 percent of the stars are formed in the burst, are ruled out at the 95 per cent confidence level. Although we do not find merger-induced differences in the DM haloes and stellar content of galaxies with our current data, application of our methods to upcoming surveys that are able to provide samples $\sim$10$\times$ larger than our current catalogue are expected to detect the weak-lensing signatures of mergers and further constrain their properties.
A 16 Myr super-Neptune in Upper-Centaurus Lupus and a preliminary survey of transiting planets in Sco-Cen with TESS
2025-02-01
Sydney Vach, George Zhou, Andrew W. Mann, Madyson G. Barber, Tyler R. Fairnington, Chelsea X. Huang, James G. Rogers, Luke G. Bouma, Joachim Krüger, Duncan Wright, Annabelle E. Niblett, Jack M. Nelson, Samuel N. Quinn, David W. Latham, Allyson Bieryla, Karen A. Collins, Michelle Kunimoto, Cristilyn N. Watkins, Richard P. Schwarz, Kevin I. Collins, Ramotholo Sefako, Keith Horne, Steve B. Howell, Catherine A. Clark, Colin Littlefield, Jessie L. Christiansen, Zahra Essack, Joshua N. Winn
Measuring the properties of planets younger than about 50 Myr helps to test different planetary formation and evolution models. NASA's Transiting Exoplanet Survey Satellite (TESS) has observed nearly the entire sky, including a wide range of star-forming regions and young stellar clusters, expanding our census of the newborn planet population. In this work, we present the discovery of the TIC 88785435 planetary system located in the Upper-Centaurus Lupus (UCL) region of the Scorpius-Centaurus OB association (Sco-Cen) and a preliminary survey of the planet population within Sco-Cen. TIC 88785435 is a pre-main sequence, K7V dwarf ($M_\star = 0.72M_\odot$, $R_\star = 0.91R_\odot$, $T_\mathrm{eff}$ = 3998K, V = 11.7 mag) located within the bounds of UCL. We investigate the distribution of rotation periods measured from the TESS long-cadence data and the Halpha and Li abundances from the spectra of TIC 88785435. TESS long-candence data reveal that TIC 88785435 hosts a transiting super-Neptune ($R_b = 5.03R_\oplus$, P = 10.51 days), TIC 88785435 b. Ground-based follow-up validates the planetary nature of TIC 88785435 b. Using the TESS data, we perform a preliminary survey to investigate how TIC 88785435 b compares to the population of newly born planets located within Sco-Cen.
Pulsatility delays the transition to sustained turbulence in quasi-two-dimensional shear flows
2025-02-01
Christopher J. Camobreco, Alban Pothérat, Gregory J. Sheard
This work investigates efficient routes to turbulence in quasi-two-dimensional shear flows. Two-dimensional disturbances require high Reynolds numbers to incite transition from a steady base flow, as transient growth is modest. With the addition of an oscillatory base flow component, this work shows that the transient growth experienced by two-dimensional initial perturbations is often well above that provided by the steady component. However, as has been shown for three-dimensional flows [Pier & Schmid J. Fluid Mech. 926, A11 (2021)], the transient growth is almost entirely composed of modal intracyclic growth, rather than a transient mechanism which takes advantage of non-normality. This lack of transient growth, relative to the severe decay induced by the favorable pressure gradient during the acceleration phase of the oscillatory base flow, only ever delays the transition to sustained turbulence. Thus, a non-oscillatory driving force remains the most efficient strategy for sustained turbulence in quasi-two-dimensional shear flows. The only benefit provided by pulsatility is that the amplitude of the initial condition required to trigger intermittent turbulence is orders of magnitude smaller.
Contrastive Forward-Forward: A Training Algorithm of Vision Transformer
2025-02-01
Hossein Aghagolzadeh, Mehdi Ezoji
Although backpropagation is widely accepted as a training algorithm for artificial neural networks, researchers are always looking for inspiration from the brain to find ways with potentially better performance. Forward-Forward is a new training algorithm that is more similar to what occurs in the brain, although there is a significant performance gap compared to backpropagation. In the Forward-Forward algorithm, the loss functions are placed after each layer, and the updating of a layer is done using two local forward passes and one local backward pass. Forward-Forward is in its early stages and has been designed and evaluated on simple multi-layer perceptron networks to solve image classification tasks. In this work, we have extended the use of this algorithm to a more complex and modern network, namely the Vision Transformer. Inspired by insights from contrastive learning, we have attempted to revise this algorithm, leading to the introduction of Contrastive Forward-Forward. Experimental results show that our proposed algorithm performs significantly better than the baseline Forward-Forward leading to an increase of up to 10% in accuracy and boosting the convergence speed by 5 to 20 times on Vision Transformer. Furthermore, if we take Cross Entropy as the baseline loss function in backpropagation, it will be demonstrated that the proposed modifications to the baseline Forward-Forward reduce its performance gap compared to backpropagation on Vision Transformer, and even outperforms it in certain conditions, such as inaccurate supervision.
The introduction of generative artificial intelligence (GenAI) has been met with a mix of reactions by higher education institutions, ranging from consternation and resistance to wholehearted acceptance. Previous work has looked at the discourse and policies adopted by universities across the U.S. as well as educators, along with the inclusion of GenAI-related content and topics in higher education. Building on previous research, this study reports findings from a survey of engineering educators on their use of and perspectives toward generative AI. Specifically, we surveyed 98 educators from engineering, computer science, and education who participated in a workshop on GenAI in Engineering Education to learn about their perspectives on using these tools for teaching and research. We asked them about their use of and comfort with GenAI, their overall perspectives on GenAI, the challenges and potential harms of using it for teaching, learning, and research, and examined whether their approach to using and integrating GenAI in their classroom influenced their experiences with GenAI and perceptions of it. Consistent with other research in GenAI education, we found that while the majority of participants were somewhat familiar with GenAI, reported use varied considerably. We found that educators harbored mostly hopeful and positive views about the potential of GenAI. We also found that those who engaged more with their students on the topic of GenAI, tend to be more positive about its contribution to learning, while also being more attuned to its potential abuses. These findings suggest that integrating and engaging with generative AI is essential to foster productive interactions between instructors and students around this technology.
The 4m International Liquid Mirror Telescope: Construction, operation, and science
2025-02-01
Jean Surdej, Paul Hickson, Kuntal Misra, Dipankar Banerjee, Bhavya Ailawadhi, Talat Akhunov, Ermanno Borra, Monalisa Dubey, Naveen Dukiya, Sara Filali, Joschua Hellemeier, Manisha Kharayat, Brajesh Kumar, Hitesh Kumar, Mukesh Kumar, T. S. Kumar, Priyanshi Kumari, Vibhore Negi, Anna Pospieszalska-Surdej, Sarath Prabhavu, Bikram Pradhan, Kumar Pranshu, Himanshu Rawat, B. Krishna Reddy, Arun Sasidharan Pillai, Khushal Singh, Suzanne Tremblay, Saakshi Turakhia, Sahaana Vijay
The International Liquid Mirror Telescope (ILMT) project was motivated by the need for an inexpensive 4 metre diameter optical telescope that could be devoted entirely to astronomical surveys. Its scientific programmes include the detection and study of transients, variable objects, asteroids, comets, space debris and low surface brightness galaxies. To this end, a collaboration was formed between the Institute of Astrophysics and Geophysics (Li\`ege University, Belgium), several Canadian universities (University of British Columbia, Laval University, University of Montreal, University of Toronto, York University, University of Victoria) and the Aryabhatta Research Institute of Observational Sciences (ARIES, India). After several years of design work in Belgium and construction in India on the ARIES Devasthal site, the telescope saw its first light on 29 April 2022. Its commissioning phase lasted from May 2022 until June 2023 (beginning of the monsoon). The ILMT was inaugurated on 21 March 2023 and has been in regular operation since October 2023. The telescope continuously observes the sky passing at the zenith using the SDSS g', r', and i' filters. This paper describes the ILMT, its operation, performance and shows some initial results.
PyLMT: A transient detection pipeline for the 4-m International Liquid Mirror Telescope
2025-02-01
Kumar Pranshu, Kuntal Misra, Bhavya Ailawadhi, Monalisa Dubey, Naveen Dukiya, Sara Filali, Paul Hickson, Brajesh Kumar, Vibhore Negi, Jean Surdej
The International Liquid Mirror Telescope (ILMT) is a 4-m aperture, zenith-pointing telescope with a field-of-view of 22', situated in the foothills of the Himalayas. The telescope operates in continuous survey mode, making it a useful instrument for time-domain astronomy, particularly for detecting transients, variable stars, active galactic nuclei variability, and asteroids. This paper presents the PyLMT transient detection pipeline to detect such transient/varying sources in the ILMT images. The pipeline utilises the image subtraction technique to compare a pair of images from the same field, identifying such sources in subtracted images with the help of convolutional neural networks (CNN) based real/bogus classifiers. The test accuracies determined for the real/bogus classifiers ranged from 94% to 98%. The resulting precision of the pipeline calculated over candidate alerts in the ILMT frames is 0.91. It also houses a CNN-aided transient candidate classifier that classifies the transient/variable candidates based on host morphology. The test accuracy of the candidate classifier is 98.6%. It has the provision to identify catalogued asteroids and other solar system objects using public databases. The median execution time of the pipeline is approximately 29 minutes per image of 17 minutes exposure. Relevant CNNs have been trained on data acquired with the ILMT during the cycle of October-November 2022. Subsequent tests on those images have confirmed the detection of numerous catalogued asteroids, variable stars, and other uncatalogued sources. The pipeline has been operational and has detected 12 extragalactic transients, including 2 new discoveries in the November 2023-May 2024 observation cycle.
Enhancing Field-Oriented Control of Electric Drives with Tiny Neural Network Optimized for Micro-controllers
2025-02-01
Martin Joel Mouk Elele, Danilo Pau, Shixin Zhuang, Tullio Facchinetti
The deployment of neural networks on resource-constrained micro-controllers has gained momentum, driving many advancements in Tiny Neural Networks. This paper introduces a tiny feed-forward neural network, TinyFC, integrated into the Field-Oriented Control (FOC) of Permanent Magnet Synchronous Motors (PMSMs). Proportional-Integral (PI) controllers are widely used in FOC for their simplicity, although their limitations in handling nonlinear dynamics hinder precision. To address this issue, a lightweight 1,400 parameters TinyFC was devised to enhance the FOC performance while fitting into the computational and memory constraints of a micro-controller. Advanced optimization techniques, including pruning, hyperparameter tuning, and quantization to 8-bit integers, were applied to reduce the model's footprint while preserving the network effectiveness. Simulation results show the proposed approach significantly reduced overshoot by up to 87.5%, with the pruned model achieving complete overshoot elimination, highlighting the potential of tiny neural networks in real-time motor control applications.
Radial Gradients and Intrinsic Scatter in MaNGA Galaxies
2025-02-01
Tathagata Pal, Guy Worthey
We derive stellar population parameters and their radial gradients within 0.65 R$_e$ for spatially resolved spectra of 2417 early-type galaxies from the MaNGA survey with stellar velocity dispersions ($\sigma$) between 50 kms$^{-1}$ and 340 kms$^{-1}$. We invert a grid of metallicity-composite stellar population models to find mean age and the abundances of C, N, Na, Mg, and Fe. These models have significant improvements compared to past models, including isochrones that respond to individual element abundances. Globally, age rises with $\sigma$ while [Fe/H] gently falls. Individual light element abundances strengthen with $\sigma$ but strengthen faster for log($\sigma$)$>$2.0. [Fe/H] shows a maximum at log($\sigma$)$\approx$2.0, falling to either side. Light element [X/Fe] anticorrelate with [Fe/H]. Heterogeneity as measured by astrophysical scatter is highest in low-$\sigma$ galaxies, most dramatically for age, Fe, and N. For galaxy-internal parameters, age shows nearly flat radial gradients in low-$\sigma$ galaxies, slightly negative at high $\sigma$. The mean radial gradient in [Fe/H] is negative and light element [X/Fe]s fall. Intrinsic scatter in gradients is highest in high-$\sigma$ galaxies, most dramatically for age and Fe. Evidently, nearly as many galaxies form inside-out as form outside-in. A near-zero radial gradient in age and light elements coupled with a mild [Fe/H] gradient supports the hierarchical merging scenario for ETG evolution. IllustrisTNG hierarchical simulations reproduce the age structure we find, show the abundance slope changes at log($\sigma$)$\approx$2.0 that we observe, and exhibit flat gradients similar to those we derive, although the abundances predicted by IllustrisTNG are significantly higher than our observations overall.
Graph Data Management and Graph Machine Learning: Synergies and Opportunities
2025-02-01
Arijit Khan, Xiangyu Ke, Yinghui Wu
The ubiquity of machine learning, particularly deep learning, applied to graphs is evident in applications ranging from cheminformatics (drug discovery) and bioinformatics (protein interaction prediction) to knowledge graph-based query answering, fraud detection, and social network analysis. Concurrently, graph data management deals with the research and development of effective, efficient, scalable, robust, and user-friendly systems and algorithms for storing, processing, and analyzing vast quantities of heterogeneous and complex graph data. Our survey provides a comprehensive overview of the synergies between graph data management and graph machine learning, illustrating how they intertwine and mutually reinforce each other across the entire spectrum of the graph data science and machine learning pipeline. Specifically, the survey highlights two crucial aspects: (1) How graph data management enhances graph machine learning, including contributions such as improved graph neural network performance through graph data cleaning, scalable graph embedding, efficient graph-based vector data management, robust graph neural networks, user-friendly explainability methods; and (2) how graph machine learning, in turn, aids in graph data management, with a focus on applications like query answering over knowledge graphs and various data science tasks. We discuss pertinent open problems and delineate crucial research directions.
Bridging Internal Probability and Self-Consistency for Effective and Efficient LLM Reasoning
2025-02-01
Zhi Zhou, Tan Yuhao, Zenan Li, Yuan Yao, Lan-Zhe Guo, Xiaoxing Ma, Yu-Feng Li
Recent advancements in large language models (LLMs) have demonstrated remarkable reasoning capabilities. However, single-shot inference often yields unreliable results for complex reasoning tasks, leading researchers to explore multiple reasoning paths through methods such as perplexity and self-consistency. In this paper, we present the first theoretical error decomposition analysis of these techniques, breaking down their error into estimation error and model error. Our analysis reveals a fundamental trade-off: perplexity methods suffer from substantial model error due to the absence of a proper consistency function, while self-consistency exhibits high estimation error due to a slow error convergence rate. To overcome these limitations, we propose Reasoning-Pruning Perplexity Consistency (RPC). This approach combines Perplexity Consistency, which seamlessly integrates LLM perplexity with self-consistency, and Reasoning Pruning, which eliminates low-probability reasoning paths to effectively prevent the degeneration of estimation error reduction. Theoretical analysis demonstrates that RPC not only accelerates the convergence rate of estimation error to an exponential level but also holds strong potential for further reducing model error. Extensive empirical evaluations on seven benchmark datasets confirm that RPC can significantly improve reasoning performance, sample efficiency, and confidence reliability.
Hadron structure via Generalized Parton Distributions
2025-02-01
Shohini Bhattacharya
Recent advancements have made it possible to approximate light-cone correlation functions in lattice QCD by computing their Euclidean counterparts. In these proceedings, we review key developments in this approach and explore their direct implications for Generalized Parton Distributions (GPDs). Furthermore, we emphasize the pivotal role of GPDs in uncovering the internal structure of hadrons and beyond.
An Inorganic Liquid Crystalline Dispersion with 2D Ferroelectric Moieties
2025-02-01
Ziyang Huang, Zehao Zhang, Rongjie Zhang, Baofu Ding, Liu Yang, Keyou Wu, Youan Xu, Gaokuo Zhong, Chuanlai Ren, Jiarong Liu, Yugan Hao, Menghao Wu, Teng Ma, Bilu Liu
Electro-optical effect based liquid crystal devices have been extensively used in optical modulation techniques, in which the Kerr coefficient reflects the sensitivity of the liquid crystals and determines the strength of the device operational electric field. The Peterlin-Stuart theory and the O'Konski model jointly indicate that a giant Kerr coefficient could be obtained in a material with both a large geometrical anisotropy and an intrinsic polarization, but such a material is not yet reported. Here we reveal a ferroelectric effect in a monolayer two-dimensional mineral vermiculite. A large geometrical anisotropy factor and a large inherent electric dipole together raise the record value of Kerr coefficient by an order of magnitude, till $3.0\times 10^{-4}$ m V$^{-2}$. This finding enables an ultra-low operational electric field of $10^2$-$10^4$ V m$^{-1}$ and the fabrication of electro-optical devices with an inch-level electrode separation, which is not practical previously. Because of its high ultraviolet stability (decay <1% under ultraviolet exposure of 1000 hours), large-scale, and energy-efficiency, prototypical displayable billboards have been fabricated for outdoor interactive scenes. The work provides new insights for both liquid crystal optics and two-dimensional ferroelectrics.
Confidence intervals for intentionally biased estimators
2025-02-01
David M. Kaplan, Xin Liu
We propose and study three confidence intervals (CIs) centered at an estimator that is intentionally biased to reduce mean squared error. The first CI simply uses an unbiased estimator's standard error; compared to centering at the unbiased estimator, this CI has higher coverage probability for confidence levels above 91.7%, even if the biased and unbiased estimators have equal mean squared error. The second CI trades some of this "excess" coverage for shorter length. The third CI is centered at a convex combination of the two estimators to further reduce length. Practically, these CIs apply broadly and are simple to compute.
UniAttn: Reducing Inference Costs via Softmax Unification for Post-Training LLMs
2025-02-01
Yizhe Xiong, Wei Huang, Xin Ye, Hui Chen, Zijia Lin, Haoran Lian, Zhenpeng Su, Jungong Han, Guiguang Ding
Post-training is essential for adapting Large Language Models (LLMs) to real-world applications. Deploying post-trained models faces significant challenges due to substantial memory overhead and noticeable inference latency. Existing work has identified significant redundancies in LLMs and proposed efficient architectures, namely intra-layer KV sharing and cross-layer KV sharing. However, intra-layer KV sharing still results in high inference costs, while cross-layer KV sharing leads to significant performance degradation. As a result, both methods remain suboptimal for post-training pre-trained LLMs. In this paper, we identify that the \texttt{Softmax} operation is a primary bottleneck for LLM inference and discover that it is actually highly redundant during post-training. We propose Softmax \textbf{Uni}fication in \textbf{Att}e\textbf{n}tion (\textbf{UniAttn}), a novel post-training method that unifies Softmax activations across transformer blocks to reduce LLM inference costs. Additionally, UniAttn adopts a linear projection to compensate for the errors induced by Softmax unification. Experiments show that UniAttn matches the performance of standard post-training while significantly reducing inference costs, outperforming existing efficient architectures during post-training. Our code will be available at \url{https://github.com/Bostoncake/UniAttn}.
Machine learning (ML) models are often integrated into ML-enabled systems to provide software functionality that would otherwise be impossible. This integration requires the selection of an appropriate ML model export format, for which many options are available. These formats are crucial for ensuring a seamless integration, and choosing a suboptimal one can negatively impact system development. However, little evidence is available to guide practitioners during the export format selection. We therefore evaluated various model export formats regarding their impact on the development of ML-enabled systems from an integration perspective. Based on the results of a preliminary questionnaire survey (n=17), we designed an extensive embedded case study with two ML-enabled systems in three versions with different technologies. We then analyzed the effect of five popular export formats, namely ONNX, Pickle, TensorFlow's SavedModel, PyTorch's TorchScript, and Joblib. In total, we studied 30 units of analysis (2 systems x 3 tech stacks x 5 formats) and collected data via structured field notes. The holistic qualitative analysis of the results indicated that ONNX offered the most efficient integration and portability across most cases. SavedModel and TorchScript were very convenient to use in Python-based systems, but otherwise required workarounds (TorchScript more than SavedModel). SavedModel also allowed the easy incorporation of preprocessing logic into a single file, which made it scalable for complex deep learning use cases. Pickle and Joblib were the most challenging to integrate, even in Python-based systems. Regarding technical support, all model export formats had strong technical documentation and strong community support across platforms such as Stack Overflow and Reddit. Practitioners can use our findings to inform the selection of ML export formats suited to their context.
MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents
2025-02-01
George Fatouros, Kostas Metaxas, John Soldatos, Manos Karathanassis
MarketSenseAI is a novel framework for holistic stock analysis which leverages Large Language Models (LLMs) to process financial news, historical prices, company fundamentals and the macroeconomic environment to support decision making in stock analysis and selection. In this paper, we present the latest advancements on MarketSenseAI, driven by rapid technological expansion in LLMs. Through a novel architecture combining Retrieval-Augmented Generation and LLM agents, the framework processes SEC filings and earnings calls, while enriching macroeconomic analysis through systematic processing of diverse institutional reports. We demonstrate a significant improvement in fundamental analysis accuracy over the previous version. Empirical evaluation on S\&P 100 stocks over two years (2023-2024) shows MarketSenseAI achieving cumulative returns of 125.9% compared to the index return of 73.5%, while maintaining comparable risk profiles. Further validation on S\&P 500 stocks during 2024 demonstrates the framework's scalability, delivering a 33.8% higher Sortino ratio than the market. This work marks a significant advancement in applying LLM technology to financial analysis, offering insights into the robustness of LLM-driven investment strategies.
Doing More with Less -- Implementing Routing Strategies in Large Language Model-Based Systems: An Extended Survey
2025-02-01
Clovis Varangot-Reille, Christophe Bouvard, Antoine Gourru, Mathieu Ciancone, Marion Schaeffer, François Jacquenet
Large Language Models (LLM)-based systems, i.e. interconnected elements that include an LLM as a central component (e.g., conversational agents), are typically monolithic static architectures that rely on a single LLM for all user queries. However, they often require different preprocessing strategies, levels of reasoning, or knowledge. Generalist LLMs (e.g. GPT-4) trained on very large multi-topic corpora can perform well in a variety of tasks. They require significant financial, energy, and hardware resources that may not be justified for basic tasks. This implies potentially investing in unnecessary costs for a given query. To overcome this problem, a routing mechanism routes user queries to the most suitable components, such as smaller LLMs or experts in specific topics. This approach may improve response quality while minimising costs. Routing can be expanded to other components of the conversational agent architecture, such as the selection of optimal embedding strategies. This paper explores key considerations for integrating routing into LLM-based systems, focusing on resource management, cost definition, and strategy selection. Our main contributions include a formalisation of the problem, a novel taxonomy of existing approaches emphasising relevance and resource efficiency, and a comparative analysis of these strategies in relation to industry practices. Finally, we identify critical challenges and directions for future research.
Influence of nanoparticulates and microgrooves on the secondary electron yield and electrical resistance of laser-treated copper surfaces
2025-02-01
P. Krkotić, T. Madarász, C. Serafim, H. Neupert, A. T. Perez-Fontenla, M. Himmerlich, S. Calatroni
Laser surface structuring has proven to be an effective technique for achieving a copper surface with secondary electron yield (SEY) values close to or below unity. However, the attributes that minimize SEY, such as moderately deep grooves and redeposited nanoparticles, may lead to undesirable consequences, including increased radio frequency surface resistance. This investigation systematically examined data about different cleaning procedures designed to eliminate redeposited adsorbed particulates. Various analysis techniques were used iteratively after each consecutive cleaning step, providing insights into the evolving surface characteristics. The collected experimental results identified distinct impacts of microgrooves, groove orientation, and associated particulates on secondary electron yield and surface resistance. Exposing the crests while retaining high particulate coverage in the grooves leads to reduced SEY values and surface resistance, suggesting that the tips of the grooves exert a more significant influence on surface current density than the groove depth. At the same time, nanoparticles in the grooves have a more significant impact on SEY values than the exposed tips at the surface.
Latent Action Learning Requires Supervision in the Presence of Distractors
2025-02-01
Alexander Nikulin, Ilya Zisman, Denis Tarasov, Nikita Lyubaykin, Andrei Polubarov, Igor Kiselev, Vladislav Kurenkov
Recently, latent action learning, pioneered by Latent Action Policies (LAPO), have shown remarkable pre-training efficiency on observation-only data, offering potential for leveraging vast amounts of video available on the web for embodied AI. However, prior work has focused on distractor-free data, where changes between observations are primarily explained by ground-truth actions. Unfortunately, real-world videos contain action-correlated distractors that may hinder latent action learning. Using Distracting Control Suite (DCS) we empirically investigate the effect of distractors on latent action learning and demonstrate that LAPO struggle in such scenario. We propose LAOM, a simple LAPO modification that improves the quality of latent actions by 8x, as measured by linear probing. Importantly, we show that providing supervision with ground-truth actions, as few as 2.5% of the full dataset, during latent action learning improves downstream performance by 4.2x on average. Our findings suggest that integrating supervision during Latent Action Models (LAM) training is critical in the presence of distractors, challenging the conventional pipeline of first learning LAM and only then decoding from latent to ground-truth actions.
3D Scene Question Answering (3D SQA) represents an interdisciplinary task that integrates 3D visual perception and natural language processing, empowering intelligent agents to comprehend and interact with complex 3D environments. Recent advances in large multimodal modelling have driven the creation of diverse datasets and spurred the development of instruction-tuning and zero-shot methods for 3D SQA. However, this rapid progress introduces challenges, particularly in achieving unified analysis and comparison across datasets and baselines. This paper presents the first comprehensive survey of 3D SQA, systematically reviewing datasets, methodologies, and evaluation metrics while highlighting critical challenges and future opportunities in dataset standardization, multimodal fusion, and task design.
The pervasiveness of the dissemination of fake news through social media platforms poses critical risks to the trust of the general public, societal stability, and democratic institutions. This challenge calls for novel methodologies in detection, which can keep pace with the dynamic and multi-modal nature of misinformation. Recent works include powering the detection using large language model advances in multimodal frameworks, methodologies using graphs, and adversarial training in the literature of fake news. Based on the different approaches which can bring success, some key highlights will be underlined: enhanced LLM-improves accuracy through more advanced semantics and cross-modality fusion for robust detections. The review further identifies critical gaps in adaptability to dynamic social media trends, real-time, and cross-platform detection capabilities, as well as the ethical challenges thrown up by the misuse of LLMs. Future directions underline the development of style-agnostic models, cross-lingual detection frameworks, and robust policies with a view to mitigating LLM-driven misinformation. This synthesis thus lays a concrete foundation for those researchers and practitioners committed to reinforcing fake news detection systems with complications that keep on growing in the digital landscape.
Simultaneous Estimation of Manipulation Skill and Hand Grasp Force from Forearm Ultrasound Images
2025-02-01
Keshav Bimbraw, Srikar Nekkanti, Daniel B. Tiller II, Mihir Deshmukh, Berk Calli, Robert D. Howe, Haichong K. Zhang
Accurate estimation of human hand configuration and the forces they exert is critical for effective teleoperation and skill transfer in robotic manipulation. A deeper understanding of human interactions with objects can further enhance teleoperation performance. To address this need, researchers have explored methods to capture and translate human manipulation skills and applied forces to robotic systems. Among these, biosignal-based approaches, particularly those using forearm ultrasound data, have shown significant potential for estimating hand movements and finger forces. In this study, we present a method for simultaneously estimating manipulation skills and applied hand force using forearm ultrasound data. Data collected from seven participants were used to train deep learning models for classifying manipulation skills and estimating grasp force. Our models achieved an average classification accuracy of 94.87 percent plus or minus 10.16 percent for manipulation skills and an average root mean square error (RMSE) of 0.51 plus or minus 0.19 Newtons for force estimation, as evaluated using five-fold cross-validation. These results highlight the effectiveness of forearm ultrasound in advancing human-machine interfacing and robotic teleoperation for complex manipulation tasks. This work enables new and effective possibilities for human-robot skill transfer and tele-manipulation, bridging the gap between human dexterity and robotic control.
Boundary conditions for probability density function transport equations in fluid mechanics
2025-02-01
Luis Valiño, Juan Hierro
The behavior of the probability density function (PDF) transport equation at the limits of the probability space is studied from the point of view of fluid mechanics. Different boundary conditions are considered depending on the nature of the variable considered (velocity, scalar, and position). A study of the implications of entrance and exit conditions is performed, showing that a new term should be added to the PDF transport equation to preserve normalization in some nonstationary processes. In practice, this term is taken into account naturally in particle methods. Finally, the existence of discontinuities at the limits is also investigated.
Progress in Normalizing Flows for 4d Gauge Theories
2025-02-01
Ryan Abbott, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
Normalizing flows have arisen as a tool to accelerate Monte Carlo sampling for lattice field theories. This work reviews recent progress in applying normalizing flows to 4-dimensional nonabelian gauge theories, focusing on two advancements: an architectural improvement referred to as learned active loops, and the application of correlated ensemble methods to QCD with $N_f=2$ dynamical fermions.
Measuring the Mental Health of Content Reviewers, a Systematic Review
2025-02-01
Alexandra Gonzalez, J. Nathan Matias
Artificial intelligence and social computing rely on hundreds of thousands of content reviewers to classify high volumes of harmful and forbidden content. Many workers report long-term, potentially irreversible psychological harm. This work is similar to activities that cause psychological harm to other kinds of helping professionals even after small doses of exposure. Yet researchers struggle to measure the mental health of content reviewers well enough to inform diagnoses, evaluate workplace improvements, hold employers accountable, or advance scientific understanding. This systematic review summarizes psychological measures from other professions and relates them to the experiences of content reviewers. After identifying 1,673 potential papers, we reviewed 143 that validate measures in related occupations. We summarize the uses of psychological measurement for content reviewing, differences between clinical and research measures, and 12 measures that are adaptable to content reviewing. We find serious gaps in measurement validity in regions where content review labor is common. Overall, we argue for reliable measures of content reviewer mental health that match the nature of the work and are culturally-relevant.
A Fast Periodicity Detection Algorithm Sensitive to Arbitrary Waveforms
2025-02-01
Douglas P. Finkbeiner, Thomas A. Prince, Samuel E. Whitebook
A reexamination of period finding algorithms is prompted by new large area astronomical sky surveys that can identify billions of individual sources having a thousand or more observations per source. This large increase in data necessitates fast and efficient period detection algorithms. In this paper, we provide an initial description of an algorithm that is being used for detection of periodic behavior in a sample of 1.5 billion objects using light curves generated from Zwicky Transient Facility (ZTF) data (Bellm et al. 2019; Masci et al. 2018). We call this algorithm "Fast Periodicity Weighting" (FPW), derived using a Gaussian Process (GP) formalism. A major advantage of the FPW algorithm for ZTF analysis is that it is agnostic to the details of the phase-folded waveform. Periodic sources in ZTF show a wide variety of waveforms, some quite complex, including eclipsing objects, sinusoidally varying objects also exhibiting eclipses, objects with cyclotron emission at various phases, and accreting objects with complex waveforms. We describe the FPW algorithm and its application to ZTF, and provide efficient code for both CPU and GPU.
Human-Precision Medicine Interaction: Public Perceptions of Polygenic Risk Score for Genetic Health Prediction
2025-01-31
Yuhao Sun, Albert Tenesa, John Vines
Precision Medicine (PM) transforms the traditional "one-drug-fits-all" paradigm by customising treatments based on individual characteristics, and is an emerging topic for HCI research on digital health. A key element of PM, the Polygenic Risk Score (PRS), uses genetic data to predict an individual's disease risk. Despite its potential, PRS faces barriers to adoption, such as data inclusivity, psychological impact, and public trust. We conducted a mixed-methods study to explore how people perceive PRS, formed of surveys (n=254) and interviews (n=11) with UK-based participants. The interviews were supplemented by interactive storyboards with the ContraVision technique to provoke deeper reflection and discussion. We identified ten key barriers and five themes to PRS adoption and proposed design implications for a responsible PRS framework. To address the complexities of PRS and enhance broader PM practices, we introduce the term Human-Precision Medicine Interaction (HPMI), which integrates, adapts, and extends HCI approaches to better meet these challenges.
Vintix: Action Model via In-Context Reinforcement Learning
2025-01-31
Andrey Polubarov, Nikita Lyubaykin, Alexander Derevyagin, Ilya Zisman, Denis Tarasov, Alexander Nikulin, Vladislav Kurenkov
In-Context Reinforcement Learning (ICRL) represents a promising paradigm for developing generalist agents that learn at inference time through trial-and-error interactions, analogous to how large language models adapt contextually, but with a focus on reward maximization. However, the scalability of ICRL beyond toy tasks and single-domain settings remains an open challenge. In this work, we present the first steps toward scaling ICRL by introducing a fixed, cross-domain model capable of learning behaviors through in-context reinforcement learning. Our results demonstrate that Algorithm Distillation, a framework designed to facilitate ICRL, offers a compelling and competitive alternative to expert distillation to construct versatile action models. These findings highlight the potential of ICRL as a scalable approach for generalist decision-making systems. Code to be released at https://github.com/dunnolab/vintix
A Spectroscopically Calibrated Prescription for Extracting PAH Flux from JWST MIRI Imaging
2025-01-31
Grant P. Donnelly, Thomas S. -Y. Lai, Lee Armus, Tanio Díaz-Santos, Kirsten L. Larson, Loreto Barcos-Muñoz, Marina Bianchin, Thomas Bohn, Torsten Böker, Victorine A. Buiten, Vassilis Charmandaris, Aaron S. Evans, Justin Howell, Hanae Inami, Darshan Kakkad, Laura Lenkić, Sean T. Linden, Cristina M. Lofaro, Matthew A. Malkan, Anne M. Medling, George C. Privon, Claudio Ricci, J. D. T. Smith, Yiqing Song, Sabrina Stierwalt, Paul P. van der Werf, Vivian U
We introduce a prescription for estimating the flux of the 7.7 micron and 11.3 micron\ polycyclic aromatic hydrocarbon (PAH) features from broadband JWST/MIRI images. Probing PAH flux with MIRI imaging data has advantages in field of view, spatial resolution, and sensitivity compared with MIRI spectral maps, but comparisons with spectra are needed to calibrate these flux estimations over a wide variety of environments. For 267 MIRI/MRS spectra from independent regions in the four luminous infrared galaxies (LIRGs) in the Great Observatories All-sky LIRG Survey (GOALS) early release science program, we derive synthetic filter photometry and directly compare estimated PAH fluxes to those measured from detailed spectral fits. We find that for probing PAH 7.7 micron, the best combination of filters is F560W, F770W, and either F1500W or F2100W, and the best for PAH 11.3 micron is F560W, F1000W, F1130W, and F1500W. The prescription with these combinations yields predicted flux densities that typically agree with values from spectral decomposition within ~7% and ~5% for PAH 7.7 and 11.3 micron, respectively.
Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models
2025-01-31
Alina Shutova, Vladimir Malinovskii, Vage Egiazarian, Denis Kuznedelev, Denis Mazur, Nikita Surkov, Ivan Ermakov, Dan Alistarh
Efficient real-world deployments of large language models (LLMs) rely on Key-Value (KV) caching for processing and generating long outputs, reducing the need for repetitive computation. For large contexts, Key-Value caches can take up tens of gigabytes of device memory, as they store vector representations for each token and layer. Recent work has shown that the cached vectors can be compressed through quantization, pruning or merging, but these techniques often compromise quality towards higher compression rates. In this work, we aim to improve Key & Value compression by exploiting two observations: 1) the inherent dependencies between keys and values across different layers, and 2) high-compression mechanisms for internal network states. We propose AQUA-KV, an adaptive quantization for Key-Value caches that relies on compact adapters to exploit existing dependencies between Keys and Values, and aims to "optimally" compress the information that cannot be predicted. AQUA-KV significantly improves compression rates, while maintaining high accuracy on state-of-the-art LLM families. On Llama 3.2 LLMs, we achieve near-lossless inference at 2-2.5 bits per value with under $1\%$ relative error in perplexity and LongBench scores. AQUA-KV is one-shot, simple, and efficient: it can be calibrated on a single GPU within 1-6 hours, even for 70B models.
2025-01-31
Sandi Klavžar, James Tuite, Ullas Chandran
Inspired by a chessboard puzzle of Dudeney, the general position problem in graph theory asks for the largest sets $S$ of vertices in a graph such that no three elements of $S$ lie on a common shortest path. The number of vertices in such a largest set is the general position number of the graph. This paper provides a survey of this rapidly growing problem, which now has an extensive literature. We cover exact results for various graph classes and the behaviour of the general position number under various graph products and operations. We also discuss interesting variations of the general position problem, for example variants corresponding to different graph convexities, as well as dynamic, fractional, colouring and game versions of the problem.
Tools of quantum and statistical field theories have ben successfully ported to turbulence. Here, we review the key results of turbulence field theory. Thermalized spectrally-truncated Euler equation is described by \textit{equilibrium field theory}, in which the equipartitioned Fourier modes generate zero energy flux. In contrast, modelling of hydrodynamic turbulence (HDT), which has small viscosity, requires \textit{nonequilibrium field theory}. In HDT, the viscosity is renormalized using field theory that leads to wavenumber-dependent viscosity and energy spectrum. Field theory calculations also yields nonzero energy flux for HDT. These field theory computations have been generalized to other systems, e.g., passive scalar and magnetohydrodynamics. In this review, we cover these aspects, along with a brief coverage of weak turbulence and intermittency.
Mechanical Properties of the Meninges: Large Language Model Assisted Systematic Review of over 25,000 Studies
2025-01-31
Brandon P. Chelstrom, Maciej P. Polak, Dane Morgan, Corinne R. Henak
Accurate constitutive models and corresponding mechanical property values for the meninges are important for predicting mechanical damage to brain tissue due to traumatic brain injury. The meninges are often oversimplified in current finite element (FE) head models due to their complex anatomy and spatially-variant mechanical behavior. This study performed a systematic review (SR) on the mechanical properties of each individual layer of the meninges to obtain benchmark data for FE modeling and to identify gaps in the current literature. Relevant studies were filtered through three stages: a broad initial search filter, a large language model classifier, and manual verification by a human reviewer. Out of over 25,000 studies initially considered, this review ultimately included 47 studies on the dura mater, 8 on the arachnoid mater, and 7 on the pia mater, representing the largest and most comprehensive SR on the mechanical properties of the meninges. Each layer was found to exhibit nonlinear rate dependence that varies with species, age, location, and orientation. This study revealed that the elastic modulus of pia mater most often used in simplified linear elastic FE models is likely underestimated by an order of magnitude and fails to consider directional dependence. Future studies investigating the mechanical properties of the meninges should focus on a wider range of loading rates as well as age effects for the arachnoid mater and pia mater, as these features are relatively understudied and expected to affect the fidelity of FE predictions.
Modeling submillimeter galaxies in cosmological simulations: Contribution to the cosmic star formation density and predictions for future surveys
2025-01-31
Ankit Kumar, M. Celeste Artale, Antonio D. Montero-Dorta, Lucia Guaita, Kyoung-Soo Lee, Alexandra Pope, Joop Schaye, Matthieu Schaller, Eric Gawiser, Ho Seong Hwang, Woong-Seob Jeong, Jaehyun Lee, Nelson Padilla, Changbom Park, Vandana Ramakrishnan, Akriti Singh, Yujin Yang
Submillimeter galaxies (SMGs) constitute a key population of bright star-forming galaxies at high redshift. These galaxies challenge galaxy formation models, particularly in reproducing their observed number counts and redshift distributions. Furthermore, although SMGs contribute significantly to the cosmic star formation rate density (SFRD), their precise role remains uncertain. Upcoming surveys, such as the Ultra Deep Survey with the TolTEC camera, are expected to offer valuable insights into SMG properties and their broader impact. Robust modeling of SMGs in a cosmologically representative volume is necessary to investigate their nature in preparation for next-generation submillimeter surveys. We implement and test parametric relations derived from radiative transfer calculations across three cosmological simulations: EAGLE, IllustrisTNG, and FLAMINGO. Particular emphasis is placed on the FLAMINGO due to their large volume and robust statistical sampling of SMGs. Based on the model that best reproduces observations, we forecast submillimeter fluxes within the simulations, analyze the properties of SMGs, and evaluate their evolution over cosmic time. Our results show that the FLAMINGO reproduces the observed redshift distribution and source number counts of SMGs without requiring a top-heavy initial mass function. On the other hand, the EAGLE and IllustrisTNG show a deficit of bright SMGs. We find that SMGs with S850 > 1 mJy contribute up to 27% of the SFRD at z=2.6 in the FLAMINGO, consistent with recent observations. Flux density functions reveal a rise in SMG abundance from z = 6 to 2.5, followed by a sharp decline in the number of brighter SMGs from z = 2.5 to 0. Leveraging the SMG population in FLAMINGO, we forecast that the TolTEC UDS will detect 80,000 sources over 0.8 deg^2 at 1.1 mm (at the 4{\sigma} detection limit), capturing about 50% of the cosmic SFRD at z=2.5.
Trust and Trustworthiness from Human-Centered Perspective in HRI -- A Systematic Literature Review
2025-01-31
Debora Firmino de Souza, Sonia Sousa, Kadri Kristjuhan-Ling, Olga Dunajeva, Mare Roosileht, Avar Pentel, Mati Mõttus, Mustafa Can Özdemir, Žanna Gratšjova
The Industry 5.0 transition highlights EU efforts to design intelligent devices that can work alongside humans to enhance human capabilities, and such vision aligns with user preferences and needs to feel safe while collaborating with such systems take priority. This demands a human-centric research vision and requires a societal and educational shift in how we perceive technological advancements. To better understand this perspective, we conducted a systematic literature review focusing on understanding how trust and trustworthiness can be key aspects of supporting this move towards Industry 5.0. This review aims to overview the most common methodologies and measurements and collect insights about barriers and facilitators for fostering trustworthy HRI. After a rigorous quality assessment following the Systematic Reviews and Meta-Analyses guidelines, using rigorous inclusion criteria and screening by at least two reviewers, 34 articles were included in the review. The findings underscores the significance of trust and safety as foundational elements for promoting secure and trustworthy human-machine cooperation. Confirm that almost 30% of the revised articles do not present a definition of trust, which can be problematic as this lack of conceptual clarity can undermine research efforts in addressing this problem from a central perspective. It highlights that the choice of domain and area of application should influence the choice of methods and approaches to fostering trust in HRI, as those choices can significantly affect user preferences and their perceptions and assessment of robot capabilities. Additionally, this lack of conceptual clarity can be a potential barrier to fostering trust in HRI and explains the sometimes contradictory findings or choice of methods and instruments used to investigate trust in robots and other autonomous systems in the literature.
Various constraints on BSM physics from extensive air showers and from ultra-high energy gamma-ray and neutrino searches
2025-01-31
O. Deligny
Various phenomena of physics beyond that of the Standard Model could occur at high scale. Ultra-high energy cosmic rays are the only particles available to explore scales above a few dozens of TeV. Although these explorations are much more limited than those carried out with colliders, they provide a series of constraints in several topics such as tests of Lorentz invariance, dark matter, phase transitions in the early universe or sterile neutrinos. Several of these constraints are reviewed in these proceedings of UHECR2024 based on searches for anomalous characteristics in extensive air showers or searches for ultra-high energy gamma rays and neutrinos.
Calabi-Yau threefolds across quadratic singularities
2025-01-31
Sébastien Picard
These are lecture notes on non-K\"ahler complex threefolds presented at the MATRIX program ``The geometry of moduli spaces in string theory''. We review some basics of Calabi-Yau geometry in Section 1, describe topological features of the conifold transition in Section 2, and survey recent developments on the geometrization of conifold transitions in Section 3.
Systematic Bias in Ionizing Radiation Escape Fraction Measurements from Foreground Large-Scale Structures
2025-01-31
C. Scarlata, W. Hu, M. J. Hayes, S. Taamoli, A. A. Khostovan, C. M. Casey, A. L. Faisst, J. S. Kartaltepe, Y. Lin, M. Salvato, M. Rafelski
We investigate the relationship between the Lyman-alpha (Lya) forest transmission in the intergalactic medium (IGM) and the environmental density of galaxies, focusing on its implications for the measurement of ionizing radiation escape fractions. Using a sample of 268 spectroscopically confirmed background galaxies at 2.7
Beyond checkmate: exploring the creative chokepoints in AI text
2025-01-31
Nafis Irtiza Tripto, Saranya Venkatraman, Mahjabin Nahar, Dongwon Lee
Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) and Artificial Intelligence (AI), unlocking unprecedented capabilities. This rapid advancement has spurred research into various aspects of LLMs, their text generation & reasoning capability, and potential misuse, fueling the necessity for robust detection methods. While numerous prior research has focused on detecting LLM-generated text (AI text) and thus checkmating them, our study investigates a relatively unexplored territory: portraying the nuanced distinctions between human and AI texts across text segments. Whether LLMs struggle with or excel at incorporating linguistic ingenuity across different text segments carries substantial implications for determining their potential as effective creative assistants to humans. Through an analogy with the structure of chess games-comprising opening, middle, and end games-we analyze text segments (introduction, body, and conclusion) to determine where the most significant distinctions between human and AI texts exist. While AI texts can approximate the body segment better due to its increased length, a closer examination reveals a pronounced disparity, highlighting the importance of this segment in AI text detection. Additionally, human texts exhibit higher cross-segment differences compared to AI texts. Overall, our research can shed light on the intricacies of human-AI text distinctions, offering novel insights for text detection and understanding.
Circumstellar and circumbinary discs in multiple stellar systems
2025-01-31
Nicolás Cuello, Antoine Alaguero, Pedro P. Poblete
The interplay between stellar multiplicity and protoplanetary discs represents a cornerstone of modern astrophysics, offering key insights into the processes of planet formation. Protoplanetary discs act as cradles for planetary systems, yet their evolution and capacity to form planets are profoundly affected by gravitational forces within multiple stellar systems. This review synthesises recent advancements in observational and theoretical studies to explore the rich diversity of circumstellar and circumbinary discs within multiple stellar systems. We examine how stellar companions shape disc morphology through truncation, spirals, and misalignment. We also outline how dust dynamics and planetesimal formation are impacted by stellar multiplicity. On top of this, observations at high angular resolution reveal detailed disc structures, while simulations offer key insights into their evolution. Last, we consider the implications of stellar multiplicity for planetary system architectures, emphasising the diversity of planetary outcomes in such environments. Looking ahead, coordinated efforts combining high-resolution observations with advanced numerical models will be critical for unravelling the role of multiple stellar systems in shaping planetary formation and evolution.
A Survey on Class-Agnostic Counting: Advancements from Reference-Based to Open-World Text-Guided Approaches
2025-01-31
Luca Ciampi, Ali Azmoudeh, Elif Ecem Akbaba, Erdi Sarıtaş, Ziya Ata Yazıcı, Hazım Kemal Ekenel, Giuseppe Amato, Fabrizio Falchi
Object counting has recently shifted towards class-agnostic counting (CAC), which addresses the challenge of counting objects across arbitrary categories, tackling a critical need in versatile counting systems. While humans effortlessly identify and count objects from diverse categories without prior knowledge, most counting methods remain restricted to enumerating instances of known classes, requiring extensive labeled datasets for training, and struggling under open-vocabulary settings. Conversely, CAC aims to count objects belonging to classes never seen during training, typically operating in a few-shot setting. In this paper, for the first time, we review advancements in CAC methodologies, categorizing them into three paradigms based on how target object classes can be specified: reference-based, reference-less, and open-world text-guided. Reference-based approaches have set performance benchmarks using exemplar-guided mechanisms. Reference-less methods eliminate exemplar dependency by leveraging inherent image patterns. Finally, open-world text-guided methods utilize vision-language models, enabling object class descriptions through textual prompts, representing a flexible and appealing solution. We analyze state-of-the-art techniques and we report their results on existing gold standard benchmarks, comparing their performance and identifying and discussing their strengths and limitations. Persistent challenges -- such as annotation dependency, scalability, and generalization -- are discussed, alongside future directions. We believe this survey serves as a valuable resource for researchers to understand the progressive developments and contributions over time and the current state-of-the-art of CAC, suggesting insights for future directions and challenges to be addressed.
Testing the Rossby Paradigm: Weakened Magnetic Braking in early K-type Stars
2025-01-31
Travis S. Metcalfe, Pascal Petit, Jennifer L. van Saders, Thomas R. Ayres, Derek Buzasi, Oleg Kochukhov, Keivan G. Stassun, Marc H. Pinsonneault, Ilya V. Ilyin, Klaus G. Strassmeier, Adam J. Finley, Rafael A. Garcia, Yuxi, Lu, Victor See
There is an intricate relationship between the organization of large-scale magnetic fields by a stellar dynamo and the rate of angular momentum loss due to magnetized stellar winds. An essential ingredient for the operation of a large-scale dynamo is the Coriolis force, which imprints organizing flows on the global convective patterns and inhibits the complete cancellation of bipolar magnetic regions. Consequently, it is natural to expect a rotational threshold for large-scale dynamo action and for the efficient angular momentum loss that it mediates through magnetic braking. Here we present new observational constraints on magnetic braking for an evolutionary sequence of six early K-type stars. To determine the wind braking torque for each of our targets, we combine spectropolarimetric constraints on the large-scale magnetic field, Ly-alpha or X-ray constraints on the mass-loss rate, as well as uniform estimates of the stellar rotation period, mass, and radius. As identified previously from similar observations of hotter stars, we find that the wind braking torque decreases abruptly by more than an order of magnitude at a critical value of the stellar Rossby number. Given that all of the stars in our sample exhibit clear activity cycles, we suggest that weakened magnetic braking may coincide with the operation of a subcritical stellar dynamo.
Mixed Feelings: Cross-Domain Sentiment Classification of Patient Feedback
2025-01-31
Egil Rønningstad, Lilja Charlotte Storset, Petter Mæhlum, Lilja Øvrelid, Erik Velldal
Sentiment analysis of patient feedback from the public health domain can aid decision makers in evaluating the provided services. The current paper focuses on free-text comments in patient surveys about general practitioners and psychiatric healthcare, annotated with four sentence-level polarity classes -- positive, negative, mixed and neutral -- while also attempting to alleviate data scarcity by leveraging general-domain sources in the form of reviews. For several different architectures, we compare in-domain and out-of-domain effects, as well as the effects of training joint multi-domain models.
Geometric characterizations of ${\sf PI}$ spaces: an overview of some modern techniques
2025-01-31
Emanuele Caputo
We survey recent results on the study of metric measure spaces satisfying a Poincar\'e inequality. We overview recent characterizations in terms of objects of dimension 1, such as pencil of curves, modulus estimates and obstacle-avoidance principles. Then, we turn our attention to characterizations in terms of objects of codimension 1, such as relative isoperimetric inequalities and separating sets, the last one obtained in collaboration with N. Cavallucci in [arXiv:2401.02762]. We propose a strategy to provide examples using our characterization in the toy-model of the Euclidean case. We also discuss a more geometric relation between separating sets and obstacle-avoidance principles, obtained in [IMRN, Vol. 2025, Issue 1, Jan. 2025, rnae276]. Finally, we recall some open questions in the field.
It's not $σ_8$ : constraining the non-linear matter power spectrum with the Dark Energy Survey Year-5 supernova sample
2025-01-31
Paul Shah, T. M. Davis, M. Vincenzi, P. Armstrong, D. Brout, R. Camilleri, L. Galbany, M. S. S. Gill, D. Huterer, N. Jeffrey, O. Lahav, J. Lee, C. Lidman, A. Möller, M. Sullivan, L. Whiteway, P. Wiseman, S. Allam, M. Aguena, J. Annis, J. Blazek, D. Brooks, A. Carnero Rosell, J. Carretero, C. Conselice, L. N. da Costa, M. E. S. Pereira, S. Desai, H. T. Diehl, P. Doel, S. Everett, I. Ferrero, B. Flaugher, J. Frieman, J. García-Bellido, E. Gaztanaga, G. Giannini, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, S. Lee, J. L. Marshall, J. Mena-Fernández, R. Miquel, A. Palmese, A. Pieres, A. A. Plazas Malagón, A. Porredon, S. Samuroff, E. Sanchez, I. Sevilla-Noarbe, M. Smith, E. Suchyta, M. E. C. Swanson, G. Tarle, D. L. Tucker, N. Weaverdyck
The weak gravitational lensing magnification of Type Ia supernovae (SNe Ia) is sensitive to the matter power spectrum on scales $k>1 h$ Mpc$^{-1}$, making it unwise to interpret SNe Ia lensing in terms of power on linear scales. We compute the probability density function of SNe Ia magnification as a function of standard cosmological parameters, plus an empirical parameter $A_{\rm mod}$ which describes the suppression or enhancement of matter power on non-linear scales compared to a cold dark matter only model. While baryons are expected to enhance power on the scales relevant to SN Ia lensing, other physics such as neutrino masses or non-standard dark matter may suppress power. Using the Dark Energy Survey Year-5 sample, we find $A_{\rm mod} = 0.77^{+0.69}_{-0.40}$ (68\% credible interval around the median). Although the median is consistent with unity there are hints of power suppression, with $A_{\rm mod} < 1.09$ at 68\% credibility.
Rapid follow-up of infant supernovae with the Gran Telescopio de Canarias
2025-01-31
Lluís Galbany, Claudia P. Gutiérrez, Lara Piscarreta, Alaa Alburai, Noor Ali, Dane Cross, Maider González-Bañuelos, Cristina Jiménez-Palau, Maria Kopsacheili, Tomás E. Müller-Bravo, Kim Phan, Ramon Sanfeliu, Maximillian Stritzinger, Chris Ashall, Eddie Baron, Gastón Folatelli, Willem Hoogendam, Saurabh Jha, Thomas de Jaeger, Thomas G. Brink, Alexei V. Filippenko, D. Andrew Howell, Daichi Hiramatsu
The first few hours of a supernova contain significant information about the progenitor system. The most modern wide-field surveys that scan the sky repeatedly every few days can discover all kinds of transients in those early epochs. At such times, some progenitor footprints may be visible, elucidating critical explosion parameters and helping to distinguish between leading explosion models. A dedicated spectroscopic classification programme using the optical spectrograph OSIRIS mounted to the Gran Telescopio de Canarias was set up to try to obtain observations of supernova at those early epochs. With the time awarded, we obtained spectra for 10 SN candidates, which we present here. Half of them were thermonuclear SNe, while the other half were core-collapse SNe. Most (70\%) were observed within the first six days of the estimated explosion, with two being captured within the first 48 hours. We present a characterization of the spectra, together with other public ancillary photometry from ZTF and ATLAS. This programme shows the need for an accompanying rapid-response spectroscopic programme to existing and future deep photometric wide-field surveys located at the right longitude to be able to trigger observations in a few hours after the discovery of the supernova candidate. Both the future La Silla Southern Supernova Survey (LS4) and the Legacy Survey of Space and Time (LSST) both located in Chile will be providing discovery and follow up of most of the transients in the southern hemisphere. This paper demonstrates that with a rapid spectroscopic programme and stringent triggering criteria, obtaining a sample of SN with spectra within a day of the explosion is possible.
Novel Magnetic Materials for Spintronic Device Technology
2025-01-31
A. Sud, A. Kumar, M. Cubukcu
Spintronics, a transformative field of research, leverages the spin of electron to revolutionize electronic devices, offering significant advantages over traditional charge-based systems. This chapter highlights the critical role of novel magnetic materials in advancing spintronic technologies by addressing their fundamental properties, fabrication methods, and applications. A diverse range of materials, including ferromagnetic, antiferromagnetic, non-collinear antiferromagnetic, synthetic antiferromagnetic, ferrimagnetic, and multiferroic systems, is explored for their unique contributions to spintronic devices. Advanced fabrication techniques, such as bulk crystal formation, thin-film deposition, and nano-structuring, are detailed along characterization methods including spin-orbit torque analysis and magnetization dynamics studies. Emerging research on three-dimensional spin textures, domain walls, skyrmions, and magnons highlighted for its potential for novel spintronic applications. Additionally, the chapter reviews applications in memory technologies, spintronic nano-oscillators, and neuromorphic computing. Finally, it concludes by examining future research directions, challenges, and opportunities in spintronics, providing insights into breakthroughs that are shaping the future of this rapidly evolving field.
New perspectives on the d'Alembertian from general relativity. An invitation
2025-01-31
Mathias Braun
This survey has multiple objectives. First, we motivate and review a new distributional notion of the d'Alembertian from mathematical relativity, more precisely, a nonlinear $p$-version thereof, where $p$ is a nonzero number less than one. This operator comes from natural Lagrangian actions introduced relatively recently. Unlike its classical linear yet hyperbolic counterpart, it is nonlinear yet has elliptic characteristics. Second, we describe recent comparison estimates for the $p$-d'Alembertian of Lorentz distance functions (notably a point or a spacelike hypersurface). Their new contribution implied by prior works on optimal transport through spacetime is a control of the timelike cut locus. Third, we illustrate exact representation formulas for these $p$-d'Alembertians employing methods from convex geometry. Fourth, several applications and open problems are presented.
2025-01-31
A. P. Kryukov, A. P. Demichev, V. A. Ilyin
The purpose of this paper is to review the most popular deep learning methods used to analyze astroparticle data obtained with Imaging Atmospheric Cherenkov Telescopes and provide references to the original papers.
Heterotic toroidal orbifolds are a powerful framework for testing our understanding of string vacua. We review recent advances in heterotic model building, from constructing new vacua to proving novel de Sitter no-go theorems. We also illustrate a potential loophole that could pave the way for the realisation of de Sitter vacua within heterotic string theories. This involves the introduction of non-perturbative, stringy, dilaton-dependent corrections to the K\"ahler potential. We discuss how recently found non-BPS heterotic instantons might provide the necessary corrections.
A Systematic Literature Review of Computer Science MOOCs for K-12 education
2025-01-31
L. M. van der Lubbe, S. P van Borkulo, J. T. Jeuring
Computer science (CS) is increasingly becoming part of the curricula of K-12 education in different countries. However, there are few K-12 CS teachers, and tools to offer K-12 CS education are often limited. Massive Open Online Courses (MOOCs) might help to temporarily address these challenges, and enable more schools to offer CS education. The goal of this systematic review is to give an overview of how CS MOOCs have been used in K-12 education. Nineteen papers from 2014 to May 2024 were included, describing thirteen different MOOCs. This review summarizes the research performed with these MOOCs and discusses directions for future research. Our findings show that most CS MOOCs target only part of the CS curriculum. When using a MOOC, a classroom teacher has an important role in supporting and managing students as they work in the MOOC. Research evaluating MOOCs is diverse, both in aims and in methods. In conclusion, MOOCs can play a valuable role in K-12 CS education, although additional teacher training to support students might be required. Moreover, additional learning material is needed to cover the full curriculum, as most MOOCs focus on programming and computational thinking.
Deep Learning Model Inversion Attacks and Defenses: A Comprehensive Survey
2025-01-31
Wencheng Yang, Song Wang, Di Wu, Taotao Cai, Yanming Zhu, Shicheng Wei, Yiying Zhang, Xu Yang, Yan Li
The rapid adoption of deep learning in sensitive domains has brought tremendous benefits. However, this widespread adoption has also given rise to serious vulnerabilities, particularly model inversion (MI) attacks, posing a significant threat to the privacy and integrity of personal data. The increasing prevalence of these attacks in applications such as biometrics, healthcare, and finance has created an urgent need to understand their mechanisms, impacts, and defense methods. This survey aims to fill the gap in the literature by providing a structured and in-depth review of MI attacks and defense strategies. Our contributions include a systematic taxonomy of MI attacks, extensive research on attack techniques and defense mechanisms, and a discussion about the challenges and future research directions in this evolving field. By exploring the technical and ethical implications of MI attacks, this survey aims to offer insights into the impact of AI-powered systems on privacy, security, and trust. In conjunction with this survey, we have developed a comprehensive repository to support research on MI attacks and defenses. The repository includes state-of-the-art research papers, datasets, evaluation metrics, and other resources to meet the needs of both novice and experienced researchers interested in MI attacks and defenses, as well as the broader field of AI security and privacy. The repository will be continuously maintained to ensure its relevance and utility. It is accessible at https://github.com/overgter/Deep-Learning-Model-Inversion-Attacks-and-Defenses.
2025-01-31
Giovanni Luca Marchetti, Vahid Shahverdi, Stefano Mereta, Matthew Trager, Kathlén Kohn
In this expository work, we promote the study of function spaces parameterized by machine learning models through the lens of algebraic geometry. To this end, we focus on algebraic models, such as neural networks with polynomial activations, whose associated function spaces are semi-algebraic varieties. We outline a dictionary between algebro-geometric invariants of these varieties, such as dimension, degree, and singularities, and fundamental aspects of machine learning, such as sample complexity, expressivity, training dynamics, and implicit bias. Along the way, we review the literature and discuss ideas beyond the algebraic domain. This work lays the foundations of a research direction bridging algebraic geometry and deep learning, that we refer to as neuroalgebraic geometry.
The Local Galactic Transient Survey Applied to an Optical Search for Directed Intelligence
2025-01-31
Alex Thomas, Natalie LeBaron, Luca Angeleri, Phillip Morgan, Varun Iyer, Prerana Kottapalli, Enda Mao, Samuel Whitebook, Jasper Webb, Dharv Patel, Rachel Darlinger, Kyle Lam, Kelvin Yip, Michael McDonald, Robby Odum, Cole Slenkovich, Yael Brynjegard-Bialik, Nicole Efstathiu, Joshua Perkins, Ryan Kuo, Audrey O'Malley, Alec Wang, Ben Fogiel, Sam Salters, Marlon Munoz, Natalie Kim, Lee Fowler, Ruiyang Wang, Philip Lubin
We discuss our transient search for directed energy systems in local galaxies, with calculations indicating the ability of modest searches to detect optical Search for Extraterrestrial Intelligence (SETI) sources in the closest galaxies. Our analysis follows Lubin (2016) where a messenger civilization follows a beacon strategy we call "intelligent targeting." We plot the required laser time to achieve an SNR of 10 and find the time for a blind transmission to target all stars in the Milky Way to be achievable for local galactic civilizations. As high cadence and sky coverage is the pathway to enable such a detection, we operate the Local Galactic Transient Survey (LGTS) targeting M31 (the Andromeda Galaxy), the Large Magellanic Cloud (LMC), and the Small Magellanic Cloud (SMC) via Las Cumbres Observatory's (LCO) network of 0.4 m telescopes. We explore the ability of modest searches like the LGTS to detect directed pulses in optical and near-infrared wavelengths from Extraterrestrial Intelligence (ETI) at these distances and conclude a civilization utilizing less powerful laser technology than we can construct in this century is readily detectable with the LGTS's observational capabilities. Data processing of 30,000 LGTS images spanning 5 years is in progress with the TRansient Image Processing Pipeline (TRIPP; Thomas et al. (2025)).
2025-01-31
Arthur Amorim, Max Taylor, Gary T. Leavens, Bill Harrison, Lance Joneckis, Trevor Kann
A compromised system component can issue message sequences that are perfectly legal while also leading the system itself into unsafe states. Such attacks are challenging to characterize, because message interfaces in standard languages define the individual messages possible but cannot express designers' intentions for how they should be used. We present initial results from ongoing work applying refined multiparty session types as a mechanism for expressing and enforcing proper message usage to exclude legal, but unsafe, sequences. We illustrate our approach by using refined multiparty session types to mitigate safety and security issues in the MAVLink protocol commonly used in UAVs.
Evaluating the Efficacy and Safety of Stereotactic Arrhythmia Radioablation in Ventricular Tachycardia: A Comprehensive Systematic Review and Meta-Analysis
2025-01-31
Keyur D. Shah, Chih-Wei Chang, Sibo Tian, Pretesh Patel, Richard Qiu, Justin Roper, Jun Zhou, Zhen Tian, Xiaofeng Yang
Purpose: Stereotactic arrhythmia radioablation (STAR) has emerged as a promising non-invasive treatment for refractory ventricular tachycardia (VT), offering a novel alternative for patients who are poor candidates for catheter ablation. This systematic review and meta-analysis evaluates the safety, efficacy, and technical aspects of STAR across preclinical studies, case reports, case series, and clinical trials. Methods and Materials: A systematic review identified 80 studies published between 2015 and 2024, including 12 preclinical studies, 47 case reports, 15 case series, and 6 clinical trials. Data on patient demographics, treatment parameters, and clinical outcomes were extracted. Meta-analyses were performed for pooled mortality rates, VT burden reduction, and acute toxicities, with subgroup analyses exploring cardiomyopathy type, age, left ventricular ejection fraction (LVEF), and treatment modality. Results: The pooled 6- and 12-month mortality rates were 16% (95% CI: 11-21%) and 32% (95% CI: 26-39%), respectively. VT burden reduction at 6 months was 75% (95% CI: 73-77%), with significant heterogeneity (I^2 = 98.8%). Grade 3+ acute toxicities were observed in 7% (95% CI: 4-11%), with pneumonitis being the most common. Subgroup analyses showed comparable outcomes between LINAC- and CyberKnife-based treatments, with minor differences based on patient characteristics and cardiomyopathy type. Conclusions: STAR demonstrates significant potential in reducing VT burden and improving patient outcomes. While favorable acute safety profiles and efficacy support clinical adoption, variability in treatment protocols underscores the need for standardized practices. Future studies should aim to optimize patient selection, establish robust dosimetric standards, and evaluate long-term safety.
Federated averaging (FedAvg) is a popular algorithm for horizontal federated learning (FL), where samples are gathered across different clients and are not shared with each other or a central server. Extensive convergence analysis of FedAvg exists for the discrete iteration setting, guaranteeing convergence for a range of loss functions and varying levels of data heterogeneity. We extend this analysis to the continuous-time setting where the global weights evolve according to a multivariate stochastic differential equation (SDE), which is the first time FedAvg has been studied from the continuous-time perspective. We use techniques from stochastic processes to establish convergence guarantees under different loss functions, some of which are more general than existing work in the discrete setting. We also provide conditions for which FedAvg updates to the server weights can be approximated as normal random variables. Finally, we use the continuous-time formulation to reveal generalization properties of FedAvg.
Text Data Augmentation for Large Language Models: A Comprehensive Survey of Methods, Challenges, and Opportunities
2025-01-31
Yaping Chai, Haoran Xie, Joe S. Qin
The increasing size and complexity of pre-trained language models have demonstrated superior performance in many applications, but they usually require large training datasets to be adequately trained. Insufficient training sets could unexpectedly make the model overfit and fail to cope with complex tasks. Large language models (LLMs) trained on extensive corpora have prominent text generation capabilities, which improve the quality and quantity of data and play a crucial role in data augmentation. Specifically, distinctive prompt templates are given in personalised tasks to guide LLMs in generating the required content. Recent promising retrieval-based techniques further improve the expressive performance of LLMs in data augmentation by introducing external knowledge to enable them to produce more grounded-truth data. This survey provides an in-depth analysis of data augmentation in LLMs, classifying the techniques into Simple Augmentation, Prompt-based Augmentation, Retrieval-based Augmentation and Hybrid Augmentation. We summarise the post-processing approaches in data augmentation, which contributes significantly to refining the augmented data and enabling the model to filter out unfaithful content. Then, we provide the common tasks and evaluation metrics. Finally, we introduce existing challenges and future opportunities that could bring further improvement to data augmentation.
The increasing prevalence of software vulnerabilities necessitates automated vulnerability repair (AVR) techniques. This Systematization of Knowledge (SoK) provides a comprehensive overview of the AVR landscape, encompassing both synthetic and real-world vulnerabilities. Through a systematic literature review and quantitative benchmarking across diverse datasets, methods, and strategies, we establish a taxonomy of existing AVR methodologies, categorizing them into template-guided, search-based, constraint-based, and learning-driven approaches. We evaluate the strengths and limitations of these approaches, highlighting common challenges and practical implications. Our comprehensive analysis of existing AVR methods reveals a diverse landscape with no single ``best'' approach. Learning-based methods excel in specific scenarios but lack complete program understanding, and both learning and non-learning methods face challenges with complex vulnerabilities. Additionally, we identify emerging trends and propose future research directions to advance the field of AVR. This SoK serves as a valuable resource for researchers and practitioners, offering a structured understanding of the current state-of-the-art and guiding future research and development in this critical domain.
Recent advances in doubly-robust weighted ordinary least squares techniques for dynamic treatment regime estimation
2025-01-31
Adel Ahmadi Nadi, Michael Wallace
A dynamic treatment regime (DTR) is an approach to delivering precision medicine that uses patient characteristics to guide treatment decisions for optimal health outcomes. Numerous methods have been proposed for DTR estimation, including dynamic weighted ordinary least squares (dWOLS), a regression-based approach that affords double robustness to model misspecification within an easy to implement analytical framework. Initially, the dWOLS approach was developed under the assumptions of continuous outcomes and binary treatment decisions. Motivated by clinical research, subsequent theoretical advancements have extended the dWOLS framework to address binary, continuous and multicategory treatments across various outcome types, including binary, continuous, and survival-type. However, certain scenarios remain unexplored. This paper summarizes the last ten years of extension and application of the dWOLS method, providing a comprehensive and detailed review of the original dWOLS method and its extensions, as well as highlighting its diverse practical applications. We also explore studies that have addressed challenges associated with dWOLS implementation, such as model validation, variable selection, and handling measurement errors. Using simulated data, we present numerical illustrations along with step-by-step implementations in the \texttt{R} environment to facilitate a deeper understanding of dWOLS-based DTR estimation methodologies.
Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review
2025-01-31
Yisong Chen, Chuqing Zhao, Yixin Xu, Chuanhao Nie
This paper systematically reviews advancements in deep learning (DL) techniques for financial fraud detection, a critical issue in the financial sector. Using the Kitchenham systematic literature review approach, 57 studies published between 2019 and 2024 were analyzed. The review highlights the effectiveness of various deep learning models such as Convolutional Neural Networks, Long Short-Term Memory, and transformers across domains such as credit card transactions, insurance claims, and financial statement audits. Performance metrics such as precision, recall, F1-score, and AUC-ROC were evaluated. Key themes explored include the impact of data privacy frameworks and advancements in feature engineering and data preprocessing. The study emphasizes challenges such as imbalanced datasets, model interpretability, and ethical considerations, alongside opportunities for automation and privacy-preserving techniques such as blockchain integration and Principal Component Analysis. By examining trends over the past five years, this review identifies critical gaps and promising directions for advancing DL applications in financial fraud detection, offering actionable insights for researchers and practitioners.
Physics-Informed Neural Network based Damage Identification for Truss Railroad Bridges
2025-01-31
Althaf Shajihan, Kirill Mechitov, Girish Chowdhary, Billie F. Spencer Jr
Railroad bridges are a crucial component of the U.S. freight rail system, which moves over 40 percent of the nation's freight and plays a critical role in the economy. However, aging bridge infrastructure and increasing train traffic pose significant safety hazards and risk service disruptions. The U.S. rail network includes over 100,000 railroad bridges, averaging one every 1.4 miles of track, with steel bridges comprising over 50% of the network's total bridge length. Early identification and assessment of damage in these bridges remain challenging tasks. This study proposes a physics-informed neural network (PINN) based approach for damage identification in steel truss railroad bridges. The proposed approach employs an unsupervised learning approach, eliminating the need for large datasets typically required by supervised methods. The approach utilizes train wheel load data and bridge response during train crossing events as inputs for damage identification. The PINN model explicitly incorporates the governing differential equations of the linear time-varying (LTV) bridge-train system. Herein, this model employs a recurrent neural network (RNN) based architecture incorporating a custom Runge-Kutta (RK) integrator cell, designed for gradient-based learning. The proposed approach updates the bridge finite element model while also quantifying damage severity and localizing the affected structural members. A case study on the Calumet Bridge in Chicago, Illinois, with simulated damage scenarios, is used to demonstrate the model's effectiveness in identifying damage while maintaining low false-positive rates. Furthermore, the damage identification pipeline is designed to seamlessly integrate prior knowledge from inspections and drone surveys, also enabling context-aware updating and assessment of bridge's condition.
Physics-informed Split Koopman Operators for Data-efficient Soft Robotic Simulation
2025-01-31
Eron Ristich, Lei Zhang, Yi Ren, Jiefeng Sun
Koopman operator theory provides a powerful data-driven technique for modeling nonlinear dynamical systems in a linear framework, in comparison to computationally expensive and highly nonlinear physics-based simulations. However, Koopman operator-based models for soft robots are very high dimensional and require considerable amounts of data to properly resolve. Inspired by physics-informed techniques from machine learning, we present a novel physics-informed Koopman operator identification method that improves simulation accuracy for small dataset sizes. Through Strang splitting, the method takes advantage of both continuous and discrete Koopman operator approximation to obtain information both from trajectory and phase space data. The method is validated on a tendon-driven soft robotic arm, showing orders of magnitude improvement over standard methods in terms of the shape error. We envision this method can significantly reduce the data requirement of Koopman operators for systems with partially known physical models, and thus reduce the cost of obtaining data.
Magnetic disk winds in protoplanetary disks: Description of the model and impact on global disk evolution
2025-01-31
Kundan Kadam, Eduard Vorobyov, Peter Woitke, Shantanu Basu, Sierk van Terwisga
Canonically, a protoplanetary disk is thought to undergo (gravito-)viscous evolution, wherein the angular momentum of the accreting material is transported outwards. However, several lines of reasoning suggest that the turbulent viscosity in a typical protoplanetary disk is insufficient to drive the observed accretion rates. An emerging paradigm suggests that radially extended magnetic disk winds may play a crucial role in the disk evolution. We propose a global model of magnetic wind-driven accretion for evolution of protoplanetary disks, based on the insights gained from local shearing box simulations. Here we develop this model and constrain its parameters with the help of theoretical expectations and comparison with observations. The magnetic wind is characterized with the associated loss of angular momentum and mass, which depend on the local disk conditions and stellar properties. We incorporate the disk winds self-consistently in the code FEOSAD and study formation and long-term evolution of protoplanetary disks. We include disk self-gravity and an adaptive turbulent alpha, while the co-evolution of dust is also considered. Synthetic observations are obtained via radiation thermo-chemical code ProDiMo. The models with inclusion of disk winds satisfy general expectations from both theory and observations. The disk wind parameters can be guided by observational constraints and the synthetic observations resulting from such a model compare favorably with the selected ALMA survey data of Class II disks. The proposed magnetic disk wind model is a significant step forward in the direction of representing a more complete disk evolution, wherein the disk experiences concurrent torques from viscous, gravitational, and magnetic wind processes.
A Comprehensive Review: Applicability of Deep Neural Networks in Business Decision Making and Market Prediction Investment
2025-01-31
Viet Trinh
Big data, both in its structured and unstructured formats, have brought in unforeseen challenges in economics and business. How to organize, classify, and then analyze such data to obtain meaningful insights are the ever-going research topics for business leaders and academic researchers. This paper studies recent applications of deep neural networks in decision making in economical business and investment; especially in risk management, portfolio optimization, and algorithmic trading. Set aside limitation in data privacy and cross-market analysis, the article establishes that deep neural networks have performed remarkably in financial classification and prediction. Moreover, the study suggests that by compositing multiple neural networks, spanning different data type modalities, a more robust, efficient, and scalable financial prediction framework can be constructed.
Coherence based on positive operator-valued measures for standard and concatenated quantum state discrimination with inconclusive results
2025-01-31
L. F. Melo, O. Jiménez, L. Neves
The optimal measurement that discriminates nonorthogonal quantum states with fixed rates of inconclusive outcomes (FRIO) can be decomposed into an assisted separation of the inputs, yielding conclusive and inconclusive outputs, followed by a minimum-error (ME) measurement for the conclusive ones (standard FRIO) or both ones (concatenated FRIO). The implementation of these measurements is underpinned by quantum resources, and here we investigate coherence based on positive operator-valued measures (POVMs) as a resource for both strategies in discriminating equally probable symmetric states of arbitrary dimension. First, we show that the POVM coherence in the assisted separation stage decomposes into the coherence of the ancillary state and the quantum discord between the system and the ancilla, evidencing coherence as a more elementary resource than quantum correlations. Next, it is demonstrated that the POVM coherence for standard and concatenated FRIO decomposes into the POVM coherence measures for state separation and ME measurement, weighted by the probabilities of occurrence of each event. Due to the ME discrimination of inconclusive states, the coherence required for the concatenated scheme is shown to be greater than that of the standard one. We discuss other general aspects of our results by characterizing the POVM coherence in the discrimination of qutrit states, with respect to the distinguishability of the inputs and the inconclusive rate. Finally, by exploiting POVM-based coherence as a quantifier of cryptographic randomness gain, we discuss the standard and concatenated FRIO strategies from the perspective of generating random bits that are secret to an eavesdropper.
New constraints on the evolution of the MHI-M* scaling relation combining CHILES and MIGHTEE-HI data
2025-01-31
Alessandro Bianchetti, Francesco Sinigaglia, Giulia Rodighiero, Ed Elson, Mattia Vaccari, D. J. Pisano, Nicholas Luber, Isabella Prandoni, Kelley Hess, Maarten Baes, Elizabeth A. K. Adams, Filippo M. Maccagni, Alvio Renzini, Laura Bisigello, Min Yun, Emmanuel Momjian, Hansung B. Gim, Hengxin Pan, Thomas A. Oosterloo, Richard Dodson, Danielle Lucero, Bradley S. Frank, Olivier Ilbert, Luke J. M. Davies, Ali A. Khostovan, Mara Salvato
The improved sensitivity of interferometric facilities to the 21-cm line of atomic hydrogen (HI) enables studies of its properties in galaxies beyond the local Universe. In this work, we perform a 21 cm line spectral stacking analysis combining the MIGHTEE and CHILES surveys in the COSMOS field to derive a robust HI-stellar mass relation at z=0.36. In particular, by stacking thousands of star-forming galaxies subdivided into stellar mass bins, we optimize the signal-to-noise ratio of targets and derive mean HI masses in the different stellar mass intervals for the investigated galaxy population. We combine spectra from the two surveys, estimate HI masses, and derive the scaling relation log10(MHI) = (0.32 +- 0.04)log10(M*) + (6.65 +- 0.36). Our findings indicate that galaxies at z=0.36 are HI richer than those at z=0, but HI poorer than those at z=1, with a slope consistent across redshift, suggesting that stellar mass does not significantly affect HI exchange mechanisms. We also observe a slower growth rate HI relative to the molecular gas, supporting the idea that the accretion of cold gas is slower than the rate of consumption of molecular gas to form stars. This study contributes to understanding the role of atomic gas in galaxy evolution and sets the stage for future development of the field in the upcoming SKA era.
Using Neural Networks to Automate the Identification of Brightest Cluster Galaxies in Large Surveys
2025-01-31
Patrick Janulewicz, Tracy M. A. Webb, Laurence Perreault-Levasseur
Brightest cluster galaxies (BCGs) lie deep within the largest gravitationally bound structures in existence. Though some cluster finding techniques identify the position of the BCG and use it as the cluster center, other techniques may not automatically include these coordinates. This can make studying BCGs in such surveys difficult, forcing researchers to either adopt oversimplified algorithms or perform cumbersome visual identification. For large surveys, there is a need for a fast and reliable way of obtaining BCG coordinates. We propose machine learning to accomplish this task and train a neural network to identify positions of candidate BCGs given no more information than multiband photometric images. We use both mock observations from The Three Hundred project and real ones from the Sloan Digital Sky Survey (SDSS), and we quantify the performance. Training on simulations yields a squared correlation coefficient, R$^2$, between predictions and ground truth of R$^2 \approx 0.94$ when testing on simulations, which decreases to R$^2 \approx 0.60$ when testing on real data due to discrepancies between datasets. Limiting the application of this method to real clusters more representative of the training data, such those with a BCG r-band magnitude $r_{\text{BCG}} \leq 16.5$, yields R$^2 \approx 0.99$. The method performs well up to a redshift of at least $z\approx 0.6$. We find this technique to be a promising method to automate and accelerate the identification of BCGs in large datasets.
Hypervelocity Stars Trace a Supermassive Black Hole in the Large Magellanic Cloud
2025-01-31
Jiwon Jesse Han, Kareem El-Badry, Scott Lucchini, Lars Hernquist, Warren Brown, Nico Garavito-Camargo, Charlie Conroy, Re'em Sari
Hypervelocity stars (HVSs) are produced by the Hills mechanism when a stellar binary is disrupted by a supermassive black hole (SMBH). The HVS Survey detected 21 unbound B-type main-sequence stars in the Milky Way's outer halo that are consistent with ejection via the Hills mechanism. We revisit the trajectories of these stars in light of proper motions from {\it Gaia} DR3 and modern constraints on the Milky Way -- Large Magellanic Cloud (LMC) orbit. We find that half of the unbound HVSs discovered by the HVS Survey trace back not the Galactic Center, but to the LMC. Motivated by this finding, we construct a forward-model for HVSs ejected from an SMBH in the LMC and observed through the selection function of the HVS Survey. The predicted spatial and kinematic distributions of simulated HVSs are remarkably similar to the observed distributions. In particular, we reproduce the conspicuous angular clustering of HVSs around the constellation Leo. This clustering occurs because HVSs from the LMC are boosted by $\sim300\,{\rm km\,s^{-1}}$ by the orbital motion of the LMC, and stars launched parallel to this motion are preferentially selected as HVS candidates. We find that the birth rate and clustering of LMC HVSs cannot be explained by supernova runaways or dynamical ejection scenarios not involving a SMBH. From the ejection velocities and relative number of Magellanic vs. Galactic HVSs, we constrain the mass of the LMC SMBH to be $10^{5.8^{+0.2}_{-0.4}} M_{\odot}$ ($\simeq 6\times10^5 M_{\odot}$).
Pulsar timing and polarimetry: results and perspectives
2025-01-31
Konstantin A. Postnov, Nataliya K. Porayko, Maxim S. Pshirkov
Pulsar timing, i.e. the analysis of the arrival times of pulses from a pulsar, is a powerful tool in modern astrophysics. It allows us to measure the time delays of an electromagnetic signal caused by a number of physical processes as the signal propagates from the source to the observer. Joint analysis of an ensemble of pulsars (Pulsar Timing Arrays, PTAs) can be used to address a variety of astrophysical challenges, including the problem of direct detection of space-time metric perturbations, in particular those induced by gravitational waves. Here we present a comprehensive review of the current state of research in the field of pulsar timing, with particular emphasis on recent advancements in the detection of stochastic background of nHz gravitational waves, reported by a number of international collaborations such as NANOGrav (North American Nanohertz Observatory for Gravitational Waves), European Pulsar Timing Array (EPTA), Chinese Pulsar Timing Array (CPTA) and Indian Pulsar Timing Array (InPTA), which are joining their efforts within the International PTA (IPTA). Additionally, this paper reviews contemporary constraints on scalar ultralight matter (pseudoscalar bosons), obtained from timing and polarimetry data of pulsars. The prospects for applying these tools to other problems in fundamental physics and cosmology are explored.
Metastable Hierarchy in Abstract Low-Temperature Lattice Models
2025-01-31
Seonwoo Kim
In this article, we review the metastable hierarchy in low-temperature lattice models. In the first part, we state that for any abstract lattice system governed by a Hamiltonian potential and evolving according to a Metropolis-type dynamics, there exists a hierarchical decomposition of the collection of stable plateaux in the system into multiple $\mathfrak{m}$ levels, such that at each level there exist tunneling metastable transitions between the stable plateaux, which can be characterized by convergence to a simple Markov chain as the inverse temperature $\beta$ tends to infinity. In the second part, we collect several examples that realize this hierarchical structure of metastability. In order to fix the ideas, we select the Ising model as our lattice system and discuss its metastable behavior under four different types of dynamics, namely the Glauber dynamics with positive/zero external fields and the Kawasaki dynamics with few/many particles. This review article is submitted to the proceedings of the event PSPDE XII, held at the University of Trieste from September 9-13, 2024.
Can AI Solve the Peer Review Crisis? A Large Scale Experiment on LLM's Performance and Biases in Evaluating Economics Papers
2025-01-31
Pat Pataranutaporn, Nattavudh Powdthavee, Pattie Maes
We investigate whether artificial intelligence can address the peer review crisis in economics by analyzing 27,090 evaluations of 9,030 unique submissions using a large language model (LLM). The experiment systematically varies author characteristics (e.g., affiliation, reputation, gender) and publication quality (e.g., top-tier, mid-tier, low-tier, AI generated papers). The results indicate that LLMs effectively distinguish paper quality but exhibit biases favoring prominent institutions, male authors, and renowned economists. Additionally, LLMs struggle to differentiate high-quality AI-generated papers from genuine top-tier submissions. While LLMs offer efficiency gains, their susceptibility to bias necessitates cautious integration and hybrid peer review models to balance equity and accuracy.
Decoding User Concerns in AI Health Chatbots: An Exploration of Security and Privacy in App Reviews
2025-01-31
Muhammad Hassan, Abdullah Ghani, Muhammad Fareed Zaffar, Masooda Bashir
AI powered health chatbot applications are increasingly utilized for personalized healthcare services, yet they pose significant challenges related to user data security and privacy. This study evaluates the effectiveness of automated methods, specifically BART and Gemini GenAI, in identifying security privacy related (SPR) concerns within these applications' user reviews, benchmarking their performance against manual qualitative analysis. Our results indicate that while Gemini's performance in SPR classification is comparable to manual labeling, both automated methods have limitations, including the misclassification of unrelated issues. Qualitative analysis revealed critical user concerns, such as data collection practices, data misuse, and insufficient transparency and consent mechanisms. This research enhances the understanding of the relationship between user trust, privacy, and emerging mobile AI health chatbot technologies, offering actionable insights for improving security and privacy practices in AI driven health chatbots. Although exploratory, our findings highlight the necessity for rigorous audits and transparent communication strategies, providing valuable guidance for app developers and vendors in addressing user security and privacy concerns.
Boosting galaxy clustering analyses with non-perturbative modelling of redshift-space distortions
2025-01-30
Alexander Eggemeier, Nanoom Lee, Román Scoccimarro, Benjamin Camacho-Quevedo, Andrea Pezzotta, Martin Crocce, Ariel G. Sánchez
Redshift-space distortions (RSD), caused by the peculiar velocities of galaxies, are a key modelling challenge in galaxy clustering analyses, limiting the scales from which cosmological information can be reliably extracted. Unlike dynamical or galaxy bias effects, RSD imprint features that are sensitive to non-linearities across all scales. Yet, no distinction between these effects is made by the state-of-the-art analytical approach - the effective field theory (EFT) - which applies the same perturbative expansion to each of them. This paper explores an alternative approach, where the non-perturbative nature of RSD is partially preserved, and compares its effectiveness against the EFT in analysing power spectrum and bispectrum multipoles from synthetic samples of luminous red galaxies, using the projected sensitivity of a Stage-IV galaxy survey. Our results demonstrate that this distinct treatment of RSD improves the robustness of model predictions for both statistics, extending the validity range of the EFT from approximately $0.2\,h\,\mathrm{Mpc}^{-1}$ to $0.35\,h\,\mathrm{Mpc}^{-1}$ for the one-loop power spectrum and from $0.1\,h\,\mathrm{Mpc}^{-1}$ to $0.14\,h\,\mathrm{Mpc}^{-1}$ for the tree-level bispectrum. This leads to a significant enhancement in the precision of cosmological parameter constraints, with uncertainties on the Hubble rate, matter density, and scalar amplitude of fluctuations reduced by $20$-$40\,\%$ for the power spectrum multipoles alone compared to the EFT, and by $25$-$50\,\%$ for joint analyses with the bispectrum. The RSD treatment proposed here may thus play a crucial role in maximising the scientific return of current and future galaxy surveys. To support this advancement, all models for the power spectrum and bispectrum used in this work are made available through an extended version of the Python package COMET.
Foundational Models for 3D Point Clouds: A Survey and Outlook
2025-01-30
Vishal Thengane, Xiatian Zhu, Salim Bouzerdoum, Son Lam Phung, Yunpeng Li
The 3D point cloud representation plays a crucial role in preserving the geometric fidelity of the physical world, enabling more accurate complex 3D environments. While humans naturally comprehend the intricate relationships between objects and variations through a multisensory system, artificial intelligence (AI) systems have yet to fully replicate this capacity. To bridge this gap, it becomes essential to incorporate multiple modalities. Models that can seamlessly integrate and reason across these modalities are known as foundation models (FMs). The development of FMs for 2D modalities, such as images and text, has seen significant progress, driven by the abundant availability of large-scale datasets. However, the 3D domain has lagged due to the scarcity of labelled data and high computational overheads. In response, recent research has begun to explore the potential of applying FMs to 3D tasks, overcoming these challenges by leveraging existing 2D knowledge. Additionally, language, with its capacity for abstract reasoning and description of the environment, offers a promising avenue for enhancing 3D understanding through large pre-trained language models (LLMs). Despite the rapid development and adoption of FMs for 3D vision tasks in recent years, there remains a gap in comprehensive and in-depth literature reviews. This article aims to address this gap by presenting a comprehensive overview of the state-of-the-art methods that utilize FMs for 3D visual understanding. We start by reviewing various strategies employed in the building of various 3D FMs. Then we categorize and summarize use of different FMs for tasks such as perception tasks. Finally, the article offers insights into future directions for research and development in this field. To help reader, we have curated list of relevant papers on the topic: https://github.com/vgthengane/Awesome-FMs-in-3D.
Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Models
2025-01-30
Hao Dong, Moru Liu, Kaiyang Zhou, Eleni Chatzi, Juho Kannala, Cyrill Stachniss, Olga Fink
In real-world scenarios, achieving domain adaptation and generalization poses significant challenges, as models must adapt to or generalize across unknown target distributions. Extending these capabilities to unseen multimodal distributions, i.e., multimodal domain adaptation and generalization, is even more challenging due to the distinct characteristics of different modalities. Significant progress has been made over the years, with applications ranging from action recognition to semantic segmentation. Besides, the recent advent of large-scale pre-trained multimodal foundation models, such as CLIP, has inspired works leveraging these models to enhance adaptation and generalization performances or adapting them to downstream tasks. This survey provides the first comprehensive review of recent advances from traditional approaches to foundation models, covering: (1) Multimodal domain adaptation; (2) Multimodal test-time adaptation; (3) Multimodal domain generalization; (4) Domain adaptation and generalization with the help of multimodal foundation models; and (5) Adaptation of multimodal foundation models. For each topic, we formally define the problem and thoroughly review existing methods. Additionally, we analyze relevant datasets and applications, highlighting open challenges and potential future research directions. We maintain an active repository that contains up-to-date literature at https://github.com/donghao51/Awesome-Multimodal-Adaptation.
Nonlinear SPDEs and Maximal Regularity: An Extended Survey
2025-01-30
Antonio Agresti, Mark Veraar
In this survey, we provide an in-depth exposition of our recent results on the well-posedness theory for stochastic evolution equations, employing maximal regularity techniques. The core of our approach is an abstract notion of critical spaces, which, when applied to nonlinear SPDEs, coincides with the concept of scaling invariant spaces. This framework leads to several sharp blow-up criteria and enables one to obtain instantaneous regularization results. Additionally, we refine and unify our previous results, while also presenting several new contributions. In the second part of the survey, we apply the abstract results to several concrete SPDEs. In particular, we give applications to stochastic perturbations of quasi-geostrophic equations, Navier-Stokes equations, and reaction-diffusion systems (including Allen--Cahn, Cahn--Hilliard and Lotka--Volterra models). Moreover, for the Navier--Stokes equations, we establish new Serrin-type blow-up criteria. While some applications are addressed using $L^2$-theory, many require a more general $L^p(L^q)$-framework. In the final section, we outline several open problems, covering both abstract aspects of stochastic evolution equations, and concrete questions in the study of linear and nonlinear SPDEs.
This paper is a review of the works devoted to understanding and reinterpretation of the theory of quantum integrable models solvable by Bethe ansatz in terms of the theory of purely classical soliton equations. Remarkably, studying polynomial solutions of the latter by methods of the classical soliton theory, one is able to develop a method to solve the spectral problem for the former which is alternative to the Bethe ansatz procedure. Our main examples are the generalized inhomogeneous spins chains with twisted boundary conditions from the quantum side and the modified Kadomtsev-Petviashvili hierarchy of nonlinear differential-difference equations from the classical side. In this paper, we restrict ourselves by quantum spin chains with rational $GL(n)$-invariant $R$-matrices (of the XXX type). Also, the connection of quantum spin chains with classical soliton equations implies a close interrelation between the spectral problem for spin chains and integrable many-body systems of classical mechanics such as Calogero-Moser and Ruijsenaars-Scheider models, which is known as the quantum-classical duality. Revisiting this topic, we suggest a simpler and more instructive proof of this kind of duality.
CryptoDNA: A Machine Learning Paradigm for DDoS Detection in Healthcare IoT, Inspired by crypto jacking prevention Models
2025-01-30
Zag ElSayed, Ahmed Abdelgawad, Nelly Elsayed
The rapid integration of the Internet of Things (IoT) and Internet of Medical (IoM) devices in the healthcare industry has markedly improved patient care and hospital operations but has concurrently brought substantial risks. Distributed Denial-of-Service (DDoS) attacks present significant dangers, jeopardizing operational stability and patient safety. This study introduces CryptoDNA, an innovative machine learning detection framework influenced by cryptojacking detection methods, designed to identify and alleviate DDoS attacks in healthcare IoT settings. The proposed approach relies on behavioral analytics, including atypical resource usage and network activity patterns. Key features derived from cryptojacking-inspired methodologies include entropy-based analysis of traffic, time-series monitoring of device performance, and dynamic anomaly detection. A lightweight architecture ensures inter-compatibility with resource-constrained IoT devices while maintaining high detection accuracy. The proposed architecture and model were tested in real-world and synthetic datasets to demonstrate the model's superior performance, achieving over 96% accuracy with minimal computational overhead. Comparative analysis reveals its resilience against emerging attack vectors and scalability across diverse device ecosystems. By bridging principles from cryptojacking and DDoS detection, CryptoDNA offers a robust, innovative solution to fortify the healthcare IoT landscape against evolving cyber threats and highlights the potential of interdisciplinary approaches in adaptive cybersecurity defense mechanisms for critical healthcare infrastructures.
The Southern Photometrical Local Universe Survey (S-PLUS): searching for metal-poor dwarf galaxies
2025-01-30
M. Grossi, D. R. Gonçalves, A. C. Krabbe, L. A. Gutiérrez Soto, E. Telles, L. S. Ribeiro, T. Signorini Gonçalves, A. R. Lopes, A. V. Smith Castelli, M. E. De Rossi, C. Lima-Dias, G. Limberg, C. E. Ferreira Lopes, J. A. Hernandez-Jimenez, P. K. Humire, A. L. Chies Santos, L. Lomelí-Nuñez, S. Torres-Flores, F. R. Herpich, G. B. Oliveira Schwarz, A. Kanaan, C. Mendes de Oliveira, T. Ribeiro, W. Schoenell
The metal content of a galaxy's interstellar medium reflects the interplay between different evolutionary processes such as feedback from massive stars and the accretion of gas from the intergalactic medium. Despite the expected abundance of low-luminosity galaxies, the low-mass and low-metallicity regime remains relatively understudied. Since the properties of their interstellar medium resemble those of early galaxies, identifying such objects in the Local Universe is crucial to understand the early stages of galaxy evolution. We used the DR3 catalog of the Southern Photometric Local Universe Survey (S-PLUS) to select low-metallicity dwarf galaxy candidates based on color selection criteria typical of metal-poor, star-forming, low-mass systems. The final sample contains approximately 50 candidates. Spectral energy distribution fitting of the 12 S-PLUS bands reveals that $\sim$ 90\% of the candidates are best fit by models with very low stellar metallicities. We obtained long-slit observations with the Gemini Multi-Object Spectrograph to follow-up a pilot sample and confirm whether these galaxies have low metallicities. We find oxygen abundances in the range $7.35<$ 12 + log(O/H) $< 7.93$ (5\% to 17\% of the solar value), confirming their metal-poor nature. Most targets are outliers in the mass-metallicity relation, i.e. they display a low metal content relative to their observed stellar masses. In some cases, perturbed optical morphologies might give evidence of dwarf-dwarf interactions or mergers. These results suggest that the low oxygen abundances may be associated with an external event causing the accretion of metal-poor gas, which dilutes the oxygen abundance in these systems.
Nonequilibrium friction and free energy estimates for kinetic coarse-graining -- Driven particles in responsive media
2025-01-30
Sebastian Milster, Joachim Dzubiella, Gerhard Stock, Steffen Wolf
Predicting the molecular friction and energy landscapes under nonequilibrium conditions is key to coarse-graining the dynamics of selective solute transport through complex, fluctuating and responsive media, e.g., polymeric materials such as hydrogels, cellular membranes or ion channels. The analysis of equilibrium ensembles already allows such a coarse-graining for very mild nonequilibrium conditions. Yet in the presence of stronger external driving and/or inhomogeneous setups, the transport process is governed apart from a potential of mean force also by a nontrivial position- and velocity-dependent friction. It is therefore important to find suitable and efficient methods to estimate the mean force and the friction landscape, which then can be used in a low-dimensional, coarse-grained Langevin framework to predict the system's transport properties and timescales. In this work, we evaluate different coarse-graining approaches based on constant-velocity constraint simulations for generating such estimates using two model systems, which are a 1D responsive barrier as a minimalistic model and a single tracer driven through a 3D bead-spring polymer membrane as a more sophisticated problem. Finally, we demonstrate that the estimates from 3D constant-velocity simulations yield the correct velocity-dependent friction, which can be directly utilized for coarse-grained (1D) Langevin simulations with constant external driving forces.
Massive Online Course on Entrepreneurship. Case Study
2025-01-30
Manuela Petrescu, Tudor Dan Mihoc
Entrepreneurship is a key component of society, and universities and major political structures have tried to support its development in recent years. The present study aims to check the perception of students (based on gender) about entrepreneurial intentions after participating in a course that had a large number of undergraduate students. There were 970 students enrolled from different faculties with various specializations. We conducted a gender-based survey on the unconventional entrepreneurial fundamentals course, where each course was delivered by a different speaker. We also compared the responses provided by computer science students with the overall responses to find differences in their perceptions related to the feasibility of teaching entrepreneurship online, determining the entrepreneurial intention of the students taking this course, and analyzing the perceptions related to the business environment and the ease of starting a business. We found that students, regardless of gender or field of study, prefer interactive online presentations based on the manner in which lectures on this subject were conducted.
Three fast-rotating Jovian Trojans identified by TESS set new population density limits
2025-01-30
Csaba Kiss, Nóra Takács, Csilla E. Kalup, Róbert Szakáts, László Molnár, Emese Plachy, Krisztián Sárneczky, Róbert Szabó, Gyula M. Szabó, Attila Bódi, András Pál
Here we report on the identification of the three fastest rotating Jovian Trojans with reliable population assignment, using light curve data from the Transiting Exoplanet Satellite Survey mission, also confirmed by Zwicky Transient Facility data. For two of our targets the rotation periods are moderately below the previously accepted ~5 h Jovian Trojan breakup limit (4.26 and 4.75 h), however, the rotation period of (13383) was found to be P = 2.926 h, leading to a density estimate of $\rho$ $\approx$ 1.6 $g cm^{-3}$, higher than the generally accepted $\leq$ 1 $g cm^{-3}$ density limit of Jovian Trojans. If associated with lower densities, this rotation rate requires considerable cohesion in the order of a few kPa. The relatively high albedo (pV $\approx$ 0.11) and fast rotation suggest that (13383) may have undergone an energetic collision that spun up the body and exposed bright material to the surface.
This article proposes a novel design for the Pinching Antenna SyStems (PASS) and advocates simple yet efficient wireless communications over the ``last meter''. First, the potential benefits of PASS are discussed in the paper by reviewing an existing prototype. Then, the fundamentals of PASS are introduced, including their physical principles, signal models, and communication designs. In contrast to existing multi-antenna systems, PASS brings a novel concept, termed \emph{Pinching Beamforming}, which is achieved by dynamically adjusting the positions of pinching antennas (PAs). Based on this concept, a couple of practical transmission architectures are proposed for realizing the full potentia of PASS, namely non-multiplexing and multiplexing architectures. More particularly, 1) the non-multiplexing architecture is featured by simple baseband signal processing and relies on the pinching beamforming only; while 2) the multiplexing architecture provides enhanced signal processing capabilities with joint baseband and pinching beamforming, which can be further divided into sub-connected, fully-connected, and phase-shifter-based fully-connected schemes. Furthermore, several emerging scenarios are put forward for integrating PASS into future wireless networks. As a further advance, by demonstrating a few numerical case studies, the significant performance gain of PASS is revealed compared to conventional multi-antenna systems. Finally, several research opportunities and open problems of PASS are highlighted.
A tutorial on conducting sample size and power calculations for detecting treatment effect heterogeneity in cluster randomized trials
2025-01-30
Mary Ryan Baumann, Monica Taljaard, Patrick J. Heagerty, Michael O. Harhay, Guangyu Tong, Rui Wang, Fan Li
Cluster-randomized trials (CRTs) are a well-established class of designs for evaluating large-scale, community-based research questions. An essential task in planning these trials is determining the required number of clusters and cluster sizes to achieve sufficient statistical power for detecting a clinically relevant effect size. Compared to methods for evaluating the average treatment effect (ATE) for the entire study population, there is more recent development of sample size methods for testing the heterogeneity of treatment effects (HTEs), i.e., modification of treatment effects by subpopulation characteristics, in CRTs. For confirmatory analyses of HTEs in CRTs, effect modifiers must be pre-specified, and ideally, accompanied by sample size or power calculations to ensure the trial has adequate power for the planned analyses. Power analysis for HTE analyses is more complex than for ATEs due to the additional design parameters that must be specified. Power and sample size formulas for HTE analyses have been separately derived under several cluster-randomized designs, including single and multi-period parallel designs, crossover designs, and stepped-wedge designs, as well as under continuous and binary outcomes. This tutorial provides a consolidated reference guide for these methods and enhances their accessibility through the development of an online R Shiny calculator. We further discuss key considerations for researchers conducting sample size and power calculations for testing pre-specified HTE hypotheses in CRTs, including the essential role of advance estimates of intracluster correlation coefficients for both outcomes and covariates on power. The sample size methodology and calculator functionality are demonstrated through real CRT examples.
Optimal Poincaré-Hardy-type Inequalities on Manifolds and Graphs
2025-01-30
Florian Fischer, Christian Rose
We review a method to obtain optimal Poincar\'e-Hardy-type inequalities on the hyperbolic spaces, and discuss briefly generalisations to certain classes of Riemannian manifolds. Afterwards, we recall a corresponding result on homogeneous regular trees and provide a new proof using the aforementioned method. The same strategy will then be applied to obtain new optimal Hardy-type inequalities on weakly spherically symmetric graphs which include fast enough growing trees and anti-trees. In particular, this yields optimal weights which are larger at infinity than the optimal weights classically constructed via the Fitzsimmons ratio of the square root of the minimal positive Green's function.
We introduce MedXpertQA, a highly challenging and comprehensive benchmark to evaluate expert-level medical knowledge and advanced reasoning. MedXpertQA includes 4,460 questions spanning 17 specialties and 11 body systems. It includes two subsets, Text for text evaluation and MM for multimodal evaluation. Notably, MM introduces expert-level exam questions with diverse images and rich clinical information, including patient records and examination results, setting it apart from traditional medical multimodal benchmarks with simple QA pairs generated from image captions. MedXpertQA applies rigorous filtering and augmentation to address the insufficient difficulty of existing benchmarks like MedQA, and incorporates specialty board questions to improve clinical relevance and comprehensiveness. We perform data synthesis to mitigate data leakage risk and conduct multiple rounds of expert reviews to ensure accuracy and reliability. We evaluate 16 leading models on MedXpertQA. Moreover, medicine is deeply connected to real-world decision-making, providing a rich and representative setting for assessing reasoning abilities beyond mathematics and code. To this end, we develop a reasoning-oriented subset to facilitate the assessment of o1-like models.
Joint Power and Spectrum Orchestration for D2D Semantic Communication Underlying Energy-Efficient Cellular Networks
2025-01-30
Le Xia, Yao Sun, Haijian Sun, Rose Qingyang Hu, Dusit Niyato, Muhammad Ali Imran
Semantic communication (SemCom) has been recently deemed a promising next-generation wireless technique to enable efficient spectrum savings and information exchanges, thus naturally introducing a novel and practical network paradigm where cellular and device-to-device (D2D) SemCom approaches coexist. Nevertheless, the involved wireless resource management becomes complicated and challenging due to the unique semantic performance measurements and energy-consuming semantic coding mechanism. To this end, this paper jointly investigates power control and spectrum reuse problems for energy-efficient D2D SemCom cellular networks. Concretely, we first model the user preference-aware semantic triplet transmission and leverage a novel metric of semantic value to identify the semantic information importance conveyed in SemCom. Then, we define the additional power consumption from semantic encoding in conjunction with basic power amplifier dissipation to derive the overall system energy efficiency (semantics/Joule). Next, we formulate an energy efficiency maximization problem for joint power and spectrum allocation subject to several SemCom-related and practical constraints. Afterward, we propose an optimal resource management solution by employing the fractional-to-subtractive problem transformation and decomposition while developing a three-stage method with theoretical analysis of its optimality guarantee and computational complexity. Numerical results demonstrate the adequate performance superiority of our proposed solution compared with different benchmarks.
Interpretability of deep-learning methods applied to large-scale structure surveys
2025-01-30
Gaspard Aymerich, Tomasz Kacprzak, Alexandre Refregier
Deep learning and convolutional neural networks in particular are powerful and promising tools for cosmological analysis of large-scale structure surveys. They are already providing similar performance to classical analysis methods using fixed summary statistics, are showing potential to break key degeneracies by better probe combination and will likely improve rapidly in the coming years as progress is made in the physical modelling through both software and hardware improvement. One key issue remains: unlike classical analysis, a convolutional neural network's decision process is hidden from the user as the network optimises millions of parameters with no direct physical meaning. This prevents a clear understanding of the potential limitations and biases of the analysis, making it hard to rely on as a main analysis method. In this work, we explore the behaviour of such a convolutional neural network through a novel method. Instead of trying to analyse a network a posteriori, i.e. after training has been completed, we study the impact on the constraining power of training the network and predicting parameters with degraded data where we removed part of the information. This allows us to gain an understanding of which parts and features of a large-scale structure survey are most important in the network's prediction process. We find that the network's prediction process relies on a mix of both Gaussian and non-Gaussian information, and seems to put an emphasis on structures whose scales are at the limit between linear and non-linear regimes.
Curvature-sensing and generation of membrane proteins: a review
2025-01-30
Hiroshi Noguchi
Membrane proteins are crucial in regulating biomembrane shapes and controlling the dynamic changes in membrane morphology during essential cellular processes. These proteins can localize to regions with their preferred curvatures (curvature sensing) and induce localized membrane curvature. Thus, this review describes the recent theoretical development in membrane remodeling performed by membrane proteins. The mean-field theories of protein binding and the resulting membrane deformations are reviewed. The effects of hydrophobic insertions on the area-difference elasticity energy and that of intrinsically disordered protein domains on the membrane bending energy are discussed. For the crescent-shaped proteins, such as Bin/Amphiphysin/Rvs superfamily proteins, anisotropic protein bending energy and orientation-dependent excluded volume significantly contribute to curvature sensing and generation. Moreover, simulation studies of membrane deformations caused by protein binding and colloidal particle adhesion are reviewed, including domain formation, budding, and tubulation.
The theory of one-relator groups: history and recent progress
2025-01-30
Marco Linton, Carl-Fredrik Nyberg-Brodda
The theory of one-relator groups is now almost a century old. The authors therefore feel that a comprehensive survey of this fascinating subject is in order, and this document is an attempt at precisely such a survey. This article is divided into two chapters, reflecting the two different phases in the story of one-relator groups. The first chapter, written by the second author, covers the historical development of the theory roughly until the advent of geometric group theory. The second chapter, written by the first author, covers the recent progress in the theory up until the present day. The two chapters can be read independently of one another and have minimal overlap.
$β$-delayed neutron spectroscopy of $^{85, 86}$As with MONSTER at IGISOL
2025-01-30
A. Pérez de Rada Fiol, T. Martínez, D. Cano-Ott, H. Penttilä, J. Agramunt, V. Alcayne, A. Algora, S. Alhomaidhi, J. Äystö, K. Banerjee, O. Beliuskina, J. Benito, C. Bhattacharya, F. Calviño, G. Cortés, R. P. de Groote, A. de Roubin, C. Delafosse, C. Domingo-Pardo, S. Geldhof, W. Gins, M. Hukkanen, A. Jokinen, A. Kankainen, J. Lerendegui-Marco, J. Llanes Gamonoso, I. Matea, E. Mendoza, A. K. Mistry, D. A. Nesterenko, J. Plaza, I. Pohjalainen, S. Rinta-Antila, P. Roy, A. Sanchez-Caballero, J. L. Taín, D. Villamarín, M. Vilen
The $\beta$-delayed neutron emission in the $^{85, 86}$As $\beta$-decays has been measured at the Ion Guide Isotope Separator On Line facility of the Accelerator Laboratory of the University of Jyv\"askyl\"a. The complete $\beta$-decays have been studied with a complex setup that consists of a plastic scintillator for $\beta$-particles, MONSTER -- the MOdular Neutron time-of-flight SpectromeTER -- for neutrons, and a high-purity germanium and four LaBr$_3$ crystals for $\gamma$-rays. The $\beta$-delayed neutron energy distributions have been determined by unfolding the time-of-flight spectra with an innovative methodology based on the iterative Bayesian unfolding method and accurate Monte Carlo simulations. The results obtained for $^{85}$As are in excellent agreement with the existing evaluated data, validating the proposed methodology. In the case of $^{86}$As, a stronger neutron intensity at higher energies than previously predicted is discovered.
Fundamental Challenges in Evaluating Text2SQL Solutions and Detecting Their Limitations
2025-01-30
Cedric Renggli, Ihab F. Ilyas, Theodoros Rekatsinas
In this work, we dive into the fundamental challenges of evaluating Text2SQL solutions and highlight potential failure causes and the potential risks of relying on aggregate metrics in existing benchmarks. We identify two largely unaddressed limitations in current open benchmarks: (1) data quality issues in the evaluation data, mainly attributed to the lack of capturing the probabilistic nature of translating a natural language description into a structured query (e.g., NL ambiguity), and (2) the bias introduced by using different match functions as approximations for SQL equivalence. To put both limitations into context, we propose a unified taxonomy of all Text2SQL limitations that can lead to both prediction and evaluation errors. We then motivate the taxonomy by providing a survey of Text2SQL limitations using state-of-the-art Text2SQL solutions and benchmarks. We describe the causes of limitations with real-world examples and propose potential mitigation solutions for each category in the taxonomy. We conclude by highlighting the open challenges encountered when deploying such mitigation strategies or attempting to automatically apply the taxonomy.
MetaWFN: A Platform for Unified Implementation of Many-Electron Wavefunctions
2025-01-30
Ning Zhang, Qingpeng Wang, Wenjian Liu
\texttt{MetaWFN} is a C++ template-based architecture designed for flexible and rapid development of wavefunction-based quantum chemical methods. It is highly modular, extendable, and efficient. This is achieved by decoupling the three distinct aspects of quantum chemical methods (i.e., nature of Hamiltonian, structure of wavefunction, and strategy of parallelization ), thereby allowing for separate treatment of them through their internal type-trait and tagging systems furnished by C++ metaprogramming. Once the second-quantized Hamiltonians, whether nonrelativistic (spin-free) or relativistic (spin-dependent), are decomposed into topologically equivalent diagrams for a unified evaluation of the basic coupling coefficients between (randomly selected) spin-free or spin-dependent configuration state functions or Slater determinants incorporating full molecular symmetry (including single or double point group and spin or time reversal symmetry), the many-electron wavefunctions, whether built up with scalar or spinor orbitals, can be assembled with the same templates. As for parallelization, \texttt{MetaWFN} supports both OpenMP and MPI, with the majority of the latter being translated automatically from its OpenMP counterparts. The whole structure of \texttt{MetaWFN} is reviewed here, with some showcases for illustrating its performance.
Code auditing is a code review process with the goal of finding bugs. Large Language Models (LLMs) have shown substantial potential in this task, offering the ability to analyze programs without compilation and enabling customized bug detection following specified prompts. However, applying LLMs to repository-level code auditing presents notable challenges. The inherent context limits and hallucinations of LLMs can lead to the low quality of bug reports. Meanwhile, the large size of software repositories introduces substantial time and token costs, hindering efficiency and scalability in real-world scenarios. This work introduces an autonomous LLM-agent, RepoAudit, designed to enable precise and efficient repository-level code auditing. Equipped with the agent memory, RepoAudit explores the code repository on demand, analyzing data-flow facts along different feasible program paths in individual functions. It also introduces the validator to check the data-flow facts for hallucination mitigation and examine the satisfiability of path conditions of potential buggy paths, which enables RepoAudit to discard false positives in the code auditing. Our experiment shows that RepoAudit powered by Claude 3.5 Sonnet successfully finds 38 true bugs in 15 real-world systems, consuming 0.44 hours and $2.54 per project on average.
In the low-energy regime, baryons with $N_f \geq 2$ have long been constructed as skyrmions or through bag models, but such constructions for $N_f = 1$ are hindered by the trivial topological structure of the meson field. Recent proposals suggest that one-flavor baryons can instead be interpreted as quantum Hall droplets on the $\eta'$ domain wall, providing a possible link to quark-hadron continuity at high density. Retrospect to the qualitative or semi-qualitative construction of one-flavor baryon on $\eta'$ domain wall, one-flavor baryons can be described as quantum Hall droplets, resemble topological solitons, akin to skyrmions. Using the effective theory on the $\eta'$ domain wall which is conjectured to be the Chern-Simons-Higgs theory, it is discussed that its vortex solution with unit baryon number naturally has spin $N_c/2$ thus can be interpreted as baryon or multi-baryon structure, while particle-vortex duality suggests that quarks carry a fractional topological charge $1/N_c$ and obey fractional statistics. In terms of chiral bag models, confinement can be attributed to the monopoles confined within the bag, and the vector meson fields on the bag surface are essential for ensuring the correct baryon number in the chiral bag framework and thus provide deeper insights into baryons as nontrivial topological structures of the meson field. In this paper, we review the progress in this development with special interests in the $\eta^\prime$ domain wall dynamics. Naive extensions to $N_f\geq 2$ are also concerned.
Reconciling fractional entropy and black hole entropy compositions
2025-01-30
Manosh T. Manoharan, N. Shaji
This study investigates the implications of adopting fractional entropy in the area law framework and demonstrates its natural alignment with an isothermal description of black hole composition. We discuss the Zeroth law compatibility of the fractional entropy and define an empirical temperature for the horizon. We highlight the distinction between the empirical and conventional Hawking temperatures associated with the black holes. Unlike the Hawking temperature, this empirical temperature appears universal, and its proximity to the Planck temperature suggests a possible quantum gravity origin. We also establish the connection between these temperatures. Furthermore, extending the conventional fractional parameter $q$, constrained between 0 and 1, we establish that any positive real number can bound $q$ under the concavity condition, provided the log of micro-state dimensionality exceeds $q-1$. Specifically, for black holes, $q = 2$, necessitating micro-state dimensionality greater than $e$, thereby excluding the construction of black hole horizon states with two level bits or qubits. We also identify the connection between the validity of the second law and information fluctuation complexity. The second law requires that the variance of information content remain smaller than the area of the black hole horizon. This constraint naturally gives rise to a Boltzmann-Gibbs-like entropy for the black hole, which, in contrast to the canonical formulation, is associated with its mass rather than its area. Equilibrium distribution analysis uncovers multiple configurations, in which the one satisfying the prerequisites of probability distribution exhibits an exponent stretched form, revealing apparent deviation from the Boltzmann distribution.
Entropy Bounds and Holographic Dark Energy: Conflicts and Consensus
2025-01-30
Manosh T. Manoharan
Cohen, Kaplan, and Nelson's influential paper established that the UV-IR cut-offs cannot be arbitrarily chosen but are constrained by the relation $\Lambda^2 L \lesssim M_p$. Here, we revisit the formulation of the CKN entropy bound and compare it with other bounds. The specific characteristics of each bound are shown to depend on the underlying scaling of entropy. Notably, employing a non-extensive scaling with the von Neumann entropy definition leads to a more stringent constraint, $S_{\text{max}} \approx \sqrt{S_{\text{BH}}}$. We also clarify distinctions between the IR cut-offs used in these frameworks. Moving to the causal entropy bound, we demonstrate that it categorises the CKN bound as matter-like, the von Neumann bound as radiation-like, and the Bekenstein bound as black hole-like systems when saturated. Emphasising cosmological implications, we confirm the consistency between the bounds and the first laws of horizon thermodynamics. We then analyse the shortcomings in standard Holographic Dark Energy (HDE) models, highlighting the challenges in constructing HDE using $\Lambda^2 L \lesssim M_p$. Specifically, using the Hubble function in HDE definitions introduces circular logic, causing dark energy to mimic the second dominant component rather than behaving as matter. We further illustrate that the potential for other IR cut-offs, like the future event horizon in an FLRW background or those involving derivatives of the Hubble function, to explain late-time acceleration stems from an integration constant that cannot be trivially set to zero. In brief, the CKN relation doesn't assign an arbitrary cosmological constant; it explains why its value is small.
TRIPP: A General Purpose Data Pipeline for Astronomical Image Processing
2025-01-30
Alex Thomas, Natalie LeBaron, Luca Angeleri, Samuel Whitebook, Rachel Darlinger, Phillip Morgan, Varun Iyer, Prerana Kottapalli, Enda Mao, Jasper Webb, Dharv Patel, Kyle Lam, Kelvin Yip, Michael McDonald, Robby Odum, Cole Slenkovich, Yael Brynjegard-Bialik, Nicole Efstathiu, Joshua Perkins, Ryan Kuo, Audrey O'Malley, Alec Wang, Ben Fogiel, Sam Salters, Marlon Munoz, Ruiyang Wang, Natalie Kim, Lee Fowler, Philip Lubin
We present the TRansient Image Processing Pipeline (TRIPP), a transient and variable source detection pipeline that employs both difference imaging and light curve analysis techniques for astronomical data. Additionally, we demonstrate TRIPP's rapid analysis capability by detecting transient candidates in near-real time. TRIPP was tested using image data of the supernova SN2023ixf and from the Local Galactic Transient Survey (LGTS, Thomas et al. (2025)) collected by the Las Cumbres Observatory's (LCO) network of 0.4 m telescopes. To verify the methods employed by TRIPP, we compare our results to published findings on the photometry of SN2023ixf. Additionally, we report the ability of TRIPP to detect transient signals from optical Search for Extra Terrestrial Intelligence (SETI) sources.
Revisiting gender bias research in bibliometrics: Standardizing methodological variability using Scholarly Data Analysis (SoDA) Cards
2025-01-30
HaeJin Lee, Shubhanshu Mishra, Apratim Mishra, Zhiwen You, Jinseok Kim, Jana Diesner
Gender biases in scholarly metrics remain a persistent concern, despite numerous bibliometric studies exploring their presence and absence across productivity, impact, acknowledgment, and self-citations. However, methodological inconsistencies, particularly in author name disambiguation and gender identification, limit the reliability and comparability of these studies, potentially perpetuating misperceptions and hindering effective interventions. A review of 70 relevant publications over the past 12 years reveals a wide range of approaches, from name-based and manual searches to more algorithmic and gold-standard methods, with no clear consensus on best practices. This variability, compounded by challenges such as accurately disambiguating Asian names and managing unassigned gender labels, underscores the urgent need for standardized and robust methodologies. To address this critical gap, we propose the development and implementation of ``Scholarly Data Analysis (SoDA) Cards." These cards will provide a structured framework for documenting and reporting key methodological choices in scholarly data analysis, including author name disambiguation and gender identification procedures. By promoting transparency and reproducibility, SoDA Cards will facilitate more accurate comparisons and aggregations of research findings, ultimately supporting evidence-informed policymaking and enabling the longitudinal tracking of analytical approaches in the study of gender and other social biases in academia.
2025-01-30
Yu-Wei Chen, Raghu Pasupathy, Jordan A. Awan
This work identifies the first privacy-aware stratified sampling scheme that minimizes the variance for general private mean estimation under the Laplace, Discrete Laplace (DLap) and Truncated-Uniform-Laplace (TuLap) mechanisms within the framework of differential privacy (DP). We view stratified sampling as a subsampling operation, which amplifies the privacy guarantee; however, to have the same final privacy guarantee for each group, different nominal privacy budgets need to be used depending on the subsampling rate. Ignoring the effect of DP, traditional stratified sampling strategies risk significant variance inflation. We phrase our optimal survey design as an optimization problem, where we determine the optimal subsampling sizes for each group with the goal of minimizing the variance of the resulting estimator. We establish strong convexity of the variance objective, propose an efficient algorithm to identify the integer-optimal design, and offer insights on the structure of the optimal design.
Branching ratio and information entropy for the p+$^{12}$C reaction in the extended-quantum-molecular-dynamics model
2025-01-30
Lei Shen, Bo-Song Huang, Yu-Gang Ma
The reactions of p+$^{12}$C with triangular 3$\alpha$ structure and spherical structure with incident energies ranging from 5 to 200 MeV/nucleon are simulated by the in the extended-quantum-molecular-dynamics (EQMD) model together with the GEMINI decay process. This paper presents the incident energy dependence of the branching ratios of fragment production and multiplicity, as well as the associated event information entropy. The results indicate that the triangular 3$\alpha$ $^{12}$C has an extra branching ratio for the quasi-elastic reaction compared to the spherical $^{12}$C. This peculiarity of the branching ratios of the triangular 3$\alpha$ $^{12}$C appears as a small dent in the curves of the event information entropy (both the fragment information entropy and the multiplicity information entropy). Therefore, we propose that the event information entropy could be a probe for detecting the $\alpha$-cluster structure.
Influence of High-Performance Image-to-Image Translation Networks on Clinical Visual Assessment and Outcome Prediction: Utilizing Ultrasound to MRI Translation in Prostate Cancer
2025-01-30
Mohammad R. Salmanpour, Amin Mousavi, Yixi Xu, William B Weeks, Ilker Hacihaliloglu
Purpose: This study examines the core traits of image-to-image translation (I2I) networks, focusing on their effectiveness and adaptability in everyday clinical settings. Methods: We have analyzed data from 794 patients diagnosed with prostate cancer (PCa), using ten prominent 2D/3D I2I networks to convert ultrasound (US) images into MRI scans. We also introduced a new analysis of Radiomic features (RF) via the Spearman correlation coefficient to explore whether networks with high performance (SSIM>85%) could detect subtle RFs. Our study further examined synthetic images by 7 invited physicians. As a final evaluation study, we have investigated the improvement that are achieved using the synthetic MRI data on two traditional machine learning and one deep learning method. Results: In quantitative assessment, 2D-Pix2Pix network substantially outperformed the other 7 networks, with an average SSIM~0.855. The RF analysis revealed that 76 out of 186 RFs were identified using the 2D-Pix2Pix algorithm alone, although half of the RFs were lost during the translation process. A detailed qualitative review by 7 medical doctors noted a deficiency in low-level feature recognition in I2I tasks. Furthermore, the study found that synthesized image-based classification outperformed US image-based classification with an average accuracy and AUC~0.93. Conclusion: This study showed that while 2D-Pix2Pix outperformed cutting-edge networks in low-level feature discovery and overall error and similarity metrics, it still requires improvement in low-level feature performance, as highlighted by Group 3. Further, the study found using synthetic image-based classification outperformed original US image-based methods.
Normative Evaluation of Large Language Models with Everyday Moral Dilemmas
2025-01-30
Pratik S. Sachdeva, Tom van Nuenen
The rapid adoption of large language models (LLMs) has spurred extensive research into their encoded moral norms and decision-making processes. Much of this research relies on prompting LLMs with survey-style questions to assess how well models are aligned with certain demographic groups, moral beliefs, or political ideologies. While informative, the adherence of these approaches to relatively superficial constructs tends to oversimplify the complexity and nuance underlying everyday moral dilemmas. We argue that auditing LLMs along more detailed axes of human interaction is of paramount importance to better assess the degree to which they may impact human beliefs and actions. To this end, we evaluate LLMs on complex, everyday moral dilemmas sourced from the "Am I the Asshole" (AITA) community on Reddit, where users seek moral judgments on everyday conflicts from other community members. We prompted seven LLMs to assign blame and provide explanations for over 10,000 AITA moral dilemmas. We then compared the LLMs' judgments and explanations to those of Redditors and to each other, aiming to uncover patterns in their moral reasoning. Our results demonstrate that large language models exhibit distinct patterns of moral judgment, varying substantially from human evaluations on the AITA subreddit. LLMs demonstrate moderate to high self-consistency but low inter-model agreement. Further analysis of model explanations reveals distinct patterns in how models invoke various moral principles. These findings highlight the complexity of implementing consistent moral reasoning in artificial systems and the need for careful evaluation of how different models approach ethical judgment. As LLMs continue to be used in roles requiring ethical decision-making such as therapists and companions, careful evaluation is crucial to mitigate potential biases and limitations.
Are Representation Disentanglement and Interpretability Linked in Recommendation Models? A Critical Review and Reproducibility Study
2025-01-30
Ervin Dervishaj, Tuukka Ruotsalo, Maria Maistro, Christina Lioma
Unsupervised learning of disentangled representations has been closely tied to enhancing the representation intepretability of Recommender Systems (RSs). This has been achieved by making the representation of individual features more distinctly separated, so that it is easier to attribute the contribution of features to the model's predictions. However, such advantages in interpretability and feature attribution have mainly been explored qualitatively. Moreover, the effect of disentanglement on the model's recommendation performance has been largely overlooked. In this work, we reproduce the recommendation performance, representation disentanglement and representation interpretability of five well-known recommendation models on four RS datasets. We quantify disentanglement and investigate the link of disentanglement with recommendation effectiveness and representation interpretability. While several existing work in RSs have proposed disentangled representations as a gateway to improved effectiveness and interpretability, our findings show that disentanglement is not necessarily related to effectiveness but is closely related to representation interpretability. Our code and results are publicly available at https://github.com/edervishaj/disentanglement-interpretability-recsys.
A General-Purpose Neuromorphic Sensor based on Spiketrum Algorithm: Hardware Details and Real-life Applications
2025-01-30
MHD Anas Alsakkal, Runze Wang, Jayawan Wijekoon, Piotr Dudek
Spiking Neural Networks (SNNs) offer a biologically inspired computational paradigm, enabling energy-efficient data processing through spike-based information transmission. Despite notable advancements in hardware for SNNs, spike encoding has largely remained software-dependent, limiting efficiency. This paper addresses the need for adaptable and resource-efficient spike encoding hardware by presenting an area-optimized hardware implementation of the Spiketrum algorithm, which encodes time-varying analogue signals into spatiotemporal spike patterns. Unlike earlier performance-optimized designs, which prioritize speed, our approach focuses on reducing hardware footprint, achieving a 52% reduction in Block RAMs (BRAMs), 31% fewer Digital Signal Processing (DSP) slices, and a 6% decrease in Look-Up Tables (LUTs). The proposed implementation has been verified on an FPGA and successfully integrated into an IC using TSMC180 technology. Experimental results demonstrate the system's effectiveness in real-world applications, including sound and ECG classification. This work highlights the trade-offs between performance and resource efficiency, offering a flexible, scalable solution for neuromorphic systems in power-sensitive applications like cochlear implants and neural devices.
Survey and Improvement Strategies for Gene Prioritization with Large Language Models
2025-01-30
Matthew Neeley, Guantong Qi, Guanchu Wang, Ruixiang Tang, Dongxue Mao, Chaozhong Liu, Sasidhar Pasupuleti, Bo Yuan, Fan Xia, Pengfei Liu, Zhandong Liu, Xia Hu
Rare diseases are challenging to diagnose due to limited patient data and genetic diversity. Despite advances in variant prioritization, many cases remain undiagnosed. While large language models (LLMs) have performed well in medical exams, their effectiveness in diagnosing rare genetic diseases has not been assessed. To identify causal genes, we benchmarked various LLMs for gene prioritization. Using multi-agent and Human Phenotype Ontology (HPO) classification, we categorized patients based on phenotypes and solvability levels. As gene set size increased, LLM performance deteriorated, so we used a divide-and-conquer strategy to break the task into smaller subsets. At baseline, GPT-4 outperformed other LLMs, achieving near 30% accuracy in ranking causal genes correctly. The multi-agent and HPO approaches helped distinguish confidently solved cases from challenging ones, highlighting the importance of known gene-phenotype associations and phenotype specificity. We found that cases with specific phenotypes or clear associations were more accurately solved. However, we observed biases toward well-studied genes and input order sensitivity, which hindered gene prioritization. Our divide-and-conquer strategy improved accuracy by overcoming these biases. By utilizing HPO classification, novel multi-agent techniques, and our LLM strategy, we improved causal gene identification accuracy compared to our baseline evaluation. This approach streamlines rare disease diagnosis, facilitates reanalysis of unsolved cases, and accelerates gene discovery, supporting the development of targeted diagnostics and therapies.
Dissociated Neuronal Cultures as Model Systems for Self-Organized Prediction
2025-01-30
Amit Yaron, Zhuo Zhang, Dai Akita, Tomoyo Isoguchi Shiramatsu, Zenas Chao, Hirokazu Takahashi
Dissociated neuronal cultures provide a simplified yet effective model system for investigating self-organized prediction and information processing in neural networks. This review consolidates current research demonstrating that these in vitro networks display fundamental computational capabilities, including predictive coding, adaptive learning, goal-directed behavior, and deviance detection. We examine how these cultures develop critical dynamics optimized for information processing, detail the mechanisms underlying learning and memory formation, and explore the relevance of the free energy principle within these systems. Building on these insights, we discuss how findings from dissociated neuronal cultures inform the design of neuromorphic and reservoir computing architectures, with the potential to enhance energy efficiency and adaptive functionality in artificial intelligence. The reduced complexity of neuronal cultures allows for precise manipulation and systematic investigation, bridging theoretical frameworks with practical implementations in bio-inspired computing. Finally, we highlight promising future directions, emphasizing advancements in three-dimensional culture techniques, multi-compartment models, and brain organoids that deepen our understanding of hierarchical and predictive processes in both biological and artificial systems. This review aims to provide a comprehensive overview of how dissociated neuronal cultures contribute to neuroscience and artificial intelligence, ultimately paving the way for biologically inspired computing solutions.
Carbon Stars From Gaia DR3 and the Space Density of Dwarf Carbon Stars
2025-01-30
Benjamin R. Roulston, Naunet Leonhardes-Barboza, Paul J. Green, Evan Portnoi
Carbon stars (with atmospheric C/O$>1$) range widely in temperature and luminosity, from low mass dwarfs to asymptotic giant branch stars (AGB). The main sequence dwarf carbon (dC) stars have inherited carbon-rich material from an AGB companion, which has since transitioned to a white dwarf. The dC stars are far more common than C giants, but no reliable estimates of dC space density have been published to date. We present results from an all-sky survey for carbon stars using the low-resolution XP spectra from Gaia DR3. We developed and measured a set of spectral indices contrasting C$_{\rm 2}$ and CN molecular band strengths in carbon stars against common absorption features found in normal (C/O$<1$) stars such as CaI, TiO and Balmer lines. We combined these indices with the XP spectral coefficients as input to supervised machine-learning algorithms trained on a vetted sample of known C stars from LAMOST. We describe the selection of the carbon candidate sample, and provide a catalog of 43,574 candidates dominated by cool C giants in the Magellanic Clouds and at low galactic latitude in the Milky Way. We report the confirmation of candidate C stars using intermediate ($R\sim 1800$) resolution optical spectroscopy from the Fred Lawrence Whipple Observatory, and provide estimates of sample purity and completeness. From a carefully-vetted sample of over 600 dCs, we measure their local space density to be $\rho_0\,=\,1.96^{+0.14}_{-0.12}\times10^{-6}\,\text{pc}^{-3}$ (about one dC in every local disk volume of radius 50\,pc), with a relatively large disk scale height of $H_z\,=\,856^{+49}_{-43}\,$pc.
Probabilistic Joint Recovery Method for CO$_2$ Plume Monitoring
2025-01-30
Zijun Deng, Rafael Orozco, Abhinav Prakash Gahlot, Felix J. Herrmann
Reducing CO$_2$ emissions is crucial to mitigating climate change. Carbon Capture and Storage (CCS) is one of the few technologies capable of achieving net-negative CO$_2$ emissions. However, predicting fluid flow patterns in CCS remains challenging due to uncertainties in CO$_2$ plume dynamics and reservoir properties. Building on existing seismic imaging methods like the Joint Recovery Method (JRM), which lacks uncertainty quantification, we propose the Probabilistic Joint Recovery Method (pJRM). By estimating posterior distributions across surveys using a shared generative model, pJRM provides uncertainty information to improve risk assessment in CCS projects.
Complex network approaches to nonlinear time series analysis
2025-01-30
Yong Zou, Reik V. Donner, Norbert Marwan, Jonathan F. Donges, Jürgen Kurths
In the last decade, there has been a growing body of literature addressing the utilization of complex network methods for the characterization of dynamical systems based on time series. While both nonlinear time series analysis and complex network theory are widely considered to be established fields of complex systems sciences with strong links to nonlinear dynamics and statistical physics, the thorough combination of both approaches has become an active field of nonlinear time series analysis, which has allowed addressing fundamental questions regarding the structural organization of nonlinear dynamics as well as the successful treatment of a variety of applications from a broad range of disciplines. In this report, we provide an in-depth review of existing approaches of time series networks, covering their methodological foundations, interpretation and practical considerations with an emphasis on recent developments. After a brief outline of the state-of-the-art of nonlinear time series analysis and the theory of complex networks, we focus on three main network approaches, namely, phase space based recurrence networks, visibility graphs and Markov chain based transition networks, all of which have made their way from abstract concepts to widely used methodologies. These three concepts, as well as several variants thereof will be discussed in great detail regarding their specific properties, potentials and limitations. More importantly, we emphasize which fundamental new insights complex network approaches bring into the field of nonlinear time series analysis. In addition, we summarize examples from the wide range of recent applications of these methods, covering rather diverse fields like climatology, fluid dynamics, neurophysiology, engineering and economics, and demonstrating the great potentials of time series networks for tackling real-world contemporary scientific problems.
The tricritical Ising CFT is the IR fixed-point of $\lambda\phi^6$ theory. It can be seen as a one-parameter family of CFTs connecting between an $\varepsilon$-expansion near the upper critical dimension 3 and the exactly solved minimal model in $d=2$. We review what is known about the tricritical Ising CFT, and study it with the numerical conformal bootstrap for various dimensions. Using a mixed system with three external operators $\{\phi\sim\sigma,\phi^2\sim \epsilon,\phi^3\sim\sigma'\}$, we find three-dimensional "bootstrap islands" in $d=2.75$ and $d=2.5$ dimensions consistent with interpolations between the perturbative estimates and the 2d exact values. In $d=2$ and $d=2.25$ the setup is not strong enough to isolate the theory. This paper also contains a survey of the perturbative spectrum and a review of results from the literature.
Electroweak clouds supported by magnetically charged black holes: Analytic treatment along the existence-line
2025-01-30
Shahar Hod
It has recently been revealed [R. Gervalle and M. S. Volkov, Phys. Rev. Lett. (2024)] that magnetically charged black holes of the composed Einstein-Weinberg-Salam field theory can support bound-state hairy configurations of electroweak fields. In the present paper we study, using {\it analytical} techniques, the physical and mathematical properties of the supported linearized electroweak fields (spatially regular electroweak 'clouds') in the dimensionless large-mass $\mu\equiv m_{\text{w}} r_+\gg1$ regime of the composed black-hole-field system (here $m_{\text{w}}$ is the mass of the supported W-boson field and $r_+$ is the outer horizon radius of the central supporting black hole). In particular, we derive a remarkably compact formula for the discrete resonance spectrum $\{\mu_k(n)\}_{k=0}^{k=\infty}$ that characterizes the composed black-hole-linearized-field configurations, where the integer $n\equiv 2Pe\in\mathbb{Z}$ characterizes the discrete charge parameter $P$ of the central magnetic Reissner-Nordstr\"om black hole and $e$ is the electron charge. The physical significance of the analytically derived resonant spectrum stems from the fact that, in the dimensionless large-charge $n\gg1$ regime, the fundamental (largest) eigenvalue $\mu_0(n)$ determines the critical existence-line of the composed Einstein-Weinberg-Salam theory, a boundary line that separates hairy magnetic-black-hole-electroweak-field bound-state configurations from bald magnetic Reissner-Nordstr\"om black holes.
The outflow impacts on the size of the narrow-line region among type-2 AGNs
2025-01-30
Kantapon Jensangjun, Suraphong Yuma
We present the study of the gas kinematics in narrow-line regions (NLRs) of 2,009 type-2 AGNs at $z<0.34$. We construct the [O III]$\lambda$5007 emission-line images using publicly available broadband images from the Sloan Digital Sky Survey (SDSS). The [O III] emission area of the samples, measured down to $1.7\times10^{-15}$ erg/s/cm$^2$/arcsec$^2$, ranges from 3.7 kpc$^2$ up to 224 kpc$^2$. With our broadband technique, we found the strong correlation between [O III] area and AGN luminosity inferred from the [O III] luminosity and the mid-infrared luminosity at the rest-frame $15\mu$m. The isophotal threshold used to determine the [O III] area affects the correlation strength in that the brighter isophote yields the stronger correlation between the [O III] area and AGN luminosity. The presence of gas outflow is examined by the ratio of the [O III] velocity dispersion to the stellar velocity dispersion ($\sigma_{\rm [O\,III]}/\sigma_\star > 1.4$) using the SDSS spectra. At the given luminosity, the objects with and without outflows exhibit the same extension of the [O III] emission. Their correlation between the [O III] area and luminosity is almost identical. It is suggested that the size of NLRs is not affected by outflow mechanisms but rather by photoionization from the central AGNS.
Novel Scalings of Neutron Star Properties from Analyzing Dimensionless Tolman--Oppenheimer--Volkoff Equations
2025-01-30
Bao-Jun Cai, Bao-An Li
The TOV equations govern the radial evolution of pressure and energy density in static neutron stars (NSs) in hydrodynamical equilibrium. Using the reduced pressure and energy density with respect to the NS central energy density, the original TOV equations can be recast into dimensionless forms. While the traditionally used integral approach for solving the original TOV equations require an input nuclear Equation of State (EOS), the dimensionless TOV equations can be anatomized by using the reduced pressure and energy density as polynomials of the reduced radial coordinate without using any input nuclear EOS. Interesting and novel perspectives about NS core EOS can be extracted directly from NS observables using this new approach based on Intrinsic and Perturbative Analyses of the Dimensionless (IPAD) TOV equations (IPAD-TOV). In this review, we first discuss the length and energy density scales of NSs as well as the dimensionless TOV equations for scaled variables and their perturbative solutions near NS cores. We then review several new insights into NS physics gained from using the IPAD-TOV. We also demonstrate that the strong-field gravity plays a fundamental role in extruding a peak in the density/radius profile of the speed of sound squared (SSS) in massive NS cores independent of the nuclear EOS. Finally, some future perspectives of NS research using the IPAD-TOV are outlined.
Machine Learning Strategies for Parkinson Tremor Classification Using Wearable Sensor Data
2025-01-30
Jesus Paucar-Escalante, Matheus Alves da Silva, Bruno De Lima Sanches, Aurea Soriano-Vargas, Laura Silveira Moriyama, Esther Luna Colombini
Parkinson's disease (PD) is a neurological disorder requiring early and accurate diagnosis for effective management. Machine learning (ML) has emerged as a powerful tool to enhance PD classification and diagnostic accuracy, particularly by leveraging wearable sensor data. This survey comprehensively reviews current ML methodologies used in classifying Parkinsonian tremors, evaluating various tremor data acquisition methodologies, signal preprocessing techniques, and feature selection methods across time and frequency domains, highlighting practical approaches for tremor classification. The survey explores ML models utilized in existing studies, ranging from traditional methods such as Support Vector Machines (SVM) and Random Forests to advanced deep learning architectures like Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM). We assess the efficacy of these models in classifying tremor patterns associated with PD, considering their strengths and limitations. Furthermore, we discuss challenges and discrepancies in current research and broader challenges in applying ML to PD diagnosis using wearable sensor data. We also outline future research directions to advance ML applications in PD diagnostics, providing insights for researchers and practitioners.
The peer-review process is broken and the problem is getting worse, especially in AI: large conferences like NeurIPS increasingly struggle to adequately review huge numbers of paper submissions. I propose a scalable solution that, foremost, recognizes reviewing as important, necessary, \emph{work} and rewards it with crypto-coins owned and managed by the conferences themselves. The idea is at its core quite simple: paper submissions require work (reviews, meta-reviews, etc.) to be done, and therefore the submitter must pay for that work. Each reviewer submits their review to be approved by some designated conference officer (e.g. PC chair, Area Chair, etc.), and upon approval is paid a single coin for a single review. If three reviews are required, the cost of submission should be three coins + a tax that covers payments to all the volunteers who organize the conference. After some one-time startup costs to fairly distribute coins, the process should be relatively stable with new coins minted only when a conference grows.
Digital Health Innovations for Screening and Mitigating Mental Health Impacts of Adverse Childhood Experiences: Narrative Review
2025-01-30
Brianna M White, Rameshwari Prasad, Nariman Ammar, Jason A Yaun, Arash Shaban-Nejad
This study presents a narrative review of the use of digital health technologies (DHTs) and artificial intelligence to screen and mitigate risks and mental health consequences associated with ACEs among children and youth. Several databases were searched for studies published from August 2017 to August 2022. Selected studies (1) explored the relationship between digital health interventions and mitigation of negative health outcomes associated with mental health in childhood and adolescence and (2) examined prevention of ACE occurrence associated with mental illness in childhood and adolescence. A total of 18 search papers were selected, according to our inclusion and exclusion criteria, to evaluate and identify means by which existing digital solutions may be useful in mitigating the mental health consequences associated with the occurrence of ACEs in childhood and adolescence and preventing ACE occurrence due to mental health consequences. We also highlighted a few knowledge gaps or barriers to DHT implementation and usability. Findings from the search suggest that the incorporation of DHTs, if implemented successfully, has the potential to improve the quality of related care provisions for the management of mental health consequences of adverse or traumatic events in childhood, including posttraumatic stress disorder, suicidal behavior or ideation, anxiety or depression, and attention-deficit/hyperactivity disorder. The use of DHTs, machine learning tools, natural learning processing, and artificial intelligence can positively help in mitigating ACEs and associated risk factors. Under proper legal regulations, security, privacy, and confidentiality assurances, digital technologies could also assist in promoting positive childhood experiences in children and young adults, bolstering resilience, and providing reliable public health resources to serve populations in need.
Multi-wavelength and Environmental Properties of Variability Selected Low Luminosity Active Galactic Nuclei
2025-01-29
Heechan Yuk, Xinyu Dai, Marko Mićić
We present the multi-wavelength and environmental properties of 37 variability-selected active galactic nuclei (AGNs), including 30 low luminosity AGNs (LLAGNs), using a high cadence time-domain survey (ASAS-SN) from a spectroscopic sample of 1218 nearby bright galaxies. We find that high-cadence time-domain surveys uniquely select LLAGNs that do not necessarily satisfy other AGN selection methods, such as X-ray, mid-IR, or BPT methods. In our sample, 3% of them pass the mid-infrared color based AGN selection, 18% pass the X-ray luminosity based AGN selection, and 60% pass the BPT selection. This result is supported by two other LLAGN samples from high-cadence time-domain surveys of TESS and PTF, suggesting that the variability selection method from well-sampled light curves can find AGNs that may not be discovered otherwise. These AGNs can have moderate to small amplitudes of variability from the accretion disk, but, of many of them, with no strong corona, emission lines from the central engine, or accretion power to dominate the mid-IR emission. The X-ray spectra of a sub-sample of bright sources are consistent with a power law model. Upon inspecting the environments of our sample, we find that LLAGNs are more common in denser environments of galaxy clusters in contrast with the trend established in the literature for luminous AGNs at low redshifts, which is broadly consistent with our analysis result for luminous AGNs limited by a smaller sample size. This contrast in environmental properties between LLAGN and luminous AGNs suggests that LLAGNs may have different trigger mechanisms.
A Comprehensive Survey on Legal Summarization: Challenges and Future Directions
2025-01-29
Mousumi Akter, Erion Çano, Erik Weber, Dennis Dobler, Ivan Habernal
This article provides a systematic up-to-date survey of automatic summarization techniques, datasets, models, and evaluation methods in the legal domain. Through specific source selection criteria, we thoroughly review over 120 papers spanning the modern `transformer' era of natural language processing (NLP), thus filling a gap in existing systematic surveys on the matter. We present existing research along several axes and discuss trends, challenges, and opportunities for future research.
Generative Unordered Flow for Set-Structured Data Generation
2025-01-29
Yangming Li, Carola-Bibiane Schönlieb
Flow-based generative models have demonstrated promising performance across a broad spectrum of data modalities (e.g., image and text). However, there are few works exploring their extension to unordered data (e.g., spatial point set), which is not trivial because previous models are mostly designed for vector data that are naturally ordered. In this paper, we present unordered flow, a type of flow-based generative model for set-structured data generation. Specifically, we convert unordered data into an appropriate function representation, and learn the probability measure of such representations through function-valued flow matching. For the inverse map from a function representation to unordered data, we propose a method similar to particle filtering, with Langevin dynamics to first warm-up the initial particles and gradient-based search to update them until convergence. We have conducted extensive experiments on multiple real-world datasets, showing that our unordered flow model is very effective in generating set-structured data and significantly outperforms previous baselines.
Looking at infrared background radiation anisotropies with Spitzer: large scale anisotropies and their implications
2025-01-29
A. Kashlinsky, Richard G. Arendt, M. L. N. Ashby, J. Kruk, N. Odegard
We use Spitzer/IRAC deep exposure data covering two significantly larger than before sky areas to construct maps suitable for evaluating source-subtracted fluctuations in the cosmic infrared background (CIB). The maps are constructed using the self-calibration methodology eliminating artifacts to sufficient accuracy and subset maps are selected in each area containing approximately uniform exposures. These maps are clipped and removed of known sources and then Fourier transformed to probe the CIB anisotropies to new larger scales. The power spectrum of the resultant CIB anisotropies is measured from the data to >1 degree revealing the component well above that from remaining known galaxies on scales >1 arcmin. The fluctuations are demonstrated to be free of Galactic and Solar System foreground contributions out to the largest scales measured. We discuss the proposed theories for the origin of the excess CIB anisotropies in light of the new data. Out of these, the model where the CIB fluctuation excess originates from the granulation power due to LIGO-observed primordial black holes as dark matter appears most successful in accounting for all observations related to the measured CIB power amplitude and spatial structure, including the measured coherence between the CIB and unresolved cosmic X-ray background (CXB). Finally we point out the use of the data to probe the CIB-CXB cross-power to new scales and higher accuracy. We also discuss the synergy of these data with future CIB programs at shorter near-IR wavelengths with deep wide surveys and sub-arcsecond angular resolution as provided by Euclid and Roman space missions.
In the world of open-source software (OSS), the number of known vulnerabilities has tremendously increased. The GitHub Advisory Database contains advisories for security risks in GitHub-hosted OSS projects. As of 09/25/2023, there are 197,609 unreviewed GitHub security advisories. Of those unreviewed, at least 63,852 are publicly documented vulnerabilities, potentially leaving many OSS projects vulnerable. Recently, bug bounty platforms have emerged to focus solely on providing bounties to help secure OSS. In this paper, we conduct an empirical study on 3,798 reviewed GitHub security advisories and 4,033 disclosed OSS bug bounty reports, a perspective that is currently understudied, because they contain comprehensive information about security incidents, e.g., the nature of vulnerabilities, their impact, and how they were resolved. We are the first to determine the explicit process describing how OSS vulnerabilities propagate from security advisories and bug bounty reports, which are the main intermediaries between vulnerability reporters, OSS maintainers, and dependent projects, to vulnerable OSS projects and entries in global vulnerability databases and possibly back. This process uncovers how missing or delayed CVE assignments for OSS vulnerabilities result in projects, both in and out of OSS, not being notified of necessary security updates promptly and corresponding bottlenecks. Based on our findings, we provide suggestions, actionable items, and future research directions to help improve the security posture of OSS projects.
Testing Research Software: An In-Depth Survey of Practices, Methods, and Tools
2025-01-29
Nasir U. Eisty, Upulee Kanewala, Jeffrey C. Carver
Context: Research software is essential for developing advanced tools and models to solve complex research problems and drive innovation across domains. Therefore, it is essential to ensure its correctness. Software testing plays a vital role in this task. However, testing research software is challenging due to the software's complexity and to the unique culture of the research software community. Aims: Building on previous research, this study provides an in-depth investigation of testing practices in research software, focusing on test case design, challenges with expected outputs, use of quality metrics, execution methods, tools, and desired tool features. Additionally, we explore whether demographic factors influence testing processes. Method: We survey research software developers to understand how they design test cases, handle output challenges, use metrics, execute tests, and select tools. Results: Research software testing varies widely. The primary challenges are test case design, evaluating test quality, and evaluating the correctness of test outputs. Overall, research software developers are not familiar with existing testing tools and have a need for new tools to support their specific needs. Conclusion: Allocating human resources to testing and providing developers with knowledge about effective testing techniques are important steps toward improving the testing process of research software. While many industrial testing tools exist, they are inadequate for testing research software due to its complexity, specialized algorithms, continuous updates, and need for flexible, custom testing approaches. Access to a standard set of testing tools that address these special characteristics will increase level of testing in research software development and reduce the overhead of distributing knowledge about software testing.
A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches
2025-01-29
Ana R. Baião, Zhaoxiang Cai, Rebecca C Poulos, Phillip J. Robinson, Roger R Reddel, Qing Zhong, Susana Vinga, Emanuel Gonçalves
The rapid advancement of high-throughput sequencing and other assay technologies has resulted in the generation of large and complex multi-omics datasets, offering unprecedented opportunities for advancing precision medicine strategies. However, multi-omics data integration presents significant challenges due to the high dimensionality, heterogeneity, experimental gaps, and frequency of missing values across data types. Computational methods have been developed to address these issues, employing statistical and machine learning approaches to uncover complex biological patterns and provide deeper insights into our understanding of disease mechanisms. Here, we comprehensively review state-of-the-art multi-omics data integration methods with a focus on deep generative models, particularly variational autoencoders (VAEs) that have been widely used for data imputation and augmentation, joint embedding creation, and batch effect correction. We explore the technical aspects of loss functions and regularisation techniques including adversarial training, disentanglement and contrastive learning. Moreover, we discuss recent advancements in foundation models and the integration of emerging data modalities, while describing the current limitations and outlining future directions for enhancing multi-modal methodologies in biomedical research.
The evolution of extragalactic peaked-spectrum sources down to 54 megahertz
2025-01-29
Sai Zhai, Anniek J. Gloudemans, Gülay Gürkan, Femke J. Ballieux, Martin J. Hardcastle, Francesco De Gasperin, Huub J. A. Röttgering
Peaked-spectrum (PS) sources, known for their distinct peaked radio spectra, represent a type of radio-loud active galactic nuclei (AGN). Among these, megahertz-peaked spectrum (MPS) sources, which exhibit a spectral peak at a frequency of a hundred megahertz, have emerged as a potential tool for identifying high-redshift candidates. However, the potential evolutionary link between the fraction of these sources and redshift remains unclear and requires further investigation. The recent, high sensitivity Low Frequency Array (LOFAR) surveys enable statistical studies of these objects to ultra-low frequencies (< 150 MHz). In this study, we first use the multiradio data to investigate the evolution of spectral index with redshift for 1,187 quasars from the SDSS 16th quasar catalog. For each quasar, we analyze available data from the LOFAR Low Band Antenna (LBA) at 54 MHz, High Band Antenna (HBA) at 144 MHz, and the Very Large Array (VLA) the Faint Images of the Radio Sky at Twenty cm (FIRST) at 1.4 GHz. We measure the spectral index ($\alpha^{144}_{54}$ and $\alpha^{1400}_{144}$) and find no significant change in their median values with the redshift. Extended sources have steeper spectral indices than compact sources, which is consistent with previous findings. Based on the spectral indices information, we identify MPS sources using these criteria: $\rm \alpha^{144}_{54} >= 0.1$ and $\rm \alpha^{1400}_{144} < 0$, and analyze their properties. We find that the fraction of MPS sources is constant with the redshift ($0.1-4.8$), bolometric luminosity ($\rm 10^{44}-10^{48} erg/s$), and supermassive black hole mass ($\rm 10^{7}-10^{10.5} M_{\odot}$), which suggests that MPS sources have relatively stable physical conditions or formation mechanisms across various evolutionary stages and environments.
NutMaat: A Python package for stellar spectral classification on the MK system
2025-01-29
R. I. El-Kholy, Z. M. Hayman
Stellar spectral classification according to the Morgan-Keenan (MK) system remains fundamental to astrophysical studies, yet modern surveys require automated, scalable tools. We present NutMaat, a Python-based package inspired by MKCLASS, designed to automate MK classification while addressing scalability and usability limitations. It employs modern computational tools for batch processing and offers a modular architecture that enables efficient, platform-independent analysis of large spectral datasets. Tested on the CFLIB and MILES libraries, NutMaat achieved spectral and luminosity classification accuracies comparable to MKCLASS, with minimal systematic offsets and a robust performance down to S/N $\approx$ 5. NutMaat successfully identified chemically peculiar stars, tested on LAMOST DR7 ACV variables, and processed the SDSS-IV MaStar library to produce a stellar catalog, demonstrating survey readiness. Future development of NutMaat will focus on extending wavelength coverage, computational acceleration via Cython, and refining peculiarity classification. As an open-source tool, NutMaat bridges traditional MK methods with modern data workflows, offering a scalable solution for current and future spectroscopic surveys.
Old Ideas for New Physicists III: String Theory Parameters are NOT Vacuum Expectation Values
2025-01-29
T. Banks
I review, for the benefit of younger physicists, arguments proposed at the beginning of the 21st century, which show that it is misleading to think of the parameters in string theory models of quantum gravity as vacuum expectation values of fields.
Enhanced dissipation by advection and applications to PDEs
2025-01-29
Anna L. Mazzucato, Yuanyuan Feng, Camilla Nobili
This survey provides a concise yet comprehensive overview on enhanced dissipation phenomena, transitioning seamlessly from the physical principles underlying the interplay between advection and diffusion to their rigorous mathematical formulation and analysis. The discussion begins with the standard theory of enhanced dissipation, highlighting key mechanisms and results, and progresses to its applications in notable nonlinear PDEs such as the Cahn-Hilliard and Kuramoto-Sivashinsky equations.
HI Intensity Mapping with the MIGHTEE Survey: First Results of the HI Power Spectrum
2025-01-29
Aishrila Mazumder, Laura Wolz, Zhaoting Chen, Sourabh Paul, Mario Santos, Matt Jarvis, Junaid Townsend, Srikrishna Sekhar, Russ Taylor
We present the first results of the HI intensity mapping power spectrum analysis with the MeerKAT International GigaHertz Tiered Extragalactic Exploration (MIGHTEE) survey. We use data covering $\sim$ 4 square degrees in the COSMOS field using a frequency range 962.5 MHz to 1008.42 MHz, equivalent to HI emission in $0.4
Heuristic-Informed Mixture of Experts for Link Prediction in Multilayer Networks
2025-01-29
Lucio La Cava, Domenico Mandaglio, Lorenzo Zangari, Andrea Tagarelli
Link prediction algorithms for multilayer networks are in principle required to effectively account for the entire layered structure while capturing the unique contexts offered by each layer. However, many existing approaches excel at predicting specific links in certain layers but struggle with others, as they fail to effectively leverage the diverse information encoded across different network layers. In this paper, we present MoE-ML-LP, the first Mixture-of-Experts (MoE) framework specifically designed for multilayer link prediction. Building on top of multilayer heuristics for link prediction, MoE-ML-LP synthesizes the decisions taken by diverse experts, resulting in significantly enhanced predictive capabilities. Our extensive experimental evaluation on real-world and synthetic networks demonstrates that MoE-ML-LP consistently outperforms several baselines and competing methods, achieving remarkable improvements of +60% in Mean Reciprocal Rank, +82% in Hits@1, +55% in Hits@5, and +41% in Hits@10. Furthermore, MoE-ML-LP features a modular architecture that enables the seamless integration of newly developed experts without necessitating the re-training of the entire framework, fostering efficiency and scalability to new experts, paving the way for future advancements in link prediction.
Understanding Trust in Authentication Methods for Icelandic Digital Public Services
2025-01-29
Brynjólfur Stefánsson, Ásta Guðrún Helgadóttir, Martin Nizon-Deladoeuille, Helmut Neukirchen, Thomas Welsh
Digital public services have revolutionised citizen and private sector interactions with governments. Certain communities are strongly dependent on such digital services for ensuring the availability of public services due to geographical isolation or the presence of adverse geophysical and weather phenomena. However, strong and effective security is key to maintaining the integrity of public records and services yet also for ensuring trust in them. Trust is essential for user uptake, particularly given a global increase in data-protection concerns and a turbulent geopolitical security environment. In this paper, we examine the case of public trust in various forms of authentication for electronic identification in Iceland, which has high availability requirements for digital public services due to its unique and dynamic geophysical characteristics. Additionally, Iceland has historically low levels of institutional trust which may conflict with the requirement for an increased need for digital public services. Through surveying the Icelandic general public, we find that there is a high-level of trust in digital identification services across all demographics. We conclude with a discussion and future research challenges towards improving the effectiveness of authentication considering the diverse groups within Icelandic society, such as the rapidly increasing population of migrants and the large and dynamic population of tourists.
The derangements subgroup in a finite permutation group and the Frobenius--Wielandt Theorem
2025-01-29
R. A. Bailey, P. J. Cameron, N. Gavioli, C. M. Scoppola
It is known that if the derangements subgroup of a transitive non-regular permutation group is a proper subgroup, then it is a Frobenius--Wielandt kernel, and, conversely, minimal Frobenius--Wielandt kernels are proper derangements subgroups. We present here a short survey of the literature on this topic, and we show that, although there are no restrictions on the structure of the $p$-groups appearing as Frobenius--Wielandt complements, a $p$-group appears as a one-point stabiliser in a transitive non-regular permutation group with a proper derangements subgroup if and only if it satisfies a certain group-theoretic condition.
Pole-zero identification refers to the obtaining of the poles and zeros of a linear (or linearized) system described by its frequency response. This is usually done using optimization techniques (such as least squares, maximum likelihood estimation, or vector fitting) that fit a given frequency response of the linear system to a transfer function defined as the ratio of two polynomials. This kind of linear system identification in the frequency domain has numerous applications in a wide variety of engineering fields (such as mechanical systems, power systems and Electromagnetic Compatibility). In the microwave domain, rational approximation is increasingly used to obtain black-box models of complex passive structures for model order reduction and efficient transient simulation. In this paper we will focus on a different application of pole-zero identification. We will review the different ways in which pole-zero identification can be applied to nonlinear circuit design (for power amplifier stability analysis and beyond). We will give a comprehensive view on recent approaches through illustrative application examples. Other uses of rational approximation techniques are beyond the scope of this paper.
Detection of Oscillation-like Patterns in Eclipsing Binary Light Curves using Neural Network-based Object Detection Algorithms
2025-01-29
Burak Ulaş, Tamás Szklenár, Róbert Szabó
The primary aim of this research is to evaluate several convolutional neural network-based object detection algorithms for identifying oscillation-like patterns in light curves of eclipsing binaries. This involves creating a robust detection framework that can effectively process both synthetic light curves and real observational data. The study employs several state-of-the-art object detection algorithms, including Single Shot MultiBox Detector, Faster Region-based Convolutional Neural Network, You Only Look Once, and EfficientDet besides a custom non-pretrained model implemented from scratch. Synthetic light curve images and images derived from observational TESS light curves of known eclipsing binaries with a pulsating component were constructed with corresponding annotation files using custom scripts. The models were trained and validated on established datasets, followed by testing on unseen {\it{Kepler}} data to assess their generalization performance. The statistical metrics are also calculated to review the quality of each model. The results indicate that the pre-trained models exhibit high accuracy and reliability in detecting the targeted patterns. Faster R-CNN and You Only Look Once, in particular, showed superior performance in terms of object detection evaluation metrics on the validation dataset such as mAP value exceeding 99\%. Single Shot MultiBox Detector, on the other hand, is the fastest although it shows slightly lower performance with a mAP of 97\%. These findings highlight the potential of these models to contribute significantly to the automated determination of pulsating components in eclipsing binary systems, facilitating more efficient and comprehensive astrophysical investigations.
2025-01-29
Omar El-Rifai, Michael Ben Ali, Imen Megdiche, André Peninou, Olivier Teste
As the industrial and commercial use of Federated Learning (FL) has expanded, so has the need for optimized algorithms. In settings were FL clients' data is non-independently and identically distributed (non-IID) and with highly heterogeneous distributions, the baseline FL approach seems to fall short. To tackle this issue, recent studies, have looked into personalized FL (PFL) which relaxes the implicit single-model constraint and allows for multiple hypotheses to be learned from the data or local models. Among the personalized FL approaches, cluster-based solutions (CFL) are particularly interesting whenever it is clear -through domain knowledge -that the clients can be separated into groups. In this paper, we study recent works on CFL, proposing: i) a classification of CFL solutions for personalization; ii) a structured review of literature iii) a review of alternative use cases for CFL. CCS Concepts: $\bullet$ General and reference $\rightarrow$ Surveys and overviews; $\bullet$ Computing methodologies $\rightarrow$ Machine learning; $\bullet$ Information systems $\rightarrow$ Clustering; $\bullet$ Security and privacy $\rightarrow$ Privacy-preserving protocols.
A Heliocentric-orbiting Objects Processing System (HOPS) for the Wide Field Survey Telescope: Architecture, Processing Workflow, and Preliminary Results
Wide-field surveys have markedly enhanced the discovery and study of solar system objects (SSOs). The 2.5-meter Wide Field Survey Telescope (WFST) represents the foremost facility dedicated to optical time-domain surveys in the northern hemisphere. To fully exploit WFST's capabilities for SSO detection, we have developed a heliocentric-orbiting objects processing system (HOPS) tailored for identifying these objects. This system integrates HelioLinC3D, an algorithm well suited for the WFST survey cadence, characterized by revisiting the same sky field twice on the majority of nights. In this paper, we outline the architecture and processing flow of our SSO processing system. The application of the system to the WFST pilot survey data collected between March and May 2024 demonstrates exceptional performance in terms of both temporal efficiency and completeness. A total of 658,489 observations encompassing 38,520 known asteroids have been documented, and 241 newly discovered asteroids have been assigned provisional designations. In particular, 27% of these new discoveries were achieved using merely two observations per night on three nights. The preliminary results not only illuminate the effectiveness of integrating HelioLinC3D within the SSO processing system, but also emphasize the considerable potential contributions of WFST to the field of solar system science.
Quantum networks promise an unprecedented leap in semi-device-independent communication and security by capitalizing on quantum steering. However, current methods for assessing quantum network steering are constrained to specific cases. In this work, we introduce the network-Clauser-Horn-Shimony-Holt-like inequality for investigating network steering independent of entanglement source characteristics. We employ this inequality to detect full network steering in both single-node and multinode repeater networks and assess the tolerance of various noise models. Under a specific noise model, our method is used to compute the bound of semi-device-independent communication distance. Through case studies, we also demonstrate that our method, as a semi-device-independent entanglement witness, is more suitable for settings requiring Bell measurements compared to network Bell inequality. These findings open new avenues for broader ways to detect quantum steering independent of entanglement sources.
A review on the novelty measurements of academic papers
2025-01-29
Yi Zhao, Chengzhi Zhang
Novelty evaluation is vital for the promotion and management of innovation. With the advancement of information techniques and the open data movement, some progress has been made in novelty measurements. Tracking and reviewing novelty measures provides a data-driven way to assess contributions, progress, and emerging directions in the science field. As academic papers serve as the primary medium for the dissemination, validation, and discussion of scientific knowledge, this review aims to offer a systematic analysis of novelty measurements for scientific papers. We began by comparing the differences between scientific novelty and four similar concepts, including originality, scientific innovation, creativity, and scientific breakthrough. Next, we reviewed the types of scientific novelty. Then, we classified existing novelty measures according to data types and reviewed the measures for each type. Subsequently, we surveyed the approaches employed in validating novelty measures and examined the current tools and datasets associated with these measures. Finally, we proposed several open issues for future studies.
Battery-powered technologies like pagers and walkie-talkies have long been integral to civilian and military operations. However, the potential for such everyday devices to be weaponized has largely been underestimated in the realm of cybersecurity. In September 2024, Lebanon experienced a series of unprecedented, coordinated explosions triggered through compromised pagers and walkie-talkies, creating a new category of attack in the domain of cyber-physical warfare. This attack not only disrupted critical communication networks but also resulted in injuries, loss of life, and exposed significant national security vulnerabilities, prompting governments and organizations worldwide to reevaluate their cybersecurity frameworks. This article provides an in-depth investigation into the infamous Pager and Walkie-Talkie attacks, analyzing both technical and non-technical dimensions. Furthermore, the study extends its scope to explore vulnerabilities in other battery-powered infrastructures, such as battery management systems, highlighting their potential exploitation. Existing prevention and detection techniques are reviewed, with an emphasis on their limitations and the challenges they face in addressing emerging threats. Finally, the article discusses emerging methodologies, particularly focusing on the role of physical inspection, as a critical component of future security measures. This research aims to provide actionable insights to bolster the resilience of cyber-physical systems in an increasingly interconnected world.
Self-Guided Virtual Reality Therapy for Anxiety: A Systematic Review
2025-01-29
Winona Graham, Russell Drinkwater, Joshua Kelson, Muhammad Ashad Kabir
Virtual reality (VR) technology can be used to treat anxiety symptoms and disorders. However, most VR interventions for anxiety have been therapist guided rather than self-guided. This systematic review aimed to examine the effectiveness and user experience (i.e., usability, acceptability, safety, and attrition rates) of self-guided VR therapy interventions in people with any anxiety condition as well as provide future research directions. Peer-reviewed journal articles reporting on self-guided VR interventions for anxiety were sought from the Cochrane Library, IEEE Explore Digital Library, PsycINFO, PubMED, Scopus, and Web of Science databases. Study data from the eligible articles were extracted, tabulated, and addressed with a narrative synthesis. A total of 21 articles met the inclusion criteria. The findings revealed that self-guided VR interventions for anxiety can provide an effective treatment of social anxiety disorder, public speaking anxiety, and specific phobias. User experiences outcomes of safety, usability, and acceptability were generally positive and the average attrition rate was low. However, there was a lack of standardised assessments to measure user experiences. Self-guided VR for anxiety can provide an engaging approach for effectively and safely treating common anxiety conditions. Nevertheless, more experimental studies are required to examine their use in underrepresented anxiety populations, their long-term treatment effects beyond 12 months, and compare their effectiveness against other self-help interventions for anxiety (e.g., internet interventions and bibliotherapy).
This article is part of a Living Literature Review exploring topics related to intellectual property, focusing on insights from the economic literature. Our aim is to provide a clear and non-technical introduction to patent rights, making them accessible to graduate students, legal scholars and practitioners, policymakers, and anyone curious about the subject.
An energetic absorption outflow in QSO J1402+2330: Analysis of DESI observations
2025-01-29
M. Dehghanian, N. Arav, M. Sharma, G. Walker, K. Johnston, M. Kaupin
Context. Quasar outflows play a significant role in the active galactic nucleus (AGN) feedback, impacting the interstellar medium and potentially influencing galaxy evolution. Characterizing these outflows is essential for understanding AGN-driven processes. Aims. We aim to analyze the physical properties of the mini-broad absorption line outflow in quasar J1402+2330 using data from the Dark Energy Spectroscopic Instrument (DESI) survey. We seek to measure the outflows location, energetics, and potential impact on AGN feedback processes. Methods. In the spectrum of J1402+2330, we identify multiple ionic absorption lines, including ground and excited states. We measure the ionic column densities and then use photoionization models to determine the total hydrogen column density and ionization parameter of the outflow. We utilized the population ratio of the excited state to the ground state of N iii and S iv to determine the electron number density. Results. The derived electron number density, combined with the ionization parameter, indicates an outflow distance of approximately 2200 pc from the central source. Having a mass outflow rate of more than one thousand solar masses per year and a kinetic energy output exceeding 5 percent of the Eddington luminosity, this outflow can significantly contribute to AGN feedback. Conclusions. Our findings suggest the absorption outflow in J1402+2330 plays a potentially significant role in AGN feedback processes. This study highlights the value of DESI data in exploring AGN feedback mechanisms.
Large Language Models Think Too Fast To Explore Effectively
2025-01-29
Lan Pan, Hanbo Xie, Robert C. Wilson
Large Language Models have emerged many intellectual capacities. While numerous benchmarks assess their intelligence, limited attention has been given to their ability to explore, an essential capacity for discovering new information and adapting to novel environments in both natural and artificial systems. The extent to which LLMs can effectively explore, particularly in open-ended tasks, remains unclear. This study investigates whether LLMs can surpass humans in exploration during an open-ended task, using Little Alchemy 2 as a paradigm, where agents combine elements to discover new ones. Results show most LLMs underperform compared to humans, except for the o1 model, with those traditional LLMs relying primarily on uncertainty driven strategies, unlike humans who balance uncertainty and empowerment. Representational analysis of the models with Sparse Autoencoders revealed that uncertainty and choices are represented at earlier transformer blocks, while empowerment values are processed later, causing LLMs to think too fast and make premature decisions, hindering effective exploration. These findings shed light on the limitations of LLM exploration and suggest directions for improving their adaptability.
COSMIC's Large-Scale Search for Technosignatures during the VLA sky Survey: Survey Description and First Results
2025-01-29
Chenoa D. Tremblay, Jared Sofair, Lucy Steffes, Talon Myburgh, Daniel Czech, Paul B. Demorest, Ross A. Donnachie, Alex W. Pollak, Mark Ruzindana, Andrew P. V. Siemion, Savin S. Varghese, Sofia Sheikh
Developing algorithms to search through data efficiently is a challenging part of searching for signs of technology beyond our solar system. We have built a digital signal processing system and computer cluster on the backend of the Karl G. Jansky Very Large Array (VLA) in New Mexico in order to search for signals throughout the Galaxy consistent with our understanding of artificial radio emissions. In our first paper, we described the system design and software pipelines. In this paper, we describe a postprocessing pipeline to identify persistent sources of interference, filter out false positives, and search for signals not immediately identifiable as anthropogenic radio frequency interference during the VLA Sky Survey. As of 01 September 2024, the Commensal Open-source Multi-mode Interferometric Cluster had observed more than 950,000 unique pointings. This paper presents the strategy we employ when commensally observing during the VLA Sky Survey and a postprocessing strategy for the data collected during the survey. To test this postprocessing pipeline, we searched toward 511 stars from the $Gaia$ catalog with coherent beams. This represents about 30 minutes of observation during VLASS, where we typically observe about 2000 sources per hour in the coherent beamforming mode. We did not detect any unidentifiable signals, setting isotropic power limits ranging from 10$^{11}$ to 10$^{16}$W.
Limits to AI Growth: The Ecological and Social Consequences of Scaling
2025-01-29
Eshta Bhardwaj, Rohan Alexander, Christoph Becker
The accelerating development and deployment of AI technologies depend on the continued ability to scale their infrastructure. This has implied increasing amounts of monetary investment and natural resources. Frontier AI applications have thus resulted in rising financial, environmental, and social costs. While the factors that AI scaling depends on reach its limits, the push for its accelerated advancement and entrenchment continues. In this paper, we provide a holistic review of AI scaling using four lenses (technical, economic, ecological, and social) and review the relationships between these lenses to explore the dynamics of AI growth. We do so by drawing on system dynamics concepts including archetypes such as "limits to growth" to model the dynamic complexity of AI scaling and synthesize several perspectives. Our work maps out the entangled relationships between the technical, economic, ecological and social perspectives and the apparent limits to growth. The analysis explains how industry's responses to external limits enables continued (but temporary) scaling and how this benefits Big Tech while externalizing social and environmental damages. To avoid an "overshoot and collapse" trajectory, we advocate for realigning priorities and norms around scaling to prioritize sustainable and mindful advancements.
As large language models (LLMs) become increasingly integrated into educational technology, their potential to assist in developing curricula has gained interest among educators. Despite this growing attention, their applicability in culturally responsive Indigenous educational settings like Hawai`i's public schools and Kaiapuni (immersion language) programs, remains understudied. Additionally, `Olelo Hawai`i, the Hawaiian language, as a low-resource language, poses unique challenges and concerns about cultural sensitivity and the reliability of generated content. Through surveys and interviews with kumu (educators), this study explores the perceived benefits and limitations of using LLMs for culturally revitalizing computer science (CS) education in Hawaiian public schools with Kaiapuni programs. Our findings highlight AI's time-saving advantages while exposing challenges such as cultural misalignment and reliability concerns. We conclude with design recommendations for future AI tools to better align with Hawaiian cultural values and pedagogical practices, towards the broader goal of trustworthy, effective, and culturally grounded AI technologies.
CIBER 4th flight fluctuation analysis: Measurements of near-IR auto- and cross-power spectra on arcminute to sub-degree scales
2025-01-29
Richard M. Feder, James J. Bock, Yun-Ting Cheng, Asantha Cooray, Phillip M. Korngut, Shuji Matsuura, Jordan Mirocha, Chi H. Nguyen, Kohji Takimoto, Kohji Tsumura, Ryan Wills, Michael Zemcov, CIBER collaboration
We present new anisotropy measurements in the near-infrared (NIR) for angular multipoles $300<\ell<10^5$ using imaging data at 1.1 $\mu$m and 1.8 $\mu$m from the fourth flight of the Cosmic Infrared Background ExpeRiment (CIBER). Using improved analysis methods and higher quality fourth flight data, we detect surface brightness fluctuations on scales $\ell<2000$ with CIBER auto-power spectra at $\sim14\sigma$ and 18$\sigma$ for 1.1 and 1.8 $\mu$m, respectively, and at $\sim10\sigma$ in cross-power spectra. The CIBER measurements pass internal consistency tests and represent a $5-10\times$ improvement in power spectrum sensitivity on several-arcminute scales relative to that of existing studies. Through cross-correlations with tracers of diffuse galactic light (DGL), we determine that scattered DGL contributes $<10\%$ to the observed fluctuation power at high confidence. On scales $\theta > 5'$, the CIBER auto- and cross-power spectra exceed predictions for integrated galactic light (IGL) and integrated stellar light (ISL) by over an order of magnitude, and are inconsistent with our baseline IGL+ISL+DGL model at high significance. We cross-correlate two of the CIBER fields with 3.6 $\mu$m and 4.5 $\mu$m mosaics from the Spitzer Deep Wide-Field Survey and find similar evidence for departures from Poisson noise in Spitzer-internal power spectra and CIBER $\times$ Spitzer cross-power spectra. A multi-wavelength analysis indicates that the auto-power of the fluctuations at low-$\ell$ is bluer than the Poisson noise from IGL and ISL; however, for $1' <\theta < 10'$, the cross-correlation coefficient $r_{\ell}$ of nearly all band combinations decreases with increasing $\theta$, disfavoring astrophysical explanations that invoke a single correlated sky component.
Engineering of Anyons on M5-Probes via Flux Quantization
2025-01-29
Hisham Sati, Urs Schreiber
These extended lecture notes survey a novel derivation of anyonic topological order (as seen in fractional quantum Hall systems) on single magnetized M5-branes probing Seifert orbi-singularities ("geometric engineering" of anyons), which we motivate from fundamental open problems in the field of quantum computing. The rigorous construction is non-Lagrangian and non-perturbative, based on previously neglected global completion of the M5-brane's tensor field by flux-quantization consistent with its non-linear self-duality and its twisting by the bulk C-field. This exists only in little-studied non-abelian generalized cohomology theories, notably in a twisted equivariant (and "twistorial") form of unstable Cohomotopy ("Hypothesis H"). As a result, topological quantum observables form Pontrjagin homology algebras of mapping spaces from the orbi-fixed worldvolume into a classifying 2-sphere. Remarkably, results from algebraic topology imply from this the quantum observables and modular functor of abelian Chern-Simons theory, as well as braid group actions on defect anyons of the kind envisioned as hardware for topologically protected quantum gates.
Constraining the Effect of Baryonic Feedback on the Matter Power Spectrum with Fast Radio Bursts
2025-01-29
Isabel Medlock, Daisuke Nagai, Daniel Anglés Alcázar, Matthew Gebhardt
In the age of large-scale galaxy and lensing surveys, such as DESI, Euclid, Roman and Rubin, we stand poised to usher in a transformative new phase of data-driven cosmology. To fully harness the capabilities of these surveys, it is critical to constrain the poorly understood influence of baryon feedback physics on the matter power spectrum. We investigate the use of a powerful and novel cosmological probe - fast radio bursts (FRBs) - to capture baryonic effects on the matter power spectrum, leveraging simulations from the CAMELS projects, including IllustrisTNG, SIMBA, and Astrid. We find that FRB statistics exhibit a strong correlation, independet of the subgrid model and cosmology, with quantities known to encapsulate baryonic impacts on the matter power spectrum, such as baryon spread and the halo baryon fraction. We propose an innovative method utilizing FRB observations to quantify the effects of feedback physics and enhance weak lensing measurements of $S_{8}$. We outline the necessary steps to prepare for the imminent detection of large FRB populations in the coming years, focusing on understanding the redshift evolution of FRB observables and mitigating the effects of cosmic variance.
Possible environmental quenching in an interacting LRD pair at $z\sim7$
2025-01-29
Rosa M. Mérida, Gaia Gaspar, Marcin Sawicki, Yoshihisa Asada, Gregor Rihtaršič, Guillaume Desprez, Jacqueline Antwi-Danso, Roberta Tripodi, Chris J. Willott, Maruša Bradač, Gabriel B. Brammer, Kartheik G. Iyer, Nicholas S. Martis, Adam Muzzin, Gaël Noirot, Ghassan T. E. Sarrouh, Vladan Markov
We report the discovery of a $z\sim7$ group of galaxies that contains two Little Red Dots (LRDs) just 3.3 kpc apart, along with three satellite galaxies, as part of the Canadian NIRISS Unbiased Cluster Survey (CANUCS). These LRDs are massive ($M_\star\sim10^{10}\,M_\odot$) and dusty (A(V) $>1$ mag) whereas the three satellites are lower-mass objects ($M_\star\sim10^{8-9}\,M_\odot$) subject to low dust attenuations. The spectral energy distributions (SEDs) of this LRD pair show strong evidence for a Balmer Break, consistent with a recent ($\sim 100$ Myr) quenching of star formation. In contrast, the satellites are compatible with a recent-onset ($\sim 100$ Myr), ongoing burst of star formation. LRD1's SED is consistent with a dust-free AGN as the source of the UV excess in the galaxy. The optical continuum would be powered by the emission from an obscured post-starburst and the AGN at a subdominant level. LRD2's SED is more ambiguous to interpret, but it could also be indicative of a dust-free AGN. The proximity of the two LRDs suggests that their interaction may be responsible for their recent star formation histories, which can be interpreted as environmental bursting and quenching in the Epoch of Reionization.
In the past five years, research has shifted from traditional Machine Learning (ML) and Deep Learning (DL) approaches to leveraging Large Language Models (LLMs) , including multimodality, for data augmentation to enhance generalization, and combat overfitting in training deep convolutional neural networks. However, while existing surveys predominantly focus on ML and DL techniques or limited modalities (text or images), a gap remains in addressing the latest advancements and multi-modal applications of LLM-based methods. This survey fills that gap by exploring recent literature utilizing multimodal LLMs to augment image, text, and audio data, offering a comprehensive understanding of these processes. We outlined various methods employed in the LLM-based image, text and speech augmentation, and discussed the limitations identified in current approaches. Additionally, we identified potential solutions to these limitations from the literature to enhance the efficacy of data augmentation practices using multimodal LLMs. This survey serves as a foundation for future research, aiming to refine and expand the use of multimodal LLMs in enhancing dataset quality and diversity for deep learning applications. (Surveyed Paper GitHub Repo: https://github.com/WSUAgRobotics/data-aug-multi-modal-llm. Keywords: LLM data augmentation, LLM text data augmentation, LLM image data augmentation, LLM speech data augmentation, audio augmentation, voice augmentation, chatGPT for data augmentation, DeepSeek R1 text data augmentation, DeepSeek R1 image augmentation, Image Augmentation using LLM, Text Augmentation using LLM, LLM data augmentation for deep learning applications)
Prompt-oriented Output of Culture-Specific Items in Translated African Poetry by Large Language Model: An Initial Multi-layered Tabular Review
2025-01-29
Adeyola Opaluwah
This paper examines the output of cultural items generated by Chat Generative PreTrained Transformer Pro in response to three structured prompts to translate three anthologies of African poetry. The first prompt was broad, the second focused on poetic structure, and the third prompt emphasized cultural specificity. To support this analysis, four comparative tables were created. The first table presents the results of the cultural items produced after the three prompts, the second categorizes these outputs based on Aixela framework of Proper nouns and Common expressions, the third table summarizes the cultural items generated by human translators, a custom translation engine, and a Large Language Model. The final table outlines the strategies employed by Chat Generative PreTrained Transformer Pro following the culture specific prompt. Compared to the outputs of cultural items from reference human translation and the custom translation engine in prior studies the findings indicate that the culture oriented prompts used with Chat Generative PreTrained Transformer Pro did not yield significant enhancements of cultural items during the translation of African poetry from English to French. Among the fifty four cultural items, the human translation produced thirty three cultural items in repetition, the custom translation engine generated Thirty eight cultural items in repetition while Chat Generative PreTrained Transformer Pro produced forty one cultural items in repetition. The untranslated cultural items revealed inconsistencies in Large language models approach to translating cultural items in African poetry from English to French.
We review three different approaches to investigate the non-equilibrium stochastic dynamics of a Josephson junction affected by L\'evy-distributed current fluctuations. First, we study the lifetime in the metastable superconducting state of current-biased short and long junctions, in the presence of Gaussian and L\'evy noise sources. We highlight the noise-induced nonmonotonic behavior of the mean switching time as a function of noise intensity and driving frequency, that is the noise enhanced stability and the stochastic resonant activation, respectively. Then, we characterize the L\'evy noise source through the average voltage drop across a current-biased junction. The voltage measurement versus the noise intensity allows to infer the value of the stability index that characterizes L\'evy-distributed fluctuations. The numerical calculation of the average voltage drop across the junction well agrees with the analytical estimate of the average velocity for L\'evy-driven escape processes from a metastable state. Finally, we look at the distribution of switching currents out of the zero-voltage state, when a L\'evy noise signal is added to a linearly ramped bias current. The analysis of the cumulative distribution function of the switching currents gives information on both the L\'evy stability index and the intensity of fluctuations. We present also a theoretical model to catch the features of the L\'evy signal from a measured distribution of switching currents. The phenomena discussed in this work can pave the way for an effective and reliable Josephson-based scheme to characterize L\'evy components eventually embedded in an unknown noisy signal.
"Ownership, Not Just Happy Talk": Co-Designing a Participatory Large Language Model for Journalism
2025-01-28
Emily Tseng, Meg Young, Marianne Aubin Le Quéré, Aimee Rinehart, Harini Suresh
Journalism has emerged as an essential domain for understanding the uses, limitations, and impacts of large language models (LLMs) in the workplace. News organizations face divergent financial incentives: LLMs already permeate newswork processes within financially constrained organizations, even as ongoing legal challenges assert that AI companies violate their copyright. At stake are key questions about what LLMs are created to do, and by whom: How might a journalist-led LLM work, and what can participatory design illuminate about the present-day challenges about adapting ``one-size-fits-all'' foundation models to a given context of use? In this paper, we undertake a co-design exploration to understand how a participatory approach to LLMs might address opportunities and challenges around AI in journalism. Our 20 interviews with reporters, data journalists, editors, labor organizers, product leads, and executives highlight macro, meso, and micro tensions that designing for this opportunity space must address. From these desiderata, we describe the result of our co-design work: organizational structures and functionality for a journalist-controlled LLM. In closing, we discuss the limitations of commercial foundation models for workplace use, and the methodological implications of applying participatory methods to LLM co-design.
Twenty years of Nešetřil's classification programme of Ramsey classes
2025-01-28
Jan Hubička, Matěj Konečný
In the 1970s, structural Ramsey theory emerged as a new branch of combinatorics. This development came with the isolation of the concepts of the $\mathbf{A}$-Ramsey property and Ramsey class. Following the influential Ne\v{s}et\v{r}il-R\"{o}dl theorem, several Ramsey classes have been identified. In the 1980s Ne\v{s}et\v{r}il, inspired by a seminar of Lachlan, discovered a crucial connection between Ramsey classes and Fra\"{\i}ss\'{e} classes and, in his 1989 paper, connected the classification programme of homogeneous structures to structural Ramsey theory. In 2005, Kechris, Pestov, and Todor\v{c}evi\'{c} revitalized the field by connecting Ramsey classes to topological dynamics. This breakthrough motivated Ne\v{s}et\v{r}il to propose a program for classifying Ramsey classes. We review the progress made on this program in the past two decades, list open problems, and discuss recent extensions to new areas, namely the extension property for partial automorphisms (EPPA), and big Ramsey structures.
Programming in Brazilian Higher Education and High School: A Systematic Literature Review
2025-01-28
Sofia C. Latini Gonçalves, Rodrigo Moreira, Larissa F. Rodrigues Moreira, André R. Backes, Adriana Zanella Martinhago
Programming, which is both economically significant and mentally stimulating, has been found to benefit the aging brain and to enhance cognitive function at various educational levels. Despite its advantages, challenges persist in standardizing and implementing programming education effectively across both the higher and secondary education levels in Brazil. To shed light on these issues, we carried out a systematic review of programming teaching methods in the Brazilian context, examining gaps, common techniques, approaches, and action opportunities in programming education. Our findings provide valuable recommendations for educational policymakers and educators to develop effective and updated national policies to teach programming.
Comprehensive Evaluation for a Large Scale Knowledge Graph Question Answering Service
2025-01-28
Saloni Potdar, Daniel Lee, Omar Attia, Varun Embar, De Meng, Ramesh Balaji, Chloe Seivwright, Eric Choi, Mina H. Farid, Yiwen Sun, Yunyao Li
Question answering systems for knowledge graph (KGQA), answer factoid questions based on the data in the knowledge graph. KGQA systems are complex because the system has to understand the relations and entities in the knowledge-seeking natural language queries and map them to structured queries against the KG to answer them. In this paper, we introduce Chronos, a comprehensive evaluation framework for KGQA at industry scale. It is designed to evaluate such a multi-component system comprehensively, focusing on (1) end-to-end and component-level metrics, (2) scalable to diverse datasets and (3) a scalable approach to measure the performance of the system prior to release. In this paper, we discuss the unique challenges associated with evaluating KGQA systems at industry scale, review the design of Chronos, and how it addresses these challenges. We will demonstrate how it provides a base for data-driven decisions and discuss the challenges of using it to measure and improve a real-world KGQA system.
The First SRG/eROSITA All-Sky Survey. Characterization of clusters of galaxies misclassified in the eRASS1 point source catalog
2025-01-28
F. Balzer, E. Bulbul, M. Kluge, A. Liu, M. Salvato, M. Fabricius, R. Seppi, E. Artis, Y. E. Bahar, R. Bender, N. Clerc, J. Comparat, V. Ghirardini, S. Grandis, S. Krippendorf, G. Lamer, N. Malavasi, A. Merloni, K. Nandra, M. E. Ramos-Ceja, J. S. Sanders, X. Zhang, S. Zelmer
The detection of the extended X-ray-emission of the intracluster medium by the first SRG/eROSITA All-Sky Survey (eRASS1), combined with optical and near-infrared follow-up, resulted in the identification of more than 12000 galaxy clusters, yielding precise constraints on cosmological parameters. However, some clusters of galaxies can be misclassified as point sources by eROSITA's source detection algorithm due to the interplay between the point-spread function, the shallow depth of the survey, compact (cool core) X-ray emission, and bright active galactic nuclei hosted in their centers or their vicinity. To identify such misclassified galaxy clusters and groups, we apply optical follow-up to the eRASS1 X-ray point sources analogously to the treatment of the extent-selected catalog. After rigorous filtering to ensure purity, we find a total of 8347 clusters of galaxies, of which 5819 are novel detections, in a redshift range $0.05 < z \lesssim 1.1$. This corresponds to a 70 % discovery rate, a fraction similar to that of the extent-selected sample. To facilitate finding new exceptional clusters such as the Phoenix cluster (which is recovered in our sample), we divide the clusters into five classes based on the optical properties of likely single-source counterparts to the X-ray emission. We further investigate potential biases in our selection process by analyzing the optical and X-ray data. With this work, we provide a catalog of galaxy clusters and groups in the eRASS1 point source catalog, including their optical and X-ray properties along with a meaningful classification.
In this study, we analyzed the close binary open clusters CWNU 2666 and HSC 224, which are in close spatial proximity, using photometric and astrometric data from the {\it Gaia} DR3 catalog. Likely member stars were identified based on a membership probability threshold ($P \geq 0.5$), resulting in 106 and 146 members for CWNU 2666 and HSC 224, respectively. The mean proper motion components ($\mu_{\alpha}\cos\delta$, $\mu_{\delta}$) were determined to be (0.646$\pm$0.155, -0.769$\pm$0.124) mas yr$^{-1}$ for CWNU 2666, and (0.665$\pm$0.131, -0.728$\pm$0.107) mas yr$^{-1}$ for HSC 224. The isochrone distances ($d_{\rm iso}$) were estimated as 1885$\pm$44 pc for CWNU 2666 and 1866$\pm$29 pc for HSC 224. The corresponding cluster ages ($t$) were derived as 160$\pm$15 Myr and 140$\pm$15 Myr, respectively. The astrometric and fundamental astrophysical parameters derived in this study demonstrate that the two open clusters are a close pair of open clusters.
BASS XLVII: 22 GHz Radio Atlas of Swift-BAT Selected AGN
2025-01-28
Macon Magno, Krista L. Smith, O. Ivy Wong, Richard Mushotzky, Stuart Vogel, Michael J. Koss, Claudio Ricci, Kyuseok Oh, Chin-Shin Chang, Loreto Barcos-Muñoz, Franz E. Bauer, Alessandro Peca, Darshan Kakkad, Turgay Caglar, Benny Trakhtenbrot, Fiona Harrison, Daniel Stern, C. Megan Urry, Merry Powell
We present the third phase of the largest high-frequency, high-resolution imaging survey of 231 nearby, hard X-ray selected AGN, with a very high $98 \pm 1\%$ detection fraction. This survey presents VLA 22 GHz radio observations with 1" spatial resolution covering over $6$ orders of magnitude in radio luminosity in nearby AGN that span $\sim4$ orders of magnitude in black hole mass and X-ray luminosity. We identify three different radio morphologies: $44 \pm 3\%$ (102/231) are compact or unresolved, $46 \pm 3\%$ (106/231) show an extended structure (star formation, possible one-sided jets, etc.), and $8 \pm 2\%$ (19/231) have a biconical or two-sided jet-like morphology. The remaining $2 \pm 1\%$ (4/231) sources are non-detections. The radio-to-X-ray luminosity ratios of the Swift-BAT AGN ($\text{L}_R/\text{L}_{14-195 \text{keV}} \sim 10^{-5.5}$ and $\text{L}_R/\text{L}_{2-10 \text{keV}} \sim 10^{-5}$) with a scatter of $\sim0.5$ dex are similar to that of coronally active stars ($\text{L}_R/\text{L}_X \sim 10^{-5}$). For most targets, extended emission in radio-quiet objects is broadly consistent with the expectation for star formation from previous FIR observations, once the contribution from the radio core has been subtracted. Our sample represents nearby analogs of distant AGN at the peak of black hole growth, and thus the high detection fraction in our work has important implications for future high frequency AGN radio surveys with the next generation VLA (ngVLA) or Square Kilometre Array (SKA), both of which should detect large fractions of more distant AGN.
Shock-cooling Constraints via Early-time Observations of the Type IIb SN 2022hnt
2025-01-28
Joseph R. Farah, D. Andrew Howell, Giacomo Terreran, Ido Irani, Jonathan Morag, Craig Pellegrino, Curtis McCully, Megan Newsome, Estefania Padilla Gonzalez, Azalee Bostroem, Griffin Hosseinzadeh
We report the results of a rapid follow-up campaign on the Type IIb Supernova (SN) 2022hnt. We present a daily, multi-band, photometric follow-up using the Las Cumbres Observatory, the Zwicky Transient Facility, the orbiting \textit{Swift} observatory, and the Asteroid Terrestrial-impact Last Alert System (ATLAS). A distinctive feature in the light curve of SN 2022hnt and other IIb SNe is an early narrow peak prior to the ${}^{56}$Ni peak caused by rapid shock cooling of the hydrogen envelope, which can serve as an important probe of the properties of the massive progenitor star in the moments before explosion. Using SN 2022hnt as a case study, we demonstrate a framework of considerations for the application of shock cooling models to type IIb SNe, outlining a consistent procedure for future surveys of Type IIb SNe progenitor and explosion properties. \hll{We fit several recent models of shock-cooling emission and obtain progenitor radii between $\sim50$ and $\sim100$ $R_\odot$, as well as hydrogen-enriched envelope masses between $\sim0.01$ and $\sim0.1$ $M_\odot$, both consistent with values for other IIb SNe. One of these models is the model of \cite{Morag2023}, marking the first time this model has been applied to a Type IIb SN.} We evaluate contrasting predictions between shock-cooling models to construct a fiducial parameter set which can be used for comparison to other SNe. Finally, we investigate the possibility of extended wind breakout or precursor emission captured in the earliest detections.
Revisiting the multi-planetary system of the nearby star HD 20794: Confirmation of a low-mass planet in the habitable zone of a nearby G-dwarf
2025-01-28
N. Nari, X. Dumusque, N. C. Hara, A. Suárez Mascareño, M. Cretignier, J. I. González Hernández, A. K. Stefanov, V. M. Passegger, R. Rebolo, F. Pepe, N. C. Santos, S. Cristiani, J. P. Faria, P. Figueira, A. Sozzetti, M. R. Zapatero Osorio, V. Adibekyan, Y. Alibert, C. Allende Prieto, F. Bouchy, S. Benatti, A. Castro-González, V. D'Odorico, M. Damasso, J. B. Delisle, P. Di Marcantonio, D. Ehrenreich, R. Génova-Santos, M. J. Hobson, B. Lavie, J. Lillo-Box, G. Lo Curto, C. Lovis, C. J. A. P. Martins, A. Mehner, G. Micela, P. Molaro, C. Mordasini, N. Nunes, E. Palle, S. P. Quanz, D. Ségransan, A. M. Silva, S. G. Sousa, S. Udry, N. Unger, J. Venturini
Close-by Earth analogs and super-Earths are of primary importance because they will be preferential targets for the next generation of direct imaging instruments. Bright and close-by G-to-M type stars are preferential targets in radial velocity surveys to find Earth analogs. We present an analysis of the RV data of the star HD 20794, a target whose planetary system has been extensively debated in the literature. The broad time span of the observations makes it possible to find planets with signal semi-amplitudes below 1 m/s in the habitable zone. We monitored the system with ESPRESSO. We joined ESPRESSO data with the HARPS data, including archival data and new measurements from a recent program. We applied the post-processing pipeline YARARA to HARPS data to correct systematics, improve the quality of RV measurements, and mitigate the impact of stellar activity. Results. We confirm the presence of three planets, with periods of 18.3142 +/- 0.0022 d, 89.68 +/- 0.10 d, and 647.6 +/- 2.6 d, along with masses of 2.15 +/- 0.17 MEarth, 2.98 +/- 0.29 MEarth, and 5.82 +/- 0.57 MEarth respectively. For the outer planet, we find an eccentricity of 0.45 +/- 0.10, whereas the inner planets are compatible with circular orbits. The latter is likely to be a rocky planet in the habitable zone of HD 20794. From the analysis of activity indicators, we find evidence of a magnetic cycle with a period around 3000 d, along with evidence pointing to a rotation period around 39 d. We have determined the presence of a system of three planets orbiting the solar-type star HD 20794. This star is bright (V=4.34 mag) and close (d = 6.04 pc), and HD 20794 d resides in the stellar habitable zone, making this system a high-priority target for future atmospheric characterization with direct imaging facilities.
Observations of the First Galaxies in the Era of JWST
2025-01-28
Daniel P. Stark, Michael W. Topping, Ryan Endsley, Mengtao Tang
We provide a review of our current knowledge of galaxies throughout the first billion years of cosmic history. This field has undergone a transformation in the last two years following the launch of $\textit{JWST}$, and we aim to deliver an observational overview of what we have learned about $z\gtrsim 5$ galaxies. We introduce the latest selection methods of high redshift galaxies and describe new measurements of the census of continuum-selected and dusty star forming galaxies at $z\gtrsim 5$. We discuss new measurements of the UV luminosity function at $z\gtrsim 10$ and associated implications for early star formation. We then summarize what is being learned about the physical properties of early galaxies, with up-to-date discussions of the sizes, masses, ages, metallicities, abundance patterns, UV colors, dust properties, and ionizing sources in $z\gtrsim 5$ galaxies. We review observational evidence for bursty star formation histories and describe prospects for characterizing the duty cycle with future observations. We provide a brief overview of the insight being gained through new detections of AGN in early galaxies. Finally we introduce the latest constraints on the contribution of galaxies to reionziation and discuss how $\textit{JWST}$ measurements of Ly$\alpha$ emission offer the potential to probe the earliest stages of the process. This review is meant to provide a broad introduction to those new to the observational study of very high redshift galaxies.
Eclipses and pulsations are the two primary ways in which the physical properties of stars can be deduced and used to improve our understanding of stellar theory. An obvious idea is to combine these two analyses into the study of pulsating stars in eclipsing binaries. This is the aim of the SWIPE project. This paper summarises the scientific arguments, current status and future plans of the project.
The CLASS Quasar Catalog: Coronal Line Activity in Type 1 SDSS Quasars
2025-01-28
Sara Doan, Shobita Satyapal, Michael Reefe, Remington O. Sexton, William Matzko, Jeffrey D. McKaig, Nathan J. Secrest, Jenna M. Cann, Ari Laor, Gabriela Canalizo
We conduct the first systematic survey of a total of eleven optical coronal lines in the spectra of a large sample of low redshift (z < 0.8) Type 1 quasars observed by the Sloan Digital Sky Survey (SDSS). We find that strong coronal line emission is rare in SDSS even in Type 1 quasars; only 885 out of 19,508 (4.5%) galaxies show at least one coronal line, with higher ionization potential lines ($>100$eV) being even rarer. The [Ne V] $\lambda$3426 line, which constitutes the majority of detections, is strongly correlated with the bolometric luminosity. These findings suggest that the optical coronal lines are significantly suppressed in the majority of local AGNs, possibly as a result of the presence of dust in the emitting regions. We find that the incidence of ionized outflows is significantly higher in coronal line emitters compared with non-coronal line emitters, possibly suggesting that dust destruction in outflows enhances coronal line emission in AGNs. Many coronal lines show line profiles that are broader than those of narrow lines, and are blue-shifted relative the lower ionization potential lines, suggesting outflows in the highly ionized gas. Given the limited number of detections, we do not find any statistically significant trends of detection statistics, or line ratios with black hole mass, Eddington ratio, or AGN bolometric luminosity. The catalog is publicly available and can provide a useful database of the coronal line properties of low redshift quasars that can be compared to the growing number of high-z AGNs discovered by JWST.
EZOA: Nançay HI follow-up observations in the Zone of Avoidance
2025-01-28
A. C. Schröder, W. van Driel, R. C. Kraan-Korteweg, C. Belleval
We present follow-up 21cm HI line observations made with the Nan\c{c}ay Radio Telescope (NRT) of 99 weak or potential detections of galaxies from the EZOA catalogue in the northern Zone of Avoidance (ZoA), which were extracted from the shallow version of the EBHIS blind HI survey performed with the Effelsberg radio telescope. The new NRT observations are on average almost three times as sensitive as those from EBHIS. Of the 99 observed sources, we confirmed 72, while three yielded inconclusive results. We find that the quality assessment of the EZOA catalogue entries correlates well with the NRT recovery rate; for instance, only four of the 22 potential detections could be confirmed. Due to the higher sensitivity as well as the large north-south extent of the NRT beam, the NRT observations also yielded five serendipitous detections, which we include here. We updated the EZOA catalogue with the improved HI parameters and detections. To test mitigation of Radio Frequency Interference signals, we also observed selected sources using a dedicated receiver and data processing system.
The interplay between super-metallicity, lithium depletion, and radial migration in nearby stars
2025-01-28
M. L. L. Dantas, R. Smiljanic, R. Boesso, H. Rocha-Pinto, L. Magrini, G. Guiglion, D. Romano
We report the discovery of a peculiar set of old super-metal-rich dwarf stars with orbits of low eccentricity that reach a maximum height from the Galactic plane between $\sim$ 0.5-1.5 kpc observed by the \emph{Gaia}-ESO Survey. These stars show lithium (Li) depletion, which is anti-correlated with their [Fe/H]. To investigate these stars' chemo-dynamical properties, we used data from the \emph{Gaia}-ESO Survey. We applied hierarchical clustering to group the stars based on their abundances (excluding Li). Orbits were integrated using \emph{Gaia} astrometry and radial velocities from \emph{Gaia}-ESO. Our analysis suggests that the high metallicity of these stars is incompatible with their formation in the solar neighbourhood. We also found that their Li envelope abundance is below the benchmark meteoritic value, in agreement with previous works. This result supports the idea that the Li abundance in old, super-metal-rich dwarf stars should not be considered a proxy for the local interstellar medium Li.
Upper critical field and colossal spin valve analogy in normal metal-superconductor-normal metal trilayers
2025-01-28
Kelsey B. Robbins, Pukar Sedai, Alexandra J. Howzen, Robert M. Klaes, Reza Loloee, Norman O. Birge, Nathan Satchell
The role of spin orbit interaction in superconducting proximity effect is an area of intense research effort. Recent theoretical and experimental works investigate the possible role of spin-orbit interaction in generating spin-triplet pair correlations. In this work, we present an experimental survey of thin normal metal-superconductor-normal metal trilayers with Nb superconductor and Al, Ti, Cu, Pt, Ta, and Au normal metals, along with single layers of Nb as reference. We aim to probe the role of spin-orbit interaction and resistivity on the normal metal proximity effect through measurements of the upper critical field. We find that the upper critical fields of the trilayers are lower than that of a single layer Nb reference sample, and that the trilayers with higher resistivity metals, Ti, Pt, and Ta, behave as 2-dimensional superconductors. We also find that compared to single layer Nb films, the trilayers show a greater suppression of critical temperature during rotation from in-plane to out-of-plane applied magnetic field. This suppression of critical temperature under field rotation is analogous to the colossal spin valve effect that can be achieved in systems with ferromagnetic materials, however in our trilayers only orbital contributions to the suppression are present.
Seasonal Influenza Vaccination Hesitancy and Digital Literacy: Evidence from the European countries
2025-01-28
Martina Celidoni, Nita Handastya, Guglielmo Weber, Nancy Zambon
This study documents the relationship between computer skills/digital literacy and influenza vaccination take-up among older adults in Europe during and after the COVID-19 pandemic. Using data from the Survey of Health, Aging and Retirement in Europe, we find a positive partial association between influenza vaccination take-up and two indicators of computer skills/digital literacy, self-assessed pre-pandemic computer skills and having used a computer at work in any pre-pandemic job. We do not estimate significant behavioural changes for individuals with better computer skills that may have been driven by spillover effects from the pandemic experience.
Large Language Models for Code Generation: The Practitioners Perspective
2025-01-28
Zeeshan Rasheed, Muhammad Waseem, Kai Kristian Kemell, Aakash Ahmad, Malik Abdul Sami, Jussi Rasku, Kari Systä, Pekka Abrahamsson
Large Language Models (LLMs) have emerged as coding assistants, capable of generating source code from natural language prompts. With the increasing adoption of LLMs in software development, academic research and industry based projects are developing various tools, benchmarks, and metrics to evaluate the effectiveness of LLM-generated code. However, there is a lack of solutions evaluated through empirically grounded methods that incorporate practitioners perspectives to assess functionality, syntax, and accuracy in real world applications. To address this gap, we propose and develop a multi-model unified platform to generate and execute code based on natural language prompts. We conducted a survey with 60 software practitioners from 11 countries across four continents working in diverse professional roles and domains to evaluate the usability, performance, strengths, and limitations of each model. The results present practitioners feedback and insights into the use of LLMs in software development, including their strengths and weaknesses, key aspects overlooked by benchmarks and metrics, and a broader understanding of their practical applicability. These findings can help researchers and practitioners make informed decisions for systematically selecting and using LLMs in software development projects. Future research will focus on integrating more diverse models into the proposed system, incorporating additional case studies, and conducting developer interviews for deeper empirical insights into LLM-driven software development.
MAUCell: An Adaptive Multi-Attention Framework for Video Frame Prediction
2025-01-28
Shreyam Gupta, P. Agrawal, Priyam Gupta
Temporal sequence modeling stands as the fundamental foundation for video prediction systems and real-time forecasting operations as well as anomaly detection applications. The achievement of accurate predictions through efficient resource consumption remains an ongoing issue in contemporary temporal sequence modeling. We introduce the Multi-Attention Unit (MAUCell) which combines Generative Adversarial Networks (GANs) and spatio-temporal attention mechanisms to improve video frame prediction capabilities. Our approach implements three types of attention models to capture intricate motion sequences. A dynamic combination of these attention outputs allows the model to reach both advanced decision accuracy along with superior quality while remaining computationally efficient. The integration of GAN elements makes generated frames appear more true to life therefore the framework creates output sequences which mimic real-world footage. The new design system maintains equilibrium between temporal continuity and spatial accuracy to deliver reliable video prediction. Through a comprehensive evaluation methodology which merged the perceptual LPIPS measurement together with classic tests MSE, MAE, SSIM and PSNR exhibited enhancing capabilities than contemporary approaches based on direct benchmark tests of Moving MNIST, KTH Action, and CASIA-B (Preprocessed) datasets. Our examination indicates that MAUCell shows promise for operational time requirements. The research findings demonstrate how GANs work best with attention mechanisms to create better applications for predicting video sequences.
The case for large-scale AGN feedback in galaxy formation simulations: insights from XFABLE
2025-01-28
Leah Bigwood, Martin A. Bourne, Vid Irsic, Alexandra Amon, Debora Sijacki
While cosmological simulations of galaxy formation have reached maturity, able to reproduce many fundamental galaxy and halo properties, no consensus has yet been reached on the impact of `baryonic feedback' on the non-linear matter power spectrum. This severely limits the precision of (and potentially biases) small-scale cosmological constraints obtained from weak lensing and galaxy surveys. Recent observational evidence indicates that `baryonic feedback' may be more extreme than commonly assumed in current cosmological hydrodynamical simulations. In this paper, we therefore explore a range of empirical AGN feedback models, within the FABLE simulation suite, with different parameterizations as a function of cosmic time, host halo properties, and/or spatial location where feedback energy is thermalized. We demonstrate that an AGN radio-mode feedback acting in a larger population of black holes, with jets thermalizing at relatively large cluster-centric distances, as exemplified by our XFABLE model, is in good agreement with the latest weak lensing + kSZ constraints across all k-scales. Furthermore, XFABLE maintains good agreement with the galaxy stellar mass function, gas fraction measurements, and all key galaxy group and cluster properties, including scaling relations and ICM radial profiles. Our work highlights the pressing need to model black hole accretion and feedback physics with a greater level of realism, including relativistic, magnetized jets in full cosmological simulations. Finally, we discuss how a range of complementary observational probes in the near future will enable us to constrain AGN feedback models, and therefore reduce `baryonic feedback' modelling uncertainty for the upcoming era of large cosmological surveys.
A totally non-compensatory multi-criteria method for evaluating and improving level of satisfaction (LoS): proposal and application on Airport Terminal of Passengers
2025-01-28
Phelipe Medeiros da Rocha, Helder Gomes Costa
To evaluate and assign a service according customer's level of satisfaction (LoS) is a relevant issue in operations management. This is a typical situation in which the evaluators, have passed by heterogeneous experiences along their life which implies they could consider different variables when evaluating a product. Despite it, the models for measuring Los usually consider a homogeneous set of criteria when facing LoS evaluation. This study applies a totally non-compensatory modeling that allows each customer to select the criteria, from a whole set of aspects, the customer wants to use for evaluating LoS. The proposal was tested in evaluating LoS regarding the services provided by Airport Terminal of Passengers (ATPs) in Brazil, with data collected in a survey involving 19,240 passengers, interviewed at 15 Brazilian international airports. The data collected was imputed into ELECTRE TRI ME algorithm to obtain the a credibility degree of sorting the instances. The values of credibility degree were them used to obtain groups of ATPs. Finally, the statistical modes of the evaluations in each group were analyzed and compared. The proposal allowed a full non-compensatory approach to obtain the credibility degree even when considering perceptions from several evaluators that could use different criteria. As a result, it was identified, for each cluster of ATP, the criteria sets to be improved and even those to be prioritized. The pioneer modeling proposed in this article for evaluating LoS plus its instancing in ATPs terminals represents an original advance in the establishment of a multi-criteria decision aid (MCDA) model to assess the quality of services and fills a relevant gap for a full non-compensatory approach able to classify the LoS in the airport context, considering perceptions of multiple evaluators even if they use different criteria in their evaluations.
We study the impact of Stack Overflow code evolution on the stability of prior research findings derived from Stack Overflow data and provide recommendations for future studies. We systematically reviewed papers published between 2005--2023 to identify key aspects of Stack Overflow that can affect study results, such as the language or context of code snippets. Our analysis reveals that certain aspects are non-stationary over time, which could lead to different conclusions if experiments are repeated at different times. We replicated six studies using a more recent dataset to demonstrate this risk. Our findings show that four papers produced significantly different results than the original findings, preventing the same conclusions from being drawn with a newer dataset version. Consequently, we recommend treating Stack Overflow as a time series data source to provide context for interpreting cross-sectional research conclusions.
Programming tools for Analogue Quantum Computing in the High-Performance Computing Context -- A Review
2025-01-28
Mateusz Meller, Vendel Szeremi, Oliver Thomson Brown
Recent advances in quantum computing have brought us closer to realizing the potential of this transformative technology. While significant strides have been made in quantum error correction, many challenges persist, particularly in the realm of noise and scalability. Analogue quantum computing schemes, such as Analogue Hamiltonian Simulation and Quantum Annealing, offer a promising approach to address these limitations. By operating at a higher level of abstraction, these schemes can simplify the development of large-scale quantum algorithms. To fully harness the power of quantum computers, they must be seamlessly integrated with traditional high-performance computing (HPC) systems. While substantial research has focused on the integration of circuit-based quantum computers with HPC, the integration of analogue quantum computers remains relatively unexplored. This paper aims to bridge this gap by contributing in the following way: Comprehensive Survey: We conduct a comprehensive survey of existing quantum software tools with analogue capabilities. Readiness Assessment: We introduce a classification and rating system to assess the readiness of these tools for HPC integration. Gap Identification and Recommendations: We identify critical gaps in the landscape of analogue quantum programming models and propose actionable recommendations for future research and development.
Medical Physics Dataset Article: a database of FLASH Murine In-Vivo Studies
2025-01-28
Mathilde Toschini, Isabella Colizzi, Antony John Lomax, Serena Psoroulas
The FLASH effect refers to a lower normal tissue damage for an equivalent tumour response, potentially widening the therapeutic window for radiotherapy. Although this effect has been demonstrated in various experiments using different types of particles and irradiation parameters, the underlying mechanism is not yet clearly understood. Uncertainties surround the conducted experiments, the explored parameter space, and the variability of reported results. To gain a better overview, we created a dataset that includes in-vivo FLASH experiments. This dataset documents all machine and biological dosimetric parameters, and for determined endpoints, it includes the outcome of the experiment. Our goal is to increase awareness of the results and their variability and provide a useful research and analysis tool for the community. The database contains peer-reviewed papers published until March 2024 on the FLASH in-vivo (murine) experiments. From each paper, previously defined parameters have been manually extracted and/or recalculated to ensure compatibility within the database entries. We provide two types of datasets: a user-friendly web-based Notion database and spreadsheets on a Zenodo repository. The database contains all the reviewed papers with extracted information in text or numeric form. Users can duplicate the database or view, search, filter, and reorganise online entries. The spreadsheets contain the data for the most analysed endpoints, allowing a comparative analysis. The study has two main applications. The web-based database will allow for a user-friendly search of information and meta-data of all published FLASH murine data. This will facilitate future research efforts to better understand the FLASH effect. The spreadsheets are a simple and useful tool for the community to conduct statistical analysis and determine the parameters associated with the FLASH effect.
Can the sound horizon-free measurement of $H_0$ constrain early new physics?
2025-01-28
Jun-Qian Jiang, Yun-Song Piao
The sound horizon-independent $H_0$ extracted by using galaxy clustering surveys data through, e.g., EFTofLSS or ShapeFit analyses, is considered to have the potential to constrain the early new physics responsible for solving the Hubble tension. Recent observations, e.g. DESI, have shown that the sound horizon-independent measurement of $H_0$ is consistent with $\Lambda$CDM. In this work, we clarify some potential misuses and misinterpretations in these analyses. On the one hand, imposing some prior from other cosmological probes is often used to strengthen the constraints on the results, however, these priors are usually derived using the assumption of $\Lambda$CDM, it is not suitable to apply these so-called $\Lambda$CDM priors (e.g., the $n_s$ prior from CMB), which would bias the results, to early new physics because these early new physics are usually accompanied by shifts of the $\Lambda$CDM parameters. On the other hand, the constraints on $H_0$ in the sound horizon-independent EFTofLSS analysis arise from not only the shape of the power spectrum ($k_\text{eq}$-based $H_0$), but also the overall amplitude (when combined with CMB lensing observations) and the relative amplitudes of the BAO wiggles, thus besides $k_\text{eq}$ other information may also play a role in constraining $H_0$. We also make forecasts for an Euclid-like survey, which suggest that ongoing observations will also have difficulty ruling out early new physics.
Time Evolution of Mg II in SDSS J2320+0024: Implications for a Subparsec Binary Supermassive Black Hole System
2025-01-28
Marta Fatović, Dragana Ilić, Andjelka B. Kovačević, Lovro Palaversa, Saša Simić, Luka Č. Popović, Karun Thanjavur, Oleksandra Razim, Željko Ivezić, Minghao Yue, Xiaohui Fan
Here we present results from our spectroscopic follow-up of SDSS J2320+0024, a candidate binary supermassive black hole (SMBH) with a suspected sub-pc separation, identified by a 278-day periodicity observed in its multi-band optical light curves. We investigate the dramatic variability of the complex Mg II emission line profile aiming to test the alignments of the observed photometric light curves and the spectroscopic signatures in the context of the binary SMBH system. We extract the pure broad Mg II line from the newly obtained Gemini and Magellan spectra and measure the emission line parameters to reveal fundamental dynamical parameters of the SMBHs binary system. We adopt the PoSKI sub-pc binary SMBH model, which includes broad-line region (BLR) around less massive component and a circumbinary BLR, to interpret the observed variability in the spectral profile. We find that the Mg II line profile has a distinctive complex shape with the asymmetry and two peaks present which is varying across recent and archival observations. The temporal variability of the Mg II line profile may be associated with the emission from the binary SMBH system consisting of components with masses $M_1 = 2 \times 10^7 \, M_{\odot}$ and $M_2 = 2 \times 10^8 \, M_{\odot}$, and eccentricity e = 0.1. With an total estimated mass of $\sim 10^9 M_{\odot}$ and a subannual orbital period, this system may be a rare example of high-mass compact candidate of SMBH binary, thus important for further investigations of the evolution of the binary system. This study is a prototype of synergies of spectroscopic follow-up and future massive time-domain photometric surveys like Vera C. Rubin Observatory Legacy Survey of Space and Time.
Star formation from low to high mass: A comparative view
2025-01-28
H. Beuther, R. Kuiper, M. Tafalla
Star formation has often been studied by separating the low- and high-mass regimes with an approximate boundary at 8M_sun. While some of the outcomes of the star-formation process are different between the two regimes, it is less clear whether the physical processes leading to these outcomes are that different at all. Here, we systematically compare low- and high-mass star formation by reviewing the most important processes and quantities from an observational and theoretical point of view. We identify three regimes where processes are either similar, quantitatively or qualitatively different between low- and high-mass star formation. Similar characteristics can be identified for the turbulent gas properties and density structures of the star-forming regions. Many of the observational characteristics also do not depend that strongly on the environment. Quantitative differences can be found for outflow, infall and accretion rates as well as mean column and volume densities. Also the multiplicity significantly rises from low- to high-mass stars. The importance of the magnetic field for the formation processes appears still less well constrained. Qualitative differences between low- and high-mass star formation relate mainly to the radiative and ionizing feedback that occurs almost exclusively in regions forming high-mass stars. Nevertheless, accretion apparently can continue via disk structures in ionized accretion flows. Finally, we discuss to what extent a unified picture of star formation over all masses is possible and which issues need to be addressed in the future.
Predator-prey models: a review on some recent adaptations
2025-01-28
Érika Diz-Pita, M. Victoria Otero-Espinar
In the last years, predator-prey systems have increased their applications and have given rise to systems which represent more accurately different biological issues that appear in the context of interacting species. Our aim in this paper is to give a state-of-art review of recent predator-prey models which include some interesting characteristics as Allee effect, fear effect, cannibalism and immigration. We compare the qualitative results obtained for each of them, particularly regarding the equilibria, local and global stability, and the existence of limit cycles.
Generalized framework for likelihood-based field-level inference of growth rate from velocity and density fields
2025-01-28
Corentin Ravoux, Bastien Carreres, Damiano Rosselli, Julian Bautista, Anthony Carr, Tyann Dummerchat, Alex G. Kim, David Parkinson, Benjamin Racine, Dominique Fouchez, Fabrice Feinstein
Measuring the growth rate of large-scale structures ($f$) as a function of redshift has the potential to break degeneracies between modified gravity and dark energy models, when combined with expansion-rate probes. Direct estimates of peculiar velocities of galaxies have gained interest to estimate $f\sigma_8$. In particular, field-level methods can be used to fit the field nuisance parameter along with cosmological parameters simultaneously. This article aims to provide the community with an unified framework for the theoretical modeling of the likelihood-based field-level inference by performing fast field covariance calculations for velocity and density fields. Our purpose is to lay the foundations for non-linear extension of the likelihood-based method at the field level. We develop a generalized framework, implemented in the dedicated software flip to perform a likelihood-based inference of $f\sigma_8$. We derive a new field covariance model, which includes wide-angle corrections. We also include the models previously described in the literature inside our framework. We compare their performance against ours, we validate our model by comparing it with the two-point statistics of a recent N-body simulation. The tests we perform allow us to validate our software and determine the appropriate wavenumber range to integrate our covariance model and its validity in terms of separation. Our framework allows for a wider wavenumber coverage used in our calculations than previous works, which is particularly interesting for non-linear model extensions. Finally, our generalized framework allows us to efficiently perform a survey geometry-dependent Fisher forecast of the $f\sigma_8$ parameter. We show that the Fisher forecast method we developed gives an error bar that is 30 % closer to a full likelihood-based estimation than a standard volume Fisher forecast.
Flow Matching: Markov Kernels, Stochastic Processes and Transport Plans
2025-01-28
Christian Wald, Gabriele Steidl
Among generative neural models, flow matching techniques stand out for their simple applicability and good scaling properties. Here, velocity fields of curves connecting a simple latent and a target distribution are learned. Then the corresponding ordinary differential equation can be used to sample from a target distribution, starting in samples from the latent one. This paper reviews from a mathematical point of view different techniques to learn the velocity fields of absolutely continuous curves in the Wasserstein geometry. We show how the velocity fields can be characterized and learned via i) transport plans (couplings) between latent and target distributions, ii) Markov kernels and iii) stochastic processes, where the latter two include the coupling approach, but are in general broader. Besides this main goal, we show how flow matching can be used for solving Bayesian inverse problems, where the definition of conditional Wasserstein distances plays a central role. Finally, we briefly address continuous normalizing flows and score matching techniques, which approach the learning of velocity fields of curves from other directions.
Misspellings in Natural Language Processing: A survey
2025-01-28
Gianluca Sperduti, Alejandro Moreo
This survey provides an overview of the challenges of misspellings in natural language processing (NLP). While often unintentional, misspellings have become ubiquitous in digital communication, especially with the proliferation of Web 2.0, user-generated content, and informal text mediums such as social media, blogs, and forums. Even if humans can generally interpret misspelled text, NLP models frequently struggle to handle it: this causes a decline in performance in common tasks like text classification and machine translation. In this paper, we reconstruct a history of misspellings as a scientific problem. We then discuss the latest advancements to address the challenge of misspellings in NLP. Main strategies to mitigate the effect of misspellings include data augmentation, double step, character-order agnostic, and tuple-based methods, among others. This survey also examines dedicated data challenges and competitions to spur progress in the field. Critical safety and ethical concerns are also examined, for example, the voluntary use of misspellings to inject malicious messages and hate speech on social networks. Furthermore, the survey explores psycholinguistic perspectives on how humans process misspellings, potentially informing innovative computational techniques for text normalization and representation. Finally, the misspelling-related challenges and opportunities associated with modern large language models are also analyzed, including benchmarks, datasets, and performances of the most prominent language models against misspellings. This survey aims to be an exhaustive resource for researchers seeking to mitigate the impact of misspellings in the rapidly evolving landscape of NLP.
Bridging spatial and temporal scales of developmental gene regulation
2025-01-28
Andrés H. Cardona, Márcia Mesquita Peixoto, Tohn Borjigin, Thomas Gregor
The development of multicellular organisms relies on the precise coordination of molecular events across multiple spatial and temporal scales. Understanding how information flows from molecular interactions to cellular processes and tissue organization during development is crucial for explaining the remarkable reproducibility of complex organisms. This review explores how chromatin-encoded information is transduced from localized transcriptional events to global gene expression patterns, highlighting the challenge of bridging these scales. We discuss recent experimental findings and theoretical frameworks, emphasizing polymer physics as a tool for describing the relationship between chromatin structure and dynamics across scales. By integrating these perspectives, we aim to clarify how gene regulation is coordinated across levels of biological organization and suggest strategies for future experimental approaches.
AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies
2025-01-28
Surojit Saha, Ross Whitaker
Automated interpretation of seismic images using deep learning methods is challenging because of the limited availability of training data. Few-shot learning is a suitable learning paradigm in such scenarios due to its ability to adapt to a new task with limited supervision (small training budget). Existing few-shot semantic segmentation (FSSS) methods fix the number of target classes. Therefore, they do not support joint training on multiple datasets varying in the number of classes. In the context of the interpretation of seismic facies, fixing the number of target classes inhibits the generalization capability of a model trained on one facies dataset to another, which is likely to have a different number of facies. To address this shortcoming, we propose a few-shot semantic segmentation method for interpreting seismic facies that can adapt to the varying number of facies across the dataset, dubbed the AdaSemSeg. In general, the backbone network of FSSS methods is initialized with the statistics learned from the ImageNet dataset for better performance. The lack of such a huge annotated dataset for seismic images motivates using a self-supervised algorithm on seismic datasets to initialize the backbone network. We have trained the AdaSemSeg on three public seismic facies datasets with different numbers of facies and evaluated the proposed method on multiple metrics. The performance of the AdaSemSeg on unseen datasets (not used in training) is better than the prototype-based few-shot method and baselines.
Laser patterning of the room temperature van der Waals ferromagnet 1$T$-CrTe$_2$
2025-01-28
Tristan Riccardi, Suman Sarkar, Anike Purbawati, Aloïs Arrighi, Marek Kostka, Abdellali Hadj-Azzem, Jan Vogel, Julien Renard, Laëtitia Marty, Amit Pawbake, Clément Faugeras, Kenji Watanabe, Takashi Taniguchi, Aurore Finco, Vincent Jacques, Lei Ren, Xavier Marie, Cedric Robert, Manuel Nuñez-Regueiro, Nicolas Rougemaille, Nedjma Bendiab, Johann Coraux
Lamellar crystalline materials, whose layers are bond by van der Waals forces, can be stacked to form ultrathin artificial heterostructures, and in particular vertical magnetic junctions when some of the stacked materials are (ferro)magnetic. Here, using the room temperature van der Waals ferromagnet 1$T$-CrTe$_2$, we report a method for patterning lateral magnetic junctions. Exploiting the heat-induced phase transformation of the material into Cr$_x$Te$_y$ compounds ($x/y>1/2$), we use local laser heating to imprint patterns at the micron-scale. Optimizing laser heat dissipation, we further demonstrate the crucial role of the substrate to control the phase transformation. If plain, unstructured poorly heat-conducting substrates allow for direct writing of magnetic patterns, structured $h$-BN layers can serve as heat stencils to draw potentially thinner patterns. Besides, $h$-BN encapsulation turns out to be heat-protective (in addition from protecting against oxidation as it is generally used for), allowing the demonstration of room temperature ferromagnetism in $<$7~nm-thick 1$T$-CrTe$_2$.
One Head Eight Arms: Block Matrix based Low Rank Adaptation for CLIP-based Few-Shot Learning
2025-01-28
Chunpeng Zhou, Qianqian Shen, Zhi Yu, Jiajun Bu, Haishuai Wang
Recent advancements in fine-tuning Vision-Language Foundation Models (VLMs) have garnered significant attention for their effectiveness in downstream few-shot learning tasks.While these recent approaches exhibits some performance improvements, they often suffer from excessive training parameters and high computational costs. To address these challenges, we propose a novel Block matrix-based low-rank adaptation framework, called Block-LoRA, for fine-tuning VLMs on downstream few-shot tasks. Inspired by recent work on Low-Rank Adaptation (LoRA), Block-LoRA partitions the original low-rank decomposition matrix of LoRA into a series of sub-matrices while sharing all down-projection sub-matrices. This structure not only reduces the number of training parameters, but also transforms certain complex matrix multiplication operations into simpler matrix addition, significantly lowering the computational cost of fine-tuning. Notably, Block-LoRA enables fine-tuning CLIP on the ImageNet few-shot benchmark using a single 24GB GPU. We also show that Block-LoRA has the more tighter bound of generalization error than vanilla LoRA. Without bells and whistles, extensive experiments demonstrate that Block-LoRA achieves competitive performance compared to state-of-the-art CLIP-based few-shot methods, while maintaining a low training parameters count and reduced computational overhead.
BASS XLV: Quantifying AGN Selection Effects in the Chandra COSMOS-Legacy Survey with BASS
2025-01-28
Yarone M. Tokayer, Michael J. Koss, C. Megan Urry, Priyamvada Natarajan, Richard Mushotzky, Mislav Balokovic, Franz E. Bauer, Peter Boorman, Alessandro Peca, Claudio Ricci, Federica Ricci, Daniel Stern, Ezequiel Treister, Benny Trakhtenbrot
Deep extragalactic X-ray surveys, such as the Chandra COSMOS-Legacy field (CCLS), are prone to be biased against active galactic nuclei (AGN) with high column densities due to their lower count rates at a given luminosity. To quantify this selection effect, we forward model nearby ($z\sim0.05$) AGN from the BAT AGN Spectroscopic Survey (BASS) with well-characterized ($\gtrsim$1000 cts) broadband X-ray spectra (0.5-195 keV) to simulate the CCLS absorption distribution. We utilize the BASS low-redshift analogs with similar luminosities to the CCLS ($L_\mathrm{2-10\ keV}^\mathrm{int}\sim10^{42-45}\ \mathrm{erg}\ \mathrm{s}^{-1}$), which are much less affected by obscuration and low-count statistics, as the seed for our simulations, and follow the spectral fitting of the CCLS. Our simulations reveal that Chandra would fail to detect the majority (53.3%; 563/1056) of obscured ($N_\mathrm{H}>10^{22}\ \mathrm{cm}^{-2}$) simulated BASS AGN given the observed redshift and luminosity distribution of the CCLS. Even for detected sources with sufficient counts ($\geq30$) for spectral modeling, the level of obscuration is significantly overestimated. This bias is most extreme for objects whose best fit indicates a high-column density AGN ($N_\mathrm{H}\geq10^{24}\ \mathrm{cm}^{-2}$), since the majority (66.7%; 18/27) of these are actually unobscured sources ($N_\mathrm{H}<10^{22}\ \mathrm{cm}^{-2}$). This implies that previous studies may have significantly overestimated the increase in the obscured fraction with redshift and the fraction of luminous obscured AGN. Our findings highlight the importance of directly considering obscuration biases and forward modeling in X-ray surveys, as well as the need for higher-sensitivity X-ray missions such as the Advanced X-ray Imaging Satellite (AXIS), and the importance of multi-wavelength indicators to estimate obscuration in distant supermassive black holes.
ATOMS: ALMA Three-millimeter Observations of massive Star-forming regions -XX. Probability distribution function of integrated intensity for dense molecular gas tracers
2025-01-28
C. Zhang, Tie Liu, Sihan Jiao, Feng-Yao Zhu, Z. -Y. Ren, H. -L. Liu, Ke Wang, J. -W. Wu, D. Li, P. García, Guido Garay, Leonardo Bronfman, Mika Juvela, Swagat das, Chang Won Lee, Feng-Wei Xu, L. V. Tóth, Prasanta Gorai, Patricio Sanhueza
We report the observations of J=1-0 of HCN, HCO+, H13CO+, and H13CN, HC3N (J=11-10) emission towards 135 massive star-forming clumps, as part of the ATOMS (ALMA Three-millimeter Observations of Massive Star-forming regions) Survey. We present the integrated intensity probability distribution function for these molecular tracers, modeled as a combination of a log-normal distribution and a power-law tail. The molecular line luminosities for the power-law tail segment, Lmol(p), have been calculated. We have investigated the correlation between the bolometric luminosity, Lbol, and the power-law part of the molecular line luminosity, Lmol(p). Our findings suggest that the scaling relationships between Lbol and Lmol(p) for HCN and HCO+ are sublinear, indicating that these molecules might not be the most effective tracers for the dense gas. In contrast, H13CN and HC3N exhibit a nearly linear relationship between Lbol and Lmol(p), indicating that they can well trace gravitationally bound dense gas. The ratios of Lbol-to-Lmol(p), serving as indicators of star formation efficiency within massive star-forming clumps, exhibit a weak anti-correlation with the power-law index in the I-PDF. In addition, the star formation efficiency is also weakly anti-correlated with the exponent U of the corresponding equivalent density distribution. Our results implie that clumps with substantial gas accumulation may still display low star formation efficiencies.
Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook
2025-01-28
Jiawei Xue, Ruichen Tan, Jianzhu Ma, Satish V. Ukkusuri
Data mining in transportation networks (DMTNs) refers to using diverse types of spatio-temporal data for various transportation tasks, including pattern analysis, traffic prediction, and traffic controls. Graph neural networks (GNNs) are essential in many DMTN problems due to their capability to represent spatial correlations between entities. Between 2016 and 2024, the notable applications of GNNs in DMTNs have extended to multiple fields such as traffic prediction and operation. However, existing reviews have primarily focused on traffic prediction tasks. To fill this gap, this study provides a timely and insightful summary of GNNs in DMTNs, highlighting new progress in prediction and operation from academic and industry perspectives since 2023. First, we present and analyze various DMTN problems, followed by classical and recent GNN models. Second, we delve into key works in three areas: (1) traffic prediction, (2) traffic operation, and (3) industry involvement, such as Google Maps, Amap, and Baidu Maps. Along these directions, we discuss new research opportunities based on the significance of transportation problems and data availability. Finally, we compile resources such as data, code, and other learning materials to foster interdisciplinary communication. This review, driven by recent trends in GNNs in DMTN studies since 2023, could democratize abundant datasets and efficient GNN methods for various transportation problems including prediction and operation.
Three Brown Dwarfs Masquerading as High-Redshift Galaxies in JWST Observations
2025-01-28
Zhijun Tu, Shu Wang, Xiaodian Chen, Jifeng Liu
We report the spectroscopic identification of three brown dwarf candidates -- o005_s41280, o006_s00089, and o006_s35616 -- discovered in the RUBIES using James Webb Space Telescope (JWST) Near-Infrared Spectrograph (NIRSpec) PRISM/CLEAR spectroscopy. We fit these sources with multiple substellar atmosphere models and present the atmospheric parameters, including effective temperature ($T_\mathrm{eff}$), surface gravity, and other derived properties. The results suggest that o005_s41280 and o006_s35616, with $T_\mathrm{eff}$ in the ranges of 2100--2300 K and 1800--2000 K, are likely L dwarfs, while o006_s00089, with $T_\mathrm{eff} < 1000$ K, is consistent with a late T dwarf classification. The best-fit model spectra provide a reasonable match to the observed spectra. However, distinct residuals exist in the $Y$, $J$, and $H$ bands for the two L dwarf candidates, particularly for o006_s35616. Incorporating the extinction parameter into the fitting process can significantly reduce these residuals. The distance estimates indicate that these candidates are about 2 kpc away. The analysis of the color-color diagram using multiple JWST NIRcam photometry suggests that cooler T dwarfs, such as o006_s00089, overlap with little red dots (LRDs), while hotter L dwarfs, like o005_s41280 and o006_s35616, tend to contaminate the high-redshift galaxy cluster. These findings suggest a brown dwarf contamination rate of approximately 0.1% in extragalactic deep field surveys, with L dwarfs being more frequently detected than cooler T and Y dwarfs.
On the Liénard's type equation: an icon of the Nonlinear Analysis
2025-01-28
Juan E. Nápoles Valdés
In this note, we review the latest qualitative results, referring to the Li\'enard Equation, in the framework of non-conformable, generalized and fractional differential operators.
FUNU: Boosting Machine Unlearning Efficiency by Filtering Unnecessary Unlearning
2025-01-28
Zitong Li, Qingqing Ye, Haibo Hu
Machine unlearning is an emerging field that selectively removes specific data samples from a trained model. This capability is crucial for addressing privacy concerns, complying with data protection regulations, and correcting errors or biases introduced by certain data. Unlike traditional machine learning, where models are typically static once trained, machine unlearning facilitates dynamic updates that enable the model to ``forget'' information without requiring complete retraining from scratch. There are various machine unlearning methods, some of which are more time-efficient when data removal requests are fewer. To decrease the execution time of such machine unlearning methods, we aim to reduce the size of data removal requests based on the fundamental assumption that the removal of certain data would not result in a distinguishable retrained model. We first propose the concept of unnecessary unlearning, which indicates that the model would not alter noticeably after removing some data points. Subsequently, we review existing solutions that can be used to solve our problem. We highlight their limitations in adaptability to different unlearning scenarios and their reliance on manually selected parameters. We consequently put forward FUNU, a method to identify data points that lead to unnecessary unlearning. FUNU circumvents the limitations of existing solutions. The idea is to discover data points within the removal requests that have similar neighbors in the remaining dataset. We utilize a reference model to set parameters for finding neighbors, inspired from the area of model memorization. We provide a theoretical analysis of the privacy guarantee offered by FUNU and conduct extensive experiments to validate its efficacy.
A Comprehensive Survey of the Lean 4 Theorem Prover: Architecture, Applications, and Advances
2025-01-28
Xichen Tang
This comprehensive survey examines Lean 4, a state-of-the-art interactive theorem prover and functional programming language. We analyze its architectural design, type system, metaprogramming capabilities, and practical applications in formal verification and mathematics. Through detailed comparisons with other proof assistants and extensive case studies, we demonstrate Lean 4's unique advantages in proof automation, performance, and usability. The paper also explores recent developments in its ecosystem, including libraries, tools, and educational applications, providing insights into its growing impact on formal methods and mathematical formalization.
Weak-emission-line QSOs (WLQs) are an enigmatic subclass of the QSO population, as their optical/UV spectra are marked by abnormally weak (or absent) emission lines. To obtain much-needed additional clues to the origin of this and other known peculiarities of WLQs, we have determined the 'ensemble' optical variability characteristics for a large, well-defined sample of 76 radio-quiet WLQs and also for a matched control sample comprising 603 normal radio-quiet QSOs. This analysis was done using their light-curves recorded in the $g$ and $r$ bands, under the ZTF survey during 2018-2024, with a typical cadence of 3 days. We find that, compared to normal QSOs, WLQs exhibit systematically milder optical variability on month/year-like time scales (by a factor of $\sim$ 1.76$\pm$0.05 in amplitude). We have independently verified this by carrying out an equivalent analysis of the V-band light-curves acquired under the CRTS during 2007- 2014, with a typical cadence of 10 days. This new observational differentiator between WLQs and normal QSOs may provide clues to understanding the intriguing nature of WLQs. It is proposed that the clumpiness of the torus material flowing into the central engine may play a key role in explaining the observed differences between the WLQs and normal QSOs.
2025-01-27
C. Frohmaier, M. Vincenzi, M. Sullivan, S. F. Hönig, M. Smith, H. Addison, T. Collett, G. Dimitriadis, R. S. Ellis, P. Gandhi, O. Graur, I. Hook, L. Kelsey, Y. L. Kim, C. Lidman, K. Maguire, L. Makrygianni, B. Martin, A. Möller, R. C. Nichol, M. Nicholl, P. Schady, B. D. Simmons, S. J. Smartt, E. Tempel, P. Wiseman, the LSST Dark Energy Science Collaboration
The Time Domain Extragalactic Survey (TiDES) conducted on the 4-metre Multi-Object Spectroscopic Telescope (4MOST) will perform spectroscopic follow-up of extragalactic transients discovered in the era of the NSF-DOE Vera C. Rubin Observatory. TiDES will conduct a 5-year survey, covering ${>}14\,000\,\mathrm{square\, degrees}$, and use around 250 000 fibre hours to address three main science goals: (i) spectroscopic observations of ${>}30 000$ live transients, (ii) comprehensive follow-up of ${>}200 000$ host galaxies to obtain redshift measurements, and (iii) repeat spectroscopic observations of Active Galactic Nuclei to enable reverberation mapping studies. The live spectra from TiDES will be used to reveal the diversity and astrophysics of both normal and exotic supernovae across the luminosity-timescale plane. The extensive host-galaxy redshift campaign will allow exploitation of the larger sample of supernovae and improve photometric classification, providing the largest-ever sample of spec-confirmed type Ia supernovae, capable of a sub-2 per cent measurement of the equation-of-state of dark energy. Finally, the TiDES reverberation mapping experiment of 700--1\,000 AGN will complement the SN Ia sample and extend the Hubble diagram to $z\sim2.5$
Evaluating The Performance of Using Large Language Models to Automate Summarization of CT Simulation Orders in Radiation Oncology
2025-01-27
Meiyun Cao, Shaw Hu, Jason Sharp, Edward Clouser, Jason Holmes, Linda L. Lam, Xiaoning Ding, Diego Santos Toesca, Wendy S. Lindholm, Samir H. Patel, Sujay A. Vora, Peilong Wang, Wei Liu
Purpose: This study aims to use a large language model (LLM) to automate the generation of summaries from the CT simulation orders and evaluate its performance. Materials and Methods: A total of 607 CT simulation orders for patients were collected from the Aria database at our institution. A locally hosted Llama 3.1 405B model, accessed via the Application Programming Interface (API) service, was used to extract keywords from the CT simulation orders and generate summaries. The downloaded CT simulation orders were categorized into seven groups based on treatment modalities and disease sites. For each group, a customized instruction prompt was developed collaboratively with therapists to guide the Llama 3.1 405B model in generating summaries. The ground truth for the corresponding summaries was manually derived by carefully reviewing each CT simulation order and subsequently verified by therapists. The accuracy of the LLM-generated summaries was evaluated by therapists using the verified ground truth as a reference. Results: About 98% of the LLM-generated summaries aligned with the manually generated ground truth in terms of accuracy. Our evaluations showed an improved consistency in format and enhanced readability of the LLM-generated summaries compared to the corresponding therapists-generated summaries. This automated approach demonstrated a consistent performance across all groups, regardless of modality or disease site. Conclusions: This study demonstrated the high precision and consistency of the Llama 3.1 405B model in extracting keywords and summarizing CT simulation orders, suggesting that LLMs have great potential to help with this task, reduce the workload of therapists and improve workflow efficiency.
RAPID: Retrieval-Augmented Parallel Inference Drafting for Text-Based Video Event Retrieval
2025-01-27
Long Nguyen, Huy Nguyen, Bao Khuu, Huy Luu, Huy Le, Tuan Nguyen, Tho Quan
Retrieving events from videos using text queries has become increasingly challenging due to the rapid growth of multimedia content. Existing methods for text-based video event retrieval often focus heavily on object-level descriptions, overlooking the crucial role of contextual information. This limitation is especially apparent when queries lack sufficient context, such as missing location details or ambiguous background elements. To address these challenges, we propose a novel system called RAPID (Retrieval-Augmented Parallel Inference Drafting), which leverages advancements in Large Language Models (LLMs) and prompt-based learning to semantically correct and enrich user queries with relevant contextual information. These enriched queries are then processed through parallel retrieval, followed by an evaluation step to select the most relevant results based on their alignment with the original query. Through extensive experiments on our custom-developed dataset, we demonstrate that RAPID significantly outperforms traditional retrieval methods, particularly for contextually incomplete queries. Our system was validated for both speed and accuracy through participation in the Ho Chi Minh City AI Challenge 2024, where it successfully retrieved events from over 300 hours of video. Further evaluation comparing RAPID with the baseline proposed by the competition organizers demonstrated its superior effectiveness, highlighting the strength and robustness of our approach.
URAG: Implementing a Unified Hybrid RAG for Precise Answers in University Admission Chatbots -- A Case Study at HCMUT
2025-01-27
Long Nguyen, Tho Quan
With the rapid advancement of Artificial Intelligence, particularly in Natural Language Processing, Large Language Models (LLMs) have become pivotal in educational question-answering systems, especially university admission chatbots. Concepts such as Retrieval-Augmented Generation (RAG) and other advanced techniques have been developed to enhance these systems by integrating specific university data, enabling LLMs to provide informed responses on admissions and academic counseling. However, these enhanced RAG techniques often involve high operational costs and require the training of complex, specialized modules, which poses challenges for practical deployment. Additionally, in the educational context, it is crucial to provide accurate answers to prevent misinformation, a task that LLM-based systems find challenging without appropriate strategies and methods. In this paper, we introduce the Unified RAG (URAG) Framework, a hybrid approach that significantly improves the accuracy of responses, particularly for critical queries. Experimental results demonstrate that URAG enhances our in-house, lightweight model to perform comparably to state-of-the-art commercial models. Moreover, to validate its practical applicability, we conducted a case study at our educational institution, which received positive feedback and acclaim. This study not only proves the effectiveness of URAG but also highlights its feasibility for real-world implementation in educational settings.
What is Formal Verification without Specifications? A Survey on mining LTL Specifications
2025-01-27
Daniel Neider, Rajarshi Roy
Virtually all verification techniques using formal methods rely on the availability of a formal specification, which describes the design requirements precisely. However, formulating specifications remains a manual task that is notoriously challenging and error-prone. To address this bottleneck in formal verification, recent research has thus focussed on automatically generating specifications for formal verification from examples of (desired and undesired) system behavior. In this survey, we list and compare recent advances in mining specifications in Linear Temporal Logic (LTL), the de facto standard specification language for reactive systems. Several approaches have been designed for learning LTL formulas, which address different aspects and settings of specification design. Moreover, the approaches rely on a diverse range of techniques such as constraint solving, neural network training, enumerative search, etc. We survey the current state-of-the-art techniques and compare them for the convenience of the formal methods practitioners.
A foundation model for human-AI collaboration in medical literature mining
2025-01-27
Zifeng Wang, Lang Cao, Qiao Jin, Joey Chan, Nicholas Wan, Behdad Afzali, Hyun-Jin Cho, Chang-In Choi, Mehdi Emamverdi, Manjot K. Gill, Sun-Hyung Kim, Yijia Li, Yi Liu, Hanley Ong, Justin Rousseau, Irfan Sheikh, Jenny J. Wei, Ziyang Xu, Christopher M. Zallek, Kyungsang Kim, Yifan Peng, Zhiyong Lu, Jimeng Sun
Systematic literature review is essential for evidence-based medicine, requiring comprehensive analysis of clinical trial publications. However, the application of artificial intelligence (AI) models for medical literature mining has been limited by insufficient training and evaluation across broad therapeutic areas and diverse tasks. Here, we present LEADS, an AI foundation model for study search, screening, and data extraction from medical literature. The model is trained on 633,759 instruction data points in LEADSInstruct, curated from 21,335 systematic reviews, 453,625 clinical trial publications, and 27,015 clinical trial registries. We showed that LEADS demonstrates consistent improvements over four cutting-edge generic large language models (LLMs) on six tasks. Furthermore, LEADS enhances expert workflows by providing supportive references following expert requests, streamlining processes while maintaining high-quality results. A study with 16 clinicians and medical researchers from 14 different institutions revealed that experts collaborating with LEADS achieved a recall of 0.81 compared to 0.77 experts working alone in study selection, with a time savings of 22.6%. In data extraction tasks, experts using LEADS achieved an accuracy of 0.85 versus 0.80 without using LEADS, alongside a 26.9% time savings. These findings highlight the potential of specialized medical literature foundation models to outperform generic models, delivering significant quality and efficiency benefits when integrated into expert workflows for medical literature mining.
Comprehensive Benchmarking Environment for Worker Flexibility in Flexible Job Shop Scheduling Problems
2025-01-27
David Hutter, Thomas Steinberger, Michael Hellwig
In Production Scheduling, the Flexible Job Shop Scheduling Problem (FJSSP) aims to optimize a sequence of operations and assign each to an eligible machine with varying processing times. For integration of the workforce, each machine also requires a worker to be present to process an operation which additionally affects the processing times. The resulting problem is called Flexible Job Shop Scheduling Problem with Worker Flexibility (FJSSP-W). The FJSSP has been approached with various problem representations, including Mixed Integer Linear Programming (MILP), Constrained Programming (CP), and Simulation-based Optimization (SBO). In the latter area in particular, there exists a large number of specialized Evolutionary Algorithms (EA) like Particle Swarm Optimization (PSO) or Genetic Algorithms (GA). Yet, the solvers are often developed for single use cases only, and validated on a few selected test instances, let alone compared with results from solvers using other problem representations. While suitable approaches do also exist, the design of the FJSSP-W instances is not standardized and the algorithms are hardly comparable. This calls for a systematic benchmarking environment that provides a comprehensive set of FJSSP(-W) instances and supports targeted algorithm development. It will facilitate the comparison of algorithmic performance in the face of different problem characteristics. The present paper presents a collection of 402 commonly accepted FJSSP instances and proposes an approach to extend these with worker flexibility. In addition, we present a detailed procedure for the evaluation of scheduling algorithms on these problem sets and provide suitable model representations for this purpose. We provide complexity characteristics for all presented instances as well as baseline results of common commercial solvers to facilitate the validation of new algorithmic developments.
Moving toward best practice when using propensity score weighting in survey observational studies
2025-01-27
Yukang Zeng, Fan Li, Guangyu Tong
Propensity score weighting is a common method for estimating treatment effects with survey data. The method is applied to minimize confounding using measured covariates that are often different between individuals in treatment and control. However, existing literature does not reach a consensus on the optimal use of survey weights for population-level inference in the propensity score weighting analysis. Under the balancing weights framework, we provided a unified solution for incorporating survey weights in both the propensity score of estimation and the outcome regression model. We derived estimators for different target populations, including the combined, treated, controlled, and overlap populations. We provide a unified expression of the sandwich variance estimator and demonstrate that the survey-weighted estimator is asymptotically normal, as established through the theory of M-estimators. Through an extensive series of simulation studies, we examined the performance of our derived estimators and compared the results to those of alternative methods. We further carried out two case studies to illustrate the application of the different methods of propensity score analysis with complex survey data. We concluded with a discussion of our findings and provided practical guidelines for propensity score weighting analysis of observational data from complex surveys.
CITYWALK: Enhancing LLM-Based C++ Unit Test Generation via Project-Dependency Awareness and Language-Specific Knowledge
2025-01-27
Yuwei Zhang, Qingyuan Lu, Kai Liu, Wensheng Dou, Jiaxin Zhu, Li Qian, Chunxi Zhang, Zheng Lin, Jun Wei
Unit testing plays a pivotal role in the software development lifecycle, as it ensures code quality. However, writing high-quality unit tests remains a time-consuming task for developers in practice. More recently, the application of large language models (LLMs) in automated unit test generation has demonstrated promising results. Existing approaches primarily focus on interpreted programming languages (e.g., Java), while mature solutions tailored to compiled programming languages like C++ are yet to be explored. The intricate language features of C++, such as pointers, templates, and virtual functions, pose particular challenges for LLMs in generating both executable and high-coverage unit tests. To tackle the aforementioned problems, this paper introduces CITYWALK, a novel LLM-based framework for C++ unit test generation. CITYWALK enhances LLMs by providing a comprehensive understanding of the dependency relationships within the project under test via program analysis. Furthermore, CITYWALK incorporates language-specific knowledge about C++ derived from project documentation and empirical observations, significantly improving the correctness of the LLM-generated unit tests. We implement CITYWALK by employing the widely popular LLM GPT-4o. The experimental results show that CITYWALK outperforms current state-of-the-art approaches on a collection of eight popular C++ projects. Our findings demonstrate the effectiveness of CITYWALK in generating high-quality C++ unit tests.
AI Agents for Computer Use: A Review of Instruction-based Computer Control, GUI Automation, and Operator Assistants
2025-01-27
Pascal J. Sager, Benjamin Meyer, Peng Yan, Rebekka von Wartburg-Kottler, Layan Etaiwi, Aref Enayati, Gabriel Nobel, Ahmed Abdulkadir, Benjamin F. Grewe, Thilo Stadelmann
Instruction-based computer control agents (CCAs) execute complex action sequences on personal computers or mobile devices to fulfill tasks using the same graphical user interfaces as a human user would, provided instructions in natural language. This review offers a comprehensive overview of the emerging field of instruction-based computer control, examining available agents -- their taxonomy, development, and respective resources -- and emphasizing the shift from manually designed, specialized agents to leveraging foundation models such as large language models (LLMs) and vision-language models (VLMs). We formalize the problem and establish a taxonomy of the field to analyze agents from three perspectives: (a) the environment perspective, analyzing computer environments; (b) the interaction perspective, describing observations spaces (e.g., screenshots, HTML) and action spaces (e.g., mouse and keyboard actions, executable code); and (c) the agent perspective, focusing on the core principle of how an agent acts and learns to act. Our framework encompasses both specialized and foundation agents, facilitating their comparative analysis and revealing how prior solutions in specialized agents, such as an environment learning step, can guide the development of more capable foundation agents. Additionally, we review current CCA datasets and CCA evaluation methods and outline the challenges to deploying such agents in a productive setting. In total, we review and classify 86 CCAs and 33 related datasets. By highlighting trends, limitations, and future research directions, this work presents a comprehensive foundation to obtain a broad understanding of the field and push its future development.
PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing
2025-01-27
Yuwei Zhang, Zhi Jin, Ying Xing, Ge Li, Fang Liu, Jiaxin Zhu, Wensheng Dou, Jun Wei
Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving software bugs. However, a noticeable gap in existing approaches lies in the oversight of collaborative facets intrinsic to bug resolution, treating the process as a single-stage endeavor. Moreover, most approaches solely take the buggy code snippet as input for LLMs during the patch generation stage. To mitigate the aforementioned limitations, we introduce a novel stage-wise framework named PATCH. Specifically, we first augment the buggy code snippet with corresponding dependence context and intent information to better guide LLMs in generating the correct candidate patches. Additionally, by taking inspiration from bug management practices, we decompose the bug-fixing task into four distinct stages: bug reporting, bug diagnosis, patch generation, and patch verification. These stages are performed interactively by LLMs, aiming to simulate the collaborative behavior of programmers during the resolution of software bugs. By harnessing these collective contributions, PATCH effectively enhances the bug-fixing capability of LLMs. We implement PATCH by employing the powerful dialogue-based LLM ChatGPT. Our evaluation on the widely used bug-fixing benchmark BFP demonstrates that PATCH has achieved better performance than state-of-the-art LLMs.
A family of sets is {\it intersecting} if every pair of its members has an element in common. Such a family of sets is called a {\it star} if some element is in every set of the family. Given a graph $G$, let $\mu(G)$ denote the size of the smallest maximal independent set of $G$. In 2005, Holroyd and Talbot conjectured the following generalization of the Erd\H{o}s-Ko-Rado Theorem: if $1\le r\le \mu(G)/2$ then there is an intersecting family of independent $r$-sets of maximum size that is a star. In this paper we present the history of this conjecture and survey the results that have supported it over the last 20 years.
Evaluation of NMT-Assisted Grammar Transfer for a Multi-Language Configurable Data-to-Text System
2025-01-27
Andreas Madsack, Johanna Heininger, Adela Schneider, Ching-Yi Chen, Christian Eckard, Robert Weißgraeber
One approach for multilingual data-to-text generation is to translate grammatical configurations upfront from the source language into each target language. These configurations are then used by a surface realizer and in document planning stages to generate output. In this paper, we describe a rule-based NLG implementation of this approach where the configuration is translated by Neural Machine Translation (NMT) combined with a one-time human review, and introduce a cross-language grammar dependency model to create a multilingual NLG system that generates text from the source data, scaling the generation phase without a human in the loop. Additionally, we introduce a method for human post-editing evaluation on the automatically translated text. Our evaluation on the SportSett:Basketball dataset shows that our NLG system performs well, underlining its grammatical correctness in translation tasks.
E-INSPIRE -- I. Bridging the gap with the local Universe: Stellar population of a statistical sample of ultra-compact massive galaxies at $z<0.3$
2025-01-27
John Mills, Chiara Spiniello, Alexey Sergeyev, Crescenzo Tortora, Vladyslav Khramtsov, Giuseppe D'Ago, Michalina Maksymowicz-Maciata, João P. V. Benedetti, Anna Ferré-Mateu, Michele Cappellari, Roger Davies, Johanna Hartke, Charles Rosen
This paper presents the first effort to Extend the Investigation of Stellar Populations In RElics (E-INSPIRE). We present a catalogue of 430 spectroscopically-confirmed ultra-compact massive galaxies (UCMGs) from the Sloan Digital Sky Survey at redshifts $0.0125\%$) of their stellar mass at $z<2$. We confirm that an efficient method of finding relics is to select UCMGs with large velocity dispersion values but we believe that the most efficient way is to select high velocity dispersion objects that also have super-solar metallicities and high [Mg/Fe].
Survey: Understand the challenges of MachineLearning Experts using Named EntityRecognition Tools
2025-01-27
Florian Freund, Philippe Tamla, Matthias Hemmje
This paper presents a survey based on Kasunic's survey research methodology to identify the criteria used by Machine Learning (ML) experts to evaluate Named Entity Recognition (NER) tools and frameworks. Comparison and selection of NER tools and frameworks is a critical step in leveraging NER for Information Retrieval to support the development of Clinical Practice Guidelines. In addition, this study examines the main challenges faced by ML experts when choosing suitable NER tools and frameworks. Using Nunamaker's methodology, the article begins with an introduction to the topic, contextualizes the research, reviews the state-of-the-art in science and technology, and identifies challenges for an expert survey on NER tools and frameworks. This is followed by a description of the survey's design and implementation. The paper concludes with an evaluation of the survey results and the insights gained, ending with a summary and conclusions.
2025-01-27
A. V. Popkova, M. S. Pshirkov, A. V. Tuntsov
Several observational lines of evidence imply that a fraction of the dark matter in the Galaxy may be comprised of small cold clouds of molecular hydrogen. Such objects are difficult to detect because of their small size and low temperature, but they can reveal themselves with gamma radiation arising in interactions between such clouds and cosmic rays or as dark shadows cast on the optical, UV and X-ray sky background. In our work we use the data of Fermi LAT 4FGL-DR4 catalogue of gamma-ray sources together with the data of GALEX UV All-Sky Survey to search for small dark clouds of molecular hydrogen in the Solar neighbourhood. This approach allows us to put an upper limit on the local concentration of such objects: $n < 2.2 \times 10^{-11} {\mathrm{AU}^{-3}}$. Constraints (upper limits) on the total amount of matter in this form bound to the Sun strongly depend on the radial profile of the clouds' distribution and reside in $0.05-30~M_{\odot}$ mass range.
Density-Functional Perturbation Theory with Numeric Atom-Centered Orbitals
2025-01-27
Connor L. Box, Reinhard J. Maurer, Honghui Shang, Matthias Scheffler, Volker Blum, Christian Carbogno, Mariana Rossi
This paper represents one contribution to a larger Roadmap article reviewing the current status of the FHI-aims code. In this contribution, the implementation of density-functional perturbation theory in a numerical atom-centered framework is summarized. Guidelines on usage and links to tutorials are provided.
The Shiny Scary Future of Automated Research Synthesis in HCI
2025-01-27
Katja Rogers
Automation and semi-automation through computational tools like LLMs are also making their way to deployment in research synthesis and secondary research, such as systematic reviews. In some steps of research synthesis, this has the opportunity to provide substantial benefits by saving time that previously was spent on repetitive tasks. The screening stages in particular may benefit from carefully vetted computational support. However, this position paper argues for additional caution when bringing in such tools to the analysis and synthesis phases, where human judgement and expertise should be paramount throughout the process.
We discuss scenarios for BSM physics near the TeV, motivated by the hierarchy problem and the flavour puzzle, and review their experimental tests at present and future colliders. Strong LHC constraints on couplings to light quarks motivate $U(2)$-like flavour symmetries as a means of lowering the new physics scale: this is demonstrated by general SMEFT analyses, and is also seen in composite Higgs solutions to the hierarchy problem. We discuss flavour non-universal gauge interactions as a possible origin for $U(2)$-like flavour symmetries which, in addition to allowing new physics to be lighter, opens up a simultaneous low-scale solution to the flavour puzzle. We focus on `flavour deconstructed' gauge interactions close to the TeV, and show how this non-universal gauge structure can be combined with Higgs compositeness in a way that better accommodates the requisite tuning in the Higgs mass.
We survey some results obtained in our quest for Fano varieties of K3 type and discuss why exploring the singular world might be interesting for discovering new K3 structures.
The Observed Luminosity Correlations of Gamma-Ray Bursts and Their Applications
2025-01-27
Chen Deng, Yong-Feng Huang, Fan Xu, Abdusattar Kurban
Gamma-ray bursts (GRBs) are among the most luminous electromagnetic transients in the universe, providing unique insights into extreme astrophysical processes and serving as promising probes for cosmology. Unlike Type Ia supernovae, which have a unified explosion mechanism, GRBs cannot directly act as standard candles for tracing cosmic evolution at high redshifts due to significant uncertainties in their underlying physical origins. Empirical correlations derived from statistical analyses involving various GRB parameters provide valuable information regarding their intrinsic properties. In this brief review, we describe various correlations among GRB parameters involving the prompt and afterglow phases, discussing possible theoretical interpretations behind them. The scarcity of low-redshift GRBs poses a major obstacle to the application of GRB empirical correlations in cosmology, referred to as the circularity problem. We present various efforts aiming at calibrating GRBs to address this challenge and leveraging established empirical correlations to constrain cosmological parameters. The pivotal role of GRB sample quality in advancing cosmological research is underscored. Some correlations that could potentially be utilized as redshift indicators are also introduced.
Stratospheric Balloon Payloads for Astronomy: the challenge of coping with rising complexity
2025-01-27
Vincent Picouet
Stratospheric balloons offer cost-effective access to space and grant the opportunity for fast scientific innovation cycles and higher-risk explorations. In addition to science pathfinders, they serve as platforms for technology advancement, and offer a unique opportunity to train future instrument scientists and PIs. However, the increase in complexity of some projects (sub-arcsecond pointing, numerous degrees of freedom, advanced cooling systems, real-time communication and data transfer, low readiness level technologies, etc.) elevates the scale of challenges. This paper discusses the challenges brought by the increase of instrument complexity in the constrained area of ballooning projects. We use the example of the multi-object slit UV spectrograph FIREBall-2 but also discuss other ambitious payloads to expose some lessons learned. We will then propose strategies for future balloon projects and potential interesting adaptations for funding agencies to accommodate these emerging complexities. The objective being to foster the added value of more ambitious balloon-borne projects, and to initiate discussion about how to generate more robust strategies for handling high-complexity undertakings. Although this paper is enriched by answers from a survey sent to balloon-borne payload PIs, it remains influenced by my personal experience on FIREBall-2. As such, it does not necessarily represent the perspectives of other members of the project, let alone the broader balloon community.
MaRginalia: Enabling In-person Lecture Capturing and Note-taking Through Mixed Reality
2025-01-27
Leping Qiu, Erin Seongyoon Kim, Sangho Suh, Ludwig Sidenmark, Tovi Grossman
Students often take digital notes during live lectures, but current methods can be slow when capturing information from lecture slides or the instructor's speech, and require them to focus on their devices, leading to distractions and missing important details. This paper explores supporting live lecture note-taking with mixed reality (MR) to quickly capture lecture information and take notes while staying engaged with the lecture. A survey and interviews with university students revealed common note-taking behaviors and challenges to inform the design. We present MaRginalia to provide digital note-taking with a stylus tablet and MR headset. Students can take notes with an MR representation of the tablet, lecture slides, and audio transcript without looking down at their device. When preferred, students can also perform detailed interactions by looking at the physical tablet. We demonstrate the feasibility and usefulness of MaRginalia and MR-based note-taking in a user study with 12 students.
2025-01-27
Nuria Aldama García, Patricia Marsà Morales, David Betancur Sánchez, Álvaro Barbero Jiménez, Marta Guerrero Nieto, Pablo Haya Coll, Patricia Martín Chozas, Elena Montiel Ponsoda
Legal corpora for Natural Language Processing (NLP) are valuable and scarce resources in languages like Spanish due to two main reasons: data accessibility and legal expert knowledge availability. INESData 2024 is a European Union funded project lead by the Universidad Polit\'ecnica de Madrid (UPM) and developed by Instituto de Ingenier\'ia del Conocimiento (IIC) to create a series of state-of-the-art NLP resources applied to the legal/administrative domain in Spanish. The goal of this paper is to present the Corpus of Legal Spanish Contract Clauses (3CEL), which is a contract information extraction corpus developed within the framework of INESData 2024. 3CEL contains 373 manually annotated tenders using 19 defined categories (4 782 total tags) that identify key information for contract understanding and reviewing.
Two-Photon Interference from an InAs Quantum Dot emitting in the Telecom C-Band
2025-01-27
Jaewon Kim, Jochen Kaupp, Yorick Reum, Giora Peniakov, Johannes Michl, Felix Kohr, Monika Emmerling, Martin Kamp, Yong-Hoon Cho, Tobias Huber-Loyola, Sven Höfling, Andreas T. Pfenning
Two-photon interference from an InAs/InAlGaAs quantum dot (QD) emitting in the telecom C-band with a raw two-photon interference visibility of $V_{HOM}=(71.9\pm0.2)$ % is demonstrated. This is achieved by a two-fold approach: an improvement of the molecular beam epitaxial growth for better QDs, and integration of the QDs into an optical circular Bragg grating resonator for a Purcell enhancement of the radiative decay rate. The quantum optical properties of the fabricated device are studied by means of time-correlated single-photon counting under quasi-resonant excitation of the charged exciton line. A reduced lifetime of $T_1=(257.5\pm0.2)$ ps is found corresponding to a Purcell factor of $F_P\geqq(4.7\pm0.5)$. Pronounced anti-bunching of the second-order autocorrelation function at zero time delay $g^{(2)} (0)=(0.0307\pm0.0004)$ confirms the single-photon emission character. The two-photon interference is demonstrated with an unbalanced Mach-Zehnder interferometer in Hong-Ou-Mandel configuration. We discuss strategies how to further improve the indistinguishability, and provide a survey of the state-of-the art.
The Extraordinary Long-lasting Infrared Echo of PS16dtm Reveals an Extremely Energetic Nuclear Outburst
2025-01-27
Ning Jiang, Di Luo, Jiazheng Zhu, Roc M. Cutri
PS16dtm is one of the earliest reported candidate tidal disruption events (TDEs) in active galactic nuclei (AGNs) and displays a remarkably bright and long-lived infrared (IR) echo revealed by multi-epoch photometry from the Wide-field Infrared Survey Explorer (WISE). After a rapid rise in the first year, the echo remains persistently at a high state from July 2017 to July 2024, the latest epoch, and keeps an almost constant color. We have fitted the extraordinary IR emission with a refined dust echo model by taking into account the dust sublimation process. The fitting suggests that an extremely giant dust structure with a new inner radius of $\sim1.6$ pc and an ultra-high peak bolometric luminosity, i.e., $\sim6\times10^{46} \rm erg~s^{-1}$ for typical 0.1$\mu$m-sized silicate grain, is required to account for the IR echo. This work highlights the distinctive value of IR echoes in measuring the accurate intrinsic bolometric luminosity, and thus the total radiated energy of TDEs, which could be severely underestimated by traditional methods, i.e. probably by more than an order of magnitude in PS16dtm. Such large energetic output compared to normal TDEs could be boosted by the pre-existing accretion disk and gas clouds around the black hole. Our model can be validated in the near future by IR time-domain surveys such as Near-Earth Object (NEO) Surveyor, given the recent retirement of WISE. In addition, the potential for spatially resolving a receding dusty torus after a TDE could also be an exciting subject in the era of advanced IR interferometry.
Transient Finite Element Simulation of Accelerator Magnets Using Thermal Thin Shell Approximation
2025-01-27
Erik Schnaubelt, Andrea Vitrano, Mariusz Wozniak, Emmanuele Ravaioli, Arjan Verweij, Sebastian Schöps
Thermal transient responses of superconducting magnets can be simulated using the finite element (FE) method. Some accelerator magnets use cables whose electric insulation is significantly thinner than the bare electric conductor. The FE discretisation of such geometries with high-quality meshes leads to many degrees of freedom. This increases the computational time, particularly since non-linear material properties are involved. In this work, we propose to use a thermal thin-shell approximation (TSA) to improve the computational efficiency when solving the heat diffusion equation in two dimensions. We apply the method to compute the thermal transient response of superconducting accelerator magnets used for CERN's Large Hadron Collider (LHC) and High-Luminosity LHC. The TSA collapses thin electrical insulation layers into lines while accurately representing the thermal gradient across the insulation's thickness. The TSA is implemented in the multipole module of the open-source Finite Element Quench Simulator (FiQuS), which can generate the multipole magnet models programmatically from input text files. First, the TSA approach is verified by comparison to classical FE simulations with meshed surface insulation regions for a simple block of four cables and a detailed model of the MBH dipole. The results show that the TSA approach reduces the computational time significantly while preserving the accuracy of the solution. Second, the quench heater (QH) delay computed with the TSA method is compared to measurements for the MBH magnet. To this end, the thermal transient simulation is coupled to a magnetostatic solution to account for magneto-resistive effects. Third, the TSA's full capabilities are showcased in non-linear magneto-thermal simulations of several LHC and HL-LHC superconducting magnet models. The full source code, including all input files, is publicly available.
This article is dedicated to the memory of Alexei Starobinsky. I begin with some recollections of him and then review the generalization of his wonderful stochastic formalism from scalar potential models to theories which interact with fermions and photons, and finally to theories with derivative interactions such as nonlinear sigma models and gravity. This entails effective potentials generated by the usual field-dependent masses, as well as by field-dependent field strengths, and by field-dependent Hubble parameters. I also discuss secular loop corrections which cannot be captured by stochastic techniques.
Autonomous Horizon-based Asteroid Navigation With Observability-constrained Maneuvers
2025-01-27
Aditya Arjun Anibha, Kenshiro Oguri
Asteroid exploration is a pertinent challenge due to the varying complexity of their dynamical environments, shape and communication delays due to distance. Thus, autonomous navigation methods are continually being developed and improved in current research to enable their safe exploration. These methods often involve using horizon-based Optical Navigation (OpNav) to determine the spacecraft's location, which is reliant on the visibility of the horizon. It is critical to ensure the reliability of this measurement such that the spacecraft may maintain an accurate state estimate throughout its mission. This paper presents an algorithm that generates control maneuvers for spacecraft to follow trajectories that allow continuously usable optical measurements to maintain system observability for safe navigation. This algorithm improves upon existing asteroid navigation capabilities by allowing the safe and robust autonomous targeting of various trajectories and orbits at a wide range of distances within optical measurement range. It is adaptable to different asteroid scenarios. Overall, the approach develops an all-encompassing system that simulates the asteroid dynamics, synthetic image generation, edge detection, horizon-based OpNav, filtering and observability-enhancing control.
The Ophiuchus DIsk Survey Employing ALMA (ODISEA): Complete Size Distributions for the 100 Brightest Disks Across Multiplicity and SED Classes
2025-01-27
Anuroop Dasgupta, Lucas A. Cieza, Camilo I. Gonzalez Ruilova, Trisha Bhowmik, Ms. Prachi Chavan, Grace Batalla-Falcon, Gregory J. Herczeg, Dary A. Ruiz-Rodriguez, Jonathan P. Williams, Anibal Sierra, Simon Casassus, Octavio M. Guilera, Sebastian Perez, Santiago Orcajo, P. H Nogueira, Antonio S. Hales, James M. Miley, Fernando R. Rannou, Alice Zurlo
The size of a protoplanetary disk is a fundamental property, yet most remain unresolved, even in nearby star-forming regions (d $\sim$ 140-200 pc). We present the complete continuum size distribution for the $105$ brightest protoplanetary disks (M$_{\text{dust}}$ $\gtrsim$ 2 M$_{\oplus}$) in the Ophiuchus cloud, obtained from ALMA Band 8 (410 GHz) observations at 0.05$^{\prime\prime}$ (7 au) to 0.15$^{\prime\prime}$ (21 au) resolution. This sample includes 54 Class II and 51 Class I and Flat Spectrum sources, providing a comprehensive distribution across evolutionary stages. We measure the Half Width at Half Maximum (HWHM) and the radius encircling $68\%$ of the flux ($R_{68\%}$) for most non-binary disks, yielding the largest flux-limited sample of resolved disks in any star-forming region. The distribution is log-normal with a median value of $\sim$14 au and a logarithmic standard deviation $\sigma_{\log} = 0.46$ (factor of 2.9 in linear scale). Disks in close binary systems ($<$ 200 au separation) have smaller radii, with median value of $\sim$5 au, indicating efficient radial drift as predicted by dust evolution models. The size distribution for young embedded objects (SED Class I and Flat Spectrum, age $\lesssim$ 1 Myr) is similar to that of Class II objects (age $\sim$ a few Myr), implying that pressure bumps must be common at early disk stages to prevent mm-sized particle migration at au scales.
Noise disturbance and lack of privacy: Modeling acoustic dissatisfaction in open-plan offices
2025-01-27
Manuj Yadav, Jungsoo Kim, Valtteri Hongisto, Densil Cabrera, Richard de Dear
Open-plan offices are well-known to be adversely affected by acoustic issues. This study aims to model acoustic dissatisfaction using measurements of room acoustics, sound environment during occupancy, and occupant surveys (n = 349) in 28 offices representing a diverse range of workplace parameters. As latent factors, the contribution of $\textit{lack of privacy}$ (LackPriv) was 25% higher than $\textit{noise disturbance}$ (NseDstrb) in predicting $\textit{acoustic dissatisfaction}$ (AcDsat). Room acoustic metrics based on sound pressure level (SPL) decay of speech ($L_{\text{p,A,s,4m}}$ and $r_{\text{C}}$) were better in predicting these factors than distraction distance ($r_{\text{D}}$) based on speech transmission index. This contradicts previous findings, and the trends for SPL-based metrics in predicting AcDsat and LackPriv go against expectations based on ISO 3382-3. For sound during occupation, $L_{\text{A,90}}$ and psychoacoustic loudness ($N_{\text{90}}$) predicted AcDsat, and a SPL fluctuation metric ($M_{\text{A,eq}}$) predicted LackPriv. However, these metrics were weaker predictors than ISO 3382-3 metrics. Medium-sized offices exhibited higher dissatisfaction than larger ($\geq$50 occupants) offices. Dissatisfaction varied substantially across parameters including ceiling heights, number of workstations, and years of work, but not between offices with fixed seating compared to more flexible and activity-based working configurations. Overall, these findings highlight the complexities in characterizing occupants' perceptions using instrumental acoustic measurements.
Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within $z < 1.4$ in the Hyper Supreme-Cam Wide Survey
2025-01-27
Chuan Tian, C. Megan Urry, Aritra Ghosh, Daisuke Nagai, Tonima T. Ananna, Meredith C. Powell, Connor Auge, Aayush Mishra, David B. Sanders, Nico Cappelluti, Kevin Schawinski
We present a composite machine learning framework to estimate posterior probability distributions of bulge-to-total light ratio, half-light radius, and flux for Active Galactic Nucleus (AGN) host galaxies within $z<1.4$ and $m<23$ in the Hyper Supreme-Cam Wide survey. We divide the data into five redshift bins: low ($0
A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks
2025-01-27
Dong Li, Guihong Wan, Xintao Wu, Xinyu Wu, Ajit J. Nirmal, Christine G. Lian, Peter K. Sorger, Yevgeniy R. Semenov, Chen Zhao
Computational pathology foundation models (CPathFMs) have emerged as a powerful approach for analyzing histopathological data, leveraging self-supervised learning to extract robust feature representations from unlabeled whole-slide images. These models, categorized into uni-modal and multi-modal frameworks, have demonstrated promise in automating complex pathology tasks such as segmentation, classification, and biomarker discovery. However, the development of CPathFMs presents significant challenges, such as limited data accessibility, high variability across datasets, the necessity for domain-specific adaptation, and the lack of standardized evaluation benchmarks. This survey provides a comprehensive review of CPathFMs in computational pathology, focusing on datasets, adaptation strategies, and evaluation tasks. We analyze key techniques, such as contrastive learning and multi-modal integration, and highlight existing gaps in current research. Finally, we explore future directions from four perspectives for advancing CPathFMs. This survey serves as a valuable resource for researchers, clinicians, and AI practitioners, guiding the advancement of CPathFMs toward robust and clinically applicable AI-driven pathology solutions.
Levi-flats in $\mathbb CP^n$: a survey for nonexperts
2025-01-27
Rasul Shafikov
This survey paper, aimed at nonexperts in the field, explores various proofs of nonexistence of real analytic Levi-flat hypersurfaces in $\mathbb CP^n$, $n>2$. Some generalizations and other related results are also discussed.
The S-PLUS 12-band photometry as a powerful tool for discovery and classification: ten cataclysmic variables in a proof-of-concept study
2025-01-27
Raimundo Lopes de Oliveira, Amanda S. de Araujo, Angela C. Krabbe, Claudia L. Mendes de Oliveira, Koji Mukai, Luis A. Gutierrez-Soto, Antonio Kanaan, Romualdo Eleuterio, Marcelo Borges Fernandes, Fredi Quispe-Huaynasi, William Schoenell, Tiago Ribeiro
Multi-band photometric surveys provide a straightforward way to discover and classify astrophysical objects systematically, enabling the study of a large number of targets at relatively low cost. Here we introduce an alternative approach to select Accreting White Dwarf (AWD) candidates following their spectral energy distribution, entirely supported by the twelve photometric bands of the Southern Photometric Local Universe Survey (S-PLUS). The method was validated with optical spectroscopic follow-up with the Gemini South telescope which unambiguously established ten systems as cataclysmic variables (CVs), alongside Swift X-ray observations of four of them. Among the ten CVs presented here are those that may be low-luminosity intermediate polars or WZ Sge-type dwarf novae with rare outbursts, two subclasses that can be easily missed in time-domain and X-ray surveys, the two methods currently dominating the discovery of new CVs. Our approach based on S-PLUS provides an important, complementary tool to uncover the total population of CVs and the complete set of its subclasses, which is an important step towards a full understanding of close binary evolution, including the origin of magnetic fields in white dwarfs and the physics of accretion. Finally, we highlight the potential of S-PLUS beyond AWDs, serving other surveys in the characterization of their sources.
Generative AI Uses and Risks for Knowledge Workers in a Science Organization
2025-01-27
Kelly B. Wagman, Matthew T. Dearing, Marshini Chetty
Generative AI could enhance scientific discovery by supporting knowledge workers in science organizations. However, the real-world applications and perceived concerns of generative AI use in these organizations are uncertain. In this paper, we report on a collaborative study with a US national laboratory with employees spanning Science and Operations about their use of generative AI tools. We surveyed 66 employees, interviewed a subset (N=22), and measured early adoption of an internal generative AI interface called Argo lab-wide. We have four findings: (1) Argo usage data shows small but increasing use by Science and Operations employees; Common current and envisioned use cases for generative AI in this context conceptually fall into either a (2) copilot or (3) workflow agent modality; and (4) Concerns include sensitive data security, academic publishing, and job impacts. Based on our findings, we make recommendations for generative AI use in science and other organizations.
Efficient inference of rankings from multi-body comparisons
2025-01-27
Jack Yeung, Daniel Kaiser, Filippo Radicchi
Many of the existing approaches to assess and predict the performance of players, teams or products in competitive contests rely on the assumption that comparisons occur between pairs of such entities. There are, however, several real contests where more than two entities are part of each comparison, e.g., sports tournaments,multiplayer board and card games, and preference surveys. The Plackett-Luce (PL) model provides a principled approach to infer the ranking of entities involved in such contests characterized by multi-body comparisons. Unfortunately, traditional algorithms used to compute PL rankings suffer from slow convergence limiting the application of the PL model to relatively small-scale systems. We present here an alternative implementation that allows for significant speed-ups and validate its efficiency in both synthetic and real-world sets of data. Further, we perform systematic cross-validation tests concerning the ability of the PL model to predict unobserved comparisons. We find that a PL model trained on a set composed of multi-body comparisons is more predictive than a PL model trained on a set of projected pairwise comparisons derived from the very same training set, emphasizing the need of properly accounting for the true multi-body nature of real-world systems whenever such an information is available.
Euclid preparation. 3-dimensional galaxy clustering in configuration space. Part I. 2-point correlation function estimation
2025-01-27
Euclid Collaboration, S. de la Torre, F. Marulli, E. Keihänen, A. Viitanen, M. Viel, A. Veropalumbo, E. Branchini, D. Tavagnacco, F. Rizzo, J. Valiviita, V. Lindholm, V. Allevato, G. Parimbelli, E. Sarpa, Z. Ghaffari, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, S. Bardelli, A. Basset, D. Bonino, M. Brescia, J. Brinchmann, A. Caillat, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, F. J. Castander, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Crocce, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, J. Dinis, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, M. Farina, S. Farrens, F. Faustini, S. Ferriol, N. Fourmanoit, M. Frailis, E. Franceschi, P. Franzetti, M. Fumana, S. Galeotta, K. George, W. Gillard, B. Gillis, C. Giocoli, P. Gómez-Alvarez, B. R. Granett, A. Grazian, F. Grupp, L. Guzzo, S. V. H. Haugan, W. Holmes, F. Hormuth, A. Hornstrup, S. Ilić, K. Jahnke, M. Jhabvala, B. Joachimi, S. Kermiche, A. Kiessling, M. Kilbinger, B. Kubik, M. Kunz, H. Kurki-Suonio, S. Ligori, P. B. Lilje, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, R. Massey, S. Maurogordato, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, M. Moresco, B. Morin, L. Moscardini, E. Munari, C. Neissner, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. Pozzetti, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, P. Schneider, T. Schrabback, M. Scodeggio, A. Secroun, E. Sefusatti, G. Seidel, M. Seiffert, S. Serrano, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, C. Surace, P. Tallada-Crespí, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, A. Tsyganov, I. Tutusaus, L. Valenziano, T. Vassallo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, E. Zucca, A. Biviano, M. Bolzonella, E. Bozzo, C. Burigana, M. Calabrese, D. Di Ferdinando, J. A. Escartin Vigo, R. Farinelli, F. Finelli, L. Gabarra, J. Gracia-Carpio, S. Matthew, N. Mauri, A. Mora, A. Pezzotta, M. Pöntinen, V. Scottez, P. Simon, A. Spurio Mancini, M. Tenti, M. Wiesmann, Y. Akrami, I. T. Andika, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, A. Balaguera-Antolinez, D. Bertacca, M. Bethermin, A. Blanchard, L. Blot, H. Böhringer, S. Borgani, M. L. Brown, S. Bruton, R. Cabanac, A. Calabro, B. Camacho Quevedo, G. Cañas-Herrera, A. Cappi, F. Caro, C. S. Carvalho, T. Castro, K. C. Chambers, F. Cogato, S. Contarini, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, S. Di Domizio, H. Dole, S. Escoffier, A. G. Ferrari, P. G. Ferreira, A. Finoguenov, A. Fontana, K. Ganga, J. García-Bellido, T. Gasparetto, V. Gautard, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, A. Gregorio, M. Guidi, C. M. Gutierrez, A. Hall, S. Hemmati, H. Hildebrandt, J. Hjorth, A. Jimenez Muñoz, S. Joudaki, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, C. C. Kirkpatrick, S. Kruk, M. Lattanzi, A. M. C. Le Brun, S. Lee, J. Le Graet, L. Legrand, M. Lembo, J. Lesgourgues, T. I. Liaudat, A. Loureiro, J. Macias-Perez, M. Magliocchetti, F. Mannucci, R. Maoli, J. Martín-Fleitas, C. J. A. P. Martins, L. Maurin, R. B. Metcalf, M. Miluzio, P. Monaco, C. Moretti, G. Morgante, C. Murray, S. Nadathur, K. Naidoo, A. Navarro-Alsina, S. Nesseris, K. Paterson, L. Patrizii, A. Pisani, V. Popa, D. Potter, P. Reimberg, I. Risso, P. -F. Rocci, M. Sahlén, A. Schneider, M. Schultheis, D. Sciotti, E. Sellentin, M. Sereno, A. Silvestri, L. C. Smith, K. Tanidis, C. Tao, N. Tessore, G. Testera, R. Teyssier, S. Toft, S. Tosi, A. Troja, M. Tucci, C. Valieri, D. Vergani, G. Verza, P. Vielzeuf, N. A. Walton
The 2-point correlation function of the galaxy spatial distribution is a major cosmological observable that enables constraints on the dynamics and geometry of the Universe. The Euclid mission aims at performing an extensive spectroscopic survey of approximately 20--30 million H$\alpha$-emitting galaxies up to about redshift two. This ambitious project seeks to elucidate the nature of dark energy by mapping the 3-dimensional clustering of galaxies over a significant portion of the sky. This paper presents the methodology and software developed for estimating the 3-dimensional 2-point correlation function within the Euclid Science Ground Segment. The software is designed to overcome the significant challenges posed by the large and complex Euclid data set, which involves millions of galaxies. Key challenges include efficient pair counting, managing computational resources, and ensuring the accuracy of the correlation function estimation. The software leverages advanced algorithms, including kd-tree, octree, and linked-list data partitioning strategies, to optimise the pair-counting process. The implementation also includes parallel processing capabilities using shared-memory open multi-processing to further enhance performance and reduce computation times. Extensive validation and performance testing of the software are presented. The results indicate that the software is robust and can reliably estimate the 2-point correlation function, which is essential for deriving cosmological parameters with high precision. Furthermore, the paper discusses the expected performance of the software during different stages of the Euclid Wide Survey observations and forecasts how the precision of the correlation function measurements will improve over the mission's timeline, highlighting the software's capability to handle large data sets efficiently.
Since 2022, generative AI (genAI) has rapidly become integrated into workplaces. Though organizations have made commitments to use this technology "responsibly", how organizations and their employees prioritize responsibility in their decision-making remains absent from extant management theorizing. In this paper, we examine how product managers - who often serve as gatekeepers in decision-making processes - implement responsible practices in their day-to-day work when using genAI. Using Institutional Theory, we illuminate the factors that constrain or support proactive responsible development and usage of genAI technologies. We employ a mixed methods research design, drawing on 25 interviews with product managers and a global survey of 300 respondents in product management-related roles. The majority of our respondents report (1) widespread uncertainty regarding what "responsibility" means or looks like, (2) diffused responsibility given assumed ethical actions by other teams, (3) lack of clear incentives and guidance within organizations, and (4) the importance of leadership buy-in and principles for navigating tensions between ethical commitments and profit motives. However, our study finds that even in highly uncertain environments, absent guidance from leadership, product managers can "recouple" ethical commitments and practices by finding responsibility "micro-moments". Product managers seek out low-risk, small-scale actions they can take without explicit buy-in from higher-level managers, such as individual or team-wide checks and reviews and safeguarding standards for data. Our research highlights how genAI poses unique challenges to organizations trying to couple ethical principles and daily practices and the role that middle-level management can play in recoupling the two.
Mapping H$α$-Excess Candidate Point Sources in the Southern Hemisphere Using S-PLUS Data
2025-01-27
L. A. Gutiérrez-Soto, R. Lopes de Oliveira, S. Akras, D. R. Gonçalves, L. F. Lomelí-Núñez, C. Mendes de Oliveira, E. Telles, A. Alvarez-Candal, M. Borges Fernandes, S. Daflon, C. E. Ferreira Lopes, M. Grossi, D. Hazarika, P. K. Humire, C. Lima-Dias, A. R. Lopes, J. L. Nilo Castellón, S. Panda, A. Kanaan, T. Ribeiro, W. Schoenell
Context. We use the Southern Photometric Local Universe Survey (S-PLUS) Fourth Data Release (DR4) to identify and classify H$\alpha$-excess point sources in the Southern Sky, combining photometric data from 12 S-PLUS filters with machine learning to improve classification of H$\alpha$-related phenomena. Aims. Our goal is to classify H$\alpha$-excess point sources by distinguishing Galactic and extragalactic objects, particularly those with redshifted emission lines, and identifying variability phenomena like RR Lyrae stars. Methods. We selected H$\alpha$-excess candidates using the ($r - J0660$) vs. ($r - i$) colour-colour diagram from the S-PLUS main survey (MS) and Galactic Disk Survey (GDS). UMAP for dimensionality reduction and HDBSCAN clustering were used to separate source types. Infrared data was incorporated, and a Random Forest model was trained on clustering results to identify key colour features. New colour-colour diagrams from S-PLUS MS and infrared data offer a preliminary classification. Results. Combining multiwavelength data with machine learning significantly improved H$\alpha$-excess source classification. We identified 6956 sources with excess in the $J0660$ filter. Cross-matching with SIMBAD explored object types, including emission-line stars, young stellar objects, nebulae, stellar binaries, cataclysmic variables, QSOs, AGNs, and galaxies. Using S-PLUS colours and machine learning, we separated RR Lyrae stars from other sources. The separation of Galactic and extragalactic sources was clearer, but distinguishing cataclysmic variables from QSOs at certain redshifts remained challenging. Infrared data refined the classification, and the Random Forest model highlighted key colour features for future follow-up spectroscopy.
In quantum field theories, spectral densities are directly related to relevant physical observables. In Lattice QCD, their non-perturbative extraction from first principles requires the Inverse Laplace transform of Euclidean-time correlation functions, a notorious ill-posed problem. Here we review our recent proposal [1,2] for a new strategy to perform this inversion both in the continuum and on the lattice, also suitable for smeared spectral densities, both in the continuum and in the discrete cases.
Improving LLM Leaderboards with Psychometrical Methodology
2025-01-27
Denis Federiakin
The rapid development of large language models (LLMs) has necessitated the creation of benchmarks to evaluate their performance. These benchmarks resemble human tests and surveys, as they consist of sets of questions designed to measure emergent properties in the cognitive behavior of these systems. However, unlike the well-defined traits and abilities studied in social sciences, the properties measured by these benchmarks are often vaguer and less rigorously defined. The most prominent benchmarks are often grouped into leaderboards for convenience, aggregating performance metrics and enabling comparisons between models. Unfortunately, these leaderboards typically rely on simplistic aggregation methods, such as taking the average score across benchmarks. In this paper, we demonstrate the advantages of applying contemporary psychometric methodologies - originally developed for human tests and surveys - to improve the ranking of large language models on leaderboards. Using data from the Hugging Face Leaderboard as an example, we compare the results of the conventional naive ranking approach with a psychometrically informed ranking. The findings highlight the benefits of adopting psychometric techniques for more robust and meaningful evaluation of LLM performance.
AMPEL workflows for LSST: Modular and reproducible real-time photometric classification
2025-01-27
Jakob Nordin, Valery Brinnel, Jakob van Santen, Simeon Reusch, Marek Kowalski
Modern time-domain astronomical surveys produce high throughput data streams which require tools for processing and analysis. This will be critical for programs making full use of the alert stream from the Vera Rubin Observatory (VRO), where spectroscopic labels will only be available for a small subset of all transients. In this context, the AMPEL toolset can work as a code-to-data platform for the development of efficient, reproducible and flexible workflows for real-time astronomical application. We here introduce three different AMPEL channels constructed to highlight different uses of alert streams: to rapidly find infant transients (SNGuess), to provide unbiased transient samples for follow-up (FollowMe) and to deliver final transient classifications (FinalBet). These pipelines already contain placeholders for mechanisms which will be essential for the optimal usage of VRO alerts: combining different classifiers, including host galaxy information, population priors and sampling non-gaussian photometric redshift distributions. Based on the ELAsTiCC simulation, all three channels are already working at a high level: SNGuess correctly tags 99% of all young supernovae, FollowMe illustrates how an unbiased subset of alerts can be selected for spectroscopic follow-up in the context of cosmological probes and FinalBet includes priors to achieve successful classifications for >~80% of all extragalactic transients. The fully functional workflows presented here are all public and can be used as starting points for any group wishing to optimize pipelines for their specific VRO science programs. AMPEL is designed to allow this to be done in accordance with FAIR principles: both software and results can be easily shared and results reproduced. The code-to-data environment ensures that models developed this way can be directly applied to the real-time LSST stream parsed by AMPEL.
Gaia-ESO Survey: massive stars in the Carina Nebula. II. The spectroscopic analysis of the O-star population
2025-01-27
S. R. Berlanas, L. Mahy, A. Herrero, J. Maíz Apellániz, R. Blomme, F. Comerón, I. Negueruela, J. A. Molina Lera, M. Pantaleoni González, S. Daflon, W. Santos, V. M. Kalari
The new census of massive stars in the Carina nebula reveals the presence of 54 apparently single O-type stars in the Car OB1 association, an extremely active star-forming region which hosts some of the most luminous stars of the Milky Way. A detailed spectroscopic analysis of the currently most complete sample of O-type stars in the association can be used to inspect the main physical properties of cluster members and test evolutionary and stellar atmospheres models. We perform quantitative spectroscopic analysis for the most complete sample of apparently single O-type stars in Car OB1 with available spectroscopic data. From the high-resolution GES and OWN spectra we obtain a reliable distribution of rotational velocities for a sample of 37 O-type stars. It shows a bimodal structure with a low velocity peak at 60 km s$^{-1}$ and a short tail of fast rotators reaching 320 km s$^{-1}$. We also perform quantitative spectroscopic analysis and derive effective temperature, surface gravity and He abundance for a sample of 47 O-type stars, now including further stars from GOSSS database. Radii, luminosities, and spectroscopic masses were also determined using $Gaia$ astrometry. We create the Hertzsprung-Russell Diagram to inspect the evolutionary status of the region and confirm the lack of stars close to the Zero Age Main Sequence (ZAMS) between $\sim$35 -- 55 M$_\odot$. We confirm a very young population with an age distribution peaking at 1 Myr, some stars close or even on the ZAMS, and a secondary peak at 4 -- 5 Myr in the age distribution. We confirm the youth of Trumpler 14, which is also the only cluster not showing the secondary peak. We also find a clear trend to evolutionary masses higher than derived spectroscopic masses for stars with evolutionary mass below 40 M$_{\odot}$.
2025-01-27
Lee Sharkey, Bilal Chughtai, Joshua Batson, Jack Lindsey, Jeff Wu, Lucius Bushnaq, Nicholas Goldowsky-Dill, Stefan Heimersheim, Alejandro Ortega, Joseph Bloom, Stella Biderman, Adria Garriga-Alonso, Arthur Conmy, Neel Nanda, Jessica Rumbelow, Martin Wattenberg, Nandi Schoots, Joseph Miller, Eric J. Michaud, Stephen Casper, Max Tegmark, William Saunders, David Bau, Eric Todd, Atticus Geiger, Mor Geva, Jesse Hoogland, Daniel Murfet, Tom McGrath
Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities in order to accomplish concrete scientific and engineering goals. Progress in this field thus promises to provide greater assurance over AI system behavior and shed light on exciting scientific questions about the nature of intelligence. Despite recent progress toward these goals, there are many open problems in the field that require solutions before many scientific and practical benefits can be realized: Our methods require both conceptual and practical improvements to reveal deeper insights; we must figure out how best to apply our methods in pursuit of specific goals; and the field must grapple with socio-technical challenges that influence and are influenced by our work. This forward-facing review discusses the current frontier of mechanistic interpretability and the open problems that the field may benefit from prioritizing.
Optimizing Neural Network Surrogate Models: Application to Black Hole Merger Remnants
2025-01-27
Lucy M. Thomas, Katerina Chatziioannou, Vijay Varma, Scott E. Field
Surrogate models of numerical relativity simulations of merging black holes provide the most accurate tools for gravitational-wave data analysis. Neural network-based surrogates promise evaluation speedups, but their accuracy relies on (often obscure) tuning of settings such as the network architecture, hyperparameters, and the size of the training dataset. We propose a systematic optimization strategy that formalizes setting choices and motivates the amount of training data required. We apply this strategy on NRSur7dq4Remnant, an existing surrogate model for the properties of the remnant of generically precessing binary black hole mergers and construct a neural network version, which we label NRSur7dq4Remnant_NN. The systematic optimization strategy results in a new surrogate model with comparable accuracy, and provides insights into the meaning and role of the various network settings and hyperparameters as well as the structure of the physical process. Moreover, NRSur7dq4Remnant_NN results in evaluation speedups of up to $8$ times on a single CPU and a further improvement of $2,000$ times when evaluated in batches on a GPU. To determine the training set size, we propose an iterative enrichment strategy that efficiently samples the parameter space using much smaller training sets than naive sampling. NRSur7dq4Remnant_NN requires $O(10^4)$ training data, so neural network-based surrogates are ideal for speeding-up models that support such large training datasets, but at the moment cannot directly be applied to numerical relativity catalogs that are $O(10^3)$ in size. The optimization strategy is available through the gwbonsai package.
Symbolic Mathematical Computation 1965--1975: The View from a Half-Century Perspective
2025-01-27
Robert M. Corless, Arthur C. Norman, Tomas Recio, William J. Turkel, Stephen M. Watt
The 2025 ISSAC conference in Guanajuato, Mexico, marks the 50th event in this significant series, making it an ideal moment to reflect on the field's history. This paper reviews the formative years of symbolic computation up to 1975, fifty years ago. By revisiting a period unfamiliar to most current participants, this survey aims to shed light on once-pressing issues that are now largely resolved and to highlight how some of today's challenges were recognized earlier than expected.
Globular clusters (GCs) are among the oldest and most luminous stellar systems in the Universe, offering unique insights into galaxy formation and evolution. While the physical processes behind their origin have long remained elusive, major theoretical and observational developments in the past decade have led to a new understanding of GCs as the natural outcome of high-pressure star formation in high-redshift galaxies. This review synthesizes recent advancements in our understanding of GC formation and aims to provide a comprehensive point of reference for leveraging the revolutionary capabilities of the current and upcoming generation of telescopes. The latest generation of GC models combine our understanding of their formation and destruction with advanced galaxy formation simulations. The next decade will provide the first-ever opportunity to test such models across their full evolutionary history, from GC formation at high redshift as seen with the James Webb Telescope, to snapshots of GC demographics at intermediate redshifts obtained with 30m-class telescopes, and eventually to the well-characterized GC populations observed at the present day. We identify the major questions that we should expect to address this way.
VEGA: Voids idEntification using Genetic Algorithm
2025-01-27
P. Ghafour, S. Tavasoli, M. R. Shojaei
Cosmic voids, the nearly empty regions nestled between walls and filaments, are recognized for their extensive applications in the field of cosmology and astrophysics. However, a consensus on the definition of voids remains elusive, as various void-finding methods identify different types of voids, each differing in shape and density based on the method that were used. In this paper, we introduce an innovative void identification method that utilizes Genetic Algorithm analysis. VEGA employs the Voronoi tessellation technique and the Convex Hull algorithm to partition the dataset plane into distinct regions and calculate the volume of each region. For the first time, VEGA integrates Genetic Algorithm analysis with the luminosity density contrast function to identify and locate the possible void region candidates. This method utilizes a set of grid points, which enhances the implementation of Voronoi tessellation and enables VEGA to more effectively access the dataset space for the identification of void regions candidates, finding the center and the ultimate structure of voids. Finally, we applied the VEGA and Aikio-M\"ah\"onen (AM) methods to the same test dataset and compared the cosmic voids identified by VEGA with those identified by the AM method. This comparison demonstrated that the VEGA void-finding method yields reliable results and can be effectively applied to various particle distributions.
I briefly review solutions to the strong CP problem based on axions, parity invariance, CP-invariance, and present a new idea based on CP as part of a spontaneously broken flavour symmetry such as a U(1) or modular invariance.
The detection of galaxy clusters, the most massive bounded structures in the universe, is crucial for cosmological analysis. Weak lensing signals allow us to track the distribution of all (dark and baryonic) matter regardless of its observable electromagnetic properties. Upcoming wide-field surveys like Euclid and LSST-Rubin will provide enhanced shape measurements of billions of background galaxies, presenting an unparalleled opportunity to detect galaxy clusters on a vast cosmic scale. The immense data volume generated by these surveys will require efficient and accurate analysis techniques. In this work, we introduce AMICO-WL, an extension of the optimal filtering algorithm implemented in AMICO, a well-tested code developed for optical cluster detection. AMICO-WL implements a specific linear optimal matched filter for weak lensing data in the AMICO infrastructure, using parallelisation and adding an efficient signal-to-noise ratio thresholding approach to set a desired sample purity and a cleaning procedure to deal with blended detections. The algorithm has been tested on a 25 deg$^2$ field of Euclid-like mock galaxy catalogue with the simulated shear signal produced using DUSTGRAIN-$pathfinder$ past-light-cones. We implemented a foreground removal procedure based on different cuts of low redshift galaxies from the input catalogue. To evaluate the performance of the method, we used an efficient matching procedure based on the 'blinking' of the simulation's individual redshift lensing planes. Cross-matching the AMICO-WL detections with the dark matter halo sample in the simulation having $M_{200} > 5 \times 10^{13}$ $M_{\odot}/h$ and considering a purity level of $\sim70\%$, the application of the foreground removal doubles the completeness from $6.5\%$ to $13.0\%$ and at the same time produces a significant decrease of spurious detections.
Dynamic Neutrino Mass Ordering and Its Imprint on the Diffuse Supernova Neutrino Background
2025-01-27
Yuber F. Perez-Gonzalez, Manibrata Sen
Neutrino masses may have evolved dynamically throughout the history of the Universe, potentially leading to a mass spectrum distinct from the normal or inverted ordering observed today. While cosmological measurements constrain the total energy density of neutrinos, they are not directly sensitive to a dynamically changing mass ordering unless future surveys achieve exceptional precision in detecting the distinct imprints of each mass eigenstate on large-scale structures. In this work, we investigate the impact of a dynamic neutrino mass spectrum on the diffuse supernova neutrino background (DSNB), which is composed of neutrinos from all supernova explosions throughout cosmic history and is on the verge of experimental detection. Since neutrino oscillations are highly sensitive to the mass spectrum, we show that the electron neutrino survival probability carries distinct signatures of the evolving neutrino mass spectrum. Our results indicate that the resulting modifications to the DSNB spectrum would exhibit unique energy-dependent features. These features are distinguishable from the effects of significant astrophysical uncertainties, providing a potential avenue for probing the dynamic nature of neutrino masses.
AI-assisted German Employment Contract Review: A Benchmark Dataset
2025-01-27
Oliver Wardas, Florian Matthes
Employment contracts are used to agree upon the working conditions between employers and employees all over the world. Understanding and reviewing contracts for void or unfair clauses requires extensive knowledge of the legal system and terminology. Recent advances in Natural Language Processing (NLP) hold promise for assisting in these reviews. However, applying NLP techniques on legal text is particularly difficult due to the scarcity of expert-annotated datasets. To address this issue and as a starting point for our effort in assisting lawyers with contract reviews using NLP, we release an anonymized and annotated benchmark dataset for legality and fairness review of German employment contract clauses, alongside with baseline model evaluations.
Aspect-Aware Decomposition for Opinion Summarization
2025-01-27
Miao Li, Jey Han Lau, Eduard Hovy, Mirella Lapata
Opinion summarization plays a key role in deriving meaningful insights from large-scale online reviews. To make this process more explainable and grounded, we propose a modular approach guided by review aspects which separates the tasks of aspect identification, opinion consolidation, and meta-review synthesis, enabling greater transparency and ease of inspection. We conduct extensive experiments across datasets representing scientific research, business, and product domains. Results show that our method generates more grounded summaries compared to strong baseline models, as verified through automated and human evaluations. Additionally, our modular approach, which incorporates reasoning based on review aspects, produces more informative intermediate outputs than knowledge-agnostic decomposed prompting. These intermediate outputs can also effectively support humans in summarizing opinions from large volumes of reviews.
2025-01-27
Oleksiy V. Arkhypov, Maxim L. Khodachenko
Time-limited space missions may miss rare occurrences of very dense clouds of lunar dust. At the same time, the information provided by the Earth-based monitoring of the Moon during at least the last three centuries still remains unused. In the present study, we fill this data analysis gap. The survey of historical reports of the 18-19 centuries about supposed lunar atmosphere manifestations, as well as the available data on too long-lasting stellar occultations by the lunar limb, enable us revealing numerous evidences of the lunar dust phenomena. By modeling of the conditions of such observations, we determine the geometrical parameters of the dust clouds, which scattered the sunlight during the particular events. Using this information, as well as the Mie scattering theory, we estimate the concentration of dust and its damaging effect at different orbits of a possible spacecraft. It was found that the some observed dust clouds of sub-micron grains could crash a space-vehicle at the low (<10 km) altitudes, similar to the incidents with landers Vikram, Beresheet, Hakuto-R M1, Luna-25, etc. The statistics of dust clouds' appearance enabled a reconstruction of a typical shape of a local dust cloud which resembles the shape of an impact plume. This, together with the revealed seasonal periodicity of observational manifestations of the dust phenomena, confirms a hypothesis on the meteoroid impact nature of the majority of the circumlunar dust clouds. At the same time, the discovered additional periodicity of the dust cloud appearance at half of synodic lunar month argue for an additional non-impact source of the circumlunar dust, connected with the lunar outgassing events, controlled by the solar tidal effects, completely unstudied. Moreover, the tendency of dust clouds to be observed during the low-level solar activity raises a question on possible dust pick up by the solar wind flow.
THOR: A Generic Energy Estimation Approach for On-Device Training
2025-01-27
Jiaru Zhang, Zesong Wang, Hao Wang, Tao Song, Huai-an Su, Rui Chen, Yang Hua, Xiangwei Zhou, Ruhui Ma, Miao Pan, Haibing Guan
Battery-powered mobile devices (e.g., smartphones, AR/VR glasses, and various IoT devices) are increasingly being used for AI training due to their growing computational power and easy access to valuable, diverse, and real-time data. On-device training is highly energy-intensive, making accurate energy consumption estimation crucial for effective job scheduling and sustainable AI. However, the heterogeneity of devices and the complexity of models challenge the accuracy and generalizability of existing estimation methods. This paper proposes THOR, a generic approach for energy consumption estimation in deep neural network (DNN) training. First, we examine the layer-wise energy additivity property of DNNs and strategically partition the entire model into layers for fine-grained energy consumption profiling. Then, we fit Gaussian Process (GP) models to learn from layer-wise energy consumption measurements and estimate a DNN's overall energy consumption based on its layer-wise energy additivity property. We conduct extensive experiments with various types of models across different real-world platforms. The results demonstrate that THOR has effectively reduced the Mean Absolute Percentage Error (MAPE) by up to 30%. Moreover, THOR is applied in guiding energy-aware pruning, successfully reducing energy consumption by 50%, thereby further demonstrating its generality and potential.
Anomalous temperature-dependent magnetization in the nearly collinear antiferromagnet Y$_2$Co$_3$
2025-01-26
Yunshu Shi, Huibo Cao, Hung-Cheng Wu, Li Yin, Neil Harrison, David S. Parker, Tushar Bhowmick, Tessa McNamee, Fatemeh Safari, Sergey L. Budko, James C. Fettinger, Susan M. Kauzlarich, Peter Klavins, Dmitry Popov, Ravhi Kumar, Russell J. Hemley, Shanti Deemyad, Taku J. Sato, Paul. C. Canfield, Valentin Taufour
Y$_2$Co$_3$ is a newly discovered antiferromagnetic (AFM) compound with distorted kagome layers. Previous investigations via bulk magnetization measurements suggested a complex noncollinear magnetic behavior, with magnetic moments primarily anti-aligned along the $b$ axis and some canting towards the $ac$ plane. In this study, we report the magnetic structure of Y$_2$Co$_3$ to be an A-type AFM structure with ferromagnetic (FM) interactions within the distorted kagome plane and an interplane antiferromagnetic interaction, as determined by single-crystal neutron diffraction. The magnetic moments align along the $b$ axis, with minimal canting towards the $c$ axis, at odds with the previous interpretation of bulk magnetization measurements. The magnetic moments on the two distinct Co sites are [0, -0.68(3), 0] $\mu_B$ and [0, 1.25(4), 0.07(1)] $\mu_B$. We attribute the previously reported "noncollinear" behavior to the considerable temperature dependence of itinerant AFM exchange interactions, induced by thermal contraction along the $b$ axis. Additionally, our examination of lattice constants through pressure studies reveals compensating effects on FM and AFM interactions, resulting in negligible pressure dependence of $T_\textrm{N}$.
An Empirical Study on Decision-Making Aspects in Responsible Software Engineering for AI
2025-01-26
Lekshmi Murali Rani, Faezeh Mohammadi, Robert Feldt, Richard Berntsson Svensson
Incorporating responsible practices into software engineering (SE) for AI is essential to ensure ethical principles, societal impact, and accountability remain at the forefront of AI system design and deployment. This study investigates the ethical challenges and complexities inherent in responsible software engineering (RSE) for AI, underscoring the need for practical,scenario-driven operational guidelines. Given the complexity of AI and the relative inexperience of professionals in this rapidly evolving field, continuous learning and market adaptation are crucial. Through qualitative interviews with seven practitioners(conducted until saturation), quantitative surveys of 51 practitioners, and static validation of results with four industry experts in AI, this study explores how personal values, emerging roles, and awareness of AIs societal impact influence responsible decision-making in RSE for AI. A key finding is the gap between the current state of the art and actual practice in RSE for AI, particularly in the failure to operationalize ethical and responsible decision-making within the software engineering life cycle for AI. While ethical issues in RSE for AI largely mirror those found in broader SE process, the study highlights a distinct lack of operational frameworks and resources to guide RSE practices for AI effectively. The results reveal that current ethical guidelines are insufficiently implemented at the operational level, reinforcing the complexity of embedding ethics throughout the software engineering life cycle. The study concludes that interdisciplinary collaboration, H-shaped competencies(Ethical-Technical dual competence), and a strong organizational culture of ethics are critical for fostering RSE practices for AI, with a particular focus on transparency and accountability.
Discovering Strong Gravitational Lenses in the Dark Energy Survey with Interactive Machine Learning and Crowd-sourced Inspection with Space Warps
2025-01-26
J. González, P. Holloway, T. Collett, A. Verma, K. Bechtol, P. Marshall, A. More, J. Acevedo Barroso, G. Cartwright, M. Martinez, T. Li, K. Rojas, S. Schuldt, S. Birrer, H. T. Diehl, R. Morgan, A. Drlica-Wagner, J. H. O'Donnell, E. Zaborowski, B. Nord, E. M. Baeten, L. C. Johnson, C. Macmillan, A. Roodman, A. Pieres, A. R. Walker, A. A. Plazas Malagón, A. Carnero Rosell, B. Santiago, B. Flaugher, D. Gruen, D. Brooks, D. L. Burke, D. J. James, D. Sanchez Cid, D. L. Hollowood, D. L. Tucker, E. Buckley-Geer, E. Gaztanaga, E. Suchyta, E. Sanchez, G. Gutierrez, G. Giannini, G. Tarle, I. Sevilla-Noarbe, J. L. Marshall, J. Carretero, J. Frieman, J. De Vicente, J. García-Bellido, J. Mena-Fernández, J. Myles, K. Honscheid, K. Kuehn, M. Lima, M. E. S. Pereira, M. Smith, M. Aguena, N. Weaverdyck, O. Lahav, P. Doel, R. Miquel, R. A. Gruendl, R. Cawthon, S. R. Hinton, S. S. Allam, S. Desai, S. Samuroff, S. Everett, S. Lee, T. M. Davis, T. M. C. Abbott, V. Vikram
We conduct a search for strong gravitational lenses in the Dark Energy Survey (DES) Year 6 imaging data. We implement a pre-trained Vision Transformer (ViT) for our machine learning (ML) architecture and adopt Interactive Machine Learning to construct a training sample with multiple classes to address common types of false positives. Our ML model reduces 236 million DES cutout images to 22,564 targets of interest, including around 85% of previously reported galaxy-galaxy lens candidates discovered in DES. These targets were visually inspected by citizen scientists, who ruled out approximately 90% as false positives. Of the remaining 2,618 candidates, 149 were expert-classified as 'definite' lenses and 516 as 'probable' lenses, with 147 of these candidates being newly identified. Additionally, we trained a second ViT to find double-source plane lens systems, finding at least one double-source system. Our main ViT excels at identifying galaxy-galaxy lenses, consistently assigning high scores to candidates with high confidence. The top 800 ViT-scored images include around 100 of our `definite' lens candidates. This selection is an order of magnitude higher in purity than previous convolutional neural network-based lens searches and demonstrates the feasibility of applying our methodology for discovering large samples of lenses in future surveys.
Remarks on "The Modification of Feynman Diagrams in Curved Space-Time"
2025-01-26
Noah M. MacKay
The arXiv preprint 2411.15164v1 \cite{Li:2024ltx} discusses how a weak Schwarzschild spacetime modifies Feynman diagram calculations and the governing equations of scalar and spinor particles. This short review offers an extended analysis on concerning points involving massless particles and limitations of weak-field approximation. Written for the Universit\"at Potsdam lecture \textit{New Developments in Astrophysics, Winter Semester 2024/25}.
Neural networks have achieved remarkable success across various fields. However, the lack of interpretability limits their practical use, particularly in critical decision-making scenarios. Post-hoc interpretability, which provides explanations for pre-trained models, is often at risk of robustness and fidelity. This has inspired a rising interest in self-interpretable neural networks, which inherently reveal the prediction rationale through the model structures. Although there exist surveys on post-hoc interpretability, a comprehensive and systematic survey of self-interpretable neural networks is still missing. To address this gap, we first collect and review existing works on self-interpretable neural networks and provide a structured summary of their methodologies from five key perspectives: attribution-based, function-based, concept-based, prototype-based, and rule-based self-interpretation. We also present concrete, visualized examples of model explanations and discuss their applicability across diverse scenarios, including image, text, graph data, and deep reinforcement learning. Additionally, we summarize existing evaluation metrics for self-interpretability and identify open challenges in this field, offering insights for future research. To support ongoing developments, we present a publicly accessible resource to track advancements in this domain: https://github.com/yangji721/Awesome-Self-Interpretable-Neural-Network.
Investigating Circularity in India's Textile Industry: Overcoming Challenges and Leveraging Digitization for Growth
2025-01-26
Suman Kumar Das
India's growing population and economy have significantly increased the demand and consumption of natural resources. As a result, the potential benefits of transitioning to a circular economic model have been extensively discussed and debated among various Indian stakeholders, including policymakers, industry leaders, and environmental advocates. Despite the numerous initiatives, policies, and transnational strategic partnerships of the Indian government, most small and medium enterprises in India face significant challenges in implementing circular economy practices. This is due to the lack of a clear pathway to measure the current state of the circular economy in Indian industries and the absence of a framework to address these challenges. This paper examines the circularity of the 93-textile industry in India using the C-Readiness Tool. The analysis comprehensively identified 9 categories with 34 barriers to adopting circular economy principles in the textile sector through a narrative literature review. The identified barriers were further compared against the findings from a C-readiness tool assessment, which revealed prominent challenges related to supply chain coordination, consumer engagement, and regulatory compliance within the industry's circularity efforts. In response to these challenges, the article proposes a strategic roadmap that leverages digital technologies to drive the textile industry towards a more sustainable and resilient industrial model.
BoKDiff: Best-of-K Diffusion Alignment for Target-Specific 3D Molecule Generation
2025-01-26
Ali Khodabandeh Yalabadi, Mehdi Yazdani-Jahromi, Ozlem Ozmen Garibay
Structure-based drug design (SBDD) leverages the 3D structure of biomolecular targets to guide the creation of new therapeutic agents. Recent advances in generative models, including diffusion models and geometric deep learning, have demonstrated promise in optimizing ligand generation. However, the scarcity of high-quality protein-ligand complex data and the inherent challenges in aligning generated ligands with target proteins limit the effectiveness of these methods. We propose BoKDiff, a novel framework that enhances ligand generation by combining multi-objective optimization and Best-of-K alignment methodologies. Built upon the DecompDiff model, BoKDiff generates diverse candidates and ranks them using a weighted evaluation of molecular properties such as QED, SA, and docking scores. To address alignment challenges, we introduce a method that relocates the center of mass of generated ligands to their docking poses, enabling accurate sub-component extraction. Additionally, we integrate a Best-of-N (BoN) sampling approach, which selects the optimal ligand from multiple generated candidates without requiring fine-tuning. BoN achieves exceptional results, with QED values exceeding 0.6, SA scores above 0.75, and a success rate surpassing 35%, demonstrating its efficiency and practicality. BoKDiff achieves state-of-the-art results on the CrossDocked2020 dataset, including a -8.58 average Vina docking score and a 26% success rate in molecule generation. This study is the first to apply Best-of-K alignment and Best-of-N sampling to SBDD, highlighting their potential to bridge generative modeling with practical drug discovery requirements. The code is provided at https://github.com/khodabandeh-ali/BoKDiff.git.
Understanding Attitudes and Trust of Generative AI Chatbots for Social Anxiety Support
2025-01-26
Yimeng Wang, Yinzhou Wang, Kelly Crace, Yixuan Zhang
Social anxiety (SA) has become increasingly prevalent. Traditional coping strategies often face accessibility challenges. Generative AI (GenAI), known for their knowledgeable and conversational capabilities, are emerging as alternative tools for mental well-being. With the increased integration of GenAI, it is important to examine individuals' attitudes and trust in GenAI chatbots' support for SA. Through a mixed-method approach that involved surveys (n = 159) and interviews (n = 17), we found that individuals with severe symptoms tended to trust and embrace GenAI chatbots more readily, valuing their non-judgmental support and perceived emotional comprehension. However, those with milder symptoms prioritized technical reliability. We identified factors influencing trust, such as GenAI chatbots' ability to generate empathetic responses and its context-sensitive limitations, which were particularly important among individuals with SA. We also discuss the design implications and use of GenAI chatbots in fostering cognitive and emotional trust, with practical and design considerations.
Engage and Mobilize! Understanding Evolving Patterns of Social Media Usage in Emergency Management
2025-01-26
Hemant Purohit, Cody Buntain, Amanda Lee Hughes, Steve Peterson, Valerio Lorini, Carlos Castillo
The work of Emergency Management (EM) agencies requires timely collection of relevant data to inform decision-making for operations and public communication before, during, and after a disaster. However, the limited human resources available to deploy for field data collection is a persistent problem for EM agencies. Thus, many of these agencies have started leveraging social media as a supplemental data source and a new venue to engage with the public. While prior research has analyzed the potential benefits and attitudes of practitioners and the public when leveraging social media during disasters, a gap exists in the critical analysis of the actual practices and uses of social media among EM agencies, across both geographical regions and phases of the EM lifecycle - typically mitigation, preparedness, response, and recovery. In this paper, we conduct a mixed-method analysis to update and fill this gap on how EM practitioners in the U.S. and Europe use social media, building on a survey study of about 150 professionals and a follow-up interview study with 11 participants. The results indicate that using social media is no longer a non-traditional practice in operational and informational processes for the decision-making of EM agencies working at both the local level (e.g., county or town) and non-local level (e.g., state/province, federal/national) for emergency management. Especially, the practitioners affiliated with agencies working at the local level have a very high perceived value of social media for situational awareness (e.g., analyzing disaster extent and impact) and public communication (e.g., disseminating timely information and correcting errors in crisis coverage). We conclude with the policy, technological, and socio-technical needs to design future social media analytics systems to support the work of EM agencies in such communication including the applications of AI.
First Real-Time Detection of Ambient Backscatters using Uplink Sounding Reference Signals of a Commercial 4G Smartphone
2025-01-26
Ahmed ElSanhoury, Islam Galal, Khaled AlKady, Aml ElKhodary, Ayman M. Hassan, Dinh-Thuy Phan-Huy
Recently, cellular Ambient Backscattering has been proposed for 4G/5G/6G networks. An Ambient backscatter tag broadcasts its message by backscattering ambient downlink waves from the closest base station according to a predefined pattern. A tag is detected by smartphones nearby. This paper presents, for the first time, a novel ambient backscatter communication system exploiting uplink ambient waves from smartphones instead of downlink waves. In this novel system, the base station connected to a smartphone monitors the uplink pilot signals and detects tags in proximity. The proposed system is implemented and tested with prototypes of tags, a commercial 4G smartphone and a 4G Software Defined Radio base station. At the base station side, a non-coherent correlator receiver is implemented, and a novel technique based on pre-correlation data processing is proposed to separate useful variations on pilot signals due to tags from variations due to time varying channel effects. To deal with collision between multiple tags, distinct Gold pseudo noise codes with minimum cross correlation are used. Tests are run in different indoor and outdoor environments. A receiver detection probability of 95% has been achieved at 0.5% False-alarm probability when the tag is at 5 meters from the UE. At the refresh rate of 2 seconds, the proposed scheme is suitable for tracking objects at moderate speeds and can therefore be used for many passive IoT-based applications.
Studying Behavioral Addiction by Combining Surveys and Digital Traces: A Case Study of TikTok
2025-01-26
Cai Yang, Sepehr Mousavi, Abhisek Dash, Krishna P. Gummadi, Ingmar Weber
Opaque algorithms disseminate and mediate the content that users consume on online social media platforms. This algorithmic mediation serves users with contents of their liking, on the other hand, it may cause several inadvertent risks to society at scale. While some of these risks, e.g., filter bubbles or dissemination of hateful content, are well studied in the community, behavioral addiction, designated by the Digital Services Act (DSA) as a potential systemic risk, has been understudied. In this work, we aim to study if one can effectively diagnose behavioral addiction using digital data traces from social media platforms. Focusing on the TikTok short-format video platform as a case study, we employ a novel mixed methodology of combining survey responses with data donations of behavioral traces. We survey 1590 TikTok users and stratify them into three addiction groups (i.e., less/moderately/highly likely addicted). Then, we obtain data donations from 107 surveyed participants. By analyzing users' data we find that, among others, highly likely addicted users spend more time watching TikTok videos and keep coming back to TikTok throughout the day, indicating a compulsion to use the platform. Finally, by using basic user engagement features, we train classifier models to identify highly likely addicted users with $F_1 \geq 0.55$. The performance of the classifier models suggests predicting addictive users solely based on their usage is rather difficult.
A general, flexible and harmonious framework to construct interpretable functions in regression analysis
2025-01-26
Tianyu Zhan, Jian Kang
An interpretable model or method has several appealing features, such as reliability to adversarial examples, transparency of decision-making, and communication facilitator. However, interpretability is a subjective concept, and even its definition can be diverse. The same model may be deemed as interpretable by a study team, but regarded as a black-box algorithm by another squad. Simplicity, accuracy and generalizability are some additional important aspects of evaluating interpretability. In this work, we present a general, flexible and harmonious framework to construct interpretable functions in regression analysis with a focus on continuous outcomes. We formulate a functional skeleton in light of users' expectations of interpretability. A new measure based on Mallows's $C_p$-statistic is proposed for model selection to balance approximation, generalizability, and interpretability. We apply this approach to derive a sample size formula in adaptive clinical trial designs to demonstrate the general workflow, and to explain operating characteristics in a Bayesian Go/No-Go paradigm to show the potential advantages of using meaningful intermediate variables. Generalization to categorical outcomes is illustrated in an example of hypothesis testing based on Fisher's exact test. A real data analysis of NHANES (National Health and Nutrition Examination Survey) is conducted to investigate relationships between some important laboratory measurements. We also discuss some extensions of this method.
Task Scheduling in Geo-Distributed Computing: A Survey
2025-01-26
Yujian Wu, Shanjiang Tang, Ce Yu, Bin Yang, Chao Sun, Jian Xiao, Hutong Wu
Geo-distributed computing, a paradigm that assigns computational tasks to globally distributed nodes, has emerged as a promising approach in cloud computing, edge computing, cloud-edge computing and supercomputer computing (HPC). It enables low-latency services, ensures data locality, and handles large-scale applications. As global computing capacity and task demands increase rapidly, scheduling tasks for efficient execution in geo-distributed computing systems has become an increasingly critical research challenge. It arises from the inherent characteristics of geographic distribution, including heterogeneous network conditions, region-specific resource pricing, and varying computational capabilities across locations. Researchers have developed diverse task scheduling methods tailored to geo-distributed scenarios, aiming to achieve objectives such as performance enhancement, fairness assurance, and fault-tolerance improvement. This survey provides a comprehensive and systematic review of task scheduling techniques across four major distributed computing environments, with an in-depth analysis of these approaches based on their core scheduling objectives. Through our analysis, we identify key research challenges and outline promising directions for advancing task scheduling in geo-distributed computing.
AI in Oncology: Transforming Cancer Detection through Machine Learning and Deep Learning Applications
2025-01-26
Muhammad Aftab, Faisal Mehmood, Chengjuan Zhang, Alishba Nadeem, Zigang Dong, Yanan Jiang, Kangdongs Liu
Artificial intelligence (AI) has potential to revolutionize the field of oncology by enhancing the precision of cancer diagnosis, optimizing treatment strategies, and personalizing therapies for a variety of cancers. This review examines the limitations of conventional diagnostic techniques and explores the transformative role of AI in diagnosing and treating cancers such as lung, breast, colorectal, liver, stomach, esophageal, cervical, thyroid, prostate, and skin cancers. The primary objective of this paper is to highlight the significant advancements that AI algorithms have brought to oncology within the medical industry. By enabling early cancer detection, improving diagnostic accuracy, and facilitating targeted treatment delivery, AI contributes to substantial improvements in patient outcomes. The integration of AI in medical imaging, genomic analysis, and pathology enhances diagnostic precision and introduces a novel, less invasive approach to cancer screening. This not only boosts the effectiveness of medical facilities but also reduces operational costs. The study delves into the application of AI in radiomics for detailed cancer characterization, predictive analytics for identifying associated risks, and the development of algorithm-driven robots for immediate diagnosis. Furthermore, it investigates the impact of AI on addressing healthcare challenges, particularly in underserved and remote regions. The overarching goal of this platform is to support the development of expert recommendations and to provide universal, efficient diagnostic procedures. By reviewing existing research and clinical studies, this paper underscores the pivotal role of AI in improving the overall cancer care system. It emphasizes how AI-enabled systems can enhance clinical decision-making and expand treatment options, thereby underscoring the importance of AI in advancing precision oncology
Semi-Supervised Learning for AVO Inversion with Strong Spatial Feature Constraints
2025-01-26
Yingtian Liu, Yong Li, Junheng Peng, Mingwei Wang
One-dimensional convolution is a widely used deep learning technique in prestack amplitude variation with offset (AVO) inversion; however, it lacks lateral continuity. Although two-dimensional convolution improves lateral continuity, due to the sparsity of well-log data, the model only learns weak spatial features and fails to explore the spatial correlations in seismic data fully. To overcome these challenges, we propose a novel AVO inversion method based on semi-supervised learning with strong spatial feature constraints (SSFC-SSL). First, two-dimensional predicted values are obtained through the inversion network, and the predicted values at well locations are sparsely represented using well-log labels. Subsequently, a label-annihilation operator is introduced, enabling the predicted values at non-well locations to learn the spatial features of well locations through the neural network. Ultimately, a two-way strong spatial feature mapping between non-well locations and well locations is achieved. Additionally, to reduce the dependence on well-log labels, we combine the semi-supervised learning strategy with a low-frequency model, further enhancing the robustness of the method. Experimental results on both synthetic example and field data demonstrate that the proposed method significantly improves lateral continuity and inversion accuracy compared to one- and two-dimensional deep learning techniques.
An Aspect Performance-aware Hypergraph Neural Network for Review-based Recommendation
2025-01-26
Junrui Liu, Tong Li, Di Wu, Zifang Tang, Yuan Fang, Zhen Yang
Online reviews allow consumers to provide detailed feedback on various aspects of items. Existing methods utilize these aspects to model users' fine-grained preferences for specific item features through graph neural networks. We argue that the performance of items on different aspects is important for making precise recommendations, which has not been taken into account by existing approaches, due to lack of data. In this paper, we propose an aspect performance-aware hypergraph neural network (APH) for the review-based recommendation, which learns the performance of items from the conflicting sentiment polarity of user reviews. Specifically, APH comprehensively models the relationships among users, items, aspects, and sentiment polarity by systematically constructing an aspect hypergraph based on user reviews. In addition, APH aggregates aspects representing users and items by employing an aspect performance-aware hypergraph aggregation method. It aggregates the sentiment polarities from multiple users by jointly considering user preferences and the semantics of their sentiments, determining the weights of sentiment polarities to infer the performance of items on various aspects. Such performances are then used as weights to aggregate neighboring aspects. Experiments on six real-world datasets demonstrate that APH improves MSE, Precision@5, and Recall@5 by an average of 2.30%, 4.89%, and 1.60% over the best baseline. The source code and data are available at https://github.com/dianziliu/APH.
The black-box nature of large language models (LLMs) necessitates the development of eXplainable AI (XAI) techniques for transparency and trustworthiness. However, evaluating these techniques remains a challenge. This study presents a general evaluation framework using four key metrics: Human-reasoning Agreement (HA), Robustness, Consistency, and Contrastivity. We assess the effectiveness of six explainability techniques from five different XAI categories model simplification (LIME), perturbation-based methods (SHAP), gradient-based approaches (InputXGradient, Grad-CAM), Layer-wise Relevance Propagation (LRP), and attention mechanisms-based explainability methods (Attention Mechanism Visualization, AMV) across five encoder-based language models: TinyBERT, BERTbase, BERTlarge, XLM-R large, and DeBERTa-xlarge, using the IMDB Movie Reviews and Tweet Sentiment Extraction (TSE) datasets. Our findings show that the model simplification-based XAI method (LIME) consistently outperforms across multiple metrics and models, significantly excelling in HA with a score of 0.9685 on DeBERTa-xlarge, robustness, and consistency as the complexity of large language models increases. AMV demonstrates the best Robustness, with scores as low as 0.0020. It also excels in Consistency, achieving near-perfect scores of 0.9999 across all models. Regarding Contrastivity, LRP performs the best, particularly on more complex models, with scores up to 0.9371.
Eigenfunctions with double exponential rate of localization
2025-01-26
S. Krymskii, A. Logunov, F. Pagano
We construct a real-valued solution to the eigenvalue problem $-\text{div}(A\nabla u)=\lambda u$, $\lambda>0,$ in the cylinder $\mathbb{T}^2\times \mathbb{R}$ with a real, uniformly elliptic, and uniformly $C^1$ matrix $A$ such that $|u(x,y,t)|\leq C e^{-c e^{c|t|}}$ for some $c,C>0$. We also construct a complex-valued solution to the heat equation $u_t=\Delta u + B \nabla u$ in a half-cylinder with continuous and uniformly bounded $B$, which also decays with double exponential speed. Related classical ideas, used in the construction of counterexamples to the unique continuation by Plis and Miller, are reviewed.
Relativistic chiral nuclear forces: status and prospects
2025-01-26
Jun-Xu Lu, Yang Xiao, Zhi-Wei Liu, Li-Sheng Geng
Understanding nuclear structure, reactions, and the properties of neutron stars from \textit{ab initio} calculations from the nucleon degrees of freedom has always been a primary goal of nuclear physics, in which the microscopic nuclear force serves as the fundamental input. So far, the Weinberg chiral nuclear force, first proposed by the Nobel laureate Weinberg, has become the \textit{de facto} standard input for nuclear \textit{ab initio} studies. However, compared to their non-relativistic counterparts, relativistic \textit{ab initio} calculations, which describe better nuclear observables, have only begun. The lack of modern relativistic nucleon-nucleon interactions is an important issue restricting their development. In this work, we briefly review the development and status of the Weinberg chiral nuclear force, as well as its limitations. We further present a concise introduction to the relativistic chiral nuclear force, show its description of the scattering phase shifts and observables such as differential cross sections, and demonstrate its unique features. Additionally, we show that the relativistic framework could be naturally extended to the antinucleon-nucleon interaction.
Large Language Models (LLMs) excel in text generation and understanding, especially in simulating socio-political and economic patterns, serving as an alternative to traditional surveys. However, their global applicability remains questionable due to unexplored biases across socio-demographic and geographic contexts. This study examines how LLMs perform across diverse populations by analyzing public surveys from Chile and the United States, focusing on predictive accuracy and fairness metrics. The results show performance disparities, with LLM consistently outperforming on U.S. datasets. This bias originates from the U.S.-centric training data, remaining evident after accounting for socio-demographic differences. In the U.S., political identity and race significantly influence prediction accuracy, while in Chile, gender, education, and religious affiliation play more pronounced roles. Our study presents a novel framework for measuring socio-demographic biases in LLMs, offering a path toward ensuring fairer and more equitable model performance across diverse socio-cultural contexts.
The search for quantum spin liquids (QSL) and chemical doping in such materials to explore superconductivity have continuously attracted intense interest. Here, we report the discovery of a potential QSL candidate, pyrochlore-lattice beta-NaYbO2. Colorless and transparent NaYbO2 single crystals, layered alpha-NaYbO2 (~250 um on edge) and octahedral beta-NaYbO2 (~50 um on edge), were grown for the first time. Synchrotron X-ray single crystal diffraction unambiguously determined that the newfound beta-NaYbO2 belongs to the three-dimensional pyrochlore structure characterized by the R-3m space group, corroborated by synchrotron X-ray and neutron powder diffraction and pair distribution function. Magnetic measurements revealed no long-range magnetic order or spin glass behavior down to 0.4 K with a low boundary spin frustration factor of 17.5, suggesting a potential QSL ground state. Under high magnetic fields, the potential QSL state was broken and spins order. Our findings reveal that NaYbO2 is a fertile playground for studying novel quantum states.
Music Generation using Human-In-The-Loop Reinforcement Learning
2025-01-25
Aju Ani Justus
This paper presents an approach that combines Human-In-The-Loop Reinforcement Learning (HITL RL) with principles derived from music theory to facilitate real-time generation of musical compositions. HITL RL, previously employed in diverse applications such as modelling humanoid robot mechanics and enhancing language models, harnesses human feedback to refine the training process. In this study, we develop a HILT RL framework that can leverage the constraints and principles in music theory. In particular, we propose an episodic tabular Q-learning algorithm with an epsilon-greedy exploration policy. The system generates musical tracks (compositions), continuously enhancing its quality through iterative human-in-the-loop feedback. The reward function for this process is the subjective musical taste of the user.
Alzheimers disease is a deadly neurological condition, impairing important memory and brain functions. Alzheimers disease promotes brain shrinkage, ultimately leading to dementia. Dementia diagnosis typically takes 2.8 to 4.4 years after the first clinical indication. Advancements in computing and information technology have led to many techniques of studying Alzheimers disease. Early identification and therapy are crucial for preventing Alzheimers disease, as early-onset dementia hits people before the age of 65, while late-onset dementia occurs after this age. According to the 2015 World Alzheimers disease Report, there are 46.8 million individuals worldwide suffering from dementia, with an anticipated 74.7 million more by 2030 and 131.5 million by 2050. Deep Learning has outperformed conventional Machine Learning techniques by identifying intricate structures in high-dimensional data. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), have achieved an accuracy of up to 96.0% for Alzheimers disease classification, and 84.2% for mild cognitive impairment (MCI) conversion prediction. There have been few literature surveys available on applying ML to predict dementia, lacking in congenital observations. However, this survey has focused on a specific data channel for dementia detection. This study evaluated Deep Learning algorithms for early Alzheimers disease detection, using openly accessible datasets, feature segmentation, and classification methods. This article also has identified research gaps and limits in detecting Alzheimers disease, which can inform future research.
Sample efficiency and systematic generalization are two long-standing challenges in reinforcement learning. Previous studies have shown that involving natural language along with other observation modalities can improve generalization and sample efficiency due to its compositional and open-ended nature. However, to transfer these properties of language to the decision-making process, it is necessary to establish a proper language grounding mechanism. One approach to this problem is applying inductive biases to extract fine-grained and informative representations from the observations, which makes them more connectable to the language units. We provide architecture-level inductive biases for modularity and sparsity mainly based on Neural Production Systems (NPS). Alongside NPS, we assign a central role to memory in our architecture. It can be seen as a high-level information aggregator which feeds policy/value heads with comprehensive information and simultaneously guides selective attention in NPS through attentional feedback. Our results in the BabyAI environment suggest that the proposed model's systematic generalization and sample efficiency are improved significantly compared to previous models. An extensive ablation study on variants of the proposed method is conducted, and the effectiveness of each employed technique on generalization, sample efficiency, and training stability is specified.
We present a sample of 12 quasar candidates with highly variable soft X-ray emission from the 4th XMM-newton Serendipitous Source Catalog (4XMM-DR13) using random forest. We obtained optical to mid-IR photometric data for the 4XMM-DR13 sources by correlating the sample with the SDSS DR18 photometric database and the AllWISE database. By cross-matching this sample with known spectral catalogs from the SDSS and LAMOST surveys, we obtained a training data set containing stars, galaxies, and quasars. The random forest algorithm was trained to classify the XMM-WISE-SDSS sample. We further filtered the classified quasar candidates with $\it{Gaia}$ proper motion to remove stellar contaminants. Finally, 53,992 quasar candidates have been classified, with 10,210 known quasars matched in SIMBAD. The quasar candidates have systematically lower X-ray fluxes than quasars in the training set, which indicates the classifier is helpful to single out fainter quasars. We constructed a sample of 12 sources from these quasars candidates which changed their soft X-ray fluxes by a factor of 10 over $\sim$ 20 years in the 4XMM-newton survey. Our selected highly variable quasar candidates extend the quasar sample, characterized by extreme soft X-ray variability, to the optically faint end with magnitudes around $r \sim 22$. None of the 12 sources were detected in ROSAT observations. Given the flux limit of ROSAT, the result suggests that quasars exhibiting variations of more than two orders of magnitudes are extremely rare.
Generalizable Deepfake Detection via Effective Local-Global Feature Extraction
2025-01-25
Jiazhen Yan, Ziqiang Li, Ziwen He, Zhangjie Fu
The rapid advancement of GANs and diffusion models has led to the generation of increasingly realistic fake images, posing significant hidden dangers and threats to society. Consequently, deepfake detection has become a pressing issue in today's world. While some existing methods focus on forgery features from either a local or global perspective, they often overlook the complementary nature of these features. Other approaches attempt to incorporate both local and global features but rely on simplistic strategies, such as cropping, which fail to capture the intricate relationships between local features. To address these limitations, we propose a novel method that effectively combines local spatial-frequency domain features with global frequency domain information, capturing detailed and holistic forgery traces. Specifically, our method uses Discrete Wavelet Transform (DWT) and sliding windows to tile forged features and leverages attention mechanisms to extract local spatial-frequency domain information. Simultaneously, the phase component of the Fast Fourier Transform (FFT) is integrated with attention mechanisms to extract global frequency domain information, complementing the local features and ensuring the integrity of forgery detection. Comprehensive evaluations on open-world datasets generated by 34 distinct generative models demonstrate a significant improvement of 2.9% over existing state-of-the-art methods.
Heat Transfer in Composite Materials: Mechanisms and Applications
2025-01-25
Mohammad Alaghemandi, Morgan Alamandi
Understanding heat transfer in composite materials is essential for optimizing their performance in critical applications across industries such as aerospace, automotive, renewable energy, and construction. This review offers a comprehensive examination of the various heat transfer mechanisms within composite materials and explores how these processes, spanning different length and time scales, are influenced by the materials' composition and structure. Both traditional and advanced analytical and numerical modeling techniques are explored, emphasizing their importance in predicting and optimizing thermal behavior across these scales. Furthermore, the review evaluates current experimental methods for measuring thermal properties, discussing their limitations and potential areas for enhancement. Significant attention is devoted to the practical applications of composite materials, from thermal management in electronic devices to heat-resistant components in aerospace engineering. Recent innovations, such as the integration of phase change materials and the development of nano-enhanced composites, are assessed for their potential to transform heat transfer capabilities. Ongoing challenges are addressed, and future research directions are outlined, highlighting the need for advancements in material science and engineering to meet emerging demands. This review aims to bridge the gap between fundamental research and practical applications, providing a comprehensive understanding of heat transfer in composite materials that is both rooted in current science and driven by future possibilities.
A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges
2025-01-25
Aitor Sánchez-Ferrera, Borja Calvo, Jose A. Lozano
Time series anomaly detection presents various challenges due to the sequential and dynamic nature of time-dependent data. Traditional unsupervised methods frequently encounter difficulties in generalization, often overfitting to known normal patterns observed during training and struggling to adapt to unseen normality. In response to this limitation, self-supervised techniques for time series have garnered attention as a potential solution to undertake this obstacle and enhance the performance of anomaly detectors. This paper presents a comprehensive review of the recent methods that make use of self-supervised learning for time series anomaly detection. A taxonomy is proposed to categorize these methods based on their primary characteristics, facilitating a clear understanding of their diversity within this field. The information contained in this survey, along with additional details that will be periodically updated, is available on the following GitHub repository: https://github.com/Aitorzan3/Awesome-Self-Supervised-Time-Series-Anomaly-Detection.
Audio-Language Models for Audio-Centric Tasks: A survey
2025-01-25
Yi Su, Jisheng Bai, Qisheng Xu, Kele Xu, Yong Dou
Audio-Language Models (ALMs), which are trained on audio-text data, focus on the processing, understanding, and reasoning of sounds. Unlike traditional supervised learning approaches learning from predefined labels, ALMs utilize natural language as a supervision signal, which is more suitable for describing complex real-world audio recordings. ALMs demonstrate strong zero-shot capabilities and can be flexibly adapted to diverse downstream tasks. These strengths not only enhance the accuracy and generalization of audio processing tasks but also promote the development of models that more closely resemble human auditory perception and comprehension. Recent advances in ALMs have positioned them at the forefront of computer audition research, inspiring a surge of efforts to advance ALM technologies. Despite rapid progress in the field of ALMs, there is still a notable lack of systematic surveys that comprehensively organize and analyze developments. In this paper, we present a comprehensive review of ALMs with a focus on general audio tasks, aiming to fill this gap by providing a structured and holistic overview of ALMs. Specifically, we cover: (1) the background of computer audition and audio-language models; (2) the foundational aspects of ALMs, including prevalent network architectures, training objectives, and evaluation methods; (3) foundational pre-training and audio-language pre-training approaches; (4) task-specific fine-tuning, multi-task tuning and agent systems for downstream applications; (5) datasets and benchmarks; and (6) current challenges and future directions. Our review provides a clear technical roadmap for researchers to understand the development and future trends of existing technologies, offering valuable references for implementation in real-world scenarios.
Mapping Galaxy Images Across Ultraviolet, Visible and Infrared Bands Using Generative Deep Learning
2025-01-25
Youssef Zaazou, Alex Bihlo, Terrence S. Tricco
We demonstrate that generative deep learning can translate galaxy observations across ultraviolet, visible, and infrared photometric bands. Leveraging mock observations from the Illustris simulations, we develop and validate a supervised image-to-image model capable of performing both band interpolation and extrapolation. The resulting trained models exhibit high fidelity in generating outputs, as verified by both general image comparison metrics (MAE, SSIM, PSNR) and specialized astronomical metrics (GINI coefficient, M20). Moreover, we show that our model can be used to predict real-world observations, using data from the DECaLS survey as a case study. These findings highlight the potential of generative learning to augment astronomical datasets, enabling efficient exploration of multi-band information in regions where observations are incomplete. This work opens new pathways for optimizing mission planning, guiding high-resolution follow-ups, and enhancing our understanding of galaxy morphology and evolution.
We give a brief review of the concept of asymptotic integrability, which means that the Hamilton equations for the propagation of short-wavelength packets along a smooth, large-scale background wave have an integral independent of the initial conditions. The existence of such an integral leads to a number of important consequences, which include, besides the direct application to the packets propagation problems, Hamiltonian theory of narrow solitons motion and generalized Bohr-Sommerfeld rule for parameters of solitons produced from an intensive initial pulse. We show that in the case of systems with two wave variables and exact fulfillment of the asymptotic integrability condition, the `quantization' of mechanical systems, associated with the additional integrals, yields the Lax pairs for a number of typical completely integrable equations, and this sheds new light on the origin of the complete integrability in nonlinear wave physics.
BitsAI-CR: Automated Code Review via LLM in Practice
2025-01-25
Tao Sun, Jian Xu, Yuanpeng Li, Zhao Yan, Ge Zhang, Lintao Xie, Lu Geng, Zheng Wang, Yueyan Chen, Qin Lin, Wenbo Duan, Kaixin Sui
Code review remains a critical yet resource-intensive process in software development, particularly challenging in large-scale industrial environments. While Large Language Models (LLMs) show promise for automating code review, existing solutions face significant limitations in precision and practicality. This paper presents BitsAI-CR, an innovative framework that enhances code review through a two-stage approach combining RuleChecker for initial issue detection and ReviewFilter for precision verification. The system is built upon a comprehensive taxonomy of review rules and implements a data flywheel mechanism that enables continuous performance improvement through structured feedback and evaluation metrics. Our approach introduces an Outdated Rate metric that can reflect developers' actual adoption of review comments, enabling automated evaluation and systematic optimization at scale. Empirical evaluation demonstrates BitsAI-CR's effectiveness, achieving 75.0% precision in review comment generation. For the Go language which has predominant usage at ByteDance, we maintain an Outdated Rate of 26.7%. The system has been successfully deployed at ByteDance, serving over 12,000 Weekly Active Users (WAU). Our work provides valuable insights into the practical application of automated code review and offers a blueprint for organizations seeking to implement automated code reviews at scale.
Evaluating the Effectiveness of Mobile Game-Based Learning for Raising Adolescent Health Awareness: The Case of "AHlam Na 2.0"
2025-01-25
Noel P. Caliston
This study addresses a critical gap in adolescent health education strategies in the Philippines, as highlighted by the Young Adult Fertility and Sexuality (YAFS) survey series, which overlooks the use of games as a medium for disseminating health information. To bridge this gap, the research introduces AHlam Na, a game-based mobile application designed to enhance adolescents' awareness and understanding of key health-related topics. Using a single-group pretest-posttest design, the study involved forty junior high school students from a randomly selected school in the Philippines. They interacted with the application that embedded adolescent health topics into its gameplay. Data collected through pretest and post-test surveys revealed a significant improvement in the student's knowledge and attitudes toward adolescent health after engaging in the game, indicating that game-based learning effectively enhances their learning experience. The positive reception and knowledge gains suggest that AHlam Na is a promising tool for promoting adolescent health awareness. Based on these findings, it is recommended that the application be integrated into the adolescent health curriculum in schools across the Philippines. Future studies should examine the long-term impact of game-based learning on health behaviors and expand the sample size to include more diverse demographic groups. This research contributes to the growing body of literature on game-based learning in health education by demonstrating the potential of digital games to address the limitations of traditional teaching methods. The successful implementation of AHlam Na underscores the importance of exploring gamified learning tools to deliver critical health information to young people effectively.
The China Space Station Telescope (CSST) is the next-generation Stage~IV survey telescope. It can simultaneously perform multi-band imaging and slitless spectroscopic wide- and deep-field surveys in ten years and an ultra-deep field (UDF) survey in two years, which are suitable for cosmological studies. Here we review several CSST cosmological probes, such as weak gravitational lensing, two-dimensional (2D) and three-dimensional (3D) galaxy clustering, galaxy cluster abundance, cosmic void, Type Ia supernovae (SNe Ia), and baryonic acoustic oscillations (BAO), and explore their capabilities and prospects in discovering new physics and opportunities in cosmology. We find that CSST will measure the matter distribution from small to large scales and the expansion history of the Universe with extremely high accuracy, which can provide percent-level stringent constraints on the property of dark energy and dark matter and precisely test the theories of gravity.
The 2.5-meter Wide Field Survey Telescope Real-time Data Processing Pipeline I: From raw data to alert distribution
2025-01-25
Minxuan Cai, Zelin Xu, Lulu Fan, Zhen Wan, Xu Kong, Weida Hu, Ji-an Jiang, Lei Hu, Qing-feng Zhu, Guoliang Li, Jie Lin, Min Fang, Yongquan Xue, Xianzhong Zhen, Tinggui Wang
The Wide Field Survey Telescope (WFST) is a dedicated photometric surveying facility built jointly by the University of Science and Technology of China (USTC) and the Purple Mountain Observatory (PMO). Since many of its scientific objectives rely on near-real-time data for effective analysis, prompt processing of WFST images is of great significance. To meet this need, we adapted the Rubin Observatory Legacy Survey of Space and Time (LSST) science pipelines to handle the data collected by WFST. This paper presents the complete data processing workflow, from ingestion of raw images to the distribution of alerts, and details the primary data products generated by our pipeline. Researchers using data processed by this pipeline can refer to this document to fully understand the data processing procedures.
On Accelerating Edge AI: Optimizing Resource-Constrained Environments
2025-01-25
Jacob Sander, Achraf Cohen, Venkat R. Dasari, Brent Venable, Brian Jalaian
Resource-constrained edge deployments demand AI solutions that balance high performance with stringent compute, memory, and energy limitations. In this survey, we present a comprehensive overview of the primary strategies for accelerating deep learning models under such constraints. First, we examine model compression techniques-pruning, quantization, tensor decomposition, and knowledge distillation-that streamline large models into smaller, faster, and more efficient variants. Next, we explore Neural Architecture Search (NAS), a class of automated methods that discover architectures inherently optimized for particular tasks and hardware budgets. We then discuss compiler and deployment frameworks, such as TVM, TensorRT, and OpenVINO, which provide hardware-tailored optimizations at inference time. By integrating these three pillars into unified pipelines, practitioners can achieve multi-objective goals, including latency reduction, memory savings, and energy efficiency-all while maintaining competitive accuracy. We also highlight emerging frontiers in hierarchical NAS, neurosymbolic approaches, and advanced distillation tailored to large language models, underscoring open challenges like pre-training pruning for massive networks. Our survey offers practical insights, identifies current research gaps, and outlines promising directions for building scalable, platform-independent frameworks to accelerate deep learning models at the edge.
Deep Learning in Wireless Communication Receiver: A Survey
2025-01-25
Shadman Rahman Doha, Ahmed Abdelhadi
The design of wireless communication receivers to enhance signal processing in complex and dynamic environments is going through a transformation by leveraging deep neural networks (DNNs). Traditional wireless receivers depend on mathematical models and algorithms, which do not have the ability to adapt or learn from data. In contrast, deep learning-based receivers are more suitable for modern wireless communication systems because they can learn from data and adapt accordingly. This survey explores various deep learning architectures such as multilayer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and autoencoders, focusing on their application in the design of wireless receivers. Key modules of a receiver such as synchronization, channel estimation, equalization, space-time decoding, demodulation, decoding, interference cancellation, and modulation classification are discussed in the context of advanced wireless technologies like orthogonal frequency division multiplexing (OFDM), multiple input multiple output (MIMO), semantic communication, task-oriented communication, and next-generation (Next-G) networks. The survey not only emphasizes the potential of deep learning-based receivers in future wireless communication but also investigates different challenges of deep learning-based receivers, such as data availability, security and privacy concerns, model interpretability, computational complexity, and integration with legacy systems.
An AI-Driven Live Systematic Reviews in the Brain-Heart Interconnectome: Minimizing Research Waste and Advancing Evidence Synthesis
2025-01-25
Arya Rahgozar, Pouria Mortezaagha, Jodi Edwards, Douglas Manuel, Jessie McGowen, Merrick Zwarenstein, Dean Fergusson, Andrea Tricco, Kelly Cobey, Margaret Sampson, Malcolm King, Dawn Richards, Alexandra Bodnaruc, David Moher
The Brain-Heart Interconnectome (BHI) combines neurology and cardiology but is hindered by inefficiencies in evidence synthesis, poor adherence to quality standards, and research waste. To address these challenges, we developed an AI-driven system to enhance systematic reviews in the BHI domain. The system integrates automated detection of Population, Intervention, Comparator, Outcome, and Study design (PICOS), semantic search using vector embeddings, graph-based querying, and topic modeling to identify redundancies and underexplored areas. Core components include a Bi-LSTM model achieving 87% accuracy for PICOS compliance, a study design classifier with 95.7% accuracy, and Retrieval-Augmented Generation (RAG) with GPT-3.5, which outperformed GPT-4 for graph-based and topic-driven queries. The system provides real-time updates, reducing research waste through a living database and offering an interactive interface with dashboards and conversational AI. While initially developed for BHI, the system's adaptable architecture enables its application across various biomedical fields, supporting rigorous evidence synthesis, efficient resource allocation, and informed clinical decision-making.
Comparable Corpora: Opportunities for New Research Directions
2025-01-24
Kenneth Church
Most conference papers present new results, but this paper will focus more on opportunities for the audience to make their own contributions. This paper is intended to challenge the community to think more broadly about what we can do with comparable corpora. We will start with a review of the history, and then suggest new directions for future research. This was a keynote at BUCC-2025, a workshop associated with Coling-2025.
State Space Models for Extractive Summarization in Low Resource Scenarios
2025-01-24
Nisrine Ait Khayi
Extractive summarization involves selecting the most relevant sentences from a text. Recently, researchers have focused on advancing methods to improve state-of-the-art results in low-resource settings. Motivated by these advancements, we propose the MPoincareSum method. This method applies the Mamba state space model to generate the semantics of reviews and sentences, which are then concatenated. A Poincare compression is used to select the most meaningful features, followed by the application of a linear layer to predict sentence relevance based on the corresponding review. Finally, we paraphrase the relevant sentences to create the final summary. To evaluate the effectiveness of MPoincareSum, we conducted extensive experiments using the Amazon review dataset. The performance of the method was assessed using ROUGE scores. The experimental results demonstrate that MPoincareSum outperforms several existing approaches in the literature
Data-NoMAD: A Tool for Boosting Confidence in the Integrity of Social Science Survey Data
2025-01-24
Sanford C. Gordon, Cyrus Samii, Zhihao Su
To safeguard against data fabrication and enhance trust in quantitative social science, we present Data Non-Manipulation Authentication Digest (Data-NoMAD). Data-NoMAD is a tool that allows researchers to certify, and others to verify, that a dataset has not been inappropriately manipulated between the point of data collection and the point at which a replication archive is made publicly available. Data-NoMAD creates and stores a column hash digest of a raw dataset upon initial download from a survey platform (the current version works with Qualtrics and SurveyCTO), but before it is subject to appropriate manipulations such as anonymity-preserving redactions. Data-NoMAD can later be used to verify the integrity of a publicly archived dataset by identifying columns that have been deleted, added, or altered. Data-NoMAD complements existing efforts at ensuring research integrity and integrates seamlessly with extant replication practices.
Automation of finding strong gravitational lenses in the Kilo Degree Survey with U-DenseLens (DenseLens + Segmentation)
2025-01-24
Bharath Chowdhary Nagam, Léon V E Koopmans, Edwin A Valentijn, Gijs Verdoes Kleijn, Jelte T A de Jong, Nicola Napolitano, Rui Li, Crescenzo Tortora, Valerio Busillo, Yue Dong
In the context of upcoming large-scale surveys like Euclid, the necessity for the automation of strong lens detection is essential. While existing machine learning pipelines heavily rely on the classification probability (P), this study intends to address the importance of integrating additional metrics, such as Information Content (IC) and the number of pixels above the segmentation threshold, to alleviate the false positive rate in unbalanced data-sets. In this work, we introduce a segmentation algorithm (U-Net) as a supplementary step in the established strong gravitational lens identification pipeline (Denselens), which primarily utilizes P$_{\rm mean}$ and IC$_{\rm mean}$ parameters for the detection and ranking. The results demonstrate that the inclusion of segmentation enables significant reduction of false positives by approximately 25 per cent in the final sample extracted from DenseLens, without compromising the identification of strong lenses. The main objective of this study is to automate the strong lens detection process by integrating these three metrics. To achieve this, a decision tree-based selection process is introduced, applied to the Kilo Degree Survey (KiDS) data. This process involves rank-ordering based on classification scores, filtering based on Information Content, and segmentation score. Additionally, the study presents 14 newly discovered strong lensing candidates identified by the U-Denselens network using the KiDS DR4 data.
Improved algorithms and novel applications of the FrankWolfe.jl library
2025-01-24
Mathieu Besançon, Sébastien Designolle, Jannis Halbey, Deborah Hendrych, Dominik Kuzinowicz, Sebastian Pokutta, Hannah Troppens, Daniel Viladrich Herrmannsdoerfer, Elias Wirth
Frank-Wolfe (FW) algorithms have emerged as an essential class of methods for constrained optimization, especially on large-scale problems. In this paper, we summarize the algorithmic design choices and progress made in the last years of the development of FrankWolfe.jl, a Julia package gathering high-performance implementations of state-of-the-art FW variants. We review key use cases of the library in the recent literature, which match its original dual purpose: first, becoming the de-facto toolbox for practitioners applying FW methods to their problem, and second, offering a modular ecosystem to algorithm designers who experiment with their own variants and implementations of algorithmic blocks. Finally, we demonstrate the performance of several FW variants on important problem classes in several experiments, which we curated in a separate repository for continuous benchmarking.
Giant Molecular Clouds in RCW 106 (G333): Galactic Mini-starbursts and Massive Star Formation Induced by Supersonic Cloud-Cloud Collisions
2025-01-24
Mikito Kohno, Rin I. Yamada, Kengo Tachihara, Shinji Fujita, Rei Enokiya, Kazuki Tokuda, Asao Habe, Hidetoshi Sano, Takahiro Hayakawa, Fumika Demachi, Takuto Ito, Kisetsu Tsuge, Atsushi Nishimura, Masato I. N. Kobayashi, Hiroaki Yamamoto, Yasuo Fukui
To reveal the origin of the mini-starbursts in the Milky Way, we carried out large-scale CO observations toward the RCW 106 giant molecular cloud (GMC) complex using the NANTEN2 4-m radio telescope operated by Nagoya University. We also analyzed the Mopra Southern Galactic plane CO survey and Herschel infrared continuum archival data. The RCW 106 GMC complex contains the radial velocity components of $-68$ km s$^{-1}$ and $-50$ km s$^{-1}$ reported by Nguyen et al. (2015). Focusing on the RCW 106 East and West region with the massive star formation having the bright infrared dust emission, we found that these regions have three different velocity components with $\sim 10$ km s$^{-1}$ differences. The two out of three velocity components show morphological correspondence with the infrared cold dust emission and connect with the bridge feature on a position-velocity diagram. Therefore, two molecular clouds (MCs) with $\sim 10$ km s$^{-1}$ differences are likely to be physically associated with massive star-forming regions in the GMC complex. Based on these observational results, we argue that mini-starbursts and massive star/cluster formation in the RCW 106 GMC complex are induced by supersonic cloud-cloud collisions in an agglomerate of molecular gas on the Scutum-Centurus arm.
Conformal prediction provides a framework for uncertainty quantification, specifically in the forms of prediction intervals and sets with distribution-free guaranteed coverage. While recent cross-conformal techniques such as CV+ and Jackknife+-after-bootstrap achieve better data efficiency than traditional split conformal methods, they incur substantial computational costs due to required pairwise comparisons between training and test samples' out-of-bag scores. Observing that these methods naturally extend from ensemble models, particularly random forests, we leverage existing optimized random forest implementations to enable efficient cross-conformal predictions. We present coverforest, a Python package that implements efficient conformal prediction methods specifically optimized for random forests. coverforest supports both regression and classification tasks through various conformal prediction methods, including split conformal, CV+, Jackknife+-after-bootstrap, and adaptive prediction sets. Our package leverages parallel computing and Cython optimizations to speed up out-of-bag calculations. Our experiments demonstrate that coverforest's predictions achieve the desired level of coverage. In addition, its training and prediction times can be faster than an existing implementation by 2--9 times. The source code for the coverforest is hosted on GitHub at https://github.com/donlapark/coverforest.
Molecular inventory of a young eruptive star's environment -- Case study of the classical FU Orionis star V1057 Cyg
2025-01-24
Zs. M. Szabó, A. Belloche, K. M. Menten, Y. Gong, Á. Kóspál, P. Ábrahám, W. Yang, C. J. Cyganowski, F. Wyrowski
Studying accretion-driven episodic outbursts in YSOs is key to understanding the later stages of star and planet formation. FU Orionis-type objects form a YSO subclass, distinguished by rapid, multi-magnitude increases in brightness at optical and near-infrared wavelengths. These outbursts may significantly impact the chemistry and molecular composition around eruptive stars. However, no comprehensive millimeter-wavelength line survey exists for more evolved (Class II) sources, unlike optical and near-infrared coverage. We conducted the first wideband millimeter spectral line survey of V1057 Cyg, a low-mass eruptive FUor with the highest observed peak accretion rate in its class. Using the IRAM 30-m telescope, we surveyed the 72-263 GHz range and complemented this with targeted spectra at 219, 227, 291, and 344 GHz with the APEX 12-m telescope. We conducted radiative transfer and population diagram analyses to get first estimates of the excitation temperatures and column densities. Several molecular species trace large-scale structures, and the position-velocity diagram of $^{12}$CO suggest episodic outburst activity, with outflow dynamical timescales on the order of tens of thousands of years. We identified simple molecules (C-, N-, O-, and S-bearing), deuterated species, molecular ions, and complex organic molecules. With over 30 molecular species (including isotopologues) detected, V1057 Cyg demonstrates rich chemistry for its evolutionary state, compared to other younger (Class 0/I) FUors. V1057 Cyg is a good candidate for future interferometric studies to resolve emission structures, to possibly constrain molecular freeze-out, and detect water and complex organic molecules. Our results highlight the importance of millimeter line surveys in complementing optical/near-infrared studies, improving statistics on molecular inventories in eruptive stars and their environments.
Next-Generation Wireless: Tracking the Evolutionary Path of 6G Mobile Communication
2025-01-24
Ekram Hossain, Angelo Vera-Rivera
The mobile communications industry is lighting-fast machinery, and discussions about 6G technologies have already begun. In this article, we intend to take the readers on a journey to discover 6G and its evolutionary stages. The journey starts with an overview of the technical constraints of 5G prompting the need for a new generation of mobile systems. The initial discussion is followed by an examination of the 6G vision, use cases, technical requirements, technology enablers, and potential network architecture. We conclude the discussion by reviewing the transformative opportunities of this technology in society, businesses and industries, along with the anticipated technological limitations of the network that will drive the development of further mobile generations. Our goal is to deliver a friendly, informative, precise, and engaging narrative that could give readers a panoramic overview of this important topic.
Active Intracellular Mechanics: A Key to Cellular Function and Organization
2025-01-24
Mohammad Amin Eskandari, Jannis Fischer, Noémie Veyret, Dorian Marx, Timo Betz
While mechanobiology has demonstrated that precise control over mechanical properties at the whole-cell level is crucial for many biological functions, comparatively little attention has been paid to the intracellular mechanical properties. Experimental tools have only recently become available to adequately measure the viscoelasticity and activity of the cytosol, revealing, revealing that the active, non-equilibrium nature of the intracellular environment must be carefully considered. To explore the interplay between active forces and viscoelastic properties, it is helpful to consider our current understanding of intracellular active mechanics. In this review, we aim not only to provide an intuitive and quantitative introduction to the relevant physical concepts, but also to offer an overview of the proteins that establish intracellular active mechanics, highlighting their spatial and temporal variation with a particular focus on the role of activity. Although we are only beginning to uncover the importance of intracellular active mechanics for cellular mechanisms, it is increasingly clear that these properties must be precisely regulated to ensure proper cellular function.
XFSC: A Catalogue of Trustable Semantic Metadata for Data Services and Providers
2025-01-24
Benedikt T. Arnold, Khalil Baydoun, Diego Collarana, Sebastian Duda, Christina Gillmann, Ahmad Hemid, Philipp Hertweck, Paul Moosmann, Denis Sukhoroslov, Christoph Lange
In dataspaces, federation services facilitate key functions such as enabling participating organizations to establish mutual trust and assisting them in discovering data and services available for consumption. Discovery is enabled by a catalogue, where participants publish metadata describing themselves and their data and service offerings as Verifiable Presentations (VPs), such that other participants may query them. This paper presents the Eclipse Cross Federation Services Components (XFSC) Catalogue, which originated as a catalogue reference implementation for the Gaia-X federated cloud service architecture but is also generally applicable to metadata required to be trustable. This implementation provides basic lifecycle management for DCAT-style metadata records and schemas. It validates submitted VPs for their cryptographic integrity and trustability, and for their conformance to an extensible collection of semantic schemas. The claims in the latest versions of valid VP submissions are extracted into a searchable graph database. The implementation scales to large numbers of records and is secure by design. Filling the catalogue with content in a maintainable way requires bindings towards where data and service offerings are coming from: connectors that expose resources hosted in an organization's IT infrastructure towards the dataspace. We demonstrate the integration of our catalogue with the widely used Eclipse Dataspace Components Connector, enabling real-world use cases of the German Culture Dataspace. In addition, we discuss potential extensions and upcoming integrations of the catalogue.
The excellent mechanical properties make graphene promising for realizing nanomechanical resonators with high resonant frequencies, large quality factors, strong nonlinearities, and the capability to effectively interface with various physical systems. Equipped with gate electrodes, it has been demonstrated that these exceptional device properties can be electrically manipulated, leading to a variety of nanomechanical/acoustic applications. Here, we review the recent progress of graphene nanomechanical resonators with a focus on their electrical tunability. First, we provide an overview of different graphene nanomechanical resonators, including their device structures, fabrication methods, and measurement setups. Then, the key mechanical properties of these devices, for example, resonant frequencies, nonlinearities, dissipations, and mode coupling mechanisms, are discussed, with their behaviors upon electrical gating being highlighted. After that, various potential classical/quantum applications based on these graphene nanomechanical resonators are reviewed. Finally, we briefly discuss challenges and opportunities in this field to offer future prospects of the ongoing studies on graphene nanomechanical resonators.
A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization
2025-01-24
Jing Wang, Anna Choromanska
As data sets grow in size and complexity, it is becoming more difficult to pull useful features from them using hand-crafted feature extractors. For this reason, deep learning (DL) frameworks are now widely popular. The Holy Grail of DL and one of the most mysterious challenges in all of modern ML is to develop a fundamental understanding of DL optimization and generalization. While numerous optimization techniques have been introduced in the literature to navigate the exploration of the highly non-convex DL optimization landscape, many survey papers reviewing them primarily focus on summarizing these methodologies, often overlooking the critical theoretical analyses of these methods. In this paper, we provide an extensive summary of the theoretical foundations of optimization methods in DL, including presenting various methodologies, their convergence analyses, and generalization abilities. This paper not only includes theoretical analysis of popular generic gradient-based first-order and second-order methods, but it also covers the analysis of the optimization techniques adapting to the properties of the DL loss landscape and explicitly encouraging the discovery of well-generalizing optimal points. Additionally, we extend our discussion to distributed optimization methods that facilitate parallel computations, including both centralized and decentralized approaches. We provide both convex and non-convex analysis for the optimization algorithms considered in this survey paper. Finally, this paper aims to serve as a comprehensive theoretical handbook on optimization methods for DL, offering insights and understanding to both novice and seasoned researchers in the field.
When considering magnetic systems in the thermodynamic limit and at low enough temperature, one finds typically magnetically ordered phases. In contrast, in the high-temperature regime, the interactions between the spin degrees of freedom become less relevant and the system loses its order: this is a paramagnet. This phenomenon of phase transition has been well understood using statistical mechanics and simple modelling. In these short lecture notes, we will review the possibility that a many-body magnetic system may remain magnetically disordered down to zero-temperature, both for classical or quantum spins. These exotic phases of matter are known, respectively, as classical and quantum spin liquids. We will address in particular the question of classification of these classical or quantum disordered phases. Indeed, while they have no local order parameter by definition, they can still possess different qualitative features related e.g. to the nature of their correlations or elementary excitations, which could be probed experimentally.
Differential privacy has emerged as the most studied framework for privacy-preserving machine learning. However, recent studies show that enforcing differential privacy guarantees can not only significantly degrade the utility of the model, but also amplify existing disparities in its predictive performance across demographic groups. Although there is extensive research on the identification of factors that contribute to this phenomenon, we still lack a complete understanding of the mechanisms through which differential privacy exacerbates disparities. The literature on this problem is muddled by varying definitions of fairness, differential privacy mechanisms, and inconsistent experimental settings, often leading to seemingly contradictory results. This survey provides the first comprehensive overview of the factors that contribute to the disparate effect of training models with differential privacy guarantees. We discuss their impact and analyze their causal role in such a disparate effect. Our analysis is guided by a taxonomy that categorizes these factors by their position within the machine learning pipeline, allowing us to draw conclusions about their interaction and the feasibility of potential mitigation strategies. We find that factors related to the training dataset and the underlying distribution play a decisive role in the occurrence of disparate impact, highlighting the need for research on these factors to address the issue.
ECTIL: Label-efficient Computational Tumour Infiltrating Lymphocyte (TIL) assessment in breast cancer: Multicentre validation in 2,340 patients with breast cancer
2025-01-24
Yoni Schirris, Rosie Voorthuis, Mark Opdam, Marte Liefaard, Gabe S Sonke, Gwen Dackus, Vincent de Jong, Yuwei Wang, Annelot Van Rossum, Tessa G Steenbruggen, Lars C Steggink, Liesbeth G. E. de Vries, Marc van de Vijver, Roberto Salgado, Efstratios Gavves, Paul J van Diest, Sabine C Linn, Jonas Teuwen, Renee Menezes, Marleen Kok, Hugo Horlings
The level of tumour-infiltrating lymphocytes (TILs) is a prognostic factor for patients with (triple-negative) breast cancer (BC). Computational TIL assessment (CTA) has the potential to assist pathologists in this labour-intensive task, but current CTA models rely heavily on many detailed annotations. We propose and validate a fundamentally simpler deep learning based CTA that can be trained in only ten minutes on hundredfold fewer pathologist annotations. We collected whole slide images (WSIs) with TILs scores and clinical data of 2,340 patients with BC from six cohorts including three randomised clinical trials. Morphological features were extracted from whole slide images (WSIs) using a pathology foundation model. Our label-efficient Computational stromal TIL assessment model (ECTIL) directly regresses the TILs score from these features. ECTIL trained on only a few hundred samples (ECTIL-TCGA) showed concordance with the pathologist over five heterogeneous external cohorts (r=0.54-0.74, AUROC=0.80-0.94). Training on all slides of five cohorts (ECTIL-combined) improved results on a held-out test set (r=0.69, AUROC=0.85). Multivariable Cox regression analyses indicated that every 10% increase of ECTIL scores was associated with improved overall survival independent of clinicopathological variables (HR 0.86, p<0.01), similar to the pathologist score (HR 0.87, p<0.001). We demonstrate that ECTIL is highly concordant with an expert pathologist and obtains a similar hazard ratio. ECTIL has a fundamentally simpler design than existing methods and can be trained on orders of magnitude fewer annotations. Such a CTA may be used to pre-screen patients for, e.g., immunotherapy clinical trial inclusion, or as a tool to assist clinicians in the diagnostic work-up of patients with BC. Our model is available under an open source licence (https://github.com/nki-ai/ectil).
Some Geometric Aspects of Curved Spacetimes Equipped with Torsionful Affinities and Einstein-Cartan's Theory in Two-Component Spinor Form
2025-01-24
J. G. Cardoso
The classical world structures borne by spacetimes endowed with torsionful affinities are reviewed. Subsequently, the definition and symmetry properties of a typical pair of Witten curvature spinors for such spacetimes are exhibited along with a comprehensive two-component spinor transcription of Einstein-Cartan's theory. A full description of the correspondence principle that interrelates Einstein-Cartan's theory and general relativity is likewise presented.
Probabilistic interpretation of quantum field theories
2025-01-24
Martin Hairer
In this note we provide a gentle introduction to the concepts and intuition behind the recent breakthrough results on the mathematically rigorous construction of a non-trivial 2D conformal field theory, namely the so-called Liouville theory. This gives us the opportunity to review Segal's axioms for conformal field theories and to discuss in some detail how the free field fits into them.
CSI-Free Low-Complexity Remote State Estimation over Wireless MIMO Fading Channels using Semantic Analog Aggregation
2025-01-24
Minjie Tang, Photios A. Stavrou, Marios Kountouris
In this work, we investigate low-complexity remote system state estimation over wireless multiple-input-multiple-output (MIMO) channels without requiring prior knowledge of channel state information (CSI). We start by reviewing the conventional Kalman filtering-based state estimation algorithm, which typically relies on perfect CSI and incurs considerable computational complexity. To overcome the need for CSI, we introduce a novel semantic aggregation method, in which sensors transmit semantic measurement discrepancies to the remote state estimator through analog aggregation. To further reduce computational complexity, we introduce a constant-gain-based filtering algorithm that can be optimized offline using the constrained stochastic successive convex approximation (CSSCA) method. We derive a closed-form sufficient condition for the estimation stability of our proposed scheme via Lyapunov drift analysis. Numerical results showcase significant performance gains using the proposed scheme compared to several widely used methods.
Passwords have been long used as the primary authentication method for web services. Weak passwords used by the users have prompted the use of password management tools and two-factor authentication to ensure better account security. While prior studies have studied their adoption individually, none of these studies focuses particularly on the Indian setting, which is culturally and economically different from the countries in which these studies have been done in the past. To this end, we conducted a survey with 90 participants residing in India to better understand the mindset of people on using password managers and two-factor authentication (2FA). Our findings suggest that a majority of the participants have used 2FA and password managers in some form, although they are sometimes unaware of their formal names. While many participants used some form of 2FA across all their accounts, browser-integrated and device-default password managers are predominantly utilized for less sensitive platforms such as e-commerce and social media rather than for more critical accounts like banking. The primary motivation for using password managers is the convenience of auto-filling. However, some participants avoid using password managers due to a lack of trust in these tools. Notably, dedicated third-party applications show low adoption for both password manager and 2FA. Despite acknowledging the importance of secure password practices, many participants still reuse passwords across multiple accounts, prefer shorter passwords, and use commonly predictable password patterns. Overall, the study suggests that Indians are more inclined to choose default settings, underscoring the need for tailored strategies to improve user awareness and strengthen password security practices.
Graph Feedback Bandits on Similar Arms: With and Without Graph Structures
2025-01-24
Han Qi, Fei Guo, Li Zhu, Qiaosheng Zhang, Xuelong Li
In this paper, we study the stochastic multi-armed bandit problem with graph feedback. Motivated by applications in clinical trials and recommendation systems, we assume that two arms are connected if and only if they are similar (i.e., their means are close to each other). We establish a regret lower bound for this problem under the novel feedback structure and introduce two upper confidence bound (UCB)-based algorithms: Double-UCB, which has problem-independent regret upper bounds, and Conservative-UCB, which has problem-dependent upper bounds. Leveraging the similarity structure, we also explore a scenario where the number of arms increases over time (referred to as the \emph{ballooning setting}). Practical applications of this scenario include Q\&A platforms (e.g., Reddit, Stack Overflow, Quora) and product reviews on platforms like Amazon and Flipkart, where answers (or reviews) continuously appear, and the goal is to display the best ones at the top. We extend these two UCB-based algorithms to the ballooning setting. Under mild assumptions, we provide regret upper bounds for both algorithms and discuss their sub-linearity. Furthermore, we propose a new version of the corresponding algorithms that do not rely on prior knowledge of the graph's structural information and provide regret upper bounds. Finally, we conduct experiments to validate the theoretical results.
A Zero-Shot LLM Framework for Automatic Assignment Grading in Higher Education
2025-01-24
Calvin Yeung, Jeff Yu, King Chau Cheung, Tat Wing Wong, Chun Man Chan, Kin Chi Wong, Keisuke Fujii
Automated grading has become an essential tool in education technology due to its ability to efficiently assess large volumes of student work, provide consistent and unbiased evaluations, and deliver immediate feedback to enhance learning. However, current systems face significant limitations, including the need for large datasets in few-shot learning methods, a lack of personalized and actionable feedback, and an overemphasis on benchmark performance rather than student experience. To address these challenges, we propose a Zero-Shot Large Language Model (LLM)-Based Automated Assignment Grading (AAG) system. This framework leverages prompt engineering to evaluate both computational and explanatory student responses without requiring additional training or fine-tuning. The AAG system delivers tailored feedback that highlights individual strengths and areas for improvement, thereby enhancing student learning outcomes. Our study demonstrates the system's effectiveness through comprehensive evaluations, including survey responses from higher education students that indicate significant improvements in motivation, understanding, and preparedness compared to traditional grading methods. The results validate the AAG system's potential to transform educational assessment by prioritizing learning experiences and providing scalable, high-quality feedback.
Advances in Temporal Point Processes: Bayesian, Deep, and LLM Approaches
2025-01-24
Feng Zhou, Quyu Kong, Yixuan Zhang
Temporal point processes (TPPs) are stochastic process models used to characterize event sequences occurring in continuous time. Traditional statistical TPPs have a long-standing history, with numerous models proposed and successfully applied across diverse domains. In recent years, advances in deep learning have spurred the development of neural TPPs, enabling greater flexibility and expressiveness in capturing complex temporal dynamics. The emergence of large language models (LLMs) has further sparked excitement, offering new possibilities for modeling and analyzing event sequences by leveraging their rich contextual understanding. This survey presents a comprehensive review of recent research on TPPs from three perspectives: Bayesian, deep learning, and LLM approaches. We begin with a review of the fundamental concepts of TPPs, followed by an in-depth discussion of model design and parameter estimation techniques in these three frameworks. We also revisit classic application areas of TPPs to highlight their practical relevance. Finally, we outline challenges and promising directions for future research.
A Classifying Space for Phases of Matrix Product States
2025-01-24
Agnes Beaudry, Michael Hermele, Markus J. Pflaum, Marvin Qi, Daniel D. Spiegel, David T. Stephen
We construct a topological space $\mathcal{B}$ consisting of translation invariant injective matrix product states (MPS) of all physical and bond dimensions and show that it has the weak homotopy type $K(\mathbb{Z}, 2) \times K(\mathbb{Z}, 3)$. The implication is that the phase of a family of such states parametrized by a space $X$ is completely determined by two invariants: a class in $H^2(X; \mathbb{Z})$ corresponding to the Chern number per unit cell and a class in $H^3(X; \mathbb{Z})$, the so-called Kapustin-Spodyneiko (KS) number. The space $\mathcal{B}$ is defined as the quotient of a contractible space $\mathcal{E}$ of MPS tensors by an equivalence relation describing gauge transformations of the tensors. We prove that the projection map $p:\mathcal{E} \rightarrow \mathcal{B}$ is a quasifibration, and this allows us to determine the weak homotopy type of $\mathcal{B}$. As an example, we review the Chern number pump-a family of MPS parametrized by $S^3$-and prove that it generates $\pi_3(\mathcal{B})$.
On the multi-$\mathbf{q}$ characteristics of magnetic ground states of honeycomb cobalt oxides
2025-01-24
Yuchen Gu, Xianghong Jin, Yuan Li
The Kitaev honeycomb model has received significant attention for its exactly solvable quantum spin liquid ground states and fractionalized excitations. For realizing the model, layered cobalt oxides have been considered a promising platform. Yet, in contrast to the conventional wisdom about single-$\mathbf{q}$ zigzag magnetic order inferred from previous studies of the Na$_2$IrO$_3$ and $\alpha$-RuCl$_3$ candidate materials, recent experiments on two of the representative honeycomb cobalt oxides, hexagonal Na$_2$Co$_2$TeO$_6$ and monoclinic Na$_3$Co$_2$SbO$_6$, have uncovered evidence for more complex multi-$\mathbf{q}$ variants of the zigzag order. This review surveys on experimental strategies to distinguish between single- and multi-$\mathbf{q}$ orders, along with the crystallographic symmetries of the cobalt oxides in comparison to the previously studied systems. General formation mechanism of multi-$\mathbf{q}$ order is also briefly discussed. The goal is to provide some rationales for examining the relevance of multi-$\mathbf{q}$ order in the honeycomb cobalt oxides, along with its implications on the microscopic model of these intriguing quantum magnets.
A brief survey of Benford's Law in dynamical systems
2025-01-24
Arno Berger, Theodore P. Hill
This article provides a brief overview on a range of basic dynamical systems that conform to the logarithmic distribution of significant digits known as Benford's law. As presented here, most theorems are special cases of known, more general results about dynamical systems whose orbits or trajectories follow this logarithmic law, in one way or another. These results span a wide variety of systems: autonomous and non-autonomous; discrete- and continuous-time; one- and multi-dimensional; deterministic and stochastic. Illustrative examples include familiar systems such as the tent map, Newton's root-finding algorithm, and geometric Brownian motion. The treatise is informal, with the goal of showcasing to the specialists the generality and universal appeal of Benford's law throughout the mathematical field of dynamical systems. References to complete proofs are provided for each known result, while one new theorem is presented in some detail.
You Only Teach Once: Learn One-Shot Bimanual Robotic Manipulation from Video Demonstrations
2025-01-24
Huayi Zhou, Ruixiang Wang, Yunxin Tai, Yueci Deng, Guiliang Liu, Kui Jia
Bimanual robotic manipulation is a long-standing challenge of embodied intelligence due to its characteristics of dual-arm spatial-temporal coordination and high-dimensional action spaces. Previous studies rely on pre-defined action taxonomies or direct teleoperation to alleviate or circumvent these issues, often making them lack simplicity, versatility and scalability. Differently, we believe that the most effective and efficient way for teaching bimanual manipulation is learning from human demonstrated videos, where rich features such as spatial-temporal positions, dynamic postures, interaction states and dexterous transitions are available almost for free. In this work, we propose the YOTO (You Only Teach Once), which can extract and then inject patterns of bimanual actions from as few as a single binocular observation of hand movements, and teach dual robot arms various complex tasks. Furthermore, based on keyframes-based motion trajectories, we devise a subtle solution for rapidly generating training demonstrations with diverse variations of manipulated objects and their locations. These data can then be used to learn a customized bimanual diffusion policy (BiDP) across diverse scenes. In experiments, YOTO achieves impressive performance in mimicking 5 intricate long-horizon bimanual tasks, possesses strong generalization under different visual and spatial conditions, and outperforms existing visuomotor imitation learning methods in accuracy and efficiency. Our project link is https://hnuzhy.github.io/projects/YOTO.
The Southern Twenty-centimetre All-sky Polarization Survey (STAPS): survey description and maps
2025-01-24
Xiaohui Sun, Marijke Haverkorn, Ettore Carretti, Tom Landecker, B. M. Gaensler, Sergio Poppi, Lister Staveley-Smith, Xuyang Gao, Jinlin Han
We present data processing and verification of the Southern Twenty-centimetre All-sky Polarization Survey (STAPS) conducted with Murriyang, the Parkes 64-m telescope. The survey covers the sky area of -89
Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration
2025-01-24
Mojtaba Safari, Zach Eidex, Chih-Wei Chang, Richard L. J. Qiu, Xiaofeng Yang
Magnetic resonance imaging (MRI) is a non-invasive imaging modality and provides comprehensive anatomical and functional insights into the human body. However, its long acquisition times can lead to patient discomfort, motion artifacts, and limiting real-time applications. To address these challenges, strategies such as parallel imaging have been applied, which utilize multiple receiver coils to speed up the data acquisition process. Additionally, compressed sensing (CS) is a method that facilitates image reconstruction from sparse data, significantly reducing image acquisition time by minimizing the amount of data collection needed. Recently, deep learning (DL) has emerged as a powerful tool for improving MRI reconstruction. It has been integrated with parallel imaging and CS principles to achieve faster and more accurate MRI reconstructions. This review comprehensively examines DL-based techniques for MRI reconstruction. We categorize and discuss various DL-based methods, including end-to-end approaches, unrolled optimization, and federated learning, highlighting their potential benefits. Our systematic review highlights significant contributions and underscores the potential of DL in MRI reconstruction. Additionally, we summarize key results and trends in DL-based MRI reconstruction, including quantitative metrics, the dataset, acceleration factors, and the progress of and research interest in DL techniques over time. Finally, we discuss potential future directions and the importance of DL-based MRI reconstruction in advancing medical imaging. To facilitate further research in this area, we provide a GitHub repository that includes up-to-date DL-based MRI reconstruction publications and public datasets-https://github.com/mosaf/Awesome-DL-based-CS-MRI.
Advances in Set Function Learning: A Survey of Techniques and Applications
2025-01-24
Jiahao Xie, Guangmo Tong
Set function learning has emerged as a crucial area in machine learning, addressing the challenge of modeling functions that take sets as inputs. Unlike traditional machine learning that involves fixed-size input vectors where the order of features matters, set function learning demands methods that are invariant to permutations of the input set, presenting a unique and complex problem. This survey provides a comprehensive overview of the current development in set function learning, covering foundational theories, key methodologies, and diverse applications. We categorize and discuss existing approaches, focusing on deep learning approaches, such as DeepSets and Set Transformer based methods, as well as other notable alternative methods beyond deep learning, offering a complete view of current models. We also introduce various applications and relevant datasets, such as point cloud processing and multi-label classification, highlighting the significant progress achieved by set function learning methods in these domains. Finally, we conclude by summarizing the current state of set function learning approaches and identifying promising future research directions, aiming to guide and inspire further advancements in this promising field.
A review of annotation classification tools in the educational domain
2025-01-24
Joaquín Gayoso-Cabada, Antonio Sarasa-Cabezuelo, José-Luis Sierra
An annotation consists of a portion of information that is associated with a piece of content in order to explain something about the content or to add more information. The use of annotations as a tool in the educational field has positive effects on the learning process. The usual way to use this instrument is to provide students with contents, usually textual, with which they must associate annotations. In most cases this task is performed in groups of students who work collaboratively. This process encourages analysis and understanding of the contents since they have to understand them in order to annotate them, and also encourages teamwork. To facilitate its use, computer applications have been devel-oped in recent decades that implement the annotation process and offer a set of additional functionalities. One of these functionalities is the classification of the annotations made. This functionality can be exploited in various ways in the learning process, such as guiding the students in the annotation process, providing information to the student about how the annotation process is done and to the teacher about how the students write and how they understand the content, as well as implementing other innovative educational processes. In this sense, the classification of annotations plays a critical role in the application of the annotation in the educational field. There are many studies of annotations, but most of them consider the classification aspect marginally only. This paper presents an initial study of the classification mech-anisms used in the annotation tools, identifying four types of cases: absence of classification mechanisms, classification based on pre-established vocabularies, classification based on extensible vocabularies, and classification based on struc-tured vocabularies.
Wireless positioning technologies hold significant value for applications in autonomous driving, extended reality (XR), unmanned aerial vehicles (UAVs), and more. With the advancement of artificial intelligence (AI), leveraging AI to enhance positioning accuracy and robustness has emerged as a field full of potential. Driven by the requirements and functionalities defined in the 3rd Generation Partnership Project (3GPP) standards, AI/machine learning (ML)-based positioning is becoming a key technology to overcome the limitations of traditional methods. This paper begins with an introduction to the fundamentals of AI and wireless positioning, covering AI models, algorithms, positioning applications, emerging wireless technologies, and the basics of positioning techniques. Subsequently, focusing on standardization progress, we provide a comprehensive review of the evolution of 3GPP positioning standards, with an emphasis on the integration of AI/ML technologies in recent and upcoming releases. Based on the AI/ML-assisted positioning and direct AI/ML positioning schemes outlined in the standards, we conduct an in-depth investigation of related research. we focus on state-of-the-art (SOTA) research in AI-based line-of-sight (LOS)/non-line-of-sight (NLOS) detection, time of arrival (TOA)/time difference of arrival (TDOA) estimation, and angle estimation techniques. For Direct AI/ML Positioning, we explore SOTA advancements in fingerprint-based positioning, knowledge-assisted AI positioning, and channel charting-based positioning. Furthermore, we introduce publicly available datasets for wireless positioning and conclude by summarizing the challenges and opportunities of AI-driven wireless positioning.
Quantum Measurement for Quantum Chemistry on a Quantum Computer
2025-01-24
Smik Patel, Praveen Jayakumar, Tzu-Ching Yen, Artur F. Izmaylov
Quantum chemistry is among the most promising applications of quantum computing, offering the potential to solve complex electronic structure problems more efficiently than classical approaches. A critical component of any quantum algorithm is the measurement step, where the desired properties are extracted from a quantum computer. This review focuses on recent advancements in quantum measurement techniques tailored for quantum chemistry, particularly within the second quantized framework suitable for current and near-term quantum hardware. We provide a comprehensive overview of measurement strategies developed primarily for the Variational Quantum Eigensolver (VQE) and its derivatives. These strategies address the inherent challenges posed by complexity of the electronic Hamiltonian operator. Additionally, we examine methods for estimating excited states and one- and two-electron properties, extending the applicability of quantum algorithms to broader chemical phenomena. Key aspects of the review include approaches for constructing measurement operators with reduced classical preprocessing and quantum implementation costs, techniques to minimize the number of measurements required for a given accuracy, and error mitigation strategies that leverage symmetries and other properties of the measurement operators. Furthermore, we explore measurement schemes rooted in Quantum Phase Estimation (QPE), which are expected to become viable with the advent of fault-tolerant quantum computing. This review emphasizes foundational concepts and methodologies rather than numerical benchmarks, serving as a resource for researchers aiming to enhance the efficiency and accuracy of quantum measurements in quantum chemistry.
A Data-Driven M Dwarf Model and Detailed Abundances for $\sim$17,000 M Dwarfs in SDSS-V
2025-01-24
Aida Behmard, Melissa K. Ness, Andrew R. Casey, Ruth Angus, Katia Cunha, Diogo Souto, Yuxi, Lu, Jennifer A. Johnson
The cool temperatures of M dwarf atmospheres enable complex molecular chemistry, making robust characterization of M dwarf compositions a long-standing challenge. Recent modifications to spectral synthesis pipelines have enabled more accurate modeling of M dwarf atmospheres, but these methods are too slow for characterizing more than a handful of stars at a time. Data-driven methods such as The Cannon are viable alternatives, and can harness the information content of many M dwarfs from large spectroscopic surveys. Here, we train The Cannon on M dwarfs with FGK binary companions from the Sloan Digital Sky Survey-V/Milky Way Mapper (SDSS-V/MWM), with spectra from the Apache Point Observatory Galactic Evolution Experiment (APOGEE). The FGK-M pairs are assumed to be chemically homogeneous and span $-$0.56 $<$ [Fe/H] $<$ 0.31 dex. The resulting model is capable of inferring M dwarf $T_{\textrm{eff}}$ and elemental abundances for Fe, Mg, Al, Si, C, N, O, Ca, Ti, Cr, and Ni with median uncertainties of 13 K and 0.018$-$0.029 dex, respectively. We test the model by verifying that it reproduces reported abundance values of M dwarfs in open clusters and benchmark M dwarf datasets, as well as expected metallicity trends from stellar evolution. We apply the model to 16,590 M dwarfs in SDSS-V/MWM and provide their detailed abundances in our accompanying catalog.
Cyclic relative difference sets and circulant weighing matrices
2025-01-24
Daniel M. Gordon
An $(m,n,k,\lambda)$-relative difference set is a lifting of a $(m,k,n\lambda)$-difference set. Lam gave a table of cyclic relative difference sets with $k \leq 50$ in 1977, all of which were liftings of $( \frac{q^d-1}{q-1},q^{d-1},q^{d-2}(q-1))$-difference sets, the parameters of complements of classical Singer difference sets. Pott found all cyclic liftings of these difference sets with $n$ odd and $k \leq 64$ in 1995. No other nontrivial difference sets are known with liftings to relative difference sets, and Pott ended his survey on relative difference sets asking whether there are any others. In this paper we extend these searches, and apply the results to the existence of circulant weighing matrices.
Clustering of functional data prone to complex heteroscedastic measurement error
2025-01-24
Andi Mai, Carmen Tekwe, Roger Zoh, Lan Xue
Several factors make clustering of functional data challenging, including the infinite-dimensional space to which observations belong and the lack of a defined probability density function for the functional random variable. To overcome these barriers, researchers either assume that observations belong to a finite-dimensional space spanned by basis functions or apply nonparametric smoothing methods to the functions prior to clustering. Although extensive literature describes clustering methods for functional data, few studies have explored the clustering of measurement error--prone function-valued data. In this work, we consider clustering methods for functional data prone to complex, heteroscedastic measurement errors. Two stage-based methods using mixed-effects models are first applied to adjust for measurement error bias, followed by cluster analysis of the measurement error--adjusted curves. Through simulations, we investigate how varying sample size, the magnitude of measurement error, and the presence of complex heteroscedastic measurement errors influence the cluster analysis of functional data. Our results indicate that failing to account for measurement errors and the correlation structures associated with frequently collected functional data reduces the accuracy of identifying the true latent groups or clusters. The method consistently produces better results regardless of the initial clustering values used. Moreover, it is flexible and can be applied to various clustering approaches, based on the specific distribution of the data. The developed methods are applied to two data sets: a school-based study of energy expenditure among elementary school-aged children in Texas and data from the National Health and Nutrition Examination Survey on participants' physical activity monitored by wearable devices at frequent intervals.
In a recent study, Dannen et al. surveyed a large parameter space to study the transition from efficient to inefficient line driving. They found that when the line force significantly weakens due to ionization, the winds are variable, with a characteristic frequency comparable to the Lamb cut-off frequency of a stratified atmosphere, {\omega}c. In this work, we present a set of simulations and perturbation analyses that elucidate the variability source and characteristics. We found that the line force adds wave energy and amplifies perturbations with frequencies near {\omega}c. This selective amplification results from the coupling between the natural tendency of velocity perturbations to grow in a stratified atmosphere and the dependence of the line force on the velocity gradient, per the Castor-Abbott-Klein line-driven wind theory. We also found that the variability stems from self-excitation that occurs in the exponential atmosphere due to the non-linearity introduced by the absolute value of the velocity gradient in the line force prescription. We conclude that self-consistently calculating ionization is insufficient for modeling the dynamics in the subsonic atmosphere. Instead future wind models should relax the Sobolev approximation, or model the radiative transfer to capture the dynamics and instabilities at the base of the wind.
Evaluation of MQTT Bridge Architectures in a Cross-Organizational Context
2025-01-24
Keila Lima, Tosin Daniel Oyetoyan, Rogardt Heldal, Wilhelm Hasselbring
The latest surveys estimate an increasing number of connected Internet-of-Things (IoT) devices (around 16 billion) despite the sector's shortage of manufacturers. All these devices deployed into the wild will collect data to guide decision-making that can be made automatically by other systems, humans, or hybrid approaches. In this work, we conduct an initial investigation of benchmark configuration options for IoT Platforms that process data ingested by such devices in real-time using the MQTT protocol. We identified metrics and related MQTT configurable parameters in the system's component deployment for an MQTT bridge architecture. For this purpose, we benchmark a real-world IoT platform's operational data flow design to monitor the surrounding environment remotely. We consider the MQTT broker solution and the system's real-time ingestion and bridge processing portion of the platform to be the system under test. In the benchmark, we investigate two architectural deployment options for the bridge component to gain insights into the latency and reliability of MQTT bridge deployments in which data is provided in a cross-organizational context. Our results indicate that the number of bridge components, MQTT packet sizes, and the topic name can impact the quality attributes in IoT architectures using MQTT protocol.
A Systematic Literature Review on Equity and Technology in HCI and Fairness: Navigating the Complexities and Nuances of Equity Research
2025-01-24
Seyun Kim, Yuanchen Bai, Haiyi Zhu, Motahhare Eslami
Equity is crucial to the ethical implications in technology development. However, implementing equity in practice comes with complexities and nuances. In response, the research community, especially the human-computer interaction (HCI) and Fairness community, has endeavored to integrate equity into technology design, addressing issues of societal inequities. With such increasing efforts, it is yet unclear why and how researchers discuss equity and its integration into technology, what research has been conducted, and what gaps need to be addressed. We conducted a systematic literature review on equity and technology, collecting and analyzing 202 papers published in HCI and Fairness-focused venues. Amidst the substantial growth of relevant publications within the past four years, we deliver three main contributions: (1) we elaborate a comprehensive understanding researchers' motivations for studying equity and technology, (2) we illustrate the different equity definitions and frameworks utilized to discuss equity, (3) we characterize the key themes addressing interventions as well as tensions and trade-offs when advancing and integrating equity to technology. Based on our findings, we elaborate an equity framework for researchers who seek to address existing gaps and advance equity in technology.
Refining local-type primordial non-Gaussianity: Sharpened $b_φ$ constraints through bias expansion
2025-01-24
Boryana Hadzhiyska, Simone Ferraro
Local-type primordial non-Gaussianity (PNG), predicted by many non-minimal models of inflation, creates a scale-dependent contribution to the power spectrum of large-scale structure (LSS) tracers. Its amplitude is characterized by the product $b_\phi f_{\rm NL}^{\rm loc}$, where $b_\phi$ is an astrophysical parameter dependent on the properties of the tracer. However, $b_\phi$ exhibits significant secondary dependence on halo concentration and other astrophysical properties, which may bias and weaken the constraints on $f_{\rm NL}^{\rm loc}$. In this work, we demonstrate that incorporating knowledge of the relation between Lagrangian bias parameters and $b_\phi$ can significantly enhance PNG constraints. We employ the Hybrid Effective Field Theory (HEFT) approach at the field-level and a linear regression model to seek a connection between the bias parameters and $b_{\phi}$ for halo and galaxy samples, constructed using the \textsc{AbacusSummit} simulation suite and mimicking the luminous red galaxies (LRGs) and quasi-stellar objects (QSOs) of the Dark Energy Spectroscopic Instrument (DESI) survey. For the fixed-mass halo samples, our full bias model reduces the uncertainty by more than 70\%, with most of that improvement coming from $b_\nabla$, which we find to be an excellent proxy for concentration. For the galaxy samples, our model reduces the uncertainty on $b_\phi$ by 80\% for all tracers. By adopting Lagrangian-bias informed priors on the parameter $b_\phi$, future analyses can thus constrain $f_{\rm NL}^{\rm loc}$ with less bias and smaller errors.
Insights from Publishing Open Data in Industry-Academia Collaboration
2025-01-24
Per Erik Strandberg, Philipp Peterseil, Julian Karoliny, Johanna Kallio, Johannes Peltola
Effective data management and sharing are critical success factors in industry-academia collaboration. This paper explores the motivations and lessons learned from publishing open data sets in such collaborations. Through a survey of participants in a European research project that published 13 data sets, and an analysis of metadata from almost 281 thousand datasets in Zenodo, we collected qualitative and quantitative results on motivations, achievements, research questions, licences and file types. Through inductive reasoning and statistical analysis we found that planning the data collection is essential, and that only few datasets (2.4%) had accompanying scripts for improved reuse. We also found that authors are not well aware of the importance of licences or which licence to choose. Finally, we found that data with a synthetic origin, collected with simulations and potentially mixed with real measurements, can be very meaningful, as predicted by Gartner and illustrated by many datasets collected in our research project.
HP2 Survey V. Ophiuchus: Filament formation in a dispersing cloud complex
2025-01-23
João Alves, Marco Lombardi, Charles Lada
We search for potential ``birthmarks'' left from the formation of filamentary molecular clouds in the Ophiuchus complex. We use high dynamic-range column density and temperature maps derived from \textit{Herschel}, \textit{Planck}, and \textit{2MASS/NICEST} extinction data. We find two distinct types of filaments based on their orientation relative to nearby massive stars: radial (R-type) and tangential (T-type). R-type filaments exhibit decreasing mass profiles away from massive stars, while T-type filaments show flat but structured profiles. We propose a scenario where both filament types originate from the dynamic interplay of compression and stretching forces exerted by a fast outflow emanating from the OB association. The two formation mechanisms leave distinct observable ``birthmarks'' (namely, filament orientation, mass distribution, and star formation location) on each filament type. Our results illustrate a complex phase in molecular cloud evolution with two simultaneous yet contrasting processes: the formation of filaments and stars via the dispersal of residual gas from a previous massive star formation event. Our approach highlights the importance of taking into account the wider context of a star-forming complex, rather than concentrating exclusively on particular subregions.
The Cosmic Microwave Background -- Secondary Anisotropies
2025-01-23
Federico Bianchini, Abhishek S. Maniyar
The cosmic microwave background (CMB), the relic radiation from the early Universe, offers a unique window into both primordial conditions and the intervening large-scale structure (LSS) it traverses. Interactions between CMB photons and the evolving Universe imprint secondary anisotropies -- modifications to the CMB's intensity and polarization caused by gravitational effects and scattering processes. These anisotropies serve as a powerful probe of fundamental physics while also revealing astrophysical processes governing the thermodynamics and distribution of baryonic matter. In this chapter, we provide a comprehensive review of the physical mechanisms underlying secondary anisotropies, their observational status, and their potential to advance precision cosmology. With the increasing sensitivity of CMB experiments and synergy with LSS surveys, secondary anisotropies are poised to unveil unprecedented insights into the Universe's composition, evolution, and dynamics.
2025-01-23
Aatman Vaidya, Tarunima Prabhakar, Denny George, Swair Shah
This report evaluates the performance of text-in text-out Large Language Models (LLMs) to understand and generate Indic languages. This evaluation is used to identify and prioritize Indic languages suited for inclusion in safety benchmarks. We conduct this study by reviewing existing evaluation studies and datasets; and a set of twenty-eight LLMs that support Indic languages. We analyze the LLMs on the basis of the training data, license for model and data, type of access and model developers. We also compare Indic language performance across evaluation datasets and find that significant performance disparities in performance across Indic languages. Hindi is the most widely represented language in models. While model performance roughly correlates with number of speakers for the top five languages, the assessment after that varies.
A Study on the Line of Sight to Galaxies Detected at Gamma-ray Energies
2025-01-23
Amy Furniss, Josepf N. Amador, Olivier Hervet, Ollie Jackson, David A. Williams
The large-scale Universal structure comprises strands of dark matter and galaxies with large under-dense volumes known as voids. We measure the fraction of the line of sight that intersects voids for active galactic nuclei (AGN) detected by Fermi Large Area Telescope (LAT) and quasars from the Sloan Digital Sky Survey (SDSS). This ``voidiness'' fraction is a rudimentary proxy for the density along the line of sight to the galaxies. The voidiness of SDSS-observed quasars (QSOs) is distinctly different from randomly distributed source populations, with a median p-value of $4.6\times10^{-5}$ and $\ll 1\times10^{-7}$, when compared with 500 simulated populations with randomly simulated locations but matching redshifts in the $0.1\leq z<0.4$ and $0.4\leq z < 0.7$ intervals, respectively. A similar comparison of the voidiness for LAT-detected AGN shows median p-values greater than 0.05 in each redshift interval. When comparing the SDSS QSO population to the LAT-detected AGN, we mitigate potential bias from a relationship between redshift and voidiness by comparing the LAT-detected AGN to a ``redshift-matched'' set of SDSS QSOs. The LAT-detected AGN between a redshift of 0.4 and 0.7 show higher voidiness compared to the redshift-matched SDSS QSO populations, with a median p-value of 2.3$\times10^{-5}$, (a $4.1\sigma$ deviation). No deviation is found when comparing the same populations between redshifts of 0.1 and 0.4 (p>0.05). We do not study possible causes of this voidiness difference. It might relate to propagation effects from lower magnetic or radiative background fields within voids or to an environment more favorable for gamma-ray production for AGN near voids.
Cold dark gas in Cygnus X: The first large-scale mapping of low-frequency carbon recombination lines
2025-01-23
Kimberly L. Emig, Pedro Salas, Loren D. Anderson, D. Anish Roshi, Lars Bonne, Alberto D. Bolatto, Isabelle A. Grenier, Rebecca C. Levy, Dylan J. Linville, Matteo Luisi, M. Riley Owens, J. Poojapriyatharsheni, Nicola Schneider, Luigi Tibaldo, Alexander G. G. M. Tielens, Stefanie K. Walch, Glenn J. White
Understanding the transition from atomic gas to molecular gas is critical to explain the formation and evolution of molecular clouds. However, the gas phases involved, cold HI and CO-dark molecular gas, are challenging to directly observe and physically characterize. We observed the Cygnus X star-forming complex in carbon radio recombination lines (CRRLs) at 274--399 MHz with the Green Bank Telescope at 21 pc (48') resolution. Of the 30 deg^2 surveyed, we detect line-synthesized C273alpha emission from 24 deg^2 and produce the first large-area maps of low-frequency CRRLs. The morphology of the C273alpha emission reveals arcs, ridges, and extended possibly sheet-like gas which are often on the outskirts of CO emission and likely transitioning from HI-to-H_2. The typical angular separation of C273alpha and 13CO emission is 12 pc, and we estimate C273alpha gas densities of n_H ~ 40 - 400 cm^3. The C273alpha line profiles are Gaussian and likely turbulent broadened, spanning a large range of FWHM from 2 to 20 km/s with a median of 10.6 km/s. Mach numbers fall within 10--30. The turbulent timescale is relatively short, 2.6 Myr, and we deduce that the turbulent pressure likely dominates the evolution of the C273alpha gas. Velocity offsets between C273alpha and 13CO components are apparent throughout the region and have a typical value of 2.9 km/s. Two regimes have emerged from the data: one regime in which C273alpha and 13CO are strongly related (at N_H ~ 4 x 10^21 cm^-2), and a second, in which C273alpha emits independently of the 13CO intensity. In the former regime, C273alpha may arise from the the envelopes of massive clouds (filaments), and in the latter, C273alpha emits from cold clumps in a more-diffuse mix of HI and H_2 gas.
Parameter-Efficient Fine-Tuning for Foundation Models
2025-01-23
Dan Zhang, Tao Feng, Lilong Xue, Yuandong Wang, Yuxiao Dong, Jie Tang
This survey delves into the realm of Parameter-Efficient Fine-Tuning (PEFT) within the context of Foundation Models (FMs). PEFT, a cost-effective fine-tuning technique, minimizes parameters and computational complexity while striving for optimal downstream task performance. FMs, like ChatGPT, DALL-E, and LLaVA specialize in language understanding, generative tasks, and multimodal tasks, trained on diverse datasets spanning text, images, and videos. The diversity of FMs guides various adaptation strategies for PEFT. Therefore, this survey aims to provide a comprehensive overview of PEFT techniques applied to diverse FMs and address critical gaps in understanding the techniques, trends, and applications. We start by providing a detailed development of FMs and PEFT. Subsequently, we systematically review the key categories and core mechanisms of PEFT across diverse FMs to offer a comprehensive understanding of trends. We also explore the most recent applications across various FMs to demonstrate the versatility of PEFT, shedding light on the integration of systematic PEFT methods with a range of FMs. Furthermore, we identify potential research and development directions for improving PEFTs in the future. This survey provides a valuable resource for both newcomers and experts seeking to understand and use the power of PEFT across FMs. All reviewed papers are listed at \url{https://github.com/THUDM/Awesome-Parameter-Efficient-Fine-Tuning-for-Foundation-Models}.
Can $ν$DM interactions solve the $S_8$ discrepancy?
2025-01-23
Lei Zu, William Giarè, Chi Zhang, Eleonora Di Valentino, Yue-Lin Sming Tsai, Sebastian Trojanowski
We present compelling evidence that Dark Matter (DM)-neutrino interactions can resolve the persistent structure growth parameter discrepancy, $S_8 = \sigma_8\,\sqrt{\Omega_m/0.3}$, between early and late universe observations. By incorporating cosmic shear measurements from current Weak Lensing (WL) surveys and leveraging an emulator based on true $N$-body simulations to account for nonlinear corrections, we demonstrate that an interaction strength of $u \sim 10^{-4}$ not only provides a coherent explanation for the high-multipole observations from the Atacama Cosmology Telescope (ACT), but also alleviates the $S_8$ discrepancy. Combining early universe constraints with DES Y3 cosmic shear data yields a nearly $3\sigma$ detection of non-zero DM-neutrino interactions, marking a significant breakthrough in cosmological research. Our findings challenge the standard $\Lambda$CDM paradigm and highlight the potential of future large-scale structure surveys, which can rigorously test this interaction and unveil the fundamental properties of dark matter.
Topic Modeling for Free-Response Text Data from a Complex Survey
2025-01-23
Namitha V. Pais, Scott H. Holan, Paul A. Parker
Topic Modeling is a popular statistical tool commonly used on textual data to identify the hidden thematic structure in a document collection based on the distribution of words. Additionally, it can be used to cluster the documents, with clusters representing distinct topics. The Mixture of Unigrams (MoU) is a standard topic model for clustering document-term data and can be particularly useful for analyzing open-ended survey responses to extract meaningful information from the underlying topics. However, with complex survey designs, where data is often collected on individual (document) characteristics, it is essential to account for the sample design in order to avoid biased estimates. To address this issue, we propose the MoU model under informative sampling using a pseudolikelihood to account for the sample design in the model by incorporating survey weights. We evaluate the effectiveness of this approach through a simulation study and illustrate its application using two datasets from the American National Election Studies (ANES). We compare our pseudolikelihood-based MoU model to the traditional MoU and assess its effectiveness in extracting meaningful topics from survey data. Additionally, we introduce a hierarchical Mixture of Unigrams (hMoU) accounting for informative sampling where topic proportions are defined as functions of document-level fixed and random effects. We demonstrate the effectiveness of the proposed model through an application to ANES data comparing topic proportions across respondent-level factors such as gender, race, age group, and state.
ATLASGAL-selected high-mass clumps in the inner Galaxy: XI. Morphology and kinematics of warm inner envelopes
2025-01-23
Thanh Dat Hoang, Min-Young Lee, Friedrich Wyrowski, Agata Karska, Felipe Navarete, Karl M. Menten
(Abridged) Massive stellar embryos are embedded in warm envelopes that provide mass reservoirs for the accretion process onto final stars. Feedback from star formation activities in return impacts the properties of the envelopes, offering us a unique opportunity to investigate star formation processes. We aim to characterise the warm envelopes of proto- or young stellar objects in different evolutionary stages based on the morphology and kinematics of the $^{13}$CO(6-5) emission and to examine their relations with star formation processes. Using the APEX telescope, we obtained maps of the mid-$J$ $^{13}$CO emission with an angular size of 80" x 80" towards 99 massive clumps from the ATLASGAL survey. Our maps are classified based on morphological complexities, and the radial structure of the emission is characterised for simple single-core sources. The velocity centroids of the emission are compared to small- and large-scale gas kinematics, aiming to shed light on the origin of envelope kinematics. $^{13}$CO(6-5) emission is detected towards sources in all stages of high-mass star formation, with a detection rate of 83% for the whole sample. The detection rate, line width, and line peak increase with evolution, and the line luminosity is strongly correlated with $L_{\mathrm{bol}}$ and $M_{\mathrm{clump}}$, indicating that the excitation of $^{13}$CO(6-5) emission is closely related to star formation processes. In addition, the radial distributions of the emission for single-core sources can be well fitted by power-law functions, suggesting a relatively simple envelope structure for many sources. As for the envelope kinematics, linear velocity gradients are common among the single-core sources. Our comparison of kinematics on different scales suggests that the origin of the linear velocity gradients in the warm envelopes is complex and unclear for many sources.
Dual-Domain Exponent of Maximum Mutual Information Decoding
2025-01-23
AmirPouya Moeini, Albert Guillén i Fàbregas
This paper provides a dual domain derivation of the error exponent of maximum mutual information (MMI) decoding with constant composition codes, showing it coincides with that of maximum likelihood decoding for discrete memoryless channels. The analysis is further extended to joint source-channel coding, demonstrating that the generalized MMI decoder achieves the same random coding error exponent as the maximum a posteriori decoder.
The vertical shear instability in protoplanetary discs as an outwardly travelling wave. I. Linear theory
2025-01-23
Gordon I. Ogilvie, Henrik N. Latter, Geoffroy Lesur
We revisit the global linear theory of the vertical shear instability (VSI) in protoplanetary discs with an imposed radial temperature gradient. We focus on the regime in which the VSI has the form of a travelling inertial wave that grows in amplitude as it propagates outwards. Building on previous work describing travelling waves in thin astrophysical discs, we develop a quantitative theory of the wave motion, its spatial structure and the physical mechanism by which the wave is amplified. We find that this viewpoint provides a useful description of the large-scale development of the VSI in global numerical simulations, which involves corrugation and breathing motions of the disc. We contrast this behaviour with that of perturbations of smaller scale, in which the VSI grows into a nonlinear regime in place without significant radial propagation.
Antiquity, Dark Ages and Renaissance of Light-Front Higher Spin Theory
2025-01-23
Anders K H Bengtsson
These notes contain -- apart from some physics -- scattered reminiscences of everyday life at the Institute for Theoretical Physics in G\"oteborg in the early eighties. The text has been in the making for many years. Some of it was written inspired by the Lars Brink 70-fest at The Solvay Institute in Brussels in 2014, but was never finished or published. Parts of the old text has now been replaced by a review of the quite unexpected recent renaissance for light-front higher spin theory. Lars sadly did not live to experience more than the very beginnings of this quite remarkable development. The text was finalized in the spring of 2023.
A search for close binary systems in the SALT survey of hydrogen-deficient stars using {\it TESS}
2025-01-23
E. J. Snowdon, C. S. Jeffery, S. Schlagenhauf, M. Dorsch
The {\it TESS} periodograms of the SALT survey catalogue of hydrogen-deficient stars were searched for evidence of short-period variability. Periodic light curve variations were identified in 16 stars out of 153 catalogue objects, of which 10 were false positives. From the remaining 6 identified variables, Ton S 415 is a known close binary system and the sixth close binary containing a hydrogen-deficient hot subdwarf. Radial velocity and SED analyses ruled out the remaining 5 as close binary systems; the causes of their variability remain uncertain. With one or more K-type companions, BPS CS 22956-0094 may be a wide binary or triple. From this SALT+{\it TESS} sample, the fraction of close binaries stands at $ 1/29 \approx 3.5\%$ for intermediate helium hot subdwarfs and $0/124 = 0\%$ for extreme helium subdwarfs.
2025-01-23
David Zwicker, Oliver W. Paulin, Cathelijne ter Burg
Droplet formation has emerged as an essential concept for the spatiotemporal organisation of biomolecules in cells. However, classical descriptions of droplet dynamics based on passive liquid-liquid phase separation cannot capture the complex situations inside cells. This review discusses three general aspects that are crucial in cells: (i) biomolecules are diverse and individually complex, implying that cellular droplets posses complex internal behaviour, e.g., in terms of their material properties; (ii) the cellular environment contains many solid-like structures that droplets can wet; (iii) cells are alive and use fuel to drive processes out of equilibrium. We illustrate how these principles control droplet nucleation, growth, position, and count to unveil possible regulatory mechanisms in biological cells and other applications of phase separation.
Spectral clustering is a powerful technique for clustering high-dimensional data, utilizing graph-based representations to detect complex, non-linear structures and non-convex clusters. The construction of a similarity graph is essential for ensuring accurate and effective clustering, making graph structure learning (GSL) central for enhancing spectral clustering performance in response to the growing demand for scalable solutions. Despite advancements in GSL, there is a lack of comprehensive surveys specifically addressing its role within spectral clustering. To bridge this gap, this survey presents a comprehensive review of spectral clustering methods, emphasizing on the critical role of GSL. We explore various graph construction techniques, including pairwise, anchor, and hypergraph-based methods, in both fixed and adaptive settings. Additionally, we categorize spectral clustering approaches into single-view and multi-view frameworks, examining their applications within one-step and two-step clustering processes. We also discuss multi-view information fusion techniques and their impact on clustering data. By addressing current challenges and proposing future research directions, this survey provides valuable insights for advancing spectral clustering methodologies and highlights the pivotal role of GSL in tackling large-scale and high-dimensional data clustering tasks.
Survey of image processing settings used for mammography systems in the United Kingdom: how variable is it?
2025-01-23
Alistair Mackenzie, John Loveland, Ruben van Engen
The aim was to undertake a national survey of the setup of mammography imaging systems in the UK, we were particularly interested in image processing and software version. We created a program that can extract selected tags from the DICOM header. 28 medical physics departments used the program on processed images of the TORMAM phantom acquired since 2023 and this produced data for 497 systems. We received data for 7 different models of mammography systems. We found that currently in use each model had between 2 and 7 different versions of software for the acquisition workstation. Each of the systems had multiple versions of image processing settings, a preliminary investigation with TORMAM demonstrated large differences in the appearance of the image for the same X-ray model. The Fujifilm, GE and Siemens systems showed differences in the setup of the dose levels. In addition to these settings there were differences in the paddles used and grid type. Our snapshot of system set up showed that there is a potential for the images to appear differently according to the settings seen in the headers. These differences may affect the outcomes of AI and also human readers. Thus the introduction of AI must take these differences into consideration and the inevitably changes of settings in the future. There are responsibilities on AI suppliers, physics, mammographic equipment manufacturers, and breast-screening units to manage the use of AI and ensure the outcomes of breast screening are not adversely affected by the set-up of equipment.
Integrated 6G TN and NTN Localization: Challenges, Opportunities, and Advancements
2025-01-23
Sharief Saleh, Pinjun Zheng, Xing Liu, Hui Chen, Musa Furkan Keskin, Basuki Priyanto, Martin Beale, Yasaman Ettefagh, Gonzalo Seco-Granados, Tareq Y. Al-Naffouri, Henk Wymeersch
The rapid evolution of cellular networks has introduced groundbreaking technologies, including large and distributed antenna arrays and reconfigurable intelligent surfaces in terrestrial networks (TNs), as well as aerial and space-based nodes in non-terrestrial networks (NTNs). These advancements enable applications beyond traditional communication, such as high-precision localization and sensing. While integrating TN and NTN enablers will lead to unparalleled opportunities for seamless global localization, such integration attempts are expected to face several challenges. To understand these opportunities and challenges, we first examine the distinctive characteristics of the key 6G enablers, evaluating their roles in localization from both technical and practical perspectives. Next, to identify developments driving TN-NTN localization, we review the latest standardization and industrial innovation progress. Finally, we discuss the opportunities and challenges of TN-NTN integration, illustrating its potential through two numerical case studies.
Vision-Based Multimodal Interfaces: A Survey and Taxonomy for Enhanced Context-Aware System Design
2025-01-23
Yongquan Hu, Jingyu Tang, Xinya Gong, Zhongyi Zhou, Shuning Zhang, Don Samitha Elvitigala, Florian 'Floyd' Mueller, Wen Hu, Aaron J. Quigley
The recent surge in artificial intelligence, particularly in multimodal processing technology, has advanced human-computer interaction, by altering how intelligent systems perceive, understand, and respond to contextual information (i.e., context awareness). Despite such advancements, there is a significant gap in comprehensive reviews examining these advances, especially from a multimodal data perspective, which is crucial for refining system design. This paper addresses a key aspect of this gap by conducting a systematic survey of data modality-driven Vision-based Multimodal Interfaces (VMIs). VMIs are essential for integrating multimodal data, enabling more precise interpretation of user intentions and complex interactions across physical and digital environments. Unlike previous task- or scenario-driven surveys, this study highlights the critical role of the visual modality in processing contextual information and facilitating multimodal interaction. Adopting a design framework moving from the whole to the details and back, it classifies VMIs across dimensions, providing insights for developing effective, context-aware systems.
Perceived Fairness of the Machine Learning Development Process: Concept Scale Development
2025-01-23
Anoop Mishra, Deepak Khazanchi
In machine learning (ML) applications, unfairness is triggered due to bias in the data, the data curation process, erroneous assumptions, and implicit bias rendered during the development process. It is also well-accepted by researchers that fairness in ML application development is highly subjective, with a lack of clarity of what it means from an ML development and implementation perspective. Thus, in this research, we investigate and formalize the notion of the perceived fairness of ML development from a sociotechnical lens. Our goal in this research is to understand the characteristics of perceived fairness in ML applications. We address this research goal using a three-pronged strategy: 1) conducting virtual focus groups with ML developers, 2) reviewing existing literature on fairness in ML, and 3) incorporating aspects of justice theory relating to procedural and distributive justice. Based on our theoretical exposition, we propose operational attributes of perceived fairness to be transparency, accountability, and representativeness. These are described in terms of multiple concepts that comprise each dimension of perceived fairness. We use this operationalization to empirically validate the notion of perceived fairness of machine learning (ML) applications from both the ML practioners and users perspectives. The multidimensional framework for perceived fairness offers a comprehensive understanding of perceived fairness, which can guide the creation of fair ML systems with positive implications for society and businesses.
Language in the Arab world presents a complex diglossic and multilingual setting, involving the use of Modern Standard Arabic, various dialects and sub-dialects, as well as multiple European languages. This diverse linguistic landscape has given rise to code-switching, both within Arabic varieties and between Arabic and foreign languages. The widespread occurrence of code-switching across the region makes it vital to address these linguistic needs when developing language technologies. In this paper, we provide a review of the current literature in the field of code-switched Arabic NLP, offering a broad perspective on ongoing efforts, challenges, research gaps, and recommendations for future research directions.
GeomGS: LiDAR-Guided Geometry-Aware Gaussian Splatting for Robot Localization
2025-01-23
Jaewon Lee, Mangyu Kong, Minseong Park, Euntai Kim
Mapping and localization are crucial problems in robotics and autonomous driving. Recent advances in 3D Gaussian Splatting (3DGS) have enabled precise 3D mapping and scene understanding by rendering photo-realistic images. However, existing 3DGS methods often struggle to accurately reconstruct a 3D map that reflects the actual scale and geometry of the real world, which degrades localization performance. To address these limitations, we propose a novel 3DGS method called Geometry-Aware Gaussian Splatting (GeomGS). This method fully integrates LiDAR data into 3D Gaussian primitives via a probabilistic approach, as opposed to approaches that only use LiDAR as initial points or introduce simple constraints for Gaussian points. To this end, we introduce a Geometric Confidence Score (GCS), which identifies the structural reliability of each Gaussian point. The GCS is optimized simultaneously with Gaussians under probabilistic distance constraints to construct a precise structure. Furthermore, we propose a novel localization method that fully utilizes both the geometric and photometric properties of GeomGS. Our GeomGS demonstrates state-of-the-art geometric and localization performance across several benchmarks, while also improving photometric performance.
The Influence of UX Design on User Retention and Conversion Rates in Mobile Apps
2025-01-23
Aaditya Shankar Majumder
This paper explores the profound impact of User Experience (UX) design on user retention and conversion rates in mobile applications. As the mobile app market becomes increasingly competitive, understanding how UX design can enhance user satisfaction, engagement, and loyalty is crucial for developers and businesses. Through a comprehensive review of existing literature and statistical insights, this study identifies key UX design principles that contribute to improved user retention and conversion rates. Intuitive navigation, appealing visuals, performance optimization, and integration of user feedback emerge as essential components of effective UX design that drive app success. Applications that prioritize these elements foster a positive user experience, leading to higher engagement and greater retention. Additionally, UX design strategies, such as personalization and customization, have been shown to significantly increase conversion rates, demonstrating the critical the role that tailored experiences play in app success. By analyzing these principles and their impact, this paper provides valuable insights for developers aiming to enhance user satisfaction, optimize app performance, and ultimately improve business outcomes.
In the field of deep learning-based computer vision, YOLO is revolutionary. With respect to deep learning models, YOLO is also the one that is evolving the most rapidly. Unfortunately, not every YOLO model possesses scholarly publications. Moreover, there exists a YOLO model that lacks a publicly accessible official architectural diagram. Naturally, this engenders challenges, such as complicating the understanding of how the model operates in practice. Furthermore, the review articles that are presently available do not delve into the specifics of each model. The objective of this study is to present a comprehensive and in-depth architecture comparison of the four most recent YOLO models, specifically YOLOv8 through YOLO11, thereby enabling readers to quickly grasp not only how each model functions, but also the distinctions between them. To analyze each YOLO version's architecture, we meticulously examined the relevant academic papers, documentation, and scrutinized the source code. The analysis reveals that while each version of YOLO has improvements in architecture and feature extraction, certain blocks remain unchanged. The lack of scholarly publications and official diagrams presents challenges for understanding the model's functionality and future enhancement. Future developers are encouraged to provide these resources.
Time Series Embedding Methods for Classification Tasks: A Review
2025-01-23
Yasamin Ghahremani, Vangelis Metsis
Time series analysis has become crucial in various fields, from engineering and finance to healthcare and social sciences. In this paper, we present a comprehensive review and evaluation of time series embedding methods for effective representations in machine learning and deep learning models. We introduce a taxonomy of embedding techniques, categorizing them based on their theoretical foundations and application contexts. Unlike previous surveys, our work provides a quantitative evaluation of representative methods from each category by assessing their performance on downstream classification tasks across diverse real-world datasets. Our experimental results demonstrate that the performance of embedding methods varies significantly depending on the dataset and classification algorithm used, highlighting the importance of careful model selection and extensive experimentation for specific applications, including engineering systems. To facilitate further research and practical applications, we provide an open-source code repository implementing these embedding methods. This study contributes to the field by offering a systematic comparison of time series embedding techniques, guiding practitioners in selecting appropriate methods for their specific applications, and providing a foundation for future advancements in time series analysis.
A review on development of eco-friendly filters in Nepal for use in cigarettes and masks and Air Pollution Analysis with Machine Learning and SHAP Interpretability
In Nepal, air pollution is a serious public health concern, especially in cities like Kathmandu where particulate matter (PM2.5 and PM10) has a major influence on respiratory health and air quality. The Air Quality Index (AQI) is predicted in this work using a Random Forest Regressor, and the model's predictions are interpreted using SHAP (SHapley Additive exPlanations) analysis. With the lowest Testing RMSE (0.23) and flawless R2 scores (1.00), CatBoost performs better than other models, demonstrating its greater accuracy and generalization which is cross validated using a nested cross validation approach. NowCast Concentration and Raw Concentration are the most important elements influencing AQI values, according to SHAP research, which shows that the machine learning results are highly accurate. Their significance as major contributors to air pollution is highlighted by the fact that high values of these characteristics significantly raise the AQI. This study investigates the Hydrogen-Alpha (HA) biodegradable filter as a novel way to reduce the related health hazards. With removal efficiency of more than 98% for PM2.5 and 99.24% for PM10, the HA filter offers exceptional defense against dangerous airborne particles. These devices, which are biodegradable face masks and cigarette filters, address the environmental issues associated with traditional filters' non-biodegradable trash while also lowering exposure to air contaminants.
The drying rate profile of granular beds can be divided into the constant rate period (CRP), which is characterized by a nearly constant drying rate, and the falling rate period (FRP), in which the drying rate rapidly decays. In order to explain this behavior quantitatively, we proposed a simple one-dimensional power law model in which the product of the water permeability and the pressure gradient is assumed to be proportional to the cube of the saturation. To test this model, we measured the drying rates of glass beads and hierarchical granular materials produced by sintering and breaking glass beads. Our results and those of previous experiments showed consistency with the power law. The obtained proportional constant of the experimental power law also shows a rough agreement with that estimated from previous studies on water permeability and capillary pressure. Drying behavior in FRP also agrees with our model in some points. The remnant deviation of the model from experimental results may be attributed to the inhomogeneity of granular media, which was qualitatively verified.
Full-Stack Optimized Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation
2025-01-23
Rong Shan, Jiachen Zhu, Jianghao Lin, Chenxu Zhu, Bo Chen, Ruiming Tang, Yong Yu, Weinan Zhang
In this paper, we address the lifelong sequential behavior incomprehension problem in large language models (LLMs) for recommendation, where LLMs struggle to extract useful information from long user behavior sequences, even within their context limits. To tackle this, we propose ReLLaX (Retrieval-enhanced Large Language models Plus), a framework offering optimization across data, prompt, and parameter levels. At the data level, we introduce Semantic User Behavior Retrieval (SUBR) to reduce sequence heterogeneity, making it easier for LLMs to extract key information. For prompt-level enhancement, we employ Soft Prompt Augmentation (SPA) to inject collaborative knowledge, aligning item representations with recommendation tasks and improving LLMs's exploration of item relationships. Finally, at the parameter level, we propose Component Fully-interactive LoRA (CFLoRA), which enhances LoRA's expressiveness by enabling interactions between its components, allowing better capture of sequential information. Moreover, we present new perspectives to compare current LoRA-based LLM4Rec methods, i.e. from both a composite and a decomposed view. We theoretically demonstrate that the ways they employ LoRA for recommendation are degraded versions of our CFLoRA, with different constraints on atom component interactions. Extensive experiments on three public datasets demonstrate ReLLaX's superiority over existing baselines and its ability to mitigate lifelong sequential behavior incomprehension effectively.
The integration of Artificial Intelligence (AI) systems into technologies used by young digital citizens raises significant privacy concerns. This study investigates these concerns through a comparative analysis of stakeholder perspectives. A total of 252 participants were surveyed, with the analysis focusing on 110 valid responses from parents/educators and 100 from AI professionals after data cleaning. Quantitative methods, including descriptive statistics and Partial Least Squares Structural Equation Modeling, examined five validated constructs: Data Ownership and Control, Parental Data Sharing, Perceived Risks and Benefits, Transparency and Trust, and Education and Awareness. Results showed Education and Awareness significantly influenced data ownership and risk assessment, while Data Ownership and Control strongly impacted Transparency and Trust. Transparency and Trust, along with Perceived Risks and Benefits, showed minimal influence on Parental Data Sharing, suggesting other factors may play a larger role. The study underscores the need for user-centric privacy controls, tailored transparency strategies, and targeted educational initiatives. Incorporating diverse stakeholder perspectives offers actionable insights into ethical AI design and governance, balancing innovation with robust privacy protections to foster trust in a digital age.
Toward Ethical AI: A Qualitative Analysis of Stakeholder Perspectives
2025-01-23
Ajay Kumar Shrestha, Sandhya Joshi
As Artificial Intelligence (AI) systems become increasingly integrated into various aspects of daily life, concerns about privacy and ethical accountability are gaining prominence. This study explores stakeholder perspectives on privacy in AI systems, focusing on educators, parents, and AI professionals. Using qualitative analysis of survey responses from 227 participants, the research identifies key privacy risks, including data breaches, ethical misuse, and excessive data collection, alongside perceived benefits such as personalized services, enhanced efficiency, and educational advancements. Stakeholders emphasized the need for transparency, privacy-by-design, user empowerment, and ethical oversight to address privacy concerns effectively. The findings provide actionable insights into balancing the benefits of AI with robust privacy protections, catering to the diverse needs of stakeholders. Recommendations include implementing selective data use, fostering transparency, promoting user autonomy, and integrating ethical principles into AI development. This study contributes to the ongoing discourse on ethical AI, offering guidance for designing privacy-centric systems that align with societal values and build trust among users. By addressing privacy challenges, this research underscores the importance of developing AI technologies that are not only innovative but also ethically sound and responsive to the concerns of all stakeholders.
Simulations of the accreted stellar halos of low-mass field galaxies
2025-01-23
Andrew P. Cooper, Carlos S. Frenk, Wojciech A. Hellwing, Sownak Bose
We predict the properties of stellar halos in galaxies of present-day virial mass $10^8 < M_{200} < 10^{12} {\rm M_\odot}$ by combining the GALFORM semi-analytic model of galaxy formation, the COCO cosmological N-body simulation, and the STINGS particle tagging technique. Galaxies in low mass halos have a wide range of stellar halo properties. Their diversity is much greater at fixed stellar mass than fixed virial mass. In the least massive DM halos capable of supporting galaxy formation, accreted mass fractions are < 10 per cent, and the typical density profile of accreted stars is similar to that of stars formed in situ. In low mass galaxies, the radial scale lengths of accreted stellar halos are smaller than those of stellar disks formed in situ, but the accreted component is still diffuse, not bulge-like. At the scale of galaxies like M33, the surface density of accreted stars exceeds that of the in situ component at ~15kpc; the accreted surface density at this radius is $3.5\lesssim \log_{10} M_\star/{\rm M_\odot} \mathrm{kpc^{-2}} \lesssim 6$, varying systematically with virial mass. We compare our predictions to observations and other cosmological simulations. A small but significant number of $\sim 10^{12} {\rm M_\odot}$ halos with exceptionally inefficient star formation -- "failed Milky Ways" -- are more prominent in our model than others; the true abundance of this population is a potential constraint on galaxy formation physics and could be measured by low surface brightness surveys targeting field galaxies with M33-like stellar mass. The stellar particle data for our simulation is publicly available.
Representing Visualization Insights as a Dense Insight Network
2025-01-23
Jane Hoffswell, Victor Soares Bursztyn, Shunan Guo, Jesse Martinez, Eunyee Koh
We propose a dense insight network framework to encode the relationships between automatically generated insights from a complex dashboard based on their shared characteristics. Our insight network framework includes five high-level categories of relationships (e.g., type, topic, value, metadata, and compound scores). The goal of this insight network framework is to provide a foundation for implementing new insight interpretation and exploration strategies, including both user-driven and automated approaches. To illustrate the complexity and flexibility of our framework, we first describe a visualization playground to directly visualize key network characteristics; this playground also demonstrates potential interactive capabilities for decomposing the dense insight network. Then, we discuss a case study application for ranking insights based on the underlying network characteristics captured by our framework, before prompting a large language model to generate a concise, natural language summary. Finally, we reflect on next steps for leveraging our insight network framework to design and evaluate new systems.
2025-01-23
Jacob Devasier, Rishabh Mediratta, Akshith Putta, Chengkai Li
We propose a novel paradigm for automatic fact-checking that leverages frame semantics to enhance the structured understanding of claims, addressing the challenges posed by misinformation in today's information ecosystem. To support this approach, we introduce a pilot dataset of real-world claims extracted from PolitiFact, specifically annotated for large-scale structured data. This dataset underpins two case studies: the first investigates voting-related claims using the Vote semantic frame, while the second explores various semantic frames and data sources from the Organisation for Economic Co-operation and Development (OECD). Our findings demonstrate the effectiveness of frame semantics in improving evidence retrieval, indicating a meaningful advancement in automatic fact-checking capabilities. Finally, we conducted a survey of frames evoked in fact-checked claims, identifying high-impact frames to guide future research.
Experience with GitHub Copilot for Developer Productivity at Zoominfo
2025-01-23
Gal Bakal, Ali Dasdan, Yaniv Katz, Michael Kaufman, Guy Levin
This paper presents a comprehensive evaluation of GitHub Copilot's deployment and impact on developer productivity at Zoominfo, a leading Go-To-Market (GTM) Intelligence Platform. We describe our systematic four-phase approach to evaluating and deploying GitHub Copilot across our engineering organization, involving over 400 developers. Our analysis combines both quantitative metrics, focusing on acceptance rates of suggestions given by GitHub Copilot and qualitative feedback given by developers through developer satisfaction surveys. The results show an average acceptance rate of 33% for suggestions and 20% for lines of code, with high developer satisfaction scores of 72%. We also discuss language-specific performance variations, limitations, and lessons learned from this medium-scale enterprise deployment. Our findings contribute to the growing body of knowledge about AI-assisted software development in enterprise settings.
An Extensive and Methodical Review of Smart Grids for Sustainable Energy Management-Addressing Challenges with AI, Renewable Energy Integration and Leading-edge Technologies
2025-01-23
Parag Biswas, Abdur Rashid, abdullah al masum, MD Abdullah Al Nasim, A. S. M Anas Ferdous, Kishor Datta Gupta, Angona Biswas
Energy management decreases energy expenditures and consumption while simultaneously increasing energy efficiency, reducing carbon emissions, and enhancing operational performance. Smart grids are a type of sophisticated energy infrastructure that increase the generation and distribution of electricity's sustainability, dependability, and efficiency by utilizing digital communication technologies. They combine a number of cutting-edge techniques and technology to improve energy resource management. A large amount of research study on the topic of smart grids for energy management has been completed in the last several years. The authors of the present study want to cover a number of topics, including smart grid benefits and components, technical developments, integrating renewable energy sources, using artificial intelligence and data analytics, cybersecurity, and privacy. Smart Grids for Energy Management are an innovative field of study aiming at tackling various difficulties and magnifying the efficiency, dependability, and sustainability of energy systems, including: 1) Renewable sources of power like solar and wind are intermittent and unpredictable 2) Defending smart grid system from various cyber-attacks 3) Incorporating an increasing number of electric vehicles into the system of power grid without overwhelming it. Additionally, it is proposed to use AI and data analytics for better performance on the grid, reliability, and energy management. It also looks into how AI and data analytics can be used to optimize grid performance, enhance reliability, and improve energy management. The authors will explore these significant challenges and ongoing research. Lastly, significant issues in this field are noted, and recommendations for further work are provided.
2025-01-23
Jeremy Goldwasser, Will Fithian, Giles Hooker
Statistical experiments often seek to identify random variables with the largest population means. This inferential task, known as rank verification, has been well-studied on Gaussian data with equal variances. This work provides the first treatment of the unequal variances case, utilizing ideas from the selective inference literature. We design a hypothesis test that verifies the rank of the largest observed value without losing power due to multiple testing corrections. This test is subsequently extended for two procedures: Identifying some number of correctly-ordered Gaussian means, and validating the top-K set. The testing procedures are validated on NHANES survey data.
Development of a Validation and Inspection Tool for Armband-based Lifelog Data (VITAL) to Facilitate the Clinical Use of Wearable Data: A Prototype and Usability Evaluation
2025-01-23
Im Eunyoung, Kang Sunghoon, Kim Hyeoneui
Background: The rise of mobile technology and health apps has increased the use of person-generated health data (PGHD). PGHD holds significant potential for clinical decision-making but remains challenging to manage. Objective: This study aimed to enhance the clinical utilization of wearable health data by developing the Validation and Inspection Tool for Armband-Based Lifelog Data (VITAL), a pipeline for data integration, visualization, and quality management, and evaluating its usability. Methods: The study followed a structured process of requirement gathering, tool implementation, and usability evaluation. Requirements were identified through input from four clinicians. Wearable health data from Samsung, Apple, Fitbit, and Xiaomi devices were integrated into a standardized dataframe at 10-minute intervals, focusing on biometrics, activity, and sleep. Features of VITAL support data integration, visualization, and quality management. Usability evaluation involved seven clinicians performing tasks, completing the Unified Theory of Acceptance and Use of Technology (UTAUT) survey, and participating in interviews to identify usability issues. Results: VITAL successfully integrated wearable data, thus enabling all participants to complete tasks with minimal errors without prior participant training. UTAUT survey results were positive, with average scores of 4.2 for performance expectancy, 3.96 for effort expectancy, and 4.14 for intention to use, indicating high user satisfaction and intent to adopt the tool. Conclusions: By enhancing wearable data integration, visualization, and quality management, the VITAL prototype shows significant potential for clinical application. Positive feedback highlights its promise, while emphasizing the need for further studies to confirm its real-world effectiveness.
Excitations of gauge bosons, and other aspects of gauge Higgs theories
2025-01-23
Jeff Greensite
I review the qualitative physical distinction between the Higgs and confinement phases of a gauge Higgs theory, and the non-local order parameter introduced by Matsuyama and myself which identifies the two phases. I then present some new results suggestive of a possible excitation spectrum of vector bosons in the electroweak sector of the Standard Model.
Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters
2025-01-23
Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi, Vineet Gundecha, Sahand Ghorbanpour, Avisek Naug, Ricardo Luna Gutierrez, Antonio Guillen
We present a Reinforcement Learning Platform for Adversarial Black-box untargeted and targeted attacks, RLAB, that allows users to select from various distortion filters to create adversarial examples. The platform uses a Reinforcement Learning agent to add minimum distortion to input images while still causing misclassification by the target model. The agent uses a novel dual-action method to explore the input image at each step to identify sensitive regions for adding distortions while removing noises that have less impact on the target model. This dual action leads to faster and more efficient convergence of the attack. The platform can also be used to measure the robustness of image classification models against specific distortion types. Also, retraining the model with adversarial samples significantly improved robustness when evaluated on benchmark datasets. The proposed platform outperforms state-of-the-art methods in terms of the average number of queries required to cause misclassification. This advances trustworthiness with a positive social impact.
The field of quantum computing is generating significant anticipation within the scientific and industrial communities due to its potential to revolutionize computing paradigms. Recognizing this potential, this paper explores the integration of transfer of knowledge techniques, traditionally used in classical artificial intelligence, into quantum computing. We present a comprehensive classification of the transfer models, focusing on Transfer Learning and Transfer Optimization. Additionally, we analyze relevant schemes in quantum computing that can benefit from knowledge sharing, and we delve into the potential synergies, supported by theoretical insights and initial experimental results. Our findings suggest that leveraging the transfer of knowledge can enhance the efficiency and effectiveness of quantum algorithms, particularly in the context of hybrid solvers. This approach not only accelerates the optimization process but also reduces the computational burden on quantum processors, making it a valuable tool for advancing quantum computing technologies.
Exploring User Perspectives on Data Collection, Data Sharing Preferences, and Privacy Concerns with Remote Healthcare Technology
2025-01-23
Daniela Napoli, Heather Molyneaux, Helene Fournier, Sonia Chiasson
Remote healthcare technology can help tackle societal issues by improving access to quality healthcare services and enhancing diagnoses through in-place monitoring. These services can be implemented through a combination of mobile devices, applications, wearable sensors, and other smart technology. It is paramount to handle sensitive data that is collected in ways that meet users' privacy expectations. We surveyed 384 people in Canada aged 20 to 93 years old to explore participants' comfort with data collection, sharing preferences, and potential privacy concerns related to remote healthcare technology. We explore these topics within the context of various healthcare scenarios including health emergencies and managing chronic health conditions.
Estimating Black Hole Masses in Obscured AGN from X-ray and Optical Emission Line Luminosities
2025-01-23
S. LaMassa, I. Farrow, C. M. Urry, B. Trakhtenbrot, C. Auge, M. J. Koss, A. Peca, D. Sanders, T. J. Turner
We test a novel method for estimating black hole masses ($M_{\rm BH}$) in obscured active galactic nuclei (AGN) that uses proxies to measure the full-width half maximum of broad H$\alpha$ (FWHM$_{\rm bH\alpha}$) and the accretion disk luminosity at 5100 Angstrom ($\lambda L_{\rm 5100 Angstrom}$). Using a published correlation, we estimate FWHM$_{\rm bH\alpha}$ from the narrow optical emission line ratio $L_{\rm [O\,III]}/L_{\rm nH\beta}$. Using a sample of 99 local obscured AGN from the Swift-BAT AGN Spectroscopic Survey, we assess the agreement between estimating $\lambda L_{\rm 5100 Angstrom}$ from the intrinsic 2-10 keV X-ray luminosity and from narrow optical emission lines. We find a mean offset of $0.32 \pm 0.68$ dex between these methods, which propagates to a factor of $\sim$2 uncertainty when estimating $M_{\rm BH}$ using a virial mass formula where $L_{\rm [O\,III]}/L_{\rm nH\beta}$ serves as a proxy of FWHM$_{\rm bH\alpha}$ ($M_{\rm BH,[O\,III]/nH\beta}$). We compare $M_{\rm BH,[O\,III]/nH\beta}$ with virial $M_{\rm BH}$ measurements from broad Paschen emission lines. For the 14 (12) BASS AGN with broad Pa$\alpha$ (Pa$\beta$) detections, we find $M_{\rm BH,[O\,III]/nH\beta}$ to be systematically higher than $M_{\rm BH,Pa\alpha}$ ($M_{\rm BH,Pa\beta}$) by a factor of 0.39 $\pm$ 0.44 dex (0.48 $\pm$ 0.51 dex). Since these offsets are within the scatter, more data are needed to assess whether $M_{\rm BH,[O\,III]/nH\beta}$ is biased high. For 151 BASS AGN with measured stellar velocity dispersions ($\sigma_{\rm *}$), we find that the $\sigma_{\rm *}$-derived $M_{\rm BH}$ agrees with $M_{\rm BH,[O\,III]/nH\beta}$ to within 0.08 dex, albeit with wide scatter (0.74 dex). The method tested here can provide estimates of $M_{\rm BH}$ in thousands of obscured AGN in spectroscopic surveys when other diagnostics are not available, though with an uncertainty of $\sim$3-5.
Global Perspectives of AI Risks and Harms: Analyzing the Negative Impacts of AI Technologies as Prioritized by News Media
2025-01-23
Mowafak Allaham, Kimon Kieslich, Nicholas Diakopoulos
Emerging AI technologies have the potential to drive economic growth and innovation but can also pose significant risks to society. To mitigate these risks, governments, companies, and researchers have contributed regulatory frameworks, risk assessment approaches, and safety benchmarks, but these can lack nuance when considered in global deployment contexts. One way to understand these nuances is by looking at how the media reports on AI, as news media has a substantial influence on what negative impacts of AI are discussed in the public sphere and which impacts are deemed important. In this work, we analyze a broad and diverse sample of global news media spanning 27 countries across Asia, Africa, Europe, Middle East, North America, and Oceania to gain valuable insights into the risks and harms of AI technologies as reported and prioritized across media outlets in different countries. This approach reveals a skewed prioritization of Societal Risks followed by Legal & Rights-related Risks, Content Safety Risks, Cognitive Risks, Existential Risks, and Environmental Risks, as reflected in the prevalence of these risk categories in the news coverage of different nations. Furthermore, it highlights how the distribution of such concerns varies based on the political bias of news outlets, underscoring the political nature of AI risk assessment processes and public opinion. By incorporating views from various regions and political orientations for assessing the risks and harms of AI, this work presents stakeholders, such as AI developers and policy makers, with insights into the AI risks categories prioritized in the public sphere. These insights my guide the development of more inclusive, safe, and responsible AI technologies that address the diverse concerns and needs across the world.
As the atomistic simulations of materials science move from traditional potentials to machine learning interatomic potential (MLIP), the field is entering the second phase focused on discovering and explaining new material phenomena. While MLIP development relies on curated data and flexible datasets from ab-initio simulations, transitioning seamlessly between ab-initio workflows and MLIP frameworks remains challenging. A global survey was conducted to understand the current standing (progress and bottleneck) of the machine learning-guided materials science research. The survey responses have been implemented to design an open-source software to reduce the access barriers of MLIP models for the global scientific community. Here, we present AtomProNet, an open-source Python package that automates obtaining atomic structures, prepares and submits ab-initio jobs, and efficiently collects batch-processed data for streamlined neural network (NN) training. Finally, we compared empirical and start-of-the-art machine learning potential, showing the practicality of using MLIPs based on computational time and resources.
Luminous Mid-IR Selected Type-2 Quasars at Cosmic Noon in SDSS Stripe82 I: Selection, Composite Photometry, and Spectral Energy Distributions
2025-01-23
Ben Wang, Joseph F. Hennawi, Zheng Cai, Gordon T. Richards, Jan-Torge Schindler, Nadia L. Zakamska, Yuzo Ishikawa, Hollis B. Akins, Zechang Sun
We analyze 23 spectroscopically confirmed Type-2 quasars (QSOs) selected from the WISE 22$\rm \mu$m band in the SDSS Stripe 82 region, focusing on their multi-band photometry and spectral energy distributions (SEDs). These objects were selected to be IR-luminous ($\rm flux_{W4} > 5mJy$, i.e., $12.62 < W4 < 14.62 \rm\ AB \, magnitude$), optically faint ($r > 23$) or with red color ($r - W4 >8.38$). Gemini/GNIRS observations were conducted for all 24 candidates, and 18/24 were also observed with Keck/LRIS. The observations confirm 23 to be real Type-2 QSOs in the redshift range $0.88 - 2.99$ (12 are at $z>2$). We collect multi-band photometry and conduct SED fitting. The composite photometry probes the wavelength from 0.1$\rm \mu$m to 10$\rm \mu$m at the rest frame. The IR emission is dominated by dust torus implying an average torus luminosity for the sample of $L_{\rm torus} 10^{46.84} \rm erg/s$. The origin of the rest-UV/optical light is not definitive, but we present three possible scenarios: scattered light, stellar emission, and the reddened accretion disk. Assuming an obscured:unobscured ratio of approximately 1:1, our targets have $L_{\rm bol} = 10^{46.28} \rm erg \,s^{-1} - 10^{47.49} \rm erg \,s^{-1}$ and around SMBH masses $\rm 10^{8.18} M_{\odot} - 10^{9.39} M_{\odot}$, assuming they accreate at the Eddington limit. Compared to previous Type-2 AGN SEDs, our targets have a brighter dust torus and redder optical-IR color. By comparing the SED to the results from JWST `little red dots' (LRDs), we find that these IR-selected Type-2 QSOs have similar SED shapes to the LRDs. This pilot Type-2 QSO survey demonstrates that mid-IR selection is an efficient way to find luminous Type-2 QSOs at $z>2$. Finally, the composite photometry and Type-2 QSOs SED model generated by this sample provide a guide for finding more Type-2 QSOs at higher redshift.
The rate of extreme coronal line emitters in the Baryon Oscillation Spectroscopic Survey LOWZ sample
2025-01-23
Joseph Callow, Or Graur, Peter Clark, Alex G. Kim, Brendan O'Connor, Jessica Aguilar, Steven Ahlen, Davide Bianchi, David Brooks, Axel de la Macorra, Arjun Dey, Peter Doel, Jaime E. Forero-Romero, Enrique Gaztañaga, Satya Gontcho A Gontcho, Gaston Gutierrez, Robert Kehoe, Andrew Lambert, Martin Landriau, Laurent Le Guillou, Aaron Meisner, Ramon Miquel, John Moustakas, Francisco Prada, Ignasi Pérez-Ràfols, Graziano Rossi, Eusebio Sanchez, Michael Schubnell, Hee-Jong Seo, David Sprayberry, Gregory Tarlé, Benjamin A. Weaver, Hu Zou
Extreme coronal line emitters (ECLEs) are a rare class of galaxy that exhibit strong, high-ionization iron coronal emission lines in their spectra. In some cases, these lines are transient and may be the result of tidal disruption event (TDEs). To test this connection, we calculate the rate of variable ECLEs (vECLEs) at redshift $\sim0.3$. We search for ECLEs in the Baryon Oscillation Spectroscopic Survey (BOSS) LOWZ sample and discover two candidate ECLEs. Using follow-up spectra from the Dark Energy Spectroscopic Instrument and Gemini Multi-Object Spectrograph, and mid-infrared observations from the Wide-field Infrared Survey Explorer, we determine that one of these galaxies is a vECLE. Using this galaxy, we calculate the galaxy-normalized vECLE rate at redshift $\sim0.3$ to be $R_\mathrm{G}=1.6~^{+3.8}_{-1.4}\times10^{-6}~\mathrm{galaxy}^{-1}~\mathrm{yr}^{-1}$ and the mass-normalized rate to be $R_\mathrm{M}=7~^{+16}_{-6}\times10^{-18}~\mathrm{M_\odot^{-1}}~\mathrm{yr}^{-1}$. This is then converted to a volumetric rate of $R_\mathrm{V}=1.8~^{+4.5}_{-1.5}\times10^{-9}~\mathrm{Mpc}^{-3}~\mathrm{yr}^{-1}$. Formally, the LOWZ vECLE rates are $2-4$ times lower than the rates calculated from the Sloan Digital Sky Survey Legacy sample at redshift $\sim0.1$. However, given the large uncertainties on both measurements, they are consistent with each other at $1\sigma$. Both the galaxy-normalized and volumetric rates are one to two orders of magnitude lower than TDE rates from the literature, consistent with vECLEs being caused by $5-20$ per cent of all TDEs.
The Three Hundred Project hydrodynamical simulations: Hydrodynamical Weak-Lensing Cluster Mass Biases and Richnesses using different hydro models
2025-01-23
Carlo Giocoli, Giulia Despali, Massimo Meneghetti, Elena Rasia, Lauro Moscardini, Stefano Borgani, Giorgio. F. Lesci, Federico Marulli, Weiguang Cui, Gustavo Yepes
The mass of galaxy clusters derived from weak-lensing observations is sensitive to projection effects, and, on average, it is biased low with respect to the true cluster mass, with a mass and redshift dependence. In this work, we leverage state-of-the-art hydrodynamical simulations of galaxy clusters carried out with GadgetX and GIZMO-SIMBA as part of the Three Hundred Project. We use them to quantify weak-lensing mass biases with respect also to the results from dark matter-only simulations. We also investigate how the biases of the weak-lensing mass estimates propagate into the richness-mass relation. Future wide-field weak-lensing surveys require per cent accuracy on the characterisation of cluster masses in order to use galaxy cluster number count and clustering as complementary probes for cosmological analyses. We aim to shed light on the effect of the presence of baryons on the weak-lensing mass bias and also whether this bias depends on the galaxy formation recipe; in addition, we seek to model the richness-mass relation that can be used as guidelines for observational experiments for cluster cosmology. We produce weak-lensing simulations of random projections to model the expected excess surface mass density profile of clusters up to redshift $z=1$. We derive the weak-lensing mass-richness relation and find consistency within 1$\sigma$ uncertainties across hydrodynamical simulations. The scatter in observed richness at a fixed weak-lensing mass, or vice versa, increases linearly with redshift at a fixed stellar mass cut. Importantly, we observe that the scatter in richness at a given true mass is smaller than at a given weak-lensing mass. Our results align well with SDSS redMaPPer cluster observations. (Abridged)
Generative models have achieved remarkable performance recently, and thus model hubs have emerged. Existing model hubs typically assume basic text matching is sufficient to search for models. However, in reality, due to different abstractions and the large number of models in model hubs, it is not easy for users to review model descriptions and example images, choosing which model best meets their needs. Therefore, it is necessary to describe model functionality wisely so that future users can efficiently search for the most suitable model for their needs. Efforts to address this issue remain limited. In this paper, we propose Conditional Generative Model Identification (CGI), which aims to provide an effective way to identify the most suitable model using user-provided example images rather than requiring users to manually review a large number of models with example images. To address this problem, we propose the PromptBased Model Identification (PMI) , which can adequately describe model functionality and precisely match requirements with specifications. To evaluate PMI approach and promote related research, we provide a benchmark comprising 65 models and 9100 identification tasks. Extensive experimental and human evaluation results demonstrate that PMI is effective. For instance, 92% of models are correctly identified with significantly better FID scores when four example images are provided.
Re-ranking Using Large Language Models for Mitigating Exposure to Harmful Content on Social Media Platforms
2025-01-23
Rajvardhan Oak, Muhammad Haroon, Claire Jo, Magdalena Wojcieszak, Anshuman Chhabra
Social media platforms utilize Machine Learning (ML) and Artificial Intelligence (AI) powered recommendation algorithms to maximize user engagement, which can result in inadvertent exposure to harmful content. Current moderation efforts, reliant on classifiers trained with extensive human-annotated data, struggle with scalability and adapting to new forms of harm. To address these challenges, we propose a novel re-ranking approach using Large Language Models (LLMs) in zero-shot and few-shot settings. Our method dynamically assesses and re-ranks content sequences, effectively mitigating harmful content exposure without requiring extensive labeled data. Alongside traditional ranking metrics, we also introduce two new metrics to evaluate the effectiveness of re-ranking in reducing exposure to harmful content. Through experiments on three datasets, three models and across three configurations, we demonstrate that our LLM-based approach significantly outperforms existing proprietary moderation approaches, offering a scalable and adaptable solution for harm mitigation.
The prevalence of harmful content on social media platforms poses significant risks to users and society, necessitating more effective and scalable content moderation strategies. Current approaches rely on human moderators, supervised classifiers, and large volumes of training data, and often struggle with scalability, subjectivity, and the dynamic nature of harmful content (e.g., violent content, dangerous challenge trends, etc.). To bridge these gaps, we utilize Large Language Models (LLMs) to undertake few-shot dynamic content moderation via in-context learning. Through extensive experiments on multiple LLMs, we demonstrate that our few-shot approaches can outperform existing proprietary baselines (Perspective and OpenAI Moderation) as well as prior state-of-the-art few-shot learning methods, in identifying harm. We also incorporate visual information (video thumbnails) and assess if different multimodal techniques improve model performance. Our results underscore the significant benefits of employing LLM based methods for scalable and dynamic harmful content moderation online.
Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review
2025-01-23
Giovanni Ciatto, Federico Sabbatini, Andrea Agiollo, Matteo Magnini, Andrea Omicini
In this paper we focus on the opacity issue of sub-symbolic machine learning predictors by promoting two complementary activities, namely, symbolic knowledge extraction (SKE) and injection (SKI) from and into sub-symbolic predictors. We consider as symbolic any language being intelligible and interpretable for both humans and computers. Accordingly, we propose general meta-models for both SKE and SKI, along with two taxonomies for the classification of SKE and SKI methods. By adopting an explainable artificial intelligence (XAI) perspective, we highlight how such methods can be exploited to mitigate the aforementioned opacity issue. Our taxonomies are attained by surveying and classifying existing methods from the literature, following a systematic approach, and by generalising the results of previous surveys targeting specific sub-topics of either SKE or SKI alone. More precisely, we analyse 132 methods for SKE and 117 methods for SKI, and we categorise them according to their purpose, operation, expected input/output data and predictor types. For each method, we also indicate the presence/lack of runnable software implementations. Our work may be of interest for data scientists aiming at selecting the most adequate SKE/SKI method for their needs, and also work as suggestions for researchers interested in filling the gaps of the current state of the art, as well as for developers willing to implement SKE/SKI-based technologies.
Accelerate High-Quality Diffusion Models with Inner Loop Feedback
2025-01-22
Matthew Gwilliam, Han Cai, Di Wu, Abhinav Shrivastava, Zhiyu Cheng
We propose Inner Loop Feedback (ILF), a novel approach to accelerate diffusion models' inference. ILF trains a lightweight module to predict future features in the denoising process by leveraging the outputs from a chosen diffusion backbone block at a given time step. This approach exploits two key intuitions; (1) the outputs of a given block at adjacent time steps are similar, and (2) performing partial computations for a step imposes a lower burden on the model than skipping the step entirely. Our method is highly flexible, since we find that the feedback module itself can simply be a block from the diffusion backbone, with all settings copied. Its influence on the diffusion forward can be tempered with a learnable scaling factor from zero initialization. We train this module using distillation losses; however, unlike some prior work where a full diffusion backbone serves as the student, our model freezes the backbone, training only the feedback module. While many efforts to optimize diffusion models focus on achieving acceptable image quality in extremely few steps (1-4 steps), our emphasis is on matching best case results (typically achieved in 20 steps) while significantly reducing runtime. ILF achieves this balance effectively, demonstrating strong performance for both class-to-image generation with diffusion transformer (DiT) and text-to-image generation with DiT-based PixArt-alpha and PixArt-sigma. The quality of ILF's 1.7x-1.8x speedups are confirmed by FID, CLIP score, CLIP Image Quality Assessment, ImageReward, and qualitative comparisons. Project information is available at https://mgwillia.github.io/ilf.
Neural Radiance Fields for the Real World: A Survey
2025-01-22
Wenhui Xiao, Remi Chierchia, Rodrigo Santa Cruz, Xuesong Li, David Ahmedt-Aristizabal, Olivier Salvado, Clinton Fookes, Leo Lebrat
Neural Radiance Fields (NeRFs) have remodeled 3D scene representation since release. NeRFs can effectively reconstruct complex 3D scenes from 2D images, advancing different fields and applications such as scene understanding, 3D content generation, and robotics. Despite significant research progress, a thorough review of recent innovations, applications, and challenges is lacking. This survey compiles key theoretical advancements and alternative representations and investigates emerging challenges. It further explores applications on reconstruction, highlights NeRFs' impact on computer vision and robotics, and reviews essential datasets and toolkits. By identifying gaps in the literature, this survey discusses open challenges and offers directions for future research.
SMART-Vision: Survey of Modern Action Recognition Techniques in Vision
2025-01-22
Ali K. AlShami, Ryan Rabinowitz, Khang Lam, Yousra Shleibik, Melkamu Mersha, Terrance Boult, Jugal Kalita
Human Action Recognition (HAR) is a challenging domain in computer vision, involving recognizing complex patterns by analyzing the spatiotemporal dynamics of individuals' movements in videos. These patterns arise in sequential data, such as video frames, which are often essential to accurately distinguish actions that would be ambiguous in a single image. HAR has garnered considerable interest due to its broad applicability, ranging from robotics and surveillance systems to sports motion analysis, healthcare, and the burgeoning field of autonomous vehicles. While several taxonomies have been proposed to categorize HAR approaches in surveys, they often overlook hybrid methodologies and fail to demonstrate how different models incorporate various architectures and modalities. In this comprehensive survey, we present the novel SMART-Vision taxonomy, which illustrates how innovations in deep learning for HAR complement one another, leading to hybrid approaches beyond traditional categories. Our survey provides a clear roadmap from foundational HAR works to current state-of-the-art systems, highlighting emerging research directions and addressing unresolved challenges in discussion sections for architectures within the HAR domain. We provide details of the research datasets that various approaches used to measure and compare goodness HAR approaches. We also explore the rapidly emerging field of Open-HAR systems, which challenges HAR systems by presenting samples from unknown, novel classes during test time.
The outer structure of old star clusters in the Small Magellanic Cloud
2025-01-22
Andrés E. Piatti
We report results on the internal dynamical evolution of old star clusters located in the outer regions of the Small Magellanic Cloud (SMC). Because the SMC has been imprinted with evidence of tidal interaction with the Large Magellanic Cloud (LMC), we investigated at what extend such an interaction has produced extra tidal structures or excess of stars beyond the clusters' tidal radii. For that purpose, we used the Survey of the Magellanic Stellar History (SMASH) DR2 data sets to build number density radial profiles of suitable star clusters, and derived their structural and internal dynamics parameters. The analysed stellar density profiles do not show any evidence of tidal effects caused by the LMC. On the contrary, the Jacobi volume of the selected SMC star clusters would seem underfilled, with a clear trend toward a smaller percentage of underfilled volume as their deprojected distance to the SMC centre increases. Moreover, the internal dynamical evolution of SMC star clusters would seem to be influenced by the SMC gravitational field, being star clusters located closer to the SMC centre in a more advanced evolutionary stage. We compared the internal dynamical evolution of SMC old star clusters with those of LMC and Milky Way globular clusters, and found that Milky Way globular clusters have dynamical evolutionary paths similar to LMC/SMC old star clusters located closer to their respective galaxy's centres. Finally, we speculate with the possibility that globular clusters belonging to Magellanic Clouds like-mass galaxies have lived a couple of times their median relaxation times.
Development of the Critical Reflection and Agency in Computing Index
2025-01-22
Aadarsh Padiyath, Mark Guzdial, Barbara Ericson
As computing's societal impact grows, so does the need for computing students to recognize and address the ethical and sociotechnical implications of their work. While there are efforts to integrate ethics into computing curricula, we lack a standardized tool to measure those efforts, specifically, students' attitudes towards ethical reflection and their ability to effect change. This paper introduces the novel framework of Critically Conscious Computing and reports on the development and content validation of the Critical Reflection and Agency in Computing Index, a novel instrument designed to assess undergraduate computing students' attitudes towards practicing critically conscious computing. The resulting index is a theoretically grounded, expert-reviewed tool to support research and practice in computing ethics education. This enables researchers and educators to gain insights into students' perspectives, inform the design of targeted ethics interventions, and measure the effectiveness of computing ethics education initiatives.
Paper Quality Assessment based on Individual Wisdom Metrics from Open Peer Review
2025-01-22
Andrii Zahorodnii, Jasper J. F. van den Bosch, Ian Charest, Christopher Summerfield, Ila R. Fiete
This study proposes a data-driven framework for enhancing the accuracy and efficiency of scientific peer review through an open, bottom-up process that estimates reviewer quality. Traditional closed peer review systems, while essential for quality control, are often slow, costly, and subject to biases that can impede scientific progress. Here, we introduce a method that evaluates individual reviewer reliability by quantifying agreement with community consensus scores and applying Bayesian weighting to refine paper quality assessments. We analyze open peer review data from two major scientific conferences, and demonstrate that reviewer-specific quality scores significantly improve the reliability of paper quality estimation. Perhaps surprisingly, we find that reviewer quality scores are unrelated to authorship quality. Our model incorporates incentive structures to recognize high-quality reviewers and encourage broader coverage of submitted papers, thereby mitigating the common "rich-get-richer" pitfall of social media. These findings suggest that open peer review, with mechanisms for estimating and incentivizing reviewer quality, offers a scalable and equitable alternative for scientific publishing, with potential to enhance the speed, fairness, and transparency of the peer review process.
A note on Strong Cosmic Censorship and its violation in Reissner-Nordström de Sitter black hole space-times
2025-01-22
Anna Chrysostomou, Alan S. Cornell, Aldo Deandrea, Seong Chan Park
Penrose's Strong Cosmic Censorship conjecture safeguards determinism in General Relativity. Within the initial value approach to General Relativity, proof of Strong Cosmic Censorship preservation is predicated on the unique evolution of the metric. For the Kerr-Newman family of black hole solutions, this requires the inextendability of the metric past the Cauchy horizon, due to the development of a "blue-shift" instability. Attempts to provide a rigorous mathematical proof of Strong Cosmic Censorship has led to the formulation of several Strong Cosmic Censorship conjectures of varying strengths, which seem to be discussed rarely outside of the mathematical relativity literature. In this note, we review some of the arguments for and against Strong Cosmic Censorship preservation, with a focus on the Reissner-Nordstr\"om de Sitter context, where the positive cosmological constant invites a "red-shift" effect that competes against the "blue-shift". We study the consequent role of quasinormal mode behaviour and illustrate the parameter space for which we consistently observe violations of the Strong Cosmic Censorship conjecture within Reissner-Nordstr\"om de Sitter black holes.
Mutations and (Non-)Euclideaness in oriented matroids
2025-01-22
Michael Wilhelmi
We call an oriented matroid Mandel if it has an extension in general position which makes all programs with that extension Euclidean. If $L$ is the minimum number of mutations adjacent to an element of the groundset, we call an oriented matroid Las Vergnas if $L > 0$. If $\frak{O}_{\mathcal{property}}$ is the class of oriented matroids having a certain property, it holds $\frak{O} \supset \frak{O}_{\mathcal{Las Vergnas}} \supset \frak{O}_{\mathcal{Mandel}} \supset \frak{O}_{\mathcal{Euclidean}} \supset \frak{O}_{\mathcal{realizable}}.$ All these inclusions are proper, we give explicit proofs/examples for the parts of this chain that were not known. For realizable hyperplane arrangements of rank $r$ we have $L = r$ which was proved by Shannon. Under the assumption that a (modified) intersection property holds we give an analogon to Shannons proof and show that uniform rank $4$ Euclidean oriented matroids with that property have $L = 4$. Using the fact that the lexicographic extension creates and destroys certain mutations, we show that for Euclidean oriented matroids holds $L \ge 3$. We give a survey of preservation of Euclideaness and prove that Euclideaness remains after a certain type of mutation-flips. This yields that a path in the mutation graph from a Euclidean oriented matroid to a totally non-Euclidean oriented matroid (which has no Euclidean oriented matroid programs) must have at least three mutation-flips. Finally, a minimal non-Euclidean or rank $4$ uniform oriented matroid is Mandel if it is connected to a Euclidean oriented matroid via one mutation-flip, hence we get many examples for Non-Euclidean but Mandel oriented matroids and have $L \le 3$ for those of rank $4$.
Discrimination and AI in insurance: what do people find fair? Results from a survey
2025-01-22
Frederik Zuiderveen Borgesius, Marvin van Bekkum, Iris van Ooijen, Gabi Schaap, Maaike Harbers, Tjerk Timan
Two modern trends in insurance are data-intensive underwriting and behavior-based insurance. Data-intensive underwriting means that insurers use and analyze more data for estimating the chance that a consumer files a claim and calculating the premium based on that estimation. Insurers analyze the new datasets with artificial intelligence (AI) to discover new correlations, with which they can estimate the policyholder's expected claims cost more precisely. Insurers also offer behavior-based insurance. For example, some car insurers use AI to follow the driving behavior of an individual policyholder in real-time and decide whether to offer that policyholder a discount. Similarly, a life insurer could track a policyholder's activity with a smart watch and offer a discount for an active lifestyle. In this paper, we report on a survey of the Dutch population (N=999) in which we asked people's opinions about examples of data-intensive underwriting and behavior-based insurance. The main results include the following. First, if survey respondents find an insurance practice unfair, they also find the practice unacceptable. Second, respondents find almost all modern insurance practices that we described unfair. Third, respondents find practices fairer if they can influence the premium. For example, respondents find behavior-based car insurance with a car tracker relatively fair. Fourth, if respondents do not see the logic of using a certain consumer characteristic, then respondents find it unfair if an insurer calculates the premium based on the characteristic. Fifth, respondents find it unfair if an insurer offers an insurance product only to a specific group, such as car insurance specifically for family doctors. Sixth, respondents find it unfair if an insurance practice leads to higher prices for poorer people. We reflect on the policy implications of the findings.
The Radiative Transfer Equation (RTE) is essential for solving the spatial distribution of light energy. It plays a crucial role in the link budget analysis of Underwater Wireless Optical Communication (UWOC). However, due to its complex integro-differential form, obtaining an exact solution is extremely challenging. This paper provides a systematic overview and comparison of key RTE solution strategies in case of UWOC scenario-including the Monte Carlo Method (MCM), Beer-Lambert Method (BLM), Beam Spread Function (BSF), Finite Element Method (FEM), and others-and analyzes how each approach balances accuracy, computational efficiency, and ease of implementation. Results show that MCM, though computationally intensive, to best match the three-dimensional spatial configuration of practical UWOC systems. BLM, while simplest, loses accuracy in turbid conditions. BSF partially corrects for scattering but yields only modest gains over BLM, and FEM struggles at longer ranges due to discretization. These findings help guide method selection for reliable estimation UWOC's system power budget.
To Measure or Not: A Cost-Sensitive, Selective Measuring Environment for Agricultural Management Decisions with Reinforcement Learning
2025-01-22
Hilmy Baja, Michiel Kallenberg, Ioannis N. Athanasiadis
Farmers rely on in-field observations to make well-informed crop management decisions to maximize profit and minimize adverse environmental impact. However, obtaining real-world crop state measurements is labor-intensive, time-consuming and expensive. In most cases, it is not feasible to gather crop state measurements before every decision moment. Moreover, in previous research pertaining to farm management optimization, these observations are often assumed to be readily available without any cost, which is unrealistic. Hence, enabling optimization without the need to have temporally complete crop state observations is important. An approach to that problem is to include measuring as part of decision making. As a solution, we apply reinforcement learning (RL) to recommend opportune moments to simultaneously measure crop features and apply nitrogen fertilizer. With realistic considerations, we design an RL environment with explicit crop feature measuring costs. While balancing costs, we find that an RL agent, trained with recurrent PPO, discovers adaptive measuring policies that follow critical crop development stages, with results aligned by what domain experts would consider a sensible approach. Our results highlight the importance of measuring when crop feature measurements are not readily available.
Exploring the heterogeneous impacts of Indonesia's conditional cash transfer scheme (PKH) on maternal health care utilisation using instrumental causal forests
2025-01-22
Vishalie Shah, Julia Hatamyar, Taufik Hidayat, Noemi Kreif
This paper uses instrumental causal forests, a novel machine learning method, to explore the treatment effect heterogeneity of Indonesia's conditional cash transfer scheme on maternal health care utilisation. Using randomised programme assignment as an instrument for enrollment in the scheme, we estimate conditional local average treatment effects for four key outcomes: good assisted delivery, delivery in a health care facility, pre-natal visits, and post-natal visits. We find significant treatment effect heterogeneity by supply-side characteristics, even though supply-side readiness was taken into account during programme development. Mothers in areas with more doctors, nurses, and delivery assistants were more likely to benefit from the programme, in terms of increased rates of good assisted delivery outcome. We also find large differences in benefits according to indicators of household poverty and survey wave, reflecting the possible impact of changes in programme design in its later years. The impact on post-natal visits in 2013 displayed the largest heterogeneity among all outcomes, with some women less likely to attend post-natal check ups after receiving the cash transfer in the long term.
2025-01-22
Vladyslav C. Usenko, Antonio Acín, Romain Alléaume, Ulrik L. Andersen, Eleni Diamanti, Tobias Gehring, Adnan A. E. Hajomer, Florian Kanitschar, Christoph Pacher, Stefano Pirandola, Valerio Pruneri
Tremendous progress in experimental quantum optics during the past decades enabled the advent of quantum technologies, one of which is quantum communication. Aimed at novel methods for more secure or efficient information transfer, quantum communication has developed into an active field of research and proceeds toward full-scale implementations and industrialization. Continuous-variable methods of multi-photon quantum state preparation, manipulation, and coherent detection, as well as the respective theoretical tools of phase-space quantum optics, offer the possibility to make quantum communication efficient, applicable and accessible, thus boosting the development of the field. We review the methodology, techniques and protocols of continuous-variable quantum communication, from the first theoretical ideas, through milestone implementations, to the recent developments, covering quantum key distribution as well as other quantum communication schemes, suggested on the basis of continuous-variable states and measurements.
Flexible tree-structured regression for clustered data with an application to quality of life in older adults
2025-01-22
Nikolai Spuck, Matthias Schmid, Moritz Berger
Tree-structured models are a powerful alternative to parametric regression models if non-linear effects and interactions are present in the data. Yet, classical tree-structured models might not be appropriate if data comes in clusters of units, which requires taking the dependence of observations into account. This is, for example, the case in cross-national studies, as presented here, where country-specific effects should not be neglected. To address this issue, we present a flexible tree-structured approach that achieves a sparse modeling of unit-specific effects and identifies subgroups (based on individual-level covariates) that differ with regard to the outcome. The methodological advances were motivated by the analysis of quality of life in older adults using data from the survey of Health, Ageing and Retirement in Europe. Application of the proposed model yields promising results and illustrated the accessibility of the approach. A comparison to alternative methods with regard to variable selection and goodness-of-fit was performed in several simulation experiments.
The Impact of Bar-induced Non-Circular Motions on the Measurement of Galactic Rotation Curves
2025-01-22
Jie Liu, Zhi Li, Juntai Shen
We study the impact of bar-induced non-circular motions on the derivation of galactic rotation curves (RCs) using hydrodynamic simulations and observational data from the PHANGS-ALMA survey. We confirm that non-circular motions induced by a bar can significantly bias RCs derived from the conventional tilted-ring method, consistent with previous findings. The shape of the derived RC depends on the position angle difference ($\Delta \phi$) between the major axes of the bar and the disk in the face-on plane. For $\left|\Delta \phi\right|\lesssim40^\circ$, non-circular motions produce a bar-induced "dip" feature (rise-drop-rise pattern) in the derived RC, which shows higher velocities near the nuclear ring and lower velocities in the bar region compared to the true RC (${\mathrm{RC_{true}}}$). We demonstrate convincingly that such dip features are very common in the PHANGS-ALMA barred galaxies sample. Hydrodynamical simulations reveal that the "dip" feature is caused by the perpendicular orientation of the gas flows in the nuclear ring and the bar; at low $\left|\Delta \phi\right|$ streamlines in the nuclear ring tend to enhance $V_\mathrm{los}$, while those in the bar tend to suppress $V_\mathrm{los}$. We use a simple {\misaell} model to qualitatively explain the general trend of RCs from the tilted-ring method (${\mathrm{RC_{tilted}}}$) and the discrepancy between ${\mathrm{RC_{tilted}}}$ and ${\mathrm{RC_{true}}}$. Furthermore, we propose a straightforward method to implement a first-order correction to the RC derived from the tilted-ring method. Our study is the first to systematically discuss the bar-induced "dip" feature in the RCs of barred galaxies combining both simulations and observations.
A Call for Critically Rethinking and Reforming Data Analysis in Empirical Software Engineering
2025-01-22
Matteo Esposito, Mikel Robredo, Murali Sridharan, Guilherme Horta Travassos, Rafael Peñaloza, Valentina Lenarduzzi
Context: Empirical Software Engineering (ESE) drives innovation in SE through qualitative and quantitative studies. However, concerns about the correct application of empirical methodologies have existed since the 2006 Dagstuhl seminar on SE. Objective: To analyze three decades of SE research, identify mistakes in statistical methods, and evaluate experts' ability to detect and address these issues. Methods: We conducted a literature survey of ~27,000 empirical studies, using LLMs to classify statistical methodologies as adequate or inadequate. Additionally, we selected 30 primary studies and held a workshop with 33 ESE experts to assess their ability to identify and resolve statistical issues. Results: Significant statistical issues were found in the primary studies, and experts showed limited ability to detect and correct these methodological problems, raising concerns about the broader ESE community's proficiency in this area. Conclusions. Despite our study's eventual limitations, its results shed light on recurring issues from promoting information copy-and-paste from past authors' works and the continuous publication of inadequate approaches that promote dubious results and jeopardize the spread of the correct statistical strategies among researchers. Besides, it justifies further investigation into empirical rigor in software engineering to expose these recurring issues and establish a framework for reassessing our field's foundation of statistical methodology application. Therefore, this work calls for critically rethinking and reforming data analysis in empirical software engineering, paving the way for our work soon.
An introduction to effective potential methods in field theory
2025-01-22
Isabella Masina, Mariano Quiros
In this chapter we give a pedagogical introduction to effective potential methods in field theories. We first review the general functional methods leading to the concept of effective action and effective potential. Focusing on the effective potential we review the methods to compute radiative contributions, starting from the original 1973 seminal paper by Sidney Coleman and Erick Weinberg on one-loop corrections, along with its improvement by the renormalization group equations, as well as some basic results from thermal field theory, including the one-loop thermal corrections to the effective potential. We concentrate on some physical applications of the effective potential: i) the possibility of spontaneous symmetry breaking by radiative corrections, ii) the metastability of the electroweak vacuum in the Standard Model, and iii) the relevance of quantum effects in the dynamics of Higgs inflation. We then apply the previous results to cosmological phase transitions in the early universe and the possibility of generating a stochastic background of gravitational waves as well as primordial black holes.
Quantum Emitters in Hexagonal Boron Nitride: Principles, Engineering and Applications
2025-01-22
Thi Ngoc Anh Mai, Md Shakhawath Hossain, Nhat Minh Nguyen, Yongliang Chen, Chaohao Chen, Xiaoxue Xu, Quang Thang Trinh, Toan Dinh, Toan Trong Tran
Solid-state quantum emitters, molecular-sized complexes releasing a single photon at a time, have garnered much attention owing to their use as a key building block in various quantum technologies. Among these, quantum emitters in hexagonal boron nitride (hBN) have emerged as front runners with superior attributes compared to other competing platforms. These attributes are attainable thanks to the robust, two-dimensional lattice of the material formed by the extremely strong B-N bonds. This review discusses the fundamental properties of quantum emitters in hBN and highlights recent progress in the field. The focus is on the fabrication and engineering of these quantum emitters facilitated by state-of-the-art equipment. Strategies to integrate the quantum emitters with dielectric and plasmonic cavities to enhance their optical properties are summarized. The latest developments in new classes of spin-active defects, their predicted structural configurations, and the proposed suitable quantum applications are examined. Despite the current challenges, quantum emitters in hBN have steadily become a promising platform for applications in quantum information science.
Cloud computing has emerged as a promising platform for running scientific workflows across various domains. Scientists can take advantage of different cloud service models, such as serverful or serverless, to execute workflows based on their specific requirements, along with diverse pricing models like on-demand, reserved, or spot instances to reduce execution costs. However, the challenge of selecting appropriate resources and pricing models, coupled with the orchestration and scheduling of workflow tasks, creates significant complexity for users. To mitigate this burden, Workflow as a Service (WaaS) brokers have been introduced to facilitate workflow execution. In recent years, numerous studies have been published, either directly or indirectly related to this research area, highlighting the need for a comprehensive and systematic review of WaaS brokers to identify key trends and challenges in this field. In this paper, we conduct a Systematic Literature Review (SLR) on WaaS brokers within cloud environments. The SLR employs a thorough 3-tier strategy (database search, backward snowballing, and forward snowballing) to answer five research questions. A total of 74 high-quality articles, published in 43 prestigious venues, are analyzed to derive a taxonomy based on the architecture of WaaS brokers. The articles are classified and surveyed according to this taxonomy, and future research directions for the design and implementation of WaaS brokers are explored. This study provides valuable insights for researchers and developers, helping them identify major trends and issues in the field of WaaS brokers.
The wide survey of the Chinese Space Station Telescope (CSST) will observe a large field of 17,500 $\text{deg}^2$. The GU, GV, and GI grism observations of CSST will cover a wavelength range from 2550 to 10000\r{A} at a resolution of $R\sim 200$ and a depth of about 22 AB magnitude for the continuum. In this paper, we present a pipeline to identify quasars and measure their physical properties with the CSST mock data. We simulate the raw images and extract the one-dimensional grism spectra for quasars, galaxies, and stars with the r-band magnitudes of $18<\text{m}_{\text{r}}<22$ using the CSST Cycle 6 simulation code. Using a convolution neural network, we separate quasars from stars and galaxies. We measure the redshifts by identifying the strong emission lines of quasars. We also fit the 1D slitless spectra with QSOFITMORE to estimate the black hole masses and Eddington ratios. Our results show that the CSST slitless spectroscopy can effectively separate quasars with redshifts $z=0-5$ from other types of objects with an accuracy of 99\%. Among those successfully classified quasars, 90\% of them could have precise redshift measurements with $\sigma_{\mathrm{NMAD}}=0.002$. The scatters of black hole masses and Eddington ratios from the spectral fittings are 0.13 and 0.15 dex, respectively. The metallicity diagnosis line ratios have a scatter of 0.1-0.2 dex. Our results show that the CSST slitless spectroscopy survey has the potential to discover about 0.9 million new quasars and provide important contributions to AGN science and cosmology.
On the equivalence between galaxy angular correlation function and power spectrum in constraining primordial non-Gaussianity
2025-01-22
Ryo Terasawa, Yue Nan, Masahiro Takada
We investigate the angular power spectrum ($C_\ell)$ and angular correlation function ($w(\theta)$) of galaxy number density field in the presence of the local-type primordial non-Gaussianity (PNG), explicitly accounting for the integral constraint in an all-sky survey. We show that the PNG signature in $C_{\ell}$ is confined to low multipoles in the linear regime, whereas its signature in $w(\theta)$ extends across a wide range of angular scales, including those below the nonlinear scale. Therefore, the equivalence between $C_\ell$ and $w(\theta)$ can be violated when scale cuts of multipoles or angular scales -- for example, to mitigate systematic effects -- are applied in the analysis. Assuming samples of photometric galaxies divided into multiple redshift bins in the range $0
The potential -- and the pitfalls -- of using pre-trained language models as cognitive science theories
2025-01-22
Raj Sanjay Shah, Sashank Varma
Many studies have evaluated the cognitive alignment of Pre-trained Language Models (PLMs), i.e., their correspondence to adult performance across a range of cognitive domains. Recently, the focus has expanded to the developmental alignment of these models: identifying phases during training where improvements in model performance track improvements in children's thinking over development. However, there are many challenges to the use of PLMs as cognitive science theories, including different architectures, different training data modalities and scales, and limited model interpretability. In this paper, we distill lessons learned from treating PLMs, not as engineering artifacts but as cognitive science and developmental science models. We review assumptions used by researchers to map measures of PLM performance to measures of human performance. We identify potential pitfalls of this approach to understanding human thinking, and we end by enumerating criteria for using PLMs as credible accounts of cognition and cognitive development.
Current Opinions on Memristor-Accelerated Machine Learning Hardware
2025-01-22
Mingrui Jiang, Yichun Xu, Zefan Li, Can Li
The unprecedented advancement of artificial intelligence has placed immense demands on computing hardware, but traditional silicon-based semiconductor technologies are approaching their physical and economic limit, prompting the exploration of novel computing paradigms. Memristor offers a promising solution, enabling in-memory analog computation and massive parallelism, which leads to low latency and power consumption. This manuscript reviews the current status of memristor-based machine learning accelerators, highlighting the milestones achieved in developing prototype chips, that not only accelerate neural networks inference but also tackle other machine learning tasks. More importantly, it discusses our opinion on current key challenges that remain in this field, such as device variation, the need for efficient peripheral circuitry, and systematic co-design and optimization. We also share our perspective on potential future directions, some of which address existing challenges while others explore untouched territories. By addressing these challenges through interdisciplinary efforts spanning device engineering, circuit design, and systems architecture, memristor-based accelerators could significantly advance the capabilities of AI hardware, particularly for edge applications where power efficiency is paramount.
Reconstructing the dark matter density, velocity, and tidal (MTV) fields from galaxy surveys is essential for advancing our understanding of the large-scale structure of the Universe. In this work, we present a machine learning-based framework using a UNet convolutional neural network to reconstruct the MTV fields from mock samples of the DESI bright galaxy survey within the redshift range $0.1 < z < 0.4$. Our approach accounts for realistic observational effects, including geometric selection, flux-limited data, and redshift space distortion (RSD) effects, thereby improving the fidelity of the reconstructed fields. Testing on mock galaxy catalogs generated from the Jiutian N-body simulation, our method achieves significant accuracy level. The reconstructed density field exhibits strong consistency with the true field, effectively eliminating most RSD effects and achieving a cross-correlation power spectrum coefficient greater than 0.985 on scales with $k < 0.1 \, h \, \mathrm{Mpc}^{-1}$. The velocity field reconstruction accurately captures large-scale coherent flows and small-scale turbulent features, exhibiting slopes of the grid-to-grid relationship close to unity and a scatter below $\sim$100 $\mathrm{km} \, \mathrm{s}^{-1}$. Additionally, the tidal field is reconstructed without bias, successfully recovering the features of the large-scale cosmic web, including clusters, filaments, sheets, and voids. Our results confirm that the proposed framework effectively captures the large-scale distribution and dynamics of dark matter while addressing key systematic challenges. These advancements provide a reliable and robust tool for analyzing current and future galaxy surveys, paving the way for new insights into cosmic structure formation and evolution.
Large language models (LLMs) have been positioned to revolutionize HCI, by reshaping not only the interfaces, design patterns, and sociotechnical systems that we study, but also the research practices we use. To-date, however, there has been little understanding of LLMs' uptake in HCI. We address this gap via a systematic literature review of 153 CHI papers from 2020-24 that engage with LLMs. We taxonomize: (1) domains where LLMs are applied; (2) roles of LLMs in HCI projects; (3) contribution types; and (4) acknowledged limitations and risks. We find LLM work in 10 diverse domains, primarily via empirical and artifact contributions. Authors use LLMs in five distinct roles, including as research tools or simulated users. Still, authors often raise validity and reproducibility concerns, and overwhelmingly study closed models. We outline opportunities to improve HCI research with and on LLMs, and provide guiding questions for researchers to consider the validity and appropriateness of LLM-related work.
In this work, we report for the first time two repeated quasi-periodic oscillations (QPOs) in the light curve of the Flat Spectrum Radio Quasar (FSRQ) S5 1044+71. This source was observed by the Transiting Exoplanet Survey Satellite (TESS) in multiple sectors. We used the generalized Lomb-Scargle periodogram method and weighted wavelet Z-transform method to search for significant periodic signals. The main results are as follows: We found QPOs of $\sim$ 7.0 days (persisted for 4 cycles, with a significance of $\sim3.5\sigma$) and $\sim$ 7.3 days (persisted for 5 cycles, with a significance of $\sim3.8\sigma$) in the light curves of Sector 47 and EP1, respectively. Considering range of error, we consider them to be the same. We discussed two likely models of these rapid quasi-periodic variations: One comes from the jet and the other from the accretion disk. For the first one, we consider kink instability of the jet as a plausible explanation. Second, the QPO is probable to come from the main hot spots in the accretion disk, which is located approximately within the innermost stable circular orbit allowed by general relativity. Based on this model, we estimate the mass of the black hole in S5 1044+71 to be $3.49 \times 10^9 M_{\odot}$.
Threat-based Security Controls to Protect Industrial Control Systems
2025-01-22
Maryam Karimi, Haritha Srinivasan
This paper analyzes the reported threats to Industrial Control Systems (ICS)/Operational Technology (OT) and identifies common tactics, techniques, and procedures (TTP) used by threat actors. The paper then uses the MITRE ATT&CK framework to map the common TTPs and provide an understanding of the security controls needed to defend against the reported ICS threats. The paper also includes a review of ICS testbeds and ideas for future research using the identified controls.
From Following to Understanding: Investigating the Role of Reflective Prompts in AR-Guided Tasks to Promote Task Understanding
2025-01-22
Nandi Zhang, Yukang Yan, Ryo Suzuki
Augmented Reality (AR) is a promising medium for guiding users through tasks, yet its impact on fostering deeper task understanding remains underexplored. This paper investigates the impact of reflective prompts -- strategic questions that encourage users to challenge assumptions, connect actions to outcomes, and consider hypothetical scenarios -- on task comprehension and performance. We conducted a two-phase study: a formative survey and co-design sessions (N=9) to develop reflective prompts, followed by a within-subject evaluation (N=16) comparing AR instructions with and without these prompts in coffee-making and circuit assembly tasks. Our results show that reflective prompts significantly improved objective task understanding and resulted in more proactive information acquisition behaviors during task completion. These findings highlight the potential of incorporating reflective elements into AR instructions to foster deeper engagement and learning. Based on data from both studies, we synthesized design guidelines for integrating reflective elements into AR systems to enhance user understanding without compromising task performance.
Investigating the Developer eXperience of LGBTQIAPN+ People in Agile Teams
2025-01-22
Edvaldo Wassouf Jr, Pedro Fukuda, Awdren Fontão
Diversity in software teams drives innovation and enhances performance, but it also introduces challenges that require intentional management. LGBTQIAPN+ professionals in the software industry face unique barriers, including discrimination, low visibility, and harassment, which can diminish satisfaction, productivity, and retention. This study investigates the Developer Experience (DX) of LGBTQIAPN+ individuals in Agile software development teams through a survey of 40 participants. Findings highlight that psychological safety and inclusive policies are critical for fostering equitable contributions and team cohesion. Agile practices, such as retrospectives, pair programming, and daily meetings, enhance collaboration and reduce biases when tailored to the needs of underrepresented groups, creating an environment of mutual respect and openness. Additionally, remote work offers significant benefits for LGBTQIAPN+ professionals, including improved psychological comfort, productivity, and work-life balance. However, challenges like isolation and insufficient virtual team interactions remain and must be addressed. This research underscores the importance of integrating inclusivity into Agile methodologies and organizational practices to support the unique needs of diverse professionals. By fostering an environment that values diversity, organizations can enable more effective and satisfied teams, ultimately driving higher-quality outcomes and improved organizational performance. This study provides actionable insights for creating more inclusive and supportive Agile work environments.
Giant planets dominate the mass of many planetary systems, including the Solar System, and represent the best-characterized class of extrasolar planets. Understanding the formation of giant planets bridges the high mass end of the planet formation process and the low mass end of processes that produce stellar and brown dwarf companions. This review examines the latest evidence supporting the formation of Solar System giant planets and most extrasolar giant planets by core accretion. Key elements of this theory and recent advances are discussed, along with the role of gravitational fragmentation of gas disks -- a mechanism more likely to produce brown dwarfs and/or similarly massive binary companions.
In the wake of major breakthroughs in General Relativity during the 1960s, Roger Penrose introduced Strong Cosmic Censorship, a profound conjecture regarding the deterministic nature of the theory. Penrose's proposal has since opened far-reaching new mathematical avenues, revealing connections to fundamental questions about black holes and the nature of gravitational singularities. We review recent advances arising from modern techniques in the theory of partial differential equations as applied to Strong Cosmic Censorship, maintaining a focus on the context of gravitational collapse that gave birth to the conjecture.
Asymptotic Freedom in Parton Language: the Birth of Perturbative QCD
2025-01-22
Stefano Forte
I review the contributions of Giorgio Parisi to perturbative QCD. Concentrated in a decade, they mark the transition of the theory of strong interactions from a set of loosely connected ideas based on models, to a quantum field theory that is now an integral part of the standard model of fundamental interactions. Parisi's contributions have established at a very early stage ideas, methods and tools that are now standard, and in several cases anticipated results that only became prominent in the XXIst century.
The Hubble Space Telescope Survey of M31 Satellite Galaxies IV. Survey Overview and Lifetime Star Formation Histories
2025-01-22
A. Savino, D. R. Weisz, A. E. Dolphin, M. J. Durbin, N. Kallivayalil, A. Wetzel, J. Anderson, G. Besla, M. Boylan-Kolchin, T. M. Brown, J. S. Bullock, A. A. Cole, M. L. M. Collins, M. C. Cooper, A. J. Deason, A. L. Dotter, M. Fardal, A. M. N. Ferguson, T. K. Fritz, M. C. Geha, K. M. Gilbert, P. Guhathakurta, R. Ibata, M. J. Irwin, M. Jeon, E. N. Kirby, G. F. Lewis, D. Mackey, S. R. Majewski, N. Martin, A. McConnachie, E. Patel, R. M. Rich, E. D. Skillman, J. D. Simon, S. T. Sohn, E. J. Tollerud, R. P. van der Marel
From $>1000$ orbits of HST imaging, we present deep homogeneous resolved star color-magnitude diagrams that reach the oldest main sequence turnoff and uniformly measured star formation histories (SFHs) of 36 dwarf galaxies ($-6 \ge M_V \ge -17$) associated with the M31 halo, and for 10 additional fields in M31, M33, and the Giant Stellar Stream. From our SFHs we find: i) the median stellar age and quenching epoch of M31 satellites correlate with galaxy luminosity and galactocentric distance. Satellite luminosity and present-day distance from M31 predict the satellite quenching epoch to within $1.8$ Gyr at all epochs. This tight relationship highlights the fundamental connection between satellite halo mass, environmental history, and star formation duration. ii) There is no difference between the median SFH of galaxies on and off the great plane of Andromeda satellites. iii) $\sim50$\% of our M31 satellites show prominent ancient star formation ($>12$ Gyr ago) followed by delayed quenching ($8-10$ Gyr ago), which is not commonly observed among the MW satellites. iv) A comparison with TNG50 and FIRE-2 simulated satellite dwarfs around M31-like hosts show that some of these trends (dependence of SFH on satellite luminosity) are reproduced in the simulations while others (dependence of SFH on galactocentric distance, presence of the delayed-quenching population) are weaker or absent. We provide all photometric catalogs and SFHs as High-Level Science Products on MAST.
On the reproducibility of discrete-event simulation studies in health research: an empirical study using open models
2025-01-22
Amy Heather, Thomas Monks, Alison Harper, Navonil Mustafee, Andrew Mayne
Reproducibility of computational research is critical for ensuring transparency, reliability and reusability. Challenges with computational reproducibility have been documented in several fields, but healthcare discrete-event simulation (DES) models have not been thoroughly examined in this context. This study assessed the computational reproducibility of eight published healthcare DES models (Python or R), selected to represent diverse contexts, complexities, and years of publication. Repositories and articles were also assessed against guidelines and reporting standards, offering insights into their relationship with reproducibility success. Reproducing results required up to 28 hours of troubleshooting per model, with 50% fully reproduced and 50% partially reproduced (12.5% to 94.1% of reported outcomes). Key barriers included the absence of open licences, discrepancies between reported and coded parameters, and missing code to produce model outputs, run scenarios, and generate tables and figures. Addressing these issues would often require relatively little effort from authors: adding an open licence and sharing all materials used to produce the article. Actionable recommendations are proposed to enhance reproducibility practices for simulation modellers and reviewers.
Applications and Challenges of AI and Microscopy in Life Science Research: A Review
2025-01-22
Himanshu Buckchash, Gyanendra Kumar Verma, Dilip K. Prasad
The complexity of human biology and its intricate systems holds immense potential for advancing human health, disease treatment, and scientific discovery. However, traditional manual methods for studying biological interactions are often constrained by the sheer volume and complexity of biological data. Artificial Intelligence (AI), with its proven ability to analyze vast datasets, offers a transformative approach to addressing these challenges. This paper explores the intersection of AI and microscopy in life sciences, emphasizing their potential applications and associated challenges. We provide a detailed review of how various biological systems can benefit from AI, highlighting the types of data and labeling requirements unique to this domain. Particular attention is given to microscopy data, exploring the specific AI techniques required to process and interpret this information. By addressing challenges such as data heterogeneity and annotation scarcity, we outline potential solutions and emerging trends in the field. Written primarily from an AI perspective, this paper aims to serve as a valuable resource for researchers working at the intersection of AI, microscopy, and biology. It summarizes current advancements, key insights, and open problems, fostering an understanding that encourages interdisciplinary collaborations. By offering a comprehensive yet concise synthesis of the field, this paper aspires to catalyze innovation, promote cross-disciplinary engagement, and accelerate the adoption of AI in life science research.
Synthetic CT image generation from CBCT: A Systematic Review
2025-01-22
Alzahra Altalib, Scott McGregor, Chunhui Li, Alessandro Perelli
The generation of synthetic CT (sCT) images from cone-beam CT (CBCT) data using deep learning methodologies represents a significant advancement in radiation oncology. This systematic review, following PRISMA guidelines and using the PICO model, comprehensively evaluates the literature from 2014 to 2024 on the generation of sCT images for radiation therapy planning in oncology. A total of 35 relevant studies were identified and analyzed, revealing the prevalence of deep learning approaches in the generation of sCT. This review comprehensively covers synthetic CT generation based on CBCT and proton-based studies. Some of the commonly employed architectures explored are convolutional neural networks (CNNs), generative adversarial networks (GANs), transformers, and diffusion models. Evaluation metrics including mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) consistently demonstrate the comparability of sCT images with gold-standard planning CTs (pCT), indicating their potential to improve treatment precision and patient outcomes. Challenges such as field-of-view (FOV) disparities and integration into clinical workflows are discussed, along with recommendations for future research and standardization efforts. In general, the findings underscore the promising role of sCT-based approaches in personalized treatment planning and adaptive radiation therapy, with potential implications for improved oncology treatment delivery and patient care.
Radiology plays a pivotal role in modern medicine due to its non-invasive diagnostic capabilities. However, the manual generation of unstructured medical reports is time consuming and prone to errors. It creates a significant bottleneck in clinical workflows. Despite advancements in AI-generated radiology reports, challenges remain in achieving detailed and accurate report generation. In this study we have evaluated different combinations of multimodal models that integrate Computer Vision and Natural Language Processing to generate comprehensive radiology reports. We employed a pretrained Vision Transformer (ViT-B16) and a SWIN Transformer as the image encoders. The BART and GPT-2 models serve as the textual decoders. We used Chest X-ray images and reports from the IU-Xray dataset to evaluate the usability of the SWIN Transformer-BART, SWIN Transformer-GPT-2, ViT-B16-BART and ViT-B16-GPT-2 models for report generation. We aimed at finding the best combination among the models. The SWIN-BART model performs as the best-performing model among the four models achieving remarkable results in almost all the evaluation metrics like ROUGE, BLEU and BERTScore.
Period Analysis of Eclipsing Cataclysmic Variable Stars
2025-01-21
Mennatalla Mahmoud Ellaqany, Valeria Garcia-Lopez, Emily S. Hatten, Mridul Agarwal, David A. Moffett
We have performed a study of the orbital properties of seven eclipsing cataclysmic variable (CV) binary systems by analyzing photometric time series from the Transiting Exoplanet Survey Satellite (TESS). We employed Python code to determine the eclipse epochs and orbital periods for each system, and constructed O-C diagrams from observed and predicted eclipse epochs. By analyzing the O-C diagrams of our target CVs, we have constrained values for changes in orbital period with time. Our targets include a sample of sources from each class of non-magnetic, eclipsing CVs: dwarf novae variables, Z Cam type, and U Gem subclasses. We include in our study classical novae variables, nova-like variables (including the VY Scl and UX UMa subclasses), and recurrent novae variable stars. We approached this project with goals of developing time series analysis techniques for future undergraduate-level studies of eclipsing CVs, and how they may contribute to the understanding of their orbital evolution.
History-Deterministic Parity Automata: Games, Complexity, and the 2-Token Theorem
2025-01-21
Aditya Prakash
History-deterministic automata are a restricted class of nondeterministic automata where the nondeterminism while reading an input can be resolved successfully based on the prefix read so far. History-deterministic automata are exponentially more succinct than deterministic automata, while still retaining some of the algorithmic properties of deterministic automata, especially in the context of reactive synthesis. This thesis focuses on the problem of checking history-determinism for parity automata. Our main result is the 2-token theorem, due to which we obtain that checking history-determinism for parity automata with a fixed parity index can be checked in PTIME. This improves the naive EXPTIME upper bound of Henzinger and Piterman that has stood since 2006. More precisely, we show that the so-called 2-token game, which can be solved in PTIME for parity automata with a fixed parity index, characterises history-determinism for parity automata. This game was introduced by Bagnol and Kuperberg in 2018, who showed that to decide if a B\"uchi automaton is history-deterministic, it suffices to find the winner of the 2-token game on it. They conjectured that this 2-token game based characterisation of history-determinism extends to parity automata. We prove Bagnol and Kuperberg's conjecture that the winner of the 2-token game characterises history-determinism on parity automata. We also give a polynomial time determinisation procedure for history-deterministic B\"uchi automata, thus solving an open problem of Kuperberg and Skrzypczak from 2015. This result is a consequence of our proof of the 2-token theorem. Finally, we also show NP-hardness for the problem of checking history-determinism for parity automata when the parity index is not fixed. This is an improvement from the lower bound of solving parity games shown by Kuperberg and Skrzypczak in 2015.
Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems
2025-01-21
Stefano Carlo Lambertenghi, Hannes Leonhard, Andrea Stocco
Advanced Driver Assistance Systems (ADAS) based on deep neural networks (DNNs) are widely used in autonomous vehicles for critical perception tasks such as object detection, semantic segmentation, and lane recognition. However, these systems are highly sensitive to input variations, such as noise and changes in lighting, which can compromise their effectiveness and potentially lead to safety-critical failures. This study offers a comprehensive empirical evaluation of image perturbations, techniques commonly used to assess the robustness of DNNs, to validate and improve the robustness and generalization of ADAS perception systems. We first conducted a systematic review of the literature, identifying 38 categories of perturbations. Next, we evaluated their effectiveness in revealing failures in two different ADAS, both at the component and at the system level. Finally, we explored the use of perturbation-based data augmentation and continuous learning strategies to improve ADAS adaptation to new operational design domains. Our results demonstrate that all categories of image perturbations successfully expose robustness issues in ADAS and that the use of dataset augmentation and continuous learning significantly improves ADAS performance in novel, unseen environments.
Video Deblurring by Sharpness Prior Detection and Edge Information
2025-01-21
Yang Tian, Fabio Brau, Giulio Rossolini, Giorgio Buttazzo, Hao Meng
Video deblurring is essential task for autonomous driving, facial recognition, and security surveillance. Traditional methods directly estimate motion blur kernels, often introducing artifacts and leading to poor results. Recent approaches utilize the detection of sharp frames within video sequences to enhance deblurring. However, existing datasets rely on fixed number of sharp frames, which may be too restrictive for some applications and may introduce a bias during model training. To address these limitations and enhance domain adaptability, this work first introduces GoPro Random Sharp (GoProRS), a new dataset where the the frequency of sharp frames within the sequence is customizable, allowing more diverse training and testing scenarios. Furthermore, it presents a novel video deblurring model, called SPEINet, that integrates sharp frame features into blurry frame reconstruction through an attention-based encoder-decoder architecture, a lightweight yet robust sharp frame detection and an edge extraction phase. Extensive experimental results demonstrate that SPEINet outperforms state-of-the-art methods across multiple datasets, achieving an average of +3.2% PSNR improvement over recent techniques. Given such promising results, we believe that both the proposed model and dataset pave the way for future advancements in video deblurring based on the detection of sharp frames.
Noise budget of a trapped on chip cold atom Rubidium 87 clock
2025-01-21
M. Dupont-Nivet, B. Wirtschafter, S. Hello, C. I. Westbrook
In this paper, we present a realisation of an on chip atomic clock using a cold cloud of Rubidium 87 atoms. This clock is based on a Ramsey interferometer with a Ramsey time around 600 ms. This is realized with large lab temperature drift during the measurement (few degrees per day) and without magnetic field shielding. We review the experimental implementation of this clock and give a full study of the known noises present in this atomic clock.
As artificial intelligence systems become increasingly integrated into daily life, the field of explainability has gained significant attention. This trend is particularly driven by the complexity of modern AI models and their decision-making processes. The advent of foundation models, characterized by their extensive generalization capabilities and emergent uses, has further complicated this landscape. Foundation models occupy an ambiguous position in the explainability domain: their complexity makes them inherently challenging to interpret, yet they are increasingly leveraged as tools to construct explainable models. In this survey, we explore the intersection of foundation models and eXplainable AI (XAI) in the vision domain. We begin by compiling a comprehensive corpus of papers that bridge these fields. Next, we categorize these works based on their architectural characteristics. We then discuss the challenges faced by current research in integrating XAI within foundation models. Furthermore, we review common evaluation methodologies for these combined approaches. Finally, we present key observations and insights from our survey, offering directions for future research in this rapidly evolving field.
X-ray emission from isolated neutron stars: latest results from XMM-Newton, NICER and eROSITA
2025-01-21
Michela Rigoselli
The X-ray spectra of isolated neutron stars (INSs) typically include a thermal component, that comes from the cooling surface, and a non-thermal component, produced by highly-relativistic particles accelerated in the stellar magnetosphere. Hot spots from returning currents can also be detected. Middle-aged pulsars exhibit a mixture of these components, but other flavours of INSs, that show a large variety of physical parameters (such as spin period, magnetic field and age) emit only thermal X-rays. Typically, these stars are detected either in large serendipitous datasets from pointed X-ray observations or from searches in the data of all-sky surveys. The connection between these thermally-emitting INSs, the ordinary pulsars, and the new emergent class of pulsars characterized by a long period, that do not show X-ray emission despite their high magnetic field, is one of the current challenges in the study of neutron stars. In this contribution I will review the latest results on several objects belonging to various INS classes, such as the XDINS RX J1308.6+2127, the enigmatic Calvera, the long period PSR J0250+5854 and the new thermal INS candidates, obtained with the X-ray observatories XMM-Newton, NICER and eROSITA.
Neutron correlations and clustering in neutron-rich nuclear systems
2025-01-21
Zaihong Yang, Yuki Kubota
In this paper, we will briefly review the recent progress on neutron correlations and clustering in neutron-rich nuclei from quasi-free scattering experiments. The quasi-free ($p$, $pn$) reaction was measured for Borromean nuclei $^{11}$Li, $^{14}$Be, and $^{17}$B. A surprisingly small $s$-wave component was found for $^{17}$B, revealing a weak neutron halo in $^{17}$B, and the comparative study of the three nuclei shows the surface localization of dineutron correlation and its universality in Borromean nuclei. The two-neutron emission of $^{16}$Be was also studied, finding strong dineutron correlation in $^{16}$Be(g.s.) but weak correlation in $^{16}$Be($2^+$). Two missing-mass measurements using $^{4}$He($^{8}$He, $^{8}$Be) and $^{8}$He($p$, $p\alpha$) reactions provided evidence for the $^{4}n$ resonance, but the $^{3}n$ resonance was not supported by the recent experiment employing the $t(t,^3$He)$^3n$ charge-exchange reaction. The quasi-free $(p,p \alpha)$ reaction has been extended to unstable nuclei, and the recent experiment unravels the $\alpha$-$2n$-$\alpha$ molecule-like cluster structure of $^{10}$Be(g.s.).
2025-01-21
Peter P. Rohde, Zixin Huang, Yingkai Ouyang, He-Liang Huang, Zu-En Su, Simon Devitt, Rohit Ramakrishnan, Atul Mantri, Si-Hui Tan, Nana Liu, Scott Harrison, Chandrashekar Radhakrishnan, Gavin K. Brennen, Ben Q. Baragiola, Jonathan P. Dowling, Tim Byrnes, William J. Munro
Following the emergence of quantum computing, the subsequent quantum revolution will be that of interconnecting individual quantum computers at global level. In the same way that classical computers only realised their full potential with the emergence of the internet, a fully realised quantum internet is the next stage of evolution for quantum computation. This work examines in detail how the quantum internet would evolve in practice, focusing not only on the technology itself but also on the implications it will have economically and politically. We present both original ideas, as well as an extensive review of relevant and related background material. This work begins with a description of classical networks before introducing the key concepts behind quantum networks, such as quantum internet protocols, quantum cryptography, and cloud quantum computing. The work is divided into technical sections (requiring only a basic knowledge of the notation of quantum mechanics), for those interested in mathematical details, as well as non-technical sections for those seeking a more general understanding. We target this work very broadly at quantum and classical computer scientists, classical computer systems, software and network engineers, physicists, economists, artists, musicians, and those just generally curious about the future of quantum technologies and what they might bring to humanity.
2025-01-21
Isabelle Bouchoule, Roberta Citro, Tim Duty, Thierry Giamarchi, Randall G. Hulet, Martin Klanjsek, Edmond Orignac, Bent Weber
The concept of a Tomonaga-Luttinger liquid (TLL) has been established as a fundamental theory for the understanding of one-dimensional quantum systems. Originally formulated as a replacement for Landau's Fermi-liquid theory, which accurately predicts the behaviour of most 3D metals but fails dramatically in 1D, the TLL description applies to a even broader class of 1D systems,including bosons and anyons. After a certain number of theoretical breakthroughs, its descriptive power has now been confirmed experimentally in different experimental platforms. They extend from organic conductors, carbon nanotubes, quantum wires, topological edge states of quantum spin Hall insulators to cold atoms, Josephson junctions, Bose liquids confined within 1D nanocapillaries and spin chains. In the ground state of such systems, quantum fluctuations become correlated on all length scales, but, counter-intuitively, no long-range order exists. In this respect, this review will illustrate the validity of conformal field theory for describing real-world systems, establishing the boundaries for its application and, on the other side will discuss the spectacular demonstration of how the quantum-critical TLL state governs the properties of many-body systems in one dimension.
Code pre-trained models have shown promising effectiveness in various software engineering tasks. Among these tasks, many tasks are related to software evolution and/or code editing. However, existing code pre-trained models often overlook the real-world code editing data and the evolutionary nature of the editing process. In this paper, to simulate the step-by-step code editing process of human developers, we propose DivoT5, a pre-trained model based on directional diffusion at the data level. In DivoT5, we adopt two categories of pre-training tasks. The first category is mask and denoising tasks augmented with a diffusion direction representing code evolution. That is, we first apply a noising process to the code snippets before evolution, and then ask the pre-training process to restore the snippets with noise into the code snippets after evolution. The second category is tasks aiming to reinforce the evolutionary direction. That is, we first generate various intermediate versions for each pair of snippets before and after evolution, and then ask the pre-training process to transform the intermediate versions into the snippet after evolution for each pair. We evaluate DivoT5 for two code-editing scenarios and one non-editing scenario using five downstream tasks. Given each downstream task, we fine-tune the pre-trained DivoT5 to evaluate its effectiveness. Our experimental results show that DivoT5 achieves state-of-the-art (SOTA) performance on most tasks in comparison to models of the same scale (220M), large scale (770M) models in fine-tuning, and billion-scale (6.7B, 8B, ChatGPT) models in few-shot settings. For one code-editing task (i.e., automated code review), DivoT5 pre-trained on top of CodeT5-small (60M) can even outperform CodeT5-base (220M) and other pre-trained models with 220M parameters except for DivoT5 pre-trained on top of CodeT5-base (220M).
Advancing Earth Observation: A Survey on AI-Powered Image Processing in Satellites
2025-01-21
Aidan Duggan, Bruno Andrade, Haithem Afli
Advancements in technology and reduction in it's cost have led to a substantial growth in the quality & quantity of imagery captured by Earth Observation (EO) satellites. This has presented a challenge to the efficacy of the traditional workflow of transmitting this imagery to Earth for processing. An approach to addressing this issue is to use pre-trained artificial intelligence models to process images on-board the satellite, but this is difficult given the constraints within a satellite's environment. This paper provides an up-to-date and thorough review of research related to image processing on-board Earth observation satellites. The significant constraints are detailed along with the latest strategies to mitigate them.
Reference-free Evaluation Metrics for Text Generation: A Survey
2025-01-21
Takumi Ito, Kees van Deemter, Jun Suzuki
A number of automatic evaluation metrics have been proposed for natural language generation systems. The most common approach to automatic evaluation is the use of a reference-based metric that compares the model's output with gold-standard references written by humans. However, it is expensive to create such references, and for some tasks, such as response generation in dialogue, creating references is not a simple matter. Therefore, various reference-free metrics have been developed in recent years. In this survey, which intends to cover the full breadth of all NLG tasks, we investigate the most commonly used approaches, their application, and their other uses beyond evaluating models. The survey concludes by highlighting some promising directions for future research.
The mass-dependent UVJ diagram at cosmic noon: A challenge for galaxy evolution models and dust radiative transfer
2025-01-21
Andrea Gebek, Benedikt Diemer, Marco Martorano, Arjen van der Wel, Lara Pantoni, Maarten Baes, Austen Gabrielpillai, Anand Utsav Kapoor, Calvin Osinga, Angelos Nersesian, Kosei Matsumoto, Karl Gordon
Context. The UVJ color-color diagram is a widely used diagnostic to separate star-forming and quiescent galaxies. Observational data from photometric surveys reveal a strong stellar mass trend, with higher-mass star-forming galaxies being systematically more dust-reddened. Aims. We analyze the UVJ diagram in the TNG100 cosmological simulation at cosmic noon ($z\approx2$). Specifically, we focus on the trend between UVJ colors and mass which has so far not been reproduced in any cosmological simulation. Methods. We apply the SKIRT dust radiative transfer code to the TNG100 simulation to generate rest-frame UVJ fluxes. These UVJ colors are then compared to observational data from several well-studied extragalactic fields from the CANDELS/3D-HST programs, augmented by recent JWST/NIRCam photometry. Results. Quiescent and low-mass ($M_\star\lesssim10^{10.5}\,\mathrm{M}_\odot$) galaxies at cosmic noon do not require significant levels of dust reddening, as opposed to massive ($M_\star\gtrsim10^{11}\,\mathrm{M}_\odot$) star-forming galaxies. An extensive range of possible dust models fall short of the required dust reddening in V-J color for massive star-forming galaxies, with the simulated galaxies being too blue by $\approx0.9\,\mathrm{mag}$. Conclusions. We find that only variations in the star-to-dust geometries of the simulated galaxies can yield V-J colors that are red enough to match the observations. A toy model with isolated dust screens around younger stellar populations (with ages below $\sim1\,\mathrm{Gyr}$) can reproduce the observational data, while all conventional dust radiative transfer models (where the dust distribution follows the metals in the interstellar medium) fail to achieve the required V-J colors.
The Dilemma of Privacy Protection for Developers in the Metaverse
2025-01-21
Argianto Rahartomo, Leonel Merino, Mohammad Ghafari, Yoshiki Ohshima
To investigate the level of support and awareness developers possess for dealing with sensitive data in the metaverse, we surveyed developers, consulted legal frameworks, and analyzed API documentation in the metaverse. Our preliminary results suggest that privacy is a major concern, but developer awareness and existing support are limited. Developers lack strategies to identify sensitive data that are exclusive to the metaverse. The API documentation contains guidelines for collecting sensitive information, but it omits instructions for identifying and protecting it. Legal frameworks include definitions that are subject to individual interpretation. These findings highlight the urgent need to build a transparent and common ground for privacy definitions, identify sensitive data, and implement usable protection measures.
The SPHERE infrared survey for exoplanets (SHINE). V. Complete observations, data reduction and analysis, detection performances, and final results
2025-01-21
A. Chomez, P. Delorme, A. -M. Lagrange, R. Gratton, O. Flasseur, G. Chauvin, M. Langlois, J. Mazoyer, A. Zurlo, S. Desidera, D. Mesa, M. Bonnefoy, M. Feldt, J. Hagelberg, M. Meyer, A. Vigan, C. Ginski, M. Kenworthy, D. Albert, S. Bergeon, J. -L. Beuzit, B. Biller, T. Bhowmik, A. Boccaletti, M. Bonavita, W. Brandner, F. Cantalloube, A. Cheetham, V. D'Orazi, C. Dominik, C. Fontanive, R. Galicher, Th. Henning, M. Janson, Q. Kral, E. Lagadec, C. Lazzoni, H. Le Coroller, R. Ligi, A. -L. Maire, G. -D. Marleau, F. Menard, S. Messina, N. Meunier, C. Mordasini, C. Moutou, A. Müller, C. Perrot, M. Samland, H. M. Schmid, T. Schmidt, V. Squicciarini, E. Sissa, M. Turatto, S. Udry, L. Abe, J. Antichi, R. Asensio-Torres, A. Baruffolo, P. Baudoz, J. Baudrand, A. Bazzon, P. Blanchard, A. J. Bohn, S. Brown Sevilla, M. Carbillet, M. Carle, E. Cascone, J. Charton, R. Claudi, A. Costille, V. De Caprio, A. Delboulbe, K. Dohlen, N. Engler, D. Fantinel, P. Feautrier, T. Fusco, P. Gigan, J. H. Girard, E. Giro, D. Gisler, L. Gluck, C. Gry, N. Hubin, E. Hugot, M. Jaquet, M. Kasper, D. Le Mignant, M. Llored, F. Madec, Y. Magnard, P. Martinez, D. Maurel, O. Möller-Nilsson, D. Mouillet, T. Moulin, A. Origné, A. Pavlov, D. Perret, C. Petit, J. Pragt, P. Puget, P. Rabou, J. Ramos, E. L. Rickman, F. Rigal, S. Rochat, R. Roelfsema, G. Rousset, A. Roux, B. Salasnich, J. -F. Sauvage, A. Sevin, C. Soenke, E. Stadler, M. Suarez, Z. Wahhaj, L. Weber, F. Wildi
During the past decade, state-of-the-art planet-finder instruments like SPHERE@VLT, coupling coronagraphic devices and extreme adaptive optics systems, unveiled, thanks to large surveys, around 20 planetary mass companions at semi-major axis greater than 10 astronomical units. Direct imaging being the only detection technique to be able to probe this outer region of planetary systems, the SPHERE infrared survey for exoplanets (SHINE) was designed and conducted from 2015 to 2021 to study the demographics of such young gas giant planets around 400 young nearby solar-type stars. In this paper, we present the observing strategy, the data quality, and the point sources analysis of the full SHINE statistical sample as well as snapSHINE. Both surveys used the SPHERE@VLT instrument with the IRDIS dual band imager in conjunction with the integral field spectrograph IFS and the angular differential imaging observing technique. All SHINE data (650 datasets), corresponding to 400 stars, including the targets of the F150 survey, are processed in a uniform manner with an advanced post-processing algorithm called PACO ASDI. An emphasis is put on the classification and identification of the most promising candidate companions. Compared to the previous early analysis SHINE F150, the use of advanced post-processing techniques significantly improved by one or 2 magnitudes (x3-x6) the contrast detection limits, which will allow us to put even tighter constraints on the radial distribution of young gas giants. This increased sensitivity directly places SHINE as the largest and deepest direct imaging survey ever conducted. We detected and classified more than 3500 physical sources. One additional substellar companion has been confirmed during the second phase of the survey (HIP 74865 B), and several new promising candidate companions are awaiting second epoch confirmations.
Hand gesture recognition has become an important research area, driven by the growing demand for human-computer interaction in fields such as sign language recognition, virtual and augmented reality, and robotics. Despite the rapid growth of the field, there are few surveys that comprehensively cover recent research developments, available solutions, and benchmark datasets. This survey addresses this gap by examining the latest advancements in hand gesture and 3D hand pose recognition from various types of camera input data including RGB images, depth images, and videos from monocular or multiview cameras, examining the differing methodological requirements of each approach. Furthermore, an overview of widely used datasets is provided, detailing their main characteristics and application domains. Finally, open challenges such as achieving robust recognition in real-world environments, handling occlusions, ensuring generalization across diverse users, and addressing computational efficiency for real-time applications are highlighted to guide future research directions. By synthesizing the objectives, methodologies, and applications of recent studies, this survey offers valuable insights into current trends, challenges, and opportunities for future research in human hand gesture recognition.
We discuss string theory methods for the study of strongly coupled holographic defect conformal field theories (CFTs) which are dual to probe-brane systems. First, we examine whether the string theory duals of such defect CFTs are classically integrable. This amounts to identifying all string boundary conditions on probe branes which preserve the integrability of the underlying Green-Schwarz sigma model. Second, we compute holographic defect correlation functions with semiclassical strings. After reviewing the computation of three and higher-point functions in strongly coupled AdS/CFT, and one-point functions in its probe-brane descendants, we outline the computation of two and higher-point correlation functions in strongly coupled defect CFTs.
Basic aspects of ferroelectricity induced by noncollinear alignment of spins
2025-01-21
I. V. Solovyev
Basic principles of ferroelectric activity induced by the noncollinear spins are reviewed. There is a fundamental reason why the inversion symmetry can be broken by magnetic order. Such situation occurs when the magnetic order simultaneously involves ferromagnetic ($F$) and antiferromagnetic ($A$) patterns, transforming under the spatial inversion $\mathcal{I}$ and time reversal $\mathcal{T}$ as $\mathcal{I}F=F$ and $\mathcal{IT}A=A$. The incompatibility of these two conditions breaks the inversion symmetry, imposing a constraint on possible dependencies of polarization on directions of spins, which can include only antisymmetric coupling and single-ion anisotropy in the from $\vec{P} = \vec{\boldsymbol{\mathcal{P}}}_{12} [ \boldsymbol{e}_{1} \times \boldsymbol{e}_{2} ] + \boldsymbol{e}_{1} \vec{\mathbb{\Pi}} \boldsymbol{e}_{1} - \boldsymbol{e}_{2} \vec{\mathbb{\Pi}} \boldsymbol{e}_{2}$. $\vec{\boldsymbol{\mathcal{P}}}_{12}$ can be evaluated in the framework of superexchange theory, resulting in $\vec{\boldsymbol{\mathcal{P}}}_{12} \sim \vec{\boldsymbol{r}}_{12}^{\phantom{0}}$, where $\vec{\boldsymbol{r}}_{12}^{\phantom{0}}$ is the part of the position operator produced by the spin-orbit coupling. $\vec{\boldsymbol{r}}_{12}$ remains invariant under $\mathcal{I}$, explaining why noncollinear spins can induce $\vec{P}$ even in the centrosymmetric case. The properties of $\vec{\boldsymbol{r}}_{12}$ are rationalized from the viewpoint of symmetry of the Kramers states. The Katsura-Nagaosa-Balatsky rule $\vec{P} \propto \vec{\epsilon}_{21} \times [\boldsymbol{e}_{1} \times \boldsymbol{e}_{2}]$ ($\vec{\epsilon}_{21}$ being the bond direction) is justified only for relatively high symmetry. The single-ion anisotropy vanishes for the spin 1/2 or if magnetic ions are located in the inversion centers. The properties of known multiferroics are reconsidered from the viewpoint of these principles.
A Hybrid Attention Framework for Fake News Detection with Large Language Models
2025-01-21
Xiaochuan Xu, Peiyang Yu, Zeqiu Xu, Jiani Wang
With the rapid growth of online information, the spread of fake news has become a serious social challenge. In this study, we propose a novel detection framework based on Large Language Models (LLMs) to identify and classify fake news by integrating textual statistical features and deep semantic features. Our approach utilizes the contextual understanding capability of the large language model for text analysis and introduces a hybrid attention mechanism to focus on feature combinations that are particularly important for fake news identification. Extensive experiments on the WELFake news dataset show that our model significantly outperforms existing methods, with a 1.5\% improvement in F1 score. In addition, we assess the interpretability of the model through attention heat maps and SHAP values, providing actionable insights for content review strategies. Our framework provides a scalable and efficient solution to deal with the spread of fake news and helps build a more reliable online information ecosystem.
Assessing Teamwork Dynamics in Software Development Projects
2025-01-21
Santiago Berrezueta-Guzman, Ivan Parmacli, Mohammad Kasra Habib, Stephan Krusche, Stefan Wagner
This study investigates teamwork dynamics in student software development projects through a mixed-method approach combining quantitative analysis of GitLab commit logs and qualitative survey data. We analyzed individual contributions across six project phases, comparing self-reported and actual contributions to measure discrepancies. Additionally, a survey captured insights on team leadership, conflict resolution, communication practices, and workload perceptions. Findings reveal that teams with minimal contribution discrepancies achieved higher project grades and exam pass rates. In contrast, teams with more significant discrepancies experienced lower performance, potentially due to role clarity and communication issues. These results underscore the value of shared leadership, structured conflict resolution, and regular feedback in fostering effective teamwork, offering educators strategies to enhance collaboration in software engineering education through self-reflection and balanced workload allocation.
A Contrastive Framework with User, Item and Review Alignment for Recommendation
2025-01-21
Hoang V. Dong, Yuan Fang, Hady W. Lauw
Learning effective latent representations for users and items is the cornerstone of recommender systems. Traditional approaches rely on user-item interaction data to map users and items into a shared latent space, but the sparsity of interactions often poses challenges. While leveraging user reviews could mitigate this sparsity, existing review-aware recommendation models often exhibit two key limitations. First, they typically rely on reviews as additional features, but reviews are not universal, with many users and items lacking them. Second, such approaches do not integrate reviews into the user-item space, leading to potential divergence or inconsistency among user, item, and review representations. To overcome these limitations, our work introduces a Review-centric Contrastive Alignment Framework for Recommendation (ReCAFR), which incorporates reviews into the core learning process, ensuring alignment among user, item, and review representations within a unified space. Specifically, we leverage two self-supervised contrastive strategies that not only exploit review-based augmentation to alleviate sparsity, but also align the tripartite representations to enhance robustness. Empirical studies on public benchmark datasets demonstrate the effectiveness and robustness of ReCAFR.
Build Optimization: A Systematic Literature Review
2025-01-21
Henri Aïdasso, Mohammed Sayagh, Francis Bordeleau
Continuous Integration (CI) consists of an automated build process involving continuous compilation, testing, and packaging of the software system. While CI comes up with several advantages related to quality and time to delivery, CI also presents several challenges addressed by a large body of research. To better understand the literature so as to help practitioners find solutions for their problems and guide future research, we conduct a systematic review of 97 studies on build optimization published between 2006 and 2024, which we summarized according to their goals, methodologies, used datasets, and leveraged metrics. The identified build optimization studies focus on two main challenges: (1) long build durations, and (2) build failures. To meet the first challenge, existing studies have developed a range of techniques, including predicting build outcome and duration, selective build execution, and build acceleration using caching or repairing performance smells. The causes of build failures have been the subject of several studies, leading to the development of techniques for predicting build script maintenance and automating repair. Recent studies have also focused on predicting flaky build failures caused by environmental issues. The majority of these techniques use machine learning algorithms and leverage build metrics, which we classify into five categories. Additionally, we identify eight publicly available build datasets for build optimization research.
Trace-free Einstein gravity as a constrained bigravity theory
2025-01-21
Merced Montesinos, Diego Gonzalez
Trace-free Einstein gravity is a prominent alternative to general relativity, which has two versions: one in which the energy-momentum conservation is assumed a priori and another in which it is not. In the first version, the cosmological constant arises as an integration constant. We report two diffeomorphism-invariant actions for trace-free Einstein gravity in which the energy-momentum conservation emerges directly from the equations of motion. The first action is based on the metric, while the second action is based on the tetrad and connection where the conservation of the energy-momentum holds if torsion vanishes. To achieve this, auxiliary fields but otherwise dynamical are introduced in the corresponding actions, which have the form a constrained bigravity theory. These results constitute a significant advance in our understanding of trace-free Einstein gravity.
Considerations with stacking absorption spectra: cold HI gas in cirrus region of the Milky Way
2025-01-21
Callum Lynn, Antoine Marchal, N. M. McClure-Griffiths, Marc-Antoine Miville-Deschênes, Claire E. Murray, Hiep Nguyen, James Dempsey, Enrico Di Teodoro, Jacco Th. van Loon, John M. Dickey, Min-Young Lee, Gilles Joncas, Yik Ki Ma, Nickolas M. Pingel, Snežana Stanimirović, Ian Kemp, Steven Gibson, Helga Dénes
We use the Milky Way neutral hydrogen (HI) absorption and emission spectra from the Galactic Australian Square Kilometre Array Pathfinder (GASKAP) Phase II Pilot survey along with toy models to investigate the effects of stacking multicomponent spectra on measurements of peak optical depth and spin temperature. Shifting spectra by the peak in emission, 'primary' components shifted to 0 km s$^{-1}$ are correctly averaged. Additional components on individual sightlines are averaged with non-centred velocities, producing a broader and shallower 'secondary' component in the resulting stack. Peak optical depths and brightness temperatures of the secondary components from stacks are lower limits of their true average values due to the velocity offset of each component. The spin temperature however is well correlated with the truth since the velocity offset of components affects the emission and absorption spectra equally. Stacking 462 GASKAP absorption-emission spectral pairs, we detect a component with a spin temperature of 1320 $\pm$ 263 K, consistent with gas from the unstable neutral medium and higher than any previous GASKAP detection in this region. We also stack 2240 pilot survey spectra containing no Milky Way absorption, revealing a primary narrow and secondary broad component, with spin temperatures belonging to the cold neutral medium (CNM). Spatially binning and stacking the non-detections across the plane-of-sky by their distance from CNM absorption detections, the primary component's optical depth decreases with distance from known locations of cold gas. The spin temperature however remains stable in both components, over an approximate physical plane-of-sky distance of $\sim$ 100 pc.
Prospects for high-resolution probes of galaxy dynamics tracing background cosmology in MaNGA
2025-01-21
Gyeong-Min Lee, Maurice H. P. M. van Putten
Large-$N$ galaxy surveys offer unprecedented opportunities to probe weak gravitation in galaxy dynamics that may contain correlations tracing background cosmology. Of particular interest is the potential of finite sensitivities to the background de Sitter scale of acceleration $a_{dS}=cH$, where $H$ is the Hubble parameter and $c$ is the velocity of light. At sufficiently large $N$, this is possibly probed by ensemble-averaged ("stacked") rotation curves (RCs) at resolutions on par with present estimates of the Hubble parameter $H_0$. Here, we consider the prospect for studies using the large $N$ Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) at APO survey. In a first and preliminary step, we consider unbiased control of sub-sample size by consistency in the three Position Angles, $\theta$ , from photometry and velocity fields of gas and stars by spectroscopy within $30^\circ$. In sub-samples of size $N=N_i(\theta)$, the scatter in-the-mean $\sigma/\sqrt{N}$ is found to reach one percent levels, differentiated over inclination angle $i$ and $\theta$. In regular propagation of uncertainties, this scatter contributes $\kappa\sigma/\sqrt{N}$ to the standard error in-the-mean to the observable, where $\kappa$ is determined by the choice of observables. As a lower bound to scatter in stacked RCs, MaNGA hereby appears promising for high-resolution analysis of sensitivity to RCs to background cosmology, notably across a sharp $C^0$-transition (van Putten 2018) of Newtonian to anomalous dynamics across $a_{dS}$ and, further out, the baryonic Tully-Fisher relation. In turn, these markers provide a novel measurement of cosmological parameters.
This study investigates the estimation of the neutral hydrogen (HI) mass function (HIMF) using a Bayesian stacking approach with simulated data for the Five-hundred-meter Aperture Spherical radio Telescope (FAST) HI intensity mapping (HIIM) drift-scan surveys. Using data from the IllustrisTNG simulation, we construct HI sky cubes at redshift $z\sim0.1$ and the corresponding optical galaxy catalogs, simulating FAST observations under various survey strategies, including pilot, deep-field, and ultradeep-field surveys. The HIMF is measured for distinct galaxy populations -- classified by optical properties into red, blue, and bluer galaxies -- and injected with systematic effects such as observational noise and flux confusion caused by the FAST beam. The results show that Bayesian stacking significantly enhances HIMF measurements. For red and blue galaxies, the HIMF can be well constrained with pilot surveys, while deeper surveys are required for the bluer galaxy population. Our analysis also reveals that sample variance dominates over observational noise, emphasizing the importance of wide-field surveys to improve constraints. Furthermore, flux confusion shifts the HIMF toward higher masses, which we address using a transfer function for correction. Finally, we explore the effects of intrinsic sample incompleteness and propose a framework to quantify its impact. This work lays the groundwork for future \hiMF studies with FAST HIIM, addressing key challenges and enabling robust analyses of HI content across galaxy populations.
We present the discovery of a binary system containing a white dwarf candidate using data from the LAMOST. Our analysis of the radial velocity data allowed us to determine an orbital period of approximately 0.953 days and a mass function of 0.129 $M_\odot$. Through spectral energy distribution (SED) fitting, we obtained the stellar parameters of the visible star. By combining these results with the mass function, we established a relationship between the mass of the invisible star and the system's inclination angle, along with the Roche lobe radius. We find that the mass of the invisible star is below the Chandrasekhar limit when the inclination angle exceeds $35^\circ$. Given that systems with large variations in radial velocity typically have high inclination angles, we classify the invisible star as a white dwarf candidate. The Roche lobe radius exceeds the physical radius of the visible star, indicating that no mass transfer occurs, which results in a weak ellipsoidal modulation effect. Additionally, we obtained light curves from the TESS, ASAS-SN, and CRTS surveys. The light curves also exhibit a periodicity of approximately 0.95 days, with ellipsoidal modulation only in the 2019 TESS observations. Coupled with the strong $\rm H_{\alpha}$ emission line observed in the LAMOST MRS spectrum, we infer that the surface of the visible star contains significant hot spots. This obscures the system's inherently weak ellipsoidal modulation, resulting in a manifestation of rotational variables. Furthermore, an analysis of the dynamical characteristics of this system indicates that it has a high inclination angle ($>60$ degrees) and its orbital properties are consistent with those of typical thin disk stars, supporting the hypothesis that the invisible object is a white dwarf.
Scientific research faces high costs and inefficiencies with traditional methods, but the rise of deep learning and large language models (LLMs) offers innovative solutions. This survey reviews LLM applications across scientific fields such as biology, medicine, chemistry, and meteorology, underscoring their role in advancing research. However, the continuous expansion of model size has led to significant memory demands, hindering further development and application of LLMs for science. To address this, we review memory-efficient training techniques for LLMs based on the transformer architecture, including distributed training, mixed precision training, and gradient checkpointing. Using AlphaFold 2 as an example, we demonstrate how tailored memory optimization methods can reduce storage needs while preserving prediction accuracy. We also discuss the challenges of memory optimization in practice and potential future directions, hoping to provide valuable insights for researchers and engineers.
Monocular Depth Estimation (MDE) is a fundamental computer vision task underpinning applications such as spatial understanding, 3D reconstruction, and autonomous driving. While deep learning-based MDE methods can predict relative depth from a single image, their lack of metric scale information often results in scale inconsistencies, limiting their utility in downstream tasks like visual SLAM, 3D reconstruction, and novel view synthesis. Monocular Metric Depth Estimation (MMDE) addresses these challenges by enabling precise, scene-scale depth inference. MMDE improves depth consistency, enhances sequential task stability, simplifies integration into downstream applications, and broadens practical use cases. This paper provides a comprehensive review of depth estimation technologies, highlighting the evolution from geometry-based methods to state-of-the-art deep learning approaches. It emphasizes advancements in scale-agnostic methods, which are crucial for enabling zero-shot generalization as the foundational capability for MMDE. Recent progress in zero-shot MMDE research is explored, focusing on challenges such as model generalization and the loss of detail at scene boundaries. Innovative strategies to address these issues include unlabelled data augmentation, image patching, architectural optimization, and generative techniques. These advancements, analyzed in detail, demonstrate significant contributions to overcoming existing limitations. Finally, this paper synthesizes recent developments in zero-shot MMDE, identifies unresolved challenges, and outlines future research directions. By offering a clear roadmap and cutting-edge insights, this work aims to deepen understanding of MMDE, inspire novel applications, and drive technological innovation.
Large Language Models with Human-In-The-Loop Validation for Systematic Review Data Extraction
2025-01-21
Noah L. Schroeder, Chris Davis Jaldi, Shan Zhang
Systematic reviews are time-consuming endeavors. Historically speaking, knowledgeable humans have had to screen and extract data from studies before it can be analyzed. However, large language models (LLMs) hold promise to greatly accelerate this process. After a pilot study which showed great promise, we investigated the use of freely available LLMs for extracting data for systematic reviews. Using three different LLMs, we extracted 24 types of data, 9 explicitly stated variables and 15 derived categorical variables, from 112 studies that were included in a published scoping review. Overall we found that Gemini 1.5 Flash, Gemini 1.5 Pro, and Mistral Large 2 performed reasonably well, with 71.17%, 72.14%, and 62.43% of data extracted being consistent with human coding, respectively. While promising, these results highlight the dire need for a human-in-the-loop (HIL) process for AI-assisted data extraction. As a result, we present a free, open-source program we developed (AIDE) to facilitate user-friendly, HIL data extraction with LLMs.
30+ Years of Source Separation Research: Achievements and Future Challenges
2025-01-21
Shoko Araki, Nobutaka Ito, Reinhold Haeb-Umbach, Gordon Wichern, Zhong-Qiu Wang, Yuki Mitsufuji
Source separation (SS) of acoustic signals is a research field that emerged in the mid-1990s and has flourished ever since. On the occasion of ICASSP's 50th anniversary, we review the major contributions and advancements in the past three decades in the speech, audio, and music SS research field. We will cover both single- and multi-channel SS approaches. We will also look back on key efforts to foster a culture of scientific evaluation in the research field, including challenges, performance metrics, and datasets. We will conclude by discussing current trends and future research directions.
Machine unlearning, as a pivotal technology for enhancing model robustness and data privacy, has garnered significant attention in prevalent web mining applications, especially in thriving graph-based scenarios. However, most existing graph unlearning (GU) approaches face significant challenges due to the intricate interactions among web-scale graph elements during the model training: (1) The gradient-driven node entanglement hinders the complete knowledge removal in response to unlearning requests; (2) The billion-level graph elements in the web scenarios present inevitable scalability issues. To break the above limitations, we open up a new perspective by drawing a connection between GU and conventional social influence maximization. To this end, we propose Node Influence Maximization (NIM) through the decoupled influence propagation model and fine-grained influence function in a scalable manner, which is crafted to be a plug-and-play strategy to identify potential nodes affected by unlearning entities. This approach enables offline execution independent of GU, allowing it to be seamlessly integrated into most GU methods to improve their unlearning performance. Based on this, we introduce Scalable Graph Unlearning (SGU) as a new fine-tuned framework, which balances the forgetting and reasoning capability of the unlearned model by entity-specific optimizations. Extensive experiments on 14 datasets, including large-scale ogbn-papers100M, have demonstrated the effectiveness of our approach. Specifically, NIM enhances the forgetting capability of most GU methods, while SGU achieves comprehensive SOTA performance and maintains scalability.
Self-Calibrated Epipolar Reconstruction for Assessment of Aneurysms in the Internal Carotid Artery Using In-Silico Biplane Angiograms
2025-01-21
Kyle A. Williams, S. V. Setlur Nagesh, Daniel R. Bednarek, Stephen Rudin, Ciprian N. Ionita
The treatment of intracranial aneurysms (IA) relies on angiography guidance using biplane views. However, accurate flow estimation and device sizing for treatment are often compromised by vessel overlap and foreshortening, which can obscure critical details. This study introduces an epipolar reconstruction approach to enhance 3D rendering of the internal carotid artery (ICA) and aneurysm dome using routinely acquired biplane angiographic data. Our method aims to improve procedural guidance by overcoming the limitations of traditional two-dimensional imaging techniques. This study employed three 3D geometries of ICA aneurysms to simulate virtual angiograms. These angiograms were generated using a computational fluid dynamics (CFD) solver, followed by the simulation of biplane angiography using a cone-beam geometry. Self-calibration was accomplished by matching contrast media position as a function of time between biplane views. Feature-matching was used to triangulate and reconstruct vascular structures in 3D. The projection data was utilized to refine the 3D estimation, including elimination of erroneous structures and ellipse-fitting. The accuracy of the reconstructions was evaluated using the Dice-Sorensen coefficient, comparing the 3D reconstructions to the original models. The proposed epipolar reconstruction method generalized well across the three tested aneurysm models, with respective Dice-Sorensen coefficients of 0.745, 0.759, and 0.654. Errors were primarily due to partial vessel overlap. The average reconstruction time for all three volumes was approximately 10 seconds. The proposed epipolar reconstruction method enhanced 3D visualization, addressing challenges such as projection-induced vessel foreshortening. This method provides a solution to the complexity of IA visualization, with the potential to provide more accurate analysis and device sizing for treatment.
Academic Case Reports Lack Diversity: Assessing the Presence and Diversity of Sociodemographic and Behavioral Factors related to Post COVID-19 Condition
2025-01-21
Juan Andres Medina Florez, Shaina Raza, Rashida Lynn, Zahra Shakeri, Brendan T. Smith, Elham Dolatabadi
Understanding the prevalence, disparities, and symptom variations of Post COVID-19 Condition (PCC) for vulnerable populations is crucial to improving care and addressing intersecting inequities. This study aims to develop a comprehensive framework for integrating social determinants of health (SDOH) into PCC research by leveraging NLP techniques to analyze disparities and variations in SDOH representation within PCC case reports. Following construction of a PCC Case Report Corpus, comprising over 7,000 case reports from the LitCOVID repository, a subset of 709 reports were annotated with 26 core SDOH-related entity types using pre-trained named entity recognition (NER) models, human review, and data augmentation to improve quality, diversity and representation of entity types. An NLP pipeline integrating NER, natural language inference (NLI), trigram and frequency analyses was developed to extract and analyze these entities. Both encoder-only transformer models and RNN-based models were assessed for the NER objective. Fine-tuned encoder-only BERT models outperformed traditional RNN-based models in generalizability to distinct sentence structures and greater class sparsity. Exploratory analysis revealed variability in entity richness, with prevalent entities like condition, age, and access to care, and underrepresentation of sensitive categories like race and housing status. Trigram analysis highlighted frequent co-occurrences among entities, including age, gender, and condition. The NLI objective (entailment and contradiction analysis) showed attributes like "Experienced violence or abuse" and "Has medical insurance" had high entailment rates (82.4%-80.3%), while attributes such as "Is female-identifying," "Is married," and "Has a terminal condition" exhibited high contradiction rates (70.8%-98.5%).
Detection of Unresolved Strongly Lensed Supernovae with 7-Dimensional Telescope
2025-01-21
Elahe Khalouei, Arman Shafieloo, Alex G. Kim, Ryan E. Keeley, William Sheu, Gregory S. H. Paek, Myungshin Im, Xiaosheng Huang, Hyung Mok Lee
Gravitationally lensed supernovae (glSNe) are a powerful tool for exploring the realms of astronomy and cosmology. Time-delay measurements and lens modeling of glSNe can provide a robust and independent method for constraining the expansion rate of the universe. The study of unresolved glSNe light curves presents a unique opportunity for utilizing small telescopes to investigate these systems. In this work, we investigate diverse observational strategies for the initial detection of glSNe using the 7-Dimensional Telescope (7DT), a multi-telescope system composed of twenty 50-cm telescopes. We implement different observing strategies on a subset of 5807 strong lensing systems and candidates identified within the Dark Energy Camera Legacy Survey (DECaLS), as reported in various publications. Our simulations under ideal observing conditions indicate the maximum expected annual detection rates for various glSNe types (Type Ia and core-collapse (CC)) using the 7DT target observing mode in the $r$-band at a depth of 22.04 mag, as follows: 8.25 events for type Ia, 3.06 for type Ic, 1.04 for type IIb, 0.66 for type IIL, 0.89 for type IIn, 5.26 for type IIP, and 1.44 for type Ib. Furthermore, in the case of medium-band filter observations (m6000) at a depth of 20.61 in the Wide-Field Survey (WFS) program, the predicted detection rate for glSNe Ia is 3.30 $yr^{-1}$. Given targeted follow-up observations of these initially detected systems with more powerful telescopes, we can apply a model-independent approach to forecast the ability to measure $H_{0}$ using a Gaussian process from Type Ia Supernovae (SNe Ia) data and time-delay distance information derived from glSNe systems, which include both Ia and CC types. We forecast that the expected detection rate of glSNe systems can achieve a $2.4\%$ precision in estimating the Hubble constant.
New TeV-emitting BL Lac candidates from the eROSITA X-ray survey
2025-01-21
Cassidy Metzger, Andrea Gokus, Manel Errando
TeV-emitting BL Lac type blazars represent the extreme end of the blazar population. They are characterized by relatively weak jets and radiatively inefficient accretion disks. Particles accelerated in these jets experience fewer radiative losses, allowing them to reach energies beyond the TeV scale and produce TeV gamma-ray emission. The study of TeV blazars is constrained by the limited number of known sources in this category. Currently, only 56 high synchrotron-peaked BL Lacs have been detected at energies above 0.1\,TeV. Searches for TeV emission from BL~Lacs typically target sources with bright X-ray emission and a synchrotron peak at or above 1\,keV. The recently released eRASS1 catalog by the eROSITA collaboration, which covers half of the sky, represents the deepest X-ray survey in the soft X-ray band to date. Utilizing the eROSITA survey, combined with infrared data from WISE and archival radio observations, we have identified 135 TeV-emitting blazar candidates. Our search introduces selection criteria based on the radio to infrared that remove quasar-like objects that have similar infrared spectra and X-ray fluxes as TeV-emitting BL~Lacs. In our search, we find 26 objects that had not been detected in the ROSAT X-ray survey and 20 that have not been previously associated with blazars. The candidates resulting from our search are suitable for follow-up observations with currently operating imaging atmospheric Cherenkov telescopes, as well as future facilities like the CTAO Observatory.
2025-01-21
I. Becker, P. Protopapas, M. Catelan, K. Pichara
In this work, we propose a novel ensemble of recurrent neural networks (RNNs) that considers the multiband and non-uniform cadence without having to compute complex features. Our proposed model consists of an ensemble of RNNs, which do not require the entire light curve to perform inference, making the inference process simpler. The ensemble is able to adapt to varying numbers of bands, tested on three real light curve datasets, namely Gaia, Pan-STARRS1, and ZTF, to demonstrate its potential for generalization. We also show the capabilities of deep learning to perform not only classification, but also regression of physical parameters such as effective temperature and radius. Our ensemble model demonstrates superior performance in scenarios with fewer observations, thus providing potential for early classification of sources from facilities such as Vera C. Rubin Observatory's LSST. The results underline the model's effectiveness and flexibility, making it a promising tool for future astronomical surveys. Our research has shown that a multitask learning approach can enrich the embeddings obtained by the models, making them instrumental to solve additional tasks, such as determining the orbital parameters of binary systems or estimating parameters for object types beyond periodic ones.
Nonverbal behaviors such as posture, gestures, and gaze are essential for conveying internal states, both consciously and unconsciously, in human interaction. For robots to interact more naturally with humans, robot movement design should likewise integrate expressive qualities, such as intention, attention, and emotions, alongside traditional functional considerations like task fulfillment and time efficiency. In this paper, we present the design and prototyping of a lamp-like robot that explores the interplay between functional and expressive objectives in movement design. Using a research-through-design methodology, we document the hardware design process, define expressive movement primitives, and outline a set of interaction scenario storyboards. We propose a framework that incorporates both functional and expressive utilities during movement generation, and implement the robot behavior sequences in different function- and social- oriented tasks. Through a user study comparing expression-driven versus function-driven movements across six task scenarios, our findings indicate that expression-driven movements significantly enhance user engagement and perceived robot qualities. This effect is especially pronounced in social-oriented tasks.
Tunable extraordinary optical transmission for integrated photonics
2025-01-21
Hira Asif, Ramazan Sahin
The propagation of light through opaque materials, served by periodic arrays of subwavelength holes, has revolutionized imaging and sensor technology with a breakthrough of extraordinary optical transmission (EOT). The enhanced optical transmission assisted by surface plasmon resonances (SPR) has become the most ingenious phenomenon in the field of light-matter interaction. Active tuning of SPR presents a new and simple way to control spectral features of the EOT signal (without the need to change the geometrical structure of the device). This provides a new possibility to integrate an active EOT device with tunable operational frequencies on a single chip of photonic integrated circuits (PIC)- a new scalable instrument in the optoelectronic industry, and quantum technology for improving subwavelength optical imaging and biomedical sensing. In this review, we discuss the fundamentals of EOT, the role of SPR, and how the active quantum plasmonic control of the EOT device makes it a feasible on-chip electro-optic programmable element for integrated photonics.
Outcome-Assisted Multiple Imputation of Missing Treatments
2025-01-21
Joseph Feldman, Jerome P. Reiter
We provide guidance on multiple imputation of missing at random treatments in observational studies. Specifically, analysts should account for both covariates and outcomes, i.e., not just use propensity scores, when imputing the missing treatments. To do so, we develop outcome-assisted multiple imputation of missing treatments: the analyst fits a regression for the outcome on the treatment indicator and covariates, which is used to sharpen the predictive probabilities for missing treatments under an estimated propensity score model. We derive an expression for the bias of the inverse probability weighted estimator for the average treatment effect under multiple imputation of missing treatments, and we show theoretically that this bias can be made small by using outcome-assisted multiple imputation. Simulations demonstrate empirically that outcome-assisted multiple imputation can offer better inferential properties than using the treatment assignment model alone. We illustrate the procedure in an analysis of data from the National Longitudinal Survey of Youth.
Wide Field Survey Telescope (WFST), with a powerful sky survey capability in the northern hemisphere, will play an important role in asteroid searching and monitoring. However, WFST is not a telescope dedicated to near-Earth asteroids (NEOs) searching. In order to improve the efficiency of finding NEOs on the premise of meeting the needs of other scientific research, we ran mock observations for WFST to study its search capability for NEOs. The NEO population model, the WFST detection model and site conditions are taken into account in our simulations. Based on the original scheduling scheme, we present two new schemes. Compared to the original scheme, the optimized scheme can improve the search capability of known and unknown NEOs by 100% and 50%. We also emphasized the importance of trailing loss and proposed an improved effective field of view model. In addition, it is predicted that adopting the clear-day ratio of 0.7 and the optimized scheme, for NEOs with absolute magnitude from 17 to 25, WFST can provide tracklets for about 1800 NEOs if their orbits are known, and in the case of blind search, more than 600 NEOs can be found by WFST. The new schemes provide valuable reference and suggestions for the WFST's regular survey strategy.
First photometric investigation of V517 Cam combined with ground-based and TESS data
2025-01-21
N. Alan, F. Alicavus, M. Alpsoy
The observations of eclipsing binary systems are of great importance in astrophysics, as they allow direct measurements of fundamental stellar parameters. By analysing high-quality space-based observations with ground-based photometric data, it becomes possible to detect these fundamental parameters with greater precision using multicolour photometry. Here, we report the first photometric analysis results of the V517 Cam eclipsing binary system by combining the Transiting Exoplanet Survey Satellite (TESS) light curve and new CCD observations in BVRI filters, obtained with a 60 cm robotic telescope (T60) at the T\"UB\.ITAK National Observatory. By means of photometric analyses, the masses and radii of the primary and secondary stars were carefully determined to be $M_{1}= 1.47\pm 0.06\,M_\odot$, $M_{2}= 0.79\pm0.05\,M_\odot$, and $R_{1}=1.43\pm 0.03\,R_\odot$, $R_{2}= 0.75\pm 0.04\,R_\odot$, respectively. Furthermore, the distance to V517 Cam was calculated to be $284\pm20$ pc. The overall age of the system is estimated to be around $63\pm15$ Myr. At this age, the primary component stands near the onset of its main-sequence evolution, near the ZAMS, whereas the secondary component remains in the pre-main-sequence evolutionary phase. To better understand the evolutionary status and nature of V517 Cam, the mass ratio and temperature values, obtained with relatively low sensitivity by photometric measurements, need to be confirmed by spectral analysis.
Survey of Radiative, Two-Temperature Magnetically Arrested Simulations of the Black Hole M87* I: Turbulent Electron Heating
2025-01-21
Andrew Chael
We present a set of eleven two-temperature, radiative, general relativistic magnetohydrodynamic (2TGRRMHD) simulations of the black hole M87* in the magnetically arrested (MAD) state, surveying different values of the black hole spin $a_*$. Our 3D simulations self-consistently evolve the temperatures of separate electron and ion populations under the effects of adiabatic compression/expansion, viscous heating, Coulomb coupling, and synchrotron, bremsstrahlung, and inverse Compton radiation. We adopt the Kawazura+ 2019 sub-grid heating prescription from gyrokinetic simulations of plasma turbulence. Our simulations have accretion rates $\dot{M}=(0.5-1.5)\times10^{-6}\dot{M}_{\rm Edd}$ and radiative efficiencies $\epsilon_{\rm rad}=2-14\%$. We compare our simulations to a fiducial set of otherwise identical single-fluid GRMHD simulations and find no significant changes in the outflow efficiency or black hole spindown parameter. Our simulations produce an effective adiabatic index for the two-temperature plasma of $\Gamma_{\rm gas}\approx1.55$, larger than the $\Gamma_{\rm gas}=13/9$ value often adopted in single-fluid GRMHD simulations. We find moderate ion-to-electron temperature ratios in the 230 GHz emitting region of $R=T_{\rm i}/T_{\rm e}{\approx}5$. While total intensity 230 GHz images from our simulations are consistent with Event Horizon Telescope (EHT) results, our images have significantly more beam-scale linear polarization ($\langle|m|\rangle\approx 30\%$) than is observed in EHT images of M87* ($\langle|m|\rangle<10\%$). We find a trend of the average linear polarization pitch angle $\angle\beta_2$ with black hole spin consistent with what is seen in single-fluid GRMHD simulations, and we provide a simple fitting function for $\angle\beta_2(a_*)$ motivated by the wind-up of magnetic field lines by black hole spin in the Blandford-Znajek mechanism.
2025-01-21
Ashley Tarrant, Jason Hinkle, Benjamin Shappee, Christopher Kochanek, Daniel Hey, Connor Auge, Anna Payne, Michael Bolish, Heechan Yuk, Xinyu Dai, Katie Auchettl, Todd Thompson, Helena Treiber
We investigate the relationship between AGN optical variability timescales, amplitudes, and supermassive black hole (SMBH) masses using homogeneous light curves from the All-Sky Automated Survey for SuperNovae (ASAS-SN). We fit a damped random walk (DRW) model to high-cadence, long-baseline ASAS-SN light curves to estimate the characteristic variability timescale ($\tau_\text{DRW}$) and amplitude ($\sigma$) for 57 AGN with precise SMBH mass measurements from reverberation mapping and dynamical methods. We confirm a significant correlation between $\tau_\text{DRW}$ and SMBH mass, and find: $\text{log}_{10}(M_\text{BH}/ \text{M}_\odot) = (1.85\pm0.20)\times\text{log}_{10} (\tau_\text{DRW}/200 \text{ days})+7.59\pm0.08$. Incorporating $\hat{\sigma}^2 = 2\sigma^2/\tau_\text{DRW}$ in a plane model significantly improves residuals, and we find: $\text{log}_{10}(M_\text{BH}/ \text{M}_\odot) = (2.27\pm0.20)\times\text{log}_{10} (\tau_\text{DRW}/200\text{ days})+(1.20\pm0.20)\times\text{log}_{10}(\hat{\sigma}/\text{1 mJy/days}^{1/2})+7.68\pm0.08$ with a scatter of 0.39 dex. We calculate $\tau_\text{DRW}$, $\hat{\sigma}$, and estimate SMBH masses for 203 bright ($V<16$ mag) AGN from the Milliquas catalog and compare these estimates with measurements from the BAT AGN Spectroscopic Survey for 42 overlapping AGN. In 10 years, LSST could extend this method to survey $7\lesssim\text{log}_{10}({M_\text{BH}/M_\odot})\lesssim9$ SMBHs out to $z\sim1$ and $\textrm{log}_{10}({M_\text{BH}/M_\odot})\sim8.0$ out to $z\sim4$, and ASAS-SN could probe $5\lesssim \textrm{log}_{10}({M_\text{BH}/M_\odot})\lesssim10.5$ SMBHs in the local universe and $\textrm{log}_{10}({M_\text{BH}/M_\odot})\sim9.0$ out to $z\sim2$. Measuring AGN variability with these datasets will provide a unique probe of SMBH evolution by making estimates of $M_\text{BH}$ spanning several orders of magnitude with photometric observations alone.
Large language models (LLMs) have demonstrated remarkable capabilities in a wide range of tasks, yet their application to specialized domains remains challenging due to the need for deep expertise. Retrieval-augmented generation (RAG) has emerged as a promising solution to customize LLMs for professional fields by seamlessly integrating external knowledge bases, enabling real-time access to domain-specific expertise during inference. Despite its potential, traditional RAG systems, based on flat text retrieval, face three critical challenges: (i) complex query understanding in professional contexts, (ii) difficulties in knowledge integration across distributed sources, and (iii) system efficiency bottlenecks at scale. This survey presents a systematic analysis of Graph-based Retrieval-Augmented Generation (GraphRAG), a new paradigm that revolutionizes domain-specific LLM applications. GraphRAG addresses traditional RAG limitations through three key innovations: (i) graph-structured knowledge representation that explicitly captures entity relationships and domain hierarchies, (ii) efficient graph-based retrieval techniques that enable context-preserving knowledge retrieval with multihop reasoning ability, and (iii) structure-aware knowledge integration algorithms that leverage retrieved knowledge for accurate and logical coherent generation of LLMs. In this survey, we systematically analyze the technical foundations of GraphRAG and examine current implementations across various professional domains, identifying key technical challenges and promising research directions. All the related resources of GraphRAG, including research papers, open-source data, and projects, are collected for the community in \textcolor{blue}{\url{https://github.com/DEEP-PolyU/Awesome-GraphRAG}}.
How Developers Choose Debugging Strategies for Challenging Web Application Defects
2025-01-20
Maryam Arab, Jenny T. Liang, Valentina Hong, Thomas D. LaToza
Effective debugging is a crucial aspect of software development, demanding problem-solving skills, expertise, and appropriate tools. Although previous research has studied expert developers' debugging strategies, the specific factors influencing strategy choice in complex scenarios remain underexplored. To investigate these contextual factors, we conducted two studies. First, we surveyed 35 developers to identify experiences with challenging debugging problems and contextual complexities. Second, we held semi-structured interviews with 16 experienced developers to gain deeper insight into strategic reasoning for complex debugging tasks. Insights from both groups enriched our understanding of debugging strategies at different expertise levels. We found that contextual factors interact in complex ways, and combinations of factors influence strategy choice, evolving throughout the debugging process. Hypothesis making is the baseline for debugging, with experience and code familiarity crucial for strategy selection. Our results show a gap between learning and effectively practicing strategies in challenging contexts, highlighting the need for carefully designed debugging tools and educational frameworks that align with problem contexts.
Distracted driving remains a significant global challenge with severe human and economic repercussions, demanding improved detection and intervention strategies. While previous studies have extensively explored single-modality approaches, recent research indicates that these systems often fall short in identifying complex distraction patterns, particularly cognitive distractions. This systematic review addresses critical gaps by providing a comprehensive analysis of machine learning (ML) and deep learning (DL) techniques applied across various data modalities - visual,, sensory, auditory, and multimodal. By categorizing and evaluating studies based on modality, data accessibility, and methodology, this review clarifies which approaches yield the highest accuracy and are best suited for specific distracted driving detection goals. The findings offer clear guidance on the advantages of multimodal versus single-modal systems and capture the latest advancements in the field. Ultimately, this review contributes valuable insights for developing robust distracted driving detection frameworks, supporting enhanced road safety and mitigation strategies.
Are generative models fair? A study of racial bias in dermatological image generation
2025-01-20
Miguel López-Pérez, Søren Hauberg, Aasa Feragen
Racial bias in medicine, particularly in dermatology, presents significant ethical and clinical challenges. It often results from the underrepresentation of darker skin tones in training datasets for machine learning models. While efforts to address bias in dermatology have focused on improving dataset diversity and mitigating disparities in discriminative models, the impact of racial bias on generative models remains underexplored. Generative models, such as Variational Autoencoders (VAEs), are increasingly used in healthcare applications, yet their fairness across diverse skin tones is currently not well understood. In this study, we evaluate the fairness of generative models in clinical dermatology with respect to racial bias. For this purpose, we first train a VAE with a perceptual loss to generate and reconstruct high-quality skin images across different skin tones. We utilize the Fitzpatrick17k dataset to examine how racial bias influences the representation and performance of these models. Our findings indicate that the VAE is influenced by the diversity of skin tones in the training dataset, with better performance observed for lighter skin tones. Additionally, the uncertainty estimates produced by the VAE are ineffective in assessing the model's fairness. These results highlight the need for improved uncertainty quantification mechanisms to detect and address racial bias in generative models for trustworthy healthcare technologies.
The present and the future of modeling eclipsing binary systems
2025-01-20
Andrej Prsa
In September 2024, eclipsing binary star practitioners gathered in Litomysl, Czech Republic, the birth town of Zdenek Kopal, one of the most celebrated pioneers of our field, to discuss the latest developments and state-of-the-art. I was invited to present my own biased view of the present and the future of modeling eclipsing binary stars. In this contribution I attempt to make a clear distinction between approaches that are suited to individual objects and approaches that aim to deliver bulk results for large datasets. I stress that our motivation should be different: individual system analysis is warranted whenever there is potential to propose or improve our understanding of the underlying physics, while bulk analysis should be used to probe stellar formation and evolution channels. I briefly discuss two examples of tools to achieve the goals: PHOEBE for individual system analysis, and PHOEBAI for bulk analysis.
Software development relies on effective collaboration between Software Development Engineers (SDEs) and User eXperience Designers (UXDs) to create software products of high quality and usability. While this collaboration issue has been explored over the past decades, anecdotal evidence continues to indicate the existence of challenges in their collaborative efforts. To understand this gap, we first conducted a systematic literature review (SLR) of 45 papers published since 2004, uncovering three key collaboration challenges and two main categories of potential best practices. We then analyzed designer and developer forums and discussions from one open-source software repository to assess how the challenges and practices manifest in the status quo. Our findings have broad applicability for collaboration in software development, extending beyond the partnership between SDEs and UXDs. The suggested best practices and interventions also act as a reference for future research, assisting in the development of dedicated collaboration tools for SDEs and UXDs.
Challenges and Opportunities of Gravitational Wave Searches above 10 kHz
2025-01-20
Nancy Aggarwal, Odylio D. Aguiar, Diego Blas, Andreas Bauswein, Giancarlo Cella, Sebastian Clesse, Adrian Michael Cruise, Valerie Domcke, Sebastian Ellis, Daniel G. Figueroa, Gabriele Franciolini, Camilo Garcia-Cely, Andrew Geraci, Maxim Goryachev, Hartmut Grote, Mark Hindmarsh, Asuka Ito, Joachim Kopp, Sung Mook Lee, Killian Martineau, Jamie McDonald, Francesco Muia, Nikhil Mukund, David Ottaway, Marco Peloso, Krisztian Peters, Fernando Quevedo, Angelo Ricciardone, Andreas Ringwald, Jessica Steinlechner, Sebastian Steinlechner, Sichun Sun, Carlos Tamarit, Michael E. Tobar, Francisco Torrenti, Caner Ünal, Graham White
The first direct measurement of gravitational waves by the LIGO and Virgo collaborations has opened up new avenues to explore our Universe. This white paper outlines the challenges and gains expected in gravitational-wave searches at frequencies above the LIGO/Virgo band. The scarcity of possible astrophysical sources in most of this frequency range provides a unique opportunity to discover physics beyond the Standard Model operating both in the early and late Universe, and we highlight some of the most promising of these sources. We review several detector concepts that have been proposed to take up this challenge, and compare their expected sensitivity with the signal strength predicted in various models. This report is the summary of a series of workshops on the topic of high-frequency gravitational wave detection, held in 2019 (ICTP, Trieste, Italy), 2021 (online) and 2023 (CERN, Geneva, Switzerland).
The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions
Research has increasingly explored the application of artificial intelligence (AI) and machine learning (ML) within the mental health domain to enhance both patient care and healthcare provider efficiency. Given that mental health challenges frequently emerge during early adolescence -- the critical years of high school and college -- investigating AI/ML-driven mental health solutions within the education domain is of paramount importance. Nevertheless, conventional AI/ML techniques follow a centralized model training architecture, which poses privacy risks due to the need for transferring students' sensitive data from institutions, universities, and clinics to central servers. Federated learning (FL) has emerged as a solution to address these risks by enabling distributed model training while maintaining data privacy. Despite its potential, research on applying FL to analyze students' mental health remains limited. In this paper, we aim to address this limitation by proposing a roadmap for integrating FL into mental health data analysis within educational settings. We begin by providing an overview of mental health issues among students and reviewing existing studies where ML has been applied to address these challenges. Next, we examine broader applications of FL in the mental health domain to emphasize the lack of focus on educational contexts. Finally, we propose promising research directions focused on using FL to address mental health issues in the education sector, which entails discussing the synergies between the proposed directions with broader human-centered domains. By categorizing the proposed research directions into short- and long-term strategies and highlighting the unique challenges at each stage, we aim to encourage the development of privacy-conscious AI/ML-driven mental health solutions.
The centimeter emission from planet-forming disks in Taurus
2025-01-20
Antonio Garufi, Carlos Carrasco-Gonzalez, Enrique Macias, Leonardo Testi, Pietro Curone, Luca Ricci, Stefano Facchini, Feng Long, Carlo F. Manara, Ilaria Pascucci, Giovanni Rosotti, Francesco Zagaria, Cathie Clarke, Gregory J. Herczeg, Andrea Isella, Alessia Rota, Karina Mauco, Nienke van der Marel, Marco Tazzari
The last decade has witnessed remarkable advances in the characterization of the (sub-)millimeter emission from planet-forming disks. Instead, the study of the (sub-)centimeter emission has made more limited progress, to the point that only a few exceptional disk-bearing objects have been characterized in the centimeter regime. This work takes a broad view of the centimeter emission from a large sample with VLA observations that is selected from previous ALMA surveys of more representative disks in brightness and extent. We report on the detection and characterization of flux at centimeter wavelengths from 21 sources in the Taurus star-forming region. Complemented by literature and archival data, the entire photometry from 0.85 mm to 6 cm is fitted by a two-component model that determines the ubiquitous presence of free-free emission entangled with the dust emission. The flux density of the free-free emission is found to scale with the accretion rate but is independent of the outer disk morphology depicted by ALMA. The dust emission at 2 cm is still appreciable, and offers the possibility to extract an unprecedented large set of dust spectral indices in the centimeter regime. A pronounced change between the median millimeter indices (2.3) and centimeter indices (2.8) suggests that a large portion of the disk emission is optically thick up to 3 mm. The comparison of both indices and fluxes with the ALMA disk extent indicates that this portion can be as large as 40 au, and suggests that the grain population within this disk region that emits the observed centimeter emission is similar in disks with different size and morphology. All these results await confirmation and dedicated dust modeling once facilities like ngVLA or SKA-mid are able to resolve the centimeter emission from planet-forming disks and disentangle the various components.
Tracing the galaxy-halo connection with galaxy clustering in COSMOS-Web from z = 0.1 to z ~ 12
2025-01-20
Louise Paquereau, Clotilde Laigle, Henry Joy McCracken, Marko Shuntov, Olivier Ilbert, Hollis B. Akins, Natalie Allen, Rafael Arango- Togo, Eddie M. Berman, Matthieu Bethermin, Caitlin M. Casey, Jacqueline McCleary, Yohan Dubois, Nicole E. Drakos, Andreas L. Faisst, Maximilien Franco, Santosh Harish, Christian K. Jespersen, Jeyhan S. Kartaltepe, Anton M. Koekemoer, Vasily Kokorev, Erini Lambrides, Rebecca Larson, Daizhong Liu, Damien Le Borgne, Joseph S. W. Lewis, Jed McKinney, Wilfried Mercier, Jason D. Rhodes, Brant E. Robertson, Sune Toft, Maxime Trebitsch, Laurence Tresse, John R. Weaver
We explore the evolving relationship between galaxies and their dark matter halos from $z \sim 0.1$ to $z \sim 12$ using mass-limited angular clustering measurements in the 0.54 deg$^2$ of the COSMOS-Web survey. This study provides the first measurements of the mass-limited two-point correlation function at $z \ge 10$ and a consistent analysis spanning 13.4 Gyr of cosmic history, setting new benchmarks for future simulations and models. Using a halo occupation distribution (HOD) framework, we derive characteristic halo masses and the stellar-to-halo mass relationship (SHMR) across redshifts and stellar mass bins. Our results first indicate that HOD models fit data at $z \ge 2.5$ best when incorporating a non-linear scale-dependent halo bias, boosting clustering at non-linear scales (r = 10-100 kpc). We find that galaxies at z > 10.5 with $\log(M_\star / M_\odot) \ge 8.85$ are hosted by halos with $M_{\rm h} \sim 10^{10.5}\,M_\odot$, achieving a star formation efficiency (SFE) $M_\star / (f_b M_{\rm h}) $ up to 1 dex higher than at $z \le 1$. The high galaxy bias at $z \ge 8$ suggests that these galaxies reside in massive halos with intrinsic high SFE. Our SHMR evolves significantly with redshift, starting high at $z \ge 10.5$, decreasing until $z \sim 2 - 3$, then increasing again until the present. Current simulations fail to reproduce both massive high-$z$ galaxies and this evolution, while semi-empirical models linking SFE to halo mass, accretion rates, and redshift align with our findings. We propose that $z > 8$ galaxies experience bursty star formation without significant feedback altering their growth, driving the rapid growth of massive galaxies observed by JWST. Over time, increasing feedback efficiency and exponential halo growth suppress star formation. At $z \sim 2 - 3$ and after, halo growth slows down while star formation continues, supported by gas reservoirs in halos.
Less Wound and More Asymmetric: JWST Confirms the Evolution of Spiral Structure in Galaxies at $z \lesssim 3$
2025-01-20
Ilia V. Chugunov, Alexander A. Marchuk, Aleksandr V. Mosenkov
Spiral galaxies are ubiquitous in the local Universe. However the properties of spiral arms in them are still not well studied, and there is even less information concerning spiral structure in distant galaxies. We aim to measure the most general parameters of spiral arms in remote galaxies and trace their changes with redshift. We perform photometric decomposition, including spiral arms, for 159 galaxies from the HST COSMOS and JWST CEERS and JADES surveys, which are imaged in optical and near-infrared rest-frame wavelengths. We confirm that, in our representative sample of spiral galaxies, the pitch angles increase, and the azimuthal lengths decrease with increasing redshift, implying that the spiral structure becomes more tightly wound over time. For the spiral-to-total luminosity ratio and the spiral width-to-disc scale length ratio, we find that band-shifting effects can be as significant as, or even stronger than, evolutionary effects. Additionally, we find that spiral structure becomes more asymmetric at higher redshifts.
Classification of HI Galaxy Profiles Using Unsupervised Learning and Convolutional Neural Networks: A Comparative Analysis and Methodological Cases of Studies
2025-01-20
Gabriel Jaimes-Illanes, Manuel Parra-Royon, Laura Darriba-Pol, Javier Moldón, Amidou Sorgho, Susana Sánchez-Expósito, Julián Garrido-Sánchez, Lourdes Verdes-Montenegro
Hydrogen, the most abundant element in the universe, is crucial for understanding galaxy formation and evolution. The 21 cm neutral atomic hydrogen - HI spectral line maps the gas kinematics within galaxies, providing key insights into interactions, galactic structure, and star formation processes. With new radio instruments, the volume and complexity of data is increasing. To analyze and classify integrated HI spectral profiles in a efficient way, this work presents a framework that integrates Machine Learning techniques, combining unsupervised methods and CNNs. To this end, we apply our framework to a selected subsample of 318 spectral HI profiles of the CIG and 30.780 profiles from the Arecibo Legacy Fast ALFA Survey catalogue. Data pre-processing involved the Busyfit package and iterative fitting with polynomial, Gaussian, and double-Lorentzian models. Clustering methods, including K-means, spectral clustering, DBSCAN, and agglomerative clustering, were used for feature extraction and to bootstrap classification we applied K-NN, SVM, and Random Forest classifiers, optimizing accuracy with CNN. Additionally, we introduced a 2D model of the profiles to enhance classification by adding dimensionality to the data. Three 2D models were generated based on transformations and normalised versions to quantify the level of asymmetry. These methods were tested in a previous analytical classification study conducted by the Analysis of the Interstellar Medium in Isolated Galaxies group. This approach enhances classification accuracy and aims to establish a methodology that could be applied to data analysis in future surveys conducted with the Square Kilometre Array (SKA), currently under construction. All materials, code, and models have been made publicly available in an open-access repository, adhering to FAIR principles.
Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applications
2025-01-20
Yuxing Lu, Sin Yee Goi, Xukai Zhao, Jinzhuo Wang
Biomedical knowledge graphs (BKGs) have emerged as powerful tools for organizing and leveraging the vast and complex data found across the biomedical field. Yet, current reviews of BKGs often limit their scope to specific domains or methods, overlooking the broader landscape and the rapid technological progress reshaping it. In this survey, we address this gap by offering a systematic review of BKGs from three core perspectives: domains, tasks, and applications. We begin by examining how BKGs are constructed from diverse data sources, including molecular interactions, pharmacological datasets, and clinical records. Next, we discuss the essential tasks enabled by BKGs, focusing on knowledge management, retrieval, reasoning, and interpretation. Finally, we highlight real-world applications in precision medicine, drug discovery, and scientific research, illustrating the translational impact of BKGs across multiple sectors. By synthesizing these perspectives into a unified framework, this survey not only clarifies the current state of BKG research but also establishes a foundation for future exploration, enabling both innovative methodological advances and practical implementations.
Artificial Neural Networks for Magnetoencephalography: A review of an emerging field
2025-01-20
Arthur Dehgan, Hamza Abdelhedi, Vanessa Hadid, Irina Rish, Karim Jerbi
Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate brain dynamics underlying cognitive processes with an unparalleled combination of high temporal and spatial precision. MEG data analytics has always relied on advanced signal processing and mathematical and statistical tools for various tasks ranging from data cleaning to probing the signals' rich dynamics and estimating the neural sources underlying the surface-level recordings. Like in most domains, the surge in Artificial Intelligence (AI) has led to the increased use of Machine Learning (ML) methods for MEG data classification. More recently, an emerging trend in this field is using Artificial Neural Networks (ANNs) to address many MEG-related tasks. This review provides a comprehensive overview of how ANNs are being used with MEG data from three vantage points: First, we review work that employs ANNs for MEG signal classification, i.e., for brain decoding. Second, we report on work that has used ANNs as putative models of information processing in the human brain. Finally, we examine studies that use ANNs as techniques to tackle methodological questions in MEG, including artifact correction and source estimation. Furthermore, we assess the current strengths and limitations of using ANNs with MEG and discuss future challenges and opportunities in this field. Finally, by establishing a detailed portrait of the field and providing practical recommendations for the future, this review seeks to provide a helpful reference for both seasoned MEG researchers and newcomers to the field who are interested in using ANNs to enhance the exploration of the complex dynamics of the human brain with MEG.
The JCMT Gould Belt Survey: First results from the Corona Australis molecular cloud and evidence of variable dust emissivity indices in the Coronet region
2025-01-20
Kate Pattle, David Bresnahan, Derek Ward-Thompson, Helen Kirk, Jason M. Kirk, David S. Berry, Hannah Broekhoven-Fiene, Jenny Hatchell, Tim Jenness, Doug Johnstone, J. C. Mottram, Ana Duarte-Cabral, James Di Francesco, M. R. Hogerheijde, Pierre Bastien, Harold Butner, Michael Chen, Antonio Chrysostomou, Simon Coudé, Malcolm J. Currie, C. J. Davis, Emily Drabek-Maunder, M. Fich, J. Fiege, Per Friberg, Rachel Friesen, Gary A. Fuller, Sarah Graves, Jane Greaves, Wayne Holland, G. Joncas, L. B. G. Knee, Steve Mairs, Ken Marsh, Brenda C. Matthews, Gerald Moriarty-Schieven, C. Mowat, Jonathan Rawlings, B. Retter, John Richer, D. Robertson, E. Rosolowsky, Sarah Sadavoy, H. Thomas, N. Tothill, Serena Viti, G. J. White, J. Wouterloot, J. Yates, M. Zhu
We present 450$\mu$m and 850$\mu$m James Clerk Maxwell Telescope (JCMT) observations of the Corona Australis (CrA) molecular cloud taken as part of the JCMT Gould Belt Legacy Survey (GBLS). We present a catalogue of 39 starless and protostellar sources, for which we determine source temperatures and masses using SCUBA-2 450$\mu$m/850$\mu$m flux density ratios for sources with reliable 450$\mu$m detections, and compare these to values determined using temperatures measured by the Herschel Gould Belt Survey (HGBS). In keeping with previous studies, we find that SCUBA-2 preferentially detects high-volume-density starless cores, which are most likely to be prestellar (gravitationally bound). We do not observe any anti-correlation between temperature and volume density in the starless cores in our sample. Finally, we combine our SCUBA-2 and Herschel data to perform SED fitting from 160-850$\mu$m across the central Coronet region, thereby measuring dust temperature $T$, dust emissivity index $\beta$ and column density $N({\rm H}_2)$ across the Coronet. We find that $\beta$ varies across the Coronet, particularly measuring $\beta = 1.55 \pm 0.35$ in the colder starless SMM-6 clump to the north of the B star R CrA. This relatively low value of $\beta$ is suggestive of the presence of large dust grains in SMM-6, even when considering the effects of $T-\beta$ fitting degeneracy and $^{12}$CO contamination of SCUBA-2 850$\mu$m data on the measured $\beta$ values.
A Survey of Exact and Approximation Algorithms for Linear-Parametric Optimization Problems
2025-01-20
Levin Nemesch, Stefan Ruzika, Clemens Thielen, Alina Wittmann
Linear-parametric optimization, where multiple objectives are combined into a single objective using linear combinations with parameters as coefficients, has numerous links to other fields in optimization and a wide range of application areas. In this survey, we provide a comprehensive overview of structural results and algorithmic strategies for solving linear-parametric optimization problems exactly and approximately. Transferring concepts from related areas such as multi-objective optimization provides further relevant results. The survey consists of two parts: First, we list strategies that work in a general fashion and do not rely on specific problem structures. Second, we look at well-studied parametric optimization problems and cover both important theoretical results and specialized algorithmic approaches for these problems. Among these problems are parametric variants of shortest path problems, minimum cost flow and maximum flow problems, spanning tree problems, the knapsack problem, and matching problems. Overall, we cover the results from 128 publications (and refer to 33 supplemental works) published between 1963 and 2024.
Fundus Image Quality Assessment and Enhancement: a Systematic Review
2025-01-20
Heng Li, Haojin Li, Mingyang Ou, Xiangyang Yu, Xiaoqing Zhang, Ke Niu, Huazhu Fu, Jiang Liu
As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions. However, fundus image degradation is common under intricate imaging environments, impacting following diagnosis and treatment. Consequently, image quality assessment (IQA) and enhancement (IQE) are essential for ensuring the clinical value and reliability of fundus images. While existing reviews offer some overview of this field, a comprehensive analysis of the interplay between IQA and IQE, along with their clinical deployment challenges, is lacking. This paper addresses this gap by providing a thorough review of fundus IQA and IQE algorithms, research advancements, and practical applications. We outline the fundamentals of the fundus photography imaging system and the associated interferences, and then systematically summarize the paradigms in fundus IQA and IQE. Furthermore, we discuss the practical challenges and solutions in deploying IQA and IQE, as well as offer insights into potential future research directions.
Understanding the relationship between population dynamics and disease-specific mortality is central to evidence-based health policy. This study introduces two novel metrics, PoPDivergence and PoPStat, one to quantify the difference between population pyramids and the other to assess the strength and nature of their association with the mortality of a given disease. PoPDivergence, based on Kullback-Leibler divergence, measures deviations between a countrys population pyramid and a reference pyramid. PoPStat is the correlation between these deviations and the log form of disease-specific mortality rates. The reference population is selected by a brute-force optimization that maximizes this correlation. Utilizing mortality data from the Global Burden of Disease 2021 and population statistics from the United Nations, we applied these metrics to 371 diseases across 204 countries. Results reveal that PoPStat outperforms traditional indicators such as median age, GDP per capita, and Human Development Index in explaining the mortality of most diseases. Noncommunicable diseases (NCDs) like neurological disorders and cancers, communicable diseases (CDs) like neglected tropical diseases, and maternal and neonatal diseases were tightly bound to the underlying demographic attributes whereas NCDs like diabetes, CDs like respiratory infections and injuries including self-harm and interpersonal violence were weakly associated with population pyramid shapes. Notably, except for diabetes, the NCD mortality burden was shared by constrictive population pyramids, while mortality of communicable diseases, maternal and neonatal causes and injuries were largely borne by expansive pyramids. Therefore, PoPStat provides insights into demographic determinants of health and empirical support for models on epidemiological transition.
MIDIS: Quantifying the AGN component of X-ray-detected galaxies
2025-01-20
Steven Gillman, John P. Pye, Almudena Alonso-Herrero, Martin J. Ward, Leindert Boogaard, Tuomo V. Tikkanen, Luis Colina, G. Östlin, Pablo G. Pérez-González, Luca Costantin, Edoardo Iani, Pierluigi Rinaldi, Javier Álvarez-Márquez, A. Bik, Sarah E. I. Bosman, Alejandro Crespo Gómez, Andreas Eckart, Macarena García-Marín, Thomas R. Greve, Jens Hjorth, A. Labiano, Danial Langeroodi, J. Melinder, Florian Peißker, Fabian Walter, M. Güdel, Thomas Henning, P. -O. Lagage, Thomas P. Ray
We combine the deepest X-ray survey from the Chandra Deep Field-South (CDF-S) `7-Ms' survey with the deepest mid-infrared (5.6$ \mu m$) image from the JWST/MIRI Deep Imaging Survey (MIDIS) in the Hubble Ultra-Deep Field (HUDF) to study the infrared counterparts and point-source emission of 31 X-ray sources with a median, intrinsic, rest-frame X-ray luminosity of $\log_{10}(L_{\rm Xc}^{\rm 0.5-7keV})$=42.04$\pm$0.22 erg $\rm s^{-1}$. The sample includes 24 AGN with a redshift range, as set by the X-ray detectability, of $z \simeq 0.5-3$. Through a multi-wavelength morphological decomposition, employing three separate classifications (visual, parametric and non-parametric) we separate (where present) the luminosity of the point-like AGN component from the remainder of the host-galaxy emission. The unprecedented mid-infrared sensitivity and imaging resolution of MIRI allows, in many cases, the direct characterisation of point-like (i.e. unresolved) components in the galaxies' emission. We establish a broad agreement between the three morphological classifications. At least 70% of the X-ray sources, including some classified as galaxies, show unresolved emission in the MIRI images, with the unresolved-to-total flux fraction at rest-frame 2$\mu m$ ranging from $\sim$0.2 to $\sim$0.9. At high X-ray luminosities ($\log_{10}(L_{\rm Xc}$)>43 erg $\rm s^{-1}$) we derive a consistent rest-frame near-infrared 2$ \mu m$ point-source luminosity to that derived for local AGN, whilst at lower X-ray luminosity we identify an excess in the 2$ \mu m$ emission compared to pre-JWST studies. We speculate this offset may be driven by a combination of Compton-thick AGN components and nuclear starburst, merger driven activity. Our observations highlight the complex nature of X-ray sources in the distant Universe and demonstrate the power of JWST/MIRI in quantifying their nuclear infrared emission. (Abridged)
On the concept of center for geometric objects and related problems
2025-01-20
M. Magdalena Martínez-Rico, L. Felipe Prieto-Martínez, R. Sánchez-Cauce
In this work, we review the concept of center of a geometric object as an equivariant map, unifying and generalizing different approaches followed by authors such as C. Kimberling or A. Edmonds. We provide examples to illustrate that this general approach encompasses many interesting spaces of geometric objects arising from different settings. Additionally, we discuss two results that characterize centers for some particular spaces of geometric objects, and we pose five open questions related to the generalization of these characterizations to other spaces. Finally, we conclude this article by briefly discussing other central objects and their relation to this concept of center.
Blockchain Developer Experience: A Multivocal Literature Review
2025-01-20
P. Soares, A. A. Araujo, G. Destefanis, R. Neykova, R. Saraiva, J. Souza
The rise of smart contracts has expanded blockchain's capabilities, enabling the development of innovative decentralized applications (dApps). However, this advancement brings its own challenges, including the management of distributed architectures and immutable data. Addressing these complexities requires a specialized approach to software engineering, with blockchain-oriented practices emerging to support development in this domain. Developer Experience (DEx) is central to this effort, focusing on the usability, productivity, and overall satisfaction of tools and frameworks from the engineers' perspective. Despite its importance, research on Blockchain Developer Experience (BcDEx) remains limited, with no systematic mapping of academic and industry efforts. To bridge this gap, we conducted a Multivocal Literature Review analyzing 62 to understand the distribution of BcDEx sources, practical implementations, and their impact. Our findings revealed that academic focus on BcDEx is limited compared to the coverage in gray literature, which primarily includes blogs (41.8%) and corporate sources (21.8%). Particularly, development efficiency, multi-network support, and usability are the most addressed aspects in tools and frameworks. In addition, we found that BcDEx is being shaped through five key perspectives: complexity abstraction, adoption facilitation, productivity enhancement, developer education, and BcDEx evaluation.
A Survey on Diffusion Models for Anomaly Detection
2025-01-20
Jing Liu, Zhenchao Ma, Zepu Wang, Yang Liu, Zehua Wang, Peng Sun, Liang Song, Bo Hu, Azzedine Boukerche, Victor C. M. Leung
Diffusion models (DMs) have emerged as a powerful class of generative AI models, showing remarkable potential in anomaly detection (AD) tasks across various domains, such as cybersecurity, fraud detection, healthcare, and manufacturing. The intersection of these two fields, termed diffusion models for anomaly detection (DMAD), offers promising solutions for identifying deviations in increasingly complex and high-dimensional data. In this survey, we review recent advances in DMAD research. We begin by presenting the fundamental concepts of AD and DMs, followed by a comprehensive analysis of classic DM architectures including DDPMs, DDIMs, and Score SDEs. We further categorize existing DMAD methods into reconstruction-based, density-based, and hybrid approaches, providing detailed examinations of their methodological innovations. We also explore the diverse tasks across different data modalities, encompassing image, time series, video, and multimodal data analysis. Furthermore, we discuss critical challenges and emerging research directions, including computational efficiency, model interpretability, robustness enhancement, edge-cloud collaboration, and integration with large language models. The collection of DMAD research papers and resources is available at https://github.com/fdjingliu/DMAD.
CME Observations -- from Sun to Impact on Geospace
2025-01-20
Manuela Temmer
Our Sun is an active star expelling dynamic phenomena known as coronal mass ejections (CMEs). The magnetic field configuration on the Sun and related solar wind structures affect the propagation behavior of CMEs, dominate its transit time and embedded magnetic field properties when impacting Earth. Since the conditions on the Sun constantly change, the impact of CMEs on the different regimes of geospace is quite variable and may differ significantly from event to event. This short review summarizes the different manifestations of CMEs on the Sun, their appearance in interplanetary space, and how CMEs trigger a cascade of reactions as they interact with Earth.
Anomaly detection from images captured using camera sensors is one of the mainstream applications at the industrial level. Particularly, it maintains the quality and optimizes the efficiency in production processes across diverse industrial tasks, including advanced manufacturing and aerospace engineering. Traditional anomaly detection workflow is based on a manual inspection by human operators, which is a tedious task. Advances in intelligent automated inspection systems have revolutionized the Industrial Anomaly Detection (IAD) process. Recent vision-based approaches can automatically extract, process, and interpret features using computer vision and align with the goals of automation in industrial operations. In light of the shift in inspection methodologies, this survey reviews studies published since 2019, with a specific focus on vision-based anomaly detection. The components of an IAD pipeline that are overlooked in existing surveys are presented, including areas related to data acquisition, preprocessing, learning mechanisms, and evaluation. In addition to the collected publications, several scientific and industry-related challenges and their perspective solutions are highlighted. Popular and relevant industrial datasets are also summarized, providing further insight into inspection applications. Finally, future directions of vision-based IAD are discussed, offering researchers insight into the state-of-the-art of industrial inspection.
Complex-scaled no-core shell model calculations of bound and unbound light nuclear states
2025-01-20
A. T. Kruppa, N. Michel, Xin-le Shang, Wei Zuo
The complex scaling method is commonly used to describe decaying states, but its applications are limited because the Hamiltonian operator must contain only relative coordinates. This has hindered the use of complex scaling in shell model, defined with laboratory single-particle coordinates. In particular, one of the most important model in low-energy nuclear physics, the no-core shell model, had never been considered in the frame of complex scaling. We will then present a straightforward procedure for introducing complex scaling in the no-core shell model in order to calculate nuclear resonance states. For that matter, the complex scaled two-body matrix elements must firstly be determined, and the resulting many-body Hamiltonian complex symmetric matrix must be diagonalized afterwards. Applications pertain to the bound ground states of the lightest nuclei $^2{\rm H}$, $^3{\rm H}$, $^3{\rm He}$, and $^4{\rm He}$, as well as the resonance ground states of $^5{\rm He}$ and $^5{\rm Li}$, whereby the realistic interaction Daejeon16 is utilized.
Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassian
2025-01-20
Wannita Takerngsaksiri, Micheal Fu, Chakkrit Tantithamthavorn, Jirat Pasuksmit, Kun Chen, Ming Wu
Programmers spend a significant amount of time reading code during the software development process. This trend is amplified by the emergence of large language models (LLMs) that automatically generate code. However, little is known about the readability of the LLM-generated code and whether it is still important from practitioners' perspectives in this new era. In this paper, we conduct a survey to explore the practitioners' perspectives on code readability in the age of LLMs and investigate the readability of our LLM-based software development agents framework, HULA, by comparing its generated code with human-written code in real-world scenarios. Overall, the findings underscore that (1) readability remains a critical aspect of software development; (2) the readability of our LLM-generated code is comparable to human-written code, fostering the establishment of appropriate trust and driving the broad adoption of our LLM-powered software development platform.
Recent breakthroughs in autonomous driving have revolutionized the way vehicles perceive and interact with their surroundings. In particular, world models have emerged as a linchpin technology, offering high-fidelity representations of the driving environment that integrate multi-sensor data, semantic cues, and temporal dynamics. Such models unify perception, prediction, and planning, thereby enabling autonomous systems to make rapid, informed decisions under complex and often unpredictable conditions. Research trends span diverse areas, including 4D occupancy prediction and generative data synthesis, all of which bolster scene understanding and trajectory forecasting. Notably, recent works exploit large-scale pretraining and advanced self-supervised learning to scale up models' capacity for rare-event simulation and real-time interaction. In addressing key challenges -- ranging from domain adaptation and long-tail anomaly detection to multimodal fusion -- these world models pave the way for more robust, reliable, and adaptable autonomous driving solutions. This survey systematically reviews the state of the art, categorizing techniques by their focus on future prediction, behavior planning, and the interaction between the two. We also identify potential directions for future research, emphasizing holistic integration, improved computational efficiency, and advanced simulation. Our comprehensive analysis underscores the transformative role of world models in driving next-generation autonomous systems toward safer and more equitable mobility.
Cybersecurity and Frequent Cyber Attacks on IoT Devices in Healthcare: Issues and Solutions
2025-01-20
Zag ElSayed, Ahmed Abdelgawad, Nelly Elsayed
Integrating Internet of Things (IoT) devices in healthcare has revolutionized patient care, offering improved monitoring, diagnostics, and treatment. However, the proliferation of these devices has also introduced significant cybersecurity challenges. This paper reviews the current landscape of cybersecurity threats targeting IoT devices in healthcare, discusses the underlying issues contributing to these vulnerabilities, and explores potential solutions. Additionally, this study offers solutions and suggestions for researchers, agencies, and security specialists to overcome these IoT in healthcare cybersecurity vulnerabilities. A comprehensive literature survey highlights the nature and frequency of cyber attacks, their impact on healthcare systems, and emerging strategies to mitigate these risks.
2025-01-20
Maciej Besta, Julia Barth, Eric Schreiber, Ales Kubicek, Afonso Catarino, Robert Gerstenberger, Piotr Nyczyk, Patrick Iff, Yueling Li, Sam Houliston, Tomasz Sternal, Marcin Copik, Grzegorz Kwaśniewski, Jürgen Müller, Łukasz Flis, Hannes Eberhard, Hubert Niewiadomski, Torsten Hoefler
Reasoning language models (RLMs), also known as Large Reasoning Models (LRMs), such as OpenAI's o1 and o3, DeepSeek-V3, and Alibaba's QwQ, have redefined AI's problem-solving capabilities by extending LLMs with advanced reasoning mechanisms. Yet, their high costs, proprietary nature, and complex architectures - uniquely combining Reinforcement Learning (RL), search heuristics, and LLMs - present accessibility and scalability challenges. To address these, we propose a comprehensive blueprint that organizes RLM components into a modular framework, based on a survey and analysis of all RLM works. This blueprint incorporates diverse reasoning structures (chains, trees, graphs, and nested forms), reasoning strategies (e.g., Monte Carlo Tree Search, Beam Search), RL concepts (policy, value models and others), supervision schemes (Outcome-Based and Process-Based Supervision), and other related concepts (e.g., Test-Time Compute, Retrieval-Augmented Generation, agent tools). We also provide detailed mathematical formulations and algorithmic specifications to simplify RLM implementation. By showing how schemes like LLaMA-Berry, QwQ, Journey Learning, and Graph of Thoughts fit as special cases, we demonstrate the blueprint's versatility and unifying potential. To illustrate its utility, we introduce x1, a modular implementation for rapid RLM prototyping and experimentation. Using x1 and a literature review, we provide key insights, such as multi-phase training for policy and value models, and the importance of familiar training distributions. Finally, we discuss scalable RLM cloud deployments and we outline how RLMs can integrate with a broader LLM ecosystem. Our work demystifies RLM construction, democratizes advanced reasoning capabilities, and fosters innovation, aiming to mitigate the gap between "rich AI" and "poor AI" by lowering barriers to RLM design and experimentation.
Tests of CP symmetry in entangled hyperon anti-hyperon pairs at BESIII
2025-01-20
Wenjing Zheng, Andrzej Kupsc, Simone Pacetti, Francesco Rosini, Nora Salone, Xiongfei Wang, Shuang-shi Fang
Decays of charmonium into hyperon and antihyperon pairs provide a pristine laboratory for exploring hyperon properties, such as their polarization and decay parameters, and for conducting tests of fundamental symmetries. This brief review highlights the significant progress made in precise tests of CP symmetry at BESIII using entangled hyperon-antihyperon pairs, including $\Lambda\bar{\Lambda}$, $\Sigma\bar{\Sigma}$, $\Xi\bar{\Xi}$ and $\Lambda\bar{\Sigma}$, selected from the high statistics of $J/\psi$ and $\psi(3686)$ events produced in $e^+e^-$ annihilations. These recent findings have sparked renewed interest in both theoretical and experimental aspects of hyperon physics, but there is still much room for improvement to reach the Standard Model expectations. To address this challenge, the prospects for future investigations on CP asymmetry at next-generation experiments are discussed.
A New Master Supernovae Ia sample and the investigation of the $H_0$ tension
2025-01-20
Maria Giovanna Dainotti, Biagio De Simone, Anargha Mondal, Kazunori Kohri, Augusto César Caligula do Espírito Santo Pedreira, Angel Bashyal, Rafid Hasan Dejrah, Shigehiro Nagataki, Giovanni Montani, Anurag Singh, Ayush Garg, Mudit Parakh, Nissim Fraija, Rohit Mandal, Hrikhes Sarkar, Tasneem Jareen, Kinshuk Jarial, Gaetano Lambiase
Modern cosmological research still thoroughly debates the discrepancy between local probes and the Cosmic Microwave Background observations in the Hubble constant ($H_0$) measurements, ranging from $4\sigma$ to $6\sigma$. In the current study we examine this tension using the Supernovae Ia (SNe Ia) data from the Pantheon, PantheonPlus, Joint Lightcurve Analysis (JLA), and Dark Energy Survey (DES) catalogs together with their combination called Master Sample containing 3789 SNe Ia, and dividing all of them into redshift-ordered bins. Two main binning techniques are presented: the equipopulation and the equispace in the $\log\, z$. We perform a Markov-Chain Monte Carlo analysis (MCMC) for each bin to determine the $H_0$ value, estimating it within the standard flat $\Lambda$CDM and the $w_{0}w_{a}$CDM models. These $H_0$ values are then fitted with the following phenomenological function: $\mathcal{H}_0(z) = \tilde{H}_0 / (1 + z)^\alpha$, where $\tilde{H}_0$ is a free parameter representing $\mathcal{H}_0(z)$ fitted at $z=0$, and $\alpha$ is the evolutionary parameter. Our results indicate a decreasing trend characterized by $\alpha \sim 0.01$ whose consistency with zero range from $1.00 \sigma$ at 3 bins in the Pantheon sample with the $w_{0}w_{a}$CDM model to $\geq 6 \sigma$ at 12 bins with the JLA and DES samples within the $\Lambda$CDM model. Such a trend in the SNe Ia catalogs could be due to evolution with redshift for the SNe Ia astrophysical variables or unveiled selection biases. Alternatively, intrinsic physics, possibly the $f(R)$ theory of gravity, could be responsible for this trend.
The evolving requirements of Internet of Things (IoT) applications are driving an increasing shift toward bringing intelligence to the edge, enabling real-time insights and decision-making within resource-constrained environments. Tiny Machine Learning (TinyML) has emerged as a key enabler of this evolution, facilitating the deployment of ML models on devices such as microcontrollers and embedded systems. However, the complexity of managing the TinyML lifecycle, including stages such as data processing, model optimization and conversion, and device deployment, presents significant challenges and often requires substantial human intervention. Motivated by these challenges, we began exploring whether Large Language Models (LLMs) could help automate and streamline the TinyML lifecycle. We developed a framework that leverages the natural language processing (NLP) and code generation capabilities of LLMs to reduce development time and lower the barriers to entry for TinyML deployment. Through a case study involving a computer vision classification model, we demonstrate the framework's ability to automate key stages of the TinyML lifecycle. Our findings suggest that LLM-powered automation holds potential for improving the lifecycle development process and adapting to diverse requirements. However, while this approach shows promise, there remain obstacles and limitations, particularly in achieving fully automated solutions. This paper sheds light on both the challenges and opportunities of integrating LLMs into TinyML workflows, providing insights into the path forward for efficient, AI-assisted embedded system development.
Recent advancements in large language models (LLMs) have demonstrated their impressive abilities in various reasoning and decision-making tasks. However, the quality and coherence of the reasoning process can still benefit from enhanced introspection and self-reflection. In this paper, we introduce Multiplex CoT (Chain of Thought), a method that enables LLMs to simulate a form of self-review while reasoning, by initiating double Chain of Thought (CoT) thinking. Multiplex CoT leverages the power of iterative reasoning, where the model generates an initial chain of thought and subsequently critiques and refines this reasoning with a second round of thought generation. This recursive approach allows for more coherent, logical, and robust answers, improving the overall decision-making process. We demonstrate how this method can be effectively implemented using simple prompt engineering in existing LLM architectures, achieving an effect similar to that of the Learning-Refinement Model (LRM) without the need for additional training. Additionally, we present a practical guide for implementing the method in Google Colab, enabling easy integration into real-world applications.
Industrial Upgrading and New Quality Productive Forces: Evidence from China's Provincial Panel Data (2003-2022)
2025-01-20
Solar Jin
Accelerating the deep transformation and upgrading of industrial structure and forming new quality productive forces are essential components for China to achieve the great rejuvenation of the Chinese Dream. After more than 40 years of rapid development, China has entered the "new normal" of development, making the advancement of new quality productive forces an urgent task. This paper reviews the evolution of China's industrial structure, argues the necessity for a new round of deep industrial transformation, and explores the impact of industrial structure transformation and upgrading on the level of new quality productive forces using various methods. The research findings are as follows:(1)The deep transformation and upgrading of the industrial structure can significantly promote the development of new quality productive forces, but there are obvious regional differences.(2)The core indicator of the improvement in the level of new quality productive forces is the enhancement of total factor productivity. Furthermore, this paper summarizes past industrial development processes and the challenges faced, and analyzes and discusses the potential challenges that may arise in promoting the development of new quality productive forces through deep industrial structure transformation, based on empirical research results.
Guided Persona-based AI Surveys: Can we replicate personal mobility preferences at scale using LLMs?
2025-01-20
Ioannis Tzachristas, Santhanakrishnan Narayanan, Constantinos Antoniou
This study explores the potential of Large Language Models (LLMs) to generate artificial surveys, with a focus on personal mobility preferences in Germany. By leveraging LLMs for synthetic data creation, we aim to address the limitations of traditional survey methods, such as high costs, inefficiency and scalability challenges. A novel approach incorporating "Personas" - combinations of demographic and behavioural attributes - is introduced and compared to five other synthetic survey methods, which vary in their use of real-world data and methodological complexity. The MiD 2017 dataset, a comprehensive mobility survey in Germany, serves as a benchmark to assess the alignment of synthetic data with real-world patterns. The results demonstrate that LLMs can effectively capture complex dependencies between demographic attributes and preferences while offering flexibility to explore hypothetical scenarios. This approach presents valuable opportunities for transportation planning and social science research, enabling scalable, cost-efficient and privacy-preserving data generation.
Advances in modeling complex materials: The rise of neuroevolution potentials
2025-01-19
Penghua Ying, Cheng Qian, Rui Zhao, Yanzhou Wang, Feng Ding, Shunda Chen, Zheyong Fan
Interatomic potentials are essential for driving molecular dynamics (MD) simulations, directly impacting the reliability of predictions regarding the physical and chemical properties of materials. In recent years, machine-learned potentials (MLPs), trained against first-principles calculations, have become a new paradigm in materials modeling as they provide a desirable balance between accuracy and computational cost. The neuroevolution potential (NEP) approach, implemented in the open-source GPUMD software, has emerged as a promising machine-learned potential, exhibiting impressive accuracy and exceptional computational efficiency. This review provides a comprehensive discussion on the methodological and practical aspects of the NEP approach, along with a detailed comparison with other representative state-of-the-art MLP approaches in terms of training accuracy, property prediction, and computational efficiency. We also demonstrate the application of the NEP approach to perform accurate and efficient MD simulations, addressing complex challenges that traditional force fields typically can not tackle. Key examples include structural properties of liquid and amorphous materials, chemical order in complex alloy systems, phase transitions, surface reconstruction, material growth, primary radiation damage, fracture in two-dimensional materials, nanoscale tribology, and mechanical behavior of compositionally complex alloys under various mechanical loadings. This review concludes with a summary and perspectives on future extensions to further advance this rapidly evolving field.
Higher-order effects in the dynamics of hierarchical triple systems. III. Astrophysical implications of second-order and dotriacontapole terms
2025-01-19
Landen Conway, Clifford M. Will
We study the long-term evolution of selected hierarchical triple systems in Newtonian gravity. We employ analytic equations derived in Paper II for the evolution of orbit-averaged orbital elements for both inner and outer orbits, which include two classes of contributions. One class consists of linear-order contributions, including quadrupole, octupole, hexadecapole and dotriacontapole orders, the latter scaling as $\epsilon^6$, where $\epsilon = a/A$, the ratio of the semimajor axes of the inner and outer orbits. The second class consists of contributions at {\em second} order in the fundamental perturbation parameter; they contribute at orders $\epsilon^{9/2}$, $\epsilon^{5}$, $\epsilon^{11/2}$ and $\epsilon^{6}$. For well studied triples such as star-planet systems perturbed by a low-mass third body (``hot Jupiters''), second-order and dotriacontapole (SOD) effects induce only small corrections. For stellar-mass binaries orbiting supermassive black holes, SOD corrections can suppress orbital flips that are generated by purely first-order effects. Planets orbiting binary star systems are susceptible to significant variations in the planetary semimajor axis, an effect that does not occur at first perturbative order. SOD effects in triple black hole systems can induce migrations of the eccentricity to significantly larger values than predicted by first-order perturbations, with implications for the gravitational-wave induced inspiral of the inner binary. We also show that in most cases, evolutions using our SOD equations are in better agreement with those from direct integration of the N-body equations of motion than those from first-order perturbations through hexadecapole order.
Efficient Frame Extraction: A Novel Approach Through Frame Similarity and Surgical Tool Tracking for Video Segmentation
2025-01-19
Huu Phong Nguyen, Shekhar Madhav Khairnar, Sofia Garces Palacios, Amr Al-Abbas, Francisco Antunes, Bernardete Ribeiro, Melissa E. Hogg, Amer H. Zureikat, Patricio M. Polanco, Herbert Zeh III, Ganesh Sankaranarayanan
The interest in leveraging Artificial Intelligence (AI) for surgical procedures to automate analysis has witnessed a significant surge in recent years. One of the primary tools for recording surgical procedures and conducting subsequent analyses, such as performance assessment, is through videos. However, these operative videos tend to be notably lengthy compared to other fields, spanning from thirty minutes to several hours, which poses a challenge for AI models to effectively learn from them. Despite this challenge, the foreseeable increase in the volume of such videos in the near future necessitates the development and implementation of innovative techniques to tackle this issue effectively. In this article, we propose a novel technique called Kinematics Adaptive Frame Recognition (KAFR) that can efficiently eliminate redundant frames to reduce dataset size and computation time while retaining useful frames to improve accuracy. Specifically, we compute the similarity between consecutive frames by tracking the movement of surgical tools. Our approach follows these steps: i) Tracking phase: a YOLOv8 model is utilized to detect tools presented in the scene, ii) Similarity phase: Similarities between consecutive frames are computed by estimating variation in the spatial positions and velocities of the tools, iii) Classification phase: A X3D CNN is trained to classify segmentation. We evaluate the effectiveness of our approach by analyzing datasets obtained through retrospective reviews of cases at two referral centers. The Gastrojejunostomy (GJ) dataset covers procedures performed between 2017 to 2021, while the Pancreaticojejunostomy (PJ) dataset spans from 2011 to 2022 at the same centers. By adaptively selecting relevant frames, we achieve a tenfold reduction in the number of frames while improving accuracy by 4.32% (from 0.749 to 0.7814).
Advanced technology in railway track monitoring using the GPR Technique: A Review
2025-01-19
Farhad Kooban, Aleksandra Radlińska, Reza Mousapour, Maryam Saraei
Subsurface evaluation of railway tracks is crucial for safe operation, as it allows for the early detection and remediation of potential structural weaknesses or defects that could lead to accidents or derailments. Ground Penetrating Radar (GPR) is an electromagnetic survey technique as advanced non-destructive technology (NDT) that can be used to monitor railway tracks. This technology is well-suited for railway applications due to the sub-layered composition of the track, which includes ties, ballast, sub-ballast, and subgrade regions. It can detect defects such as ballast pockets, fouled ballast, poor drainage, and subgrade settlement. The paper reviews recent works on advanced technology and interpretations of GPR data collected for different layers. Further, this paper demonstrates the current techniques for using synthetic modeling to calibrate real-world GPR data, enhancing accuracy in identifying subsurface features like ballast conditions and structural anomalies and applying various algorithms to refine GPR data analysis. These include Support Vector Machine (SVM) for classifying railway ballast types, Fuzzy C-means, and Generalized Regression Neural Networks for high-accuracy defect classification. Deep learning techniques, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are also highlighted for their effectiveness in recognizing patterns associated with defects in GPR images. The article specifically focuses on the development of a Convolutional Recurrent Neural Network (CRNN) model, which combines CNN and RNN architectures for efficient processing of GPR data. This model demonstrates enhanced detection capabilities and faster processing compared to traditional object detection models like Faster R-CNN.
Clinical trial cohort selection using Large Language Models on n2c2 Challenges
2025-01-19
Chi-en Amy Tai, Xavier Tannier
Clinical trials are a critical process in the medical field for introducing new treatments and innovations. However, cohort selection for clinical trials is a time-consuming process that often requires manual review of patient text records for specific keywords. Though there have been studies on standardizing the information across the various platforms, Natural Language Processing (NLP) tools remain crucial for spotting eligibility criteria in textual reports. Recently, pre-trained large language models (LLMs) have gained popularity for various NLP tasks due to their ability to acquire a nuanced understanding of text. In this paper, we study the performance of large language models on clinical trial cohort selection and leverage the n2c2 challenges to benchmark their performance. Our results are promising with regard to the incorporation of LLMs for simple cohort selection tasks, but also highlight the difficulties encountered by these models as soon as fine-grained knowledge and reasoning are required.
The JWST EXCELS survey: an extremely metal-poor galaxy at $z=8.271$ hosting an unusual population of massive stars
2025-01-19
F. Cullen, A. C. Carnall, D. Scholte, D. J. McLeod, R. J. McLure, K. Z. Arellano-Córdova, T. M Stanton, C. T. Donnan, J. S. Dunlop, A. E. Shapley, L. Barrufet, R. Begley, C. Bondestam, M. Cirasuolo, H. -H. Leung, C. L. Pollock, S. Stevenson
We present an analysis of the rest-frame optical ($\lambda \simeq 3100-5600 \,$\r{A}) spectrum of a $\mathrm{log}_{10}(M_*/\mathrm{M_\odot}) = 8.6$ star-forming galaxy at $z=8.271$ (EXCELS-63107) from JWST/NIRSpec medium-resolution observations taken as part of the EXCELS survey. EXCELS-63107 is a compact object consistent with the size of local star-forming cluster complexes ($r_e < 200 \, \rm{pc}$) and has an extremely steep UV continuum measured from JWST/NIRCam photometry ($\beta=-3.3\pm0.3$). The JWST/NIRSpec G395M spectrum of EXCELS-63107 is notable for its strong [OIII]$\lambda4363$ auroral-line emission relative to the [OIII]$\lambda5007$ forbidden line. Via a detailed emission-line and photoionization-modelling analysis, we find that the the observed properties of EXCELS-63107 are consistent with the presence of an ionizing source with an effective temperature of $T_{\rm eff} \gtrsim 80 \, 000\,\rm{K}$ heating ionized gas with a density of $n_e < 10^4 \, \rm{cm}^{-3}$ to a volume-averaged electron temperature of $T_e \simeq 34 \, 000\,\rm{K}$. Crucially, we find that stellar population models assuming a standard IMF are not capable of producing the required heating. We determine an oxygen abundance of ${12+\mathrm{log(O/H)}= 6.89^{+0.26}_{-0.21}}$ which is one of the lowest directly constrained oxygen abundances measured in any galaxy to date, and $\simeq 10 \times$ lower than is typical for $z\simeq8$ galaxies with the same stellar mass. The extremely low metallicity of EXCELS-63107 places it in a regime in which theoretical models expect a transition to a top-heavy IMF, and we speculate that a $\simeq 10-30 \, \times$ excess of $M > 50 \, \rm{M}_{\odot}$ stars is one plausible explanation for its observed properties. However, more exotic scenarios, such as Pop III star formation within a mildly enriched halo, are also consistent with the observations.
Reproducibility review of "Why Not Other Classes": Towards Class-Contrastive Back-Propagation Explanations
2025-01-19
Arvid Eriksson, Anton Israelsson, Mattias Kallhauge
"Why Not Other Classes?": Towards Class-Contrastive Back-Propagation Explanations (Wang & Wang, 2022) provides a method for contrastively explaining why a certain class in a neural network image classifier is chosen above others. This method consists of using back-propagation-based explanation methods from after the softmax layer rather than before. Our work consists of reproducing the work in the original paper. We also provide extensions to the paper by evaluating the method on XGradCAM, FullGrad, and Vision Transformers to evaluate its generalization capabilities. The reproductions show similar results as the original paper, with the only difference being the visualization of heatmaps which could not be reproduced to look similar. The generalization seems to be generally good, with implementations working for Vision Transformers and alternative back-propagation methods. We also show that the original paper suffers from issues such as a lack of detail in the method and an erroneous equation which makes reproducibility difficult. To remedy this we provide an open-source repository containing all code used for this project.
Dynamic semantic networks for exploration of creative thinking
2025-01-19
Danko D. Georgiev, Georgi V. Georgiev
Human creativity originates from brain cortical networks that are specialized in idea generation, processing, and evaluation. The concurrent verbalization of our inner thoughts during the execution of a design task enables the use of dynamic semantic networks as a tool for investigating, evaluating, and monitoring creative thought. The primary advantage of using lexical databases such as WordNet for reproducible information-theoretic quantification of convergence or divergence of design ideas in creative problem solving is the simultaneous handling of both words and meanings, which enables interpretation of the constructed dynamic semantic networks in terms of underlying functionally active brain cortical regions involved in concept comprehension and production. In this study, the quantitative dynamics of semantic measures computed with a moving time window is investigated empirically in the DTRS10 dataset with design review conversations and detected divergent thinking is shown to predict success of design ideas. Thus, dynamic semantic networks present an opportunity for real-time computer-assisted detection of critical events during creative problem solving, with the goal of employing this knowledge to artificially augment human creativity.
Generative AI-driven Cross-layer Covert Communication: Fundamentals, Framework and Case Study
2025-01-19
Tianhao Liu, Jiqiang Liu, Tao Zhang, Jian Wang, Jiacheng Wang, Jiawen Kang, Dusit Niyato, Shiwen Mao
Ensuring end-to-end cross-layer communication security in military networks by selecting covert schemes between nodes is a key solution for military communication security. With the development of communication technology, covert communication has expanded from the physical layer to the network and application layers, utilizing methods such as artificial noise, private networks, and semantic coding to transmit secret messages. However, as adversaries continuously eavesdrop on specific communication channels, the accumulation of sufficient data may reveal underlying patterns that influence concealment, and establishing a cross-layer covert communication mechanism emerges as an effective strategy to mitigate these regulatory challenges. In this article, we first survey the communication security solution based on covert communication, specifically targeting three typical scenarios: device-to-device, private network communication, and public network communication, and analyze their application scopes. Furthermore, we propose an end-to-end cross-layer covert communication scheme driven by Generative Artificial Intelligence (GenAI), highlighting challenges and their solutions. Additionally, a case study is conducted using diffusion reinforcement learning to sovle cloud edge internet of things cross-layer secure communication.
Bridging the Security Gap: Lessons from 5G and What 6G Should Do Better
2025-01-19
Isabella D. Lutz, Matthew C. Valenti
The security requirements for future 6G mobile networks are anticipated to be significantly more complex and demanding than those of 5G. This increase stems from several factors: the proliferation of massive machine-type communications will dramatically increase the density of devices competing for network access; secure ultra-reliable low-latency communication will impose stringent requirements on security, latency, and reliability; and the widespread deployment of small cells and non-terrestrial networks, including satellite mega-constellations, will result in more frequent handovers. This paper provides a set of security recommendations for 6G networks, with a particular focus on access and handover procedures, which often lack encryption and integrity protection, making them more vulnerable to exploitation. Since 6G is expected to be a backward-compatible extension of 5G, and given that secure systems cannot be effectively designed without a clear understanding of their goals, it is imperative to first evaluate the limitations of the current generation. To this end, the paper begins by reviewing existing 5G access and authentication mechanisms, highlighting several critical vulnerabilities in these procedures. It then examines potential 6G challenges and concludes with actionable recommendations to enhance the security, resilience, and robustness of 6G access and handover mechanisms.
ScaMaha: A Tool for Parsing, Analyzing, and Visualizing Object-Oriented Software Systems
2025-01-19
Ra'Fat Al-Msie'deen
Reverse engineering tools are required to handle the complexity of software products and the unique requirements of many different tasks, like software analysis and visualization. Thus, reverse engineering tools should adapt to a variety of cases. Static Code Analysis (SCA) is a technique for analyzing and exploring software source code without running it. Manual review of software source code puts additional effort on software developers and is a tedious, error-prone, and costly job. This paper proposes an original approach (called ScaMaha) for Object-Oriented (OO) source code analysis and visualization based on SCA. ScaMaha is a modular, flexible, and extensible reverse engineering tool. ScaMaha revolves around a new meta-model and a new code parser, analyzer, and visualizer. ScaMaha parser extracts software source code based on the Abstract Syntax Tree (AST) and stores this code as a code file. The code file includes all software code identifiers, relations, and structural information. ScaMaha analyzer studies and exploits the code files to generate useful information regarding software source code. The software metrics file gives unique metrics regarding software systems, such as the number of method access relations. Software source code visualization plays an important role in software comprehension. Thus, ScaMaha visualizer exploits code files to visualize different aspects of software source code. The visualizer generates unique graphs about software source code, like the visualization of inheritance relations. ScaMaha tool was applied to several case studies from small to large software systems, such as drawing shapes, mobile photo, health watcher, rhino, and ArgoUML. Results show the scalability, performance, soundness, and accuracy of ScaMaha tool. Evaluation metrics, such as precision and recall, demonstrate the accuracy of ScaMaha ...
One of the deadliest cancers, lung cancer necessitates an early and precise diagnosis. Because patients have a better chance of recovering, early identification of lung cancer is crucial. This review looks at how to diagnose lung cancer using sophisticated machine learning techniques like Random Forest (RF) and Support Vector Machine (SVM). The Chi-squared test is one feature selection strategy that has been successfully applied to find related features and enhance model performance. The findings demonstrate that these techniques can improve detection efficiency and accuracy while also assisting in runtime reduction. This study produces recommendations for further research as well as ideas to enhance diagnostic techniques. In order to improve healthcare and create automated methods for detecting lung cancer, this research is a critical first step.
Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond
2025-01-19
Weiyu Chen, Xiaoyuan Zhang, Baijiong Lin, Xi Lin, Han Zhao, Qingfu Zhang, James T. Kwok
Multi-objective optimization (MOO) in deep learning aims to simultaneously optimize multiple conflicting objectives, a challenge frequently encountered in areas like multi-task learning and multi-criteria learning. Recent advancements in gradient-based MOO methods have enabled the discovery of diverse types of solutions, ranging from a single balanced solution to finite or even infinite Pareto sets, tailored to user needs. These developments have broad applications across domains such as reinforcement learning, computer vision, recommendation systems, and large language models. This survey provides the first comprehensive review of gradient-based MOO in deep learning, covering algorithms, theories, and practical applications. By unifying various approaches and identifying critical challenges, it serves as a foundational resource for driving innovation in this evolving field. A comprehensive list of MOO algorithms in deep learning is available at \url{https://github.com/Baijiong-Lin/Awesome-Multi-Objective-Deep-Learning}.
Generative Artificial Intelligence (AI) has rapidly advanced the field of computer vision by enabling machines to create and interpret visual data with unprecedented sophistication. This transformation builds upon a foundation of generative models to produce realistic images, videos, and 3D or 4D content. Traditionally, generative models primarily focus on visual fidelity while often neglecting the physical plausibility of generated content. This gap limits their effectiveness in applications requiring adherence to real-world physical laws, such as robotics, autonomous systems, and scientific simulations. As generative AI evolves to increasingly integrate physical realism and dynamic simulation, its potential to function as a "world simulator" expands-enabling the modeling of interactions governed by physics and bridging the divide between virtual and physical realities. This survey systematically reviews this emerging field of physics-aware generative AI in computer vision, categorizing methods based on how they incorporate physical knowledge-either through explicit simulation or implicit learning. We analyze key paradigms, discuss evaluation protocols, and identify future research directions. By offering a comprehensive overview, this survey aims to help future developments in physically grounded generation for vision. The reviewed papers are summarized at https://github.com/BestJunYu/Awesome-Physics-aware-Generation.
A Note on the Conversion of Nonnegative Integers to the Canonical Signed-digit Representation
2025-01-19
R. J. Cintra
This note addresses the signed-digit representation of non-negative integer binary numbers. We review and revisit popular literature methods for canonical signed-digit representation. A method based on string substitution is discussed.
Cities are some of the most intricate and advanced creations of humanity. Most objects in cities are perfectly synchronised to coordinate activities like jobs, education, transportation, entertainment or waste management. Although each city has its characteristics, some commonalities can be observed across most cities, like issues related to noise, pollution, segregation and others. Further, some of these issues might be accentuated in larger or smaller cities. For example, with more people, a city might experience more competition for space so rents would be higher. The urban scaling theory gives a framework to analyse cities in the context of their size. New data for analysing urban scaling theory allow an understanding of how urban metrics change with their population size, whether they apply across most regions or if patterns correspond only to some country or region. Yet, reducing a city and all its complexity into a single indicator might simplify urban areas to an extent where their disparities and variations are overlooked. Often, the differences between the living conditions in different parts of the same city are bigger than the degree of variation observed between cities. For example, in terms of rent or crime, within-city variations might be more significant than between cities. Here, we review some urban scaling principles and explore ways to analyse variations within the same city.
A Comprehensive Survey on Integrating Large Language Models with Knowledge-Based Methods
2025-01-19
Lilian Some, Wenli Yang, Michael Bain, Byeong Kang
The rapid development of artificial intelligence has brought about substantial advancements in the field. One promising direction is the integration of Large Language Models (LLMs) with structured knowledge-based systems. This approach aims to enhance AI capabilities by combining the generative language understanding of LLMs with the precise knowledge representation of structured systems. This survey explores the synergy between LLMs and knowledge bases, focusing on real-world applications and addressing associated technical, operational, and ethical challenges. Through a comprehensive literature review, the study identifies critical issues and evaluates existing solutions. The paper highlights the benefits of integrating generative AI with knowledge bases, including improved data contextualization, enhanced model accuracy, and better utilization of knowledge resources. The findings provide a detailed overview of the current state of research, identify key gaps, and offer actionable recommendations. These insights contribute to advancing AI technologies and support their practical deployment across various sectors.
Canada's research productivity in Library and Information Science (LIS) is significant: studies have found that Canada ranks third globally in terms of output. As the LIS field continues to grow, the pace of output accelerates, and the scope of this work expands. The recently launched Canadian Publications in Library and Information Science Database compiles all Canadian scientific publications, including those authored by faculty members and academic librarians. This database offers the advantage of encompassing articles and librarian publications that may not be typically included in traditional bibliometric surveys, such as those conducted using databases like Web of Science, Scopus, and Library and Information Science Abstracts (LISA). Using this data, this study maps the scholarly contributions of Canadian LIS scholars and academic librarians to the field of LIS and examines whether Canadian LIS research is characterized by silos. This paper examines the similarities and differences in research output, impact, topics, and publication venues between academic librarians and scholars in Canada, as well as the extent to which academics and practitioners engage in research collaborations or reference each other's work. We find that while there is some degree of overlap in research topics and publication venues between LIS academics and academic librarians, the two groups appear to act as distinct research communities with distinct topical foci and publishing habits. The two groups also do not appear to engage with each other strongly, either through collaboration or citing each other's work.
Symmetries and Anomalies of Hamiltonian Staggered Fermions
2025-01-18
Simon Catterall, Arnab Pradhan
We review the shift, time reversal and phase symmetries of Hamiltonian staggered fermions and their connection to continuum symmetries concentrating in particular on the case of massless fermions and (3+1) dimensions. We construct operators using the staggered fields that implement these symmetries on finite lattices. We show that it is possible to build shift symmetries that commute with time reversal by combining the elementary shifts with the phase symmetry. In general these symmetries do not form a mutually commuting set and the system suffers from 't Hooft anomalies. However, in the case where the phase symmetry is broken to $Z_4$ by four fermion interactions we find that these lattice anomalies cancel. The naive continuum limit of this anomaly free lattice model has the symmetries and matter representations of the Pati-Salam GUT.
Enterprise ontology serves as a foundational framework for semantically comprehending the nature of organizations and the essential components that uphold their integrity. The systematic and conceptual understanding of organizations has garnered significant attention from researchers due to its pivotal role in various domains, including business modeling, enterprise architecture, business process management, context-aware systems, application development, interoperability across diverse systems and platforms, knowledge management, organizational learning and innovation, and conflict resolution within organizations. Achieving a consensus on the concepts related to the fundamental elements that constitute an organization is therefore critical. This study aims to conduct a comprehensive analysis and comparison of existing conceptual models of enterprises as documented in scholarly articles published over the past decade. We discuss the strengths and weaknesses of each model and introduce a robust framework for their evaluation. To facilitate this evaluation, we propose several pertinent criteria derived from established methodologies for assessing ontologies. Furthermore, we identify contemporary challenges and issues that have been overlooked in prior studies, offering insights and suggestions for future research directions in enterprise modeling. This article ultimately presents a roadmap for enhancing the systematic understanding of organizations through refined enterprise ontology frameworks.
The digitalization of public administration has advanced significantly on a global scale. Many governments now view digital platforms as essential for improving the delivery of public services and fostering direct communication between citizens and public institutions. However, this view overlooks the role played by digital intermediaries significantly shape the provision of e-government services. Using Chile as a case study, we analyze these intermediaries through a national survey on digitalization, we find five types of intermediaries: family members, peers, political figures, bureaucrats, and community leaders. The first two classes comprise close intermediaries, while the latter three comprise hierarchical intermediaries. Our findings suggest that all these intermediaries are a critical but underexplored element in the digitalization of public administration.
Experimental models for cohesive granular materials: a review
2025-01-18
Ram Sudhir Sharma, Alban Sauret
Granular materials are involved in most industrial and environmental processes, as well as many civil engineering applications. Although significant advances have been made in understanding the statics and dynamics of cohesionless grains over the past decades, most granular systems we encounter often display some adhesive forces between grains. The presence of cohesion has effects at distances substantially larger than the closest neighbors and consequently can greatly modify their overall behavior. While considerable progress has been made in understanding and describing cohesive granular systems through idealized numerical simulations, controlled experiments corroborating and expanding the wide range of behavior remain challenging to perform. In recent years, various experimental approaches have been developed to control inter-particle adhesion that now pave the way to further our understanding of cohesive granular flows. This article reviews different approaches for making particles sticky, controlling their relative stickiness, and thereby studying their granular and bulk mechanics. Some recent experimental studies relying on model cohesive grains are synthesized, and opportunities and perspectives in this field are discussed.
A comprehensive survey on RPL routing-based attacks, defences and future directions in Internet of Things
2025-01-18
Anil K Prajapati, Emmanuel S Pilli, Ramesh B Battula, Vijay Varadharajan, Abhishek Verma, R C Joshi
The Internet of Things (IoT) is a network of digital devices like sensors, processors, embedded and communication devices that can connect to and exchange data with other devices and systems over the internet. IoT devices have limitations on power, memory, and computational resources. Researchers have developed the IPv6 Over Low-power Wireless Personal Area Network (6LoWPAN) protocols to provide wireless connectivity among these devices while overcoming the constraints on resources. 6LoWPAN has been approved subsequently by the Internet Engineering Task Force (IETF). The IETF Routing Over Low-power and Lossy Networks (ROLL) standardized the Routing Protocol for LLNs known as RPL (IETF RFC 6550), which is part of the 6LoWPAN stack. However, IoT devices are vulnerable to various attacks on RPL-based routing. This survey provides an in depth study of existing RPL-based attacks and defense published from year 2011 to 2024 from highly reputed journals and conferences. By thematic analysis of existing routing attacks on RPL, we developed a novel attack taxonomy which focuses on the nature of routing attacks and classifies them into 12 major categories. Subsequently, the impact of each attack on the network is analyzed and discussed real life scenarios of these attacks. Another contribution of this survey proposed a novel taxonomy for classification of defense mechanisms into 8 major categories against routing attacks based on type of defense strategy. The detailed analysis of each defense mechanism with real life applicability is explained. Furthermore, evaluation tools such as testbeds and simulators for RPL-based attack and defense are discussed and critically analyzed in terms of real world applicability. Finally, open research challenges are presented on the basis of research gaps of existing literature along with research directions for practitioners and researchers.
A Non-Stop Aurora? The Intriguing Radio Emission from the Rapidly Rotating Magnetic Massive Star HR 5907
2025-01-18
Ayan Biswas, Barnali Das, James A. Barron, Gregg A. Wade, Gonzalo Holgado
HR 5907 (HD 142184) stands out among magnetic OB stars for its rapid rotation, exceptionally hard X-ray emission, and strong magnetic field. High-frequency (>5 GHz) radio emission from the star exhibits an approximately flat spectrum that can be attributed to gyrosynchrotron emission from a dense centrifugal magnetosphere. In a survey of radio emission from massive stars at sub-GHz frequencies, we noticed remarkable low-frequency radio emission from this star, characterized by high circular polarization and brightness temperature, which is inconsistent with the gyrosynchrotron model. We present a follow-up low-frequency radio study of this star with the upgraded Giant Metrewave Radio Telescope (uGMRT) in search of emission mechanisms that can go undiagnosed at higher frequencies. We detect variable radio emission characterized by varying degrees of circular polarization (15-45%) throughout the rotation cycle. The broad-band spectral fitting also suggests additional emission components at lower frequencies. We show that the observed emission is likely auroral emission via electron cyclotron maser emission (ECME), and identify this star as a Main-sequence Radio Pulse emitter (MRP). For MRPs, ECME is usually observed as short polarized enhancements near the magnetic nulls of the star. The detection of a high degree of circular polarization (>15%) at all times makes HR 5907 unique among MRPs. This is only the second MRP after $\rho$ Oph C (detected polarization fraction: 0-60%) that exhibits persistent coherent radio emission attributed to the nearly aligned stellar magnetic and rotational axes.
Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption
2025-01-18
Jinyuan Liu, Guanyao Wu, Zhu Liu, Di Wang, Zhiying Jiang, Long Ma, Wei Zhong, Xin Fan, Risheng Liu
Infrared-visible image fusion (IVIF) is a critical task in computer vision, aimed at integrating the unique features of both infrared and visible spectra into a unified representation. Since 2018, the field has entered the deep learning era, with an increasing variety of approaches introducing a range of networks and loss functions to enhance visual performance. However, challenges such as data compatibility, perception accuracy, and efficiency remain. Unfortunately, there is a lack of recent comprehensive surveys that address this rapidly expanding domain. This paper fills that gap by providing a thorough survey covering a broad range of topics. We introduce a multi-dimensional framework to elucidate common learning-based IVIF methods, from visual enhancement strategies to data compatibility and task adaptability. We also present a detailed analysis of these approaches, accompanied by a lookup table clarifying their core ideas. Furthermore, we summarize performance comparisons, both quantitatively and qualitatively, focusing on registration, fusion, and subsequent high-level tasks. Beyond technical analysis, we discuss potential future directions and open issues in this area. For further details, visit our GitHub repository: https://github.com/RollingPlain/IVIF_ZOO.
Skyrmions, merons, bimerons, and skyrmionic hopfions are quasiparticles which have stable topological textures. They have been observed across multiple physical domains including nucleons, water waves, magnetic materials, electromagnetic fields, condensed-matter physics, liquid crystals, and Bose-Einstein condensates. The nontrivial topological texture of these quasiparticles exhibited promising applications across several research fields including cosmology, particle physics, superfluids, high-energy physics, optics, condensed matter, early-universe cosmology, cold quantum matter, and liquid crystals. These quasiparticles have most recently been observed in paraxial vector beams. Due to the inhomogeneous polarization distribution of vector beams, the quasiparticle textures reside in the Stokes vector fields. Here, we review recent developments in paraxial quasiparticle research and provide detailed information on their fundamental properties and future prospects. We discuss various techniques which have been used to experimentally generate these quasiparticles and we examine a number of their proposed applications. Notably, this review offers insight into the concepts and techniques which can be applied to the generation of optical skyrmions in paraxial laser beams, and investigates their intriguing applications across both fundamental and applied optics.
Studying the Baryon Acoustic Oscillations using photometric redshifts from the DESI Legacy Imaging survey DR9
2025-01-18
Christoph Saulder, Yong-Seon Song, Minji Oh, Yi Zheng, Ashley J. Ross, Rongpu Zhou, Jeffrey A. Newman, Chia-Hsun Chuang, Jessica Nicole Aguilar, Steven Ahlen, Robert Blum, David Brooks, Todd Claybaugh, Axel de la Macorra, Biprateep Dey, Zhejie Ding, Peter Doel, Jaime E. Forero-Romero, Enrique Gaztañaga, Satya Gontcho A Gontcho, Gaston Gutierrez, Stephanie Juneau, David Kirkby, Theodore Kisner, Anthony Kremin, Andrew Lambert, Martin Landriau, Laurent Le Guillou, Michael Levi, Aaron Meisner, Eva-Maria Mueller, Andrea Muñoz-Gutiérrez, Gustavo Niz, Francisco Prada, Mehdi Rezaie, Graziano Rossi, Eusebio Sanchez, Michael Schubnell, Joseph Harry Silber, David Sprayberry, Gregory Tarlé, Francisco Valdes, Benjamin Alan Weaver, Hu Zou
Context. The DESI Legacy Imaging Survey DR9, with its extensive dataset of galaxy locations and photometric redshifts, presents an opportunity to study Baryon Acoustic Oscillations (BAO) in the region covered by the ongoing DESI spectroscopic survey. Aims. We aim to investigate differences between different parts of the DR9 footprint. Furthermore, we want to measure the BAO scale for luminous red galaxies within them. Our selected redshift range of 0.6 to 0.8 corresponds to the bin in which a tension between DESI Y1 and eBOSS was found. Methods. We calculated the anisotropic two-point correlation function in a modified binning scheme to detect the BAO in DR9 data. We then use template fits based on simulations to measure the BAO scale in the imaging data. Results. Our analysis revealed the expected correlation function shape in most of the footprint areas, showing a BAO scale consistent with Planck's observations. Aside from identified mask-related data issues in the southern region of the South Galactic Cap, we also find a notable variance between the different footprints. Conclusions. We find that this variance is consistent with the difference between the DESI Y1 and eBOSS data and it supports the argument that that tension is caused by sample variance. Additionally, we also uncovered systematic biases not previously accounted for in photometric BAO studies. We emphasize the necessity of adjusting for the systematic shift in the BAO scale associated with typical photometric redshift uncertainties to ensure accurate measurements.
Development of Application-Specific Large Language Models to Facilitate Research Ethics Review
2025-01-18
Sebastian Porsdam Mann, Joel Seah Jiehao, Stephen R. Latham, Julian Savulescu, Mateo Aboy, Brian D. Earp
Institutional review boards (IRBs) play a crucial role in ensuring the ethical conduct of human subjects research, but face challenges including inconsistency, delays, and inefficiencies. We propose the development and implementation of application-specific large language models (LLMs) to facilitate IRB review processes. These IRB-specific LLMs would be fine-tuned on IRB-specific literature and institutional datasets, and equipped with retrieval capabilities to access up-to-date, context-relevant information. We outline potential applications, including pre-review screening, preliminary analysis, consistency checking, and decision support. While addressing concerns about accuracy, context sensitivity, and human oversight, we acknowledge remaining challenges such as over-reliance on AI and the need for transparency. By enhancing the efficiency and quality of ethical review while maintaining human judgment in critical decisions, IRB-specific LLMs offer a promising tool to improve research oversight. We call for pilot studies to evaluate the feasibility and impact of this approach.
Computational Discovery of Chiasmus in Ancient Religious Text
2025-01-18
Hope McGovern, Hale Sirin, Tom Lippincott
Chiasmus, a debated literary device in Biblical texts, has captivated mystics while sparking ongoing scholarly discussion. In this paper, we introduce the first computational approach to systematically detect chiasmus within Biblical passages. Our method leverages neural embeddings to capture lexical and semantic patterns associated with chiasmus, applied at multiple levels of textual granularity (half-verses, verses). We also involve expert annotators to review a subset of the detected patterns. Despite its computational efficiency, our method achieves robust results, with high inter-annotator agreement and system precision@k of 0.80 at the verse level and 0.60 at the half-verse level. We further provide a qualitative analysis of the distribution of detected chiasmi, along with selected examples that highlight the effectiveness of our approach.
In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review
2025-01-18
Amelia Jiménez-Sánchez, Natalia-Rozalia Avlona, Sarah de Boer, Víctor M. Campello, Aasa Feragen, Enzo Ferrante, Melanie Ganz, Judy Wawira Gichoya, Camila González, Steff Groefsema, Alessa Hering, Adam Hulman, Leo Joskowicz, Dovile Juodelyte, Melih Kandemir, Thijs Kooi, Jorge del Pozo Lérida, Livie Yumeng Li, Andre Pacheco, Tim Rädsch, Mauricio Reyes, Théo Sourget, Bram van Ginneken, David Wen, Nina Weng, Jack Junchi Xu, Hubert Dariusz Zając, Maria A. Zuluaga, Veronika Cheplygina
Datasets play a critical role in medical imaging research, yet issues such as label quality, shortcuts, and metadata are often overlooked. This lack of attention may harm the generalizability of algorithms and, consequently, negatively impact patient outcomes. While existing medical imaging literature reviews mostly focus on machine learning (ML) methods, with only a few focusing on datasets for specific applications, these reviews remain static -- they are published once and not updated thereafter. This fails to account for emerging evidence, such as biases, shortcuts, and additional annotations that other researchers may contribute after the dataset is published. We refer to these newly discovered findings of datasets as research artifacts. To address this gap, we propose a living review that continuously tracks public datasets and their associated research artifacts across multiple medical imaging applications. Our approach includes a framework for the living review to monitor data documentation artifacts, and an SQL database to visualize the citation relationships between research artifact and dataset. Lastly, we discuss key considerations for creating medical imaging datasets, review best practices for data annotation, discuss the significance of shortcuts and demographic diversity, and emphasize the importance of managing datasets throughout their entire lifecycle. Our demo is publicly available at http://130.226.140.142.
Microwave-driven synthesis and modification of nanocarbons and hybrids in liquid and solid phases
2025-01-18
Nagaraj Nandihalli
Over the past 20 years, nanocarbons have become more significant as nanostructured fillers in composites and, more recently, as functional elements in a brand-new class of hybrid materials. Microwave-assisted synthesis and processing is a burgeoning subject matter in materials research with significant strides in the realm of nanocarbon during the last decade. The review examines recent approaches to producing various nanocarbons using microwaves as energy sources, the characterization of such materials for various applications, and their results. The underlying factors supporting the increased performance of such materials or their composites are analyzed and reaction mechanisms are presented wherever necessary. In particular, the recently developed and verified approaches to produce porous carbon materials, CNTs and fibers, carbon nanospheres, carbon dots, CQDs, reduced graphene oxide, nanocarbon hybrid materials, and the purification and modification of CNTs are discussed. The reduction of graphene oxide and the preparation of graphene derivative hybrids using solid-state and liquid-state routes such as polyopl, mixed solvents, ionic liquids, and microwave-assisted hydrothermal/solvothermal methods are analyzed in detail. In addition, the principles of microwave heating in liquid and solid states, the use of metals particles as arcing agents or catalysts, and carbonaceous materials as internal or external susceptors during synthesis and modifications are presented in detail.
Algorithmic Derivation of Human Spatial Navigation Indices From Eye Movement Data
2025-01-18
Sobhan Teymouri, Fatemeh Alizadehziri, Mobina Zibandehpoor, Mehdi Delrobaei
Spatial navigation is a complex cognitive function involving sensory inputs, such as visual, auditory, and proprioceptive information, to understand and move within space. This ability allows humans to create mental maps, navigate through environments, and process directional cues, crucial for exploring new places and finding one's way in unfamiliar surroundings. This study takes an algorithmic approach to extract indices relevant to human spatial navigation using eye movement data. Leveraging electrooculography signals, we analyzed statistical features and applied feature engineering techniques to study eye movements during navigation tasks. The proposed work combines signal processing and machine learning approaches to develop indices for navigation and orientation, spatial anxiety, landmark recognition, path survey, and path route. The analysis yielded five subscore indices with notable accuracy. Among these, the navigation and orientation subscore achieved an R2 score of 0.72, while the landmark recognition subscore attained an R2 score of 0.50. Additionally, statistical features highly correlated with eye movement metrics, including blinks, saccades, and fixations, were identified. The findings of this study can lead to more cognitive assessments and enable early detection of spatial navigation impairments, particularly among individuals at risk of cognitive decline.
Softplus and Neural Architectures for Enhanced Negative Binomial INGARCH Modeling
2025-01-18
Divya Kuttenchalil Andrews, N. Balakrishna
The study addresses a significant gap in the literature by introducing the Softplus negative binomial Integer-valued Generalized Autoregressive Conditional Heteroskedasticity (sp NB- INGARCH) model and establishing its stationarity properties, alongside methodology for parameter estimation. Building upon this foundation, the Neural negative binomial INGARCH (neu - NB-INGARCH) model is proposed, designed to enhance predictive accuracy while accommodating moderate non-stationarity in count time series data. A simulation study and data analysis demonstrate the efficacy of the sp NB-INGARCH model, while the practical utility of the neu - NB - INGARCH model is showcased through a comprehensive analysis of a healthcare data. Additionally, a thorough literature review is presented, focusing on the application of neural networks in time series modeling, with particular emphasis on count time series. In short, this work contributes to advancing the theoretical understanding and practical application of neural network-based models in count time series forecasting.
Ensuring safety alignment has become a critical requirement for large language models (LLMs), particularly given their widespread deployment in real-world applications. However, LLMs remain susceptible to jailbreak attacks, which exploit system vulnerabilities to bypass safety measures and generate harmful outputs. Although numerous defense mechanisms based on adversarial training have been proposed, a persistent challenge lies in the exacerbation of over-refusal behaviors, which compromise the overall utility of the model. To address these challenges, we propose a Latent-space Adversarial Training with Post-aware Calibration (LATPC) framework. During the adversarial training phase, LATPC compares harmful and harmless instructions in the latent space and extracts safety-critical dimensions to construct refusal features attack, precisely simulating agnostic jailbreak attack types requiring adversarial mitigation. At the inference stage, an embedding-level calibration mechanism is employed to alleviate over-refusal behaviors with minimal computational overhead. Experimental results demonstrate that, compared to various defense methods across five types of jailbreak attacks, LATPC framework achieves a superior balance between safety and utility. Moreover, our analysis underscores the effectiveness of extracting safety-critical dimensions from the latent space for constructing robust refusal feature attacks.
Unified Flow Rule of Undeveloped and Fully Developed Dense Granular Flows Down Rough Inclines
2025-01-18
Yanbin Wu, Thomas Pähtz, Zixiao Guo, Lu Jing, Zhao Duan, Zhiguo He
We report on chute measurements of the free-surface velocity $v$ in dense flows of spheres and diverse sands and spheres-sand mixtures down rough inclines. These and previous measurements are inconsistent with standard flow rules, in which the Froude number $v/\sqrt{gh}$ scales linearly with $h/h_s$ or $(\tan\theta/\mu_r)^2h/h_s$, where $\mu_r$ is the dynamic friction coefficient, $h$ the flow thickness, and $h_s(\theta)$ its smallest value that permits a steady, uniform dense flow state at a given inclination angle $\theta$. This is because the characteristic length $L$ a flow needs to fully develop can exceed the chute or travel length $l$ and because neither rule is universal for fully developed flows across granular materials. We use a dimensional analysis motivated by a recent unification of sediment transport to derive a flow rule that solves both problems in accordance with our and previous measurements: $v=v_\infty[1-\exp(-l/L)]^{1/2}$, with $v_\infty\propto\mu_r^{3/2}\left[(\tan\theta-\mu_r)h\right]^{4/3}$ and $L\propto\mu_r^3\left[(\tan\theta-\mu_r)h\right]^{5/3}h$.
Effects of particle elongation on dense granular flows down a rough inclined plane
2025-01-18
Jixiong Liu, Lu Jing, Thomas Pähtz, Yifei Cui, Gordon G. D. Zhou, Xudong Fu
Granular materials in nature are nearly always non-spherical, but particle shape effects in granular flow remain largely elusive. This study uses discrete element method simulations to investigate how elongated particle shapes affect the mobility of dense granular flows down a rough incline. For a range of systematically varied particle length-to-diameter aspect ratios (AR), we run simulations with various flow thicknesses $h$ and slope angles $\theta$ to extract the well-known $h_\textrm{stop}(\theta)$ curves (below which the flow ceases) and the $Fr$-$h/h_\textrm{stop}$ relations following Pouliquen's approach, where $Fr=u/\sqrt{gh}$ is the Froude number, $u$ is the mean flow velocity, and $g$ is the gravitational acceleration. The slope $\beta$ of the $Fr$-$h/h_\textrm{stop}$ relations shows an intriguing S-shaped dependence on AR, with two plateaus at small and large AR, respectively, transitioning with a sharp increase. We understand this S-shaped dependence by examining statistics of particle orientation, alignment, and hindered rotation. We find that the rotation ability of weakly elongated particles ($\textrm{AR}\lesssim1.3$) remains similar to spheres, leading to the first plateau in the $\beta$-AR relation, whereas the effects of particle orientation saturates beyond $\textrm{AR}\approx2.0$, explaining the second plateau. An empirical sigmoidal function is proposed to capture this non-linear dependence. The findings are expected to enhance our understanding of how particle shape affects the flow of granular materials from both the flow- and particle-scale perspectives.
A pioneering experiment combining single-antenna and aperture-synthesis data to measure Faraday rotation with GMIMS and the CGPS
2025-01-18
Anna Ordog, Jo-Anne C. Brown, T. L. Landecker, Alex S. Hill, Roland Kothes, Jennifer L. West, John M. Dickey, Marijke Haverkorn, Ettore Carretti, Alec J. M. Thomson, Andrea Bracco, D. A. Del Rizzo, Ryan R. Ransom, Robert I. Reid
Structures in the magneto-ionic medium exist across a wide range of angular sizes due to large-scale magnetic fields coherent over the Galactic spiral arms combined with small-scale fluctuations in the magnetic field and electron density resulting from energy injection processes such as supernovae. For the first time, we produce diffuse Galactic synchrotron emission Faraday rotation maps covering all spatial scales down to $3'$ resolution for Galactic magnetic field studies. These maps complement standard total and polarized intensity maps combining single-antenna and interferometric data that have been produced, for example, for the Canadian Galactic Plane Survey (CGPS). Such combined maps have sensitivity to large scales from the single-antenna component, and the resolution from the interferometric component. We combine Global Magneto-Ionic Medium Survey High-Band-North (GMIMS-HBN) single-antenna and CGPS aperture-synthesis polarization data after spatial filtering, producing Stokes $Q$ and $U$ maps individually for the four CGPS frequency channels. We calculate Rotation Measures (RM) for all pixels using a linear fit to polarization angle versus wavelength squared. The RM maps show magnetic field structures on the full range of scales probed by this dataset. Regions of smooth polarized emission require the large-scale sensitivity of the single-antenna data to illuminate the Faraday depths, while the aperture-synthesis data reveal small-scale variability in the Faraday rotation. Despite the limitation of the 35 MHz bandwidth of the CGPS, we demonstrate that useful information on Faraday rotation structures can be obtained by combining single-antenna and aperture-synthesis data, highlighting the important synergy between future broadband interferometric and single-antenna polarization surveys.
Large language models (LLMs) have significantly impacted human society, influencing various domains. Among them, academia is not simply a domain affected by LLMs, but it is also the pivotal force in the development of LLMs. In academic publications, this phenomenon is represented during the incorporation of LLMs into the peer review mechanism for reviewing manuscripts. We proposed the concept of automated scholarly paper review (ASPR) in our previous paper. As the incorporation grows, it now enters the coexistence phase of ASPR and peer review, which is described in that paper. LLMs hold transformative potential for the full-scale implementation of ASPR, but they also pose new issues and challenges that need to be addressed. In this survey paper, we aim to provide a holistic view of ASPR in the era of LLMs. We begin with a survey to find out which LLMs are used to conduct ASPR. Then, we review what ASPR-related technological bottlenecks have been solved with the incorporation of LLM technology. After that, we move on to explore new methods, new datasets, new source code, and new online systems that come with LLMs for ASPR. Furthermore, we summarize the performance and issues of LLMs in ASPR, and investigate the attitudes and reactions of publishers and academia to ASPR. Lastly, we discuss the challenges associated with the development of LLMs for ASPR. We hope this survey can serve as an inspirational reference for the researchers and promote the progress of ASPR for its actual implementation.
New Fashion Products Performance Forecasting: A Survey on Evolutions, Models and Emerging Trends
2025-01-17
Andrea Avogaro, Luigi Capogrosso, Andrea Toaiari, Franco Fummi, Marco Cristani
The fast fashion industry's insatiable demand for new styles and rapid production cycles has led to a significant environmental burden. Overproduction, excessive waste, and harmful chemicals have contributed to the negative environmental impact of the industry. To mitigate these issues, a paradigm shift that prioritizes sustainability and efficiency is urgently needed. Integrating learning-based predictive analytics into the fashion industry represents a significant opportunity to address environmental challenges and drive sustainable practices. By forecasting fashion trends and optimizing production, brands can reduce their ecological footprint while remaining competitive in a rapidly changing market. However, one of the key challenges in forecasting fashion sales is the dynamic nature of consumer preferences. Fashion is acyclical, with trends constantly evolving and resurfacing. In addition, cultural changes and unexpected events can disrupt established patterns. This problem is also known as New Fashion Products Performance Forecasting (NFPPF), and it has recently gained more and more interest in the global research landscape. Given its multidisciplinary nature, the field of NFPPF has been approached from many different angles. This comprehensive survey wishes to provide an up-to-date overview that focuses on learning-based NFPPF strategies. The survey is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological flow, allowing for a systematic and complete literature review. In particular, we propose the first taxonomy that covers the learning panorama for NFPPF, examining in detail the different methodologies used to increase the amount of multimodal information, as well as the state-of-the-art available datasets. Finally, we discuss the challenges and future directions.
Sign Switching in Dark Sector Coupling Interactions as a Candidate for Resolving Cosmological Tensions
2025-01-17
Miguel A. Sabogal, Emanuelly Silva, Rafael C. Nunes, Suresh Kumar, Eleonora Di Valentino
The $\Lambda$CDM model has successfully explained a wide range of cosmological observations, but is increasingly challenged by the emergence of cosmological tensions, particularly the Hubble Tension $H_0$ and the $S_8$ tension. The Hubble Tension, with a significance above 5$\sigma$, and the $S_8$ tension, showing a discrepancy of approximately 2-4$\sigma$, highlight inconsistencies between measurements of the local and early universe. This paper expands a well-established Interacting Dark Energy (IDE) phenomenological scenario, where dark matter (DM) can transfer energy to dark energy (DE) or vice versa, depending on the sign of the coupling parameter $\xi$. The novel feature consists in a transition mechanism which reverses the direction of the energy-momentum transfer after the redshift where the densities of the dark species are the same. We evaluate this model using a comprehensive set of recent observational data, including Baryon Acoustic Oscillations (BAO) from the DESI survey, Type Ia Supernovae from the PantheonPlus, DESY5 and Union3 samples, and Cosmic Microwave Background (CMB) data from Planck. Our analysis shows that this scenario can potentially relax both the $H_0$ and $S_8$ tensions simultaneously. We find the new model to be weakly preferred over $\Lambda$CDM by BAO-DESI data. However, we show that the IDE model features positive Bayesian evidence compared to $\Lambda$CDM only when Cepheid distance calibration in the SH0ES sample is used to calibrate SNIa data from PantheonPlus.
Natural Language Processing (NLP) is an essential subset of artificial intelligence. It has become effective in several domains, such as healthcare, finance, and media, to identify perceptions, opinions, and misuse, among others. Privacy is no exception, and initiatives have been taken to address the challenges of usable privacy notifications to users with the help of NLP. To this aid, we conduct a literature review by analyzing 109 papers at the intersection of NLP and privacy policies. First, we provide a brief introduction to privacy policies and discuss various facets of associated problems, which necessitate the application of NLP to elevate the current state of privacy notices and disclosures to users. Subsequently, we a) provide an overview of the implementation and effectiveness of NLP approaches for better privacy policy communication; b) identify the methodologies that can be further enhanced to provide robust privacy policies; and c) identify the gaps in the current state-of-the-art research. Our systematic analysis reveals that several research papers focus on annotating and classifying privacy texts for analysis but need to adequately dwell on other aspects of NLP applications, such as summarization. More specifically, ample research opportunities exist in this domain, covering aspects such as corpus generation, summarization vectors, contextualized word embedding, identification of privacy-relevant statement categories, fine-grained classification, and domain-specific model tuning.
An Ontology for Social Determinants of Education (SDoEd) based on Human-AI Collaborative Approach
2025-01-17
Navya Martin Kollapally, James Geller, Patricia Morreale, Daehan Kwak
The use of computational ontologies is well-established in the field of Medical Informatics. The topic of Social Determinants of Health (SDoH) has also received extensive attention. Work at the intersection of ontologies and SDoH has been published. However, a standardized framework for Social Determinants of Education (SDoEd) is lacking. In this paper, we are closing the gap by introducing an SDoEd ontology for creating a precise conceptualization of the interplay between life circumstances of students and their possible educational achievements. The ontology was developed utilizing suggestions from ChatGPT-3.5-010422 and validated using peer-reviewed research articles. The first version of developed ontology was evaluated by human experts in the field of education and validated using standard ontology evaluation software. This version of the SDoEd ontology contains 231 domain concepts, 10 object properties, and 24 data properties
No evidence that the binary black hole mass distribution evolves with redshift
2025-01-17
Max Lalleman, Kevin Turbang, Thomas Callister, Nick van Remortel
The mass distribution of merging binary black holes is generically predicted to evolve with redshift, reflecting systematic changes in their astrophysical environment, stellar progenitors, and/or dominant formation channels over cosmic time. Whether or not such an effect is observed in gravitational-wave data, however, remains an open question, with some contradictory results present in the literature. In this paper, we study the ensemble of binary black holes within the latest GWTC-3 catalog released by the LIGO-Virgo-KAGRA Collaboration, systematically surveying for possible evolution of their mass distribution with redshift. We specifically focus on two key features present in the binary black hole primary mass distribution -- (1) an excess of $35\,M_\odot$ black holes and (2) a broad power-law continuum ranging from 10 to $\gtrsim 80 M_\odot$ -- and ask if one or both of these features are observed to vary with redshift. We find no evidence that either the Gaussian peak or power-law continuum components of the mass distribution change with redshift. In some cases, we place somewhat stringent bounds on the degree of allowed redshift evolution. Most notably, we find that the mean location of the $35\,M_\odot$ peak and the slope of the power-law continuum are constrained to remain approximately constant below redshift $z\approx 1$. The data remain more agnostic about other forms of redshift dependence, such as evolution in the height of the $35\,M_\odot$ excess or the minimum and maximum black hole masses. In all cases, we conclude that a redshift-dependent mass spectrum remains possible, but that it is not required by current data.
The year 2024 marks two anniversaries: The 70th anniversary of CERN and the 50th anniversary of the $J/\Psi$ discovery. At this occasion I have been asked to give review talks on the significance of these anniversaries. This article is an expanded version of these talks.
Provably Safeguarding a Classifier from OOD and Adversarial Samples: an Extreme Value Theory Approach
2025-01-17
Nicolas Atienza, Christophe Labreuche, Johanne Cohen, Michele Sebag
This paper introduces a novel method, Sample-efficient Probabilistic Detection using Extreme Value Theory (SPADE), which transforms a classifier into an abstaining classifier, offering provable protection against out-of-distribution and adversarial samples. The approach is based on a Generalized Extreme Value (GEV) model of the training distribution in the classifier's latent space, enabling the formal characterization of OOD samples. Interestingly, under mild assumptions, the GEV model also allows for formally characterizing adversarial samples. The abstaining classifier, which rejects samples based on their assessment by the GEV model, provably avoids OOD and adversarial samples. The empirical validation of the approach, conducted on various neural architectures (ResNet, VGG, and Vision Transformer) and medium and large-sized datasets (CIFAR-10, CIFAR-100, and ImageNet), demonstrates its frugality, stability, and efficiency compared to the state of the art.
Generative Artificial Intelligence: Implications for Biomedical and Health Professions Education
2025-01-17
William Hersh
Generative AI has had a profound impact on biomedicine and health, both in professional work and in education. Based on large language models (LLMs), generative AI has been found to perform as well as humans in simulated situations taking medical board exams, answering clinical questions, solving clinical cases, applying clinical reasoning, and summarizing information. Generative AI is also being used widely in education, performing well in academic courses and their assessments. This review summarizes the successes of LLMs and highlights some of their challenges in the context of education, most notably aspects that may undermines the acquisition of knowledge and skills for professional work. It then provides recommendations for best practices overcoming shortcomings for LLM use in education. Although there are challenges for use of generative AI in education, all students and faculty, in biomedicine and health and beyond, must have understanding and be competent in its use.
In recent years, Semantic Communication (SemCom), which aims to achieve efficient and reliable transmission of meaning between agents, has garnered significant attention from both academia and industry. To ensure the security of communication systems, encryption techniques are employed to safeguard confidentiality and integrity. However, traditional cryptography-based encryption algorithms encounter obstacles when applied to SemCom. Motivated by this, this paper explores the feasibility of applying homomorphic encryption to SemCom. Initially, we review the encryption algorithms utilized in mobile communication systems and analyze the challenges associated with their application to SemCom. Subsequently, we employ scale-invariant feature transform to demonstrate that semantic features can be preserved in homomorphic encrypted ciphertext. Based on this finding, we propose a task-oriented SemCom scheme secured through homomorphic encryption. We design the privacy preserved deep joint source-channel coding (JSCC) encoder and decoder, and the frequency of key updates can be adjusted according to service requirements without compromising transmission performance. Simulation results validate that, when compared to plaintext images, the proposed scheme can achieve almost the same classification accuracy performance when dealing with homomorphic ciphertext images. Furthermore, we provide potential future research directions for homomorphic encrypted SemCom.
Unveiling High-dimensional Backstage: A Survey for Reliable Visual Analytics with Dimensionality Reduction
2025-01-17
Hyeon Jeon, Hyunwook Lee, Yun-Hsin Kuo, Taehyun Yang, Daniel Archambault, Sungahn Ko, Takanori Fujiwara, Kwan-Liu Ma, Jinwook Seo
Dimensionality reduction (DR) techniques are essential for visually analyzing high-dimensional data. However, visual analytics using DR often face unreliability, stemming from factors such as inherent distortions in DR projections. This unreliability can lead to analytic insights that misrepresent the underlying data, potentially resulting in misguided decisions. To tackle these reliability challenges, we review 133 papers that address the unreliability of visual analytics using DR. Through this review, we contribute (1) a workflow model that describes the interaction between analysts and machines in visual analytics using DR, and (2) a taxonomy that identifies where and why reliability issues arise within the workflow, along with existing solutions for addressing them. Our review reveals ongoing challenges in the field, whose significance and urgency are validated by five expert researchers. This review also finds that the current research landscape is skewed toward developing new DR techniques rather than their interpretation or evaluation, where we discuss how the HCI community can contribute to broadening this focus.
CSSDM Ontology to Enable Continuity of Care Data Interoperability
2025-01-17
Subhashis Das, Debashis Naskar, Sara Rodriguez Gonzalez, Pamela Hussey
The rapid advancement of digital technologies and recent global pandemic scenarios have led to a growing focus on how these technologies can enhance healthcare service delivery and workflow to address crises. Action plans that consolidate existing digital transformation programs are being reviewed to establish core infrastructure and foundations for sustainable healthcare solutions. Reforming health and social care to personalize home care, for example, can help avoid treatment in overcrowded acute hospital settings and improve the experiences and outcomes for both healthcare professionals and service users. In this information-intensive domain, addressing the interoperability challenge through standards-based roadmaps is crucial for enabling effective connections between health and social care services. This approach facilitates safe and trustworthy data workflows between different healthcare system providers. In this paper, we present a methodology for extracting, transforming, and loading data through a semi-automated process using a Common Semantic Standardized Data Model (CSSDM) to create personalized healthcare knowledge graph (KG). The CSSDM is grounded in the formal ontology of ISO 13940 ContSys and incorporates FHIR-based specifications to support structural attributes for generating KGs. We propose that the CSSDM facilitates data harmonization and linking, offering an alternative approach to interoperability. This approach promotes a novel form of collaboration between companies developing health information systems and cloud-enabled health services. Consequently, it provides multiple stakeholders with access to high-quality data and information sharing.
Prediction Sets and Conformal Inference with Censored Outcomes
2025-01-17
Weiguang Liu, Áureo de Paula, Elie Tamer
Given data on a scalar random variable $Y$, a prediction set for $Y$ with miscoverage level $\alpha$ is a set of values for $Y$ that contains a randomly drawn $Y$ with probability $1 - \alpha$, where $\alpha \in (0,1)$. Among all prediction sets that satisfy this coverage property, the oracle prediction set is the one with the smallest volume. This paper provides estimation methods of such prediction sets given observed conditioning covariates when $Y$ is censored or measured in intervals. We first characterise the oracle prediction set under interval censoring and develop a consistent estimator for the shortest prediction interval that satisfies this coverage property. We then extend these consistency results to accommodate cases where the prediction set consists of multiple disjoint intervals. Second, we use conformal inference to construct a prediction set that achieves a particular notion of finite-sample validity under censoring and maintains consistency as sample size increases. This notion exploits exchangeability to obtain finite sample guarantees on coverage using a specially constructed conformity score function. The procedure accomodates the prediction uncertainty that is irreducible (due to the stochastic nature of outcomes), the modelling uncertainty due to partial identification and also sampling uncertainty that gets reduced as samples get larger. We conduct a set of Monte Carlo simulations and an application to data from the Current Population Survey. The results highlight the robustness and efficiency of the proposed methods.
2025-01-17
Akitaka Ariga, Jamie Boyd, Felix Kling, Albert De Roeck
The proton-proton collisions at the Large Hadron Collider (LHC) produce an intense, high-energy beam of neutrinos of all flavors, collimated in the forward direction. Recently two dedicated neutrino experiments, FASER and SND@LHC, have started operating to take advantage of the TeV energy LHC neutrino beam, with first results released in 2023 and further results released in 2024. The first detection of neutrinos produced at a particle collider opens up a new avenue of research, allowing to study the highest energy neutrinos produced in a controlled laboratory environment, with an associated broad and rich physics program. Neutrino measurements at the LHC will provide important contributions to QCD, neutrino and BSM physics, with impactful implications for astro-particle physics. This review article summarizes the physics motivation, status and plans of, present and future neutrino experiments at the LHC.
A Survey on LLM Test-Time Compute via Search: Tasks, LLM Profiling, Search Algorithms, and Relevant Frameworks
2025-01-17
Xinzhe Li
LLM test-time compute (or LLM inference) via search has emerged as a promising research area with rapid developments. However, current frameworks often adopt distinct perspectives on three key aspects (task definition, LLM profiling, and search procedures), making direct comparisons challenging. Moreover, the search algorithms employed often diverge from standard implementations, and their specific characteristics are not thoroughly specified. In this survey, we provide a comprehensive technical review that unifies task definitions and provides modular definitions of LLM profiling and search procedures. The definitions enable precise comparisons of various LLM inference frameworks while highlighting their departures from conventional search algorithms. We also discuss the applicability, performance, and efficiency of these methods. For further details and ongoing updates, please refer to our GitHub repository: https://github.com/xinzhel/LLM-Agent-Survey/blob/main/search.md
MSTS: A Multimodal Safety Test Suite for Vision-Language Models
2025-01-17
Paul Röttger, Giuseppe Attanasio, Felix Friedrich, Janis Goldzycher, Alicia Parrish, Rishabh Bhardwaj, Chiara Di Bonaventura, Roman Eng, Gaia El Khoury Geagea, Sujata Goswami, Jieun Han, Dirk Hovy, Seogyeong Jeong, Paloma Jeretič, Flor Miriam Plaza-del-Arco, Donya Rooein, Patrick Schramowski, Anastassia Shaitarova, Xudong Shen, Richard Willats, Andrea Zugarini, Bertie Vidgen
Vision-language models (VLMs), which process image and text inputs, are increasingly integrated into chat assistants and other consumer AI applications. Without proper safeguards, however, VLMs may give harmful advice (e.g. how to self-harm) or encourage unsafe behaviours (e.g. to consume drugs). Despite these clear hazards, little work so far has evaluated VLM safety and the novel risks created by multimodal inputs. To address this gap, we introduce MSTS, a Multimodal Safety Test Suite for VLMs. MSTS comprises 400 test prompts across 40 fine-grained hazard categories. Each test prompt consists of a text and an image that only in combination reveal their full unsafe meaning. With MSTS, we find clear safety issues in several open VLMs. We also find some VLMs to be safe by accident, meaning that they are safe because they fail to understand even simple test prompts. We translate MSTS into ten languages, showing non-English prompts to increase the rate of unsafe model responses. We also show models to be safer when tested with text only rather than multimodal prompts. Finally, we explore the automation of VLM safety assessments, finding even the best safety classifiers to be lacking.
A Family of Controllable Momentum Coefficients for Forward-Backward Accelerated Algorithms
2025-01-17
Mingwei Fu, Bin Shi
Nesterov's accelerated gradient method (NAG) marks a pivotal advancement in gradient-based optimization, achieving faster convergence compared to the vanilla gradient descent method for convex functions. However, its algorithmic complexity when applied to strongly convex functions remains unknown, as noted in the comprehensive review by Chambolle and Pock [2016]. This issue, aside from the critical step size, was addressed by Li et al. [2024b], with the monotonic case further explored by Fu and Shi [2024]. In this paper, we introduce a family of controllable momentum coefficients for forward-backward accelerated methods, focusing on the critical step size $s=1/L$. Unlike traditional linear forms, the proposed momentum coefficients follow an $\alpha$-th power structure, where the parameter $r$ is adaptively tuned to $\alpha$. Using a Lyapunov function specifically designed for $\alpha$, we establish a controllable $O\left(1/k^{2\alpha} \right)$ convergence rate for the NAG-$\alpha$ method, provided that $r > 2\alpha$. At the critical step size, NAG-$\alpha$ achieves an inverse polynomial convergence rate of arbitrary degree by adjusting $r$ according to $\alpha > 0$. We further simplify the Lyapunov function by expressing it in terms of the iterative sequences $x_k$ and $y_k$, eliminating the need for phase-space representations. This simplification enables us to extend the controllable $O \left(1/k^{2\alpha} \right)$ rate to the monotonic variant, M-NAG-$\alpha$, thereby enhancing optimization efficiency. Finally, by leveraging the fundamental inequality for composite functions, we extended the controllable $O\left(1/k^{2\alpha} \right)$ rate to proximal algorithms, including the fast iterative shrinkage-thresholding algorithm (FISTA-$\alpha$) and its monotonic counterpart (M-FISTA-$\alpha$).
The role of dry mergers in shaping the scaling relations of galaxies
2025-01-17
Mauro D'Onofrio, Cesare Chiosi, Francesco Brevi
In the context of the hierarchical formation of galaxies, we investigated the role played by mergers in shaping the scaling relations of galaxies, that is the projections of their Fundamental Plane onto the Ie-Re, Ie-Sigma, Ms-Re and L-Sigma planes. To this aim, based on the scalar Virial Theorem, we developed a simple theory of multiple dry mergers to read both the large scale simulations and the companion scaling relations. The aim was to compare the results of this approach with the observational data and with two of the most recent and detailed numerical cosmo-hydro-dynamical simulations, that is Illustris-TNG and EAGLE (Evolution and Assembly of GaLaxies and their Environments). We derived the above scaling relations for the galaxies of the MaNGA (Mapping Nearby Galaxies at APO) and WINGS (Wide-field Imaging of Nearby Galaxy-Clusters Survey) databases and compared them with the observational data, the numerical simulations, and the results of our simple theory of dry mergers. The multiple dry merging mechanism is able to explain all the main characteristics of the observed scaling relations of galaxies, such as slopes, scatters, curvatures and zones of exclusion. The distribution of galaxies in these planes is continuously changing across time because of the merging activity and other physical processes, such as star formation, quenching, energy feedback, and so forth. The simple merger theory presented here yields the correct distribution of galaxies in the main scaling relations at all cosmic epochs. The precision is comparable with that obtained by the modern cosmo-hydro-dynamical simulations, with the advantage of providing a rapid exploratory response on the consequences engendered by different physical effects.
Exploring Code Comprehension in Scientific Programming: Preliminary Insights from Research Scientists
2025-01-17
Alyssia Chen, Carol Wong, Bonita Sharif, Anthony Peruma
Scientific software-defined as computer programs, scripts, or code used in scientific research, data analysis, modeling, or simulation-has become central to modern research. However, there is limited research on the readability and understandability of scientific code, both of which are vital for effective collaboration and reproducibility in scientific research. This study surveys 57 research scientists from various disciplines to explore their programming backgrounds, practices, and the challenges they face regarding code readability. Our findings reveal that most participants learn programming through self-study or on the-job training, with 57.9% lacking formal instruction in writing readable code. Scientists mainly use Python and R, relying on comments and documentation for readability. While most consider code readability essential for scientific reproducibility, they often face issues with inadequate documentation and poor naming conventions, with challenges including cryptic names and inconsistent conventions. Our findings also show low adoption of code quality tools and a trend towards utilizing large language models to improve code quality. These findings offer practical insights into enhancing coding practices and supporting sustainable development in scientific software.
Spatially Multiplexed Interferometric Microscopy with one-dimensional diffraction grating
2025-01-17
Jose Angel Picazo-Bueno, Maciej Trusiak, Javier Garcia, Vicente Mico
Digital holographic microscopy (DHM) applied to quantitative phase imaging (QPI) has been successfully demonstrated as a powerful label-free method to analyse the optical properties of cells. Spatially multiplexed interferometric microscopy (SMIM) is a DHM technique that implements a common-path interferometric layout in the embodiment of a standard microscope to achieve QPI. More concretely, SMIM introduces three minimal modifications: 1) replaces the broadband illumination of the microscope by a coherent or partially coherent light source, 2) divides the input plane into two or three regions for transmission in parallel of both imaging and reference beams, and 3) includes a one-dimensional diffraction grating or a beam splitter cube for holographic recording. Hence, SMIM is a cost-effective, extremely simple, and highly stable manner of converting a standard bright field microscope into a holographic one. The goal of this contribution is to provide a review of the SMIM approaches implemented using a one-dimensional (1D) diffraction grating, and highlight vast range of capabilities including super-resolved, reflective, transflective, noise-reduced and single-shot slightly off-axis amplitude and phase imaging.
On understanding and overcoming spectral biases of deep neural network learning methods for solving PDEs
2025-01-17
Zhi-Qin John Xu, Lulu Zhang, Wei Cai
In this review, we survey the latest approaches and techniques developed to overcome the spectral bias towards low frequency of deep neural network learning methods in learning multiple-frequency solutions of partial differential equations. Open problems and future research directions are also discussed.
Explainable artificial intelligence (XAI): from inherent explainability to large language models
2025-01-17
Fuseini Mumuni, Alhassan Mumuni
Artificial Intelligence (AI) has continued to achieve tremendous success in recent times. However, the decision logic of these frameworks is often not transparent, making it difficult for stakeholders to understand, interpret or explain their behavior. This limitation hinders trust in machine learning systems and causes a general reluctance towards their adoption in practical applications, particularly in mission-critical domains like healthcare and autonomous driving. Explainable AI (XAI) techniques facilitate the explainability or interpretability of machine learning models, enabling users to discern the basis of the decision and possibly avert undesirable behavior. This comprehensive survey details the advancements of explainable AI methods, from inherently interpretable models to modern approaches for achieving interpretability of various black box models, including large language models (LLMs). Additionally, we review explainable AI techniques that leverage LLM and vision-language model (VLM) frameworks to automate or improve the explainability of other machine learning models. The use of LLM and VLM as interpretability methods particularly enables high-level, semantically meaningful explanations of model decisions and behavior. Throughout the paper, we highlight the scientific principles, strengths and weaknesses of state-of-the-art methods and outline different areas of improvement. Where appropriate, we also present qualitative and quantitative comparison results of various methods to show how they compare. Finally, we discuss the key challenges of XAI and directions for future research.
Unveiling the Interior Structure and Thermal Evolution of Super-Earth GJ 486b
2025-01-17
Chandan K. Sahu, Liton Majumdar, Sudipta Mridha, Harshit Krishna
Recent ground- and space-based surveys have shown that planets between Earth and Neptune in size, known as "super-Earths," are among the most frequently found planets in the Galaxy. Although the JWST era has provided high-quality atmospheric data on several such super-Earths, modeling tools are crucial for understanding their unobservable interiors. Consequently, interior studies represent the next essential step in gaining a comprehensive understanding of this class of exoplanets. This study investigates the interior structure, thermal evolution, and atmospheric dynamics of the super-Earth GJ 486b using SERPINT, a 1-D self-consistent coupled interior structure and evolution model, aiming to understand the planet's thermal evolution based on an Earth-like structure. Our results indicate that GJ 486b's core is approximately 1.34 times larger than Earth's, with a core pressure of about 1171 GPa. The thermal evolution model predicts that the planet's mantle cools and solidifies over approximately 0.93 million years. As the magma ocean cools, water is released from the melt, forming a water-rich atmosphere during early solidification. Photolysis of water vapor and subsequent hydrogen escape lead to oxygen accumulation, forming a water- and oxygen-rich secondary atmosphere. Future high-sensitivity JWST observations, with improved wavelength coverage and the detection of additional trace gases, will enable a detailed analysis of the planet's atmospheric composition, providing crucial insights into the interior, surface, and subsurface properties of GJ 486b.
Multi-turn interaction in the dialogue system research refers to a system's ability to maintain context across multiple dialogue turns, enabling it to generate coherent and contextually relevant responses. Recent advancements in large language models (LLMs) have significantly expanded the scope of multi-turn interaction, moving beyond chatbots to enable more dynamic agentic interactions with users or environments. In this paper, we provide a focused review of the multi-turn capabilities of LLMs, which are critical for a wide range of downstream applications, including conversational search and recommendation, consultation services, and interactive tutoring. This survey explores four key aspects: (1) the core model capabilities that contribute to effective multi-turn interaction, (2) how multi-turn interaction is evaluated in current practice, (3) the general algorithms used to enhance multi-turn interaction, and (4) potential future directions for research in this field.
Indigenous Languages Spoken in Argentina: A Survey of NLP and Speech Resources
2025-01-17
Belu Ticona, Fernando Carranza, Viviana Cotik
Argentina has a diverse, yet little-known, Indigenous language heritage. Most of these languages are at risk of disappearing, resulting in a significant loss of world heritage and cultural knowledge. Currently, no unified information on speakers and computational tools is available for these languages. In this work, we present a systematization of the Indigenous languages spoken in Argentina, along with national demographic data on the country's Indigenous population. The languages are classified into seven families: Mapuche, Tup\'i-Guaran\'i, Guaycur\'u, Quechua, Mataco-Mataguaya, Aymara, and Chon. We also provide an introductory survey of the computational resources available for these languages, whether or not they are specifically developed for Argentine varieties.
The Evolution of Unobserved Skill Returns in the U.S.: A New Approach Using Panel Data
2025-01-17
Lance Lochner, Youngmin Park, Youngki Shin
Economists disagree about the factors driving the substantial increase in residual wage inequality in the US over the past few decades. To identify changes in the returns to unobserved skills, we make a novel assumption about the dynamics of skills rather than about the stability of skill distributions across cohorts, as is standard. We show that our assumption is supported by data on test score dynamics for older workers in the HRS. Using survey data from the PSID and administrative data from the IRS and SSA, we estimate that the returns to unobserved skills $declined$ substantially in the late-1980s and 1990s despite an increase in residual inequality. Accounting for firm-specific pay differences yields similar results. Extending our framework to consider occupational differences in returns to skill and multiple unobserved skills, we further show that skill returns display similar patterns for workers employed in each of cognitive, routine, and social occupations. Finally, our results suggest that increasing skill dispersion, driven by rising skill volatility, explains most of the growth in residual wage inequality since the 1980s.
Discovery and Multi-Wavelength Analysis of a New Dissociative Galaxy Merger: The Champagne Cluster
2025-01-17
Faik Bouhrik, Rodrigo Stancioli, David Wittman
We report the discovery of a new binary galaxy cluster merger, the Champagne Cluster (RM J130558.9+263048.4), using a detection method that identifies dynamically active clusters in the redMaPPer SDSS DR8 photometric galaxy cluster catalog. The Champagne Cluster exhibits the classic X-ray morphology of a post-pericenter dissociative galaxy cluster merger: an X-ray peak located between two galaxy overdensities at the same redshift. We conducted a Keck/DEIMOS survey and obtained redshifts for 103 member galaxies. The redshift analysis indicates a relative velocity of 411 $\pm$ 180 km/s between the two subclusters, which suggests that the merger is happening near the plane of the sky. We used cosmological simulations to find analogous systems to constrain the time since pericenter (74-250 Myr) and the angle the merger axis makes with the plane of the sky (62$^\circ$-90$^\circ$) at the 68$\%$ confidence level. We estimated the bulk temperature (8.20 $\pm 1.2$ keV) and total X-ray luminosity (5.2 $\pm$ 0.8 $\times$ $10^{44}$ erg $\times$ $s^{-1}$) of the intracluster medium using $\textit{Chandra}$ archival data.
Learning port maneuvers from data for automatic guidance of Unmanned Surface Vehicles
2025-01-17
Yeyson A. Becerra-Mora, José Ángel Acosta, Ángel Rodríguez Castaño
At shipping ports, some repetitive maneuvering tasks such as entering/leaving port, transporting goods inside it or just making surveillance activities, can be efficiently and quickly carried out by a domestic pilot according to his experience. This know-how can be seized by Unmanned Surface Vehicles (USV) in order to autonomously replicate the same tasks. However, the inherent nonlinearity of ship trajectories and environmental perturbations as wind or marine currents make it difficult to learn a model and its respective control. We therefore present a data-driven learning and control methodology for USV, which is based on Gaussian Mixture Model, Gaussian Mixture Regression and the Sontag's universal formula. Our approach is capable to learn the nonlinear dynamics as well as guarantee the convergence toward the target with a robust controller. Real data have been collected through experiments with a vessel at the port of Ceuta. The complex trajectories followed by an expert have been learned including the robust controller. The effect of the controller over noise/perturbations are presented, a measure of error is used to compare estimates and real data trajectories, and finally, an analysis of computational complexity is performed.
The increasing availability of traffic videos functioning on a 24/7/365 time scale has the great potential of increasing the spatio-temporal coverage of traffic accidents, which will help improve traffic safety. However, analyzing footage from hundreds, if not thousands, of traffic cameras in a 24/7/365 working protocol remains an extremely challenging task, as current vision-based approaches primarily focus on extracting raw information, such as vehicle trajectories or individual object detection, but require laborious post-processing to derive actionable insights. We propose SeeUnsafe, a new framework that integrates Multimodal Large Language Model (MLLM) agents to transform video-based traffic accident analysis from a traditional extraction-then-explanation workflow to a more interactive, conversational approach. This shift significantly enhances processing throughput by automating complex tasks like video classification and visual grounding, while improving adaptability by enabling seamless adjustments to diverse traffic scenarios and user-defined queries. Our framework employs a severity-based aggregation strategy to handle videos of various lengths and a novel multimodal prompt to generate structured responses for review and evaluation and enable fine-grained visual grounding. We introduce IMS (Information Matching Score), a new MLLM-based metric for aligning structured responses with ground truth. We conduct extensive experiments on the Toyota Woven Traffic Safety dataset, demonstrating that SeeUnsafe effectively performs accident-aware video classification and visual grounding by leveraging off-the-shelf MLLMs. Source code will be available at \url{https://github.com/ai4ce/SeeUnsafe}.
Understanding Computational Science and Domain Science Skills Development in National Laboratory Graduate Internships
2025-01-17
Morgan M. Fong, Hilary Egan, Marc Day, Kristin Potter, Michael J. Martin
Contribution: This study presents an evaluation of federally-funded graduate internship outcomes in computational science at a national laboratory. Additionally, we present a survey instrument that may be used for other internship programs with a similar focus. Background: There is ongoing demand for computational scientists to grapple with large-scale problems such as climate change. Internships may help provide additional training and access to greater compute capabilities for graduate students. However, little work has been done to quantify the learning outcomes of such internships. Background: There is ongoing demand for computational scientists to grapple with large-scale problems such as climate change. Internships may help provide additional training and access to greater compute capabilities for graduate students. However, little work has been done to quantify the learning outcomes of such internships. Research Questions: What computational skills, research skills, and professional skills do graduate students improve through their internships at NREL, the national laboratory selected for the study? What sustainability and renewable energy topics do graduate students gain more familiarity with through their internships at NREL? Do graduate students' career interests change after their internships at NREL? Methodology: We developed a survey and collected responses from past participants of five federally-funded internship programs and compare participant ratings of their prior experience to their internship experience. Findings: Our results indicate participants improve their computational skills, familiarity with sustainability and renewable energy topics, and are more interested in working at national labs. Additionally, participants go on to degree programs and positions related to sustainability and renewable energy after their internships.
Testing Local Lorentz Invariance with Laser Tracking of the LAGEOS II satellite
2025-01-17
David Lucchesi, Massimo Visco, Roberto Peron, Giuseppe Pucacco, Luciano Anselmo, Massimo Bassan, Marco Cinelli, Alessandro Di Marco, Marco Lucente, Carmelo Magnafico, Carmen Pardini, José C. Rodriguez, Feliciana Sapio
A breakdown of Lorentz Invariance is predicted by some theories as a result of quantum physics applied to gravity or for the presence of vector/tensor fields, beside the metric tensor of General Relativity, mediating the gravitational interaction. A violation of Lorentz Invariance would manifest itself, in the gravitational sector, mainly through a non-zero value of the post-Newtonian parameter $\alpha_1$, that is null in General Relativity. In this paper we describe a test of Local Lorentz Invariance considering the effect of a possible preferred frame, that of the cosmic microwave background, on the orbit of the LAGEOS II satellite tracked by the Satellite Laser Ranging technique. We constrain this parameter down to the level of $3\times10^{-5}$, improving a previous limit obtained through the Lunar Laser Ranging technique.
Polymer-based solid-state electrolytes for lithium sulfur batteries
2025-01-17
Praveen Balaji T, Soumyadip Choudhury, Ernesto E. Marinero
Lithium-sulfur (Li-S) batteries offer substantial theoretical energy density gains over Li-ion bat-teries, a crucial factor for transportation electrification. In addition, sulfur is an earth-abundant, inexpensive material obtainable from multiple resources; thus, Li-S batteries are envisioned to provide environmentally sustainable solutions to the growing demand for energy storage. A crit-ical roadblock to the realization of commercial Li-S batteries is the formation of polysulfides and their secondary reactions with liquid organic electrolytes, resulting in low coulombic efficiency for charging and fast self-discharge rates. The realization of solid-state electrolytes for Li-S bat-teries provides potential pathways to address the safety concerns of liquid electrolytes and inhib-it the formation of polysulfides and/or prevent their diffusion into the anode electrode. However, current solid-state electrolytes are limited by low ionic conductivity, inadequate electrode inter-facial compatibility, and restricted electrochemical windows. This review discusses the status of polymer-based electrolytes for Li-S batteries, and outlines current methods for their fabrication, their transport characteristics and ongoing research aimed at overcoming material properties hindering the development of all-solid-state Li-S batteries.
WALLABY Pilot Survey: kNN identification of perturbed galaxies through HI morphometrics
2025-01-17
B. W. Holwerda, Helga Dénes, J. Rhee, D. Leahy, B. Koribalsky, N. Yu, N. Deg, T. Westmeier, K. Lee-Waddell, Y. Ascasibar, M. Saraf, X. Lin, B. Catinella, K. Hess
Galaxy morphology in stellar light can be described by a series of "non-parametric" or "morphometric" parameters, such as concentration-asymmetry-smoothness, Gini, $M_{20}$, and Sersic fit. These parameters can be applied to column density maps of atomic hydrogen (HI). The HI distribution is susceptible to perturbations by environmental effects, e.g. inter-galactic medium pressure and tidal interactions. Therefore, HI morphology can potentially identify galaxies undergoing ram-pressure stripping or tidal interactions. We explore three fields in the WALLABY Pilot HI survey and identify perturbed galaxies based on a k-nearest Neighbor (kNN) algorithm using an HI morphometric feature space. For training, we used labeled galaxies in the combined NGC 4808 and NGC 4636 fields with six HI morphometrics to train and test a kNN classifier. The kNN classification is proficient in classifying perturbed galaxies with all metrics -- accuracy, precision and recall -- at 70-80%. By using the kNN method to identify perturbed galaxies in the deployment field, the NGC 5044 mosaic, we find that in most regards, the scaling relations of perturbed and unperturbed galaxies have similar distribution in the scaling relations of stellar mass vs star formation rate and the Baryonic Tully-Fisher relation, but the HI and stellar mass relation flatter than of the unperturbed galaxies. Our results for NGC 5044 provide a prediction for future studies on the fraction of galaxies undergoing interaction in this catalogue and to build a training sample to classify such galaxies in the full WALLABY survey.
REBELS-IFU: Evidence for metal-rich massive galaxies at z~6-8
2025-01-17
Lucie E. Rowland, Mauro Stefanon, Rychard Bouwens, Jacqueline Hodge, Hiddo Algera, Rebecca Fisher, Pratika Dayal, Andrea Pallottini, Daniel P. Stark, Kasper E. Heintz, Manuel Aravena, Rebecca Bowler, Karin Cescon, Ryan Endsley, Andrea Ferrara, Valentino Gonzalez, Luca Graziani, Cindy Gulis, Thomas Herard-Demanche, Hanae Inami, Andrès Laza-Ramos, Ivana van Leeuwen, Ilse de Looze, Themiya Nanayakkara, Pascal Oesch, Katherine Ormerod, Nina S. Sartorio, Sander Schouws, Renske Smit, Laura Sommovigo, Sune Toft, John R. Weaver, Paul van der Werf
Metallicity is a crucial tracer of galaxy evolution, providing insights into gas accretion, star formation, and feedback. At high redshift, these processes reveal how early galaxies assembled and enriched their interstellar medium. In this work, we present rest-frame optical spectroscopy of 12 massive ($\log(M_*/\mathrm{M_{\odot}})>9$) galaxies at $z\sim 6$-$8$ from the REBELS ALMA large program, observed with JWST NIRSpec/IFU in the prism mode. These observations span emission lines from [OII]$\lambda$3727,9 to [SII]$\lambda$6716,31, providing key information on nebular dust attenuation, ionisation states, and chemical abundances. We find lower O32 ratios (average $\sim3.7$) and [OIII]$\lambda$5007 equivalent widths (average ${EW_{[OIII]}}\sim390$\AA) than are generally found in existing large spectroscopic surveys at $z>6$, indicating less extreme ionising conditions. Strong-line diagnostics suggest that these systems are some of the most metal-rich galaxies observed at $z>6$ (average $Z_{\mathrm{gas}}\sim 0.4 Z_{\odot}$), including sources with near-solar oxygen abundances, in line with their high stellar masses (average $\log{M_*/\mathrm{M_{\odot}}}\sim9.5$). Supplementing with literature sources at lower masses, we investigate the mass-metallicity and fundamental metallicity relations (MZR and FMR, respectively) over a 4 dex stellar mass range at $63$ studies.
Tabular data is one of the most widely used formats across industries, driving critical applications in areas such as finance, healthcare, and marketing. In the era of data-centric AI, improving data quality and representation has become essential for enhancing model performance, particularly in applications centered around tabular data. This survey examines the key aspects of tabular data-centric AI, emphasizing feature selection and feature generation as essential techniques for data space refinement. We provide a systematic review of feature selection methods, which identify and retain the most relevant data attributes, and feature generation approaches, which create new features to simplify the capture of complex data patterns. This survey offers a comprehensive overview of current methodologies through an analysis of recent advancements, practical applications, and the strengths and limitations of these techniques. Finally, we outline open challenges and suggest future perspectives to inspire continued innovation in this field.
Diffusion Models in Recommendation Systems: A Survey
2025-01-17
Ting-Ruen Wei, Yi Fang
Recommender systems remain an essential topic due to its wide application in various domains and the business potential behind them. With the rise of deep learning, common solutions have leveraged neural networks to facilitate collaborative filtering, and some have turned to generative adversarial networks to augment the dataset and tackle the data sparsity issue. However, they are limited in learning the complex user and item distribution and still suffer from model collapse. Given the great generation capability exhibited by diffusion models in computer vision recently, many recommender systems have adopted diffusion models and found improvements in performance for various tasks. Diffusion models in recommender systems excel in managing complex user and item distributions and do not suffer from mode collapse. With these advantages, the amount of research in this domain have been growing rapidly and calling for a systematic survey. In this survey paper, we present and propose a taxonomy on past research papers in recommender systems that utilize diffusion models. Distinct from a prior survey paper that categorizes based on the role of the diffusion model, we categorize based on the recommendation task at hand. The decision originates from the rationale that after all, the adoption of diffusion models is to enhance the recommendation performance, not vice versa: adapting the recommendation task to enable diffusion models. Nonetheless, we offer a unique perspective for diffusion models in recommender systems complementary to existing surveys. We present the foundation algorithms in diffusion models and their applications in recommender systems to summarize the rapid development in this field. Finally, we discuss open research directions to prepare and encourage further efforts to advance the field. We compile the relevant papers in a public GitHub repository.
REBELS-IFU: Dust attenuation curves of 12 massive galaxies at $z\simeq7$
2025-01-17
Rebecca Fisher, Rebecca A. A. Bowler, Mauro Stefanon, Lucie E. Rowland, Hiddo S. B. Algera, Manuel Aravena, Rychard Bouwens, Pratika Dayal, Andrea Ferrara, Yoshinobu Fudamoto, Jacqueline A. Hodge, Hanae Inami, Katherine Ormerod, Andrea Pallottini, Siân G. Phillips, Nina S. Sartorio, Renske Smit, Laura Sommovigo, Dan P. Stark, Paul P. van der Werf
We present measurements of the dust attenuation curves of 12 massive ($9~<~\log$($M_{\star}/{M}_{\odot})$ $<~10$) Lyman-break galaxies at $z=6.5-7.7$ derived from James Webb Space Telescope (JWST) NIRSpec integral field unit (IFU) spectroscopy. The galaxies are drawn from the Atacama Large Millimeter/submillimeter Array (ALMA) Reionization Era Bright Emission Line Survey (REBELS) large program. The dust attenuation curves were obtained by fitting spectral energy distribution (SED) models with a flexible dust law to the full galaxy spectra over observed wavelengths $0.6-5.3$ $\mu$m. These attenuation curves show a range of recovered slopes ($-0.39\leq\delta\leq0.08$) that are on average slightly flatter than seen in local sources of the same stellar masses, with none exhibiting very steep slopes. Three galaxies exhibit evidence for a 2175 \r{A} dust bump ($>4\sigma$) and we find SED fitting excluding the bump can overestimate derived stellar masses by up to $0.4$ dex. Correcting for the dust attenuation with our best-fit attenuation curves we recover a range of intrinsic UV-slopes ($-2.5\leq\beta_0\leq-2.2$). The galaxies show moderate reddening ($A_V~=~0.1-0.6$ mag) and the $A_V$ to stellar mass relation is consistent with local sources. The attenuation law slope is found to correlate with $A_V$, while we see no strong correlation with stellar mass, ${M_{\rm UV}}$, or gas-phase metallicity. Overall, our results show little evolution in dust properties in the REBELS sources compared to the local Universe. Comparing our recovered trends to empirical models suggests that the most important factor driving the variation in the attenuation curves in our sample is the dust-star geometry, not the properties of the dust grains themselves.
Active filtering: a predictive function of recurrent circuits of sensory cortex
2025-01-17
Mark H. Histed
Our brains encode many features of the sensory world into memories: we can sing along with songs we have heard before, interpret spoken and written language composed of words we have learned, and recognize faces and objects. Where are these memories stored? Each area of the cerebral cortex has a huge number of local, recurrent, excitatory-excitatory synapses, as many as 500 million per cubic millimeter. Here I review evidence that cortical recurrent connectivity in sensory cortex is a substrate for sensory memories. Evidence suggests that the local recurrent network encodes the structure of natural sensory input, and that it does so via active filtering, transforming network inputs to boost or select those associated with natural sensation. This is a form of predictive processing, in which the cortical recurrent network selectively amplifies some input patterns and attenuates others, and a form of memory.
REBELS-IFU: Dust Build-up in Massive Galaxies at Redshift 7
2025-01-17
Hiddo Algera, Lucie Rowland, Mauro Stefanon, Marco Palla, Laura Sommovigo, Hanae Inami, Rychard Bouwens, Manuel Aravena, Rebecca Bowler, Pratika Dayal, Ilse De Looze, Andrea Ferrara, Rebecca Fisher, Luca Graziani, Cindy Gulis, Kasper Heintz, Jacqueline Hodge, Ivana van Leeuwen, Andrea Pallottini, Siân Phillips, Sander Schouws, Renske Smit, Daniel Stark, Paul van der Werf
In recent years, observations with the JWST have started to map out the rapid metal enrichment of the early Universe, while (sub)millimeter observations have simultaneously begun to reveal the ubiquity of dust beyond $z\gtrsim6$. However, the pathways that led to the assembly of early dust reservoirs remain poorly quantified, and require pushing our understanding of key scaling relations between dust, gas and metals into the early Universe. We investigate the dust build-up in twelve $6.5 \lesssim z \lesssim 7.7$ galaxies drawn from the REBELS survey that benefit from (i) JWST/NIRSpec strong-line metallicity measurements, (ii) ALMA [CII]-based redshifts and gas masses, and (iii) dust masses from single- or multi-band ALMA continuum observations. Combining these measurements, we investigate the dust-to-gas (DtG), dust-to-metal (DtM), and dust-to-stellar mass (DtS) ratios of our sample as a function of metallicity. While our analysis is limited by systematic uncertainties related to the [CII]-to-H$_2$ conversion factor and dust temperature, we explore a wide range of possible values, and carefully assess their impact on our results. Under a fiducial set of assumptions, we find an average $\log(\mathrm{DtG}) = -3.02 \pm 0.23$, only slightly below that of local metal-rich galaxies. On the other hand, at fixed metallicity our average $\log(\mathrm{DtS}) = -2.15 \pm 0.42$ is significantly larger than that of low-redshift galaxies. Finally, through a comparison to various theoretical models of high-redshift dust production, we find that assembling the dust reservoirs in massive galaxies at $z\approx7$ likely requires the combination of rapid supernova enrichment and efficient ISM dust growth.
High-Significance Detection of Correlation Between the Unresolved Gamma-Ray Background and the Large Scale Cosmic Structure
2025-01-17
B. Thakore, M. Negro, M. Regis, S. Camera, D. Gruen, N. Fornengo, A. Roodman, A. Porredon, T. Schutt, A. Cuoco, A. Alarcon, A. Amon, K. Bechtol, M. R. Becker, G. M. Bernstein, A. Campos, A. Carnero Rosell, M. Carrasco Kind, R. Cawthon, C. Chang, R. Chen, A. Choi, J. Cordero, C. Davis, J. DeRose, H. T. Diehl, S. Dodelson, C. Doux, A. Drlica-Wagner, K. Eckert, J. Elvin-Poole, S. Everett, A. Ferté, M. Gatti, G. Giannini, R. A. Gruendl, I. Harrison, W. G. Hartley, E. M. Huff, M. Jarvis, N. Kuropatkin, P. -F. Leget, N. MacCrann, J. McCullough, J. Myles, A. Navarro-Alsina, S. Pandey, J. Prat, M. Raveri, R. P. Rollins, A. J. Ross, E. S. Rykoff, C. Sánchez, L. F. Secco, I. Sevilla-Noarbe, E. Sheldon, T. Shin, M. A. Troxel, I. Tutusaus, B. Yanny, B. Yin, Y. Zhang, M. Aguena, D. Brooks, J. Carretero, L. N. da Costa, T. M. Davis, J. De Vicente, S. Desai, P. Doel, B. Flaugher, J. Frieman, J. García-Bellido, E. Gaztanaga, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, K. Kuehn, O. Lahav, S. Lee, M. Lima, J. L. Marshall, J. Mena-Fernández, R. Miquel, R. L. C. Ogando, A. Palmese, A. Pieres, A. A. Plazas Malagón, S. Samuroff, E. Sanchez, D. Sanchez Cid, M. Smith, E. Suchyta, G. Tarle, V. Vikram, A. R. Walker, N. Weaverdyck
Our understanding of the $\gamma$-ray sky has improved dramatically in the past decade, however, the unresolved $\gamma$-ray background (UGRB) still has a potential wealth of information about the faintest $\gamma$-ray sources pervading the Universe. Statistical cross-correlations with tracers of cosmic structure can indirectly identify the populations that most characterize the $\gamma$-ray background. In this study, we analyze the angular correlation between the $\gamma$-ray background and the matter distribution in the Universe as traced by gravitational lensing, leveraging more than a decade of observations from the Fermi-Large Area Telescope (LAT) and 3 years of data from the Dark Energy Survey (DES). We detect a correlation at signal-to-noise ratio of 8.9. Most of the statistical significance comes from large scales, demonstrating, for the first time, that a substantial portion of the UGRB aligns with the mass clustering of the Universe as traced by weak lensing. Blazars provide a plausible explanation for this signal, especially if those contributing to the correlation reside in halos of large mass ($\sim 10^{14} M_{\odot}$) and account for approximately 30-40 % of the UGRB above 10 GeV. Additionally, we observe a preference for a curved $\gamma$-ray energy spectrum, with a log-parabolic shape being favored over a power-law. We also discuss the possibility of modifications to the blazar model and the inclusion of additional $gamma$-ray sources, such as star-forming galaxies or particle dark matter.
A Bayesian Approach to Inferring Accretion Signatures in Young Stellar Objects: A Case Study with VIRUS
2025-01-17
Lauren Halstead Willett, Joe P. Ninan, Suvrath Mahadevan, Gregory R. Zeimann, Steven Janowiecki, Gary J. Hill
The mass accretion rates of young stellar objects (YSOs) are key to understanding how stars form, how their circumstellar disks evolve, and even how planets form. We develop a Bayesian framework to determine the accretion rates of a sample of 15 YSOs using archival data from the VIRUS spectrograph ($R \sim 800$, 3500-5500\r{A}) on the Hobby-Eberly Telescope. We are publicly releasing our developed tool, dubbed nuts-for-ysos, as a Python package which can also be applied to other spectroscopic datasets. The nuts-for-ysos code fits a simple accretion model to the near-UV and optical continuum of each VIRUS spectrum. Our Bayesian approach aims to identify correlations between model parameters using the No U-Turn Sampler (NUTS). Moreover, this approach self-consistently incorporates all parameter uncertainties, allowing for a thorough estimation of the probability distribution for accretion rate not accomplished in previous works. Using nuts-for-ysos, we derive accretion rates of each YSO. We then verify the reliability of our method by comparing to results separately derived from only the spectral emission lines, and to results from earlier studies of the Lupus, Chamaeleon I, and NGC1333 regions. Finally, we discuss what qualitative trends, covariances, and degeneracies were found among model parameters. The technique developed in this paper is a useful improvement that can be applied in the future to larger samples of YSOs observed by VIRUS or other spectrographs.
Understanding User Preference -- Comparison between Linear and Directional Top-K Query results
2025-01-17
Xiaolei Jiang
This paper investigates user preferences for Linear Top-k Queries and Directional Top-k Queries, two methods for ranking results in multidimensional datasets. While Linear Queries prioritize weighted sums of attributes, Directional Queries aim to deliver more balanced results by incorporating the spatial relationship between data points and a user-defined preference line. The study explores how preferences for these methods vary across different contexts by focusing on two real-world topics: used cars (e-commerce domain) and football players (personal interest domain). A user survey involving 106 participants was conducted to evaluate preferences, with results visualized as scatter plots for comparison. The findings reveal a significant preference for directional queries in the used cars topic, where balanced results align better with user goals. In contrast, preferences in the football players topic were more evenly distributed, influenced by user expertise and familiarity with the domain. Additionally, the study demonstrates that the two specific topics selected for this research exhibit significant differences in their impact on user preferences. This research reveals authentic user preferences, highlighting the practical utility of Directional Queries for lifestyle-related applications and the subjective nature of preferences in specialized domains. These insights contribute to advancing personalized database technologies, guiding the development of more user-centric ranking systems.
This study was aimed at finding out if journalists in South East Nigeria have knowledge of Google Translate Application and also utilise it. It adopted a survey design with a sample size of 320 which was determined using Krejcie & Morgan (1970). Its objectives were to ascertain the extent journalists in South East Nigeria know about Google Translate Application, assess the utilisation of Google Translate Application among journalists in South East Nigeria, and identify the challenges affecting the journalists in South East Nigeria while using Google Translate Application. The theoretical underpin was Knowledge Attitude and Practise Model (KAP). The findings showed that journalists in South East Nigeria have knowledge of Google Translate Application but apply it mostly outside the region. It concludes that journalists in South East Nigeria have the knowledge of the App. but apply it outside the zone. The study recommends increased usage of the App. within South East Nigeria.
Spatial reasoning is an essential problem in embodied AI research. Efforts to enhance spatial reasoning abilities through supplementary spatial data and fine-tuning have proven limited and ineffective when addressing complex embodied tasks, largely due to their dependence on language-based outputs. While some approaches have introduced a point-based action space to mitigate this issue, they fall short in managing more intricate tasks within complex environments. This deficiency arises from their failure to fully exploit the inherent thinking and reasoning capabilities that are fundamental strengths of Vision-Language Models (VLMs). To address these limitations, we propose a novel approach named SpatialCoT, specifically designed to bolster the spatial reasoning capabilities of VLMs. Our approach comprises two stages: spatial coordinate bi-directional alignment, which aligns vision-language inputs with spatial coordinates, and chain-of-thought spatial grounding, which harnesses the reasoning capabilities of language models for advanced spatial reasoning. We evaluate SpatialCoT on challenging navigation and manipulation tasks, both in simulation and real-world settings. Experimental results demonstrate that our method significantly outperforms previous state-of-the-art approaches in both tasks.
Language has long been conceived as an essential tool for human reasoning. The breakthrough of Large Language Models (LLMs) has sparked significant research interest in leveraging these models to tackle complex reasoning tasks. Researchers have moved beyond simple autoregressive token generation by introducing the concept of "thought" -- a sequence of tokens representing intermediate steps in the reasoning process. This innovative paradigm enables LLMs' to mimic complex human reasoning processes, such as tree search and reflective thinking. Recently, an emerging trend of learning to reason has applied reinforcement learning (RL) to train LLMs to master reasoning processes. This approach enables the automatic generation of high-quality reasoning trajectories through trial-and-error search algorithms, significantly expanding LLMs' reasoning capacity by providing substantially more training data. Furthermore, recent studies demonstrate that encouraging LLMs to "think" with more tokens during test-time inference can further significantly boost reasoning accuracy. Therefore, the train-time and test-time scaling combined to show a new research frontier -- a path toward Large Reasoning Model. The introduction of OpenAI's o1 series marks a significant milestone in this research direction. In this survey, we present a comprehensive review of recent progress in LLM reasoning. We begin by introducing the foundational background of LLMs and then explore the key technical components driving the development of large reasoning models, with a focus on automated data construction, learning-to-reason techniques, and test-time scaling. We also analyze popular open-source projects at building large reasoning models, and conclude with open challenges and future research directions.
This tutorial provides an in-depth guide on inference-time guidance and alignment methods for optimizing downstream reward functions in diffusion models. While diffusion models are renowned for their generative modeling capabilities, practical applications in fields such as biology often require sample generation that maximizes specific metrics (e.g., stability, affinity in proteins, closeness to target structures). In these scenarios, diffusion models can be adapted not only to generate realistic samples but also to explicitly maximize desired measures at inference time without fine-tuning. This tutorial explores the foundational aspects of such inference-time algorithms. We review these methods from a unified perspective, demonstrating that current techniques -- such as Sequential Monte Carlo (SMC)-based guidance, value-based sampling, and classifier guidance -- aim to approximate soft optimal denoising processes (a.k.a. policies in RL) that combine pre-trained denoising processes with value functions serving as look-ahead functions that predict from intermediate states to terminal rewards. Within this framework, we present several novel algorithms not yet covered in the literature. Furthermore, we discuss (1) fine-tuning methods combined with inference-time techniques, (2) inference-time algorithms based on search algorithms such as Monte Carlo tree search, which have received limited attention in current research, and (3) connections between inference-time algorithms in language models and diffusion models. The code of this tutorial on protein design is available at https://github.com/masa-ue/AlignInversePro
We introduce the Serendipitous H-ATLAS-fields Observations of Radio Extragalactic Sources (SHORES) multiple pencil beam survey that observed at 2.1 GHz with the Australia Telescope Compact Array (ATCA) 29 fields in total intensity and polarization within the Herschel-ATLAS Southern Galactic Field. This paper presents the observations, calibration and analysis of the 27 shallow fields that cover an overall area of $\sim 26$ square degree with increasing sensitivity towards the phase centers of each pointing according to the ATCA 22 m dish response function, down to $\sigma\lesssim 33\, \mu$Jy. Two additional (deep) fields have been observed to even higher sensitivity. All the SHORES observations have been calibrated to account also for linear polarization. Polarization and deeper field analysis will be presented in future papers. The SHORES shallow-field sample considered in the present paper counts $2294$ sources detected with BLOBCAT to signal-to-noise ratio $SNR\gtrsim 4.5$. Simulations determined that our procedure and final catalog is 95% reliable above $497.5\, \mu$Jy and $95\%$ complete to the $SNR\gtrsim 4.5$ significance level. By exploiting ATCA E-W 6 km configuration we reached resolutions of $3.2\times 7.2$ arcsec, to which level $81\%$ of our sources are unresolved. We determined source counts down to the $150\, \mu$Jy level. For the sources with a counterpart in H-ATLAS, the FIR-radio correlation is calculated and discussed.
Within the rapidly evolving domain of Electronic Design Automation (EDA), Large Language Models (LLMs) have emerged as transformative technologies, offering unprecedented capabilities for optimizing and automating various aspects of electronic design. This survey provides a comprehensive exploration of LLM applications in EDA, focusing on advancements in model architectures, the implications of varying model sizes, and innovative customization techniques that enable tailored analytical insights. By examining the intersection of LLM capabilities and EDA requirements, the paper highlights the significant impact these models have on extracting nuanced understandings from complex datasets. Furthermore, it addresses the challenges and opportunities in integrating LLMs into EDA workflows, paving the way for future research and application in this dynamic field. Through this detailed analysis, the survey aims to offer valuable insights to professionals in the EDA industry, AI researchers, and anyone interested in the convergence of advanced AI technologies and electronic design.
In many real-world applications, agents must make sequential decisions in environments where conditions are subject to change due to various exogenous factors. These non-stationary environments pose significant challenges to traditional decision-making models, which typically assume stationary dynamics. Non-stationary Markov decision processes (NS-MDPs) offer a framework to model and solve decision problems under such changing conditions. However, the lack of standardized benchmarks and simulation tools has hindered systematic evaluation and advance in this field. We present NS-Gym, the first simulation toolkit designed explicitly for NS-MDPs, integrated within the popular Gymnasium framework. In NS-Gym, we segregate the evolution of the environmental parameters that characterize non-stationarity from the agent's decision-making module, allowing for modular and flexible adaptations to dynamic environments. We review prior work in this domain and present a toolkit encapsulating key problem characteristics and types in NS-MDPs. This toolkit is the first effort to develop a set of standardized interfaces and benchmark problems to enable consistent and reproducible evaluation of algorithms under non-stationary conditions. We also benchmark six algorithmic approaches from prior work on NS-MDPs using NS-Gym. Our vision is that NS-Gym will enable researchers to assess the adaptability and robustness of their decision-making algorithms to non-stationary conditions.
Advancements and Challenges in Sintering Cr4+:YAG Ceramics: A Comprehensive Review on Transparent Ceramic Technology for Q-switched Lasers
2025-01-16
Mykhailo Chaika
The resent progress in the technology of transparent ceramics extends the application of CW and pulsed lasers. The parameters of the transparent ceramics are comparable with the single crystals both for active elements (Nd:YAG, Yb:YAG, etc.) and passive Q-switchers such as Cr4+:YAG. Despite the reaching of considerable progress, the sintering of Cr4+:YAG remains a challenge up to now. Up to the present the influence of the CaO, MgO and Cr2O3 additives on the sintering process is poorly understander. Moreover, there is an absence of the unified model of Cr4+ ions formations in YAG ceramics. This factor limits the progress in the development of Cr4+:YAG ceramics. This review focused on the influence of the single additives and their combinations on the sintering trajectory of tetravalent chromium - doped YAG ceramics and the Cr4+ ions formation.
Polarons in atomic gases and two-dimensional semiconductors
2025-01-16
Pietro Massignan, Richard Schmidt, Grigori E. Astrakharchik, Ataç İmamoglu, Martin Zwierlein, Jan J. Arlt, Georg M. Bruun
In this work we provide a comprehensive review of theoretical and experimental studies of the properties of polarons formed by mobile impurities strongly interacting with quantum many-body systems. We present a unified perspective on the universal concepts and theoretical techniques used to characterize polarons in two distinct platforms, ultracold atomic gases and atomically-thin transition metal dichalcogenides, which are linked by many deep parallels. We review polarons in both fermionic and bosonic environments, highlighting their similarities and differences including the intricate interplay between few- and many-body physics. Various kinds of polarons with long-range interactions or in magnetic backgrounds are discussed, and the theoretical and experimental progress towards understanding interactions between polarons is described. We outline how polaron physics, regarded as the low density limit of quantum mixtures, provides fundamental insights regarding the phase diagram of complex condensed matter systems. Furthermore, we describe how polarons may serve as quantum sensors of many-body physics in complex environments. Our work highlights the open problems, identifies new research directions and provides a comprehensive framework for this rapidly evolving research field.
Local US officials' views on the impacts and governance of AI: Evidence from 2022 and 2023 survey waves
2025-01-16
Sophia Hatz, Noemi Dreksler, Kevin Wei, Baobao Zhang
This paper presents a survey of local US policymakers' views on the future impact and regulation of AI. Our survey provides insight into US policymakers' expectations regarding the effects of AI on local communities and the nation, as well as their attitudes towards specific regulatory policies. Conducted in two waves (2022 and 2023), the survey captures changes in attitudes following the release of ChatGPT and the subsequent surge in public awareness of AI. Local policymakers express a mix of concern, optimism, and uncertainty about AI's impacts, anticipating significant societal risks such as increased surveillance, misinformation, and political polarization, alongside potential benefits in innovation and infrastructure. Many also report feeling underprepared and inadequately informed to make AI-related decisions. On regulation, a majority of policymakers support government oversight and favor specific policies addressing issues such as data privacy, AI-related unemployment, and AI safety and fairness. Democrats show stronger and more consistent support for regulation than Republicans, but the latter experienced a notable shift towards majority support between 2022 and 2023. Our study contributes to understanding the perspectives of local policymakers-key players in shaping state and federal AI legislation-by capturing evolving attitudes, partisan dynamics, and their implications for policy formation. The findings highlight the need for capacity-building initiatives and bi-partisan coordination to mitigate policy fragmentation and build a cohesive framework for AI governance in the US.
Effective fall risk assessment is critical for post-stroke patients. The present study proposes a novel, data-informed fall risk assessment method based on the instrumented Timed Up and Go (ITUG) test data, bringing in many mobility measures that traditional clinical scales fail to capture. IFRA, which stands for Instrumented Fall Risk Assessment, has been developed using a two-step process: first, features with the highest predictive power among those collected in a ITUG test have been identified using machine learning techniques; then, a strategy is proposed to stratify patients into low, medium, or high-risk strata. The dataset used in our analysis consists of 142 participants, out of which 93 were used for training (15 synthetically generated), 17 for validation and 32 to test the resulting IFRA scale (22 non-fallers and 10 fallers). Features considered in the IFRA scale include gait speed, vertical acceleration during sit-to-walk transition, and turning angular velocity, which align well with established literature on the risk of fall in neurological patients. In a comparison with traditional clinical scales such as the traditional Timed Up & Go and the Mini-BESTest, IFRA demonstrates competitive performance, being the only scale to correctly assign more than half of the fallers to the high-risk stratum (Fischer's Exact test p = 0.004). Despite the dataset's limited size, this is the first proof-of-concept study to pave the way for future evidence regarding the use of IFRA tool for continuous patient monitoring and fall prevention both in clinical stroke rehabilitation and at home post-discharge.
The White Dwarf Pareto: Tracing Mass Loss in Binary Systems
2025-01-16
Sahar Shahaf
The white dwarf mass distribution has been studied primarily at two extremes: objects that presumably evolved as single stars and members of close binaries that likely underwent substantial interaction. This work considers the intermediate separation regime of ~1 au and demonstrates how binary interaction affects white dwarf masses. The binary mass ratio distribution is utilized for this purpose. Modeled as a truncated Pareto profile, this distribution provides insights into the populations' properties and evolutionary history. When applied to homogeneous samples of binaries with giant primaries of similar age, the distribution's shape constrains the fraction of white dwarf companions, the white dwarf mass distribution, and the properties of their progenitors. As a test case, this method is applied to a small spectroscopic sample of binaries in open clusters with red giant primaries and orbital periods between 0.5 and 20 years. The analysis reveals that white dwarfs in these systems are ~20% less massive than their isolated counterparts, with a typical mass of ~0.55 Msun. Their progenitors likely lost 80-85% of their mass, with binary interactions enhancing mass loss by an additional ~0.2 Msun. These findings highlight the utility of this approach for studying binary evolution and improving population models, particularly with future datasets from Gaia and other large-scale surveys.
Theoretical Models for Tension-Dependent DNA Looping Time
2025-01-16
Wout Laeremans, Wouter G Ellenbroek
The influence of tension on DNA looping has been studied both experimentally and theoretically in the past. However, different theoretical models have yielded different predictions, leaving uncertainty about their validity. We briefly review the predictions of those models and propose a novel model that demonstrates exceptional agreement with simulations for long semiflexible chains. Additionally, we elucidate the relationship between our result and that of the previously proposed two-state model, highlighting the distinct interpretative approach that underpins our framework. Our findings offer predictive insights that pave the way for future experimental validation.
An Intermediate-mass Black Hole Lurking in A Galactic Halo Caught Alive during Outburst
2025-01-16
C. -C. Jin, D. -Y. Li, N. Jiang, L. -X. Dai, H. -Q. Cheng, J. -Z. Zhu, C. -W. Yang, A. Rau, P. Baldini, T. -G. Wang, H. -Y. Zhou, W. Yuan, C. Zhang, X. -W. Shu, R. -F. Shen, Y. -L. Wang, S. -X. Wen, Q. -Y. Wu, Y. -B. Wang, L. L. Thomsen, Z. -J. Zhang, W. -J. Zhang, A. Coleiro, R. Eyles-Ferris, X. Fang, L. C. Ho, J. -W. Hu, J. -J. Jin, W. -X. Li, B. -F. Liu, F. -K. Liu, M. -J. Liu, Z. Liu, Y. -J. Lu, A. Merloni, E. -L. Qiao, R. Saxton, R. Soria, S. Wang, Y. -Q. Xue, H. -N. Yang, B. Zhang, W. -D. Zhang, Z. -M. Cai, F. -S. Chen, H. -L. Chen, T. -X. Chen, W. Chen, Y. -H. Chen, Y. -F. Chen, Y. Chen, B. Cordier, C. -Z. Cui, W. -W. Cui, Y. -F. Dai, H. -C. Ding, D. -W. Fan, Z. Fan, H. Feng, J. A. Garcia, J. Guan, D. -W. Han, D. -J. Hou, H. -B. Hu, M. -H. Huang, J. Huo, S. -M. Jia, Z. -Q. Jia, B. -W. Jiang, G. Jin, X. Kong, E. Kuulkers, W. -H. Lei, C. -K. Li, J. -F. Li, L. -H. Li, M. -S. Li, W. Li, Z. -D. Li, T. -Y. Lian, Z. -X. Ling, C. -Z. Liu, H. -Y Liu, H. -Q. Liu, J. -F. Liu, Y. Liu, F. -J. Lu, L. -D. Luo, J. Ma, X. Mao, H. -Y. Mu, K. Nandra, P. O'Brien, H. -W. Pan, X. Pan, G. -J. Qin, N. Rea, J. Sanders, L. -M. Song, H. Sun, S. -L. Sun, X. -J. Sun, Y. -Y. Tan, Q. -J. Tang, Y. -H. Tao, B. -C. Wang, J. Wang, J. -F. Wang, L. Wang, W. -X. Wang, Y. -S. Wang, Z. -X. Wang, Q. -W. Wu, X. -F. Wu, H. -T. Xu, J. -J. Xu, X. -P. Xu, Y. -F. Xu, Z. Xu, C. -B. Xue, S. -J. Xue, Y. -L. Xue, A. -L. Yan, X. -T. Yang, Y. -J. Yang, J. Zhang, M. Zhang, S. -N. Zhang, Y. -H. Zhang, Z. Zhang, Z. Zhang, Z. -L. Zhang, D. -H. Zhao, H. -S. Zhao, X. -F. Zhao, Z. -J. Zhao, J. Zheng, Q. -F. Zhu, Y. -X. Zhu, Z. -C. Zhu, H. Zou
Stellar-mass and supermassive black holes abound in the Universe, whereas intermediate-mass black holes (IMBHs) of ~10^2-10^5 solar masses in between are largely missing observationally, with few cases found only. Here we report the real-time discovery of a long-duration X-ray transient, EP240222a, accompanied by an optical flare with prominent H and He emission lines revealed by prompt follow-up observations. Its observed properties evidence an IMBH located unambiguously in the halo of a nearby galaxy and flaring by tidally disrupting a star -- the only confirmed off-nucleus IMBH-tidal disruption event so far. This work demonstrates the potential of sensitive time-domain X-ray surveys, complemented by timely multi-wavelength follow-ups, in probing IMBHs, their environments, demographics, origins and connections to stellar-mass and supermassive black holes.
A New Teacher-Reviewer-Student Framework for Semi-supervised 2D Human Pose Estimation
2025-01-16
Wulian Yun, Mengshi Qi, Fei Peng, Huadong Ma
Conventional 2D human pose estimation methods typically require extensive labeled annotations, which are both labor-intensive and expensive. In contrast, semi-supervised 2D human pose estimation can alleviate the above problems by leveraging a large amount of unlabeled data along with a small portion of labeled data. Existing semi-supervised 2D human pose estimation methods update the network through backpropagation, ignoring crucial historical information from the previous training process. Therefore, we propose a novel semi-supervised 2D human pose estimation method by utilizing a newly designed Teacher-Reviewer-Student framework. Specifically, we first mimic the phenomenon that human beings constantly review previous knowledge for consolidation to design our framework, in which the teacher predicts results to guide the student's learning and the reviewer stores important historical parameters to provide additional supervision signals. Secondly, we introduce a Multi-level Feature Learning strategy, which utilizes the outputs from different stages of the backbone to estimate the heatmap to guide network training, enriching the supervisory information while effectively capturing keypoint relationships. Finally, we design a data augmentation strategy, i.e., Keypoint-Mix, to perturb pose information by mixing different keypoints, thus enhancing the network's ability to discern keypoints. Extensive experiments on publicly available datasets, demonstrate our method achieves significant improvements compared to the existing methods.
Core Hours and Carbon Credits: Incentivizing Sustainability in HPC
2025-01-16
Alok Kamatar, Maxime Gonthier, Valerie Hayot-Sasson, Andre Bauer, Marcin Copik, Torsten Hoefler, Raul Castro Fernandez, Kyle Chard, Ian Foster
Realizing a shared responsibility between providers and consumers is critical to manage the sustainability of HPC. However, while cost may motivate efficiency improvements by infrastructure operators, broader progress is impeded by a lack of user incentives. We conduct a survey of HPC users that reveals fewer than 30 percent are aware of their energy consumption, and that energy efficiency is among users' lowest priority concerns. One explanation is that existing pricing models may encourage users to prioritize performance over energy efficiency. We propose two transparent multi-resource pricing schemes, Energy- and Carbon-Based Accounting, that seek to change this paradigm by incentivizing more efficient user behavior. These two schemes charge for computations based on their energy consumption or carbon footprint, respectively, rewarding users who leverage efficient hardware and software. We evaluate these two pricing schemes via simulation, in a prototype, and a user study.
Quantitative phase microscopy (QPM), a technique combining phase imaging and microscopy, enables visualization of the 3D topography in reflective samples, as well as the inner structure or refractive index distribution of transparent and translucent samples. Similar to other imaging modalities, QPM is constrained by the conflict between numerical aperture (NA) and field of view (FOV): an imaging system with a low NA has to be employed to maintain a large FOV. This fact severely limits the resolution in QPM up to being the illumination wavelength. Consequently, finer structures of samples cannot be resolved by using modest NA objectives in QPM. Aimed to that, many approaches, such as oblique illumination, structured illumination, and speckle illumination (just to cite a few), have been proposed to improve the spatial resolution (or the space bandwidth product) in phase microscopy by restricting other degrees of freedom (mostly time). This paper aims to provide an up to date review on the resolution enhancement approaches in QPM, discussing the pros and cons of each technique as well as the confusion on resolution definition claims on QPM and other coherent microscopy methods. Through this survey, we will review the most appealing and useful techniques for superresolution in coherent microscopy, working with and without lenses and with special attention to QPM.
WALLABY Pilot Survey & ASymba: Comparing HI Detection Asymmetries to the SIMBA Simulation
2025-01-16
Mathieu Perron-Cormier, Nathan Deg, Kristine Spekkens, Mark L. A. Richardson, Marcin Glowacki, Kyle A. Oman, Marc A. W. Verheijen, Nadine A. N. Hank, Sarah Blyth, Helga Dénes, Jonghwan Rhee, Ahmed Elagali, Austin Xiaofan Shen, Wasim Raja, Karen Lee-Waddell, Luca Cortese, Barbara Catinella, Tobias Westmeier
An avenue for understanding cosmological galaxy formation is to compare morphometric parameters in observations and simulations of galaxy assembly. In this second paper of the ASymba: Asymmetries of HI in SIMBA Galaxies series, we measure atomic gas HI asymmetries in spatially-resolved detections from the untargetted WALLABY survey, and compare them to realizations of WALLABY-like mock samples from the SIMBA cosmological simulations. We develop a Scanline Tracing method to create mock galaxy HI datacubes which minimizes shot noise along the spectral dimension compared to particle-based methods, and therefore spurious asymmetry contributions. We compute 1D and 3D asymmetries for spatially-resolved WALLABY Pilot Survey detections, and find that the highest 3D asymmetries A3D>0.5 stem from interacting systems or detections with strong bridges or tails. We then construct a series of WALLABY-like mock realizations drawn from the SIMBA 50 Mpc simulation volume, and compare their asymmetry distributions. We find that the incidence of high A3D detections is higher in WALLABY than in the SIMBA mocks, but that difference is not statistically significant (p-value = 0.05). The statistical power of quantitative comparisons of asymmetries such as the one presented here will improve as the WALLABY survey progresses, and as simulation volumes and resolutions increase.
Make yourself comfortable: Nudging urban heat and noise mitigation with smartwatch-based Just-in-time Adaptive Interventions (JITAI)
2025-01-16
Clayton Miller, Yun Xuan Chua, Matias Quintana, Binyu Lei, Filip Biljecki, Mario Frei
Humans can play a more active role in improving their comfort in the built environment if given the right information at the right place and time. This paper outlines the use of Just-in-Time Adaptive Interventions (JITAI) implemented in the context of the built environment to provide information that helps humans minimize the impact of heat and noise on their daily lives. This framework builds upon the open-source Cozie iOS smartwatch platform. It includes data collection through micro-surveys and intervention messages triggered by environmental, contextual, and personal history conditions. An eight-month deployment of the method was completed in Singapore with 103 participants who submitted over 12,000 micro-surveys and delivered over 3,600 JITAI intervention messages. A weekly survey conducted during two deployment phases revealed an overall increase in perceived usefulness ranging from 8-19% over the first three weeks of data collection. For noise-related interventions, participants showed an overall increase in location changes ranging from 4-11% and a 2-17% increase in earphone use to mitigate noise distractions. For thermal comfort-related interventions, participants demonstrated a 3-13% increase in adjustments to their location or thermostat to feel more comfortable. The analysis found evidence that personality traits (such as conscientiousness), gender, and environmental preferences could be factors in determining the perceived helpfulness of JITAIs and influencing behavior change. These findings underscore the importance of tailoring intervention strategies to individual traits and environmental conditions, setting the stage for future research to refine the delivery, timing, and content of intervention messages.
Quantum Machine Learning represents a paradigm shift at the intersection of Quantum Computing and Machine Learning, leveraging quantum phenomena such as superposition, entanglement, and quantum parallelism to address the limitations of classical approaches in processing high-dimensional and large-scale datasets. This survey provides a comprehensive analysis of Quantum Machine Learning, detailing foundational concepts, algorithmic advancements, and their applications across domains such as healthcare, finance, and quantum chemistry. Key techniques, including Quantum Support Vector Machine, Quantum Neural Network, Quantum Decision Trees, and hybrid quantum-classical models, are explored with a focus on their theoretical foundations, computational benefits, and comparative performance against classical counterparts. While the potential for exponential speedups and enhanced efficiency is evident, the field faces significant challenges, including hardware constraints, noise, and limited qubit coherence in the current era of Noisy Intermediate-Scale Quantum devices. Emerging solutions, such as error mitigation techniques, hybrid frameworks, and advancements in quantum hardware, are discussed as critical enablers for scalable and fault-tolerant Quantum Machine Learning systems. By synthesizing state-of-the-art developments and identifying research gaps, this survey aims to provide a foundational resource for advancing Quantum Machine Learning toward practical, real-world applications in tackling computationally intensive problems.
Dynamic security assessment (DSA) is crucial for ensuring the reliable operation of power systems. However, conventional DSA approaches are becoming intractable for future power systems, driving interest in more computationally efficient data-driven methods. Efficient dataset generation is a cornerstone of these methods. While importance and generic sampling techniques often focus on operating points near the system's security boundary, systematic methods for sampling in this region remain scarce. Furthermore, the impact of sampling near the security boundary on the performance of data-driven DSA methods has yet to be established. This paper highlights the critical role of accurately capturing security boundaries for effective security assessment. As such, we propose a novel method for generating a high number of samples close to the security boundary, considering both AC feasibility and small-signal stability. Case studies on the PGLib-OPF 39-bus and PGLib-OPF 162-bus systems demonstrate the importance of including boundary-adjacent operating points in training datasets while maintaining a balanced distribution of secure and insecure points.
Multimodal Marvels of Deep Learning in Medical Diagnosis: A Comprehensive Review of COVID-19 Detection
2025-01-16
Md Shofiqul Islam, Khondokar Fida Hasan, Hasibul Hossain Shajeeb, Humayan Kabir Rana, Md Saifur Rahmand, Md Munirul Hasan, AKM Azad, Ibrahim Abdullah, Mohammad Ali Moni
This study presents a comprehensive review of the potential of multimodal deep learning (DL) in medical diagnosis, using COVID-19 as a case example. Motivated by the success of artificial intelligence applications during the COVID-19 pandemic, this research aims to uncover the capabilities of DL in disease screening, prediction, and classification, and to derive insights that enhance the resilience, sustainability, and inclusiveness of science, technology, and innovation systems. Adopting a systematic approach, we investigate the fundamental methodologies, data sources, preprocessing steps, and challenges encountered in various studies and implementations. We explore the architecture of deep learning models, emphasising their data-specific structures and underlying algorithms. Subsequently, we compare different deep learning strategies utilised in COVID-19 analysis, evaluating them based on methodology, data, performance, and prerequisites for future research. By examining diverse data types and diagnostic modalities, this research contributes to scientific understanding and knowledge of the multimodal application of DL and its effectiveness in diagnosis. We have implemented and analysed 11 deep learning models using COVID-19 image, text, and speech (ie, cough) data. Our analysis revealed that the MobileNet model achieved the highest accuracy of 99.97% for COVID-19 image data and 93.73% for speech data (i.e., cough). However, the BiGRU model demonstrated superior performance in COVID-19 text classification with an accuracy of 99.89%. The broader implications of this research suggest potential benefits for other domains and disciplines that could leverage deep learning techniques for image, text, and speech analysis.
Understanding emotions accurately is essential for fields like human-computer interaction. Due to the complexity of emotions and their multi-modal nature (e.g., emotions are influenced by facial expressions and audio), researchers have turned to using multi-modal models to understand human emotions rather than single-modality. However, current video multi-modal large language models (MLLMs) encounter difficulties in effectively integrating audio and identifying subtle facial micro-expressions. Furthermore, the lack of detailed emotion analysis datasets also limits the development of multimodal emotion analysis. To address these issues, we introduce a self-reviewed dataset and a human-reviewed dataset, comprising 24,137 coarse-grained samples and 3,500 manually annotated samples with detailed emotion annotations, respectively. These datasets allow models to learn from diverse scenarios and better generalize to real-world applications. Moreover, in addition to the audio modeling, we propose to explicitly integrate facial encoding models into the existing advanced Video MLLM, enabling the MLLM to effectively unify audio and the subtle facial cues for emotion understanding. By aligning these features within a unified space and employing instruction tuning in our proposed datasets, our Omni-Emotion achieves state-of-the-art performance in both emotion recognition and reasoning tasks.
The impact of Artificial Intelligence (AI) is transforming various aspects of urban life, including, governance, policy and planning, healthcare, sustainability, economics, entrepreneurship, etc. Although AI immense potential for positively impacting urban living, its success depends on overcoming significant challenges, particularly in telecommunications infrastructure. Smart city applications, such as, federated learning, Internet of Things (IoT), and online financial services, require reliable Quality of Service (QoS) from telecommunications networks to ensure effective information transfer. However, with over three billion people underserved or lacking access to internet, many of these AI-driven applications are at risk of either remaining underutilized or failing altogether. Furthermore, many IoT and video-based applications in densely populated urban areas require high-quality connectivity. This paper explores these issues, focusing on the challenges that need to be mitigated to make AI succeed in emerging countries, where more than 80% of the world population resides and urban migration grows. In this context, an overview of a case study conducted in Kathmandu, Nepal, highlights citizens' aspirations for affordable, high-quality internet-based services. The findings underscore the pressing need for advanced telecommunication networks to meet diverse user requirements while addressing investment and infrastructure gaps. This discussion provides insights into bridging the digital divide and enabling AI's transformative potential in urban areas.
From Complexification to Self-Similarity: New Aspects of Quantum Criticality
2025-01-16
Yang Liu, Erhai Zhao, Haiyuan Zou
Quantum phase transitions are a fascinating area of condensed matter physics. The extension through complexification not only broadens the scope of this field but also offers a new framework for understanding criticality and its statistical implications. This mini review provides a concise overview of recent developments in complexification, primarily covering finite temperature and equilibrium quantum phase transitions, as well as their connection with dynamical quantum phase transitions and non-Hermitian physics, with a particular focus on the significance of Fisher zeros. Starting from the newly discovered self-similarity phenomenon associated with complex partition functions, we further discuss research on self-similar systems briefly. Finally, we offer a perspective on these aspects.
Large Language Models (LLMs) hold great promise in the task of code translation. However, the lack of explainability complicates the identification of the inevitable translation errors. In this paper, we propose tHinter, a debugging tool to locate translation errors in auto-translated code. The core idea of tHinter is that correctly translated, the source and translated code should present the same functionalities, giving the same output for the same input. Hence, lines in the translated code responsible for output differences are possibly translation errors. First, tHinter employs fuzzing to generate diverse test cases that thoroughly explore the translated code. Then, tHinter relies on a heuristic algorithm to pinpoint translation errors from coverage information and differential testing execution results of those test cases. This heuristic algorithm is designed to leverage both the statistics and the expertise of developers. Comprehensive experiments with real code show its effectiveness. It reduces 71% lines developers need to review during debugging and increases the likelihood of the LLM fixing translation errors in a single query by 59%. Developers generally consider it satisfactory and helpful.
Sensorimotor Control Strategies for Tactile Robotics
2025-01-16
Enrico Donato, Matteo Lo Preti, Lucia Beccai, Egidio Falotico
How are robots becoming smarter at interacting with their surroundings? Recent advances have reshaped how robots use tactile sensing to perceive and engage with the world. Tactile sensing is a game-changer, allowing robots to embed sensorimotor control strategies to interact with complex environments and skillfully handle heterogeneous objects. Such control frameworks plan contact-driven motions while staying responsive to sudden changes. We review the latest methods for building perception and control systems in tactile robotics while offering practical guidelines for their design and implementation. We also address key challenges to shape the future of intelligent robots.
While large language models (LLMs) present significant potential for supporting numerous real-world applications and delivering positive social impacts, they still face significant challenges in terms of the inherent risk of privacy leakage, hallucinated outputs, and value misalignment, and can be maliciously used for generating toxic content and unethical purposes after been jailbroken. Therefore, in this survey, we present a comprehensive review of recent advancements aimed at mitigating these issues, organized across the four phases of LLM development and usage: data collecting and pre-training, fine-tuning and alignment, prompting and reasoning, and post-processing and auditing. We elaborate on the recent advances for enhancing the performance of LLMs in terms of privacy protection, hallucination reduction, value alignment, toxicity elimination, and jailbreak defenses. In contrast to previous surveys that focus on a single dimension of responsible LLMs, this survey presents a unified framework that encompasses these diverse dimensions, providing a comprehensive view of enhancing LLMs to better serve real-world applications.
Homoepitaxial step-flow growth of high-quality $\beta$-Ga$_{2}$O$_{3}$ thin films is essential for the advancement of high-performance Ga$_{2}$O$_{3}$-based devices. In this work, the step-flow growth mechanism of $\beta$-Ga$_{2}$O$_{3}$ (100) facet is explored by machine-learning molecular dynamics simulations and density functional theory calculations. Our results reveal that Ga adatoms and Ga-O adatom pairs, with their high mobility, are the primary atomic species responsible for efficient surface migration on the (100) facet. The asymmetric monoclinic structure of $\beta$-Ga$_{2}$O$_{3}$ induces a distinct two-stage Ehrlich-Schwoebel barrier for Ga adatoms at the [00$\overline{1}$] step edge, contributing to the suppression of double-step and hillock formation. Furthermore, a miscut towards [00$\overline{1}$] does not induce the nucleation of stable twin boundaries, whereas a miscut towards [001] leads to the spontaneous formation of twin boundaries. This research provides meaningful insights not only for high-quality $\beta$-Ga$_{2}$O$_{3}$ homoepitaxy but also the step-flow growth mechanism of other similar systems.
A practical review on promoting connectivity in topology optimization
2025-01-16
Vanessa Cool, Niels Aage, Ole Sigmund
Topology optimization facilitates the automated design of high-performance structures across various engineering fields but, if unconstrained, often produces designs that are complex and difficult to manufacture. A key attribute of the resulting designs is connectivity, which involves controlling the presence of solid and/or void islands of material. This manuscript provides a comprehensive overview of existing connectivity constraints developed for continuous design representations and highlights their advantages and limitations in influencing design outcomes and performance. The review further includes a practical comparison of five different connectivity constraints using a topology optimization framework for sandwich panels that balances acoustic and structural performance. With Pareto-front analyses, the constraints are evaluated based on computational cost, monotonicity, parameter dependency, and their impact on the optimized designs, their performance, and underlying dynamics. From the comparison, practical insights and rule of thumbs have been derived. The findings emphasize the critical role of selecting appropriate connectivity constraints, given their significant effect on the optimization results.
The Mini SiTian Array: Prospects of Searching for Tidal Disruption Events
2025-01-16
Bei-chuan Wang, Jun-jie Jin, Yu Zhang, Yanan Wang, Song Wang, Hong-rui Gu, Min He, Hai-yang Mu, Kai Xiao, Zhi-rui Li, Zhou Fan, Liang Ge, Jian-feng Tian, Yang Huang, Jie Zheng, Hong Wu
We assess the detectability of tidal disruption events (TDEs) using mock observations from the Mini SiTian array. We select 100 host galaxy samples from a simulated galaxy catalog based on specific criteria such as redshift, BH mass, and event rate. Taking into account the site conditions and survey strategy, we simulate observations over a 440 deg$^2$ field. The results indicate that $0.53\pm 0.73$ TDEs can be detected per year when observing in both $g$ and $r$ bands with 300-second exposures every 3 days. Applying this method to the SiTian project, we expect to discover approximately 204 TDEs annually, heralding a new era in TDE science.
Image segmentation, a key task in computer vision, has traditionally relied on convolutional neural networks (CNNs), yet these models struggle with capturing complex spatial dependencies, objects with varying scales, need for manually crafted architecture components and contextual information. This paper explores the shortcomings of CNN-based models and the shift towards transformer architectures -to overcome those limitations. This work reviews state-of-the-art transformer-based segmentation models, addressing segmentation-specific challenges and their solutions. The paper discusses current challenges in transformer-based segmentation and outlines promising future trends, such as lightweight architectures and enhanced data efficiency. This survey serves as a guide for understanding the impact of transformers in advancing segmentation capabilities and overcoming the limitations of traditional models.
Asymptotic phase and amplitudes are fundamental concepts in the analysis of limit-cycle oscillators. In this paper, we briefly review the definition of these quantities, particularly a generalization to stochastic oscillatory systems from the viewpoint of Koopman operator theory, and discuss a data-driven approach to estimate the asymptotic phase and amplitude functions from time-series data of stochastic oscillatory systems. We demonstrate that the standard Extended dynamic mode decomposition (EDMD) can successfully reconstruct the phase and amplitude functions of the noisy FitzHugh-Nagumo neuron model only from the time-series data.
Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning
2025-01-16
Seohyun Lee, Wenzhi Fang, Anindya Bijoy Das, Seyyedali Hosseinalipour, David J. Love, Christopher G. Brinton
Federated learning (FL) is vulnerable to backdoor attacks, where adversaries alter model behavior on target classification labels by embedding triggers into data samples. While these attacks have received considerable attention in horizontal FL, they are less understood for vertical FL (VFL), where devices hold different features of the samples, and only the server holds the labels. In this work, we propose a novel backdoor attack on VFL which (i) does not rely on gradient information from the server and (ii) considers potential collusion among multiple adversaries for sample selection and trigger embedding. Our label inference model augments variational autoencoders with metric learning, which adversaries can train locally. A consensus process over the adversary graph topology determines which datapoints to poison. We further propose methods for trigger splitting across the adversaries, with an intensity-based implantation scheme skewing the server towards the trigger. Our convergence analysis reveals the impact of backdoor perturbations on VFL indicated by a stationarity gap for the trained model, which we verify empirically as well. We conduct experiments comparing our attack with recent backdoor VFL approaches, finding that ours obtains significantly higher success rates for the same main task performance despite not using server information. Additionally, our results verify the impact of collusion on attack performance.
Understanding Mental Health Content on Social Media and Its Effect Towards Suicidal Ideation
2025-01-16
Mohaiminul Islam Bhuiyan, Nur Shazwani Kamarudin, Nur Hafieza Ismail
This review underscores the critical need for effective strategies to identify and support individuals with suicidal ideation, exploiting technological innovations in ML and DL to further suicide prevention efforts. The study details the application of these technologies in analyzing vast amounts of unstructured social media data to detect linguistic patterns, keywords, phrases, tones, and contextual cues associated with suicidal thoughts. It explores various ML and DL models like SVMs, CNNs, LSTM, neural networks, and their effectiveness in interpreting complex data patterns and emotional nuances within text data. The review discusses the potential of these technologies to serve as a life-saving tool by identifying at-risk individuals through their digital traces. Furthermore, it evaluates the real-world effectiveness, limitations, and ethical considerations of employing these technologies for suicide prevention, stressing the importance of responsible development and usage. The study aims to fill critical knowledge gaps by analyzing recent studies, methodologies, tools, and techniques in this field. It highlights the importance of synthesizing current literature to inform practical tools and suicide prevention efforts, guiding innovation in reliable, ethical systems for early intervention. This research synthesis evaluates the intersection of technology and mental health, advocating for the ethical and responsible application of ML, DL, and NLP to offer life-saving potential worldwide while addressing challenges like generalizability, biases, privacy, and the need for further research to ensure these technologies do not exacerbate existing inequities and harms.
K-geodetic graphs (K capital) are defined as graphs in which each pair of nonadjacent vertices has at most K paths of minimum length between them. A K-geodetic graph is geodetic if K=1, bigeodetic if K=2 and trigeodetic if K=3. K-geodetic graphs are applied effectively to the solution of several practical problems in distinct areas of computer science, hence the importance of their study. Four problems are central to the study of K-geodetic graphs, namely, characterization, construction, enumeration and classification. The problems of finding the general classification of K-geodetic graphs for each of their classes K=1,2,3 are open. The present paper is a survey dedicated to the construction of K-geodetic graphs for K=1,2,3 using balanced incomplete block designs (BIBDs). To this purpose, we use block designs as combinatorial structures defined in terms of completely predetermined parameters, which is essential for the easy construction of the K-geodetic graphs described in this survey.
Bilayer nickelate thin film superconductors discovered under ambient pressure enable vast possibilities for investigating electronic structures of the superconducting state. Here, we report angle-resolved photoemission spectroscopy (ARPES) measurements of heterointerfaces between 1, 2, and 3 unit-cell epitaxial La$_{2.85}$Pr$_{0.15}$Ni$_2$O$_7$ films and SrLaAlO$_4$ substates, through pure-oxygen in situ sample transportation. Evidence obtained using photons with distinct probing depths shows that conduction is localized primarily at the first unit cell near the interface. Scanning transmission electron microscopy (STEM), together with energy-dispersive X-ray spectroscopy (EDS) and electron energy loss spectroscopy (EELS), indicates that interfacial Sr diffusion and pronounced oxygen 2p hybridization gradient may collectively account for the interfacial confinement of conduction. Fermi surface maps reveal hole doping compared to non-superconducting ambient-pressure bulk crystals. Measurements of dispersive band structures suggest the contributions from both Ni $d_{x^2-y^2}$ and $d_{z^2}$ orbitals at the Fermi level. Density functional theory (DFT) + U calculations capture qualitative features of the ARPES results, consistent with a hole-doped scenario. These findings constrain theoretical models of the superconducting mechanism and suggest pathways for enhancing superconductivity in nickelates under ambient pressure.
Dispersive dark excitons in van der Waals ferromagnet CrI3
2025-01-16
W. He, J. Sears, F. Barantani, T. Kim, J. W. Villanova, T. Berlijn, M. Lajer, M. A. McGuire, J. Pelliciari, V. Bisogni, S. Johnston, E. Baldini, M. Mitrano, M. P. M. Dean
Spin-flip dark excitons are optical-dipole-forbidden quasiparticles with remarkable potential in optoelectronics, especially when they are realized within cleavable van der Waals materials. Despite this potential, dark excitons have not yet been definitively identified in ferromagnetic van der Waals materials. Here, we report two dark excitons in a model ferromagnetic material CrI3 using high-resolution resonant inelastic x-ray scattering (RIXS) and show that they feature narrower linewidths compared to the bright excitons previously reported in this material. These excitons are shown to have spin-flip character, to disperse as a function of momentum, and to change through the ferromagnetic transition temperature. Given the versatility of van der Waals materials, these excitons hold promise for new types of magneto-optical functionality.
Simons Observatory: Characterization of the Large Aperture Telescope Receiver
2025-01-16
Tanay Bhandarkar, Saianeesh K. Haridas, Jeff Iuliano, Anna Kofman, Alex Manduca, Karen Perez Sarmiento, John Orlowski-Scherer, Thomas P. Satterthwaite, Yuhan Wang, Zeeshan Ahmed, Jason E. Austermann, Kyuyoung Bae, Gabriele Coppi, Mark J. Devlin, Simon R Dicker, Peter N. Dow, Shannon M. Duff, Daniel Dutcher, Nicholas Galitzki, Jon E. Gudmundsson, Shawn W. Henderson, Johannes Hubmayr, Bradley R. Johnson, Matthew A. Koc, Brian J. Koopman, Michele Limon, Michael J Link, Tammy J. Lucas, Jenna E. Moore, Federico Nati, Michael D. Niemack, Carlos E. Sierra, Max Silva-Feaver, Robinjeet Singh, Suzanne T. Staggs, Rita F. Sonka, Robert J. Thornton, Tran Tsan, Jeff L. Van Lanen, Eve M. Vavagiakis, Michael R Vissers, Liam Walters, Mario Zannoni, Kaiwen Zheng
The Simons Observatory (SO) is a ground-based cosmic microwave background (CMB) survey experiment that currently consists of three 0.42m small-aperture telescopes (SATs) and one 6m large-aperture telescope (LAT), located at an elevation of 5200m in the Atacama Desert in Chile. At the LAT's focal plane, SO will install >62,000 transition-edge sensor detectors across 13 optics tubes (OTs) within the Large Aperture Telescope Receiver (LATR), the largest cryogenic camera ever built to observe the CMB. Here we report on the validation of the LATR in the laboratory and the subsequent dark testing and validation within the LAT. We show that the LATR meets cryogenic, optical, and detector specifications required for high-sensitivity measurements of the CMB. At the time of writing, the LATR is installed in the LAT with six OTs (corresponding to >31,000 detectors), and the LAT mirrors and remaining seven OTs are undergoing development.
AI-based Identity Fraud Detection: A Systematic Review
2025-01-16
Chuo Jun Zhang, Asif Q. Gill, Bo Liu, Memoona J. Anwar
With the rapid development of digital services, a large volume of personally identifiable information (PII) is stored online and is subject to cyberattacks such as Identity fraud. Most recently, the use of Artificial Intelligence (AI) enabled deep fake technologies has significantly increased the complexity of identity fraud. Fraudsters may use these technologies to create highly sophisticated counterfeit personal identification documents, photos and videos. These advancements in the identity fraud landscape pose challenges for identity fraud detection and society at large. There is a pressing need to review and understand identity fraud detection methods, their limitations and potential solutions. This research aims to address this important need by using the well-known systematic literature review method. This paper reviewed a selected set of 43 papers across 4 major academic literature databases. In particular, the review results highlight the two types of identity fraud prevention and detection methods, in-depth and open challenges. The results were also consolidated into a taxonomy of AI-based identity fraud detection and prevention methods including key insights and trends. Overall, this paper provides a foundational knowledge base to researchers and practitioners for further research and development in this important area of digital identity fraud.
Bent-tail radio galaxies (BTRGs) are characterized by bent radio lobes. This unique shape is mainly caused by the movement of the galaxy within a cluster, during which the radio jets are deflected by the intra-cluster medium. A combined method, which involves a deep learning-based radio source finder along with visual inspection, has been utilized to search for BTRGs from the Faint Images of the Radio Sky at Twenty-centimeters survey images. Consequently, a catalog of 4876 BTRGs has been constructed, among which 3871 are newly discovered. Based on the classification scheme of the opening angle between the two jets of the galaxy, BTRGs are typically classified as either wide-angle-tail (WAT) sources or narrow-angle-tail (NAT) sources. Our catalog comprises 4424 WATs and 652 NATs. Among these, optical counterparts are identified for 4193 BTRGs. This catalog covers luminosities in the range of $1.91\times10^{20} \leq L_{\rm 1.4\,GHz} \leq 1.45\times10^{28}$ ${\rm W\,Hz^{-1}}$ and redshifts from $z = 0.0023$ to $z = 3.43$. Various physical properties of these BTRGs and their statistics are presented. Particularly, by the nearest neighbor method, we found that 1825 BTRGs in this catalog belong to galaxy clusters reported in literature.
G-functions, motives, and unlikely intersections -- old and new
2025-01-16
Yves André
In this survey, we outline the role of G-functions in arithmetic geometry, notably their link with Picard-Fuchs differential equations and periods. We explain how polynomial relations between special values of G-functions arising from a pencil of algebraic varieties may occur at a parameter where the fiber has more ``motivic" symmetries; and how Bombieri's principle of global relations can be used to control the height of such parameters (which was also one of the origins of the Andr\'e-Oort conjecture). At the end, we sketch the recent revival of the G-function method in the context of unlikely intersections and the Zilber-Pink conjecture.
Empirical Evaluation of Embedding Models in the Context of Text Classification in Document Review in Construction Delay Disputes
2025-01-16
Fusheng Wei, Robert Neary, Han Qin, Qiang Mao, Jianping Zhang
Text embeddings are numerical representations of text data, where words, phrases, or entire documents are converted into vectors of real numbers. These embeddings capture semantic meanings and relationships between text elements in a continuous vector space. The primary goal of text embeddings is to enable the processing of text data by machine learning models, which require numerical input. Numerous embedding models have been developed for various applications. This paper presents our work in evaluating different embeddings through a comprehensive comparative analysis of four distinct models, focusing on their text classification efficacy. We employ both K-Nearest Neighbors (KNN) and Logistic Regression (LR) to perform binary classification tasks, specifically determining whether a text snippet is associated with 'delay' or 'not delay' within a labeled dataset. Our research explores the use of text snippet embeddings for training supervised text classification models to identify delay-related statements during the document review process of construction delay disputes. The results of this study highlight the potential of embedding models to enhance the efficiency and accuracy of document analysis in legal contexts, paving the way for more informed decision-making in complex investigative scenarios.
Adaptive Weighted Total Variation boosted by learning techniques in few-view tomographic imaging
2025-01-16
Elena Morotti, Davide Evangelista, Andrea Sebastiani, Elena Loli Piccolomini
This study presents the development of a spatially adaptive weighting strategy for Total Variation regularization, aimed at addressing under-determined linear inverse problems. The method leverages the rapid computation of an accurate approximation of the true image (or its gradient magnitude) through a neural network. Our approach operates without requiring prior knowledge of the noise intensity in the data and avoids the iterative recomputation of weights. Additionally, the paper includes a theoretical analysis of the proposed method, establishing its validity as a regularization approach. This framework integrates advanced neural network capabilities within a regularization context, thereby making the results of the networks interpretable. The results are promising as they enable high-quality reconstructions from limited-view tomographic measurements.
The field of molecular excitons and related supramolecular systems has largely focused on aggregates where nearest-neighbour couplings dominate. We propose that radically different states can be produced by moving beyond that paradigm. In practice, how to accomplish this task remains an open challenge because it requires development of ways to couple networks molecules more densely. In the present work we motivate why it would be worthwhile. We describe a merger of work developed in the field of discrete mathematics with concepts and needs for the field of molecular excitons. We discuss the reasons for exciton localization and posit how systems where the spectrum contains a gap can be robust to disorder, and thus maintain coherence, or delocalization. We propose that certain kinds of structures (expander graphs) specifying how molecules are coupled to each other, show such a gap and thus resilience to decoherence. We review relevant background known from graph theory. This perspective suggests a fascinating scope of new properties possible by demonstrating expander graph inspired excitonics.
Torque Responsive Metamaterials Enable High Payload Soft Robot Arms
2025-01-16
Ian Good, Srivatsan Balaji, David Oh, Sawyer Thomas, Jeffrey I. Lipton
Soft robots have struggled to support large forces and moments while also supporting their own weight against gravity. This limits their ability to reach certain configurations necessary for tasks such as inspection and pushing objects up. We have overcome this limitation by creating an electrically driven metamaterial soft arm using handed shearing auxetics (HSA) and bendable extendable torque resistant (BETR) shafts. These use the large force and torque capacity of HSAs and the nestable torque transmission of BETRs to create a strong soft arm. We found that the HSA arm was able to push 2.3 kg vertically and lift more than 600 g when positioned horizontally, supporting 0.33 Nm of torque at the base. The arm is able to move between waypoints while carrying the large payload and demonstrates consistent movement with path variance below 5 mm. The HSA arm's ability to perform active grasping with HSA grippers was also demonstrated, requiring 20 N of pull force to dislodge the object. Finally, we test the arm in a pipe inspection task. The arm is able to locate all the defects while sliding against the inner surface of the pipe, demonstrating its compliance.
2025-01-16
Alasdair I. Fletcher, Cillian Harney, Masoud Ghalaii, Panagiotis Papanastasiou, Alexandros Mountogiannakis, Gaetana Spedalieri, Adnan A. E. Hajomer, Tobias Gehring, Stefano Pirandola
As quantum key distribution (QKD) emerges as a robust defense against quantum computer threats, significant advancements have been realized by researchers. A pivotal focus has been the development of protocols that not only simplify hardware implementation like the use of continuous-variable (CV) systems, but also negate the necessity for trusted nodes, as seen with the measurement-device independent (MDI) approach. This paper delves into the integration of these methodologies in the CV-MDI-QKD protocol, offering an in-depth exploration of its evolution, primary characteristics, and the latest advancements in both theory and experiment.
The cosmic evolution of FRBs inferred from CHIME/FRB Catalog 1
2025-01-16
Om Gupta, Paz Beniamini, Pawan Kumar, Steven L. Finkelstein
Redshift and luminosity distributions are essential for understanding the cosmic evolution of extragalactic objects and phenomena, such as galaxies, gamma-ray bursts, and fast radio bursts (FRBs). For FRBs, these distributions are primarily estimated using the fluence and the Dispersion Measure (DM). Calibrating their joint distribution has been challenging due to a lack of accurate fluences in the intensity data of the CHIME/FRB survey. Using the baseband update of CHIME/FRB Catalog 1, we calibrate the 2D fluence-DM distribution for the first time. We find the energy distribution is described well by a Schechter function with power-law slope of $-1.94^{+0.14}_{-0.12}$. Testing two types of redshift evolution models suggests a likely combination of young and old formation channels. $31^{+31}_{-21}$\% of FRB sources may track star formation, or correspondingly, FRB sources may have delay times of $1.94^{+1.54}_{-1.31}$ Gyr. A pure star formation tracking population is excluded by only one model at $> 2\sigma$ confidence. An updated cosmic star formation rate density evolution up to redshift 14 is constrained by compiling results from several JWST studies. Using this information, we expect next-generation radio telescopes to detect a few dozen bursts per year at $z \gtrsim 6$. A radio telescope with a system-equivalent flux density of $\leq 1$ Jy (equivalent to a detection threshold of 14 mJy ms) and instantaneous sky coverage of $\gtrsim 200$ square degrees should be able to detect $1.4^{+1.6}_{-1.0}\times 10^3$ FRBs year$^{-1}$ at $z \gtrsim 6$ and $234^{+250}_{-162}$ FRBs year$^{-1}$ at $z\gtrsim 8$, which is sufficient to differentiate between reionization histories.
Kinematically induced dipole anisotropy in line-emitting galaxy number counts and line intensity maps
2025-01-16
Kyungjin Ahn
The motion of the solar system against an isotropic radiation background, such as the cosmic microwave background, induces a dipole anisotropy in the background due to the Doppler effect. Flux-limited observation of the continuum radiation from galaxies also has been studied extensively to show a dipole anisotropy due to the Doppler effect and the aberration effect. We show that a similar dipole anisotropy exists in spectral-line intensity maps, represented as either galaxy number counts or the diffuse intensity maps. The amplitude of these dipole anisotropies is determined by not only the solar velocity against the large-scale structures but also the temporal evolution of the monopole (sky-average) component. Measuring the dipole at multiple frequencies, which have mutually independent origins due to their occurrence from multiple redshifts, can provide a very accurate measure of the solar velocity thanks to the redundant information. We find that such a measurement can even constrain astrophysical parameters in the nearby universe. We explore the potential for dipole measurement of existing and upcoming surveys, and conclude that the spectral number count of galaxies through SPHEREx will be optimal for the first measurement of the dipole anisotropy in the spectral-line galaxy distribution. LIM surveys with reasonable accuracy are also found to be promising. We also discuss whether these experiments might reveal a peculiar nature of our local universe, that seems to call for a non-standard cosmology other than the simple LambdaCDM model as suggested by recent measures of the baryon acoustic oscillation signatures and the Alcock-Paczynski tests.
Cepheids in spectroscopic binary systems -- current status and recent discoveries
2025-01-16
Bogumił Pilecki
We present a summary of the current knowledge about Cepheids in binary systems. We focus on the most recent findings and discoveries, such as the highly increasing number of confirmed and candidate spectroscopic binary Cepheids and the progress in determining their physical parameters. This includes new and newly analyzed binary Cepheids in the Milky Way and Magellanic Clouds. We will provide an update on the project to increase the number of the most valuable Cepheids in double-lined binary (SB2) systems from six to more than 100. To date, we have confirmed 60 SB2 systems, including detecting a significant orbital motion for 37. We identified systems with orbital periods up to five times shorter than the shortest period reported before and systems with mass ratios significantly different from unity (suggesting past binary interactions, including merger events). Both features are essential to understanding how multiplicity affects the formation and destruction of Cepheid progenitors and how this influences global Cepheid properties. We will also present nine new systems composed of two Cepheids. Only one such double Cepheid system was known before.
Detecting dark matter sub-halos in the Galactic plane with the Cherenkov Telescope Array Observatory
2025-01-16
Christopher Eckner, Veronika Vodeb, Tejas Satheesh, Francesca Calore, Moritz Hütten, Pierrick Martin, Gabrijela Zaharijas
Numerous observations confirm the existence of dark matter (DM) at astrophysical and cosmological scales. Theory and simulations of galaxy formation predict that DM should cluster on small scales in bound structures called sub-halos or DM clumps. While the most massive DM sub-halos host baryonic matter, less massive, unpopulated sub-halos could be abundant in the Milky Way (MW), as well and yield high-energy gamma rays as final products of DM annihilation. Recently, it has been highlighted that the brightest halos should also have a sizeable extension in the sky. In this study, we examine the prospects offered by the Cherenkov Telescope Array Observatory (CTAO), a next-generation gamma-ray instrument, for detecting and characterizing such objects. Previous studies have primarily focused on high-latitude observations; here, we assess the potential impact of the CTAO's Galactic Plane Survey, which will provide unprecedentedly deep survey data for the inner five degrees of the Galactic plane. Our modeling accounts for tidal effects on the sub-halo population, examining the conditions under which DM sub-halos can be detected and distinguished from conventional astrophysical sources. We find that regions a few degrees above or below the Galactic plane offer the highest likelihood for DM sub-halo detection. For an individual sub-halo -- the brightest from among various realizations of the MW subhalo population -- we find that detection at the 5$\sigma$ level is achievable for an annihilation cross section of $\langle \sigma v \rangle \sim 3\times10^{-25}$ cm$^3$/s for TeV-scale DM annihilating into $b\bar{b}$. For a full population study, depending on the distribution and luminosity model of Galactic sub-halos, yet unconstrained cross sections in the range $\langle \sigma v \rangle \sim 10^{-23}-10^{-22}$ cm$^3$/s for TeV DM candidates are necessary for the brightest sub-halos to be detected.
Search for $K_{S,L}$ oscillations and invisible decays into the dark sector at NA64
2025-01-16
S. N. Gninenko, N. V. Krasnikov, V. A. Matveev
The decays $K_{S,L}$ have never been experimentally tested. In the Standard Model their branching ratios for the decay into two neutrinos are predicted to be extremely small, $Br(K_{S,L} \to \nu \bar{\nu}) \lesssim 10^{-16}$. We consider several natural extensions of the SM, such as two-Higgs-doublet (2HDM), 2HDM and light scalar, and dark mirror sector models, that allow to enhance the $Br(K_{S,L})$ up to a measurable level. We briefly discuss the possible search for $K_{S,L}$ decays and $K_{S,L}$ oscillations into the dark sector at the NA64 experiment at CERN with the sensitivity to $Br(K_{S,L}) \lesssim 10^{-7}-10^{-5}$.
The foundation model (FM) paradigm is transforming Machine Learning Force Fields (MLFFs), leveraging general-purpose representations and scalable training to perform a variety of computational chemistry tasks. Although MLFF FMs have begun to close the accuracy gap relative to first-principles methods, there is still a strong need for faster inference speed. Additionally, while research is increasingly focused on general-purpose models which transfer across chemical space, practitioners typically only study a small subset of systems at a given time. This underscores the need for fast, specialized MLFFs relevant to specific downstream applications, which preserve test-time physical soundness while maintaining train-time scalability. In this work, we introduce a method for transferring general-purpose representations from MLFF foundation models to smaller, faster MLFFs specialized to specific regions of chemical space. We formulate our approach as a knowledge distillation procedure, where the smaller "student" MLFF is trained to match the Hessians of the energy predictions of the "teacher" foundation model. Our specialized MLFFs can be up to 20 $\times$ faster than the original foundation model, while retaining, and in some cases exceeding, its performance and that of undistilled models. We also show that distilling from a teacher model with a direct force parameterization into a student model trained with conservative forces (i.e., computed as derivatives of the potential energy) successfully leverages the representations from the large-scale teacher for improved accuracy, while maintaining energy conservation during test-time molecular dynamics simulations. More broadly, our work suggests a new paradigm for MLFF development, in which foundation models are released along with smaller, specialized simulation "engines" for common chemical subsets.
In this article, we survey recent progress on self-similar $p$-energy forms on self-similar fractals, where $p\in(1,\infty)$. While for $p=2$ the notion of such forms coincides with that of self-similar Dirichlet forms and there have been plenty of studies on them since the late 1980s, studies on the case of $p\in(1,\infty)\setminus\{2\}$ was initiated much later in 2004 by Herman, Peirone and Strichartz [Potential Anal. 20 (2004), 125--148] and Strichartz and Wong [Nonlinearity 17 (2004), 595--616] and no essential progress on this case had been made since then until a few years ago. The recent progress by Kigami, Shimizu, Cao--Gu--Qiu and Murugan--Shimizu has established the existence of such $p$-energy forms on general post-critically finite (p.-c.f.) self-similar sets and on large classes of low-dimensional infinitely ramified self-similar sets, and the authors have proved further detailed properties of these forms and associated $p$-harmonic functions, mainly for p.-c.f. self-similar sets. This article is devoted to a review of these results, focusing on the most recent developments by the authors and illustrating them in the simplest non-trivial setting of the two-dimensional standard Sierpi\'{n}ski gasket.
Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models
2025-01-15
Karukriti Kaushik Ghosh, Chiranjib Sur
Aspect-based sentiment analysis (ASBA) is a refined approach to sentiment analysis that aims to extract and classify sentiments based on specific aspects or features of a product, service, or entity. Unlike traditional sentiment analysis, which assigns a general sentiment score to entire reviews or texts, ABSA focuses on breaking down the text into individual components or aspects (e.g., quality, price, service) and evaluating the sentiment towards each. This allows for a more granular level of understanding of customer opinions, enabling businesses to pinpoint specific areas of strength and improvement. The process involves several key steps, including aspect extraction, sentiment classification, and aspect-level sentiment aggregation for a review paragraph or any other form that the users have provided. ABSA has significant applications in areas such as product reviews, social media monitoring, customer feedback analysis, and market research. By leveraging techniques from natural language processing (NLP) and machine learning, ABSA facilitates the extraction of valuable insights, enabling companies to make data-driven decisions that enhance customer satisfaction and optimize offerings. As ABSA evolves, it holds the potential to greatly improve personalized customer experiences by providing a deeper understanding of sentiment across various product aspects. In this work, we have analyzed the strength of LLMs for a complete cross-domain aspect-based sentiment analysis with the aim of defining the framework for certain products and using it for other similar situations. We argue that it is possible to that at an effectiveness of 92\% accuracy for the Aspect Based Sentiment Analysis dataset of SemEval-2015 Task 12.
Properties of high-redshift Type II supernovae discovered by the JADES transient survey
2025-01-15
Takashi J. Moriya, David A. Coulter, Christa DeCoursey, Justin D. R. Pierel, Kevin Hainline, Matthew R. Siebert, Armin Rest, Eiichi Egami, Sebastian Gomez, Robert M. Quimby, Ori D. Fox, Michael Engesser, Fengwu Sun, Wenlei Chen, Yossef Zenati, Suvi Gezari, Bhavin A. Joshi, Melissa Shahbandeh, Louis-Gregory Strolger, Qinan Wang, Stacey Alberts, Rachana Bhatawdekar, Andrew J. Bunker, Pierluigi Rinaldi, Brant E. Robertson, Sandro Tacchella
In this work we estimate the explosion and progenitor properties of six Type II supernovae (SNe) at 0.675 <= z <= 3.61 discovered by the JWST Advanced Deep Extragalactic Survey (JADES) transient survey by modeling their light curves. This high-redshift Type II SN sample allows us to compare low-redshift Type II SNe to their high-redshift counterparts. Two Type II SNe are found to have high explosion energies of 3e51 erg, while the other four Type II SNe are estimated to have typical explosion energies found in the local Universe [(0.5-2)e51 erg]. The fraction of Type II SNe with high explosion energies might be higher at high redshifts because of, e.g., lower metallicity, but it is still difficult to draw a firm conclusion because of the small sample size and potential observational biases. We found it difficult to constrain the progenitor masses for Type II SNe in our sample because of the sparse light-curve data. We found two Type II SN light curves can be better reproduced by introducing confined, dense circumstellar matter. Thus, the confined, dense circumstellar matter frequently observed in nearby Type II SNe is likely to exist in Type II SNe at high redshifts as well. Two Type II SNe are estimated to have high host galaxy extinctions, showing the ability of JWST to discover dust-obscured SNe at high redshifts. More high-redshift Type II SNe are required to investigate the differences in the properties of Type II SNe near and far, but here we show the first glimpse into the high-redshift population of Type II SNe.
Reconstructing Time-of-Flight Detector Values of Angular Streaking Using Machine Learning
2025-01-15
David Meier, Wolfram Helml, Thorsten Otto, Bernhard Sick, Jens Viefhaus, Gregor Hartmann
Angular streaking experiments enable for experimentation in the attosecond regions. However, the deployed Time-of-flight detectors are susceptible to noise and failure. These shortcomings make the outputs of the Time-of-flight detectors hard to understand for humans and further processing, such as for example the extraction of beam properties. In this article, we present an approach to remove high noise levels and reconstruct up to three failed Time-of-flight detectors from an arrangement of 16 Time-of-flight detectors. Due to its fast evaluation time, the presented method is applicable online during a running experiment. It is trained with simulation data, and we show the results of denoising and reconstruction of our method on real-world experiment data.
String Theory is a hot topic of physics and mathematics. For the former, it stands as a huge sandbox where the formulation of difficult problems can be simplified and their hard computations carried out. For the latter, it stands as a direct contribution upstream, going from physics to maths, rather than the usual other way around. These notes provide a comprehensive review of the bosonic string action, the entering point of String Theory, and also covers the resulting equations of motion and the effective mass of open and closed strings.
Taint Analysis for Graph APIs Focusing on Broken Access Control
2025-01-15
Leen Lambers, Lucas Sakizloglou, Taisiya Khakharova, Fernando Orejas
Graph APIs are capable of flexibly retrieving or manipulating graph-structured data over the web. This rather novel type of APIs presents new challenges when it comes to properly securing the APIs against the usual web application security risks, e.g., broken access control. A prominent security testing approach is taint analysis, which traces tainted, i.e., security-relevant, data from sources (where tainted data is inserted) to sinks (where the use of tainted data may lead to a security risk), over the information flow in an application. We present a first systematic approach to static and dynamic taint analysis for Graph APIs focusing on broken access control. The approach comprises the following. We taint nodes in the Graph API if they represent data requiring specific privileges in order to be retrieved or manipulated, and identify API calls which are related to sources and sinks. Then, we statically analyze whether tainted information flow between API source and sink calls occurs. To this end, we model the API calls using graph transformation rules. We subsequently use critical pair analysis to automatically analyze potential dependencies between rules representing source calls and rules representing sink calls. We distinguish direct from indirect tainted information flow and argue under which conditions the CPA is able to detect not only direct, but also indirect tainted flow. The static taint analysis (i) identifies flows that need to be further reviewed, since tainted nodes may be created by an API call and used or manipulated by another API call later without having the necessary privileges, and (ii) can be used to systematically design dynamic security tests for broken access control. The dynamic taint analysis checks if potential broken access control risks detected during the static taint analysis really occur. We apply the approach to a part of the GitHub GraphQL API.
We have proposed using laser interferometric gravitational wave detectors to search for ultralight vector and axion dark matter. Vector dark matter can be probed through oscillating forces on suspended mirrors, while axion dark matter can be detected via oscillating polarization rotation of laser beams. This paper reviews these searches with the KAGRA detector in Japan, including the first vector dark matter search with KAGRA's 2020 data and installation of polarization optics for axion dark matter search during the upcoming 2025 observing run.
Software Testing for Extended Reality Applications: A Systematic Mapping Study
2025-01-15
Ruizhen Gu, José Miguel Rojas, Donghwan Shin
Extended Reality (XR) is an emerging technology spanning diverse application domains and offering immersive user experiences. However, its unique characteristics, such as six degrees of freedom interactions, present significant testing challenges distinct from traditional 2D GUI applications, demanding novel testing techniques to build high-quality XR applications. This paper presents the first systematic mapping study on software testing for XR applications. We selected 34 studies focusing on techniques and empirical approaches in XR software testing for detailed examination. The studies are classified and reviewed to address the current research landscape, test facets, and evaluation methodologies in the XR testing domain. Additionally, we provide a repository summarising the mapping study, including datasets and tools referenced in the selected studies, to support future research and practical applications. Our study highlights open challenges in XR testing and proposes actionable future research directions to address the gaps and advance the field of XR software testing.
On the abiotic origin of dimethyl sulfide: discovery of DMS in the Interstellar Medium
2025-01-15
Miguel Sanz-Novo, Víctor M. Rivilla, Christian P. Endres, Valerio Lattanzi, Izaskun Jiménez-Serra, Laura Colzi, Shaoshan Zeng, Andrés Megías, Álvaro López-Gallifa, Antonio Martínez-Henares, David San Andrés, Belén Tercero, Pablo de Vicente, Sergio Martín, Miguel A. Requena-Torres, Paola Caselli, Jesús Martín-Pintado
Following the discovery of dimethyl sulfide (CH$_3$SCH$_3$, DMS) signatures in comet 67P/Churyumov-Gerasimenko, we report the first detection of this organosulfur species in the interstellar medium, during the exploration of an ultradeep molecular line survey performed toward the Galactic Center molecular cloud G+0.693-0.027 with the Yebes 40$\,$m and IRAM 30$\,$m telescopes. We derive a molecular column density of $N$ = (2.6 $\pm$ 0.3)$\times$10$^{13}$ cm$^{-2}$, yielding a fractional abundance relative to H$_2$ of $\sim$1.9$\times$10$^{-10}$. This implies that DMS is a factor of $\sim$1.6 times less abundant than its structural isomer CH$_3$CH$_2$SH and $\sim$30 times less abundant than its O-analogue dimethyl ether (CH$_3$OCH$_3$) toward this cloud, in excellent agreement with previous results on various O/S pairs. Furthermore, we find a remarkable resemblance between the relative abundance of DMS/CH$_3$OH in G+0.693-0.027 ($\sim$1.7$\times$10$^{-3}$) and in the comet ($\sim$1.3$\times$10$^{-3}$), further strengthening the connection between the chemical inventory of the interstellar medium and that of the minor bodies of the Solar System. Although the chemistry of DMS beyond Earth is yet to be fully disclosed, this discovery provides conclusive observational evidence on its efficient abiotic production in the interstellar medium, casting doubts about using DMS as a reliable biomarker in exoplanet science.
Smart Contract Fuzzing Towards Profitable Vulnerabilities
2025-01-15
Ziqiao Kong, Cen Zhang, Maoyi Xie, Ming Hu, Yue Xue, Ye Liu, Haijun Wang, Yang Liu
Billions of dollars are transacted through smart contracts, making vulnerabilities a major financial risk. One focus in the security arms race is on profitable vulnerabilities that attackers can exploit. Fuzzing is a key method for identifying these vulnerabilities. However, current solutions face two main limitations: a lack of profit-centric techniques for expediting detection, and insufficient automation in maximizing the profitability of discovered vulnerabilities, leaving the analysis to human experts. To address these gaps, we have developed VERITE, a profit-centric smart contract fuzzing framework that not only effectively detects those profitable vulnerabilities but also maximizes the exploited profits. VERITE has three key features: 1) DeFi action-based mutators for boosting the exploration of transactions with different fund flows; 2) potentially profitable candidates identification criteria, which checks whether the input has caused abnormal fund flow properties during testing; 3) a gradient descent-based profit maximization strategy for these identified candidates. VERITE is fully developed from scratch and evaluated on a dataset consisting of 61 exploited real-world DeFi projects with an average of over 1.1 million dollars loss. The results show that VERITE can automatically extract more than 18 million dollars in total and is significantly better than state-of-the-art fuzzer ITYFUZZ in both detection (29/9) and exploitation (58 times more profits gained on average). Remarkbly, in 12 targets, it gains more profits than real-world attacking exploits (1.01 to 11.45 times more). VERITE is also applied by auditors in contract auditing, where 6 (5 high severity) zero-day vulnerabilities are found with over $2,500 bounty rewards.
In this work, we offer a historical stroll through the vast topic of binary quadratic forms. We begin with a quick review of their history and then an overview of contemporary algebraic developments on the subject.
A cusp of a Hecke group $G_q$ has two natural lifts to the ring of integers of a cyclotomic field. These lifts are called here odd vanishing cycles. All lifts of all cusps together form a discrete subset of ${\mathbb C}$ of some exquisite beauty. They form one or two or four orbits of a certain subgroup of the matrix Hecke group. The subgroup can be considered as a monodromy group and is an analog of a rank 2 Coxeter group, so of a dihedral group. The paper has a research part and a larger survey part.
The 3D structure of the Nucleon in momentum space: TMD phenomenology
2025-01-15
Marco Radici
I give a brief overview of our current understanding of the internal partonic 3D structure of nucleons in momentum space. I discuss some recent extractions of transverse-momentum-dependent distributions for quarks, whose analyses in the unpolarized case are reaching a theoretical precision comparable to collinear parton distribution functions. On the contrary, gluon transverse-momentum-dependent distributions are poorly known from a phenomenological point of view. I briefly review their general properties and sketch a recent model calculation covering all (un)polarized combinations at leading twist.
Sentiment analysis is one of the most crucial tasks in Natural Language Processing (NLP), involving the training of machine learning models to classify text based on the polarity of opinions. Pre-trained Language Models (PLMs) can be applied to downstream tasks through fine-tuning, eliminating the need to train the model from scratch. Specifically, PLMs have been employed for Sentiment Analysis, a process that involves detecting, analyzing, and extracting the polarity of text sentiments. Numerous models have been proposed to address this task, with pre-trained PhoBERT-V2 models standing out as the state-of-the-art language models for Vietnamese. The PhoBERT-V2 pre-training approach is based on RoBERTa, optimizing the BERT pre-training method for more robust performance. In this paper, we introduce a novel approach that combines PhoBERT-V2 and SentiWordnet for Sentiment Analysis of Vietnamese reviews. Our proposed model utilizes PhoBERT-V2 for Vietnamese, offering a robust optimization for the prominent BERT model in the context of Vietnamese language, and leverages SentiWordNet, a lexical resource explicitly designed to support sentiment classification applications. Experimental results on the VLSP 2016 and AIVIVN 2019 datasets demonstrate that our sentiment analysis system has achieved excellent performance in comparison to other models.
2025-01-15
Yaroslav V. Kartashov, Fangwei Ye, Vladimir V. Konotop
We report on the topological pumping of quadratic optical solitons, observed through their quantized transport in a dynamic optical potential. A distinctive feature of this system is that the two fields with different frequencies, which together form the quadratic soliton, evolve in separate yet topologically equivalent dynamic optical potentials. Pumping in this system exhibits several notable differences from pumping in cubic media. While Chern indices characterizing quantized transport for uncoupled fundamental and second harmonic waves are nonzero, small-amplitude solitons with narrow spectra do not move, thus revealing a non-topological phase. As the nonlinearity increases, the system undergoes a sharp transition, depending on the velocity of one of the sublattices forming dynamical potential, into the phase where the quantized transport of quadratic solitons governed by nonzero Chern numbers is observed. The power level at which this transition occurs increases with increase of pumping velocity, and the transition is observed even in the regime when the adiabatic approximation no longer applies. Unlike in cubic media, in a quadratic medium neither breakup of topological pumping nor fractional pumping at high power levels are observed.
Invariant and non-invariant almost complex structures on compact quotients of Lie groups
2025-01-15
Lorenzo Sillari, Adriano Tomassini
In this paper we briefly survey the classical problem of understanding which Lie algebras admit a complex structure, put in the broader perspective of almost complex structures with special properties. We focus on the different behavior of invariant and non-invariant structures, with a special attention to their canonical bundle and Kodaira dimension. We provide new examples of computations of Kodaira dimension of invariant and non-invariant structures.
2025-01-15
Andrés Meseguer Valenzuela, Francisco Blanes Noguera
Mobile robot fleets are currently used in different scenarios such as medical environments or logistics. The management of these systems provides different challenges that vary from the control of the movement of each robot to the allocation of tasks to be performed. Task Allocation (TA) problem is a key topic for the proper management of mobile robot fleets to ensure the minimization of energy consumption and quantity of necessary robots. Solutions on this aspect are essential to reach economic and environmental sustainability of robot fleets, mainly in industry applications such as warehouse logistics. The minimization of energy consumption introduces TA problem as an optimization issue which has been treated in recent studies. This work focuses on the analysis of current trends in solving TA of mobile robot fleets. Main TA optimization algorithms are presented, including novel methods based on Artificial Intelligence (AI). Additionally, this work showcases most important results extracted from simulations, including frameworks utilized for the development of the simulations. Finally, some conclusions are obtained from the analysis to target on gaps that must be treated in the future.
Pixelization effects in cosmic shear angular power spectra
2025-01-15
Alex Hall, Nicolas Tessore
We conduct a comprehensive study into the impact of pixelization on cosmic shear, uncovering several sources of bias in standard pseudo-$C_\ell$ estimators based on discrete catalogues. We derive models that can bring residual biases to the percent level on small scales. We elucidate the impact of aliasing and the varying shape of HEALPix pixels on power spectra and show how the HEALPix pixel window function approximation is made in the discrete spin-2 setting. We propose several improvements to the standard estimator and its modelling, based on the principle that source positions and weights are to be considered fixed. We show how empty pixels can be accounted for either by modifying the mixing matrices or applying correction factors that we derive. We introduce an approximate interlacing scheme for the HEALPix grid and show that it can mitigate the effects of aliasing. We introduce bespoke pixel window functions adapted to the survey footprint and show that, for band-limited spectra, biases from using an isotropic window function can be effectively reduced to zero. This work partly intends to serve as a useful reference for pixel-related effects in angular power spectra, which are of relevance for ongoing and forthcoming lensing and clustering surveys.
Neuroblastoma: nutritional strategies as supportive care in pediatric oncology
2025-01-15
Hafida Hamdache, Alexia Gazeu, Marion Gambart, Nathalie Bendriss-Vermare, Vera Pancaldi
Neuroblastoma, is a highly heterogeneous pediatric tumour and is responsible for 15% of pediatric cancer-related deaths. The clinical outcomes can vary from spontaneous regression to high metastatic disease. This extracranial tumour arises from a neural crest-derived cell and can harbor different phenotypes. Its heterogeneity may result from variations in differentiation states influenced by genetic and epigenetic factors and individual patient characteristics. This leads downstream to disruption of homeostasis and a metabolic shift in response to the tumour needs. Nutrition can play a key role in influencing various aspects of a tumour behaviour. This review provides an in-depth exploration of the aetiology of neuroblastoma and the different avenues of disease progression, which can be targeted with individualized nutrition intervention strategies to improve the well-being of children and optimize clinical outcomes.
2025-01-15
Kareth M. León-López, Abderrahim Halimi, Jean-Yves Tourneret, Herwig Wendt
Multifractal analysis (MFA) provides a framework for the global characterization of image textures by describing the spatial fluctuations of their local regularity based on the multifractal spectrum. Several works have shown the interest of using MFA for the description of homogeneous textures in images. Nevertheless, natural images can be composed of several textures and, in turn, multifractal properties associated with those textures. This paper introduces a Bayesian multifractal segmentation method to model and segment multifractal textures by jointly estimating the multifractal parameters and labels on images. For this, a computationally and statistically efficient multifractal parameter estimation model for wavelet leaders is firstly developed, defining different multifractality parameters to different regions of an image. Then, a multiscale Potts Markov random field is introduced as a prior to model the inherent spatial and scale correlations between the labels of the wavelet leaders. A Gibbs sampling methodology is employed to draw samples from the posterior distribution of the parameters. Numerical experiments are conducted on synthetic multifractal images to evaluate the performance of the proposed segmentation approach. The proposed method achieves superior performance compared to traditional unsupervised segmentation techniques as well as modern deep learning-based approaches, showing its effectiveness for multifractal image segmentation.
Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching
2025-01-15
Chuangtao Ma, Sriom Chakrabarti, Arijit Khan, Bálint Molnár
Traditional similarity-based schema matching methods are incapable of resolving semantic ambiguities and conflicts in domain-specific complex mapping scenarios due to missing commonsense and domain-specific knowledge. The hallucination problem of large language models (LLMs) also makes it challenging for LLM-based schema matching to address the above issues. Therefore, we propose a Knowledge Graph-based Retrieval-Augmented Generation model for Schema Matching, referred to as the KG-RAG4SM. In particular, KG-RAG4SM introduces novel vector-based, graph traversal-based, and query-based graph retrievals, as well as a hybrid approach and ranking schemes that identify the most relevant subgraphs from external large knowledge graphs (KGs). We showcase that KG-based retrieval-augmented LLMs are capable of generating more accurate results for complex matching cases without any re-training. Our experimental results show that KG-RAG4SM outperforms the LLM-based state-of-the-art (SOTA) methods (e.g., Jellyfish-8B) by 35.89% and 30.50% in terms of precision and F1 score on the MIMIC dataset, respectively; KG-RAG4SM with GPT-4o-mini outperforms the pre-trained language model (PLM)-based SOTA methods (e.g., SMAT) by 69.20% and 21.97% in terms of precision and F1 score on the Synthea dataset, respectively. The results also demonstrate that our approach is more efficient in end-to-end schema matching, and scales to retrieve from large KGs. Our case studies on the dataset from the real-world schema matching scenario exhibit that the hallucination problem of LLMs for schema matching is well mitigated by our solution.
Empirical Study on the Factors Influencing Stock Market Volatility in China
2025-01-15
Jingchu Zhang
This paper mainly utilizes the ARDL model and principal component analysis to investigate the relationship between the volatility of China's Shanghai Composite Index returns and the variables of exchange rate and domestic and foreign bond yields in an internationally integrated stock market. This paper uses a daily data set for the period from July 1, 2010 to April 30, 2024, in which the dependent variable is the Shanghai Composite Index return, and the main independent variables are the spot exchange rate of the RMB against the US dollar, the 10-year treasury bond yields in China and the United States and their lagged variables, with the effect of the time factor added. Firstly, the development of the stock, foreign exchange and bond markets and the basic theories are reviewed, and then each variable is analyzed by descriptive statistics, the correlation between the independent variables and the dependent variable is expanded theoretically, and the corresponding empirical analyses are briefly introduced, and then the empirical analyses and modeling of the relationship between the independent variables and the dependent variable are carried out on the basis of the theoretical foundations mentioned above with the support of the daily data, and the model conclusions are analyzed economically through a large number of tests, then the model conclusions are analyzed economically. economic analysis of the model conclusions, and finally, the author proposes three suggestions to enhance the stability and return of the Chinese stock market, respectively. Key Words: Chinese Stock Market, Volatility, GARCH, ARDL Model
Facial recognition models are increasingly employed by commercial enterprises, government agencies, and cloud service providers for identity verification, consumer services, and surveillance. These models are often trained using vast amounts of facial data processed and stored in cloud-based platforms, raising significant privacy concerns. Users' facial images may be exploited without their consent, leading to potential data breaches and misuse. This survey presents a comprehensive review of current methods aimed at preserving facial image privacy in cloud-based services. We categorize these methods into two primary approaches: image obfuscation-based protection and adversarial perturbation-based protection. We provide an in-depth analysis of both categories, offering qualitative and quantitative comparisons of their effectiveness. Additionally, we highlight unresolved challenges and propose future research directions to improve privacy preservation in cloud computing environments.
ODIN: Star Formation Histories Reveal Formative Starbursts Experienced by Lyman Alpha Emitting Galaxies at Cosmic Noon
2025-01-15
Nicole M. Firestone, Eric Gawiser, Kartheik G. Iyer, Kyoung-Soo Lee, Vandana Ramakrishnan, Francisco Valdes, Changbom Park, Yujin Yang, Anahita Alavi, Robin Ciardullo, Norman Grogin, Caryl Gronwall, Lucia Guaita, Sungryong Hong, Ho Seong Hwang, Sang Hyeok Im, Woong-Seob Jeong, Seongjae Kim, Anton M. Koekemoer, Ankit Kumar, Jaehyun Lee, Vihang Mehta, Gautam Nagaraj, Julie Nantais, Laura Prichard, Marc Rafelski, Hyunmi Song, Ben Sunnquist, Harry I. Teplitz, Xin Wang
In this work, we test the conventional assumption that Lyman Alpha Emitting galaxies (LAEs) are experiencing their first major burst of star formation at the time of observation. To this end, we identify 74 LAEs from the ODIN Survey with rest-UV-through-NIR photometry from UVCANDELS. For each LAE, we perform non-parametric star formation history (SFH) reconstruction using the Dense Basis Gaussian process-based method of spectral energy distribution fitting. We find that a strong majority (67%) of our LAE SFHs align with the conventional archetype of a first major star formation burst, with at most modest star formation rates (SFRs) in the past. However, the rest of our LAE SFHs have significant amounts of star formation in the past, with 28% exhibiting earlier bursts of star formation with the ongoing burst having the highest SFR (dominant bursts), and the final 5% having experienced their highest SFR in the past (non-dominant bursts). Combining the SFHs indicating first and dominant bursts, 95% of LAEs are experiencing their largest burst yet -- a formative burst. This suggests that LAEs have more complicated stellar mass assembly than expected. We also find that the fraction of total stellar mass created in the last 200 Myr is ~1.33 times higher in LAEs than in control Lyman Break Galaxy (LBG) samples, and that a majority of LBGs are experiencing dominant bursts, reaffirming that LAEs differ from other star forming galaxies. Overall, our results suggest that multiple evolutionary paths can produce galaxies with strong observed Ly$\alpha$ emission.
Boundary-induced Majorana coupling in a planar topological Josephson junction
2025-01-15
Hyeongseop Kim, Sang-Jun Choi, H. -S. Sim, Sunghun Park
Understanding environmental effects in a topological Josephson junction is vital for identifying signatures of Majorana modes. We consider a planar Josephson junction formed on the surface of a three-dimensional topological insulator, which possesses Majorana modes inside the junction and boundary modes outside. We find that tunneling between the inner and outer modes gives rise to effective coupling between the inner Majorana modes, and hence induces energy splitting of their states even in the absence of the direct spatial overlap of their wave functions. The energy splitting is obtained analytically in the weak tunneling limit and is numerically investigated for an arbitrary tunneling strength. We discuss in detail the evolution of the energy splitting with an external perpendicular magnetic field and its effect on the shape of the Fraunhofer pattern.
LAMOST medium-resolution spectroscopic survey of Galactic Open Clusters (LAMOST-MRS-O): An overview of survey plan and preliminary results
2025-01-15
Xi Zhang, Chengzhi Liu, Jing Zhong, Li Chen, Ali Luo, Jian-Rong Shi, Chao Liu, JianJun Chen, Haotong Zhang, Jinliang Hou
As part of the LAMOST medium-resolution spectroscopic survey, the LAMOST-MRS-O is a non-time domain survey that aims to perform medium-resolution spectral observations for member stars in the open cluster area. This survey plans to obtain the spectroscopic parameters such as radial velocity and metal abundances of member stars and provide data support for further study on the chemical and dynamical characteristics and evolution of open clusters in combination with Gaia data. We have completed the observations on ten open cluster fields and obtained 235184 medium-resolution spectra of 133792 stars. Based on the data analyzed of LAMOST DR11V1.1, for some clusters of particular concern, it is found that the sampling ratio of members stars with Gmag < 15 mag can reach 70%, which indicates that the LAMOST-MRS-O has reached our initial design goal.
Discovery of 19 strongly-lensed quasars, dual and projected quasars in DESI-LS
2025-01-15
Zizhao He, Qihang Chen, Limeng Deng, Yiping Shu, Rui Li, Nan Li, Dongdong Shi, Guoliang Li
We report the follow-up spectroscopic confirmation of two lensed quasars, six dual quasars, and eleven projected quasars that were previously identified as lensed-quasar candidates in \cite{He2023}. The spectroscopic data were obtained from two different sources: the P200/DBSP in California and publicly available datasets, including SDSS and DESI-EDR. The two lensed quasars (both pairs) have the following properties: $\theta_E$ = 1.208'', $z_s$ = 3.105; $\theta_E$ = 0.749, $z_s$ = 2.395. The six dual quasars have redshifts ranging from 0.58 to 3.28 and projected separations ranging from 15.44 to 22.54 kpc, with a mean separation of 17.95 kpc. The eleven projected quasars have projected separations ranging from 10.96 to 39.07 kpc, with a mean separation of 22.64 kpc. Additionally, there are three likely lensed quasars that cannot be definitively confirmed, contributed by two reasons. Firstly, their image separations (0.83'', 0.98'', and 0.93'') are small compared to the seeing conditions during our observations (around 1.2''). Secondly, no high SNR lensing galaxy can be detected in the Legacy Survey Imaging. Better spectroscopy and (or) imaging are needed to confirm their lensing nature.
Deep inference of simulated strong lenses in ground-based surveys
2025-01-15
Jason Poh, Ashwin Samudre, Aleksandra Ćiprijanović, Joshua Frieman, Gourav Khullar, Brian D. Nord
The large number of strong lenses discoverable in future astronomical surveys will likely enhance the value of strong gravitational lensing as a cosmic probe of dark energy and dark matter. However, leveraging the increased statistical power of such large samples will require further development of automated lens modeling techniques. We show that deep learning and simulation-based inference (SBI) methods produce informative and reliable estimates of parameter posteriors for strong lensing systems in ground-based surveys. We present the examination and comparison of two approaches to lens parameter estimation for strong galaxy-galaxy lenses -- Neural Posterior Estimation (NPE) and Bayesian Neural Networks (BNNs). We perform inference on 1-, 5-, and 12-parameter lens models for ground-based imaging data that mimics the Dark Energy Survey (DES). We find that NPE outperforms BNNs, producing posterior distributions that are more accurate, precise, and well-calibrated for most parameters. For the 12-parameter NPE model, the calibration is consistently within $<$10\% of optimal calibration for all parameters, while the BNN is rarely within 20\% of optimal calibration for any of the parameters. Similarly, residuals for most of the parameters are smaller (by up to an order of magnitude) with the NPE model than the BNN model. This work takes important steps in the systematic comparison of methods for different levels of model complexity.
A Survey on IBR Penetrated Power System Stability Analysis Using Frequency Scanning
2025-01-15
Shuvangkar Chandra Das, Lokesh Saravana, Le Minh Vu, Manh Bui, Tuyen Vu, Jianhua Zhang, Thomas Ortmeyer
The rapid rise in inverter-based renewable resources has heightened concerns over subsynchronous resonance and oscillations, thereby challenging grid stability. This paper reviews approaches to identify and mitigate these issues, focusing on frequency scanning methods for stability assessment. It categorizes white-, black-, and gray-box modeling techniques, compares positive-sequence, dq-frame, and alpha-beta domain scanning, and examines perturbation shapes like step, ramp, and chirp. A comparative study highlights their strengths, limitations, and suitability for specific scenarios. By summarizing past events and surveying available tools, this work guides operators and researchers toward more effective, reliable stability analysis methods in grids with high renewable penetration.
Exploring the spatially resolved initial mass function in SAMI star-forming galaxies
2025-01-15
Diego Salvador, Andrew Hopkins, Matt Owers, Themiya Nanayakkara, Scott Croom
The initial mass function (IMF) is a construct that describes the distribution of stellar masses for a newly formed population of stars. It is a fundamental element underlying all of star and galaxy formation, and has been the subject of extensive investigation for more than 60 years. In the past few decades there has been a growing, and now substantial, body of evidence supporting the need for a variable IMF. In this light, it is crucial to investigate the IMF's characteristics across different spatial scales and to understand the factors driving its variability. We make use of spatially resolved spectroscopy to examine the high-mass IMF slope of star-forming galaxies within the SAMI survey. By applying the Kennicutt method and stellar population synthesis models, we estimated both the spaxel-resolved ($\alpha_{res}$) and galaxy-integrated ($\alpha_{int}$) high-mass IMF slopes of these galaxies. Our findings indicate that the resolved and integrated IMF slopes exhibit a near 1:1 relationship for $\alpha_{int}> -2.7$. We observe a wide range of $\alpha_{res}$ distributions within galaxies. To explore the sources of this variability, we analyse the relationships between the resolved and integrated IMF slopes and both the star formation rate (SFR) and SFR surface density ($\Sigma_{SFR}$). Our results reveal a strong correlation where flatter/steeper slopes are associated with higher/lower SFR and $\Sigma_{SFR}$. This trend is qualitatively similar for resolved and global scales. Additionally, we identify a mass dependency in the relationship with SFR, though none was found in the relation between the resolved slope and $\Sigma_{SFR}$. These findings suggest an scenario where the formation of high-mass stars is favoured in regions with more concentrated star formation. This may be a consequence of the reduced fragmentation of molecular clouds, which nonetheless accrete more material.
Cosmological distance forecasts for the CSST Galaxy Survey using BAO peaks
2025-01-15
Feng Shi, Jieyi Tian, Zhejie Ding, Xiaohu Yang, Yizhou Gu, Christoph Saulder, Xiaoping Li, Yanming Liu, Zitong Wang, Hu Zhan, Ming Li, Xiaolei Li, Hong Guo, Yan Gong, Yunkun Han, Cheng Li, Yipeng Jing, Jipeng Sui, Run Wen, Gong-Bo Zhao, Hu Zou, Pengjie Zhang, Xianzhong Zheng, Xingchen Zhou
The measurement of cosmological distances using baryon acoustic oscillations (BAO) is crucial for studying the universe's expansion. The Chinese Space Station Telescope (CSST) galaxy redshift survey, with its vast volume and sky coverage, provides an opportunity to address key challenges in cosmology. However, redshift uncertainties in galaxy surveys can degrade both angular and radial distance estimates. In this study, we forecast the precision of BAO distance measurements using mock CSST galaxy samples, applying a two-point correlation function (2PCF) wedge approach to mitigate redshift errors. We simulate redshift uncertainties of $\sigma_0 = 0.003$ and $\sigma_0 = 0.006$, representative of expected CSST errors, and examine their effects on the BAO peak and distance scaling factors, $\alpha_\perp$ and $\alpha_\parallel$, across redshift bins within $0.0 < z \leqslant 1.0$. The wedge 2PCF method proves more effective in detecting the BAO peak compared to the monopole 2PCF, particularly for $\sigma_0 = 0.006$. Constraints on the BAO peaks show that $\alpha_\perp$ is well constrained around 1.0, regardless of $\sigma_0$, with precision between 1% and 3% across redshift bins. In contrast, $\alpha_\parallel$ measurements are more sensitive to increases in $\sigma_0$. For $\sigma_0 = 0.003$, the results remain close to the fiducial value, with uncertainties ranging between 4% and 9%; for $\sigma_0 = 0.006$, significant deviations from the fiducial value are observed. We also study the ability to measure parameters $(\Omega_m, H_0r_\mathrm{d})$ using distance measurements, proving robust constraints as a cosmological probe under CSST-like redshift uncertainties.
Visual Network Analysis in Immersive Environments: A Survey
2025-01-15
Lucas Joos, Maximilian T. Fischer, Julius Rauscher, Daniel A. Keim, Tim Dwyer, Falk Schreiber, Karsten Klein
The increasing complexity and volume of network data demand effective analysis approaches, with visual exploration proving particularly beneficial. Immersive technologies, such as augmented reality, virtual reality, and large display walls, have enabled the emerging field of immersive analytics, offering new opportunities to enhance user engagement, spatial awareness, and problem-solving. A growing body of work explores immersive environments for network visualisation, ranging from design studies to fully integrated applications across various domains. Despite these advancements, the field remains fragmented, with diverse methodologies, hardware setups, and evaluation criteria, often lacking clear connections to prior work. This fragmentation complicates the comparability and generalisability of findings. To address this, we present a structured survey of visual network analysis in immersive environments. We systematically categorise and analyse existing approaches, revealing connections and coverage within the design space. By synthesising findings of experiments and evaluating current applications, we identify key achievements, challenges, and research gaps. Additionally, we provide an interactive online resource for exploring and updating results, aiming to guide researchers and practitioners in advancing the field. This work provides a comprehensive overview of the research landscape and proposes actionable insights to foster innovation in immersive network analysis.
Combining Movement Primitives with Contraction Theory
2025-01-15
Moses C. Nah, Johannes Lachner, Neville Hogan, Jean-Jacques Slotine
This paper presents a modular framework for motion planning using movement primitives. Central to the approach is Contraction Theory, a modular stability tool for nonlinear dynamical systems. The approach extends prior methods by achieving parallel and sequential combinations of both discrete and rhythmic movements, while enabling independent modulation of each movement. This modular framework enables a divide-and-conquer strategy to simplify the programming of complex robot motion planning. Simulation examples illustrate the flexibility and versatility of the framework, highlighting its potential to address diverse challenges in robot motion planning.
Background exoplanet candidates in the original Kepler field
2025-01-15
John Bienias, Robert Szabo
Context. During the primary Kepler mission, between 2009 and 2013, about 150,000 pre-selected targets were observed with a 29.42 minute-long cadence. However, a survey of background stars that fall within the field of view (FOV) of the downloaded apertures of the primary targets has revealed a number of interesting objects. In previous papers we have presented surveys of short period Eclipsing Binaries and RR Lyrae stars. Aims. The current survey of the Kepler background is concentrated on identifying longer-period eclipsing binaries and pulsating stars. These will be the subject of later papers. In the course of this survey, in addition to eclipsing binaries and pulsating stars, seven exoplanet candidates have been uncovered and in this paper we report on these candidates. Methods. We use Lomb-Scargle (LS), light curve transit search and Phase Dispersion Minimisation (PDM) methods to reveal pixels that show significant periodicities, resulting in the identification of the seven exoplanet candidates. We have prepared the light curves for analysis using Pytransit software and cross matched the pixel coordinates with Gaia and other catalogues to identify the sources. Results.We identify seven Hot Jupiter exoplanet candidates with planet radii ranging from 0.8878 to 1.5174 RJup and periods ranging from 2.5089 to 4.7918 days.
Cosmology from UNIONS weak lensing profiles of galaxy clusters
2025-01-15
Charlie T. Mpetha, James E. Taylor, Yuba Amoura, Roan Haggar, Thomas de Boer, Sacha Guerrini, Axel Guinot, Fabian Hervas Peters, Hendrik Hildebrandt, Michael J. Hudson, Martin Kilbinger, Tobias Liaudat, Alan McConnachie, Ludovic Van Waerbeke, Anna Wittje
Cosmological information is encoded in the structure of galaxy clusters. In Universes with less matter and larger initial density perturbations, clusters form earlier and have more time to accrete material, leading to a more extended infall region. Thus, measuring the mean mass distribution in the infall region provides a novel cosmological test. The infall region is largely insensitive to baryonic physics, and provides a cleaner structural test than other measures of cluster assembly time such as concentration. We consider cluster samples from three publicly available galaxy cluster catalogues: the Spectrsopic Identification of eROSITA Sources (SPIDERS) catalogue, the X-ray and Sunyaev-Zeldovich effect selected clusters in the meta-catalogue M2C, and clusters identified in the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Survey. Using a preliminary shape catalogue from the Ultraviolet Near Infrared Optical Northern Survey (UNIONS), we derive excess surface mass density profiles for each sample. We then compare the mean profile for the DESI Legacy sample, which is the most complete, to predictions from a suite of simulations covering a range of $\Omega_{\rm m}$ and $\sigma_8$, obtaining constraints of $\Omega_{\rm m}=0.29\pm 0.05$ and $\sigma_8=0.80 \pm 0.04$. We also measure mean (comoving) splashback radii for SPIDERS, M2C and DESI Legacy Imaging Survey clusters of $1.59^{+0.16}_{-0.13} {\rm cMpc}/h$, $1.30^{+0.25}_{-0.13} {\rm cMpc}/h$ and $1.45\pm0.11 {\rm cMpc}/h$ respectively. Performing this analysis with the final UNIONS shape catalogue and the full sample of spectroscopically observed clusters in DESI, we can expect to improve on the best current constraints from cluster abundance studies by a factor of 2 or more.
The Science of Urban Metabolism and Sustainability
2025-01-15
Mariana Brüning-González, José Ignacio Arroyo, Pablo A. Marquet, Horacio Samaniego
Understanding the quantitative patterns behind scientific disciplines is fundamental for informed research policy. While many fields have been studied from this perspective, Urban Science (USc) and its subfields remain underexplored. As organisms, urban systems rely on materials and energy inputs and transformation (i.e. metabolism) to sustain essential dynamics. This concept has been adopted by various disciplines, including architecture and sociology, and by those focused on metabolic processes, such as ecology and industrial ecology. This study addresses the structure and evolution of Urban Metabolism (UM) and Sustainability research, analyzing articles by disciplines, study subjects (e.g., cities, regions), methodologies, and author diversity (nationality and gender). Our review suggests that UM is an emerging field that grew until 2019, primarily addressed by environmental science and ecology. Common methods include Ecological Network Analysis, and Life Cycle Assessment, and Material Flow Analysis, focusing flows over stocks, ecosystem dynamics and evolutionary perspectives of the urban system. Authors are predominantly from China and the USA, and there are less gender gaps compared to general science research. Our analysis identifies relevant challenges that have become evident in the statistical properties of this scientific field and which might be helpful for the design of improved research policies.
Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
2025-01-15
Aditi Singh, Abul Ehtesham, Saket Kumar, Tala Talaei Khoei
Large Language Models (LLMs) have revolutionized artificial intelligence (AI) by enabling human like text generation and natural language understanding. However, their reliance on static training data limits their ability to respond to dynamic, real time queries, resulting in outdated or inaccurate outputs. Retrieval Augmented Generation (RAG) has emerged as a solution, enhancing LLMs by integrating real time data retrieval to provide contextually relevant and up-to-date responses. Despite its promise, traditional RAG systems are constrained by static workflows and lack the adaptability required for multistep reasoning and complex task management. Agentic Retrieval-Augmented Generation (Agentic RAG) transcends these limitations by embedding autonomous AI agents into the RAG pipeline. These agents leverage agentic design patterns reflection, planning, tool use, and multiagent collaboration to dynamically manage retrieval strategies, iteratively refine contextual understanding, and adapt workflows to meet complex task requirements. This integration enables Agentic RAG systems to deliver unparalleled flexibility, scalability, and context awareness across diverse applications. This survey provides a comprehensive exploration of Agentic RAG, beginning with its foundational principles and the evolution of RAG paradigms. It presents a detailed taxonomy of Agentic RAG architectures, highlights key applications in industries such as healthcare, finance, and education, and examines practical implementation strategies. Additionally, it addresses challenges in scaling these systems, ensuring ethical decision making, and optimizing performance for real-world applications, while providing detailed insights into frameworks and tools for implementing Agentic RAG
The emission of interpulses by a 6.45-hour period coherent radio transient
2025-01-15
Y. W. J. Lee, M. Caleb, Tara Murphy, E. Lenc, D. L. Kaplan, L. Ferrario, Z. Wadiasingh, A. Anumarlapudi, N. Hurley-Walker, V. Karambelkar, S. K. Ocker, S. McSweeney, H. Qiu, K. M. Rajwade, A. Zic, K. W. Bannister, N. D. R. Bhat, A. Deller, D. Dobie, L. N. Driessen, K. Gendreau, M. Glowacki, V. Gupta, J. N. Jahns-Schindler, A. Jaini, C. W. James, M. M. Kasliwal, M. E. Lower, R. M. Shannon, P. A. Uttarkar, Y. Wang, Z. Wang
Long-period radio transients are a novel class of astronomical objects characterised by prolonged periods ranging from 18 minutes to 54 minutes. They exhibit highly polarised, coherent, beamed radio emission lasting only 10--100 seconds. The intrinsic nature of these objects is subject to speculation, with highly magnetised white dwarfs and neutron stars being the prevailing candidates. Here we present ASKAP J183950.5-075635.0 (hereafter, ASKAP J1839-0756), boasting the longest known period of this class at 6.45 hours. It exhibits emission characteristics of an ordered dipolar magnetic field, with pulsar-like bright main pulses and weaker interpulses offset by about half a period are indicative of an oblique or orthogonal rotator. This phenomenon, observed for the first time in a long-period radio transient, confirms that the radio emission originates from both magnetic poles and that the observed period corresponds to the rotation period. The spectroscopic and polarimetric properties of ASKAP J1839-0756 are consistent with a neutron star origin, and this object is a crucial piece of evidence in our understanding of long-period radio sources and their links to neutron stars.
5G Network Slicing as a Service Enabler for theAutomotive Sector
2025-01-15
David Candal-Ventureira, José Manuel Rúa-Estévez, Pablo Fondo-Ferreiro, Felipe Gil-Castiñeira, Antonio Fernández-Barciela, Francisco Javier González-Castaño, Emilio Diéguez-Pazo, Luis Fernández-Ferreira
Network slicing, a key technology introduced in 5G standards, enables mobile networks to simultaneously support a wide range ofheterogeneous use cases with diverse quality of service (QoS) requirements. This work discusses the potential benefits of networkslicing for the automotive sector, encompassing manufacturing processes and vehicular communications. The review of the stateof the art reveals a clear gap regarding the application of network slicing from the perspective of industrial verticals such asautomotive use cases and their specific requirements. Departing from this observation, we first identify limitations of previouscellular technologies and open challenges for supporting the data services required. Then we describe network slicing as an enablerto face these challenges. We present an analysis of the cost equilibrium for network slicing to be effective for car manufacturers,and tests in real 5G networks that demonstrate the performance improvement in OTA updates coexisting with other services.
RAILS: A Synthetic Sampling Weights for Volunteer-Based National Biobanks -- A Case Study with the All of Us Research Program
2025-01-15
Huiding Chen, Andrew Guide, Lina Sulieman, Robert M Cronin, Thomas Lumley, Qingxia Chen
While national biobanks are essential for advancing medical research, their non-probability sampling designs limit their representativeness of the target population. This paper proposes a method that leverages high-quality national surveys to create synthetic sampling weights for non-probabilistic cohort studies, aiming to improve representativeness. Specifically, we focus on deriving more accurate base weights, which enhance calibration by meeting population constraints, and on automating data-supported selection of cross-tabulations for calibration. This approach combines a pseudo-design-based model with a novel Last-In-First-Out criterion, enhancing the accuracy and stability of the estimates. Extensive simulations demonstrate that our method, named RAILS, reduces bias, improves efficiency, and strengthens inference compared to existing approaches. We apply the proposed method to the All of Us Research Program, using data from the National Health Interview Survey 2020 and the American Community Survey 2022 and comparing prevalence estimates for common phenotypes against national benchmarks. The results underscore our method's ability to effectively reduce selection bias in non-probability samples, offering a valuable tool for enhancing biobank representativeness. Using the developed sampling weights for the All of Us Research Program, we can estimate the prevalence of the United States population for phenotypes and genotypes not captured by national probability studies.
Mantis Shrimp: Exploring Photometric Band Utilization in Computer Vision Networks for Photometric Redshift Estimation
2025-01-15
Andrew Engel, Nell Byler, Adam Tsou, Gautham Narayan, Emmanuel Bonilla, Ian Smith
We present Mantis Shrimp, a multi-survey deep learning model for photometric redshift estimation that fuses ultra-violet (GALEX), optical (PanSTARRS), and infrared (UnWISE) imagery. Machine learning is now an established approach for photometric redshift estimation, with generally acknowledged higher performance in areas with a high density of spectroscopically identified galaxies over template-based methods. Multiple works have shown that image-based convolutional neural networks can outperform tabular-based color/magnitude models. In comparison to tabular models, image models have additional design complexities: it is largely unknown how to fuse inputs from different instruments which have different resolutions or noise properties. The Mantis Shrimp model estimates the conditional density estimate of redshift using cutout images. The density estimates are well calibrated and the point estimates perform well in the distribution of available spectroscopically confirmed galaxies with (bias = 1e-2), scatter (NMAD = 2.44e-2) and catastrophic outlier rate ($\eta$=17.53$\%$). We find that early fusion approaches (e.g., resampling and stacking images from different instruments) match the performance of late fusion approaches (e.g., concatenating latent space representations), so that the design choice ultimately is left to the user. Finally, we study how the models learn to use information across bands, finding evidence that our models successfully incorporates information from all surveys. The applicability of our model to the analysis of large populations of galaxies is limited by the speed of downloading cutouts from external servers; however, our model could be useful in smaller studies such as generating priors over redshift for stellar population synthesis.
Observations of massive supermassive black holes (SMBHs) in the early universe challenge existing black hole formation models. We propose that soliton cores in fuzzy dark matter (FDM) offer a potential solution to this timing problem. Our FDM cosmological zoom-in simulations confirm that for a particle mass $m_{\rm FDM}\sim 10^{-22}~{\rm eV}$, solitons are well developed at redshift $z \sim 7$ with masses of $\sim10^9~M_\odot$, comparable to the observed SMBHs. We then demonstrate using hydrodynamic simulations that, compared to cold dark matter, these high-$z$ massive FDM solitons with mass $M_s$ can provide additional gravitational potential to accrete gas and boost the Bondi accretion rate of a growing black hole seed with mass $M_{\rm BH}$ by up to two to four orders of magnitude, in the regime of efficient cooling and negligible radiation pressure. This accretion boosting mechanism is effective for $10^{-22}~{\rm eV} \lesssim m_{\rm FDM} \lesssim 10^{-20}~{\rm eV}$ and potentially beyond as long as $M_s > M_{\rm BH}$. The simulation code GAMER is accessible at https://github.com/gamer-project/gamer.
A Pair of Dynamically Interacting Sub-Neptunes Around TOI-6054
2025-01-15
Maxwell A. Kroft, Thomas G. Beatty, Ian J. M. Crossfield, Joseph R. Livesey, Juliette Becker, Jacob K. Luhn, Paul Robertson, Allyson Bieryla, David R. Ciardi, Catherine A. Clark, Maria V. Goliguzova, Steve B. Howell, Jack J. Lissauer, Colin Littlefield, Michael B. Lund, Boris S. Safonov, Joseph M. Akana Murphy, Natalie M. Batalha, Malik Bossett, Jonathan Brande, Tansu Daylan, Courtney Dressing, Anna Gagnebin, Daniel Huber, Howard Isaacson, Stephen R. Kane, Laura Kreidberg, David W. Latham, Rafael Luque, Alex S. Polanski, Pranav H. Premnath, Maleah Rhem, Claire J. Rogers, Emma V. Turtelboom
We confirm the planetary nature of a pair of transiting sub-Neptune exoplanets orbiting the bright F-type sub-giant star TOI-6054 ($V=8.02$, $K=6.673$) as a part of the OrCAS radial velocity survey using WIYN/NEID observations. We find that TOI-6054b and TOI-6054c have radii of $2.65 \pm 0.15$ $R_{\oplus}$ and $2.81 \pm 0.18$ $R_{\oplus}$, respectively, and masses of $12.4 \pm 1.7$ $M_{\oplus}$ and $9.2 \pm 2.0$ $M_{\oplus}$. The planets have zero-albedo equilibrium temperatures of $1360 \pm 33$ K and $1144 \pm 28$ K. The host star has expanded and will evolve off of the Main Sequence within the next $\sim$500 Myr, and the resulting increase in stellar luminosity has more than doubled the stellar flux the two planets receive compared to the start of the host star's main sequence phase. Consequently, TOI-6054b may be losing some of its primordial H/He atmosphere -- if it has one. Based on dynamical simulations performed using the orbital parameters of the two planets, TOI-6054b, and TOI-6054c are very likely in a 5:3 mean motion resonance. The TOI-6054 system thus has the potential to be an excellent candidate for future atmospheric follow-up observations, with two similarly sized sub-Neptunes around a bright star. We also estimate that if TOI-6054b is currently losing its H/He atmosphere this should be observable from space and from the ground.
TeV afterglow emission from a multi-component GRB jet using the kinetic approach
2025-01-15
John P. Hope, Hendrik J. van Eerten, Sayan Kundu, Patricia Schady
Recent years have seen a growing sample of TeV emission detections in gamma-ray burst afterglows, as well as an increasing role for structured jets in afterglow modelling. Using a kinetic approach, we show that the structure of an afterglow jet impacts its TeV emission, with jets where the energy falls off more sharply with angle showing a decrease in Inverse Compton (IC) peak flux relative to synchrotron peak flux. We use a modified version of the code katu, to which we have added adiabatic expansion and a fully self-consistent treatment of IC cooling both for the electron and photon populations. We compare our results to the semi-analytical code afterglowpy, finding a good agreement with our model except at early times off axis where the effects of baryon loading are important. We compare electron cooling in the cases where there is no IC cooling, Thomson cooling and an inclusion of Klein-Nishina effects, finding that the spectra can only be distinguished if the Compton potential is significantly increased. The smooth and gradual transition of our KN cooling also leads to a disparity in the cooling between our results and semi-analytical solutions based on asymptotic limits. Finally, we combine best fit parameters from afterglowpy to reproduce the light curves of GRB 170817A in our model. For our choice of parameters, we find that GRB 170817 would not have been detected in the TeV domain if seen on-axis, even by the upcoming Cherenkov Telescope Array Observatory.
We provide an accessible review to the eta problem in brane-antibrane inflation and a recently proposed solution. The description of the antibrane by means of nonlinearly realized supersymmetry suggests a simple stabilization mechanism for the volume mode, based on the most general Kahler and superpotentials compatible with the symmetries of the problem. This work is a contribution to the proceedings of the second general meeting of the COST Action CA21106 (Cosmic WISPers) and is based on arXiv:2410.00097 .
Searching for Quiescent Galaxies over $3 < z < 6$ in JWST Surveys Using Manifold Learning
2025-01-15
Alexander de la Vega, Mitchell D. Babcock, Bahram Mobasher, Dominik A. Riemann, Nima Chartab, Shoubaneh Hemmati, Arianna S. Long, Sogol Sanjaripour
Quiescent galaxies over $34$, although our measurements are consistent within errors. This technique may be applied to other JWST surveys.
REsolved ALMA and SMA Observations of Nearby Stars (REASONS): A population of 74 resolved planetesimal belts at millimetre wavelengths
2025-01-15
L. Matrà, S. Marino, D. J. Wilner, G. M. Kennedy, M. Booth, A. V. Krivov, J. P. Williams, A. M. Hughes, C. del Burgo, J. Carpenter, C. L. Davies, S. Ertel, Q. Kral, J. -F. Lestrade, J. P. Marshall, J. Milli, K. I. Öberg, N. Pawellek, A. G. Sepulveda, M. C. Wyatt, B. C. Matthews, M. MacGregor
Planetesimal belts are ubiquitous around nearby stars, and their spatial properties hold crucial information for planetesimal and planet formation models. We present resolved dust observations of 74 planetary systems as part of the REsolved ALMA and SMA Observations of Nearby Stars (REASONS) survey and archival reanalysis. We uniformly modelled interferometric visibilities for the entire sample to obtain the basic spatial properties of each belt, and combined these with constraints from multi-wavelength photometry. We report key findings from a first exploration of this legacy dataset: (1) Belt dust masses are depleted over time in a radially dependent way, with dust being depleted faster in smaller belts, as predicted by collisional evolution. (2) Most belts are broad discs rather than narrow rings, with much broader fractional widths than rings in protoplanetary discs. We link broad belts to either unresolved substructure or broad planetesimal discs produced if protoplanetary rings migrate. (3) The vertical aspect ratios (h = H/R) of 24 belts indicate orbital inclinations of 1-20 degrees, implying relative particle velocities of 0.1-4 km/s, and no clear evolution of heights with system age. This could be explained by early stirring within the belt by large bodies (with sizes of at least 140 km to the size of the Moon), by inheritance of inclinations from the protoplanetary disc stage, or by a diversity in evolutionary pathways and gravitational stirring mechanisms. We release the REASONS legacy multidimensional sample of millimetre-resolved belts to the community as a valuable tool for follow-up multi-wavelength observations and population modelling studies.
The COSMOS-Web deep galaxy group catalog up to $z=3.7$
2025-01-15
Greta Toni, Ghassem Gozaliasl, Matteo Maturi, Lauro Moscardini, Alexis Finoguenov, Gianluca Castignani, Fabrizio Gentile, Kaija Virolainen, Caitlin M. Casey, Jeyhan S. Kartaltepe, Hollis B. Akins, Natalie Allen, Rafael C. Arango-Toro, Arif Babul, Malte Brinch, Nicole E. Drakos, Andreas L. Faisst, Maximilien Franco, Richard E. Griffiths, Santosh Harish, Günther Hasinger, Olivier Ilbert, Shuowen Jin, Ali Ahmad Khostovan, Anton M. Koekemoer, Maarit Korpi-Lagg, Rebecca L. Larson, Jitrapon Lertprasertpong, Daizhong Liu, Georgios Magdis, Richard Massey, Henry J. McCracken, Jed McKinney, Louise Paquereau, Jason Rhodes, Brant E. Robertson, Mark Sargent, Marko Shuntov, Masayuki Tanaka, Sina Taamoli, Elmo Tempel, Sune Toft, Eleni Vardoulaki, Lilan Yang
Galaxy groups with $M_{tot} \lesssim 10^{14}$ $M_\odot$ and up to a few tens of members are the most common galaxy environment, marking the transition between field and massive clusters. Identifying groups plays a crucial role in understanding structure formation and galaxy evolution. Modern deep surveys allow us to build well-characterized samples of groups up to the regime where structures were taking shape. We aimed to build the largest deep catalog of galaxy groups to date over the COSMOS-Web field effective area of 0.45 deg$^2$, leveraging the deep high quality data of the new COSMOS-Web photometric catalog resulted from the James Webb Space Telescope observations of the COSMOS-Web field. We performed the group search with the AMICO algorithm, a linear matched filter based on an analytical model for the group signal. AMICO has already been tested in wide and deep field surveys, including COSMOS data up to $z=2$. In this work, we tested the algorithm performances at even higher redshift and searched for protocluster cores at $z>2$. We compiled a list of known protoclusters in COSMOS at $2 \leq z \leq 3.7$, matched them with our detections and studied the clustering of the detected cores. We estimated purity and completeness of our sample by creating data-driven mocks with the SinFoniA code and linked signal-to-noise to purity. We detected 1678 groups in the COSMOS-Web field up to $z=3.7$, including lists of members extending nearly two magnitudes deeper than the previous AMICO-COSMOS catalog. 756 groups were detected with purity of 80\%. More than 500 groups have their redshift confirmed by assigning spectroscopic counterparts. This group catalog offers a unique opportunity to explore galaxy evolution in different environments spanning $\sim$12 Gyr and to study groups, from the least rich population to the formation of the most massive clusters.
Manipulation of viscous liquids is an essential kitchen activity - from pouring golden syrup onto a pancake to decorating a cake with whipped cream frosting, from streaming ketchup on top of French fries to dispensing molten chocolate onto a strawberry. Typical viscosities in these and many other kitchen flows, and the heights from which the streams are dispensed, make such jets susceptible to the coiling instability. Indeed, the coiling of a thin thread of poured maple syrup is a source of fascination for children and adults alike, whereas the folding of the stream of ketchup squeezed out from a plastic bottle is a phenomenon familiar to all. In this paper, we review the fluid dynamics of such kitchen flows and discuss separately the case when the substrate is stationary (honey on a toast), and when it translates (cookies on a conveyor belt) or rotates (a pancake on a spinning hot plate). It is hoped that this may encourage experimentation and enjoyment of physics in the kitchen, and perhaps even lead to more elegant if not more tasty culinary results.
A Survey on Pedophile Attribution Techniques for Online Platforms
2025-01-14
Hiba Fallatah, Ching Suen, Olga Ormandjieva
Reliance on anonymity in social media has increased its popularity on these platforms among all ages. The availability of public Wi-Fi networks has facilitated a vast variety of online content, including social media applications. Although anonymity and ease of access can be a convenient means of communication for their users, it is difficult to manage and protect its vulnerable users against sexual predators. Using an automated identification system that can attribute predators to their text would make the solution more attainable. In this survey, we provide a review of the methods of pedophile attribution used in social media platforms. We examine the effect of the size of the suspect set and the length of the text on the task of attribution. Moreover, we review the most-used datasets, features, classification techniques and performance measures for attributing sexual predators. We found that few studies have proposed tools to mitigate the risk of online sexual predators, but none of them can provide suspect attribution. Finally, we list several open research problems.
Little impact of mergers and galaxy morphology on the production and escape of ionizing photons in the early Universe
2025-01-14
S. Mascia, L. Pentericci, M. Llerena, A. Calabrò, J. Matthee, S. Flury, F. Pacucci, A. Jaskot, R. O. Amorín, R. Bhatawdekar, M. Castellano, N. Cleri, L. Costantin, K. Davis, C. Di Cesare, M. Dickinson, A. Fontana, Y. Guo, M. Giavalisco, B. W. Holwerda, W. Hu, M. Huertas-Company, Intae Jung, J. Kartaltepe, D. Kashino, Anton M. Koekemoer, R. A. Lucas, J. Lotz, L. Napolitano, S. Jogee, S. Wilkins
Compact, star-forming galaxies with high star formation rate surface densities ($\Sigma_{\text{SFR}}$) are often efficient Lyman continuum (LyC) emitters at $z\leq 4.5$, likely as intense stellar feedback creates low-density channels that allow photons to escape. Irregular or disturbed morphologies, such as those resulting from mergers, can also facilitate LyC escape by creating anisotropic gas distributions. We investigate the influence of galaxy morphology on LyC production and escape at redshifts $5 \leq z \leq 7$ using observations from various \textit{James Webb Space Telescope} (JWST) surveys. Our sample consists of 436 sources, which are predominantly low-mass ($\sim 10^{8.15} M_\odot$), star-forming galaxies with ionizing photon efficiency ($\xi_{\rm ion}$) values consistent with canonical expectations. Since direct measurements of $f_{\rm esc}$ are not possible during the Epoch of Reionization (EoR), we predict $f_{\rm esc}$ for high-redshift galaxies by applying survival analysis to a subsample of LyC emitters from the Low-Redshift Lyman Continuum Survey (LzLCS), selected to be direct analogs of reionization-era galaxies. We find that these galaxies exhibit on average modest predicted escape fractions ($\sim 0.04$). Additionally, we assess the correlation between morphological features and LyC emission. Our findings indicate that neither $\xi_{\rm ion}$ nor the predicted $f_{\rm esc}$ values show a significant correlation with the presence of merger signatures. This suggests that in low-mass galaxies at $z \geq 5$, strong morphological disturbances are not the primary mechanism driving LyC emission and leakage. Instead, compactness and star formation activity likely play a more pivotal role in regulating LyC escape.
Cloud Operations (CloudOps) is a rapidly growing field focused on the automated management and optimization of cloud infrastructure which is essential for organizations navigating increasingly complex cloud environments. MontyCloud Inc. is one of the major companies in the CloudOps domain that leverages autonomous bots to manage cloud compliance, security, and continuous operations. To make the platform more accessible and effective to the customers, we leveraged the use of GenAI. Developing a GenAI-based solution for autonomous CloudOps for the existing MontyCloud system presented us with various challenges such as i) diverse data sources; ii) orchestration of multiple processes; and iii) handling complex workflows to automate routine tasks. To this end, we developed MOYA, a multi-agent framework that leverages GenAI and balances autonomy with the necessary human control. This framework integrates various internal and external systems and is optimized for factors like task orchestration, security, and error mitigation while producing accurate, reliable, and relevant insights by utilizing Retrieval Augmented Generation (RAG). Evaluations of our multi-agent system with the help of practitioners as well as using automated checks demonstrate enhanced accuracy, responsiveness, and effectiveness over non-agentic approaches across complex workflows.
Cognitive Assessment and Training in Extended Reality: Multimodal Systems, Clinical Utility, and Current Challenges
2025-01-14
Palmira Victoria González-Erena, Sara Fernández-Guinea, Panagiotis Kourtesis
Extended reality (XR) technologies-encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR) are transforming cognitive assessment and training by offering immersive, interactive environments that simulate real-world tasks. XR enhances ecological validity while enabling real-time, multimodal data collection through tools such as galvanic skin response (GSR), electroencephalography (EEG), eye tracking (ET), hand tracking, and body tracking. This allows for a more comprehensive understanding of cognitive and emotional processes, as well as adaptive, personalized interventions for users. Despite these advancements, current XR applications often underutilize the full potential of multimodal integration, relying primarily on visual and auditory inputs. Challenges such as cybersickness, usability concerns, and accessibility barriers further limit the widespread adoption of XR tools in cognitive science and clinical practice. This review examines XR-based cognitive assessment and training, focusing on its advantages over traditional methods, including ecological validity, engagement, and adaptability. It also explores unresolved challenges such as system usability, cost, and the need for multimodal feedback integration. The review concludes by identifying opportunities for optimizing XR tools to improve cognitive evaluation and rehabilitation outcomes, particularly for diverse populations, including older adults and individuals with cognitive impairments.
Discovery of 26 new Galactic radio transients by MeerTRAP
2025-01-14
James Dennis Turner, Ben W. Stappers, Jun Tian, Mechiel C. Bezuidenhout, Manisha Caleb, Laura N. Driessen, Fabian Jankowski, Inés Pastor-Marazuela, Kaustubh M. Rajwade, Mayuresh Surnis, Michael Kramer, Ewan D. Barr, Marina Berezina
Radio searches for single pulses provide the opportunity to discover one-off events, fast transients and some pulsars that might otherwise be missed by conventional periodicity searches. The MeerTRAP real-time search pipeline operates commensally to observations with the MeerKAT telescope. Here, we report on 26 new Galactic radio transients, mostly rotating radio transients (RRATs) and also the detection of one RRAT and two pulsars that were independently discovered by other surveys. The dispersion measures of two of the new sources marginally exceed the Galactic contribution depending on the electron density model used. Using a simple method of fitting a Gaussian function to individual pulses, and obtaining positions of arcsecond accuracy from image-based localisations using channelised voltage data from our transient buffer, we have derived timing solutions spanning multiple years for five sources. The timing parameters imply ages of several Myr and low surface magnetic field strengths which is characteristic of RRATs. We were able to measure spin periods for eight more transients, including one source which appears to rotate every 17.5 seconds. A majority of the sources have only been seen in one observation, sometimes despite multiple return visits to the field. Some sources exhibit complex emission features like component switching and periodic microstructure.
The VMC Survey : LI. Classifying extragalactic sources using a probabilistic random forest supervised machine learning algorithm
2025-01-14
Clara M. Pennock, Jacco Th. van Loon, Maria-Rosa L. Cioni, Chandreyee Maitra, Joana M. Oliveira, Jessica E. M. Craig, Valentin D. Ivanov, James Aird, Joy. O. Anih, Nicholas J. G. Cross, Francesca Dresbach, Richard de Grijs, Martin A. T. Groenewegen
We used a supervised machine learning algorithm (probabilistic random forest) to classify ~130 million sources in the VISTA Survey of the Magellanic Clouds (VMC). We used multi-wavelength photometry from optical to far-infrared as features to be trained on, and spectra of Active Galactic Nuclei (AGN), galaxies and a range of stellar classes including from new observations with the Southern African Large Telescope (SALT) and SAAO 1.9m telescope. We also retain a label for sources that remain unknown. This yielded average classifier accuracies of ~79% (SMC) and ~87% (LMC). Restricting to the 56,696,719 sources with class probabilities (P$_{class}$) > 80% yields accuracies of ~90% (SMC) and ~98% (LMC). After removing sources classed as 'Unknown', we classify a total of 707,939 (SMC) and 397,899 (LMC) sources, including > 77,600 extragalactic sources behind the Magellanic Clouds. The extragalactic sources are distributed evenly across the field, whereas the Magellanic sources concentrate at the centres of the Clouds, and both concentrate in optical/IR colour-colour/magnitude diagrams as expected. We also test these classifications using independent datasets, finding that, as expected, the majority of X-ray sources are classified as AGN (554/883) and the majority of radio sources are classed as AGN (1756/2694) or galaxies (659/2694), where the relative AGN-galaxy proportions vary substantially with radio flux density. We have found: > 49,500 hitherto unknown AGN candidates, likely including more AGN dust dominated sources which are in a critical phase of their evolution; > 26,500 new galaxy candidates and > 2800 new Young Stellar Object (YSO) candidates.
Architecture Classification for Extrasolar Planetary Systems
2025-01-14
Alex R. Howe, Juliette C. Becker, Christopher C. Stark, Fred C. Adams
This paper presents a classification framework for the architectures of planetary systems based on a complete survey of the confirmed exoplanet population. With nearly 6000 confirmed exoplanets discovered, including more than 300 multiplanet systems with three or more planets, the current observational sample has reached the point where it is both feasible and useful to build a classification system that divides the observed population into meaningful categories. This framework provides a criterion to split planetary systems into inner and outer regimes, and then further divides inner systems into dynamical classes. The resulting categories include "peas-in-a-pod systems" with uniformly small planets and "warm Jupiter systems" with a mix of large and small planets, as well as "closely-spaced systems" and "gapped systems," with further subdivisions based on the locations of gaps and other features. These categories can classify nearly all of the confirmed systems with three or more planets with minimal ambiguity. We qualitatively examine the relative prevalence of each type of system, subject to observational selection effects, as well as other notable features such as the presence of hot Jupiters. A small number of outlier systems are also discussed. Potential additional classes of systems yet to be discovered are proposed.
Potential and Perils of Large Language Models as Judges of Unstructured Textual Data
2025-01-14
Rewina Bedemariam, Natalie Perez, Sreyoshi Bhaduri, Satya Kapoor, Alex Gil, Elizabeth Conjar, Ikkei Itoku, David Theil, Aman Chadha, Naumaan Nayyar
Rapid advancements in large language models have unlocked remarkable capabilities when it comes to processing and summarizing unstructured text data. This has implications for the analysis of rich, open-ended datasets, such as survey responses, where LLMs hold the promise of efficiently distilling key themes and sentiments. However, as organizations increasingly turn to these powerful AI systems to make sense of textual feedback, a critical question arises, can we trust LLMs to accurately represent the perspectives contained within these text based datasets? While LLMs excel at generating human-like summaries, there is a risk that their outputs may inadvertently diverge from the true substance of the original responses. Discrepancies between the LLM-generated outputs and the actual themes present in the data could lead to flawed decision-making, with far-reaching consequences for organizations. This research investigates the effectiveness of LLMs as judge models to evaluate the thematic alignment of summaries generated by other LLMs. We utilized an Anthropic Claude model to generate thematic summaries from open-ended survey responses, with Amazon's Titan Express, Nova Pro, and Meta's Llama serving as LLM judges. The LLM-as-judge approach was compared to human evaluations using Cohen's kappa, Spearman's rho, and Krippendorff's alpha, validating a scalable alternative to traditional human centric evaluation methods. Our findings reveal that while LLMs as judges offer a scalable solution comparable to human raters, humans may still excel at detecting subtle, context-specific nuances. This research contributes to the growing body of knowledge on AI assisted text analysis. We discuss limitations and provide recommendations for future research, emphasizing the need for careful consideration when generalizing LLM judge models across various contexts and use cases.
Evaluating Policy Effects through Network Dynamics and Sampling
2025-01-14
Eugene T. Y. Ang, Yong Sheng Soh
In the process of enacting or introducing a new policy, policymakers frequently consider the population's responses. These considerations are critical for effective governance. There are numerous methods to gauge the ground sentiment from a subset of the population; examples include surveys or listening to various feedback channels. Many conventional approaches implicitly assume that opinions are static; however, in reality, the population will discuss and debate these new policies among themselves, and reform new opinions in the process. In this paper, we pose the following questions: Can we quantify the effect of these social dynamics on the broader opinion towards a new policy? Given some information about the relationship network that underlies the population, how does overall opinion change post-discussion? We investigate three different settings in which the policy is revealed: respondents who do not know each other, groups of respondents who all know each other, and respondents chosen randomly. By controlling who the policy is revealed to, we control the degree of discussion among the population. We quantify how these factors affect the changes in policy beliefs via the Wasserstein distance between the empirically observed data post-discussion and its distribution pre-discussion. We also provide several numerical analyses based on generated network and real-life network datasets. Our work aims to address the challenges associated with network topology and social interactions, and provide policymakers with a quantitative lens to assess policy effectiveness in the face of resource constraints and network complexities.
Automating Explanation Need Management in App Reviews: A Case Study from the Navigation App Industry
2025-01-14
Martin Obaidi, Nicolas Voß, Jakob Droste, Hannah Deters, Marc Herrmann, Jannik Fischbach, Kurt Schneider
Providing explanations in response to user reviews is a time-consuming and repetitive task for companies, as many reviews present similar issues requiring nearly identical responses. To improve efficiency, this paper proposes a semi-automated approach to managing explanation needs in user reviews. The approach leverages taxonomy categories to classify reviews and assign them to relevant internal teams or sources for responses. 2,366 app reviews from the Google Play Store and Apple App Store were scraped and analyzed using a word and phrase filtering system to detect explanation needs. The detected needs were categorized and assigned to specific internal teams at the company Graphmasters GmbH, using a hierarchical assignment strategy that prioritizes the most relevant teams. Additionally, external sources, such as existing support articles and past review responses, were integrated to provide comprehensive explanations. The system was evaluated through interviews and surveys with the Graphmasters support team, which consists of four employees. The results showed that the hierarchical assignment method improved the accuracy of team assignments, with correct teams being identified in 79.2% of cases. However, challenges in interrater agreement and the need for new responses in certain cases, particularly for Apple App Store reviews, were noted. Future work will focus on refining the taxonomy and enhancing the automation process to reduce manual intervention further.
Reflection-dominated Compton-thick AGN Candidates in the SRG/eROSITA Lockman Hole Survey
2025-01-14
M. I. Belvedersky, S. D. Bykov, M. R. Gilfanov, P. S. Medvedev, R. A. Sunyaev
We search for reflection-dominated Compton-thick active galactic nuclei (CT AGN) candidates in the Lockman Hole region using the data of SRG/eROSITA Lockman Hole survey. We selected sources with anomalously hard photon indices in the $0.3 - 8.0$ keV band, untypical for type I AGN. In particular, we required that the upper end of the $90\%$ error interval did not exceed a fiducial boundary of $\Gamma=1.3$. We found 291 sources which constitute a rare subpopulation among extragalactic X-ray sources detected by eROSITA in the Lockman Hole field, $\approx 5\%$. These sources constitute the eROSITA sample of CT AGN candidates in the Lockman Hole field. We further divide the sources into three categories depending on the availability of reliable redshift and statistically significant detection of intrinsic absorption. We present two catalogues: the bright sample (37 sources) and the faint one (254). We estimate the fraction and sky density of reflection-dominated CT AGN candidates. We show examples of individual spectra and use stacking analysis to search for possible redshift evolution of their properties with redshift. We analyse combined eROSITA spectra of bright sources of different categories with a physically motivated spectral model UXCLUMPY and find them fully consistent with the fits to the about $\sim 1$ Msec XMM-Newton data for one of our reflection-dominated CT candidates, Type 2 galaxy $\text{SRGe J105348.6+573032}$. The catalogues of CT AGN candidates could be a good starting point for planning future studies and follow-ups at all wavelengths.
Pressure and chemical potentials in the inner crust of a cold neutron star within Hartree-Fock and extended Thomas-Fermi methods
2025-01-14
Nicolas Chamel, Nikolai N. Shchechilin, Andrey I. Chugunov
Self-consistent mean-field methods with Skyrme-type effective interactions and semiclassical approximations, such as the Thomas-Fermi approach and its extensions are particularly well-suited for describing in a thermodynamically consistent way the various phases of the dense matter present in the interior of neutron stars. These methods have been applied to predict the composition of the different regions, including the inner crust constituted by nuclear clusters coexisting with free neutrons and electrons. Because of the computational cost, the energy is typically calculated for a few selected average baryon number densities, and the results are interpolated to obtain the pressure numerically. However, this may introduce systematic errors in the calculations of the global structure of a neutron star and its dynamical evolution. In this paper, we show how the full equation of state can be consistently calculated within the same framework by deriving exact formulas for the chemical potentials and for the pressure that can be easily implemented in existing computer codes. These formulas are applicable to both catalyzed and accreted crusts. We discuss in each case the suitable conditions to impose to determine the composition. Numerical examples are also presented and discussed. Results from refined calculations of the BSk24 equation of state for the inner crust of nonaccreted neutron stars and the corresponding adiabatic index are provided.
Misinformation is a challenging problem. This paper provides the first systematic interdisciplinary investigation of technical and non-technical interventions against misinformation. It combines interviews and a survey to understand which interventions are accepted across academic disciplines and approved by misinformation experts. Four interventions are supported by more than two in three misinformation experts: promoting media literacy, education in schools and universities, finding information about claims, and finding sources for claims. The most controversial intervention is deleting misinformation. We discuss the potentials and risks of all interventions. Education-based interventions are perceived as the most helpful by misinformation experts. Interventions focused on providing evidence are also widely perceived as helpful. We discuss them as scalable and always available interventions that empower users to independently identify misinformation. We also introduce the Phase Model of Misinformation Interventions that helps practitioners make informed decisions about which interventions to focus on and how to best combine interventions.
Strong coupling Møller-Plesset perturbation theory
2025-01-14
Yassir El Moutaoukal, Rosario R. Riso, Matteo Castagnola, Enrico Ronca, Henrik Koch
Perturbative approaches are methods to efficiently tackle many-body problems, offering both intuitive insights and analysis of correlation effects. However, their application to systems where light and matter are strongly coupled is non-trivial. Specifically, the definition of suitable orbitals for the zeroth-order Hamiltonian represents a significant theoretical challenge. While reviewing previously investigated orbital choices, this work presents an alternative polaritonic orbital basis suitable for the strong coupling regime. We develop a quantum electrodynamical (QED) M{\o}ller-Plesset perturbation theory using orbitals obtained from the strong coupling QED Hartree-Fock. We assess the strengths and limitations of the different approaches and emphasize the essential role of using a consistent molecular orbital framework to achieve an accurate description of cavity-induced electron-photon correlation effects.
Building Symbiotic AI: Reviewing the AI Act for a Human-Centred, Principle-Based Framework
2025-01-14
Miriana Calvano, Antonio Curci, Giuseppe Desolda, Andrea Esposito, Rosa Lanzilotti, Antonio Piccinno
Artificial Intelligence (AI) spreads quickly as new technologies and services take over modern society. The need to regulate AI design, development, and use is strictly necessary to avoid unethical and potentially dangerous consequences to humans. The European Union (EU) has released a new legal framework, the AI Act, to regulate AI by undertaking a risk-based approach to safeguard humans during interaction. At the same time, researchers offer a new perspective on AI systems, commonly known as Human-Centred AI (HCAI), highlighting the need for a human-centred approach to their design. In this context, Symbiotic AI (a subtype of HCAI) promises to enhance human capabilities through a deeper and continuous collaboration between human intelligence and AI. This article presents the results of a Systematic Literature Review (SLR) that aims to identify principles that characterise the design and development of Symbiotic AI systems while considering humans as the core of the process. Through content analysis, four principles emerged from the review that must be applied to create Human-Centred AI systems that can establish a symbiotic relationship with humans. In addition, current trends and challenges were defined to indicate open questions that may guide future research for the development of SAI systems that comply with the AI Act.
On the features of great Forbush effect during May 2024 extreme geomagnetic storm
2025-01-14
M. A. Abunina, N. S. Shlyk, A. V. Belov, S. M. Belov, A. A. Abunin
The work investigates the features of galactic cosmic ray density and anisotropy behavior and their relation to solar sources, interplanetary and geomagnetic disturbances from May 8 to May 13, 2024. During this time, powerful solar flares and fast CMEs were recorded, leading to registration of an extreme geomagnetic storm along with one of the most significant Forbush effects for the entire observation period. All the calculations of cosmic ray characteristics are made using the data of global neutron monitor network and unique methods maintained at IZMIRAN: the Global Survey Method and the Ring of Stations Method. It is determined that the magnitude of Forbush effect under study was 15.7% (for particles with 10 GV rigidity) and as an extreme geomagnetic storm was recorded there was a significant magnetospheric effect observed in the data of neutron monitors (~4%).
Periodic Gamma-ray Modulation of the blazar PG 1553+113 Confirmed by Fermi-LAT and Multi-wavelength Observations
2025-01-14
S. Abdollahi, L. Baldini, G. Barbiellini, R. Bellazzini, B. Berenji, E. Bissaldi, R. D. Blandford, R. Bonino, P. Bruel, S. Buson, R. A. Cameron, P. A. Caraveo, F. Casaburo, E. Cavazzuti, C. C. Cheung, G. Chiaro, S. Ciprini, G. Cozzolongo, P. Cristarella Orestano, S. Cutini, F. D'Ammando, N. Di Lalla, F. Dirirsa, L. Di Venere, A. Domínguez, S. J. Fegan, E. C. Ferrara, A. Fiori, Y. Fukazawa, S. Funk, P. Fusco, F. Gargano, S. Garrappa, D. Gasparrini, S. Germani, N. Giglietto, F. Giordano, M. Giroletti, D. Green, I. A. Grenier, S. Guiriec, E. Hays, D. Horan, M. Kuss, S. Larsson, M. Laurenti, J. Li, I. Liodakis, F. Longo, F. Loparco, B. Lott, M. N. Lovellette, P. Lubrano, S. Maldera, D. Malyshev, A. Manfreda, L. Marcotulli, G. Martí-Devesa, M. N. Mazziotta, I. Mereu, P. F. Michelson, W. Mitthumsiri, T. Mizuno, M. E. Monzani, A. Morselli, I. V. Moskalenko, M. Negro, N. Omodei, M. Orienti, E. Orlando, J. F. Ormes, D. Paneque, M. Perri, M. Persic, M. Pesce-Rollins, T. A. Porter, G. Principe, S. Rainò, R. Rando, B. Rani, M. Razzano, A. Reimer, O. Reimer, P. M. Saz Parkinson, L. Scotton, D. Serini, A. Sesana, C. Sgrò, E. J. Siskind, G. Spandre, P. Spinelli, D. J. Suson, H. Tajima, M. N. Takahashi, D. Tak, J. B. Thayer, D. J. Thompson, D. F. Torres, J. Valverde, F. Verrecchia, G. Zaharijas
A 2.1-year periodic oscillation of the gamma-ray flux from the blazar PG 1553+113 has previously been tentatively identified in almost 7 year of data from the Fermi Large Area Telescope. After 15 years of Fermi sky-survey observations, doubling the total time range, we report >7 cycle gamma-ray modulation with an estimated significance of 4 sigma against stochastic red noise. Independent determinations of oscillation period and phase in the earlier and the new data are in close agreement (chance probability <0.01). Pulse timing over the full light curve is also consistent with a coherent periodicity. Multiwavelength new data from Swift X-Ray Telescope, Burst Alert Telescope, and UVOT, and from KAIT, Catalina Sky Survey, All-Sky Automated Survey for Supernovae, and Owens Valley Radio Observatory ground-based observatories as well as archival Rossi X-Ray Timing Explorer satellite-All Sky Monitor data, published optical data of Tuorla, and optical historical Harvard plates data are included in our work. Optical and radio light curves show clear correlations with the gamma-ray modulation, possibly with a nonconstant time lag for the radio flux. We interpret the gamma-ray periodicity as possibly arising from a pulsational accretion flow in a sub-parsec binary supermassive black hole system of elevated mass ratio, with orbital modulation of the supplied material and energy in the jet. Other astrophysical scenarios introduced include instabilities, disk and jet precession, rotation or nutation, and perturbations by massive stars or intermediate-mass black holes in polar orbit.
Strongly Periodic Modules and Perverse Autoequivalences
2025-01-14
Alfred Dabson
We introduce a notion of strong periodicity of a module over a finite-dimensional algebra over a field. We prove that the existence of such modules over certain idempotent algebras is both a necessary and sufficient condition for the existence of a two-step self-perverse equivalence of a finite-dimensional algebra. We survey some applications to the setting of the symmetric groups.
Mapping reionization bubbles in the JWST era II: inferring the position and characteristic size of individual bubbles
2025-01-14
Ivan Nikolić, Andrei Mesinger, Charlotte A. Mason, Ting-Yi Lu, Mengtao Tang, David Prelogović, Samuel Gagnon-Hartman, Daniel P. Stark
The James Webb Space Telescope (JWST) is discovering an increasing number of galaxies well into the early stages of the Epoch of Reionization (EoR). Many of these galaxies are clustered with strong Lyman alpha (Ly$\alpha$) emission, motivating the presence of surrounding cosmic HII regions that would facilitate Ly$\alpha$ transmission through the intergalactic medium (IGM). Detecting these HII "bubbles" would allow us to connect their growth to the properties of the galaxies inside them. Here we develop a new forward-modeling framework to estimate the local HII region size and location from Ly$\alpha$ spectra of galaxy groups in the early stages of the EoR. Our model takes advantage of the complementary information provided by neighboring sightlines through the IGM. Our forward models sample the main sources of uncertainty, including: (i) the global neutral fraction; (ii) EoR morphology; (iii) emergent Ly$\alpha$ emission; and (iv) NIRSpec instrument noise. Depending on the availability of complementary nebular lines, $\sim$ 0.006 $\unicode{x2013}$ 0.01 galaxies per cMpc$^3$, are required to be $\gtrsim$95\% confident that the HII bubble location and size recovered by our method is accurate to within $\sim$ 1 comoving Mpc. This corresponds roughly to tens of galaxies at $z\sim7\unicode{x2013}8$ in $\sim$2x2 tiled pointing with JWST NIRSpec. Such a sample is achievable with a targeted survey with completeness down to $M_{\rm UV}^{\rm min}\lesssim$ -19 $\unicode{x2013}$ -17, depending on the over-density of the field. We test our method on 3D EoR simulations as well as misspecified equivalent width distributions, in both cases accurately recovering the HII region surrounding targeted galaxy groups.
High-Contrast Imaging: Hide and Seek with Exoplanets
2025-01-14
Riccardo Claudi, Dino Mesa
So far, most of the about 5700 exoplanets have been discovered mainly with radial velocity and transit methods. These techniques are sensitive to planets in close orbits, not being able to probe large star--planet separations. $\mu$-lensing is the indirect method that allows us to probe the planetary systems at the snow-line and beyond, but it is not a repeatable observation. On the contrary, direct imaging (DI) allows for the detection and characterization of low mass companions at wide separation (\mbox{$\leq$ 5--6 au}). The main challenge of DI is that a typical planet--star contrast ranges from $10^{-6}$, for a young Jupiter in emitted light, to $10^{-9}$ for Earth in reflected light. In the last two decades, a lot of efforts have been dedicated to combining large (D $\geq$ 5 m) telescopes (to reduce the impact of diffraction) with coronagraphs and high-order adaptive optics (to correct phase errors induced by atmospheric turbulence), with sophisticated image post-processing, to reach such a contrast between the star and the planet in order to detect and characterize cooler and closer companions to nearby stars. Building on the first pioneering instrumentation, the second generation of high-contrast imagers, SPHERE, GPI, and SCExAO, allowed us to probe hundreds of stars (e.g., 500--600 stars using SHINE and GPIES), contributing to a better understanding of the demography and the occurrence of planetary systems. The DI offers a possible clear vision for studying the formation and physical properties of gas giant planets and brown dwarfs, and the future DI (space and ground-based) instruments with deeper detection limits will enhance this vision. In this paper, we briefly review the methods, the instruments, the main sample of targeted stars, the remarkable results, and the perspective of this rising technique.
This chapter provides a comprehensive overview of weak gravitational lensing and its current applications in cosmology. We begin by introducing the fundamental concepts of gravitational lensing and derive the key equations for the deflection angle, lensing potential, convergence, and shear. We explore how weak lensing can be used as a cosmological probe, discussing cosmic shear, galaxy-galaxy lensing, and their combination with galaxy clustering in the 3$\times$2pt analysis. The chapter covers the theoretical framework for modeling lensing observables, shear estimation techniques, and major systematic effects such as intrinsic alignments and baryonic feedback. We review the current results of weak lensing cosmology from major surveys and outline prospects for future advancements in the field.
2025-01-14
I. D. Karachentsev, V. E. Karachentseva, K. V. Vladimirova, K. A. Kozyrev
We present the results of searching for new dwarf galaxies in the Local Volume. We found 40 satellite candidates in the double-virial-radius regions of 20 Milky Way-like and LMC-like galaxies in the southern sky using DESI Legacy Imaging Surveys, 10 of which were known but not clearly associated with the Local Volume previously. Among the 40 satellite candidates, 8 are supposed members of the NGC6744 group and 13 are located in the vicinity of the Sombrero galaxy. Based on seven companions to the giant spiral galaxy NGC\,6744 with measured radial velocities, we estimate that the total mass of the group is $M_T = (1.88\pm0.71)\times 10^{12} M_{\odot}$ and the total mass-to-K-luminosity ratio $M_T/L_K = (16.1\pm6.0) M_{\odot}/L_{\odot}$. We reproduce a distribution of 68 early- and late-type galaxies in the Local Volume situated around the Sombrero, noting their strong morphological segregation and also the presence of a foreground diffuse association of dwarf galaxies at 8 degrees to SE from the Sombrero.
Assessment of Personalized Learning in Immersive and Intelligent Virtual Classroom on Student Engagement
2025-01-14
Ying Weng, Yiming Zhang
As trends in education evolve, personalized learning has transformed individuals' engagement with knowledge and skill development. In the digital age, state-of-the-art technologies have been increasingly integrated into classrooms to support intelligent education and foster personalized learning experiences. One promising approach is the use of eye-tracking technology to evaluate student engagement in intelligent virtual classrooms. This paper explores the assessment of personalized learning in the virtual classroom and its impact on student engagement through the eye movement paradigm. The study aims to provide insights into how personalized learning approaches can enhance student participation, motivation, and academic performance in the online learning environment. Through a comprehensive literature review, case study, and data analysis, the paper examines the key elements of personalized learning, the methods of assessment, and the resulting effects on student engagement. The findings suggest that the eye movement paradigm has the potential to assess student engagement and promote better educational outcomes.
Using the Kaniadakis horizon entropy in the presence of neutrinos to alleviate the Hubble and $ S_{8} $ Tensions
2025-01-14
Muhammad Yarahmadi, Amin Salehi
The $H_{0}$ tension stands as a prominent challenge in cosmology, serving as a primary driver for exploring alternative models of dark energy. Another tension arises from measurements of the $ S_{8} $ parameter, which is characterize the amplitude of matter fluctuations in the universe. In this study, we address the alleviation of both the Hubble tension and $ S_{8} $ tension by incorporating Kaniadakis horizon entropy. We investigate two scenarios to explore the impact of this entropy on cosmological parameters. In the first scenario, utilizing modified Friedmann equations through Kaniadakis entropy, we estimate the values of $H_{0}$ and $ S_{8} $. In the subsequent scenario, we introduce the neutrino term and assess its effect on mitigating the Hubble and $ S_{8} $ tensions. Our findings reveal that when considering the first scenario, the results closely align with Planck's 2018 outcomes for Hubble and $ S_{8} $ tensions. Moreover, with the inclusion of neutrinos, these tensions are alleviated to approximately 2$\sigma$, and the $ S_{8} $ value is in full agreement with the results from the KiDS and DES survey. Furthermore, we impose a constraint on the parameter $K$ in each scenario. Our analysis yields $K = 0.12\pm 0.41$ for Kaniadakis entropy without neutrinos and $K = 0.39\pm 0.4$ for the combined dataset considering Kaniadakis entropy in the presence of neutrinos. We demonstrate that the value of K may be affected by neutrino mass, which can cause energy transfer between different parts of the universe and alter the Hubble parameter value.
Image Super-Resolution (SR) aims to recover a high-resolution image from its low-resolution counterpart, which has been affected by a specific degradation process. This is achieved by enhancing detail and visual quality. Recent advancements in transformer-based methods have remolded image super-resolution by enabling high-quality reconstructions surpassing previous deep-learning approaches like CNN and GAN-based. This effectively addresses the limitations of previous methods, such as limited receptive fields, poor global context capture, and challenges in high-frequency detail recovery. Additionally, the paper reviews recent trends and advancements in transformer-based SR models, exploring various innovative techniques and architectures that combine transformers with traditional networks to balance global and local contexts. These neoteric methods are critically analyzed, revealing promising yet unexplored gaps and potential directions for future research. Several visualizations of models and techniques are included to foster a holistic understanding of recent trends. This work seeks to offer a structured roadmap for researchers at the forefront of deep learning, specifically exploring the impact of transformers on super-resolution techniques.
Using the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we have performed an Ursa Major cluster HI Survey (FUMaS) covering the entire UMa region centered at RA=11$^h$59$^m$28$^s$.3, DEC=49\degr05\arcmin18\arcsec with a radius of 7.5\degr. We have obtained the most complete catalog of HIsources in the UMa cluster, containing 179 HI sources with velocities in the range 625-1213.4 km~s$^{-1}$ and masses in the range 10$^{6.0}$-10$^{10.1}$ M$_{\odot}$ assuming a distance of 17.4 Mpc. Among them, 55 HI sources were detected for the first time. 32 HI sources do not have optical counterparts with known redshifts, and we found 25 possible counterparts in the multicolor optical image, and another 7 may be HI clouds. We also detected HI distributions in some interacting systems, e.g. the overlapping gas disks between NGC 3992 and its three companion galaxies, filaments around NGC 4026 and NGC 4111, and debris-like gas around NGC 3998. We computed the HIMF of the UMa cluster using the 1/V$_\mathrm{max}$ method and fitting it with the non-linear least squares (NLLS) and modified maximum likelihood (MML) method for the parameters: log$_{10}$($\phi_*$/Mpc$^{-3}$) = -0.86 $\pm$ 0.18, $\alpha$ = -1.10 $\pm$ 0.08 and log$_{10}$($M_*$/$M_{\odot}$) = 9.92 $\pm$ 0.23 for the NLLS method, and log$_{10}$($\phi_*$/Mpc$^{-3}$) = -0.78 $\pm$ 0.11, $\alpha$ = -1.10 $\pm$ 0.05 and log$_{10}$($M_*$/$M_{\odot}$) = 9.88 $\pm$ 0.14 for the MML method. This result is similar to that derived with VLA data, but the slope is steeper at the low-mass end because we detected more low-mass galaxies. The slope is flatter than that of the global HIMF, which is in agreement with the theoretical prediction that galaxies in high-density regions are stripped of gas due to interactions.
We measure the void size function (VSF) from the Baryon Oscillation Spectroscopic Survey (BOSS DR16) and perform the cosmological constraints. The BOSS DR16 galaxy sample is selected in the redshift range from $z = 0.2$ to 0.8, considering the selection criteria based on galaxy number density. We identify non-spherical voids from this galaxy catalog using the Voronoi tessellation and watershed algorithm without assuming any void shape. We select the void samples based on the void ellipticity, and derive the VSFs in two redshift bins, i.e. $z=0.2-0.5$ and $0.5-0.8$. The VSF model we use is based on the excursion-set theory, including the void linear underdensity threshold $\delta_{\rm v}$ and the redshift space distortion (RSD) parameter $\beta$. The Markov Chain Monte Carlo (MCMC) method is applied to perform the joint constraints on the cosmological and void parameters. We find that the VSF measurement from BOSS DR16 gives $w = -1.214_{-0.375}^{+0.293}$, $\Omega_{\rm m} = 0.280_{-0.047}^{+0.056}$, and $\sigma_8 = 0.874_{-0.210}^{+0.203}$, which can be a good complementary probe to galaxy clustering measurements. Our method demonstrates the potential of using the VSF to study cosmological models, and it can provide a reference for future VSF analysis in the upcoming galaxy spectroscopic surveys.
Generic framework for non-perturbative QCD in light hadrons
2025-01-14
Wei-Yang Liu
This paper aims to serve as an introductory resource for disseminating the concept to individuals with interests in quantum chromodynamics (QCD) for hadrons. We discuss several topological aspects of the QCD vacuum and briefly review recent progress on this intuitive unifying framework for the lowlying hadron physics rooted in QCD by introducing the vacuum as a liquid of instantons and anti-instantons. We develop systematic density expansion on the dilute vacuum with diagrammatical Feynman rules to calculate the vacuum expectation values and generalize the calculations to hadronic matrix element (charges), and hadronic form factors using the instanton liquid model (ILM). The ILM prediction are well-consistent with those of recent lattice QCD calculations.
Large Language Models for Knowledge Graph Embedding Techniques, Methods, and Challenges: A Survey
2025-01-14
Bingchen Liu, Xin Li
Large Language Models (LLMs) have attracted a lot of attention in various fields due to their superior performance, aiming to train hundreds of millions or more parameters on large amounts of text data to understand and generate natural language. As the superior performance of LLMs becomes apparent, they are increasingly being applied to knowledge graph embedding (KGE) related tasks to improve the processing results. As a deep learning model in the field of Natural Language Processing (NLP), it learns a large amount of textual data to predict the next word or generate content related to a given text. However, LLMs have recently been invoked to varying degrees in different types of KGE related scenarios such as multi-modal KGE and open KGE according to their task characteristics. In this paper, we investigate a wide range of approaches for performing LLMs-related tasks in different types of KGE scenarios. To better compare the various approaches, we summarize each KGE scenario in a classification. In addition to the categorization methods, we provide a tabular overview of the methods and their source code links for a more direct comparison. In the article we also discuss the applications in which the methods are mainly used and suggest several forward-looking directions for the development of this new research area.
The NEMESIS Catalogue of Young Stellar Objects for the Orion Star Formation Complex. I. General description of data curation
2025-01-14
J. Roquette, M. Audard, D. Hernandez, I. Gezer, G. Marton, C. Mas, M. Madarász, O. Dionatos
The past decade has seen a rise in the use of Machine Learning methods in the study of young stellar evolution. This trend has led to a growing need for a comprehensive database of young stellar objects (YSO) that goes beyond survey-specific biases and that can be employed for training, validation, and refining the physical interpretation of machine learning outcomes. We reviewed the literature focused on the Orion Star Formation complex (OSFC) to compile a thorough catalogue of previously identified YSO candidates in the region including the curation of observables relevant to probe their youth. Starting from the NASA/ADS database, we assembled YSO candidates from more than 200 peer-reviewed publications. We collated data products relevant to the study of YSOs into a dedicated catalogue, which was complemented with data from large photometric and spectroscopic surveys and in the Strasbourg astronomical Data Center. We also added significant value to the catalogue by homogeneously deriving YSO infrared classification labels and through a comprehensive curation of labels concerning sources' multiplicity. Finally, we used a panchromatic approach to derive the probabilities that the sources in our catalogue were contaminant extragalactic sources or giant stars. We present the NEMESIS catalogue of YSOs for the OSFC, which includes data collated for 27879 sources covering the whole mass spectrum and the various stages of pre-Main Sequence evolution from protostars to diskless young stars. The catalogue includes a collection of panchromatic photometric data processed into spectral energy distributions, stellar parameters, infrared classes, equivalent widths of emission lines related to YSOs accretion and star-disk interaction, and absorption lines such as lithium and lines related to source's gravity, X-ray emission observables, photometric variability observables, and multiplicity labels.
An Updated Detection Pipeline for Precursor Emission in Type II Supernova 2020tlf
2025-01-14
Wynn Jacobson-Galán, Sebastian Gonzalez, Shreyas Patel, Luc Dessart, David Jones, Deanne Coppejans, Georgios Dimitriadis, Ryan J. Foley, Charles D. Kilpatrick, David Matthews, Sofia Rest, Giacomo Terreran, Patrick D. Aleo, Katie Auchettl, Peter K. Blanchard, David A. Coulter, Kyle W. Davis, Thomas de Boer, Lindsay DeMarchi, Maria R. Drout, Nicholas Earl, Alexander Gagliano, Christa Gall, Jens Hjorth, Mark E. Huber, Adaeze L. Ibik, Danny Milisavljevic, Yen-Chen Pan, Armin Rest, Ryan Ridden-Harper, Cesar Rojas-Bravo, Matthew R. Siebert, Ken W. Smith, Kirsty Taggart, Samaporn Tinyanont, Qinan Wang, Yossef Zenati
We present a new photometric pipeline for the detection of pre-supernova (pre-SN) emission in the Young Supernova Experiment (YSE) sky survey. The method described is applied to SN 2020tlf, a type II SN (SN II) with precursor emission in the last ~100 days before first light. We re-analyze the YSE griz-band light curves of SN 2020tlf and provide revised pre-explosion photometry that includes a robust list of confident detection and limiting magnitudes. Compared to the results of Jacobson-Galan et al. 2022a, this new analysis yields fewer total r/i/z-band pre-SN detections at phases > -100 days. Furthermore, we discourage the use of the blackbody modeling of the pre-explosion spectral energy distribution, the pre-SN bolometric light curve and the blackbody model parameters presented in Jacobson-Galan et al. 2022a. Nevertheless, binned photometry of SN 2020tlf confirms a consistent progenitor luminosity of ~10$^{40}$ erg s$^{-1}$ before explosion.
The Theater Stage as Laboratory: Review of Real-Time Comedy LLM Systems for Live Performance
2025-01-14
Piotr Wojciech Mirowski, Boyd Branch, Kory Wallace Mathewson
In this position paper, we review the eclectic recent history of academic and artistic works involving computational systems for humor generation, and focus specifically on live performance. We make the case that AI comedy should be evaluated in live conditions, in front of audiences sharing either physical or online spaces, and under real-time constraints. We further suggest that improvised comedy is therefore the perfect substrate for deploying and assessing computational humor systems. Using examples of successful AI-infused shows, we demonstrate that live performance raises three sets of challenges for computational humor generation: 1) questions around robotic embodiment, anthropomorphism and competition between humans and machines, 2) questions around comedic timing and the nature of audience interaction, and 3) questions about the human interpretation of seemingly absurd AI-generated humor. We argue that these questions impact the choice of methodologies for evaluating computational humor, as any such method needs to work around the constraints of live audiences and performance spaces. These interrogations also highlight different types of collaborative relationship of human comedians towards AI tools.
The MAGPI Survey: the subtle role of environment and not-so-subtle impact of generations of stars on galaxy dynamics
2025-01-14
Caroline Foster, Sabine Bellstedt, Francesco DEugenio, Adriano Poci, Ryan Bagge, Katherine Harborne, Thomas Venville, J. Trevor Mendel, Claudia Del P. Lagos, Emily Wisnioski, Tania M. Barone, Andrew J. Battisti, Stefania Barsanti, Sarah Brough, Scott M. Croom, Caro Derkenne, Lucas C. Kimmig, Anilkumar Mailvaganam, Rhea-Silvia Remus, Gauri Sharma, Sarah M. Sweet, Sabine Thater, Lucas M. Valenzuela, Jesse van de Sande, Sam P. Vaughan, Bodo Ziegler
The stellar age and mass of galaxies have been suggested as the primary determinants for the dynamical state of galaxies, with environment seemingly playing no or only a very minor role. We use a sample of 77 galaxies at intermediate redshift (z~0.3) in the Middle-Ages Galaxies Properties with Integral field spectroscopy (MAGPI) Survey to study the subtle impact of environment on galaxy dynamics. We use a combination of statistical techniques (simple and partial correlations and principal component analysis) to isolate the contribution of environment on galaxy dynamics, while explicitly accounting for known factors such as stellar age, star formation histories and stellar masses. We consider these dynamical parameters: high-order kinematics of the line-of-sight velocity distribution (parametrised by the Gauss-Hermite coefficients $h_3$ and $h_4$), kinematic asymmetries $V_{\rm asym}$ derived using kinemetry and the observational spin parameter proxy $\lambda_{R_e}$. Of these, the mean $h_4$ is the only parameter found to have a significant correlation with environment as parametrised by group dynamical mass. This correlation exists even after accounting for age and stellar mass trends. Finally, we confirm that variations in the spin parameter $\lambda_{R_e}$ are most strongly (anti-)correlated with age as seen in local studies, and show that this dependence is well-established by z~0.3.
Large Language Models For Text Classification: Case Study And Comprehensive Review
2025-01-14
Arina Kostina, Marios D. Dikaiakos, Dimosthenis Stefanidis, George Pallis
Unlocking the potential of Large Language Models (LLMs) in data classification represents a promising frontier in natural language processing. In this work, we evaluate the performance of different LLMs in comparison with state-of-the-art deep-learning and machine-learning models, in two different classification scenarios: i) the classification of employees' working locations based on job reviews posted online (multiclass classification), and 2) the classification of news articles as fake or not (binary classification). Our analysis encompasses a diverse range of language models differentiating in size, quantization, and architecture. We explore the impact of alternative prompting techniques and evaluate the models based on the weighted F1-score. Also, we examine the trade-off between performance (F1-score) and time (inference response time) for each language model to provide a more nuanced understanding of each model's practical applicability. Our work reveals significant variations in model responses based on the prompting strategies. We find that LLMs, particularly Llama3 and GPT-4, can outperform traditional methods in complex classification tasks, such as multiclass classification, though at the cost of longer inference times. In contrast, simpler ML models offer better performance-to-time trade-offs in simpler binary classification tasks.
Nonlinear Modeling of a PEM Fuel Cell System; a Practical Study with Experimental Validation
2025-01-14
Seyed Mehdi Rakhtala, Roja Eini
In this paper, a nonlinear six order model is proposed for a proton exchange membrane fuel cell (PEMFC) as a control-oriented electrochemical model. Its validation is performed on a specific single cell PEMFC with effective dimension of 5 cm5 cm. This model is described in the nonlinear state space form with 6 state variables. Load current and DC voltage are considered as measurable disturbance and control input respectively. Besides, the model includes fuel cell stack and its auxiliary components as well. In this survey, a nonlinear state space representation is derived by arranging nonlinear equations and combining them with auxiliary components model. The proposed model can be successfully used to design nonlinear controller and nonlinear observer systems. The analyzed PEMFC system consists of air compressor motor dynamic equations, air and fuel supply subsystems, a perfect air humidifier and a fuel cell stack. An experimentally validated nonlinear model that reproduces the most typical features of a laboratory PEMFC system is presented. This model is derived based on physics law in stack, including system gases dynamics. The objective of this paper is to introduce a fully analytical model which has been fully validated on a fuel cell system and its auxiliary components. The proposed method can be used as a general modeling guideline for control-oriented purposes. Moreover, it can be successfully implemented in composing a dynamic subsystem, like hydrogen subsystem, as part of the whole nonlinear model.
BiDepth Multimodal Neural Network: Bidirectional Depth Deep Learning Arcitecture for Spatial-Temporal Prediction
2025-01-14
Sina Ehsani, Fenglian Pan, Qingpei Hu, Jian Liu
Accurate prediction of spatial-temporal (ST) information in dynamic systems, such as urban mobility and weather patterns, is a crucial yet challenging problem. The complexity stems from the intricate interplay between spatial proximity and temporal relevance, where both long-term trends and short-term fluctuations are present in convoluted patterns. Existing approaches, including traditional statistical methods and conventional neural networks, may provide inaccurate results due to the lack of an effective mechanism that simultaneously incorporates information at variable temporal depths while maintaining spatial context, resulting in a trade-off between comprehensive long-term historical analysis and responsiveness to short-term new information. To bridge this gap, this paper proposes the BiDepth Multimodal Neural Network (BDMNN) with bidirectional depth modulation that enables a comprehensive understanding of both long-term seasonality and short-term fluctuations, adapting to the complex ST context. Case studies with real-world public data demonstrate significant improvements in prediction accuracy, with a 12% reduction in Mean Squared Error for urban traffic prediction and a 15% improvement in rain precipitation forecasting compared to state-of-the-art benchmarks, without demanding extra computational resources.
A Comparative Evaluation of Bayesian Model-Assisted Two-Stage Designs for Phase I/II Clinical Trials
2025-01-14
Hao Sun, Jerry Li
The primary goal of a two-stage Phase I/II trial is to identify the optimal dose for the following large-scale Phase III trial. Recently, Phase I dose-finding designs have shifted from identifying the maximum tolerated dose (MTD) to the optimal biological dose (OBD). Typically, several doses are selected as recommended Phase II doses (RP2D) for further evaluation. In Phase II dose optimization trials, each RP2D is evaluated independently to determine its "go/no-go" decision. The optimal RP2D is then chosen from the remaining RP2Ds as the recommended Phase III dose (RP3D). The effectiveness of both dose-finding and dose optimization designs at two stages impacts RP3D selection. This paper reviews and compares fifteen Bayesian model-assisted two-stage designs, combining five Phase I dose-finding designs (BOIN, TITE-BOIN, BF-BOIN, BOIN12, and TITE-BOIN12) with three Phase II dose optimization designs (TS, BOP2, and TOP). We conduct extensive simulation studies to evaluate their performance under different dose-response scenarios, with and without the existence of the OBD. Based on our results, we recommend the TITE-BOIN12 + TOP combination as the optimal two-stage design for Phase I/II trials.
Multi-axion like description of the dark sector in light of the Hubble and $σ_8$ tensions
2025-01-14
John Dumancic, Richard Gass, Ennis Mawas, L. C. R. Wijewardhana
Local methods of direct determination of the Hubble constant and $\sigma_8$ seem to conflict with the predictions made from the cosmic microwave background and $\Lambda$CDM. We propose a proof-of-concept model that models portions of the dark sector as several coupled axion-like fields, resulting in both early and late time departures from $\Lambda$CDM. We determine that the model successfully eliminates both the Hubble and $\sigma_8$ tensions, while remaining consistent with both the DESI survey and the BAO sound horizon.
Complete Hamiltonian Framework of Relativistic Hierarchical Triple Systems: Capabilities and Limitations of Secular Perturbation Theory
2025-01-14
Kaye Jiale Li, Kinwah Wu, Ziri Younsi, Tjonnie G. F. Li
Relativistic secular perturbation theory has ignited significant interest in uncovering intricate cross-term effects, especially the interplay between 1PN and quadrupole terms. While most existing studies rely on the Lagrangian planetary perturbation method for computing cross terms, a comprehensive Hamiltonian framework for the field has been missing. In this work, we introduce a framework based on von Zeipel transformation, utilizing two sequential canonical transformations to systematically compute cross terms to arbitrary orders. Our results reveal secular cross terms up to quadrupole-squared order, showcasing remarkable consistency with both the Lagrangian method [1] and the effective-field-theory approach [2]. We present leading-order periodic cross terms arising from the interactions between 1PN and quadrupole, and present estimates of higher-order cross terms. It is demonstrated that this method not only accurately predicts the long-term evolution of hierarchical systems but also captures fast oscillations observed in N-body simulations. We identify and validate resonances caused by quadrupole-squared effects, highlighting both consistencies and discrepancies when compared to N-body simulations. These discrepancies underscore the importance of mean-motion resonances, a factor overlooked in current secular perturbation frameworks. Finally, we provide a comprehensive review of the subtleties and limitations inherent to secular perturbation theory, paving the way for future research and advancements in this field.
The intracluster light (ICL) is an important tracer of a galaxy cluster's history and past interactions. However, only small samples have been studied to date due to its very low surface brightness and the heavy manual involvement required for the majority of measurement algorithms. Upcoming large imaging surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time are expected to vastly expand available samples of deep cluster images. However, to process this increased amount of data, we need faster, fully automated methods to streamline the measurement process. This paper presents a machine learning model designed to automatically measure the ICL fraction in large samples of images, with no manual preprocessing required. We train the fully supervised model on a training dataset of 50,000 images with injected artificial ICL profiles. We then transfer its learning onto real data by fine-tuning with a sample of 101 real clusters with their ICL fraction measured manually using the surface brightness threshold method. With this process, the model is able to effectively learn the task and then adapt its learning to real cluster images. Our model can be directly applied to Hyper Suprime-Cam images, processing up to 500 images in a matter of seconds on a single GPU, or fine-tuned for other imaging surveys such as LSST, with the fine-tuning process taking just 3 minutes. The model could also be retrained to match other ICL measurement methods. Our model and the code for training it is made available on GitHub.
Antimatter Gravity Experiments, the Astronomical Challenges to Lambda-CDM Cosmology and the Quantum Vacuum as a Possible Source of Gravity in the Universe
2025-01-14
Dragan S Hajdukovic
This review is motivated by the first result of the ALPHA-g experiment at CERN, which indicates that atoms and anti-atoms have different gravitational charges; according to measurements, the gravitational acceleration of anti-atoms is only 0.75 of that of ordinary matter. If confirmed by more precise measurements, this will greatly increase the plausibility of the emerging cosmological model, which is based on the working hypothesis that quantum vacuum fluctuations are virtual gravitational dipoles; a hypothesis that opens up the possibility that the quantum vacuum is a major source of gravity in the universe (which could eventually eliminate the need for the hypothetical dark matter and dark energy). This laboratory challenge to general relativity and Lambda-CDM cosmology is complemented by astronomical challenges (the Hubble tension, very fast initial growth of structures in the Universe, dark energy deviating from the cosmological constant...). The intriguing question is: do the antimatter gravity experiments at CERN and the recent astronomical observations point to the same (highly unexpected) new physics? With this question in mind, we briefly review the antimatter gravity experiments at CERN and elsewhere, together with the major astronomical challenges and the emerging Quantum Vacuum cosmology, which seems to be compatible with the ALPHA-g result and with the preliminary astronomical challenges. The laboratory and astronomical challenges have suddenly taken us into terra incognita, where we need absolutely unprecedented imagination and open-minded thinking.
Physics-Informed Machine Learning for Microscale Drying of Plant-Based Foods: A Systematic Review of Computational Models and Experimental Insights
2025-01-14
C. P. Batuwatta-Gamage, H. Jeong, HCP Karunasena, M. A. Karim, C. M. Rathnayaka, Y. T. Gu
This review examines the current state of research on microscale cellular changes during the drying of plant-based food materials (PBFM), with particular emphasis on computational modelling approaches. The review addresses the critical need for advanced computational methods in microscale investigations. We systematically analyse experimental studies in PBFM drying, highlighting their contributions and limitations in capturing cellular-level phenomena, including challenges in data acquisition and measurement accuracy under varying drying conditions. The evolution of computational models for microstructural investigations is thoroughly examined, from traditional numerical methods to contemporary state-of-the-art approaches, with specific focus on their ability to handle the complex, nonlinear properties of plant cellular materials. Special attention is given to the emergence of data-driven models and their limitations in predicting microscale cellular behaviour during PBFM drying, particularly addressing challenges in dataset acquisition and model generalization. The review provides an in-depth analysis of Physics-Informed Machine Learning (PIML) frameworks, examining their theoretical foundations, current applications in related fields, and unique advantages in combining physical principles with neural network architectures. Through this comprehensive assessment, we identify critical gaps in existing methodologies, evaluate the trade-offs between different modelling approaches, and provide insights into future research directions for improving our understanding of cellular-level transformations during PBFM drying processes. The review concludes with recommendations for integrating experimental and computational approaches to advance the field of food preservation technology.
Model-independent forecasts for the cosmological anisotropic stress
2025-01-13
Ziad Sakr, Ziyang Zheng, Santiago Casas
The effective anisotropic stress $\eta$ is a key variable in the characterization of many classes of modified gravity theories, as it allows the testing for a long-range force additional to gravity. In this paper we forecast the precision with which future large surveys can determine $\eta$ in a way that only relies on directly observable quantities obtained from the spectroscopic measurements of the clustering of galaxies and the photometric based observation of the projected lensing and galaxy clustering correlations and their cross signal. Our method does not require further assumptions about the initial power spectrum, the modified gravity model, the expansion rate, or the bias. We consider various cases: $\eta$ free to vary in space and time, or with only redshift dependence, or constant. We take as a reference specifications that approximate a Euclid-like photometric or a combined one with a DESI-like spectroscopic survey. Among our results, we find that a future large-scale lensing and clustering survey can constrain $\eta$ to at least 30\% if $z$, $k$ independent, and to less than 10\% on average for the $z$ dependence only, to finally reach 5\% values in the constant case.
Quantifying Polarization: A Comparative Study of Measures and Methods
2025-01-13
Edoardo Di Martino, Matteo Cinelli, Roy Cerqueti, Walter Quattrociocchi
Political polarization, a key driver of social fragmentation, has drawn increasing attention for its role in shaping online and offline discourse. Despite significant efforts, accurately measuring polarization within ideological distributions remains a challenge. This study evaluates five widely used polarization measures, testing their strengths and weaknesses with synthetic datasets and a real-world case study on YouTube discussions during the 2020 U.S. Presidential Election. Building on these findings, we present a novel adaptation of Kleinberg's burst detection algorithm to improve mode detection in polarized distributions. By offering both a critical review and an innovative methodological tool, this work advances the analysis of ideological patterns in social media discourse.
Healthcare systems worldwide face persistent challenges in efficiency, accessibility, and personalization. Powered by modern AI technologies such as multimodal large language models and world models, Embodied AI (EmAI) represents a transformative frontier, offering enhanced autonomy and the ability to interact with the physical world to address these challenges. As an interdisciplinary and rapidly evolving research domain, "EmAI in healthcare" spans diverse fields such as algorithms, robotics, and biomedicine. This complexity underscores the importance of timely reviews and analyses to track advancements, address challenges, and foster cross-disciplinary collaboration. In this paper, we provide a comprehensive overview of the "brain" of EmAI for healthcare, wherein we introduce foundational AI algorithms for perception, actuation, planning, and memory, and focus on presenting the healthcare applications spanning clinical interventions, daily care & companionship, infrastructure support, and biomedical research. Despite its promise, the development of EmAI for healthcare is hindered by critical challenges such as safety concerns, gaps between simulation platforms and real-world applications, the absence of standardized benchmarks, and uneven progress across interdisciplinary domains. We discuss the technical barriers and explore ethical considerations, offering a forward-looking perspective on the future of EmAI in healthcare. A hierarchical framework of intelligent levels for EmAI systems is also introduced to guide further development. By providing systematic insights, this work aims to inspire innovation and practical applications, paving the way for a new era of intelligent, patient-centered healthcare.
Model compression is essential in the deployment of large Computer Vision models on embedded devices. However, static optimization techniques (e.g. pruning, quantization, etc.) neglect the fact that different inputs have different complexities, thus requiring different amount of computations. Dynamic Neural Networks allow to condition the number of computations to the specific input. The current literature on the topic is very extensive and fragmented. We present a comprehensive survey that synthesizes and unifies existing Dynamic Neural Networks research in the context of Computer Vision. Additionally, we provide a logical taxonomy based on which component of the network is adaptive: the output, the computation graph or the input. Furthermore, we argue that Dynamic Neural Networks are particularly beneficial in the context of Sensor Fusion for better adaptivity, noise reduction and information prioritization. We present preliminary works in this direction.
Functional Linear Cox Regression Model with Frailty
2025-01-13
Deniz Inan, Ufuk Beyaztas, Carmen D. Tekwe, Xiwei Chen, Roger S. Zoh
This paper presents a functional linear Cox regression model with frailty to tackle unobserved heterogeneity in survival data with functional covariates. While traditional Cox models are common, they struggle to incorporate frailty effects that represent individual differences not captured by observed covariates. Our model combines scalar and functional covariates with a frailty term to address these unmeasured influences, creating a robust framework for high-dimensional survival analysis. We estimate parameters using functional principal component analysis and apply penalized partial likelihood for the frailty structure. A simulation study shows that our model outperforms traditional approaches in estimation accuracy and predictive capacity, especially with high frailty. We also analyze data from the National Health and Nutrition Examination Survey, highlighting significant links between physical activity and mortality in frail subpopulations. Our findings demonstrate the model's effectiveness in managing complex survival data, with potential applications in biomedical research related to unobserved heterogeneity. The method is available as an R package.
Nonreciprocal charge transport in polar Dirac metals with tunable spin-valley coupling
2025-01-13
M. Kondo, M. Kimata, M. Ochi, T. Kaneko, K. Kuroki, K. Sudo, S. Sakaguchi, H. Murakawa, N. Hanasaki, H. Sakai
Nonreciprocal charge transport in solids, where resistance is different between rightward and leftward currents, is a key function of rectifying devices in the modern electronics, as exemplified by $p$-$n$ semiconductor junctions. Recently, this was also demonstrated in noncentrosymmetric materials in magnetic fields, since their band structure exhibits spin polarization coupled to the position of momentum space due to the antisymmetric spin-orbit coupling. To enhance the magnitude of nonreciprocal effect, it is essential to tune such spin-momentum coupling, which has been hampered in the conventional materials owing to the difficulty in controlling the broken inversion symmetry built into the lattice and interfacial structures. Here we report large nonreciprocal resistivity in layered polar metal BaMn$X_2$ ($X$=Sb, Bi), where the spin-polarized Dirac dispersion depends on the in-plane polarization tunable by chemical substitution of the $X$ site. For $X$=Sb with a pair of single-type valleys, the nonreciprocal resistivity increases monotonically with decreasing temperature, while for $X$=Bi with multiple types of valleys it is reduced by about an order of magnitude and exhibits a peak at a low temperature. Theoretical calculations indicate that the nonreciprocal resistivity is sensitive not only to the spin-momentum (spin-valley) coupling, but also to the Fermi energy and the Dirac dispersion. The observed significant variation of nonreciprocal transport in the same series of materials might be of great use in the design of junction-free rectifying devices and circuits.
A flux-limited sample of dusty star-forming galaxies from the Atacama Cosmology Telescope: physical properties and the case for multiplicity
2025-01-13
Kirsten R. Hall, Jake Hassan, Richard M. Feder, Tobias A. Marriage, Michael Zemcov
We report the modeling of the millimeter and far-infrared spectral energy distributions of 71 dusty star-forming galaxies (DSFGs) selected by the Atacama Cosmology Telescope (ACT) with a lower flux-density limit of 8 mJy at 220 GHz (1.4 mm). All sources were cross-identified with Herschel surveys at 500, 350, and 250 {\mu}m, and nineteen of our sources were observed at with the Submillimeter Array. A probabilistic cataloging algorithm, PCAT, favors multiple unresolved flux components in the Herschel data for the majority of ACT-selected DSFGs. We compare the derived physical properties of the DSFGs obtained from modeling the flux densities with those from similar studies of both lensed and unlensed DSFG populations. We find the median, 16th and 84th percentiles for the following model parameters: redshift zphot=3.3(+0.7)(-0.6), apparent size {\mu}d=5.2(+0.9)(-2.4) kpc, apparent dust mass log10({\mu}Md/Msun)=9.14(+0.12)(-0.04) and cutoff temperature Tc=35.6(+4.8)(-1.6) K, and the corresponding apparent far-infrared luminosity log10({\mu}LIR/Lsun)=13.6(+0.2)(-0.3), where {\mu} is lensing magnification. While many of the properties broadly agree with those of samples of primarily lensed DSFGs, we exercise caution in interpreting them. ACT's lower flux limit, the PCAT decomposition, and the higher-resolution SMA observations all suggest that some fraction of these DSFGs are likely to be unlensed and possibly multiples. The SMA data indicate that at least fourteen out of nineteen sources are such, either via "missing" flux in comparison to the ensemble model or detection of additional sources in the fields. Additional high-resolution follow-up and targeted redshift observations are needed to better understand this flux-limited sample of DSFGs.
The GRACE project: Hard X-ray giant radio galaxies and their duty cycle
2025-01-13
G. Bruni, F. Panessa, L. Bassani, M. Brienza, M. Fanelli, F. Ursini, F. Massaro, A. Malizia, M. Molina, L. Hernández-García, C. J. Riseley, E. K. Mahony, M. Janssen, D. Dallacasa, T. Venturi, R. D. Baldi, M. Persic
The advent of new generation radio telescopes is opening new possibilities on the classification and study of extragalactic high-energy sources, specially the underrepresented ones like radio galaxies. Among these, Giant Radio Galaxies (GRG, larger than 0.7 Mpc) are among the most extreme manifestations of the accretion/ejection processes on supermassive black holes. Our recent studies have shown that GRG can be up to four times more abundant in hard X-ray selected (i.e. from INTEGRAL/IBIS and Swift/BAT at $>$20 keV) samples and, most interestingly, the majority of them present signs of restarted radio activity. This makes them the ideal test-bed to study the so far unknown duty cycle of jets in active galactic nuclei. Open questions in the field include: How and when jets are restarted? How jets evolve and what's their dynamic? What is the jet's duty cycle and what triggers them? Our group has recently collected a wealth of radio data on these high-energy selected GRGs, allowing us to study their jet formation and evolution from the pc to kpc scales, across different activity epochs. In particular, thanks to our EVN large programme, we were able to probe the new radio phase in the core of these giants. Furthermore, we are devoting an effort to the exploitation of new radio surveys data for the discovery of new classes of counterparts of Fermi/LAT catalogues. In particular, we are unveiling the hidden population of radio galaxies associated with gamma-ray sources.
Is planetary inward migration responsible for GJ 504's fast rotation and bright X-ray luminosity? New constraints from eROSITA
2025-01-13
C. Pezzotti, G. Buldgen, E. Magaudda, M. Farnir, V. Van Grootel, S. Bellotti, K. Poppenhaeger
The discovery of an increasing variety of exoplanets in very close orbits around their host stars raised many questions about how stars and planets interact, and to which extent host stars' properties may be influenced by the presence of close-by companions. Understanding how the evolution of stars is impacted by the interactions with their planets is fundamental to disentangle their intrinsic evolution from Star-Planet Interactions (SPI)-induced phenomena. GJ 504 is a promising candidate for a star that underwent strong SPI. Its unusually short rotational period (3.4 days), while being in contrast with what is expected by single-star models, could result from the inward migration of a close-by, massive companion, pushed starward by tides. Moreover, its brighter X-ray luminosity may hint at a rejuvenation of the dynamo process sustaining the stellar magnetic field, consequent to the SPI-induced spin-up. We aim to study the evolution of GJ 504 and establish whether by invoking the engulfment of a planetary companion we can better reproduce its rotational period and X-ray luminosity. We simulate the past evolution assuming two different scenarios: 'Star without close-by planet', 'Star with close-by planet'. In the second scenario, we investigate how inward migration and planetary engulfment driven by tides spin up the stellar surface and rejuvenate its dynamo. We compare our tracks with rotational period and X-ray data collected from the all-sky surveys of the ROentgen Survey with an Imaging Telescope Array (eROSITA) on board the Russian Spektrum-Roentgen-Gamma mission (SRG). Despite the very uncertain stellar age, we found that the second evolutionary scenario is in better agreement with the short rotational period and the bright X-ray luminosity of GJ 504, thus strongly favouring the inward migration scenario over the one in which close-by planets have no tidal impact on the star.
The Essentials of AI for Life and Society: An AI Literacy Course for the University Community
2025-01-13
Joydeep Biswas, Don Fussell, Peter Stone, Kristin Patterson, Kristen Procko, Lea Sabatini, Zifan Xu
We describe the development of a one-credit course to promote AI literacy at The University of Texas at Austin. In response to a call for the rapid deployment of class to serve a broad audience in Fall of 2023, we designed a 14-week seminar-style course that incorporated an interdisciplinary group of speakers who lectured on topics ranging from the fundamentals of AI to societal concerns including disinformation and employment. University students, faculty, and staff, and even community members outside of the University, were invited to enroll in this online offering: The Essentials of AI for Life and Society. We collected feedback from course participants through weekly reflections and a final survey. Satisfyingly, we found that attendees reported gains in their AI literacy. We sought critical feedback through quantitative and qualitative analysis, which uncovered challenges in designing a course for this general audience. We utilized the course feedback to design a three-credit version of the course that is being offered in Fall of 2024. The lessons we learned and our plans for this new iteration may serve as a guide to instructors designing AI courses for a broad audience.
This paper discusses three key themes in forecasting for monetary policy highlighted in the Bernanke (2024) review: the challenges in economic forecasting, the conditional nature of central bank forecasts, and the importance of forecast evaluation. In addition, a formal evaluation of the Bank of England's inflation forecasts indicates that, despite the large forecast errors in recent years, they were still accurate relative to common benchmarks.
2025-01-13
Weimin Yuan, Lixin Dai, Hua Feng, Chichuan Jin, Peter Jonker, Erik Kuulkers, Yuan Liu, Kirpal Nandra, Paul O'Brien, Luigi Piro, Arne Rau, Nanda Rea, Jeremy Sanders, Lian Tao, Junfeng Wang, Xuefeng Wu, Bing Zhang, Shuangnan Zhang, Shunke Ai, Johannes Buchner, Esra Bulbul, Hechao Chen, Minghua Chen, Yong Chen, Yu-Peng Chen, Alexis Coleiro, Francesco Coti Zelati, Zigao Dai, Xilong Fan, Zhou Fan, Susanne Friedrich, He Gao, Chong Ge, Mingyu Ge, Jinjun Geng, Giancarlo Ghirlanda, Giulia Gianfagna, Lijun Gou, Sébastien Guillot, Xian Hou, Jingwei Hu, Yongfeng Huang, Long Ji, Shumei Jia, S. Komossa, Albert K. H. Kong, Lin Lan, An Li, Ang Li, Chengkui Li, Dongyue Li, Jian Li, Zhaosheng Li, Zhixing Ling, Ang Liu, Jinzhong Liu, Liangduan Liu, Zhu Liu, Jiawei Luo, Ruican Ma, Pierre Maggi, Chandreyee Maitra, Alessio Marino, Stephen Chi-Yung Ng, Haiwu Pan, Surangkhana Rukdee, Roberto Soria, Hui Sun, Pak-Hin Thomas Tam, Aishwarya Linesh Thakur, Hui Tian, Eleonora Troja, Wei Wang, Xiangyu Wang, Yanan Wang, Junjie Wei, Sixiang Wen, Jianfeng Wu, Ting Wu, Di Xiao, Dong Xu, Renxin Xu, Yanjun Xu, Yu Xu, Haonan Yang, Bei You, Heng Yu, Yunwei Yu, Binbin Zhang, Chen Zhang, Guobao Zhang, Liang Zhang, Wenda Zhang, Yu Zhang, Ping Zhou, Zecheng Zou
The Einstein Probe (EP) is an interdisciplinary mission of time-domain and X-ray astronomy. Equipped with a wide-field lobster-eye X-ray focusing imager, EP will discover cosmic X-ray transients and monitor the X-ray variability of known sources in 0.5-4 keV, at a combination of detecting sensitivity and cadence that is not accessible to the previous and current wide-field monitoring missions. EP can perform quick characterisation of transients or outbursts with a Wolter-I X-ray telescope onboard. In this paper, the science objectives of the Einstein Probe mission are presented. EP is expected to enlarge the sample of previously known or predicted but rare types of transients with a wide range of timescales. Among them, fast extragalactic transients will be surveyed systematically in soft X-rays, which include {\gamma}-ray bursts and their variants, supernova shock breakouts, and the predicted X-ray transients associated with binary neutron star mergers. EP will detect X-ray tidal disruption events and outbursts from active galactic nuclei, possibly at an early phase of the flares for some. EP will monitor the variability and outbursts of X-rays from white dwarfs, neutron stars and black holes in our and neighbouring galaxies at flux levels fainter than those detectable by the current instruments, and is expected to discover new objects. A large sample of stellar X-ray flares will also be detected and characterised. In the era of multi-messenger astronomy, EP has the potential of detecting the possible X-ray counterparts of gravitational wave events, neutrino sources, and ultra-high energy {\gamma}-ray and cosmic ray sources. EP is expected to help advance the studies of extreme objects/phenomena and their underlying physical processes revealed in the dynamic X-ray universe, as well as studies in other areas of X-ray astronomy.
A multi-wavelength view of the isolated neutron star eRASSU J065715.3+260428
2025-01-13
J. Kurpas, A. M. Pires, A. D. Schwope, Z. C. Pan, Z. L. Zhang, L. Qian, F. Haberl, L. Ji, I. Traulsen
The X-ray source eRASSU J065715.3+260428 was identified as a likely thermally emitting isolated neutron star in a search in the SRG/eROSITA All-Sky Survey. We investigated the nature and evolutionary state of the source through a dedicated multi-wavelength follow-up campaign with XMM-Newton, NICER, FAST, and ESO-VLT, complemented by the analysis of archival Fermi-LAT observations. The X-ray observations unveiled the rotation period, $P=261.085400(4)$ ms, and spin-down rate, $\dot{P}=6^{+11}_{-4}\times10^{-15}$ s s$^{-1}$, of the source. No optical counterparts are detected down to 27.3 mag ($5\sigma$, R band), implying a large X-ray-to-optical flux ratio above 5200. The X-ray spectrum of the source is best described by a composite phenomenological model consisting of two thermal components, either a double blackbody continuum with temperatures 90 eV and 220 eV or a hydrogen neutron star atmosphere of temperature $\log(T/\mathrm{K})\sim 5.8$ combined with a hot blackbody of 250 eV, in both cases modified by an absorption feature at low energies ($\sim0.3$ keV). The presence of faint non-thermal hard X-ray tails is ruled out above $(2.1\pm1.8)$% of the source unabsorbed flux. Radio searches at $1-1.5$ GHz with FAST yielded negative results, with a deep upper limit on the pulsed flux of 1.4 $\mu$Jy ($10\sigma$). Similarly, no significant spatial or pulsed signals were detected in sixteen years of Fermi-LAT observations. The source is most likely a middle-aged spin-powered pulsar and can also be identified as PSR J0657+2604. The absence of non-thermal X-ray, radio, or gamma-ray emission within current limits suggests either an unfavourable viewing geometry or unusual magnetospheric properties. Additional observations are needed to check for faint hard X-ray tails, investigate the presence of diffuse emission from a pulsar-wind nebula, and obtain a more accurately sampled timing solution.
Galaxy mass-size segregation in the cosmic web from the CAVITY parent sample
2025-01-13
I. Perez, L. Gil, A. Ferre-Mateu, G. Torres-Rios, A. Zurita, M. Argudo-Fernandez, B. Bidaran, L. Sanchez-Menguiano, T. Ruiz-Lara, J. Dominguez-Gomez, S. Duarte Puertas, D. Espada, J. Falcon-Barroso, E. Florido, R. Garcia-Benito, A. Jimenez, R. F. Peletier, J. Román, P. Sanchez Alarcon, P. Sanchez-Blazquez, P. Vasquez-Bustos
The mass-size relation is a fundamental galaxy scaling law closely tied to galaxy formation and evolution. Using added-value products of the Calar Alto Void Integral-field Treasury surveY (CAVITY) and SDSS DR16 images, we examine the effect of large-scale environments on the stellar mass-size relation. We analyse the Petrosian R50 and R90 radii of approximately 140000 galaxies in voids, filaments, and clusters, with a mass range of $10^{8.5} - 10^{11} M_{\odot}$. We explore the relation in terms of galaxy morphology and star formation history, parametrised by T50, T70, and T90. We find that early-type void galaxies are, on average, 10-20% smaller than their counterparts in denser environments, regardless of their mass assembly history. Moreover, the mass-size relation for massive early-type void galaxies has a shallower slope compared to those in denser regions. In contrast, early-type galaxies in filaments, and clusters show a more uniform mass-size relation. Late-type cluster galaxies with stellar masses $log(M_{\star} / M_{\odot}) = 9 - 10.5$ are smaller and more concentrated than their counterparts in lower-density environments, such as filaments, and voids. We conclude that large-scale environments influence the mass-size relation. Early-type galaxies appear to grow most of their mass during the initial formation phase. Subsequent size growth in voids is less significant, likely due to slower evolution, reduced minor merger activity, fewer accretion events, or a combination. The flatter slope for massive void galaxies indicates a lower rate of minor accretion, a trend also observed in late-type void galaxies with $\approx 10^{10.5} M_{\odot}$, where minor mergers contribute to size growth. Conversely, late-type quenched cluster galaxies are smaller due to environmental interactions, with early infallers being most affected.
Affirmative Hackathon for Software Developers with Disabilities: An Industry Initiative
2025-01-13
Thayssa Rocha, Nicole Davila, Rafaella Vaccari, Nicoly Menezes, Marcelle Mota, Edward Monteiro, Cleidson de Souza, Gustavo Pinto
People with disabilities (PWD) often encounter several barriers to becoming employed. A growing body of evidence in software development highlights the benefits of diversity and inclusion in the field. However, recruiting, hiring, and fostering a supportive environment for PWD remains challenging. These challenges are exacerbated by the lack of skilled professionals with experience in inclusive hiring and management, which prevents companies from effectively increasing PWD representation on software development teams. Inspired by the strategy adopted in some technology companies that attract talent through hackathons and training courses, this paper reports the experience of Zup Innovation, a Brazilian software company, in hosting a fully remote affirmative hackathon with 50 participants to attract PWD developers. This event resulted in 10 new hires and 146 people added to the company's talent pool. Through surveys with participants, we gathered attendees' perceptions and experiences, aiming to improve future hackathons and similar initiatives by providing insights on accessibility and collaboration. Our findings offer lessons for other companies seeking to address similar challenges and promote greater inclusion in tech teams.
Am I Infected? Lessons from Operating a Large-Scale IoT Security Diagnostic Service
2025-01-13
Takayuki Sasaki, Tomoya Inazawa, Youhei Yamaguchi, Simon Parkin, Michel van Eeten, Katsunari Yoshioka, Tsutomu Matsumoto
There is an expectation that users of home IoT devices will be able to secure those devices, but they may lack information about what they need to do. In February 2022, we launched a web service that scans users' IoT devices to determine how secure they are. The service aims to diagnose and remediate vulnerabilities and malware infections of IoT devices of Japanese users. This paper reports on findings from operating this service drawn from three studies: (1) the engagement of 114,747 users between February, 2022 - May, 2024; (2) a large-scale evaluation survey among service users (n=4,103), and; (3) an investigation and targeted survey (n=90) around the remediation actions of users of non-secure devices. During the operation, we notified 417 (0.36%) users that one or more of their devices were detected as vulnerable, and 171 (0.15%) users that one of their devices was infected with malware. The service found no issues for 99% of users. Still, 96% of all users evaluated the service positively, most often for it providing reassurance, being free of charge, and short diagnosis time. Of the 171 users with malware infections, 67 returned to the service later for a new check, with 59 showing improvement. Of the 417 users with vulnerable devices, 151 users revisited and re-diagnosed, where 75 showed improvement. We report on lessons learned, including a consideration of the capabilities that non-expert users will assume of a security scan.
Lifelong learning, also known as continual or incremental learning, is a crucial component for advancing Artificial General Intelligence (AGI) by enabling systems to continuously adapt in dynamic environments. While large language models (LLMs) have demonstrated impressive capabilities in natural language processing, existing LLM agents are typically designed for static systems and lack the ability to adapt over time in response to new challenges. This survey is the first to systematically summarize the potential techniques for incorporating lifelong learning into LLM-based agents. We categorize the core components of these agents into three modules: the perception module for multimodal input integration, the memory module for storing and retrieving evolving knowledge, and the action module for grounded interactions with the dynamic environment. We highlight how these pillars collectively enable continuous adaptation, mitigate catastrophic forgetting, and improve long-term performance. This survey provides a roadmap for researchers and practitioners working to develop lifelong learning capabilities in LLM agents, offering insights into emerging trends, evaluation metrics, and application scenarios. Relevant literature and resources are available at \href{this url}{https://github.com/qianlima-lab/awesome-lifelong-llm-agent}.
An Extended Survey and a Comparison Framework for Dataflow Models of Computation and Communication
2025-01-13
Guillaume Roumage, Selma Azaiez, Cyril Faure, Stéphane Louise
Dataflow Model of Computation and Communications (DF MoCCs) is a formalism used to specify the behavior of Cyber-Physical Systems (CPSs). DF MoCCs are widely used in the design of CPSs, as they provide a high-level of abstraction to specify the system's behavior. DF MoCCs rules give semantics to a dataflow specification of a CPS, and static analysis algorithms rely on these semantics to guarantee safety properties of the dataflow specification, such as bounded memory usage and deadlock freeness. A wide range of DF MoCCs exists, each with its own characteristics and static analyses. This paper presents a survey of those DF MoCCs and a classification in eight categories. In addition, DF MoCCs are characterized by a comprehensive list of features and static analyses, which reflect their expressiveness and analyzability. Based on this characterization, a framework is proposed to compare the expressiveness and the analyzability of DF MoCCs quantitatively.
Review and analysis of thermophysical properties of a sulfuric acid-water electrolyte
2025-01-13
Laura Oca, Jose Miguel Campillo, Mounir M. Bou-Ali
In this work, we have performed a critical review of the thermophysical properties of sulfuric acid-water mixtures. The thermophysical properties analyzed are density, viscosity, refraction index, thermal expansion coefficient, and mass expansion coefficient. The density of the sulfuric acid-water mixture has been measured for different mass fractions, w = 0.1-0.4, over a broad temperatura range, T = 273.15-333.15 K. A new parametrization of the density has been performed using a least-squares method. In this parametrization, the number of coefficients has been reduced with respect to previous works. Moreover, this new equation has been analyzed in an extended region of temperature and concentration in the following ranges: w = 0-0.1 and T = 233.15-373.15 K. The coefficients of thermal and mass expansion have also been calculated experimentally, and a linear relation of the density has been determined as a function of the variations in temperature and concentration. In addition, viscosity and refraction index measurements have been performed at 298.15 and 293.15 K, respectively. Our results have been checked against measurements in the literature (review) and show good correspondence.
MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework
2025-01-13
Ping Guo, Cheng Gong, Xi Lin, Fei Liu, Zhichao Lu, Qingfu Zhang, Zhenkun Wang
Crafting adversarial examples is crucial for evaluating and enhancing the robustness of Deep Neural Networks (DNNs), presenting a challenge equivalent to maximizing a non-differentiable 0-1 loss function. However, existing single objective methods, namely adversarial attacks focus on a surrogate loss function, do not fully harness the benefits of engaging multiple loss functions, as a result of insufficient understanding of their synergistic and conflicting nature. To overcome these limitations, we propose the Multi-Objective Set-based Attack (MOS Attack), a novel adversarial attack framework leveraging multiple loss functions and automatically uncovering their interrelations. The MOS Attack adopts a set-based multi-objective optimization strategy, enabling the incorporation of numerous loss functions without additional parameters. It also automatically mines synergistic patterns among various losses, facilitating the generation of potent adversarial attacks with fewer objectives. Extensive experiments have shown that our MOS Attack outperforms single-objective attacks. Furthermore, by harnessing the identified synergistic patterns, MOS Attack continues to show superior results with a reduced number of loss functions.
Eigenstate Thermalization Hypothesis: A Short Review
2025-01-13
Mohsen Alishahiha, Mohammad Javad Vasli
Understanding how an isolated quantum system evolves toward a thermal state from an initial state far from equilibrium such as one prepared by a global quantum quench has attracted significant interest in recent years. This phenomenon can be elucidated through the Eigenstate Thermalization Hypothesis (ETH), which has had a profound impact across various fields, from high-energy physics to condensed matter physics. The purpose of this review article is to present the fundamental concepts of quantum equilibrium and the ETH to a broad audience within the physics community, particularly for those in high-energy physics who seek a comprehensive understanding of these important topics.
Teaching Empirical Research Methods in Software Engineering: An Editorial Introduction
2025-01-13
Daniel Mendez, Paris Avgeriou, Marcos Kalinowski, Nauman bin Ali
Empirical Software Engineering has received much attention in recent years and became a de-facto standard for scientific practice in Software Engineering. However, while extensive guidelines are nowadays available for designing, conducting, reporting, and reviewing empirical studies, similar attention has not yet been paid to teaching empirical software engineering. Closing this gap is the scope of this edited book. In the following editorial introduction, we, the editors, set the foundation by laying out the larger context of the discipline for a positioning of the remainder of this book.
Epitaxial thin films of pyrochlore iridates: a forward looking approach
2025-01-13
Araceli Gutiérrez-Llorente
Topological quantum materials that show strongly correlated electrons as well as topological order, for which spin-orbit coupling is a key ingredient, exhibit novel states of matter. One such example is the family of pyrochlore iridates, featuring strong spin-orbital coupling, strong electron interactions as well as geometric frustration, making them an ideal platform to study novel topological phases. High-quality epitaxial pyrochlore iridate films, although challenging to produce, provide a pathway to explore unconventional behaviours and unravel the intrinsic properties of these largely unexplored materials. Additionally, designing interfaces with specific properties is crucial to create multilayered devices that can achieve significant technological breakthroughs using topological states of these materials. This article reviews experimental work on epitaxial pyrochlore iridate thin films, discussing evidence of topological phases found in them. Future research directions are outlined, which include exploring the rich tunability offered by chemical doping, especially when combined with the design of epitaxial heterostructures.
Rotation periods of asteroids from light curves of TESS data
2025-01-13
D. E. Vavilov, B. Carry
Understanding the dynamical evolution of asteroids through the secular Yarkovsky effect requires the determination of many physical properties, including the rotation period. We propose a method aimed at obtaining a robust determination of the rotation period of asteroids, while avoiding the pitfalls of aliases. We applied this approach to thousands of asteroid light curves measured by the NASA TESS mission. We developed a robust period-analysis algorithm based on a Fourier series. Our approach includes a comparison of the results from multiple orders and tests on the number of extremes to identify and reject potential aliases. We also provide the uncertainty interval for the result as well as additional periods that may be plausible. We report the rotation period for 4521 asteroids within a precision of 10%. A comparison with the literature (whenever available) reveals a very good agreement and validates the approach presented here. Our approach also highlights cases for which the determination of the period should be considered invalid. The dataset presented here confirms the apparent small number of asteroids with a rotation between 50 and 100 h and correlated with diameter. The amplitude of the light curves is found to increase toward smaller diameters, as asteroids become less and less spherical. Finally, there is a systematic difference between the broad C and S complex in the amplitude-period, revealing the statistically lower density of C-types compared to S-type asteroids. Conclusions. Our approach to the determination of asteroid rotation period is based on simple concepts, yet it is nonetheless robust. It can be applied to large corpora of time series photometry, such as those extracted from exoplanet transit surveys.
CureGraph: Contrastive Multi-Modal Graph Representation Learning for Urban Living Circle Health Profiling and Prediction
2025-01-13
Jinlin Li, Xiao Zhou
The early detection and prediction of health status decline among the elderly at the neighborhood level are of great significance for urban planning and public health policymaking. While existing studies affirm the connection between living environments and health outcomes, most rely on single data modalities or simplistic feature concatenation of multi-modal information, limiting their ability to comprehensively profile the health-oriented urban environments. To fill this gap, we propose CureGraph, a contrastive multi-modal representation learning framework for urban health prediction that employs graph-based techniques to infer the prevalence of common chronic diseases among the elderly within the urban living circles of each neighborhood. CureGraph leverages rich multi-modal information, including photos and textual reviews of residential areas and their surrounding points of interest, to generate urban neighborhood embeddings. By integrating pre-trained visual and textual encoders with graph modeling techniques, CureGraph captures cross-modal spatial dependencies, offering a comprehensive understanding of urban environments tailored to elderly health considerations. Extensive experiments on real-world datasets demonstrate that CureGraph improves the best baseline by $28\%$ on average in terms of $R^2$ across elderly disease risk prediction tasks. Moreover, the model enables the identification of stage-wise chronic disease progression and supports comparative public health analysis across neighborhoods, offering actionable insights for sustainable urban development and enhanced quality of life. The code is publicly available at https://github.com/jinlin2021/CureGraph.
The LHC is not only the most powerful collider built to date but also the source of an intense beam of the most energetic neutrinos ever produced by humankind. After nearly 15 years of LHC operation, these neutrinos have been observed for the first time by the FASER and SND@LHC experiments. This breakthrough marks the dawn of a new field: collider neutrino physics. Further neutrino measurements at the LHC will offer novel opportunities to advance neutrino physics, constrain the strong interaction in uncharted kinematic regimes, provide critical input for addressing outstanding questions in astroparticle physics, and search for phenomena predicted by scenarios of physics beyond the Standard Model. This proceeding reviews the existing and proposed neutrino detectors, presents their first results, and summarizes their physics potential.
The diverse physical origins of stars in the dynamically hot bulge: CALIFA vs. IllustrisTNG
2025-01-13
Le Zhang, Ling Zhu, Annalisa Pillepich, Min Du, Fangzhou Jiang, Jesús Falcón-Barroso
We compare the internal stellar structures of central galaxies in the TNG50 and TNG100 simulations and field galaxies in the CALIFA survey. The luminosity fractions of the dynamically cold, warm, and hot components in both TNG50 and TNG100 galaxies exhibit general consistency with those observed in CALIFA galaxies. For example, they all exhibit a minimum luminosity fraction of the dynamically hot component in galaxies with intermediate stellar masses, and the morphology of each orbital component in the TNG50 and TNG100 galaxies closely resembles that found in the CALIFA galaxies. We therefore use the simulations to quantify the physical origins of the different components, focusing on the dynamically hot component in TNG50. We identify three primary regimes and thus physical processes: (1) in low mass galaxies that have not experienced major mergers, stars are born with a wide range of circularity distributions and have remained relatively unchanged until the present day. Consequently, hot stars in such galaxies at redshift 0 are predominantly born hot. (2) In higher mass galaxies lacking major mergers, most stars are initially born cold but are subsequently heated through secular evolution. (3) In galaxies across the entire mass range, mergers, if they occurred, significantly increased the hot orbital fraction. As a result, the dynamically hot bulge within $R_e$ of present-day galaxies does not indicate their past merger histories; instead, the hot stars in the outer regions are mostly heated or accreted by mergers, thus indicating galaxy merger history. The massive galaxies are initially born with cold, rotationally supported structures, consistent with recent observations from the James Webb Space Telescope (JWST) regarding high-redshift galaxies.
Duality identities in random matrix theory for products and powers of characteristic polynomials, and for moments, are reviewed. The structure of a typical duality identity for the average of a positive integer power $k$ of the characteristic polynomial for particular ensemble of $N \times N$ matrices is that it is expressed as the average of the power $N$ of the characteristic polynomial of some other ensemble of random matrices, now of size $k \times k$. With only a few exceptions, such dualities involve (the $\beta$ generalised) classical Gaussian, Laguerre and Jacobi ensembles Hermitian ensembles, the circular Jacobi ensemble, or the various non-Hermitian ensembles relating to Ginibre random matrices. In the case of unitary symmetry in the Hermitian case, they can be studied using the determinantal structure. The $\beta$ generalised case requires the use of Jack polynomial theory, and in particular Jack polynomial based hypergeometric functions. Applications to the computation of the scaling limit of various $\beta$ ensemble correlation and distribution functions are also reviewed. The non-Hermitian case relies on the particular cases of Jack polynomials corresponding to zonal polynomials, and their integration properties when their arguments are eigenvalues of certain matrices. The main tool to study dualities for moments of the spectral density, and generalisations, is the loop equations.
The large scale structure of complete $4$-manifolds with nonnegative Ricci curvature and Euclidean volume growth
2025-01-13
Daniele Semola
We survey the implications of our joint work with E. Bru\`e and A. Pigati on the structure of blow-downs for a smooth, complete, Riemannian $4$-manifold with nonnegative Ricci curvature and Euclidean volume growth. Very imprecisely, any such manifold looks like a cone over a spherical space form at infinity. We present some open questions and discuss possible future directions along the way.
Visual Question Answering (VQA) is an interdisciplinary field that bridges the gap between computer vision (CV) and natural language processing(NLP), enabling Artificial Intelligence(AI) systems to answer questions about images. Since its inception in 2015, VQA has rapidly evolved, driven by advances in deep learning, attention mechanisms, and transformer-based models. This survey traces the journey of VQA from its early days, through major breakthroughs, such as attention mechanisms, compositional reasoning, and the rise of vision-language pre-training methods. We highlight key models, datasets, and techniques that shaped the development of VQA systems, emphasizing the pivotal role of transformer architectures and multimodal pre-training in driving recent progress. Additionally, we explore specialized applications of VQA in domains like healthcare and discuss ongoing challenges, such as dataset bias, model interpretability, and the need for common-sense reasoning. Lastly, we discuss the emerging trends in large multimodal language models and the integration of external knowledge, offering insights into the future directions of VQA. This paper aims to provide a comprehensive overview of the evolution of VQA, highlighting both its current state and potential advancements.
A Deep Search for a Strong Diffuse Interstellar Band in the Circumgalactic Medium
2025-01-13
Chih-Yuan Chang, Ting-Wen Lan
We investigate the absorption signals of a strong diffuse interstellar band, DIB$\lambda4430$, in the circumgalactic medium (CGM) traced by MgII absorption lines. To this end, we make use of approximately 60,000 MgII absorption line spectra within $0.4
From trees to traits: A review of advances in PhyloG2P methods and future directions
2025-01-13
Arlie R. Macdonald, Maddie E. James, Jonathan D. Mitchell, Barbara R. Holland
Mapping genotypes to phenotypes (G2P) is a fundamental goal in biology. So called PhyloG2P methods are a relatively new set of tools that leverage replicated evolution in phylogenetically independent lineages to identify genomic regions associated with traits of interest. Here, we review recent developments in PhyloG2P methods, focusing on three key areas: methods based on replicated amino acid substitutions, methods detecting changes in evolutionary rates, and methods analysing gene duplication and loss. We discuss how the definition and measurement of traits impacts the utility of these methods, arguing that focusing on simple rather than compound traits will lead to more meaningful genotype-phenotype associations. We advocate for the use of methods that work with continuous traits directly rather than collapsing them to binary representations. We examine the strengths and limitations of different approaches to modeling genetic replication, highlighting the importance of explicit modeling of evolutionary processes. Finally, we outline promising future directions, including the integration of population-level variation, as well as epigenetic and environmental information. No one method is likely to identify all genomic regions of interest, so we encourage users to apply multiple methods that are capable of detecting a wide range of associations. The overall aim of this review is to provide practitioners a roadmap for understanding and applying PhyloG2P methods.
A Weighted Similarity Metric for Community Detection in Sparse Data
2025-01-13
Yong Zhang, Eric Herrison Gyamfi
Many Natural Language Processing (NLP) related applications involves topics and sentiments derived from short documents such as consumer reviews and social media posts. Topics and sentiments of short documents are highly sparse because a short document generally covers a few topics among hundreds of candidates. Imputation of missing data is sometimes hard to justify and also often unpractical in highly sparse data. We developed a method for calculating a weighted similarity for highly sparse data without imputation. This weighted similarity is consist of three components to capture similarities based on both existence and lack of common properties and pattern of missing values. As a case study, we used a community detection algorithm and this weighted similarity to group different shampoo brands based on sparse topic sentiments derived from short consumer reviews. Compared with traditional imputation and similarity measures, the weighted similarity shows better performance in both general community structures and average community qualities. The performance is consistent and robust across metrics and community complexities.
This is the abstract of a series of lectures given during the XIIIth School on Geometry and Physics, Bialystok (Poland), in July 2024. In this minicourse, we first examine the algebraic aspects of barycentric algebras. Then, we focus on various examples and applications, reviewing the pertinence of the barycentric algebra structure.
Graph Contrastive Learning on Multi-label Classification for Recommendations
2025-01-13
Jiayang Wu, Wensheng Gan, Huashen Lu, Philip S. Yu
In business analysis, providing effective recommendations is essential for enhancing company profits. The utilization of graph-based structures, such as bipartite graphs, has gained popularity for their ability to analyze complex data relationships. Link prediction is crucial for recommending specific items to users. Traditional methods in this area often involve identifying patterns in the graph structure or using representational techniques like graph neural networks (GNNs). However, these approaches encounter difficulties as the volume of data increases. To address these challenges, we propose a model called Graph Contrastive Learning for Multi-label Classification (MCGCL). MCGCL leverages contrastive learning to enhance recommendation effectiveness. The model incorporates two training stages: a main task and a subtask. The main task is holistic user-item graph learning to capture user-item relationships. The homogeneous user-user (item-item) subgraph is constructed to capture user-user and item-item relationships in the subtask. We assessed the performance using real-world datasets from Amazon Reviews in multi-label classification tasks. Comparative experiments with state-of-the-art methods confirm the effectiveness of MCGCL, highlighting its potential for improving recommendation systems.
Data Enrichment Work and AI Labor in Latin America and the Caribbean
2025-01-13
Gianna Williams, Maya De Los Santos, Alexandra To, Saiph Savage
The global AI surge demands crowdworkers from diverse languages and cultures. They are pivotal in labeling data for enabling global AI systems. Despite global significance, research has primarily focused on understanding the perspectives and experiences of US and India crowdworkers, leaving a notable gap. To bridge this, we conducted a survey with 100 crowdworkers across 16 Latin American and Caribbean countries. We discovered that these workers exhibited pride and respect for their digital labor, with strong support and admiration from their families. Notably, crowd work was also seen as a stepping stone to financial and professional independence. Surprisingly, despite wanting more connection, these workers also felt isolated from peers and doubtful of others' labor quality. They resisted collaboration and gender-based tools, valuing gender-neutrality. Our work advances HCI understanding of Latin American and Caribbean crowdwork, offering insights for digital resistance tools for the region.
Boosting Sclera Segmentation through Semi-supervised Learning with Fewer Labels
2025-01-13
Guanjun Wang, Lu Wang, Ning Niu, Qiaoyi Yao, Yixuan Wang, Sufen Ren, Shengchao Chen
Sclera segmentation is crucial for developing automatic eye-related medical computer-aided diagnostic systems, as well as for personal identification and verification, because the sclera contains distinct personal features. Deep learning-based sclera segmentation has achieved significant success compared to traditional methods that rely on hand-crafted features, primarily because it can autonomously extract critical output-related features without the need to consider potential physical constraints. However, achieving accurate sclera segmentation using these methods is challenging due to the scarcity of high-quality, fully labeled datasets, which depend on costly, labor-intensive medical acquisition and expertise. To address this challenge, this paper introduces a novel sclera segmentation framework that excels with limited labeled samples. Specifically, we employ a semi-supervised learning method that integrates domain-specific improvements and image-based spatial transformations to enhance segmentation performance. Additionally, we have developed a real-world eye diagnosis dataset to enrich the evaluation process. Extensive experiments on our dataset and two additional public datasets demonstrate the effectiveness and superiority of our proposed method, especially with significantly fewer labeled samples.
A Review on the Security Vulnerabilities of the IoMT against Malware Attacks and DDoS
2025-01-13
Lily Dzamesi, Nelly Elsayed
The Internet of Medical Things (IoMT) has transformed the healthcare industry by connecting medical devices in monitoring treatment outcomes of patients. This increased connectivity has resulted to significant security vulnerabilities in the case of malware and Distributed Denial of Service (DDoS) attacks. This literature review examines the vulnerabilities of IoMT devices, focusing on critical threats and exploring mitigation strategies. We conducted a comprehensive search across leading databases such as ACM Digital Library, IEEE Xplore, and Elsevier to analyze peer-reviewed studies published within the last five years (from 2019 to 2024). The review shows that inadequate encryption protocols, weak authentication methods, and irregular firmware updates are the main causes of risks associated with IoMT devices. We have identified emerging solutions like machine learning algorithms, blockchain technology, and edge computing as promising approaches to enhance IoMT security. This review emphasizes the pressing need to develop lightweight security measures and standardized protocols to protect patient data and ensure the integrity of healthcare services.
Overview of the latest ATLAS and ATLAS-AFP photoproduction results
2025-01-13
Andre Sopczak
A key focus of the physics program at the LHC is the study of head-on proton-proton collisions. However, an important class of physics can be studied for cases where the protons narrowly miss one another and remain intact. In such cases, the electromagnetic fields surrounding the protons can interact producing high-energy photon-photon collisions. Alternatively, interactions mediated by the strong force can also result in intact forward scattered protons, providing probes of quantum chromodynamics (QCD). In order to aid identification and provide unique information about these rare interactions, instrumentation to detect and measure protons scattered through very small angles is installed in the beam pipe far downstream of the interaction point. We review photoproduction results from data collected with the ATLAS Forward Proton (AFP) and Absolute Luminosity For ATLAS (ALFA) detectors in proton-proton and heavy ion collisions.
An Investigation of Experiences Engaging the Margins in Data-Centric Innovation
2025-01-13
Gabriella Thompson, Ebtesam Al Haque, Paulette Blanc, Meme Styles, Denae Ford, Angela D. R. Smith, Brittany Johnson
Data-centric technologies provide exciting opportunities, but recent research has shown how lack of representation in datasets, often as a result of systemic inequities and socioeconomic disparities, can produce inequitable outcomes that can exclude or harm certain demographics. In this paper, we discuss preliminary insights from an ongoing effort aimed at better understanding barriers to equitable data-centric innovation. We report findings from a survey of 261 technologists and researchers who use data in their work regarding their experiences seeking adequate, representative datasets. Our findings suggest that age and identity play a significant role in the seeking and selection of representative datasets, warranting further investigation into these aspects of data-centric research and development.
Practitioners use Infrastructure as Code (IaC) scripts to efficiently configure IT infrastructures through machine-readable definition files. However, during the development of these scripts, some code patterns or deployment choices may lead to sustainability issues like inefficient resource utilization or redundant provisioning for example. We call this type of patterns sustainability smells. These inefficiencies pose significant environmental and financial challenges, given the growing scale of cloud computing. This research focuses on Terraform, a widely adopted IaC tool. Our study involves defining seven sustainability smells and validating them through a survey with 19 IaC practitioners. We utilized a dataset of 28,327 Terraform scripts from 395 open-source repositories. We performed a detailed qualitative analysis of a randomly sampled 1,860 Terraform scripts from the original dataset to identify code patterns that correspond to the sustainability smells and used the other 26,467 Terraform scripts to study the prevalence of the defined sustainability smells. Our results indicate varying prevalence rates of these smells across the dataset. The most prevalent smell is Monolithic Infrastructure, which appears in 9.67\% of the scripts. Additionally, our findings highlight the complexity of conducting root cause analysis for sustainability issues, as these smells often arise from a confluence of script structures, configuration choices, and deployment contexts.
Towards nonlinearity. The p-regularity theory. Applications and developments
2025-01-13
E. Bednarczuk, O. Brezhneva, K. Leśniewski, A. Prusińska, A. Tret'yakov
We present recent advances in the analysis of nonlinear equations with singular operators and nonlinear optimization problems with constraints given by singular mappings. The results are obtained within the framework of $p$-regularity theory, which has developed successfully over the last forty years. We illustrate the theory with its applications to degenerate problems in various areas of mathematics. In particular, we address the problem of describing the tangent cone to the solution set of nonlinear equations in a singular case. The structure of p-factor operators is used to propose optimality conditions and construct numerical methods for solving degenerate nonlinear equations and optimization problems. The methods presented in the paper can be considered as the first numerical approaches targeting solutions of degenerate problems, such as the Van der Pol differential equation, boundary-value problems with a small parameter, partial differential equations where Poincar\'e's method of small parameter fails, nonlinear degenerate dynamical systems, and others. There are various practical applications for the theory of p-regularity, including structural engineering, composite materials, and material design. For instance, the theory can be applied to analyze the behavior of materials with irregular or complex properties. By considering higher-order derivatives, it becomes possible to model and predict the response of materials to external forces, such as stress or temperature variations. In geophysics, the $p$-regularity theory can be utilized to analyze and interpret complex data obtained from seismic surveys, gravity measurements, or electromagnetic surveys. The theory also finds applications in the analysis of nonlinear differential equations arising in control systems, geometric and topological analysis, biomechanics, and many other fields.
Deep Neural Networks (DNNs) have grown increasingly large in size to achieve state of the art performance across a wide range of tasks. However, their high computational requirements make them less suitable for resource-constrained applications. Also, real-world datasets often consist of a mixture of easy and complex samples, necessitating adaptive inference mechanisms that account for sample difficulty. Early exit strategies offer a promising solution by enabling adaptive inference, where simpler samples are classified using the initial layers of the DNN, thereby accelerating the overall inference process. By attaching classifiers at different layers, early exit methods not only reduce inference latency but also improve the model robustness against adversarial attacks. This paper presents a comprehensive survey of early exit methods and their applications in NLP.
PSZ2 G181.06+48.47 I: X-ray exploration of a low-mass cluster with exceptionally-distant radio relics
2025-01-13
Andra Stroe, Kamlesh Rajpurohit, Zhenlin Zhu, Lorenzo Lovisari, Aurora Simionescu, Ewan O'Sullivan, Scott Randall, William Forman, Hiroki Akamatsu, Reinout van Weeren, M. James Jee, Wonki Lee, Hyejeon Cho, Eunmo Ahn, Kyle Finner
Relics are diffuse, highly-polarized radio sources that trace merger-driven shocks at the periphery of merging galaxy clusters. The LOFAR survey recently discovered a rare example of double relics in the low-mass cluster PSZ2 G181.06+48.47. Through a detailed exploration of new Chandra and XMM-Newton observations, we reveal that PSZ2 G181.06+48.47 has a lower mass ($M_{500,X}=2.57^{+0.37}_{-0.38}\times10^{14}$ M$_{\odot}$) than previously thought. Despite its cool global temperature of $kT_{500}=3.86^{+0.27}_{-0.29}$ keV, PSZ2 G181.06+48.47 is one of the most disturbed clusters in the Planck sample, with a complex morphological and thermodynamic structure. We discover a set of three discontinuities within <500 kpc of the cluster center, and, from a surface brightness analysis, place $5\sigma$ upper limits of $M_{NE}<1.43$ and $M_{SW}<1.57$ for any shock associated with the relic locations. We also revise established scaling relations for double radio-relics by adding 12 new systems not included in previous work. The PSZ2 G181.06+48.47 relics have the widest separation (scaled for $r_{500}$) of all known double-relic systems. The exceptional distance from the cluster center ($>r_{200}$), indicates the relics may be associated with shocks in the ``run-away" phase. We propose that this late-stage, post-apocenter merger is captured as the two subclusters with a mass ratio of 1.2-1.4 fall back into each other. The outer relic shocks were likely produced at the first core passage, while the inner discontinuities are associated with the second infall.
The uncommon intracluster medium features of the first massive clusters selected independently of their baryon content
2025-01-13
S. Andreon, M. Radovich, A. Moretti, F. -X. Desert, T. Hamana, M. Pizzardo, C. Romero, H. Roussel, G. Trinchieri
Our current knowledge of the thermodynamic properties of galaxy clusters comes primarily from detailed studies of clusters selected by their minority components: hot baryons. Most of these studies select the clusters using the component that is being investigated, the intracluster medium (ICM), making the sample choice prone to selection effects. Weak-gravitational lensing allows us to select clusters by the total mass component and, being independent of the type of matter, makes the sample choice unbiased with respect to the baryon content. In this paper, we study four galaxy clusters at intermediate redshift ($0.252\sigma$) features, while we observed on average two rare features in each one of the seven explored properties: richness, core-excised luminosity, Compton parameter, pressure and electron pressure profiles, and central values of them. The abundance of rare and unique features in such a small sample indicates a fundamental bias in our knowledge of the thermodynamic properties of clusters when derived from ICM-selected samples.
Inadequate turbulent support in low-metallicity molecular clouds
2025-01-13
Lingrui Lin, Zhi-Yu Zhang, Junzhi Wang, Padelis P. Papadopoulos, Yong Shi, Yan Gong, Yan Sun, Yichen Sun, Thomas G. Bisbas, Donatella Romano, Di Li, Hauyu Baobab Liu, Keping Qiu, Lijie Liu, Gan Luo, Chao-Wei Tsai, Jingwen Wu, Siyi Feng, Bo Zhang
The dynamic properties of molecular clouds are set by the interplay of their self-gravity, turbulence, external pressure and magnetic fields. Extended surveys of Galactic molecular clouds typically find that their kinetic energy ($E_{\rm k}$) counterbalances their self-gravitational energy ($E_{\rm g}$), setting their virial parameter $\alpha_{\rm vir}=2E_{\rm k}/|E_{\rm g}|\approx1$. However, past studies either have been biased by the use of optically-thick lines or have been limited within the solar neighborhood and the inner Galaxy (Galactocentric radius $R_{\rm gc}
A novel Bayesian approach for decomposing the radio emission of quasars: II. Link between quasar radio emission and black hole mass
2025-01-13
B. -H. Yue, K. J. Duncan, P. N. Best, M. I. Arnaudova, L. K. Morabito, J. W. Petley, H. J. A. Röttgering, S. Shenoy, D. J. B. Smith
Whether the mass of supermassive black hole ($M_\mathrm{BH}$) is directly linked to the quasar radio luminosity remains a long-debated issue, and understanding the role of $M_\mathrm{BH}$ in the evolution of quasars is pivotal to unveiling the mechanism of AGN feedback. In this work, based on a two-component Bayesian model, we examine how $M_\mathrm{BH}$ affects the radio emission from quasars, separating the contributions from host galaxy star formation (SF) and AGN activity. By modelling the radio flux density distribution of Sloan Digital Sky Survey (SDSS) quasars from the LOFAR Two-metre Sky Survey Data Release 2, we find no correlation between $M_\mathrm{BH}$ and SF rate (SFR) at any mass for quasars at a given redshift and bolometric luminosity. The same holds for AGN activity across most $M_\mathrm{BH}$ values; however, quasars with the top 20\% most massive SMBHs are 2 to 3 times more likely to host strong radio jets than those with lower-mass SMBHs at similar redshift and luminosity. We suggest defining radio quasar populations by their AGN and SF contributions instead of radio loudness; our new definition unifies previously divergent observational results on the role of $M_\mathrm{BH}$ in quasar radio emissions. We further demonstrate that this radio enhancement in quasars with the 20\% most massive SMBHs affects only the $\sim5\%$ most radio bright quasars at a given redshift and bolometric luminosity. We discuss possible physical origins of this radio excess in the most massive and radio-bright quasar population, which remains an interest for future study.
Cross-correlating the patchy screening and kinetic Sunyaev-Zel'dovich effects as a new probe of reionization
2025-01-13
Darby Kramer, Alexander van Engelen, Christopher Cain, Niall MacCrann, Hy Trac, Skylar Grayson, Evan Scannapieco, Blake Sherwin
The kinetic Sunyaev-Zel'dovich effect (kSZ) and patchy screening effect are two complementary cosmic microwave background (CMB) probes of the reionization era. The kSZ effect is a relatively strong signal, but is difficult to disentangle from other sources of temperature anisotropy, whereas patchy screening is weaker but can be reconstructed using the cleaner polarization channel. Here, we explore the potential of using upcoming CMB surveys to correlate a reconstructed map of patchy screening with (the square of) the kSZ map, and what a detection of this cross-correlation would mean for reionization science. To do this, we use simulations and theory to quantify the contributions to this signal from different redshifts. We then use the expected survey properties for CMB-S4 and CMB-HD to make detection forecasts before exploring how the signal depends on reionization parameters. We forecast that CMB-S4 will obtain a hint of this signal at up to 1.8$\sigma$ and CMB-HD will detect it at up to 14$\sigma$. We find that this signal contains unique information about the timing and morphology of reionization, particularly sensitivity to the first half of reionization and to the bispectrum of the ionized gas distribution.
Discovery of Ancient Globular Cluster Candidates in The Relic, a Quiescent Galaxy at z=2.5
2025-01-13
Katherine E. Whitaker, Sam E. Cutler, Rupali Chandar, Richard Pan, David J. Setton, Lukas J. Furtak, Rachel Bezanson, Ivo Labbé, Joel Leja, Katherine A. Suess, Bingjie Wang, John R. Weaver, Hakim Atek, Gabriel B. Brammer, Robert Feldmann, Natascha M. Förster Schreiber, Karl Glazebrook, Anna de Graaff, Jenny E. Greene, Gourav Khullar, Danilo Marchesini, Michael V. Maseda, Tim B. Miller, Houjun Mo, Lamiya A. Mowla, Themiya Nanayakkara, Erica J. Nelson, Sedona H. Price, Francesca Rizzo, Pieter van Dokkum, Christina C. Williams, Yanzhe Zhang, Yunchong Zhang, Adi Zitrin
Globular clusters (GCs) are some of the oldest bound structures in the Universe, holding clues to the earliest epochs of star formation and galaxy assembly. However, accurate age measurements of ancient clusters are challenging due to the age-metallicity degeneracy. Here, we report the discovery of 42 compact stellar systems within the 'Relic', a massive, quiescent galaxy at $z=2.53$. The Relic resides in an over-density behind the Abell 2744 cluster, with a prominent tidal tail extending towards two low-mass companions. Using deep data from the UNCOVER/MegaScience JWST Surveys, we find that clusters formed in age intervals ranging from 8 Myr up to $\sim2$ Gyr, suggesting a rich formation history starting at $z\sim10$. While the cluster-based star formation history is broadly consistent with the high past star formation rates derived from the diffuse host galaxy light, one potential discrepancy is a tentative $\sim2-3\times$ higher rate in the cluster population for the past Gyr. Taken together with the spatial distribution and low inferred metallicities of these young-to-intermediate age clusters, we may be seeing direct evidence for the accretion of star clusters in addition to their early in-situ formation. The cluster masses are high, $\sim10^6-10^7~M_{\odot}$, which may explain why we are able to detect them around this likely post-merger galaxy. Overall, the Relic clusters are consistent with being precursors of the most massive present-day GCs. This unique laboratory enables the first connection between long-lived, high-redshift clusters and local stellar populations, offering insights into the early stages of GC evolution and the broader processes of galaxy assembly.
The claim that at the so-called Planck scale our current physics breaks down and a new theory of quantum gravity is required is ubiquitous, but the evidence is shakier than the confidence of those assertions warrants. In this paper, I survey five arguments in favour of this claim - based on dimensional analysis, quantum black holes, generalised uncertainty principles, the nonrenormalisability of quantum gravity, and theories beyond the standard model - but find that none of them succeeds. The argument from nonrenormalisability is the most convincing, yet it requires the unwarranted assumption that the same constant of action occurs in every quantum field theory. Therefore, our theories don't (yet) predict that quantum gravity happens at the Planck scale.
4D fabrication of shape-changing systems for tissue engineering: state of the art and perspectives
2025-01-13
Lorenzo Bonetti, Giulia Scalet
In recent years, four-dimensional (4D) fabrication has emerged as a powerful technology capable of revolutionizing the field of tissue engineering. This technology represents a shift in perspective from traditional tissue engineering approaches, which generally rely on static-or passive-structures (e.g., scaffolds, constructs) unable of adapting to changes in biological environments. In contrast, 4D fabrication offers the unprecedented possibility of fabricating complex designs with spatiotemporal control over structure and function in response to environment stimuli, thus mimicking biological processes. In this review, an overview of the state of the art of 4D fabrication technology for the obtainment of cellularized constructs is presented, with a focus on shape-changing soft materials. First, the approaches to obtain cellularized constructs are introduced, also describing conventional and non-conventional fabrication techniques with their relative advantages and limitations. Next, the main families of shape-changing soft materials, namely shape-memory polymers and shape-memory hydrogels are discussed and their use in 4D fabrication in the field of tissue engineering is described. Ultimately, current challenges and proposed solutions are outlined, and valuable insights into future research directions of 4D fabrication for tissue engineering are provided to disclose its full potential.
We give a concise and pedagogical introduction to Feynman diagrams. After discussing a toy model which requires only undergraduate mathematics, we focus on relativistic quantum field theory. We review the derivation of Feynman rules from the Lagrangian of the theory and we discuss modern methods to compute tree and loop diagrams.
A Preliminary Survey of Semantic Descriptive Model for Images
2025-01-13
Chengxi Yan, Jie Jian, Yang Li
Considering the lack of a unified framework for image description and deep cultural analysis at the subject level in the field of Ancient Chinese Paintings (ACP), this study utilized the Beijing Palace Museum's ACP collections to develop a semantic model integrating the iconological theory with a new workflow for term extraction and mapping. Our findings underscore the model's effectiveness. SDM can be used to support further art-related knowledge organization and cultural exploration of ACPs.
An Adaptive Collocation Point Strategy For Physics Informed Neural Networks via the QR Discrete Empirical Interpolation Method
2025-01-13
Adrian Celaya, David Fuentes, Beatrice Riviere
Physics-informed neural networks (PINNs) have gained significant attention for solving forward and inverse problems related to partial differential equations (PDEs). While advancements in loss functions and network architectures have improved PINN accuracy, the impact of collocation point sampling on their performance remains underexplored. Fixed sampling methods, such as uniform random sampling and equispaced grids, can fail to capture critical regions with high solution gradients, limiting their effectiveness for complex PDEs. Adaptive methods, inspired by adaptive mesh refinement from traditional numerical methods, address this by dynamically updating collocation points during training but may overlook residual dynamics between updates, potentially losing valuable information. To overcome this limitation, we propose an adaptive collocation point selection strategy utilizing the QR Discrete Empirical Interpolation Method (QR-DEIM), a reduced-order modeling technique for efficiently approximating nonlinear functions. Our results on benchmark PDEs, including the wave, Allen-Cahn, and Burgers' equations, demonstrate that our QR-DEIM-based approach improves PINN accuracy compared to existing methods, offering a promising direction for adaptive collocation point strategies.
Fluid antenna multiple access (FAMA), enabled by the fluid antenna system (FAS), offers a new and straightforward solution to massive connectivity. Previous results on FAMA were primarily based on narrowband channels. This paper studies the adoption of FAMA within the fifth-generation (5G) orthogonal frequency division multiplexing (OFDM) framework, referred to as OFDM-FAMA, and evaluate its performance in broadband multipath channels. We first design the OFDM-FAMA system, taking into account 5G channel coding and OFDM modulation. Then the system's achievable rate is analyzed, and an algorithm to approximate the FAS configuration at each user is proposed based on the rate. Extensive link-level simulation results reveal that OFDM-FAMA can significantly improve the multiplexing gain over the OFDM system with fixed-position antenna (FPA) users, especially when robust channel coding is applied and the number of radio-frequency (RF) chains at each user is small.
AAS2RTO: Automated Alert Streams to Real-Time Observations: Preparing for rapid follow-up of transient objects in the era of LSST
2025-01-12
Aidan Sedgewick, Christa Gall, Luca Izzo, Adriano Agnello, Charlotte R. Angus, Jens Hjorth, Arthur Kadela
The upcoming Vera C. Rubin Legacy Survey of Space and Time (LSST) will discover tens of thousands of astrophysical transients per night, far outpacing available spectroscopic follow-up capabilities. Carefully prioritising candidates for follow-up observations will maximise the scientific return from small telescopes with a single-object spectrograph. We introduce AAS2RTO, an astrophysical transient candidate prioritisation tool written in Python. AAS2RTO is flexible in that any number of criteria that consider observed properties of transients can be implemented. The visibility of candidates from a given observing site is also considered. The prioritised list of candidates provided by AAS2RTO is continually updated when new transient data are made available. Therefore, it can be applied to observing campaigns with a wide variety of scientific motivations. AAS2RTO uses a greedy algorithm to prioritise candidates. Candidates are represented by a single numerical value, or `score'. Scores are computed by constructing simple numerical factors which individually consider the competing facets of a candidate which make it suitable for follow-up observation. AAS2RTO is currently configured to work primarily with photometric data from the Zwicky Transient Facility (ZTF), distributed by certified LSST community brokers. We provide an example of how AAS2RTO can be used by defining a set of criteria to prioritise observations of type Ia supernovae (SNe Ia) close to peak brightness, in preparation for observations with the spectrograph at the Danish-1.54m telescope. Using a sample of archival alerts from ZTF, we evaluate the criteria we have designed to estimate the number of SNe Ia that we will be able to observe with a 1.5m telescope. Finally, we evaluate the performance of our criteria when applied to mock LSST observations of SNe Ia.
OpenGERT: Open Source Automated Geometry Extraction with Geometric and Electromagnetic Sensitivity Analyses for Ray-Tracing Propagation Models
2025-01-12
Serhat Tadik, Rajib Bhattacharjea, Johnathan Corgan, David Johnson, Jacobus Van der Merwe, Gregory D. Durgin
Accurate RF propagation modeling in urban environments is critical for developing digital spectrum twins and optimizing wireless communication systems. We introduce OpenGERT, an open-source automated Geometry Extraction tool for Ray Tracing, which collects and processes terrain and building data from OpenStreetMap, Microsoft Global ML Building Footprints, and USGS elevation data. Using the Blender Python API, it creates detailed urban models for high-fidelity simulations with NVIDIA Sionna RT. We perform sensitivity analyses to examine how variations in building height, position, and electromagnetic material properties affect ray-tracing accuracy. Specifically, we present pairwise dispersion plots of channel statistics (path gain, mean excess delay, delay spread, link outage, and Rician K-factor) and investigate how their sensitivities change with distance from transmitters. We also visualize the variance of these statistics for selected transmitter locations to gain deeper insights. Our study covers Munich and Etoile scenes, each with 10 transmitter locations. For each location, we apply five types of perturbations: material, position, height, height-position, and all combined, with 50 perturbations each. Results show that small changes in permittivity and conductivity minimally affect channel statistics, whereas variations in building height and position significantly alter all statistics, even with noise standard deviations of 1 meter in height and 0.4 meters in position. These findings highlight the importance of precise environmental modeling for accurate propagation predictions, essential for digital spectrum twins and advanced communication networks. The code for geometry extraction and sensitivity analyses is available at github.com/serhatadik/OpenGERT/.
Harnessing Large Language Models for Disaster Management: A Survey
2025-01-12
Zhenyu Lei, Yushun Dong, Weiyu Li, Rong Ding, Qi Wang, Jundong Li
Large language models (LLMs) have revolutionized scientific research with their exceptional capabilities and transformed various fields. Among their practical applications, LLMs have been playing a crucial role in mitigating threats to human life, infrastructure, and the environment. Despite growing research in disaster LLMs, there remains a lack of systematic review and in-depth analysis of LLMs for natural disaster management. To address the gap, this paper presents a comprehensive survey of existing LLMs in natural disaster management, along with a taxonomy that categorizes existing works based on disaster phases and application scenarios. By collecting public datasets and identifying key challenges and opportunities, this study aims to guide the professional community in developing advanced LLMs for disaster management to enhance the resilience against natural disasters.
Physics-based numerical models have been the bedrock of atmospheric sciences for decades, offering robust solutions but often at the cost of significant computational resources. Deep learning (DL) models have emerged as powerful tools in meteorology, capable of analyzing complex weather and climate data by learning intricate dependencies and providing rapid predictions once trained. While these models demonstrate promising performance in weather prediction, often surpassing traditional physics-based methods, they still face critical challenges. This paper presents a comprehensive survey of recent deep learning and foundation models for weather prediction. We propose a taxonomy to classify existing models based on their training paradigms: deterministic predictive learning, probabilistic generative learning, and pre-training and fine-tuning. For each paradigm, we delve into the underlying model architectures, address major challenges, offer key insights, and propose targeted directions for future research. Furthermore, we explore real-world applications of these methods and provide a curated summary of open-source code repositories and widely used datasets, aiming to bridge research advancements with practical implementations while fostering open and trustworthy scientific practices in adopting cutting-edge artificial intelligence for weather prediction. The related sources are available at https://github.com/JimengShi/ DL-Foundation-Models-Weather.
Virtual Reality-Based Telerehabilitation for Upper Limb Recovery Post-Stroke: A Systematic Review of Design Principles, Monitoring, Safety, and Engagement Strategies
2025-01-12
Pedro Rodrigues, Claudia Quaresma, Maria Costa, Filipe Luz, Maria Micaela Fonseca
Stroke rehabilitation continues to face challenges in accessibility and patient engagement, where traditional approaches often fall short. Virtual reality (VR)-based telerehabilitation offers a promising avenue, by enabling home-based recovery through immersive environments and gamification. This systematic review evaluates current VR solutions for upper-limb post-stroke recovery, focusing on design principles, safety measures, patient-therapist communication, and strategies to promote motivation and adherence. Following PRISMA 2020 guidelines, a comprehensive search was conducted across PubMed, IEEE Xplore, and ScienceDirect. The review reveals a scarcity of studies meeting the inclusion criteria, possibly reflecting the challenges inherent in the current paradigm of VR telerehabilitation systems. Although these systems have potential to enhance accessibility and patient autonomy, they often lack standardized safety protocols and reliable real-time monitoring. Human-centered design principles are evident in some solutions, but inconsistent patient involvement during the development process limits their usability and clinical relevance. Furthermore, communication between patients and therapists remains constrained by technological barriers, although advancements in real-time feedback and adaptive systems offer promising solutions. This review underscores the potential of VR telerehabilitation to address critical needs in upper-limb stroke recovery while highlighting the importance of addressing existing limitations to ensure broader clinical implementation and improved patient outcomes.
Correcting Annotator Bias in Training Data: Population-Aligned Instance Replication (PAIR)
2025-01-12
Stephanie Eckman, Bolei Ma, Christoph Kern, Rob Chew, Barbara Plank, Frauke Kreuter
Models trained on crowdsourced labels may not reflect broader population views when annotator pools are not representative. Since collecting representative labels is challenging, we propose Population-Aligned Instance Replication (PAIR), a method to address this bias through statistical adjustment. Using a simulation study of hate speech and offensive language detection, we create two types of annotators with different labeling tendencies and generate datasets with varying proportions of the types. Models trained on unbalanced annotator pools show poor calibration compared to those trained on representative data. However, PAIR, which duplicates labels from underrepresented annotator groups to match population proportions, significantly reduces bias without requiring new data collection. These results suggest statistical techniques from survey research can help align model training with target populations even when representative annotator pools are unavailable. We conclude with three practical recommendations for improving training data quality.
This study introduces a novel method that employs tag annotation coupled with the ChatGPT language model to analyze student learning behaviors and generate personalized feedback. Central to this approach is the conversion of complex student data into an extensive set of tags, which are then decoded through tailored prompts to deliver constructive feedback that encourages rather than discourages students. This methodology focuses on accurately feeding student data into large language models and crafting prompts that enhance the constructive nature of feedback. The effectiveness of this approach was validated through surveys conducted with over 20 mathematics teachers, who confirmed the reliability of the generated reports. This method can be seamlessly integrated into intelligent adaptive learning systems or provided as a tool to significantly reduce the workload of teachers, providing accurate and timely feedback to students. By transforming raw educational data into interpretable tags, this method supports the provision of efficient and timely personalized learning feedback that offers constructive suggestions tailored to individual learner needs.
With the rapid growth of dynamic vision sensor (DVS) data, constructing a low-energy, efficient data retrieval system has become an urgent task. Hash learning is one of the most important retrieval technologies which can keep the distance between hash codes consistent with the distance between DVS data. As spiking neural networks (SNNs) can encode information through spikes, they demonstrate great potential in promoting energy efficiency. Based on the binary characteristics of SNNs, we first propose a novel supervised hashing method named Spikinghash with a hierarchical lightweight structure. Spiking WaveMixer (SWM) is deployed in shallow layers, utilizing a multilevel 3D discrete wavelet transform (3D-DWT) to decouple spatiotemporal features into various low-frequency and high frequency components, and then employing efficient spectral feature fusion. SWM can effectively capture the temporal dependencies and local spatial features. Spiking Self-Attention (SSA) is deployed in deeper layers to further extract global spatiotemporal information. We also design a hash layer utilizing binary characteristic of SNNs, which integrates information over multiple time steps to generate final hash codes. Furthermore, we propose a new dynamic soft similarity loss for SNNs, which utilizes membrane potentials to construct a learnable similarity matrix as soft labels to fully capture the similarity differences between classes and compensate information loss in SNNs, thereby improving retrieval performance. Experiments on multiple datasets demonstrate that Spikinghash can achieve state-of-the-art results with low energy consumption and fewer parameters.
Hold On! Is My Feedback Useful? Evaluating the Usefulness of Code Review Comments
2025-01-12
Sharif Ahmed, Nasir U. Eisty
Context: In collaborative software development, the peer code review process proves beneficial only when the reviewers provide useful comments. Objective: This paper investigates the usefulness of Code Review Comments (CR comments) through textual feature-based and featureless approaches. Method: We select three available datasets from both open-source and commercial projects. Additionally, we introduce new features from software and non-software domains. Moreover, we experiment with the presence of jargon, voice, and codes in CR comments and classify the usefulness of CR comments through featurization, bag-of-words, and transfer learning techniques. Results: Our models outperform the baseline by achieving state-of-the-art performance. Furthermore, the result demonstrates that the commercial gigantic LLM, GPT-4o, or non-commercial naive featureless approach, Bag-of-Word with TF-IDF, is more effective for predicting the usefulness of CR comments. Conclusion: The significant improvement in predicting usefulness solely from CR comments escalates research on this task. Our analyses portray the similarities and differences of domains, projects, datasets, models, and features for predicting the usefulness of CR comments.
Sensing and edge artificial intelligence (AI) are envisioned as two essential and interconnected functions in sixth-generation (6G) mobile networks. On the one hand, sensing-empowered applications rely on powerful AI models to extract features and understand semantics from ubiquitous wireless sensors. On the other hand, the massive amount of sensory data serves as the fuel to continuously refine edge AI models. This deep integration of sensing and edge AI has given rise to a new task-oriented paradigm known as integrated sensing and edge AI (ISEA), which features a holistic design approach to communication, AI computation, and sensing for optimal sensing-task performance. In this article, we present a comprehensive survey for ISEA. We first provide technical preliminaries for sensing, edge AI, and new communication paradigms in ISEA. Then, we study several use cases of ISEA to demonstrate its practical relevance and introduce current standardization and industrial progress. Next, the design principles, metrics, tradeoffs, and architectures of ISEA are established, followed by a thorough overview of ISEA techniques, including digital air interface, over-the-air computation, and advanced signal processing. Its interplay with various 6G advancements, e.g., new physical-layer and networking techniques, are presented. Finally, we present future research opportunities in ISEA, including the integration of foundation models, convergence of ISEA and integrated sensing and communications (ISAC), and ultra-low-latency ISEA.
The Harmonic Pitching NACA 0018 Airfoil in Low Reynolds Number Flow
2025-01-12
Krzysztof Rogowski, Maciej Śledziewski, Jan Michna
This study investigates the aerodynamic performance of the symmetric NACA 0018 airfoil under harmonic pitching motions at low Reynolds numbers, a regime characterized by the presence of laminar separation bubbles and their impact on aerodynamic forces. The analysis encompasses oscillation frequencies of 1 Hz, 2 Hz, and 13.3 Hz, with amplitudes of 4{\deg} and 8{\deg}, along with steady-state simulations conducted for angles of attack up to 20{\deg} to validate the numerical model. The results reveal that the Transition SST turbulence model provides improved predictions of aerodynamic forces at higher Reynolds numbers but struggles at lower Reynolds numbers, where laminar flow effects dominate. The inclusion of the 13.3 Hz frequency, relevant to Darrieus vertical-axis wind turbines, demonstrates the model's effectiveness in capturing dynamic hysteresis loops and reduced oscillations, contrasting with the \(k-\omega\) SST model. Comparisons with XFOIL further highlight challenges in accurately modeling laminar-to-turbulent transitions and dynamic flow phenomena. These findings offer valuable insights into the aerodynamic behavior of thick airfoils under low Reynolds number conditions and contribute to advancing the understanding of turbulence modeling, particularly in applications involving vertical-axis wind turbines.
BATSRUS GPU: Faster-than-Real-Time Magnetospheric Simulations with a Block-Adaptive Grid Code
2025-01-12
Yifu An, Yuxi Chen, Hongyang Zhou, Alexander Gaenko, Gábor Tóth
BATSRUS, our state-of-the-art extended magnetohydrodynamic code, is the most used and one of the most resource-consuming models in the Space Weather Modeling Framework. It has always been our objective to improve its efficiency and speed with emerging techniques, such as GPU acceleration. To utilize the GPU nodes on modern supercomputers, we port BATSRUS to GPUs with the OpenACC API. Porting the code to a single GPU requires rewriting and optimizing the most used functionalities of the original code into a new solver, which accounts for around 1% of the entire program in length. To port it to multiple GPUs, we implement a new message passing algorithm to support its unique block-adaptive grid feature. We conduct weak scaling tests on as many as 256 GPUs and find good performance. The program has 50-60% parallel efficiency on up to 256 GPUs, and up to 95% efficiency within a single node (4 GPUs). Running large problems on more than one node has reduced efficiency due to hardware bottlenecks. We also demonstrate our ability to run representative magnetospheric simulations on GPUs. The performance for a single A100 GPU is about the same as 270 AMD "Rome" CPU cores, and it runs 3.6 times faster than real time. The simulation can run 6.9 times faster than real time on four A100 GPUs.
ELIZA Reanimated: The world's first chatbot restored on the world's first time sharing system
2025-01-12
Rupert Lane, Anthony Hay, Arthur Schwarz, David M. Berry, Jeff Shrager
ELIZA, created by Joseph Weizenbaum at MIT in the early 1960s, is usually considered the world's first chatbot. It was developed in MAD-SLIP on MIT's CTSS, the world's first time-sharing system, on an IBM 7094. We discovered an original ELIZA printout in Prof. Weizenbaum's archives at MIT, including an early version of the famous DOCTOR script, a nearly complete version of the MAD-SLIP code, and various support functions in MAD and FAP. Here we describe the reanimation of this original ELIZA on a restored CTSS, itself running on an emulated IBM 7094. The entire stack is open source, so that any user of a unix-like OS can run the world's first chatbot on the world's first time-sharing system.
Generative AI in Education: From Foundational Insights to the Socratic Playground for Learning
2025-01-12
Xiangen Hu, Sheng Xu, Richard Tong, Art Graesser
This paper explores the synergy between human cognition and Large Language Models (LLMs), highlighting how generative AI can drive personalized learning at scale. We discuss parallels between LLMs and human cognition, emphasizing both the promise and new perspectives on integrating AI systems into education. After examining challenges in aligning technology with pedagogy, we review AutoTutor-one of the earliest Intelligent Tutoring Systems (ITS)-and detail its successes, limitations, and unfulfilled aspirations. We then introduce the Socratic Playground, a next-generation ITS that uses advanced transformer-based models to overcome AutoTutor's constraints and provide personalized, adaptive tutoring. To illustrate its evolving capabilities, we present a JSON-based tutoring prompt that systematically guides learner reflection while tracking misconceptions. Throughout, we underscore the importance of placing pedagogy at the forefront, ensuring that technology's power is harnessed to enhance teaching and learning rather than overshadow it.
Correlation effects in two-dimensional MX_2 and MA_2Z_4 (M= Nb, Ta; X= S, Se, Te; A=Si, Ge; Z=N, P) cold metals: Implications for device applications
2025-01-11
W. Beida, E. Sasioglu, M. Tas, C. Friedrich, S. Blugel, I. Mertig, I. Galanakis
Cold metals, characterized by their distinctive band structures, hold promise for innovative electronic devices such as tunnel diodes with negative differential resistance (NDR) effect and field-effect transistors (FETs) with sub-60 mV/dec subthreshold swing (SS). In this study, we employ the GW approximation and HSE06 hybrid functional to investigate the correlation effects on the electronic band structure of two-dimensional (2D) cold metallic materials, specifically focusing on MX_2 and MA_2Z_4 (M=Nb, Ta; X=S, Se, Te; A=Si, Ge; Z= N, P) compounds in 1H structure. These materials exhibit a unique band structure with an isolated metallic band around the Fermi energy, denoted as W_m, as well as two energy gaps: the internal gap E^I_g below the Fermi level and the external gap E^E_g above the Fermi level. These three electronic structure parameters play a decisive role in determining the current-voltage (I-V) characteristics of tunnel diodes, the nature of the NDR effect, and the transfer characteristics and SS value of FETs. Our calculations reveal that both GW and HSE06 methods yield consistent electronic structure properties for all studied compounds. We observed a consistent increase in both internal and external band gaps, as well as metallic bandwidths, across all pn-type cold metal systems. Notably, the internal band gap E^I_g exhibits the most substantial enhancement, highlighting the sensitivity of these materials to correlation effects. In contrast, the changes in the metallic bandwidth W_m and external band gap E^E_g are relatively modest. These findings offer valuable insights for designing and optimizing cold metal-based devices. Materials like NbSi_2N_4, NbGe_2N_4, and TaSi_2N_4 show particular promise for high-performance NDR tunnel diodes and sub-60 mV/dec SS FETs.
Dual use issues in the field of Natural Language Generation
2025-01-11
Emiel van Miltenburg
This report documents the results of a recent survey in the SIGGEN community, focusing on Dual Use issues in Natural Language Generation (NLG). SIGGEN is the Special Interest Group (SIG) of the Association for Computational Linguistics (ACL) for researchers working on NLG. The survey was prompted by the ACL executive board, which asked all SIGs to provide an overview of dual use issues within their respective subfields. The survey was sent out in October 2024 and the results were processed in January 2025. With 23 respondents, the survey is presumably not representative of all SIGGEN members, but at least this document offers a helpful resource for future discussions. This report is open to feedback from the SIGGEN community. Let me know if you have any questions or comments!
The tardigrade as an emerging model organism for systems neuroscience
2025-01-11
Ana M. Lyons, Saul Kato
We present the case for developing the tardigrade (Hypsibius exemplaris) into a model organism for systems neuroscience. These microscopic, transparent animals (~300-500 microns) are among the smallest known to possess both limbs (eight) and eyes (two), with a nervous system of only a few hundred neurons organized into a multi-lobed brain, ventral nerve cord, and a series of ganglia along the body. Despite their neuroanatomical simplicity, tardigrades exhibit complex behaviors, including multi-limbed walking gaits, individual limb grasping, phototaxis, and transitions between active and dormant states. These behaviors position tardigrades as a uniquely powerful system for addressing certain fundamental questions in systems neuroscience, such as: How do nervous systems coordinate multi-limbed behaviors? How are top-down and bottom-up motor control systems integrated? How is stereovision-guided navigation implemented? What mechanisms underlie neural resilience and recovery during environmental stress? We review current knowledge of tardigrade neuroanatomy, behavior, and genomics, and we identify opportunities and challenges for leveraging their unique biology. We propose developing essential neuroscientific tools for tardigrades, including genetic engineering and live neuroimaging, alongside behavioral assays linking neural activity to outputs. Leveraging their evolutionary ties to Caenorhabditis elegans and Drosophila melanogaster, we can adapt existing toolkits to accelerate tardigrade research - providing a bridge between simpler invertebrate systems and more complex neural architectures.
RoboHorizon: An LLM-Assisted Multi-View World Model for Long-Horizon Robotic Manipulation
2025-01-11
Zixuan Chen, Jing Huo, Yangtao Chen, Yang Gao
Efficient control in long-horizon robotic manipulation is challenging due to complex representation and policy learning requirements. Model-based visual reinforcement learning (RL) has shown great potential in addressing these challenges but still faces notable limitations, particularly in handling sparse rewards and complex visual features in long-horizon environments. To address these limitations, we propose the Recognize-Sense-Plan-Act (RSPA) pipeline for long-horizon tasks and further introduce RoboHorizon, an LLM-assisted multi-view world model tailored for long-horizon robotic manipulation. In RoboHorizon, pre-trained LLMs generate dense reward structures for multi-stage sub-tasks based on task language instructions, enabling robots to better recognize long-horizon tasks. Keyframe discovery is then integrated into the multi-view masked autoencoder (MAE) architecture to enhance the robot's ability to sense critical task sequences, strengthening its multi-stage perception of long-horizon processes. Leveraging these dense rewards and multi-view representations, a robotic world model is constructed to efficiently plan long-horizon tasks, enabling the robot to reliably act through RL algorithms. Experiments on two representative benchmarks, RLBench and FurnitureBench, show that RoboHorizon outperforms state-of-the-art visual model-based RL methods, achieving a 23.35% improvement in task success rates on RLBench's 4 short-horizon tasks and a 29.23% improvement on 6 long-horizon tasks from RLBench and 3 furniture assembly tasks from FurnitureBench.
Transforming Social Science Research with Transfer Learning: Social Science Survey Data Integration with AI
2025-01-11
Ali Amini
Large-N nationally representative surveys, which have profoundly shaped American politics scholarship, represent related but distinct domains -a key condition for transfer learning applications. These surveys are related through their shared demographic, party identification, and ideological variables, yet differ in that individual surveys often lack specific policy preference questions that researchers require. Our study introduces a novel application of transfer learning (TL) to address these gaps, marking the first systematic use of TL paradigms in the context of survey data. Specifically, models pre-trained on the Cooperative Election Study (CES) dataset are fine-tuned for use in the American National Election Studies (ANES) dataset to predict policy questions based on demographic variables. Even with a naive architecture, our transfer learning approach achieves approximately 92 percentage accuracy in predicting missing variables across surveys, demonstrating the robust potential of this method. Beyond this specific application, our paper argues that transfer learning is a promising framework for maximizing the utility of existing survey data. We contend that artificial intelligence, particularly transfer learning, opens new frontiers in social science methodology by enabling systematic knowledge transfer between well-administered surveys that share common variables but differ in their outcomes of interest.
Physics-Informed Neuro-Evolution (PINE): A Survey and Prospects
2025-01-11
Jian Cheng Wong, Abhishek Gupta, Chin Chun Ooi, Pao-Hsiung Chiu, Jiao Liu, Yew-Soon Ong
Deep learning models trained on finite data lack a complete understanding of the physical world. On the other hand, physics-informed neural networks (PINNs) are infused with such knowledge through the incorporation of mathematically expressible laws of nature into their training loss function. By complying with physical laws, PINNs provide advantages over purely data-driven models in limited-data regimes. This feature has propelled them to the forefront of scientific machine learning, a domain characterized by scarce and costly data. However, the vision of accurate physics-informed learning comes with significant challenges. This review examines PINNs for the first time in terms of model optimization and generalization, shedding light on the need for new algorithmic advances to overcome issues pertaining to the training speed, precision, and generalizability of today's PINN models. Of particular interest are the gradient-free methods of neuroevolution for optimizing the uniquely complex loss landscapes arising in PINN training. Methods synergizing gradient descent and neuroevolution for discovering bespoke neural architectures and balancing multiple conflicting terms in physics-informed learning objectives are positioned as important avenues for future research. Yet another exciting track is to cast neuroevolution as a meta-learner of generalizable PINN models.
Design and analysis for constrained order-of-addition experiments
2025-01-11
Jianbin Chen, Dennis K. J. Lin, Nicholas Rios, Xueru Zhang
In an order-of-addition (OofA) experiment, the sequence of m different components can significantly impact the experiment's response. In many OofA experiments, the components are subject to constraints, where certain orders are impossible. For example, in survey design and job scheduling, the components are often arranged into groups, and these groups of components must be placed in a fixed order. If two components are in different groups, their pairwise order is determined by the fixed order of their groups. Design and analysis are needed for these pairwise-group constrained OofA experiments. A new model is proposed to accommodate pairwise-group constraints. This paper also introduces a model for mixed-pairwise constrained OofA experiments, which allows one pair of components within each group to have a pre-determined pairwise order. It is proven that the full design, which uses all feasible orders exactly once, is D- and G-optimal under the proposed models. Systematic construction methods are used to find optimal fractional designs for pairwise-group and mixed-pairwise constrained OofA experiments. The proposed methods efficiently assess the impact of question order in a survey dataset, where participants answered generalized intelligence questions in a randomly assigned order under mixed-pairwise constraints.
Spoken language datasets are vital for advancing linguistic research, Natural Language Processing, and speech technology. However, resources dedicated to Italian, a linguistically rich and diverse Romance language, remain underexplored compared to major languages like English or Mandarin. This survey provides a comprehensive analysis of 66 spoken Italian datasets, highlighting their characteristics, methodologies, and applications. The datasets are categorized by speech type, source and context, and demographic and linguistic features, with a focus on their utility in fields such as Automatic Speech Recognition, emotion detection, and education. Challenges related to dataset scarcity, representativeness, and accessibility are discussed alongside recommendations for enhancing dataset creation and utilization. The full dataset inventory is publicly accessible via GitHub and archived on Zenodo, serving as a valuable resource for researchers and developers. By addressing current gaps and proposing future directions, this work aims to support the advancement of Italian speech technologies and linguistic research.
Dispersion Measures as Predictors of Lexical Decision Time, Word Familiarity, and Lexical Complexity
2025-01-11
Adam Nohejl, Taro Watanabe
Various measures of dispersion have been proposed to paint a fuller picture of a word's distribution in a corpus, but only little has been done to validate them externally. We evaluate a wide range of dispersion measures as predictors of lexical decision time, word familiarity, and lexical complexity in five diverse languages. We find that the logarithm of range is not only a better predictor than log-frequency across all tasks and languages, but that it is also the most powerful additional variable to log-frequency, consistently outperforming the more complex dispersion measures. We discuss the effects of corpus part granularity and logarithmic transformation, shedding light on contradictory results of previous studies.
Determination of galaxy photometric redshifts using Conditional Generative Adversarial Networks (CGANs)
2025-01-11
M. Garcia-Fernandez
Accurate and reliable photometric redshifts determination is one of the key aspects for wide-field photometric surveys. Determination of photometric redshift for galaxies, has been traditionally solved by use of machine-learning and artificial intelligence techniques trained on a calibration sample of galaxies, where both photometry and spectrometry are determined. On this paper, we present a new algorithmic approach for determining photometric redshifts of galaxies using Conditional Generative Adversarial Networks (CGANs). Proposed CGAN implementation, approaches photometric redshift determination as a probabilistic regression, where instead of determining a single value for the estimated redshift of the galaxy, a full probability density is computed. The methodology proposed, is tested with data from Dark Energy Survey (DES) Y1 data and compared with other existing algorithm such as a Random Forest regressor.
Advancements in UAV-based Integrated Sensing and Communication: A Comprehensive Survey
2025-01-11
Manzoor Ahmed, Ali Arshad Nasir, Mudassir Masood, Kamran Ali Memon, Khurram Karim Qureshi, Feroz Khan, Wali Ullah Khan, Fang Xu, Zhu Han
Unmanned aerial vehicle (UAV)-based integrated sensing and communication (ISAC) systems are poised to revolutionize next-generation wireless networks by enabling simultaneous sensing and communication (S\&C). This survey comprehensively reviews UAV-ISAC systems, highlighting foundational concepts, key advancements, and future research directions. We explore recent advancements in UAV-based ISAC systems from various perspectives and objectives, including advanced channel estimation (CE), beam tracking, and system throughput optimization under joint sensing and communication S\&C constraints. Additionally, we examine weighted sum rate (WSR) and sensing trade-offs, delay and age of information (AoI) minimization, energy efficiency (EE), and security enhancement. These applications highlight the potential of UAV-based ISAC systems to improve spectrum utilization, enhance communication reliability, reduce latency, and optimize energy consumption across diverse domains, including smart cities, disaster relief, and defense operations. The survey also features summary tables for comparative analysis of existing methodologies, emphasizing performance, limitations, and effectiveness in addressing various challenges. By synthesizing recent advancements and identifying open research challenges, this survey aims to be a valuable resource for developing efficient, adaptive, and secure UAV-based ISAC systems.
Not real or too soft? On the challenges of publishing interdisciplinary software engineering research
2025-01-11
Sonja M. Hyrynsalmi, Grischa Liebel, Ronnie de Souza Santos, Sebastian Baltes
The discipline of software engineering (SE) combines social and technological dimensions. It is an interdisciplinary research field. However, interdisciplinary research submitted to software engineering venues may not receive the same level of recognition as more traditional or technical topics such as software testing. For this paper, we conducted an online survey of 73 SE researchers and used a mixed-method data analysis approach to investigate their challenges and recommendations when publishing interdisciplinary research in SE. We found that the challenges of publishing interdisciplinary research in SE can be divided into topic-related and reviewing-related challenges. Furthermore, while our initial focus was on publishing interdisciplinary research, the impact of current reviewing practices on marginalized groups emerged from our data, as we found that marginalized groups are more likely to receive negative feedback. In addition, we found that experienced researchers are less likely to change their research direction due to feedback they receive. To address the identified challenges, our participants emphasize the importance of highlighting the impact and value of interdisciplinary work for SE, collaborating with experienced researchers, and establishing clearer submission guidelines and new interdisciplinary SE publication venues. Our findings contribute to the understanding of the current state of the SE research community and how we could better support interdisciplinary research in our field.
High-pressure growth effect on the properties of high-Tc iron-based superconductors: A short review
2025-01-11
Priya Singh, Manasa Manasa, Mohammad Azam, Shiv J. Singh
The high-pressure growth technique is a vital approach that facilitates the stabilization of new phases and allows for meticulous control of structural parameters, which significantly impact electronic and magnetic properties. We present a short review of our ongoing investigations into various families of iron-based superconductors (IBS), employing the high-gas pressure and high-temperature synthesis (HP-HTS) method. This technique is capable of producing the gas pressures up to 1.8 GPa and a heating temperature of up to 1700 {\deg}C through a three-zone furnace within a cylindrical chamber. Different kinds of IBS samples are prepared using HPHTS and characterized through various measurements to reach the final conclusions. The results demonstrate that the high-pressure growth technique significantly enhances the properties of IBS, including the transition temperature, critical current density, and pinning force. In addition, the quality of the samples and their density are improved through the intergrain connections. Furthermore, the comprehensive evaluations and investigations prove that a growth pressure of 0.5 GPa is sufficient for producing high-quality IBS bulks under the optimized synthesis conditions.
Online Algorithm for Aggregating Experts' Predictions with Unbounded Quadratic Loss
2025-01-11
Alexander Korotin, Vladimir V'yugin, Evgeny Burnaev
We consider the problem of online aggregation of expert predictions with the quadratic loss function. We propose an algorithm for aggregating expert predictions which does not require a prior knowledge of the upper bound on the losses. The algorithm is based on the exponential reweighing of expert losses.
Study Self-lensing/Eclipsing Signals in Edge-on Double White-Dwarf Systems
2025-01-11
Sedighe Sajadian
Stellar lightcurves from edge-on double white dwarf systems(DWDs) have periodic lensing/eclipsing signals at times of alignment between two components as seen by the observer. Here, we study the characterization and detection of these signals. In common DWDs, the Einstein radii have similar orders of magnitude with WDs' radii, and the projected source and lens radii normalized to the Einstein radius ($\rho_{\star}$, and $\rho_{\rm l}$) are $\sim 1$. Both of them are reduced with the orbital period and the lens mass. If $\rho_{\rm l}\simeq 1$ the lensing-induced minor image is always blocked by the lens which results lower magnification factors. If $\rho_{\rm l}\lesssim 1$ and in transit events the finite-lens effects decrease the lightcurves' width. When $\rho_{\rm l}\gtrsim1$ (happens for close DWDs including one low-mass and one massive WD) deep or complete eclipses dominate to lensing effects. The self-lensing signals maximize for massive DWDs in wide orbits. We study the detect-ability of lensing/eclipsing signals in edge-on DWDs in observations by The NASA's Transiting Exoplanet Survey Satellite(TESS), The Vera Rubin Observatory(LSST) and The Nancy Grace Roman Space Telescope. We simulate stellar lightcurves due to edge-on DWDs and generate synthetic data points based on their observing strategies. Detection efficiency maximizes for extremely low-mass WDs in close orbits, and the numbers of DWDs within 100 pc and an observing cone with detectable lensing/eclipsing signals in one $27.4$-day TESS and $62$-day Roman observing window are $\sim1$ and $<1$, respectively. Detecting these signals by LSST is barely possible because of its long cadence.
TopoFormer: Integrating Transformers and ConvLSTMs for Coastal Topography Prediction
2025-01-11
Santosh Munian, Oktay Karakuş, William Russell, Gwyn Nelson
This paper presents \textit{TopoFormer}, a novel hybrid deep learning architecture that integrates transformer-based encoders with convolutional long short-term memory (ConvLSTM) layers for the precise prediction of topographic beach profiles referenced to elevation datums, with a particular focus on Mean Low Water Springs (MLWS) and Mean Low Water Neaps (MLWN). Accurate topographic estimation down to MLWS is critical for coastal management, navigation safety, and environmental monitoring. Leveraging a comprehensive dataset from the Wales Coastal Monitoring Centre (WCMC), consisting of over 2000 surveys across 36 coastal survey units, TopoFormer addresses key challenges in topographic prediction, including temporal variability and data gaps in survey measurements. The architecture uniquely combines multi-head attention mechanisms and ConvLSTM layers to capture both long-range dependencies and localized temporal patterns inherent in beach profiles data. TopoFormer's predictive performance was rigorously evaluated against state-of-the-art models, including DenseNet, 1D/2D CNNs, and LSTMs. While all models demonstrated strong performance, \textit{TopoFormer} achieved the lowest mean absolute error (MAE), as low as 2 cm, and provided superior accuracy in both in-distribution (ID) and out-of-distribution (OOD) evaluations.
Assessing instructor-AI cooperation for grading essay-type questions in an introductory sociology course
2025-01-11
Francisco Olivos, Tobias Kamelski, Sebastián Ascui-Gac
This study explores the use of artificial intelligence (AI) as a complementary tool for grading essay-type questions in higher education, focusing on its consistency with human grading and potential to reduce biases. Using 70 handwritten exams from an introductory sociology course, we evaluated generative pre-trained transformers (GPT) models' performance in transcribing and scoring students' responses. GPT models were tested under various settings for both transcription and grading tasks. Results show high similarity between human and GPT transcriptions, with GPT-4o-mini outperforming GPT-4o in accuracy. For grading, GPT demonstrated strong correlations with the human grader scores, especially when template answers were provided. However, discrepancies remained, highlighting GPT's role as a "second grader" to flag inconsistencies for assessment reviewing rather than fully replace human evaluation. This study contributes to the growing literature on AI in education, demonstrating its potential to enhance fairness and efficiency in grading essay-type questions.
This study introduces an advanced methodology for automatically identifying minor deformations in flat walls caused by vibrations from nearby railway tracks. It leverages high-density Terrestrial Laser Scanner (TLS) LiDAR surveys and AI/ML techniques to collect and analyze data. The scan data is processed into a detailed point cloud, which is segmented to distinguish ground points, trees, buildings, and other objects. The analysis focuses on identifying sections along flat walls and estimating their deformations relative to the ground orientation. Findings from the study, conducted at the RGIPT campus, reveal significant deformations in walls close to the railway corridor, with the highest deformations ranging from 7 to 8 cm and an average of 3 to 4 cm. In contrast, walls further from the corridor show negligible deformations. The developed automated process for feature extraction and deformation monitoring demonstrates potential for structural health monitoring. By integrating LiDAR data with machine learning, the methodology provides an efficient system for identifying and analyzing structural deformations, highlighting the importance of continuous monitoring for ensuring structural integrity and public safety in urban infrastructure. This approach represents a substantial advancement in automated feature extraction and deformation analysis, contributing to more effective management of urban infrastructure.
Tagged particle dynamics in supercooled quantum liquid
2025-01-11
Ankita Das, Gopika Krishnan, Eran Rabani, Upendra Harbola
We analyze dynamics of quantum supercooled liquids in terms of tagged particle dynamics. Unlike the classical case, uncertainty in the position of a particle in quantum liquid leads to qualitative changes. We demonstrate these effects in the dynamics of the first two moments of displacements, namely, the mean-squared displacement, $\langle \Delta r^2(t)\rangle$, and $\langle \Delta r^4(t)\rangle$. Results are presented for a hard sphere liquid using mode-coupling theory (MCT) formulation and simulation on a binary Lennard-Jones liquid. As the quantumness (controlled by the de-Broglie thermal wavelength) is increased, a non-zero value of the moments at zero time leads to significant deviations from the classical behavior in the initial dynamics. Initial displacement shows ballistic behavior $\langle \Delta r^2(t)\rangle\sim t^2$, but, as a result of large uncertainty in the position, the dynamical effects become weaker with increasing quantumness over this time scale.
Alexei Alexandrovich Starobinsky was outstanding theoretical physicist who made fundamental contributions to gravitational theory and cosmology, based on geometrical ideas in physics, in the spirit of Einstein. One of his greatest achievements is the famous Starobinsky model of cosmological inflation in the early universe, proposed in 1979-1980. In this paper, the Starobinsky inflation model is systematically reviewed from the modern perspective. Its deformation to include production of primordial black holes is proposed, and possible quantum corrections in the context of superstring theory and the Swampland Program are discussed. Starobinsky inflation also leads to the universal reheating mechanism for particle production after inflation.
2025-01-11
Nathan Secrest, Sebastian von Hausegger, Mohamed Rameez, Roya Mohayaee, Subir Sarkar
Modern cosmology is built on the assumption that the Universe is homogeneous and isotropic on large scales - but this is challenged by results of the Ellis-Baldwin test that show an unexplained anomaly in the distribution of distant galaxies and quasars.
Towards User-Focused Cross-Domain Testing: Disentangling Accessibility, Usability, and Fairness
2025-01-11
Matheus de Morais Leça, Ronnie de Souza Santos
Fairness testing is increasingly recognized as fundamental in software engineering, especially in the domain of data-driven systems powered by artificial intelligence. However, its practical integration into software development may pose challenges, given its overlapping boundaries with usability and accessibility testing. In this tertiary study, we explore these complexities using insights from 12 systematic reviews published in the past decade, shedding light on the nuanced interactions among fairness, usability, and accessibility testing and how they intersect within contemporary software development practices.
The Radiowave Hunt for Young Stellar Object Emission and Demographics (RADIOHEAD): A Radio Luminosity${-}$Spectral Type Dependence in Taurus${-}$Auriga YSOs
2025-01-10
Ramisa Akther Rahman, Joshua Bennett Lovell, Eric W. Koch, David J. Wilner, Sean M. Andrews, Kristina Monsch, Dan Ha
We measure the radio continuum fluxes at the locations of all Gaia${-}$confirmed members of Taurus${-}$Auriga using Karl G. Jansky Very Large Array Sky Survey data (VLASS; 2${-}$4 GHz, $\sigma_{\rm{VLASS}}{\sim}110{-}140 \mu$Jy, $2.5''$ resolution) spanning 3 VLASS epochs (2019, 2021, and 2023). We present 35 detections coincident with young Taurus${-}$Auriga stars (29 in individual VLASS images, 6 via stacking). We find a strong dependence on spectral type, wherein the fractional detection rate of radio emission coincident with early-type young stellar objects (YSOs) is systematically higher than late-type YSOs, ranging from 25% $\pm$ 13% for B${-}$F YSOs, 21% $\pm$ 11% for G YSOs, 18.4% $\pm$ 6.3% for K0${-}$K4 YSOs, 15.5% $\pm$ 5.4% for K5${-}$K9 YSOs, 7.0% $\pm$ 2.7% for M0${-}$M2 YSOs, 2.3% $\pm$ 0.9% for M3${-}$M6 YSOs, and 1.9% $\pm$ 1.9% for YSOs with SpTs later than M7. We present cumulative density distributions of radio luminosity densities that demonstrate a significant luminosity enhancement for early- versus late-type YSOs. We find 25% of the detected sources to be significantly variable. We discuss possible interpretations of this dependence, which may reflect stellar magnetic activity, binary interactions, or stellar flaring. We find that mid-infrared YSO class is a strong indicator of radio detectability consistent with higher frequency Taurus-Auriga VLA surveys, with class III stars detected at a rate of 8.8% $\pm$ 1.6%, class IIs at 2.0% $\pm$ 1.2%, and combined class 0s, Is and Fs at 8.0% $\pm$ 5.4%.
We review the recent development of machine-learning (ML) force-field frameworks for Landau-Lifshitz-Gilbert (LLG) dynamics simulations of itinerant electron magnets, focusing on the general theory and implementations of symmetry-invariant representations of spin configurations. The crucial properties that such magnetic descriptors must satisfy are differentiability with respect to spin rotations and invariance to both lattice point-group symmetry and internal spin rotation symmetry. We propose an efficient implementation based on the concept of reference irreducible representations, modified from the group-theoretical power-spectrum and bispectrum methods. The ML framework is demonstrated using the s-d models, which are widely applied in spintronics research. We show that LLG simulations based on local fields predicted by the trained ML models successfully reproduce representative non-collinear spin structures, including 120$^\circ$, tetrahedral, and skyrmion crystal orders of the triangular-lattice s-d models. Large-scale thermal quench simulations enabled by ML models further reveal intriguing freezing dynamics and glassy stripe states consisting of skyrmions and bi-merons. Our work highlights the utility of ML force-field approach to dynamical modeling of complex spin orders in itinerant electron magnets.
Statistical Challenges in Analyzing Migrant Backgrounds Among University Students: a Case Study from Italy
2025-01-10
Lorenzo Giammei, Laura Terzera, Fulvia Mecatti
The methodological issues and statistical complexities of analyzing university students with migrant backgrounds is explored, focusing on Italian data from the University of Milano-Bicocca. With the increasing size of migrant populations and the growth of the second and middle generations, the need has risen for deeper knowledge of the various strata of this population, including university students with migrant backgrounds. This presents challenges due to inconsistent recording in university datasets. By leveraging both administrative records and an original targeted survey we propose a methodology to fully identify the study population of students with migrant histories, and to distinguish relevant subpopulations within it such as second-generation born in Italy. Traditional logistic regression and machine learning random forest models are used and compared to predict migrant status. The primary contribution lies in creating an expanded administrative dataset enriched with indicators of students' migrant backgrounds and status. The expanded dataset provides a critical foundation for analyzing the characteristics of students with migration histories across all variables routinely registered in the administrative data set. Additionally, findings highlight the presence of selection bias in the targeted survey data, underscoring the need of further research.
A strong-coupling effective-field theory for asymmetrically charged plates with counterions only
2025-01-10
L. Samaj, E. Trizac, M. Trulsson
We are interested in rationalizing the phenomenon of like-charge attraction between charged bodies, such as a pair of colloids, in the strong coupling regime. The two colloids are modelled as uniformly charged parallel plates, neutralized by mobile counterions. In an earlier work [Palaia et al., J. Phys. Chem. B 126, 3143 (2022)], we developed an effective-field theory for symmetric plates, stemming from the ground-state description that holds at infinite couplings. Here, we generalize the approach to the asymmetric case, where the plates bear charges of the same sign, but of different values. In the symmetric situation, the mobile ions, which are localized in the vicinity of the two plates, share equally between both of them. Here, the sharing is non-trivial, depending both on the coupling parameter and the distance between the plates. We thus introduce a counterion occupation parameter, that is determined variationally to ensure minimum of the free energy. The resulting analytical results for the pressure as a function of the plate-plate distance $d$ agree well with our Monte Carlo data, in a large interval of strong and intermediate coupling constants $\Xi$. We show in particular that within this description, there exists a range of large distances at which the attractive pressure features a $1/d^2$ behavior.
The GALAH survey: Improving chemical abundances using star clusters
2025-01-10
Janez Kos, Sven Buder, Kevin L. Beeson, Joss Bland-Hawthorn, Gayandhi M. De Silva, Valentina D'Orazi, Ken Freeman, Michael Hayden, Geraint F. Lewis, Karin Lind, Sarah L. Martell, Sanjib Sharma, Daniel B. Zucker, Tomaž Zwitter, Dennis Stello, Richard de Grijs
Large spectroscopic surveys aim to consistently compute stellar parameters of very diverse stars while minimizing systematic errors. We explore the use of stellar clusters as benchmarks to verify the precision of spectroscopic parameters in the fourth data release (DR4) of the GALAH survey. We examine 58 open and globular clusters and associations to validate measurements of temperature, gravity, chemical abundances, and stellar ages. We focus on identifying systematic errors and understanding trends between stellar parameters, particularly temperature and chemical abundances. We identify trends by stacking measurements of chemical abundances against effective temperature and modelling them with splines. We also refitted spectra in three clusters with the Spectroscopy Made Easy and Korg packages to reproduce the trends in DR4 and to search for their origin by varying temperature and gravity priors, linelists, and spectral continuum. Trends are consistent between clusters of different ages and metallicities, can reach amplitudes of ~0.5 dex and differ for dwarfs and giants. We use the derived trends to correct the DR4 abundances of 24 and 31 chemical elements for dwarfs and giants, respectively, and publish a detrended catalogue. While we couldn't pinpoint the trends' origins, we found that: i) photometric priors affect derived abundances, ii) temperature, metallicity, and continuum levels are degenerate in spectral fitting, and it is hard to break the degeneracy even by using independent measurements, iii) the extent of the linelist used in spectral synthesis is essential for cool stars, and iv) different spectral fitting codes produce significantly different iron abundances for stars of all temperatures. We conclude that clusters can be used to characterise the systematic errors of parameters produced in large surveys, but further research is needed to explain the origin of the trends.
2025-01-10
Wolfgang Tittel, Mikael Afzelius, Adam Kinos, Lars Rippe, Andreas Walther
We review concepts and recent work related to creating light-matter interfaces for future quantum networks based on rare-earth ion-doped crystals. More precisely, we explore their unique suitability for creating photon sources, optical quantum memories for light, and qubits that allow quantum information processing. In addition, we review the state-of-the-art of elementary quantum repeater links, and provide suggestions for future research.
Towards Developing Socially Compliant Automated Vehicles: State of the Art, Experts Expectations, and A Conceptual Framework
2025-01-10
Yongqi Dong, Bart van Arem, Haneen Farah
Automated Vehicles (AVs) hold promise for revolutionizing transportation by improving road safety, traffic efficiency, and overall mobility. Despite the steady advancement in high-level AVs in recent years, the transition to full automation entails a period of mixed traffic, where AVs of varying automation levels coexist with human-driven vehicles (HDVs). Making AVs socially compliant and understood by human drivers is expected to improve the safety and efficiency of mixed traffic. Thus, ensuring AVs compatibility with HDVs and social acceptance is crucial for their successful and seamless integration into mixed traffic. However, research in this critical area of developing Socially Compliant AVs (SCAVs) remains sparse. This study carries out the first comprehensive scoping review to assess the current state of the art in developing SCAVs, identifying key concepts, methodological approaches, and research gaps. An expert interview was also conducted to identify critical research gaps and expectations towards SCAVs. Based on the scoping review and expert interview input, a conceptual framework is proposed for the development of SCAVs. The conceptual framework is evaluated using an online survey targeting researchers, technicians, policymakers, and other relevant professionals worldwide. The survey results provide valuable validation and insights, affirming the significance of the proposed conceptual framework in tackling the challenges of integrating AVs into mixed-traffic environments. Additionally, future research perspectives and suggestions are discussed, contributing to the research and development agenda of SCAVs.
A monthly sub-national Harmonized Food Insecurity Dataset for comprehensive analysis and predictive modeling
2025-01-10
Mélissande Machefer, Michele Ronco, Anne-Claire Thomas, Michael Assouline, Melanie Rabier, Christina Corbane, Felix Rembold
Food security is a complex, multidimensional concept challenging to measure comprehensively. Effective anticipation, monitoring, and mitigation of food crises require timely and comprehensive global data. This paper introduces the Harmonized Food Insecurity Dataset (HFID), an open-source resource consolidating four key data sources: the Integrated Food Security Phase Classification (IPC)/Cadre Harmonis\'e (CH) phases, the Famine Early Warning Systems Network (FEWS NET) IPC-compatible phases, and the World Food Program's (WFP) Food Consumption Score (FCS) and reduced Coping Strategy Index (rCSI). Updated monthly and using a common reference system for administrative units, the HFID offers extensive spatial and temporal coverage. It serves as a vital tool for food security experts and humanitarian agencies, providing a unified resource for analyzing food security conditions and highlighting global data disparities. The scientific community can also leverage the HFID to develop data-driven predictive models, enhancing the capacity to forecast and prevent future food crises.
The ultraviolet luminosity function of star-forming galaxies between redshifts of 0.4 and 0.6
2025-01-10
M. J. Page, T. Dwelly, I. McHardy, N. Seymour, K. O. Mason, M. Sharma, J. A. Kennea, T. P. Sasseen, A. A. Breeveld, A. E. Matthews
We combine ultraviolet imaging of the 13H survey field, taken with the XMM-Newton Optical Monitor telescope (XMM-OM) and the Neil Gehrels Swift Observatory Ultraviolet and Optical Telescope (UVOT) in the UVM2 band, to measure rest-frame ultraviolet 1500A luminosity functions of star-forming galaxies with redshifts between 0.4 and 0.6. In total the UVM2 imaging covers a sky area of 641 square arcmin, and we detect 273 galaxies in the UVM2 image with 0.4
Distilling Calibration via Conformalized Credal Inference
2025-01-10
Jiayi Huang, Sangwoo Park, Nicola Paoletti, Osvaldo Simeone
Deploying artificial intelligence (AI) models on edge devices involves a delicate balance between meeting stringent complexity constraints, such as limited memory and energy resources, and ensuring reliable performance in sensitive decision-making tasks. One way to enhance reliability is through uncertainty quantification via Bayesian inference. This approach, however, typically necessitates maintaining and running multiple models in an ensemble, which may exceed the computational limits of edge devices. This paper introduces a low-complexity methodology to address this challenge by distilling calibration information from a more complex model. In an offline phase, predictive probabilities generated by a high-complexity cloud-based model are leveraged to determine a threshold based on the typical divergence between the cloud and edge models. At run time, this threshold is used to construct credal sets -- ranges of predictive probabilities that are guaranteed, with a user-selected confidence level, to include the predictions of the cloud model. The credal sets are obtained through thresholding of a divergence measure in the simplex of predictive probabilities. Experiments on visual and language tasks demonstrate that the proposed approach, termed Conformalized Distillation for Credal Inference (CD-CI), significantly improves calibration performance compared to low-complexity Bayesian methods, such as Laplace approximation, making it a practical and efficient solution for edge AI deployments.
Periodic Variability of the Central Stars of Planetary Nebulae Surveyed through the Zwicky Transient Facility
2025-01-10
Pinjian Chen, Xuan Fang, Xiaodian Chen, Jifeng Liu
A consensus has been reached in recent years that binarity plays an important role in the formation and evolution of a significant fraction of planetary nebulae (PNe). Utilizing the archived photometric data from the Zwicky Transient Facility survey, we conducted a comprehensive data mining in search for brightness variations in a large sample of Galactic PNe. This effort leads to identification of 39 PNe, whose central stars exhibit periodic variation in light curves. Among these objects, 20 are known binary central stars of PNe, while the remaining 19 are new discoveries. Additionally, we identified 14 PNe with central stars displaying anomalous variation in light curves, as well as eight variables based on the high-cadence photometric data. Among the new discoveries of periodicity, we found compelling evidence of binary systems at the centres of two archetypal quadrupolar PNe. We also report on very peculiar brightness variation observed in the central core of the compact PN NGC6833. Several PNe in our sample deserve follow-up observations, both high-dispersion spectroscopy and high-precision photometry, to reveal the true nature of their central binarity or even multiplicity.
In this paper we review the Takeuchi correspondence between right coideal subalgebras and left $H$-module factor coalgebras of a Hopf algebra with bijective antipode. We are especially interested to describe the situation when the faithfulness assumption in the conditions of flatness and coflatness is dropped. This study was motivated by several questions which remain open.
Bound-State Beta Decay of $\mathbf{\mathrm{^{205}{Tl}^{81+}}}$ Ions and the LOREX Project
2025-01-10
R. S. Sidhu, G. Leckenby, R. J. Chen, R. Mancino, Yu. A. Litvinov, G. Martínez-Pinedo, G. Amthauer, M. Bai, K. Blaum, B. Boev, F. Bosch, C. Brandau, V. Cvetković, T. Dickel, I. Dillmann, D. Dmytriiev, T. Faestermann, O. Forstner, B. Franczak, H. Geissel, R. Gernhäuser, J. Glorius, C. Griffin, A. Gumberidze, E. Haettner, P. -M. Hillenbrand, P. Kienle, W. Korten, Ch. Kozhuharov, N. Kuzminchuk, K. Langanke, S. Litvinov, E. Menz, T. Morgenroth, T. Neff, C. Nociforo, F. Nolden, M. K. Pavícević, N. Petridis, U. Popp, S. Purushothaman, R. Reifarth, M. S. Sanjari, C. Scheidenberger, U. Spillmann, M. Steck, Th. Stöhlker, Y. K. Tanaka, M. Trassinelli, S. Trotsenko, L. Varga, M. Wang, H. Weick, P. J. Woods, T. Yamaguchi, Y. H. Zhang, J. Zhao, K. Zuber
Stable $^{205}$Tl ions have the lowest known energy threshold for capturing electron neutrinos ($\nu_e$) of ${ E}_{\nu_e}\ge50.6$\,keV. The Lorandite Experiment (LOREX), proposed in the 1980s, aims at obtaining the longtime averaged solar neutrino flux by utilizing natural deposits of Tl-bearing lorandite ores. To determine the $\nu_e$ capture cross section, it is required to know the strength of the weak transition connecting the ground state of $^{205}$Tl and the 2.3 keV first excited state in $^{205}$Pb. The only way to experimentally address this transition is to measure the bound-state beta decay ($\beta_{b}$) of fully ionized $\mathrm{^{205}Tl^{81+}}$ ions. After three decades of meticulous preparation, the half-life of the $\beta_{b}$ decay of $\mathrm{^{205}Tl^{81+}}$ has been measured to be $291_{-27}^{+33}$ days using the Experimental Storage Ring (ESR) at GSI, Darmstadt. The longer measured half-life compared to theoretical estimates reduces the expected signal-to-noise ratio in the LOREX, thus challenging its feasibility.
Doubly-Robust Functional Average Treatment Effect Estimation
2025-01-10
Lorenzo Testa, Tobia Boschi, Francesca Chiaromonte, Edward H. Kennedy, Matthew Reimherr
Understanding causal relationships in the presence of complex, structured data remains a central challenge in modern statistics and science in general. While traditional causal inference methods are well-suited for scalar outcomes, many scientific applications demand tools capable of handling functional data -- outcomes observed as functions over continuous domains such as time or space. Motivated by this need, we propose DR-FoS, a novel method for estimating the Functional Average Treatment Effect (FATE) in observational studies with functional outcomes. DR-FoS exhibits double robustness properties, ensuring consistent estimation of FATE even if either the outcome or the treatment assignment model is misspecified. By leveraging recent advances in functional data analysis and causal inference, we establish the asymptotic properties of the estimator, proving its convergence to a Gaussian process. This guarantees valid inference with simultaneous confidence bands across the entire functional domain. Through extensive simulations, we show that DR-FoS achieves robust performance under a wide range of model specifications. Finally, we illustrate the utility of DR-FoS in a real-world application, analyzing functional outcomes to uncover meaningful causal insights in the SHARE (Survey of Health, Aging and Retirement in Europe) dataset.
Nonlinear partial differential equations in neuroscience: from modelling to mathematical theory
2025-01-10
José A Carrillo, Pierre Roux
Many systems of partial differential equations have been proposed as simplified representations of complex collective behaviours in large networks of neurons. In this survey, we briefly discuss their derivations and then review the mathematical methods developed to handle the unique features of these models, which are often nonlinear and non-local. The first part focuses on parabolic Fokker-Planck equations: the Nonlinear Noisy Leaky Integrate and Fire neuron model, stochastic neural fields in PDE form with applications to grid cells, and rate-based models for decision-making. The second part concerns hyperbolic transport equations, namely the model of the Time Elapsed since the last discharge and the jump-based Leaky Integrate and Fire model. The last part covers some kinetic mesoscopic models, with particular attention to the kinetic Voltage-Conductance model and FitzHugh-Nagumo kinetic Fokker-Planck systems.
Designing culturally relevant questionnaires for ICTD research is challenging, particularly when adapting surveys for populations to non-western contexts. Prior work adapted questionnaires through expert reviews and pilot studies, which are resource-intensive and time-consuming. To address these challenges, we propose using large language models (LLMs) to automate the questionnaire pretesting process in cross-cultural settings. Our study used LLMs to adapt a U.S.-focused climate opinion survey for a South African audience. We then tested the adapted questionnaire with 116 South African participants via Prolific, asking them to provide feedback on both versions. Participants perceived the LLM-adapted questions as slightly more favorable than the traditional version. Our note opens discussions on the potential role of LLMs in adapting surveys and facilitating cross-cultural questionnaire design.
Quantum signal processing (QSP) relies on a historically costly pre-processing step, "QSP-processing/phase-factor finding." QSP-processing is now a developed topic within quantum algorithms literature, and a beginner accessible review of QSP-processing is overdue. This work provides a whirlwind tour through QSP conventions and pre-processing methods, beginning from a pedagogically accessible QSP convention. We then review QSP conventions associated with three common polynomial types: real polynomials with definite parity, sums of reciprocal/anti-reciprocal Chebyshev polynomials, and complex polynomials. We demonstrate how the conventions perform with respect to three criteria: circuit length, polynomial conditions, and pre-processing methods. We then review the recently introduced Wilson method for QSP-processing and give conditions where it can succeed with bound error. Although the resulting bound is not computationally efficient, we demonstrate that the method succeeds with linear error propagation for relevant target polynomials and precision regimes, including the Jacobi-Anger expansion used in Hamiltonian simulation algorithms. We then apply our benchmarks to three QSP-processing methods for QSP circuits and show that a method introduced by Berntson and S\"underhauf outperforms both the Wilson method and the standard optimization strategy for complex polynomials.
Service robots are increasingly entering the home to provide domestic tasks for residents. However, when working in an open, dynamic, and unstructured home environment, service robots still face challenges such as low intelligence for task execution and poor long-term autonomy (LTA), which has limited their deployment. As the basis of robotic task execution, environment modeling has attracted significant attention. This integrates core technologies such as environment perception, understanding, and representation to accurately recognize environmental information. This paper presents a comprehensive survey of environmental modeling from a new task-executionoriented perspective. In particular, guided by the requirements of robots in performing domestic service tasks in the home environment, we systematically review the progress that has been made in task-execution-oriented environmental modeling in four respects: 1) localization, 2) navigation, 3) manipulation, and 4) LTA. Current challenges are discussed, and potential research opportunities are also highlighted.
Expressing One's Identity Online: Left-Right and cross EU-country variation in self-representation in social media
2025-01-10
Carlo Romano Marcello Alessandro Santagiustina, Jean-Philippe Cointet, Pedro Ramaciotti Morales
We examine how social media users from eight European Union (EU) member states express their socio-political identities, focusing on users' online self-presentation and group identity cues conveyed through bios. Our goal is to explore commonalities and differences in topics discussed in social media profiles, across Left-and Right-wing user groups, within and across EU countries. Through a novel approach we map how identity-related discourse varies by country and political orientation, revealing how group identity is expressed within the EU. We find that topics related to democracy, national way of life, and decentralization emerge as particularly divisive, showing considerable variation both within and between EU countries. A subset of topics, which includes education, environmentalism, sustainability, equality, freedom & human rights, and traditional morality, among others, clearly differentiate Left-from Right-leaning user groups. These partisan topics are relevant as they could be leveraged for mobilizing ideological groups and highlight Left-Right identitarian differences at the EU level. Finally, we show that our Left-Right identity similarity metrics reflect aspects of real-world political fragmentation, which are closely aligned to the perceptions of political conflict intensity by country, as measured by the 2022 PEW survey.
LLMs Reproduce Stereotypes of Sexual and Gender Minorities
2025-01-10
Ruby Ostrow, Adam Lopez
A large body of research has found substantial gender bias in NLP systems. Most of this research takes a binary, essentialist view of gender: limiting its variation to the categories _men_ and _women_, conflating gender with sex, and ignoring different sexual identities. But gender and sexuality exist on a spectrum, so in this paper we study the biases of large language models (LLMs) towards sexual and gender minorities beyond binary categories. Grounding our study in a widely used psychological framework -- the Stereotype Content Model -- we demonstrate that English-language survey questions about social perceptions elicit more negative stereotypes of sexual and gender minorities from LLMs, just as they do from humans. We then extend this framework to a more realistic use case: text generation. Our analysis shows that LLMs generate stereotyped representations of sexual and gender minorities in this setting, raising concerns about their capacity to amplify representational harms in creative writing, a widely promoted use case.
The New Anticipatory Governance Culture for Innovation: Regulatory Foresight, Regulatory Experimentation and Regulatory Learning
2025-01-10
Deirdre Ahern
With the rapid pace of technological innovation, traditional methods of policy formation and legislating are becoming conspicuously anachronistic. The need for regulatory choices to be made to counter the deadening effect of regulatory lag is more important to developing markets and fostering growth than achieving one off regulatory perfection. This article advances scholarship on innovation policy and the regulation of technological innovation in the European Union. It does so by considering what building an agile yet robust anticipatory governance regulatory culture involves. It systematically excavates a variety of tools and elements that are being put into use in inventive ways and argues that these need to be more cohesively and systemically integrated into the regulatory toolbox. Approaches covered include strategic foresight, the critical embrace of iterative policy development and regulatory learning in the face of uncertainty and the embrace of bottom up approaches to cocreation of policy such as Policy Labs and the testing and regulatory learning through pilot regulation and experimentation. The growing use of regulatory sandboxes as an EU policy tool to boost innovation and navigate regulatory complexity as seen in the EU AI Act is also probed
2025-01-10
José María Buades Rubio, Antoni Jaume-i-Capó, David López González, Gabriel Moyà Alcover
Nonograms are logic puzzles in which cells in a grid must be colored or left blank according to the numbers that are located in its headers. In this study, we analyze different techniques to solve this type of logical problem using an Heuristic Algorithm, Genetic Algorithm, and Heuristic Algorithm with Neural Network. Furthermore, we generate a public dataset to train the neural networks. We published this dataset and the code of the algorithms. Combination of the heuristic algorithm with a neural network obtained the best results. From state of the art review, no previous works used neural network to solve nonograms, nor combined a network with other algorithms to accelerate the resolution process.
We survey some recent results and open questions on the approaching geodesics property and its application to the study of the Gromov and horofunction compactifications of a proper geodesic Gromov metric space. We obtain results on the dynamics of isometries and we exhibit an example of a Gromov hyperbolic domain of $\mathbb{C}$ which does not satisfy the approaching geodesic property.
Causal survival analysis, Estimation of the Average Treatment Effect (ATE): Practical Recommendations
2025-01-10
Charlotte Voinot, Clément Berenfeld, Imke Mayer, Bernard Sebastien, Julie Josse
Causal survival analysis combines survival analysis and causal inference to evaluate the effect of a treatment or intervention on a time-to-event outcome, such as survival time. It offers an alternative to relying solely on Cox models for assessing these effects. In this paper, we present a comprehensive review of estimators for the average treatment effect measured with the restricted mean survival time, including regression-based methods, weighting approaches, and hybrid techniques. We investigate their theoretical properties and compare their performance through extensive numerical experiments. Our analysis focuses on the finite-sample behavior of these estimators, the influence of nuisance parameter selection, and their robustness and stability under model misspecification. By bridging theoretical insights with practical evaluation, we aim to equip practitioners with both state-of-the-art implementations of these methods and practical guidelines for selecting appropriate estimators for treatment effect estimation. Among the approaches considered, G-formula two-learners, AIPCW-AIPTW, Buckley-James estimators, and causal survival forests emerge as particularly promising.
Large $N$ limits of supersymmetric quantum field theories: A pedagogical overview
2025-01-10
Leonardo Santilli
The different large $N$ limits of supersymmetric quantum field theories in three, four, and five dimensions are reviewed. We distinguish between the planar limit of SQCD theories, the M-theory limit suited in three and five dimensions, and the long quiver limit. The method to solve exactly the sphere partition functions in each type of limit is spelled out in a pedagogical way. After a comprehensive general treatment of the saddle point approximation in the large $N$ limit, we present an extensive list of examples and detail the calculations. The scope of this overview is to provide an entry-level, computation-oriented understanding of the techniques featured in the field theory side of the AdS/CFT correspondence.
Dark Energy Survey Year 6 Results: Point-Spread Function Modeling
2025-01-10
T. Schutt, M. Jarvis, A. Roodman, A. Amon, M. R. Becker, R. A. Gruendl, M. Yamamoto, K. Bechtol, G. M. Bernstein, M. Gatti, E. S. Rykoff, E. Sheldon, M. A. Troxel, T. M. C. Abbott, M. Aguena, F. Andrade-Oliveira, D. Brooks, A. Carnero Rosell, J. Carretero, C. Chang, A. Choi, L. N. da Costa, T. M. Davis, J. De Vicente, S. Desai, H. T. Diehl, P. Doel, A. Ferté, J. Frieman, J. García-Bellido, E. Gaztanaga, D. Gruen, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, K. Kuehn, O. Lahav, S. Lee, M. Lima, J. L. Marshall, J. Mena-Fernández, R. Miquel, J. J. Mohr, J. Myles, R. L. C. Ogando, A. Pieres, A. A. Plazas Malagón, A. Porredon, S. Samuroff, E. Sanchez, D. Sanchez Cid, I. Sevilla-Noarbe, M. Smith, E. Suchyta, G. Tarle, V. Vikram, A. R. Walker, N. Weaverdyck
We present the point-spread function (PSF) modeling for weak lensing shear measurement using the full six years of the Dark Energy Survey (DES Y6) data. We review the PSF estimation procedure using the PIFF (PSFs In the Full FOV) software package and describe the key improvements made to PIFF and modeling diagnostics since the DES year three (Y3) analysis: (i) use of external Gaia and infrared photometry catalogs to ensure higher purity of the stellar sample used for model fitting, (ii) addition of color-dependent PSF modeling, the first for any weak lensing analysis, and (iii) inclusion of model diagnostics inspecting fourth-order moments, which can bias weak lensing measurements to a similar degree as second-order modeling errors. Through a comprehensive set of diagnostic tests, we demonstrate the improved accuracy of the Y6 models evident in significantly smaller systematic errors than those of the Y3 analysis, in which all $g$ band data were excluded due to insufficiently accurate PSF models. For the Y6 weak lensing analysis, we include $g$ band photometry data in addition to the $riz$ bands, providing a fourth band for photometric redshift estimation. Looking forward to the next generation of wide-field surveys, we describe several ongoing improvements to PIFF, which will be the default PSF modeling software for weak lensing analyses for the Vera C. Rubin Observatory's Legacy Survey of Space and Time.
Semantic Mapping in Indoor Embodied AI -- A Comprehensive Survey and Future Directions
2025-01-10
Sonia Raychaudhuri, Angel X. Chang
Intelligent embodied agents (e.g. robots) need to perform complex semantic tasks in unfamiliar environments. Among many skills that the agents need to possess, building and maintaining a semantic map of the environment is most crucial in long-horizon tasks. A semantic map captures information about the environment in a structured way, allowing the agent to reference it for advanced reasoning throughout the task. While existing surveys in embodied AI focus on general advancements or specific tasks like navigation and manipulation, this paper provides a comprehensive review of semantic map-building approaches in embodied AI, specifically for indoor navigation. We categorize these approaches based on their structural representation (spatial grids, topological graphs, dense point-clouds or hybrid maps) and the type of information they encode (implicit features or explicit environmental data). We also explore the strengths and limitations of the map building techniques, highlight current challenges, and propose future research directions. We identify that the field is moving towards developing open-vocabulary, queryable, task-agnostic map representations, while high memory demands and computational inefficiency still remaining to be open challenges. This survey aims to guide current and future researchers in advancing semantic mapping techniques for embodied AI systems.
Dark Energy Survey Year 6 Results: Photometric Data Set for Cosmology
2025-01-10
K. Bechtol, I. Sevilla-Noarbe, A. Drlica-Wagner, B. Yanny, R. A. Gruendl, E. Sheldon, E. S. Rykoff, J. De Vicente, M. Adamow, D. Anbajagane, M. R. Becker, G. M. Bernstein, A. Carnero Rosell, J. Gschwend, M. Gorsuch, W. G. Hartley, M. Jarvis, T. Jeltema, R. Kron, T. A. Manning, J. O'Donnell, A. Pieres, M. Rodríguez-Monroy, D. Sanchez Cid, M. Tabbutt, L. Toribio San Cipriano, D. L. Tucker, N. Weaverdyck, M. Yamamoto, T. M. C. Abbott, M. Aguena, A. Alarcón, S. Allam, A. Amon, F. Andrade-Oliveira, S. Avila, P. H. Bernardinelli, E. Bertin, J. Blazek, D. Brooks, D. L. Burke, J. Carretero, F. J. Castander, R. Cawthon, C. Chang, A. Choi, C. Conselice, M. Costanzi, M. Crocce, L. N. da Costa, T. M. Davis, S. Desai, H. T. Diehl, S. Dodelson, P. Doel, C. Doux, A. Ferté, B. Flaugher, P. Fosalba, J. Frieman, J. García-Bellido, M. Gatti, E. Gaztanaga, G. Giannini, D. Gruen, G. Gutierrez, K. Herner, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. Huterer, N. Jeffrey, E. Krause, K. Kuehn, O. Lahav, S. Lee, C. Lidman, M. Lima, H. Lin, J. L. Marshall, J. Mena-Fernández, R. Miquel, J. J. Mohr, J. Muir, J. Myles, R. L. C. Ogando, A. Palmese, A. A. Plazas Malagón, A. Porredon, J. Prat, M. Raveri, A. K. Romer, A. Roodman, S. Samuroff, E. Sanchez, V. Scarpine, M. Smith, M. Soares-Santos, E. Suchyta, G. Tarle, M. A. Troxel, V. Vikram, A. R. Walker, J. Weller, P. Wiseman, Y. Zhang
We describe the photometric data set assembled from the full six years of observations by the Dark Energy Survey (DES) in support of static-sky cosmology analyses. DES Y6 Gold is a curated data set derived from DES Data Release 2 (DR2) that incorporates improved measurement, photometric calibration, object classification and value added information. Y6 Gold comprises nearly $5000~{\rm deg}^2$ of $grizY$ imaging in the south Galactic cap and includes 669 million objects with a depth of $i_{AB} \sim 23.4$ mag at S/N $\sim 10$ for extended objects and a top-of-the-atmosphere photometric uniformity $< 2~{\rm mmag}$. Y6 Gold augments DES DR2 with simultaneous fits to multi-epoch photometry for more robust galaxy shapes, colors, and photometric redshift estimates. Y6 Gold features improved morphological star-galaxy classification with efficiency $98.6\%$ and contamination $0.8\%$ for galaxies with $17.5 < i_{AB} < 22.5$. Additionally, it includes per-object quality information, and accompanying maps of the footprint coverage, masked regions, imaging depth, survey conditions, and astrophysical foregrounds that are used for cosmology analyses. After quality selections, benchmark samples contain 448 million galaxies and 120 million stars. This paper will be complemented by online data access and documentation.
Relativistic magnetohydrodynamics in the early Universe
2025-01-10
Alberto Roper Pol, Antonino Salvino Midiri
We review the conservation laws of magnetohydrodynamics (MHD) in an expanding homogeneous and isotropic Universe that can be applied to the study of early Universe physics during the epoch of radiation domination. The conservation laws for a conducting perfect fluid with relativistic bulk velocities in an expanding background are presented, extending previous results that apply in the limit of subrelativistic bulk motion. Furthermore, it is shown that the subrelativistic limit presents new corrections that have not been considered in previous work. Imperfect relativistic fluids are briefly described but their detailed study is not included in this work. We review the propagation of sound waves, Alfv\'en waves, and magnetosonic waves, as well as the Boris correction for relativistic Alfv\'en speeds. This review is an extension, including new results, of part of the lectures presented at the minicourse ``Simulations of Early Universe Magnetohydrodynamics'' lectured by A. Roper Pol and J. Schober at EPFL, as part of the six-week program ``Generation, evolution, and observations of cosmological magnetic fields'' at the Bernoulli Center in May 2024.
Collective effects in strong interaction processes: experimental highlights
2025-01-10
V. A. Okorokov
Collective effects are reviewed for collisions of various systems - from proton-proton to heavy ion - in wide energy range. In proton-proton interactions studies of hadron jets devote to the better understanding of some basic features of strong interaction and search for the physics beyond of Standard Model. First results have been obtained for massive gauge bosons and antitop-top pair production in proton-nuclear and heavy ion collisions at multi-TeV energies. The collectivity has been observed for various particle and beam species, in particular, in collision of small systems. Experimental results obtained for discrete symmetries of strong interaction at finite temperature confirm indirectly the topologically non-trivial structure of the vacuum. The recent measurements of femtoscopic correlations provide, in particular, the indirect estimations for parameters of hyperon-nucleon potentials which are essential for study of inner structure of compact astrophysical objects. Novel mechanism for multiparticle production due to collectivity can be expected in very high energy nuclear collisions and it may be helpful for better understanding of the nature of the muon puzzle in ultra-high energy cosmic ray measurements. Thus studies of collective effects in strong interaction processes provide new important results for relativistic astrophysics, cosmology and cosmic ray physics, i.e. have interdisciplinary significance.
How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond
2025-01-10
Chen Huang, Yang Deng, Wenqiang Lei, Jiancheng Lv, Tat-Seng Chua, Jimmy Xiangji Huang
With the advancement of large language models (LLMs), intelligent models have evolved from mere tools to autonomous agents with their own goals and strategies for cooperating with humans. This evolution has birthed a novel paradigm in NLP, i.e., human-model cooperation, that has yielded remarkable progress in numerous NLP tasks in recent years. In this paper, we take the first step to present a thorough review of human-model cooperation, exploring its principles, formalizations, and open challenges. In particular, we introduce a new taxonomy that provides a unified perspective to summarize existing approaches. Also, we discuss potential frontier areas and their corresponding challenges. We regard our work as an entry point, paving the way for more breakthrough research in this regard.
The nonlinear acoustic valley Hall effect (AVHE), a recently discovered novel acoustically driven phenomena, has sparked extensive interests in valleytronics. So far, only the intrinsic contributions from band structure (Berry curvature or asymmetric energy dispersions) to nonlinear AVHE have been investigated. Here, we theoretically investigate the nonlinear AVHE from both intrinsic and extrinsic contributions in two-dimensional (2D) hexagonal massive Dirac materials with disorders based on the Boltzmann formalism and also concretely analyse the behaviours of nonlinear AVHE in disordered monolayer MoS2. It's found that the extrinsic contributions (side jump and skew scattering) can also give rise to a pure nonlinear AVHE in the 2D hexagonal massive Dirac materials. Remarkably, the extrinsic mechanisms dominate the nonlinear AVHE in the disordered monolayer MoS2.
Dark Energy Survey Year 6 Results: Synthetic-source Injection Across the Full Survey Using Balrog
2025-01-10
D. Anbajagane, M. Tabbutt, J. Beas-Gonzalez, B. Yanny, S. Everett, M. R. Becker, M. Yamamoto, E. Legnani, J. De Vicente, K. Bechtol, J. Elvin-Poole, G. M. Bernstein, A. Choi, M. Gatti, G. Giannini, R. A. Gruendl, M. Jarvis, S. Lee, J. Mena-Fernández, A. Porredon, M. Rodriguez-Monroy, E. Rozo, E. S. Rykoff, T. Schutt, E. Sheldon, M. A. Troxel, N. Weaverdyck, V. Wetzell, M. Aguena, A. Alarcon, S. Allam, A. Amon, F. Andrade-Oliveira, D. Brooks, A. Carnero Rosell, J. Carretero, C. Chang, M. Crocce, L. N. da Costa, M. E. S. Pereira, T. M. Davis, S. Desai, H. T. Diehl, S. Dodelson, P. Doel, A. Drlica-Wagner, A. Ferté, J. Frieman, J. García-Bellido, E. Gaztanaga, D. Gruen, G. Gutierrez, W. G. Hartley, K. Herner, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. Huterer, D. J. James, E. Krause, K. Kuehn, O. Lahav, J. L. Marshall, R. Miquel, J. Muir, J. Myles, A. Pieres, A. A. Plazas Malagón, J. Prat, M. Raveri, S. Samuroff, E. Sanchez, D. Sanchez Cid, I. Sevilla-Noarbe, M. Smith, E. Suchyta, G. Tarle, D. L. Tucker, A. R. Walker, P. Wiseman, Y. Zhang
Synthetic source injection (SSI), the insertion of sources into pixel-level on-sky images, is a powerful method for characterizing object detection and measurement in wide-field, astronomical imaging surveys. Within the Dark Energy Survey (DES), SSI plays a critical role in characterizing all necessary algorithms used in converting images to catalogs, and in deriving quantities needed for the cosmology analysis, such as object detection rates, galaxy redshift estimation, galaxy magnification, star-galaxy classification, and photometric performance. We present here a source injection catalog of $146$ million injections spanning the entire 5000 deg$^2$ DES footprint, generated using the Balrog SSI pipeline. Through this sample, we demonstrate that the DES Year 6 (Y6) image processing pipeline provides accurate estimates of the object properties, for both galaxies and stars, at the percent-level, and we highlight specific regimes where the accuracy is reduced. We then show the consistency between SSI and data catalogs, for all galaxy samples developed within the weak lensing and galaxy clustering analyses of DES Y6. The consistency between the two catalogs also extends to their correlations with survey observing properties (seeing, airmass, depth, extinction, etc.). Finally, we highlight a number of applications of this catalog to the DES Y6 cosmology analysis. This dataset is the largest SSI catalog produced at this fidelity and will serve as a key testing ground for exploring the utility of SSI catalogs in upcoming surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time.
Dark Energy Survey Year 6 Results: Cell-based Coadds and Metadetection Weak Lensing Shape Catalogue
2025-01-10
M. Yamamoto, M. R. Becker, E. Sheldon, M. Jarvis, R. A. Gruendl, F. Menanteau, E. S. Rykoff, S. Mau, T. Schutt, M. Gatti, M. A. Troxel, A. Amon, D. Anbajagane, G. M. Bernstein, D. Gruen, E. M. Huff, M. Tabbutt, A. Tong, B. Yanny, T. M. C. Abbott, M. Aguena, A. Alarcon, F. Andrade-Oliveira, K. Bechtol, J. Blazek, D. Brooks, A. Carnero Rosell, J. Carretero, C. Chang, A. Choi, M. Costanzi, M. Crocce, L. N. da Costa, T. M. Davis, J. De Vicente, S. Desai, H. T. Diehl, S. Dodelson, P. Doel, C. Doux, A. Drlica-Wagner, A. Ferté, B. Flaugher, J. Frieman, J. García-Bellido, E. Gaztanaga, G. Giannini, G. Gutierrez, W. G. Hartley, K. Herner, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. Huterer, E. Krause, K. Kuehn, O. Lahav, M. Lima, J. L. Marshall, J. Mena-Fernández, R. Miquel, J. J. Mohr, J. Muir, J. Myles, R. L. C. Ogando, A. Pieres, A. A. Plazas Malagón, A. Porredon, J. Prat, M. Raveri, M. Rodriguez-Monroy, A. Roodman, S. Samuroff, E. Sanchez, D. Sanchez Cid, V. Scarpine, I. Sevilla-Noarbe, M. Smith, E. Suchyta, G. Tarle, V. Vikram, N. Weaverdyck, P. Wiseman, Y. Zhang
We present the Metadetection weak lensing galaxy shape catalogue from the six-year Dark Energy Survey (DES Y6) imaging data. This dataset is the final release from DES, spanning 4422 deg$^2$ of the southern sky. We describe how the catalogue was constructed, including the two new major processing steps, cell-based image coaddition and shear measurements with Metadetection. The DES Y6 Metadetection weak lensing shape catalogue consists of 151,922,791 galaxies detected over riz bands, with an effective number density of $n_{\rm eff}$ =8.22 galaxies per arcmin$^2$ and shape noise of $\sigma_e$ = 0.29. We carry out a suite of validation tests on the catalogue, including testing for PSF leakage, testing for the impact of PSF modeling errors, and testing the correlation of the shear measurements with galaxy, PSF, and survey properties. In addition to demonstrating that our catalogue is robust for weak lensing science, we use the DES Y6 image simulation suite (Mau, Becker et al. 2025) to estimate the overall multiplicative shear bias of our shear measurement pipeline. We find no detectable multiplicative bias at the roughly half-percent level, with m = (3.4 $\pm$ 6.1) x $10^{-3}$, at 3$\sigma$ uncertainty. This is the first time both cell-based coaddition and Metadetection algorithms are applied to observational data, paving the way to the Stage-IV weak lensing surveys.
2025-01-10
Adam Moss, Mukremin Kilic, Pierre Bergeron, Gracyn Jewett, Warren Brown
We report our findings on a spectroscopic survey of seven unresolved DA+DB binary white dwarf candidates. We have discovered extreme spectroscopic variations in one of these candidates, SDSS J084716.21+484220.40. Previous analysis failed to reproduce the optical spectrum using a single object with a homogeneous atmosphere. Our time-resolved spectroscopy reveals a double-faced white dwarf that switches between a DBA and DA spectral type over 6.5 or 8.9 hours due to varying surface abundances. We also provide time-series spectroscopy of the magnetic DBA, SDSS J085618.94+161103.6 (LB 8915), and confirm an inhomogeneous atmosphere. We employ an atmosphere model with hydrogen caps and a helium belt that yields excellent fits to our time-resolved spectra. We use the oblique rotator model to derive the system geometry for both targets. With the addition of these two objects, the emerging class of double-faced white dwarfs now consists of seven members. We summarize the properties of this new class of objects, and discuss how magnetism impacts the convective processes and leads to the formation of double-faced white dwarfs. We identify cooler versions of white dwarfs with inhomogeneous atmospheres among the cool magnetic DA white dwarf sample, where the H$\alpha$ line is shallower than expected based on pure hydrogen atmosphere models.
OpenUniverse2024: A shared, simulated view of the sky for the next generation of cosmological surveys
2025-01-10
OpenUniverse, The LSST Dark Energy Science Collaboration, The Roman HLIS Project Infrastructure Team, The Roman RAPID Project Infrastructure Team, The Roman Supernova Cosmology Project Infrastructure Team, A. Alarcon, L. Aldoroty, G. Beltz-Mohrmann, A. Bera, J. Blazek, J. Bogart, G. Braeunlich, A. Broughton, K. Cao, J. Chiang, N. E. Chisari, V. Desai, Y. Fang, L. Galbany, A. Hearin, K. Heitmann, C. Hirata, R. Hounsell, B. Jain, M. Jarvis, J. Jencson, A. Kannawadi, M. K. Kasliwal, R. Kessler, A. Kiessling, R. Knop, E. Kovacs, R. Laher, K. Laliotis, C. Lin, I. Lopes, A. Mahabal, R. Mandelbaum, J. Masiero, S. Mau, C. Meehan, J. Meyers, B. Moraes, R. Paladini, A. Pearl, A. Plazas Malagon, B. Rose, D. Rubin, B. Rusholme, A. Santos, N. Šarčević, D. Scolnic, M. A. Troxel, N. Van Alfen, S. Van Dyke, C. W. Walter, T. Wu, M. Yamamoto, Y. Yan, T. Zhang
The OpenUniverse2024 simulation suite is a cross-collaboration effort to produce matched simulated imaging for multiple surveys as they would observe a common simulated sky. Both the simulated data and associated tools used to produce it are intended to uniquely enable a wide range of studies to maximize the science potential of the next generation of cosmological surveys. We have produced simulated imaging for approximately 70 deg$^2$ of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Wide-Fast-Deep survey and the Nancy Grace Roman Space Telescope High-Latitude Wide-Area Survey, as well as overlapping versions of the ELAIS-S1 Deep-Drilling Field for LSST and the High-Latitude Time-Domain Survey for Roman. OpenUniverse2024 includes i) an early version of the updated extragalactic model called Diffsky, which substantially improves the realism of optical and infrared photometry of objects, compared to previous versions of these models; ii) updated transient models that extend through the wavelength range probed by Roman and Rubin; and iii) improved survey, telescope, and instrument realism based on up-to-date survey plans and known properties of the instruments. It is built on a new and updated suite of simulation tools that improves the ease of consistently simulating multiple observatories viewing the same sky. The approximately 400 TB of synthetic survey imaging and simulated universe catalogs are publicly available, and we preview some scientific uses of the simulations.
Concerns and Values in Human-Robot Interactions: A Focus on Social Robotics
2025-01-10
Giulio Antonio Abbo, Tony Belpaeme, Micol Spitale
Robots, as AI with physical instantiation, inhabit our social and physical world, where their actions have both social and physical consequences, posing challenges for researchers when designing social robots. This study starts with a scoping review to identify discussions and potential concerns arising from interactions with robotic systems. Two focus groups of technology ethics experts then validated a comprehensive list of key topics and values in human-robot interaction (HRI) literature. These insights were integrated into the HRI Value Compass web tool, to help HRI researchers identify ethical values in robot design. The tool was evaluated in a pilot study. This work benefits the HRI community by highlighting key concerns in human-robot interactions and providing an instrument to help researchers design robots that align with human values, ensuring future robotic systems adhere to these values in social applications.
AFRIDOC-MT: Document-level MT Corpus for African Languages
2025-01-10
Jesujoba O. Alabi, Israel Abebe Azime, Miaoran Zhang, Cristina España-Bonet, Rachel Bawden, Dawei Zhu, David Ifeoluwa Adelani, Clement Oyeleke Odoje, Idris Akinade, Iffat Maab, Davis David, Shamsuddeen Hassan Muhammad, Neo Putini, David O. Ademuyiwa, Andrew Caines, Dietrich Klakow
This paper introduces AFRIDOC-MT, a document-level multi-parallel translation dataset covering English and five African languages: Amharic, Hausa, Swahili, Yor\`ub\'a, and Zulu. The dataset comprises 334 health and 271 information technology news documents, all human-translated from English to these languages. We conduct document-level translation benchmark experiments by evaluating neural machine translation (NMT) models and large language models (LLMs) for translations between English and these languages, at both the sentence and pseudo-document levels. These outputs are realigned to form complete documents for evaluation. Our results indicate that NLLB-200 achieved the best average performance among the standard NMT models, while GPT-4o outperformed general-purpose LLMs. Fine-tuning selected models led to substantial performance gains, but models trained on sentences struggled to generalize effectively to longer documents. Furthermore, our analysis reveals that some LLMs exhibit issues such as under-generation, repetition of words or phrases, and off-target translations, especially for African languages.
From Stability to Instability: Characterizing the Eccentricities of Multi-planet Systems in the California Kepler Survey as a Means of Studying Stability
2025-01-10
Matthew J. Doty, Lauren M. Weiss, Matthias Y. He, Antoine C. Petit
Understanding the stability of exoplanet systems is crucial for constraining planetary formation and evolution theories. We use the machine-learning stability indicator, SPOCK, to characterize the stability of 126 high-multiplicity systems from the California Kepler Survey (CKS). We constrain the range of stable eccentricities for each system, adopting the value associated with a 50% chance of stability as the characteristic eccentricity. We confirm characteristic eccentricities via a small suite of N-body integrations. In studying correlations between characteristic eccentricity and various planet-pair and system-level metrics we find that minimum period ratio correlates most strongly with characteristic eccentricity. These characteristic eccentricities are approximately 20% of the eccentricities necessary for two-body mean-motion resonance overlap, suggesting three-body dynamics are needed to drive future instabilities. Systems in which the eccentricities would need to be high (> 0.15) to drive instability are likely dynamically relaxed and might be the fossils of a previous epoch of giant impacts that increased the typical planet-planet spacing.
Understanding the motivations underlying the human inclination to automate tasks is vital to developing truly helpful robots integrated into daily life. Accordingly, we ask: are individuals more inclined to automate chores based on the time they consume or the feelings experienced while performing them? This study explores these preferences and whether they vary across different social groups (i.e., gender category and income level). Leveraging data from the BEHAVIOR-1K dataset, the American Time-Use Survey, and the American Time-Use Survey Well-Being Module, we investigate the relationship between the desire for automation, time spent on daily activities, and their associated feelings - Happiness, Meaningfulness, Sadness, Painfulness, Stressfulness, or Tiredness. Our key findings show that, despite common assumptions, time spent does not strongly relate to the desire for automation for the general population. For the feelings analyzed, only happiness and pain are key indicators. Significant differences by gender and economic level also emerged: Women prefer to automate stressful activities, whereas men prefer to automate those that make them unhappy; mid-income individuals prioritize automating less enjoyable and meaningful activities, while low and high-income show no significant correlations. We hope our research helps motivate technologies to develop robots that match the priorities of potential users, moving domestic robotics toward more socially relevant solutions. We open-source all the data, including an online tool that enables the community to replicate our analysis and explore additional trends at https://hri1260.github.io/why-automate-this.
The Missing Link of Sulfur Chemistry in TMC-1: The Detection of c-C3H2S from the GOTHAM Survey
2025-01-10
Anthony J. Remijan, P. Bryan Changala, Ci Xue, Elsa Q. H. Yuan, Miya Duffy, Haley N. Scolati, Christopher N. Shingledecker, Thomas H. Speak, Ilsa R. Cooke, Ryan Loomis, Andrew M. Burkhardt, Zachary T. P. Fried, Gabi Wenzel, Andrew Lipnicky, Michael C. McCarthy, Brett A. McGuire
We present the spectroscopic characterization of cyclopropenethione (c-C3H2S) in the laboratory and detect it in space using the Green Bank Telescope (GBT) Observations of TMC-1: Hunting Aromatic Molecules (GOTHAM) survey. The detection of this molecule - the missing link in understanding the C3H2S isomeric family in TMC-1 - completes the detection of all 3 low-energy isomers of C3H2S as both CH2CCS and HCCCHS have been previously detected in this source. The total column density of this molecule (N_T of 5.72+2.65/-1.61x10^10 cm^-2 at an excitation temperature of 4.7+1.3/-1.1 K) is smaller than both CH2CCS and HCCCHS and follows nicely the relative dipole principle (RDP), a kinetic rule-of-thumb for predicting isomer abundances which suggests that, all other chemistry among a family of isomers being the same, the member with the smallest dipole should be the most abundant. The RDP now holds for the astronomical abundance ratios of both the S-bearing and O-bearing counterparts observed in TMC-1; however, CH2CCO continues to elude detection in any astronomical source.
The TESS-Keck Survey XXIV: Outer Giants may be More Prevalent in the Presence of Inner Small Planets
2025-01-10
Judah Van Zandt, Erik A. Petigura, Jack Lubin, Lauren M. Weiss, Emma V. Turtelboom, Tara Fetherolf, Joseph M. Akana Murphy, Ian J. M. Crossfield, Greg Gilbert, Teo Mocnik, Natalie M. Batalha, Courtney Dressing, Benjamin Fulton, Andrew W. Howard, Daniel Huber, Howard Isaacson, Stephen R. Kane, Paul Robertson, Arpita Roy, Isabel Angelo, Aida Behmard, Corey Beard, Ashley Chontos, Fei Dai, Paul A. Dalba, Steven Giacalone, Michelle L. Hill, Lea A. Hirsch, Rae Holcomb, Steve B. Howell, Andrew W. Mayo, Mason G. MacDougall, Daria Pidhorodetska, Alex S. Polanski, James Rogers, Lee J. Rosenthal, Ryan A. Rubenzahl, Nicholas Scarsdale, Dakotah Tyler, Samuel W. Yee, Jon Zink
We present the results of the Distant Giants Survey, a three-year radial velocity (RV) campaign to search for wide-separation giant planets orbiting Sun-like stars known to host an inner transiting planet. We defined a distant giant to have $a$ = 1--10 AU and $M_{p} \sin i = 70-4000$ \mearth~ = 0.2-12.5 \mj, and required transiting planets to have $a<1$ AU and $R_{p} = 1-4$ \rearth. We assembled our sample of 47 stars using a single selection function, and observed each star at monthly intervals to obtain $\approx$30 RV observations per target. The final catalog includes a total of twelve distant companions: four giant planets detected during our survey, two previously known giant planets, and six objects of uncertain disposition identified through RV/astrometric accelerations. Statistically, half of the uncertain objects are planets and the remainder are stars/brown dwarfs. We calculated target-by-target completeness maps to account for missed planets. We found evidence for a moderate enhancement of distant giants (DG) in the presence of close-in small planets (CS), P(DG|CS) = $30^{+14}_{-12}\%$, over the field rate of P(DG) = $16^{+2}_{-2}\%$. No enhancement is disfavored ($p \sim$ 8%). In contrast to a previous study, we found no evidence that stellar metallicity enhances P(DG|CS). We found evidence that distant giant companions are preferentially found in systems with multiple transiting planets and have lower eccentricities than randomly selected giant planets. This points toward dynamically cool formation pathways for the giants that do not disturb the inner systems.
Morphological Classification of Galaxies Through Structural and Star Formation Parameters Using Machine Learning
2025-01-10
G. Aguilar-Argüello, G. Fuentes-Pineda, H. M. Hernández-Toledo, L. A. Martínez-Vázquez, J. A. Vázquez-Mata, S. Brough, R. Demarco, A. Ghosh, Y. Jiménez-Teja, G. Martin, W. J. Pearson, C. Sifón
We employ the XGBoost machine learning (ML) method for the morphological classification of galaxies into two (early-type, late-type) and five (E, S0--S0a, Sa--Sb, Sbc--Scd, Sd--Irr) classes, using a combination of non-parametric ($C,\,A,\,S,\,A_S,\,\mathrm{Gini},\,M_{20},\,c_{5090}$), parametric (S\'ersic index, $n$), geometric (axial ratio, $BA$), global colour ($g-i,\,u-r,\,u-i$), colour gradient ($\Delta (g - i)$), and asymmetry gradient ($\Delta A_{9050}$) information, all estimated for a local galaxy sample ($z<0.15$) compiled from the Sloan Digital Sky Survey (SDSS) imaging data. We train the XGBoost model and evaluate its performance through multiple standard metrics. Our findings reveal better performance when utilizing all fourteen parameters, achieving accuracies of 88\% and 65\% for the two-class and five-class classification tasks, respectively. In addition, we investigate a hierarchical classification approach for the five-class scenario, combining three XGBoost classifiers. We observe comparable performance to the ``direct'' five-class classification, with discrepancies of only up to 3\%. Using SHAP (an advanced interpretation tool), we analyse how galaxy parameters impact the model's classifications, providing valuable insights into the influence of these features on classification outcomes. Finally, we compare our results with previous studies and find them consistently aligned.
Isometric simplices for high-order anisotropic mesh adaptation. Part I: Definition and existence of isometric triangulations
2025-01-10
Arthur Bawin, André Garon, Jean-François Remacle
Anisotropic mesh adaptation with Riemannian metrics has proven effective for generating straight-sided meshes with anisotropy induced by the geometry of interest and/or the resolved physics. Within the continuous mesh framework, anisotropic meshes are thought of as discrete counterparts to Riemannian metrics. Ideal, or unit, simplicial meshes consist only of simplices whose edges exhibit unit or quasi-unit length with respect to a given Riemannian metric. Recently, mesh adaptation with high-order (i.e., curved) elements has grown in popularity in the meshing community, as the additional flexibility of high-order elements can further reduce the approximation error. However, a complete and competitive methodology for anisotropic and high-order mesh adaptation is not yet available. The goal of this paper is to address a key aspect of metric-based high-order mesh adaptation, namely, the adequacy between a Riemannian metric and high-order simplices. This is done by extending the notions of unit simplices and unit meshes, central to the continuous mesh framework, to high-order elements. The existing definitions of a unit simplex are reviewed, then a broader definition involving Riemannian isometries is introduced to handle curved and high-order simplices. Similarly, the notion of quasi-unitness is extended to curved simplices to tackle the practical generation of high-order meshes. Proofs of concept for unit and (quasi-)isometric meshes are presented in two dimensions.
Multi-Agent Collaboration Mechanisms: A Survey of LLMs
2025-01-10
Khanh-Tung Tran, Dung Dao, Minh-Duong Nguyen, Quoc-Viet Pham, Barry O'Sullivan, Hoang D. Nguyen
With recent advances in Large Language Models (LLMs), Agentic AI has become phenomenal in real-world applications, moving toward multiple LLM-based agents to perceive, learn, reason, and act collaboratively. These LLM-based Multi-Agent Systems (MASs) enable groups of intelligent agents to coordinate and solve complex tasks collectively at scale, transitioning from isolated models to collaboration-centric approaches. This work provides an extensive survey of the collaborative aspect of MASs and introduces an extensible framework to guide future research. Our framework characterizes collaboration mechanisms based on key dimensions: actors (agents involved), types (e.g., cooperation, competition, or coopetition), structures (e.g., peer-to-peer, centralized, or distributed), strategies (e.g., role-based or model-based), and coordination protocols. Through a review of existing methodologies, our findings serve as a foundation for demystifying and advancing LLM-based MASs toward more intelligent and collaborative solutions for complex, real-world use cases. In addition, various applications of MASs across diverse domains, including 5G/6G networks, Industry 5.0, question answering, and social and cultural settings, are also investigated, demonstrating their wider adoption and broader impacts. Finally, we identify key lessons learned, open challenges, and potential research directions of MASs towards artificial collective intelligence.
Network-centric optimal hybrid sensing hole recovery and self-healing in IPV6 WSNs
2025-01-10
Kwadwo Asante, Yaw Marfo Missah, Frimpong Twum. Michael Asante
In our earlier work, Network-Centric Optimal Hybrid Mobility for IPv6 wireless sensor networks, in which the work sought to control mobility of sensor nodes from an external network was proposed. It was a major improvement on earlier works such as Cluster Sensor Proxy Mobile IPv6 (CSPMIPv6) and Network of Proxies (NoP). In this work, the Network-Centric optimal hybrid mobility scenario was used to detect and fill sensing holes occurring as a result damaged or energy depleted sensing nodes. Various sensor networks self-healing and recovery, and deployment algorithms such as Enhanced Virtual Forces Algorithm with Boundary Forces (EVFA-B); Coverage - Aware Sensor Automation protocol (CASA); Sensor Self-Organizing Algorithm (SSOA); VorLag and the use of the use of anchor and relay nodes were reviewed. With node density thresholds set for various scenarios, the recovery efficiency using various parameters were measured. Comparably, our method provides the most efficient node relocation and self-healing mechanism for sensor networks. Compared to Sensor Self-Organizing Algorithm (SSOA), Hybrid Mobile IP showed superiority in coverage, shorter period of recovery, less computational cost and lower energy depletion. With processing and mobility costs shifted to the external network, Hybrid Mobile IP extends the life span of the network.
VIRAC2: NIR Astrometry and Time Series Photometry for 500M+ Stars from the VVV and VVVX Surveys
2025-01-10
Leigh C. Smith, Philip W. Lucas, Sergey E. Koposov, Carlos González-Fernández, Javier Alonso-García, Dante Minniti, Jason L. Sanders, Luigi R. Bedin, Vasily Belokurov, N. Wyn Evans, Maren Hempel, Valentin D. Ivanov, Radostin G. Kurtev, Roberto K. Saito
We present VIRAC2, a catalogue of positions, proper motions, parallaxes and $Z$, $Y$, $J$, $H$, and $K_s$ near-infrared photometric time series of 545 346 537 unique stars. The catalogue is based on a point spread function fitting reduction of nearly a decade of VISTA VVV and VVVX images, which cover $560~{\rm deg}^2$ of the Southern Galactic plane and bulge. The catalogue is complete at the $>90$ per cent level for $11
Goku: A 10-Parameter Simulation Suite for Cosmological Emulation
2025-01-10
Yanhui Yang, Simeon Bird, Ming-Feng Ho
We present Goku, a suite of cosmological $N$-body simulations, and the corresponding 10-dimensional emulator, GokuEmu, for the nonlinear matter power spectrum. The simulations span the base parameters of $\Lambda$CDM cosmology and its extensions, including dynamical dark energy ($w_0$, $w_a$), the sum of the neutrino masses ($\sum m_\nu$), the effective number of neutrinos ($N_\text{eff}$), and the running of the scalar spectral index ($\alpha_\text{s}$). Designed within the MF-Box framework, which integrates multi-scale and multi-fidelity simulations, the suite includes high-fidelity simulations evolving $3000^3$ particles in $1\,(\text{Gpc}/h)^3$ volumes and low-fidelity simulations with $750^3$ particles across varying box sizes. This approach achieves percent-level accuracy in high-likelihood regions and 5% accuracy across broader parameter ranges, while reducing computational costs by over 90% compared to single-fidelity methods. The simulations adopt an accurate treatment of massive neutrinos, enhancing predictions of the matter power spectrum on nonlinear scales. Key innovations include an adaptive sampling strategy and the use of beam search to optimize generalization accuracy. The emulator is valid for redshifts $z \leq 3$ and scales $0.01 \lesssim k / (h \, \text{Mpc}^{-1}) \lesssim 10$. Beyond the matter power spectrum, the simulations also support analyses of other statistical measures, such as the halo mass function. The emulator and its training data are publicly available at https://github.com/astro-YYH/GokuEmu, providing a valuable resource for cosmological parameter inference and model testing.
A Catalog of Stellar and Dust Properties for 500,000 Stars in the Southwest Bar of the Small Magellanic Cloud
2025-01-10
Petia Yanchulova Merica-Jones, Karl Gordon, Karin Sandstrom, Claire E. Murray, L. Clifton Johnson, Julianne J. Dalcanton, Julia Roman-Duval, Jeremy Chastenet, Benjamin F. Williams, Daniel R. Weisz, Andrew E. Dolphin
We present a catalog of individual stellar and dust extinction properties along close to 500,000 sight lines in the southwest bar of the Small Magellanic Cloud (SMC). The catalog is based on multiband Hubble Space Telescope photometric data spanning near-ultraviolet to near-infrared wavelengths from the Small Magellanic Cloud Investigation of Dust and Gas Evolution survey (SMIDGE) covering a 100 x 200 pc area. We use the probabilistic technique of the Bayesian Extinction And Stellar Tool (BEAST) to model the spectral energy distributions of individual stars in SMIDGE and include the effects of observational uncertainties in the data. We compare BEAST-derived dust extinction properties with tracers of the interstellar medium, such as the emission from the 12CO (2-1) transition (I(CO)), the dust mass surface density ({\Sigma}dust) from far-IR emission, the H I column density (N(HI)) from the 21cm transition, and the mass fraction of polycyclic aromatic hydrocarbons (PAHs; qPAH, derived from IR emission). We find that the dust extinction (A(V )) in the SMIDGE field is strongly correlated with {\Sigma}dust and I(CO), and less so with N(HI) and qPAH, and suggest potential explanations. Our extinction measurements are also sensitive to the presence of the 2175 {\AA} bump in the extinction curve toward UV bright stars. While most do not show evidence for the bump, we identify ~200 lines of sight that are 2175 {\AA} bump candidates. Furthermore, we find distinct structures in the dust extinction-distance distributions that provide insights into the 3D geometry of the SMC.
Visualizing Uncertainty in Image Guided Surgery a Review
2025-01-10
Mahsa Geshvadi
During tumor resection surgery, surgeons rely on neuronavigation to locate tumors and other critical structures in the brain. Most neuronavigation is based on preoperative images, such as MRI and ultrasound, to navigate through the brain. Neuronavigation acts like GPS for the brain, guiding neurosurgeons during the procedure. However, brain shift, a dynamic deformation caused by factors such as osmotic concentration, fluid levels, and tissue resection, can invalidate the preoperative images and introduce registration uncertainty. Considering and effectively visualizing this uncertainty has the potential to help surgeons trust the navigation again. Uncertainty has been studied in various domains since the 19th century. Considering uncertainty requires two essential components: 1) quantifying uncertainty; and 2) conveying the quantified values to the observer. There has been growing interest in both of these research areas during the past few decades.
In recent years, there has been a surge of research focused on underwater image enhancement using Generative Adversarial Networks (GANs), driven by the need to overcome the challenges posed by underwater environments. Issues such as light attenuation, scattering, and color distortion severely degrade the quality of underwater images, limiting their use in critical applications. Generative Adversarial Networks (GANs) have emerged as a powerful tool for enhancing underwater photos due to their ability to learn complex transformations and generate realistic outputs. These advancements have been applied to real-world applications, including marine biology and ecosystem monitoring, coral reef health assessment, underwater archaeology, and autonomous underwater vehicle (AUV) navigation. This paper explores all major approaches to underwater image enhancement, from physical and physics-free models to Convolutional Neural Network (CNN)-based models and state-of-the-art GAN-based methods. It provides a comprehensive analysis of these methods, evaluation metrics, datasets, and loss functions, offering a holistic view of the field. Furthermore, the paper delves into the limitations and challenges faced by current methods, such as generalization issues, high computational demands, and dataset biases, while suggesting potential directions for future research.
2025-01-10
Wei Ruan, Yanjun Lyu, Jing Zhang, Jiazhang Cai, Peng Shu, Yang Ge, Yao Lu, Shang Gao, Yue Wang, Peilong Wang, Lin Zhao, Tao Wang, Yufang Liu, Luyang Fang, Ziyu Liu, Zhengliang Liu, Yiwei Li, Zihao Wu, Junhao Chen, Hanqi Jiang, Yi Pan, Zhenyuan Yang, Jingyuan Chen, Shizhe Liang, Wei Zhang, Terry Ma, Yuan Dou, Jianli Zhang, Xinyu Gong, Qi Gan, Yusong Zou, Zebang Chen, Yuanxin Qian, Shuo Yu, Jin Lu, Kenan Song, Xianqiao Wang, Andrea Sikora, Gang Li, Xiang Li, Quanzheng Li, Yingfeng Wang, Lu Zhang, Yohannes Abate, Lifang He, Wenxuan Zhong, Rongjie Liu, Chao Huang, Wei Liu, Ye Shen, Ping Ma, Hongtu Zhu, Yajun Yan, Dajiang Zhu, Tianming Liu
With the rapid advancements in large language model (LLM) technology and the emergence of bioinformatics-specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications. This survey aims to address this need by providing a thorough review of BioLMs, focusing on their evolution, classification, and distinguishing features, alongside a detailed examination of training methodologies, datasets, and evaluation frameworks. We explore the wide-ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development, highlighting their impact and transformative potential in bioinformatics. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities. Finally, we highlight emerging trends and future directions, offering valuable insights to guide researchers and clinicians toward advancing BioLMs for increasingly sophisticated biological and clinical applications.
A survey of textual cyber abuse detection using cutting-edge language models and large language models
2025-01-09
Jose A. Diaz-Garcia, Joao Paulo Carvalho
The success of social media platforms has facilitated the emergence of various forms of online abuse within digital communities. This abuse manifests in multiple ways, including hate speech, cyberbullying, emotional abuse, grooming, and sexting. In this paper, we present a comprehensive analysis of the different forms of abuse prevalent in social media, with a particular focus on how emerging technologies, such as Language Models (LMs) and Large Language Models (LLMs), are reshaping both the detection and generation of abusive content within these networks. We delve into the mechanisms through which social media abuse is perpetuated, exploring the psychological and social impact. Additionally, we examine the dual role of advanced language models-highlighting their potential to enhance automated detection systems for abusive behavior while also acknowledging their capacity to generate harmful content. This paper aims to contribute to the ongoing discourse on online safety and ethics, offering insights into the evolving landscape of cyberabuse and the technological innovations that both mitigate and exacerbate it.
Background: The field of Artificial Intelligence has undergone cyclical periods of growth and decline, known as AI summers and winters. Currently, we are in the third AI summer, characterized by significant advancements and commercialization, particularly in the integration of Symbolic AI and Sub-Symbolic AI, leading to the emergence of Neuro-Symbolic AI. Methods: The review followed the PRISMA methodology, utilizing databases such as IEEE Explore, Google Scholar, arXiv, ACM, and SpringerLink. The inclusion criteria targeted peer-reviewed papers published between 2020 and 2024. Papers were screened for relevance to Neuro-Symbolic AI, with further inclusion based on the availability of associated codebases to ensure reproducibility. Results: From an initial pool of 1,428 papers, 167 met the inclusion criteria and were analyzed in detail. The majority of research efforts are concentrated in the areas of learning and inference (63%), logic and reasoning (35%), and knowledge representation (44%). Explainability and trustworthiness are less represented (28%), with Meta-Cognition being the least explored area (5%). The review identifies significant interdisciplinary opportunities, particularly in integrating explainability and trustworthiness with other research areas. Conclusion: Neuro-Symbolic AI research has seen rapid growth since 2020, with concentrated efforts in learning and inference. Significant gaps remain in explainability, trustworthiness, and Meta-Cognition. Addressing these gaps through interdisciplinary research will be crucial for advancing the field towards more intelligent, reliable, and context-aware AI systems.
A Disintegrating Rocky Planet with Prominent Comet-like Tails Around a Bright Star
2025-01-09
Marc Hon, Saul Rappaport, Avi Shporer, Andrew Vanderburg, Karen A. Collins, Cristilyn N. Watkins, Richard P. Schwarz, Khalid Barkaoui, Samuel W. Yee, Joshua N. Winn, Alex S. Polanski, Emily A. Gilbert, David R. Ciardi, Jeroen Audenaert, William Fong, Jack Haviland, Katharine Hesse, Daniel Muthukrishna, Glen Petitpas, Ellie Hadjiyska Schmelzer, Norio Narita, Akihiko Fukui, Sara Seager, George R. Ricker
We report the discovery of BD+05$\,$4868$\,$Ab, a transiting exoplanet orbiting a bright ($V=10.16$) K-dwarf (TIC 466376085) with a period of 1.27 days. Observations from NASA's Transiting Exoplanet Survey Satellite (TESS) reveal variable transit depths and asymmetric transit profiles that are characteristic of comet-like tails formed by dusty effluents emanating from a disintegrating planet. Unique to BD+05$\,$4868$\,$Ab is the presence of prominent dust tails in both the trailing and leading directions that contribute to the extinction of starlight from the host star. By fitting the observed transit profile and analytically modeling the drift of dust grains within both dust tails, we infer large grain sizes ($\sim1-10\,\mu$m) and a mass loss rate of $10\,M_{\rm \oplus}\,$Gyr$^{-1}$, suggestive of a lunar-mass object with a disintegration timescale of only several Myr. The host star is probably older than the Sun and is accompanied by an M-dwarf companion at a projected physical separation of 130 AU. The brightness of the host star, combined with the planet's relatively deep transits ($0.8-2.0\%$), presents BD+05$\,$4868$\,$Ab as a prime target for compositional studies of rocky exoplanets and investigations into the nature of catastrophically evaporating planets.
We give a short non-technical introduction to the Ising model, and review some successes as well as challenges which have emerged from its study in probability and mathematical physics. This includes the infinite-volume theory of phase transitions, and ideas like scaling, renormalization group, universality, SLE, and random symmetry breaking in disordered systems and networks. This note is based on a talk given on 15 August 2024, as part of the Ising lecture during the 11th Bernoulli-IMS world congress, Bochum.
Developing a Foundation of Vector Symbolic Architectures Using Category Theory
2025-01-09
Nolan P Shaw, P Michael Furlong, Britt Anderson, Jeff Orchard
At the risk of overstating the case, connectionist approaches to machine learning, i.e. neural networks, are enjoying a small vogue right now. However, these methods require large volumes of data and produce models that are uninterpretable to humans. An alternative framework that is compatible with neural networks and gradient-based learning, but explicitly models compositionality, is Vector Symbolic Architectures (VSAs). VSAs are a family of algebras on high-dimensional vector representations. They arose in cognitive science from the need to unify neural processing and the kind of symbolic reasoning that humans perform. While machine learning methods have benefited from category theoretical analyses, VSAs have not yet received similar treatment. In this paper, we present a first attempt at applying category theory to VSAs. Specifically, we conduct a brief literature survey demonstrating the lacking intersection of these two topics, provide a list of desiderata for VSAs, and propose that VSAs may be understood as a (division) rig in a category enriched over a monoid in Met (the category of Lawvere metric spaces). This final contribution suggests that VSAs may be generalised beyond current implementations. It is our hope that grounding VSAs in category theory will lead to more rigorous connections with other research, both within and beyond, learning and cognition.
An ESO-SKAO Synergistic Approach to Galaxy Formation and Evolution Studies
2025-01-09
Isabella Prandoni, Mark Sargent, Elizabeth A. K. Adams, Barbara Catinella, Michele Cirasuolo, Eric Emsellem, Andrew Hopkins, Natasha Maddox, Vincenzo Mainieri, Emily Wisnioski, Matthew Colless
We highlight the potential benefits of a synergistic use of SKAO and ESO facilities for galaxy evolution studies, focusing on the role that ESO spectroscopic surveys can play in supporting next-generation radio continuum and atomic hydrogen (HI) surveys. More specifically we illustrate the role that currently available or soon to be operational ESO multiplex spectrographs can play for three classes of projects: large/deep redshift survey campaigns, integral field unit/Atacama Large Millimeter/submillimeter Array (IFU/ALMA) surveys of selected regions of sky, and IFU/ALMA follow-ups of selected samples. We conclude with some general recommendations for an efficient joint exploitation of ESO-SKAO surveys.
We propose new ways to compare two latent distributions when only ordinal data are available and without imposing parametric assumptions on the underlying continuous distributions. First, we contribute identification results. We show how certain ordinal conditions provide evidence of between-group inequality, quantified by particular quantiles being higher in one latent distribution than in the other. We also show how other ordinal conditions provide evidence of higher within-group inequality in one distribution than in the other, quantified by particular interquantile ranges being wider in one latent distribution than in the other. Second, we propose an "inner" confidence set for the quantiles that are higher for the first latent distribution. We also describe frequentist and Bayesian inference on features of the ordinal distributions relevant to our identification results. Our contributions are illustrated by empirical examples with mental health and general health.
Modal Force Partitioning -- A Method for Determining the Aerodynamic Loads for Decomposed Flow Modes with Application to Aeroacoustic Noise
2025-01-09
Suryansh Prakhar, Jung-Hee Seo, Rajat Mittal
Aerodynamic loads play a central role in many fluid dynamics applications, and we present a method for identifying the structures (or modes) in a flow that make dominant contributions to the time-varying aerodynamic loads in a flow. The method results from the combination of the force partitioning method (Menon and Mittal, J. Fluid Mech., 907:A37, 2021) and modal decomposition techniques such as Reynolds decomposition, triple decomposition, and proper orthogonal decomposition, and is applied here to three distinct flows - two-dimensional flows past a circular cylinder and an airfoil, and the three-dimensional flow over a revolving rectangular wing. We show that force partitioning applied to modal decomposition of velocity fields results in complex, and difficult to interpret inter-modal interactions. We therefore propose and apply modal decomposition directly to the $Q$-field associated with these flows. The variable $Q$ is a non-linear observable that is typically used to identify vortices in a flow, and we find that the direct decomposition of $Q$ leads to results that are more amenable to interpretation. We also demonstrate that this modal force partitioning can be extended to provide insights into the far-field aeroacoustic loading noise of these flows.
Distributed Learning and Inference Systems: A Networking Perspective
2025-01-09
Hesham G. Moussa, Arashmid Akhavain, S. Maryam Hosseini, Bill McCormick
Machine learning models have achieved, and in some cases surpassed, human-level performance in various tasks, mainly through centralized training of static models and the use of large models stored in centralized clouds for inference. However, this centralized approach has several drawbacks, including privacy concerns, high storage demands, a single point of failure, and significant computing requirements. These challenges have driven interest in developing alternative decentralized and distributed methods for AI training and inference. Distribution introduces additional complexity, as it requires managing multiple moving parts. To address these complexities and fill a gap in the development of distributed AI systems, this work proposes a novel framework, Data and Dynamics-Aware Inference and Training Networks (DA-ITN). The different components of DA-ITN and their functions are explored, and the associated challenges and research areas are highlighted.
Forecast of gravitationally lensed Type Ia supernovae time delay measurement by Muztage-Ata 1.93m Synergy Telescope
2025-01-09
Guanhua Rui, Wenwen Zheng, Zizhao He, Yiping Shu, Xinzhong Er, Guoliang Li, Bin Hu
Strong lensing time delay measurement is a promising method to address the Hubble tension, offering a completely independent approach compared to both the cosmic microwave background analysis and the local distance ladder. As a third-party examination of the Hubble tension, this method provides a unique perspective. Strongly lensed quasar (glQSO) systems have demonstrated significant potential in tackling this issue, achieving an impressive \(2\%\) accuracy level. However, advancing to \(1\%\) or sub-percent accuracy is challenging due to several intrinsic limitations of glQSOs. Fortunately, strongly lensed supernovae (glSNe) offer a more robust solution, thanks to their characteristic light curve, significant brightness variations, and additional advantages. The Muztagh-Ata 1.93m Synergy Telescope (MOST) is an exceptional instrument for monitoring strong lensing time delays. In this study, we simulate the follow-up multi-band light curve monitoring for glSNe Ia systems, which are expected to be firstly discovered by the Chinese Survey Space Telescope (CSST). Our results show that with \(300s \times 9\) exposures in each epoch, MOST can achieve a signal-to-noise ratio (SNR) of approximately 50 for the brightest images of glSNe Ia, while even the faintest images maintain an SNR of at least 7. Using a standard SNe Ia light curve template for fitting, we measured the time delays. With a 2-day cadence, MOST achieves a time delay error of only a few hours, with the bias typically remaining below one hour. This study highlights the capability of MOST to significantly advance the precision of time delay measurements, offering a promising path toward resolving the Hubble tension.
Detection of Rumors and Their Sources in Social Networks: A Comprehensive Survey
2025-01-09
Otabek Sattarov, Jaeyoung Choi
With the recent advancements in social network platform technology, an overwhelming amount of information is spreading rapidly. In this situation, it can become increasingly difficult to discern what information is false or true. If false information proliferates significantly, it can lead to undesirable outcomes. Hence, when we receive some information, we can pose the following two questions: $(i)$ Is the information true? $(ii)$ If not, who initially spread that information? % The first problem is the rumor detection issue, while the second is the rumor source detection problem. A rumor-detection problem involves identifying and mitigating false or misleading information spread via various communication channels, particularly online platforms and social media. Rumors can range from harmless ones to deliberately misleading content aimed at deceiving or manipulating audiences. Detecting misinformation is crucial for maintaining the integrity of information ecosystems and preventing harmful effects such as the spread of false beliefs, polarization, and even societal harm. Therefore, it is very important to quickly distinguish such misinformation while simultaneously finding its source to block it from spreading on the network. However, most of the existing surveys have analyzed these two issues separately. In this work, we first survey the existing research on the rumor-detection and rumor source detection problems with joint detection approaches, simultaneously. % This survey deals with these two issues together so that their relationship can be observed and it provides how the two problems are similar and different. The limitations arising from the rumor detection, rumor source detection, and their combination problems are also explained, and some challenges to be addressed in future works are presented.
MoSi$_2$N$_4$-like crystals -- the new family of two-dimensional materials
2025-01-09
Tatiana Latychevskaia, Denis A. Bandurin, Kostya S. Novoselov
Recently-synthesised MoSi$_2$N$_4$ is the first septuple layer two-dimensional material, which doesn't naturally occurs as a layered crystal, and has been obtained with CVD growth. It can be considered as MoN$_2$ crystal (with a crystal structure of MoS2) intercalating Si2N2 two-dimensional layer (with the structure similar to InSe). Such classification gave rise to the understanding of the electronic properties of the material, but also to the prediction of other members of the family (many dozens of them) as well as to the way to classify those. Whereas the originally-synthesised MoSi$_2$N$_4$ is a semiconductor, some of the members of the family are also metallic and some even demonstrate magnetic properties. Interestingly, the room-temperature mobility predicted for such crystals can be as high few thousands cm$^2$/V*s (hole mobility typically higher than electron) with some record cases as high as $5\times 10^4$ cm$^2$/V*s, making these materials strong contenders for future electronic applications. The major interest towards these materials is coming from the septuple layer structure, which allows multiple crystal phases, but also complex compositions, in particular those with broken mirror-reflection symmetry against the layer of metal atoms.
Research on quantum compilation of neutral atom quantum computing platform
2025-01-09
Chongyuan Xu
Quantum compilation is the process of decomposing high-level quantum algorithms or arbitrary unitary operations into quantum circuits composed of a specific set of quantum gates. Neutral atom quantum computing platform is a quantum computing implementation method with high controllability and scalability, but its quantum compilation method is not mature. We systematically review the quantum compilation methods based on matrix decomposition, and propose a compilation algorithm suitable for neutral atom quantum computing, which can effectively decompose any unitary operation into a series of quantum gates suitable for the neutral atom platform, and ensure that the generated quantum circuits can run directly on the platform.
Implementation Pitfalls for Carbonate Mineral Dissolution -- a Technical Note
2025-01-09
Fiona J. Weiss, Leon Keim, Kai Wendel, Holger Class
In systems with slow reaction kinetics, such as mineral dissolution processes, chemical equilibrium cannot be assumed and an accurate understanding of reaction rates is essential; discrepancies in parameter reporting can greatly affect simulation results. This technical note identifies an issue with the reporting of rate parameters for carbonate mineral dissolution in a widely used database for reactive transport modeling based on Palandri and Kharaka 2004. This misrepresentation leads to a considerable overestimation of reaction timescales. Using the simulators Reaktoro and DuMuX, we simulated a simple calcite dissolution batch test and compared the results to experimental data. By adjusting the parameter to align with established literature, we demonstrate an improved fit between simulated and experimental data. Discrepancies in reaction timescales were reduced by an order of magnitude, emphasizing the importance of regular validation of simulations with experimental data.
A Thermodynamic Theory of Proximity Ferroelectricity
2025-01-09
Eugene A. Eliseev, Anna N. Morozovska, Jon-Paul Maria, Long-Qing Chen, Venkatraman Gopalan
Proximity ferroelectricity has recently been reported as a new design paradigm for inducing ferroelectricity, where a non-ferroelectric polar material becomes a ferroelectric by interfacing with a thin ferroelectric layer. Strongly polar materials, such as AlN and ZnO, which were previously unswitchable with an external field below their dielectric breakdown fields, can now be switched with practical coercive fields when they are in intimate proximity to a switchable ferroelectric. Here, we develop a general Landau-Ginzburg theory of proximity ferroelectricity in multilayers of non-ferroelectrics and ferroelectrics to analyze their switchability and coercive fields. The theory predicts regimes of both "proximity switching" where the multilayers collectively switch, as well as "proximity suppression" where they collectively do not switch. The mechanism of the proximity ferroelectricity is an internal electric field determined by the polarization of the layers and their relative thickness in a self-consistent manner that renormalizes the double-well ferroelectric potential to lower the steepness of the switching barrier. Further reduction in the coercive field emerges from charged defects in the bulk that act as nucleation centers. The application of the theory to proximity ferroelectricity in Alx-1ScxN/AlN and Zn1-xMgxO/ZnO bilayers is demonstrated. The theory further predicts that multilayers of dielectric/ferroelectric and paraelectric/ferroelectric layers can potentially result in induced ferroelectricity in the dielectric or paraelectric layers, resulting in the entire stack being switched, an exciting avenue for new discoveries. This thawing of "frozen ferroelectrics", paraelectrics and potentially dielectrics, promises a large class of new ferroelectrics with exciting prospects for previously unrealizable domain-patterned optoelectronic and memory technologies.
Deep Assessment of Code Review Generation Approaches: Beyond Lexical Similarity
2025-01-09
Yanjie Jiang, Hui Liu, Tianyi Chen, Fu Fan, Chunhao Dong, Kui Liu, Lu Zhang
Code review is a standard practice for ensuring the quality of software projects, and recent research has focused extensively on automated code review. While significant advancements have been made in generating code reviews, the automated assessment of these reviews remains less explored, with existing approaches and metrics often proving inaccurate. Current metrics, such as BLEU, primarily rely on lexical similarity between generated and reference reviews. However, such metrics tend to underestimate reviews that articulate the expected issues in ways different from the references. In this paper, we explore how semantic similarity between generated and reference reviews can enhance the automated assessment of code reviews. We first present a benchmark called \textit{GradedReviews}, which is constructed by collecting real-world code reviews from open-source projects, generating reviews using state-of-the-art approaches, and manually assessing their quality. We then evaluate existing metrics for code review assessment using this benchmark, revealing their limitations. To address these limitations, we propose two novel semantic-based approaches for assessing code reviews. The first approach involves converting both the generated review and its reference into digital vectors using a deep learning model and then measuring their semantic similarity through Cosine similarity. The second approach generates a prompt based on the generated review and its reference, submits this prompt to ChatGPT, and requests ChatGPT to rate the generated review according to explicitly defined criteria. Our evaluation on the \textit{GradedReviews} benchmark indicates that the proposed semantic-based approaches significantly outperform existing state-of-the-art metrics in assessing generated code review, improving the correlation coefficient between the resulting scores and human scores from 0.22 to 0.47.
Bringing Order Amidst Chaos: On the Role of Artificial Intelligence in Secure Software Engineering
2025-01-09
Matteo Esposito
Context. Developing secure and reliable software remains a key challenge in software engineering (SE). The ever-evolving technological landscape offers both opportunities and threats, creating a dynamic space where chaos and order compete. Secure software engineering (SSE) must continuously address vulnerabilities that endanger software systems and carry broader socio-economic risks, such as compromising critical national infrastructure and causing significant financial losses. Researchers and practitioners have explored methodologies like Static Application Security Testing Tools (SASTTs) and artificial intelligence (AI) approaches, including machine learning (ML) and large language models (LLMs), to detect and mitigate these vulnerabilities. Each method has unique strengths and limitations. Aim. This thesis seeks to bring order to the chaos in SSE by addressing domain-specific differences that impact AI accuracy. Methodology. The research employs a mix of empirical strategies, such as evaluating effort-aware metrics, analyzing SASTTs, conducting method-level analysis, and leveraging evidence-based techniques like systematic dataset reviews. These approaches help characterize vulnerability prediction datasets. Results. Key findings include limitations in static analysis tools for identifying vulnerabilities, gaps in SASTT coverage of vulnerability types, weak relationships among vulnerability severity scores, improved defect prediction accuracy using just-in-time modeling, and threats posed by untouched methods. Conclusions. This thesis highlights the complexity of SSE and the importance of contextual knowledge in improving AI-driven vulnerability and defect prediction. The comprehensive analysis advances effective prediction models, benefiting both researchers and practitioners.
Detection of binary companions below the diffraction limit with lucky imaging
2025-01-09
Miguel A. Cagigas, R. Clavero, Manuel P. Cagigal, David Nespral, A. A. Djupvik, D. Jones, Pedro J. Valle, Vidal F. Canales, E. Soria, R. López, O. Zamora, A. Oscoz, J. Marco
We provide a technique for resolving intermediate-separation binaries stars with medium-sized telescopes (i.e. diameter less than or equal to 2.5 m) at wavelengths around 825 nm in the super-resolution range (i.e. below the limit defined by the Rayleigh criterion). We combined two well-known algorithms that have been applied to reduce the halo in lucky imaging observations: COvariancE of Lucky Images (COELI) and the Lucky Imaging Speckle Suppression Algorithm (LISSA). We reviewed the fundamentals of both algorithms and describe a new technique called Lucky Imaging Super resolution Technique (LIST), which is optimized for peak highlighting within the first ring of the Airy pattern. To validate the technique, we carried out several observing campaigns of well-known binary stars with FastCam on the 1.52 m TCS and 2.56 m NOT. The projected angular separation between objects was resolved by applying LIST with a result below 0.15". It can go down to approximately 0.05", given the limitations of the detector plate scale. This is, to our knowledge, the first time that binary companions with such small angular separations have been detected using only lucky imaging at optical wavelengths. The average accuracy achieved for the angular separation measurement is 16 mas with NOT and is 20 mas with TCS. The average accuracy obtained for the position angle measurement is $9.5^o$ for NOT and $11^o$ for TCS. We also made an attempt to measure the relative brightnesses of the binary components, obtaining results that are compatible with literature measurements. Lucky imaging, in combination with speckle suppression and a covariance analysis, can allow the resolution of multiple point sources below the diffraction limit of 2-m class telescopes. However, it should be noted that measurements in the super-resolution regime are less sensitive than those above the first Airy ring.
A Systematic Literature Review on Deep Learning-based Depth Estimation in Computer Vision
2025-01-09
Ali Rohan, Md Junayed Hasan, Andrei Petrovski
Depth estimation (DE) provides spatial information about a scene and enables tasks such as 3D reconstruction, object detection, and scene understanding. Recently, there has been an increasing interest in using deep learning (DL)-based methods for DE. Traditional techniques rely on handcrafted features that often struggle to generalise to diverse scenes and require extensive manual tuning. However, DL models for DE can automatically extract relevant features from input data, adapt to various scene conditions, and generalise well to unseen environments. Numerous DL-based methods have been developed, making it necessary to survey and synthesize the state-of-the-art (SOTA). Previous reviews on DE have mainly focused on either monocular or stereo-based techniques, rather than comprehensively reviewing DE. Furthermore, to the best of our knowledge, there is no systematic literature review (SLR) that comprehensively focuses on DE. Therefore, this SLR study is being conducted. Initially, electronic databases were searched for relevant publications, resulting in 1284 publications. Using defined exclusion and quality criteria, 128 publications were shortlisted and further filtered to select 59 high-quality primary studies. These studies were analysed to extract data and answer defined research questions. Based on the results, DL methods were developed for mainly three different types of DE: monocular, stereo, and multi-view. 20 publicly available datasets were used to train, test, and evaluate DL models for DE, with KITTI, NYU Depth V2, and Make 3D being the most used datasets. 29 evaluation metrics were used to assess the performance of DE. 35 base models were reported in the primary studies, and the top five most-used base models were ResNet-50, ResNet-18, ResNet-101, U-Net, and VGG-16. Finally, the lack of ground truth data was among the most significant challenges reported by primary studies.
Detection of thioacetaldehyde (CH3CHS) in TMC-1: sulfur-oxygen differentiation along the hydrogenation sequence
2025-01-09
M. Agundez, G. Molpeceres, C. Cabezas, N. Marcelino, B. Tercero, R. Fuentetaja, P. de Vicente, J. Cernicharo
In recent years the chemistry of sulfur in the interstellar medium has experienced a renewed interest due to the detection of a large variety of molecules containing sulfur. Here we report the first identification in space of a new S-bearing molecule, thioacetaldehyde (CH3CHS), which is the sulfur counterpart of acetaldehyde (CH3CHO). The astronomical observations are part of QUIJOTE, a Yebes 40m Q band line survey of the cold dense cloud TMC-1. We detected seven individual lines corresponding to A and E components of the four most favorable rotational transitions of CH3CHS covered in the Q band (31.0-50.3 GHz). Assuming a rotational temperature of 9 K, we derive a column density of 9.8e10 cm-2 for CH3CHS, which implies that it is 36 times less abundant than its oxygen counterpart CH3CHO. By comparing the column densities of the O- and S-bearing molecules detected in TMC-1, we find that as molecules increase their degree of hydrogenation, sulfur-bearing molecules become less abundant compared to their oxygen analog. That is, hydrogenation seems to be less favored for S-bearing molecules than for O-bearing ones in cold sources like TMC-1. We explored potential formation pathways to CH3CHS and implemented them into a chemical model, which however underestimates by several orders of magnitude the observed abundance of thioacetaldehyde. Quantum chemical calculations carried out for one of the potential formation pathways, the S + C2H5 reaction, indicate that formation of CH3CHS is only a minor channel in this reaction.
The ESO SupJup Survey V: Exploring Atmospheric Variability and Orbit of the Super-Jupiter AB Pictoris b with CRIRES+
2025-01-09
Siddharth Gandhi, Sam de Regt, Ignas Snellen, Paulina Palma-Bifani, Idriss Abdoulwahab, Gaël Chauvin, Darío González Picos, Yapeng Zhang, Rico Landman, Tomas Stolker, Aurora Kesseli, Willeke Mulder, Antoine Chomez, Anne-Marie Lagrange, Alice Zurlo
A growing number of directly-imaged companions have been recently characterised, with robust constraints on carbon-to-oxygen ratios and even isotopic ratios. Many companions and isolated targets have also shown spectral variability. In this work we observed the super-Jupiter AB~Pictoris~b across four consecutive nights using VLT/CRIRES+ as part of the ESO SupJup survey, exploring how the constraints on chemical composition and temperature profile change over time using spectral line shape variations between nights. We performed atmospheric retrievals of the high-resolution observations and found broadly consistent results across all four nights, but there were differences for some parameters. We clearly detect H$_2$O, $^{12}$CO and $^{13}$CO in each night, but abundances varied by $\sim2\sigma$, which was correlated to the deep atmosphere temperature profiles. We also found differences in the $^{12}$C$/^{13}$C ratios in each night by up to $\sim3\sigma$, which seemed to be correlated with the cloud deck pressure. Our combined retrieval simultaneously analysing all nights together constrained broadly the average of each night individually, with the C/O$=0.59\pm0.01$, consistent with solar composition, and $^{12}$C$/^{13}$C~$ = 102\pm8$, slightly higher than the ISM and Solar System values. We also find a low projected rotational velocity, suggesting that AB~Pictoris~b is either intrinsically a slow rotator due to its young age or that the spin axis is observed pole-on with a $\sim90^\circ$ misalignment with its orbit inclination. Future observations will be able to further explore the variability and orbit of AB~Pictoris~b as well as for other companions.
Advancing ALS Applications with Large-Scale Pre-training: Dataset Development and Downstream Assessment
2025-01-09
Haoyi Xiu, Xin Liu, Taehoon Kim, Kyoung-Sook Kim
The pre-training and fine-tuning paradigm has revolutionized satellite remote sensing applications. However, this approach remains largely underexplored for airborne laser scanning (ALS), an important technology for applications such as forest management and urban planning. In this study, we address this gap by constructing a large-scale ALS point cloud dataset and evaluating its impact on downstream applications. Our dataset comprises ALS point clouds collected across the contiguous United States, provided by the United States Geological Survey's 3D Elevation Program. To ensure efficient data collection while capturing diverse land cover and terrain types, we introduce a geospatial sampling method that selects point cloud tiles based on land cover maps and digital elevation models. As a baseline self-supervised learning model, we adopt BEV-MAE, a state-of-the-art masked autoencoder for 3D outdoor point clouds, and pre-train it on the constructed dataset. The pre-trained models are subsequently fine-tuned for downstream tasks, including tree species classification, terrain scene recognition, and point cloud semantic segmentation. Our results show that the pre-trained models significantly outperform their scratch counterparts across all downstream tasks, demonstrating the transferability of the representations learned from the proposed dataset. Furthermore, we observe that scaling the dataset using our geospatial sampling method consistently enhances performance, whereas pre-training on datasets constructed with random sampling fails to achieve similar improvements. These findings highlight the utility of the constructed dataset and the effectiveness of our sampling strategy in the pre-training and fine-tuning paradigm. The source code and pre-trained models will be made publicly available at \url{https://github.com/martianxiu/ALS_pretraining}.
In a wide-area spectroscopic survey of galaxies, it is nearly impossible to obtain a homogeneous sample of galaxies with respect to galaxy properties such as stellar mass and host halo mass across a range of redshifts. Despite the selection effect, theoretical templates in most analyses assume single tracers when compared with the measured clustering quantities. We demonstrate analytically that the selection effect inevitably introduces a bias in the redshift-space power spectrum on scales from linear to nonlinear scales. To quantitatively assess the impact of the selection effect, we construct mock galaxy catalogs from halos in N-body simulations by selecting halos above redshift-dependent mass thresholds such that the resulting redshift distribution of the halos, $n(z)$, matches that of SDSS-like galaxies. We find that the selection effect causes fractional changes of up to only 1% and 2% in the monopole and quadrupole moments of the redshift-space power spectrum at $k\lesssim 0.3~h{\rm{Mpc}}^{-1}$, respectively, compared to the moments for the single mass-threshold (therefore single tracer) sample, for $n_{\rm g}(z)$ of the SDSS-like galaxy samples. We also argue that the selection effect is unlikely to cause a significant bias in the estimation of cosmological parameters using the Fisher matrix method, provided that the redshift-dependent selection effect is modest.
The continual production of gravitons during inflation endows loop corrections with secular logarithms which grow nonperturbatively large during a prolonged period of inflation. The physics behind these effects is reviewed, along with a catalog of the examples which have so far been found. Resummation can be accomplished by combining a variant of Starobinsky's stochastic formalism with a variant of the renormalization group. The issue of gauge independence is also addressed.
Fully comprehensive diagnostic of galaxy activity using principal components of visible spectra: implementation on nearby S0s
2025-01-09
J. L. Tous, J. M. Solanes, J. D. Perea
We introduce a novel galaxy classification methodology based on the visible spectra of a sample of over 68,000 nearby ($z\leq 0.1$) Sloan Digital Sky Survey lenticular (S0) galaxies. Unlike traditional diagnostic diagrams, which rely on a limited set of emission lines and class dividers to identify ionizing sources, our approach provides a comprehensive framework for characterizing galaxies regardless of their activity level. By projecting galaxies into the 2D latent space defined by the first three principal components (PCs) of their entire visible spectra, our method remains robust even when data from individual emission lines are missing. We employ Gaussian kernel density estimates of the classical Baldwin-Phillips-Terlevich (BPT) activity classes in the new classification subspace, adjusted according to their relative abundance in our S0 sample, to generate probability maps for star-forming, Seyfert, composite, and LINER galaxies. These maps closely mirror the canonical distribution of BPT classes shown by the entire galaxy population, demonstrating that our PC-based taxonomy effectively predicts the dominant ionizing mechanisms through a probabilistic approach that provides a realistic reflection of galaxy activity and allows for refined class membership. Our analysis further reveals that flux-limited BPT-like diagrams are inherently biased against composite and star-forming galaxies due to their weaker [OIII] emission. Besides, it suggests that although most low-activity galaxies excluded from these diagnostics exhibit visual spectra with LINER-like characteristics, their remaining activity is likely driven by mechanisms unrelated to either star formation or supermassive black hole accretion. A machine-readable catalogue listing BPT-class probabilities for the galaxies analysed is available online at the CDS website.
Time-domain study of coupled collective excitations in quantum materials
2025-01-09
Chenhang Xu, Alfred Zong
Quantum materials hold immense promises for future applications due to their intriguing electronic, magnetic, thermal, and mechanical properties that are often traced to a complex interplay among different microscopic degrees of freedom. Important insights of such interactions come from studying the collective excitations of electrons, spins, orbitals, and lattice, whose cooperative motions play a crucial role in determining the novel behavior of these systems and offer us a key tuning knob to modify material properties on-demand through external perturbations. In this regard, ultrafast light-matter interaction has shown great potential in controlling the couplings of collective excitations, and rapid progress in a plethora of time-resolved techniques down to the attosecond regime has significantly advanced our understanding of the coupling mechanism and guided us in manipulating the dynamical property of quantum materials. This review aims to highlight recent experiments on visualizing collective excitations in the time domain, focusing on the coupling mechanism between different collective modes such as phonon-phonon, phonon-magnon, phonon-exciton, magnon-magnon, magnon-exciton, and various polaritons. We introduce how these collective modes are excited by an ultrashort laser pulse and probed by different ultrafast techniques, and we explain how the coupling between collective excitations governs the ensuing nonequilibrium dynamics. We also provide some perspectives on future studies that can lead to further discoveries regarding the emergent properties of quantum materials both in and out of equilibrium.
Euclid: Detecting Solar System objects in Euclid images and classifying them using Kohonen self-organising maps
2025-01-09
A. A. Nucita, L. Conversi, A. Verdier, A. Franco, S. Sacquegna, M. Pöntinen, B. Altieri, B. Carry, F. De Paolis, F. Strafella, V. Orofino, M. Maiorano, V. Kansal, R. D. Vavrek, M. Miluzio, M. Granvik, V. Testa, N. Aghanim, S. Andreon, N. Auricchio, M. Baldi, S. Bardelli, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, G. Congedo, C. J. Conselice, Y. Copin, F. Courbin, H. M. Courtois, A. Da Silva, H. Degaudenzi, A. M. Di Giorgio, J. Dinis, F. Dubath, X. Dupac, S. Dusini, M. Farina, S. Farrens, S. Ferriol, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, B. Gillis, C. Giocoli, P. Gómez-Alvarez, A. Grazian, F. Grupp, S. V. H. Haugan, J. Hoar, W. Holmes, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, E. Keihänen, S. Kermiche, A. Kiessling, M. Kilbinger, R. Kohley, B. Kubik, M. Kümmel, H. Kurki-Suonio, R. Laureijs, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, N. Martinet, F. Marulli, R. Massey, D. C. Masters, E. Medinaceli, S. Mei, Y. Mellier, M. Meneghetti, G. Meylan, M. Moresco, L. Moscardini, R. Nakajima, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, D. Sapone, B. Sartoris, M. Schirmer, P. Schneider, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, J. Skottfelt, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, A. Zacchei, E. Zucca, M. Bolzonella, C. Burigana, V. Scottez
The ESA Euclid mission will survey more than 14,000 deg$^2$ of the sky in visible and near-infrared wavelengths, mapping the extra-galactic sky to constrain our cosmological model of the Universe. Although the survey focusses on regions further than 15 deg from the ecliptic, it should allow for the detection of more than about $10^5$ Solar System objects (SSOs). After simulating the expected signal from SSOs in Euclid images acquired with the visible camera (VIS), we describe an automated pipeline developed to detect moving objects with an apparent velocity in the range of 0.1-10 arcsec/h, typically corresponding to sources in the outer Solar System (from Centaurs to Kuiper-belt objects). In particular, the proposed detection scheme is based on Sourcextractor software and on applying a new algorithm capable of associating moving objects amongst different catalogues. After applying a suite of filters to improve the detection quality, we study the expected purity and completeness of the SSO detections. We also show how a Kohonen self-organising neural network can be successfully trained (in an unsupervised fashion) to classify stars, galaxies, and SSOs. By implementing an early-stopping method in the training scheme, we show that the network can be used in a predictive way, allowing one to assign the probability of each detected object being a member of each considered class.
We report the results of molecular line observations with the Atacama Large Millimeter/submillimeter Array (ALMA) towards two peculiar icy objects, which were discovered serendipitously by infrared spectroscopic survey of the Galactic plane with the AKARI satellite. Previous infrared observations have reported that both objects show deep ice and dust absorption features that are often seen in embedded young stellar objects (YSOs) or background stars sitting behind dense clouds, however, they are located neither in known star-forming regions nor in known dense clouds. Their infrared spectral energy distributions (SEDs) show a peak around 5 micron, which are incompatible with existing SED models of typical embedded YSOs. The present ALMA observations have detected compact emission of CO(3-2) and SiO(8-7) at the positions of the icy objects. The observed large column ratios of gas-phase SiO/CO (~10^-3) in both objects, as well as their broad line widths (8-14 km/s), imply that they are associated with shocked gas. Although a large dust extinction (Av ~100 mag) is expected from their deep dust/ice absorption, no dust continuum emission is detected, which would suggest a large beam dilution effect due to their compact source sizes. Their systemic velocities are clearly separated from the surrounding CO clouds, suggesting that they are isolated. The characteristics of their SEDs, the presence of deep dust/ice absorption features, compact source size, and SiO-dominated broad molecular line emission, cannot easily be accounted for by any of known interstellar ice-absorption sources. They may represent a previously unknown type of isolated icy objects.
The Rapid ASKAP Continuum Survey (RACS) VI: The RACS-high 1655.5 MHz images and catalogue
2025-01-09
S. W. Duchesne, K. Ross, A. J. M. Thomson, E. Lenc, Tara Murphy, T. J. Galvin, A. W. Hotan, V. A. Moss, Matthew T. Whiting
We have conducted a widefield, wideband, snapshot survey using the Australian SKA Pathfinder (ASKAP) referred to as the Rapid ASKAP Continuum Survey (RACS). RACS covers $\approx$ 90% of the sky, with multiple observing epochs in three frequency bands sampling the ASKAP frequency range of 700 to 1800 MHz. This paper describes the third major epoch at 1655.5 MHz, RACS-high, and the subsequent imaging and catalogue data release. The RACS-high observations at 1655.5 MHz are otherwise similar to the previously released RACS-mid (at 1367.5 MHz), and were calibrated and imaged with minimal changes. From the 1493 images covering the sky up to declination $\approx$ +48$^\circ$, we present a catalogue of 2 677 509 radio sources. The catalogue is constructed from images with a median root-mean-square noise of $\approx$ 195 $\mu$Jy PSF$^{-1}$ (point-spread function) and a median angular resolution of 11.8" by 8.1". The overall reliability of the catalogue is estimated to be 99.18%, and we find a decrease in reliability as angular resolution improves. We estimate the brightness scale to be accurate to 10%, and the astrometric accuracy to be within $\approx$ 0.6" in right ascension and $\approx$ 0.7" in declination after correction of a systematic declination-dependent offset. All data products from RACS-high, including calibrated visibility datasets, images from individual observations, full-sensitivity mosaics, and the all-sky catalogue are available at the CSIRO ASKAP Science Data Archive.
Influence of photometric galaxies redshift distribution in BAO estimation
2025-01-09
Paula S. Ferreira, Ribamar R. R. Reis
Photometric surveys are good solutions for large-scale structure studies. The Baryon Acoustic Oscillations (BAO) benefit from photometric redshift survey observations due to faster coverage and a higher number of observed objects. In the present study, we use the Dark Energy Survey Year 3 catalog of Luminous Red Galaxies (LRG) to use the realistic galaxies' Probability Distribution Function (PDF) that describes each galaxy redshift to select samples and include in the fitting model as the input for the redshift uncertainty. We used four different photo-z estimators \texttt{ANNz2}, \texttt{BPZ}, \texttt{ENF}, and \texttt{DNF} to compare how they affect the BAO feature constraint, the catalogs called \texttt{Pz Cats}. Furthermore, for each algorithm, we selected two different samples according to the PDFs shape, one where the PDFs are nearly Gaussian and the other that chooses the least noisy PDFs in which the PDFs have one pronounced peak. We computed the correlation function $\xi_\perp(z_p)$ by getting the bin pairs transversal to each other using \texttt{CAMB}. The kernel window function is the $f(z|z_p)$ which is the selection of the PDF value when the photometric redshift is nearly the same as the spectroscopic redshift estimated by the matched spectroscopic sample. For compatible $z_{eff}$, We concluded that the shape of the galaxy redshift PDF could shift the BAO feature position either by including the PDF in the model or not We also learnt that, given the same spectroscopic sample, \texttt{ANNz2} is the best photo-z estimator for BAO analysis considering the realistic full probability distribution function of each galaxy.
MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain
Deep neural network (DNN) based perception models are indispensable in the development of autonomous vehicles (AVs). However, their reliance on large-scale, high-quality data is broadly recognized as a burdensome necessity due to the substantial cost of data acquisition and labeling. Further, the issue is not a one-time concern, as AVs might need a new dataset if they are to be deployed to another region (real-target domain) that the in-hand dataset within the real-source domain cannot incorporate. To mitigate this burden, we propose leveraging synthetic environments as an auxiliary domain where the characteristics of real domains are reproduced. This approach could enable indirect experience about the real-target domain in a time- and cost-effective manner. As a practical demonstration of our methodology, nuScenes and South Korea are employed to represent real-source and real-target domains, respectively. That means we construct digital twins for several regions of South Korea, and the data-acquisition framework of nuScenes is reproduced. Blending the aforementioned components within a simulator allows us to obtain a synthetic-fusion domain in which we forge our novel driving dataset, MORDA: Mixture Of Real-domain characteristics for synthetic-data-assisted Domain Adaptation. To verify the value of synthetic features that MORDA provides in learning about driving environments of South Korea, 2D/3D detectors are trained solely on a combination of nuScenes and MORDA. Afterward, their performance is evaluated on the unforeseen real-world dataset (AI-Hub) collected in South Korea. Our experiments present that MORDA can significantly improve mean Average Precision (mAP) on AI-Hub dataset while that on nuScenes is retained or slightly enhanced.
Low-mass galaxies are the building blocks of massive galaxies in the framework of hierarchical structure formation. To enable detailed studies of galactic ecosystems in dwarf galaxies by spatially resolving different galactic components, we have carried out the Dwarf Galaxy Integral-field Survey (DGIS). This survey aims to acquire observations with spatial resolutions as high as 10 to 100 pc while maintaining reasonably high signal-to-noise ratios with VLT/MUSE and ANU-2.3m/WiFeS. The whole sample will be composed of 65 dwarf galaxies with $M_{\rm \ast}$ $<$ 10$^{9}$ $\rm M_{\odot}$, selected from the Spitzer Local Volume Legacy Survey. The overall scientific goals include studying baryonic cycles in dwarf galaxies, searching for off-nuclear (intermediate)-massive black holes, and quantifying the inner density profiles of dark matter. In this work, we describe the sample selection, data reduction, and high-level data products. By integrating the spectra over the field of view for each galaxy, we obtained the integrated gas-phase metallicity and discussed its dependence on stellar mass and SFR. We find that the overall relation between metallicity and stellar mass of our DGIS nearly follows the extrapolation from the higher mass end. Its dispersion does not decrease by invoking the dependence on SFR.
The Catalogue of Virtual Early-Type Galaxies from IllustrisTNG: Validation and Real Observation Consistency
2025-01-09
Pedro de Araujo Ferreira, Nicola R. Napolitano, Luciano Casarini, Crescenzo Tortora, Rodrigo von Marttens, Sirui Wu
Early-type galaxies (ETGs) are reference systems to understand galaxy formation and evolution processes. The physics of their collapse and internal dynamics are codified in well-known scaling relations. Cosmological hydrodynamical simulations play an important role, providing insights into the 3D distribution of matter and galaxy formation mechanisms, as well as validating methods to infer the properties of real objects. In this work, we present the closest-to-reality sample of ETGs from the IllustrisTNG100-1 simulation, dubbed "virtual-ETGs," based on an observational-like algorithm that combines standard projected and three-dimensional galaxy structural parameters. We extract 2D photometric information by projecting the galaxies' light into three planes and modeling them via S\'ersic profiles. Aperture velocity dispersions, corrected for softened central dynamics, are calculated along the line-of-sight orthogonal to the photometric projection plane. Central mass density profiles assume a power-law model, while 3D masses remain unmodified from the IllustrisTNG catalogue. The final catalogue includes $10121$ galaxies at redshifts $z \leq 0.1$. By comparing the virtual properties with observations, we find that the virtual-ETG scaling relations (e.g., size-mass, size-central surface brightness, and Faber-Jackson), central density slopes, and scaling relations among total density slopes and galaxy structural parameters are generally consistent with observations. We make the virtual-ETG publicly available for galaxy formation studies and plan to use this sample as a training set for machine learning tools to infer galaxy properties in future imaging and spectroscopic surveys.
2025-01-09
C. S. Abelson, Carles Badenes, Laura Chomiuk, Benjamin F. Williams, Katelyn Breivik, Lluís Galbany, Cristina Jimenez Palau
We present the first characterization of the statistical relationship between a large sample of novae in M31 and their progenitor stellar populations in the form of a delay time distribution. To this end, we leverage the spatially resolved stellar age distribution of the M31 disk derived from deep HST photometry by the Panchromatic Hubble Andromeda Treasury (PHAT) survey and a large catalog of novae in M31. Our delay time distribution has two statistically significant detections: one population of nova progenitors, ages between 2 and 3.2 Gyr, with an unnormalized rate of ($3.7^{+6.8}_{-3.5} \pm 2.1) \cdot 10^{-9}$ events / $M_{\odot}$, and another of ages between 7.9 Gyr and the age of the Universe with ($4.8^{+1.0}_{-0.9} \pm 0.2) \cdot 10^{-9}$ events / $M_{\odot}$ (uncertainties are statistical and systematic, respectively). Together with the upper limits we derive at other time bins, these detections are consistent with either a constant production efficiency or a higher production efficiency of novae at earlier delay times.
2025-01-09
Jake H. Lee, Michael Kiper, David R. Thompson, Philip G. Brodrick
Current and upcoming generations of visible-shortwave infrared (VSWIR) imaging spectrometers promise unprecedented capacity to quantify Earth System processes across the globe. However, reliable cloud screening remains a fundamental challenge for these instruments, where traditional spatial and temporal approaches are limited by cloud variability and limited temporal coverage. The Spectroscopic Transformer (SpecTf) addresses these challenges with a spectroscopy-specific deep learning architecture that performs cloud detection using only spectral information (no spatial or temporal data are required). By treating spectral measurements as sequences rather than image channels, SpecTf learns fundamental physical relationships without relying on spatial context. Our experiments demonstrate that SpecTf significantly outperforms the current baseline approach implemented for the EMIT instrument, and performs comparably with other machine learning methods with orders of magnitude fewer learned parameters. Critically, we demonstrate SpecTf's inherent interpretability through its attention mechanism, revealing physically meaningful spectral features the model has learned. Finally, we present SpecTf's potential for cross-instrument generalization by applying it to a different instrument on a different platform without modifications, opening the door to instrument agnostic data driven algorithms for future imaging spectroscopy tasks.
Another piece to the puzzle: radio detection of a JWST discovered AGN candidate
2025-01-09
Anniek J. Gloudemans, Kenneth J. Duncan, Anna-Christina Eilers, Emanuele Paolo Farina, Yuichi Harikane, Kohei Inayoshi, Erini Lambrides, Eleni Vardoulaki
Radio observations can provide crucial insight into the nature of a new abundant and mysterious population of dust-reddened active galactic nuclei (AGN) candidates discovered by the James Webb Space Telescope (JWST), including ``Little Red Dots" (LRDs). In this study, we search for radio bright sources in a large sample of $\sim$700 JWST discovered AGN candidates ($z\sim2-11$) in the 0.144-3 GHz frequency range, utilizing deep radio imaging in COSMOS, GOODS-N, and GOODS-S. Only one source is significantly detected in our radio surveys, which is PRIMER-COS 3866 at $z=4.66$. Its radio properties are consistent with both an AGN and star formation origin with a spectral index of $\alpha=-0.76^{+0.11}_{-0.09}$ and a radio-loudness of $R\approx0.5$. The derived brightness temperature limit of PRIMER-COS 3866 of $T_b \gtrsim 10^{3}$ K is too low to confirm its AGN nature. Our stacking results yield non-detections in all fields with the most constraining 3$\sigma$ limit $L_{1.3\text{GHz}} < 1.3\times10^{39}$ erg s$^{-1}$ (rms of $\sim$0.15 $\mu$Jy beam$^{-1}$ at $z_{\text{median}}=6.1$) obtained for photometrically selected AGN candidates in the COSMOS field. This result is still consistent with expectations from the empirical $L_X - L_{\text{H}\alpha}$ and $L_X - L_R$ correlations established for local AGN. We conclude that current radio observations have insufficient depth to claim JWST discovered AGN candidates are radio-weak. We project that future surveys carried out by the SKA and ngVLA should be able to obtain significant detections within a few hours, providing crucial measurements of their brightness temperature, which would allow for distinguishing between AGN and starburst-driven origins of this new abundant population.
Online Continual Learning: A Systematic Literature Review of Approaches, Challenges, and Benchmarks
2025-01-09
Seyed Amir Bidaki, Amir Mohammadkhah, Kiyan Rezaee, Faeze Hassani, Sadegh Eskandari, Maziar Salahi, Mohammad M. Ghassemi
Online Continual Learning (OCL) is a critical area in machine learning, focusing on enabling models to adapt to evolving data streams in real-time while addressing challenges such as catastrophic forgetting and the stability-plasticity trade-off. This study conducts the first comprehensive Systematic Literature Review (SLR) on OCL, analyzing 81 approaches, extracting over 1,000 features (specific tasks addressed by these approaches), and identifying more than 500 components (sub-models within approaches, including algorithms and tools). We also review 83 datasets spanning applications like image classification, object detection, and multimodal vision-language tasks. Our findings highlight key challenges, including reducing computational overhead, developing domain-agnostic solutions, and improving scalability in resource-constrained environments. Furthermore, we identify promising directions for future research, such as leveraging self-supervised learning for multimodal and sequential data, designing adaptive memory mechanisms that integrate sparse retrieval and generative replay, and creating efficient frameworks for real-world applications with noisy or evolving task boundaries. By providing a rigorous and structured synthesis of the current state of OCL, this review offers a valuable resource for advancing this field and addressing its critical challenges and opportunities. The complete SLR methodology steps and extracted data are publicly available through the provided link: https://github.com/kiyan-rezaee/ Systematic-Literature-Review-on-Online-Continual-Learning
Introducing the generalized gamma distribution: a flexible distribution for index standardization
2025-01-09
Jillian C. Dunic, Jason Conner, Sean C. Anderson, James T. Thorson
Fisheries scientists use regression models to estimate population quantities, such as biomass or abundance, for use in climate, habitat, stock, and ecosystem assessments. However, these models are sensitive to the chosen probability distribution used to characterize observation error. Here, we introduce the generalized gamma distribution (GGD), which has not been widely used in fisheries science. The GGD has useful properties: (1) it reduces to the lognormal distribution when the shape parameter approaches zero; (2) it reduces to the gamma distribution when the shape and scale parameters are equal; and (3) the coefficient of variation is independent of the mean. We assess the relative performance and robustness of the GGD to estimate biomass density across different observation error types in a simulation experiment. When fit to data generated from the GGD, lognormal, gamma, and Tweedie families, the GGD had low bias and high predictive accuracy. Finally, we fit spatiotemporal index standardization models using the R package sdmTMB to 15 species from three trawl surveys from the Gulf of Alaska and coast of British Columbia, Canada. When the Akaike information criterion (AIC) weight was compared among fits using the lognormal, gamma, and Tweedie families the GGD was the most commonly selected model.
Quantum spin liquids (QSLs) represent exotic states of matter where quantum spins interact strongly yet evade long-range magnetic order down to absolute zero. Characterized by non-local quantum entanglement and resultant fractionalized excitations, QSLs have emerged as a frontier in condensed matter physics, bolstered by the recent identification of several candidate materials. This field holds profound implications for understanding strong correlations, topological order, and emergent phenomena in quantum materials. Among them, the spin-1/2 Kitaev honeycomb model, featuring bond-directional Ising interactions, provides a rare exactly solvable QSL example. Its ground state is a topological QSL, with spin degrees of freedom fractionalized into emergent Majorana fermions. Under an applied magnetic field, the Kitaev QSL transitions to a topologically non-trivial chiral spin liquid state with non-Abelian anyons, offering potential resources for topological quantum computation. The non-Abelian character of these anyons in the Kitaev QSL demonstrates a profound connection to certain topological superconductors and even-denominator fractional quantum Hall states. Since the theoretical prediction that the Kitaev model could manifest in spin-orbit-coupled materials such as honeycomb iridates and ruthenates, research has focused on identifying candidate compounds. In particular, experimental evidence suggests spin fractionalization and topological phenomena akin to the Kitaev model in the spin-orbit Mott insulator $\alpha$-RuCl$_3$. However, results and interpretations remain actively debated. This review presents a comprehensive survey of existing studies on Kitaev candidate materials, with a particular focus on $\alpha$-RuCl$_3$. Rather than offering conclusive remarks, our aim is to inspire future research by examining several key aspects of the current literature and perspectives.
The LSPE-Strip Pointing Reconstruction and Star Tracker
2025-01-09
Michele Maris, Maurizio Tomasi, Matteo Baratto, Fabio Paonessa, Cristian Franceschet, Daniele Tavagnacco, Oscar Antonio Peverini, Fabrizio Villa, Mario Zannoni, Marco Bersanelli, Barbara Caccianiga, Stefano Mandelli, Aniello Mennella, Federico Nati, Stefano Sartor, Ricardo T. Génova-Santos, Jose A. Rubino-Martin, Francesco Cuttaia, Francesco Cavaliere, Luciano Mandelli, Massimo Gervasi, Andrea Zacchei
This paper aims to describe the Pointing Reconstruction Model (PRM) and the prototype Star Tracker, which will be mounted on LSPE-Strip, a microwave Q- and W-band CMB telescope planned for installation at the "Observatorio del Teide" in Tenerife. The PRM integrates information on the instantaneous attitude provided by the telescope control system to determine the actual pointing direction and focal plane orientation of the telescope. It accounts for various non-idealities in the telescope setup, represented by eight configuration angles, which will be calibrated using the Star Tracker. Following the derivation of the PRM formalism and its implementation, we investigate the pointing errors caused by incorrect calibration of these configuration angles to validate the required 1 arcminute maximum systematic pointing error for the LSPE-Strip survey. This paper also describes the main structure and operations of the Star Tracker and presents the results of a campaign of actual sky observations conducted with a prototype. The results demonstrate a Star Tracker RMS accuracy of approximately 3 arcseconds, while systematic errors remain below 10 arcseconds. Based on these results, we analyzed the problem of reconstructing the PRM configuration angles. Two methods for intercalibrating the Star Tracker's pointing direction with respect to the focal plane's pointing direction were examined: (1) observations of planets and (2) observations of a drone carrying both an optical beacon and a radio beacon. In the first case, an intercalibration accuracy between 1/3 arcminute and 1 arcminute is achievable. In the second case, the expected intercalibration accuracy ranges from 0.25 arcminute to 1 arcminute.
The Multifaceted Nature of Mentoring in OSS: Strategies, Qualities, and Ideal Outcomes
2025-01-09
Zixuan Feng, Igor Steinmacher, Marco Gerosa, Tyler Menezes, Alexander Serebrenik, Reed Milewicz, Anita Sarma
Mentorship in open source software (OSS) is a vital, multifaceted process that includes onboarding newcomers, fostering skill development, and enhancing community building. This study examines task-focused mentoring strategies that help mentees complete their tasks and the ideal personal qualities and outcomes of good mentorship in OSS communities. We conducted two surveys to gather contributor perceptions: the first survey, with 70 mentors, mapped 17 mentoring challenges to 21 strategies that help support mentees. The second survey, with 85 contributors, assessed the importance of personal qualities and ideal mentorship outcomes. Our findings not only provide actionable strategies to help mentees overcome challenges and become successful contributors but also guide current and future mentors and OSS communities in understanding the personal qualities that are the cornerstone of good mentorship and the outcomes that mentor-mentee pairs should aspire to achieve.
The Impact of Question Framing on the Precision of Automatic Occupation Coding
2025-01-09
Olga Kononykhina, Malte Schierholz, Frauke Kreuter
Occupational data play a vital role in research, official statistics, and policymaking, yet their collection and accurate classification remain a persistent challenge. This study investigates the effects of occupational question wording on data variability and the performance of automatic coding tools. Through a series of survey experiments conducted and replicated in Germany, we tested two widely-used occupational question formats: one focusing on "job title" (Berufsbezeichnung) and another on "occupational tasks" (berufliche T\"atigkeit). Our analysis reveals that automatic coding tools, such as CASCOT and OccuCoDe, exhibit significant sensitivity to the form and origin of the data. Specifically, these tools performed more efficiently when coding responses to the job title question format compared to the occupational task format. Additionally, we found that including examples of main tasks and duties in the questions led respondents to provide more detailed but less linguistically diverse responses. This reduced diversity may negatively affect the precision of automatic coding. These findings highlight the importance of tailoring automatic coding tools to the specific structure and origin of the data they are applied to. We emphasize the need for further research to optimize question design and coding tools for greater accuracy and applicability in occupational data collection.
Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
2025-01-09
Gert Aarts, Kenji Fukushima, Tetsuo Hatsuda, Andreas Ipp, Shuzhe Shi, Lingxiao Wang, Kai Zhou
The integration of deep learning techniques and physics-driven designs is reforming the way we address inverse problems, in which accurate physical properties are extracted from complex data sets. This is particularly relevant for quantum chromodynamics (QCD), the theory of strong interactions, with its inherent limitations in observational data and demanding computational approaches. This perspective highlights advances and potential of physics-driven learning methods, focusing on predictions of physical quantities towards QCD physics, and drawing connections to machine learning(ML). It is shown that the fusion of ML and physics can lead to more efficient and reliable problem-solving strategies. Key ideas of ML, methodology of embedding physics priors, and generative models as inverse modelling of physical probability distributions are introduced. Specific applications cover first-principle lattice calculations, and QCD physics of hadrons, neutron stars, and heavy-ion collisions. These examples provide a structured and concise overview of how incorporating prior knowledge such as symmetry, continuity and equations into deep learning designs can address diverse inverse problems across different physical sciences.
A New Approach for Constraining Large-Scale Temperature Fluctuations in the Intergalactic Medium
2025-01-09
Saba Etezad-Razavi, Sarah E. I. Bosman, Frederick B. Davies
The reionization of helium is thought to occur at $2.5\lesssim z\lesssim4$, marking the last phase transition and final global heating event of the intergalactic medium (IGM). Since it is driven by rare quasars, helium reionization should give rise to strong temperature fluctuations in the IGM between neutral and recently-ionized regions of order $\sigma (\ln T) \sim \Delta T/T = 20-50\%$. We introduce a novel method to search for reionization-induced temperature fluctuations in the IGM by using the effective optical depths of the Lyman-$\alpha$ forest towards a large number of background quasars. Higher IGM temperatures give rise to lower effective optical depths in the Lyman-$\alpha$ forest, implying that temperature fluctuations will broaden the observed optical depth distribution. We measured the distributions of effective Lyman-$\alpha$ forest optical depths across $71$ X-Shooter spectra from the XQ-100 survey in four redshift bins from $z=3.76$ to $z=4.19$ and compared them to a large-volume cosmological hydrodynamical simulation. A good agreement is found between the observations and the simulation, which does not include temperature fluctuations; therefore, we do not detect a signature of helium reionization. We then post-process the simulations to include an increasing amount of temperature fluctuations until the model becomes inconsistent with the observations. We obtain tight constraints on $\sigma (\ln T) < 0.29 \ (<0.40)$ at $2 \sigma\ (3 \sigma)$ at $z=3.76$ when averaging over scales of $100$ comoving Mpc, and weaker constraints for higher redshifts and smaller scales. Our constraints are the tightest to date, and imply that either the IGM temperature contrast caused by helium reionization is less than $\sim30\%$, or that the process has not yet significantly started at $z=3.76$.
Bayesian Model Selection for Network Discrimination and Risk-informed Decision Making in Material Flow Analysis
2025-01-09
Jiankan Liao, Xun Huan, Daniel Cooper
Material flow analyses (MFAs) provide insight into supply chain level opportunities for resource efficiency. MFAs can be represented as networks with nodes that represent materials, processes, sectors or locations. MFA network structure uncertainty (i.e., the existence or absence of flows between nodes) is pervasive and can undermine the reliability of the flow predictions. This article investigates MFA network structure uncertainty by proposing candidate node-and-flow structures and using Bayesian model selection to identify the most suitable structures and Bayesian model averaging to quantify the parametric mass flow uncertainty. The results of this holistic approach to MFA uncertainty are used in conjunction with the input-output (I/O) method to make risk-informed resource efficiency recommendation. These techniques are demonstrated using a case study on the U.S. steel sector where 16 candidate structures are considered. Model selection highlights 2 networks as most probable based on data collected from the United States Geological Survey and the World Steel Association. Using the I/O method, we then show that the construction sector accounts for the greatest mean share of domestic U.S. steel industry emissions while the automotive and steel products sectors have the highest mean emissions per unit of steel used in the end-use sectors. The uncertainty in the results is used to analyze which end-use sector should be the focus of demand reduction efforts under different appetites for risk. This article's methods generate holistic and transparent MFA uncertainty that account for structural uncertainty, enabling decisions whose outcomes are more robust to the uncertainty.
The MeerKAT Fornax Survey IV. A close look at the cluster physics through the densest rotation measure grid
2025-01-09
Francesca Loi, Paolo Serra, Matteo Murgia, Federica Govoni, Valentina Vacca, Filippo Maccagni, Dane Kleiner, Peter Kamphuis
Using the Square Kilometre Array (SKA) mid precursor MeerKAT, we acquired broadband spectro-polarimetric data in the context of the MeerKAT Fornax Survey to study the Fornax cluster's magnetic fields in detail by building the densest rotation measure (RM) grid to date. Here, we present the survey, the analysis, and a discussion of the RM grid properties. We analyzed a circular region centered on the Fornax cluster center with a radius of $\sim1.4^\circ$; that is, $\rm\sim 0.73 R_{vir}$. The mosaics have a resolution of 13arcsec and cover the frequencies between 900\,MHz and 1.4\,GHz, reaching an average noise of 16$\mu$Jy beam$^{-1}$ in total intensity and 3$\mu$Jy beam$^{-1}$ in the Q and U Stokes images. With these data, we detected 508 polarized sources over an area of $\sim$6.35 deg$^2$ corresponding to a density of $\sim$80 polarized sources/deg$^2$. This is the densest RM grid ever built. Of the polarized sources, five are cluster sources. Excluding the cluster sources, we built the Euclidean-normalized differential source counts in polarization and we went a factor of ten deeper than previous surveys. We tentatively detect for the first time an increment in the differential source counts at low polarized flux densities; that is, $\sim$9\,$\mu$Jy at 1.4\,GHz. The average degree of polarization of about 3--4\% suggests that the sub$-\mu$Jansky population is not dominated by star-forming galaxies, typically showing a degree of polarization lower than 1\%. The majority of the polarized sources are Faraday simple; in other words, their polarization plane rotates linearly with the wavelength squared. The RM shows the typical decrement going from the center to the outskirts of the Fornax cluster. However, interesting features are observed both in the RM grid and in the RM radial profiles across different directions. A combination of the ...
JWST NIRCam simulations and observations of AGN ionization cones in cosmic noon galaxies
2025-01-09
Sophie Lebowitz, Kevin Hainline, Stéphanie Juneau, Jianwei Lyu, Christina Williams, Stacey Alberts, Xiaohui Fan, Marcia Rieke
The extended narrow line region (NLR) of Active Galactic Nuclei (AGN) provides a valuable laboratory for exploring the relationship between AGN and their host galaxies, often appearing as an ''ionization cone'' that can extend out to the very edge of the galaxy. We use new James Webb Space Telescope (JWST) NIRCam imaging to study the morphologies and sizes of NLRs traced by [O III] at cosmic noon ($z\sim2-3$), measurements which were previously only well-studied at $z\sim0$ with IFU spectroscopy. To determine NIRCam's ability to probe the properties of the NLR in cosmic noon galaxies, we present simulated images of AGN at $z=2-3$ created with archival data cubes from the Multi Unit Spectroscopic Explorer (MUSE) of nine nearby ($z<0.05$) AGN host galaxies with previously confirmed extended NLRs. Our simulated images show that NIRCam is able to resolve the morphologies of NLRs at $z=2-3$ with narrow- and medium-band observations. We then search for extended NLRs with NIRCam medium-band observations targeting the [O III] emission in twenty-seven previously identified AGN at $z=2.4-3.4$ in the Great Observatories Origins Deep Survey South (GOODS-S) field. We detect six galaxies in our observed sample with [O III] morphologies consistent with AGN ionization cones with characteristic NLR sizes of $1-2.5$ kpc. Thanks to our simulated data, we can predict the effects of cosmological surface brightness dimming and instrument noise on the NLR size measurements at high redshift, which has the biasing effect of lowering the NLR size-AGN Luminosity trend that is observed at low redshift by a factor of $\sim 2$.
Discovery of a likely Type II SN at $z$=3.6 with JWST
2025-01-09
D. A. Coulter, J. D. R. Pierel, C. DeCoursey, T. J. Moriya, M. R. Siebert, B. A. Joshi, M. Engesser, A. Rest, E. Egami, M. Shahbandeh, W. Chen, O. D. Fox, L. G. Strolger, Y. Zenati, A. J. Bunker, P. A. Cargile, M. Curti, D. J. Eisenstein, S. Gezari, S. Gomez, M. Guolo, K. Hainline, J. Jencson, B. D. Johnson, M. Karmen, R. Maiolino, R. M. Quimby, P. Rinaldi, B. Robertson, S. Tacchella, F. Sun, Q. Wang, T. Wevers
Transient astronomy in the early, high-redshift (z>3) Universe is an unexplored regime that offers the possibility of probing the first stars and the Epoch of Reionization. During Cycles 1 and 2 of the James Webb Space Telescope (JWST), the JWST Advanced Deep Extragalactic Survey (JADES) program enabled one of the first searches for transients in deep images (~ $30$ AB mag) over a relatively wide area ($25$ arcmin$^2$). One transient, AT 2023adsv, was discovered with an F200W magnitude of $28.04$ AB mag, and subsequent JWST observations revealed that the transient is a likely supernova (SN) in a host with $z_{\rm spec}=3.613\pm0.001$, a host mass of log(M$_{*}$/M$_\odot$) = $8.41^{+0.12}_{-0.12}$, and an inferred metallicity at the position of the SN of Z$_{*}$ = $0.3\pm0.1$ Z$_\odot$. At this redshift, the first detections in F115W and F150W show that AT 2023adsv had bright rest-frame ultraviolet flux at the time of discovery. The multi-band light curve of AT 2023adsv is best matched by a template of an SN IIP, with a peak absolute magnitude of $-18.3$ AB mag in the rest-frame $B$-band. We model AT 2023adsv's light curve and find a good match to a $20M_\odot$ red supergiant progenitor star with an explosion energy of $2.0\times 10^{51}$ ergs, likely higher than normally observed in the local Universe, but consistent with SNe IIP drawn from local, lower metallicity environments. AT 2023adsv is the most distant photometrically classified SN IIP yet discovered with a spectroscopic redshift measurement, and may represent a global shift in SNe IIP properties as a function of redshift. This discovery, and the ones sure to follow, demonstrate the continued need for facilities like JWST to build a statistical sample of core-collapse SNe to understand the evolution of their properties, and to constrain the poorly understood relationship between progenitor metallicity and massive star evolution.
A Survey of Interactive Verifiable Computing: Utilizing Low-degree Polynomials
2025-01-09
Angold Wang
This survey provides a comprehensive examination of verifiable computing, tracing its evolution from foundational complexity theory to modern zero-knowledge succinct non-interactive arguments of knowledge (ZK-SNARKs). We explore key developments in interactive proof systems, knowledge complexity, and the application of low-degree polynomials in error detection and verification protocols. The survey delves into essential mathematical frameworks such as the Cook-Levin Theorem, the sum-check protocol, and the GKR protocol, highlighting their roles in enhancing verification efficiency and soundness. By systematically addressing the limitations of traditional NP-based proof systems and then introducing advanced interactive proof mechanisms to overcome them, this work offers an accessible step-by-step introduction for newcomers while providing detailed mathematical analyses for researchers. Ultimately, we synthesize these concepts to elucidate the GKR protocol, which serves as a foundation for contemporary verifiable computing models. This survey not only reviews the historical and theoretical advancements in verifiable computing over the past three decades but also lays the groundwork for understanding recent innovations in the field.
A study of the frequency and characteristics of stellar companions and Jupiter-like planets in nearby open clusters
2025-01-08
R. Gratton, M. Bonavita, D. Mesa, S. Desidera, A. Zurlo, S. Marino, V. D'Orazi, E. Rigliaco, V. Nascimbeni, D. Barbato, G. Columba, V. Squicciarini
Observations of companions of solar-type stars in nearby young moving groups (NYMGs) show that they split into two groups: stellar and brown dwarf companions (mass ratio $q>0.05$) and Jupiter-like (JL) planets ($q<0.02$). The frequency of JL planets in NYMGs appears to be higher than that obtained from radial velocity (RV) surveys. We extended the search for companions to three nearby clusters of intermediate age: Hyades, Coma Berenices, and Ursa Major. They are older and formed in more massive events than the NYMGs. The sample of host stars is complete for the core of the clusters, while we considered only a fraction of the tidal tails. We used the same methods considered for the members of NYMGs. We obtained a fairly complete sample of stellar companions and detected six massive JL planets. We found a lower frequency of equal-mass companions than in the NYMGs; this might be related to how binaries form in these environments. We also observed a concentration of stellar binaries in the cores of Ursa Major and Coma Berenices; we attribute this to the selective loss of low-mass systems. The observed scarcity of wide companions in Hyades can be due to the destruction of binaries in close encounters. The frequency of JL planets is lower than in the NYMGs but similar to that obtained from RV surveys. This extends the correlation with age and mass previously found for NYMGs. Results of this study alone do not indicate whether age or mass are the factors driving the observed correlation. A comparison of the frequencies of free-floating planets from microlenses and in young associations favours mass as the main driving parameter. Once the initial cluster mass function is considered, the frequency of JL planets in NYMGs is consistent with the results obtained using RVs.
URSA: Understanding and Verifying Chain-of-thought Reasoning in Multimodal Mathematics
2025-01-08
Ruilin Luo, Zhuofan Zheng, Yifan Wang, Yiyao Yu, Xinzhe Ni, Zicheng Lin, Jin Zeng, Yujiu Yang
Chain-of-thought (CoT) reasoning has been widely applied in the mathematical reasoning of Large Language Models (LLMs). Recently, the introduction of derivative process supervision on CoT trajectories has sparked discussions on enhancing scaling capabilities during test time, thereby boosting the potential of these models. However, in multimodal mathematical reasoning, the scarcity of high-quality CoT training data has hindered existing models from achieving high-precision CoT reasoning and has limited the realization of reasoning potential during test time. In this work, we propose a three-module synthesis strategy that integrates CoT distillation, trajectory-format rewriting, and format unification. It results in a high-quality CoT reasoning instruction fine-tuning dataset in multimodal mathematics, MMathCoT-1M. We comprehensively validate the state-of-the-art (SOTA) performance of the trained URSA-7B model on multiple multimodal mathematical benchmarks. For test-time scaling, we introduce a data synthesis strategy that automatically generates process annotation datasets, known as DualMath-1.1M, focusing on both interpretation and logic. By further training URSA-7B on DualMath-1.1M, we transition from CoT reasoning capabilities to robust supervision abilities. The trained URSA-RM-7B acts as a verifier, effectively enhancing the performance of URSA-7B at test time. URSA-RM-7B also demonstrates excellent out-of-distribution (OOD) verifying capabilities, showcasing its generalization. Model weights, training data and code will be open-sourced.
A Novel Highly Parallelizable Machine-Learning Based Method for the Fast Solution of Integral Equations for Electromagnetic Scattering Problems
2025-01-08
Enes Koç, Mert Kalfa, Secil E. Dogan, Vakur B. Ertürk
We propose a novel method for the efficient and accurate iterative solution of frequency domain integral equations (IEs) that are used for large/multi-scale electromagnetic scattering problems. The proposed method uses a novel group-by-group interaction strategy to accurately evaluate far-zone interactions within the framework of the one-box-buffer scheme during the matrix-vector multiplication at each iteration. Briefly, subdomain basis functions that are used to model the scatterer at each box are represented by a fixed number of uniformly distributed and arbitrarily oriented Hertzian dipoles (referred to as uniform basis functions), and then the dipole-to-dipole interactions are predicted in a group-wise manner by employing machine learning algorithms, thereby showcasing efficiency, strong scalability for parallelization and accuracy without the low-frequency breakdown (LFB) problem. Since the dipole representation is independent of the underlying material properties of the scatterer, the proposed method is valid for all types of IEs (surface or volume). Moreover, because the training is performed offline, the resulting networks can be used for any scatterer under any IE, without extra training, as long as the size of, and the distances among the boxes are preserved. The efficiency and accuracy of the proposed method are assessed by comparing our results with those obtained from the conventional multilevel fast multipole algorithm for various scattering problems. The proposed method's parallelization performance is showcased through scalability tests, and its resilience to LFB is demonstrated.
2025-01-08
Pedro R. A. S. Bassi, Mehmet Can Yavuz, Kang Wang, Xiaoxi Chen, Wenxuan Li, Sergio Decherchi, Andrea Cavalli, Yang Yang, Alan Yuille, Zongwei Zhou
With over 85 million CT scans performed annually in the United States, creating tumor-related reports is a challenging and time-consuming task for radiologists. To address this need, we present RadGPT, an Anatomy-Aware Vision-Language AI Agent for generating detailed reports from CT scans. RadGPT first segments tumors, including benign cysts and malignant tumors, and their surrounding anatomical structures, then transforms this information into both structured reports and narrative reports. These reports provide tumor size, shape, location, attenuation, volume, and interactions with surrounding blood vessels and organs. Extensive evaluation on unseen hospitals shows that RadGPT can produce accurate reports, with high sensitivity/specificity for small tumor (<2 cm) detection: 80/73% for liver tumors, 92/78% for kidney tumors, and 77/77% for pancreatic tumors. For large tumors, sensitivity ranges from 89% to 97%. The results significantly surpass the state-of-the-art in abdominal CT report generation. RadGPT generated reports for 17 public datasets. Through radiologist review and refinement, we have ensured the reports' accuracy, and created the first publicly available image-text 3D medical dataset, comprising over 1.8 million text tokens and 2.7 million images from 9,262 CT scans, including 2,947 tumor scans/reports of 8,562 tumor instances. Our reports can: (1) localize tumors in eight liver sub-segments and three pancreatic sub-segments annotated per-voxel; (2) determine pancreatic tumor stage (T1-T4) in 260 reports; and (3) present individual analyses of multiple tumors--rare in human-made reports. Importantly, 948 of the reports are for early-stage tumors.
This paper presents Recorder, a parallel I/O tracing tool designed to capture comprehensive I/O information on HPC applications. Recorder traces I/O calls across various I/O layers, storing all function parameters for each captured call. The volume of stored information scales linearly the application's execution scale. To address this, we present a sophisticated pattern-recognition-based compression algorithm. This algorithm identifies and compresses recurring I/O patterns both within individual processes and across multiple processes, significantly reducing space and time overheads. We evaluate the proposed compression algorithm using I/O benchmarks and real-world applications, demonstrating that Recorder can store more information while requiring approximately 12x less storage space compared to its predecessor. Notably, for applications with typical parallel I/O patterns, Recorder achieves a constant trace size regardless of execution scale. Additionally, a comparison with the profiling tool Darshan shows that Recorder captures detailed I/O information without incurring substantial overhead. The richer data collected by Recorder enables new insights and facilitates more in-depth I/O studies, offering valuable contributions to the I/O research community.
Large-scale Grid Optimization: The Workhorse of Future Grid Computations
2025-01-08
Amritanshu Pandey, Mads Almassalkhi, Sam Chevalier
Purpose: The computation methods for modeling, controlling and optimizing the transforming grid are evolving rapidly. We review and systemize knowledge for a special class of computation methods that solve large-scale power grid optimization problems. Summary: Large-scale grid optimizations are pertinent for, amongst other things, hedging against risk due to resource stochasticity, evaluating aggregated DERs' impact on grid operation and design, and improving the overall efficiency of grid operation in terms of cost, reliability, and carbon footprint. We attribute the continual growth in scale and complexity of grid optimizations to a large influx of new spatial and temporal features in both transmission (T) and distribution (D) networks. Therefore, to systemize knowledge in the field, we discuss the recent advancements in T and D systems from the viewpoint of mechanistic physics-based and emerging data-driven methods. Findings: We find that while mechanistic physics-based methods are leading the science in solving large-scale grid optimizations, data-driven techniques, especially physics-constrained ones, are emerging as an alternative to solve otherwise intractable problems. We also find observable gaps in the field and ascertain these gaps from the paper's literature review and by collecting and synthesizing feedback from industry experts.
Pressure inside hadrons: criticism, conjectures, and all that
2025-01-08
Cédric Lorcé, Peter Schweitzer
The interpretation of the energy-momentum tensor form factor $D(t)$ of hadrons in terms of pressure and shear force distributions is discussed, concerns raised in the literature are reviewed, and ways to reconcile the concerns with the interpretation are indicated.
Incoherent horizontal emittance growth due to the interplay of beam-beam and longitudinal wakefield in crab-waist colliders
2025-01-08
Peter Kicsiny, Demin Zhou, Xavier Buffat, Tatiana Pieloni, Mike Seidel
In this paper, we investigate quadrupolar sychrobetatron resonances caused by beam-beam collisions and their interplay with longitudinal wakefields in the context of crab-waist colliders. We present a comprehensive theoretical review of the established theory of sychrobetatron resonances and extend the formalism to explore horizontal sychrobetatron resonances specific to crab-waist colliders. As a case study, we examine incoherent horizontal emittance growth at the SuperKEKB and demonstrate through simulations that the interplay between beam-beam and longitudinal wakefields leads to a horizontal blowup of the bunch size and that the study of the dynamics can be reduced to the horizontal-longitudinal plane, independent of the motion in the vertical dimension. We present extensive simulation results using the codes BBWS, PyHEADTAIL and Xsuite, connect our analytical findings with these findings, and propose strategies to mitigate horizontal blowup.
Identity-Preserving Video Dubbing Using Motion Warping
2025-01-08
Runzhen Liu, Qinjie Lin, Yunfei Liu, Lijian Lin, Ye Zhu, Yu Li, Chuhua Xian, Fa-Ting Hong
Video dubbing aims to synthesize realistic, lip-synced videos from a reference video and a driving audio signal. Although existing methods can accurately generate mouth shapes driven by audio, they often fail to preserve identity-specific features, largely because they do not effectively capture the nuanced interplay between audio cues and the visual attributes of reference identity . As a result, the generated outputs frequently lack fidelity in reproducing the unique textural and structural details of the reference identity. To address these limitations, we propose IPTalker, a novel and robust framework for video dubbing that achieves seamless alignment between driving audio and reference identity while ensuring both lip-sync accuracy and high-fidelity identity preservation. At the core of IPTalker is a transformer-based alignment mechanism designed to dynamically capture and model the correspondence between audio features and reference images, thereby enabling precise, identity-aware audio-visual integration. Building on this alignment, a motion warping strategy further refines the results by spatially deforming reference images to match the target audio-driven configuration. A dedicated refinement process then mitigates occlusion artifacts and enhances the preservation of fine-grained textures, such as mouth details and skin features. Extensive qualitative and quantitative evaluations demonstrate that IPTalker consistently outperforms existing approaches in terms of realism, lip synchronization, and identity retention, establishing a new state of the art for high-quality, identity-consistent video dubbing.
The Impostor is Among Us: Can Large Language Models Capture the Complexity of Human Personas?
2025-01-08
Christopher Lazik, Christopher Katins, Charlotte Kauter, Jonas Jakob, Caroline Jay, Lars Grunske, Thomas Kosch
Large Language Models (LLMs) created new opportunities for generating personas, which are expected to streamline and accelerate the human-centered design process. Yet, AI-generated personas may not accurately represent actual user experiences, as they can miss contextual and emotional insights critical to understanding real users' needs and behaviors. This paper examines the differences in how users perceive personas created by LLMs compared to those crafted by humans regarding their credibility for design. We gathered ten human-crafted personas developed by HCI experts according to relevant attributes established in related work. Then, we systematically generated ten personas and compared them with human-crafted ones in a survey. The results showed that participants differentiated between human-created and AI-generated personas, with the latter being perceived as more informative and consistent. However, participants noted that the AI-generated personas tended to follow stereotypes, highlighting the need for a greater emphasis on diversity when utilizing LLMs for persona creation.
2025-01-08
Steven R. Schofield, Andrew Fisher, Eran Ginossar, Joseph W. Lyding, Richard Silver, Fan Fei, Pradeep Namboodiri, Jonathan Wyrick, M. G. Masteghin, D. C. Cox, B. N. Murdin, S. K Clowes, Joris Keizer, Michelle Simmons, Holly G. Stemp, Andrea Morello, Benoit Voisin, Sven Rogge, Robert Wolkow, Lucian Livadaru, Jason Pitters, Taylor Stock, Neil Curson, Robert E. Butera, Tatiana Pavlova, A. M. Jakob, D. Spemann, P. Räcke, F. Schmidt-Kaler, D. N. Jamieson, Utkarsh Pratiush, Gerd Duscher, Sergei Kalinin, Dimitrios Kazazis, Procopios Constantinou, Gabriel Aeppli, Yasin Ekinci, James H. G. Owen, Emma Fowler, Reza Moheimani, John Randall, Shashank Misra, Jeffrey Ivie, Christopher R. Allemang, Evan M. Anderson, Ezra Bussmann, Quinn Campbell, Xujiao Gao, Tzu-Ming Lu, Scott W. Schmucker
Spin states in semiconductors provide exceptionally stable and noise-resistant environments for qubits, positioning them as optimal candidates for reliable quantum computing technologies. The proposal to use nuclear and electronic spins of donor atoms in silicon, introduced by Kane in 1998, sparked a new research field focused on the precise positioning of individual impurity atoms for quantum devices, utilising scanning tunnelling microscopy and ion implantation. This roadmap article reviews the advancements in the 25 years since Kane's proposal, the current challenges, and the future directions in atomic-scale semiconductor device fabrication and measurement. It covers the quest to create a silicon-based quantum computer and expands to include diverse material systems and fabrication techniques, highlighting the potential for a broad range of semiconductor quantum technological applications. Key developments include phosphorus in silicon devices such as single-atom transistors, arrayed few-donor devices, one- and two-qubit gates, three-dimensional architectures, and the development of a toolbox for future quantum integrated circuits. The roadmap also explores new impurity species like arsenic and antimony for enhanced scalability and higher-dimensional spin systems, new chemistry for dopant precursors and lithographic resists, and the potential for germanium-based devices. Emerging methods, such as photon-based lithography and electron beam manipulation, are discussed for their disruptive potential. This roadmap charts the path toward scalable quantum computing and advanced semiconductor quantum technologies, emphasising the critical intersections of experiment, technological development, and theory.
2025-01-08
Kasra Amini, Cornelius Wittig, Sofia Saoncella, Outi Tammisola, Fredrik Lundell, Shervin Bagheri
Optical Coherence Tomography (OCT) has become an indispensable tool for investigating mesoscopic features in soft matter and fluid mechanics. Its ability to provide high-resolution, non-invasive measurements in both spatial and temporal domains bridges critical gaps in experimental instrumentation, enabling the study of complex, confined, and dynamic systems. This review serves as both an introduction to OCT and a practical guide for researchers seeking to adopt this technology. A set of tutorials, complemented by Python scripts, are provided for both intensity- and Doppler-based techniques. The versatility of OCT is illustrated through case studies, including time-resolved velocimetry, particle-based velocity measurements, slip velocity characterization, detection of shear-induced structures, and analysis of fluid-fluid and fluid-structure interactions. Drawing on our experiences, we also present a set of practical guidelines for avoiding common pitfalls.
The Cygnus Allscale Survey of Chemistry and Dynamical Environments: CASCADE. IV. Unveiling the hidden structures in DR18
2025-01-08
W. -J. Kim, H. Beuther, F. Wyrowski, K. M. Menten, N. Schneider, Á. Sánchez-Monge, A. Brunthaler, T. Csengeri, C. Romero, N. Cunningham, L. Bouscasse, J. M. Winters, F. Comerón, V. S. Veena, A. Ginsburg, D. Semenov, C. Gieser, A. Hernández-Gómez, S. A. Dzib, I. -M. Skretas, I. B. Christensen, P. Schilke
The Cygnus-X complex is a massive, nearby (1.4 kpc) star-forming region with several OB associations. As part of the Cygnus Allscale Survey of Chemistry and Dynamical Environments (CASCADE) program, we carried out 3.6 millimeter (mm) continuum and spectral line high-resolution observations ($\sim$ 3 - 4$''$) toward DR18, covering several molecular species with the Northern Extended Millimeter Array (NOEMA) and the Institut de Radioastronomie Millim\'etrique (IRAM) 30m telescope. In addition, multi-wavelength archival datasets were used to provide a comprehensive analysis of the region. A comparison of the 3.6mm and 6 cm continuum emission confirms that a B2 star (DR18-05) shapes the cometary HII region in the DR18 cavity, with ionized gas escaping toward the OB2 association. On the other hand, the extended 3.6mm and 6 cm continuum emission are likely to trace photoevaporating ionized gas from ultraviolet radiation from the Cyg OB2 association, not from DR18-05. The shell structure around DR18-05 indicates photodissociation regions (PDRs) formed by the expanding HII region and photo-erosion from DR18-05 and OB2 stars. We also identified 18 compact cores with N$_2$H$^+$ emission, half of which are gravitationally bound and mostly located in colder regions behind the PDRs. The SiO emission is found only in PDRs, with narrow-line widths ( 0.8 - 2.0 km s$^{-1}$) and lower abundances (X(SiO) $\sim$ 5$\times$10$^{-11}$ - 1$\times$10$^{-10}$). Comparing with the UV irradiated shock models, we suggest that the SiO emission partially encompassing the HII region arises from the molecular gas region, marginally compressed by low-velocity shocks with $\sim$ 5 km s$^{-1}$, irradiated by external UV radiation (G$_{\rm 0} \sim 10^{2} - 10^{3}$), as they traverse through a medium with $n_{\rm H} \sim 10^{4}$ to 10$^5$ cm$^{-3}$.
Searching the non-accreting white dwarf population in eROSITA data
2025-01-08
S. Friedrich, C. Maitra, K. Dennerl, A. Schwope, K. Werner, B. Stelzer
eROSITA is the soft X-ray instrument aboard the Spectrum Roentgen Gamma (SRG) satellite that is most sensitive in the energy range between 0.2 and 2.3 keV. Between December 2019 and December 2021, eROSITA completed four all-sky surveys producing all-sky X-ray source lists and sky maps of unprecedented depth. In the energy range between 0.2 keV and 1 keV, we detected about 38,000 sources with a hardness ratio below -0.94, covering a small sample of known white dwarfs found with eROSITA in the dataset to which the German eROSITA consortium has rights (half sky). 264 of these soft sources have a probability of more than 90 % to be a white dwarf. This is more than the 175 white dwarfs ROSAT found in the whole sky. Here we present the results of a pilot study to increase the sensitivity of eROSITA for soft sources by extending the detection threshold down to 0.1 keV. First tests with dedicated sky regions are promising.
The Role of Machine Learning in Congenital Heart Disease Diagnosis: Datasets, Algorithms, and Insights
2025-01-08
Khalil Khan, Farhan Ullah, Ikram Syed, Irfan Ullah
Congenital heart disease is among the most common fetal abnormalities and birth defects. Despite identifying numerous risk factors influencing its onset, a comprehensive understanding of its genesis and management across diverse populations remains limited. Recent advancements in machine learning have demonstrated the potential for leveraging patient data to enable early congenital heart disease detection. Over the past seven years, researchers have proposed various data-driven and algorithmic solutions to address this challenge. This paper presents a systematic review of congential heart disease recognition using machine learning, conducting a meta-analysis of 432 references from leading journals published between 2018 and 2024. A detailed investigation of 74 scholarly works highlights key factors, including databases, algorithms, applications, and solutions. Additionally, the survey outlines reported datasets used by machine learning experts for congenital heart disease recognition. Using a systematic literature review methodology, this study identifies critical challenges and opportunities in applying machine learning to congenital heart disease.
Understanding, Implementing, and Supporting Security Assurance Cases in Safety-Critical Domains
2025-01-08
Mazen Mohamad
The increasing demand for connectivity in safety-critical domains has made security assurance a crucial consideration. In safety-critical industry, software, and connectivity have become integral to meeting market expectations. Regulatory bodies now require security assurance cases (SAC) to verify compliance, as demonstrated in ISO/SAE-21434 for automotive. However, existing approaches for creating SACs do not adequately address industry-specific constraints and requirements. In this thesis, we present CASCADE, an approach for creating SACs that aligns with ISO/SAE-21434 and integrates quality assurance measures. CASCADE is developed based on insights from industry needs and a systematic literature review. We explore various factors driving SAC adoption, both internal and external to companies in safety-critical domains, and identify gaps in the existing literature. Our approach addresses these gaps and focuses on asset-driven methodology and quality assurance. We provide an illustrative example and evaluate CASCADE's suitability and scalability in an automotive OEM. We evaluate the generalizability of CASCADE in the medical domain, highlighting its benefits and necessary adaptations. Furthermore, we support the creation and management of SACs by developing a machine-learning model to classify security-related requirements and investigating the management of security evidence. We identify deficiencies in evidence management practices and propose potential areas for automation. Finally, our work contributes to the advancement of security assurance practices and provides practical support for practitioners in creating and managing SACs.
Multidimensional integrable systems from contact geometry
2025-01-08
A. Sergyeyev
Upon having presented a bird's eye view of history of integrable systems, we give a brief review of certain earlier advances (arXiv:1401.2122 & arXiv:1812.02263) in the longstanding problem of search for partial differential systems in four independent variables, often referred to as (3+1)-dimensional or 4D systems, that are integrable in the sense of soliton theory. Namely, we review a recent construction for a large new class of (3+1)-dimensional integrable systems with Lax pairs involving contact vector fields. This class contains inter alia two infinite families of such systems, thus establishing that there is significantly more integrable (3+1)-dimensional systems than it was believed for a long time. In fact, the construction under study yields (3+1)-dimensional integrable generalizations of many well-known dispersionless integrable (2+1)-dimensional systems like the dispersionless KP equation, as well as a first example of a (3+1)-dimensional integrable system with an algebraic, rather than rational, nonisospectral Lax pair. To demonstrate the versatility of the construction in question, we employ it here to produce novel integrable (3+1)-dimensional generalizations for the following (2+1)-dimensional integrable systems: dispersionless BKP, dispersionless asymmetric Nizhnik--Veselov--Novikov, dispersionless Gardner, and dispersionless modified KP equations, and the generalized Benney system.
Thermodynamics-Like Formalism for Immiscible and Incompressible Two-Phase Flow in Porous Media
2025-01-08
Alex Hansen, Santanu Sinha
It is possible to formulate immiscible and incompressible two-phase flow in porous media in a mathematical framework resembling thermodynamics based on the Jaynes generalization of statistical mechanics. We review this approach and discuss the meaning of the emergent variables that appear, agiture, flow derivative and flow pressure, which are conjugate to the configurational entropy, the saturation and the porosity respectively. We conjecture that the agiture, the temperature-like variable, is directly related to the pressure gradient. This has as a consequence that the configurational entropy, a measure of how the fluids are distributed within the porous media and the accompanying velocity field, and the mobility of the fluids are related. We also develop elements of another version of the thermodynamics-like formalism where fractional flow rather than saturation is the control variable, since this is typically the natural control variable in experiments.
Assuming isospin conservation, the decay of a $c\bar c$ vector meson into the $\Lambda\bar\Sigma^0+\mathrm{c.c.}$ final state is purely electromagnetic. At the leading order, the $c\bar c$ vector meson first converts into a virtual photon that, then produces the $\Lambda\bar\Sigma^0+\mathrm{c.c.}$ final state. Moreover, such a mechanism, i.e., the virtual photon coupling to $\Lambda\bar\Sigma^0+\mathrm{c.c.}$, is the solely intermediate process through which, in Born approximation, the reaction $e^+e^-\to\Lambda\bar\Sigma^0+\mathrm{c.c.}$ does proceed. It follows that any significant difference between the amplitudes of the processes $c\bar c\to\Lambda\bar\Sigma^0+\mathrm{c.c.}$ and $e^+e^-\to\Lambda\bar\Sigma^0+\mathrm{c.c.}$ at the $c\bar c$ mass must be ascribed to an isospin-violating contribution in the $c\bar c$ decay. In Eur. Phys. J. C $\boldsymbol{80}$, 903 (2020) we studied the decay of the $\psi(2S)$ vector meson into $\Lambda\bar\Sigma^0+\mathrm{c.c.}$ and, on the light of the large branching fraction ${\rm BR}_{18}(\psi(2S)\to\Lambda\bar\Sigma^0+\mathrm{c.c.})=(1.23\pm0.24)\times 10^{-5}$, published in the 2018 edition of the Review of Particle Physics Phys. Rev. D $\boldsymbol{98}$, 030001 (2018), we claimed either the presence of a significant isospin-violating contribution or, with a lesser emphasis, a "not complete reliability of the only available datum". In any case, we propose a new measurement. Apparently, our second and considered less serious hypothesis was the right one, indeed the branching fraction published in the 2024 edition of the Review of Particle Physics Phys. Rev. D $\boldsymbol{110}$, 030001 (2024) is $ {\rm BR}(\psi(2S)\to\Lambda\bar\Sigma^0+\mathrm{c.c.})=(1.6\pm0.7)\times 10^{-6}$, more than seven times lower with the error that increased from $\sim 20\%$ to $\sim 45\%$.
A Survey on Path Planning Problem of Rolling Contacts: Approaches, Applications and Future Challenges
2025-01-08
Seyed Amir Tafrishi, Mikhail Svinin, Kenji Tahara
This paper explores an eclectic range of path-planning methodologies engineered for rolling surfaces. Our focus is on the kinematic intricacies of rolling contact systems, which are investigated through a motion planning lens. Beyond summarizing the approaches to single-contact rotational surfaces, we explore the challenging domain of spin-rolling multi-contact systems. Our work proposes solutions for the higher-dimensional problem of multiple rotating objects in contact. Venturing beyond kinematics, these methodologies find application across a spectrum of domains, including rolling robots, reconfigurable swarm robotics, micro/nano manipulation, and nonprehensile manipulations. Through meticulously examining established planning strategies, we unveil their practical implementations in various real-world scenarios, from intricate dexterous manipulation tasks to the nimble manoeuvring of rolling robots and even shape planning of multi-contact swarms of particles. This study introduces the persistent challenges and unexplored frontiers of robotics, intricately linked to both path planning and mechanism design. As we illuminate existing solutions, we also set the stage for future breakthroughs in this dynamic and rapidly evolving field by highlighting the critical importance of addressing rolling contact problems.
Intelligent Transportation Systems (ITS) are crucial for the development and operation of smart cities, addressing key challenges in efficiency, productivity, and environmental sustainability. This paper comprehensively reviews the transformative potential of Large Language Models (LLMs) in optimizing ITS. Initially, we provide an extensive overview of ITS, highlighting its components, operational principles, and overall effectiveness. We then delve into the theoretical background of various LLM techniques, such as GPT, T5, CTRL, and BERT, elucidating their relevance to ITS applications. Following this, we examine the wide-ranging applications of LLMs within ITS, including traffic flow prediction, vehicle detection and classification, autonomous driving, traffic sign recognition, and pedestrian detection. Our analysis reveals how these advanced models can significantly enhance traffic management and safety. Finally, we explore the challenges and limitations LLMs face in ITS, such as data availability, computational constraints, and ethical considerations. We also present several future research directions and potential innovations to address these challenges. This paper aims to guide researchers and practitioners through the complexities and opportunities of integrating LLMs in ITS, offering a roadmap to create more efficient, sustainable, and responsive next-generation transportation systems.
On Conway's Numbers and Games, the Von Neumann Universe, and Pure Set Theory
2025-01-08
Wolfgang Bertram
We take up Dedekind's question ''Was sind und was sollen die Zahlen?'' (''What are numbers, and would should they be?''), with the aim to describe the place that Conway's (Surreal) Numbers and Games take, or deserve to take, in the whole of mathematics. Rather than just reviewing the work of Conway, and subsequent one by Gonshor, Alling, Ehrlich, and others, we propose a new setting which puts the theory of surreal numbers onto the firm ground of ''pure'' set theory. This approach is closely related to Gonshor's one by ''sign expansions'', but appears to be significantly simpler and clearer, and hopefully may contribute to realizing that ''surreal'' numbers are by no means surrealistic, goofy or wacky. They could, and probably should, play a central role in mathematics. We discuss the interplay between the various approaches to surreal numbers, and analyze the link with Conway's original approach via Combinatorial Game Theory (CGT). To clarify this, we propose to call pure set theory the algebraic theory of pure sets, or in other terms, of the algebraic structures of the von Neumann universe. This topic may be interesting in its own right: it puts CGT into a broad context which has a strong ''quantum flavor'', and where Conway's numbers (as well as their analogue, the nimbers) arise naturally.
Modern Hardware Security: A Review of Attacks and Countermeasures
2025-01-08
Jyotiprakash Mishra, Sanjay K. Sahay
With the exponential rise in the use of cloud services, smart devices, and IoT devices, advanced cyber attacks have become increasingly sophisticated and ubiquitous. Furthermore, the rapid evolution of computing architectures and memory technologies has created an urgent need to understand and address hardware security vulnerabilities. In this paper, we review the current state of vulnerabilities and mitigation strategies in contemporary computing systems. We discuss cache side-channel attacks (including Spectre and Meltdown), power side-channel attacks (such as Simple Power Analysis, Differential Power Analysis, Correlation Power Analysis, and Template Attacks), and advanced techniques like Voltage Glitching and Electromagnetic Analysis to help understand and build robust cybersecurity defense systems and guide further research. We also examine memory encryption, focusing on confidentiality, granularity, key management, masking, and re-keying strategies. Additionally, we cover Cryptographic Instruction Set Architectures, Secure Boot, Root of Trust mechanisms, Physical Unclonable Functions, and hardware fault injection techniques. The paper concludes with an analysis of the RISC-V architecture's unique security challenges. The comprehensive analysis presented in this paper is essential for building resilient hardware security solutions that can protect against both current and emerging threats in an increasingly challenging security landscape.
The unbearable lightness of Restricted Boltzmann Machines: Theoretical Insights and Biological Applications
2025-01-08
Giovanni di Sarra, Barbara Bravi, Yasser Roudi
Restricted Boltzmann Machines are simple yet powerful neural networks. They can be used for learning structure in data, and are used as a building block of more complex neural architectures. At the same time, their simplicity makes them easy to use, amenable to theoretical analysis, yielding interpretable models in applications. Here, we focus on reviewing the role that the activation functions, describing the input-output relationship of single neurons in RBM, play in the functionality of these models. We discuss recent theoretical results on the benefits and limitations of different activation functions. We also review applications to biological data analysis, namely neural data analysis, where RBM units are mostly taken to have sigmoid activation functions and binary units, to protein data analysis and immunology where non-binary units and non-sigmoid activation functions have recently been shown to yield important insights into the data. Finally, we discuss open problems addressing which can shed light on broader issues in neural network research.
Neighborhood Disparities in Smart City Service Adoption
2025-01-08
Shahaf Donio, Eran Toch
While local governments have invested heavily in smart city infrastructure, significant disparities in adopting these services remain in urban areas. The success of many user-facing smart city technologies requires understanding barriers to adoption, including persistent inequalities in urban areas. An analysis of a random sample telephone survey (n=489) in four neighborhoods of Tel Aviv merged with digital municipal services usage data found that neighborhood residency influences the reasons why residents adopt resident-facing smart city services, as well as individual-level factors. Structured Equation Modeling shows that neighborhood residency is related to digital proficiency and privacy perceptions beyond demographic factors and that those influence the adoption of smart-city services. We summarize the paper by discussing why and how place effects must be considered in further research in smart cities and the study and mitigation of digital inequality.
Sustainability in Telecommunication Networks and Key Value Indicators: a Survey
2025-01-08
Lucia Pintor, Luigi Atzori, Antonio Iera
Telecommunication technologies are important enablers for both digital and ecological transitions. By offering digital alternatives to traditional modes of transportation and communication, they help reduce carbon footprints while improving access to fundamental services. Particularly in rural and remote areas, telecommunications facilitate access to education, healthcare, and employment, helping to bridge the digital divide. Additionally, telecommunications can promote sustainability by supporting renewable energy usage, gender equality, and circular economies. However, defining the role of telecommunications in sustainability remains complex due to the historical focus on performance rather than long-term societal goals. Given the significance of this theme, this paper aims to provide the reader with a deeper look at the concept of sustainability within the telecommunications sector by examining relevant initiatives and projects. It reviews the major approaches for measuring sustainability and outlines practical approaches for implementing these assessments. Furthermore, the paper explores the proposed network architectures that incorporate Key Value Indicators and discusses major technologies in this area, such as Network Digital Twins and Intent-Based Networking. Through this analysis, the paper aims to contribute to creating sustainable telecommunication networks and broader industries.
Gravitational physics in the context of Indian astronomy: A vision document
2025-01-08
P. Ajith, K. G. Arun, Sukanta Bose, Sumanta Chakraborty, Shantanu Desai, A. Gopakumar, Sanved Kolekar, Rajesh Nayak, Archana Pai, Sudipta Sarkar, Jasjeet Singh Bagla, Patrick Das Gupta, Rahul Kashyap, Prashant Kocherlakota, Prayush Kumar, Banibrata Mukhopadhyay
Contributions from the Indian gravity community have played a significant role in shaping several branches of astronomy and astrophysics. This document reviews some of the most important contributions and presents a vision for gravity research in the context of astronomy \& astrophysics in India. This is an expanded version of one of the chapters in the recently released Vision Document of the Astronomical Society of India.
Neutrinos provide a compelling avenue to explore physics beyond the Standard Model. This proceeding is a brief summary of a plenary talk given at the 12th Neutrino Oscillation Workshop (NOW2024). We present a discussion on various topics on neutrino non-standard properties, such as the origin of neutrino mass, their decay modes, interaction mechanisms, and a potential connection to dark matter. Constraints and observational results from cosmology, astrophysics, and laboratory experiments are reviewed to illustrate the interplay between neutrino physics and broader questions about the universe.
In recent years, the rapid advancement of Large Language Models (LLMs) has transformed the landscape of scientific research, offering unprecedented support across various stages of the research cycle. This paper presents the first systematic survey dedicated to exploring how LLMs are revolutionizing the scientific research process. We analyze the unique roles LLMs play across four critical stages of research: hypothesis discovery, experiment planning and implementation, scientific writing, and peer reviewing. Our review comprehensively showcases the task-specific methodologies and evaluation benchmarks. By identifying current challenges and proposing future research directions, this survey not only highlights the transformative potential of LLMs, but also aims to inspire and guide researchers and practitioners in leveraging LLMs to advance scientific inquiry. Resources are available at the following repository: https://github.com/du-nlp-lab/LLM4SR
Research on the Interstellar Medium and Star Formation in the Galaxy: An Indian Perspective
2025-01-08
Bhaswati Mookerjea, Maheswar G., Kinsuk Acharyya, Tapas Baug, Prasun Datta, Jessy Jose, D. K. Ojha, Jagadheep D. Pandian, Nirupam Roy, Manash Samal, Saurabh Sharma, Archana Soam, Sarita Vig, Ankan Das, Lokesh Dewangan, Somnath Dutta, C. Eswariah, Liton Majumdar, Kshitiz Kumar Mallick, Soumen Mondal, Joe P. Ninan, Neelam Panwar, Amit Pathak, Shantanu Rastogi, Dipen Sahu, Anandmayee Tej, Veena V. S
Although the star formation process has been studied for decades, many important aspects of the physics involved remain unsolved. Recent advancement of instrumentation in the infrared, far-infrared and sub-millimetre wavelength regimes have contributed to a significantly improved understanding of processes in the interstellar medium (ISM) leading to star formation. The future of research on the ISM and star formation looks exciting with instruments like the JWST, ALMA, etc., already contributing to the topic by gathering high-resolution high-sensitivity data and with several larger ground- and space-bound facilities either being planned or constructed. India has a sizable number of astronomers engaged in research on topics related to the ISM and star formation. In this white paper invited by the Astronomical Society of India to prepare a vision document for Indian astronomy, we review the Indian contributions to the global understanding of the star formation process and suggest areas that require focused efforts both in creating observing facilities and in theoretical front in India, in order to improve the impact of our research in the coming decades.
Transient astrophysical events are characterized by short timescales, high energy, and multi-wavelength radiation, often accompanied by violent energy releases. These phenomena are a major focus of modern astronomical research. To reveal their underlying physical mechanisms, near-real-time, multi-wavelength, and multi-messenger follow-up observations are essential. However, current transient alert systems face multiple challenges, including fragmented messages, inconsistent formats, and difficulties in retrospective analysis, all of which hinder the efficiency of triggering observations. This paper presents \textbf{TransientVerse}, an innovative real-time database platform to integrate and disseminate transient alerts. The platform uses an automated pipeline to integrate real-time alerts from multiple sources (e.g., ATel, VOEvent, and GCN). It structures unstructured text data into a dual-format database for transient alerts by using open-source large language models. TransientVerse offers retrospective searches, data visualization, literature reviews, and customized subscriptions for efficient event tracking and analysis. Additionally, for Fast Radio Bursts (FRBs), the platform provides real-time statistics on repeat burst rates across different time intervals and alerts astronomers about high-frequency burst sources, enabling rapid follow-up observations and optimizing the use of limited observation windows. TransientVerse improves the efficiency of acquiring transient events in real time, lowers the technical barriers for simultaneous observations, and provides robust technical support for multi-wavelength, multi-messenger time-domain astronomy and astrophysics studies.
Agent Laboratory: Using LLM Agents as Research Assistants
2025-01-08
Samuel Schmidgall, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu, Zicheng Liu, Emad Barsoum
Historically, scientific discovery has been a lengthy and costly process, demanding substantial time and resources from initial conception to final results. To accelerate scientific discovery, reduce research costs, and improve research quality, we introduce Agent Laboratory, an autonomous LLM-based framework capable of completing the entire research process. This framework accepts a human-provided research idea and progresses through three stages--literature review, experimentation, and report writing to produce comprehensive research outputs, including a code repository and a research report, while enabling users to provide feedback and guidance at each stage. We deploy Agent Laboratory with various state-of-the-art LLMs and invite multiple researchers to assess its quality by participating in a survey, providing human feedback to guide the research process, and then evaluate the final paper. We found that: (1) Agent Laboratory driven by o1-preview generates the best research outcomes; (2) The generated machine learning code is able to achieve state-of-the-art performance compared to existing methods; (3) Human involvement, providing feedback at each stage, significantly improves the overall quality of research; (4) Agent Laboratory significantly reduces research expenses, achieving an 84% decrease compared to previous autonomous research methods. We hope Agent Laboratory enables researchers to allocate more effort toward creative ideation rather than low-level coding and writing, ultimately accelerating scientific discovery.
eRO-ExTra: eROSITA extragalactic non-AGN X-ray transients and variables in eRASS1 and eRASS2
2025-01-08
Iuliia Grotova, Arne Rau, Mara Salvato, Johannes Buchner, Adelle J. Goodwin, Zhu Liu, Adam Malyali, Andrea Merloni, Dusán Tubín-Arenas, David Homan, Mirko Krumpe, Kirpal Nandra, Gemma E. Anderson, Riccardo Arcodia, Sabina Bahic, Pietro Baldini, David A. H. Buckley, Stefano Ciroi, Adela Kawka, Megan Masterson, James C. A. Miller-Jones, Francesco Di Mille
(Abridged) While previous X-ray studies showed the dominance of regular active galactic nuclei (AGN) variability, a small fraction of sources arise from more exotic phenomena such as tidal disruption events (TDEs), quasi-periodic eruptions, or other short-lived events associated with supermassive black hole accretion. This paper describes the systematic selection of X-ray extragalactic transients found in the first two eROSITA all-sky surveys (eRASS) that are not associated with known AGN prior to eROSITA observations. We generated a variability sample from eRASS1 and eRASS2 (Dec. 2019-Dec. 2020), which includes sources with a variability significance and a fractional amplitude larger than four, located in the Legacy Survey DR10 (LS10) footprint. The properties of LS10 counterparts were used to exclude stars and known AGN. The sample was additionally cleaned using pre-eROSITA classifications, archival optical spectra, and archival X-ray data. The final catalog eRO-ExTra includes 304 extragalactic eROSITA transients and variables not associated with known AGN. More than 90% of sources have reliable LS10 optical counterparts. For each source, we provide archival X-ray data from Swift, ROSAT, and XMM-Newton; the eROSITA long-term light curve (2-2.5 years) with a light curve classification; as well as the best power law fit spectral results at the peak eROSITA epoch. Reliable spectroscopic and photometric redshifts are provided for more than 80% of the sample. Several sources in the catalog are known TDE candidates discovered by eROSITA. In addition, 31 sources are radio detected. The eRO-ExTra transients constitute a relatively clean parent sample of non-AGN variability phenomena associated with massive black holes. More than 95% of eRO-ExTra sources were discovered in X-rays with eROSITA for the first time, which makes it a valuable resource for studying unique nuclear transients.
Clusterization aspects in the states of non-rotating and fast rotating nuclei
2025-01-08
A. V. Afanasjev
The understanding of clustering aspects at the ground state of nuclei and in fast rotating ones within the framework of covariant density functional theory has been reviewed and reanalyzed. The appearance of many exotic nuclear shapes in nuclear chart can be inferred from combined analysis of nodal structure of the densities of the single-particle states and the evolution of such states in the Nilsson diagram with deformation and particle number. Such analysis which is supported by fully self-consistent calculations allows to predict the existence of nuclear configurations with specific shape or cluster properties at ground state and at high spin. For example, it indicates that in a given shell with principal quantum number $N$ only the lowest in energy two-fold degenerate deformed state can contribute to the formation of linear chain $\alpha$ cluster structures.
Signal Temporal Logic (STL) offers a concise yet expressive framework for specifying and reasoning about spatio-temporal behaviors of robotic systems. Attractively, STL admits the notion of robustness, the degree to which an input signal satisfies or violates an STL specification, thus providing a nuanced evaluation of system performance. Notably, the differentiability of STL robustness enables direct integration to robotics workflows that rely on gradient-based optimization, such as trajectory optimization and deep learning. However, existing approaches to evaluating and differentiating STL robustness rely on recurrent computations, which become inefficient with longer sequences, limiting their use in time-sensitive applications. In this paper, we present STLCG++, a masking-based approach that parallelizes STL robustness evaluation and backpropagation across timesteps, achieving more than 1000x faster computation time than the recurrent approach. We also introduce a smoothing technique for differentiability through time interval bounds, expanding STL's applicability in gradient-based optimization tasks over spatial and temporal variables. Finally, we demonstrate STLCG++'s benefits through three robotics use cases and provide open-source Python libraries in JAX and PyTorch for seamless integration into modern robotics workflows.
Identifying Flare Locations Through Exoplanet Transit Occultations
2025-01-08
Tayt Armitage, David V. Martin, Romy Rodríguez Martínez
M dwarfs are the most common stars in the galaxy, with long lifespans, a high occurrence rate of rocky planets, and close-in habitable zones. However, high stellar activity in the form of frequent flaring and any associated coronal mass ejections may drive atmospheric escape with the bombardment of radiation and high-energy particles, drastically impacting the habitability of these systems. The stellar latitude where flares and coronal mass ejections occur determines the space weather that exoplanets are subject to, with high-energy particle events associated with equatorial flares producing significant atmospheric erosion. However, the flaring latitudes for M dwarfs remain largely unconstrained. To aid in the effort to locate these flaring regions we explore the applicability of flare occultations using optical photometry to identify the latitudes of flares. As a planet transits in front of an ongoing flare the timing and geometry of the transit can be used to constrain the latitude and longitude of the flare. We predict the probability of detecting an occultation for known transiting planets and eclipsing binaries. From this, we estimate 3-22 detectable occultations exist within the TESS primary mission photometry, with the majority occurring in eclipsing binary observations. To demonstrate this technique, we analyze a candidate flare occultation event for the eclipsing binary CM Draconis.
Structures of various types of symmetry in the solar activity cycle
2025-01-08
V. N. Obridko, A. S. Shibalova, D. D. Sokoloff
The solar cycle is a complex phenomenon. To comprehensively understand it, we have to study various tracers. The most important component of this complex is the solar dynamo, which is understood as self-excitation of the solar magnetic field in the form of traveling waves somewhere in the convection zone. Along with the solar dynamo, the formation of the solar cycle involves other processes that are associated with the dynamo but are not its necessary part. We give a review of such phenomena that have not yet been explained in terms of dynamo theory. We consider the manifestations of the solar cycle in harmonics of the solar large-scale surface magnetic field, including zonal, sectorial, and tesseral harmonics; analyze their contribution to magnetic energy; and identify phases of the activity cycle using harmonics of different types of symmetry. The universal magnetic scenario of a solar activity cycle does not depend on its number and height. At the beginning of the cycle on the photosphere, the zonal harmonics account for 37-42% of the total energy (not 100%, as assumed in simplified descriptions). Sectorial harmonics do not disappear at all but account for 5-10% of the total energy. At this stage, the greatest energy (about 40%) is contained in the tesseral harmonics. As the cycle develops, the relative energy of zonal harmonics gradually decreases, reaching a minimum of 15-18% immediately before the onset of the sunspot maximum. The relative energy of sectorial harmonics increases and reaches a maximum (60-65%) somewhat later than the calendar date of the sunspot maximum. A particular feature of the tesseral harmonics is that their relative energy index changes in a much narrower range and never falls below 40% even at the cycle minimum. This is due to active regions and nonglobal magnetic fields. It is possible that tesseral harmonics are formed in shallow subphotospheric layers.
Turbulent mesoscale convection in the Boussinesq limit and beyond
2025-01-08
Shadab Alam, Dmitry Krasnov, Ambrish Pandey, John Panickacheril John, Roshan J. Samuel, Philipp P. Vieweg, Jörg Schumacher
Mesoscale convection covers an intermediate scale range between small-scale turbulence and the global organization of the convection flow. It is often characterized by an order of the convection patterns despite very high Rayleigh numbers and strong turbulent fluctuations. In this review, we discuss several aspects of mesoscale convection, which have been investigated by three-dimensional direct numerical simulations. The numerical studies are performed in a characteristic configuration of a plane layer that is heated from below and cooled from above or subject to constant heat flux at the top and bottom boundaries. We discuss the role of the thermal and mechanical boundary conditions for structure formation and study the impact of the domain shape as well as the Prandtl number. With respect to the latter, we focus on low values that arise in astrophysical convection and are partly not anymore accessible in laboratory experiments with liquid metals. Beside these experiments in the Boussinesq approximation, we report studies of non-Boussinesq mesoscale convection. This is done by investigating effects of compressibility and temperature dependence of material properties. The kinetic energy dissipation rate turns out to remain a central quantity for the turbulent mixing in compressible convection. Their different components, statistics, relation to the turbulent viscosity, and the multifractal properties are discussed.
The Evolution of Half-Mass Radii and Color Gradients for Young and Old Quiescent Galaxies at $0.5 < z < 3$ with JWST/PRIMER
2025-01-08
Maike Clausen, Ivelina Momcheva, Katherine E. Whitaker, Sam E. Cutler, Rachel S. Bezanson, James S. Dunlop, Norman A. Grogin, Anton M. Koekemoer, Derek McLeod, Ross McLure, Tim B. Miller, Erica Nelson, Arjen van der Wel, David Wake, Stijn Wuyts
We present a study of the size growth of the red sequence between $0.510$, whereas half-mass radii are derived from the F444W profiles together with the F277W-F444W color-$M_*$/L relation. We investigate the dependence of the ratio $r_{e, \mathrm{mass}}/r_{e, \mathrm{light}}$ on redshift, stellar mass, and the wavelength used to measure $r_{e, \mathrm{light}}$, also separating the sample into younger and older quiescent galaxies. Our data demonstrate that rest-frame infrared sizes accurately trace mass-weighted sizes while sizes measured at rest-frame optical wavelengths (0.5-0.7$\mu$m) are 0.1-0.2 dex larger, with only minor variations in redshift. We find that the average size of young quiescent galaxies agrees with that of old quiescent galaxies at intermediate masses, $10<$log($M_*/M_{\odot}$)$<11$, within their respective uncertainties in all observed-frame half-light, rest-frame half-light and half-mass radius measurements. At face value, our results point to a combination of progenitor bias and minor mergers driving the size growth of intermediate-mass quiescent galaxies at $0.5
Deep Photometric Observations of Ultra-Faint Milky Way Satellites Centaurus I and Eridanus IV
2025-01-08
Quinn O. Casey, Burçin Mutlu-Pakdil, David J. Sand, Andrew B. Pace, Denija Crnojevic, Amandine Doliva-Dolinsky, William Cerny, Mairead E. Heiger, Alex H. Riley, Alexander P. Ji, Guilherme Limberg, Laurella Marin, Clara E. Martínez-Vázquez, Gustavo E. Medina, Ting S. Li, Sasha N. Campana, Astha Chaturvedi, Joanna D. Sakowska, Alfredo Zenteno, Julio A. Carballo-Bello, Mahdieh Navabi, Clecio R. Bom
We present deep Magellan$+$Megacam imaging of Centaurus I (Cen I) and Eridanus IV (Eri IV), two recently discovered Milky Way ultra-faint satellites. Our data reach $\sim2-3$ magnitudes deeper than the discovery data from the DECam Local Volume Exploration (DELVE) Survey. We use these data to constrain their distances, structural properties (e.g., half-light radii, ellipticity, and position angle), and luminosities. We investigate whether these systems show signs of tidal disturbance, and identify new potential member stars using Gaia EDR3. Our deep color-magnitude diagrams show that Cen I and Eri IV are consistent with an old ($\tau\sim 13.0$ Gyr) and metal-poor ($\text{[Fe/H]}\le-2.2$) stellar population. We find Cen I to have a half-light radius of $r_{h}=2.60\pm0.30'$ ($90.6\pm11$ pc), an ellipticity of $\epsilon=0.36\pm0.05$, a distance of $D=119.8\pm4.1$ kpc ($m-M=20.39\pm0.08$ mag), and an absolute magnitude of $M_{V}=-5.39\pm0.19$. Similarly, Eri IV has $r_{h}=3.24\pm0.48'$ ($65.9\pm10$ pc), $\epsilon=0.26\pm0.09$, $D=69.9\pm3.6$ kpc ($m-M=19.22\pm0.11$ mag), and $M_{V}=-3.55\pm0.24$. These systems occupy a space on the size-luminosity plane consistent with other known Milky Way dwarf galaxies which supports the findings from our previous spectroscopic follow-up. Cen I has a well-defined morphology which lacks any clear evidence of tidal disruption, whereas Eri IV hosts a significant extended feature with multiple possible interpretations.
Lyman Alpha Forest - Halo Cross-Correlations in Effective Field Theory
2025-01-08
Anton Chudaykin, Mikhail M. Ivanov
We provide a perturbative effective field theory (EFT) description for anisotropic (redshift-space) correlations between the Lyman alpha forest and a generic biased tracer of matter, which could be represented by quasars, high-redshift galaxies, or dark matter halos. We compute one-loop EFT power spectrum predictions for the combined analysis of the Lyman alpha and biased tracers' data and test them on the publicly available high fidelity Sherwood simulations. We use massive and light dark matter halos at redshift $z=2.8$ as proxies for quasars and high-redshift galaxies, respectively. In both cases, we demonstrate that our EFT model can consistently describe the complete data vector consisting of the Lyman alpha forest auto spectrum, the halo auto spectrum, and the Lyman alpha -- halo cross spectrum. We show that the addition of cross-correlations significantly sharpens constraints on EFT parameters of the Lyman alpha forest and halos. In the combined analysis, our EFT model fits the simulated cross-spectra with a percent level accuracy at $k_{\rm max}= 1~h$Mpc$^{-1}$, which represents a significant improvement over previous analytical models. Thus, our work provides precision theoretical tools for full-shape analyses of Lyman alpha - quasar cross-correlations with ongoing and upcoming spectroscopic surveys.
Video Summarisation with Incident and Context Information using Generative AI
2025-01-08
Ulindu De Silva, Leon Fernando, Kalinga Bandara, Rashmika Nawaratne
The proliferation of video content production has led to vast amounts of data, posing substantial challenges in terms of analysis efficiency and resource utilization. Addressing this issue calls for the development of robust video analysis tools. This paper proposes a novel approach leveraging Generative Artificial Intelligence (GenAI) to facilitate streamlined video analysis. Our tool aims to deliver tailored textual summaries of user-defined queries, offering a focused insight amidst extensive video datasets. Unlike conventional frameworks that offer generic summaries or limited action recognition, our method harnesses the power of GenAI to distil relevant information, enhancing analysis precision and efficiency. Employing YOLO-V8 for object detection and Gemini for comprehensive video and text analysis, our solution achieves heightened contextual accuracy. By combining YOLO with Gemini, our approach furnishes textual summaries extracted from extensive CCTV footage, enabling users to swiftly navigate and verify pertinent events without the need for exhaustive manual review. The quantitative evaluation revealed a similarity of 72.8%, while the qualitative assessment rated an accuracy of 85%, demonstrating the capability of the proposed method.
DAREK -- Distance Aware Error for Kolmogorov Networks
2025-01-08
Masoud Ataei, Mohammad Javad Khojasteh, Vikas Dhiman
In this paper, we provide distance-aware error bounds for Kolmogorov Arnold Networks (KANs). We call our new error bounds estimator DAREK -- Distance Aware Error for Kolmogorov networks. Z. Liu et al. provide error bounds, which may be loose, lack distance-awareness, and are defined only up to an unknown constant of proportionality. We review the error bounds for Newton's polynomial, which is then generalized to an arbitrary spline, under Lipschitz continuity assumptions. We then extend these bounds to nested compositions of splines, arriving at error bounds for KANs. We evaluate our method by estimating an object's shape from sparse laser scan points. We use KAN to fit a smooth function to the scans and provide error bounds for the fit. We find that our method is faster than Monte Carlo approaches, and that our error bounds enclose the true obstacle shape reliably.
Traditional Celluloid (Cel) Animation production pipeline encompasses multiple essential steps, including storyboarding, layout design, keyframe animation, inbetweening, and colorization, which demand substantial manual effort, technical expertise, and significant time investment. These challenges have historically impeded the efficiency and scalability of Cel-Animation production. The rise of generative artificial intelligence (GenAI), encompassing large language models, multimodal models, and diffusion models, offers innovative solutions by automating tasks such as inbetween frame generation, colorization, and storyboard creation. This survey explores how GenAI integration is revolutionizing traditional animation workflows by lowering technical barriers, broadening accessibility for a wider range of creators through tools like AniDoc, ToonCrafter, and AniSora, and enabling artists to focus more on creative expression and artistic innovation. Despite its potential, issues such as maintaining visual consistency, ensuring stylistic coherence, and addressing ethical considerations continue to pose challenges. Furthermore, this paper discusses future directions and explores potential advancements in AI-assisted animation. For further exploration and resources, please visit our GitHub repository: https://github.com/yunlong10/Awesome-AI4Animation
A Survey on Algorithmic Developments in Optimal Transport Problem with Applications
2025-01-08
Sina Moradi
Optimal Transport (OT) has established itself as a robust framework for quantifying differences between distributions, with applications that span fields such as machine learning, data science, and computer vision. This paper offers a detailed examination of the OT problem, beginning with its theoretical foundations, including the classical formulations of Monge and Kantorovich and their extensions to modern computational techniques. It explores cutting-edge algorithms, including Sinkhorn iterations, primal-dual strategies, and reduction-based approaches, emphasizing their efficiency and scalability in addressing high-dimensional problems. The paper also highlights emerging trends, such as integrating OT into machine learning frameworks, the development of novel problem variants, and ongoing theoretical advancements. Applications of OT are presented across a range of domains, with particular attention to its innovative application in time series data analysis via Optimal Transport Warping (OTW), a robust alternative to methods like Dynamic Time Warping. Despite the significant progress made, challenges related to scalability, robustness, and ethical considerations remain, necessitating further research. The paper underscores OT's potential to bridge theoretical depth and practical utility, fostering impactful advancements across diverse disciplines.
2025-01-07
Mohan Li, Martin Gjoreski, Pietro Barbiero, Gašper Slapničar, Mitja Luštrek, Nicholas D. Lane, Marc Langheinrich
Human Sensing, a field that leverages technology to monitor human activities, psycho-physiological states, and interactions with the environment, enhances our understanding of human behavior and drives the development of advanced services that improve overall quality of life. However, its reliance on detailed and often privacy-sensitive data as the basis for its machine learning (ML) models raises significant legal and ethical concerns. The recently proposed ML approach of Federated Learning (FL) promises to alleviate many of these concerns, as it is able to create accurate ML models without sending raw user data to a central server. While FL has demonstrated its usefulness across a variety of areas, such as text prediction and cyber security, its benefits in Human Sensing are under-explored, given the particular challenges in this domain. This survey conducts a comprehensive analysis of the current state-of-the-art studies on FL in Human Sensing, and proposes a taxonomy and an eight-dimensional assessment for FL approaches. Through the eight-dimensional assessment, we then evaluate whether the surveyed studies consider a specific FL-in-Human-Sensing challenge or not. Finally, based on the overall analysis, we discuss open challenges and highlight five research aspects related to FL in Human Sensing that require urgent research attention. Our work provides a comprehensive corpus of FL studies and aims to assist FL practitioners in developing and evaluating solutions that effectively address the real-world complexities of Human Sensing.
Thermal Transport Properties of Magnons on the $α$-T$_3$ Lattice
2025-01-07
Luqman Saleem, Hasan M. Abdullah, Udo Schwingenschlogl, Aurelien Manchon
We theoretically investigate magnons on the $\alpha$-T$_3$ lattice. Atomistic spin dynamics simulations show that next-nearest neighbor hopping and easy-axis anisotropy stabilize ferromagnetic order in the presence of Dzyaloshinskii-Moriya interaction. We identify one topologically trivial magnon insulator phase and three magnon Chern insulator phases. The topologically trivial magnon insulator phase exhibits a small but non-zero magnon thermal Hall conductivity, while in the magnon Chern insulator phases the Chern number of the lowest magnon band dominates the magnon thermal Hall conductivity. The sign of the magnon thermal Hall conductivity does not change at the topological phase boundaries, but distinct changes are observed in the magnitude.
A novel experimental approach to uncover the nature of cosmic-ray Deuterium
2025-01-07
Francesco Dimiccoli, Francesco Maria Follega
Studying the isotopic composition of cosmic-rays (CRs) provides crucial insights into the galactic environment and helps improve existing propagation models. Special attention is given to the secondary-to-primary ratios of light isotopic components in CRs, as these measurements can offer complementary data compared to traditional secondary-to-primary ratios like B/C. Recently, a precision measurement of the Deuterium (D) abundance in CR in the 2-21 GV rigidity range provided by the AMS02 experiment unexpectedly detected an excess of D with respect to its expected secondary nature, opening the field for new measurements at high rigidity to determine how the spectrum evolves and whether there is confirmation of a primary or primary plus secondary origin. While there are theoretical models that attempt to explain this excess, the experimental uncertainties on D production cross-sections and on CR propagation models remain significant, and only new and precise measurements can dissipate existing doubts. In this work we review the current experimental scenario and we propose a dedicated experiment able to extend the D abundance measurement up to 100 GeV/nucl without the need of a magnetic spectrometer, using a multiple scattering based technique for the measurement of particle momentum. The expected performances of the proposed detector were assessed through a dedicated simulation using the GEANT4 package, and its role in the current particle physics scenario is discussed.
2025-01-07
Amy Steier, Lipika Ramaswamy, Andre Manoel, Alexa Haushalter
Recent advancements in generative AI have made it possible to create synthetic datasets that can be as accurate as real-world data for training AI models, powering statistical insights, and fostering collaboration with sensitive datasets while offering strong privacy guarantees. Effectively measuring the empirical privacy of synthetic data is an important step in the process. However, while there is a multitude of new privacy metrics being published every day, there currently is no standardization. In this paper, we review the pros and cons of popular metrics that include simulations of adversarial attacks. We also review current best practices for amending generative models to enhance the privacy of the data they create (e.g. differential privacy).
Visual question answering: from early developments to recent advances -- a survey
2025-01-07
Ngoc Dung Huynh, Mohamed Reda Bouadjenek, Sunil Aryal, Imran Razzak, Hakim Hacid
Visual Question Answering (VQA) is an evolving research field aimed at enabling machines to answer questions about visual content by integrating image and language processing techniques such as feature extraction, object detection, text embedding, natural language understanding, and language generation. With the growth of multimodal data research, VQA has gained significant attention due to its broad applications, including interactive educational tools, medical image diagnosis, customer service, entertainment, and social media captioning. Additionally, VQA plays a vital role in assisting visually impaired individuals by generating descriptive content from images. This survey introduces a taxonomy of VQA architectures, categorizing them based on design choices and key components to facilitate comparative analysis and evaluation. We review major VQA approaches, focusing on deep learning-based methods, and explore the emerging field of Large Visual Language Models (LVLMs) that have demonstrated success in multimodal tasks like VQA. The paper further examines available datasets and evaluation metrics essential for measuring VQA system performance, followed by an exploration of real-world VQA applications. Finally, we highlight ongoing challenges and future directions in VQA research, presenting open questions and potential areas for further development. This survey serves as a comprehensive resource for researchers and practitioners interested in the latest advancements and future
ALMA chemical survey of disk-outflow sources in Taurus (ALMA-DOT) VII: the layered molecular outflow from HL Tau and its relationship with the ringed disk
2025-01-07
F. Bacciotti, T. Nony, L. Podio, C. Dougados, A. Garufi, S. Cabrit, C. Codella, N. Zimniak, J. Ferreira
The ringed disk around HL Tau stands out as the iconic signature of planet formation, but the origin of the substructures is still debated. The HL Tau system also drives a powerful bipolar wind, and we analyze its outermost component traced by CO emission, to determine the relationship of the flow with the disk and its substructures. We use ALMA observations of the ${}^{12}$CO (2-1) line at 1.3 mm, with 0.2 km/s and ~ 0.28" resolution, conducted within the ALMA-DOT project. The channel maps and position-velocity diagrams show a rich structure of concatenated bubble- and arc-shaped features, whose size and distance from the source increase with velocity. The superposition of the features generates the apparent conical shape. The tomographic reconstruction of the morphology and kinematics of the red-shifted lobe suggests the presence of distinct nested shells having higher velocity and faster acceleration going toward the axis, rotating in the same sense of the disk. Such configuration can be justified by different classes of models. In this paper we compare the derived wind parameters with the predictions of magnetohydrodynamic (MHD) disk winds. Under this hypothesis, the launch radii of the three outermost shells are found to be at 55, 67 and 86 au from the star, coincident with regions of enhanced gas density in the disk. The wind may be capable of removing angular momentum from the outer disk, and we derive a magnetic lever arm of $\lambda \sim 4 - 5$, higher than that commonly adopted for MHD winds from these regions. Interpretations are discussed. The arrangement of the wind in nested shells with brighter emission rooted in rings of enhanced gas density could support the results of non-ideal MHD simulations according to which magnetic instabilities can generate the disk ring-gap system with a connected layered wind, alternatively to the action of yet undetected protoplanets.
SELMA3D challenge: Self-supervised learning for 3D light-sheet microscopy image segmentation
2025-01-07
Ying Chen, Rami Al-Maskari, Izabela Horvath, Mayar Ali, Luciano Höher, Kaiyuan Yang, Zengming Lin, Zhiwei Zhai, Mengzhe Shen, Dejin Xun, Yi Wang, Tony Xu, Maged Goubran, Yunheng Wu, Ali Erturk, Johannes C. Paetzold
Recent innovations in light sheet microscopy, paired with developments in tissue clearing techniques, enable the 3D imaging of large mammalian tissues with cellular resolution. Combined with the progress in large-scale data analysis, driven by deep learning, these innovations empower researchers to rapidly investigate the morphological and functional properties of diverse biological samples. Segmentation, a crucial preliminary step in the analysis process, can be automated using domain-specific deep learning models with expert-level performance. However, these models exhibit high sensitivity to domain shifts, leading to a significant drop in accuracy when applied to data outside their training distribution. To address this limitation, and inspired by the recent success of self-supervised learning in training generalizable models, we organized the SELMA3D Challenge during the MICCAI 2024 conference. SELMA3D provides a vast collection of light-sheet images from cleared mice and human brains, comprising 35 large 3D images-each with over 1000^3 voxels-and 315 annotated small patches for finetuning, preliminary testing and final testing. The dataset encompasses diverse biological structures, including vessel-like and spot-like structures. Five teams participated in all phases of the challenge, and their proposed methods are reviewed in this paper. Quantitative and qualitative results from most participating teams demonstrate that self-supervised learning on large datasets improves segmentation model performance and generalization. We will continue to support and extend SELMA3D as an inaugural MICCAI challenge focused on self-supervised learning for 3D microscopy image segmentation.
Machine learning applications in archaeological practices: a review
2025-01-07
Mathias Bellat, Jordy D. Orellana Figueroa, Jonathan S. Reeves, Ruhollah Taghizadeh-Mehrjardi, Claudio Tennie, Thomas Scholten
Artificial intelligence and machine learning applications in archaeology have increased significantly in recent years, and these now span all subfields, geographical regions, and time periods. The prevalence and success of these applications have remained largely unexamined, as recent reviews on the use of machine learning in archaeology have only focused only on specific subfields of archaeology. Our review examined an exhaustive corpus of 135 articles published between 1997 and 2022. We observed a significant increase in the number of relevant publications from 2019 onwards. Automatic structure detection and artefact classification were the most represented tasks in the articles reviewed, followed by taphonomy, and archaeological predictive modelling. From the review, clustering and unsupervised methods were underrepresented compared to supervised models. Artificial neural networks and ensemble learning account for two thirds of the total number of models used. However, if machine learning is gaining in popularity it remains subject to misunderstanding. We observed, in some cases, poorly defined requirements and caveats of the machine learning methods used. Furthermore, the goals and the needs of machine learning applications for archaeological purposes are in some cases unclear or poorly expressed. To address this, we proposed a workflow guide for archaeologists to develop coherent and consistent methodologies adapted to their research questions, project scale and data. As in many other areas, machine learning is rapidly becoming an important tool in archaeological research and practice, useful for the analyses of large and multivariate data, although not without limitations. This review highlights the importance of well-defined and well-reported structured methodologies and collaborative practices to maximise the potential of applications of machine learning methods in archaeology.
Around the topological classification problem of polynomial maps: A survey
2025-01-07
Boulos El Hilany
The study of the topology of polynomial maps originates from classical questions in affine geometry, such as the Jacobian Conjecture, as well as from works of Whitney, Thom, and Mather in the 1950-70s on diffeomorphism types of smooth maps. During that period, Thom came up with a famous construction of a one-dimensional family of real polynomial maps having fixed degree nine and infinitely many topological types. In his convention, a topological type of a map is preserved precisely when it is composed with a homeomorphism on the source space, and one on the target space. Thom also conjectured that for each pair $(n,d)$, any family of $n$--variate, degree--$d$ (complex, or real) polynomial functions has at most finitely-many topological types. Soon after, a collection of results by several mathematicians throughout the 1970s and 1980s settled this conjecture, and solved its subsequent generalization to polynomial maps. In this survey, we outline the historical context and highlight a range of significant works from the 1950s to the present day that lead to the current state of the art in the study of polynomial maps' topology. The focal point of this survey is to shed some light on the ensuing classification problem of topological types of polynomial maps. The presentation is achieved by making a gentle introduction to several other prominent questions in affine geometry, all of which are recounted through the lens of the above classification problem.
High-cadence stellar variability studies of RR Lyrae stars with DECam: New multi-band templates
2025-01-07
K. Baeza-Villagra, N. Rodriguez-Segovia, M. Catelan, A. Rest, A. Papageorgiou, C. E. Martinez-Vazquez, A. A. R. Valcarce, C. E. Ferreira Lopes, F. B. Bianco
We present the most extensive set to date of high-quality RR Lyrae light curve templates in the griz bands, based on time-series observations of the Dark Energy Camera Plane Survey (DECaPS) East field, located in the Galactic bulge at coordinates (RA, DEC)(J2000) = (18:03:34, -29:32:02), obtained with the Dark Energy Camera (DECam) on the 4-m Blanco telescope at the Cerro Tololo Inter-American Observatory (CTIO). Our templates, which cover both fundamental-mode (RRab) and first-overtone (RRc) pulsators, can be especially useful when there is insufficient data for accurately calculating the average magnitudes and colors, hence distances, as well as to inform multi-band light curve classifiers, as will be required in the case of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). In this paper, we describe in detail the procedures that were adopted in producing these templates, including a novel approach to account for the presence of outliers in photometry. Our final sample comprises 136 RRab and 144 RRc templates, all of which are publicly available. Lastly, in this paper we study the inferred Fourier parameters and other light curve descriptors, including rise time,skewness, and kurtosis, as well as their correlations with the pulsation mode, period, and effective wavelength.
TOI-5108 b and TOI 5786 b: Two transiting sub-Saturns detected and characterized with TESS, MaHPS and SOPHIE
2025-01-07
Luis Thomas, Guillaume Hébrard, Hanna Kellermann, Judith Korth, Neda Heidari, Thierry Forveille, Sérgio G. Sousa, Laura Schöller, Arno Riffeser, Claus Gössl, Juan Serrano Bell, Flavien Kiefer, Nathan Hara, Frank Grupp, Juliana Ehrhardt, Felipe Murgas, Karen A. Collins, Allyson Bieryla, Hannu Parviainen, Alexandr A. Belinski, Emma Esparza-Borges, David R. Ciardi, Catherine A. Clark, Akihiko Fukui, Emily A. Gilbert, Ulrich Hopp, Kai Ikuta, Jon M. Jenkins, David W. Latham, Norio Narita, Louise D. Nielsen, Samuel N. Quinn, Enric Palle, Jan-Niklas Pippert, Alex S. Polanski, Christoph Ries, Michael Schmidt, Richard P. Schwarz, Sara Seager, Ivan A. Strakhov, Stephanie Striegel, Julian C. van Eyken, Noriharu Watanabe, Cristilyn N. Watkins, Joshua N. Winn, Carl Ziegler, Raphael Zöller
We report the discovery and characterization of two sub-Saturns from the Transiting Exoplanet Survey Satellite (\textit{TESS}) using high-resolution spectroscopic observations from the MaHPS spectrograph at the Wendelstein Observatory and the SOPHIE spectrograph at the Haute-Provence Observatory. Combining photometry from TESS, KeplerCam, LCOGT, and MuSCAT2 with the radial velocity measurements from MaHPS and SOPHIE we measure precise radii and masses for both planets. TOI-5108 b is a sub-Saturn with a radius of $6.6 \pm 0.1$ $R_\oplus$ and a mass of $32 \pm 5$ $M_\oplus$. TOI-5786 b is similar to Saturn with a radius of $8.54 \pm 0.13$ $R_\oplus$ and a mass of $73 \pm 9$ $M_\oplus$. The host star for TOI-5108 b is a moderately bright (Vmag 9.75) G-type star. TOI-5786 is a slightly dimmer (Vmag 10.2) F-type star. Both planets are close to their host stars with periods of 6.75 days and 12.78 days respectively. This puts TOI-5108 b just inside the bounds of the Neptune desert while TOI-5786 b is right above the upper edge. We estimate hydrogen-helium envelope mass fractions of $38 \%$ for TOI-5108 b and $74 \% $ for TOI-5786 b. However, using a model for the interior structure that includes tidal effects the envelope fraction of TOI-5108 b could be much lower ($\sim 20\,\%$) depending on the obliquity. We estimate mass-loss rates between 1.0 * $10^9$ g/s and 9.8 * $10^9$ g/s for TOI-5108 b and between 3.6 * $10^8$ g/s and 3.5 * $10^9$ g/s for TOI-5786 b. Given their masses, this means that both planets are stable against photoevaporation. We also detect a transit signal for a second planet candidate in the TESS data of TOI-5786 with a period of 6.998 days and a radius of $3.83 \pm 0.16$ $R_\oplus$. Using our RV data and photodynamical modeling, we are able to provide a 3-$\sigma$ upper limit of 26.5 $M_\oplus$ for the mass of the potential inner companion to TOI-5786 b.
A common framework for test ideals, closure operations, and their duals
2025-01-07
Neil Epstein, Rebecca R. G., Janet Vassilev
Closure operations such as tight and integral closure and test ideals have appeared frequently in the study of commutative algebra. This articles serves as a survey of the authors' prior results connecting closure operations, test ideals, and interior operations via the more general structure of pair operations. Specifically, we describe a duality between closure and interior operations generalizing the duality between tight closure and its test ideal, provide methods for creating pair operations that are compatible with taking quotient modules or submodules, and describe a generalization of core and its dual. Throughout, we discuss how these ideas connect to common constructions in commutative algebra.
Self-Adaptive ERP: Embedding NLP into Petri-Net creation and Model Matching
2025-01-07
Ahmed Maged, Gamal Kassem
Enterprise Resource Planning (ERP) consultants play a vital role in customizing systems to meet specific business needs by processing large amounts of data and adapting functionalities. However, the process is resource-intensive, time-consuming, and requires continuous adjustments as business demands evolve. This research introduces a Self-Adaptive ERP Framework that automates customization using enterprise process models and system usage analysis. It leverages Artificial Intelligence (AI) & Natural Language Processing (NLP) for Petri nets to transform business processes into adaptable models, addressing both structural and functional matching. The framework, built using Design Science Research (DSR) and a Systematic Literature Review (SLR), reduces reliance on manual adjustments, improving ERP customization efficiency and accuracy while minimizing the need for consultants.
Ultra-cold atoms as quantum simulators for relativistic phenomena
2025-01-07
Ralf Schützhold
The goal of this article is to review developments regarding the use of ultra-cold atoms as quantum simulators. Special emphasis is placed on relativistic quantum phenomena, which are presumably most interesting for the audience of this journal. After a brief introduction into the main idea of quantum simulators and the basic physics of ultra-cold atoms, relativistic quantum phenomena of linear fields are discussed, including Hawking radiation, the Unruh effect, cosmological particle creation, the Gibbons-Hawking and Ginzburg effects, super-radiance, Sauter-Schwinger and Breit-Wheeler pair creation, as well as the dynamical Casimir effect. After that, the focus is shifted to phenomena of non-linear fields, such as the sine-Gordon model, the Kibble-Zurek mechanism, false-vacuum decay, and quantum back-reaction. In order to place everything into proper context, the basic underlying mechanisms of these phenomena are briefly recapitulated before their simulators are discussed. Even though effort is made to provide a review as fair as possible, there can be co claim of completeness and the selection as well as the relative weights of the topics may well reflect some personal bias.
Investigation of bremsstrahlung emission in an electron cyclotron resonance ion source and its dependence on the magnetic confinement
2025-01-07
Andrea Cernuschi, Thomas Thuillier
A Monte Carlo (MC) code is used to investigate the bremsstrahlung x-ray emission of an electron cyclotron resonance ion source (ECRIS) and its dependence on the axial magnetic confinement. The x-ray spectral temperature Ts measured with the simulations is in fair agreement with previous experiments. The dependence of Ts on the minimum magnetic field of the configuration Bmin is corroborated, also observing that the ion extraction peak field Bext has no influence on temperature. Details on the mechanism generating the hot electron population responsible for the change in spectral x-ray temperature as a function of Bmin are proposed, based on an in-depth investigation of the electron population with the MC code using a high statistics.
Implications of the intriguing constant inner mass surface density observed in dark matter halos
2025-01-07
Jorge Sanchez Almeida
It is known for long that the observed mass surface density of cored dark matter (DM) halos is approximately constant, independently of the galaxy mass (i.e., rhoc X rc simeq constant}, with rhoc and rc the central volume density and the radius of the core, respectively). Here we review the evidence supporting this empirical fact as well as its theoretical interpretation. It seems to be an emergent law resulting from the concentration-halo mass relation predicted by the current cosmological model, where the DM is made of collisionless cold DM particles (CDM). We argue that the prediction rhoc X rc simeq constant is not specific to this particular model of DM but holds for any other DM model (e.g., self-interacting) or process (e.g., stellar or AGN feedback) that redistributes the DM within halos conserving its CDM mass. In addition, the fact that rhoc X rc simeq constant is shown to allow the estimate of the core DM mass and baryon fraction from stellar photometry alone, particularly useful when the observationally-expensive conventional spectroscopic techniques are unfeasible.
Goodness-of-fit tests for spatial point processes: A review
2025-01-07
Chiara Fend, Claudia Redenbach
In this review, the state-of-the-art for goodness-of-fit testing for spatial point processes is summarized. Test statistics based on classical functional summary statistics and recent contributions from topological data analysis are considered. Different approaches to derive test statistics from functional summary statistics are categorized in a unifying notation. We discuss additional aspects such as the graphical representation in terms of global envelopes and the selection of the parameters in the individual tests.
Illuminating extracellular vesicles biology with super resolution microscopy: Insights into morphology and composition
2025-01-07
Lucile Alexandre, Daniele D Arrigo, Nicolas Kuszla, Andre Cronemberger Andrade, Amanda K A Silva, Cataldo Schietroma, Florence Gazeau, Quentin Lubart, Stephanie Mangenot
Super-Resolution Microscopy (SRM) is emerging as a powerful and innovative tool for imaging, characterizing, and understanding the structure of Extracellular Vesicles (EVs). By addressing the need for single-particle analysis with the high resolution required to study the composition and organization of these nanoparticles, SRM provides unique insights into EV biology. However, its application is accompanied by significant challenges, ranging from experimental setup to data analysis. This review outlines the fundamentals of SRM and its position within the broader field of EV research. We then explore its applications in evaluating (i) the morphological structure of EVs, (ii) their molecular composition, and (iii) their roles in biological systems. By offering practical guidance and an overview of critical parameters for standardization, this review aims at providing researchers with the tools and insights necessary to effectively apply SRM to EV investigation.
Bacterial cells use run-and-tumble motion to climb up attractant concentration gradient in their environment. By extending the uphill runs and shortening the downhill runs the cells migrate towards the higher attractant zones. Motivated by this, we formulate a reinforcement learning (RL) algorithm where an agent moves in one dimension in the presence of an attractant gradient. The agent can perform two actions: either persistent motion in the same direction or reversal of direction. We assign costs for these actions based on the recent history of the agent's trajectory. We ask the question: which RL strategy works best in different types of attractant environment. We quantify efficiency of the RL strategy by the ability of the agent (a) to localize in the favorable zones after large times, and (b) to learn about its complete environment. Depending on the attractant profile and the initial condition, we find an optimum balance is needed between exploration and exploitation to ensure the most efficient performance.
Single-molecule Scale Magnetic Resonance Spectroscopy using Nitrogen-Vacancy Centers in Diamond
2025-01-07
Jiangfeng Du, Fazhan Shi, Xi Kong, Fedor Jekezko, Jörg Wrachtrup
Single-molecule technology stands as a powerful tool, enabling the characterization of intricate structural and dynamic information that would otherwise remain concealed within the averaged behaviors of numerous molecules. This technology finds extensive application across diverse fields including physics, chemistry, biology, and medicine. Quantum sensing, particularly leveraging nitrogen-vacancy (NV) centers within diamond structures, presents a promising avenue for single-molecule magnetic resonance, offering prospects for sensing and imaging technology at the single-molecule level. Notably, while significant strides have been made in single-molecule scale magnetic resonance using NV centers over the past two decades, current approaches still exhibit limitations in magnetic sensitivity, spectral resolution, and spatial resolution. Particularly, the full reconstruction of three-dimensional positions of nuclear spins within single molecules remains an unattained goal. This review provides a comprehensive overview of the current state-of-the-art in single-molecule scale magnetic resonance, encompassing an analysis of various relevant techniques involving NV centers. Additionally, it explores the optimization of technical parameters associated with these methods. This detailed analysis serves as a foundation for the development of new technologies and the exploration of potential applications.
Is social media hindering or helping Academic Performance? A case study of Walter Sisulu University Buffalo City Campus
2025-01-07
Jose M. Lukose, Abayomi O. Agbeyangi
Social media platforms are popular among higher education students and have seen increased usage for academic purposes, especially during the COVID-19 pandemic. However, excessive use of social media can negatively impact students' academic performance. This preliminary study examines social media's impact on students' academic performance at Walter Sisulu University (WSU), Buffalo City campus. Using a positivist paradigm and a quantitative approach, randomly sampled data were collected from 71 students through a survey to identify trends and generate preliminary insights. Results indicate that while social media can facilitate academic work, it predominantly acts as a distraction, negatively affecting academic performance, particularly for first-year students. Notably, 84.5% of the students spend more than four hours daily on social media, and 39.4% agree that it negatively impacts their assignment completion. The study underscores the need for students to balance their social media use and academic responsibilities, highlighting the importance of this issue. Recommendations for achieving this balance, such as adopting time management strategies and integrating social media into teaching methodologies, are discussed.
Applying Large Language Models in Knowledge Graph-based Enterprise Modeling: Challenges and Opportunities
2025-01-07
Benedikt Reitemeyer, Hans-Georg Fill
The role of large language models (LLMs) in enterprise modeling has recently started to shift from academic research to that of industrial applications. Thereby, LLMs represent a further building block for the machine-supported generation of enterprise models. In this paper we employ a knowledge graph-based approach for enterprise modeling and investigate the potential benefits of LLMs in this context. In addition, the findings of an expert survey and ChatGPT-4o-based experiments demonstrate that LLM-based model generations exhibit minimal variability, yet remain constrained to specific tasks, with reliability declining for more intricate tasks. The survey results further suggest that the supervision and intervention of human modeling experts are essential to ensure the accuracy and integrity of the generated models.
The generalized phase retrieval problem over compact groups
2025-01-07
Tamir Bendory, Dan Edidin
The classical phase retrieval problem involves estimating a signal from its Fourier magnitudes (power spectrum) by leveraging prior information about the desired signal. This paper extends the problem to compact groups, addressing the recovery of a set of matrices from their Gram matrices. In this broader context, the missing phases in Fourier space are replaced by missing unitary or orthogonal matrices arising from the action of a compact group on a finite-dimensional vector space. This generalization is driven by applications in multi-reference alignment and single-particle cryo-electron microscopy, a pivotal technology in structural biology. We define the generalized phase retrieval problem over compact groups and explore its underlying algebraic structure. We survey recent results on the uniqueness of solutions, focusing on the significant class of semialgebraic priors. Furthermore, we present a family of algorithms inspired by classical phase retrieval techniques. Finally, we propose a conjecture on the stability of the problem based on bi-Lipschitz analysis, supported by numerical experiments.
Tabular data remains one of the most prevalent data types across a wide range of real-world applications, yet effective representation learning for this domain poses unique challenges due to its irregular patterns, heterogeneous feature distributions, and complex inter-column dependencies. This survey provides a comprehensive review of state-of-the-art techniques in tabular data representation learning, structured around three foundational design elements: training data, neural architectures, and learning objectives. Unlike prior surveys that focus primarily on either architecture design or learning strategies, we adopt a holistic perspective that emphasizes the universality and robustness of representation learning methods across diverse downstream tasks. We examine recent advances in data augmentation and generation, specialized neural network architectures tailored to tabular data, and innovative learning objectives that enhance representation quality. Additionally, we highlight the growing influence of self-supervised learning and the adaptation of transformer-based foundation models for tabular data. Our review is based on a systematic literature search using rigorous inclusion criteria, encompassing 127 papers published since 2020 in top-tier conferences and journals. Through detailed analysis and comparison, we identify emerging trends, critical gaps, and promising directions for future research, aiming to guide the development of more generalizable and effective tabular data representation methods.
A Review of Cognitive Readiness, Wearable Devices, and Prospects
2025-01-07
Tasnim Irtifa Chowdhury, Andrew Vargo, Chris Blakely, Benjamin Tag, Koichi Kise
In Human-Computer Interaction (HCI) and Ubiquitous Computing, the objective of optimizing device interactions and personalizing user experiences has placed a new emphasis on accurately evaluating cognitive readiness using wearable devices. Interpreting cognitive readiness in real-world scenarios is complex due to the plethora of potential physiological measures, individual variability, and the limitations of wearable devices. In this review, we present a systematic overview of key physiological measures that can be used for an in-depth assessment of cognitive readiness. These measures can serve as proxies for detailed assessments of cognitive readiness. This review serves as a tool for assessing cognitive readiness for diverse applications, with special focus on in-the-wild research settings. In addition, due to the complexity of measurements and devices, we propose the development of robust catalog for cognitive readiness measurements.
2025-01-07
Shakeel A. Sheikh, Md. Sahidullah, Ina Kodrasi
Advancements in spoken language technologies for neurodegenerative speech disorders are crucial for meeting both clinical and technological needs. This overview paper is vital for advancing the field, as it presents a comprehensive review of state-of-the-art methods in pathological speech detection, automatic speech recognition, pathological speech intelligibility enhancement, intelligibility and severity assessment, and data augmentation approaches for pathological speech. It also high-lights key challenges, such as ensuring robustness, privacy, and interpretability. The paper concludes by exploring promising future directions, including the adoption of multimodal approaches and the integration of graph neural networks and large language models to further advance speech technology for neurodegenerative speech disorders
In this paper, a progress recognition method consider occlusion using deep metric learning is proposed to visualize the product assembly process in a factory. First, the target assembly product is detected from images acquired from a fixed-point camera installed in the factory using a deep learning-based object detection method. Next, the detection area is cropped from the image. Finally, by using a classification method based on deep metric learning on the cropped image, the progress of the product assembly work is estimated as a rough progress step. As a specific progress estimation model, we propose an Anomaly Triplet-Net that adds anomaly samples to Triplet Loss for progress estimation considering occlusion. In experiments, an 82.9% success rate is achieved for the progress estimation method using Anomaly Triplet-Net. We also experimented with the practicality of the sequence of detection, cropping, and progression estimation, and confirmed the effectiveness of the overall system.
The S-PLUS Ultra-Short Survey: Photometric Re-calibration with the BEst STar Database
2025-01-07
Xiaolu Li, Kai Xiao, Yang Huang, Haibo Yuan, Yanke Tang, Timothy C. Beers, Bowen Huang, Mingyang Ma, Pedro K. Humire, Alvaro Alvarez-Candal, Federico Sestito, Ning Gai, Yongna Mao, Hongrui Gu, Zhenzhao Tao, Lin Yang, Shuai Xu, Rong Hu
We present an independent validation and comprehensive re-calibration of S-PLUS Ultra-Short Survey (USS) DR1 12-band photometry using about 30,000--70,000 standard stars from the BEst STar (BEST) database. We identify spatial variation of zero-point offsets, up to 30--40\,mmag for blue filters ($u$, $J0378$, $J0395$) and 10\,mmag for others, predominantly due to the higher uncertainties of the technique employed in the original USS calibration. Moreover, we detect large- and medium-scale CCD position-dependent systematic errors, up to 50\,mmag, primarily caused by different aperture and flat-field corrections. We then re-calibrate the USS DR1 photometry by correcting the systematic shifts for each tile using second-order two-dimensional polynomial fitting combined with a numerical stellar flat-field correction method. The re-calibrated results from the XPSP and the SCR standards are consistent within 6\,mmag in the USS zero-points, demonstrating both the typical precision of re-calibrated USS photometry and a sixfold improvement in USS zero-point precision. Further validation using SDSS and Pan-STARRS1, as well as LAMOST DR10 and Gaia photometry, also confirms this precision for the re-calibrated USS photometry. Our results clearly demonstrate the capability and the efficiency of the BEST database in improving calibration precision to the milli-magnitude level for wide-field photometric surveys. The re-calibrated USS DR1 photometry is publicly available: doi: 10.12149/101503 (https://nadc.china-vo.org/res/r101504/).
Assessing the Impact of Binary Systems on Microlensing Using SPISEA and PopSyCLE Population Simulations
2025-01-07
Natasha S. Abrams, Jessica R. Lu, Casey Y. Lam, Michael S. Medford, Matthew W. Hosek, Jr., Sam Rose
Gravitational microlensing provides a unique opportunity to probe the mass distribution of stars, black holes, and other objects in the Milky Way. Population simulations are necessary to interpret results from microlensing surveys. The contribution from binary objects is often neglected or minimized in analysis of observations and simulations despite the high percentage of binary systems and microlensing's ability to probe binaries. To simulate the population effects we added multiple systems to Stellar Population Interface for Stellar Evolution and Atmospheres (SPISEA), which simulates stellar clusters. We then inject these multiples into Population Synthesis for Compact-object Lensing Events (PopSyCLE), which simulates Milky Way microlensing surveys. When making OGLE observational selection criteria, we find that 55% of observed microlensing events involve a binary system. Specifically, 14.5% of events have a multiple-lens and a single source, 31.7% have a single lens and a multiple-source, and 8.8% have a multiple-lens and a multiple-source. The majority of these events have photometric lightcurves that appear single and are fit well by a single-lens, single-source model. This suggests that binary source and binary lens-binary source models should be included more frequently in event analysis. The mean Einstein crossing time shifts from 19.1 days for single events only to 21.3 days for singles and multiple events, after cutting binary events with multiple peaks. The Einstein crossing time distribution of singles and single-peaked multiple events is better aligned with observed distributions from OGLE (arXiv:1707.07634) than singles alone, indicating that multiple systems are a significant missing piece between simulations and reality.
ISSR: Iterative Selection with Self-Review for Vocabulary Test Distractor Generation
2025-01-07
Yu-Cheng Liu, An-Zi Yen
Vocabulary acquisition is essential to second language learning, as it underpins all core language skills. Accurate vocabulary assessment is particularly important in standardized exams, where test items evaluate learners' comprehension and contextual use of words. Previous research has explored methods for generating distractors to aid in the design of English vocabulary tests. However, current approaches often rely on lexical databases or predefined rules, and frequently produce distractors that risk invalidating the question by introducing multiple correct options. In this study, we focus on English vocabulary questions from Taiwan's university entrance exams. We analyze student response distributions to gain insights into the characteristics of these test items and provide a reference for future research. Additionally, we identify key limitations in how large language models (LLMs) support teachers in generating distractors for vocabulary test design. To address these challenges, we propose the iterative selection with self-review (ISSR) framework, which makes use of a novel LLM-based self-review mechanism to ensure that the distractors remain valid while offering diverse options. Experimental results show that ISSR achieves promising performance in generating plausible distractors, and the self-review mechanism effectively filters out distractors that could invalidate the question.
2025-01-07
Xiao Wang, Fuling Wang, Haowen Wang, Bo Jiang, Chuanfu Li, Yaowei Wang, Yonghong Tian, Jin Tang
X-ray image based medical report generation achieves significant progress in recent years with the help of the large language model, however, these models have not fully exploited the effective information in visual image regions, resulting in reports that are linguistically sound but insufficient in describing key diseases. In this paper, we propose a novel associative memory-enhanced X-ray report generation model that effectively mimics the process of professional doctors writing medical reports. It considers both the mining of global and local visual information and associates historical report information to better complete the writing of the current report. Specifically, given an X-ray image, we first utilize a classification model along with its activation maps to accomplish the mining of visual regions highly associated with diseases and the learning of disease query tokens. Then, we employ a visual Hopfield network to establish memory associations for disease-related tokens, and a report Hopfield network to retrieve report memory information. This process facilitates the generation of high-quality reports based on a large language model and achieves state-of-the-art performance on multiple benchmark datasets, including the IU X-ray, MIMIC-CXR, and Chexpert Plus. The source code of this work is released on \url{https://github.com/Event-AHU/Medical_Image_Analysis}.
Physics-Constrained Generative Artificial Intelligence for Rapid Takeoff Trajectory Design
2025-01-07
Samuel Sisk, Xiaosong Du
To aid urban air mobility (UAM), electric vertical takeoff and landing (eVTOL) aircraft are being targeted. Conventional multidisciplinary analysis and optimization (MDAO) can be expensive, while surrogate-based optimization can struggle with challenging physical constraints. This work proposes physics-constrained generative adversarial networks (physicsGAN), to intelligently parameterize the takeoff control profiles of an eVTOL aircraft and to transform the original design space to a feasible space. Specifically, the transformed feasible space refers to a space where all designs directly satisfy all design constraints. The physicsGAN-enabled surrogate-based takeoff trajectory design framework was demonstrated on the Airbus A3 Vahana. The physicsGAN generated only feasible control profiles of power and wing angle in the feasible space with around 98.9% of designs satisfying all constraints. The proposed design framework obtained 99.6% accuracy compared with simulation-based optimal design and took only 2.2 seconds, which reduced the computational time by around 200 times. Meanwhile, data-driven GAN-enabled surrogate-based optimization took 21.9 seconds using a derivative-free optimizer, which was around an order of magnitude slower than the proposed framework. Moreover, the data-driven GAN-based optimization using gradient-based optimizers could not consistently find the optimal design during random trials and got stuck in an infeasible region, which is problematic in real practice. Therefore, the proposed physicsGAN-based design framework outperformed data-driven GAN-based design to the extent of efficiency (2.2 seconds), optimality (99.6% accurate), and feasibility (100% feasible). According to the literature review, this is the first physics-constrained generative artificial intelligence enabled by surrogate models.
Learning to Transfer Human Hand Skills for Robot Manipulations
2025-01-07
Sungjae Park, Seungho Lee, Mingi Choi, Jiye Lee, Jeonghwan Kim, Jisoo Kim, Hanbyul Joo
We present a method for teaching dexterous manipulation tasks to robots from human hand motion demonstrations. Unlike existing approaches that solely rely on kinematics information without taking into account the plausibility of robot and object interaction, our method directly infers plausible robot manipulation actions from human motion demonstrations. To address the embodiment gap between the human hand and the robot system, our approach learns a joint motion manifold that maps human hand movements, robot hand actions, and object movements in 3D, enabling us to infer one motion component from others. Our key idea is the generation of pseudo-supervision triplets, which pair human, object, and robot motion trajectories synthetically. Through real-world experiments with robot hand manipulation, we demonstrate that our data-driven retargeting method significantly outperforms conventional retargeting techniques, effectively bridging the embodiment gap between human and robotic hands. Website at https://rureadyo.github.io/MocapRobot/.
Graph transformations definable in logic can be described using the notion of transductions. By understanding transductions as a basic embedding mechanism, which captures the possibility of encoding one graph in another graph by means of logical formulas, we obtain a new perspective on the landscape of graph classes and of their properties. The aim of this survey is to give a comprehensive presentation of this angle on structural graph theory. We first give a logic-focused overview of classic graph-theoretic concepts, such as treedepth, shrubdepth, treewidth, cliquewidth, twin-width, bounded expansion, and nowhere denseness. Then, we present recent developments related to notions defined purely through transductions, such as monadic stability, monadic dependence, and classes of structurally sparse graphs.
2025-01-07
Agathe Chouippe, Aman G. Kidanemariam, Jos Derksen, Anthony Wachs, Markus Uhlmann
We review some of the results obtained to date with the aid of the PR-DNS approach to turbulent particulate flows. It is shown that the method has matured to a point which allows to apply it successfully to a wide variety of fluid/particle configurations, albeit still at a relatively large computational cost. Due to the availability of high-fidelity space-and-time-resolved data, a number of challenging open questions have already been addressed in unprecedented detail.
Quantum entanglement as an extremal Kirkwood-Dirac nonreality
2025-01-07
Agung Budiyono
Understanding the relationship between various different forms of nonclassicality and their resource character is of great importance in quantum foundation and quantum information. Here, we discuss a quantitative link between quantum entanglement and the anomalous/nonclassical nonreal values of Kirkwood-Dirac (KD) quasiprobability, in a bipartite setting. We first construct an entanglement monotone for a pure bipartite state based on the nonreality of the KD quasiprobability defined over a pair of orthonormal bases in which one of them is a product, and optimizations over these bases. It admits a closed expression as a Schur-concave function of the state of the subsystem having a form of nonadditive quantum entropy. We then construct a bipartite entanglement monotone for generic quantum states using the convex roof extension. Its normalized value is upper bounded by the concurrence of formation, and for two-qubit systems, they are equal. We also derive lower and upper bounds in terms of different forms of uncertainty in the subsystem quantified respectively by an extremal trace-norm asymmetry and a nonadditive quantum entropy. The entanglement monotone can be expressed as the minimum total state disturbance due to a nonselective local binary measurement. Finally, we discuss its estimation using weak value measurement and classical optimization, and its connection with strange weak value and quantum contextuality.
Exploiting Instabilities to Enable Large Shape Transformations in Dielectric Elastomers
2025-01-07
Daniel Katusele, Carmel Majidi, Pradeep Sharma, Kaushik Dayal
Dielectric elastomers have significant potential for new technologies ranging from soft robots to biomedical devices, driven by their ability to display complex shape changes in response to electrical stimulus. However, an important shortcoming of current realizations is that large voltages are required for useful actuation strains. This work proposes, and demonstrates through theory and numerical simulations, a strategy to achieve large and controlled actuation by exploiting the electromechanical analog of the Treloar-Kearsley (TK) instability. The key idea is to use the fact that the TK instability is a symmetry-breaking bifurcation, which implies the existence of a symmetry-driven constant-energy region in the energy landscape. This provides for nonlinear soft modes with large deformations that can be accessed with very small external stimulus, which is achieved here by applying a small in-plane electric field. First, the bifurcation and post-bifurcation behavior of the electromechanical TK instability are established theoretically in the idealized setting of uniform deformation and electric field. Next, building on this, a finite element analysis of a realistic geometry with patterned top and bottom electrodes is applied to demonstrate large and soft shape changes driven by small voltage differences across the electrodes.
Bridging Impulse Control of Piecewise Deterministic Markov Processes and Markov Decision Processes: Frameworks, Extensions, and Open Challenges
2025-01-07
Alice Cleynen, Benoîte de Saporta, Orlane Rossini, Régis Sabbadin, Amélie Vernay
Control theory plays a pivotal role in understanding and optimizing the behavior of complex dynamical systems across various scientific and engineering disciplines. Two key frameworks that have emerged for modeling and solving control problems in stochastic systems are piecewise deterministic Markov processes (PDMPs) and Markov decision processes (MDPs). Each framework has its unique strengths, and their intersection offers promising opportunities for tackling a broad class of problems, particularly in the context of impulse controls and decision-making in complex systems. The relationship between PDMPs and MDPs is a natural subject of exploration, as embedding impulse control problems for PDMPs into the MDP framework could open new avenues for their analysis and resolution. Specifically, this integration would allow leveraging the computational and theoretical tools developed for MDPs to address the challenges inherent in PDMPs. On the other hand, PDMPs can offer a versatile and simple paradigm to model continuous time problems that are often described as discrete-time MDPs parametrized by complex transition kernels. This transformation has the potential to bridge the gap between the two frameworks, enabling solutions to previously intractable problems and expanding the scope of both fields. This paper presents a comprehensive review of two research domains, illustrated through a recurring medical example. The example is revisited and progressively formalized within the framework of thevarious concepts and objects introduced
\texttt{Pz Cats}: Photometric redshift catalogs based on DES Y3 BAO sample
2025-01-07
Paula S. Ferreira, Ribamar R. R. Reis
The photometric redshift estimation (photo-z) has been developed over the years with various methods. In this work, we analyse four different photo-z estimators using the Dark Energy Survey Y3 BAO Sample: \texttt{ANNz2}, \texttt{BPZ}, \texttt{ENF}, and \texttt{DNF}. Unlike what is usually found in the literature, we investigate the possibility of selecting the best galaxies according to their redshift Probability Distribution Function (PDF). We selected 25,760 galaxies from four different spectroscopic surveys and cross-matched them with the photo-z sample. These galaxies served to understand the redshift bias and its 68th percentile $\sigma_{68}$. We found that within a range of $0.79
Nonlinear phononics in Bi$_2$Te$_3$ from first-principles
2025-01-07
A. Levchuk, R. Busselez, G. Vaudel, P. Ruello, V. Juvé, B. Arnaud
Density Functional Theory (DFT) calculations not only allow to predict the vibrational and optical properties of solids but also to understand and disentangle the mechanisms playing a key role in the generation of coherent optical phonons. Recent experiments performed on a Bi$_2$Te$_3$ nanofilm have shown that a THz pulse launches at least a coherent $A_{1g}^1$ phonon as the transient transmittance measured using an isotropic detection scheme displays oscillations with a frequency matching the frequency of the $A_{1g}^1$ mode measured in Raman experiments. Such an observation can be explained by invoking either a sum frequency process or cubic/quartic phonon-phonon couplings as considered for Bi$_2$Se$_3$, a parent compound of Bi$_2$Te$_3$. By resorting to group theory and calculating energy surfaces from first-principles, the main phonon-phonon couplings can be identified. Furthermore, a minimal model can be built to compute the dynamics of the Raman active modes coupled to the infrared active mode driven by the experimental THz pulse. Our model firmly establishes that cubic phonon-phonon interactions are relevant as the agreement between the computed and experimental transmittance is noteworthy.
The LOFAR Two-metre Sky Survey: Deep Fields Data Release 2. I. The ELAIS-N1 field
2025-01-07
T. W. Shimwell, C. L. Hale, P. N. Best, A. Botteon, A. Drabent, M. J. Hardcastle, V. Jelić, J. M. G. H. J. de Jong, R. Kondapally, H. J. A. Röttgering, C. Tasse, R. J. van Weeren, W. L. Williams, A. Bonafede, M. Bondi, M. Brüggen, G. Brunetti, J. R. Callingham, F. De Gasperin, K. J. Duncan, C. Horellou, S. Iyer, I. de Ruiter, K. Małek, D. G. Nair, L. K. Morabito, I. Prandoni, A. Rowlinson, J. Sabater, A. Shulevski, D. J. B. Smith, F. Sweijen
We present the final 6'' resolution data release of the ELAIS-N1 field from the LOw-Frequency ARray (LOFAR) Two-metre Sky Survey Deep Fields project (LoTSS Deep). The 144MHz images are the most sensitive achieved to date at this frequency and were created from 290 TB of data obtained from 505 hrs on-source observations taken over 7.5 years. The data were processed following the strategies developed for previous LoTSS and LoTSS Deep data releases. The resulting images span 24.53 square degrees and, using a refined source detection approach, we identified 154,952 radio sources formed from 182,184 Gaussian components within this area. The maps reach a noise level of 10.7 $\mu$Jy/beam at 6'' resolution where approximately half of the noise is due to source confusion. In about 7.4% of the image our limited dynamic range around bright sources results in a further > 5% increase in the noise. The images have a flux density scale accuracy of about 9% and the standard deviation of offsets between our source positions and those from Pan-STARRS is 0.2'' in RA and Dec for high significance detections. We searched individual epoch images for variable sources, identifying 39 objects with considerable variation. We also searched for circularly polarised sources achieving three detections of previously known emitters (two stars and one pulsar) whilst constraining the typical polarisation fraction plus leakage to be less than 0.045%.
Streams, Shells, and Substructures in the Accretion-Built Stellar Halo of NGC 300
2025-01-07
Catherine E. Fielder, David J. Sand, Michael G. Jones, Denija Crnojević, Alex Drlica-Wagner, Paul Bennet, Jeffrey L. Carlin, William Cerny, Amandine Doliva-Dolinsky, Laura C. Hunter, Ananthan Karunakaran, Guilherme Limberg, Burçin Mutlu-Pakdil, Andrew B. Pace, Sarah Pearson, Adam Smercina, Kristine Spekkens, Tjitske Starkenburg, Jay Strader, Guy S. Stringfellow, Erik Tollerud, Clecio R. Bom, Julio A. Carballo-Bello, Astha Chaturvedi, Yumi Choi, David J. James, Clara E. Martínez-Vázquez, Alex Riley, Joanna Sakowska, Kathy Vivas
We present deep optical observations of the stellar halo of NGC 300, an LMC-mass galaxy, acquired with the DEEP sub-component of the DECam Local Volume Exploration survey (DELVE) using the 4 m Blanco Telescope. Our resolved star analysis reveals a large, low surface brightness stellar stream ($M_{V}\sim-8.5$; [Fe/H] $= -1.4\pm0.15$) extending more than 40 kpc north from the galaxy's center. We also find other halo structures, including potentially an additional stream wrap to the south, which may be associated with the main stream. The morphology and derived low metallicities of the streams and shells discovered surrounding NGC 300 are highly suggestive of a past accretion event. Assuming a single progenitor, the accreted system is approximately Fornax-like in luminosity, with an inferred mass ratio to NGC 300 of approximately $1:15$. We also present the discovery of a metal-poor globular cluster ($R_{\rm{proj}}=23.3$~kpc; $M_{V}=-8.99\pm0.16$; [Fe/H] $\approx-1.6\pm0.6$) in the halo of NGC 300, the furthest identified globular cluster associated with NGC 300. The stellar structures around NGC 300 represent the richest features observed in a Magellanic Cloud analog to date, strongly supporting the idea that accretion and subsequent disruption is an important mechanism in the assembly of dwarf galaxy stellar halos.
The Cosmic Evolution Early Release Science Survey (CEERS)
2025-01-07
Steven L. Finkelstein, Micaela B. Bagley, Pablo Arrabal Haro, Mark Dickinson, Henry C. Ferguson, Jeyhan S. Kartaltepe, Dale D. Kocevski, Anton M. Koekemoer, Jennifer M. Lotz, Casey Papovich, Pablo G. Perez-Gonzalez, Nor Pirzkal, Rachel S. Somerville, Jonathan R. Trump, Guang Yang, L. Y. Aaron Yung, Adriano Fontana, Andrea Grazian, Norman A. Grogin, Lisa J. Kewley, Allison Kirkpatrick, Rebecca L. Larson, Laura Pentericci, Swara Ravindranath, Stephen M. Wilkins, Omar Almaini, Ricardo O. Amorin, Guillermo Barro, Rachana Bhatawdekar, Laura Bisigello, Madisyn Brooks, Fernando Buitrago, Antonello Calabro, Marco Castellano, Yingjie Cheng, Nikko J. Cleri, Justin W. Cole, M. C. Cooper, Olivia R. Cooper, Luca Costantin, Isa G. Cox, Darren Croton, Emanuele Daddi, Kelcey Davis, Avishai Dekel, David Elbaz, Vital Fernandez, Seiji Fujimoto, Giovanni Gandolfi, Jonathan P. Gardner, Eric Gawiser, Mauro Giavalisco, Carlos Gomez-Guijarro, Yuchen Guo, Ansh R. Gupta, Nimish P. Hathi, Santosh Harish, Aurelien Henry, Michaela Hirschmann, Weida Hu, Taylor A. Hutchison, Kartheik G. Iyer, Anne E. Jaskot, Saurabh W. Jha, Intae Jung, Vasily Kokorev, Peter Kurczynski, Gene C. K. Leung, Mario Llerena, Arianna S. Long, Ray A. Lucas, Shiying Lu, Elizabeth J. McGrath, Daniel H. McIntosh, Emiliano Merlin, Alexa M. Morales, Lorenzo Napolitano, Fabio Pacucci, Viraj Pandya, Marc Rafelski, Giulia Rodighiero, Caitlin Rose, Paola Santini, Lise-Marie Seille, Raymond C. Simons, Lu Shen, Amber N. Straughn, Sandro Tacchella, Brittany N. Vanderhoof, Jesus Vega-Ferrero, Benjamin J. Weiner, Christopher N. A. Willmer, Peixin Zhu, Eric F. Bell, Stijn Wuyts, Benne W. Holwerda, Xin Wang, Weichen Wang, Jorge A. Zavala
We present the Cosmic Evolution Early Release Science (CEERS) Survey, a 77.2 hour Director's Discretionary Early Release Science Program. CEERS demonstrates, tests, and validates efficient extragalactic surveys using coordinated, overlapping parallel observations with the JWST instrument suite, including NIRCam and MIRI imaging, NIRSpec low (R~100) and medium (R~1000) resolution spectroscopy, and NIRCam slitless grism (R~1500) spectroscopy. CEERS targets the Hubble Space Telescope-observed region of the Extended Groth Strip (EGS) field, supported by a rich set of multiwavelength data. CEERS facilitated immediate community science in both of the extragalactic core JWST science drivers ``First Light" and ``Galaxy Assembly," including: 1) The discovery and characterization of large samples of galaxies at z >~ 10 from ~90 arcmin^2 of NIRCam imaging, constraining their abundance and physical nature; 2) Deep spectra of >1000 galaxies, including dozens of galaxies at 63; and 4) Characterizing galaxy mid-IR emission with MIRI to study dust-obscured star-formation and supermassive black hole growth at z~1-3. As a legacy product for the community, the CEERS team has provided several data releases, accompanied by detailed notes on the data reduction procedures and notebooks to aid in reproducibility. In addition to an overview of the survey and quality of the data, we provide science highlights from the first two years with CEERS data.
Galaxy cluster characterization with machine learning techniques
2025-01-07
Maria Sadikov, Julie Hlavacek-Larrondo, Laurence Perreault Levasseur, Carter Lee Rhea, Michael McDonald, Michelle Ntampaka, John ZuHone
We present an analysis of the X-ray properties of the galaxy cluster population in the z=0 snapshot of the IllustrisTNG simulations, utilizing machine learning techniques to perform clustering and regression tasks. We examine five properties of the hot gas (the central cooling time, the central electron density, the central entropy excess, the concentration parameter, and the cuspiness) which are commonly used as classification metrics to identify cool core (CC), weak cool core (WCC) and non cool core (NCC) clusters of galaxies. Using mock Chandra X-ray images as inputs, we first explore an unsupervised clustering scheme to see how the resulting groups correlate with the CC/WCC/NCC classification based on the different criteria. We observe that the groups replicate almost exactly the separation of the galaxy cluster images when classifying them based on the concentration parameter. We then move on to a regression task, utilizing a ResNet model to predict the value of all five properties. The network is able to achieve a mean percentage error of 1.8% for the central cooling time, and a balanced accuracy of 0.83 on the concentration parameter, making them the best-performing metrics. Finally, we use simulation-based inference (SBI) to extract posterior distributions for the network predictions. Our neural network simultaneously predicts all five classification metrics using only mock Chandra X-ray images. This study demonstrates that machine learning is a viable approach for analyzing and classifying the large galaxy cluster datasets that will soon become available through current and upcoming X-ray surveys, such as eROSITA.
2025-01-07
Marshall Hobson-Ritz, Jessica Birky, Leah Peterson, Peter Gwartney, Rachel Wong, John Delker, Tyler Gordon, Samantha Gilbert, James R. A. Davenport, Rory Barnes
Tidal synchronization plays a fundamental role in the evolution of binary star systems. However, key details such as the timescale of synchronization, efficiency of tidal dissipation, final rotational period, and dependence on stellar mass are not well constrained. We present a catalog of rotation periods, orbital periods, and eccentricities from eclipsing binaries (EBs) that can be used to study the role of tides in the rotational evolution of low-mass dwarf (FGKM spectral type) binaries. This study presents the largest catalog of EB orbital and rotational periods (Porb and Prot) measured from the Transiting Exoplanet Satellite Survey (TESS). We first classify 4584 light curves from the TESS Eclipsing Binary Catalog according to out-of-eclipse stellar variability type: starspot modulation, ellipsoidal variability, non-periodic variability, and "other" variability (e.g. pulsations). We then manually validate each light curve classification, resulting in a sample of 1039 candidates with 584 high confidence EBs that exhibit detectable star-spot modulation. From there, we measure and compare the rotation period of each starspot-modulated EB using three methods: Lomb-Scargle periodograms, autocorrelation function, and phase dispersion minimization. We find that our period distributions are consistent with previous work that used a sample of 816 starspot EBs from Kepler to identify two populations: a synchronous population (with Porb \approx Prot) and a subsynchronous population (with 8Porb \approx 7Prot). Using Bayesian model comparison, we find that a bimodal distribution is a significantly better fit than a unimodal distribution for Kepler and TESS samples, both individually or combined, confirming that the subsynchronous population is statistically significant.
Detecting Exomoons in Free-Floating-Planet Events from Space-based Microlensing Surveys
2025-01-07
Hao-Zhu Fu, Subo Dong
When a planet is ejected from its star-planet system due to dynamical interactions, its satellite may remain gravitationally bound to the planet. The Chinese Space Station Telescope (CSST) will be capable of detecting a large number of low-mass free-floating planet events (FFPs) from a bulge microlensing survey. We assess the feasibility of detecting satellites (a.k.a., exomoons) orbiting FFPs by simulating CSST light curves and calculating the detection efficiency as a function of satellite-to-planet mass ratios (q) and projected separations (s) in units of the Einstein radius. For a Neptune-class FFP in the Galactic disk with a Sun-like star as the microlensed source, CSST can detect Earth-mass satellites over a decade of separations (from ~0.01 to ~0.1 AU) and has sensitivity down to Moon-mass satellites (q~1e-3) at s~1. CSST also has some sensitivity to detect Moon-mass satellites at s~2 (~0.02 AU) orbiting an Earth-mass FFP in the disk. CSST has substantially reduced sensitivity for detecting satellites when the source star is an M dwarf, compared to a Sun-like source. We also calculate the satellite detection efficiency for the dedicated microlensing survey of the Roman Space Telescope (Roman), which demonstrates greater sensitivity than CSST, particularly for M-dwarf sources. Notably, some of the Neptune-Earth systems detectable by CSST and Roman may exhibit significant tidal heating.
The Gaia mission has triggered major developments in the field of Galactic dynamics in recent years, which we discuss in this review. The structure and kinematics of all Galactic components - disc, bar/bulge and halo - are now mapped in great detail not only in the Solar neighbourhood, but across a large part of the Milky Way. The dramatic improvements in the coverage and precision of observations revealed various disequilibrium processes, such as perturbations in the Galactic disc and the deformations of the outer halo, which are partly attributed to the interaction with satellite galaxies. The knowledge of the gravitational potential at all scales has also advanced considerably, but we are still far from having a consistent view on the key properties of the Galaxy, such as the bar pattern speed or the mass profile and shape of the dark halo. The complexity and interplay of several dynamical processes makes the interpretation of observational data challenging, and it is fair to say that more theoretical effort is needed to fully reap the fruit of the Gaia revolution.
Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends
2025-01-07
Duy M. H. Nguyen, Hasan Md Tusfiqur Alam, Tai Nguyen, Devansh Srivastav, Hans-Juergen Profitlich, Ngan Le, Daniel Sonntag
The emergence of artificial intelligence (AI), particularly deep learning (DL), has marked a new era in the realm of ophthalmology, offering transformative potential for the diagnosis and treatment of posterior segment eye diseases. This review explores the cutting-edge applications of DL across a range of ocular conditions, including diabetic retinopathy, glaucoma, age-related macular degeneration, and retinal vessel segmentation. We provide a comprehensive overview of foundational ML techniques and advanced DL architectures, such as CNNs, attention mechanisms, and transformer-based models, highlighting the evolving role of AI in enhancing diagnostic accuracy, optimizing treatment strategies, and improving overall patient care. Additionally, we present key challenges in integrating AI solutions into clinical practice, including ensuring data diversity, improving algorithm transparency, and effectively leveraging multimodal data. This review emphasizes AI's potential to improve disease diagnosis and enhance patient care while stressing the importance of collaborative efforts to overcome these barriers and fully harness AI's impact in advancing eye care.
Advances in RNA secondary structure prediction and RNA modifications: Methods, data, and applications
2025-01-07
Shu Yang, Nhat Truong Pham, Ziyang Li, Jae Young Baik, Joseph Lee, Tianhua Zhai, Weicheng Yu, Bojian Hou, Tianqi Shang, Weiqing He, Duy Duong-Tran, Mayur Naik, Li Shen
Due to the hierarchical organization of RNA structures and their pivotal roles in fulfilling RNA functions, the formation of RNA secondary structure critically influences many biological processes and has thus been a crucial research topic. This review sets out to explore the computational prediction of RNA secondary structure and its connections to RNA modifications, which have emerged as an active domain in recent years. We first examine the progression of RNA secondary structure prediction methodology, focusing on a set of representative works categorized into thermodynamic, comparative, machine learning, and hybrid approaches. Next, we survey the advances in RNA modifications and computational methods for identifying RNA modifications, focusing on the prominent modification types. Subsequently, we highlight the interplay between RNA modifications and secondary structures, emphasizing how modifications such as m6A dynamically affect RNA folding and vice versa. In addition, we also review relevant data sources and provide a discussion of current challenges and opportunities in the field. Ultimately, we hope our review will be able to serve as a cornerstone to aid in the development of innovative methods for this emerging topic and foster therapeutic applications in the future.
A trailing lognormal approximation of the Lyman-$α$ forest: comparison with full hydrodynamic simulations at $2.2\leq z\leq 2.7$
2025-01-07
Bhaskar Arya
Observations of the Lyman-$\alpha$ forest in distant quasar spectra with upcoming surveys are expected to provide significantly larger and higher-quality datasets. To interpret these datasets, it is imperative to develop efficient simulations. One such approach is based on the assumption that baryonic densities in the intergalactic medium (IGM) follow a lognormal distribution. We know from our previous work that while scaling the linear density contrast by parameter $\nu$ can recover the thermal parameters within $\sim 1-\sigma$ of best-fit values, the recovery of $\Gamma_{12}$ remains discrepant at $\gtrsim 3-\sigma$ for $z > 2.2$. Keeping this in mind, we extend our earlier work to improve the lognormal model of the Lyman-$\alpha$ (Ly$\alpha$) forest in recovering the parameters characterizing IGM state, particularly the hydrogen photoionization rate ($\Gamma_{12}$), between $2.2 \leq z \leq 2.7$, by simulating the model spectra at a slightly lower redshift than the Sherwood smooth particle hydrodynamical simulations (SPH) data. The recovery of thermal parameters, namely, the mean-density IGM temperature ($T_0$) and the slope of the temperature-density relation ($\gamma$) is also alleviated. These parameters are estimated through a Markov Chain Monte Carlo (MCMC) technique, using the mean and power spectrum of the transmitted flux. We find that the usual lognormal distribution of IGM densities tend to over-predict the number of Ly$\alpha$ absorbers seen in SPH simulation. A lognormal model simulated at a lower redshift than SPH data can address this limitation to a certain extent. We show that with such a "trailing" model of lognormal distribution, values of $\Gamma_{12}$ are recovered at $\lesssim 1-\sigma$. We argue that this model can be useful for constraining cosmological parameters.
After more than half a century after the first predictions of the so-called "island of stability of superheavy nuclei", exploring the limits of nuclear stability at highest atomic numbers is still one of the most prominent challenges in low-energy nuclear physics. These exotic nuclear species reveal their character and details of some of their properties by their induced or spontaneous disintegration. The achievements in the field superheavy nuclei (SHN) research have been reported in a number of review papers throughout the decades. This review is an attempt to summarize the progress that has been made in recent years by employing the versatile tool park of Decay Spectroscopy After Separation (DSAS) for the heaviest isotopes from Z=99 (einsteinium) to Z=118 (oganesson). In addition, references to earlier reviews and the relevant papers, together with the major decay properties of all these isotopes are given.
Implicit Guidance and Explicit Representation of Semantic Information in Points Cloud: A Survey
2025-01-07
Jingyuan Tang, Yuhuan Zhao, Songlin Sun, Yangang Cai
Point clouds, a prominent method of 3D representation, are extensively utilized across industries such as autonomous driving, surveying, electricity, architecture, and gaming, and have been rigorously investigated for their accuracy and resilience. The extraction of semantic information from scenes enhances both human understanding and machine perception. By integrating semantic information from two-dimensional scenes with three-dimensional point clouds, researchers aim to improve the precision and efficiency of various tasks. This paper provides a comprehensive review of the diverse applications and recent advancements in the integration of semantic information within point clouds. We explore the dual roles of semantic information in point clouds, encompassing both implicit guidance and explicit representation, across traditional and emerging tasks. Additionally, we offer a comparative analysis of publicly available datasets tailored to specific tasks and present notable observations. In conclusion, we discuss several challenges and potential issues that may arise in the future when fully utilizing semantic information in point clouds, providing our perspectives on these obstacles. The classified and organized articles related to semantic based point cloud tasks, and continuously followed up on relevant achievements in different fields, which can be accessed through https://github.com/Jasmine-tjy/Semantic-based-Point-Cloud-Tasks.
Synthetic accessibility and sodium ion conductivity of the Na$_{8-x}$A$^{x}$P$_2$O$_9$ (NAP) high-temperature sodium superionic conductor framework
2025-01-06
Lauren N. Walters, Yuxing Fei, Bernardus Rendy, Xiaochen Yang, Mouhamad Diallo, KyuJung Jun, Grace Wei, Matthew J. McDermott, Andrea Giunto, Tara Mishra, Fengyu Shen, David Milsted, May Sabai Oo, Haegyeom Kim, Michael C. Tucker, Gerbrand Ceder
Advancement of solid state electrolytes (SSEs) for all solid state batteries typically focuses on modification of a parent structural framework for improved conductivity, \textit{e.g.} cation substitution for an immobile ion or varying the concentration of the mobile ion. Therefore, novel frameworks can be disruptive by enabling fast ion conduction aided by different structure and diffusion mechanisms, and unlocking optimal conductors with different properties (\textit{e.g.} mechanical properties, sintering needs, electrochemical stability) than previously published. Herein, we perform a high throughput survey of an understudied structural framework for sodium ion conduction, Na$_{8-x}$A$^x$P$_2$O$_9$ (NAP), to understand the family's thermodynamic stability, synthesizability, and ionic conduction. We first show that the parent phase Na$_4$TiP$_2$O$_9$ (NTP) undergoes a structural distortion (with accompanying conductivity transition) due to unstable phonons from a pseduo-Jahn Teller mode in the 1D titanium chains. Then, screening of cation-substituted structural candidates with \textit{ab initio} and machine-learned potential calculations reveal a number of candidates that are thermodynamically stable, likely synthesizable, and have high predicted ionic conductivities. High throughput experimental trials and subsequent methodology optimization of one Na$_4$SnP$_2$O$_9$ (NSP) highlight collective challenges to the synthesis pathways for sodium phosphate materials via solid state synthesis. Our results demonstrate that NAP is a highly tunable conduction framework whose high temperature conductivity transition has heretofore eliminated it from significant research interest. By expanding the structural toolkit for SSE design, we increase the number of useful sodium ion electrolytes for integration into safe and accessible solid state batteries.
powerROC: An Interactive Web Tool for Sample Size Calculation in Assessing Models' Discriminative Abilities
2025-01-06
François Grolleau, Robert Tibshirani, Jonathan H. Chen
Rigorous external validation is crucial for assessing the generalizability of prediction models, particularly by evaluating their discrimination (AUROC) on new data. This often involves comparing a new model's AUROC to that of an established reference model. However, many studies rely on arbitrary rules of thumb for sample size calculations, often resulting in underpowered analyses and unreliable conclusions. This paper reviews crucial concepts for accurate sample size determination in AUROC-based external validation studies, making the theory and practice more accessible to researchers and clinicians. We introduce powerROC, an open-source web tool designed to simplify these calculations, enabling both the evaluation of a single model and the comparison of two models. The tool offers guidance on selecting target precision levels and employs flexible approaches, leveraging either pilot data or user-defined probability distributions. We illustrate powerROC's utility through a case study on hospital mortality prediction using the MIMIC database.
Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches
2025-01-06
Alhassan Mumuni, Fuseini Mumuni
Generative artificial intelligence (AI) systems based on large-scale pretrained foundation models (PFMs) such as vision-language models, large language models (LLMs), diffusion models and vision-language-action (VLA) models have demonstrated the ability to solve complex and truly non-trivial AI problems in a wide variety of domains and contexts. Multimodal large language models (MLLMs), in particular, learn from vast and diverse data sources, allowing rich and nuanced representations of the world and, thereby, providing extensive capabilities, including the ability to reason, engage in meaningful dialog; collaborate with humans and other agents to jointly solve complex problems; and understand social and emotional aspects of humans. Despite this impressive feat, the cognitive abilities of state-of-the-art LLMs trained on large-scale datasets are still superficial and brittle. Consequently, generic LLMs are severely limited in their generalist capabilities. A number of foundational problems -- embodiment, symbol grounding, causality and memory -- are required to be addressed for LLMs to attain human-level general intelligence. These concepts are more aligned with human cognition and provide LLMs with inherent human-like cognitive properties that support the realization of physically-plausible, semantically meaningful, flexible and more generalizable knowledge and intelligence. In this work, we discuss the aforementioned foundational issues and survey state-of-the art approaches for implementing these concepts in LLMs. Specifically, we discuss how the principles of embodiment, symbol grounding, causality and memory can be leveraged toward the attainment of artificial general intelligence (AGI) in an organic manner.
2025-01-06
Aghiles Ait Messaoud, Sonia Ben Mokhtar, Anthony Simonet-Boulogne
Key-Value Stores (KVSs) are No-SQL databases that store data as key-value pairs and have gained popularity due to their simplicity, scalability, and fast retrieval capabilities. However, storing sensitive data in KVSs requires strong security properties to prevent data leakage and unauthorized tampering. While software (SW)-based encryption techniques are commonly used to maintain data confidentiality and integrity, they suffer from several drawbacks. They strongly assume trust in the hosting system stack and do not secure data during processing unless using performance-heavy techniques (e.g., homomorphic encryption). Alternatively, Trusted Execution Environments (TEEs) provide a solution that enforces the confidentiality and integrity of code and data at the CPU level, allowing users to build trusted applications in an untrusted environment. They also secure data in use by providing an encapsulated processing environment called enclave. Nevertheless, TEEs come with their own set of drawbacks, including performance issues due to memory size limitations and CPU context switching. This paper examines the state of the art in TEE-based confidential KVSs and highlights common design strategies used in KVSs to leverage TEE security features while overcoming their inherent limitations. This work aims to provide a comprehensive understanding of the use of TEEs in KVSs and to identify research directions for future work.
Interaction of Electromagnetic Radiation with Cometary Dust
2025-01-06
ludmilla Kolokolova, Michael S. P. Kelley, Hiroshi Kimura, Thiem Hoang
The chapter overviews the recent developments in the remote sensing of cometary dust using visible, near-infrared, and thermal-infrared radiation, as well as interaction of the dust with electromagnetic radiation, which affects the dynamics of dust particles. It considers photometric, polarimetric, and spectral studies of cometary dust, focusing on those observables and correlations between them that allow revealing the composition, size, and structure of the dust particles. The analysis includes the observed brightness and polarization phase curves, color and polarimetric color of the cometary dust, and near- and thermal-infrared spectra. Special attention is paid to the role of gas contamination in the polarimetric and photometric data. A review of modeling efforts to interpret the observational results is also provided, describing the most popular (and some novel) techniques used in the computer modeling of light scattering by dust particles with a focus on modeling the most complex type of cometary particles: fluffy and porous agglomerates. The chapter also considers how properties of the dust particles affect their photoelectric emission and their response to the radiation pressure and radiative torque, including alignment and fragmentation of particles. Results of computer and some laboratory modeling are analyzed for their consistency with the observational and in situ data. Also discussed is how the modeling results can be combined with in situ data for better characterization of the cometary dust.
The NewAthena mission concept in the context of the next decade of X-ray astronomy
2025-01-06
Mike Cruise, Matteo Guainazzi, James Aird, Francisco J. Carrera, Elisa Costantini, Lia Corrales, Thomas Dauser, Dominique Eckert, Fabio Gastaldello, Hironori Matsumoto, Rachel Osten, Pierre-Olivier Petrucci, Delphine Porquet, Gabriel W. Pratt, Nanda Rea, Thomas H. Reiprich, Aurora Simionescu, Daniele Spiga, Eleonora Troja
Large X-ray observatories such as Chandra and XMM-Newton have been delivering scientific breakthroughs in research fields as diverse as our Solar System, the astrophysics of stars, stellar explosions and compact objects, accreting super-massive black holes, and large-scale structures traced by the hot plasma permeating and surrounding galaxy groups and clusters. The recently launched observatory XRISM is opening in earnest the new observational window of non-dispersive high-resolution spectroscopy. However, several quests are left open, such as the effect of the stellar radiation field on the habitability of nearby planets, the Equation-of-State regulating matter in neutron stars, the origin and distribution of metals in the Universe, the processes driving the cosmological evolution of the baryons locked in the gravitational potential of Dark Matter and the impact of supermassive black hole growth on galaxy evolution, just to mention a few. Furthermore, X-ray astronomy is a key player in multi-messenger astrophysics. Addressing these quests experimentally requires an order-of-magnitude leap in sensitivity, spectroscopy and survey capabilities with respect to existing X-ray observatories. This paper succinctly summarizes the main areas where high-energy astrophysics is expected to contribute to our understanding of the Universe in the next decade and describes a new mission concept under study by the European Space Agency, the scientific community worldwide and two International Partners (JAXA and NASA), designed to enable transformational discoveries: NewAthena. This concept inherits its basic payload design from a previous study carried out until 2022, Athena.
RIS-Driven Resource Allocation Strategies for Diverse Network Environments: A Comprehensive Review
2025-01-06
Manzoor Ahmed, Fang Xu, Yuanlin Lyu, Aized Amin Soofi, Yongxiao Li, Feroz Khan, Wali Ullah Khan, Muhammad Sheraz, Teong Chee Chuah, Min Deng
This comprehensive survey examines how Reconfigurable Intelligent Surfaces (RIS) revolutionize resource allocation in various network frameworks. It begins by establishing a theoretical foundation with an overview of RIS technologies, including passive RIS, active RIS, and Simultaneously Transmitting and Reflecting RIS (STAR-RIS). The core of the survey focuses on RIS's role in optimizing resource allocation within Single-Input Multiple-Output (SIMO), Multiple-Input Single-Output (MISO), and Multiple-Input Multiple-Output (MIMO) systems. It further explores RIS integration in complex network environments, such as Heterogeneous Wireless Networks (HetNets) and Non-Orthogonal Multiple Access (NOMA) frameworks. Additionally, the survey investigates RIS applications in advanced communication domains like Terahertz (THz) networks, Vehicular Communication (VC), and Unmanned Aerial Vehicle (UAV) communications, highlighting the synergy between RIS and Artificial Intelligence (AI) for enhanced network efficiency. Summary tables provide comparative insights into various schemes. The survey concludes with lessons learned, future research directions, and challenges, emphasizing critical open issues.
On statistical and causal models associated with acyclic directed mixed graphs
2025-01-06
Qingyuan Zhao
Causal models in statistics are often described using acyclic directed mixed graphs (ADMGs), which contain directed and bidirected edges and no directed cycles. This article surveys various interpretations of ADMGs, discusses their relations in different sub-classes of ADMGs, and argues that one of them -- nonparametric equation system (the E model below) -- should be used as the default interpretation. The E model is closely related to but different from the interpretation of ADMGs as directed acyclic graphs (DAGs) with latent variables that is commonly found in the literature. Our endorsement of the E model is based on two observations. First, in a subclass of ADMGs called unconfounded graphs (which retain most of the good properties of directed acyclic graphs and bidirected graphs), the E model is equivalent to many other interpretations including the global Markov and nested Markov models. Second, the E model for an arbitrary ADMG is exactly the union of that for all unconfounded expansions of that graph. This property is referred to as completeness, as it shows that the model does not commit to any specific latent variable explanation. In proving that the E model is nested Markov, we also develop an ADMG-based theory for causality. Finally, we criticize the common interpretation of ADMG as a convenient shortcut to represent some unspecified large causal DAG that generate the data. We argue that the "latent DAG" interpretation is mathematically unnecessary, makes obscure ontological assumptions, and discourages practitioners from deliberating over important structural assumptions.
Production of linear alkanes via the solid-state hydrogenation of interstellar polyynes
2025-01-06
G. Fedoseev, X. Li, G. A. Baratta, M. E. Palumbo, K. -J. Chuang
Highly unsaturated carbon chains, including polyynes, have been detected in many astronomical regions and planetary systems. With the success of the QUIJOTE survey of the TMC-1, the community has seen a "boom" in the number of detected carbon chains. On the other hand, the Rosetta mission revealed the release of fully saturated hydrocarbons, C$_3$H$_8$, C$_4$H$_{10}$, C$_5$H$_{12}$, and (under specific conditions) C$_6$H$_{14}$ with C$_7$H$_{16}$, from the comet 67P/Churyumov-Gerasimenko. The detection of the latter two is attributed to dust-rich events. Similarly, the analysis of samples returned from asteroid Ryugu by Hayabusa2 mission indicates the presence of long saturated aliphatic chains in Ryugu's organic matter. The surface chemistry of unsaturated carbon chains under conditions resembling those of molecular clouds can provide a possible link among these independent observations. However, laboratory-based investigations to validate such a chemistry is still lacking. In the present study, we aim to experimentally verify the formation of fully saturated hydrocarbons by the surface hydrogenation of C$_{2n}$H$_2$ ($n>1$) polyynes under ultra-high vacuum conditions at 10 K. We undertook a two-step experimental technique. First, a thin layer of C$_2$H$_2$ ice was irradiated by UV-photons ($\geq$ 121 nm) to achieve a partial conversion of C$_2$H$_2$ into larger polyynes: C$_4$H$_2$ and C$_6$H$_2$. Afterwards, the obtained photoprocessed ice was exposed to H atoms to verify the formation of various saturated hydrocarbons. In addition to C$_2$H$_6$, which was investigated previously, the formation of larger alkanes, including C$_4$H$_{10}$ and (tentatively) C$_6$H$_{14}$, is confirmed by our study. A qualitative analysis of the obtained kinetic data indicates that hydrogenation of HCCH and HCCCCH triple bonds proceeds at comparable rates, given a surface temperature of 10 K.}
Symmetries of Grothendieck rings in representation theory
2025-01-06
David Hernandez
This is a written version of the invited lecture at the 9th European Congress of Mathematics in July 2024 in Sevilla. We review certain new symmetries of Grothendieck rings that have emerged in representation theory.
Characterizing Measurement Error in the German Socio-Economic Panel Using Linked Survey and Administrative Data
2025-01-06
Nico Thurow
This paper exploits the linkage of German administrative social security data (GER: Integrierte Erwerbsbiografien) and survey data from the socio-economic panel (GER: Sozio-\"okonomisches Panel, SOEP) for the characterization of measurement error in metrics quantifying individual-specific labor earnings in Germany. We find that survey participants' decision whether to consent to linkage is non-random based on observables. In that sense, the studied sample does not constitute a random sample of SOEP. Measurement error is not classical: we observe underreporting of income on average, autocorrelation, and non-zero correlation with the true signal and other observable characteristics. In levels, calculated reliability ratios above 0.94 hint at a relaitvely small attenuation bias in simple linear univariate regressions with earnings as the explanatory variable. For changes in income, i.e. first differences, the bias from measurement error is exacerbated.
I review the status of chiral dynamics. Topics include pion-pion scattering, dynamically generated states in the hadron spectrum and the emergence of two-pole structures, chiral symmetry in nuclear physics and chiral dynamics in the Big Bang.
The study of gas bubble dynamics in liquids is justified by the numerous applications and natural phenomena where this two-phase flow is encountered. Gas bubbles move as forces are applied to them; their dynamics are full of nuances that need to be addressed carefully. Since the mass of gas bubbles is practically negligible, in comparison to that of the surrounding liquid, their reaction to the fluid is controlled by the added mass acceleration and is thus impacted by all the forces arising from the fluid action. Furthermore, since their surface can be deformed by the same forces acting on them, their shape may change leading to changes in their resistance to move, the drag force, and therefore affecting their speed and their interaction with the surrounding flow which is often turbulent. The liquid rheology, as well as its surfactant content can also affect the bubble shape and motion as well. Understanding these issues, in addition to the effect of interactions with other bubbles, walls, and non-uniform flows, provides sufficient elements to model and predict bubble behavior through the solution of dynamic equations. In this review, we cover the key aspects of non-condensable gas bubble dynamics. We survey classical references on the subject and provide an overview of the main findings in the past 20 years. We conclude with a scope and suggestions for future research directions, with special attention to the dynamics of bubble in turbulence, in non-Newtonian fluid and/or in the presence of electrolytes.
A Bio-Inspired Research Paradigm of Collision Perception Neurons Enabling Neuro-Robotic Integration: The LGMD Case
2025-01-06
Ziyan Qin, Jigen Peng, Shigang Yue, Qinbing Fu
Compared to human vision, insect visual systems excel at rapid and precise collision detection, despite relying on only tens of thousands of neurons organized through a few neuropils. This efficiency makes them an attractive model system for developing artificial collision-detecting systems. Specifically, researchers have identified collision-selective neurons in the locust's optic lobe, called lobula giant movement detectors (LGMDs), which respond specifically to approaching objects. Research upon LGMD neurons began in the early 1970s. Initially, due to their large size, these neurons were identified as motion detectors, but their role as looming detectors was recognized over time. Since then, progress in neuroscience, computational modeling of LGMD's visual neural circuits, and LGMD-based robotics has advanced in tandem, each field supporting and driving the others. Today, with a deeper understanding of LGMD neurons, LGMD-based models have significantly improved collision-free navigation in mobile robots including ground and aerial robots. This review highlights recent developments in LGMD research from the perspectives of neuroscience, computational modeling, and robotics. It emphasizes a biologically plausible research paradigm, where insights from neuroscience inform real-world applications, which would in turn validate and advance neuroscience. With strong support from extensive research and growing application demand, this paradigm has reached a mature stage and demonstrates versatility across different areas of neuroscience research, thereby enhancing our understanding of the interconnections between neuroscience, computational modeling, and robotics. Furthermore, other motion-sensitive neurons have also shown promising potential for adopting this research paradigm.
Evolution of the Sérsic Index up to z=2.5 from JWST and HST
2025-01-06
Marco Martorano, Arjen van der Wel, Maarten Baes, Eric F. Bell, Gabriel Brammer, Marijn Franx, Andrea Gebek, Sharon E. Meidt, Tim B. Miller, Erica Nelson, Angelos Nersesian, Sedona H. Price, Pieter van Dokkum, Katherine Whitaker, Stijn Wuyts
The James Webb Space Telescope (JWST) is unveiling the rest-frame near-IR structure of galaxies. We measure the evolution with redshift of the rest-frame optical and near-IR S\'ersic index ($n$), and examine the dependence on stellar mass and star-formation activity across the redshift range $0.5\leq z\leq2.5$. We infer rest-frame near-IR S\'ersic profiles for $\approx 15.000$ galaxies in publicly available NIRCam imaging mosaics from the COSMOS-Web and PRIMER surveys. We augment these with rest-frame optical S\'ersic indices, previously measured from HST imaging mosaics. The median S\'ersic index evolves slowly or not at all with redshift, except for very high-mass galaxies ($M_\star > 10^{11}~{\text{M}}_\odot$), which show an increase from $n\approx 2.5$ to $n\approx 4$ at $z<1$. High-mass galaxies have higher $n$ than lower-mass galaxies ($M_\star=10^{9.5}~{\text{M}}_\odot$) at all redshifts, with a stronger dependence in the rest-frame near-IR than in the rest-frame optical at $z>1$. This wavelength dependence is caused by star-forming galaxies that have lower optical than near-IR $n$ at z>1 (but not at z<1). Both at optical and near-IR wavelengths, star-forming galaxies have lower $n$ than quiescent galaxies, fortifying the connection between star-formation activity and radial stellar mass distribution. At $z>1$ the median near-IR $n$ varies strongly with star formation activity, but not with stellar mass. The scatter in near-IR $n$ is higher in the green valley (0.25 dex) than on the star-forming sequence and among quiescent galaxies (0.18 dex) -- this trend is not seen in the optical because dust and young stars contribute to the variety in optical light profiles. Our newly measured rest-frame near-IR radial light profiles motivate future comparisons with radial stellar mass profiles of simulated galaxies as a stringent constraint on processes that govern galaxy formation.
In recent years, vision language models (VLMs) have made significant advancements in video understanding. However, a crucial capability - fine-grained motion comprehension - remains under-explored in current benchmarks. To address this gap, we propose MotionBench, a comprehensive evaluation benchmark designed to assess the fine-grained motion comprehension of video understanding models. MotionBench evaluates models' motion-level perception through six primary categories of motion-oriented question types and includes data collected from diverse sources, ensuring a broad representation of real-world video content. Experimental results reveal that existing VLMs perform poorly in understanding fine-grained motions. To enhance VLM's ability to perceive fine-grained motion within a limited sequence length of LLM, we conduct extensive experiments reviewing VLM architectures optimized for video feature compression and propose a novel and efficient Through-Encoder (TE) Fusion method. Experiments show that higher frame rate inputs and TE Fusion yield improvements in motion understanding, yet there is still substantial room for enhancement. Our benchmark aims to guide and motivate the development of more capable video understanding models, emphasizing the importance of fine-grained motion comprehension. Project page: https://motion-bench.github.io .
2025-01-06
Samuel J. Gershman, Ila Fiete, Kazuki Irie
Classical models of memory in psychology and neuroscience rely on similarity-based retrieval of stored patterns, where similarity is a function of retrieval cues and the stored patterns. While parsimonious, these models do not allow distinct representations for storage and retrieval, despite their distinct computational demands. Key-value memory systems, in contrast, distinguish representations used for storage (values) and those used for retrieval (keys). This allows key-value memory systems to optimize simultaneously for fidelity in storage and discriminability in retrieval. We review the computational foundations of key-value memory, its role in modern machine learning systems, related ideas from psychology and neuroscience, applications to a number of empirical puzzles, and possible biological implementations.
Towards Quantitative Interpretation of 3D Atomic Force Microscopy at Solid-Liquid Interfaces
2025-01-06
Qian Ai, Lalith Krishna Samanth Bonagiri, Amir Farokh Payam, Narayana R. Aluru, Yingjie Zhang
Three-dimensional atomic force microscopy (3D-AFM) has been a powerful tool to probe the atomic-scale structure of solid-liquid interfaces. As a nanoprobe moves along the 3D volume of interfacial liquid, the probe-sample interaction force is sensed and mapped, providing information on not only the solid morphology, but also the liquid density distribution. To date 3D-AFM force maps of a diverse set of solid-liquid interfaces have been recorded, revealing remarkable force oscillations that are typically attributed to solvation layers or electrical double layers. However, despite the high resolution down to sub-angstrom level, quantitative interpretation of the 3D force maps has been an outstanding challenge. Here we will review the technical details of 3D-AFM and the existing approaches for quantitative data interpretation. Based on evidences in recent literature, we conclude that the perturbation-induced AFM force paradoxically represents the intrinsic, unperturbed liquid density profile. We will further discuss how the oscillatory force profiles can be attributed to the probe-modulation of the liquid configurational entropy, and how the quantitative, atomic-scale liquid density distribution can be derived from the force maps.
The puzzle of isolated and quenched dwarf galaxies in cosmic voids
2025-01-06
Bahar Bidaran, Isabel Pérez, Laura Sánchez-Menguiano, María Argudo-Fernández, Anna Ferré-Mateu, Julio F. Navarro, Reynier F. Peletier, Tomás Ruiz-Lara, Glenn van de Ven, Simon Verley, Almudena Zurita, Salvador Duarte Puertas, Jesús Falcón-Barroso, Patricia Sánchez-Blázquez, Andoni Jiménez
We report, for the first time, the detection of a sample of quenched and isolated dwarf galaxies (with 8.9 $<$ log(M$_{\rm \star}$/M$_{\rm \odot}$) $<$ 9.5) in the least dense regions of the cosmic web, including voids, filaments, and walls. These dwarfs have no neighbouring galaxy within 1.0~Mpc in projected distance. Based on the full spectral fitting of their central spectra using Sloan Digital Sky Survey data, these galaxies are gas-deprived, exhibit stellar mass assembly very similar to dwarfs in the central regions of galaxy clusters, and have experienced no significant star formation in the past 2 Gyr. Additionally, analysis of r-band images from the Dark Energy Camera Legacy Survey showed that these dwarf galaxies host a central Nuclear Star Cluster (NSC). Detecting quenched, isolated dwarf galaxies in cosmic voids indicates that environmental factors are not the sole drivers of their quenching. Internal mechanisms, such as feedback from in-situ star formation, also contributing to the NSC formation, black holes, or variations in conditions during their formation, offer potential explanations for star formation suppression in these galaxies. These findings highlight the need for a significant revision in our understanding of baryonic physics, particularly concerning the formation and evolution of low-mass galaxies.
Testing General Relativity using Large Scale Structures Photometric Redshift Surveys and Cosmic Microwave Background Lensing Effect
2025-01-06
Shang Li, Jun-Qing Xia
The $E_G$ statistic provides a valuable tool for evaluating predictions of General Relativity (GR) by probing the relationship between gravitational potential and galaxy clustering on cosmological scales within the observable universe. In this study, we constrain the $E_G$ statistic using photometric redshift data from the Dark Energy Survey (DES) MagLim sample in combination with the Planck 2018 Cosmic Microwave Background (CMB) lensing map. Unlike spectroscopic redshift surveys, photometric redshift measurements are subject to significant redshift uncertainties, making it challenging to constrain the redshift distortion parameter $\beta$ with high precision. We adopt a new definition for this parameter, $\beta(z) = {f\sigma_8(z)}/{b\sigma_8(z)}$. In this formulation, we reconstruct the growth rate of structure, $f\sigma_8(z)$, using Artificial Neural Networks (ANN) method, while simultaneously utilizing model-independent constraints on the parameter $b\sigma_8(z)$, directly obtained from the DES collaboration. After obtaining the angular power spectra $C_\ell^{gg}$ (galaxy-galaxy) and $C_\ell^{g\kappa}$ (galaxy-CMB lensing) from the combination of DES photometric data and Planck lensing, we derive new measurements of the $E_G$ statistic: $E_G = 0.354 \pm 0.146$, $0.452 \pm 0.092$, $0.414 \pm 0.069$, and $0.296 \pm 0.069$ (68$\%$ C.L.) across four redshift bins: $z = 0.30, 0.47, 0.63$, and $0.80$, respectively, which are consistent with the predictions of the standard $\Lambda$CDM model. Finally, we forecast the $E_G$ statistic using future photometric redshift data from the China Space Station Telescope, combined with lensing measurements from the CMB-S4 project, indicating an achievable constraint on $E_G$ of approximately 1$\%$, improving the precision of tests for GR on cosmological scales.
Large Language Models for Video Surveillance Applications
2025-01-06
Ulindu De Silva, Leon Fernando, Billy Lau Pik Lik, Zann Koh, Sam Conrad Joyce, Belinda Yuen, Chau Yuen
The rapid increase in video content production has resulted in enormous data volumes, creating significant challenges for efficient analysis and resource management. To address this, robust video analysis tools are essential. This paper presents an innovative proof of concept using Generative Artificial Intelligence (GenAI) in the form of Vision Language Models to enhance the downstream video analysis process. Our tool generates customized textual summaries based on user-defined queries, providing focused insights within extensive video datasets. Unlike traditional methods that offer generic summaries or limited action recognition, our approach utilizes Vision Language Models to extract relevant information, improving analysis precision and efficiency. The proposed method produces textual summaries from extensive CCTV footage, which can then be stored for an indefinite time in a very small storage space compared to videos, allowing users to quickly navigate and verify significant events without exhaustive manual review. Qualitative evaluations result in 80% and 70% accuracy in temporal and spatial quality and consistency of the pipeline respectively.
With the rapid growth of the Internet of Things and Cyber-Physical Systems, widespread sensor deployment has become essential. However, the high costs of building sensor networks limit their scale and coverage, making fine-grained deployment challenging. Inductive Spatio-Temporal Kriging (ISK) addresses this issue by introducing virtual sensors. Based on graph neural networks (GNNs) extracting the relationships between physical and virtual sensors, ISK can infer the measurements of virtual sensors from physical sensors. However, current ISK methods rely on conventional message-passing mechanisms and network architectures, without effectively extracting spatio-temporal features of physical sensors and focusing on representing virtual sensors. Additionally, existing graph construction methods face issues of sparse and noisy connections, destroying ISK performance. To address these issues, we propose DarkFarseer, a novel ISK framework with three key components. First, we propose the Neighbor Hidden Style Enhancement module with a style transfer strategy to enhance the representation of virtual nodes in a temporal-then-spatial manner to better extract the spatial relationships between physical and virtual nodes. Second, we propose Virtual-Component Contrastive Learning, which aims to enrich the node representation by establishing the association between the patterns of virtual nodes and the regional patterns within graph components. Lastly, we design a Similarity-Based Graph Denoising Strategy, which reduces the connectivity strength of noisy connections around virtual nodes and their neighbors based on their temporal information and regional spatial patterns. Extensive experiments demonstrate that DarkFarseer significantly outperforms existing ISK methods.
Large Language Models (LLMs) have demonstrated strong performance across various reasoning tasks, yet building a single model that consistently excels across all domains remains challenging. This paper addresses this problem by exploring strategies to integrate multiple domain-specialized models into an efficient pivot model.We propose two fusion strategies to combine the strengths of multiple LLMs: (1) a pairwise, multi-step fusion approach that sequentially distills each source model into the pivot model, followed by a weight merging step to integrate the distilled models into the final model. This method achieves strong performance but requires substantial training effort; and (2) a unified fusion approach that aggregates all source models' outputs simultaneously.To improve the fusion process, we introduce a novel Rate-Skewness Adaptive Fusion (RSAF) technique, which dynamically adjusts top-K ratios during parameter merging for enhanced flexibility and stability.Furthermore, we propose an uncertainty-based weighting method for the unified approach, which dynamically balances the contributions of source models and outperforms other logits/distribution ensemble methods.We achieved accuracy improvements of 9.27%, 8.80%, and 8.89% on the GSM8K, MATH, and HumanEval tasks, respectively.
Document retrieval techniques form the foundation for the development of large-scale information systems. The prevailing methodology is to construct a bi-encoder and compute the semantic similarity. However, such scalar similarity is difficult to reflect enough information and impedes our comprehension of the retrieval results. In addition, this computational process mainly emphasizes the global semantics and ignores the fine-grained semantic relationship between the query and the complex text in the document. In this paper, we propose a new method called $\textbf{Ge}$neration $\textbf{A}$ugmented $\textbf{R}$etrieval ($\textbf{GeAR}$) that incorporates well-designed fusion and decoding modules. This enables GeAR to generate the relevant text from documents based on the fused representation of the query and the document, thus learning to "focus on" the fine-grained information. Also when used as a retriever, GeAR does not add any computational burden over bi-encoders. To support the training of the new framework, we have introduced a pipeline to efficiently synthesize high-quality data by utilizing large language models. GeAR exhibits competitive retrieval and localization performance across diverse scenarios and datasets. Moreover, the qualitative analysis and the results generated by GeAR provide novel insights into the interpretation of retrieval results. The code, data, and models will be released after completing technical review to facilitate future research.
Visual-language models (VLM) have emerged as a powerful tool for learning a unified embedding space for vision and language. Inspired by large language models, which have demonstrated strong reasoning and multi-task capabilities, visual large language models (VLLMs) are gaining increasing attention for building general-purpose VLMs. Despite the significant progress made in VLLMs, the related literature remains limited, particularly from a comprehensive application perspective, encompassing generalized and specialized applications across vision (image, video, depth), action, and language modalities. In this survey, we focus on the diverse applications of VLLMs, examining their using scenarios, identifying ethics consideration and challenges, and discussing future directions for their development. By synthesizing these contents, we aim to provide a comprehensive guide that will pave the way for future innovations and broader applications of VLLMs. The paper list repository is available: https://github.com/JackYFL/awesome-VLLMs.
This paper reviews the calculation of nuclear Schiff moments, which one must know in order to interpret experiments that search for time-reversal-violating electric dipole moments in certain atoms and molecules. After briefly reviewing the connection between dipole moments and CP violation in and beyond the Standard Model of particle physics, Schiff's theorem, which concerns the screening of nuclear electric dipole moments by electrons, Schiff moments, and experiments to measure dipole moments in atoms and molecules, the paper examines attempts to compute Schiff moments in nuclei such as $^{199}$Hg and octupole-deformed isotopes such as $^{225}$Ra, which are particularly useful in experiments. It then turns to ab initio nuclear-structure theory, describing ways in which both the In-Medium Similarity Renormalization Group and coupled-cluster theory can be used to compute important Schiff moments more accurately than the less controlled methods that have been applied so far.
Precision measurements of muonium and muonic helium hyperfine structure at J-PARC
2025-01-06
Patrick Strasser, Mitsushi Abe, Kanta Asai, Seiso Fukumura, Mahiro Fushihara, Yu Goto, Takashi Ino, Ryoto Iwai, Sohtaro Kanda, Shiori Kawamura, Masaaki Kitaguchi, Shoichiro Nishimura, Takayuki Oku, Takuya Okudaira, Adam Powell, Ken-ichi Sasaki, Hirohiko M. Shimizu, Koichiro Shimomura, Hiroki Tada, Hiroyuki A. Torii, Takashi Yamanaka, Takayuki Yamazaki
At the J-PARC Muon Science Facility (MUSE), the MuSEUM collaboration is now performing new precision measurements of the ground state hyperfine structure (HFS) of both muonium and muonic helium atoms. High-precision measurements of the muonium ground-state HFS are recognized as one of the most sensitive tools for testing bound-state quantum electrodynamics theory to precisely probe the standard model and determine fundamental constants of the positive muon magnetic moment and mass. The same technique can also be employed to measure muonic helium HFS, obtain the negative muon magnetic moment and mass, and test and improve the theory of the three-body atomic system. Measurements at zero magnetic field have already yielded more accurate results than previous experiments for both muonium and muonic helium atoms. High-field measurements are now ready to start collecting data using the world's most intense pulsed muon beam at the MUSE H-line. We aim to improve the precision of previous measurements ten times for muonium and a hundred times or more for muonic helium. We review all the key developments for these new measurements, focusing on the high-field experiment, and report the latest results and prospects.
Graph Machine Learning is essential for understanding and analyzing relational data. However, privacy-sensitive applications demand the ability to efficiently remove sensitive information from trained graph neural networks (GNNs), avoiding the unnecessary time and space overhead caused by retraining models from scratch. To address this issue, Graph Unlearning (GU) has emerged as a critical solution, with the potential to support dynamic graph updates in data management systems and enable scalable unlearning in distributed data systems while ensuring privacy compliance. Unlike machine unlearning in computer vision or other fields, GU faces unique difficulties due to the non-Euclidean nature of graph data and the recursive message-passing mechanism of GNNs. Additionally, the diversity of downstream tasks and the complexity of unlearning requests further amplify these challenges. Despite the proliferation of diverse GU strategies, the absence of a benchmark providing fair comparisons for GU, and the limited flexibility in combining downstream tasks and unlearning requests, have yielded inconsistencies in evaluations, hindering the development of this domain. To fill this gap, we present OpenGU, the first GU benchmark, where 16 SOTA GU algorithms and 37 multi-domain datasets are integrated, enabling various downstream tasks with 13 GNN backbones when responding to flexible unlearning requests. Based on this unified benchmark framework, we are able to provide a comprehensive and fair evaluation for GU. Through extensive experimentation, we have drawn $8$ crucial conclusions about existing GU methods, while also gaining valuable insights into their limitations, shedding light on potential avenues for future research.
Artificial Intelligence in Creative Industries: Advances Prior to 2025
2025-01-06
Nantheera Anantrasirichai, Fan Zhang, David Bull
The rapid advancements in artificial intelligence (AI), particularly in generative AI and large language models (LLMs), have profoundly impacted the creative industries by enabling innovative content creation, enhancing workflows, and democratizing access to creative tools. This paper explores the significant technological shifts since our previous review in 2022, highlighting how these developments have expanded creative opportunities and efficiency. These technological advancements have enhanced the capabilities of text-to-image, text-to-video, and multimodal generation technologies. In particular, key breakthroughs in LLMs have established new benchmarks in conversational AI, while advancements in image generators have revolutionized content creation. We also discuss AI integration into post-production workflows, which has significantly accelerated and refined traditional processes. Despite these innovations, challenges remain, particularly for the media industry, due to the demands on communication traffic from creative content. We therefore include data compression and quality assessment in this paper. Furthermore, we highlight the trend toward unified AI frameworks capable of addressing multiple creative tasks and underscore the importance of human oversight to mitigate AI-generated inaccuracies. Finally, we explore AI's future potential in the creative sector, stressing the need to navigate emerging challenges to maximize its benefits while addressing associated risks.
Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation
2025-01-06
Anh Tu Ngo, Chuan Song Heng, Nandish Chattopadhyay, Anupam Chattopadhyay
Deep Neural Networks (DNNs) have gained considerable traction in recent years due to the unparalleled results they gathered. However, the cost behind training such sophisticated models is resource intensive, resulting in many to consider DNNs to be intellectual property (IP) to model owners. In this era of cloud computing, high-performance DNNs are often deployed all over the internet so that people can access them publicly. As such, DNN watermarking schemes, especially backdoor-based watermarks, have been actively developed in recent years to preserve proprietary rights. Nonetheless, there lies much uncertainty on the robustness of existing backdoor watermark schemes, towards both adversarial attacks and unintended means such as fine-tuning neural network models. One reason for this is that no complete guarantee of robustness can be assured in the context of backdoor-based watermark. In this paper, we extensively evaluate the persistence of recent backdoor-based watermarks within neural networks in the scenario of fine-tuning, we propose/develop a novel data-driven idea to restore watermark after fine-tuning without exposing the trigger set. Our empirical results show that by solely introducing training data after fine-tuning, the watermark can be restored if model parameters do not shift dramatically during fine-tuning. Depending on the types of trigger samples used, trigger accuracy can be reinstated to up to 100%. Our study further explores how the restoration process works using loss landscape visualization, as well as the idea of introducing training data in fine-tuning stage to alleviate watermark vanishing.
The Quasar Feedback Survey: zooming into the origin of radio emission with e-MERLIN
2025-01-06
Ann Njeri, Chris M. Harrison, Preeti Kharb, Robert Beswick, Gabriela Calistro-Rivera, Chiara Circosta, Vincenzo Mainieri, Stephen Molyneux, James Mullaney, Silpa Sasikumar
We present 6 GHz e-MERLIN observations of 42 $z<0.2$ type 1 and type 2 mostly radio-quiet quasars ($L_{\rm[OIII]}\gtrsim10^{42}$ erg s$^{-1}$; $L_{\rm AGN}\gtrsim10^{45}$ erg s$^{-1}$) from the Quasar Feedback Survey. The nature and origin of radio emission in these types of sources is typically ambiguous based on all-sky, low-resolution surveys. With e-MERLIN, we investigate radio emission on sub-kiloparsec scales ($\sim$10s-100s pc). We find 37/42 quasars are detected, with a diversity of radio morphologies, including compact cores, knots and extended jet-like structures, with sizes of 30-540 pc. Based on morphology and brightness temperature, we classify 76 per cent of the quasars as radio-AGN, compared to the $\sim$57 per cent identified as radio-AGN at the $\sim$1-60 kpc scales probed in prior studies. Combining results from e-MERLIN and the Very Large Array, 86 per cent reveal a radio-AGN. On average, $\sim$60 per cent of the total radio flux is resolved away in the e-MERLIN maps, and is likely dominated by jet-driven lobes and outflow-driven shocks. We find no significant differences in measured radio properties between type 1 and type 2 quasars, and estimate sub-relativistic jet speeds of $\sim$0.2-0.3c and modest jet powers of $P_\mathrm{jet} \approx \times$10$^{43}$ erg s$^{-1}$ for the few targets, where these measurements were possible. These quasars share characteristics with compact radio-selected populations, and the global radio emission likely traces strong interactions between the AGN (jets/outflows) and their host galaxy ISM from 10s parsec to 10s kiloparsec scales.
This paper presents a brief exposition of Soergel bimodules with applications to some topics in Kazhdan-Lusztig theory. We ultimately exposit a few of Soergel's main results, which allowed him to give alternative proofs, using his theory, of the Kazhdan-Lusztig conjectures. This paper should be viewed as a (very) condensed outline following the work of Elias, Makisumi, Thiel, and Williamson in their lovely book Introduction to Soergel Bimodules, and is meant for a reader wishing to survey a quite vast subject.
2025-01-06
Nikita Belenkov, Valerian Callens, Alexandr Murashkin, Kacper Bak, Martin Derka, Jan Gorzny, Sung-Shine Lee
Blockchain technology has revolutionized industries by enabling secure and decentralized transactions. However, the isolated nature of blockchain ecosystems hinders the seamless transfer of digital assets across different chains. Cross-chain bridges have emerged as vital web3 infrastructure to address this challenge by facilitating interoperability between distinct blockchains. Cross-chain bridges remain vulnerable to various attacks despite sophisticated designs and security measures. The industry has experienced a surge in bridge attacks, resulting in significant financial losses. The largest hack impacted Axie Infinity Ronin Bridge, with a loss of almost \$600 million USD. This paper analyzes recent cross-chain bridge hacks in 2022 and 2023 and examines the exploited vulnerabilities. By understanding the attack nature and underlying weaknesses, the paper aims to enhance bridge security and propose potential countermeasures. The findings contribute to developing industry-wide standards for bridge security and operational resilience. Addressing the vulnerabilities and weaknesses exploited in recent cross-chain bridge hacks fosters trust and confidence in cross-chain interoperability.
Rapid Experimentation with Python Considering Optional and Hierarchical Inputs
2025-01-06
Neil Ranly, Torrey Wagner
Space-filling experimental design techniques are commonly used in many computer modeling and simulation studies to explore the effects of inputs on outputs. This research presents raxpy, a Python package that leverages expressive annotation of Python functions and classes to simplify space-filling experimentation. It incorporates code introspection to derive a Python function's input space and novel algorithms to automate the design of space-filling experiments for spaces with optional and hierarchical input dimensions. In this paper, we review the criteria for design evaluation given these types of dimensions and compare the proposed algorithms with numerical experiments. The results demonstrate the ability of the proposed algorithms to create improved space-filling experiment designs. The package includes support for parallelism and distributed execution. raxpy is available as free and open-source software under a MIT license.
Geophysical flows over topography, a playground for laboratory experiments
2025-01-06
Jérémie Vidal, Jérôme Noir, David Cébron, Fabian Burmann, Rémy Monville, Vadim Giraud, Yoann Charles
Physicists face major challenges in modelling multi-scale phenomena that are observed in geophysical flows (e.g. in the Earth's oceans and atmosphere, or liquid planetary cores). In particular, complexities arise because geophysical fluids are rotating and subject to density variations, but also because the fluid boundaries have complex geometries (e.g. the ocean floor) with wavelengths ranging from metres to thousands of kilometres. Dynamical models of planetary fluid layers are thus often constrained by observations, whose interpretation necessitates a comprehensive understanding of the underlying physics. To this end, geophysical studies often combine cutting-edge experiments across a wide range of parameters, together with theory and numerical simulations, to derive predictive scaling laws applicable for planetary settings. In this review, we discuss experimental efforts that have contributed to our understanding of geophysical flows with topography. More specifically, we focus on (i) the flow response to mechanical (orbital) forcings in the presence of a large-scale (ellipsoidal) topography, (ii) some effects of small-scale topography onto bulk flows and boundary-layer dynamics, and (iii) the interaction between convection and roughness. The geophysical context is briefly introduced for each case, and some experimental perspectives are drawn.
Tensor-structured PCG for finite difference solver of domain patterns in ferroelectric material
2025-01-06
Věnceslav Chumchal, Pavel Marton, Martin Plešinger, Martina Šimůnková
This paper presents a case study of application of the preconditioned method of conjugate gradients (CG) on a problem with operator resembling the structure of sum of Kronecker products. In particular, we are solving the Poisson's equation on a sample of homogeneous isotropic ferroelectric material of cuboid shape, where the Laplacian is discretized by finite difference. We present several preconditioners that fits the Kronecker structure and thus can be efficiently implemented and applied. Preconditioner based on the Moore--Penrose pseudoinverse is extremely efficient for this particular problem, and also applicable (if we are able to store the dense right-hand side of our problem). We briefly analyze the computational cost of the method and individual preconditioners, and illustrate effectiveness of the chosen one by numerical experiments. Although we describe our method as preconditioned CG with pseudoinverse-based preconditioner, it can also be seen as pseudoinverse-based direct solver with iterative refinement by CG iteration. This work is motivated by real application, the method was already implemented in C/C++ code Ferrodo2 and first results were published in Physical Review B 107(9) (2023), paper id 094102.
Strong Lensing analysis of SPT-CLJ2325$-$4111 and SPT-CLJ0049$-$2440, two Powerful Cosmic Telescopes ($R_E > 40''$) from the SPT Clusters Sample
2025-01-06
Guillaume Mahler, Keren Sharon, Matthew Bayliss, Lindsey. E. Bleem, Mark Brodwin, Benjamin Floyd, Raven Gassis, Michael D. Gladders, Gourav Khullar, Juan D. Remolina Gonzalez, Arnab Sarkar
We report the results from a study of two massive ($M_{500c} > 6.0 \times 10^{14} M_{\odot}$) strong lensing clusters selected from the South Pole Telescope cluster survey for their high Einstein radius ($R_E > 40''$), SPT-CLJ2325$-$4111 and SPT-CLJ0049$-$2440. Ground-based and shallow HST imaging indicated extensive strong lensing evidence in these fields, with giant arcs spanning 18\arcsec\ and 31\arcsec, respectively, motivating further space-based imaging followup. Here, we present multiband HST imaging and ground-based Magellan spectroscopy of the fields, from which we compile detailed strong lensing models. The lens models of SPT-CL\,J2325$-$4111 and SPT-CL\,J0049$-$2440 were optimized using 9, and 8 secure multiple-imaged systems with a final image-plane rms of 0\farcs63 and 0\farcs73, respectively. From the lensing analysis, we measure the projected mass density within 500~kpc of $M(<500 ~{\rm kpc}) = 7.30\pm0.07 \times 10^{14}$$M_{\odot}$, and $M(<500 ~{\rm kpc})=7.12^{+0.16}_{-0.19}\times 10^{14}$ $M_{\odot}$ for these two clusters, and a sub-halos mass ratio of $0.12\pm{0.01}$ and $0.21^{+0.07}_{-0.05}$, respectively. Both clusters produce a large area with high magnification ($\mu\geq 3$) for a source at $z=9$, $A^{lens}_{| \mu | \geq 3 }=4.93^{+0.03}_{-0.04} arcmin^2$, and $A^{lens}_{| \mu | \geq 3 }=3.64^{+0.14}_{-0.10} arcmin^2$ respectively, placing them in the top tier of strong lensing clusters. We conclude that these clusters are spectacular sightlines for further observations that will reduce the systematic uncertainties due to cosmic variance. This paper provides the community with two additional well-calibrated cosmic telescopes, as strong as the Frontier Fields, suitable for studies of the highly magnified background Universe.
Multi-Wavelength Analysis of AT 2023sva: a Luminous Orphan Afterglow With Evidence for a Structured Jet
2025-01-06
Gokul P. Srinivasaragavan, Daniel A. Perley, Anna Y. Q. Ho, Brendan O'Connor, Antonio de Ugarte Postigo, Nikhil Sarin, S. Bradley Cenko, Jesper Sollerman, Lauren Rhodes, David A. Green, Dmitry S. Svinkin, Varun Bhalerao, Gaurav Waratkar, A. J. Nayana, Poonam Chandra, M. Coleman Miller, Daniele B. Malesani, Geoffrey Ryan, Suryansh Srijan, Eric C. Bellm, Eric Burns, David J. Titterington, Maria B. Stone, Josiah Purdum, Tomás Ahumada, G. C. Anupama, Sudhanshu Barway, Michael W. Coughlin, Andrew Drake, Rob Fender, José F. AgüÍ Fernández, Dmitry D. Frederiks, Stefan Geier, Matthew J. Graham, Mansi M. Kasliwal, S. R. Kulkarni, Harsh Kumar, Maggie L. Li, Russ R. Laher, Alexandra L. Lysenko, Gopal Parwani, Richard A. Perley, Anna V. Ridnaia, Anirudh Salgundi, Roger Smith, Niharika Sravan, Vishwajeet Swain, Christina C. Thöne, Anastasia E. Tsvetkova, Mikhail V. Ulanov, Jada Vail, Jacob L. Wise, Avery Wold
We present multi-wavelength analysis of ZTF23abelseb (AT 2023sva), an optically discovered fast-fading ($\Delta m_r = 2.2$ mag in $\Delta t = 0.74 $ days), luminous ($M_r \sim -30.0$ mag) and red ($g-r = 0.50$ mag) transient at $z = 2.28$ with accompanying luminous radio emission. AT 2023sva does not possess a $\gamma$-ray burst (GRB) counterpart to an isotropic equivalent energy limit of $E_{\rm{\gamma, \, iso}} < 1.6 \times 10^{52}$ erg, determined through searching $\gamma$-ray satellite archives between the last non-detection and first detection, making it the sixth example of an optically-discovered afterglow with a redshift measurement and no detected GRB counterpart. We analyze AT 2023sva's optical, radio, and X-ray observations to characterize the source. From radio analyses, we find the clear presence of strong interstellar scintillation (ISS) 72 days after the initial explosion, allowing us to place constraints on the source's angular size and bulk Lorentz factor. When comparing the source sizes derived from ISS of orphan events to those of the classical GRB population, we find orphan events have statistically smaller source sizes. We also utilize Bayesian techniques to model the multi-wavelength afterglow. Within this framework, we find evidence that AT 2023sva possesses a shallow power-law structured jet viewed slightly off-axis ($\theta_{\rm{v}} = 0.07 \pm 0.02$) just outside of the jet's core opening angle ($\theta_{\rm{c}} = 0.06 \pm 0.02$). We determine this is likely the reason for the lack of a detected GRB counterpart, but also investigate other scenarios. AT 2023sva's evidence for possessing a structured jet stresses the importance of broadening orphan afterglow search strategies to a diverse range of GRB jet angular energy profiles, to maximize the return of future optical surveys.
A multi-wavelength investigation of spiral structures in $z > 1$ galaxies with JWST
2025-01-06
Boris S. Kalita, Si-Yue Yu, John D. Silverman, Emanuele Daddi, Luis C. Ho, Andreas L. Faisst, Miroslava Dessauges-Zavadsky, Annagrazia Puglisi, Simon Birrer, Daichi Kashino, Xuheng Ding, Jeyhan S. Kartaltepe, Darshan Kakkad, Francesco Valentino, Olivier Ilbert, Georgios Magdis, Arianna S. Long, Shuowen Jin, Anton M. Koekemoer, Richard Massey
Recent JWST observations have revealed the prevalence of spiral structures at $z > 1$. Unlike in the local Universe, the origin and the consequence of spirals at this epoch remain unexplored. We use public JWST/NIRCam data from the COSMOS-Web survey to map spiral structures in eight massive ($> 10^{10.5}\,\rm M_{\odot}$) star-forming galaxies at $z_{\rm spec} \sim 1.5$. We present a method for systematically quantifying spiral arms at $z>1$, enabling direct measurements of flux distributions. Using rest-frame near-IR images, we construct morphological models accurately tracing spiral arms. We detect offsets ($\sim 0.2 - 0.8\,\rm kpc$) between the rest-frame optical and near-IR flux distributions across most arms. Drawing parallels to the local Universe, we conclude that these offsets reflect the presence of density waves. For nine out of eighteen arms, the offsets indicate spiral shocks triggered by density waves. Five arms have offsets in the opposite direction and are likely associated with tidal interactions. For the remaining cases with no detected offsets, we suggest that stochastic 'clumpy' star formation is the primary driver of their formation. In conclusion, we find a multi-faceted nature of spiral arms at $z > 1$, similar to that in the local Universe.
A very crucial part of Bangladeshi people's employment, GDP contribution, and mainly livelihood is agriculture. It plays a vital role in decreasing poverty and ensuring food security. Plant diseases are a serious stumbling block in agricultural production in Bangladesh. At times, humans can't detect the disease from an infected leaf with the naked eye. Using inorganic chemicals or pesticides in plants when it's too late leads in vain most of the time, deposing all the previous labor. The deep-learning technique of leaf-based image classification, which has shown impressive results, can make the work of recognizing and classifying all diseases trouble-less and more precise. In this paper, we've mainly proposed a better model for the detection of leaf diseases. Our proposed paper includes the collection of data on three different kinds of crops: bell peppers, tomatoes, and potatoes. For training and testing the proposed CNN model, the plant leaf disease dataset collected from Kaggle is used, which has 17,430 images. The images are labeled with 14 separate classes of damage. The developed CNN model performs efficiently and could successfully detect and classify the tested diseases. The proposed CNN model may have great potency in crop disease management.
Lattice Effective Field Theory Simulations of Nuclei
2025-01-06
Dean Lee
Lattice effective field theory applies the principles of effective field theory in a lattice framework where space and time are discretized. Nucleons are placed on the lattice sites, and the interactions are tuned to replicate the observed features of the nuclear force. Monte Carlo simulations are then employed to predict the properties of nuclear few- and many-body systems. We review the basic methods and several theoretical and algorithmic advances that have been used to further our understanding of atomic nuclei.
A weak $f$-structure on a smooth manifold, introduced by the author and R. Wolak (2022), generalizes K. Yano's (1961) $f$-structure. This generalization allows us to revisit classical theory and discover new applications related to Killing vector fields, totally geodesic foliations, Ricci-type solitons, and Einstein-type metrics. This article reviews the results on weak metric $f$-manifolds, where the complex structure on the contact distribution of a metric $f$-structure is replaced with a nonsingular skew-symmetric tensor, and explores its distinguished classes.
From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering
2025-01-05
Wen-ran Li, Xavier F. Cadet, David Medina-Ortiz, Mehdi D. Davari, Ramanathan Sowdhamini, Cedric Damour, Yu Li, Alain Miranville, Frederic Cadet
Protein design with desirable properties has been a significant challenge for many decades. Generative artificial intelligence is a promising approach and has achieved great success in various protein generation tasks. Notably, diffusion models stand out for their robust mathematical foundations and impressive generative capabilities, offering unique advantages in certain applications such as protein design. In this review, we first give the definition and characteristics of diffusion models and then focus on two strategies: Denoising Diffusion Probabilistic Models and Score-based Generative Models, where DDPM is the discrete form of SGM. Furthermore, we discuss their applications in protein design, peptide generation, drug discovery, and protein-ligand interaction. Finally, we outline the future perspectives of diffusion models to advance autonomous protein design and engineering. The E(3) group consists of all rotations, reflections, and translations in three-dimensions. The equivariance on the E(3) group can keep the physical stability of the frame of each amino acid as much as possible, and we reflect on how to keep the diffusion model E(3) equivariant for protein generation.
Predicting plant species composition in specific spatiotemporal contexts plays an important role in biodiversity management and conservation, as well as in improving species identification tools. Our work utilizes 88,987 plant survey records conducted in specific spatiotemporal contexts across Europe. We also use the corresponding satellite images, time series data, climate time series, and other rasterized environmental data such as land cover, human footprint, bioclimatic, and soil variables as training data to train the model to predict the outcomes of 4,716 plant surveys. We propose a feature construction and result correction method based on the graph structure. Through comparative experiments, we select the best-performing backbone networks for feature extraction in both temporal and image modalities. In this process, we built a backbone network based on the Swin-Transformer Block for extracting temporal Cubes features. We then design a hierarchical cross-attention mechanism capable of robustly fusing features from multiple modalities. During training, we adopt a 10-fold cross-fusion method based on fine-tuning and use a Threshold Top-K method for post-processing. Ablation experiments demonstrate the improvements in model performance brought by our proposed solution pipeline.
Trust and Dependability in Blockchain & AI Based MedIoT Applications: Research Challenges and Future Directions
2025-01-05
Ellis Solaiman, Christa Awad
This paper critically reviews the integration of Artificial Intelligence (AI) and blockchain technologies in the context of Medical Internet of Things (MedIoT) applications, where they collectively promise to revolutionize healthcare delivery. By examining current research, we underscore AI's potential in advancing diagnostics and patient care, alongside blockchain's capacity to bolster data security and patient privacy. We focus particularly on the imperative to cultivate trust and ensure reliability within these systems. Our review highlights innovative solutions for managing healthcare data and challenges such as ensuring scalability, maintaining privacy, and promoting ethical practices within the MedIoT domain. We present a vision for integrating AI-driven insights with blockchain security in healthcare, offering a comprehensive review of current research and future directions. We conclude with a set of identified research gaps and propose that addressing these is crucial for achieving the dependable, secure, and patient -centric MedIoT applications of tomorrow.
On the Independence Assumption in Quasi-Cyclic Code-Based Cryptography
2025-01-05
Maxime Bombar, Nicolas Resch, Emiel Wiedijk
Cryptography based on the presumed hardness of decoding codes -- i.e., code-based cryptography -- has recently seen increased interest due to its plausible security against quantum attackers. Notably, of the four proposals for the NIST post-quantum standardization process that were advanced to their fourth round for further review, two were code-based. The most efficient proposals -- including HQC and BIKE, the NIST submissions alluded to above -- in fact rely on the presumed hardness of decoding structured codes. Of particular relevance to our work, HQC is based on quasi-cyclic codes, which are codes generated by matrices consisting of two cyclic blocks. In particular, the security analysis of HQC requires a precise understanding of the Decryption Failure Rate (DFR), whose analysis relies on the following heuristic: given random ``sparse'' vectors $e_1,e_2$ (say, each coordinate is i.i.d. Bernoulli) multiplied by fixed ``sparse'' quasi-cyclic matrices $A_1,A_2$, the weight of resulting vector $e_1A_1+e_2A_2$ is very concentrated around its expectation. In the documentation, the authors model the distribution of $e_1A_1+e_2A_2$ as a vector with independent coordinates (and correct marginal distribution). However, we uncover cases where this modeling fails. While this does not invalidate the (empirically verified) heuristic that the weight of $e_1A_1+e_2A_2$ is concentrated, it does suggest that the behavior of the noise is a bit more subtle than previously predicted. Lastly, we also discuss implications of our result for potential worst-case to average-case reductions for quasi-cyclic codes.
Primordial Black Hole stellar microlensing constraints: understanding their dependence on the density and velocity distributions
2025-01-05
Anne M. Green
Microlensing surveys of stars in the Large Magellanic Cloud constrain the fraction of the Milky Way halo in Primordial Black Holes (PBHs) with mass $10^{-9} \lesssim M/M_{\odot} \lesssim 10^{4}$. Various studies have reached different conclusions on the uncertainties in these constraints due to uncertainties in the Dark Matter (DM) distribution. We therefore revisit the dependence of the microlensing differential event rate, and hence exclusion limits, on the DM density and velocity distributions. The constraints on the abundance of low- and high-mass PBHs depend, respectively, on the long- and short-duration tails of the differential event rate distribution. Long-duration events are due to PBHs moving close to the line of sight and their rate (and hence the constraints on low-mass PBHs) has a fairly weak ($\sim 10\%$) dependence on the DM density and velocity distributions. Short-duration events are due to PBHs close to the observer and their rate (and hence the constraints on moderate- and high-mass PBHs) depends much more strongly on the DM velocity distribution. An accurate calculation of the local DM velocity distribution is therefore crucial for accurately calculating PBH stellar microlensing constraints.
Strong lensing systems, expected to be abundantly discovered by next-generation surveys, offer a powerful tool for studying cosmology and galaxy evolution. The connection between galaxy structure and cosmology through distance ratios highlights the need to examine the evolution of lensing galaxy mass density profiles. We propose a novel, dark energy-model-independent method to investigate the mass density slopes of lensing galaxies and their redshift evolution using an extended power-law (EPL) model. We employ a non-parametric approach based on Artificial Neural Networks (ANNs) trained on Type Ia Supernovae (SNIa) data to reconstruct distance ratios of strong lensing systems. These ratios are compared with theoretical predictions to estimate the evolution of EPL model parameters. Analyses conducted at three levels, including the combined sample, individual lenses, and binned groups, ensure robust and reliable estimates. A negative trend in the mass density slope with redshift is observed, quantified as $\partial\gamma/\partial z = -0.20 \pm 0.12$ under a triangular prior for anisotropy. This study demonstrates that the redshift evolution of density slopes in lensing galaxies can be determined independently of dark energy models. Simulations based on LSST Rubin Observatory forecasts, which anticipate 100,000 strong lenses, show that spectroscopic follow-up of just 10 percent of these systems can constrain the redshift evolution coefficient with uncertainty ($\Delta\partial\gamma/\partial z$) to 0.021. This precision distinguishes evolving and non-evolving density slopes, providing new insights into galaxy evolution and cosmology.
A review on reinforcement learning methods for mobility on demand systems
2025-01-05
Tarek Chouaki, Sebastian Hörl, Jakob Puchinger
Mobility on Demand (MoD) refers to mobility systems that operate on the basis of immediate travel demand. Typically, such a system consists of a fleet of vehicles that can be booked by customers when needed. The operation of these services consists of two main tasks: deciding how vehicles are assigned to requests (vehicle assignment); and deciding where vehicles move (including charging stations) when they are not serving a request (rebalancing). A field of research is emerging around the design of operation strategies for MoD services, and an increasingly popular trend is the use of learning based (most often Reinforcement Learning) approaches. We review, in this work, the literature on algorithms for operation strategies of MoD systems that use approaches based on Reinforcement Learning with a focus on the types of algorithms being used. The novelty of our review stands in three aspects: First, the algorithmic details are discussed and the approaches classified in a unified framework for sequential decision-making. Second, the use cases on which approaches are tested and their features are taken into account. Finally, validation methods that can be found across the literature are discussed. The review aims at advancing the state of the art by identifying similarities and differences between approaches and highlighting current research directions.
Reevaluating Specificity in Neuroimaging: Implications for the Salience Network and Methodological Rigor
2025-01-05
Tommaso Costa, Franco Cauda
The accurate assessment of neuroimaging specificity is critical for advancing our understanding of brain disorders. Current methodologies often rely on frequentist approaches and limited cross-pathology comparisons, leading to potential overestimations of specificity. This study critiques these limitations, highlighting the inherent shortcomings of frequentist methods in specificity calculations and the necessity of comprehensive control conditions. Through a review of the Bayesian framework, we demonstrate its superiority in evaluating specificity by incorporating probabilistic modeling and robust reverse inference. The work also emphasizes the pivotal role of well-defined control conditions in mitigating overlap among brain pathologies, particularly within shared networks like the salience network. By applying Bayesian tools such as BACON (Bayes fACtor mOdeliNg), we validate the ability to derive disease-specific patterns, contrasting these with the narrower findings of frequentist analyses. This paper underscores the importance of Bayesian methodologies and extensive meta-analytic datasets in overcoming existing challenges, ultimately paving the way for more precise neuroimaging studies.
Polarisation, Born Effective Charges, and Topological Invariants via a Berry-Phase Approach
2025-01-05
Christian Carbogno, Nikita Rybin, Sara Panahian Jand, Alaa Akkoush, Carlos Mera Acosta, Zhenkun Yuan, Mariana Rossi
This paper represents one contribution to a larger Roadmap article reviewing the current status of the FHI-aims code. In this contribution, the implementation of polarization, Born-effective charges and topological invariants using a Berry-phase approach in a all-electron, numeric atom-centered orbitals framework is summarized. Guidelines on usage and links to tutorials are provided.
Towards New Benchmark for AI Alignment & Sentiment Analysis in Socially Important Issues: A Comparative Study of Human and LLMs in the Context of AGI
2025-01-05
Ljubisa Bojic, Dylan Seychell, Milan Cabarkapa
With the expansion of neural networks, such as large language models, humanity is exponentially heading towards superintelligence. As various AI systems are increasingly integrated into the fabric of societies-through recommending values, devising creative solutions, and making decisions-it becomes critical to assess how these AI systems impact humans in the long run. This research aims to contribute towards establishing a benchmark for evaluating the sentiment of various Large Language Models in socially importan issues. The methodology adopted was a Likert scale survey. Seven LLMs, including GPT-4 and Bard, were analyzed and compared against sentiment data from three independent human sample populations. Temporal variations in sentiment were also evaluated over three consecutive days. The results highlighted a diversity in sentiment scores among LLMs, ranging from 3.32 to 4.12 out of 5. GPT-4 recorded the most positive sentiment score towards AGI, whereas Bard was leaning towards the neutral sentiment. The human samples, contrastingly, showed a lower average sentiment of 2.97. The temporal comparison revealed differences in sentiment evolution between LLMs in three days, ranging from 1.03% to 8.21%. The study's analysis outlines the prospect of potential conflicts of interest and bias possibilities in LLMs' sentiment formation. Results indicate that LLMs, akin to human cognitive processes, could potentially develop unique sentiments and subtly influence societies' perceptions towards various opinions formed within the LLMs.
Test-time Computing: from System-1 Thinking to System-2 Thinking
2025-01-05
Yixin Ji, Juntao Li, Hai Ye, Kaixin Wu, Jia Xu, Linjian Mo, Min Zhang
The remarkable performance of the o1 model in complex reasoning demonstrates that test-time computing scaling can further unlock the model's potential, enabling powerful System-2 thinking. However, there is still a lack of comprehensive surveys for test-time computing scaling. We trace the concept of test-time computing back to System-1 models. In System-1 models, test-time computing addresses distribution shifts and improves robustness and generalization through parameter updating, input modification, representation editing, and output calibration. In System-2 models, it enhances the model's reasoning ability to solve complex problems through repeated sampling, self-correction, and tree search. We organize this survey according to the trend of System-1 to System-2 thinking, highlighting the key role of test-time computing in the transition from System-1 models to weak System-2 models, and then to strong System-2 models. We also point out a few possible future directions.
IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors
2025-01-05
Noé Dia, M. J. Yantovski-Barth, Alexandre Adam, Micah Bowles, Laurence Perreault-Levasseur, Yashar Hezaveh, Anna Scaife
Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challenge in radio astronomy. In this work, we introduce Imaging for Radio Interferometry with Score-based models (IRIS). We use score-based models trained on optical images of galaxies as an expressive prior in combination with a Gaussian likelihood in the uv-space to infer images of protoplanetary disks from visibility data of the DSHARP survey conducted by ALMA. We demonstrate the advantages of this framework compared with traditional radio interferometry imaging algorithms, showing that it produces plausible posterior samples despite the use of a misspecified galaxy prior. Through coverage testing on simulations, we empirically evaluate the accuracy of this approach to generate calibrated posterior samples.
Experiences and attitudes toward working remotely from home in a time of pandemic: A snapshot from a New Zealand-based online survey
2025-01-05
Edgar Pacheco
Due to the Covid-19 pandemic, employees from around the world were compelled to work remotely from home and, in many cases, without much preparation. A substantial body of international research has been conducted on the experiences and attitudes of remote workers as well as the implications of this phenomenon for organisations. While New Zealand research evidence is growing, most existing inquiry is qualitative. This paper provides a quantitative snapshot of remote working using survey data from participants whose jobs can be done from home (n=415). Data collection took place when the country was facing Covid-related measures. Based on descriptive and inferential statistics, it was found that, not only was remote working common, but that hybrid working arrangements were also more prevalent. While half of the participants wanted to work from home more frequently, age, but not gender, was significantly associated with this preference. Another relevant finding is that perceived change in the workplace culture due to flexible work arrangements was significantly associated with preference for working remotely more often. Finally, the most common perceived barriers to working from home were slow internet speed, the need to attend face-to-face meetings, and limited space at home to work. The implications of the results are discussed and some directions for future research are proposed.
From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence
2025-01-05
Tianyang Wang, Yunze Wang, Jun Zhou, Benji Peng, Xinyuan Song, Charles Zhang, Xintian Sun, Qian Niu, Junyu Liu, Silin Chen, Keyu Chen, Ming Li, Pohsun Feng, Ziqian Bi, Ming Liu, Yichao Zhang, Cheng Fei, Caitlyn Heqi Yin, Lawrence KQ Yan
Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems, particularly in high-risk domains such as healthcare, autonomous systems, and financial technology, where decision-making processes must account for uncertainty. This review explores the evolution of uncertainty quantification techniques in AI, distinguishing between aleatoric and epistemic uncertainties, and discusses the mathematical foundations and methods used to quantify these uncertainties. We provide an overview of advanced techniques, including probabilistic methods, ensemble learning, sampling-based approaches, and generative models, while also highlighting hybrid approaches that integrate domain-specific knowledge. Furthermore, we examine the diverse applications of UQ across various fields, emphasizing its impact on decision-making, predictive accuracy, and system robustness. The review also addresses key challenges such as scalability, efficiency, and integration with explainable AI, and outlines future directions for research in this rapidly developing area. Through this comprehensive survey, we aim to provide a deeper understanding of UQ's role in enhancing the reliability, safety, and trustworthiness of AI systems.
Skewness, crossing number and Euler's bound for graphs on surfaces
2025-01-04
Paul C. Kainen
For every connected graph $G$ and surface $S$, we consider the well-known string of inequalities $\delta_S(G) \leq \mu_S(G) \leq \nu_S(G)$, where $\mu$ and $\nu$ denote skewness and crossing number and $\delta$ is the Euler-formula lower bound. Recent developments are surveyed; new results are given for the ``folded'' cube including its genus.
In 1980 Ian G. Macdonald established an explicit bijection between the isomorphism classes of the irreducible representations of ${\mathrm{GL}}_n(k)$, where $k$ is a finite field, and inertia equivalence classes of admissible tamely ramified $n$-dimensional Weil-Deligne representations of $W_F$, where $F$ is a non-archimedean local field with residue field $k$ and $W_F$ the absolute Weil group of $F$. We describe a construction of the Macdonald correspondence based on the specialization to ${\mathrm{GL}}_n(k)$ of Lusztig's classification of irreducible representations of finite groups of Lie type, and review some properties of the correspondence. We define $\epsilon$-factors for pairs of irreducible cuspidal representations of finite general linear groups, and show that they match with the expected Deligne $\epsilon$-factors under the Macdonald correspondence. We use these $\epsilon$-factors for pairs to obtain a characterization of the Macdonald correspondence for the irreducible cuspidal representations
Concept camera for the next-generation mm-wave cosmological surveys
2025-01-04
Attila Kovács, Garrett Keating, Timothy Norton
Past millimeter-wave galaxy surveys have probed the brightest starburst galaxies only and suffered heavily from confusion. The interpretation of existing surveys has also been hindered by the lack of reliable redshift indicators for measuring distances for the entire sample. Thanks to recent advances in mm-wave detector technologies we can now overcome these limitations, and conduct the first truly volumetric surveys of star-forming galaxies at mm-wavelengths down to the L* luminosities of typical galaxies, with ~1000 redshift slices spanning most of the Cosmic star-forming volume (z ~ 1--12) with nearly uniform mass and luminosity selection. We describe an instrument concept capable of delivering such surveys with the technologies available today, which can be built and operated on a ground-based mm-wave facility in the near future. Such spectrometer cameras can resolve and redshift identify up to to 25,000 star-forming galaxies per year even when operated on a 10-m class telescope. On a larger aperture it can do the same faster or probe even deeper. We propose a loose, open-source collaboration to design, build, and operate one or several such cameras through the shared contributions of leading experts and telescopes from around the globe.
Chirality, a pervasive phenomenon in nature, is widely studied across diverse fields including the origins of life, chemical catalysis, drug discovery, and physical optoelectronics. The investigations of natural chiral materials have been constrained by their intrinsically weak chiral effects. Recently, significant progress has been made in the fabrication and assembly of low-dimensional micro and nanoscale chiral materials and their architectures, leading to the discovery of novel optoelectronic phenomena such as circularly polarized light emission, spin and charge flip, advocating great potential for applications in quantum information, quantum computing, and biosensing. Despite these advancements, the fundamental mechanisms underlying the generation, propagation, and amplification of chirality in low-dimensional chiral materials and architectures remain largely unexplored. To tackle these challenges, we focus on employing ultrafast spectroscopy to investigate the dynamics of chirality evolution, with the aim of attaining a more profound understanding of the microscopic mechanisms governing chirality generation and amplification. This review thus provides a comprehensive overview of the chiral micro-/nano-materials, including two-dimensional transition metal dichalcogenides (TMDs), chiral halide perovskites, and chiral metasurfaces, with a particular emphasis on the physical mechanism. This review further explores the advancements made by ultrafast chiral spectroscopy research, thereby paving the way for innovative devices in chiral photonics and optoelectronics.
2025-01-04
Kong Yue, Yang haokun, Nie Rong, Zhang Xuxiang, Zhang Hongtao, Nian Xin
Obesity is defined as the excessive accumulation or abnormal distribution of body fat. According to data from World Obesity Atlas 2024, the increase in prevalence of obesity has become a major worldwide health problem in adults as well as among children and adolescents. Although an increasing number of drugs have been approved for the treatment of obesity in recent years, many of these drugs have inevitable side effects which have increased the demand for new safe, accessible and effective drugs for obesity and prompt interest in natural products. Berberine (BBR) and its metabolites, known for their multiple pharmacological effects. Recent studies have emphatically highlighted the anti-obesity benefits of BBR and the underlying mechanisms have been gradually elucidated. However, its clinical application is limited by poor oral absorption and low bioavailability. Based on this, this review summarizes current research on the anti-obesity effects of BBR and its metabolites, including advancements in clinical trail results, understanding potential molecular mechanisms and absorption and bioavailability. As a natural compound derived from plants, BBR holds potential as an alternative approach for managing obesity.
TOI-6038 A b: A dense sub-Saturn in the transition regime between the Neptunian ridge and savanna
2025-01-04
Sanjay Baliwal, Rishikesh Sharma, Abhijit Chakraborty, K. J. Nikitha, A. Castro-González, Hareesh G. Bhaskar, Akanksha Khandelwal, David W. Latham, Allyson Bieryla, Vincent Bourrier, Neelam J. S. S. V. Prasad, Kapil K. Bharadwaj, Kevikumar A. Lad, Ashirbad Nayak, Vishal Joshi, Jason D. Eastman
We present the discovery and characterization of a sub-Saturn exoplanet, TOI-6038~A~b, using the PARAS-2 spectrograph. The planet orbits a bright ($m_V=9.9$), metal-rich late F-type star, TOI-6038~A, with $T_{\rm{eff}}=6110\pm100~\mathrm{K}$, $\log{g}=4.118^{+0.015}_{-0.025}$, and $[{\rm{Fe/H}}]=0.124^{+0.079}_{-0.077}$ dex. The system also contains a wide-orbit binary companion, TOI-6038~B, an early K-type star at a projected separation of $\approx3217$ AU. We combined radial velocity data from PARAS-2 with photometric data from the Transiting Exoplanet Survey Satellite (TESS) for joint modeling. TOI-6038~A~b has a mass of $78.5^{+9.5}_{-9.9}~M_\oplus$ and a radius of $6.41^{+0.20}_{-0.16}~R_\oplus$, orbiting in a circular orbit with a period of $5.8267311^{+0.0000074}_{-0.0000068}$ days. Internal structure modeling suggests that $\approx74\%$ of the planet's mass is composed of dense materials, such as rock and iron, forming a core, while the remaining mass consists of a low-density H/He envelope. TOI-6038~A~b lies at the transition regime between the recently identified Neptunian ridge and savanna. Having a density of $\rho_{\rm{P}}=1.62^{+0.23}_{-0.24}\rm~g\,cm^{-3}$, TOI-6038~A~b is compatible with the population of dense ridge planets ($\rho_{\rm{P}}\simeq$ 1.5-2.0 $\rm~g\,cm^{-3}$), which have been proposed to have reached their close-in locations through high-eccentricity tidal migration (HEM). First-order estimates suggest that the secular perturbations induced by TOI-6038~B may be insufficient to drive the HEM of TOI-6038~A~b. Therefore, it is not clear whether HEM driven by a still undetected companion, or early disk-driven migration, brought TOI-6038~A~b to its present-day close-in orbit. Its bright host star makes TOI-6038~A~b a prime target for atmospheric escape and orbital architecture observations, which will help us to better understand its overall evolution.
The growth of a large-scale magnetic field in the Sun and stars is usually possible when the dynamo number (D) is above a critical value Dc. As the star ages, its rotation rate and thus D decrease. Hence, the question is how far the solar dynamo is from the critical dynamo transition. To answer this question, we have performed a set of simulations using Babcock-Leighton type dynamo models at different values of dynamo supercriticality and analyzed various features of magnetic cycle. By comparing the recovery rates of the dynamo from the Maunder minimum and statistics (numbers and durations) of the grand minima and maxima with that of observations and we show that the solar dynamo is only about two times critical and thus not highly supercritical. The observed correlation between the polar field proxy and the following cycle amplitudes and Gnevyshev-Ohl rule are also compatible with this conclusion.
Survey on Question Answering over Visually Rich Documents: Methods, Challenges, and Trends
2025-01-04
Camille Barboule, Benjamin Piwowarski, Yoan Chabot
Using Large Language Models (LLMs) for Visually-rich Document Understanding (VrDU) has significantly improved performance on tasks requiring both comprehension and generation, such as question answering, albeit introducing new challenges. This survey explains how VrDU models enhanced by LLMs function, covering methods for integrating VrD features into LLMs and highlighting key challenges.
Recommender systems have become increasingly vital in our daily lives, helping to alleviate the problem of information overload across various user-oriented online services. The emergence of Large Language Models (LLMs) has yielded remarkable achievements, demonstrating their potential for the development of next-generation recommender systems. Despite these advancements, LLM-based recommender systems face inherent limitations stemming from their LLM backbones, particularly issues of hallucinations and the lack of up-to-date and domain-specific knowledge. Recently, Retrieval-Augmented Generation (RAG) has garnered significant attention for addressing these limitations by leveraging external knowledge sources to enhance the understanding and generation of LLMs. However, vanilla RAG methods often introduce noise and neglect structural relationships in knowledge, limiting their effectiveness in LLM-based recommendations. To address these limitations, we propose to retrieve high-quality and up-to-date structure information from the knowledge graph (KG) to augment recommendations. Specifically, our approach develops a retrieval-augmented framework, termed K-RagRec, that facilitates the recommendation generation process by incorporating structure information from the external KG. Extensive experiments have been conducted to demonstrate the effectiveness of our proposed method.
A Comprehensive Catalog of Emission-line Nebulae, Star Clusters, and Supergiants in M31 from the LAMOST Spectroscopic Survey
2025-01-04
Pinjian Chen, Bingqiu Chen, Xuan Fang, Haibo Yuan, Baisong Zhang, Xiangwei Zhang, Jiarui Sun, Xiaowei Liu
Spectroscopic observations of various tracers in nearby galaxies, such as Andromeda (M31), play a crucial role in identifying and classifying individual stellar populations and nebular objects, thereby enhancing our understanding of galactic composition, environment, and dynamics as well as stellar evolution. While the LAMOST (Large Sky Area Multi-Object Fibre Spectroscopic Telescope) survey of M31 has produced extensive datasets, a comprehensive catalog of emission-line nebulae, star clusters, and supergiants is yet to be completed. In this paper, we present a final catalog of 384 emission-line nebulae, 380 star clusters, and 375 supergiants and candidates in M31, as carefully selected and identified from the LAMOST spectroscopic database. These objects were classified using a random forest algorithm, followed by thorough visual examinations of their spectral characteristics as well as morphologies revealed by archive images. For emission-line nebulae, we measured radial velocities and relative fluxes of emission lines, enabling further classification of planetary nebulae and HII regions. Additionally, we identified 245 emission-line nebulae in M33. This work lays the data foundation for the study of M31, and offers valuable tracers to investigate M31's structure and evolution.
2025-01-04
Ramanpreet Singh, Athul C N, H. K. Jassal
Cosmological parameter fitting remains crucial, especially with the abundance of available data. While many parameters have been tightly constrained, discrepancies-most notably the Hubble tension-persist between measurements obtained from different observational datasets. In this paper, we re-examine the Pantheon supernova dataset to explore deviations in the distribution of distance modulus residuals from the Gaussian distribution, which is typically the underlying assumption. We do this analysis for the concordant cosmological constant model and for a variety of dynamical dark energy models. It has been shown earlier that the residuals in this dataset are better fit to a logistic distribution. We compare the residual distributions assuming both Gaussian and Logistic likelihoods on the complete dataset, as well as various subsets of the data. The results, validated through various statistical tests, demonstrate that the Logistic likelihood provides a better fit for the full dataset and lower redshift bins, while higher redshift bins fit Gaussian and Logistic likelihoods similarly. Furthermore, the findings indicate a preference for a cosmological constant model. However analysing individual surveys within the Pantheon dataset reveals inconsistencies among subsets. The level of agreement between surveys varies depending upon the underlying likelihood function.
Benchmark Evaluations, Applications, and Challenges of Large Vision Language Models: A Survey
2025-01-04
Zongxia Li, Xiyang Wu, Hongyang Du, Huy Nghiem, Guangyao Shi
Multimodal Vision Language Models (VLMs) have emerged as a transformative technology at the intersection of computer vision and natural language processing, enabling machines to perceive and reason about the world through both visual and textual modalities. For example, models such as CLIP, Claude, and GPT-4V demonstrate strong reasoning and understanding abilities on visual and textual data and beat classical single modality vision models on zero-shot classification. Despite their rapid advancements in research and growing popularity in applications, a comprehensive survey of existing studies on VLMs is notably lacking, particularly for researchers aiming to leverage VLMs in their specific domains. To this end, we provide a systematic overview of VLMs in the following aspects: model information of the major VLMs developed over the past five years (2019-2024); the main architectures and training methods of these VLMs; summary and categorization of the popular benchmarks and evaluation metrics of VLMs; the applications of VLMs including embodied agents, robotics, and video generation; the challenges and issues faced by current VLMs such as hallucination, fairness, and safety. Detailed collections including papers and model repository links are listed in https://github.com/zli12321/Awesome-VLM-Papers-And-Models.git.
The integration of Large Language Models into recommendation frameworks presents key advantages for personalization and adaptability of experiences to the users. Classic methods of recommendations, such as collaborative filtering and content-based filtering, are seriously limited in the solution of cold-start problems, sparsity of data, and lack of diversity in information considered. LLMs, of which GPT-4 is a good example, have emerged as powerful tools that enable recommendation frameworks to tap into unstructured data sources such as user reviews, social interactions, and text-based content. By analyzing these data sources, LLMs improve the accuracy and relevance of recommendations, thereby overcoming some of the limitations of traditional approaches. This work discusses applications of LLMs in recommendation systems, especially in electronic commerce, social media platforms, streaming services, and educational technologies. This showcases how LLMs enrich recommendation diversity, user engagement, and the system's adaptability; yet it also looks into the challenges connected to their technical implementation. This can also be presented as a study that shows the potential of LLMs for changing user experiences and making innovation possible in industries.
The Integration of Blockchain and Artificial Intelligence for Secure Healthcare Systems
2025-01-04
Umar Safdar, Simon Gabrael
Verisign reported a 125 percent increase in data breaches within the healthcare sector in the United States during 2022, with 18.2 million patient records being impacted. Growing healthcare data volumes and diversification mean that medical information is becoming more valuable. Many Health Centers use various technologies to ease the classification, storage, and exchange of big data. This use can also make the health data of the users at risk and vulnerable. AI and blockchain are among the leading technologies at hand. With AI, data-driven operations and big data efficiency have been improved with respect to traditional techniques. Due to its potential to bring about improvements in health services and lower medical costs, this AI technology is regularly used in healthcare. Blockchain helps protect transactions on sharing information and private privacy as long as the exchange of knowledge is that of the standard. The objective of this analysis is to investigate the research and unique contributions since 2008 regarding blockchain-integrated AI and healthcare systems. The work sheds light on applied AI-based healthcare schemes with machine, ballistic, and acrylic learning and disparate blockchain structures. The use of technology in order to ensure patient data security and manage medical information effectively in healthcare settings offers a highly successful position for both healthcare providers and patients. From 2018 to 2021, the best year was 2021 to grow, enhancing everything to examine the download of the device and the counting of Google Academies, for which the joining perspective was borrowed; local research experts were asked, identified articles in recent years, and read reviews of large research grants.
Optimizing Edge AI: A Comprehensive Survey on Data, Model, and System Strategies
2025-01-04
Xubin Wang, Weijia Jia
The emergence of 5G and edge computing hardware has brought about a significant shift in artificial intelligence, with edge AI becoming a crucial technology for enabling intelligent applications. With the growing amount of data generated and stored on edge devices, deploying AI models for local processing and inference has become increasingly necessary. However, deploying state-of-the-art AI models on resource-constrained edge devices faces significant challenges that must be addressed. This paper presents an optimization triad for efficient and reliable edge AI deployment, including data, model, and system optimization. First, we discuss optimizing data through data cleaning, compression, and augmentation to make it more suitable for edge deployment. Second, we explore model design and compression methods at the model level, such as pruning, quantization, and knowledge distillation. Finally, we introduce system optimization techniques like framework support and hardware acceleration to accelerate edge AI workflows. Based on an in-depth analysis of various application scenarios and deployment challenges of edge AI, this paper proposes an optimization paradigm based on the data-model-system triad to enable a whole set of solutions to effectively transfer ML models, which are initially trained in the cloud, to various edge devices for supporting multiple scenarios.
Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap
2025-01-03
Weizhi Zhang, Yuanchen Bei, Liangwei Yang, Henry Peng Zou, Peilin Zhou, Aiwei Liu, Yinghui Li, Hao Chen, Jianling Wang, Yu Wang, Feiran Huang, Sheng Zhou, Jiajun Bu, Allen Lin, James Caverlee, Fakhri Karray, Irwin King, Philip S. Yu
Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recommendations. Due to the diversification of internet platforms and the exponential growth of users and items, the importance of cold-start recommendation (CSR) is becoming increasingly evident. At the same time, large language models (LLMs) have achieved tremendous success and possess strong capabilities in modeling user and item information, providing new potential for cold-start recommendations. However, the research community on CSR still lacks a comprehensive review and reflection in this field. Based on this, in this paper, we stand in the context of the era of large language models and provide a comprehensive review and discussion on the roadmap, related literature, and future directions of CSR. Specifically, we have conducted an exploration of the development path of how existing CSR utilizes information, from content features, graph relations, and domain information, to the world knowledge possessed by large language models, aiming to provide new insights for both the research and industrial communities on CSR. Related resources of cold-start recommendations are collected and continuously updated for the community in https://github.com/YuanchenBei/Awesome-Cold-Start-Recommendation.
Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning
2025-01-03
Anjitha John William, Priyanka Jalan, Maciej Bilicki, Wojciech A. Hellwing, Hareesh Thuruthipilly, Szymon J. Nakoneczny
We employ deep learning (DL) to improve photometric redshifts (photo-$z$s) in the Kilo-Degree Survey Data Release 4 Bright galaxy sample (KiDS-Bright DR4). This dataset, used as a foreground for KiDS lensing and clustering studies, is flux-limited to $r<20$ mag with mean $z=0.23$ and covers 1000 deg$^2$. Its photo-$z$s were previously derived with artificial neural networks from the ANNz2 package, trained on the Galaxy And Mass Assembly (GAMA) spectroscopy. Here we considerably improve over these previous redshift estimations by building a DL model, Hybrid-z, which combines four-band KiDS images with nine-band magnitudes from KiDS+VIKING. The Hybrid-z framework provides photo-$z$s for KiDS-Bright, with negligible mean residuals of O($10^{-4}$) and scatter at the level of $0.014(1+z)$ -- reduction by 20% over the previous nine-band derivations with ANNz2. We check our photo-$z$ model performance on test data drawn from GAMA, as well as from other KiDS-overlapping wide-angle spectroscopic surveys, namely SDSS, 2dFLenS, and 2dFGRS. We find stable behavior and consistent improvement over ANNz2 throughout. We finally apply Hybrid-z trained on GAMA to the entire KiDS-Bright DR4 sample of 1.2 million galaxies. For these final predictions, we design a method of smoothing the input redshift distribution of the training set, to avoid propagation of features present in GAMA, related to its small sky area and large-scale structure imprint in its fields. Our work paves the way towards the best-possible photo-$z$s achievable with machine learning for any galaxy type both for the final KiDS-Bright DR5 data and for future deeper imaging, such as from the Legacy Survey of Space and Time.
Identification of the interstellar 1-cyano propargyl radical (HCCCHCN) in TMC-1
2025-01-03
C. Cabezas, M. Agúndez, N. Marcelino, C. H. Chang, R. Fuentetaja, B. Tercero, M. Nakajima, Y. Endo, P. de Vicente, J. Cernicharo
We report the first detection in interstellar medium of the 1-cyano propargyl radical, HC$_3$HCN. This species is an isomer of the 3-cyano propargyl radical (CH$_2$C$_3$N), which was recently discovered in TMC-1. The 1-cyano propargyl radical was observed in the cold dark cloud TMC-1 using data from the ongoing QUIJOTE line survey, which is being carried out with the Yebes 40m telescope. A total of seven rotational transitions with multiple hyperfine components were detected in the 31.0-50.4 GHz range. We derived a column density of (2.2$\pm$0.2)$\times$10$^{11}$ cm$^{-2}$ and a rotational temperature of 7$\pm$1\,K. The abundance ratio between HC$_3$HCN and CH$_2$C$_3$N is 1.4. The almost equal abundance of these isomers indicates that the two species may be produced in the same reaction with a similar efficiency, probably in the reaction C + CH$_2$CHCN and perhaps also in the reaction C$_2$ + CH$_3$CN and the dissociative recombination with electrons of CH$_2$C$_3$NH$^+$
Abstractive Text Summarization for Contemporary Sanskrit Prose: Issues and Challenges
2025-01-03
Shagun Sinha
This thesis presents Abstractive Text Summarization models for contemporary Sanskrit prose. The first chapter, titled Introduction, presents the motivation behind this work, the research questions, and the conceptual framework. Sanskrit is a low-resource inflectional language. The key research question that this thesis investigates is what the challenges in developing an abstractive TS for Sanskrit. To answer the key research questions, sub-questions based on four different themes have been posed in this work. The second chapter, Literature Review, surveys the previous works done. The third chapter, data preparation, answers the remaining three questions from the third theme. It reports the data collection and preprocessing challenges for both language model and summarization model trainings. The fourth chapter reports the training and inference of models and the results obtained therein. This research has initiated a pipeline for Sanskrit abstractive text summarization and has reported the challenges faced at every stage of the development. The research questions based on every theme have been answered to answer the key research question.
Structural and Statistical Audio Texture Knowledge Distillation (SSATKD) for Passive Sonar Classification
2025-01-03
Jarin Ritu, Amirmohammad Mohammadi, Davelle Carreiro, Alexandra Van Dine, Joshua Peeples
Knowledge distillation has been successfully applied to various audio tasks, but its potential in underwater passive sonar target classification remains relatively unexplored. Existing methods often focus on high-level contextual information while overlooking essential low-level audio texture features needed to capture local patterns in sonar data. To address this gap, the Structural and Statistical Audio Texture Knowledge Distillation (SSATKD) framework is proposed for passive sonar target classification. SSATKD combines high-level contextual information with low-level audio textures by utilizing an Edge Detection Module for structural texture extraction and a Statistical Knowledge Extractor Module to capture signal variability and distribution. Experimental results confirm that SSATKD improves classification accuracy while optimizing memory and computational resources, making it well-suited for resource-constrained environments.
I make a brief review about the QCD phases and the equation of state inferred from the neutron star data. Along the temperature axis at low baryon density, the QCD phase transition is a smooth crossover, and it is a natural extension of our imagination to postulate a similar crossover along the density axis at low temperature. Even without phase transitions, the inferred thermodynamic properties of neutron star matter turn out to be highly nontrivial already at twice of the nuclear saturation density. I also give some discussions about the substantiation of quark matter by means of the gravitational wave signals including the multi-messenger prospect.
This is a light and short review on the geometry of hypermultiplets coupled to supergravity and its appearance in string theory. As is long known, the target space of the hypermultiplet sigma-model is a quaternion-K\"ahler (QK) manifold of negative scalar curvature. We review some aspects of hypermultiplet moduli spaces, but also study aspects of QK-geometries in relation to the swampland, including some concrete examples of swampland QK-manifolds.
Autonomous vehicles (AVs) can significantly promote the advances in road transport mobility in terms of safety, reliability, and decarbonization. However, ensuring safety and efficiency in interactive during within dynamic and diverse environments is still a primary barrier to large-scale AV adoption. In recent years, deep reinforcement learning (DRL) has emerged as an advanced AI-based approach, enabling AVs to learn decision-making strategies adaptively from data and interactions. DRL strategies are better suited than traditional rule-based methods for handling complex, dynamic, and unpredictable driving environments due to their adaptivity. However, varying driving scenarios present distinct challenges, such as avoiding obstacles on highways and reaching specific exits at intersections, requiring different scenario-specific decision-making algorithms. Many DRL algorithms have been proposed in interactive decision-making. However, a rationale review of these DRL algorithms across various scenarios is lacking. Therefore, a comprehensive evaluation is essential to assess these algorithms from multiple perspectives, including those of vehicle users and vehicle manufacturers. This survey reviews the application of DRL algorithms in autonomous driving across typical scenarios, summarizing road features and recent advancements. The scenarios include highways, on-ramp merging, roundabouts, and unsignalized intersections. Furthermore, DRL-based algorithms are evaluated based on five rationale criteria: driving safety, driving efficiency, training efficiency, unselfishness, and interpretability (DDTUI). Each criterion of DDTUI is specifically analyzed in relation to the reviewed algorithms. Finally, the challenges for future DRL-based decision-making algorithms are summarized.
A review of long lasting activities of the central engine of gamma-ray bursts
2025-01-03
Bruce Gendre
Gamma-ray bursts are known to display various features on top of their canonical behavior. In this short review, we will describe and discuss two of them: the ultra-long gamma-ray bursts, which are defined by an extreme duration of their prompt phase, and the plateau phase, which is defined by a steady phase of large duration at the start of the afterglow. We will review the main properties of those two phenomena, and will discuss their possible origin, in light of the standard fireball model of gamma-ray bursts. A final section will discuss the future missions which could bring new evidences to the study of those objects.
Electronic band structures of topological kagome materials
2025-01-03
Man Li, Huan Ma, Rui Lou, Shancai Wang
The kagome lattice has garnered significant attention due to its ability to host quantum spin Fermi liquid states. Recently, the combination of unique lattice geometry, electron-electron correlations, and adjustable magnetism in solid kagome materials has led to the discovery of numerous fascinating quantum properties. These include unconventional superconductivity, charge and spin density waves (CDW/SDW), pair density waves (PDW), and Chern insulator phases. These emergent states are closely associated with the distinctive characteristics of the kagome lattice's electronic structure, such as van Hove singularities, Dirac fermions, and flat bands, which can exhibit exotic quasi-particle excitations under different symmetries and magnetic conditions. Recently, various quantum kagome materials have been developed, typically consisting of kagome layers stacked along the $z$-axis with atoms either filling the geometric centers of the kagome lattice or embedded between the layers. In this topical review, we begin by introducing the fundamental properties of several kagome materials. To gain an in-depth understanding of the relationship between topology and correlation, we then discuss the complex phenomena observed in these systems. These include the simplest kagome metal $T_3X$, kagome intercalation metal $TX$, and the ternary compounds $AT_6X_6$ and $RT_3X_5$ ($A$ = Li, Mg, Ca, or rare earth; $T$ = V, Cr, Mn, Fe, Co, Ni; $X$ = Sn, Ge; $R$ = K, Rb, Cs). Finally, we provide a perspective on future experimental work in this field.
Leveraging Sustainable Systematic Literature Reviews
2025-01-03
Vinicius dos Santos, Rick Kazman, Elisa Yumi Nakagawa
Systematic Literature Reviews (SLRs) are a widely employed research method in software engineering. However, there are several problems with SLRs, including the enormous time and effort to conduct them and the lack of obvious impacts of SLR results on software engineering practices and industry projects. To address these problems, the concepts of \textit{sustainability} and \textit{sustainable SLR} have been proposed, aiming to raise awareness among researchers about the importance of dealing with SLR problems in a consistent way; however, practical and concrete actions are still lacking. This paper presents concrete directions towards sustainable SLRs. We first identified 18 ``green drivers'' (GD) that could directly impact SLR sustainability, and we distilled 25 sustainability indicators (SI) associated with the GD to assess SLRs regarding their sustainability. A preliminary evaluation was conducted on the ten top-cited SLRs in software engineering published over the last decade. From this analysis, we synthesized our insights into 12 leverage points for sustainability. Our results indicate that even in high-quality reviews, there are threats to sustainability, such as: flaws in the search process, lack of essential details in the documentation, weak collaboration with stakeholders, poor knowledge management, lack of use of supporting tools, and a dearth of practical insights for software engineering practitioners. The good news is that moving towards sustainable SLRs only requires some simple actions, which can pave the way for a profound change in the software engineering community's mindset about how to create and sustain SLRs.
In affine formation control problems, the construction of the framework with universal rigidity and affine localizability is a critical prerequisite, but it has not yet been well addressed, especially when additional agents join the formation or link/agent failures emerge. Motivated by this observation, we investigate the problem of constructing affine formation frameworks in three scenarios, including vertex addition, edge deletion and vertex deletion. Our approach starts from the original affine formation and uses geometric methods to locally adjust the structure of the weighted graph to describe the topology, so that the modified framework maintains the universal rigidity and affine localizability. Notably, the developed strategies only utilize local measurements and exhibit distributed characteristics, laying the foundation for applications in multi-agent systems. To demonstrate the compatibility with affine formation control proposals, we present a case study on affine formation tracking in a multi-UAV formation, demonstrating the effectiveness of our algorithms in constructing eligible frameworks in aforementioned scenarios. Moreover, a comparative simulations is also conducted to highlight the low time complexity of our distributed algorithm relative to the centralized optimization-based method.
The ESO SupJup Survey IV. Unveiling the carbon isotope ratio of GQ Lup B and its host star
2025-01-03
D. González Picos, I. A. G. Snellen, S. de Regt, R. Landman, Y. Zhang, S. Gandhi, A. Sánchez-López
The carbon isotope ratio ($^{12}\mathrm{C}/^{13}\mathrm{C}$) is a potential tracer of giant planet and brown dwarf formation. The GQ Lup system, hosting the K7 T Tauri star GQ Lup A and its substellar companion GQ Lup B, offers a unique opportunity to investigate this ratio in a young system. We aim to characterise the atmosphere of GQ Lup B, determining its temperature, chemical composition, spin, surface gravity, and $^{12}\mathrm{C}/^{13}\mathrm{C}$, while also measuring the same ratio for its host star, GQ Lup A. High-resolution K-band spectra of GQ Lup were obtained using CRIRES+ at the VLT. We modelled the starlight contribution from GQ Lup A and fitted GQ Lup B\'s spectrum with atmospheric models from petitRADTRANS. The $^{12}\mathrm{C}/^{13}\mathrm{C}$ ratio for GQ Lup A was derived using isotope-sensitive PHOENIX models. Atmospheric analysis of GQ Lup B revealed abundances of H$_2$O, $^{12}$CO, $^{13}$CO, HF, Na, Ca, and Ti, along with a C/O ratio of $0.50 \pm 0.01$, consistent with the solar value. The carbon isotope ratio was measured as $^{12}\mathrm{C}/^{13}\mathrm{C} = 53^{+7}_{-6}$ for GQ Lup B and $^{12}\mathrm{C}/^{13}\mathrm{C} = 51^{+10}_{-8}$ for GQ Lup A. Strong veiling of the photospheric lines of GQ Lup A was identified and accounted for. The similar $^{12}\mathrm{C}/^{13}\mathrm{C}$ values in GQ Lup A and B suggest a common origin from a shared material reservoir, supporting formation via disc fragmentation or gravitational collapse.
Gas and dust in the Galactic Center are subjected to energetic processing by intense UV radiation fields, widespread shocks, enhanced rates of cosmic-rays and X-rays, and strong magnetic fields. The Giant Molecular Clouds in the Galactic Center present a rich chemistry in a wide variety of chemical compounds, some of which are prebiotic. We have conducted unbiased, ultrasensitive and broadband spectral surveys toward the G+0.693-0.027 molecular cloud located in the Galactic Center, which have yielded the discovery of new complex organic molecules proposed as precursors of the "building blocks" of life. I will review our current understanding of the chemistry in Galactic Center molecular clouds, and summarize the recent detections toward G+0.693-0.027 of key precursors of prebiotic chemistry. All this suggests that the ISM is an important source of prebiotic material that could have contributed to the process of the origin of life on Earth and elsewhere in the Universe.
Long time behavior of the stochastic 2D Navier-Stokes equations
2025-01-03
Benedetta Ferrario, Margherita Zanella
We review some basic results on existence and uniqueness of the invariant measure for the two-dimensional stochastic Navier-Stokes equations. A large part of the literature concerns the additive noise case; after revising these models, we consider our recent result, arXiv:2307.03483, with a multiplicative noise.
Multi-modal classification of forest biodiversity potential from 2D orthophotos and 3D airborne laser scanning point clouds
2025-01-03
Simon B. Jensen, Stefan Oehmcke, Andreas Møgelmose, Meysam Madadi, Christian Igel, Sergio Escalera, Thomas B. Moeslund
Accurate assessment of forest biodiversity is crucial for ecosystem management and conservation. While traditional field surveys provide high-quality assessments, they are labor-intensive and spatially limited. This study investigates whether deep learning-based fusion of close-range sensing data from 2D orthophotos (12.5 cm resolution) and 3D airborne laser scanning (ALS) point clouds (8 points/m^2) can enhance biodiversity assessment. We introduce the BioVista dataset, comprising 44.378 paired samples of orthophotos and ALS point clouds from temperate forests in Denmark, designed to explore multi-modal fusion approaches for biodiversity potential classification. Using deep neural networks (ResNet for orthophotos and PointVector for ALS point clouds), we investigate each data modality's ability to assess forest biodiversity potential, achieving mean accuracies of 69.4% and 72.8%, respectively. We explore two fusion approaches: a confidence-based ensemble method and a feature-level concatenation strategy, with the latter achieving a mean accuracy of 75.5%. Our results demonstrate that spectral information from orthophotos and structural information from ALS point clouds effectively complement each other in forest biodiversity assessment.
Proposing Hierarchical Goal-Conditioned Policy Planning in Multi-Goal Reinforcement Learning
2025-01-03
Gavin B. Rens
Humanoid robots must master numerous tasks with sparse rewards, posing a challenge for reinforcement learning (RL). We propose a method combining RL and automated planning to address this. Our approach uses short goal-conditioned policies (GCPs) organized hierarchically, with Monte Carlo Tree Search (MCTS) planning using high-level actions (HLAs). Instead of primitive actions, the planning process generates HLAs. A single plan-tree, maintained during the agent's lifetime, holds knowledge about goal achievement. This hierarchy enhances sample efficiency and speeds up reasoning by reusing HLAs and anticipating future actions. Our Hierarchical Goal-Conditioned Policy Planning (HGCPP) framework uniquely integrates GCPs, MCTS, and hierarchical RL, potentially improving exploration and planning in complex tasks.
Theory-of-Mind (ToM) is a fundamental psychological capability that allows humans to understand and interpret the mental states of others. Humans infer others' thoughts by integrating causal cues and indirect clues from broad contextual information, often derived from past interactions. In other words, human ToM heavily relies on the understanding about the backgrounds and life stories of others. Unfortunately, this aspect is largely overlooked in existing benchmarks for evaluating machines' ToM capabilities, due to their usage of short narratives without global backgrounds. In this paper, we verify the importance of understanding long personal backgrounds in ToM and assess the performance of LLMs in such realistic evaluation scenarios. To achieve this, we introduce a novel benchmark, CharToM-QA, comprising 1,035 ToM questions based on characters from classic novels. Our human study reveals a significant disparity in performance: the same group of educated participants performs dramatically better when they have read the novels compared to when they have not. In parallel, our experiments on state-of-the-art LLMs, including the very recent o1 model, show that LLMs still perform notably worse than humans, despite that they have seen these stories during pre-training. This highlights the limitations of current LLMs in capturing the nuanced contextual information required for ToM reasoning.
For the first time, we develop a simulation-based model for the Minkowski functionals (MFs) of large-scale structure, which allows us to extract the full information available from the MFs (including both the Gaussian and non-Gaussian part), and apply it to the BOSS DR12 CMASS galaxy sample. Our model is based on high-fidelity mock galaxy catalogs constructed from the \textsc{Abacus}\textsc{Summit} simulations using the halo occupation distribution (HOD) framework, which include the redshift-space distortions and Alcock-Paczynski distortions, incorporate survey realism, including survey geometry and veto masks, and account for angular plus radial selection effects. The cosmological and HOD parameter dependence of the MFs is captured with a neural network emulator trained from the galaxy mocks with various cosmological and HOD parameters. To benchmark the constraining power of the MFs, we also train an emulator for the galaxy 2-point correlation function (2PCF) using the same pipeline. Having validated our approach through successful parameter recovery tests on both internal and external mocks, including non-HOD forward models of the halo-galaxy connection, we apply our forward model to analyze the CMASS data in the redshift range $0.45
Finding Large Sets Without Arithmetic Progressions of Length Three: An Empirical View and Survey II
2025-01-03
William Gasarch, James Glenn, Clyde Kruskal
There has been much work on the following question: given n how large can a subset of {1,...,n} be that has no arithmetic progressions of length 3. We call such sets 3-free. Most of the work has been asymptotic. In this paper we sketch applications of large 3-free sets, review the literature of how to construct large 3-free sets, and present empirical studies on how large such sets actually are. The two main questions considered are (1) How large can a 3-free set be when n is small, and (2) How do the methods in the literature compare to each other? In particular, when do the ones that are asymptotically better actually yield larger sets? (This paper overlaps with our previous paper with the title { Finding Large 3-Free Sets I: the Small n Case}.)
Revisiting Data Analysis with Pre-trained Foundation Models
2025-01-03
Chen Liang, Donghua Yang, Zheng Liang, Zhiyu Liang, Tianle Zhang, Boyu Xiao, Yuqing Yang, Wenqi Wang, Hongzhi Wang
Data analysis focuses on harnessing advanced statistics, programming, and machine learning techniques to extract valuable insights from vast datasets. An increasing volume and variety of research emerged, addressing datasets of diverse modalities, formats, scales, and resolutions across various industries. However, experienced data analysts often find themselves overwhelmed by intricate details in ad-hoc solutions or attempts to extract the semantics of grounded data properly. This makes it difficult to maintain and scale to more complex systems. Pre-trained foundation models (PFMs), grounded with a large amount of grounded data that previous data analysis methods can not fully understand, leverage complete statistics that combine reasoning of an admissible subset of results and statistical approximations by surprising engineering effects, to automate and enhance the analysis process. It pushes us to revisit data analysis to make better sense of data with PFMs. This paper provides a comprehensive review of systematic approaches to optimizing data analysis through the power of PFMs, while critically identifying the limitations of PFMs, to establish a roadmap for their future application in data analysis.
Treatment of Thermal Non-Equilibrium Dissociation Rates: Application to $\rm H_2$
2025-01-03
Alex T. Carroll, Jacob Wolmer, Guillaume Blanquart, Aaron M. Brandis, Brett A. Cruden
This work presents a detailed description of the thermochemical non-equilibrium dissociation of diatomic molecules, and applies this theory to the case of $\rm H_2$ dissociation. The master equations are used to derive corresponding aggregate rate constant expressions that hold for any degree of thermochemical non-equilibrium. These general expressions are analyzed in three key limits/ regimes: the thermal equilibrium limit, the quasi-steady-state (QSS) regime, and the pre-QSS regime. Under several simplifying assumptions, an analytical source term expression that holds in all of these regimes, and is only a function of the translational temperature, $T_{\rm t}$, and the fraction of dissociation, $\phi_{\rm A}$, is proposed. This expression has two input parameters: the QSS dissociation rate constant in the absence of recombination, $k_{\rm d,nr}(T_{\rm t})$, and a pre-QSS correction factor, $\eta(T_{\rm t})$. The value of $\eta(T_{\rm t})$ is evaluated by comparing the predictions of the proposed expression against existing master equation simulations of a 0-D isothermal and isochoric reactor for the case of $\rm H_2$ dissociation with the third-bodies $\rm H_2$, $\rm H$, and $\rm He$. Despite its simple functional form, the proposed expression is able to reproduce the master equation results for the majority of the tested conditions. The best fit of $k_{\rm d,nr}(T_{\rm t})$ is then evaluated by conducting a detailed literature review. Data from a wide range of experimental and computational studies are considered for the third-bodies $\rm H_2$, $\rm H$, and inert gases, and fits that are valid from 200 to 20,000 K are proposed. From this review, the uncertainty of the proposed fits are estimated to be less than a factor of two.
Fire and Ice in the Whirlpool: Spatially Resolved Scaling Relations between X-ray Emitting Hot Gas and Cold Molecular Gas in M51
2025-01-03
Chunyi Zhang, Junfeng Wang, Tian-Wen Cao
The cold and hot interstellar medium (ISM) in star forming galaxies resembles the reservoir for star formation and associated heating by stellar winds and explosions during stellar evolution, respectively. We utilize data from deep $Chandra$ observations and archival millimeter surveys to study the interconnection between these two phases and the relation to star formation activities in M51 on kiloparsec scales. A sharp radial decrease is present in the hot gas surface brightness profile within the inner 2 kpc of M51. The ratio between the total infrared luminosity ($L_{\rm IR}$) and the hot gas luminosity ($L_{\rm 0.5 - 2\,keV}^{\rm gas}$) shows a positive correlation with the galactic radius in the central region. For the entire galaxy, a twofold correlation is revealed in the $L_{\rm 0.5 - 2\,keV}^{\rm gas}$${-}$$L_{\rm IR}$ diagram, where $L_{\rm 0.5 - 2\,keV}^{\rm gas}$ sharply increases with $L_{\rm IR}$ in the center but varies more slowly in the disk. The best fit gives a steep relation of ${\rm log}(L_{\rm 0.5-2\,keV}^{\rm gas} /{\rm erg\,s^{-1}})=1.82\,{\rm log}(L_{\rm IR} /{L_{\rm \odot}})+22.26$ for the center of M51. The similar twofold correlations are also found in the $L_{\rm 0.5 - 2\,keV}^{\rm gas}$${-}$molecular line luminosity ($L^\prime_{\rm gas}$) relations for the four molecular emission lines CO(1-0), CO(2-1), HCN(1-0), and HCO$^+$(1-0). We demonstrate that the core-collapse supernovae (SNe) are the primary source of energy for heating gas in the galactic center of M51, leading to the observed steep $L_{\rm 0.5 - 2\,keV}^{\rm gas}$${-}$$L_{\rm IR}$ and $L_{\rm 0.5 - 2\,keV}^{\rm gas}$${-}$$L^\prime_{\rm gas}$ relations, as their X-ray radiation efficiencies ($\eta$ $\equiv$ $L_{\rm 0.5 - 2\,keV}^{\rm gas}$/$\dot{E}_\mathrm{SN}$) increase with the star formation rate surface densities, where $\dot{E}_\mathrm{SN}$ is the SN mechanical energy input rate.
Advances in imaging techniques for the study of individual bacteria and their pathophysiology
2025-01-03
Dohyeon Lee, Hyun-Seung Lee, Moosung Lee, Minhee Kang, Geon Kim, Tae Yeul Kim, Nam Yong Lee, YongKeun Park
Bacterial heterogeneity is pivotal for adaptation to diverse environments, posing significant challenges in microbial diagnostics and therapeutic interventions. Recent advancements in high-resolution optical microscopy have revolutionized our ability to observe and characterize individual bacteria, offering unprecedented insights into their metabolic states and behaviors at the single-cell level. This review discusses the transformative impact of various high-resolution imaging techniques, including fluorescence and label-free imaging, which have enhanced our understanding of bacterial pathophysiology. These methods provide detailed visualizations that are crucial for developing targeted treatments and improving clinical diagnostics. We highlight the integration of these imaging techniques with computational tools, which has facilitated rapid, accurate pathogen identification and real-time monitoring of bacterial responses to treatments. The ongoing development of these optical imaging technologies promises to significantly advance our understanding of microbiology and to catalyze the translation of these insights into practical healthcare solutions.
Millihertz Oscillations Near the Innermost Orbit of a Supermassive Black Hole
2025-01-03
Megan Masterson, Erin Kara, Christos Panagiotou, William N. Alston, Joheen Chakraborty, Kevin Burdge, Claudio Ricci, Sibasish Laha, Iair Arcavi, Riccardo Arcodia, S. Bradley Cenko, Andrew C. Fabian, Javier A. García, Margherita Giustini, Adam Ingram, Peter Kosec, Michael Loewenstein, Eileen T. Meyer, Giovanni Miniutti, Ciro Pinto, Ronald A. Remillard, Dev R. Sadaula, Onic I. Shuvo, Benny Trakhtenbrot, Jingyi Wang
Recent discoveries from time-domain surveys are defying our expectations for how matter accretes onto supermassive black holes (SMBHs). The increased rate of short-timescale, repetitive events around SMBHs, including the newly-discovered quasi-periodic eruptions (QPEs), are garnering further interest in stellar-mass companions around SMBHs and the progenitors to mHz frequency gravitational wave events. Here we report the discovery of a highly significant mHz Quasi-Periodic Oscillation (QPO) in an actively accreting SMBH, 1ES 1927+654, which underwent a major optical, UV, and X-ray outburst beginning in 2018. The QPO was first detected in 2022 with a roughly 18-minute period, corresponding to coherent motion on scales of less than 10 gravitational radii, much closer to the SMBH than typical QPEs. The period decreased to 7.1 minutes over two years with a decelerating period evolution ($\ddot{P} > 0$). This evolution has never been seen in SMBH QPOs or high-frequency QPOs in stellar mass black holes. Models invoking orbital decay of a stellar-mass companion struggle to explain the period evolution without stable mass transfer to offset angular momentum losses, while the lack of a direct analog to stellar mass black hole QPOs means that many instability models cannot explain all of the observed properties of the QPO in 1ES 1927+654. Future X-ray monitoring will test these models, and if it is a stellar-mass orbiter, the Laser Interferometer Space Antenna (LISA) should detect its low-frequency gravitational wave emission.
Tree-NET: Enhancing Medical Image Segmentation Through Efficient Low-Level Feature Training
2025-01-03
Orhan Demirci, Bulent Yilmaz
This paper introduces Tree-NET, a novel framework for medical image segmentation that leverages bottleneck feature supervision to enhance both segmentation accuracy and computational efficiency. While previous studies have employed bottleneck feature supervision, their applications have largely been limited to the training phase, offering no computational benefits during training or evaluation. To the best of our knowledge, this study is the first to propose a framework that incorporates two additional training phases for segmentation models, utilizing bottleneck features at both input and output stages. This approach significantly improves computational performance by reducing input and output dimensions with a negligible addition to parameter count, without compromising accuracy. Tree-NET features a three-layer architecture comprising Encoder-Net and Decoder-Net, which are autoencoders designed to compress input and label data, respectively, and Bridge-Net, a segmentation framework that supervises the bottleneck features. By focusing on dense, compressed representations, Tree-NET enhances operational efficiency and can be seamlessly integrated into existing segmentation models without altering their internal structures or increasing model size. We evaluate Tree-NET on two critical segmentation tasks -- skin lesion and polyp segmentation -- using various backbone models, including U-NET variants and Polyp-PVT. Experimental results demonstrate that Tree-NET reduces FLOPs by a factor of 4 to 13 and decreases memory usage, while achieving comparable or superior accuracy compared to the original architectures. These findings underscore Tree-NET's potential as a robust and efficient solution for medical image segmentation.
Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning
2025-01-03
Zhaoyuan Gu, Junheng Li, Wenlan Shen, Wenhao Yu, Zhaoming Xie, Stephen McCrory, Xianyi Cheng, Abdulaziz Shamsah, Robert Griffin, C. Karen Liu, Abderrahmane Kheddar, Xue Bin Peng, Yuke Zhu, Guanya Shi, Quan Nguyen, Gordon Cheng, Huijun Gao, Ye Zhao
Humanoid robots have great potential to perform various human-level skills. These skills involve locomotion, manipulation, and cognitive capabilities. Driven by advances in machine learning and the strength of existing model-based approaches, these capabilities have progressed rapidly, but often separately. Therefore, a timely overview of current progress and future trends in this fast-evolving field is essential. This survey first summarizes the model-based planning and control that have been the backbone of humanoid robotics for the past three decades. We then explore emerging learning-based methods, with a focus on reinforcement learning and imitation learning that enhance the versatility of loco-manipulation skills. We examine the potential of integrating foundation models with humanoid embodiments, assessing the prospects for developing generalist humanoid agents. In addition, this survey covers emerging research for whole-body tactile sensing that unlocks new humanoid skills that involve physical interactions. The survey concludes with a discussion of the challenges and future trends.
Human and Machine: How Software Engineers Perceive and Engage with AI-Assisted Code Reviews Compared to Their Peers
2025-01-03
Adam Alami, Neil A. Ernst
The integration of artificial intelligence (AI) continues to increase and evolve, including in software engineering (SE). This integration involves processes traditionally entrusted to humans, such as coding. However, the impact on socio-technical processes like code review remains underexplored. In this interview-based study (20 interviewees), we investigate how software engineers perceive and engage with Large Language Model (LLM)-assisted code reviews compared to human peer-led reviews. In this inherently human-centric process, we aim to understand how software engineers navigate the introduction of AI into collaborative workflows. We found that engagement in code review is multi-dimensional, spanning cognitive, emotional, and behavioral dimensions. The introduction of LLM-assisted review impacts some of these attributes. For example, there is less need for emotional regulation and coping mechanisms when dealing with an LLM compared to peers. However, the cognitive load sometimes is higher in dealing with LLM-generated feedback due to its excessive details. Software engineers use a similar sense-making process to evaluate and adopt feedback suggestions from their peers and the LLM. However, the LLM feedback adoption is constrained by trust and lack of context in the review. Our findings contribute to a deeper understanding of how AI tools are impacting SE socio-technical processes and provide insights into the future of AI-human collaboration in SE practices.
On the Statistical Complexity for Offline and Low-Adaptive Reinforcement Learning with Structures
2025-01-03
Ming Yin, Mengdi Wang, Yu-Xiang Wang
This article reviews the recent advances on the statistical foundation of reinforcement learning (RL) in the offline and low-adaptive settings. We will start by arguing why offline RL is the appropriate model for almost any real-life ML problems, even if they have nothing to do with the recent AI breakthroughs that use RL. Then we will zoom into two fundamental problems of offline RL: offline policy evaluation (OPE) and offline policy learning (OPL). It may be surprising to people that tight bounds for these problems were not known even for tabular and linear cases until recently. We delineate the differences between worst-case minimax bounds and instance-dependent bounds. We also cover key algorithmic ideas and proof techniques behind near-optimal instance-dependent methods in OPE and OPL. Finally, we discuss the limitations of offline RL and review a burgeoning problem of \emph{low-adaptive exploration} which addresses these limitations by providing a sweet middle ground between offline and online RL.
2025-01-03
Kai-Xuan Zhang, Giung Park, Youjin Lee, Beom Hyun Kim, Je-Geun Park
The magnetoelectric (ME) effect is a fundamental concept in modern condensed matter physics and represents the electrical control of magnetic polarisations or vice versa. Two-dimensional (2D) van-der-Waals (vdW) magnets have emerged as a new class of materials and exhibit novel ME effects with diverse manifestations. This review emphasizes some important recent discoveries unique to vdW magnets: multiferroicity on two dimensions, spin-charge correlation, atomic ME effect and current-induced intrinsic spin-orbit torque, and electrical gating control and magnetic control of their electronic properties. We also highlight the promising route of utilizing quantum magnetic hetero- or homo-structures to engineer the ME effect and corresponding spintronic and optoelectronic device applications. Due to the intrinsic two-dimensionality, vdW magnets with those ME effects are expected to form a new, exciting research direction.
Sporadic Dips from Extended Debris Transiting the Metal-Rich White Dwarf SBSS 1232+563
2025-01-03
J. J. Hermes, Joseph A. Guidry, Zachary P. Vanderbosch, Mariona Badenas-Agusti, Siyi Xu, Malia L. Kao, Antonio C. Rodriguez, Keith Hawkins
We present the discovery of deep but sporadic transits in the flux of SBSS 1232+563, a metal-rich white dwarf polluted by disrupted exoplanetary debris. Nearly 25 years of photometry from multiple sky surveys reveal evidence of occasional dimming of the white dwarf, most notably evident in an 8-months-long event in 2023 that caused a >40% drop in flux from the star. In-transit follow-up shows additional short-timescale (minutes- to hours-long) dimming events. TESS photometry suggests a coherent 14.842-hr signal that could represent the dominant orbital period of debris. Six low-resolution spectra collected at various transit depths over two decades show no evidence of significant changes in the observed elemental abundances. SBSS 1232+563 demonstrates that debris transits around white dwarfs can be sporadic, with many years of inactivity before large-amplitude dimming events.
Inferring and predicting Fried physical frailty phenotype deficits
2025-01-03
Glen Pridham, Kenneth Rockwood, Andrew Rutenberg
We predict the Fried physical frailty phenotype health deficits (FPFP5: slow gait, weakness, weight loss, low activity, and exhaustion) using two measures of frailty: frailty index (FI) or frailty phenotype (FP). The FP theorizes that the FPFP5 are mutually dependent through shared etiology and positive feedbacks, so that the total number of FPFP5 deficits (NFPFP5) should be highly predictive of existing deficits. Alternatively, the FI theorizes that strong mutual dependencies exist between \emph{all} age-related health deficits, so that the FI would be more predictive. We investigated predictive models of FPFP5 using FI or NFPFP5 in the Health and Retirement Study (HRS), the English Longitudinal Study of Aging (ELSA), and the National Health and Nutrition Examination Survey (NHANES). We find that the FI, chronological age, and current deficit state are all important predictors of future FPFP5 deficits. Notably, the FI consistently out-performed NFPFP5, raising questions regarding FPFP5 causal connections and how best to measure the physical component of frailty. We discuss implications for both FPFP5 forecasting, and inference when data are missing or incomplete.
A Survey on Large Language Models with some Insights on their Capabilities and Limitations
2025-01-03
Andrea Matarazzo, Riccardo Torlone
The rapid advancement of artificial intelligence, particularly with the development of Large Language Models (LLMs) built on the transformer architecture, has redefined the capabilities of natural language processing. These models now exhibit remarkable performance across various language-related tasks, such as text generation, question answering, translation, and summarization, often rivaling human-like comprehension. More intriguingly, LLMs have demonstrated emergent abilities extending beyond their core functions, showing proficiency in tasks like commonsense reasoning, code generation, and arithmetic. This survey paper explores the foundational components, scaling mechanisms, and architectural strategies that drive these capabilities. Emphasizing models like GPT and LLaMA, we analyze the impact of exponential data and computational growth on LLM performance, while also addressing the trade-offs associated with scaling. We also examine LLM applications across sectors, such as healthcare, finance, education, and law, highlighting their adaptability and potential to solve domain-specific challenges. Central to this work are the questions of how LLMs generalize across diverse tasks, exhibit planning, and reasoning abilities, and whether these emergent abilities can be systematically elicited or enhanced. In particular, we provide some insights into the CoT (Chain of Thought) and PoT (Plan of Thought) abilities within LLMs, focusing on how pre-training data influences their emergence. Additionally, we investigate LLM-modulo frameworks that integrate external systems, allowing LLMs to handle complex, dynamic tasks. By analyzing these factors, this paper aims to foster the ongoing discussion on the capabilities and limits of LLMs, promoting their responsible development and application in novel and increasingly complex environments.
We present an updated analysis of cosmological models with very low reheating scenarios ($T_\text{RH} \sim \mathcal{O}(\text{MeV})$). Our study includes a more precise computation of neutrino distribution functions, leveraging the latest datasets from cosmological surveys. We perform a joint analysis that combines constraints from Big Bang Nucleosynthesis, the Cosmic Microwave Background, and galaxy surveys, alongside separate investigations of these datasets, carefully assessing the impact of different choices of priors. At the $95\%$ confidence level, we establish a lower bound on the reheating temperature of $T_\text{RH} > 5.96 \; \text{MeV} $, representing the most stringent constraint to date.
Tracking behavioural differences across chronotypes: A case study in Finland using Oura rings
2025-01-02
Chandreyee Roy, Kunal Bhattacharya, Kimmo Kaski
Non-invasive mobile wearables like fitness trackers, smart watches and rings allow an easy and less expensive approach to study everyday human behaviour. This alternative approach not only supplements clinical studies, but also provides an opportunity to overcome some of the limitations in them. One of the major challenges faced by them is studying long-term human health and behaviour in realistic settings. Here we have utilised Oura rings to obtain granular data from nineteen healthy participants over the span of one year (October 2023 - September 2024) along with monthly surveys for nine months to track their subjective stress within the duration of the study. We have studied longitudinal sleep and activity patterns of three chronotype groups of participating individuals: morning type (MT), neither type (NT) and evening type (ET). We find that while ET individuals do not seem to lead as healthy life as the MT or NT individuals, they have seemingly improved their habits during the duration of the study. We also show that the Daylight Saving Time changes affect the chronotypes differently. Finally, by utilising mixed effects regression model, we have shown that the stress an individual experiences has a significant correlation with his or her total sleep duration, monthly survey response time, and age.
Large Vision-Language Model Alignment and Misalignment: A Survey Through the Lens of Explainability
2025-01-02
Dong Shu, Haiyan Zhao, Jingyu Hu, Weiru Liu, Lu Cheng, Mengnan Du
Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in processing both visual and textual information. However, the critical challenge of alignment between visual and linguistic representations is not fully understood. This survey presents a comprehensive examination of alignment and misalignment in LVLMs through an explainability lens. We first examine the fundamentals of alignment, exploring its representational and behavioral aspects, training methodologies, and theoretical foundations. We then analyze misalignment phenomena across three semantic levels: object, attribute, and relational misalignment. Our investigation reveals that misalignment emerges from challenges at multiple levels: the data level, the model level, and the inference level. We provide a comprehensive review of existing mitigation strategies, categorizing them into parameter-frozen and parameter-tuning approaches. Finally, we outline promising future research directions, emphasizing the need for standardized evaluation protocols and in-depth explainability studies.
Inequality of opportunity under current China's license plate policy
2025-01-02
Nina Xiaochun Sun, Zaifang He, Yi Pang
Measuring inequality of opportunities has long been a challenging and open problem, primarily due to the limitations associated with individual-level data. In this study, we utilize data obtained from vehicle license plates in a comprehensive survey (17258 vehicles from 6 major cities in China) to evaluate the inequality of opportunities in the country. In our context, we define inequality of opportunity as the scenario where relatively expensive vehicles have a higher likelihood of being paired with license plates featuring 'Lucky Numbers'. To quantify this, we propose a lucky-number-based opportunity Gini coefficient. Through the calculation of the opportunity Gini coefficient, we observe a significant and positive correlation between opportunity inequality and income inequality. Particularly noteworthy is our finding that the advancement of technology, exemplified by the widespread adoption of new energy vehicles, can substantially reduce the inequality of opportunity. Taking incorporation of a random lottery process before acquiring a motor vehicle in Beijing and Shanghai as a natural experiment, our empirical results support the argument that, in terms of equality, employing random drawing is a fair and equitable approach for allocating scarce resources.
Language Models for Code Optimization: Survey, Challenges and Future Directions
2025-01-02
Jingzhi Gong, Vardan Voskanyan, Paul Brookes, Fan Wu, Wei Jie, Jie Xu, Rafail Giavrimis, Mike Basios, Leslie Kanthan, Zheng Wang
Language models (LMs) built upon deep neural networks (DNNs) have recently demonstrated breakthrough effectiveness in software engineering tasks such as code generation, completion, and repair. This has paved the way for the emergence of LM-based code optimization techniques, which are crucial for enhancing the performance of existing programs, such as accelerating program execution time. However, a comprehensive survey dedicated to this specific application has been lacking. To fill this gap, we present a systematic literature review of over 50 primary studies, identifying emerging trends and addressing 11 specialized questions. Our findings reveal five critical open challenges, such as balancing model complexity with practical usability, cross-language/performance generalizability, and building trust in AI-driven solutions. Furthermore, we provide eight future research directions to facilitate more efficient, robust, and reliable LM-based code optimization. Thereby, this study aims to provide actionable insights and foundational references for both researchers and practitioners in this rapidly evolving field.
Digital Guardians: Can GPT-4, Perspective API, and Moderation API reliably detect hate speech in reader comments of German online newspapers?
2025-01-02
Manuel Weber, Moritz Huber, Maximilian Auch, Alexander Döschl, Max-Emanuel Keller, Peter Mandl
In recent years, toxic content and hate speech have become widespread phenomena on the internet. Moderators of online newspapers and forums are now required, partly due to legal regulations, to carefully review and, if necessary, delete reader comments. This is a labor-intensive process. Some providers of large language models already offer solutions for automated hate speech detection or the identification of toxic content. These include GPT-4o from OpenAI, Jigsaw's (Google) Perspective API, and OpenAI's Moderation API. Based on the selected German test dataset HOCON34k, which was specifically created for developing tools to detect hate speech in reader comments of online newspapers, these solutions are compared with each other and against the HOCON34k baseline. The test dataset contains 1,592 annotated text samples. For GPT-4o, three different promptings are used, employing a Zero-Shot, One-Shot, and Few-Shot approach. The results of the experiments demonstrate that GPT-4o outperforms both the Perspective API and the Moderation API, and exceeds the HOCON34k baseline by approximately 5 percentage points, as measured by a combined metric of MCC and F2-score.
About the Keplerization of motion in any central force field
2025-01-02
Christian Carimalo
The method of keplerization of one-body motion in any central force field, introduced by Martinusi and Gurfil in 2012, is reviewed and reformulated into a general homogenization method which applies to any kind of bounded motion. It is also shown how this extended method provides a proof of the existence of a dynamical symmetry group and how it can be used to extend that group to a global symmetry group, for any such system
This paper provides an in-depth review and discussion of the state of the art in redundancy mitigation for the vehicular Collective Perception Service (CPS). We focus on the evolutionary differences between the redundancy mitigation rules proposed in 2019 in ETSI TR 103 562 versus the 2023 technical specification ETSI TS 103 324, which uses a Value of Information (VoI) based mitigation approach. We also critically analyse the academic literature that has sought to quantify the communication challenges posed by the CPS and present a unique taxonomy of the redundancy mitigation approaches proposed using three distinct classifications: object inclusion filtering, data format optimisation, and frequency management. Finally, this paper identifies open research challenges that must be adequately investigated to satisfactorily deploy CPS redundancy mitigation measures. Our critical and comprehensive evaluation serves as a point of reference for those undertaking research in this area.
Science of a coffee cup: a physicist walks into a bar...
2025-01-02
Aleksi Bossart, Romain Fleury, Benjamin Apffel
... and annoys everyone with unsolicited experiments. The present paper proposes a short pedagogical review of the various phenomena that can be observed in a coffee cup with little to no equipment. The physical domains spanned include acoustics, optics and, of course, fluid mechanics. The variety of experimental and theoretical techniques introduced throughout the paper makes it suitable for a broad audience. For each topic, we first propose an experimental realization before introducing a minimal model to explain the observations. We end each section by discussing more advanced works existing in the literature as well as related applications. We provide detailed experimental procedures and videos of the experiments that can be freely used for teaching purposes. The phenomena presented here also show remarkable efficiency as icebreakers for morning coffee in laboratories or conferences.
Deep Learning in Palmprint Recognition-A Comprehensive Survey
2025-01-02
Chengrui Gao, Ziyuan Yang, Wei Jia, Lu Leng, Bob Zhang, Andrew Beng Jin Teoh
Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition.
Graphs are data structures used to represent irregular networks and are prevalent in numerous real-world applications. Previous methods directly model graph structures and achieve significant success. However, these methods encounter bottlenecks due to the inherent irregularity of graphs. An innovative solution is converting graphs into textual representations, thereby harnessing the powerful capabilities of Large Language Models (LLMs) to process and comprehend graphs. In this paper, we present a comprehensive review of methodologies for applying LLMs to graphs, termed LLM4graph. The core of LLM4graph lies in transforming graphs into texts for LLMs to understand and analyze. Thus, we propose a novel taxonomy of LLM4graph methods in the view of the transformation. Specifically, existing methods can be divided into two paradigms: Graph2text and Graph2token, which transform graphs into texts or tokens as the input of LLMs, respectively. We point out four challenges during the transformation to systematically present existing methods in a problem-oriented perspective. For practical concerns, we provide a guideline for researchers on selecting appropriate models and LLMs for different graphs and hardware constraints. We also identify five future research directions for LLM4graph.
It has long been argued that the action of the Schwinger mechanism in the gauge sector of Quantum Chromodynamics leads to the generation of a gluon mass scale. Within this scenario, the analytic structure of the fundamental vertices is modified by the creation of scalar colored excitations with vanishing mass. In the limit of zero momentum transfer, these terms act as massless poles, providing the required conditions for the infrared stabilization of the gluon propagator, and producing a characteristic displacement to the associated Ward identities. In this article we offer an extensive overview of the salient notions and techniques underlying this dynamical picture. We place particular emphasis on recent developments related to the exact renormalization of the mass, the nonlinear nature of the pole equation, and the key role played by Fredholm's alternatives theorem.
SpecPT (Spectroscopy Pre-trained Transformer) Model for Extragalactic Spectroscopy: I. Architecture and Automated Redshift Measurement
2025-01-02
Rohan Pattnaik, Jeyhan S. Kartaltepe, Clive Binu
We introduce the Spectroscopy Pre-trained Transformer (SpecPT), a transformer-based model designed to analyze spectroscopic data, with applications in spectrum reconstruction and redshift measurement. Using the Early Data Release (EDR) of the DESI survey, we evaluate SpecPT's performance on two distinct datasets: the Bright Galaxy Survey (BGS) and Emission Line Galaxy (ELG) samples. SpecPT successfully reconstructs spectra, accurately capturing emission lines, absorption features, and continuum shapes while effectively reducing noise. For redshift prediction, SpecPT achieves competitive accuracy, with Normalized Median Absolute Deviation (NMAD) values of 0.0006 and 0.0008, and catastrophic outlier fractions of 0.20% and 0.80% for BGS and ELG, respectively. Notably, SpecPT performs consistently well across the full redshift range ($0 < z < 1.6$), demonstrating its versatility and robustness. By leveraging its learned latent representations, SpecPT lays the groundwork for a foundational spectroscopic model, with potential applications in outlier detection, interstellar medium (ISM) property estimation, and transfer learning to other datasets. This work represents a first step in building a generalized framework for spectroscopic analysis, capable of scaling to the full DESI dataset and beyond.
Higher serum 25(OH)D concentration is associated with lower risk of metabolic syndrome among Aboriginal and Torres Strait Islander peoples in Australia
2025-01-02
Belinda Neo, Dale Tilbrook, Noel Nannup, John Jacky, Carol Michie, Cindy Prior, Eleanor Dunlop, Brad Farrant, Won Sun Chen, Carrington C. J. Shepherd, Lucinda J. Black, .
Although previous observational studies have shown associations between serum 25-hydroxyvitamin D (25(OH)D) concentration and metabolic syndrome, this association has not yet been investigated among Aboriginal and Torres Strait Islander peoples. We aimed to investigate the association between serum 25(OH)D concentration and metabolic syndrome and its risk factors in this population group. We used cross-sectional data from the 2012-2013 Australian Aboriginal and Torres Strait Islander Health Survey. Metabolic syndrome is defined as having 3 or more risk factors: elevated waist circumference, elevated triglycerides, low high-density lipoprotein (HDL) cholesterol, elevated blood pressure, or elevated fasting blood glucose. We used binomial logistic regression to test associations between serum 25(OH)D concentration and metabolic syndrome, and multiple linear regression to test associations between serum 25(OH)D concentration and each risk factor. We included the following covariates: age, sex, smoking status, education level, socio-economic status, remoteness of location, season, and body mass index (BMI). After adjusting for covariates, we found that each 10 nmol/L increase in serum 25(OH)D concentration was statistically significantly associated with a 16% lower risk of metabolic syndrome (odds ratio: 0.84, 95% confidence interval: 0.76, 0.92) and a 2.1 cm (95% confidence interval: 1.65, 2.57) lower waist circumference (BMI was not included in the model for waist circumference). We found small inverse associations between serum 25(OH)D concentration and all other risk factors except systolic blood pressure. Given that higher serum 25(OH)D concentration may confer metabolic health benefits, promoting vitamin D sufficiency may be beneficial for this population.
ValuesRAG: Enhancing Cultural Alignment Through Retrieval-Augmented Contextual Learning
2025-01-02
Wonduk Seo, Zonghao Yuan, Yi Bu
Cultural values alignment in Large Language Models (LLMs) is a critical challenge due to their tendency to embed Western-centric biases from training data, leading to misrepresentations and fairness issues in cross-cultural contexts. Recent approaches, such as role-assignment and few-shot learning, often struggle with reliable cultural alignment as they heavily rely on pre-trained knowledge, lack scalability, and fail to capture nuanced cultural values effectively. To address these issues, we propose ValuesRAG, a novel and effective framework that applies Retrieval-Augmented Generation (RAG) with in-context learning to integrate cultural and demographic knowledge dynamically during text generation. Leveraging the World Values Survey (WVS) dataset, ValuesRAG first generates summaries of values for each individual. Subsequently, we curated several representative regional datasets to serve as test datasets and retrieve relevant summaries of values based on demographic features, followed by a reranking step to select the top-k relevant summaries. ValuesRAG consistently outperforms baseline methods, both in the main experiment and in the ablation study where only the values summary was provided, highlighting ValuesRAG's potential to foster culturally aligned AI systems and enhance the inclusivity of AI-driven applications.
Reasoning is fundamental to human intelligence, and critical for problem-solving, decision-making, and critical thinking. Reasoning refers to drawing new conclusions based on existing knowledge, which can support various applications like clinical diagnosis, basic education, and financial analysis. Though a good number of surveys have been proposed for reviewing reasoning-related methods, none of them has systematically investigated these methods from the viewpoint of their dependent knowledge base. Both the scenarios to which the knowledge bases are applied and their storage formats are significantly different. Hence, investigating reasoning methods from the knowledge base perspective helps us better understand the challenges and future directions. To fill this gap, this paper first classifies the knowledge base into symbolic and parametric ones. The former explicitly stores information in human-readable symbols, and the latter implicitly encodes knowledge within parameters. Then, we provide a comprehensive overview of reasoning methods using symbolic knowledge bases, parametric knowledge bases, and both of them. Finally, we identify the future direction toward enhancing reasoning capabilities to bridge the gap between human and machine intelligence.
State-of-the-art AI-based Learning Approaches for Deepfake Generation and Detection, Analyzing Opportunities, Threading through Pros, Cons, and Future Prospects
2025-01-02
Harshika Goyal, Mohammad Saif Wajid, Mohd Anas Wajid, Akib Mohi Ud Din Khanday, Mehdi Neshat, Amir Gandomi
The rapid advancement of deepfake technologies, specifically designed to create incredibly lifelike facial imagery and video content, has ignited a remarkable level of interest and curiosity across many fields, including forensic analysis, cybersecurity and the innovative creation of digital characters. By harnessing the latest breakthroughs in deep learning methods, such as Generative Adversarial Networks, Variational Autoencoders, Few-Shot Learning Strategies, and Transformers, the outcomes achieved in generating deepfakes have been nothing short of astounding and transformative. Also, the ongoing evolution of detection technologies is being developed to counteract the potential for misuse associated with deepfakes, effectively addressing critical concerns that range from political manipulation to the dissemination of fake news and the ever-growing issue of cyberbullying. This comprehensive review paper meticulously investigates the most recent developments in deepfake generation and detection, including around 400 publications, providing an in-depth analysis of the cutting-edge innovations shaping this rapidly evolving landscape. Starting with a thorough examination of systematic literature review methodologies, we embark on a journey that delves into the complex technical intricacies inherent in the various techniques used for deepfake generation, comprehensively addressing the challenges faced, potential solutions available, and the nuanced details surrounding manipulation formulations. Subsequently, the paper is dedicated to accurately benchmarking leading approaches against prominent datasets, offering thorough assessments of the contributions that have significantly impacted these vital domains. Ultimately, we engage in a thoughtful discussion of the existing challenges, paving the way for continuous advancements in this critical and ever-dynamic study area.
Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review
2025-01-02
Yan Gu, Zhaoze Liu, Shuhong Dai, Cong Liu, Ying Wang, Shen Wang, Georgios Theodoropoulos, Long Cheng
Cloud computing has revolutionized the provisioning of computing resources, offering scalable, flexible, and on-demand services to meet the diverse requirements of modern applications. At the heart of efficient cloud operations are job scheduling and resource management, which are critical for optimizing system performance and ensuring timely and cost-effective service delivery. However, the dynamic and heterogeneous nature of cloud environments presents significant challenges for these tasks, as workloads and resource availability can fluctuate unpredictably. Traditional approaches, including heuristic and meta-heuristic algorithms, often struggle to adapt to these real-time changes due to their reliance on static models or predefined rules. Deep Reinforcement Learning (DRL) has emerged as a promising solution to these challenges by enabling systems to learn and adapt policies based on continuous observations of the environment, facilitating intelligent and responsive decision-making. This survey provides a comprehensive review of DRL-based algorithms for job scheduling and resource management in cloud computing, analyzing their methodologies, performance metrics, and practical applications. We also highlight emerging trends and future research directions, offering valuable insights into leveraging DRL to advance both job scheduling and resource management in cloud computing.
These lecture notes are intended to cover some introductory topics in stochastic simulation for scientific computing courses offered by the IT department at Uppsala University, as taught by the author. Basic concepts in probability theory are provided in the Appendix A, which you may review before starting the upcoming sections or refer to as needed throughout the text.
Photometric Objects Around Cosmic Webs (PAC). VII. Disentangling Mass and Environment Quenching with the Aid of Galaxy-halo Connection in Simulations
2025-01-02
Yun Zheng, Kun Xu, Donghai Zhao, Y. P. Jing, Hongyu Gao, Xiaolin Luo, Ming Li
Star formation quenching in galaxies is a critical process in galaxy formation. It is widely believed that the quenching process is dominated by the mass of galaxies and/or their environment. In Paper V, we addressed the challenge to disentangle the effects of mass and environment by employing the PAC method, which combines spectroscopic and deep photometric surveys. This approach enabled us to measure the excess surface density of blue and red galaxies around massive central galaxies down to $10^{9.0}M_{\odot}$. However, it is not straightforward to completely separate the two effects.To address this issue, in this paper, we derive the average quenched fraction of central (isolated) galaxies, $\bar{f}_{\mathrm{q}}^{\mathrm{cen}}(M_{*})$, by combining the 3D quenched fraction distribution $f^{\mathrm{sat}}_{\mathrm{q}}(r; M_{*,\mathrm{cen}}, M_{*,\mathrm{sat}})$, reconstructed from the $\bar{n}_2w_{\mathrm{p}}(r_{\mathrm{p}})$ measurements, with the stellar mass-halo mass relation in N-body simulations from Paper IV, and the observed total quenched fraction, $\bar{f}_{\mathrm{q}}^{\mathrm{all}}(M_{*})$. Using $f^{\mathrm{sat}}_{\mathrm{q}}(r;M_{*,\mathrm{cen}},M_{*,\mathrm{sat}})$, $\bar{f}_{\mathrm{q}}^{\mathrm{cen}}(M_{*})$, and the galaxy-halo connection, we assign a quenched probability to each (sub)halo in the simulation, enabling a comprehensive study of galaxy quenching. We find that the mass-quenched fraction increases from 0.3 to 0.87 across the stellar mass range $[10^{9.5}, 10^{11.0}]M_{\odot}$, while the environmental quenched fraction decreases from 0.17 to 0.03. The mass effect dominates galaxy quenching across the entire stellar mass range we studied. Moreover, more massive host halos are more effective at quenching their satellite galaxies, while satellite stellar mass has minimal influence on environmental quenching.
The $z \gtrsim 9$ galaxy UV luminosity function from the JWST Advanced Deep Extragalactic Survey: insights into early galaxy evolution and reionization
2025-01-02
Lily Whitler, Daniel P. Stark, Michael W. Topping, Brant Robertson, Marcia Rieke, Kevin N. Hainline, Ryan Endsley, Zuyi Chen, William M. Baker, Rachana Bhatawdekar, Andrew J. Bunker, Stefano Carniani, Stéphane Charlot, Jacopo Chevallard, Emma Curtis-Lake, Eiichi Egami, Daniel J. Eisenstein, Jakob M. Helton, Zhiyuan Ji, Benjamin D. Johnson, Pablo G. Pérez-González, Pierluigi Rinaldi, Sandro Tacchella, Christina C. Williams, Christopher N. A. Willmer, Chris Willott, Joris Witstok
The high-redshift UV luminosity function provides important insights into the evolution of early galaxies. JWST has revealed an unexpectedly large population of bright ($M_\mathrm{UV} \lesssim -20$) galaxies at $z\gtrsim10$, implying fundamental changes in the star forming properties of galaxies at increasingly early times. However, constraining the fainter population ($M_\mathrm{UV} \gtrsim -18$) has been more challenging. In this work, we present the $z\gtrsim9$ UV luminosity function from the JWST Advanced Deep Extragalactic Survey. We calculate the UV luminosity function from several hundred $z\gtrsim9$ galaxy candidates that reach UV luminosities of $M_\mathrm{UV}\sim-17$ in redshift bins of $z\sim9-12$ (309 candidates) and $z\sim12-16$ (63 candidates). We search for candidates at $z\sim16-22.5$ and find none. We also estimate the $z\sim14-16$ luminosity function from the $z\geq14$ subset of the $z\sim12-16$ sample. Consistent with other measurements, we find an excess of bright galaxies that is in tension with many theoretical models, especially at $z\gtrsim12$. However, we also find high number densities at $-18\lesssim M_\mathrm{UV} \lesssim-17$, suggesting that there is a larger population of faint galaxies than expected, as well as bright ones. From our parametric fits for the luminosity function, we find steep faint end slopes of $-2.5\lesssim\alpha\lesssim-2.3$, suggesting a large population of faint ($M_\mathrm{UV} \gtrsim -17$) galaxies. Combined, the high normalization and steep faint end slope of the luminosity function could imply that the reionization process is appreciably underway as early as $z=10$.
Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice
2025-01-02
Federico Ravenda, Seyed Ali Bahrainian, Andrea Raballo, Antonietta Mira, Noriko Kando
In psychological practice, standardized questionnaires serve as essential tools for assessing mental constructs (e.g., attitudes, traits, and emotions) through structured questions (aka items). With the increasing prevalence of social media platforms where users share personal experiences and emotions, researchers are exploring computational methods to leverage this data for rapid mental health screening. In this study, we propose a novel adaptive Retrieval-Augmented Generation (RAG) approach that completes psychological questionnaires by analyzing social media posts. Our method retrieves the most relevant user posts for each question in a psychological survey and uses Large Language Models (LLMs) to predict questionnaire scores in a zero-shot setting. Our findings are twofold. First we demonstrate that this approach can effectively predict users' responses to psychological questionnaires, such as the Beck Depression Inventory II (BDI-II), achieving performance comparable to or surpassing state-of-the-art models on Reddit-based benchmark datasets without relying on training data. Second, we show how this methodology can be generalized as a scalable screening tool, as the final assessment is systematically derived by completing standardized questionnaires and tracking how individual item responses contribute to the diagnosis, aligning with established psychometric practices.
Photometry of outer Solar System objects from the Dark Energy Survey II: a joint analysis of trans-Neptunian absolute magnitudes, colors, lightcurves and dynamics
2025-01-02
Pedro H. Bernardinelli, Gary M. Bernstein, T. M. C. Abbott, M. Aguena, S. S. Allam, D. Brooks, A. Carnero Rosell, J. Carretero, L. N. da Costa, M. E. S. Pereira, T. M. Davis, J. De Vicente, S. Desai, H. T. Diehl, P. Doel, S. Everett, B. Flaugher, J. Frieman, J. García-Bellido, E. Gaztanaga, R. A. Gruendl, G. Gutierrez, K. Herner, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, K. Kuehn, O. Lahav, S. Lee, J. L. Marshall, J. Mena-Fernández, R. Miquel, J. Myles, A. A. Plazas Malagón, S. Samuroff, E. Sanchez, B. Santiago, I. Sevilla-Noarbe, M. Smith, E. Suchyta, G. Tarle, D. L. Tucker, V. Vikram, A. R. Walker, N. Weaverdyck
For the 696 trans-Neptunian objects (TNOs) with absolute magnitudes $5.5 < H_r < 8.2$ detected in the Dark Energy Survey (DES), we characterize the relationships between their dynamical state and physical properties -- namely $H_r$, indicating size; colors, indicating surface composition; and flux variation semi-amplitude $A$, indicating asphericity and surface inhomogeneity. We seek ``birth'' physical distributions that can recreate these parameters in every dynamical class. We show that the observed colors of these TNOs are consistent with 2 Gaussian distributions in $griz$ space, ``near-IR bright'' (NIRB) and ``near-IR faint'' (NIRF), presumably an inner and outer birth population, respectively. We find a model in which both the NIRB and NIRF $H_r$ and $A$ distributions are independent of current dynamical states, supporting their assignment as birth populations. All objects are consistent with a common rolling $p(H_r)$, but NIRF objects are significantly more variable. Cold classicals (CCs) are purely NIRF, while hot classical (HC), scattered, and detached TNOs are consistent with $\approx70\%$ NIRB, and resonances' NIRB fractions show significant variation. The NIRB component of the HCs and of some resonances have broader inclination distributions than the NIRFs, i.e. their current dynamics retains information about birth location. We find evidence for radial stratification within the birth NIRB population, in that HC NIRBs are on average redder than detached or scattered NIRBs; a similar effect distinguishes CCs from other NIRFs. We estimate total object counts and masses of each class within our $H_r$ range. These results will strongly constrain models of the outer solar system.
Bounds and Optimal Results for the Total Irregularity Measure
2025-01-02
Akbar Ali, Darko Dimitrov, Tamás Réti, Abeer M. Albalahi, Amjad E. Hamza
A (molecular) graph in which all vertices have the same degree is known as a regular graph. According to Gutman, Hansen, and M\'elot [J. Chem. Inf. Model. 45 (2005) 222-230], it is of interest to measure the irregularity of nonregular molecular graphs both for descriptive purposes and for QSAR/QSPR studies. The graph invariants that can be used to measure the irregularity of graphs are referred to as irregularity measures. One of the well-studied irregularity measures is the ``total irregularity'' measure, which was introduced about a decade ago. Bounds and optimization problems for this measure have already been extensively studied. A considerable number of existing results (concerning this measure) also hold for molecular graphs; particularly, the ones regarding lower bounds and minimum values of the mentioned measure. The primary objective of the present review article is to collect the existing bounds and optimal results concerning the total irregularity measure. Several open problems related to the existing results on the total irregularity measure are also given.
Review Article: Integral Role of Physics in Advancing Pharmacy Education and Research
2025-01-02
İzzet Sakallı
Physics plays a fundamental role in advancing pharmacy education and research, providing theoretical underpinnings and practical tools necessary to address complex challenges in drug development, delivery, and diagnostics. This review explores the integration of physics into the pharmacy curriculum, highlighting how principles such as fluid dynamics, thermodynamics, and spectroscopy (Tokgoz and Sakalli, 2018) enhance students' critical thinking and problem-solving skills. Additionally, it examines the pivotal contributions of physics to pharmaceutical research, including molecular modeling, imaging technologies like MRI and PET, and nanotechnology-driven drug delivery systems. Despite challenges in interdisciplinary collaboration and resource allocation, innovative teaching strategies and laboratory based learning are shown significant promise. Looking forward, the convergence of artificial intelligence and physics, as highlighted by recent Nobel Prize achievements in attosecond physics and bioorthogonal chemistry, is set to revolutionize pharmaceutical sciences, offering unprecedented precision and efficiency in drug discovery and personalized medicine.
Maximum force conjecture in curved spacetimes of stable self-gravitating matter configurations
2025-01-02
Shahar Hod
Gibbons and Schiller have raised the physically interesting conjecture that forces in general relativity are bounded from above by the mathematically compact relation ${\cal F}\leq c^4/4G$. In the present compact paper we explicitly prove, using the non-linearly coupled Einstein-matter field equations, that the force function ${\cal F}\equiv 4\pi r^2 p(r)$ in {\it stable} self-gravitating horizonless matter configurations is characterized by the upper bound ${\cal F}\leq c^4/G$ [here $p(r)$ is the radial pressure inside the self-gravitating matter configuration].
Gravitational Waves of GUT Phase Transition during Inflation
2025-01-02
Xi-He Hu, Ye-Ling Zhou
GUT phase transition during inflation solves the GUT monopole problem on one hand. On the other hand, gravitational waves (GWs) from such a phase transition, if it is first-order, are redshifted and deformed, and might be observed today in GW observatories. We review the formalism of inflated GWs and derive the general deformation function between inflated and uninflated GW spectra in the instant-source application. It is valid for any e-folding number of instant source. Applying the formalism to GUT phase transition, we find that the e-folding number at 15 or 25 can shift the GWs to 10 Hz or mHz hands, respectively, which might be tested in the future ground-based or space-based interferometers. We further generalise the discussion to inflated GWs via phase transition below the GUT scale. It is worth mentioning that, due to the deformation of the spectrum, the peak of inflated GWs is not simply a redshift of the peak of uninflated GWs.
A Link Between White Dwarf Pulsars and Polars: Multiwavelength Observations of the 9.36-Minute Period Variable Gaia22ayj
2025-01-02
Antonio C. Rodriguez, Kareem El-Badry, Pasi Hakala, Pablo Rodríguez-Gil, Tong Bao, Ilkham Galiullin, Jacob A. Kurlander, Casey J. Law, Ingrid Pelisoli, Matthias R. Schreiber, Kevin Burdge, Ilaria Caiazzo, Jan van Roestel, Paula Szkody, Andrew J. Drake, David A. H. Buckley, Stephen B. Potter, Boris Gaensicke, Kaya Mori, Eric C. Bellm, Shrinivas R. Kulkarni, Thomas A. Prince, Matthew Graham, Mansi M. Kasliwal, Sam Rose, Yashvi Sharma, Tomás Ahumada, Shreya Anand, Akke Viitanen, Avery Wold, Tracy X. Chen, Reed Riddle, Roger Smith
White dwarfs (WDs) are the most abundant compact objects, and recent surveys have suggested that over a third of WDs in accreting binaries host a strong (B $\gtrsim$ 1 MG) magnetic field. However, the origin and evolution of WD magnetism remain under debate. Two WD pulsars, AR Sco and J191213.72-441045.1 (J1912), have been found, which are non-accreting binaries hosting rapidly spinning (1.97-min and 5.30-min, respectively) magnetic WDs. The WD in AR Sco is slowing down on a $P/\dot{P}\approx 5.6\times 10^6$ yr timescale. It is believed they will eventually become polars, accreting systems in which a magnetic WD (B $\approx 10-240$ MG) accretes from a Roche lobe-filling donor spinning in sync with the orbit ($\gtrsim 78$ min). Here, we present multiwavelength data and analysis of Gaia22ayj, which outbursted in March 2022. We find that Gaia22ayj is a magnetic accreting WD that is rapidly spinning down ($P/\dot{P} = 6.1^{+0.3}_{-0.2}\times 10^6$ yr) like WD pulsars, but shows clear evidence of accretion, like polars. Strong linear polarization (40%) is detected in Gaia22ayj; such high levels have only been seen in the WD pulsar AR Sco and demonstrate the WD is magnetic. High speed photometry reveals a 9.36-min period accompanying a high amplitude ($\sim 2$ mag) modulation. We associate this with a WD spin or spin-orbit beat period, not an orbital period as was previously suggested. Fast (60-s) optical spectroscopy reveals a broad ``hump'', reminiscent of cyclotron emission in polars, between 4000-8000 Angstrom. We find an X-ray luminosity of $L_X = 2.7_{-0.8}^{+6.2}\times10^{32} \textrm{ erg s}^{-1}$ in the 0.3-8 keV energy range, while two VLA radio campaigns resulted in a non-detection with a $F_r < 15.8\mu\textrm{Jy}$ 3$ \sigma$ upper limit. The shared properties of both WD pulsars and polars suggest that Gaia22ayj is a missing link between the two classes of magnetic WD binaries.
A Survey of Deep Learning Methods in Protein Bioinformatics and its Impact on Protein Design
2025-01-02
Weihang Dai
Proteins are sequences of amino acids that serve as the basic building blocks of living organisms. Despite rapidly growing databases documenting structural and functional information for various protein sequences, our understanding of proteins remains limited because of the large possible sequence space and the complex inter- and intra-molecular forces. Deep learning, which is characterized by its ability to learn relevant features directly from large datasets, has demonstrated remarkable performance in fields such as computer vision and natural language processing. It has also been increasingly applied in recent years to the data-rich domain of protein sequences with great success, most notably with Alphafold2's breakout performance in the protein structure prediction. The performance improvements achieved by deep learning unlocks new possibilities in the field of protein bioinformatics, including protein design, one of the most difficult but useful tasks. In this paper, we broadly categorize problems in protein bioinformatics into three main categories: 1) structural prediction, 2) functional prediction, and 3) protein design, and review the progress achieved from using deep learning methodologies in each of them. We expand on the main challenges of the protein design problem and highlight how advances in structural and functional prediction have directly contributed to design tasks. Finally, we conclude by identifying important topics and future research directions.
This brief review, intended for high energy and astrophysics researchers, explores the implications of recent theoretical advances in string theory and the Swampland program for understanding bounds on the structure of positive potentials allowed in quantum gravity. This has a bearing on both inflationary models for the early universe as well as the fate of our universe. The paper includes a review of the dS conjecture as well as the TransPlanckian Censorship Conjecture (TCC) and its relation to the species scale. We provide evidence for these principles as well as what they may lead to in terms of phenomenological predictions. (Talk presented at Lemaitre Conference 2024)
2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining
2025-01-01
Wenqi Zhang, Hang Zhang, Xin Li, Jiashuo Sun, Yongliang Shen, Weiming Lu, Deli Zhao, Yueting Zhuang, Lidong Bing
Compared to image-text pair data, interleaved corpora enable Vision-Language Models (VLMs) to understand the world more naturally like humans. However, such existing datasets are crawled from webpage, facing challenges like low knowledge density, loose image-text relations, and poor logical coherence between images. On the other hand, the internet hosts vast instructional videos (e.g., online geometry courses) that are widely used by humans to learn foundational subjects, yet these valuable resources remain underexplored in VLM training. In this paper, we introduce a high-quality \textbf{multimodal textbook} corpus with richer foundational knowledge for VLM pretraining. It collects over 2.5 years of instructional videos, totaling 22,000 class hours. We first use an LLM-proposed taxonomy to systematically gather instructional videos. Then we progressively extract and refine visual (keyframes), audio (ASR), and textual knowledge (OCR) from the videos, and organize as an image-text interleaved corpus based on temporal order. Compared to its counterparts, our video-centric textbook offers more coherent context, richer knowledge, and better image-text alignment. Experiments demonstrate its superb pretraining performance, particularly in knowledge- and reasoning-intensive tasks like ScienceQA and MathVista. Moreover, VLMs pre-trained on our textbook exhibit outstanding interleaved context awareness, leveraging visual and textual cues in their few-shot context for task solving~\footnote{Our code are available at \url{https://github.com/DAMO-NLP-SG/multimodal_textbook}}.
Incremental Dialogue Management: Survey, Discussion, and Implications for HRI
2025-01-01
Casey Kennington, Pierre Lison, David Schlangen
Efforts towards endowing robots with the ability to speak have benefited from recent advancements in NLP, in particular large language models. However, as powerful as current models have become, they still operate on sentence or multi-sentence level input, not on the word-by-word input that humans operate on, affecting the degree of responsiveness that they offer, which is critical in situations where humans interact with robots using speech. In this paper, we review the literature on interactive systems that operate incrementally (i.e., at the word level or below it). We motivate the need for incremental systems, survey incremental modeling of important aspects of dialogue like speech recognition and language generation. Primary focus is on the part of the system that makes decisions, known as the dialogue manager. We find that there is very little research on incremental dialogue management, offer some requirements for practical incremental dialogue management, and the implications of incremental dialogue for embodied, robotic platforms.
These notes review the theory of Fisher information, especially its use in kinetic theory of gases and plasmas. The recent monotonicity theorem by Guillen--Silvestre for the Landau--Coulomb equation is put in perspective and generalised. Following my joint work with Imbert and Silvestre, it is proven that Fisher information is decaying along the spatially homogeneous Boltzmann equation, for all relevant interactions, and from this the once longstanding problem of regularity estimates for very singular collision kernels (very soft potentials) is solved.
The extraordinary success of recent Large Language Models (LLMs) on a diverse array of tasks has led to an explosion of scientific and philosophical theorizing aimed at explaining how they do what they do. Unfortunately, disagreement over fundamental theoretical issues has led to stalemate, with entrenched camps of LLM optimists and pessimists often committed to very different views of how these systems work. Overcoming stalemate requires agreement on fundamental questions, and the goal of this paper is to address one such question, namely: is LLM behavior driven partly by representation-based information processing of the sort implicated in biological cognition, or is it driven entirely by processes of memorization and stochastic table look-up? This is a question about what kind of algorithm LLMs implement, and the answer carries serious implications for higher level questions about whether these systems have beliefs, intentions, concepts, knowledge, and understanding. I argue that LLM behavior is partially driven by representation-based information processing, and then I describe and defend a series of practical techniques for investigating these representations and developing explanations on their basis. The resulting account provides a groundwork for future theorizing about language models and their successors.
Paternalism and Deliberation: An Experiment on Making Formal Rules
2025-01-01
Max R. P. Grossmann
This paper studies the relationship between soft and hard paternalism by examining two kinds of restriction: a waiting period and a hard limit (cap) on risk-seeking behavior. Mandatory waiting periods have been instituted for medical procedures, gun purchases and other high-stakes decisions. Are these policies substitutes for hard restrictions, and are delayed decisions more respected? In an experiment, decision-makers are informed about an impending high-stakes decision. Treatments define when the decision is made: on the spot or after one day, and whether the initial decision can be revised. In a general population survey experiment, another class of subjects (Choice Architects) is granted the opportunity to make rules for decision-makers. Given a decision's temporal structure, Choice Architects can decide on a cap to the decision-maker's risk taking. In another treatment, Choice Architects can implement a mandatory waiting period in addition to the cap. This allows us to study the substitutional relationship between waiting periods and paternalistic action and the effect of deliberation on the autonomy afforded to the decision-maker. Our highly powered experiment reveals that exogenous deliberation has no effect on the cap. Moreover, endogenously prescribed waiting periods represent add-on restrictions that do not substitute for the cap. Choice Architects believe that, with time, the average decision-maker will take less risk and -- because of the distribution of Choice Architects' bliss points -- come closer to Choice Architects' subjective ideal choice. These findings highlight the complementarity of policy tools in targeting various parts of a distribution of decision-makers.
Advances in UAV Avionics Systems Architecture, Classification and Integration: A Comprehensive Review and Future Perspectives
2025-01-01
Hashim A. Hashim
Avionics systems of an Unmanned Aerial Vehicle (UAV) or drone are the critical electronic components found onboard that regulate, navigate, and control UAV travel while ensuring public safety. Contemporary UAV avionics work together to facilitate success of UAV missions by enabling stable communication, secure identification protocols, novel energy solutions, multi-sensor accurate perception and autonomous navigation, precise path planning, that guarantees collision avoidance, reliable trajectory control, and efficient data transfer within the UAV system. Moreover, special consideration must be given to electronic warfare threats prevention, detection, and mitigation, and the regulatory framework associated with UAV operations. This review presents the role and taxonomy of each UAV avionics system while covering shortcomings and benefits of available alternatives within each system. UAV communication systems, antennas, and location communication tracking are surveyed. Identification systems that respond to air-to-air or air-to-ground interrogating signals are presented. UAV classical and more innovative power sources are discussed. The rapid development of perception systems improves UAV autonomous navigation and control capabilities. The paper reviews common perception systems, navigation techniques, path planning approaches, obstacle avoidance methods, and tracking control. Modern electronic warfare uses advanced techniques and has to be counteracted by equally advanced methods to keep the public safe. Consequently, this work presents a detailed overview of common electronic warfare threats and state-of-the-art countermeasures and defensive aids. UAV safety occurrences are analyzed in the context of national regulatory framework and the certification process. Databus communication and standards for UAVs are reviewed as they enable efficient and fast real-time data transfer.
Scale-wise Bidirectional Alignment Network for Referring Remote Sensing Image Segmentation
2025-01-01
Kun Li, George Vosselman, Michael Ying Yang
The goal of referring remote sensing image segmentation (RRSIS) is to extract specific pixel-level regions within an aerial image via a natural language expression. Recent advancements, particularly Transformer-based fusion designs, have demonstrated remarkable progress in this domain. However, existing methods primarily focus on refining visual features using language-aware guidance during the cross-modal fusion stage, neglecting the complementary vision-to-language flow. This limitation often leads to irrelevant or suboptimal representations. In addition, the diverse spatial scales of ground objects in aerial images pose significant challenges to the visual perception capabilities of existing models when conditioned on textual inputs. In this paper, we propose an innovative framework called Scale-wise Bidirectional Alignment Network (SBANet) to address these challenges for RRSIS. Specifically, we design a Bidirectional Alignment Module (BAM) with learnable query tokens to selectively and effectively represent visual and linguistic features, emphasizing regions associated with key tokens. BAM is further enhanced with a dynamic feature selection block, designed to provide both macro- and micro-level visual features, preserving global context and local details to facilitate more effective cross-modal interaction. Furthermore, SBANet incorporates a text-conditioned channel and spatial aggregator to bridge the gap between the encoder and decoder, enhancing cross-scale information exchange in complex aerial scenarios. Extensive experiments demonstrate that our proposed method achieves superior performance in comparison to previous state-of-the-art methods on the RRSIS-D and RefSegRS datasets, both quantitatively and qualitatively. The code will be released after publication.
Semantic communication (SemCom) is regarded as a promising and revolutionary technology in 6G, aiming to transcend the constraints of ``Shannon's trap" by filtering out redundant information and extracting the core of effective data. Compared to traditional communication paradigms, SemCom offers several notable advantages, such as reducing the burden on data transmission, enhancing network management efficiency, and optimizing resource allocation. Numerous researchers have extensively explored SemCom from various perspectives, including network architecture, theoretical analysis, potential technologies, and future applications. However, as SemCom continues to evolve, a multitude of security and privacy concerns have arisen, posing threats to the confidentiality, integrity, and availability of SemCom systems. This paper presents a comprehensive survey of the technologies that can be utilized to secure SemCom. Firstly, we elaborate on the entire life cycle of SemCom, which includes the model training, model transfer, and semantic information transmission phases. Then, we identify the security and privacy issues that emerge during these three stages. Furthermore, we summarize the techniques available to mitigate these security and privacy threats, including data cleaning, robust learning, defensive strategies against backdoor attacks, adversarial training, differential privacy, cryptography, blockchain technology, model compression, and physical-layer security. Lastly, this paper outlines future research directions to guide researchers in related fields.
Current optical flow methods exploit the stable appearance of frame (or RGB) data to establish robust correspondences across time. Event cameras, on the other hand, provide high-temporal-resolution motion cues and excel in challenging scenarios. These complementary characteristics underscore the potential of integrating frame and event data for optical flow estimation. However, most cross-modal approaches fail to fully utilize the complementary advantages, relying instead on simply stacking information. This study introduces a novel approach that uses a spatially dense modality to guide the aggregation of the temporally dense event modality, achieving effective cross-modal fusion. Specifically, we propose an event-enhanced frame representation that preserves the rich texture of frames and the basic structure of events. We use the enhanced representation as the guiding modality and employ events to capture temporally dense motion information. The robust motion features derived from the guiding modality direct the aggregation of motion information from events. To further enhance fusion, we propose a transformer-based module that complements sparse event motion features with spatially rich frame information and enhances global information propagation. Additionally, a mix-fusion encoder is designed to extract comprehensive spatiotemporal contextual features from both modalities. Extensive experiments on the MVSEC and DSEC-Flow datasets demonstrate the effectiveness of our framework. Leveraging the complementary strengths of frames and events, our method achieves leading performance on the DSEC-Flow dataset. Compared to the event-only model, frame guidance improves accuracy by 10\%. Furthermore, it outperforms the state-of-the-art fusion-based method with a 4\% accuracy gain and a 45\% reduction in inference time.
Complexity of Finite Semigroups: History and Decidability
2025-01-01
StuarT Margolis, John Rhodes, Anne Schilling
In recent papers, Margolis, Rhodes and Schilling proved that the complexity of a finite semigroup is computable. This solved a problem that had been open for more than 50 years. The purpose of this paper is to survey the basic results of Krohn-Rhodes complexity of finite semigroups and to outline the proof of its computability.
This is a review of gravitational instantons -- solutions to Riemannian Einstein or Einstein-Maxwell equations in four dimensions which yield complete metrics on non-compact four-manifolds, and which asymptotically `look like' flat space. The review focuses on examples, and is based on lectures given by the author at the Cracow School of Theoretical Physics held in Zakopane in June 2024.
Unraveling Indirect In-Context Learning Using Influence Functions
2025-01-01
Hadi Askari, Shivanshu Gupta, Terry Tong, Fei Wang, Anshuman Chhabra, Muhao Chen
This work introduces a novel paradigm for generalized In-Context Learning (ICL), termed Indirect In-Context Learning. In Indirect ICL, we explore demonstration selection strategies tailored for two distinct real-world scenarios: Mixture of Tasks and Noisy Demonstrations. We systematically evaluate the effectiveness of Influence Functions (IFs) as a selection tool for these settings, highlighting the potential for IFs to better capture the informativeness of examples within the demonstration pool. For the Mixture of Tasks setting, demonstrations are drawn from 28 diverse tasks, including MMLU, BigBench, StrategyQA, and CommonsenseQA. We demonstrate that combining BertScore-Recall (BSR) with an IF surrogate model can significantly improve performance, leading to average absolute accuracy gains of 0.37\% and 1.45\% for 3-shot and 5-shot setups when compared to traditional ICL metrics. In the Noisy Demonstrations setting, we examine scenarios where demonstrations might be mislabeled. Our experiments show that reweighting traditional ICL selectors (BSR and Cosine Similarity) with IF-based selectors boosts accuracy by an average of 2.90\% for Cosine Similarity and 2.94\% for BSR on noisy GLUE benchmarks. In sum, we propose a robust framework for demonstration selection that generalizes beyond traditional ICL, offering valuable insights into the role of IFs for Indirect ICL.
Multi-Center Study on Deep Learning-Assisted Detection and Classification of Fetal Central Nervous System Anomalies Using Ultrasound Imaging
2025-01-01
Yang Qi, Jiaxin Cai, Jing Lu, Runqing Xiong, Rongshang Chen, Liping Zheng, Duo Ma
Prenatal ultrasound evaluates fetal growth and detects congenital abnormalities during pregnancy, but the examination of ultrasound images by radiologists requires expertise and sophisticated equipment, which would otherwise fail to improve the rate of identifying specific types of fetal central nervous system (CNS) abnormalities and result in unnecessary patient examinations. We construct a deep learning model to improve the overall accuracy of the diagnosis of fetal cranial anomalies to aid prenatal diagnosis. In our collected multi-center dataset of fetal craniocerebral anomalies covering four typical anomalies of the fetal central nervous system (CNS): anencephaly, encephalocele (including meningocele), holoprosencephaly, and rachischisis, patient-level prediction accuracy reaches 94.5%, with an AUROC value of 99.3%. In the subgroup analyzes, our model is applicable to the entire gestational period, with good identification of fetal anomaly types for any gestational period. Heatmaps superimposed on the ultrasound images not only provide a visual interpretation for the algorithm but also provide an intuitive visual aid to the physician by highlighting key areas that need to be reviewed, helping the physician to quickly identify and validate key areas. Finally, the retrospective reader study demonstrates that by combining the automatic prediction of the DL system with the professional judgment of the radiologist, the diagnostic accuracy and efficiency can be effectively improved and the misdiagnosis rate can be reduced, which has an important clinical application prospect.
Unexpected Rise in Nuclear Collectivity from Short-Range Physics
2024-12-31
Kevin S. Becker, Kristina D. Launey, Andreas Ekström, Tomáš Dytrych, Daniel Langr, Grigor H. Sargsyan, Jerry P. Draayer
We discover a surprising relation between the collective motion of nucleons within atomic nuclei, traditionally understood to be driven by long-range correlations, and short-range nucleon-nucleon interactions. Specifically, we find that quadrupole collectivity in low-lying states of $^6$Li and $^{12}$C, calculated with state-of-the-art ab initio techniques, is significantly influenced by two opposing $S$-wave contact couplings that subtly alter the surface oscillations of one largely deformed nuclear shape, without changing that shape's overall contribution within the nucleus. The results offer new insights into the nature of emergent nuclear collectivity and its link to the underlying nucleon-nucleon interaction at short distances.
Why Are Positional Encodings Nonessential for Deep Autoregressive Transformers? Revisiting a Petroglyph
2024-12-31
Kazuki Irie
Do autoregressive Transformer language models require explicit positional encodings (PEs)? The answer is "no" as long as they have more than one layer -- they can distinguish sequences with permuted tokens without requiring explicit PEs. This property has been known since early efforts (those contemporary with GPT-2) adopting the Transformer for language modeling. However, this result does not appear to have been well disseminated and was even rediscovered recently. This may be partially due to a sudden growth of the language modeling community after the advent of GPT-2, but perhaps also due to the lack of a clear explanation in prior publications, despite being commonly understood by practitioners in the past. Here we review this long-forgotten explanation why explicit PEs are nonessential for multi-layer autoregressive Transformers (in contrast, one-layer models require PEs to discern order information of their input tokens). We also review the origin of this result, and hope to re-establish it as a common knowledge.
Efficient Standardization of Clinical Notes using Large Language Models
2024-12-31
Daniel B. Hier, Michael D. Carrithers, Thanh Son Do, Tayo Obafemi-Ajayi
Clinician notes are a rich source of patient information but often contain inconsistencies due to varied writing styles, colloquialisms, abbreviations, medical jargon, grammatical errors, and non-standard formatting. These inconsistencies hinder the extraction of meaningful data from electronic health records (EHRs), posing challenges for quality improvement, population health, precision medicine, decision support, and research. We present a large language model approach to standardizing a corpus of 1,618 clinical notes. Standardization corrected an average of $4.9 +/- 1.8$ grammatical errors, $3.3 +/- 5.2$ spelling errors, converted $3.1 +/- 3.0$ non-standard terms to standard terminology, and expanded $15.8 +/- 9.1$ abbreviations and acronyms per note. Additionally, notes were re-organized into canonical sections with standardized headings. This process prepared notes for key concept extraction, mapping to medical ontologies, and conversion to interoperable data formats such as FHIR. Expert review of randomly sampled notes found no significant data loss after standardization. This proof-of-concept study demonstrates that standardization of clinical notes can improve their readability, consistency, and usability, while also facilitating their conversion into interoperable data formats.
The Bayesian elastic net regression model is characterized by the regression coefficient prior distribution, the negative log density of which corresponds to the elastic net penalty function. While Markov chain Monte Carlo (MCMC) methods exist for sampling from the posterior of the regression coefficients given the penalty parameters, full Bayesian inference that incorporates uncertainty about the penalty parameters remains a challenge due to an intractable integrable in the posterior density function. Though sampling methods have been proposed that avoid computing this integral, all correctly-specified methods for full Bayesian inference that have appeared in the literature involve at least one "Metropolis-within-Gibbs" update, requiring tuning of proposal distributions. The computational landscape is complicated by the fact that two forms of the Bayesian elastic net prior have been introduced, and two representations (with and without data augmentation) of the prior suggest different MCMC algorithms. We review the forms and representations of the prior, discuss all combinations of these different treatments for the first time, and introduce one combination of form and representation that has yet to appear in the literature. We introduce MCMC algorithms for full Bayesian inference for all treatments of the prior. The algorithms allow for direct sampling of all parameters without any "Metropolis-within-Gibbs" steps. The key to the new approach is a careful transformation of the parameter space and an analysis of the resulting full conditional density functions that allows for efficient rejection sampling. We make empirical comparisons between our approaches and existing MCMC samplers for different data structures.
Setting Standards in Turkish NLP: TR-MMLU for Large Language Model Evaluation
2024-12-31
M. Ali Bayram, Ali Arda Fincan, Ahmet Semih Gümüş, Banu Diri, Savaş Yıldırım, Öner Aytaş
Language models have made remarkable advancements in understanding and generating human language, achieving notable success across a wide array of applications. However, evaluating these models remains a significant challenge, particularly for resource-limited languages such as Turkish. To address this gap, we introduce the Turkish MMLU (TR-MMLU) benchmark, a comprehensive evaluation framework designed to assess the linguistic and conceptual capabilities of large language models (LLMs) in Turkish. TR-MMLU is constructed from a carefully curated dataset comprising 6200 multiple-choice questions across 62 sections, selected from a pool of 280000 questions spanning 67 disciplines and over 800 topics within the Turkish education system. This benchmark provides a transparent, reproducible, and culturally relevant tool for evaluating model performance. It serves as a standard framework for Turkish NLP research, enabling detailed analyses of LLMs' capabilities in processing Turkish text and fostering the development of more robust and accurate language models. In this study, we evaluate state-of-the-art LLMs on TR-MMLU, providing insights into their strengths and limitations for Turkish-specific tasks. Our findings reveal critical challenges, such as the impact of tokenization and fine-tuning strategies, and highlight areas for improvement in model design. By setting a new standard for evaluating Turkish language models, TR-MMLU aims to inspire future innovations and support the advancement of Turkish NLP research.
AI and Quantum Computing in Binary Photocatalytic Hydrogen Production
2024-12-31
Dennis Delali Kwesi Wayo, Leonardo Goliatt, Darvish Ganji
Photocatalytic water splitting has emerged as a sustainable pathway for hydrogen production, leveraging sunlight to drive chemical reactions. This review explores the integration of density functional theory (DFT) with machine learning (ML) to accelerate the discovery, optimization, and design of photocatalysts. DFT provides quantum-mechanical insights into electronic structures and reaction mechanisms, while ML algorithms enable high-throughput analysis of material properties, prediction of catalytic performance, and inverse design. This paper emphasizes advancements in binary photocatalytic systems, highlighting materials like $TiO_2$, $BiVO_4$, and $g-C_3N_4$, as well as novel heterojunctions and co-catalysts that improve light absorption and charge separation efficiency. Key breakthroughs include the use of ML architectures such as random forests, support vector regression, and neural networks, trained on experimental and computational datasets to optimize band gaps, surface reactions, and hydrogen evolution rates. Emerging techniques like quantum machine learning (QML) and generative models (GANs, VAEs) demonstrate the potential to explore hypothetical materials and enhance computational efficiency. The review also highlights advanced light sources, such as tunable LEDs and solar simulators, for experimental validation of photocatalytic systems. Challenges related to data standardization, scalability, and interpretability are addressed, proposing collaborative frameworks and open-access repositories to democratize DFT-AI tools. By bridging experimental and computational methodologies, this synergistic approach offers transformative potential for achieving scalable, cost-effective hydrogen production, paving the way for sustainable energy solutions.
Angle-resolved photoemission of topological materials
2024-12-31
Jaime Sánchez-Barriga, Oliver J. Clark, Oliver Rader
Topological materials have gained significant attention in condensed matter physics due to their unique electronic and transport properties. Three-dimensional (3D) topological materials are characterized by robust electronic states that are protected by symmetries and exhibit peculiar spin textures. They offer a rich platform for for future information technology including spintronics and topological quantum computing. Here, we review the investigation by angle-resolved photoelectron spectroscopy (ARPES) of topological phases such as strong topological insulators, topological crystalline insulators, magnetic topological insulators, and 3D Dirac, Weyl, nodal, and chiral semimetals and address the status of correlated topological insulators and topological superconductors. A special emphasis is laid on examples from the transition metal dichalcogenide family. Moreover, insights from ultrafast pump-probe experiments are reviewed and a brief outlook is provided.
Efficient support ticket resolution using Knowledge Graphs
2024-12-31
Sherwin Varghese, James Tian
A review of over 160,000 customer cases indicates that about 90% of time is spent by the product support for solving around 10% of subset of tickets where a trivial solution may not exist. Many of these challenging cases require the support of several engineers working together within a "swarm", and some also need to go to development support as bugs. These challenging customer issues represent a major opportunity for machine learning and knowledge graph that identifies the ideal engineer / group of engineers(swarm) that can best address the solution, reducing the wait times for the customer. The concrete ML task we consider here is a learning-to-rank(LTR) task that given an incident and a set of engineers currently assigned to the incident (which might be the empty set in the non-swarming context), produce a ranked list of engineers best fit to help resolve that incident. To calculate the rankings, we may consider a wide variety of input features including the incident description provided by the customer, the affected component(s), engineer ratings of their expertise, knowledge base article text written by engineers, response to customer text written by engineers, and historic swarming data. The central hypothesis test is that by including a holistic set of contextual data around which cases an engineer has solved, we can significantly improve the LTR algorithm over benchmark models. The article proposes a novel approach of modelling Knowledge Graph embeddings from multiple data sources, including the swarm information. The results obtained proves that by incorporating this additional context, we can improve the recommendations significantly over traditional machine learning methods like TF-IDF.
A Novel Survey for Young Substellar Objects with the W-band Filter. VII. Water-Bearing Objects in the Core of the Rho Ophiuchi Cloud Complex
2024-12-31
Tanvi Sharma, Wen-Ping Chen, Beth Biller, Loic Albert, Belinda Damian, Jessy Jose, Bhavana Lalchand, Michael C. Liu, Yumiko Oasa
We present a study of very low-mass stars and brown dwarfs in the rich star-forming core of the Rho Ophiuchi cloud complex. The selection of the sample relies on detecting the inherent water absorption characteristic in young substellar objects. Of the 22 water-bearing candidates selected, 15 have a spectral type of M6 or later. Brown dwarf candidates too faint for membership determination by Gaia have their proper motions derived by deep-infrared images spanning six years. Astrometric analysis confirms 21/22 sources as members, one identified as a contaminant. Infrared colors and the spectral energy distribution of each water-bearing candidate are used to diagnose the mass, age, and possible existence of circumstellar dust. 15 sources exhibit evidence of disks in their spectral energy distributions, as late as in M8-type objects. Spectroscopy for bright candidates has confirmed one as an M8 member and verified two sources (with disks) exhibiting signatures of magnetospheric accretion.
Topic Review: Hatsugai-Kohmoto models: Exactly solvable playground for Mottness and Non-Fermi Liquid
2024-12-31
Miaomiao Zhao, Wei-Wei Yang, Yin Zhong
This pedagogic review aims to give a gentle introduction to an exactly solvable model, the Hatsugai-Kohmoto (HK) model, which has infinite-ranged interaction but conserves the center of mass. Although this model is invented in 1992, intensive studies on its properties ranging from unconventional superconductivity, topological ordered states to non-Fermi liquid behaviors are made since 2020. We focus on its emergent non-Fermi liquid behavior and provide discussion on its thermodynamics, single-particle and two-particle correlation functions. Perturbation around solvable limit has also been explored with the help of perturbation theory, renormalization group and exact diagonalization calculation. We hope the present review will be helpful for graduate students or researchers interested in HK-like models or more generic strongly correlated electron systems.
The rapid advancement of foundation modelslarge-scale neural networks trained on diverse, extensive datasetshas revolutionized artificial intelligence, enabling unprecedented advancements across domains such as natural language processing, computer vision, and scientific discovery. However, the substantial parameter count of these models, often reaching billions or trillions, poses significant challenges in adapting them to specific downstream tasks. Low-Rank Adaptation (LoRA) has emerged as a highly promising approach for mitigating these challenges, offering a parameter-efficient mechanism to fine-tune foundation models with minimal computational overhead. This survey provides the first comprehensive review of LoRA techniques beyond large Language Models to general foundation models, including recent techniques foundations, emerging frontiers and applications of low-rank adaptation across multiple domains. Finally, this survey discusses key challenges and future research directions in theoretical understanding, scalability, and robustness. This survey serves as a valuable resource for researchers and practitioners working with efficient foundation model adaptation.
Spin waves in magnetic nanodisks, nanorings, and 3D nanovolcanoes
2024-12-31
Oleksandr Dobrovolskiy, Gleb Kakazei
Patterned magnetic nanostructures are advanced materials characterized by their unique magnetic properties at the nanoscale, which are the result of tailored geometric configurations and compositional engineering. As interest in nanotechnology continues to grow exponentially, the exploration of patterned magnetic nanostructures turns into a vibrant and critical area of study for both industry professionals and academic researchers. Here, we review investigations of standing spin waves (collective spin precessions) in magnetic elements by the technique of ferromagnetic resonance (FMR). The presentation encompasses earlier studies of arrays of magnetic nanodisks and nanoringes by cavity-based FMR as well as more recent studies of individual nanodisks and 3D nanovolcanoes by a broadband FMR method which implies the use of a coplanar waveguide. Overall, the manuscript outlines the development of FMR studies along the two major lines: (i) downscaling from multiple to individual magnetic nanoelements and (ii) extending planar nanomagnets into the third dimension.
Recent advancements in foundation models have enhanced AI systems' capabilities in autonomous tool usage and reasoning. However, their ability in location or map-based reasoning - which improves daily life by optimizing navigation, facilitating resource discovery, and streamlining logistics - has not been systematically studied. To bridge this gap, we introduce MapEval, a benchmark designed to assess diverse and complex map-based user queries with geo-spatial reasoning. MapEval features three task types (textual, API-based, and visual) that require collecting world information via map tools, processing heterogeneous geo-spatial contexts (e.g., named entities, travel distances, user reviews or ratings, images), and compositional reasoning, which all state-of-the-art foundation models find challenging. Comprising 700 unique multiple-choice questions about locations across 180 cities and 54 countries, MapEval evaluates foundation models' ability to handle spatial relationships, map infographics, travel planning, and navigation challenges. Using MapEval, we conducted a comprehensive evaluation of 28 prominent foundation models. While no single model excelled across all tasks, Claude-3.5-Sonnet, GPT-4o, and Gemini-1.5-Pro achieved competitive performance overall. However, substantial performance gaps emerged, particularly in MapEval, where agents with Claude-3.5-Sonnet outperformed GPT-4o and Gemini-1.5-Pro by 16% and 21%, respectively, and the gaps became even more amplified when compared to open-source LLMs. Our detailed analyses provide insights into the strengths and weaknesses of current models, though all models still fall short of human performance by more than 20% on average, struggling with complex map images and rigorous geo-spatial reasoning. This gap highlights MapEval's critical role in advancing general-purpose foundation models with stronger geo-spatial understanding.
Retrieval-Augmented Generation with Graphs (GraphRAG)
2024-12-31
Haoyu Han, Yu Wang, Harry Shomer, Kai Guo, Jiayuan Ding, Yongjia Lei, Mahantesh Halappanavar, Ryan A. Rossi, Subhabrata Mukherjee, Xianfeng Tang, Qi He, Zhigang Hua, Bo Long, Tong Zhao, Neil Shah, Amin Javari, Yinglong Xia, Jiliang Tang
Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information, such as knowledge, skills, and tools from external sources. Graph, by its intrinsic "nodes connected by edges" nature, encodes massive heterogeneous and relational information, making it a golden resource for RAG in tremendous real-world applications. As a result, we have recently witnessed increasing attention on equipping RAG with Graph, i.e., GraphRAG. However, unlike conventional RAG, where the retriever, generator, and external data sources can be uniformly designed in the neural-embedding space, the uniqueness of graph-structured data, such as diverse-formatted and domain-specific relational knowledge, poses unique and significant challenges when designing GraphRAG for different domains. Given the broad applicability, the associated design challenges, and the recent surge in GraphRAG, a systematic and up-to-date survey of its key concepts and techniques is urgently desired. Following this motivation, we present a comprehensive and up-to-date survey on GraphRAG. Our survey first proposes a holistic GraphRAG framework by defining its key components, including query processor, retriever, organizer, generator, and data source. Furthermore, recognizing that graphs in different domains exhibit distinct relational patterns and require dedicated designs, we review GraphRAG techniques uniquely tailored to each domain. Finally, we discuss research challenges and brainstorm directions to inspire cross-disciplinary opportunities. Our survey repository is publicly maintained at https://github.com/Graph-RAG/GraphRAG/.
A novel analysis of contamination in Lyman-break galaxy samples at $\boldsymbol{z\sim6-8}$: spatial correlation with intermediate-redshift galaxies at $\boldsymbol{z\sim1.3-2}$
2024-12-31
Miftahul Hilmi, Nicha Leethochawalit, Michele Trenti, Benjamin Metha
Potential contamination from low/intermediate-redshift galaxies, such as objects with a prominent Balmer break, affects the photometric selection of high-redshift galaxies through identification of a Lyman break. Traditionally, contamination is estimated from spectroscopic follow-up and/or simulations. Here, we introduce a novel approach to estimating contamination for Lyman-break galaxy (LBG) samples based on measuring spatial correlation with the parent population of lower redshift interlopers. We propose two conceptual approaches applicable to different survey strategies: a single large contiguous field and a survey consisting of multiple independent lines of sight. For a large single field, we compute the cross-correlation function between galaxies at redshift $z \sim 6$ and intermediate-redshift galaxies at $z \sim 1.3$. We apply the method to the CANDELS GOODS-S and XDF surveys and compare the measurement with simulated mock observations, finding that the contamination level in both cases is not measurable and lies below $5.5\%$ (at $90\%$ confidence). For random-pointing multiple field surveys, we measure instead the number count correlation between high-redshift galaxies and interlopers, as a two-point correlation analysis is not generally feasible. We show an application to the LBG samples at redshift $z \sim 8$ and the possible interloper population at $z \sim 2$ in the Brightest of Reionizing Galaxies (BoRG) survey. By comparing the Pearson correlation coefficient with the result from Monte Carlo simulations, we estimate a contamination fraction of $62^{+13}_{-39}\%$, consistent with previous estimates in the literature. These results validate the proposed approach and demonstrate its utility as an independent check of contamination in photometrically selected samples of high-redshift galaxies.
A review of faithfulness metrics for hallucination assessment in Large Language Models
2024-12-31
Ben Malin, Tatiana Kalganova, Nikoloas Boulgouris
This review examines the means with which faithfulness has been evaluated across open-ended summarization, question-answering and machine translation tasks. We find that the use of LLMs as a faithfulness evaluator is commonly the metric that is most highly correlated with human judgement. The means with which other studies have mitigated hallucinations is discussed, with both retrieval augmented generation (RAG) and prompting framework approaches having been linked with superior faithfulness, whilst other recommendations for mitigation are provided. Research into faithfulness is integral to the continued widespread use of LLMs, as unfaithful responses can pose major risks to many areas whereby LLMs would otherwise be suitable. Furthermore, evaluating open-ended generation provides a more comprehensive measure of LLM performance than commonly used multiple-choice benchmarking, which can help in advancing the trust that can be placed within LLMs.
Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS
2024-12-31
Hany Ragab, Sidney Givigi, Aboelmagd Noureldin
Modern on-road navigation systems heavily depend on integrating speed measurements with inertial navigation systems (INS) and global navigation satellite systems (GNSS). Telemetry-based applications typically source speed data from the On-Board Diagnostic II (OBD-II) system. However, the method of deriving speed, as well as the types of sensors used to measure wheel speed, differs across vehicles. These differences result in varying error characteristics that must be accounted for in navigation and autonomy applications. This paper addresses this gap by examining the diverse speed-sensing technologies employed in standard automotive systems and alternative techniques used in advanced systems designed for higher levels of autonomy, such as Advanced Driver Assistance Systems (ADAS), Autonomous Driving (AD), or surveying applications. We propose a method to identify the type of speed sensor in a vehicle and present strategies for accurately modeling its error characteristics. To validate our approach, we collected and analyzed data from three long real road trajectories conducted in urban environments in Toronto and Kingston, Ontario, Canada. The results underscore the critical role of integrating multiple sensor modalities to achieve more accurate speed estimation, thus improving automotive navigation state estimation, particularly in GNSS-denied environments.
CancerKG.ORG A Web-scale, Interactive, Verifiable Knowledge Graph-LLM Hybrid for Assisting with Optimal Cancer Treatment and Care
2024-12-31
Michael Gubanov, Anna Pyayt, Aleksandra Karolak
Here, we describe one of the first Web-scale hybrid Knowledge Graph (KG)-Large Language Model (LLM), populated with the latest peer-reviewed medical knowledge on colorectal Cancer. It is currently being evaluated to assist with both medical research and clinical information retrieval tasks at Moffitt Cancer Center, which is one of the top Cancer centers in the U.S. and in the world. Our hybrid is remarkable as it serves the user needs better than just an LLM, KG or a search-engine in isolation. LLMs as is are known to exhibit hallucinations and catastrophic forgetting as well as are trained on outdated corpora. The state of the art KGs, such as PrimeKG, cBioPortal, ChEMBL, NCBI, and other require manual curation, hence are quickly getting stale. CancerKG is unsupervised and is capable of automatically ingesting and organizing the latest medical findings. To alleviate the LLMs shortcomings, the verified KG serves as a Retrieval Augmented Generation (RAG) guardrail. CancerKG exhibits 5 different advanced user interfaces, each tailored to serve different data modalities better and more convenient for the user.
Debunking the CUDA Myth Towards GPU-based AI Systems
2024-12-31
Yunjae Lee, Juntaek Lim, Jehyeon Bang, Eunyeong Cho, Huijong Jeong, Taesu Kim, Hyungjun Kim, Joonhyung Lee, Jinseop Im, Ranggi Hwang, Se Jung Kwon, Dongsoo Lee, Minsoo Rhu
With the rise of AI, NVIDIA GPUs have become the de facto standard for AI system design. This paper presents a comprehensive evaluation of Intel Gaudi NPUs as an alternative to NVIDIA GPUs for AI model serving. First, we create a suite of microbenchmarks to compare Intel Gaudi-2 with NVIDIA A100, showing that Gaudi-2 achieves competitive performance not only in primitive AI compute, memory, and communication operations but also in executing several important AI workloads end-to-end. We then assess Gaudi NPU's programmability by discussing several software-level optimization strategies to employ for implementing critical FBGEMM operators and vLLM, evaluating their efficiency against GPU-optimized counterparts. Results indicate that Gaudi-2 achieves energy efficiency comparable to A100, though there are notable areas for improvement in terms of software maturity. Overall, we conclude that, with effective integration into high-level AI frameworks, Gaudi NPUs could challenge NVIDIA GPU's dominance in the AI server market, though further improvements are necessary to fully compete with NVIDIA's robust software ecosystem.
Unraveling the switching dynamics in a quantum double-well potential
2024-12-31
Qile Su, Rodrigo G. Cortiñas, Jayameenakshi Venkatraman, Shruti Puri
The spontaneous switching of a quantum particle between the wells of a double-well potential is a phenomenon of general interest to physics and chemistry. It was broadly believed that the switching rate decreases steadily with the size of the energy barrier. This view was challenged by a recent experiment on a driven superconducting Kerr nonlinear oscillator (often called the Kerr-cat qubit or the Kerr parametric oscillator), whose energy barrier can be increased by ramping up the drive. Remarkably, as the drive amplitude increases, the switching rate exhibits a step-like decrease termed the "staircase". The view challenged by the experiment demands a deep review of our understanding of quantum effects in double wells. In this work, we derive a semi-analytical formula for the switching rate that resolves a continuous transition between tunneling- and dissipation-dominated dynamics. These two dynamics are observed respectively in the flat and the steep parts of each step in the staircase. Our formula exposes two distinct dissipative processes that limit tunneling: dephasing and decay. This allows us to predict the critical drive amplitudes where steps occur. In addition, we show that in the regime of a few states in the well and under moderate to low temperatures, highly excited states are populated predominantly via cascaded and direct thermal heating rather than quantum heating. At very low temperatures, however, the perturbation induced by the nonhermitian Hamiltonian becomes important and facilitates a new form of quantum heating. We numerically map the activation mechanism as a function of drive amplitude, damping rate, and temperature. Our theory deepens the understanding of switching dynamics between metastable quantum states, highlights the importance of a general interplay between tunneling and dissipation, and identifies a novel quantum regime in activated transitions.
Parallel I/O Characterization and Optimization on Large-Scale HPC Systems: A 360-Degree Survey
2024-12-31
Hammad Ather, Jean Luca Bez, Chen Wang, Hank Childs, Allen D. Malony, Suren Byna
Driven by artificial intelligence, data science, and high-resolution simulations, I/O workloads and hardware on high-performance computing (HPC) systems have become increasingly complex. This complexity can lead to large I/O overheads and overall performance degradation. These inefficiencies are often mitigated using tools and techniques for characterizing, analyzing, and optimizing the I/O behavior of HPC applications. That said, the myriad number of tools and techniques available makes it challenging to navigate to the best approach. In response, this paper surveys 131 papers from the ACM Digital Library, IEEE Xplore, and other reputable journals to provide a comprehensive analysis, synthesized in the form of a taxonomy, of the current landscape of parallel I/O characterization, analysis, and optimization of large-scale HPC systems. We anticipate that this taxonomy will serve as a valuable resource for enhancing I/O performance of HPC applications.
Towards Unraveling and Improving Generalization in World Models
2024-12-31
Qiaoyi Fang, Weiyu Du, Hang Wang, Junshan Zhang
World models have recently emerged as a promising approach to reinforcement learning (RL), achieving state-of-the-art performance across a wide range of visual control tasks. This work aims to obtain a deep understanding of the robustness and generalization capabilities of world models. Thus motivated, we develop a stochastic differential equation formulation by treating the world model learning as a stochastic dynamical system, and characterize the impact of latent representation errors on robustness and generalization, for both cases with zero-drift representation errors and with non-zero-drift representation errors. Our somewhat surprising findings, based on both theoretic and experimental studies, reveal that for the case with zero drift, modest latent representation errors can in fact function as implicit regularization and hence result in improved robustness. We further propose a Jacobian regularization scheme to mitigate the compounding error propagation effects of non-zero drift, thereby enhancing training stability and robustness. Our experimental studies corroborate that this regularization approach not only stabilizes training but also accelerates convergence and improves accuracy of long-horizon prediction.
2024-12-31
D. Thirumalai, Naoto Hori, Hung T. Nguyen
The increasing importance of RNA as a prime player in biology can hardly be overstated. It is suspected that the functions of RNA are linked to their structures and dynamics. Many of the problems in RNA, such as folding and RNA-RNA interactions that drive phase separation even in the absence of proteins, require cations. Because experiments alone cannot directly reveal the dynamics of cation-RNA interactions, well calibrated theory and computations are needed to predict how ions control the behavior of RNA. In this review, we outline the development of coarse-grained models at different resolutions with application to phase separation in low complexity sequences. We also describe folding of ribozymes and riboswitches with a focus on the impact of monovalent and divalent cations. We outline major challenges that need to be overcome to simulate complex problems such as assembly of ribosomes.
2024-12-30
Jacob L. Bourjaily, Michael Plesser, Cristian Vergu
We study the colour-dependence of scattering amplitudes in Yang-Mills theory with arbitrary (but fixed) gauge group and various representations of charged matter. When the rank of the gauge theory is taken arbitrarily large compared to the number of particles involved in an amplitude, it is well known that the number of independent colour-structure tensors grows factorially with multiplicity; however, for any fixed gauge group, this number grows at most exponentially with multiplicity. We review how this counting arises in representation theory and survey its implications for a wide variety of specific gauge groups with various representations of charged matter, uncovering several surprising structures along the way.
2024-12-30
Kristan Jensen, Suvrat Raju, Antony J. Speranza
We study the algebra of observables in a time band on the boundary of anti-de Sitter space in a theory of quantum gravity. Strictly speaking this algebra does not have a commutant because products of operators within the time band give rise to operators outside the time band. However, we show that in a state where the bulk contains a macroscopic observer, it is possible to define a coarse-grained version of this algebra with a non-trivial commutant, and a resolution limited by the observer's characteristics. This algebra acts on a little Hilbert space that describes excitations about the observer's state and time-translated versions of this state. Our construction requires a choice of dressing that determines how elements of the algebra transform under the Hamiltonian. At leading order in gravitational perturbation theory, and with a specific choice of dressing, our construction reduces to the modular crossed-product described previously in the literature. We also prove a theorem showing that this is the only crossed product of a type III$_1$ algebra resulting in an algebra with a trace. This trace can be used to define entropy differences between states in the little Hilbert space that are insensitive to the properties of the observer. We discuss some technical challenges in extending this construction to higher orders in perturbation theory. Lastly, we review the construction of interior operators in the eternal black hole and show that they can be written as elements of a crossed product algebra.
Open-Source 5G Core Platforms: A Low-Cost Solution and Performance Evaluation
2024-12-30
Maria Barbosa, Marcelo Silva, Ednelson Cavalcanti, Kelvin Dias
An essential component for the Fifth Generation of Mobile Networks deployments is the 5G Core (5GC), which bridges the 5G Radio Access Network (RAN) to the rest of the Internet. Some open-source platforms for the 5GC have emerged and been deployed in Common Off-the-Shelf (COTS)-based setups. Despite these open-source 5GC initiatives following the 3GPP specifications, they differ in implementing some features and their stages in the timeline of 3GPP releases. Besides that, they may yield different performance to metrics related to the data and control planes. This article reviews the major open-source 5GC platforms and evaluates their performance in a 5G Standalone (SA) COTS-based testbed. The results indicate that Open5GS provides the best latencies for control plane procedures, OpenAirInterface offers the highest data rates, and Free5GC has the lowest resource consumption.
Optimal bound for survival time of the SIRS process on star graphs
2024-12-30
Phuc Lam, Oanh Nguyen, Iris Yang
We analyze the Susceptible-Infected-Recovered-Susceptible (SIRS) process, a continuous-time Markov chain frequently employed in epidemiology to model the spread of infections on networks. In this framework, infections spread as infected vertices recover at rate 1, infect susceptible neighbors independently at rate $\lambda$, and recovered vertices become susceptible again at rate $\alpha$. This model presents a significantly greater analytical challenge compared to the SIS model, which has consequently inspired a much more extensive and rich body of mathematical literature for the latter. Understanding the survival time, the duration before the infection dies out completely, is a fundamental question in this context. On general graphs, survival time heavily depends on the infection's persistence around high-degree vertices (known as hubs or stars), as long persistence enables transmission between hubs and prolongs the process. In contrast, short persistence leads to rapid extinction, making the dynamics on star graphs, which serve as key representatives of hubs, particularly important to study. In the 2016 paper by Ferreira, Sander, and Pastor-Satorras, published in {\it Physical Review E}, it was conjectured, based on intuitive arguments, that the survival time for SIRS on a star graph with $n$ leaves is bounded above by $(\lambda^2 n)^\alpha$ for large $n$. Later, in the seemingly first mathematically rigorous result for SIRS (\cite{friedrich2022analysis}) provided an upper bound of $n^\alpha \log n$, with contains an additional $\log n$ and no dependence on $\lambda$. We resolve this conjecture by proving that the survival time is indeed of order $(\lambda^2 n)^\alpha$, with matching upper and lower bounds. Additionally, we show that this holds even in the case where only the root undergoes immunization, while the leaves revert to susceptibility immediately after recovery.
Galaxy Spectra Networks (GaSNet). III. Generative pre-trained network for spectrum reconstruction, redshift estimate and anomaly detection
2024-12-30
Fucheng Zhong, Nicola R. Napolitano, Caroline Heneka, Jens-Kristian Krogager, Ricardo Demarco, Nicolas F. Bouché, Jonathan Loveday, Alexander Fritz, Aurélien Verdier, Boudewijn F. Roukema, Cristóbal Sifón, Letizia P. Cassará, Roberto J. Assef, Steve Ardern
Classification of spectra (1) and anomaly detection (2) are fundamental steps to guarantee the highest accuracy in redshift measurements (3) in modern all-sky spectroscopic surveys. We introduce a new Galaxy Spectra Neural Network (GaSNet-III) model that takes advantage of generative neural networks to perform these three tasks at once with very high efficiency. We use two different generative networks, an autoencoder-like network and U-Net, to reconstruct the rest-frame spectrum (after redshifting). The autoencoder-like network operates similarly to the classical PCA, learning templates (eigenspectra) from the training set and returning modeling parameters. The U-Net, in contrast, functions as an end-to-end model and shows an advantage in noise reduction. By reconstructing spectra, we can achieve classification, redshift estimation, and anomaly detection in the same framework. Each rest-frame reconstructed spectrum is extended to the UV and a small part of the infrared (covering the blueshift of stars). Owing to the high computational efficiency of deep learning, we scan the chi-squared value for the entire type and redshift space and find the best-fitting point. Our results show that generative networks can achieve accuracy comparable to the classical PCA methods in spectral modeling with higher efficiency, especially achieving an average of $>98\%$ classification across all classes ($>99.9\%$ for star), and $>99\%$ (stars), $>98\%$ (galaxies) and $>93\%$ (quasars) redshift accuracy under cosmology research requirements. By comparing different peaks of chi-squared curves, we define the ``robustness'' in the scanned space, offering a method to identify potential ``anomalous'' spectra. Our approach provides an accurate and high-efficiency spectrum modeling tool for handling the vast data volumes from future spectroscopic sky surveys.
A polynomial affine model of gravity: after ten years
2024-12-30
Oscar Castillo-Felisola, Bastian Grez, Manuel Morocho-López, Jose Perdiguero, Aureliano Skirzewski, Jefferson Vaca, Nicolas Zambra-Gómez
The polynomial affine model of gravity was proposed as an alternative to metric and metric-affine gravitational models. What at the beginning was thought as a source of unpredictability, the presence of many terms in the action, turned out to be a milestone, since it contains all possible combinations of the fields compatible with the covariance under diffeomorphisms. Here, we present a review of the advances in the analysis of the model after ten years of its proposal, and sketch the guideline of our future perspectives.
Impact of Fourth Industrial Revolution (4IR) on Small and Medium Enterprises (SMEs) and Employment in Bangladesh: Opportunities and Challenges
2024-12-30
Toukir Ahammed, Moumita Asad, Kazi Sakib
The Fourth Industrial Revolution (4IR) is transforming industries and economies worldwide, presenting both opportunities and challenges for Small and Medium Enterprises (SMEs) and employment. This study qualitatively explores the impact of 4IR on the SME sector in Bangladesh. Initially, secondary data sources are reviewed to establish the context and to prepare the questionnaire for primary data collection. Then, the primary data is collected through Key Informant Interviews and Focus Group Discussions with different stakeholders including SME owners, association representatives, and government officials. The study reveals that while most of the participants have only a superficially awareness of 4IR, they view it as a blessing for the SME sector. Despite being in early adoption stages in Bangladesh, SMEs anticipate numerous benefits including enhanced customer experiences, reduced production times, improved quality, etc. Regarding employment, most participants believe that adopting 4IR in the SME sector of Bangladesh will create new job opportunities. However, participants express concern about challenges during the transition to 4IR, including a lack of technical knowledge, financial constraints, inadequate training, safety and security issues, etc. To fully harness 4IR's potential benefits for SMEs in Bangladesh, several key recommendations emerge that include analyzing of the current SME landscape, establishing a collaborative information sharing platform, organizing effective training and workshops, promoting resource sharing, encouraging local innovation, attracting foreign clients, ensuring proper policy implementation and fostering collaboration among government, associations, and academia. By addressing these challenges and implementing the recommended strategies, Bangladesh can effectively embrace the transformative benefits of 4IR, simultaneously improving its SME sector.
A Simplified Theory of External Occulters for Solar Coronagraphs
2024-12-30
Craig E. DeForest, Nicholas F. Erickson, Matthew N. Beasley, Steven N. Osterman, Travis J. Smith, Mary H. Hanson
We present a first-principles analytic treatment of modern multi-vane occulters in circular (coronagraph) and linear (heliospheric imager) geometry, develop a simplified theory that is useful for designing and predicting their performance, explain certain visual artifacts, and explore the performance limits of multi-vane occulters. Multi-vane occulters are challenging to design in part because they violate the conditions for both the Fraunhofer and Fresnel approximations to diffraction theory, and new designs have therefore generally required explicit simulation, empirical measurement, "guesstimation", or all three. Starting from the Kirchoff diffraction integral, we develop a "sequential plane wave" approximate analytic theory that is suitable for predicting performance of multi-vane occulters, and use it to derive closed-form expressions for the performance of new designs. We review the fundamental 2-D system of an occulter edge, discuss how it applies to real 3-D systems by extrusion or revolution, present the reason for observed bright quasi-achromatic fringing around coronagraph occulters, develop the sequential plane wave approximation in 2-D and explore its limits, describe the relevance of the 2-D theory to practical 3-D instruments, and discuss implications for multi-vane occulter design in current and future instruments.
2024-12-30
Chengcheng Han, Hong-Jian He, Linghao Song, Jingtao You
The tiny neutrino masses are most naturally explained by the seesaw mechanism through singlet right-handed neutrinos, which can further explain the matter-antimatter asymmetry in the universe. In this work, we propose a new approach to study cosmological signatures of neutrino seesaw through the interaction between inflaton and right-handed neutrinos. After inflation the inflaton predominantly decays into right-handed neutrinos and its decay rate is modulated by the fluctuations of Higgs field which act as the source of curvature perturbations. We demonstrate that this modulation produces primordial non-Gaussian signatures, which can be measured by the forthcoming large-scale structure surveys. We find that these surveys have the potential to probe a large portion of the neutrino seesaw parameter space, opening up a new window for testing the high scale seesaw mechanism.
Resolving Pleiades binary stars with Gaia and speckle interferometric observations
2024-12-30
Dmitry Chulkov, Ivan Strakhov, Boris Safonov
The Pleiades is the most prominent open star cluster visible from Earth and an important benchmark for simple stellar populations, unified by common origin, age, and distance. Binary stars are its essential ingredient, yet their contribution remains uncertain due to heavy observational biases. A resolved multiplicity survey was conducted for a magnitude-limited G < 15mag sample of 423 potential cluster members, including sources with poorly fitted astrometric solutions in Gaia DR3. Speckle interferometric observations at the 2.5 meter telescope of SAI MSU observatory were combined with Gaia data, enabling the identification of 61 resolved binary or multiple systems within the 0.04 - 10 arcsec (5 - 1350 au) separation range. With speckle observations, we discovered 21 components in 20 systems. The existence of a Merope (23 Tau) companion is confirmed after several previous unsuccessful attempts. We show that the Gaia multipeak fraction is a strong predictor of subarcsecond multiplicity, as all sources with ipd_frac_multi_peak > 4% are successfully resolved. We found that 10% of Pleiades stars have a companion with a mass ratio q > 0.5 within projected separation of 27 < s < 1350 au, and confirm a deficit of wide binaries with s > 300 au. An observed dearth of wide pairs with large mass ratio (q > 0.55) may imprint the transition from hard to soft binaries regime at the early stages of cluster evolution. The total binary fraction for q > 0.5 systems is extrapolated to be around 25%.
On Analytical Solutions in Witten's Cubic Open String Field Theory
2024-12-30
Ilmar Gahramanov
This short review is based on the lecture given by the author at the Feza G\"ursey Physics Days School 2024. Here we briefly review analytic solutions (in particular, the Erler-Schnabl solution) of open cubic string field theory for the tachyon vacuum.
The reduced-width amplitude in nuclear cluster physics
2024-12-30
De-Ye Tao, Bo Zhou
The reduced-width amplitude, as a cluster overlap amplitude, is one important physical quantity for analyzing clustering in the nucleus depending on specified channels and has been calculated and applied widely in nuclear cluster physics. In this review, we briefly revisit the theoretical framework for calculating the reduced-width amplitude, as well as the outlines of cluster models to obtain the microscopic or semi-microscopic cluster wave functions. Besides, we also introduce recent progress related to the cluster overlap amplitudes, such as implementation for cross-section estimation and extension to three-body clustering analysis. Comprehensive examples are provided to show the applications of the reduced-width amplitude in analyzing cluster structures.
Enhanced Multimodal RAG-LLM for Accurate Visual Question Answering
2024-12-30
Junxiao Xue, Quan Deng, Fei Yu, Yanhao Wang, Jun Wang, Yuehua Li
Multimodal large language models (MLLMs), such as GPT-4o, Gemini, LLaVA, and Flamingo, have made significant progress in integrating visual and textual modalities, excelling in tasks like visual question answering (VQA), image captioning, and content retrieval. They can generate coherent and contextually relevant descriptions of images. However, they still face challenges in accurately identifying and counting objects and determining their spatial locations, particularly in complex scenes with overlapping or small objects. To address these limitations, we propose a novel framework based on multimodal retrieval-augmented generation (RAG), which introduces structured scene graphs to enhance object recognition, relationship identification, and spatial understanding within images. Our framework improves the MLLM's capacity to handle tasks requiring precise visual descriptions, especially in scenarios with challenging perspectives, such as aerial views or scenes with dense object arrangements. Finally, we conduct extensive experiments on the VG-150 dataset that focuses on first-person visual understanding and the AUG dataset that involves aerial imagery. The results show that our approach consistently outperforms existing MLLMs in VQA tasks, which stands out in recognizing, localizing, and quantifying objects in different spatial contexts and provides more accurate visual descriptions.
On some new metric characterisations of Hilbert spaces
2024-12-30
M. A. Sofi
In the literature surrounding the theory of Banach spaces, considerable effort has been invested in exploring the conditions on a Banach space X that characterise X as being an inner product space or as a linearly isomorphic copy of a Hilbert space. On the other hand, a different theory emerges when the class of Banach spaces is looked upon as a Lipschitz category where Lipschitz maps are used as morphisms in the new category in place of the familiar bounded linear maps in the linear theory. This paper provides a short survey of recent results involving the appropriate Lipschitz analogues of certain well known results from the linear theory characterizing Hilbert spaces. Whereas isometric description of Hilbert spaces has all along been a popular theme in this line of investigations, we shall concentrate mainly on isomorphic characterisations which entail the existence of an equivalent norm on the underlying space arising from an inner product.
Constraining the modified symmetric teleparallel gravity using cosmological data
2024-12-30
Shambel Sahlu, Amare Abebe
This paper examines the late-time accelerating Universe and the formation of large-scale structures within the modified symmetric teleparallel gravity framework, specifically using the $f(Q)$-gravity model, in light of recent cosmological data. After reviewing the background history of the Universe, and the linear cosmological perturbations, we consider the toy model $F(Q) = \alpha\sqrt{Q}+\beta$ ( where $Q$ represents nonmetricity, $\alpha$ and $\beta$ are model parameters) for further analysis. To evaluate the cosmological viability of this model, we utilize 57 Observational Hubble Data (OHD) points, 1048 supernovae distance modulus measurements (SNIa), their combined analysis (OHD+SNIa), 14 growth rate data points (f-data), and 30 redshift-space distortions (f$\sigma_8$) datasets. Through a detailed statistical analysis, the comparison between our model and $\Lambda$CDM has been conducted after we compute the best-fit values through the Markov Chain Monte Carlo (MCMC) simulations. Based on the results, we obtain the Hubble parameter, $H_0 = 69.20^{+4.40}_{{-}2.10}$ and the amplitude of the matter power spectrum normalization $\sigma_8 = 0.827^{+0.03}_{{-}0.01}$. These values suggest that our model holds significant promise in addressing the cosmological tensions.
Event cameras are biologically inspired sensors that emit events asynchronously with remarkable temporal resolution, garnering significant attention from both industry and academia. Mainstream methods favor frame and voxel representations, which reach a satisfactory performance while introducing time-consuming transformation, bulky models, and sacrificing fine-grained temporal information. Alternatively, Point Cloud representation demonstrates promise in addressing the mentioned weaknesses, but it ignores the polarity information, and its models have limited proficiency in abstracting long-term events' features. In this paper, we propose a frequency-aware network named FECNet that leverages Event Cloud representations. FECNet fully utilizes 2S-1T-1P Event Cloud by innovating the event-based Group and Sampling module. To accommodate the long sequence events from Event Cloud, FECNet embraces feature extraction in the frequency domain via the Fourier transform. This approach substantially extinguishes the explosion of Multiply Accumulate Operations (MACs) while effectively abstracting spatial-temporal features. We conducted extensive experiments on event-based object classification, action recognition, and human pose estimation tasks, and the results substantiate the effectiveness and efficiency of FECNet.
Robust Matrix Completion for Discrete Rating-Scale Data
2024-12-30
Aurore Archimbaud, Andreas Alfons, Ines Wilms
Matrix completion has gained considerable interest in recent years. The goal of matrix completion is to predict the unknown entries of a partially observed matrix using its known entries. Although common applications feature discrete rating-scale data, such as user-product rating matrices in recommender systems or surveys in the social and behavioral sciences, methods for matrix completion are almost always designed for and studied in the context of continuous data. Furthermore, only a small subset of the literature considers matrix completion in the presence of corrupted observations despite their common occurrence in practice. Examples include attacks on recommender systems (i.e., malicious users deliberately manipulating ratings to influence the recommender system to their advantage), or careless respondents in surveys (i.e., respondents providing answers irrespective of what the survey asks of them due to a lack of attention). We introduce a matrix completion algorithm that is tailored towards the discrete nature of rating-scale data and robust to the presence of corrupted observations. In addition, we investigate the performance of the proposed method and its competitors with discrete rating-scale (rather than continuous) data as well as under various missing data mechanisms and types of corrupted observations.
The Pristine survey. XXVI. Chemical abundances of subgiant stars of the extremelymetal-poor stream C-19
2024-12-30
P. Bonifacio, E. Caffau, P. François, N. Martin, R. Ibata, Z. Yuan, G. Kordopatis, J. I. González Hernández, D. S. Aguado, G. F. Thomas, A. Viswanathan, E. Dodd, F. Gran, E. Starkenburg, C. Lardo, R. Errani, M. Fouesneau, J. F. Navarro, K. A. Venn, K. Malhan
Context: The C-19 stellar stream is the most metal-poor stream known to date. While its width and velocity dispersion indicate a dwarf galaxy origin, its metallicity spread and abundance patterns are more similar to those of globular clusters (GCs). If it is indeed of GC origin, its extremely low metallicity ([Fe/H]=-3.4, estimated from giant stars) implies that these stellar systems can form out of gas that is as extremely poor in metals as this. Previously, only giant stream stars were observed spectroscopically, although the majority of stream stars are unevolved stars. Aims: We pushed the spectroscopic observations to the subgiant branch stars ($G\approx 20$) in order to consolidate the chemical and dynamical properties of C-19. Methods: We used the high-efficiency spectrograph X-shooter fed by the ESO 8.2 m VLT telescope to observe 15 candidate subgiant C-19 members. The spectra were used to measure radial velocities and to determine chemical abundances using the \mygi\ code. Results; We developed a likelihood model that takes metallicity and radial velocities into account. We conclude that 12 stars are likely members of C-19, while 3 stars (S05, S12, and S13) are likely contaminants. When these 3 stars are excluded, our model implies a mean metallicity $\rm \langle [Fe/H]\rangle = -3.1\pm 0.1$, the mean radial velocity is $\langle v_r\rangle = -192\pm3$ kms$^{-1}$, and the velocity dispersion is $\sigma_{vr} = 5.9^{+3.6}_{-5.9}$ kms$^{-1}$. This all agrees within errors with previous studies. The A(Mg) of a sample of 15 C-19 members, including 6 giant stars, shows a standard deviation of 0.44 dex, and the mean uncertainty on Mg is 0.25 dex. Conclusions: Our preferred interpretation of the current data is that C-19 is a disrupted GC. We cannot completely rule out the possibility that the GC could have belonged to a dwarf galaxy that contained more metal-rich stars, however. This scenario would explain the radial velocity members at higher metallicity, as well as the width and velocity dispersion of the stream. In either case, a GC formed out of gas as poor in metals as these stars seems necessary to explain the existence of C-19. The possibility that no GC was associated with C-19 cannot be ruled out either.
Overview of the development of smart classrooms under information technology: development and innovation of hardware and software
2024-12-30
Yanying Cheng
With the rapid development of information and communication technology (ICT), smart classroom has become an important trend in education modernization. This article reviews the development of smart classrooms from the hardware and software levels. The hardware describes the transformation from the construction of basic ICT facilities in single mode to a multi-modal information cloud platform. In terms of software, we look at the evolution of related supporting algorithms and technologies from the platform construction technology to the integration of advanced artificial intelligence (AI) technology from the perspectives of learning analysis and data mining. Provide guidance and suggestions for future educators, researchers and policymakers on the future direction of smart classrooms.
This survey accompanies a lecture on the paper ``Interlacing Families I: Bipartite Ramanujan Graphs of All Degrees'' by A. Marcus, D. Spielman, and N. Srivastava at the 2024 International Congress of Basic Science (ICBS) in July, 2024. Its purpose is to explain the developments surrounding this work over the past ten or so years, with an emphasis on connections to other areas of mathematics. Earlier surveys about the interlacing families method by the same authors focused on applications in functional analysis, whereas the focus here is on applications in spectral graph theory.
Air-ground collaborative robots have shown great potential in the field of fire and rescue, which can quickly respond to rescue needs and improve the efficiency of task execution. Mapping and navigation, as the key foundation for air-ground collaborative robots to achieve efficient task execution, have attracted a great deal of attention. This growing interest in collaborative robot mapping and navigation is conducive to improving the intelligence of fire and rescue task execution, but there has been no comprehensive investigation of this field to highlight their strengths. In this paper, we present a systematic review of the ground-to-ground cooperative robots for fire and rescue from a new perspective of mapping and navigation. First, an air-ground collaborative robots framework for fire and rescue missions based on unmanned aerial vehicle (UAV) mapping and unmanned ground vehicle (UGV) navigation is introduced. Then, the research progress of mapping and navigation under this framework is systematically summarized, including UAV mapping, UAV/UGV co-localization, and UGV navigation, with their main achievements and limitations. Based on the needs of fire and rescue missions, the collaborative robots with different numbers of UAVs and UGVs are classified, and their practicality in fire and rescue tasks is elaborated, with a focus on the discussion of their merits and demerits. In addition, the application examples of air-ground collaborative robots in various firefighting and rescue scenarios are given. Finally, this paper emphasizes the current challenges and potential research opportunities, rounding up references for practitioners and researchers willing to engage in this vibrant area of air-ground collaborative robots.
Differentiable Convex Optimization Layers in Neural Architectures: Foundations and Perspectives
2024-12-30
Calder Katyal
The integration of optimization problems within neural network architectures represents a fundamental shift from traditional approaches to handling constraints in deep learning. While it is long known that neural networks can incorporate soft constraints with techniques such as regularization, strict adherence to hard constraints is generally more difficult. A recent advance in this field, however, has addressed this problem by enabling the direct embedding of optimization layers as differentiable components within deep networks. This paper surveys the evolution and current state of this approach, from early implementations limited to quadratic programming, to more recent frameworks supporting general convex optimization problems. We provide a comprehensive review of the background, theoretical foundations, and emerging applications of this technology. Our analysis includes detailed mathematical proofs and an examination of various use cases that demonstrate the potential of this hybrid approach. This work synthesizes developments at the intersection of optimization theory and deep learning, offering insights into both current capabilities and future research directions in this rapidly evolving field.
There are several interweaving notions of discrete curvature on graphs. Many approaches utilize the optimal transportation metric on its probability simplex or the distance matrix of the graph. Since each of these ingredients is well understood when the underlying graph is a tree, in this note, we review existing and obtain new formulas for three different types of curvature on graphs. Along the way we obtain: a comparison result for the curvatures under consideration, a degree-diameter theorem for trees, and a combinatorial identity for certain sums of distances on trees.
2024-12-30
Rosario D. Laureano, Diana A. Mendes, Manuel Alberto M. Ferreira
This article provides a self-contained comprehensive review of the phenomenon of synchronization in dynamical systems, with a particular focus on chaotic systems in both continuous-time and discrete-time contexts. Synchronization, initially observed by Christiaan Huygens in 1665, has evolved from the study of periodic signals to encompass chaotic systems, where the sensitive dependence on initial conditions poses unique challenges. This review pointed to both theoretical foundations and contributions (concepts and methods) and practical insights, reinforcing the relevance of chaos synchronization in physics, biology, engineering, social sciences, economics and communication systems. The study investigates various coupling schemes, such as unidirectional and bidirectional coupling, and presents stability criteria under different configurations. In a very concise way, some ongoing research carried out by the authors is also indicated, using Lorenz, Rossler and hyperchaotic Rossler systems, and quadratic maps as case studies with parameter values leading to chaotic behavior. Special attention is given to the stability of synchronized states and the role of multi-stability and bifurcations, and its implications to loss of synchronization. We highlight the role of Lyapunov exponents, eigenvalues, and Lyapunov functions in guaranteeing local and global stability of the synchronized state. We aim to contribute to a broader understanding of chaos synchronization and its practical applications in diverse fields of knowledge. This text shed light on the control and stability of coupled chaotic systems, offering new perspectives on the synchronization of non-identical systems and the emergence of complex synchronization dynamics.
Graph Neural Networks for Next-Generation-IoT: Recent Advances and Open Challenges
2024-12-30
Nguyen Xuan Tung, Le Tung Giang, Bui Duc Son, Seon Geun Jeong, Trinh Van Chien, Won Joo Hwang, Lajos Hanzo
Graph Neural Networks (GNNs) have emerged as a critical tool for optimizing and managing the complexities of the Internet of Things (IoT) in next-generation networks. This survey presents a comprehensive exploration of how GNNs may be harnessed in 6G IoT environments, focusing on key challenges and opportunities through a series of open questions. We commence with an exploration of GNN paradigms and the roles of node, edge, and graph-level tasks in solving wireless networking problems and highlight GNNs' ability to overcome the limitations of traditional optimization methods. This guidance enhances problem-solving efficiency across various next-generation (NG) IoT scenarios. Next, we provide a detailed discussion of the application of GNN in advanced NG enabling technologies, including massive MIMO, reconfigurable intelligent surfaces, satellites, THz, mobile edge computing (MEC), and ultra-reliable low latency communication (URLLC). We then delve into the challenges posed by adversarial attacks, offering insights into defense mechanisms to secure GNN-based NG-IoT networks. Next, we examine how GNNs can be integrated with future technologies like integrated sensing and communication (ISAC), satellite-air-ground-sea integrated networks (SAGSIN), and quantum computing. Our findings highlight the transformative potential of GNNs in improving efficiency, scalability, and security within NG-IoT systems, paving the way for future advances. Finally, we propose a set of design guidelines to facilitate the development of efficient, scalable, and secure GNN models tailored for NG IoT applications.
Electromotive field in space and astrophysical plasmas
2024-12-30
Philippe-A. Bourdin, Yasuhito Narita
The concept of electromotive field appears in various applications in space and astrophysical plasmas. A review is given on the electromotive field highlighting our current understanding of the theoretical picture and the spacecraft observations in interplanetary space. The electromotive field is a key concept to successfully close the set of turbulent magnetohydrodynamic equations and also to construct a more complete picture of space plasma turbulence. Applications to astrophysical cases (Earth magnetosphere, heliospheric shocks, interstellar medium, and relativistic jets) are also briefly introduced, as well.
The Extreme Space Weather Event of 1872 February: Sunspots, Magnetic Disturbance, and Auroral Displays
2024-12-30
Hisashi Hayakawa, Edward W. Cliver, Frédéric Clette, Yusuke Ebihara, Shin Toriumi, Ilaria Ermolli, Theodosios Chatzistergos, Kentaro Hattori, Delores J. Knipp, Séan P. Blake, Gianna Cauzzi, Kevin Reardon, Philippe-A. Bourdin, Dorothea Just, Mikhail Vokhmyanin, Keitaro Matsumoto, Yoshizumi Miyoshi, José R. Ribeiro, Ana P. Correia, David M. Willis, Matthew N. Wild, Sam M. Silverman
We review observations of solar activity, geomagnetic variation, and auroral visibility for the extreme geomagnetic storm on 1872 February 4. The extreme storm (referred to here as the Chapman-Silverman storm) apparently originated from a complex active region of moderate area (\approx 500 {\mu}sh) that was favorably situated near disk center (S19{\deg} E05{\deg}). There is circumstantial evidence for an eruption from this region at 9--10 UT on 1872 February 3, based on the location, complexity, and evolution of the region, and on reports of prominence activations, which yields a plausible transit time of \approx29 hr to Earth. Magnetograms show that the storm began with a sudden commencement at \approx14:27 UT and allow a minimum Dst estimate of {\pounds} -834 nT. Overhead aurorae were credibly reported at Jacobabad (British India) and Shanghai (China), both at 19{\deg}.9 in magnetic latitude (MLAT) and 24{\deg}. 2 in invariant latitude (ILAT). Auroral visibility was reported from 13 locations with MLAT below |20|{\deg} for the 1872 storm (ranging from |10{\deg}. 0|--|19{\deg}. 9| MLAT) versus one each for the 1859 storm (|17{\deg}. 3| MLAT) and the 1921 storm (|16.{\deg}2| MLAT). The auroral extension and conservative storm intensity indicate a magnetic storm of comparable strength to the extreme storms of 1859 September (25{\deg}.1 \pm 0{\deg}.5 ILAT and -949 \pm 31 nT) and 1921 May (27{\deg}.1 ILAT and -907 \pm 132 nT), which places the 1872 storm among the three largest magnetic storms yet observed.
The Text Classification Pipeline: Starting Shallow going Deeper
2024-12-30
Marco Siino, Ilenia Tinnirello, Marco La Cascia
Text Classification (TC) stands as a cornerstone within the realm of Natural Language Processing (NLP), particularly when viewed through the lens of computer science and engineering. The past decade has seen deep learning revolutionize TC, propelling advancements in text retrieval, categorization, information extraction, and summarization. The scholarly literature is rich with datasets, models, and evaluation criteria, with English being the predominant language of focus, despite studies involving Arabic, Chinese, Hindi, and others. The efficacy of TC models relies heavily on their ability to capture intricate textual relationships and nonlinear correlations, necessitating a comprehensive examination of the entire TC pipeline. This monograph provides an in-depth exploration of the TC pipeline, with a particular emphasis on evaluating the impact of each component on the overall performance of TC models. The pipeline includes state-of-the-art datasets, text preprocessing techniques, text representation methods, classification models, evaluation metrics, current results and future trends. Each chapter meticulously examines these stages, presenting technical innovations and significant recent findings. The work critically assesses various classification strategies, offering comparative analyses, examples, case studies, and experimental evaluations. These contributions extend beyond a typical survey, providing a detailed and insightful exploration of TC.
Enhanced modeling of microlensing variations in light curves of strongly lensed quasars improves measurements of cosmological time delays, the Hubble Constant, and quasar structure. Traditional methods for modeling extra-galactic microlensing rely on computationally expensive magnification map generation. With large datasets expected from wide-field surveys like the Vera C. Rubin Legacy Survey of Space and Time, including thousands of lensed quasars and hundreds of multiply imaged supernovae, faster approaches become essential. We introduce a deep-learning model that is trained on pre-computed magnification maps covering the parameter space on a grid of k, g, and s. Our autoencoder creates a low-dimensional latent space representation of these maps, enabling efficient map generation. Quantifying the performance of magnification map generation from a low dimensional space is an essential step in the roadmap to develop neural network-based models that can replace traditional feed-forward simulation at much lower computational costs. We develop metrics to study various aspects of the autoencoder generated maps and show that the reconstruction is reliable. Even though we observe a mild loss of resolution in the generated maps, we find this effect to be smaller than the smoothing effect of convolving the original map with a source of a plausible size for its accretion disk in the red end of the optical spectrum and larger wavelengths and particularly one suitable for studying the Broad-Line Region of quasars. Used to generate large samples of on-demand magnification maps, our model can enable fast modeling of microlensing variability in lensed quasars and supernovae.
2024-12-30
Jiabin Yu, B. Andrei Bernevig, Raquel Queiroz, Enrico Rossi, Päivi Törmä, Bohm-Jung Yang
Quantum geometry, characterized by the quantum geometric tensor, is pivotal in diverse physical phenomena in quantum materials. In condensed matter systems, quantum geometry refers to the geoemtric properties of Bloch states in the Brillouin zone. This pedagogical review provides an accessible introduction to the concept of quantum geometry, emphasizing its extensive implications across multiple domains. Specifically, we discuss the role of quantum geometry in optical responses, Landau levels, and fractional Chern insulators, as well as its influence on superfluid weight, spin stiffness, exciton condensates, electron-phonon coupling, etc. By integrating these topics, we underscore the pervasive significance of quantum geometry in understanding emergent behaviors in quantum materials. Finally, we present an outlook on open questions and potential future directions, highlighting the need for continued exploration in this rapidly developing field.
This paper provides a comprehensive review of the design and implementation of automatically generated assessment reports (AutoRs) for formative use in K-12 Science, Technology, Engineering, and Mathematics (STEM) classrooms. With the increasing adoption of technology-enhanced assessments, there is a critical need for human-computer interactive tools that efficiently support the interpretation and application of assessment data by teachers. AutoRs are designed to provide synthesized, interpretable, and actionable insights into students' performance, learning progress, and areas for improvement. Guided by cognitive load theory, this study emphasizes the importance of reducing teachers' cognitive demands through user-centered and intuitive designs. It highlights the potential of diverse information presentation formats such as text, visual aids, and plots and advanced functionalities such as live and interactive features to enhance usability. However, the findings also reveal that many existing AutoRs fail to fully utilize these approaches, leading to high initial cognitive demands and limited engagement. This paper proposes a conceptual framework to inform the design, implementation, and evaluation of AutoRs, balancing the trade-offs between usability and functionality. The framework aims to address challenges in engaging teachers with technology-enhanced assessment results, facilitating data-driven decision-making, and providing personalized feedback to improve the teaching and learning process.
The Effects of Anthropogenic Air and Light Pollution on Astrophysics and Society
2024-12-30
Maya Nunez
Light and air pollution are the two main forms of pollution, containing the highest concentration in urban areas. I set out to investigate the effects of anthropogenic air and light pollution on the night sky, how this affects the astronomical data-collecting process, and how the general public perceives both forms of pollution. This paper utilizes primary and secondary research, referring to different sources: data collected through my telescope, astrophotography, other scientists' research, and survey responses from over 40 people across 2 areas: Southern California and Guadalajara, Mexico. Through this, I have found strong correlations between increased aerosol particles from air pollutants and increased night sky brightness, increased aerosol particles amplifying cloud formation, which in turn increases light reflection, and a correlation between different areas and what they believe about pollution, with many being misinformed on air and light pollution themselves.
Beam test performance of a prototype muon trigger detector for the PSI muEDM experiment
2024-12-30
Tianqi Hu, Jun Kai Ng, Guan Ming Wong, Cheng Chen, Kim Siang Khaw, Meng Lyu, Angela Papa, Philipp Schmidt-Wellenburg, David Staeger, Bastiano Vitali
We report on the performance evaluation of a prototype muon trigger detector for the PSI muEDM experiment, conducted as a proof-of-principle test at the $\pi$E1 beamline of the Paul Scherrer Institute (PSI) using \SI{27.5}{MeV/c} muons. The detector is designed to identify muons within the acceptance phase space of a compact storage solenoid and activate a pulsed magnetic kicker for muon storage; it was tested without the application of a magnetic field. It comprises a telescope made up of four scintillators in anticoincidence with a gate scintillator, all read out by silicon photomultipliers. The study focused on characterizing the detector's response to various muon trajectories and the light yield of its plastic scintillators. Experimental results demonstrated strong agreement with Geant4 Monte Carlo simulations that incorporate optical photon modeling, confirming the detector's concept and its potential for meeting the stringent requirements of the muEDM experiment.
Bridging the Gap: A Decade Review of Time-Series Clustering Methods
2024-12-29
John Paparrizos, Fan Yang, Haojun Li
Time series, as one of the most fundamental representations of sequential data, has been extensively studied across diverse disciplines, including computer science, biology, geology, astronomy, and environmental sciences. The advent of advanced sensing, storage, and networking technologies has resulted in high-dimensional time-series data, however, posing significant challenges for analyzing latent structures over extended temporal scales. Time-series clustering, an established unsupervised learning strategy that groups similar time series together, helps unveil hidden patterns in these complex datasets. In this survey, we trace the evolution of time-series clustering methods from classical approaches to recent advances in neural networks. While previous surveys have focused on specific methodological categories, we bridge the gap between traditional clustering methods and emerging deep learning-based algorithms, presenting a comprehensive, unified taxonomy for this research area. This survey highlights key developments and provides insights to guide future research in time-series clustering.
Scattering amplitudes in the high-energy limit can be described in terms of their singularity structure in the complex angular momentum plane, consisting of Regge poles and cuts. In QCD, gluon Reggeization has long been understood as a manifestation of a Regge pole, but until recently Reggeization violation remained largely obscure. New methods, based on iterative solution of rapidity evolution equations, facilitate direct computation of components of the amplitude which are mediated by multi-Reggeon exchange, a manifestation of Regge cuts. Upon disentangling the Regge cut from the pole we are now able to extract the pole parameters from state-of-the-art fixed-order computations (3 loops) and make predictions regarding certain components of the amplitude to higher loop orders. In this talk I review the key ideas which led to this progress, describe where we stand in exploring the structure of 2 -> 2 and 2 -> 3 amplitudes in the (multi-) Regge limit, and comment on the interplay between this research and the study of infrared factorization.
2024-12-29
John Paparrizos, Haojun Li, Fan Yang, Kaize Wu, Jens E. d'Hondt, Odysseas Papapetrou
Distance measures have been recognized as one of the fundamental building blocks in time-series analysis tasks, e.g., querying, indexing, classification, clustering, anomaly detection, and similarity search. The vast proliferation of time-series data across a wide range of fields has increased the relevance of evaluating the effectiveness and efficiency of these distance measures. To provide a comprehensive view of this field, this work considers over 100 state-of-the-art distance measures, classified into 7 categories: lock-step measures, sliding measures, elastic measures, kernel measures, feature-based measures, model-based measures, and embedding measures. Beyond providing comprehensive mathematical frameworks, this work also delves into the distinctions and applications across these categories for both univariate and multivariate cases. By providing comprehensive collections and insights, this study paves the way for the future development of innovative time-series distance measures.
Mott and Wiedemann Franz Law for Monolayer Graphene for Different Scattering Mechanisms
2024-12-29
Purnendu Ray, Kingshuk Sarkar
In this study, we conducted a comprehensive review and analysis of the thermoelectric responses exhibited by monolayer pristine graphene in response to temperature variations. Employing the Boltzmann transport theory, we rigorously examined and evaluated various thermoelectric coefficients, with particular emphasis on elucidating their behavior under different scattering mechanisms. We derived the analytical expressions for electrical conductivity, thermopower, and thermal conductivity at low temperatures by Sommerfeld expansion of the Fermi integral. We demonstrated that our numerically obtained values are consistent with the analytical calculations at low temperatures and hence obeying Mott and Wiedeman Franz law. However, the deviation was observed at higher temperatures. Furthermore, we performed theoretical calculations of chemical potential at both low and high temperatures and compared them with our numerically evaluated results at all temperatures. Through extensive calculations and meticulous evaluation, our study contributes to a deeper understanding of the intricate thermoelectric properties inherent in monolayer pristine graphene.
A comparison of HPC-based quantum computing simulators using Quantum Volume
2024-12-29
Lourens van Niekerk, Dhiraj Kumar, Aasish Kumar Sharma, Tino Meisel, Martin Leandro Paleico, Christian Boehme
This paper compares quantum computing simulators running on a single CPU or GPU-based HPC node using the Quantum Volume benchmark commonly proposed for comparing NISQ systems. As simulators do not suffer from noise, the metric used in the comparison is the time required to simulate a set Quantum Volume. The results are important to estimate the feasibility of proof of concept studies and debugging of quantum algorithms on HPC systems. Besides benchmarks of some commonly used simulators, this paper also offers an overview of their main features, a review of the state of quantum computing simulation and quantum computing benchmarking, and some insight into the theory of Quantum Volume.
Dive into Time-Series Anomaly Detection: A Decade Review
2024-12-29
Paul Boniol, Qinghua Liu, Mingyi Huang, Themis Palpanas, John Paparrizos
Recent advances in data collection technology, accompanied by the ever-rising volume and velocity of streaming data, underscore the vital need for time series analytics. In this regard, time-series anomaly detection has been an important activity, entailing various applications in fields such as cyber security, financial markets, law enforcement, and health care. While traditional literature on anomaly detection is centered on statistical measures, the increasing number of machine learning algorithms in recent years call for a structured, general characterization of the research methods for time-series anomaly detection. This survey groups and summarizes anomaly detection existing solutions under a process-centric taxonomy in the time series context. In addition to giving an original categorization of anomaly detection methods, we also perform a meta-analysis of the literature and outline general trends in time-series anomaly detection research.
(Invited Review) According to modern definition, a symbiotic nova is an otherwise normal nova (i.e. powered by explosive thermonuclear burning) that erupts within a symbiotic star, which is a binary where a WD accretes from a cool giant companion. Guided primarily by the very well observed eruptions of RS Oph in 2006 and 2021, and that of V407 Cyg in 2010, we investigate the main multi-wavelength properties of symbiotic novae and their relation to classical novae, and propose a 3D model structure that identifies the emitting source location for hard and supersoft X-rays, radio syncrothron and thermal, permitted and forbidden emission lines. Very few symbiotic novae are known in the Galaxy, and we compile a revised catalog based on firm astrometric identification. The exciting prospect of an imminent new outburst of T CrB is also discussed.
A Comprehensive Framework for Reliable Legal AI: Combining Specialized Expert Systems and Adaptive Refinement
2024-12-29
Sidra Nasir, Qamar Abbas, Samita Bai, Rizwan Ahmed Khan
This article discusses the evolving role of artificial intelligence (AI) in the legal profession, focusing on its potential to streamline tasks such as document review, research, and contract drafting. However, challenges persist, particularly the occurrence of "hallucinations" in AI models, where they generate inaccurate or misleading information, undermining their reliability in legal contexts. To address this, the article proposes a novel framework combining a mixture of expert systems with a knowledge-based architecture to improve the precision and contextual relevance of AI-driven legal services. This framework utilizes specialized modules, each focusing on specific legal areas, and incorporates structured operational guidelines to enhance decision-making. Additionally, it leverages advanced AI techniques like Retrieval-Augmented Generation (RAG), Knowledge Graphs (KG), and Reinforcement Learning from Human Feedback (RLHF) to improve the system's accuracy. The proposed approach demonstrates significant improvements over existing AI models, showcasing enhanced performance in legal tasks and offering a scalable solution to provide more accessible and affordable legal services. The article also outlines the methodology, system architecture, and promising directions for future research in AI applications for the legal sector.
Multi-Epoch precise photometry from the ground: MUDEHaR, magnetic stars and everything around
2024-12-29
G. Holgado, J. Maíz Apellániz, J. A. Caballero
MUDEHaR is an on-going multi-epoch photometric survey with two narrow filters in H$\alpha$ and the calcium triplet window that uses the T80Cam wide-field imager at the JAST/T80 telescope at Spanish Javalambre astronomical observatory. It is obtaining 100 epochs/year per field for 20 fields in the Galactic disk, each of 2\,deg$^2$, for a total of 40\,deg$^2$. Focused on stellar clusters and HII regions including bright stars, its main objective is to detect tens of thousands OB stars that present emission variability in H$\alpha$ on days-months-years scale. The observed targets include magnetic massive stars, pulsating stars, and all kinds of variable stars. Among our driving scientific objectives of MUDEHaR observations is to identify potential magnetic candidates in massive stars. Only 10--20\,\% of OB stars display a measurable magnetic field, and its origin is still in debate. We outline here the multi-step process involved in identifying OB magnetic stars, highlighting the significance of MUDEHaR observations in this process.
Robot foundation models, particularly Vision-Language-Action (VLA) models, have garnered significant attention for their ability to enhance robot policy learning, greatly improving robot generalization and robustness. OpenAI recent model, o1, showcased impressive capabilities in solving complex problems by utilizing extensive reasoning chains. This prompts an important question: can robot models achieve better performance in multi-task, complex environments by reviewing prior observations and then providing task-specific reasoning to guide action prediction? In this paper, we introduce \textbf{Chain-of-Affordance (CoA)}, a novel approach to scaling robot models by incorporating reasoning in the format of sequential robot affordances to facilitate task completion. Specifically, we prompt the model to consider the following four types of affordances before taking action: a) object affordance - what object to manipulate and where it is; b) grasp affordance - the specific object part to grasp; c) spatial affordance - the optimal space to place the object; and d) movement affordance - the collision-free path for movement. By integrating this knowledge into the policy model, the robot gains essential context, allowing it to act with increased precision and robustness during inference. Our experiments demonstrate that CoA achieves superior performance than state-of-the-art robot foundation models, such as OpenVLA and Octo. Additionally, CoA shows strong generalization to unseen object poses, identifies free space, and avoids obstacles in novel environments.
Cool, But What About Oracles? An Oracle-Based Perspective on Blockchain Integration in the Accounting Field
2024-12-29
Giulio Caldarelli
The Bitcoin Network is a sophisticated accounting system that allows its underlying cryptocurrency to be trusted even in the absence of a reliable financial authority. Given its undeniable success, the technology, generally referred to as blockchain, has also been proposed as a means to improve legacy accounting systems. Accounting for real-world data, however, requires the intervention of a third party known as an Oracle, which, having not the same characteristics as a blockchain, could potentially reduce the expected integration benefit. Through a systematic review of the literature, this study aims to investigate whether the papers concerning blockchain integration in accounting consider and address the limitations posed by oracles. A broad overview of the limitations that emerged in the literature is provided and distinguished according to the specific accounting integration. Results support the view that although research on the subject counts numerous articles, actual studies considering oracle limitations are lacking. Interestingly, despite the scarce production of papers addressing oracles in various accounting sectors, reporting for ESG already shows interesting workarounds for oracle limitations, with permissioned chains envisioned as a valid support for the safe storage of sustainability data.
Stiffening of matter in quark-hadron continuity: a mini-review
2024-12-29
Toru Kojo
Recent observations of neutron stars, combined with causality, thermodynamic stability, and nuclear constraints, indicate rapid stiffening of QCD matter at density slightly above nuclear saturation density ($n_0 \simeq 0.16\,{\rm fm}^{-3}$). The evolution of the stiffening is quicker than expected from purely nucleonic models with many-body repulsion. Taking into account the quark substructure of baryons, we argue that the saturation of quarks states occur at $\sim$ 2-3$n_0$, driving the quark matter formation even before baryonic cores of the radii $\sim$0.5 fm spatially overlap. We describe the continuous transitions from hadronic to quark matter are described within a quarkyonic matter model in which gluons are assumed to be confining at density of interest. To obtain analytic insights for the transient regime, we construct an ideal model of quarkyonic matter, IdylliQ model, in which one can freely switch from baryonic to quark languages and vice versa.
The financial sector, a pivotal force in economic development, increasingly uses the intelligent technologies such as natural language processing to enhance data processing and insight extraction. This research paper through a review process of the time span of 2018-2023 explores the use of text mining as natural language processing techniques in various components of the financial system including asset pricing, corporate finance, derivatives, risk management, and public finance and highlights the need to address the specific problems in the discussion section. We notice that most of the research materials combined probabilistic with vector-space models, and text-data with numerical ones. The most used technique regarding information processing is the information classification technique and the most used algorithms include the long-short term memory and bidirectional encoder models. The research noticed that new specific algorithms are developed and the focus of the financial system is mainly on asset pricing component. The research also proposes a path from engineering perspective for researchers who need to analyze financial text. The challenges regarding text mining perspective such as data quality, context-adaption and model interpretability need to be solved so to integrate advanced natural language processing models and techniques in enhancing financial analysis and prediction. Keywords: Financial System (FS), Natural Language Processing (NLP), Software and Text Engineering, Probabilistic, Vector-Space, Models, Techniques, TextData, Financial Analysis.
Artificial Intelligence for Quantum Error Correction: A Comprehensive Review
2024-12-29
Zihao Wang, Hao Tang
Quantum Error Correction (QEC) is the process of detecting and correcting errors in quantum systems, which are prone to decoherence and quantum noise. QEC is crucial for developing stable and highly accurate quantum computing systems, therefore, several research efforts have been made to develop the best QEC strategy. Recently, Google's breakthrough shows great potential to improve the accuracy of the existing error correction methods. This survey provides a comprehensive review of advancements in the use of artificial intelligence (AI) tools to enhance QEC schemes for existing Noisy Intermediate Scale Quantum (NISQ) systems. Specifically, we focus on machine learning (ML) strategies and span from unsupervised, supervised, semi-supervised, to reinforcement learning methods. It is clear from the evidence, that these methods have recently shown superior efficiency and accuracy in the QEC pipeline compared to conventional approaches. Our review covers more than 150 relevant studies, offering a comprehensive overview of progress and perspective in this field. We organized the reviewed literature on the basis of the AI strategies employed and improvements in error correction performance. We also discuss challenges ahead such as data sparsity caused by limited quantum error datasets and scalability issues as the number of quantum bits (qubits) in quantum systems kept increasing very fast. We conclude the paper with summary of existing works and future research directions aimed at deeper integration of AI techniques into QEC strategies.
With the rapid evolution of large language models (LLM), reinforcement learning (RL) has emerged as a pivotal technique for code generation and optimization in various domains. This paper presents a systematic survey of the application of RL in code optimization and generation, highlighting its role in enhancing compiler optimization, resource allocation, and the development of frameworks and tools. Subsequent sections first delve into the intricate processes of compiler optimization, where RL algorithms are leveraged to improve efficiency and resource utilization. The discussion then progresses to the function of RL in resource allocation, emphasizing register allocation and system optimization. We also explore the burgeoning role of frameworks and tools in code generation, examining how RL can be integrated to bolster their capabilities. This survey aims to serve as a comprehensive resource for researchers and practitioners interested in harnessing the power of RL to advance code generation and optimization techniques.
There has been a growing interest in using Large Language Models (LLMs) for code review thanks to their proven proficiency in code comprehension. The primary objective of most review scenarios is to generate desired review comments (DRCs) that explicitly identify issues to trigger code fixes. However, existing LLM-based solutions are not so effective in generating DRCs for various reasons such as hallucination. To enhance their code review ability, they need to be fine-tuned with a customized dataset that is ideally full of DRCs. Nevertheless, such a dataset is not yet available, while manual annotation of DRCs is too laborious to be practical. In this paper, we propose a dataset distillation method, Desiview, which can automatically construct a distilled dataset by identifying DRCs from a code review dataset. Experiments on the CodeReviewer dataset comprising more than 150K review entries show that Desiview achieves an impressive performance of 88.93%, 80.37%, 86.67%, and 84.44% in terms of Precision, Recall, Accuracy, and F1, respectively, surpassing state-of-the-art methods. To validate the effect of such a distilled dataset on enhancing LLMs' code review ability, we first fine-tune the latest LLaMA series (i.e., LLaMA 3 and LLaMA 3.1) to build model Desiview4FT. We then enhance the model training effect through KTO alignment by feeding those review comments identified as non-DRCs to the LLMs, resulting in model Desiview4FA. Verification results indicate that Desiview4FA slightly outperforms Desiview4FT, while both models have significantly improved against the base models in terms of generating DRCs. Human evaluation confirms that both models identify issues more accurately and tend to generate review comments that better describe the issues contained in the code than the base LLMs do.
2024-12-29
Core Francisco Park, Andrew Lee, Ekdeep Singh Lubana, Yongyi Yang, Maya Okawa, Kento Nishi, Martin Wattenberg, Hidenori Tanaka
Recent work has demonstrated that semantics specified by pretraining data influence how representations of different concepts are organized in a large language model (LLM). However, given the open-ended nature of LLMs, e.g., their ability to in-context learn, we can ask whether models alter these pretraining semantics to adopt alternative, context-specified ones. Specifically, if we provide in-context exemplars wherein a concept plays a different role than what the pretraining data suggests, do models reorganize their representations in accordance with these novel semantics? To answer this question, we take inspiration from the theory of conceptual role semantics and define a toy "graph tracing" task wherein the nodes of the graph are referenced via concepts seen during training (e.g., apple, bird, etc.) and the connectivity of the graph is defined via some predefined structure (e.g., a square grid). Given exemplars that indicate traces of random walks on the graph, we analyze intermediate representations of the model and find that as the amount of context is scaled, there is a sudden re-organization from pretrained semantic representations to in-context representations aligned with the graph structure. Further, we find that when reference concepts have correlations in their semantics (e.g., Monday, Tuesday, etc.), the context-specified graph structure is still present in the representations, but is unable to dominate the pretrained structure. To explain these results, we analogize our task to energy minimization for a predefined graph topology, providing evidence towards an implicit optimization process to infer context-specified semantics. Overall, our findings indicate scaling context-size can flexibly re-organize model representations, possibly unlocking novel capabilities.
ELECTRA and GPT-4o: Cost-Effective Partners for Sentiment Analysis
2024-12-29
James P. Beno
Bidirectional transformers excel at sentiment analysis, and Large Language Models (LLM) are effective zero-shot learners. Might they perform better as a team? This paper explores collaborative approaches between ELECTRA and GPT-4o for three-way sentiment classification. We fine-tuned (FT) four models (ELECTRA Base/Large, GPT-4o/4o-mini) using a mix of reviews from Stanford Sentiment Treebank (SST) and DynaSent. We provided input from ELECTRA to GPT as: predicted label, probabilities, and retrieved examples. Sharing ELECTRA Base FT predictions with GPT-4o-mini significantly improved performance over either model alone (82.74 macro F1 vs. 79.29 ELECTRA Base FT, 79.52 GPT-4o-mini) and yielded the lowest cost/performance ratio (\$0.12/F1 point). However, when GPT models were fine-tuned, including predictions decreased performance. GPT-4o FT-M was the top performer (86.99), with GPT-4o-mini FT close behind (86.77) at much less cost (\$0.38 vs. \$1.59/F1 point). Our results show that augmenting prompts with predictions from fine-tuned encoders is an efficient way to boost performance, and a fine-tuned GPT-4o-mini is nearly as good as GPT-4o FT at 76% less cost. Both are affordable options for projects with limited resources.
No Preference Left Behind: Group Distributional Preference Optimization
2024-12-28
Binwei Yao, Zefan Cai, Yun-Shiuan Chuang, Shanglin Yang, Ming Jiang, Diyi Yang, Junjie Hu
Preferences within a group of people are not uniform but follow a distribution. While existing alignment methods like Direct Preference Optimization (DPO) attempt to steer models to reflect human preferences, they struggle to capture the distributional pluralistic preferences within a group. These methods often skew toward dominant preferences, overlooking the diversity of opinions, especially when conflicting preferences arise. To address this issue, we propose Group Distribution Preference Optimization (GDPO), a novel framework that aligns language models with the distribution of preferences within a group by incorporating the concept of beliefs that shape individual preferences. GDPO calibrates a language model using statistical estimation of the group's belief distribution and aligns the model with belief-conditioned preferences, offering a more inclusive alignment framework than traditional methods. In experiments using both synthetic controllable opinion generation and real-world movie review datasets, we show that DPO fails to align with the targeted belief distributions, while GDPO consistently reduces this alignment gap during training. Moreover, our evaluation metrics demonstrate that GDPO outperforms existing approaches in aligning with group distributional preferences, marking a significant advance in pluralistic alignment.
"Feeling that I was Collaborating with Them": A 20 years Systematic Literature Review of Social Virtual Reality Leveraging Collaboration
2024-12-28
Niloofar Sayadi, Sadie Co, Diego Gomez-Zara
As more people meet, interact, and socialize online, Social Virtual Reality (VR) emerges as a promising technology that can bridge the gap between traditional face-to-face and online communication. Compared to traditional screen-based applications, Social VR provides immersive, physical, and three-dimensional social interactions, making it a promising communication tool for enhancing collaborations. Despite the growing interest and research in Social VR, how it has supported collaborations and teamwork has not been explored in depth. To map the existing research in this domain, we conducted a 20-year systematic literature review to characterize how Social VR has been employed for collaboration. After screening 1,123 articles, we identified 51 articles that specifically addressed how Social VR has leveraged collaboration among remote users. Our findings show that Social VR can enhance team collaboration on three key levels: enhancing individual perceptions and experiences within their groups, fostering team dynamics with virtual elements that enable realistic interactions, and employing VR affordances that augment users' spaces. However, the research requires a deeper understanding of how Social VR can support long-term collaboration and trust-building in collaborative settings. This review highlights the current practices, challenges, and future research opportunities within CSCW, offering insights for theorizing the impact of Social VR on team collaboration and for designing new applications that effectively support remote collaborations.
Interconnection is crucial for computing systems. However, the current interconnection performance between processors and devices, such as memory devices and accelerators, significantly lags behind their computing performance, severely limiting the overall performance. To address this challenge, Intel proposes Compute Express Link (CXL), an open industry-standard interconnection. With memory semantics, CXL offers low-latency, scalable, and coherent interconnection between processors and devices. This paper introduces recent advances in CXL-based interconnection systems with memory semantics. We classify the existing research into three categories: Pooling Memory, Distributed Shared Memory, and Unified Memory. Pooling Memory interconnects processors and memory, aims to address memory wall challenge. Distributed shared memory interconnects processors across nodes, aims to synchronize the cluster. Unified memory interconnects processors and accelerators, aims to enhance collaboration in heterogeneous computing systems. Finally, we discuss the future research and envision memory-centric computing with CXL.
Visual Grounding is also known as Referring Expression Comprehension and Phrase Grounding. It involves localizing a natural number of specific regions within an image based on a given textual description. The objective of this task is to emulate the prevalent referential relationships in social conversations, equipping machines with human-like multimodal comprehension capabilities. Consequently, it has extensive applications in various domains. However, since 2021, visual grounding has witnessed significant advancements, with emerging new concepts such as grounded pre-training, grounding multimodal LLMs, generalized visual grounding, and giga-pixel grounding, which have brought numerous new challenges. In this survey, we initially examine the developmental history of visual grounding and provide an overview of essential background knowledge. We systematically track and summarize the advancements and meticulously organize the various settings in visual grounding, thereby establishing precise definitions of these settings to standardize future research and ensure a fair comparison. Additionally, we delve into several advanced topics and highlight numerous applications of visual grounding. Finally, we outline the challenges confronting visual grounding and propose valuable directions for future research, which may serve as inspiration for subsequent researchers. By extracting common technical details, this survey encompasses the representative works in each subtopic over the past decade. To the best, this paper presents the most comprehensive overview currently available in the field of grounding. This survey is designed to be suitable for both beginners and experienced researchers, serving as an invaluable resource for understanding key concepts and tracking the latest research developments. We keep tracing related works at https://github.com/linhuixiao/Awesome-Visual-Grounding.
Vector Extrapolation Methods Applied To Geometric Multigrid Solvers For Isogeometric Analysis
2024-12-28
Abdellatif Mouhssine, Ahmed Ratnani, Hassane Sadok
In the present work, we study how to develop an efficient solver for the fast resolution of large and sparse linear systems that occur while discretizing elliptic partial differential equations using isogeometric analysis. Our new approach combines vector extrapolation methods with geometric multigrid schemes. Using polynomial-type extrapolation methods to speed up the multigrid iterations is our main focus. Several numerical tests are given to demonstrate the efficiency of these polynomial extrapolation methods in improving multigrid solvers in the context of isogeometric analysis.
Weakly Supervised Monitoring Anomaly Detection (WSMAD) utilizes weak supervision learning to identify anomalies, a critical task for smart city monitoring. However, existing multimodal approaches often fail to meet the real-time and interpretability requirements of edge devices due to their complexity. This paper presents TCVADS (Two-stage Cross-modal Video Anomaly Detection System), which leverages knowledge distillation and cross-modal contrastive learning to enable efficient, accurate, and interpretable anomaly detection on edge devices.TCVADS operates in two stages: coarse-grained rapid classification and fine-grained detailed analysis. In the first stage, TCVADS extracts features from video frames and inputs them into a time series analysis module, which acts as the teacher model. Insights are then transferred via knowledge distillation to a simplified convolutional network (student model) for binary classification. Upon detecting an anomaly, the second stage is triggered, employing a fine-grained multi-class classification model. This stage uses CLIP for cross-modal contrastive learning with text and images, enhancing interpretability and achieving refined classification through specially designed triplet textual relationships. Experimental results demonstrate that TCVADS significantly outperforms existing methods in model performance, detection efficiency, and interpretability, offering valuable contributions to smart city monitoring applications.
2024-12-28
Yijun Bei, Teng Ma, Dongxiang Zhang, Sai Wu, Kian-Lee Tan, Gang Chen
Discovering co-movement patterns from urban-scale video data sources has emerged as an attractive topic. This task aims to identify groups of objects that travel together along a common route, which offers effective support for government agencies in enhancing smart city management. However, the previous work has made a strong assumption on the accuracy of recovered trajectories from videos and their co-movement pattern definition requires the group of objects to appear across consecutive cameras along the common route. In practice, this often leads to missing patterns if a vehicle is not correctly identified from a certain camera due to object occlusion or vehicle mis-matching. To address this challenge, we propose a relaxed definition of co-movement patterns from video data, which removes the consecutiveness requirement in the common route and accommodates a certain number of missing captured cameras for objects within the group. Moreover, a novel enumeration framework called MaxGrowth is developed to efficiently retrieve the relaxed patterns. Unlike previous filter-and-refine frameworks comprising both candidate enumeration and subsequent candidate verification procedures, MaxGrowth incurs no verification cost for the candidate patterns. It treats the co-movement pattern as an equivalent sequence of clusters, enumerating candidates with increasing sequence length while avoiding the generation of any false positives. Additionally, we also propose two effective pruning rules to efficiently filter the non-maximal patterns. Extensive experiments are conducted to validate the efficiency of MaxGrowth and the quality of its generated co-movement patterns. Our MaxGrowth runs up to two orders of magnitude faster than the baseline algorithm. It also demonstrates high accuracy in real video dataset when the trajectory recovery algorithm is not perfect.
An Undergraduate Course on the Statistical Principles of Research Study Design
2024-12-28
Lee Kennedy-Shaffer
The undergraduate curriculum in statistics and data science is undergoing changes to accommodate new methods, newly interested students, and the changing role of statistics in society. Because of this, it is more important than ever that students understand the role of study design and how to formulate meaningful scientific and statistical research questions. While the traditional Design of Experiments course is still extremely valuable for students heading to industry and research careers, a broader study design course that incorporates survey sampling, observational studies, and the basics of causal inference with randomized experiment design is particularly useful for students with a wide range of applied interests. Here, I describe such a course at a small liberal arts college, along with ways to adapt it to meet different student and instructor background and interests. The course serves as a valuable bridge to advanced statistical coursework, meets key statistical literacy and communication learning goals, and can be tailored to the desired level of computational and mathematical fluency. Through reading, discussing, and critiquing actual published research studies, students learn that statistics is a living discipline with real consequences and become better consumers and producers of scientific research and data-driven insights.
Pattern recognition leveraging both RGB and Event cameras can significantly enhance performance by deploying deep neural networks that utilize a fine-tuning strategy. Inspired by the successful application of large models, the introduction of such large models can also be considered to further enhance the performance of multi-modal tasks. However, fully fine-tuning these models leads to inefficiency and lightweight fine-tuning methods such as LoRA and Adapter have been proposed to achieve a better balance between efficiency and performance. To our knowledge, there is currently no work that has conducted parameter-efficient fine-tuning (PEFT) for RGB-Event recognition based on pre-trained foundation models. To address this issue, this paper proposes a novel PEFT strategy to adapt the pre-trained foundation vision models for the RGB-Event-based classification. Specifically, given the RGB frames and event streams, we extract the RGB and event features based on the vision foundation model ViT with a modality-specific LoRA tuning strategy. The frame difference of the dual modalities is also considered to capture the motion cues via the frame difference backbone network. These features are concatenated and fed into high-level Transformer layers for efficient multi-modal feature learning via modality-shared LoRA tuning. Finally, we concatenate these features and feed them into a classification head to achieve efficient fine-tuning. The source code and pre-trained models will be released on \url{https://github.com/Event-AHU/VELoRA}.
Humblebragging is a phenomenon where individuals present self-promotional statements under the guise of modesty or complaints. For example, a statement like, "Ugh, I can't believe I got promoted to lead the entire team. So stressful!", subtly highlights an achievement while pretending to be complaining. Detecting humblebragging is important for machines to better understand the nuances of human language, especially in tasks like sentiment analysis and intent recognition. However, this topic has not yet been studied in computational linguistics. For the first time, we introduce the task of automatically detecting humblebragging in text. We formalize the task by proposing a 4-tuple definition of humblebragging and evaluate machine learning, deep learning, and large language models (LLMs) on this task, comparing their performance with humans. We also create and release a dataset called HB24, containing 3,340 humblebrags generated using GPT-4o. Our experiments show that detecting humblebragging is non-trivial, even for humans. Our best model achieves an F1-score of 0.88. This work lays the foundation for further exploration of this nuanced linguistic phenomenon and its integration into broader natural language understanding systems.
Proceedings Eleventh International Conference on Non-Classical Logics. Theory and Applications
2024-12-28
Andrzej Indrzejczak, Michał Zawidzki
Non-Classical Logics. Theory and Applications (NCL) is an international conference which aims to present novel results and survey works in widely understood non-classical logics and their applications. This year's edition was also an opportunity to hold a special session devoted to the ERC-funded project "ExtenDD" devoted to complex terms and term-forming operators.
Children's Acquisition of Tail-recursion Sequences: A Review of Locative Recursion and Possessive Recursion as Examples
2024-12-28
Xiaoyi Wang, Chenxi Fu, Caimei Yang, Ziman Zhuang
Recursion is the nature of human natural language. Since Chomsky proposed generative grammar, many scholars have studied recursion either theoretically or empirically. However, by observing children's acquisition of tail recursion sequences, we can verify the nativism of language supported by universal grammar and reveal the cognitive mechanism of human brain. To date, our understanding of children's acquisition path of recursion and influencing factors still remain controversial. This systematic review summarizes the research of tail recursive sequence by taking possessive recursion and locative recursion as examples, focusing on the experimental methods, acquisition paths, and influencing factors of tail recursive sequence. The current behavioural experiments reveal that, the debate about children's performance revolves around: 1) Gradual acquisition or synchronous acquisition. 2) symmetry or asymmetry between the acquisition of locative recursion sequences and possessive recursion sequences. We presume that children can acquire recursion quickly in a short period of time thanks to the language acquisition device, though there are also scholars who believe that a third factor also plays a role.
Uncertainty Quantified Deep Learning and Regression Analysis Framework for Image Segmentation of Skin Cancer Lesions
2024-12-28
Elhoucine Elfatimi, Pratik Shah
Deep learning models (DLMs) frequently achieve accurate segmentation and classification of tumors from medical images. However, DLMs lacking feedback on their image segmentation mechanisms, such as Dice coefficients and confidence in their performance, face challenges when processing previously unseen images in real-world clinical settings. Uncertainty estimates to identify DLM predictions at the cellular or single-pixel level that require clinician review can enhance trust. However, their deployment requires significant computational resources. This study reports two DLMs, one trained from scratch and another based on transfer learning, with Monte Carlo dropout or Bayes-by-backprop uncertainty estimations to segment lesions from the publicly available The International Skin Imaging Collaboration-19 dermoscopy image database with cancerous lesions. A novel approach to compute pixel-by-pixel uncertainty estimations of DLM segmentation performance in multiple clinical regions from a single dermoscopy image with corresponding Dice scores is reported for the first time. Image-level uncertainty maps demonstrated correspondence between imperfect DLM segmentation and high uncertainty levels in specific skin tissue regions, with or without lesions. Four new linear regression models that can predict the Dice performance of DLM segmentation using constants and uncertainty measures, either individually or in combination from lesions, tissue structures, and non-tissue pixel regions critical for clinical diagnosis and prognostication in skin images (Spearman's correlation, p < 0.05), are reported for the first time for low-compute uncertainty estimation workflows.
Comprehensive Review of EEG-to-Output Research: Decoding Neural Signals into Images, Videos, and Audio
2024-12-28
Yashvir Sabharwal, Balaji Rama
Electroencephalography (EEG) is an invaluable tool in neuroscience, offering insights into brain activity with high temporal resolution. Recent advancements in machine learning and generative modeling have catalyzed the application of EEG in reconstructing perceptual experiences, including images, videos, and audio. This paper systematically reviews EEG-to-output research, focusing on state-of-the-art generative methods, evaluation metrics, and data challenges. Using PRISMA guidelines, we analyze 1800 studies and identify key trends, challenges, and opportunities in the field. The findings emphasize the potential of advanced models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers, while highlighting the pressing need for standardized datasets and cross-subject generalization. A roadmap for future research is proposed that aims to improve decoding accuracy and broadening real-world applications.
Low altitude economy (LAE) holds immense potential to drive urban development across various sectors. However, LAE also faces challenges in data collection and processing efficiency, flight control precision, and network performance. The challenges could be solved by realizing an integration of sensing, communications, computation, and control (ISC3) for LAE. In this regard, embodied artificial intelligence (EAI), with its unique perception, planning, and decision-making capabilities, offers a promising solution to realize ISC3. Specifically, this paper investigates an application of EAI into ISC3 to support LAE, exploring potential research focuses, solutions, and case study. We begin by outlining rationales and benefits of introducing EAI into LAE, followed by reviewing research directions and solutions for EAI in ISC3. We then propose a framework of an EAI-enabled ISC3 for LAE. The framework's effectiveness is evaluated through a case study of express delivery utilizing an EAI-enabled UAV. Finally, we discuss several future research directions for advancing EAI-enabled LAE.
From Generalist to Specialist: A Survey of Large Language Models for Chemistry
2024-12-28
Yang Han, Ziping Wan, Lu Chen, Kai Yu, Xin Chen
Large Language Models (LLMs) have significantly transformed our daily life and established a new paradigm in natural language processing (NLP). However, the predominant pretraining of LLMs on extensive web-based texts remains insufficient for advanced scientific discovery, particularly in chemistry. The scarcity of specialized chemistry data, coupled with the complexity of multi-modal data such as 2D graph, 3D structure and spectrum, present distinct challenges. Although several studies have reviewed Pretrained Language Models (PLMs) in chemistry, there is a conspicuous absence of a systematic survey specifically focused on chemistry-oriented LLMs. In this paper, we outline methodologies for incorporating domain-specific chemistry knowledge and multi-modal information into LLMs, we also conceptualize chemistry LLMs as agents using chemistry tools and investigate their potential to accelerate scientific research. Additionally, we conclude the existing benchmarks to evaluate chemistry ability of LLMs. Finally, we critically examine the current challenges and identify promising directions for future research. Through this comprehensive survey, we aim to assist researchers in staying at the forefront of developments in chemistry LLMs and to inspire innovative applications in the field.
Generalized Grade-of-Membership Estimation for High-dimensional Locally Dependent Data
2024-12-27
Ling Chen, Chengzhu Huang, Yuqi Gu
This work focuses on the mixed membership models for multivariate categorical data widely used for analyzing survey responses and population genetics data. These grade of membership (GoM) models offer rich modeling power but present significant estimation challenges for high-dimensional polytomous data. Popular existing approaches, such as Bayesian MCMC inference, are not scalable and lack theoretical guarantees in high-dimensional settings. To address this, we first observe that data from this model can be reformulated as a three-way (quasi-)tensor, with many subjects responding to many items with varying numbers of categories. We introduce a novel and simple approach that flattens the three-way quasi-tensor into a "fat" matrix, and then perform a singular value decomposition of it to estimate parameters by exploiting the singular subspace geometry. Our fast spectral method can accommodate a broad range of data distributions with arbitrarily locally dependent noise, which we formalize as the generalized-GoM models. We establish finite-sample entrywise error bounds for the generalized-GoM model parameters. This is supported by a new sharp two-to-infinity singular subspace perturbation theory for locally dependent and flexibly distributed noise, a contribution of independent interest. Simulations and applications to data in political surveys, population genetics, and single-cell sequencing demonstrate our method's superior performance.
Complement or substitute? How AI increases the demand for human skills
2024-12-27
Elina Mäkelä, Fabian Stephany
The question of whether AI substitutes or complements human work is central to debates on the future of work. This paper examines the impact of AI on skill demand and compensation in the U.S. economy, analysing 12 million online job vacancies from 2018 to 2023. It investigates internal effects (within-job substitution and complementation) and external effects (across occupations, industries, and regions). Our findings reveal a significant increase in demand for AI-complementary skills, such as digital literacy, teamwork, and resilience, alongside rising wage premiums for these skills in AI roles like Data Scientist. Conversely, substitute skills, including customer service and text review, have declined in both demand and value within AI-related positions. Examining external effects, we find a notable rise in demand for complementary skills in non-AI roles linked to the growth of AI-related jobs in specific industries or regions. At the same time, there is a moderate decline in non-AI roles requiring substitute skills. Overall, AI's complementary effect is up to 50% larger than its substitution effect, resulting in net positive demand for skills. These results, replicated for the UK and Australia, highlight AI's transformative impact on workforce skill requirements. They suggest reskilling efforts should prioritise not only technical AI skills but also complementary skills like ethics and digital literacy.
Physics of 2D magnets and magnetic thin films: Surface structure and surface phase transition, criticality and skyrmions
2024-12-27
Hung T. Diep
Recently, there is an increasing renewed interest in 2D magnetism such as Van der Waals magnets. The physics of 2D magnetism and ultra-thin magnetic films has a long history. This chapter is a review devoted to some fundamental theoretical properties of 2D magnets and and magnetic thin films including frustrated systems and topological spin textures. These properties allow to understand macroscopic behaviors experimentally observed in thin films and superlattices where the surface and the interface play a crucial role. The chapter begins with a review on 2D magnets, their spin structures and phase transitions. Next, the case of thin films is considered. The theory of surface spin waves is discussed in various situations with and without surface reconstruction of spin ordering. Various interactions are taken into account: surface interaction different from the bulk one, competing interactions, Dzyaloshinskii-Moriya interaction. Surface phase transitions are shown in some particularly striking cases. Finally, some cases of topological spin textures called "skyrmions" are reviewed. All the results shown in this chapter have been published in various research papers cited in the text. Therefore, we will discuss some important results but avoid to enter complicated methods. Instead, the reader is referred to original papers for detailed demonstrations.
Fully-relativistic evolution of vacuum tensor inhomogeneities during inflation
2024-12-27
Ericka Florio, E. Paul S. Shellard
We present a complete method for the initialisation and extraction of first-order inflationary tensor perturbations for fully relativistic simulations which incorporate gravitational back-reaction. We outline a correspondence between the Cosmological Perturbation Theory (CPT) framework and the numerical relativity BSSN variables in the appropriate limit. We describe a generation method for stochastic tensoral initial conditions, inspired by the standard scalar initial condition used from inflation and implemented in lattice cosmology. We discuss the implementation of this procedure in the GRChombo/GRTeclyn code, and demonstrate the detailed quantitative correspondence between the linearised and fully-nonlinear solutions in the perturbative limit, through the evolution of the background and the tensor power spectrum. We also validate the methodology by showing that energy and momentum constraints are introduced and preserved to second-order or better. We provide some preliminary indicative results probing tensoral non-Gaussianity using the skewness and kurtosis. The computational pipeline presented here will be used to study the emergence of a primordial tensor bispectra and cross-spectra that incorporate the effect of nonlinear gravitational couplings with the metric, which has potential applications for the analysis of next-generation CMB surveys.
Trading Off Energy Storage and Payload -- An Analytical Model for Freight Train Configuration
2024-12-27
Max T. M. Ng, Adrian Hernandez, Pablo L. Durango-Cohen, Hani S. Mahmassani
To support planning of alternative fuel technology (e.g., battery-electric locomotives) deployment for decarbonizing non-electrified freight rail, we develop a convex optimization formulation with a closed-form solution to determine the optimal number of energy storage tender cars in a train. The formulation shares a similar structure to an Economic Order Quantity (EOQ) model. For given market characteristics, cost forecasts, and technology parameters, our model captures the trade-offs between inventory carrying costs associated with trip times (including delays due to charging/refueling) and ordering costs associated with train dispatch and operation (energy, amortized equipment, and labor costs). To illustrate the framework, we find the optimal number of battery-electric energy tender cars in 22,501 freight markets (origin-destination pairs and commodities) for U.S. Class I railroads. The results display heterogeneity in optimal configurations with lighter, yet more time-sensitive shipments (e.g., intermodal) utilizing more battery tender cars. For heavier commodities (e.g., coal) with lower holding costs, single battery tender car configurations are generally optimal. The results also show that the optimal train configurations are sensitive to delays associated with recharging or swapping tender cars.
From prediction to explanation: managing influential negative reviews through explainable AI
2024-12-27
Rongping Shen
The profound impact of online reviews on consumer decision-making has made it crucial for businesses to manage negative reviews. Recent advancements in artificial intelligence (AI) technology have offered businesses novel and effective ways to manage and analyze substantial consumer feedback. In response to the growing demand for explainablility and transparency in AI applications, this study proposes a novel explainable AI (XAI) algorithm aimed at identifying influential negative reviews. The experiments conducted on 101,338 restaurant reviews validate the algorithm's effectiveness and provides understandable explanations from both the feature-level and word-level perspectives. By leveraging this algorithm, businesses can gain actionable insights for predicting, perceiving, and strategically responding to online negative feedback, fostering improved customer service and mitigating the potential damage caused by negative reviews.
A Review on the Integration of Artificial Intelligence and Medical Imaging in IVF Ovarian Stimulation
2024-12-27
Jana Zakall, Birgit Pohn, Antonia Graf, Daniel Kovatchki, Arezoo Borji, Ragib Shahriar Islam, Hossam Haick, Heinz Strohmer, Sepideh Hatamikia
Artificial intelligence (AI) has emerged as a powerful tool to enhance decision-making and optimize treatment protocols in in vitro fertilization (IVF). In particular, AI shows significant promise in supporting decision-making during the ovarian stimulation phase of the IVF process. This review evaluates studies focused on the applications of AI combined with medical imaging in ovarian stimulation, examining methodologies, outcomes, and current limitations. Our analysis of 13 studies on this topic reveals that, reveal that while AI algorithms demonstrated notable potential in predicting optimal hormonal dosages, trigger timing, and oocyte retrieval outcomes, the medical imaging data utilized predominantly came from two-dimensional (2D) ultrasound which mainly involved basic quantifications, such as follicle size and number, with limited use of direct feature extraction or advanced image analysis techniques. This points to an underexplored opportunity where advanced image analysis approaches, such as deep learning, and more diverse imaging modalities, like three-dimensional (3D) ultrasound, could unlock deeper insights. Additionally, the lack of explainable AI (XAI) in most studies raises concerns about the transparency and traceability of AI-driven decisions - key factors for clinical adoption and trust. Furthermore, many studies relied on single-center designs and small datasets, which limit the generalizability of their findings. This review highlights the need for integrating advanced imaging analysis techniques with explainable AI methodologies, as well as the importance of leveraging multicenter collaborations and larger datasets. Addressing these gaps has the potential to enhance ovarian stimulation management, paving the way for efficient, personalized, and data-driven treatment pathways that improve IVF outcomes.
We use Matsuki's decomposition for symmetric pairs $(G, H)$ of (not necessarily compact) reductive Lie groups to construct the radial parts for invariant differential operators acting on matrix-spherical functions. As an application, we employ this machinery to formulate an alternative, mathematically rigorous approach to obtaining radial parts of Casimir operators that appear in the theory of conformal blocks, which avoids poorly defined analytical continuations from the compact quotient cases. To exemplify how this works, after reviewing the presentation of conformal 4-point correlation functions via matrix-spherical functions for the corresponding symmetric pair, we for the first time provide a complete analysis of the Casimir radial part decomposition in the case of Lorentzian signature. As another example, we revisit the Casimir reduction in the case of conformal blocks for two scalar defects of equal dimension. We argue that Matsuki's decomposition thus provides a proper mathematical framework for analysing the correspondence between Casimir equations and the Calogero-Sutherland-type models, first discovered by one of the authors and Schomerus.
Multipole moments in general relativity serve as a powerful tool for characterising the gravitational field. In this paper, we review the construction of the Geroch--Hansen multipole moments for stationary asymptotically flat vacuum spacetimes. A particular focus is placed on the well-definedness of these moments, which hinges on the uniqueness of the one-point conformal completion in Geroch's asymptotic flatness definition. Based on Geroch's approach, we formulate and prove a revised uniqueness result, thereby filling in some gaps in the original approach. Uniqueness holds up to certain conformal transformations, and we discuss how the multipole moments behave under such transformations.
Quasicrystal problem -- on rigidity of non-periodic structures from statistical mechanics point of view
2024-12-27
Jacek Miȩkisz
We present a brief history of quasicrystals and a short introduction to classical lattice-gas models of interacting particles. We discuss stability of non-periodic tilings and one-dimensional sequences of symbols seen as ground states of some hamiltonians. We argue that some sort of homogeneity, the so-called Strict Boundary Condition, is necessary for stability of non-periodic ground states against small perturbations of interactions and thermal fluctuations.
Muonium fine structure: theory update, tests of Lorentz violation and experimental prospects
2024-12-27
Philipp Blumer, Svenja Geissmann, Arnaldo J. Vargas, Gianluca Janka, Ben Ohayon, Paolo Crivelli
We review the status of the QED calculations for the muonium $2S_{1/2}-2P_{3/2}$ energy interval and provide the updated theoretical value of $9874.357\pm0.001\,\mathrm{MHz}$. Additionally, we present a model for probing Lorentz-violating coefficients within the Standard Model Extension framework using the fine structure measurement in the presence and absence of a weak external magnetic field, enabling novel tests of CPT and Lorentz symmetry. Using Monte Carlo simulations, we estimate that a precision of $\sim 10\,\mathrm{kHz}$ on the isolated $2S_{1/2}, F=1 - 2P_{3/2}, F=1$ transition could be achievable employing Ramsey's separate oscillatory fields (SOF) technique. Collecting the required statics will become feasible with the upcoming High-Intensity Muon Beam (HiMB) at the Paul Scherrer Institute (PSI) in Switzerland. These advancements will enable precise tests of radiative QED corrections and nuclear self-energy contributions, while also providing tests of new physics and sensitivity to unconstrained coefficients for Lorentz violation within the Standard Model Extension framework.
The extremely luminous infrared galaxy (ELIRG), WISE J090924.01+000211.1 (hereafter; WISE J0909+0002, $z=1.87$) is an extraordinary object with a quasar aspect. This study performs monitoring observations of WISE J0909+0002 with the 105 cm Murikabushi telescope, Okayama and Akeno 50 cm telescopes/MITSuME ($g'$, $R_{\rm c}$, and $I_{\rm c}$ bands), and the SaCRA 55 cm telescope/MuSaSHI ($r$, $i$, and $z$ bands). We obtain the following results by combining the UV/optical light curves of the CRTS, Pan-STARRS, and ZTF archive data, and our observational data: (1) the light curves of WISE J0909+0002 present quasi-periodic (sinusoidal) oscillations with the rest-frame period of $\sim$ 660$-$689 day; (2) the structure functions of WISE J0909+0002 do not show a damped random walk (DRW) trend; (3) the mock DRW light curves present periodic-like trend on rare occasions in 10000 simulations; (4) the relativistic boost scenario is favored, since the relation between variability amplitude and power-law slope ratio is consistent with the theoretical prediction of this scenario, and a substantial parameter space exists between the inclination angles and the black hole mass; (5) the circumbinary disk model is difficult to explain the spectral energy distribution of our target; (6) the significant radio flux density of WISE J0909+0002 is not detected from the VLA FIRST Survey, thus the radio jet precession scenario is ruled out. From our results, the Doppler boost scenario is likely as a cause of the periodic variability, consequently the quasi-periodic oscillations in WISE J0909+0002 is possibly interpreted by a supermassive blackhole binary. Additional observations to investigate the continuity of the periodic trend would bring new insights into mechanisms of the quasi-periodic oscillations and/or ELIRGs.
Structural Similarity in Deep Features: Image Quality Assessment Robust to Geometrically Disparate Reference
2024-12-27
Keke Zhang, Weiling Chen, Tiesong Zhao, Zhou Wang
Image Quality Assessment (IQA) with references plays an important role in optimizing and evaluating computer vision tasks. Traditional methods assume that all pixels of the reference and test images are fully aligned. Such Aligned-Reference IQA (AR-IQA) approaches fail to address many real-world problems with various geometric deformations between the two images. Although significant effort has been made to attack Geometrically-Disparate-Reference IQA (GDR-IQA) problem, it has been addressed in a task-dependent fashion, for example, by dedicated designs for image super-resolution and retargeting, or by assuming the geometric distortions to be small that can be countered by translation-robust filters or by explicit image registrations. Here we rethink this problem and propose a unified, non-training-based Deep Structural Similarity (DeepSSIM) approach to address the above problems in a single framework, which assesses structural similarity of deep features in a simple but efficient way and uses an attention calibration strategy to alleviate attention deviation. The proposed method, without application-specific design, achieves state-of-the-art performance on AR-IQA datasets and meanwhile shows strong robustness to various GDR-IQA test cases. Interestingly, our test also shows the effectiveness of DeepSSIM as an optimization tool for training image super-resolution, enhancement and restoration, implying an even wider generalizability. \footnote{Source code will be made public after the review is completed.
With the increasing complexity of industrial production systems, accurate fault diagnosis is essential to ensure safe and efficient system operation. However, due to changes in production demands, dynamic process adjustments, and complex external environmental disturbances, multiple operating conditions frequently arise during production. The multi-condition characteristics pose significant challenges to traditional fault diagnosis methods. In this context, multi-condition fault diagnosis has gradually become a key area of research, attracting extensive attention from both academia and industry. This paper aims to provide a systematic and comprehensive review of existing research in the field. Firstly, the mathematical definition of the problem is presented, followed by an overview of the current research status. Subsequently, the existing literature is reviewed and categorized from the perspectives of single-model and multi-model approaches. In addition, standard evaluation metrics and typical real-world application scenarios are summarized and analyzed. Finally, the key challenges and prospects in the field are thoroughly discussed.
Semantic communication (SemCom) is an emerging paradigm aiming at transmitting only task-relevant semantic information to the receiver, which can significantly improve communication efficiency. Recent advancements in generative artificial intelligence (GenAI) have empowered GenAI-enabled SemCom (GenSemCom) to further expand its potential in various applications. However, current GenSemCom systems still face challenges such as semantic inconsistency, limited adaptability to diverse tasks and dynamic environments, and the inability to leverage insights from past transmission. Motivated by the success of retrieval-augmented generation (RAG) in the domain of GenAI, this paper explores the integration of RAG in GenSemCom systems. Specifically, we first provide a comprehensive review of existing GenSemCom systems and the fundamentals of RAG techniques. We then discuss how RAG can be integrated into GenSemCom. Following this, we conduct a case study on semantic image transmission using an RAG-enabled diffusion-based SemCom system, demonstrating the effectiveness of the proposed integration. Finally, we outline future directions for advancing RAG-enabled GenSemCom systems.
Large Language Models (LLMs) have revolutionized a wide range of domains such as natural language processing, computer vision, and multi-modal tasks due to their ability to comprehend context and perform logical reasoning. However, the computational and memory demands of LLMs, particularly during inference, pose significant challenges when scaling them to real-world, long-context, and real-time applications. Key-Value (KV) cache management has emerged as a critical optimization technique for accelerating LLM inference by reducing redundant computations and improving memory utilization. This survey provides a comprehensive overview of KV cache management strategies for LLM acceleration, categorizing them into token-level, model-level, and system-level optimizations. Token-level strategies include KV cache selection, budget allocation, merging, quantization, and low-rank decomposition, while model-level optimizations focus on architectural innovations and attention mechanisms to enhance KV reuse. System-level approaches address memory management, scheduling, and hardware-aware designs to improve efficiency across diverse computing environments. Additionally, the survey provides an overview of both text and multimodal datasets and benchmarks used to evaluate these strategies. By presenting detailed taxonomies and comparative analyses, this work aims to offer useful insights for researchers and practitioners to support the development of efficient and scalable KV cache management techniques, contributing to the practical deployment of LLMs in real-world applications. The curated paper list for KV cache management is in: \href{https://github.com/TreeAI-Lab/Awesome-KV-Cache-Management}{https://github.com/TreeAI-Lab/Awesome-KV-Cache-Management}.
Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detection
2024-12-27
Mansour El Alami, Nouhaila Innan, Muhammad Shafique, Mohamed Bennai
As financial fraud becomes increasingly complex, effective detection methods are essential. Quantum Machine Learning (QML) introduces certain capabilities that may enhance both accuracy and efficiency in this area. This study examines how different quantum feature map and ansatz configurations affect the performance of three QML-based classifiers-the Variational Quantum Classifier (VQC), the Sampler Quantum Neural Network (SQNN), and the Estimator Quantum Neural Network (EQNN)-when applied to two non-standardized financial fraud datasets. Different quantum feature map and ansatz configurations are evaluated, revealing distinct performance patterns. The VQC consistently demonstrates strong classification results, achieving an F1 score of 0.88, while the SQNN also delivers promising outcomes. In contrast, the EQNN struggles to produce robust results, emphasizing the challenges presented by non-standardized data. These findings highlight the importance of careful model configuration in QML-based financial fraud detection. By showing how specific feature maps and ansatz choices influence predictive success, this work guides researchers and practitioners in refining QML approaches for complex financial applications.
The Rendezvous Between Extreme Value Theory and Next-generation Networks
2024-12-27
Srinivas Sagar, Athira Subhash, Chen-Feng Liu, Ahmed Elzanaty, Yazan H. Al-Badarneh, Sheetal Kalyani, Mohamed-Slim Alouini, Mehdi Bennis, Lajos Hanzo
Promising technologies such as massive multiple-input and multiple-output, reconfigurable intelligent reflecting surfaces, non-terrestrial networks, millimetre wave communication, ultra-reliable lowlatency communication are envisioned as the enablers for next-generation (NG) networks. In contrast to conventional communication systems meeting specific average performance requirements, NG systems are expected to meet quality-of-service requirements in extreme scenarios as well. In this regard, extreme value theory (EVT) provides a powerful framework for the design of communication systems. In this paper, we provide a comprehensive survey of advances in communication that utilize EVT to characterize the extreme order statistics of interest. We first give an overview of the history of EVT and then elaborate on the fundamental theorems. Subsequently, we discuss different problems of interest in NG communication systems and how EVT can be utilized for their analysis. We finally point out the open challenges and future directions of EVT in NG communication systems.
This study investigates Sri Lankan ICT teachers' readiness to teach AI in schools, focusing on self-efficacy. A survey of over 1,300 teachers assessed their self-efficacy using a scale developed based on Bandura's theory. PLS-SEM analysis revealed that teachers' self-efficacy was low, primarily influenced by emotional and physiological states and imaginary experiences related to AI instruction. Mastery experiences had a lesser impact, and vicarious experiences and verbal persuasion showed no significant effect. The study highlights the need for a systemic approach to teacher professional development, considering the limitations in teachers' AI expertise and social capital. Further research is recommended to explore a socio-technical systems perspective for effective AI teacher training.
Introduction to Graph Neural Networks: A Starting Point for Machine Learning Engineers
2024-12-27
James H. Tanis, Chris Giannella, Adrian V. Mariano
Graph neural networks are deep neural networks designed for graphs with attributes attached to nodes or edges. The number of research papers in the literature concerning these models is growing rapidly due to their impressive performance on a broad range of tasks. This survey introduces graph neural networks through the encoder-decoder framework and provides examples of decoders for a range of graph analytic tasks. It uses theory and numerous experiments on homogeneous graphs to illustrate the behavior of graph neural networks for different training sizes and degrees of graph complexity.
The Hobby-Eberly Telescope Dark Energy Experiment Survey (HETDEX) Active Galactic Nuclei Catalog: the Fourth Data Release
2024-12-27
Chenxu Liu, Karl Gebhardt, Erin Mentuch Cooper, Dustin Davis, Donald P. Schneider, Matt J. Jarvis, Daniel J. Farrow, Steven L. Finkelstein, Oscar A. Chavez Ortiz, The HETDEX Collaboration
We present the Active Galactic Nuclei (AGN) catalog from the fourth data release (HDR4) of the Hobby-Eberly Telescope Dark Energy Experiment Survey (HETDEX). HETDEX is an untargeted spectroscopic survey. HDR4 contains 345,874 Integral Field Unit (IFU) observations from January 2017 to August 2023 covering an effective area of 62.9 deg2. With no imaging pre-selection, our spectroscopic confirmed AGN sample includes low-luminosity AGN, narrow-line AGN, and/or red AGN down to g~25. This catalog has 15,940 AGN across the redshifts of z=0.1~4.6, giving a raw AGN number density of 253.4 deg-2. Among them, 10,499 (66%) have redshifts either confirmed by line pairs or matched to the Sloan Digital Sky Survey Quasar Catalog. For the remaining 5,441 AGN, 2,083 are single broad line AGN candidates, while the remaining 3,358 are single intermediate broad line (full width at half maximum, FWHM ~ 1200 km s-1) AGN candidates. A total of 4,060 (39%) of the 10,499 redshift-confirmed AGN have emission-line regions $3\sigma$ more extended than the image quality which could be strong outflows blowing into the outskirts of the host galaxies or ionized intergalactic medium.
Joint Optimization of Multimodal Transit Frequency and Shared Autonomous Vehicle Fleet Size with Hybrid Metaheuristic and Nonlinear Programming
2024-12-27
Max T. M. Ng, Hani S. Mahmassani, Draco Tong, Omer Verbas, Taner Cokyasar
This paper presents an optimization framework for the joint multimodal transit frequency and shared autonomous vehicle (SAV) fleet size optimization, a problem variant of the transit network frequency setting problem (TNFSP) that explicitly considers mode choice behavior and route selection. To address the non-linear non-convex optimization problem, we develop a hybrid solution approach that combines metaheuristics (particle swarm optimization, PSO) with local nonlinear programming (NLP) improvement, incorporating approximation models for SAV waiting time, multimodal route choice, and mode choice. Applied to the Chicago metropolitan area, our method achieves a 33.3% increase in transit ridership.
Two-echelon Electric Vehicle Routing Problem in Parcel Delivery: A Literature Review
2024-12-27
Nima Moradi, Niloufar Mirzavand Boroujeni, Navid Aftabi, Amin Aslani
Multi-echelon parcel delivery systems using electric vehicles (EVs) are crucial for managing urban logistics complexity and promoting sustainability. In multi-echelon systems, particularly within two-stage systems, larger vehicles transport parcels from a central depot to satellite hubs, where smaller EVs pick up the parcels and carry out last-mile deliveries. This system could increase efficiency, reduce emissions, and improve service reliability. The two-echelon electric vehicle routing problem (2E-EVRP), an extension of the traditional two-echelon vehicle routing problem (2E-VRP), addresses EV-specific challenges such as battery constraints and recharging stations to tackle environmental impacts, urban congestion, and e-commerce demands. While effectively reducing costs, energy use, and emissions, the 2E-EVRP faces modeling challenges due to multi-echelon structures, EV limitations, and recharging station selection. This paper systematically reviews 2E-EVRP literature, analyzing key studies. It proposes a classification scheme to categorize the papers based on the problem variants, objectives, constraints, and solution methods. It identifies gaps such as delivery tardiness, environmental trade-offs, multi-objective optimization, multiple depots, split deliveries, and time-dependent travel conditions. Future research directions include aligning models with urban policies, integrating parcel lockers, enabling same-day delivery, and incorporating advanced technologies like autonomous vehicles. Methodological advancements suggest using machine learning, reinforcement learning, and simulation-based approaches to enhance dynamic routing and real-time decision-making. These directions aim to expand the 2E-EVRP applicability, addressing theoretical and practical challenges in sustainable urban logistics for future works.
Hydrodynamic Description of the Quark-Gluon Plasma
2024-12-27
Ulrich Heinz, Björn Schenke
We review the history and success of applying relativistic hydrodynamics to high-energy heavy-ion collisions. We emphasize the important role hydrodynamics has played in the discovery of the quark-gluon plasma and its quantitative exploration.
Evidence for $CP$ violation and measurement of $CP$-violating parameters in B$^0_\mathrm{s}$ $\to$ J/$ψ\,φ$(1020) decays in pp collisions at $\sqrt{s} =$ 13 TeV
2024-12-27
CMS Collaboration
A pioneering machine-learning-based flavor-tagging algorithm combining same-side and opposite-side tagging is used to obtain the equivalent of 27$\,$000 tagged B$^0_\mathrm{s}$ $\to$ J/$\psi\, \phi$(1020) decays from pp collisions at $\sqrt{s} =$ 13 TeV, collected by the CMS experiment and corresponding to an integrated luminosity of 96.5 fb$^{-1}$. A time- and flavor-dependent angular analysis of the $\mu^+\mu^-$K$^+$K$^-$ final state is used to measure parameters of the $\mathrm{B}^0_\mathrm{s}$-$\overline{\mathrm{B}}^0_\mathrm{s}$ system. The weak phase is measured to be $\phi_\mathrm{s}$ = $-$73 $\pm$ 23 (stat) $\pm$ 7 (syst) mrad, which, combined with a $\sqrt{s}$ = 8 TeV CMS result, gives $\phi_\mathrm{s}$ = $-$74 $\pm$ 23 mrad. This value differs from zero by 3.2 standard deviations, providing evidence for $CP$ violation in B$^0_\mathrm{s}$ $\to$ J/$\psi\,\phi$(1020) decays. All measured physics parameters are found to agree with standard model predictions where available.
Hidformer: Transformer-Style Neural Network in Stock Price Forecasting
2024-12-27
Kamil Ł. Szydłowski, Jarosław A. Chudziak
This paper investigates the application of Transformer-based neural networks to stock price forecasting, with a special focus on the intersection of machine learning techniques and financial market analysis. The evolution of Transformer models, from their inception to their adaptation for time series analysis in financial contexts, is reviewed and discussed. Central to our study is the exploration of the Hidformer model, which is currently recognized for its promising performance in time series prediction. The primary aim of this paper is to determine whether Hidformer will also prove itself in the task of stock price prediction. This slightly modified model serves as the framework for our experiments, integrating the principles of technical analysis with advanced machine learning concepts to enhance stock price prediction accuracy. We conduct an evaluation of the Hidformer model's performance, using a set of criteria to determine its efficacy. Our findings offer additional insights into the practical application of Transformer architectures in financial time series forecasting, highlighting their potential to improve algorithmic trading strategies, including human decision making.
Due to the exponential growth of information and the need for efficient information consumption the task of summarization has gained paramount importance. Evaluating summarization accurately and objectively presents significant challenges, particularly when dealing with long and unstructured texts rich in content. Existing methods, such as ROUGE (Lin, 2004) and embedding similarities, often yield scores that have low correlation with human judgements and are also not intuitively understandable, making it difficult to gauge the true quality of the summaries. LLMs can mimic human in giving subjective reviews but subjective scores are hard to interpret and justify. They can be easily manipulated by altering the models and the tones of the prompts. In this paper, we introduce a novel evaluation methodology and tooling designed to address these challenges, providing a more comprehensive, accurate and interpretable assessment of summarization outputs. Our method (SumAutoEval) proposes and evaluates metrics at varying granularity levels, giving objective scores on 4 key dimensions such as completeness, correctness, Alignment and readability. We empirically demonstrate, that SumAutoEval enhances the understanding of output quality with better human correlation.
A new constraint on galaxy-halo connections of [O II] emitters via HOD modelling with angular clustering and luminosity functions from the Subaru HSC survey
2024-12-27
Shogo Ishikawa, Teppei Okumura, Masao Hayashi, Tsutomu T. Takeuchi
Establishing a robust connection model between emission-line galaxies (ELGs) and their host dark haloes is of paramount importance in anticipation of upcoming redshift surveys. In this paper, we propose a novel halo occupation distribution (HOD) framework that incorporates galaxy luminosity, a key observable reflecting ELG star-formation activity, into the galaxy occupation model. This innovation enables prediction of galaxy luminosity functions (LFs) and facilitates joint analyses using both angular correlation functions (ACFs) and LFs. Using physical information from luminosity, our model provides more robust constraints on the ELG-halo connection compared to methods relying solely on ACF and number density constraints. Our model was applied to [O II]-emitting galaxies observed at two redshift slices at $z=1.193$ and $1.471$ from the Subaru Hyper Suprime-Cam PDR2. Our model effectively reproduces observed ACFs and LFs observed in both redshift slices. Compared to the established \citeauthor{geach12} HOD model, our approach offers a more nuanced depiction of ELG occupation across halo mass ranges, suggesting a more realistic representation of ELG environments. Our findings suggest that ELGs at $z\sim1.4$ may evolve into Milky-Way-like galaxies, highlighting their role as potential building blocks in galaxy formation scenarios. By incorporating the LF as a constraint linking galaxy luminosity to halo properties, our HOD model provides a more precise understanding of ELG-host halo relationships. Furthermore, this approach facilitates the generation of high-quality ELG mock catalogues of for future surveys. As the LF is a fundamental observable, our framework is potentially applicable to diverse galaxy populations, offering a versatile tool for analysing data from next-generation galaxy surveys.
Biological Insights from Integrative Modeling of Intrinsically Disordered Protein Systems
2024-12-27
Zi Hao Liu, Maria Tsanai, Oufan Zhang, Teresa Head-Gordon, Julie Forman-Kay
Intrinsically disordered proteins and regions are increasingly appreciated for their abundance in the proteome and the many functional roles they play in the cell. In this short review, we describe a variety of approaches used to obtain biological insight from the structural ensembles of disordered proteins, regions, and complexes and the integrative biology challenges that arise from combining diverse experiments and computational models. Importantly, we highlight findings regarding structural and dynamic characterization of disordered regions involved in binding and phase separation, as well as drug targeting of disordered regions, using a broad framework of integrative modeling approaches.
The general theory of relativity is currently established as the most precise theory of gravity supported by observations, and its application is diverse ranging from astronomy to cosmology, while its application to astrophysics has been restricted only to compact stars due to the assumption that the Newtonian approximation is sufficient for celestial bodies with medium density such as the sun. Surprisingly, the recent research of the author has implied that this long-held assumption is not valid, and that non-perturbative effects significantly change relevant results obtained by Newtonian gravity. In particular, local physical quantities inside the sun are newly predicted to exhibit power law differently from the so-called standard solar model. This surprising result is reviewed including brief discussion of physics behind the discrepancy and a new application of the new mass formula to gas planets.
The Internet of Value: Integrating Blockchain and Lightning Network Micropayments for Knowledge Markets
2024-12-26
Ellis Solaiman, Jorge Robins
Q&A websites rely on user-generated responses, with incentives such as reputation scores or monetary rewards often offered. While some users may find it intrinsically rewarding to assist others, studies indicate that payment can improve the quality and speed of answers. However, traditional payment processors impose minimum thresholds that many Q&A inquiries fall below. The introduction of Bitcoin enabled direct digital value transfer, yet frequent micropayments remain challenging. Recent advancements like the Lightning Network now allow frictionless micropayments by reducing costs and minimising reliance on intermediaries. This development fosters an "Internet of Value," where transferring even small amounts of money is as simple as sharing data. This study investigates integrating Lightning Network-based micropayment strategies into Q&A platforms, aiming to create a knowledge market free of minimum payment barriers. A survey was conducted to address the gap below the $2 payment level identified in prior research. Responses confirmed that incentives for asking and answering weaken as payments decrease. Findings reveal even minimal payments, such as {\pounds}0.01, significantly encourage higher quality and effort in responses. The study recommends micropayment incentives for service-oriented applications, particularly Q&A platforms. By leveraging the Lightning Network to remove barriers, a more open marketplace can emerge, improving engagement and outcomes. Further research is needed to confirm if users follow through on reported intentions when spending funds.
A Review of Resilience Enhancement Measures for Hydrogen-penetrated Multi-energy Systems
2024-12-26
Liang Yu, Haoyu Fang, Goran Strbac, Dawei Qiu, Dong Yue, Xiaohong Guan, Gerhard P. Hancke
Energy supply for electricity and heat sectors accounts for more than 40% of global carbon emissions in 2023, which brings great pressure for achieving net-zero carbon emission targets in the future. Under the above background, hydrogen-penetrated multi-energy systems (HMESs) have received wide attention due to their potential low-carbon attribute. However, HMESs still face the following challenge, i.e., how to survive and quickly recover from extreme and unexpected events (e.g., natural disasters, extreme weather, and cyber-physical attacks). To enable the above resilience attribute, many existing works on HMES resilience enhancement have been done. However, there lacks a systematic overview of different resilience enhancement measures for HMESs. To fill the research gap, this paper provides a comprehensive overview of resilience enhancement strategies for HMESs from the perspective of hydrogen-related planning and operation. To be specific, we propose a comprehensive resilience enhancement framework for HEMSs. Under the proposed framework, the widely used resilience metrics and event-oriented contingency models in existing works are summarized. Then, we classify the hydrogen-related planning measures for HMES resilience enhancement according to the type of hydrogen-related facilities and provide some insights for planning problem formulation framework. Moreover, we categorize the hydrogen-related operation measures for HMES resilience enhancement according to the three kinds of operation response stages involved, including preventive response, emergency response, and restoration response. Finally, we identify some research gaps and point out possible future directions.
Large Language Models for Market Research: A Data-augmentation Approach
2024-12-26
Mengxin Wang, Dennis J. Zhang, Heng Zhang
Large Language Models (LLMs) have transformed artificial intelligence by excelling in complex natural language processing tasks. Their ability to generate human-like text has opened new possibilities for market research, particularly in conjoint analysis, where understanding consumer preferences is essential but often resource-intensive. Traditional survey-based methods face limitations in scalability and cost, making LLM-generated data a promising alternative. However, while LLMs have the potential to simulate real consumer behavior, recent studies highlight a significant gap between LLM-generated and human data, with biases introduced when substituting between the two. In this paper, we address this gap by proposing a novel statistical data augmentation approach that efficiently integrates LLM-generated data with real data in conjoint analysis. Our method leverages transfer learning principles to debias the LLM-generated data using a small amount of human data. This results in statistically robust estimators with consistent and asymptotically normal properties, in contrast to naive approaches that simply substitute human data with LLM-generated data, which can exacerbate bias. We validate our framework through an empirical study on COVID-19 vaccine preferences, demonstrating its superior ability to reduce estimation error and save data and costs by 24.9\% to 79.8\%. In contrast, naive approaches fail to save data due to the inherent biases in LLM-generated data compared to human data. Another empirical study on sports car choices validates the robustness of our results. Our findings suggest that while LLM-generated data is not a direct substitute for human responses, it can serve as a valuable complement when used within a robust statistical framework.
Mixed-precision numerics in scientific applications: survey and perspectives
2024-12-26
Aditya Kashi, Hao Lu, Wesley Brewer, David Rogers, Michael Matheson, Mallikarjun Shankar, Feiyi Wang
The explosive demand for artificial intelligence (AI) workloads has led to a significant increase in silicon area dedicated to lower-precision computations on recent high-performance computing hardware designs. However, mixed-precision capabilities, which can achieve performance improvements of 8x compared to double-precision in extreme compute-intensive workloads, remain largely untapped in most scientific applications. A growing number of efforts have shown that mixed-precision algorithmic innovations can deliver superior performance without sacrificing accuracy. These developments should prompt computational scientists to seriously consider whether their scientific modeling and simulation applications could benefit from the acceleration offered by new hardware and mixed-precision algorithms. In this article, we review the literature on relevant applications, existing mixed-precision algorithms, theories, and the available software infrastructure. We then offer our perspective and recommendations on the potential of mixed-precision algorithms to enhance the performance of scientific simulation applications. Broadly, we find that mixed-precision methods can have a large impact on computational science in terms of time-to-solution and energy consumption. This is true not only for a few arithmetic-dominated applications but also, to a more moderate extent, to the many memory bandwidth-bound applications. In many cases, though, the choice of algorithms and regions of applicability will be domain-specific, and thus require input from domain experts. It is helpful to identify cross-cutting computational motifs and their mixed-precision algorithms in this regard. Finally, there are new algorithms being developed to utilize AI hardware and and AI methods to accelerate first-principles computational science, and these should be closely watched as hardware platforms evolve.
Anvendelse av kunstig intelligens (KI) i Norge i norsk offentlig sektor 2024
2024-12-26
John Krogstie
There are great expectations for the use of AI in Norway. On the other hand, it is reported that the adoption of AI in Norway is slower than expected in both the private and public sectors. Using responses from NOKIOS Technology Radar 2017-2021, IT in Practice surveys conducted by Ramboll in 2021-2024, as well as another national survey as part of a five-year cycle, this article looks at reported and planned use of AI with a focus on local (municipalities) and national government agencies. IT in practice is distributed to a large number of Norwegian public agencies, with a response rate of over 5o percent. The most recent data (2024) presented in this article is based on responses from 335 public organizations, with 237 municipalities, and 98 public organizations at the national or regional level. The survey confirms that the use of AI is still at an early stage, although expectations are high for future use. -- Det er store forventninger til bruk av KI i Norge. P{\aa} den annen side rapporteres det at adopsjonen av KI i Norge g{\aa}r tregere enn forventet b{\aa}de i privat og offentlig sektor. Ved hjelp av svar fra NOKIOS teknologiradar 2017-2021, IT i Praksis unders{\o}kelser utf{\o}rt av Ramb{\o}ll i 2021-2024, samt en annen nasjonal unders{\o}kelse som en del av en fem{\aa}rig syklus, ser vi i denne artikkelen p{\aa} rapportert og planlagt bruk av KI med fokus p{\aa} lokale (kommuner) og nasjonale offentlige etater. IT i praksis distribueres til en lang rekke norske offentlige virksomheter, med en svarprosent p{\aa} over 50 prosent. De nyeste dataene (2024) presentert i denne artikkelen er basert p{\aa} svar fra 335 offentlige organisasjoner, med 237 kommuner, og 98 offentlige organisasjoner p{\aa} nasjonalt eller regionalt niv{\aa}. Unders{\o}kelsen bekrefter at bruken av KI fortsatt er p{\aa} et tidlig stadium, selv om forventningene er h{\o}ye til fremtidig bruk.
Search for a neutral gauge boson with nonuniversal fermion couplings in vector boson fusion processes in proton-proton collisions at $\sqrt{s}$ = 13 TeV
2024-12-26
CMS Collaboration
The first search for a heavy neutral spin-1 gauge boson (Z') with nonuniversal fermion couplings produced via vector boson fusion processes and decaying to tau leptons or W bosons is presented. The analysis is performed using LHC data at $\sqrt{s}$ = 13 TeV, collected from 2016 to 2018 and corresponding to an integrated luminosity of 138 fb$^{-1}$. The data are consistent with the standard model predictions. Upper limits are set on the product of the cross section for production of the Z' boson and its branching fraction to $\tau\tau$ or WW. The presence of a Z' boson decaying to $\tau^+\tau^-$ (W$^+$W$^-$) is excluded for masses up to 2.45 (1.60) TeV, depending on the Z' boson coupling to SM weak bosons, and assuming a Z' $\to$ $\tau^+\tau^-$ (W$^+$W$^-$) branching fraction of 50%.
Graph mining is an important area in data mining and machine learning that involves extracting valuable information from graph-structured data. In recent years, significant progress has been made in this field through the development of graph neural networks (GNNs). However, GNNs are still deficient in generalizing to diverse graph data. Aiming to this issue, Large Language Models (LLMs) could provide new solutions for graph mining tasks with their superior semantic understanding. In this review, we systematically review the combination and application techniques of LLMs and GNNs and present a novel taxonomy for research in this interdisciplinary field, which involves three main categories: GNN-driving-LLM, LLM-driving-GNN, and GNN-LLM-co-driving. Within this framework, we reveal the capabilities of LLMs in enhancing graph feature extraction as well as improving the effectiveness of downstream tasks such as node classification, link prediction, and community detection. Although LLMs have demonstrated their great potential in handling graph-structured data, their high computational requirements and complexity remain challenges. Future research needs to continue to explore how to efficiently fuse LLMs and GNNs to achieve more powerful graph learning and reasoning capabilities and provide new impetus for the development of graph mining techniques.
Rigorous Computation of Classical Horizontal Geodetic Networks
2024-12-26
Sandi Berk, Bojan Stopar
This paper examines mathematical models for processing classical horizontal geodetic (triangulation and trilateration) networks. Two rigorous parametric adjustment models are discussed. The first one is a well-known model of adjustment in the geodetic coordinate system. This model is completely rigorous (functional and stochastic parts) and uses unreduced distance and direction observations. The proposed alternative is a model of planar network adjustment with observations rigorously reduced directly to the mapping plane. These ground-to-grid reductions are simple and universal, regardless of which map projection is used. Slightly different results of the planar network adjustment are obtained. The differences are attributed to a non-rigorous stochastic model. In theory, the stochastic properties of the reduced observations should also be adapted. However, these differences are very small and can always be neglected in geodetic and surveying practice.
Multi-Attribute Constraint Satisfaction via Language Model Rewriting
2024-12-26
Ashutosh Baheti, Debanjana Chakraborty, Faeze Brahman, Ronan Le Bras, Ximing Lu, Nouha Dziri, Yejin Choi, Mark Riedl, Maarten Sap
Obeying precise constraints on top of multiple external attributes is a common computational problem underlying seemingly different domains, from controlled text generation to protein engineering. Existing language model (LM) controllability methods for multi-attribute constraint satisfaction often rely on specialized architectures or gradient-based classifiers, limiting their flexibility to work with arbitrary black-box evaluators and pretrained models. Current general-purpose large language models, while capable, cannot achieve fine-grained multi-attribute control over external attributes. Thus, we create Multi-Attribute Constraint Satisfaction (MACS), a generalized method capable of finetuning language models on any sequential domain to satisfy user-specified constraints on multiple external real-value attributes. Our method trains LMs as editors by sampling diverse multi-attribute edit pairs from an initial set of paraphrased outputs. During inference, LM iteratively improves upon its previous solution to satisfy constraints for all attributes by leveraging our designed constraint satisfaction reward. We additionally experiment with reward-weighted behavior cloning to further improve the constraint satisfaction rate of LMs. To evaluate our approach, we present a new Fine-grained Constraint Satisfaction (FineCS) benchmark, featuring two challenging tasks: (1) Text Style Transfer, where the goal is to simultaneously modify the sentiment and complexity of reviews, and (2) Protein Design, focusing on modulating fluorescence and stability of Green Fluorescent Proteins (GFP). Our empirical results show that MACS achieves the highest threshold satisfaction in both FineCS tasks, outperforming strong domain-specific baselines. Our work opens new avenues for generalized and real-value multi-attribute control, with implications for diverse applications spanning NLP and bioinformatics.
Two main ingredients of current particle physics such as local gauge symmetry and mass generation via the Higgs mechanism being basic ground of the Standard Model are widely confirmed by experimental data. However, some problems such as neutrino masses, dark matter, baryon asymmetry of Universe have clearly indicated that the Standard Model cannot be the ultimate theory of nature. To surpass the mentioned puzzles, many extensions of the Standard Model (called beyond Standard Model) have been proposed. Among beyond Standard Models, the 3-3-1 models have some intriguing features and they get wide attention. The pioneer models develop in some directions. In this paper, %some new main versions of the 3-3-1 models and their consequences are presented.
Synchronization is an emergent phenomenon in coupled dynamical networks. The Master Stability Function (MSF) is a highly elegant and powerful tool for characterizing the stability of synchronization states. However, a significant challenge lies in determining the MSF for complex dynamical networks driven by nonlinear interaction mechanisms. These mechanisms introduce additional complexity through the intricate connectivity of interacting elements within the network and the intrinsic dynamics, which are governed by nonlinear processes with diverse parameters and higher dimensionality of systems. Over the past 25 years, extensive research has focused on determining the MSF for pairwise coupled identical systems with diffusive coupling. Our literature survey highlights two significant advancements in recent years: the consideration of multilayer networks instead of single-layer networks and the extension of MSF analysis to incorporate higher-order interactions alongside pairwise interactions. In this review article, we revisit the analysis of the MSF for diffusively pairwise coupled dynamical systems and extend this framework to more general coupling schemes. Furthermore, we systematically derive the MSF for multilayer dynamical networks and single-layer coupled systems by incorporating higher-order interactions alongside pairwise interactions. The primary focus of our review is on the analytical derivation and numerical computation of the MSF for complex dynamical networks. Finally, we demonstrate the application of the MSF in data science, emphasizing its relevance and potential in this rapidly evolving field.
Today, manga has gained worldwide popularity. However, the question of how various elements of manga, such as characters, text, and panel layouts, reflect the uniqueness of a particular work, or even define it, remains an unexplored area. In this paper, we aim to quantitatively and qualitatively analyze the visual characteristics of manga works, with a particular focus on panel layout features. As a research method, we used facing page images of manga as input to train a deep learning model for predicting manga titles, examining classification accuracy to quantitatively analyze these features. Specifically, we conducted ablation studies by limiting page image information to panel frames to analyze the characteristics of panel layouts. Through a series of quantitative experiments using all 104 works, 12 genres, and 10,122 facing page images from the Manga109 dataset, as well as qualitative analysis using Grad-CAM, our study demonstrates that the uniqueness of manga works is strongly reflected in their panel layouts.
How Can Haptic Feedback Assist People with Blind and Low Vision (BLV): A Systematic Literature Review
2024-12-26
Chutian Jiang, Emily Kuang, Mingming Fan
People who are blind or have low vision (BLV) encounter numerous challenges in their daily lives and work. To support them, various haptic assistive tools have been developed. Despite these advancements, the effective utilization of these tools -- including the optimal haptic feedback and on-body stimulation positions for different tasks along with their limitations -- remains poorly understood. Recognizing these gaps, we conducted a systematic literature review spanning two decades (2004-2024) to evaluate the development of haptic assistive tools within the HCI community. Our findings reveal that these tools are primarily used for understanding graphical information, providing guidance and navigation, and facilitating education and training, among other life and work tasks. We identified three main limitations: hardware limitations, functionality limitations, and UX and evaluation methods limitations. Based on these insights, we discuss potential research avenues and offer suggestions for enhancing the effectiveness of future haptic assistive technologies.
The role of potential energy landscape research in the development of new electrolyte solutions
2024-12-26
Vitaly V. Chaban
The development of new electrolyte solutions with improved characteristics is a key challenge for creating high-performance batteries, fuel cells, supercapacitors, and other electrochemical devices. The study of the potential energy landscape (PEL) plays an important role in this process, providing information about the interactions between solution components at the molecular level. In this work, we review the practice of applying PEL research methods based on classical and quantum-chemical algorithms to analyze the structure, dynamics, and thermodynamic properties of electrolyte solutions. Intermolecular and ion-molecular interactions at the microscopic level, which determine the macroscopic properties of the electrolyte solution, are considered in detail. The importance of identifying stable configurations of ions and their solvates is emphasized. PEL analysis allows for the systematic determination of the most probable structures and complexes formed in solution, which is important for understanding ion transport mechanisms. The study of the PEL allows for the determination of the energy barriers that must be overcome for ion migration, which is related to the conductivity of the electrolyte. The application of PEL research methods in combination with experimental data opens up new possibilities for the rational design of electrolyte solutions with desired physicochemical properties.
High-dimensional quantum entanglement is an essential resource in quantum technology since it provides benefits in increasing the information capacity and processing speed. Thus, the controlled harnessing of high-dimensional entanglement has long been hailed as a necessary prerequisite towards practical quantum applications. By using a deterministic quantum state filter that implemented through quantum interference, we present a generalised formulation for the complete high-dimensional symmetric and anti-symmetric Bell basis, and experimentally prepare four-dimensional orbital angular momentum Bell states that provide the well-behaved symmetric or anti-symmetric properties. Additionally, we use a concise yet efficient scan of temporal delay to directly observe high-dimensional two-photon interference effects in spatial modes. These results provide an alternative way for harnessing high-dimensional entanglement, and may facilitate the use of quantum interference for more complex quantum information processing tasks that beyond qubits.
TravelAgent: Generative Agents in the Built Environment
2024-12-25
Ariel Noyman, Kai Hu, Kent Larson
Understanding human behavior in built environments is critical for designing functional, user centered urban spaces. Traditional approaches, such as manual observations, surveys, and simplified simulations, often fail to capture the complexity and dynamics of real world behavior. To address these limitations, we introduce TravelAgent, a novel simulation platform that models pedestrian navigation and activity patterns across diverse indoor and outdoor environments under varying contextual and environmental conditions. TravelAgent leverages generative agents integrated into 3D virtual environments, enabling agents to process multimodal sensory inputs and exhibit human-like decision-making, behavior, and adaptation. Through experiments, including navigation, wayfinding, and free exploration, we analyze data from 100 simulations comprising 1898 agent steps across diverse spatial layouts and agent archetypes, achieving an overall task completion rate of 76%. Using spatial, linguistic, and sentiment analyses, we show how agents perceive, adapt to, or struggle with their surroundings and assigned tasks. Our findings highlight the potential of TravelAgent as a tool for urban design, spatial cognition research, and agent-based modeling. We discuss key challenges and opportunities in deploying generative agents for the evaluation and refinement of spatial designs, proposing TravelAgent as a new paradigm for simulating and understanding human experiences in built environments.
Experimental particle production in time-dependent spacetimes: a one-dimensional scattering problem
2024-12-25
Marius Sparn, Elinor Kath, Nikolas Liebster, Jelte Duchene, Christian F. Schmidt, Mireia Tolosa-Simeón, Álvaro Parra-López, Stefan Floerchinger, Helmut Strobel, Markus K. Oberthaler
We experimentally study cosmological particle production in a two-dimensional Bose-Einstein condensate, whose density excitations map to an analog cosmology. The expansion of spacetime is realized with tunable interactions. The particle spectrum can be understood through an analogy to quantum mechanical scattering, in which the dynamics of the spacetime metric determine the shape of the scattering potential. Hallmark scattering phenomena such as resonant forward scattering and Bragg reflection are connected to their cosmological counterparts, namely linearly expanding space and bouncing universes. We compare our findings to a theoretical description that extends beyond the acoustic approximation, which enables us to apply the model to high-momentum excitations.
Diffusion-based text-to-image models have shown immense potential for various image-related tasks. However, despite their prominence and popularity, customizing these models using unauthorized data also brings serious privacy and intellectual property issues. Existing methods introduce protective perturbations based on adversarial attacks, which are applied to the customization samples. In this systematization of knowledge, we present a comprehensive survey of protective perturbation methods designed to prevent unauthorized data usage in diffusion-based image generation. We establish the threat model and categorize the downstream tasks relevant to these methods, providing a detailed analysis of their designs. We also propose a completed evaluation framework for these perturbation techniques, aiming to advance research in this field.
Integrating Zero-Shot Classification to Advance Long COVID Literature: A Systematic Social Media-Centered Review
2024-12-25
Nirmalya Thakur
Long COVID continues to challenge public health by affecting a significant segment of individuals who have recovered from acute SARS-CoV-2 infection yet endure prolonged and often debilitating symptoms. Social media has emerged as a vital resource for those seeking real-time information, peer support, and validating their health concerns related to Long COVID. This paper examines recent works focusing on mining, analyzing, and interpreting user-generated content on social media platforms such as Twitter, Reddit, Facebook, and YouTube to capture the broader discourse on persistent post-COVID conditions. A novel transformer-based zero-shot learning approach serves as the foundation for classifying research papers in this area into four primary categories: Clinical or Symptom Characterization, Advanced NLP or Computational Methods, Policy, Advocacy, or Public Health Communication, and Online Communities and Social Support. This methodology showcases the adaptability of advanced language models in categorizing research papers without predefined training labels, thus enabling a more rapid and scalable assessment of existing literature. This review highlights the multifaceted nature of Long COVID research, where computational techniques applied to social media data reveal insights into narratives of individuals suffering from Long COVID. This review also demonstrates the capacity of social media analytics to inform clinical practice and contribute to policy-making related to Long COVID.
Identifying New $γ$-Ray Sources in All-Sky Surveys Based on Fermipy's Advanced Algorithm
2024-12-25
Y. C. Xiang, P. Feng, X. F. Lan
We employ an efficient method for identifying gamma-ray sources across the entire sky, leveraging advanced algorithms from Fermi p y, and cleverly utilizing the Galactic diffuse background emission model to partition the entire sky into 72 regions,thereby greatly enhancing the efficiency of discovering new sources throughout the sky through multi-threaded parallel computing. After confirming the reliability of the new method, we applied it for the first time to analyze data from the Fermi Large Area Telescope encompassing approximately 15.41yr of all sky surveys. Through this analysis, we successfully identified 1379 new sources with levels exceeding 4sigma, of which 497 sources exhibited higher significance levels exceeding 5sigma. Subsequently, we performed a systematic analysis of the spatial extension, spectra, and light variation characteristics of these newly identified sources. We identified 21 extended sources and 23 sources exhibiting spectral curvature above 10GeV. Additionally, we identified 44 variable sources above 1GeV.
Data clustering: an essential technique in data science
2024-12-25
Wong Hauchi, Daniil Lisik, Tai Dinh
This paper provides a comprehensive exploration of data clustering, emphasizing its methodologies and applications across different fields. Traditional techniques, including partitional and hierarchical clustering, are discussed alongside other approaches such as data stream, subspace and network clustering, highlighting their role in addressing complex, high-dimensional datasets. The paper also reviews the foundational principles of clustering, introduces common tools and methods, and examines its diverse applications in data science. Finally, the discussion concludes with insights into future directions, underscoring the centrality of clustering in driving innovation and enabling data-driven decision making.
Towards a Statistical Understanding of Neural Networks: Beyond the Neural Tangent Kernel Theories
2024-12-25
Haobo Zhang, Jianfa Lai, Yicheng Li, Qian Lin, Jun S. Liu
A primary advantage of neural networks lies in their feature learning characteristics, which is challenging to theoretically analyze due to the complexity of their training dynamics. We propose a new paradigm for studying feature learning and the resulting benefits in generalizability. After reviewing the neural tangent kernel (NTK) theory and recent results in kernel regression, which address the generalization issue of sufficiently wide neural networks, we examine limitations and implications of the fixed kernel theory (as the NTK theory) and review recent theoretical advancements in feature learning. Moving beyond the fixed kernel/feature theory, we consider neural networks as adaptive feature models. Finally, we propose an over-parameterized Gaussian sequence model as a prototype model to study the feature learning characteristics of neural networks.
Instead of writing a review article on the state of the field, I'm going to instead write a retrospective from the year 2040. I'll tell you how this whole "quantum computing thing" turned out.
Red supergiant stars in binary systems II. Confirmation of B-type companions of red supergiants in the Small Magellanic Cloud using Hubble ultra-violet spectroscopy
2024-12-24
L. R. Patrick, D. J. Lennon, A. Schootemeijer, L. Bianchi, I. Negueruela, N. Langer, D. Thilker, R. Dorda
Red supergiant stars (RSGs) represent the final evolutionary phase of the majority of massive stars and hold a unique role in testing the physics of stellar models. Eighty eight RSGs in the Small Magellanic Cloud (SMC) were recently found to have an ultra-violet excess that was attributed to a B-type companion. We present follow-up Hubble Space Telescope (HST) Space Telescope Imaging Spectrograph (STIS) ultra-violet (1700 -- 3000\,\AA) spectroscopy for 16 of these stars to investigate the nature of the UV excess and confirm the presence of a hot companion. In all cases we are able to confirm that the companion is a main-sequence B-type star based on the near-UV continuum. We determine effective temperatures, radii and luminosities from fitting the UV continuum with TLUSTY models and find stellar parameters in the expected range of SMC B-type stars. We display these results on a Hertzsprung--Russell diagram and assess the previously determined stellar parameters using UV photometry alone. From this comparison we conclude that UV photometric surveys are vital to identify such companions and UV spectroscopy is similarly vital to characterise the hot companions. From a comparison with IUE spectra of 32 Cyg, a well known RSG binary system in the Galaxy, four targets display evidence of being embedded in the wind of the RSG, like 32 Cyg, although none to the more extreme extent of VV Cep. The ages of six targets, determined via the stellar parameters of the hot companions, are found to be in tension with the ages determined for the RSG. A solution to this problem could be binary mass-transfer or red straggler stars.
Fine-grained sentiment analysis (FSA) aims to extract and summarize user opinions from vast opinionated text. Recent studies demonstrate that large language models (LLMs) possess exceptional sentiment understanding capabilities. However, directly deploying LLMs for FSA applications incurs high inference costs. Therefore, this paper investigates the distillation of fine-grained sentiment understanding from LLMs into small language models (SLMs). We prompt LLMs to examine and interpret the sentiments of given reviews and then utilize the generated content to pretrain SLMs. Additionally, we develop a comprehensive FSA benchmark to evaluate both SLMs and LLMs. Extensive experiments on this benchmark reveal that: (1) distillation significantly enhances the performance of SLMs in FSA tasks, achieving a 6.00\% improvement in $F_1$-score, and the distilled model can outperform Llama-2-7b with only 220M parameters; (2) distillation equips SLMs with excellent zero-shot sentiment classification capabilities, enabling them to match or even exceed their teacher models. These results suggest that distillation from LLMs is a highly promising direction for FSA. We will release our code, data, and pretrained model weights at https://github.com/HITSZ-HLT/FSA-Distillation.
Post-pandemic social contacts in Italy: implications for social distancing measures on in-person school and work attendance
2024-12-24
Lorenzo Lucchini, Valentina Marziano, Filippo Trentini, Chiara Chiavenna, Elena D'Agnese, Vittoria Offeddu, Mattia Manica, Piero Poletti, Duilio Balsamo, Giorgio Guzzetta, Marco Aielli, Alessia Melegaro, Stefano Merler
The collection of updated data on social contact patterns following the COVID-19 pandemic disruptions is crucial for future epidemiological assessments and evaluating non-pharmaceutical interventions (NPIs) based on physical distancing. We conducted two waves of an online survey in March 2022 and March 2023 in Italy, gathering data from a representative population sample on direct (verbal/physical interactions) and indirect (prolonged co-location in indoor spaces) contacts. Using a generalized linear mixed model, we examined determinants of individuals' total social contacts and evaluated the potential impact of work-from-home and distance learning on the transmissibility of respiratory pathogens. In-person attendance at work or school emerged as a primary driver of social contacts. Adults attending in person reported a mean of 1.69 (95% CI: 1.56-1.84) times the contacts of those staying home; among children and adolescents, this ratio increased to 2.38 (95% CI: 1.98-2.87). We estimated that suspending all non-essential work alone would marginally reduce transmissibility. However, combining distance learning for all education levels with work-from-home policies could decrease transmissibility by up to 23.7% (95% CI: 18.2%-29.0%). Extending these measures to early childcare services would yield only minimal additional benefits. These results provide useful data for modelling the transmission of respiratory pathogens in Italy after the end of the COVID-19 emergency. They also provide insights into the potential epidemiological effectiveness of social distancing interventions targeting work and school attendance, supporting considerations on the balance between the expected benefits and their heavy societal costs.
The massive fast spinning white dwarf in the HD 49798/RX J0648.0-4418 binary
2024-12-24
Sandro Mereghetti
I review the properties and discuss some of the puzzling aspects of the unique binary system composed of the luminous hot subdwarf HD 49798 and a white dwarf with mass of 1.2 solar masses and spin period of 13.2 s. This is one of the few massive white dwarfs with a dynamically measured mass and the one with the shortest spin period. It emits pulsed X-rays with a very soft spectrum, powered by accretion from the tenuous stellar wind of its companion of sdO spectral type. The current level of mass accretion cannot provide enough angular momentum to explain the small, but precisely measured, spin-up rate of 72 nanoseconds per year, which is instead best interpreted as the result of the radial contraction of this young white dwarf. The higher mass transfer rate expected during the future evolutionary stages of HD 49798 will drive the white dwarf above the Chandrasekhar limit, but the final fate, a type Ia SN explosion or the collapse to a millisecond pulsar, is uncertain.
Advancing Deformable Medical Image Registration with Multi-axis Cross-covariance Attention
2024-12-24
Mingyuan Meng, Michael Fulham, Lei Bi, Jinman Kim
Deformable image registration is a fundamental requirement for medical image analysis. Recently, transformers have been widely used in deep learning-based registration methods for their ability to capture long-range dependency via self-attention (SA). However, the high computation and memory loads of SA (growing quadratically with the spatial resolution) hinder transformers from processing subtle textural information in high-resolution image features, e.g., at the full and half image resolutions. This limits deformable registration as the high-resolution textural information is crucial for finding precise pixel-wise correspondence between subtle anatomical structures. Cross-covariance Attention (XCA), as a "transposed" version of SA that operates across feature channels, has complexity growing linearly with the spatial resolution, providing the feasibility of capturing long-range dependency among high-resolution image features. However, existing XCA-based transformers merely capture coarse global long-range dependency, which are unsuitable for deformable image registration relying primarily on fine-grained local correspondence. In this study, we propose to improve existing deep learning-based registration methods by embedding a new XCA mechanism. To this end, we design an XCA-based transformer block optimized for deformable medical image registration, named Multi-Axis XCA (MAXCA). Our MAXCA serves as a general network block that can be embedded into various registration network architectures. It can capture both global and local long-range dependency among high-resolution image features by applying regional and dilated XCA in parallel via a multi-axis design. Extensive experiments on two well-benchmarked inter-/intra-patient registration tasks with seven public medical datasets demonstrate that our MAXCA block enables state-of-the-art registration performance.
2024-12-24
Umut Cihan, Vahid Haratian, Arda İçöz, Mert Kaan Gül, Ömercan Devran, Emircan Furkan Bayendur, Baykal Mehmet Uçar, Eray Tüzün
Code review is a widespread practice to improve software quality and transfer knowledge. It is often seen as time-consuming due to the need for manual effort and potential delays. Several AI-assisted tools, such as Qodo, GitHub Copilot, and Coderabbit, provide automated reviews using large language models (LLMs). The effects of such tools in the industry are yet to be examined. This study examines the impact of LLM-based automated code review tools in an industrial setting. The study was conducted within a software development environment that adopted an AI-assisted review tool (based on open-source Qodo PR Agent). Around 238 practitioners across ten projects had access to the tool. We focused on three projects with 4,335 pull requests, 1,568 of which underwent automated reviews. Data collection comprised three sources: (1) a quantitative analysis of pull request data, including comment labels indicating whether developers acted on the automated comments, (2) surveys sent to developers regarding their experience with reviews on individual pull requests, and (3) a broader survey of 22 practitioners capturing their general opinions on automated reviews. 73.8% of automated comments were resolved. However, the average pull request closure duration increased from five hours 52 minutes to eight hours 20 minutes, with varying trends across projects. Most practitioners reported a minor improvement in code quality due to automated reviews. The LLM-based tool proved useful in software development, enhancing bug detection, increasing awareness of code quality, and promoting best practices. However, it also led to longer pull request closure times and introduced drawbacks like faulty reviews, unnecessary corrections, and irrelevant comments.
Accelerating process control and optimization via machine learning: A review
2024-12-24
Ilias Mitrai, Prodromos Daoutidis
Process control and optimization have been widely used to solve decision-making problems in chemical engineering applications. However, identifying and tuning the best solution algorithm is challenging and time-consuming. Machine learning tools can be used to automate these steps by learning the behavior of a numerical solver from data. In this paper, we discuss recent advances in (i) the representation of decision-making problems for machine learning tasks, (ii) algorithm selection, and (iii) algorithm configuration for monolithic and decomposition-based algorithms. Finally, we discuss open problems related to the application of machine learning for accelerating process optimization and control.
Generating Explanations for Autonomous Robots: a Systematic Review
2024-12-24
David Sobrín-Hidalgo, Ángel Manuel Guerrero-Higueras, Vicente Matellán-Olivera
Building trust between humans and robots has long interested the robotics community. Various studies have aimed to clarify the factors that influence the development of user trust. In Human-Robot Interaction (HRI) environments, a critical aspect of trust development is the robot's ability to make its behavior understandable. The concept of an eXplainable Autonomous Robot (XAR) addresses this requirement. However, giving a robot self-explanatory abilities is a complex task. Robot behavior includes multiple skills and diverse subsystems. This complexity led to research into a wide range of methods for generating explanations about robot behavior. This paper presents a systematic literature review that analyzes existing strategies for generating explanations in robots and studies the current XAR trends. Results indicate promising advancements in explainability systems. However, these systems are still unable to fully cover the complex behavior of autonomous robots. Furthermore, we also identify a lack of consensus on the theoretical concept of explainability, and the need for a robust methodology to assess explainability methods and tools has been identified.
Elevating Information System Performance: A Deep Dive into Quality Metrics
2024-12-24
Dana A Abdullah, Hewir A. Khidir, Ismail Y. Maolood, Aso K. Ameen, Dana Rasul Hamad, Hakem Saed Beitolahi, Abdulhady Abas Abdullah, Tarik Ahmed Rashid, Mohammed Y. Shakor
In today's digital age, information systems (IS) are indispensable tools for organizations of all sizes. The quality of these systems, encompassing system, information, and service dimensions, significantly impacts organizational performance. This study investigates the intricate relationships between these three quality dimensions and their collective influence on key performance indicators such as customer satisfaction and operational efficiency. By conducting a comparative analysis of various quality metrics, we aim to identify the most effective indicators for assessing IS quality. Our research contributes to the field by providing actionable insights for researchers or practitioners to develop the implementation, evaluation and design of information systems. Also, a quantitative study employing a structured questionnaire survey was conducted to achieve primary data from respondents across various sectors. Statistical analysis, including Cronbach's Alpha (0.953) and factor analysis (KMO = 0.965, Bartlett's Test p < 0.000), revealed strong interdependencies among System Quality (SQ), Information Quality (IQ), and Service Quality (SerQ). The results demonstrate that high SQ leads to improved IQ, which in turn contributes to enhanced SerQ and user satisfaction. While all three qualities are crucial, SerQ emerges as the most relevant indicator of overall system performance due to its broader representation of quality dimensions
Study of the physical and chemical properties of dense clumps at different evolutionary stages in several regions of massive star and stellar cluster formation
2024-12-24
A. G. Pazukhin, I. I. Zinchenko, E. A. Trofimova
Massive stars play an important role in the Universe. Unlike low-mass stars, the formation of these objects located at great distances is still unclear. It is expected to be governed by some combination of self-gravity, turbulence, and magnetic fields. In this work, we aim to study the chemical and physical conditions of dense clumps at different evolutionary stages. We performed observations towards 5 regions of massive star and stellar cluster formation (L1287, S187, S231, DR 21(OH), NGC 7538) with the IRAM-30m telescope. We covered the 2 and 3$-$4 mm wavelength bands and analysed the lines of HCN, HNC, HCO$^+$, HC$_3$N, HNCO, OCS, CS, SiO, SO$_2$, and SO. Using astrodendro algorithm on the 850 $\mu$m dust emission data from the SCUBA Legacy catalogue, we determined the masses, H$_2$ column densities, and sizes of the clumps. Furthermore, the kinetic temperatures, molecular abundances, and dynamical state were obtained. The Red Midcourse Space Experiment Source survey (RMS) was used to determine the clump types. A total of 20 clumps were identified. Three clumps were found to be associated with the Hii regions, 10 with young stellar objects (YSOs), and 7 with submillimetre emission. The clumps have typical sizes of about 0.2 pc and masses ranging from 1 to $10^{2}\,M_\odot$, kinetic temperatures ranging from 20 to 40 K and line widths of $\rm H^{13}CO^{+} (1-0)$ approximately 2 $\rm km\,s^{-1}$. We found no significant correlation in the line width$-$size and the line width$-$mass relationships. However, a strong correlation is observed in mass$-$size relationships. The virial analysis indicated that three clumps are gravitationally bound. Furthermore, we suggested that magnetic fields of about 1 mG provide additional support for clump stability. The molecular abundances relative to H$_2$ are approximately $10^{-10}-10^{-8}$.
How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation System?
2024-12-24
Sara Papi, Peter Polak, Ondřej Bojar, Dominik Macháček
Simultaneous speech-to-text translation (SimulST) translates source-language speech into target-language text concurrently with the speaker's speech, ensuring low latency for better user comprehension. Despite its intended application to unbounded speech, most research has focused on human pre-segmented speech, simplifying the task and overlooking significant challenges. This narrow focus, coupled with widespread terminological inconsistencies, is limiting the applicability of research outcomes to real-world applications, ultimately hindering progress in the field. Our extensive literature review of 110 papers not only reveals these critical issues in current research but also serves as the foundation for our key contributions. We 1) define the steps and core components of a SimulST system, proposing a standardized terminology and taxonomy; 2) conduct a thorough analysis of community trends, and 3) offer concrete recommendations and future directions to bridge the gaps in existing literature, from evaluation frameworks to system architectures, for advancing the field towards more realistic and effective SimulST solutions.
Field-theoretic approach to flat polymerized membranes
2024-12-24
Simon Metayer, Sofian Teber
We review the field-theoretic renormalization-group approach to critical properties of flat polymerized membranes. We start with a presentation of the flexural effective model that is entirely expressed in terms of a transverse (flexural) field with non-local interactions. We then provide a detailed account of the full three-loop computations of the renormalization-group functions of the model within the dimensional regularization scheme. The latter allows us to consider the general case of a $d$-dimensional membrane embedded in $D$-dimensional space. Focusing on the critical flat phase of two-dimensional membranes $(d = 2)$ in three-dimensional space $(D = 3)$, we analyse the corresponding flow diagram and present the derivation of the anomalous stiffness. The latter controls all the other critical exponents of the theory such as the roughness exponent and the scaling of the elastic constants. State-of-the-art four-loop results as well as discussions on the structure of the perturbative series and comparison with other approaches are also provided.
Research shows that phishing emails often utilize persuasion techniques, such as social proof, liking, consistency, authority, scarcity, and reciprocity to gain trust to obtain sensitive information or maliciously infect devices. The link between principles of persuasion and social engineering attacks, particularly in phishing email attacks, is an important topic in cyber security as they are the common and effective method used by cybercriminals to obtain sensitive information or access computer systems. This survey paper concluded that spear phishing, a targeted form of phishing, has been found to be specifically effective as attackers can tailor their messages to the specific characteristics, interests, and vulnerabilities of their targets. Understanding the uses of the principles of persuasion in spear phishing is key to the effective defence against it and eventually its elimination. This survey paper systematically summarizes and presents the current state of the art in understanding the use of principles of persuasion in phishing. Through a systematic review of the existing literature, this survey paper identifies a significant gap in the understanding of the impact of principles of persuasion as a social engineering strategy in phishing attacks and highlights the need for further research in this area.
PonziLens+: Visualizing Bytecode Actions for Smart Ponzi Scheme Identification
2024-12-24
Xiaolin Wen, Tai D. Nguyen, Shaolun Ruan, Qiaomu Shen, Jun Sun, Feida Zhu, Yong Wang
With the prevalence of smart contracts, smart Ponzi schemes have become a common fraud on blockchain and have caused significant financial loss to cryptocurrency investors in the past few years. Despite the critical importance of detecting smart Ponzi schemes, a reliable and transparent identification approach adaptive to various smart Ponzi schemes is still missing. To fill the research gap, we first extract semantic-meaningful actions to represent the execution behaviors specified in smart contract bytecodes, which are derived from a literature review and in-depth interviews with domain experts. We then propose PonziLens+, a novel visual analytic approach that provides an intuitive and reliable analysis of Ponzi-scheme-related features within these execution behaviors. PonziLens+ has three visualization modules that intuitively reveal all potential behaviors of a smart contract, highlighting fraudulent features across three levels of detail. It can help smart contract investors and auditors achieve confident identification of any smart Ponzi schemes. We conducted two case studies and in-depth user interviews with 12 domain experts and common investors to evaluate PonziLens+. The results demonstrate the effectiveness and usability of PonziLens+ in achieving an effective identification of smart Ponzi schemes.
Detection of an Orphan X-ray Flare from a Blazar Candidate EP240709a with Einstein Probe
2024-12-24
Mingjun Liu, Yijia Zhang, Yun Wang, Rui Xue, David Buckley, D. Andrew Howell, Chichuan Jin, Wenxiong Li, Itumeleng Monageng, Haiwu Pan, Ning-Chen Sun, Samaporn Tinyanont, Lingzhi Wang, Weimin Yuan, Jie An, Moira Andrews, Rungrit Anutarawiramkul, Pathompong Butpan, Huaqing Cheng, Cui-Yuan Dai, Lixin Dai, Joseph Farah, Hua Feng, Shaoyu Fu, Zhen Guo, Shuaiqing Jiang, An Li, Dongyue Li, Yifang Liang, Heyang Liu, Xing Liu, Yuan Liu, Jirong Mao, Curtis McCully, Megan Newsome, Estefania Padilla Gonzalez, Xin Pan, Xinxiang Sun, Giacomo Terreran, Ze-Rui Wang, Qinyu Wu, Hubing Xiao, Dingrong Xiong, Dong Xu, Xinpeng Xu, Suijian Xue, Haonan Yang, Jun Yang, Jin Zhang, Wenda Zhang, Wenjie Zhang, Hu Zou
Blazars are often observed to flare across multiple wavelengths. Orphan flares from blazars have been only detected a few times, providing an opportunity to understand the structure of the jet in the accreting system. We report a remarkable orphan X-ray flare from a blazar candidate EP240709a, detected by Einstein Probe (EP) in July 2024. The multi-band spectral properties and variability support EP240709a as a high-energy peaked BL Lacertae-type object. The flux in 0.5-10 keV increases by at least 28 times to the value of low state in 2020, with non-detection of remarkable flaring in other bands during the same period. EP240709a exhibits the harder-when-brighter tendency in the X-ray band during the orphan flare, while its infrared-optical spectra are featureless. We employ one-zone and two-zone leptonic synchrotron self-Compton models to perform the spectral energy distribution fitting. Detecting this rare orphan flare shows the potential of EP in discovering peculiar activities from AGN in high-cadence X-ray sky surveys.
2024-12-24
Denis Bernardes, Orlando Verducci Junior, Francisco Rodrigues, Claudia Vilega Rodrigues, Luciano Fraga, Eder Martioli, Clemens D. Gneiding, André Luiz de Moura Alves, Juliano Romão, Laerte Andrade, Leandro de Almeida, Ana Carolina Mattiuci, Flavio Felipe Ribeiro, Wagner Schlindwein, Jesulino Bispo dos Santos, Francisco Jose Jablonski, Julio Cesar Neves Campagnolo, Rene Laporte
SPARC4 is a new astronomical instrument developed entirely by Brazilian institutions, currently installed on the 1.6-m Perkin-Elmer telescope of the Pico dos Dias Observatory. It allows the user to perform photometric or polarimetric observations simultaneously in the four SDSS bands (g, r, i, and z). In this paper, we describe the control system developed for SPARC4. This system is composed of S4ACS, S4ICS, and S4GUI softwares and associated hardware. S4ACS is responsible for controlling the four EMCCD scientific cameras (one for each instrument band). S4ICS controls the sensors and motors responsible for the moving parts of SPARC4. Finally, S4GUI is the interface used to perform observations, which includes the choice of instrument configuration and image acquisition parameters. S4GUI communicates with the instrument subsystems and with some observatory facilities, needed during the observations. Bench tests were performed for the determination of the overheads added by SPARC4 control system in the acquisition of photometric and polarimetric series of images. In the photometric mode, SPARC4 allows the acquisition of a series of 1400 full-frame images, with a deadtime of 4.5 ms between images. Besides, several image series can be concatenated with a deadtime of 450 ms plus the readout time of the last image. For the polarimetric mode, measurements can be obtained with a deadtime of 1.41 s plus the image readout time between subsequent waveplate positions. For both photometric and polarimetric modes, the user can choose among operating modes with image readout times between 5.9 ms and 1.24 s, which ultimately defines the instrument temporal performance.
Unveiling the Threat of Fraud Gangs to Graph Neural Networks: Multi-Target Graph Injection Attacks against GNN-Based Fraud Detectors
2024-12-24
Jinhyeok Choi, Heehyeon Kim, Joyce Jiyoung Whang
Graph neural networks (GNNs) have emerged as an effective tool for fraud detection, identifying fraudulent users, and uncovering malicious behaviors. However, attacks against GNN-based fraud detectors and their risks have rarely been studied, thereby leaving potential threats unaddressed. Recent findings suggest that frauds are increasingly organized as gangs or groups. In this work, we design attack scenarios where fraud gangs aim to make their fraud nodes misclassified as benign by camouflaging their illicit activities in collusion. Based on these scenarios, we study adversarial attacks against GNN-based fraud detectors by simulating attacks of fraud gangs in three real-world fraud cases: spam reviews, fake news, and medical insurance frauds. We define these attacks as multi-target graph injection attacks and propose MonTi, a transformer-based Multi-target one-Time graph injection attack model. MonTi simultaneously generates attributes and edges of all attack nodes with a transformer encoder, capturing interdependencies between attributes and edges more effectively than most existing graph injection attack methods that generate these elements sequentially. Additionally, MonTi adaptively allocates the degree budget for each attack node to explore diverse injection structures involving target, candidate, and attack nodes, unlike existing methods that fix the degree budget across all attack nodes. Experiments show that MonTi outperforms the state-of-the-art graph injection attack methods on five real-world graphs.
Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning
2024-12-24
Safa Ben Atitallah, Chaima Ben Rabah, Maha Driss, Wadii Boulila, Anis Koubaa
Graph Mamba, a powerful graph embedding technique, has emerged as a cornerstone in various domains, including bioinformatics, social networks, and recommendation systems. This survey represents the first comprehensive study devoted to Graph Mamba, to address the critical gaps in understanding its applications, challenges, and future potential. We start by offering a detailed explanation of the original Graph Mamba architecture, highlighting its key components and underlying mechanisms. Subsequently, we explore the most recent modifications and enhancements proposed to improve its performance and applicability. To demonstrate the versatility of Graph Mamba, we examine its applications across diverse domains. A comparative analysis of Graph Mamba and its variants is conducted to shed light on their unique characteristics and potential use cases. Furthermore, we identify potential areas where Graph Mamba can be applied in the future, highlighting its potential to revolutionize data analysis in these fields. Finally, we address the current limitations and open research questions associated with Graph Mamba. By acknowledging these challenges, we aim to stimulate further research and development in this promising area. This survey serves as a valuable resource for both newcomers and experienced researchers seeking to understand and leverage the power of Graph Mamba.
Quo Vadis, Anomaly Detection? LLMs and VLMs in the Spotlight
2024-12-24
Xi Ding, Lei Wang
Video anomaly detection (VAD) has witnessed significant advancements through the integration of large language models (LLMs) and vision-language models (VLMs), addressing critical challenges such as interpretability, temporal reasoning, and generalization in dynamic, open-world scenarios. This paper presents an in-depth review of cutting-edge LLM-/VLM-based methods in 2024, focusing on four key aspects: (i) enhancing interpretability through semantic insights and textual explanations, making visual anomalies more understandable; (ii) capturing intricate temporal relationships to detect and localize dynamic anomalies across video frames; (iii) enabling few-shot and zero-shot detection to minimize reliance on large, annotated datasets; and (iv) addressing open-world and class-agnostic anomalies by using semantic understanding and motion features for spatiotemporal coherence. We highlight their potential to redefine the landscape of VAD. Additionally, we explore the synergy between visual and textual modalities offered by LLMs and VLMs, highlighting their combined strengths and proposing future directions to fully exploit the potential in enhancing video anomaly detection.
DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation
2024-12-24
Junyi Lu, Xiaojia Li, Zihan Hua, Lei Yu, Shiqi Cheng, Li Yang, Fengjun Zhang, Chun Zuo
Code review is a vital but demanding aspect of software development, generating significant interest in automating review comments. Traditional evaluation methods for these comments, primarily based on text similarity, face two major challenges: inconsistent reliability of human-authored comments in open-source projects and the weak correlation of text similarity with objectives like enhancing code quality and detecting defects. This study empirically analyzes benchmark comments using a novel set of criteria informed by prior research and developer interviews. We then similarly revisit the evaluation of existing methodologies. Our evaluation framework, DeepCRCEval, integrates human evaluators and Large Language Models (LLMs) for a comprehensive reassessment of current techniques based on the criteria set. Besides, we also introduce an innovative and efficient baseline, LLM-Reviewer, leveraging the few-shot learning capabilities of LLMs for a target-oriented comparison. Our research highlights the limitations of text similarity metrics, finding that less than 10% of benchmark comments are high quality for automation. In contrast, DeepCRCEval effectively distinguishes between high and low-quality comments, proving to be a more reliable evaluation mechanism. Incorporating LLM evaluators into DeepCRCEval significantly boosts efficiency, reducing time and cost by 88.78% and 90.32%, respectively. Furthermore, LLM-Reviewer demonstrates significant potential of focusing task real targets in comment generation.
Investigating Large Language Models for Code Vulnerability Detection: An Experimental Study
2024-12-24
Xuefeng Jiang, Lvhua Wu, Sheng Sun, Jia Li, Jingjing Xue, Yuwei Wang, Tingting Wu, Min Liu
Code vulnerability detection (CVD) is essential for addressing and preventing system security issues, playing a crucial role in ensuring software security. Previous learning-based vulnerability detection methods rely on either fine-tuning medium-size sequence models or training smaller neural networks from scratch. Recent advancements in large pre-trained language models (LLMs) have showcased remarkable capabilities in various code intelligence tasks including code understanding and generation. However, the effectiveness of LLMs in detecting code vulnerabilities is largely under-explored. This work aims to investigate the gap by fine-tuning LLMs for the CVD task, involving four widely-used open-source LLMs. We also implement other five previous graph-based or medium-size sequence models for comparison. Experiments are conducted on five commonly-used CVD datasets, including both the part of short samples and long samples. In addition, we conduct quantitative experiments to investigate the class imbalance issue and the model's performance on samples of different lengths, which are rarely studied in previous works. To better facilitate communities, we open-source all codes and resources of this study in https://github.com/SakiRinn/LLM4CVD and https://huggingface.co/datasets/xuefen/VulResource.
Graph neural networks (GNNs) have emerged as state-of-the-art methods to learn from graph-structured data for recommendation. However, most existing GNN-based recommendation methods focus on the optimization of model structures and learning strategies based on pre-defined graphs, neglecting the importance of the graph construction stage. Earlier works for graph construction usually rely on speciffic rules or crowdsourcing, which are either too simplistic or too labor-intensive. Recent works start to utilize large language models (LLMs) to automate the graph construction, in view of their abundant open-world knowledge and remarkable reasoning capabilities. Nevertheless, they generally suffer from two limitations: (1) invisibility of global view (e.g., overlooking contextual information) and (2) construction inefficiency. To this end, we introduce AutoGraph, an automatic graph construction framework based on LLMs for recommendation. Specifically, we first use LLMs to infer the user preference and item knowledge, which is encoded as semantic vectors. Next, we employ vector quantization to extract the latent factors from the semantic vectors. The latent factors are then incorporated as extra nodes to link the user/item nodes, resulting in a graph with in-depth global-view semantics. We further design metapath-based message aggregation to effectively aggregate the semantic and collaborative information. The framework is model-agnostic and compatible with different backbone models. Extensive experiments on three real-world datasets demonstrate the efficacy and efffciency of AutoGraph compared to existing baseline methods. We have deployed AutoGraph in Huawei advertising platform, and gain a 2.69% improvement on RPM and a 7.31% improvement on eCPM in the online A/B test. Currently AutoGraph has been used as the main trafffc model, serving hundreds of millions of people.
Artificial Intelligence Generated Content (AIGC) has gained significant popularity for creating diverse content. Current AIGC models primarily focus on content quality within a centralized framework, resulting in a high service delay and negative user experiences. However, not only does the workload of an AIGC task depend on the AIGC model's complexity rather than the amount of data, but the large model and its multi-layer encoder structure also result in a huge demand for computational and memory resources. These unique characteristics pose new challenges in its modeling, deployment, and scheduling at edge networks. Thus, we model an offloading problem among edges for providing real AIGC services and propose LAD-TS, a novel Latent Action Diffusion-based Task Scheduling method that orchestrates multiple edge servers for expedited AIGC services. The LAD-TS generates a near-optimal offloading decision by leveraging the diffusion model's conditional generation capability and the reinforcement learning's environment interaction ability, thereby minimizing the service delays under multiple resource constraints. Meanwhile, a latent action diffusion strategy is designed to guide decision generation by utilizing historical action probability, enabling rapid achievement of near-optimal decisions. Furthermore, we develop DEdgeAI, a prototype edge system with a refined AIGC model deployment to implement and evaluate our LAD-TS method. DEdgeAI provides a real AIGC service for users, demonstrating up to 29.18% shorter service delays than the current five representative AIGC platforms. We release our open-source code at https://github.com/ChangfuXu/DEdgeAI/.
Learning Sign Language Representation using CNN LSTM, 3DCNN, CNN RNN LSTM and CCN TD
2024-12-24
Nikita Louison, Wayne Goodridge, Koffka Khan
Existing Sign Language Learning applications focus on the demonstration of the sign in the hope that the student will copy a sign correctly. In these cases, only a teacher can confirm that the sign was completed correctly, by reviewing a video captured manually. Sign Language Translation is a widely explored field in visual recognition. This paper seeks to explore the algorithms that will allow for real-time, video sign translation, and grading of sign language accuracy for new sign language users. This required algorithms capable of recognizing and processing spatial and temporal features. The aim of this paper is to evaluate and identify the best neural network algorithm that can facilitate a sign language tuition system of this nature. Modern popular algorithms including CNN and 3DCNN are compared on a dataset not yet explored, Trinidad and Tobago Sign Language as well as an American Sign Language dataset. The 3DCNN algorithm was found to be the best performing neural network algorithm from these systems with 91% accuracy in the TTSL dataset and 83% accuracy in the ASL dataset.
India's vast linguistic diversity presents unique challenges and opportunities for technological advancement, especially in the realm of Natural Language Processing (NLP). While there has been significant progress in NLP applications for widely spoken languages, the regional languages of India, such as Marathi and Hindi, remain underserved. Research in the field of NLP for Indian regional languages is at a formative stage and holds immense significance. The paper aims to build a platform which enables the user to use various features like text anonymization, abstractive text summarization and spell checking in English, Hindi and Marathi language. The aim of these tools is to serve enterprise and consumer clients who predominantly use Indian Regional Languages.
2024-12-24
Yusuke Ide, Joshua Tanner, Adam Nohejl, Jacob Hoffman, Justin Vasselli, Hidetaka Kamigaito, Taro Watanabe
Multiword expressions (MWEs) refer to idiomatic sequences of multiple words. MWE identification, i.e., detecting MWEs in text, can play a key role in downstream tasks such as machine translation. Existing datasets for MWE identification are inconsistently annotated, limited to a single type of MWE, or limited in size. To enable reliable and comprehensive evaluation, we created CoAM: Corpus of All-Type Multiword Expressions, a dataset of 1.3K sentences constructed through a multi-step process to enhance data quality consisting of human annotation, human review, and automated consistency checking. MWEs in CoAM are tagged with MWE types, such as Noun and Verb, to enable fine-grained error analysis. Annotations for CoAM were collected using a new interface created with our interface generator, which allows easy and flexible annotation of MWEs in any form, including discontinuous ones. Through experiments using CoAM, we find that a fine-tuned large language model outperforms the current state-of-the-art approach for MWE identification. Furthermore, analysis using our MWE type tagged data reveals that Verb MWEs are easier than Noun MWEs to identify across approaches.
In this paper, we study a system in which a sensor forwards status updates to a receiver through an error-prone channel, while the receiver sends the transmission results back to the sensor via a reliable channel. Both channels are subject to random delays. To evaluate the timeliness of the status information at the receiver, we use the Age of Information (AoI) metric. The objective is to design a sampling policy that minimizes the expected time-average AoI, even when the channel statistics (e.g., delay distributions) are unknown. We first review the threshold structure of the optimal offline policy under known channel statistics and then reformulate the design of the online algorithm as a stochastic approximation problem. We propose a Robbins-Monro algorithm to solve this problem and demonstrate that the optimal threshold can be approximated almost surely. Moreover, we prove that the cumulative AoI regret of the online algorithm increases with rate $\mathcal{O}(\ln K)$, where $K$ is the number of successful transmissions. In addition, our algorithm is shown to be minimax order optimal, in the sense that for any online learning algorithm, the cumulative AoI regret up to the $K$-th successful transmissions grows with the rate at least $\Omega(\ln K)$ in the worst case delay distribution. Finally, we improve the stability of the proposed online learning algorithm through a momentum-based stochastic gradient descent algorithm. Simulation results validate the performance of our proposed algorithm.
A Review of 3D Particle Tracking and Flow Diagnostics Using Digital Holography
2024-12-24
Shyam Kumar M, Jiarong Hong
Advanced three-dimensional (3D) tracking methods are essential for studying particle dynamics across a wide range of complex systems, including multiphase flows, environmental and atmospheric sciences, colloidal science, biological and medical research, and industrial manufacturing processes. This review provides a comprehensive summary of 3D particle tracking and flow diagnostics using Digital Holography (DH). We begin by introducing the principles of DH, accompanied by a detailed discussion on numerical reconstruction. The review then explores various hardware setups used in DH, including inline, off-axis, and dual or multiple-view configurations, outlining their advantages and limitations. We also delve into different hologram processing methods, categorized into traditional multi-step, inverse, and machine learning-based approaches, providing in-depth insights into their applications for 3D particle tracking and flow diagnostics across multiple studies. The review concludes with a discussion on future prospects, emphasizing the significant role of machine learning in enabling accurate DH-based particle tracking and flow diagnostic techniques across diverse fields, such as manufacturing, environmental monitoring, and biological sciences.
Agents on the Bench: Large Language Model Based Multi Agent Framework for Trustworthy Digital Justice
2024-12-24
Cong Jiang, Xiaolei Yang
The justice system has increasingly employed AI techniques to enhance efficiency, yet limitations remain in improving the quality of decision-making, particularly regarding transparency and explainability needed to uphold public trust in legal AI. To address these challenges, we propose a large language model based multi-agent framework named AgentsBench, which aims to simultaneously improve both efficiency and quality in judicial decision-making. Our approach leverages multiple LLM-driven agents that simulate the collaborative deliberation and decision making process of a judicial bench. We conducted experiments on legal judgment prediction task, and the results show that our framework outperforms existing LLM based methods in terms of performance and decision quality. By incorporating these elements, our framework reflects real-world judicial processes more closely, enhancing accuracy, fairness, and society consideration. AgentsBench provides a more nuanced and realistic methods of trustworthy AI decision-making, with strong potential for application across various case types and legal scenarios.
Video Is Worth a Thousand Images: Exploring the Latest Trends in Long Video Generation
2024-12-24
Faraz Waseem, Muhammad Shahzad
An image may convey a thousand words, but a video composed of hundreds or thousands of image frames tells a more intricate story. Despite significant progress in multimodal large language models (MLLMs), generating extended videos remains a formidable challenge. As of this writing, OpenAI's Sora, the current state-of-the-art system, is still limited to producing videos that are up to one minute in length. This limitation stems from the complexity of long video generation, which requires more than generative AI techniques for approximating density functions essential aspects such as planning, story development, and maintaining spatial and temporal consistency present additional hurdles. Integrating generative AI with a divide-and-conquer approach could improve scalability for longer videos while offering greater control. In this survey, we examine the current landscape of long video generation, covering foundational techniques like GANs and diffusion models, video generation strategies, large-scale training datasets, quality metrics for evaluating long videos, and future research areas to address the limitations of the existing video generation capabilities. We believe it would serve as a comprehensive foundation, offering extensive information to guide future advancements and research in the field of long video generation.
Gamma-ray bursts: what do we know today that we did not know 10 years ago?
2024-12-24
Asaf Pe'er
I discuss here the progress made in the last decade on few of the key open problems in GRB physics. These include: (1) the nature of GRB progenitors, and the outliers found to the collapsar/merger scenarios; (2) Jet structures, whose existence became evident following GRB/GW170817; (3) the great progress made in understanding the GRB jet launching mechanisms, enabled by general-relativistic magneto-hydrodynamic (GR-MHD) codes; (4) recent studies of magnetic reconnection as a valid energy dissipation mechanism; (5) the early afterglow, which may be highly affected by a wind bubble, as well as recent indication that in many GRBs, the Lorentz factor is only a few tens, rather than few hundreds. I highlight some recent observational progress, including major breakthrough in detecting TeV photons and the on-going debate about their origin, polarization measurements, as well as the pair annihilation line recently detected in GRB 221009A, and its implications on the prompt emission physics. I point into some open questions that I anticipate would be at the forefront of GRB research in the next decade.
State-of-the-Art Underwater Vehicles and Technologies Enabling Smart Ocean: Survey and Classifications
2024-12-24
Jiajie Xu, Xabier Irigoien, Mohamed-Slim Alouini
The exploration and sustainable use of marine environments have become increasingly critical as oceans cover over 70% of surface of Earth. This paper provides a comprehensive survey and classification of state-of-the-art underwater vehicles (UVs) and supporting technologies essential for enabling a smart ocean. We categorize UVs into several types, including remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), hybrid underwater vehicles (HUVs), unmanned surface vehicles (USVs), and underwater bionic vehicles (UBVs). These technologies are fundamental in a wide range of applications, such as environmental monitoring, deep-sea exploration, defense, and underwater infrastructure inspection. Additionally, the paper explores advancements in underwater communication technologies, namely acoustic, optical, and hybrid systems, as well as key support facilities, including submerged buoys, underwater docking stations, and wearable underwater localization systems. By classifying the vehicles and analyzing their technological capabilities and limitations, this work aims to guide future developments in underwater exploration and monitoring, addressing challenges such as energy efficiency, communication limitations, and environmental adaptability. The paper concludes by discussing the integration of artificial intelligence and machine learning in enhancing the autonomy and operational efficiency of these systems, paving the way for the realization of a fully interconnected and sustainable Smart Ocean.
A Review of Latent Representation Models in Neuroimaging
2024-12-24
C. Vázquez-García, F. J. Martínez-Murcia, F. Segovia Román, Juan M. Górriz
Neuroimaging data, particularly from techniques like MRI or PET, offer rich but complex information about brain structure and activity. To manage this complexity, latent representation models - such as Autoencoders, Generative Adversarial Networks (GANs), and Latent Diffusion Models (LDMs) - are increasingly applied. These models are designed to reduce high-dimensional neuroimaging data to lower-dimensional latent spaces, where key patterns and variations related to brain function can be identified. By modeling these latent spaces, researchers hope to gain insights into the biology and function of the brain, including how its structure changes with age or disease, or how it encodes sensory information, predicts and adapts to new inputs. This review discusses how these models are used for clinical applications, like disease diagnosis and progression monitoring, but also for exploring fundamental brain mechanisms such as active inference and predictive coding. These approaches provide a powerful tool for both understanding and simulating the brain's complex computational tasks, potentially advancing our knowledge of cognition, perception, and neural disorders.
Effects of Turbulence Modeling and Parcel Approach on Dispersed Two-Phase Swirling Flow
2024-12-24
Osama A. Marzouk, E. David Huckaby
Several numerical simulations of a co-axial particle-laden swirling air flow in a vertical circular pipe were performed. The air flow was modeled using the unsteady Favre-averaged Navier-Stokes equations. A Lagrangian model was used for the particle motion. The gas and particles are coupled through two-way momentum exchange. The results of the simulations using three versions of the k-epsilon turbulence model (standard, re-normalization group (RNG), and realizable) are compared with experimental mean velocity profiles. The standard model achieved the best overall performance. The realizable model was unable to satisfactorily predict the radial velocity; it is also the most computationally-expensive model. The simulations using the RNG model predicted additional recirculation zones. We also compared the particle and parcel approaches in solving the particle motion. In the latter, multiple similar particles are grouped in a single parcel, thereby reducing the amount of computation.
ResearchTown: Simulator of Human Research Community
2024-12-23
Haofei Yu, Zhaochen Hong, Zirui Cheng, Kunlun Zhu, Keyang Xuan, Jinwei Yao, Tao Feng, Jiaxuan You
Large Language Models (LLMs) have demonstrated remarkable potential in scientific domains, yet a fundamental question remains unanswered: Can we simulate human research communities with LLMs? Addressing this question can deepen our understanding of the processes behind idea brainstorming and inspire the automatic discovery of novel scientific insights. In this work, we propose ResearchTown, a multi-agent framework for research community simulation. Within this framework, the human research community is simplified and modeled as an agent-data graph, where researchers and papers are represented as agent-type and data-type nodes, respectively, and connected based on their collaboration relationships. We also introduce TextGNN, a text-based inference framework that models various research activities (e.g., paper reading, paper writing, and review writing) as special forms of a unified message-passing process on the agent-data graph. To evaluate the quality of the research simulation, we present ResearchBench, a benchmark that uses a node-masking prediction task for scalable and objective assessment based on similarity. Our experiments reveal three key findings: (1) ResearchTown can provide a realistic simulation of collaborative research activities, including paper writing and review writing; (2) ResearchTown can maintain robust simulation with multiple researchers and diverse papers; (3) ResearchTown can generate interdisciplinary research ideas that potentially inspire novel research directions.
Multimodal learning, a rapidly evolving field in artificial intelligence, seeks to construct more versatile and robust systems by integrating and analyzing diverse types of data, including text, images, audio, and video. Inspired by the human ability to assimilate information through many senses, this method enables applications such as text-to-video conversion, visual question answering, and image captioning. Recent developments in datasets that support multimodal language models (MLLMs) are highlighted in this overview. Large-scale multimodal datasets are essential because they allow for thorough testing and training of these models. With an emphasis on their contributions to the discipline, the study examines a variety of datasets, including those for training, domain-specific tasks, and real-world applications. It also emphasizes how crucial benchmark datasets are for assessing models' performance in a range of scenarios, scalability, and applicability. Since multimodal learning is always changing, overcoming these obstacles will help AI research and applications reach new heights.
Warped accretion disks and quasars with episodic periodicity of long-term variations
2024-12-23
Yue-Chang Peng, Jian-Min Wang, Pu Du, Shuo Zhai, Yan-Rong Li
It has been found that some quasars are undergoing quasi-periodic variations (most of them with damped amplitudes) in optical bands from long-term monitoring campaigns, but how to explain the origin of such light curve variations still remains an open question. In this paper, we use the warped accretion disks model to explain the quasi-periodical variations. This model employs a free-bending wave traveling in an accretion disk which causes the orientation of the central part of the disk to oscillate from the line of sight, resulting in a quasi-periodical variation. We numerically solve the governing equation of warp propagation and calculate the simulated R-band light curves, finding that the periodical light curves generated by this model have damped amplitudes. To compare with observations, we select SDSSJ134820.42+194831.5 as a preliminary example from a sample of periodic quasar candidates by combining CRTS with other public survey data, and fitted its light curve with different observational angles. Our result gives a reduced $\chi^{2}\simeq 2.4$, implying that the model might give insights to future application of warped disk model.
A Tale of Three: Magnetic Fields along the Orion Integral-Shaped Filament as Revealed by JCMT BISTRO survey
2024-12-23
Jintai Wu, Keping Qiu, Frederick Poidevin, Pierre Bastien, Junhao Liu, Tao-Chung Ching, Tyler L. Bourke, Derek Ward-Thompson, Kate Pattle, Doug Johnstone, Patrick M. Koch, Doris Arzoumanian, Chang Won Lee, Lapo Fanciullo, Takashi Onaka, Jihye Hwang, Valentin J. M. Le Gouellec, Archana Soam, Motohide Tamura, Mehrnoosh Tahani, Chakali Eswaraiah, Hua-Bai Li, David Berry, Ray S. Furuya, Simon Coude, Woojin Kwon, Sheng-Jun Lin, Jia-Wei Wang, Tetsuo Hasegawa, Shih-Ping Lai, Do-Young Byun, Zhiwei Chen, Huei-Ru Vivien Chen, Wen Ping Chen, Mike Chen, Jungyeon Cho, Youngwoo Choi, Yunhee Choi, Minho Choi, Antonio Chrysostomou, Eun Jung Chung, Sophia Dai, James Di Francesco, Pham Ngoc Diep, Yasuo Doi, Hao-Yuan Duan, Yan Duan, David Eden, Jason Fiege, Laura M. Fissel, Erica Franzmann, Per Friberg, Rachel Friesen, Gary Fuller, Tim Gledhill, Sarah Graves, Jane Greaves, Matt Griffin, Qilao Gu, Ilseung Han, Saeko Hayashi, Thiem Hoang, Martin Houde, Tsuyoshi Inoue, Shu-ichiro Inutsuka, Kazunari Iwasaki, Il-Gyo Jeong, Vera Konyves, Ji-hyun Kang, Miju Kang, Janik Karoly, Akimasa Kataoka, Koji Kawabata, Shinyoung Kim, Mi-Ryang Kim, Kyoung Hee Kim, Kee-Tae Kim, Jongsoo Kim, Hyosung Kim, Gwanjeong Kim, Florian Kirchschlager, Jason Kirk, Masato I. N. Kobayashi, Takayoshi Kusune, Jungmi Kwon, Kevin Lacaille, Chi-Yan Law, Hyeseung Lee, Chin-Fei Lee, Sang-Sung Lee, Jeong-Eun Lee, Dalei Li, Di Li, Guangxing Li, Sheng-Yuan Liu, Tie Liu, Hong-Li Liu, Xing Lu, A-Ran Lyo, Steve Mairs, Masafumi Matsumura, Brenda Matthews, Gerald Moriarty-Schieven, Tetsuya Nagata, Fumitaka Nakamura, Hiroyuki Nakanishi, Nguyen Bich Ngoc, Nagayoshi Ohashi, Geumsook Park, Harriet Parsons, Nicolas Peretto, Felix Priestley, Tae-Soo Pyo, Lei Qian, Ramprasad Rao, Jonathan Rawlings, Mark Rawlings, Brendan Retter, John Richer, Andrew Rigby, Sarah Sadavoy, Hiro Saito, Giorgio Savini, Masumichi Seta, Ekta Sharma, Yoshito Shimajiri, Hiroko Shinnaga, Ya-Wen Tang, Xindi Tang, Hoang Duc Thuong, Kohji Tomisaka, Le Ngoc Tram, Yusuke Tsukamoto, Serena Viti, Hongchi Wang, Anthony Whitworth, Jinjin Xie, Meng-Zhe Yang, Hsi-Wei Yen, Hyunju Yoo, Jinghua Yuan, Hyeong-Sik Yun, Tetsuya Zenko, Guoyin Zhang, Chuan-Peng Zhang, Yapeng Zhang, Jianjun Zhou, Lei Zhu, Ilse de Looze, Philippe Andre, C. Darren Dowell, Stewart Eyres, Sam Falle, Jean-Francois Robitaille, Sven van Loo
As part of the BISTRO survey, we present JCMT 850 $\mu$m polarimetric observations towards the Orion Integral-Shaped Filament (ISF) that covers three portions known as OMC-1, OMC-2, and OMC-3. The magnetic field threading the ISF seen in the JCMT POL-2 map appears as a tale of three: pinched for OMC-1, twisted for OMC-2, and nearly uniform for OMC-3. A multi-scale analysis shows that the magnetic field structure in OMC-3 is very consistent at all the scales, whereas the field structure in OMC-2 shows no correlation across different scales. In OMC-1, the field retains its mean orientation from large to small scales, but shows some deviations at small scales. Histograms of relative orientations between the magnetic field and filaments reveal a bimodal distribution for OMC-1, a relatively random distribution for OMC-2, and a distribution with a predominant peak at 90$^\circ$ for OMC-3. Furthermore, the magnetic fields in OMC-1 and OMC-3 both appear to be aligned perpendicular to the fibers, which are denser structures within the filament, but the field in OMC-2 is aligned along with the fibers. All these suggest that gravity, turbulence, and magnetic field are each playing a leading role in OMC-1, 2, and 3, respectively. While OMC-2 and 3 have almost the same gas mass, density, and non-thermal velocity dispersion, there are on average younger and fewer young stellar objects in OMC-3, providing evidence that a stronger magnetic field will induce slower and less efficient star formation in molecular clouds.
The rapid development and deployment of large language models (LLMs) have introduced a new frontier in artificial intelligence, marked by unprecedented capabilities in natural language understanding and generation. However, the increasing integration of these models into critical applications raises substantial safety concerns, necessitating a thorough examination of their potential risks and associated mitigation strategies. This survey provides a comprehensive overview of the current landscape of LLM safety, covering four major categories: value misalignment, robustness to adversarial attacks, misuse, and autonomous AI risks. In addition to the comprehensive review of the mitigation methodologies and evaluation resources on these four aspects, we further explore four topics related to LLM safety: the safety implications of LLM agents, the role of interpretability in enhancing LLM safety, the technology roadmaps proposed and abided by a list of AI companies and institutes for LLM safety, and AI governance aimed at LLM safety with discussions on international cooperation, policy proposals, and prospective regulatory directions. Our findings underscore the necessity for a proactive, multifaceted approach to LLM safety, emphasizing the integration of technical solutions, ethical considerations, and robust governance frameworks. This survey is intended to serve as a foundational resource for academy researchers, industry practitioners, and policymakers, offering insights into the challenges and opportunities associated with the safe integration of LLMs into society. Ultimately, it seeks to contribute to the safe and beneficial development of LLMs, aligning with the overarching goal of harnessing AI for societal advancement and well-being. A curated list of related papers has been publicly available at https://github.com/tjunlp-lab/Awesome-LLM-Safety-Papers.
Gravitational algebras and applications to nonequilibrium physics
2024-12-23
Michele Cirafici
This note aims to offer a non-technical and self-contained introduction to gravitational algebras and their applications in the nonequilibrium physics of gravitational systems. We begin by presenting foundational concepts from operator algebra theory and exploring their relevance to perturbative quantum gravity. Additionally, we provide a brief overview of the theory of nonequilibrium dynamical systems in finite dimensions and discuss its generalization to gravitational algebras. Specifically, we focus on entropy production in black hole backgrounds and fluctuation theorems in de Sitter spacetime.
Euclid: Early Release Observations of diffuse stellar structures and globular clusters as probes of the mass assembly of galaxies in the Dorado group
2024-12-23
M. Urbano, P. -A. Duc, T. Saifollahi, E. Sola, A. Lançon, K. Voggel, F. Annibali, M. Baes, H. Bouy, Michele Cantiello, D. Carollo, J. -C. Cuillandre, P. Dimauro, P. Erwin, A. M. N. Ferguson, R. Habas, M. Hilker, L. K. Hunt, M. Kluge, S. S. Larsen, Q. Liu, O. Marchal, F. R. Marleau, D. Massari, O. Müller, R. F. Peletier, M. Poulain, M. Rejkuba, M. Schirmer, C. Stone, R. Zöller, B. Altieri, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, A. Basset, P. Battaglia, E. Branchini, M. Brescia, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, H. Degaudenzi, G. De Lucia, F. Dubath, X. Dupac, S. Dusini, M. Farina, S. Farrens, F. Faustini, S. Ferriol, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, B. Gillis, C. Giocoli, P. Gómez-Alvarez, A. Grazian, F. Grupp, L. Guzzo, S. V. H. Haugan, J. Hoar, H. Hoekstra, W. Holmes, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, E. Keihänen, S. Kermiche, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, E. Medinaceli, S. Mei, M. Melchior, M. Meneghetti, E. Merlin, G. Meylan, L. Moscardini, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, D. Sapone, B. Sartoris, R. Scaramella, P. Schneider, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, T. Vassallo, G. Verdoes Kleijn, Y. Wang, J. Weller, O. R. Williams, E. Zucca, M. Bolzonella, C. Burigana, A. Mora, V. Scottez
Deep surveys reveal tidal debris and associated compact stellar systems. Euclid's unique combination of capabilities (spatial resolution, depth, and wide sky coverage) will make it a groundbreaking tool for galactic archaeology in the local Universe, bringing low surface brightness (LSB) science into the era of large-scale astronomical surveys. Euclid's Early Release Observations (ERO) demonstrate this potential with a field of view that includes several galaxies in the Dorado group. In this paper, we aim to derive from this image a mass assembly scenario for its main galaxies: NGC 1549, NGC 1553, and NGC 1546. We detect internal and external diffuse structures, and identify candidate globular clusters (GCs). By analysing the colours and distributions of the diffuse structures and candidate GCs, we can place constraints on the galaxies' mass assembly and merger histories. The results show that feature morphology, surface brightness, colours, and GC density profiles are consistent with galaxies that have undergone different merger scenarios. We classify NGC 1549 as a pure elliptical galaxy that has undergone a major merger. NGC 1553 appears to have recently transitioned from a late-type galaxy to early type, after a series of radial minor to intermediate mergers. NGC 1546 is a rare specimen of galaxy with an undisturbed disk and a prominent diffuse stellar halo, which we infer has been fed by minor mergers and then disturbed by the tidal effect from NGC 1553. Finally, we identify limitations specific to the observing conditions of this ERO, in particular stray light in the visible and persistence in the near-infrared bands. Once these issues are addressed and the extended emission from LSB objects is preserved by the data-processing pipeline, the Euclid Wide Survey will allow studies of the local Universe to be extended to statistical ensembles over a large part of the extragalactic sky.
Editing Implicit and Explicit Representations of Radiance Fields: A Survey
2024-12-23
Arthur Hubert, Gamal Elghazaly, Raphael Frank
Neural Radiance Fields (NeRF) revolutionized novel view synthesis in recent years by offering a new volumetric representation, which is compact and provides high-quality image rendering. However, the methods to edit those radiance fields developed slower than the many improvements to other aspects of NeRF. With the recent development of alternative radiance field-based representations inspired by NeRF as well as the worldwide rise in popularity of text-to-image models, many new opportunities and strategies have emerged to provide radiance field editing. In this paper, we deliver a comprehensive survey of the different editing methods present in the literature for NeRF and other similar radiance field representations. We propose a new taxonomy for classifying existing works based on their editing methodologies, review pioneering models, reflect on current and potential new applications of radiance field editing, and compare state-of-the-art approaches in terms of editing options and performance.
Facial Expression Analysis and Its Potentials in IoT Systems: A Contemporary Survey
2024-12-23
Zixuan Shanggua, Yanjie Dong, Song Guo, Victor C. M. Leung, M. Jamal Deen, Xiping Hu
Facial expressions convey human emotions and can be categorized into macro-expressions (MaEs) and micro-expressions (MiEs) based on duration and intensity. While MaEs are voluntary and easily recognized, MiEs are involuntary, rapid, and can reveal concealed emotions. The integration of facial expression analysis with Internet-of-Thing (IoT) systems has significant potential across diverse scenarios. IoT-enhanced MaE analysis enables real-time monitoring of patient emotions, facilitating improved mental health care in smart healthcare. Similarly, IoT-based MiE detection enhances surveillance accuracy and threat detection in smart security. This work aims at providing a comprehensive overview of research progress in facial expression analysis and explores its integration with IoT systems. We discuss the distinctions between our work and existing surveys, elaborate on advancements in MaE and MiE techniques across various learning paradigms, and examine their potential applications in IoT. We highlight challenges and future directions for the convergence of facial expression-based technologies and IoT systems, aiming to foster innovation in this domain. By presenting recent developments and practical applications, this study offers a systematic understanding of how facial expression analysis can enhance IoT systems in healthcare, security, and beyond.
Superoutbursts and Positive Superhumps are Detected During the Standstill in AT Cnc
2024-12-23
Qi-Bin Sun, Sheng-Bang Qian, Li-Ying Zhu, Qin-Mei Li Fu-Xing Li, Min-Yu Li, Ping Li
The disk instability model suggests that the disk remains in a hot state during standstill. The thermal-tidal instability model posits that superoutbursts require a mass ratio of ( q = M2/M2 < 0.25-0.33). However, this paper presents evidence of superoutbursts and positive superhumps (PSHs) during a standstill of AT Cnc (q > 0.33 ), based on both space- and ground-based photometric data from sky surveys. Notably, the PSHs evolve gradually prior to the onset of a superoutburst, suggesting that an eccentric, prograde-precessing disk forms first, with the superoutburst occurring as the radius of the accretion disk continues to expand. These observations indicate that, during the standstill, the disk radius in AT Cnc not only surpasses the tidal truncation radius and the 3:1 resonance radius, but is still undergoing expansion.Our analysis further reveals that superoutbursts are triggered by oscillations, which appear to be a characteristic feature of special dwarf nova outbursts. Additionally, we observe that special outbursts develop from normal outbursts, implying that certain stages of the disk may not reach a fully hot state during the standstill. Furthermore, the PSH amplitude was found to be correlated with special outbursts, revealing that the localized thermal instability had a significant impact on the evolution of the eccentric disk. These findings provide the first detailed observational evidence of superoutbursts and PSHs occurring during standstill, offering important new insights into the classification of dwarf novae and the underlying mechanisms of outbursts.
Investigating Length Issues in Document-level Machine Translation
2024-12-23
Ziqian Peng, Rachel Bawden, François Yvon
Transformer architectures are increasingly effective at processing and generating very long chunks of texts, opening new perspectives for document-level machine translation (MT). In this work, we challenge the ability of MT systems to handle texts comprising up to several thousands of tokens. We design and implement a new approach designed to precisely measure the effect of length increments on MT outputs. Our experiments with two representative architectures unambiguously show that (a)~translation performance decreases with the length of the input text; (b)~the position of sentences within the document matters and translation quality is higher for sentences occurring earlier in a document. We further show that manipulating the distribution of document lengths and of positional embeddings only marginally mitigates such problems. Our results suggest that even though document-level MT is computationally feasible, it does not yet match the performance of sentence-based MT.
The neutron numbers N = 32 and 34 are new magic numbers suggested in neutron-rich $pf$-shell nuclei. In this article, we discuss the experimental observables and state-of-the-art theoretical calculations that characterize and explain the shell evolution leading to new magic numbers. Particular focus shall be afforded to the experimental progress of the shell migration study at and beyond N = 32, 34 in Ar, K, Ca, and Sc isotopes at the RIBF using direct reactions with liquid hydrogen targets over the past ten years. The results prove the double magicity of \ts{52,54}Ca, and support the persistence of the N = 34 subshell closure below Z = 20 with a sharp weakening beyond Z = 20. Future measurements of intruder bands of N = 32, 34 nuclei and shell evolution towards N = 40 are discussed within the context of an upgraded RIBF facility and the development of novel detection systems.
Search for charged Higgs bosons produced in top-quark decays or in association with top quarks and decaying via $H^{\pm} \to τ^{\pm}ν_τ$ in 13 TeV $pp$ collisions with the ATLAS detector
2024-12-23
ATLAS Collaboration
Charged Higgs bosons produced either in top-quark decays or in association with a top-quark, subsequently decaying via $H^{\pm} \to \tau^{\pm}\nu_{\tau}$, are searched for in 140 $\text{fb}^{-1}$ of proton-proton collision data at $\sqrt{s}=13$ TeV recorded with the ATLAS detector. Depending on whether the top-quark produced together with the $H^{\pm}$ decays hadronically or semi-leptonically, the search targets $\tau$+jets or $\tau$+lepton final states, in both cases with a $\tau$-lepton decaying into a neutrino and hadrons. No significant excess over the Standard Model background expectation is observed. For the mass range of $80 \leq m_{H^{\pm}} \leq 3000$ GeV, upper limits at 95% confidence level are set on the production cross-section of the charged Higgs boson times the branching fraction $\mathrm{\cal{B}}(H^{\pm} \to \tau^{\pm}\nu_{\tau})$ in the range 4.5 pb-0.4 fb. In the mass range 80-160 GeV, assuming the Standard Model cross-section for $t\bar{t}$ production, this corresponds to upper limits between 0.27% and 0.02% on $\mathrm{\cal{B}}(t\to bH^{\pm}) \times \mathrm{\cal{B}}(H^{\pm} \to \tau^{\pm}\nu_{\tau})$.
A Survey of Query Optimization in Large Language Models
2024-12-23
Mingyang Song, Mao Zheng
\textit{Query Optimization} (QO) refers to techniques aimed at enhancing the efficiency and quality of Large Language Models (LLMs) in understanding and answering queries, especially complex ones in scenarios like Retrieval-Augmented Generation (RAG). Specifically, RAG mitigates the limitations of LLMs by dynamically retrieving and leveraging up-to-date relevant information, which provides a cost-effective solution to the challenge of LLMs producing plausible but potentially inaccurate responses. Recently, as RAG evolves and incorporates multiple components that influence its performance, QO has emerged as a critical element, playing a pivotal role in determining the effectiveness of RAG's retrieval stage in accurately sourcing the necessary multiple pieces of evidence to answer queries correctly. In this paper, we trace the evolution of QO techniques by summarizing and analyzing significant studies. Through an organized framework and categorization, we aim to consolidate existing QO techniques in RAG, elucidate their technological foundations, and highlight their potential to enhance the versatility and applications of LLMs.
This is the text of a talk given by the first author at the Harish-Chandra centenary meeting held in Allahabad in October 2023. It reviews Harish-Chandra's isomorphism and its many applications to representation theory and mathematical physics. It also announces the existence and uniqueness of nonsymmetric shift operators for an arbitrary root system. These are differential-reflection operators with a transmutation property relative to Dunkl-Cherednik operators: they shift the parameter k of these operators by 1, and restrict on symmetric functions to the hypergeometric shift operators introduced by the first author.
Uncertainties of Satellite-based Essential Climate Variables from Deep Learning
2024-12-23
Junyang Gou, Arnt-Børre Salberg, Mostafa Kiani Shahvandi, Mohammad J. Tourian, Ulrich Meyer, Eva Boergens, Anders U. Waldeland, Isabella Velicogna, Fredrik Dahl, Adrian Jäggi, Konrad Schindler, Benedikt Soja
Accurate uncertainty information associated with essential climate variables (ECVs) is crucial for reliable climate modeling and understanding the spatiotemporal evolution of the Earth system. In recent years, geoscience and climate scientists have benefited from rapid progress in deep learning to advance the estimation of ECV products with improved accuracy. However, the quantification of uncertainties associated with the output of such deep learning models has yet to be thoroughly adopted. This survey explores the types of uncertainties associated with ECVs estimated from deep learning and the techniques to quantify them. The focus is on highlighting the importance of quantifying uncertainties inherent in ECV estimates, considering the dynamic and multifaceted nature of climate data. The survey starts by clarifying the definition of aleatoric and epistemic uncertainties and their roles in a typical satellite observation processing workflow, followed by bridging the gap between conventional statistical and deep learning views on uncertainties. Then, we comprehensively review the existing techniques for quantifying uncertainties associated with deep learning algorithms, focusing on their application in ECV studies. The specific need for modification to fit the requirements from both the Earth observation side and the deep learning side in such interdisciplinary tasks is discussed. Finally, we demonstrate our findings with two ECV examples, snow cover and terrestrial water storage, and provide our perspectives for future research.
Is ChatGPT Massively Used by Students Nowadays? A Survey on the Use of Large Language Models such as ChatGPT in Educational Settings
2024-12-23
Jérémie Sublime, Ilaria Renna
The rapid adoption of Generative AI (GenAI) based on Large Language Models (LLMs) such as ChatGPT has recently and profoundly impacted education, offering transformative opportunities while raising significant concerns. In this study we present the results of a survey that investigates how 395 students aged 13 to 25 years old in France and Italy integrate LLMs into their educational routines. Key findings include the widespread use of these tools across all age groups and disciplines, with older students and male students demonstrating higher usage frequencies, particularly in scientific contexts. The results also show gender disparities, raising concerns about an emerging AI literacy and technological gender gap. Additionally, while most students utilise LLMs constructively, the lack of systematic proofreading and critical evaluation among younger users suggests potential risks to cognitive skills development, including critical thinking and foundational knowledge. The survey results underscore the need for educational institutions to adapt their curricula to integrate AI tools effectively, promoting ethical use, critical thinking, and awareness of AI limitations and environmental costs. This paper provides actionable recommendations for fostering equitable and effective cohabitation of LLMs and education while addressing emerging challenges.
Multi-generative agent systems (MGASs) have become a research hotspot since the rise of large language models (LLMs). However, with the continuous influx of new related works, the existing reviews struggle to capture them comprehensively. This paper presents a comprehensive survey of these studies. We first discuss the definition of MGAS, a framework encompassing much of previous work. We provide an overview of the various applications of MGAS in (i) solving complex tasks, (ii) simulating specific scenarios, and (iii) evaluating generative agents. Building on previous studies, we also highlight several challenges and propose future directions for research in this field.
Interplay of Kitaev Interaction and Off-diagonal Exchanges: Exotic Phases and Quantum Phase Diagrams
2024-12-23
Qiang Luo, Jize Zhao, Xiaoqun Wang
Aligning with the everlasting search for quantum spin liquids (QSLs), identifying the QSL in Kitaev magnets has garnered great research interest during the past decade and remains nevertheless an enormous challenge. One of the major difficulties lies in that Kitaev QSL is typically fragile against competing interactions like off-diagonal exchanges, which are ubiquitous in real materials due to spin-orbit coupling and crystal-field effect. This, in turn, gives rise to many intriguing field-induced novel phases and thermal Hall effect. In this review, we will focus on the interplay of Kitaev interaction and off-diagonal $\Gamma$ and $\Gamma'$ exchanges from a numerical perspective. This review discusses some representative exotic phases such as $\Gamma$ spin liquid, nematic ferromagnet, spin-flop phase, and distinct chiral-spin states with spontaneously time-reversal symmetry breaking. It also presents quantum phase diagrams of anisotropic Kitaev-$\Gamma$ chains that exhibit kaleidoscopes of both ordered and disordered phases.
Bismuth doping induced enhancement of the spin-orbit coupling strength in the prototype dilute ferromagnetic semiconductor (Ga,Mn)As: a review
2024-12-23
Tadeusz Wosinski
Extensive studies on the impact of bismuth incorporation into the (Ga,Mn)As prototype dilute ferromagnetic semiconductor (DFS) on its structural, magnetic and magnetotransport properties are summarized in this review. Thin epitaxial layers of the quaternary (Ga,Mn)(Bi,As) compound, containing up to 1% Bi and 6% Mn atoms, and the reference ternary (Ga,Mn)As compound, have been grown under either a compressive or tensile biaxial misfit strain by the low-temperature molecular-beam epitaxy technique with precisely optimized growth conditions. The high-resolution X-ray diffractometry measurements and transmission electron microscopy imaging of cross-sections across the sample interfaces have evidenced for high structural perfection of the DFS layers and sharp interfaces with the substrate. An addition of bismuth into the layers causes a small decrease in their ferromagnetic Curie temperature and a distinct increase in the coercive fields, as revealed by the superconducting quantum interference device magnetometry investigations. Most of all, the incorporation of a small atomic fraction of heavy Bi atoms, substituting As atoms in the layer, predominantly enhances the spin-orbit coupling strength in its valence band, considerably affecting electromagnetic properties of the layers. Investigations of magnetotransport properties of the DFS layers, performed on micro-Hall-bars prepared from the layers using electron-beam lithography patterning, reveal, as a result of Bi addition to the layers, significantly enhanced magnitudes of magnetoresistance, anomalous and planar Hall effects as well as the spin-orbit torque effect. The latter effect is of special interest for applications to the next generation non-volatile data storage and logic spintronic devices, utilizing electrically controlled magnetization reversal.
2024-12-23
Manuel S. Mariani, Federico Battiston, Emőke-Ágnes Horvát, Giacomo Livan, Federico Musciotto, Dashun Wang
Understanding the collective dynamics behind the success of ideas, products, behaviors, and social actors is critical for decision-making across diverse contexts, including hiring, funding, career choices, and the design of interventions for social change. Methodological advances and the increasing availability of big data now allow for a broader and deeper understanding of the key facets of success. Recent studies unveil regularities beneath the collective dynamics of success, pinpoint underlying mechanisms, and even enable predictions of success across diverse domains, including science, technology, business, and the arts. However, this research also uncovers troubling biases that challenge meritocratic views of success. This review synthesizes the growing, cross-disciplinary literature on the collective dynamics behind success and calls for further research on cultural influences, the origins of inequalities, the role of algorithms in perpetuating them, and experimental methods to further probe causal mechanisms behind success. Ultimately, these efforts may help to better align success with desired societal values.
Biomarker combination based on the Youden index with and without gold standard
2024-12-23
Ao Sun, Yanting Li, Xiao-Hua Zhou
In clinical practice, multiple biomarkers are often measured on the same subject for disease diagnosis, and combining them can improve diagnostic accuracy. Existing studies typically combine multiple biomarkers by maximizing the Area Under the ROC Curve (AUC), assuming a gold standard exists or that biomarkers follow a multivariate normal distribution. However, practical diagnostic settings require both optimal combination coefficients and an effective cutoff value, and the reference test may be imperfect. In this paper, we propose a two-stage method for identifying the optimal linear combination and cutoff value based on the Youden index. First, it maximizes an approximation of the empirical AUC to estimate the optimal linear coefficients for combining multiple biomarkers. Then, it maximizes the empirical Youden index to determine the optimal cutoff point for disease classification. Under the semiparametric single index model and regularity conditions, the estimators for the linear coefficients, cutoff point, and Youden index are consistent. This method is also applicable when the reference standard is imperfect. We demonstrate the performance of our method through simulations and apply it to construct a diagnostic scale for Chinese medicine.
Finiteness of piecewise flat quantum gravity with matter
2024-12-23
Aleksandar Mikovic
We review the approach to quantum gravity which is based on the assumption that the short-distance structure of the spacetime is given by a piecewise flat manifold corresponding to a triangulation of a smooth manifold. We then describe the coupling of the Standard Model to this quantum gravity theory and show that the corresponding path integral is finite when the negative power of the product of the edge lengths squared in the path-integral measure is chossen to be grater than 52,5. The implications of this result are discussed, which include a relationship between the effective action and a wavefunction of the universe, the existence of the non-perturbative effective action, the correct value of the cosmological constant and the natural appearence of the Starobinsky inflation.
The Role of XAI in Transforming Aeronautics and Aerospace Systems
2024-12-23
Francisco Javier Cantero Zorita, Mikel Galafate, Javier M. Moguerza, Isaac Martín de Diego, M. Teresa Gonzalez, Gema Gutierrez Peña
Recent advancements in Artificial Intelligence (AI) have transformed decision-making in aeronautics and aerospace. These advancements in AI have brought with them the need to understand the reasons behind the predictions generated by AI systems and models, particularly by professionals in these sectors. In this context, the emergence of eXplainable Artificial Intelligence (XAI) has helped bridge the gap between professionals in the aeronautical and aerospace sectors and the AI systems and models they work with. For this reason, this paper provides a review of the concept of XAI is carried out defining the term and the objectives it aims to achieve. Additionally, the paper discusses the types of models defined within it and the properties these models must fulfill to be considered transparent, as well as the post-hoc techniques used to understand AI systems and models after their training. Finally, various application areas within the aeronautical and aerospace sectors will be presented, highlighting how XAI is used in these fields to help professionals understand the functioning of AI systems and models.
Hereditary properties of finite ultrametric spaces
2024-12-23
Evgeniy A. Petrov
A characterization of finite homogeneous ultrametric spaces and finite ultrametric spaces generated by unrooted labeled trees is found in terms of representing trees. A characterization of finite ultrametric spaces having perfect strictly $n$-ary trees is found in terms of special graphs connected with the space. Further, we give a detailed survey of some special classes of finite ultrametric spaces, which were considered in the last ten years, and study their hereditary properties. More precisely, we are interested in the following question. Let $X$ be an arbitrary finite ultrametric space from some given class. Does every subspace of $X$ also belong to this class?
From modelling to understanding: the signals in nerves
2024-12-23
Jüri Engelbrecht, Kert Tamm, Tanel Peets
This paper attempts to review our studies on the propagation of signals in nerves over the past decade. The need for interdisciplinary studies is stressed that helps to understand the physical mechanisms of coupling the electrical, mechanical, and thermal effects in nerves. Based on the analysis of structural properties of axons and possible mechanisms of interaction between different physical phenomena, a set of assumptions and hypotheses is formulated. As a proof of concept, a rather general mathematical model is presented for describing a wave ensemble in unmyelinated axons. This model is composed of several governing equations ("building blocks") which are coupled by forces describing the interaction between the effects. The numerical simulation using the dimensionless variables demonstrated a rather good qualitative match with experiments. The further generalisation of this model in physical units for the processes in myelinated axons permits a closer match to measurements. Based on modelling and in silico experiments, the guidelines for modelling such a complex electrophysiological process are formulated. These guidelines reflect the importance of following the physical principles in modelling together with interdisciplinary knowledge from continuum mechanics and mathematics.
This note is an expository contribution for a proceedings volume of the workshop "Higher Segal Spaces and their Applications to Algebraic K-theory, Hall Algebras, and Combinatorics". We survey various versions of Waldhausen's S-construction and the role they play in defining K-theory, and we discuss their 2-Segality properties.
Deep Learning in Proteomics Informatics: Applications, Challenges, and Future Directions
2024-12-23
Yindan Luo, Jiaxin Cai
Deep learning is an advanced technology that relies on large-scale data and complex models for feature extraction and pattern recognition. It has been widely applied across various fields, including computer vision, natural language processing, and speech recognition. In recent years, deep learning has demonstrated significant potential in the realm of proteomics informatics, particularly in deciphering complex biological information. The introduction of this technology not only accelerates the processing speed of protein data but also enhances the accuracy of predictions regarding protein structure and function. This provides robust support for both fundamental biology research and applied biotechnological studies. Currently, deep learning is primarily focused on applications such as protein sequence analysis, three-dimensional structure prediction, functional annotation, and the construction of protein interaction networks. These applications offer numerous advantages to proteomic research. Despite its growing prevalence in this field, deep learning faces several challenges including data scarcity, insufficient model interpretability, and computational complexity; these factors hinder its further advancement within proteomics. This paper comprehensively reviews the applications of deep learning in proteomics along with the challenges it encounters. The aim is to provide a systematic theoretical discussion and practical basis for research in this domain to facilitate ongoing development and innovation of deep learning technologies within proteomics.
Hierarchical Dirichlet Process Mixture of Products of Multinomial Distributions: Applications to Survey Data with Potentially Missing Values
2024-12-23
Chayut Wongkamthong
In social science research, understanding latent structures in populations through survey data with categorical responses is a common and important task. Traditional methods like Factor Analysis and Latent Class Analysis have limitations, particularly in handling categorical data and accommodating mixed memberships in latent structures, respectively. Moreover, choosing the number of factors or latent classes is often subjective and can be challenging in the presence of missing values. This study introduces a Hierarchical Dirichlet Process Mixture of Products of Multinomial Distributions (HDPMPM) model, which leverages the flexibility of nonparametric Bayesian methods to address these limitations. The HDPMPM model allows for multiple latent classes within individuals and avoids fixing the number of mixture components at an arbitrary number. Additionally, it incorporates missing data imputation directly into the model's Gibbs sampling process. By applying a truncated stick-breaking representation of the Dirichlet process, we can derive a Gibbs sampling scheme for posterior inference. An application of the HDPMPM model to the 2016 American National Election Study (ANES) data demonstrates its effectiveness in identifying political profiles and handling missing data scenarios, including those that are missing at random (MAR) and missing completely at random (MCAR). The results show that the HDPMPM model successfully recovers dominant profiles and manages complex latent structures in survey data, providing an alternative tool for social science researchers in dealing with categorical data with missing values.
SoK: The Design Paradigm of Safe and Secure Defaults
2024-12-23
Jukka Ruohonen
In security engineering, including software security engineering, there is a well-known design paradigm telling to prefer safe and secure defaults. The paper presents a systematization of knowledge (SoK) of this paradigm by the means of a systematic mapping study and a scoping review of relevant literature. According to the mapping and review, the paradigm has been extensively discussed, used, and developed further since the late 1990s. Partially driven by the insecurity of the Internet of things, the volume of publications has accelerated from the circa mid-2010s onward. The publications reviewed indicate that the paradigm has been adopted in numerous different contexts. It has also been expanded with security design principles not originally considered when the paradigm was initiated in the mid-1970s. Among the newer principles are an "off by default" principle, various overriding and fallback principles, as well as those related to the zero trust model. The review also indicates obstacles developers and others have faced with the~paradigm.
Feature Based Methods Domain Adaptation for Object Detection: A Review Paper
2024-12-23
Helia Mohamadi, Mohammad Ali Keyvanrad, Mohammad Reza Mohammadi
Domain adaptation, a pivotal branch of transfer learning, aims to enhance the performance of machine learning models when deployed in target domains with distinct data distributions. This is particularly critical for object detection tasks, where domain shifts (caused by factors such as lighting conditions, viewing angles, and environmental variations) can lead to significant performance degradation. This review delves into advanced methodologies for domain adaptation, including adversarial learning, discrepancy-based, multi-domain, teacher-student, ensemble, and VLM techniques, emphasizing their efficacy in reducing domain gaps and enhancing model robustness. Feature-based methods have emerged as powerful tools for addressing these challenges by harmonizing feature representations across domains. These techniques, such as Feature Alignment, Feature Augmentation/Reconstruction, and Feature Transformation, are employed alongside or as integral parts of other domain adaptation strategies to minimize domain gaps and improve model performance. Special attention is given to strategies that minimize the reliance on extensive labeled data and using unlabeled data, particularly in scenarios involving synthetic-to-real domain shifts. Applications in fields such as autonomous driving and medical imaging are explored, showcasing the potential of these methods to ensure reliable object detection in diverse and complex settings. By providing a thorough analysis of state-of-the-art techniques, challenges, and future directions, this work offers a valuable reference for researchers striving to develop resilient and adaptable object detection frameworks, advancing the seamless deployment of artificial intelligence in dynamic environments.
Free-viewpoint Human Animation with Pose-correlated Reference Selection
2024-12-23
Fa-Ting Hong, Zhan Xu, Haiyang Liu, Qinjie Lin, Luchuan Song, Zhixin Shu, Yang Zhou, Duygu Ceylan, Dan Xu
Diffusion-based human animation aims to animate a human character based on a source human image as well as driving signals such as a sequence of poses. Leveraging the generative capacity of diffusion model, existing approaches are able to generate high-fidelity poses, but struggle with significant viewpoint changes, especially in zoom-in/zoom-out scenarios where camera-character distance varies. This limits the applications such as cinematic shot type plan or camera control. We propose a pose-correlated reference selection diffusion network, supporting substantial viewpoint variations in human animation. Our key idea is to enable the network to utilize multiple reference images as input, since significant viewpoint changes often lead to missing appearance details on the human body. To eliminate the computational cost, we first introduce a novel pose correlation module to compute similarities between non-aligned target and source poses, and then propose an adaptive reference selection strategy, utilizing the attention map to identify key regions for animation generation. To train our model, we curated a large dataset from public TED talks featuring varied shots of the same character, helping the model learn synthesis for different perspectives. Our experimental results show that with the same number of reference images, our model performs favorably compared to the current SOTA methods under large viewpoint change. We further show that the adaptive reference selection is able to choose the most relevant reference regions to generate humans under free viewpoints.
Emerging Microelectronic Materials by Design: Navigating Combinatorial Design Space with Scarce and Dispersed Data
2024-12-23
Hengrui Zhang, Alexandru B. Georgescu, Suraj Yerramilli, Christopher Karpovich, Daniel W. Apley, Elsa A. Olivetti, James M. Rondinelli, Wei Chen
The increasing demands of sustainable energy, electronics, and biomedical applications call for next-generation functional materials with unprecedented properties. Of particular interest are emerging materials that display exceptional physical properties, making them promising candidates in energy-efficient microelectronic devices. As the conventional Edisonian approach becomes significantly outpaced by growing societal needs, emerging computational modeling and machine learning (ML) methods are employed for the rational design of materials. However, the complex physical mechanisms, cost of first-principles calculations, and the dispersity and scarcity of data pose challenges to both physics-based and data-driven materials modeling. Moreover, the combinatorial composition-structure design space is high-dimensional and often disjoint, making design optimization nontrivial. In this Account, we review a team effort toward establishing a framework that integrates data-driven and physics-based methods to address these challenges and accelerate materials design. We begin by presenting our integrated materials design framework and its three components in a general context. We then provide an example of applying this materials design framework to metal-insulator transition (MIT) materials, a specific type of emerging materials with practical importance in next-generation memory technologies. We identify multiple new materials which may display this property and propose pathways for their synthesis. Finally, we identify some outstanding challenges in data-driven materials design, such as materials data quality issues and property-performance mismatch. We seek to raise awareness of these overlooked issues hindering materials design, thus stimulating efforts toward developing methods to mitigate the gaps.
Coupled differential-algebraic equations framework for modeling six-degree-of-freedom flight dynamics of asymmetric fixed-wing aircraft
2024-12-23
Osama A. Marzouk
This study presents a comprehensive mathematical framework for modeling the flight dynamics of a six-degree-of-freedom fixed-wing aircraft as a rigid body with three control surfaces: rudder, elevators, and ailerons. The framework consists of 35 differential-algebraic equations (DAEs) and requires 30 constants to be specified. It supports both direct and inverse flight dynamics analyses. In direct dynamics, the historical profiles of control inputs (deflection angles and engine thrust) are specified, and the resulting flight trajectory is predicted. In inverse dynamics, the desired flight trajectory and an additional constraint are specified to determine the required control inputs. The framework employs wind axes for linear-momentum equations and body axes for angular-momentum equations, incorporates two flight path angles, and provides formulas for aerodynamic force and moment coefficients. Key advantages include improved computational efficiency, elimination of Euler angle singularities, and independence from symmetry assumptions with regard to the aircraft's moments of inertia. The model also accounts for nonlinear air density variations with altitude, up to 20 km above mean sea level, making it suitable for accurate and efficient flight dynamics simulations.
In this contribution, which introduces the "Crossing the Portal" session of the NOW 2024 meeting, I discuss the value of the concepts of interdisciplinarity and innovation for neutrino physics. After some historical considerations, which provide an initial illustration of the significant role of these concepts, I review some well-known cases of neutrino science, involving both astrophysics and particle physics, which allow us to deepen the analysis. The importance of a harmonious relationship between theoretical elaborations and experiments emerges: effective collaboration between theoretical and experimental physicists played a key role in many of the successful cases and proved marginal or defective in the doubtful ones. The discussion highlights also the need to proceed armed with a patience, a virtue that Feynman himself indicated as essential to science, all the more necessary when investigating interesting but elusive particles such as neutrinos.
X-Ray Views of Galactic Accreting Pulsars in High-Mass X-Ray Binaries
2024-12-23
Shan-Shan Weng, Long Ji
Accreting X-ray pulsars, located in X-ray binaries, are neutron stars with magnetic fields as strong as $B\sim10^{12\text{--}13}$ G. This review offers a concise overview of the accretion and radiation processes of X-ray pulsars and summarizes their rich observational features, particularly focusing on complex and variable temporal phenomena, spectral properties, and evolution, the new window for X-ray polarimetry and multi-wavelength advances. We also briefly discuss other related systems, i.e., gamma-ray binaries and pulsating ultraluminous X-ray sources.
We consider a surface embedded in the Euclidean 3-space and fix a tangential vector $v$ at a given point $p$ on the surface. In this paper, we first review a history of the formula obtained by Mannheim, d'Ocagne and Koenderink, which asserts that the Gaussian curvature of the surface at $p$ can be obtained if one knows "the normal curvature at $p$ with respect to $v$" and "the curvature of the contour line $L$ of the surface at $p$" with respect to the orthogonal projection induced by $v$. Unfortunately, this formula does not work when $v$ points in an asymptotic direction. When $v$ is just the case, we give anlogues of the formula, which include an invariant of cusp singular points of $L$.
Decomposition of thermal light with a flat spectrum into localized pulses
2024-12-23
Yunkai Wang, Yujie Zhang, Virginia O. Lorenz
Previous work [Physical Review Letters 114, 213601 (2015)] has shown that thermal light with a blackbody spectrum cannot be decomposed as a mixture of independent localized pulses. We find that thermal light with a flat spectrum, which can occur when spectral filtering is applied, can be decomposed into a direct product of independent localized pulses. Furthermore, we show in the weak-source limit that the first non-vacuum term of thermal light with a flat spectrum is a mixture of independent localized pulses. We quantify the deviation from independent pulses when the spectrum is not flat.
Astronomical X-ray Polarimetry as a diagnostic for questions of fundamental Physics. What we learned from the Imaging X-Ray Polarimetry Explorer (IXPE)
2024-12-23
Paolo Soffitta, Enrico Costa
X-ray Astrophysics, which addresses extreme physics in extreme conditions, is particularly well suited for answering questions related to known physics. Reversely tiny effects, but integrated along sidereal distances, allow to probe extensions of known physics or even new physics. The new window into polarimetry in this energy band, opened by the Imaging X-ray Polarimetry Explorer (IXPE) a NASA-ASI Small Explorer mission launched on 9th December 2021 enables an entirely novel approach, whether used alone or in combination with standard observables such as light curves and spectra and with data in other wavelengths. In this paper, we review IXPE's results after nearly three years of successful operation, focusing on their implications for key questions in Fundamental Physics.
Franck-Condon electron emission from polar semiconductor photocathodes
2024-12-23
W. Andreas Schroeder, L. A. Angeloni, I-J. Shan, L. B. Jones
A presented analytical formulation of (optical)phonon-mediated and momentum-resonant Franck-Condon emission of photoexcited electrons from polar semiconductors is shown to be very consistent with (i) the observed emission properties of a Cesiated GaAs(001) photocathode at 808nm [J. Phys. D: Appl. Phys. 54, 205301 (2021)] and (ii) the measured spectral emission properties of a GaN(0001) photocathode from just below its bandgap energy to 5eV. The theoretical analysis in the parabolic band approximation predicts the form of both the quantum efficiency and mean transverse energy of photoemission as a function of the photocathode's electron affinity and the electron temperature in the vicinity of its emission face. The good agreement between theory and experimental data also suggests that sub-10nm rms surface roughness effects are not significant for polar semiconductor photocathodes.
2024-12-23
Sidong Chen, Frank Browne, Tomás R. Rodríguez, Volker Werner
Extensive gamma-ray spectroscopy of very neutron-rich nuclei of isotopes between the Ni and Sn isotopic chains was facilitated by the high luminosity LH2 target system, MINOS. Results show a persistence of deformation when going beyond the N = 60 threshold of the transition between spherical to deformed ground states at N < 60 and N > 60, respectively. Close to 78Ni, a more detailed image of the N > 50 Zn isotopes shows an erosion of the N = 50 shell closure, with core-breaking effects needed from theoretical models to replicate observation. As well as the experimental results indicating collective effects, the projected generator coordinate method is discussed in detail within the context of the neutron-rich Ge isotopes.
The Quest for the Origins of Ultra-High-Energy Cosmic Rays
2024-12-23
Rafael Alves Batista
Significant progress has been made over the past decades towards unveiling the sources of the most energetic particles in nature, the ultra-high-energy cosmic rays (UHECRs). Despite these advancements, the exact astrophysical sites capable of accelerating these particles to such extreme energies remain largely unknown. Moreover, the mechanisms by which they achieve these extreme energies are poorly understood. Here, I provide a concise overview of the theory underlying the acceleration and propagation of UHECRs. I then critically discuss three recent results that could help unveil their origins: the reported excess around Centaurus A, the correlation with starburst galaxies, and the efforts to jointly model the energy spectrum, composition, and arrival directions. Finally, I discuss strategies for advancing this field, emphasising the need for refined theoretical models, the challenges in building them, and the potential for new observatories to shed light on the mysteries of UHECRs.
Assessing UML Models by ChatGPT: Implications for Education
2024-12-23
Chong Wang, Beian Wang, Peng Liang, Jie Liang
In software engineering (SE) research and practice, UML is well known as an essential modeling methodology for requirements analysis and software modeling in both academia and industry. In particular, fundamental knowledge of UML modeling and practice in creating high-quality UML models are included in SE-relevant courses in the undergraduate programs of many universities. This leads to a time-consuming and labor-intensive task for educators to review and grade a large number of UML models created by the students. Recent advancements in generative AI techniques, such as ChatGPT, have paved new ways to automate many SE tasks. However, current research or tools seldom explore the capabilities of ChatGPT in evaluating the quality of UML models. This paper aims to investigate the feasibility and effectiveness of ChatGPT in assessing the quality of UML use case diagrams, class diagrams, and sequence diagrams. First, 11 evaluation criteria with grading details were proposed for these UML models. Next, a series of experiments were designed and conducted on 40 students' UML modeling reports to explore the performance of ChatGPT in evaluating and grading these UML diagrams. The research findings reveal that ChatGPT performed well in this assessing task because the scores that ChatGPT gives to the UML models are similar to the ones by human experts, and there are three evaluation discrepancies between ChatGPT and human experts, but varying in different evaluation criteria used in different types of UML models.
Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review
2024-12-23
Yidong Gan, Maciej Rybinski, Ben Hachey, Jonathan K. Kummerfeld
Clinical coding is crucial for healthcare billing and data analysis. Manual clinical coding is labour-intensive and error-prone, which has motivated research towards full automation of the process. However, our analysis, based on US English electronic health records and automated coding research using these records, shows that widely used evaluation methods are not aligned with real clinical contexts. For example, evaluations that focus on the top 50 most common codes are an oversimplification, as there are thousands of codes used in practice. This position paper aims to align AI coding research more closely with practical challenges of clinical coding. Based on our analysis, we offer eight specific recommendations, suggesting ways to improve current evaluation methods. Additionally, we propose new AI-based methods beyond automated coding, suggesting alternative approaches to assist clinical coders in their workflows.
Optimization of Convolutional Neural Network Hyperparameter for Medical Image Diagnosis using Metaheuristic Algorithms: A short Recent Review (2019-2022)
Convolutional Neural Networks (CNNs) have been successfully utilized in the medical diagnosis of many illnesses. Nevertheless, identifying the optimal architecture and hyperparameters among the available possibilities might be a substantial challenge. Typically, CNN hyperparameter selection is performed manually. Nonetheless, this is a computationally costly procedure, as numerous rounds of hyperparameter settings must be evaluated to determine which produces the best results. Choosing the proper hyperparameter settings has always been a crucial and challenging task, as it depends on the researcher's knowledge and experience. This study will present work done in recent years on the usage of metaheuristic optimization algorithms in the CNN optimization process. It looks at a number of recent studies that focus on the use of optimization methods to optimize hyperparameters in order to find high-performing CNNs. This helps researchers figure out how to set hyperparameters efficiently.
Reinforcement Learning for Motor Control: A Comprehensive Review
2024-12-23
Danial Kazemikia
Electric motors are crucial in many applications, but traditional control methods struggle with nonlinearities, parameter uncertainties, and external disturbances. Reinforcement Learning (RL) offers a promising solution as a data-driven approach that can learn optimal control strategies without an explicit model. This review paper examines the current state of RL in motor control, exploring various RL algorithms and applications. The review highlights RL's advantages, including model-free control, adaptability to changing conditions, and the ability to optimize for complex objectives. It also addresses challenges in applying RL to motor control, such as sim-to-real transfer, safety and stability concerns, scalability, and computational complexity. By providing a comprehensive overview of the field, this review aims to deepen understanding of RL's potential to revolutionize motor control and drive advancements across industries.
Contemporary implementations of spiking bio-inspired neural networks
2024-12-23
Andrey E. Schegolev, Marina V. Bastrakova, Michael A. Sergeev, Anastasia A. Maksimovskaya, Nikolay V. Klenov, Igor I. Soloviev
The extensive development of the field of spiking neural networks has led to many areas of research that have a direct impact on people's lives. As the most bio-similar of all neural networks, spiking neural networks not only allow the solution of recognition and clustering problems (including dynamics), but also contribute to the growing knowledge of the human nervous system. Our analysis has shown that the hardware implementation is of great importance, since the specifics of the physical processes in the network cells affect their ability to simulate the neural activity of living neural tissue, the efficiency of certain stages of information processing, storage and transmission. This survey reviews existing hardware neuromorphic implementations of bio-inspired spiking networks in the "semiconductor", "superconductor" and "optical" domains. Special attention is given to the possibility of effective "hybrids" of different approaches
Methods for differential network estimation: an empirical comparison
2024-12-23
Anna Plaksienko, Magne Thoresen, Vera Djordjilović
We provide a review and a comparison of methods for differential network estimation in Gaussian graphical models with focus on structure learning. We consider the case of two datasets from distributions associated with two graphical models. In our simulations, we use five different methods to estimate differential networks. We vary graph structure and sparsity to explore their influence on performance in terms of power and false discovery rate. We demonstrate empirically that presence of hubs proves to be a challenge for all the methods, as well as increased density. We suggest local and global properties that are associated with this challenge. Direct estimation with lasso penalized D-trace loss is shown to perform the best across all combinations of network structure and sparsity.
Quasi-free scattering of atomic nuclei away from the stability line has reached several milestones over the past decade. The advent of gamma, charged particles, and neutron detection devices for inverse kinematics, especially in combination with RI beams, has opened new horizons in nuclear physics. Research is progressing with detection devices optimized to explore these new and challenging area of physics. While some of the new detection developments aim for high energy and angular resolution, others focus on the increasing detection efficiency or enhancing large angular acceptance. As high-intensity RI beams become available worldwide, we reflect on past detectors and provide a review of the future development of the detection devices.
Exploring Modular Mobility: Industry Advancements, Research Trends, and Future Directions on Modular Autonomous Vehicles
2024-12-23
Lanhang Ye, Toshiyuki Yamamoto
Modular autonomous vehicles (MAVs) represent a transformative paradigm in the rapidly advancing field of autonomous vehicle technology. The integration of modularity offers numerous advantages, poised to reshape urban mobility systems and foster innovation in this emerging domain. Although publications on MAVs have only gained traction in the past five years, these pioneering efforts are critical for envisioning the future of modular mobility. This work provides a comprehensive review of industry and academic contributions to MAV development up to 2024, encompassing conceptualization, design, and applications in both passenger and logistics transport. The review systematically defines MAVs and outlines their technical framework, highlighting groundbreaking efforts in vehicular conceptualization, system design, and business models by the automotive industry and emerging mobility service providers. It also synthesizes academic research on key topics, including passenger and logistics transport, and their integration within future mobility ecosystems. The review concludes by identifying challenges, summarizing the current state of the art, and proposing future research directions to advance the development of modular autonomous mobility systems.
Survey on Abstractive Text Summarization: Dataset, Models, and Metrics
2024-12-22
Gospel Ozioma Nnadi, Flavio Bertini
The advancements in deep learning, particularly the introduction of transformers, have been pivotal in enhancing various natural language processing (NLP) tasks. These include text-to-text applications such as machine translation, text classification, and text summarization, as well as data-to-text tasks like response generation and image-to-text tasks such as captioning. Transformer models are distinguished by their attention mechanisms, pretraining on general knowledge, and fine-tuning for downstream tasks. This has led to significant improvements, particularly in abstractive summarization, where sections of a source document are paraphrased to produce summaries that closely resemble human expression. The effectiveness of these models is assessed using diverse metrics, encompassing techniques like semantic overlap and factual correctness. This survey examines the state of the art in text summarization models, with a specific focus on the abstractive summarization approach. It reviews various datasets and evaluation metrics used to measure model performance. Additionally, it includes the results of test cases using abstractive summarization models to underscore the advantages and limitations of contemporary transformer-based models. The source codes and the data are available at https://github.com/gospelnnadi/Text-Summarization-SOTA-Experiment.
Unveiling the Potential of NOMA: A Journey to Next Generation Multiple Access
2024-12-22
Adeel Ahmed, Wang Xingfu, Ammar Hawbani, Weijie Yuan, Hina Tabassum, Yuanwei Liu, Muhammad Umar Farooq Qaisar, Zhiguo Ding, Naofal Al-Dhahir, Arumugam Nallanathan, Derrick Wing Kwan Ng
Revolutionary sixth-generation wireless communications technologies and applications, notably digital twin networks (DTN), connected autonomous vehicles (CAVs), space-air-ground integrated networks (SAGINs), zero-touch networks, industry 5.0, and healthcare 5.0, are driving next-generation wireless networks (NGWNs). These technologies generate massive data, requiring swift transmission and trillions of device connections, fueling the need for sophisticated next-generation multiple access (NGMA) schemes. NGMA enables massive connectivity in the 6G era, optimizing NGWN operations beyond current multiple access (MA) schemes. This survey showcases non-orthogonal multiple access (NOMA) as NGMA's frontrunner, exploring What has NOMA delivered?, What is NOMA providing?, and What lies ahead?. We present NOMA variants, fundamental operations, and applicability in multi-antenna systems, machine learning, reconfigurable intelligent surfaces (RIS), cognitive radio networks (CRN), integrated sensing and communications (ISAC), terahertz networks, and unmanned aerial vehicles (UAVs). Additionally, we explore NOMA's interplay with state-of-the-art wireless technologies, highlighting its advantages and technical challenges. Finally, we unveil NOMA research trends in the 6G era and provide design recommendations and future perspectives for NOMA as the leading NGMA solution for NGWNs.
Early detection of cancer is critical in improving treatment outcomes and increasing survival rates, particularly for common cancers such as lung, breast, and prostate which collectively contribute to a significant global mortality burden. With advancements in imaging technologies and data processing, Convolutional Neural Networks (CNNs) have emerged as a powerful tool for analyzing and classifying medical images, enabling more precise cancer detection. This paper provides a comprehensive review of recent studies leveraging CNN models for detecting ten different types of cancer. Each study employs distinct CNN architectures to identify patterns associated with these cancers, utilizing diverse datasets. Key differences and strengths of these architectures are meticulously compared and analyzed, highlighting their efficacy in improving early detection. Beyond reviewing the performance and limitations of CNN-based cancer detection methods, this study explores the feasibility of integrating CNNs into clinical settings as an early detection tool, potentially complementing or replacing traditional methods. Despite significant progress, challenges remain, including data diversity, result interpretation, and ethical considerations. By identifying the best-performing CNN architectures and providing a comparative analysis, this study aims to contribute a comprehensive perspective on the application of CNNs in cancer detection and their role in advancing diagnostic capabilities in healthcare.
Lies, Damned Lies, and Distributional Language Statistics: Persuasion and Deception with Large Language Models
2024-12-22
Cameron R. Jones, Benjamin K. Bergen
Large Language Models (LLMs) can generate content that is as persuasive as human-written text and appear capable of selectively producing deceptive outputs. These capabilities raise concerns about potential misuse and unintended consequences as these systems become more widely deployed. This review synthesizes recent empirical work examining LLMs' capacity and proclivity for persuasion and deception, analyzes theoretical risks that could arise from these capabilities, and evaluates proposed mitigations. While current persuasive effects are relatively small, various mechanisms could increase their impact, including fine-tuning, multimodality, and social factors. We outline key open questions for future research, including how persuasive AI systems might become, whether truth enjoys an inherent advantage over falsehoods, and how effective different mitigation strategies may be in practice.
We consider the problem of maximizing the stored energy for a given charging duration in a quantum battery composed of a pair of spins-$1/2$ with Ising coupling starting from the spin-down state, using bounded transverse field control. We map this problem to an optimal control problem on a single qubit and using optimal control theory we show that, although a single bang pulse can quickly achieve considerable charging levels for relatively large upper control bounds, higher levels of stored energy including complete charging are accomplished by a bang-singular-bang pulse-sequence, where the intermediate singular pulse is an Off pulse. If the control is restricted between zero and a maximum value, the initial and final bang pulses attain the maximum bound but have different durations, while if it is restricted between symmetric negative and positive boundaries, the bang pulses have the same duration but opposite boundary values. For both cases we provide transcendental equations from which the durations of the individual pulses in the optimal pulse-sequence can be calculated. For the case of full charging we surprisingly find that the three ``switching" functions for the equivalent qubit problem become zero while the adjoint ket does not, in consistency with optimal control theory.
ViLBias: A Framework for Bias Detection using Linguistic and Visual Cues
2024-12-22
Shaina Raza, Caesar Saleh, Emrul Hasan, Franklin Ogidi, Maximus Powers, Veronica Chatrath, Marcelo Lotif, Roya Javadi, Anam Zahid, Vahid Reza Khazaie
The integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) opens new avenues for addressing complex challenges in multimodal content analysis, particularly in biased news detection. This study introduces ViLBias, a framework that leverages state of the art LLMs and VLMs to detect linguistic and visual biases in news content, addressing the limitations of traditional text-only approaches. Our contributions include a novel dataset pairing textual content with accompanying visuals from diverse news sources and a hybrid annotation framework, combining LLM-based annotations with human review to enhance quality while reducing costs and improving scalability. We evaluate the efficacy of LLMs and VLMs in identifying biases, revealing their strengths in detecting subtle framing and text-visual inconsistencies. Empirical analysis demonstrates that incorporating visual cues alongside text enhances bias detection accuracy by 3 to 5 %, showcasing the complementary strengths of LLMs in generative reasoning and Small Language Models (SLMs) in classification. This study offers a comprehensive exploration of LLMs and VLMs as tools for detecting multimodal biases in news content, highlighting both their potential and limitations. Our research paves the way for more robust, scalable, and nuanced approaches to media bias detection, contributing to the broader field of natural language processing and multimodal analysis. (The data and code will be made available for research purposes).
Modular Conversational Agents for Surveys and Interviews
2024-12-22
Jiangbo Yu, Jinhua Zhao, Luis Miranda-Moreno, Matthew Korp
Surveys and interviews (structured, semi-structured, or unstructured) are widely used for collecting insights on emerging or hypothetical scenarios. Traditional human-led methods often face challenges related to cost, scalability, and consistency. Recently, various domains have begun to explore the use of conversational agents (chatbots) powered by large language models (LLMs). However, as public investments and policies on infrastructure and services often involve substantial public stakes and environmental risks, there is a need for a rigorous, transparent, privacy-preserving, and cost-efficient development framework tailored for such major decision-making processes. This paper addresses this gap by introducing a modular approach and its resultant parameterized process for designing conversational agents. We detail the system architecture, integrating engineered prompts, specialized knowledge bases, and customizable, goal-oriented conversational logic in the proposed approach. We demonstrate the adaptability, generalizability, and efficacy of our modular approach through three empirical studies: (1) travel preference surveys, highlighting multimodal (voice, text, and image generation) capabilities; (2) public opinion elicitation on a newly constructed, novel infrastructure project, showcasing question customization and multilingual (English and French) capabilities; and (3) transportation expert consultation about future transportation systems, highlighting real-time, clarification request capabilities for open-ended questions, resilience in handling erratic inputs, and efficient transcript post-processing. The results show the effectiveness of this modular approach and how it addresses key ethical, privacy, security, and token consumption concerns, setting the stage for the next-generation surveys and interviews.
On the Spectral Shape of the Structural Relaxation in Deeply Supercooled Liquids
2024-12-22
Till Böhmer, Florian Pabst, Jan Gabriel, Rolf Zeißler, Thomas Blochowicz
Structural relaxation in deeply supercooled liquids is non-exponential. In susceptibility representation, $\chi^{\prime\prime}(\nu)$, the spectral shape of the structural relaxation is observed as an asymmetrically broadened peak with a $\nu^{1}$ low- and $\nu^{-\beta}$ high-frequency behavior. In this perspective article we discuss common notions, recent results and open questions regarding the spectral shape of the structural relaxation. In particular, we focus on the observation that a high-frequency behavior of $\nu^{-1/2}$ appears to be a generic feature in a broad range of different deeply supercooled liquids. Moreover, we review extensive evidence that contributions from orientational cross-correlations can lead to deviations from the generic spectral shape in certain substances, in particular in dielectric loss spectra. Additionally, intramolecular dynamics can contribute significantly to the spectral shape in substances containing more complex and flexible molecules. Finally, we discuss the open questions regarding potential physical origins of the generic $\nu^{-1/2}$ behavior and the evolution of the spectral shape towards higher temperatures.
Robustness of Large Language Models Against Adversarial Attacks
2024-12-22
Yiyi Tao, Yixian Shen, Hang Zhang, Yanxin Shen, Lun Wang, Chuanqi Shi, Shaoshuai Du
The increasing deployment of Large Language Models (LLMs) in various applications necessitates a rigorous evaluation of their robustness against adversarial attacks. In this paper, we present a comprehensive study on the robustness of GPT LLM family. We employ two distinct evaluation methods to assess their resilience. The first method introduce character-level text attack in input prompts, testing the models on three sentiment classification datasets: StanfordNLP/IMDB, Yelp Reviews, and SST-2. The second method involves using jailbreak prompts to challenge the safety mechanisms of the LLMs. Our experiments reveal significant variations in the robustness of these models, demonstrating their varying degrees of vulnerability to both character-level and semantic-level adversarial attacks. These findings underscore the necessity for improved adversarial training and enhanced safety mechanisms to bolster the robustness of LLMs.
Competition of the shell closure and deformations across the doubly magic $^{78}$Ni
2024-12-22
Ryo Taniuchi
The properties of the neutron-rich isotope $^{78}$Ni, long postulated to be doubly magic, have been extensively explored through recent experimental and theoretical studies. Confirmations of robust shell closures at $Z=28$ and $N=50$ as well as hints of competing deformations in neighboring isotopes have been obtained. Innovations of a thick liquid hydrogen target system with vertex reconstructions and the in-beam $\gamma$-ray spectroscopy technique have facilitated detailed investigations into the nuclear structure of these extreme systems. Proton knockout reactions conducted at relativistic energies have provided the first experimental evidence of shape coexistence at the cornerstone nucleus $^{78}$Ni and its vicinity. As the nuclear structure around $^{78}$Ni influences the description of very neutron-rich systems and r-process nucleosynthesis, these findings underscore the importance of further investigations. This review encapsulates the recent results concerning the nuclear structure at the vicinity of $^{78}$Ni on both experimental and theoretical aspects. It outlines prospective research directions that could further illuminate this complex and intriguing area of the nuclear chart.
Our understanding of the structure of atomic nuclei largely derives from the nuclear shell model, which has proven widely successful. Further test to our interpretation of the nuclear properties is provided by the study of shell evolution. Increasing experimental information has shown that the nuclear energy shells change when going towards the most exotic nuclei, in turn making some shell closures disappear while others arise. In particular, the $N=40$ sub-shell closure has been the subject of extensive research due to the emergence of a so-called Island of Inversion, where deformed intruder configurations dominate the wave function of the ground state. An overview of recent experimental results in the $N=40$ Island of Inversion, particularly those performed with the combination of the MINOS hydrogen target and the DALI2 $\gamma$-ray array at the RIBF are discussed.
Photometric Stellar Parameters for 195,478 Kepler Input Catalog (KIC) Stars
2024-12-22
Bowen Zhang, Yang Huang, Timothy C. Beers, Kai Xiao, Jifeng Liu, Lei Jia, Henggeng Han, Zhirui Li, Chuanjie Zheng, Yongkang Sun, Ruifeng Shi, Hongrui Gu
The stellar atmospheric parameters and physical properties of stars in the Kepler Input Catalog (KIC) are of great significance for the study of exoplanets, stellar activity, and asteroseismology. However, despite extensive effort over the past decades, accurate spectroscopic estimates of these parameters are available for only about half of the stars in the full KIC catalog. In our work, by training relationships between photometric colors and spectroscopic stellar parameters from Gaia DR3, the Kepler Issac-Newton Survey, LAMOST DR10, and APOGEE DR17, we have obtained atmospheric-parameter estimates for over 195,000 stars, accounting for 97$\%$ of the total sample of KIC stars. We obtain 1$\sigma$ uncertainties of 0.1 dex on metallicity [Fe/H], 100 K on effective temperature $T_{\rm eff}$, and 0.2 dex on surface gravity log $g$. In addition, based on these atmospheric parameters, we estimated the ages, masses, radii, and surface gravities of these stars using the commonly adopted isochrone-fitting approach. External comparisons indicate that the resulting precision for turn-off stars is 20$\%$ in age; for dwarf stars, it is 0.07 $M_{\odot}$ in mass, 0.05 $R_{\odot}$ in radius, and 0.12 dex in surface gravity; and for giant stars, it is 0.14 $M_{\odot}$ in mass, 0.73 $R_{\odot}$ in radius, and 0.11 dex in surface gravity.
Learning an Adaptive Fall Recovery Controller for Quadrupeds on Complex Terrains
2024-12-22
Yidan Lu, Yinzhao Dong, Ji Ma, Jiahui Zhang, Peng Lu
Legged robots have shown promise in locomotion complex environments, but recovery from falls on challenging terrains remains a significant hurdle. This paper presents an Adaptive Fall Recovery (AFR) controller for quadrupedal robots on challenging terrains such as rocky, breams, steep slopes, and irregular stones. We leverage deep reinforcement learning to train the AFR, which can adapt to a wide range of terrain geometries and physical properties. Our method demonstrates improvements over existing approaches, showing promising results in recovery scenarios on challenging terrains. We trained our method in Isaac Gym using the Go1 and directly transferred it to several mainstream quadrupedal platforms, such as Spot and ANYmal. Additionally, we validated the controller's effectiveness in Gazebo. Our results indicate that the AFR controller generalizes well to complex terrains and outperforms baseline methods in terms of success rate and recovery speed.
Trees that can be grown in "too many" ways: A review of Bouch's construction
2024-12-22
Hal Tasaki
We carefully review the hierarchical construction by Bouch [1] of trees on the square lattice that can be grown from its root in $L!/C^L$ distinct ways, where $L$ denotes the number of bonds constituting the tree, and $C>1$ is a constant. (As discussed in Section IV.A of [2] and Appendix A.3 of [3], this result has an implication on the operator growth in quantum spin systems in two or higher dimensions.)
Technical Status Report on Plasma Components and Systems in the context of EuPRAXIA
2024-12-22
A. Biagioni, N. Bourgeois, F. Brandi, K. Cassou, L. Corner, L. Crincoli, B. Cros, S. Dobosz Dufrénoy, D. Douillet, P. Drobniak, J. Faure, G. Gatti, G. Grittani, S. Lorenz, H. Jones, B. Lucas, F. Massimo, B. Mercier, A. Molodozhentsev, J. Monzac, R. Pattathil, G. Sarri, P. Sasorov, R. J. Shalloo, L. Steyn, M. Streeter, D. Symes, C. Thaury, A. Vernier, J. C. Wood
The EuPRAXIA project aims to construct two state-of-the-art accelerator facilities based on plasma accelerator technology. Plasma-based accelerators offer the possibility of a significant reduction in facility size and cost savings over current radio frequency (RF) accelerators. The two facilities - one laser-driven one a beam-driven - are envisioned to provide electron beams with an energy in the range of 1-5 GeV and beam quality comparable to existing RF machines. This will enable a versatile portfolio of applications from compact free-electron laser (FEL) drivers to sources for medical and industrial imaging. At the heart of both facilities is the use of plasma-based accelerator components and systems which encompass not only the accelerating medium itself, but also a range of auxiliary systems such as plasma-based electron beam optics and plasma-based mirrors for high-intensity lasers. From a technical standpoint, a high-degree of control over these plasma devices will be essential for EuPRAXIA to achieve its target performance goals. The ability to diagnose and characterize these plasma devices and to simulate their operation will be further essential success factors. Additionally, compatibility with extended operation at high-repetition rates and integration into the accelerator beamline will also prove crucial. In this work, we aim to review the current status of plasma components and related systems for both laser-driven and beam-driven plasma accelerators and to assess challenges to be addressed regarding implementation at future EuPRAXIA facilities.
Lightweight Design and Optimization methods for DCNNs: Progress and Futures
2024-12-22
Hanhua Long, Wenbin Bi, Jian Sun
Lightweight design, as a key approach to mitigate disparity between computational requirements of deep learning models and hardware performance, plays a pivotal role in advancing application of deep learning technologies on mobile and embedded devices, alongside rapid development of smart home, telemedicine, and autonomous driving. With its outstanding feature extracting capabilities, Deep Convolutional Neural Networks (DCNNs) have demonstrated superior performance in computer vision tasks. However, high computational costs and large network architectures severely limit the widespread application of DCNNs on resource-constrained hardware platforms such as smartphones, robots, and IoT devices. This paper reviews lightweight design strategies for DCNNs and examines recent research progress in both lightweight architectural design and model compression. Additionally, this paper discusses current limitations in this field of research and propose prospects for future directions, aiming to provide valuable guidance and reflection for lightweight design philosophy on deep neural networks in the field of computer vision.
Aims. We reported the discovery that a changing-look AGN SDSS J101152.98+544206.4 (J1011+5442 for short) gradually returns to the type 1 state after a short period between 2014 and 2019 in the faint type 1.9 state. Methods. Motivated by the rebrightening in optical and mid-infrared light curves from ZTF and WISE, we obtained the new spectroscopic observations by Xinglong 2.16-m, Lijiang 2.4-m, and MMT 6.5-m optical telescopes in 2024. Results. After changing the optical AGN type from 1 to 1.9 between 2003 and 2015 based on the repeat spectroscopy from the Time Domain Spectroscopic Survey, J1011+5442 returns to its type 1 state in 2024. We detect the significant and very broad Hbeta lines (FWHM > 5000 km/s) based on the new spectra, which suggests that J1011+5442 is in the intermediate state between the dim state in 2015 and the bright state in 2003. The long-term optical and mid-infrared light curves also show a brightening trend between 2019 and 2024 as the broad Hbeta line appears. The time lag of about 100 days between the mid-infrared and optical variability is consistent with the prediction of dust reverberation mapping. Conclusions. The behaviors of the photometric and spectroscopic observations of J1011+5442 are consistent with the argument that the repeating changing-look phenomenon is regulated by the variation of accretion rate.
Normalized eigenfunctions of parametrically factored Schroedinger equations
2024-12-22
J. de la Cruz, H. C. Rosu
The factorizations using the general Riccati solution constructed from a given particular solution by means of the Bernoulli ansatz initiated in 1984 by Mielnik and Fernandez C. for the cases of the quantum harmonic oscillator and the radial Hydrogen equation, respectively, are briefy reviewed. The issue of the eigenfunction normalization of the obtained one-parameter Darboux-deformed Hamiltonians is addressed here.
Fatigue Monitoring Using Wearables and AI: Trends, Challenges, and Future Opportunities
2024-12-22
Kourosh Kakhi, Senthil Kumar Jagatheesaperumal, Abbas Khosravi, Roohallah Alizadehsani, U Rajendra Acharya
Monitoring fatigue is essential for improving safety, particularly for people who work long shifts or in high-demand workplaces. The development of wearable technologies, such as fitness trackers and smartwatches, has made it possible to continuously analyze physiological signals in real-time to determine a person level of exhaustion. This has allowed for timely insights into preventing hazards associated with fatigue. This review focuses on wearable technology and artificial intelligence (AI) integration for tiredness detection, adhering to the PRISMA principles. Studies that used signal processing methods to extract pertinent aspects from physiological data, such as ECG, EMG, and EEG, among others, were analyzed as part of the systematic review process. Then, to find patterns of weariness and indicators of impending fatigue, these features were examined using machine learning and deep learning models. It was demonstrated that wearable technology and cutting-edge AI methods could accurately identify weariness through multi-modal data analysis. By merging data from several sources, information fusion techniques enhanced the precision and dependability of fatigue evaluation. Significant developments in AI-driven signal analysis were noted in the assessment, which should improve real-time fatigue monitoring while requiring less interference. Wearable solutions powered by AI and multi-source data fusion present a strong option for real-time tiredness monitoring in the workplace and other crucial environments. These developments open the door for more improvements in this field and offer useful tools for enhancing safety and reducing fatigue-related hazards.
2024-12-22
Onyinye Dibia, Brad Stenger, Steven Baldasty, Mako Bates, Ivoline C. Ngong, Yuanyuan Feng, Joseph P. Near
Differential Privacy (DP) has emerged as a pivotal approach for safeguarding individual privacy in data analysis, yet its practical adoption is often hindered by challenges in usability in implementation and communication of the privacy protection levels. This paper presents a comprehensive systematization of existing research on the usability of and communication about DP, synthesizing insights from studies on both the practical use of DP tools and strategies for conveying DP parameters that determine the privacy protection levels such as epsilon. By reviewing and analyzing these studies, we identify core usability challenges, best practices, and critical gaps in current DP tools that affect adoption across diverse user groups, including developers, data analysts, and non-technical stakeholders. Our analysis highlights actionable insights and pathways for future research that emphasizes user-centered design and clear communication, fostering the development of more accessible DP tools that meet practical needs and support broader adoption.
Ion-Scale Solitary Structures in the Solar Wind Observed by Solar Orbiter and Parker Solar Probe
2024-12-22
Yufei Yang, Timothy S. Horbury, Domenico Trotta, Lorenzo Matteini, Joseph Wang, Andrey Fedorov, Philippe Louarn, Stuart Bale, Marc Pulupa, Davin E. Larson, Michael Stevens, Milan Maksimovic, Yuri Khotyaintsev, Andrea Larosa
We investigate a class of ion-scale magnetic solitary structures in the solar wind, characterized by distinct magnetic field enhancements and bipolar rotations over spatial scales of several proton inertial lengths. Previously tentatively identified as Alfv\'enic solitons, these structures are revisited using high-resolution data from the Solar Orbiter and Parker Solar Probe missions. Using a machine learning-based method, we identified nearly a thousand such structures, providing new insights into their evolution and physical properties. Statistical analysis shows that these structures are more abundant closer to the Sun, with occurrence rates peaking around 30-40 solar radii and declining at greater distances, suggesting that they decay. High-cadence measurements reveal that these structures are predominantly found in low-beta environments, with consistent fluctuations in density, velocity, and magnetic field. Magnetic field enhancements are often accompanied by plasma density drops, which, under near pressure balance, limit field increases. This leads to small fractional field enhancements near the Sun (approximately 0.01 at 20 solar radii), making detection challenging. Magnetic field variance analysis indicates that these structures are primarily oblique to the local magnetic field. Alfv\'enic velocity-magnetic field correlations suggest that most of these structures propagate sunward in the plasma frame, distinguishing them from typical solar wind fluctuations. We compare these findings with previous studies, discussing possible generation mechanisms and their implications for the turbulent cascade in the near-Sun Alfv\'enic solar wind. Further high-resolution observations and simulations are needed to fully understand their origins and impacts.
IMVB7t: A Multi-Modal Model for Food Preferences based on Artificially Produced Traits
2024-12-21
Mushfiqur Rahman Abir, Md. Tanzib Hosain, Md. Abdullah-Al-Jubair, M. F. Mridha
Human behavior and interactions are profoundly influenced by visual stimuli present in their surroundings. This influence extends to various aspects of life, notably food consumption and selection. In our study, we employed various models to extract different attributes from the environmental images. Specifically, we identify five key attributes and employ an ensemble model IMVB7 based on five distinct models for some of their detection resulted 0.85 mark. In addition, we conducted surveys to discern patterns in food preferences in response to visual stimuli. Leveraging the insights gleaned from these surveys, we formulate recommendations using decision tree for dishes based on the amalgamation of identified attributes resulted IMVB7t 0.96 mark. This study serves as a foundational step, paving the way for further exploration of this interdisciplinary domain.
The southern shore of the island of inversion studied via quasi-free scattering
2024-12-21
J. Kahlbow
Neutron-rich nuclei exhibit a variety of intriguing features associated with nuclear structure evolution, deformation, and other phenomena. Particularly interesting is the region in the chart of nuclides around Z = 12 and N = 20, commonly referred to as "Island of Inversion", which is profoundly influenced by these features. Recent cutting-edge experiments performed at SAMURAI/RIBF have investigated the structure of the most neutron-rich O and F isotopes, including 27,28O and 28-30F, utilizing quasi-free scattering and invariant-mass spectroscopy techniques. This experimental campaign manifests the breakdown of the N = 20 magicity for O and F isotopes, placing them within the "Island of Inversion", as is discussed in this review article. The results are further supported by theoretical analyses employing state-of-the-art shell-model and ab-initio calculations. These nuclei serve as corner stones for the study of weak binding and continuum coupling, deformation, and halo formation. Signatures for the establishment of a superfluid regime in 28O and 29F are found. Future experimental and theoretical studies are needed to examine details.
SubData: A Python Library to Collect and Combine Datasets for Evaluating LLM Alignment on Downstream Tasks
2024-12-21
Leon Fröhling, Pietro Bernardelle, Gianluca Demartini
With the release of ever more capable large language models (LLMs), researchers in NLP and related disciplines have started to explore the usability of LLMs for a wide variety of different annotation tasks. Very recently, a lot of this attention has shifted to tasks that are subjective in nature. Given that the latest generations of LLMs have digested and encoded extensive knowledge about different human subpopulations and individuals, the hope is that these models can be trained, tuned or prompted to align with a wide range of different human perspectives. While researchers already evaluate the success of this alignment via surveys and tests, there is a lack of resources to evaluate the alignment on what oftentimes matters the most in NLP; the actual downstream tasks. To fill this gap we present SubData, a Python library that offers researchers working on topics related to subjectivity in annotation tasks a convenient way of collecting, combining and using a range of suitable datasets.
SoK: Understanding the Attack Surface in Device Driver Isolation Frameworks
2024-12-21
Yongzhe Huang, Kaiming Huang, Matthew Ennis, Vikram Narayanan, Anton Burtsev, Trent Jaeger, Gang Tan
Device driver isolation is a promising approach for protecting the kernel from faulty or malicious drivers, but the actual security provided by such frameworks is often not well understood. Recent research has identified Compartment Interface Vulnerabilities (CIVs) in userspace compartmentalized applications, yet their impact on driver isolation frameworks remains poorly understood. This paper provides a comprehensive survey of the design and security guarantees of existing driver isolation frameworks and systemizes existing CIV classifications, evaluating them under driver isolation. The analysis shows that different classes of CIVs are prevalent across the studied drivers under a baseline threat model, with large drivers having more than 100 instances of different CIVs and an average of 33 instances across the studied drivers. Enforcing extra security properties, such as CFI, can reduce the number of CIVs to around 28 instances on average. This study provides insights for understanding existing driver isolation security and the prevalence of CIVs in the driver isolation context, and extracts useful insights that can provide security guidance for future driver isolation systems.
Business Analysis: User Attitude Evaluation and Prediction Based on Hotel User Reviews and Text Mining
2024-12-21
Ruochun Zhao, Yue Hao, Xuechen Li
In the post-pandemic era, the hotel industry plays a crucial role in economic recovery, with consumer sentiment increasingly influencing market trends. This study utilizes advanced natural language processing (NLP) and the BERT model to analyze user reviews, extracting insights into customer satisfaction and guiding service improvements. By transforming reviews into feature vectors, the BERT model accurately classifies emotions, uncovering patterns of satisfaction and dissatisfaction. This approach provides valuable data for hotel management, helping them refine service offerings and improve customer experiences. From a financial perspective, understanding sentiment is vital for predicting market performance, as shifts in consumer sentiment often correlate with stock prices and overall industry performance. Additionally, the study addresses data imbalance in sentiment analysis, employing techniques like oversampling and undersampling to enhance model robustness. The results offer actionable insights not only for the hotel industry but also for financial analysts, aiding in market forecasts and investment decisions. This research highlights the potential of sentiment analysis to drive business growth, improve financial outcomes, and enhance competitive advantage in the dynamic tourism and hospitality sectors, thereby contributing to the broader economic landscape.
Near a gravitating compact object, massive particles traveling along timelike geodesics are gravitationally deflected similarly to light. In this paper, we study the deflection angles of these particles in the strong deflection limit. This analytical approximation applies when particles in unbound orbits approach the compact object very closely, circle around it at a radius close to that of the unstable circular orbit, and eventually escape. While previous studies have provided results for particular metrics, we offer a general solution applicable to any static, spherically symmetric and asymptotically flat spacetime. After briefly reviewing the exact expression for the deflection angle of massive particles, we present a strong deflection limit analysis for this general case. The developed formulas are then applied to three particular metrics: Schwarzschild, Reissner-Nordstr\"om and Janis-Newman-Winicour.
Shielding of breathers for the focusing nonlinear Schrödinger equation
2024-12-21
Gregorio Falqui, Tamara Grava, Christian Puntini
We study a deterministic gas of breathers for the Focusing Nonlinear Schr\"odinger equation. The gas of breathers is obtained from a $N$-breather solution in the limit $N\to \infty$.\\ The limit is performed at the level of scattering data by letting the $N$-breather spectrum to fill uniformly a suitable compact domain of the complex plane in the limit $N\to\infty$. The corresponding norming constants are interpolated by a smooth function and scaled as $1/N$. For particular choices of the domain and the interpolating function, the gas of breathers behaves as finite breathers solution. This extends the shielding effect discovered in "M. Bertola, T. Grava, and G. Orsatti - Physical Review Letters, 130.12 (2023): 1" for a soliton gas also to a breather gas.
This paper proposes a novel approach to develop an open-domain and long-form Over-The-Top (OTT) Question-Answering (QA) dataset, DragonVerseQA, specifically oriented to the fantasy universe of "House of the Dragon" and "Game Of Thrones" TV series. Most existing QA datasets focus on short, fact-based answers sourced almost solely from Wikipedia articles, devoid of depth and contextual richness for sophisticated narrative understanding. We curate a dataset that combines full episode summaries sourced from HBO and fandom wiki websites, user reviews from sources like IMDb and Rotten Tomatoes, and high-quality, open-domain, legally admissible sources, and structured data from repositories like WikiData into one dataset. The dataset provides a multi-dimensional context, reflecting complex character dynamics and plot developments from these varied sources. That means, on equal footing, only after heavy data preprocessing and filtering methods will meaningful, non-spam unbiased reviews be available in this enriched dataset. The comprehensive insights are given through the long-form answers generated from this enriched context. This is what makes this valuable dataset for improving conversational AI, narrative analysis, sentiment analysis, summarization techniques, and relation extraction. A comparative analysis with state-of-the-art QA datasets such as SQuAD 2.0, TriviaQA, and Natural Questions brings to light the unique advantages of our dataset in terms of contextual complexity and answer length. Detailed reviews add layers to audience sentiment and narrative interpretation, raising the bar for domain-specific QA with a new quality benchmark. Our work also allows a deeper understanding of entertainment-industry content and opens the door to more knowledgeable and creative AI-driven interactions within digital media environments.
The Y Dwarf Population with HST: unlocking the secrets of our coolest neighbours -- II. Parallaxes and Proper Motions
2024-12-21
Clémence Fontanive, Luigi R. Bedin, Loïc Albert, Daniella C. Bardalez Gagliuffi
We present astrometric results from a Hubble Space Telescope (HST) campaign aimed at determining precise distances for cold Y-type brown dwarfs. Combining observations from a dedicated HST/WFC3 program with archival data, we derive astrometric solutions for 15 nearby Y dwarfs, by linking the high-precision relative astrometry from Hubble to the high-accuracy Gaia DR3 absolute reference system, using stars present in both to anchor the two frames of reference. We reach uncertainties on parallaxes below the 1-mas level for half of the sample, and down to 3 mas for two thirds of the targets, or relative precisions <1% in most cases and 2-5x improvements over previous measurements. For the remaining targets, we achieved slightly lower precisions on parallaxes (5-12 mas, 5-10%), correlated with the lower signal-to-noise of the faintest targets. The precision reached in our derived proper motions is around 0.1-0.4 mas/yr for most targets, and up to 1-2 mas/yr for less precise cases. Our estimated parallaxes and proper motions are generally in good agreement with literature values, and consistent to 1-2 sigma with recent Spitzer-derived parallaxes in most cases. These new astrometric solutions provide important validation of these objects' distances and sky motions, especially given the large disparities seen in previous estimates. Our results demonstrate the power of HST combined with Gaia to measure highly-precise absolute astrometry for faint brown dwarfs, and highlights the limitations reached for the reddest and coldest objects, for which JWST will certainly provide a favourable platform to improve these results.
Decades after their initial observation in prion-infected brain tissues, the identities of virus-like dense particles, varicose tubules, and oval bodies containing parallel bands and fibrils have remained elusive. Our recent work revealed that a phenotype of dilation of the endoplasmic reticulum (ER), most notable for the perinuclear space (PNS), contributes to spongiform degeneration. To assess the significance of this phenotype for the etiology of prion diseases, we explored whether it can be functionally linked to other neuropathological hallmarks observed in these diseases, as this would indicate it to be a central event. Having surveyed the neuropathological record and other distant literature niches, we propose a model in which pathogenic forms of the prion protein poison raft domains, including essential Na+, K+-ATPases (NKAs) embedded within them, thereby triggering an ER-centered cellular rescue program coordinated by the unfolded protein response (UPR). The execution of this program stalls general protein synthesis, causing the deterioration of synaptic spines. As the disease progresses, cells selectively increase sterol biosynthesis, along with ribosome and ER biogenesis. These adaptive rescue attempts cause morphological changes to the ER which manifest as ER dilation or ER hypertrophy in a manner that is influenced by Ca2+ influx into the cell. The nuclear-to-cytoplasmic transport of mRNAs and tRNAs interrupts in late stage disease, thereby depriving ribosomes of supplies and inducing them to aggregate into a paracrystalline form. In support of this model, we share previously reported data, whose features are consistent with the interpretation that 1) the phenotype of ER dilation is observed in major prion diseases, 2) varicose tubules and oval bodies represent ER hypertrophy, and 3) virus-like dense particles are paracrystalline aggregates of inactive ribosomes.
Privacy and security are central to the design of information systems endowed with sound data protection and cyber resilience capabilities. Still, developers often struggle to incorporate these properties into software projects as they either lack proper cybersecurity training or do not consider them a priority. Prior work has tried to support privacy and security engineering activities through threat modeling methods for scrutinizing flaws in system architectures. Moreover, several techniques for the automatic identification of vulnerabilities and the generation of secure code implementations have also been proposed in the current literature. Conversely, such as-code approaches seem under-investigated in the privacy domain, with little work elaborating on (i) the automatic detection of privacy properties in source code or (ii) the generation of privacy-friendly code. In this work, we seek to characterize the current research landscape of Privacy as Code (PaC) methods and tools by conducting a rapid literature review. Our results suggest that PaC research is in its infancy, especially regarding the performance evaluation and usability assessment of the existing approaches. Based on these findings, we outline and discuss prospective research directions concerning empirical studies with software practitioners, the curation of benchmark datasets, and the role of generative AI technologies.
Reaction mechanism of quasi-free knockout processes in exotic RI beam era
2024-12-21
Kazuki Yoshida, Junki Tanaka
The quasi-free nucleon knockout reaction has been revealed the single-particle nature of nuclei. Thanks to the advances in experimental techniques and reaction theory, various new aspects of nuclei are being revealed by knockout reactions. In this article, we review the basic concept of the quasi-free knockout reaction, and recent achievements in the SEASTAR project using the MINOS system. We also present our new findings on the low-energy nucleon knockout reaction and the $\alpha$ knockout reaction. The combination of the (microscopic) structure theory, reaction theory and experiments will be the key to a complete understanding of the $\alpha$ formation and its universality in the coming decades. Noble clusters, e.g., $d$, $t$, $^{3}$He, etc. are in the scope of the ONOKORO project. The implementation of the two (and more) nucleon correlation in the reaction theory is essential to connect the properties of such clusters and the reaction observables. A new framework, CDCCIA, is introduced for this purpose, which will also be applicable to the two-nucleon knockout reactions, e.g., $(p,3p)$, $(p,2pn)$, and $(p,p2n)$.
A Systems Thinking Approach to Algorithmic Fairness
2024-12-21
Chris Lam
Systems thinking provides us with a way to model the algorithmic fairness problem by allowing us to encode prior knowledge and assumptions about where we believe bias might exist in the data generating process. We can then model this using a series of causal graphs, enabling us to link AI/ML systems to politics and the law. By treating the fairness problem as a complex system, we can combine techniques from machine learning, causal inference, and system dynamics. Each of these analytical techniques is designed to capture different emergent aspects of fairness, allowing us to develop a deeper and more holistic view of the problem. This can help policymakers on both sides of the political aisle to understand the complex trade-offs that exist from different types of fairness policies, providing a blueprint for designing AI policy that is aligned to their political agendas.
The rapid advancements in satellite remote sensing have enhanced the capability to monitor and analyze the Earth's surface. Among the many variables captured through satellite sensors, Land Surface Temperature (LST) plays a critical role in understanding key environmental processes. However, obtaining high-resolution LST data remains a challenge, as satellite sensors often face a trade-off between spatial and temporal resolutions. In response, Spatio-Temporal Fusion (STF) has emerged as a powerful method to integrate two satellite data sources, one providing high spatial but low temporal resolution, and the other offering high temporal but low spatial resolution. Although a range of STF techniques have been proposed, from traditional methods to cutting-edge deep learning (DL) models, most have focused on surface reflectance, with limited application to LST estimation. DL approaches, in particular, show promise in improving the spatial and temporal resolutions of LST by capturing complex, non-linear relationships between input and output LST data. This paper offers a comprehensive review of the latest advancements in DL-based STF techniques for LST estimation. We analyze key research developments, mathematically formulate the STF problem, and introduce a novel taxonomy for DL-based STF methods. Furthermore, we discuss the challenges faced by current methods and highlight future research directions. In addition, we present the first open-source benchmark STF dataset for LST estimation, consisting of 51 pairs of MODIS-Landsat images spanning from 2013 to 2024. To support our findings, we conduct extensive experiments on state-of-the-art methods and present both quantitative and qualitative assessments. This is the first survey paper focused on DL-based STF for LST estimation. We hope it serves as a valuable reference for researchers and paves the way for future research in this field.
The discovery of bilayer nickelate superconductors under high pressure has opened a new chapter in high-transition temperature (high-TC) superconductivity. Here, we report ambient-pressure superconductivity onset above the McMillan limit (40 K) in bilayer nickelate epitaxial ultrathin films. Three-unit-cell (3UC) thick La2.85Pr0.15Ni2O7 single-phase-crystalline films are grown using the gigantic-oxidative atomic-layer-by-layer epitaxy (GOALL-Epitaxy) on SrLaAlO4 substrates. Resistivity measurements and magnetic-field responses indicate onset TC = 45 K. The transition to zero resistance exhibits characteristics consistent with a Berezinskii-Kosterlitz-Thouless (BKT)-like behavior, with TBKT = 9 K. Meissner diamagnetic effect is observed at TM = 8.5 K via a mutual inductance setup, in agreement with the BKT-like transition. In-plane and out-of-plane critical magnetic fields exhibit anisotropy. Scanning transmission electron microscopy (STEM) images and X-ray reciprocal space mappings (RSMs) show that the films maintain a tetragonal phase with coherent epitaxial compressive strain ~2% in the NiO2 planes relative to the bulk. Our findings pave the way for comprehensive investigations of nickelate superconductors under ambient pressure conditions and for exploring superconductivity at higher transition temperature through strain engineering in heterostructures.
Trade-off between coherence and dissipation for excitable phase oscillators
2024-12-21
Chunming Zheng
Thermodynamic uncertainty relation (TUR) bounds coherence in stochastic oscillatory systems. In this paper, we show that both dynamical and thermodynamic bounds play important roles for the excitable oscillators, e.g. neurons. Firstly, we investigate the trade-off between coherence and dissipation both in the sub and super-threshold regions for a single excitable unit, where both the TUR and the SNIC bounds constrain the fluctuation of inter-spike intervals. Secondly, we show that the widely studied phenomenon called coherence resonance, where there exists a noise strength to make the oscillatory responses of the system most coherent, is also bounded by the TUR in the one-dimensional excitable phase model. Finally, we study the coherence-dissipation relation in ensembles of strongly coupled excitable oscillators.
2024-12-21
Marco Pangallo, R. Maria del Rio-Chanona
Economic agent-based models (ABMs) are becoming more and more data-driven, establishing themselves as increasingly valuable tools for economic research and policymaking. We propose to classify the extent to which an ABM is data-driven based on whether agent-level quantities are initialized from real-world micro-data and whether the ABM's dynamics track empirical time series. This paper discusses how making ABMs data-driven helps overcome limitations of traditional ABMs and makes ABMs a stronger alternative to equilibrium models. We review state-of-the-art methods in parameter calibration, initialization, and data assimilation, and then present successful applications that have generated new scientific knowledge and informed policy decisions. This paper serves as a manifesto for data-driven ABMs, introducing a definition and classification and outlining the state of the field, and as a guide for those new to the field.
A Generalizable 3D Diffusion Framework for Low-Dose and Few-View Cardiac SPECT
2024-12-21
Huidong Xie, Weijie Gan, Wei Ji, Xiongchao Chen, Alaa Alashi, Stephanie L. Thorn, Bo Zhou, Qiong Liu, Menghua Xia, Xueqi Guo, Yi-Hwa Liu, Hongyu An, Ulugbek S. Kamilov, Ge Wang, Albert J. Sinusas, Chi Liu
Myocardial perfusion imaging using SPECT is widely utilized to diagnose coronary artery diseases, but image quality can be negatively affected in low-dose and few-view acquisition settings. Although various deep learning methods have been introduced to improve image quality from low-dose or few-view SPECT data, previous approaches often fail to generalize across different acquisition settings, limiting their applicability in reality. This work introduced DiffSPECT-3D, a diffusion framework for 3D cardiac SPECT imaging that effectively adapts to different acquisition settings without requiring further network re-training or fine-tuning. Using both image and projection data, a consistency strategy is proposed to ensure that diffusion sampling at each step aligns with the low-dose/few-view projection measurements, the image data, and the scanner geometry, thus enabling generalization to different low-dose/few-view settings. Incorporating anatomical spatial information from CT and total variation constraint, we proposed a 2.5D conditional strategy to allow the DiffSPECT-3D to observe 3D contextual information from the entire image volume, addressing the 3D memory issues in diffusion model. We extensively evaluated the proposed method on 1,325 clinical 99mTc tetrofosmin stress/rest studies from 795 patients. Each study was reconstructed into 5 different low-count and 5 different few-view levels for model evaluations, ranging from 1% to 50% and from 1 view to 9 view, respectively. Validated against cardiac catheterization results and diagnostic comments from nuclear cardiologists, the presented results show the potential to achieve low-dose and few-view SPECT imaging without compromising clinical performance. Additionally, DiffSPECT-3D could be directly applied to full-dose SPECT images to further improve image quality, especially in a low-dose stress-first cardiac SPECT imaging protocol.
Mathematics and Machine Creativity: A Survey on Bridging Mathematics with AI
2024-12-21
Shizhe Liang, Wei Zhang, Tianyang Zhong, Tianming Liu
This paper presents a comprehensive overview on the applications of artificial intelligence (AI) in mathematical research, highlighting the transformative role AI has begun to play in this domain. Traditionally, AI advancements have heavily relied on theoretical foundations provided by mathematics and statistics. However, recent developments in AI, particularly in reinforcement learning (RL) and large language models (LLMs), have demonstrated the potential for AI to contribute back to mathematics by offering flexible algorithmic frameworks and powerful inductive reasoning capabilities that support various aspects of mathematical research. This survey aims to establish a bridge between AI and mathematics, providing insights into the mutual benefits and fostering deeper interdisciplinary understanding. In particular, we argue that while current AI and LLMs may struggle with complex deductive reasoning, their "inherent creativity", the ability to generate outputs at high throughput based on recognition of shallow patterns, holds significant potential to support and inspire mathematical research. This creative capability, often overlooked, could be the key to unlocking new perspectives and methodologies in mathematics. Furthermore, we address the lack of cross-disciplinary communication: mathematicians may not fully comprehend the latest advances in AI, while AI researchers frequently prioritize benchmark performance over real-world applications in frontier mathematical research. This paper seeks to close that gap, offering a detailed exploration of AI fundamentals, its strengths, and its emerging applications in the mathematical sciences.
Stellar Contamination Correction Using Back-to-Back Transits of TRAPPIST-1 b and c
2024-12-21
Alexander D. Rathcke, Lars A. Buchhave, Julien De Wit, Benjamin V. Rackham, Prune C. August, Hannah Diamond-Lowe, João M. Mendonça, Aaron Bello-Arufe, Mercedes López-Morales, Daniel Kitzmann, Kevin Heng
Stellar surface heterogeneities, such as spots and faculae, often contaminate exoplanet transit spectra, hindering precise atmospheric characterization. We demonstrate a novel, epoch-based, model-independent method to mitigate stellar contamination, applicable to multi-planet systems with at least one airless planet. We apply this method using quasi-simultaneous transits of TRAPPIST-1 b and TRAPPIST-1 c observed on July 9, 2024, with JWST NIRSpec PRISM. These two planets, with nearly identical radii and impact parameters, are likely either bare rocks or possess thin, low-pressure atmospheres, making them ideal candidates for this technique, as variations in their transit spectra would be primarily attributed to stellar activity. Our observations reveal their transit spectra exhibit consistent features, indicating similar levels of stellar contamination. We use TRAPPIST-1 b to correct the transit spectrum of TRAPPIST-1 c, achieving a 2.5x reduction in stellar contamination at shorter wavelengths. At longer wavelengths, lower SNR prevents clear detection of contamination or full assessment of mitigation. Still, out-of-transit analysis reveals variations across the spectrum, suggesting contamination extends into the longer wavelengths. Based on the success of the correction at shorter wavelengths, we argue that contamination is also reduced at longer wavelengths to a similar extent. This shifts the challenge of detecting atmospheric features to a predominantly white noise issue, which can be addressed by stacking observations. This method enables epoch-specific stellar contamination corrections, allowing co-addition of planetary spectra for reliable searches of secondary atmospheres with signals of 60-250 ppm. Additionally, we identify small-scale cold (2000 K) and warm (2600 K) regions almost uniformly distributed on TRAPPIST-1, with overall covering fractions varying by 0.1% per hour.
Privacy in Fine-tuning Large Language Models: Attacks, Defenses, and Future Directions
2024-12-21
Hao Du, Shang Liu, Lele Zheng, Yang Cao, Atsuyoshi Nakamura, Lei Chen
Fine-tuning has emerged as a critical process in leveraging Large Language Models (LLMs) for specific downstream tasks, enabling these models to achieve state-of-the-art performance across various domains. However, the fine-tuning process often involves sensitive datasets, introducing privacy risks that exploit the unique characteristics of this stage. In this paper, we provide a comprehensive survey of privacy challenges associated with fine-tuning LLMs, highlighting vulnerabilities to various privacy attacks, including membership inference, data extraction, and backdoor attacks. We further review defense mechanisms designed to mitigate privacy risks in the fine-tuning phase, such as differential privacy, federated learning, and knowledge unlearning, discussing their effectiveness and limitations in addressing privacy risks and maintaining model utility. By identifying key gaps in existing research, we highlight challenges and propose directions to advance the development of privacy-preserving methods for fine-tuning LLMs, promoting their responsible use in diverse applications.
The emergence of large language models (LLMs) has sparked the possibility of about Artificial Superintelligence (ASI), a hypothetical AI system surpassing human intelligence. However, existing alignment paradigms struggle to guide such advanced AI systems. Superalignment, the alignment of AI systems with human values and safety requirements at superhuman levels of capability aims to addresses two primary goals -- scalability in supervision to provide high-quality guidance signals and robust governance to ensure alignment with human values. In this survey, we examine scalable oversight methods and potential solutions for superalignment. Specifically, we explore the concept of ASI, the challenges it poses, and the limitations of current alignment paradigms in addressing the superalignment problem. Then we review scalable oversight methods for superalignment. Finally, we discuss the key challenges and propose pathways for the safe and continual improvement of ASI systems. By comprehensively reviewing the current literature, our goal is provide a systematical introduction of existing methods, analyze their strengths and limitations, and discuss potential future directions.
2024-12-21
Irene Hou, Hannah Vy Nguyen, Owen Man, Stephen MacNeil
Help-seeking is a critical aspect of learning and problem-solving for computing students. Recent research has shown that many students are aware of generative AI (GenAI) tools; however, there are gaps in the extent and effectiveness of how students use them. With over two years of widespread GenAI usage, it is crucial to understand whether students' help-seeking behaviors with these tools have evolved and how. This paper presents findings from a repeated cross-sectional survey conducted among computing students across North American universities (n=95). Our results indicate shifts in GenAI usage patterns. In 2023, 34.1% of students (n=47) reported never using ChatGPT for help, ranking it fourth after online searches, peer support, and class forums. By 2024, this figure dropped sharply to 6.3% (n=48), with ChatGPT nearly matching online search as the most commonly used help resource. Despite this growing prevalence, there has been a decline in students' hourly and daily usage of GenAI tools, which may be attributed to a common tendency to underestimate usage frequency. These findings offer new insights into the evolving role of GenAI in computing education, highlighting its increasing acceptance and solidifying its position as a key help resource.
Uncertainty Quantification in Continual Open-World Learning
2024-12-21
Amanda S. Rios, Ibrahima J. Ndiour, Parual Datta, Jaroslaw Sydir, Omesh Tickoo, Nilesh Ahuja
AI deployed in the real-world should be capable of autonomously adapting to novelties encountered after deployment. Yet, in the field of continual learning, the reliance on novelty and labeling oracles is commonplace albeit unrealistic. This paper addresses a challenging and under-explored problem: a deployed AI agent that continuously encounters unlabeled data - which may include both unseen samples of known classes and samples from novel (unknown) classes - and must adapt to it continuously. To tackle this challenge, we propose our method COUQ "Continual Open-world Uncertainty Quantification", an iterative uncertainty estimation algorithm tailored for learning in generalized continual open-world multi-class settings. We rigorously apply and evaluate COUQ on key sub-tasks in the Continual Open-World: continual novelty detection, uncertainty guided active learning, and uncertainty guided pseudo-labeling for semi-supervised CL. We demonstrate the effectiveness of our method across multiple datasets, ablations, backbones and performance superior to state-of-the-art.
Artificial Intelligence, Scientific Discovery, and Product Innovation
2024-12-21
Aidan Toner-Rodgers
This paper studies the impact of artificial intelligence on innovation, exploiting the randomized introduction of a new materials discovery technology to 1,018 scientists in the R&D lab of a large U.S. firm. AI-assisted researchers discover 44% more materials, resulting in a 39% increase in patent filings and a 17% rise in downstream product innovation. These compounds possess more novel chemical structures and lead to more radical inventions. However, the technology has strikingly disparate effects across the productivity distribution: while the bottom third of scientists see little benefit, the output of top researchers nearly doubles. Investigating the mechanisms behind these results, I show that AI automates 57% of "idea-generation" tasks, reallocating researchers to the new task of evaluating model-produced candidate materials. Top scientists leverage their domain knowledge to prioritize promising AI suggestions, while others waste significant resources testing false positives. Together, these findings demonstrate the potential of AI-augmented research and highlight the complementarity between algorithms and expertise in the innovative process. Survey evidence reveals that these gains come at a cost, however, as 82% of scientists report reduced satisfaction with their work due to decreased creativity and skill underutilization.
Strongly interacting matter in extreme magnetic fields
2024-12-21
Prabal Adhikari, Martin Ammon, Sidney S. Avancini, Alejandro Ayala, Aritra Bandyopadhyay, David Blaschke, Fabio L. Braghin, Pavel Buividovich, Rafael P. Cardoso, Casey Cartwright, Jorge David Castaño-Yepes, Maxim Chernodub, M. Coppola, Mayusree Das, Mariana Dutra, Gergely Endrődi, Jianjun Fang, Ricardo L. S. Farias, Eduardo S. Fraga, Arthur Frazon, Kenji Fukushima, Juan D. García-Muñoz, Eduardo Garnacho-Velasco, D. Gomez Dumm, Sebastian Grieninger, Francesca Gulminelli, Juan Hernandez, Chowdhury Aminul Islam, Matthias Kaminski, Andrey Kotov, Gastão Krein, Jing Li, Pok Man Lo, Marcelo Loewe, Odilon Lourenço, Gergely Markó, Kau D. Marquez, Ana Mizher, Banibrata Mukhopadhyay, Enrique Muñoz, S. Noguera, Rodrigo M. Nunes, Helena Pais, Letícia F. Palhares, Constança Providência, Alfredo Raya, Tulio Restrepo, Juan Cristóbal Rojas, N. N. Scoccola, Luigi Scurto, Armen Sedrakian, Dominik Smith, William Rafael Tavares, Maria E. Tejeda-Yeomans, Varese S. Timóteo, Laura Tolos, Cristian Villavicencio, Fridolin Weber, Shigehiro Yasui, Renato Zamora, Zenia Zuraiq
Magnetic fields are ubiquitous across different physical systems of current interest; from the early Universe, compact astrophysical objects and heavy-ion collisions to condensed matter systems. A proper treatment of the effects produced by magnetic fields during the dynamical evolution of these systems, can help to understand observables that otherwise show a puzzling behavior. Furthermore, when these fields are comparable to or stronger than \Lambda_QCD, they serve as excellent probes to help elucidate the physics of strongly interacting matter under extreme conditions of temperature and density. In this work we provide a comprehensive review of recent developments on the description of QED and QCD systems where magnetic field driven effects are important. These include the modification of meson static properties such as masses and form factors, the chiral magnetic effect, the description of anomalous transport coefficients, superconductivity in extreme magnetic fields, the properties of neutron stars, the evolution of heavy-ion collisions, as well as effects on the QCD phase diagram. We describe recent theory and phenomenological developments using effective models as well as LQCD methods. The work represents a state-of-the-art review of the field, motivated by presentations and discussions during the "Workshop on Strongly Interacting Matter in Strong Electromagnetic Fields" that took place in the European Centre for Theoretical Studies in Nuclear Physics and Related Areas (ECT*) in the city of Trento, Italy, September 25-29, 2023.
MPI-Rockstar: a Hybrid MPI and OpenMP Parallel Implementation of the Rockstar Halo finder
2024-12-21
Tomoyuki Tokuue, Tomoaki Ishiyama, Ken Osato, Satoshi Tanaka, Peter Behroozi
MPI-Rockstar is a massively parallel halo finder based on the Rockstar phase-space temporal halo finder code, which is one of the most extensively used halo finding codes. Compared to the original code, parallelized by a primitive socket communication library, we parallelized it in a hybrid way using MPI and OpenMP, which is suitable for analysis on the hybrid shared and distributed memory environments of modern supercomputers. This implementation can easily handle the analysis of more than a trillion particles on more than 100,000 parallel processes, enabling the production of a huge dataset for the next generation of cosmological surveys. As new functions to the original Rockstar code, MPI-Rockstar supports HDF5 as an output format and can output additional halo properties such as the inertia tensor.
Quantitative classicality in cosmological interactions during inflation
2024-12-20
Yoann L. Launay, Gerasimos I. Rigopoulos, E. Paul S. Shellard
We examine the classical and quantum evolution of inflationary cosmological perturbations from quantum initial conditions, using the on-shell and off-shell contributions to correlators to investigate the signatures of interactions. In particular, we calculate the Keldysh contributions to the leading order bispectrum from past infinity, showing that the squeezed limit is dominated by the on-shell evolution. By truncating the time integrals in the analytic expressions for contributions to the bispectrum, we define a `quantum interactivity' and quantitatively identify scales and times for which it is sufficient to only assume classical evolution, given a fixed precision. In contrast to common perceptions inspired by free two-point functions, we show that common non-linear terms of inflationary perturbations can be well-described by classical evolution even prior to horizon crossing. The insights gained here can pave the way for quantitative criteria for justifying the validity of numerically simulating the generation and evolution of quantum fluctuations in inflation. In particular, we comment on the validity of using stochastic inflation to reproduce known in-in perturbative results. An extensive appendix provides a review of the Keldysh formulation of the in-in formalism with the initial state set at a finite, as opposed to infinite past, emphasizing the importance of considering temporal boundary terms and the initial state for correctly obtaining the propagators. We also show how stochastic dynamics can emerge as a sufficient approximation to the full quantum evolution. This becomes particularly transparent in the Keldysh description.
NeRF-To-Real Tester: Neural Radiance Fields as Test Image Generators for Vision of Autonomous Systems
2024-12-20
Laura Weihl, Bilal Wehbe, Andrzej Wąsowski
Autonomous inspection of infrastructure on land and in water is a quickly growing market, with applications including surveying constructions, monitoring plants, and tracking environmental changes in on- and off-shore wind energy farms. For Autonomous Underwater Vehicles and Unmanned Aerial Vehicles overfitting of controllers to simulation conditions fundamentally leads to poor performance in the operation environment. There is a pressing need for more diverse and realistic test data that accurately represents the challenges faced by these systems. We address the challenge of generating perception test data for autonomous systems by leveraging Neural Radiance Fields to generate realistic and diverse test images, and integrating them into a metamorphic testing framework for vision components such as vSLAM and object detection. Our tool, N2R-Tester, allows training models of custom scenes and rendering test images from perturbed positions. An experimental evaluation of N2R-Tester on eight different vision components in AUVs and UAVs demonstrates the efficacy and versatility of the approach.
Robust and fine localization algorithms are crucial for autonomous driving. For the production of such vehicles as a commodity, affordable sensing solutions and reliable localization algorithms must be designed. This work considers scenarios where the sensor data comes from images captured by an inexpensive camera mounted on the vehicle and where the vehicle contains a fine global map. Such localization algorithms typically involve finding the section in the global map that best matches the captured image. In harsh environments, both the global map and the captured image can be noisy. Because of physical constraints on camera placement, the image captured by the camera can be viewed as a noisy perspective transformed version of the road in the global map. Thus, an optimal algorithm should take into account the unequal noise power in various regions of the captured image, and the intrinsic uncertainty in the global map due to environmental variations. This article briefly reviews two matching methods: (i) standard inner product (SIP) and (ii) normalized mutual information (NMI). It then proposes novel and principled modifications to improve the performance of these algorithms significantly in noisy environments. These enhancements are inspired by the physical constraints associated with autonomous vehicles. They are grounded in statistical signal processing and, in some context, are provably better. Numerical simulations demonstrate the effectiveness of such modifications.
Can LLMs Obfuscate Code? A Systematic Analysis of Large Language Models into Assembly Code Obfuscation
2024-12-20
Seyedreza Mohseni, Seyedali Mohammadi, Deepa Tilwani, Yash Saxena, Gerald Ndawula, Sriram Vema, Edward Raff, Manas Gaur
Malware authors often employ code obfuscations to make their malware harder to detect. Existing tools for generating obfuscated code often require access to the original source code (e.g., C++ or Java), and adding new obfuscations is a non-trivial, labor-intensive process. In this study, we ask the following question: Can Large Language Models (LLMs) potentially generate a new obfuscated assembly code? If so, this poses a risk to anti-virus engines and potentially increases the flexibility of attackers to create new obfuscation patterns. We answer this in the affirmative by developing the MetamorphASM benchmark comprising MetamorphASM Dataset (MAD) along with three code obfuscation techniques: dead code, register substitution, and control flow change. The MetamorphASM systematically evaluates the ability of LLMs to generate and analyze obfuscated code using MAD, which contains 328,200 obfuscated assembly code samples. We release this dataset and analyze the success rate of various LLMs (e.g., GPT-3.5/4, GPT-4o-mini, Starcoder, CodeGemma, CodeLlama, CodeT5, and LLaMA 3.1) in generating obfuscated assembly code. The evaluation was performed using established information-theoretic metrics and manual human review to ensure correctness and provide the foundation for researchers to study and develop remediations to this risk. The source code can be found at the following GitHub link: https://github.com/mohammadi-ali/MetamorphASM.
Logical Consistency of Large Language Models in Fact-checking
2024-12-20
Bishwamittra Ghosh, Sarah Hasan, Naheed Anjum Arafat, Arijit Khan
In recent years, large language models (LLMs) have demonstrated significant success in performing varied natural language tasks such as language translation, question-answering, summarizing, fact-checking, etc. Despite LLMs' impressive ability to generate human-like texts, LLMs are infamous for their inconsistent responses -- a meaning-preserving change in the input query results in an inconsistent response and attributes to vulnerabilities of LLMs such as hallucination, jailbreaking, etc. Consequently, existing research focuses on simple paraphrasing-based consistency assessment of LLMs, and ignores complex queries that necessitates an even better understanding of logical reasoning by an LLM. Our work therefore addresses the logical inconsistency of LLMs under complex logical queries with primitive logical operators, e.g., negation, conjunction, and disjunction. As a test bed, we consider retrieval-augmented LLMs on a fact-checking task involving propositional logic queries from real-world knowledge graphs (KGs). Our contributions are three-fold. Benchmark: We introduce three logical fact-checking datasets over KGs for community development towards logically consistent LLMs. Assessment: We propose consistency measures of LLMs on propositional logic queries as input and demonstrate that existing LLMs lack logical consistency, specially on complex queries. Improvement: We employ supervised fine-tuning to improve the logical consistency of LLMs on the complex fact-checking task with KG contexts.
Group interactions are a natural part of our daily life, and as robots become more integrated into society, they must be able to socially interact with multiple people at the same time. However, group human-robot interaction (HRI) poses unique computational challenges often overlooked in the current HRI literature. We conducted a scoping review including 44 group HRI papers from the last decade (2015-2024). From these papers, we extracted variables related to perception and behaviour generation challenges, as well as factors related to the environment, group, and robot capabilities that influence these challenges. Our findings show that key computational challenges in perception included detection of groups, engagement, and conversation information, while challenges in behaviour generation involved developing approaching and conversational behaviours. We also identified research gaps, such as improving detection of subgroups and interpersonal relationships, and recommended future work in group HRI to help researchers address these computational challenges
The Evolution of LLM Adoption in Industry Data Curation Practices
2024-12-20
Crystal Qian, Michael Xieyang Liu, Emily Reif, Grady Simon, Nada Hussein, Nathan Clement, James Wexler, Carrie J. Cai, Michael Terry, Minsuk Kahng
As large language models (LLMs) grow increasingly adept at processing unstructured text data, they offer new opportunities to enhance data curation workflows. This paper explores the evolution of LLM adoption among practitioners at a large technology company, evaluating the impact of LLMs in data curation tasks through participants' perceptions, integration strategies, and reported usage scenarios. Through a series of surveys, interviews, and user studies, we provide a timely snapshot of how organizations are navigating a pivotal moment in LLM evolution. In Q2 2023, we conducted a survey to assess LLM adoption in industry for development tasks (N=84), and facilitated expert interviews to assess evolving data needs (N=10) in Q3 2023. In Q2 2024, we explored practitioners' current and anticipated LLM usage through a user study involving two LLM-based prototypes (N=12). While each study addressed distinct research goals, they revealed a broader narrative about evolving LLM usage in aggregate. We discovered an emerging shift in data understanding from heuristic-first, bottom-up approaches to insights-first, top-down workflows supported by LLMs. Furthermore, to respond to a more complex data landscape, data practitioners now supplement traditional subject-expert-created 'golden datasets' with LLM-generated 'silver' datasets and rigorously validated 'super golden' datasets curated by diverse experts. This research sheds light on the transformative role of LLMs in large-scale analysis of unstructured data and highlights opportunities for further tool development.
Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation
2024-12-20
Timur Sattarov, Marco Schreyer, Damian Borth
The increasing demand for privacy-preserving data analytics in finance necessitates solutions for synthetic data generation that rigorously uphold privacy standards. We introduce DP-Fed-FinDiff framework, a novel integration of Differential Privacy, Federated Learning and Denoising Diffusion Probabilistic Models designed to generate high-fidelity synthetic tabular data. This framework ensures compliance with stringent privacy regulations while maintaining data utility. We demonstrate the effectiveness of DP-Fed-FinDiff on multiple real-world financial datasets, achieving significant improvements in privacy guarantees without compromising data quality. Our empirical evaluations reveal the optimal trade-offs between privacy budgets, client configurations, and federated optimization strategies. The results affirm the potential of DP-Fed-FinDiff to enable secure data sharing and robust analytics in highly regulated domains, paving the way for further advances in federated learning and privacy-preserving data synthesis.
Cloud-scale elemental abundance variations and the CO-to-dust-mass conversion factor in M31
2024-12-20
Chloe Bosomworth, Jan Forbrich, Charles J. Lada, Nelson Caldwell, Chiaki Kobayashi, Sébastien Viaene
From a spectroscopic survey of candidate H II regions in the Andromeda galaxy (M31) with MMT/Hectospec, we have identified 294 H II regions using emission line ratios and calculated elemental abundances from strong-line diagnostics (values ranging from sub-solar to super-solar) producing both Oxygen and Nitrogen radial abundance gradients. The Oxygen gradient is relatively flat, while the Nitrogen gradient is significantly steeper, indicating a higher N/O ratio in M31's inner regions, consistent with recent simulations of galaxy chemical evolution. No strong evidence was found of systematic galaxy-scale trends beyond the radial gradient. After subtracting the radial gradient from abundance values, we find an apparently stochastic and statistically significant scatter of standard deviation 0.06 dex, which exceeds measurement uncertainties. One explanation includes a possible collision with M32 200 - 800 Myrs ago. Using the two-point correlation function of the Oxygen abundance, we find that, similar to other spiral galaxies, M31 is well-mixed on sub-kpc scales but less so on larger (kpc) scales, which could be a result of an exponential decrease in mixing speed with spatial scale, and the aforementioned recent merger. Finally, the MMT spectroscopy is complemented by a dust continuum and CO survey of individual Giant Molecular Clouds, conducted with the Submillimeter Array. By combining the MMT and SMA observations, we obtain a unique direct test of the Oxygen abundance dependence of the $\alpha^{\prime}(^{12}\mathrm{CO})$ factor which is crucial to convert CO emission to dust mass. Our results suggest that within our sample there is no trend of the $\alpha^{\prime}(^{12}\mathrm{CO})$ with Oxygen abundance.
Segmentation of arbitrary features in very high resolution remote sensing imagery
2024-12-20
Henry Cording, Yves Plancherel, Pablo Brito-Parada
Very high resolution (VHR) mapping through remote sensing (RS) imagery presents a new opportunity to inform decision-making and sustainable practices in countless domains. Efficient processing of big VHR data requires automated tools applicable to numerous geographic regions and features. Contemporary RS studies address this challenge by employing deep learning (DL) models for specific datasets or features, which limits their applicability across contexts. The present research aims to overcome this limitation by introducing EcoMapper, a scalable solution to segment arbitrary features in VHR RS imagery. EcoMapper fully automates processing of geospatial data, DL model training, and inference. Models trained with EcoMapper successfully segmented two distinct features in a real-world UAV dataset, achieving scores competitive with prior studies which employed context-specific models. To evaluate EcoMapper, many additional models were trained on permutations of principal field survey characteristics (FSCs). A relationship was discovered allowing derivation of optimal ground sampling distance from feature size, termed Cording Index (CI). A comprehensive methodology for field surveys was developed to ensure DL methods can be applied effectively to collected data. The EcoMapper code accompanying this work is available at https://github.com/hcording/ecomapper .
Full Parity-Violating Trispectrum in Axion Inflation: Reduction to Low-D Integrals
2024-12-20
Matthew Reinhard, Zachary Slepian, Jiamin Hou, Alessandro Greco
Recent measurements of the galaxy 4-Point Correlation Function (4PCF) have seemingly detected non-zero parity-odd modes at high significance. Since gravity, the primary driver of galaxy formation and evolution is parity-even, any parity violation, if genuine, is likely to have been produced by some new parity-violating mechanism in the early Universe. Here we investigate an inflationary model with a Chern-Simons interaction between an axion and a $U(1)$ gauge field, where the axion itself is the inflaton field. Evaluating the trispectrum (Fourier-space analog of the 4PCF) of the primordial curvature perturbations is an involved calculation with very high-dimensional loop integrals. We demonstrate how to simplify these integrals and perform all angular integrations analytically by reducing the integrals to convolutions and exploiting the Convolution Theorem. This leaves us with low-dimensional radial integrals that are much more amenable to efficient numerical evaluation. This paper is the first in a series in which we will use these results to compute the full late-time 4PCF for axion inflation, thence enabling constraints from upcoming 3D spectroscopic surveys such as Dark Energy Spectroscopic Instrument (DESI), Euclid, or Roman.
The Only Way is Ethics: A Guide to Ethical Research with Large Language Models
2024-12-20
Eddie L. Ungless, Nikolas Vitsakis, Zeerak Talat, James Garforth, Björn Ross, Arno Onken, Atoosa Kasirzadeh, Alexandra Birch
There is a significant body of work looking at the ethical considerations of large language models (LLMs): critiquing tools to measure performance and harms; proposing toolkits to aid in ideation; discussing the risks to workers; considering legislation around privacy and security etc. As yet there is no work that integrates these resources into a single practical guide that focuses on LLMs; we attempt this ambitious goal. We introduce 'LLM Ethics Whitepaper', which we provide as an open and living resource for NLP practitioners, and those tasked with evaluating the ethical implications of others' work. Our goal is to translate ethics literature into concrete recommendations and provocations for thinking with clear first steps, aimed at computer scientists. 'LLM Ethics Whitepaper' distils a thorough literature review into clear Do's and Don'ts, which we present also in this paper. We likewise identify useful toolkits to support ethical work. We refer the interested reader to the full LLM Ethics Whitepaper, which provides a succinct discussion of ethical considerations at each stage in a project lifecycle, as well as citations for the hundreds of papers from which we drew our recommendations. The present paper can be thought of as a pocket guide to conducting ethical research with LLMs.
Less is More: Towards Green Code Large Language Models via Unified Structural Pruning
2024-12-20
Guang Yang, Yu Zhou, Xiangyu Zhang, Wei Cheng, Ke Liu, Xiang Chen, Terry Yue Zhuo, Taolue Chen
The extensive application of Large Language Models (LLMs) in generative coding tasks has raised concerns due to their high computational demands and energy consumption. Unlike previous structural pruning methods designed for classification models that deal with lowdimensional classification logits, generative Code LLMs produce high-dimensional token logit sequences, making traditional pruning objectives inherently limited. Moreover, existing single component pruning approaches further constrain the effectiveness when applied to generative Code LLMs. In response, we propose Flab-Pruner, an innovative unified structural pruning method that combines vocabulary, layer, and Feed-Forward Network (FFN) pruning. This approach effectively reduces model parameters while maintaining performance. Additionally, we introduce a customized code instruction data strategy for coding tasks to enhance the performance recovery efficiency of the pruned model. Through extensive evaluations on three state-of-the-art Code LLMs across multiple generative coding tasks, the results demonstrate that Flab-Pruner retains 97% of the original performance after pruning 22% of the parameters and achieves the same or even better performance after post-training. The pruned models exhibit significant improvements in storage, GPU usage, computational efficiency, and environmental impact, while maintaining well robustness. Our research provides a sustainable solution for green software engineering and promotes the efficient deployment of LLMs in real-world generative coding intelligence applications.
Background: Recent advances in large language models highlight the need for high-quality multilingual medical datasets. While Japan leads globally in CT scanner deployment and utilization, the lack of large-scale Japanese radiology datasets has hindered the development of specialized language models for medical imaging analysis. Objective: To develop a comprehensive Japanese CT report dataset through machine translation and establish a specialized language model for structured finding classification. Additionally, to create a rigorously validated evaluation dataset through expert radiologist review. Methods: We translated the CT-RATE dataset (24,283 CT reports from 21,304 patients) into Japanese using GPT-4o mini. The training dataset consisted of 22,778 machine-translated reports, while the validation dataset included 150 radiologist-revised reports. We developed CT-BERT-JPN based on "tohoku-nlp/bert-base-japanese-v3" architecture for extracting 18 structured findings from Japanese radiology reports. Results: Translation metrics showed strong performance with BLEU scores of 0.731 and 0.690, and ROUGE scores ranging from 0.770 to 0.876 for Findings and from 0.748 to 0.857 for Impression sections. CT-BERT-JPN demonstrated superior performance compared to GPT-4o in 11 out of 18 conditions, including lymphadenopathy (+14.2%), interlobular septal thickening (+10.9%), and atelectasis (+7.4%). The model maintained F1 scores exceeding 0.95 in 14 out of 18 conditions and achieved perfect scores in four conditions. Conclusions: Our study establishes a robust Japanese CT report dataset and demonstrates the effectiveness of a specialized language model for structured finding classification. The hybrid approach of machine translation and expert validation enables the creation of large-scale medical datasets while maintaining high quality.
In software applications, user models can be used to specify the profile of the typical users of the application, including personality traits, preferences, skills, etc. In theory, this would enable an adaptive application behavior that could lead to a better user experience. Nevertheless, user models do not seem to be part of standard modeling languages nor common in current model-driven engineering (MDE) approaches. In this paper, we conduct a systematic literature review to analyze existing proposals for user modeling in MDE and identify their limitations. The results showcase that there is a lack of a unified and complete user modeling perspective. Instead, we observe a lot of fragmented and partial proposals considering only simple user dimensions and with lack of proper tool support. This limits the implementation of richer user interfaces able to better support the user-specific needs. Therefore, we hope this analysis triggers a discussion on the importance of user models and their inclusion in MDE pipelines. Especially in a context where, thanks to the rise of AI techniques, personalization, based on a rich number of user dimensions, is becoming more and more of a possibility.
Multi-phase HI clouds in the Small Magellanic Cloud halo
2024-12-20
F. Buckland-Willis, M. A. Miville-Deschenes, A. Marchal, J. R. Dawson, H. Denes, E. M. Di Teodoro, J. M. Dickey, S. J. Gibson, I. P. Kemp, C. Lynn, Y. K. Ma, N. M. McClure-Griffiths, C. E. Murray, N. M. Pingel, S. Stanimirovic, J. Th. Van Loon
Context. The Galactic ASKAP collaboration (GASKAP) is undertaking an HI emission survey of the 21cm line to map the Magellanic system and the Galactic plane with the Australian Square Kilometre Array Pathfinder (ASKAP). One of the first areas observed in the Pilot Phase I of the survey was the Small Magellanic Cloud (SMC). Previous surveys of the SMC have uncovered new structures in the periphery of the SMC, along relatively low column density lines of sight. Aims. In this work we aimed to uncover the phase distribution of three distinct structures in the periphery of the SMC. This work will add to the constraints we have on the existence and survival of the cold neutral medium (CNM) in the SMC. Methods. We used ROHSA, a Gaussian decomposition algorithm, to model the emission across each cloud and classify the HI emission into their respective phases based on the linewidths of the fitted Gaussians. We created maps of velocity and column density of each phase of the HI across these three clouds. We measured the HI mass and CNM number density for each cloud. We also compared the HI results across the different phases with other gas tracers. Results. We find that in two clouds, the ends of each cloud are almost completely CNM dominated. Analysis of these two clouds indicates they are experiencing a compressive force from the direction of the SMC main body. In the third cloud we find a uniform CNM distribution along one wall of what is likely a supershell structure. Comparison with previous measurements of CO clumps in two of the clouds show the CO and HI are co-moving within a few km/s in regions of high HI column density, particularly when considering just the CNM.
Enriching Social Science Research via Survey Item Linking
2024-12-20
Tornike Tsereteli, Daniel Ruffinelli, Simone Paolo Ponzetto
Questions within surveys, called survey items, are used in the social sciences to study latent concepts, such as the factors influencing life satisfaction. Instead of using explicit citations, researchers paraphrase the content of the survey items they use in-text. However, this makes it challenging to find survey items of interest when comparing related work. Automatically parsing and linking these implicit mentions to survey items in a knowledge base can provide more fine-grained references. We model this task, called Survey Item Linking (SIL), in two stages: mention detection and entity disambiguation. Due to an imprecise definition of the task, existing datasets used for evaluating the performance for SIL are too small and of low-quality. We argue that latent concepts and survey item mentions should be differentiated. To this end, we create a high-quality and richly annotated dataset consisting of 20,454 English and German sentences. By benchmarking deep learning systems for each of the two stages independently and sequentially, we demonstrate that the task is feasible, but observe that errors propagate from the first stage, leading to a lower overall task performance. Moreover, mentions that require the context of multiple sentences are more challenging to identify for models in the first stage. Modeling the entire context of a document and combining the two stages into an end-to-end system could mitigate these problems in future work, and errors could additionally be reduced by collecting more diverse data and by improving the quality of the knowledge base. The data and code are available at https://github.com/e-tornike/SIL .
AI Apology: A Critical Review of Apology in AI Systems
2024-12-20
Hadassah Harland, Richard Dazeley, Hashini Senaratne, Peter Vamplew, Francisco Cruz, Bahareh Nakisa
Apologies are a powerful tool used in human-human interactions to provide affective support, regulate social processes, and exchange information following a trust violation. The emerging field of AI apology investigates the use of apologies by artificially intelligent systems, with recent research suggesting how this tool may provide similar value in human-machine interactions. Until recently, contributions to this area were sparse, and these works have yet to be synthesised into a cohesive body of knowledge. This article provides the first synthesis and critical analysis of the state of AI apology research, focusing on studies published between 2020 and 2023. We derive a framework of attributes to describe five core elements of apology: outcome, interaction, offence, recipient, and offender. With this framework as the basis for our critique, we show how apologies can be used to recover from misalignment in human-AI interactions, and examine trends and inconsistencies within the field. Among the observations, we outline the importance of curating a human-aligned and cross-disciplinary perspective in this research, with consideration for improved system capabilities and long-term outcomes.
Coherent Interactions of Free Electrons and Matter: Toward Tunable Compact X-ray Sources
2024-12-20
Amnon Balanov, Alexey Gorlach, Vladimir Baryshevsky, Ilya Feranchuk, Hideo Nitta, Yasushi Hayakawa, Alexander Shchagin, Yuichi Takabayashi, Yaron Danon, Liang Jie Wong, Ido Kaminer
Compact laboratory-scale X-ray sources still rely on the same fundamental principles as in the first X-ray tubes developed more than a century ago. In recent years, significant research and development have focused on large-scale X-ray sources such as synchrotrons and free-electron lasers, leading to the generation of high-brightness coherent X-rays. However, the large size and high costs of such sources prevent their widespread use. The quest for a compact and coherent Xray source has long been a critical objective in modern physics, gaining further importance in recent years for industrial applications and fundamental scientific research. Here, we review the physical mechanisms governing compact coherent X-ray generation. Of current interest are coherent periodic interactions of free electrons in crystalline materials, creating hard X-rays via a mechanism known as parametric X-ray radiation (PXR). Over the past decade, X-ray sources leveraging this mechanism have demonstrated state-of-the-art tunability, directionality, and broad spatial coherence, enabling X-ray phase-contrast imaging on a compact scale. The coming years are expected to show substantial miniaturization of compact X-ray sources, facilitated by progress in electron beam technologies. This review compares the most promising mechanisms used for hard-X-ray generation, contrasting parametric X-ray radiation with inverse Compton scattering and characteristic radiation from a liquid-jet anode. We cover the most recent advancements, including the development of new materials, innovative geometrical designs, and specialized optimization techniques, aiming toward X-ray flux levels suitable for medical imaging and X-ray spectroscopy in compact scales.
Critique of Impure Reason: Unveiling the reasoning behaviour of medical Large Language Models
2024-12-20
Shamus Sim, Tyrone Chen
Background: Despite the current ubiquity of Large Language Models (LLMs) across the medical domain, there is a surprising lack of studies which address their reasoning behaviour. We emphasise the importance of understanding reasoning behaviour as opposed to high-level prediction accuracies, since it is equivalent to explainable AI (XAI) in this context. In particular, achieving XAI in medical LLMs used in the clinical domain will have a significant impact across the healthcare sector. Results: Therefore, we define the concept of reasoning behaviour in the specific context of medical LLMs. We then categorise and discuss the current state of the art of methods which evaluate reasoning behaviour in medical LLMs. Finally, we propose theoretical frameworks which can empower medical professionals or machine learning engineers to gain insight into the low-level reasoning operations of these previously obscure models. Conclusion: The subsequent increased transparency and trust in medical machine learning models by clinicians as well as patients will accelerate the integration, application as well as further development of medical AI for the healthcare system as a whole
On the integrability of the supremum of stochastic volatility models and other martingales
2024-12-20
Stefan Gerhold, Julian Pachschwöll, Johannes Ruf
We propose a method to determine the expectation of the supremum of the price process in stochastic volatility models. It can be applied to the rough Bergomi model, avoiding the need to discuss finiteness of higher moments. Our motivation stems from the theory of American option pricing, as an integrable supremum implies the existence of an optimal stopping time for any linearly bounded payoff. Moreover, we survey the literature on martingales with non-integrable supremum, and give a new construction that yields uniformly integrable martingales with this property.
From Model Based to Learned Regularization in Medical Image Registration: A Comprehensive Review
2024-12-20
Anna Reithmeir, Veronika Spieker, Vasiliki Sideri-Lampretsa, Daniel Rueckert, Julia A. Schnabel, Veronika A. Zimmer
Image registration is fundamental in medical imaging applications, such as disease progression analysis or radiation therapy planning. The primary objective of image registration is to precisely capture the deformation between two or more images, typically achieved by minimizing an optimization problem. Due to its inherent ill-posedness, regularization is a key component in driving the solution toward anatomically meaningful deformations. A wide range of regularization methods has been proposed for both conventional and deep learning-based registration. However, the appropriate application of regularization techniques often depends on the specific registration problem, and no one-fits-all method exists. Despite its importance, regularization is often overlooked or addressed with default approaches, assuming existing methods are sufficient. A comprehensive and structured review remains missing. This review addresses this gap by introducing a novel taxonomy that systematically categorizes the diverse range of proposed regularization methods. It highlights the emerging field of learned regularization, which leverages data-driven techniques to automatically derive deformation properties from the data. Moreover, this review examines the transfer of regularization methods from conventional to learning-based registration, identifies open challenges, and outlines future research directions. By emphasizing the critical role of regularization in image registration, we hope to inspire the research community to reconsider regularization strategies in modern registration algorithms and to explore this rapidly evolving field further.
Does the brain behave like a (complex) network? I. Dynamics
2024-12-20
D. Papo, J. M. Buldú
Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network structure does not entail that the brain actually works as a network. Asking whether the brain behaves as a network means asking whether network properties count. From the viewpoint of neurophysiology and, possibly, of brain physics, the most substantial issues a network structure may be instrumental in addressing relate to the influence of network properties on brain dynamics and to whether these properties ultimately explain some aspects of brain function. Here, we address the dynamical implications of complex network, examining which aspects and scales of brain activity may be understood to genuinely behave as a network. To do so, we first define the meaning of networkness, and analyse some of its implications. We then examine ways in which brain anatomy and dynamics can be endowed with a network structure and discuss possible ways in which network structure may be shown to represent a genuine organisational principle of brain activity, rather than just a convenient description of its anatomy and dynamics.
Code Review Automation Via Multi-task Federated LLM -- An Empirical Study
2024-12-20
Jahnavi Kumar, Sridhar Chimalakonda
Code review is a crucial process before deploying code to production, as it validates the code, provides suggestions for improvements, and identifies errors such as missed edge cases. In projects with regular production releases, the effort required for peer code-reviews remains high. Consequently, there has been significant interest from software engineering (SE) researchers in automating the code review process. Previous research on code review automation has typically approached the task as three independent sub-tasks: review necessity prediction, review comment generation, and code refinement. Our study attempts to (i) leverage the relationships between the sub-tasks of code review automation, by developing a multi-task model that addresses all tasks in an integrated manner, and (ii) increase model robustness on unseen data via collaborative large language model (LLM) modeling, while retaining the proprietary nature of code, by using federated learning (FL). The study explores five simple techniques for multi-task training, including two sequential methods, one parallel method, and two cumulative methods. The results indicate that sequentially training a federated LLM (FedLLM) for our code review multi-task use case is less efficient in terms of time, computation, and performance metrics, compared to training separate models for each task. Because sequential training demonstrates catastrophic forgetting, alternatively cumulative fine-tuning for multi-task training performs better than training models for individual tasks. This study highlights the need for research focused on effective fine-tuning of multi-task FedLLMs for SE tasks.
This paper thoroughly surveys machine learning (ML) algorithms acceleration in hardware accelerators, focusing on Field-Programmable Gate Arrays (FPGAs). It reviews 287 out of 1138 papers from the past six years, sourced from four top FPGA conferences. Such selection underscores the increasing integration of ML and FPGA technologies and their mutual importance in technological advancement. Research clearly emphasises inference acceleration (81\%) compared to training acceleration (13\%). Additionally, the findings reveals that CNN dominates current FPGA acceleration research while emerging models like GNN show obvious growth trends. The categorization of the FPGA research papers reveals a wide range of topics, demonstrating the growing relevance of ML in FPGA research. This comprehensive analysis provides valuable insights into the current trends and future directions of FPGA research in the context of ML applications.
Gender Disparities in Contributions, Leadership, and Collaboration: An Exploratory Study on Software Systems Research
2024-12-20
Shamse Tasnim Cynthia, Saikat Mondal, Joy Krishan Das, Banani Roy
Gender diversity enhances research by bringing diverse perspectives and innovative approaches. It ensures equitable solutions that address the needs of diverse populations. However, gender disparity persists in research where women remain underrepresented, which might limit diversity and innovation. Many even leave scientific careers as their contributions often go unnoticed and undervalued. Therefore, understanding gender-based contributions and collaboration dynamics is crucial to addressing this gap and creating a more inclusive research environment. In this study, we analyzed 2,000 articles published over the past decade in the Journal of Systems and Software (JSS). From these, we selected 384 articles that detailed authors' contributions and contained both female and male authors to investigate gender-based contributions. Our contributions are fourfold. First, we analyzed women's engagement in software systems research. Our analysis showed that only 32.74% of the total authors are women and female-led or supervised studies were fewer than those of men. Second, we investigated female authors' contributions across 14 major roles. Interestingly, we found that women contributed comparably to men in most roles, with more contributions in conceptualization, writing, and reviewing articles. Third, we explored the areas of software systems research and found that female authors are more actively involved in human-centric research domains. Finally, we analyzed gender-based collaboration dynamics. Our findings revealed that female supervisors tended to collaborate locally more often than national-level collaborations. Our study highlights that females' contributions to software systems research are comparable to those of men. Therefore, the barriers need to be addressed to enhance female participation and ensure equity and inclusivity in research.
A Fusion Approach of Dependency Syntax and Sentiment Polarity for Feature Label Extraction in Commodity Reviews
2024-12-20
Jianfei Xu
This study analyzes 13,218 product reviews from JD.com, covering four categories: mobile phones, computers, cosmetics, and food. A novel method for feature label extraction is proposed by integrating dependency parsing and sentiment polarity analysis. The proposed method addresses the challenges of low robustness in existing extraction algorithms and significantly enhances extraction accuracy. Experimental results show that the method achieves an accuracy of 0.7, with recall and F-score both stabilizing at 0.8, demonstrating its effectiveness. However, challenges such as dependence on matching dictionaries and the limited scope of extracted feature tags require further investigation in future research.
Image to point cloud global localization is crucial for robot navigation in GNSS-denied environments and has become increasingly important for multi-robot map fusion and urban asset management. The modality gap between images and point clouds poses significant challenges for cross-modality fusion. Current cross-modality global localization solutions either require modality unification, which leads to information loss, or rely on engineered training schemes to encode multi-modality features, which often lack feature alignment and relation consistency. To address these limitations, we propose, SaliencyI2PLoc, a novel contrastive learning based architecture that fuses the saliency map into feature aggregation and maintains the feature relation consistency on multi-manifold spaces. To alleviate the pre-process of data mining, the contrastive learning framework is applied which efficiently achieves cross-modality feature mapping. The context saliency-guided local feature aggregation module is designed, which fully leverages the contribution of the stationary information in the scene generating a more representative global feature. Furthermore, to enhance the cross-modality feature alignment during contrastive learning, the consistency of relative relationships between samples in different manifold spaces is also taken into account. Experiments conducted on urban and highway scenario datasets demonstrate the effectiveness and robustness of our method. Specifically, our method achieves a Recall@1 of 78.92% and a Recall@20 of 97.59% on the urban scenario evaluation dataset, showing an improvement of 37.35% and 18.07%, compared to the baseline method. This demonstrates that our architecture efficiently fuses images and point clouds and represents a significant step forward in cross-modality global localization. The project page and code will be released.
Thin films as practical quantum materials: a status quo and beyond
2024-12-20
Chaehyeong Ha, Yoon Jang Chung
Quantum materials have been in the limelight for several years now. These materials exhibit intriguing quantum phenomena, which when harnessed properly, promise extraordinary advancements across various scientific and technological domains. To fully exploit their potential, it is imperative to synthesize such quantum materials in thin film form so that they are compatible with well-established device fabrication techniques. In this perspective, an overview of the current status and future directions of thin film quantum material synthesis is provided. The criteria for quantum materials are discussed, as well as the many benefits of preparing them as thin films. Prominent deposition techniques such as molecular beam epitaxy and chemical vapor deposition are reviewed along with potential contenders. Despite challenges, progress in thin film quantum material technology holds the potential to realize practical devices with unprecedented functionalities.
Spatial Clustering of Citizen Science Data Improves Downstream Species Distribution Models
2024-12-20
Nahian Ahmed, Mark Roth, Tyler A. Hallman, W. Douglas Robinson, Rebecca A. Hutchinson
Citizen science biodiversity data present great opportunities for ecology and conservation across vast spatial and temporal scales. However, the opportunistic nature of these data lacks the sampling structure required by modeling methodologies that address a pervasive challenge in ecological data collection: imperfect detection, i.e., the likelihood of under-observing species on field surveys. Occupancy modeling is an example of an approach that accounts for imperfect detection by explicitly modeling the observation process separately from the biological process of habitat selection. This produces species distribution models that speak to the pattern of the species on a landscape after accounting for imperfect detection in the data, rather than the pattern of species observations corrupted by errors. To achieve this benefit, occupancy models require multiple surveys of a site across which the site's status (i.e., occupied or not) is assumed constant. Since citizen science data are not collected under the required repeated-visit protocol, observations may be grouped into sites post hoc. Existing approaches for constructing sites discard some observations and/or consider only geographic distance and not environmental similarity. In this study, we compare ten approaches for site construction in terms of their impact on downstream species distribution models for 31 bird species in Oregon, using observations recorded in the eBird database. We find that occupancy models built on sites constructed by spatial clustering algorithms perform better than existing alternatives.
NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning
2024-12-20
Zheyuan Zhang, Yiyang Li, Nhi Ha Lan Le, Zehong Wang, Tianyi Ma, Vincent Galassi, Keerthiram Murugesan, Nuno Moniz, Werner Geyer, Nitesh V Chawla, Chuxu Zhang, Yanfang Ye
Diet plays a critical role in human health, yet tailoring dietary reasoning to individual health conditions remains a major challenge. Nutrition Question Answering (QA) has emerged as a popular method for addressing this problem. However, current research faces two critical limitations. On one hand, the absence of datasets involving user-specific medical information severely limits \textit{personalization}. This challenge is further compounded by the wide variability in individual health needs. On the other hand, while large language models (LLMs), a popular solution for this task, demonstrate strong reasoning abilities, they struggle with the domain-specific complexities of personalized healthy dietary reasoning, and existing benchmarks fail to capture these challenges. To address these gaps, we introduce the Nutritional Graph Question Answering (NGQA) benchmark, the first graph question answering dataset designed for personalized nutritional health reasoning. NGQA leverages data from the National Health and Nutrition Examination Survey (NHANES) and the Food and Nutrient Database for Dietary Studies (FNDDS) to evaluate whether a food is healthy for a specific user, supported by explanations of the key contributing nutrients. The benchmark incorporates three question complexity settings and evaluates reasoning across three downstream tasks. Extensive experiments with LLM backbones and baseline models demonstrate that the NGQA benchmark effectively challenges existing models. In sum, NGQA addresses a critical real-world problem while advancing GraphQA research with a novel domain-specific benchmark.
From Galaxy Zoo DECaLS to BASS/MzLS: detailed galaxy morphology classification with unsupervised domain adaption
2024-12-20
Renhao Ye, Shiyin Shen, Rafael S. de Souza, Quanfeng Xu, Mi Chen, Zhu Chen, Emille E. O. Ishida, Alberto Krone-Martins, Rupesh Durgesh
The DESI Legacy Imaging Surveys (DESI-LIS) comprise three distinct surveys: the Dark Energy Camera Legacy Survey (DECaLS), the Beijing-Arizona Sky Survey (BASS), and the Mayall z-band Legacy Survey (MzLS). The citizen science project Galaxy Zoo DECaLS 5 (GZD-5) has provided extensive and detailed morphology labels for a sample of 253,287 galaxies within the DECaLS survey. This dataset has been foundational for numerous deep learning-based galaxy morphology classification studies. However, due to differences in signal-to-noise ratios and resolutions between the DECaLS images and those from BASS and MzLS (collectively referred to as BMz), a neural network trained on DECaLS images cannot be directly applied to BMz images due to distributional mismatch. In this study, we explore an unsupervised domain adaptation (UDA) method that fine-tunes a source domain model trained on DECaLS images with GZD-5 labels to BMz images, aiming to reduce bias in galaxy morphology classification within the BMz survey. Our source domain model, used as a starting point for UDA, achieves performance on the DECaLS galaxies' validation set comparable to the results of related works. For BMz galaxies, the fine-tuned target domain model significantly improves performance compared to the direct application of the source domain model, reaching a level comparable to that of the source domain. We also release a catalogue of detailed morphology classifications for 248,088 galaxies within the BMz survey, accompanied by usage recommendations.
Challenges in Forming Millisecond Pulsar-Black Holes from Isolated Binaries
2024-12-20
Camille Liotine, Vicky Kalogera, Jeff J. Andrews, Simone S. Bavera, Max Briel, Tassos Fragos, Seth Gossage, Konstantinos Kovlakas, Matthias U. Kruckow, Kyle A. Rocha, Philipp M. Srivastava, Meng Sun, Elizabeth Teng, Zepei Xing, Emmanouil Zapartas
Binaries harboring a millisecond pulsar (MSP) and a black hole (BH) are a key observing target for current and upcoming pulsar surveys. We model the formation and evolution of such binaries in isolation at solar metallicity using the next-generation binary population synthesis code POSYDON. We examine neutron star (NS)-BH binaries where the NS forms first (labeled NSBH), as the NS must be able to spin-up to MSP rotation periods before the BH forms in these systems. We find that NSBHs are very rare and have a birth rate < 1 Myr$^{-1}$ for a Milky Way-like galaxy in our typical models. The NSBH birth rate is 2-3 orders of magnitude smaller than that for NS-BHs where the BH forms first (labeled BHNS). These rates are also sensitive to model assumptions about the supernova (SN) remnant masses, natal kicks, and common-envelope efficiency. We find that 100% of NSBHs undergo a mass ratio reversal before the first SN and up to 64% of NSBHs undergo a double common envelope phase after the mass ratio reversal occurs. Most importantly, no NSBH binaries in our populations undergo a mass transfer phase, either stable or unstable, after the first SN. This implies that there is no possibility of pulsar spin-up via accretion, and thus MSP-BH binaries cannot form. Thus, dynamical environments and processes may provide the only formation channels for such MSP-BH binaries.
Humanlike Cognitive Patterns as Emergent Phenomena in Large Language Models
2024-12-20
Zhisheng Tang, Mayank Kejriwal
Research on emergent patterns in Large Language Models (LLMs) has gained significant traction in both psychology and artificial intelligence, motivating the need for a comprehensive review that offers a synthesis of this complex landscape. In this article, we systematically review LLMs' capabilities across three important cognitive domains: decision-making biases, reasoning, and creativity. We use empirical studies drawing on established psychological tests and compare LLMs' performance to human benchmarks. On decision-making, our synthesis reveals that while LLMs demonstrate several human-like biases, some biases observed in humans are absent, indicating cognitive patterns that only partially align with human decision-making. On reasoning, advanced LLMs like GPT-4 exhibit deliberative reasoning akin to human System-2 thinking, while smaller models fall short of human-level performance. A distinct dichotomy emerges in creativity: while LLMs excel in language-based creative tasks, such as storytelling, they struggle with divergent thinking tasks that require real-world context. Nonetheless, studies suggest that LLMs hold considerable potential as collaborators, augmenting creativity in human-machine problem-solving settings. Discussing key limitations, we also offer guidance for future research in areas such as memory, attention, and open-source model development.
Hidden Figures in Software Engineering: A Replication Study Exploring Undergraduate Software Students' Awareness of Distinguished Scientists from Underrepresented Groups
2024-12-20
Ronnie de Souza Santos, Italo Santos, Robson Santos, Cleyton Magalhaes
Technology is a cornerstone of modern life, yet the software engineering field struggles to reflect the diversity of contemporary society. This lack of diversity and inclusivity within the software industry can be traced back to limited representation in software engineering academic settings, where students from underrepresented groups are often stigmatized despite the field's rich history of contributions from scientists from diverse backgrounds. Over the years, studies have revealed that women, LGBTQIA+ individuals, and Black students frequently encounter unwelcoming environments in software engineering programs. However, similar to other fields, increasing awareness of notable individuals from marginalized backgrounds could inspire students and foster a more inclusive environment. This study reports the findings from a replicated global survey with undergraduate software engineering students, exploring their knowledge of distinguished scientists from underrepresented groups. These findings show that students have limited awareness of these figures and their contributions, highlighting the need to improve diversity awareness and develop educational practices that celebrate the achievements of historically marginalized groups in software engineering. Index Terms-EDi in software engineering, software engineering education, diversity.
A remaining challenge in multirotor drone flight is the autonomous identification of viable landing sites in unstructured environments. One approach to solve this problem is to create lightweight, appearance-based terrain classifiers that can segment a drone's RGB images into safe and unsafe regions. However, such classifiers require data sets of images and masks that can be prohibitively expensive to create. We propose a pipeline to automatically generate synthetic data sets to train these classifiers, leveraging modern drones' ability to survey terrain automatically and the ability to automatically calculate landing safety masks from terrain models derived from such surveys. We then train a U-Net on the synthetic data set, test it on real-world data for validation, and demonstrate it on our drone platform in real-time.
A Review of the Marathi Natural Language Processing
2024-12-20
Asang Dani, Shailesh R Sathe
Marathi is one of the most widely used languages in the world. One might expect that the latest advances in NLP research in languages like English reach such a large community. However, NLP advancements in English didn't immediately reach Indian languages like Marathi. There were several reasons for this. They included diversity of scripts used, lack of (publicly available) resources like tokenization strategies, high quality datasets \& benchmarks, and evaluation metrics. In addition to this, the morphologically rich nature of Marathi, made NLP tasks challenging. Advances in Neural Network (NN) based models and tools since the early 2000s helped improve this situation and make NLP research more accessible. In the past 10 years, significant efforts were made to improve language resources for all 22 scheduled languages of India. This paper presents a broad overview of evolution of NLP research in Indic languages with a focus on Marathi and state-of-the-art resources and tools available to the research community. It also provides an overview of tools \& techniques associated with Marathi NLP tasks.
The Landscape of College-level Data Visualization Courses, and the Benefits of Incorporating Statistical Thinking
2024-12-20
Zach Branson, Monica Paz Parra, Ronald Yurko
Data visualization is a core part of statistical practice and is ubiquitous in many fields. Although there are numerous books on data visualization, instructors in statistics and data science may be unsure how to teach data visualization, because it is such a broad discipline. To give guidance on teaching data visualization from a statistical perspective, we make two contributions. First, we conduct a survey of data visualization courses at top colleges and universities in the United States, in order to understand the landscape of data visualization courses. We find that most courses are not taught by statistics and data science departments and do not focus on statistical topics, especially those related to inference. Instead, most courses focus on visual storytelling, aesthetic design, dashboard design, and other topics specialized for other disciplines. Second, we outline three teaching principles for incorporating statistical inference in data visualization courses, and provide several examples that demonstrate how instructors can follow these principles. The dataset from our survey allows others to explore the diversity of data visualization courses, and our teaching principles give guidance to instructors and departments who want to encourage statistical thinking via data visualization. In this way, statistics-related departments can provide a valuable perspective on data visualization that is unique to current course offerings.
A Comprehensive Study of the Dust Declines in R Coronae Borealis Stars
2024-12-20
Courtney L. Crawford, Jamie Soon, Geoffrey C. Clayton, Patrick Tisserand, Timothy R. Bedding, Caleb J. Clark, Chung-Uk Lee
The R Coronae Borealis (RCB) variables are rare, hydrogen-deficient, carbon-rich supergiants known for large, erratic declines in brightness due to dust formation. Recently, the number of known RCB stars in the Milky Way and Magellanic Clouds has increased from $\sim$30 to 162. We use all-sky and targeted photometric surveys to create the longest possible light curves for all known RCB stars and systematically study their declines. Our study, the largest of its kind, includes measurements of decline activity levels, morphologies, and periodicities for nearly all RCB stars. We confirm previous predictions that cool RCB stars exhibit more declines than warm RCBs, supporting a relationship between dust formation and condensation temperatures. We also find evidence for two distinct dust production mechanisms. R CrB and SU Tau show decline onsets consistent with a Poisson process, suggesting their dust production is driven by stochastic processes, such as convection. In contrast, RY Sgr's declines correlate with its pulsation period, suggesting that its dust production is driven by pulsationally-induced shocks. Finally, we show that the dust properties of the related class of DY~Per variables differ from those of the RCB stars, suggesting differences in their evolutionary status.
2024-12-20
Paul Duetting, Michal Feldman, Inbal Talgam-Cohen
A contract is an economic tool used by a principal to incentivize one or more agents to exert effort on her behalf, by defining payments based on observable performance measures. A key challenge addressed by contracts -- known in economics as moral hazard -- is that, absent a properly set up contract, agents might engage in actions that are not in the principal's best interest. Another common feature of contracts is limited liability, which means that payments can go only from the principal -- who has the deep pocket -- to the agents. With classic applications of contract theory moving online, growing in scale, and becoming more data-driven, tools from contract theory become increasingly important for incentive-aware algorithm design. At the same time, algorithm design offers a whole new toolbox for reasoning about contracts, ranging from additional tools for studying the tradeoff between simple and optimal contracts, through a language for discussing the computational complexity of contracts in combinatorial settings, to a formalism for analyzing data-driven contracts. This survey aims to provide a computer science-friendly introduction to the basic concepts of contract theory. We give an overview of the emerging field of "algorithmic contract theory" and highlight work that showcases the potential for interaction between the two areas. We also discuss avenues for future research.
FairREAD: Re-fusing Demographic Attributes after Disentanglement for Fair Medical Image Classification
2024-12-20
Yicheng Gao, Jinkui Hao, Bo Zhou
Recent advancements in deep learning have shown transformative potential in medical imaging, yet concerns about fairness persist due to performance disparities across demographic subgroups. Existing methods aim to address these biases by mitigating sensitive attributes in image data; however, these attributes often carry clinically relevant information, and their removal can compromise model performance-a highly undesirable outcome. To address this challenge, we propose Fair Re-fusion After Disentanglement (FairREAD), a novel, simple, and efficient framework that mitigates unfairness by re-integrating sensitive demographic attributes into fair image representations. FairREAD employs orthogonality constraints and adversarial training to disentangle demographic information while using a controlled re-fusion mechanism to preserve clinically relevant details. Additionally, subgroup-specific threshold adjustments ensure equitable performance across demographic groups. Comprehensive evaluations on a large-scale clinical X-ray dataset demonstrate that FairREAD significantly reduces unfairness metrics while maintaining diagnostic accuracy, establishing a new benchmark for fairness and performance in medical image classification.
This systematic literature review investigates perceptions, concerns, and expectations of young digital citizens regarding privacy in artificial intelligence (AI) systems, focusing on social media platforms, educational technology, gaming systems, and recommendation algorithms. Using a rigorous methodology, the review started with 2,000 papers, narrowed down to 552 after initial screening, and finally refined to 108 for detailed analysis. Data extraction focused on privacy concerns, data-sharing practices, the balance between privacy and utility, trust factors in AI, transparency expectations, and strategies to enhance user control over personal data. Findings reveal significant privacy concerns among young users, including a perceived lack of control over personal information, potential misuse of data by AI, and fears of data breaches and unauthorized access. These issues are worsened by unclear data collection practices and insufficient transparency in AI applications. The intention to share data is closely associated with perceived benefits and data protection assurances. The study also highlights the role of parental mediation and the need for comprehensive education on data privacy. Balancing privacy and utility in AI applications is crucial, as young digital citizens value personalized services but remain wary of privacy risks. Trust in AI is significantly influenced by transparency, reliability, predictable behavior, and clear communication about data usage. Strategies to improve user control over personal data include access to and correction of data, clear consent mechanisms, and robust data protection assurances. The review identifies research gaps and suggests future directions, such as longitudinal studies, multicultural comparisons, and the development of ethical AI frameworks.
Fermi-Liquid $T^2$ Resistivity: Dynamical Mean-Field Theory Meets Experiment
2024-12-20
Jeremy Lee-Hand, Harrison LaBollita, Fabian B. Kugler, Lorenzo Van Muñoz, Jason Kaye, Sophie Beck, Alexander Hampel, Antoine Georges, Cyrus E. Dreyer
Direct-current resistivity is a key probe for the physical properties of materials. In metals, Fermi-liquid (FL) theory serves as the basis for understanding transport. A $T^2$ behavior of the resistivity is often taken as a signature of FL electron-electron scattering. However, the presence of impurity and phonon scattering as well as material-specific aspects such as Fermi surface geometry can complicate this interpretation. We demonstrate how density-functional theory combined with dynamical mean-field theory can be used to elucidate the FL regime. We take as examples SrVO$_{3}$ and SrMoO$_{3}$, two moderately correlated perovskite oxides, and establish a precise framework to analyze the FL behavior of the self-energy at low energy and temperature. Reviewing published low-temperature resistivity measurements, we find agreement between our calculations and experiments performed on samples with exceptionally low residual resistivity. This comparison emphasizes the need for further theoretical, synthesis, and characterization developments in these and other FL materials.
The aim of this review paper is to discuss some applications of orthogonal polynomials in quantum information processing. The hope is to keep the paper self contained so that someone wanting a brief introduction to the theory of orthogonal polynomials and continuous time quantum walks on graphs may find it in one place. In particular, we focus on the associated Jacobi operators and discuss how these can be used to detect perfect state transfer. We also discuss how orthogonal polynomials have been used to give results which are analogous to those given by Karlin and McGregor when studying classical birth and death processes. Finally, we show how these ideas have been extended to quantum walks with more than nearest neighbor interactions using exceptional orthogonal polynomials. We also provide a (non-exhaustive) list of related open questions.
Precise determination of pomeron intercept via scaling entropy analysis
2024-12-20
Lucas Soster Moriggi, Magno Valério Trindade Machado
In this work, we confront the geometrical scaling properties of inclusive DIS cross section ($e+p\rightarrow e +X$) with the scaling entropy obtained from event multiplicity. We show that these two quantities are equivalent in the kinematic range probed by H1 Collaboration data. We propose that scaling entropy associated with partonic interactions is a more efficient way to detect scaling in experimental data. We used a combined analysis of the inclusive cross section and entropy obtained from multiplicities $P(N)$ of final-state hadrons to accurately determine the value of the Pomeron intercept. The approach could provide new constraints for future hadron collider experiments and deepen our understanding of parton saturation.
Generalizing causal effect estimates to larger populations while accounting for (uncertainty in) effect modifiers using a scaled Bayesian bootstrap with application to estimating the effect of family planning on employment in Nigeria
2024-12-20
Lucas Godoy Garraza, Ilene Speizer, Leontine Alkema
Strategies are needed to generalize causal effects from a sample that may differ systematically from the population of interest. In a motivating case study, interest lies in the causal effect of family planning on empowerment-related outcomes among urban Nigerian women, while estimates of this effect and its variation by covariates are available only from a sample of women in six Nigerian cities. Data on covariates in target populations are available from a complex sampling design survey. Our approach, analogous to the plug-in g-formula, takes the expectation of conditional average treatment effects from the source study over the covariate distribution in the target population. This method leverages generalizability literature from randomized trials, applied to a source study using principal stratification for identification. The approach uses a scaled Bayesian bootstrap to account for the complex sampling design. We also introduce checks for sensitivity to plausible departures of assumptions. In our case study, the average effect in the target population is higher than in the source sample based on point estimates and sensitivity analysis shows that a strong omitted effect modifier must be present in at least 40% of the target population for the 95% credible interval to include the null effect.
Interacting dark sector with quadratic coupling: theoretical and observational viability
2024-12-20
Jaelsson S. Lima, Rodrigo von Marttens, Luciano Casarini
Models proposing a non-gravitational interaction between dark energy (DE) and dark matter (CDM) have been extensively studied as alternatives to the standard cosmological model. A common approach to describing the DE-CDM coupling assumes it to be linearly proportional to the dark energy density. In this work, we consider the model with interaction term $Q=3H\gamma{\rho_{x}^{2}}/{(\rho_{c}+\rho_{x})}$. We show that for positive values of $\gamma$ this model predicts a future violation of the Weak Energy Condition (WEC) for the dark matter component, and for a specific range of negative values of $\gamma$ the CDM energy density can be negative in the past. We perform a parameter selection analysis for this model using data from Type Ia supernovae, Cosmic Chronometers, Baryon Acoustic Oscillations, and CMB combined with the Hubble constant $H_0$ prior. Imposing a prior to ensure that the WEC is not violated, our model is consistent with $\Lambda$CDM in 2$\sigma$ C.L.. In reality, the WEC prior shifts the constraints towards smaller values of $H_0$, highlighting an increase in the tension on the Hubble parameter. However, it significantly improves the parameter constraints, with a preference for smaller values of $\sigma_8$, alleviating the $\sigma_8$ tension between the CMB results from Planck 2018 and the weak gravitational lensing observations from the KiDS-1000 cosmic shear survey. In the case without the WEC prior, our model seems to alleviate the $H_0$ tension, which is related to the positive value of the interaction parameter $\gamma$.
A Probabilistic Model to Estimate Number Densities from Column Densities in Molecular Clouds
2024-12-20
Brandt A. L. Gaches, Michael Y. Grudić
Constraining the physical and chemical evolution of molecular clouds is essential to our understanding of star formation. These investigations often necessitate knowledge of some local representative number density of the gas along the line of sight. However, constraining the number density is a difficult endeavor. Robust constraints of the number density often require line observations of specific molecules along with radiation transfer modeling, which provides densities traced by that specific molecule. Column density maps of molecular clouds are more readily available, with many high-fidelity maps calculated from dust emission and extinction, in particular from surveys conducted with the Herschel Space Observatory. We introduce a new probabilistic model which is based on the assumption that the total hydrogen nuclei column density along a line of sight can be decomposed into a turbulent component and a gravitationally-dominated component. Therefore, for each pixel in a column density map, the line of sight is decomposed into characteristic diffuse (dubbed ``turbulent'') and dense (dubbed ``gravitational'') gas number densities from column density maps. The method thus exploits a physical model of turbulence to decouple the random turbulent column from gas in dense bound structures empirically using the observed column density maps. We find the model produces reasonable turbulent and gravitational densities in the Taurus L1495/B213 and Polaris Flare clouds. The model can also be used to infer an effective attenuating column density into the cloud, which is useful for astrochemical models of the clouds. We conclude by demonstrating an application of this method by predicting the emission of the [C II], [C I], and CO (J = 1-0) lines across the Taurus L1495/B213 region at the native resolution of the column density map utilizing a grid of photodissociation-region models.
LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks
2024-12-19
Yushi Bai, Shangqing Tu, Jiajie Zhang, Hao Peng, Xiaozhi Wang, Xin Lv, Shulin Cao, Jiazheng Xu, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li
This paper introduces LongBench v2, a benchmark designed to assess the ability of LLMs to handle long-context problems requiring deep understanding and reasoning across real-world multitasks. LongBench v2 consists of 503 challenging multiple-choice questions, with contexts ranging from 8k to 2M words, across six major task categories: single-document QA, multi-document QA, long in-context learning, long-dialogue history understanding, code repository understanding, and long structured data understanding. To ensure the breadth and the practicality, we collect data from nearly 100 highly educated individuals with diverse professional backgrounds. We employ both automated and manual review processes to maintain high quality and difficulty, resulting in human experts achieving only 53.7% accuracy under a 15-minute time constraint. Our evaluation reveals that the best-performing model, when directly answers the questions, achieves only 50.1% accuracy. In contrast, the o1-preview model, which includes longer reasoning, achieves 57.7%, surpassing the human baseline by 4%. These results highlight the importance of enhanced reasoning ability and scaling inference-time compute to tackle the long-context challenges in LongBench v2. The project is available at https://longbench2.github.io.
Language Models as Continuous Self-Evolving Data Engineers
2024-12-19
Peidong Wang, Ming Wang, Zhiming Ma, Xiaocui Yang, Shi Feng, Daling Wang, Yifei Zhang
Large Language Models (LLMs) have demonstrated remarkable capabilities on various tasks, while the further evolvement is limited to the lack of high-quality training data. In addition, traditional training approaches rely too much on expert-labeled data, setting an upper limit on the performance of LLMs. To address this issue, we propose a novel paradigm that enables LLMs to train itself by autonomously generating, cleaning, reviewing, and annotating data with preference information, named LANCE. Our approach demonstrates that LLMs can serve as continuous self-evolving data engineers, significantly reducing the time and cost of the post-training data construction process. Through iterative fine-tuning on different variants of the Qwen2, we validate the effectiveness of LANCE across various tasks, showing that it can continuously improve model performance and maintain high-quality data generation. Across eight benchmark dimensions, LANCE resulted in an average score enhancement of 3.36 for Qwen2-7B and 2.70 for Qwen2-7B-Instruct. This training paradigm with autonomous data construction not only reduces the reliance on human experts or external models but also ensures that the data aligns with human values and preferences, paving the way for the development of future superintelligent systems that can exceed human capabilities.
Primordial Gravitational Wave Probes of Non-Standard Thermal Histories
2024-12-19
Annet Konings, Mariia Marinichenko, Oleksii Mikulenko, Subodh P. Patil
Primordial gravitational waves propagate almost unimpeded from the moment they are generated to the present epoch. Nevertheless, they are subject to convolution with a non-trivial transfer function. Within the standard thermal history, shifts in the temperature-redshift relation combine with damping effects by free streaming neutrinos to non-trivially process different wavelengths during radiation domination, with subsequently negligible effects at later times. Presuming a nearly scale invariant primordial spectrum, one obtains a characteristic late time spectrum, deviations from which would indicate departures from the standard thermal history. Given the paucity of probes of the early universe physics before nucleosynthesis, it is useful to classify how deviations from the standard thermal history of the early universe can be constrained from observations of the late time stochastic background. The late time spectral density has a plateau at high frequencies that can in principle be significantly enhanced or suppressed relative to the standard thermal history depending on the equation of state of the epoch intervening reheating and the terminal phase of radiation domination, imprinting additional features from bursts of entropy production, and additional damping at intermediate scales via anisotropic stress production. In this paper, we survey phenomenologically motivated scenarios of early matter domination, kination, and late time decaying particles as representative non-standard thermal histories, elaborate on their late time stochastic background, and discuss constraints on different model scenarios.
A review of high order strong stability preserving two-derivative explicit, implicit, and IMEX methods
2024-12-19
Sigal Gottlieb, Zachary J. Grant
High order strong stability preserving (SSP) time discretizations ensure the nonlinear non-inner-product strong stability properties of spatial discretizations suited for the stable simulation of hyperbolic PDEs. Over the past decade multiderivative time-stepping have been used for the time-evolution hyperbolic PDEs, so that the strong stability properties of these methods have become increasingly relevant. In this work we review sufficient conditions for a two-derivative multistage method to preserve the strong stability properties of spatial discretizations in a forward Euler and different conditions on the second derivative. In particular we present the SSP theory for explicit and implicit two-derivative Runge--Kutta schemes, and discuss a special condition on the second derivative under which these implicit methods may be unconditionally SSP. This condition is then used in the context of implicit-explicit (IMEX) multi-derivative Runge--Kutta schemes, where the time-step restriction is independent of the stiff term. Finally, we present the SSP theory for implicit-explicit (IMEX) multi-derivative general linear methods, and some novel second and third order methods where the time-step restriction is independent of the stiff term.
Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignment
2024-12-19
Qiyang Sun, Yupei Li, Emran Alturki, Sunil Munthumoduku Krishna Murthy, Björn W. Schuller
As Artificial Intelligence (AI) continues to advance rapidly, Friendly AI (FAI) has been proposed to advocate for more equitable and fair development of AI. Despite its importance, there is a lack of comprehensive reviews examining FAI from an ethical perspective, as well as limited discussion on its potential applications and future directions. This paper addresses these gaps by providing a thorough review of FAI, focusing on theoretical perspectives both for and against its development, and presenting a formal definition in a clear and accessible format. Key applications are discussed from the perspectives of eXplainable AI (XAI), privacy, fairness and affective computing (AC). Additionally, the paper identifies challenges in current technological advancements and explores future research avenues. The findings emphasise the significance of developing FAI and advocate for its continued advancement to ensure ethical and beneficial AI development.
Review-Then-Refine: A Dynamic Framework for Multi-Hop Question Answering with Temporal Adaptability
2024-12-19
Xiangsen Chen, Xuming Hu, Nan Tang
Retrieve-augmented generation (RAG) frameworks have emerged as a promising solution to multi-hop question answering(QA) tasks since it enables large language models (LLMs) to incorporate external knowledge and mitigate their inherent knowledge deficiencies. Despite this progress, existing RAG frameworks, which usually follows the retrieve-then-read paradigm, often struggle with multi-hop QA with temporal information since it has difficulty retrieving and synthesizing accurate time-related information. To address the challenge, this paper proposes a novel framework called review-then-refine, which aims to enhance LLM performance in multi-hop QA scenarios with temporal information. Our approach begins with a review phase, where decomposed sub-queries are dynamically rewritten with temporal information, allowing for subsequent adaptive retrieval and reasoning process. In addition, we implement adaptive retrieval mechanism to minimize unnecessary retrievals, thus reducing the potential for hallucinations. In the subsequent refine phase, the LLM synthesizes the retrieved information from each sub-query along with its internal knowledge to formulate a coherent answer. Extensive experimental results across multiple datasets demonstrate the effectiveness of our proposed framework, highlighting its potential to significantly improve multi-hop QA capabilities in LLMs.
ConfliBERT: A Language Model for Political Conflict
2024-12-19
Patrick T. Brandt, Sultan Alsarra, Vito J. D`Orazio, Dagmar Heintze, Latifur Khan, Shreyas Meher, Javier Osorio, Marcus Sianan
Conflict scholars have used rule-based approaches to extract information about political violence from news reports and texts. Recent Natural Language Processing developments move beyond rigid rule-based approaches. We review our recent ConfliBERT language model (Hu et al. 2022) to process political and violence related texts. The model can be used to extract actor and action classifications from texts about political conflict. When fine-tuned, results show that ConfliBERT has superior performance in accuracy, precision and recall over other large language models (LLM) like Google's Gemma 2 (9B), Meta's Llama 3.1 (7B), and Alibaba's Qwen 2.5 (14B) within its relevant domains. It is also hundreds of times faster than these more generalist LLMs. These results are illustrated using texts from the BBC, re3d, and the Global Terrorism Dataset (GTD).
GA-NIFS: interstellar medium properties and tidal interactions in the evolved massive merging system B14-65666 at z = 7.152
2024-12-19
Gareth C. Jones, Rebecca Bowler, Andrew J. Bunker, Santiago Arribas, Stefano Carniani, Stephane Charlot, Michele Perna, Bruno Rodríguez Del Pino, Hannah Übler, Chris J. Willott, Jacopo Chevallard, Giovanni Cresci, Eleonora Parlanti, Jan Scholtz, Giacomo Venturi
We present JWST/NIRSpec IFU observations of the z=7.152 galaxy system B14-65666, as part of the GA-NIFS survey. Line and continuum emission in this massive system (log10(M*/Msol)=9.8+/-0.2) is resolved into two strong cores, two weaker clumps, and a faint arc, as seen in recent JWST/NIRCam imaging. Our dataset contains detections of [OII]3727,3729, [NeIII]3869,3968, Balmer lines (HBeta, HGamma, HDelta, HEpsilon, HZeta), [OIII]5007, and weak [OIII]4363. Each spectrum is fit with a model that consistently incorporates interstellar medium conditions (i.e., electron temperature, T_e, electron density, n_e, and colour excess, E(B-V)). The resulting line fluxes are used to constrain the gas-phase metallicity (~0.3-0.4 solar) and HBeta-based SFR (310+/-40 Msol/yr) for each region. Common line ratio diagrams (O32-R23, R3-R2, Ne3O2-R23) reveal that each line-emitting region lies at the intersection of local and high-redshift galaxies, suggesting low ionisation and higher metallicity compared to the predominantly lower-mass galaxies studied with the JWST/NIRSpec IFU so far at z>5.5. Spaxel-by-spaxel fits reveal evidence for both narrow (FWHM<400 km/s) and broad (FWHM >500 km/s) line emission, the latter of which likely represents tidal interaction or outflows. Comparison to ALMA [CII]158um and [OIII]88um data shows a similar velocity structure, and optical-far infrared diagnostics suggest regions of high Lyman continuum escape fraction and n_e. This source lies on the mass-metallicity relation at z>4, suggesting an evolved nature. The two core galaxies show contrasting properties (e.g., SFR, M*, gas-phase metallicity), suggesting distinct evolutionary pathways. Combining the NIRSpec IFU and ALMA data sets, our analysis opens new windows into the merging system B14-65666.
To find crystals of $\mathfrak{sl}_2$ representations of the form $\Lambda^n\text{Sym}^r\mathbb{C}^2$ it suffices to solve the combinatorial problem of decomposing Young's lattice into symmetric, saturated chains. We review the literature on this latter problem, and present a strategy to solve it. For $n \le 4$, the strategy recovers recently discovered solutions. We obtain (i) counting formulas for plethystic coefficients, (ii) new recursive formulas for plethysms of Schur functions, and (iii) formulas for the number of constituents of $\Lambda^n\text{Sym}^r\mathbb{C}^2$.
Large Language Models and Code Security: A Systematic Literature Review
2024-12-19
Enna Basic, Alberto Giaretta
Large Language Models (LLMs) have emerged as powerful tools for automating various programming tasks, including security-related ones, such as detecting and fixing vulnerabilities. Despite their promising capabilities, when required to produce or modify pre-existing code, LLMs could introduce vulnerabilities unbeknown to the programmer. When analyzing code, they could miss clear vulnerabilities or signal nonexistent ones. In this Systematic Literature Review (SLR), we aim to investigate both the security benefits and potential drawbacks of using LLMs for a variety of code-related tasks. In particular, first we focus on the types of vulnerabilities that could be introduced by LLMs, when used for producing code. Second, we analyze the capabilities of LLMs to detect and fix vulnerabilities, in any given code, and how the prompting strategy of choice impacts their performance in these two tasks. Last, we provide an in-depth analysis on how data poisoning attacks on LLMs can impact performance in the aforementioned tasks.
EPOCHS XI: The Structure and Morphology of Galaxies in the Epoch of Reionization to z ~ 12.5
2024-12-19
Lewi Westcott, Christopher J. Conselice, Thomas Harvey, Duncan Austin, Nathan Adams, Fabricio Ferrari, Leonardo Ferreira, James Trussler, Qiong Li, Vadim Rusakov, Qiao Duan, Honor Harris, Caio Goolsby, Thomas J. Broadhurst, Dan Coe, Seth H. Cohen, Simon P. Driver, Jordan C. J. D'Silva, Brenda Frye, Norman A. Grogin, Nimish P. Hathi, Rolf A. Jansen, Anton M. Koekemoer, Madeline A. Marshall, Rafael Ortiz III, Nor Pirzkal, Aaron Robotham, Russell E. Ryan Jr., Jake Summers, Christopher N. A. Willmer, Rogier A. Windhorst, Haojing Yan
We present a structural analysis of 521 galaxy candidates at 6.5 < z < 12.5, with $SNR > 10\sigma$ in the F444W filter, taken from the EPOCHS v1 sample, consisting of uniformly reduced deep JWST NIRCam data, covering the CEERS, JADES GOOD-S, NGDEEP, SMACS0723, GLASS and PEARLS surveys. We use standard software to fit single S\'ersic models to each galaxy in the rest-frame optical and extract their parametric structural parameters (S\'ersic index, half-light radius and axis-ratio), and \texttt{Morfometryka} to measure their non-parametric concentration and asymmetry parameters. We find a wide range of sizes for these early galaxies, but with a strong galaxy-size mass correlation up to $z \sim 12$ such that galaxy sizes continue to get progressively smaller in the high-redshift regime, following $R_{e} = 2.74 \pm 0.49 \left( 1 + z \right) ^{-0.79 \pm 0.08}$ kpc. Using non-parametric methods we find that galaxy merger fractions, classified through asymmetry parameters, at these redshifts remain consistent with those in literature, maintaining a value of $f_{m} \sim 0.12 \pm 0.07$ showing little dependence with redshift when combined with literature at $z > 4$. We find that galaxies which are smaller in size also appear rounder, with an excess of high axis-ratio objects. Finally, we artificially redshift a subsample of our objects to determine how robust the observational trends we see are, determining that observed trends are due to real evolutionary effects, rather than being a consequence of redshift effects.
The impact of rotation on the stochastic excitation of stellar acoustic modes in solar-like pulsators
2024-12-19
Leïla Bessila, Adrien Deckx van Ruys, Valentin Buriasco, Stéphane Mathis, Lisa Bugnet, Rafael A. García, Savita Mathur
Recent observational results from asteroseismic studies show that an important fraction of solar-like stars do not present detectable stochastically excited acoustic oscillations. This non-detectability seems to correlate with a high rotation rate in the convective envelope and a high surface magnetic activity. At the same time, the properties of stellar convection are affected by rotation and magnetism. We investigate the role of rotation in the excitation of acoustic modes in the convective envelope of solar-like stars, to evaluate its impact on the energy injected in the oscillations. We derive theoretical prescriptions for the excitation of acoustic waves in the convective envelope of rotating solar-like stars. We adopt the Rotating Mixing-Length Theory to model the influence of rotation on convection. We use the MESA stellar evolution code and the GYRE stellar oscillation code to estimate the power injected in the oscillations from our theoretical prescriptions. We demonstrate that the power injected in the acoustic modes is insensitive to the rotation if a Gaussian time-correlation function is assumed, while it can decrease by up to 60 % for a Lorentzian time-correlation function, for a $20 \Omega_{\odot}$ rotation rate. This result can allow us to better constrain the properties of stellar convection by studying observationally acoustic modes excitation. These results demonstrate how important it is to take into account the modification of stellar convection by rotation when evaluating the amplitude of the stellar oscillations it stochastically excites. They open the path for understanding the large variety of observed acoustic-mode amplitudes at the surface of solar-like stars as a function of surface rotation rates.
Lie algebras and the (co)homology of configuration spaces
2024-12-19
Ben Knudsen
We survey decades of research identifying the (co)homology of configuration spaces with Lie algebra (co)homology. The different routes to this one proto-theorem offer genuinely different explanations of its truth, and we attempt to convey some sense of the conceptual core of each perspective. We close with a list of problems.
Enhancing the dimensionless figure of merit zT is central to developing better thermoelectric materials and advancing thermoelectric generation technology. However, the intrinsic interdependence between electrical conductivity, the Seebeck coefficient, and thermal conductivity presents a significant challenge. Over time, various strategies have emerged, but the literature remains difficult to navigate due to its widespread distribution across numerous sources. This short review highlights the key approaches to improving zT, offering a clear and concise guide to help researchers understand the major ideas and breakthroughs in the field.
2024-12-19
Zhiyuan Li, Tingyu Xia, Yi Chang, Yuan Wu
The Receptance Weighted Key Value (RWKV) model offers a novel alternative to the Transformer architecture, merging the benefits of recurrent and attention-based systems. Unlike conventional Transformers, which depend heavily on self-attention, RWKV adeptly captures long-range dependencies with minimal computational demands. By utilizing a recurrent framework, RWKV addresses some computational inefficiencies found in Transformers, particularly in tasks with long sequences. RWKV has recently drawn considerable attention for its robust performance across multiple domains. Despite its growing popularity, no systematic review of the RWKV model exists. This paper seeks to fill this gap as the first comprehensive review of the RWKV architecture, its core principles, and its varied applications, such as natural language generation, natural language understanding, and computer vision. We assess how RWKV compares to traditional Transformer models, highlighting its capability to manage long sequences efficiently and lower computational costs. Furthermore, we explore the challenges RWKV encounters and propose potential directions for future research and advancement. We consistently maintain the related open-source materials at: https://github.com/MLGroupJLU/RWKV-Survey.
The J-PLUS collaboration. Additive versus multiplicative systematics in surveys of the large scale structure of the Universe
2024-12-19
C. Hernández-Monteagudo, G. Aricò, J. Chaves-Montero, L. R. Abramo, P. Arnalte-Mur, A. Hernán-Caballero, C. López-Sanjuan, V. Marra, R. von Marttens, E. Tempel, J. Cenarro, D. Cristóbal-Hornillos, A. Marín-Franch, M. Moles, J. Varela, H. Vázquez Ramió, J. Alcaniz, R. Dupke, A. Ederoclite, L. Sodré Jr., R. E. Angulo
Observational and/or astrophysical systematics modulating the observed number of luminous tracers can constitute a major limitation in the cosmological exploitation of surveys of the large scale structure of the universe. Part of this limitation arises on top of our ignorance on how such systematics actually impact the observed galaxy/quasar fields. In this work we develop a generic, hybrid model for an arbitrary number of systematics that may modulate observations in both an additive and a multiplicative way. This model allows us devising a novel algorithm that addresses the identification and correction for either additive and/or multiplicative contaminants. We test this model on galaxy mocks and systematics templates inspired from data of the third data release of the {\it Javalambre Photometric Local Universe Survey} (J-PLUS). We find that our method clearly outperforms standard methods that assume either an additive or multiplicative character for all contaminants in scenarios where both characters are actually acting on the observed data. In simpler scenarios where only an additive or multiplicative imprint on observations is considered, our hybrid method does not lie far behind the corresponding simplified, additive/multiplicative methods. Nonetheless, in scenarios of mild/low impact of systematics, we find that our hybrid approach converges towards the standard method that assumes additive contamination, as predicted by our model describing systematics. Our methodology also allows for the estimation of biases induced by systematics residuals on different angular scales and under different observational configurations, although these predictions necessarily restrict to the subset of {\em known/identified} potential systematics, and say nothing about ``unknown unknowns" possibly impacting the data.
J-PLUS: Tomographic analysis of galaxy angular density and redshift fluctuations in Data Release 3. Constraints on photo-$z$ errors, linear bias, and peculiar velocities
2024-12-19
C. Hernández-Monteagudo, A. Balaguera-Antolínez, R. von Marttens, A. del Pino, A. Hernán-Caballero, L. R. Abramo, J. Chaves-Montero, C. López-Sanjuan, V. Marra, E. Tempel, G. Aricò, J. Cenarro, D. Cristóbal-Hornillos, A. Marín-Franch, M. Moles, J. Varela, H. Vázquez Ramió, J. Alcaniz, R. Dupke, A. Ederoclite, L. Sodré Jr., R. E. Angulo
The {\it Javalambre Photometric Local Universe Survey} (J-PLUS) is a {\it spectro-photometric} survey covering about 3,000~deg$^2$ in its third data release (DR3), and containing about 300,000 galaxies with high quality ({\it odds}$>0.8$) photometric redshifts (hereafter photo-$z$s). We use this galaxy sample to conduct a tomographic study of the counts and redshift angular fluctuations under Gaussian shells sampling the redshift range $z\in[0.05,0.25]$. We confront the angular power spectra of these observables measured under shells centered on 11 different redshifts with theoretical expectations derived from a linear Boltzmann code ({\tt ARFCAMB}). Overall we find that J-PLUS DR3 data are well reproduced by our linear, simplistic model. We obtain that counts (or density) angular fluctuations (hereafter ADF) are very sensitive to the linear galaxy bias $b_g(z)$, although weakly sensitive to radial peculiar velocities of the galaxy field, while suffering from systematics residuals for $z>0.15$. Angular redshift fluctuations (ARF), instead, show higher sensitivity to radial peculiar velocities and also higher sensitivity to the average uncertainty in photo-$z$s ($\sigma_{\rm Err}$), with no obvious impact from systematics. For $z<0.15$ both ADF and ARF agree on measuring a monotonically increasing linear bias varying from $b_g(z=0.05)\simeq 0.9\pm 0.06$ up to $b_g(z=0.15)\simeq 1.5\pm 0.05$, while, by first time, providing consistent measurements of $\sigma_{\rm Err}(z)\sim 0.014$ that are $\sim 40~\%$ higher than estimates from the photo-$z$ code {\tt LePhare}, ($\sigma_{\rm Err}^{\rm LePhare}=0.010$). As expected, this photo-$z$ uncertainty level prevents the detection of radial peculiar velocities in the modest volume sampled by J-PLUS DR3, although prospects for larger galaxy surveys of similar (and higher) photo-$z$ precision are promising.
Revision of upper bound on volume-filling intergalactic magnetic fields with LOFAR
2024-12-19
A. Neronov, F. Vazza, S. Mtchedlidze, E. Carretti
Magnetic fields present in the Large Scale Structure (LSS) of the Universe change polarization of radio waves arriving from distant extragalactic sources through the effect of Faraday rotation. This effect has been recently used to detect magnetic field in the LSS filaments based on the Rotation Measure data of the LOFAR Two-Meter Sky Survey (LoTSS). We notice that the same data also constrain the strength of the volume-filling magnetic field in the voids of the LSS. We use the LoTSS data to to derive an improved upper bound on the volume-filling field. The new upper bound provides an order of magnitude improvement on the previous Faraday rotation bounds. The new Faraday Rotation bound on the scale-invariant field that may originate from the epoch of inflation is also an order of magnitude lower than the bound on such field derived from the anisotropy analysis of the Cosmic Microwave Background.
ResoFilter: Fine-grained Synthetic Data Filtering for Large Language Models through Data-Parameter Resonance Analysis
2024-12-19
Zeao Tu, Xiangdi Meng, Yu He, Zihan Yao, Tianyu Qi, Jun Liu, Ming Li
Large language models (LLMs) have shown remarkable effectiveness across various domains, with data augmentation methods utilizing GPT for synthetic data generation becoming prevalent. However, the quality and utility of augmented data remain questionable, and current methods lack clear metrics for evaluating data characteristics. To address these challenges, we propose ResoFilter, a novel method that integrates models, data, and tasks to refine datasets. ResoFilter leverages the fine-tuning process to obtain Data-Parameter features for data selection, offering improved interpretability by representing data characteristics through model weights. Our experiments demonstrate that ResoFilter achieves comparable results to full-scale fine-tuning using only half the data in mathematical tasks and exhibits strong generalization across different models and domains. This method provides valuable insights for constructing synthetic datasets and evaluating high-quality data, offering a promising solution for enhancing data augmentation techniques and improving training dataset quality for LLMs. For reproducibility, we will release our code and data upon acceptance.
A General Control Method for Human-Robot Integration
2024-12-19
Maddalena Feder, Giorgio Grioli, Manuel G. Catalano, Antonio Bicchi
This paper introduces a new generalized control method designed for multi-degrees-of-freedom devices to help people with limited motion capabilities in their daily activities. The challenge lies in finding the most adapted strategy for the control interface to effectively map user's motions in a low-dimensional space to complex robotic assistive devices, such as prostheses, supernumerary limbs, up to remote robotic avatars. The goal is a system which integrates the human and the robotic parts into a unique system, moving so as to reach the targets decided by the human while autonomously reducing the user's effort and discomfort. We present a framework to control general multi DoFs assistive systems, which translates user-performed compensatory motions into the necessary robot commands for reaching targets while canceling or reducing compensation. The framework extends to prostheses of any number of DoF up to full robotic avatars, regarded here as a sort of whole-body prosthesis of the person who sees the robot as an artificial extension of their own body without a physical link but with a sensory-motor integration. We have validated and applied this control strategy through tests encompassing simulated scenarios and real-world trials involving a virtual twin of the robotic parts (prosthesis and robot) and a physical humanoid avatar.
2024-12-19
Roderick Murray-Smith, John H. Williamson, Sebastian Stein
Active Inference is a closed-loop computational theoretical basis for understanding behaviour, based on agents with internal probabilistic generative models that encode their beliefs about how hidden states in their environment cause their sensations. We review Active Inference and how it could be applied to model the human-computer interaction loop. Active Inference provides a coherent framework for managing generative models of humans, their environments, sensors and interface components. It informs off-line design and supports real-time, online adaptation. It provides model-based explanations for behaviours observed in HCI, and new tools to measure important concepts such as agency and engagement. We discuss how Active Inference offers a new basis for a theory of interaction in HCI, tools for design of modern, complex sensor-based systems, and integration of artificial intelligence technologies, enabling it to cope with diversity in human users and contexts. We discuss the practical challenges in implementing such Active Inference-based systems.
Advances in Artificial Intelligence forDiabetes Prediction: Insights from a Systematic Literature Review
2024-12-19
Pir Bakhsh Khokhar, Carmine Gravino, Fabio Palomba
This systematic review explores the use of machine learning (ML) in predicting diabetes, focusing on datasets, algorithms, training methods, and evaluation metrics. It examines datasets like the Singapore National Diabetic Retinopathy Screening program, REPLACE-BG, National Health and Nutrition Examination Survey, and Pima Indians Diabetes Database. The review assesses the performance of ML algorithms like CNN, SVM, Logistic Regression, and XGBoost in predicting diabetes outcomes. The study emphasizes the importance of interdisciplinary collaboration and ethical considerations in ML-based diabetes prediction models.
Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools
2024-12-19
James Prather, Juho Leinonen, Natalie Kiesler, Jamie Gorson Benario, Sam Lau, Stephen MacNeil, Narges Norouzi, Simone Opel, Vee Pettit, Leo Porter, Brent N. Reeves, Jaromir Savelka, David H. Smith IV, Sven Strickroth, Daniel Zingaro
Generative AI (GenAI) is advancing rapidly, and the literature in computing education is expanding almost as quickly. Initial responses to GenAI tools were mixed between panic and utopian optimism. Many were fast to point out the opportunities and challenges of GenAI. Researchers reported that these new tools are capable of solving most introductory programming tasks and are causing disruptions throughout the curriculum. These tools can write and explain code, enhance error messages, create resources for instructors, and even provide feedback and help for students like a traditional teaching assistant. In 2024, new research started to emerge on the effects of GenAI usage in the computing classroom. These new data involve the use of GenAI to support classroom instruction at scale and to teach students how to code with GenAI. In support of the former, a new class of tools is emerging that can provide personalized feedback to students on their programming assignments or teach both programming and prompting skills at the same time. With the literature expanding so rapidly, this report aims to summarize and explain what is happening on the ground in computing classrooms. We provide a systematic literature review; a survey of educators and industry professionals; and interviews with educators using GenAI in their courses, educators studying GenAI, and researchers who create GenAI tools to support computing education. The triangulation of these methods and data sources expands the understanding of GenAI usage and perceptions at this critical moment for our community.
Cosmology and Source Redshift Distributions from Combining Radio Weak Lensing with CMB Lensing
2024-12-19
Alba Kalaja, Ian Harrison, William R Coulton
Measurements of weak gravitational lensing using the cosmic microwave background and the shapes of galaxies have refined our understanding of the late-time history of the Universe. While optical surveys have been the primary source for cosmic shear measurements, radio continuum surveys offer a promising avenue. Relevant radio sources, principally star-forming galaxies, have populations with higher mean redshifts and are less affected by dust extinction compared to optical sources. We focus on the future mid frequency SKA radio telescope and explore the cross-correlation between radio cosmic shear and CMB lensing convergence ($\gamma_\mathrm{R}\times \kappa_\mathrm{CMB}$). We investigate its potential in constraining the redshift distribution of radio galaxy samples and improving cosmological parameter constraints, including the neutrino sector. Using simulations of the first phase of the SKA and the Simons Observatory as a CMB experiment, we show how this $\gamma_\mathrm{R}\times \kappa_\mathrm{CMB}$ cross-correlation can provide $\sim1 - 10\%$ calibration of the overall radio source redshift distribution, which in turn can significantly tighten otherwise degenerate measurements of radio galaxy bias. For the case of the next-generation full SKA, we find that the cross-correlation becomes more powerful than the equivalent with a \textit{Euclid}-like survey, with constraints $30\%$ tighter on $\Lambda$CDM parameters and narrower bounds on sum of neutrino masses at the level of $\sim 24\%$. These constraints are also driven by higher redshifts and larger scales than other galaxy-CMB cross-correlations, potentially shedding light on different physical models. Our findings demonstrate the potential of radio weak lensing in improving constraints, and establish the groundwork for future joint analyses of CMB experiments and radio continuum surveys.
Smart spaces are ubiquitous computing environments that integrate diverse sensing and communication technologies to enhance space functionality, optimize energy utilization, and improve user comfort and well-being. The integration of emerging AI methodologies into these environments facilitates the formation of AI-driven smart spaces, which further enhance functionalities of the spaces by enabling advanced applications such as personalized comfort settings, interactive living spaces, and automatization of the space systems, all resulting in enhanced indoor experiences of the users. In this paper, we present a systematic survey of existing research on the foundational components of AI-driven smart spaces, including sensor technologies, data communication protocols, sensor network management and maintenance strategies, as well as the data collection, processing and analytics. Given the pivotal role of AI in establishing AI-powered smart spaces, we explore the opportunities and challenges associated with traditional machine learning (ML) approaches, such as deep learning (DL), and emerging methodologies including large language models (LLMs). Finally, we provide key insights necessary for the development of AI-driven smart spaces, propose future research directions, and sheds light on the path forward.
Mitigating Nonlinear Systematics in Weak Lensing Surveys: The BNT Approach
2024-12-19
Shiming Gu, Ludovic van Waerbeke, Francis Bernardeau, Roohi Dalal
Weak lensing surveys, along with most other late-Universe probes, have consistently measured a lower amplitude of the matter fluctuation spectrum, denoted by the parameter $S_8$, compared to predictions from early-Universe measurements in cosmic microwave background data. Improper modelling of nonlinear scales may partially explain these discrepancies in lensing surveys. This study investigates whether the conventional approach to addressing small-scale biases remains optimal for Stage-IV lensing surveys. We demonstrate that conventional weak lensing estimators are affected by scale leakage from theoretical biases at nonlinear scales, which influence all observed scales. Using the BNT transform, we propose an $\ell$-cut methodology that effectively controls this leakage. The BNT transform reorganizes weak lensing data in $\ell$ space, aligning it with $k$ space, thereby reducing the mixing of nonlinear scales and providing a more accurate interpretation of the data. We evaluate the BNT approach by comparing HMcode, Halofit, Baryon Correction Model and AxionHMcode mass power spectrum models using Euclid-like survey configurations. Additionally, we introduce a new estimator to quantify scale leakage in both the BNT and noBNT approaches. Our findings show that BNT outperforms traditional methods, preserving cosmological constraints while significantly mitigating theoretical biases.
A brief history of quantum vs classical computational advantage
2024-12-19
Ryan LaRose
In this review article we summarize all experiments claiming quantum computational advantage to date. Our review highlights challenges, loopholes, and refutations appearing in subsequent work to provide a complete picture of the current statuses of these experiments. In addition, we also discuss theoretical computational advantage in example problems such as approximate optimization and recommendation systems. Finally, we review recent experiments in quantum error correction -- the biggest frontier to reach experimental quantum advantage in Shor's algorithm.
The development of laser science and technology have stimulated the study of condensed matter physics, especially, dynamical or non-equilibrium nature in solids. The laser technique in terahertz (THz) regime, whose photon energy is comparable to those of typical collective modes in solids such as magnetic excitations, phonons, etc., has remarkably proceeded in the last decade. Theoretical tools for non-equilibrium states have also progressed. Thanks to these backgrounds, magneto-optics, especially, the study of controlling magnetism with laser, now enters a new stage. For such controls, Floquet engineering is a key concept, which means the method of controlling static properties of targets with high-frequency external fields like laser. I review the theoretical foundation of Floquet engineering and its application to magnetic insulators. Basic magnetic quantities such as magnetization, spin chirality, and spin current are shown to be controlled with intense THz laser or wave.
The Ly$\alpha$ emission line is a characteristic feature found in high-$z$ galaxies, serving as a probe of cosmic reionization. While previous works present various correlations between Ly$\alpha$ emission and physical properties of host galaxies, it is still unclear which characteristics predominantly determine the Ly$\alpha$ emission. In this study, we introduce a neural network approach to simultaneously handle multiple properties of galaxies. The neural-network-based prediction model that identifies Ly$\alpha$ emitters (LAEs) from six physical properties: star formation rate (SFR), stellar mass, UV absolute magnitude $M_\mathrm{UV}$, age, UV slope $\beta$, and dust attenuation $E(B-V)$, obtained by the SED fitting. The network is trained with galaxy samples from the VANDELS and MUSE spectroscopic surveys and achieves the performance of 77% true positive rate and 14% false positive rate. The permutation feature importance method shows that $\beta$, $M_\mathrm{UV}$, and $M_*$ are important for the prediction of LAEs. As an independent validation, we find that 91% of LAEs spectroscopically confirmed by the James Webb Space Telescope (JWST) have a probability of LAE higher than 70% in this model. This prediction model enables the efficient construction of a large LAE sample in a wide and continuous redshift space using only photometric data. We apply the prediction model to the JWST photometric galaxy sample and obtain Ly$\alpha$ fraction consistent with previous studies. Moreover, we demonstrate that the difference between the distributions of LAEs predicted by the model and the spectroscopically identified LAEs provides a strong constraint on the HII bubble size.
Galaxy-Point Spread Function correlations as a probe of weak-lensing systematics with UNIONS data
2024-12-19
Sacha Guerrini, Martin Kilbinger, Hubert Leterme, Axel Guinot, Jingwei Wang, Fabian Hervas Peters, Hendrik Hildebrandt, Michael J. Hudson, Alan McConnachie
Weak gravitational lensing requires precise measurements of galaxy shapes and therefore an accurate knowledge of the PSF model. The latter can be a source of systematics that affect the shear two-point correlation function. A key stake of weak lensing analysis is to forecast the systematics due to the PSF. Correlation functions of galaxies and the PSF, the so-called $\rho$- and $\tau$-statistics, are used to evaluate the level of systematics coming from the PSF model and PSF corrections, and contributing to the two-point correlation function used to perform cosmological inference. Our goal is to introduce a fast and simple method to estimate this level of systematics and assess its agreement with state-of-the-art approaches. We introduce a new way to estimate the covariance matrix of the $\tau$-statistics using analytical expressions. The covariance allows us to estimate parameters directly related to the level of systematics associated with the PSF and provides us with a tool to validate the PSF model used in a weak-lensing analysis. We apply those methods to data from the Ultraviolet Near-Infrared Optical Northern Survey (UNIONS). We show that the semi-analytical covariance yields comparable results than using covariances obtained from simulations or jackknife resampling. It requires less computation time and is therefore well suited for rapid comparison of the systematic level obtained from different catalogs. We also show how one can break degeneracies between parameters with a redefinition of the $\tau$-statistics. The methods developed in this work will be useful tools in the analysis of current weak-lensing data but also of Stage IV surveys such as Euclid, LSST or Roman. They provide fast and accurate diagnostics on PSF systematics that are crucial to understand in the context of cosmic shear studies.
This survey is devoted to the geography problem of log-surfaces constructed as pairs consisting of a smooth projective surface and a reduced boundary divisor. In the first part we focus on the geography problem for log-surfaces associated with pairs of the form $(\mathbb{P}^{2}, C)$, where $C$ is an arrangement of smooth plane curves admitting ordinary singularities. In particular, we focus on the case where $C$ is an arrangement of smooth rational curves. In the second part, containing original new results, we study log surfaces constructed as pairs consisting of a $K3$ surface and a rational curve arrangement. In particular, we provide some combinatorial conditions for such pairs to have the log-Chern slope equal to $3$. Our survey is illustrated with many explicit examples of log-surfaces.
Fruit tree image segmentation is an essential problem in automating a variety of agricultural tasks such as phenotyping, harvesting, spraying, and pruning. Many research papers have proposed a diverse spectrum of solutions suitable to specific tasks and environments. The review scope of this paper is confined to the front views of fruit trees and based on 158 relevant papers collected using a newly designed crawling review method. These papers are systematically reviewed based on a taxonomy that sequentially considers the method, image, task, and fruit. This taxonomy will assist readers to intuitively grasp the big picture of these research activities. Our review reveals that the most noticeable deficiency of the previous studies was the lack of a versatile dataset and segmentation model that could be applied to a variety of tasks and environments. Six important future research tasks are suggested, with the expectation that these will pave the way to building a versatile tree segmentation module.
2024-12-19
Paula Boubel, Matthew Colless, Khaled Said, Lister Staveley-Smith
The cosmological principle asserting the large-scale uniformity of the Universe is a testable assumption of the standard cosmological model. We explore the constraints on anisotropic expansion provided by measuring directional variation in the Hubble constant, $H_0$, derived from differential zeropoint measurements of the Tully-Fisher distance estimator. We fit various models for directional variation in $H_0$ using the Tully-Fisher dataset from the all-sky Cosmicflows-4 catalog. The best-fit dipole variation has an amplitude of 0.063 $\pm$ 0.016 mag in the direction ($\ell,b$) = (142 $\pm$ 30$^{\circ}$, 52 $\pm$ 10$^{\circ}$). If this were due to anisotropic expansion it would imply a 3% variation in $H_0$, corresponding to $\Delta H_0$ = 2.10 $\pm$ 0.53 km/s/Mpc if $H_0$ = 70 km/s/Mpc, with a significance of 3.9$\sigma$. A model that includes this $H_0$ dipole is only weakly favored relative to a model with a constant $H_0$ and a bulk motion of the volume sampled by Cosmicflows-4 that is consistent with the standard $\Lambda$CDM cosmology. However, we show that with the expected Tully-Fisher data from the WALLABY and DESI surveys it should be possible to detect a 1% $H_0$ dipole anisotropy at 5.8$\sigma$ confidence and to distinguish it from the typical bulk flow predicted by $\Lambda$CDM over the volume of these surveys.
With the advent of large language models (LLMs) in the artificial intelligence (AI) area, the field of software engineering (SE) has also witnessed a paradigm shift. These models, by leveraging the power of deep learning and massive amounts of data, have demonstrated an unprecedented capacity to understand, generate, and operate programming languages. They can assist developers in completing a broad spectrum of software development activities, encompassing software design, automated programming, and maintenance, which potentially reduces huge human efforts. Integrating LLMs within the SE landscape (LLM4SE) has become a burgeoning trend, necessitating exploring this emergent landscape's challenges and opportunities. The paper aims at revisiting the software development life cycle (SDLC) under LLMs, and highlighting challenges and opportunities of the new paradigm. The paper first summarizes the overall process of LLM4SE, and then elaborates on the current challenges based on a through discussion. The discussion was held among more than 20 participants from academia and industry, specializing in fields such as software engineering and artificial intelligence. Specifically, we achieve 26 key challenges from seven aspects, including software requirement & design, coding assistance, testing code generation, code review, code maintenance, software vulnerability management, and data, training, and evaluation. We hope the achieved challenges would benefit future research in the LLM4SE field.
Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics.
Unconventional Superconductivity in Correlated, Multiband, and Topological Systems
2024-12-19
Grgur Palle
In this thesis, we theoretically examine the pairing mechanisms and the identification of the pairing symmetry of unconventional superconductors whose normal states are correlated, multiband, or topological. In the first part, we investigate whether fluctuating intra-unit-cell loop currents can drive unconventional superconductivity. For general systems, we find that even-parity loop currents are not an effective pairing glue, whereas odd-parity loop currents, such as those proposed to explain the cuprates, are strong pair-breakers, suppressing rather than enhancing Cooper pairing. By employing the same methodology, we also analyze quantum-critical pairing due to other intra-unit-cell orders. For cuprates, we review the evidence for intra-unit-cell loop currents in the pseudogap, we classify the possible loop-current and particle-hole orders in the Emery model, and we analyze the pairing due to the various possible loop-current orders. In the second part, we present a novel electronic pairing mechanism that is based on electric monopole-dipole interactions. We show that these interactions become enhanced in quasi-2D systems with strong parity-mixing and spin-orbit coupling, such as doped Bi$_2$Se$_3$ or SnTe, and that they induce unconventional odd-parity superconductivity. In addition, we establish that the proposed pairing glue is measurable in the out-of-plane optical conductivity. In the last part, we reexamine the pairing symmetry of Sr$_2$RuO$_4$ in light of recent experiments. By theoretically analyzing recent $T_c$ and elastocaloric measurements under uniaxial stress, we demonstrate that the pairing state includes $s$, $d_{x^2-y^2}$, or body-centered $d_{xz} + i d_{yz}$ admixtures and that a bulk two-component superconductivity requires a great deal of fine-tuning to be consistent with ultrasound experiments.
As one type of blue early-type galaxies, the evolutionary history and fate of star-forming lenticular galaxies (S0s) remain elusive. We selected 134 star-forming S0s from the SDSS-IV MaNGA survey and found that they have steep and warped size-mass relations, similar to quiescent S0s and red spirals, indicating that they may have similar gas dissipation scenarios. These galaxies have a higher central stellar mass surface density than normal blue spirals. The radial profiles of $D_{\rm n}4000$ and [Mgb/Fe] show that red spirals and quiescent S0s have similar old central populations and high [Mgb/Fe] values, suggesting rapid bulge formation, though red spirals exhibit a steeper gradient possibly due to residual star formation (SF) in outer regions. In contrast, star-forming S0s exhibit profiles between quiescent S0s/red spirals and normal blue spirals, with relatively flat $D_{\rm n}4000$ and [Mgb/Fe] gradients. More long-term SF history causes normal blue spirals to have very flat $D_{\rm n}4000$ and [Mgb/Fe] profiles, and the majority of them (79 $\pm$ 5 $\%$) have S$\acute{\rm e}$rsic index $<$ 2. We also found that the halo mass of star-forming S0s resembles that of quiescent S0s/red spirals, with 82 $\pm$ 5 $\%$ exceeding the critical mass ($M_{\rm halo} = 10^{12}$$M_{\odot}$h$^{-1}$). To supplement previous H\,{\sc i} detection of star-forming S0s covered by H\,{\sc i}MaNGA, we obtained new observation for H\,{\sc i} emission from 41 star-forming S0s in our sample using the Five-hundred-meter Aperture Spherical Radio Telescope. We found that the H\,{\sc i} mass distribution of star-forming S0s matches that of normal blue spirals, although both star-forming S0s and red spirals are relatively gas-poor, resulting in varying atomic gas depletion times due to different SF levels. Based on these observational results, we discuss the possible evolutionary scenarios of star-forming S0s.
Nuclear and Star Formation Activities in Nearby Galaxies: Roles of Gas Supply and AGN Feedback
2024-12-19
Huynh Anh N. Le, Yongquan Xue
We analyzed a sample of $\sim$113,000 galaxies ($\rm z < 0.3$) from the Sloan Digital Sky Survey, divided into star-forming, composite, Seyfert, and LINER types, to explore the relationships between UV-to-optical colors ($\rm u-r$), star formation rates (SFRs), specific star formation rates (sSFRs), stellar velocity dispersions ($\rm \sigma_{*}$), mass accretion rates onto the black hole ($\rm L_{[OIII]}/\sigma_{*}^{4}$), and Eddington ratios. Star-forming galaxies predominantly feature young, blue stars along the main-sequence (MS) line, while composite, Seyfert, and LINER galaxies deviate from this line, displaying progressively older stellar populations and lower SFRs. $\rm L_{[OIII]}/\sigma_{*}^{4}$ and Eddington ratios are highest in Seyfert galaxies, moderate in composite galaxies, and lowest in LINERs, with higher ratios associated with bluer colors, indicating a younger stellar population and stronger active galactic nucleus (AGN) activity. These trends suggest a strong correlation between sSFRs and Eddington ratios, highlighting a close connection between AGN and star formation activities. These results may imply an evolutionary sequence where galaxies transition from blue star-forming galaxies to red LINERs, passing through composite and Seyfert phases, driven primarily by gas supply, with AGN feedback playing a secondary role. While both radio luminosities ($\rm L_{1.4GHz}$) and Eddington ratios correlate with SFRs, their trends differ on the SFR$-$stellar mass ($\rm M_{*}$) plane, with radio luminosities increasing with stellar mass along the MS line, and no direct connection between radio luminosities and Eddington ratios. These findings may provide new insights into the interplay between star formation, AGN activity, and radio emission in galaxies, shedding light on their evolutionary pathways.
Radio Galaxy Zoo Data Release 1: 100,185 radio source classifications from the FIRST and ATLAS surveys
2024-12-19
O. Ivy Wong, A. F. Garon, M. J. Alger, L. Rudnick, S. S. Shabala, K. W. Willett, J. K. Banfield, H. Andernach, R. P. Norris, J. Swan, M. J. Hardcastle, C. J. Lintott, S. V. White, N. Seymour, A. D. Kapińska, H. Tang, B. D. Simmons, K. Schawinski
Radio galaxies can extend far beyond the stellar component of their originating host galaxies, and their radio emission can consist of multiple discrete components. Furthermore, the apparent source structure will depend on survey sensitivity, resolution and the observing frequency. Associated discrete radio components and their originating host galaxy are typically identified through a visual comparison of radio and mid-infrared survey images. We present the first data release of Radio Galaxy Zoo, an online citizen science project that enlists the help of citizen scientists to cross-match extended radio sources from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) and the Australia Telescope Large Area Survey (ATLAS) surveys, often with complex structure, to host galaxies in 3.6 um infrared images from the Wide-field Infrared Survey Explorer (WISE) and the Spitzer Space Telescope. This first data release consists of 100,185 classifications for 99,146 radio sources from the FIRST survey and 583 radio sources from the ATLAS survey. We include two tables for each of the FIRST and ATLAS surveys: 1)the identification of all components making up each radio source; and 2) the cross-matched host galaxies. These classifications have an average reliability of 0.83 based on the weighted consensus levels of our citizen scientists. The reliability of the DR1 catalogue has been further demonstrated through several parallel studies which used the pre-release versions of this catalogue to train and prototype machine learning-based classifiers. We also include a brief description of the radio source populations catalogued by RGZ DR1.
Enhanced detection and identification of satellites using an all-sky multi-frequency survey with prototype SKA-Low stations
2024-12-19
Dylan Grigg, Steven Tingay, Steve Prabu, Marcin Sokolowski, Balthasar Indermuehle
With the low Earth orbit environment becoming increasingly populated with artificial satellites, rockets, and debris, it is important to understand the effects they have on radio astronomy. In this work, we undertake a multi-frequency, multi-epoch survey with two SKA-Low station prototypes located at the SKA-Low site, to identify and characterise radio frequency emission from orbiting objects and consider their impact on radio astronomy observations. We identified 152 unique satellites across multiple passes in low and medium Earth orbits from 1.6 million full-sky images across 13 selected ${\approx}1$ MHz frequency bands in the SKA-Low frequency range, acquired over almost 20 days of data collection. Our algorithms significantly reduce the rate of satellite misidentification, compared to previous work, validated through simulations to be $<1\%$. Notably, multiple satellites were detected transmitting unintended electromagnetic radiation, as well as several decommissioned satellites likely transmitting when the Sun illuminates their solar panels. We test alternative methods of processing data, which will be deployed for a larger, more systematic survey at SKA-Low frequencies in the near future. The current work establishes a baseline for monitoring satellite transmissions, which will be repeated in future years to assess their evolving impact on radio astronomy observations.
Why do we build local large language models (LLMs)? What should a local LLM learn from the target language? Which abilities can be transferred from other languages? Do language-specific scaling laws exist? To explore these research questions, we evaluated 35 Japanese, English, and multilingual LLMs on 19 evaluation benchmarks for Japanese and English, taking Japanese as a local language. Adopting an observational approach, we analyzed correlations of benchmark scores, and conducted principal component analysis (PCA) on the scores to derive \textit{ability factors} of local LLMs. We found that training on English text can improve the scores of academic subjects in Japanese (JMMLU). In addition, it is unnecessary to specifically train on Japanese text to enhance abilities for solving Japanese code generation, arithmetic reasoning, commonsense, and reading comprehension tasks. In contrast, training on Japanese text could improve question-answering tasks about Japanese knowledge and English-Japanese translation, which indicates that abilities for solving these two tasks can be regarded as \textit{Japanese abilities} for LLMs. Furthermore, we confirmed that the Japanese abilities scale with the computational budget for Japanese text.
From Human Annotation to LLMs: SILICON Annotation Workflow for Management Research
2024-12-19
Xiang Cheng, Raveesh Mayya, João Sedoc
Unstructured text data annotation and analysis are fundamental to management research, often relying on human annotators through crowdsourcing platforms. While Large Language Models (LLMs) promise to provide a cost-effective and efficient alternative to human annotation, there lacks a systematic workflow that evaluate when LLMs are suitable or how to proceed with LLM-based text annotation in a reproducible manner. This paper addresses this methodological gap by introducing the ``SILICON" (\textbf{S}ystematic \textbf{I}nference with \textbf{L}LMs for \textbf{I}nformation \textbf{C}lassificati\textbf{o}n and \textbf{N}otation) workflow. The workflow integrates established principles of human annotation with systematic prompt optimization and model selection, addressing challenges such as developing robust annotation guidelines, establishing high-quality human baselines, optimizing prompts, and ensuring reproducibility across LLMs. We validate the SILICON workflow through seven case studies covering common management research tasks, including business proposal evaluation, dialog intent and breakdown analysis, review attribute detection. Our findings highlight the importance of validating annotation guideline agreement, the superiority of expert-developed human baselines over crowdsourced ones, the iterative nature of prompt optimization, and the necessity of testing multiple LLMs. Notably, we propose a regression-based methodology to empirically compare LLM outputs across prompts and models. Our workflow advances management research by establishing reproducible processes for LLM-based annotation that maintain scientific rigor. We provide practical guidance for researchers to effectively navigate the evolving landscape of generative AI tools effectively while maintaining transparency and reproducibility.
Instability of the Environment as a Necessary Condition for Optimal Control of an Economic Object
2024-12-19
Sergey Masaev
A review of economic approaches showed the lack of a universal method for assessing management decisions in the face of an increasing volume of analyzed data and changing parameters of the external environment. The method of integral indicators is proposed. Integral indicators are one of the modern methods for researching the behavior of an enterprise. It provides an assessment of the impact of the external environment. It shows the ability of the enterprise to adapt to new conditions. The dynamics of the correlation indicator shows the reaction of the enterprise to the impact of external factors. The purpose of the scientific work was achieved: the optimal control of the enterprise was carried out in the conditions of changing the parameters of the external environment For this, the model of the economic object and the method of its analysis are formalized. The structure of an economic object (enterprise) is given. The characteristics of the parameters of the external environment are given. The state of an economic object (enterprise) is modeled taking into account the influence of the external environment. With the help of the software package created by the author, six optimal options for control decisions have been analyzed. The state of an economic object has been modeled depending on the state of the external environment by 5,000 parameters. The research showed significant changes in the values of the correlation of the parameters of the system and the intensity of business processes when the conditions for the functioning of the system change. The optimal control of an economic object (enterprise) is selected according to the integral indicator.
The sensitive axes of atom gravimeters are defined by the directions of the respective Raman lasers. Any tilt of the Raman lasers with respect to the vertical direction introduces errors in gravity measurements. In this work, we report a fast determination of the tilt of Raman lasers, where the fringe of the atom interferometer is scanned by varying the tilt, rather than the phase, of the Raman lasers. Unlike the periodic cosine fringes typically used in atom interferometers, the fringe obtained by changing the tilt, referred to as the tilt-scanned fringe, is aperiodic and symmetric with respect to zero tilt. The tilt-scanned fringe is highly sensitive to asymmetries caused by non-zero tilt, enabling fast and precise determination of the Raman laser tilt in atom gravimeters. We demonstrate that one tilt-scanned fringe, corresponding to a measurement cycle time of 13 s, can determine the tilt with a typical precision of about 30 $\mu$rad in our developed atom gravimeter. Further investigation proves that the tilt-scanned fringe approach shortens the measurement cycle time by over an order of magnitude while keeping comparable precision with conventional tilt determination techniques. The fast tilt determination presented here is significant for the application of atom gravimeters, particularly in absolute gravity surveys.
The technology for generating robot actions has significantly contributed to the automation and efficiency of tasks. However, the ability to adapt to objects of different shapes and hardness remains a challenge for general industrial robots. Motion reproduction systems (MRS) replicate previously acquired actions using position and force control, but generating actions for significantly different environments is difficult. Furthermore, methods based on machine learning require the acquisition of a large amount of motion data. This paper proposes a new method that matches the impedance of two pre-recorded action data with the current environmental impedance to generate highly adaptable actions. This method recalculates the command values for position and force based on the current impedance to improve reproducibility in different environments. Experiments conducted under conditions of extreme action impedance, such as position control and force control, confirmed the superiority of the proposed method over MRS. The advantages of this method include using only two sets of motion data, significantly reducing the burden of data acquisition compared to machine learning-based methods, and eliminating concerns about stability by using existing stable control systems. This study contributes to improving robots' environmental adaptability while simplifying the action generation method.
In the recent past, so-called pig-butchering scams are on the rise. This term is based on a translation of the Chinese term "Sha Zhu Pan", where scammers refer to victims as "pig" which are to be "fattened up before slaughter" so that scammer can siphon off as much monetary value as possible. In this type of scam, attackers perform social engineering tricks on victims over an extended period to build credibility or relationships. After a certain period, when victims transfer larger amounts of money to scammers, the fraudsters' platforms or profiles go permanently offline and the victims' money is lost. In this work, we provide the first comprehensive study of pig-butchering scams from multiple vantage points. Our study analyzes the direct victims' narratives shared on multiple social media platforms, public abuse report databases, and case studies from news outlets. Between March 2024 to October 2024, we collected data related to pig butchering scams from (i) four social media platforms comprised of more than 430,000 social media accounts and 770,000 posts; (ii) more than 3,200 public abuse reports narratives, and (iii) about 1,000 news articles. Through automated and qualitative evaluation, we provide an evaluation of victims of pig-butchering scams, finding 146 social media scammed users, 2,570 abuse reports narratives, and 50 case studies of 834 souls from news outlets. In total, we approximated losses of over \$521 million related to such scams. To complement this analysis, we performed a survey on crowdsourcing platforms with 584 users to broaden the insights on comparative analysis of pig-butchering scams with other types of scams. Our research highlights that these attacks are sophisticated and often require multiple entities, including policymakers and law enforcement, to work together alongside user education to create a proactive detection of such scams.
Decade of Natural Language Processing in Chronic Pain: A Systematic Review
2024-12-19
Swati Rajwal
In recent years, the intersection of Natural Language Processing (NLP) and public health has opened innovative pathways for investigating various domains, including chronic pain in textual datasets. Despite the promise of NLP in chronic pain, the literature is dispersed across various disciplines, and there is a need to consolidate existing knowledge, identify knowledge gaps in the literature, and inform future research directions in this emerging field. This review aims to investigate the state of the research on NLP-based interventions designed for chronic pain research. A search strategy was formulated and executed across PubMed, Web of Science, IEEE Xplore, Scopus, and ACL Anthology to find studies published in English between 2014 and 2024. After screening 132 papers, 26 studies were included in the final review. Key findings from this review underscore the significant potential of NLP techniques to address pressing challenges in chronic pain research. The past 10 years in this field have showcased the utilization of advanced methods (transformers like RoBERTa and BERT) achieving high-performance metrics (e.g., F1>0.8) in classification tasks, while unsupervised approaches like Latent Dirichlet Allocation (LDA) and k-means clustering have proven effective for exploratory analyses. Results also reveal persistent challenges such as limited dataset diversity, inadequate sample sizes, and insufficient representation of underrepresented populations. Future research studies should explore multimodal data validation systems, context-aware mechanistic modeling, and the development of standardized evaluation metrics to enhance reproducibility and equity in chronic pain research.
Autonomous Vehicle Security: A Deep Dive into Threat Modeling
2024-12-19
Amal Yousseef, Shalaka Satam, Banafsheh Saber Latibari, Jesus Pacheco, Soheil Salehi, Salim Hariri, Partik Satam
Autonomous vehicles (AVs) are poised to revolutionize modern transportation, offering enhanced safety, efficiency, and convenience. However, the increasing complexity and connectivity of AV systems introduce significant cybersecurity challenges. This paper provides a comprehensive survey of AV security with a focus on threat modeling frameworks, including STRIDE, DREAD, and MITRE ATT\&CK, to systematically identify and mitigate potential risks. The survey examines key components of AV architectures, such as sensors, communication modules, and electronic control units (ECUs), and explores common attack vectors like wireless communication exploits, sensor spoofing, and firmware vulnerabilities. Through case studies of real-world incidents, such as the Jeep Cherokee and Tesla Model S exploits, the paper highlights the critical need for robust security measures. Emerging technologies, including blockchain for secure Vehicle-to-Everything (V2X) communication, AI-driven threat detection, and secure Over-The-Air (OTA) updates, are discussed as potential solutions to mitigate evolving threats. The paper also addresses legal and ethical considerations, emphasizing data privacy, user safety, and regulatory compliance. By combining threat modeling frameworks, multi-layered security strategies, and proactive defenses, this survey offers insights and recommendations for enhancing the cybersecurity of autonomous vehicles.
Non-equatorial scalar rings supported by rapidly spinning Gauss-Bonnet black holes
2024-12-19
Shahar Hod
Black-hole spacetimes that possess stationary equatorial matter rings are known to exist in general relativity. We here reveal the existence of black-hole spacetimes that support {\it non}-equatorial matter rings. In particular, it is proved that rapidly-rotating Kerr black holes in the dimensionless large-spin regime ${\bar a}>{\bar a}_{\text{crit}}= \sqrt{\big\{{{7+\sqrt{7}\cos\big[{1\over3}\arctan\big(3\sqrt{3}\big)\big]- \sqrt{21}\sin\big[{1\over3}\arctan\big(3\sqrt{3}\big)\big]}\big\}/12}}\simeq0.78$ can support a pair of non-equatorial massive scalar rings which are negatively coupled to the Gauss-Bonnet curvature invariant of the spinning spacetime (here ${\bar a}\equiv J/M^2$ is the dimensionless angular momentum of the central supporting black hole). We explicitly prove that these non-equatorial scalar rings are characterized by the dimensionless functional relation $-{{57+28\sqrt{21}\cos\big[{1\over3}\arctan\big({{1}\over{3\sqrt{3}}}\big)\big]} \over{8(1+\sqrt{1-{\bar a}^2})^6}} \cdot{{\bar\eta}\over{{\bar\mu}^2}}\to 1^{+}$ in the large-mass ${\bar\mu}\equiv M\mu\gg1$ regime (here $\{{\bar\eta}<0,\mu\}$ are respectively the non-trivial coupling parameter of the composed Einstein-Gauss-Bonnet-massive-scalar field theory and the proper mass of the supported non-minimally coupled scalar field).
On the Double: Two Luminous Flares from the Nearby Tidal Disruption Event ASASSN-22ci (AT2022dbl) and Connections to Repeating TDE Candidates
2024-12-19
Jason T. Hinkle, Katie Auchettl, Willem B. Hoogendam, Anna V. Payne, Thomas W. -S. Holoien, Benjamin J. Shappee, Michael A. Tucker, Christopher S. Kochanek, K. Z. Stanek, Patrick J. Vallely, Charlotte R. Angus, Chris Ashall, Thomas de Jaeger, Dhvanil D. Desai, Aaron Do, Michael M. Fausnaugh, Mark E. Huber, Ryan J. Rickards Vaught, Jennifer Shi
We present observations of ASASSN-22ci (AT2022dbl), a nearby tidal disruption event (TDE) discovered by the All-Sky Automated Survey for Supernovae (ASAS-SN) at a distance of d$_L \simeq 125$ Mpc. Roughly two years after the initial ASAS-SN discovery, a second flare was detected coincident with ASASSN-22ci. UV/optical photometry and optical spectroscopy indicate that both flares are likely powered by TDEs. The striking similarity in flare properties suggests that these flares result from subsequent disruptions of the same star. Each flare rises on a timescale of $\sim$30 days, has a temperature of $\approx$30,000 K, a peak bolometric luminosity of $L_{UV/Opt} = 10^{43.6 - 43.9} \textrm{ erg} \textrm{ s}^{-1}$, and exhibits a blue optical spectrum with broad H, He, and N lines. No X-ray emission is detected during either flare, but X-ray emission with an unabsorbed luminosity of $L_{X} = 3\times10^{41} \textrm{ erg} \textrm{ s}^{-1}$ and $kT = 0.042$ eV is observed between the flares. Pre-discovery survey observations rule out the existence of earlier flares within the past $\approx$6000 days, indicating that the discovery of ASASSN-22ci likely coincides with the first flare. If the observed flare separation of $720 \pm 4.7$ days is the orbital period, the next flare of ASASSN-22ci should occur near MJD 61075 (2026 February 04). Finally, we find that the existing sample of repeating TDE candidates is consistent with Hills capture of a star initially in a binary with a total mass between $\sim$$1 - 4$ M$_{\odot}$ and a separation of $\sim$$0.01 - 0.1$ AU.
von Neumann entropy and quantum version of thermodynamic entropy
2024-12-19
Smitarani Mishra, Shaon Sahoo
The debate whether the von Neumann (VN) entropy is a suitable quantum version of the thermodynamic (TH) entropy has a long history. In this regard, we briefly review some of the main reservations about the VN entropy and explain that the objections about its time-invariance and subadditivity properties can be either avoided or addressed convincingly. In a broader context, we analyze here whether and when the VN entropy is the same or equivalent to the TH entropy. For a thermalized isolated or open system, the VN entropy for an appropriately chosen density operator is the same as the quantum version of, respectively, the Boltzmann or Gibbs entropy (these latter entropies are equivalent to the TH entropy for large thermalized systems). Since the quantum thermalization is essentially defined for a subsystem of a much larger system, it is important to investigate if the VN entropy of a subsystem is equivalent to its TH entropy. We here show that the VN entropy of a small subsystem of a large thermalized system is proportional to the quantum statistical entropy (Boltzmann's version) of the subsystem. For relevant numerical results, we take a one-dimensional spin-1/2 chain with next-nearest neighbor interactions.
Coronal kink oscillations and photospheric driving: combining SolO/EUI and SST/CRISP high-resolution observations
2024-12-19
Nicolas Poirier, Sanja Danilovic, Petra Kohutova, Carlos J. Díaz Baso, Luc Rouppe van der Voort, Daniele Calchetti, Jonas Sinjan
The driving and excitation mechanisms of decay-less kink oscillations in coronal loops remain under debate. We aim to quantify and provide simple observational constraints on the photospheric driving of oscillating coronal loops in a few typical active region configurations: sunspot, plage, pores and enhanced-network regions. We then aim to investigate the possible interplay between photospheric driving and properties of kink oscillations in connected coronal loops. We analyse two unique datasets of the corona and photosphere taken at a high resolution during the first coordinated observation campaign between Solar Orbiter and the Swedish 1-m Solar Telescope (SST). A local correlation tracking method is applied on the SST/CRISP data to quantify the photospheric motions at the base of coronal loops. The same loops are then analysed in the corona by exploiting data from the Extreme Ultraviolet Imager on Solar Orbiter, and by using a wavelet analysis to characterize the kink oscillations. Each photospheric region shows dynamics with an overall increase in strength going from pore, plage, enhanced-network to sunspot regions. Differences are also seen in the kink-mode amplitudes of the corresponding coronal loops. This suggests the photosphere is involved in the driving of coronal kink oscillations. However, the few samples available does not allow to further establish the excitation mechanism yet. Despite oscillating coronal loops being anchored in seemingly "static" strong magnetic field regions as seen from coronal EUV observations, photospheric observations provide evidence for a continuous and significant driving at their base. The precise connection between photospheric driving and coronal kink oscillations remains to be further investigated. This study finally provides critical constraints on photospheric driving that can be tested in existing numerical models of coronal loops.
Enhancing the detection of low-energy M dwarf flares: wavelet-based denoising of CHEOPS data
2024-12-19
J. Poyatos, O. Fors, J. M. Gómez Cama, I. Ribas
Stellar flares are powerful bursts of electromagnetic radiation triggered by magnetic reconnection in the chromosphere of stars, occurring frequently and intensely on active M dwarfs. While missions like TESS and Kepler have studied regular and super-flares, their detection of flares with energies below $10^{30}$ erg remains incomplete. Extending flare studies to include these low-energy events could enhance flare formation models and provide insight into their impacts on exoplanetary atmospheres. This study investigates CHEOPS's capacity to detect low-energy flares in M dwarf light curves. Using CHEOPS's high photometric precision and observing cadence, along with a tailored wavelet-based denoising algorithm, we aim to improve detection completeness and refine flare statistics for low-energy events. We conducted a flare injection and recovery process to optimise denoising parameters, applied it to CHEOPS light curves to maximise detection rates, and used a flare breakdown algorithm to analyse complex structures. Our analysis recovered 349 flares with energies ranging from $2.2\times10^{26}$ to $8.1\times10^{30}$ erg across 63 M dwarfs, with $\sim$40% exhibiting complex, multi-peaked structures. The denoising algorithm improved flare recovery by $\sim$34%, though it marginally extended the lower boundary of detectable energies. For the full sample, the power-law index $\alpha$ was $1.92\pm0.07$, but a log-normal distribution fit better, suggesting multiple flare formation scenarios. While CHEOPS's observing mode is not ideal for large-scale surveys, it captures weaker flares than TESS or Kepler, expanding the observed energy range. Wavelet-based denoising enhances low-energy event recovery, enabling exploration of the micro-flaring regime. Expanding low-energy flare observations could refine flare generation models and improve the understanding of their role in star-planet interactions.
Majority voting (MV) is the prototypical ``wisdom of the crowd'' algorithm. Theorems considering when MV is optimal for group decisions date back to Condorcet's 1785 jury decision theorem. The same assumption of error independence used by Condorcet is used here to prove a jury evaluation theorem that does purely algebraic evaluation (AE). Three or more binary jurors are enough to obtain the only two possible statistics of their correctness on a joint test they took. AE is shown to be superior to MV since it allows one to choose the minority vote depending on how the jurors agree or disagree. In addition, AE is self-alarming about the failure of the error-independence assumption. Experiments labeling demographic datasets from the American Community Survey are carried out to compare MV and AE on nearly error-independent ensembles. In general, using algebraic evaluation leads to better classifier evaluations and group labeling decisions.
Transfer learning aims to transfer knowledge or information from a source domain to a relevant target domain. In this paper, we understand transfer learning from the perspectives of knowledge transferability and trustworthiness. This involves two research questions: How is knowledge transferability quantitatively measured and enhanced across domains? Can we trust the transferred knowledge in the transfer learning process? To answer these questions, this paper provides a comprehensive review of trustworthy transfer learning from various aspects, including problem definitions, theoretical analysis, empirical algorithms, and real-world applications. Specifically, we summarize recent theories and algorithms for understanding knowledge transferability under (within-domain) IID and non-IID assumptions. In addition to knowledge transferability, we review the impact of trustworthiness on transfer learning, e.g., whether the transferred knowledge is adversarially robust or algorithmically fair, how to transfer the knowledge under privacy-preserving constraints, etc. Beyond discussing the current advancements, we highlight the open questions and future directions for understanding transfer learning in a reliable and trustworthy manner.
Joint Perception and Prediction for Autonomous Driving: A Survey
2024-12-18
Lucas Dal'Col, Miguel Oliveira, Vítor Santos
Perception and prediction modules are critical components of autonomous driving systems, enabling vehicles to navigate safely through complex environments. The perception module is responsible for perceiving the environment, including static and dynamic objects, while the prediction module is responsible for predicting the future behavior of these objects. These modules are typically divided into three tasks: object detection, object tracking, and motion prediction. Traditionally, these tasks are developed and optimized independently, with outputs passed sequentially from one to the next. However, this approach has significant limitations: computational resources are not shared across tasks, the lack of joint optimization can amplify errors as they propagate throughout the pipeline, and uncertainty is rarely propagated between modules, resulting in significant information loss. To address these challenges, the joint perception and prediction paradigm has emerged, integrating perception and prediction into a unified model through multi-task learning. This strategy not only overcomes the limitations of previous methods, but also enables the three tasks to have direct access to raw sensor data, allowing richer and more nuanced environmental interpretations. This paper presents the first comprehensive survey of joint perception and prediction for autonomous driving. We propose a taxonomy that categorizes approaches based on input representation, scene context modeling, and output representation, highlighting their contributions and limitations. Additionally, we present a qualitative analysis and quantitative comparison of existing methods. Finally, we discuss future research directions based on identified gaps in the state-of-the-art.
A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future
2024-12-18
Shilin Sun, Wenbin An, Feng Tian, Fang Nan, Qidong Liu, Jun Liu, Nazaraf Shah, Ping Chen
Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challenges in interpreting the "black-box" nature of AI models. To address these concerns, eXplainable AI (XAI) has emerged with a focus on transparency and interpretability to enhance human understanding and trust in AI decision-making processes. In the context of multimodal data fusion and complex reasoning scenarios, the proposal of Multimodal eXplainable AI (MXAI) integrates multiple modalities for prediction and explanation tasks. Meanwhile, the advent of Large Language Models (LLMs) has led to remarkable breakthroughs in natural language processing, yet their complexity has further exacerbated the issue of MXAI. To gain key insights into the development of MXAI methods and provide crucial guidance for building more transparent, fair, and trustworthy AI systems, we review the MXAI methods from a historical perspective and categorize them across four eras: traditional machine learning, deep learning, discriminative foundation models, and generative LLMs. We also review evaluation metrics and datasets used in MXAI research, concluding with a discussion of future challenges and directions. A project related to this review has been created at https://github.com/ShilinSun/mxai_review.
What can we learn from the Parton Branching method in QCD?
2024-12-18
S. Taheri Monfared
This work reviews recent developments in the Parton Branching (PB) method, focusing on its application to Transverse Momentum Dependent (TMD) parton distributions and the implementation of TMD evolution equations in Monte Carlo generators. Key advancements include the inclusion of photon and heavy electroweak boson radiation in the evolution equations and their impact on collinear and TMD distributions. A detailed comparison of PB and Collins-Soper-Sterman formalisms highlights improvements in the accuracy of PB Sudakov form factors. The role of soft gluons, intrinsic transverse momentum, and the $z_M$ parameter in modelling non-perturbative effects is emphasized, with implications for inclusive distributions and Drell-Yan transverse momentum spectra. This review also addresses challenges in achieving consistency between forward and backward evolution.
2024-12-18
Santosh K. Das, Prabhakar Palni, Amal Sarkar, Vineet Kumar Agotiya, Aritra Bandyopadhyay, Partha Pratim Bhaduri, Saumen Datta, Vaishnavi Desai, Debarshi Dey, Vincenzo Greco, Mohammad Yousuf Jamal, Gurleen Kaur, Manisha Kumari, Monideepa Maity, Subrata Pal, Binoy Krishna Patra, Pooja, Jai Prakash, Manaswini Priyadarshini, Vyshakh B R, Marco Ruggieri, Nihar Ranjan Sahoo, Raghunath Sahoo, Om Shahi, Devanshu Sharma, Rishabh Sharma, Rishi Sharma
The hot and dense QCD matter, known as the Quark-Gluon Plasma (QGP), is explored through heavy-ion collision experiments at the LHC and RHIC. Jets and heavy flavors, produced from the initial hard scattering, are used as hard probes to study the properties of the QGP. Recent experimental observations on jet quenching and heavy-flavor suppression have strengthened our understanding, allowing for fine-tuning of theoretical models in hard probes. The second conference, HOT QCD Matter 2024, was organized to bring the community together for discussions on key topics in the field. This article comprises 15 sections, each addressing various aspects of hard probes in relativistic heavy-ion collisions, offering a snapshot of current experimental observations and theoretical advancements. The article begins with a discussion on memory effects in the quantum evolution of quarkonia in the quark-gluon plasma, followed by an experimental review, new insights on jet quenching at RHIC and LHC, and concludes with a machine learning approach to heavy flavor production at the Large Hadron Collider.
Turbulent solutions of the binormal flow and the 1D cubic Schrödinger equation
2024-12-18
Valeria Banica, Luis Vega
In the last three decades there is an intense activity on the exploration of turbulent phenomena of dispersive equations, as for instance the growth of Sobolev norms since the work of Bourgain in the 90s. In general the 1D cubic Schr\"odinger equation has been left aside because of its complete integrability. In a series of papers of the last six years that we survey here for the special issue of the ICMP 2024 ([12],[13],[14],[15],[16],[7],[8]), we considered, together with the 1D cubic Schr\"odinger equation, the binormal flow, which is a geometric flow explicitly related to it. We displayed rigorously a large range of complex behavior as creation of singularities and unique continuation, Fourier growth, Talbot effects, intermittency and multifractality, justifying in particular some previous numerical observations. To do so we constructed a class of well-posedness for the 1D cubic Schr\"odinger equation included in the critical Fourier-Lebesgue space $\mathcal FL^\infty$ and in supercritical Sobolev spaces with respect to scaling. Last but not least we recall that the binormal flow is a classical model for the dynamics of a vortex filament in a 3D fluid or superfluid, and that vortex motions are a key element of turbulence.
Evaluation of 'Dunkelflaute' event detection methods considering grid operators' needs
2024-12-18
Benjamin Biewald, Bastien Cozian, Laurent Dubus, William Zappa, Laurens Stoop
Weather conditions associated with low electricity production from renewable energy sources (RES) can result in challenging 'dunkelflaute' events, where 'dunkel' means dark and 'flaute' refers to low windspeeds. In a power system relying significantly on RES, such events can pose a risk for maintaining resource adequacy, i.e. the balance between generation and demand, particularly if they occur over a large geographical area and for an extended period of time. This risk is further emphasized in periods of cold ('kalte') temperature, known as 'kalte dunkelflaute'. In this paper, we perform a literature review of different methods to identify dunkelflaute events from hourly RES production and load data alone. We then validate three of these methods by comparing their results with periods of shortage identified from a detailed power system simulation model used by grid operators (ERAA2023). Strengths and weaknesses of these methods are discussed in terms of their data requirements, ease of application, and skill in detecting dunkelflaute events. We find that all three 'dunkelflaute' event detection methods have some ability to identify potential energy shortages, but none are able to detect all events. Most likely other factors such as the presence of energy storage capacity, non-weather-dependent outages, and model-related factors limit the skill of these methods. We find that all three methods perform best if the residual load is used as input, rather than hourly RES production or load alone. Overall, we find that Otero'22 is the method that yields the best results while being straightforward to implement and requiring only data with daily resolution. The results hold for countries relying on a small or a large share of RES production in their electricity mix.
Hypersonic flow conditions pose exceptional challenges for Reynolds-Averaged Navier-Stokes (RANS) turbulence modeling. Critical phenomena include compressibility effects, shock/turbulent boundary layer interactions, turbulence-chemistry interaction in thermo-chemical non-equilibrium, and ablation-induced surface roughness and blowing effects. This comprehensive review synthesizes recent developments in adapting turbulence models to hypersonic applications, examining approaches ranging from empirical modifications to physics-based reformulations and novel data-driven methodologies. We provide a systematic evaluation of current RANS-based turbulence modeling capabilities, comparing eddy viscosity and Reynolds stress transport formulations in their ability to predict engineering quantities of interest such as separation characteristics and wall heat transfer. Our analysis encompasses the latest experimental and direct numerical simulation datasets for validation, specifically addressing two- and three-dimensional equilibrium turbulent boundary layers and shock/turbulent boundary layer interactions across both smooth and rough surfaces. Key multi-physics considerations including catalysis and ablation phenomena along with the integration of conjugate heat transfer into a RANS solver for efficient design of a thermal protection system are also discussed. We conclude by identifying the critical gaps in the available validation databases and limitations of the existing turbulence models and suggest potential areas for future research to improve the fidelity of turbulence modeling in the hypersonic regime.
Model-Agnostic Cosmological Inference with SDSS-IV eBOSS: Simultaneous Probing for Background and Perturbed Universe
2024-12-18
Purba Mukherjee, Anjan A. Sen
Here we explore certain subtle features imprinted in data from the completed Sloan Digital Sky Survey IV (SDSS-IV) extended Baryon Oscillation Spectroscopic Survey (eBOSS) as a combined probe for the background and perturbed Universe. We reconstruct the baryon Acoustic Oscillation (BAO) and Redshift Space Distortion (RSD) observables as functions of redshift, using measurements from SDSS alone. We apply the Multi-Task Gaussian Process (MTGP) framework to model the interdependencies of cosmological observables $D_M(z)/r_d$, $D_H(z)/r_d$, and $f\sigma_8(z)$, and track their evolution across different redshifts. Subsequently, we obtain constrained three-dimensional phase space containing $D_M(z)/r_d$, $D_H(z)/r_d$, and $f\sigma_8(z)$ at different redshifts probed by the SDSS-IV eBOSS survey. Furthermore, assuming the $\Lambda$CDM model, we obtain constraints on model parameters $\Omega_{m}$, $H_{0}r_{d}$, $\sigma_{8}$ and $S_{8}$ at each redshift probed by SDSS-IV eBOSS. This indicates redshift-dependent trends in $H_0$, $\Omega_m$, $\sigma_8$ and $S_8$ in the $\Lambda$CDM model, suggesting a possible inconsistency in the $\Lambda$CDM model. Ours is a template for model-independent extraction of information for both background and perturbed Universe using a single galaxy survey taking into account all the existing correlations between background and perturbed observables and this can be easily extended to future DESI-3YR as well as Euclid results.
Gravitational wave astronomy and the expansion history of the Universe
2024-12-18
Massimo Giovannini
The timeline of the expansion rate ultimately defines the interplay between high energy physics, astrophysics and cosmology. The guiding theme of this topical review is provided by the scrutiny of the early history of the space-time curvature through the diffuse backgrounds of gravitational radiation that are sensitive to all the stages of the evolution of the plasma. Due to their broad spectrum (extending from the aHz region to the THz domain) they bridge the macroworld described by general relativity and the microworld of the fundamental constituents of matter. It is argued that during the next score year the analysis of the relic gravitons may infirm or confirm the current paradigm where a radiation plasma is assumed to dominate the whole post-inflationary epoch. The role of high frequency and ultra-high frequency signals between the MHz and the THz is emphasized in the perspective of quantum sensing. The multiparticle final state of the relic gravitons and its macroscopic quantumness is also discussed with particular attention to the interplay between the entanglement entropy and the maximal frequency of the spectrum.
Weak ergodicity breaking with isolated integrable sectors
2024-12-18
Hosho Katsura, Chihiro Matsui, Chiara Paletta, Balázs Pozsgay
We consider spin chain models with local Hamiltonians that display weak ergodicity breaking. In these models, the majority of the eigenstates are thermal, but there is a distinguished subspace of the Hilbert space in which ergodicity is broken. We achieve such a weak breaking by embedding selected integrable models into larger Hilbert spaces of otherwise chaotic models. The integrable subspaces do not have a tensor product structure with respect to any spatial bipartition, therefore our constructions differ from certain trivial embeddings. We consider multiple mechanisms for such an embedding, and we also review previous examples in the literature. Curiously, all our examples can be seen as perturbations of models with Hilbert space fragmentation, such that the perturbed models are not fragmented anymore.
Restricted subgraphs of edge-colored graphs and applications
2024-12-18
Benny Sudakov
A properly edge-colored graph is a graph with a coloring of its edges such that no vertex is incident to two or more edges of the same color. A subgraph is called rainbow if all its edges have different colors. The problem of finding rainbow subgraphs or other restricted structures in edge-colored graphs has a long history, dating back to Euler's work on Latin squares. It has also proven to be a powerful method for studying several well-known questions in other areas. In this survey, we will provide a brief introduction to this topic, discuss several results in this area, and demonstrate their applications to problems in graph decomposition, additive combinatorics, theoretical computer science, and coding theory.
Security and Privacy of Digital Twins for Advanced Manufacturing: A Survey
2024-12-18
Alexander D. Zemskov, Yao Fu, Runchao Li, Xufei Wang, Vispi Karkaria, Ying-Kuan Tsai, Wei Chen, Jianjing Zhang, Robert Gao, Jian Cao, Kenneth A. Loparo, Pan Li
In Industry 4.0, the digital twin is one of the emerging technologies, offering simulation abilities to predict, refine, and interpret conditions and operations, where it is crucial to emphasize a heightened concentration on the associated security and privacy risks. To be more specific, the adoption of digital twins in the manufacturing industry relies on integrating technologies like cyber-physical systems, the Industrial Internet of Things, virtualization, and advanced manufacturing. The interactions of these technologies give rise to numerous security and privacy vulnerabilities that remain inadequately explored. Towards that end, this paper analyzes the cybersecurity threats of digital twins for advanced manufacturing in the context of data collection, data sharing, machine learning and deep learning, and system-level security and privacy. We also provide several solutions to the threats in those four categories that can help establish more trust in digital twins.
Development of a High-Resolution, High-Dynamic-Range Charge Detector for Ion Beam Monitoring
2024-12-18
O. Adriani, E. Berti, P. Betti, J. Casaus, R. DAlessandro, S. Detti, C. Diaz, J. Marin, G. Martinez, N. Mori, L. Pacini, C. Pizzolotto, A. Tiberio, M. Scaringella, O. Starodubtsev, G. Zampa, N. Zampa
We present an innovative charge detector with high resolution and wide dynamic range designed to fulfill the requirements of a monitoring system for a high energy ion beam. The detector prototype, constructed using Si photodiodes and a custom readout electronics, underwent extensive testing during HERD and AMS beam tests at CERN SPS facilities. Initial testing showcased the detector's exceptional performance, emphasizing both high resolution and a dynamic range capable of measuring nuclei with atomic numbers ranging from 1 to 80. The prototype's compatibility with fast, quasi real-time data analysis qualifies it as an ideal candidate for online applications. This article presents the results from the testing phase of the prototype, highlighting its capabilities and performance. Ongoing detector development, potential applications, and future developments aimed at enhancing the detector's functionality and versatility are also discussed.
Deep learning solutions are instrumental in cybersecurity, harnessing their ability to analyze vast datasets, identify complex patterns, and detect anomalies. However, malevolent actors can exploit these capabilities to orchestrate sophisticated attacks, posing significant challenges to defenders and traditional security measures. Adversarial attacks, particularly those targeting vulnerabilities in deep learning models, present a nuanced and substantial threat to cybersecurity. Our study delves into adversarial learning threats such as Data Poisoning, Test Time Evasion, and Reverse Engineering, specifically impacting Network Intrusion Detection Systems. Our research explores the intricacies and countermeasures of attacks to deepen understanding of network security challenges amidst adversarial threats. In our study, we present insights into the dynamic realm of adversarial learning and its implications for network intrusion. The intersection of adversarial attacks and defenses within network traffic data, coupled with advances in machine learning and deep learning techniques, represents a relatively underexplored domain. Our research lays the groundwork for strengthening defense mechanisms to address the potential breaches in network security and privacy posed by adversarial attacks. Through our in-depth analysis, we identify domain-specific research gaps, such as the scarcity of real-life attack data and the evaluation of AI-based solutions for network traffic. Our focus on these challenges aims to stimulate future research efforts toward the development of resilient network defense strategies.
Achieving Dispatchability in Data Centers: Carbon and Cost-Aware Sizing of Energy Storage and Local Photovoltaic Generation
2024-12-18
Enea Figini, Mario Paolone
Data centers are large electricity consumers due to the high consumption needs of servers and their cooling systems. Given the current crypto-currency and artificial intelligence trends, the data center electricity demand is bound to grow significantly. With the electricity sector being responsible for a large share of global greenhouse gas (GHG) emissions, it is important to lower the carbon footprint of data centers to meet GHG emissions targets set by international agreements. Moreover, uncontrolled integration of data centers in power distribution grids contributes to increasing the stochasticity of the power system demand, thus increasing the need for capacity reserves, which leads to economic and environmental inefficiencies in the power grid operation. This work provides a method to size a PhotoVoltaic (PV) system and an Energy Storage System (ESS) for an existing data center looking to reduce both its carbon footprint and demand stochasticity via dispatching. The proposed scenario-based optimization framework allows to size the ESS and the PV system to minimize the expected operational and capital costs, along with the carbon footprint of the data center complex. The life cycle assessment of the resources, as well as the dynamic carbon emissions of the upstream power distribution grid, are accounted for while computing the day-ahead planning of the data center aggregated demand and PV generation. Case studies in different Swiss cantons and regions of Germany emphasize the need for location-aware sizing processes since the obtained optimal solutions strongly depend on the local electricity carbon footprint, cost and on the local irradiance conditions. Some regions show potential in carbon footprint reduction, while other regions do not.
As the adoption of machine learning (ML) systems continues to grow across industries, concerns about fairness and bias in these systems have taken center stage. Fairness toolkits, designed to mitigate bias in ML models, serve as critical tools for addressing these ethical concerns. However, their adoption in the context of software development remains underexplored, especially regarding the cognitive and behavioral factors driving their usage. As a deeper understanding of these factors could be pivotal in refining tool designs and promoting broader adoption, this study investigates the factors influencing the adoption of fairness toolkits from an individual perspective. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT2), we examined the factors shaping the intention to adopt and actual use of fairness toolkits. Specifically, we employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data from a survey study involving practitioners in the software industry. Our findings reveal that performance expectancy and habit are the primary drivers of fairness toolkit adoption. These insights suggest that by emphasizing the effectiveness of these tools in mitigating bias and fostering habitual use, organizations can encourage wider adoption. Practical recommendations include improving toolkit usability, integrating bias mitigation processes into routine development workflows, and providing ongoing support to ensure professionals see clear benefits from regular use.
Traditional mathematical models used in designing next-generation communication systems often fall short due to inherent simplifications, narrow scope, and computational limitations. In recent years, the incorporation of deep learning (DL) methodologies into communication systems has made significant progress in system design and performance optimisation. Autoencoders (AEs) have become essential, enabling end-to-end learning that allows for the combined optimisation of transmitters and receivers. Consequently, AEs offer a data-driven methodology capable of bridging the gap between theoretical models and real-world complexities. The paper presents a comprehensive survey of the application of AEs within communication systems, with a particular focus on their architectures, associated challenges, and future directions. We examine 120 recent studies across wireless, optical, semantic, and quantum communication fields, categorising them according to transceiver design, channel modelling, digital signal processing, and computational complexity. This paper further examines the challenges encountered in the implementation of AEs, including the need for extensive training data, the risk of overfitting, and the requirement for differentiable channel models. Through data-driven approaches, AEs provide robust solutions for end-to-end system optimisation, surpassing traditional mathematical models confined by simplifying assumptions. This paper also summarises the computational complexity associated with AE-based systems by conducting an in-depth analysis employing the metric of floating-point operations per second (FLOPS). This analysis encompasses the evaluation of matrix multiplications, bias additions, and activation functions. This survey aims to establish a roadmap for future research, emphasising the transformative potential of AEs in the formulation of next-generation communication systems.
Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey
2024-12-18
Yichen Li, Haozhao Wang, Wenchao Xu, Tianzhe Xiao, Hong Liu, Minzhu Tu, Yuying Wang, Xin Yang, Rui Zhang, Shui Yu, Song Guo, Ruixuan Li
Non-Centralized Continual Learning (NCCL) has become an emerging paradigm for enabling distributed devices such as vehicles and servers to handle streaming data from a joint non-stationary environment. To achieve high reliability and scalability in deploying this paradigm in distributed systems, it is essential to conquer challenges stemming from both spatial and temporal dimensions, manifesting as distribution shifts, catastrophic forgetting, heterogeneity, and privacy issues. This survey focuses on a comprehensive examination of the development of the non-centralized continual learning algorithms and the real-world deployment across distributed devices. We begin with an introduction to the background and fundamentals of non-centralized learning and continual learning. Then, we review existing solutions from three levels to represent how existing techniques alleviate the catastrophic forgetting and distribution shift. Additionally, we delve into the various types of heterogeneity issues, security, and privacy attributes, as well as real-world applications across three prevalent scenarios. Furthermore, we establish a large-scale benchmark to revisit this problem and analyze the performance of the state-of-the-art NCCL approaches. Finally, we discuss the important challenges and future research directions in NCCL.
Fool's gold: ligand-receptor interactions and the origins of life
2024-12-18
Betony Adams, Angela Illing, Francesco Petruccione
The origins of life is a question that continues to intrigue scientists across disciplines. One theory - the iron-sulphur theory - suggests that reactions essential to the synthesis of biological materials got their catalytic 'spark' from mineral surfaces such as iron pyrite, commonly known as fool's gold. Additionally, the binding affinity of the ligands synthesised in this 'surface metabolism' acted as an early version of natural selection: more strongly-binding ligands were accumulated into further autocatalytic reactions and the aggregation of complex biological materials. Ligand-receptor binding is thus fundamental to the origins of life. In this paper, we use the iron-sulphur theory as a lens through which to review ligand-receptor interactions as they are more commonly understood today. In particular we focus on the electron tunnelling theory of receptor activation that has emerged from research into quantum biology. We revisit criticism against this theory, particularly the lack of evidence for electron transfer in receptors, to see what insights might be offered by ligand-receptor interactions mediated by iron pyrite at the origins of life. What emerges from this comparison is the central importance of redox activity in receptors, in particular with respect to the recurring presence of the disulphide bond. While the paper is a speculative exercise, we conclude that conductivity in biomolecules, particularly the selective conductivity conferred by appropriate ligand-receptor binding, is a powerful tool for understanding diverse phenomena such as pharmacological potency and viral infection. As such it deserves further investigation.
Recent studies have shown that large vision-language models (LVLMs) often suffer from the issue of object hallucinations (OH). To mitigate this issue, we introduce an efficient method that edits the model weights based on an unsafe subspace, which we call HalluSpace in this paper. With truthful and hallucinated text prompts accompanying the visual content as inputs, the HalluSpace can be identified by extracting the hallucinated embedding features and removing the truthful representations in LVLMs. By orthogonalizing the model weights, input features will be projected into the Null space of the HalluSpace to reduce OH, based on which we name our method Nullu. We reveal that HalluSpaces generally contain statistical bias and unimodal priors of the large language models (LLMs) applied to build LVLMs, which have been shown as essential causes of OH in previous studies. Therefore, null space projection suppresses the LLMs' priors to filter out the hallucinated features, resulting in contextually accurate outputs. Experiments show that our method can effectively mitigate OH across different LVLM families without extra inference costs and also show strong performance in general LVLM benchmarks. Code is released at \url{https://github.com/Ziwei-Zheng/Nullu}.
SimADFuzz: Simulation-Feedback Fuzz Testing for Autonomous Driving Systems
2024-12-18
Huiwen Yang, Yu Zhou, Taolue Chen
Autonomous driving systems (ADS) have achieved remarkable progress in recent years. However, ensuring their safety and reliability remains a critical challenge due to the complexity and uncertainty of driving scenarios. In this paper, we focus on simulation testing for ADS, where generating diverse and effective testing scenarios is a central task. Existing fuzz testing methods face limitations, such as overlooking the temporal and spatial dynamics of scenarios and failing to leverage simulation feedback (e.g., speed, acceleration and heading) to guide scenario selection and mutation. To address these issues, we propose SimADFuzz, a novel framework designed to generate high-quality scenarios that reveal violations in ADS behavior. Specifically, SimADFuzz employs violation prediction models, which evaluate the likelihood of ADS violations, to optimize scenario selection. Moreover, SimADFuzz proposes distance-guided mutation strategies to enhance interactions among vehicles in offspring scenarios, thereby triggering more edge-case behaviors of vehicles. Comprehensive experiments demonstrate that SimADFuzz outperforms state-of-the-art fuzzers by identifying 32 more unique violations, including 4 reproducible cases of vehicle-vehicle and vehicle-pedestrian collisions. These results demonstrate SimADFuzz's effectiveness in enhancing the robustness and safety of autonomous driving systems.
LLM-SEM: A Sentiment-Based Student Engagement Metric Using LLMS for E-Learning Platforms
2024-12-18
Ali Hamdi, Ahmed Abdelmoneim Mazrou, Mohamed Shaltout
Current methods for analyzing student engagement in e-learning platforms, including automated systems, often struggle with challenges such as handling fuzzy sentiment in text comments and relying on limited metadata. Traditional approaches, such as surveys and questionnaires, also face issues like small sample sizes and scalability. In this paper, we introduce LLM-SEM (Language Model-Based Student Engagement Metric), a novel approach that leverages video metadata and sentiment analysis of student comments to measure engagement. By utilizing recent Large Language Models (LLMs), we generate high-quality sentiment predictions to mitigate text fuzziness and normalize key features such as views and likes. Our holistic method combines comprehensive metadata with sentiment polarity scores to gauge engagement at both the course and lesson levels. Extensive experiments were conducted to evaluate various LLM models, demonstrating the effectiveness of LLM-SEM in providing a scalable and accurate measure of student engagement. We fine-tuned TXLM-RoBERTa using human-annotated sentiment datasets to enhance prediction accuracy and utilized LLama 3B, and Gemma 9B from Ollama.
The Mira discovery problem -- Observations by David Fabricius in 1596 and 1609 (and by others before?): Positional accuracy, brightness, color index, and period
2024-12-18
R. Neuhäuser, D. L. Neuhäuser, M. Mugrauer, D. Luge, J. Chapman
The pulsating variable star Mira (omikron Ceti) was observed by David Fabricius (Frisia) in 1596 and 1609. We review suggested previous detections (e.g. China, Hipparchos). We analyze all Mira records from Fabricius in their historical context. Fabricius measured the separation of Mira to other stars to \pm 1.6-1.7'. From his texts, we derive a brightness (slightly brighter than Hamal) of ca. 1.9 \pm 0.1 mag and a color index B-V \simeq 1.3-1.4 mag (`like Mars') for 1596 Aug 3 (jul.). Mira started to fainten 19 days later and was observed until mid/late Oct. We show why such a red star cannot be followed by the naked eye until ca. 6 mag: For Mira's color at disappearance and altitude from Frisia, the limit is reduced by ca. 1.0 mag. Since Fabricius connected the Mira brightening with the close-by prograde Jupiter, he re-detected it only 12 years later, probably shortly before a relatively bright maximum - discoveries are strongly affected by biases. A Mira period of 330.2 days is consistent with both the oldest data (from Fabricius 1596 to Hevelius 1660) and the most current data (VSX 2004-2023), so that we see no evidence for secular period or phase shifts. (We also present Fabricius' observations of P Cygni in 1602.)
User-Generated Content and Editors in Games: A Comprehensive Survey
2024-12-18
Yuyue Liu, Haihan Duan, Wei Cai
User-Generated Content (UGC) refers to any form of content, such as posts and images, created by users rather than by professionals. In recent years, UGC has become an essential part of the evolving video game industry, influencing both game culture and community dynamics. The ability for users to actively contribute to the games they engage with has shifted the landscape of gaming from a one-directional entertainment experience into a collaborative, user-driven ecosystem. Therefore, this growing trend highlights the urgent need for summarizing the current UGC development in game industry. Our conference paper has systematically classified the existing UGC in games and the UGC editors separately into four types. However, the previous survey lacks the depth and precision necessary to capture the wide-ranging and increasingly complex nature of UGC. To this end, as an extension of previous work, this paper presents a refined and expanded classification of UGC and UGC editors within video games, offering a more robust and comprehensive framework with representative cases that better reflects the diversity and nuances of contemporary user-generated contributions. Moreover, we provide our insights on the future of UGC, involving game culture, game genre and user creative tendencies, artificial intelligence, its potential ethical considerations, and relationship between games, users and communities.
MedCoT: Medical Chain of Thought via Hierarchical Expert
2024-12-18
Jiaxiang Liu, Yuan Wang, Jiawei Du, Joey Tianyi Zhou, Zuozhu Liu
Artificial intelligence has advanced in Medical Visual Question Answering (Med-VQA), but prevalent research tends to focus on the accuracy of the answers, often overlooking the reasoning paths and interpretability, which are crucial in clinical settings. Besides, current Med-VQA algorithms, typically reliant on singular models, lack the robustness needed for real-world medical diagnostics which usually require collaborative expert evaluation. To address these shortcomings, this paper presents MedCoT, a novel hierarchical expert verification reasoning chain method designed to enhance interpretability and accuracy in biomedical imaging inquiries. MedCoT is predicated on two principles: The necessity for explicit reasoning paths in Med-VQA and the requirement for multi-expert review to formulate accurate conclusions. The methodology involves an Initial Specialist proposing diagnostic rationales, followed by a Follow-up Specialist who validates these rationales, and finally, a consensus is reached through a vote among a sparse Mixture of Experts within the locally deployed Diagnostic Specialist, which then provides the definitive diagnosis. Experimental evaluations on four standard Med-VQA datasets demonstrate that MedCoT surpasses existing state-of-the-art approaches, providing significant improvements in performance and interpretability.
3D point cloud registration is a fundamental problem in computer vision, computer graphics, robotics, remote sensing, and etc. Over the last thirty years, we have witnessed the amazing advancement in this area with numerous kinds of solutions. Although a handful of relevant surveys have been conducted, their coverage is still limited. In this work, we present a comprehensive survey on 3D point cloud registration, covering a set of sub-areas such as pairwise coarse registration, pairwise fine registration, multi-view registration, cross-scale registration, and multi-instance registration. The datasets, evaluation metrics, method taxonomy, discussions of the merits and demerits, insightful thoughts of future directions are comprehensively presented in this survey. The regularly updated project page of the survey is available at https://github.com/Amyyyy11/3D-Registration-in-30-Years-A-Survey.
We investigate cosmological constraints on local position invariance (LPI), a key aspect of the Einstein equivalence principle (EEP), through asymmetric galaxy clustering. The LPI asserts that the outcomes of the non-gravitational experiments are identical regardless of location in spacetime and has been tested through measurements of the gravitational redshift effect. Therefore, measuring the gravitational redshift effect encoded in galaxy clustering provides a powerful and novel cosmological probe of the LPI. Recent work by Saga et al. proposed its validation using the cross-correlation function between distinct galaxy samples, but their analysis focused solely on the dipole moment. In this paper, we extend their work by further analyzing a higher-order odd multipole moment, the octupole moment, in the constraints on the LPI-violating parameter, $\alpha$, expected from galaxy surveys such as Dark Energy Spectroscopic Instrument, Euclid space telescope, Subaru Prime Focus Spectrograph, and Square Kilometre Array. We demonstrate that combining the octupole and dipole moments significantly improves the constraints, particularly when the analysis is restricted to larger scales, characterized by a large minimum separation $s_{\rm min}$. For a conservative setup with $s_{\rm min}=15 {\rm Mpc}/h$, we find an average improvement of 11$\%$ compared to using the dipole moment alone. Our results highlight the importance of higher-order multipoles in constraining $\alpha$, providing a more robust approach to testing the EEP on cosmological scales.
Splitting criteria for ordinal decision trees: an experimental study
2024-12-18
Rafael Ayllón-Gavilán, Francisco José Martínez-Estudillo, David Guijo-Rubio, César Hervás-Martínez, Pedro Antonio Gutiérrez
Ordinal Classification (OC) is a machine learning field that addresses classification tasks where the labels exhibit a natural order. Unlike nominal classification, which treats all classes as equally distinct, OC takes the ordinal relationship into account, producing more accurate and relevant results. This is particularly critical in applications where the magnitude of classification errors has implications. Despite this, OC problems are often tackled using nominal methods, leading to suboptimal solutions. Although decision trees are one of the most popular classification approaches, ordinal tree-based approaches have received less attention when compared to other classifiers. This work conducts an experimental study of tree-based methodologies specifically designed to capture ordinal relationships. A comprehensive survey of ordinal splitting criteria is provided, standardising the notations used in the literature for clarity. Three ordinal splitting criteria, Ordinal Gini (OGini), Weighted Information Gain (WIG), and Ranking Impurity (RI), are compared to the nominal counterparts of the first two (Gini and information gain), by incorporating them into a decision tree classifier. An extensive repository considering 45 publicly available OC datasets is presented, supporting the first experimental comparison of ordinal and nominal splitting criteria using well-known OC evaluation metrics. Statistical analysis of the results highlights OGini as the most effective ordinal splitting criterion to date. Source code, datasets, and results are made available to the research community.
Optical aberrations in autonomous driving: Physics-informed parameterized temperature scaling for neural network uncertainty calibration
2024-12-18
Dominik Werner Wolf, Alexander Braun, Markus Ulrich
'A trustworthy representation of uncertainty is desirable and should be considered as a key feature of any machine learning method' (Huellermeier and Waegeman, 2021). This conclusion of Huellermeier et al. underpins the importance of calibrated uncertainties. Since AI-based algorithms are heavily impacted by dataset shifts, the automotive industry needs to safeguard its system against all possible contingencies. One important but often neglected dataset shift is caused by optical aberrations induced by the windshield. For the verification of the perception system performance, requirements on the AI performance need to be translated into optical metrics by a bijective mapping (Braun, 2023). Given this bijective mapping it is evident that the optical system characteristics add additional information about the magnitude of the dataset shift. As a consequence, we propose to incorporate a physical inductive bias into the neural network calibration architecture to enhance the robustness and the trustworthiness of the AI target application, which we demonstrate by using a semantic segmentation task as an example. By utilizing the Zernike coefficient vector of the optical system as a physical prior we can significantly reduce the mean expected calibration error in case of optical aberrations. As a result, we pave the way for a trustworthy uncertainty representation and for a holistic verification strategy of the perception chain.
Clio: Privacy-Preserving Insights into Real-World AI Use
2024-12-18
Alex Tamkin, Miles McCain, Kunal Handa, Esin Durmus, Liane Lovitt, Ankur Rathi, Saffron Huang, Alfred Mountfield, Jerry Hong, Stuart Ritchie, Michael Stern, Brian Clarke, Landon Goldberg, Theodore R. Sumers, Jared Mueller, William McEachen, Wes Mitchell, Shan Carter, Jack Clark, Jared Kaplan, Deep Ganguli
How are AI assistants being used in the real world? While model providers in theory have a window into this impact via their users' data, both privacy concerns and practical challenges have made analyzing this data difficult. To address these issues, we present Clio (Claude insights and observations), a privacy-preserving platform that uses AI assistants themselves to analyze and surface aggregated usage patterns across millions of conversations, without the need for human reviewers to read raw conversations. We validate this can be done with a high degree of accuracy and privacy by conducting extensive evaluations. We demonstrate Clio's usefulness in two broad ways. First, we share insights about how models are being used in the real world from one million Claude.ai Free and Pro conversations, ranging from providing advice on hairstyles to providing guidance on Git operations and concepts. We also identify the most common high-level use cases on Claude.ai (coding, writing, and research tasks) as well as patterns that differ across languages (e.g., conversations in Japanese discuss elder care and aging populations at higher-than-typical rates). Second, we use Clio to make our systems safer by identifying coordinated attempts to abuse our systems, monitoring for unknown unknowns during critical periods like launches of new capabilities or major world events, and improving our existing monitoring systems. We also discuss the limitations of our approach, as well as risks and ethical concerns. By enabling analysis of real-world AI usage, Clio provides a scalable platform for empirically grounded AI safety and governance.
Learning to Control an Android Robot Head for Facial Animation
2024-12-18
Marcel Heisler, Christian Becker-Asano
The ability to display rich facial expressions is crucial for human-like robotic heads. While manually defining such expressions is intricate, there already exist approaches to automatically learn them. In this work one such approach is applied to evaluate and control a robot head different from the one in the original study. To improve the mapping of facial expressions from human actors onto a robot head, it is proposed to use 3D landmarks and their pairwise distances as input to the learning algorithm instead of the previously used facial action units. Participants of an online survey preferred mappings from our proposed approach in most cases, though there are still further improvements required.
Are LLMs Good Literature Review Writers? Evaluating the Literature Review Writing Ability of Large Language Models
2024-12-18
Xuemei Tang, Xufeng Duan, Zhenguang G. Cai
The literature review is a crucial form of academic writing that involves complex processes of literature collection, organization, and summarization. The emergence of large language models (LLMs) has introduced promising tools to automate these processes. However, their actual capabilities in writing comprehensive literature reviews remain underexplored, such as whether they can generate accurate and reliable references. To address this gap, we propose a framework to assess the literature review writing ability of LLMs automatically. We evaluate the performance of LLMs across three tasks: generating references, writing abstracts, and writing literature reviews. We employ external tools for a multidimensional evaluation, which includes assessing hallucination rates in references, semantic coverage, and factual consistency with human-written context. By analyzing the experimental results, we find that, despite advancements, even the most sophisticated models still cannot avoid generating hallucinated references. Additionally, different models exhibit varying performance in literature review writing across different disciplines.
Single-cell spatial (scs) omics: Recent developments in data analysis
2024-12-18
José Camacho, Michael Sorochan Armstrong, Luz García-Martínez, Caridad Díaz, Carolina Gómez-Llorente
Over the past few years, technological advances have allowed for measurement of omics data at the cell level, creating a new type of data generally referred to as single-cell (sc) omics. On the other hand, the so-called spatial omics are a family of techniques that generate biological information in a spatial domain, for instance, in the volume of a tissue. In this survey, we are mostly interested in the intersection between sc and spatial (scs) omics and in the challenges and opportunities that this new type of data pose for downstream data analysis methodologies. Our goal is to cover all major omics modalities, including transcriptomics, genomics, epigenomics, proteomics and metabolomics.
Extragalactic globular cluster near-infrared spectroscopy: II. The stellar population synthesis model zero-point problem extends to the near-infrared
2024-12-18
E. Eftekhari, A. Vazdekis, R. Riffel, L. G. Dahmer-Hahn, A. L. Chies-Santos, M. A. Beasley, A. Villaume, E. Zanatta
Many recent studies have pointed out significant discrepancies between observations and models of stellar populations in the near-infrared (NIR). With current and future observing facilities being focused in this wavelength range, properly assessing and solving these issues is of utmost importance. Here, we present the first application of the extragalactic globular cluster (GC) near-infrared spectroscopy survey, and present evidence that these GCs reveal an age zero-point problem of stellar population synthesis (SPS) models. This problem has already been identified in the optical range for the GCs of the Milky Way. Such an issue arises when derived GC spectroscopic ages appear older than the Universe itself. We extend this discussion for the first time to the NIR, specifically using the Pa$_{\rm\beta}$ line at 1.28~microns. We focus on the GCs of the nearby Centaurus~A galaxy using their NIR spectra. This work broadens our understanding of the age zero-point problem and emphasises the necessity to revisit and refine SPS models, especially in the NIR domain.
Extragalactic globular cluster near-infrared spectroscopy: I. Integrated near-infrared spectra of Centaurus A/NGC 5128
2024-12-18
L. G. Dahmer-Hahn, A. L. Chies-Santos, E. Eftekhari, E. Zanatta, R. Riffel, A. Vazdekis, A. Villaume, M. A. Beasley, A. E. Lassen
One way to constrain the evolutionary histories of galaxies is to analyse their stellar populations. In the local Universe, our understanding of the stellar population properties of galaxies has traditionally relied on the study of optical absorption and emission-line features. In order to overcome limitations intrinsic to this wavelength range, such as the age-metallicity degeneracy and the high sensitivity to dust reddening, we must use wavelength ranges beyond the optical. The near-infrared (NIR) offers a possibility to extract information on spectral signatures that are not as obvious in traditional optical bands. Moreover, with the current and forthcoming generation of instrumentation focusing on the NIR, it is mandatory to explore possibilities within this wavelength range for nearby-Universe galaxies. However, although the NIR shows great potential, we are only beginning to understand it. Widely used techniques such as a full spectral fitting and line strength indices need to be tested on systems that are as close to simple stellar populations as possible, and the result from the techniques need to be compared to the yields from a traditional optical analysis. We present a NIR spectral survey of extragalactic globular clusters (GCs). The set was composed of 21 GCs from NGC5128 that were obtained with SOAR/TripleSpec4, which covered the ~1.0-2.4$\mu$m range with a spectral resolution R=$\lambda/\Delta\lambda$ of 3500. These spectra cover H$\beta$ equivalent widths between 0.98$\r{A}$ and 4.32$\r{A}$, and [MgFe]' between 0.24$\r{A}$ and 3.76$\r{A}$. This set is ideal for performing absorption band measurements and a full spectral fitting, and it can be used for kinematic studies and age and abundance measurements. With this library, we expect to be able to probe the capabilities of NIR models, as well as to further improve stellar population estimates for the GCs around NGC5128.
Which Imputation Fits Which Feature Selection Method? A Survey-Based Simulation Study
2024-12-18
Jakob Schwerter, Andrés Romero, Florian Dumpert, Markus Pauly
Tree-based learning methods such as Random Forest and XGBoost are still the gold-standard prediction methods for tabular data. Feature importance measures are usually considered for feature selection as well as to assess the effect of features on the outcome variables in the model. This also applies to survey data, which are frequently encountered in the social sciences and official statistics. These types of datasets often present the challenge of missing values. The typical solution is to impute the missing data before applying the learning method. However, given the large number of possible imputation methods available, the question arises as to which should be chosen to achieve the 'best' reflection of feature importance and feature selection in subsequent analyses. In the present paper, we investigate this question in a survey-based simulation study for eight state-of-the art imputation methods and three learners. The imputation methods comprise listwise deletion, three MICE options, four \texttt{missRanger} options as well as the recently proposed mixGBoost imputation approach. As learners, we consider the two most common tree-based methods, Random Forest and XGBoost, and an interpretable linear model with regularization.
We develop the framework of Indirect Query Bayesian Optimization (IQBO), a new class of Bayesian optimization problems where the integrated feedback is given via a conditional expectation of the unknown function $f$ to be optimized. The underlying conditional distribution can be unknown and learned from data. The goal is to find the global optimum of $f$ by adaptively querying and observing in the space transformed by the conditional distribution. This is motivated by real-world applications where one cannot access direct feedback due to privacy, hardware or computational constraints. We propose the Conditional Max-Value Entropy Search (CMES) acquisition function to address this novel setting, and propose a hierarchical search algorithm to address the multi-resolution setting and improve the computational efficiency. We show regret bounds for our proposed methods and demonstrate the effectiveness of our approaches on simulated optimization tasks.
An Analysis of the Relationship Between the Characteristics of Innovative Consumers and the Degree of Serious Leisure in User Innovation
2024-12-18
Taichi Abe, Yasunobu Morita
This study examines the relationship between the concept of serious leisure and user innovation. We adopted the characteristics of innovative consumers identified by Luthje (2004)-product use experience, information exchange, and new product adoption speed-to analyze their correlation with serious leisure engagement. The analysis utilized consumer behavior survey data from the "Marketing Analysis Contest 2023" sponsored by Nomura Research Institute, examining the relationship between innovative consumer characteristics and the degree of serious leisure (Serious Leisure Inventory and Measure: SLIM). Since the contest data did not directly measure innovative consumer characteristics or serious leisure engagement, we established alternative variables for quantitative analysis. The results showed that the SLIM alternative variable had positive correlations with diverse product experiences and early adoption of new products. However, no clear relationship was found with information exchange among consumers. These findings suggest that serious leisure practice may serve as a potential antecedent to user innovation. The leisure career perspective of the serious leisure concept may capture the motivations of user innovators that Okada and Nishikawa (2019) identified.
Lyapunov Analysis For Monotonically Forward-Backward Accelerated Algorithms
2024-12-18
Mingwei Fu, Bin Shi
In the realm of gradient-based optimization, Nesterov's accelerated gradient method (NAG) is a landmark advancement, achieving an accelerated convergence rate that outperforms the vanilla gradient descent method for convex function. However, for strongly convex functions, whether NAG converges linearly remains an open question, as noted in the comprehensive review by Chambolle and Pock [2016]. This issue, aside from the critical step size, was addressed by Li et al. [2024a] using a high-resolution differential equation framework. Furthermore, Beck [2017, Section 10.7.4] introduced a monotonically convergent variant of NAG, referred to as M-NAG. Despite these developments, the Lyapunov analysis presented in [Li et al., 2024a] cannot be directly extended to M-NAG. In this paper, we propose a modification to the iterative relation by introducing a gradient term, leading to a new gradient-based iterative relation. This adjustment allows for the construction of a novel Lyapunov function that excludes kinetic energy. The linear convergence derived from this Lyapunov function is independent of both the parameters of the strongly convex functions and the step size, yielding a more general and robust result. Notably, we observe that the gradient iterative relation derived from M-NAG is equivalent to that from NAG when the position-velocity relation is applied. However, the Lyapunov analysis does not rely on the position-velocity relation, allowing us to extend the linear convergence to M-NAG. Finally, by utilizing two proximal inequalities, which serve as the proximal counterparts of strongly convex inequalities, we extend the linear convergence to both the fast iterative shrinkage-thresholding algorithm (FISTA) and its monotonic counterpart (M-FISTA).
2024-12-18
Dang Nguyen, Jian Chen, Yu Wang, Gang Wu, Namyong Park, Zhengmian Hu, Hanjia Lyu, Junda Wu, Ryan Aponte, Yu Xia, Xintong Li, Jing Shi, Hongjie Chen, Viet Dac Lai, Zhouhang Xie, Sungchul Kim, Ruiyi Zhang, Tong Yu, Mehrab Tanjim, Nesreen K. Ahmed, Puneet Mathur, Seunghyun Yoon, Lina Yao, Branislav Kveton, Thien Huu Nguyen, Trung Bui, Tianyi Zhou, Ryan A. Rossi, Franck Dernoncourt
Graphical User Interface (GUI) agents, powered by Large Foundation Models, have emerged as a transformative approach to automating human-computer interaction. These agents autonomously interact with digital systems or software applications via GUIs, emulating human actions such as clicking, typing, and navigating visual elements across diverse platforms. Motivated by the growing interest and fundamental importance of GUI agents, we provide a comprehensive survey that categorizes their benchmarks, evaluation metrics, architectures, and training methods. We propose a unified framework that delineates their perception, reasoning, planning, and acting capabilities. Furthermore, we identify important open challenges and discuss key future directions. Finally, this work serves as a basis for practitioners and researchers to gain an intuitive understanding of current progress, techniques, benchmarks, and critical open problems that remain to be addressed.
T$^3$-S2S: Training-free Triplet Tuning for Sketch to Scene Generation
2024-12-18
Zhenhong Sun, Yifu Wang, Yonhon Ng, Yunfei Duan, Daoyi Dong, Hongdong Li, Pan Ji
Scene generation is crucial to many computer graphics applications. Recent advances in generative AI have streamlined sketch-to-image workflows, easing the workload for artists and designers in creating scene concept art. However, these methods often struggle for complex scenes with multiple detailed objects, sometimes missing small or uncommon instances. In this paper, we propose a Training-free Triplet Tuning for Sketch-to-Scene (T3-S2S) generation after reviewing the entire cross-attention mechanism. This scheme revitalizes the existing ControlNet model, enabling effective handling of multi-instance generations, involving prompt balance, characteristics prominence, and dense tuning. Specifically, this approach enhances keyword representation via the prompt balance module, reducing the risk of missing critical instances. It also includes a characteristics prominence module that highlights TopK indices in each channel, ensuring essential features are better represented based on token sketches. Additionally, it employs dense tuning to refine contour details in the attention map, compensating for instance-related regions. Experiments validate that our triplet tuning approach substantially improves the performance of existing sketch-to-image models. It consistently generates detailed, multi-instance 2D images, closely adhering to the input prompts and enhancing visual quality in complex multi-instance scenes. Code is available at https://github.com/chaos-sun/t3s2s.git.
Transducer Tuning: Efficient Model Adaptation for Software Tasks Using Code Property Graphs
2024-12-18
Imam Nur Bani Yusuf, Lingxiao Jiang
Large language models have demonstrated promising performance across various software engineering tasks. While fine-tuning is a common practice to adapt these models for downstream tasks, it becomes challenging in resource-constrained environments due to increased memory requirements from growing trainable parameters in increasingly large language models. We introduce \approach, a technique to adapt large models for downstream code tasks using Code Property Graphs (CPGs). Our approach introduces a modular component called \transducer that enriches code embeddings with structural and dependency information from CPGs. The Transducer comprises two key components: Graph Vectorization Engine (GVE) and Attention-Based Fusion Layer (ABFL). GVE extracts CPGs from input source code and transforms them into graph feature vectors. ABFL then fuses those graphs feature vectors with initial code embeddings from a large language model. By optimizing these transducers for different downstream tasks, our approach enhances the models without the need to fine-tune them for specific tasks. We have evaluated \approach on three downstream tasks: code summarization, assert generation, and code translation. Our results demonstrate competitive performance compared to full parameter fine-tuning while reducing up to 99\% trainable parameters to save memory. \approach also remains competitive against other fine-tuning approaches (e.g., LoRA, Prompt-Tuning, Prefix-Tuning) while using only 1.5\%-80\% of their trainable parameters. Our findings show that integrating structural and dependency information through Transducer Tuning enables more efficient model adaptation, making it easier for users to adapt large models in resource-constrained settings.
Inducing Berry Curvature Dipole in Multilayer Graphene through Inhomogeneous Interlayer Sliding
2024-12-18
Jie Pan, Huanhuan Wang, Lin Zou, Haibo Xie, Yi Ding, Yuze Zhang, Aiping Fang, Zhe Wang
Breaking lattice symmetry is crucial for generating a nonzero Berry curvature. While manipulating twisting angles between adjacent layers has successfully broken lattice symmetry through strain field and generated nonzero Berry curvature, interlayer sliding in principle offers a promising alternative route. However, realizing uniform interlayer sliding faces experimental challenges due to its energetic instability. In this work, we introduce an experimentally feasible method, using a corrugated substrate to induce an inhomogeneous but energetically more stable interlayer sliding in multilayer graphene. Our simulations demonstrate that inhomogeneous interlayer sliding produces a sizable Berry curvature dipole, which can be further tuned by varying the interlayer sliding distances and potential differences. The resulting Berry curvature dipole magnitude is remarkably up to 100 times greater than the maximum displacement involved in the inhomogeneous sliding. Our results highlight inhomogeneous interlayer sliding as a viable and effective method to induce a significant Berry curvature dipole in graphene systems and propose the experimentally feasible way to realize it.
Mass Acquisition of Dirac Fermions in Bi4I4 by Spontaneous Symmetry Breaking
2024-12-18
Ming Yang, Wenxuan Zhao, Dan Mu, Zhijian Shi, Jingyuan Zhong, Yaqi Li, Yundan Liu, Jianxin Zhong, Ningyan Cheng, Wei Zhou, Jianfeng Wang, Yan Shi, Ying Sun, Weichang Hao, Lexian Yang, Jincheng Zhuang, Yi Du
Massive Dirac fermions, which are essential for realizing novel topological phenomena, are expected to be generated from massless Dirac fermions by breaking the related symmetry, such as time-reversal symmetry (TRS) in topological insulators or crystal symmetry in topological crystalline insulators. Here, we report scanning tunneling microscopy and angle-resolved photoemission spectroscopy studies of {\alpha}-Bi4I4, which reveals the realization of massive Dirac fermions in the (100) surface states without breaking the TRS. Combined with first-principle calculations, our experimental results indicate that the spontaneous symmetry breaking engenders two nondegenerate edges states at the opposite sides of monolayer Bi4I4 after the structural phase transition, imparting mass to the Dirac fermions after taking the interlayer coupling into account. Our results not only demonstrate the formation of the massive Dirac fermions by spontaneous symmetry breaking, but also imply the potential for the engineering of Dirac fermions for device applications.
Toward an Insider Threat Education Platform: A Theoretical Literature Review
2024-12-18
Haywood Gelman, John D. Hastings, David Kenley, Eleanor Loiacono
Insider threats (InTs) within organizations are small in number but have a disproportionate ability to damage systems, information, and infrastructure. Existing InT research studies the problem from psychological, technical, and educational perspectives. Proposed theories include research on psychological indicators, machine learning, user behavioral log analysis, and educational methods to teach employees recognition and mitigation techniques. Because InTs are a human problem, training methods that address InT detection from a behavioral perspective are critical. While numerous technological and psychological theories exist on detection, prevention, and mitigation, few training methods prioritize psychological indicators. This literature review studied peer-reviewed, InT research organized by subtopic and extracted critical theories from psychological, technical, and educational disciplines. In doing so, this is the first study to comprehensively organize research across all three approaches in a manner which properly informs the development of an InT education platform.
Foundation model (FM) powered agent services are regarded as a promising solution to develop intelligent and personalized applications for advancing toward Artificial General Intelligence (AGI). To achieve high reliability and scalability in deploying these agent services, it is essential to collaboratively optimize computational and communication resources, thereby ensuring effective resource allocation and seamless service delivery. In pursuit of this vision, this paper proposes a unified framework aimed at providing a comprehensive survey on deploying FM-based agent services across heterogeneous devices, with the emphasis on the integration of model and resource optimization to establish a robust infrastructure for these services. Particularly, this paper begins with exploring various low-level optimization strategies during inference and studies approaches that enhance system scalability, such as parallelism techniques and resource scaling methods. The paper then discusses several prominent FMs and investigates research efforts focused on inference acceleration, including techniques such as model compression and token reduction. Moreover, the paper also investigates critical components for constructing agent services and highlights notable intelligent applications. Finally, the paper presents potential research directions for developing real-time agent services with high Quality of Service (QoS).
Large Language Model (LLM) has transformative potential in various domains, including recommender systems (RS). There have been a handful of research that focuses on empowering the RS by LLM. However, previous efforts mainly focus on LLM as RS, which may face the challenge of intolerant inference costs by LLM. Recently, the integration of LLM into RS, known as LLM-Enhanced Recommender Systems (LLMERS), has garnered significant interest due to its potential to address latency and memory constraints in real-world applications. This paper presents a comprehensive survey of the latest research efforts aimed at leveraging LLM to enhance RS capabilities. We identify a critical shift in the field with the move towards incorporating LLM into the online system, notably by avoiding their use during inference. Our survey categorizes the existing LLMERS approaches into three primary types based on the component of the RS model being augmented: Knowledge Enhancement, Interaction Enhancement, and Model Enhancement. We provide an in-depth analysis of each category, discussing the methodologies, challenges, and contributions of recent studies. Furthermore, we highlight several promising research directions that could further advance the field of LLMERS.
A review of scientific literature showed the relevance of the issue of assessing the training of an engineering specialist. Engineering includes a variety of works that relate to production issues. To assess the training of an engineering specialist, the digital twin of the enterprise is used. The digital twin of an enterprise includes all major pre-production, production and production support processes. A method for assessing the competencies received by an engineering specialist has been selected. The set of competencies is associated with the ongoing processes of the digital twin of an industrial enterprise. The normal mode of execution of the enterprise production processes is measured. The mode of operation of the processes was measured taking into account the new competencies of an engineering specialist. The study proposes a methodology for assessing the competence of an engineering specialist. The assessment of the effectiveness of the training passed by him.
Dark Matter: Explanatory Unification and Historical Continuity
2024-12-18
Simon Allzén
In recent years, the hope to confirm the existence of dark matter by experimentally detecting it has diminished significantly. After more than 30 years of experimental searches, many of the most promising candidates have since been ruled out, leaving the epistemic and scientific condition of dark matter in a state of suspension. In efforts to improve the epistemic justification for the dark-matter hypothesis, physicists have turned to philosophical arguments and historical narratives. In this paper, I explicate two such strategies -- explanatory unification and historical continuity -- applied in the context of dark matter. I argue that greater care and attention should be invested in the explanatory arguments to increase their strength, and that a survey of primary historical sources in astronomy renders the historical evidence for the continuity of dark matter substantially weaker. The quality and rigor of the philosophical and historical arguments which physicists are constructing could be substantially improved by increasing interdisciplinary practices.
Dynamic charge order from strong correlations in the cuprates
2024-12-18
Eduardo H. da Silva Neto, Alex Frano, Fabio Boschini
Charge order has been a central focus in the study of cuprate high-temperature superconductors due to its intriguing yet not fully understood connection to superconductivity. Recent advances in resonant inelastic x-ray scattering (RIXS) in the soft x-ray regime have enabled the first momentum-resolved studies of dynamic charge order correlations in the cuprates. This progress has opened a window for a more nuanced investigation into the mechanisms behind the formation of charge order (CO) correlations. This review provides an overview of RIXS-based measurements of dynamic CO correlations in various cuprate materials. It specifically focuses on electron-doped cuprates and Bi-based hole-doped cuprates, where the CO-related RIXS signals may reveal signatures of the effective Coulomb interactions. This aims to explore a connection between two central phenomena in the cuprates: strong Coulomb correlations and CO-forming tendencies. Finally, we discuss current open questions and potential directions for future RIXS studies as the technique continues to improve and mature, along with other probes of dynamic correlations that would provide a more comprehensive picture.
Network Modelling in Analysing Cyber-related Graphs
2024-12-18
Vesa Kuikka, Lauri Pykälä, Tuomas Takko, Kimmo Kaski
In order to improve the resilience of computer infrastructure against cyber attacks and finding ways to mitigate their impact we need to understand their structure and dynamics. Here we propose a novel network-based influence spreading model to investigate event trajectories or paths in various types of attack and causal graphs, which can be directed, weighted, and / or cyclic. In case of attack graphs with acyclic paths, only self-avoiding attack chains are allowed. In the framework of our model a detailed probabilistic analysis beyond the traditional visualisation of attack graphs, based on vulnerabilities, services, and exploitabilities, can be performed. In order to demonstrate the capabilities of the model, we present three use cases with cyber-related graphs, namely two attack graphs and a causal graph. The model can be of benefit to cyber analysts in generating quantitative metrics for prioritisation, summaries, or analysis of larger graphs.
The Role of Patents: Incentivizing Innovation or Hindering Progress?
2024-12-18
Gaétan de Rassenfosse
This article is part of a Living Literature Review exploring topics related to intellectual property, focusing on insights from the economic literature. Our aim is to provide a clear and non-technical introduction to patent rights, making them accessible to graduate students, legal scholars and practitioners, policymakers, and anyone curious about the subject.
Uncertainty Awareness in Wireless Communications, Sensing, and Learning
2024-12-18
Shixiong Wang, Wei Dai, Geoffrey Ye Li
Wireless communications and sensing (WCS) establish the backbone of modern information exchange and environment perception. Typical applications range from mobile networks and the Internet of Things to radar and sensor grids. The incorporation of machine learning further expands WCS's boundaries, unlocking automated and high-quality data analytics, together with advisable and efficient decision-making. Despite transformative capabilities, wireless systems often face numerous uncertainties in design and operation, such as modeling errors due to incomplete physical knowledge, statistical errors arising from data scarcity, measurement errors caused by sensor imperfections, computational errors owing to resource limitation, and unpredictability of environmental evolution. Once ignored, these uncertainties can lead to severe outcomes, e.g., performance degradation, system untrustworthiness, inefficient resource utilization, and security vulnerabilities. As such, this article reviews mature and emerging architectural, computational, and operational countermeasures, encompassing uncertainty-aware designs of signals and systems (e.g., diversity, adaptivity, modularity), as well as uncertainty-aware modeling and computational frameworks (e.g., risk-informed optimization, robust signal processing, and trustworthy machine learning). Trade-offs to employ these methods, e.g., robustness vs optimality, are also highlighted.
2024-12-18
Xiangjue Dong, Maria Teleki, James Caverlee
Techniques that enhance inference through increased computation at test-time have recently gained attention. In this survey, we investigate the current state of LLM Inference-Time Self-Improvement from three different perspectives: Independent Self-improvement, focusing on enhancements via decoding or sampling methods; Context-Aware Self-Improvement, leveraging additional context or datastore; and Model-Aided Self-Improvement, achieving improvement through model collaboration. We provide a comprehensive review of recent relevant studies, contribute an in-depth taxonomy, and discuss challenges and limitations, offering insights for future research.
2024-12-18
Kenneth Church, Raman Chandrasekar, John E. Ortega, Ibrahim Said Ahmad
How effective is peer-reviewing in identifying important papers? We treat this question as a forecasting task. Can we predict which papers will be highly cited in the future based on venue and "early returns" (citations soon after publication)? We show early returns are more predictive than venue. Finally, we end with constructive suggestions to address scaling challenges: (a) too many submissions and (b) too few qualified reviewers.
2024-12-18
Luca Delle Monache, Daniel F. Steinhoff, Rachel Weihs, Matthew Simpson, Mohammadvaghef Ghazvinian, Vesta Afzali Gorooh, Kevin M. Lupo, Patrick Mulrooney, Caroline Papadopoulos, F. Martin Ralph
A 200-member ensemble developed at the Center for Western Weather and Water Extremes based on the Weather Research and Forecast atmospheric model tailored for the prediction of atmospheric rivers and associated heavy-to-extreme precipitation events over the Western US (West-WRF) is presented. The ensemble (WW200En) is generated with initial and boundary conditions from the US National Center for Environmental Prediction's Global Ensemble Forecast System (GEFS) and the European Centre for Medium-Range Weather Forecasts' Ensemble Prediction System (EPS), 100 unique combinations of microphysics, planetary boundary layer, and cumulus schemes, as well as perturbations applied to each of the 200 members based on the stochastic kinetic-energy backscatter scheme. Each member is run with 9-km horizontal increments and 60 vertical levels for a 10-month period spanning two winters. The performance of WW200En is compared to GEFS and EPS for probabilistic forecasts of 24-h precipitation, integrated water vapor transport (IVT), and for several thresholds including high percentiles of the observed climatological distribution. The WW200En precipitation forecast skill is better than GEFS at nearly all thresholds and lead times, and comparable or better than the EPS. For larger rainfall thresholds WW200En typically exhibits the best forecast skill. Additionally, WW200En has a better spread-skill relationship than the global systems, and an improved overall reliability and resolution of the probabilistic prediction. The results for IVT are qualitatively similar to those for precipitation forecasts. A sensitivity analysis of the physics parameterizations and the number of ensemble members provides insights into possible future developments of WW200En.
Detection of the Temperature Dependence of the White Dwarf Mass-Radius Relation with Gravitational Redshifts
2024-12-18
Nicole R. Crumpler, Vedant Chandra, Nadia L. Zakamska, Gautham Adamane Pallathadka, Stefan Arseneau, Nicola Gentile Fusillo, J. J. Hermes, Carles Badenes, Priyanka Chakraborty, Boris T. Gänsicke, Stephen P. Schmidt
Models predict that the well-studied mass-radius relation of white dwarf stars depends on the temperature of the star, with hotter white dwarfs having larger masses at a given radius than cooler stars. In this paper, we use a catalog of 26,041 DA white dwarfs observed in Sloan Digital Sky Survey Data Releases 1-19. We measure the radial velocity, effective temperature, surface gravity, and radius for each object. By binning this catalog in radius or surface gravity, we average out the random motion component of the radial velocities for nearby white dwarfs to isolate the gravitational redshifts for these objects and use them to directly measure the mass-radius relation. For gravitational redshifts measured from binning in either radius or surface gravity, we find strong evidence for a temperature-dependent mass-radius relation, with warmer white dwarfs consistently having greater gravitational redshifts than cool objects at a fixed radius or surface gravity. For warm white dwarfs, we find that their mean radius is larger and mean surface gravity is smaller than those of cool white dwarfs at 5.2{\sigma} and 6.0{\sigma} significance, respectively. Selecting white dwarfs with similar radii or surface gravities, the significance of the difference in mean gravitational redshifts between the warm and cool samples is >6.1{\sigma} and >3.6{\sigma} for measurements binned in radius and surface gravity, respectively, in the direction predicted by theory. This is an improvement over previous implicit detections, and our technique can be expanded to precisely test the white dwarf mass-radius relation with future surveys.
ZTF SN Ia DR2: Properties of the low-mass host galaxies of Type Ia supernovae in a volume-limited sample
2024-12-18
U. Burgaz, K. Maguire, G. Dimitriadis, M. Smith, J. Sollerman, L. Galbany, M. Rigault, A. Goobar, J. Johansson, Y. -L. Kim, A. Alburai, M. Amenouche, M. Deckers, M. Ginolin, L. Harvey, T. E. Muller-Bravo, J. Nordin, K. Phan, P. Rosnet, P. E. Nugent, J. H. Terwel, M. Graham, D. Hale, M. M. Kasliwal, R. R. Laher, J. D. Neill, J. Purdum, B. Rusholme
In this study, we explore the characteristics of `low-mass' ($\log(M_{\star}/M_{\odot}) \leq 8$) and `intermediate-mass' ($8 \lt \log(M_{\star}/M_{\odot}) \leq 10$) host galaxies of Type Ia supernovae (SNe Ia) from the second data release (DR2) of the Zwicky Transient Facility survey and investigate their correlations with different sub-types of SNe Ia. We use the photospheric velocities measured from the Si II $\lambda$6355 feature, SALT2 light-curve stretch ($x_1$) and host-galaxy properties of SNe Ia to re-investigate the existing relationship between host galaxy mass and Si II $\lambda$6355 velocities. We also investigate sub-type preferences for host populations and show that while the more energetic and brighter 91T-like SNe Ia tends to populate the younger host populations, 91bg-like SNe Ia populate in the older populations. Our findings suggest High Velocity SNe Ia (HV SNe Ia) not only comes from the older populations but they also come from young populations as well. Therefore, while our findings can partially provide support for HV SNe Ia relating to single degenerate progenitor models, they indicate that HV SNe Ia other than being a different population, might be a continued distribution with different explosion mechanisms. We lastly investigate the specific rate of SNe Ia in the volume-limited SN Ia sample of DR2 and compare with other surveys.
Discovery of dual "little red dots" indicates excess clustering on kilo-parsec scales
2024-12-18
Takumi S. Tanaka, John D. Silverman, Kazuhiro Shimasaku, Junya Arita, Hollis B. Akins, Kohei Inayoshi, Xuheng Ding, Masafusa Onoue, Zhaoxuan Liu, Caitlin M. Casey, Erini Lambrides, Vasily Kokorev, Shuowen Jin, Andreas L. Faisst, Nicole Drakos, Yue Shen, Junyao Li, Mingyang Zhuang, Qinyue Fei, Kei Ito, Wenke Ren, Suin Matsui, Makoto Ando, Shun Hatano, Michiko S. Fujii, Jeyhan S. Kartaltepe, Anton M. Koekemoer, Daizhong Liu, Henry Joy McCracken, Jason Rhodes, Brant E. Robertson, Maximilien Franco, Irham T. Andika, Aidan P. Cloonan, Xiaohui Fan, Ghassem Gozaliasl, Santosh Harish, Christopher C. Hayward, Marc Huertas-Company, Darshan Kakkad, Tomoya Kinugawa, Namrata Roy, Marko Shuntov, Margherita Talia, Sune Toft, Aswin P. Vijayan, Yiyang Zhang
``Little Red Dots'' (LRDs) are an abundant high-redshift population newly discovered by the James Webb Space Telescope (JWST). They are characterized by a red color in the rest-frame optical band, compact morphology, and broad Balmer emission lines (${\rm FWHM} \gtrsim 1000~{\rm km\,s^{-1}}$) that suggest an AGN nature. Using a method of pixel-by-pixel color selection and relaxing the compactness criteria, we identify three of the first dual LRD candidates in the COSMOS-Web survey with projected separations of $0.\!\!^{\prime\prime}2-0.\!\!^{\prime\prime}4$ (1-2 pkpc at their photometric redshifts). A comparison between existing LRD samples and mock data reveals that the projected separations of these dual LRD candidates are unlikely to result from chance projections of objects at different redshifts. In one case (CW-B5-15958), the dual LRD includes two bright sources ($m_{\rm F444W}=24.3$ and $24.8$) with characteristic V-shape spectral energy distribution (SEDs) and photometric redshifts consistent with each other. We find that CW-B5-15958 has a faint off-centered component and a companion galaxy. In the other two dual systems, the brighter LRD exhibits a V-shape SED, while the fainter LRD ($m_{\rm F444W}\gtrsim26$) is undetected in both F115W and F150W. These discoveries suggest that the angular auto-correlation function (ACF) of LRDs exhibits a significant excess ($\sim3\times10^2$ times) on sub-arcsec (kilo-parsec) separations compared to the extrapolation of a power-law ACF of JWST-found AGNs measured over $10^{\prime\prime}-100^{\prime\prime}$. Follow-up spectroscopic confirmation of their redshifts and the construction of a larger sample are essential to advance our understanding of the evolution of supermassive black holes and the importance of mergers in the early universe.
The Weyl-Heisenberg symmetries originate from translation invariances of various manifolds viewed as phase spaces, e.g. Euclidean plane, semi-discrete cylinder, torus, in the two-dimensional case, and higher-dimensional generalisations. In this review we describe, on an elementary level, how this symmetry emerges through displacement operators and standard Fourier analysis, and how their unitary representations are used both in Signal Analysis (time-frequency techniques, Gabor transform) and in quantum formalism (covariant integral quantizations and semi-classical portraits). An example of application of the formalism to the Majorana stellar constellation in the plane is presented.
The Effect of Age Introduced to Virtual Reality on Susceptibility to Motion Sickness
2024-12-18
Michael Abia
Every human with a functioning vestibular system is capable of feeling motion sickness, but some are more vulnerable than others. Based on the leading theories explaining this condition, vulnerability should be predicted by a person's years of real-life experience before using a VR device and years of VR experience after. A questionnaire was filled out on susceptibility to motion sickness in VR by people on VR-related forums. Results from the survey show that the condition has a significant relationship with age or experience outside the environment.
Recent observations by the Large High Altitude Air Shower Observatory (LHAASO) detected Ultra High Energy (UHE) photons in the range 100 TeV to 1.4 PeV from twelve sources including Crab nebula. The detection of these photons demands the presence of at least PeV energy particle in the source. It is important to understand particle acceleration and radiation emission processes in such source. One of those twelve sources, LHAASO J2108+5157 does not show any association or counterparts at any other wavelength. In search of counterpart, we surveyed the region with Giant Metrewave Radio Telescope (GMRT) at 650 MHz frequency. GMRT observation revel radio emission from an extended source within the PSF of LHAASO which shows disk-jet morphology. Considering the spatial association and extent of the source, it is plausible that particle acceleration to PeV energies originates from this source.
In recent years, data science agents powered by Large Language Models (LLMs), known as "data agents," have shown significant potential to transform the traditional data analysis paradigm. This survey provides an overview of the evolution, capabilities, and applications of LLM-based data agents, highlighting their role in simplifying complex data tasks and lowering the entry barrier for users without related expertise. We explore current trends in the design of LLM-based frameworks, detailing essential features such as planning, reasoning, reflection, multi-agent collaboration, user interface, knowledge integration, and system design, which enable agents to address data-centric problems with minimal human intervention. Furthermore, we analyze several case studies to demonstrate the practical applications of various data agents in real-world scenarios. Finally, we identify key challenges and propose future research directions to advance the development of data agents into intelligent statistical analysis software.
The emergence of large-scale Mixture of Experts (MoE) models has marked a significant advancement in artificial intelligence, offering enhanced model capacity and computational efficiency through conditional computation. However, the deployment and inference of these models present substantial challenges in terms of computational resources, latency, and energy efficiency. This comprehensive survey systematically analyzes the current landscape of inference optimization techniques for MoE models across the entire system stack. We first establish a taxonomical framework that categorizes optimization approaches into model-level, system-level, and hardware-level optimizations. At the model level, we examine architectural innovations including efficient expert design, attention mechanisms, various compression techniques such as pruning, quantization, and knowledge distillation, as well as algorithm improvement including dynamic routing strategies and expert merging methods. At the system level, we investigate distributed computing approaches, load balancing mechanisms, and efficient scheduling algorithms that enable scalable deployment. Furthermore, we delve into hardware-specific optimizations and co-design strategies that maximize throughput and energy efficiency. This survey not only provides a structured overview of existing solutions but also identifies key challenges and promising research directions in MoE inference optimization. Our comprehensive analysis serves as a valuable resource for researchers and practitioners working on large-scale deployment of MoE models in resource-constrained environments. To facilitate ongoing updates and the sharing of cutting-edge advances in MoE inference optimization research, we have established a repository accessible at \url{https://github.com/MoE-Inf/awesome-moe-inference/}.
Development and Adoption of SATD Detection Tools: A State-of-practice Report
2024-12-18
Edi Sutoyo, Andrea Capiluppi
Self-Admitted Technical Debt (SATD) refers to instances where developers knowingly introduce suboptimal solutions into code and document them, often through textual artifacts. This paper provides a comprehensive state-of-practice report on the development and adoption of SATD detection tools. Through a systematic review of the available literature and tools, we examined their overall accessibility. Our findings reveal that, although SATD detection tools are crucial for maintaining software quality, many face challenges such as technological obsolescence, poor maintenance, and limited platform compatibility. Only a small number of tools are actively maintained, hindering their widespread adoption. This report discusses common anti-patterns in tool development, proposes corrections, and highlights the need for implementing Findable, Accessible, Interoperable, and Reusable (FAIR) principles and fostering greater collaboration between academia and industry to ensure the sustainability and efficacy of these tools. The insights presented here aim to drive more robust management of technical debt and enhance the reliability of SATD tools.
Binary properties of the globular cluster 47 Tuc (NGC 104). A dearth of short-period binaries
2024-12-17
Johanna Müller-Horn, Fabian Göttgens, Stefan Dreizler, Sebastian Kamann, Sven Martens, Sara Saracino, Claire S. Ye
Spectroscopic observations of binary stars in globular clusters are essential to shed light on the poorly constrained period, eccentricity, and mass ratio distributions and to develop an understanding of the formation of peculiar stellar objects. 47 Tuc (NGC 104) is one of the most massive Galactic globular clusters, with a large population of blue stragglers and with many predicted but as-yet elusive stellar-mass black holes. This makes it an exciting candidate for binary searches. We present a multi-epoch spectroscopic survey of 47 Tuc with the VLT/MUSE integral field spectrograph to determine radial velocity variations for 21,699 stars. We find a total binary fraction in the cluster of $(2.4\pm1.0)\%$, consistent with previous photometric estimates, and an increased binary fraction among blue straggler stars, approximately three times higher than the cluster average. We find very few binaries with periods below three days, and none with massive dark companions. A comparison with predictions from state-of-the-art models shows that the absence of such short-period binaries and of binaries with massive companions is surprising, highlighting the need to improve our understanding of stellar and dynamical evolution in binary systems.
How can we predict future interaction trajectories of human hands in a scene given high-level colloquial task specifications in the form of natural language? In this paper, we extend the classic hand trajectory prediction task to two tasks involving explicit or implicit language queries. Our proposed tasks require extensive understanding of human daily activities and reasoning abilities about what should be happening next given cues from the current scene. We also develop new benchmarks to evaluate the proposed two tasks, Vanilla Hand Prediction (VHP) and Reasoning-Based Hand Prediction (RBHP). We enable solving these tasks by integrating high-level world knowledge and reasoning capabilities of Vision-Language Models (VLMs) with the auto-regressive nature of low-level ego-centric hand trajectories. Our model, HandsOnVLM is a novel VLM that can generate textual responses and produce future hand trajectories through natural-language conversations. Our experiments show that HandsOnVLM outperforms existing task-specific methods and other VLM baselines on proposed tasks, and demonstrates its ability to effectively utilize world knowledge for reasoning about low-level human hand trajectories based on the provided context. Our website contains code and detailed video results https://www.chenbao.tech/handsonvlm/
How the cool-core population transitions from galaxy groups to massive clusters: A comparison of the largest Magneticum simulation with eROSITA, XMM-Newton, Chandra and LOFAR observations
2024-12-17
Justo Antonio Gonzalez Villalba, Klaus Dolag, Veronica Biffi
Our aim is to understand how the interplay between AGN feedback and merge processes can effectively turn cool-core galaxy clusters into hot-core clusters in the modern universe. Additionally, we also aim to clarify which parameters of the AGN feedback model used in simulations can cause an excess of feedback at the scale of galaxy groups while not efficiently suppressing star formation at the scale of galaxy clusters. To obtain robust statistics of the cool-core population, we compare the modern Universe snapshot (z=0.25) of the largest Magneticum simulation (Box2b/hr) with the eROSITA eFEDS survey and Planck SZ-selected clusters observed with XMM-Newton. Additionally, we compare the AGN feedback injected by the simulation in radio mode with Chandra observations of X-ray cavities, and LOFAR observations of radio emission. We confirm a decreasing trend in cool-core fractions towards the most massive galaxy clusters, which is well reproduced by the Magneticum simulations. This evolution is connected with an increased merge activity that injects high-energy particles into the core region, but it also requires thermalization and conductivity to enhance mixing through the ICM core, where both factors are increasingly efficient towards the high mass end. On the other hand, AGN feedback remains as the dominant factor at the scale of galaxy groups, while its relative impact decreases towards the most massive clusters. The problems suppressing star formation in simulations are not caused by low AGN feedback efficiencies. They root in the definition of the black hole sphere of influence used to distribute the feedback, which decreases as density and accretion rate increase. Actually, a decreasing AGN feedback efficiency towards low-mass galaxy groups is required to prevent overheating.
Algorithmic Fidelity of Large Language Models in Generating Synthetic German Public Opinions: A Case Study
2024-12-17
Bolei Ma, Berk Yoztyurk, Anna-Carolina Haensch, Xinpeng Wang, Markus Herklotz, Frauke Kreuter, Barbara Plank, Matthias Assenmacher
In recent research, large language models (LLMs) have been increasingly used to investigate public opinions. This study investigates the algorithmic fidelity of LLMs, i.e., the ability to replicate the socio-cultural context and nuanced opinions of human participants. Using open-ended survey data from the German Longitudinal Election Studies (GLES), we prompt different LLMs to generate synthetic public opinions reflective of German subpopulations by incorporating demographic features into the persona prompts. Our results show that Llama performs better than other LLMs at representing subpopulations, particularly when there is lower opinion diversity within those groups. Our findings further reveal that the LLM performs better for supporters of left-leaning parties like The Greens and The Left compared to other parties, and matches the least with the right-party AfD. Additionally, the inclusion or exclusion of specific variables in the prompts can significantly impact the models' predictions. These findings underscore the importance of aligning LLMs to more effectively model diverse public opinions while minimizing political biases and enhancing robustness in representativeness.
Linkage between core-collapse supernovae (SNe) and their progenitors is not fully understood and ongoing effort of searching and identifying the progenitors is needed. $\mathrm{SN\,2024abfl}$ is a recent Type II supernova exploded in the nearby star-bursting galaxy $\mathrm{NGC\,2146}$, which is also the host galaxy of $\mathrm{SN\,2018zd}$. From archival Hubble Space Telescope (HST) data, we have found a red source ($\mathrm{m_{F814W} \sim 25}$) near the location (angular distance $\leq 0.2"$) of $\mathrm{SN\,2024abfl}$ before its explosion. With F814W and F606W photometry, we found that the properties of this source matched a typical red supergiant (RSG) moderately reddened by interstellar dust at the distance of the host galaxy. We conclude that the $\mathrm{SN\,2024abfl}$ had an RSG progenitor with initial mass of $\mathrm{10M_{\odot}}$--$\mathrm{16\,M_{\odot}}$.
Redshift tomography of the kinematic matter dipole
2024-12-17
Sebastian von Hausegger, Charles Dalang
The dipole anisotropy induced by our peculiar motion in the sky distribution of cosmologically distant sources is an important consistency test of the standard FLRW cosmology. In this work, we formalize how to compute the kinematic matter dipole in redshift bins. Apart from the usual terms arising from angular aberration and flux boosting, there is a contribution from the boosting of the redshifts that becomes important when considering a sample selected on observed redshift, leading to non-vanishing correction terms. We discuss examples and provide expressions to incorporate arbitrary redshift selection functions. We also discuss the effect of redshift measurement uncertainties in this context, in particular in upcoming surveys for which we provide estimates of the correction terms. Depending on the shape of a sample's redshift distribution and on the applied redshift cuts, the correction terms can become substantial, even to the degree that the direction of the dipole is reversed. Lastly, we discuss how cuts on variables correlated with observed redshift, such as color, can induce additional correction terms.
BanglishRev: A Large-Scale Bangla-English and Code-mixed Dataset of Product Reviews in E-Commerce
2024-12-17
Mohammad Nazmush Shamael, Sabila Nawshin, Swakkhar Shatabda, Salekul Islam
This work presents the BanglishRev Dataset, the largest e-commerce product review dataset to date for reviews written in Bengali, English, a mixture of both and Banglish, Bengali words written with English alphabets. The dataset comprises of 1.74 million written reviews from 3.2 million ratings information collected from a total of 128k products being sold in online e-commerce platforms targeting the Bengali population. It includes an extensive array of related metadata for each of the reviews including the rating given by the reviewer, date the review was posted and date of purchase, number of likes, dislikes, response from the seller, images associated with the review etc. With sentiment analysis being the most prominent usage of review datasets, experimentation with a binary sentiment analysis model with the review rating serving as an indicator of positive or negative sentiment was conducted to evaluate the effectiveness of the large amount of data presented in BanglishRev for sentiment analysis tasks. A BanglishBERT model is trained on the data from BanglishRev with reviews being considered labeled positive if the rating is greater than 3 and negative if the rating is less than or equal to 3. The model is evaluated by being testing against a previously published manually annotated dataset for e-commerce reviews written in a mixture of Bangla, English and Banglish. The experimental model achieved an exceptional accuracy of 94\% and F1 score of 0.94, demonstrating the dataset's efficacy for sentiment analysis. Some of the intriguing patterns and observations seen within the dataset and future research directions where the dataset can be utilized is also discussed and explored. The dataset can be accessed through https://huggingface.co/datasets/BanglishRev/bangla-english-and-code-mixed-ecommerce-review-dataset.
Studies have shown that toxic behavior can cause contributors to leave, and hinder newcomers' (especially from underrepresented communities) participation in Open Source Software (OSS) projects. Thus, detection of toxic language plays a crucial role in OSS collaboration and inclusivity. Off-the-shelf toxicity detectors are ineffective when applied to OSS communications, due to the distinct nature of toxicity observed in these channels (e.g., entitlement and arrogance are more frequently observed on GitHub than on Reddit or Twitter). In this paper, we investigate a machine learning-based approach for the automatic detection of toxic communications in OSS. We leverage psycholinguistic lexicons, and Moral Foundations Theory to analyze toxicity in two types of OSS communication channels; issue comments and code reviews. Our evaluation indicates that our approach can achieve a significant performance improvement (up to 7% increase in F1 score) over the existing domain-specific toxicity detector. We found that using moral values as features is more effective than linguistic cues, resulting in 67.50% F1-measure in identifying toxic instances in code review data and 64.83% in issue comments. While the detection accuracy is far from accurate, this improvement demonstrates the potential of integrating moral and psycholinguistic features in toxicity detection models. These findings highlight the importance of context-specific models that consider the unique communication styles within OSS, where interpersonal and value-driven language dynamics differ markedly from general social media platforms. Future work could focus on refining these models to further enhance detection accuracy, possibly by incorporating community-specific norms and conversational context to better capture the nuanced expressions of toxicity in OSS environments.
Empirical Analysis of Pull Requests for Google Summer of Code
2024-12-17
Saheed Popoola
Internship and industry-affiliated capstone projects are popular ways to expose students to real world experience and bridge the gap between academic training and industry requirements. However, these two approaches often require active industry collaboration and many students often struggle to find industry placements. Open-source contributions is a crucial alternative to gain real world experience, earn publicly verifiable contribution with real world impact, and learn from experienced open-source contributors. The Google Summer of Code is a global initiative that matches students or new contributors with experienced mentors to work on open-source projects. The goal of the program is to introduce the students to open-source, help gain valuable skills under the guidance of a mentor, and hopefully continue to contribute to open source development; thereby, provide a continuous pool of talented new contributors necessary for maintaining an open source project. This study presents an empirical analysis of pull requests created by interns during the Google Summer of Code program. We extracted and analysed 17,232 pull requests from 2456 interns across 1937 open-source projects. The results show that majority of the tasks involve both code-intensive tasks like adding new features and fixing bugs as well as non-code tasks like updating the documentation and restructuring the code base. The feedback from reviewers covers code functionality and programming logic, testing coverage, error handling, code readability, and adopting best practices. Finally, we discuss the implications of these results for software engineering education.
Equity in the Use of ChatGPT for the Classroom: A Comparison of the Accuracy and Precision of ChatGPT 3.5 vs. ChatGPT4 with Respect to Statistics and Data Science Exams
2024-12-17
Monnie McGee, Bivin Sadler
A college education historically has been seen as method of moving upward with regards to income brackets and social status. Indeed, many colleges recognize this connection and seek to enroll talented low income students. While these students might have their education, books, room, and board paid; there are other items that they might be expected to use that are not part of most college scholarship packages. One of those items that has recently surfaced is access to generative AI platforms. The most popular of these platforms is ChatGPT, and it has a paid version (ChatGPT4) and a free version (ChatGPT3.5). We seek to explore differences in the free and paid versions in the context of homework questions and data analyses as might be seen in a typical introductory statistics course. We determine the extent to which students who cannot afford newer and faster versions of generative AI programs would be disadvantaged in terms of writing such projects and learning these methods.
2024-12-17
Boris Houska, Matthias A. Müller, Mario E. Villanueva
In this article, we survey the primary research on polyhedral computing methods for constrained linear control systems. Our focus is on the modeling power of convex optimization, featured to design set-based robust and optimal controllers. In detail, we review the state-of-the-art techniques for computing geometric structures such as robust control invariant polytopes. Moreover, we survey recent methods for constructing control Lyapunov functions with polyhedral epigraphs as well as the extensive literature on robust model predictive control. The article concludes with a discussion of both the complexity and potential of polyhedral computing methods that rely on large-scale convex optimization.
Color profiles of disk galaxies at $z=1$-$3$ observed with JWST: Implications for outer-disk formation histories
2024-12-17
Si-Yue Yu, Dewang Xu, Boris S. Kalita, Sijia Li, John D. Silverman, Xinyue Liang, Taotao Fang
We investigate the deconvolved color profiles of 223 disk galaxies at redshifts of $z=1$-3 observed by the James Webb Space Telescope (JWST) as part of the Cosmic Evolution Early Release Science survey (CEERS). The filters were selected to approximate the rest-frame $B-Y$ color, which is used to identify U-shaped color profiles -- those becoming progressively bluer with increasing radius, then turning redder beyond a specific point. We find that 36% of Type II (down-bending) disks exhibit U-shaped color profiles with a minimum at or near the disk break. In contrast, no Type I (single-exponential) disks and only 9% of Type III (up-bending) disks show such a profile. The presence of U-shaped color profiles in Type II disks likely arises from the interplay between a star-formation threshold and spiral- or bar-driven secular radial migration of older stars outward. The fraction of Type II disks exhibiting a U-shaped color profile remains almost consistent across two redshift bins, $z=1$-$2$ and $z=2$-$3$, but is significantly lower than that observed in the local Universe, likely because the secular process of radial migration at high redshift may not have had sufficient time to significantly influence the disk structure. The absence of U-shaped color profiles in Type II disks could point to rapid rather than secular radial star migration potentially caused by violent clump instabilities, transporting both younger and older stars to the outer disk. Our results provide useful constraints on the formation and evolution models of disk galaxies in the early Universe.
In this Letter, we use the latest results from the Dark Energy Spectroscopic Instrument (DESI) survey to measure the Hubble constant. Baryon acoustic oscillation (BAO) observations released by the DESI survey, allow us to determine $H_0$ from the first principles. Our method is purely data-driven and relies on unanchored luminosity distances reconstructed from SN Ia data and $H(z)$ reconstruction from cosmic chronometers. Thus it circumvents calibrations related to the value of the sound horizon size at the baryon drag epoch or intrinsic luminosity of SN Ia. We find $H_0=68.4^{+1.0}_{-0.8}~{\rm km~s^{-1}~Mpc^{-1}}$ at 68% C.L., which provides the Hubble constant at an accuracy of 1.3% with minimal assumptions. Our assessments of this fundamental cosmological quantity using the BAO data spanning the redshift range $z=0.51-2.33$ agree very well with Planck's results and TRGB results within $1\sigma$. This result is still in a $4.3\sigma$ tension with the results of the Supernova H0 for the Equation of State (SH0ES).
Epithelial tissues are continuously exposed to cyclic stretch. Physiological stretching has been found to regulate soft tissue function at the molecular, cellular, and tissue scales, allowing tissues to preserve their homeostasis and adapt to challenges. In contrast, dysregulated or pathological stretching can induce damage and tissue fragilisation. Many mechanisms have been described for the repair of epithelial tissues across a range of time-scales. In this review, we present the timescales of (i) physiological cyclic loading regimes, (ii) strain-regulated remodelling and damage accumulation, and (iii) repair mechanisms in epithelial tissues. We discuss how fatigue in biological tissues differs from synthetic materials, in that damage can be partially or fully reversed by repair mechanisms acting on timescales shorter than cyclic loading. We highlight that timescales are critical to understanding the interplay between damage and repair in tissues that experience cyclic loading, opening up new avenues for exploring soft tissue homeostasis.
We give a brief survey of the theory of hadronic atoms, which represent important sources of information for studying hadron interactions at very low energy. It will be namely demonstrated that a systematic expansion of the observables of hadronic atoms (the energy levels and the decay width) in terms of the fine-structure constant can be obtained, using the framework of non-relativistic effective Lagrangians. We also present a pedagogical introduction to the non-relativistic effective theories that includes a review of the main concepts, such as the scale separation, construction of the Lagrangian, power counting and matching.
Boosting Test Performance with Importance Sampling--a Subpopulation Perspective
2024-12-17
Hongyu Shen, Zhizhen Zhao
Despite empirical risk minimization (ERM) is widely applied in the machine learning community, its performance is limited on data with spurious correlation or subpopulation that is introduced by hidden attributes. Existing literature proposed techniques to maximize group-balanced or worst-group accuracy when such correlation presents, yet, at the cost of lower average accuracy. In addition, many existing works conduct surveys on different subpopulation methods without revealing the inherent connection between these methods, which could hinder the technology advancement in this area. In this paper, we identify important sampling as a simple yet powerful tool for solving the subpopulation problem. On the theory side, we provide a new systematic formulation of the subpopulation problem and explicitly identify the assumptions that are not clearly stated in the existing works. This helps to uncover the cause of the dropped average accuracy. We provide the first theoretical discussion on the connections of existing methods, revealing the core components that make them different. On the application side, we demonstrate a single estimator is enough to solve the subpopulation problem. In particular, we introduce the estimator in both attribute-known and -unknown scenarios in the subpopulation setup, offering flexibility in practical use cases. And empirically, we achieve state-of-the-art performance on commonly used benchmark datasets.
Periodic layer potentials and domain perturbations
2024-12-17
Roberto Bramati, Matteo Dalla Riva, Paolo Luzzini, Paolo Musolino
In this paper, we review the construction of periodic fundamental solutions and periodic layer potentials for various differential operators. Specifically, we focus on the Laplace equation, the Helmholtz equation, the Lam\'e system, and the heat equation. We then describe how these layer potentials can be applied to analyze domain perturbation problems. In particular, we present applications to the asymptotic behavior of quasi-periodic solutions for a Dirichlet problem for the Helmholtz equation in an unbounded domain with small periodic perforations. Additionally, we investigate the dependence of spatially periodic solutions of an initial value Dirichlet problem for the heat equation on regular perturbations of the base of a parabolic cylinder.
Stellar flare morphology with TESS across the main sequence
2024-12-17
Bálint Seli, Krisztián Vida, Katalin Oláh, Anna Görgei, Szabolcs Soós, András Pál, Levente Kriskovics, Zsolt Kővári
Stellar flares are abundant in space photometric light curves. As they are now available in large enough numbers, the statistical study of their overall temporal morphology is timely. We use light curves from the Transiting Exoplanet Survey Satellite (TESS) to study the shapes of stellar flares beyond a simple parameterization by duration and amplitude, and reveal possible connections to astrophysical parameters. We retrain and use the flatwrm2 long-short term memory neural network to find stellar flares in 2-min cadence TESS light curves from the first five years of the mission (sectors 1-69). We scale these flares to a comparable standard shape, and use principal component analysis to describe their temporal morphology in a concise way. We investigate how the flare shapes change along the main sequence, and test whether individual flares hold any information about their host stars. We also apply similar techniques to solar flares, using extreme ultraviolet irradiation time series. Our final catalog contains ~120,000 flares on ~14,000 stars. Due to the strict filtering and the final manual vetting, this sample contains virtually no false positives, although at the expense of reduced completeness. Using this flare catalog, we detect a dependence of the average flare shape on the spectral type. These changes are not apparent for individual flares, only when averaging thousands of events. We find no strong clustering in the flare shape space. We create new analytical flare templates for different types of stars, present a technique to sample realistic flares, and a method to locate flares with similar shapes. The flare catalog, along with the extracted flare shapes, and the data used to train flatwrm2 are publicly available.
2024-12-17
Andrea Gabrielli, Diego Garlaschelli, Subodh P. Patil, M. Ángeles Serrano
The renormalization group (RG) is a powerful theoretical framework developed to consistently transform the description of configurations of systems with many degrees of freedom, along with the associated model parameters and coupling constants, across different levels of resolution. It also provides a way to identify critical points of phase transitions and study the system's behaviour around them by distinguishing between relevant and irrelevant details, the latter being unnecessary to describe the emergent macroscopic properties. In traditional physical applications, the RG largely builds on the notions of homogeneity, symmetry, geometry and locality to define metric distances, scale transformations and self-similar coarse-graining schemes. More recently, various approaches have tried to extend RG concepts to the ubiquitous realm of complex networks where explicit geometric coordinates do not necessarily exist, nodes and subgraphs can have very different properties, and homogeneous lattice-like symmetries are absent. The strong heterogeneity of real-world networks significantly complicates the definition of consistent renormalization procedures. In this review, we discuss the main attempts, the most important advances, and the remaining open challenges on the road to network renormalization.
How much earlier would LSST have discovered currently known long-period comets?
2024-12-17
Laura Inno, Margherita Scuderi, Ivano Bertini, Marco Fulle, Elena Mazzotta Epifani, Vincenzo Della Corte, Alice Maria Piccirillo, Antonio Vanzanella, Pedro Lacerda, Chiara Grappasonni, Eleonora Ammanito, Giuseppe Sindoni, Alessandra Rotundi
Among solar system objects, comets coming from the Oort Cloud are an elusive population, intrinsically rare and difficult to detect. Nonetheless, as the more pristine objects we can observe, they encapsulate critical cues on the formation of planetary systems and are the focus of many scientific investigations and science missions. The Legacy Survey of Space and Time (LSST), which will start to operate from the Vera C. Rubin Observatory in 2025, is expected to dramatically improve our detection ability of these comets by performing regular monitoring of the Southern sky deep down to magnitude 24.5 with excellent astrometry. However, making straightforward predictions on future LSST detection rates is challenging due to our biased knowledge of the underlying population. This is because identifications to date have been conducted by various surveys or individual observers, often without detailed information on their respective selection functions. Recent efforts to predict incoming flux of Long Period Comets still suffer of the lack of systematic, well-characterized, homogeneous cometary surveys. Here, we adopt a different point of view by asking how much earlier~on known comets on long-period or hyperbolic orbits would have been discovered by a LSST-like survey if it was already in place 10 years prior to their perihelion epoch. In this case, we are not simulating a real flux of incoming comet, as all comets in our sample reach the perihelion simultaneously, but we can analyze the impact of a LSST-like survey on individual objects. We find that LSST would have found about 40% of comets in our sample at least 5 years prior to their perihelion epoch, and at double (at least) the distance at which they were actually discovered. Based on this approach, we find that LSST has the potentiality to at least twofold the current discovery rate of long-period and hyperbolic comets.
Meta-analysis models relaxing the random effects normality assumption: methodological systematic review and simulation study
2024-12-17
Kanella Panagiotopoulou, Theodoros Evrenoglou, Christopher H Schmid, Silvia Metelli, Anna Chaimani
Random effects meta-analysis is widely used for synthesizing studies under the assumption that underlying effects come from a normal distribution. However, under certain conditions the use of alternative distributions might be more appropriate. We conducted a systematic review to identify articles introducing alternative meta-analysis models assuming non-normal between-study distributions. We identified 27 eligible articles suggesting 24 alternative meta-analysis models based on long-tail and skewed distributions, on mixtures of distributions, and on Dirichlet process priors. Subsequently, we performed a simulation study to evaluate the performance of these models and to compare them with the standard normal model. We considered 22 scenarios varying the amount of between-study variance, the shape of the true distribution, and the number of included studies. We compared 15 models implemented in the Frequentist or in the Bayesian framework. We found small differences with respect to bias between the different models but larger differences in the level of coverage probability. In scenarios with large between-study variance, all models were substantially biased in the estimation of the mean treatment effect. This implies that focusing only on the mean treatment effect of random effects meta-analysis can be misleading when substantial heterogeneity is suspected or outliers are present.
Although experimental design and methodological surveys for mono-molecular activity/property has been extensively investigated, those for chemical composition have received little attention, with the exception of a few prior studies. In this study, we configured three simple DNN regressors to predict essential oil property based on chemical composition. Despite showing overfitting due to the small size of dataset, all models were trained effectively in this study.
DESI and SNe: Dynamical Dark Energy, $Ω_m$ Tension or Systematics?
2024-12-17
Eoin Ó Colgáin, M. M. Sheikh-Jabbari
Dark Energy Spectroscopic Instrument (DESI) observations, when combined with Cosmic Microwave Background (CMB) and Type Ia supernovae (SNe), have led to statistically significant dynamical dark energy (DDE) claims. In this letter we reconstruct $\Lambda$CDM parameter $\Omega_m$ from the $w_0 w_a$CDM cosmologies advocated by the DESI collaboration. We identify i) a mild increasing $\Omega_m$ trend at high redshifts and ii) a sharp departure from $\Lambda$CDM at low redshift. The latter, which is statistically significant, is driven by SNe that are $1.9 \sigma- 2.5 \sigma$ discrepant with DESI full-shape galaxy clustering in overlapping redshift ranges. We identify a low redshift subsample of the Dark Energy Survey (DES) SNe sample that is discrepant with DESI at $3.4 \sigma$ despite both observables probing the same effective redshift. This ``$\Omega_m$ tension'' may point to unexplored systematics. SNe and BAO/full-shape modeling should not disagree on $\Omega_m$ at the same effective redshift. If they do, DDE claims are premature.
We review the status of baryon form factors with a special focus on the nucleon electromagnetic form factors which are known best. First, we give an introduction into the dispersive analyses and emphasize the role of unitarity and analyticity in the construction of the isoscalar and isovector spectral functions. Second, we present the state of the art in our understanding of nucleon form factors and radii including reliable uncertainty estimates from bootstrap and Bayesian methods. Third, we discuss the physics of the time-like form factors and point out further issues to be addressed in this framework. Finally, we review the status of hyperon form factors and comment on the pion cloud.
Adding TESS to CRÉME. Light curves and masses of 300+ eclipsing binaries
2024-12-17
Krzysztof G. Hełminiak, Ayush Moharana, Tilak B. Pawar, Ganesh Pawar
The Comprehensive Research with Echelles on the Most interesting Eclipsing binaries (CR\'EME) projects was aimed to collect high-resolutions spectra of about 380 detached eclipsing binaries (DEBs), which mostly do not have literature RV data. From this vast observational material we were able to estimate masses of components of 325 double-lined system. Since the launch of the TESS mission we have been collecting 2-min cadence photometry for the CR\'EME targets through successful GI proposals. As by Sector 85, we obtained data for $>$330 of them. We are thus now in the process of comprehensively analyzing our targets. This paper presents the recent status of the CR\'EME project and its space photometry counterpart, and describes several sub-projects within CR\'EME that focus on specific classes of targets.
2024-12-17
Jiaqi Wang, Liutao Yu, Liwei Huang, Chenlin Zhou, Han Zhang, Zhenxi Song, Min Zhang, Zhengyu Ma, Zhiguo Zhang
The intrinsic dynamics and event-driven nature of spiking neural networks (SNNs) make them excel in processing temporal information by naturally utilizing embedded time sequences as time steps. Recent studies adopting this approach have demonstrated SNNs' effectiveness in speech command recognition, achieving high performance by employing large time steps for long time sequences. However, the large time steps lead to increased deployment burdens for edge computing applications. Thus, it is important to balance high performance and low energy consumption when detecting temporal patterns in edge devices. Our solution comprises two key components. 1). We propose a high-performance fully spike-driven framework termed SpikeSCR, characterized by a global-local hybrid structure for efficient representation learning, which exhibits long-term learning capabilities with extended time steps. 2). To further fully embrace low energy consumption, we propose an effective knowledge distillation method based on curriculum learning (KDCL), where valuable representations learned from the easy curriculum are progressively transferred to the hard curriculum with minor loss, striking a trade-off between power efficiency and high performance. We evaluate our method on three benchmark datasets: the Spiking Heidelberg Dataset (SHD), the Spiking Speech Commands (SSC), and the Google Speech Commands (GSC) V2. Our experimental results demonstrate that SpikeSCR outperforms current state-of-the-art (SOTA) methods across these three datasets with the same time steps. Furthermore, by executing KDCL, we reduce the number of time steps by 60% and decrease energy consumption by 54.8% while maintaining comparable performance to recent SOTA results. Therefore, this work offers valuable insights for tackling temporal processing challenges with long time sequences in edge neuromorphic computing systems.
From An LLM Swarm To A PDDL-Empowered HIVE: Planning Self-Executed Instructions In A Multi-Modal Jungle
2024-12-17
Kaustubh Vyas, Damien Graux, Yijun Yang, Sébastien Montella, Chenxin Diao, Wendi Zhou, Pavlos Vougiouklis, Ruofei Lai, Yang Ren, Keshuang Li, Jeff Z. Pan
In response to the call for agent-based solutions that leverage the ever-increasing capabilities of the deep models' ecosystem, we introduce Hive -- a comprehensive solution for selecting appropriate models and subsequently planning a set of atomic actions to satisfy the end-users' instructions. Hive operates over sets of models and, upon receiving natural language instructions (i.e. user queries), schedules and executes explainable plans of atomic actions. These actions can involve one or more of the available models to achieve the overall task, while respecting end-users specific constraints. Notably, Hive handles tasks that involve multi-modal inputs and outputs, enabling it to handle complex, real-world queries. Our system is capable of planning complex chains of actions while guaranteeing explainability, using an LLM-based formal logic backbone empowered by PDDL operations. We introduce the MuSE benchmark in order to offer a comprehensive evaluation of the multi-modal capabilities of agent systems. Our findings show that our framework redefines the state-of-the-art for task selection, outperforming other competing systems that plan operations across multiple models while offering transparency guarantees while fully adhering to user constraints.
A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions
2024-12-17
Yuyuan Li, Xiaohua Feng, Chaochao Chen, Qiang Yang
Recommender systems have become increasingly influential in shaping user behavior and decision-making, highlighting their growing impact in various domains. Meanwhile, the widespread adoption of machine learning models in recommender systems has raised significant concerns regarding user privacy and security. As compliance with privacy regulations becomes more critical, there is a pressing need to address the issue of recommendation unlearning, i.e., eliminating the memory of specific training data from the learned recommendation models. Despite its importance, traditional machine unlearning methods are ill-suited for recommendation unlearning due to the unique challenges posed by collaborative interactions and model parameters. This survey offers a comprehensive review of the latest advancements in recommendation unlearning, exploring the design principles, challenges, and methodologies associated with this emerging field. We provide a unified taxonomy that categorizes different recommendation unlearning approaches, followed by a summary of widely used benchmarks and metrics for evaluation. By reviewing the current state of research, this survey aims to guide the development of more efficient, scalable, and robust recommendation unlearning techniques. Furthermore, we identify open research questions in this field, which could pave the way for future innovations not only in recommendation unlearning but also in a broader range of unlearning tasks across different machine learning applications.
Noise-based Local Learning using Stochastic Magnetic Tunnel Junctions
2024-12-17
Kees Koenders, Leo Schnitzpan, Fabian Kammerbauer, Sinan Shu, Gerhard Jakob, Mathis Kläui, Johan Mentink, Nasir Ahmad, Marcel van Gerven
Brain-inspired learning in physical hardware has enormous potential to learn fast at minimal energy expenditure. One of the characteristics of biological learning systems is their ability to learn in the presence of various noise sources. Inspired by this observation, we introduce a novel noise-based learning approach for physical systems implementing multi-layer neural networks. Simulation results show that our approach allows for effective learning whose performance approaches that of the conventional effective yet energy-costly backpropagation algorithm. Using a spintronics hardware implementation, we demonstrate experimentally that learning can be achieved in a small network composed of physical stochastic magnetic tunnel junctions. These results provide a path towards efficient learning in general physical systems which embraces rather than mitigates the noise inherent in physical devices.
Digital technologies and performance incentives: Evidence from businesses in the Swiss economy
2024-12-17
Johannes Lehmann, Michael Beckmann
Using novel survey data from Swiss firms, this paper empirically examines the relationship between the use of digital technologies and the prevalence of performance incentives. We argue that digital technologies tend to reduce the cost of organizational monitoring through improved measurement of employee behavior and performance, as well as through employee substitution in conjunction with a reduced agency problem. While we expect the former mechanism to increase the prevalence of performance incentives, the latter is likely to decrease it. Our doubly robust ATE estimates show that companies using business software and certain key technologies of Industry 4.0 increasingly resort to performance incentives, suggesting that the improved measurement effect dominates the employee substitution effect. In addition, we find that companies emerging as technology-friendly use performance incentives more frequently than their technology-averse counterparts. Both findings hold for managerial and non-managerial employees. Our estimation results are robust to a variety of sensitivity checks and suggest that Swiss businesses leverage digital technologies to enhance control over production or service processes, allowing them to intensify their management of employees through performance incentives.
Different from most conventional recommendation problems, sequential recommendation focuses on learning users' preferences by exploiting the internal order and dependency among the interacted items, which has received significant attention from both researchers and practitioners. In recent years, we have witnessed great progress and achievements in this field, necessitating a new survey. In this survey, we study the SR problem from a new perspective (i.e., the construction of an item's properties), and summarize the most recent techniques used in sequential recommendation such as pure ID-based SR, SR with side information, multi-modal SR, generative SR, LLM-powered SR, ultra-long SR and data-augmented SR. Moreover, we introduce some frontier research topics in sequential recommendation, e.g., open-domain SR, data-centric SR, could-edge collaborative SR, continuous SR, SR for good, and explainable SR. We believe that our survey could be served as a valuable roadmap for readers in this field.
Large Language Models (LLMs) demonstrate remarkable performance in semantic understanding and generation, yet accurately assessing their output reliability remains a significant challenge. While numerous studies have explored calibration techniques, they primarily focus on White-Box LLMs with accessible parameters. Black-Box LLMs, despite their superior performance, pose heightened requirements for calibration techniques due to their API-only interaction constraints. Although recent researches have achieved breakthroughs in black-box LLMs calibration, a systematic survey of these methodologies is still lacking. To bridge this gap, we presents the first comprehensive survey on calibration techniques for black-box LLMs. We first define the Calibration Process of LLMs as comprising two interrelated key steps: Confidence Estimation and Calibration. Second, we conduct a systematic review of applicable methods within black-box settings, and provide insights on the unique challenges and connections in implementing these key steps. Furthermore, we explore typical applications of Calibration Process in black-box LLMs and outline promising future research directions, providing new perspectives for enhancing reliability and human-machine alignment. This is our GitHub link: https://github.com/LiangruXie/Calibration-Process-in-Black-Box-LLMs
Ultra-long period compact sources: a glimpse into observational breakthroughs and theoretical challenges
2024-12-17
Francesco Coti Zelati, Alice Borghese
At the Seventeenth Marcel Grossman meeting, researchers gathered to discuss significant advances in the study of ultra-long period sources. Presentations covered key aspects, including emission properties, evolutionary scenarios, and models for their emission. In this proceeding, we summarize key observational breakthroughs and touch upon the proposed evolutionary pathways and state-of-the-art models that seek to explain these sources. Finally, we outline future directions, including the potential of ongoing and upcoming surveys, improved detection algorithms, and multiwavelength observations to significantly expand the known population of these mysterious sources.
Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection
2024-12-17
Debajyoti Mazumder, Aakash Kumar, Jasabanta Patro
In this paper, we reported our experiments with various strategies to improve code-mixed humour and sarcasm detection. We did all of our experiments for Hindi-English code-mixed scenario, as we have the linguistic expertise for the same. We experimented with three approaches, namely (i) native sample mixing, (ii) multi-task learning (MTL), and (iii) prompting very large multilingual language models (VMLMs). In native sample mixing, we added monolingual task samples in code-mixed training sets. In MTL learning, we relied on native and code-mixed samples of a semantically related task (hate detection in our case). Finally, in our third approach, we evaluated the efficacy of VMLMs via few-shot context prompting. Some interesting findings we got are (i) adding native samples improved humor (raising the F1-score up to 6.76%) and sarcasm (raising the F1-score up to 8.64%) detection, (ii) training MLMs in an MTL framework boosted performance for both humour (raising the F1-score up to 10.67%) and sarcasm (increment up to 12.35% in F1-score) detection, and (iii) prompting VMLMs couldn't outperform the other approaches. Finally, our ablation studies and error analysis discovered the cases where our model is yet to improve. We provided our code for reproducibility.
Using LLM-Generated Draft Replies to Support Human Experts in Responding to Stakeholder Inquiries in Maritime Industry: A Real-World Case Study of Industrial AI
2024-12-17
Tita Alissa Bach, Aleksandar Babic, Narae Park, Tor Sporsem, Rasmus Ulfsnes, Henrik Smith-Meyer, Torkel Skeie
The maritime industry requires effective communication among diverse stakeholders to address complex, safety-critical challenges. Industrial AI, including Large Language Models (LLMs), has the potential to augment human experts' workflows in this specialized domain. Our case study investigated the utility of LLMs in drafting replies to stakeholder inquiries and supporting case handlers. We conducted a preliminary study (observations and interviews), a survey, and a text similarity analysis (LLM-as-a-judge and Semantic Embedding Similarity). We discover that while LLM drafts can streamline workflows, they often require significant modifications to meet the specific demands of maritime communications. Though LLMs are not yet mature enough for safety-critical applications without human oversight, they can serve as valuable augmentative tools. Final decision-making thus must remain with human experts. However, by leveraging the strengths of both humans and LLMs, fostering human-AI collaboration, industries can increase efficiency while maintaining high standards of quality and precision tailored to each case.
MINDS. JWST-MIRI reveals a peculiar CO$_2$-rich chemistry in the drift-dominated disk CX Tau
2024-12-17
Marissa Vlasblom, Milou Temmink, Sierra L. Grant, Nicolas Kurtovic, Andrew D. Sellek, Ewine F. van Dishoeck, Manuel Güdel, Thomas Henning, Pierre-Olivier Lagage, David Barrado, Alessio Caratti o Garatti, Adrian M. Glauser, Inga Kamp, Fred Lahuis, Göran Olofsson, Aditya M. Arabhavi, Valentin Christiaens, Danny Gasman, Hyerin Jang, Maria Morales-Calderón, Giulia Perotti, Kamber Schwarz, Benoît Tabone
Radial drift of icy pebbles can have a large impact on the chemistry of the inner regions of protoplanetary disks. Compact dust disks ($\lesssim$50 au) are suggested to have a higher (cold) H$_2$O flux than more extended disks, likely due to efficient radial drift bringing H$_2$O-rich material to the inner disk, where it can be observed with JWST. We present JWST MIRI/MRS observations of the disk CX Tau taken as a part of the Mid-INfrared Disk Survey (MINDS) GTO program, a prime example of a drift-dominated disk. This compact disk seems peculiar: the source possesses a bright CO$_2$ feature instead of the bright H$_2$O expected based on its efficient radial drift. We aim to provide an explanation for this finding. We detect molecular emission from H$_2$O, $^{12}$CO$_2$, $^{13}$CO$_2$, C$_2$H$_2$, HCN, and OH in this disk, and even demonstrate a potential detection of CO$^{18}$O. Analysis of the $^{12}$CO$_2$ and $^{13}$CO$_2$ emission shows the former to be tracing a temperature of $\sim$450 K, whereas the $^{13}$CO$_2$ traces a significantly colder temperature ($\sim$200 K). H$_2$O is also securely detected both at shorter and longer wavelengths, tracing a similar temperature of $\sim$500-600 K as the CO$_2$ emission. We also find evidence for a colder, $\sim$200 K H$_2$O component at longer wavelengths, which is in line with this disk having strong radial drift. The cold $^{13}$CO$_2$ and H$_2$O emission indicate that radial drift of ices likely plays an important role in setting the chemistry of the inner disk of CX Tau. Potentially, the H$_2$O-rich gas has already advected onto the central star, which is now followed by an enhancement of comparatively CO$_2$-rich gas reaching the inner disk, explaining the enhancement of CO$_2$ emission in CX Tau. The comparatively weaker H$_2$O emission can be explained by the source's low accretion luminosity. (abridged)
Accelerating lensed quasars discovery and modeling with physics-informed variational autoencoders
2024-12-17
Irham T. Andika, Stefan Schuldt, Sherry H. Suyu, Satadru Bag, Raoul Cañameras, Alejandra Melo, Claudio Grillo, James H. H. Chan
Strongly lensed quasars provide valuable insights into the rate of cosmic expansion, the distribution of dark matter in foreground deflectors, and the characteristics of quasar hosts. However, detecting them in astronomical images is difficult due to the prevalence of non-lensing objects. To address this challenge, we developed a generative deep learning model called VariLens, built upon a physics-informed variational autoencoder. This model seamlessly integrates three essential modules: image reconstruction, object classification, and lens modeling, offering a fast and comprehensive approach to strong lens analysis. VariLens is capable of rapidly determining both (1) the probability that an object is a lens system and (2) key parameters of a singular isothermal ellipsoid (SIE) mass model -- including the Einstein radius ($\theta_\mathrm{E}$), lens center, and ellipticity -- in just milliseconds using a single CPU. A direct comparison of VariLens estimates with traditional lens modeling for 20 known lensed quasars within the Subaru Hyper Suprime-Cam (HSC) footprint shows good agreement, with both results consistent within $2\sigma$ for systems with $\theta_\mathrm{E}<3$ arcsecs. To identify new lensed quasar candidates, we begin with an initial sample of approximately 80 million sources, combining HSC data with multiwavelength information from various surveys. After applying a photometric preselection aimed at locating $z>1.5$ sources, the number of candidates is reduced to 710,966. Subsequently, VariLens highlights 13,831 sources, each showing a high likelihood of being a lens. A visual assessment of these objects results in 42 promising candidates that await spectroscopic confirmation. These results underscore the potential of automated deep learning pipelines to efficiently detect and model strong lenses in large datasets.
With the rapid development of high-throughput sequencing platforms, an increasing number of omics technologies, such as genomics, metabolomics, and transcriptomics, are being applied to disease genetics research. However, biological data often exhibit high dimensionality and significant noise, making it challenging to effectively distinguish disease subtypes using a single-omics approach. To address these challenges and better capture the interactions among DNA, RNA, and proteins described by the central dogma, numerous studies have leveraged artificial intelligence to develop multi-omics models for disease research. These AI-driven models have improved the accuracy of disease prediction and facilitated the identification of genetic loci associated with diseases, thus advancing precision medicine. This paper reviews the mathematical definitions of multi-omics, strategies for integrating multi-omics data, applications of artificial intelligence and deep learning in multi-omics, the establishment of foundational models, and breakthroughs in multi-omics technologies, drawing insights from over 130 related articles. It aims to provide practical guidance for computational biologists to better understand and effectively utilize AI-based multi-omics machine learning algorithms in the context of central dogma.
Local optical conductivity of strain solitons in bilayer graphene with arbitrary soliton angle
2024-12-17
Lu Wen, Xinyu Lv, Zhiqiang Li
We theoretically study the electronic band structure and local optical conductivity of domain wall solitons in bilayer graphene (as well as twisted bilayer graphene) with arbitrary soliton angle, which characterizes the local strain direction. We demonstrate that the soliton angle provides an important yet underexplored degree of freedom that can strongly modify the local optical conductivity. The conductivity spectrum features resonance peaks associated with interband transitions involving the topological as well as high-energy soliton states. Two most prominent peaks exhibit continuous suppression and enhancement, respectively, with the soliton angle. The dependence of the peaks on Fermi energy provides important information about the soliton band structure. The local optical conductivity exhibits substantial spatial dependence, which can be used to study the spatial distribution of the soliton states. Furthermore, we show that the conductivity spectra for all soliton angles are broadly tunable by external pressure, which can double the energies of the resonance peaks in experimentally achievable pressure ranges.
As the time-domain survey telescope of the highest survey power in the northern hemisphere currently, Wide Field Survey Telescope (WFST) is scheduled to hourly/daily/semi-weekly scan northern sky up to ~23 mag in four optical (ugri) bands. Unlike the observation cadences in the forthcoming regular survey missions, WFST performed "staring" observations toward Galactic plane in a cadence of $\approx$1 minute for a total on-source time of about 13 hours, during the commissioning and pilot observation phases. Such an observation cadence is well applied in producing densely sampling light curves and hunting for stars exhibiting fast stellar variabilities. Here we introduce the primary methodologies in detecting variability, periodicity, and stellar flares among a half million sources from the minute-cadence observations, and present the WFST g-/r-band light curves generated from periodic variable stars and flaring stars. Benefit from high photometric precisions and deep detection limits of WFST, the observations have captured several rare variable stars, such as a variable hot white dwarf (WD) and an ellipsoidal WD binary candidate. By surveying the almost unexplored parameter spaces for variables, WFST will lead to new opportunities in discovering unique variable stars in the northern sky.
Cosmic Multipoles in Galaxy Surveys Part I: How Inferences Depend on Source Counts and Masks
2024-12-17
Oliver T. Oayda, Vasudev Mittal, Geraint F. Lewis
We present a new approach to constructing and fitting dipoles and higher-order multipoles in synthetic galaxy samples over the sky. Within our Bayesian paradigm, we illustrate that this technique is robust to masked skies, allowing us to make credible inferences about the relative contributions of each multipole. We also show that dipoles can be recovered in surveys with small footprints, determining the requisite source counts required for concrete estimation of the dipole parameters. This work is motivated by recent probes of the cosmic dipole in galaxy catalogues. Namely, the kinematic dipole of the Cosmic Microwave Background, as arising from the motion of our heliocentric frame at $\approx 370\ \text{km}\,\text{s}^{-1}$, implies that an analogous dipole should be observed in the number counts of galaxies in flux-density-limited samples. Recent studies have reported a dipole aligning with the kinematic dipole but with an anomalously large amplitude. Accordingly, our new technique will be important as forthcoming galaxy surveys are made available and for revisiting previous data.
LLMs are Also Effective Embedding Models: An In-depth Overview
2024-12-17
Chongyang Tao, Tao Shen, Shen Gao, Junshuo Zhang, Zhen Li, Zhengwei Tao, Shuai Ma
Large language models (LLMs) have revolutionized natural language processing by achieving state-of-the-art performance across various tasks. Recently, their effectiveness as embedding models has gained attention, marking a paradigm shift from traditional encoder-only models like ELMo and BERT to decoder-only, large-scale LLMs such as GPT, LLaMA, and Mistral. This survey provides an in-depth overview of this transition, beginning with foundational techniques before the LLM era, followed by LLM-based embedding models through two main strategies to derive embeddings from LLMs. 1) Direct prompting: We mainly discuss the prompt designs and the underlying rationale for deriving competitive embeddings. 2) Data-centric tuning: We cover extensive aspects that affect tuning an embedding model, including model architecture, training objectives, data constructions, etc. Upon the above, we also cover advanced methods, such as handling longer texts, and multilingual and cross-modal data. Furthermore, we discuss factors affecting choices of embedding models, such as performance/efficiency comparisons, dense vs sparse embeddings, pooling strategies, and scaling law. Lastly, the survey highlights the limitations and challenges in adapting LLMs for embeddings, including cross-task embedding quality, trade-offs between efficiency and accuracy, low-resource, long-context, data bias, robustness, etc. This survey serves as a valuable resource for researchers and practitioners by synthesizing current advancements, highlighting key challenges, and offering a comprehensive framework for future work aimed at enhancing the effectiveness and efficiency of LLMs as embedding models.
Distributed satellite information networks: Architecture, enabling technologies, and trends
2024-12-17
Qinyu Zhang, Liang Xu, Jianhao Huang, Tao Yang, Jian Jiao, Ye Wang, Yao Shi, Chiya Zhang, Xingjian Zhang, Ke Zhang, Yupeng Gong, Na Deng, Nan Zhao, Zhen Gao, Shujun Han, Xiaodong Xu, Li You, Dongming Wang, Shan Jiang, Dixian Zhao, Nan Zhang, Liujun Hu, Xiongwen He, Yonghui Li, Xiqi Gao, Xiaohu You
Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current satellite systems struggling to accommodate the escalating demands of next-generation intelligent applications. In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services. This survey first provides a profound discussion about innovative network architectures of DSIN, encompassing distributed regenerative satellite network architecture, distributed satellite computing network architecture, and reconfigurable satellite formation flying, to enable flexible and scalable communication, computing and control. The DSIN faces challenges from network heterogeneity, unpredictable channel dynamics, sparse resources, and decentralized collaboration frameworks. To address these issues, a series of enabling technologies is identified, including channel modeling and estimation, cloud-native distributed MIMO cooperation, grant-free massive access, network routing, and the proper combination of all these diversity techniques. Furthermore, to heighten the overall resource efficiency, the cross-layer optimization techniques are further developed to meet upper-layer deterministic, adaptive and secure information services requirements. In addition, emerging research directions and new opportunities are highlighted on the way to achieving the DSIN vision.
Probing Environmental Dependence of High-Redshift Galaxy Properties with the Marked Correlation Function
2024-12-17
Emy Mons, Charles Jose
In hierarchical structure formation, correlations between galaxy properties and their environments reveal important clues about galaxy evolution, emphasizing the importance of measuring these relationships. We probe the environmental dependence of Lyman-break galaxy (LBG) properties in the redshift range of $3$ to $5$ using marked correlation function statistics with galaxy samples from the Hyper Suprime-Cam Subaru Strategic Program and the Canada--France--Hawaii Telescope U-band surveys. We find that the UV magnitude and color of magnitude-selected LBG samples are strongly correlated with their environment, making these properties effective tracers of it. In contrast, the star formation rate and stellar mass of LBGs exhibit a weak environmental dependence. For UV magnitudes and color, the correlation is stronger in brighter galaxy samples across all redshifts and extends to scales far beyond the size of typical dark matter halos. This suggests that within a given sample, LBGs with high UV magnitudes or colors are more likely to form pairs at these scales than predicted by the two-point angular correlation function. Moreover, the amplitude of the marked correlation function is generally higher for LBG samples compared to that of $z \sim 0$ galaxies from previous studies.We also find that for LBG samples selected by the same absolute threshold magnitude or average halo mass, the correlation between UV magnitudes and the environment generally becomes more pronounced as the redshift decreases. On the other hand, for samples with the same effective large-scale bias at $z\sim 4$ and $5$, the marked correlation functions are similar on large scales.
EXIT: Context-Aware Extractive Compression for Enhancing Retrieval-Augmented Generation
2024-12-17
Taeho Hwang, Sukmin Cho, Soyeong Jeong, Hoyun Song, SeungYoon Han, Jong C. Park
We introduce EXIT, an extractive context compression framework that enhances both the effectiveness and efficiency of retrieval-augmented generation (RAG) in question answering (QA). Current RAG systems often struggle when retrieval models fail to rank the most relevant documents, leading to the inclusion of more context at the expense of latency and accuracy. While abstractive compression methods can drastically reduce token counts, their token-by-token generation process significantly increases end-to-end latency. Conversely, existing extractive methods reduce latency but rely on independent, non-adaptive sentence selection, failing to fully utilize contextual information. EXIT addresses these limitations by classifying sentences from retrieved documents - while preserving their contextual dependencies - enabling parallelizable, context-aware extraction that adapts to query complexity and retrieval quality. Our evaluations on both single-hop and multi-hop QA tasks show that EXIT consistently surpasses existing compression methods and even uncompressed baselines in QA accuracy, while also delivering substantial reductions in inference time and token count. By improving both effectiveness and efficiency, EXIT provides a promising direction for developing scalable, high-quality QA solutions in RAG pipelines. Our code is available at https://github.com/ThisIsHwang/EXIT
Ultrafast demagnetization in ferromagnetic materials: Origins and progress
2024-12-17
Xiaowen Chen, Roman Adam, Daniel E. Bürgler, Fangzhou Wang, Zhenyan Lu, Lining Pan, Sarah Heidtfeld, Christian Greb, Meihong Liu, Qingfang Liu, Jianbo Wang, Claus M. Schneider, Derang Cao
Since the discovery of ultrafast demagnetization in Ni thin films in 1996, laser-induced ultrafast spin dynamics have become a prominent research topic in the field of magnetism and spintronics. This development offers new possibilities for the advancement of spintronics and magnetic storage technology. The subject has drawn a substantial number of researchers, leading to a series of research endeavors. Various models have been proposed to elucidate the physical processes underlying laser-induced ultrafast spin dynamics in ferromagnetic materials. However, the potential origins of these processes across different material systems and the true contributions of these different origins remain challenging in the realm of ultrafast spin dynamics. This predicament also hinders the development of spintronic terahertz emitters. In this review, we initially introduce the different experimental methods used in laser-induced ultrafast spin dynamics. We then systematically explore the magnetization precession process and present seven models of ultrafast demagnetization in ferromagnetic materials. Subsequently, we discuss the physical processes and research status of four ultrafast demagnetization origins (including spin-flipping, spin transport, non-thermal electronic distribution, and laser-induced lattice strain). Since attosecond laser technique and antiferromagnetic materials exhibit promising applications in ultrahigh-frequency spintronics, we acknowledge the emerging studies used by attosecond pules and studies on ultrafast spin dynamics in antiferromagnets, noting the significant challenges that need to be addressed in these burgeoning field.
The DESI-Lensing Mock Challenge: large-scale cosmological analysis of 3x2-pt statistics
2024-12-17
C. Blake, C. Garcia-Quintero, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, A. de la Macorra, J. DeRose, A. Dey, P. Doel, N. Emas, S. Ferraro, J. E. Forero-Romero, G. Gutierrez, S. Heydenreich, K. Honscheid, C. Howlett, M. Ishak, E. Jullo, R. Kehoe, D. Kirkby, A. Kremin, A. Krolewski, M. Landriau, J. U. Lange, A. Leauthaud, M. E. Levi, M. Manera, R. Miquel, J. Moustakas, G. Niz, W. J. Percival, I. Pérez-Ràfols, A. Porredon, G. Rossi, R. Ruggeri, E. Sanchez, C. Saulder, D. Schlegel, D. Sprayberry, Z. Sun, G. Tarlé, B. A. Weaver
The current generation of large galaxy surveys will test the cosmological model by combining multiple types of observational probes. Realising the statistical promise of these new datasets requires rigorous attention to all aspects of analysis including cosmological measurements, modelling, covariance and parameter likelihood. In this paper we present the results of an end-to-end simulation study designed to test the analysis pipeline for the combination of the Dark Energy Spectroscopic Instrument (DESI) Year 1 galaxy redshift dataset and separate weak gravitational lensing information from the Kilo-Degree Survey, Dark Energy Survey and Hyper-Suprime-Cam Survey. Our analysis employs the 3x2-pt correlation functions including cosmic shear and galaxy-galaxy lensing, together with the projected correlation function of the spectroscopic DESI lenses. We build realistic simulations of these datasets including galaxy halo occupation distributions, photometric redshift errors, weights, multiplicative shear calibration biases and magnification. We calculate the analytical covariance of these correlation functions including the Gaussian, noise and super-sample contributions, and show that our covariance determination agrees with estimates based on the ensemble of simulations. We use a Bayesian inference platform to demonstrate that we can recover the fiducial cosmological parameters of the simulation within the statistical error margin of the experiment, investigating the sensitivity to scale cuts. This study is the first in a sequence of papers in which we present and validate the large-scale 3x2-pt cosmological analysis of DESI-Y1.
The ALMA-ATOMS survey: Vibrationally excited HC$_3$N lines in hot cores
2024-12-17
Li Chen, Sheng-Li Qin, Tie Liu, Paul F. Goldsmith, Xunchuan Liu, Yaping Peng, Xindi Tang, Guido Garay, Zhiping Kou, Mengyao Tang, Patricio Sanhueza, Ziyang Li, Prasanta Gorai, Swagat R. Das, Leonardo Bronfman, Lokesh Dewangan, Pablo García, Shanghuo Li, Chang Won Lee, Hong-Li Liu, L. Viktor Tóth, James O. Chibueze, Jihye Hwang, Xiaohu Li, Fengwei Xu, Jiahang Zou, Wenyu Jiao, Zhenying Zhang, Yong Zhang
Interstellar molecules are excellent tools for studying the physical and chemical environments of massive star-forming regions. In particular, vibrationally excited HC$_3$N (HC$_3$N*) lines are the key tracers for probing hot cores environments. We present the Atacama Large Millimeter/submillimeter Array (ALMA) 3 mm observations of HC$_3$N* lines in 60 hot cores, aiming to investigate how physical conditions affect the excitation of HC$_3$N* transitions. We have used the XCLASS for line identification. Under the assumption of local thermodynamic equilibrium (LTE), we derived the rotation temperature and column density of HC$_3$N* transitions in hot cores. Additionally, we calculated the H$_2$ column density and number density, along with the abundance of HC$_3$N* relative to H$_2$, to enable a comparison of the physical properties of hot cores with different numbers of HC$_3$N* states. We have detected HC$_3$N* lines in 52 hot cores, in which 29 cores showing more than one vibrationally excited state. Hot cores with higher gas temperatures have more detections of these vibrationally excited lines. The excitation of HC$_3$N* requires dense environments, with its spatial distribution influenced by the presence of UC Hii regions. The observed column density of HC$_3$N* contributes to the number of HC$_3$N* states in hot core environments. After analyzing the various factors influencing HC$_3$N* excitation in hot cores, we conclude that the excitation of HC$_3$N* is mainly driven by mid-IR pumping, while collisional excitation is ineffective.
When to Speak, When to Abstain: Contrastive Decoding with Abstention
2024-12-17
Hyuhng Joon Kim, Youna Kim, Sang-goo Lee, Taeuk Kim
Large Language Models (LLMs) demonstrate exceptional performance across diverse tasks by leveraging both pre-trained knowledge (i.e., parametric knowledge) and external knowledge (i.e., contextual knowledge). While substantial efforts have been made to leverage both forms of knowledge, scenarios in which the model lacks any relevant knowledge remain underexplored. Such limitations can result in issues like hallucination, causing reduced reliability and potential risks in high-stakes applications. To address such limitations, this paper extends the task scope to encompass cases where the user's request cannot be fulfilled due to the lack of relevant knowledge. To this end, we introduce Contrastive Decoding with Abstention (CDA), a training-free decoding method that empowers LLMs to generate responses when relevant knowledge is available and to abstain otherwise. CDA evaluates the relevance of each knowledge for a given query, adaptively determining which knowledge to prioritize or which to completely ignore. Extensive experiments with four LLMs on three question-answering datasets demonstrate that CDA can effectively perform accurate generation and abstention simultaneously. These findings highlight CDA's potential to broaden the applicability of LLMs, enhancing reliability and preserving user trust.
Ambient IoT towards 6G: Standardization, Potentials, and Challenges
2024-12-17
Kan Zheng, Rongtao Xu, Jie Mei, Haojun Yang, Lei Lei, Xianbin Wang
The Ambient Internet of Things (A-IoT) has emerged as a critical direction for achieving effective connectivity as the IoT system evolves to 6G. However, the introduction of A-IoT technologies, particularly involving backscatter modulation, poses numerous challenges for system design and network operations. This paper surveys current standardization efforts, highlights potential challenges, and explores future directions for A-IoT. It begins with a comprehensive overview of ongoing standardization initiatives by the 3rd Generation Partnership Project (3GPP) on A-IoT, providing a solid foundation for further technical research in both industry and academia. Building upon this groundwork, the paper conducts an analysis of critical enabling technologies for A-IoT. Moreover, a comprehensive A-IoT demonstration system is designed to showcase the practical viability and efficiency of A-IoT techniques, supported by field experiments. We finally address ongoing challenges associated with A-IoT technologies, providing valuable insights for future research endeavors.
Many computer systems are now being redesigned to incorporate LLM-powered agents, enabling natural language input and more flexible operations. This paper focuses on handling database transactions created by large language models (LLMs). Transactions generated by LLMs may include semantic errors, requiring systems to treat them as long-lived. This allows for human review and, if the transaction is incorrect, removal from the database history. Any removal action must ensure the database's consistency (the "C" in ACID principles) is maintained throughout the process. We propose a novel middleware framework based on Invariant Satisfaction (I-Confluence), which ensures consistency by identifying and coordinating dependencies between long-lived transactions and new transactions. This middleware buffers suspicious or compensating transactions to manage coordination states. Using the TPC-C benchmark, we evaluate how transaction generation frequency, user reviews, and invariant completeness impact system performance. For system researchers, this study establishes an interactive paradigm between LLMs and database systems, providing an "undoing" mechanism for handling incorrect operations while guaranteeing database consistency. For system engineers, this paper offers a middleware design that integrates removable LLM-generated transactions into existing systems with minimal modifications.
Human-in-the-Loop Generation of Adversarial Texts: A Case Study on Tibetan Script
2024-12-17
Xi Cao, Yuan Sun, Jiajun Li, Quzong Gesang, Nuo Qun, Tashi Nyima
DNN-based language models perform excellently on various tasks, but even SOTA LLMs are susceptible to textual adversarial attacks. Adversarial texts play crucial roles in multiple subfields of NLP. However, current research has the following issues. (1) Most textual adversarial attack methods target rich-resourced languages. How do we generate adversarial texts for less-studied languages? (2) Most textual adversarial attack methods are prone to generating invalid or ambiguous adversarial texts. How do we construct high-quality adversarial robustness benchmarks? (3) New language models may be immune to part of previously generated adversarial texts. How do we update adversarial robustness benchmarks? To address the above issues, we introduce HITL-GAT, a system based on a general approach to human-in-the-loop generation of adversarial texts. HITL-GAT contains four stages in one pipeline: victim model construction, adversarial example generation, high-quality benchmark construction, and adversarial robustness evaluation. Additionally, we utilize HITL-GAT to make a case study on Tibetan script which can be a reference for the adversarial research of other less-studied languages.
Although large language models (LLMs) store vast amount of knowledge in their parameters, they still have limitations in the memorization and utilization of certain knowledge, leading to undesired behaviors such as generating untruthful and inaccurate responses. This highlights the critical need to understand the knowledge boundary of LLMs, a concept that remains inadequately defined in existing research. In this survey, we propose a comprehensive definition of the LLM knowledge boundary and introduce a formalized taxonomy categorizing knowledge into four distinct types. Using this foundation, we systematically review the field through three key lenses: the motivation for studying LLM knowledge boundaries, methods for identifying these boundaries, and strategies for mitigating the challenges they present. Finally, we discuss open challenges and potential research directions in this area. We aim for this survey to offer the community a comprehensive overview, facilitate access to key issues, and inspire further advancements in LLM knowledge research.
Fermi Unassociated Sources in the MeerKAT Absorption Line Survey
2024-12-17
Morgan Himes, Preshanth Jagannathan, Dale Frail, Frank Schinzel, Neeraj Gupta, S. A. Balashev, F. Combes, P. P. Deka, H. -R Klockner, Emmanuel Momjian, Jonah Wagenveld
Over 2000 Gamma ray sources identified by the Large Area Telescope (LAT) on NASA's Fermi Gamma-ray Space Telescope are considered unassociated, meaning that they have no known counterparts in any other frequency regime. We have carried out an image-based search for steep spectrum radio sources, with in-band spectral index less than -1.4, within the error regions of Fermi unassociated sources using 1 to 1.4 GHz radio data from the MeerKAT Absorption Line Survey (MALS) Data Release. MALS DR1 with a median rms noise of 22 to 25 microJy and 735,649 sources is a significant advance over past image-based searches with improvements in sensitivity, resolution and bandwidth. Steep spectrum candidates were identified using a combination of in-band spectral indices from MALS and existing radio surveys. We developed an optical and infrared source classification scheme in order to distinguish between galactic pulsars and radio galaxies. In total, we identify nine pulsar candidates towards six Fermi sources that are worthy of follow-up for pulsation searches. We also report 41 steep spectrum radio galaxy candidates that may be of interest in searches for high-redshift radio galaxies. We show that MALS due to its excellent continuum sensitivity can detect 80 percent of the known pulsar population. This exhibits the promise of identifying exotic pulsar candidates with future image-based surveys with the Square Kilometre and its precursors.
Topic modeling is widely used for uncovering thematic structures within text corpora, yet traditional models often struggle with specificity and coherence in domain-focused applications. Guided approaches, such as SeededLDA and CorEx, incorporate user-provided seed words to improve relevance but remain labor-intensive and static. Large language models (LLMs) offer potential for dynamic topic refinement and discovery, yet their application often incurs high API costs. To address these challenges, we propose the LLM-assisted Iterative Topic Augmentation framework (LITA), an LLM-assisted approach that integrates user-provided seeds with embedding-based clustering and iterative refinement. LITA identifies a small number of ambiguous documents and employs an LLM to reassign them to existing or new topics, minimizing API costs while enhancing topic quality. Experiments on two datasets across topic quality and clustering performance metrics demonstrate that LITA outperforms five baseline models, including LDA, SeededLDA, CorEx, BERTopic, and PromptTopic. Our work offers an efficient and adaptable framework for advancing topic modeling and text clustering.
With the increasing prevalence of cross-domain Text-Attributed Graph (TAG) Data (e.g., citation networks, recommendation systems, social networks, and ai4science), the integration of Graph Neural Networks (GNNs) and Large Language Models (LLMs) into a unified Model architecture (e.g., LLM as enhancer, LLM as collaborators, LLM as predictor) has emerged as a promising technological paradigm. The core of this new graph learning paradigm lies in the synergistic combination of GNNs' ability to capture complex structural relationships and LLMs' proficiency in understanding informative contexts from the rich textual descriptions of graphs. Therefore, we can leverage graph description texts with rich semantic context to fundamentally enhance Data quality, thereby improving the representational capacity of model-centric approaches in line with data-centric machine learning principles. By leveraging the strengths of these distinct neural network architectures, this integrated approach addresses a wide range of TAG-based Task (e.g., graph learning, graph reasoning, and graph question answering), particularly in complex industrial scenarios (e.g., supervised, few-shot, and zero-shot settings). In other words, we can treat text as a medium to enable cross-domain generalization of graph learning Model, allowing a single graph model to effectively handle the diversity of downstream graph-based Task across different data domains. This work serves as a foundational reference for researchers and practitioners looking to advance graph learning methodologies in the rapidly evolving landscape of LLM. We consistently maintain the related open-source materials at \url{https://github.com/xkLi-Allen/Awesome-GNN-in-LLMs-Papers}.
Assessing the Limitations of Large Language Models in Clinical Fact Decomposition
2024-12-17
Monica Munnangi, Akshay Swaminathan, Jason Alan Fries, Jenelle Jindal, Sanjana Narayanan, Ivan Lopez, Lucia Tu, Philip Chung, Jesutofunmi A. Omiye, Mehr Kashyap, Nigam Shah
Verifying factual claims is critical for using large language models (LLMs) in healthcare. Recent work has proposed fact decomposition, which uses LLMs to rewrite source text into concise sentences conveying a single piece of information, as an approach for fine-grained fact verification. Clinical documentation poses unique challenges for fact decomposition due to dense terminology and diverse note types. To explore these challenges, we present FactEHR, a dataset consisting of full document fact decompositions for 2,168 clinical notes spanning four types from three hospital systems. Our evaluation, including review by clinicians, highlights significant variability in the quality of fact decomposition for four commonly used LLMs, with some LLMs generating 2.6x more facts per sentence than others. The results underscore the need for better LLM capabilities to support factual verification in clinical text. To facilitate future research in this direction, we plan to release our code at \url{https://github.com/som-shahlab/factehr}.
Testing Colour-magnitude Pattern as A Method in the Search for Changing-Look AGNs
2024-12-17
Li-tao Zhu, Zhongxiang Wang, P. U. Devanand, Alok C. Gupta, Karan Dogra, Jie Li, Ju-Jia Zhang, Shun-hao Ji, Si-si Sun
We develop a simple method to search for changing-look (CL) active galactic nucleus (AGN) candidates, and conduct a test run. In this method, optical variations of AGNs are monitored and CL-AGNs may appear to have a pattern of being bluer when in brightening flare-like events. Applying this method, previously-classified type 2 AGNs that show the bluer-when-brighter (BWB) pattern are selected. Among more than ten thousands type 2 AGNs classified in the Sloan Digital Sky Survey (SDSS), we find 73 candidates with possibly the strongest BWB pattern. We note that 13 of them have previously been reported as CL-AGNs. We have observed nine candidates, and found that five among them showed the CL transition from type 2 to type 1. In addition, we also test extending the selection to previously-classified type 1 AGNs in the SDSS by finding sources with a possible redder-when-brighter pattern, but none of the three sources observed by us is found to show the transition from type 1 to type 2. We discuss the variation properties in both the success and failure cases, and plan to observe more candidates selected with the method. From the observational results, a detailed comparison between the CL-AGNs and none CL-AGNs will help quantitatively refine the selection criteria and in turn allow us to configure the general properties of CLAGNs.
Improving essay peer grading accuracy in MOOCs using personalized weights from student's engagement and performance
2024-12-17
Carlos García-Martínez, Rebeca Cerezo, Manuel Bermúdez, Cristóbal Romero
Most MOOC platforms either use simple schemes for aggregating peer grades, e.g., taking the mean or the median, or apply methodologies that increase students' workload considerably, such as calibrated peer review. To reduce the error between the instructor and students' aggregated scores in the simple schemes, without requiring demanding grading calibration phases, some proposals compute specific weights to compute a weighted aggregation of the peer grades. In this work, and in contrast to most previous studies, we analyse the use of students' engagement and performance measures to compute personalized weights and study the validity of the aggregated scores produced by these common functions, mean and median, together with two other from the information retrieval field, namely the geometric and harmonic means. To test this procedure we have analysed data from a MOOC about Philosophy. The course had 1059 students registered, and 91 participated in a peer review process that consisted in writing an essay and rating three of their peers using a rubric. We calculated and compared the aggregation scores obtained using weighted and non-weighted versions. Our results show that the validity of the aggregated scores and their correlation with the instructors grades can be improved in relation to peer grading, when using the median and weights are computed according to students' performance in chapter tests.
Evolution of the "long tail" concept for scientific data
2024-12-17
Gretchen R. Stahlman, Inna Kouper
This review paper explores the evolution of discussions about "long-tail" scientific data in the scholarly literature. The "long-tail" concept, originally used to explain trends in digital consumer goods, was first applied to scientific data in 2007 to refer to a vast array of smaller, heterogeneous data collections that cumulatively represent a substantial portion of scientific knowledge. However, these datasets, often referred to as "long-tail data," are frequently mismanaged or overlooked due to inadequate data management practices and institutional support. This paper examines the changing landscape of discussions about long-tail data over time, situated within broader ecosystems of research data management and the natural interplay between "big" and "small" data. The review also bridges discussions on data curation in Library & Information Science (LIS) and domain-specific contexts, contributing to a more comprehensive understanding of the long-tail concept's utility for effective data management outcomes. The review aims to provide a more comprehensive understanding of this concept, its terminological diversity in the literature, and its utility for guiding data management, overall informing current and future information science research and practice.
Maximal supergravities in ten and eleven dimensions admit consistent truncations on particular spheres to maximal supergravities in lower dimensions. Concurrently, the truncation to singlets under any subgroup of the sphere isometry group leads to consistent truncations with less or no supersymmetry. We review the relation between these truncations in the framework of exceptional field theory. As an application, we derive three new G$_2$-invariant solutions of $D=11$ supergravity. Their geometry is of the form AdS$_4\times \Sigma_7$ where $\Sigma_7$ is a deformed seven-sphere, preserving SO(7) isometries.
A family of instanton-invariants for four-manifolds and their relation to Khovanov homology
2024-12-17
Michael Bleher
This article reviews Witten's gauge-theoretic approach to Khovanov homology from the perspective of Haydys-Witten instanton Floer theory. Expanding on Witten's arguments, we introduce a one-parameter family of instanton Floer homology groups $HF_{\theta}(W^4)$, which, based on physical arguments, are expected to be topological invariants of the four-manifold $W^4$. In analogy to the original Yang-Mills instanton Floer theory, these groups are defined by the solutions of the $\theta$-Kapustin-Witten equations on $W^4$ modulo instanton solutions of the Haydys-Witten equations that interpolate between them on the five-dimensional cylinder $\mathbb{R}_s \times W^4$. The relation to knot invariants arises when the four-manifold is the geometric blowup $W^4 = [X^3 \times \mathbb{R}^+, K]$ along a knot $K \subset X^3 \times \{0\}$ embedded in its three-dimensional boundary. The boundaries and corners of this manifold require the specification of boundary conditions that preserve the topological invariance of the construction and are fundamentally linked to various dimensional reductions of the Haydys-Witten equations. We provide a comprehensive discussion of these dimensional reductions and relate them to well-known gauge-theoretic equations in lower dimensions, including the $\theta$-Kapustin-Witten equations, twisted extended Bogomolny equations, and twisted octonionic Nahm equations. Along the way, we record novel results on the elliptic regularity of the Haydys-Witten equations with twisted Nahm pole boundary conditions. The upshot of the article is a tentative definition of Haydys-Witten Floer theory and a precise restatement of Witten's conjecture: an equality between the Haydys-Witten Floer homology $HF^\bullet_{\pi/2}([S^3 \times \mathbb{R}^+, K])$ and Khovanov homology $Kh^\bullet(K)$.
Tunable mechanical properties and air-based lubrication in an acoustically levitated granular material
2024-12-17
Nina M. Brown, Bryan VanSaders, Jason M. Kronenfeld, Joseph M. DeSimone, Heinrich M. Jaeger
Cohesive granular materials are found in many natural and industrial environments, but experimental platforms for exploring the innate mechanical properties of these materials are often limited by the difficulty of adjusting cohesion strength. Granular particles levitated in an acoustic cavity form a model system to address this. Such particles self-assemble into free-floating, quasi-two-dimensional raft structures which are held together by acoustic scattering forces; the strength of this attraction can be changed simply by modifying the sound field. We investigate the mechanical properties of acoustically bound granular rafts using substrate-free micro-scale shear tests. We first demonstrate deformation of rafts of spheres and the dependence of this deformation on acoustic pressure. We then apply these methods to rafts composed of anisotropic sand grains and smaller spheres, in which the smaller spheres have a thin layer of air separating them from other grain surfaces. These spheres act as soft, effectively frictionless particles that populate the interstices between the larger grains, which enables us to investigate the effect of lubricating the mixture in the presence of large-grain cohesion.
PANORAMIC: Discovery of an Ultra-Massive Grand-Design Spiral Galaxy at $z\sim5.2$
2024-12-17
Mengyuan Xiao, Christina C. Williams, Pascal A. Oesch, David Elbaz, Miroslava Dessauges-Zavadsky, Rui Marques Coelho Chaves, Longji Bing, Zhiyuan Ji, Andrea Weibel, Rachel Bezanson, Gabriel Brammer, Caitlin Casey, Aidan P. Cloonan, Emanuele Daddi, Pratika Dayal, Andreas L. Faisst, Marijn Franx, Karl Glazebrook, Anne Hutter, Jeyhan S. Kartaltepe, Ivo Labbe, Guilaine Lagache, Seunghwan Lim, Benjamin Magnelli, Felix Martinez, Michael V. Maseda, Themiya Nanayakkara, Daniel Schaerer, Katherine E. Whitaker
We report the discovery of an ultra-massive grand-design red spiral galaxy, named Zh\'ul\'ong (Torch Dragon), at $z_{\rm phot} = 5.2^{+0.3}_{-0.2}$ in the JWST PANORAMIC survey, identified as the most distant bulge+disk galaxy with spiral arms known to date. Zh\'ul\'ong displays an extraordinary combination of properties: 1) a classical bulge centered in a large, face-on exponential stellar disk (half-light radius of $R_{\rm e} = 3.7 \pm 0.1 \, \mathrm{kpc}$), with spiral arms extending across 19 kpc; 2) a clear transition from the red, quiescent core ($F150W-F444W=3.1$ mag) with high stellar mass surface density ($\log(\Sigma M_{\star}/M_{\odot} \, \mathrm{kpc}^{-2}) = 9.91_{-0.09}^{+0.11}$) to the star-forming outer regions, as revealed by spatially resolved SED analysis, which indicates significant inside-out galaxy growth; 3) an extremely high stellar mass at its redshift, with $\log (M_{\star}/M_{\odot})=11.03_{-0.08}^{+0.10}$ comparable to the Milky Way, and an implied baryon-to-star conversion efficiency ($\epsilon \sim 0.3$) that is 1.5 times higher than even the most efficient galaxies at later epochs; 4) despite an active disk, a relatively modest overall star formation rate ($\mathrm{SFR} =66_{-46}^{+89} ~M_{\odot} \, \mathrm{yr}^{-1}$), which is $>$0.5 dex below the star formation main sequence at $z \sim 5.2$ and $>$10 times lower than ultra-massive dusty galaxies at $z=5-6$. Altogether, Zh\'ul\'ong shows that mature galaxies emerged much earlier than expected in the first billion years after the Big Bang through rapid galaxy formation and morphological evolution. Our finding offers key constraints for models of massive galaxy formation and the origin of spiral structures in the early universe.
Accurate absolute photometric calibration by fitting the system transmission
2024-12-17
S. Garrappa, E. O. Ofek, S. Ben-Ami, D. Polishook, A. Gal-Yam, Y. Shvartzvald, A. Krassilchtchikov, R. Konno, E. Segre, Y. M. Shani, Y. Sofer-Rimalt, A. Blumenzweig
Transforming the instrumental photometry of ground-based telescopes into a calibrated physical flux in a well-defined passband is a major challenge in astronomy. Along with the intrinsic instrumental difference between telescopes sharing the same filter, the effective transmission is continuously modified by the effects of the variable atmosphere of the Earth. We have developed a new approach to the absolute photometric calibration that simultaneously treats instrumental and atmospheric effects on an image-by-image basis by fitting the system transmission. This approach aims at breaking the 1% absolute photometric accuracy which limits current calibration methods for ground-based observatories. We fit the transmission, as a function of wavelength, for each image. The fit is done by comparing the instrumental fluxes of stars in the image to the synthetic photometry of the stars given their spectrum and the transmission function which have free parameters. A key element that enables this approach is the set of about 220 million low-resolution spectra measured by Gaia, which provides a large number of stellar calibrators in the image. We demonstrate the method using data from the Large Array Survey Telescope (LAST). We show that the residuals between observations and synthetic photometry of the Gaia spectra in the fitted transmission have a standard deviation $<$1% on an image-by-image basis, with no spatial and color dependencies. The median accuracy of the zero-point throughout the image is between 3-5 mmag, depending on the total image exposure. Furthermore we show that this method provides high stability over long temporal scales.
A comprehensive review of assistive technologies for children with dyslexia
2024-12-17
Sansrit Paudel, Subek Acharya
Dyslexia is a learning disability that primarily disrupts one's ability to read, write and spell. About 15-20% of the world population is affected by dyslexia, which signals it is a concerning subject. A systematic literature review was conducted on this research to evaluate trending interventions, common accessibility features, and potential barriers to existing assistive devices and to identify any research gaps. Papers published over the period (2015-2023) of 8 years were reviewed, and the data was meticulously analyzed. This research shows that technological-based interventions are leading interventions, especially with the use of mobile apps and augmentative realities. More innovative technologies like virtual reality, NLP, haptic, and tangible user interfaces are emerging to provide unique solutions addressing the user's needs. Non-computing devices are less effective compared to modern digital solutions however, they provide a promising alternative.
Logic-Constrained Shortest Paths for Flight Planning
2024-12-17
Ricardo Euler, Pedro Maristany de las Casas, Ralf Borndörfer
The Logic-Constrained Shortest Path Problem (LCSP) combines a one-to-one shortest path problem with satisfiability constraints imposed on the routing graph. This setting arises in flight planning, where air traffic control (ATC) authorities are enforcing a set of traffic flow restrictions (TFRs) on aircraft routes in order to increase safety and throughput. We propose a new branch and bound-based algorithm for the LCSP. The resulting algorithm has three main degrees of freedom: the node selection rule, the branching rule and the conflict. While node selection and branching rules have been long studied in the MIP and SAT communities, most of them cannot be applied out of the box for the LCSP. We review the existing literature and develop tailored variants of the most prominent rules. The conflict, the set of variables to which the branching rule is applied, is unique to the LCSP. We analyze its theoretical impact on the B&B algorithm. In the second part of the paper, we show how to model the Flight Planning Problem with TFRs as an LCSP and solve it using the branch and bound algorithm. We demonstrate the algorithm's efficiency on a dataset consisting of a global flight graph and a set of around 20000 real TFRs obtained from our industry partner Lufthansa Systems GmbH. We make this dataset publicly available. Finally, we conduct an empirical in-depth analysis of node selection rules, branching rules and conflicts. Carefully choosing an appropriate combination yields an improvement of an order of magnitude compared to an uninformed choice.
Creating an LLM-based AI-agent: A high-level methodology towards enhancing LLMs with APIs
2024-12-17
Ioannis Tzachristas
Large Language Models (LLMs) have revolutionized various aspects of engineering and science. Their utility is often bottlenecked by the lack of interaction with the external digital environment. To overcome this limitation and achieve integration of LLMs and Artificial Intelligence (AI) into real-world applications, customized AI agents are being constructed. Based on the technological trends and techniques, we extract a high-level approach for constructing these AI agents, focusing on their underlying architecture. This thesis serves as a comprehensive guide that elucidates a multi-faceted approach for empowering LLMs with the capability to leverage Application Programming Interfaces (APIs). We present a 7-step methodology that begins with the selection of suitable LLMs and the task decomposition that is necessary for complex problem-solving. This methodology includes techniques for generating training data for API interactions and heuristics for selecting the appropriate API among a plethora of options. These steps eventually lead to the generation of API calls that are both syntactically and semantically aligned with the LLM's understanding of a given task. Moreover, we review existing frameworks and tools that facilitate these processes and highlight the gaps in current attempts. In this direction, we propose an on-device architecture that aims to exploit the functionality of carry-on devices by using small models from the Hugging Face community. We examine the effectiveness of these approaches on real-world applications of various domains, including the generation of a piano sheet. Through an extensive analysis of the literature and available technologies, this thesis aims to set a compass for researchers and practitioners to harness the full potential of LLMs augmented with external tool capabilities, thus paving the way for more autonomous, robust, and context-aware AI agents.
Physics-model-guided Worst-case Sampling for Safe Reinforcement Learning
2024-12-17
Hongpeng Cao, Yanbing Mao, Lui Sha, Marco Caccamo
Real-world accidents in learning-enabled CPS frequently occur in challenging corner cases. During the training of deep reinforcement learning (DRL) policy, the standard setup for training conditions is either fixed at a single initial condition or uniformly sampled from the admissible state space. This setup often overlooks the challenging but safety-critical corner cases. To bridge this gap, this paper proposes a physics-model-guided worst-case sampling strategy for training safe policies that can handle safety-critical cases toward guaranteed safety. Furthermore, we integrate the proposed worst-case sampling strategy into the physics-regulated deep reinforcement learning (Phy-DRL) framework to build a more data-efficient and safe learning algorithm for safety-critical CPS. We validate the proposed training strategy with Phy-DRL through extensive experiments on a simulated cart-pole system, a 2D quadrotor, a simulated and a real quadruped robot, showing remarkably improved sampling efficiency to learn more robust safe policies.
Integrating Evidence into the Design of XAI and AI-based Decision Support Systems: A Means-End Framework for End-users in Construction
2024-12-17
Peter . E. D. Love, Jane Matthews, Weili Fang, Hadi Mahamivanan
A narrative review is used to develop a theoretical evidence-based means-end framework to build an epistemic foundation to uphold explainable artificial intelligence instruments so that the reliability of outcomes generated from decision support systems can be assured and better explained to end-users. The implications of adopting an evidence-based approach to designing decision support systems in construction are discussed with emphasis placed on evaluating the strength, value, and utility of evidence needed to develop meaningful human explanations for end-users. While the developed means-end framework is focused on end-users, stakeholders can also utilize it to create meaningful human explanations. However, they will vary due to their different epistemic goals. Including evidence in the design and development of explainable artificial intelligence and decision support systems will improve decision-making effectiveness, enabling end-users' epistemic goals to be achieved. The proposed means-end framework is developed from a broad spectrum of literature. Thus, it is suggested that it can be used in construction and other engineering domains where there is a need to integrate evidence into the design of explainable artificial intelligence and decision support systems.
A Comprehensive Review on Traffic Datasets and Simulators for Autonomous Vehicles
2024-12-17
Supriya Sarker, Brent Maples, Weizi Li
Autonomous driving has rapidly developed and shown promising performance due to recent advances in hardware and deep learning techniques. High-quality datasets are fundamental for developing reliable autonomous driving algorithms. Previous dataset surveys either focused on a limited number or lacked detailed investigation of dataset characteristics. Besides, we analyze the annotation processes, existing labeling tools, and the annotation quality of datasets, showing the importance of establishing a standard annotation pipeline. On the other hand, we thoroughly analyze the impact of geographical and adversarial environmental conditions on the performance of autonomous driving systems. Moreover, we exhibit the data distribution of several vital datasets and discuss their pros and cons accordingly. Additionally, this paper provides a comprehensive analysis of publicly available traffic simulators. In addition to informing about traffic datasets, it is also the goal of this paper to provide context and information on the current capabilities of traffic simulators for their specific contributions to autonomous vehicle simulation and development. Furthermore, this paper discusses future directions and the increasing importance of synthetic data generation in simulators to enhance the training and creation of effective simulations. Finally, we discuss the current challenges and the development trend of future autonomous driving datasets.
Geometry of 3-dimensional del Pezzo fibrations in positive characteristic
2024-12-16
Fabio Bernasconi
We survey some of the recent works on the geometry of del Pezzo surfaces over imperfect fields, with applications to 3-dimensional del Pezzo fibrations in positive characteristic. We place particular emphasis on cases where the general fibres of the fibrations are not smooth
DarkNESS: developing a skipper-CCD instrument to search for Dark Matter from Low Earth Orbit
2024-12-16
Phoenix Alpine, Samriddhi Bhatia, Fernando Chierchie, Alex Drlica-Wagner, Rouven Essig, Juan Estrada, Erez Etzion, Roni Harnik, Michael Lembeck, Nathan Saffold, Sho Uemura
The DarkNESS (Dark Matter Nano-satellite Equipped with Skipper Sensors) mission aims to deploy a skipper-CCD CubeSat Observatory to search for dark matter (DM) from Low Earth Orbit. This mission will employ novel skipper-CCDs to investigate O(keV) X-rays from decaying DM, as well as electron recoils from strongly-interacting sub-GeV DM. The DarkNESS mission will be the first space deployment of skipper-CCDs, and the DarkNESS team is developing a skipper-CCD instrument that is compatible with the CubeSat platform. DarkNESS has recently progressed from laboratory validation to a Critical Design Review (CDR) phase, with a launch opportunity anticipated in late 2025. The implementation of the DarkNESS skipper-CCD payload on the CubeSat platform will pave the way for future demonstrators of space-based imagers for X-ray and single-electron counting applications.
$X+y$: insights on gas thermodynamics from the combination of X-ray and thermal Sunyaev-Zel'dovich data cross-correlated with cosmic shear
2024-12-16
Adrien La Posta, David Alonso, Nora Elisa Chisari, Tassia Ferreira, Carlos García-García
We measure the cross-correlation between cosmic shear from the third-year release of the Dark Energy Survey, thermal Sunyaev-Zel'dovich (tSZ) maps from Planck, and X-ray maps from ROSAT. We investigate the possibility of developing a physical model able to jointly describe both measurements, simultaneously constraining the spatial distribution and thermodynamic properties of hot gas. We find that a relatively simple model is able to describe both sets of measurements and to make reasonably accurate predictions for other observables (the tSZ auto-correlation, its cross-correlation with X-rays, and tomographic measurements of the bias-weighted mean gas pressure). We show, however, that contamination from X-ray AGN, as well as the impact of non-thermal pressure support, must be incorporated in order to fully resolve tensions in parameter space between different data combinations. We obtain simultaneous constraints on the mass scale at which half of the gas content has been expelled from the halo, $\mathrm{log}_{10}(M_c)=14.83^{+0.16}_{-0.23}$, on the polytropic index of the gas, $\Gamma=1.144^{+0.016}_{-0.013}$, and on the ratio of the central gas temperature to the virial temperature $\alpha_T=1.30^{+0.15}_{-0.28}$.
Existing research on AI-based traffic management systems, utilizing techniques such as fuzzy logic, reinforcement learning, deep neural networks, and evolutionary algorithms, demonstrates the potential of AI to transform the traffic landscape. This article endeavors to review the topics where AI and traffic management intersect. It comprises areas like AI-powered traffic signal control systems, automatic distance and velocity recognition (for instance, in autonomous vehicles, hereafter AVs), smart parking systems, and Intelligent Traffic Management Systems (ITMS), which use data captured in real-time to keep track of traffic conditions, and traffic-related law enforcement and surveillance using AI. AI applications in traffic management cover a wide range of spheres. The spheres comprise, inter alia, streamlining traffic signal timings, predicting traffic bottlenecks in specific areas, detecting potential accidents and road hazards, managing incidents accurately, advancing public transportation systems, development of innovative driver assistance systems, and minimizing environmental impact through simplified routes and reduced emissions. The benefits of AI in traffic management are also diverse. They comprise improved management of traffic data, sounder route decision automation, easier and speedier identification and resolution of vehicular issues through monitoring the condition of individual vehicles, decreased traffic snarls and mishaps, superior resource utilization, alleviated stress of traffic management manpower, greater on-road safety, and better emergency response time.
The Impact of AI Assistance on Radiology Reporting: A Pilot Study Using Simulated AI Draft Reports
2024-12-16
Julián N. Acosta, Siddhant Dogra, Subathra Adithan, Kay Wu, Michael Moritz, Stephen Kwak, Pranav Rajpurkar
Radiologists face increasing workload pressures amid growing imaging volumes, creating risks of burnout and delayed reporting times. While artificial intelligence (AI) based automated radiology report generation shows promise for reporting workflow optimization, evidence of its real-world impact on clinical accuracy and efficiency remains limited. This study evaluated the effect of draft reports on radiology reporting workflows by conducting a three reader multi-case study comparing standard versus AI-assisted reporting workflows. In both workflows, radiologists reviewed the cases and modified either a standard template (standard workflow) or an AI-generated draft report (AI-assisted workflow) to create the final report. For controlled evaluation, we used GPT-4 to generate simulated AI drafts and deliberately introduced 1-3 errors in half the cases to mimic real AI system performance. The AI-assisted workflow significantly reduced average reporting time from 573 to 435 seconds (p=0.003), without a statistically significant difference in clinically significant errors between workflows. These findings suggest that AI-generated drafts can meaningfully accelerate radiology reporting while maintaining diagnostic accuracy, offering a practical solution to address mounting workload challenges in clinical practice.
LeARN: Learnable and Adaptive Representations for Nonlinear Dynamics in System Identification
2024-12-16
Arunabh Singh, Joyjit Mukherjee
System identification, the process of deriving mathematical models of dynamical systems from observed input-output data, has undergone a paradigm shift with the advent of learning-based methods. Addressing the intricate challenges of data-driven discovery in nonlinear dynamical systems, these methods have garnered significant attention. Among them, Sparse Identification of Nonlinear Dynamics (SINDy) has emerged as a transformative approach, distilling complex dynamical behaviors into interpretable linear combinations of basis functions. However, SINDy relies on domain-specific expertise to construct its foundational "library" of basis functions, which limits its adaptability and universality. In this work, we introduce a nonlinear system identification framework called LeARN that transcends the need for prior domain knowledge by learning the library of basis functions directly from data. To enhance adaptability to evolving system dynamics under varying noise conditions, we employ a novel meta-learning-based system identification approach that uses a lightweight deep neural network (DNN) to dynamically refine these basis functions. This not only captures intricate system behaviors but also adapts seamlessly to new dynamical regimes. We validate our framework on the Neural Fly dataset, showcasing its robust adaptation and generalization capabilities. Despite its simplicity, our LeARN achieves competitive dynamical error performance compared to SINDy. This work presents a step toward the autonomous discovery of dynamical systems, paving the way for a future where machine learning uncovers the governing principles of complex systems without requiring extensive domain-specific interventions.
Control of a Josephson Digital Phase Detector via an SFQ-based Flux Bias Driver
2024-12-16
Laura Di Marino, Luigi Di Palma, Michele Riccio, Francesco Fienga, Marco Arzeo, Oleg Mukhanov
Quantum computation requires high-fidelity qubit readout, preserving the quantum state. In the case of superconducting (SC) qubits, readout is typically performed using a complex analog experimental setup operated at room temperature, which poses significant technological and economic barriers to large system scalability. An alternative approach is to perform a cryogenic on-chip qubit readout based on a Josephson Digital Phase Detector (JDPD): a flux switchable device capable of digitizing the phase sign of a coherent input. The readout operation includes the flux excitation of the JDPD to evolve from a single to a double-minima potential. In this work, the effect of the flux bias characteristics on the JDPD performances is studied numerically. To meet the identified requirements that maximize detection fidelity and tackle the engineering challenges, a cryogenic on-chip Single Flux Quantum based flux bias driver is proposed and discussed.
OpenReviewer: A Specialized Large Language Model for Generating Critical Scientific Paper Reviews
2024-12-16
Maximilian Idahl, Zahra Ahmadi
We present OpenReviewer, an open-source system for generating high-quality peer reviews of machine learning and AI conference papers. At its core is Llama-OpenReviewer-8B, an 8B parameter language model specifically fine-tuned on 79,000 expert reviews from top ML conferences. Given a PDF paper submission and review template as input, OpenReviewer extracts the full text, including technical content like equations and tables, and generates a structured review following conference-specific guidelines. Our evaluation on 400 test papers shows that OpenReviewer produces significantly more critical and realistic reviews compared to general-purpose LLMs like GPT-4 and Claude-3.5. While other LLMs tend toward overly positive assessments, OpenReviewer's recommendations closely match the distribution of human reviewer ratings. The system provides authors with rapid, constructive feedback to improve their manuscripts before submission, though it is not intended to replace human peer review. OpenReviewer is available as an online demo and open-source tool.
Euclid: Field-level inference of primordial non-Gaussianity and cosmic initial conditions
2024-12-16
A. Andrews, J. Jasche, G. Lavaux, F. Leclercq, F. Finelli, Y. Akrami, M. Ballardini, D. Karagiannis, J. Valiviita, N. Bartolo, G. Cañas-Herrera, S. Casas, B. R. Granett, F. Pace, D. Paoletti, N. Porqueres, Z. Sakr, D. Sapone, N. Aghanim, A. Amara, S. Andreon, C. Baccigalupi, M. Baldi, S. Bardelli, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, J. Dinis, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, M. Farina, S. Farrens, F. Faustini, S. Ferriol, M. Frailis, E. Franceschi, S. Galeotta, B. Gillis, C. Giocoli, P. Gómez-Alvarez, A. Grazian, F. Grupp, S. V. H. Haugan, W. Holmes, F. Hormuth, A. Hornstrup, P. Hudelot, S. Ilić, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, M. Kunz, H. Kurki-Suonio, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, E. Medinaceli, S. Mei, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, M. Moresco, L. Moscardini, C. Neissner, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, A. G. Sánchez, B. Sartoris, M. Schirmer, P. Schneider, T. Schrabback, A. Secroun, E. Sefusatti, S. Serrano, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, G. Zamorani, E. Zucca, C. Burigana, V. Scottez, A. Spurio Mancini, M. Viel
A primary target of the \Euclid space mission is to constrain early-universe physics by searching for deviations from a primordial Gaussian random field. A significant detection of primordial non-Gaussianity would rule out the simplest models of cosmic inflation and transform our understanding of the origin of the Universe. This paper forecasts how well field-level inference of galaxy redshift surveys can constrain the amplitude of local primordial non-Gaussianity ($f_{NL}$), within a Bayesian hierarchical framework, in the upcoming \Euclid data. We design and simulate mock data sets and perform Markov chain Monte Carlo analyses using a full-field forward modelling approach. By including the formation history of the cosmic matter field in the analysis, the method takes into account all available probes of primordial non-Gaussianity, and goes beyond statistical summary estimators of $f_{NL}$. Probes include, for example, two-point and higher-order statistics, peculiar velocity fields, and scale-dependent galaxy biases. Furthermore, the method simultaneously handles systematic survey effects, such as selection effects, survey geometries, and galaxy biases. The forecast shows that the method can reach precision levels of up to $\sigma (f_{NL}) = 2.3$ (68.3\% CI, and at the grid resolution $\Delta L = 62.5\,h^{-1}$Mpc) with \Euclid data. We also provide data products, including realistic $N$-body simulations with nonzero values of $f_{NL}$ and maps of adiabatic curvature fluctuations. The results underscore the feasibility and advantages of field-level inference to constrain $f_{NL}$ in galaxy redshift surveys. Our approach consistently captures all the information available in the large-scale structure to constrain $f_{NL}$, and resolves the degeneracy between early-universe physics and late-time gravitational effects, while mitigating the impact of systematic and observational effects.
SEAGraph: Unveiling the Whole Story of Paper Review Comments
2024-12-16
Jianxiang Yu, Jiaqi Tan, Zichen Ding, Jiapeng Zhu, Jiahao Li, Yao Cheng, Qier Cui, Yunshi Lan, Xiang Li
Peer review, as a cornerstone of scientific research, ensures the integrity and quality of scholarly work by providing authors with objective feedback for refinement. However, in the traditional peer review process, authors often receive vague or insufficiently detailed feedback, which provides limited assistance and leads to a more time-consuming review cycle. If authors can identify some specific weaknesses in their paper, they can not only address the reviewer's concerns but also improve their work. This raises the critical question of how to enhance authors' comprehension of review comments. In this paper, we present SEAGraph, a novel framework developed to clarify review comments by uncovering the underlying intentions behind them. We construct two types of graphs for each paper: the semantic mind graph, which captures the author's thought process, and the hierarchical background graph, which delineates the research domains related to the paper. A retrieval method is then designed to extract relevant content from both graphs, facilitating coherent explanations for the review comments. Extensive experiments show that SEAGraph excels in review comment understanding tasks, offering significant benefits to authors.
A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges
2024-12-16
Yibo Yan, Jiamin Su, Jianxiang He, Fangteng Fu, Xu Zheng, Yuanhuiyi Lyu, Kun Wang, Shen Wang, Qingsong Wen, Xuming Hu
Mathematical reasoning, a core aspect of human cognition, is vital across many domains, from educational problem-solving to scientific advancements. As artificial general intelligence (AGI) progresses, integrating large language models (LLMs) with mathematical reasoning tasks is becoming increasingly significant. This survey provides the first comprehensive analysis of mathematical reasoning in the era of multimodal large language models (MLLMs). We review over 200 studies published since 2021, and examine the state-of-the-art developments in Math-LLMs, with a focus on multimodal settings. We categorize the field into three dimensions: benchmarks, methodologies, and challenges. In particular, we explore multimodal mathematical reasoning pipeline, as well as the role of (M)LLMs and the associated methodologies. Finally, we identify five major challenges hindering the realization of AGI in this domain, offering insights into the future direction for enhancing multimodal reasoning capabilities. This survey serves as a critical resource for the research community in advancing the capabilities of LLMs to tackle complex multimodal reasoning tasks.
Generalised Fermat equation: a survey of solved cases
2024-12-16
Ashleigh Wilcox, Bogdan Grechuk
Generalised Fermat equation (GFE) is the equation of the form $ax^p+by^q=cz^r$, where $a,b,c,p,q,r$ are positive integers. If $1/p+1/q+1/r<1$, GFE is known to have at most finitely many primitive integer solutions $(x,y,z)$. A large body of the literature is devoted to finding such solutions explicitly for various six-tuples $(a,b,c,p,q,r)$, as well as for infinite families of such six-tuples. This paper surveys the families of parameters for which GFE has been solved. Although the proofs are not discussed here, collecting these references in one place will make it easier for the readers to find the relevant proof techniques in the original papers. Also, this survey will help the readers to avoid duplicate work by solving the already solved cases.
The cosmic history of Primordial Black Hole accretion and its uncertainties
2024-12-16
Pratibha Jangra, Daniele Gaggero, Bradley J. Kavanagh, J. M. Diego
Primordial Black Holes (PBHs) have not been experimentally detected so far, but their existence would provide important insights about the early Universe and serve as one of the possible candidates of dark matter (DM). In this work, we explore the accretion of radiation and matter by PBHs, with relevance for the growth of PBH seeds to form early Supermassive Black Holes; the emission from accreting PBHs; and constraints from gravitational wave observations, among others. We study the growth of PBH masses in the early Universe due to the accretion of radiation, highlighting uncertainties which arise from estimates of the PBH formation time. For baryonic accretion, we review the traditional Bondi-Hoyle-Lyttleton (BHL) and its refined version known as the Park-Ricotti (PR) model, which also includes radiative feedback. We find that in the BHL model, PBHs heavier than $\sim 100 \,\mathrm{M_{\odot}}$ can grow in mass by several orders of magnitude by $z \lesssim 10$, though only when surrounded by DM halos and only when the accretion efficiency is large. By contrast, the inclusion of radiation feedback in the PR model can drastically suppress the baryonic accretion rate of PBHs, leading to a negligible change in PBH mass over cosmic time. Furthermore our calculations show that the accretion rate depends sensitively on the modelling of various parameters such as the speed of sound in the baryonic gas and the velocity of PBHs. These findings highlight the uncertainties associated with accretion onto PBHs, and we find that a large increase in the PBH mass due to accretion is by no means guaranteed.
What Can Youth Learn About in One Hour? Examining How Hour of Code Activities Address the Five Big Ideas of Artificial Intelligence
2024-12-16
Luis Morales-Navarro, Yasmin B. Kafai, Eric Yang, Asep Suryana
The prominence of artificial intelligence and machine learning in everyday life has led to efforts to foster AI literacy for all K-12 students. In this paper, we review how Hour of Code activities engage with the five big ideas of AI, in particular with machine learning and societal impact. We found that a large majority of activities focus on perception and machine learning, with little attention paid to representation and other topics. A surprising finding was the increased attention paid to critical aspects of computing. However, we also observed a limited engagement with hands-on activities. In the discussion, we address how future introductory activities could be designed to offer a broader array of topics, including the development of tools to introduce novices to artificial intelligence and machine learning and the design of more unplugged and collaborative activities.
Playground of Lognormal Seminumerical Simulations of the Lyman $α$ Forest: Thermal History of the Intergalactic Medium
2024-12-16
Tomas Ondro, Bhaskar Arya, Rudolf Galis
This study aims to test a potential application of lognormal seminumerical simulations to recover the thermal parameters and Jeans length. This could be suitable for generating large number of synthetic spectra with various input data and parameters, and thus ideal for interpreting the high-quality data obtained from QSO absorption spectra surveys. We use a seminumerical approach to simulate absorption spectra of quasars at redshifts $ 3 \leq z \leq 5$. These synthetic spectra are compared with the 1D flux power spectra and using the Markov Chain Monte Carlo analysis method we determine the temperature at mean density, slope of the temperature-density relation and Jeans length. Our best-fit model is also compared with the evolution of the temperature of the intergalactic medium from various UVB models. We show that the lognormal simulations can effectively recover thermal parameters and Jeans length. Besides, by comparing the synthetic flux power spectra with observations from Baryon Oscillation Spectroscopy Survey we found, that such an approach can be also used for the cosmological parameter inference.
Modelling chemical clocks -- Theoretical evidences of the space and time evolution of [s/alpha] in the Galactic disc with Gaia-ESO survey
2024-12-16
Marta Molero, Laura Magrini, Marco Palla, Gabriele Cescutti, Carlos Viscasillas Vázquez, Giada Casali, Emanuele Spitoni, Francesca Matteucci, Sofia Randich
Chemical clocks based on [s-process elements/alpha-elements] ratios are widely used to estimate ages of Galactic stellar populations. However, the [s/alpha] vs. age relations are not universal, varying with metallicity, location in the Galactic disc, and specific s-process elements. Current Galactic chemical evolution models struggle to reproduce the observed [s/alpha] increase at young ages. We provide chemical evolution models for the Milky Way disc to identify the conditions required to reproduce the observed [s/H], [s/Fe], and [s/alpha] vs. age relations. We adopt a multi-zone chemical evolution model including state-of-the-art nucleosynthesis prescriptions for neutron-capture elements (AGB stars, rotating massive stars, neutron star mergers, magneto-driven supernovae). We explore variations in gas infall, AGB yield dependencies on progenitor stars, and rotational velocity distributions for massive stars. Results are compared with open cluster data from the Gaia-ESO survey. A three-infall scenario for disc formation captures the rise of [s/alpha] with age in the outer regions but fails in the inner ones, especially for second s-process peak elements. Ba production in the last 3 Gyr of chemical evolution would need to increase by half to match observations. S-process contributions from low-mass AGB stars improve predictions but require increases not supported by nucleosynthesis calculations, even with potential i-process contribution. Variations in the metallicity dependence of AGB yields show inconsistent effects across elements. Distributions of massive star rotational velocities fail to improve results due to balanced effects on elements. We confirm that there is no single relationship [s/alpha] vs. age, but that it varies along the MW disc. Current prescriptions for neutron-capture element yields cannot fully capture the complexity of evolution, particularly in the inner disc.
The Selection Problem in Multi-Query Optimization: a Comprehensive Survey
2024-12-16
Sergey Zinchenko, Denis Ponomaryov
View materialization, index selection, and plan caching are well-known techniques for optimization of query processing in database systems. The essence of these tasks is to select and save a subset of the most useful candidates (views/indexes/plans) for reuse within given space/time budget constraints. In this paper, based on the View Selection Problem, we propose a unified view on these problems. We identify the root causes of the complexity of these selection problems and provide a detailed analysis of techniques to cope with them. Our survey provides a modern classification of selection algorithms known in the literature, including the latest ones based on Machine Learning. We provide a ground for the reuse of the selection techniques between different optimization scenarios and highlight challenges and promising directions in the field.
Advancements and Challenges in Bangla Question Answering Models: A Comprehensive Review
2024-12-16
Md Iftekhar Islam Tashik, Abdullah Khondoker, Enam Ahmed Taufik, Antara Firoz Parsa, S M Ishtiak Mahmud
The domain of Natural Language Processing (NLP) has experienced notable progress in the evolution of Bangla Question Answering (QA) systems. This paper presents a comprehensive review of seven research articles that contribute to the progress in this domain. These research studies explore different aspects of creating question-answering systems for the Bangla language. They cover areas like collecting data, preparing it for analysis, designing models, conducting experiments, and interpreting results. The papers introduce innovative methods like using LSTM-based models with attention mechanisms, context-based QA systems, and deep learning techniques based on prior knowledge. However, despite the progress made, several challenges remain, including the lack of well-annotated data, the absence of high-quality reading comprehension datasets, and difficulties in understanding the meaning of words in context. Bangla QA models' precision and applicability are constrained by these challenges. This review emphasizes the significance of these research contributions by highlighting the developments achieved in creating Bangla QA systems as well as the ongoing effort required to get past roadblocks and improve the performance of these systems for actual language comprehension tasks.
Discrete Poincaré inequalities: a review on proofs, equivalent formulations, and behavior of constants
2024-12-16
Alexandre Ern, Johnny Guzmán, Pratyush Potu, Martin Vohralík
We investigate discrete Poincar\'e inequalities on piecewise polynomial subspaces of the Sobolev spaces H(curl) and H(div) in three space dimensions. We characterize the dependence of the constants on the continuous-level constants, the shape regularity and cardinality of the underlying tetrahedral mesh, and the polynomial degree. One important focus is on meshes being local patches (stars) of tetrahedra from a larger tetrahedral mesh. We also review various equivalent results to the discrete Poincar\'e inequalities, namely stability of discrete constrained minimization problems, discrete inf-sup conditions, bounds on operator norms of piecewise polynomial vector potential operators (Poincar\'e maps), and existence of graph-stable commuting projections.
The SOFIA Massive (SOMA) Star Formation Survey. V. Clustered Protostars
2024-12-16
Zoie Telkamp, Ruben Fedriani, Jonathan C. Tan, Chi-Yan Law, Yichen Zhang, Adele Plunkett, Samuel Crowe, Yao-Lun Yang, James M. De Buizer, Maria T. Beltran, Melisse Bonfand, Ryan Boyden, Giuliana Cosentino, Prasanta Gorai, Mengyao Liu, Viviana Rosero, Kotomi Taniguchi, Kei E. I. Tanaka
We present $\sim8-40\,\mu$m SOFIA-FORCAST images of seven regions of ``clustered" star formation as part of the SOFIA Massive (SOMA) Star Formation Survey. We identify a total of 34 protostar candidates and build their spectral energy distributions (SEDs). We fit these SEDs with a grid of radiative transfer models based on the Turbulent Core Accretion (TCA) theory to derive key protostellar properties, including initial core mass, $M_c$, clump environment mass surface density, $\Sigma_{\rm cl}$, and current protostellar mass, $m_*$. We also carry out empirical graybody (GB) estimation of $\Sigma_{\rm cl}$, which allows a case of restricted SED fitting within the TCA model grid. We also release version 2.0 of the open-source Python package \emph{sedcreator}, designed to automate the aperture photometry and SED building and fitting process for sources in clustered environments, where flux contamination from close neighbors typically complicates the process. Using these updated methods, SED fitting yields values of $M_c\sim30-200\:M_{\odot}$, $\Sigma_{\text{cl,SED}}\sim0.1-3\:{\rm{g\:cm}}^{-2}$, and $m_*\sim4-50\:M_{\odot}$. The graybody fitting yields smaller values of $\Sigma_{\text{cl,GB}}\lesssim1\:{\rm{g\:cm}}^{-2}$. From these results, we do not find evidence for a critical $\Sigma_{\rm{cl}}$ needed to form massive ($\gtrsim 8\:M_\odot$) stars. However, we do find tentative evidence for a dearth of the most massive ($m_*\gtrsim30\:M_\odot$) protostars in the clustered regions suggesting a potential impact of environment on the stellar initial mass function.
A Method for Detecting Legal Article Competition for Korean Criminal Law Using a Case-augmented Mention Graph
2024-12-16
Seonho An, Young Yik Rhim, Min-Soo Kim
As social systems become increasingly complex, legal articles are also growing more intricate, making it progressively harder for humans to identify any potential competitions among them, particularly when drafting new laws or applying existing laws. Despite this challenge, no method for detecting such competitions has been proposed so far. In this paper, we propose a new legal AI task called Legal Article Competition Detection (LACD), which aims to identify competing articles within a given law. Our novel retrieval method, CAM-Re2, outperforms existing relevant methods, reducing false positives by 20.8% and false negatives by 8.3%, while achieving a 98.2% improvement in precision@5, for the LACD task. We release our codes at https://github.com/asmath472/LACD-public.
Interplay of epidemic spreading and vaccine uptake under complex social contagion
2024-12-16
Alfonso de Miguel-Arribas, Alberto Aleta, Yamir Moreno
Modeling human behavior is essential to accurately predict epidemic spread, with behaviors like vaccine hesitancy complicating control efforts. While epidemic spread is often treated as a simple contagion, vaccine uptake may follow complex contagion dynamics, where individuals' decisions depend on multiple social contacts. Recently, the concept of complex contagion has received strong theoretical underpinnings thanks to the generalization of spreading phenomena from pairwise to higher-order interactions. Although several potential applications have been suggested, examples of complex contagions motivated by real data remain scarce. Surveys on COVID-19 vaccine hesitancy in the US suggest that vaccination attitudes may indeed depend on the vaccination status of social peers, aligning with complex contagion principles. In this work, we examine the interactions between epidemic spread, vaccination, and vaccine uptake attitudes under complex contagion. Using the SIR model with a dynamic, threshold-based vaccination campaign, we simulate scenarios on an age-structured multilayer network informed by US contact data. Our results offer insights into the role of social dynamics in shaping vaccination behavior and epidemic outcomes.
Efficient LiDAR Bundle Adjustment for Multi-Scan Alignment Utilizing Continuous-Time Trajectories
2024-12-16
Louis Wiesmann, Elias Marks, Saurabh Gupta, Tiziano Guadagnino, Jens Behley, Cyrill Stachniss
Constructing precise global maps is a key task in robotics and is required for localization, surveying, monitoring, or constructing digital twins. To build accurate maps, data from mobile 3D LiDAR sensors is often used. Mapping requires correctly aligning the individual point clouds to each other to obtain a globally consistent map. In this paper, we investigate the problem of multi-scan alignment to obtain globally consistent point cloud maps. We propose a 3D LiDAR bundle adjustment approach to solve the global alignment problem and jointly optimize the available data. Utilizing a continuous-time trajectory allows us to consider the ego-motion of the LiDAR scanner while recording a single scan directly in the least squares adjustment. Furthermore, pruning the search space of correspondences and utilizing out-of-core circular buffer enables our approach to align thousands of point clouds efficiently. We successfully align point clouds recorded with a handheld LiDAR, as well as ones mounted on a vehicle, and are able to perform multi-session alignment.
The aim of this survey papier is to present a result due to Eisenstein, to prove a generalized version of it, and to present some applications of this Eisenstein's Theorem, in particular to the study of the algebraic closure of the field of power series in several indeterminates.
Density of states in liquids: quadratic or linear, and what each means?
2024-12-16
V. V. Brazhkin
A lot has been said about the vibrational density of states (DoS) in liquids. A more recent discussion introduces contradictions with earlier results, and here I briefly review several pieces of evidence from modeling, experiments and theory showing this. I then show that the origin of contradictions often comes from misinterpreting the nature of excitations in liquids. Classic quadratic DoS corresponds to propagating (albeit damped) phonons as in solids and applies to any medium at low frequency, whereas the linear DoS comes from overdamped modes, localised particle motions. This has implications for interpreting simulations, experiments and a theory of liquids. I also introduce a new mechanism where the exponent of quadratic DoS reduces to close to 1 due to the asymmetry of the scattering intensity, reducing the effective phonon frequency.
On Large Language Models in Mission-Critical IT Governance: Are We Ready Yet?
2024-12-16
Matteo Esposito, Francesco Palagiano, Valentina Lenarduzzi, Davide Taibi
Context. The security of critical infrastructure has been a fundamental concern since the advent of computers, and this concern has only intensified in today's cyber warfare landscape. Protecting mission-critical systems (MCSs), including essential assets like healthcare, telecommunications, and military coordination, is vital for national security. These systems require prompt and comprehensive governance to ensure their resilience, yet recent events have shown that meeting these demands is increasingly challenging. Aim. Building on prior research that demonstrated the potential of GAI, particularly Large Language Models (LLMs), in improving risk analysis tasks, we aim to explore practitioners' perspectives, specifically developers and security personnel, on using generative AI (GAI) in the governance of IT MCSs seeking to provide insights and recommendations for various stakeholders, including researchers, practitioners, and policymakers. Method. We designed a survey to collect practical experiences, concerns, and expectations of practitioners who develop and implement security solutions in the context of MCSs. Analyzing this data will help identify key trends, challenges, and opportunities for introducing GAIs in this niche domain. Conclusions and Future Works. Our findings highlight that the safe use of LLMs in MCS governance requires interdisciplinary collaboration. Researchers should focus on designing regulation-oriented models and focus on accountability; practitioners emphasize data protection and transparency, while policymakers must establish a unified AI framework with global benchmarks to ensure ethical and secure LLMs-based MCS governance.
From Specific-MLLM to Omni-MLLM: A Survey about the MLLMs alligned with Multi-Modality
2024-12-16
Shixin Jiang, Jiafeng Liang, Ming Liu, Bing Qin
From the Specific-MLLM, which excels in single-modal tasks, to the Omni-MLLM, which extends the range of general modalities, this evolution aims to achieve understanding and generation of multimodal information. Omni-MLLM treats the features of different modalities as different "foreign languages," enabling cross-modal interaction and understanding within a unified space. To promote the advancement of related research, we have compiled 47 relevant papers to provide the community with a comprehensive introduction to Omni-MLLM. We first explain the four core components of Omni-MLLM for unified modeling and interaction of multiple modalities. Next, we introduce the effective integration achieved through "alignment pretraining" and "instruction fine-tuning," and discuss open-source datasets and testing of interaction capabilities. Finally, we summarize the main challenges facing current Omni-MLLM and outline future directions.
Self-Adaptive Paraphrasing and Preference Learning for Improved Claim Verifiability
2024-12-16
Amelie Wührl, Roman Klinger
In fact-checking, structure and phrasing of claims critically influence a model's ability to predict verdicts accurately. Social media content in particular rarely serves as optimal input for verification systems, which necessitates pre-processing to extract the claim from noisy context before fact checking. Prior work suggests extracting a claim representation that humans find to be checkworthy and verifiable. This has two limitations: (1) the format may not be optimal for a fact-checking model, and (2), it requires annotated data to learn the extraction task from. We address both issues and propose a method to extract claims that is not reliant on labeled training data. Instead, our self-adaptive approach only requires a black-box fact checking model and a generative language model (LM). Given a tweet, we iteratively optimize the LM to generate a claim paraphrase that increases the performance of a fact checking model. By learning from preference pairs, we align the LM to the fact checker using direct preference optimization. We show that this novel setup extracts a claim paraphrase that is more verifiable than their original social media formulations, and is on par with competitive baselines. For refuted claims, our method consistently outperforms all baselines.
A comprehensive GeoAI review: Progress, Challenges and Outlooks
2024-12-16
Anasse Boutayeb, Iyad Lahsen-cherif, Ahmed El Khadimi
In recent years, Geospatial Artificial Intelligence (GeoAI) has gained traction in the most relevant research works and industrial applications, while also becoming involved in various fields of use. This paper offers a comprehensive review of GeoAI as a synergistic concept applying Artificial Intelligence (AI) methods and models to geospatial data. A preliminary study is carried out, identifying the methodology of the work, the research motivations, the issues and the directions to be tracked, followed by exploring how GeoAI can be used in various interesting fields of application, such as precision agriculture, environmental monitoring, disaster management and urban planning. Next, a statistical and semantic analysis is carried out, followed by a clear and precise presentation of the challenges facing GeoAI. Then, a concrete exploration of the future prospects is provided, based on several informations gathered during the census. To sum up, this paper provides a complete overview of the correlation between AI and the geospatial domain, while mentioning the researches conducted in this context, and emphasizing the close relationship linking GeoAI with other advanced concepts such as geographic information systems (GIS) and large-scale geospatial data, known as big geodata. This will enable researchers and scientific community to assess the state of progress in this promising field, and will help other interested parties to gain a better understanding of the issues involved.
Data analysis of the NISP instrument for the Euclid mission: from the first validation tests on ground up to the verification phase after launch
2024-12-16
Louis Gabarra
The Euclid telescope, launched from Cape Canaveral on July 1st, 2023, is dedicated to studying dark matter and dark energy from its orbit at the Sun-Earth Lagrangian point L2. It is equipped with two instruments: the visual imager (VIS) and the Near-Infrared Spectrometer and Photometer (NISP). The Euclid Wide Survey (Scaramella et al. 2022) will cover approximately 14500 deg2 of the extragalactic sky, and will be complemented by the Euclid Deep Survey that will cover about 40 deg2 and achieve two magnitudes fainter depth. Several ground-test campaigns were performed to assess NISP instrument basic functionalities and performances, some highlights will be reviewed in this thesis. The analysis comprises two key aspects: the evaluation of NISP image data acquired during ground-tests and during commissioning, and the verification of instrument performance in spectroscopic mode through the use of pixel-level simulated images. The analysis includes the characterisation of NISP detectors, from instrumental background to point-spread function (PSF) evaluations, confirming that NISP performance is well within the Euclid requirement. We introduce a calibration methodology to estimate the filter wheel assembly position from PSF distortions that have been successfully applied during the commissioning and will continue to be used during the mission for calibration purpose. Additionally, we investigated noise events that occur during image acquisition, with a particular focus on \v{S}snowballs\v{S} and cosmic ray tracks. Finally, we present a spectroscopic pixel-level simulation campaign of the NISP instrument, where spectral energy distributions were generated and processed by the Euclid spectroscopic channel simulator.
Monitoring blazar variability to understand extragalactic jets
2024-12-16
Claudia M. Raiteri
We review the main topics in the field of blazar multiwavelength variability as a tool to understand the physics and structure of extragalactic jets and their central engine. We address issues such as the cross-correlation between flux variations at different frequencies, the mechanisms to explain the long-term and short-term variability, the size of the jet emitting regions, the polarization behaviour, and the periodicities detected in the multiwavelength light curves.
Minute-cadence Observations of the LAMOST Fields with the TMTS: VI. Absolute Physical Parameters of Contact Binaries
2024-12-16
Qiqi Xia, Xiaofeng Wang, Kai Li, Xiang Gao, Fangzhou Guo, Jie Lin, Cheng Liu, Jun Mo, Haowei Peng, Qichun Liu, Gaobo Xi, Shengyu Yan, Xiaojun Jiang, Jicheng Zhang, Cui-Ying Song, Jianrong Shi, Xiaoran Ma, Danfeng Xiang, Wenxiong Li
With the development of wide-field surveys, a large amount of data on short-period W UMa contact binaries have been obtained. Continuous and uninterrupted light curves as well as high-resolution spectroscopic data are crucial in determining the absolute physical parameters. Targets with both TMTS light curves and LAMOST medium-resolution spectra were selected. The absolute physical parameters were inferred with the W-D code for ten systems, all of them are W-type shallow or medium contact binaries. The O'Connell effect observed in the light curves can be explained by adding a spot on the primary or secondary component in the models. According to O-C analysis, the orbital periods exhibit a long-term increasing or decreasing trend, amongst which J0132, J1300, and J1402 show periodic variations that may be attributed to the presence of a third body or magnetic activity cycles. Spectral subtraction analysis revealed that the equivalent width of H$\alpha$ indicates strong magnetic activity in J0047, J0305, J0638, and J1402. Among the 10 selected binary systems, except for J0132 and J0913, the more massive components are found to be main-sequence stars while the less massive components have evolved off the main sequence. In J0132, both components are in the main sequence, whereas both components of J0913 lie above the terminal-age main sequence. Based on the relationship between orbital angular momentum and total mass for these two systems, as well as their low fill-out factors, it is possible that these two systems are newly formed contact binaries, having recently evolved from the detached configuration.
Android App Feature Extraction: A review of approaches for malware and app similarity detection
2024-12-16
Simon Torka, Sahin Albayrak
This paper reviews work published between 2002 and 2022 in the fields of Android malware, clone, and similarity detection. It examines the data sources, tools, and features used in existing research and identifies the need for a comprehensive, cross-domain dataset to facilitate interdisciplinary collaboration and the exploitation of synergies between different research areas. Furthermore, it shows that many research papers do not publish the dataset or a description of how it was created, making it difficult to reproduce or compare the results. The paper highlights the necessity for a dataset that is accessible, well-documented, and suitable for a range of applications. Guidelines are provided for this purpose, along with a schematic method for creating the dataset.
Near Large Far Small: Relative Distance Based Partition Learning for UAV-view Geo-Localization
2024-12-16
Quan Chen, Tingyu Wang, Rongfeng Lu, Bolun Zheng, Zhedong Zheng, Chenggang Yan
UAV-view Geo-Localization (UVGL) presents substantial challenges, primarily due to appearance differences between drone-view and satellite-view. Existing methods develop partition learning strategies aimed at mining more comprehensive information by constructing diverse part-level feature representations, which rely on consistent cross-view scales. However, variations of UAV flight state leads to the scale mismatch of cross-views, resulting in serious performance degradation of partition-based methods. To overcome this issue, we propose a partition learning framework based on relative distance, which alleviates the dependence on scale consistency while mining fine-grained features. Specifically, we propose a distance guided dynamic partition learning strategy (DGDPL), consisting of a square partition strategy and a dynamic-guided adjustment strategy. The former is utilized to extract fine-grained features and global features in a simple manner. The latter calculates the relative distance ratio between drone- and satellite-view to adjust the partition size, thereby aligning the semantic information between partition pairs. Furthermore, we propose a saliency-guided refinement strategy to refine part-level features, so as to further improve the retrieval accuracy. Extensive experiments show that our approach achieves superior geo-localization accuracy across various scale-inconsistent scenarios, and exhibits remarkable robustness against scale variations. The code will be released.
DART: An AIGT Detector using AMR of Rephrased Text
2024-12-16
Hyeonchu Park, Byungjun Kim, Bugeun Kim
As large language models (LLMs) generate more human-like texts, concerns about the side effects of AI-generated texts (AIGT) have grown. So, researchers have developed methods for detecting AIGT. However, two challenges remain. First, the performance on detecting black-box LLMs is low, because existing models have focused on syntactic features. Second, most AIGT detectors have been tested on a single-candidate setting, which assumes that we know the origin of an AIGT and may deviate from the real-world scenario. To resolve these challenges, we propose DART, which consists of four steps: rephrasing, semantic parsing, scoring, and multiclass classification. We conducted several experiments to test the performance of DART by following previous work. The experimental result shows that DART can discriminate multiple black-box LLMs without using syntactic features and knowing the origin of AIGT.
Sub-quadratic scalable approximate linear converter using multi-plane light conversion with low-entropy mode mixers
2024-12-16
Yoshitaka Taguchi
Optical computing is emerging as a promising platform for energy-efficient, high-throughput hardware in deep learning. A key challenge lies in the realization of optical matrix-vector multiplication, which often requires $O(N^2)$ phase shifters for exact synthesis of $N \times N$ matrices, limiting scalability. In this study, we propose an approximate matrix realization method using multi-plane light conversion (MPLC) that reduces both the system size and the number of phase shifters while maintaining acceptable error bounds. This approach uses low-entropy mode mixers, allowing more compact implementations compared to conventional mixers. We introduce Shannon matrix entropy as a measure of mode coupling strength in mixers and demonstrate that low-entropy mixers can preserve computational accuracy while reducing the requirements for the mixers. The approximation quality is evaluated using the maximum norm between the target and realized matrices. Numerical results show that the proposed method achieves sub-quadratic scaling of phase shifters by tolerating predefined error thresholds. To identify efficient architectures for implementing general linear matrices, we compare block-encoding (BE) and singular-value decomposition (SVD) schemes for realizing general linear matrices using unitary converters based on MPLC. Results indicate that BE exhibits superior iterative configuration properties beyond the unitary group. By characterizing the trade-offs between matrix entropy, number of phase shifter layers, and the error tolerance, this study provides a framework for designing scalable and efficient approximate optical converters.
Anyon condensation in wormhole geometries is investigated in the Virasoro TQFT (VTQFT) formulation. We first review some elementary techniques of VTQFT and verify its consistency by showing that it reproduces semiclassical results, including the Hawking-Page phase transition and the Bekenstein-Hawking entropy of BTZ blackhole. We then summarize a gauging scheme for non-invertible symmetries referred to as anyon condensation and exhibit that it is applicable to VTQFT even though the category of Wilson lines associated with it is not strictly a modular tensor category (MTC).
As model parameter sizes reach the billion-level range and their training consumes zettaFLOPs of computation, components reuse and collaborative development are become increasingly prevalent in the Machine Learning (ML) community. These components, including models, software, and datasets, may originate from various sources and be published under different licenses, which govern the use and distribution of licensed works and their derivatives. However, commonly chosen licenses, such as GPL and Apache, are software-specific and are not clearly defined or bounded in the context of model publishing. Meanwhile, the reused components may also have free-content licenses and model licenses, which pose a potential risk of license noncompliance and rights infringement within the model production workflow. In this paper, we propose addressing the above challenges along two lines: 1) For license analysis, we have developed a new vocabulary for ML workflow management and encoded license rules to enable ontological reasoning for analyzing rights granting and compliance issues. 2) For standardized model publishing, we have drafted a set of model licenses that provide flexible options to meet the diverse needs of model publishing. Our analysis tool is built on Turtle language and Notation3 reasoning engine, envisioned as a first step toward Linked Open Model Production Data. We have also encoded our proposed model licenses into rules and demonstrated the effects of GPL and other commonly used licenses in model publishing, along with the flexibility advantages of our licenses, through comparisons and experiments.
Optimal interpolation in Hardy and Bergman spaces: a reproducing kernel Banach space approach
2024-12-16
Gilbert J. Groenewald, Sanne ter Horst, Hugo J. Woerdeman
After a review of the reproducing kernel Banach space framework and semi-inner products, we apply the techniques to the setting of Hardy spaces $H^p$ and Bergman spaces $A^p$, $1
HResFormer: Hybrid Residual Transformer for Volumetric Medical Image Segmentation
2024-12-16
Sucheng Ren, Xiaomeng Li
Vision Transformer shows great superiority in medical image segmentation due to the ability in learning long-range dependency. For medical image segmentation from 3D data, such as computed tomography (CT), existing methods can be broadly classified into 2D-based and 3D-based methods. One key limitation in 2D-based methods is that the intra-slice information is ignored, while the limitation in 3D-based methods is the high computation cost and memory consumption, resulting in a limited feature representation for inner-slice information. During the clinical examination, radiologists primarily use the axial plane and then routinely review both axial and coronal planes to form a 3D understanding of anatomy. Motivated by this fact, our key insight is to design a hybrid model which can first learn fine-grained inner-slice information and then generate a 3D understanding of anatomy by incorporating 3D information. We present a novel \textbf{H}ybrid \textbf{Res}idual trans\textbf{Former} \textbf{(HResFormer)} for 3D medical image segmentation. Building upon standard 2D and 3D Transformer backbones, HResFormer involves two novel key designs: \textbf{(1)} a \textbf{H}ybrid \textbf{L}ocal-\textbf{G}lobal fusion \textbf{M}odule \textbf{(HLGM)} to effectively and adaptively fuse inner-slice information from 2D Transformer and intra-slice information from 3D volumes for 3D Transformer with local fine-grained and global long-range representation. \textbf{(2)} a residual learning of the hybrid model, which can effectively leverage the inner-slice and intra-slice information for better 3D understanding of anatomy. Experiments show that our HResFormer outperforms prior art on widely-used medical image segmentation benchmarks. This paper sheds light on an important but neglected way to design Transformers for 3D medical image segmentation.
Optimized Quran Passage Retrieval Using an Expanded QA Dataset and Fine-Tuned Language Models
2024-12-16
Mohamed Basem, Islam Oshallah, Baraa Hikal, Ali Hamdi, Ammar Mohamed
Understanding the deep meanings of the Qur'an and bridging the language gap between modern standard Arabic and classical Arabic is essential to improve the question-and-answer system for the Holy Qur'an. The Qur'an QA 2023 shared task dataset had a limited number of questions with weak model retrieval. To address this challenge, this work updated the original dataset and improved the model accuracy. The original dataset, which contains 251 questions, was reviewed and expanded to 629 questions with question diversification and reformulation, leading to a comprehensive set of 1895 categorized into single-answer, multi-answer, and zero-answer types. Extensive experiments fine-tuned transformer models, including AraBERT, RoBERTa, CAMeLBERT, AraELECTRA, and BERT. The best model, AraBERT-base, achieved a MAP@10 of 0.36 and MRR of 0.59, representing improvements of 63% and 59%, respectively, compared to the baseline scores (MAP@10: 0.22, MRR: 0.37). Additionally, the dataset expansion led to improvements in handling "no answer" cases, with the proposed approach achieving a 75% success rate for such instances, compared to the baseline's 25%. These results demonstrate the effect of dataset improvement and model architecture optimization in increasing the performance of QA systems for the Holy Qur'an, with higher accuracy, recall, and precision.
Efficient Multiagent Planning via Shared Action Suggestions
2024-12-16
Dylan M. Asmar, Mykel J. Kochenderfer
Decentralized partially observable Markov decision processes with communication (Dec-POMDP-Com) provide a framework for multiagent decision making under uncertainty, but the NEXP-complete complexity renders solutions intractable in general. While sharing actions and observations can reduce the complexity to PSPACE-complete, we propose an approach that bridges POMDPs and Dec-POMDPs by communicating only suggested joint actions, eliminating the need to share observations while maintaining performance comparable to fully centralized planning and execution. Our algorithm estimates joint beliefs using shared actions to prune infeasible beliefs. Each agent maintains possible belief sets for other agents, pruning them based on suggested actions to form an estimated joint belief usable with any centralized policy. This approach requires solving a POMDP for each agent, reducing computational complexity while preserving performance. We demonstrate its effectiveness on several Dec-POMDP benchmarks showing performance comparable to centralized methods when shared actions enable effective belief pruning. This action-based communication framework offers a natural avenue for integrating human-agent cooperation, opening new directions for scalable multiagent planning under uncertainty, with applications in both autonomous systems and human-agent teams.
Looking At the Distant Universe with the MeerKAT Array: the HI Mass Function in the Local Universe
2024-12-16
Amir Kazemi-Moridani, Andrew J. Baker, Marc Verheijen, Eric Gawiser, Sarah-Louise Blyth, Danail Obreschkow, Laurent Chemin, Jordan D. Collier, Kyle W. Cook, Jacinta Delhaize, Ed Elson, Bradley S. Frank, Marcin Glowacki, Kelley M. Hess, Benne W. Holwerda, Zackary L. Hutchens, Matt J. Jarvis, Melanie Kaasinen, Sphesihle Makhathini, Abhisek Mohapatra, Hengxing Pan, Anja C. Schröder, Leyya Stockenstroom, Mattia Vaccari, Tobias Westmeier, John F. Wu, Martin Zwaan
We present measurements of the neutral atomic hydrogen (HI) mass function (HIMF) and cosmic HI density ($\Omega_{\rm HI}$) at $0 \leq z \leq 0.088$ from the Looking at the Distant Universe with MeerKAT Array (LADUMA) survey. Using LADUMA Data Release 1 (DR1), we analyze the HIMF via a new "recovery matrix" (RM) method that we benchmark against a more traditional Modified Maximum Likelihood (MML) method. Our analysis, which implements a forward modeling approach, corrects for survey incompleteness and uses extensive synthetic source injections to ensure robust estimates of the HIMF parameters and their associated uncertainties. This new method tracks the recovery of sources in mass bins different from those in which they were injected and incorporates a Poisson likelihood in the forward modeling process, allowing it to correctly handle uncertainties in bins with few or no detections. The application of our analysis to a high-purity subsample of the LADUMA DR1 spectral line catalog in turn mitigates any possible biases that could result from the inconsistent treatment of synthetic and real sources. For the surveyed redshift range, the recovered Schechter function normalization, low-mass slope, and "knee" mass are $\phi_\ast = 3.56_{-1.92}^{+0.97} \times 10^{-3}$ Mpc$^{-3}$ dex$^{-1}$, $\alpha = -1.18_{-0.19}^{+0.08}$, and $\log(M_\ast/M_\odot) = 10.01_{-0.12}^{+0.31}$, respectively, which together imply a comoving cosmic HI density of $\Omega_{\rm HI}=3.09_{-0.47}^{+0.65}\times 10^{-4}$. Our results show consistency between RM and MML methods and with previous low-redshift studies, giving confidence that the cosmic volume probed by LADUMA, even at low redshifts, is not an outlier in terms of its HI content.
The role of Enhanced Geothermal Systems in the energy transition at Cornell -- Report of a workshop held at Cornell University, Ithaca, October 23-24, 2024
2024-12-16
Chloé Arson, Dominic Balog-Way, Koenraad Beckers, Wayne Bezner-Kerr, Sarah Carson, Stacey Edwards, Patrick Fulton, Michael Gillenwater, Trystan Goetze, Olaf Gustafson, Tony Ingraffea, Terry Jordan, Katherine McComas, Sheila Olmstead, Seth Saltiel, Jeff Tester, Cole Tucker, Marguerite Wells
To review the lessons learnt from recent deep geothermal case studies and plan strategically the research, development, regulation, and communication work required for the implementation of an Enhanced Geothermal System (EGS) at Cornell University, a group of engineers and scholars convened a two-day workshop on the Ithaca campus, on October 23-24, 2024. The event was funded by Cornell Atkinson Center for Sustainability. This report is a summary of the content of the presentations and discussions that took place during the workshop. The first section focuses on philosophical, sociological, economic, and regulatory questions posed by EGS deployment as a means to mitigate climate change. The second section tackles the scientific and technological research areas associated with EGS. The third section aims to assess the feasibility of developing EGS for heat direct use at Cornell University, based on results and information available to date. The report concludes with a summary of the most salient technological and scientific breakthroughs, and a plan for future technological and academic engagement in EGS projects at Cornell.
Privacy-Preserving Brain-Computer Interfaces: A Systematic Review
2024-12-16
K. Xia, W. Duch, Y. Sun, K. Xu, W. Fang, H. Luo, Y. Zhang, D. Sang, X. Xu, F-Y Wang, D. Wu
A brain-computer interface (BCI) establishes a direct communication pathway between the human brain and a computer. It has been widely used in medical diagnosis, rehabilitation, education, entertainment, etc. Most research so far focuses on making BCIs more accurate and reliable, but much less attention has been paid to their privacy. Developing a commercial BCI system usually requires close collaborations among multiple organizations, e.g., hospitals, universities, and/or companies. Input data in BCIs, e.g., electroencephalogram (EEG), contain rich privacy information, and the developed machine learning model is usually proprietary. Data and model transmission among different parties may incur significant privacy threats, and hence privacy protection in BCIs must be considered. Unfortunately, there does not exist any contemporary and comprehensive review on privacy-preserving BCIs. This paper fills this gap, by describing potential privacy threats and protection strategies in BCIs. It also points out several challenges and future research directions in developing privacy-preserving BCIs.
This research provides a comprehensive overview of adversarial attacks on AI and ML models, exploring various attack types, techniques, and their potential harms. We also delve into the business implications, mitigation strategies, and future research directions. To gain practical insights, we employ the Adversarial Robustness Toolbox (ART) [1] library to simulate these attacks on real-world use cases, such as self-driving cars. Our goal is to inform practitioners and researchers about the challenges and opportunities in defending AI systems against adversarial threats. By providing a comprehensive comparison of different attack methods, we aim to contribute to the development of more robust and secure AI systems.
PSR J1631-4722: The Discovery of a Young and Energetic Pulsar in the Supernova Remnant G336.7+0.5
2024-12-16
A. Ahmad, S. Dai, S. Lazarević, M. D. Filipović, S. Johnston, M. Kerr, D. Li, C. Maitra, R. N. Manchester
Detecting a pulsar associated with a supernova remnant (SNR) and/or pulsar wind nebula (PWN) is crucial for unraveling its formation history and pulsar wind dynamics, yet the association with a radio pulsar is observed only in a small fraction of known SNRs and PWNe. In this paper, we report the discovery of a young pulsar J1631$-$4722, associated with the Galactic SNR G336.7$+$0.5 using Murriyang, CSIRO's Parkes radio telescope. It is also potentially associated with a PWN revealed by the Rapid ASKAP (Australian Square Kilometre Array Pathfinder) Continuum Survey (RACS). This 118 ms pulsar has a high dispersion measure of 873 $\mathrm{pc\,cm^{-3}}$ and a rotation measure of $-$1004 $\mathrm{rad\,m^{-2}}$. Because of such a high DM, at frequencies below 2 GHz, the pulse profile is significantly scattered, making it effectively undetectable in previous pulsar surveys at $\sim$1.4 GHz. Follow-up observations yield a period derivative of $\dot{P} = 3.6 \times 10^{-15}$, implying a characteristic age, $\tau_{c} = 33\,$kyr, and spin-down luminosity, $\dot{E} = 1.3\times10^{36}\,$erg$\,s^{-1}$. PSR$\,$J1631$-$4722, with its high spin-down luminosity and potential link to a PWN, stands out as a promising source of the high-energy $\gamma$-ray emission observed in the region.
Generalized entropy calibration for analyzing voluntary survey data
2024-12-16
Yonghyun Kwon, Jae Kwang Kim, Yumou Qiu
Statistical analysis of voluntary survey data is an important area of research in survey sampling. We consider a unified approach to voluntary survey data analysis under the assumption that the sampling mechanism is ignorable. Generalized entropy calibration is introduced as a unified tool for calibration weighting to control the selection bias. We first establish the relationship between the generalized calibration weighting and its dual expression for regression estimation. The dual relationship is critical in identifying the implied regression model and developing model selection for calibration weighting. Also, if a linear regression model for an important study variable is available, then two-step calibration method can be used to smooth the final weights and achieve the statistical efficiency. Asymptotic properties of the proposed estimator are investigated. Results from a limited simulation study are also presented.
Perturbation theory scope for predicting vibronic selectivity by entangled two photon absorption
2024-12-16
C. D. Rodriguez-Camargo, H. O. Gestsson, C. Nation, A. R. Jones, A. Olaya-Castro
Using second-order perturbation theory in the light-matter interaction, we derive an analytical approximation for the vibronic populations of a diatomic system excited by ultrabroadband frequency entangled photons and evaluate the population dynamics for different degrees of entanglement between photon pairs. Our analytical approach make the same predictions as previously derived via numerical solutions of the complete Schr\"odinger equation [H. Oka, Physical Review A 97, 063859 (2018)], with the added advantage of providing clear physical insights into the vibronic selectivity as a function of the degree of photon correlations while requiring significantly reduced computational effort. Specifically, our analytical expression for the probability of vibronic excitation includes a factor which predicts the enhancement of vibrational selectivity as a function of the degree correlation between the entangled photon pairs, the targeted vibrational energy level, and the vibrational molecular structure encoded in the Franck-Condon factors. Our results illustrate the importance of going beyond the usual approximations in second-order perturbation theory to capture the relevance of the vibrational structure of the molecular system of interest in order to gain a deeper understanding of the possible quantum-enhancement provided by the interaction between quantum light and matter.
Virtual Realities: Is there only one advanced image display that astronomers need?
2024-12-16
Christopher J. Fluke, Hugo K. Walsh, Lewis de Zoete Grundy, Brian Brady
Data visualisation is an essential ingredient of scientific analysis, discovery, and communication. Along with a human (to do the looking) and the data (something to look at), an image display device is a key component of any data visualisation workflow. For the purpose of this work, standard displays include combinations of laptop displays, peripheral monitors, tablet and smartphone screens, while the main categories of advanced displays are stereoscopic displays, tiled display walls, digital domes, virtual/mixed reality (VR/MR) head-mounted displays, and CAVE/CAVE2-style immersive rooms. We present the results of the second Advanced Image Displays for Astronomy (AIDA) survey, advertised to the membership of the Astronomical Society of Australia (ASA) during June-August 2021. The goal of this survey was to gather background information on the level of awareness and usage of advanced displays in astronomy and astrophysics research. From 17 complete survey responses, sampled from a population of ~750 ASA members, we infer that: (1) a high proportion of ASA members use standard displays but do not use advanced displays; (2) a moderate proportion have seen a VR/MR HMD, and may also have used one -- but not for research activities; and (3) there is a need for improved knowledge in general about advanced displays, along with relevant software or applications that can target specific science needs. We expect that this is compatible with the experiences of much of the international astronomy and astrophysics research community. We suggest that VR/MR head-mounted displays have now reached a level of technical maturity such that they could be used to replicate or replace the functionality of most other advanced displays.
Diversity in Software Engineering Education: Exploring Motivations, Influences, and Role Models Among Undergraduate Students
2024-12-16
Ronnie de Souza Santos, Italo Santos, Robson Santos, Cleyton Magalhaes
Software engineering (SE) faces significant diversity challenges in both academia and industry, with underrepresented students encountering hostile environments, limited representation, and systemic biases that hinder their academic and professional success. Despite significant research on the exclusion experienced by students from underrepresented groups in SE education, there is limited understanding of the specific motivations, influences, and role models that drive underrepresented students to pursue and persist in the field. This study explores the motivations and influences shaping the career aspirations of students from underrepresented groups in SE, and it investigates how role models and mentorship impact their decisions to stay in the field. We conducted a cross-sectional survey with undergraduate SE students and related fields, focusing on their motivations, influences, and the impact of mentorship and role models on their career paths. We identified eight motivations for pursuing SE, with career advancement, technological enthusiasm, and personal growth being the most common. Family members, tech influencers, teachers, and friends were key influences, though 64\% of students reported no specific individual influence. Role models, particularly tech influencers and family members play a critical role in sustaining interest in the field, especially for underrepresented groups. This study provides insights into the varied motivations and influences that guide underrepresented students' decisions to pursue SE. It emphasizes the importance of role models and highlights the need for intersectional approaches to better support diversity in the field.
A second candidate magnetic helium core white dwarf and 3 other variable white dwarfs in the globular cluster NGC 6397
2024-12-16
Manuel Pichardo Marcano, Liliana E. Rivera Sandoval, Thomas J. Maccarone, Rene D. Rohrmann, Leandro G. Althaus, Craig O. Heinke, Diogo Belloni, Arash Bahramian
Using archival Hubble Space Telescope observations, we report the discovery of four variable low-mass white dwarfs ($0.18 \, M_\odot \leq M \leq 0.5 \,M_\odot$) in the globular cluster NGC 6397. One source exhibits a periodic optical modulation of $5.21 \pm 0.02$ hours, which we interpret as potentially due to the rotation of a magnetic helium core WD (He WD). This makes this candidate the second magnetic He WD in NGC 6397, and one of the few He WDs with a measured rotation period. The other three candidates show aperiodic variability, with a change in magnitude ranging from $\sim 0.11-0.6$. These discoveries highlight the importance of high-cadence photometric surveys in dense stellar environments. Follow-up spectroscopic observations are needed to confirm the nature of the variability of these systems.
Positivity of state, trace, and moment polynomials, and applications in quantum information
2024-12-16
Felix Huber, Victor Magron, Jurij Volčič
State, trace, and moment polynomials are polynomial expressions in several operator or random variables and positive functionals on their products (states, traces or expectations). While these concepts, and in particular their positivity and optimization, arose from problems in quantum information theory, yet they naturally fit under the umbrella of multivariate operator theory. This survey presents state, trace, and moment polynomials in a concise and unified way, and highlights their similarities and differences. The focal point is their positivity and optimization. Sums of squares certificates for unconstrained and constrained positivity (Positivstellens\"atze) are given, and parallels with their commutative and freely noncommutative analogs are discussed. They are used to design a convergent hierarchy of semidefinite programs for optimization of state, trace, and moment polynomials. Finally, circling back to the original motivation behind the derived theory, multiple applications in quantum information theory are outlined.
The CIViL* Survey: The Discovery of a C IV Dichotomy in the CGM of L* Galaxies
2024-12-16
Samantha L. Garza, Jessica K. Werk, Trystyn A. M. Berg, Yakov Faerman, Benjamin D. Oppenheimer, Rongmon Bordoloi, Sara L. Ellison
This paper investigates C IV absorption in the circumgalactic medium (CGM) of L* galaxies and its relationship with galaxy star formation rates. We present new observations from the C IV in L* survey (CIViL*; PID$\#$17076) using the Hubble Space Telescope/Cosmic Origins Spectrograph. By combining these measurements with archival C IV data (46 observations total), we estimate detection fractions for star-forming (sSFR $>$ 10$^{-11}$ yr$^{-1}$) and passive galaxies (sSFR $\leq$ 10$^{-11}$ yr$^{-1}$) to be 72$_{-18}^{+14}$\% [21/29] and 23$_{-15}^{+27}$\% [3/13], respectively. This indicates a significant dichotomy in C IV presence between L* star-forming and passive galaxies, with over 99% confidence. This finding aligns with Tumlinson et al. (2011), which noted a similar dichotomy in O VI absorption. Our results imply a substantial carbon reservoir in the CGM of L* galaxies, suggesting a minimum carbon mass of $\gtrsim$ 3.03 $\times$ 10$^{6}$ M$_{\odot}$ out to 120 kpc. Together, these findings highlight a strong connection between star formation in galaxies and the state of their CGM, providing insight into the mechanisms governing galaxy evolution.
An Open Effective Field Theory for light in a medium
2024-12-16
Santiago Agui Salcedo, Thomas Colas, Enrico Pajer
In many scenarios of interest, a quantum system interacts with an unknown environment, necessitating the use of open quantum system methods to capture dissipative effects and environmental noise. With the long-term goal of developing a perturbative theory for open quantum gravity, we take an important step by studying Abelian gauge theories within the Schwinger-Keldysh formalism. We begin with a pedagogical review of general results for open free theories, setting the stage for our primary focus: constructing the most general open effective field theory for electromagnetism in a medium. We assume locality in time and space, but allow for an arbitrary finite number of derivatives. Crucially, we demonstrate that the two copies of the gauge group associated with the two branches of the Schwinger-Keldysh contour are not broken but are instead deformed by dissipative effects. We provide a thorough discussion of gauge fixing, define covariant gauges, and calculate the photon propagators, proving that they yield gauge-invariant results. A notable result is the discovery that gauge invariance is accompanied by non-trivial constraints on noise fluctuations. We derive these constraints through three independent methods, highlighting their fundamental significance for the consistent formulation of open quantum gauge theories.
CARMENES input catalogue of M dwarfs IX. Multiplicity from close spectroscopic binaries to ultra-wide systems
2024-12-16
C. Cifuentes, J. A. Caballero, J. González-Payo, P. J. Amado, V. J. S. Béjar, A. J. Burgasser, M. Cortés-Contreras, N. Lodieu, D. Montes, A. Quirrenbach, A. Reiners, I. Ribas, J. Sanz-Forcada, W. Seifert, M. R. Zapatero Osorio
Multiplicity studies greatly benefit from focusing on M dwarfs because they are often paired in a variety of configurations with both stellar and substellar objects, including exoplanets. We aim to address the observed multiplicity of M dwarfs by conducting a systematic analysis using the latest available astrophotometric data. For every star in a sample of 2214 M dwarfs from the CARMENES catalogue, we investigated the existence of resolved and unresolved physical companions in the literature and in all-sky surveys, especially in Gaia DR3 data products. We covered a very wide range of separations, from known spectroscopic binaries in tight arrangements $\sim$0.01 au to remarkably separated ultra-wide pairs ($\sim$10$^5$ au). We identified 835 M dwarfs in 720 multiple systems, predominantly binaries. Thus, we propose 327 new binary candidates based on Gaia data. If these candidates are finally confirmed, we expect the multiplicity fraction of M dwarfs to be 40.3$^{+2.1}_{-2.0}$ %. When only considering the systems already identified, the multiplicity fraction is reduced to 27.8$^{+1.9}_{-1.8}$ %. This result is in line with most of the values published in the literature. We also identified M-dwarf multiple systems with FGK, white dwarf, ultra-cool dwarf, and exoplanet companions, as well as those in young stellar kinematic groups. We studied their physical separations, orbital periods, binding energies, and mass ratios. We argue that based on reliable astrometric data and spectroscopic investigations from the literature (even when considering detection biases), the multiplicity fraction of M dwarfs could still be significantly underestimated. This calls for further high-resolution follow-up studies to validate these findings.
Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies
2024-12-16
Li Li
Deep learning (DL) has significantly transformed cybersecurity, enabling advancements in malware detection, botnet identification, intrusion detection, user authentication, and encrypted traffic analysis. However, the rise of adversarial examples (AE) poses a critical challenge to the robustness and reliability of DL-based systems. These subtle, crafted perturbations can deceive models, leading to severe consequences like misclassification and system vulnerabilities. This paper provides a comprehensive review of the impact of AE attacks on key cybersecurity applications, highlighting both their theoretical and practical implications. We systematically examine the methods used to generate adversarial examples, their specific effects across various domains, and the inherent trade-offs attackers face between efficacy and resource efficiency. Additionally, we explore recent advancements in defense mechanisms, including gradient masking, adversarial training, and detection techniques, evaluating their potential to enhance model resilience. By summarizing cutting-edge research, this study aims to bridge the gap between adversarial research and practical security applications, offering insights to fortify the adoption of DL solutions in cybersecurity.
X-ray properties of coronal emission in radio quiet Active Galactic Nuclei
2024-12-15
Sibasish Laha, Claudio Ricci, John C. Mather, Ehud Behar, Luigi C. Gallo, Frederic Marin, Rostom Mbarek, Amelia Hankla
Active galactic nuclei (AGN) are powerful sources of panchromatic radiation. All AGN emit in X-rays, contributing around $\sim 5-10\%$ of the AGN bolometric luminosity. The X-ray emitting region, popularly known as the corona, is geometrically and radiatively compact with a size typically $\lesssim 10 \, R_{\rm G}$ (gravitational radii). The rapid and extreme variability in X-rays also suggest that the corona must be a dynamic structure. Decades of X-ray studies have shed much light on the topic, but the nature and origin of AGN corona are still not clearly understood. This is mostly due to the complexities involved in several physical processes at play in the high-gravity, high-density and high-temperature region in the vicinity of the supermassive black hole (SMBH). It is still not clear how exactly the corona is energetically and physically sustained near a SMBH. Since corona is ubiquitously found in AGN, is there something fundamental about the accretion process that produces it? In this review we discuss the X-ray observational properties of the corona in radio quiet AGN.
VAR models with an index structure: A survey with new results
2024-12-15
Gianluca Cubadda
The main aim of this paper is to review recent advances in the multivariate autoregressive index model [MAI], originally proposed by reinsel1983some, and their applications to economic and financial time series. MAI has recently gained momentum because it can be seen as a link between two popular but distinct multivariate time series approaches: vector autoregressive modeling [VAR] and the dynamic factor model [DFM]. Indeed, on the one hand, the MAI is a VAR model with a peculiar reduced-rank structure; on the other hand, it allows for identification of common components and common shocks in a similar way as the DFM. The focus is on recent developments of the MAI, which include extending the original model with individual autoregressive structures, stochastic volatility, time-varying parameters, high-dimensionality, and cointegration. In addition, new insights on previous contributions and a novel model are also provided.
Complex investigations of an active star-formation region in southern part of Mon R2
2024-12-15
T. A. Movsessian, J. Bally, T. Yu. Magakian, A. V. Moiseev
We continue to present the results of a Byurakan Narrow Band Imaging Survey (BNBIS). In this work we present the results of the search and further detailed investigation of the objects, found in the course of the BNBIS survey in the southern part of the Mon R2 association. For the search of HH objects the narrow band images, obtained with the 1-m Schmidt telescope of the Byurakan Observatory, were used. Newly found objects were imaged in optical and near-IR range with the Apache Point Observatory 3.5 meter telescope, and observed spectrally with long-slit spectrograph and scanning Fabry-Perot interferometer on 6 m telescope of Special Astrophysical Observatory of the Russian Academy of Sciences using SCORPIO-2. We found three new HH groups: HH 1233, HH 1234 and HH 1235, two of them represent extended collimated flows. HH 1233 is the C-shape bipolar outflow system associated with the 2MASS 06084223$-$0657385 source star. HH 1234 is the helical chain of HH knots near the star V963 Mon. HH 1235 is a separate compact knot, connected with the visible only in mid- and far-IR source WISE J060856.57$-$070103.5. We found also several molecular hydrogen outflows, one of which coincides with HH 1233 and two other are associated with the deeply embedded IR sources in the same field. One more probable bipolar H$_2$ outflow is related to WISE J060856.57$-$070103.5. The emission spectra and spectral energy distributions of the source stars were analyzed. According to them they should be under rather early evolutional stage.
Surveying the landscape of optically addressable spin qubits for quantum information and sensing technology
2024-12-15
Calysta A. Tesiman, Mark Oxborrow, Max Attwood
Quantum technologies offer ways to solve certain tasks more quickly, efficiently, and with greater sensitivity than their classical counterparts. Yet substantial challenges remain in the construction of sufficiently error-free and scaleable quantum platforms that are needed to unlock any real benefits to society. Acknowledging that this hardware can take vastly different forms, our review here focuses on so-called spintronic (\textit{i.e.}~spin-electronic) materials that use electronic or nuclear spins to embody qubits. Towards helping the reader to spot trends and pick winners, we have surveyed the various families of optically addressable spin qubits and attempted to benchmark and identify the most promising ones in each group. We reveal further trends that demonstrate how qubit lifetimes depend on the material's synthesis, the concentration/distribution of its embedded qubits, and the experimental conditions.
Deep Learning-based Approaches for State Space Models: A Selective Review
2024-12-15
Jiahe Lin, George Michailidis
State-space models (SSMs) offer a powerful framework for dynamical system analysis, wherein the temporal dynamics of the system are assumed to be captured through the evolution of the latent states, which govern the values of the observations. This paper provides a selective review of recent advancements in deep neural network-based approaches for SSMs, and presents a unified perspective for discrete time deep state space models and continuous time ones such as latent neural Ordinary Differential and Stochastic Differential Equations. It starts with an overview of the classical maximum likelihood based approach for learning SSMs, reviews variational autoencoder as a general learning pipeline for neural network-based approaches in the presence of latent variables, and discusses in detail representative deep learning models that fall under the SSM framework. Very recent developments, where SSMs are used as standalone architectural modules for improving efficiency in sequence modeling, are also examined. Finally, examples involving mixed frequency and irregularly-spaced time series data are presented to demonstrate the advantage of SSMs in these settings.
Leveraging Large Language Models for Active Merchant Non-player Characters
2024-12-15
Byungjun Kim, Minju Kim, Dayeon Seo, Bugeun Kim
We highlight two significant issues leading to the passivity of current merchant non-player characters (NPCs): pricing and communication. While immersive interactions have been a focus, negotiations between merchant NPCs and players on item prices have not received sufficient attention. First, we define passive pricing as the limited ability of merchants to modify predefined item prices. Second, passive communication means that merchants can only interact with players in a scripted manner. To tackle these issues and create an active merchant NPC, we propose a merchant framework based on large language models (LLMs), called MART, which consists of an appraiser module and a negotiator module. We conducted two experiments to guide game developers in selecting appropriate implementations by comparing different training methods and LLM sizes. Our findings indicate that finetuning methods, such as supervised finetuning (SFT) and knowledge distillation (KD), are effective in using smaller LLMs to implement active merchant NPCs. Additionally, we found three irregular cases arising from the responses of LLMs. We expect our findings to guide developers in using LLMs for developing active merchant NPCs.
A Comprehensive Survey of Action Quality Assessment: Method and Benchmark
2024-12-15
Kanglei Zhou, Ruizhi Cai, Liyuan Wang, Hubert P. H. Shum, Xiaohui Liang
Action Quality Assessment (AQA) quantitatively evaluates the quality of human actions, providing automated assessments that reduce biases in human judgment. Its applications span domains such as sports analysis, skill assessment, and medical care. Recent advances in AQA have introduced innovative methodologies, but similar methods often intertwine across different domains, highlighting the fragmented nature that hinders systematic reviews. In addition, the lack of a unified benchmark and limited computational comparisons hinder consistent evaluation and fair assessment of AQA approaches. In this work, we address these gaps by systematically analyzing over 150 AQA-related papers to develop a hierarchical taxonomy, construct a unified benchmark, and provide an in-depth analysis of current trends, challenges, and future directions. Our hierarchical taxonomy categorizes AQA methods based on input modalities (video, skeleton, multi-modal) and their specific characteristics, highlighting the evolution and interrelations across various approaches. To promote standardization, we present a unified benchmark, integrating diverse datasets to evaluate the assessment precision and computational efficiency. Finally, we review emerging task-specific applications and identify under-explored challenges in AQA, providing actionable insights into future research directions. This survey aims to deepen understanding of AQA progress, facilitate method comparison, and guide future innovations. The project web page can be found at https://ZhouKanglei.github.io/AQA-Survey.
We have witnessed superhuman intelligence thanks to the fast development of large language models and multimodal language models. As the application of such superhuman models becomes more and more common, a critical question rises here: how can we ensure superhuman models are still safe, reliable and aligned well to human values? In this position paper, we discuss the concept of superalignment from the learning perspective to answer this question by outlining the learning paradigm shift from large-scale pretraining, supervised fine-tuning, to alignment training. We define superalignment as designing effective and efficient alignment algorithms to learn from noisy-labeled data (point-wise samples or pair-wise preference data) in a scalable way when the task becomes very complex for human experts to annotate and the model is stronger than human experts. We highlight some key research problems in superalignment, namely, weak-to-strong generalization, scalable oversight, and evaluation. We then present a conceptual framework for superalignment, which consists of three modules: an attacker which generates adversary queries trying to expose the weaknesses of a learner model; a learner which will refine itself by learning from scalable feedbacks generated by a critic model along with minimal human experts; and a critic which generates critics or explanations for a given query-response pair, with a target of improving the learner by criticizing. We discuss some important research problems in each component of this framework and highlight some interesting research ideas that are closely related to our proposed framework, for instance, self-alignment, self-play, self-refinement, and more. Last, we highlight some future research directions for superalignment, including identification of new emergent risks and multi-dimensional alignment.
Decoding Drug Discovery: Exploring A-to-Z In silico Methods for Beginners
2024-12-15
Hezha O. Rasul, Dlzar D. Ghafour, Bakhtyar K. Aziz, Bryar A. Hassan, Tarik A. Rashid, Arif Kivrak
The drug development process is a critical challenge in the pharmaceutical industry due to its time-consuming nature and the need to discover new drug potentials to address various ailments. The initial step in drug development, drug target identification, often consumes considerable time. While valid, traditional methods such as in vivo and in vitro approaches are limited in their ability to analyze vast amounts of data efficiently, leading to wasteful outcomes. To expedite and streamline drug development, an increasing reliance on computer-aided drug design (CADD) approaches has merged. These sophisticated in silico methods offer a promising avenue for efficiently identifying viable drug candidates, thus providing pharmaceutical firms with significant opportunities to uncover new prospective drug targets. The main goal of this work is to review in silico methods used in the drug development process with a focus on identifying therapeutic targets linked to specific diseases at the genetic or protein level. This article thoroughly discusses A-to-Z in silico techniques, which are essential for identifying the targets of bioactive compounds and their potential therapeutic effects. This review intends to improve drug discovery processes by illuminating the state of these cutting-edge approaches, thereby maximizing the effectiveness and duration of clinical trials for novel drug target investigation.
Rethinking Software Misconfigurations in the Real World: An Empirical Study and Literature Analysis
2024-12-15
Yuhao Liu, Yingnan Zhou, Hanfeng Zhang, Zhiwei Chang, Sihan Xu, Yan Jia, Wei Wang, Zheli Liu
Software misconfiguration has consistently been a major reason for software failures. Over the past twenty decades, much work has been done to detect and diagnose software misconfigurations. However, there is still a gap between real-world misconfigurations and the literature. It is desirable to investigate whether existing taxonomy and tools are applicable for real-world misconfigurations in modern software. In this paper, we conduct an empirical study on 823 real-world misconfiguration issues, based on which we propose a novel classification of the root causes of software misconfigurations, i.e., constraint violation, resource unavailability, component-dependency error, and misunderstanding of configuration effects. Then, we systematically review the literature on misconfiguration troubleshooting, and study the trends of research and the practicality of the tools and datasets in this field. We find that the research targets have changed from fundamental software to advanced applications (e.g., cloud service). In the meanwhile, the research on non-crash misconfigurations such as performance degradation and security risks also has a significant growth. Despite the progress, a majority of studies lack reproducibility due to the unavailable tools and evaluation datasets. In total, only six tools and two datasets are publicly available. However, the adaptability of these tools limit their practical use on real-world misconfigurations. We also summarize the important challenges and several suggestions to facilitate the research on software misconfiguration.
Populating cellular metamaterials on the extrema of attainable elasticity through neuroevolution
2024-12-15
Maohua Yan, Ruicheng Wang, Ke Liu
The trade-offs between different mechanical properties of materials pose fundamental challenges in engineering material design, such as balancing stiffness versus toughness, weight versus energy-absorbing capacity, and among the various elastic coefficients. Although gradient-based topology optimization approaches have been effective in finding specific designs and properties, they are not efficient tools for surveying the vast design space of metamaterials, and thus unable to reveal the attainable bound of interdependent material properties. Other common methods, such as parametric design or data-driven approaches, are limited by either the lack of diversity in geometry or the difficulty to extrapolate from known data, respectively. In this work, we formulate the simultaneous exploration of multiple competing material properties as a multi-objective optimization (MOO) problem and employ a neuroevolution algorithm to efficiently solve it. The Compositional Pattern-Producing Networks (CPPNs) is used as the generative model for unit cell designs, which provide very compact yet lossless encoding of geometry. A modified Neuroevolution of Augmenting Topologies (NEAT) algorithm is employed to evolve the CPPNs such that they create metamaterial designs on the Pareto front of the MOO problem, revealing empirical bounds of different combinations of elastic properties. Looking ahead, our method serves as a universal framework for the computational discovery of diverse metamaterials across a range of fields, including robotics, biomedicine, thermal engineering, and photonics.
The standard model for planet formation is a bottom-up process in which the origin of rocky and gaseous planets can be traced back to the collision of micron-sized dust grains within the gas-rich environment of protoplanetary disks. Key milestones along the way include disk formation, grain growth, planetesimal formation, core growth, gas accretion, and planetary system evolution. I provide an introductory overview of planet formation, emphasizing the main ideas and reviewing current theoretical understanding. Many of the phases of planet formation have a well-developed physical understanding, though the complexity of the problem means that few can be quantitatively modeled with complete confidence. Transformative advances in disk imaging and exoplanet detection provide the first direct information on the initial conditions for planet formation, and have motivated new formation models that are faster, more efficient, and lead to a more diverse set of architectures than their Solar System inspired forebears. Much remains to be learned, and I close with a personal, incomplete list, of open problems.
We survey some recent progress on the Erd\H{o}s similarity conjecture, proposed by P. Erd\H{o}s in 1974. The conjecture remains open for exponentially decaying sequences as well as Cantor sets that have both Newhouse thickness and Hausdorff dimension zero. We also review progress on some new variants of the original problem: namely, the bi-Lipschitz variant, the topological variant, and a variant ``in the large''. These problems were recently studied by the authors and their collaborators. Each of them offers new perspectives on the original conjecture.
In this work, we proposed AirwayAtlas, which is an end-to-end pipeline for automatic extraction of airway anatomies with lobar, segmental and subsegmental labeling. A compact representation, AirwaySign, is generated based on diverse features of airway branches. Experiments on multi-center datasets validated the effectiveness of AirwayAtlas. We also demonstrated that AirwaySign is a powerful tool for correlation analysis on pulmonary diseases.
SightGlow: A Web Extension to Enhance Color Perception and Interaction for Vision Deficiency
2024-12-15
Sansrit Paudel
SightGlow is a web extension tailored to improve color perception accuracy for individuals with red-green color blindness. The research was focused on evaluating whether personalized color adjustment and selective zoom enhance user interaction and satisfaction for individuals with low vision and color vision impairment. The system was developed as an iterative process by conducting a pilot user survey. Existing web extensions were limited in addressing challenges faced by low vision and color blindness; hence this application provides additional features, including selective zoom and color controls, which make it unique. Most participants responded that the application's flexibility to adjust the color balance for any images or video graphic content enhanced their user experience, hence resulting in the effectiveness of the system.
AI-Driven Innovations in Volumetric Video Streaming: A Review
2024-12-15
Erfan Entezami, Hui Guan
Recent efforts to enhance immersive and interactive user experiences have driven the development of volumetric video, a form of 3D content that enables 6 DoF. Unlike traditional 2D content, volumetric content can be represented in various ways, such as point clouds, meshes, or neural representations. However, due to its complex structure and large amounts of data size, deploying this new form of 3D data presents significant challenges in transmission and rendering. These challenges have hindered the widespread adoption of volumetric video in daily applications. In recent years, researchers have proposed various AI-driven techniques to address these challenges and improve the efficiency and quality of volumetric content streaming. This paper provides a comprehensive overview of recent advances in AI-driven approaches to facilitate volumetric content streaming. Through this review, we aim to offer insights into the current state-of-the-art and suggest potential future directions for advancing the deployment of volumetric video streaming in real-world applications.
This review article summarizes two decades of laboratory research aimed at understanding the dynamics of accretion disks, with particular emphasis on magnetohydrodynamic experiments involving liquid metals and plasmas. First, the Taylor-Couette experiments demonstrated the generation of magnetorotational instability (MRI) in liquid metals, and highlighted how this instability is critically influenced by boundary conditions and the geometry of the applied magnetic field. These experiments also highlight the nonlinear transition to turbulence in accretion disks, and their link with other MHD instabilities in centrifugally-stable flows. A complementary approach, involving laboratory experiments with volumetric fluid driving rather than rotating boundaries, enables a quantitative study of angular momentum transport by Keplerian turbulence. Collectively, these various laboratory studies offer new constraints on the theoretical models designed to explain the dynamics of accretion disks. This is particularly true with regard to the role of Keplerian turbulence in protoplanetary disks, where recent observations from the ALMA telescope have considerably revised previously expected values of the magnitude of the turbulent fluctuations. Finally, the paper discusses outstanding questions and future prospects in laboratory modeling of accretion disks.
A multi-theoretical kernel-based approach to social network-based recommendation
2024-12-15
Xin Li, Mengyue Wang, T. -P. Liang
Recommender systems are a critical component of e-commercewebsites. The rapid development of online social networking services provides an opportunity to explore social networks together with information used in traditional recommender systems, such as customer demographics, product characteristics, and transactions. It also provides more applications for recommender systems. To tackle this social network-based recommendation problem, previous studies generally built trust models in light of the social influence theory. This study inspects a spectrumof social network theories to systematicallymodel themultiple facets of a social network and infer user preferences. In order to effectively make use of these heterogonous theories, we take a kernel-based machine learning paradigm, design and select kernels describing individual similarities according to social network theories, and employ a non-linear multiple kernel learning algorithm to combine the kernels into a unified model. This design also enables us to consider multiple theories' interactions in assessing individual behaviors. We evaluate our proposed approach on a real-world movie review data set. The experiments show that our approach provides more accurate recommendations than trust-based methods and the collaborative filtering approach. Further analysis shows that kernels derived from contagion theory and homophily theory contribute a larger portion of the model.
Multiple Imputation for Nonresponse in Complex Surveys Using Design Weights and Auxiliary Margins
2024-12-14
Kewei Xu, Jerome P. Reiter
Survey data typically have missing values due to unit and item nonresponse. Sometimes, survey organizations know the marginal distributions of certain categorical variables in the survey. As shown in previous work, survey organizations can leverage these distributions in multiple imputation for nonignorable unit nonresponse, generating imputations that result in plausible completed-data estimates for the variables with known margins. However, this prior work does not use the design weights for unit nonrespondents; rather, it relies on a set of fabricated weights for these units. We extend this previous work to utilize the design weights for all sampled units. We illustrate the approach using simulation studies.
MedG-KRP: Medical Graph Knowledge Representation Probing
2024-12-14
Gabriel R. Rosenbaum, Lavender Yao Jiang, Ivaxi Sheth, Jaden Stryker, Anton Alyakin, Daniel Alexander Alber, Nicolas K. Goff, Young Joon Fred Kwon, John Markert, Mustafa Nasir-Moin, Jan Moritz Niehues, Karl L. Sangwon, Eunice Yang, Eric Karl Oermann
Large language models (LLMs) have recently emerged as powerful tools, finding many medical applications. LLMs' ability to coalesce vast amounts of information from many sources to generate a response-a process similar to that of a human expert-has led many to see potential in deploying LLMs for clinical use. However, medicine is a setting where accurate reasoning is paramount. Many researchers are questioning the effectiveness of multiple choice question answering (MCQA) benchmarks, frequently used to test LLMs. Researchers and clinicians alike must have complete confidence in LLMs' abilities for them to be deployed in a medical setting. To address this need for understanding, we introduce a knowledge graph (KG)-based method to evaluate the biomedical reasoning abilities of LLMs. Essentially, we map how LLMs link medical concepts in order to better understand how they reason. We test GPT-4, Llama3-70b, and PalmyraMed-70b, a specialized medical model. We enlist a panel of medical students to review a total of 60 LLM-generated graphs and compare these graphs to BIOS, a large biomedical KG. We observe GPT-4 to perform best in our human review but worst in our ground truth comparison; vice-versa with PalmyraMed, the medical model. Our work provides a means of visualizing the medical reasoning pathways of LLMs so they can be implemented in clinical settings safely and effectively.
Reconstructing meshes from point clouds is an important task in fields such as robotics, autonomous systems, and medical imaging. This survey examines state-of-the-art learning-based approaches to mesh reconstruction, categorizing them into five paradigms: PointNet family, autoencoder architectures, deformation-based methods, point-move techniques, and primitive-based approaches. Each paradigm is explored in depth, detailing the primary approaches and their underlying methodologies. By comparing these techniques, our study serves as a comprehensive guide, and equips researchers and practitioners with the knowledge to navigate the landscape of learning-based mesh reconstruction techniques. The findings underscore the transformative potential of these methods, which often surpass traditional techniques in allowing detailed and efficient reconstructions.
2024-12-14
Radhika Prasad, Sanjana Wanare, Suman Karan, Mritunjay K. Joshi, Abhinandan Bhattacharjee, Anand K. Jha
Structured optical fields have led to several ground-breaking techniques in classical imaging and microscopy. At the same time, in the quantum domain, position-momentum entangled photon fields have been shown to have several unique features that can lead to beyond-classical imaging and microscopy capabilities. Therefore, it is natural to expect that position-momentum entangled two-photon fields that are structured can push the boundaries of quantum imaging and microscopy even further beyond. Nonetheless, the existing experimental schemes are able to produce either structured two-photon fields without position-momentum entanglement, or position-momentum entangled two-photon fields without structures. In this article, by manipulating the phase-matching condition of the spontaneous parametric down-conversion process, we report experimental generation of two-photon fields with various structures in their spatial correlations. We experimentally measure the minimum bound on the entanglement of formation and thereby verify the position-momentum entanglement of the structured two-photon field. We expect this work to have important implications for quantum technologies related to imaging and sensing.
Automatically Detecting Checked-In Secrets in Android Apps: How Far Are We?
2024-12-14
Kevin Li, Lin Ling, Jinqiu Yang, Lili Wei
Mobile apps are predominantly integrated with cloud services to benefit from enhanced functionalities. Adopting authentication using secrets such as API keys is crucial to ensure secure mobile-cloud interactions. However, developers often overlook the proper storage of such secrets, opting to put them directly into their projects. These secrets are checked into the projects and can be easily extracted and exploited by malicious adversaries. While many researchers investigated the issue of checked-in secret in open-source projects, there is a notable research gap concerning checked-in secrets in Android apps deployed on platforms such as Google Play Store. Unlike open-source projects, the lack of direct access to the source code and the presence of obfuscation complicates the checked-in secret detection for Android apps. This motivates us to conduct an empirical analysis to measure and compare the performance of different checked-in secret detection tools on Android apps. We first conducted a literature review to find all the checked-in secret detection tools that can be applied to Android apps. Then, we evaluate three representative tools on 5,135 Android apps, comparing their performance and analyzing their limitations. Our experiment reveals 2,142 checked-in secrets affecting 2,115 Android apps. We also disclose that the current checked-in secret detection techniques suffer from key limitations. All of the evaluated tools can miss a significant number of checked-in secrets in Android apps. Nevertheless, we observed that the tools are complimentary, suggesting the possibility of developing a more effective checked-in secret detection tool by combining their insights. Additionally, we propose that analyzing string groups within methods containing checked-in secrets may provide a more effective strategy to overcome obfuscation challenges.
Adaptive Reward Design for Reinforcement Learning in Complex Robotic Tasks
2024-12-14
Minjae Kwon, Ingy ElSayed-Aly, Lu Feng
There is a surge of interest in using formal languages such as Linear Temporal Logic (LTL) and finite automata to precisely and succinctly specify complex tasks and derive reward functions for reinforcement learning (RL) in robotic applications. However, existing methods often assign sparse rewards (e.g., giving a reward of 1 only if a task is completed and 0 otherwise), necessitating extensive exploration to converge to a high-quality policy. To address this limitation, we propose a suite of reward functions that incentivize an RL agent to make measurable progress on tasks specified by LTL formulas and develop an adaptive reward shaping approach that dynamically updates these reward functions during the learning process. Experimental results on a range of RL-based robotic tasks demonstrate that the proposed approach is compatible with various RL algorithms and consistently outperforms baselines, achieving earlier convergence to better policies with higher task success rates and returns.
Improving Numerical Stability and Accuracy in Partitioned Methods with Algebraic Prediction
2024-12-14
Ahmad Ali, Haya Monawwar, Hantao Cui
The partitioned approach for the numerical integration of power system differential algebraic equations faces inherent numerical stability challenges due to delays between the computation of state and algebraic variables. Such delays can compromise solution accuracy and computational efficiency, particularly in large-scale system simulations. We present an $O(h^2)$-accurate prediction scheme for algebraic variables based on forward and backward difference formulas, applied before the correction step of numerical integration. The scheme improves the numerical stability of the partitioned approach while maintaining computational efficiency. Through numerical simulations on a lightly damped single machine infinite bus system and a large-scale 140-bus network, we demonstrate that the proposed method, when combined with variable time-stepping, significantly enhances the numerical stability, solution accuracy, and computational performance of the simulation. Results show reduced step rejections, fewer nonlinear solver iterations, and improved accuracy compared to conventional approaches, making the method particularly valuable for large-scale power system dynamic simulations.
CEKER: A Generalizable LLM Framework for Literature Analysis with a Case Study in Unikernel Security
2024-12-14
Alex Wollman, John Hastings
Literature reviews are a critical component of formulating and justifying new research, but are a manual and often time-consuming process. This research introduces a novel, generalizable approach to literature analysis called CEKER which uses a three-step process to streamline the collection of literature, the extraction of key insights, and the summarized analysis of key trends and gaps. Leveraging Large Language Models (LLMs), this methodology represents a significant shift from traditional manual literature reviews, offering a scalable, flexible, and repeatable approach that can be applied across diverse research domains. A case study on unikernel security illustrates CEKER's ability to generate novel insights validated against previous manual methods. CEKER's analysis highlighted reduced attack surface as the most prominent theme. Key security gaps included the absence of Address Space Layout Randomization, missing debugging tools, and limited entropy generation, all of which represent important challenges to unikernel security. The study also revealed a reliance on hypervisors as a potential attack vector and emphasized the need for dynamic security adjustments to address real-time threats.
Interharmonic Power: A New Concept for Power System Oscillation Source Location
2024-12-14
Wilsun Xu, Jing Yong, Horacio J. Marquez, Chun Li
Power system oscillations are a significant concern for system operators, a problem that has grown due to the interconnection of inverter-based resources. To address this issue, various methods have been proposed to locate the sources of oscillations, which is essential for effective mitigation actions. A common characteristic of these methods is that they rely on phasor representation of oscillation phenomena. This paper takes a different approach by examining the actual voltage and current waveforms underlying the phasors. It is found that the presence of interharmonic components is both the necessary and sufficient condition for phasor oscillations. Moreover, the generation and propagation of interharmonic powers are identified as the true culprits behind power system oscillations and oscillatory instability. Based on these insights, two new methods are developed for locating oscillation sources: one for measurement-based monitoring applications and another for model-based system studies. These findings are validated through four field data-based and one simulation-based case studies.
Large-format imaging systems based on solid-state detectors in optical astronomy
2024-12-14
V V Vlasyuk, I V Afanasieva, V I Ardilanov, V A Murzin, N G Ivaschenko, M A Pritychenko, S N Dodonov
The development of technologies for creating various types of solid-state detectors for optical astronomy is reviewed. The principles of designing astronomical photodetecting systems with large-format sensors based on charge-coupled device (CCD) and complementary metal oxide semiconductor (CMOS) structures are analyzed. Examples of the most advanced projects to which they have been applied are given. The history of the creation of optical detectors for telescopes operated in Russia is described, and a brief description and characteristics of the developed systems are provided. The results of testing in real research are displayed. The prospects for creating large-format systems based on CCD and CMOS detectors manufactured in Russia and abroad are discussed.
Low-density and unbound stellar groups, OB associations have been historically delineated through their bright and massive members. They have been analysed for decades, but the arrival of Hipparcos, and more recently of Gaia led to a change of paradigm by allowing the identification of more reliable members using parallaxes and proper motions. This renewed interest offers an opportunity to emphasize the role of OB associations across many areas of astronomy. In this review, I highlight their importance across multiple scales: how OB associations constitute suitable sites to study massive stars and stellar multiplicity, their relation with star clusters, their interactions with the interstellar medium through the feedback of their massive members, and how they shape the structure and evolution of the Milky Way and beyond.
By analyzing data from DESI Legacy Imaging Survey of the dwarf galaxies in the Arecibo Legacy Fast Alfa Survey, we have identified five ultra-diffuse galaxies (UDGs) featuring central pseudo-bulges. These UDGs display blue pseudo-bulges with S\'ersic indices $n<2.5$ and effective radii spanning 300-700 pc, along with bluer thin stellar disks exhibiting low surface brightness and expansive effective radii that align with the UDG definition. The rotation velocities of these UDGs, determined using HI line widths and optical inclinations, exceed those of most dwarf galaxies of similar mass, suggesting the high halo spins or substantial dark matter halos. We propose that these UDGs likely formed through mergers of dwarf galaxies lacking old stars in their progenitors, resulting in the development of central bulge-like structures during starbursts triggered by the mergers, while also enhancing their halo spin. Subsequent gas accretion facilitated the formation of extended stellar disks. It is also worth noting the possibility that these UDGs could alternatively represent ``failed $L^{\star}$ galaxies'' with massive dark matter halos but reduced star formation efficiencies. If future high-resolution HI observations confirm the presence of massive halos around these UDGs, they may have formed due to intense AGN feedback in the early universe, and may be the descendants of ``little red dots'' observed by the James Webb Space Telescope, which are characterized by heightened central black hole masses and intensified accretion and feedback processes in the early universe.
We provide an in-depth study of tripartite entanglement of qudits. We start with a short review of tripartite entanglement invariants, prove a theorem about the complete list of all allowed values of three (out of the total of four) such invariants, and give several bounds on the allowed values of the fourth invariant. After introducing several operations on entangled states (that allow us to build new states from old states) and deriving general properties pertaining to their invariants, we arrive at the decomposition theorem as one of our main results. The theorem relates the algebraic invariants of any entanglement class with the invariants of its corresponding components in each of its direct sum decompositions. This naturally leads to the definition of reducible and irreducible entanglement classes. We explicitly compute algebraic invariants for several families of irreducible classes and show how the decomposition theorem allows computations of invariants for compounded classes to be carried out efficiently. This theorem also allows us to compute the invariants for the infinite number of entanglement classes constructed from irreducible components. We proceed with the complete list of the entanglement classes for three tribits with decompositions of each class into irreducible components, and provide a visual guide to interrelations of these decompositions. We conclude with numerous examples of building classes for higher-spin qudits.
A Post a Day Keeps the Doctor Away: Sharing Personal Information on Self-Diagnosis Platforms
2024-12-14
Roopa Bhat, Lord Crawford, Nicole Hong
For many, it can be intimidating or even impossible to seek professional medical help if they have symptoms of an illness. As such, some people approach platforms like Reddit or Quora for a community-based conversation in an attempt to diagnose themselves. In this paper, we unearth what motivates people to share personal health information on these platforms. From an online survey and in-depth interviews, we present who this population of users are, and what, where, and why they are posting. Our evaluation finds that tech-savvy young adults are more likely to post on online platforms about potentially sensitive or highly specific topics for convenience, fast response, and a sense of community. Most importantly, we found that anonymity, distrust of physicians, and prior experience with platforms were key factors that affected behavior.
Negative feedback signals are crucial to guardrail content recommendations and improve user experience. When these signals are effectively integrated into recommendation systems, they play a vital role in preventing the promotion of harmful or undesirable content, thereby contributing to a healthier online environment. However, the challenges associated with negative signals are noteworthy. Due to the limited visibility of options for users to express negative feedback, these signals are often sparse compared to positive signals. This imbalance can lead to a skewed understanding of user preferences, resulting in recommendations that prioritize short-term engagement over long-term satisfaction. Moreover, an over-reliance on positive signals can create a filter bubble, where users are continuously exposed to content that aligns with their immediate preferences but may not be beneficial in the long run. This scenario can ultimately lead to user attrition as audiences become disillusioned with the quality of the content provided. Additionally, existing user signals frequently fail to meet specific customized requirements, such as understanding the underlying reasons for a user's likes or dislikes regarding a video. This lack of granularity hinders our ability to tailor content recommendations effectively, as we cannot identify the particular attributes of content that resonate with individual users.
VibrantVS: A high-resolution multi-task transformer for forest canopy height estimation
2024-12-13
Tony Chang, Kiarie Ndegwa, Andreas Gros, Vincent A. Landau, Luke J. Zachmann, Bogdan State, Mitchell A. Gritts, Colton W. Miller, Nathan E. Rutenbeck, Scott Conway, Guy Bayes
This paper explores the application of a novel multi-task vision transformer (ViT) model for the estimation of canopy height models (CHMs) using 4-band National Agriculture Imagery Program (NAIP) imagery across the western United States. We compare the effectiveness of this model in terms of accuracy and precision aggregated across ecoregions and class heights versus three other benchmark peer-reviewed models. Key findings suggest that, while other benchmark models can provide high precision in localized areas, the VibrantVS model has substantial advantages across a broad reach of ecoregions in the western United States with higher accuracy, higher precision, the ability to generate updated inference at a cadence of three years or less, and high spatial resolution. The VibrantVS model provides significant value for ecological monitoring and land management decisions for wildfire mitigation.
2024-12-13
Divya Shanmugam, Monica Agrawal, Rajiv Movva, Irene Y. Chen, Marzyeh Ghassemi, Maia Jacobs, Emma Pierson
The increased capabilities of generative AI have dramatically expanded its possible use cases in medicine. We provide a comprehensive overview of generative AI use cases for clinicians, patients, clinical trial organizers, researchers, and trainees. We then discuss the many challenges -- including maintaining privacy and security, improving transparency and interpretability, upholding equity, and rigorously evaluating models -- which must be overcome to realize this potential, and the open research directions they give rise to.
Applied Statistics in the Era of Artificial Intelligence: A Review and Vision
2024-12-13
Jie Min, Xinyi Song, Simin Zheng, Caleb B. King, Xinwei Deng, Yili Hong
The advent of artificial intelligence (AI) technologies has significantly changed many domains, including applied statistics. This review and vision paper explores the evolving role of applied statistics in the AI era, drawing from our experiences in engineering statistics. We begin by outlining the fundamental concepts and historical developments in applied statistics and tracing the rise of AI technologies. Subsequently, we review traditional areas of applied statistics, using examples from engineering statistics to illustrate key points. We then explore emerging areas in applied statistics, driven by recent technological advancements, highlighting examples from our recent projects. The paper discusses the symbiotic relationship between AI and applied statistics, focusing on how statistical principles can be employed to study the properties of AI models and enhance AI systems. We also examine how AI can advance applied statistics in terms of modeling and analysis. In conclusion, we reflect on the future role of statisticians. Our paper aims to shed light on the transformative impact of AI on applied statistics and inspire further exploration in this dynamic field.
The Critical 2d Stochastic Heat Flow and Related Models
2024-12-13
Francesco Caravenna, Rongfeng Sun, Nikos Zygouras
In these lecture notes, we review recent progress in the study of the stochastic heat equation and its discrete analogue, the directed polymer model, in spatial dimension 2. It was discovered that a phase transition emerges on an intermediate disorder scale, with Edwards-Wilkinson (Gaussian) fluctuations in the sub-critical regime. In the critical window, a unique scaling limit has been identified and named the critical 2d stochastic heat flow. This gives a meaning to the solution of the stochastic heat equation in the critical dimension 2, which lies beyond existing solution theories for singular SPDEs. We outline the proof ideas, introduce the key ingredients, and discuss related literature on disordered systems and singular SPDEs. A list of open questions is also provided.
Simulation-based inference on warm dark matter from HERA forecasts
2024-12-13
Quentin Decant, Androniki Dimitriou, Laura Lopez Honorez, Bryan Zaldivar
The redshifted 21cm signal from Cosmic Dawn promises to open a new window into the early history of our universe and enable the probing of an unprecedented comoving survey volume. In this work, we revisit the imprint of Warm Dark Matter (WDM) on the 21cm signal power spectrum using an updated implementation of the WDM effect in the public code $\texttt{21cmFast}$ and considering a single population of cosmic dawn galaxies. By focusing on inferring the WDM mass, we analyze the degeneracies between the latter and the astrophysics parameters characterizing star formation and X-ray heating and we emphasize the role of the threshold mass for star-forming galaxies, $M_{\rm turn}$. We study the capability of the recently built HERA telescope to reconstruct the WDM mass by adopting the statistical approach of simulation-based inference. We include a comparison of the per-parameter reconstruction quality for different number of simulations used in the training of the algorithm. Our results indicate that HERA could surpass current Lyman-$\alpha$ forest constraints if Cosmic Dawn galaxies exhibit a threshold mass $M_{\rm turn}\lesssim 10^{8}\, M_\odot$. The X-ray source properties considered in this study may also influence the strength of the WDM constraint for lower threshold masses.
A Note On Square-free Sequences and Anti-unification Type
2024-12-13
David M. Cerna
Anti-unification is a fundamental operation used for inductive inference. It is abstractly defined as a process deriving from a set of symbolic expressions a new symbolic expression possessing certain commonalities shared between its members. We consider anti-unification over term algebras where some function symbols are interpreted as associative-idempotent ($f(x,f(y,z)) = f(f(x,y),z)$ and $f(x,x)=x$, respectively) and show that there exists generalization problems for which a minimal complete set of solutions does not exist (Nullary), that is every complete set must contain comparable elements with respect to the generality relation. In contrast to earlier techniques for showing the nullarity of a generalization problem, we exploit combinatorial properties of complete sets of solutions to show that comparable elements are not avoidable. We show that every complete set of solutions contains an infinite chain of comparable generalizations whose structure is isomorphic to a subsequence of an infinite square-free sequence over three symbols.
Stochastic relativistic viscous hydrodynamics from the Metropolis algorithm
2024-12-13
Jay Bhambure, Rajeev Singh, Derek Teaney
We propose an algorithm for simulating stochastic relativistic fluid dynamics based on Metropolis updates. Each step of the algorithm begins with an update based on ideal hydrodynamics. This is followed by proposing random (spatial) momentum transfers between fluid cells, keeping the total energy fixed. These proposals are then accepted or rejected using the change in entropy as a statistical weight. The algorithm reproduces relativistic viscous hydrodynamics in the ``Density Frame", which is a formulation of viscous hydrodynamics we review and clarify here. This formulation is first order in time and requires no auxiliary dynamical fields such as $\Pi^{\mu\nu}$. The only parameters are the shear and bulk viscosities and the equation of state. By adopting the 3+1 split of general relativity, we extend the Metropolis algorithm to general space-time coordinates, such as Bjorken coordinates, which are commonly used to simulate heavy-ion collisions.
Constraints on primordial non-Gaussianity from the cross-correlation of DESI Luminous Red Galaxies and $Planck$ CMB lensing
2024-12-13
J. R. Bermejo-Climent, R. Demina, A. Krolewski, E. Chaussidon, M. Rezaie, S. Ahlen, S. Bailey, D. Bianchi, D. Brooks, E. Burtin, T. Claybaugh, A. de la Macorra, Arjun Dey, P. Doel, G. Farren, S. Ferraro, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, C. Hahn, K. Honscheid, C. Howlett, R. Kehoe, D. Kirkby, T. Kisner, M. Landriau, L. Le Guillou, M. E. Levi, M. Manera, A. Meisner, R. Miquel, J. Moustakas, J. A. Newman, G. Niz, N. Palanque-Delabrouille, W. J. Percival, F. Prada, I. Pérez-Ràfols, D. Rabinowitz, A. J. Ross, G. Rossi, E. Sanchez, D. Schlegel, D. Sprayberry, G. Tarlé, B. A. Weaver, M. White, C. Yèche, P. Zarrouk
We use the angular cross-correlation between a Luminous Red Galaxy (LRG) sample from the DR9 DESI Legacy Survey and the $Planck$ PR4 CMB lensing maps to constrain the local primordial non-Gaussianity parameter $f_{\rm NL}$ using the scale-dependent galaxy bias effect. The galaxy sample covers $\sim$ 40% of the sky and contains galaxies up to $z \sim 1.4$, and is calibrated with the LRG spectra that have been observed for the DESI Survey Validation. We apply a nonlinear imaging systematics treatment based on neural networks to remove observational effects that could potentially bias the $f_{\rm NL}$ measurement. Our measurement is performed without blinding, but the full analysis pipeline is tested with simulations including systematics. Using the two-point angular cross-correlation between LRG and CMB lensing only ($C_\ell^{\kappa G}$) we find $f_{\rm NL} = 39_{-38}^{+40}$ at 68% confidence level, and our result is robust in terms of systematics and cosmology assumptions. If we combine this information with the autocorrelation of LRG ($C_\ell^{GG}$) applying a $\ell_{\rm min}$ scale cut to limit the impact of systematics, we find $f_{\rm NL} = 24_{-21}^{+20}$ at 68% confidence level. Our results motivate the use of CMB lensing cross-correlations for measuring $f_{\rm NL}$ with future datasets given its stability in terms of observational systematics compared to the angular auto-correlation.
On the Significance of Intermediate Latents: Distinguishing Quantum Causal Scenarios with Indistinguishable Classical Analogs
2024-12-13
Daniel Centeno, Elie Wolfe
The use of graphical models to represent causal hypotheses has enabled revolutionary progress in the study of the foundations of quantum theory. Here we consider directed acyclic graphs each of which contains both nodes representing observed variables as well as nodes representing latent or hidden variables. When comparing distinct causal structure, a natural question to ask is if they can explain distinct sets of observable distributions or not. Statisticians have developed a great variety of tools for resolving such questions under the assumption that latent nodes be interpreted classically. Here we highlight how the change to a quantum interpretation of the latent nodes induces distinctions between causal scenarios that would be classically indistinguishable. We especially concentrate on quantum scenarios containing latent nodes with at least one latent parent, a.k.a. possessing intermediate latents. This initial survey demonstrates that many such quantum processes can be operationally distinguished by considerations related to monogamy of nonlocality, especially when computationally aided by a hierarchy of semidefinite relaxations which we tailor for the study such scenarios. We conclude by clarifying the challenges that prevent the generalization of this work, calling attention to open problems regarding observational (in)equivalence of quantum causal structures with intermediate latents.
How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives
2024-12-13
Timour Ichmoukhamedov, James Hinns, David Martens
A rapidly developing application of LLMs in XAI is to convert quantitative explanations such as SHAP into user-friendly narratives to explain the decisions made by smaller prediction models. Evaluating the narratives without relying on human preference studies or surveys is becoming increasingly important in this field. In this work we propose a framework and explore several automated metrics to evaluate LLM-generated narratives for explanations of tabular classification tasks. We apply our approach to compare several state-of-the-art LLMs across different datasets and prompt types. As a demonstration of their utility, these metrics allow us to identify new challenges related to LLM hallucinations for XAI narratives.
Impact of Mixing on Flavor and Aroma Development in Fermented Foods
2024-12-13
Azarmidokht Gholamipour-Shirazi, Endre Joachim Lerheim Mossige
The flavor and aroma development in fermented foods is intricately tied to the mixing dynamics during fermentation. This review explores how variations in mixing influence the physical, chemical, and microbial interactions within fermentation systems, ultimately affecting sensory characteristics such as flavor and aroma. Factors like rheology, shear forces, and fluid flow patterns are critical in mass transfer, microbial activity, and the release of volatile compounds, contributing to fermented products' sensory profile. Examples from common fermented foods -- including bread, yogurt, beer, wine, and cheese -- highlight how controlled mixing can optimize the release of desirable flavor compounds, improve biosynthesis yields, and reduce technological complexity. Understanding these physical interactions is essential for advancing fermentation processes in the food industry, leading to higher product quality, better flavor retention, and enhanced consumer satisfaction.
A catalogue of complex radio sources in the Rapid ASKAP Continuum Survey created using a Self-Organising Map
2024-12-13
Afrida Alam, Kevin A. Pimbblet, Yjan A. Gordon
Next generations of radio surveys are expected to identify tens of millions of new sources, and identifying and classifying their morphologies will require novel and more efficient methods. Self-Organising Maps (SOMs), a type of unsupervised machine learning, can be used to address this problem. We map 251,259 multi-Gaussian sources from Rapid ASKAP Continuum Survey (RACS) onto a SOM with discrete neurons. Similarity metrics, such as Euclidean distances, can be used to identify the best-matching neuron or unit (BMU) for each input image. We establish a reliability threshold by visually inspecting a subset of input images and their corresponding BMU. We label the individual neurons based on observed morphologies and these labels are included in our value-added catalogue of RACS sources. Sources for which the Euclidean distance to their BMU is $\lesssim$ 5 (accounting for approximately 79$\%$ of sources) have an estimated $>90\%$ reliability for their SOM-derived morphological labels. This reliability falls to less than 70$\%$ at Euclidean distances $\gtrsim$ 7. Beyond this threshold it is unlikely that the morphological label will accurately describe a given source. Our catalogue of complex radio sources from RACS with their SOM-derived morphological labels from this work will be made publicly available.
Research Integrity and GenAI: A Systematic Analysis of Ethical Challenges Across Research Phases
2024-12-13
Sonja Bjelobaba, Lorna Waddington, Mike Perkins, Tomáš Foltýnek, Sabuj Bhattacharyya, Debora Weber-Wulff
Background: The rapid development and use of generative AI (GenAI) tools in academia presents complex and multifaceted ethical challenges for its users. Earlier research primarily focused on academic integrity concerns related to students' use of AI tools. However, limited information is available on the impact of GenAI on academic research. This study aims to examine the ethical concerns arising from the use of GenAI across different phases of research and explores potential strategies to encourage its ethical use for research purposes. Methods: We selected one or more GenAI platforms applicable to various research phases (e.g. developing research questions, conducting literature reviews, processing data, and academic writing) and analysed them to identify potential ethical concerns relevant for that stage. Results: The analysis revealed several ethical concerns, including a lack of transparency, bias, censorship, fabrication (e.g. hallucinations and false data generation), copyright violations, and privacy issues. These findings underscore the need for cautious and mindful use of GenAI. Conclusions: The advancement and use of GenAI are continuously evolving, necessitating an ongoing in-depth evaluation. We propose a set of practical recommendations to support researchers in effectively integrating these tools while adhering to the fundamental principles of ethical research practices.
TIGRE v3: Efficient and easy to use iterative computed tomographic reconstruction toolbox for real datasets
2024-12-13
Ander Biguri, Tomoyuki Sadakane, Reuben Lindroos, Yi Liu, Malena Sabaté Landman, Yi Du, Manasavee Lohvithee, Stefanie Kaser, Sepideh Hatamikia, Robert Bryll, Emilien Valat, Sarinrat Wonglee, Thomas Blumensath, Carola-Bibiane Schönlieb
Computed Tomography (CT) has been widely adopted in medicine and it is increasingly being used in scientific and industrial applications. Parallelly, research in different mathematical areas concerning discrete inverse problems has led to the development of new sophisticated numerical solvers that can be applied in the context of CT. The Tomographic Iterative GPU-based Reconstruction (TIGRE) toolbox was born almost a decade ago precisely in the gap between mathematics and high performance computing for real CT data, providing user-friendly open-source software tools for image reconstruction. However, since its inception, the tools' features and codebase have had over a twenty-fold increase, and are now including greater geometric flexibility, a variety of modern algorithms for image reconstruction, high-performance computing features and support for other CT modalities, like proton CT. The purpose of this work is two-fold: first, it provides a structured overview of the current version of the TIGRE toolbox, providing appropriate descriptions and references, and serving as a comprehensive and peer-reviewed guide for the user; second, it is an opportunity to illustrate the performance of several of the available solvers showcasing real CT acquisitions, which are typically not be openly available to algorithm developers.
AMUSE: Adaptive Model Updating using a Simulated Environment
2024-12-13
Louis Chislett, Catalina A. Vallejos, Timothy I. Cannings, James Liley
Prediction models frequently face the challenge of concept drift, in which the underlying data distribution changes over time, weakening performance. Examples can include models which predict loan default, or those used in healthcare contexts. Typical management strategies involve regular model updates or updates triggered by concept drift detection. However, these simple policies do not necessarily balance the cost of model updating with improved classifier performance. We present AMUSE (Adaptive Model Updating using a Simulated Environment), a novel method leveraging reinforcement learning trained within a simulated data generating environment, to determine update timings for classifiers. The optimal updating policy depends on the current data generating process and ongoing drift process. Our key idea is that we can train an arbitrarily complex model updating policy by creating a training environment in which possible episodes of drift are simulated by a parametric model, which represents expectations of possible drift patterns. As a result, AMUSE proactively recommends updates based on estimated performance improvements, learning a policy that balances maintaining model performance with minimizing update costs. Empirical results confirm the effectiveness of AMUSE in simulated data.
MALAMUTE: A Multilingual, Highly-granular, Template-free, Education-based Probing Dataset
2024-12-13
Sagi Shaier, George Arthur Baker, Chiranthan Sridhar, Lawrence E Hunter, Katharina von der Wense
Language models (LMs) have excelled in various broad domains. However, to ensure their safe and effective integration into real-world educational settings, they must demonstrate proficiency in specific, granular areas of knowledge. Existing cloze-style benchmarks, commonly used to evaluate LMs' knowledge, have three major limitations. They: 1) do not cover the educational domain; 2) typically focus on low-complexity, generic knowledge or broad domains, which do not adequately assess the models' knowledge in specific subjects; and 3) often rely on templates that can bias model predictions. Here, we introduce MALAMUTE, a multilingual, template-free, and highly granular probing dataset comprising expert-written, peer-reviewed probes from 71 university-level textbooks across three languages (English, Spanish, and Polish). MALAMUTE is the first education-based cloze-style dataset. It covers eight domains, each with up to 14 subdomains, further broken down into concepts and concept-based prompts, totaling 33,361 university curriculum concepts and 116,887 prompts. MALAMUTE's fine granularity, educational focus, and inclusion of both sentence-level and paragraph-level prompts make it an ideal tool for evaluating LMs' course-related knowledge. Our evaluation of masked and causal LMs on MALAMUTE shows that despite overall proficiency, they have significant gaps in knowledge when examined closely on specific subjects, hindering their safe use in classrooms and underscoring the need for further development.
Detecting sarcasm effectively requires a nuanced understanding of context, including vocal tones and facial expressions. The progression towards multimodal computational methods in sarcasm detection, however, faces challenges due to the scarcity of data. To address this, we present AMuSeD (Attentive deep neural network for MUltimodal Sarcasm dEtection incorporating bi-modal Data augmentation). This approach utilizes the Multimodal Sarcasm Detection Dataset (MUStARD) and introduces a two-phase bimodal data augmentation strategy. The first phase involves generating varied text samples through Back Translation from several secondary languages. The second phase involves the refinement of a FastSpeech 2-based speech synthesis system, tailored specifically for sarcasm to retain sarcastic intonations. Alongside a cloud-based Text-to-Speech (TTS) service, this Fine-tuned FastSpeech 2 system produces corresponding audio for the text augmentations. We also investigate various attention mechanisms for effectively merging text and audio data, finding self-attention to be the most efficient for bimodal integration. Our experiments reveal that this combined augmentation and attention approach achieves a significant F1-score of 81.0% in text-audio modalities, surpassing even models that use three modalities from the MUStARD dataset.
Artificial intelligence (AI) is revolutionizing research by enabling the efficient analysis of large datasets and the discovery of hidden patterns. In astrophysics, AI has become essential, transforming the classification of celestial sources, data modeling, and the interpretation of observations. In this review, I highlight examples of AI applications in astrophysics, including source classification, spectral energy distribution modeling, and discuss the advancements achievable through generative AI. However, the use of AI introduces challenges, including biases, errors, and the "black box" nature of AI models, which must be resolved before their application. These issues can be addressed through the concept of Human-Guided AI (HG-AI), which integrates human expertise and domain-specific knowledge into AI applications. This approach aims to ensure that AI is applied in a robust, interpretable, and ethical manner, leading to deeper insights and fostering scientific excellence.
A Survey on Knowledge Graph Structure and Knowledge Graph Embeddings
2024-12-13
Jeffrey Sardina, John D. Kelleher, Declan O'Sullivan
Knowledge Graphs (KGs) and their machine learning counterpart, Knowledge Graph Embedding Models (KGEMs), have seen ever-increasing use in a wide variety of academic and applied settings. In particular, KGEMs are typically applied to KGs to solve the link prediction task; i.e. to predict new facts in the domain of a KG based on existing, observed facts. While this approach has been shown substantial power in many end-use cases, it remains incompletely characterised in terms of how KGEMs react differently to KG structure. This is of particular concern in light of recent studies showing that KG structure can be a significant source of bias as well as partially determinant of overall KGEM performance. This paper seeks to address this gap in the state-of-the-art. This paper provides, to the authors' knowledge, the first comprehensive survey exploring established relationships of Knowledge Graph Embedding Models and Graph structure in the literature. It is the hope of the authors that this work will inspire further studies in this area, and contribute to a more holistic understanding of KGs, KGEMs, and the link prediction task.
Lower-Hybrid Drift Instabilities in a magnetic nozzle
2024-12-13
Matteo Ripoli, Eduardo Ahedo, Mario Merino
Magnetic nozzles are a key component of electrodeless plasma thrusters, acting as their main acceleration stage. Non-stationary phenomena common to the entire range of $E \times B$ devices, such as oscillations and instabilities, are likely to exist in the magnetic nozzle, according to the mounting experimental evidence. These mechanisms could lead to anomalous cross-field transport, either enhancing the plasma plume divergence or favoring electron detachment. In this work we present a local linear analysis of fluid instabilities relevant for said devices, expanding on previous works with the addition of plasma inhomogeneities in the direction parallel to the magnetic field, with a rigorous inclusion of the effects of magnetic curvature, finite Larmor radius and $3$D wave propagation, allowing for a general formulation of drift-driven instabilities in partially magnetized plasmas. Instability conditions are first studied analytically, and then applied to simulation data of a helicon plasma thruster. Finally, the effect of instabilities on wave-driven cross-field electron transport is assessed by means of quasi-linear analysis. This study predicts the onset of essentially-azimuthal instabilities in the $1$ kHz--$1$ MHz range, in qualitative agreement with some of the available experimental data, and highlights the importance of including parallel inhomogeneities in the formulation of the dispersion relation of an $E \times B$ plasma, as these gradients may drive instabilities even in the absence of axial propagation. Lastly, quasi-linear analysis suggests that the induced cross-field transport acts to smooth out the zeroth-order drifts which cause the plasma to destabilize in the first place.
A Relativistic Tensorial Model for Fractional Interaction between Dark Matter and Gravity
2024-12-13
Francesco Benetti, Andrea Lapi, Samuele Silveravalle, Stefano Liberati, Balakrishna S. Haridasu, Yacer Boumechta, Minahil Adil Butt, Carlo Baccigalupi
In a series of recent papers it was shown that several aspects of Dark Matter (DM) phenomenology, such as the velocity profiles of individual dwarfs and spiral galaxies, the scaling relations observed in the latter, and the pressure and density profiles of galaxy clusters, can be explained by assuming the DM component in virialized halos to feel a non-local fractional interaction mediated by gravity. Motivated by the remarkable success of this model, in a recent work we have looked for a general relativistic extension, proposing a theory, dubbed Relativistic Scalar Fractional Gravity or RSFG, in which the trace of the DM stress-energy tensor couples to the scalar curvature via a non-local operator constructed with a fractional power of the d'Alembertian. In this work we construct an extension of that model in which also a non-local coupling between the Ricci tensor and the DM stress energy tensor is present. In the action we encode the normalization between these scalar and tensorial term into two operators $F_0(\Box)$ and $F_2(\Box)$, and we derive the general field equations. We then take the weak field limit of the latter, showing that they reduce to general relativity sourced by an effective stress energy tensor, featuring a non local isotropic pressure and anisotropic stress, even if one starts with the assumption of a pressureless DM fluid. Finally, after having worked out the lensing theory in our setup, we test particularly interesting realizations of our framework against the measured convergence profiles of the individual and stacked clusters of the CLASH sample, finding remarkable consistency with the data.
The p-Laplacian: phenomenological modelling of the flow in porous media and CFD simulations
2024-12-13
Petr Girg, Lukáš Kotrla, Anežka Švandová
The aim of this paper is to discuss several aspects of connections between the p-Laplacian and mathematical models in hydrology. At first we present models of groundwater flow in phreatic aquifers and models of irrigation and drainage that lead to quasilinear parabolic equations involving the p-Laplacian. Next, we survey conditions of validity of Strong Maximum Principle and Strong Comparison Principle for this type of problems. Finally, we employ computer fluid dynamics simulations to realistic scenario of fracture networks to estimate values of the parameters of constitutive laws governing groundwater flow in the context of fractured hard-rock aquifers.
Visual Object Tracking (VOT) is an attractive and significant research area in computer vision, which aims to recognize and track specific targets in video sequences where the target objects are arbitrary and class-agnostic. The VOT technology could be applied in various scenarios, processing data of diverse modalities such as RGB, thermal infrared and point cloud. Besides, since no one sensor could handle all the dynamic and varying environments, multi-modal VOT is also investigated. This paper presents a comprehensive survey of the recent progress of both single-modal and multi-modal VOT, especially the deep learning methods. Specifically, we first review three types of mainstream single-modal VOT, including RGB, thermal infrared and point cloud tracking. In particular, we conclude four widely-used single-modal frameworks, abstracting their schemas and categorizing the existing inheritors. Then we summarize four kinds of multi-modal VOT, including RGB-Depth, RGB-Thermal, RGB-LiDAR and RGB-Language. Moreover, the comparison results in plenty of VOT benchmarks of the discussed modalities are presented. Finally, we provide recommendations and insightful observations, inspiring the future development of this fast-growing literature.
Nap-induced modulations of tinnitus -a cross-sectional database analysis
2024-12-13
Robin Guillard, Martin Schecklmann, Jorge Simoes, Berthold Langguth, Alain Londero, Marco Congedo, Sarah Michiels, Markku Vesala, Hazel Goedhart, Thomas Wetter, Franziska C Weber
The influence of naps on tinnitus was systematically assessed by exploring the frequency, clinical and demographic characteristics of this phenomenon. 9,724 data from two different tinnitus databases (Tinnitus Hub: $n = 6115$; Tinnitus Research Initiative (TRI): $n = 3627$) were included. After separate analysis of the databases, these results were then compared with each other. In the Tinnitus Hub survey database, a total of 31.1% reported an influence on tinnitus by taking a nap (26.9% in the TRI database), with much more frequent worsening after a nap than improvement (23.0% a little or a lot worse; TRI: 17.7% worse; 8.1% a little or a lot better; TRI: 9.2% better). The influence of napping on tinnitus was associated in both databases with other clinical features, such as the dependence of tinnitus on night quality, stress and somatosensory maneuvers. The present study confirms the clinical observation that more tinnitus sufferers report worsening after a nap than tinnitus sufferers reporting an improvement. It was consistently shown that tinnitus sufferers reporting nap-induced modulation of tinnitus also report more frequently an influence of night sleep on their tinnitus. Further clinical and polysomnographic research is warranted to better understand the interaction between sleep and tinnitus.
User interactions in recommender systems are inherently complex, often involving behaviors that go beyond simple acceptance or rejection. One particularly common behavior is hesitation, where users deliberate over recommended items, signaling uncertainty. Our large-scale surveys, with 6,644 and 3,864 responses respectively, confirm that hesitation is not only widespread but also has a profound impact on user experiences. When users spend additional time engaging with content they are ultimately uninterested in, this can lead to negative emotions, a phenomenon we term as tolerance. The surveys reveal that such tolerance behaviors often arise after hesitation and can erode trust, satisfaction, and long-term loyalty to the platform. For instance, a click might reflect a need for more information rather than genuine interest, and prolonged exposure to unsuitable content amplifies frustration. This misalignment between user intent and system interpretation introduces noise into recommendation training, resulting in suggestions that increase uncertainty and disengagement. To address these issues, we identified signals indicative of tolerance behavior and analyzed datasets from both e-commerce and short-video platforms. The analysis shows a strong correlation between increased tolerance behavior and decreased user activity. We integrated these insights into the training process of a recommender system for a major short-video platform. Results from four independent online A/B experiments demonstrated significant improvements in user retention, achieved with minimal additional computational costs. These findings underscore the importance of recognizing hesitation as a ubiquitous user behavior and addressing tolerance to enhance satisfaction, build trust, and sustain long-term engagement in recommender systems.
The Three Hundred Project: The relationship between the shock and splashback radii of simulated galaxy clusters
2024-12-13
M. Zhang, K. Walker, A. Sullivan, C. Power, W. Cui, Y. Li, X. Zhang
Observations of the intracluster medium (ICM) in the outskirts of galaxy clusters reveal shocks associated with gas accretion from the cosmic web. Previous work based on non-radiative cosmological hydrodynamical simulations have defined the shock radius, $r_\text{shock}$, using the ICM entropy, $K \propto T/{n_\mathrm{e}}^{2/3}$, where $T$ and $n_\text{e}$ are the ICM temperature and electron density respectively; the $r_\text{shock}$ is identified with either the radius at which $K$ is a maximum or at which its logarithmic slope is a minimum. We investigate the relationship between $r_\text{shock}$, which is driven by gravitational hydrodynamics and shocks, and the splashback radius, $r_\text{splash}$, which is driven by the gravitational dynamics of cluster stars and dark matter and is measured from their mass profile. Using 324 clusters from {\small The Three Hundred} project of cosmological galaxy formation simulations, we quantify statistically how $r_\text{shock}$ relates to $r_\text{splash}$. Depending on our definition, we find that the median $r_\text{shock} \simeq 1.38 r_\text{splash} (2.58 R_{200})$ when $K$ reaches its maximum and $r_\text{shock} \simeq 1.91 r_\text{splash} (3.54 R_{200})$ when its logarithmic slope is a minimum; the best-fit linear relation increases as $r_\text{shock} \propto 0.65 r_\text{splash}$. We find that $r_\text{shock}/R_{200}$ and $r_\text{splash}/R_{200}$ anti-correlate with virial mass, $M_{200}$, and recent mass accretion history, and $r_\text{shock}/r_\text{splash}$ tends to be larger for clusters with higher recent accretion rates. We discuss prospects for measuring $r_\text{shock}$ observationally and how the relationship between $r_\text{shock}$ and $r_\text{splash}$ can be used to improve constraints from radio, X-ray, and thermal Sunyaev-Zeldovich surveys that target the interface between the cosmic web and clusters.
LinGen: Towards High-Resolution Minute-Length Text-to-Video Generation with Linear Computational Complexity
2024-12-13
Hongjie Wang, Chih-Yao Ma, Yen-Cheng Liu, Ji Hou, Tao Xu, Jialiang Wang, Felix Juefei-Xu, Yaqiao Luo, Peizhao Zhang, Tingbo Hou, Peter Vajda, Niraj K. Jha, Xiaoliang Dai
Text-to-video generation enhances content creation but is highly computationally intensive: The computational cost of Diffusion Transformers (DiTs) scales quadratically in the number of pixels. This makes minute-length video generation extremely expensive, limiting most existing models to generating videos of only 10-20 seconds length. We propose a Linear-complexity text-to-video Generation (LinGen) framework whose cost scales linearly in the number of pixels. For the first time, LinGen enables high-resolution minute-length video generation on a single GPU without compromising quality. It replaces the computationally-dominant and quadratic-complexity block, self-attention, with a linear-complexity block called MATE, which consists of an MA-branch and a TE-branch. The MA-branch targets short-to-long-range correlations, combining a bidirectional Mamba2 block with our token rearrangement method, Rotary Major Scan, and our review tokens developed for long video generation. The TE-branch is a novel TEmporal Swin Attention block that focuses on temporal correlations between adjacent tokens and medium-range tokens. The MATE block addresses the adjacency preservation issue of Mamba and improves the consistency of generated videos significantly. Experimental results show that LinGen outperforms DiT (with a 75.6% win rate) in video quality with up to 15$\times$ (11.5$\times$) FLOPs (latency) reduction. Furthermore, both automatic metrics and human evaluation demonstrate our LinGen-4B yields comparable video quality to state-of-the-art models (with a 50.5%, 52.1%, 49.1% win rate with respect to Gen-3, LumaLabs, and Kling, respectively). This paves the way to hour-length movie generation and real-time interactive video generation. We provide 68s video generation results and more examples in our project website: https://lineargen.github.io/.
Deep Learning for Spectrum Prediction in Cognitive Radio Networks: State-of-the-Art, New Opportunities, and Challenges
2024-12-13
Guangliang Pan, David K. Y. Yau, Bo Zhou, Qihui Wu
Spectrum prediction is considered to be a promising technology that enhances spectrum efficiency by assisting dynamic spectrum access (DSA) in cognitive radio networks (CRN). Nonetheless, the highly nonlinear nature of spectrum data across time, frequency, and space domains, coupled with the intricate spectrum usage patterns, poses challenges for accurate spectrum prediction. Deep learning (DL), recognized for its capacity to extract nonlinear features, has been applied to solve these challenges. This paper first shows the advantages of applying DL by comparing with traditional prediction methods. Then, the current state-of-the-art DL-based spectrum prediction techniques are reviewed and summarized in terms of intra-band and crossband prediction. Notably, this paper uses a real-world spectrum dataset to prove the advancements of DL-based methods. Then, this paper proposes a novel intra-band spatiotemporal spectrum prediction framework named ViTransLSTM. This framework integrates visual self-attention and long short-term memory to capture both local and global long-term spatiotemporal dependencies of spectrum usage patterns. Similarly, the effectiveness of the proposed framework is validated on the aforementioned real-world dataset. Finally, the paper presents new related challenges and potential opportunities for future research.
Prospects for Systematic Planetary Nebulae Detection with the Census of the Local Universe Narrowband Survey
2024-12-13
Rong Du, David O. Cook, Soumyadeep Bhattacharjee, Shrinivas R. Kulkarni, Christoffer Fremling, David L. Kaplan, Mansi M. Kasliwal, Russ R. Laher, Frank J. Masci, David L. Shupe, Chaoran Zhang
We investigate the efficacy of a systematic planetary nebula (PN) search in the Census of the Local Universe (CLU) narrowband (H$\alpha$) survey that covers a considerably larger sky region of above declination $-20^\circ$ than most previous surveys. Using PNe observed by the Isaac Newton Telescope Photometric H$\alpha$ Survey (IPHAS) as validation, we are able to visually recover 432 out of 441 cataloged PNe (98\%) within the CLU dataset, with 5 sources having unusable CLU images and 4 missed due to limitations of imaging quality. Moreover, the reference PNe are conventionally divided into three PN classes in decreasing order of identification confidence given their spectra and morphologies. We record consistently high recovery rate across all classes: 95\% of True, 71\% of Likely, and 81\% of Possible sources are readily recovered. To further demonstrate the ability of CLU to find new PNe, we undertake a preliminary search of compact PNe within a sub-region of the validation catalog, mainly utilizing the significance of narrow-band colors ($\Sigma$) as a metric for identification. In a $200\,\rm deg^2$ region, we search the CLU source catalog and find 31 PN candidates after automated and visual scrutiny, of which 12 are new sources not appearing in previous studies. As a demonstration of our ongoing follow-up campaign, we present medium-resolution optical spectra of six candidates and notice that four of them show emission signatures characteristic of confirmed PNe. As we refine our selection methods, CLU promises to provide a systematic catalog of PNe spanning $2/3$ of the sky.
Which cycling environment appears safer? Learning cycling safety perceptions from pairwise image comparisons
2024-12-13
Miguel Costa, Manuel Marques, Carlos Lima Azevedo, Felix Wilhelm Siebert, Filipe Moura
Cycling is critical for cities to transition to more sustainable transport modes. Yet, safety concerns remain a critical deterrent for individuals to cycle. If individuals perceive an environment as unsafe for cycling, it is likely that they will prefer other means of transportation. Yet, capturing and understanding how individuals perceive cycling risk is complex and often slow, with researchers defaulting to traditional surveys and in-loco interviews. In this study, we tackle this problem. We base our approach on using pairwise comparisons of real-world images, repeatedly presenting respondents with pairs of road environments and asking them to select the one they perceive as safer for cycling, if any. Using the collected data, we train a siamese-convolutional neural network using a multi-loss framework that learns from individuals' responses, learns preferences directly from images, and includes ties (often discarded in the literature). Effectively, this model learns to predict human-style perceptions, evaluating which cycling environments are perceived as safer. Our model achieves good results, showcasing this approach has a real-life impact, such as improving interventions' effectiveness. Furthermore, it facilitates the continuous assessment of changing cycling environments, permitting short-term evaluations of measures to enhance perceived cycling safety. Finally, our method can be efficiently deployed in different locations with a growing number of openly available street-view images.
2024-12-13
Nicole L. Thomas, Imogen H. Whittam, Catherine L. Hale, Leah K. Morabito, Romeel Davé, Matt J. Jarvis, Robin H. W. Cook
We present a qualitative comparison between the host and black hole properties of radio galaxies in the MeerKAT GigaHertz Tiered Extragalactic Exploration~(MIGHTEE) survey with the radio galaxy population in the SIMBA suite of cosmological hydrodynamical simulations. The MIGHTEE data includes a $\sim$1deg$^{2}$ pointing of the COSMOS field observed at 1.28GHz with the MeerKAT radio telescope and cross-matched with multi-wavelength counterparts to provide classifications of high and low excitation radio galaxies (HERGs and LERGs) along with their corresponding host properties. We compare the properties of the MIGHTEE HERGs and LERGs with that predicted by the SIMBA simulations where HERGs and LERGs are defined as radio galaxies dominated by cold or hot mode accretion respectively. We consider stellar masses $\;{M}_{*}$, star formation rates SFR, AGN bolometric luminosity $L_{\rm bol}$, and Eddington fraction $f_{\rm Edd}$, as a function of 1.4GHz radio luminosity and redshift. In both MIGHTEE and SIMBA, the properties of HERGs and LERGs are similar across all properties apart from SFRs due to differences in host cold gas content in SIMBA. We predict a population of HERGs with low $f_{\rm Edd}$ in SIMBA that are confirmed in the MIGHTEE observations and tied to the faint population at low $z$. The predictions from SIMBA with the MIGHTEE observations describe a regime where our understanding of the radio galaxy dichotomy breaks down, challenging our understanding of the role of AGN accretion and feedback in the faint population of radio galaxies.
A systematic review of norm emergence in multi-agent systems
2024-12-13
Carmengelys Cordova, Joaquin Taverner, Elena Del Val, Estefania Argente
Multi-agent systems (MAS) have gained relevance in the field of artificial intelligence by offering tools for modelling complex environments where autonomous agents interact to achieve common or individual goals. In these systems, norms emerge as a fundamental component to regulate the behaviour of agents, promoting cooperation, coordination and conflict resolution. This article presents a systematic review, following the PRISMA method, on the emergence of norms in MAS, exploring the main mechanisms and factors that influence this process. Sociological, structural, emotional and cognitive aspects that facilitate the creation, propagation and reinforcement of norms are addressed. The findings highlight the crucial role of social network topology, as well as the importance of emotions and shared values in the adoption and maintenance of norms. Furthermore, opportunities are identified for future research that more explicitly integrates emotional and ethical dynamics in the design of adaptive normative systems. This work provides a comprehensive overview of the current state of research on norm emergence in MAS, serving as a basis for advancing the development of more efficient and flexible systems in artificial and real-world contexts.
Advances in Transformers for Robotic Applications: A Review
2024-12-13
Nikunj Sanghai, Nik Bear Brown
The introduction of Transformers architecture has brought about significant breakthroughs in Deep Learning (DL), particularly within Natural Language Processing (NLP). Since their inception, Transformers have outperformed many traditional neural network architectures due to their "self-attention" mechanism and their scalability across various applications. In this paper, we cover the use of Transformers in Robotics. We go through recent advances and trends in Transformer architectures and examine their integration into robotic perception, planning, and control for autonomous systems. Furthermore, we review past work and recent research on use of Transformers in Robotics as pre-trained foundation models and integration of Transformers with Deep Reinforcement Learning (DRL) for autonomous systems. We discuss how different Transformer variants are being adapted in robotics for reliable planning and perception, increasing human-robot interaction, long-horizon decision-making, and generalization. Finally, we address limitations and challenges, offering insight and suggestions for future research directions.
A Survey of Rédei-Berge Symmetric Functions via Matrix Algebra
2024-12-13
John Irving, Mohamed Omar
This survey provides a detailed exposition of the R\'{e}dei-Berge symmetric function $\mathcal{U}_D$ for digraphs $D$, a specialization of Chow's path-cycle symmetric function. Through the lens of matrix algebra, we consolidate and expand on the work of Chow, Grinberg and Stanley, and Lass concerning the resolution of $\mathcal{U}_D$ in the power sum and Schur bases. Along the way we also revisit various results on Hamiltonian paths in digraphs.
The JWST EXCELS survey: direct estimates of C, N, and O abundances in two relatively metal-rich galaxies at $\mathbf{z\simeq5}$
2024-12-13
K. Z. Arellano-Córdova, F. Cullen, A. C. Carnall, D. Scholte, T. M. Stanton, C. Kobayashi, Z. Martinez, D. A. Berg, L. Barrufet, R. Begley, C. T. Donnan, J. S. Dunlop, M. L. Hamadouche, D. J. McLeod, R. J. McLure, K. Rowlands, A. E. Shapley
We present a spectroscopic analysis of two star-forming galaxies at z~5 observed with JWST/NIRSpec as part of the Early eXtragalactic Continuum and Emission Line Science (EXCELS) survey. The detection of the C III]$\lambda\lambda$1906,09, [O II]$\lambda\lambda$3726,29, [O III]$\lambda\lambda$4363,5007, and [N II]$\lambda$6584 nebular emission lines enables investigation of the C/O, N/O, and C/N abundance ratios using the temperature-sensitive method. The two galaxies have stellar masses of log($M_{\star}$/M$_{\odot}$ ) = 8.13$\pm$0.09 and log($M_{\star}$/M$_{\odot}$ )=8.52$\pm$0.13 and corresponding metallicities of Z~0.2Z$_{\odot}$ and Z~0.3Z$_{\odot}$. These metallicities are somewhat higher than is typical for other z>5 galaxies with similar stellar mass and are in fact comparable to high-redshift analogue galaxies at z~0. Both galaxies display evidence for N/O enhancement with respect to the z~0 sample, with log(N/O)=-1.07$\pm$0.17 and log(N/O)=-0.86$\pm$0.15 respectively. In contrast, we find low C abundances, with log(C/O)=-0.82$\pm$0.22 and log(C/O)=-1.02$\pm$0.22, consistent with the predicted yields of core-collapse supernovae. Following the trend observed in other high-redshift sources, we find that the C/N ratios are lower at fixed O/H compared to the majority of local galaxies. In contrast to the top-heavy IMF invoked in some studies to explain low C/N ratios in metal-poor galaxies, we find, via comparison to chemical evolution models, that a standard or bottom-heavy IMF better explains the observed abundance ratios in more enriched systems due to an increase in N-enrichment from intermediate mass (4-7M$_{\odot}$) stars. Our results demonstrate that robust measurements of CNO abundances with JWST can reveal unique enrichment pathways in galaxies as a function of both metallicity and redshift.
Predictive Pattern Recognition Techniques Towards Spatiotemporal Representation of Plant Growth in Simulated and Controlled Environments: A Comprehensive Review
2024-12-13
Mohamed Debbagh, Shangpeng Sun, Mark Lefsrud
Accurate predictions and representations of plant growth patterns in simulated and controlled environments are important for addressing various challenges in plant phenomics research. This review explores various works on state-of-the-art predictive pattern recognition techniques, focusing on the spatiotemporal modeling of plant traits and the integration of dynamic environmental interactions. We provide a comprehensive examination of deterministic, probabilistic, and generative modeling approaches, emphasizing their applications in high-throughput phenotyping and simulation-based plant growth forecasting. Key topics include regressions and neural network-based representation models for the task of forecasting, limitations of existing experiment-based deterministic approaches, and the need for dynamic frameworks that incorporate uncertainty and evolving environmental feedback. This review surveys advances in 2D and 3D structured data representations through functional-structural plant models and conditional generative models. We offer a perspective on opportunities for future works, emphasizing the integration of domain-specific knowledge to data-driven methods, improvements to available datasets, and the implementation of these techniques toward real-world applications.
Uncertainty estimation for time series classification: Exploring predictive uncertainty in transformer-based models for variable stars
2024-12-13
Martina Cádiz-Leyton, Guillermo Cabrera-Vives, Pavlos Protopapas, Daniel Moreno-Cartagena, Cristobal Donoso-Oliva, Ignacio Becker
Classifying variable stars is key for understanding stellar evolution and galactic dynamics. With the demands of large astronomical surveys, machine learning models, especially attention-based neural networks, have become the state-of-the-art. While achieving high accuracy is crucial, enhancing model interpretability and uncertainty estimation is equally important to ensure that insights are both reliable and comprehensible. We aim to enhance transformer-based models for classifying astronomical light curves by incorporating uncertainty estimation techniques to detect misclassified instances. We tested our methods on labeled datasets from MACHO, OGLE-III, and ATLAS, introducing a framework that significantly improves the reliability of automated classification for the next-generation surveys. We used Astromer, a transformer-based encoder designed for capturing representations of single-band light curves. We enhanced its capabilities by applying three methods for quantifying uncertainty: Monte Carlo Dropout (MC Dropout), Hierarchical Stochastic Attention (HSA), and a novel hybrid method combining both approaches, which we have named Hierarchical Attention with Monte Carlo Dropout (HA-MC Dropout). We compared these methods against a baseline of deep ensembles (DEs). To estimate uncertainty estimation scores for the misclassification task, we selected Sampled Maximum Probability (SMP), Probability Variance (PV), and Bayesian Active Learning by Disagreement (BALD) as uncertainty estimates. In predictive performance tests, HA-MC Dropout outperforms the baseline, achieving macro F1-scores of 79.8+-0.5 on OGLE, 84+-1.3 on ATLAS, and 76.6+-1.8 on MACHO. When comparing the PV score values, the quality of uncertainty estimation by HA-MC Dropout surpasses that of all other methods, with improvements of 2.5+-2.3 for MACHO, 3.3+-2.1 for ATLAS and 8.5+-1.6 for OGLE-III.
Semantic maps are fundamental for robotics tasks such as navigation and manipulation. They also enable yield prediction and phenotyping in agricultural settings. In this paper, we introduce an efficient and scalable approach for active semantic mapping in horticultural environments, employing a mobile robot manipulator equipped with an RGB-D camera. Our method leverages probabilistic semantic maps to detect semantic targets, generate candidate viewpoints, and compute corresponding information gain. We present an efficient ray-casting strategy and a novel information utility function that accounts for both semantics and occlusions. The proposed approach reduces total runtime by 8% compared to previous baselines. Furthermore, our information metric surpasses other metrics in reducing multi-class entropy and improving surface coverage, particularly in the presence of segmentation noise. Real-world experiments validate our method's effectiveness but also reveal challenges such as depth sensor noise and varying environmental conditions, requiring further research.
Dense gas scaling relations at kiloparsec scale across nearby galaxies with the ALMA ALMOND and IRAM 30-m EMPIRE surveys
2024-12-13
Lukas Neumann, Maria J. Jimenez-Donaire, Adam K. Leroy, Frank Bigiel, Antonio Usero, Jiayi Sun, Eva Schinnerer, Miguel Querejeta, Sophia K. Stuber, Ivana Beslic, Ashley Barnes, Jakob den Brok, Yixian Cao, Cosima Eibensteiner, Hao He, Ralf S. Klessen, Fu-Heng Liang, Daizhong Liu, Hsi-An Pan, Thomas G. Williams
Dense, cold gas is the key ingredient for star formation. Over the last two decades, HCN(1-0) emission has been utilised as the most accessible dense gas tracer to study external galaxies. We present new measurements tracing the relationship between dense gas tracers, bulk molecular gas tracers, and star formation in the ALMA ALMOND survey, the largest sample of resolved (1-2 kpc resolution) HCN maps of galaxies in the local universe (d < 25 Mpc). We measure HCN/CO, a line ratio sensitive to the physical density distribution, and SFR/HCN, a proxy for the dense gas star formation efficiency, as a function of molecular gas surface density, stellar mass surface density, and dynamical equilibrium pressure across 31 galaxies, increasing the number of galaxies by a factor of > 3 over the previous largest such study (EMPIRE). HCN/CO increases (slope of ~ 0.5 and scatter of ~ 0.2 dex), while SFR/HCN decreases (slope of ~ -0.6 and scatter of ~ 0.4 dex) with increasing molecular gas surface density, stellar mass surface density and pressure. Galaxy centres with high stellar mass surface density show a factor of a few higher HCN/CO and lower SFR/HCN compared to the disc average, but both environments follow the same average trend. Our results emphasise that molecular gas properties vary systematically with the galactic environment and demonstrate that the scatter in the Gao-Solomon relation (SFR against HCN) is of physical origin.
Fitness landscape for quantum state tomography from neutron scattering
2024-12-13
Tymoteusz Tula, Jorge Quintanilla, Gunnar Möller
Recently, a direct connection between static structure factors and quantum ground states for two-spin interaction Hamiltonians was proven. This suggests the possibility of quantum state tomography from neutron scattering. Here, we investigate the associated fitness landscape numerically. We find a linear relationship between the mean square distances of the structure factors and the associated state overlaps, implying a well-behaved fitness landscape. Furthermore, we find evidence suggesting that the approach can be generalized to thermal equilibrium states. We also extend the arguments to the cases of applied magnetic fields and finite clusters.
Benchmarking large language models for materials synthesis: the case of atomic layer deposition
2024-12-13
Angel Yanguas-Gil, Matthew T. Dearing, Jeffrey W. Elam, Jessica C. Jones, Sungjoon Kim, Adnan Mohammad, Chi Thang Nguyen, Bratin Sengupta
In this work we introduce an open-ended question benchmark, ALDbench, to evaluate the performance of large language models (LLMs) in materials synthesis, and in particular in the field of atomic layer deposition, a thin film growth technique used in energy applications and microelectronics. Our benchmark comprises questions with a level of difficulty ranging from graduate level to domain expert current with the state of the art in the field. Human experts reviewed the questions along the criteria of difficulty and specificity, and the model responses along four different criteria: overall quality, specificity, relevance, and accuracy. We ran this benchmark on an instance of OpenAI's GPT-4o. The responses from the model received a composite quality score of 3.7 on a 1 to 5 scale, consistent with a passing grade. However, 36% of the questions received at least one below average score. An in-depth analysis of the responses identified at least five instances of suspected hallucination. Finally, we observed statistically significant correlations between the difficulty of the question and the quality of the response, the difficulty of the question and the relevance of the response, and the specificity of the question and the accuracy of the response as graded by the human experts. This emphasizes the need to evaluate LLMs across multiple criteria beyond difficulty or accuracy.
A Survey on Web Application Testing: A Decade of Evolution
2024-12-13
Tao Li, Rubing Huang, Chenhui Cui, Dave Towey, Lei Ma, Yuan-Fang Li, Wen Xia
As one of the most popular software applications, a web application is a program, accessible through the web, to dynamically generate content based on user interactions or contextual data, for example, online shopping platforms, social networking sites, and financial services. Web applications operate in diverse environments and leverage web technologies such as HTML, CSS, JavaScript, and Ajax, often incorporating features like asynchronous operations to enhance user experience. Due to the increasing user and popularity of web applications, approaches to their quality have become increasingly important. Web Application Testing (WAT) plays a vital role in ensuring web applications' functionality, security, and reliability. Given the speed with which web technologies are evolving, WAT is especially important. Over the last decade, various WAT approaches have been developed. The diversity of approaches reflects the many aspects of web applications, such as dynamic content, asynchronous operations, and diverse user environments. This paper provides a comprehensive overview of the main achievements during the past decade: It examines the main steps involved in WAT, including test-case generation and execution, and evaluation and assessment. The currently available tools for WAT are also examined. The paper also discusses some open research challenges and potential future WAT work.
Internet connectivity in schools is critical to provide students with the digital literary skills necessary to compete in modern economies. In order for governments to effectively implement digital infrastructure development in schools, accurate internet connectivity information is required. However, traditional survey-based methods can exceed the financial and capacity limits of governments. Open-source Earth Observation (EO) datasets have unlocked our ability to observe and understand socio-economic conditions on Earth from space, and in combination with Machine Learning (ML), can provide the tools to circumvent costly ground-based survey methods to support infrastructure development. In this paper, we present our work on school internet connectivity prediction using EO and ML. We detail the creation of our multi-modal, freely-available satellite imagery and survey information dataset, leverage the latest geographically-aware location encoders, and introduce the first results of using the new European Space Agency phi-lab geographically-aware foundational model to predict internet connectivity in Botswana and Rwanda. We find that ML with EO and ground-based auxiliary data yields the best performance in both countries, for accuracy, F1 score, and False Positive rates, and highlight the challenges of internet connectivity prediction from space with a case study in Kigali, Rwanda. Our work showcases a practical approach to support data-driven digital infrastructure development in low-resource settings, leveraging freely available information, and provide cleaned and labelled datasets for future studies to the community through a unique collaboration between UNICEF and the European Space Agency phi-lab.
Emergent electrostatics in planar XY spin models: the bridge connecting topological order/nonergodicity with broken $U(1)$ symmetry
2024-12-13
Michael F. Faulkner
Topological phases have been a central focus of condensed-matter physics for over 50 years. Along with many experimental applications, they have provided much intellectual interest due to their characterization via some form of topological ordering, as opposed to the symmetry-breaking ordering of conventional continuous phase transitions. This distinction is most subtle in the case of the Berezinskii-Kosterlitz-Thouless (BKT) transition as its experimental realizations appear to break U(1) symmetry at low temperature. It also presents two further paradoxes: i) its prototypical short-range interacting planar XY spin model behaves as an emergent long-range interacting electrolyte; ii) its topological ordering is not accompanied by a topological nonergodicity within the BKT picture. This review paper addresses these three interconnected questions. We review a series of papers that demonstrate that U(1) symmetry is indeed broken, but within a broader framework than that traditionally used to characterize broken symmetry. We discuss recovery of this symmetry by breaking velocity-symmetry in a deterministic Markov process. We then expand on a modern field theory of the emergent electrolyte that maps directly from the spin field to an emergent lattice electric field governed by an augmented electrostatic Boltzmann distribution. This local model of electrolyte physics resolves both the short-range-long-range paradox and the question of topological nonergodicity - as in contrast with the BKT picture, it describes global topological defects and their nonergodic freezing by the topological ordering. It also connects the broken U(1) symmetry with the topological ordering - providing a comprehensive framework for broken symmetry at the transition. We introduce long-time topological stability as a measure of topological nonergodicity - within a general framework for weakly broken ergodicity.
JWST PRIMER: strong evidence for the environmental quenching of low-mass galaxies out to $\mathbf{\textit{z} \simeq 2}$
2024-12-12
M. L. Hamadouche, R. J. McLure, A. Carnall, D. J. McLeod, J. S. Dunlop, K. Whitaker, C. T. Donnan, R. Begley, T. M. Stanton, O. Almaini, J. Aird, F. Cullen, S. Cutler, A. M. Koekemoer
We present the results of a study investigating the galaxy stellar-mass function (GSMF), size-mass relations and morphological properties of star-forming and quiescent galaxies over the redshift range $0.25
A Theoretical Analysis of Soft-Label vs Hard-Label Training in Neural Networks
2024-12-12
Saptarshi Mandal, Xiaojun Lin, R. Srikant
Knowledge distillation, where a small student model learns from a pre-trained large teacher model, has achieved substantial empirical success since the seminal work of \citep{hinton2015distilling}. Despite prior theoretical studies exploring the benefits of knowledge distillation, an important question remains unanswered: why does soft-label training from the teacher require significantly fewer neurons than directly training a small neural network with hard labels? To address this, we first present motivating experimental results using simple neural network models on a binary classification problem. These results demonstrate that soft-label training consistently outperforms hard-label training in accuracy, with the performance gap becoming more pronounced as the dataset becomes increasingly difficult to classify. We then substantiate these observations with a theoretical contribution based on two-layer neural network models. Specifically, we show that soft-label training using gradient descent requires only $O\left(\frac{1}{\gamma^2 \epsilon}\right)$ neurons to achieve a classification loss averaged over epochs smaller than some $\epsilon > 0$, where $\gamma$ is the separation margin of the limiting kernel. In contrast, hard-label training requires $O\left(\frac{1}{\gamma^4} \cdot \ln\left(\frac{1}{\epsilon}\right)\right)$ neurons, as derived from an adapted version of the gradient descent analysis in \citep{ji2020polylogarithmic}. This implies that when $\gamma \leq \epsilon$, i.e., when the dataset is challenging to classify, the neuron requirement for soft-label training can be significantly lower than that for hard-label training. Finally, we present experimental results on deep neural networks, further validating these theoretical findings.
A glitch in gravity: cosmic Lorentz-violation from fiery Big Bang to glacial heat death
2024-12-12
Robin Y. Wen, Lukas T. Hergt, Niayesh Afshordi, Douglas Scott
One regime where we might see departures from general relativity is at the largest accessible scales, with a natural choice in cosmology being the cosmological horizon (or Hubble) scale. We investigate a single-parameter extension to the standard cosmological model with a different strength of gravity above and below this scale -- a "cosmic glitch" in gravity. Cosmic microwave background observations, and Baryonic Acoustic Oscillations (including the recent DESI Y1) favour weaker superhorizon gravity, at nearly a percent (or 2$\sigma$ level), easing both the Hubble and clustering tensions with other cosmological data. This compounds evidence for an even stronger glitch during Big Bang nucleosynthesis (from helium abundance observations), suggesting that symmetries of general relativity are maximally violated at the Big Bang, but gradually recovered as we approach the present-day cosmological de Sitter scale, associated with the observed dark energy.
Two-dimensional orbital-obstructed insulators with higher-order band topology
2024-12-12
Olga Arroyo-Gascon, Sergio Bravo, Monica Pacheco, Leonor Chico
Obstructed atomic phases, with their realizations in systems of diverse dimensionality, have recently arisen as one of the topological states with greatest potential to show higher-order phenomena. In this work we report a special type of obstruction, known as orbital-mediated atomic obstruction, in monolayers of materials with spatial symmetry described by the space group P-3m1. By means of a minimal tight-binding model and first-principles calculations, we show that this obstructed phase is related to the mismatch of the charge centers coming from the atomic limit with respect to the centers that are obtained from the reciprocal space description. Although we find atomic limits that correspond with occupied atomic sites, orbital-mediated atomic obstruction requires the presence of orbitals that have no support in real space. In order to demonstrate the nontrivial character of the obstruction, we confirm the presence of a filling anomaly for finite geometries that is directly associated with the bulk configuration, and discuss the role of the boundary states and their underlying mechanism. Several material examples are presented to illustrate the ubiquity of these nontrivial responses and, in turn, to discuss the differences related to the particular ground state configuration. In addition, we perform a survey of materials and elaborate a list of candidate systems which will host this obstructed phase in monolayer form.
2024-12-12
Víctor Bresó, Gudrun Heinrich, Vitaly Magerya, Anton Olsson
While the calculation of scattering amplitudes at higher orders in perturbation theory has reached a high degree of maturity, their usage to produce physical predictions within Monte Carlo programs is often hampered by the slow evaluation of the multi-loop amplitudes, especially so for amplitudes calculated numerically. As a remedy, interpolation frameworks have been successfully used in lower dimensions, but for higher-dimensional problems, such as the five dimensions of a 2 -> 3 phase space, efficient and reliable solutions are sparse. This work aims to fill this gap by reviewing state-of-the-art interpolation methods, and by assessing their performance for concrete examples drawn from particle physics. Specifically, we investigate interpolation methods based on polynomials, splines, spatially adaptive sparse grids, and neural networks (multilayer perceptron and Lorentz-Equivariant Geometric Algebra Transformer). Our additional aim is to motivate further studies of the interpolation of scattering amplitudes among both physicists and mathematicians.
The Extended Crosswise Model Adjusted for Random Answering
2024-12-12
Khadiga H. A. Sayed, Maarten J. L. F. Cruyff, Andrea Petróczi, Peter G. M. van der Heijden
The Extended Crosswise Model is a popular randomized response design that employs a sensitive and a randomized innocuous statement, and asks respondents if one of these statements is true, or that none or both are true. The model has a degree of freedom to test for response biases, but is unable to detect random answering. In this paper, we propose two new methods to indirectly estimate and correct for random answering. One method uses a non-sensitive control statement and a quasi-randomized innocuous statement to which both answers are known to estimate the proportion of random respondents. The other method assigns less weight in the estimation procedure to respondents who complete the survey in an unrealistically short time. For four surveys among elite athletes, we use these methods to correct the prevalence estimates of doping use for random answering.
Nonlinear control and stability analysis of a unified Tethered UAV-winder system
2024-12-12
Samuel Folorunsho, Maggie Ni, William Norris
This paper presents the development of a comprehensive dynamics and stabilizing control architecture for Tethered Unmanned Aerial Vehicle (TUAV) systems. The proposed architecture integrates both onboard and ground-based controllers, employing nonlinear backstepping control techniques to achieve asymptotic stability of the TUAV's equilibrium. The onboard controllers are responsible for the position and attitude control of the TUAV, while the ground controllers regulate the winder mechanism to maintain the desired tether length, ensuring it retains its catenary form. Simulation results demonstrate the ability of the TUAV system to accurately track linear and circular trajectories, ensuring robust performance under various operational scenarios. The code and movies demonstrating the performance of the system can be found at https://github.com/sof-danny/TUAV\_system\_control.
We describe the puzzles that arise in the quantum theory of black holes, and explain how they are resolved in string theory. We review how the Bekenstein entropy is obtained through the count of brane bound states. We describe the fuzzball construction of black hole microstates. These states have no horizon and radiate from their surface like a normal body, so there is no information puzzle. We explain how the semiclassical approximation is violated in gravitational collapse even though curvatures are low at the horizon. This violation happens because the collapse leads to a stretching of space that is {\it fast}: light does not have time to travel across the collapsing region to establish the `vecro' correlations needed in the quantum gravitational vacuum. These vecro correlations arise from the existence of virtual fuzzball fluctuations in the gravitational vacuum, and are significant because of the large degeneracy of fuzzball states implied by the Bekenstein entropy. It is plausible that similar effects of fast expansion may be responsible for effects like dark energy and the Early Dark Energy postulated to explain the Hubble tension.
2024-12-12
Matthew Houtput, Luigi Ranalli, Carla Verdi, Serghei Klimin, Stefano Ragni, Cesare Franchini, Jacques Tempere
Describing electron-phonon interactions in a solid requires knowledge of the electron-phonon matrix elements in the Hamiltonian. State-of-the-art first-principles calculations for the electron-phonon interaction are limited to the 1-electron-1-phonon matrix element, which is suitable for harmonic materials. However, there is no first-principles theory for 1-electron-2-phonon interactions, which occur in anharmonic materials with significant electron-phonon interaction such as halide perovskites and quantum paraelectrics. Here, we derive an analytical expression for the long-range part of the 1-electron-2-phonon matrix element, written in terms of microscopic quantities that can be calculated from first principles. We show that the long-range 1-electron-2-phonon interaction is described by the derivative of the phonon dynamical matrix with respect to an external electric field. We calculate the quasiparticle energy of a large polaron including 1-electron-2-phonon interaction, and show that it can be written in terms of a 1-electron-2-phonon spectral function $\mathcal{T}_{\alpha \beta}(\omega)$. We demonstrate how to calculate this spectral function for the benchmark material LiF. The first-principles framework developed in this article is general, paving the way for future calculations of 1-electron-2-phonon interactions from first principles.
The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspective
2024-12-12
Javier de la Rosa, Vladislav Mikhailov, Lemei Zhang, Freddy Wetjen, David Samuel, Peng Liu, Rolv-Arild Braaten, Petter Mæhlum, Magnus Breder Birkenes, Andrey Kutuzov, Tita Enstad, Svein Arne Brygfjeld, Jon Atle Gulla, Stephan Oepen, Erik Velldal, Wilfred Østgulen, Liljia Øvrelid, Aslak Sira Myhre
The use of copyrighted materials in training generative language models raises critical legal and ethical questions. This paper presents a framework for and the results of empirically assessing the impact of copyrighted materials on the performance of large language models (LLMs) for Norwegian. We found that both books and newspapers contribute positively when the models are evaluated on a diverse set of Norwegian benchmarks, while fiction works possibly lead to decreased performance. Our experiments could inform the creation of a compensation scheme for authors whose works contribute to AI development.
First-principles theory of nonlinear long-range electron-phonon interaction
2024-12-12
Matthew Houtput, Luigi Ranalli, Carla Verdi, Serghei Klimin, Stefano Ragni, Jacques Tempere, Cesare Franchini
Electron-phonon interactions in solids are crucial for understanding many interesting phenomena, such as conventional superconductivity, temperature-dependent band-gap renormalization, and polarons. For harmonic materials, the linear interaction of one electron with one phonon is sufficient to quantitatively describe these properties. However, in anharmonic materials such as quantum paraelectrics, halide perovskites, and high-pressure hydrides, the nonlinear electron-phonon interactions may play an important role. Currently, the only available Hamiltonians for nonlinear electron-phonon interaction are model Hamiltonians, written in terms of phenomenological parameters. Here, we present a microscopic theory for long-range nonlinear electron-phonon interactions, which can be combined with first-principles calculations. We provide a semi-analytical expression for the long-range part of the 1-electron-2-phonon matrix element. We show that in contrast to the long-range 1-electron-1-phonon interaction, the continuum approximation is not sufficient and the entire phonon dispersion must be taken into account. Additionally, we show that the quasiparticle energies can be written in terms of a 1-electron-2-phonon spectral function. To demonstrate the method, we calculate the 1-electron-2-phonon spectral function for LiF and KTaO$_3$ from first principles. Our framework is a step forward toward complete first-principles calculations of nonlinear electron-phonon interactions in solids.
A review of scientific papers has shown that digital twins are very common for modeling the states of physical objects. It is relevant to consider the creation of a digital twin of an enterprise to obtain various assessments in management, taking into account the human factor. In the research, the impact of the human factor is assessed by the digital twin and staff competencies. The system of goals of staff competencies stands for: cognitive, affective, psychomotor. An enterprise model is created from the description of the set of events taking place on it. Each event is mapped to an element of staff competencies target system. The resulting set of staff competencies is estimated by a universal (integral) indicator. The dynamics of the universal indicator characterizes two modes of operation of the enterprise. The first operating mode is normal. The second mode of operation is based on the implementation of staff competencies. Taking into account the competencies of personnel by Bloom's taxonomy allows you to determine their interconnection: cognitive, affective, psychomotor. It correlation depends on the work performed by the employee and the influence of the external environment. It depends on the efforts of the employee as a learner.
From Intention To Implementation: Automating Biomedical Research via LLMs
2024-12-12
Yi Luo, Linghang Shi, Yihao Li, Aobo Zhuang, Yeyun Gong, Ling Liu, Lin Chen
Conventional biomedical research is increasingly labor-intensive due to the exponential growth of scientific literature and datasets. Artificial intelligence (AI), particularly Large Language Models (LLMs), has the potential to revolutionize this process by automating various steps. Still, significant challenges remain, including the need for multidisciplinary expertise, logicality of experimental design, and performance measurements. This paper introduces BioResearcher, the first end-to-end automated system designed to streamline the entire biomedical research process involving dry lab experiments. BioResearcher employs a modular multi-agent architecture, integrating specialized agents for search, literature processing, experimental design, and programming. By decomposing complex tasks into logically related sub-tasks and utilizing a hierarchical learning approach, BioResearcher effectively addresses the challenges of multidisciplinary requirements and logical complexity. Furthermore, BioResearcher incorporates an LLM-based reviewer for in-process quality control and introduces novel evaluation metrics to assess the quality and automation of experimental protocols. BioResearcher successfully achieves an average execution success rate of 63.07% across eight previously unmet research objectives. The generated protocols averagely outperform typical agent systems by 22.0% on five quality metrics. The system demonstrates significant potential to reduce researchers' workloads and accelerate biomedical discoveries, paving the way for future innovations in automated research systems.
AI Predicts AGI: Leveraging AGI Forecasting and Peer Review to Explore LLMs' Complex Reasoning Capabilities
2024-12-12
Fabrizio Davide, Pietro Torre, Andrea Gaggioli
We tasked 16 state-of-the-art large language models (LLMs) with estimating the likelihood of Artificial General Intelligence (AGI) emerging by 2030. To assess the quality of these forecasts, we implemented an automated peer review process (LLM-PR). The LLMs' estimates varied widely, ranging from 3% (Reka- Core) to 47.6% (GPT-4o), with a median of 12.5%. These estimates closely align with a recent expert survey that projected a 10% likelihood of AGI by 2027, underscoring the relevance of LLMs in forecasting complex, speculative scenarios. The LLM-PR process demonstrated strong reliability, evidenced by a high Intraclass Correlation Coefficient (ICC = 0.79), reflecting notable consistency in scoring across the models. Among the models, Pplx-70b-online emerged as the top performer, while Gemini-1.5-pro-api ranked the lowest. A cross-comparison with external benchmarks, such as LMSYS Chatbot Arena, revealed that LLM rankings remained consistent across different evaluation methods, suggesting that existing benchmarks may not encapsulate some of the skills relevant for AGI prediction. We further explored the use of weighting schemes based on external benchmarks, optimizing the alignment of LLMs' predictions with human expert forecasts. This analysis led to the development of a new, 'AGI benchmark' designed to highlight performance differences in AGI-related tasks. Our findings offer insights into LLMs' capabilities in speculative, interdisciplinary forecasting tasks and emphasize the growing need for innovative evaluation frameworks for assessing AI performance in complex, uncertain real-world scenarios.
Searching for New Physics in Ultradense Environment: a Review on Dark Matter Admixed Neutron Stars
2024-12-12
Francesco Grippa, Gaetano Lambiase, Tanmay Kumar Poddar
Neutron Stars (NSs), among the densest objects in the Universe, are exceptional laboratories for investigating Dark Matter (DM) properties. Recent theoretical and observational developments have heightened interest in exploring the impact of DM on NS structure, giving rise to the concept of Dark Matter Admixed Neutron Stars (DANSs). This review examines how NSs can accumulate DM over time, potentially altering their fundamental properties. We explore leading models describing DM behavior within NSs, focusing on the effects of both bosonic and fermionic candidates on key features such as mass, radius, and tidal deformability. Additionally, we review how DM can modify the cooling and heating processes, trigger the formation of a black hole, and impact Gravitational Waves (GWs) emissions from binary systems. By synthesizing recent research, this work highlights how DANSs might produce observable signatures, offering new opportunities to probe DM properties through astrophysical phenomena.
From Bench to Bedside: A Review of Clinical Trialsin Drug Discovery and Development
2024-12-12
Tianyang Wang, Ming Liu, Benji Peng, Xinyuan Song, Charles Zhang, Xintian Sun, Qian Niu, Junyu Liu, Silin Chen, Keyu Chen, Ming Li, Pohsun Feng, Ziqian Bi, Yunze Wang, Yichao Zhang, Cheng Fei, Lawrence KQ Yan
Clinical trials are an indispensable part of the drug development process, bridging the gap between basic research and clinical application. During the development of new drugs, clinical trials are used not only to evaluate the safety and efficacy of the drug but also to explore its dosage, treatment regimens, and potential side effects. This review discusses the various stages of clinical trials, including Phase I (safety assessment), Phase II (preliminary efficacy evaluation), Phase III (large-scale validation), and Phase IV (post-marketing surveillance), highlighting the characteristics of each phase and their interrelationships. Additionally, the paper addresses the major challenges encountered in clinical trials, such as ethical issues, subject recruitment difficulties, diversity and representativeness concerns, and proposes strategies for overcoming these challenges. With the advancement of technology, innovative technologies such as artificial intelligence, big data, and digitalization are gradually transforming clinical trial design and implementation, improving trial efficiency and data quality. The article also looks forward to the future of clinical trials, particularly the impact of emerging therapies such as gene therapy and immunotherapy on trial design, as well as the importance of regulatory reforms and global collaboration. In conclusion, the core role of clinical trials in drug development will continue to drive the progress of innovative drug development and clinical treatment.
A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis
2024-12-12
Maria Eleftheria Vlontzou, Maria Athanasiou, Kalliopi Dalakleidi, Ioanna Skampardoni, Christos Davatzikos, Konstantina Nikita
An interpretable machine learning (ML) framework is introduced to enhance the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) by ensuring robustness of the ML models' interpretations. The dataset used comprises volumetric measurements from brain MRI and genetic data from healthy individuals and patients with MCI/AD, obtained through the Alzheimer's Disease Neuroimaging Initiative. The existing class imbalance is addressed by an ensemble learning approach, while various attribution-based and counterfactual-based interpretability methods are leveraged towards producing diverse explanations related to the pathophysiology of MCI/AD. A unification method combining SHAP with counterfactual explanations assesses the interpretability techniques' robustness. The best performing model yielded 87.5% balanced accuracy and 90.8% F1-score. The attribution-based interpretability methods highlighted significant volumetric and genetic features related to MCI/AD risk. The unification method provided useful insights regarding those features' necessity and sufficiency, further showcasing their significance in MCI/AD diagnosis.
2024-12-12
S. Bellotti, P. Petit, S. V. Jeffers, S. C. Marsden, J. Morin, A. A. Vidotto, C. P. Folsom, V. See, J. -D. do Nascimento Jr
The magnetic cycle on the Sun consists of two consecutive 11-yr sunspot cycles and exhibits a polarity reversal around sunspot maximum. Although solar dynamo theories have progressively become more sophisticated, the details as to how the dynamo sustains magnetic fields are still subject of research. Observing the magnetic fields of Sun-like stars are useful to contextualise the solar dynamo. The BCool survey studies the evolution of surface magnetic fields to understand how dynamo-generated processes are influenced by key ingredients, like mass and rotation. Here, we focus on six Sun-like stars with mass between 1.02 and 1.06 MSun and with 3.5-21 d rotation period. We analysed high-resolution spectropolarimetric data collected with ESPaDOnS, Narval and Neo-Narval. We measured the longitudinal magnetic field from least-squares deconvolution line profiles and inspected its long-term behaviour with a Lomb-Scargle periodogram and a Gaussian process. We applied Zeeman-Doppler imaging to reconstruct the large-scale magnetic field geometry at the stellar surface for different epochs. Two stars, namely HD 9986 and HD 56124 (Prot ~ 20 d) exhibit repeating polarity reversals of the radial or toroidal field component on time scales of 5 to 6 yr. HD 73350 (Prot = 12 d) has one polarity reversal of the toroidal component and HD 76151 (Prot=17 d) may have short-term evolution (2.5 yr) modulated by the long-term (16 yr) chromospheric cycle. HD 166435 and HD 175726 (Prot =3-5 d), manifest complex magnetic fields without cyclic evolution. Our findings indicate the potential dependence of the magnetic cycles nature with stellar rotation period. For the two stars with likely cycles, the polarity reversal time scale seems to decrease with decreasing rotation period or Rossby number. These results represent important observational constraints for dynamo models of solar-like stars.
Is cosmological data suggesting a nonminimal coupling between matter and gravity?
2024-12-12
Miguel Barroso Varela, Orfeu Bertolami
Theoretical predictions from a modified theory of gravity with a nonminimal coupling between matter and curvature are compared to data from recent cosmological surveys. We use type Ia supernovae data from the Pantheon+ sample and the recent 5-year Dark Energy Survey (DES) data release along with baryon acoustic oscillation measurements from the Dark Energy Spectroscopic Instrument (DESI) and extended Baryon Oscillation Spectroscopic Survey (eBOSS) to constrain the modified model's parameters and to compare its fit quality to the Flat-$\Lambda$CDM model. We find moderate to strong evidence for a preference of the nonminimally coupled theory over the current standard model for all dataset combinations. Although the modified model is shown to be capable of matching early-time observations from the cosmic microwave background and late-time supernovae data, we find that there is still some incoherence with respect to the conclusions drawn from baryon acoustic oscillation observations.
A first glimpse at the MeerKAT DEEP2 field at S-band
2024-12-12
S. Ranchod, J. D. Wagenveld, H. -R. Klöckner, O. Wucknitz, R. P. Deane, S. S. Sridhar, E. Barr, S. Buchner, F. Camilo, A. Damas-Segovia, C. Kasemann, M. Kramer, L. S. Legodi, S. A. Mao, K. Menten, I. Rammala, M. R. Rugel, G. Wieching
We present the first widefield extragalactic continuum catalogue with the MeerKAT S-band (2.5 GHz), of the radio-selected DEEP2 field. The combined image over the S1 (1.96 - 2.84 GHz) and S4 (2.62 - 3.50 GHz) sub-bands has an angular resolution of 6.8''$\times$3.6'' (4.0''$\times$2.4'') at a robust weighting of $R = 0.3$ ($R=-0.5$) and a sensitivity of 4.7 (7.5) $\mu$Jy beam$^{-1}$ with an on-source integration time of 70 minutes and a minimum of 52 of the 64 antennas, for respective observations. We present the differential source counts for this field, as well as a morphological comparison of resolved sources between S-band and archival MeerKAT L-band images. We find consistent source counts with the literature and provide spectral indices fitted over a combined frequency range of 1.8 GHz. These observations provide an important first demonstration of the capabilities of MeerKAT S-band imaging with relatively short integration times, as well as a comparison with existing S-band surveys, highlighting the rich scientific potential with future MeerKAT S-band surveys.
A Comprehensive Survey of Data Reduction Rules for the Maximum Weighted Independent Set Problem
2024-12-12
Ernestine Großmann, Kenneth Langedal, Christian Schulz
The Maximum Weight Independent Set (MWIS) problem, as well as its related problems such as Minimum Weight Vertex Cover, are fundamental NP-hard problems with numerous practical applications. Due to their computational complexity, a variety of data reduction rules have been proposed in recent years to simplify instances of these problems, enabling exact solvers and heuristics to handle them more effectively. Data reduction rules are polynomial time procedures that can reduce an instance while ensuring that an optimal solution on the reduced instance can be easily extended to an optimal solution for the original instance. Data reduction rules have proven to be especially useful in branch-and-reduce methods, where successful reductions often lead to problem instances that can be solved exactly. This survey provides a comprehensive overview of data reduction rules for the MWIS problem. We also provide a reference implementation for these reductions. This survey will be updated as new reduction techniques are developed, serving as a centralized resource for researchers and practitioners.
Nonlinear Hall Effect in Two-dimensional Materials
2024-12-12
Shuo Wang, Wei Niu, Yue-Wen Fang
Symmetry is a cornerstone of condensed matter physics, fundamentally shaping the behavior of electronic systems and inducing the emergence of novel phenomena. The Hall effect, a key concept in this field, demonstrates how symmetry breaking, particularly of time-reversal symmetry, influences electronic transport properties. Recently, the nonlinear Hall effect has extended this understanding by generating a transverse voltage that modulates at twice the frequency of the driving alternating current without breaking time-reversal symmetry. This effect is closely tied to the symmetry and quantum geometric properties of materials, offering a new approach to probing the Berry curvature and quantum metric. Here, we provide a review of the theoretical insights and experimental advancements in the nonlinear Hall effect, particularly focusing on its realization in two-dimensional materials. We discuss the challenges still ahead, look at potential applications for devices, and explore how these ideas might apply to other nonlinear transport phenomena. By elucidating these aspects, this review aims to advance the understanding of nonlinear transport effects and their broader implications for future technologies.
Background: Logging is an important part of modern software projects; logs are used in several tasks such as debugging and testing. Due to the complex nature of logging, it remains a difficult task with several pitfalls that could have serious consequences. Several other domains of software engineering have mitigated such threats by identifying the early signs of more serious issues, i.e., "smells". However, this concept is not yet properly defined for logging. Objective: The goal of this study is to create a taxonomy of log smells that can help developers write better logging code. To further help the developers and to identify issues that need more attention from the research community, we also map the identified smells to existing tools addressing them. Methods: We identified logging issues and tools by conducting a survey of the scientific literature. After extracting relevant data from 45 articles, we used them to define logging issues using open coding technique and classified the defined issues using card sorting. We classify the tools based on their reported output. Results: The paper presents a taxonomy of ten log smells, describing several facets for each of them. We also review existing tools addressing some of these facets, highlighting the lack of tools addressing some log smells and identifying future research opportunities to close this gap.
Learning to Solve Domain-Specific Calculation Problems with Knowledge-Intensive Programs Generator
2024-12-12
Chengyuan Liu, Shihang Wang, Lizhi Qing, Jun Lin, Ji Zhang, Fei Wu, Kun Kuang
Domain Large Language Models (LLMs) are developed for domain-specific tasks based on general LLMs. But it still requires professional knowledge to facilitate the expertise for some domain-specific tasks. In this paper, we investigate into knowledge-intensive calculation problems. We find that the math problems to be challenging for LLMs, when involving complex domain-specific rules and knowledge documents, rather than simple formulations of terminologies. Therefore, we propose a pipeline to solve the domain-specific calculation problems with Knowledge-Intensive Programs Generator more effectively, named as KIPG. It generates knowledge-intensive programs according to the domain-specific documents. For each query, key variables are extracted, then outcomes which are dependent on domain knowledge are calculated with the programs. By iterative preference alignment, the code generator learns to improve the logic consistency with the domain knowledge. Taking legal domain as an example, we have conducted experiments to prove the effectiveness of our pipeline, and extensive analysis on the modules. We also find that the code generator is also adaptable to other domains, without training on the new knowledge.
Knowledge Tracing (KT) is a research field that aims to estimate a student's knowledge state through learning interactions-a crucial component of Intelligent Tutoring Systems (ITSs). Despite significant advancements, no current KT models excel in both predictive accuracy and interpretability. Meanwhile, Large Language Models (LLMs), pre-trained on vast natural language datasets, have emerged as powerful tools with immense potential in various educational applications. This systematic review explores the intersections, opportunities, and challenges of combining KT models and LLMs in educational contexts. The review first investigates LLM applications in education, including their adaptability to domain-specific content and ability to support personalized learning. It then examines the development and current state of KT models, from traditional to advanced approaches, aiming to uncover potential challenges that LLMs could mitigate. The core of this review focuses on integrating LLMs with KT, exploring three primary functions: addressing general concerns in KT fields, overcoming specific KT model limitations, and performing as KT models themselves. Our findings reveal that LLMs can be customized for specific educational tasks through tailor-making techniques such as in-context learning and agent-based approaches, effectively managing complex and unbalanced educational data. These models can enhance existing KT models' performance and solve cold-start problems by generating relevant features from question data. However, both current models depend heavily on structured, limited datasets, missing opportunities to use diverse educational data that could offer deeper insights into individual learners and support various educational settings.
The believable simulation of multi-user behavior is crucial for understanding complex social systems. Recently, large language models (LLMs)-based AI agents have made significant progress, enabling them to achieve human-like intelligence across various tasks. However, real human societies are often dynamic and complex, involving numerous individuals engaging in multimodal interactions. In this paper, taking e-commerce scenarios as an example, we present LMAgent, a very large-scale and multimodal agents society based on multimodal LLMs. In LMAgent, besides freely chatting with friends, the agents can autonomously browse, purchase, and review products, even perform live streaming e-commerce. To simulate this complex system, we introduce a self-consistency prompting mechanism to augment agents' multimodal capabilities, resulting in significantly improved decision-making performance over the existing multi-agent system. Moreover, we propose a fast memory mechanism combined with the small-world model to enhance system efficiency, which supports more than 10,000 agent simulations in a society. Experiments on agents' behavior show that these agents achieve comparable performance to humans in behavioral indicators. Furthermore, compared with the existing LLMs-based multi-agent system, more different and valuable phenomena are exhibited, such as herd behavior, which demonstrates the potential of LMAgent in credible large-scale social behavior simulations.
We tackle the challenging problem of Open-Set Object Detection (OSOD), which aims to detect both known and unknown objects in unlabelled images. The main difficulty arises from the absence of supervision for these unknown classes, making it challenging to distinguish them from the background. Existing OSOD detectors either fail to properly exploit or inadequately leverage the abundant unlabeled unknown objects in training data, restricting their performance. To address these limitations, we propose UADet, an Uncertainty-Aware Open-Set Object Detector that considers appearance and geometric uncertainty. By integrating these uncertainty measures, UADet effectively reduces the number of unannotated instances incorrectly utilized or omitted by previous methods. Extensive experiments on OSOD benchmarks demonstrate that UADet substantially outperforms previous state-of-the-art (SOTA) methods in detecting both known and unknown objects, achieving a 1.8x improvement in unknown recall while maintaining high performance on known classes. When extended to Open World Object Detection (OWOD), our method shows significant advantages over the current SOTA method, with average improvements of 13.8% and 6.9% in unknown recall on M-OWODB and S-OWODB benchmarks, respectively. Extensive results validate the effectiveness of our uncertainty-aware approach across different open-set scenarios.
CleanComedy: Creating Friendly Humor through Generative Techniques
2024-12-12
Dmitry Vikhorev, Daria Galimzianova, Svetlana Gorovaia, Elizaveta Zhemchuzhina, Ivan P. Yamshchikov
Humor generation is a challenging task in natural language processing due to limited resources and the quality of existing datasets. Available humor language resources often suffer from toxicity and duplication, limiting their effectiveness for training robust models. This paper proposes CleanComedy, a specialized, partially annotated toxicity-filtered corpus of English and Russian jokes collected from various sources. We study the effectiveness of our data filtering approach through a survey on humor and toxicity levels in various joke groups. In addition, we study advances in computer humor generation by comparing jokes written by humans with various groups of generative jokes, including our baseline models trained on the CleanComedy datasets.
Text embedding has become a foundational technology in natural language processing (NLP) during the deep learning era, driving advancements across a wide array of downstream tasks. While many natural language understanding challenges can now be modeled using generative paradigms and leverage the robust generative and comprehension capabilities of large language models (LLMs), numerous practical applications, such as semantic matching, clustering, and information retrieval, continue to rely on text embeddings for their efficiency and effectiveness. In this survey, we categorize the interplay between LLMs and text embeddings into three overarching themes: (1) LLM-augmented text embedding, enhancing traditional embedding methods with LLMs; (2) LLMs as text embedders, utilizing their innate capabilities for embedding generation; and (3) Text embedding understanding with LLMs, leveraging LLMs to analyze and interpret embeddings. By organizing these efforts based on interaction patterns rather than specific downstream applications, we offer a novel and systematic overview of contributions from various research and application domains in the era of LLMs. Furthermore, we highlight the unresolved challenges that persisted in the pre-LLM era with pre-trained language models (PLMs) and explore the emerging obstacles brought forth by LLMs. Building on this analysis, we outline prospective directions for the evolution of text embedding, addressing both theoretical and practical opportunities in the rapidly advancing landscape of NLP.
Point-set registration in bounded domains via the Fokker-Planck equation
2024-12-12
Angelo Iollo, Tommaso Taddei
We present a point set registration method in bounded domains based on the solution to the Fokker Planck equation. Our approach leverages (i) density estimation based on Gaussian mixture models; (ii) a stabilized finite element discretization of the Fokker Planck equation; (iii) a specialized method for the integration of the particles. We review relevant properties of the Fokker Planck equation that provide the foundations for the numerical method. We discuss two strategies for the integration of the particles and we propose a regularization technique to control the distance of the particles from the boundary of the domain. We perform extensive numerical experiments for two two-dimensional model problems to illustrate the many features of the method.
We formalize the quantum arithmetic, i.e. a relationship between number theory and operator algebras. Namely, it is proved that rational projective varieties are dual to the $C^*$-algebras with real multiplication. Our construction fits all axioms of the quantum arithmetic conjectured by Manin and others. Applications to elliptic curves, Shafarevich-Tate groups of abelian varieties and height functions are reviewed.
2024-12-12
Meng Li, Xiang Wang, Liming Nie, Chenglin Li, Yang Liu, Yangyang Zhao, Lei Xue, Kabir Sulaiman Said
As digital interfaces become increasingly prevalent, certain manipulative design elements have emerged that may harm user interests, raising associated ethical concerns and bringing dark patterns into focus as a significant research topic. Manipulative design strategies are widely used in user interfaces (UI) primarily to guide user behavior in ways that favor service providers, often at the cost of the users themselves. This paper addresses three main challenges in dark pattern research: inconsistencies and incompleteness in classification, limitations of detection tools, and insufficient comprehensiveness in existing datasets. In this study, we propose a comprehensive analytical framework--the Dark Pattern Analysis Framework (DPAF). Using this framework, we developed a taxonomy comprising 68 types of dark patterns, each annotated in detail to illustrate its impact on users, potential scenarios, and real-world examples, validated through industry surveys. Furthermore, we evaluated the effectiveness of current detection tools and assessed the completeness of available datasets. Our findings indicate that, among the 8 detection tools studied, only 31 types of dark patterns are identifiable, resulting in a coverage rate of just 45.5%. Similarly, our analysis of four datasets, encompassing 5,561 instances, reveals coverage of only 30 types of dark patterns, with an overall coverage rate of 44%. Based on the available datasets, we standardized classifications and merged datasets to form a unified image dataset and a unified text dataset. These results highlight significant room for improvement in the field of dark pattern detection. This research not only deepens our understanding of dark pattern classification and detection tools but also offers valuable insights for future research and practice in this domain.
Revisiting the Linear Chain Trick in epidemiological models: Implications of underlying assumptions for numerical solutions
2024-12-12
Lena Plötzke, Anna Wendler, René Schmieding, Martin J. Kühn
In order to simulate the spread of infectious diseases, many epidemiological models use systems of ordinary differential equations (ODEs) to describe the underlying dynamics. These models incorporate the implicit assumption, that the stay time in each disease state follows an exponential distribution. However, a substantial number of epidemiological, data-based studies indicate that this assumption is not plausible. One method to alleviate this limitation is to employ the Linear Chain Trick (LCT) for ODE systems, which realizes the use of Erlang distributed stay times. As indicated by data, this approach allows for more realistic models while maintaining the advantages of using ODEs. In this work, we propose an advanced LCT model incorporating eight infection states with demographic stratification. We review key properties of LCT models and demonstrate that predictions derived from a simple ODE-based model can be significantly distorted, potentially leading to wrong political decisions. Our findings demonstrate that the influence of distribution assumptions on the behavior at change points and on the prediction of epidemic peaks is substantial, while the assumption has no effect on the final size of the epidemic. As the corresponding ODE systems are often solved by adaptive Runge-Kutta methods such as the Cash Karp method, we also study the implications on the time-to-solution using Cash Karp 5(4) for different LCT models. Eventually and for the application side, we highlight the importance of incorporating a demographic stratification by age groups to improve the prediction performance of the model. We validate our model by showing that realistic infection dynamics are better captured by LCT models than by a simple ODE model.
The Stroh formalism for acoustic waves in 1D-inhomogeneous media: unified background and some applications
2024-12-12
A. L. Shuvalov
The aim of the review is to consolidate the core aspects of the Stroh formalism, trace the development of the impedance matrix concept, and outline the range of acoustic problems that have been treated by these methods over the last two decades.
Chemodynamic evolution of Sun-like stars in nearby moving groups
2024-12-12
Christian Lehmann, Michael T. Murphy, Fan Liu, Chris Flynn
Sun-like stars are well represented in the solar neighbourhood but are currently under-utilised, with many studies of chemical and kinematic evolution focusing on red giants (which can be observed further away) or turn-off stars (which have well measured ages). Recent surveys (e.g. GALAH) provide spectra for large numbers of nearby Sun-like stars, which provides an opportunity to apply our newly developed method for measuring metallicities, temperatures, and surface gravities - the EPIC algorithm - which yields improved ages via isochrone fitting. We test this on moving groups, by applying it to the large GALAH DR3 sample. This defines a sample of 72,288 solar analogue targets for which the stellar parameter measurements are most precise and reliable. These stars are used to estimate, and test the accuracy and precision of, age measurements derived with the SAMD isochrone fitting algorithm. Using these ages, we recover the age-metallicity relationships for nearby (<= 1 kpc) moving groups, traced by solar analogues, and analyse them with respect to the stellar kinematics. In particular, we found that the age-metallicity relationships of all moving groups follows a particular trend of young (age < 6 Gyr) stars having constant metallicity and older (age >= 6 Gyr) stars decreasing in metallicity with increasing age. The Hercules stream carries the highest fraction of metal-rich young stars (~ 0.1 dex) in our sample, which is consistent with a migrating population of stars from the inner Galaxy, and we discuss the possible causes of this migration in the context of our results.
An Algorithm-Centered Approach To Model Streaming Data
2024-12-12
Fabian Hinder, Valerie Vaquet, David Komnick, Barbara Hammer
Besides the classical offline setup of machine learning, stream learning constitutes a well-established setup where data arrives over time in potentially non-stationary environments. Concept drift, the phenomenon that the underlying distribution changes over time poses a significant challenge. Yet, despite high practical relevance, there is little to no foundational theory for learning in the drifting setup comparable to classical statistical learning theory in the offline setting. This can be attributed to the lack of an underlying object comparable to a probability distribution as in the classical setup. While there exist approaches to transfer ideas to the streaming setup, these start from a data perspective rather than an algorithmic one. In this work, we suggest a new model of data over time that is aimed at the algorithm's perspective. Instead of defining the setup using time points, we utilize a window-based approach that resembles the inner workings of most stream learning algorithms. We compare our framework to others from the literature on a theoretical basis, showing that in many cases both model the same situation. Furthermore, we perform a numerical evaluation and showcase an application in the domain of critical infrastructure.
Oversight in Action: Experiences with Instructor-Moderated LLM Responses in an Online Discussion Forum
2024-12-12
Shuying Qiao, Paul Denny, Nasser Giacaman
The integration of large language models (LLMs) into computing education offers many potential benefits to student learning, and several novel pedagogical approaches have been reported in the literature. However LLMs also present challenges, one of the most commonly cited being that of student over-reliance. This challenge is compounded by the fact that LLMs are always available to provide instant help and solutions to students, which can undermine their ability to independently solve problems and diagnose and resolve errors. Providing instructor oversight of LLM-generated content can mitigate this problem, however it is often not practical in real-time learning contexts. Online class discussion forums, which are widely used in computing education, present an opportunity for exploring instructor oversight because they operate asynchronously. Unlike real-time interactions, the discussion forum format aligns with the expectation that responses may take time, making oversight not only feasible but also pedagogically appropriate. In this practitioner paper, we present the design, deployment, and evaluation of a `bot' module that is controlled by the instructor, and integrated into an online discussion forum. The bot assists the instructor by generating draft responses to student questions, which are reviewed, modified, and approved before release. Key features include the ability to leverage course materials, access archived discussions, and publish responses anonymously to encourage open participation. We report our experiences using this tool in a 12-week second-year software engineering course on object-oriented programming. Instructor feedback confirmed the tool successfully alleviated workload but highlighted a need for improvement in handling complex, context-dependent queries. We report the features that were viewed as most beneficial, and suggest avenues for future exploration.
Researches on leveraging big artificial intelligence model (BAIM) technology to drive the intelligent evolution of wireless networks are emerging. However, since the breakthrough in generalization brought about by BAIM techniques mainly occurs in natural language processing, there is still a lack of a clear technical roadmap on how to efficiently apply BAIM techniques to wireless systems with many additional peculiarities. To this end, this paper first reviews recent research works on BAIM for wireless and assesses the current research situation. Then, this paper analyzes and compares the differences between language intelligence and wireless intelligence on multiple levels, including scientific foundations, core usages, and technical details. It highlights the necessity and scientific significance of developing BAIM technology in a wireless-native way, as well as new issues that need to be considered in specific technical implementation. Finally, by synthesizing the evolutionary laws of language models with the particularities of wireless system, this paper provides several instructive methodologies for how to develop wireless-native BAIM.
We present MS2Mesh-XR, a novel multi-modal sketch-to-mesh generation pipeline that enables users to create realistic 3D objects in extended reality (XR) environments using hand-drawn sketches assisted by voice inputs. In specific, users can intuitively sketch objects using natural hand movements in mid-air within a virtual environment. By integrating voice inputs, we devise ControlNet to infer realistic images based on the drawn sketches and interpreted text prompts. Users can then review and select their preferred image, which is subsequently reconstructed into a detailed 3D mesh using the Convolutional Reconstruction Model. In particular, our proposed pipeline can generate a high-quality 3D mesh in less than 20 seconds, allowing for immersive visualization and manipulation in run-time XR scenes. We demonstrate the practicability of our pipeline through two use cases in XR settings. By leveraging natural user inputs and cutting-edge generative AI capabilities, our approach can significantly facilitate XR-based creative production and enhance user experiences. Our code and demo will be available at: https://yueqiu0911.github.io/MS2Mesh-XR/
We applied machine learning to the entire data history of ESO's High Accuracy Radial Velocity Planet Searcher (HARPS) instrument. Our primary goal was to recover the physical properties of the observed objects, with a secondary emphasis on simulating spectra. We systematically investigated the impact of various factors on the accuracy and fidelity of the results, including the use of simulated data, the effect of varying amounts of real training data, network architectures, and learning paradigms. Our approach integrates supervised and unsupervised learning techniques within autoencoder frameworks. Our methodology leverages an existing simulation model that utilizes a library of existing stellar spectra in which the emerging flux is computed from first principles rooted in physics and a HARPS instrument model to generate simulated spectra comparable to observational data. We trained standard and variational autoencoders on HARPS data to predict spectral parameters and generate spectra. Our models excel at predicting spectral parameters and compressing real spectra, and they achieved a mean prediction error of approximately 50 K for effective temperatures, making them relevant for most astrophysical applications. Furthermore, the models predict metallicity ([M/H]) and surface gravity (log g) with an accuracy of approximately 0.03 dex and 0.04 dex, respectively, underscoring their broad applicability in astrophysical research. The models' computational efficiency, with processing times of 779.6 ms on CPU and 3.97 ms on GPU, makes them valuable for high-throughput applications like massive spectroscopic surveys and large archival studies. By achieving accuracy comparable to classical methods with significantly reduced computation time, our methodology enhances the scope and efficiency of spectroscopic analysis.
An empirical model of the extragalactic radio background
2024-12-12
Fangyou Gao, Tao Wang, Yijun Wang
Radio observations provide a powerful tool to constrain the assembly of galaxies over cosmic time. Recent deep and wide radio continuum surveys have improved significantly our understanding on radio emission properties of AGNs and SFGs across $0 < z < 4$. This allows us to derive an empirical model of the radio continuum emission of galaxies based on their SFR and the probability of hosting an radio AGN. We make use of the Empirical Galaxy Generator (EGG) to generate a near-infrared-selected, flux-limited multi-wavelength catalog to mimic real observations. Then we assign radio continuum flux densities to galaxies based on their SFRs and the probability of hosting a radio-AGN of specific 1.4 GHz luminosity. We also apply special treatments to reproduce the clustering signal of radio AGNs.Our empirical model successfully recovers the observed 1.4 GHz radio luminosity functions (RLFs) of both AGN and SFG populations, as well as the differential number counts at various radio bands. The uniqueness of this approach also allows us to directly link radio flux densities of galaxies to other properties, including redshifts, stellar masses, and magnitudes at various photometric bands. We find that roughly half of the radio continuum sources to be detected by SKA at $z \sim 4-6$ will be too faint to be detected in the optical survey ($r \sim 27.5$) carried out by Rubin observatory. Unlike previous studies which utilized RLFs to reproduce ERB, our work starts from a simulated galaxy catalog with realistic physical properties. It has the potential to simultaneously, and self-consistently reproduce physical properties of galaxies across a wide range of wavelengths, from optical, NIR, FIR to radio wavelengths. Our empirical model can shed light on the contribution of different galaxies to the extragalactic background light, and greatly facilitates designing future multiwavelength galaxy surveys.
Type-I/Type-II superconductivity in noncentrosymmetric compound Ir$_2$Ga$_9$
2024-12-12
J. C. Jiao, K. W. Chen, O. O. Bernal, P. -C. Ho, L. Shu
We have performed magnetization, specific heat, and muon spin relaxation ($\mu$SR) measurements on single crystals of the noncentrosymmetric superconductor Ir$_{2}$Ga$_{9}$. The isothermal magnetization measurements show that there is a crossover from Type-I to Type-II superconductivity with decreasing temperature. Potential multi-band superconductivity of Ir$_{2}$Ga$_{9}$~is observed in the specific heat data. $\mu$SR~measurement is performed to map the phase diagram of Ir$_{2}$Ga$_{9}$, and both Type-I and Type-II superconductivity characteristics are obtained. Most importantly, a more unique region with the coexistence of Type-I and Type-II $\mu$SR signals is observed. In addition, time reversal symmetry is found to be preserved in Ir$_{2}$Ga$_{9}$ by zero field $\mu$SR measurement.
EmoDubber: Towards High Quality and Emotion Controllable Movie Dubbing
2024-12-12
Gaoxiang Cong, Jiadong Pan, Liang Li, Yuankai Qi, Yuxin Peng, Anton van den Hengel, Jian Yang, Qingming Huang
Given a piece of text, a video clip, and a reference audio, the movie dubbing task aims to generate speech that aligns with the video while cloning the desired voice. The existing methods have two primary deficiencies: (1) They struggle to simultaneously hold audio-visual sync and achieve clear pronunciation; (2) They lack the capacity to express user-defined emotions. To address these problems, we propose EmoDubber, an emotion-controllable dubbing architecture that allows users to specify emotion type and emotional intensity while satisfying high-quality lip sync and pronunciation. Specifically, we first design Lip-related Prosody Aligning (LPA), which focuses on learning the inherent consistency between lip motion and prosody variation by duration level contrastive learning to incorporate reasonable alignment. Then, we design Pronunciation Enhancing (PE) strategy to fuse the video-level phoneme sequences by efficient conformer to improve speech intelligibility. Next, the speaker identity adapting module aims to decode acoustics prior and inject the speaker style embedding. After that, the proposed Flow-based User Emotion Controlling (FUEC) is used to synthesize waveform by flow matching prediction network conditioned on acoustics prior. In this process, the FUEC determines the gradient direction and guidance scale based on the user's emotion instructions by the positive and negative guidance mechanism, which focuses on amplifying the desired emotion while suppressing others. Extensive experimental results on three benchmark datasets demonstrate favorable performance compared to several state-of-the-art methods.
Is Contrastive Distillation Enough for Learning Comprehensive 3D Representations?
2024-12-12
Yifan Zhang, Junhui Hou
Cross-modal contrastive distillation has recently been explored for learning effective 3D representations. However, existing methods focus primarily on modality-shared features, neglecting the modality-specific features during the pre-training process, which leads to suboptimal representations. In this paper, we theoretically analyze the limitations of current contrastive methods for 3D representation learning and propose a new framework, namely CMCR, to address these shortcomings. Our approach improves upon traditional methods by better integrating both modality-shared and modality-specific features. Specifically, we introduce masked image modeling and occupancy estimation tasks to guide the network in learning more comprehensive modality-specific features. Furthermore, we propose a novel multi-modal unified codebook that learns an embedding space shared across different modalities. Besides, we introduce geometry-enhanced masked image modeling to further boost 3D representation learning. Extensive experiments demonstrate that our method mitigates the challenges faced by traditional approaches and consistently outperforms existing image-to-LiDAR contrastive distillation methods in downstream tasks. Code will be available at https://github.com/Eaphan/CMCR.
Deep learning has transformed AI applications but faces critical security challenges, including adversarial attacks, data poisoning, model theft, and privacy leakage. This survey examines these vulnerabilities, detailing their mechanisms and impact on model integrity and confidentiality. Practical implementations, including adversarial examples, label flipping, and backdoor attacks, are explored alongside defenses such as adversarial training, differential privacy, and federated learning, highlighting their strengths and limitations. Advanced methods like contrastive and self-supervised learning are presented for enhancing robustness. The survey concludes with future directions, emphasizing automated defenses, zero-trust architectures, and the security challenges of large AI models. A balanced approach to performance and security is essential for developing reliable deep learning systems.
Swarmalators are entities that combine the swarming behavior of particles with the oscillatory dynamics of coupled phase oscillators and represent a novel and rich area of study within the field of complex systems. Unlike traditional models that treat spatial movement and phase synchronization separately, swarmalators exhibit a unique coupling between their positions and internal phases, leading to emergent behaviors that include clustering, pattern formation, and the coexistence of synchronized and desynchronized states etc. This paper presents a comprehensive analysis of a two-dimensional swarmalator model in the presence of a predator-like agent that we call a contrarian. The positions and the phases of the swarmalators are influenced by the contrarian and we observe the emergence of intriguing collective states. We find that swarmalator phases are synchronized even with negative coupling strength when their interaction with the contrarian is comparatively strong. Through a combination of analytical methods and simulations, we demonstrate how varying these parameters can lead to transitions between different collective states.
While the capacity to self-regulate has been found to be crucial for secondary school students, prior studies often rely on self-report surveys and think-aloud protocols that present notable limitations in capturing self-regulated learning (SRL) processes. This study advances the understanding of SRL in secondary education by using trace data to examine SRL processes during multi-source writing tasks, with higher education participants included for comparison. We collected fine-grained trace data from 66 secondary school students and 59 university students working on the same writing tasks within a shared SRL-oriented learning environment. The data were labelled using Bannert's validated SRL coding scheme to reflect specific SRL processes, and we examined the relationship between these processes, essay performance, and educational levels. Using epistemic network analysis (ENA) to model and visualise the interconnected SRL processes in Bannert's coding scheme, we found that: (a) secondary school students predominantly engaged in three SRL processes -- Orientation, Re-reading, and Elaboration/Organisation; (b) high-performing secondary students engaged more in Re-reading, while low-performing students showed more Orientation process; and (c) higher education students exhibited more diverse SRL processes such as Monitoring and Evaluation than their secondary education counterparts, who heavily relied on following task instructions and rubrics to guide their writing. These findings highlight the necessity of designing scaffolding tools and developing teacher training programs to enhance awareness and development of SRL skills for secondary school learners.
ESpRESSO -- Forward modeling Roman Space Telescope spectroscopy
2024-12-12
Austen Gabrielpillai, Isak G. B. Wold, Sangeeta Malhotra, James Rhoads, Guangjun Gao, Mainak Singha, Anton M. Koekemoer
We describe the software package $\texttt{ESpRESSO}$ - [E]xtragalactic [Sp]ectroscopic [R]oman [E]mulator and [S]imulator of [S]ynthetic [O]bjects, created to emulate the slitless spectroscopic observing modes of the Nancy Grace Roman Space Telescope (Roman) Wide Field Instrument (WFI). We combine archival Hubble Space Telescope (HST) imaging data of comparable spatial resolution with model spectral energy distributions to create a data-cube of flux density as a function of position and wavelength. This data-cube is used for simulating a nine detector grism observation, producing a crowded background scene which model field angle dependent optical distortions expected for the grism. We also demonstrate the ability to inject custom sources using the described tools and pipelines. In addition, we show that spectral features such as emission line pairs are unlikely to be mistaken as off order contaminating features and vice versa. Our result is a simulation suite of half of the eighteen detector array, with a realistic background scene and injected Ly$\alpha$ emitter (LAE) galaxies, realized at 25 position angles (PAs), 12 with analogous positive and negative dithers, Using an exposure time of 10ks per PA, the full PA set can be used as a mock deep Roman grism survey with high (synthetic) LAE completeness for developing future spectral data analysis tools.
EL CVn-type systems represent a rare evolutionary stage in binary star evolution, providing ideal laboratories for investigating stable mass transfer processes and the formation of extremely low-mass white dwarfs (ELM WDs). The Transiting Exoplanet Survey Satellite (TESS) has delivered an extensive collection of high-precision time-domain photometric data, which is invaluable for studying EL CVn binaries. In this study, we identified 29 EL CVn systems from the TESS eclipsing binary catalogs (sectors 1-65), 11 of which are newly discovered. These systems consist of smaller, hotter pre-He white dwarfs and A/F main-sequence stars. The orbital periods of these binaries range from 0.64 to 2.5 days. Utilizing TESS light curves, Gaia distances, and multi-band photometric data (e.g., GALEX, 2MASS, WISE, SkyMapper), we modeled the light curves and spectral energy distributions to derive system parameters, including effective temperatures, masses, and radii. These systems were then compared with the white dwarf mass-period relation and the evolutionary tracks of ELM WDs. The comparison reveals that these binaries are consistent with the expected mass-period relation for white dwarfs and align well with the evolutionary tracks on the Teff-logg diagram for ELM WDs. This result suggests that these EL CVn systems likely formed through stable mass transfer processes. We provide a catalog of complete parameters for 29 EL CVn systems identified from the TESS survey. This catalog will serve as an essential resource for studying binary mass transfer, white dwarf formation, and pulsation phenomena in EL CVn-type systems.
Formal justification of a continuum relaxation model for one-dimensional moiré materials
2024-12-12
Jingzhi, Zhou, Alexander B. Watson
Mechanical relaxation in moir\'e materials is often modeled by a continuum model where linear elasticity is coupled to a stacking penalty known as the Generalized Stacking Fault Energy (GSFE). We review and compute minimizers of a one-dimensional version of this model, and then show how it can be formally derived from a natural atomistic model. Specifically, we show that the continuum model emerges in the limit $\epsilon \downarrow 0$ and $\delta \downarrow 0$ while holding the ratio $\eta := \frac{\epsilon^2}{\delta}$ fixed, where $\epsilon$ is the ratio of the monolayer lattice constant to the moir\'e lattice constant and $\delta$ is the ratio of the typical stacking energy to the monolayer stiffness.
ALMA/SCUBA-2 COSMOS Survey: Properties of X-ray- and SED-selected AGNs in Bright Submillimeter Galaxies
2024-12-12
Ryosuke Uematsu, Yoshihiro Ueda, David M. Alexander, A. M. Swinbank, Ian Smail, Carolina Andonie, Chian-Chou Chen, Ugne Dudzeviciute, Soh Ikarashi, Kotaro Kohno, Yuichi Matsuda, Annagrazia Puglisi, Hideki Umehata, Wei-Hao Wang
We investigate the properties of active galactic nuclei (AGNs) in the brightest submillimeter galaxies (SMGs) in the COSMOS field. We utilize the bright sample of ALMA/SCUBA-2 COSMOS Survey (AS2COSMOS), which consists of 260 SMGs with $S_{\mathrm{870}\, \mu \mathrm{m}}=0.7\text{--}19.2\,\mathrm{mJy}$ at $z=0\text{--}6$. We perform optical to millimeter spectral energy distribution (SED) modeling for the whole sample. We identify 24 AGN-host galaxies from the SEDs. Supplemented by 23 X-ray detected AGNs (X-ray AGNs), we construct an overall sample of 40 AGN-host galaxies. The X-ray luminosity upper bounds indicate that the X-ray undetected SED-identified AGNs are likely to be nearly Compton thick or have unusually suppressed X-ray emission. From visual classification, we identify $25^{+6}_{-5}$\% of the SMGs without AGNs as major merger candidates. This fraction is almost consistent with the general galaxy population at $z\sim2$, suggesting that major mergers are not necessarily required for the enhanced star formation in SMGs. We also identify $47^{+16}_{-15}$\% of the AGN hosts as major merger candidates, which is about twice as high as that in the SMGs without AGNs. This suggests that major mergers play a key role in triggering AGN activity in bright SMGs.
The Multiband Imaging Survey for High-Alpha PlanetS (MISHAPS) I: Preliminary Constraints on the Occurrence Rate of Hot Jupiters in 47 Tucanae
2024-12-12
Alison L. Crisp, Jonas Klüter, Marz L. Newman, Matthew T. Penny, Thomas G. Beatty, L. Ilsedore Cleeves, Karen A. Collins, Jennifer A. Johnson, Marshall C. Johnson, Michael B. Lund, Clara E. Martínez-Vázquez, Melissa K. Ness, Joseph E. Rodriguez, Robert Siverd, Daniel J. Stevens, Steven Villanueva, Carl Ziegler
The first generation of transiting planet searches in globular clusters yielded no detections, and in hindsight, only placed occurrence rate limits slightly higher than the measured occurrence rate in the higher-metallicity Galactic thick disk. To improve these limits, we present the first results of a new wide field search for transiting hot Jupiters in the globular cluster 47~Tucanae. We have observed 47~Tuc as part of the Multiband Imaging Survey for High-Alpha Planets (MISHAPS). Using 24 partial and full nights of observations taken with the Dark Energy Camera on the 4-m Blanco telescope at CTIO, we perform a search on 19,930 stars in the outer regions of the cluster. Though we find no clear planet detections, by combining our result with the upper limit enabled by Gilliland et al.'s 2000 Hubble search for planets around an independent sample of 34,091 stars in the inner cluster, we place the strongest limit to date on hot Jupiters with periods of $0.8 \leq P \leq 8.3$ days and $0.5~R_{\rm Jup} \leq R_{\rm P} \leq 2.0~R_{\rm Jup}$ of $f_{\rm HJ} < 0.11\%$, a factor of ${\sim}$4 below the occurrence rate in the \textit{Kepler} field. Our search found 35 transiting planet candidates, though we are ultimately able to rule out each without follow-up observations. We also found 4 eclipsing binaries, including 3 previously-uncataloged detached eclipsing binary stars.
Neutrinos from explosive transients at the dawn of multi-messenger astronomy
2024-12-12
Irene Tamborra
With the advent of time-domain astronomy and the game-changing next generation of telescopes, we have unprecedented opportunities to explore the most energetic events in our Universe through electromagnetic radiation, gravitational waves, and neutrinos. These are elementary particles, which exist in three different flavors and change the latter as they propagate in the dense core of astrophysical sources as well as en route to Earth. To capitalize on existing and upcoming multi-messenger opportunities, it is crucial to understand: 1. the role of neutrinos in explosive transient sources as well as in the synthesis of the elements heavier than iron; 2. the impact of neutrino physics on the multi-messenger observables; 3. the information on the source physics carried by the detectable neutrino signal. In this review, the status of this exciting and fast-moving field is outlined, focusing on astrophysical sources linked to collapsing massive stars and neutron-star mergers. In light of the upcoming plethora of multi-messenger data, outstanding open issues concerning the optimization of multi-messenger detection strategies are discussed.
Towards the phase diagram of fermions coupled with $SO(3)$ quantum links in $(2+1)$-D
2024-12-12
Graham Van Goffrier, Debasish Banerjee, Bipasha Chakraborty, Emilie Huffman, Sandip Maiti
Quantum link models (QLMs) are generalizations of Wilson's lattice gauge theory formulated with finite-dimensional link Hilbert spaces. In certain cases, the non-Abelian Gauss Law constraint can be exactly solved, and the gauge invariant subspace embedded onto local spin Hamiltonians for efficient quantum simulation. In $(1+1)d$ previous studies of the $SO(3)$ QLM coupled to adjoint fermionic matter have been shown to reflect key properties of QCD and nuclear physics, including distinct confining/deconfining phases and hadronic bound states. We extend the model to $(2+1)d$ dimensions for the first time, and report on our initial results. We review the construction of gauge-invariant state space for the proposed models, and study the single-plaquette ground state via exact-diagonalisation. We provide indications of a rich phase diagram which shows both spontaneous and explicit chiral symmetry breaking, confinement, and distinct magnetic phases characterised by different plaquette expectation values.
OGLE-2015-BLG-1609Lb: Sub-jovian planet orbiting a low-mass stellar or brown dwarf host
2024-12-12
M. J. Mróz, R. Poleski, A. Udalski, T. Sumi, Y. Tsapras, M. Hundertmark, P. Pietrukowicz, M. K. Szymański, J. Skowron, P. Mróz, M. Gromadzki, P. Iwanek, S. Kozłowski, M. Ratajczak, K. A. Rybicki, D. M. Skowron, I. Soszyński, K. Ulaczyk, M. Wrona, F. Abe, K. Bando, D. P. Bennett, A. Bhattacharya, I. A. Bond, A. Fukui, R. Hamada, S. Hamada, N. Hamasaki, Y. Hirao, S. Ishitani Silva, Y. Itow, N. Koshimoto, Y. Matsubara, S. Miyazaki, Y. Muraki, T. Nagai, K. Nunota, G. Olmschenk, C. Ranc, N. J. Rattenbury, Y. Satoh, D. Suzuki, S. K. Terry, P. J. Tristram, A. Vandorou, H. Yama, R. A. Street, E. Bachelet, M. Dominik, A. Cassan, R. Figuera Jaimes, K. Horne, R. Schmidt, C. Snodgrass, J. Wambsganss, I. A. Steele, J. Menzies, U. G. Jørgensen, P. Longa-Peña, N. Peixinho, J. Skottfelt, J. Southworth, M. I. Andersen, V. Bozza, M. J. Burgdorf, G. D'Ago, T. C. Hinse, E. Kerins, H. Korhonen, M. Küffmeier, L. Mancini, M. Rabus, S. Rahvar
We present a comprehensive analysis of a planetary microlensing event OGLE-2015-BLG-1609. The planetary anomaly was detected by two survey telescopes, OGLE and MOA. Each of these surveys collected enough data over the planetary anomaly to allow for an unambiguous planet detection. Such survey detections of planetary anomalies are needed to build a robust sample of planets that could improve studies on the microlensing planetary occurrence rate by reducing biases and statistical uncertainties. In this work, we examined different methods for modeling microlensing events using individual datasets, particularly we incorporated a Galactic model prior to better constrain poorly defined microlensing parallax. Ultimately, we fitted a comprehensive model to all available data, identifying three potential typologies, with two showing comparably high Bayesian evidence. Our analysis indicates that the host of the planet is a brown dwarf with a probability of 34%, or a low-mass stellar object (M-dwarf) with the probability of 66%.
Milky Way dark matter distribution or MOND test from vertical stellar kinematics with Gaia DR3
2024-12-12
Martin Lopez-Corredoira
Vertical stellar kinematics+density can be used to trace the dark matter distribution [or the equivalent phantom mass in a Modified Newtonian Dynamics (MOND) scenario] through Jeans equations. In this paper, we want to improve this type of analysis by making use of the recent data of the 6D information from the Gaia-DR3 survey in the anticenter and the Galactic poles to obtain the dynamical mass distribution near plane regions, including extended kinematics over a wide region of 8 kpc$R_\odot$, $z=1.05$ kpc: $\Sigma=28.7\pm 9.6$, $23.0\pm 5.7$, $16.9\pm 5.8$, $11.4\pm 6.6$ M$_\odot$ pc$^{-2}$, respectively, for $R=10,13,16,19$ kpc, compatible with visible matter alone. Larger error bars in comparison with previous works are not due to worse data or a more awkward technique but to a stricter modeling of the stellar distribution.
Deep learning-based image generation has undergone a paradigm shift since 2021, marked by fundamental architectural breakthroughs and computational innovations. Through reviewing architectural innovations and empirical results, this paper analyzes the transition from traditional generative methods to advanced architectures, with focus on compute-efficient diffusion models and vision transformer architectures. We examine how recent developments in Stable Diffusion, DALL-E, and consistency models have redefined the capabilities and performance boundaries of image synthesis, while addressing persistent challenges in efficiency and quality. Our analysis focuses on the evolution of latent space representations, cross-attention mechanisms, and parameter-efficient training methodologies that enable accelerated inference under resource constraints. While more efficient training methods enable faster inference, advanced control mechanisms like ControlNet and regional attention systems have simultaneously improved generation precision and content customization. We investigate how enhanced multi-modal understanding and zero-shot generation capabilities are reshaping practical applications across industries. Our analysis demonstrates that despite remarkable advances in generation quality and computational efficiency, critical challenges remain in developing resource-conscious architectures and interpretable generation systems for industrial applications. The paper concludes by mapping promising research directions, including neural architecture optimization and explainable generation frameworks.
Motion Generation Review: Exploring Deep Learning for Lifelike Animation with Manifold
2024-12-12
Jiayi Zhao, Dongdong Weng, Qiuxin Du, Zeyu Tian
Human motion generation involves creating natural sequences of human body poses, widely used in gaming, virtual reality, and human-computer interaction. It aims to produce lifelike virtual characters with realistic movements, enhancing virtual agents and immersive experiences. While previous work has focused on motion generation based on signals like movement, music, text, or scene background, the complexity of human motion and its relationships with these signals often results in unsatisfactory outputs. Manifold learning offers a solution by reducing data dimensionality and capturing subspaces of effective motion. In this review, we present a comprehensive overview of manifold applications in human motion generation, one of the first in this domain. We explore methods for extracting manifolds from unstructured data, their application in motion generation, and discuss their advantages and future directions. This survey aims to provide a broad perspective on the field and stimulate new approaches to ongoing challenges.
This paper delves into the applications of generative artificial intelligence (GAI) in semantic communication (SemCom) and presents a thorough study. Three popular SemCom systems enabled by classical GAI models are first introduced, including variational autoencoders, generative adversarial networks, and diffusion models. For each system, the fundamental concept of the GAI model, the corresponding SemCom architecture, and the associated literature review of recent efforts are elucidated. Then, a novel generative SemCom system is proposed by incorporating the cutting-edge GAI technology-large language models (LLMs). This system features two LLM-based AI agents at both the transmitter and receiver, serving as "brains" to enable powerful information understanding and content regeneration capabilities, respectively. This innovative design allows the receiver to directly generate the desired content, instead of recovering the bit stream, based on the coded semantic information conveyed by the transmitter. Therefore, it shifts the communication mindset from "information recovery" to "information regeneration" and thus ushers in a new era of generative SemCom. A case study on point-to-point video retrieval is presented to demonstrate the superiority of the proposed generative SemCom system, showcasing a 99.98% reduction in communication overhead and a 53% improvement in retrieval accuracy compared to the traditional communication system. Furthermore, four typical application scenarios for generative SemCom are delineated, followed by a discussion of three open issues warranting future investigation. In a nutshell, this paper provides a holistic set of guidelines for applying GAI in SemCom, paving the way for the efficient implementation of generative SemCom in future wireless networks.
An Improved Precision Calculation of the $0νββ$ Contact Term within Chiral Effective Field Theory
2024-12-11
Graham Van Goffrier
Neutrinoless double-beta ($0\nu\beta\beta$) decay is an as-yet unobserved nuclear process, which stands to provide crucial insights for model-building beyond the Standard Model of particle physics. Its detection would simultaneously confirm the hypothesis that neutrinos are Majorana fermions, thus violating lepton-number conservation, and provide the first measurement of the absolute neutrino mass scale. This work aims to improve the estimation within chiral effective field theory of the so-called ``contact term'' for $0\nu\beta\beta$-decay, a short-range two-nucleon effect which is unaccounted for in traditional nuclear approaches to the process. We conduct a thorough review of the justifications for this contact term and the most precise computation of its size to date $g_\nu^{NN}$ = 1.3(6) at renormalisation point $\mu=m_\pi$), whose precision is limited by a truncation to elastic intermediate hadronic states. We then perform an extension of this analysis to a subleading class of inelastic intermediate states which we characterise, delivering an updated figure for the contact coefficient ($g_\nu^{NN}$ = 1.4(3) at $\mu=m_\pi$) with uncertainty reduced by half. Such ab initio nuclear results, especially with enhanced precision, show promise for the resolution of disagreements between estimates of $0\nu\nu\beta\beta$ from different many-body methods.
DMin: Scalable Training Data Influence Estimation for Diffusion Models
2024-12-11
Huawei Lin, Yingjie Lao, Weijie Zhao
Identifying the training data samples that most influence a generated image is a critical task in understanding diffusion models, yet existing influence estimation methods are constrained to small-scale or LoRA-tuned models due to computational limitations. As diffusion models scale up, these methods become impractical. To address this challenge, we propose DMin (Diffusion Model influence), a scalable framework for estimating the influence of each training data sample on a given generated image. By leveraging efficient gradient compression and retrieval techniques, DMin reduces storage requirements from 339.39 TB to only 726 MB and retrieves the top-k most influential training samples in under 1 second, all while maintaining performance. Our empirical results demonstrate DMin is both effective in identifying influential training samples and efficient in terms of computational and storage requirements.
We present a test of the equivalence principle on cosmological scales involving minimal assumptions. Our approach relies on the cross-correlation of two different galaxy populations with large-scale relativistic corrections. We construct a measurable quantity $E_P$ acting as a null test, i.e. deviating from unity whenever the weak equivalence principle is violated. We provide forecasts with the DESI Bright Galaxy Sample and with the Square Kilometre Array Phase 2 (SKA2). The relativistic corrections can be detected with high significance by both surveys, while $E_P$ can only be measured by SKA2. We forecast a precision around 10-15$\%$ across the redshift range between 0.25 and 0.75.
A 1% accurate method to include baryonic effects in galaxy-galaxy lensing models
2024-12-11
Matteo Zennaro, Giovanni Aricò, Carlos García-García, Raúl E. Angulo, Lurdes Ondaro-Mallea, Sergio Contreras, Andrina Nicola, Matthieu Schaller, Joop Schaye
Galaxy clustering and galaxy-galaxy lensing are two of the main observational probes in Stage-IV large-scale structure surveys. Unfortunately, the complicated relationship between galaxies and matter limits the exploitation of this data. Galaxy bias models -- such as the hybrid Lagrangian bias expansion -- allow describing galaxy clustering down to scales as small as $k = 0.7h$/Mpc. However, the galaxy-matter cross-power spectra are already affected by baryons on these scales, directly impacting the modelling of galaxy-galaxy lensing. We propose to extend models of the galaxy-matter cross-power spectrum $P_{\rm gm}(k)$ (currently only accounting for dark matter) by including a baryonic correction inferred from the matter component ($S_{\rm mm}(k)$), so that $P_{\rm gm, full \, physics} (k) = \sqrt{S_{\rm mm}} P_{\rm gm, gravity \, only}$. We use the FLAMINGO simulations to measure the effect of baryons on the galaxy-matter cross-power spectrum and to assess the performance of our model. We perform a Bayesian analysis of synthetic data, implementing a model based on BACCO's hybrid Lagrangian bias expansion (for the nonlinear galaxy bias) and Baryon Correction Model. Ignoring baryons in the galaxy-matter cross-power spectrum leads to a biased inference of the galaxy bias, while ignoring baryons in both the galaxy-matter and matter-matter power spectra leads to a biased inference of both the galaxy bias and cosmological parameters. In contrast, our method is 1% accurate compared to all physics variations in FLAMINGO and on all scales described by hybrid perturbative models ($k < 0.7h$/Mpc). Moreover, our model leads to inferred bias and cosmological parameters compatible within 1$\sigma$ with their reference values. We anticipate that our method will be a promising candidate for analysing forthcoming Stage-IV survey data.
Enhancing the use of family planning service statistics using a Bayesian modelling approach to inform estimates of modern contraceptive use in low- and middle-income countries
Monitoring family planning indicators, such as modern contraceptive prevalence rate (mCPR), is essential for family planning programming. The Family Planning Estimation Tool (FPET) uses survey data to estimate and forecast family planning indicators, including mCPR, over time. However, sole reliance on large-scale surveys, carried out on average every 3-5 years, can lead to data gaps. Service statistics are a readily available data source, routinely collected in conjunction with service delivery. Various service statistics data types can be used to derive a family planning indicator called Estimated Modern Use (EMU). In a number of countries, annual rates of change in EMU have been found to be predictive of true rates of change in mCPR. However, it has been challenging to capture the varying levels of uncertainty associated with the EMU indicator across different countries and service statistics data types and to subsequently quantify this uncertainty when using EMU in FPET. We present a new approach to using EMUs in FPET to inform mCPR estimates, using annual EMU rates of change as input, and accounting for uncertainty associated with the EMU derivation process. The approach also considers additional country-type-specific uncertainty. We assess the EMU type-specific uncertainty at the country level, via a Bayesian hierarchical modelling approach. Validation results and anonymised country-level case studies highlight improved predictive performance and provide insights into the impact of including EMU data on mCPR estimates compared to using survey data alone. Together, they demonstrate that EMUs can help countries monitor progress toward their family planning goals more effectively.
Preference Discerning with LLM-Enhanced Generative Retrieval
2024-12-11
Fabian Paischer, Liu Yang, Linfeng Liu, Shuai Shao, Kaveh Hassani, Jiacheng Li, Ricky Chen, Zhang Gabriel Li, Xialo Gao, Wei Shao, Xue Feng, Nima Noorshams, Sem Park, Bo Long, Hamid Eghbalzadeh
Sequential recommendation systems aim to provide personalized recommendations for users based on their interaction history. To achieve this, they often incorporate auxiliary information, such as textual descriptions of items and auxiliary tasks, like predicting user preferences and intent. Despite numerous efforts to enhance these models, they still suffer from limited personalization. To address this issue, we propose a new paradigm, which we term preference discerning. In preference dscerning, we explicitly condition a generative sequential recommendation system on user preferences within its context. To this end, we generate user preferences using Large Language Models (LLMs) based on user reviews and item-specific data. To evaluate preference discerning capabilities of sequential recommendation systems, we introduce a novel benchmark that provides a holistic evaluation across various scenarios, including preference steering and sentiment following. We assess current state-of-the-art methods using our benchmark and show that they struggle to accurately discern user preferences. Therefore, we propose a new method named Mender ($\textbf{M}$ultimodal Prefer$\textbf{en}$ce $\textbf{d}$iscern$\textbf{er}$), which improves upon existing methods and achieves state-of-the-art performance on our benchmark. Our results show that Mender can be effectively guided by human preferences even though they have not been observed during training, paving the way toward more personalized sequential recommendation systems. We will open-source the code and benchmarks upon publication.
CCSNscore: A multi-input deep learning tool for classification of core-collapse supernovae using SED-Machine spectra
2024-12-11
Yashvi Sharma, Ashish A. Mahabal, Jesper Sollerman, Christoffer Fremling, S. R. Kulkarni, Nabeel Rehemtulla, Adam A. Miller, Marie Aubert, Tracy X. Chen, Michael W. Coughlin, Matthew J. Graham, David Hale, Mansi M. Kasliwal, Young-Lo Kim, James D. Neill, Josiah N. Purdum, Ben Rusholme, Avinash Singh, Niharika Sravan
Supernovae (SNe) come in various flavors and are classified into different types based on emission and absorption lines in their spectra. SN candidates are now abundant with the advent of large systematic dynamic sky surveys like the Zwicky Transient Facility (ZTF), however, the identification bottleneck lies in their spectroscopic confirmation and classification. Fully robotic telescopes with dedicated spectrographs optimized for SN follow-up have eased the burden of data acquisition. However, the task of classifying the spectra still largely rests with the astronomers. Automating this classification step reduces human effort, and can make the SN type available sooner to the public. For this purpose, we have developed a deep-learning based program for classifying core-collapse supernovae (CCSNe) with ultra-low resolution spectra obtained with the SED-Machine IFU spectrograph on the Palomar 60-inch telescope. The program consists of hierarchical classification task layers, with each layer composed of multiple binary classifiers which are run in parallel to produce a reliable classification. The binary classifiers utilize RNN and CNN architecture and are designed to take multiple inputs, to supplement spectra with g- and r-band photometry from ZTF. On non-host-contaminated and good quality SEDM spectra ("gold" test set), CCSNscore is ~94% accurate (correct classifications) in distinguishing between hydrogen-rich (Type II) and hydrogen-poor (Type Ibc) CCSNe. With the help of light curve input, CCSNscore classifies ~83% of the gold set with high confidence (score >= 0.8 and score-error < 0.05), with ~98% accuracy.
Machine Learning Information Retrieval and Summarisation to Support Systematic Review on Outcomes Based Contracting
2024-12-11
Iman Munire Bilal, Zheng Fang, Miguel Arana-Catania, Felix-Anselm van Lier, Juliana Outes Velarde, Harry Bregazzi, Eleanor Carter, Mara Airoldi, Rob Procter
As academic literature proliferates, traditional review methods are increasingly challenged by the sheer volume and diversity of available research. This article presents a study that aims to address these challenges by enhancing the efficiency and scope of systematic reviews in the social sciences through advanced machine learning (ML) and natural language processing (NLP) tools. In particular, we focus on automating stages within the systematic reviewing process that are time-intensive and repetitive for human annotators and which lend themselves to immediate scalability through tools such as information retrieval and summarisation guided by expert advice. The article concludes with a summary of lessons learnt regarding the integrated approach towards systematic reviews and future directions for improvement, including explainability.
Identifiability of the instrumental variable model with the treatment and outcome missing not at random
2024-12-11
Shuozhi Zuo, Peng Ding, Fan Yang
The instrumental variable model of Imbens and Angrist (1994) and Angrist et al. (1996) allow for the identification of the local average treatment effect, also known as the complier average causal effect. However, many empirical studies are challenged by the missingness in the treatment and outcome. Generally, the complier average causal effect is not identifiable without further assumptions when the treatment and outcome are missing not at random. We study its identifiability even when the treatment and outcome are missing not at random. We review the existing results and provide new findings to unify the identification analysis in the literature.
The reranker and generator are two critical components in the Retrieval-Augmented Generation (i.e., RAG) pipeline, responsible for ranking relevant documents and generating responses. However, due to differences in pre-training data and objectives, there is an inevitable gap between the documents ranked as relevant by the reranker and those required by the generator to support answering the query. To address this gap, we propose RADIO, a novel and practical preference alignment framework with RAtionale DIstillatiOn. Specifically, We first propose a rationale extraction method that leverages the reasoning capabilities of Large Language Models (LLMs) to extract the rationales necessary for answering the query. Subsequently, a rationale-based alignment process is designed to rerank the documents based on the extracted rationales, and fine-tune the reranker to align the preferences. We conduct extensive experiments on two tasks across three datasets to demonstrate the effectiveness of our approach compared to baseline methods. Our code is released online to ease reproduction.
Feature selection is crucial in recommender systems for improving model efficiency and predictive performance. Traditional methods rely on agency models, such as decision trees or neural networks, to estimate feature importance. However, this approach is inherently limited, as the agency models may fail to learn effectively in all scenarios due to suboptimal training conditions (e.g., feature collinearity, high-dimensional sparsity, and data insufficiency). In this paper, we propose AltFS, an Agency-light Feature Selection method for deep recommender systems. AltFS integrates semantic reasoning from Large Language Models (LLMs) with task-specific learning from agency models. Initially, LLMs will generate a semantic ranking of feature importance, which is then refined by an agency model, combining world knowledge with task-specific insights. Extensive experiments on three public datasets from real-world recommender platforms demonstrate the effectiveness of AltFS. Our code is publicly available for reproducibility.
Comparative Opinion Mining in Product Reviews: Multi-perspective Prompt-based Learning
2024-12-11
Hai-Yen Thi Nguyen, Cam-Van Thi Nguyen
Comparative reviews are pivotal in understanding consumer preferences and influencing purchasing decisions. Comparative Quintuple Extraction (COQE) aims to identify five key components in text: the target entity, compared entities, compared aspects, opinions on these aspects, and polarity. Extracting precise comparative information from product reviews is challenging due to nuanced language and sequential task errors in traditional methods. To mitigate these problems, we propose MTP-COQE, an end-to-end model designed for COQE. Leveraging multi-perspective prompt-based learning, MTP-COQE effectively guides the generative model in comparative opinion mining tasks. Evaluation on the Camera-COQE (English) and VCOM (Vietnamese) datasets demonstrates MTP-COQE's efficacy in automating COQE, achieving superior performance with a 1.41% higher F1 score than the previous baseline models on the English dataset. Additionally, we designed a strategy to limit the generative model's creativity to ensure the output meets expectations. We also performed data augmentation to address data imbalance and to prevent the model from becoming biased towards dominant samples.
Time discretization of Quadratic Forward-Backward SDEs with singular drifts
2024-12-11
Rhoss Likibi Pellat, Emmanuel Che Fonka, Olivier Menoukeu Pamen
We investigate the convergence rate for the time discretization of a class of quadratic backward SDEs -- potentially involving path-dependent terminal values -- when coupled with non-standard Lipschitz-type forward SDEs. In our review of the explicit time-discretization schemes in the spirit of Pag\`es \& Sagna (see \cite{PaSa18}), we achieve an error control close to $\frac{1}{2}$, even under the modest assumptions considered in this work (see \cite{ChaRichou16}, for comparison). A central element of our approach is a thorough re-examination of Zhang's $L^2\text{-time regularity}$ of the martingale integrand $Z$ which follows from an extension of the first-order variational regularity for this class of singular forward-backward SDEs with non-uniform Cauchy-Lipschitz drivers. This is complemented by the recently introduced caracterisation of stochastic processes of {\it bounded mean oscillation} (abbreviated as $\bmo$) by K. L\^e (see \cite{Le22}) which we used to derive an $L^p\text{-version}$ of the strong approximation of SDEs with singular drifts from Dareiotis \& Gerencs\'er (see \cite{DaGe20}). As such, this study addresses a crucial gap in the numerical analysis of forward-backward SDEs (FBSDEs). To our knowledge, for the first time, the impact of regularization by noise on Euler-Maruyama numerical schemes for singular forward SDEs has been successfully transferred to enhance the convergence rate of the discrete time approximations for solutions to backward SDEs.
The high speeds seen in rapidly rotating pulsars after supernova explosions present a longstanding puzzle in astrophysics. Numerous theories have been suggested over the years to explain this sudden "kick" imparted to the neutron star, yet each comes with its own set of challenges and limitations. Key explanations for pulsar kicks include hydrodynamic instabilities in supernovae, anisotropic neutrino emission, asymmetries in the magnetic field, binary system disruption, and physics beyond the Standard Model. Unraveling the origins of pulsar kicks not only enhances our understanding of supernova mechanisms but also opens up possibilities for exploring new physics. In this brief review, we will introduce pulsar kicks, examine the leading hypotheses, and explore future directions for this intriguing phenomenon.
Assessing Personalized AI Mentoring with Large Language Models in the Computing Field
2024-12-11
Xiao Luo, Sean O'Connell, Shamima Mithun
This paper provides an in-depth evaluation of three state-of-the-art Large Language Models (LLMs) for personalized career mentoring in the computing field, using three distinct student profiles that consider gender, race, and professional levels. We evaluated the performance of GPT-4, LLaMA 3, and Palm 2 using a zero-shot learning approach without human intervention. A quantitative evaluation was conducted through a custom natural language processing analytics pipeline to highlight the uniqueness of the responses and to identify words reflecting each student's profile, including race, gender, or professional level. The analysis of frequently used words in the responses indicates that GPT-4 offers more personalized mentoring compared to the other two LLMs. Additionally, a qualitative evaluation was performed to see if human experts reached similar conclusions. The analysis of survey responses shows that GPT-4 outperformed the other two LLMs in delivering more accurate and useful mentoring while addressing specific challenges with encouragement languages. Our work establishes a foundation for developing personalized mentoring tools based on LLMs, incorporating human mentors in the process to deliver a more impactful and tailored mentoring experience.
Searching for dual/lensed quasar candidates with spectroscopic surveys
2024-12-11
Xiang Ji, Zhen-Ya Zheng, Ru-Qiu Lin, Hai-Cheng Feng
Dual and lensed quasars are valuable astrophysical targets in many aspects. Dual quasars, considered as the precursors of supermassive black hole binaries, can provide crucial insights into how black hole mergers drive the growth of supermassive black holes and influence the evolution of galaxies. Lensed quasars, formed by the gravitational deflection of a background quasar's light by a massive foreground object, can address key cosmological questions, particularly in refining measurements of the Hubble constant. Despite their significance, the number of confirmed dual and lensed quasars remains limited. Here in this work, we propose a systematic search for dual/lensed quasars using broad emission line profile diagnostics. Our parent sample consists of spectroscopic quasars from the Dark Energy Spectroscopic Instrument Early Data Release (DESI EDR) and the SDSS DR17 catalog. We identify 30 lensed quasar candidates with similar broad emission line profiles, as well as 36 dual quasar candidates with different profiles. Cross-matching these 66 targets with the HST archival database, we find four overlapping targets, including three previously reported lensed quasars and one newly identified dual quasar candidate. We estimate the black hole masses for the two cores in the same system. The mass ratios are similar in the lensed quasar scenario but vary widely for dual quasars, consistent with the physical nature of these two types. In particular, we identified a dual quasar candidate with the mass ratio exceeding 100 times. We aim to discover more dual/lensed quasar candidates using our method with the upcoming future spectroscopic surveys.
Spin-glass dynamics: experiment, theory and simulation
2024-12-11
E. D. Dahlberg, I. González-Adalid Pemartín, E. Marinari, V. Martin-Mayor, J. Moreno-Gordo, R. L. Orbach, I. Paga, G. Parisi, F. Ricci-Tersenghi, J. J. Ruiz-Lorenzo, D. Yllanes
The study of spin-glass dynamics, long considered the paradigmatic complex system, has reached important milestones. The availability of high-quality single crystals has allowed the experimental measurement of spin-glass coherence lengths of almost macroscopic dimensions, while the advent of special-purpose massive computers enables dynamical simulations that approach experimental time and length scales. This review provides an account of the quantitative convergence of these two avenues of research, with precise experimental measurements of the expected scaling laws and numerical reproduction of classic experimental results, such as memory and rejuvenation. The article opens with a brief review of the defining spin-glass properties, randomness and frustration, and their experimental consequences. These apparently simple characteristics are shown to generate rich and complex physics. Models are introduced that enable quantitative descriptions. After a summary of the main numerical results in equilibrium, paying particular attention to the concept of temperature chaos, this review examines off-equilibrium dynamics in the absence of a magnetic field and shows how it can be related to equilibrium structures through the fluctuation-dissipation relations. The nonlinear response at a given temperature is then developed, including experiments and scaling in the vicinity of the transition temperature $T_\mathrm{g}$. The consequences of temperature change -- including temperature chaos, rejuvenation, and memory -- are reviewed. The interpretation of these phenomena requires identifying several length scales relevant to dynamics, which, in turn, generate new insights. Finally, issues for future investigations are introduced, including what is to be nailed down theoretically, why the Ising Edwards-Anderson model is so successful at modeling spin-glass dynamics, and experiments yet to be undertaken.
Comparison of Static Analysis Architecture Recovery Tools for Microservice Applications
2024-12-11
Simon Schneider, Alexander Bakhtin, Xiaozhou Li, Jacopo Soldani, Antonio Brogi, Tomas Cerny, Riccardo Scandariato, Davide Taibi
Architecture recovery tools help software engineers obtain an overview of the structure of their software systems during all phases of the software development life cycle. This is especially important for microservice applications because they consist of multiple interacting microservices, which makes it more challenging to oversee the architecture. Various tools and techniques for architecture recovery (also called architecture reconstruction) have been presented in academic and gray literature sources, but no overview and comparison of their accuracy exists. This paper presents the results of a multivocal literature review with the goal of identifying architecture recovery tools for microservice applications and a comparison of the identified tools' architectural recovery accuracy. We focused on static tools since they can be integrated into fast-paced CI/CD pipelines. 13 such tools were identified from the literature and nine of them could be executed and compared on their capability of detecting different system characteristics. The best-performing tool exhibited an overall F1-score of 0.86. Additionally, the possibility of combining multiple tools to increase the recovery correctness was investigated, yielding a combination of four individual tools that achieves an F1-score of 0.91. Registered report: The methodology of this study has been peer-reviewed and accepted as a registered report at MSR'24: arXiv:2403.06941
On BRST Lagrangian formulation of massless higher spin fields
2024-12-11
I. L. Buchbinder, S. A. Fedoruk, A. P. Isaev, V. A. Krykhtin
The paper is dedicated to the blessed memory of Professor Vladislav Gavrilovich Bagrov, an outstanding Russian scientist in the area of theoretical and mathematical physics. He had a great influence on the formation of the scientific interests dozens of scientists in Tomsk and Russia as a whole. Two of the authors of this paper (I.L.B and V.A.K) are to one degree or another grateful to Professor V.G. Bagrov for comprehensive support in the initial period of their scientific career. Two other authors (S.A.F. and A.P.I.) are familiar with and use the work of scientists from the Tomsk School of Theoretical Physics, founded by Professor V.G. Bagrov. The paper is devoted to certain aspects of the higher-spin field theory, which were mainly initiated and continued during of I.L.B and V.A.K work in Tomsk. We demonstrate in details the simplicity and clearity of the Lagrangian formulation for free four-dimensional massless higher-spin fields within the universal BRST approach, while describing these fields in terms of two-component spin-tensors.
2024-12-11
Nishat Raihan, Christian Newman, Marcos Zampieri
Large language models (LLMs) have demonstrated remarkable capabilities across various NLP tasks and have recently expanded their impact to coding tasks, bridging the gap between natural languages (NL) and programming languages (PL). This taxonomy-based survey provides a comprehensive analysis of LLMs in the NL-PL domain, investigating how these models are utilized in coding tasks and examining their methodologies, architectures, and training processes. We propose a taxonomy-based framework that categorizes relevant concepts, providing a unified classification system to facilitate a deeper understanding of this rapidly evolving field. This survey offers insights into the current state and future directions of LLMs in coding tasks, including their applications and limitations.
Towards Precision in Bolted Joint Design: A Preliminary Machine Learning-Based Parameter Prediction
2024-12-11
Ines Boujnah, Nehal Afifi, Andreas Wettstein, Sven Matthiesen
Bolted joints are critical in engineering for maintaining structural integrity and reliability. Accurate prediction of parameters influencing their function and behavior is essential for optimal performance. Traditional methods often fail to capture the non-linear behavior of bolted joints or require significant computational resources, limiting accuracy and efficiency. This study addresses these limitations by combining empirical data with a feed-forward neural network to predict load capacity and friction coefficients. Leveraging experimental data and systematic preprocessing, the model effectively captures nonlinear relationships, including rescaling output variables to address scale discrepancies, achieving 95.24% predictive accuracy. While limited dataset size and diversity restrict generalizability, the findings demonstrate the potential of neural networks as a reliable, efficient alternative for bolted joint design. Future work will focus on expanding datasets and exploring hybrid modeling techniques to enhance applicability.
The supermassive black hole population from seeding via collisions in Nuclear Star Clusters
2024-12-11
M. Liempi, D. R. G. Schleicher, A. Benson, A. Escala, M. C. Vergara
The coexistence of nuclear star clusters (NSCs) and supermassive black holes (SMBHs) in galaxies with stellar masses $\sim 10^{10}~$M$_\odot$, the scaling relations between their properties and properties of the host galaxy (e.g., $M_{NSC}^{stellar}-M_{galaxy}^{stellar}$, $M_{BH}-M_{galaxy}^{stellar}$), and the fact that NSCs seem to take on the role of SMBHs in less massive galaxies and vice versa in the more massive ones, suggest that the origin of NSCs and SMBHs is related. In this study, we implement an 'in-situ' NSC formation scenario, where NSCs are formed in the center of galaxies due to star formation in the accumulated gas. We explore the impact of the free parameter $A_{res}$ which regulates the amount of gas transferred to the NSC reservoir, playing a crucial role in shaping the cluster's growth. Simultaneously, we include a BH seed formation recipe based on stellar collisions within NSCs in the Semi-Analytical Model (SAM) Galacticus to explore the resulting population of SMBHs. We determine the parameter space of the NSCs that form a BH seed and find that in initially more compact NSCs the formation of these BH seeds is more favorable, leading to the formation of light, medium and heavy BH seeds which finally reach masses up to $\sim 10^9$~M$_\odot$ and is comparable with the observed SMBH mass function at masses above $10^8$~M$_\odot$. Additionally, we compare the resulting population of NSCs with a derived NSC mass function from the stellar mass function of galaxies from the GAMA survey at $z<0.06$ finding a well agreement in shape terms. We also find a considerable overlap in the observed scaling relations between the NSC mass, and the host galaxy stellar mass and velocity dispersion which is independent of the value of $A_{res}$. However, the chi-square analysis suggests that the model requires further refinement to achieve better quantitative agreement.
Efficient estimation of error bounds for quantum multiparametric imaging with constraints
2024-12-11
Alexander Mikhalychev, Saif Almazrouei, Svetlana Mikhalycheva, Abdellatif Bouchalkha, Dmitri Mogilevtsev, Bobomurat Ahmedov
Advanced super-resolution imaging techniques require specific approaches for accurate and consistent estimation of the achievable spatial resolution. Fisher information supplied to Cramer-Rao bound (CRB) has proved to be a powerful and efficient tool for resolution analysis and optical setups optimization. However, the standard CRB is not applicable to constrained problems violating the unbiasedness condition, while such models are frequently encountered in quantum imaging of complex objects. Complimentary to the existing approaches based on modifying CRB, we propose a practical algorithm for approximate construction of a modified Fisher information matrix, which takes the constraints into account and can be supplied to the standard CRB. We demonstrate the efficiency of the proposed technique by applying it to 1-, 2-, and multi-parameter model problems in quantum imaging. The approach provides quantitative explanation of previous results with successful experimental reconstruction of objects with the spatial scale smaller than the theoretical limit predicted by the standard CRB.
Active galactic nucleus time-variability analysis and its caveats
2024-12-11
Sofia Kankkunen, Merja Tornikoski, Talvikki Hovatta
In this study, we demonstrate some of the caveats in common statistical methods used for analysing astronomical variability timescales. We consider these issues specifically in the context of active galactic nuclei (AGNs) and use a more practical approach compared to mathematics literature, where the number of formulae may sometimes be overwhelming. We conducted a thorough literature review both on the statistical properties of light-curve data, specifically in the context of sampling effects, as well as on the methods used to analyse them. We simulated a wide range of data to test some of the known issues in AGN variability analysis as well as to investigate previously unknown or undocumented caveats. We discovered problems with some commonly used methods and confirmed how challenging it is to identify timescales from observed data. We find that interpolation of a light curve with biased sampling, specifically with bias towards flaring events, affects its measured power spectral density in a different manner than those of simulated light curves. We also find that an algorithm aiming to match the probability density function of a light curve has often been used incorrectly. These new issues appear to have been mostly overlooked and not necessarily addressed before, especially in astronomy literature.
Hadronic particle acceleration in the supernova remnant SN 1006 as traced by Fermi-LAT observations
2024-12-11
M. Lemoine-Goumard, F. Acero, J. Ballet, M. Miceli
The supernova remnant SN 1006 is a source of high-energy particles detected at radio, X-rays, and tera-electronvolt gamma rays. It was also announced as a source of gamma rays by Fermi-LAT but only the north-east (NE) limb was detected at more than $5\sigma$ significance level. Using 15 years of Fermi-LAT observation and a thorough morphological analysis above 1 GeV, we report the detection of the NE rim at the $6\sigma$ level and the south-west (SW) rim at the $5.5\sigma$ level using radio templates from the GLEAM survey. The spectral analysis performed between 100 MeV and 1 TeV allows the detection of a hard spectral index for the NE limb of $1.7 \pm 0.1 \pm 0.1$ while the emission detected in the SW is well reproduced with a steeper spectral index of $2.2 \pm 0.1 \pm 0.1$. A marginal detection (~$3\sigma$) of emission coincident with the bright north-west (NW) H$\alpha$ filament is also described with a similar spectral index of ~2.1. We successfully characterized the non-thermal multi-wavelength emission of the NE and SW limbs with a model in which inverse-Compton emission dominates in the NE while proton-proton interactions becomes significant in the SW due to the enhanced density of the medium.
CRAFTS for HI cosmology: I. data analysis and preliminary results
2024-12-11
Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen
We present the results from calibrating the data of the Commensal Radio Astronomy FAST Survey (CRAFTS) for \HI intensity mapping by the Five-hundred-meter Aperture Spherical Radio Telescope (FAST). Using 70 hours of drift-scan observation with the L-band (1.05-1.45GHz) 19-beam receiver, we obtain the data covering $270\,\rm deg^2$ sky area. We employ both the pulsar backend and the spectrum backend to calibrate the spectral time-ordered-data (TOD) before projecting them onto HEALPix maps. We produce calibrated TOD with frequency resolution of 30 kHz and time resolution of 1 s and the map data-cube with frequency resolution of 30kHz and spatial resolution of $2.95\,\rm arcmin^2$. We carefully examine the pointing errors, noise overflow, RFI contamination and their effect on the data quality. The resulting noise level is $\sim$ 5.7 mJy for the calibrated TOD and 1.6 mJy for the map, which is consistent with the theoretical predictions within 5\% at RFI-free channels. We also validate the data by Principal Components Analysis (PCA) and find most foreground components are concentrated in the first 30 modes. We identify 447 isolated bright continuum sources in our data matching the NRAO-VLA Sky Survey (NVSS) catalog, with relative flux error of 8.3\% for TOD and 11.9\% for the map-level. We also measure the \HI emission of 90 galaxies with redshift $z<0.07$ and compare with \HI-MaNGA spectra from the Green Bank Telescope (GBT), yielding an overall relative error of the \HI integral flux of 16.7\%. Our results confirm the feasibility of conducting cosmological \HI signal detection with CRAFTS.
The exploration of various vision-language tasks, such as visual captioning, visual question answering, and visual commonsense reasoning, is an important area in artificial intelligence and continuously attracts the research community's attention. Despite the improvements in overall performance, classic challenges still exist in vision-language tasks and hinder the development of this area. In recent years, the rise of pre-trained models is driving the research on vision-language tasks. Thanks to the massive scale of training data and model parameters, pre-trained models have exhibited excellent performance in numerous downstream tasks. Inspired by the powerful capabilities of pre-trained models, new paradigms have emerged to solve the classic challenges. Such methods have become mainstream in current research with increasing attention and rapid advances. In this paper, we present a comprehensive overview of how vision-language tasks benefit from pre-trained models. First, we review several main challenges in vision-language tasks and discuss the limitations of previous solutions before the era of pre-training. Next, we summarize the recent advances in incorporating pre-trained models to address the challenges in vision-language tasks. Finally, we analyze the potential risks associated with the inherent limitations of pre-trained models and discuss possible solutions, attempting to provide future research directions.
The intrinsic alignment (IA) of galaxy shapes probes the underlying gravitational tidal field, thus offering cosmological information complementary to galaxy clustering. In this paper, we perform a Fisher forecast to assess the benefit of IA in improving cosmological parameter constraints, for the first time, leveraging the full-shape (FS) information of IA statistics. Our forecast is based on PFS-like and Euclid-like surveys as examples of deep and wide galaxy surveys, respectively. We explore various cosmological models, with the most comprehensive one simultaneously including dynamical dark energy, curvature, massive neutrinos, and modified gravity (MG). We find that adding FS IA information significantly tightens cosmological constraints relative to the FS clustering-only cases, particularly for dynamical dark energy and nonflat-MG models. For a deep galaxy survey, the Figure-of-Merit for the dark energy equation of state parameters is improved by at least more than $40\%$ in all dynamical dark energy models investigated. For nonflat-MG models, parameter constraints are tightened by $6-28\%$, except for the dark matter density and spectral index parameters. For a wide galaxy survey, improvements with IA become milder, although its joint constraints are tighter than those from the deep survey. Our findings highlight the efficacy of the galaxy IA as a complementary cosmological probe to galaxy clustering.
A Review of Intelligent Device Fault Diagnosis Technologies Based on Machine Vision
2024-12-11
Guiran Liu, Binrong Zhu
This paper provides a comprehensive review of mechanical equipment fault diagnosis methods, focusing on the advancements brought by Transformer-based models. It details the structure, working principles, and benefits of Transformers, particularly their self-attention mechanism and parallel computation capabilities, which have propelled their widespread application in natural language processing and computer vision. The discussion highlights key Transformer model variants, such as Vision Transformers (ViT) and their extensions, which leverage self-attention to improve accuracy and efficiency in visual tasks. Furthermore, the paper examines the application of Transformer-based approaches in intelligent fault diagnosis for mechanical systems, showcasing their superior ability to extract and recognize patterns from complex sensor data for precise fault identification. Despite these advancements, challenges remain, including the reliance on extensive labeled datasets, significant computational demands, and difficulties in deploying models on resource-limited devices. To address these limitations, the paper proposes future research directions, such as developing lightweight Transformer architectures, integrating multimodal data sources, and enhancing adaptability to diverse operational conditions. These efforts aim to further expand the application of Transformer-based methods in mechanical fault diagnosis, making them more robust, efficient, and suitable for real-world industrial environments.
2024-12-11
Yang Li, Xinyu Zhou, Yitong Wang, Liangxin Qian, Jun Zhao
Transformer models have revolutionized AI, enabling applications like content generation and sentiment analysis. However, their use in Machine Learning as a Service (MLaaS) raises significant privacy concerns, as centralized servers process sensitive user data. Private Transformer Inference (PTI) addresses these issues using cryptographic techniques such as Secure Multi-Party Computation (MPC) and Homomorphic Encryption (HE), enabling secure model inference without exposing inputs or models. This paper reviews recent advancements in PTI, analyzing state-of-the-art solutions, their challenges, and potential improvements. We also propose evaluation guidelines to assess resource efficiency and privacy guarantees, aiming to bridge the gap between high-performance inference and data privacy.
A Systematic Literature Review on the NIS2 Directive
2024-12-11
Jukka Ruohonen
A directive known as NIS2 was enacted in the European Union (EU) in late 2022. It deals particularly with European critical infrastructures, enlarging their scope substantially from an older directive that only considered the energy and transport sectors as critical. The directive's focus is on cyber security of critical infrastructures, although together with other new EU laws it expands to other security domains as well. Given the importance of the directive and most of all the importance of critical infrastructures, the paper presents a systematic literature review on academic research addressing the NIS2 directive either explicitly or implicitly. According to the review, existing research has often framed and discussed the directive with the EU's other cyber security laws. In addition, existing research has often operated in numerous contextual areas, including industrial control systems, telecommunications, the energy and water sectors, and infrastructures for information sharing and situational awareness. Despite the large scope of existing research, the review reveals noteworthy research gaps and worthwhile topics to examine in further research.
Face-swapping techniques have advanced rapidly with the evolution of deep learning, leading to widespread use and growing concerns about potential misuse, especially in cases of fraud. While many efforts have focused on detecting swapped face images or videos, these methods are insufficient for tracing the malicious users behind fraudulent activities. Intrusive watermark-based approaches also fail to trace unmarked identities, limiting their practical utility. To address these challenges, we introduce FaceTracer, the first non-intrusive framework specifically designed to trace the identity of the source person from swapped face images or videos. Specifically, FaceTracer leverages a disentanglement module that effectively suppresses identity information related to the target person while isolating the identity features of the source person. This allows us to extract robust identity information that can directly link the swapped face back to the original individual, aiding in uncovering the actors behind fraudulent activities. Extensive experiments demonstrate FaceTracer's effectiveness across various face-swapping techniques, successfully identifying the source person in swapped content and enabling the tracing of malicious actors involved in fraudulent activities. Additionally, FaceTracer shows strong transferability to unseen face-swapping methods including commercial applications and robustness against transmission distortions and adaptive attacks.
A Survey of Open-Source Power System Dynamic Simulators with Grid-Forming Inverter for Machine Learning Applications
2024-12-11
Tong Su, Jiangkai Peng, Alaa Selim, Junbo Zhao, Jin Tan
The emergence of grid-forming (GFM) inverter technology and the increasing role of machine learning in power systems highlight the need for evaluating the latest dynamic simulators. Open-source simulators offer distinct advantages in this field, being both free and highly customizable, which makes them well-suited for scientific research and validation of the latest models and methods. This paper provides a comprehensive survey and comparison of the latest open-source simulators that support GFM, with a focus on their capabilities and performance in machine-learning applications.
Go-Oracle: Automated Test Oracle for Go Concurrency Bugs
2024-12-11
Foivos Tsimpourlas, Chao Peng, Carlos Rosuero, Ping Yang, Ajitha Rajan
The Go programming language has gained significant traction for developing software, especially in various infrastructure systems. Nonetheless, concurrency bugs have become a prevalent issue within Go, presenting a unique challenge due to the language's dual concurrency mechanisms-communicating sequential processes and shared memory. Detecting concurrency bugs and accurately classifying program executions as pass or fail presents an immense challenge, even for domain experts. We conducted a survey with expert developers at Bytedance that confirmed this challenge. Our work seeks to address the test oracle problem for Go programs, to automatically classify test executions as pass or fail. This problem has not been investigated in the literature for Go programs owing to its distinctive programming model. Our approach involves collecting both passing and failing execution traces from various subject Go programs. We capture a comprehensive array of execution events using the native Go execution tracer. Subsequently, we preprocess and encode these traces before training a transformer-based neural network to effectively classify the traces as either passing or failing. The evaluation of our approach encompasses 8 subject programs sourced from the GoBench repository. These subject programs are routinely used as benchmarks in an industry setting. Encouragingly, our test oracle, Go-Oracle, demonstrates high accuracies even when operating with a limited dataset, showcasing the efficacy and potential of our methodology. Developers at Bytedance strongly agreed that they would use the Go-Oracle tool over the current practice of manual inspections to classify tests for Go programs as pass or fail.
Surveying Facial Recognition Models for Diverse Indian Demographics: A Comparative Analysis on LFW and Custom Dataset
2024-12-11
Pranav Pant, Niharika Dadu, Harsh V. Singh, Anshul Thakur
Facial recognition technology has made significant advances, yet its effectiveness across diverse ethnic backgrounds, particularly in specific Indian demographics, is less explored. This paper presents a detailed evaluation of both traditional and deep learning-based facial recognition models using the established LFW dataset and our newly developed IITJ Faces of Academia Dataset (JFAD), which comprises images of students from IIT Jodhpur. This unique dataset is designed to reflect the ethnic diversity of India, providing a critical test bed for assessing model performance in a focused academic environment. We analyze models ranging from holistic approaches like Eigenfaces and SIFT to advanced hybrid models that integrate CNNs with Gabor filters, Laplacian transforms, and segmentation techniques. Our findings reveal significant insights into the models' ability to adapt to the ethnic variability within Indian demographics and suggest modifications to enhance accuracy and inclusivity in real-world applications. The JFAD not only serves as a valuable resource for further research but also highlights the need for developing facial recognition systems that perform equitably across diverse populations.
The Grunsky operator and quasiconformality: old and new
2024-12-11
Samuel L. Krushkal
The Grunsky operator arises from univalence and plays a crucial role in geometric function theory. This operator also implies quasiconformal extendibility and has an intrinsic connection with Teichmuller space theory and its interactions with complex analysis and pluripotential theory. This paper surveys recent results in this field and simultaneously provides solutions of two old problems.
Survey on Human-Vehicle Interactions and AI Collaboration for Optimal Decision-Making in Automated Driving
2024-12-11
Abu Jafar Md Muzahid, Xiaopeng Zhao, Zhenbo Wang
The capabilities of automated vehicles are advancing rapidly, yet achieving full autonomy remains a significant challenge, requiring ongoing human cognition in decision-making processes. Incorporating human cognition into control algorithms has become increasingly important, as researchers work to develop strategies that minimize conflicts between human drivers and AI systems. Despite notable progress, many challenges persist, underscoring the need for further innovation and refinement in this field. This review covers recent progress in human-vehicle interaction (HVI) and AI collaboration for vehicle control. First, we start by looking at how HVI has evolved, pointing out key developments and identifying persistent problems. Second, we discuss the existing techniques, including methods for integrating human intuition and cognition into decision-making processes and developing systems that can mimic human behavior to enable optimal driving strategies and achieve safer and more efficient transportation. This review aims to contribute to the development of more effective and adaptive automated driving systems by enhancing human-AI collaboration.
Why so young? A curious connection between the broadband flux and neutral gas content of AGN
2024-12-11
Emily F. Kerrison, Elaine M. Sadler, Vanessa A. Moss, Elizabeth K. Mahony
We present here a study of the broadband spectral properties of 33 sources detected in HI absorption as part of the ASKAP-FLASH Pilot Surveys. We outline our approach to spectral classification and discuss the correlation seen between spectral shape and the detection of HI absorption. We further consider the implications of the observed correlation on the spatial distribution of the neutral gas, and on the jet-gas interactions. Our results are evaluated in the context of the forthcoming, full ASKAP-FLASH survey and other large, untargeted searches of the radio sky.
Axiomatic approach to measures of total correlations
2024-12-11
Gabriel L. Moraes, Renato M. Angelo, Ana C. S. Costa
Correlations play a pivotal role in various fields of science, particularly in quantum mechanics, yet their proper quantification remains a subject of debate. In this work, we aim to discuss the challenge of defining a reliable measure of total correlations. We first outline essential properties that an effective correlation measure should satisfy and review existing measures, including quantum mutual information, the p-norm of the correlation matrix, and the recently defined quantum Pearson correlation coefficient. Additionally, we introduce new measures based on R\'enyi and Tsallis relative entropies, as well as the Kullback-Leibler divergence. Our analysis reveals that while quantum mutual information, the p-norm, and the Pearson measure exhibit equivalence for two-qubit systems, they all suffer from an ordering problem. Despite criticisms regarding its reliability, we argue that quantum mutual information remains a valid measure of total correlations.
Evidence of enhanced two-level system loss suppression in high-Q, thin film aluminum microwave resonators
2024-12-11
Carolyn G. Volpert, Emily M. Barrentine, Alberto D. Bolatto, Ari Brown, Jake A. Connors, Thomas Essinger-Hileman, Larry A. Hess, Vilem Mikula, Thomas R. Stevenson, Eric R. Switzer
As superconducting kinetic inductance detectors (KIDs) continue to grow in popularity for sensitive sub-mm detection and other applications, there is a drive to advance toward lower loss devices. We present measurements of diagnostic thin film aluminum coplanar waveguide (CPW) resonators designed to inform ongoing KID development at NASA Goddard Space Flight Center. The resonators span $\rm f_0 = 3.5 - 4$\,GHz and include both quarter-wave and half-wave resonators with varying coupling capacitor designs. We present measurements of the device film properties and an analysis of the dominant mechanisms of loss in the resonators measured in a dark environment. We demonstrate quality factors of $\rm Q_i^{-1} \approx 3.64 - 8.57 \times10^{-8}$, and observe enhanced suppression of two-level system (TLS) loss in our devices at high internal microwave power levels before the onset of quasiparticle dissipation from microwave heating. We observe deviations from the standard TLS loss model at low powers and temperatures below 60 mK, and use a modified model to describe this behavior.
DAmodel: Hierarchical Bayesian Modelling of DA White Dwarfs for Spectrophotometric Calibration
2024-12-11
Benjamin M. Boyd, Gautham Narayan, Kaisey S. Mandel, Matthew Grayling, Aidan Berres, Mai Li, Aaron Do, Abhijit Saha, Tim Axelrod, Thomas Matheson, Edward W. Olszewski, Ralph C. Bohlin, Annalisa Calamida, Jay B. Holberg, Ivan Hubeny, John W. Mackenty, Armin Rest, Elena Sabbi, Christopher W. Stubbs
We use hierarchical Bayesian modelling to calibrate a network of 32 all-sky faint DA white dwarf (DA WD) spectrophotometric standards ($16.5 < V < 19.5$) alongside the three CALSPEC standards, from 912 \r{A} to 32 $\mu$m. The framework is the first of its kind to jointly infer photometric zeropoints and WD parameters ($\log g$, $T_{\text{eff}}$, $A_V$, $R_V$) by simultaneously modelling both photometric and spectroscopic data. We model panchromatic HST/WFC3 UVIS and IR fluxes, HST/STIS UV spectroscopy and ground-based optical spectroscopy to sub-percent precision. Photometric residuals for the sample are the lowest yet yielding $<0.004$ mag RMS on average from the UV to the NIR, achieved by jointly inferring time-dependent changes in system sensitivity and WFC3/IR count-rate nonlinearity. Our GPU-accelerated implementation enables efficient sampling via Hamiltonian Monte Carlo, critical for exploring the high-dimensional posterior space. The hierarchical nature of the model enables population analysis of intrinsic WD and dust parameters. Inferred SEDs from this model will be essential for calibrating the James Webb Space Telescope as well as next-generation surveys, including Vera Rubin Observatory's Legacy Survey of Space and Time, and the Nancy Grace Roman Space Telescope.
Multiple metamagnetic transitions in helical antiferromagnet CeVGe$_3$
2024-12-11
Hanshang Jin, Eun Sang Choi, Hung-Cheng Wu, N. J. Curro, K. Nawa, T. J. Sato, R. Kiyanagi, T. Ohhara, Peter Klavins, Valentin Taufour
We report on neutron diffraction, magnetoresistance, magnetization, and magnetic torque measurements under high magnetic field in the helical antiferromagnet CeVGe$_3$. This compound exhibits Kondo lattice coherence and helical antiferromagnetic (AFM) ordering at ambient pressure, similar to the well-studied CeRhIn$_5$. Our measurements reveal that CeVGe$_3$ undergoes a magnetic transition from an incommensurate (ICM) AFM state to an up-up-down-down commensurate (CM) AFM structure, followed by a transition to a novel phase at higher fields. A quantum phase transition occurs around 21.3 T. This rich magnetic field phase diagram closely resembles that of CeRhIn$_5$. Furthermore, angle-dependent magnetoresistance measurements reveal that all transitions in CeVGe$_3$ occur from the field component along the $ab$ plane. These findings highlight the intricate interplay among exchange interactions, crystal field effects, ground state properties, and crystalline symmetries.
$\texttt{galsbi}$: A Python package for the GalSBI galaxy population model
2024-12-11
Silvan Fischbacher, Beatrice Moser, Tomasz Kacprzak, Joerg Herbel, Luca Tortorelli, Uwe Schmitt, Alexandre Refregier, Adam Amara
Large-scale structure surveys measure the shape and position of millions of galaxies in order to constrain the cosmological model with high precision. The resulting large data volume poses a challenge for the analysis of the data, from the estimation of photometric redshifts to the calibration of shape measurements. We present GalSBI, a model for the galaxy population, to address these challenges. This phenomenological model is constrained by observational data using simulation-based inference (SBI). The $\texttt{galsbi}$ Python package provides an easy interface to generate catalogs of galaxies based on the GalSBI model, including their photometric properties, and to simulate realistic images of these galaxies using the $\texttt{UFig}$ package.
$\texttt{UFig v1}$: The ultra-fast image generator
2024-12-11
Silvan Fischbacher, Beatrice Moser, Tomasz Kacprzak, Luca Tortorelli, Joerg Herbel, Claudio Bruderer, Uwe Schmitt, Alexandre Refregier, Joel Berge, Lukas Gamper, Adam Amara
With the rise of simulation-based inference (SBI) methods, simulations need to be fast as well as realistic. $\texttt{UFig v1}$ is a public Python package that generates simulated astronomical images with exceptional speed - taking approximately the same time as source extraction. This makes it particularly well-suited for simulation-based inference (SBI) methods where computational efficiency is crucial. To render an image, $\texttt{UFig}$ requires a galaxy catalog and a description of the point spread function (PSF). It can also add background noise, sample stars using the Besan\c{c}on model of the Milky Way, and run $\texttt{SExtractor}$ to extract sources from the rendered image. The extracted sources can be matched to the intrinsic catalog, flagged based on $\texttt{SExtractor}$ output and survey masks, and emulators can be used to bypass the image simulation and extraction steps. A first version of $\texttt{UFig}$ was presented in Berg\'e et al. (2013) and the software has since been used and further developed in a variety of forward modelling applications.
Probing the major driver of stellar population properties over sub-galaxy scales with SDSS MaNGA IFU spectroscopy
2024-12-11
Ignacio Ferreras, Marina Trevisan, Ofer Lahav, Reinaldo R. de Carvalho, Joseph Silk
Thanks to Integral Field Unit survey data it is possible to explore in detail the link between the formation of the stellar content in galaxies and the drivers of evolution. Traditionally, scaling relations have connected galaxy-wide parameters such as stellar mass (M$_s$), morphology or average velocity dispersion ($\sigma$) to the star formation histories (SFHs). We study a high quality sample of SDSS-MaNGA spectra to test the possibility that sub-galaxy ($\sim$2\,kpc) scales are dominant, instead of galaxy-wide parameters. We find a strong correlation between local velocity dispersion and key line strengths that depend on the SFHs, allowing us to make the ansatz that this indicator - that maps the local gravitational potential - is the major driver of star formation in galaxies, whereas larger scales play a role of a secondary nature. Galactocentric distance has a weaker correlation, suggesting that the observed radial gradients effectively reflect local variations of velocity dispersion. In our quest for a cause, instead of a correlation, we contrast $\sigma$ with local stellar mass, that appears less correlated with population properties. We conclude that the inherently higher uncertainty in M$_s$ may explain its lower correlation with respect to $\sigma$, but the extra uncertainty needed for $\sigma$ to have similar correlations as M$_s$ is rather high. Therefore we posit local velocity dispersion as the major driver of evolution, a result that should be reproduced by hydrodynamical models at the proper resolution.
GalSBI: Phenomenological galaxy population model for cosmology using simulation-based inference
2024-12-11
Silvan Fischbacher, Tomasz Kacprzak, Luca Tortorelli, Beatrice Moser, Alexandre Refregier, Patrick Gebhardt, Daniel Gruen
We present GalSBI, a phenomenological model of the galaxy population for cosmological applications using simulation-based inference. The model is based on analytical parametrizations of galaxy luminosity functions, morphologies and spectral energy distributions. Model constraints are derived through iterative Approximate Bayesian Computation, by comparing Hyper Suprime-Cam deep field images with simulations which include a forward model of instrumental, observational and source extraction effects. We developed an emulator trained on image simulations using a normalizing flow. We use it to accelerate the inference by predicting detection probabilities, including blending effects and photometric properties of each object, while accounting for background and PSF variations. This enables robustness tests for all elements of the forward model and the inference. The model demonstrates excellent performance when comparing photometric properties from simulations with observed imaging data for key parameters such as magnitudes, colors and sizes. The redshift distribution of simulated galaxies agrees well with high-precision photometric redshifts in the COSMOS field within $1.5\sigma$ for all magnitude cuts. Additionally, we demonstrate how GalSBI's redshifts can be utilized for splitting galaxy catalogs into tomographic bins, highlighting its potential for current and upcoming surveys. GalSBI is fully open-source, with the accompanying Python package, $\texttt{galsbi}$, offering an easy interface to quickly generate realistic, survey-independent galaxy catalogs.
New Technologies for Axion and Dark Photon Searches
2024-12-11
Asher Berlin, Yonatan Kahn
The search for dark matter and physics beyond the Standard Model has grown to encompass a highly interdisciplinary approach. In this review, we survey recent searches for light, weakly-coupled particles - axions and dark photons - over the past decade, focusing on new experimental results and the incorporation of technologies and techniques from fields as diverse as quantum science, microwave engineering, precision magnetometry, and condensed matter physics. We also review theoretical progress which has been useful in identifying new experimental directions, and identify the areas of most rapid experimental progress and the technological advances required to continue exploring the parameter space for axions and dark photons.
Multiprobe Cosmology from the Abundance of SPT Clusters and DES Galaxy Clustering and Weak Lensing
2024-12-10
S. Bocquet, S. Grandis, E. Krause, C. To, L. E. Bleem, M. Klein, J. J. Mohr, T. Schrabback, A. Alarcon, O. Alves, A. Amon, F. Andrade-Oliveira, E. J. Baxter, K. Bechtol, M. R. Becker, G. M. Bernstein, J. Blazek, H. Camacho, A. Campos, A. Carnero Rosell, M. Carrasco Kind, R. Cawthon, C. Chang, R. Chen, A. Choi, J. Cordero, M. Crocce, C. Davis, J. DeRose, H. T. Diehl, S. Dodelson, C. Doux, A. Drlica-Wagner, K. Eckert, T. F. Eifler, F. Elsner, J. Elvin-Poole, S. Everett, X. Fang, A. Ferté, P. Fosalba, O. Friedrich, J. Frieman, M. Gatti, G. Giannini, D. Gruen, R. A. Gruendl, I. Harrison, W. G. Hartley, K. Herner, H. Huang, E. M. Huff, D. Huterer, M. Jarvis, N. Kuropatkin, P. -F. Leget, P. Lemos, A. R. Liddle, N. MacCrann, J. McCullough, J. Muir, J. Myles, A. Navarro-Alsina, S. Pandey, Y. Park, A. Porredon, J. Prat, M. Raveri, R. P. Rollins, A. Roodman, R. Rosenfeld, E. S. Rykoff, C. Sánchez, J. Sanchez, L. F. Secco, I. Sevilla-Noarbe, E. Sheldon, T. Shin, M. A. Troxel, I. Tutusaus, T. N. Varga, N. Weaverdyck, R. H. Wechsler, H. -Y. Wu, B. Yanny, B. Yin, Y. Zhang, J. Zuntz, T. M. C. Abbott, P. A. R. Ade, M. Aguena, S. Allam, S. W. Allen, A. J. Anderson, B. Ansarinejad, J. E. Austermann, M. Bayliss, J. A. Beall, A. N. Bender, B. A. Benson, F. Bianchini, M. Brodwin, D. Brooks, L. Bryant, D. L. Burke, R. E. A. Canning, J. E. Carlstrom, J. Carretero, F. J. Castander, C. L. Chang, P. Chaubal, H. C. Chiang, T-L. Chou, R. Citron, C. Corbett Moran, M. Costanzi, T. M. Crawford, A. T. Crites, L. N. da Costa, M. E. S. Pereira, T. M. Davis, T. de Haan, M. A. Dobbs, P. Doel, W. Everett, A. Farahi, B. Flaugher, A. M. Flores, B. Floyd, J. Gallicchio, E. Gaztanaga, E. M. George, M. D. Gladders, N. Gupta, G. Gutierrez, N. W. Halverson, S. R. Hinton, J. Hlavacek-Larrondo, G. P. Holder, D. L. Hollowood, W. L. Holzapfel, J. D. Hrubes, N. Huang, J. Hubmayr, K. D. Irwin, D. J. James, F. Kéruzoré, G. Khullar, K. Kim, L. Knox, R. Kraft, K. Kuehn, O. Lahav, A. T. Lee, S. Lee, D. Li, C. Lidman, M. Lima, A. Lowitz, G. Mahler, A. Mantz, J. L. Marshall, M. McDonald, J. J. McMahon, J. Mena-Fernández, S. S. Meyer, R. Miquel, J. Montgomery, T. Natoli, J. P. Nibarger, G. I. Noble, V. Novosad, R. L. C. Ogando, S. Padin, P. Paschos, S. Patil, A. A. Plazas Malagón, C. Pryke, C. L. Reichardt, J. Roberson, A. K. Romer, C. Romero, J. E. Ruhl, B. R. Saliwanchik, L. Salvati, S. Samuroff, E. Sanchez, B. Santiago, A. Sarkar, A. Saro, K. K. Schaffer, K. Sharon, C. Sievers, G. Smecher, M. Smith, T. Somboonpanyakul, M. Sommer, B. Stalder, A. A. Stark, J. Stephen, V. Strazzullo, E. Suchyta, M. E. C. Swanson, G. Tarle, D. Thomas, C. Tucker, D. L. Tucker, T. Veach, J. D. Vieira, A. von der Linden, G. Wang, N. Whitehorn, W. L. K. Wu, V. Yefremenko, M. Young, J. A. Zebrowski, H. Zohren, DES Collaboration, SPT Collaboration
Cosmic shear, galaxy clustering, and the abundance of massive halos each probe the large-scale structure of the universe in complementary ways. We present cosmological constraints from the joint analysis of the three probes, building on the latest analyses of the lensing-informed abundance of clusters identified by the South Pole Telescope (SPT) and of the auto- and cross-correlation of galaxy position and weak lensing measurements (3$\times$2pt) in the Dark Energy Survey (DES). We consider the cosmological correlation between the different tracers and we account for the systematic uncertainties that are shared between the large-scale lensing correlation functions and the small-scale lensing-based cluster mass calibration. Marginalized over the remaining $\Lambda$CDM parameters (including the sum of neutrino masses) and 52 astrophysical modeling parameters, we measure $\Omega_\mathrm{m}=0.300\pm0.017$ and $\sigma_8=0.797\pm0.026$. Compared to constraints from Planck primary CMB anisotropies, our constraints are only 15% wider with a probability to exceed of 0.22 ($1.2\sigma$) for the two-parameter difference. We further obtain $S_8\equiv\sigma_8(\Omega_\mathrm{m}/0.3)^{0.5}=0.796\pm0.013$ which is lower than the Planck measurement at the $1.6\sigma$ level. The combined SPT cluster, DES 3$\times$2pt, and Planck datasets mildly prefer a non-zero positive neutrino mass, with a 95% upper limit $\sum m_\nu<0.25~\mathrm{eV}$ on the sum of neutrino masses. Assuming a $w$CDM model, we constrain the dark energy equation of state parameter $w=-1.15^{+0.23}_{-0.17}$ and when combining with Planck primary CMB anisotropies, we recover $w=-1.20^{+0.15}_{-0.09}$, a $1.7\sigma$ difference with a cosmological constant. The precision of our results highlights the benefits of multiwavelength multiprobe cosmology.
VLASS-based survey of transition state galaxies and their relationship to compact peaked-spectrum radio sources
2024-12-10
M. Kunert-Bajraszewska, A. Krauze, A. E. Kimball, L. Stawarz, P. Kharb, D. Stern, K. Mooley, K. Nyland, D. Koziel-Wierzbowska
We present multi-frequency and high-resolution studies of a sample of 24 radio transients sources discovered by comparing the NRAO VLA Sky Survey (NVSS) and Very Large Array Sky Survey (VLASS) surveys. All of them are characterized by a significant increase in radio flux density over the last two decades.Their convex spectra, small sizes and high brightness temperatures are typical for young gigahertz-peaked spectrum (GPS) radio sources and indicative of an AGN buried in the host galaxy. On the other hand, they are much weaker than the archetypical GPS objects and their parsec-scale radio structures, although indicating the presence of young radio jets, are similar to radio-quiet AGNs like Seyfert and low-ionization nuclear emission-line region (LINER) galaxies. Based on the distribution of these objects in power$-$size ($P - D$) and peak frequency$-$size ($\nu_p - D$) diagrams, we suggest that after stabilizing their radio activity, some of the GHz-peaked radio transients (galaxies and quasars) will develop into radio-intermediate and radio-quiet (RI/RQ) quasars and low-frequency peaked-spectrum (PS) objects. We discuss several possible origins for the transient radio emission in our sources and conclude that changes in the accretion rate combined with low-power radio ejecta are the most probable cause. This is the scenario we also propose for one of our sources, 101841$-$13, which was independently identified as a candidate tidal disruption event (TDE) based on its infrared variability. However, we cannot exclude that 101841$-$13 or other sources in our sample are TDEs.
The CARMENES search for exoplanets around M dwarfs. The impact of rotation and magnetic fields on the radial velocity jitter in cool stars
2024-12-10
H. L. Ruh, M. Zechmeister, A. Reiners, E. Nagel, Y. Shan, C. Cifuentes, S. V. Jeffers, L. Tal-Or, V. J. S. Béjar, P. J. Amado, J. A. Caballero, A. Quirrenbach, I. Ribas, J. Aceituno, A. P. Hatzes, Th. Henning, A. Kaminski, D. Montes, J. C. Morales, P. Schöfer, A. Schweitzer, R. Varas
Radial velocity (RV) jitter represents an intrinsic limitation on the precision of Doppler searches for exoplanets that can originate from both instrumental and astrophysical sources. We aim to determine the RV jitter floor in M dwarfs and investigate the stellar properties that lead to RV jitter induced by stellar activity. We determined the RV jitter in 239 M dwarfs from the CARMENES survey that are predominantly of mid to late spectral type and solar metallicity. We also investigated the correlation between stellar rotation and magnetic fields with RV jitter. The median jitter in the CARMENES sample is 3.1 m/s, and it is 2.3 m/s for stars with an upper limit of 2 km/s on their projected rotation velocities. We provide a relation between the stellar equatorial rotation velocity and RV jitter in M dwarfs based on a subsample of 129 well-characterized CARMENES stars. RV jitter induced by stellar rotation dominates for stars with equatorial rotation velocities greater than 1 km/s. A jitter floor of 2 m/s dominates in stars with equatorial rotation velocities below 1 km/s. This jitter floor likely contains contributions from stellar jitter, instrumental jitter, and undetected companions. We study the impact of the average magnetic field and the distributions of magnetic filling factors on the RV jitter. We find a series of stars with excess RV jitter and distinctive distributions of magnetic filling factors. These stars are characterized by a dominant magnetic field component between 2-4 kG. An RV jitter floor can be distinguished from RV jitter induced by activity and rotation based on the stellar equatorial rotation velocity. RV jitter induced by activity and rotation primarily depends on the equatorial rotation velocity. This RV jitter is also related to the distribution of magnetic filling factors, and this emphasizes the role of the magnetic field in the generation of RV jitter.
Two Distinct Populations of Dark Comets Delineated by Orbits and Sizes
2024-12-10
Darryl Z. Seligman, Davide Farnocchia, Marco Micheli, Olivier R. Hainaut, Henry H. Hsieh, Adina D. Feinstein, Steven R. Chesley, Aster G. Taylor, Joseph Masiero, Karen J. Meech
Small bodies are capable of delivering essential prerequisites for the development of life, such as volatiles and organics, to the terrestrial planets. For example, empirical evidence suggests that water was delivered to the Earth by hydrated planetesimals from distant regions of the Solar System. Recently, several morphologically inactive near-Earth objects (NEOs) were reported to experience significant nongravitational accelerations inconsistent with radiation-based effects, and possibly explained by volatile-driven outgassing. However, these "dark comets" display no evidence of comae in archival images, which are the defining feature of cometary activity. Here we report detections of nongravitational accelerations on seven additional objects previously classified as inactive (doubling the population) that could also be explainable by asymmetric mass loss. A detailed search of archival survey and targeted data rendered no detection of dust activity in any of these objects in individual or stacked images. We calculate dust production limits of $\sim10$, $0.1$, and $0.1$ kg s$^{-1}$ for 1998 FR$_{11}$, 2001 ME$_{1}$, and 2003 RM with these data, indicating little or no dust surrounding the objects during the observations. This set of dark comets reveals the delineation between two distinct populations: larger, "outer" dark comets on eccentric orbits that are end members of a continuum in activity level of comets, and smaller, "inner" dark comets on near-circular orbits that could signify a new population. These objects may trace various stages in the life cycle of a previously undetected, but potentially numerous, volatile-rich population that may have provided essential material to the Earth.
Proceedings 17th Interaction and Concurrency Experience
2024-12-10
Clément Aubert, Cinzia Di Giusto, Simon Fowler, Violet Ka I Pun
This volume contains the proceedings of ICE'24, the 17th Interaction and Concurrency Experience, which was held on Friday 21th June 2024 at the University of Groningen in Groningen, The Netherlands, as a satellite workshop of DisCoTec 2024. The ICE workshop series features a distinguishing review and selection procedure: PC members are encouraged to interact, anonymously, with authors. The 2024 edition of ICE included double blind reviewing of original research papers, in order to increase fairness and avoid bias in reviewing. Each paper was reviewed by three PC members, and altogether 3 papers were accepted for publication plus 1 oral communication, which was accepted for presentation at the workshop. We were proud to host one invited talk, by Jorge A. P\'erez. The abstract of his talk is included in this volume, together with the final versions of the research papers, which take into account the discussion at the workshop and during the review process.
Electromagnetic form factors of hyperons in the timelike region: A short review
2024-12-10
Ling-Yun Dai, Johann Haidenbauer, Ulf-G. Meißner
We review recent experimental and theoretical results for the electromagnetic form factors of hyperons (Y) in the timelike region, accessible in the reactions $e^+e^-\to \bar YY$. Specifically, we focus on the final states $\bar \Lambda\Lambda$, $\bar\Lambda\Sigma^0$/$\bar \Sigma^0\Lambda$, $\bar \Sigma\Sigma$, $\bar \Xi\Xi$, and $\bar\Omega\Omega$. The $\bar \Lambda_c\Lambda_c$ system is also discussed.
RIS-aided Wireless-Powered Backscatter Communications for Sustainable Internet of Underground Things
2024-12-10
Kaiqiang Lin, Yijie Mao
Wireless-powered underground sensor networks (WPUSNs), which enable wireless energy transfer to sensors located underground, is a promising approach for establishing sustainable internet of underground things (IoUT). To support urgent information transmission and improve resource utilization within WPUSNs, backscatter communication (BC) is introduced, resulting in what is known as wireless-powered backscatter underground sensor networks (WPBUSNs). Nevertheless, the performance of WPBUSNs is significantly limited by severe channel impairments caused by the underground soil and the blockage of direct links. To overcome this challenge, in this work, we propose integrating reconfigurable intelligent surface (RIS) with WPBUSNs, leading to the development of RIS-aided WPBUSNs. We begin by reviewing the recent advancements in BC-WPUSNs and RIS. Then, we propose a general architecture of RIS-aided WPBUSNs across various IoUT scenarios, and discuss its advantages and implementation challenges. To illustrate the effectiveness of RIS-aided WPBUSNs, we focus on a realistic farming case study, demonstrating that our proposed framework outperforms the three benchmarks in terms of the sum throughput. Finally, we discuss the open challenges and future research directions for translating this study into practical IoUT applications.
The reproducibility of computational pipelines is an expectation in biomedical science, particularly in critical domains like human health. In this context, reporting next generation genome sequencing methods used in precision medicine spurred the development of the IEEE 2791-2020 standard for Bioinformatics Analyses Generated by High Throughput Sequencing (HTS), known as the BioCompute Object (BCO). Championed by the USA's Food and Drug Administration, the BCO is a pragmatic framework for documenting pipelines; however, it has not been systematically assessed for its reproducibility claims. This study uses the PRIMAD model, a conceptual framework for describing computational experiments for reproducibility purposes, to systematically review the BCO for depth and coverage. A meticulous mapping of BCO and PRIMAD elements onto a published BCO use case reveals potential omissions and necessary extensions within both frameworks. This underscores the significance of systematically validating claims of reproducibility for published digital objects, thereby enhancing the reliability of scientific research in bioscience and related disciplines. This study, along with its artifacts, is reported as a RO-Crate, providing a structured reporting approach, which is available at https://doi.org/10.5281/zenodo.14317922.
In recent years, there has been increasing collaboration between the fields of quantum computing and high energy physics, including using LHC processes such as top (anti-)quark pair production to perform high energy tests of quantum entanglement. In this proceeding, I will review another interesting property from quantum computing ("magic"), that is needed to make quantum computers with genuine computational advantage over their classical counterparts. How to make and enhance magic in general quantum systems is an open question, such that new insights are always useful. To this end, I will show that the LHC naturally produces magic top quarks, providing a novel playground for further study in this area.
This ia a review of anomalies applied to a bottom-up approach to standard model and gravity. It is divided in two parts. The first consists in a review proper of anomalies in quantum field theories. Anomalies are analyzed according to three different methods: a perturbative one based on Feynman diagram, a non-perturbative one relying on the Schwinger-DeWitt approach and, third, the one hinging on the Atiyah-Singer family's index theorem. The three methods are applied both to chiral gauge anomalies and trace anomalies. The fundamental distinction that our presentation leads to is between obstructive (O) and non-obstructive (NO) anomalies. The former are tied to the non-existence of fermion propagators, which fatally maim the corresponding theory. In the second part we apply this analysis to the SM and various of its extensions immersed in a gravitational background, and find that they all are plagued by a residual chiral trace anomaly. To completely eliminate all kind of dangerous anomalies in SM-like theories we propose a somewhat unconventional scheme, and exemplify it by means of an explicit model. The latter is a left-right symmetric model. We embed it in a Weyl geometry to render it conformal invariant. We then deal with some of its quantum aspects, in particular its even (NO) trace anomalies and the means to preserve its confomal invariance at the quantum level. We briefly review renormalization and unitarity in the framework of similar models discussed in the existing literature. Finally we present a possible (conjectural) application of the model to describe the junction between cosmology and quantum field theory.
Moving to the suburbs? Exploring the potential impact of work-from-home on suburbanization in Poland
2024-12-10
Beata Woźniak-Jęchorek, Sławomir Kuźmar, David Bole
The main goal of this paper is to assess the likelihood of office workers relocating to the suburbs due to work-from-home opportunities and the key factors influencing these preferences. Our study focuses on Poland, a country with different cultural individualism at work, which can impact work-from-home preferences and, indirectly, home relocation desires. Given the methodological limitations of official data on remote work practices in Poland, we conducted an original survey, gathering primary data from a nationally representative sample of office workers living in cities with populations exceeding 100,000. To investigate the factors shaping employees' preferences for suburban relocation driven by remote work, we utilized logistic regression to analyze the effects of socio-economic and employment characteristics, commuting experiences, and reported changes in work productivity. Our findings reveal that age, mode of commuting, perceived changes in work productivity, and sector ownership are significant determinants, strongly affecting home relocation preferences in response to work-from-home opportunities. These results align with previous research, typically based on data from countries with different cultural frameworks and more developed work-from-home practices.
Identity theft and societal acceptability of electronic identity in Europe and in the United States
2024-12-10
Marek Tiits, Tarmo Kalvet, David McBee
This paper addresses critical questions surrounding the security of government-issued identity documents and their potential misuse, with an emphasis on understanding the perspectives of ordinary citizens across Europe and the United States of America. Drawing upon research on technology acceptance and diffusion, the research focuses on understanding the factors that influence users' adoption of novel identity management solutions. Our methodology includes a comprehensive, census-representative survey spanning citizens from France, Germany, Italy, Spain, the United Kingdom, and the USA. The paper's findings underscore a robust confidence in government-issued identity documents, contrasted by a lower trust in private sector services, including social media platforms and email accounts. The adoption of artificial intelligence for identity verification remains contested, with a significant percentage of respondents undecided, indicating a need for explicit explanation and transparency about its implementation and related risks. Public sentiment leans towards acceptance of government data collection for identification purposes; however, the sharing of this data with private entities elicits more apprehension.
A Robust Sustainability Assessment Methodology for Aircraft Parts: Application to a Fuselage Panel
2024-12-10
Aikaterini A. Anagnostopoulou, Dimitris G. Sotiropoulos, Konstantinos I. Tserpes
The paper presents a cradle-to-gate sustainability assessment methodology specifically designed to evaluate aircraft components in a robust and systematic manner. This methodology integrates multi-criteria decision-making (MCDM) analysis across ten criteria, categorized under environmental impact, cost, and performance. Environmental impact is analyzed through life cycle assessment and cost through life cycle costing, with both analyses facilitated by SimaPro software. Performance is measured in terms of component mass and specific stiffness. The robustness of this methodology is tested through various MCDM techniques, normalization approaches, and objective weighting methods. To demonstrate the methodology, the paper assesses the sustainability of a fuselage panel, comparing nine variants that differ in materials, joining techniques, and part thicknesses. All approaches consistently identify thermoplastic CFRP panels as the most sustainable option, with the geometric mean aggregation of weights providing balanced criteria consideration across environmental, cost, and performance aspects. The adaptability of this proposed methodology is illustrated, showing its applicability to any aircraft component with the requisite data. This structured approach offers critical insights to support sustainable decision-making in aircraft component design and procurement.
In today's world, stress is a big problem that affects people's health and happiness. More and more people are feeling stressed out, which can lead to lots of health issues like breathing problems, feeling overwhelmed, heart attack, diabetes, etc. This work endeavors to forecast stress and non-stress occurrences among college students by applying various machine learning algorithms: Decision Trees, Random Forest, Support Vector Machines, AdaBoost, Naive Bayes, Logistic Regression, and K-nearest Neighbors. The primary objective of this work is to leverage a research study to predict and mitigate stress and non-stress based on the collected questionnaire dataset. We conducted a workshop with the primary goal of studying the stress levels found among the students. This workshop was attended by Approximately 843 students aged between 18 to 21 years old. A questionnaire was given to the students validated under the guidance of the experts from the All India Institute of Medical Sciences (AIIMS) Raipur, Chhattisgarh, India, on which our dataset is based. The survey consists of 28 questions, aiming to comprehensively understand the multidimensional aspects of stress, including emotional well-being, physical health, academic performance, relationships, and leisure. This work finds that Support Vector Machines have a maximum accuracy for Stress, reaching 95\%. The study contributes to a deeper understanding of stress determinants. It aims to improve college student's overall quality of life and academic success, addressing the multifaceted nature of stress.
A Review of Challenges in Speech-based Conversational AI for Elderly Care
2024-12-10
Willemijn Klaassen, Bram van Dijk, Marco Spruit
Artificially intelligent systems optimized for speech conversation are appearing at a fast pace. Such models are interesting from a healthcare perspective, as these voice-controlled assistants may support the elderly and enable remote health monitoring. The bottleneck for efficacy, however, is how well these devices work in practice and how the elderly experience them, but research on this topic is scant. We review elderly use of voice-controlled AI and highlight various user- and technology-centered issues, that need to be considered before effective speech-controlled AI for elderly care can be realized.
An Abstract Account of Up-to Techniques for Inductive Behavioural Relations
2024-12-10
Davide Sangiorgi
Up-to techniques' represent enhancements of the coinduction proof method and are widely used on coinductive behavioural relations such as bisimilarity. Abstract formulations of these coinductive techniques exist, using fixed-points or category theory. A proposal has been recently put forward for transporting the enhancements onto the concrete realms of inductive behavioural relations, i.e., relations defined from inductive observables, such as traces or enriched forms of traces. The abstract meaning of such 'inductive enhancements', however, has not been explored. In this paper, we review the theory, and then propose an abstract account of it, using fixed-point theory in complete lattices.
Controlled Locomotion of a Minimal Soft Structure by Stick-Slip Nonlinearity
2024-12-10
Thomas Barois, A. Boucherie, L. Tadrist, H. Kellay
We present a locomotion mechanism that uses the stick-slip transition of a soft passive structure with an internal mechanical resonance. The structure is harmonically driven by a global vertical shaking and, because of its resonance dephasing and the threshold response of stick-slip transition, it can either move forward or backward. We establish a relation for the motion acceleration threshold that we experimentally validate. We identify a non-trivial regime close to the resonance with a velocity inversion for a constant excitation frequency and an increasing driving amplitude. We finally show that we can achieve a controlled multi-modal motion by combining multiple internal resonances.
Tides on Lava Worlds: Application to Close-in Exoplanets and the Early Earth-Moon System
2024-12-10
Mohammad Farhat, Pierre Auclair-Desrotour, Gwenaël Boué, Tim Lichtenberg, Jacques Laskar
Understanding the physics of planetary magma oceans has been the subject of growing efforts, in light of the increasing abundance of Solar system samples and extrasolar surveys. A rocky planet harboring such an ocean is likely to interact tidally with its host star, planetary companions, or satellites. To date, however, models of the tidal response and heat generation of magma oceans have been restricted to the framework of weakly viscous solids, ignoring the dynamical fluid behavior of the ocean beyond a critical melt fraction. Here we provide a handy analytical model that accommodates this phase transition, allowing for a physical estimation of the tidal response of lava worlds. We apply the model in two settings: The tidal history of the early Earth-Moon system in the aftermath of the giant impact; and the tidal interplay between short-period exoplanets and their host stars. For the former, we show that the fluid behavior of the Earth's molten surface drives efficient early Lunar recession to ${\sim} 25$ Earth radii within $10^4{-} 10^5$ years, in contrast with earlier predictions. For close-in exoplanets, we report on how their molten surfaces significantly change their spin-orbit dynamics, allowing them to evade spin-orbit resonances and accelerating their track towards tidal synchronization from a Gyr to Myr timescale. Moreover, we re-evaluate the energy budgets of detected close-in exoplanets, highlighting how the surface thermodynamics of these planets are likely controlled by enhanced, fluid-driven tidal heating, rather than vigorous insolation, and how this regime change substantially alters predictions for their surface temperatures.
Detecting the Black Hole Candidate Population in M51's Young Massive Star Clusters: Constraints on Accreting Intermediate Mass Black Holes
2024-12-10
Kristen C. Dage, Evangelia Tremou, Bolivia Cuevas Otahola, Eric W. Koch, Kwangmin Oh, Richard M. Plotkin, Vivian L. Tang, Muhammad Ridha Aldhalemi, Zainab Bustani, Mariam Ismail Fawaz, Hans J. Harff, Amna Khalyleh, Timothy McBride, Jesse Mason, Anthony Preston, Cortney Rinehart, Ethan Vinson, Gemma Anderson, Edward M. Cackett, Shih Ching Fu, Sebastian Kamann, Teresa Panurach, Renuka Pechetti, Payaswini Saikia, Susmita Sett, Ryan Urquhart, Christopher Usher
Intermediate mass black holes (10^2 < M_BH< 10^5 Msun) are an open question in our understanding of black hole evolution and growth. They have long been linked to dense star cluster environments thanks to cluster dynamics, but there are a limited number of secure detections. We leverage existing X-ray observations from Chandra X-ray Observatory and optical catalogs from Hubble Space Telescope with new radio observations from the Karl G. Jansky Very Large Array to search for any evidence of accreting black holes in young massive clusters in the nearby galaxy M51. We find that of 43 bright ($L_X > 10^{38}$ erg/s) X-ray point sources in M51, 24 had probable matches to objects including possible associated star clusters in the HST Legacy Extragalactic UV Survey catalog, seven of which were classified as contaminants (background galaxies or foreground stars). We explore the optical properties of the remaining 17 sources, including cluster age and mass estimates, and search for radio counterparts in the 8-12 GHz band. The lack of radio counterparts to X-ray sources we know to be associated with young massive clusters in M51 suggests that we do not significantly detect hard-state IMBHs ~ 10^4 Msun or above. However, more sensitive radio facilities like the Square Kilometre Array and next generation Very Large Array may be able to provide evidence for IMBHs with masses down to ~ 10^3 Msun.
Human-Computer Interaction and Human-AI Collaboration in Advanced Air Mobility: A Comprehensive Review
2024-12-10
Fatma Yamac Sagirli, Xiaopeng Zhao, Zhenbo Wang
The increasing rates of global urbanization and vehicle usage are leading to a shift of mobility to the third dimension-through Advanced Air Mobility (AAM)-offering a promising solution for faster, safer, cleaner, and more efficient transportation. As air transportation continues to evolve with more automated and autonomous systems, advancements in AAM require a deep understanding of human-computer interaction and human-AI collaboration to ensure safe and effective operations in complex urban and regional environments. There has been a significant increase in publications regarding these emerging applications; thus, there is a need to review developments in this area. This paper comprehensively reviews the current state of research on human-computer interaction and human-AI collaboration in AAM. Specifically, we focus on AAM applications related to the design of human-machine interfaces for various uses, including pilot training, air traffic management, and the integration of AI-assisted decision-making systems with immersive technologies such as extended, virtual, mixed, and augmented reality devices. Additionally, we provide a comprehensive analysis of the challenges AAM encounters in integrating human-computer frameworks, including unique challenges associated with these interactions, such as trust in AI systems and safety concerns. Finally, we highlight emerging opportunities and propose future research directions to bridge the gap between human factors and technological advancements in AAM.
Stochastic Integration Based Estimator: Robust Design and Stone Soup Implementation
2024-12-10
Jindrich Dunik, Jakub Matousek, Ondrej Straka, Erik Blasch, John Hiles, Ruixin Niu
This paper deals with state estimation of nonlinear stochastic dynamic models. In particular, the stochastic integration rule, which provides asymptotically unbiased estimates of the moments of nonlinearly transformed Gaussian random variables, is reviewed together with the recently introduced stochastic integration filter (SIF). Using SIF, the respective multi-step prediction and smoothing algorithms are developed in full and efficient square-root form. The stochastic-integration-rule-based algorithms are implemented in Python (within the Stone Soup framework) and in MATLAB and are numerically evaluated and compared with the well-known unscented and extended Kalman filters using the Stone Soup defined tracking scenario.
The identity of a speaker significantly influences spoken language comprehension by affecting both perception and expectation. This review explores speaker effects, focusing on how speaker information impacts language processing. We propose an integrative model featuring the interplay between bottom-up perception-based processes driven by acoustic details and top-down expectation-based processes driven by a speaker model. The acoustic details influence lower-level perception, while the speaker model modulates both lower-level and higher-level processes such as meaning interpretation and pragmatic inferences. We define speaker-idiosyncrasy and speaker-demographics effects and demonstrate how bottom-up and top-down processes interact at various levels in different scenarios. This framework contributes to psycholinguistic theory by offering a comprehensive account of how speaker information interacts with linguistic content to shape message construction. We suggest that speaker effects can serve as indices of a language learner's proficiency and an individual's characteristics of social cognition. We encourage future research to extend these findings to AI speakers, probing the universality of speaker effects across humans and artificial agents.
BrokerChain: A Blockchain Sharding Protocol by Exploiting Broker Accounts
2024-12-10
Huawei Huang, Zhaokang Yin, Qinde Chen, Guang Ye, Xiaowen Peng, Yue Lin, Zibin Zheng, Song Guo
State-of-the-art blockchain sharding solutions such as Monoxide, can cause severely imbalanced distribution of transaction (TX) workloads across all blockchain shards due to the deployment policy of their accounts. Imbalanced TX distributions then produce hot shards, in which the cross-shard TXs may experience an unlimited confirmation latency. Thus, how to address the hot-shard issue and how to reduce crossshard TXs become significant challenges of blockchain sharding. Through reviewing the related studies, we find that a crossshard TX protocol that can achieve workload balance among all shards and simultaneously reduce the quantity of crossshard TXs is still absent from the literature. To this end, we propose BrokerChain, which is a cross-shard blockchain protocol dedicated to account-based state sharding. Essentially, BrokerChain exploits fine-grained state partition and account segmentation. We also elaborate on how BrokerChain handles cross-shard TXs through broker accounts. The security issues and other properties of BrokerChain are analyzed rigorously. Finally, we conduct comprehensive evaluations using an opensource blockchain sharding prototype named BlockEmulator. The evaluation results show that BrokerChain outperforms other baselines in terms of transaction throughput, transaction confirmation latency, the queue size of the transaction pool, and workload balance.
A Review on the Applications of Transformer-based language models for Nucleotide Sequence Analysis
2024-12-10
Nimisha Ghosh, Daniele Santoni, Indrajit Saha, Giovanni Felici
In recent times, Transformer-based language models are making quite an impact in the field of natural language processing. As relevant parallels can be drawn between biological sequences and natural languages, the models used in NLP can be easily extended and adapted for various applications in bioinformatics. In this regard, this paper introduces the major developments of Transformer-based models in the recent past in the context of nucleotide sequences. We have reviewed and analysed a large number of application-based papers on this subject, giving evidence of the main characterizing features and to different approaches that may be adopted to customize such powerful computational machines. We have also provided a structured description of the functioning of Transformers, that may enable even first time users to grab the essence of such complex architectures. We believe this review will help the scientific community in understanding the various applications of Transformer-based language models to nucleotide sequences. This work will motivate the readers to build on these methodologies to tackle also various other problems in the field of bioinformatics.
Effective Reward Specification in Deep Reinforcement Learning
2024-12-10
Julien Roy
In the last decade, Deep Reinforcement Learning has evolved into a powerful tool for complex sequential decision-making problems. It combines deep learning's proficiency in processing rich input signals with reinforcement learning's adaptability across diverse control tasks. At its core, an RL agent seeks to maximize its cumulative reward, enabling AI algorithms to uncover novel solutions previously unknown to experts. However, this focus on reward maximization also introduces a significant difficulty: improper reward specification can result in unexpected, misaligned agent behavior and inefficient learning. The complexity of accurately specifying the reward function is further amplified by the sequential nature of the task, the sparsity of learning signals, and the multifaceted aspects of the desired behavior. In this thesis, we survey the literature on effective reward specification strategies, identify core challenges relating to each of these approaches, and propose original contributions addressing the issue of sample efficiency and alignment in deep reinforcement learning. Reward specification represents one of the most challenging aspects of applying reinforcement learning in real-world domains. Our work underscores the absence of a universal solution to this complex and nuanced challenge; solving it requires selecting the most appropriate tools for the specific requirements of each unique application.
The first exploration of the correlations between \textit{WISE} 12 \micron\ and CO emission in early-type galaxies
2024-12-10
Yang Gao, Enci Wang, Qing-Hua Tan, Timothy A. Davis, Fu-Heng Liang, Xue-Jian Jiang, Ning Gai, Qian Jiao, DongDong Shi, Shuai Feng, Yanke Tang, Shijie Li, Yi-Fan Wang
We present the analysis of a comprehensive sample of 352 early-type galaxies using public data, to investigate the correlations between CO luminosities and mid-infrared luminosities observed by \textit{Wide-field Infrared Survey Explorer} (\textit{WISE}). We find strong correlations between both CO (1-0) and CO (2-1) luminosities and 12 \micron\ luminosity, boasting a correlation coefficient greater than 0.9 and an intrinsic scatter smaller than 0.1 dex. The consistent slopes observed for the relationships of CO (1-0) and CO (2-1) suggest that the line ratio R21 lacks correlation with mid-infrared emission in early-type galaxies, which is significantly different from star-forming galaxies. Moreover, the slopes of $L_{\rm CO (1-0)}$--$L_{\mbox{12\micron}}$ and $L_{\rm CO (2-1)}$--$L_{\mbox{12\micron}}$ relations in early-type galaxies are steeper than those observed in star-forming galaxies. Given the absence of correlation with color, morphology or sSFR, the correlation between deviations and the molecular gas mass surface density could be eliminated by correcting the possible 12 \micron\ emission from old stars or adopting a systematically different $\alpha_{\rm CO}$. The latter, on average, is equivalent to adding an constant CO brightness density, specifically ${2.8{_{-0.6}}\!\!\!\!\!\!\!\!\!^{+0.8}}~[\mathrm{K~km~s^{-1}}]$ and ${4.4{_{-1.4}}\!\!\!\!\!\!\!\!\!^{+2.2}}~[\mathrm{K~km~s^{-1}}]$ for CO (1-0) and (2-1) respectively. These explorations will serve as useful tools for estimating the molecular gas content in gas-poor galaxies and understanding associated quenching processes.
A Bayesian Mixture Model Approach to Examining Neighborhood Social Determinants of Health Disparities in Endometrial Cancer Care in Massachusetts
2024-12-10
Carmen B. Rodriguez, Stephanie M. Wu, Stephanie Alimena, Alecia J McGregor, Briana JK Stephenson
Many studies have examined social determinants of health (SDoH) factors independently, overlooking their interconnected and intersectional nature. Our study takes a multifactorial approach to construct a neighborhood level measure of SDoH and explores how neighborhood residency impacts care received by endometrial cancer patients in Massachusetts. We used a Bayesian multivariate Bernoulli mixture model to create and characterize neighborhood SDoH (NSDoH) profiles using the 2015-2019 American Community Survey at the census tract level (n=1478), incorporating 18 variables across four domains: housing conditions and resources, economic security, educational attainment, and social and community context. We linked these profiles to Massachusetts Cancer Registry data to estimate the odds of receiving optimal care for endometrial cancer using Bayesian multivariate logistic regression. The model identified eight NSDoH profiles. Profiles 1 and 2 accounted for 27% and 25% of census tracts, respectively. Profile 1 featured neighborhoods with high homeownership, above median incomes, and high education, while Profile 2 showed higher probabilities of limited English proficiency, renters, lower education, and working class jobs. After adjusting for sociodemographic and clinical characteristics, we found no statistically significant association between NSDoH profiles and receipt of optimal care. However, compared to patients in NSDoH Profile 1, those in Profile 2 had lower odds of receiving optimal care, OR = 0.77, 95% CI (0.56, 1.07). Our results demonstrate the interconnected and multidimensional nature of NSDoH, underscoring the importance of modeling them accordingly. This study also highlights the need for targeted interventions at the neighborhood level to address underlying drivers of health disparities, ensure equitable healthcare delivery, and foster better outcomes for all patients.
A Review of Human Emotion Synthesis Based on Generative Technology
2024-12-10
Fei Ma, Yukan Li, Yifan Xie, Ying He, Yi Zhang, Hongwei Ren, Zhou Liu, Wei Yao, Fuji Ren, Fei Richard Yu, Shiguang Ni
Human emotion synthesis is a crucial aspect of affective computing. It involves using computational methods to mimic and convey human emotions through various modalities, with the goal of enabling more natural and effective human-computer interactions. Recent advancements in generative models, such as Autoencoders, Generative Adversarial Networks, Diffusion Models, Large Language Models, and Sequence-to-Sequence Models, have significantly contributed to the development of this field. However, there is a notable lack of comprehensive reviews in this field. To address this problem, this paper aims to address this gap by providing a thorough and systematic overview of recent advancements in human emotion synthesis based on generative models. Specifically, this review will first present the review methodology, the emotion models involved, the mathematical principles of generative models, and the datasets used. Then, the review covers the application of different generative models to emotion synthesis based on a variety of modalities, including facial images, speech, and text. It also examines mainstream evaluation metrics. Additionally, the review presents some major findings and suggests future research directions, providing a comprehensive understanding of the role of generative technology in the nuanced domain of emotion synthesis.
Recent progress on the solid-state materials for photocatalysis
2024-12-10
L. D. Tamang, S. Gurung, R. Zosiamliana, L. Celestine, B. Chettri, Jitendra Pal Singh, A. Laref, Mukhriddin E. Tursunov, Avazbek T. Dekhkonov, D. P. Rai
Hydrogen is considered an alternative source of energy to fossil fuels for the fulfilment of current energy demands. Photocatalysis initiates the hydrogen evolution reaction which is believed to be the greenest approach to produce hydrogen through clean, safe, and environmentally friendly methods. In this Review, we focus mainly on the comprehensive analysis of the 2D and 3D bulk materials on the basis of their superior photocatalytic activities. However, several literatures have reported the superiority of 2D material over the bulk counterpart in terms of photocatalytic performance owing to their ultrathin layered structures, offer a higher surface-to-volume ratio, flexibility, large active sites for incoming H2O molecules, etc. We have thoroughly analysed the drawbacks of various hydrogen production methods focusing on the photocatalysis mechanism and the processes of evolution of hydrogen. In addition to this, a short overview of the various solid-state materials for photocatalysis that have been developed so far and their mechanisms are discussed. Lastly, we have discussed the recent developments in 2D materials and their composites as promising photocatalysts.
The High Time Resolution Universe Pulsar Survey-XIX. A coherent GPU accelerated reprocessing and the discovery of 71 pulsars in the Southern Galactic plane
2024-12-10
R. Sengar, M. Bailes, V. Balakrishnan, E. D. Barr, N. D. R. Bhat, M. Burgay, M. C. i Bernadich, A. D. Cameron, D. J. Champion, W. Chen, C. M. L. Flynn, A. Jameson, S. Johnston, M. J. Keith, M. Kramer, V. Morello, C. Ng, A. Possenti, S. Stevenson, R. M. Shannon, W. van Straten, J. Wongphechauxsorn
We have conducted a GPU accelerated reprocessing of $\sim 87\%$ of the archival data from the High Time Resolution Universe South Low Latitude (HTRU-S LowLat) pulsar survey by implementing a pulsar search pipeline that was previously used to reprocess the Parkes Multibeam pulsar survey (PMPS). We coherently searched the full 72-min observations of the survey with an acceleration search range up to $|50|\, \rm m\,s^{-2}$, which is most sensitive to binary pulsars experiencing nearly constant acceleration during 72 minutes of their orbital period. Here we report the discovery of 71 pulsars, including 6 millisecond pulsars (MSPs) of which five are in binary systems, and seven pulsars with very high dispersion measures (DM $>800 \, \rm pc \, cm^{-3}$). These pulsar discoveries largely arose by folding candidates to a much lower spectral signal-to-noise ratio than previous surveys, and exploiting the coherence of folding over the incoherent summing of the Fourier components to discover new pulsars as well as candidate classification techniques. We show that these pulsars could be fainter and on average more distant as compared to both the previously reported 100 HTRU-S LowLat pulsars and background pulsar population in the survey region. We have assessed the effectiveness of our search method and the overall pulsar yield of the survey. We show that through this reprocessing we have achieved the expected survey goals including the predicted number of pulsars in the survey region and discuss the major causes as to why these pulsars were missed in previous processings of the survey.
M87: a cosmic laboratory for deciphering black hole accretion and jet formation
2024-12-10
Kazuhiro Hada, Keiichi Asada, Masanori Nakamura, Motoki Kino
Over the past decades, there has been significant progress in our understanding of accreting supermassive black holes (SMBHs) that drive active galactic nuclei (AGNs), both from observational and theoretical perspectives. As an iconic target for this area of study, the nearby giant elliptical galaxy M87 has received special attention thanks to its proximity, large mass of the central black hole and bright emission across the entire electromagnetic spectrum from radio to very-high-energy gamma rays. In particular, recent global millimeter-very-long-baseline-interferometer observations towards this nucleus have provided the first-ever opportunity to image the event-horizon-scale structure of an AGN, opening a new era of black hole astrophysics. On large scales, M87 exhibits a spectacular jet propagating far beyond the host galaxy, maintaining its narrowly collimated shape over seven orders of magnitude in distance. Elucidating the generation and propagation, as well as the internal structure, of powerful relativistic jets remains a longstanding challenge in radio-loud AGNs. M87 offers a privileged opportunity to examine such a jet with unprecedented detail. In this review, we provide a comprehensive overview of the observational knowledge accumulated about the M87 black hole across various wavelengths. We cover both accretion and ejection processes at spatial scales ranging from outside the Bondi radius down to the event horizon. By compiling these observations and relevant theoretical studies, we aim to highlight our current understanding of accretion and jet physics for this specific object.
The Atacama Cosmology Telescope: Semi-Analytic Covariance Matrices for the DR6 CMB Power Spectra
2024-12-10
Zachary Atkins, Zack Li, David Alonso, J. Richard Bond, Erminia Calabrese, Adriaan J. Duivenvoorden, Jo Dunkley, Serena Giardiello, Carlos Hervías-Caimapo, J. Colin Hill, Hidde T. Jense, Joshua Kim, Michael D. Niemack, Lyman Page, Adrien La Posta, Thibaut Louis, Kavilan Moodley, Thomas W. Morris, Sigurd Naess, Cristóbal Sifón, Edward J. Wollack
The Atacama Cosmology Telescope Data Release 6 (ACT DR6) power spectrum is expected to provide state-of-the-art cosmological constraints, with an associated need for precise error modeling. In this paper we design, and evaluate the performance of, an analytic covariance matrix prescription for the DR6 power spectrum that sufficiently accounts for the complicated ACT map properties. We use recent advances in the literature to handle sharp features in the signal and noise power spectra, and account for the effect of map-level anisotropies on the covariance matrix. In including inhomogeneous survey depth information, the resulting covariance matrix prescription is structurally similar to that used in the $\textit{Planck}$ Cosmic Microwave Background (CMB) analysis. We quantify the performance of our prescription using comparisons to Monte Carlo simulations, finding better than $3\%$ agreement. This represents an improvement from a simpler, pre-existing prescription, which differs from simulations by $\sim16\%$. We develop a new method to correct the analytic covariance matrix using simulations, after which both prescriptions achieve better than $1\%$ agreement. This correction method outperforms a commonly used alternative, where the analytic correlation matrix is assumed to be accurate when correcting the covariance. Beyond its use for ACT, this framework should be applicable for future high resolution CMB experiments including the Simons Observatory (SO).
Data-Driven Assessment of Vehicle-to-Grid Capabilities in Supporting Grid During Emergencies: Case Study of Travis County, TX
2024-12-10
Kelsey Nelson, Javad Mohammadi
As extreme weather events become more common and threaten power grids, the continuing adoption of electric vehicles (EVs) introduces a growing opportunity for their use as a distributed energy storage resource. This energy storage can be used as backup generation through the use of vehicle-to-grid (V2G) technology, where electricity is sent back from EV batteries to the grid. With enough participation from EV owners, V2G can mitigate outages during grid emergencies. In order to investigate a practical application of V2G, this study leverages a vast array of real-world data, such as survey results on V2G participation willingness, historical outage data within ERCOT, current EV registrations, and demographic data. This data informs realistic emergency grid scenarios with V2G support using a synthetic transmission grid for Travis County. The results find that as EV ownership rises in the coming years, the simultaneous facilitation of bidirectional charging availability would allow for V2G to play a substantial role in preventing involuntary load shed as a result of emergencies like winter storms.
Deep learning models often struggle with generalization when deploying on real-world data, due to the common distributional shift to the training data. Test-time adaptation (TTA) is an emerging scheme used at inference time to address this issue. In TTA, models are adapted online at the same time when making predictions to test data. Neighbor-based approaches have gained attention recently, where prototype embeddings provide location information to alleviate the feature shift between training and testing data. However, due to their inherit limitation of simplicity, they often struggle to learn useful patterns and encounter performance degradation. To confront this challenge, we study the TTA problem from a geometric point of view. We first reveal that the underlying structure of neighbor-based methods aligns with the Voronoi Diagram, a classical computational geometry model for space partitioning. Building on this observation, we propose the Test-Time adjustment by Voronoi Diagram guidance (TTVD), a novel framework that leverages the benefits of this geometric property. Specifically, we explore two key structures: 1) Cluster-induced Voronoi Diagram (CIVD): This integrates the joint contribution of self-supervision and entropy-based methods to provide richer information. 2) Power Diagram (PD): A generalized version of the Voronoi Diagram that refines partitions by assigning weights to each Voronoi cell. Our experiments under rigid, peer-reviewed settings on CIFAR-10-C, CIFAR-100-C, ImageNet-C, and ImageNet-R shows that TTVD achieves remarkable improvements compared to state-of-the-art methods. Moreover, extensive experimental results also explore the effects of batch size and class imbalance, which are two scenarios commonly encountered in real-world applications. These analyses further validate the robustness and adaptability of our proposed framework.
Torsion Resistant Strain Limiting Layers Enable High Grip Strength of Electrically-Driven Handed Shearing Auxetic Grippers
2024-12-10
Ian Good, Srivatsan Balaji, Jeffrey I. Lipton
Soft grippers have demonstrated a strong ability to successfully pick and manipulate many objects. A key limitation to their wider adoption is their inability to grasp larger payloads due to objects slipping out of grasps. We have overcome this limitation by introducing a torsionally rigid strain limiting layer (TR-SLL). This reduces out-of-plane bending while maintaining the gripper's softness and in-plane flexibility. We characterize the design space of the strain limiting layer and Handed Shearing Auxetic (HSA) actuators for a soft gripper using simulation and experiment. The inclusion of the TR-SLL with HSAs enables HSA grippers to be made with a single digit. We found that the use of our TR-SLL HSA gripper enabled pinch grasping of payloads over 1 kg. We demonstrate a lifting capacity of 5 kg when loading using the TR-SLL. We also demonstrate a peak pinch grasp force of 5.8 N, and a peak planar caging force of 14.5 N. Finally, we test the TR-SLL gripper on a suite of 43 YCB objects. We show success on 37 objects demonstrating significant capabilities.
From Lived Experience to Insight: Unpacking the Psychological Risks of Using AI Conversational Agents
2024-12-10
Mohit Chandra, Suchismita Naik, Denae Ford, Ebele Okoli, Munmun De Choudhury, Mahsa Ershadi, Gonzalo Ramos, Javier Hernandez, Ananya Bhattacharjee, Shahed Warreth, Jina Suh
Recent gain in popularity of AI conversational agents has led to their increased use for improving productivity and supporting well-being. While previous research has aimed to understand the risks associated with interactions with AI conversational agents, these studies often fall short in capturing the lived experiences. Additionally, psychological risks have often been presented as a sub-category within broader AI-related risks in past taxonomy works, leading to under-representation of the impact of psychological risks of AI use. To address these challenges, our work presents a novel risk taxonomy focusing on psychological risks of using AI gathered through lived experience of individuals. We employed a mixed-method approach, involving a comprehensive survey with 283 individuals with lived mental health experience and workshops involving lived experience experts to develop a psychological risk taxonomy. Our taxonomy features 19 AI behaviors, 21 negative psychological impacts, and 15 contexts related to individuals. Additionally, we propose a novel multi-path vignette based framework for understanding the complex interplay between AI behaviors, psychological impacts, and individual user contexts. Finally, based on the feedback obtained from the workshop sessions, we present design recommendations for developing safer and more robust AI agents. Our work offers an in-depth understanding of the psychological risks associated with AI conversational agents and provides actionable recommendations for policymakers, researchers, and developers.
Quantum Hydrodynamics Meets Plasmonics and Nonlinear Optics in Two-Dimensional Materials
2024-12-10
Simão S. Cardoso, A. J. Chaves, N. Asger Mortensen, N. M. R. Peres
This paper explores the application of quantum-hydrodynamic models to study two-dimensional electron gases, with a focus on nonlocal plasmonics and nonlinear optics. We begin by reviewing the derivation of the Madelung equations from the Wigner distribution function. Using the Madelung equations in conjunction with Poisson's equation, we calculate the spectrum of magnetoplasmons and the magneto-optical conductivity in the electrostatic regime, incorporating nonlocal corrections due to the Fermi pressure. In the absence of a magnetic field, we analyze nonlinear and nonlocal second-harmonic generation, demonstrating how plasmon excitation enhances this process. We further discuss the emergence of self-modulation phenomena driven by nonlinearity, leading to the renormalization of the plasmon dispersion. Notably, we show that nonlinearity amplifies nonlocal effects and, leveraging the hydrodynamic formalism, derive a simple analytic expression for the renormalized spectra. Additionally, we examine the role of the quantum potential, interpreted as a gradient correction to the Thomas--Fermi kinetic energy. Our results provide new insights into quantum effects in plasmonic systems, with significant implications for future advances in nanophotonics through the lens of hydrodynamic theory.
Big Bang Nucleosynthesis (BBN), the process of creation of lightest elements in the early universe, is a highly robust, precise, and ultimately successful theory that forms one of the three pillars of the standard hot-Big-Bang cosmological model. Existing theoretical treatments of BBN and the associated computer codes are accurate and flexible, but are typically highly technical and opaque, and not suitable for pedagogical understanding of the BBN. Here we present BBN-simple -- a from-scratch numerical calculation of the lightest element abundances pitched at an advanced undergraduate or beginning graduate level. We review the physics of the early universe relevant for BBN, provide information about the reaction rates, and discuss computational-mathematics background that is essential in setting up a BBN calculation. We calculate the abundances of the principal nuclear species in a standard cosmological model, and find a reasonably good agreement with public precision-level BBN codes.
Protocol Learning, Decentralized Frontier Risk and the No-Off Problem
2024-12-10
Alexander Long
Frontier models are currently developed and distributed primarily through two channels: centralized proprietary APIs or open-sourcing of pre-trained weights. We identify a third paradigm - Protocol Learning - where models are trained across decentralized networks of incentivized participants. This approach has the potential to aggregate orders of magnitude more computational resources than any single centralized entity, enabling unprecedented model scales and capabilities. However, it also introduces novel challenges: heterogeneous and unreliable nodes, malicious participants, the need for unextractable models to preserve incentives, and complex governance dynamics. To date, no systematic analysis has been conducted to assess the feasibility of Protocol Learning or the associated risks, particularly the 'No-Off Problem' arising from the inability to unilaterally halt a collectively trained model. We survey recent technical advances that suggest decentralized training may be feasible - covering emerging communication-efficient strategies and fault-tolerant methods - while highlighting critical open problems that remain. Contrary to the notion that decentralization inherently amplifies frontier risks, we argue that Protocol Learning's transparency, distributed governance, and democratized access ultimately reduce these risks compared to today's centralized regimes.
Convergence estimates for the Magnus expansion IE. Finite dimensional Banach algebras
2024-12-10
Gyula Lakos
We review and provide simplified proofs related to the Magnus expansion, and improve convergence estimates. Observations and improvements concerning the Baker--Campbell--Hausdorff expansion are also made. In this Part IE, we consider the case of finite dimensional Banach algebras. We show that Magnus expansion is convergent (and works in logarithmic sense) if the cumulative norm $< 2+\varepsilon_{n}$, where $\varepsilon_{n}$ is a positive number depending on the dimension $n$ of the Banach algebra. We also show concrete finite-dimensional counterexamples of multiple Baker--Campbell--Hausdorff type for any cumulative norm $>2$ (necessarily of possibly great dimension).
2024-12-10
Mohamed Kaber El Alem, Zohra Guessoum, Abdelkader Tatachak
Let $(X_N)_{N\geq 1}$ denote a sequence of real random variables and let $\vartheta$ be the mode of the random variable of interest $X$. In this paper, we study the kernel mode estimator (say) $\vartheta_n$ when the data are widely orthant dependent (WOD) and subject to Random Left Truncation (RLT) mechanism. We establish the uniform consistency rate of the density estimator (say) $f_n$ of the underlying density $f$ as well as the almost sure convergence rate of $\vartheta_n$. The performance of the estimators are illustrated via some simulation studies and applied on a real dataset of car brake pads.
Tracing the earliest stages of star and cluster formation in 19 nearby galaxies with PHANGS-JWST and HST: compact 3.3 $μ$m PAH emitters and their relation to the optical census of star clusters
2024-12-10
M. Jimena Rodríguez, Janice C. Lee, Remy Indebetouw, B. C. Whitmore, Daniel Maschmann, Thomas G. Williams, Rupali Chandar, A. T. Barnes, Oleg Y. Gnedin, Karin M. Sandstrom, Erik Rosolowsky, Jiayi Sun, Ralf S. Klessen, Brent Groves, Aida Wofford, Médéric Boquien, Daniel A. Dale, Adam K. Leroy, David A. Thilker, Hwihyun Kim, Rebecca C. Levy, Sumit K. Sarbadhicary, Leonardo Ubeda, Kirsten L. Larson, Kelsey E. Johnson, Frank Bigiel, Hamid Hassani, Kathryn Grasha
The earliest stages of star and cluster formation are hidden within dense cocoons of gas and dust, limiting their detection at optical wavelengths. With the unprecedented infrared capabilities of JWST, we can now observe dust-enshrouded star formation with $\sim$10 pc resolution out to $\sim$20 Mpc. Early findings from PHANGS-JWST suggest that 3.3 $\mu$m polycyclic aromatic hydrocarbon (PAH) emission can identify star clusters in their dust-embedded phases. Here, we extend this analysis to 19 galaxies from the PHANGS-JWST Cycle 1 Treasury Survey, providing the first characterization of compact sources exhibiting 3.3$\mu$m PAH emission across a diverse sample of nearby star-forming galaxies. We establish selection criteria, a median color threshold of F300M-F335M=0.67 at F335M=20, and identify of 1816 sources. These sources are predominantly located in dust lanes, spiral arms, rings, and galaxy centers, with $\sim$87% showing concentration indices similar to optically detected star clusters. Comparison with the PHANGS-HST catalogs suggests that PAH emission fades within $\sim$3 Myr. The H$\alpha$ equivalent width of PAH emitters is 1-2.8 times higher than that of young PHANGS-HST clusters, providing evidence that PAH emitters are on average younger. Analysis of the bright portions of luminosity functions (which should not suffer from incompleteness) shows that young dusty clusters may increase the number of optically visible $\leq$ 3 Myr-old clusters in PHANGS-HST by a factor between $\sim$1.8x-8.5x.
X-Sifter: detecting transients in X-ray data using the optimal Poisson matched filter
2024-12-10
Maayane T. Soumagnac, Eran O. Ofek, Shachar S. Israeli, Guy Nir, Imri A. Dickstein
We present X-sifter, a software package designed for near-optimal detection of sources in X-ray images and other forms of photon images in the Poisson-noise regime. The code is based on the Poisson-noise-matched filter (Ofek & Zackay), which provides an efficient method for calculating the delta log-likelihood function for source detection. The software accounts for several complexities inherent in real data, including variations in both the instrumental Point Spread Function (PSF) and background across the detector and as a function of energy. We validate the pipeline using real data with simulated source injections, as well as actual Chandra images. A comparison between the sources detected by our pipeline and those in the Chandra Source Catalog (CSC) suggests an approximate ~30% increase in the number of detected (real) sources. Near the detection limit, the reported S/N of our pipeline is approximately 1.3x higher than that of the CSC. This corresponds to a factor of 1.8 increase in survey speed.
Constraining cosmological parameters using density split lensing and the conditional stellar mass function
2024-12-10
Pierre A. Burger, Darshak A. Patel, Michael J. Hudson
In this work, we develop a simulation-based model to predict the excess surface mass density (ESD) depending on the local density environment. Using a conditional stellar mass function, our foreground galaxies are tailored toward the bright galaxy sample of the early data release of the Dark Energy Spectroscopic Instrument (DESI). Due to the nature of the ESD measurement, our derived model is directly applicable to all DESI data. To build this model, we use the $\texttt{AbacusSummit}$ N-body simulation suite from which we measure all necessary statistics and train an emulator based on $\texttt{CosmoPower}$. Finally, we present a cosmological parameter forecast for a possible combined analysis of DESI and the Ultraviolet Near Infrared Optical Northern Survey.
The SARAO MeerKAT Galactic Plane Survey filamentary source catalogue
2024-12-10
Gwenllian M. Williams, Mark A. Thompson, Mubela Mutale, Andrew J. Rigby, Cristobal Bordiu, Simone Riggi, Michael Bietenholz, Loren D. Anderson, Fernando Camilo, Sharmila Goedhart, Sarah E. Jaffa, Willice O. Obonyo, Corrado Trigilio, Grazia Umana
We present a catalogue of filamentary structures identified in the SARAO (South African Radio Astronomy Observatory) MeerKAT 1.3 GHz Galactic Plane Survey (SMGPS). We extract 933 filaments across the survey area, 803 of which (~86%) are associated with extended radio structures (e.g. supernova remnants and HII regions), whilst 130 (~14%) are largely isolated. We classify filaments as thermal or non-thermal via their associated mid-infrared emission and find 77/130 (~59%) of the isolated sources are likely to be non-thermal, and are therefore excellent candidates for the first isolated, non-thermal radio filaments observed outside of the Galactic Centre (GC). Comparing the morphological properties of these non-thermal candidates to the non-thermal filaments observed towards the GC we find the GC filaments are on the whole angularly narrower and shorter than those across the SMGPS, potentially an effect of distance. The SMGPS filaments have flux densities similar to those of the GC, however the distribution of the latter extends to higher flux densities. If the SMGPS filaments were closer than the GC population, it would imply a more energetic population of cosmic ray electrons in the GC. We find the filament position angles in the SMGPS are uniformly distributed, implying that the local magnetic field traced by the filaments does not follow the large-scale Galactic field. Finally, although we have clearly shown that filaments are not unique to the GC, the GC nevertheless has the highest density of filaments in the Milky Way.
Group field theory is a background-independent approach to quantum gravity whose starting point is the definition of a quantum field theory on an auxiliary group manifold (not interpreted as spacetime). Group field theory models can be seen as an extension of matrix and tensor models by additional data, and are traditionally defined through a functional integral whose perturbative expansion generates a sum over discrete geometries. More recently, some efforts have been directed towards formulations of group field theory based on a Hilbert space and operators, in particular in applications to cosmology. This is an attempt to review some of these formulations and their main ideas, to disentangle these constructions as much as possible from applications and phenomenology, and to put them into a wider context of quantum gravity research.
Evidence that pre-processing in filaments drives the anisotropic quenching of satellite galaxies in massive clusters
2024-12-10
Harry Stephenson, John Stott, Joseph Butler, Molly Webster, Jonathan Head
We use a sample of 11 $z\approx0.2-0.5$ ($z_{\text{med.}} = 0.36$) galaxy clusters from the Cluster Lensing And Supernovae survey with Hubble (CLASH) to analyse the angular dependence of satellite galaxy colour $(B-R)$ and passive galaxy fractions ($f_{\text{pass.}}$) with respect to the major axis of the brightest cluster galaxy (BCG). This phenomenon has been dubbed as \say{anisotropic quenching}, \say{angular conformity} or \say{angular segregation}, and it describes how satellite galaxies along the major axis of the BCG are more likely to be quenched than those along the minor axis. We are the first to measure anisotropic quenching out to $3R_{200}$ ($R_{200\text{, med.}} \approx 933$ \si{\kilo\parsec}) from the cluster centre. A highly significant anisotropic quenching signal is found for satellites with a peak in $(B-R)$ and $f_{\text{pass.}}$ along the major axis. We find that the anisotropic quenching signal is significant out to at least $2.5R_{200}$, and the amplitude of the sinusoidal fit peaks at $\approx1.25R_{200}$. This is the first time the radial peak of the anisotropic quenching signal has been measured directly. Finally, we find that $f_{\text{pass.}}$ is significantly higher along the major axis for fixed values of local surface density. The density drops less rapidly along the major axis and so satellites spend more time being pre-processed here compared to the minor axis. We therefore conclude that pre-processing in large-scale structure, and not active galactic nuclei (AGN) outflows, is the likely cause of the anisotropic quenching signal in massive galaxy clusters, however this may not be the cause in lower mass halos.
Baryonic Ecosystem in Galaxies (BEINGMgII). Host Galaxies of Ultra-strong MgII Absorbers in Subaru Hyper Suprime-Cam Survey
2024-12-10
Ravi Joshi, Sarbeswar Das, Michele Fumagalli, Matteo Fossati, Céline Péroux, Reena Chaudhary, Hassen M. Yesuf, Luis C. Ho
We study the galaxies hosting ultra-strong MgII (USMgII) absorbers at small impact parameters of $\sim$2" (5 - 20 kpc), spanning a redshift range of $0.4 \le z \le 1.7$, using deep, high-resolution images from Hyper Suprime-Cam Subaru Strategic Survey and spectra from SDSS survey. From a total of 418 USMgII absorbers with $W_{2796}\ \ge 3 \mathring{A}$, along 412 quasar sightlines, we detect 50 galaxies based on [O II] $\lambda\lambda$3727,3729 nebular emission detected at $\ge 2\sigma$ level. Utilizing the [O II] emission from the stacked spectrum and employing the best-fit galaxy SED template, we further identify 86 galaxies, leading to a total of 136 bona fide USMgII galaxies. With a prerequisite of having a minimum of four HSC passbands available, we find a detection rate of $\sim$38% at an average impact parameter of 11.4 kpc. We find that galaxies hosting USMgII systems are typically star-forming main sequence galaxies, with 21% exhibiting a starburst nature. The non-zero [O II] emission along the `clear' sightlines, with no stellar counterpart, hints that the USMgII absorbers may likely emanate from the unseen faint galaxies near the quasar. The USMgII absorbers preferentially align along the major and minor axes of the galaxy, which suggests that they originate in the disk or large-scale wind. We show that the distribution of $W_{2796}$ as a function of impact parameter indicates a discernible radial dependence for the `disk' and `wind' subsets, with the observed large scatter in $W_{2796}$ potentially attributed to large-scale outflows. The quasar sightline hosting USMgII systems show a factor three higher galaxy surface density at impact parameters of $\lesssim 50$kpc, highlights the multiple pathways giving rise to USMgII absorption.
Resolved mass assembly and star formation in Milky Way Progenitors since $z = 5$ from JWST/CANUCS: From clumps and mergers to well-ordered disks
2024-12-10
Vivian Yun Yan Tan, Adam Muzzin, Ghassan T. E. Sarrouh, Jacqueline Antwi-Danso, Visal Sok, Naadiyah Jagga, Roberto Abraham, Yoshihisa Asada, Guillaume Desprez, Kartheik Iyer, Nicholas S. Martis, Rosa M. Mérida, Lamiya A. Mowla, Gaël Noirot, Kiyoaki Christopher Omori, Marcin Sawicki, Roberta Tripodi, Chris J. Willott
We present a resolved study of $>900$ progenitors of Milky Way Analogs (MWAs) at $0.3
While large vision-language models (LVLMs) have shown impressive capabilities in generating plausible responses correlated with input visual contents, they still suffer from hallucinations, where the generated text inaccurately reflects visual contents. To address this, recent approaches apply contrastive decoding to calibrate the model's response via contrasting output distributions with original and visually distorted samples, demonstrating promising hallucination mitigation in a training-free manner. However, the potential of changing information in visual inputs is not well-explored, so a deeper investigation into the behaviors of visual contrastive decoding is of great interest. In this paper, we first explore various methods for contrastive decoding to change visual contents, including image downsampling and editing. Downsampling images reduces the detailed textual information while editing yields new contents in images, providing new aspects as visual contrastive samples. To further study benefits by using different contrastive samples, we analyze probability-level metrics, including entropy and distribution distance. Interestingly, the effect of these samples in mitigating hallucinations varies a lot across LVLMs and benchmarks. Based on our analysis, we propose a simple yet effective method to combine contrastive samples, offering a practical solution for applying contrastive decoding across various scenarios. Extensive experiments are conducted to validate the proposed fusion method among different benchmarks.
XRZoo: A Large-Scale and Versatile Dataset of Extended Reality (XR) Applications
2024-12-09
Shuqing Li, Chenran Zhang, Cuiyun Gao, Michael R. Lyu
The rapid advancement of Extended Reality (XR, encompassing AR, MR, and VR) and spatial computing technologies forms a foundational layer for the emerging Metaverse, enabling innovative applications across healthcare, education, manufacturing, and entertainment. However, research in this area is often limited by the lack of large, representative, and highquality application datasets that can support empirical studies and the development of new approaches benefiting XR software processes. In this paper, we introduce XRZoo, a comprehensive and curated dataset of XR applications designed to bridge this gap. XRZoo contains 12,528 free XR applications, spanning nine app stores, across all XR techniques (i.e., AR, MR, and VR) and use cases, with detailed metadata on key aspects such as application descriptions, application categories, release dates, user review numbers, and hardware specifications, etc. By making XRZoo publicly available, we aim to foster reproducible XR software engineering and security research, enable cross-disciplinary investigations, and also support the development of advanced XR systems by providing examples to developers. Our dataset serves as a valuable resource for researchers and practitioners interested in improving the scalability, usability, and effectiveness of XR applications. XRZoo will be released and actively maintained.
Hydrogen intensity mapping with MeerKAT: Preserving cosmological signal by optimising contaminant separation
2024-12-09
Isabella P. Carucci, José L. Bernal, Steven Cunnington, Mario G. Santos, Jingying Wang, José Fonseca, Keith Grainge, Melis O. Irfan, Yichao Li, Alkistis Pourtsidou, Marta Spinelli, Laura Wolz
Removing contaminants is a delicate yet crucial step in neutral hydrogen (HI) intensity mapping, often considered the technique's greatest challenge. Here, we address this challenge by analysing HI intensity maps of about $100$ deg$^2$ at redshift $z\approx0.4$ collected by the MeerKAT radio telescope, a SKA Observatory (SKAO) precursor, with a combined 10.5-hour observation. Using unsupervised statistical methods, we remove the contaminating foreground emission and systematically test step-by-step common pre-processing choices to facilitate the cleaning process. We also introduce and test a novel multiscale approach, where data is redundantly decomposed into subsets referring to different spatial scales (large and small), and the cleaning procedure is performed independently. We confirm the detection of the HI cosmological signal in cross-correlation with an ancillary galactic data set without the need to correct for signal loss. In the best set-up reached, we constrain the HI distribution through the combination of its cosmic abundance ($\Omega_{\rm HI}$) and linear clustering bias ($b_{\rm HI}$) up to a cross-correlation coefficient ($r$) and measure $\Omega_{\rm HI}b_{\rm HI}r = [0.93 \pm 0.17]\,\times\,10^{-3}$ with $\approx6\sigma$ confidence. The measurement is independent of scale cuts at both edges of the probed scale range ($0.04 \lesssim k \lesssim 0.3 \,h$Mpc$^{-1}$), corroborating its robustness. Our new pipeline has successfully found an optimal compromise in separating contaminants without incurring a catastrophic signal loss, instilling more confidence in the outstanding science we can deliver with MeerKAT on the path towards HI intensity mapping surveys with the full SKAO.
Partially Observed Optimal Stochastic Control: Regularity, Optimality, Approximations, and Learning
2024-12-09
Ali Devran Kara, Serdar Yuksel
In this review/tutorial article, we present recent progress on optimal control of partially observed Markov Decision Processes (POMDPs). We first present regularity and continuity conditions for POMDPs and their belief-MDP reductions, where these constitute weak Feller and Wasserstein regularity and controlled filter stability. These are then utilized to arrive at existence results on optimal policies for both discounted and average cost problems, and regularity of value functions. Then, we study rigorous approximation results involving quantization based finite model approximations as well as finite window approximations under controlled filter stability. Finally, we present several recent reinforcement learning theoretic results which rigorously establish convergence to near optimality under both criteria.
Adiabatic regularization for spin $1/2$ fields and the renormalized stress-energy tensor
2024-12-09
Adrian del Rio, Jose Navarro-Salas
In this article we briefly review the adiabatic renormalization program for spin 1/2 fields in expanding universes. We introduce the method and provide explicit expressions for the renormalized vacuum expectation value of the stress-energy tensor. Then, we discuss its application to some cosmological scenario of physical interest. We end up sketching out the proof that adiabatic and DeWitt-Schwinger point-splitting schemes provide the same renormalized expectation values of the stress-energy tensor for Dirac fields.
Facade: High-Precision Insider Threat Detection Using Deep Contextual Anomaly Detection
2024-12-09
Alex Kantchelian, Casper Neo, Ryan Stevens, Hyungwon Kim, Zhaohao Fu, Sadegh Momeni, Birkett Huber, Elie Bursztein, Yanis Pavlidis, Senaka Buthpitiya, Martin Cochran, Massimiliano Poletto
We present Facade (Fast and Accurate Contextual Anomaly DEtection): a high-precision deep-learning-based anomaly detection system deployed at Google (a large technology company) as the last line of defense against insider threats since 2018. Facade is an innovative unsupervised action-context system that detects suspicious actions by considering the context surrounding each action, including relevant facts about the user and other entities involved. It is built around a new multi-modal model that is trained on corporate document access, SQL query, and HTTP/RPC request logs. To overcome the scarcity of incident data, Facade harnesses a novel contrastive learning strategy that relies solely on benign data. Its use of history and implicit social network featurization efficiently handles the frequent out-of-distribution events that occur in a rapidly changing corporate environment, and sustains Facade's high precision performance for a full year after training. Beyond the core model, Facade contributes an innovative clustering approach based on user and action embeddings to improve detection robustness and achieve high precision, multi-scale detection. Functionally what sets Facade apart from existing anomaly detection systems is its high precision. It detects insider attackers with an extremely low false positive rate, lower than 0.01%. For single rogue actions, such as the illegitimate access to a sensitive document, the false positive rate is as low as 0.0003%. To the best of our knowledge, Facade is the only published insider risk anomaly detection system that helps secure such a large corporate environment.
Overview of Indigenous rights and outer space for the IAU-CPS Policy Hub
2024-12-09
Hilding Neilson
As part of the mission of the International Astronomical Union Centre for the Protection of the Dark and Quiet Sky from Satellite Constellation Interference (IAU-CPS) Policy Hub to consider national and international regulations about the usage and sustainability in outer space, we also included discussion specific to the rights of Indigenous peoples with respect to outer space under the context of the United Nations Declaration for the Rights of Indigenous Peoples (UNDRIP). In this work, we review how some of the articles of UNDRIP require various actors in the use and exploitation of outer space including satellite companies, nation states, and professional/academic astronomy to consult and support Indigenous peoples/nations and respect Indigenous sovereignties. This work is concluded with recommendations for consulting and collaborating with Indigenous peoples and recommendations for moving from the traditional colonial exploitation of outer space and building an anti-colonial future in relationship with outer space.
Indigenous knowledges and kinship as a model for our future in outer space
2024-12-09
Hilding Neilson
Commercial endeavours have already compromised our relationship with space. The Artemis Accords are creating a framework that will commercialize the Moon and further impact that relation. To confront that impact, a number of organizations have begun to develop new principles of sustainability in space, many of which are borne out of the capitalist and colonial frameworks that have harmed water, nature, peoples and more on Earth. Indigenous methodologies and ways of knowing offer different paths for living in relationship with space and the Moon. While Indigenous knowledges are not homogeneous, there are lessons we can use from some of common methods. In this talk we will review some Indigenous methodologies, including the concept of kinship and discuss how kinship can inform our actions both on Earth and in space.
Recent developments on Gromov-Witten theory of Hilbert schemes of points on certain surfaces
2024-12-09
Mazen Alhwaimel
In This paper, we survey recent progress on the theory of Gromov- Witten invariants on Hilbert schemes of points mainly on elliptic surfaces and simply connected minimal surface of general type. In particular, we focus on the aspects of computational progress that has been done in the cases of lower genus. Then, we discuss some important conjectures that have been proposed and gather all available information and progress toward answering them.
Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Dataset
2024-12-09
Xiao Wang, Yu Jin, Wentao Wu, Wei Zhang, Lin Zhu, Bo Jiang, Yonghong Tian
Object detection in event streams has emerged as a cutting-edge research area, demonstrating superior performance in low-light conditions, scenarios with motion blur, and rapid movements. Current detectors leverage spiking neural networks, Transformers, or convolutional neural networks as their core architectures, each with its own set of limitations including restricted performance, high computational overhead, or limited local receptive fields. This paper introduces a novel MoE (Mixture of Experts) heat conduction-based object detection algorithm that strikingly balances accuracy and computational efficiency. Initially, we employ a stem network for event data embedding, followed by processing through our innovative MoE-HCO blocks. Each block integrates various expert modules to mimic heat conduction within event streams. Subsequently, an IoU-based query selection module is utilized for efficient token extraction, which is then channeled into a detection head for the final object detection process. Furthermore, we are pleased to introduce EvDET200K, a novel benchmark dataset for event-based object detection. Captured with a high-definition Prophesee EVK4-HD event camera, this dataset encompasses 10 distinct categories, 200,000 bounding boxes, and 10,054 samples, each spanning 2 to 5 seconds. We also provide comprehensive results from over 15 state-of-the-art detectors, offering a solid foundation for future research and comparison. The source code of this paper will be released on: https://github.com/Event-AHU/OpenEvDET
Shocked POststarburst Galaxy Survey. IV. Outflows in Shocked Post-Starburst Galaxies Are Not Responsible For Quenching
2024-12-09
Antoniu Fodor, Taylor Tomko, Mary Braun, Anne M. Medling, Thomas M. Johnson, Alexander Thompson, Victor D. Johnston, Matthew Newhouse, Yuanze Luo, K. Decker French, Justin A. Otter, Akshat Tripathi, Margaret E. Verrico, Katherine Alatalo, Kate Rowlands, Timothy Heckman
Shocked POst-starburst Galaxies (SPOGs) exhibit both emission lines suggestive of shock-heated gas and post-starburst-like stellar absorption, resulting in a unique subset for galaxy evolution studies. We have observed 77 galaxies that fulfilled the SPOGs criteria selection using the DeVeny Spectrograph on the Lowell Discovery Telescope. Our long-slit minor axis spectra detect H$\alpha$ and [O III] in some SPOGs out to 6 kpc above the galactic plane. We find extraplanar ionized gas in 31 targets of our sample overall. Using their internal and external kinematics, we argue that 22 galaxies host outflows with ionized gas masses ranging from $10^2 M_{\odot}$ to $10^5 M_{\odot}$. The rest are likely extended diffuse ionized gas. A positive correlation exists between AGN luminosity and the extraplanar gas extent, velocity dispersion, and mass$\unicode{x2013}\unicode{x2013}$suggesting that the AGN may indeed drive the outflows detected in AGN hosts. The low masses of the extraplanar gas suggest that these outflows are not depleting each galaxy's gas reserves. The outflows, therefore, are not likely a significant quenching mechanism in these SPOGs.
Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusions
2024-12-09
Jhih-Yi, Hsieh, Aditi Raghunathan, Nihar B. Shah
In the peer review process of top-tier machine learning (ML) and artificial intelligence (AI) conferences, reviewers are assigned to papers through automated methods. These assignment algorithms consider two main factors: (1) reviewers' expressed interests indicated by their bids for papers, and (2) reviewers' domain expertise inferred from the similarity between the text of their previously published papers and the submitted manuscripts. A significant challenge these conferences face is the existence of collusion rings, where groups of researchers manipulate the assignment process to review each other's papers, providing positive evaluations regardless of their actual quality. Most efforts to combat collusion rings have focused on preventing bid manipulation, under the assumption that the text similarity component is secure. In this paper, we demonstrate that even in the absence of bidding, colluding reviewers and authors can exploit the machine learning based text-matching component of reviewer assignment used at top ML/AI venues to get assigned their target paper. We also highlight specific vulnerabilities within this system and offer suggestions to enhance its robustness.
Examining the Use and Impact of an AI Code Assistant on Developer Productivity and Experience in the Enterprise
2024-12-09
Justin D. Weisz, Shraddha Kumar, Michael Muller, Karen-Ellen Browne, Arielle Goldberg, Ellice Heintze, Shagun Bajpai
AI assistants are being created to help software engineers conduct a variety of coding-related tasks, such as writing, documenting, and testing code. We describe the use of the watsonx Code Assistant (WCA), an LLM-powered coding assistant deployed internally within IBM. Through surveys of two user cohorts (N=669) and unmoderated usability testing (N=15), we examined developers' experiences with WCA and its impact on their productivity. We learned about their motivations for using (or not using) WCA, we examined their expectations of its speed and quality, and we identified new considerations regarding ownership of and responsibility for generated code. Our case study characterizes the impact of an LLM-powered assistant on developers' perceptions of productivity and it shows that although such tools do often provide net productivity increases, these benefits may not always be experienced by all users.
Towards Controllable Speech Synthesis in the Era of Large Language Models: A Survey
2024-12-09
Tianxin Xie, Yan Rong, Pengfei Zhang, Li Liu
Text-to-speech (TTS), also known as speech synthesis, is a prominent research area that aims to generate natural-sounding human speech from text. Recently, with the increasing industrial demand, TTS technologies have evolved beyond synthesizing human-like speech to enabling controllable speech generation. This includes fine-grained control over various attributes of synthesized speech such as emotion, prosody, timbre, and duration. Besides, advancements in deep learning, such as diffusion and large language models, have significantly enhanced controllable TTS over the past several years. In this paper, we conduct a comprehensive survey of controllable TTS, covering approaches ranging from basic control techniques to methods utilizing natural language prompts, aiming to provide a clear understanding of the current state of research. We examine the general controllable TTS pipeline, challenges, model architectures, and control strategies, offering a comprehensive and clear taxonomy of existing methods. Additionally, we provide a detailed summary of datasets and evaluation metrics and shed some light on the applications and future directions of controllable TTS. To the best of our knowledge, this survey paper provides the first comprehensive review of emerging controllable TTS methods, which can serve as a beneficial resource for both academic researchers and industry practitioners.
Primordial non-Gaussianity -- the effects of relativistic and wide-angle corrections to the power spectrum
2024-12-09
Sêcloka L. Guedezounme, Sheean Jolicoeur, Roy Maartens
Wide-angle and relativistic corrections to the Newtonian and flat-sky approximations are important for accurate modelling of the galaxy power spectrum of next-generation galaxy surveys. In addition to Doppler and Sachs-Wolfe relativistic corrections, we include the effects of lensing convergence, time delay and integrated Sachs-Wolfe. We investigate the impact of these corrections on measurements of the local primordial non-Gaussianity parameter $f_{\rm NL}$, using two futuristic spectroscopic galaxy surveys, planned for SKAO2 and MegaMapper. In addition to the monopole, we include the quadrupole of the galaxy Fourier power spectrum. The quadrupole is much more sensitive to the corrections than the monopole. The combination with the quadrupole improves the precision on $f_{\rm NL}$ by $\sim {40}\%$ and $\sim {60}\%$ for SKAO2 and MegaMapper respectively. Neglecting the wide-angle and relativistic corrections produces a shift in $f_{\rm NL}$ of $\sim {0.1}\sigma$ and $\sim {0.2}\sigma$ for SKAO2 and MegaMapper. The shift in $f_{\rm NL}$ is very sensitive to the magnification bias and the redshift evolution of the comoving number density. For these surveys, the contributions to the shift from integrated and non-integrated effects partly cancel. We point out that some of the approximations made in the corrections may artificially suppress the shift in $f_{\rm NL}$.
Small Languages, Big Models: A Study of Continual Training on Languages of Norway
2024-12-09
David Samuel, Vladislav Mikhailov, Erik Velldal, Lilja Øvrelid, Lucas Georges Gabriel Charpentier, Andrey Kutuzov
Training large language models requires vast amounts of data, posing a challenge for less widely spoken languages like Norwegian and even more so for truly low-resource languages like S\'ami. To address this issue, we present a novel three-stage continual training approach. We also experiment with combining causal and masked language modeling to get more flexible models. Based on our findings, we train, evaluate, and openly release a new large generative language model for Norwegian Bokm\r{a}l, Nynorsk, and Northern S\'ami with 11.4 billion parameters: NorMistral-11B.
An inferential measure of dependence between two systems using Bayesian model comparison
2024-12-09
Guillaume Marrelec, Alain Giron
We propose to quantify dependence between two systems $X$ and $Y$ in a dataset $D$ based on the Bayesian comparison of two models: one, $H_0$, of statistical independence and another one, $H_1$, of dependence. In this framework, dependence between $X$ and $Y$ in $D$, denoted $B(X,Y|D)$, is quantified as $P(H_1|D)$, the posterior probability for the model of dependence given $D$, or any strictly increasing function thereof. It is therefore a measure of the evidence for dependence between $X$ and $Y$ as modeled by $H_1$ and observed in $D$. We review several statistical models and reconsider standard results in the light of $B(X,Y|D)$ as a measure of dependence. Using simulations, we focus on two specific issues: the effect of noise and the behavior of $B(X,Y|D)$ when $H_1$ has a parameter coding for the intensity of dependence. We then derive some general properties of $B(X,Y|D)$, showing that it quantifies the information contained in $D$ in favor of $H_1$ versus $H_0$. While some of these properties are typical of what is expected from a valid measure of dependence, others are novel and naturally appear as desired features for specific measures of dependence, which we call inferential. We finally put these results in perspective; in particular, we discuss the consequences of using the Bayesian framework as well as the similarities and differences between $B(X,Y|D)$ and mutual information.
Fast time optical variability in Supergiant X-ray binaries
2024-12-09
D. Di Filippantonio, P. Reig, J. Fabregat
The main goal of this work is to investigate the fast photometric variability of the optical counterparts to supergiant X-ray binaries and to compare the general patterns of such variability with the Galactic population of other early-type stars. We analyzed a sample of 14 high-mass X-ray binaries with supergiant companions observed by the Transiting Exoplanet Survey Satellite (TESS) and 4 Be/X-ray binaries with persistent X-ray emission for comparison. All sources exhibit fast time aperiodic light variations. The shape of the periodogram is well described by a red noise component at intermediate frequencies ($\sim 1-10$ d$^{-1}$). At lower frequencies the noise level flattens while at higher frequencies the periodogram is dominated by white noise. We find that the patterns of variability of the massive companions in supergiant X-ray binaries agree with that of single early-type evolved stars in terms of the general shape of the periodograms. However, they exhibit higher amplitude at low frequencies and lower characteristic frequencies than Be/X-ray binaries. Unlike Be/X-ray binaries, supergiant X-ray binaries exhibit a total lack of coherent signals at high frequencies. Most sources have been analyzed over multiple TESS sectors, spanning a duration of 4 years. We do not find any significant variation over time in the low-frequency variability characteristics. This study reveals that stochastic low-frequency variability is a very common, if not ubiquitous, feature intrinsic to supergiant optical companions in X-ray binaries.
StarWhisper Telescope: Agent-Based Observation Assistant System to Approach AI Astrophysicist
2024-12-09
Cunshi Wang, Xinjie Hu, Yu Zhang, Xunhao Chen, Pengliang Du, Yiming Mao, Rui Wang, Yuyang Li, Ying Wu, Hang Yang, Yansong Li, Beichuan Wang, Haiyang Mu, Zheng Wang, Jianfeng Tian, Liang Ge, Yongna Mao, Shengming Li, Xiaomeng Lu, Jinhang Zou, Yang Huang, Ningchen Sun, Jie Zheng, Min He, Yu Bai, Junjie Jin, Hong Wu, Chaohui Shang, Jifeng Liu
With the rapid advancements in Large Language Models (LLMs), LLM-based agents have introduced convenient and user-friendly methods for leveraging tools across various domains. In the field of astronomical observation, the construction of new telescopes has significantly increased astronomers' workload. Deploying LLM-powered agents can effectively alleviate this burden and reduce the costs associated with training personnel. Within the Nearby Galaxy Supernovae Survey (NGSS) project, which encompasses eight telescopes across three observation sites, aiming to find the transients from the galaxies in 50 mpc, we have developed the \textbf{StarWhisper Telescope System} to manage the entire observation process. This system automates tasks such as generating observation lists, conducting observations, analyzing data, and providing feedback to the observer. Observation lists are customized for different sites and strategies to ensure comprehensive coverage of celestial objects. After manual verification, these lists are uploaded to the telescopes via the agents in the system, which initiates observations upon neutral language. The observed images are analyzed in real-time, and the transients are promptly communicated to the observer. The agent modifies them into a real-time follow-up observation proposal and send to the Xinglong observatory group chat, then add them to the next-day observation lists. Additionally, the integration of AI agents within the system provides online accessibility, saving astronomers' time and encouraging greater participation from amateur astronomers in the NGSS project.
A multi-technique detection of an eccentric giant planet around accelerating star HD 57625
2024-12-09
D. Barbato, D. Mesa, V. D'Orazi, S. Desidera, A. Ruggieri, J. Farinato, L. Marafatto, E. Carolo, D. Vassallo, S. Ertel, J. Hom, R. M. Anche, F. Battaini, A. Becker, M. Bergomi, F. Biondi, A. Cardwell, P. Cerpelloni, G. Chauvin, S. Chinellato, C. Desgrange, S. Di Filippo, M. Dima, T. S. Gomes Machado, R. Gratton, D. Greggio, Th. Henning, M. Kenworthy, F. Laudisio, C. Lazzoni, J. Leisenring, L. Lessio, A. Lorenzetto, L. Mohr, M. Montoya, G. Rodeghiero, J. Patience, J. Power, D. Ricci, K. K. R. Santhakumari, A. Sozzetti, G. Umbriaco, M. Vega Pallauta, V. Viotto, K. Wagner
The synergy between different detection methods is a key asset in exoplanetology, allowing for both precise characterization of detected exoplanets and robust constraints even in the case of non-detection. Recently, the interplay between imaging, radial velocities and astrometry has produced significant advancements in exoplanetary science. We report a first result of an ongoing survey performed with SHARK-NIR, the new high-contrast near-infrared imaging camera at the Large Binocular Telescope, in parallel with LBTI/LMIRCam in order to detect planetary companions around stars with significant proper motion anomaly. In this work we focus on HD 57625, a F8 star for which we determine a $4.8^{+3.7}_{-2.9}$Ga age, exhibiting significant astrometric acceleration and for which archival radial velocities hint at the presence of a previously undetected massive long-period companion. We analyse the imaging data we collected with SHARK-NIR and LMIRCam in synergy with the available public SOPHIE radial velocity time series and Hipparcos-Gaia proper motion anomaly. With this joint multi-technique analysis, we aim at characterizing the companion responsible for the astrometric and radial velocity signals. The imaging observations result in a non-detection, indicating the companion to be in the substellar regime. This is confirmed by the synergic analysis of archival radial velocity and astrometric measurements resulting in the detection of HD 57625 b, a ${8.43}_{-0.91}^{+1.10}$M$_{\rm Jup}$ planetary companion with an orbital separation of ${5.70}_{-0.13}^{+0.14}$au and ${0.52}_{-0.03}^{+0.04}$ eccentricity. HD 57625 b joins the small but growing population of giant planets in outer orbits with true mass determination provided by the synergic usage of multiple detection methods, proving once again the importance of multi-technique analysis in providing robust characterization of planetary companions.
Estimation of Classical Cepheid's Physical Parameters from NIR Light Curves
2024-12-09
Lajos G. Balázs, Gábor B. Kovács
Recent space-borne and ground-based observations provide photometric measurements as time series. The effect of interstellar dust extinction in the near-infrared range is only 10% of that measured in the V band. However, the sensitivity of the light curve shape to the physical parameters in the near-infrared is much lower. So, interpreting these types of data sets requires new approaches like the different large-scale surveys, which create similar problems with big data. Using a selected data set, we provide a method for applying routines implemented in R to extract most information of measurements to determine physical parameters, which can also be used in automatic classification schemes and pipeline processing. We made a multivariate classification of 131 Cepheid light curves (LC) in J, H, and K colors, where all the LCs were represented in 20D parameter space in these colors separately. Performing a Principal Component Analysis (PCA), we got an orthogonal coordinate system and squared Euclidean distances between LCs, with 6 significant eigenvalues, reducing the 20-dimension to 6. We also estimated the optimal number of partitions of similar objects and found it to be equal to 7 in each color; their dependence on the period, absolute magnitude, amplitude, and metallicity are also discussed. We computed the Spearman rank correlations, showing that periods and absolute magnitudes correlate with the first three PCs significantly. The first two PC are also found to have a relationship with the amplitude, but the metallicity effects are only marginal. The method shown can be generalized and implemented in unsupervised classification schemes and analysis of mixed and biased samples. The analysis of our Classical Cepheid near-infrared LC sample showed that the J, H, K curves are insufficient for determination of stellar metallicity, with mass being the key factor shaping them.
Stable and tempered stable distributions and processes: an overview toward trajectory simulation
2024-12-09
Taher Jalal
Stable distributions are a celebrated class of probability laws used in various fields. The $\alpha$-stable process, and its exponentially tempered counterpart, the Classical Tempered Stable (CTS) process, are also prominent examples of L\'evy processes. Simulating these processes is critical for many applications, yet it remains computationally challenging, due to their infinite jump activity. This survey provides an overview of the key properties of these objects offering a roadmap for practitioners. The first part is a review of the stability property, sampling algorithms are provided along with numerical illustrations. Then CTS processes are presented, with the Baeumer-Meerschaert algorithm for increment simulation, and a computational analysis is provided with numerical illustrations across different time scales.
Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi
2024-12-09
F. Bredell, H. A. Engelbrecht, J. C. Schoeman
The card game Hanabi is considered a strong medium for the testing and development of multi-agent reinforcement learning (MARL) algorithms, due to its cooperative nature, hidden information, limited communication and remarkable complexity. Previous research efforts have explored the capabilities of MARL algorithms within Hanabi, focusing largely on advanced architecture design and algorithmic manipulations to achieve state-of-the-art performance for a various number of cooperators. However, this often leads to complex solution strategies with high computational cost and requiring large amounts of training data. For humans to solve the Hanabi game effectively, they require the use of conventions, which often allows for a means to implicitly convey ideas or knowledge based on a predefined, and mutually agreed upon, set of ``rules''. Multi-agent problems containing partial observability, especially when limited communication is present, can benefit greatly from the use of implicit knowledge sharing. In this paper, we propose a novel approach to augmenting the action space using conventions, which act as special cooperative actions that span over multiple time steps and multiple agents, requiring agents to actively opt in for it to reach fruition. These conventions are based on existing human conventions, and result in a significant improvement on the performance of existing techniques for self-play and cross-play across a various number of cooperators within Hanabi.
Recent development of optical electric current transformer and its obstacles
2024-12-09
Yu-Xuan Chen, Jing Sun, Bo-Qi Meng
Conventional electromagnetic induction-based current transformers suffer from issues such as bulky and complex structures, slow response times, and low safety levels. Consequently, researchers have explored combining various sensing technologies with optical fibers to develop optical current transformers that could become the primary choice for power systems in the future. With the maturation of optoelectronic technology, optical current transformers have emerged. They offer outstanding advantages, including high sensitivity, integration, stability, and the ability to operate in complex environments. This review categorizes optical current transformers based on different principles, including all-fiber current transformers, those based on magnetostrictive effects, magneto-optic effects, and thermal effects. It also discusses their principles, structures, manufacturing techniques, and signal processing, while forecasting their future development trends.
Omni-Scene: Omni-Gaussian Representation for Ego-Centric Sparse-View Scene Reconstruction
2024-12-09
Dongxu Wei, Zhiqi Li, Peidong Liu
Prior works employing pixel-based Gaussian representation have demonstrated efficacy in feed-forward sparse-view reconstruction. However, such representation necessitates cross-view overlap for accurate depth estimation, and is challenged by object occlusions and frustum truncations. As a result, these methods require scene-centric data acquisition to maintain cross-view overlap and complete scene visibility to circumvent occlusions and truncations, which limits their applicability to scene-centric reconstruction. In contrast, in autonomous driving scenarios, a more practical paradigm is ego-centric reconstruction, which is characterized by minimal cross-view overlap and frequent occlusions and truncations. The limitations of pixel-based representation thus hinder the utility of prior works in this task. In light of this, this paper conducts an in-depth analysis of different representations, and introduces Omni-Gaussian representation with tailored network design to complement their strengths and mitigate their drawbacks. Experiments show that our method significantly surpasses state-of-the-art methods, pixelSplat and MVSplat, in ego-centric reconstruction, and achieves comparable performance to prior works in scene-centric reconstruction. Furthermore, we extend our method with diffusion models, pioneering feed-forward multi-modal generation of 3D driving scenes.
Does Task Complexity Moderate the Benefits of Liveness? A Controlled Experiment
2024-12-09
Patrick Rein, Stefan Ramson, Tom Beckmann, Robert Hirschfeld
Live programming features can be found in a range of programming environments, from individual prototypes to widely used environments. While liveness is generally considered a useful property, there is little empirical evidence on when and how liveness can be beneficial. Even though there are few experimental studies, their results are largely inconclusive. We reviewed existing experiments and related studies to gather a collection of potential effects of liveness and moderating factors. Based on this collection, we concluded that **task complexity** and **prior experience addressing liveness** are potentially essential factors neglected in previous experiments. To fill this gap, we devised and conducted a controlled experiment (N = 37) testing the hypothesis that task complexity moderates the effects of live introspection tools on participants? debugging efficiency, given participants with prior experience with liveness. Our results do not support the hypothesis that task complexity moderates the effect of live introspection tools. This non-significant moderation effect might result from the low number of participants, as the data suggests a moderation effect. The results also show that in our experiment setting, live introspection tools significantly reduced the time participants took to debug the tasks. For researchers interested in the effects of liveness, our findings suggest that studies on liveness should make conscious choices about task complexity and participants' prior experience with liveness. For designers of programming environments, the results of our experiment are a step toward understanding when and how programming tools should be live to become more helpful to programmers.
2024-12-09
Yaron Lipman, Marton Havasi, Peter Holderrieth, Neta Shaul, Matt Le, Brian Karrer, Ricky T. Q. Chen, David Lopez-Paz, Heli Ben-Hamu, Itai Gat
Flow Matching (FM) is a recent framework for generative modeling that has achieved state-of-the-art performance across various domains, including image, video, audio, speech, and biological structures. This guide offers a comprehensive and self-contained review of FM, covering its mathematical foundations, design choices, and extensions. By also providing a PyTorch package featuring relevant examples (e.g., image and text generation), this work aims to serve as a resource for both novice and experienced researchers interested in understanding, applying and further developing FM.
Advancing Extended Reality with 3D Gaussian Splatting: Innovations and Prospects
2024-12-09
Shi Qiu, Binzhu Xie, Qixuan Liu, Pheng-Ann Heng
3D Gaussian Splatting (3DGS) has attracted significant attention for its potential to revolutionize 3D representation, rendering, and interaction. Despite the rapid growth of 3DGS research, its direct application to Extended Reality (XR) remains underexplored. Although many studies recognize the potential of 3DGS for XR, few have explicitly focused on or demonstrated its effectiveness within XR environments. In this paper, we aim to synthesize innovations in 3DGS that show specific potential for advancing XR research and development. We conduct a comprehensive review of publicly available 3DGS papers, with a focus on those referencing XR-related concepts. Additionally, we perform an in-depth analysis of innovations explicitly relevant to XR and propose a taxonomy to highlight their significance. Building on these insights, we propose several prospective XR research areas where 3DGS can make promising contributions, yet remain rarely touched. By investigating the intersection of 3DGS and XR, this paper provides a roadmap to push the boundaries of XR using cutting-edge 3DGS techniques.
Plagiarism is an act of using someone else's work without proper acknowledgment, and this sin is seen to cut across various arenas including the academy, publishing, and other similar arenas. The traditional methods of plagiarism detection through keyword matching and review by humans usually fail to cope with increasingly sophisticated techniques used to mask copy pasted content. This paper aims to introduce a plagiarism detection approach based on machine learning that utilizes natural language processing and complex classification algorithms toward efficient detection of similarities between the documents. The developed model has the capability to detect both exact and paraphrased plagiarism accurately using advanced feature extraction techniques with supervised learning algorithms. We adapted and tested our model on an extensive text sample dataset. And we demonstrated some promising results about precision, recall, and detection accuracy. These outcomes showed that applying machine learning techniques can significantly enhance the functionalities of plagiarism detection systems and improve traditional ones with robust scalability. Future work would include enlargement of the dataset and fine-tuning of the model toward more complicated cases of disguised plagiarism.
2024-12-09
Qihang Ai, Hanxiao Feng, Xinyu Yang, Mengxi Tan, Xingyuan Xu, Roberto Morandotti, Donglin Su, David J. Moss
The simultaneous progress of integrated optical frequency comb (OFC) and radio frequency (RF) photonic signal processing technique have promoted the rapid development of real-time signal processing. Integrated optical frequency comb offer multiple wavelengths as a powerful source for RF photonic signal transversal filter. Here, we review development of real-time signal processing system consisting of integrated OFC and RF photonic signal transversal filter in chronological order, and focus on the applications of this system such as differentiator, integrator, Hilbert transformer, and image processor. We also discuss and present our outlook on more parallel functions and further integration of real-time signal processing system.
A Scalable Decentralized Reinforcement Learning Framework for UAV Target Localization Using Recurrent PPO
2024-12-09
Leon Fernando, Billy Pik Lik Lau, Chau Yuen, U-Xuan Tan
The rapid advancements in unmanned aerial vehicles (UAVs) have unlocked numerous applications, including environmental monitoring, disaster response, and agricultural surveying. Enhancing the collective behavior of multiple decentralized UAVs can significantly improve these applications through more efficient and coordinated operations. In this study, we explore a Recurrent PPO model for target localization in perceptually degraded environments like places without GNSS/GPS signals. We first developed a single-drone approach for target identification, followed by a decentralized two-drone model. Our approach can utilize two types of sensors on the UAVs, a detection sensor and a target signal sensor. The single-drone model achieved an accuracy of 93%, while the two-drone model achieved an accuracy of 86%, with the latter requiring fewer average steps to locate the target. This demonstrates the potential of our method in UAV swarms, offering efficient and effective localization of radiant targets in complex environmental conditions.
Graph neural networks (GNNs) are powerful machine learning models designed to handle irregularly structured data. However, their generic design often proves inadequate for analyzing brain connectomes in Alzheimer's Disease (AD), highlighting the need to incorporate domain knowledge for optimal performance. Infusing AD-related knowledge into GNNs is a complicated task. Existing methods typically rely on collaboration between computer scientists and domain experts, which can be both time-intensive and resource-demanding. To address these limitations, this paper presents a novel self-guided, knowledge-infused multimodal GNN that autonomously incorporates domain knowledge into the model development process. Our approach conceptualizes domain knowledge as natural language and introduces a specialized multimodal GNN capable of leveraging this uncurated knowledge to guide the learning process of the GNN, such that it can improve the model performance and strengthen the interpretability of the predictions. To evaluate our framework, we curated a comprehensive dataset of recent peer-reviewed papers on AD and integrated it with multiple real-world AD datasets. Experimental results demonstrate the ability of our method to extract relevant domain knowledge, provide graph-based explanations for AD diagnosis, and improve the overall performance of the GNN. This approach provides a more scalable and efficient alternative to inject domain knowledge for AD compared with the manual design from the domain expert, advancing both prediction accuracy and interpretability in AD diagnosis.
Sound2Vision: Generating Diverse Visuals from Audio through Cross-Modal Latent Alignment
2024-12-09
Kim Sung-Bin, Arda Senocak, Hyunwoo Ha, Tae-Hyun Oh
How does audio describe the world around us? In this work, we propose a method for generating images of visual scenes from diverse in-the-wild sounds. This cross-modal generation task is challenging due to the significant information gap between auditory and visual signals. We address this challenge by designing a model that aligns audio-visual modalities by enriching audio features with visual information and translating them into the visual latent space. These features are then fed into the pre-trained image generator to produce images. To enhance image quality, we use sound source localization to select audio-visual pairs with strong cross-modal correlations. Our method achieves substantially better results on the VEGAS and VGGSound datasets compared to previous work and demonstrates control over the generation process through simple manipulations to the input waveform or latent space. Furthermore, we analyze the geometric properties of the learned embedding space and demonstrate that our learning approach effectively aligns audio-visual signals for cross-modal generation. Based on this analysis, we show that our method is agnostic to specific design choices, showing its generalizability by integrating various model architectures and different types of audio-visual data.
Applying Machine Learning Tools for Urban Resilience Against Floods
2024-12-09
Mahla Ardebili Pour, Mohammad B. Ghiasi, Ali Karkehabadi
Floods are among the most prevalent and destructive natural disasters, often leading to severe social and economic impacts in urban areas due to the high concentration of assets and population density. In Iran, particularly in Tehran, recurring flood events underscore the urgent need for robust urban resilience strategies. This paper explores flood resilience models to identify the most effective approach for District 6 in Tehran. Through an extensive literature review, various resilience models were analyzed, with the Climate Disaster Resilience Index (CDRI) emerging as the most suitable model for this district due to its comprehensive resilience dimensions: Physical, Social, Economic, Organizational, and Natural Health resilience. Although the CDRI model provides a structured approach to resilience measurement, it remains a static model focused on spatial characteristics and lacks temporal adaptability. An extensive literature review enhances the CDRI model by integrating data from 2013 to 2022 in three-year intervals and applying machine learning techniques to predict resilience dimensions for 2025. This integration enables a dynamic resilience model that can accommodate temporal changes, providing a more adaptable and data driven foundation for urban flood resilience planning. By employing artificial intelligence to reflect evolving urban conditions, this model offers valuable insights for policymakers and urban planners to enhance flood resilience in Tehrans critical District 6.
Modeling, Planning, and Control for Hybrid UAV Transition Maneuvers
2024-12-09
Spencer Folk
Small unmanned aerial vehicles (UAVs) have become standard tools in reconnaissance and surveying for both civilian and defense applications. In the future, UAVs will likely play a pivotal role in autonomous package delivery, but current multi-rotor candidates suffer from poor energy efficiency leading to insufficient endurance and range. In order to reduce the power demands of package delivery UAVs while still maintaining necessary hovering capabilities, companies like Amazon are experimenting with hybrid Vertical Take-Off and Landing (VTOL) platforms. Tailsitter VTOLs offer a mechanically simple and cost-effective solution compared to other hybrid VTOL configurations, and while advances in hardware and microelectronics have optimized the tailsitter for package delivery, the software behind its operation has largely remained a critical barrier to industry adoption. Tailsitters currently lack a generic, computationally efficient method of control that can provide strong safety and robustness guarantees over the entire flight domain. Further, tailsitters lack a closed-form method of designing dynamically feasible transition maneuvers between hover and cruise. In this paper, we survey the modeling and control methods currently implemented on small-scale tailsitter UAVs, and attempt to leverage a nonlinear dynamic model to design physically realizable, continuous-pitch transition maneuvers at constant altitude. Primary results from this paper isolate potential barriers to constant-altitude transition, and a novel approach to bypassing these barriers is proposed. While initial results are unsuccessful at providing feasible transition, this work acts as a stepping stone for future efforts to design new transition maneuvers that are safe, robust, and computationally efficient.
Classification of Periodic Variable Stars from TESS
2024-12-09
Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu
The number of known periodic variable stars has increased rapidly in recent years. As an all-sky transit survey, the Transiting Exoplanet Survey Satellite (TESS) plays an important role in detecting low-amplitude variable stars. Using 2-minute cadence data from the first 67 sectors of TESS, we find 72,505 periodic variable stars. We used 19 parameters including period, physical parameters, and light curve (LC) parameters to classify periodic variable stars into 12 sub-types using random forest method. Pulsating variable stars and eclipsing binaries are distinguished mainly by period, LC parameters and physical parameters. GCAS, ROT, UV, YSO are distinguished mainly by period and physical parameters. Compared to previously published catalogs, 63,106 periodic variable stars (87.0$\%$) are newly classified, including 13 Cepheids, 27 RR Lyrae stars, $\sim$4,600 $\delta$ Scuti variable stars, $\sim$1,600 eclipsing binaries, $\sim$34,000 rotational variable stars, and about 23,000 other types of variable stars. The purity of eclipsing binaries and pulsation variable stars ranges from 94.2$\%$ to 99.4$\%$ when compared to variable star catalogs of Gaia DR3 and ZTF DR2. The purity of ROT is relatively low at 83.3$\%$. The increasing number of variables stars is helpful to investigate the structure of the Milky Way, stellar physics, and chromospheric activity.
Membership Inference Attacks and Defenses in Federated Learning: A Survey
2024-12-09
Li Bai, Haibo Hu, Qingqing Ye, Haoyang Li, Leixia Wang, Jianliang Xu
Federated learning is a decentralized machine learning approach where clients train models locally and share model updates to develop a global model. This enables low-resource devices to collaboratively build a high-quality model without requiring direct access to the raw training data. However, despite only sharing model updates, federated learning still faces several privacy vulnerabilities. One of the key threats is membership inference attacks, which target clients' privacy by determining whether a specific example is part of the training set. These attacks can compromise sensitive information in real-world applications, such as medical diagnoses within a healthcare system. Although there has been extensive research on membership inference attacks, a comprehensive and up-to-date survey specifically focused on it within federated learning is still absent. To fill this gap, we categorize and summarize membership inference attacks and their corresponding defense strategies based on their characteristics in this setting. We introduce a unique taxonomy of existing attack research and provide a systematic overview of various countermeasures. For these studies, we thoroughly analyze the strengths and weaknesses of different approaches. Finally, we identify and discuss key future research directions for readers interested in advancing the field.
A Hyperdimensional One Place Signature to Represent Them All: Stackable Descriptors For Visual Place Recognition
2024-12-09
Connor Malone, Somayeh Hussaini, Tobias Fischer, Michael Milford
Visual Place Recognition (VPR) enables coarse localization by comparing query images to a reference database of geo-tagged images. Recent breakthroughs in deep learning architectures and training regimes have led to methods with improved robustness to factors like environment appearance change, but with the downside that the required training and/or matching compute scales with the number of distinct environmental conditions encountered. Here, we propose Hyperdimensional One Place Signatures (HOPS) to simultaneously improve the performance, compute and scalability of these state-of-the-art approaches by fusing the descriptors from multiple reference sets captured under different conditions. HOPS scales to any number of environmental conditions by leveraging the Hyperdimensional Computing framework. Extensive evaluations demonstrate that our approach is highly generalizable and consistently improves recall performance across all evaluated VPR methods and datasets by large margins. Arbitrarily fusing reference images without compute penalty enables numerous other useful possibilities, three of which we demonstrate here: descriptor dimensionality reduction with no performance penalty, stacking synthetic images, and coarse localization to an entire traverse or environmental section.
Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder
2024-12-09
Kamala Devi Kannan, Senthil Kumar Jagatheesaperumal, Rajesh N. V. P. S. Kandala, Mojtaba Lotfaliany, Roohallah Alizadehsanid, Mohammadreza Mohebbi
For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) has started playing a significant role. By evaluating complex data from imaging, genetics, and behavioral assessments, these technologies have the potential to significantly improve clinical outcomes. However, they also present unique challenges related to data integration and ethical issues. This survey reviews the development of ML and DL methods for the early diagnosis and treatment of mental health issues. It examines a range of applications, with a particular emphasis on behavioral assessments, genetic and biomarker analysis, and medical imaging for diagnosing diseases like depression, bipolar disorder, and schizophrenia. Predictive modeling for illness progression is further discussed, focusing on the role of risk prediction models and longitudinal studies. Key findings highlight how ML and DL can improve diagnostic accuracy and treatment outcomes while addressing methodological inconsistencies, data integration challenges, and ethical concerns. The study emphasizes the importance of building real-time monitoring systems for individualized treatment, enhancing data fusion techniques, and fostering interdisciplinary collaboration. Future research should focus on overcoming these obstacles to ensure the valuable and ethical application of ML and DL in mental health services.
The Development of Low-Q Cavity Type Beam Position Monitor with a Position Resolution of Nanometer for Future Colliders
2024-12-09
S. W. Jang, E. -S. Kim, T. Tauchi, N. Terunuma, P. N. Burrows, N. Blaskovic Kraljevic, P. Bambade, S. Wallon, O. Blanco
The nano-meter beam size in future linear colliders requires very high resolution beam position monitor since higher resolution allows more accurate position measurement in the interaction point. We developed and tested a low-Q C-band beam position monitor with position resolution of nanometer. The C-band BPM was tested for the fast beam feedback system at the interaction point of ATF2 in KEK, in which C-band beam position monitor is called to IPBPM (Interaction Point Beam Position Monitor). The average position resolution of the developed IPBPMs was measured to be 10.1 nm at a nominal beam charge of $87\%$ of ATF2. From the measured beam position resolution, we can expect beam position resolution of around 8.8 nm and 4.4 nm with nominal ATF2 and ILC beam charge conditions, respectively, in which the position resolution is below the vertical beam size in ILC. In this paper, we describe the development of the IPBPM and the beam test results at the nanometer level in beam position resolution
Spiking Neural Networks for Radio Frequency Interference Detection in Radio Astronomy
2024-12-09
Nicholas J. Pritchard, Andreas Wicenec, Mohammed Bennamoun, Richard Dodson
Spiking Neural Networks (SNNs) promise efficient spatio-temporal data processing owing to their dynamic nature. This paper addresses a significant challenge in radio astronomy, Radio Frequency Interference (RFI) detection, by reformulating it as a time-series segmentation task inherently suited for SNN execution. Automated RFI detection systems capable of real-time operation with minimal energy consumption are increasingly important in modern radio telescopes. We explore several spectrogram-to-spike encoding methods and network parameters, applying first-order leaky integrate-and-fire SNNs to tackle RFI detection. To enhance the contrast between RFI and background information, we introduce a divisive normalisation-inspired pre-processing step, which improves detection performance across multiple encoding strategies. Our approach achieves competitive performance on a synthetic dataset and compelling results on real data from the Low-Frequency Array (LOFAR) instrument. To our knowledge, this work is the first to train SNNs on real radio astronomy data successfully. These findings highlight the potential of SNNs for performing complex time-series tasks, paving the way for efficient, real-time processing in radio astronomy and other data-intensive fields.
Privacy-Preserving Large Language Models: Mechanisms, Applications, and Future Directions
2024-12-09
Guoshenghui Zhao, Eric Song
The rapid advancement of large language models (LLMs) has revolutionized natural language processing, enabling applications in diverse domains such as healthcare, finance and education. However, the growing reliance on extensive data for training and inference has raised significant privacy concerns, ranging from data leakage to adversarial attacks. This survey comprehensively explores the landscape of privacy-preserving mechanisms tailored for LLMs, including differential privacy, federated learning, cryptographic protocols, and trusted execution environments. We examine their efficacy in addressing key privacy challenges, such as membership inference and model inversion attacks, while balancing trade-offs between privacy and model utility. Furthermore, we analyze privacy-preserving applications of LLMs in privacy-sensitive domains, highlighting successful implementations and inherent limitations. Finally, this survey identifies emerging research directions, emphasizing the need for novel frameworks that integrate privacy by design into the lifecycle of LLMs. By synthesizing state-of-the-art approaches and future trends, this paper provides a foundation for developing robust, privacy-preserving large language models that safeguard sensitive information without compromising performance.
Dark Matter halo properties have been studied extensively within the virial radius of host halo systems, and previous research shows that there are correlations between host halo properties and subhalo occupation. This work explores how the correlation would change when one extends the definition of subhalo occupation out to 1.5 Mpc for Milky Way-mass host halos. We compute the correlations between four host halo properties (half-mass scale, concentration, peak-mass scale, and spin) and subhalo occupation with varying halo radius definitions. We find that the host halo properties impact satellite occupation beyond the virial radius and the locations at which the correlation peaks do not typically align with the virial radius or splashback radius. The behavior of the subhalo occupation variation as a function of radius, especially in the outskirts, is connected to the effect of halo assembly bias. However, there is no universal behavior in the subhalo occupation variation as the halo radius definition changes. We further find that using a ratio of the number of subhalos within an individual host system rather than total number counts can, to some extent, eliminate its concentration dependence. This method shows promise for using observed satellite counts to estimate host halo mass. Our analysis highlights the utility of extending observational surveys of satellite galaxies to beyond the virial radius.
Advancing clinical trial outcomes using deep learning and predictive modelling: bridging precision medicine and patient-centered care
2024-12-09
Sydney Anuyah, Mallika K Singh, Hope Nyavor
The integration of artificial intelligence [AI] into clinical trials has revolutionized the process of drug development and personalized medicine. Among these advancements, deep learning and predictive modelling have emerged as transformative tools for optimizing clinical trial design, patient recruitment, and real-time monitoring. This study explores the application of deep learning techniques, such as convolutional neural networks [CNNs] and transformerbased models, to stratify patients, forecast adverse events, and personalize treatment plans. Furthermore, predictive modelling approaches, including survival analysis and time-series forecasting, are employed to predict trial outcomes, enhancing efficiency and reducing trial failure rates. To address challenges in analysing unstructured clinical data, such as patient notes and trial protocols, natural language processing [NLP] techniques are utilized for extracting actionable insights. A custom dataset comprising structured patient demographics, genomic data, and unstructured text is curated for training and validating these models. Key metrics, including precision, recall, and F1 scores, are used to evaluate model performance, while trade-offs between accuracy and computational efficiency are examined to identify the optimal model for clinical deployment. This research underscores the potential of AI-driven methods to streamline clinical trial workflows, improve patient-centric outcomes, and reduce costs associated with trial inefficiencies. The findings provide a robust framework for integrating predictive analytics into precision medicine, paving the way for more adaptive and efficient clinical trials. By bridging the gap between technological innovation and real-world applications, this study contributes to advancing the role of AI in healthcare, particularly in fostering personalized care and improving overall trial success rates.
The rapid advancement of Generative AI (Gen AI) technologies, particularly tools like ChatGPT, is significantly impacting the labor market by reshaping job roles and skill requirements. This study examines the demand for ChatGPT-related skills in the U.S. labor market by analyzing job advertisements collected from major job platforms between May and December 2023. Using text mining and topic modeling techniques, we extracted and analyzed the Gen AI-related skills that employers are hiring for. Our analysis identified five distinct ChatGPT-related skill sets: general familiarity, creative content generation, marketing, advanced functionalities (such as prompt engineering), and product development. In addition, the study provides insights into job attributes such as occupation titles, degree requirements, salary ranges, and other relevant job characteristics. These findings highlight the increasing integration of Gen AI across various industries, emphasizing the growing need for both foundational knowledge and advanced technical skills. The study offers valuable insights into the evolving demands of the labor market, as employers seek candidates equipped to leverage generative AI tools to improve productivity, streamline processes, and drive innovation.
Learning About Algorithm Auditing in Five Steps: Scaffolding How High School Youth Can Systematically and Critically Evaluate Machine Learning Applications
2024-12-09
Luis Morales-Navarro, Yasmin B. Kafai, Lauren Vogelstein, Evelyn Yu, Danaë Metaxa
While there is widespread interest in supporting young people to critically evaluate machine learning-powered systems, there is little research on how we can support them in inquiring about how these systems work and what their limitations and implications may be. Outside of K-12 education, an effective strategy in evaluating black-boxed systems is algorithm auditing-a method for understanding algorithmic systems' opaque inner workings and external impacts from the outside in. In this paper, we review how expert researchers conduct algorithm audits and how end users engage in auditing practices to propose five steps that, when incorporated into learning activities, can support young people in auditing algorithms. We present a case study of a team of teenagers engaging with each step during an out-of-school workshop in which they audited peer-designed generative AI TikTok filters. We discuss the kind of scaffolds we provided to support youth in algorithm auditing and directions and challenges for integrating algorithm auditing into classroom activities. This paper contributes: (a) a conceptualization of five steps to scaffold algorithm auditing learning activities, and (b) examples of how youth engaged with each step during our pilot study.
The presence, distribution and kinematics of atomic neutral hydrogen in the central regions of galaxies can be traced by the HI 21~cm line observed in absorption. Depending only on the strength of the radio continuum, the associated absorption can trace the gas down to pc scale, which is ideal for exploring the HI in the nuclear regions of radio AGN. This paper gives a brief overview of the main recent findings with particular focus on the AGN-driven HI outflows. Absorption has made possible the discovery of fast and massive HI outflows and their clumpy structure on pc scales. The similarities with the predictions of numerical simulations confirm the impact of young radio jets in the feedback cycle and galaxy evolution. The field of HI absorption is rapidly expanding thanks to new ``blind" surveys and the increased spectral capabilities of the radio telescopes. This opens many possibilities for future discoveries and complements the studies of HI emission.
Do we owe our existence to a Gravitational Anomaly?
2024-12-09
Nick E. Mavromatos
I review a mechanism for a potential explanation of the dominance of matter over antimatter in the Universe (``our existence''), based on a (3+1)-dimensional string-inspired cosmological model, characterised by (uncancelled) gravitational Chern-Simons (gCS) anomalies in the premordial epochs. The model is consistent with general covariance but entails spontaneous Lorentz (LV) and CPT Violation (CPTV), due to appropriate backgrounds of axion fields, which couple to the anomaly terms. Upon condensation of the gCS terms, due to primordial gravitational waves, these axions acquire a linear potential, which drives inflation of a Running-Vacuum-Model (RVM) type, while the effective action is also characterised by imaginary parts as well, which imply metastability of this RVM inflation. The axion backgrounds remain undiluted until the exit from inflation, and are responsible for yielding unconventional LV and CPTV Leptogenesis, which is then communicated to the baryon sector (Baryogenesis) via sphaleron processes. The model provides a phenomenologically consistent cosmological evolution from inflation till the modern era.
JWST UNCOVERs the Optical Size - Stellar Mass Relation at $4
2024-12-09
Tim B. Miller, Katherine A. Suess, David J. Setton, Sedona H. Price, Ivo Labbe, Rachel Bezanson, Gabriel Brammer, Sam E. Cutler, Lukas J. Furtak, Joel Leja, Richard Pan, Bingjie Wang, John R. Weaver, Katherine E. Whitaker, Pratika Dayal, Anna de Graaff, Robert Feldmann, Jenny E. Greene, S. Fujimoto, Michael V. Maseda, Themiya Nanayakkara, Erica J. Nelson, Pieter van Dokkum, Adi Zitrin
We study the rest-frame optical and ultraviolet morphology of galaxies in the first billion years of the Universe. Using JWST data from the UNCOVER and MegaScience surveys targeting the lensing cluster Abell 2744 we present multi-band morphological measurements for a sample of 995 galaxies selected using 20-band NIRCam photometry and 35 using NIRSpec Prism spectroscopy over the redshift range of $4
Photometric Selection of type 1 Quasars in the XMM-LSS Field with Machine Learning and the Disk-Corona Connection
2024-12-09
Jian Huang, Bin Luo, W. N. Brandt, Ying Chen, Qingling Ni, Yongquan Xue, Zijian Zhang
We present photometric selection of type 1 quasars in the $\approx5.3~{\rm deg}^{2}$ XMM-Large Scale Structure (XMM-LSS) survey field with machine learning. We constructed our training and \hbox{blind-test} samples using spectroscopically identified SDSS quasars, galaxies, and stars. We utilized the XGBoost machine learning method to select a total of 1\,591 quasars. We assessed the classification performance based on the blind-test sample, and the outcome was favorable, demonstrating high reliability ($\approx99.9\%$) and good completeness ($\approx87.5\%$). We used XGBoost to estimate photometric redshifts of our selected quasars. The estimated photometric redshifts span a range from 0.41 to 3.75. The outlier fraction of these photometric redshift estimates is $\approx17\%$ and the normalized median absolute deviation ($\sigma_{\rm NMAD}$) is $\approx0.07$. To study the quasar disk-corona connection, we constructed a subsample of 1\,016 quasars with HSC $i<22.5$ after excluding radio-loud and potentially X-ray-absorbed quasars. The relation between the optical-to-X-ray power-law slope parameter ($\alpha_{\rm OX}$) and the 2500 Angstrom monochromatic luminosity ($L_{2500}$) for this subsample is $\alpha_{\rm OX}=(-0.156\pm0.007)~{\rm log}~{L_{\rm 2500}}+(3.175\pm0.211)$ with a dispersion of 0.159. We found this correlation in good agreement with the correlations in previous studies. We explored several factors which may bias the $\alpha_{\rm OX}$-$L_{\rm 2500}$ relation and found that their effects are not significant. We discussed possible evolution of the $\alpha_{\rm OX}$-$L_{\rm 2500}$ relation with respect to $L_{\rm 2500}$ or redshift.
Using Large Language Models to Assign Partial Credit to Students' Explanations of Problem-Solving Process: Grade at Human Level Accuracy with Grading Confidence Index and Personalized Student-facing Feedback
2024-12-09
Zhongzhou Chen, Tong Wan
This study examines the feasibility and potential advantages of using large language models, in particular GPT-4o, to perform partial credit grading of large numbers of student written responses to introductory level physics problems. Students were instructed to write down verbal explanations of their reasoning process when solving one conceptual and two numerical calculation problems on in class exams. The explanations were then graded according to a 3-item rubric with each item grades as binary (1 or 0). We first demonstrate that machine grading using GPT-4o with no examples nor reference answer can reliably agree with human graders on 70%-80% of all cases, which is equal to or higher than the level at which two human graders agree with each other. Two methods are essential for achieving this level of accuracy: 1. Adding explanation language to each rubric item that targets the errors of initial machine grading. 2. Running the grading process 5 times and taking the most frequent outcome. Next, we show that the variation in outcomes across 5 machine grading attempts as measured by the Shannon Entropy can serve as a grading confidence index, allowing a human instructor to identify ~40% of all potentially incorrect gradings by reviewing just 10 - 15% of all responses. Finally, we show that it is straightforward to use GPT-4o to write clear explanations of the partial credit grading outcomes. Those explanations can be used as feedback for students, which will allow students to understand their grades and raise different opinions when necessary. Almost all feedback messages generated were rated 3 or above on a 5-point scale by two experienced instructors. The entire grading and feedback generating process cost roughly $5 per 100 student answers, which shows immense promise for automating labor-intensive grading process by a combination of machine grading with human input and supervision.
Time to Sparkler. Accurate ages of lensed globular clusters at $z=1.4$ with JWST photometry
2024-12-09
Elena Tomasetti, Michele Moresco, Carmela Lardo, Frédéric Courbin, Raul Jimenez, Licia Verde, Martin Millon, Andrea Cimatti
Determining reliable ages for old stellar objects at different redshifts offers a powerful means to constrain cosmology without relying on a specific cosmological model: this is known as the cosmic clocks method. Globular clusters (GCs), long recognised as hosts of the Universe's oldest stars, have served as the archetypical cosmic clocks. However, their age estimates have traditionally been confined to redshift z=0, limiting their role to constraining the present-day age of the Universe. Here we explore how to measure reliable ages of GCs well beyond $z=0$, leveraging their potential to extend cosmic clock measurements to earlier epochs. Specifically, we use 6-band JWST/NIRCam high-precision photometry of candidate stellar clusters in the Sparkler galaxy, located at redshift $z$=1.378 and strongly lensed by the galaxy cluster SMACS J0723.3-7327. By employing stellar population models within a Bayesian inference framework, we constrain the GCs' ages, star formation histories, metallicities, and dust attenuation. The five compact sources previously identified as GCs, based on their red spectral energy distributions being consistent with the colours of old stellar systems, yield a formation age of $1.9\pm0.4$ Gyr on average. This result implies a total age of the Universe that aligns well with the $\Lambda$CDM model derived from Planck18 data. Recent space-based observations have uncovered a wealth of lensed GCs as well as globulars within the member galaxies of the clusters themselves. These findings suggest that the pool of objects available for cosmic clock studies is enormous. A systematic multi-band photometric survey of GCs in and behind galaxy clusters, using facilities like Euclid and JWST, would therefore be a powerful tool for estimating cluster ages across a large range of redshifts, allowing the Universe to be dated across an unprecedented range of epochs.
The Pristine Inner Galaxy Survey (PIGS) XI: Revealing the chemical evolution of the interacting Sagittarius dwarf galaxy
2024-12-09
Sara Vitali, Alvaro Rojas-Arriagada, Paula Jofré, Federico Sestito, Joshua Povick, Vanessa Hill, Emma Fernández-Alvar, Anke Ardern-Arentsen, Pascale Jablonka, Nicolas F. Martin, Else Starkenburg, David Aguado
The Sagittarius dwarf spheroidal galaxy (Sgr dSph) is a satellite orbiting the Milky Way that has experienced multiple stripping events due to tidal interactions with our Galaxy. Its accretion history has led to a distinct stellar overdensity, which is the remnant of the core of the progenitor. We present a complete chemical analysis of 111 giant stars in the core of Sgr dSph to investigate the chemical evolution and enrichment history of this satellite. Employing the metallicity-sensitive Ca H&K photometry from the Pristine Inner Galaxy Survey, we selected stars spanning a wide metallicity range and obtained high-resolution spectra with the ESO FLAMES/GIRAFFE multi-object spectrograph. For the stellar sample covering $-2.13 < \rm{[Fe/H] < -0.35}$, we derived abundances for up to 14 chemical elements with average uncertainties of $\sim 0.09$ dex and a set of stellar ages which allowed us to build an age-metallicity relation (AMR) for the entire sample. With the most comprehensive set of chemical species measured for the core of Sgr, we studied several [X/Fe] ratios. Most trends align closely with Galactic chemical trends, but notable differences emerge in the heavy $n$-capture elements, which offer independent insights into the star formation history of a stellar population. The deficiency in the $\alpha$-elements with respect the Milky Way suggests a slower, less efficient early star formation history, similar to other massive satellites. $S$-process element patterns indicate significant enrichment from AGB stars over time. The AMR and chemical ratios point to an extended star formation history, with a rapid early phase in the first Gyr, followed by declining activity and later star-forming episodes. These findings are consistent with Sgr hosting multiple stellar populations, from young ($\sim 4$ Gyr) to old, metal-poor stars ($\sim 10$ Gyr)
A SPectroscopic survey of biased halos In the Reionization Era (ASPIRE): Spectroscopically Complete Census of Obscured Cosmic Star Formation Rate Density at $z=4-6$
2024-12-09
Fengwu Sun, Feige Wang, Jinyi Yang, Jaclyn B. Champagne, Roberto Decarli, Xiaohui Fan, Eduardo Bañados, Zheng Cai, Luis Colina, Eiichi Egami, Joseph F. Hennawi, Xiangyu Jin, Hyunsung D. Jun, Yana Khusanova, Mingyu Li, Zihao Li, Xiaojing Lin, Weizhe Liu, Romain A. Meyer, Maria A. Pudoka, George H. Rieke, Yue Shen, Wei Leong Tee, Bram Venemans, Fabian Walter, Yunjing Wu, Huanian Zhang, Siwei Zou
We present a stringent measurement of the dust-obscured star-formation rate density (SFRD) at $z=4-6$ from the ASPIRE JWST Cycle-1 medium and ALMA Cycle-9 large program. We obtained JWST/NIRCam grism spectroscopy and ALMA 1.2-mm continuum map along 25 independent quasar sightlines, covering a total survey area of $\sim$35 arcmin$^2$ where we search for dusty star-forming galaxies (DSFGs) at $z = 0 - 7$. We identify eight DSFGs in seven fields at $z=4-6$ through the detection of H$\alpha$ or [O III] $\lambda$5008 lines, including fainter lines such as H$\beta$, [O III] $\lambda$4960, [N II] $\lambda$6585, [S II] $\lambda\lambda$6718,6733 for six sources. With this spectroscopically complete DSFG sample at $z=4-6$ and negligible impact from cosmic variance (shot noise), we measure the infrared luminosity function (IRLF) down to $L_\mathrm{IR} \sim 2\times10^{11}$ $L_\odot$. We find flattening of IRLF at $z=4-6$ towards the faint end (power-law slope $\alpha = 0.59_{-0.45}^{+0.39}$). We determine the dust-obscured cosmic SFRD at this epoch as $\log[\rho_\mathrm{SFR,IR} / (\mathrm{M}_\odot\,\mathrm{yr}^{-1}\,\mathrm{Mpc}^{-3})] = -1.52_{-0.13}^{+0.14}$. This is significantly higher than previous determination using ALMA data in the Hubble Ultra Deep Field, which is void of DSFGs at $z=4-6$ because of strong cosmic variance (shot noise). We conclude that the majority ($66\pm7$%) of cosmic star formation at $z \sim 5$ is still obscured by dust. We also discuss the uncertainty of SFRD propagated from far-IR spectral energy distribution and IRLF at the bright end, which will need to be resolved with future ALMA and JWST observations.
The ACCEL2 Project: Precision Measurements of EFT Parameters and BAO Peak Shifts for the Lyman-$α$ Forest
2024-12-09
Roger de Belsunce, Shi-Fan Chen, Mikhail M. Ivanov, Corentin Ravoux, Solene Chabanier, Jean Sexton, Zarija Lukic
We present precision measurements of the bias parameters of the one-loop power spectrum model of the Lyman-alpha (Lya) forest, derived within the effective field theory of large-scale structure (EFT). We fit our model to the three-dimensional flux power spectrum measured from the ACCEL2 hydrodynamic simulations. The EFT model fits the data with an accuracy of below 2 percent up to a wavenumber of k = 2 h/Mpc. Further, we analytically derive how non-linearities in the three-dimensional clustering of the Lya forest introduce biases in measurements of the Baryon Acoustic Oscillations (BAO) scaling parameters in radial and transverse directions. From our EFT parameter measurements, we obtain a theoretical error budget of -0.2 (-0.3) percent for the radial (transverse) parameters at redshift two. This corresponds to a shift of -0.3 (0.1) percent for the isotropic (anisotropic) distance measurements. We provide an estimate for the shift of the BAO peak for Lya-quasar cross-correlation measurements assuming analytical and simulation-based scaling relations for the non-linear quasar bias parameters resulting in a shift of -0.2 (-0.1) percent for the radial (transverse) dilation parameters, respectively. This analysis emphasizes the robustness of Lya forest BAO measurements to the theory modeling. We provide informative priors and an error budget for measuring the BAO feature -- a key science driver of the currently observing Dark Energy Spectroscopic Instrument (DESI). Our work paves the way for full-shape cosmological analyses of Lya forest data from DESI and upcoming surveys such as the Prime Focus Spectrograph, WEAVE-QSO, and 4MOST.
The Sloan Digital Sky Survey Reverberation Mapping Project: Insights on Maximizing Efficiency in Lag Measurements and Black-Hole Masses
2024-12-09
Y. Homayouni, Yuanzhe Jiang, W. N. Brandt, C. J. Grier, Jonathan R. Trump, Yue Shen, Keith Horne, Patrick B. Hall, Scott F. Anderson, Luis C. Ho, D. P. Schneider
Multi-year observations from the Sloan Digital Sky Survey Reverberation Mapping (SDSS-RM) project have significantly increased the number of quasars with reliable reverberation-mapping lag measurements. We statistically analyze target properties, light-curve characteristics, and survey design choices to identify factors crucial for successful and efficient RM surveys. Analyzing 172 high-confidence ("gold") lag measurements from SDSS-RM for the H$\beta$, MgII, and CIV emission lines, we find that the Durbin-Watson statistic (a statistical test for residual correlation) is the most significant predictor of light curves suitable for lag detection. Variability signal-to-noise ratio and emission-line placement on the detector also correlate with successful lag measurements. We further investigate the impact of observing cadence on survey design by analyzing the effect of reducing observations in the first year of SDSS-RM. Our results demonstrate that a modest reduction in observing cadence to $\sim$1.5 weeks between observations can retain approximately 90% of the lag measurements compared to twice-weekly observations in the initial year. Provided similar and uniform sampling in subsequent years, this adjustment has a minimal effect on the overall recovery of lags across all emission lines. These results provide valuable inputs for optimizing future RM surveys.
How do uncertainties in galaxy formation physics impact field-level galaxy bias?
2024-12-09
Mahlet Shiferaw, Nickolas Kokron, Risa H. Wechsler
Our ability to extract cosmological information from galaxy surveys is limited by uncertainties in the galaxy-dark matter halo relationship for a given galaxy population, which are governed by the intricacies of galaxy formation. To quantify these uncertainties, we examine quenched and star-forming galaxies using two distinct approaches to modeling galaxy formation: UniverseMachine, an empirical semi-analytic model, and the IllustrisTNG hydrodynamical simulation. We apply a second-order hybrid N-body perturbative bias expansion to each galaxy sample, enabling direct comparison of modeling approaches and revealing how uncertainties in galaxy formation and the galaxy-halo connection affect bias parameters and non-Poisson noise across number density and redshift. Notably, we find that quenched and star-forming galaxies occupy distinct parts of bias parameter spacce, and that the scatter induced from these entirely different galaxy formation models is small when conditioned on similar selections of galaxies. We also detect a signature of assembly bias in our samples; this leads to small but significant deviations from predictions of the analytic bias, while samples with assembly bias removed match these predictions well. This work indicates that galaxy samples from a spectrum of reasonable, physically motivated models for galaxy formation roughly spanning our current understanding give a relatively small range of field-level galaxy bias parameters and relations. We estimate a set of priors from this set of models that should be useful in extracting cosmological information from LRG- and ELG-like samples. Looking forward, this indicates that careful estimates of the range of impacts of galaxy formation for a given sample and cosmological analysis will be an essential ingredient for extracting the most precise cosmological information from current and future large galaxy surveys.
SafeWatch: An Efficient Safety-Policy Following Video Guardrail Model with Transparent Explanations
2024-12-09
Zhaorun Chen, Francesco Pinto, Minzhou Pan, Bo Li
With the rise of generative AI and rapid growth of high-quality video generation, video guardrails have become more crucial than ever to ensure safety and security across platforms. Current video guardrails, however, are either overly simplistic, relying on pure classification models trained on simple policies with limited unsafe categories, which lack detailed explanations, or prompting multimodal large language models (MLLMs) with long safety guidelines, which are inefficient and impractical for guardrailing real-world content. To bridge this gap, we propose SafeWatch, an efficient MLLM-based video guardrail model designed to follow customized safety policies and provide multi-label video guardrail outputs with content-specific explanations in a zero-shot manner. In particular, unlike traditional MLLM-based guardrails that encode all safety policies autoregressively, causing inefficiency and bias, SafeWatch uniquely encodes each policy chunk in parallel and eliminates their position bias such that all policies are attended simultaneously with equal importance. In addition, to improve efficiency and accuracy, SafeWatch incorporates a policy-aware visual token pruning algorithm that adaptively selects the most relevant video tokens for each policy, discarding noisy or irrelevant information. This allows for more focused, policy-compliant guardrail with significantly reduced computational overhead. Considering the limitations of existing video guardrail benchmarks, we propose SafeWatch-Bench, a large-scale video guardrail benchmark comprising over 2M videos spanning six safety categories which covers over 30 tasks to ensure a comprehensive coverage of all potential safety scenarios. SafeWatch outperforms SOTA by 28.2% on SafeWatch-Bench, 13.6% on benchmarks, cuts costs by 10%, and delivers top-tier explanations validated by LLM and human reviews.
Safety Monitoring of Machine Learning Perception Functions: a Survey
2024-12-09
Raul Sena Ferreira, Joris Guérin, Kevin Delmas, Jérémie Guiochet, Hélène Waeselynck
Machine Learning (ML) models, such as deep neural networks, are widely applied in autonomous systems to perform complex perception tasks. New dependability challenges arise when ML predictions are used in safety-critical applications, like autonomous cars and surgical robots. Thus, the use of fault tolerance mechanisms, such as safety monitors, is essential to ensure the safe behavior of the system despite the occurrence of faults. This paper presents an extensive literature review on safety monitoring of perception functions using ML in a safety-critical context. In this review, we structure the existing literature to highlight key factors to consider when designing such monitors: threat identification, requirements elicitation, detection of failure, reaction, and evaluation. We also highlight the ongoing challenges associated with safety monitoring and suggest directions for future research.
This work focus on how to stabilize and lossless model compression, aiming to reduce model complexity and enhance efficiency without sacrificing performance due to compression errors. A key challenge is effectively leveraging compression errors and defining the boundaries for lossless compression to minimize model loss. i.e., compression for better. Currently, there is no systematic approach to determining this error boundary or understanding its specific impact on model performance. We propose a general \textbf{L}oss\textbf{L}ess \textbf{C}ompression theoretical framework (\textbf{LLC}), which further delineates the compression neighborhood and higher-order analysis boundaries through the total differential, thereby specifying the error range within which a model can be compressed without loss. To verify the effectiveness of LLC, we apply various compression techniques, including quantization and decomposition. Specifically, for quantization, we reformulate the classic quantization search problem as a grouped knapsack problem within the lossless neighborhood, achieving lossless quantization while improving computational efficiency. For decomposition, LLC addresses the approximation problem under low-rank constraints, automatically determining the rank for each layer and producing lossless low-rank models. We conduct extensive experiments on multiple neural network architectures on different datasets. The results show that without fancy tricks, LLC can effectively achieve lossless model compression. Our code will be made publicly.
Low-rank factorization is a popular model compression technique that minimizes the error $\delta$ between approximated and original weight matrices. Despite achieving performances close to the original models when $\delta$ is optimized, a performance discrepancy remains due to the separate optimization processes for low-rank factorization and model performance, resulting in unavoidable losses. We address this issue by introducing a novel joint optimization strategy for lossless low-rank weight factorization, which, for the first time, enhances the model's performance beyond the original. Our approach begins with a theoretical analysis of the relationship between low-rank factorization and model optimization objectives, establishing a precise perturbation range for matrix factorization errors on model performance. This challenge is then reformulated as a numerical rank deficiency problem with inequality constraints and develop a joint objective that simultaneously addresses factorization error and model performance. Based on the above analysis, we propose two optimization algorithms: \textbf{a lossless optimization algorithm} that maximizes model accuracy while ensuring compression, and \textbf{a compact optimization algorithm} that minimizes model size while preserving performance. These algorithms do not require fine-tuning and can directly compress numerous deep models to achieve lossless results. Our methods demonstrate robust efficacy across various vision and language tasks. For example, the compressed model reduced by 70\% on ResNext50 outperforms the original. Our code will be made public.
FP=xINT:A Low-Bit Series Expansion Algorithm for Post-Training Quantization
2024-12-09
Boyang Zhang, Daning Cheng, Yunquan Zhang, Fangmin Liu
Post-Training Quantization (PTQ) converts pre-trained Full-Precision (FP) models into quantized versions without training. While existing methods reduce size and computational costs, they also significantly degrade performance and quantization efficiency at extremely low settings due to quantization noise. We introduce a deep model series expansion framework to address this issue, enabling rapid and accurate approximation of unquantized models without calibration sets or fine-tuning. This is the first use of series expansion for neural network quantization. Specifically, our method expands the FP model into multiple low-bit basis models. To ensure accurate quantization, we develop low-bit basis model expansions at different granularities (tensor, layer, model), and theoretically confirm their convergence to the dense model, thus restoring FP model accuracy. Additionally, we design AbelianAdd/Mul operations between isomorphic models in the low-bit expansion, forming an Abelian group to ensure operation parallelism and commutativity. The experiments show that our algorithm achieves state-of-the-art performance in low-bit settings; for example, 4-bit quantization of ResNet-50 surpasses the original accuracy, reaching 77.03%. The code will be made public.
Black Hole Information, Replica Wormholes, and Macroscopic Entanglement
2024-12-09
Xavier Calmet, Stephen D. H. Hsu
In this invited review, we discuss the evaporation of a black hole, with emphasis on the resulting macroscopically distinct patterns of Hawking radiation. The density matrix of this radiation can approach a pure final state in the form of a highly entangled macroscopic superposition state. We note that this exact property is exhibited in replica wormhole calculations, and also in quantum hair effects on Hawking radiation amplitudes. Finally, we revisit the information paradox (Mathur's theorem and firewalls), showing that it can be resolved by macroscopic entanglement.
Exploring Barred Galaxies in the Young Universe at $z\sim$2 Using $\textit{JWST}$ CEERS Data
2024-12-08
Keith Pritchett Jr., Shardha Jogee, Yuchen Guo
Studying barred galaxies at early epochs can shed light on the early evolution of stellar bars, their impact on secular evolution and the star formation activity of young galaxies, and the origins of present-day barred galaxies like the Milky Way. We analyze data from the James Webb Space Telescope (JWST) Cosmic Evolution Early Release Science (CEERS) Survey to explore the impact of rest-frame wavelength and spatial resolution on detecting and characterizing some of the youngest barred galaxies known to date. We apply both visual classification and ellipse-fitting to JWST F115W, F200W, and F444W images of the barred galaxy CEERS-30155 at $z\sim$2.136, an epoch when the universe was only $\sim$22$\%$ of its current age. We find that the stellar bar in CEERS-30155 is not visible in the F115W image, which traces rest-frame ultraviolet (UV) light at $z\sim$2, a rest-frame wavelength highly obscured by dust. The stellar bar is visible in the F200W image, but is most prominent in the F444W image, likely due to the F444W image tracing rest-frame near-infrared (NIR) light at $z\sim$2. Rest-frame NIR light is not obscured by dust and traces low-mass, long-lived stars that dominate the stellar mass in galaxies. However, ellipse fits of the F444W image only robustly detect stellar bars whose semimajor axis are at least one PSF ($\sim$ 0.16" or $\sim$ 1.4 kpc at $z\sim$2). At $z\sim$2, stellar bars smaller than 1.5 kpc will be more robustly detected in the sharper F200W image (PSF $\sim$ 0.08" or $\sim$0.7 kpc at $z\sim$2), provided that the rest-frame optical light it traces is not overly impacted by dust and can still unveil the bar structure. Using a combination of both JWST F200W and F444W images can improve the detection of barred galaxies at $z\sim$2 to 4. At even higher redshifts (z > 4), the Giant Magellan Telescope will be a cornerstone facility to explore young barred galaxies.
Cloud Platforms for Developing Generative AI Solutions: A Scoping Review of Tools and Services
2024-12-08
Dhavalkumar Patel, Ganesh Raut, Satya Narayan Cheetirala, Girish N Nadkarni, Robert Freeman, Benjamin S. Glicksberg, Eyal Klang, Prem Timsina
Generative AI is transforming enterprise application development by enabling machines to create content, code, and designs. These models, however, demand substantial computational power and data management. Cloud computing addresses these needs by offering infrastructure to train, deploy, and scale generative AI models. This review examines cloud services for generative AI, focusing on key providers like Amazon Web Services (AWS), Microsoft Azure, Google Cloud, IBM Cloud, Oracle Cloud, and Alibaba Cloud. It compares their strengths, weaknesses, and impact on enterprise growth. We explore the role of high-performance computing (HPC), serverless architectures, edge computing, and storage in supporting generative AI. We also highlight the significance of data management, networking, and AI-specific tools in building and deploying these models. Additionally, the review addresses security concerns, including data privacy, compliance, and AI model protection. It assesses the performance and cost efficiency of various cloud providers and presents case studies from healthcare, finance, and entertainment. We conclude by discussing challenges and future directions, such as technical hurdles, vendor lock-in, sustainability, and regulatory issues. Put together, this work can serve as a guide for practitioners and researchers looking to adopt cloud-based generative AI solutions, serving as a valuable guide to navigating the intricacies of this evolving field.
Probing Gravity -- Fundamental Aspects of Metric Theories and their Implications for Tests of General Relativity
2024-12-08
Jann Zosso
Guided by the Einstein equivalence principle that identifies the phenomenon of gravitation as a manifestation of the dynamics of spacetime in contrast to a localizable force, we review and explore its consequences on formulating a theory of gravity. The resulting space of metric theories of gravity may address open conceptual and observational puzzles through a wealth of effects beyond general relativity, whose traces can be searched for within today's and tomorrow's gravitational testing grounds. Above all, we offer a generic metric theory generalization of Isaacson's approach to the leading-order field equations of physical perturbations with a well-defined notion of energy-momentum carried by the gravitational waves. Within this framework, we identify the backreaction of the Isaacson energy-momentum flux onto the background spacetime with the displacement memory effect that induces a permanent distortion of space after the passage of a gravitational wave. This effect is a well-known prediction of GR whose dominant contribution captures its inherent non-linear nature, manifest in the ability of gravity to gravitate. However, the novel interpretation of memory as naturally arising within the Isaacson approach to gravitational waves comes with two main advantages. Firstly, it allows for a unified understanding of both the null and the ordinary memory effect, which are respectively sourced by unbound energy fluxes that do and do not reach asymptotic null infinity. Secondly, and most importantly, this approach allows for a consistent derivation of the memory formula for a large class of metric theories with considerable lessons to be learned for upcoming future measurements of the memory effect.
Teleoperation of Continuum Instruments: Investigation of Linear vs. Angular Commands through Task-Priority Analysis
2024-12-08
Ehsan Nasiri, Long Wang
This paper addresses the challenge of teleoperating continuum instruments for minimally invasive surgery (MIS). We develop and adopt a novel task-priority-based kinematic formulation to quantitatively investigate teleoperation commands for continuum instruments under remote center of motion (RCM) constraints. Using redundancy resolution methods, we investigate the kinematic performance during teleoperation, comparing linear and angular commands within a task-priority scheme. For experimental validation, an instrument module (IM) was designed and integrated with a 7-DoF manipulator. Assessments, simulations, and experimental validations demonstrated the effectiveness of the proposed framework. The experiments involved several tasks: trajectory tracking of the IM tip along multiple paths with varying priorities for linear and angular teleoperation commands, pushing a ball along predefined paths on a silicon board, following a pattern on a pegboard, and guiding the continuum tip through rings on a ring board using a standard surgical kit.
Regarding the concepts of urban management, digital transformation, and smart cities, various issues are presented. Currently, we like to attend to location allocation problems that can be a new part of digital transformation in urban management (such as locating and placing facilities, locating and arranging centers such as aid and rescue centers, or even postal hubs, telecommunications, electronic equipment, and data centers, and routing in transportation optimization). These issues, which are seemingly simple but in practice complex, are important in urban environments, and the issue of accurate location allocation based on existing criteria directly impacts cost management, profit, efficiency, and citizen satisfaction. In recent years, researchers have used or presented various models and methods for location allocation problems, some of which will be mentioned in this article. Given the nature of these problems, which are optimization problems, this article will also examine existing research from an optimization perspective in summary. Finally, a brief conclusion will be made of the existing methods and their weaknesses, and suggestions will be made for continuing the path and improving scientific and practical research in this field.
Interior and Gravity Field Models for Uranus Suggest Mixed-composition Interior: Implications for the Uranus Orbiter and Probe
2024-12-08
Zifan Lin, Sara Seager, Benjamin P. Weiss
The interior composition and structure of Uranus are ambiguous. It is unclear whether Uranus is composed of fully differentiated layers dominated by an icy mantle or has smooth compositional gradients. The Uranus Orbiter and Probe (UOP), the next NASA Flagship mission prioritized by the Planetary Science and Astrobiology Survey 2023-2032, will constrain the planet's interior by measuring its gravity and magnetic fields. To characterize the Uranian interior, here we present CORGI, a newly developed planetary interior and gravity model. We confirm that high degrees of mixing are required for Uranus interior models to be consistent with the $J_2$ and $J_4$ gravity harmonics measured by Voyager 2. Empirical models, which have smooth density profiles that require extensive mixing, can reproduce the Voyager 2 measurements. Distinct-layer models with mantles composed of H$_2$O-H/He or H$_2$O-CH$_4$-NH$_3$ mixtures are consistent with the Voyager 2 measurements if the heavy element mass fraction, $Z$, in the mantle $\lesssim85\%$, or if atmospheric $Z$ $\gtrsim25\%$. Our gravity harmonics model shows that UOP $J_2$ and $J_4$ measurements can distinguish between high ($Z\geq25\%$) and low ($Z=12.5\%$) atmospheric metallicity scenarios. The UOP can robustly constrain $J_6$ and potentially $J_8$ given polar orbits within rings. An ice-rich composition can naturally explain the source of Uranus' magnetic field. However, because the physical properties of rock-ice mixtures are poorly known, magnetic field generation by a rock-rich composition cannot be ruled out. Future experiments and simulations on realistic planetary building materials will be essential for refining Uranus interior models.
Hallucination-aware Optimization for Large Language Model-empowered Communications
2024-12-08
Yinqiu Liu, Guangyuan Liu, Ruichen Zhang, Dusit Niyato, Zehui Xiong, Dong In Kim, Kaibin Huang, Hongyang Du
Large Language Models (LLMs) have significantly advanced communications fields, such as Telecom Q\&A, mathematical modeling, and coding. However, LLMs encounter an inherent issue known as hallucination, i.e., generating fact-conflicting or irrelevant content. This problem critically undermines the applicability of LLMs in communication systems yet has not been systematically explored. Hence, this paper provides a comprehensive review of LLM applications in communications, with a particular emphasis on hallucination mitigation. Specifically, we analyze hallucination causes and summarize hallucination mitigation strategies from both model- and system-based perspectives. Afterward, we review representative LLM-empowered communication schemes, detailing potential hallucination scenarios and comparing the mitigation strategies they adopted. Finally, we present a case study of a Telecom-oriented LLM that utilizes a novel hybrid approach to enhance the hallucination-aware service experience. On the model side, we publish a Telecom hallucination dataset and apply direct preference optimization to fine-tune LLMs, resulting in a 20.6\% correct rate improvement. Moreover, we construct a mobile-edge mixture-of-experts architecture for optimal LLM expert activation. Our research aims to propel the field of LLM-empowered communications forward by detecting and minimizing hallucination impacts.
A Cross-Validation Study of Turkish Sentiment Analysis Datasets and Tools
2024-12-08
Şevval Çakıcı, Dilara Karaduman, Mehmet Akif Çırlan, Ali Hürriyetoğlu
In recent years, sentiment analysis has gained increasing significance, prompting researchers to explore datasets in various languages, including Turkish. However, the limited availability of Turkish datasets has led to their multifaceted usage in different studies, yielding diverse outcomes. To overcome this challenge, a rigorous review was conducted of research articles published between 2012 and 2022. 31 studies were listed, and 23 Turkish datasets obtained from publicly available sources and email requests used in these studies were collected. We labeled these 31 studies using a taxonomy. We provide a map of sentiment analysis datasets according to this taxonomy in Turkish over 10 years. Moreover, we run state-of-the-art sentiment analysis tools on these datasets and analyzed performance across popular Turkish sentiment datasets. We observed that the performance of the sentiment analysis tools significantly depends on the characteristics of the target text. Our study fosters a more nuanced understanding of sentiment analysis in the Turkish language.
An overview of the stability of Sobolev inequalities on Riemannian manifolds with Ricci lower bounds
2024-12-08
Francesco Nobili
We review recent results regarding the problem of the stability of Sobolev inequalities on Riemannian manifolds with Ricci curvature lower bounds. We shall describe techniques and methods from smooth and non-smooth geometry, the fruitful combination of which revealed particularly effective. Furthermore, we present a self-contained overview of the proof of the stability of the Sobolev inequality on manifolds with non-negative Ricci curvature and Euclidean volume growth, adopting a direct strategy tailored to this setting. Finally, we discuss related stability results and present some open problems.
Testing for isospin symmetry breaking with extensive calculations of isotope shift factors in potassium
2024-12-08
Vaibhav Katyal, A. Chakraborty, B. K. Sahoo, Ben Ohayon, Chien-Yeah Seng, Mikhail Gorchtein, John Behr
Precise evaluation of the isotope shift (IS) factors for seven low-lying potassium (K) states is achieved using relativistic coupled-cluster (RCC) theory. The energies of these states are assessed and compared with experimental data to confirm the accuracy of the wave functions calculated at varying RCC theory approximations and highlight the significance of many-body and relativistic effects in determining the energies and IS factors of K. Various methods are used to compute the IS factors, with the finite-field (FF) approach yielding results that align with observed and semi-empirical data. This consistency is attributed to orbital relaxation effects that are naturally present in the FF method but emerge only through complex interactions in other techniques. Using the IS factors derived from FF, we review the mean square radius difference between $^{38m}$K and $^{39}$K. From this difference and muonic atom x-ray spectroscopy, we deduce the absolute radius of $^{38m}$K using an updated calculation of the nuclear polarizability effect. Finally, we evaluate the isospin symmetry breaking (ISB) in this isotriplet by integrating the radius of $^{38m}$K with an updated radius of $^{38}$Ca, concluding that the ISB is compatible with zero. This finding offers a stringent benchmark for nuclear model calculations of ISB corrections in nuclear beta decay, which play a key role in determining the $V_{ud}$ matrix element.
A review of low-rank methods for time-dependent kinetic simulations
2024-12-08
Lukas Einkemmer, Katharina Kormann, Jonas Kusch, Ryan G. McClarren, Jing-Mei Qiu
Time-dependent kinetic models are ubiquitous in computational science and engineering. The underlying integro-differential equations in these models are high-dimensional, comprised of a six--dimensional phase space, making simulations of such phenomena extremely expensive. In this article we demonstrate that in many situations, the solution to kinetics problems lives on a low dimensional manifold that can be described by a low-rank matrix or tensor approximation. We then review the recent development of so-called low-rank methods that evolve the solution on this manifold. The two classes of methods we review are the dynamical low-rank (DLR) method, which derives differential equations for the low-rank factors, and a Step-and-Truncate (SAT) approach, which projects the solution onto the low-rank representation after each time step. Thorough discussions of time integrators, tensor decompositions, and method properties such as structure preservation and computational efficiency are included. We further show examples of low-rank methods as applied to particle transport and plasma dynamics.
The Internet of Things (IoT) has transformed healthcare, facilitating remote patient monitoring, enhanced medication adherence, and chronic disease management. However, this interconnected ecosystem faces significant vulnerabilities with the advent of quantum computing, which threatens to break existing encryption standards protecting sensitive patient data in IoT-enabled medical devices. This chapter examines the quantum threat to healthcare IoT security, highlighting the potential impacts of compromised encryption, including privacy breaches, device failures, and manipulated medical records. It introduces post-quantum cryptography (PQC) and quantum-resistant techniques like quantum key distribution (QKD), addressing their application in resource-constrained healthcare IoT devices such as pacemakers, monitoring tools, and telemedicine systems. The chapter further explores the challenges of integrating these solutions and reviews global efforts in mitigating quantum risks, offering insights into suitable PQC primitives for various healthcare use cases.
A Review on Multisensor Data Fusion for Wearable Health Monitoring
2024-12-08
Arlene John, Barry Cardiff, Deepu John
The growing demand for accurate, continuous, and non-invasive health monitoring has propelled multi-sensor data fusion to the forefront of healthcare technology. This review aims to provide an overview of the development of fusion frameworks in the literature and common terminology used in fusion literature. The review introduces the fusion classification standards and methods that are most relevant from an algorithm development perspective. Applications of the reviewed fusion frameworks in fields such as defense, autonomous driving, robotics, and image fusion are also discussed to provide contextual information on the various fusion methodologies that have been developed in this field. This review provides a comprehensive analysis of multi-sensor data fusion methods applied to health monitoring systems, focusing on key algorithms, applications, challenges, and future directions. We examine commonly used fusion techniques, including Kalman filters, Bayesian networks, and machine learning models. By integrating data from various sources, these fusion approaches enhance the reliability, accuracy, and resilience of health monitoring systems. However, challenges such as data quality and differences in acquisition systems exist, calling for intelligent fusion algorithms in recent years. The review finally converges on applications of fusion algorithms in biomedical inference tasks like heartbeat detection, respiration rate estimation, sleep apnea detection, arrhythmia detection, and atrial fibrillation detection.
DiTer++: Diverse Terrain and Multi-modal Dataset for Multi-Robot SLAM in Multi-session Environments
2024-12-08
Juwon Kim, Hogyun Kim, Seokhwan Jeong, Youngsik Shin, Younggun Cho
We encounter large-scale environments where both structured and unstructured spaces coexist, such as on campuses. In this environment, lighting conditions and dynamic objects change constantly. To tackle the challenges of large-scale mapping under such conditions, we introduce DiTer++, a diverse terrain and multi-modal dataset designed for multi-robot SLAM in multi-session environments. According to our datasets' scenarios, Agent-A and Agent-B scan the area designated for efficient large-scale mapping day and night, respectively. Also, we utilize legged robots for terrain-agnostic traversing. To generate the ground-truth of each robot, we first build the survey-grade prior map. Then, we remove the dynamic objects and outliers from the prior map and extract the trajectory through scan-to-map matching. Our dataset and supplement materials are available at https://sites.google.com/view/diter-plusplus/.
Semi-Supervised Contrastive Learning for Controllable Video-to-Music Retrieval
2024-12-08
Shanti Stewart, Gouthaman KV, Lie Lu, Andrea Fanelli
Content creators often use music to enhance their videos, from soundtracks in movies to background music in video blogs and social media content. However, identifying the best music for a video can be a difficult and time-consuming task. To address this challenge, we propose a novel framework for automatically retrieving a matching music clip for a given video, and vice versa. Our approach leverages annotated music labels, as well as the inherent artistic correspondence between visual and music elements. Distinct from previous cross-modal music retrieval works, our method combines both self-supervised and supervised training objectives. We use self-supervised and label-supervised contrastive learning to train a joint embedding space between music and video. We show the effectiveness of our approach by using music genre labels for the supervised training component, and our framework can be generalized to other music annotations (e.g., emotion, instrument, etc.). Furthermore, our method enables fine-grained control over how much the retrieval process focuses on self-supervised vs. label information at inference time. We evaluate the learned embeddings through a variety of video-to-music and music-to-video retrieval tasks. Our experiments show that the proposed approach successfully combines self-supervised and supervised objectives and is effective for controllable music-video retrieval.
Recent studies have proposed integrating Chain-of-Thought (CoT) reasoning to further enhance the reliability of Code Language Models (CLMs) in generating code, a step-by-step approach that breaks down complex programming tasks into manageable sub-problems. Advances in this area have introduced CoT models, specifically designed to integrate CoT reasoning effectively into language models, achieving notable improvements in code generation. Despite these advancements, the security of CoT models has not been systematically studied. In this study, we aim to fill this gap by investigating the vulnerability of CoT models to backdoor injection in code generation tasks. To address this, we propose a model-agnostic backdoor attack method SABER (\textbf{S}elf-\textbf{A}ttention-\textbf{B}as\textbf{E}d backdoo\textbf{R}) based on the self-attention mechanism. SABER begins by selecting a malicious output as the backdoor using code mutation operations. It then identifies tokens most relevant to poisoned content by analyzing self-attention scores in the CodeBERT model. Finally, it applies semantic-preserving perturbations to generate adaptive and natural triggers. Our experiments on HumanEval-CoT and OpenEval-CoT test sets demonstrate that CoT models are susceptible to backdoor attacks via data poisoning. Taking the OpenEval-CoT dataset as an example, SABER achieves an ASR of 76.19%, representing an improvement of 14.29% over RIPPLe and a substantial 23.08% enhancement compared to BadPre. Further evaluations using ONION for automated detection and human studies reveal that SABER is stealthier and harder to detect, bypassing 77.27% of automated detection, with a human detection rate of just 3.17%. Our findings reveal that backdoors can be injected into CoT models to manipulate downstream code generation tasks.
Risk factor identification and classification of malnutrition among under-five children in Bangladesh: Machine learning and statistical approach
2024-12-08
Tasfin Mahmud, Tayab Uddin Wara, Chironjeet Das Joy
This study aims to understand the factors that resulted in under-five children's malnutrition from the Multiple Indicator Cluster (MICS-2019) nationwide surveys and classify different malnutrition stages based on the four well-established machine learning algorithms, namely - Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP) neural network. Accuracy, precision, recall, and F1 scores are obtained to evaluate the performance of each model. The statistical Pearson correlation coefficient analysis is also done to understand the significant factors related to a child's malnutrition. The eligible data sample for analysis was 21,858 among 24,686 samples from the dataset. Satisfactory and insightful results were obtained in each case and, the RF and MLP performed extraordinarily well. For RF, the accuracy was 98.55%, average precision 98.3%, recall value 95.68%, and F1 score 97.13%. For MLP, the accuracy was 98.69%, average precision 97.62%, recall 90.96%, and F1 score of 97.39%. From the Pearson co-efficient, all negative correlation results are enlisted, and the most significant impacts are found for the WAZ2 (Weight for age Z score WHO) (-0.828"), WHZ2 (Weight for height Z score WHO) (-0.706"), ZBMI (BMI Z score WHO) (-0.656"), BD3 (whether child is still being breastfed) (-0.59"), HAZ2 (Height for age Z score WHO) (-0.452"), CA1 (whether child had diarrhea in last 2 weeks) (-0.34"), Windex5 (Wealth index quantile) (-0.161"), melevel (Mother's education) (-0.132"), and CA14/CA16/CA17 (whether child had illness with fever, cough, and breathing) (-0.04) in successive order.
A persistent challenge in astronomical machine learning is a systematic bias where predictions compress the dynamic range of true values -- high values are consistently predicted too low while low values are predicted too high. Understanding this bias has important consequences for astronomical measurements and our understanding of physical processes in astronomical inference. Through analytical examination of linear regression, we show that this bias arises naturally from measurement uncertainties in input features and persists regardless of training sample size, label accuracy, or parameter distribution. In the univariate case, we demonstrate that attenuation becomes important when the ratio of intrinsic signal range to measurement uncertainty ($\sigma_{\text{range}}/\sigma_x$) is below O(10) -- a regime common in astronomy. We further extend the theoretical framework to multivariate linear regression and demonstrate its implications using stellar spectroscopy as a case study. Even under optimal conditions -- high-resolution APOGEE-like spectra (R=24,000) with high signal-to-noise ratios (SNR=100) and multiple correlated features -- we find percent-level bias. The effect becomes even more severe for modern-day low-resolution surveys like LAMOST and DESI due to the lower SNR and resolution. These findings have broad implications, providing a theoretical framework for understanding and addressing this limitation in astronomical data analysis with machine learning.
On 1 January 2024, devastating tsunamis caused by the Noto Peninsula earthquake hit coastal areas within several minutes, but only two tsunami casualties were officially reported. Despite its importance, the cause of this unexpectedly low human loss was unclear because of the limited access to the peninsula and the presence of many visitors during the holiday, which made conducting conventional surveys infeasible. Here, we reveal evacuation behaviour during the 2024 Noto Peninsula tsunami using massive geolocation data collected from a smartphone app. By analysing these massive data, which include over 1.5 million records collected on this day, we find that the evacuation was extremely fast, occurring within 2--6 minutes after the origin time. Further analyses suggest that these fast departures were driven mainly by strong ground shaking; the fact that the tsunami occurred during the family-oriented New Year holiday was also a key factor. Additionally, the long-term analysis of the data reveals that people started returning to the coastal area 20--100 minutes after the origin time, which was long before the downgrading and cancellation of the tsunami warnings. These results highlight the utility of the innovative data-driven approach to evacuation surveys, which addresses the limitations of conventional evacuation surveys.
A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
2024-12-08
Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
The Breakthrough Listen program is, to date, the most extensive search for technological life beyond Earth. As part of this goal, over the past nine years it has surveyed thousands of nearby stars, close to 100 nearby galaxies, and a variety of exotic and solar system objects with telescopes around the world, including the Robert C. Byrd Green Bank Telescope (GBT) in West Virginia. The goal is to find evidence of technosignatures of other civilizations, such as narrowband Doppler drifting radio signals. Despite the GBT's location in a radio-quiet zone, the primary challenge of this search continues to be the high quantities of human-generated radio-frequency interference (RFI), and the ability to pick out genuinely promising candidates from it. Here we present a novel search method aimed at finding these `needle-in-a-haystack' type signals, applied to 9,684 observation cadences of 3,077 stars (each observed with one or more of the L, S, C, and X band receivers) from the GBT archive. We implement a low-complexity statistical process to vet out RFI and highlight signals that, upon visual inspection, appear more promising than those from previous analyses. Our work returns candidate signals found previously using both traditional and machine learning algorithms, as well as many promising ones not previously identified. This analysis represents the largest dataset searched for technosignatures to date, and highlights the efficacy that traditional (non-machine-learning) algorithms continue to have in these types of technosignature searches. We find that less than 1% of stars host transmitters brighter than 0.3 Arecibo radar equivalents broadcasting in our direction over the frequency band covered.
Recent research on integrating Large Language Models (LLMs) with Graph Neural Networks (GNNs) typically follows two approaches: LLM-centered models, which convert graph data into tokens for LLM processing, and GNN-centered models, which use LLMs to encode text features into node and edge representations for GNN input. LLM-centered models often struggle to capture graph structures effectively, while GNN-centered models compress variable-length textual data into fixed-size vectors, limiting their ability to understand complex semantics. Additionally, GNN-centered approaches require converting tasks into a uniform, manually-designed format, restricting them to classification tasks and preventing language output. To address these limitations, we introduce a new architecture that deeply integrates GNN with LLM, featuring three key innovations: (1) Structure-Aware Transformers, which incorporate GNN's message-passing capabilities directly into LLM's transformer layers, allowing simultaneous processing of textual and structural information and generating outputs from both GNN and LLM; (2) Graph-Text Cross-Attention, which processes full, uncompressed text from graph nodes and edges, ensuring complete semantic integration; and (3) GNN-LLM Twin Predictor, enabling LLM's flexible autoregressive generation alongside GNN's scalable one-pass prediction. GL-Fusion achieves outstand performance on various tasks. Notably, it achieves state-of-the-art performance on OGBN-Arxiv and OGBG-Code2.
This review article provides an overview of random matrix theory (RMT) with a focus on its growing impact on the formulation and inference of statistical models and methodologies. Emphasizing applications within high-dimensional statistics, we explore key theoretical results from RMT and their role in addressing challenges associated with high-dimensional data. The discussion highlights how advances in RMT have significantly influenced the development of statistical methods, particularly in areas such as covariance matrix inference, principal component analysis (PCA), signal processing, and changepoint detection, demonstrating the close interplay between theory and practice in modern high-dimensional statistical inference.
Exploring the Impact of Emotional Voice Integration in Sign-to-Speech Translators for Deaf-to-Hearing Communication
2024-12-07
Hyunchul Lim, Minghan Gao, Franklin Mingzhe Li, Nam Anh Dang, Ianip Sit, Michelle M Olson, Cheng Zhang
Emotional voice communication plays a crucial role in effective daily interactions. Deaf and hard-of-hearing (DHH) individuals often rely on facial expressions to supplement sign language to convey emotions, as the use of voice is limited. However, in American Sign Language (ASL), these facial expressions serve not only emotional purposes but also as linguistic markers, altering sign meanings and often confusing non-signers when interpreting a signer's emotional state. Most existing ASL translation technologies focus solely on signs, neglecting the role of emotional facial expressions in the translated output (e.g., text, voice). This paper present studies which 1) confirmed the challenges for non-signers of interpreting emotions from facial expressions in ASL communication, of facial expressions, and 2) how integrating emotional voices into translation systems can enhance hearing individuals' comprehension of a signer's emotions. An online survey conducted with 45 hearing participants (Non-ASL Signers) revealed that they frequently misinterpret signers' emotions when emotional and linguistic facial expressions are used simultaneously. The findings indicate that incorporating emotional voice into translation systems significantly improves the recognition of signers' emotions by 32%. Additionally, further research involving 6 DHH participants discusses design considerations for the emotional voice feature from both perspectives, emphasizing the importance of integrating emotional voices in translation systems to bridge communication gaps between DHH and hearing communities.
A Scoping Review of ChatGPT Research in Accounting and Finance
2024-12-07
Mengming Michael Dong, Theophanis C. Stratopoulos, Victor Xiaoqi Wang
This paper provides a review of recent publications and working papers on ChatGPT and related Large Language Models (LLMs) in accounting and finance. The aim is to understand the current state of research in these two areas and identify potential research opportunities for future inquiry. We identify three common themes from these earlier studies. The first theme focuses on applications of ChatGPT and LLMs in various fields of accounting and finance. The second theme utilizes ChatGPT and LLMs as a new research tool by leveraging their capabilities such as classification, summarization, and text generation. The third theme investigates implications of LLM adoption for accounting and finance professionals, as well as for various organizations and sectors. While these earlier studies provide valuable insights, they leave many important questions unanswered or partially addressed. We propose venues for further exploration and provide technical guidance for researchers seeking to employ ChatGPT and related LLMs as a tool for their research.
Diffuse Ionized Gas in the Anti-center of the Milky Way
2024-12-07
Shiming Wen, Wei Zhang, Lin Ma, Yunning Zhao, Man I. Lam, Chaojian Wu, Juanjuan Ren, Jianjun Chen, Yuzhong Wu, Guozhen Hu, Yonghui Hou, Yongheng Zhao, Hong Wu
Using data from the LAMOST Medium-Resolution Spectroscopic Survey of Nebulae, we create a sample of 17,821 diffuse ionized gas (DIG) spectra in the anti-center region of the Milky Way, by excluding fibers in the directions of H II regions and supernova remnants. We then analyze the radial and vertical distributions of three line ratios ([N II]/H$\alpha$, [S II]/H$\alpha$, and [S II]/[N II]), as well as the oxygen abundance. [N II]/H$\alpha$ and [S II]/H$\alpha$ do not exhibit a consistent, monotonic decrease with increasing Galactocentric distance (R$_{gal}$). Instead, they show enhancement within the interarm region, positioned between the Local Arm and the Perseus Arm. [S II]/[N II] has a radial gradient of 0.1415 $\pm$ 0.0646 kpc$^{-1}$ for the inner disk (8.34 $ < R_{gal} < $ 9.65 kpc), and remains nearly flat for the outer disk ($R_{gal} > $ 9.65 kpc). In the vertical direction, [N II]/H$\alpha$, [S II]/H$\alpha$, and [S II]/[N II] increase with increasing Galactic disk height ($|z|$) in both southern and northern disks. Based on the N2S2H$\alpha$ method, which combines [S II]/[N II] and [N II]/H$\alpha$, we estimate the oxygen abundance. The oxygen abundance exhibits a consistent radial gradient with R$_{gal}$, featuring a slope of -0.0559 $\pm$ 0.0209 dex kpc$^{-1}$ for the inner disk and a similar slope of -0.0429 $\pm$ 0.0599 dex kpc$^{-1}$ for the outer disk. A single linear fitting to the entire disk yields a slope of -0.0317 $\pm$ 0.0124 dex kpc$^{-1}$. In the vertical direction, the oxygen abundance decreases with increasing $|z|$ in both southern and northern disks.
Plasmonic Electro-Optic Modulators based on Epsilon-Near-Zero Materials: Comparing the Classical Drift-Diffusion and Schrödinger-Poisson Coupling Models
2024-12-07
Masoud Shabaninezhad, Hamid Mehrvar, Eric Bernier, Lora Ramunno, Pierre Berini
We present the design, modeling, and optimization of high-performance plasmonic electro-optic modulators leveraging voltage-gated carrier density in indium tin oxide (ITO) where the gated carrier density is modeled using both the Classical Drift-Diffusion (CDD) and Schr\"odinger-Poisson Coupling (SPC) methods. The latter ensures a more detailed and precise description of carrier distributions under various gate voltages which gains particular significance when applied to an epsilon-near-zero (ENZ) medium such as ITO. Combining the nanoscale confinement and field enhancement enabled by surface plasmon polaritons with the ENZ effect in ITO, modulator designs integrated with silicon waveguides and optimized for operation at {\lambda}0 = 1550 nm achieve a 3-dB bandwidth of 210 GHz, an insertion loss of 3 dB, and an extinction ratio of 5 dB for an overall length of < 4 {\mu}m as predicted by the SPC model. Our results illustrate trade-offs between high-speed modulator operation and low insertion loss, vs. extinction ratio, and the need for precise modelling of carrier distributions in ENZ materials.
Enhancing Research Methodology and Academic Publishing: A Structured Framework for Quality and Integrity
2024-12-07
Md. Jalil Piran, Nguyen H. Tran
Following a brief introduction to research, research processes, research types, papers, reviews, and evaluations, this paper presents a structured framework for addressing inconsistencies in research methodology, technical writing, quality assessment, and publication standards across academic disciplines. Using a four-dimensional evaluation model that focuses on 1) technical content, 2) structural coherence, 3) writing precision, and 4) ethical integrity, this framework not only standardizes review and publication processes but also serves as a practical guide for authors in preparing high-quality manuscripts. Each of these four dimensions cannot be compromised for the sake of another. Following that, we discuss the components of a research paper adhering to the four-dimensional evaluation model in detail by providing guidelines and principles. By aligning manuscripts with journal standards, reducing review bias, and enhancing transparency, the framework contributes to more reliable and reproducible research results. Moreover, by strengthening cross-disciplinary credibility, improving publication consistency, and fostering public trust in academic literature, this initiative is expected to positively influence both research quality and scholarly publishing's reputation.
2024-12-07
Rory Conlin, Jonathan Schilling, Daniel W. Dudt, Dario Panici, Rogerio Jorge, Egemen Kolemen
In this work we consider the free boundary inverse equilibrium problem for 3D ideal MHD. We review boundary conditions for both fixed and free boundary solutions and under what circumstances a sheet current may exist at the plasma-vacuum interface. We develop an efficient and accurate algorithm for computing the residual of these boundary conditions and use it to compute free boundary equilibria in the DESC code both in vacuum and at finite plasma beta, with and without sheet currents.
Physical properties of stars such as luminosity, surface temperature, distance, or mass are measured from observations. These physical properties are of paramount importance to understand how stars are born, live, and die in the universe near and far. This chapter discusses the basic concepts used by astronomers to derive key information about stars from the light they emit. We present through a pedagogical approach the methods required for determining stellar brightness (apparent and absolute magnitudes), surface temperature (via black-body radiation and spectral classification), and distance (using parallax and standard candles). We finally review techniques for estimating stellar mass and radius, including the use of binary star systems and stellar evolution models.
Sulfide-based photocatalysts (PC) are promising materials for efficiently producing hydrogen (H2). This chapter aims to provide a detailed survey of the recent advancements in sulfide-based photocatalysts and emphasize their enhanced performance and pathways to efficient H2 production. A detailed summary has been given, including several metal sulfides, such as cadmium sulfide (CdS), zinc sulfide (ZnS), molybdenum disulfide (MoS2), tungsten disulfide (WS2), lead sulfide (PbS), nickel sulfides (NiS/NiS2), iron disulfide (FeS2), copper sulfides (CuS/Cu2S), cobalt sulfides (CoS/CoS2), tin disulfide (SnS2), indium sulfide (In2S3), bismuth sulfide (Bi2S3), zinc cadmium sulfide (ZnxCd1-xS), manganese cadmium sulfide (MnxCd1-xS), zinc indium sulfide (ZnIn2S4), and cadmium indium sulfide (CdIn2S4). This chapter will focus on the latest advancements in metal-sulfide-based materials for photocatalytic hydrogen evolution reactions (HER), taking its accelerated growth and excellent research into account. After briefly outlining the basic properties, the chapter will showcase the cutting-edge strategies and recent research progress, including the construction of heterojunctions, defect engineering, co-catalyst loading, elemental doping, and single-atom engineering, which improve the electronic structure and charge separation capabilities of metal sulfides for photocatalytic hydrogen production. A future perspective and outlook have been proposed, focusing on some key points and a standard protocol. With this knowledge, we hope sulfide-based photocatalysts can be modified and engineered to improve their efficiency and stability in future research.
NGC 5128 (Centaurus A), the closest giant elliptical galaxy outside the Local Group to the Milky Way, is one of the brightest extragalactic radio sources. It is distinguished by a prominent dust lane and powerful jets, driven by a supermassive black hole at its core. Using previously identified long-period variable (LPV) stars from the literature, this study aims to reconstruct the star formation history (SFH) of two distinct regions in the halo of NGC 5128. These regions reveal remarkably similar SFHs, despite being located about 28 kpc apart on opposite sides of the galaxy's center. In Field 1, star formation rates (SFRs) show notable increases at approximately 800 Myr and 3.8 Gyr ago. Field 2 exhibits similar peaks at these times, along with an additional rise around 6.3 Gyr ago. The increase in SFR around 800 Myr ago is consistent with earlier research suggesting a merger event. Since no LPV catalog exists for the central region of NGC 5128, we focused our investigation on its outer regions, which has provided new insights into the complex evolutionary history of this cornerstone galaxy. The SFH traced by LPVs supports a scenario in which multiple events of nuclear activity have triggered episodic, jet-induced star formation.
On the detectability and parameterisation of binary stars through spectral energy distributions
2024-12-07
Vikrant V. Jadhav
This study examines the characterization of binary star systems using Spectral Energy Distributions (SEDs), a technique increasingly essential with the rise of large-scale astronomical surveys. Binaries can emit flux at different regions of the electromagnetic spectrum, making SEDs a valuable tool in identifying and characterising unresolved binary systems. However, fitting multi-component models to SEDs and recovering accurate stellar parameters remains challenging due to nonlinear fitting methods and inherent uncertainties in the data and the spectral models. In this work, a simplified approach was used to model stars as blackbodies and we tested the accuracy of parameter recovery from SEDs, particularly focusing on secondary stars. We explored a range of primary properties, filter sets and noise models. Special attention was given to two case studies: one examining the detection of unresolved binaries using Gaia XP spectra, and the other focusing on identifying hotter companions in binary systems using UV-IR SEDs. Although an analytic prescription for recoverability is not possible, we present a simplified model and the necessary Python tools to analyse any potential binary system. Finally, we propose using blackbody models as a baseline for error estimation in SED fitting, offering a potential method for measuring fitting errors and improving the precision of binary star characterisations.
The rapid advancements in Artificial Intelligence, specifically Machine Learning (ML) and Deep Learning (DL), have opened new prospects in medical sciences for improved diagnosis, prognosis, and treatment of severe health conditions. This paper focuses on the development of an ML model with high predictive accuracy to classify arrhythmic electrocardiogram (ECG) signals. The ECG signals datasets utilized in this study were sourced from the PhysioNet and MIT-BIH databases. The research commenced with binary classification, where an optimized Bidirectional Long Short-Term Memory (Bi-LSTM) model yielded excellent results in differentiating normal and atrial fibrillation signals. A pivotal aspect of this research was a survey among medical professionals, which not only validated the practicality of AI-based ECG classifiers but also identified areas for improvement, including accuracy and the inclusion of more arrhythmia types. These insights drove the development of an advanced Convolutional Neural Network (CNN) system capable of classifying five different types of ECG signals with better accuracy and precision. The CNN model's robust performance was ensured through rigorous stratified 5-fold cross validation. A web portal was also developed to demonstrate real-world utility, offering access to the trained model for real-time classification. This study highlights the potential applications of such models in remote health monitoring, predictive healthcare, assistive diagnostic tools, and simulated environments for educational training and interdisciplinary collaboration between data scientists and medical personnel.
LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods
2024-12-07
Haitao Li, Qian Dong, Junjie Chen, Huixue Su, Yujia Zhou, Qingyao Ai, Ziyi Ye, Yiqun Liu
The rapid advancement of Large Language Models (LLMs) has driven their expanding application across various fields. One of the most promising applications is their role as evaluators based on natural language responses, referred to as ''LLMs-as-judges''. This framework has attracted growing attention from both academia and industry due to their excellent effectiveness, ability to generalize across tasks, and interpretability in the form of natural language. This paper presents a comprehensive survey of the LLMs-as-judges paradigm from five key perspectives: Functionality, Methodology, Applications, Meta-evaluation, and Limitations. We begin by providing a systematic definition of LLMs-as-Judges and introduce their functionality (Why use LLM judges?). Then we address methodology to construct an evaluation system with LLMs (How to use LLM judges?). Additionally, we investigate the potential domains for their application (Where to use LLM judges?) and discuss methods for evaluating them in various contexts (How to evaluate LLM judges?). Finally, we provide a detailed analysis of the limitations of LLM judges and discuss potential future directions. Through a structured and comprehensive analysis, we aim aims to provide insights on the development and application of LLMs-as-judges in both research and practice. We will continue to maintain the relevant resource list at https://github.com/CSHaitao/Awesome-LLMs-as-Judges.
A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions
2024-12-07
Ola Shorinwa, Zhiting Mei, Justin Lidard, Allen Z. Ren, Anirudha Majumdar
The remarkable performance of large language models (LLMs) in content generation, coding, and common-sense reasoning has spurred widespread integration into many facets of society. However, integration of LLMs raises valid questions on their reliability and trustworthiness, given their propensity to generate hallucinations: plausible, factually-incorrect responses, which are expressed with striking confidence. Previous work has shown that hallucinations and other non-factual responses generated by LLMs can be detected by examining the uncertainty of the LLM in its response to the pertinent prompt, driving significant research efforts devoted to quantifying the uncertainty of LLMs. This survey seeks to provide an extensive review of existing uncertainty quantification methods for LLMs, identifying their salient features, along with their strengths and weaknesses. We present existing methods within a relevant taxonomy, unifying ostensibly disparate methods to aid understanding of the state of the art. Furthermore, we highlight applications of uncertainty quantification methods for LLMs, spanning chatbot and textual applications to embodied artificial intelligence applications in robotics. We conclude with open research challenges in uncertainty quantification of LLMs, seeking to motivate future research.
ULMRec: User-centric Large Language Model for Sequential Recommendation
2024-12-07
Minglai Shao, Hua Huang, Qiyao Peng, Hongtao Liu
Recent advances in Large Language Models (LLMs) have demonstrated promising performance in sequential recommendation tasks, leveraging their superior language understanding capabilities. However, existing LLM-based recommendation approaches predominantly focus on modeling item-level co-occurrence patterns while failing to adequately capture user-level personalized preferences. This is problematic since even users who display similar behavioral patterns (e.g., clicking or purchasing similar items) may have fundamentally different underlying interests. To alleviate this problem, in this paper, we propose ULMRec, a framework that effectively integrates user personalized preferences into LLMs for sequential recommendation. Considering there has the semantic gap between item IDs and LLMs, we replace item IDs with their corresponding titles in user historical behaviors, enabling the model to capture the item semantics. For integrating the user personalized preference, we design two key components: (1) user indexing: a personalized user indexing mechanism that leverages vector quantization on user reviews and user IDs to generate meaningful and unique user representations, and (2) alignment tuning: an alignment-based tuning stage that employs comprehensive preference alignment tasks to enhance the model's capability in capturing personalized information. Through this design, ULMRec achieves deep integration of language semantics with user personalized preferences, facilitating effective adaptation to recommendation. Extensive experiments on two public datasets demonstrate that ULMRec significantly outperforms existing methods, validating the effectiveness of our approach.
AI Planning: A Primer and Survey (Preliminary Report)
2024-12-07
Dillon Z. Chen, Pulkit Verma, Siddharth Srivastava, Michael Katz, Sylvie Thiébaux
Automated decision-making is a fundamental topic that spans multiple sub-disciplines in AI: reinforcement learning (RL), AI planning (AP), foundation models, and operations research, among others. Despite recent efforts to ``bridge the gaps'' between these communities, there remain many insights that have not yet transcended the boundaries. Our goal in this paper is to provide a brief and non-exhaustive primer on ideas well-known in AP, but less so in other sub-disciplines. We do so by introducing the classical AP problem and representation, and extensions that handle uncertainty and time through the Markov Decision Process formalism. Next, we survey state-of-the-art techniques and ideas for solving AP problems, focusing on their ability to exploit problem structure. Lastly, we cover subfields within AP for learning structure from unstructured inputs and learning to generalise to unseen scenarios and situations.
2024-12-07
Timothy Sudijono, Simon Ejdemyr, Apoorva Lal, Martin Tingley
Experimentation in online digital platforms is used to inform decision making. Specifically, the goal of many experiments is to optimize a metric of interest. Null hypothesis statistical testing can be ill-suited to this task, as it is indifferent to the magnitude of effect sizes and opportunity costs. Given access to a pool of related past experiments, we discuss how experimentation practice should change when the goal is optimization. We survey the literature on empirical Bayes analyses of A/B test portfolios, and single out the A/B Testing Problem (Azevedo et al., 2020) as a starting point, which treats experimentation as a constrained optimization problem. We show that the framework can be solved with dynamic programming and implemented by appropriately tuning $p$-value thresholds. Furthermore, we develop several extensions of the A/B Testing Problem and discuss the implications of these results on experimentation programs in industry. For example, under no-cost assumptions, firms should be testing many more ideas, reducing test allocation sizes, and relaxing $p$-value thresholds away from $p = 0.05$.
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System
2024-12-07
Pengyu Li, Zhijie Zhong, Tong Zhang, Zhiwen Yu, C. L. Philip Chen, Kaixiang Yang
Time series anomaly detection (TSAD) has been a research hotspot in both academia and industry in recent years. Deep learning methods have become the mainstream research direction due to their excellent performance. However, new viewpoints have emerged in recent TSAD research. Deep learning is not required for TSAD due to limitations such as slow deep learning speed. The Broad Learning System (BLS) is a shallow network framework that benefits from its ease of optimization and speed. It has been shown to outperform machine learning approaches while remaining competitive with deep learning. Based on the current situation of TSAD, we propose the Contrastive Patch-based Broad Learning System (CPatchBLS). This is a new exploration of patching technique and BLS, providing a new perspective for TSAD. We construct Dual-PatchBLS as a base through patching and Simple Kernel Perturbation (SKP) and utilize contrastive learning to capture the differences between normal and abnormal data under different representations. To compensate for the temporal semantic loss caused by various patching, we propose CPatchBLS with model level integration, which takes advantage of BLS's fast feature to build model-level integration and improve model detection. Using five real-world series anomaly detection datasets, we confirmed the method's efficacy, outperforming previous deep learning and machine learning methods while retaining a high level of computing efficiency.
Exotic properties and manipulation in 2D semimetal Mn2B2(OH)2: a theoretical study
2024-12-07
Pingwei Liu, Dan Liu, Shixin Song, Kang Li, Xueyong Yuan, Jie Guan
Most functional materials possess one single outstanding property and are limited to be used for a particular purpose. Instead of integrating materials with different functions into one module, designing materials with controllable multi-functions is more promising for the electronic industry. In this study, we investigate an unexplored alpha-phase of two-dimensional (2D) Mn2B2(OH)2 theoretically. Eighteen distinct electrical polarizations, characterized by three different magnitudes and twelve different directions, are found in this phase. The switch of the electrical polarizations is also linked to an observed splitting of band structures between different spin states and the ferroelasticity of the system. The manipulation of these properties can be realized through controlling the alignment of Mn-OH-Mn chains. Additionally, the approximately honeycomb lattice for the atomic layer of boron indicate the potential superconductivity in the system. The diverse and tunable properties make the proposed material as an outstanding candidate for sensing applications at the 2D limit.
Non-linear Transport in Non-centrosymmetric Systems: From Fundamentals to Applications
2024-12-06
Manuel Suárez-Rodríguez, Fernando De Juan, Ivo Souza, Marco Gobbi, Fèlix Casanova, Luis E. Hueso
Ohm's law has been a cornerstone of electronics since its experimental discovery. This law establishes that in a conductive system, the voltage is directly proportional to the current. Even when time-reversal symmetry is disrupted, leading to the emergence of magnetoresistance and Hall effects, the linear relationship between voltage and current remains intact. However, recent experiments have demonstrated a breakdown of Ohm's law in non-centrosymmetric structures. In these systems, non-linear transport effects are permitted with quadratic scaling between voltages and currents. Here, we review the main demonstrations of non-linear transport in non-centrosymmetric systems, analyzing the connection between non-linear behavior and the system's symmetry. Additionally, we delve into the microscopic mechanisms driving these effects, such as Berry curvature dipole and Berry connection polarizability. Finally, we highlight potential applications of non-linear transport in spintronics and energy harvesting.
Object detection is a fundamental task in computer vision and image understanding, with the goal of identifying and localizing objects of interest within an image while assigning them corresponding class labels. Traditional methods, which relied on handcrafted features and shallow models, struggled with complex visual data and showed limited performance. These methods combined low-level features with contextual information and lacked the ability to capture high-level semantics. Deep learning, especially Convolutional Neural Networks (CNNs), addressed these limitations by automatically learning rich, hierarchical features directly from data. These features include both semantic and high-level representations essential for accurate object detection. This paper reviews object detection frameworks, starting with classical computer vision methods. We categorize object detection approaches into two groups: (1) classical computer vision techniques and (2) CNN-based detectors. We compare major CNN models, discussing their strengths and limitations. In conclusion, this review highlights the significant advancements in object detection through deep learning and identifies key areas for further research to improve performance.
This paper presents a novel approach to compute food composition data for Indian recipes using a knowledge graph for Indian food (FKG.in) and LLMs. The primary focus is to provide a broad overview of an automated food composition analysis workflow and describe its core functionalities: nutrition data aggregation, food composition analysis, and LLM-augmented information resolution. This workflow aims to complement FKG.in and iteratively supplement food composition data from verified knowledge bases. Additionally, this paper highlights the challenges of representing Indian food and accessing food composition data digitally. It also reviews three key sources of food composition data: the Indian Food Composition Tables, the Indian Nutrient Databank, and the Nutritionix API. Furthermore, it briefly outlines how users can interact with the workflow to obtain diet-based health recommendations and detailed food composition information for numerous recipes. We then explore the complex challenges of analyzing Indian recipe information across dimensions such as structure, multilingualism, and uncertainty as well as present our ongoing work on LLM-based solutions to address these issues. The methods proposed in this workshop paper for AI-driven knowledge curation and information resolution are application-agnostic, generalizable, and replicable for any domain.
2024-12-06
Sepideh Bazazi, Jurgis Karpus, Taha Yasseri
Cooperation between humans and machines is increasingly vital as artificial intelligence (AI) becomes more integrated into daily life. Research indicates that people are often less willing to cooperate with AI agents than with humans, more readily exploiting AI for personal gain. While prior studies have shown that giving AI agents human-like features influences people's cooperation with them, the impact of AI's assigned gender remains underexplored. This study investigates how human cooperation varies based on gender labels assigned to AI agents with which they interact. In the Prisoner's Dilemma game, 402 participants interacted with partners labelled as AI (bot) or humans. The partners were also labelled male, female, non-binary, or gender-neutral. Results revealed that participants tended to exploit female-labelled and distrust male-labelled AI agents more than their human counterparts, reflecting gender biases similar to those in human-human interactions. These findings highlight the significance of gender biases in human-AI interactions that must be considered in future policy, design of interactive AI systems, and regulation of their use.
A Survey of Large Language Model-Based Generative AI for Text-to-SQL: Benchmarks, Applications, Use Cases, and Challenges
2024-12-06
Aditi Singh, Akash Shetty, Abul Ehtesham, Saket Kumar, Tala Talaei Khoei
Text-to-SQL systems facilitate smooth interaction with databases by translating natural language queries into Structured Query Language (SQL), bridging the gap between non-technical users and complex database management systems. This survey provides a comprehensive overview of the evolution of AI-driven text-to-SQL systems, highlighting their foundational components, advancements in large language model (LLM) architectures, and the critical role of datasets such as Spider, WikiSQL, and CoSQL in driving progress. We examine the applications of text-to-SQL in domains like healthcare, education, and finance, emphasizing their transformative potential for improving data accessibility. Additionally, we analyze persistent challenges, including domain generalization, query optimization, support for multi-turn conversational interactions, and the limited availability of datasets tailored for NoSQL databases and dynamic real-world scenarios. To address these challenges, we outline future research directions, such as extending text-to-SQL capabilities to support NoSQL databases, designing datasets for dynamic multi-turn interactions, and optimizing systems for real-world scalability and robustness. By surveying current advancements and identifying key gaps, this paper aims to guide the next generation of research and applications in LLM-based text-to-SQL systems.
Go-or-Grow Models in Biology: a Monster on a Leash
2024-12-06
R. Thiessen, M. Conte, T. L. Stepien, T. Hillen
Go-or-grow approaches represent a specific class of mathematical models used to describe populations where individuals either migrate or reproduce, but not both simultaneously. These models have a wide range of applications in biology and medicine, chiefly among those the modeling of brain cancer spread. The analysis of go-or-grow models has inspired new mathematics, and it is the purpose of this review to highlight interesting and challenging mathematical properties of reaction--diffusion models of the go-or-grow type. We provide a detailed review of biological and medical applications before focusing on key results concerning solution existence and uniqueness, pattern formation, critical domain size problems, and traveling waves. We present new general results related to the critical domain size and traveling wave problems, and we connect these findings to the existing literature. Moreover, we demonstrate the high level of instability inherent in go-or-grow models. We argue that there is currently no accurate numerical solver for these models, and emphasize that special care must be taken when dealing with the "monster on a leash".
Americans' Support for AI Development -- Measured Daily with Open Data and Methods
2024-12-06
Jason Jeffrey Jones
A confluence of maturing Web technologies and Web platforms affords a new form of scientific communication: free and open nowcasting of public opinion. Here, I present the first open-source system to do so. The automated system gathers new human responses to survey items daily, anonymizes and publicly distributes microdata, and presents analyses through a publicly viewable Web dashboard. A demonstration implementation tracked support for further development of artificial intelligence at daily resolution. As of 2024-11-17, the system had autonomously produced 214 daily estimates of support. I argue that more scientists should adopt the method of open nowcasting, because it encourages transparency in research design and eases replication.
The reliability of gas-phase metallicities immediately adjacent to non-star-forming spaxels in MaNGA
2024-12-06
Jillian M. Scudder, Aidan Khelil, Jonah Z. Ordower
In this work, we use gas phase metallicities calculated from the Sloan Digital Sky Survey (SDSS) Mapping Nearby Galaxies at Apache Point (MaNGA) Data Release 17 (DR17) to assess the extent of potential biases in spaxels which are spatially adjacent to spaxels identified as non-star forming (non-SF) on a BPT diagram. We identify a sample of $\sim21,000$ such spaxels with calculable metallicities from the full metallicity catalogue ($\sim$1.57 million), representing a small fraction ($\sim1.3$ per cent) of the full metallicity sample. $\sim$23 per cent of all galaxies with at least one spaxel with a calculable metallicity also contain at least one spaxel with a calculated metallicity adjacent to a non-SF spaxel, with a typical galaxy hosting 9 non-SF-adjacent spaxels. From our suite of 6 different metallicity calibrations, we find that only the metallicity calibrations based entirely on the [NII]$_{6584}$/H$\alpha$ ratio are affected, showing systematic offsets to higher metallicities by up to $\sim$0.04 dex if they are located adjacent to a non-SF flagged spaxel, relative to a radially matched control sample. The inclusion of additional diagnostic diagrams (based on [OI]$_{6300}$~\&/or [SII]$_{6717+6731}$) is insufficient to remove the observed offset in the [NII]$_{6584}$/H$\alpha$ based calibrations. Using a stricter diagnostic line on the BPT diagram removes $\sim$94 per cent of identified bordering spaxels with metallicities for all metallicity calibrations, and removes the residual offset to higher metallicity values seen in [NII]$_{6584}$/H$\alpha$ calibrations. If science cases demand an exceptionally clean metallicity sample, we recommend either a stricter BPT cut, and/or a non-[NII]$_{6584}$/H$\alpha$ based metallicity calibration.
Probabilistic Galaxy Field Generation with Diffusion Models
2024-12-06
Tanner Sether, Elena Giusarma, Mauricio Reyes-Hurtado
In the era of precision cosmology, the ability to generate accurate and large-scale galaxy catalogs is crucial for advancing our understanding of the universe. With the flood of cosmological data from current and upcoming missions, generating theoretical predictions to compare with these observations is essential for constraining key cosmological parameters. While traditional methods, such as the Halo-Occupation Distribution (HOD), have provided foundational insights, they struggle to balance the need for both accuracy and computational efficiency. High-fidelity hydrodynamic simulations offer improved precision but are computationally expensive and resource-intensive. In this work, we introduce a novel machine learning approach that harnesses Convolutional Neural Networks (CNNs) and Diffusion Models, trained on the CAMELS simulation suite, to bridge the gap between computationally inexpensive dark matter simulations and the galaxy distributions of more costly hydrodynamic simulations. Our method not only outperforms traditional HOD techniques in accuracy but also significantly accelerates the simulation process, offering a scalable solution for next-generation cosmological surveys. This advancement has the potential to revolutionize galaxy catalog generation, enabling more precise, data-driven cosmological analyses.
The Prompt Canvas: A Literature-Based Practitioner Guide for Creating Effective Prompts in Large Language Models
2024-12-06
Michael Hewing, Vincent Leinhos
The rise of large language models (LLMs) has highlighted the importance of prompt engineering as a crucial technique for optimizing model outputs. While experimentation with various prompting methods, such as Few-shot, Chain-of-Thought, and role-based techniques, has yielded promising results, these advancements remain fragmented across academic papers, blog posts and anecdotal experimentation. The lack of a single, unified resource to consolidate the field's knowledge impedes the progress of both research and practical application. This paper argues for the creation of an overarching framework that synthesizes existing methodologies into a cohesive overview for practitioners. Using a design-based research approach, we present the Prompt Canvas, a structured framework resulting from an extensive literature review on prompt engineering that captures current knowledge and expertise. By combining the conceptual foundations and practical strategies identified in prompt engineering, the Prompt Canvas provides a practical approach for leveraging the potential of Large Language Models. It is primarily designed as a learning resource for pupils, students and employees, offering a structured introduction to prompt engineering. This work aims to contribute to the growing discourse on prompt engineering by establishing a unified methodology for researchers and providing guidance for practitioners.
Immigration and aging have always been significant topics of discussion in society, concerning the stability and future development of a country and its people. Research in the field of HCI on immigration and aging has primarily focused on their practical needs but has paid less attention to the adaptability issues of older internal migrants moving with their families. In this study, we investigate the challenges older internal migrants face in adapting socially, using metadata surveys and semi-structured interviews to delve into their life struggles and resilience sources. Our findings highlight the older internal migrants' remarkable resilience, particularly evident in their reminiscences. We explore the integration of reminiscences with the metaverse, identifying the necessary conditions to create a "Metamemory". We introduce a novel design for a metaverse scene that bridges past and present experiences. This aims to encourage discussions on enhancing older internal migrants' reminiscence, leveraging the metaverse's positive potential, and devising strategies to more effectively address older internal migrants' concerns in the future.
Prospects for Observing Astrophysical Transients with GeV Neutrinos
2024-12-06
Angelina Partenheimer, Jessie Thwaites, Ke Fang, Justin Vandenbroucke, Brian D. Metzger
Although Cherenkov detectors of high-energy neutrinos in ice and water are often optimized to detect TeV-PeV neutrinos, they may also be sensitive to transient neutrino sources in the 1-100~GeV energy range. A wide variety of transient sources have been predicted to emit GeV neutrinos. In light of the upcoming IceCube-Upgrade, which will extend the IceCube detector's sensitivity down to a few GeV, as well as improve its angular resolution, we survey a variety of transient source models and compare their predicted neutrino fluences to detector sensitivities, in particular those of IceCube-DeepCore and the IceCube Upgrade. We consider the ranges of neutrino fluence from transients powered by non-relativistic shocks, such as novae, supernovae, fast blue optical transients, and tidal disruption events. We also consider fast radio bursts and relativistic outflows of high- and low-luminosity gamma-ray bursts. Our study sheds light on the prospects of observing GeV transients with existing and upcoming neutrino facilities.
The Survey of Surveys: machine learning for stellar parametrization
2024-12-06
A. Turchi, E. Pancino, F. Rossi, A. Avdeeva, P. Marrese, S. Marinoni, N. Sanna, M. Tsantaki, G. Fanari
We present a machine learning method to assign stellar parameters (temperature, surface gravity, metallicity) to the photometric data of large photometric surveys such as SDSS and SKYMAPPER. The method makes use of our previous effort in homogenizing and recalibrating spectroscopic data from surveys like APOGEE, GALAH, or LAMOST into a single catalog, which is used to inform a neural network. We obtain spectroscopic-quality parameters for millions of stars that have only been observed photometrically. The typical uncertainties are of the order of 100K in temperature, 0.1 dex in surface gravity, and 0.1 dex in metallicity and the method performs well down to low metallicity, were obtaining reliable results is known to be difficult.
2024-12-06
David García García, Jose A. R. Cembranos
This work explores the intersection of quantum mechanics and curved spacetime by employing the Wigner formalism to investigate quantum systems in the vicinity of black holes. Specifically, we study the quantum dynamics of a probe particle bound to a Schwarzschild black hole using a phase-space representation of quantum mechanics. The analysis begins with a review of the covariant Wigner function in curved spacetime, highlighting its application to spherically symmetric, uncharged black holes. We then derive an effective potential from the Schwarzschild metric, which defines the Hamiltonian for the electron. Relativistic corrections are treated perturbatively to estimate energy levels and associated Wigner functions for the bound state. Additionally, we compare the results obtained through the Schrodinger equation with those derived directly using the symplectic formalism, demonstrating the consistency and versatility of the phase-space approach. The study sheds light on quantum behavior near black holes and suggests new avenues for combining quantum kinetic theory with relativistic gravitational settings.
Steps are all you need: Rethinking STEM Education with Prompt Engineering
2024-12-06
Krishnasai Addala, Kabir Dev Paul Baghel, Chhavi Kirtani, Avinash Anand, Rajiv Ratn Shah
Few shot and Chain-of-Thought prompting have shown promise when applied to Physics Question Answering Tasks, but are limited by the lack of mathematical ability inherent to LLMs, and are prone to hallucination. By utilizing a Mixture of Experts (MoE) Model, along with analogical prompting, we are able to show improved model performance when compared to the baseline on standard LLMs. We also survey the limits of these prompting techniques and the effects they have on model performance. Additionally, we propose Analogical CoT prompting, a prompting technique designed to allow smaller, open source models to leverage Analogical prompting, something they have struggled with, possibly due to a lack of specialist training data.
D&R is a statistical approach designed to handle large and complex datasets. It partitions the dataset into several manageable subsets and subsequently applies the analytic method to each subset independently to obtain results. Finally, the results from each subset are combined to yield the results for the entire dataset. D&R strategies can be implemented to fit GLMs to datasets too large for conventional methods. Several D&R strategies are available for different GLMs, some of which are theoretically justified but lack practical validation. A significant limitation is the theoretical and practical justification for estimating combined standard errors and confidence intervals. This paper reviews D&R strategies for GLMs and proposes a method to determine the combined standard error for D&R-based estimators. In addition to the traditional dataset division procedures, we propose a different division method named sequential partitioning for D&R-based estimators on GLMs. We show that the obtained D&R estimator with the proposed standard error attains equivalent efficiency as the full data estimate. We illustrate this on a large synthetic dataset and verify that the results from D&R are accurate and identical to those from other available R packages.
Eta-Earth Revisited II: Deriving a Maximum Number of Earth-like Habitats in the Galactic Disk
2024-12-06
Manuel Scherf, Helmut Lammer, Laurenz Sproß
In Lammer et al. 2024, we defined Earth-like Habitats (EH) as rocky planets in the habitable zone of complex life (HZCL) on which Earth-like N$_2$-O$_2$-dominated atmospheres with minor amounts of CO$_2$ can exist and derived a formula for estimating their maximum number in the Galaxy. Here, we apply this formula by considering only requirements that are already scientifically quantifiable. By implementing models for star formation rate, initial mass function, and galactic mass distribution, we calculate the spatial distribution of disk stars as functions of stellar mass and birth age. We apply models for the GHZ and evaluate the thermal stability of Earth-like atmospheres with various CO$_2$ mixing ratios by implementing the newest stellar evolution and upper atmosphere models. In addition, we include the rocky exoplanet frequency, the availability of oceans and subaerial land, and the potential large moon requirement by evaluating their importance and implementing these criteria from minima to maxima values. We also discuss factors that are not yet scientifically quantifiable but may be requirements for EHs to evolve. We find that EHs are rare by obtaining maximum numbers of $2.5^{+71.6}_{-2.4}\times10^{5}$ and $0.6^{+27.1}_{-0.59}\times10^{5}$ planets that can potentially host N$_2$-Earth-like atmospheres with maximum CO$_2$ mixing ratios of 10\% and 1\%, respectively, implying that a minimum of $\sim 10^3 - 10^6$ rocky HZCL planets are needed for 1 EH to evolve. Their actual number, however, may be substantially lower as several requirements are not included in our model; this also implies ETIs are significantly rarer still. Our results illustrate that neither every star can host EHs, nor that each rocky HZCL planet evolves such that it may be able to host complex animal-like life. The Copernican Principle therefore cannot be applied to infer that such life is common in the Galaxy.
'Debunk-It-Yourself': Health Professionals' Strategies for Responding to Misinformation on TikTok
2024-12-06
Filipo Sharevski, Jennifer Vander Loop, Amy Devine, Peter Jachim, Sanchari Das
Misinformation is "sticky" in nature, requiring a considerable effort to undo its influence. One such effort is debunking or exposing the falsity of information. As an abundance of misinformation is on social media, platforms do bear some debunking responsibility in order to preserve their trustworthiness as information providers. A subject of interpretation, platforms poorly meet this responsibility and allow dangerous health misinformation to influence many of their users. This open route to harm did not sit well with health professional users, who recently decided to take the debunking into their own hands. To study this individual debunking effort - which we call 'Debunk-It-Yourself (DIY)' - we conducted an exploratory survey n=14 health professionals who wage a misinformation counter-influence campaign through videos on TikTok. We focused on two topics, nutrition and mental health, which are the ones most often subjected to misinformation on the platform. Our thematic analysis reveals that the counterinfluence follows a common process of initiation, selection, creation, and "stitching" or duetting a debunking video with a misinformation video. The 'Debunk-It-Yourself' effort was underpinned by three unique aspects: (i) it targets trending misinformation claims perceived to be of direct harm to people's health; (ii) it offers a symmetric response to the misinformation; and (iii) it is strictly based on scientific evidence and claimed clinical experience. Contrasting the 'Debunk-It-Yourself' effort with the one TikTok and other platforms (reluctantly) put in moderation, we offer recommendations for a structured response against the misinformation's influence by the users themselves.
Neutrino masses from large-scale structures: future sensitivity and theory dependence
2024-12-06
Davide Racco, Pierre Zhang, Henry Zheng
In the incoming years, cosmological surveys aim at measuring the sum of neutrino masses $\Sigma m_\nu$, complementing the determination of their mass ordering from laboratory experiments. In order to assess the full potential of large-scale structures (LSS), we employ state-of-the-art predictions from the effective field theory of LSS (EFTofLSS) at one loop to perform Fisher forecasts on the sensitivity (combining power spectrum and bispectrum) of ongoing and future surveys (DESI, MegaMapper) in combination with CMB measurements (Planck, Litebird and Stage-4). We find that the 1$\sigma$ sensitivity on $\Sigma m_\nu$ is expected to be 15 meV with Planck+DESI, and 7 meV with S4+MegaMapper, where $\sim 10\%$ and $30\%$ of the constraints are brought by the one-loop bispectrum respectively. To understand how robust are these bounds, we explore how they are relaxed when considering extensions to the standard model, dubbed `new physics'. We find that the shift induced on $\Sigma m_\nu$ by a $1\sigma$ shift on new physics parameters (we consider extra relativistic species, neutrino self-interactions, curvature or a time-evolving electron mass) could be $\mathcal O(10)$ meV for Planck+DESI, but it will be suppressed down to $\mathcal O(1)$ meV in S4+MegaMapper. Our study highlights the quantitative impact of including the bispectrum at one loop in the EFTofLSS, and the robustness of the sensitivity to $\Sigma m_\nu$ against potential new physics thanks to the synergy of cosmological probes.
Five-years Altitude Statistics of Noctilucent Clouds Based on Multi-Site Wide-Field Camera Survey
2024-12-06
Oleg S. Ugolnikov, Nikolay N. Pertsev, Vladimir I. Perminov, Ilya S. Yankovsky, Dmitry N. Aleshin, Ekaterina N. Tipikina, Alexander A. Ilyukhin, Egor O. Ugolnikov, Stanislav A. Korotkiy, Olga Yu. Golubeva, Andrey M. Tatarnikov, Sergey G. Zheltoukhov, Alexey V. Popov, Alexey M. Sushkov, Egor A. Volkov, Natalya S. Krapkina, Damir I. Yalyshev
The results of simultaneous measurements of noctilucent clouds (NLC) position in a number of ground-based locations are presented. Observational data of 14 bright NLC events over 5 years is used for building the altitude maps of cloud fields using triangulation technique updated for multi-location case. Statistical distribution of NLC altitude and its change during the summer season is considered. Mean NLC altitudes are compared with colorimetric technique based on the same data and simple radiation transfer model. This can be used to check the model and estimate the accuracy of single-camera technique of NLC altitude measurements. Results and methods are suggested for net ground-based survey of noctilucent clouds.
2024-12-06
Valeria Banica, Daniel Eceizabarrena, Andrea. R. Nahmod, Luis Vega
In this proceedings article we survey the results in [5] and their motivation, as presented at the 50th Journ\'ees EDP 2024. With the aim of quantifying turbulent behaviors of vortex filaments, we study the multifractality of a family of generalized Riemann's non-differentiable functions. These functions represent, in a certain limit, the trajectory of regular polygonal vortex filaments that evolve according to the binormal flow, the classical model for vortex filaments dynamics. We explain how we determined their spectrum of singularities through a careful design of Diophantine sets, which we study by using the Duffin-Schaeffer theorem and the Mass Transference Principle.
Near- and mid-infrared excitation of ultrafast demagnetization in a cobalt multilayer system
2024-12-06
Katherine Légaré, Guillaume Barrette, Laurent Giroux, Jean-Michel Parent, Elissa Haddad, Heide Ibrahim, Philippe Lassonde, Emmanuelle Jal, Boris Vodungbo, Jan Lüning, Fabio Boschini, Nicolas Jaouen, François Légaré
In the last few decades, ultrafast demagnetization elicited by ultrashort laser pulses has been the subject of a large body of work that aims to better understand and control this phenomenon. Although specific magnetic materials' properties play a key role in defining ultrafast demagnetization dynamics, features of the driving laser pulse such as its duration and photon energy might also contribute. Here, we report ultrafast demagnetization of a cobalt/platinum multilayer in a broad spectral range spanning from the near-infrared (near-IR) to the mid-infrared (mid-IR), with wavelengths between 0.8 and 8.7 $\mu$m. The ultrafast dynamics of the macroscopic magnetization is tracked via the time-resolved magneto-optical Kerr effect. We show that the ultrafast demagnetization of the sample can be efficiently induced over that entire excitation spectrum with minimal dependence on the excitation wavelength. Instead, we confirm that the temporal profile of the pump excitation pulse is an important factor influencing ultrafast demagnetization dynamics.
A grand-design spiral galaxy 1.5 billion years after the Big Bang with JWST
2024-12-06
Rashi Jain, Yogesh Wadadekar
We report the discovery of a large ($\sim 10$ kpc diameter), massive ($\log(M_\star/M_\odot) = 10.15^{+0.01}_{-0.01}$), grand-design spiral galaxy with photometric redshift $z_{\text{phot}} = 4.03$ in the UNCOVER and Medium Band Mega Science surveys with JWST. This is the highest redshift spiral galaxy discovered with JWST so far. In the rest-frame near-UV and far-UV, we clearly see the beads-on-a-string pattern of star formation; in the rest-frame visible bands, each string appears as an arm. Spectral energy distribution modeling using the Bagpipes code is strongly constrained by detections and flux measurements in 21 JWST and HST filters. The stellar mass-weighted age is 228 Myr, implying that 50% of the stars in the galaxy formed after $z \sim 4.5$. This is a highly star-forming galaxy with a star formation rate (SFR) of $57.57^{+1.80}_{-1.90} \, M_\odot\, \text{yr}^{-1}$. We detect strong H-$\alpha$ + [NII] emission from the entire disk. The detection of a spiral galaxy at $z \sim 4$ indicates that massive and large spiral galaxies and disks were already in place merely 1.5 billion years after the Big Bang.
Cosmological constraints on dark energy models using DESI BAO 2024
2024-12-06
Jie Zheng, Da-Chun Qiang, Zhi-Qiang You
Recently, the measurements of baryon acoustic oscillations (BAO) by the Dark Energy Spectroscopic Instrument (DESI) indicate a potential deviation from the standard $\Lambda$CDM model. Some studies suggest that the data points from the luminous red galaxies (LRG) survey in DESI BAO data may contribute to this discrepancy. In this work, our main goal is to investigate whether this deviation is caused by the parameterization of the equation of state (EoS) of dark energy (DE). Hence, we have examined four popular parameterized dark energy models in our analysis: the Chevallier-Polarski-Linder (CPL), Barboza-Alcaniz (BA), Jassal-Bagla-Padmanabhan (JBP), and Feng-Shen-Li-Li (FSLL) parameterizations. Considering that LRG1 and LRG2 data points may lead to deviation from the $\Lambda$CDM model, we use two versions of DESI BAO data, differing in whether these data points are included. Additionally, to break the parameter degeneracies and obtain robust constraint results, we introduce Type Ia supernovae (SNe Ia) and quasars (QSO) in our analysis. Our findings indicate that in these parameterizations, the deviation from ($w_0$,$w_{1}$)=(-1,0) becomes more pronounced when using the combined data from DESI BAO, SNe Ia, and QSO compilations. Here, $w_{0}$ and $w_{1}$ represent the EoS of DE. It suggests that the parameterizations of the EoS of DE have little impact on the deviation from the $\Lambda$CDM model. Besides, our analysis potentially hints that dark energy may have dynamic properties. In addition, the results obtained from different BAO datasets demonstrate that the LRG1 and LRG2 data points do indeed contribute to a deviation from the $\Lambda$CDM model. Finally, according to the statistical criteria, the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), the joint constraints provide substantial observational support to the BA and FSLL models.
Assessing Similarity Measures for the Evaluation of Human-Robot Motion Correspondence
2024-12-06
Charles Dietzel, Patrick J. Martin
One key area of research in Human-Robot Interaction is solving the human-robot correspondence problem, which asks how a robot can learn to reproduce a human motion demonstration when the human and robot have different dynamics and kinematic structures. Evaluating these correspondence problem solutions often requires the use of qualitative surveys that can be time consuming to design and administer. Additionally, qualitative survey results vary depending on the population of survey participants. In this paper, we propose the use of heterogeneous time-series similarity measures as a quantitative evaluation metric for evaluating motion correspondence to complement these qualitative surveys. To assess the suitability of these measures, we develop a behavioral cloning-based motion correspondence model, and evaluate it with a qualitative survey as well as quantitative measures. By comparing the resulting similarity scores with the human survey results, we identify Gromov Dynamic Time Warping as a promising quantitative measure for evaluating motion correspondence.
Automatic Prediction of Stroke Treatment Outcomes: Latest Advances and Perspectives
2024-12-06
Zeynel A. Samak, Philip Clatworthy, Majid Mirmehdi
Stroke is a major global health problem that causes mortality and morbidity. Predicting the outcomes of stroke intervention can facilitate clinical decision-making and improve patient care. Engaging and developing deep learning techniques can help to analyse large and diverse medical data, including brain scans, medical reports and other sensor information, such as EEG, ECG, EMG and so on. Despite the common data standardisation challenge within medical image analysis domain, the future of deep learning in stroke outcome prediction lie in using multimodal information, including final infarct data, to achieve better prediction of long-term functional outcomes. This article provides a broad review of recent advances and applications of deep learning in the prediction of stroke outcomes, including (i) the data and models used, (ii) the prediction tasks and measures of success, (iii) the current challenges and limitations, and (iv) future directions and potential benefits. This comprehensive review aims to provide researchers, clinicians, and policy makers with an up-to-date understanding of this rapidly evolving and promising field.
In this work, we perform a detailed analysis to constrain the Hu-Sawicki $f(R)$ gravity model, using cosmic shear data from three prominent Stage-III weak lensing surveys: DES-Y3, KiDS-1000, and HSC-Y3. To accurately model the nonlinear matter clustering in the analysis of cosmic shear signals, we employ FREmu, a recently developed power spectrum emulator for the $f(R)$ gravity trained on the Quijote-MG simulations. This emulator achieves precise predictions, limiting the errors to 5% on scales of $0.009h\,{\rm Mpc}^{-1} < k < 0.5h\,{\rm Mpc}^{-1}$. Our findings reveal that cosmic shear data alone impose only weak constraints on the $f(R)$ parameter $\log_{10}|f_{R_0}|$. To improve these constraints, we incorporate state-of-the-art external observations, including data from the cosmic microwave background and baryon acoustic oscillations. The inclusion of these external datasets significantly enhances the constraints, yielding an upper limit of $\log_{10}|f_{R_0}| < -4.79$ at the 95% confidence level.
Anomaly detection (AD) is a critical machine learning task with diverse applications in web systems, including fraud detection, content moderation, and user behavior analysis. Despite its significance, AD in natural language processing (NLP) remains underexplored, limiting advancements in detecting anomalies in text data such as harmful content, phishing attempts, or spam reviews. In this paper, we introduce NLP-ADBench, the most comprehensive benchmark for NLP anomaly detection (NLP-AD), comprising eight curated datasets and evaluations of nineteen state-of-the-art algorithms. These include three end-to-end methods and sixteen two-step algorithms that apply traditional anomaly detection techniques to language embeddings generated by bert-base-uncased and OpenAI's text-embedding-3-large models. Our results reveal critical insights and future directions for NLP-AD. Notably, no single model excels across all datasets, highlighting the need for automated model selection. Moreover, two-step methods leveraging transformer-based embeddings consistently outperform specialized end-to-end approaches, with OpenAI embeddings demonstrating superior performance over BERT embeddings. By releasing NLP-ADBench at https://github.com/USC-FORTIS/NLP-ADBench, we provide a standardized framework for evaluating NLP-AD methods, fostering the development of innovative approaches. This work fills a crucial gap in the field and establishes a foundation for advancing NLP anomaly detection, particularly in the context of improving the safety and reliability of web-based systems.
A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges
2024-12-06
Aditi Singh, Nirmal Prakashbhai Patel, Abul Ehtesham, Saket Kumar, Tala Talaei Khoei
Large Language Models (LLMs) have transformed numerous domains by providing advanced capabilities in natural language understanding, generation, and reasoning. Despite their groundbreaking applications across industries such as research, healthcare, and creative media, their rapid adoption raises critical concerns regarding sustainability. This survey paper comprehensively examines the environmental, economic, and computational challenges associated with LLMs, focusing on energy consumption, carbon emissions, and resource utilization in data centers. By synthesizing insights from existing literature, this work explores strategies such as resource-efficient training, sustainable deployment practices, and lifecycle assessments to mitigate the environmental impacts of LLMs. Key areas of emphasis include energy optimization, renewable energy integration, and balancing performance with sustainability. The findings aim to guide researchers, practitioners, and policymakers in developing actionable strategies for sustainable AI systems, fostering a responsible and environmentally conscious future for artificial intelligence.
MusE GAs FLOw and Wind (MEGAFLOW) XIII. Cool gas traced by MgII around isolated galaxies
2024-12-06
Maxime Cherrey, Nicolas F. Bouché, Johannes Zabl, Ilane Schroetter, Martin Wendt, Ivanna Langan, Joop Schaye, Lutz Wisotzki, Yucheng Guo, Ismael Pessa
The circumgalactic medium (CGM) is a key component needed to understand the physical processes governing the flows of gas around galaxies. Quantifying its evolution and its dependence on galaxy properties is particularly important for our understanding of accretion and feedback mechanisms. We select a volume-selected sample of 66 {\it isolated} star-forming galaxies (SFGs) at $0.4< z <1.5$ with $\log(M_\star/M_{\odot})> 9$ from the MusE GAs FLOw and Wind (MEGAFLOW) survey. Using MgII 2796,2803 absorptions in background quasars, we measure the covering fraction $f_c$ and quantify how the cool gas profile depends on galaxy properties (such as star-formation rate (SFR), stellar mass ($M_\star$) or azimuthal angle relative to the line of sight) and how these dependencies evolve with redshift. The MgII covering fraction of isolated galaxies is a strong function of impact parameter, and is steeper than previously reported. The impact parameter $b_{50}$ at which $f_c = $50\% is $b_{50}=50\pm7$kpc ($65\pm7$ kpc) for $W_r^{2796}>$0.5 \AA ($W_r^{2796}>0.1$ \AA), respectively. It is weakly correlated with SFR ($\propto$ SFR$^{0.08\pm0.09}$) and decreases with cosmic time ($\propto (1+z)^{0.8 \pm 0.7}$), contrary to the expectation of increasingly larger halos with time. The covering fraction is also higher along the minor axis than along the major axis at the $\approx 2 \sigma$ level. The CGM traced by \MgII{} is similar across the isolated galaxy population. Indeed, among the isolated galaxies with an impact parameter below 55 kpc, all have associated MgII absorption with $W_r^{2796}>$0.3\AA, resulting in a steep covering fraction $f_c(b)$.
'Being there together for health': A Systematic Review on the Feasibility, Effectiveness and Design Considerations of Immersive Collaborative Virtual Environments in Health Applications
2024-12-06
Tohid Zarei, Michelle Emery, Dimitrios Saredakis, Gun A. Lee, Ben Stubbs, Ancret Szpak, Tobias Loetscher
Effectively using immersive multi-user environments for digital applications (via virtual, augmented and mixed reality technologies) beckons the future of healthcare delivery in the metaverse. We aimed to evaluate the feasibility and effectiveness of these environments used in health applications, while identifying their design features. We systematically searched MEDLINE, PsycINFO, and Emcare databases for peer-reviewed original reports, published in English, without date restrictions until Aug 30, 2023, and conducted manual citation searching in Feb 2024. All studies using fully immersive extended reality technologies (e.g., head-mounted displays, smart glasses) while engaging more than one participant in an intervention with direct health benefits were included. A qualitative synthesis of findings is reported. The quality of research was assessed using JBI Critical Checklists. The review was pre-registered on PROSPERO (CRD42023479155). Of 2862 identified records, 10 studies were eligible. Included studies were mostly conducted with healthy young adults (five studies) and older adults (four studies). While they all used different models of Oculus/Meta headsets, their environments' designs were distinctive and aligned with their objectives. Findings indicated varying degrees of positive health outcomes, for engagement in rehabilitation, meaningful interactions across distances, positive affect, transformative experiences, mental health therapies, and motor skill learning. Participants reported high usability, motivation, enjoyment, presence and copresence. They also expressed the need for more training time with technology. Adopting an intentional intervention design, considering factors affecting presence and copresence, as well as integrating co-creation of the program with participants, seems integral to achieving positive health outcomes.
Machine learning algorithms to predict the risk of rupture of intracranial aneurysms: a systematic review
2024-12-06
Karan Daga, Siddharth Agarwal, Zaeem Moti, Matthew BK Lee, Munaib Din, David Wood, Marc Modat, Thomas C Booth
Purpose: Subarachnoid haemorrhage is a potentially fatal consequence of intracranial aneurysm rupture, however, it is difficult to predict if aneurysms will rupture. Prophylactic treatment of an intracranial aneurysm also involves risk, hence identifying rupture-prone aneurysms is of substantial clinical importance. This systematic review aims to evaluate the performance of machine learning algorithms for predicting intracranial aneurysm rupture risk. Methods: MEDLINE, Embase, Cochrane Library and Web of Science were searched until December 2023. Studies incorporating any machine learning algorithm to predict the risk of rupture of an intracranial aneurysm were included. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). PROSPERO registration: CRD42023452509. Results: Out of 10,307 records screened, 20 studies met the eligibility criteria for this review incorporating a total of 20,286 aneurysm cases. The machine learning models gave a 0.66-0.90 range for performance accuracy. The models were compared to current clinical standards in six studies and gave mixed results. Most studies posed high or unclear risks of bias and concerns for applicability, limiting the inferences that can be drawn from them. There was insufficient homogenous data for a meta-analysis. Conclusions: Machine learning can be applied to predict the risk of rupture for intracranial aneurysms. However, the evidence does not comprehensively demonstrate superiority to existing practice, limiting its role as a clinical adjunct. Further prospective multicentre studies of recent machine learning tools are needed to prove clinical validation before they are implemented in the clinic.
Question Answering for Decisionmaking in Green Building Design: A Multimodal Data Reasoning Method Driven by Large Language Models
2024-12-06
Yihui Li, Xiaoyue Yan, Hao Zhou, Borong Lin
In recent years, the critical role of green buildings in addressing energy consumption and environmental issues has become widely acknowledged. Research indicates that over 40% of potential energy savings can be achieved during the early design stage. Therefore, decision-making in green building design (DGBD), which is based on modeling and performance simulation, is crucial for reducing building energy costs. However, the field of green building encompasses a broad range of specialized knowledge, which involves significant learning costs and results in low decision-making efficiency. Many studies have already applied artificial intelligence (AI) methods to this field. Based on previous research, this study innovatively integrates large language models with DGBD, creating GreenQA, a question answering framework for multimodal data reasoning. Utilizing Retrieval Augmented Generation, Chain of Thought, and Function Call methods, GreenQA enables multimodal question answering, including weather data analysis and visualization, retrieval of green building cases, and knowledge query. Additionally, this study conducted a user survey using the GreenQA web platform. The results showed that 96% of users believed the platform helped improve design efficiency. This study not only effectively supports DGBD but also provides inspiration for AI-assisted design.
BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English
2024-12-06
Dipankar Srirag, Aditya Joshi, Jordan Painter, Diptesh Kanojia
Despite large language models (LLMs) being known to exhibit bias against non-mainstream varieties, there are no known labeled datasets for sentiment analysis of English. To address this gap, we introduce BESSTIE, a benchmark for sentiment and sarcasm classification for three varieties of English: Australian (en-AU), Indian (en-IN), and British (en-UK). Using web-based content from two domains, namely, Google Place reviews and Reddit comments, we collect datasets for these language varieties using two methods: location-based and topic-based filtering. Native speakers of the language varieties manually annotate the datasets with sentiment and sarcasm labels. Subsequently, we fine-tune nine large language models (LLMs) (representing a range of encoder/decoder and mono/multilingual models) on these datasets, and evaluate their performance on the two tasks. Our results reveal that the models consistently perform better on inner-circle varieties (i.e., en-AU and en-UK), with significant performance drops for en-IN, particularly in sarcasm detection. We also report challenges in cross-variety generalisation, highlighting the need for language variety-specific datasets such as ours. BESSTIE promises to be a useful evaluative benchmark for future research in equitable LLMs, specifically in terms of language varieties. The BESSTIE datasets, code, and models are currently available on request, while the paper is under review. Please email aditya.joshi@unsw.edu.au.
Cell-free (CF) architecture and full-duplex (FD) communication are leading candidates for next-generation wireless networks. The CF framework removes cell boundaries in traditional cell-based systems, thereby mitigating inter-cell interference and improving coverage probability. In contrast, FD communication allows simultaneous transmission and reception on the same frequency-time resources, effectively doubling the spectral efficiency (SE). The integration of these technologies, known as CF FD communication, leverages the advantages of both approaches to enhance the spectral and energy efficiency in wireless networks. CF FD communication is particularly promising due to the low-power and cost-effective FD-enabled access points (APs), which are ideal for short-range transmissions between APs and users. Despite its potential, a comprehensive survey or tutorial on CF FD communication has been notably absent. This paper aims to address this gap in the literature. It begins with an overview of FD communication fundamentals, self-interference cancellation techniques, and CF technology principles, including their implications for current wireless networks. The discussion then moves to the integration and compatibility of CF and FD technologies, focusing on channel estimation, performance analysis, and resource allocation in CF FD massive multiple-input multiple-output (mMIMO) networks, supported by an extensive literature review and case studies.
Hamiltonian Theory: generalizations to higher dimensions, supersymmetry and modified gravity
2024-12-06
Norbert Bodendorfer, Konstantin Eder, Xiangdong Zhang
Loop quantum gravity in its Hamiltonian form relies on a connection formulation of the gravitational phase space with three key properties: 1.) a compact gauge group, 2.) real variables, and 3.) canonical Poisson brackets. In conjunction, these properties allow to construct a well defined kinematical quantization of the holonomy flux-algebra on top of which the remaining constraints can be implemented. While this idea has traditionally been mainly used for Einstein gravity, any gravitational theory with the above properties can be accommodated. In this paper, we are going to review three strands of work building on this observation, namely the study of higher-dimensional loop quantum gravity, supersymmetric extensions of loop quantum gravity, as well as the quantization of modified gravitational theories.
Enlargement of symmetry groups in physics: a practitioner's guide
2024-12-06
Lehel Csillag, Julio Marny Hoff da Silva, Tudor Patuleanu
Wigner's classification has led to the insight that projective unitary representations play a prominent role in quantum mechanics. The physics literature often states that the theory of projective unitary representations can be reduced to the theory of ordinary unitary representations by enlarging the group of physical symmetries. Nevertheless, the enlargement process is not always described explicitly: it is unclear in which cases the enlargement has to be done to the universal cover, a central extension, or to a central extension of the universal cover. On the other hand, in the mathematical literature, projective unitary representations were extensively studied, and famous theorems such as the theorems of Bargmann and Cassinelli have been achieved. The present article bridges the two: we provide a precise, step-by-step guide on describing projective unitary representations as unitary representations of the enlarged group. Particular focus is paid to the difference between algebraic and topological obstructions. To build the bridge mentioned above, we present a detailed review of the difference between group cohomology and Lie group cohomology. This culminates in classifying Lie group central extensions by smooth cocycles around the identity. Finally, the take-away message is a hands-on algorithm that takes the symmetry group of a given quantum theory as input and provides the enlarged group as output. This algorithm is applied to several cases of physical interest. We also briefly outline a generalization of Bargmann's theory to time-dependent phases using Hilbert bundles.
From Principles to Practice: A Deep Dive into AI Ethics and Regulations
2024-12-06
Nan Sun, Yuantian Miao, Hao Jiang, Ming Ding, Jun Zhang
In the rapidly evolving domain of Artificial Intelligence (AI), the complex interaction between innovation and regulation has become an emerging focus of our society. Despite tremendous advancements in AI's capabilities to excel in specific tasks and contribute to diverse sectors, establishing a high degree of trust in AI-generated outputs and decisions necessitates meticulous caution and continuous oversight. A broad spectrum of stakeholders, including governmental bodies, private sector corporations, academic institutions, and individuals, have launched significant initiatives. These efforts include developing ethical guidelines for AI and engaging in vibrant discussions on AI ethics, both among AI practitioners and within the broader society. This article thoroughly analyzes the ground-breaking AI regulatory framework proposed by the European Union. It delves into the fundamental ethical principles of safety, transparency, non-discrimination, traceability, and environmental sustainability for AI developments and deployments. Considering the technical efforts and strategies undertaken by academics and industry to uphold these principles, we explore the synergies and conflicts among the five ethical principles. Through this lens, work presents a forward-looking perspective on the future of AI regulations, advocating for a harmonized approach that safeguards societal values while encouraging technological advancement.
Hydroxyl Lines and Moonlight: a High Spectral Resolution Investigation of NIR skylines from Maunakea to guide NIR spectroscopic surveys
2024-12-06
Frederick Dauphin, Andreea Petric, Étienne Artigau, Andrew W. Stephens, Neil James Cook, Steven Businger, Nicolas Flagey, Jennifer Marshall, Michelle Ntampaka, Swara Ravindranath, Laurie Rousseau-Nepton
Subtracting the changing sky contribution from the near-infrared (NIR) spectra of faint astronomical objects is challenging and crucial to a wide range of science cases such as estimating the velocity dispersions of dwarf galaxies, studying the gas dynamics in faint galaxies, measuring accurate redshifts, and any spectroscopic studies of faint targets. Since the sky background varies with time and location, NIR spectral observations, especially those employing fiber spectrometers and targeting extended sources, require frequent sky-only observations for calibration. However, sky subtraction can be optimized with sufficient a priori knowledge of the sky's variability. In this work, we explore how to optimize sky subtraction by analyzing 1075 high-resolution NIR spectra from the CFHT's SPIRou on Maunakea, and we estimate the variability of 481 hydroxyl (OH) lines. These spectra were collected during two sets of three nights dedicated to obtaining sky observations every five and a half minutes. During the first set, we observed how the Moon affects the NIR, which has not been accurately measured at these wavelengths. We suggest accounting for the Moon contribution at separation distances less than 10 degrees when 1) reconstructing the sky using principal component analysis 2) observing targets at Y JHK mags fainter than ~15 and 3) attempting a sky subtraction better than 1%. We also identified 126 spectral doublets, or OH lines that split into at least two components, at SPIRou's resolution. In addition, we used Lomb-Scargle Periodograms and Gaussian process regression to estimate that most OH lines vary on similar timescales, which provides a valuable input for IR spectroscopic survey strategies. The data and code developed for this study are publicly available.
The Status of Neutrino Cosmology: Standard $Λ$CDM, Extensions, and Tensions
2024-12-06
Helena García Escudero, Kevork N. Abazajian
We examine the performance of the six-parameter $\Lambda$CDM model and its extensions in light of recent cosmological observations, with particular focus on neutrino properties inferred from cosmology. Using a broad suite of nine combinations of datasets, with three separate analyses of the Planck Cosmic Microwave Background (CMB) data, and three separate supernovae (SNe) survey data, plus the recent DESI baryon acoustic oscillation (BAO) scale results, we derive constraints on the sum of neutrino masses ($\Sigma m_\nu$). Our results show upper limits in the range of $\Sigma m_\nu < 76.9\,\mathrm{meV}$ to $\Sigma m_\nu < 108\,\mathrm{meV}$ (95\% CL). The variation in the limits on $\Sigma m_\nu$ arises from the separate analyses of the Planck CMB data and the separate supernovae datasets, as they relate to the inferred matter density and its relation to the sensitivity of the BAO scale and CMB lensing to $\Sigma m_\nu$. In the context of hierarchical mass models in $\Lambda$CDM, we find a $1.47\sigma$ preference for normal ordering (NO) over inverted ordering (IO), with similar values of preference across all datasets. Despite the strong constraints, an inclination towards massless neutrinos over NO remains weak at $1.36\sigma$. We find that a ``negative'' neutrino mass, inferred from the shape of the likelihood in the physical regime, $\Sigma m_\nu > 0$, is only present at less than $2\sigma$. We confirm that models allowing extra relativistic degrees of freedom, with $N_{\rm eff} \approx 3.5$, alleviate the Hubble tension. Significantly, we find a $3.3\sigma$ preference for a 0.1 eV partially thermalized sterile neutrino when the SH0ES $H_0$ measurement is included, a scale of interest in short-baseline oscillation experiment results. [abridged]
AdS/CFT Duality and Holographic Renormalization Group: A Review
2024-12-06
Han Huang
In this paper we review aspects of anti de Sitter/conformal field theory (AdS/CFT) duality and the notion of holographic renormalization group (RG) flow. We start by discussing supersymmetry and construct the N = 4 super Yang-Mills theory in d = 4 by Kaluza-Klein dimensional reduction method. Then, we study the large-N limit and how it leads to the AdS/CFT duality. Using AdS/CFT, we then study the super-gravitational dual flows to the RG flows in CFT generated by marginal and relevant deformations, using the N = 4 super Yang-Mills theory as an example. Then, we prove the Zamolodchikov C-theorem holographically. Finally, we discuss the Wilsonian holographic renormalization.
From Voice to Value: Leveraging AI to Enhance Spoken Online Reviews on the Go
2024-12-06
Kavindu Ravishan, Dániel Szabó, Niels van Berkel, Aku Visuri, Chi-Lan Yang, Koji Yatani, Simo Hosio
Online reviews help people make better decisions. Review platforms usually depend on typed input, where leaving a good review requires significant effort because users must carefully organize and articulate their thoughts. This may discourage users from leaving comprehensive and high-quality reviews, especially when they are on the go. To address this challenge, we developed Vocalizer, a mobile application that enables users to provide reviews through voice input, with enhancements from a large language model (LLM). In a longitudinal study, we analysed user interactions with the app, focusing on AI-driven features that help refine and improve reviews. Our findings show that users frequently utilized the AI agent to add more detailed information to their reviews. We also show how interactive AI features can improve users self-efficacy and willingness to share reviews online. Finally, we discuss the opportunities and challenges of integrating AI assistance into review-writing systems.
In connection with recent discoveries of heavy-quark containing exotic states publications discussing $Qq$ diquarks ($Q,q$ stand for a heavy and light quarks, respectively) proliferated in the literature. After a brief summary of the diquark concept I review various general reasons why the $Qq$ diquark (with sufficinetly heavy $Q$) does not exist. Then I argue (this is the focus of my talk) that the most direct way to confirm non-existence of the $Qq$ diquarks is the study of pre-asymptotic corrections in the inclusive decays of $Qqq$ baryons, e.g. $\Lambda_b$. Since the $c$ quarks are much lighter than $b$, namely, $m_b^2/m_c^2\sim 11$, traces of the $cq$ attraction in the color anti-triplet spin-0 state may or may not be present in the $cqq$ baryons.
The Compton-thick AGN luminosity function in the local Universe: A robust estimate combining BAT detections and NuSTAR spectra
2024-12-06
I. Georgantopoulos, E. Pouliasis, A. Ruiz, A. Akylas
The Compton-thick Active Galactic Nuclei (AGN) arguably constitute the most elusive class of sources as they are absorbed by large column densities above logN_H(cm^-2)=24. These extreme absorptions hamper the detection of the central source even in hard X-ray energies. In this work, we use both SWIFT and NuSTAR observations in order to derive the most accurate yet Compton-thick AGN luminosity function. We, first, compile a sample of candidate Compton-thick AGN (logN_H(cm^-2)= 24-25) detected in the Swift BAT all-sky survey in the 14-195 keV band. We confirm that they are Compton-thick sources by using the follow-up NuSTAR observations already presented in the literature. Our sample is composed of 44 sources, consistent with a column density of logN_H(cm^-2)=24-25 at the 90% confidence level. These have intrinsic luminosities higher than L(10-50 keV) ~ 3x10^41 erg/s and are found up to a redshift of z=0.05 (200 Mpc). We derive the luminosity function of Compton-thick AGN using a Bayesian methodology where both the full column density and the luminosity distributions are taken into account. The faint end of the luminosity function is flat, having a slope of 0.01(+0.51,-0.74), rather arguing against a numerous population of low luminosity Compton-thick AGN. Based on our luminosity function, we estimate that the fraction of Compton-thick AGN relative to the total number of AGN is of the order of 24 (+5,-5) % in agreement with previous estimates in the local Universe based on BAT samples.
Detectability prospects for long-lived gamma-ray emission from r-process events
2024-12-06
Benjamin Amend, Christopher L. Fryer, Matthew R. Mumpower, Oleg Korobkin
Detecting gamma-ray emission from radioactive decay in r-process-enriched kilonova and supernova remnants offers a direct method for probing heavy element synthesis in the Milky Way. We assess the feasibility of such detections through an all-sky survey using mock instruments similar to the COmpton Spectrometer and Imager (COSI) and targeted observations with sensitive, low field-of-view instruments similar to the High Energy X-ray Probe (HEX-P). By modeling the spatial distribution of potential remnants and generating synthetic time-evolving gamma-ray spectra, we compare predicted fluxes to the sensitivity limits of each instrument. Our findings suggest that the likelihood of detecting kilonova remnants with COSI-like instruments over a 24-month observing cycle is extremely low (~1%), highlighting the need for instruments with at least ten times greater sensitivity to make such detections more probable. The superior sensitivity of a HEX-P-like instrument offers higher chances of detection, provided suitable targets are identified. We propose methods for optimizing target selection and outline the observational advancements needed to improve detectability prospects. Both detections and non-detections carry important implications for our understanding of galactic r-process nucleosynthesis, influencing future observational strategies.
Effect of electric field on excitons in wide quantum wells
2024-12-06
Shiming Zheng, E. S. Khramtsov, I. V. Ignatiev
A microscopic model of a heterostructure with a quantum well (QW) is proposed to study the exciton behavior in an external electric field. The effect of an electric field ranging from 0 to 6 kV/cm applied to the GaAs/AlGaAs QW structure in the growth direction is studied for several QWs of various widths up to 100 nm. The three-dimensional Schr\"odinger equation (SE) of exciton is numerically solved using the finite difference method. Wave functions and energies for several states of the heavy-hole and light-hole excitons are calculated. Dependencies of the exciton state energy, the binding energy, the radiative broadening, and the static dipole moment on the applied electric fields are determined. The threshold of exciton dissociation for the 100-nm QW is also determined. In addition, we found the electric-field-induced shift of the center of mass of the heavy-hole and light-hole exciton in the QWs. Finally, we have modeled reflection spectra of heterostructures with the GaAs/AlGaAs QWs in the electric field using the calculated energies and radiative broadenings of excitons.
Newly discovered magnetic phase: A brief review on Altermagnets
2024-12-06
R. Tamang, Shivraj Gurung, D. P. Rai, Samy Brahimi, Samir Lounis
Recently, a new magnetic phase, termed altermagnetism, has caught the attention of the magnetism and spintronics community. This newly discovered magnetic phenomenon differs from traditional ferromagnetism and antiferromagnetic. It generally lacks net magnetization and is characterized by unusual non-relativistic spin-splitting and broken time-reversal symmetry. This leads to novel transport properties such as the anomalous Hall effect, the crystal Nernst effect, and spin-dependent phenomena that cannot be fully explained by traditional magnetic theories. Spin-dependent phenomena such as spin currents, spin-splitter torques, and high-frequency dynamics emerge as key characteristics in altermagnets. This paper reviews the main aspects pertaining to altermagnets by providing an overview of theoretical investigations and experimental realizations. We discuss the most recent developments in altermagnetism, its comparison to other magnetic orders, and future prospects for exploiting its unique properties in next-generation devices.
Physical Origins of Outflowing Cold Clouds in Local Star-forming Dwarf Galaxies
2024-12-06
Zixuan Peng, Crystal L. Martin, Zirui Chen, Drummond B. Fielding, Xinfeng Xu, Timothy Heckman, Lise Ramambason, Yuan Li, Cody Carr, Weida Hu, Zuyi Chen, Claudia Scarlata, Alaina Henry
We study the physical origins of outflowing cold clouds in a sample of 14 low-redshift dwarf ($M_{\ast} \lesssim 10^{10}$ $M_{\odot}$) galaxies from the COS Legacy Archive Spectroscopic SurveY (CLASSY) using Keck/ESI data. Outflows are traced by broad (FWHM ~ 260 $\rm{km}$ $\rm{s^{-1}}$) and very-broad (VB; FWHM ~ 1200 $\rm{km}$ $\rm{s^{-1}}$) velocity components in strong emission lines like [O III] $\lambda 5007$ and $\rm{H}\alpha$. The maximum velocities ($v_{\rm{max}}$) of broad components correlate positively with SFR, unlike the anti-correlation observed for VB components, and are consistent with superbubble models. In contrast, supernova-driven galactic wind models better reproduce the $v_{\rm{max}}$ of VB components. Direct radiative cooling from a hot wind significantly underestimates the luminosities of both broad and VB components. A multi-phase wind model with turbulent radiative mixing reduces this discrepancy to at least one dex for most VB components. Stellar photoionization likely provides additional energy since broad components lie in the starburst locus of excitation diagnostic diagrams. We propose a novel interpretation of outflow origins in star-forming dwarf galaxies$-$broad components trace expanding superbubble shells, while VB components originate from galactic winds. One-zone photoionization models fail to explain the low-ionization lines ([S II] and [O I]) of broad components near the maximal starburst regime, which two-zone photoionization models with density-bounded channels instead reproduce. These two-zone models indicate anisotropic leakage of Lyman continuum photons through low-density channels formed by expanding superbubbles. Our study highlights extreme outflows ($v_{\rm{max}} \gtrsim 1000$ $\rm{km}$ $\rm{s^{-1}}$) in 9 out of 14 star-forming dwarf galaxies, comparable to AGN-driven winds.
2024-12-06
Harry Addison, Chris Frohmaier, Kate Maguire, Robert C. Nichol, Isobel Hook, Stephen J. Smartt
Early-time spectroscopy of supernovae (SNe), acquired within days of explosion, yields crucial insights into their outermost ejecta layers, facilitating the study of their environments, progenitor systems, and explosion mechanisms. Recent efforts in early discovery and follow-up of SNe have shown the potential insights that can be gained from early-time spectra. The Time-Domain Extragalactic Survey (TiDES), conducted with the 4-meter Multi-Object Spectroscopic Telescope (4MOST), will provide spectroscopic follow-up of transients discovered by the Legacy Survey of Space and Time (LSST). Current simulations indicate that early-time spectroscopic studies conducted with TiDES data will be limited by the current SN selection criteria. To enhance TiDES's capability for early-time SN spectroscopic studies, we propose an additional set of selection criteria focusing on early-time (young) SNe (YSNe). Utilising the Zwicky Transient Facility live transient alerts, we developed criteria to select YSNe while minimising the sample's contamination rate to 28 percent. The developed criteria were applied to LSST simulations, yielding a sample of 1167 Deep Drilling Field survey SNe and 67388 Wide Fast Deep survey SNe for follow-up with 4MOST. We demonstrate that our criteria enables the selection of SNe at early-times, enhancing TiDES's future early-time spectroscopic SN studies. Finally, we investigated 4MOST-like observing strategies to increase the sample of spectroscopically observed YSNe. We propose that a 4MOST-like observing strategy that follows LSST with a delay of 3 days is optimal for the TiDES SN survey, while a 1 day delay is most optimal for enhancing the early-time science in conjunction with our YSN selection criteria.
2024-12-06
The VEXAG Exploration Strategy Study Analysis Workgroup
The 2023-2032 Planetary Science and Astrobiology Decadal Survey Origins, Worlds, and Life recommended that "NASA develop scientific exploration strategies, as it has for Mars, in areas of broad scientific importance, e.g., Venus... that have an increasing number of U.S. missions and international collaboration opportunities" (OWL, p.22-10). In NASA's initial responses to that Decadal Survey, the agency asserted that "...specific scientific exploration strategies should be community generated by bodies such as the Analysis Groups," thus placing the onus on the planetary community to generate and support these exploration strategies. In late 2022, the Venus Exploration Analysis Group began a project to develop a new exploration strategy for Venus, reflecting the 2021 selections of the VERITAS, DAVINCI, and EnVision missions and the sweeping comparative planetology recommendations relevant to Venus in Origins, Worlds, and Life. This is that strategy. Taking a broad look at the scientific, technological, and programmatic advances required to address the key outstanding questions that Venus poses, and predicated on VERITAS, DAVINCI, and EnVision flying as planned in the early 2030s, this report outlines a set of actions available to NASA, VEXAG, and the planetary science community at large to establish a sustained program of Venus exploration in the years and decades ahead. Key to this approach is recognizing Venus as a unique setting where multiple, cross-disciplinary, Decadal-level planetary, Earth, heliophysics, and exoplanet science questions can be addressed, as well as being a worthy target of exploration in its own right. This report offers Assessments of the current state of Venus exploration, and Actions for the U.S. and international Venus community, as well as NASA, to consider. This strategy is a living document and should be updated as warranted.
Enhancing LLMs for Impression Generation in Radiology Reports through a Multi-Agent System
2024-12-06
Fang Zeng, Zhiliang Lyu, Quanzheng Li, Xiang Li
This study introduces "RadCouncil," a multi-agent Large Language Model (LLM) framework designed to enhance the generation of impressions in radiology reports from the finding section. RadCouncil comprises three specialized agents: 1) a "Retrieval" Agent that identifies and retrieves similar reports from a vector database, 2) a "Radiologist" Agent that generates impressions based on the finding section of the given report plus the exemplar reports retrieved by the Retrieval Agent, and 3) a "Reviewer" Agent that evaluates the generated impressions and provides feedback. The performance of RadCouncil was evaluated using both quantitative metrics (BLEU, ROUGE, BERTScore) and qualitative criteria assessed by GPT-4, using chest X-ray as a case study. Experiment results show improvements in RadCouncil over the single-agent approach across multiple dimensions, including diagnostic accuracy, stylistic concordance, and clarity. This study highlights the potential of utilizing multiple interacting LLM agents, each with a dedicated task, to enhance performance in specialized medical tasks and the development of more robust and adaptable healthcare AI solutions.
Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum
2024-12-05
Tristan Hoellinger, Florent Leclercq
The next generation of galaxy surveys has the potential to substantially deepen our understanding of the Universe. This potential hinges on our ability to rigorously address systematic uncertainties. Until now, diagnosing systematic effects prior to inferring cosmological parameters has been out of reach in field-based implicit likelihood cosmological inference frameworks. As a solution, we aim to diagnose a variety of systematic effects in galaxy surveys prior to inferring cosmological parameters, using the inferred initial matter power spectrum. Our approach is built upon a two-step framework. First, we employ the Simulator Expansion for Likelihood-Free Inference (SELFI) algorithm to infer the initial matter power spectrum, which we utilise to thoroughly investigate the impact of systematic effects. This investigation relies on a single set of N-body simulations. Second, we obtain a posterior on cosmological parameters via implicit likelihood inference, recycling the simulations from the first step for data compression. For demonstration, we rely on a model of large-scale spectroscopic galaxy surveys that incorporates fully non-linear gravitational evolution and simulates multiple systematic effects encountered in real surveys. We provide a practical guide on how the SELFI posterior can be used to assess the impact of misspecified galaxy bias parameters, selection functions, survey masks, inaccurate redshifts, and approximate gravity models on the inferred initial matter power spectrum. We show that a subtly misspecified model can lead to a bias exceeding $2\sigma$ in the $(\Omega_\mathrm{m},\sigma_8)$ plane, which we are able to detect and avoid prior to inferring the cosmological parameters. This framework has the potential to significantly enhance the robustness of physical information extraction from full-forward models of large-scale galaxy surveys such as DESI, Euclid, and LSST.
Efficient Task Grouping Through Samplewise Optimisation Landscape Analysis
2024-12-05
Anshul Thakur, Yichen Huang, Soheila Molaei, Yujiang Wang, David A. Clifton
Shared training approaches, such as multi-task learning (MTL) and gradient-based meta-learning, are widely used in various machine learning applications, but they often suffer from negative transfer, leading to performance degradation in specific tasks. While several optimisation techniques have been developed to mitigate this issue for pre-selected task cohorts, identifying optimal task combinations for joint learning - known as task grouping - remains underexplored and computationally challenging due to the exponential growth in task combinations and the need for extensive training and evaluation cycles. This paper introduces an efficient task grouping framework designed to reduce these overwhelming computational demands of the existing methods. The proposed framework infers pairwise task similarities through a sample-wise optimisation landscape analysis, eliminating the need for the shared model training required to infer task similarities in existing methods. With task similarities acquired, a graph-based clustering algorithm is employed to pinpoint near-optimal task groups, providing an approximate yet efficient and effective solution to the originally NP-hard problem. Empirical assessments conducted on 8 different datasets highlight the effectiveness of the proposed framework, revealing a five-fold speed enhancement compared to previous state-of-the-art methods. Moreover, the framework consistently demonstrates comparable performance, confirming its remarkable efficiency and effectiveness in task grouping.
We conducted a review of the fundamental aspects of describing and detecting the Baryon Acoustic Oscillation (BAO) feature in galaxy surveys, emphasizing the optimal tools for constraining this probe based on the type of observation. Additionally, we included new results with two spectroscopic datasets to determine the best-fit model for the power spectrum, $P(k)$. Using the framework described in a previous analysis, we applied this to a different sub-sample of the BOSS survey, specifically galaxies with redshifts $0.3
Gruenhage spaces and their influence on Banach space renorming theory
2024-12-05
Richard J. Smith
In a paper from 1987, Gruenhage defined a class of topological spaces that now bear his name, and used it to solve a problem of Talagrand on the existence of dense $G_\delta$ metrizable subsets of Gul'ko compact spaces. Gruenhage's paper became highly influential among researchers in renorming theory, a branch of Banach space theory. In this paper we survey Gruenhage and related spaces, and their interactions with renorming theory.
Optical constraints on the coldest metal-poor population
2024-12-05
Jerry Jun-Yan Zhang, Nicolas Lodieu, Eduardo L. Martín, María Rosa Zapatero Osorio, Victor J. S. Béjar, Valentin D. Ivanov, Henri M. J. Boffin, Tariq Shahbaz, Yakiv V. Pavlenko, Rafael Rebolo, Bartosz Gauza, Nafise Sedighi, Carlos Quezada
The coldest metal-poor population made of T and Y dwarfs are archaeological tracers of our Galaxy because they are very old and have kept the pristine material. The optical properties of these objects are important to characterise their atmospheric properties. We aim at characterising further the optical properties of ultracool metal-poor population with deep far-red optical images and parallax determinations. We solve trigonometric parallaxes of five metal-poor T dwarf candidates using 2-year monitoring with Calar-Alto 3.5-m telescope. We obtain $z'$-band photometry for the other 12 metal-poor T dwarf candidates using the 10.4-m GTC, the 8.2-m VLT, and the DES, increasing the sample of T subdwarfs with optical photometry from 12 to 24. We report a 3-$\sigma$ limit for the Accident in five optical bands using the 10.4-m GTC. We confirm four T subdwarfs and the Accident as a Y subdwarf, and propose two more Y subdwarf candidates. We emphasise that the $z_{PS1}-W1$ colour combining with the $W1-W2$ colour could break the metallicity-temperature degeneracy for T and possibly for Y dwarfs. The $z_{PS1}-W1$ colour shifts redward when metallicity decreases for a certain temperature, which is not predicted by state-of-the-art ultracool models. The Accident has the reddest $z_{PS1}-W1$ colour among our sample. The $z_{PS1}-W1$ colour will be useful to search for other examples of this cold and old population in upcoming and existing deep optical and infrared large-area surveys.
Search for heavy neutral resonances decaying to tau lepton pairs in proton-proton collisions at $\sqrt{s}$ = 13 TeV
2024-12-05
CMS Collaboration
A search for heavy neutral gauge bosons (Z') decaying into a pair of tau leptons is performed in proton-proton collisions at $\sqrt{s}$ = 13 TeV at the CERN LHC. The data were collected with the CMS detector and correspond to an integrated luminosity of 138 fb$^{-1}$. The observations are found to be in agreement with the expectation from standard model processes. Limits at 95% confidence level are set on the product of the Z' production cross section and its branching fraction to tau lepton pairs for a range of Z' boson masses. For a narrow resonance in the sequential standard model scenario, a Z' boson with a mass below 3.5 TeV is excluded. This is the most stringent limit to date from this type of search.
The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation
2024-12-05
Fredrik Carlsson, Fangyu Liu, Daniel Ward, Murathan Kurfali, Joakim Nivre
This paper introduces the counter-intuitive generalization results of overfitting pre-trained large language models (LLMs) on very small datasets. In the setting of open-ended text generation, it is well-documented that LLMs tend to generate repetitive and dull sequences, a phenomenon that is especially apparent when generating using greedy decoding. This issue persists even with state-of-the-art LLMs containing billions of parameters, trained via next-token prediction on large datasets. We find that by further fine-tuning these models to achieve a near-zero training loss on a small set of samples -- a process we refer to as hyperfitting -- the long-sequence generative capabilities are greatly enhanced. Greedy decoding with these Hyperfitted models even outperform Top-P sampling over long-sequences, both in terms of diversity and human preferences. This phenomenon extends to LLMs of various sizes, different domains, and even autoregressive image generation. We further find this phenomena to be distinctly different from that of Grokking and double descent. Surprisingly, our experiments indicate that hyperfitted models rarely fall into repeating sequences they were trained on, and even explicitly blocking these sequences results in high-quality output. All hyperfitted models produce extremely low-entropy predictions, often allocating nearly all probability to a single token.
Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems
2024-12-05
Yuwei Cao, Liangwei Yang, Zhiwei Liu, Yuqing Liu, Chen Wang, Yueqing Liang, Hao Peng, Philip S. Yu
Graph-based and sequential methods are two popular recommendation paradigms, each excelling in its domain but lacking the ability to leverage signals from the other. To address this, we propose a novel method that integrates both approaches for enhanced performance. Our framework uses Graph Neural Network (GNN)-based and sequential recommenders as separate submodules while sharing a unified embedding space optimized jointly. To enable positive knowledge transfer, we design a loss function that enforces alignment and uniformity both within and across submodules. Experiments on three real-world datasets demonstrate that the proposed method significantly outperforms using either approach alone and achieves state-of-the-art results. Our implementations are publicly available at https://github.com/YuweiCao-UIC/GSAU.git.
Scoping review of methodology for aiding generalisability and transportability of clinical prediction models
2024-12-05
Kritchavat Ploddi, Matthew Sperrin, Glen P. Martin, Maurice M. O'Connell
Generalisability and transportability of clinical prediction models (CPMs) refer to their ability to maintain predictive performance when applied to new populations. While CPMs may show good generalisability or transportability to a specific new population, it is rare for a CPM to be developed using methods that prioritise good generalisability or transportability. There is an emerging literature of such techniques; therefore, this scoping review aims to summarise the main methodological approaches, assumptions, advantages, disadvantages and future development of methodology aiding the generalisability/transportability. Relevant articles were systematically searched from MEDLINE, Embase, medRxiv, arxiv databases until September 2023 using a predefined set of search terms. Extracted information included methodology description, assumptions, applied examples, advantages and disadvantages. The searches found 1,761 articles; 172 were retained for full text screening; 18 were finally included. We categorised the methodologies according to whether they were data-driven or knowledge-driven, and whether are specifically tailored for target population. Data-driven approaches range from data augmentation to ensemble methods and density ratio weighting, while knowledge-driven strategies rely on causal methodology. Future research could focus on comparison of such methodologies on simulated and real datasets to identify their strengths specific applicability, as well as synthesising these approaches for enhancing their practical usefulness.
BEACON: JWST NIRCam Pure-parallel Imaging Survey. I. Survey Design and Initial Results
2024-12-05
Takahiro Morishita, Charlotte A. Mason, Kimi C. Kreilgaard, Michele Trenti, Tommaso Treu, Benedetta Vulcani, Yechi Zhang, Abdurro'uf, Anahita Alavi, Hakim Atek, Yannick Bahe, Marusa Bradac, Larry D. Bradley, Andrew J. Bunker, Dan Coe, James Colbert, Viola Gelli, Matthew J. Hayes, Tucker Jones, Tadayuki Kodama, Nicha Leethochawalit, Zhaoran Liu, Matthew A. Malkan, Vihang Mehta, Benjamin Metha, Andrew B. Newman, Marc Rafelski, Guido Roberts-Borsani, Michael J. Rutkowski, Claudia Scarlata, Massimo Stiavelli, Ryo A. Sutanto, Kosuke Takahashi, Harry I. Teplitz, Xin Wang
We introduce the Bias-free Extragalactic Analysis for Cosmic Origins with NIRCam (BEACON) survey, a JWST Cycle2 program allocated up to 600 pure-parallel hours of observations. BEACON explores high-latitude areas of the sky with JWST/NIRCam over $\sim100$ independent sightlines, totaling $\sim0.3$deg$^2$, reaching a median F444W depth of $\approx28.2$AB mag (5$\sigma$). Based on existing JWST observations in legacy fields, we estimate that BEACON will photometrically identify 25--150 galaxies at $z>10$ and 500--1000 at $z\sim7$--10 uniquely enabled by an efficient multiple filter configuration spanning $0.9$--5.0$\mu$m. The expected sample size of $z>10$ galaxies will allow us to obtain robust number density estimates and to discriminate between different models of early star formation. In this paper, we present an overview of the survey design and initial results using the first 19 fields. We present 129 galaxy candidates at $z>7$ identified in those fields, including 11 galaxies at $z>10$ and several UV-luminous ($M_{\rm UV}<-21$mag) galaxies at $z\sim8$. The number densities of $z<13$ galaxies inferred from the initial fields are overall consistent with those in the literature. Despite reaching a considerably large volume ($\sim10^5$Mpc$^3$), however, we find no galaxy candidates at $z>13$, providing us with a complimentary insight into early galaxy evolution with minimal cosmic variance. We publish imaging and catalog data products for these initial fields. Upon survey completion, all BEACON data will be coherently processed and distributed to the community along with catalogs for redshift and other physical quantities.
Predictive Strategies for the Control of Complex Motor Skills: Recent Insights into Individual and Joint Actions
2024-12-05
Marta Russo, Antonella Maselli, Dagmar Sternad, Giovanni Pezzulo
Humans can perform exquisite sensorimotor skills, both individually and in teams, from athletes performing rhythmic gymnastics to everyday tasks like carrying a cup of coffee. The "predictive brain" framework suggests that mastering these tasks relies on predictive mechanisms, raising the question of how we deploy such predictions for real-time control and coordination. This review highlights two lines of research: one showing that during the control of complex objects people make the interaction with 'tools' predictable; the second one examines dyadic coordination showing that people make their behavior predictable for their partners. These studies demonstrate that to achieve sophisticated motor skills, we play "prediction tricks": we select subspaces of predictable solutions and make sensorimotor interactions more predictable and legible by and for others. This synthesis underscores the critical role of predictability in optimizing control strategies across various contexts and establishes a link between predictive processing and closed-loop control theories of behavior.
Multi-Layer Privacy-Preserving Record Linkage with Clerical Review based on gradual information disclosure
2024-12-05
Florens Rohde, Victor Christen, Martin Franke, Erhard Rahm
Privacy-Preserving Record linkage (PPRL) is an essential component in data integration tasks of sensitive information. The linkage quality determines the usability of combined datasets and (machine learning) applications based on them. We present a novel privacy-preserving protocol that integrates clerical review in PPRL using a multi-layer active learning process. Uncertain match candidates are reviewed on several layers by human and non-human oracles to reduce the amount of disclosed information per record and in total. Predictions are propagated back to update previous layers, resulting in an improved linkage performance for non-reviewed candidates as well. The data owners remain in control of the amount of information they share for each record. Therefore, our approach follows need-to-know and data sovereignty principles. The experimental evaluation on real-world datasets shows considerable linkage quality improvements with limited labeling effort and privacy risks.
DHOST theories as disformal gravity: From black holes to radiative spacetimes
2024-12-05
Jibril Ben Achour
This manuscript reviews the construction of exact solutions describing both (rotating) black holes and non-linear radiative spacetimes in the context of degenerate higher order scalar-tensor (DHOST) theories. We start be reviewing the structure of the DHOST theory space, the notion of degeneracy conditions and the stability of these degeneracy classes under disformal field redefinition (DFR). Then we discuss several key notions related to stationary and axi-symmetric black holes, and in particular the no-hair theorems derived in GR and in its scalar-tensor extensions. The rest of the chapters are devoted to the disformal solution generating map use to construct new hairy black holes solutions. A brief review of the rotating black holes solutions found so far in this context and a detailed description of the disformed Kerr black hole, we further comment on on-going efforts to construct rotating black hole solutions mimicking the closest the Kerr geometry. Then, we discuss how DFR affects the algebraic properties of a gravitational field and in particular its Petrov type. This provides a first systematic characterization of this effect, paving the road for constructing new solutions with a fixed Petrov type. Finally, we review more recent works aiming at characterizing the effect of a DFR on non-linear radiative geometries. We derive the general conditions for the generation of disformal tensorial gravitational wave and we study in detail a concrete example in DHOST gravity describing the new radiative effect induced by a time-dependent scalar monopoly in such higher-order theory. While most of the material presented here is a re-organized and augmented version of our published works, we have included new results and also new proposals to construct phenomenologically interesting solutions. This manuscript can be used as a review on the construction of exact solutions in DHOST gravity.
Recent advances in generative AI have sparked renewed interest and expanded possibilities for music generation. However, the performance and versatility of these systems across musical genres are heavily influenced by the availability of training data. We conducted an extensive analysis of over one million hours of audio datasets used in AI music generation research and manually reviewed more than 200 papers from eleven prominent AI and music conferences and organizations (AAAI, ACM, EUSIPCO, EURASIP, ICASSP, ICML, IJCAI, ISMIR, NeurIPS, NIME, SMC) to identify a critical gap in the fair representation and inclusion of the musical genres of the Global South in AI research. Our findings reveal a stark imbalance: approximately 86% of the total dataset hours and over 93% of researchers focus primarily on music from the Global North. However, around 40% of these datasets include some form of non-Western music, genres from the Global South account for only 14.6% of the data. Furthermore, approximately 51% of the papers surveyed concentrate on symbolic music generation, a method that often fails to capture the cultural nuances inherent in music from regions such as South Asia, the Middle East, and Africa. As AI increasingly shapes the creation and dissemination of music, the significant underrepresentation of music genres in datasets and research presents a serious threat to global musical diversity. We also propose some important steps to mitigate these risks and foster a more inclusive future for AI-driven music generation.
Towards a multi-tracer neutrino mass measurement with line-intensity mapping
2024-12-05
Gali Shmueli, Sarah Libanore, Ely D. Kovetz
Accurately determining neutrino masses is a main objective of contemporary cosmology. Since massive neutrinos affect structure formation and evolution, probes of large scale structure are sensitive to the sum of their masses. In this work, we explore future constraints on $\sum m_\nu$ utilizing line-intensity mapping (LIM) as a promising emerging probe of the density of our Universe, focusing on the fine-structure [CII] line as an example, and compare these constraints with those derived from traditional galaxy surveys. Additionally, we perform a multi-tracer analysis using velocity tomography via the kinetic Sunyaev-Zeldovich and moving lens effects to reconstruct the three-dimensional velocity field. Our forecasts indicate that the next-generation AtLAST detector by itself can achieve $\sigma_{\Sigma m_\nu} \sim 50$ meV sensitivity. Velocity tomography will further improve these constraints by 4%. Incorporating forecasts for CMB-S4 and DESI-BAO in a comprehensive multi-tracer analysis, while setting a prior on the optical depth to reionization $\tau$ derived using 21-cm forecasted observations, to break degeneracies, we find that a $\gtrsim5\sigma$ detection of $\sum m_\nu\!\sim\! 60$ meV, under the normal hierarchy, is within reach with LIM. Even without a $\tau$ prior, our combined forecast reaches $\sigma_{\Sigma m_\nu} \!\sim\! 18$ meV.
Air pollution kills 7 million people annually. The brick kiln sector significantly contributes to economic development but also accounts for 8-14\% of air pollution in India. Policymakers have implemented compliance measures to regulate brick kilns. Emission inventories are critical for air quality modeling and source apportionment studies. However, the largely unorganized nature of the brick kiln sector necessitates labor-intensive survey efforts for monitoring. Recent efforts by air quality researchers have relied on manual annotation of brick kilns using satellite imagery to build emission inventories, but this approach lacks scalability. Machine-learning-based object detection methods have shown promise for detecting brick kilns; however, previous studies often rely on costly high-resolution imagery and fail to integrate with governmental policies. In this work, we developed a scalable machine-learning pipeline that detected and classified 30638 brick kilns across five states in the Indo-Gangetic Plain using free, moderate-resolution satellite imagery from Planet Labs. Our detections have a high correlation with on-ground surveys. We performed automated compliance analysis based on government policies. In the Delhi airshed, stricter policy enforcement has led to the adoption of efficient brick kiln technologies. This study highlights the need for inclusive policies that balance environmental sustainability with the livelihoods of workers.
The physical and chemical structure of Sagittarius B2, VIII. Full molecular line survey of hot cores
2024-12-05
T. Möller, P. Schilke, Á. Sánchez-Monge, A. Schmiedeke
The giant molecular cloud complex Sagittarius B2 (Sgr~B2) in the central molecular zone of our Galaxy hosts several high-mass star formation sites, with Sgr~B2(M) and Sgr~B2(N) being the main centers of activity. This analysis aims to comprehensively model each core spectrum, considering molecular lines, dust attenuation, and free-free emission interactions. We describe the molecular content analysis of each hot core and identify the chemical composition of detected sources. Using ALMA's high sensitivity, we aim to characterize the hot core population in Sgr~B2(M) and N, gaining a better understanding of the different evolutionary phases of star formation processes in this complex. We conducted an unbiased ALMA spectral line survey of 47 sources in band 6 (211-275 GHz). Chemical composition and column densities were derived using XCLASS, assuming local thermodynamic equilibrium. Quantitative descriptions for each molecule were determined, considering all emission and absorption features across the spectral range. Temperature and velocity distributions were analyzed, and derived abundances were compared with other spectral line surveys. We identified 65 isotopologs from 48 different molecules, ranging from light molecules to complex organic compounds, originating from various environments. Most sources in the Sgr~B2 complex were assigned different evolutionary phases of high-mass star formation. Sgr~B2(N) hot cores show more complex molecules such as CH$_3$OH, CH$_3$OCHO, and CH$_3$OCH$_3$, while M cores contain lighter molecules such as SO$_2$, SO, and NO. Some sulfur-bearing molecules are more abundant in N than in M. The derived molecular abundances can be used for comparison and to constrain astrochemical models. Inner sources in both regions were generally more developed than outer sources, with some exceptions.
Quality Control in Open-Ended Crowdsourcing: A Survey
2024-12-05
Lei Chai, Hailong Sun, Jing Zhang
Crowdsourcing provides a flexible approach for leveraging human intelligence to solve large-scale problems, gaining widespread acceptance in domains like intelligent information processing, social decision-making, and crowd ideation. However, the uncertainty of participants significantly compromises the answer quality, sparking substantial research interest. Existing surveys predominantly concentrate on quality control in Boolean tasks, which are generally formulated as simple label classification, ranking, or numerical prediction. Ubiquitous open-ended tasks like question-answering, translation, and semantic segmentation have not been sufficiently discussed. These tasks usually have large to infinite answer spaces and non-unique acceptable answers, posing significant challenges for quality assurance. This survey focuses on quality control methods applicable to open-ended tasks in crowdsourcing. We propose a two-tiered framework to categorize related works. The first tier introduces a holistic view of the quality model, encompassing key aspects like task, worker, answer, and system. The second tier refines the classification into more detailed categories, including quality dimensions, evaluation metrics, and design decisions, providing insights into the internal structures of the quality control framework in each aspect. We thoroughly investigate how these quality control methods are implemented in state-of-the-art works and discuss key challenges and potential future research directions.
Kinematic distances of galaxies in the Local Volume
2024-12-05
I. D. Karachentsev, A. A. Popova
We consider the kinematic distances to nearby galaxies obtained by the Numerical Action Method (NAM) based on the Cosmic-flow-3 survey data. NAM distances are compared with 418 high-precision distances measured by the Tip of the Red Giant Branch (TRGB) method using the Hubble Space Telescope. We estimated the average difference = -0.30 +- 0.08 Mpc and the standard deviation of 1.57 Mpc. Approximately the same difference in the distance scale is obtained in comparison with less accurate distance estimates through the membership of galaxies in known groups or from the Tully-Fisher relation. We conclude that the NAM method provides distance estimates with an accuracy of 20% within the Local Volume, which is valid for ~90% of the sky, except for the regions of the Virgo cluster and the Coma-I group.
In the manufacturing industry, defect detection is an essential but challenging task aiming to detect defects generated in the process of production. Though traditional YOLO models presents a good performance in defect detection, they still have limitations in capturing high-order feature interrelationships, which hurdles defect detection in the complex scenarios and across the scales. To this end, we introduce hypergraph computation into YOLO framework, dubbed HyperDefect-YOLO (HD-YOLO), to improve representative ability and semantic exploitation. HD-YOLO consists of Defect Aware Module (DAM) and Mixed Graph Network (MGNet) in the backbone, which specialize for perception and extraction of defect features. To effectively aggregate multi-scale features, we propose HyperGraph Aggregation Network (HGANet) which combines hypergraph and attention mechanism to aggregate multi-scale features. Cross-Scale Fusion (CSF) is proposed to adaptively fuse and handle features instead of simple concatenation and convolution. Finally, we propose Semantic Aware Module (SAM) in the neck to enhance semantic exploitation for accurately localizing defects with different sizes in the disturbed background. HD-YOLO undergoes rigorous evaluation on public HRIPCB and NEU-DET datasets with significant improvements compared to state-of-the-art methods. We also evaluate HD-YOLO on self-built MINILED dataset collected in real industrial scenarios to demonstrate the effectiveness of the proposed method. The source codes are at https://github.com/Jay-zzcoder/HD-YOLO.
Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation
2024-12-05
Hao Zhu, Yan Zhu, Jiayu Xiao, Tianxiang Xiao, Yike Ma, Yucheng Zhang, Feng Dai
Automated crop mapping through Satellite Image Time Series (SITS) has emerged as a crucial avenue for agricultural monitoring and management. However, due to the low resolution and unclear parcel boundaries, annotating pixel-level masks is exceptionally complex and time-consuming in SITS. This paper embraces the weakly supervised paradigm (i.e., only image-level categories available) to liberate the crop mapping task from the exhaustive annotation burden. The unique characteristics of SITS give rise to several challenges in weakly supervised learning: (1) noise perturbation from spatially neighboring regions, and (2) erroneous semantic bias from anomalous temporal periods. To address the above difficulties, we propose a novel method, termed exploring space-time perceptive clues (Exact). First, we introduce a set of spatial clues to explicitly capture the representative patterns of different crops from the most class-relative regions. Besides, we leverage the temporal-to-class interaction of the model to emphasize the contributions of pivotal clips, thereby enhancing the model perception for crop regions. Build upon the space-time perceptive clues, we derive the clue-based CAMs to effectively supervise the SITS segmentation network. Our method demonstrates impressive performance on various SITS benchmarks. Remarkably, the segmentation network trained on Exact-generated masks achieves 95% of its fully supervised performance, showing the bright promise of weakly supervised paradigm in crop mapping scenario. Our code will be publicly available.
Our peculiar velocity imprints a dipole on galaxy density maps derived from redshift surveys. The dipole gives rise to an oscillatory signal in the multipole moments of the observed power spectrum which we indicate as the finger-of-the-observer (FOTO) effect. Using a suite of large mock catalogues mimicking ongoing and future $\textrm{H}\alpha$- and $\textrm{H}\scriptstyle\mathrm{I}$-selected surveys, we demonstrate that the oscillatory features can be measured with a signal-to-noise ratio of up to 7 (depending on the sky area coverage and provided that observational systematics are kept under control on large scales). We also show that the FOTO effect cannot be erased by correcting the individual galaxy redshifts. On the contrary, by leveraging the power of the redshift corrections, we propose a novel method to determine both the magnitude and the direction of our peculiar velocity. After applying this technique to our mock catalogues, we conclude that it can be used to either test the kinematic interpretation of the temperature dipole in the cosmic microwave background or to extract cosmological information such as the matter density parameter and the equation of state of dark energy.
Perspective on 2D perovskite ferroelectrics and multiferroics
2024-12-05
Junting Zhang, Yu Xie, Ke Ji, Xiaofan Shen
Two-dimensional (2D) ferroelectrics and multiferroics have attracted considerable scientific and technological interest in recent years due to the increasing demands for miniaturization and low energy consumption of electronic devices. At present, the research on 2D ferroelectrics and multiferroics is still focused on van der Waals materials, while the known bulk ferroelectric and multiferroic materials are mostly found in perovskite systems. The ability to prepare and transfer 2D perovskite oxides has provided unprecedented opportunities for developing ferroelectrics and multiferroics based on 2D perovskites. In this Perspective, we review the research progress on 2D ferroelectrics and multiferroics in inorganic perovskites in terms of different ferroelectric and magnetoelectric coupling mechanisms. The improper ferroelectricity and novel magnetoelectric coupling mechanisms discovered in 2D perovskites are emphasized, and then, the main challenges and future development direction are put forward.
Virtual models of human gait, or digital twins, offer a promising solution for studying mobility without the need for labor-intensive data collection. However, challenges such as the sim-to-real gap and limited adaptability to diverse walking conditions persist. To address these, we developed and validated a framework to create a skeletal humanoid agent capable of adapting to varying walking speeds while maintaining biomechanically realistic motions. The framework combines a synthetic data generator, which produces biomechanically plausible gait kinematics from open-source biomechanics data, and a training system that uses adversarial imitation learning to train the agent's walking policy. We conducted comprehensive analyses comparing the agent's kinematics, synthetic data, and the original biomechanics dataset. The agent achieved a root mean square error of 5.24 +- 0.09 degrees at varying speeds compared to ground-truth kinematics data, demonstrating its adaptability. This work represents a significant step toward developing a digital twin of human locomotion, with potential applications in biomechanics research, exoskeleton design, and rehabilitation.
The rapid development of Artificial Intelligence (AI) has led to the creation of powerful text generation models, such as large language models (LLMs), which are widely used for diverse applications. However, concerns surrounding AI-generated content, including issues of originality, bias, misinformation, and accountability, have become increasingly prominent. This paper offers a comprehensive overview of AI text generators (AITGs), focusing on their evolution, capabilities, and ethical implications. This paper also introduces Retrieval-Augmented Generation (RAG), a recent approach that improves the contextual relevance and accuracy of text generation by integrating dynamic information retrieval. RAG addresses key limitations of traditional models, including their reliance on static knowledge and potential inaccuracies in handling real-world data. Additionally, the paper reviews detection tools that help differentiate AI-generated text from human-written content and discusses the ethical challenges these technologies pose. The paper explores future directions for improving detection accuracy, supporting ethical AI development, and increasing accessibility. The paper contributes to a more responsible and reliable use of AI in content creation through these discussions.
Privacy-Preserving in Medical Image Analysis: A Review of Methods and Applications
2024-12-05
Yanming Zhu, Xuefei Yin, Alan Wee-Chung Liew, Hui Tian
With the rapid advancement of artificial intelligence and deep learning, medical image analysis has become a critical tool in modern healthcare, significantly improving diagnostic accuracy and efficiency. However, AI-based methods also raise serious privacy concerns, as medical images often contain highly sensitive patient information. This review offers a comprehensive overview of privacy-preserving techniques in medical image analysis, including encryption, differential privacy, homomorphic encryption, federated learning, and generative adversarial networks. We explore the application of these techniques across various medical image analysis tasks, such as diagnosis, pathology, and telemedicine. Notably, we organizes the review based on specific challenges and their corresponding solutions in different medical image analysis applications, so that technical applications are directly aligned with practical issues, addressing gaps in the current research landscape. Additionally, we discuss emerging trends, such as zero-knowledge proofs and secure multi-party computation, offering insights for future research. This review serves as a valuable resource for researchers and practitioners and can help advance privacy-preserving in medical image analysis.
A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios
2024-12-05
Xiachong Feng, Longxu Dou, Ella Li, Qinghao Wang, Haochuan Wang, Yu Guo, Chang Ma, Lingpeng Kong
Game-theoretic scenarios have become pivotal in evaluating the social intelligence of Large Language Model (LLM)-based social agents. While numerous studies have explored these agents in such settings, there is a lack of a comprehensive survey summarizing the current progress. To address this gap, we systematically review existing research on LLM-based social agents within game-theoretic scenarios. Our survey organizes the findings into three core components: Game Framework, Social Agent, and Evaluation Protocol. The game framework encompasses diverse game scenarios, ranging from choice-focusing to communication-focusing games. The social agent part explores agents' preferences, beliefs, and reasoning abilities. The evaluation protocol covers both game-agnostic and game-specific metrics for assessing agent performance. By reflecting on the current research and identifying future research directions, this survey provides insights to advance the development and evaluation of social agents in game-theoretic scenarios.
Truly SubNyquist Multicomponent Linear FM Signal Decomposition Method
2024-12-05
Huigaung Zhang
Accurate extraction of multicomponent linear frequency modulation (LFM) signal parameters, such as onset frequency, linear modulation frequency, amplitude, and initial phase, is of great importance in the fields of ISAR, cognitive radio, electronic countermeasures, and star-ground communications. However, the task of accurately extracting the characteristic parameters of a signal is challenging when it has an extraordinarily large bandwidth as well as cross or neighboring components in the time-frequency domain. In this paper, we first review the main current methods used for multicomponent LFM signal decomposition and their challenges, and then propose a novel multi-parameter feature parameter extraction algorithm. The algorithmrealizes the direct and accurate extraction of thefeature parameters of multicomponent LFM signals at ultra-low sub-Nyquist sampling rate for the first time. Moreover, the algorithm is optimized for the computational complexity and anti-noise problems in practical applications, so that it has high accuracy, high efficiency and good noise robustness. We also compare the algorithm with innovative and existing methods, and the results show that the algorithm has excellent performance in feature parameter extraction accuracy, noise immunity and computational speed.
Macroscopic arrow of time from multiscale perspectives
2024-12-05
Mahendra K. Verma
Fundamental laws of physics are symmetric under time reversal ($T$) symmetry, but the $T$ symmetry is strongly broken in the macroscopic world. In this Perspective, I review $T$ symmetry breaking frameworks: \textit{second law of thermodynamics, multiscale energy transfer}, and \textit{open systems}. In driven dissipative nonequilibrium systems, including turbulence, the multiscale energy flux from large scales to small scales helps determine the arrow of time. In addition, open systems are often irreversible due to particle and energy exchanges between the system and the environment. Causality is another important factor that breaks the $T$ symmetry.
2024-12-05
Fangyuan Luo, Honglei Zhang, Tong Li, Jun Wu
With the explosive growth of users and items, Recommender Systems (RS) are facing unprecedented challenges on both retrieval efficiency and storage cost. Fortunately, Learning to Hash (L2H) techniques have been shown as a promising solution to address the two dilemmas, whose core idea is encoding high-dimensional data into compact hash codes. To this end, L2H for RS (HashRec for short) has recently received widespread attention to support large-scale recommendations. In this survey, we present a comprehensive review of current HashRec algorithms. Specifically, we first introduce the commonly used two-tower models in the recall stage and identify two search strategies frequently employed in L2H. Then, we categorize prior works into two-tier taxonomy based on: (i) the type of loss function and (ii) the optimization strategy. We also introduce some commonly used evaluation metrics to measure the performance of HashRec algorithms. Finally, we shed light on the limitations of the current research and outline the future research directions. Furthermore, the summary of HashRec methods reviewed in this survey can be found at \href{https://github.com/Luo-Fangyuan/HashRec}{https://github.com/Luo-Fangyuan/HashRec}.
Fine-Grained Sentiment Analysis of Electric Vehicle User Reviews: A Bidirectional LSTM Approach to Capturing Emotional Intensity in Chinese Text
2024-12-05
Shuhao Chen, Chengyi Tu
The rapid expansion of the electric vehicle (EV) industry has highlighted the importance of user feedback in improving product design and charging infrastructure. Traditional sentiment analysis methods often oversimplify the complexity of user emotions, limiting their effectiveness in capturing nuanced sentiments and emotional intensities. This study proposes a Bidirectional Long Short-Term Memory (Bi-LSTM) network-based sentiment scoring model to analyze user reviews of EV charging infrastructure. By assigning sentiment scores ranging from 0 to 5, the model provides a fine-grained understanding of emotional expression. Leveraging a dataset of 43,678 reviews from PC Auto, the study employs rigorous data cleaning and preprocessing, including tokenization and stop word removal, to optimize input for deep learning. The Bi-LSTM model demonstrates significant improvements over traditional approaches like SnowNLP across key evaluation metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Explained Variance Score (EVS). These results highlight the model's superior capability to capture nuanced sentiment dynamics, offering valuable insights for targeted product and service enhancements in the EV ecosystem.
Isolated neutron stars as Science Validation for XMM2ATHENA: Ensuring robust data for future X-ray Astronomy
2024-12-05
Adriana Mancini Pires, Christian Motch, Axel Schwope, Iris Traulsen, Jean Ballet, Sudip Chakraborty, David Homan, Jan Kurpas, Ada Nebot Gomez-Moran, Francois-Xavier Pineau, Hugo Tranin, Natalie Webb
The discovery of radio-quiet, X-ray thermally emitting isolated neutron stars (XINSs) in the ROSAT All-Sky Survey revealed a previously overlooked component of the neutron star population. Advancements in X-ray instrumentation and the availability of deep, wide-area optical surveys now enable us to explore XINSs at fainter X-ray fluxes and greater distances. In this study, we investigated candidates selected from the 4XMM-DR9 catalogue using XMM-Newton, focusing on long-term flux stability, spectral characterisation, and astrometry. By leveraging resources from the XMM2ATHENA project -- including updated catalogues, multiwavelength characterisation and machine learning classification -- we refined our understanding of this sample of soft X-ray emitters. Our findings enhance the characterisation of XINS candidates, laying the groundwork for more targeted investigations and future catalogue searches.
How Good is ChatGPT in Giving Adaptive Guidance Using Knowledge Graphs in E-Learning Environments?
2024-12-05
Patrick Ocheja, Brendan Flanagan, Yiling Dai, Hiroaki Ogata
E-learning environments are increasingly harnessing large language models (LLMs) like GPT-3.5 and GPT-4 for tailored educational support. This study introduces an approach that integrates dynamic knowledge graphs with LLMs to offer nuanced student assistance. By evaluating past and ongoing student interactions, the system identifies and appends the most salient learning context to prompts directed at the LLM. Central to this method is the knowledge graph's role in assessing a student's comprehension of topic prerequisites. Depending on the categorized understanding (good, average, or poor), the LLM adjusts its guidance, offering advanced assistance, foundational reviews, or in-depth prerequisite explanations, respectively. Preliminary findings suggest students could benefit from this tiered support, achieving enhanced comprehension and improved task outcomes. However, several issues related to potential errors arising from LLMs were identified, which can potentially mislead students. This highlights the need for human intervention to mitigate these risks. This research aims to advance AI-driven personalized learning while acknowledging the limitations and potential pitfalls, thus guiding future research in technology and data-driven education.
2024-12-05
Xiang-dong Shi, Sheng-bang Qian, Li-ying Zhu, Lin-jia Li
This paper reports the discovery of new slowly pulsating B-type stars. Based on the photometric, spectral, and astrometric data of TESS, LAMOST, and Gaia surveys, we have found 286 new slowly pulsating B-type stars (SPB stars) and 21 candidates. Among these, 20 are Be stars or candidates with emission line profiles. It is shown that these SPB stars have luminosities between 40 and 2850 $L_{\odot}$ and effective temperatures ranging from 10000K to 21000K. Their pulsation periods are from 0.14 to 6.5 days with amplitude ranges of 0.2-20 mmag in TESS band. It is indicated that these targets follow the distribution of the SPB stars in the period-luminosity (P-L) and the period-temperature (P-T) diagrams. Their positions on the H-R diagram reveal that most of these pulsators are distributed in the instability region of SPB stars, in the main-sequence evolutionary stage, and with mass ranges of 2.5-7 $M_{\odot}$. However, there are some targets beyond the red edge of the theoretical instability region, which should be caused by the rapid rotation reducing the measured effective temperature. The discovery of these new SPB stars increases the total number by over 60\%, which are significant samples for further investigating the structure and evolution of intermediate-mass and even massive stars by asteroseismology.
Unified analysis of phase-field models for cohesive fracture
2024-12-05
Jian-Ying Wu
We address in this review unified analysis of phase-field models for cohesive fracture. Aiming to regularize the Barenblatt (1959) cohesive zone model, all the discussed models are distinguished by three characteristic functions, i.e., the geometric function dictating the crack profile, the degradation function for the constitutive relation and the dissipation function defining the crack driving force. The latter two functions coincide in the associated formulation, while in the non-associated one they are designed to be different. Distinct from the counterpart for brittle fracture, in the phase-field model for cohesive fracture the regularization length parameter has to be properly incorporated into the dissipation and/or degradation functions such that the failure strength and traction-separation softening curve are both well-defined. Moreover, the resulting crack bandwidth needs to be non-decreasing during failure in order that imposition of the crack irreversibility condition does not affect the anticipated traction-separation law (TSL). With a truncated degradation function that is proportional to the length parameter, the Conti et al.(2016) model and the latter improved versions can deal with crack nucleation only in the vanishing limit and capture cohesive fracture only with a particular TSL. Owing to a length scale dependent degradation function of rational fraction, these deficiencies are largely overcome in the phase-field cohesive zone model (PF-CZM). Among many variants in the literature, only with the optimal geometric function, can the associated PF-CZM apply to general non-concave softening laws and the non-associated uPF-CZM to (almost) any arbitrary one. Some mis-interpretations are clarified and representative numerical examples are presented.
Information Geometry for Maximum Diversity Distributions
2024-12-05
Shinto Eguchi
In recent years, biodiversity measures have gained prominence as essential tools for ecological and environmental assessments, particularly in the context of increasingly complex and large-scale datasets. We provide a comprehensive review of diversity measures, including the Gini-Simpson index, Hill numbers, and Rao's quadratic entropy, examining their roles in capturing various aspects of biodiversity. Among these, Rao's quadratic entropy stands out for its ability to incorporate not only species abundance but also functional and genetic dissimilarities. The paper emphasizes the statistical and ecological significance of Rao's quadratic entropy under the information geometry framework. We explore the distribution maximizing such a diversity measure under linear constraints that reflect ecological realities, such as resource competition or habitat suitability. Furthermore, we discuss a unified approach of the Leinster-Cobbold index combining Hill numbers and Rao's entropy, allowing for an adaptable and similarity-sensitive measure of biodiversity. Finally, we discuss the information geometry associated with the maximum diversity distribution focusing on the cross diversity measures such as the cross-entropy.
Industrial anomaly classification (AC) is an indispensable task in industrial manufacturing, which guarantees quality and safety of various product. To address the scarcity of data in industrial scenarios, lots of few-shot anomaly detection methods emerge recently. In this paper, we propose an effective few-shot anomaly classification (FSAC) framework with one-stage training, dubbed CLIP-FSAC++. Specifically, we introduce a cross-modality interaction module named Anomaly Descriptor following image and text encoders, which enhances the correlation of visual and text embeddings and adapts the representations of CLIP from pre-trained data to target data. In anomaly descriptor, image-to-text cross-attention module is used to obtain image-specific text embeddings and text-to-image cross-attention module is used to obtain text-specific visual embeddings. Then these modality-specific embeddings are used to enhance original representations of CLIP for better matching ability. Comprehensive experiment results are provided for evaluating our method in few-normal shot anomaly classification on VisA and MVTEC-AD for 1, 2, 4 and 8-shot settings. The source codes are at https://github.com/Jay-zzcoder/clip-fsac-pp
The O- and B-type (OB-type) pulsating stars are important objects to study the structure and evolution of massive stars through asteroseismology. A large amount of data from various sky surveys provide an unprecedented opportunity to search for and study this kind of variable star. We identify 155 OB-type pulsating stars or candidates, including 38 Oe/Be stars or candidates, from the data observed by TESS, LAMOST, and GAIA, which are almost new. Among the 155 objects, 87 samples are identified as SPB stars including 37 objects with pure low-frequency and 50 objects with both low- and high-frequency pulsation, and 14 samples are identified as BCEP stars with both low- and high-frequency pulsation. The H-R diagram shows that these SPB and BCEP stars are mainly located in their instability regions and in the evolutionary stage of the main-sequence with a mass range of 2.5-20 $M_{\odot}$ and 7-20 $M_{\odot}$. Two special objects show fourier spectra similar to BCEP stars but with different positions in H-R, Period-Temperature (P-T), and Period-Luminosity (P-L) diagrams. Meanwhile, 52 other targets are identified as candidates of OB-type pulsating stars. We also derive the preliminary results of the P-L relation for SPB and BCEP stars, respectively. This work also indicates that in addition to the H-R diagram, P-T and P-L diagrams are also very useful for the classification of SPB and BCEP. Further detailed analysis of these objects can dramatically increase our understanding of theories of evolution and structure for massive OB-type pulsating stars.
Detecting Redundant Health Survey Questions Using Language-agnostic BERT Sentence Embedding (LaBSE)
2024-12-05
Sunghoon Kang, Hyeoneui Kim, Hyewon Park, Ricky Taira
The goal of this work was to compute the semantic similarity among publicly available health survey questions in order to facilitate the standardization of survey-based Person-Generated Health Data (PGHD). We compiled various health survey questions authored in both English and Korean from the NIH CDE Repository, PROMIS, Korean public health agencies, and academic publications. Questions were drawn from various health lifelog domains. A randomized question pairing scheme was used to generate a Semantic Text Similarity (STS) dataset consisting of 1758 question pairs. Similarity scores between each question pair were assigned by two human experts. The tagged dataset was then used to build three classifiers featuring: Bag-of-Words, SBERT with BERT-based embeddings, and SBRET with LaBSE embeddings. The algorithms were evaluated using traditional contingency statistics. Among the three algorithms, SBERT-LaBSE demonstrated the highest performance in assessing question similarity across both languages, achieving an Area Under the Receiver Operating Characteristic (ROC) and Precision-Recall Curves of over 0.99. Additionally, it proved effective in identifying cross-lingual semantic similarities.The SBERT-LaBSE algorithm excelled at aligning semantically equivalent sentences across both languages but encountered challenges in capturing subtle nuances and maintaining computational efficiency. Future research should focus on testing with larger multilingual datasets and on calibrating and normalizing scores across the health lifelog domains to improve consistency. This study introduces the SBERT-LaBSE algorithm for calculating semantic similarity across two languages, showing it outperforms BERT-based models and the Bag of Words approach, highlighting its potential to improve semantic interoperability of survey-based PGHD across language barriers.
Synergizing LLMs and Knowledge Graphs: A Novel Approach to Software Repository-Related Question Answering
2024-12-05
Samuel Abedu, SayedHassan Khatoonabadi, Emad Shihab
Software repositories contain valuable information for gaining insights into their development process. However, extracting insights from these repository data is time-consuming and requires technical expertise. While software engineering chatbots have been developed to facilitate natural language interactions with repositories, they struggle with understanding natural language and accurately retrieving relevant data. This study aims to improve the accuracy of LLM-based chatbots in answering repository-related questions by augmenting them with knowledge graphs. We achieve this in a two-step approach; (1) constructing a knowledge graph from the repository data and (2) synergizing the knowledge graph with LLM to allow for the natural language questions and answers. We curated a set of 20 questions with different complexities and evaluated our approach on five popular open-source projects. Our approach achieved an accuracy of 65%. We further investigated the limitations and identified six key issues, with the majority relating to the reasoning capability of the LLM. We experimented with a few-shot chain-of-thought prompting to determine if it could enhance our approach. This technique improved the overall accuracy to 84%. Our findings demonstrate the synergy between LLMs and knowledge graphs as a viable solution for making repository data accessible to both technical and non-technical stakeholders.
Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review
2024-12-05
Sofiane Ennadir, Gabriela Zarzar Gandler, Filip Cornell, Lele Cao, Oleg Smirnov, Tianze Wang, Levente Zólyomi, Björn Brinne, Sahar Asadi
Graphs are ubiquitous in real-world applications, ranging from social networks to biological systems, and have inspired the development of Graph Neural Networks (GNNs) for learning expressive representations. While most research has centered on static graphs, many real-world scenarios involve dynamic, temporally evolving graphs, motivating the need for Continuous-Time Dynamic Graph (CTDG) models. This paper provides a comprehensive review of Graph Representation Learning (GRL) on CTDGs with a focus on Self-Supervised Representation Learning (SSRL). We introduce a novel theoretical framework that analyzes the expressivity of CTDG models through an Information-Flow (IF) lens, quantifying their ability to propagate and encode temporal and structural information. Leveraging this framework, we categorize existing CTDG methods based on their suitability for different graph types and application scenarios. Within the same scope, we examine the design of SSRL methods tailored to CTDGs, such as predictive and contrastive approaches, highlighting their potential to mitigate the reliance on labeled data. Empirical evaluations on synthetic and real-world datasets validate our theoretical insights, demonstrating the strengths and limitations of various methods across long-range, bi-partite and community-based graphs. This work offers both a theoretical foundation and practical guidance for selecting and developing CTDG models, advancing the understanding of GRL in dynamic settings.
Learning for Layered Safety-Critical Control with Predictive Control Barrier Functions
2024-12-05
William D. Compton, Max H. Cohen, Aaron D. Ames
Safety filters leveraging control barrier functions (CBFs) are highly effective for enforcing safe behavior on complex systems. It is often easier to synthesize CBFs for a Reduced order Model (RoM), and track the resulting safe behavior on the Full order Model (FoM) -- yet gaps between the RoM and FoM can result in safety violations. This paper introduces \emph{predictive CBFs} to address this gap by leveraging rollouts of the FoM to define a predictive robustness term added to the RoM CBF condition. Theoretically, we prove that this guarantees safety in a layered control implementation. Practically, we learn the predictive robustness term through massive parallel simulation with domain randomization. We demonstrate in simulation that this yields safe FoM behavior with minimal conservatism, and experimentally realize predictive CBFs on a 3D hopping robot.
A practical guide to feedback control for Pound-Drever-Hall laser linewidth narrowing
2024-12-05
Wance Wang, Sarthak Subhankar, Joseph W. Britton
The Pound-Drever-Hall (PDH) technique for laser linewidth narrowing is widely used by AMO experimentalists. However, achieving a high-performance PDH locking requires substantial engineering experience, which is scattered across literature and often lacks a cohesive control-theory perspective. Excellent pedagogical papers exist on the theory of the PDH error signal but they rarely cover feedback control. General-purpose control theory literature seldom discuss PDH laser locking specifically. Although excellent PDH review articles provide thorough knowledge and practice on both aspects but they are not reader-friendly. We extend prior works by addressing component choice and loop tuning using modern tools like a vector network analyzer. We organize multifaceted engineering considerations systematically, grounded in feedback control principles. Our target reader is researchers setting up a PDH laser lock for the first time; we eschew advanced topics like minimizing residual amplitude modulation (RAM). Our guidance is illustrated by step-by-step optimization of the lock for a 1650 nm ECDL.
The 100 pc White Dwarf Sample in the SDSS Footprint II. A New Look at the Spectral Evolution of White Dwarfs
2024-12-05
Mukremin Kilic, Pierre Bergeron, Simon Blouin, Adam Moss, Warren R. Brown, Antoine Bedard, Gracyn Jewett, Marcel A. Agueros
We increase the spectroscopic completeness of the 100 pc white dwarf sample in the SDSS footprint with 840 additional spectra. Our spectroscopy is 86% complete for white dwarfs hotter than $T_{\rm eff}= 5000$ K, where H$\alpha$ remains visible and provides reliable constraints on the atmospheric composition. We identify 2108 DA white dwarfs with pure hydrogen atmospheres, and show that ultramassive DA white dwarfs with $M\geq1.1~M_{\odot}$ are an order of magnitude less common below 10,000 K. This is consistent with a fraction of them getting stuck on the crystallization sequence due to $^{22}$Ne distillation. In addition, there are no ultramassive DA white dwarfs with $M\geq1.1~M_{\odot}$ and $T_{\rm eff}\leq6000$ K in our sample, likely because Debye cooling makes them rapidly fade away. We detect a significant trend in the fraction of He-atmosphere white dwarfs as a function of temperature; the fraction increases from 9% at 20,000 K to 32% at 6000 K. This provides direct evidence of convective mixing in cool DA white dwarfs. Finally, we detect a relatively tight sequence of low-mass DQ white dwarfs in color-magnitude diagrams for the first time. We discuss the implications of this tight DQ sequence, and conclude with a discussion of the future prospects from the upcoming ULTRASAT mission and the large-scale multi-fiber spectroscopic surveys.
2024-12-05
Francois Chollet, Mike Knoop, Gregory Kamradt, Bryan Landers
As of December 2024, the ARC-AGI benchmark is five years old and remains unbeaten. We believe it is currently the most important unsolved AI benchmark in the world because it seeks to measure generalization on novel tasks -- the essence of intelligence -- as opposed to skill at tasks that can be prepared for in advance. This year, we launched ARC Prize, a global competition to inspire new ideas and drive open progress towards AGI by reaching a target benchmark score of 85\%. As a result, the state-of-the-art score on the ARC-AGI private evaluation set increased from 33\% to 55.5\%, propelled by several frontier AGI reasoning techniques including deep learning-guided program synthesis and test-time training. In this paper, we survey top approaches, review new open-source implementations, discuss the limitations of the ARC-AGI-1 dataset, and share key insights gained from the competition.
A compact group lens modeled with GIGA-Lens: Enhanced inference for complex systems
2024-12-05
F. Urcelay, E. Jullo, L. F. Barrientos, X. Huang, J. Hernandez
In the era of large-scale astronomical surveys, fast modeling of strong lens systems has become increasingly vital. While significant progress has been made for galaxy-scale lenses, the development of automated methods for modeling larger systems, such as groups and clusters, is not as extensive. Our study aims to extend the capabilities of the GIGA-Lens code, enhancing its efficiency in modeling multi-galaxy strong lens systems. We focus on demonstrating the potential of GPU-accelerated Bayesian inference in handling complex lensing scenarios with a high number of free parameters. We employ an improved inference approach that combines image position and pixelated data with an annealing sampling technique to obtain the posterior distribution of complex models. This method allows us to overcome the challenge of limited prior information, a high number of parameters, and memory usage. Our process is exemplified through the analysis of the compact group lens system DES J0248-3955, for which we present VLT/X-shooter spectra. We measure a redshift of $z = 0.69 \pm 0.04$ for the group, and $z = 1.2722 \pm 0.0005$ for one of the extended arcs. Our enhanced method successfully constrained a lens model with 29 free parameters and lax priors in a remarkably short time. The mass of the lens is well described by a single dark-matter halo with a velocity dispersion of $\sigma_v = (690 \pm 30) \, km \, s^{-1}$. The model predicts the presence of a second source at the same redshift and a third source at approximately $z \sim 2.7$. Our study demonstrates the effectiveness of our lens modeling technique for dealing with a complex system in a short time using ground-based data. This presents considerable potential within the context of large surveys such as LSST.
X-raying CAMELS: Constraining Baryonic Feedback in the Circum-Galactic Medium with the CAMELS simulations and eRASS X-ray Observations
2024-12-05
Erwin T. Lau, Daisuke Nagai, Ákos Bogdán, Isabel Medlock, Benjamin D. Oppenheimer, Nicholas Battaglia, Daniel Anglés-Alcázar, Shy Genel, Yueying Ni, Francisco Villaescusa-Navarro
The circumgalactic medium (CGM) around massive galaxies plays a crucial role in regulating star formation and feedback. Using the CAMELS simulation suite, we develop emulators for the X-ray surface brightness profile and the X-ray luminosity--stellar mass scaling relation to investigate how stellar and AGN feedback shape the X-ray properties of the hot CGM. Our analysis shows that at CGM scales ($10^{12} \lesssim M_{\rm halo}/M_\odot \lesssim 10^{13}$, $10\lesssim r/{\rm kpc} \lesssim 400$), stellar feedback more significantly impacts the X-ray properties than AGN feedback within the parameters studied. Comparing the emulators to recent eROSITA All-Sky Survey observations, it was found that stronger feedback than currently implemented in the IllustrisTNG, SIMBA, and Astrid simulations is required to match observed CGM properties. However, adopting these enhanced feedback parameters causes deviations in the stellar-mass-halo-mass relations from observational constraints below the group mass scale. This tension suggests possible unaccounted systematics in X-ray CGM observations or inadequacies in the feedback models of cosmological simulations.
The flavor puzzles remain among the most compelling open questions in particle physics. The striking hierarchies observed in the masses and mixing of charged fermions define the Standard Model (SM) flavor puzzle, a profound structural enigma pointing to physics beyond the SM. Simultaneously, the absence of deviations from SM predictions in precision measurements of flavor-changing neutral currents imposes severe constraints on new physics at the TeV scale, giving rise to the new physics flavor puzzle. This review article provides an overview of a selection of recent advancements in flavor model building, with a particular focus on attempts to address one or both of these puzzles within the quark sector.
EMPRESS. X. Spatially resolved mass-metallicity relation in extremely metal-poor galaxies: evidence of episodic star-formation fueled by a metal-poor gas infall
2024-12-05
Kimihiko Nakajima, Masami Ouchi, Yuki Isobe, Yi Xu, Shinobu Ozaki, Tohru Nagao, Akio K. Inoue, Michael Rauch, Haruka Kusakabe, Masato Onodera, Moka Nishigaki, Yoshiaki Ono, Yuma Sugahara, Takashi Hattori, Yutaka Hirai, Takuya Hashimoto, Ji Hoon Kim, Takashi J. Moriya, Hiroto Yanagisawa, Shohei Aoyama, Seiji Fujimoto, Hajime Fukushima, Keita Fukushima, Yuichi Harikane, Shun Hatano, Kohei Hayashi, Tsuyoshi Ishigaki, Masahiro Kawasaki, Takashi Kojima, Yutaka Komiyama, Shuhei Koyama, Yusei Koyama, Chien-Hsiu Lee, Akinori Matsumoto, Ken Mawatari, Kentaro Motohara, Kai Murai, Kentaro Nagamine, Minami Nakane, Tomoki Saito, Rin Sasaki, Takatoshi Shibuya, Akihiro Suzuki, Tsutomu T. Takeuchi, Hiroya Umeda, Masayuki Umemura, Kuria Watanabe, Kiyoto Yabe, Hidenobu Yajima, Yechi Zhang
Using the Subaru/FOCAS IFU capability, we examine the spatially resolved relationships between gas-phase metallicity, stellar mass, and star-formation rate surface densities (Sigma_* and Sigma_SFR, respectively) in extremely metal-poor galaxies (EMPGs) in the local universe. Our analysis includes 24 EMPGs, comprising 9,177 spaxels, which span a unique parameter space of local metallicity (12+log(O/H) = 6.9 to 7.9) and stellar mass surface density (Sigma_* ~ 10^5 to 10^7 Msun/kpc^2), extending beyond the range of existing large integral-field spectroscopic surveys. Through spatially resolved emission line diagnostics based on the [NII] BPT-diagram, we verify the absence of evolved active galactic nuclei in these EMPGs. Our findings reveal that, while the resolved mass-metallicity relation exhibits significant scatter in the low-mass regime, this scatter is closely correlated with local star-formation surface density. Specifically, metallicity decreases as Sigma_SFR increases for a given Sigma_*. Notably, half of the EMPGs show a distinct metal-poor horizontal branch on the resolved mass-metallicity relation. This feature typically appears at the peak clump with the highest Sigma_* and Sigma_SFR and is surrounded by a relatively metal-enriched ambient region. These findings support a scenario in which metal-poor gas infall fuels episodic star formation in EMPGs, consistent with the kinematic properties observed in these systems. In addition, we identify four EMPGs with exceptionally low central metallicities (12+log(O/H) <~ 7.2), which display only a metal-poor clump without a surrounding metal-rich region. This suggests that such ultra-low metallicity EMPGs, at less than a few percent of the solar metallicity, may serve as valuable analogs for galaxies in the early stages of galaxy evolution.
Efficient Ionizers with Low H$\boldsymbolβ$+[OIII] Equivalent Widths: JADES Spectroscopy of a Peculiar High-z Population
2024-12-05
Isaac H. Laseter, Michael V. Maseda, Charlotte Simmonds, Ryan Endsley, Daniel Stark, Andrew J. Bunker, Rachana Bhatawdekar, Kristan Boyett, Alex J. Cameron, Stefano Carniani, Mirko Curti, Zhiyuan Ji, Pierluigi Rinaldi, Aayush Saxena, Sandro Tacchella, Chris Willott, Joris Witstok, Yongda Zhu
Early JWST photometric studies discovered a population of UV faint ($\rm 700$\r{A}) exclude the most metal-poor efficient ionizers and favor 1) more chemically enriched systems with comparable extreme radiation fields and 2) older starbursting systems. In contrast, metallicity degeneracies are reduced in H$\alpha$ space, enabling the identification of these metal-poor efficient ionizers by their specific star-formation rate.
Traditional sociological research often relies on human participation, which, though effective, is expensive, challenging to scale, and with ethical concerns. Recent advancements in large language models (LLMs) highlight their potential to simulate human behavior, enabling the replication of individual responses and facilitating studies on many interdisciplinary studies. In this paper, we conduct a comprehensive survey of this field, illustrating the recent progress in simulation driven by LLM-empowered agents. We categorize the simulations into three types: (1) Individual Simulation, which mimics specific individuals or demographic groups; (2) Scenario Simulation, where multiple agents collaborate to achieve goals within specific contexts; and (3) Society Simulation, which models interactions within agent societies to reflect the complexity and variety of real-world dynamics. These simulations follow a progression, ranging from detailed individual modeling to large-scale societal phenomena. We provide a detailed discussion of each simulation type, including the architecture or key components of the simulation, the classification of objectives or scenarios and the evaluation method. Afterward, we summarize commonly used datasets and benchmarks. Finally, we discuss the trends across these three types of simulation. A repository for the related sources is at {\url{https://github.com/FudanDISC/SocialAgent}}.
We review the suggestion that it is possible to eliminate the Big Bang curvature singularity of the Friedman cosmological solution by considering a particular type of degenerate spacetime metric. Specifically, we take the 4-dimensional spacetime metric to have a 3-dimensional defect with a vanishing determinant of the metric. This new solution suggests the existence of another "side" of the Big Bang (perhaps a more appropriate description than "pre-big-bang" phase used in our original paper). The corresponding new solution for defect wormholes is also briefly discussed.
A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences
2024-12-04
Gabriel Lino Garcia, João Renato Ribeiro Manesco, Pedro Henrique Paiola, Lucas Miranda, Maria Paola de Salvo, João Paulo Papa
The rapid advancement of large language models (LLMs) has opened new boundaries in the extraction and synthesis of medical knowledge, particularly within evidence synthesis. This paper reviews the state-of-the-art applications of LLMs in the biomedical domain, exploring their effectiveness in automating complex tasks such as evidence synthesis and data extraction from a biomedical corpus of documents. While LLMs demonstrate remarkable potential, significant challenges remain, including issues related to hallucinations, contextual understanding, and the ability to generalize across diverse medical tasks. We highlight critical gaps in the current research literature, particularly the need for unified benchmarks to standardize evaluations and ensure reliability in real-world applications. In addition, we propose directions for future research, emphasizing the integration of state-of-the-art techniques such as retrieval-augmented generation (RAG) to enhance LLM performance in evidence synthesis. By addressing these challenges and utilizing the strengths of LLMs, we aim to improve access to medical literature and facilitate meaningful discoveries in healthcare.
Microwave Remote Sensing of Soil Moisture, Above Ground Biomass and Freeze-Thaw Dynamic: Modeling and Empirical Approaches
2024-12-04
Laura Angeloni, Domenico Daniele Bloisi, Paolo Burghignoli, Davide Comite, Danilo Costarelli, Michele Piconi, Anna Rita Sambucini, Alessio Troiani, Alessandro Veneri
Human actions have accelerated changes in global temperature, precipitation patterns, and other critical Earth systems. Key markers of these changes can be linked to the dynamic of Essential Climate Variables (ECVs) and related quantities, such as Soil Moisture (SM), Above Ground Biomass (AGB), and Freeze-Thaw (FT) Dynamics. These variables are crucial for understanding global climate changes, hydrological and carbon cycles included. Monitoring these variables helps to validate climate models and inform policy decisions. Technologies like microwave remote sensing provide critical tools for monitoring the effects of human activities on these variables at a global scale. Other than proper tachenological developments, the study of ECVs requires suitable theoretical retrieval tools, which leads to the solutions of inverse problems. In this brief survey, we analyze and summarize the main retrieval techniques available in the literature for SM, AGB, and FT, performed on data collected with microwave remote sensing sensors. Such methods will be some of the fundamental algorithms that can find applications in the research activities of the interdisciplinary, curiosity-driven, project {\it REmote sensing daTa INversion with multivariate functional modeling for essential climAte variables characterization (RETINA)}, recently funded by the European Union under the Italian National Recovery and Resilience Plan of NextGenerationEU, under the Italian Ministry of University and Research. The main goal of RETINA, in which three research units from three different italian universities are involved, is to create innovative techniques for analyzing data generated by the interaction of electromagnetic waves with the Earth's surface, applying theoretical insights to address real-world challenges.
2024-12-04
Le Chen, Cheng Ouyang, Samy Tindel, Panqiu Xia
In this note we review several situations in which stochastic PDEs exhibit ergodic properties. We begin with the basic dissipative conditions, as stated by Da Prato and Zabczyk in their classical monograph. Then we describe the singular case of SPDEs with reflection. Next we move to some degenerate (and thus more demanding) settings. Namely we recall some results obtained around 2006, concerning stochastic Navier-Stokes equations with a very degenerate noise. We finish the article by handling some cases with degenerate coefficients. This includes a new result about the parabolic Anderson model in dimension $d\ge 3$, driven by a general class of noises and fairly general initial conditions. In this context, a phase transition is observed, expressed in terms of the noise intensity.
Adequately generating and evaluating prediction models based on supervised machine learning (ML) is often challenging, especially for less experienced users in applied research areas. Special attention is required in settings where the model generation process involves hyperparameter tuning, i.e. data-driven optimization of different types of hyperparameters to improve the predictive performance of the resulting model. Discussions about tuning typically focus on the hyperparameters of the ML algorithm (e.g., the minimum number of observations in each terminal node for a tree-based algorithm). In this context, it is often neglected that hyperparameters also exist for the preprocessing steps that are applied to the data before it is provided to the algorithm (e.g., how to handle missing feature values in the data). As a consequence, users experimenting with different preprocessing options to improve model performance may be unaware that this constitutes a form of hyperparameter tuning - albeit informal and unsystematic - and thus may fail to report or account for this optimization. To illuminate this issue, this paper reviews and empirically illustrates different procedures for generating and evaluating prediction models, explicitly addressing the different ways algorithm and preprocessing hyperparameters are typically handled by applied ML users. By highlighting potential pitfalls, especially those that may lead to exaggerated performance claims, this review aims to further improve the quality of predictive modeling in ML applications.
CUBES, the Cassegrain U-Band Efficient Spectrograph: towards final design review
2024-12-04
Matteo Genoni, Hans Dekker, Stefano Covino, Roberto Cirami, Marcello Agostino Scalera, Lawrence Bissel, Walter Seifert, Ariadna Calcines, Gerardo Avila, Julian Stuermer, Christopher Ritz, David Lunney, Chris Miller, Stephen Watson, Chris Waring, Bruno Vaz Castilho, Marcio De Arruda, Orlando Verducci, Igor Coretti, Luca Oggioni, Giorgio Pariani, Edoardo Alberto Maria Redaelli, Matteo D'Ambrogio, Giorgio Calderone, Matteo Porru, Ingo Stilz, Rodolfo Smiljanic, Guido Cupani, Mariagrazia Franchini, Andrea Scaudo, Vincent Geers, Vincenzo De Caprio, Domenico D' Auria, Mina Sibalic, Cyrielle Opitom, Gabriele Cescutti, Valentina D' Odorico, Ruben Sanchez Janssen, Andreas Quirrenbach, Beatriz Barbuy, Stefano Cristiani, Paolo Di Marcantonio
In the era of Extremely Large Telescopes, the current generation of 8-10m facilities are likely to remain competitive at ground-UV wavelengths for the foreseeable future. The Cassegrain U-Band Efficient Spectrograph (CUBES) has been designed to provide high instrumental efficiency ( $>$ 37\%) observations in the near UV (305-400 nm requirement, 300-420 nm goal) at a spectral resolving power of R $>$ 20, 000 (with a lower-resolution, sky-limited mode of R $\sim$ 7, 000). With the design focusing on maximizing the instrument throughput (ensuring a Signal to Noise Ratio -SNR- $\sim$ 20 per spectral resolution element at 313 nm for U $\sim$ 17.5 mag objects in 1h of observations), it will offer new possibilities in many fields of astrophysics: i) access to key lines of stellar spectra (e.g. lighter elements, in particular Beryllium), extragalactic studies (e.g. circumgalactic medium of distant galaxies, cosmic UV background) and follow-up of explosive transients. We present the CUBES instrument design, currently in Phase-C and approaching the final design review, summarizing the hardware architecture and interfaces between the different subsystems as well as the relevant technical requirements. We describe the optical, mechanical, electrical design of the different subsystems (from the telescope adapter and support structure, through the main opto-mechanical path, including calibration unit, detector devices and cryostat control, main control electronics), detailing peculiar instrument functions like the Active Flexure Compensation (AFC). Furthermore, we outline the AITV concept and the main instrument operations giving an overview of its software ecosystem. Installation at the VLT is planned for 2028-2029 and first science operations in late 2029.
Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasets
2024-12-04
Shir Bar, Or Hirschorn, Roi Holzman, Shai Avidan
In the study of animal behavior, researchers often record long continuous videos, accumulating into large-scale datasets. However, the behaviors of interest are often rare compared to routine behaviors. This incurs a heavy cost on manual annotation, forcing users to sift through many samples before finding their needles. We propose a pipeline to efficiently sample rare behaviors from large datasets, enabling the creation of training datasets for rare behavior classifiers. Our method only needs an unlabeled animal pose or acceleration dataset as input and makes no assumptions regarding the type, number, or characteristics of the rare behaviors. Our pipeline is based on a recent graph-based anomaly detection model for human behavior, which we apply to this new data domain. It leverages anomaly scores to automatically label normal samples while directing human annotation efforts toward anomalies. In research data, anomalies may come from many different sources (e.g., signal noise versus true rare instances). Hence, the entire labeling budget is focused on the abnormal classes, letting the user review and label samples according to their needs. We tested our approach on three datasets of freely-moving animals, acquired in the laboratory and the field. We found that graph-based models are particularly useful when studying motion-based behaviors in animals, yielding good results while using a small labeling budget. Our method consistently outperformed traditional random sampling, offering an average improvement of 70% in performance and creating datasets even when the behavior of interest was only 0.02% of the data. Even when the performance gain was minor (e.g., when the behavior is not rare), our method still reduced the annotation effort by half.
From Words to Workflows: Automating Business Processes
2024-12-04
Laura Minkova, Jessica López Espejel, Taki Eddine Toufik Djaidja, Walid Dahhane, El Hassane Ettifouri
As businesses increasingly rely on automation to streamline operations, the limitations of Robotic Process Automation (RPA) have become apparent, particularly its dependence on expert knowledge and inability to handle complex decision-making tasks. Recent advancements in Artificial Intelligence (AI), particularly Generative AI (GenAI) and Large Language Models (LLMs), have paved the way for Intelligent Automation (IA), which integrates cognitive capabilities to overcome the shortcomings of RPA. This paper introduces Text2Workflow, a novel method that automatically generates workflows from natural language user requests. Unlike traditional automation approaches, Text2Workflow offers a generalized solution for automating any business process, translating user inputs into a sequence of executable steps represented in JavaScript Object Notation (JSON) format. Leveraging the decision-making and instruction-following capabilities of LLMs, this method provides a scalable, adaptable framework that enables users to visualize and execute workflows with minimal manual intervention. This research outlines the Text2Workflow methodology and its broader implications for automating complex business processes.
We study crossed modules in the context of algebras over an operad. To do so, in the first section, we adapt the methods of Janelidze by reviewing the notions of internal actions, precrossed modules and crossed modules in the operadic case. Moreover, we extract the Peiffer relations, well known in the Lie case, for precrossed modules over an arbitrary operad. We prove that our notion of crossed modules is equivalent to the one of Janelidze by proving that our crossed modules of algebras over a fixed operad are equivalent to categories internal to algebras over this fixed operad. In the second section, we study the notion of crossed modules of algebras over an operad as introduced by Leray-Riviere-Wagemann in arXiv:2411.04614 and prove it to be equivalent to the already existing notions of crossed modules. Roughly speaking, a crossed module in the sense of Leray-Rivi\`ere-Wagemann is an algebra structure on a chain complex concentrated in degrees 0 and 1. We highlight a "codescent" process which allows us to endow the degree one and zero terms of the chain complex with algebra structures such that the differential is an algebra morphism. Moreover, we prove it to be a crossed module. In fact we prove that the approach to crossed modules of Leray-Riviere-Wagemann is equivalent to the one of Janelidze. This new approach, together with the codescent process, allows us to introduce higher crossed modules in a very concise and explicit way, and we prove them to be equivalent to n-fold categories internal to algebras, for some n.
Ground-based Cislunar Space Surveillance Demonstrations at Los Alamos National Laboratory
2024-12-04
Yancey Sechrest, Marion Vance, Christian Ward, William Priedhorsky, Robert Hill, W Thomas Vestrand, Przemyslaw Wozniak
Surveillance of objects in the cislunar domain is challenging due primarily to the large distances (10x the Geosynchronous orbit radius) and total volume of space to be covered. Ground-based electro-optical observations are further hindered by high background levels due to scattered moonlight. In this paper, we report on ground-based demonstrations of space surveillance for targets in the cislunar domain exploiting the remarkable performance of 36cm, F2.2 class telescopes equipped with current generation, back-side illuminated, full-frame CMOS imager. The demonstrations leverage advantageous viewing conditions for the Artemis Orion vehicle during its return to earth, and the total lunar eclipse of November 8th 2022 for viewing the CAPSTONE vehicle. Estimated g-band magnitudes for vehicles were 19.57 at a range of 4.4e5 km and 15.53 at a range of 3.2e5 km for CAPSTONE and Artemis Orion, respectively. In addition to observations, we present reflectance signature modeling implemented in the LunaTK space-sensing simulation framework and compare calculated apparent magnitudes to observed. Design of RApid Telescopes for Optical Response (RAPTOR) instruments, observing campaigns of Artemis Orion and CAPSTONE missions, and initial comparisons to electro-optical modeling are reviewed.
Pb transport on Si(111)-(7x7) following tip induced imbalance
2024-12-04
Paul Philip Schmidt, Felix Hartmann, Lea Faber, Ralf Metzler, Janet Anders, Regina Hoffmann-Vogel
Pb on Si(111)-(7x7) shows surprising nucleation and mass transport dynamics at odds with standard theories. To create local imbalances on stable Pb islands we use the tip of a scanning force microscope. We enforce a short, local contact between the island and our tip. The subsequent island height growth and the local contact potential difference are studied via scanning force microscopy and Kelvin probe force microscopy. Though the island has a large volume increase after the contact, we observe that its surrounding wetting layer shows the same Pb density decrease as the global wetting layer. This indicates a collective density thinning of the wetting layer.
Specific heat at low temperatures in quasiplanar molecular crystals: Where do glassy anomalies in minimally disordered crystals come from?
2024-12-04
Daria Szewczyk, Manuel Moratalla, Grzegorz Chajewski, Jonathan F. Gebbia, Andrzej Jeżowski, Alexander I. Krivchikov, María Barrio, Josep Ll. Tamarit, Miguel A. Ramos
We present low-temperature specific heat (Cp) measurements of a monoclinic P2_{1}/c crystal formed by quasiplanar molecules of tetrachloro-m-xylene. The dynamic disorder frozen at low-temperature of the asymmetric unit (formed by a half molecule) consists of reorientation around a three-fold-like axis perpendicular to the benzene ring. Such a minimal disorder gives rise to typical glassy anomalies, as a linear in contribution in Cp ascribed to two-level systems and a broad maximum around 6.6 K in Cp/T^3 (the boson peak). We discuss these results in the framework of other quasiplanar molecular crystals with different accountable number of in-plane molecular orientations We find that the density of two-level systems does not correlate with the degree of orientational disorder. Rather, it is the molecular asymmetry that seems to play a relevant role in the thermal anomalies. Furthermore, we discuss the suggested correlation between the boson peak and Debye temperatures. We find that a linear correlation between the boson peak and Debye temperatures holds for many -- but not all -- structural glasses and strikingly holds even better for some disordered crystals, including our studied quasiplanar molecular crystals.
Searches for exotic spin-dependent interactions with spin sensors
2024-12-04
Min Jiang, Haowen Su, Yifan Chen, Man Jiao, Ying Huang, Yuanhong Wang, Xing Rong, Xinhua Peng, Jiangfeng Du
Numerous theories have postulated the existence of exotic spin-dependent interactions beyond the Standard Model of particle physics. Spin-based quantum sensors, which utilize the quantum properties of spins to enhance measurement precision, emerge as powerful tools for probing these exotic interactions. These sensors encompass a wide range of technologies, such as optically pumped magnetometers, atomic comagnetometers, spin masers, nuclear magnetic resonance, spin amplifiers, and nitrogen-vacancy centers. These technologies stand out for their ultrahigh sensitivity, compact tabletop design, and cost-effectiveness, offering complementary approaches to the large-scale particle colliders and astrophysical observations. This article reviews the underlying physical principles of various spin sensors and highlights the recent theoretical and experimental progress in the searches for exotic spin-dependent interactions with these quantum sensors. Investigations covered include the exotic interactions of spins with ultralight dark matter, exotic spin-dependent forces, electric dipole moment, spin-gravity interactions, and among others. Ongoing and forthcoming experiments using advanced spin-based sensors to investigate exotic spin-dependent interactions are discussed.
Diverse methods and practical aspects in controlling single semiconductor qubits: a review
2024-12-04
Jia-Ao Peng, Chu-Dan Qiu, Wen-Long Ma, Jun-Wei Luo
Quantum control allows a wide range of quantum operations employed in molecular physics, nuclear magnetic resonance and quantum information processing. Thanks to the existing microelectronics industry, semiconducting qubits, where quantum information is encoded in spin or charge degree freedom of electrons or nuclei in semiconductor quantum dots, constitute a highly competitive candidate for scalable solid-state quantum technologies. In quantum information processing, advanced control techniques are needed to realize quantum manipulations with both high precision and noise resilience. In this review, we first introduce the basics of various widely-used control methods, including resonant excitation, adabatic passage, shortcuts to adiabaticity, composite pulses, and quantum optimal control. Then we review the practical aspects in applying these methods to realize accurate and robust quantum gates for single semiconductor qubits, such as Loss-DiVincenzo spin qubit, spinglet-triplet qubit, exchange-only qubit and charge qubit.
For the central values of the relevant experimental inputs, that is the strong coupling constant and the top quark and Higgs masses, the effective Higgs potential displays two minima, one at the electroweak scale and a deeper one at high energies. We review the phenomenology of the Higgs inflation model, extending the Standard Model to include a non-minimal coupling to gravity; as recently shown, even configurations that would be metastable in the Standard Model, become viable for inflation if the non-minimal coupling is large enough to flatten the Higgs potential at field values below the barrier between the minima.
EAP-FIDO: A Novel EAP Method for Using FIDO2 Credentials for Network Authentication
2024-12-04
Martiño Rivera-Dourado, Christos Xenakis, Alejandro Pazos, Jose Vázquez-Naya
The adoption of FIDO2 authentication by major tech companies in web applications has grown significantly in recent years. However, we argue FIDO2 has broader potential applications. In this paper, we introduce EAP-FIDO, a novel Extensible Authentication Protocol (EAP) method for use in IEEE 802.1X-protected networks. This allows organisations with WPA2/3-Enterprise wireless networks or MACSec-enabled wired networks to leverage FIDO2's passwordless authentication in compliance with existing standards. Additionally, we provide a comprehensive security and performance analysis to support the feasibility of this approach.
Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges
2024-12-04
Minghao Shao, Abdul Basit, Ramesh Karri, Muhammad Shafique
Large Language Models (LLMs) represent a class of deep learning models adept at understanding natural language and generating coherent responses to various prompts or queries. These models far exceed the complexity of conventional neural networks, often encompassing dozens of neural network layers and containing billions to trillions of parameters. They are typically trained on vast datasets, utilizing architectures based on transformer blocks. Present-day LLMs are multi-functional, capable of performing a range of tasks from text generation and language translation to question answering, as well as code generation and analysis. An advanced subset of these models, known as Multimodal Large Language Models (MLLMs), extends LLM capabilities to process and interpret multiple data modalities, including images, audio, and video. This enhancement empowers MLLMs with capabilities like video editing, image comprehension, and captioning for visual content. This survey provides a comprehensive overview of the recent advancements in LLMs. We begin by tracing the evolution of LLMs and subsequently delve into the advent and nuances of MLLMs. We analyze emerging state-of-the-art MLLMs, exploring their technical features, strengths, and limitations. Additionally, we present a comparative analysis of these models and discuss their challenges, potential limitations, and prospects for future development.
Constraints on dark matter annihilation in the Large Magellanic Cloud from multiple low-frequency radio observations
2024-12-04
Zhanfang Chen, Feng Huang, Taotao Fang
Low-frequency radio emission from the Large Magellanic Cloud~(LMC) is assumed to be dominated by nonthermal synchrotron radiation from energy loss of energetic $e^+/e^-$ in magnetic field. Two different kinds of sources of $e^+/e^-$, dark matter~(DM) annihilation and cosmic rays~(CR) related to massive stars, are taken into account in this paper. We fit the multiple low-frequency radio observations, from 19.7 MHz to 1.4 GHz, with a double power-law model $S_{nth} =S_{DM}(\frac {\nu}{\nu_{\star}})^{-\alpha_{DM}}+S_{CR}( \frac {\nu}{\nu_{\star}})^{-\alpha_{CR}} $. $\nu_{\star}$ is set to be $1.4$ GHz and $S_{CR}$ could be determined from the 24 $\mu m$ luminosity based on the global radio-infrared correlation. Our best fit with a fixed $\alpha_{CR}$ changing from $0.80$ to $0.55$ yields $\alpha_{DM}$ ranging from $0.21$ to $0.66$. Given a fixed value of $\alpha_{CR}$, we derive the upper limits of synchrotron emission induced by dark matter annihilation at different radio frequencies. Larger value of $\alpha_{CR}$ represents for a harder $e^+/e^-$ spectrum from cosmic rays, which leads to a smaller value of $\alpha_{DM}$ and allow less synchrotron emission resulted from dark matter annihilation in lower frequency. Under the same assumption on the magnetic field, we find that the lower the frequency, the stronger the restriction on DM parameter space. Meanwhile, as the peak frequency of synchrotron radiation decrease with the energy of $e^+/e^-$, constraints on DM properties obtained from lower frequency are more severe in the case of DM with lower mass. Future low-frequency radio survey should be considered a promising and powerful way to constrain DM.
LLM-Mirror: A Generated-Persona Approach for Survey Pre-Testing
2024-12-04
Sunwoong Kim, Jongho Jeong, Jin Soo Han, Donghyuk Shin
Surveys are widely used in social sciences to understand human behavior, but their implementation often involves iterative adjustments that demand significant effort and resources. To this end, researchers have increasingly turned to large language models (LLMs) to simulate human behavior. While existing studies have focused on distributional similarities, individual-level comparisons remain underexplored. Building upon prior work, we investigate whether providing LLMs with respondents' prior information can replicate both statistical distributions and individual decision-making patterns using Partial Least Squares Structural Equation Modeling (PLS-SEM), a well-established causal analysis method. We also introduce the concept of the LLM-Mirror, user personas generated by supplying respondent-specific information to the LLM. By comparing responses generated by the LLM-Mirror with actual individual survey responses, we assess its effectiveness in replicating individual-level outcomes. Our findings show that: (1) PLS-SEM analysis shows LLM-generated responses align with human responses, (2) LLMs, when provided with respondent-specific information, are capable of reproducing individual human responses, and (3) LLM-Mirror responses closely follow human responses at the individual level. These findings highlight the potential of LLMs as a complementary tool for pre-testing surveys and optimizing research design.
Successive magnetic transitions in the spin-5/2 easy-axis triangular-lattice antiferromagnet Na$_2$BaMn(PO$_4$)$_2$: A neutron diffraction study
2024-12-04
Chuandi Zhang, Junsen Xiang, Cheng Su, Denis Sheptyakov, Xinyang Liu, Yuan Gao, Peijie Sun, Wei Li, Gang Su, Wentao Jin
Motivated by the recent observations of various exotic quantum states in the equilateral triangular-lattice phosphates Na$_2$BaCo(PO$_4$)$_2$ with $J\rm_{eff}$ = 1/2 and Na$_2$BaNi(PO$_4$)$_2$ with $S$ = 1, the magnetic properties of spin-5/2 antiferromagnet Na$_2$BaMn(PO$_4$)$_2$, their classical counterpart, are comprehensively investigated experimentally. DC magnetization and specific heat measurements on polycrystalline samples indicate two successive magnetic transitions at $T\rm_{N1}$ $\approx$ 1.13 K and $T\rm_{N2}$ $\approx$ 1.28 K, respectively. Zero-field neutron powder diffraction measurement at 67 mK reveals a Y-like spin configuration as its ground-state magnetic structure, with both the $ab$-plane and $c$-axis components of the Mn$^{2+}$ moments long-range ordered. The incommensurate magnetic propagation vector $k$ shows a dramatic change for the intermediate phase between $T\rm_{N1}$ and $T\rm_{N2}$, in which the spin state is speculated to change into a collinear structure with only the $c$-axis moments ordered, as stabilized by thermal fluctuations. The successive magnetic transitions observed in Na$_2$BaMn(PO$_4$)$_2$ are in line with the expectation for a triangle-lattice antiferromagnet with an easy-axis magnetic anisotropy.
Primordial non-Gaussianity systematics from redshift mismatch with SPHEREx
2024-12-04
Chandra Shekhar Saraf, David Parkinson
The ability to differentiate between different models of inflation through the imprint of primordial non-Gaussianity (PNG) requires a tight constraint on the local PNG parameter $f_{\text{NL}}^{\text{loc}}$. Future large scale structure surveys like \textit{Euclid}, Vera C. Rubin Observatory, and the Spectro-Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer (SPHEREx) will play a crucial role in advancing our understanding of the inflationary epoch. In this light, we present forecasts on PNG with tomographic angular power spectrum from simulations of SPHEREx. We put forward the effects of redshift bin mismatch of galaxies as a source of systematic error when estimating $f_{\text{NL}}^{\text{loc}}$ and galaxy linear halo bias. We %use \texttt{GLASS} to simulate $500$ SPHEREx-like galaxy density fields, and divide the galaxies into $13$ redshift bins assuming Gaussian photometric redshift errors. We show that the misclassification of galaxies in redshift bins can result in strong apparent tensions on $f_{\text{NL}}^{\text{loc}}$ up to $\sim 3\sigma$ and up to $\sim 9\sigma$ on galaxy bias. We propose a scattering matrix formalism to mitigate bin mismatch of galaxies and to obtain unbiased estimates of cosmological parameters from tomographic angular clustering measurements.
Wireless sensing technology has become prevalent in healthcare, smart homes, and autonomous driving due to its non-contact operation, penetration capabilities, and cost-effectiveness. As its applications expand, the technology faces mounting security challenges: sensing systems can be attack targets, signals can be weaponized, or signals can function as security shields. Despite these security concerns significantly impacting the technology's development, a systematic review remains lacking. This paper presents the first comprehensive survey of wireless sensing security through a role-based perspective. Analyzing over 200 publications from 2020-2024, we propose a novel classification framework that systematically categorizes existing research into three main classes: (1) wireless systems as victims of attacks, (2) wireless signals as weapons for attacks, and (3) wireless signals as shields for security applications. This role-based classification method is not only intuitive and easy to understand, but also reflects the essential connection between wireless signals and security issues. Through systematic literature review and quantitative analysis, this paper outlines a panoramic view of wireless sensing security, revealing key technological trends and innovation opportunities, thereby helping to promote the development of this field. Project page: \url{https://github.com/Intelligent-Perception-Lab/Awesome-WS-Security}.
The FAST Galactic Plane Pulsar Snapshot Survey: VII. Six millisecond pulsars in compact orbits with massive white dwarf companions
2024-12-04
Z. L. Yang, J. L. Han, T. Wang, P. F. Wang, W. Q. Su, W. C. Chen, C. Wang, D. J. Zhou, Y. Yan, W. C. Jing, N. N. Cai, L. Xie, J. Xu, H. G. Wang, R. X. Xu
Binary millisecond pulsars with a massive white dwarf (WD) companion are intermediate-mass binary pulsars (IMBPs). They are formed via the Case BB Roche-lobe overflow (RLO) evolution channel if they are in compact orbits with an orbital period of less than 1 day. They are fairly rare in the known pulsar population, only five such IMBPs have been discovered before, and one of them is in a globular cluster. Here we report six IMBPs in a compact orbit, PSRs J0416+5201, J0520+3722, J1919+1341, J1943+2210, J1947+2304 and J2023+2853, discovered during the Galactic Plane Pulsar Snapshot (GPPS) survey by using the Five-hundred-meter Aperture Spherical radio Telescope (FAST), doubling the number of such IMBPs due to the high survey sensitivity in the short survey time of 5 minutes. Follow-up timing observations show that they all have either a CO WD or an ONeMg WD companion with a mass greater than about 0.8~M$_\odot$ in a very circular orbit with an eccentricity in the order of $\lesssim10^{-5}$. PSR J0416+5201 should be an ONeMg WD companion with a remarkable minimum mass of 1.28 M$_\odot$. These massive white dwarf companions lead to a detectable Shapiro delay for PSRs J0416+5201, J0520+3722, J1943+2210, and J2023+2853, indicating that their orbits are highly inclined. From the measurement of the Shapiro delay, the pulsar mass of J1943+2210 was constrained to be 1.84$^{\,+0.11}_{-0.09}$~M$_\odot$, and that of PSR J2023+2853 to be 1.28$^{\,+0.06}_{-0.05}$~M$_\odot$.
The FAST Galactic Plane Pulsar Snapshot survey: VIII. 112 binary pulsars
2024-12-04
P. F. Wang, J. L. Han, Z. L. Yang, T. Wang, C. Wang, W. Q. Su, J. Xu, D. J. Zhou, Yi Yan, W. C. Jing, N. N. Cai, J. P. Yuan, R. X. Xu, H. G. Wang, X. P. You
Finding pulsars in binaries are important for measurements of the masses of neutron stars, for tests of gravity theories, and for studies of star evolution. We are carrying out the Galactic Plane Pulsar Snapshot survey (GPPS) by using the the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Here we present the Keplerian parameters for 112 newly discovered pulsars in the FAST GPPS survey, and obtain timing solutions for 27 pulsars. Companions of these pulsars are He white dwarfs, CO/ONe white dwarfs, neutron stars, main sequence stars and ultra light objects or even planets. Our observations uncover eclipses of 8 binary systems. The optical counterpart for the companion of PSR J1908+1036 is identified. The Post-Keplerian parameter \Dot{\omega} for the double neutron star systems PSR J0528+3529 and J1844-0128 have been determined, with which the total masses of the binary systems are determined.
We report our experiences for the development of a neighborhood algorithm implemented via tree-codes to optimize the performance of a discrete element method (DEM) for convex polytopes. Our implementation of the two-dimensional tree code needs $N\log N$, as does the sort and sweep approach. For our choice of boundary conditions (a rotating drum) and system sizes (up to several thousand particles), the performance of the tree-code is slightly better, but the algorithm is considerably more complicated than the sort and sweep approach.
Unveiling Saving and Credit Dynamics: Insights from Financial Diaries and Surveys among Low-Income Households in Unauthorized Colonies in Delhi
2024-12-04
Divya Sharma
The paper presents findings from a comprehensive study examining the saving and credit behaviors of low-income households residing in unauthorized colonies within a metropolitan area. Utilizing a dual approach, the study engaged in prolonged fieldwork, including repeated fortnightly interviews with selected households and a one-time primary survey with a larger sample size. The research meticulously analyzed the financial lives of these households, focusing on their saving and credit behaviors and assessing the accessibility and intensity of usage of financial instruments available to them. Through suitable regression models, the study identified key factors influencing the usage of financial instruments among low-income households. Transaction costs, convenience, and financial knowledge emerged as significant determinants impacting both usage decisions and the intensity of usage. The research underscores the importance of addressing demand side factors to ensure widespread financial services usage among low-income groups. Efforts to reduce time costs, enhance product accessibility and liquidity, and augment financial literacy are essential for fostering financial inclusion in unauthorized colonies. The findings highlight the imperative of moving beyond mere financial access towards promoting universal usage to realize the full benefits of financial inclusion.
Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models
2024-12-04
Alex Havrilla, Andrew Dai, Laura O'Mahony, Koen Oostermeijer, Vera Zisler, Alon Albalak, Fabrizio Milo, Sharath Chandra Raparthy, Kanishk Gandhi, Baber Abbasi, Duy Phung, Maia Iyer, Dakota Mahan, Chase Blagden, Srishti Gureja, Mohammed Hamdy, Wen-Ding Li, Giovanni Paolini, Pawan Sasanka Ammanamanchi, Elliot Meyerson
Synthetic data generation with Large Language Models is a promising paradigm for augmenting natural data over a nearly infinite range of tasks. Given this variety, direct comparisons among synthetic data generation algorithms are scarce, making it difficult to understand where improvement comes from and what bottlenecks exist. We propose to evaluate algorithms via the makeup of synthetic data generated by each algorithm in terms of data quality, diversity, and complexity. We choose these three characteristics for their significance in open-ended processes and the impact each has on the capabilities of downstream models. We find quality to be essential for in-distribution model generalization, diversity to be essential for out-of-distribution generalization, and complexity to be beneficial for both. Further, we emphasize the existence of Quality-Diversity trade-offs in training data and the downstream effects on model performance. We then examine the effect of various components in the synthetic data pipeline on each data characteristic. This examination allows us to taxonomize and compare synthetic data generation algorithms through the components they utilize and the resulting effects on data QDC composition. This analysis extends into a discussion on the importance of balancing QDC in synthetic data for efficient reinforcement learning and self-improvement algorithms. Analogous to the QD trade-offs in training data, often there exist trade-offs between model output quality and output diversity which impact the composition of synthetic data. We observe that many models are currently evaluated and optimized only for output quality, thereby limiting output diversity and the potential for self-improvement. We argue that balancing these trade-offs is essential to the development of future self-improvement algorithms and highlight a number of works making progress in this direction.
Nonlinear transport plays a vital role in probing the quantum geometry of Bloch electrons, valley chirality, and carrier scattering mechanisms. The nonlinear Hall effect, characterized by a nonlinear scaling of Hall voltage with longitudinal current, has been explored to reveal the Berry curvature and quantum metric related physics. In this work, we extend the study of nonlinear transport to spin and valley degrees of freedom. Using bilayer graphene devices with Fe3GeTe2 contacts, we observe a second-order nonlinear spin current exhibiting spin valve-like behaviors. By tracking magnetic moment precession under an in-plane magnetic field, we identify a significantly enhanced critical magnetic field required for in-plane rotation, suggesting out-of-plane valley polarization induced by ferromagnetic proximity. These findings offer deep insights into the interplay of valley and spin in second-order nonlinear transport, opening avenues for promising device applications.
In materials with spin-momentum locked spin textures, such as Rashba states and topological surface states, the current-induced shift of the Fermi contour in the k space leads to spin polarization, known as the Edelstein effect, which depends linearly on the applied current. However, its nonlinear counterpart has not yet been discovered. Here, we report the observation of the nonlinear Edelstein effect in few-layer WTe2. Under a current bias, an out-of-plane magnetization is induced in WTe2, which is electrically probed using an Fe3GeTe2 electrode, a van der Waals ferromagnet with perpendicular magnetic anisotropy. Notably, with an applied ac at frequency {\omega}, an induced magnetization with second-harmonic response at frequency 2{\omega} is observed, and its magnitude demonstrates a quadratic dependence on the applied current, characteristic of the nonlinear Edelstein effect. This phenomenon is well explained by the current-induced orbital magnetization via the Berry connection polarizability tensors in WTe2. The orbital degree of freedom plays the primary role in the observed nonlinear Edelstein effect, that is, the nonlinear orbital Edelstein effect. This can, in turn, give rise to a nonlinear spin Edelstein effect through spin-orbit coupling.
We report on the observation of the linear anomalous Hall effect (AHE) in the nonmagnetic Weyl semimetal TaIrTe4. This is achieved by applying a direct current Idc and an alternating current Iac (Iac<
A Benchmark for Math Misconceptions: Bridging Gaps in Middle School Algebra with AI-Supported Instruction
2024-12-04
Otero Nancy, Druga Stefania, Lan Andrew
This study introduces an evaluation benchmark for middle school algebra to be used in artificial intelligence(AI) based educational platforms. The goal is to support the design of AI systems that can enhance learner conceptual understanding of algebra by taking into account their current level of algebra comprehension. The data set comprises 55 misconceptions about algebra, common errors, and 220 diagnostic examples identified in previous peer-reviewed studies. We provide an example application using a large language model, observing a range of precision and recall scores depending on the topic and experimental setup that reaches 83.9% when including educator feedback and restricting it by topic. We found that topics such as ratios and proportions prove as difficult for LLMs as they are for students. We included a human assessment of LLMs results and feedback from five middle school math educators on the clarity and occurrence of misconceptions in the dataset and the potential use of AI in conjunction with the dataset. Most educators (80% or more) indicated that they encounter these misconceptions among their students, suggesting the relevance of the data set to teaching middle school algebra. Despite varying familiarity with AI tools, four out of five educators expressed interest in using the data set with AI to diagnose student misconceptions or train teachers. The results emphasize the importance of topic-constrained testing, the need for multimodal approaches, and the relevance of human expertise to gain practical insights when using AI for human learning.
Recent advances in auto-regressive large language models (LLMs) have shown their potential in generating high-quality text, inspiring researchers to apply them to image and video generation. This paper explores the application of LLMs to video continuation, a task essential for building world models and predicting future frames. In this paper, we tackle challenges including preventing degeneration in long-term frame generation and enhancing the quality of generated images. We design a scheme named ARCON, which involves training our model to alternately generate semantic tokens and RGB tokens, enabling the LLM to explicitly learn and predict the high-level structural information of the video. We find high consistency in the RGB images and semantic maps generated without special design. Moreover, we employ an optical flow-based texture stitching method to enhance the visual quality of the generated videos. Quantitative and qualitative experiments in autonomous driving scenarios demonstrate our model can consistently generate long videos.
Graph-based machine learning methods are useful tools in the identification and prediction of variation in genetic data. In particular, the comprehension of phenotypic effects at the cellular level is an accelerating research area in pharmacogenomics. In this article, a novel graph theoretic approach is proposed to infer a co-occurrence network from 16S microbiome data. The approach is specialised to handle datasets containing a small number of samples. Small datasets exacerbate the significant challenges faced by biological data, which exhibit properties such as sparsity, compositionality, and complexity of interactions. Methodologies are also proposed to enrich and statistically filter the inferred networks. The utility of the proposed method lies in that it extracts an informative network from small sampled data that is not only feature-rich, but also biologically meaningful and statistically significant. Although specialised for small data sets, which are abundant, it can be generally applied to any small-sampled dataset, and can also be extended to integrate multi-omics data. The proposed methodology is tested on a data set of chickens vaccinated against and challenged by the protozoan parasite Eimeria tenella. The raw genetic reads are processed, and networks inferred to describe the ecosystems of the chicken intestines under three different stages of disease progression. Analysis of the expression of network features derive biologically intuitive conclusions from purely statistical methods. For example, there is a clear evolution in the distribution of node features in line with the progression of the disease. The distributions also reveal clusters of species interacting mutualistically and parasitically, as expected. Moreover, a specific sub-network is found to persist through all experimental conditions, representative of a persistent microbiome.
Improving Optical Photo-z Estimates Using Submillimeter Photometry
2024-12-04
Pouya Tanouri, Ryley Hill, Douglas Scott, Edward L. Chapin
Estimating the redshifts of distant galaxies is critical for determining their intrinsic properties, as well as for using them as cosmological probes. Measuring redshifts spectroscopically is accurate, but expensive in terms of telescope time, hence it has become common to measure `photometric' redshifts, which are fits to photometry taken in a number of filters using templates of galaxy spectral energy distributions (SEDs). However, most photometric methods rely on optical and near-infrared (NIR) photometry, neglecting longer wavelength data in the far-infrared (FIR) and millimeter. Since the ultimate goal of future surveys is to obtain redshift estimates for all galaxies, it is important to improve photometric redshift algorithms for cases where optical/NIR fits fail to produce reliable results. For specific subsets of galaxies, in particular dusty star-forming galaxies (DSFGs), it can be particularly hard to obtain good optical photometry and thus reliable photometric redshift estimates, while these same galaxies are often bright at longer wavelengths. Here we describe a new method for independently incorporating FIR-to-millimeter photometry to the outputs of standard optical/NIR SED-fitting codes to help improve redshift estimation, in particular of DSFGs. We test our method with the H-ATLAS catalog, which contains FIR photometry from Herschel-SPIRE cross-matched to optical and NIR observations, and show that our approach reduces the number of catastrophic outliers by a factor of three compared to standard optical and NIR SED-fitting routines alone.
Long-lived population inversion in resonantly driven excitonic antiferromagnet
2024-12-04
Jacob A. Warshauer, Huyongqing Chen, Daniel Alejandro Bustamante Lopez, Qishuo Tan, Jing Tang, Xi Ling, Wanzheng Hu
Van der Waals magnets are an emerging material family for investigating light-matter interactions and spin-correlated excitations. Here, we report the discovery of a photo-induced state with a lifetime of 17 ps in the van der Waals antiferromagnet NiPS$_3$, which appears exclusively with resonant pumping at 1.476 eV in the antiferromagnetic state. The long-lived state comes with a negative photoconductivity, a characteristic optical response of population inversion. Our findings demonstrate a promising pathway to potentially achieve long-lived lasing at terahertz frequencies in reduced dimensions.
Constraining the link between the 2175Å dust absorption feature and PAHs in Nearby Star-Forming Galaxies using Swift/UVOT and JWST/MIRI
2024-12-04
A. J. Battisti, I. Shivaei, H. -J. Park, M. Decleir, D. Calzetti, J. Mathew, E. Wisnioski, Elisabete da Cunha
The 2175{\AA} bump is a prominent absorption feature at ultraviolet (UV) wavelengths in dust extinction and attenuation curves. Understanding the relative strength of this feature is important for accurate dust corrections at both low- and high-redshift. This feature is postulated to arise from polycyclic aromatic hydrocarbon (PAH) dust grains; however, the carrier has not been definitively established. We present results on the correlation between the 2175{\AA} feature and PAH abundances in a spatially-resolved manner for 15 local galaxies in the PHANGS-JWST survey that have NUV and mid-IR imaging data from Swift/UVOT and JWST/MIRI, respectively. We find a moderate positive correlation between the 2175{\AA} feature strength and PAH abundance, albeit with large intrinsic scatter. However, most of this trend can be attributed to a stronger negative correlation of both quantities with SFR surface density and specific-SFR (proxies of ionising radiation). The latter trends are consistent with previous findings that both the 2175{\AA} carrier and PAHs are small grains that are easily destroyed by UV photons, although the proxy for PAH abundance could also be influenced by dust heating. When controlling for SFR surface density, we find weaker correlations between the 2175{\AA} feature and PAH abundances, disfavouring a direct link. However, analyses based on spectroscopic measurements of the 2175{\AA} feature and PAH features are required to verify our findings. No significant trends with gas-phase metallicity are found for the 2175{\AA} feature and PAHs, however the metallicity range of our sample is limited. We provide prescriptions for the strength of the 2175{\AA} feature and PAHs in local massive (metal-rich) galaxies with SFR surface density and specific-SFR, however the former should be used with caution since bump strengths measured from Swift/UVOT are expected to be underestimated.
Assessing Changes in Thinking about Troubleshooting in Physical Computing: A Clinical Interview Protocol with Failure Artifacts Scenarios
2024-12-04
Luis Morales-Navarro, Deborah A. Fields, Yasmin B. Kafai, Deepali Barapatre
Purpose: The purpose of this paper is to examine how a clinical interview protocol with failure artifact scenarios can capture changes in high school students' explanations of troubleshooting processes in physical computing activities. We focus on physical computing since finding and fixing hardware and software bugs is a highly contextual practice that involves multiple interconnected domains and skills. Approach: We developed and piloted a "failure artifact scenarios" clinical interview protocol. Youth were presented with buggy physical computing projects over video calls and asked for suggestions on how to fix them without having access to the actual project or its code. We applied this clinical interview protocol before and after an eight-week-long physical computing (more specifically, electronic textiles) unit. We analyzed matching pre- and post-interviews from 18 students at four different schools. Findings: Our findings demonstrate how the protocol can capture change in students' thinking about troubleshooting by eliciting students' explanations of specificity of domain knowledge of problems, multimodality of physical computing, iterative testing of failure artifact scenarios, and concreteness of troubleshooting and problem solving processes. Originality: Beyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, our "failure artifact scenarios" clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students' abilities over time.
Tribute to Henry Primakoff:Tests of Chiral Perturbation Theory via Primakoff Reactions
2024-12-04
Murray Moinester
Henry Primakoff founded a field of study based on the scattering of very high energy particles (pions, kaons, $\gamma$ gamma-rays) from photon targets, $\gamma^*$ virtual photons in the Coulomb field of an atomic nucleus. Primakoff was the first to describe how to determine the $\pi^0$ lifetime by measuring the $\gamma\gamma^*\to\pi^0$ production cross section. For the case that a high energy (190 GeV/c) pion beam is used, Primakoff scattering, viewed in the rest frame of the pion, is effectively low-energy soft scattering of ~250 MeV gamma rays from a pion at rest. Together, low-energy soft scattering and high-energy hard scattering studies play crucial complementary roles in testing the Quantum Chromodynamics (QCD) framework. A short description is presented of Primakoff's scientific career and personal life. Here, we focus on a review of Primakoff Scattering studies of pion polarizability and the $\gamma\to\pi\pi\pi$ chiral anomaly at CERN COMPASS, and of the {\pi}0 lifetime at Jefferson Laboratory (Jlab). Following a brief review of Chiral Perturbation Theory (ChPT), we discuss the good agreement of the results of these studies with 2-flavor $(u,d)$ ChPT predictions. We discuss why Primakoff studies at CERN AMBER for kaon polarizability and $\gamma\to\pi\pi\eta$ and $\gamma\to KK\pi$ chiral anomalies, and at Jlab for the $\pi^0$ and $\eta$ lifetimes, are important for validating the theoretical framework of 3-flavor $(u,d,s)$ ChPT. The transition from 2-flavor to 3-flavor ChPT incorporates the strange quark, which is crucial for understanding the full dynamics of the light mesons (pions, kaons, and etas). By comparing 3-flavor ChPT predictions with data for kaons, $\pi^0$ and $\eta$, it will be possible to assess how well the model captures the interplay of the additional flavor and the effects of strange quarks.
The Growth of Galaxy Stellar Haloes Over $0.2 \leq z \leq 1.1$
2024-12-04
Devin J. Williams, Ivana Damjanov, Marcin Sawicki, Harrison Souchereau, Lingjian Chen, Guillaume Desprez, Angelo George, Marianna Annunziatella, Stephen Gwyn
Galaxies are predicted to assemble their stellar haloes through the accretion of stellar material from interactions with their cosmic environment. Observations that trace stellar halo buildup probe the processes that drive galaxy size and stellar mass growth. We investigate stellar halo assembly over $0.2 \leq z \leq 1.1$ in a mass-complete ($M_{\star} \geq 10^{9.5}M_{\odot}$) sample of 242,456 star-forming and 88,421 quiescent galaxies (SFGs and QGs) from the CLAUDS and HSC-SSP surveys. We extract galaxy rest-frame $g$-band surface brightness ($\mu_g$) profiles to study faint, extended emission in galaxy outskirts. We examine trends in galaxy assembly by analyzing the median $\mu_g$ profiles in different SFG and QG \msS ranges with decreasing redshift and connecting evolution in galaxy $\mu_g$ profiles with the underlying stellar mass growth in galaxies. Since $z=1.1$, the majority of evolution in the median $\mu_g$ profiles of galaxies ($\sim$64$\%$ in SFGs and $\sim$71$\%$ in QGs) occurs throughout their stellar halo regions (2-10$R_e$). More massive galaxies assemble stellar halo material more rapidly at $0.2 \leq z \leq 1.1$. Over this period, QGs grow a larger fraction of their stellar haloes than SFGs at fixed $M_{\star}$ (factor of $\sim$1.2). Although star formation can account for the stellar halo growth observed in low-mass SFGs ($10^{9.5}M_\odot \leq M_\star < 10^{10.5}M_\odot$), high-mass SFGs ($M_\star \geq 10^{10.5}M_\odot$) and both low- and high-mass QGs require an additional assembly mechanism. Our results suggest accretion via minor mergers drives additional stellar halo growth in these galaxies. The contribution from accretion is larger in more massive galaxies (over $M_{\star} \geq 10^{9.5}M_{\odot}$), and QGs exhibit larger fractional increases to their ex-situ fractions over $0.2 \leq z \leq 1.1$ than SFGs at fixed $M_{\star}$.
In this paper, we develop the first comprehensive tutorial on designing future 6G networks that synergistically integrate notions of resilience and criticality from the ground up. While resilience refers to the ability to absorb, adapt to, and recover from adversarial or challenging conditions, criticality indicates the degree of importance or urgency assigned to a particular service or component. Despite a spiking interest in designing resilient wireless networks, most prior works do not provide a unified resilience definition, nor harness the intricate interplay between resilience and criticality. In order to fill this gap, in this paper, we highlight the importance of a criticality-aware approach as a key enabler for providing reliable and resilient service functionality. Moreover, we delve into the unique challenges and opportunities of the envisioned 6G features pertaining to resilience and (mixed) criticality. After reviewing resilience definitions, we present a core resilience strategy, a unified resilience metric, different criteria for service criticality, and prioritization frameworks, that augment the 6G resilience prospects. Afterwards, we explore the opportunities presented by promising technologies that enable a resilient 6G network design from a radio access network protocol stack perspective. We briefly revisit state-of-the-art network architectures, establish a rough connection to the Open-RAN Alliance vision, and discuss opportunities, existing techniques, and promising enabling mechanisms for 6G at each layer. Finally, the article discusses important research directions and open problems concerning resilience and criticality in 6G.
Image-Constrained Modeling with Hubble and Keck Images Reveals that OGLE-2012-BLG-0563Lb is a Jupiter-Mass planet Orbiting a K Dwarf
2024-12-04
David P. Bennett, Aparna Bhattacharya, Jean-Philippe Beaulieu, Naoki Koshimoto, Joshua W. Blackman, Ian A. Bond, Clement Ranc, Natalia Rektsini, Sean K. Terry, Aikaterini Vandorou
We present high angular resolution imaging from the {\sl Hubble Space Telescope} combined with adaptive optics imaging results from the {\sl Keck}-II telescope to determine the mass of the OGLE-2012-BLG-0563L host star and planet to be $M_{\rm host} = 0.801\pm 0.033M_\odot$ and $M_{\rm planet} = 1.116 \pm 0.087 M_{\rm Jupiter}$, respectively, located at a distance of $D_L = 5.46\pm 0.56\,$kpc. There is a close-wide degeneracy in the light curve models that indicates star-planet projected separation of $1.50\pm 0.16\,$AU for the close model and $8.41\pm 0.87\,$AU for the wide model. We used the image-constrained modeling method to analyze the light curve data with constraints from this high angular resolution image analysis. This revealed systematic errors in some of the ground-based light curve photometry that led to an estimate of the angular Einstein Radius, $\theta_E$, that was too large by a factor of $\sim 2$. The host star mass is a factor of 2.4 larger than the value presented in the \citet{fukui15} discovery paper. Although most systematic photometry errors seen in ground-based microlensing light curve photometry will not be repeated in data from the {\sl Roman Space Telescope}'s Galactic Bulge Time Domain Survey, we argue that image constrained modeling will be a valuable method to identify possible systematic errors in {\sl Roman} photometry.
Automated galaxy sizes in Euclid images using the Segment Anything Model
2024-12-04
J. Vega-Ferrero, F. Buitrago, J. Fernández-Iglesias, S. Raji, B. Sahelices, H. Domínguez Sánchez
Stellar disk truncations, also referred to as galaxy edges, are key indicators of galactic size, determined by the radial location of the gas density threshold for star formation. Accurately measuring galaxy sizes for millions of galaxies is essential for understanding the physical processes driving galaxy evolution over cosmic time. In this study, we aim to explore the potential of the Segment Anything Model (SAM), a foundation model designed for image segmentation, to automatically identify disk truncations in galaxy images. With the Euclid Wide Survey poised to deliver vast datasets, our goal is to assess SAM's capability to measure galaxy sizes in a fully automated manner. SAM was applied to a labeled dataset of 1,047 disk-like galaxies with $M_* > 10^{10} M_{\odot}$ at redshifts up to $z \sim 1$, sourced from the HST CANDELS fields. We 'euclidized' the HST galaxy images by creating composite RGB images, using the F160W (H-band), F125W (J-band), and F814W + F606W (I-band + V-band) HST filters, respectively. Using these processed images as input for SAM, we retrieved various truncation masks for each galaxy image under different configurations of the input data. We find excellent agreement between the galaxy sizes identified by SAM and those measured manually (i.e., by using the radial positions of the stellar disk edges in galaxy light profiles), with an average deviation of approximately $3\%$. This error reduces to about $1\%$ when excluding problematic cases. Our results highlight the strong potential of SAM for detecting disk truncations and measuring galaxy sizes across large datasets in an automated way. SAM performs well without requiring extensive image preprocessing, labeled training datasets for truncations (used only for validation), fine-tuning, or additional domain-specific adaptations such as transfer learning.
In this work, we leverage CMB data from the Atacama Cosmology Telescope (ACT) and LSS data from the imaging survey conducted by the Dark Energy Spectroscopic Instrument (DESI) to study the distribution of gas around galaxy groups at low redshift, $z \approx 0.3$, via the kinematic Sunyaev-Zel'dovich (kSZ) effect. In particular, we perform velocity-weighted stacking on the photometric Bright Galaxy Sample (BGS) to isolate the monopole and quadrupole of the kSZ signal, orienting the stacked images along 2D filaments identified using the Hessian of the projected gravitational potential. We find a 7.2$\sigma$ detection in the monopole of the signal (i.e., the gas density profile) and a 4$\sigma$ detection in the quadrupole ($m = 2$), constituting the first measurement of the alignment between gas distribution and the cosmic web through the kSZ effect. As it is a linear probe of the local gas density, the kSZ has heightened sensitivity to the warm-hot intergalactic medium (WHIM), which is believed to house the majority of the ``missing baryons.'' Mapping out the gas density at low redshifts, as enabled by our measurements, is crucial for weak lensing surveys, for which the impact of baryons on small scales is a major impediment. We compare the anisotropic signal against two hydrodynamical simulations, TNG300-1 and Illustris, which have very different baryonic feedback prescriptions. We find that the anisotropic signal measured in the data is comparable but slightly larger and more extended compared with the simulations. This suggests that there is excess accretion and feedback taking place through the filaments, hinting at the possible presence of spin-filament alignment of the BGS objects.
The development of single-cell and spatial transcriptomics has revolutionized our capacity to investigate cellular properties, functions, and interactions in both cellular and spatial contexts. However, the analysis of single-cell and spatial omics data remains challenging. First, single-cell sequencing data are high-dimensional and sparse, often contaminated by noise and uncertainty, obscuring the underlying biological signals. Second, these data often encompass multiple modalities, including gene expression, epigenetic modifications, and spatial locations. Integrating these diverse data modalities is crucial for enhancing prediction accuracy and biological interpretability. Third, while the scale of single-cell sequencing has expanded to millions of cells, high-quality annotated datasets are still limited. Fourth, the complex correlations of biological tissues make it difficult to accurately reconstruct cellular states and spatial contexts. Traditional feature engineering-based analysis methods struggle to deal with the various challenges presented by intricate biological networks. Deep learning has emerged as a powerful tool capable of handling high-dimensional complex data and automatically identifying meaningful patterns, offering significant promise in addressing these challenges. This review systematically analyzes these challenges and discusses related deep learning approaches. Moreover, we have curated 21 datasets from 9 benchmarks, encompassing 58 computational methods, and evaluated their performance on the respective modeling tasks. Finally, we highlight three areas for future development from a technical, dataset, and application perspective. This work will serve as a valuable resource for understanding how deep learning can be effectively utilized in single-cell and spatial transcriptomics analyses, while inspiring novel approaches to address emerging challenges.
2024-12-04
Qi Zheng, Yibo Fan, Leilei Huang, Tianyu Zhu, Jiaming Liu, Zhijian Hao, Shuo Xing, Chia-Ju Chen, Xiongkuo Min, Alan C. Bovik, Zhengzhong Tu
Video quality assessment (VQA) is an important processing task, aiming at predicting the quality of videos in a manner highly consistent with human judgments of perceived quality. Traditional VQA models based on natural image and/or video statistics, which are inspired both by models of projected images of the real world and by dual models of the human visual system, deliver only limited prediction performances on real-world user-generated content (UGC), as exemplified in recent large-scale VQA databases containing large numbers of diverse video contents crawled from the web. Fortunately, recent advances in deep neural networks and Large Multimodality Models (LMMs) have enabled significant progress in solving this problem, yielding better results than prior handcrafted models. Numerous deep learning-based VQA models have been developed, with progress in this direction driven by the creation of content-diverse, large-scale human-labeled databases that supply ground truth psychometric video quality data. Here, we present a comprehensive survey of recent progress in the development of VQA algorithms and the benchmarking studies and databases that make them possible. We also analyze open research directions on study design and VQA algorithm architectures.
Exploring the Diversity of Faint Satellites in the M81 Group
2024-12-03
Katya Gozman, Eric F. Bell, In Sung Jang, Jose Marco Arias, Jeremy Bailin, Roelof S. de Jong, Richard D'Souza, Oleg Y. Gnedin, Antonela Monachesi, Paul A. Price, Vaishnav V. Rao, Adam Smercina
In the last decade, we have been able to probe further down the galaxy luminosity function than ever before and expand into the regime of ultra-faint dwarfs (UFDs), which are some of the best probes we have of small-scale cosmology and galaxy formation. Digital sky surveys have enabled the discovery and study of these incredibly low-mass, highly dark-matter dominated systems around the Local Group, but it is critical that we expand the satellite census further out to understand if Milky Way and M31 satellites are representative of dwarf populations in the local Universe. Using data from HST/ACS, we present updated characterization of four satellite systems in the M81 group. These systems - D1005+68, D1006+69, DWJ0954+6821, and D1009+68 - were previously discovered using ground-based Subaru HSC data as overdensities in M81's halo and are now confirmed with HST/ACS by this work. These are all faint (M_V >= -7.9) and consistent with old (~13 Gyr), metal-poor ([M/H] < -1.5) populations. Each system possesses relatively unusual features - including one of the most concentrated satellite galaxies with a Sersic index of n ~ 5, one of the most elliptical galaxies outside the Local Group with an e ~ 0.6, and one of the most compact galaxies for its magnitude. Two of the satellites have very low surface brightness, lower than most known galaxies in this absolute magnitude range. This work previews the scientific promise of the upcoming Rubin Observatory and Roman Telescope for illuminating the diversity of UFDs in the Local Volume and beyond.
2024-12-03
F. B. M. dos Santos, G. Rodrigues, R. de Souza, J. S. Alcaniz
We report forecasted constraints on warm inflation in the light of future cosmic microwave background (CMB) surveys, with data expected to be available in the coming decade. These observations could finally give us the missing information necessary to unveil the production of gravitational waves during inflation, reflected by detecting a non-zero tensor-to-scalar ratio crucial to the B-mode power spectrum of the CMB. We consider the impact of three future surveys, namely the CMB-S4, Simons Observatory, and the space-borne $\textit{LiteBIRD}$, in restricting the parameter space of four typical warm inflationary models in the context of a quartic potential, which is well motivated theoretically. We find that all three surveys significantly improve the models' parameter space, compared to recent results obtained with current $\textit{Planck}$+BICEP/Keck Array data. Moreover, the combination of ground-based and space-borne (CMB-S4+$\textit{LiteBIRD}$) tightens the constraints so that we expect to distinguish even better warm inflation scenarios. This result becomes clear when we compare the models' predictions with a $\Lambda$CDM+$r$ forecast, compatible with $r=0$, in which one of them already becomes excluded by data.
Molecular gas and dust properties in $z>7$ quasar hosts
2024-12-03
Francesco Salvestrini, Chiara Feruglio, Roberta Tripodi, Fabio Fontanot, Manuela Bischetti, Gabriella De Lucia, Fabrizio Fiore, Michaela Hirschmann, Umberto Maio, Enrico Piconcelli, Ivano Saccheo, Alessia Tortosa, Rosa Valiante, Lizhi Xie, Luca Zappacosta
Observational campaigns hunting the elusive reservoirs of cold gas in the host galaxies of quasars at the Epoch of Reionization (EoR) are crucial to study the formation and evolution of the first massive systems at early epochs. We present new Northern Extended Millimetre Array (NOEMA) observations tracing CO(6--5), CO(7--6) emission lines, and the underlying continuum in five of the eight quasars at redshift $z>7$ known to date, thus completing the survey of the cold molecular gas reservoir in the host galaxies of the first quasars. Combining NOEMA observations with archival Atacama Large Millimeter Array (ALMA) data available, we model the far-infrared spectral energy distribution with a modified blackbody to measure dust properties and star formation rates. We use CO and [CII] lines to derive molecular gas masses, which we compare with results from semi-analytical models and observations of galaxies at different epochs. No statistically significant detection of CO emission lines was reported for the five quasars in this sample, resulting in a relatively low amount of cold molecular gas in the host when compared with galaxies at later epochs. Nonetheless, gas-to-dust ratios are consistent with the local value, suggesting that the scaling relation between dust and cold gas holds up to $z>7$. Quasars at the EoR show star formation efficiencies which are among the highest observed so far, but comparable with that observed in luminous quasar at Cosmic Noon and that predicted for the brightest ($L_{bol}>3\times10^{46}$ erg s$^{-1}$) quasar objects drawn from the semi-analytical model GAEA. Quasar host galaxies at the EoR are undergoing an intense phase of star formation, which suggests a strong coupling between the luminous phase of the quasar and the rapid growth of the host.
Soft QCD Physics at the LHC: highlights and opportunities
2024-12-03
Peter Christiansen, Pierre Van Mechelen
The Large Hadron Collider (LHC) at CERN in Switzerland became operational in 2009 and has since then produced a plethora of results for proton-proton (pp) collisions. This short review covers results that relates to soft QCD focusing on non-diffractive physics at mid-rapidity. Most of the presented results comes from transverse momentum ($p_{\rm T}$) spectra and related/derived observables such as multiplicity, $\langle p_{\rm T} \rangle$ and ratios, but also the observed ``ridge'' and the questions of quark-gluon plasma in pp collisions will be discussed. The goal of the review is on one hand to introduce the topics and provide references for scientists joining the LHC program while at the same time highlighting what we consider the most interesting results and open questions to inspire novel measurements.
The Broader Landscape of Robustness in Algorithmic Statistics
2024-12-03
Gautam Kamath
The last decade has seen a number of advances in computationally efficient algorithms for statistical methods subject to robustness constraints. An estimator may be robust in a number of different ways: to contamination of the dataset, to heavy-tailed data, or in the sense that it preserves privacy of the dataset. We survey recent results in these areas with a focus on the problem of mean estimation, drawing technical and conceptual connections between the various forms of robustness, showing that the same underlying algorithmic ideas lead to computationally efficient estimators in all these settings.
An unbiased statistical study of the metallicity of core-collapse supernovae based on VLT/MUSE integral-field-unit spectroscopy
2024-12-03
Qiang Xi, Ning-Chen Sun, Yi-Han Zhao, Justyn R. Maund, Zexi Niu, Adam J. Singleton, Jifeng Liu
Metallicity plays a crucial role in the evolution of massive stars and their final core-collapse supernova (CCSN) explosions. Integral-field-unit (IFU) spectroscopy can provide a spatially resolved view of SN host galaxies and serve as a powerful tool to study SN metallicities. Early transient surveys targeted bright galaxies with high star formation and SN rates; as a result, the discovered SNe are significantly biased toward high metallicities. More recently, the untargeted, wide-field transient surveys, such as ASAS-SN and ZTF, have discovered a large number of SNe without such a bias. In this work, we construct a large and unbiased sample of SNe discovered by (quasi-) untargted searches, consisting of 209 SNe of Types II (with unknown subtypes), IIP, IIn, IIb, Ib and Ic at z $\leq$ 0.02 with VLT/MUSE observations. This is currently the largest CCSN sample with IFU observations. With the strong-line method, we reveal the spatially-resolved metallicity maps of the SN host galaxies and acquire accurate metallicity measurements for the SN sites. Our results show that the SN metallicities range from 12 + log(O/H) = 8.1 to 8.7 dex, and the metallicity distributions for different SN types are very close to each other, with mean and median values of 8.4 - 8.5 dex. We carefully analysed the stochastic sampling effect, showing that our large sample size narrows the 1$\sigma$ uncertainty down to only 0.05 dex. The apparent metallicity differences among SN types are all within 3$\sigma$ uncertainties and the metallicity distributions for different SN types are all consistent with being randomly drawn from the same reference distribution. This suggests that metallicity plays a minor role in the origin of different CCSN types and some other metallicity-insensitive processes, such as binary interaction, dominate the distinction of CCSN types.
Scaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education
2024-12-03
Karen D. Wang, Zhangyang Wu, L'Nard Tufts II, Carl Wieman, Shima Salehi, Nick Haber
Developing problem-solving competency is central to Science, Technology, Engineering, and Mathematics (STEM) education, yet translating this priority into effective approaches to problem-solving instruction and assessment remain a significant challenge. The recent proliferation of generative artificial intelligence (genAI) tools like ChatGPT in higher education introduces new considerations about how these tools can help or hinder students' development of STEM problem-solving competency. Our research examines these considerations by studying how and why college students use genAI tools in their STEM coursework, focusing on their problem-solving support. We surveyed 40 STEM college students from diverse U.S. institutions and 28 STEM faculty to understand instructor perspectives on effective genAI tool use and guidance in STEM courses. Our findings reveal high adoption rates and diverse applications of genAI tools among STEM students. The most common use cases include finding explanations, exploring related topics, summarizing readings, and helping with problem-set questions. The primary motivation for using genAI tools was to save time. Moreover, over half of student participants reported simply inputting problems for AI to generate solutions, potentially bypassing their own problem-solving processes. These findings indicate that despite high adoption rates, students' current approaches to utilizing genAI tools often fall short in enhancing their own STEM problem-solving competencies. The study also explored students' and STEM instructors' perceptions of the benefits and risks associated with using genAI tools in STEM education. Our findings provide insights into how to guide students on appropriate genAI use in STEM courses and how to design genAI-based tools to foster students' problem-solving competency.
Costs of Bayesian Parameter Estimation in Third-Generation Gravitational Wave Detectors: a Review of Acceleration Methods
2024-12-03
Qian Hu, John Veitch
Bayesian inference with stochastic sampling has been widely used to obtain the properties of gravitational wave (GW) sources. Although computationally intensive, its cost remains manageable for current second-generation GW detectors because of the relatively low event rate and signal-to-noise ratio (SNR). The third-generation (3G) GW detectors are expected to detect hundreds of thousands of compact binary coalescence events every year with substantially higher SNR and longer signal duration, presenting significant computational challenges. In this study, we systematically evaluate the computational costs of source parameter estimation (PE) in the 3G era by modeling the PE time cost as a function of SNR and signal duration. We examine the standard PE method alongside acceleration methods including relative binning, multibanding, and reduced order quadrature. We predict that PE for a one-month-observation catalog with 3G detectors could require billions to quadrillions of CPU core hours with the standard PE method, whereas acceleration techniques can reduce this demand to millions of core hours. These findings highlight the necessity for more efficient PE methods to enable cost-effective and environmentally sustainable data analysis for 3G detectors. In addition, we assess the accuracy of accelerated PE methods, emphasizing the need for careful treatment in high-SNR scenarios.
A Bidirectional Long Short Term Memory Approach for Infrastructure Health Monitoring Using On-board Vibration Response
2024-12-03
R. R. Samani, A. Nunez, B. De Schutter
The growing volume of available infrastructural monitoring data enables the development of powerful datadriven approaches to estimate infrastructure health conditions using direct measurements. This paper proposes a deep learning methodology to estimate infrastructure physical parameters, such as railway track stiffness, using drive-by vibration response signals. The proposed method employs a Long Short-term Memory (LSTM) feature extractor accounting for temporal dependencies in the feature extraction phase, and a bidirectional Long Short-term Memory (BiLSTM) networks to leverage bidirectional temporal dependencies in both the forward and backward paths of the drive-by vibration response in condition estimation phase. Additionally, a framing approach is employed to enhance the resolution of the monitoring task to the beam level by segmenting the vibration signal into frames equal to the distance between individual beams, centering the frames over the beam nodes. The proposed LSTM-BiLSTM model offers a versatile tool for various bridge and railway infrastructure conditions monitoring using direct drive-by vibration response measurements. The results demonstrate the potential of incorporating temporal analysis in the feature extraction phase and emphasize the pivotal role of bidirectional temporal information in infrastructure health condition estimation. The proposed methodology can accurately and automatically estimate railway track stiffness and identify local stiffness reductions in the presence of noise using drive-by measurements. An illustrative case study of vehicle-track interaction simulation is used to demonstrate the performance of the proposed model, achieving a maximum mean absolute percentage error of 1.7% and 0.7% in estimating railpad and ballast stiffness, respectively.
Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions
2024-12-03
Kai Sun, Siyan Xue, Fuchun Sun, Haoran Sun, Yu Luo, Ling Wang, Siyuan Wang, Na Guo, Lei Liu, Tian Zhao, Xinzhou Wang, Lei Yang, Shuo Jin, Jun Yan, Jiahong Dong
Recent advancements in deep learning have significantly revolutionized the field of clinical diagnosis and treatment, offering novel approaches to improve diagnostic precision and treatment efficacy across diverse clinical domains, thus driving the pursuit of precision medicine. The growing availability of multi-organ and multimodal datasets has accelerated the development of large-scale Medical Multimodal Foundation Models (MMFMs). These models, known for their strong generalization capabilities and rich representational power, are increasingly being adapted to address a wide range of clinical tasks, from early diagnosis to personalized treatment strategies. This review offers a comprehensive analysis of recent developments in MMFMs, focusing on three key aspects: datasets, model architectures, and clinical applications. We also explore the challenges and opportunities in optimizing multimodal representations and discuss how these advancements are shaping the future of healthcare by enabling improved patient outcomes and more efficient clinical workflows.
Impact of blending on weak lensing measurements with the Legacy Survey of Space and Time
2024-12-03
Manon Ramel, Cyrille Doux, Marine Kuna
Upcoming deep optical surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will scan the sky to unprecedented depths, detecting billions of galaxies. However, this amount of detections will lead to the apparent superposition of galaxies in the images, a phenomenon known as blending, that can affect the accurate measurement of individual galaxy properties. In particular, galaxy shapes play a crucial role in estimating the masses of large-scale structures, such as galaxy clusters, through weak gravitational lensing. This proceeding introduces a new catalog matching algorithm, friendly, designed for detecting and characterizing blends in simulated LSST data for the Dark Energy Science Collaboration (DESC) Data Challenge 2. The aim of this algorithm is to combine several matching procedures, as well as a probabilistic method to quantify blended systems. By removing the resulted 27% of galaxies affected by blending from the dataset, we demonstrate that the amplitude of the excess surface mass density weak lensing profile - potentially biased low due to blending - may be partially corrected.
Neutral hydrogen (HI) intensity mapping (IM) presents great promise for future cosmological large-scale structure surveys. However, a major challenge for HIIM cosmological studies is to accurately subtract the foreground contamination. An accurate beam model is crucial for improving foreground subtraction accuracy. In this work, we develop a stacking-based beam reconstruction method utilizing the radio continuum point sources within the drift-scan field. Based on the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we employ two series of drift-scan survey data and merge the measurements to construct the beam patterns of the FAST L-band 19 feeds. To model the beams, we utilize the Zernike polynomial (ZP), which effectively captures % the side lobes and asymmetric features of the side lobes. asymmetric features of the main beam and the different side lobes. Due to the symmetric location of the beams, the main features of the beams are closely related to the distance from the center of the feed array, e.g., as the distance increases, side lobes become more pronounced. This modeling pipeline leverages the stable drift-scan data to extract beam patterns exclude the reflector's changing effects, and provides a more accurate measurement beam and a more precise model beam for FAST HIIM cosmology surveys.
Remote Sensing Temporal Vision-Language Models: A Comprehensive Survey
2024-12-03
Chenyang Liu, Jiafan Zhang, Keyan Chen, Man Wang, Zhengxia Zou, Zhenwei Shi
Temporal image analysis in remote sensing has traditionally centered on change detection, which identifies regions of change between images captured at different times. However, change detection remains limited by its focus on visual-level interpretation, often lacking contextual or descriptive information. The rise of Vision-Language Models (VLMs) has introduced a new dimension to remote sensing temporal image analysis by integrating visual information with natural language, creating an avenue for advanced interpretation of temporal image changes. Remote Sensing Temporal VLMs (RSTVLMs) allow for dynamic interactions, generating descriptive captions, answering questions, and providing a richer semantic understanding of temporal images. This temporal vision-language capability is particularly valuable for complex remote sensing applications, where higher-level insights are crucial. This paper comprehensively reviews the progress of RSTVLM research, with a focus on the latest VLM applications for temporal image analysis. We categorize and discuss core methodologies, datasets, and metrics, highlight recent advances in temporal vision-language tasks, and outline key challenges and future directions for research in this emerging field. This survey fills a critical gap in the literature by providing an integrated overview of RSTVLM, offering a foundation for further advancements in remote sensing temporal image understanding. We will keep tracing related works at \url{https://github.com/Chen-Yang-Liu/Awesome-RS-Temporal-VLM}
ComPair-2: A Next Generation Medium Energy Gamma-ray Telescope Prototype
2024-12-03
Regina Caputo, Carolyn Kierans, Nicholas Cannady, Abe Falcone, Yasushi Fukazawa, Manoj Jadhav, Matthew Kerr, Nicholas Kirschner, Kavic Kumar, Adrien Laviron, Richard Leys, Julie McEnery, Jessica Metcalfe, Zachary Metzler, Nathan Miller, John Mitchell, Lucas Parker, Ivan Peric, Jeremy Perkins, Bernard Phlips, Judith Racusin, Makoto Sasaki, Kenneth N. Segal, Daniel Shy, Amanda L. Steinhebel, Nicolas Striebig, Yusuke Suda, Hiroyasu Tajima, Janeth Valverde, Daniel P. Violette, Richard Woolf, Andreas Zoglauer
Many questions posed in the Astro2020 Decadal survey in both the New Messengers and New Physics and the Cosmic Ecosystems science themes require a gamma-ray mission with capabilities exceeding those of existing (e.g. Fermi, Swift) and planned (e.g. COSI) observatories. ComPair, the Compton Pair telescope, is a prototype of such a next-generation gamma-ray mission. It had its inaugural balloon flight from Ft. Sumner, New Mexico in August 2023. To continue the goals of the ComPair project to develop technologies that will enable a future gamma-ray mission, the next generation of ComPair (ComPair-2) will be upgraded to increase the sensitivity and low-energy transient capabilities of the instrument. These advancements are enabled by AstroPix, a silicon monolithic active pixel sensor, in the tracker and custom dual-gain silicon photomultipliers and front-end electronics in the calorimeter. This effort builds on design work for the All-sky Medium Energy Gamma-ray Observatory eXplorer (AMEGO-X) concept that was submitted the 2021 MIDEX Announcement of Opportunity. Here we describe the ComPair-2 prototype design and integration and testing plans to advance the readiness level of these novel technologies.
Quadrupolar Density Structures in Driven Magnetic Reconnection Experiments with a Guide Field
2024-12-03
T. W. O. Varnish, J. Chen, S. Chowdhry, R. Datta, G. V. Dowhan, L. S. Horan IV, N. M. Jordan, E. R. Neill, A. P. Shah, B. J. Sporer, R. Shapovalov, R. D. McBride, J. D. Hare
Magnetic reconnection is a ubiquitous process in plasma physics, driving rapid and energetic events such as coronal mass ejections. Reconnection between magnetic fields with arbitrary shear can be decomposed into an anti-parallel, reconnecting component, and a non-reconnecting guide-field component which is parallel to the reconnecting electric field. This guide field modifies the structure of the reconnection layer and the reconnection rate. We present results from experiments on the MAIZE pulsed-power generator (500 kA peak current, 200 ns rise-time) which use two exploding wire arrays, tilted in opposite directions, to embed a guide field in the plasma flows with a relative strength $b\equiv B_g/B_{rec}=\text{0, 0.4, or 1}$. The reconnection layers in these experiments have widths which are less than the ion skin depth, $d_i=c/\omega_{pi}$, indicating the importance of the Hall term, which generates a distinctive quadrupolar magnetic field structure along the separatrices of the reconnection layer. Using laser imaging interferometry, we observe quadrupolar structures in the line-integrated electron density, consistent with the interaction of the embedded guide field with the quadrupolar Hall field. Our measurements extend over much larger length scales ($40 d_i$) at higher $\beta$ ($\sim 1$) than previous experiments, providing an insight into the global structure of the reconnection layer.
The ALPINE-ALMA [CII] Survey: Unveiling the baryon evolution in the ISM of $z\sim5$ star-forming galaxies
2024-12-03
P. Sawant, A. Nanni, M. Romano, D. Donevski, G. Bruzual, N. Ysard, B. C. Lemaux, H. Inami, F. Calura, F. Pozzi, K. Małek, Junais, M. Boquien, A. L. Faisst, M. Hamed, M. Ginolfi, G. Zamorani, G. Lorenzon, J. Molina, S. Bardelli, E. Ibar, D. Vergani, C. Di Cesare, M. Béthermin, D. Burgarella, P. Cassata, M. Dessauges-Zavadsky, E. D'Onghia, Y. Dubois, G. E. Magdis, H. Mendez-Hernandez
Recent observations reveal a rapid dust build-up in high-redshift galaxies (z > 4), challenging current models of galaxy formation. While our understanding of dust production and destruction in the interstellar medium (ISM) is advancing, probing baryonic processes in the early Universe remains a complex task. We characterize the evolution of 98 z~5 star-forming galaxies observed as part of the ALPINE survey by constraining the physical processes underpinning the gas and dust production, consumption, and destruction in their ISM. We make use of chemical evolution models to simultaneously reproduce the observed dust and gas content. For each galaxy, we estimate initial gas mass, inflows and outflows, and efficiencies of dust growth and destruction. We test the models with the canonical Chabrier and top-heavy initial mass functions (IMFs), with the latter enabling rapid dust production on shorter timescales. Our models successfully reproduce gas and dust content in older galaxies (> 600 Myr) regardless of the IMF, with Type II SNe as the primary dust source and no dust growth in ISM with moderate inflow of primordial gas. In case of intermediate-age galaxies (300 - 600 Myr), we reproduce the gas and dust content through Type II SNe and dust growth in ISM, though we observe an over-prediction of dust mass in older galaxies, potentially indicating an unaccounted dust destruction mechanism and/or an overestimation of the observed dust masses. The number of young galaxies (< 300 Myr) reproduced, increases for models assuming top-heavy IMF but with maximal prescriptions of dust production. Galactic outflows are necessary to reproduce observed gas and dust masses. The Chabrier IMF models reproduce 65% of galaxies, while top-heavy IMF models improve this to 93%, easing tensions with observations. Upcoming JWST data will refine these models by resolving degeneracies in intrinsic galaxy properties.
We report the synergy between orbital and spin-orbit torques in WTe2/Fe3GeTe2 heterostructures characterized by a Berry curvature dipole. By applying a current along the a axis in WTe2, we detect an out-of-plane magnetization in the system, which we attribute to nonequilibrium orbital magnetization linked to the Berry curvature dipole based on first-principles calculations, manifesting as the orbital Edelstein effect. This effect generates orbital torques that enable field-free perpendicular magnetization switching. Furthermore, by applying a relatively small current along the a axis and a pulsed current along the b axis in WTe2, we demonstrate controllable field-free magnetization switching of the adjacent Fe3GeTe2 layer, independently manipulating the orbital and spin-orbit torques. Our findings not only enhance the understanding of the collaborative dynamics between these torques but also suggest potential applications in magnetoresistive random-access memory.
Current-induced out of plane magnetization has been utilized for field-free switching of ferromagnets with perpendicular magnetic anisotropy. Identifying systems capable of energy-efficiently converting charge currents into out of plane orbit- or spin-polarized currents is crucial for advancing magnetic memory technologies. Here we introduce the Berry curvature dipole as a key evaluation factor, directly measurable through nonlinear Hall effects. In the Weyl semimetal TaIrTe4 used in our experiments, applying a current parallel to the Berry curvature dipole results in out of plane orbital magnetization, which governs the field-free perpendicular magnetization switching in TaIrTe4/Fe3GaTe2 heterostructures. Notably, all-electric control of van der Waals magnetoresistive memory at room temperature has been achieved with a low critical current density 2x10^6A/cm2 for data writing. Our findings reveal the connection between nonlinear Hall effects and field-free magnetization switching, highlighting the potential of the Berry curvature dipole in advancing orbitronics.
Numerical approaches to compute spectra of non-self adjoint operators in dimensions two and three
2024-12-03
Fatima Aboud, François Jauberteau, Didier Robert
In this article we are interested for the numerical computation of spectra of non-self adjoint quadratic operators, in two and three spatial dimensions. Indeed, in the multidimensional case very few results are known on the location of the eignevalues. This leads to solve nonlinear eigenvalue problems. In introduction we begin with a review of theoretical results and numerical results obtained for the one dimensional case. Then we present the numerical methods developed to compute the spectra (finite difference discretization) for the two and three dimensional cases. The numerical results obtained are presented and analyzed. One difficulty here is that we have to compute eigenvalues of strongly non-self-adjoint operators which are unstable. This work is in continuity of a previous work in one spatial dimension.
Temporal-spatial distribution of YSOs nearby Taurus region
2024-12-03
Jiaming Liu, Min Fang, Chao Liu, Xiaolong Wang, Wenyuan Cui, Licai Deng
The Taurus region is one of the most extensively studied star-forming regions. Surveys indicate that the young stars in this region are comprised of Young Stellar Objects (YSOs) that cluster in groups associated with the molecular cloud (Grouped Young Stellar Objects, GYSOs), and some older ones that are sparsely distributed throughout the region (Distributed Young Stellar Objects, DYSOs). To bridge the age gap between the GYSOs ($\le$5 Myr) and the DYSOs (10-20 Myr), we conducted a survey to search for new YSOs in this direction. Based on infrared color excesses and Li I absorption lines, we identified 145 new YSOs. Combining these with the previously identified GYSOs and DYSOs, we constructed a sample of 519 YSOs that encompasses the entire region. Subsequently, we calculated the ages of the sample based on their proximity to the local bubble. The age versus Distance to the Local Bubble ($D_{\rm LB}$) relationship for the DYSOs shows a clear trend: the farther they are from the Local Bubble, the younger they are, which is consistent with the supernovae-driven formation scenario of the Local Bubble. The GYSOs also exhibit a mild age versus $D_{\rm LB}$ trend. However, they are significantly younger and are mostly confined to distances of 120 to 220 pc. Considering their distribution in the age versus $D_{\rm LB}$ space is well separated from the older DYSOs, they may also be products of the Local Bubble but formed in more recent and localized events.
Nature versus nurture in galaxy formation: the effect of environment on star formation with causal machine learning
2024-12-03
Sunil Mucesh, William G. Hartley, Ciarán M. Gilligan-Lee, Ofer Lahav
Understanding how galaxies form and evolve is at the heart of modern astronomy. With the advent of large-scale surveys and simulations, remarkable progress has been made in the last few decades. Despite this, the physical processes behind the phenomena, and particularly their importance, remain far from known, as correlations have primarily been established rather than the underlying causality. We address this challenge by applying the causal inference framework. Specifically, we tackle the fundamental open question of whether galaxy formation and evolution depends more on nature (i.e., internal processes) or nurture (i.e., external processes), by estimating the causal effect of environment on star-formation rate in the IllustrisTNG simulations. To do so, we develop a comprehensive causal model and employ cutting-edge techniques from epidemiology to overcome the long-standing problem of disentangling nature and nurture. We find that the causal effect is negative and substantial, with environment suppressing the SFR by a maximal factor of $\sim100$. While the overall effect at $z=0$ is negative, in the early universe, environment is discovered to have a positive impact, boosting star formation by a factor of $\sim10$ at $z\sim1$ and by even greater amounts at higher redshifts. Furthermore, we show that: (i) nature also plays an important role, as ignoring it underestimates the causal effect in intermediate-density environments by a factor of $\sim2$, (ii) controlling for the stellar mass at a snapshot in time, as is common in the literature, is not only insufficient to disentangle nature and nurture but actually has an adverse effect, though (iii) stellar mass is an adequate proxy of the effects of nature. Finally, this work may prove a useful blueprint for extracting causal insights in other fields that deal with dynamical systems with closed feedback loops, such as the Earth's climate.
Autism spectrum disorder (ASD) is a pervasive developmental disorder of the central nervous system, which occurs most frequently in childhood and is characterized by unusual and repetitive ritualistic behaviors. Currently, diagnostic methods primarily rely on questionnaire surveys and behavioral observation, which may lead to misdiagnoses due to the subjective evaluation and measurement used in these traditional methods. With the advancement in medical imaging, MR imaging-based diagnosis has become an alternative and more objective approach. In this paper, we propose a Hybrid neural Network model for ASD identification, termded ASD-HNet, to hierarchically extract features on the functional brain networks based on resting-state functional magnetic resonance imaging data. This hierarchical method can better extract brain representations, improve the diagnostic accuracy, and help us better locate brain regions related to ASD. Specifically, features are extracted from three scales: local regions of interest (ROIs) scale, community-clustering scale, and the whole-communities scale. For the ROI scale, graph convolution is used to transfer features between ROIs. At the community cluster scale, functional gradients are introduced, the clustering algorithm K-Means is used to automatically cluster ROIs with similar functional gradients into several communities, and features of ROIs belonging to the same community are extracted to characterize these communities. At global information integration scale, we extract global features from community-scale brain networks to characterize the whole brain networks. We validate the effectiveness of our method using the public dataset of Autism Brain Imaging Data Exchange I (ABIDE I), and elucidate the interpretability of the method. Experimental results demonstrate that the proposed ASD-HNet can yield superior performance than compared methods.
We present VISTA (Visualization of Internal States and Their Associations), a novel pipeline for visually exploring and interpreting neural network representations. VISTA addresses the challenge of analyzing vast multidimensional spaces in modern machine learning models by mapping representations into a semantic 2D space. The resulting collages visually reveal patterns and relationships within internal representations. We demonstrate VISTA's utility by applying it to sparse autoencoder latents uncovering new properties and interpretations. We review the VISTA methodology, present findings from our case study ( https://got.drib.net/latents/ ), and discuss implications for neural network interpretability across various domains of machine learning.
The classification of real and bogus transients using active learning and semi-supervised learning
2024-12-03
Yating Liu, Lulu Fan, Lei Hu, Junqiang Lu, Yan Lu, Zelin Xu, Jiazheng Zhu, Haochen Wang, Xu Kong
Deep-learning-based methods have been favored in astrophysics owing to their adaptability and remarkable performance and have been applied to the task of the classification of real and bogus transients. Different from most existing approaches which necessitate massive yet expensive annotated data, We aim to leverage training samples with only 1000 labels available to discover real sources that vary in brightness over time in the early stage of the WFST 6-year survey. Methods. We present a novel deep-learning method that combines active learning and semi-supervised learning to construct a competitive real/bogus classifier. Our method incorporates an active learning stage, where we actively select the most informative or uncertain samples for annotation. This stage aims to achieve higher model performance by leveraging fewer labeled samples, thus reducing annotation costs and improving the overall learning process efficiency. Furthermore, our approach involves a semi-supervised learning stage that exploits the unlabeled data to enhance the model's performance and achieve superior results compared to using only the limited labeled data.
2024-12-03
Adam Wróbel, Mikołaj Janusz, Bartosz Zieliński, Dawid Rymarczyk
Deep Learning (DL) models are often black boxes, making their decision-making processes difficult to interpret. This lack of transparency has driven advancements in eXplainable Artificial Intelligence (XAI), a field dedicated to clarifying the reasoning behind DL model predictions. Among these, attribution-based methods such as LRP and GradCAM are widely used, though they rely on approximations that can be imprecise. To address these limitations, we introduce One Matrix to Explain Neural Networks (OMENN), a novel post-hoc method that represents a neural network as a single, interpretable matrix for each specific input. This matrix is constructed through a series of linear transformations that represent the processing of the input by each successive layer in the neural network. As a result, OMENN provides locally precise, attribution-based explanations of the input across various modern models, including ViTs and CNNs. We present a theoretical analysis of OMENN based on dynamic linearity property and validate its effectiveness with extensive tests on two XAI benchmarks, demonstrating that OMENN is competitive with state-of-the-art methods.
Estimating Photometric Redshifts for Galaxies from the DESI Legacy Imaging Surveys with Bayesian Neural Networks Trained by DESI EDR
2024-12-03
Xingchen Zhou, Nan Li, Hu Zou, Yan Gong, Furen Deng, Xuelei Chen, Qian Yu, Zizhao He, Boyi Ding
We present a catalogue of photometric redshifts for galaxies from DESI Legacy Imaging Surveys, which includes $\sim0.18$ billion sources covering 14,000 ${\rm deg}^2$. The photometric redshifts, along with their uncertainties, are estimated through galaxy images in three optical bands ($g$, $r$ and $z$) from DESI and two near-infrared bands ($W1$ and $W2$) from WISE using a Bayesian Neural Network (BNN). The training of BNN is performed by above images and their corresponding spectroscopic redshifts given in DESI Early Data Release (EDR). Our results show that categorizing galaxies into individual groups based on their inherent characteristics and estimating their photo-$z$s within their group separately can effectively improve the performance. Specifically, the galaxies are categorized into four distinct groups based on DESI's target selection criteria: Bright Galaxy Sample (BGS), Luminous Red Galaxies (LRG), Emission Line Galaxies (ELG) and a group comprising the remaining sources, referred to as NON. As measured by outliers of $|\Delta z| > 0.15 (1 + z_{\rm true})$, accuracy $\sigma_{\rm NMAD}$ and mean uncertainty $\overline{E}$ for BNN, we achieve low outlier percentage, high accuracy and low uncertainty: 0.14%, 0.018 and 0.0212 for BGS and 0.45%, 0.026 and 0.0293 for LRG respectively, surpassing results without categorization. However, the photo-$z$s for ELG cannot be reliably estimated, showing result of $>15\%$, $\sim0.1$ and $\sim0.1$ irrespective of training strategy. On the other hand, NON sources can reach 1.9%, 0.039 and 0.0445 when a magnitude cut of $z<21.3$ is applied. Our findings demonstrate that estimating photo-$z$s directly from galaxy images is significantly potential, and to achieve high-quality photo-$z$ measurement for ongoing and future large-scale imaging survey, it is sensible to implement categorization of sources based on their characteristics.
Use of surrogate endpoints in health technology assessment: a review of selected NICE technology appraisals in oncology
2024-12-03
Lorna Wheaton, Sylwia Bujkiewicz
Objectives: Surrogate endpoints, used to substitute for and predict final clinical outcomes, are increasingly being used to support submissions to health technology assessment agencies. The increase in use of surrogate endpoints has been accompanied by literature describing frameworks and statistical methods to ensure their robust validation. The aim of this review was to assess how surrogate endpoints have recently been used in oncology technology appraisals by the National Institute for Health and Care Excellence (NICE) in England and Wales. Methods: This paper identified technology appraisals in oncology published by NICE between February 2022 and May 2023. Data are extracted on methods for the use and validation of surrogate endpoints. Results: Of the 47 technology appraisals in oncology available for review, 18 (38 percent) utilised surrogate endpoints, with 37 separate surrogate endpoints being discussed. However, the evidence supporting the validity of the surrogate relationship varied significantly across putative surrogate relationships with 11 providing RCT evidence, 7 providing evidence from observational studies, 12 based on clinical opinion and 7 providing no evidence for the use of surrogate endpoints. Conclusions: This review supports the assertion that surrogate endpoints are frequently used in oncology technology appraisals in England and Wales. Despite increasing availability of statistical methods and guidance on appropriate validation of surrogate endpoints, this review highlights that use and validation of surrogate endpoints can vary between technology appraisals which can lead to uncertainty in decision-making.
TSCheater: Generating High-Quality Tibetan Adversarial Texts via Visual Similarity
2024-12-03
Xi Cao, Quzong Gesang, Yuan Sun, Nuo Qun, Tashi Nyima
Language models based on deep neural networks are vulnerable to textual adversarial attacks. While rich-resource languages like English are receiving focused attention, Tibetan, a cross-border language, is gradually being studied due to its abundant ancient literature and critical language strategy. Currently, there are several Tibetan adversarial text generation methods, but they do not fully consider the textual features of Tibetan script and overestimate the quality of generated adversarial texts. To address this issue, we propose a novel Tibetan adversarial text generation method called TSCheater, which considers the characteristic of Tibetan encoding and the feature that visually similar syllables have similar semantics. This method can also be transferred to other abugidas, such as Devanagari script. We utilize a self-constructed Tibetan syllable visual similarity database called TSVSDB to generate substitution candidates and adopt a greedy algorithm-based scoring mechanism to determine substitution order. After that, we conduct the method on eight victim language models. Experimentally, TSCheater outperforms existing methods in attack effectiveness, perturbation magnitude, semantic similarity, visual similarity, and human acceptance. Finally, we construct the first Tibetan adversarial robustness evaluation benchmark called AdvTS, which is generated by existing methods and proofread by humans.
Dynamic Prompt Middleware: Contextual Prompt Refinement Controls for Comprehension Tasks
2024-12-03
Ian Drosos, Jack Williams, Advait Sarkar, Nicholas Wilson
Effective prompting of generative AI is challenging for many users, particularly in expressing context for comprehension tasks such as explaining spreadsheet formulas, Python code, and text passages. Prompt middleware aims to address this barrier by assisting in prompt construction, but barriers remain for users in expressing adequate control so that they can receive AI-responses that match their preferences. We conduct a formative survey (n=38) investigating user needs for control over AI-generated explanations in comprehension tasks, which uncovers a trade-off between standardized but predictable support for prompting, and adaptive but unpredictable support tailored to the user and task. To explore this trade-off, we implement two prompt middleware approaches: Dynamic Prompt Refinement Control (Dynamic PRC) and Static Prompt Refinement Control (Static PRC). The Dynamic PRC approach generates context-specific UI elements that provide prompt refinements based on the user's prompt and user needs from the AI, while the Static PRC approach offers a preset list of generally applicable refinements. We evaluate these two approaches with a controlled user study (n=16) to assess the impact of these approaches on user control of AI responses for crafting better explanations. Results show a preference for the Dynamic PRC approach as it afforded more control, lowered barriers to providing context, and encouraged exploration and reflection of the tasks, but that reasoning about the effects of different generated controls on the final output remains challenging. Drawing on participant feedback, we discuss design implications for future Dynamic PRC systems that enhance user control of AI responses. Our findings suggest that dynamic prompt middleware can improve the user experience of generative AI workflows by affording greater control and guide users to a better AI response.
Characterizing Information Shared by Participants to Coding Challenges: The Case of Advent of Code
2024-12-03
Francesco Cauteruccio, Enrico Corradini, Luca Virgili
Advent of Code (AoC from now on) is a popular coding challenge requiring to solve programming puzzles for a variety of skill sets and levels. AoC follows the advent calendar, therefore it is an annual challenge that lasts for 25 days. AoC participants usually post their solutions on social networks and discuss them online. These challenges are interesting to study since they could highlight the adoption of new tools, the evolution of the developer community, or the technological requirements of well-known companies. For these reasons, we first create a dataset of the 2019-2021 AoC editions containing the discussion threads made on the subreddit {\tt /r/adventofcode}. Then, we propose a model based on stream graphs to best study this context, where we represent its most important actors through time: participants, comments, and programming languages. Thanks to our model, we investigate user participation, adoption of new programming languages during a challenge and between two of them, and resiliency of programming languages based on a Stack Overflow survey. We find that the top-used programming languages are almost the same in the three years, pointing out their importance. Moreover, participants tend to keep the same programming language for the whole challenge, while the ones attending two AoCs usually change it in the next one. Finally, we observe interesting results about the programming languages that are ``Popular'' or ``Loved'' according to the Stack Overflow survey. Firstly, these are the ones adopted for the longest time in an AoC edition, thanks to which users have a high chance of reaching the end of the challenge. Secondly, they are the most chosen when a participant decides to change programming language during the same challenge.
Selective Reviews of Bandit Problems in AI via a Statistical View
2024-12-03
Pengjie Zhou, Haoyu Wei, Huiming Zhang
Reinforcement Learning (RL) is a widely researched area in artificial intelligence that focuses on teaching agents decision-making through interactions with their environment. A key subset includes stochastic multi-armed bandit (MAB) and continuum-armed bandit (SCAB) problems, which model sequential decision-making under uncertainty. This review outlines the foundational models and assumptions of bandit problems, explores non-asymptotic theoretical tools like concentration inequalities and minimax regret bounds, and compares frequentist and Bayesian algorithms for managing exploration-exploitation trade-offs. We also extend the discussion to $K$-armed contextual bandits and SCAB, examining their methodologies, regret analyses, and discussing the relation between the SCAB problems and the functional data analysis. Finally, we highlight recent advances and ongoing challenges in the field.
Medical imaging is essential in healthcare to provide key insights into patient anatomy and pathology, aiding in diagnosis and treatment. Non-invasive techniques such as X-ray, Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasound (US), capture detailed images of organs, tissues, and abnormalities. Effective analysis of these images requires precise segmentation to delineate regions of interest (ROI), such as organs or lesions. Traditional segmentation methods, relying on manual feature-extraction, are labor-intensive and vary across experts. Recent advancements in Artificial Intelligence (AI) and Deep Learning (DL), particularly convolutional models such as U-Net and its variants (U-Net++ and U-Net 3+), have transformed medical image segmentation (MIS) by automating the process and enhancing accuracy. These models enable efficient, precise pixel-wise classification across various imaging modalities, overcoming the limitations of manual segmentation. This review explores various medical imaging techniques, examines the U-Net architectures and their adaptations, and discusses their application across different modalities. It also identifies common challenges in MIS and proposes potential solutions.
Stimulated Raman Scattering in Nonlinear Silicon Nanophotonic Waveguides: Theory and Applications in Photonic Integrated Circuits
2024-12-03
Abdurrahman Javid Shaikh, Othman Sidek
Photonics caught world attention since channel capacity limit of metallic interconnects approached due to research and design in high speed digital processors. Use of dielectrics, instead, suitable for light propagation was more attractive due to its extremely wide bandwidth. Many of the devices, both active and passive, have been demonstrated using these insulating materials. Due to its excellent optical characteristics, established fabrication history, and cheaper throughput, silicon found its place in photonics arena. However, due to its indirect band structure, efficient light sources are not possible using silicon as the base material. Nevertheless, techniques such as stimulated Raman scattering and third-harmonic generation have made it possible to avoid this natural hurdle in the path of silicon as a light source. This paper reviews basic theory of stimulated Raman scattering, its applications in the context of silicon based photonic integrated circuits and describes ways to improve this nonlinear effect. This paper also covers few of the most important demonstrations of stimulated Raman scattering published in literature from the last decade.
Optical losses as a function of beam position on the mirrors in a 285-m suspended Fabry-Perot cavity
2024-12-03
Y. Zhao, M. Vardaro, E. Capocasa, J. Ding, Y. Guo, M. Lequime, M. Barsuglia
Reducing optical losses is crucial for reducing quantum noise in gravitational-wave detectors. Losses are the main source of degradation of the squeezed vacuum. Frequency dependent squeezing obtained via a filter cavity is currently used to reduce quantum noise in the whole detector bandwidth. Such filter cavities are required to have high finesse in order to produce the optimal squeezing angle rotation and the presence of losses is particularly detrimental for the squeezed beam, as it does multiple round trip within the cavity. Characterising such losses is crucial to assess the quantum noise reduction achievable. In this paper we present an in-situ measurement of the optical losses, done for different positions of the beam on the mirrors of the Virgo filter cavity. We implemented an automatic system to map the losses with respect to the beam position on the mirrors finding that optical losses depend clearly on the beam hitting position on input mirror, varying from 42 ppm to 87 ppm, while they are much more uniform when we scan the end mirror (53 ppm to 61 ppm). We repeated the measurements on several days, finding a statistical error smaller than 4 ppm. The lowest measured losses are not much different with respect to those estimated from individual mirror characterisation performed before the installation (30.3 - 39.3 ppm). This means that no major loss mechanism has been neglected in the estimation presented here. The larger discrepancy found for some beam positions is likely to be due to contamination. In addition to a thorough characterisation of the losses, the methodology described in this paper allowed to find an optimal cavity axis position for which the cavity round trip losses are among the lowest ever measured. This work can contribute to achieve the very challenging losses goals for the optical cavities of the future gravitational-wave detectors.
Analyzing the Impact of AI Tools on Student Study Habits and Academic Performance
2024-12-03
Ben Ward, Deepshikha Bhati, Fnu Neha, Angela Guercio
This study explores the effectiveness of AI tools in enhancing student learning, specifically in improving study habits, time management, and feedback mechanisms. The research focuses on how AI tools can support personalized learning, adaptive test adjustments, and provide real-time classroom analysis. Student feedback revealed strong support for these features, and the study found a significant reduction in study hours alongside an increase in GPA, suggesting positive academic outcomes. Despite these benefits, challenges such as over-reliance on AI and difficulties in integrating AI with traditional teaching methods were also identified, emphasizing the need for AI tools to complement conventional educational strategies rather than replace them. Data were collected through a survey with a Likert scale and follow-up interviews, providing both quantitative and qualitative insights. The analysis involved descriptive statistics to summarize demographic data, AI usage patterns, and perceived effectiveness, as well as inferential statistics (T-tests, ANOVA) to examine the impact of demographic factors on AI adoption. Regression analysis identified predictors of AI adoption, and qualitative responses were thematically analyzed to understand students' perspectives on the future of AI in education. This mixed-methods approach provided a comprehensive view of AI's role in education and highlighted the importance of privacy, transparency, and continuous refinement of AI features to maximize their educational benefits.
Leveraging Large Language Models for Comparative Literature Summarization with Reflective Incremental Mechanisms
2024-12-03
Fernando Gabriela Garcia, Spencer Burns, Harrison Fuller
In this paper, we introduce ChatCite, a novel method leveraging large language models (LLMs) for generating comparative literature summaries. The ability to summarize research papers with a focus on key comparisons between studies is an essential task in academic research. Existing summarization models, while effective at generating concise summaries, fail to provide deep comparative insights. ChatCite addresses this limitation by incorporating a multi-step reasoning mechanism that extracts critical elements from papers, incrementally builds a comparative summary, and refines the output through a reflective memory process. We evaluate ChatCite on a custom dataset, CompLit-LongContext, consisting of 1000 research papers with annotated comparative summaries. Experimental results show that ChatCite outperforms several baseline methods, including GPT-4, BART, T5, and CoT, across various automatic evaluation metrics such as ROUGE and the newly proposed G-Score. Human evaluation further confirms that ChatCite generates more coherent, insightful, and fluent summaries compared to these baseline models. Our method provides a significant advancement in automatic literature review generation, offering researchers a powerful tool for efficiently comparing and synthesizing scientific research.
A short review on TBA equation and scattering amplitude/Wilson loop duality
2024-12-03
Hongfei Shu
In this review (written in Chinese), we introduce the computation of the minimal surface area in the scattering amplitude/Wilson loop duality, where the minimal surface ends on a light-like polygonal Wilson loop at the boundary of anti-de Sitter space (AdS). Due to its nonlinearity and the complexity of the boundary conditions, directly solving the equations of motion to compute the area is highly challenging. This paper reviews an alternative approach that bypasses the direct solution of the equations of motion and instead uses integrable systems to compute the area. We will provide boundary conditions for the Hitchin system, which is equivalent to the equations of motion, to describe the light-like polygonal boundary of the minimal surface. Starting from the solution of the Hitchin system, we will further derive the Y-system and the Thermodynamic Bethe Ansatz (TBA) equations, whose free energy provides the nontrivial part of the minimal surface area. Finally, we will discuss recent developments in this field and provide an outlook for future research.
Personalized Multimodal Large Language Models: A Survey
2024-12-03
Junda Wu, Hanjia Lyu, Yu Xia, Zhehao Zhang, Joe Barrow, Ishita Kumar, Mehrnoosh Mirtaheri, Hongjie Chen, Ryan A. Rossi, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Namyong Park, Sungchul Kim, Huanrui Yang, Subrata Mitra, Zhengmian Hu, Nedim Lipka, Dang Nguyen, Yue Zhao, Jiebo Luo, Julian McAuley
Multimodal Large Language Models (MLLMs) have become increasingly important due to their state-of-the-art performance and ability to integrate multiple data modalities, such as text, images, and audio, to perform complex tasks with high accuracy. This paper presents a comprehensive survey on personalized multimodal large language models, focusing on their architecture, training methods, and applications. We propose an intuitive taxonomy for categorizing the techniques used to personalize MLLMs to individual users, and discuss the techniques accordingly. Furthermore, we discuss how such techniques can be combined or adapted when appropriate, highlighting their advantages and underlying rationale. We also provide a succinct summary of personalization tasks investigated in existing research, along with the evaluation metrics commonly used. Additionally, we summarize the datasets that are useful for benchmarking personalized MLLMs. Finally, we outline critical open challenges. This survey aims to serve as a valuable resource for researchers and practitioners seeking to understand and advance the development of personalized multimodal large language models.
In recent years, Large Language Models (LLMs) have garnered considerable attention for their remarkable abilities in natural language processing tasks. However, their widespread adoption has raised concerns pertaining to trust and safety. This systematic review investigates the current research landscape on trust and safety in LLMs, with a particular focus on the novel application of LLMs within the field of Trust and Safety itself. We delve into the complexities of utilizing LLMs in domains where maintaining trust and safety is paramount, offering a consolidated perspective on this emerging trend.\ By synthesizing findings from various studies, we identify key challenges and potential solutions, aiming to benefit researchers and practitioners seeking to understand the nuanced interplay between LLMs and Trust and Safety. This review provides insights on best practices for using LLMs in Trust and Safety, and explores emerging risks such as prompt injection and jailbreak attacks. Ultimately, this study contributes to a deeper understanding of how LLMs can be effectively and responsibly utilized to enhance trust and safety in the digital realm.
Machine Learning Methods for Automated Interstellar Object Classification with LSST
2024-12-03
Richard Cloete, Peter Vereš, Abraham Loeb
The Legacy Survey of Space and Time, to be conducted with the Vera C. Rubin Observatory, is poised to revolutionize our understanding of the Solar System by providing an unprecedented wealth of data on various objects, including the elusive interstellar objects (ISOs). Detecting and classifying ISOs is crucial for studying the composition and diversity of materials from other planetary systems. However, the rarity and brief observation windows of ISOs, coupled with the vast quantities of data to be generated by LSST, create significant challenges for their identification and classification. This study aims to address these challenges by exploring the application of machine learning algorithms to the automated classification of ISO tracklets in simulated LSST data. We employed various machine learning algorithms, including random forests (RFs), stochastic gradient descent (SGD), gradient boosting machines (GBMs), and neural networks (NNs), to classify ISO tracklets in simulated LSST data. We demonstrate that GBM and RF algorithms outperform SGD and NN algorithms in accurately distinguishing ISOs from other Solar System objects. RF analysis shows that many derived Digest2 values are more important than direct observables in classifying ISOs from the LSST tracklets. The GBM model achieves the highest precision, recall, and F1 score, with values of 0.9987, 0.9986, and 0.9987, respectively. These findings lay the foundation for the development of an efficient and robust automated system for ISO discovery using LSST data, paving the way for a deeper understanding of the materials and processes that shape planetary systems beyond our own. The integration of our proposed machine learning approach into the LSST data processing pipeline will optimize the survey's potential for identifying these rare and valuable objects, enabling timely follow-up observations and further characterization.
Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey
2024-12-03
Yunkai Dang, Kaichen Huang, Jiahao Huo, Yibo Yan, Sirui Huang, Dongrui Liu, Mengxi Gao, Jie Zhang, Chen Qian, Kun Wang, Yong Liu, Jing Shao, Hui Xiong, Xuming Hu
The rapid development of Artificial Intelligence (AI) has revolutionized numerous fields, with large language models (LLMs) and computer vision (CV) systems driving advancements in natural language understanding and visual processing, respectively. The convergence of these technologies has catalyzed the rise of multimodal AI, enabling richer, cross-modal understanding that spans text, vision, audio, and video modalities. Multimodal large language models (MLLMs), in particular, have emerged as a powerful framework, demonstrating impressive capabilities in tasks like image-text generation, visual question answering, and cross-modal retrieval. Despite these advancements, the complexity and scale of MLLMs introduce significant challenges in interpretability and explainability, essential for establishing transparency, trustworthiness, and reliability in high-stakes applications. This paper provides a comprehensive survey on the interpretability and explainability of MLLMs, proposing a novel framework that categorizes existing research across three perspectives: (I) Data, (II) Model, (III) Training \& Inference. We systematically analyze interpretability from token-level to embedding-level representations, assess approaches related to both architecture analysis and design, and explore training and inference strategies that enhance transparency. By comparing various methodologies, we identify their strengths and limitations and propose future research directions to address unresolved challenges in multimodal explainability. This survey offers a foundational resource for advancing interpretability and transparency in MLLMs, guiding researchers and practitioners toward developing more accountable and robust multimodal AI systems.
Crash Severity Risk Modeling Strategies under Data Imbalance
2024-12-03
Abdullah Al Mamun, Abyad Enan, Debbie A. Indah, Judith Mwakalonge, Gurcan Comert, Mashrur Chowdhury
This study investigates crash severity risk modeling strategies for work zones involving large vehicles (i.e., trucks, buses, and vans) when there are crash data imbalance between low-severity (LS) and high-severity (HS) crashes. We utilized crash data, involving large vehicles in South Carolina work zones for the period between 2014 and 2018, which included 4 times more LS crashes compared to HS crashes. The objective of this study is to explore crash severity prediction performance of various models under different feature selection and data balancing techniques. The findings of this study highlight a disparity between LS and HS predictions, with less-accurate prediction of HS crashes compared to LS crashes due to class imbalance and feature overlaps between LS and HS crashes. Combining features from multiple feature selection techniques: statistical correlation, feature importance, recursive elimination, statistical tests, and mutual information, slightly improves HS crash prediction performance. Data balancing techniques such as NearMiss-1 and RandomUnderSampler, maximize HS recall when paired with certain prediction models, such as Bayesian Mixed Logit (BML), NeuralNet, and RandomForest, making them suitable for HS crash prediction. Conversely, RandomOverSampler, HS Class Weighting, and Kernel-based Synthetic Minority Oversampling (K-SMOTE), used with certain prediction models such as BML, CatBoost, and LightGBM, achieve a balanced performance, defined as achieving an equitable trade-off between LS and HS prediction performance metrics. These insights provide safety analysts with guidance to select models, feature selection techniques, and data balancing techniques that align with their specific safety objectives, offering a robust foundation for enhancing work-zone crash severity prediction.
The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis
2024-12-03
Kee Siong Ng, Samuel Yang-Zhao, Timothy Cadogan-Cowper
The AI safety literature is full of examples of powerful AI agents that, in blindly pursuing a specific and usually narrow objective, ends up with unacceptable and even catastrophic collateral damage to others. In this paper, we consider the problem of social harms that can result from actions taken by learning and utility-maximising agents in a multi-agent environment. The problem of measuring social harms or impacts in such multi-agent settings, especially when the agents are artificial generally intelligent (AGI) agents, was listed as an open problem in Everitt et al, 2018. We attempt a partial answer to that open problem in the form of market-based mechanisms to quantify and control the cost of such social harms. The proposed setup captures many well-studied special cases and is more general than existing formulations of multi-agent reinforcement learning with mechanism design in two ways: (i) the underlying environment is a history-based general reinforcement learning environment like in AIXI; (ii) the reinforcement-learning agents participating in the environment can have different learning strategies and planning horizons. To demonstrate the practicality of the proposed setup, we survey some key classes of learning algorithms and present a few applications, including a discussion of the Paperclips problem and pollution control with a cap-and-trade system.
Theory of monochromatic advanced-wave picture and applications in biphoton optics
2024-12-03
Yi Zheng, Jin-Shi Xu, Chuan-Feng Li, Guang-Can Guo
Klyshko's advanced-wave picture (AWP) is mainly interpreted by replacing the nonlinear crystal producing biphotons via spontaneous parametric down-conversion (SPDC) by a mirror in quantum imaging protocols with thin crystals, where the biphotons are perfectly correlated in position at the crystal. To better explain the biphoton spatial states produced by arbitrary crystals and pump beams, we develop a formal theory of AWP with monochromatic lights that the conditional wave function of one photon is calculated by propagation, multiplication, and another propagation. The case of more general photon postselection or no detection and the inclusion of polarization are studied. Then, we explain the form of biphoton state from SPDC with a bulk crystal and its free-space propagation. By treating the biphoton wave function as an impulse response function of a classical optical setup, we analyze quantum imaging with undetected photons and quantum holography with polarization entanglement, where properties like the spatial resolution can be concisely deduced. This method can be employed to design nonlinear materials or novel quantum imaging techniques. Finally, we discuss Klyshko's original proposal beyond monochromatic lights with the Hong-Ou-Mandel effect as an example.
Three Warm Jupiters orbiting TOI-6628, TOI-3837, TOI-5027 and one sub-Saturn orbiting TOI-2328
2024-12-03
Marcelo Tala Pinto, Andrés Jordán, Lorena Acuña, Matías Jones, Rafael Brahm, Yared Reinarz, Jan Eberhardt, Néstor Espinoza, Thomas Henning, Melissa Hobson, Felipe Rojas, Martin Schlecker, Trifon Trifonov, Gaspar Bakos, Gavin Boyle, Zoltan Csubry, Joel Hartmann, Benjamin Knepper, Laura Kreidberg, Vincent Suc, Johanna Teske, R. Paul Butler, Jeffrey Crane, Steve Schectman, Ian Thompson, Dave Osip, George Ricker, Karen A. Collins, Cristilyn N. Watkins, Allyson Bieryla, Chris Stockdale, Gavin Wang, Roberto Zambelli, Sara Seager, Joshua Winn, Mark E. Rose, Malena Rice, Zahra Essack
We report the discovery and characterization of three new transiting giant planets orbiting TOI-6628, TOI-3837 and TOI-5027, and one new warm sub-Saturn orbiting TOI-2328, whose transits events were detected in the lightcurves of the Transiting Exoplanet Survey Satellite \textbf{(TESS)} space mission. By combining TESS lightcurves with ground-based photometric and spectroscopic follow-up observations we confirm the planetary nature of the observed transits and radial velocity variations. TOI-6628~$b$ has a mass of 0.75$\pm$0.06~$M_\mathrm{J}$, a radius of 0.98$\pm$0.05~$R_J$ and is orbiting a metal-rich star with a period of 18.18424$\pm{0.00001}$ days and an eccentricity of 0.667$\pm0.016$, making it one of the most eccentric orbits of all known warm giants. TOI-3837~$b$ has a mass of 0.59$\pm$0.06~$M_\mathrm{J}$, a radius of 0.96$\pm$0.05~$R_J$ and orbits its host star every 11.88865$\pm$0.00003~days, with a moderate eccentricity of 0.198$^{+0.046}_{-0.058}$. With a mass of 2.01$\pm$0.13~$M_\mathrm{J}$ and a radius of 0.99$^{+0.07}_{-0.12}$ $R_J$, TOI-5027~$b$ orbits its host star in an eccentric orbit with $e$~=~0.395$^{+0.032}_{-0.029}$ every 10.24368$\pm{0.00001}$~days. TOI-2328~$b$ is a Saturn-like planet with a mass of 0.16$\pm$0.02~$M_\mathrm{J}$ and a radius of 0.89$\pm$0.04~$R_J$, orbiting its host star in a nearly circular orbit with $e$~=~0.057$^{+0.046}_{-0.029}$ at an orbital period of 17.10197$\pm{0.00001}$ days. All four planets have orbital periods above 10 days, and our planet interior structure models are consistsent a rocky-icy core with a H/He envelope, providing evidence supporting the core accretion model of planet formation for this kind of planets.
SuperLoc: The Key to Robust LiDAR-Inertial Localization Lies in Predicting Alignment Risks
2024-12-03
Shibo Zhao, Honghao Zhu, Yuanjun Gao, Beomsoo Kim, Yuheng Qiu, Aaron M. Johnson, Sebastian Scherer
Map-based LiDAR localization, while widely used in autonomous systems, faces significant challenges in degraded environments due to lacking distinct geometric features. This paper introduces SuperLoc, a robust LiDAR localization package that addresses key limitations in existing methods. SuperLoc features a novel predictive alignment risk assessment technique, enabling early detection and mitigation of potential failures before optimization. This approach significantly improves performance in challenging scenarios such as corridors, tunnels, and caves. Unlike existing degeneracy mitigation algorithms that rely on post-optimization analysis and heuristic thresholds, SuperLoc evaluates the localizability of raw sensor measurements. Experimental results demonstrate significant performance improvements over state-of-the-art methods across various degraded environments. Our approach achieves a 54% increase in accuracy and exhibits the highest robustness. To facilitate further research, we release our implementation along with datasets from eight challenging scenarios
Were You Helpful -- Predicting Helpful Votes from Amazon Reviews
2024-12-03
Emin Kirimlioglu, Harrison Kung, Dominic Orlando
This project investigates factors that influence the perceived helpfulness of Amazon product reviews through machine learning techniques. After extensive feature analysis and correlation testing, we identified key metadata characteristics that serve as strong predictors of review helpfulness. While we initially explored natural language processing approaches using TextBlob for sentiment analysis, our final model focuses on metadata features that demonstrated more significant correlations, including the number of images per review, reviewer's historical helpful votes, and temporal aspects of the review. The data pipeline encompasses careful preprocessing and feature standardization steps to prepare the input for model training. Through systematic evaluation of different feature combinations, we discovered that metadata elements we choose using a threshold provide reliable signals when combined for predicting how helpful other Amazon users will find a review. This insight suggests that contextual and user-behavioral factors may be more indicative of review helpfulness than the linguistic content itself.
Data-driven analysis of dipole strength functions using artificial neural networks
2024-12-03
Weiguang Jiang, Tim Egert, Sonia Bacca, Francesca Bonaiti, Peter von Neumann Cosel
We present a data-driven analysis of dipole strength functions across the nuclear chart, employing an artificial neural network to model and predict nuclear dipole responses. We train the network on a dataset of experimentally measured dipole strength functions for 216 different nuclei. To assess its predictive capability, we test the trained model on an additional set of 10 new nuclei, where experimental data exist. Our results demonstrate that the artificial neural network not only accurately reproduces known data but also identifies potential inconsistencies in certain experimental datasets, indicating which results may warrant further review or possible rejection. Additionally, for nuclei where experimental data are sparse or unavailable, the network confirms theoretical calculations, reinforcing its utility as a predictive tool in nuclear physics. Finally, utilizing the predicted electric dipole polarizability, we extract the value of the symmetry energy at saturation density and find it consistent with results from the literature.
Active phase separation: new phenomenology from non-equilibrium physics
2024-12-03
M. E. Cates, C. Nardini
In active systems, whose constituents have non-equilibrium dynamics at local level, fluid-fluid phase separation is widely observed. Examples include the formation of membraneless organelles within cells; the clustering of self-propelled colloidal particles in the absence of attractive forces, and some types of ecological segregation. A schematic understanding of such active phase separation was initially borrowed from what is known for the equilibrium case, in which detailed balance holds at microscopic level. However it has recently become clear that in active systems the absence of detailed balance, although it leave phase separation qualitatively unchanged in some regimes (e.g. domain growth driven by interfacial tension via Ostwald ripening), can in other regimes radically alter its phenomenology at mechanistic level. For example, microphase separation can be caused by reverse Ostwald ripening, a process that is hard to imagine from an equilibrium perspective. This and other new phenomena arise because, instead of having a single, positive interfacial tension like their equilibrium counterparts, the fluid-fluid interfaces created by active phase separation can have several distinct interfacial tensions governing different properties, some of which can be negative. These phenomena can be broadly understood by studying continuum field theories for a single conserved scalar order parameter (the fluid density), supplemented with a velocity field if momentum conservation is also present. More complex regimes arise in systems described by multiple scalar order parameters (especially with nonreciprocal interactions between these); or when an order parameter undergoes both conserved and non-conserved dynamics (such that the combination breaks detailed balance); or in systems that support orientational long-range order in one or more of the coexisting phases. In this Review [...]
Grand Challenges in Immersive Technologies for Cultural Heritage
2024-12-03
Hanbing Wang, Junyan Du, Yue Li, Lie Zhang, Xiang Li
Cultural heritage, a testament to human history and civilization, has gained increasing recognition for its significance in preservation and dissemination. The integration of immersive technologies has transformed how cultural heritage is presented, enabling audiences to engage with it in more vivid, intuitive, and interactive ways. However, the adoption of these technologies also brings a range of challenges and potential risks. This paper presents a systematic review, with an in-depth analysis of 177 selected papers. We comprehensively examine and categorize current applications, technological approaches, and user devices in immersive cultural heritage presentations, while also highlighting the associated risks and challenges. Furthermore, we identify areas for future research in the immersive presentation of cultural heritage. Our goal is to provide a comprehensive reference for researchers and practitioners, enhancing understanding of the technological applications, risks, and challenges in this field, and encouraging further innovation and development.
Block MedCare: Advancing healthcare through blockchain integration with AI and IoT
2024-12-03
Oliver Simonoski, Dijana Capeska Bogatinoska
This research explores the integration of blockchain technology in healthcare, focusing on enhancing the security and efficiency of Electronic Health Record (EHR) management. We propose a novel Ethereum-based system that empowers patients with secure control over their medical data. Our approach addresses key challenges in healthcare blockchain implementation, including scalability, privacy, and regulatory compliance. The system incorporates digital signatures, Role-Based Access Control, and a multi-layered architecture to ensure secure, controlled access. We developed a decentralized application (dApp) with user-friendly interfaces for patients, doctors, and administrators, demonstrating the practical application of our solution. A survey among healthcare professionals and IT experts revealed strong interest in blockchain adoption, while also highlighting concerns about integration costs. The study explores future enhancements, including integration with IoT devices and AI-driven analytics, contributing to the evolution of secure, efficient, and interoperable healthcare systems that leverage cutting-edge technologies for improved patient care.
Constraining differential rotation in gamma Doradus stars from inertial dips properties
2024-12-03
L. Barrault, L. Bugnet, S. Mathis
The presence of dips in the gravity modes period spacing versus period diagram of gamma Doradus stars is now well established by recent asteroseismic studies. Such Lorentzian-shaped inertial dips arise from the interaction of gravito-inertial modes in the radiative envelope of intermediate-mass main sequence stars with pure inertial modes in their convective core. They allow to study stellar internal properties. This window on stellar internal dynamics is extremely valuable in the context of the understanding of angular momentum transport inside stars, since it allows us to probe rotation in their core. We investigate the signature and the detectability of a differential rotation between the convective core and the near-core region inside gamma Doradus stars from the inertial dip properties. We study the coupling between gravito-inertial modes in the radiative zone and pure inertial modes in the convective core in the sub-inertial regime, allowing for a two-zones differential rotation from the two sides of the boundary. Taking a bi-layer rotation profile, we derive the wave equations in the convective core and the radiative envelope. We solve the coupling equation numerically and match the result to an analytical derivation of the Lorentzian dip. We then use typical values of measured near-core rotation and buoyancy travel time to infer ranges of parameters for which differential core to near-core rotation would be detectable in current Kepler data. We show that increasing the convective core rotation with respect to the near-core rotation leads to a shift of the period of the observed dip to lower periods. In addition, the dip gets deeper and thinner as the convective core rotation increases. We demonstrate that such a signature is detectable in Kepler data, given appropriate dip parameter ranges and near-core structural properties.
2024-12-03
Ben R Gordon, Helena Buschermöhle, Wata Tubthong, David V. Martin, Sean Smallets, Grace Masiello, Liz Bergeron
Exomoon discovery is on the horizon. Although several exomoon candidates exist around single stars, there are currently no candidates around circumbinary planets (CBPs). Circumbinary planets are predicted to have migrated to their current location from a farther region of the protoplanetary disc where they formed. An exomoon of a CBP therefore represents a fascinating yet complex and evolving four-body system. Their existence (or absence) would shed light on the robustness of moon formation in more dynamically active planetary systems. In this work, we simulate exomoons around migrating CBPs. We show that for fully migrated CBPs a moon is capable of surviving the migration if it is formed within ~5-10% of the planet's Hill Radius. Roughly 30-40% of the surviving moons are in the habitable zone, giving credence to circumbinary habitability even if the known CBPs all gas giants. The majority of moons fall off of their host planet early in the migration and become long-period CBPs (i.e a multi-planet circumbinary system). A subset of exomoons are ejected from the system entirely. This last class presents a new mechanism for producing free-floating planetary mass objects, like those discovered recently and expected in the Roman microlensing survey.
Environmental Evidence for Overly Massive Black Holes in Low Mass Galaxies and a Black Hole - Halo Mass Relation at $z \sim 5$
2024-12-03
Jorryt Matthee, Rohan P. Naidu, Gauri Kotiwale, Lukas J. Furtak, Ivan Kramarenko, Ruari Mackenzie, Jenny Greene, Angela Adamo, Rychard J. Bouwens, Claudia Di Cesare, Anna-Christina Eilers, Anna de Graaff, Kasper E. Heintz, Daichi Kashino, Michael V. Maseda, Sandro Tacchella, Alberto Torralba
JWST observations have unveiled faint active galactic nuclei (AGN) at high-redshift that provide insights on the formation of supermassive black holes (SMBHs) and their coevolution with galaxies. However, disentangling stellar from AGN light in these sources is challenging. Here, we use an empirical approach to infer the average stellar mass of 6 faint broad line (BL) Halpha emitters at z = 4 - 5 with BH masses ~ 6 (4 - 15)x10^6 Msun, with a method independent of their spectral energy distribution (SED). We use the deep JWST/NIRcam grism survey ALT to measure the over-densities around BL-Halpha emitters and around a spectroscopic reference sample of ~300 galaxies. In our reference sample, we find that Mpc-scale over-density correlates with stellar mass, while pair counts are flat below ~50 kpc due to satellites. Their large-scale environments suggest that BL-Halpha emitters are hosted by galaxies with stellar masses ~5x10^7 Msun, ~40 times lower than those inferred from galaxy-only SED fits. Adding measurements around more luminous z~6 AGNs, we find tentative correlations between line width, BH mass and the over-density, suggestive of a steep BH to halo mass relation. The main implications are (1) when BH masses are taken at face value, we confirm extremely high BH to stellar mass ratios of ~10 %, (2) the low stellar mass galaxies hosting growing SMBHs are in tension with typical hydrodynamical simulations, except those without feedback, (3) a 1 % duty cycle implied by the host mass hints at super-Eddington accretion, which may imply over-estimated SMBH masses, (4) the masses are at odds with a high stellar density interpretation of the line broadening, (5) our results imply a diversity of galaxy masses, environments and SEDs among AGN samples, depending on their luminosity.
Although open-vocabulary classification models like Contrastive Language Image Pretraining (CLIP) have demonstrated strong zero-shot learning capabilities, their robustness to common image corruptions remains poorly understood. Through extensive experiments, we show that zero-shot CLIP lacks robustness to common image corruptions at increasing severity levels during test-time, necessitating the adaptation of CLIP to unlabeled corrupted images using test-time adaptation (TTA). However, we found that existing TTA methods have severe limitations in adapting CLIP due to their unimodal nature. To address these limitations, we propose \framework, a bimodal TTA method specially designed to improve CLIP's robustness to common image corruptions. The key insight of our approach is not only to adapt the visual encoders for better image feature extraction but also to strengthen the alignment between image and text features by promoting a stronger association between the image class prototype, computed using pseudo-labels, and the corresponding text feature. We evaluate our approach on benchmark image corruption datasets and achieve state-of-the-art results in TTA for CLIP, specifically for domains involving image corruption. Particularly, with a ViT-B/16 vision backbone, we obtain mean accuracy improvements of 9.7%, 5.94%, and 5.12% for CIFAR-10C, CIFAR-100C, and ImageNet-C, respectively.
Commensal Transient Searches with MeerKAT in Gamma-Ray Burst and Supernova Fields
2024-12-03
S. I. Chastain, A. J. van der Horst, A. Horesh, A. Rowlinson, A. Andersson, R. Diretse, M. Vaccari, R. P. Fender, P. A. Woudt
The sensitivity and field of view of the MeerKAT radio telescope provides excellent opportunities for commensal transient searches. We carry out a commensal transient search in supernova and short gamma-ray burst fields using methodologies established in Chastain et al. (2023). We search for transients in MeerKAT L-band images with 30 minute integration times, finding 13 variable sources. We compare these sources to the VLASS and RACS survey data, and examine possible explanation for the variability. We find that 12 of these sources are consistent with variability due to interstellar scintillation. The remaining source could possibly have some intrinsic variability. We also split the MeerKAT L-band into an upper and lower half, and search for transients in images with an 8 second integration time. We find a source with a duration of 8 to 16 seconds that is highly polarized at the lowest frequencies. We conclude that this source may be consistent with a stellar flare. Finally, we calculate accurate upper and lower limits on the transient rate using transient simulations.
Galaxy evolution in the post-merger regime. III -- The triggering of active galactic nuclei peaks immediately after coalescence
2024-12-03
Sara L. Ellison, Leonardo Ferreira, Robert Bickley, Tess Grindlay, Samir Salim, Shoshannah Byrne-Mamahit, Shobita Satyapal, David R. Patton, Jillian M. Scudder
Galaxy mergers have been shown to trigger AGN in the nearby universe, but the timescale over which this process happens remains unconstrained. The Multi-Model Merger Identifier (MUMMI) machine vision pipeline has been demonstrated to provide reliable predictions of time post-merger (T_PM) for galaxies selected from the Ultraviolet Near Infrared and Optical Northern Survey (UNIONS) up to T_PM=1.76 Gyr after coalescence. By combining the post-mergers identified in UNIONS with pre-coalescence galaxy pairs, we can study the triggering of AGN throughout the merger sequence. AGN are identified using a range of complementary metrics: mid-IR colours, narrow emission lines and broad emission lines, which can be combined to provide insight into the demographics of dust and luminosity of the AGN population. Our main results are: 1) Regardless of the metric used, we find that the peak AGN excess (compared with a matched control sample) occurs immediately after coalescence, at 0 < T_PM < 0.16 Gyr. 2) The excess of AGN is observed until long after coalescence; both the mid-IR selected AGN and broad line AGN are more common than in the control sample even in the longest time bin of our sample (0.96 < T_PM < 1.76 Gyr). 3) The AGN excess is larger for more luminous and bolometrically dominant AGN, and we find that AGN in post-mergers are generally more luminous than secularly triggered events. 4) A deficit of broad line AGN in the pre-merger phase, that evolves into an excess in post-mergers is consistent with evolution of the covering fraction of nuclear obscuring material. Before coalescence, tidally triggered inflows increase the covering fraction of nuclear dust; in the post-merger regime feedback from the AGN clears (at least some of) this material. 5) The statistical peak in the triggering of starbursts occurs contemporaneously with AGN, within 0.16 Gyr of coalescence.
A Simulated Galaxy Laboratory: Exploring the Observational Effects on UV Spectral Absorption Line Measurements
2024-12-03
R. Michael Jennings, Alaina Henry, Valentin Mauerhofer, Timothy Heckman, Claudia Scarlata, Cody Carr, Xinfeng Xu, Mason Huberty, Simon Gazagnes, Anne E. Jaskot, Jeremy Blaizot, Anne Verhamme, Sophia R. Flury, Alberto Saldana-Lopez, Matthew J. Hayes, Maxime Trebitsch
Ultraviolet absorption line spectroscopy is a sensitive diagnostic for the properties of interstellar and circumgalactic gas. Down-the-barrel observations, where the absorption is measured against the galaxy itself, are commonly used to study feedback from galactic outflows and to make predictions about the leakage of HI ionizing photons into the intergalactic medium. Nonetheless, the interpretation of these observations is challenging and observational compromises are often made in terms of signal-to-noise, spectral resolution, or the use of stacking analyses. In this paper, we present a novel quantitative assessment of UV absorption line measurement techniques by using mock observations of a hydrodynamical simulation. We use a simulated galaxy to create 22,500 spectra in the commonly used SiII lines while also modeling the signal-to-noise and spectral resolution of recent rest-frame UV galaxy surveys at both high and low redshifts. We show that the residual flux of absorption features is easily overestimated for single line measurements and for stacked spectra. Additionally, we explore the robustness of the partial covering model for estimating column densities from spectra and find under-predictions on average of 1.25 dex. We show that the under-prediction is likely caused by high-column-density sight-lines that are optically-thick to dust making them invisible in UV spectra.
Exploring the Potential of Llama Models in Automated Code Refinement: A Replication Study
2024-12-03
Genevieve Caumartin, Qiaolin Qin, Sharon Chatragadda, Janmitsinh Panjrolia, Heng Li, Diego Elias Costa
Code reviews are an integral part of software development and have been recognized as a crucial practice for minimizing bugs and favouring higher code quality. They serve as an important checkpoint before committing code and play an essential role in knowledge transfer between developers. However, code reviews can be time-consuming and can stale the development of large software projects. In a recent study, Guo et al. assessed how ChatGPT3.5 can help the code review process. They evaluated the effectiveness of ChatGPT in automating the code refinement tasks, where developers recommend small changes in the submitted code. While Guo et al. 's study showed promising results, proprietary models like ChatGPT pose risks to data privacy and incur extra costs for software projects. In this study, we explore alternatives to ChatGPT in code refinement tasks by including two open-source, smaller-scale large language models: CodeLlama and Llama 2 (7B parameters). Our results show that, if properly tuned, the Llama models, particularly CodeLlama, can achieve reasonable performance, often comparable to ChatGPT in automated code refinement. However, not all code refinement tasks are equally successful: tasks that require changing existing code (e.g., refactoring) are more manageable for models to automate than tasks that demand new code. Our study highlights the potential of open-source models for code refinement, offering cost-effective, privacy-conscious solutions for real-world software development.
Discovery and Characterization of an Eccentric, Warm Saturn Transiting the Solar Analog TOI-4994
2024-12-03
Romy Rodriguez Martinez, Jason D. Eastman, Karen Collins, Joseph Rodriguez, David Charbonneau, Samuel Quinn, David W. Latham, Carl Ziegler, Rafael Brahm, Tyler Fairnington, Solene Ulmer-Moll, Keivan Stassun, Olga Suarez, Tristan Guillot, Melissa Hobson, Joshua N. Winn, Shubham Kanodia, Martin Schlecker, R. P. Butler, Jeffrey D. Crane, Steve Shectman, Johanna K. Teske, David Osip, Yuri Beletsky, Matthew P. Battley, Angelica Psaridi, Pedro Figueira, Monika Lendl, Francois Bouche, Stephane Udry, Michelle Kunimoto, Dejamel Mekarnia, Lyu Abe, Trifonov Trifonov, Marcelo T. Pinto, Jan Eberhardt, Nestor Espinoza, Thomas Henning, Andres Jordan, Felipe I. Rojas, Khalid Barkaoui, Howard M. Relles, Gregor Srdoc, Kevin I. Collins, Sara Seager, Avi Shporer, Michael Vezie, Christina L. Hedges, Ismael Mireles
We present the detection and characterization of TOI-4994b (TIC 277128619b), a warm Saturn-sized planet discovered by the NASA Transiting Exoplanet Survey Satellite (TESS). TOI-4994b transits a G-type star (V = 12.6 mag) with a mass, radius, and effective temperature of $M_{\star} =1.005^{+0.064}_{-0.061} M_{\odot}$, $R_{\star} = 1.055^{+0.040}_{-0.037} R_{\odot}$, and $T_{\rm eff} = 5640 \pm 110$ K. We obtained follow-up ground-based photometry from the Las Cumbres Observatory (LCO) and the Antarctic Search for Transiting ExoPlanets (ASTEP) telescopes, and we confirmed the planetary nature of TOI-4994b with multiple radial velocity observations from the PFS, CHIRON, HARPS, FEROS, and CORALIE instruments. From a global fit to the photometry and radial velocities, we determine that TOI-4994b is in a 21.5-day, eccentric orbit ($e = 0.32 \pm 0.04$) and has a mass of $M_{P}= 0.280^{+0.037}_{-0.034} M_{J}$, a radius of $R_{P}= 0.762^{+0.030}_{-0.027}R_{J}$, and a Saturn-like bulk density of $\rho_{p} = 0.78^{+0.16}_{-0.14}$ $\rm g/cm^3$. We find that TOI-4994 is a potentially viable candidate for follow-up stellar obliquity measurements. TOI-4994b joins the small sample of warm Saturn analogs and thus sheds light on our understanding of these rare and unique worlds.
The importance of gas starvation in driving satellite quenching in galaxy groups at $z\sim 0.8$
2024-12-03
Devontae C. Baxter, Sean P. Fillingham, Alison L. Coil, Michael C. Cooper
We present results from a Keck/DEIMOS survey to study satellite quenching in group environments at $z \sim 0.8$ within the Extended Groth Strip (EGS). We target $11$ groups in the EGS with extended X-ray emission. We obtain high-quality spectroscopic redshifts for group member candidates, extending to depths over an order of magnitude fainter than existing DEEP2/DEEP3 spectroscopy. This depth enables the first spectroscopic measurement of the satellite quiescent fraction down to stellar masses of $\sim 10^{9.5}~{\rm M}_{\odot}$ at this redshift. By combining an infall-based environmental quenching model, constrained by the observed quiescent fractions, with infall histories of simulated groups from the IllustrisTNG100-1-Dark simulation, we estimate environmental quenching timescales ($\tau_{\mathrm{quench}}$) for the observed group population. At high stellar masses (${M}_{\star}=10^{10.5}~{\rm M}_{\odot}$) we find that $\tau_{\mathrm{quench}} = 2.4\substack{+0.2 \\ -0.2}$ Gyr, which is consistent with previous estimates at this epoch. At lower stellar masses (${M}_{\star}=10^{9.5}~{\rm M}_{\odot}$), we find that $\tau_{\mathrm{quench}}=3.1\substack{+0.5 \\ -0.4}$ Gyr, which is shorter than prior estimates from photometry-based investigations. These timescales are consistent with satellite quenching via starvation, provided the hot gas envelope of infalling satellites is not stripped away. We find that the evolution in the quenching timescale between $0 \lt z \lt 1$ aligns with the evolution in the dynamical time of the host halo and the total cold gas depletion time. This suggests that the doubling of the quenching timescale in groups since $z\sim1$ could be related to the dynamical evolution of groups or a decrease in quenching efficiency via starvation with decreasing redshift.
The imprint of cosmic voids from the DESI Legacy Survey DR9 LRGs in the Planck 2018 lensing map through spectroscopically calibrated mocks
2024-12-03
S. Sartori, P. Vielzeuf, S. Escoffier, M. C. Cousinou, A. Kovács, J. DeRose, S. Ahlen, D. Bianchi, D. Brooks, E. Burtin, T. Claybaugh, A. de la Macorra, J. E. Forero-Romero, J. Garcia-Bellido, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, R. Kehoe, D. Kirkby, T. Kisner, M. Landriau, M. E. Levi, A. Meisner, R. Miquel, J. Moustakas, J. A. Newman, N. Palanque-Delabrouille, I. Pérez-Ràfols, F. Prada, G. Rossi, E. Sanchez, D. Sprayberry, G. Tarlé, B. A. Weaver
The cross-correlation of cosmic voids with the lensing convergence ($\kappa$) map of the Cosmic Microwave Background (CMB) fluctuations provides a powerful tool to refine our understanding of the cosmological model. However, several studies have reported a moderate tension between the lensing imprint of cosmic voids on the observed CMB and the simulated $\mathrm{\Lambda}$CDM signal. To address this "lensing-is-low" tension and to obtain new, precise measurements, we exploit the large DESI Legacy Survey Luminous Red Galaxy (LRG) dataset, covering approximately 19,500 $\deg^2$ of the sky and including about 10 million LRGs at $z < 1.05$. Our $\mathrm{\Lambda}$CDM template was created using the Buzzard mocks, which we specifically calibrated to match the clustering properties of the observed galaxy sample by exploiting more than one million DESI spectra. We identified our catalogs of 3D voids in the range $0.35 < z < 0.95$, dividing the sample into bins according to the redshift and $\lambda_\mathrm{v}$ values of the voids. We report a 14$\sigma$ detection of the lensing signal, with $A_\kappa = 1.016 \pm 0.054$, which increases to 17$\sigma$ when considering the void-in-void ($A_\kappa = 0.944 \pm 0.064$) and the void-in-cloud ($A_\kappa = 0.975 \pm 0.060$) populations individually, the highest detection significance for studies of this kind. We observe a full agreement between the observations and $\mathrm{\Lambda}$CDM predictions across all redshift bins, sky regions, and void populations considered. In addition to these findings, our analysis highlights the importance of matching sparseness and redshift error distributions between mocks and observations, as well as the role of $\lambda_\mathrm{v}$ in enhancing the signal-to-noise ratio.
At the core of the Gaia mission is a multi-epoch survey consisting of astrometric, photometric, spectrophotometric, and spectroscopic measurements. The astrometric time series provides parallaxes and proper motions, along with information on astrometric binary systems. The photometric time series offers a means to investigate the variability of the sources. Due to their whole-sky, multi-epoch nature, multiple instruments, their magnitude range covering 21 magnitudes, and their remarkable photometric precision, these data allow us to describe the variability of celestial phenomena in an unprecedented manner. For the third Gaia Data Release (DR3), the data collection spanned 34 months, with a median number of field-of-view measurements in the G band of about 44, reaching up to 270. At publication time, DR3 delivered the largest collection of variable sources with an associated classification across the entire sky. All these sources have their $G$, $G_{BP}$, $G_{RP}$ epoch data published and accessible in the Gaia ESA archive. In summary, there are 10.5 million variable sources, including 9.5 million variable stars and 1 million QSOs. Additionally, 2.5 million galaxies were identified thanks to spurious variability caused by the non-axisymmetric nature of galaxies and the way Gaia collects data. Moreover, all the epoch data and time series of nearly 1.3 million sources in a pencil beam around the Andromeda galaxy are published, regardless of their status (constant or variable); This dataset is known as the Gaia Andromeda Photometric Survey. We also introduce the citizen science project, GaiaVari to classify variable stars, the Focused Product Release delivered on October 10, 2023. In the future, DR4 will cover 66 months, and we hope DR5 will have accumulated 10.5 years of data.
Synoptic Wide-field EVN--e-MERLIN Public Survey (SWEEPS) -- I. First steps towards commensal surveys with VLBI
2024-12-03
Célestin Herbé-George, J. P. Mckean, Raffaella Morganti, Jack F. Radcliffe
The high angular resolution and sensitivity of Very Long Baseline Interferometry (VLBI) offer a unique tool to identify and study active galactic nuclei and star-formation activity over cosmic time. However, despite recent technical advances, such as multiple phase centre correlation, VLBI surveys have thus far been limited to either a few well-studied deep-fields or wide-areas to a relatively shallow depth. To enter the era of extensive statistical studies at high angular resolution, a significantly larger area of the sky must be observed to much better sensitivity with VLBI. The Synoptic Wide-field EVN--e-MERLIN Public Survey (SWEEPS) is a proposed commensal observing mode for the EVN and e-MERLIN, where single-target principle investigator-led observations are re-correlated at the position of known radio sources within 12 arcmin of the pointing centre. Here, we demonstrate a proof-of-concept of this methodology by detecting a 5.6 mJy core-jet object at 1.7 GHz that would have otherwise been lost from the parent data set. This is the first object to be recovered as part of the SWEEPS pilot programme, which highlights the potential for increasing sample sizes of VLBI-detected radio sources with commensal observing modes in the near future.
The FLAMINGO project: cosmology with the redshift dependence of weak gravitational lensing peaks
2024-12-03
Jeger C. Broxterman, Matthieu Schaller, Henk Hoekstra, Joop Schaye, Robert J. McGibbon, Victor J. Forouhar Moreno, Roi Kugel, Willem Elbers
Weak gravitational lensing (WL) convergence peaks contain valuable cosmological information in the regime of non-linear collapse. Using the FLAMINGO suite of cosmological hydrodynamical simulations, we study the physical origin and redshift distributions of the objects generating WL peaks selected from a WL convergence map mimicking a $\textit{Euclid}$ signal. We match peaks to individual haloes and show that the high signal-to-noise ratio (SNR$~>~5$) WL peaks measured by Stage IV WL surveys primarily trace $M_{\mathrm{200c}} > 10^{14}~\mathrm{M_\odot}$ haloes. We find that the WL peak sample can compete with the purity and completeness of state-of-the-art X-ray and Sunyaev-Zel'dovich cluster abundance inferences. By comparing the distributions predicted by simulation variations that have been calibrated to the observed gas fractions of local clusters and the present-day galaxy stellar mass function, or shifted versions of these, we illustrate that the shape of the redshift distribution of SNR$~>~5$ peaks is insensitive to baryonic physics while it does change with cosmology. The difference highlights the potential of using WL peaks to constrain cosmology. As the number density of WL peaks scales differently with cosmology and baryonic feedback, WL peak statistics can simultaneously calibrate baryonic feedback and constrain cosmology.
Picture Perfect: Photometric Transient Classification Using the ParSNIP Model with Roman Hourglass Simulations
2024-12-03
Belal Abdelhadi, David Rubin
The Roman Space Telescope, equipped with a 2.4 meter primary mirror and optical--NIR wide-field camera, promises to revolutionize our understanding of dark energy, exoplanets, and infrared astrophysics. One of the Roman Core Community Surveys is the High Latitude Time Domain Survey (HLTDS), which will measure more than 10,000 SN Ia light curves but obtain a fraction of this number with spectra. The remaining SNe will have to be photometrically classified to achieve the full potential of the Roman HLTDS. To investigate transient yields and classifications, Rose et al. (in prep.) updated the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) framework (originally developed for the Vera Rubin Observatory) for the Roman HLTDS. This study leverages this Roman Hourglass dataset to train and evaluate the ParSNIP (Parameterized Supernova Identification Pipeline) model. We employ this model to classify various transient types from photometric data, paying particular attention to the types most represented in the dataset: normal SNe Ia, 91bg-like SNe Ia, SNe Iax, and CC SNe. The ParSNIP model performance is assessed through confusion matrices and ROC curves across different redshift ranges. Our analysis reveals that while the model performs robustly at higher redshifts (with the AUC for classification varying between 0.9 and 0.95 in the range 0.5 < z < 2), its accuracy dips at the lowest redshifts of the survey, likely due to limited training data. These findings underscore the importance of ensuring adequate representation of classes in the training set. This work underscores the value of machine learning models for next-generation surveys, paving the way for future studies with the Roman Space Telescope for survey optimization, cosmological forecasts, and synergies with other surveys.
Social Media Informatics for Sustainable Cities and Societies: An Overview of the Applications, associated Challenges, and Potential Solutions
2024-12-03
Jebran Khan, Kashif Ahmad, Senthil Kumar Jagatheesaperumal, Nasir Ahmad, Kyung-Ah Sohn
In the modern world, our cities and societies face several technological and societal challenges, such as rapid urbanization, global warming & climate change, the digital divide, and social inequalities, increasing the need for more sustainable cities and societies. Addressing these challenges requires a multifaceted approach involving all the stakeholders, sustainable planning, efficient resource management, innovative solutions, and modern technologies. Like other modern technologies, social media informatics also plays its part in developing more sustainable and resilient cities and societies. Despite its limitations, social media informatics has proven very effective in various sustainable cities and society applications. In this paper, we review and analyze the role of social media informatics in sustainable cities and society by providing a detailed overview of its applications, associated challenges, and potential solutions. This work is expected to provide a baseline for future research in the domain.
Dynamic Prediction of High-density Generalized Functional Data with Fast Generalized Functional Principal Component Analysis
2024-12-02
Ying Jin, Andrew Leroux
Dynamic prediction, which typically refers to the prediction of future outcomes using historical records, is often of interest in biomedical research. For datasets with large sample sizes, high measurement density, and complex correlation structures, traditional methods are often infeasible because of the computational burden associated with both data scale and model complexity. Moreover, many models do not directly facilitate out-of-sample predictions for generalized outcomes. To address these issues, we develop a novel approach for dynamic predictions based on a recently developed method estimating complex patterns of variation for exponential family data: fast Generalized Functional Principal Components Analysis (fGFPCA). Our method is able to handle large-scale, high-density repeated measures much more efficiently with its implementation feasible even on personal computational resources (e.g., a standard desktop or laptop computer). The proposed method makes highly flexible and accurate predictions of future trajectories for data that exhibit high degrees of nonlinearity, and allows for out-of-sample predictions to be obtained without reestimating any parameters. A simulation study is designed and implemented to illustrate the advantages of this method. To demonstrate its practical utility, we also conducted a case study to predict diurnal active/inactive patterns using accelerometry data from the National Health and Nutrition Examination Survey (NHANES) 2011-2014. Both the simulation study and the data application demonstrate the better predictive performance and high computational efficiency of the proposed method compared to existing methods. The proposed method also obtains more personalized prediction that improves as more information becomes available, which is an essential goal of dynamic prediction that other methods fail to achieve.
Measuring the expansion history of the Universe with cosmic chronometers
2024-12-02
Michele Moresco
As revealed by Hubble in 1928, our Universe is expanding. This discovery was fundamental to widening our horizons and our conception of space, and since then determining the rate at which our Universe is expanding has become one of the crucial measurements in cosmology. At the beginning of this century, these measurements revealed the unexpected behavior that this expansion is accelerating and allowed us to have a first glimpse of the dark components that constitute $\sim$95\% of our Universe. Cosmic chronometers represent a novel technique to obtain a cosmology-independent determination of the expansion of the Universe, based on the differential age dating of a population of very massive and passively evolving galaxies. Currently, with this new cosmological probe it is possible to constrain the Hubble parameter with an accuracy of around 5\% at $z\sim0.5$ up to 10-20\% at $z\sim2$. In this Chapter, the cosmic chronometers approach is presented, describing the method and how an optimal sample can be selected; it is then discussed how the most recent measurements of the expansion history of the Universe have been obtained with this approach, as well as the cosmological constraints that can be derived. Particular attention will be given to the systematics involved in this approach and the treatment to properly take them into account. We conclude by presenting forecasts that show how future spectroscopic surveys will significantly boost the accuracy of this method and open the possibility to a percent determination of the Hubble constant, making cosmic chronometers a powerful independent tool to derive information on the expansion history of the Universe.
Time-periodic (Floquet) systems in classical wave physics and engineering: Opinion
2024-12-02
Francesco Monticone, Dimitrios Sounas, Matteo Ciabattoni
The study of classical waves in time-periodic systems is experiencing a resurgence of interest, motivated by their rich physics and the new engineering opportunities they enable, with several analogies to parallel efforts in other branches of physics, e.g., Floquet-engineered quantum materials and time crystals. Here, we first briefly review some of the most prominent features enabled by time-periodic modulations, and we then focus on two specific areas, namely, time-varying systems to break reciprocity and to overcome various theoretical limitations and performance bounds, discussing their current status, challenges, and opportunities.
The use of large language models to enhance cancer clinical trial educational materials
2024-12-02
Mingye Gao, Aman Varshney, Shan Chen, Vikram Goddla, Jack Gallifant, Patrick Doyle, Claire Novack, Maeve Dillon-Martin, Teresia Perkins, Xinrong Correia, Erik Duhaime, Howard Isenstein, Elad Sharon, Lisa Soleymani Lehmann, David Kozono, Brian Anthony, Dmitriy Dligach, Danielle S. Bitterman
Cancer clinical trials often face challenges in recruitment and engagement due to a lack of participant-facing informational and educational resources. This study investigated the potential of Large Language Models (LLMs), specifically GPT4, in generating patient-friendly educational content from clinical trial informed consent forms. Using data from ClinicalTrials.gov, we employed zero-shot learning for creating trial summaries and one-shot learning for developing multiple-choice questions, evaluating their effectiveness through patient surveys and crowdsourced annotation. Results showed that GPT4-generated summaries were both readable and comprehensive, and may improve patients' understanding and interest in clinical trials. The multiple-choice questions demonstrated high accuracy and agreement with crowdsourced annotators. For both resource types, hallucinations were identified that require ongoing human oversight. The findings demonstrate the potential of LLMs "out-of-the-box" to support the generation of clinical trial education materials with minimal trial-specific engineering, but implementation with a human-in-the-loop is still needed to avoid misinformation risks.
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
2024-12-02
Philippe Brouillard, Chandler Squires, Jonas Wahl, Konrad P. Kording, Karen Sachs, Alexandre Drouin, Dhanya Sridhar
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientific disciplines. However, its real-world applications remain limited. Current methods often rely on unrealistic assumptions and are evaluated only on simple synthetic toy datasets, often with inadequate evaluation metrics. In this paper, we substantiate these claims by performing a systematic review of the recent causal discovery literature. We present applications in biology, neuroscience, and Earth sciences - fields where causal discovery holds promise for addressing key challenges. We highlight available simulated and real-world datasets from these domains and discuss common assumption violations that have spurred the development of new methods. Our goal is to encourage the community to adopt better evaluation practices by utilizing realistic datasets and more adequate metrics.
The Evolution and Future Perspectives of Artificial Intelligence Generated Content
2024-12-02
Chengzhang Zhu, Luobin Cui, Ying Tang, Jiacun Wang
Artificial intelligence generated content (AIGC), a rapidly advancing technology, is transforming content creation across domains, such as text, images, audio, and video. Its growing potential has attracted more and more researchers and investors to explore and expand its possibilities. This review traces AIGC's evolution through four developmental milestones-ranging from early rule-based systems to modern transfer learning models-within a unified framework that highlights how each milestone contributes uniquely to content generation. In particular, the paper employs a common example across all milestones to illustrate the capabilities and limitations of methods within each phase, providing a consistent evaluation of AIGC methodologies and their development. Furthermore, this paper addresses critical challenges associated with AIGC and proposes actionable strategies to mitigate them. This study aims to guide researchers and practitioners in selecting and optimizing AIGC models to enhance the quality and efficiency of content creation across diverse domains.
The Origin of Supermassive Black Holes from Pop III.1 Seeds
2024-12-02
Jonathan C. Tan, Jasbir Singh, Vieri Cammelli, Mahsa Sanati, Maya Petkova, Devesh Nandal, Pierluigi Monaco
The origin of supermassive black holes (SMBHs) is a key open question for contemporary astrophysics and cosmology. Here we review the features of a cosmological model of SMBH formation from Pop III.1 seeds, i.e., remnants of metal-free stars forming in locally-isolated minihalos, where energy injection from dark matter particle annihilation alters the structure of the protostar allowing growth to supermassive scales (Banik et al. 2019; Singh et al. 2023; Cammelli et al. 2024). The Pop III.1 model explains the paucity of intermediate-mass black holes (IMBHs) via a characteristic SMBH seed mass of $\sim10^5\:M_\odot$ that is set by the baryonic content of minihalos. Ionization feedback from supermassive Pop III.1 stars sets the cosmic number density of SMBHs to be $n_{\rm SMBH}\lesssim 0.2\:{\rm Mpc}^{-3}$. The model then predicts that all SMBHs form by $z\sim20$ with a spatial distribution that is initially unclustered. SMBHs at high redshifts $z\gtrsim7$ should all be single objects, with SMBH binaries and higher order multiples emerging only at lower redshifts. We also discuss the implications of this model for SMBH host galaxy properties, occupation fractions, gravitational wave emission, cosmic reionization, and the nature of dark matter. These predictions are compared to latest observational results, especially from HST, JWST and pulsar timing array observations.
UNIONS: a direct measurement of intrinsic alignment with BOSS/eBOSS spectroscopy
2024-12-02
Fabian Hervas Peters, Martin Kilbinger, Romain Paviot, Lucie Baumont, Elisa Russier, Ziwen Zhang, Calum Murray, Valeria Pettorino, Thomas de Boer, Sébastien Fabbro, Sacha Guerrini, Hendrik Hildebrandt, Mike Hudson, Ludovic Van Waerbeke, Anna Wittje
During their formation, galaxies are subject to tidal forces, which create correlations between their shapes and the large-scale structure of the Universe, known as intrinsic alignment. This alignment is a contamination for cosmic-shear measurements as one needs to disentangle correlations induced by external lensing effects from those intrinsically present in galaxies. We constrain the amplitude of intrinsic alignment and test models by making use of the overlap between the Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) covering $3500 \, \mathrm{deg}^2$, and spectroscopic data from the Baryon Oscillation Spectroscopic Survey (BOSS/eBOSS). By comparing our results to measurements from other lensing surveys on the same spectroscopic tracers, we can test the reliability of these estimates and verify they are not survey dependent. We measure projected correlation functions between positions and ellipticities, which we model with perturbation theory to constrain the commonly used non-linear alignment model and its higher-order expansion. Using the non-linear alignment model, we obtain a $13\sigma$ detection with CMASS galaxies, a $3\sigma$ detection with LRGs, and a detection compatible with the null hypothesis for ELGs. We test the tidal alignment and tidal torque model, a higher-order alignment model, which we find to be in good agreement with the non-linear alignment prediction and for which we can constrain the second-order parameters. We show a strong scaling of our intrinsic alignment amplitude with luminosity. We demonstrate that the UNIONS sample is robust against systematic contributions, particularly concerning PSF biases. We reached a reasonable agreement when comparing our measurements to other lensing samples for the same spectroscopic samples. We take this agreement as an indication that direct measurements of intrinsic alignment are mature for stage IV priors.
Type Ia supernova progenitors: a contemporary view of a long-standing puzzle
2024-12-02
Ashley J. Ruiter, Ivo R. Seitenzahl
Type Ia supernovae (SNe Ia) are runaway thermonuclear explosions in white dwarfs that result in the disruption of the white dwarf star, and possibly its nearby stellar companion. SNe Ia occur over an immense range of stellar population age and host galaxy environments, and play a critical role in the nucleosynthesis of intermediate-mass and iron-group elements, primarily the production of nickel, iron, cobalt, chromium, and manganese. Though the nature of their progenitors is still not well-understood, SNe Ia are unique among stellar explosions in that the majority of them exhibit a systematic lightcurve relation: more luminous supernovae dim more slowly over time than less luminous supernovae in optical light (intrinsically brighter SNe Ia have broader lightcurves). This feature, unique to SNe Ia, is rather remarkable and allows their peak luminosities to be determined with fairly high accuracy out to cosmological distances via measurement of their lightcurve decline. Further, studying SNe Ia gives us important insights into binary star evolution physics, since it is widely agreed that the progenitors of SNe Ia are binary (possibly multiple) star systems. In this review, we give a current update on the different proposed Type Ia supernova progenitors, including descriptions of possible binary star configurations, and their explosion mechanisms, from a theoretical perspective. We additionally give a brief overview of the historical (focusing on the more recent) observational work that has helped the astronomical community to understand the nature of the most important distance indicators in cosmology.
Vector-Like Quarks at the LHC: A Unified Perspective from ATLAS and CMS Exclusion Limits
2024-12-02
Rachid Benbrik, Mohammed Boukidi, Mohamed Ech-chaouy, Stefano Moretti, Khawla Salime, Qi-Shu Yan
In this work, we present a comprehensive review of the most up-to-date exclusion limits on Vector-Like Quarks (VLQs) derived from ATLAS and CMS data at the Large Hadron Collider (LHC). Our analysis encompasses both pair and single production modes, systematically comparing results from the two collaborations to identify and employ the most stringent bounds at each mass point. We evaluate the excluded parameter space for VLQs under singlet, doublet, and triplet representations. For top-like VLQs ($T$), the exclusion limits rule out masses up to 1.49 TeV in singlet scenarios, while single production constrains the mixing parameter $\kappa$ to values below 0.26 at $m_T \sim 1.5$ TeV and up to 0.42 for $m_T \sim 2$ TeV. For bottom-like VLQs ($B$), the strongest exclusion limits from pair production exclude masses up to 1.52 TeV in doublet configurations, with single production constraining $\kappa$ values between 0.2 and 0.7 depending on the mass. For exotic VLQs, such as $X$ and $Y$, pair production excludes masses up to 1.46 TeV and 1.7 TeV, respectively. The constraints on $\kappa$ from these analyses become increasingly restrictive at higher masses, reflecting the enhanced sensitivity of single production channels in this regime. For $X$, $\kappa$ is constrained below 0.16 for masses between 0.8 and 1.6 TeV and further tightens to $\kappa < 0.2$ as the mass approaches 1.8 TeV. Similarly, for $Y$, $\kappa$ values are constrained below 0.26 around $m_Y \sim 1.7$ TeV, with exclusions gradually relaxing at higher masses. These exclusion regions, derived from the most stringent LHC search results, offer a unified and up-to-date perspective on VLQ phenomenology. The results were computed using \texttt{VLQBounds}, a new Python-based tool specifically developed for this purpose.
Human-Machine Interfaces for Subsea Telerobotics: From Soda-straw to Natural Language Interactions
2024-12-02
Adnan Abdullah, Ruo Chen, David Blow, Thanakon Uthai, Eric Jing Du, Md Jahidul Islam
This review explores the evolution of human-machine interfaces (HMIs) for subsea telerobotics, tracing back the transition from traditional first-person "soda-straw" consoles (narrow field-of-view camera feed) to advanced interfaces powered by gesture recognition, virtual reality, and natural language models. First, we discuss various forms of subsea telerobotics applications, current state-of-the-art (SOTA) interface systems, and the challenges they face in robust underwater sensing, real-time estimation, and low-latency communication. Through this analysis, we highlight how advanced HMIs facilitate intuitive interactions between human operators and robots to overcome these challenges. A detailed review then categorizes and evaluates the cutting-edge HMI systems based on their offered features from both human perspectives (e.g., enhancing operator control and situational awareness) and machine perspectives (e.g., improving safety, mission accuracy, and task efficiency). Moreover, we examine the literature on bidirectional interaction and intelligent collaboration in terms of sensory feedback and intuitive control mechanisms for both physical and virtual interfaces. The paper concludes by identifying critical challenges, open research questions, and future directions, emphasizing the need for multidisciplinary collaboration in subsea telerobotics.
Strong gravitational lensing with upcoming wide-field radio surveys
2024-12-02
Samuel McCarty, Liam Connor
The number of strong lensing systems will soon increase by orders of magnitude thanks to sensitive, wide-field optical and infrared imaging surveys such as Euclid, Rubin-LSST, and Roman. A dramatic increase in strong lenses will also occur at radio wavelengths. The 2000-antenna Deep Synoptic Array (DSA-2000) will detect over $10^9$ continuum sources in the Northern Hemisphere with a high mean redshift ($\langle z_s \rangle \approx2$) and the Square Kilometer Array (SKA) will observe a large sample of extragalactic sources in the South with sub-arcsecond resolution. We forecast lensing rates, finding that the DSA-2000 will discover $\mathcal{O}(10^5)$ strongly lensed systems, many of which will be galaxy group and cluster lenses. We propose strategies for strong lensing discovery in the limit where the Einstein radii are comparable to the PSF angular scale, taking advantage of modern computer vision techniques and multi-survey data. We also forecast synergies with optical and infrared surveys, which will provide redshifts as well as multiwavelength information about the lens systems. Finally, we describe applications of radio strong lensing systems, including time-delay cosmography with transient and variable sources. We find that $\sim$100 time-variable flat-spectrum AGN discovered by the DSA-2000 could be used to constrain $H_0$ at the percent level with the appropriate follow-up.
Physical motion has long been at the heart of what humans identify as life. Early thinkers about what differentiates life from inanimate matter connected agency to autonomous motion, a line of thinking that continued until Robert Brown's experiments showed decisively that autonomous motion, and many other physical behaviors and organizational principles, belong to inanimate matter. An incredible trajectory of inquiry for the next 100 years showed that life is characterized by the ability to reproduce, to inherit, to adapt. But throughout this arc, a persistent search continued for that fundamental process, that gain in function from matter to life. Subsequent decades showed that genetic sequence is not enough. The leap occurs somewhere in cells' watery milieu - physicochemical functions unique to life. In this Review, we explore how advances in the last decade have embraced the deep coupling of physics and cellular biochemistry to shed light on the matter/life nexus. Brownian flow physics are more than implicated but rather help instantiate life. The reader will observe in this review many references to works in cell biology - as many or more than in biophysics or fluid mechanics. This is perhaps not as intentional as it is unavoidable in the effort to see how matter becomes life inside the watery compartment of the cell.
Observations of massive contact binaries in the local universe
2024-12-02
Michael Abdul-Masih
The contact phase represents a crossroad in the evolution of massive binary stars. Depending on the internal physics, the predicted end products can vary greatly including various exotic objects such as Be stars, magnetic massive stars, LBVs and gravitational wave sources. This phase also offers a unique observational laboratory to study binary interaction physics. Here, I review the current state of the field of massive contact binary observations. I summarize the techniques available to identify and characterize these systems as well as the limitations of each and the potential biases that they introduce. I present the sample of known confirmed systems and what the bulk statistics can tell us about their formation and evolution. Next I discuss the challenges that these systems pose from a characterization point of view and how we can overcome these. Finally I discuss the future direction of the field on the observational side.
Smart Contract Vulnerabilities, Tools, and Benchmarks: An Updated Systematic Literature Review
2024-12-02
Gerardo Iuliano, Dario Di Nucci
Smart contracts are self-executing programs on blockchain platforms like Ethereum, which have revolutionized decentralized finance by enabling trustless transactions and the operation of decentralized applications. Despite their potential, the security of smart contracts remains a critical concern due to their immutability and transparency, which expose them to malicious actors. The connections of contracts further complicate vulnerability detection. This paper presents a systematic literature review that explores vulnerabilities in Ethereum smart contracts, focusing on automated detection tools and benchmark evaluation. We reviewed 1,888 studies from five digital libraries and five major software engineering conferences, applying a structured selection process that resulted in 131 high-quality studies. The key results include a hierarchical taxonomy of 101 vulnerabilities grouped into ten categories, a comprehensive list of 144 detection tools with corresponding functionalities, methods, and code transformation techniques, and a collection of 102 benchmarks used for tool evaluation. We conclude with insights on the current state of Ethereum smart contract security and directions for future research.
Scholarly peer review is a cornerstone of scientific advancement, but the system is under strain due to increasing manuscript submissions and the labor-intensive nature of the process. Recent advancements in large language models (LLMs) have led to their integration into peer review, with promising results such as substantial overlaps between LLM- and human-generated reviews. However, the unchecked adoption of LLMs poses significant risks to the integrity of the peer review system. In this study, we comprehensively analyze the vulnerabilities of LLM-generated reviews by focusing on manipulation and inherent flaws. Our experiments show that injecting covert deliberate content into manuscripts allows authors to explicitly manipulate LLM reviews, leading to inflated ratings and reduced alignment with human reviews. In a simulation, we find that manipulating 5% of the reviews could potentially cause 12% of the papers to lose their position in the top 30% rankings. Implicit manipulation, where authors strategically highlight minor limitations in their papers, further demonstrates LLMs' susceptibility compared to human reviewers, with a 4.5 times higher consistency with disclosed limitations. Additionally, LLMs exhibit inherent flaws, such as potentially assigning higher ratings to incomplete papers compared to full papers and favoring well-known authors in single-blind review process. These findings highlight the risks of over-reliance on LLMs in peer review, underscoring that we are not yet ready for widespread adoption and emphasizing the need for robust safeguards.
Andreev non-Hermitian Hamiltonian for open Josephson junctions from Green's functions
2024-12-02
Roberto Capecelatro, Marco Marciani, Gabriele Campagnano, Procolo Lucignano
We investigate the transport properties of open Josephson junctions (JJs) through a minimal effective non-Hermitian (NH) approach derived from the equilibrium Green's function (GF) formalism. Specifically, we consider a JJ with a quantum dot barrier coupled to a normal metal reservoir. The coupling introduces an imaginary self-energy term in the JJ Hamiltonian which can be naturally accounted for in the NH formalism. While most approaches to similar problems work with the full junction Hamiltonian we propose a scheme for deriving an effective NH Hamiltonian for the Andreev levels only, that we compute from the singular part of the barrier GF. To establish the range of applicability of this NH model we benchmark our results for both the dot density of states and the supercurrent against exact GF predictions in different transport regimes. We find that, as a rule of thumb, the Andreev NH description is accurate when the spectral overlap between the Andreev bound states (ABS) and the near-gap continuum states is negligible, i.e. when the ABS energies lie sufficiently far from the superconducting gap relative to their line-width. This method not only highlights the effective physics of the JJ but also offers a scalable framework for studying large-size devices.
A Well-Characterized Survey for Centaurs in Pan-STARRS1
2024-12-02
Jacob A. Kurlander, Matthew J. Holman, Pedro H. Bernardinelli, Mario Juric, Aren N. Heinze, Matthew J. Payne
To prepare for the upcoming Legacy Survey of Space and Time, we develop methods for quantifying the selection function of a wide-field survey as a function of all six orbital parameters and absolute magnitude. We perform a HelioLinC3D search for Centaurs in the Pan-STARRS1 detection catalog and use a synthetic debiasing population to characterize our survey's selection function. We find nine new objects, including Centaur 2010 RJ$_{226}$, among 320 real objects, along with $\sim$70,000 debiasing objects. We use the debiasing population to fit a selection function and apply the selection function to a model Centaur population with literature orbital and size distributions. We confirm the model's marginal distributions but reject its joint distribution, and estimate an intrinsic population of 21,400$^{+3,400}_{-2,800}$ Centaurs with $H_r < 13.7$. The discovery of only nine new objects in archival data verifies that the Pan-STARRS discovery pipeline had high completeness, but also shows that new linking algorithms can contribute even to traditional single-tracklet surveys. As the first systematic application of HelioLinC3D to a survey with extensive sky coverage, this project proves the viability of HelioLinC3D as a discovery algorithm for big-data wide-field surveys.
NGC 628 in SIGNALS: Explaining the Abundance-Ionization Correlation in HII Regions
2024-12-02
Ray Garner III, Robert Kennicutt Jr, Laurie Rousseau-Nepton, Grace M. Olivier, David Fernández-Arenas, Carmelle Robert, René Pierre Martin, Philippe Amram
The variations of oxygen abundance and ionization parameter in HII regions are usually thought to be the dominant factors that produced variations seen in observed emission line spectra. However, if and how these two quantities are physically related is hotly debated in the literature. Using emission line data of NGC 628 observed with SITELLE as part of the Star-formation, Ionized Gas, and Nebular Abundances Legacy Survey (SIGNALS), we use a suite of photoionization models to constrain the abundance and ionization parameters for over 1500 HII regions throughout its disk. We measure an anti-correlation between these two properties, consistent with expectations, although with considerable scatter. Secondary trends with dust extinction and star formation rate surface density potentially explain the large scatter observed. We raise concerns throughout regarding various modeling assumptions and their impact on the observed correlations presented in the literature.
Gravitational waves and galaxies cross-correlations: a forecast on GW biases for future detectors
2024-12-02
Stefano Zazzera, José Fonseca, Tessa Baker, Chris Clarkson
Gravitational waves (GWs) have rapidly become important cosmological probes since their first detection in 2015. As the number of detected events continues to rise, upcoming instruments like the Einstein Telescope (ET) and Cosmic Explorer (CE) will observe millions of compact binary (CB) mergers. These detections, coupled with galaxy surveys by instruments such as DESI, Euclid, and the Vera Rubin Observatory, will provide unique information on the large-scale structure of the universe by cross-correlating GWs with the distribution of galaxies which host them. In this paper, we focus on how these cross-correlations constrain the clustering bias of GWs emitted by the coalescence of binary black holes (BBH). This parameter links BBHs to the underlying dark matter distribution, hence informing us how they populate galaxies. Using a multi-tracer approach, we forecast the precision of these measurements under different survey combinations. Our results indicate that current GW detectors will have limited precision, with measurement errors as high as $\sim50\%$. However, third-generation detectors like ET, when cross-correlated with LSST data, can improve clustering bias measurements to within $2.5\%$. Furthermore, we demonstrate that these cross-correlations can enable a percent-level measurement of the magnification lensing effect on GWs. Despite this, there is a degeneracy between magnification and evolution biases, which hinders the precision of both. This degeneracy is most effectively addressed by assuming knowledge of one bias or targeting an optimal redshift range of $1 < z < 2.5$. Our analysis opens new avenues for studying the distribution of BBHs and testing the nature of gravity through large-scale structure.
2024-12-02
Mark Hofstadter, Ravit Helled, David J. Stevenson, Bethany Ehlmann, Mandy Bethkenhagen, Hao Cao, Junjie Dong, Maryame El Moutamid, Anton Ermakov, Jim Fuller, Tristan Guillot, Benjamin Idini, Andre Izidoro, Yohai Kaspi, Tanja Kovacevic, Valéry Lainey, Steve Levin, Jonathan Lunine, Christopher Mankovich, Stephen Markham, Marius Millot, Olivier Mousis, Simon Müller, Nadine Nettelmann, Francis Nimmo, Marzia Parisi, Krista Soderland, Lars Stixrude, Nick Teanby, Allona Vazan
Determining the internal structure of Uranus is a key objective for planetary science. Knowledge of Uranus's bulk composition and the distribution of elements is crucial to understanding its origin and evolutionary path. In addition, Uranus represents a poorly understood class of intermediate-mass planets (intermediate in size between the relatively well studied terrestrial and gas giant planets), which appear to be very common in the Galaxy. As a result, a better characterization of Uranus will also help us to better understand exoplanets in this mass and size regime. Recognizing the importance of Uranus, a Keck Institute for Space Studies (KISS) workshop was held in September 2023 to investigate how we can improve our knowledge of Uranus's internal structure in the context of a future Uranus mission that includes an orbiter and a probe. The scientific goals and objectives of the recently released Planetary Science and Astrobiology Decadal Survey were taken as our starting point. We reviewed our current knowledge of Uranus's interior and identified measurement and other mission requirements for a future Uranus spacecraft, providing more detail than was possible in the Decadal Survey's mission study and including new insights into the measurements to be made. We also identified important knowledge gaps to be closed with Earth-based efforts in the near term that will help guide the design of the mission and interpret the data returned.
Federated Learning (FL) has been becoming a popular interdisciplinary research area in both applied mathematics and information sciences. Mathematically, FL aims to collaboratively optimize aggregate objective functions over distributed datasets while satisfying a variety of privacy and system constraints.Different from conventional distributed optimization methods, FL needs to address several specific issues (e.g., non-i.i.d. data distributions and differential private noises), which pose a set of new challenges in the problem formulation, algorithm design, and convergence analysis. In this paper, we will systematically review existing FL optimization research including their assumptions, formulations, methods, and theoretical results. Potential future directions are also discussed.
This chapter reviews the three-dimensional structure, age, kinematics, and chemistry of the Milky Way (MW) region within ~2 kpc from its center (hereafter referred to as the 'bulge') from an observational perspective. While not exhaustive in citations, this review provides historical context and discusses the main controversies and limitations in the current consensus. The nuclear bulge region, within $\sim$200 pc from the Galactic center, has been excluded from this review. This very complex region, hosting dense molecular clouds and active star formation, would deserve a dedicated paper.
Due to their quantum nature, single-photon emitters generate individual photons in bursts or streams. They are paramount in emerging quantum technologies such as quantum key distribution, quantum repeaters, and measurement-based quantum computing. Many such systems have been reported in the last three decades, from Rubidium atoms coupled to cavities to semiconductor quantum dots and color centers implanted in waveguides. This review article highlights different material systems with deterministic and controlled single photon generation. We discuss and compare the performance metrics, such as purity and indistinguishability, for these sources and evaluate their potential for different applications. Finally, a new potential single-photon source, based on the Rydberg exciton in solid state metal oxide thin films, is introduced, briefly discussing its promising qualities and advantages in fabricating quantum chips for quantum photonic applications.
Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art
2024-12-02
Sebastian Peitz, Sedjro Salomon Hotegni
Simultaneously considering multiple objectives in machine learning has been a popular approach for several decades, with various benefits for multi-task learning, the consideration of secondary goals such as sparsity, or multicriteria hyperparameter tuning. However - as multi-objective optimization is significantly more costly than single-objective optimization - the recent focus on deep learning architectures poses considerable additional challenges due to the very large number of parameters, strong nonlinearities and stochasticity. This survey covers recent advancements in the area of multi-objective deep learning. We introduce a taxonomy of existing methods - based on the type of training algorithm as well as the decision maker's needs - before listing recent advancements, and also successful applications. All three main learning paradigms supervised learning, unsupervised learning and reinforcement learning are covered, and we also address the recently very popular area of generative modeling.
Fast Radio Bursts and the radio perspective on multi-messenger gravitational lensing
2024-12-02
Inés Pastor-Marazuela
Fast Radio Bursts (FRBs) are extragalactic millisecond-duration radio transients whose nature remains unknown. The advent of numerous facilities conducting dedicated FRB searches has dramatically revolutionised the field: hundreds of new bursts have been detected, and some are now known to repeat. Using interferometry, it is now possible to localise FRBs to their host galaxies, opening up new avenues for using FRBs as astrophysical probes. One promising application is studying gravitationally lensed FRBs. This review outlines the requirements for identifying a lensed FRB, taking into account their propagation effects and the importance of capturing the amplitude and phase of the signal. It also explores the different lens masses that could be probed with FRBs throughout the duration of an FRB survey, from stellar masses to individual galaxies. This highlights the unique cosmological applications of gravitationally lensed FRBs, including measurements of the Hubble constant and the compact object content of dark matter. Finally, we discuss future radio interferometers and the prospects for finding gravitationally lensed FRBs.
Take Your Steps: Hierarchically Efficient Pulmonary Disease Screening via CT Volume Compression
2024-12-02
Qian Shao, Kai Zhang, Bang Du, Zepeng Li, Yixuan Wu, Qiyuan Chen, Jian Wu, Jintai Chen, Honghao Gao, Hongxia Xu
Deep learning models are widely used to process Computed Tomography (CT) data in the automated screening of pulmonary diseases, significantly reducing the workload of physicians. However, the three-dimensional nature of CT volumes involves an excessive number of voxels, which significantly increases the complexity of model processing. Previous screening approaches often overlook this issue, which undoubtedly reduces screening efficiency. Towards efficient and effective screening, we design a hierarchical approach to reduce the computational cost of pulmonary disease screening. The new approach re-organizes the screening workflows into three steps. First, we propose a Computed Tomography Volume Compression (CTVC) method to select a small slice subset that comprehensively represents the whole CT volume. Second, the selected CT slices are used to detect pulmonary diseases coarsely via a lightweight classification model. Third, an uncertainty measurement strategy is applied to identify samples with low diagnostic confidence, which are re-detected by radiologists. Experiments on two public pulmonary disease datasets demonstrate that our approach achieves comparable accuracy and recall while reducing the time by 50%-70% compared with the counterparts using full CT volumes. Besides, we also found that our approach outperforms previous cutting-edge CTVC methods in retaining important indications after compression.
On the Square Speed of Sound in High-Energy Collisions: Range of Values and How to Understand It
2024-12-02
Ting-Ting Duan, Fu-Hu Liu, Khusniddin K. Olimov
After reviewing the sound speeds in various forms and conditions of matter, we investigate the sound speed of hadronic matter that has decoupled from the hot and dense system formed during high-energy collisions. We comprehensively consider factors such as energy loss of the incident beam, rapidity shift of leading nucleons, and the Landau hydrodynamic model for hadron production. The sound speed is related to the width or standard deviation of the Gaussian rapidity distribution of hadrons. The extracted square speed of sound lies within a range from 0 to 1/3 in most cases. For scenarios exceeding this limit, we also provide an explanation.
A comprehensive review of datasets and deep learning techniques for vision in Unmanned Surface Vehicles
2024-12-02
Linh Trinh, Siegfried Mercelis, Ali Anwar
Unmanned Surface Vehicles (USVs) have emerged as a major platform in maritime operations, capable of supporting a wide range of applications. USVs can help reduce labor costs, increase safety, save energy, and allow for difficult unmanned tasks in harsh maritime environments. With the rapid development of USVs, many vision tasks such as detection and segmentation become increasingly important. Datasets play an important role in encouraging and improving the research and development of reliable vision algorithms for USVs. In this regard, a large number of recent studies have focused on the release of vision datasets for USVs. Along with the development of datasets, a variety of deep learning techniques have also been studied, with a focus on USVs. However, there is a lack of a systematic review of recent studies in both datasets and vision techniques to provide a comprehensive picture of the current development of vision on USVs, including limitations and trends. In this study, we provide a comprehensive review of both USV datasets and deep learning techniques for vision tasks. Our review was conducted using a large number of vision datasets from USVs. We elaborate several challenges and potential opportunities for research and development in USV vision based on a thorough analysis of current datasets and deep learning techniques.
Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey
2024-12-02
Mridula Vijendran, Jingjing Deng, Shuang Chen, Edmond S. L. Ho, Hubert P. H. Shum
Artificial Intelligence significantly enhances the visual art industry by analyzing, identifying and generating digitized artistic images. This review highlights the substantial benefits of integrating geometric data into AI models, addressing challenges such as high inter-class variations, domain gaps, and the separation of style from content by incorporating geometric information. Models not only improve AI-generated graphics synthesis quality, but also effectively distinguish between style and content, utilizing inherent model biases and shared data traits. We explore methods like geometric data extraction from artistic images, the impact on human perception, and its use in discriminative tasks. The review also discusses the potential for improving data quality through innovative annotation techniques and the use of geometric data to enhance model adaptability and output refinement. Overall, incorporating geometric guidance boosts model performance in classification and synthesis tasks, providing crucial insights for future AI applications in the visual arts domain.
My Voice, Your Voice, Our Voice: Attitudes Towards Collective Governance of a Choral AI Dataset
2024-12-02
Jennifer Ding, Eva Jäger, Victoria Ivanova, Mercedes Bunz
Data grows in value when joined and combined; likewise the power of voice grows in ensemble. With 15 UK choirs, we explore opportunities for bottom-up data governance of a jointly created Choral AI Dataset. Guided by a survey of chorister attitudes towards generative AI models trained using their data, we explore opportunities to create empowering governance structures that go beyond opt in and opt out. We test the development of novel mechanisms such as a Trusted Data Intermediary (TDI) to enable governance of the dataset amongst the choirs and AI developers. We hope our findings can contribute to growing efforts to advance collective data governance practices and shape a more creative, empowering future for arts communities in the generative AI ecosystem.
A Top-down Graph-based Tool for Modeling Classical Semantic Maps: A Crosslinguistic Case Study of Supplementary Adverbs
2024-12-02
Zhu Liu, Cunliang Kong, Ying Liu, Maosong Sun
Semantic map models (SMMs) construct a network-like conceptual space from cross-linguistic instances or forms, based on the connectivity hypothesis. This approach has been widely used to represent similarity and entailment relationships in cross-linguistic concept comparisons. However, most SMMs are manually built by human experts using bottom-up procedures, which are often labor-intensive and time-consuming. In this paper, we propose a novel graph-based algorithm that automatically generates conceptual spaces and SMMs in a top-down manner. The algorithm begins by creating a dense graph, which is subsequently pruned into maximum spanning trees, selected according to metrics we propose. These evaluation metrics include both intrinsic and extrinsic measures, considering factors such as network structure and the trade-off between precision and coverage. A case study on cross-linguistic supplementary adverbs demonstrates the effectiveness and efficiency of our model compared to human annotations and other automated methods. The tool is available at \url{https://github.com/RyanLiut/SemanticMapModel}.
Chaos Engineering: A Multi-Vocal Literature Review
2024-12-02
Joshua Owotogbe, Indika Kumara, Willem-Jan Van Den Heuvel, Damian Andrew Tamburri
Organizations, particularly medium and large enterprises, typically today rely heavily on complex, distributed systems to deliver critical services and products. However, the growing complexity of these systems poses challenges in ensuring service availability, performance, and reliability. Traditional resilience testing methods often fail to capture modern systems' intricate interactions and failure modes. Chaos Engineering addresses these challenges by proactively testing how systems in production behave under turbulent conditions, allowing developers to uncover and resolve potential issues before they escalate into outages. Though chaos engineering has received growing attention from researchers and practitioners alike, we observed a lack of a comprehensive literature review. Hence, we performed a Multivocal Literature Review (MLR) on chaos engineering to fill this research gap by systematically analyzing 88 academic and grey literature sources published from January 2019 to April 2024. We first used the selected sources to derive a unified definition of chaos engineering and to identify key capabilities, components, and adoption drivers. We also developed a taxonomy for chaos engineering and compared the relevant tools using it. Finally, we analyzed the state of the current chaos engineering research and identified several open research issues.
The rapid advancements in machine learning have made its application to anomalous diffusion analysis both essential and inevitable. This review systematically introduces the integration of machine learning techniques for enhanced analysis of anomalous diffusion, focusing on two pivotal aspects: single trajectory characterization via machine learning and representation learning of anomalous diffusion. We extensively compare various machine learning methods, including both classical machine learning and deep learning, used for the inference of diffusion parameters and trajectory segmentation. Additionally, platforms such as the Anomalous Diffusion Challenge that serve as benchmarks for evaluating these methods are highlighted. On the other hand, we outline three primary strategies for representing anomalous diffusion: the combination of predefined features, the feature vector from the penultimate layer of neural network, and the latent representation from the autoencoder, analyzing their applicability across various scenarios. This investigation paves the way for future research, offering valuable perspectives that can further enrich the study of anomalous diffusion and advance the application of artificial intelligence in statistical physics and biophysics.
A Survey on Deep Neural Networks in Collaborative Filtering Recommendation Systems
2024-12-02
Pang Li, Shahrul Azman Mohd Noah, Hafiz Mohd Sarim
This survey provides an examination of the use of Deep Neural Networks (DNN) in Collaborative Filtering (CF) recommendation systems. As the digital world increasingly relies on data-driven approaches, traditional CF techniques face limitations in scalability and flexibility. DNNs can address these challenges by effectively modeling complex, non-linear relationships within the data. We begin by exploring the fundamental principles of both collaborative filtering and deep neural networks, laying the groundwork for understanding their integration. Subsequently, we review key advancements in the field, categorizing various deep learning models that enhance CF systems, including Multilayer Perceptrons (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Graph Neural Networks (GNN), autoencoders, Generative Adversarial Networks (GAN), and Restricted Boltzmann Machines (RBM). The paper also discusses evaluation protocols, various publicly available auxiliary information, and data features. Furthermore, the survey concludes with a discussion of the challenges and future research opportunities in enhancing collaborative filtering systems with deep learning.
An overview of diffusion models for generative artificial intelligence
2024-12-02
Davide Gallon, Arnulf Jentzen, Philippe von Wurstemberger
This article provides a mathematically rigorous introduction to denoising diffusion probabilistic models (DDPMs), sometimes also referred to as diffusion probabilistic models or diffusion models, for generative artificial intelligence. We provide a detailed basic mathematical framework for DDPMs and explain the main ideas behind training and generation procedures. In this overview article we also review selected extensions and improvements of the basic framework from the literature such as improved DDPMs, denoising diffusion implicit models, classifier-free diffusion guidance models, and latent diffusion models.
The ionizing photon production efficiency of star-forming galaxies at $z\sim 4-10$
2024-12-02
M. Llerena, L. Pentericci, L. Napolitano, S. Mascia, R. Amorín, A. Calabrò, M. Castellano, N. J. Cleri, M. Giavalisco, N. A. Grogin, N. P. Hathi, M. Hirschmann, A. M. Koekemoer, T. Nanayakkara, F. Pacucci, L. Shen, S. M. Wilkins, I. Yoon, L. Y. A. Yung, R. Bhatawdekar, R. A. Lucas, X. Wang, P. Arrabal Haro, M. B. Bagley, S. L. Finkelstein, J. S. Kartaltepe, E. Merlin, C. Papovich, N. Pirzkal
Investigating the ionizing emission of star-forming galaxies is critical to understanding their contribution to reionization and their impact on the surrounding environment. The number of ionizing photons available to reionize the intergalactic medium (IGM) depends not only on the abundance of galaxies but also on their efficiency in producing ionizing photons ($\xi_{ion}$). We aim to estimate the $\xi_{ion}$ using Balmer lines in a sample of 731 galaxies at $4\leq z \leq 10$ selected from different JWST surveys. We used the available HST and JWST photometry to perform a SED fitting in the sample to determine their physical properties. We used the BAGPIPES code and assumed a delayed exponential model for the star formation history. We used the NIRSpec spectra from prism or grating configurations to estimate Balmer luminosities and then constrained $\xi_{ion}$ values after dust correction. We find a mean value of 10$^{25.23}$Hz erg$^{-1}$ for $\xi_{ion}$ in the sample with an observed scatter of 0.42dex. We find an increase in the median values of $\xi_{ion}$ which confirms the redshift evolution of $\xi_{ion}$ found in other works. Regarding the relation with physical properties, we find a decrease of $\xi_{ion}$ with increasing stellar mass, indicating that low-mass galaxies are efficient producers of ionizing photons. We also find an increase of $\xi_{ion}$ with increasing specific star formation rate (sSFR) and increasing UV absolute magnitude, which indicates that faint galaxies and with high sSFR are also efficient producers. We also investigated the relation of $\xi_{ion}$ with the EW([OIII]$\lambda$5007) and find that galaxies with the higher EW([OIII]) are the more efficient producers of ionizing photons. Similarly, we find that galaxies with higher O32 ratios and lower gas-phase metallicities (based on the R23 calibration) show higher $\xi_{ion}$ values.
This paper investigates the evolving landscape of decentralized finance (DeFi) by examining its foundational concepts, research trends, and ecosystem. A bibliometric analysis was conducted to identify thematic clusters and track the evolution of DeFi research. Additionally, a thematic review was performed to analyze the roles and interactions of key participants within the DeFi ecosystem, focusing on its opportunities and inherent risks. The bibliometric analysis identified a progression in research priorities, transitioning from an initial focus on technological innovation to addressing sustainability, environmental impacts, and regulatory challenges. Key thematic clusters include decentralization, smart contracts, tokenization, and sustainability concerns. The analysis of participants highlighted the roles of developers, liquidity providers, auditors, and regulators while identifying critical risks such as smart contract vulnerabilities, liquidity constraints, and regulatory uncertainties. The study underlines the transformative potential of DeFi to enhance financial inclusion and transparency while emphasizing the need for robust security frameworks and regulatory oversight to ensure long-term stability. This paper comprehensively explains the DeFi ecosystem by integrating bibliometric and thematic analyses. It offers valuable insights for researchers, practitioners, and policymakers, contributing to the ongoing discourse on the sustainable development and integration of DeFi into the global financial system.
Gauge invariant perturbations in teleparallel Horndeski gravity
2024-12-02
Bobomurat Ahmedov, Maria Caruana, Jackson Levi Said, Konstantinos F. Dialektopoulos, Abdurakhmon Nosirov, Zinovia Oikonomopoulou, Odil Yunusov
We present in the form of a catalogue of the cosmological perturbations within the Bahamonde- Dialektopoulos-Levi Said (BDLS) theory, which serves as the teleparallel counterpart of Horndeski gravity. To understand structure formation in cosmological models, it is essential to study both the background and perturbative aspects of their cosmology. While extensive analysis of both Horndeski gravity and its teleparallel analog exists in the literature, a quantitative understanding requires a detailed examination of their cosmological perturbations. We review here all the different gauges for the scalar, vector and tensor perturbations of a cosmological background up to second order and we hope this will help people who work with observations, to incorporate it in existing codes.
Hierarchical Object-Oriented POMDP Planning for Object Rearrangement
2024-12-02
Rajesh Mangannavar, Alan Fern, Prasad Tadepalli
We present an online planning framework for solving multi-object rearrangement problems in partially observable, multi-room environments. Current object rearrangement solutions, primarily based on Reinforcement Learning or hand-coded planning methods, often lack adaptability to diverse challenges. To address this limitation, we introduce a novel Hierarchical Object-Oriented Partially Observed Markov Decision Process (HOO-POMDP) planning approach. This approach comprises of (a) an object-oriented POMDP planner generating sub-goals, (b) a set of low-level policies for sub-goal achievement, and (c) an abstraction system converting the continuous low-level world into a representation suitable for abstract planning. We evaluate our system on varying numbers of objects, rooms, and problem types in AI2-THOR simulated environments with promising results.
Can Large Language Models Serve as Evaluators for Code Summarization?
2024-12-02
Yang Wu, Yao Wan, Zhaoyang Chu, Wenting Zhao, Ye Liu, Hongyu Zhang, Xuanhua Shi, Philip S. Yu
Code summarization facilitates program comprehension and software maintenance by converting code snippets into natural-language descriptions. Over the years, numerous methods have been developed for this task, but a key challenge remains: effectively evaluating the quality of generated summaries. While human evaluation is effective for assessing code summary quality, it is labor-intensive and difficult to scale. Commonly used automatic metrics, such as BLEU, ROUGE-L, METEOR, and BERTScore, often fail to align closely with human judgments. In this paper, we explore the potential of Large Language Models (LLMs) for evaluating code summarization. We propose CODERPE (Role-Player for Code Summarization Evaluation), a novel method that leverages role-player prompting to assess the quality of generated summaries. Specifically, we prompt an LLM agent to play diverse roles, such as code reviewer, code author, code editor, and system analyst. Each role evaluates the quality of code summaries across key dimensions, including coherence, consistency, fluency, and relevance. We further explore the robustness of LLMs as evaluators by employing various prompting strategies, including chain-of-thought reasoning, in-context learning, and tailored rating form designs. The results demonstrate that LLMs serve as effective evaluators for code summarization methods. Notably, our LLM-based evaluator, CODERPE , achieves an 81.59% Spearman correlation with human evaluations, outperforming the existing BERTScore metric by 17.27%.
In this paper, we design indices of economic fluctuation narratives derived from economic surveys. Companies, governments, and investors rely on key metrics like GDP and industrial production indices to predict economic trends. However, they have yet to effectively leverage the wealth of information contained in economic text, such as causal relationships, in their economic forecasting. Therefore, we design indices of economic fluctuation from economic surveys by using our previously proposed narrative framework. From the evaluation results, it is observed that the proposed indices had a stronger correlation with cumulative lagging diffusion index than other types of diffusion indices.
Over the last several decades, extensive research has been conducted on the baryon cycles within cosmic structures, encompassing a broad mass range from dwarf galaxies to galaxy clusters. However, a notable gap in understanding the cosmic baryon cycle is the poor galaxy groups with halo masses around $10^{13}\ M_{\odot}$ (e.g., McGaugh et al. 2010). Poor galaxy groups, like our own Local Group, are prevalent throughout the universe, yet robust detection of their hot, X-ray emitting intragroup medium (IGrM) has remained elusive. The presence of this hot IGrM is crucial for addressing the long-standing "missing baryons" problem. Previous ROSAT-based studies were limited by a small number of X-ray bright samples, thus restricting the scope of their findings. Here we show a robust detection of this hot IGrM in a large, optically selected poor groups sample, based on the stacked X-ray images from the eROSITA Final Equatorial Depth Survey. These groups are identified in DESI LS with a mass range of log($M_\mathrm{halo}/h^{-1}M_{\odot}$) = 11.5-13.5 and a redshift range of z = 0.1-0.5. Additionally, our results indicate that despite its presence in virtually groups at all sizes, this gas component is still not sufficient to recover the universal baryon fraction, and hence the "missing baryons" problem still persists in poor galaxy groups.
Investigations of MWISP Filaments. I. Filament Identification and Analysis Algorithms, and Source Catalogue
2024-12-02
Yu Jiang
Filaments play a crucial role in providing the necessary environmental conditions for star formation, actively participating in the process. To facilitate the identification and analysis of filaments, we introduce DPConCFil (Directional and Positional Consistency between Clumps and Filaments), a suite of algorithms comprising one identification method and two analysis methods. The first method, the consistency-based identification approach, uses directional and positional consistency among neighboring clumps and local filament axes to identify filaments in the PPV datacube. The second method employs a graph-based skeletonization technique to extract the filament intensity skeletons. The third method, a graph-based substructuring approach, allows the decomposition of complex filaments into simpler sub-filaments. We demonstrate the effectiveness of DPConCFil by applying the identification method to the clumps detected in the Milky Way Imaging Scroll Painting (MWISP) survey dataset by FacetClumps, successfully identifying a batch of filaments across various scales within $10^{\circ} \leq l \leq 20^{\circ}$, $-5.25^{\circ} \leq b \leq 5.25^{\circ}$ and -200 km s$^{-1}$ $\leq v \leq$ 200 km s$^{-1}$. Subsequently, we apply the analysis methods to the identified filaments, presenting a catalog with basic parameters and conducting statistics of their galactic distribution and properties. DPConCFil is openly available on GitHub, accompanied by a manual.
Best Practices for Large Language Models in Radiology
2024-12-02
Christian Bluethgen, Dave Van Veen, Cyril Zakka, Katherine Link, Aaron Fanous, Roxana Daneshjou, Thomas Frauenfelder, Curtis Langlotz, Sergios Gatidis, Akshay Chaudhari
At the heart of radiological practice is the challenge of integrating complex imaging data with clinical information to produce actionable insights. Nuanced application of language is key for various activities, including managing requests, describing and interpreting imaging findings in the context of clinical data, and concisely documenting and communicating the outcomes. The emergence of large language models (LLMs) offers an opportunity to improve the management and interpretation of the vast data in radiology. Despite being primarily general-purpose, these advanced computational models demonstrate impressive capabilities in specialized language-related tasks, even without specific training. Unlocking the potential of LLMs for radiology requires basic understanding of their foundations and a strategic approach to navigate their idiosyncrasies. This review, drawing from practical radiology and machine learning expertise and recent literature, provides readers insight into the potential of LLMs in radiology. It examines best practices that have so far stood the test of time in the rapidly evolving landscape of LLMs. This includes practical advice for optimizing LLM characteristics for radiology practices along with limitations, effective prompting, and fine-tuning strategies.
Advances and Challenges of Hexagonal Boron Nitride-based Anticorrosion Coatings
2024-12-02
Onurcan Kaya, Luca Gabatel, Sebastiano Bellani, Fabrizio Barberis, Francesco Bonaccorso, Ivan Cole, Stephan Roche
The corrosion of metallic surfaces poses significant challenges across industries such as petroleum, energy, and biomedical sectors, leading to structural degradation, safety risks, and substantial maintenance costs. Traditional organic and metallic coatings provide some protection, but their limited durability and susceptibility to harsh environmental conditions necessitate the development of more advanced and efficient solutions. This has driven significant interest in two-dimensional (2D) materials, with graphene extensively studied for its exceptional mechanical strength and impermeability to gases and ions. However, while graphene offers short-term corrosion protection, its high electrical conductivity presents a long-term issue by promoting galvanic corrosion on metal surfaces. In contrast, hexagonal boron nitride (h-BN) has emerged as a promising alternative for anticorrosion coatings. h-BN combines exceptional chemical stability, impermeability, and electrical insulation, making it particularly suited for long-term protection in highly corrosive or high-temperature environments. While h-BN holds promise as anticorrosion material, challenges such as structural defects, agglomeration of nanosheets, and poor dispersion within coatings limit its performance. This review provides a comprehensive analysis of recent advancements in addressing these challenges, including novel functionalization strategies, scalable synthesis methods, and hybrid systems that integrate h-BN with complementary materials. By bridging the gap between fundamental research and industrial applications, this review outlines the potential for h-BN to revolutionize anticorrosion technologies. These obstacles necessitate advanced strategies such as surface functionalization to improve compatibility with polymer matrices and dispersion optimization to minimize agglomeration.
The Simons Observatory: Design, Integration, and Current Status of Small Aperture Telescopes
2024-12-02
Aashrita Mangu, Lance Corbett, Sanah Bhimani, Fred Carl, Samuel Day-Weiss, Brooke DiGia, Josquin Errard, Nicholas Galitzki, Masashi Hazumi, Shawn W. Henderson, Varun Kabra, Amber Miller, Jenna Moore, Xue Song, Tran Tsan, Yuhan Wang, Andrea Zonca
The Simons Observatory (SO) is a cosmic microwave background (CMB) survey experiment located in the Atacama Desert in Chile at an elevation of 5200 meters, nominally consisting of an array of three 0.42-meter small aperture telescopes (SATs) and one 6-meter large aperture telescope (LAT). SO will make accurate measurements of the CMB temperature and polarization spanning six frequency bands ranging from 27 to 280 GHz, fielding a total of $\sim$68,000 detectors covering angular scales between one arcminute to tens of degrees. In this paper, we focus on the SATs, which are tailored to search for primordial gravitational waves, with the primary science goal of measuring the primordial tensor-to-scalar ratio \textit{r} at a target level of $\sigma(r) \approx 0.003$. We discuss the design drivers, scientific impact, and current deployment status of the three SATs, which are scheduled to start taking data in the coming year. The SATs aim to map 10\% of the sky at a 2 $\mu$K-arcmin noise level observing at mid-frequencies (93/145 GHz), with additional ultra-high-frequency (225/280 GHz) and low-frequency (27/39 GHz) targets to yield galactic foreground-subtracted measurements.
When learning stable linear dynamical systems from data, three important properties are desirable: i) predictive accuracy, ii) provable stability, and iii) computational efficiency. Unconstrained minimization of reconstruction errors leads to high accuracy and efficiency but cannot guarantee stability. Existing methods to remedy this focus on enforcing stability while also ensuring accuracy, but do so only at the cost of increased computation. In this work, we investigate if a straightforward approach can simultaneously offer all three desiderata of learning stable linear systems. Specifically, we consider a post-hoc approach that manipulates the spectrum of the learned system matrix after it is learned in an unconstrained fashion. We call this approach spectrum clipping (SC) as it involves eigen decomposition and subsequent reconstruction of the system matrix after clipping all of its eigenvalues that are larger than one to one (without altering the eigenvectors). Through detailed experiments involving two different applications and publicly available benchmark datasets, we demonstrate that this simple technique can simultaneously learn highly accurate linear systems that are provably stable. Notably, we demonstrate that SC can achieve similar or better performance than strong baselines while being orders-of-magnitude faster. We also show that SC can be readily combined with Koopman operators to learn stable nonlinear dynamics, such as those underlying complex dexterous manipulation skills involving multi-fingered robotic hands. Our codes and dataset can be found at https://github.com/GT-STAR-Lab/spec_clip.
Representation Learning for Time-Domain High-Energy Astrophysics: Discovery of Extragalactic Fast X-ray Transient XRT 200515
2024-12-02
Steven Dillmann, Rafael Martínez-Galarza, Roberto Soria, Rosanne Di Stefano, Vinay L. Kashyap
We present a novel representation learning method for downstream tasks such as anomaly detection and unsupervised transient classification in high-energy datasets. This approach enabled the discovery of a new fast X-ray transient (FXT) in the Chandra archive, XRT 200515, a needle-in-the-haystack event and the first Chandra FXT of its kind. Recent serendipitous breakthroughs in X-ray astronomy, including FXTs from binary neutron star mergers and an extragalactic planetary transit candidate, highlight the need for systematic transient searches in X-ray archives. We introduce new event file representations, E-t Maps and E-t-dt Cubes, designed to capture both temporal and spectral information, effectively addressing the challenges posed by variable-length event file time series in machine learning applications. Our pipeline extracts low-dimensional, informative features from these representations using principal component analysis or sparse autoencoders, followed by clustering in the embedding space with DBSCAN. New transients are identified within transient-dominant clusters or through nearest-neighbor searches around known transients, producing a catalog of 3,539 candidates (3,427 flares and 112 dips). XRT 200515 exhibits unique temporal and spectral variability, including an intense, hard <10 s initial burst followed by spectral softening in an ~800 s oscillating tail. We interpret XRT 200515 as either the first giant magnetar flare observed at low X-ray energies or the first extragalactic Type I X-ray burst from a faint LMXB in the LMC. Our method extends to datasets from other observatories such as XMM-Newton, Swift-XRT, eROSITA, Einstein Probe, and upcoming missions like AXIS.
Decoding Financial Behaviour: An Analysis of urbanised households in India using AIDIS 77th round
2024-12-02
Divya Sharma
This research paper delves into the financial behavior of urbanized households in India, specifically focusing on million-plus agglomerations. Using data from the 77th round of the All India Debt and Investment Survey, the study analyzes assets, borrowing patterns, and the usage of financial instruments like bank accounts, e-wallets, and life insurance. The research explores demographic factors, household structures, and district-level parameters to understand the intricate financial landscape. With a focus on low-income households, the study identifies income, education, type of employment, and branches per capita as significant factors influencing financial behavior. The study contributes valuable insights for financial institutions, policymakers, and researchers seeking a comprehensive understanding of the financial lives of urban households in India.
Given a system $\mathcal{G} =\{G_1,G_2,\dots,G_m\}$ of graphs/digraphs/hypergraphs on the common vertex set $V$ of size $n$, an $m$-edge graph/digraph/hypergraph $H$ on $V$ is transversal in $\mathcal{G}$ if there exists a bijection $\phi :E(H)\rightarrow [m]$ such that $e \in E(G_{\phi(e)})$ for all $e\in E(H)$. In this survey, we consider extremal problems for transversal structures in graph systems. More precisely, we summarize some sufficient conditions that ensure the existence of transversal structures in graph/digraph/hypergraph systems, which generalize several classical theorems in extremal graph theory to transversal version. We also include a number of conjectures and open problems.
Object Tracking in a $360^o$ View: A Novel Perspective on Bridging the Gap to Biomedical Advancements
2024-12-02
Mojtaba S. Fazli, Shannon Quinn
Object tracking is a fundamental tool in modern innovation, with applications in defense systems, autonomous vehicles, and biomedical research. It enables precise identification, monitoring, and spatiotemporal analysis of objects across sequential frames, providing insights into dynamic behaviors. In cell biology, object tracking is vital for uncovering cellular mechanisms, such as migration, interactions, and responses to drugs or pathogens. These insights drive breakthroughs in understanding disease progression and therapeutic interventions. Over time, object tracking methods have evolved from traditional feature-based approaches to advanced machine learning and deep learning frameworks. While classical methods are reliable in controlled settings, they struggle in complex environments with occlusions, variable lighting, and high object density. Deep learning models address these challenges by delivering greater accuracy, adaptability, and robustness. This review categorizes object tracking techniques into traditional, statistical, feature-based, and machine learning paradigms, with a focus on biomedical applications. These methods are essential for tracking cells and subcellular structures, advancing our understanding of health and disease. Key performance metrics, including accuracy, efficiency, and adaptability, are discussed. The paper explores limitations of current methods and highlights emerging trends to guide the development of next-generation tracking systems for biomedical research and broader scientific domains.
Spectral theory of effective transport for continuous uniaxial polycrystalline materials
2024-12-02
N. Benjamin Murphy, Daniel Hallman, Elena Cherkaev, Kenneth M. Golden
Following seminal work in the early 1980s that established the existence and representations of the homogenized transport coefficients for two phase random media, we develop a mathematical framework that provides Stieltjes integral representations for the bulk transport coefficients for uniaxial polycrystalline materials, involving spectral measures of self-adjoint random operators, which are compositions of non-random and random projection operators. We demonstrate the same mathematical framework also describes two-component composites, with a simple substitution of the random projection operator, making the mathematical descriptions of these two distinct physical systems directly analogous to one another. A detailed analysis establishes the operators arising in each setting are indeed self-adjoint on an $L^2$-type Hilbert space, providing a rigorous foundation to the formal spectral theoretic framework established by Golden and Papanicolaou in 1983. An abstract extension of the Helmholtz theorem also leads to integral representations for the inverses of effective parameters, e.g., effective conductivity and resistivity. An alternate formulation of the effective parameter problem in terms of a Sobolev-type Hilbert space provides a rigorous foundation for an approach first established by Bergman and Milton. We show that the correspondence between the two formulations is a one-to-one isometry. Rigorous bounds that follow from such Stieltjes integrals and partial knowledge about the material geometry are reviewed and validated by numerical calculations of the effective parameters for polycrystalline media.
FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear Systems under Uncertain Disturbances
2024-12-02
Takahito Fujimori
Linear Quadratic Regulator (LQR) is often combined with feedback linearization (FBL) for nonlinear systems that have the nonlinearity additive to the input. Conventional approaches estimate and cancel the nonlinearity based on the first principle or data-driven methods such as Gaussian Processes (GPs). However, the former needs an elaborate modeling process, and the latter provides a fixed learned model, which may be suffering when the model dynamics are changing. In this letter, we take a Deep Neural Network (DNN) using a real-time-updated dataset to approximate the unknown nonlinearity while the controller is running. Spectrally normalizing the weights in each time-step, we stably incorporate the DNN prediction to an LQR controller and compensate for the nonlinear term. Leveraging the property of the bounded Lipschitz constant of the DNN, we provide theoretical analysis and locally exponential stability of the proposed controller. Simulation results show that our controller significantly outperforms Baseline controllers in trajectory tracking cases.
Log anomaly detection has become a common practice for software engineers to analyze software system behavior. Despite significant research efforts in log anomaly detection over the past decade, it remains unclear what are practitioners' expectations on log anomaly detection and whether current research meets their needs. To fill this gap, we conduct an empirical study, surveying 312 practitioners from 36 countries about their expectations on log anomaly detection. In particular, we investigate various factors influencing practitioners' willingness to adopt log anomaly detection tools. We then perform a literature review on log anomaly detection, focusing on publications in premier venues from 2014 to 2024, to compare practitioners' needs with the current state of research. Based on this comparison, we highlight the directions for researchers to focus on to develop log anomaly detection techniques that better meet practitioners' expectations.
e-Fold Cross-Validation for Recommender-System Evaluation
2024-12-02
Moritz Baumgart, Lukas Wegmeth, Tobias Vente, Joeran Beel
To combat the rising energy consumption of recommender systems we implement a novel alternative for k-fold cross validation. This alternative, named e-fold cross validation, aims to minimize the number of folds to achieve a reduction in power usage while keeping the reliability and robustness of the test results high. We tested our method on 5 recommender system algorithms across 6 datasets and compared it with 10-fold cross validation. On average e-fold cross validation only needed 41.5% of the energy that 10-fold cross validation would need, while it's results only differed by 1.81%. We conclude that e-fold cross validation is a promising approach that has the potential to be an energy efficient but still reliable alternative to k-fold cross validation.
The pursuit of leaderboard rankings in Large Language Models (LLMs) has created a fundamental paradox: models excel at standardized tests while failing to demonstrate genuine language understanding and adaptability. Our systematic analysis of NLP evaluation frameworks reveals pervasive vulnerabilities across the evaluation spectrum, from basic metrics to complex benchmarks like GLUE and MMLU. These vulnerabilities manifest through benchmark exploitation, dataset contamination, and evaluation bias, creating a false perception of progress in language understanding capabilities. Through extensive review of contemporary evaluation approaches, we identify significant limitations in static benchmark designs, human evaluation protocols, and LLM-as-judge frameworks, all of which compromise the reliability of current performance assessments. As LLM capabilities evolve and existing benchmarks become redundant, we lay the groundwork for new evaluation methods that resist manipulation, minimize data contamination, and assess domain-specific tasks. This requires frameworks that are adapted dynamically, addressing current limitations and providing a more accurate reflection of LLM performance.
Competition Dynamics Shape Algorithmic Phases of In-Context Learning
2024-12-01
Core Francisco Park, Ekdeep Singh Lubana, Itamar Pres, Hidenori Tanaka
In-Context Learning (ICL) has significantly expanded the general-purpose nature of large language models, allowing them to adapt to novel tasks using merely the inputted context. This has motivated a series of papers that analyze tractable synthetic domains and postulate precise mechanisms that may underlie ICL. However, the use of relatively distinct setups that often lack a sequence modeling nature to them makes it unclear how general the reported insights from such studies are. Motivated by this, we propose a synthetic sequence modeling task that involves learning to simulate a finite mixture of Markov chains. As we show, models trained on this task reproduce most well-known results on ICL, hence offering a unified setting for studying the concept. Building on this setup, we demonstrate we can explain a model's behavior by decomposing it into four broad algorithms that combine a fuzzy retrieval vs. inference approach with either unigram or bigram statistics of the context. These algorithms engage in a competition dynamics to dominate model behavior, with the precise experimental conditions dictating which algorithm ends up superseding others: e.g., we find merely varying context size or amount of training yields (at times sharp) transitions between which algorithm dictates the model behavior, revealing a mechanism that explains the transient nature of ICL. In this sense, we argue ICL is best thought of as a mixture of different algorithms, each with its own peculiarities, instead of a monolithic capability. This also implies that making general claims about ICL that hold universally across all settings may be infeasible.
LOFAR Deep Fields: Probing the sub-mJy regime of polarized extragalactic sources in ELAIS-N1. II. Analysis
2024-12-01
S. Piras, C. Horellou, J. Conway, M. Thomasson, T. W. Shimwell, S. P. O'Sullivan, E. Carretti, V. Vacca, A. Bonafede, I. Prandoni
Deep polarization surveys at low radio frequencies are key to cosmic magnetism studies: Larger catalogs of polarized extragalactic sources and increased precision on Faraday rotation measures (RMs) make it possible to probe the magneto-ionic medium along the lines of sight of the sources and to construct denser RM grids. In a first paper, we presented a search for polarized sources in deep observations of the 25 square degree area of the European Large Area ISO Survey North 1 (ELAIS-N1) field with the LOw Frequency ARray (LOFAR) in the range 114.9 to 177.4 MHz. In this paper, we investigate the properties of the polarized radio galaxies and use the catalog to produce an RM grid of the field. After identifying the host galaxies and collecting redshift information, we characterized the radio galaxies in terms of their radio morphologies, rest frame radio luminosities, and linear sizes. We calculated residual rotation measures (RRMs) by removing the Galactic RM and studied the variation in the RRMs with redshift and degree of polarization. We produced an RRM grid of the field and compared the positions of the polarized sources with those of galaxy clusters and superclusters. The radio galaxies show a variety of morphologies, including diffuse emission; Fanaroff Riley type II sources make up about half of the sample. Using available multiband catalogs, we found redshifts for the hosts of all polarized sources in the range of 0.06 to 1.9. Polarized emission is detected mainly from large radio galaxies. The RRM values have a median close to zero, and they appear to be independent of redshift and degree of polarization. The sources in the lines of sight of clusters of galaxies and of a supercluster are indistinguishable in their polarization and RRM properties from the population of sources that are not behind these structures.
Quantum Information Processing, Sensing and Communications: Their Myths, Realities and Futures
2024-12-01
Lajos Hanzo, Zunaira Babar, Zhenyu Cai, Daryus Chandra, Ivan B. Djordjevic, Balint Koczor, Soon Xin Ng, Mohsen Razavi, Osvaldo Simeone
The recent advances in quantum information processing, sensing and communications are surveyed with the objective of identifying the associated knowledge gaps and formulating a roadmap for their future evolution. Since the operation of quantum systems is prone to the deleterious effects of decoherence, which manifests itself in terms of bit-flips, phase-flips or both, the pivotal subject of quantum error mitigation is reviewed both in the presence and absence of quantum coding. The state-of-the-art, knowledge gaps and future evolution of quantum machine learning are also discussed, followed by a discourse on quantum radar systems and briefly hypothesizing about the feasibility of integrated sensing and communications in the quantum domain. Finally, we conclude with a set of promising future research ideas in the field of ultimately secure quantum communications with the objective of harnessing ideas from the classical communications field.
TGTOD: A Global Temporal Graph Transformer for Outlier Detection at Scale
2024-12-01
Kay Liu, Jiahao Ding, MohamadAli Torkamani, Philip S. Yu
While Transformers have revolutionized machine learning on various data, existing Transformers for temporal graphs face limitations in (1) restricted receptive fields, (2) overhead of subgraph extraction, and (3) suboptimal generalization capability beyond link prediction. In this paper, we rethink temporal graph Transformers and propose TGTOD, a novel end-to-end Temporal Graph Transformer for Outlier Detection. TGTOD employs global attention to model both structural and temporal dependencies within temporal graphs. To tackle scalability, our approach divides large temporal graphs into spatiotemporal patches, which are then processed by a hierarchical Transformer architecture comprising Patch Transformer, Cluster Transformer, and Temporal Transformer. We evaluate TGTOD on three public datasets under two settings, comparing with a wide range of baselines. Our experimental results demonstrate the effectiveness of TGTOD, achieving AP improvement of 61% on Elliptic. Furthermore, our efficiency evaluation shows that TGTOD reduces training time by 44x compared to existing Transformers for temporal graphs. To foster reproducibility, we make our implementation publicly available at https://github.com/kayzliu/tgtod.
The WALOP-North Instrument I: Optical Design, Filter Design, Calibration
2024-12-01
John A. Kypriotakis, Siddharth Maharana, Ramya M. Anche, Chaitanya V. Rajarshi, A. N. Ramaprakash, Bhushan Joshi, Artem Basyrov, Dmitry Blinov, Tuhin Ghosh, Eirik Gjerlow, Sebastian Kiehlmann, Nikolaos Mandarakas, Georgia V. Panopoulou, Katerina Papadaki, Vasiliki Pavlidou, Timothy J. Pearson, Vincent Pelgrims, Stephen B. Potter, Anthony C. S. Readhead, Raphael Skalidis, Konstantinos Tassis
The Wide Area Linear Optical Polarimeter North (WALOP-North) is an optical polarimeter designed for the needs of the PASIPHAE survey. It will be installed on the 1.3m telescope at the Skinakas Observatory in Crete, Greece. After commissioning, it will measure the polarization of millions of stars at high Galactic latitude, aiming to measure hundreds of stars per $deg^2$. The astronomical filter used in the instrument is a modified, polarimetrically-neutral broadband SDSS-r. This instrument will be pioneering one due to its large field-of-view (FoV) of $30\times 30$ $arcmin^2$ and high accuracy polarimetry measurements. The accuracy and sensitivity of the instrument in polarization fraction will be at the 0.1\% and 0.05\% level, respectively. Four separate 4k$\times$4k CCDs will be used as the instrument detectors, each imaging one of the $0\deg{}, 45\deg{}, 90\deg{}$ and $135\deg{}$ polarized FoV separately, therefore making the instrument a four-channel, one-shot polarimeter. Here, we present the overall optical design of the instrument, emphasizing on the aspects of the instrument that are different from WALOP-South. We also present a novel design of filters appropriate for polarimetry along with details on the management of the instrument size and its polarimetric calibration.
LLMs as mirrors of societal moral standards: reflection of cultural divergence and agreement across ethical topics
2024-12-01
Mijntje Meijer, Hadi Mohammadi, Ayoub Bagheri
Large language models (LLMs) have become increasingly pivotal in various domains due the recent advancements in their performance capabilities. However, concerns persist regarding biases in LLMs, including gender, racial, and cultural biases derived from their training data. These biases raise critical questions about the ethical deployment and societal impact of LLMs. Acknowledging these concerns, this study investigates whether LLMs accurately reflect cross-cultural variations and similarities in moral perspectives. In assessing whether the chosen LLMs capture patterns of divergence and agreement on moral topics across cultures, three main methods are employed: (1) comparison of model-generated and survey-based moral score variances, (2) cluster alignment analysis to evaluate the correspondence between country clusters derived from model-generated moral scores and those derived from survey data, and (3) probing LLMs with direct comparative prompts. All three methods involve the use of systematic prompts and token pairs designed to assess how well LLMs understand and reflect cultural variations in moral attitudes. The findings of this study indicate overall variable and low performance in reflecting cross-cultural differences and similarities in moral values across the models tested, highlighting the necessity for improving models' accuracy in capturing these nuances effectively. The insights gained from this study aim to inform discussions on the ethical development and deployment of LLMs in global contexts, emphasizing the importance of mitigating biases and promoting fair representation across diverse cultural perspectives.
Large Language Models as Mirrors of Societal Moral Standards
2024-12-01
Evi Papadopoulou, Hadi Mohammadi, Ayoub Bagheri
Prior research has demonstrated that language models can, to a limited extent, represent moral norms in a variety of cultural contexts. This research aims to replicate these findings and further explore their validity, concentrating on issues like 'homosexuality' and 'divorce'. This study evaluates the effectiveness of these models using information from two surveys, the WVS and the PEW, that encompass moral perspectives from over 40 countries. The results show that biases exist in both monolingual and multilingual models, and they typically fall short of accurately capturing the moral intricacies of diverse cultures. However, the BLOOM model shows the best performance, exhibiting some positive correlations, but still does not achieve a comprehensive moral understanding. This research underscores the limitations of current PLMs in processing cross-cultural differences in values and highlights the importance of developing culturally aware AI systems that better align with universal human values.
Fuzzy Galaxies or Cirrus? Decomposition of Galactic Cirrus in Deep Wide-Field Images
2024-12-01
Qing Liu, Roberto Abraham, Peter G. Martin, William P. Bowman, Pieter van Dokkum, Shany Danieli, Ekta Patel, Steven R. Janssens, Zili Shen, Seery Chen, Ananthan Karunakaran, Michael A. Keim, Deborah Lokhorst, Imad Pasha, Douglas L. Welch
Diffuse Galactic cirrus, or Diffuse Galactic Light (DGL), can be a prominent component in the background of deep wide-field imaging surveys. The DGL provides unique insights into the physical and radiative properties of dust grains in our Milky Way, and it also serves as a contaminant on deep images, obscuring the detection of background sources such as low surface brightness galaxies. However, it is challenging to disentangle the DGL from other components of the night sky. In this paper, we present a technique for the photometric characterization of Galactic cirrus, based on (1) extraction of its filamentary or patchy morphology and (2) incorporation of color constraints obtained from Planck thermal dust models. Our decomposition method is illustrated using a $\sim$10 deg$^2$ imaging dataset obtained by the Dragonfly Telephoto Array, and its performance is explored using various metrics which characterize the flatness of the sky background. As a concrete application of the technique, we show how removal of cirrus allows low surface brightness galaxies to be identified on cirrus-rich images. We also show how modeling the cirrus in this way allows optical DGL intensities to be determined with high radiometric precision.
I review the formalism of patch bosonization of Fermi surfaces, with a focus on the problem of a two-dimensional metal at a quantum critical point. I argue that this formalism is fundamentally inapplicable to the problem, except in synthetic limits. One such limit is the small-$N$ limit, which was already discussed in early studies of the problem; a similar but slightly less unphysical large-$N$ limit is proposed. I show that it is at least formally possible to construct perturbative expansions around these synthetic limits. However, I argue that nonperturbative effects become important when $N\sim1$.
Continuous Topological Insulators Classification and Bulk Edge Correspondence
2024-12-01
Guillaume Bal
This paper reviews recent results on the classification of partial differential operators modeling bulk and interface topological insulators in Euclidean spaces. Our main objective is the mathematical analysis of the unusual, robust-to-perturbations, asymmetric transport that necessarily appears at interfaces separating topological insulators in different phases. The central element of the analysis is an interface current observable describing this asymmetry. We show that this observable may be computed explicitly by spectral flow when the interface Hamiltonian is explicitly diagonalizable. We review the classification of bulk phases for Landau and Dirac operators and provide a general classification of elliptic interface pseudo-differential operators by means of domain walls and a corresponding bulk-difference invariant (BDI). The BDI is simple to compute by the Fedosov-H\"ormander formula implementing in a Euclidean setting an Atiyah-Singer index theory. A generalized bulk-edge correspondence then states that the interface current observable and the BDI agree on elliptic operator, whereas this is not necessarily the case for non-elliptic operators.
Classical elliptic integrable systems from the moduli space of instantons
2024-12-01
Andrei Grekov
This paper is intended to serve as a review of a series of papers with Nikita Nekrasov, where we achieved several important results concerning the relation between the moduli space of instantons and classical integrable systems. We derive I. Krichever's Lax matrix for the elliptic Calogero-Moser system from the equivariant cohomology of the moduli space of instantons. This result also has K-theoretic and elliptic cohomology counterparts. Our methods rely upon the so-called $\theta$-transform of the $qq$-characters vev's, defined as integrals of certain classes in these cohomology theories. The key step is the non-commutative Jacobi-like product formula for them. We also obtained a natural answer for the eigenvector of the Lax matrix and the horizontal section for the associated isomonodromic connection in terms of the partition function of folded instantons. As an application of our formula, we demonstrate some progress towards the spectral duality of the many-body systems in question, as well as give a new look at the quantum-classical duality between their trigonometric version and the corresponding spin chains.
On a revised concept of an event that allows linking microdosimetry in nanometric sites and nanodosimetry with macroscopic dosimetry
2024-12-01
Hans Rabus, Leo Thomas
Microdosimetry and nanodosimetry study the track structure of charged particles, i.e., the stochastics of radiation interaction on the microscopic scale. The (different) concepts developed in micro- and nanodosimetry are matched to the experimental approaches in the two fields. This work reviews the concepts of an event used in the two fields and explores a common denominator between the two methodologies to characterize particle track structure at the nanometric level. An approach to harmonize the concepts of microdosimetry and nanodosimetry for nanometric sites and linking them to macroscopic fluence is theoretically formulated and tested on proton tracks with energies between 1 MeV and 100 MeV. Unification of microdosimetry and nanodosimetry for the same target seems to require three elements: (1) the definition of an event as relating to a beam volume within which interactions of the primary particle can result in energy deposits or ionizations in the site, (2) the definition of a nanodosimetric analogue to a microdosimetric event, and (3) the redefinition of the microdosimetric concept of an event to include only events in which an ionization occurs in the site. The range of impact parameters of particle tracks that contributes to energy imparted in a site appears not to depend on whether any energy deposits or only energy deposits by ionizations are considered. Since there is no longer a one-to-one correspondence between tracks passing a site and the occurrence of an event, it is proposed to use the fluence for which on average one event occurs as a substitute for single events.
In this paper, a novel continuous-aperture array (CAPA)-based wireless communication architecture is proposed, which relies on an electrically large aperture with a continuous current distribution. First, an existing prototype of CAPA is reviewed, followed by the potential benefits and key motivations for employing CAPAs in wireless communications. Then, three practical hardware implementation approaches for CAPAs are introduced based on electronic, optical, and acoustic materials. Furthermore, several beamforming approaches are proposed to optimize the continuous current distributions of CAPAs, which are fundamentally different from those used for conventional spatially discrete arrays (SPDAs). Numerical results are provided to demonstrate their key features in low complexity and near-optimality. Based on these proposed approaches, the performance gains of CAPAs over SPDAs are revealed in terms of channel capacity as well as diversity-multiplexing gains. Finally, several open research problems in CAPA are highlighted.
Ionized Gas Outflows in the Galaxy And Mass Assembly (GAMA) Survey: Signatures of AGN Feedback in Low-Mass Galaxies
2024-12-01
Sheyda Salehirad, Amy E. Reines, Mallory Molina
We present a sample of 398 galaxies with ionized gas outflow signatures in their spectra from the Galaxy and Mass Assembly (GAMA) Survey Data Release 4, including 45 low-mass galaxies with stellar masses $M_*<10^{10}$ $M_\odot$. We assemble our sample by systematically searching for the presence of a second velocity component in the [O III]$\lambda\lambda 4959, 5007$ doublet emission line in 39,612 galaxies with redshifts $z<0.3$. The host galaxies are classified using the BPT diagram, with $\sim$89% identified as AGNs and composites and 11% as star-forming (SF) galaxies. The outflows are typically faster in AGNs with a median velocity of 936 km s$^{-1}$ compared to 655 km s$^{-1}$ in the SF objects. Of particular interest are the 45 galaxies in the low-mass range, of which a third are classified as AGNs/composites. The outflows from the low-mass AGNs are also faster and more blueshifted compared to those in the low-mass SF galaxies. This indicates that black hole outflows can affect host galaxies in the low-mass range and that AGN feedback in galaxies with $M_*<10^{10}$ $ M_\odot$ should be considered in galaxy evolution models.
A new way to find symbiotic stars: accretion disc detection with optical survey photometry
2024-12-01
A. B. Lucy, J. L. Sokoloski, G. J. M. Luna, K. Mukai, N. E. Nuñez, D. A. H. Buckley, H. Breytenbach, B. Paul, S. B. Potter, R. Manick, D. A. Howell, C. Wolf, C. A. Onken
Symbiotic stars are binaries in which a cool and evolved star of luminosity class I-III accretes onto a smaller companion. However, direct accretion signatures like disc flickering and boundary layer X-rays are typically outshone or suppressed by the luminous giant, shell burning on the accreting white dwarf, and the illuminated wind nebula. We present a new way to find symbiotics that is less biased against directly-detectable accretion discs than methods based on narrow-band H$\alpha$ photometry or objective prism plate surveys. We identified outliers in SkyMapper survey photometry, using reconstructed uvg snapshot colours and rapid variability among the three exposures of each 20-minute SkyMapper Main Survey filter sequence, from a sample of 366,721 luminous red objects. We found that SkyMapper catalog colours of large-amplitude pulsating giants must be corrected for variability, and that flickering is detectable with only three data points. Our methods probed a different region of parameter space than a recent search for accreting-only symbiotics in the GALAH survey, while being surprisingly concordant with another survey's infrared detection algorithm. We discovered 12 new symbiotics, including four with optical accretion disc flickering. Two of the optical flickerers exhibited boundary-layer hard X-rays. We also identified 10 symbiotic candidates, and discovered likely optical flickering in the known symbiotic V1044 Cen (CD-36 8436). We conclude that at least 20% of the true population of symbiotics exhibit detectable optical flickering from the inner accretion disc, the majority of which do not meet the H$\alpha$ detection thresholds used to find symbiotics in typical narrow-band surveys.
In \citep{Qin+}, we attempted to reconstruct the weak lensing convergence map $\hat{\kappa}$ from cosmic magnification by linearly weighting the DECaLS galaxy overdensities in different magnitude bins of $grz$ photometry bands. The $\hat{\kappa}$ map is correlated with cosmic shear at 20-$\sigma$ significance. However, the low galaxy number density in the DECaLS survey prohibits the measurement of $\hat{\kappa}$ auto-correlation. In this paper, we apply the reconstruction method to the Dark Energy Survey Year 3 (DES Y3) galaxies from the DES Data Release 2 (DR2). With greater survey depth and higher galaxy number density, convergence-shear cross-correlation signals are detected with $S/N\approx 9,16,20$ at $0.4
Non-Terrestrial Networking for 6G: Evolution, Opportunities, and Future Directions
2024-12-01
Feng Wang, Shengyu Zhang, Huiting Yang, Tony Q. S. Quek
From 5G onwards, Non-Terrestrial Networks (NTNs) have emerged as a key component of future network architectures. Leveraging Low Earth Orbit (LEO) satellite constellations, NTNs are capable of building a space Internet and present a paradigm shift in delivering mobile services to even the most remote regions on Earth. However, the extensive coverage and rapid movement of LEO satellites pose unique challenges for NTN networking, including user equipment (UE) access and inter-satellite delivery, which directly impact the quality of service (QoS) and data transmission continuity. This paper offers an in-depth review of advanced NTN management technologies in the context of 6G evolution, focusing on radio resource management, mobility management, and dynamic network slicing. Building on this foundation and considering the latest trends in NTN development, we then present some innovative perspectives to emerging challenges in satellite beamforming, handover mechanisms, and inter-satellite transmissions. Lastly, we identify open research issues and propose future directions aimed at advancing satellite Internet deployment and enhancing NTN performance.
Categorical Keypoint Positional Embedding for Robust Animal Re-Identification
2024-12-01
Yuhao Lin, Lingqiao Liu, Javen Shi
Animal re-identification (ReID) has become an indispensable tool in ecological research, playing a critical role in tracking population dynamics, analyzing behavioral patterns, and assessing ecological impacts, all of which are vital for informed conservation strategies. Unlike human ReID, animal ReID faces significant challenges due to the high variability in animal poses, diverse environmental conditions, and the inability to directly apply pre-trained models to animal data, making the identification process across species more complex. This work introduces an innovative keypoint propagation mechanism, which utilizes a single annotated image and a pre-trained diffusion model to propagate keypoints across an entire dataset, significantly reducing the cost of manual annotation. Additionally, we enhance the Vision Transformer (ViT) by implementing Keypoint Positional Encoding (KPE) and Categorical Keypoint Positional Embedding (CKPE), enabling the ViT to learn more robust and semantically-aware representations. This provides more comprehensive and detailed keypoint representations, leading to more accurate and efficient re-identification. Our extensive experimental evaluations demonstrate that this approach significantly outperforms existing state-of-the-art methods across four wildlife datasets. The code will be publicly released.
Does chat change LLM's mind? Impact of Conversation on Psychological States of LLMs
2024-12-01
Junhyuk Choi, Yeseon Hong, Minju Kim, Bugeun Kim
The recent growth of large language models (LLMs) has enabled more authentic, human-centered interactions through multi-agent systems. However, investigation into how conversations affect the psychological states of LLMs is limited, despite the impact of these states on the usability of LLM-based systems. In this study, we explored whether psychological states change during multi-agent interactions, focusing on the effects of conversation depth, topic, and speaker. We experimentally investigated the behavior of 10 LLMs in open-domain conversations. We employed 14 questionnaires and a topic-analysis method to examine the behavior of LLMs across four aspects: personality, interpersonal relationships, motivation, and emotion. The results revealed distinct psychological trends influenced by conversation depth and topic, with significant variations observed between different LLM families and parameter sizes.
Detection of the Long Period Variable Stars of And II Dwarf Satellite galaxy
2024-12-01
Hedieh Abdollahi, Atefeh Javadi, Jacco Th. van Loon, Iain McDonald, Mahdi Abdollahi, Elham Saremi, Habib G. Khosroshahi, Hamidreza Mahani
We conducted an extensive study of the spheroidal dwarf satellite galaxies around the Andromeda galaxy to produce an extensive catalog of LPV stars. The optical monitoring project consists of 55 dwarf galaxies and four globular clusters that are members of the Local Group. We have made observations of these galaxies using the WFC mounted on the 2.5 m INT in nine different periods, both in the i-band filter Sloan and in the filter V-band Harris. We aim to select AGB stars with brightness variations larger than 0.2 mag to investigate the evolutionary processes in these dwarf galaxies. The resulting catalog of LPV stars in Andromeda's satellite galaxies offers updated information on features like half-light radii, TRGB magnitudes, and distance moduli. This manuscript will review the results obtained for And II galaxy. Using the Sobel filter, we have calculated the distance modulus for this satellite galaxy, which ranges from 23.90 to 24.11 mag.
We survey Kondrat'ev--Landis' conjecture, providing an up-to-date account of the main advances and describing the techniques developed. We complement the overview with references and formulations of the problem in further closely connected contexts.
Post-Vaccination COVID-19 Data Analysis: Privacy and Ethics
2024-12-01
Sankha Das, Amit Dua
The COVID-19 pandemic has severely affected the world in terms of health, economy and peace. Fortunately, the countries are trying to overcome the situation by actively carrying out vaccinations. However, like any other massive operation involving humans such as human resource management, elections, surveys, etc., the vaccination process raises several questions about citizen privacy and misuse of personal data. In most of the countries, few attempts have been made to verify the vaccination statistics as reported by the health centers. These issues collectively require the solutions of anonymity of citizens' personal information, immutability of vaccination data and easy yet restricted access by adversarial bodies such as the government for the verification and analysis of the data. This paper introduces a blockchain-based application to simulate and monitor the vaccination process. The structure of data model used in the proposed system is based on the IEEE Standard for Data Format for Blockchain Systems 2418.2TM-2020. The proposed system enables authorized stakeholders to share and access relevant information for vaccination process chain while preserving citizen privacy and accountability of the system. It is implemented on the Ethereum blockchain and uses a Python API for the simulation and validation of each step of the vaccination process.
2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification
2024-12-01
Jingwei Zhang, Anh Tien Nguyen, Xi Han, Vincent Quoc-Huy Trinh, Hong Qin, Dimitris Samaras, Mahdi S. Hosseini
Efficiently modeling large 2D contexts is essential for various fields including Giga-Pixel Whole Slide Imaging (WSI) and remote sensing. Transformer-based models offer high parallelism but face challenges due to their quadratic complexity for handling long sequences. Recently, Mamba introduced a selective State Space Model (SSM) with linear complexity and high parallelism, enabling effective and efficient modeling of wide context in 1D sequences. However, extending Mamba to vision tasks, which inherently involve 2D structures, results in spatial discrepancies due to the limitations of 1D sequence processing. On the other hand, current 2D SSMs inherently model 2D structures but they suffer from prohibitively slow computation due to the lack of efficient parallel algorithms. In this work, we propose 2DMamba, a novel 2D selective SSM framework that incorporates the 2D spatial structure of images into Mamba, with a highly optimized hardware-aware operator, adopting both spatial continuity and computational efficiency. We validate the versatility of our approach on both WSIs and natural images. Extensive experiments on 10 public datasets for WSI classification and survival analysis show that 2DMamba~improves up to $2.48\%$ in AUC, $3.11\%$ in F1 score, $2.47\%$ in accuracy and $5.52\%$ in C-index. Additionally, integrating our method with VMamba for natural imaging yields $0.5$ to $0.7$ improvements in mIoU on the ADE20k semantic segmentation dataset, and $0.2\%$ accuracy improvement on ImageNet-1K classification dataset. Our code is available at https://github.com/AtlasAnalyticsLab/2DMamba.
A Comprehensive Survey on Dynamic Software Updating Techniques in IoTs
2024-12-01
Madhav Neupane
This comprehensive survey paper provides an in-depth analysis of Dynamic Software Updating (DSU) techniques in the Internet of Things (IoT). This study critically examines eight significant research papers that employ diverse methodologies to address the challenges of DSU in IoT devices. The primary objectives include comparative analysis to identify the application domains of DSU tools, classification of program alterations accommodated by these systems, evaluation of the advantages and disadvantages of various DSU tools, and identification of potential paths for future research. This paper emphasizes the critical function of DSU in improving energy efficiency, extending operational durability, and bolstering security within IoT environments that demand high availability, including applications in smart cities and connected vehicles. It delves into the basic approaches and mechanisms of DSU, ranging from traditional methods to advanced practices like Over-the-Air updates and container-based solutions. This survey highlights the evolving nature of DSU techniques, balancing operational efficiency, security, and adaptability amidst the complexities of diverse IoT applications. Through this exploration, the paper aims to guide future developments in DSU strategies, enhancing IoT devices' resilience, functionality, and sustainability in a connected world. The insights from this survey are pivotal for researchers, practitioners, and policymakers in shaping effective DSU strategies to meet the growing needs of the IoT ecosystem.
Adding RR Lyrae $y$-Band Template Light Curves to gatspy
2024-12-01
Chow-Choong Ngeow, Chia-Yu Cheng
We complement the $y$-band template light curves for RR Lyrae to the well-established template light curves in the $ugriz$-band, where the latter have been adopted in the astroML/gatspy python package as one of the period-search methods for RR Lyrae. These $y$-band template light curves were constructed based on the $z$-band time series data taken from the Sloan Digital Sky Survey (SDSS), the $y$-band light curves from the Pan-STARRS1 survey, and dedicated $y$-band observations using the Lulin One-meter Telescope for RR Lyrae located in the SDSS Stripe 82 region. These $y$-band template light curves, 9 for the ab-type RR Lyrae and 3 for the c-type RR Lyrae, can be applied in conjunction to the $ugriz$-band template light curves for upcoming sky surveys involving the $y$-band, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST).
Optimization of Delivery Routes for Fresh E-commerce in Pre-warehouse Mode
2024-12-01
Alice Harward, Junjie Lin, Yun Wang, Xiaoke Xie
With the development of the economy, fresh food e-commerce has experienced rapid growth. One of the core competitive advantages of fresh food e-commerce platforms lies in selecting an appropriate logistics distribution model. This study focuses on the front warehouse model, aiming to minimize distribution costs. Considering the perishable nature and short shelf life of fresh food, a distribution route optimization model is constructed, and the saving mileage method is designed to determine the optimal distribution scheme. The results indicate that under certain conditions, different distribution schemes significantly impact the performance of fresh food e-commerce platforms. Based on a review of domestic and international research, this paper takes Dingdong Maicai as an example to systematically introduce the basic concepts of distribution route optimization in fresh food e-commerce platforms under the front warehouse model, analyze the advantages of logistics distribution, and thoroughly examine the importance of distribution routes for fresh products.
ARChef: An iOS-Based Augmented Reality Cooking Assistant Powered by Multimodal Gemini LLM
2024-12-01
Rithik Vir, Parsa Madinei
Cooking meals can be difficult, causing many to use cookbooks and online recipes, which results in missing ingredients, nutritional hazards, unsatisfactory meals. Using Augmented Reality (AR) can address this issue, however, current AR cooking applications have poor user interfaces and limited accessibility. This paper proposes a prototype of an iOS application that integrates AR and Computer Vision (CV) into the cooking process. We leverage Google's Gemini Large Language Model (LLM) to identify ingredients based on the camera's field of vision, and generate recipe choices with their nutritional information. Additionally, this application uses Apple's ARKit to create an AR user interface compatible with iOS devices. Users can personalize their meal suggestions by inputting their dietary preferences and rating each meal. The application's effectiveness is evaluated through user experience surveys. This application contributes to the field of accessible cooking assistance technologies, aiming to reduce food wastage and improve the meal planning experience.
Spline-FRIDA: Towards Diverse, Humanlike Robot Painting Styles with a Sample-Efficient, Differentiable Brush Stroke Model
2024-11-30
Lawrence Chen, Peter Schaldenbrand, Tanmay Shankar, Lia Coleman, Jean Oh
A painting is more than just a picture on a wall; a painting is a process comprised of many intentional brush strokes, the shapes of which are an important component of a painting's overall style and message. Prior work in modeling brush stroke trajectories either does not work with real-world robotics or is not flexible enough to capture the complexity of human-made brush strokes. In this work, we introduce Spline-FRIDA which can model complex human brush stroke trajectories. This is achieved by recording artists drawing using motion capture, modeling the extracted trajectories with an autoencoder, and introducing a novel brush stroke dynamics model to the existing robotic painting platform FRIDA. We conducted a survey and found that our open-source Spline-FRIDA approach successfully captures the stroke styles in human drawings and that Spline-FRIDA's brush strokes are more human-like, improve semantic planning, and are more artistic compared to existing robot painting systems with restrictive B\'ezier curve strokes.
This paper introduces Opus, a novel framework for generating and optimizing Workflows tailored to complex Business Process Outsourcing (BPO) use cases, focusing on cost reduction and quality enhancement while adhering to established industry processes and operational constraints. Our approach generates executable Workflows from Intention, defined as the alignment of Client Input, Client Output, and Process Context. These Workflows are represented as Directed Acyclic Graphs (DAGs), with nodes as Tasks consisting of sequences of executable Instructions, including tools and human expert reviews. We adopt a two-phase methodology: Workflow Generation and Workflow Optimization. In the Generation phase, Workflows are generated using a Large Work Model (LWM) informed by a Work Knowledge Graph (WKG) that encodes domain-specific procedural and operational knowledge. In the Optimization phase, Workflows are transformed into Workflow Graphs (WFGs), where optimal Workflows are determined through path optimization. Our experiments demonstrate that state-of-the-art Large Language Models (LLMs) face challenges in reliably retrieving detailed process data as well as generating industry-compliant workflows. The key contributions of this paper include: - The integration of a Work Knowledge Graph (WKG) into a Large Work Model (LWM), enabling the generation of context-aware, semantically aligned, structured and auditable Workflows. - A two-phase approach that combines Workflow Generation from Intention with graph-based Workflow Optimization. - Opus Alpha 1 Large and Opus Alpha 1 Small, models that outperform state-of-the-art LLMs by 38\% and 29\% respectively in Workflow Generation for a Medical Coding use case.
MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption
2024-11-30
Siddhant Dutta, Nouhaila Innan, Sadok Ben Yahia, Muhammad Shafique, David Esteban Bernal Neira
The integration of fully homomorphic encryption (FHE) in federated learning (FL) has led to significant advances in data privacy. However, during the aggregation phase, it often results in performance degradation of the aggregated model, hindering the development of robust representational generalization. In this work, we propose a novel multimodal quantum federated learning framework that utilizes quantum computing to counteract the performance drop resulting from FHE. For the first time in FL, our framework combines a multimodal quantum mixture of experts (MQMoE) model with FHE, incorporating multimodal datasets for enriched representation and task-specific learning. Our MQMoE framework enhances performance on multimodal datasets and combined genomics and brain MRI scans, especially for underrepresented categories. Our results also demonstrate that the quantum-enhanced approach mitigates the performance degradation associated with FHE and improves classification accuracy across diverse datasets, validating the potential of quantum interventions in enhancing privacy in FL.
From Audio Deepfake Detection to AI-Generated Music Detection -- A Pathway and Overview
2024-11-30
Yupei Li, Manuel Milling, Lucia Specia, Björn W. Schuller
As Artificial Intelligence (AI) technologies continue to evolve, their use in generating realistic, contextually appropriate content has expanded into various domains. Music, an art form and medium for entertainment, deeply rooted into human culture, is seeing an increased involvement of AI into its production. However, the unregulated use of AI music generation (AIGM) tools raises concerns about potential negative impacts on the music industry, copyright and artistic integrity, underscoring the importance of effective AIGM detection. This paper provides an overview of existing AIGM detection methods. To lay a foundation to the general workings and challenges of AIGM detection, we first review general principles of AIGM, including recent advancements in deepfake audios, as well as multimodal detection techniques. We further propose a potential pathway for leveraging foundation models from audio deepfake detection to AIGM detection. Additionally, we discuss implications of these tools and propose directions for future research to address ongoing challenges in the field.
Particles are accelerated to very high, non-thermal energies in space, solar, and astrophysical plasma environments. In cosmic ray physics, the "Hillas limit" is often used as a rough estimate (or the necessary condition) of the maximum energy of particles. This limit is based on the concepts of one-shot direct acceleration by a system-wide motional electric field, as well as stochastic and diffusive acceleration in strongly turbulent environments. However, it remains unclear how well this limit explains the actual observed maximum energies of particles. Here we show, based on a systematic review, that the observed maximum energy of particles -- those in space, solar, astrophysical, and laboratory environments -- often reach the energy predicted by the Hillas limit. We also found several exceptions, such as electrons in solar flares and jet-terminal lobes of radio galaxies, as well as protons in planetary radiation belts, where deviations from this limit occur. We discuss possible causes of such deviations, and we argue in particular that there is a good chance of detecting ultra-high-energy ($\sim$100 GeV) solar flare electrons that have not yet been detected. We anticipate that this study will facilitate further interdisciplinary discussions on the maximum energy of particles and the underlying mechanisms of particle acceleration in diverse plasma environments.
Exact Certification of (Graph) Neural Networks Against Label Poisoning
2024-11-30
Mahalakshmi Sabanayagam, Lukas Gosch, Stephan Günnemann, Debarghya Ghoshdastidar
Machine learning models are highly vulnerable to label flipping, i.e., the adversarial modification (poisoning) of training labels to compromise performance. Thus, deriving robustness certificates is important to guarantee that test predictions remain unaffected and to understand worst-case robustness behavior. However, for Graph Neural Networks (GNNs), the problem of certifying label flipping has so far been unsolved. We change this by introducing an exact certification method, deriving both sample-wise and collective certificates. Our method leverages the Neural Tangent Kernel (NTK) to capture the training dynamics of wide networks enabling us to reformulate the bilevel optimization problem representing label flipping into a Mixed-Integer Linear Program (MILP). We apply our method to certify a broad range of GNN architectures in node classification tasks. Thereby, concerning the worst-case robustness to label flipping: $(i)$ we establish hierarchies of GNNs on different benchmark graphs; $(ii)$ quantify the effect of architectural choices such as activations, depth and skip-connections; and surprisingly, $(iii)$ uncover a novel phenomenon of the robustness plateauing for intermediate perturbation budgets across all investigated datasets and architectures. While we focus on GNNs, our certificates are applicable to sufficiently wide NNs in general through their NTK. Thus, our work presents the first exact certificate to a poisoning attack ever derived for neural networks, which could be of independent interest.
Simon Says: Exploring the Importance of Notification Design Formats on User Engagement
2024-11-30
Hans Matthew Abello, Maxine Beatriz Badiola, Mark John Custer, Lorane Bernadeth Fausto, Patrick Josh Leonida, Denzel Bryan Yongco, Jordan Aiko Deja
Push notifications are brief messages that users frequently encounter in their daily lives. However, the volume of notifications can lead to information overload, making it challenging for users to engage effectively. This study investigates how notification behavior and color influence user interaction and perception. To explore this, we developed an app prototype that tracks user interactions with notifications, categorizing them as accepted, dismissed, or ignored. After each interaction, users were asked to complete a survey regarding their perception of the notifications. The study focused on how different notification colors might affect the likelihood of acceptance and perceived importance. The results reveal that certain colors were more likely to be accepted and were perceived as more important compared to others, suggesting that both color and behavior play significant roles in shaping user engagement with notifications.
CDEMapper: Enhancing NIH Common Data Element Normalization using Large Language Models
2024-11-30
Yan Wang, Jimin Huang, Huan He, Vincent Zhang, Yujia Zhou, Xubing Hao, Pritham Ram, Lingfei Qian, Qianqian Xie, Ruey-Ling Weng, Fongci Lin, Yan Hu, Licong Cui, Xiaoqian Jiang, Hua Xu, Na Hong
Common Data Elements (CDEs) standardize data collection and sharing across studies, enhancing data interoperability and improving research reproducibility. However, implementing CDEs presents challenges due to the broad range and variety of data elements. This study aims to develop an effective and efficient mapping tool to bridge the gap between local data elements and National Institutes of Health (NIH) CDEs. We propose CDEMapper, a large language model (LLM) powered mapping tool designed to assist in mapping local data elements to NIH CDEs. CDEMapper has three core modules: (1) CDE indexing and embeddings. NIH CDEs were indexed and embedded to support semantic search; (2) CDE recommendations. The tool combines Elasticsearch (BM25 similarity methods) with state of the art GPT services to recommend candidate CDEs and their permissible values; and (3) Human review. Users review and select the NIH CDEs and values that best match their data elements and value sets. We evaluate the tool recommendation accuracy against manually annotated mapping results. CDEMapper offers a publicly available, LLM-powered, and intuitive user interface that consolidates essential and advanced mapping services into a streamlined pipeline. It provides a step by step, quality assured mapping workflow designed with a user-centered approach. The evaluation results demonstrated that augmenting BM25 with GPT embeddings and a ranker consistently enhances CDEMapper mapping accuracy in three different mapping settings across four evaluation datasets. This work opens up the potential of using LLMs to assist with CDE recommendation and human curation when aligning local data elements with NIH CDEs. Additionally, this effort enhances clinical research data interoperability and helps researchers better understand the gaps between local data elements and NIH CDEs.
Probing Cosmic Isotropy with the FAST All Sky HI Survey
2024-11-30
Yi-Wen Wu, Jun-Qing Xia
This paper leverages the first released catalog from the FAST All Sky \textsc{Hi} Survey (FASHI) to examine the hypothesis of cosmic isotropy in the local Universe. Given the design of the overall FAST survey, the inhomogeneous detection sensitivity of FASHI is likely to introduce significant biases in the statistical properties of the catalog. To mitigate the potential influence of spurious clustering effects due to these sensitivity variations, we focus on extragalactic \textsc{Hi} sources within the sensitivity range of $[0.65, 1.0]$. This refined subsample is divided into ten distinct sky regions, for which we compute the two-point angular correlation functions (2PACF) over angular scales of $0.5^\circ < \theta < 10^\circ$. We apply the Markov chain Monte Carlo method to fit these 2PACFs with a power-law model and assess the statistical significance of the best-fit parameters for the ten FASHI sky regions by comparing them against results from mock catalogs generated under the assumptions of homogeneity and isotropy. Our findings indicate that the local Universe, as traced by the \textsc{Hi} sources in the FASHI survey, aligns with the cosmic isotropy hypothesis within a $2\sigma$ confidence level. We do not detect any statistically significant deviations from cosmic isotropy in the FASHI survey data.
AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models
2024-11-30
Yutong Zhou, Masahiro Ryo
We introduce AgriBench, the first agriculture benchmark designed to evaluate MultiModal Large Language Models (MM-LLMs) for agriculture applications. To further address the agriculture knowledge-based dataset limitation problem, we propose MM-LUCAS, a multimodal agriculture dataset, that includes 1,784 landscape images, segmentation masks, depth maps, and detailed annotations (geographical location, country, date, land cover and land use taxonomic details, quality scores, aesthetic scores, etc), based on the Land Use/Cover Area Frame Survey (LUCAS) dataset, which contains comparable statistics on land use and land cover for the European Union (EU) territory. This work presents a groundbreaking perspective in advancing agriculture MM-LLMs and is still in progress, offering valuable insights for future developments and innovations in specific expert knowledge-based MM-LLMs.
Signal Processing over Time-Varying Graphs: A Systematic Review
2024-11-30
Yi Yan, Jiacheng Hou, Zhenjie Song, Ercan Engin Kuruoglu
As irregularly structured data representations, graphs have received a large amount of attention in recent years and have been widely applied to various real-world scenarios such as social, traffic, and energy settings. Compared to non-graph algorithms, numerous graph-based methodologies benefited from the strong power of graphs for representing high-dimensional and non-Euclidean data. In the field of Graph Signal Processing (GSP), analogies of classical signal processing concepts, such as shifting, convolution, filtering, and transformations are developed. However, many GSP techniques usually postulate the graph is static in both signal and typology. This assumption hinders the effectiveness of GSP methodologies as the assumption ignores the time-varying properties in numerous real-world systems. For example, in the traffic network, the signal on each node varies over time and contains underlying temporal correlation and patterns worthy of analysis. To tackle this challenge, more and more work are being done recently to investigate the processing of time-varying graph signals. They cope with time-varying challenges from three main directions: 1) graph time-spectral filtering, 2) multi-variate time-series forecasting, and 3) spatiotemporal graph data mining by neural networks, where non-negligible progress has been achieved. Despite the success of signal processing and learning over time-varying graphs, there is no survey to compare and conclude the current methodology for GSP and graph learning. To compensate for this, in this paper, we aim to review the development and recent progress on signal processing and learning over time-varying graphs, and compare their advantages and disadvantages from both the methodological and experimental side, to outline the challenges and potential research directions for future research.
Sarcasm is hard to interpret as human beings. Being able to interpret sarcasm is often termed as a sign of intelligence, given the complex nature of sarcasm. Hence, this is a field of Natural Language Processing which is still complex for computers to decipher. This Literature Survey delves into different aspects of sarcasm detection, to create an understanding of the underlying problems faced during detection, approaches used to solve this problem, and different forms of available datasets for sarcasm detection.
Cosmological Models in Lovelock Gravity: An Overview of Recent Progress
2024-11-30
Sergey Pavluchenko
In the current review, we provide a summary of the recent progress made in the cosmological aspect of extra-dimensional Lovelock gravity. Our review covers a wide variety of particular model/matter source combinations: Einstein--Gauss--Bonnet as well as cubic Lovelock gravities with vacuum, cosmological constant, perfect fluid, spatial curvature, and some of their combinations. Our analysis suggests that it is possible to set constraints on the parameters of the above-mentioned models from the simple requirement of the existence of a smooth transition from the initial singularity to a realistic low-energy regime. Initially, anisotropic space naturally evolves into a configuration with two isotropic subspaces, and if one of these subspaces is three-dimensional and is expanding while another is contracting, we call it realistic compactification. Of course, the process is not devoid of obstacles, and in our paper, we review the results of the compactification occurrence investigation for the above-mentioned models. In particular, for vacuum and $\Lambda$-term EGB models, compactification is not suppressed (but is not the only possible outcome either) if the number of extra dimensions is $D \geqslant 2$; for vacuum cubic Lovelock gravities it is always present (however, cubic Lovelock gravity is defined only for $D \geqslant 3$ number of extra dimensions); for the EGB model with perfect fluid it is present for $D=2$ (we have not considered this model in higher dimensions yet), and in the presence of spatial curvature, the realistic stabilization of extra dimensions is always present (however, such a model is well-defined only in $D \geqslant 4$ number of extra dimensions).
Search for and analysis of eclipsing binaries in the LAMOST Medium-Resolution Survey field. I. RA: $\textbf{23}^h$$\textbf{01}^m$$\textbf{51}^s$, Dec: +34$^\circ$36$^\prime$45$^{\prime \prime}$
Eclipsing binaries (EBs) play an important astrophysical role in studying stellar properties and evolution. By analyzing photometric data in the LAMOST Medium-Resolution Survey field, RA: $23^h$$01^m$$51.00^s$, Dec: +34$^\circ$36$^\prime$45$^{\prime \prime}$, 48 EBs are detected and 2 are newly discovered. This specific field has been observed 52 times by the LAMOST Medium-Resolution Survey DR 9, which facilitates a comprehensive analysis of the EBs. For EBs with LAMOST medium-resolution spectra, radial velocity curves were obtained, and their precise orbital parameters were determined by simultaneously analyzing photometric light curves and radial velocity curves. For the other EBs with only photometric light curves, we used the q-search or the temperature ratio method to determine their initial mass ratios and then determined the orbital parameters. It is found that 15 EBs belong to detached systems, 1 to semi-detached systems, and 32 to contact systems. Based on the O-C analysis for 26 EBs with sufficient eclipsing times, we found a long-term decrease in the orbital period of 11 EBs and a continuous increase of 5 EBs, which are due to the material transfer between the two components. The O-C curve of 1 EB shows a distinct periodic variation, which is caused by the light travel time effect, and the third body is likely to be a black hole. By applying the spectral subtraction method to 13 EBs with LAMOST medium-resolution spectra, 10 systems exhibit distinct H$\alpha$ emission lines, in which 1 system exhibits double-peaked lines near phases 0.25 and 0.75, implying strong chromospheric activity. In the mass-luminosities and mass-radius distributions, most of the more massive components are less evolved than the less massive ones.
Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Survey
2024-11-30
Wei Zhou, Lei Zhao, Runyu Zhang, Yifan Cui, Hongpu Huang, Kun Qie, Chen Wang
Traffic Surveillance Systems (TSS) have become increasingly crucial in modern intelligent transportation systems, with vision-based technologies playing a central role for scene perception and understanding. While existing surveys typically focus on isolated aspects of TSS, a comprehensive analysis bridging low-level and high-level perception tasks, particularly considering emerging technologies, remains lacking. This paper presents a systematic review of vision-based technologies in TSS, examining both low-level perception tasks (object detection, classification, and tracking) and high-level perception applications (parameter estimation, anomaly detection, and behavior understanding). Specifically, we first provide a detailed methodological categorization and comprehensive performance evaluation for each task. Our investigation reveals five fundamental limitations in current TSS: perceptual data degradation in complex scenarios, data-driven learning constraints, semantic understanding gaps, sensing coverage limitations and computational resource demands. To address these challenges, we systematically analyze five categories of potential solutions: advanced perception enhancement, efficient learning paradigms, knowledge-enhanced understanding, cooperative sensing frameworks and efficient computing frameworks. Furthermore, we evaluate the transformative potential of foundation models in TSS, demonstrating their unique capabilities in zero-shot learning, semantic understanding, and scene generation. This review provides a unified framework bridging low-level and high-level perception tasks, systematically analyzes current limitations and solutions, and presents a structured roadmap for integrating emerging technologies, particularly foundation models, to enhance TSS capabilities.
Fusing Physics-Driven Strategies and Cross-Modal Adversarial Learning: Toward Multi-Domain Applications
2024-11-30
Hana Satou, Alan Mitkiy
The convergence of cross-modal adversarial learning and physics-driven methods represents a cutting-edge direction for tackling challenges in complex multi-modal tasks and scientific computing. This review focuses on systematically analyzing how these two approaches can be synergistically integrated to enhance performance and robustness across diverse application domains. By addressing key obstacles such as modality discrepancies, limited data availability, and insufficient model robustness, this paper highlights the role of physics-based optimization frameworks in facilitating efficient and interpretable adversarial perturbation generation. The review also explores significant advancements in cross-modal adversarial learning, including applications in tasks such as image cross-modal retrieval (e.g., infrared and RGB matching), scientific computing (e.g., solving partial differential equations), and optimization under physical consistency constraints in vision systems. By examining theoretical foundations and experimental outcomes, this study demonstrates the potential of combining these approaches to handle complex scenarios and improve the security of multi-modal systems. Finally, we outline future directions, proposing a novel framework that unifies physical principles with adversarial optimization, providing a pathway for researchers to develop robust and adaptable cross-modal learning methods with both theoretical and practical significance.
Large Language Models (LLMs) are trained on large corpora written by humans and demonstrate high performance on various tasks. However, as humans are susceptible to cognitive biases, which can result in irrational judgments, LLMs can also be influenced by these biases, leading to irrational decision-making. For example, changing the order of options in multiple-choice questions affects the performance of LLMs due to order bias. In our research, we first conducted an extensive survey of existing studies examining LLMs' cognitive biases and their mitigation. The mitigation techniques in LLMs have the disadvantage that they are limited in the type of biases they can apply or require lengthy inputs or outputs. We then examined the effectiveness of two mitigation methods for humans, SoPro and AwaRe, when applied to LLMs, inspired by studies in crowdsourcing. To test the effectiveness of these methods, we conducted experiments on GPT-3.5 and GPT-4 to evaluate the influence of six biases on the outputs before and after applying these methods. The results demonstrate that while SoPro has little effect, AwaRe enables LLMs to mitigate the effect of these biases and make more rational responses.
It has been almost 40 years since the proposal of the idea that Hawking radiation of black holes does not lead to a complete evaporation but rather a "remnant" state. Though traditionally viewed with great criticisms especially from the high energy physics community, in recent years, various approaches have demonstrated that black hole remnants remain a viable possibility. In this review, which is primarily aimed as an introduction to the subject, we will discuss some possible routes to forming remnants and their respective properties and challenges.
An ALMA spectroscopic survey of the Planck high-redshift object PLCK G073.4-57.5 confirms two protoclusters
2024-11-30
Ryley Hill, Maria del Carmen Polletta, Matthieu Bethermin, Herve Dole, Ruediger Kneissl, Douglas Scott
Planck observed the whole sky between 350um and 3mm, discovering thousands of unresolved peaks in the cosmic infrared background. The nature of these peaks are still poorly understood - while some are strong gravitational lenses, the majority are spatial overdensities of star-forming galaxies, but with almost no redshift constraints. PLCK G073.4-57.5 (G073) is one of these Planck-selected peaks. G073 was previously observed by ALMA, with the results suggesting the presence of two structures (one around redshift 1.5 and one around redshift 2) aligned along the line of sight, but the results lacked robust spectroscopic confirmation. Characterizing the full redshift distribution of the galaxies within G073 is needed in order to better understand this representative example of these objects, and connect them to the emergence of galaxy clusters. We use ALMA spectral scans to search for CO and CI(1-0) line emission, targeting eight red Herschel-SPIRE sources in the field and four bright SCUBA-2 sources. We find 15 emission lines in 13 galaxies, and we secure the spectroscopic redshifts of all 13 galaxies. 11 of these galaxies are SPIRE-selected and lie in two structures at z=1.53 and z=2.31, while the two SCUBA-2-selected galaxies are at z=2.61. Using multiwavelength photometry we constrain stellar masses and star-formation rates, and using the CO and CI emission lines we constrain gas masses. Our protocluster galaxies exhibit typical depletion timescales compared to field galaxies at the same redshifts, but enhanced gas-to-stellar mass ratios, potentially driven by emission line selection effects. We find that the two structures confirmed in our survey are reproduced in cosmological simulations of star-forming halos at high redshift; the simulated halos have a 60-70% probability of collapsing into galaxy clusters, implying that the two structures in G073 are genuinely protoclusters.
The hot circumgalactic medium in the eROSITA All-Sky Survey III. Star-forming and quiescent galaxies
2024-11-29
Yi Zhang, Johan Comparat, Gabriele Ponti, Andrea Merloni, Kirpal Nandra, Frank Haberl, Nhut Truong, Annalisa Pillepich, Paola Popesso, Nicola Locatelli, Xiaoyuan Zhang, Jeremy Sanders, Xueying Zheng, Ang Liu, Teng Liu, Peter Predehl, Mara Salvato, Marcus Bruggen, Soumya Shreeram, Michael C. H. Yeung
The circumgalactic medium (CGM), as the gas repository for star formation, might contain the answer to the mysterious galaxy quenching and bimodal galaxy population origin. We measured the X-ray emission of the hot CGM around star-forming and quiescent galaxies. We detect extended X-ray emission from the hot CGM around star-forming galaxies with $\log(M_*/M_\odot)>11.0$ and quiescent galaxies with $\log(M_*/M_\odot)>10.5$, extending out to $R_{\rm 500c}$. $L_{\rm X, CGM}$ of star-forming galaxies with median stellar masses $\log(M_{\rm *,med}/M_\odot) = 10.7, 11.1, 11.3$ are approximately $0.8\,, 2.3\,, 4.0 \times 10^{40}\,\rm erg/s$, while for quiescent galaxies with $\log(M_{\rm *,med}/M_\odot) = 10.8, 11.1, 11.4$, they are $1.1\,, 6.2\,, 30 \times 10^{40}\,\rm erg/s$. Notably, quiescent galaxies with $\log(M_{\rm *,med}/M_\odot) > 11.0$ exhibit brighter hot CGM than their star-forming counterparts. In halo mass bins, we detect similar X-ray emission around star-forming and quiescent galaxies with $\log(M_{\rm 200m}/M_\odot) > 12.5$, suggesting that galaxies in the same mass dark matter halos host equally bright hot CGM. We emphasize the observed $L_{\rm X, CGM} - M_{\rm 500c}$ relations of star-forming and quiescent galaxies are sensitive to the stellar-to-halo mass relation (SHMR). A comparison with cosmological hydrodynamical simulations (EAGLE, TNG100, and SIMBA) reveals varying degrees of agreement, contingent on the simulation and the specific stellar or halo mass ranges considered. Either selected in stellar mass or halo mass, the star-forming galaxies do not host brighter stacked X-ray emission from the hot CGM than their quiescent counterparts at the same mass range. The result provides useful constraints on the extent of feedback's impacts as a mechanism for quenching star formation as implemented in current cosmological simulations.
Interacting Dark Sector (ETHOS $n=0$): Cosmological Constraints from SPT Cluster Abundance with DES and HST Weak Lensing Data
2024-11-29
Asmaa Mazoun, Sebastian Bocquet, Joseph J. Mohr, Mathias Garny, Henrique Rubira, Matthias Klein, Lindsey Bleem, Sebastian Grandis, Tim Schrabback
We use galaxy cluster abundance measurements from the South Pole Telescope (SPT) enhanced by Multi-Component Matched Filter (MCMF) confirmation and complemented with mass information obtained using weak-lensing data from Dark Energy Survey Year~3 (DES Y3) and targeted Hubble Space Telescope (HST) observations for probing deviations from the cold dark matter paradigm. Concretely, we consider a class of dark sector models featuring interactions between dark matter (DM) and a dark radiation (DR) component within the framework of the Effective Theory of Structure Formation (ETHOS). We focus on scenarios that lead to power suppression over a wide range of scales, and thus can be tested with data sensitive to large scales, as realized for example for DM$-$DR interactions following from an unbroken non-Abelian $SU(N)$ gauge theory (interaction rate with power-law index $n=0$ within the ETHOS parameterization). Cluster abundance measurements are mostly sensitive to the amount of DR interacting with DM, parameterized by the ratio of DR temperature to the cosmic microwave background (CMB) temperature, $\xi_{\rm DR}=T_{\rm DR}/T_{\rm CMB}$. We find an upper limit $\xi_{\rm DR}<17\%$ at $95\%$ credibility. When the cluster data are combined with Planck 2018 CMB data along with baryon acoustic oscillation (BAO) measurements we find $\xi_{\rm DR}<10\%$, corresponding to a limit on the abundance of interacting DR that is around three times tighter than that from CMB+BAO data alone. We also discuss the complementarity of weak lensing informed cluster abundance studies with probes sensitive to smaller scales, explore the impact on our analysis of massive neutrinos, and comment on a slight preference for the presence of a non-zero interacting DR abundance, which enables a physical solution to the $S_8$ tension.
What fifty-one years of Linguistics and Artificial Intelligence research tell us about their correlation: A scientometric review
2024-11-29
Mohammed Q. Shormani
There is a strong correlation between linguistics and artificial intelligence (AI), best manifested by deep learning language models. This study provides a thorough scientometric analysis of this correlation, synthesizing the intellectual production during 51 years, from 1974 to 2024. It involves 5750 Web of Science-indexed articles published in 2124 journals, which are written by 20835 authors belonging to 13773 research centers in 794 countries. Two powerful software, viz., CiteSpace and VOSviewer, were used to generate mapping visualizations of the intellectual landscape, trending issues and (re)emerging hotspots. The results indicate that in the 1980s and 1990s, linguistics and AI research was not robust, characterized by unstable publication over time. It has, however, witnessed a remarkable increase of publication since then, reaching 1478 articles in 2023, and 546 articles in January-March timespan in 2024, involving emerging issues and hotspots, addressing new horizons, new topics, and launching new applications and powerful deep learning language models including ChatGPT.
Artificial intelligence contribution to translation industry: looking back and forward
2024-11-29
Mohammed Q. Shormani
This study provides a comprehensive analysis of artificial intelligence (AI) contribution to translation industry (ACTI) research, synthesizing it over forty-one years from 1980-2024. 13220 articles were retrieved from three sources, namely WoS, Scopus, and Lens. We provided two types of analysis, viz., scientometric and thematic, focusing on cluster, subject categories, keywords, burstness, centrality and research centers as for the former. For the latter, we thematically review 18 articles, selected purposefully from the articles involved, centering on purpose, approach, findings, and contribution to ACTI future directions. The findings reveal that in the past AI contribution to translation industry was not rigorous, resulting in rule-based machine translation and statistical machine translation whose output was not satisfactory. However, the more AI develops, the more machine translation develops, incorporating Neural Networking Algorithms and (Deep) Language Learning Models like ChatGPT whose translation output has developed considerably. However, much rigorous research is still needed to overcome several problems encountering translation industry, specifically concerning low-source languages, multi-dialectical and free word order languages, and cultural and religious registers.
Neuroplasticity and Psychedelics: a comprehensive examination of classic and non-classic compounds in pre and clinical models
2024-11-29
Claudio Agnorelli, Meg Spriggs, Kate Godfrey, Gabriela Sawicka, Bettina Bohl, Hannah Douglass, Andrea Fagiolini, Hashemi Parastoo, Robin Carhart-Harris, David Nutt, David Erritzoe
Neuroplasticity, the ability of the nervous system to adapt throughout an organism's lifespan, offers potential as both a biomarker and treatment target for neuropsychiatric conditions. Psychedelics, a burgeoning category of drugs, are increasingly prominent in psychiatric research, prompting inquiries into their mechanisms of action. Distinguishing themselves from traditional medications, psychedelics demonstrate rapid and enduring therapeutic effects after a single or few administrations, believed to stem from their neuroplasticity-enhancing properties. This review examines how classic psychedelics (e.g., LSD, psilocybin, N,N-DMT) and non-classic psychedelics (e.g., ketamine, MDMA) influence neuroplasticity. Drawing from preclinical and clinical studies, we explore the molecular, structural, and functional changes triggered by these agents. Animal studies suggest psychedelics induce heightened sensitivity of the nervous system to environmental stimuli (meta-plasticity), re-opening developmental windows for long-term structural changes (hyper-plasticity), with implications for mood and behavior. Translating these findings to humans faces challenges due to limitations in current imaging techniques. Nonetheless, promising new directions for human research are emerging, including the employment of novel positron-emission tomography (PET) radioligands, non-invasive brain stimulation methods, and multimodal approaches. By elucidating the interplay between psychedelics and neuroplasticity, this review informs the development of targeted interventions for neuropsychiatric disorders and advances understanding of psychedelics' therapeutic potential.
Feedback-driven object detection and iterative model improvement
2024-11-29
Sönke Tenckhoff, Mario Koddenbrock, Erik Rodner
Automated object detection has become increasingly valuable across diverse applications, yet efficient, high-quality annotation remains a persistent challenge. In this paper, we present the development and evaluation of a platform designed to interactively improve object detection models. The platform allows uploading and annotating images as well as fine-tuning object detection models. Users can then manually review and refine annotations, further creating improved snapshots that are used for automatic object detection on subsequent image uploads - a process we refer to as semi-automatic annotation resulting in a significant gain in annotation efficiency. Whereas iterative refinement of model results to speed up annotation has become common practice, we are the first to quantitatively evaluate its benefits with respect to time, effort, and interaction savings. Our experimental results show clear evidence for a significant time reduction of up to 53% for semi-automatic compared to manual annotation. Importantly, these efficiency gains did not compromise annotation quality, while matching or occasionally even exceeding the accuracy of manual annotations. These findings demonstrate the potential of our lightweight annotation platform for creating high-quality object detection datasets and provide best practices to guide future development of annotation platforms. The platform is open-source, with the frontend and backend repositories available on GitHub.
2024-11-29
Louisa A Mason, Michael A Garrett, Kelvin Wandia, Andrew P V Siemion
The Atacama Millimeter/Submillimeter Array (ALMA) remains unparalleled in sensitivity at radio frequencies above 35 GHz. In this paper, we explore ALMA's potential for narrowband technosignature detection, considering factors such as the interferometer's undistorted field of view, signal dilution due to significant drift rates at high frequencies and the possibility of spectral confusion. We present the first technosignature survey using archival ALMA data in Band 3, focusing on two spectral windows centred on 90.642 GHz and 93.151 GHz. Our survey places new limits at these frequencies on the prevalence of extraterrestrial transmitters for 28 galactic stars, selected from the Gaia DR3 catalogue. We employ a stellar 'bycatch' method to sample these objects within the undistorted field of view of four ALMA calibrators. For the closest star in our sample, we find no evidence of transmitters with EIRP_min > 7 x 10^17 W. To the best of our knowledge, this represents the first technosignature search conducted using ALMA data.
On operators preserving inequalities between polynomials
2024-11-29
S. Gulzar, Ravinder Kumar, Mudassir A Bhat
In this review paper, we explore operator aspects in extremal properties of Bernstein-type polynomial inequalities. We shall also see that a linear operator which send polynomials to polynomials and have zero-preserving property naturally preserve Bernstein's inequality.
Superintegrability and geometry: a review of the extended Hamiltonian approach
2024-11-29
Claudia Maria Chanu, Giovanni Rastelli
We review the results of several of our papers about the procedure of extension of Hamiltonians, allowing the construction of families of superintegrable systems with non-trivial polynomial first integrals (or symmetry operators) of arbitrarily high degree. In particular, we focus on the geometric structures involved by the procedure: warped manifolds and Riemannian coverings. Examples of superintegrable systems, classical and quantum, with the structure of extended Hamiltonians are anisotropic harmonic oscillators, Kepler-Coulomb, Tremblay-Turbiner-Winternitz and Post-Winternitz systems.
Tuning the Legacy Survey of Space and Time (LSST) Observing Strategy for Solar System Science: Incremental Templates in Year 1
2024-11-29
James E. Robinson, Megan E. Schwamb, R. Lynne Jones, Mario Jurić, Peter Yoachim, Bryce T. Bolin, Colin O. Chandler, Steven R. Chesley, Grigori Fedorets, Wesley C. Fraser, Sarah Greenstreet, Henry H. Hsieh, Stephanie R. Merritt, Cyrielle Opitom, John K. Parejko
The Vera C. Rubin Observatory is due to commence the 10-year Legacy Survey of Space and Time (LSST) at the end of 2025. To detect transient/variable sources and identify solar system objects (SSOs), the processing pipelines require templates of the static sky to perform difference imaging. During the first year of the LSST, templates must be generated as the survey progresses, otherwise SSOs cannot be discovered nightly. The incremental template generation strategy has not been finalized; therefore, we use the Metric Analysis Framework (MAF) and a simulation of the survey cadence (one_snap_v4.0_10yrs}) to explore template generation in Year 1. We have assessed the effects of generating templates over timescales of days-weeks, when at least four images of sufficient quality are available for $\geq90\%$ of the visit. We predict that SSO discoveries will begin $\sim$2-3 months after the start of the survey. We find that the ability of the LSST to discover SSOs in real-time is reduced in Year 1. This is especially true for detections in areas of the sky that receive fewer visits, such as the North Ecliptic Spur (NES), and in less commonly used filters, such as the $u$ and $g$-bands. The lack of templates in the NES dominates the loss of real-time SSO discoveries; across the whole sky the MAF Main-Belt asteroid (MBA) discovery metric decreases by up to $63\%$ compared to the baseline observing strategy, whereas the metric decreases by up to $79\%$ for MBAs in the NES alone.
Extensive analysis of reconstruction algorithms for DESI 2024 baryon acoustic oscillations
2024-11-29
X. Chen, Z. Ding, E. Paillas, S. Nadathur, H. Seo, S. Chen, N. Padmanabhan, M. White, A. de Mattia, P. McDonald, A. J. Ross, A. Variu, A. Carnero Rosell, B. Hadzhiyska, M. M. S Hanif, D. Forero-Sánchez, S. Ahlen, O. Alves, U. Andrade, S. BenZvi, D. Bianchi, D. Brooks, E. Chaussidon, T. Claybaugh, A. de la Macorra, Biprateep Dey, K. Fanning, S. Ferraro, A. Font-Ribera, J. E. Forero-Romero, C. Garcia-Quintero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, C. Hahn, K. Honscheid, S. Juneau, R. Kehoe, D. Kirkby, T. Kisner, A. Kremin, M. E. Levi, A. Meisner, J. Mena-Fernández, R. Miquel, J. Moustakas, A. Muñoz-Gutiérrez, F. Nikakhtar, N. Palanque-Delabrouille, W. J. Percival, F. Prada, I. Pérez-Ràfols, M. Rashkovetskyi, G. Rossi, R. Ruggeri, E. Sanchez, C. Saulder, D. Schlegel, M. Schubnell, A. Smith, D. Sprayberry, G. Tarlé, D. Valcin, M. Vargas-Magaña, B. A. Weaver, S. Yuan, R. Zhou
Reconstruction of the baryon acoustic oscillation (BAO) signal has been a standard procedure in BAO analyses over the past decade and has helped to improve the BAO parameter precision by a factor of ~2 on average. The Dark Energy Spectroscopic Instrument (DESI) BAO analysis for the first year (DR1) data uses the ``standard'' reconstruction framework, in which the displacement field is estimated from the observed density field by solving the linearized continuity equation in redshift space, and galaxy and random positions are shifted in order to partially remove nonlinearities. There are several approaches to solving for the displacement field in real survey data, including the multigrid (MG), iterative Fast Fourier Transform (iFFT), and iterative Fast Fourier Transform particle (iFFTP) algorithms. In this work, we analyze these algorithms and compare them with various metrics including two-point statistics and the displacement itself using realistic DESI mocks. We focus on three representative DESI samples, the emission line galaxies (ELG), quasars (QSO), and the bright galaxy sample (BGS), which cover the extreme redshifts and number densities, and potential wide-angle effects. We conclude that the MG and iFFT algorithms agree within 0.4% in post-reconstruction power spectrum on BAO scales with the RecSym convention, which does not remove large-scale redshift space distortions (RSDs), in all three tracers. The RecSym convention appears to be less sensitive to displacement errors than the RecIso convention, which attempts to remove large-scale RSDs. However, iFFTP deviates from the first two; thus, we recommend against using iFFTP without further development. In addition, we provide the optimal settings for reconstruction for five years of DESI observation. The analyses presented in this work pave the way for DESI DR1 analysis as well as future BAO analyses.
2024-11-29
Liming Nie, Hao Liu, Jing Sun, Kabir Sulaiman Said, Shanshan Hong, Lei Xue, Zhiyuan Wei, Yangyang Zhao, Meng Li
There is an increasing global emphasis on information accessibility, with numerous researchers actively developing automated tools to detect and repair accessibility issues, thereby ensuring that individuals with diverse abilities can independently access software products and services. However, current research still encounters significant challenges in two key areas: the absence of a comprehensive taxonomy of accessibility issue types, and the lack of comprehensive analysis of the capabilities of detection and repair tools, as well as the status of corresponding datasets. To address these challenges, this paper introduces the Accessibility Issue Analysis (AIA) framework. Utilizing this framework, we develop a comprehensive taxonomy that categorizes 55 types of accessibility issues across four pivotal dimensions: Perceivability, Operability, Understandability, and Robustness. This taxonomy has been rigorously recognized through a questionnaire survey (n=130). Building on this taxonomy, we conduct an in-depth analysis of existing detection and repair tools, as well as the status of corresponding datasets. In terms of tools, our findings indicate that 14 detection tools can identify 31 issue types, achieving a 56.3% rate (31/55). Meanwhile, 9 repair tools address just 13 issue types, with a 23.6% rate. In terms of datasets, those for detection tools cover 21 issue types, at a 38.1% coverage rate, whereas those for repair tools cover only 7 types, at a 12.7% coverage rate.
A Statistical Study of Lopsided Galaxies using Random Forest
2024-11-29
Valentina Fontirroig, Facundo A. Gomez, Marcelo Jaque Arancibia, Arianna Dolfi, Nicolas Monsalves
Lopsided galaxies are late-type galaxies with a non-axisymmetric disc due to an uneven distribution of their stellar mass. Despite being a relatively common perturbation, several questions regarding its origin and the information that can be extracted from them about the evolutionary history of late-type galaxies. The advent of several large multi-band photometric surveys will allow us to statistically analyze this perturbation, with information that was not previously available. Given the strong correlation between lopsidedness and the structural properties of the galaxies, this paper aims to develop a method to automatically classify late-type galaxies between lopsided and symmetric. We seek to explore if an accurate classification can be obtain by only considering their internal properties, without additional information about the environment. We select 8000 late-type galaxies from TNG50. A Fourier decomposition of their stellar mass surface density is used to label galaxies as lopsided and symmetric. We trained a Random Forest classifier to rapidly and automatically identify this type of perturbations, exclusively using galaxies internal properties. We test different algorithms to deal with the imbalance of our data and select the most suitable approach based on the considered metrics. We show that our trained algorithm can provide a very accurate and rapid classification of lopsided galaxies. The excellent results obtained by our classifier strongly supports the hypothesis that lopsidedness is mainly a tracer of galaxies internal structures. We show that similar results can be obtained using observable quantities, readily obtainable from multi-bad photometric surveys. Our results show it allows a rapid and accurate classification of lopsided galaxies, allowing us to explore whether lopsidedness in present-day disc galaxies is connected to galaxies specific evolutionary histories.
Challenges and Opportunities for Global Cellular Connectivity
2024-11-29
Viktoria Vomhoff, Hyunseok Daniel Jang, Matteo Varvello, Stefan Geißler, Yasir Zaki, Tobias Hoßfeld, Andra Lutu
Traditional cellular service was designed for global connectivity, but business and logistical constraints led to its fragmentation, with deployments limited to individual countries and regions. Initiatives like Mobile Virtual Network Operators (MVNOs), Mobile Network Aggregators (MNAs), and regulations like ''roam-like-at-home'' have partially restored global service potential, though often at high costs in terms of user bills, application performance, and traffic efficiency. This paper makes two key contributions: first, it surveys the global cellular ecosystem, analyzing the strengths and weaknesses of major players using data from prior research, proprietary datasets, and public sources. Second, it argues that the technology for seamless global service exists in Local Breakout (LBO), a roaming architecture which allows user traffic to be routed directly to the Internet through the visited network, bypassing the home network and/or third-party infrastructures. However, LBO adoption is hindered by issues such as policy enforcement, billing, and Quality of Service (QoS) guarantees, rooted in a lack of trust between operators. The paper concludes by exploring technological advances that could enable LBO, and pave the way for truly global cellular connectivity.
Gated-Attention Feature-Fusion Based Framework for Poverty Prediction
2024-11-29
Muhammad Umer Ramzan, Wahab Khaddim, Muhammad Ehsan Rana, Usman Ali, Manohar Ali, Fiaz ul Hassan, Fatima Mehmood
This research paper addresses the significant challenge of accurately estimating poverty levels using deep learning, particularly in developing regions where traditional methods like household surveys are often costly, infrequent, and quickly become outdated. To address these issues, we propose a state-of-the-art Convolutional Neural Network (CNN) architecture, extending the ResNet50 model by incorporating a Gated-Attention Feature-Fusion Module (GAFM). Our architecture is designed to improve the model's ability to capture and combine both global and local features from satellite images, leading to more accurate poverty estimates. The model achieves a 75% R2 score, significantly outperforming existing leading methods in poverty mapping. This improvement is due to the model's capacity to focus on and refine the most relevant features, filtering out unnecessary data, which makes it a powerful tool for remote sensing and poverty estimation.
MIMDE: Exploring the Use of Synthetic vs Human Data for Evaluating Multi-Insight Multi-Document Extraction Tasks
2024-11-29
John Francis, Saba Esnaashari, Anton Poletaev, Sukankana Chakraborty, Youmna Hashem, Jonathan Bright
Large language models (LLMs) have demonstrated remarkable capabilities in text analysis tasks, yet their evaluation on complex, real-world applications remains challenging. We define a set of tasks, Multi-Insight Multi-Document Extraction (MIMDE) tasks, which involves extracting an optimal set of insights from a document corpus and mapping these insights back to their source documents. This task is fundamental to many practical applications, from analyzing survey responses to processing medical records, where identifying and tracing key insights across documents is crucial. We develop an evaluation framework for MIMDE and introduce a novel set of complementary human and synthetic datasets to examine the potential of synthetic data for LLM evaluation. After establishing optimal metrics for comparing extracted insights, we benchmark 20 state-of-the-art LLMs on both datasets. Our analysis reveals a strong correlation (0.71) between the ability of LLMs to extracts insights on our two datasets but synthetic data fails to capture the complexity of document-level analysis. These findings offer crucial guidance for the use of synthetic data in evaluating text analysis systems, highlighting both its potential and limitations.
State of the Art on Stacked Intelligent Metasurfaces: Communication, Sensing and Computing in the Wave Domain
2024-11-29
Marco Di Renzo
Stacked intelligent metasurface (SIM) is an emerging technology that capitalizes on reconfigurable metasurfaces for several applications in wireless communications. SIM is considered an enabler for integrating communication, sensing and computing in a unique platform. In this paper, we offer a survey on the state of the art of SIM for wireless communications.
MIRI Deep Imaging Survey (MIDIS) of the Hubble Ultra Deep Field
2024-11-29
Göran Östlin, Pablo G. Pérez-González, Jens Melinder, Steven Gillman, Edoardo Iani, Luca Costantin, Leindert A. Boogaard, Pierluigi Rinaldi, Luis Colina, Hans Ulrik Nørgaard-Nielsen, Daniel Dicken, Thomas R. Greve, Gillian Wright, Almudena Alonso-Herrero, Javier Alvarez-Marquez, Marianna Annunziatella, Arjan Bik, Sarah E. I. Bosman, Karina I. Caputi, Alejandro Crespo Gomez, Andreas Eckart, Macarena Garcia-Marin, Jens Hjorth, Olivier Ilbert, Iris Jermann, Sarah Kendrew, Alvaro Labiano, Danial Langeroodi, Olivier Le Fevre, Mattia Libralato, Romain A. Meyer, Thibaud Moutard, Florian Peissker, John P. Pye, Tuomo V. Tikkanen, Martin Topinka, Fabian Walter, Martin Ward, Paul van der Werf, Ewine F. van Dishoeck, Manuel Güdel Thomas Henning, Pierre-Olivier Lagage, Tom P. Ray, Bart Vandenbussche
The recently launched James Webb Space Telescope (JWST) is opening new observing windows on the distant universe. Among JWST's instruments, the Mid Infrared Instrument (MIRI) offers the unique capability of imaging observations at wavelengths $\lambda > 5\mu$m. This enables unique access to the rest frame near infra-red (NIR, $\lambda \ge 1$\mum) emission from galaxies at redshifts $z>4$ and the visual ($\lambda \gtrsim 5000$\AA) rest frame for $z>9$. We here report on the guaranteed time observations (GTO) from the MIRI European Consortium, of the Hubble Ultra Deep Field (HUDF), forming the MIRI Deep Imaging Survey (MIDIS), consisting of an on source integration time of $\sim41$ hours in the MIRI/F560W (5.6 $\mu$m) filter. To our knowledge, this constitutes the longest single filter exposure obtained with JWST of an extragalactic field as yet.
Long-term stability and dynamical spacing of compact planetary systems
2024-11-29
Antoine C. Petit
Exoplanet detection surveys revealed the existence of numerous multi-planetary systems packed close to their stability limit. In this proceeding, we review the mechanism driving the instability of compact systems, originally published in Petit et al. (2020). Compact systems dynamics are dominated by the interactions between resonances involving triplets of planets. The complex network of three-planet mean motion resonances drives a slow chaotic semi-major axes diffusion, leading to a fast and destructive scattering phase. This model reproduces quantitatively the instability timescale found numerically. We can observe signpost of this process on exoplanet systems architecture. The critical spacing ensuring stability scales as the planet-to star mass ratio to the power 1/4. It explains why the Hill radius is not an adapted measure of dynamical compactness of exoplanet systems, particularly for terrestrial planets. We also provide some insight on the theoretical tools developed in the original work and how they can be of interest in other problems.
Enhancing Sentiment Analysis in Bengali Texts: A Hybrid Approach Using Lexicon-Based Algorithm and Pretrained Language Model Bangla-BERT
2024-11-29
Hemal Mahmud, Hasan Mahmud
Sentiment analysis (SA) is a process of identifying the emotional tone or polarity within a given text and aims to uncover the user's complex emotions and inner feelings. While sentiment analysis has been extensively studied for languages like English, research in Bengali, remains limited, particularly for fine-grained sentiment categorization. This work aims to connect this gap by developing a novel approach that integrates rule-based algorithms with pre-trained language models. We developed a dataset from scratch, comprising over 15,000 manually labeled reviews. Next, we constructed a Lexicon Data Dictionary, assigning polarity scores to the reviews. We developed a novel rule based algorithm Bangla Sentiment Polarity Score (BSPS), an approach capable of generating sentiment scores and classifying reviews into nine distinct sentiment categories. To assess the performance of this method, we evaluated the classified sentiments using BanglaBERT, a pre-trained transformer-based language model. We also performed sentiment classification directly with BanglaBERT on the original data and evaluated this model's results. Our analysis revealed that the BSPS + BanglaBERT hybrid approach outperformed the standalone BanglaBERT model, achieving higher accuracy, precision, and nuanced classification across the nine sentiment categories. The results of our study emphasize the value and effectiveness of combining rule-based and pre-trained language model approaches for enhanced sentiment analysis in Bengali and suggest pathways for future research and application in languages with similar linguistic complexities.
A Review of LLM-based Explanations in Recommender Systems
2024-11-29
Alan Said
The rise of Large Language Models (LLMs), such as LLaMA and ChatGPT, has opened new opportunities for enhancing recommender systems through improved explainability. This paper provides a systematic literature review focused on leveraging LLMs to generate explanations for recommendations -- a critical aspect for fostering transparency and user trust. We conducted a comprehensive search within the ACM Guide to Computing Literature, covering publications from the launch of ChatGPT (November 2022) to the present (November 2024). Our search yielded 232 articles, but after applying inclusion criteria, only six were identified as directly addressing the use of LLMs in explaining recommendations. This scarcity highlights that, despite the rise of LLMs, their application in explainable recommender systems is still in an early stage. We analyze these select studies to understand current methodologies, identify challenges, and suggest directions for future research. Our findings underscore the potential of LLMs improving explanations of recommender systems and encourage the development of more transparent and user-centric recommendation explanation solutions.
Are Stellar Embryos in Perseus Radio-Synchrotron Emitters? Statistical data analysis with Herschel and LOFAR paving the way for the SKA
2024-11-29
Andrea Bracco, Marco Padovani, Daniele Galli, Stefania Pezzuto, Alexandre Cipriani, Alexander Drabent
Cosmic rays (CRs) are fundamental to the chemistry and physics of star-forming regions, influencing molecular gas ionization, mediating interactions with interstellar magnetic fields, and regulating star formation from the diffuse interstellar medium to the creation of stellar cores. The electronic GeV component of CRs is expected to produce non-thermal synchrotron radiation detectable at radio frequencies, yet such emissions from Galactic star-forming regions remain elusive. This study reports the first statistical attempt to detect synchrotron emission at 144 MHz using the LOw Frequency ARray (LOFAR) in the nearby Perseus molecular cloud (300 pc). By median-stacking 353 prestellar and 132 protostellar cores from the Herschel Gould Belt Survey and using LOFAR Two-Meter Sky Survey (LoTSS) data (20" resolution), 18 protostellar and 5 prestellar radio candidates were initially identified. However, these were likely extragalactic contaminants within the Herschel catalog. Stacked analyses did not reveal significant radio counterparts for prestellar and protostellar cores, with upper limits of $5\, \mu$Jy beam$^{-1}$ and $8\, \mu$Jy beam$^{-1}$, respectively. Non-detections suggest strong extinction mechanisms like free-free absorption and the Razin-Tsytovich effect for protostellar cores. For prestellar cores, analytical magnetostatic-isothermal models constrain the maximum ordered magnetic-field strength to 100 $\mu$G. Future predictions suggest that Square Kilometre Array-Low (SKA-Low) arrays could detect this emission in 9 hours (AA*) or 4 hours (AA4), enabling more sensitive constraints on synchrotron radiation in star-forming cores.
Contextual Checkerboard Denoise -- A Novel Neural Network-Based Approach for Classification-Aware OCT Image Denoising
2024-11-29
Md. Touhidul Islam, Md. Abtahi M. Chowdhury, Sumaiya Salekin, Aye T. Maung, Akil A. Taki, Hafiz Imtiaz
In contrast to non-medical image denoising, where enhancing image clarity is the primary goal, medical image denoising warrants preservation of crucial features without introduction of new artifacts. However, many denoising methods that improve the clarity of the image, inadvertently alter critical information of the denoised images, potentially compromising classification performance and diagnostic quality. Additionally, supervised denoising methods are not very practical in medical image domain, since a \emph{ground truth} denoised version of a noisy medical image is often extremely challenging to obtain. In this paper, we tackle both of these problems by introducing a novel neural network based method -- \emph{Contextual Checkerboard Denoising}, that can learn denoising from only a dataset of noisy images, while preserving crucial anatomical details necessary for image classification/analysis. We perform our experimentation on real Optical Coherence Tomography (OCT) images, and empirically demonstrate that our proposed method significantly improves image quality, providing clearer and more detailed OCT images, while enhancing diagnostic accuracy.
Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook
2024-11-29
Florinel-Alin Croitoru, Andrei-Iulian Hiji, Vlad Hondru, Nicolae Catalin Ristea, Paul Irofti, Marius Popescu, Cristian Rusu, Radu Tudor Ionescu, Fahad Shahbaz Khan, Mubarak Shah
With the recent advancements in generative modeling, the realism of deepfake content has been increasing at a steady pace, even reaching the point where people often fail to detect manipulated media content online, thus being deceived into various kinds of scams. In this paper, we survey deepfake generation and detection techniques, including the most recent developments in the field, such as diffusion models and Neural Radiance Fields. Our literature review covers all deepfake media types, comprising image, video, audio and multimodal (audio-visual) content. We identify various kinds of deepfakes, according to the procedure used to alter or generate the fake content. We further construct a taxonomy of deepfake generation and detection methods, illustrating the important groups of methods and the domains where these methods are applied. Next, we gather datasets used for deepfake detection and provide updated rankings of the best performing deepfake detectors on the most popular datasets. In addition, we develop a novel multimodal benchmark to evaluate deepfake detectors on out-of-distribution content. The results indicate that state-of-the-art detectors fail to generalize to deepfake content generated by unseen deepfake generators. Finally, we propose future directions to obtain robust and powerful deepfake detectors. Our project page and new benchmark are available at https://github.com/CroitoruAlin/biodeep.
Heterogeneity of tumor biophysical properties and their potential role as prognostic markers
2024-11-29
Anja Madleine Markl, Daniel Nieder, Diana Isabel Sandoval-Bojorquez, Anna Taubenberger, Jean-François Berret, Artur Yakimovich, Eduardo Sergio Oliveros- Mata, Larysa Baraban, Anna Dubrovska
Progress in our knowledge of tumor mechanisms and complexity led to the understanding of the physical parameters of cancer cells and their microenvironment, including the mechanical, thermal, and electrical properties, solid stress, and liquid pressure, as critical regulators of tumor progression and potential prognostic traits associated with clinical outcomes. The biological hallmarks of cancer and physical abnormalities of tumors are mutually reinforced, promoting a vicious cycle of tumor progression. A comprehensive analysis of the biological and physical tumor parameters is critical for developing more robust prognostic and diagnostic markers and improving treatment efficiency. Like the biological tumor traits, physical tumor features are characterized by inter-and intratumoral heterogeneity. The dynamic changes of physical tumor traits during tumor progression and as a result of tumor treatment highlight the necessity of their spatial and temporal analysis in clinical settings. This review focuses on the biological basis of the tumor-specific physical traits, the state-of-the-art methods of their analyses, and the perspective of clinical translation. The importance of tumor physical parameters for disease progression and therapy resistance, as well as current treatment strategies to monitor and target tumor physical traits in clinics, is highlighted.
Radio-loudness statistics of quasars from Quaia-VLASS
2024-11-29
Nestor Arsenov, Sandor Frey, András Kovács, Lyuba Slavcheva-Mihova
Quasars are objects of high interest for applications in extragalactic astrophysics, cosmology, and astrometry. One of their useful qualities is owed to their relativistic jets: they can be radio-loud. However, the fraction of radio-loud vs. radio-quiet quasars is subject to ongoing investigations, where the statistical power is limited by the low number of known quasars with radio counterparts. In this analysis, we revisited the radio-loudness statistics of quasars by significantly expanding the pool of known sources. Our main goal was to create a new, value-added catalogue of quasars with information about their extinction-corrected magnitudes, radio flux density, possible contamination levels, and other data flags, besides their sky coordinates and photometric redshifts. We cross-matched the optical Quaia catalogue of about 1.3 million quasars (selected from the Gaia data set) with 1.9 million sources from the Very Large Array Sky Survey (VLASS) radio catalogue. We explored different thresholds for the matching radius, balancing the completeness and purity of the resulting Quaia-VLASS catalogue, and found that 1.5 arc seconds is a sufficient choice. Our main finding is that the radio-loud fraction of quasars is in good agreement with previous works (< 10%), and there is no significant large-scale pattern in radio-loudness across the sky. The exact estimate depends on the G-band magnitude limit, and we observed some weak trends with redshift and absolute optical magnitude, possibly indicating remnant systematic effects in our data sets. The cross-matched Quaia-VLASS catalogue with 43,650 sources is available to the public for future analyses. This latest census of QSOs with radio counterparts will facilitate further investigations of the dichotomy of radio-loud and radio-quiet quasars, and it may also support other lines of investigations using quasars in cosmology and astrophysics.
2024-11-29
K. Perger, J. Fogasy, S. Frey, K. É. Gabányi
To investigate the radio properties of the recently found high-redshift population, we collected a sample of $919$ little red dots (LRDs) from the literature. By cross-matching their coordinates with the radio catalogues based on the first- and second-epoch observations of the Very Large Array Sky Survey (VLASS) and the Faint Images of the Radio Sky at Twenty-centimeters (FIRST) survey, we found no radio counterparts coinciding with any of the LRDs. To uncover possible sub-mJy level weak radio emission, we performed mean and median image stacking analyses of empty-field 'Quick Look' VLASS and FIRST image cutouts centred on the LRD positions. We found no radio emission above $3\sigma$ noise levels ($\sim11$ and $\sim18~\mu$Jy~beam$^{-1}$ for the VLASS and FIRST maps, respectively) in either of the stacked images for the LRD sample, while the noise levels of the single-epoch images are comparable to those found earlier in the stacking of high-redshift radio-quiet active galactic nuclei (AGNs). The non-detection of radio emission in LRDs suggests these sources host weaker (or no) radio AGNs.
Generative AI as a Tool or Leader? Exploring AI-Augmented Thinking in Student Programming Tasks
2024-11-29
Tianlong Zhong, Gaoxia Zhu, Kang You Lim, Yew Soon Ong
The increasing use of Generative Artificial Intelligence (GAI) tools in education highlights the need to understand their influence on individuals' thinking processes and agency. This research explored 20 university students' interaction with GAI during programming. Participants completed surveys, recorded their screens during an hour-long programming session, and reflected on their GAI use. To analyse the data, we developed an AI-augmented thinking coding scheme with four dimensions: Question Formulation, Solution Development, Solution Analysis and Evaluation, and Solution Refinement. Participants were categorised into human-led and AI-led groups based on the time ratio of human-generating source code versus copying source code from GAI. T-tests indicated that the human-led group spent significantly more time on Solution Development and Solution Refinement than the AI-led group. Sequential pattern mining revealed distinct patterns of the two groups: the human-led group often refined GAI outputs, while the AI-led group frequently relied on direct answers from GAI. Correlation analyses found that positive attitudes towards AI, critical thinking, and programming self-efficacy positively correlated with Question Formulation; critical thinking was positively related to Solution Refinement; and programming self-efficacy was negatively associated with Solution Analysis and Evaluation. This study enhances understanding of the thinking process in GAI-supported programming.
ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions -- XIX. The origin of SiO emission
2024-11-29
Rong Liu, Tie Liu, Izaskun Jiménez-Serra, Jin-Zeng Li, Jesús Martín-Pintado, Xunchuan Liu, Chang Won Lee, Patricio Sanhueza, James O. Chibueze, Víctor M. Rivilla, Mika Juvela, Laura Colzi, Leonardo Bronfman, Hong-Li Liu, Miguel Sanz-Novo, Álvaro López-Gallifa, Shanghuo Li, Andrés Megías, David San Andrés, Guido Garay, Jihye Hwang, Jianwen Zhou, Fengwei Xu, Antonio Martínez-Henares, Anindya Saha, Hafiz Nazeer
The production of silicon monoxide (SiO) can be considered as a fingerprint of shock interaction. In this work, we use high-sensitivity observations of the SiO (2-1) and H$^{13}$CO$^{+}$ (1-0) emission to investigate the broad and narrow SiO emission toward 146 massive star-forming regions in the ATOMS survey. We detected SiO emission in 136 regions and distinguished broad and narrow components across the extension of 118 sources (including 58 UC $H_{II}$ regions) with an average angular resolution of 2.5$^{\prime}$$^{\prime}$. The derived SiO luminosity ($L_{SiO}$) across the whole sample shows that the majority of $L_{SiO}$ (above 66$\%$) can be attributed to broad SiO, indicating its association with strong outflows. The comparison of the ALMA SiO images with the filamentary skeletons identified from H$^{13}$CO$^{+}$ and in the infrared data (at 4.5, 8, and 24 $mu$m), further confirms that most SiO emission originates from outflows. However, note that for nine sources in our sample, the observed SiO emission may be generated by expanding UC $H_{II}$ regions. There is a moderate positive correlation between the bolometric luminosity ($L_{bol}$) and $L_{SiO}$ for both components (narrow and broad). The UC $H_{II}$ sources show a weaker positive correlation between $L_{bol}$ and $L_{SiO}$ and higher $L_{SiO}$ compared to the sources without UC $H_{II}$ regions. These results imply that the SiO emission from UC $H_{II}$ sources might be affected by UV-photochemistry induced by UC $H_{II}$ regions.
Peculiar velocities are the motions of galaxies due to the gravitational influence of large-scale structure, and thus are an important cosmological probe of the underlying matter density field. In recent years the number of surveys designed to measure peculiar velocities has increased, to the point that it is plausible that we will have completely mapped out the peculiar velocity field in the local universe within the next decade. Such an abundance of data will enable us to place precise constraints on the growth rate of large-scale structure which in turn will inform us about the true nature of the laws of gravity and the standard cosmological model. In this chapter, the physics governing the generation of peculiar velocities, the methods of measuring them, and the statistical tools used to extract cosmological information from them are described. It will also cover a swathe of current and future surveys dedicated to collecting peculiar velocities, what their aims are, and what these datasets may mean for the future of cosmological analyses.
Micro frontend (MFE) architectures have gained significant popularity for promoting independence and modularity in development. Despite their widespread adoption, the field remains relatively unexplored, especially concerning identifying problems and documenting best practices. Drawing on both established microservice (MS) anti-patterns and the analysis of real problems faced by software development teams that adopt MFE, this paper presents a catalog of 12 MFE anti-patterns. We composed an initial version of the catalog by recognizing parallels between MS anti-patterns and recurring issues in MFE projects to map and adapt MS anti-patterns to the context of MFE. To validate the identified problems and proposed solutions, we conducted a survey with industry practitioners, collecting valuable feedback to refine the anti-patterns. Additionally, we asked participants if they had encountered these problems in practice and to rate their harmfulness on a 10-point Likert scale. The survey results revealed that participants had encountered all the proposed anti-patterns in real-world MFE architectures, with only one reported by less than 50\% of participants. They stated that the catalog can serve as a valuable guide for both new and experienced developers, with the potential to enhance MFE development quality. The collected feedback led to the development of an improved version of the anti-patterns catalog. Furthermore, we developed a web application designed to not only showcase the anti-patterns but also to actively foster collaboration and engagement within the MFE community. The proposed catalog is a valuable resource for identifying and mitigating potential pitfalls in MFE development. It empowers developers of all experience levels to create more robust, maintainable, and well-designed MFE applications.
2024-11-29
Saloni Deepak, J. Christopher Howk, Nicolas Lehner, Céline Péroux
We present a global census of metals in the Universe and their evolution with cosmic time, synthesizing robust estimates of metals in stars, hot intra-cluster gas, and gaseous absorbers tracing neutral gas as well as ionized gas in the circumgalactic and intergalactic media. We observe a 13-fold increase in the stellar metal mass density from z~2.5 to 0.7, over which time stars emerge as the most important metal reservoir at low redshifts, housing ~31% of the total expected metal density at z~0.1. Hot virialized intracluster/intragroup gas accounts for ~15% and 10% of metals at z~0.1 and 0.7, respectively. Using metallicity measurements from CCC, KODIAQ-Z, and HD-LLS surveys covering redshifts z<1 to z~2-3.5 we investigate the global distribution of metals in extragalactic ionized gas as a function of HI column density. During the period from z~3 to z<1, the global metal density of cool (T~10^{4-5} K) gas has doubled. However, the fractional contribution of the ionized gas to the total expected metal density decreased from ~20% at z~3 to ~4% at z<1. The cosmic metal density of all gas phases has increased with cosmic time, reflecting an "inside-out" metal dispersion by feedback mechanisms and galactic outflows.
Streamlining the review process: AI-generated annotations in research manuscripts
2024-11-29
Oscar Díaz, Xabier Garmendia, Juanan Pereira
The increasing volume of research paper submissions poses a significant challenge to the traditional academic peer-review system, leading to an overwhelming workload for reviewers. This study explores the potential of integrating Large Language Models (LLMs) into the peer-review process to enhance efficiency without compromising effectiveness. We focus on manuscript annotations, particularly excerpt highlights, as a potential area for AI-human collaboration. While LLMs excel in certain tasks like aspect coverage and informativeness, they often lack high-level analysis and critical thinking, making them unsuitable for replacing human reviewers entirely. Our approach involves using LLMs to assist with specific aspects of the review process. This paper introduces AnnotateGPT, a platform that utilizes GPT-4 for manuscript review, aiming to improve reviewers' comprehension and focus. We evaluate AnnotateGPT using a Technology Acceptance Model (TAM) questionnaire with nine participants and generalize the findings. Our work highlights annotation as a viable middle ground for AI-human collaboration in academic review, offering insights into integrating LLMs into the review process and tuning traditional annotation tools for LLM incorporation.
On the transfer of the angular momentum of a structured laser pulse to an ensemble of charged particles
2024-11-29
E. Dmitriev, Ph. Korneev
A structure of a laser pulse may significantly influence the dynamics of interacting particles. In the case of dilute plasma the particle dynamics may be considered in the single particle approximation. In this paper the problem of the angular momentum gain of a single particle in a focused structured pulse is considered for some certain cases, including radial and azimuthal polarizations, in the frameworks of the theoretical model, presented in [E. Dmitriev and Ph. Korneev, 'Angular momentum gain by electrons under the action of intense structured light', Physical Review A, vol. 110, p. 013514, (2024)], which includes solving of Maxwell's equations with a required accuracy and the high order perturbation theory. The obtained analytical results are compared to the results of numerical simulations. Angular dependence in the structure of the laser pulse is shown to be critically important for the orbital angular momentum gain of an ensemble of charged particles in a structured wave.
Examining a Quantum System Subject to Energy Decoherence
2024-11-29
Henry Crumley
This paper studies the energy decoherence of an interacting quantum system. It first reviews the experiments that motivated the postulates of quantum mechanics. It then discusses a decoherence that occurs dynamically in a closed system. This effect is studied in interacting quantum systems consisting of an oscillator and spins using analytical and numerical methods. The subsequent results are contrasted with cases with no decoherence. Connections of energy decoherence with thermodynamics are explored.
Generating a Low-code Complete Workflow via Task Decomposition and RAG
2024-11-29
Orlando Marquez Ayala, Patrice Béchard
AI technologies are moving rapidly from research to production. With the popularity of Foundation Models (FMs) that generate text, images, and video, AI-based systems are increasing their complexity. Compared to traditional AI-based software, systems employing FMs, or GenAI-based systems, are more difficult to design due to their scale and versatility. This makes it necessary to document best practices, known as design patterns in software engineering, that can be used across GenAI applications. Our first contribution is to formalize two techniques, Task Decomposition and Retrieval-Augmented Generation (RAG), as design patterns for GenAI-based systems. We discuss their trade-offs in terms of software quality attributes and comment on alternative approaches. We recommend to AI practitioners to consider these techniques not only from a scientific perspective but also from the standpoint of desired engineering properties such as flexibility, maintainability, safety, and security. As a second contribution, we describe our industry experience applying Task Decomposition and RAG to build a complex real-world GenAI application for enterprise users: Workflow Generation. The task of generating workflows entails generating a specific plan using data from the system environment, taking as input a user requirement. As these two patterns affect the entire AI development cycle, we explain how they impacted the dataset creation, model training, model evaluation, and deployment phases.
Peer Effects and Herd Behavior: An Empirical Study Based on the "Double 11" Shopping Festival
2024-11-29
Hambur Wang
This study employs a Bayesian Probit model to empirically analyze peer effects and herd behavior among consumers during the "Double 11" shopping festival, using data collected through a questionnaire survey. The results demonstrate that peer effects significantly influence consumer decision-making, with the probability of participation in the shopping event increasing notably when roommates are involved. Additionally, factors such as gender, online shopping experience, and fashion consciousness significantly impact consumers' herd behavior. This research not only enhances the understanding of online shopping behavior among college students but also provides empirical evidence for e-commerce platforms to formulate targeted marketing strategies. Finally, the study discusses the fragility of online consumption activities, the need for adjustments in corporate marketing strategies, and the importance of promoting a healthy online culture.
Clinical Document Corpora and Assorted Domain Proxies: A Survey of Diversity in Corpus Design, with Focus on German Text Data
2024-11-29
Udo Hahn
We survey clinical document corpora, with focus on German textual data. Due to rigid data privacy legislation in Germany these resources, with only few exceptions, are stored in safe clinical data spaces and locked against clinic-external researchers. This situation stands in stark contrast with established workflows in the field of natural language processing where easy accessibility and reuse of data collections are common practice. Hence, alternative corpus designs have been examined to escape from this data poverty. Besides machine translation of English clinical datasets and the generation of synthetic corpora with fictitious clinical contents, several other types of domain proxies have come up as substitutes for authentic clinical documents. Common instances of close proxies are medical journal publications, clinical therapy guidelines, drug labels, etc., more distant proxies include online encyclopedic medical articles or medical contents from social media channels. After PRISM-conformant screening of 359 hits from four bibliographic systems, 75 relevant documents were finally selected for this review and 59 distinct corpora were determined. We identified 24 real clinical corpora (from 40 publications) out of which only 5 are publicly distributable. 2 translations of real corpora and 3 synthetic ones complement the set of clinical corpora. 14 corpora were categorized as close domain proxies, 16 as distant ones. There is a clear divide between the large number of non-accessible authentic clinical German-language corpora and their publicly accessible substitutes: translated or synthetic, close or more distant proxies. So on first sight, the data bottleneck seems broken. Intuitively yet, differences in genre-specific writing style, wording and medical domain expertise in this typological space are also obvious. This raises the question how valid alternative corpus designs really are.
Digital Twin in Industries: A Comprehensive Survey
2024-11-29
Md Bokhtiar Al Zami, Shaba Shaon, Vu Khanh Quy, Dinh C. Nguyen
Industrial networks are undergoing rapid transformation driven by the convergence of emerging technologies that are revolutionizing conventional workflows, enhancing operational efficiency, and fundamentally redefining the industrial landscape across diverse sectors. Amidst this revolution, Digital Twin (DT) emerges as a transformative innovation that seamlessly integrates real-world systems with their virtual counterparts, bridging the physical and digital realms. In this article, we present a comprehensive survey of the emerging DT-enabled services and applications across industries, beginning with an overview of DT fundamentals and its components to a discussion of key enabling technologies for DT. Different from literature works, we investigate and analyze the capabilities of DT across a wide range of industrial services, including data sharing, data offloading, integrated sensing and communication, content caching, resource allocation, wireless networking, and metaverse. In particular, we present an in-depth technical discussion of the roles of DT in industrial applications across various domains, including manufacturing, healthcare, transportation, energy, agriculture, space, oil and gas, as well as robotics. Throughout the technical analysis, we delve into real-time data communications between physical and virtual platforms to enable industrial DT networking. Subsequently, we extensively explore and analyze a wide range of major privacy and security issues in DT-based industry. Taxonomy tables and the key research findings from the survey are also given, emphasizing important insights into the significance of DT in industries. Finally, we point out future research directions to spur further research in this promising area.
A Survey of Magnetic Field Properties in Bok Globules
2024-11-29
Tamojeet Roychowdhury, Thushara G. S. Pillai, Claudia Vilega-Rodrigues, Jens Kauffmann, Le Ngoc Tram, Tyler L. Bourke, Victor de Souza Magalhaes
Bok globules are small, dense clouds that act as isolated precursors for the formation of single or binary stars. Although recent dust polarization surveys, primarily with Planck, have shown that molecular clouds are strongly magnetized, the significance of magnetic fields in Bok globules has largely been limited to individual case studies, lacking a broader statistical understanding. In this work, we introduce a comprehensive optical polarimetric survey of 21 Bok globules. Using Gaia and near-IR photometric data, we produce extinction maps for each target. Using the radiative torque alignment model customized to the physical properties of the Bok globule, we characterize the polarization efficiency of one representative globule as a function of its visual extinction. We thus find our optical polarimetric data to be a good probe of the globule's magnetic field. Our statistical analysis of the orientation of elongated extinction structures relative to the plane-of-sky magnetic field orientations shows they do not align strictly parallel or perpendicular. Instead, the data is best explained by a bimodal distribution, with structures oriented at projected angles that are either parallel or perpendicular. The plane-of-sky magnetic field strengths on the scales probed by optical polarimetric data are measured using the Davis-Chandrasekhar-Fermi technique. We then derive magnetic properties such as Alfv\'en Mach numbers and mass-to-magnetic flux ratios. Our findings statistically place the large-scale (Av < 7 mag) magnetic properties of Bok globules in a dynamically important domain.
Interactive Multimodal Integral Field Spectroscopy
2024-11-29
Adrián García Riber, Rubén García-Benito, Francisco Serradilla
Using sonification on scientific data analysis provides additional dimensions to visualization, potentially increasing researchers' analytical capabilities and fostering inclusion and accessibility. This research explores the potential of multimodal Integral Field Spectroscopy (IFS) applied to galaxy analysis through the development and evaluation of a tool that complements the visualization of datacubes with sound. The proposed application, \textit{ViewCube}, provides interactive visualizations and sonifications of spectral information across a two-dimensional field-of-view, and its architecture is designed to incorporate future sonification approaches. The first sonification implementation described in this article uses a deep learning module to generate binaural unsupervised auditory representations. The work includes a qualitative and quantitative user study based on an online questionnaire, aimed at both specialized and non-specialized participants, focusing on the case study of datacubes of galaxies from the Calar Alto Integral Field Spectroscopy Area (CALIFA) survey. Out of 67 participants who completed the questionnaire, 42 had the opportunity to test the application in person prior to filling out the online survey. 81\% of these 42 participants expressed the good interactive response of the tool, 79.1\% of the complete sample found the application "Useful", and 58.2\% rated its aesthetics as "Good". The quantitative results suggest that all participants were able to retrieve information from the sonifications, pointing to previous experience in the analysis of sound events as more helpful than previous knowledge of the data for the proposed tasks, and highlighting the importance of training and attention to detail for the understanding of complex auditory information.
High energy neutrino astronomy has seen significant progress in the past few years. This includes the detection of neutrino flux from the Galactic plane, as well as strong evidence for neutrino emission from the active galaxy NGC 1068, both reported by IceCube. New results start coming from the two km$^3$-scale neutrino telescopes under construction in the Northern hemisphere: KM3NeT in the Mediterranean Sea and Baikal-GVD in Lake Baikal. After briefly reviewing the status of the field, we present the current status of the Baikal-GVD neutrino telescope and its recent results, including observations of atmospheric and astrophysical neutrinos.
The Baryonic Mass Estimates of the Milky Way Halo in the form of High Velocity Clouds
2024-11-29
Noraiz Tahir, Martin Lopez Corredoira, Francesco De Paolis
The halo of our Galaxy is populated with a significant number of high-velocity clouds (HVCs) moving with a speed up to $500$ km/s. It is suggested that these HVCs might contain a non-negligible fraction of the missing baryons. The main aim of the current paper is to estimate the baryonic mass of the Milky Way halo in the form of HVCs in order to constrain a fraction of missing baryons in the form of these clouds. Such findings would give substantial help in the studying halo dynamics of our Galaxy. We first estimate the HVCs distance. We consider the most recent and updated HVC catalog, namely the Galactic All Sky Survey (GASS), which, however, covers the southern sky declinations, south of $b \leq 60^\circ$. Following a model presented in the literature, we assume that most of the HVCs (not all of the HVCs in the Milky Way) were ejected from the Magellanic Clouds (MCls) which is at a distance of about 50 kpc. We assume that the HVCs have a temperature in the range of about $10^2 - 10^4$ K, and are distributed in the Galactic halo as the Navarro-Frenk-White (NFW) profile. Since the GASS survey covers a small portion of the sky, we estimate the number of missing clouds by using Monte Carlo (MC) simulations. The next step will be to estimate the total mass of the Milky Way contained in the form of these HVCs. The total mass resulted to be $\sim (7 \pm 2) \times 10^{9} M_{\odot}$ in the form of HVCs and compact high-velocity clouds (CHVCs).
On the detectability of gravitational waves emitted from head-on collisions of $\ell$-boson stars
2024-11-28
Mariana Lira, Laura O. Villegas, Javier M. Antelis, Victor Jaramillo, Claudia Moreno, Darío Núñez
In this work, we investigate head-on collisions of $\ell$-boson stars, potential candidates for dark matter compact objects. We begin with a review of the general properties and features of these stars, leveraging results from prior studies to analyze the gravitational wave signals generated by such collisions. Considering a maximum distance of 100 Mpc for potential events, we identify the range of masses and scalar field frequencies for these stars that would render the gravitational waves detectable by current gravitational wave observatories. Additionally, we process the resulting signals to generate simulated observatory images, highlighting their similarities and differences compared to those produced by black hole collisions.
Unveiling AGN Outflows: [O iii] Outflow Detection Rates and Correlation with Low-Frequency Radio Emission
2024-11-28
Emmy L. Escott, Leah K. Morabito, Jan Scholtz, Ryan C. Hickox, Chris M. Harrison, David M. Alexander, Marina I. Arnaudova, Daniel J. B. Smith, Kenneth J. Duncan, James Petley, Rohit Kondapally, Gabriela Calistro Rivera, Sthabile Kolwa
Some Active Galactic Nuclei (AGN) host outflows which have the potential to alter the host galaxy's evolution (AGN feedback). These outflows have been linked to enhanced radio emission. Here we investigate the connection between low-frequency radio emission using the International LOFAR Telescope and [O III] $\lambda$5007 ionised gas outflows using the Sloan Digital Sky Survey. Using the LOFAR Two-metre Sky Survey (LoTSS) Deep Fields, we select 198 AGN with optical spectra, 115 of which are detected at 144 MHz, and investigate their low-frequency radio emission properties. The majority of our sample do not show a radio excess when considering radio luminosity - SFR relationship, and are therefore not driven by powerful jets. We extract the [O III] $\lambda$5007 kinematics and remove AGN luminosity dependencies by matching the radio detected and non-detected AGN in $L_{\mathrm{6\mu m}}$ and redshift. Using both spectral fitting and $W_{80}$ measurements, we find radio detected AGN have a higher outflow rate (67.2$\pm$3.4 percent) than the radio non-detected AGN (44.6$\pm$2.7 percent), indicating a connection between ionised outflows and the presence of radio emission. For spectra where there are two components of the [O III] emission line present, we normalise all spectra by the narrow component and find that the average broad component in radio detected AGN is enhanced compared to the radio non-detected AGN. This could be a sign of higher gas content, which is suggestive of a spatial relationship between [O III] outflows and radio emission in the form of either low-powered jets or shocks from AGN winds.
Perspective of Software Engineering Researchers on Machine Learning Practices Regarding Research, Review, and Education
2024-11-28
Anamaria Mojica-Hanke, David Nader Palacio, Denys Poshyvanyk, Mario Linares-Vásquez, Steffen Herbold
Context: Machine Learning (ML) significantly impacts Software Engineering (SE), but studies mainly focus on practitioners, neglecting researchers. This overlooks practices and challenges in teaching, researching, or reviewing ML applications in SE. Objective: This study aims to contribute to the knowledge, about the synergy between ML and SE from the perspective of SE researchers, by providing insights into the practices followed when researching, teaching, and reviewing SE studies that apply ML. Method: We analyzed SE researchers familiar with ML or who authored SE articles using ML, along with the articles themselves. We examined practices, SE tasks addressed with ML, challenges faced, and reviewers' and educators' perspectives using grounded theory coding and qualitative analysis. Results: We found diverse practices focusing on data collection, model training, and evaluation. Some recommended practices (e.g., hyperparameter tuning) appeared in less than 20\% of literature. Common challenges involve data handling, model evaluation (incl. non-functional properties), and involving human expertise in evaluation. Hands-on activities are common in education, though traditional methods persist. Conclusion: Despite accepted practices in applying ML to SE, significant gaps remain. By enhancing guidelines, adopting diverse teaching methods, and emphasizing underrepresented practices, the SE community can bridge these gaps and advance the field.
On the Rich Landscape of Complete Intersection Monomial Curves
2024-11-28
Patricio Almirón
The aim of this survey is to explore complete intersection monomial curves from a contemporary perspective. The main goal is to help readers understand the intricate connections within the field and its potential applications. The properties of any monomial curve singularity will be first reviewed, highlighting the interaction between combinatorial and algebraic properties. Next, we will revisit the two main characterizations of complete intersection monomial curves. One is based on deep algebraic properties given by Herzog and Kunz in 1971, while the other is based on a combinatorial approach given by Delorme in 1976. Our aim is to bridge the gap between these perspectives present in the current literature. Then, we will focus on recent advances that show an intriguing connection between numerical semigroups and Alexander polynomials of knots. Finally, we will revisit the deformation theory of these curves and provide an answer to a question posed by Buchweitz and Greuel regarding curve deformations.
Non-parametric reconstructions of cosmic curvature: current constraints and forecasts
2024-11-28
Mariana L. S. Dias, Antônio F. B. da Cunha, Carlos A. P. Bengaly, Rodrigo S. Gonçalves, Jonathan Morais
The assumption of a flat universe that follows the cosmological principle, i.e., that the universe is statistically homogeneous and isotropic at large scales, comprises one of the core foundations of the standard cosmological model -- namely, the $\Lambda$CDM paradigm. Nevertheless, it has been seldom tested in the literature. In this work, we assess the validity of this hypothesis by reconstructing the cosmic curvature with currently available observations, such as Type Ia Supernova and Cosmic Chronometers. We do so by means of null tests, given by consistency relations within the standard model scenario, using a non-parametric method -- which allows us to circumvent prior assumptions on the underlying cosmology. We find no statistically significant departure from the cosmological principle and null curvature in our analysis. In addition, we show that future cosmological observations, such as those expected from Hubble parameter measurements from redshift surveys, along with gravitational wave observations as standard sirens, will be able to significantly reduce the uncertainties of current reconstructions.
Night-Side Relativistic Electron Precipitation Bursts in the Outer Radiation Belt: Insights from ELFIN and THEMIS
2024-11-28
Xi Lu, Xiao-Jia Zhang, Anton V. Artemyev, Vassilis Angelopoulos, Jacob Bortnik
Electromagnetic whistler-mode waves play a crucial role in the acceleration and precipitation of radiation belt electrons. Statistical surveys of wave characteristics suggest that these waves should preferentially scatter and precipitate relativistic electrons on the day side. However, the night-side region is expected to be primarily associated with electron acceleration. The recent low-altitude observations reveal relativistic electron precipitation in the night-side region. In this paper, we present statistical surveys of night-side relativistic electron losses due to intense precipitation bursts. We demonstrate that such bursts are associated with storm time substorm injections and are likely related to relativistic electron scattering by ducted whistler-mode waves. We also speculate on the role of injections in creating conditions favorable for relativistic electron precipitation.
2024-11-28
Khrystyna O. Levchenko, Kristýna Davídková, Jan Mikkelsen, Andrii V. Chumak
This review explores the progress of spin-wave technology, emphasizing magnonics as a potential solution to modern challenges in radio frequency (RF) communication systems. To ensure clarity, the first two sections of the review reiterate premises of the magnonics, outline key milestones in spin-wave research, briefly review the state-of-the-art for technology and materials, as well as highlight potential directions for future developments. The third section is dedicated to spin-wave RF applications developed over the past 50 years, focusing on selected passive fundamental devices (filters, power limiters, delay lines, phase shifters, directional couplers, resonators, etc) and their operating principles. In the last section, the review further discusses the main advantages of spin-waves for RF applications while highlighting potential concerns, such as insertion losses, linearity and power capacity. Finally, potential solutions to address these challenges are proposed.
The General Data Protection Regulation (GDPR) is the benchmark in the European Union (EU) for privacy and data protection standards. Substantial research has been conducted in the requirements engineering (RE) literature investigating the elicitation, representation, and verification of privacy requirements in GDPR. Software systems including mobile apps must comply with the GDPR. With the growing pervasiveness of mobile apps and their increasing demand for personal data, privacy concerns have acquired further interest within the software engineering (SE) community at large. Despite the extensive literature on GDPR-relevant privacy concerns in mobile apps, there is no secondary study that describes, analyzes, and categorizes the current focus. Research gaps and persistent challenges are thus left unnoticed. In this article, we aim to systematically review existing primary studies highlighting various GDPR concepts and how these concepts are addressed in mobile apps research. The objective is to reconcile the existing work on GDPR in the RE literature with the research on GDPR-related privacy concepts in mobile apps in the SE literature. Our findings show that the current research landscape reflects a rather shallow understanding of GDPR requirements. Some GDPR concepts such as data subject rights (i.e., the rights of individuals over their personal data) are fundamental to GDPR, yet under-explored in the literature. In this article, we highlight future directions to be pursued by the SE community for supporting the development of GDPR-compliant mobile apps.
Tracking Progress Towards Sustainable Development Goal 6 Using Satellite Imagery
2024-11-28
Othmane Echchabi, Nizar Talty, Josh Manto, Aya Lahlou, Ka Leung Lam
Clean water and sanitation are essential for health, well-being, and sustainable development, yet significant global disparities remain. Although the United Nations' Sustainable Development Goal 6 has clear targets for universal access to clean water and sanitation, data coverage and openness remain obstacles for tracking progress in many countries. Nontraditional data sources are needed to fill this gap. This study incorporated Afrobarometer survey data, satellite imagery (Landsat 8 and Sentinel-2), and deep learning techniques (Meta's DINO model) to develop a modelling framework for evaluating access to piped water and sewage systems across diverse African regions. The modelling framework demonstrated high accuracy, achieving over 96% and 97% accuracy in identifying areas with piped water access and sewage system access respectively using satellite imagery. It can serve as a screening tool for policymakers and stakeholders to potentially identify regions for more targeted and prioritized efforts to improve water and sanitation infrastructure. When coupled with spatial population data, the modelling framework can also estimate and track the national-level percentages of the population with access to piped water and sewage systems. In the future, this approach could potentially be extended to evaluate other SDGs, particularly those related to critical infrastructure.
Can we detect super-Chandrasekhar white dwarfs via continuous gravitational waves?
2024-11-28
Mayusree Das, Banibrata Mukhopadhyay
The existence of massive white dwarfs (WDs) containing more than Chandrasekhar's maximum mass has been suggested via the detection of peculiar type Ia Supernovae. It had been crucial to directly detect those 'super' (more massive)-Chandrasekhar WDs to confirm their existence. The WD's small size and cold internal environment have been a great disadvantage for detecting them via electromagnetic surveys like GAIA and SDSS. Although many WDs are detected in optical observations, none of the super-Chandrasekhar WDs was observed directly. Mukhopadhyay and his group proposed a decade ago that one of the possibilities to explain the high mass of super-Chandrasekhar WD is to exercise the existence of a strong magnetic field in the WD interior. The group also recently proposed that a high magnetic field, in turn, can nontrivially deform the star from a spherical shape. In the presence of rotation and obliquity angle, such WDs can radiate continuous gravitational waves (CGW). This opens up the exciting possibility of detecting the super-Chandrasekhar WDs with the upcoming detector LISA. However, the gravitational wave (GW) amplitude will decay with time due to electromagnetic radiation and quadrupolar radiation. Altogether, those decay mechanisms will set a timescale for detecting WDs via CGW. Even with a timescale bound, we can expect to detect a few super-Chandrasekhar WDs with a couple of years of cumulative detection, leading to the direct detection of super-Chandrasekhar WDs.
2024-11-28
Francisco R. V. Guimarães, José Mairton B. da Silva Jr., Charles Casimiro Cavalcante, Gabor Fodor, Mats Bengtsson, Carlo Fischione
The 5th generation (5G) of wireless systems is being deployed with the aim to provide many sets of wireless communication services, such as low data rates for a massive amount of devices, broadband, low latency, and industrial wireless access. Such an aim is even more complex in the next generation wireless systems (6G) where wireless connectivity is expected to serve any connected intelligent unit, such as software robots and humans interacting in the metaverse, autonomous vehicles, drones, trains, or smart sensors monitoring cities, buildings, and the environment. Because of the wireless devices will be orders of magnitude denser than in 5G cellular systems, and because of their complex quality of service requirements, the access to the wireless spectrum will have to be appropriately shared to avoid congestion, poor quality of service, or unsatisfactory communication delays. Spectrum sharing methods have been the objective of intense study through model-based approaches, such as optimization or game theories. However, these methods may fail when facing the complexity of the communication environments in 5G, 6G, and beyond. Recently, there has been significant interest in the application and development of data-driven methods, namely machine learning methods, to handle the complex operation of spectrum sharing. In this survey, we provide a complete overview of the state-of-theart of machine learning for spectrum sharing. First, we map the most prominent methods that we encounter in spectrum sharing. Then, we show how these machine learning methods are applied to the numerous dimensions and sub-problems of spectrum sharing, such as spectrum sensing, spectrum allocation, spectrum access, and spectrum handoff. We also highlight several open questions and future trends.
Prediction of high-Tc superconductivity in ternary actinium beryllium hydrides at low pressure
2024-11-28
Kun Gao, Wenwen Cui, Jingming Shi, Artur P. Durajski, Jian Hao, Silvana Botti, Miguel A. L. Marques, Yinwei Li
Hydrogen-rich superconductors are promising candidates to achieve room-temperature superconductivity. However, the extreme pressures needed to stabilize these structures significantly limit their practical applications. An effective strategy to reduce the external pressure is to add a light element M that binds with H to form MHx units, acting as a chemical precompressor. We exemplify this idea by performing ab initio calculations of the Ac-Be-H phase diagram, proving that the metallization pressure of Ac-H binaries, for which critical temperatures as high as 200 K were predicted at 200 GPa, can be significantly reduced via beryllium incorporation. We identify three thermodynamically stable (AcBe2H10, AcBeH8, and AcBe2H14) and four metastable compounds (fcc AcBeH8, AcBeH10, AcBeH12 and AcBe2H16). All of them are superconductors. In particular, fcc AcBeH8 remains dynamically stable down to 10 GPa, where it exhibits a superconducting transition temperature Tc of 181 K. The Be-H bonds are responsible for the exceptional properties of these ternary compounds and allow them to remain dynamically stable close to ambient pressure. Our results suggest that high-Tc superconductivity in hydrides is achievable at low pressure and may stimulate experimental synthesis of ternary hydrides.
The expanding influence of social media platforms over the past decade has impacted the way people communicate. The level of obscurity provided by social media and easy accessibility of the internet has facilitated the spread of hate speech. The terms and expressions related to hate speech gets updated with changing times which poses an obstacle to policy-makers and researchers in case of hate speech identification. With growing number of individuals using their native languages to communicate with each other, hate speech in these low-resource languages are also growing. Although, there is awareness about the English-related approaches, much attention have not been provided to these low-resource languages due to lack of datasets and online available data. This article provides a detailed survey of hate speech detection in low-resource languages around the world with details of available datasets, features utilized and techniques used. This survey further discusses the prevailing surveys, overlapping concepts related to hate speech, research challenges and opportunities.
Prediction of high-Tc superconductivity under submegabar pressure in ternary actinium borohydrides
2024-11-28
Tingting Gu, Wenwen Cui, Jian Hao, Jingming Shi, Artur P. Durajski, Hanyu Liu, Yinwei Li
Ternary hydrides are considered as the ideal candidates with high critical temperature (Tc) stabilized at submegabar pressure, evidenced by the recent discoveries in LaBeH8 (110 K at 80 GPa) and LaB2H8 (106 K at 90 GPa). Here, we investigate the crystal structures and superconductivity of an Ac-B-H system under pressures of 100 and 200 GPa by using an advanced structure method combined with first-principles calculations. As a result, nine stable compounds were identified, where B atoms are bonded with H atoms in the formation with diverse BHx motifs, e.g., methanelike (BH4), polythenelike, (BH2)n,andBH6 octahedron. Among them, seven Ac-B-H compounds were found to become superconductive. In particular, AcBH7 was estimated to have a Tc of 122 K at 70 GPa. Our in-depth analysis reveals that the B-H interactions in the BH6 units play a key role in its high superconductivity and stability at submegabar pressure. Our current results provide a guidance for future experiments to synthesize ternary hydride superconductors with high-Tc at moderate pressure.
Based on the relativistic mean field (RMF) model with Thomas-Fermi approximation, we investigate the elastic properties of neutron star matter. The elastic constants are estimated by introducing deformations on the nuclear pasta structures in $\beta$-equilibrium, where various crystalline configurations are considered in a fully three-dimensional geometry without the Wigner-Seitz approximation. Two scenarios with different symmetry energy slope ($L = 41.34$ and 89.39 MeV) are examined, where the the elastic constants can vary by ten times. By fitting to the numerical results, we improve the analytic formulae for the elastic properties of nuclear pasta by introducing damping factors.
High-$z$ radio galaxies (HzRGs) are considered important objects for understanding the formation and evolution of massive galaxies in the early universe. However, till date, detailed studies of the stellar population of HzRGs such as the star-formation history have been scarce. Therefore, this study conducted a new survey to establish a less-biased sample of HzRGs and consequently investigate their properties. We utilized a sample of $g$-dropout Lyman break galaxies (LBGs) obtained from an optical wide and deep imaging survey made by Subaru Hyper Suprime-Cam (HSC). Based on the cross-matching of this LBG sample with the VLA FIRST radio survey data, we constructed a photometric sample of high-redshift radio galaxies (HzRGs) at $z \sim 4$ for $\sim$560 deg$^2$ survey field. Consequently, we identified 146 HzRG candidates. To analyze the characteristics of these candidates, we focus on objects exhibiting the near-infrared photometry of VIKING or UKIDSS and the mid-infrared photometry of unWISE (28 objects). The results indicate that 7 objects exhibit SEDs consistent with galaxies at $z \sim 4$. The HzRG candidates have very large stellar masses with $\sim 4.2 \times 10^{11} M_{\odot}$ on average. This stellar mass is similar to that of previously discovered USS HzRGs at $z \sim 4$, though our sample is affected by a sample selection bias that selects only HzRGs with $M_{\star} > 10^{11} M_{\odot}$. Further, the SEDs of those HzRG candidates suggest a past fast quenching with a rough timescale of $\sim$0.1 Gyr, as evidenced from the rest-frame UVJ diagram.
EzSQL: An SQL intermediate representation for improving SQL-to-text Generation
2024-11-28
Meher Bhardwaj, Hrishikesh Ethari, Dennis Singh Moirangthem
The SQL-to-text generation task traditionally uses template base, Seq2Seq, tree-to-sequence, and graph-to-sequence models. Recent models take advantage of pre-trained generative language models for this task in the Seq2Seq framework. However, treating SQL as a sequence of inputs to the pre-trained models is not optimal. In this work, we put forward a new SQL intermediate representation called EzSQL to align SQL with the natural language text sequence. EzSQL simplifies the SQL queries and brings them closer to natural language text by modifying operators and keywords, which can usually be described in natural language. EzSQL also removes the need for set operators. Our proposed SQL-to-text generation model uses EzSQL as the input to a pre-trained generative language model for generating the text descriptions. We demonstrate that our model is an effective state-of-the-art method to generate text narrations from SQL queries on the WikiSQL and Spider datasets. We also show that by generating pretraining data using our SQL-to-text generation model, we can enhance the performance of Text-to-SQL parsers.
In the era of big data, managing evolving graph data poses substantial challenges due to storage costs and privacy issues. Training graph neural networks (GNNs) on such evolving data usually causes catastrophic forgetting, impairing performance on earlier tasks. Despite existing continual graph learning (CGL) methods mitigating this to some extent, they predominantly operate in centralized architectures and overlook the potential of distributed graph databases to harness collective intelligence for enhanced performance optimization. To address these challenges, we present a pioneering study on Federated Continual Graph Learning (FCGL), which adapts GNNs to multiple evolving graphs within decentralized settings while adhering to storage and privacy constraints. Our work begins with a comprehensive empirical analysis of FCGL, assessing its data characteristics, feasibility, and effectiveness, and reveals two principal challenges: local graph forgetting (LGF), where local GNNs forget prior knowledge when adapting to new tasks, and global expertise conflict (GEC), where the global GNN exhibits sub-optimal performance in both adapting to new tasks and retaining old ones, arising from inconsistent client expertise during server-side parameter aggregation. To tackle these, we propose the POWER framework, which mitigates LGF by preserving and replaying experience nodes with maximum local-global coverage at each client and addresses GEC by using a pseudo prototype reconstruction strategy and trajectory-aware knowledge transfer at the central server. Extensive evaluations across multiple graph datasets demonstrate POWER's superior performance over straightforward federated extensions of the centralized CGL algorithms and vision-focused federated continual learning algorithms. Our code is available at https://github.com/zyl24/FCGL_POWER.
MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network
2024-11-28
Yu-Tung Liu, Kuan-Chen Wang, Rong Chao, Sabato Marco Siniscalchi, Ping-Cheng Yeh, Yu Tsao
Surface electromyography (sEMG) recordings can be contaminated by electrocardiogram (ECG) signals when the monitored muscle is closed to the heart. Traditional signal-processing-based approaches, such as high-pass filtering and template subtraction, have been used to remove ECG interference but are often limited in their effectiveness. Recently, neural-network-based methods have shown greater promise for sEMG denoising, but they still struggle to balance both efficiency and effectiveness. In this study, we introduce MSEMG, a novel system that integrates the Mamba State Space Model with a convolutional neural network to serve as a lightweight sEMG denoising model. We evaluated MSEMG using sEMG data from the Non-Invasive Adaptive Prosthetics database and ECG signals from the MIT-BIH Normal Sinus Rhythm Database. The results show that MSEMG outperforms existing methods, generating higher-quality sEMG signals with fewer parameters. The source code for MSEMG is available at https://github.com/tonyliu0910/MSEMG.
Deformed algebraic structure of angular momenta: GUP perspective
2024-11-28
Gaurav Bhandari, S. D. Pathak, Manabendra Sharma, Anzhong Wang
The prediction of a minimal length scale by various quantum gravity candidates (such as string/M theory, Doubly Special Relativity, Loop Quantum Gravity and others) have suggested modification of Heisenberg Uncertainty Principle (HUP), resulting in the Generalized Uncertainty Principle (GUP). In this short review, we investigate the origins of the GUP and examine higher-order models, focusing on the linear plus quadratic form of the GUP. We extend the concept of minimal length to minimal angular resolution, which plays a crucial role in modifying angular momentum and its associated algebra. A comparison is made between the standard angular momentum commutator algebra and that modified by the GUP. Finally, we review its application in the hydrogen atom spectra and and discuss future endeavors.
Textured As-Is BIM via GIS-informed Point Cloud Segmentation
2024-11-28
Mohamed S. H. Alabassy
Creating as-is models from scratch is to this day still a time- and money-consuming task due to its high manual effort. Therefore, projects, especially those with a big spatial extent, could profit from automating the process of creating semantically rich 3D geometries from surveying data such as Point Cloud Data (PCD). An automation can be achieved by using Machine and Deep Learning Models for object recognition and semantic segmentation of PCD. As PCDs do not usually include more than the mere position and RGB colour values of points, tapping into semantically enriched Geoinformation System (GIS) data can be used to enhance the process of creating meaningful as-is models. This paper presents a methodology, an implementation framework and a proof of concept for the automated generation of GIS-informed and BIM-ready as-is Building Information Models (BIM) for railway projects. The results show a high potential for cost savings and reveal the unemployed resources of freely accessible GIS data within.
CovHuSeg: An Enhanced Approach for Kidney Pathology Segmentation
2024-11-28
Huy Trinh, Khang Tran, Nam Nguyen, Tri Cao, Binh Nguyen
Segmentation has long been essential in computer vision due to its numerous real-world applications. However, most traditional deep learning and machine learning models need help to capture geometric features such as size and convexity of the segmentation targets, resulting in suboptimal outcomes. To resolve this problem, we propose using a CovHuSeg algorithm to solve the problem of kidney glomeruli segmentation. This simple post-processing method is specified to adapt to the segmentation of ball-shaped anomalies, including the glomerulus. Unlike other post-processing methods, the CovHuSeg algorithm assures that the outcome mask does not have holes in it or comes in unusual shapes that are impossible to be the shape of a glomerulus. We illustrate the effectiveness of our method by experimenting with multiple deep-learning models in the context of segmentation on kidney pathology images. The results show that all models have increased accuracy when using the CovHuSeg algorithm.
Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges
2024-11-28
Majid Ghasemi, Amir Hossein Mousavi, Dariush Ebrahimi
Reinforcement Learning (RL) has emerged as a powerful paradigm in Artificial Intelligence (AI), enabling agents to learn optimal behaviors through interactions with their environments. Drawing from the foundations of trial and error, RL equips agents to make informed decisions through feedback in the form of rewards or penalties. This paper presents a comprehensive survey of RL, meticulously analyzing a wide range of algorithms, from foundational tabular methods to advanced Deep Reinforcement Learning (DRL) techniques. We categorize and evaluate these algorithms based on key criteria such as scalability, sample efficiency, and suitability. We compare the methods in the form of their strengths and weaknesses in diverse settings. Additionally, we offer practical insights into the selection and implementation of RL algorithms, addressing common challenges like convergence, stability, and the exploration-exploitation dilemma. This paper serves as a comprehensive reference for researchers and practitioners aiming to harness the full potential of RL in solving complex, real-world problems.
New pulsating stars detected in EA-type eclipsing binary systems based on TESS data
2024-11-28
Xiang-Dong Shi, Sheng-Bang Qian, Lin-Jia Li
Pulsating stars in eclipsing binaries are very important to understand stellar interior structures through astroseismology because their absolute parameters such as the masses and radii can be determined in high precision based on photometric and spectroscopic data. The high-precision and continuously time-series photometric data of the Transiting Exoplanet Survey Satellite (TESS) provides an unprecedented opportunity to search for and study this kind of variable stars in the whole sky. About 1626 Algol type (EA-type) eclipsing binary systems were observed by TESS in the 1-45 sectors with 2-minutes short cadence. By analyzing those TESS data, we found 57 new pulsating stars in EA-type binary stars. The preliminary results show that those binary systems have orbital periods in the range from 0.4 to 27 days, while the periods of pulsating components are in the range from 0.02 to 5 days. It is detected that 43 targets follow the correlation between pulsation and orbital periods of oscillating eclipsing binaries of Algol type (oEA), which may indicate that they are typical oEA stars. The other 14 targets may be other types of variable stars in eclipsing binary systems. These objects are a very interesting source to investigate the binary structures and evolutions as well as to understand the influences of tidal forces and mass transfer on stellar pulsations.
Measuring Risk of Bias in Biomedical Reports: The RoBBR Benchmark
2024-11-28
Jianyou Wang, Weili Cao, Longtian Bao, Youze Zheng, Gil Pasternak, Kaicheng Wang, Xiaoyue Wang, Ramamohan Paturi, Leon Bergen
Systems that answer questions by reviewing the scientific literature are becoming increasingly feasible. To draw reliable conclusions, these systems should take into account the quality of available evidence, placing more weight on studies that use a valid methodology. We present a benchmark for measuring the methodological strength of biomedical papers, drawing on the risk-of-bias framework used for systematic reviews. The four benchmark tasks, drawn from more than 500 papers, cover the analysis of research study methodology, followed by evaluation of risk of bias in these studies. The benchmark contains 2000 expert-generated bias annotations, and a human-validated pipeline for fine-grained alignment with research paper content. We evaluate a range of large language models on the benchmark, and find that these models fall significantly short of expert-level performance. By providing a standardized tool for measuring judgments of study quality, the benchmark can help to guide systems that perform large-scale aggregation of scientific data. The dataset is available at https://github.com/RoBBR-Benchmark/RoBBR.
The task of Raga classification in Indian Art Music (IAM) is constrained by the limited availability of labeled datasets, resulting in many Ragas being unrepresented during the training of machine learning models. Traditional Raga classification methods rely on supervised learning, and assume that for a test audio to be classified by a Raga classification model, it must have been represented in the training data, which limits their effectiveness in real-world scenarios where novel, unseen Ragas may appear. To address this limitation, we propose a method based on Novel Class Discovery (NCD) to detect and classify previously unseen Ragas. Our approach utilizes a feature extractor trained in a supervised manner to generate embeddings, which are then employed within a contrastive learning framework for self-supervised training, enabling the identification of previously unseen Raga classes. The results demonstrate that the proposed method can accurately detect audio samples corresponding to these novel Ragas, offering a robust solution for utilizing the vast amount of unlabeled music data available online. This approach reduces the need for manual labeling while expanding the repertoire of recognized Ragas, and other music data in Music Information Retrieval (MIR).
Variability of hot sub-luminous stars and binaries: Machine learning analysis of Gaia DR3 multi-epoch photometry
2024-11-27
P. Ranaivomanana, M. Uzundag, C. Johnston, P. J. Groot, T. Kupfer, C. Aerts
Hot sub-luminous stars represent a population of stripped and evolved red giants located at the Extreme Horizontal Branch (EHB). Since they exhibit a wide range of variability due to pulsations or binary interactions, unveiling their intrinsic and extrinsic variability is crucial for understanding the physical processes responsible for their formation. In the Hertzsprung-Russell diagram, they overlap with interacting binaries such as Cataclysmic Variables (CVs). By leveraging cutting-edge clustering algorithm tools, we investigate the variability of 1,576 hot subdwarf variable candidates using comprehensive data from Gaia DR3 multi-epoch photometry and Transiting Exoplanet Survey Satellite (TESS) observations. We present a novel approach that utilises the t-distributed stochastic neighbor embedding (t-SNE) and the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction algorithms to facilitate the identification and classification of different populations of variable hot subdwarfs and Cataclysmic Variables in a large dataset. In addition to the Gaia time-series statistics table, we adopt extra statistical features that enhance the performance of the algorithms. The clustering results lead to the identification of 85 new hot subdwarf variables based on Gaia and TESS lightcurves and 108 new variables based on Gaia lightcurves alone, including reflection-effect systems, HW Vir, ellipsoidal variables, and high-amplitude pulsating variables. A significant number of known Cataclysmic Variables (140) distinctively cluster in the 2-D feature space among an additional 152 objects that we consider new Cataclysmic Variable candidates. This study paves the way for more efficient and comprehensive analyses of stellar variability from both ground and space-based observations, as well as the application of machine learning classifications of variable candidates in large surveys.
This research presents and compares multiple approaches to automate the generation of literature reviews using several Natural Language Processing (NLP) techniques and retrieval-augmented generation (RAG) with a Large Language Model (LLM). The ever-increasing number of research articles provides a huge challenge for manual literature review. It has resulted in an increased demand for automation. Developing a system capable of automatically generating the literature reviews from only the PDF files as input is the primary objective of this research work. The effectiveness of several Natural Language Processing (NLP) strategies, such as the frequency-based method (spaCy), the transformer model (Simple T5), and retrieval-augmented generation (RAG) with Large Language Model (GPT-3.5-turbo), is evaluated to meet the primary objective. The SciTLDR dataset is chosen for this research experiment and three distinct techniques are utilized to implement three different systems for auto-generating the literature reviews. The ROUGE scores are used for the evaluation of all three systems. Based on the evaluation, the Large Language Model GPT-3.5-turbo achieved the highest ROUGE-1 score, 0.364. The transformer model comes in second place and spaCy is at the last position. Finally, a graphical user interface is created for the best system based on the large language model.
Surveying the space of descriptions of a composite system with machine learning
2024-11-27
Kieran A. Murphy, Yujing Zhang, Dani S. Bassett
Multivariate information theory provides a general and principled framework for understanding how the components of a complex system are connected. Existing analyses are coarse in nature -- built up from characterizations of discrete subsystems -- and can be computationally prohibitive. In this work, we propose to study the continuous space of possible descriptions of a composite system as a window into its organizational structure. A description consists of specific information conveyed about each of the components, and the space of possible descriptions is equivalent to the space of lossy compression schemes of the components. We introduce a machine learning framework to optimize descriptions that extremize key information theoretic quantities used to characterize organization, such as total correlation and O-information. Through case studies on spin systems, Sudoku boards, and letter sequences from natural language, we identify extremal descriptions that reveal how system-wide variation emerges from individual components. By integrating machine learning into a fine-grained information theoretic analysis of composite random variables, our framework opens a new avenues for probing the structure of real-world complex systems.
2024-11-27
Simon Yves, Romain Fleury, Gal Shmuel, Vincenzo Vitelli, Michael R. Haberman, Andrea Alù
Phonons are quasi-particles associated with mechanical vibrations in materials, at the root of the propagation of sound, elastic / mechanical waves, and of thermal phenomena, common to our every day life and many technologies. The fundamental understanding and control over phonon responses in natural and artificial media is of major importance in the context of telecommunications, shielding, energy harvesting and control, sensing and imaging, across multiple scales. In this context, it has been recently realized that controlling different classes of symmetries at the microscopic and mesoscopic scale offers a powerful rational tool to precisely tailor phononic responses, leading to advanced acoustic and elastodynamic wave control. In this paper, we review the recent advances in the design and synthesis of artificial phononic media, namely phononic metamaterials, guided by symmetry principles. Starting from tailored broken spatial symmetries, we discuss their interplay with time symmetries for non-reciprocity and non-conservative phenomena, and finally address broader concepts that combine multiple symmetry classes to support exotic phononic wave transport.
The definition of the homotopy limit of a diagram of left Quillen functors of model categories has been useful in a number of applications. In this paper we review its definition and summarize some of these applications. We conclude with a discussion of why we could work with right Quillen functors instead, but cannot work with a combination of the two.
Personalised Serious Games and Gamification in Healthcare: Survey and Future Research Directions
2024-11-27
Stéphanie Carlier, Femke De Backere, Filip De Turck
Serious games and gamification (SGG) in eHealth have positive health impacts, but a personalized approach is needed due to diverse user contexts. This introduces challenges in achieving personalization in SGG. A literature search on Web of Science and PubMed identified 31 articles: 22 on serious games and 9 on gamification. These strategies are most applied in behavior change and rehabilitation, with machine learning and AI showing promise for personalization. Reusability of personalisation algorithms and domain knowledge is underemphasized, reported in only 10 articles. Future research should standardize personalized SGG development, focusing on component reuse to streamline design and enhance evaluation.
Robust dark-field signal extraction for modulation-based X-ray tensor tomography
2024-11-27
Ginevra Lautizi, Vittorio Di Trapani, Alain Studer, Marie-Christine Zdora, Fabio De Marco, Jisoo Kim, Federica Marone, Marco Stampanoni, Pierre Thibault
We demonstrate a robust signal extraction method for X-ray speckle-based tensor tomography. We validate the effectiveness of the method for several carbon fiber composites, highlighting its potential for industrial applications. The proposed method can be adapted to various acquisition schemes and wavefront-marking optical elements, making it a versatile and robust tool for X-ray scattering tensor tomography.
2024-11-27
Xuandong Zhao, Sam Gunn, Miranda Christ, Jaiden Fairoze, Andres Fabrega, Nicholas Carlini, Sanjam Garg, Sanghyun Hong, Milad Nasr, Florian Tramer, Somesh Jha, Lei Li, Yu-Xiang Wang, Dawn Song
As the outputs of generative AI (GenAI) techniques improve in quality, it becomes increasingly challenging to distinguish them from human-created content. Watermarking schemes are a promising approach to address the problem of distinguishing between AI and human-generated content. These schemes embed hidden signals within AI-generated content to enable reliable detection. While watermarking is not a silver bullet for addressing all risks associated with GenAI, it can play a crucial role in enhancing AI safety and trustworthiness by combating misinformation and deception. This paper presents a comprehensive overview of watermarking techniques for GenAI, beginning with the need for watermarking from historical and regulatory perspectives. We formalize the definitions and desired properties of watermarking schemes and examine the key objectives and threat models for existing approaches. Practical evaluation strategies are also explored, providing insights into the development of robust watermarking techniques capable of resisting various attacks. Additionally, we review recent representative works, highlight open challenges, and discuss potential directions for this emerging field. By offering a thorough understanding of watermarking in GenAI, this work aims to guide researchers in advancing watermarking methods and applications, and support policymakers in addressing the broader implications of GenAI.
Strongly Coupled PT-Symmetric Models in Holography
2024-11-27
Daniel Arean, David Garcia-Fariña, Karl Landsteiner
Non-Hermitian quantum field theories are a promising tool to study open quantum systems. These theories preserve unitarity if PT-symmetry is respected, and in that case an equivalent Hermitian description exists via the so-called Dyson map. Generically, PT-symmetric non-Hermitian theories can also feature phases where PT-symmetry is broken and unitarity is lost. We review the construction of holographic duals to strongly coupled PT-symmetric quantum field theories and the study of their phase diagram. We next focus on spacetime-dependent non-Hermitian couplings: non-Hermitian quenches and lattices. They violate the null energy condition in the gravity dual. The lattices realize phases supporting an imaginary current that breaks PT-symmetry spontaneously. Remarkably, these non-Hermitian lattices flow to a PT-symmetric fixed point in the IR.
Stars are mostly found in binary and multiple systems, as at least 50% of all solar-like stars have companions - a fraction that goes up to 100% for the most massive stars. Moreover, a large fraction of them will interact in some way or another over the course of their lives. Such interactions can, and often will, alter the structure and evolution of both components in the system. This will, in turn, lead to the production of exotic objects whose existence cannot be explained by standard single star evolution models, including gravitational wave progenitors, blue stragglers, symbiotic and barium stars, novae, and supernovae. More generally, binary stars prove crucial in many aspects, ranging from cultural ones, to constraining models of stellar evolution, star formation, and even, possibly, of gravity itself. They also provide a quasi-model independent way to determine stellar masses, radii, and luminosities. We here provide a brief summary of the importance of binary stars.
This series of three lectures was presented at ``Escola de Cosmologia e Gravita\c{c}\~ao" \url{https://cosmosecontexto.org.br/ecg-inscricoes} and webcast at \url{https://shorturl.at/2ZSI7} -- in Portuguese, but slides in English. We will go through a brief review on $f(R)$ theories (in the metric approach) and the usual requirements for successful modifications of General Relativity. Then we will open a large parenthesis to talk about a non-standard approach to Phase Transitions: the Catastrophe Theory. Finally, we will connect the previous lectures to introduce a new Thermodynamic interpretation of $f(R)$ theories.
Advancements in Myocardial Infarction Detection and Classification Using Wearable Devices: A Comprehensive Review
2024-11-27
Abhijith S, Arjun Rajesh, Mansi Manoj, Sandra Davis Kollannur, Sujitta R V, Jerrin Thomas Panachakel
Myocardial infarction (MI), commonly known as a heart attack, is a critical health condition caused by restricted blood flow to the heart. Early-stage detection through continuous ECG monitoring is essential to minimize irreversible damage. This review explores advancements in MI classification methodologies for wearable devices, emphasizing their potential in real-time monitoring and early diagnosis. It critically examines traditional approaches, such as morphological filtering and wavelet decomposition, alongside cutting-edge techniques, including Convolutional Neural Networks (CNNs) and VLSI-based methods. By synthesizing findings on machine learning, deep learning, and hardware innovations, this paper highlights their strengths, limitations, and future prospects. The integration of these techniques into wearable devices offers promising avenues for efficient, accurate, and energy-aware MI detection, paving the way for next-generation wearable healthcare solutions.
Mapping the Cosmic Gamma-ray Horizon: The 1CGH Catalogue of Fermi-LAT detections above 10 GeV
2024-11-27
Bruno Arsioli, Yu-Ling Chang, Luca Ighina
We present the First Cosmic Gamma-ray Horizon (1CGH) catalogue, featuring $\gamma$-ray detections above 10 GeV based on 16 years of observations with the Fermi-LAT satellite. After carefully selecting a sample of blazars and blazar candidates from catalogues in the literature, we performed a binned likelihood analysis and identified about 2900 $\gamma$-ray emitters above 10 GeV, including 69 reported here for the first time. For each source, we estimated the mean energy of the highest-energy bin and analysed them in the context of the cosmic gamma-ray horizon. By adopting a reference model for the Extragalactic Background Light (EBL), we identified a subsample of about 500 sources where moderate to severe $\gamma$-ray absorption could be detected across the redshift range of 0 to 3.0. This work provides the most up-to-date compilation of detections above 10 GeV, along with their redshift information. We condense extensive results from the literature, including reports on observational campaigns dedicated to blazars and $\gamma$-ray sources, thereby delivering an unprecedented review of the redshift information for sources detected above 10 GeV. Additionally, we highlight key 1CGH sources where redshift information remains incomplete, offering guidance for future optical observation campaigns. The 1CGH catalogue aims to track the most significant sources for understanding the $\gamma$-ray transparency of the universe. Furthermore, it provides a targeted subsample where the EBL optical depth, $\tau_{(E,z)}$, can be effectively measured using Fermi-LAT data.
We deal with the category of finitely generated modules over an artin algebra $A$. Recall that an object in an abelian category is said to be a brick provided its endomorphism ring is a division ring. Simple modules are, of course, bricks, but in case $A$ is connected and not local, there do exist bricks which are not simple. The aim of this survey is to focus the attention to filtrations of modules where all factors are bricks, with bricks being ordered in some definite way. In general, a module category will have many oriented cycles. Recently, Demonet has proposed to look at so-called brick chains in order to deal with a very interesting directedness feature of a module category. These are the orderings of bricks which we will use. This is a survey which relies on recent investigations by a quite large group of mathematicians. We have singled out some important observations and have reordered them in order to obtain a completely self-contained (and elementary) treatment of the relevance of bricks in a module category. (Most of the papers we rely on are devoted to what is called $\tau$-tilting theory, but for the results we are interested in, there is no need to deal with $\tau$-tilting, or even with the Auslander-Reiten translation $\tau$).
Collectivity in ultra-peripheral heavy-ion and e+A collisions
2024-11-27
Björn Schenke, Chun Shen, Wenbin Zhao
We review recent theoretical progress in describing collective effects in photon+nucleus collisions. The approaches considered range from the color glass condensate where correlations are encoded in the initial state, to hydrodynamic frameworks, where a strong final state response to the initial geometry of the collision is the key ingredient to generate momentum-space correlations.
Exploring the Impact of Rewards on Developers' Proactive AI Accountability Behavior
2024-11-27
L. H. Nguyen, S. Lins, G. Du, A. Sunyaev
The rapid integration of Artificial Intelligence (AI)-based systems offers benefits for various domains of the economy and society but simultaneously raises concerns due to emerging scandals. These scandals have led to the increasing importance of AI accountability to ensure that actors provide justification and victims receive compensation. However, AI accountability has a negative connotation due to its emphasis on penalizing sanctions, resulting in reactive approaches to emerging concerns. To counteract the prevalent negative view and offer a proactive approach to facilitate the AI accountability behavior of developers, we explore rewards as an alternative mechanism to sanctions. We develop a theoretical model grounded in Self-Determination Theory to uncover the potential impact of rewards and sanctions on AI developers. We further identify typical sanctions and bug bounties as potential reward mechanisms by surveying related research from various domains, including cybersecurity.
Topic Modeling and Sentiment Analysis on Japanese Online Media's Coverage of Nuclear Energy
2024-11-27
Yifan Sun, Hirofumi Tsuruta, Masaya Kumagai, Ken Kurosaki
Thirteen years after the Fukushima Daiichi nuclear power plant accident, Japan's nuclear energy accounts for only approximately 6% of electricity production, as most nuclear plants remain shut down. To revitalize the nuclear industry and achieve sustainable development goals, effective communication with Japanese citizens, grounded in an accurate understanding of public sentiment, is of paramount importance. While nationwide surveys have traditionally been used to gauge public views, the rise of social media in recent years has provided a promising new avenue for understanding public sentiment. To explore domestic sentiment on nuclear energy-related issues expressed online, we analyzed the content and comments of over 3,000 YouTube videos covering topics related to nuclear energy. Topic modeling was used to extract the main topics from the videos, and sentiment analysis with large language models classified user sentiments towards each topic. Additionally, word co-occurrence network analysis was performed to examine the shift in online discussions during August and September 2023 regarding the release of treated water. Overall, our results provide valuable insights into the online discourse on nuclear energy and contribute to a more comprehensive understanding of public sentiment in Japan.
SoK: Privacy Personalised -- Mapping Personal Attributes \& Preferences of Privacy Mechanisms for Shoulder Surfing
2024-11-27
Habiba Farzand, Karola Marky, Mohamed Khamis
Shoulder surfing is a byproduct of smartphone use that enables bystanders to access personal information (such as text and photos) by making screen observations without consent. To mitigate this, several protection mechanisms have been proposed to protect user privacy. However, the mechanisms that users prefer remain unexplored. This paper explores correlations between personal attributes and properties of shoulder surfing protection mechanisms. For this, we first conducted a structured literature review and identified ten protection mechanism categories against content-based shoulder surfing. We then surveyed N=192 users and explored correlations between personal attributes and properties of shoulder surfing protection mechanisms. Our results show that users agreed that the presented mechanisms assisted in protecting their privacy, but they preferred non-digital alternatives. Among the mechanisms, participants mainly preferred an icon overlay mechanism followed by a tangible mechanism. We also found that users who prioritized out-of-device privacy and a high tendency to interact with technology favoured the personalisation of protection mechanisms. On the contrary, age and smartphone OS did not impact users' preference for perceived usefulness and personalisation of mechanisms. Based on the results, we present key takeaways to support the design of future protection mechanisms.
Accurate electron correlation-energy functional: Expansion in an interaction renormalized by the random-phase approximation
2024-11-27
Mario Benites, Angel Rosado, Efstratios Manousakis
We present an accurate local density-functional for electronic-structure calculations within the density functional theory (DFT). The functional is derived by analyzing the structure of the standard perturbative expansion of the correlation energy of the interacting uniform electron gas. Then, the expansion is partially re-summed and reorganized as a self-consistent series in powers of a renormalized electron-electron interaction vertex based on the screened frequency-momentum dependent dielectric matrix given by the well-known random-phase approximation. First, we demonstrate that the range of $r_s$, where this reorganized and renormalized series converges, contains and is significantly larger than the average range realized in real crystalline materials. Using a combination of analytical, numerical, and stochastic integration techniques we are able to calculate all the diagrams which have contribution up to the same leading order. We benchmarked the functional using the Quantum ESPRESSO implementation of the DFT applied to the same list of materials, selected previously by other authors, in its entirety without any modification of the list. We find that for ground-state properties in general, such as, equilibrium atomic distances and bulk moduli, the functional presented here is more accurate than the currently available most popular one.
We review the Gasperini-Veneziano scale factor duality symmetry for the dilaton field in scalar-tensor theory and its extension in teleparallelism. Within the framework of symmetric teleparallel scalar-tensor theory, we consider a spatially flat Friedmann--Lema\^{\i}tre--Robertson--Walker metric cosmology. For the three possible connections, we write the corresponding point-like Lagrangians for the gravitational field equations, and we construct discrete transformations which generalize the Gasperini-Veneziano scale factor duality symmetry. The discrete transformations depend on the parameter which defines the coupling between the scalar field and the nonmetricity scalar. The Gasperini-Veneziano duality symmetry is recovered for a specific limit of this free parameter. Furthermore, we derive the conservation laws for the classical field equations for these models, and we present the origin of the discrete transformations. Finally, we discuss the integrability properties of the model, and exact solutions are determined.
First characterisation of the MAGO cavity, a superconducting RF detector for kHz-MHz gravitational waves
2024-11-27
Lars Fischer, Bianca Giaccone, Ivan Gonin, Anna Grassellino, Wolfgang Hillert, Timergali Khabiboulline, Tom Krokotsch, Gudrid Moortgat-Pick, Andrea Muhs, Yuriy Orlov, Krisztian Peters, Sam Posen, Oleg Pronitchev, Marc Wenskat
Heterodyne detection using microwave cavities is a promising method for detecting high-frequency gravitational waves or ultralight axion dark matter. In this work, we report on studies conducted on a spherical 2-cell cavity developed by the MAGO collaboration for high-frequency gravitational waves detection. Although fabricated around 20 years ago, the cavity had not been used since. Due to deviations from the nominal geometry, we conducted a mechanical survey and performed room-temperature plastic tuning. Measurements and simulations of the mechanical resonances and electromagnetic properties were carried out, as these are critical for estimating the cavity's gravitational wave coupling potential. Based on these results, we plan further studies in a cryogenic environment. The cavity characterisation does not only provide valuable experience for a planned physics run but also informs the future development of improved cavity designs.
Anisotropic, multiband, and strong-coupling superconductivity of the Pb0.64Bi0.36 alloy
2024-11-27
Sylwia Gutowska, Karolina Górnicka, Paweł Wójcik, Tomasz Klimczuk, Bartlomiej Wiendlocha
This paper presents theoretical and experimental studies on the superconductivity of Pb${_{0.64}}$Bi$_{0.36}$ alloy, which is a prototype of strongly coupled superconductors and exhibits the strongest coupling under ambient pressure among the materials studied so far. The critical temperature, the specific heat in the superconducting state, and the magnetic critical fields are experimentally determined. Deviations from the single-gap s-wave BCS-like behavior are observed. The electronic structure, phonons and electron-phonon interactions are analyzed in relation to the metallic Pb, explaining why the Pb-Bi alloy exhibits such a large value of the electron-phonon coupling parameter $\lambda \simeq 2$. Superconductivity is studied using the isotropic Eliashberg formalism as well as the anisotropic density functional theory for superconductors. We find that while Pb is a two-gap superconductor with well-defined separate superconducting gaps, in the Pb-Bi alloy an overlapped three-gap-like structure is formed with a strong anisotropy. Furthermore, the chemical disorder, inherent to this alloy, leads to strong electron scattering, which is found to reduce the critical temperature.
GUIs have long been central to human-computer interaction, providing an intuitive and visually-driven way to access and interact with digital systems. The advent of LLMs, particularly multimodal models, has ushered in a new era of GUI automation. They have demonstrated exceptional capabilities in natural language understanding, code generation, and visual processing. This has paved the way for a new generation of LLM-brained GUI agents capable of interpreting complex GUI elements and autonomously executing actions based on natural language instructions. These agents represent a paradigm shift, enabling users to perform intricate, multi-step tasks through simple conversational commands. Their applications span across web navigation, mobile app interactions, and desktop automation, offering a transformative user experience that revolutionizes how individuals interact with software. This emerging field is rapidly advancing, with significant progress in both research and industry. To provide a structured understanding of this trend, this paper presents a comprehensive survey of LLM-brained GUI agents, exploring their historical evolution, core components, and advanced techniques. We address research questions such as existing GUI agent frameworks, the collection and utilization of data for training specialized GUI agents, the development of large action models tailored for GUI tasks, and the evaluation metrics and benchmarks necessary to assess their effectiveness. Additionally, we examine emerging applications powered by these agents. Through a detailed analysis, this survey identifies key research gaps and outlines a roadmap for future advancements in the field. By consolidating foundational knowledge and state-of-the-art developments, this work aims to guide both researchers and practitioners in overcoming challenges and unlocking the full potential of LLM-brained GUI agents.
In the era of 5G communication, the knowledge of channel state information (CSI) is crucial for enhancing network performance. This paper explores the utilization of language models for spatial CSI prediction within MIMO-OFDM systems. We begin by outlining the significance of accurate CSI in enabling advanced functionalities such as adaptive modulation. We review existing methodologies for CSI estimation, emphasizing the shift from traditional to data-driven approaches. Then a novel framework for spatial CSI prediction using realistic environment information is proposed, and experimental results demonstrate the effectiveness. This research paves way for innovative strategies in managing wireless networks.
"What you think is private is no longer" -- Investigating the Aftermath of Shoulder Surfing on Smartphones in Everyday Life through the Eyes of the Victims
2024-11-27
Habiba Farzand, Shaun Macdonald, Karola Marky, Mohamed Khamis
Shoulder surfing has been studied extensively, however, it remains unexplored whether and how it impacts users. Understanding this is important as it determines whether shoulder surfing poses a significant concern and, if so, how best to address it. By surveying smartphone users in the UK, we explore how shoulder surfing impacts a) the privacy perceptions of victim users and b) their interaction with smartphones. We found that the impact of being shoulder surfed is highly individual. It is perceived as unavoidable and frequently occurring, leading to increased time for task completion. Individuals are concerned for their own and other peoples privacy, seeing shoulder surfing as a gateway to more serious threats like identity or device theft. Participants expressed a willingness to alter their behaviour and use software based protective measures to prevent shoulder surfing, yet, this comes with a set of user defined criteria, such as effectiveness, affordability, reliability, and availability. We discuss future work directions for user-centred shoulder surfing mitigation.
Exotic hadrons beyond the conventional quark model have been discovered in the past two decades. Investigations of these states can lead to deep understanding of nonperturbative dynamics of the strong interaction. In this concise review, we focus on the productions of exotic hadrons in $pp$, $p\bar p$, and nuclear collisions. Experimental observations of light nuclei and hypernuclei, as prototypes of hadronic molecules, in heavy ion collisions will also be briefly discussed.
Leveraging Transfer Learning for Astronomical Image Analysis
2024-11-27
Stefano Cavuoti, Lars Doorenbos, Demetra De Cicco, Gianluca Sasanelli, Massimo Brescia, Giuseppe Longo, Maurizio Paolillo, Olena Torbaniuk, Giuseppe Angora, Crescenzo Tortora
The exponential growth of astronomical data from large-scale surveys has created both opportunities and challenges for the astrophysics community. This paper explores the possibilities offered by transfer learning techniques in addressing these challenges across various domains of astronomical research. We present a set of recent applications of transfer learning methods for astronomical tasks based on the usage of a pre-trained convolutional neural networks. The examples shortly discussed include the detection of candidate active galactic nuclei (AGN), the possibility of deriving physical parameters for galaxies directly from images, the identification of artifacts in time series images, and the detection of strong lensing candidates and outliers. We demonstrate how transfer learning enables efficient analysis of complex astronomical phenomena, particularly in scenarios where labeled data is scarce. This kind of method will be very helpful for upcoming large-scale surveys like the Rubin Legacy Survey of Space and Time (LSST). By showcasing successful implementations and discussing methodological approaches, we highlight the versatility and effectiveness of such techniques.
The ViCTORIA project: description of a multi-frequency radio survey of the Virgo galaxy cluster
2024-11-27
F. de Gasperin, H. W. Edler, A. Boselli, P. Serra, M. Fossati, V. Heesen, A. Merloni, M. Murgia, T. H. Reiprich, A. Spasic, N. Zabel
The Virgo cluster is the closest richest nearby galaxy cluster. It is in the formation process, with a number of sub-clusters undergoing merging and interactions. Although a great laboratory to study galaxy evolution and cluster formation, its large apparent size and the severe dynamic range limitations due to the presence of the bright radio source Virgo A (M 87) reduced the ability of past wide-area radio surveys to image the region with high sensitivity and fidelity. In this paper we describe the "Virgo Cluster multi-Telescope Observations in Radio of Interacting galaxies and AGN" (ViCTORIA) project. The survey and its data reduction strategy are designed to mitigate the challenges of this field and deliver: images from 42 MHz to 1.7 GHz frequencies of the Virgo cluster, about 60 times deeper than existing data, in full polarisation, and including a blind HI survey that aims at mapping seven times more galaxies than previous experiments and without selection biases. Data have been collected with the Low-Frequency Array (LOFAR) and with MeerKAT in L-band, including polarisation and enough frequency resolution to conduct local HI studies. At the distance of Virgo, current radio instruments have the resolution to probe scales of ~500 pc and the sensitivity to study dwarf galaxies, the most fragile systems given their shallow gravitational potential wells, making Virgo a unique laboratory to study galaxy evolution and AGN feedback in a rich environment. In this work, we present some preliminary results, including high resolution images of the radio emission surrounding M 87, that show that the lobes are filled with filamentary structures. The combination of the presented radio surveys with state-of-the-art optical, UV, X-ray surveys will massively increase the scientific output from the studies of the Virgo cluster, making the ViCTORIA Project's legacy value outstanding.
The Galaxy Activity, Torus, and Outflow Survey (GATOS). Black hole mass estimation using machine learning
2024-11-27
R. Poitevineau, F. Combes, S. Garcia-Burillo, D. Cornu, A. Alonso Herrero, C. Ramos Almeida, A. Audibert, E. Bellocchi, P. G. Boorman, A. J. Bunker, R. Davies, T. Díaz-Santos, I. García-Bernete, B. García-Lorenzo, O. González-Martín, E. K. S. Hicks, S. F. Hönig, L. K. Hunt, M. Imanishi, M. Pereira-Santaella, C. Ricci, D. Rigopoulou, D. J. Rosario, D. Rouan, M. Villar Martin, M. Ward
The detailed feeding and feedback mechanisms of Active Galactic Nuclei (AGN) are not yet well known. For low-luminosity and obscured AGN, as well as late-type galaxies, determining the central black hole (BH) masses is challenging. Our goal with the GATOS sample is to study circum-nuclear regions and better estimate BH masses with more precision than scaling relations offer. Using ALMA's high spatial resolution, we resolve CO(3-2) emissions within ~100 pc around the supermassive black hole (SMBH) in seven GATOS galaxies to estimate their BH masses when sufficient gas is present. We study seven bright ($L_{AGN}(14-150\mathrm{keV}) \geq 10^{42}\mathrm{erg/s}$), nearby (<28 Mpc) galaxies from the GATOS core sample. For comparison, we searched the literature for previous BH mass estimates and made additional calculations using the \mbh~ - $\sigma$ relation and the fundamental plane of BH activity. We developed a supervised machine learning method to estimate BH masses from position-velocity diagrams or first-moment maps using ALMA CO(3-2) observations. Numerical simulations with a wide range of parameters created the training, validation, and test sets. Seven galaxies provided enough gas for BH mass estimations: NGC4388, NGC5506, NGC5643, NGC6300, NGC7314, NGC7465, and NGC~7582. Our BH masses, ranging from 6.39 to 7.18 log$(M_{BH}/M_\odot)$, align with previous estimates. Additionally, our machine learning method provides robust error estimations with confidence intervals and offers greater potential than scaling relations. This work is a first step toward an automated \mbh estimation method using machine learning.
Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges
2024-11-27
Milin Zhang, Mohammad Abdi, Venkat R. Dasari, Francesco Restuccia
Semantic Edge Computing (SEC) and Semantic Communications (SemComs) have been proposed as viable approaches to achieve real-time edge-enabled intelligence in sixth-generation (6G) wireless networks. On one hand, SemCom leverages the strength of Deep Neural Networks (DNNs) to encode and communicate the semantic information only, while making it robust to channel distortions by compensating for wireless effects. Ultimately, this leads to an improvement in the communication efficiency. On the other hand, SEC has leveraged distributed DNNs to divide the computation of a DNN across different devices based on their computational and networking constraints. Although significant progress has been made in both fields, the literature lacks a systematic view to connect both fields. In this work, we fulfill the current gap by unifying the SEC and SemCom fields. We summarize the research problems in these two fields and provide a comprehensive review of the state of the art with a focus on their technical strengths and challenges.
Rediscovering the Milky Way with orbit superposition approach and APOGEE data III. Panoramic view of the bulge
2024-11-27
Sergey Khoperskov, Paola Di Matteo, Matthias Steinmetz, Bridget Ratcliffe, Glenn van de Ven, Tristan Boin, Misha Haywood, Nikolay Kacharov, Ivan Minchev, Davor Krajnovic, Marica Valentini, Roelof S. de Jong
The innermost parts of the Milky Way (MW) are very difficult to observe due to the high extinction along the line of sight, especially close to the disc mid-plane. However, this region contains the most massive complex stellar component of the MW, the bulge, primarily composed of disc stars whose structure is (re-)shaped by the evolution of the bar. In this work, we extend the application of the orbit superposition method to explore the present-day 3D structure, orbital composition, chemical abundance trends and kinematics of the MW bulge. Thanks to our approach, we are able to transfer astrometry from Gaia and stellar parameters from APOGEE DR 17 to map the inner MW without obscuration by the survey footprint and selection function. We demonstrate that the MW bulge is made of two main populations originating from a metal-poor, high-{\alpha} thick disc and a metal-rich, low-{\alpha} thin disc, with a mass ratio of 4:3, seen as two major components in the MDF. Finer MDF structures hint at multiple sub-populations associated with different orbital families of the bulge, which, however, have broad MDFs themselves. Decomposition using 2D GMMs in [Fe/H] -[Mg/Fe] identifies five components including a population with ex-situ origin. Two dominant ones correspond to the thin and thick discs and two in between trace the transition between them. We show that no universal metallicity gradient value can characterise the MW bulge. The radial gradients closely trace the X-shaped bulge density structure, while the vertical gradient variations follow the boxy component. While having, on average, subsolar metallicity, the MW bulge populations are more metal-rich compared to the surrounding disc, in agreement with extragalactic observations and state-of-the-art simulations reinforcing its secular origin.
A survey on cutting-edge relation extraction techniques based on language models
2024-11-27
Jose A. Diaz-Garcia, Julio Amador Diaz Lopez
This comprehensive survey delves into the latest advancements in Relation Extraction (RE), a pivotal task in natural language processing essential for applications across biomedical, financial, and legal sectors. This study highlights the evolution and current state of RE techniques by analyzing 137 papers presented at the Association for Computational Linguistics (ACL) conferences over the past four years, focusing on models that leverage language models. Our findings underscore the dominance of BERT-based methods in achieving state-of-the-art results for RE while also noting the promising capabilities of emerging large language models (LLMs) like T5, especially in few-shot relation extraction scenarios where they excel in identifying previously unseen relations.
Timing analysis of rotating radio transients discovered with MeerKAT
2024-11-27
Thulo Letsele, Mechiel Christiaan Bezuidenhout, the MeerTRAP collaboration
Pulsars are rapidly rotating neutron stars that emit pulses of radiation at regular intervals, typically ranging from milliseconds to seconds. The precise recording and modelling of the arrival times of pulsar emission is known as timing analysis. Rotating radio transients (RRATs) are a subclass of pulsars that emit pulses very sporadically. Because of the sparse pulse times of arrival (ToAs) typically available for these sources, they are much more difficult to time than regular pulsars, to the extent that few RRATs currently have coherent timing solutions. In this work, we present the results of timing analyses for four RRATs discovered by the MeerTRAP transient survey using MeerKAT. We incorporated additional pulse ToAs from each source that have been detected since their original analysis. We confirmed the known timing solution for PSR J1843$-$0757, with a period of $P=2.03$ seconds, and a period derivative of $\dot{P}=4,13\times10^{-15}$. However, our analysis did not comport with the solution of MTP0005, which we conclude may have been mistakenly identified with the known PSR J1840$-$0815 in the original analysis. Finally, the spin period for MTP0007 was determined to be $1.023(1)$ seconds using a brute-force period fitting approach.
Online Knowledge Integration for 3D Semantic Mapping: A Survey
2024-11-27
Felix Igelbrink, Marian Renz, Martin Günther, Piper Powell, Lennart Niecksch, Oscar Lima, Martin Atzmueller, Joachim Hertzberg
Semantic mapping is a key component of robots operating in and interacting with objects in structured environments. Traditionally, geometric and knowledge representations within a semantic map have only been loosely integrated. However, recent advances in deep learning now allow full integration of prior knowledge, represented as knowledge graphs or language concepts, into sensor data processing and semantic mapping pipelines. Semantic scene graphs and language models enable modern semantic mapping approaches to incorporate graph-based prior knowledge or to leverage the rich information in human language both during and after the mapping process. This has sparked substantial advances in semantic mapping, leading to previously impossible novel applications. This survey reviews these recent developments comprehensively, with a focus on online integration of knowledge into semantic mapping. We specifically focus on methods using semantic scene graphs for integrating symbolic prior knowledge and language models for respective capture of implicit common-sense knowledge and natural language concepts
Using different sources of ground truths and transfer learning to improve the generalization of photometric redshift estimation
2024-11-27
Jonathan Soriano, Srinath Saikrishnan, Vikram Seenivasan, Bernie Boscoe, Jack Singal, Tuan Do
In this work, we explore methods to improve galaxy redshift predictions by combining different ground truths. Traditional machine learning models rely on training sets with known spectroscopic redshifts, which are precise but only represent a limited sample of galaxies. To make redshift models more generalizable to the broader galaxy population, we investigate transfer learning and directly combining ground truth redshifts derived from photometry and spectroscopy. We use the COSMOS2020 survey to create a dataset, TransferZ, which includes photometric redshift estimates derived from up to 35 imaging filters using template fitting. This dataset spans a wider range of galaxy types and colors compared to spectroscopic samples, though its redshift estimates are less accurate. We first train a base neural network on TransferZ and then refine it using transfer learning on a dataset of galaxies with more precise spectroscopic redshifts (GalaxiesML). In addition, we train a neural network on a combined dataset of TransferZ and GalaxiesML. Both methods reduce bias by $\sim$ 5x, RMS error by $\sim$ 1.5x, and catastrophic outlier rates by 1.3x on GalaxiesML, compared to a baseline trained only on TransferZ. However, we also find a reduction in performance for RMS and bias when evaluated on TransferZ data. Overall, our results demonstrate these approaches can meet cosmological requirements.
A primer on the formation and evolution of hydrogen deficient Central Stars of Planetary Nebulæ and Related Objects
2024-11-27
Marcelo M. Miller Bertolami
We present a brief review on the formation and evolution of hydrogen deficient central stars of planetary nebulae. We include a detailed description of the main observable features of both the central stars and their surrounding nebulae and review their main classifications. We also provide a brief description of the possible progenitor systems of hydrogen deficient central stars, as well as, of transients closely connected to the formation of these stars. In particular we offer a detailed theoretical explanation of the main evolutionary scenarios, both single and binary, devised to explain these stars and nebulae. Particular emphasis is made in the description of the so-called born again scenario, their quantitative predictions and uncertainties. Finally we discuss the pros and cons of both binary and single evolution channels, draw some conclusions and discuss open questions in the field.
HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction
2024-11-27
Wei Zhang, Qing Cheng, David Skuddis, Niclas Zeller, Daniel Cremers, Norbert Haala
We present HI-SLAM2, a geometry-aware Gaussian SLAM system that achieves fast and accurate monocular scene reconstruction using only RGB input. Existing Neural SLAM or 3DGS-based SLAM methods often trade off between rendering quality and geometry accuracy, our research demonstrates that both can be achieved simultaneously with RGB input alone. The key idea of our approach is to enhance the ability for geometry estimation by combining easy-to-obtain monocular priors with learning-based dense SLAM, and then using 3D Gaussian splatting as our core map representation to efficiently model the scene. Upon loop closure, our method ensures on-the-fly global consistency through efficient pose graph bundle adjustment and instant map updates by explicitly deforming the 3D Gaussian units based on anchored keyframe updates. Furthermore, we introduce a grid-based scale alignment strategy to maintain improved scale consistency in prior depths for finer depth details. Through extensive experiments on Replica, ScanNet, and ScanNet++, we demonstrate significant improvements over existing Neural SLAM methods and even surpass RGB-D-based methods in both reconstruction and rendering quality. The project page and source code will be made available at https://hi-slam2.github.io/.
Improved implicit diffusion model with knowledge distillation to estimate the spatial distribution density of carbon stock in remote sensing imagery
2024-11-27
Zhenyu Yu
The forest serves as the most significant terrestrial carbon stock mechanism, effectively reducing atmospheric CO$_2$ concentrations and mitigating climate change. Remote sensing provides high data accuracy and enables large-scale observations. Optical images facilitate long-term monitoring, which is crucial for future carbon stock estimation studies. This study focuses on Huize County, Qujing City, Yunnan Province, China, utilizing GF-1 WFV satellite imagery. The KD-VGG and KD-UNet modules were introduced for initial feature extraction, and the improved implicit diffusion model (IIDM) was proposed. The results showed: (1) The VGG module improved initial feature extraction, improving accuracy, and reducing inference time with optimized model parameters. (2) The Cross-attention + MLPs module enabled effective feature fusion, establishing critical relationships between global and local features, achieving high-accuracy estimation. (3) The IIDM model, a novel contribution, demonstrated the highest estimation accuracy with an RMSE of 12.17\%, significantly improving by 41.69\% to 42.33\% compared to the regression model. In carbon stock estimation, the generative model excelled in extracting deeper features, significantly outperforming other models, demonstrating the feasibility of AI-generated content in quantitative remote sensing. The 16-meter resolution estimates provide a robust basis for tailoring forest carbon sink regulations, enhancing regional carbon stock management.
DeepDISC-photoz: Deep Learning-Based Photometric Redshift Estimation for Rubin LSST
2024-11-27
Grant Merz, Xin Liu, Samuel Schmidt, Alex I. Malz, Tianqing Zhang, Doug Branton, Colin J. Burke, Melissa Delucchi, Yaswant Sai Ejjagiri, Jeremy Kubica, Yichen Liu, Olivia Lynn, Drew Oldag, The LSST Dark Energy Science Collaboration
Photometric redshifts will be a key data product for the Rubin Observatory Legacy Survey of Space and Time (LSST) as well as for future ground and space-based surveys. The need for photometric redshifts, or photo-zs, arises from sparse spectroscopic coverage of observed galaxies. LSST is expected to observe billions of objects, making it crucial to have a photo-z estimator that is accurate and efficient. To that end, we present DeepDISC photo-z, a photo-z estimator that is an extension of the DeepDISC framework. The base DeepDISC network simultaneously detects, segments, and classifies objects in multi-band coadded images. We introduce photo-z capabilities to DeepDISC by adding a redshift estimation Region of Interest head, which produces a photo-z probability distribution function for each detected object. On simulated LSST images, DeepDISC photo-z outperforms traditional catalog-based estimators, in both point estimate and probabilistic metrics. We validate DeepDISC by examining dependencies on systematics including galactic extinction, blending and PSF effects. We also examine the impact of the data quality and the size of the training set and model. We find that the biggest factor in DeepDISC photo-z quality is the signal-to-noise of the imaging data, and see a reduction in photo-z scatter approximately proportional to the image data signal-to-noise. Our code is fully public and integrated in the RAIL photo-z package for ease of use and comparison to other codes at https://github.com/LSSTDESC/rail_deepdisc
Cocycles in Lie Groups, Cochains and Regularity Problem
2024-11-27
Rosário D. Laureano
After the fundamental work of Livschitz in [1; 2], various research directions emerged, among which the following stand out: (i) the study of cocycles with values in groups and semigroups beyond R, as well as the investigation of corresponding regularity results; (ii) the analysis of how a certain degree of regularity of the cocycle can confer corresponding regularity to the solution of the cohomological equation; and (iii) the study of higher-dimensional cohomology naturally associated with the action of groups other than Z or R. The aim of this article is to present, as self-contained as possible, a review of the natural generalizations of the notions of cocycles and cochains, as well as their corresponding results, in the study of cohomological equations.
Stellar atmospheric parameters and chemical abundances of about 5 million stars from S-PLUS multi-band photometry
2024-11-27
C. E. Ferreira Lopes, L. A. Gutiérrez-Soto, V. S. Ferreira Alberice, N. Monsalves, D. Hazarika, M. Catelan, V. M. Placco, G. Limberg, F. Almeida-Fernandes, H. D. Perottoni, A. V. Smith Castelli, S. Akras, J. Alonso-García, V. Cordeiro, M. Jaque Arancibia, S. Daflon, B. Dias, D. R. Gonçalves, E. Machado-Pereira, A. R. Lopes, C. R. Bom, R. C. Thom de Souza, N. G. de Isídio, A. Alvarez-Candal, M. E. De Rossi, C. J. Bonatto, B. Cubillos Palma, M. Borges Fernandes, P. K. Humire, G. B. Oliveira Schwarz, W. Schoenell, A. Kanaan, C. Mendes de Oliveira
Context. Spectroscopic surveys like APOGEE, GALAH, and LAMOST have significantly advanced our understanding of the Milky Way by providing extensive stellar parameters and chemical abundances. Complementing these, photometric surveys with narrow/medium-band filters, such as the Southern Photometric Local Universe Survey (S-PLUS), offer the potential to estimate stellar parameters and abundances for a much larger number of stars. Aims. This work develops methodologies to extract stellar atmospheric parameters and selected chemical abundances from S-PLUS photometric data, which spans ~3000 square degrees using seven narrowband and five broadband filters. Methods. Using 66 S-PLUS colors, we estimated parameters based on training samples from LAMOST, APOGEE, and GALAH, applying Cost-Sensitive Neural Networks (NN) and Random Forests (RF). We tested for spurious correlations by including abundances not covered by the S-PLUS filters and evaluated NN and RF performance, with NN consistently outperforming RF. Including Teff and log g as features improved accuracy by ~3%. We retained only parameters with a goodness-of-fit above 50%. Results. Our approach provides reliable estimates of fundamental parameters (Teff, log g, [Fe/H]) and abundance ratios such as [{\alpha}/Fe], [Al/Fe], [C/Fe], [Li/Fe], and [Mg/Fe] for ~5 million stars, with goodness-of-fit >60%. Additional ratios like [Cu/Fe], [O/Fe], and [Si/Fe] were derived but are less accurate. Validation using star clusters, TESS, and J-PLUS data confirmed the robustness of our methodology. Conclusions. By leveraging S-PLUS photometry and machine learning, we present a cost-effective alternative to high-resolution spectroscopy for deriving stellar parameters and abundances, enabling insights into Milky Way stellar populations and supporting future classification efforts.
The Galaxy Activity, Torus, and Outflow Survey (GATOS). VII. The 20-214 $μ$m imaging atlas of active galactic nuclei using SOFIA
2024-11-27
Lindsay Fuller, Enrique Lopez-Rodriguez, Ismael Garcia-Bernete, Cristina Ramos Almeida, Almudena Alonso-Herrero, Chris Packham, Lulu Zhang, Mason Leist, Nancy Levenson, Masa Imanishi, Sebastian Hoenig, Marko Stalevski, Claudio Ricci, Erin Hicks, Enrica Bellocchi, Francoise Combes, Ric Davies, Santiago Garcia Burillo, Omaira GonzalezMartin, TakumaIzumi, Alvaro Labiano, Miguel Pereira Santaella, Dimitra Rigopoulou, David Rosario, Daniel Rouan, Taro Shimizu, Martin Ward
We present a 19.7 - 214 $\mu$m imaging atlas of local (4 - 181 Mpc; median 43 Mpc) active galactic nuclei (AGN) observed with FORCAST and HAWC+ on board the SOFIA telescope with angular resolutions ~ 3"- 20". This atlas comprises 22 Seyferts (17 Type 2 and 5 Type 1) with a total of 69 images, 41 of which have not been previously published. The AGN span a range of luminosities of log$_{10}$ ($L_{bol}$ [erg/s]) = [42, 46] with a median of log$_{10}$ ($L_{bol}$ [erg/s]) = 44.1 $\pm$ 1.0. We provide total fluxes of our sample using aperture photometry for point source objects and a 2-D Gaussian fitting for objects with extended host galaxy emission, which was used to estimate the unresolved nuclear component. Most galaxies in our sample are point-like sources, however, four sources (Centaurus A, Circinus, NGC 1068, and NGC 4388) show extended emission in all wavelengths. The 30 - 40 $\mu$m extended emission in NGC 4388 is coincident with the narrow line region at PA ~ 50$^{\circ}$, while the dusty extension at longer wavelengths arises from the host galaxy at PA ~ 90$^{\circ}$. Our new observations allow us to construct the best sampled spectral energy distributions (SEDs) available between 30 - 500 $\mu$m for a sample of nearby AGN. We estimate that the average peak wavelength of the nuclear SEDs is ~ 40 $\mu$m in $\nu$F$_{\nu}$ , which we associate with an unresolved extended dusty region heated by the AGN.
Foundation Models in Radiology: What, How, When, Why and Why Not
2024-11-27
Magdalini Paschali, Zhihong Chen, Louis Blankemeier, Maya Varma, Alaa Youssef, Christian Bluethgen, Curtis Langlotz, Sergios Gatidis, Akshay Chaudhari
Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Such models, typically referred to as foundation models, are trained on extensive corpora of unlabeled data and demonstrate high performance across various tasks. Foundation models have recently received extensive attention from academic, industry, and regulatory bodies. Given the potentially transformative impact that foundation models can have on the field of radiology, this review aims to establish a standardized terminology concerning foundation models, with a specific focus on the requirements of training data, model training paradigms, model capabilities, and evaluation strategies. We further outline potential pathways to facilitate the training of radiology-specific foundation models, with a critical emphasis on elucidating both the benefits and challenges associated with such models. Overall, we envision that this review can unify technical advances and clinical needs in the training of foundation models for radiology in a safe and responsible manner, for ultimately benefiting patients, providers, and radiologists.
Tentative Blazar Candidate EP240709A Associated with 4FGL J0031.5-5648: NICER and Archival Multiwavelength Observations
2024-11-27
Mason Ng, Jeremy Hare, Gaurava K. Jaisawal, Christian Malacaria, Craig B. Markwardt, Andrea Sanna
We report on follow-up observations of the recently discovered transient by the Einstein Probe, EP240709A, with the Neutron star Interior Composition Explorer (NICER). We also incorporated archival multiwavelength survey data from the Neil Gehrels Swift Observatory (X-ray), Gaia (optical), the Fermi Gamma-ray Space Telescope (gamma-ray), and the Wide-field Infrared Survey Explorer (infrared) to distinguish between blazars and stellar systems. We suggest that EP240709A is likely an active blazar.
Addressing bias in Recommender Systems: A Case Study on Data Debiasing Techniques in Mobile Games
2024-11-27
Yixiong Wang, Maria Paskevich, Hui Wang
The mobile gaming industry, particularly the free-to-play sector, has been around for more than a decade, yet it still experiences rapid growth. The concept of games-as-service requires game developers to pay much more attention to recommendations of content in their games. With recommender systems (RS), the inevitable problem of bias in the data comes hand in hand. A lot of research has been done on the case of bias in RS for online retail or services, but much less is available for the specific case of the game industry. Also, in previous works, various debiasing techniques were tested on explicit feedback datasets, while it is much more common in mobile gaming data to only have implicit feedback. This case study aims to identify and categorize potential bias within datasets specific to model-based recommendations in mobile games, review debiasing techniques in the existing literature, and assess their effectiveness on real-world data gathered through implicit feedback. The effectiveness of these methods is then evaluated based on their debiasing quality, data requirements, and computational demands.
Embracing AI in Education: Understanding the Surge in Large Language Model Use by Secondary Students
2024-11-27
Tiffany Zhu, Kexun Zhang, William Yang Wang
The impressive essay writing and problem-solving capabilities of large language models (LLMs) like OpenAI's ChatGPT have opened up new avenues in education. Our goal is to gain insights into the widespread use of LLMs among secondary students to inform their future development. Despite school restrictions, our survey of over 300 middle and high school students revealed that a remarkable 70% of students have utilized LLMs, higher than the usage percentage among young adults, and this percentage remains consistent across 7th to 12th grade. Students also reported using LLMs for multiple subjects, including language arts, history, and math assignments, but expressed mixed thoughts on their effectiveness due to occasional hallucinations in historical contexts and incorrect answers for lack of rigorous reasoning. The survey feedback called for LLMs better adapted for students, and also raised questions to developers and educators on how to help students from underserved communities leverage LLMs' capabilities for equal access to advanced education resources. We propose a few ideas to address such issues, including subject-specific models, personalized learning, and AI classrooms.
Using the chi-square test to compare asteroid and laboratory reflectance spectra
2024-11-27
Latika Joshi, Ines Belkhodja, Livneh Naaman, Thomas Burbine, Brian Burt
This study uses the Pearson's chi-square test to analyze the VNIR reflectance spectra of seven asteroids and look for spectral matches among approximately 11,000 laboratory spectra of meteoritic, terrestrial, synthetic, Apollo, and Luna samples. First, we use the chi-square method to analyze three well-studied asteroids - (4) Vesta, (6) Hebe, and (19) Fortuna - to establish the technique's reliability by attempting to confirm previously predicted spectral matches. The chi-square test is then applied to four other asteroids: the Mars trojans (5261) Eureka, (101429) 1998 VF31, (311999) 2007 NS2, and (385250) 2001 DH47. This study focuses on Mars trojans because of their undetermined origin and possible relationship to Mars. For asteroids that may have undergone space weathering, reddening effects are removed from the spectra to allow for a more accurate chi-square analysis. The top chi-square matches among the Mars trojans reveal significant spectral similarities with Martian meteorites and minerals found on Mars, supporting the hypothesis of a Martian origin for the analyzed Mars trojans. However, the possibility that these Mars trojans could be fragments of a disrupted differentiated body or bodies cannot be ruled out. We find that using the chi-square test with a large number of laboratory spectra is a very useful initial technique for identifying spectral analogs to asteroids.
HOLISMOKES XV. Search for strong gravitational lenses combining ground-based and space-based imaging
2024-11-27
A. Melo, R. Cañameras, S. Schuldt, S. H. Suyu, Irham T. Andika, S. Bag, S. Taubenberger
In the past, researchers have mostly relied on single-resolution images from individual telescopes to detect gravitational lenses. We propose a search for galaxy-scale lenses that, for the first time, combines high-resolution single-band images (in our case the Hubble Space Telescope, HST) with lower-resolution multi-band images (in our case Legacy survey, LS) using machine learning. This methodology aims to simulate the operational strategies that will be employed by future missions, such as combining the images of Euclid and the Rubin Observatory's Legacy Survey of Space and Time (LSST). To compensate for the scarcity of lensed galaxy images for network training, we have generated mock lenses by superimposing arc features onto HST images, saved the lens parameters, and replicated the lens system in the LS images. We test four architectures based on ResNet-18: (1) using single-band HST images, (2) using three bands of LS images, (3) stacking these images after interpolating the LS images to HST pixel scale for simultaneous processing, and (4) merging a ResNet branch of HST with a ResNet branch of LS before the fully connected layer. We compare these architecture performances by creating Receiver Operating Characteristic (ROC) curves for each model and comparing their output scores. At a false-positive rate of $10^{-4}$, the true-positive rate is $\sim$0.41, $\sim$0.45, $\sim$0.51 and $\sim$0.55, for HST, LS, stacked images and merged branches, respectively. Our results demonstrate that models integrating images from both the HST and LS significantly enhance the detection of galaxy-scale lenses compared to models relying on data from a single instrument. These results show the potential benefits of using both Euclid and LSST images, as wide-field imaging surveys are expected to discover approximately 100,000 lenses.
SIRIUS: Identifying Metal-poor Stars Enriched by a Single Supernova in a Star-by-star Cosmological Zoom-in Simulation of a Dwarf Galaxy
2024-11-27
Yutaka Hirai, Takayuki R. Saitoh, Michiko S. Fujii, Katsuhiro Kaneko, Timothy C. Beers
Metal-poor stars enriched by a single supernova (mono-enriched stars) are direct proof (and provide valuable probes) of supernova nucleosynthesis. Photometric and spectroscopic observations have shown that metal-poor stars have a wide variety of chemical compositions; the star's chemical composition reflects the nucleosynthesis process(es) that occurred before the star's formation. While the identification of mono-enriched stars enables us to study the ejecta properties of a single supernova, the fraction of mono-enriched stars among metal-poor stars remains unknown. Here we identify mono-enriched stars in a star-by-star cosmological zoom-in simulation of a dwarf galaxy. We find that the fraction of mono-enriched stars is higher for lower metallicity, stars with [Fe/H] $< -2.5$. The percentages of mono-enriched stars are 11% at [Fe/H] = $-$5.0 and 1% at [Fe/H] = $-$2.5, suggesting that most metal-poor stars are affected by multiple supernovae. We also find that mono-enriched stars tend to be located near the center of the simulated dwarf. Such regions will be explored in detail in upcoming surveys such as the Prime Focus Spectrograph (PFS) on the Subaru telescope.
Generating long-duration videos has always been a significant challenge due to the inherent complexity of spatio-temporal domain and the substantial GPU memory demands required to calculate huge size tensors. While diffusion based generative models achieve state-of-the-art performance in video generation task, they are typically trained with predefined video resolutions and lengths. During inference, a noise tensor with specific resolution and length should be specified at first, and the model will perform denoising on the entire video tensor simultaneously, all the frames together. Such approach will easily raise an out-of-memory (OOM) problem when the specified resolution and/or length exceed a certain limit. One of the solutions to this problem is to generate many short video chunks autoregressively with strong inter-chunk spatio-temporal relation and then concatenate them together to form a long video. In this approach, a long video generation task is divided into multiple short video generation subtasks, and the cost of each subtask is reduced to a feasible level. In this paper, we conduct a detailed survey on long video generation with the autoregressive chunk-by-chunk strategy. We address common problems caused by applying short image-to-video models to long video tasks and design an efficient $k$-step search solution to mitigate these problems.
How do Multimodal Foundation Models Encode Text and Speech? An Analysis of Cross-Lingual and Cross-Modal Representations
2024-11-26
Hyunji Lee, Danni Liu, Supriti Sinhamahapatra, Jan Niehues
Multimodal foundation models aim to create a unified representation space that abstracts away from surface features like language syntax or modality differences. To investigate this, we study the internal representations of three recent models, analyzing the model activations from semantically equivalent sentences across languages in the text and speech modalities. Our findings reveal that: 1) Cross-modal representations converge over model layers, except in the initial layers specialized at text and speech processing. 2) Length adaptation is crucial for reducing the cross-modal gap between text and speech, although current approaches' effectiveness is primarily limited to high-resource languages. 3) Speech exhibits larger cross-lingual differences than text. 4) For models not explicitly trained for modality-agnostic representations, the modality gap is more prominent than the language gap.
The JCMT BISTRO Survey: The magnetised evolution of star-forming cores in the Ophiuchus Molecular Cloud interpreted using Histograms of Relative Orientation
2024-11-26
James P. Perry, Kate Pattle, Doug Johnstone, Woojin Kwon, Tyler Bourke, Eun Jung Chung, Simon Coudé, Yasuo Doi, Lapo Fanciullo, Jihye Hwang, Zacariyya A. Khan, Jungmi Kwon, Shih-Ping Lai, Valentin J. M. Le Gouellec, Chang Won Lee, Nagayoshi Ohashi, Sarah Sadavoy, Giorgio Savini, Ekta Sharma, Motohide Tamura
The relationship between B-field orientation and density structure in molecular clouds is often assessed using the Histogram of Relative Orientations (HRO). We perform a plane-of-the-sky geometrical analysis of projected B-fields, by interpreting HROs in dense, spheroidal, prestellar and protostellar cores. We use James Clerk Maxwell Telescope (JCMT) POL-2 850 $\mu$m polarisation maps and Herschel column density maps to study dense cores in the Ophiuchus molecular cloud complex. We construct two-dimensional core models, assuming Plummer column density profiles and modelling both linear and hourglass B-fields. We find high-aspect-ratio ellipsoidal cores produce strong HRO signals, as measured using the shape parameter $\xi$. Cores with linear fields oriented $< 45^{\circ}$ from their minor axis produce constant HROs with $-1 < \xi < 0$, indicating fields are preferentially parallel to column density gradients. Fields parallel to the core minor axis produce the most negative value of $\xi$. For low-aspect-ratio cores, $\xi \approx 0$ for linear fields. Hourglass fields produce a minimum in $\xi$ at intermediate densities in all cases, converging to the minor-axis-parallel linear field value at high and low column densities. We create HROs for six dense cores in Ophiuchus. $\rho$ Oph A and IRAS 16293 have high aspect ratios and preferentially negative HROs, consistent with moderately strong-field behaviour. $\rho$ Oph C, L1689A and L1689B have low aspect ratios, and $\xi \approx 0$. $\rho$ Oph B is too complex to be modelled using a simple spheroidal field geometry. We see no signature of hourglass fields, agreeing with previous findings that dense cores generally exhibit linear fields on these size scales.
Association between Recurrent Novae and Nova Super-Remnants
2024-11-26
Michael William Healy-Kalesh
Nova super-remnants (NSRs) are substantially extended structures (>100 parsecs) encompassing recurrent novae. NSRs grow as a result of frequent nova eruptions transporting vast quantities of the locally surrounding interstellar medium away from the binary system over many millennia into a thin high-density shell, as the central white dwarf grows towards the Chandrasekhar limit. The prototypical NSR, first identified as such in 2014, is situated in the Andromeda Galaxy and belongs to the annually erupting nova, M31N 2008-12a (or '12a'). In this short review, modelling of evolving NSRs (including the 12a NSR) will be outlined as motivation towards searching for more of these phenomena in the Galaxy and beyond. The latest developments in this upcoming subfield of nova research will then be presented including the discovery of two new Galactic nova super-remnants (and their consequent modelling) and the first survey undertaken with the sole purpose of finding NSRs in the Andromeda Galaxy and the Large Magellanic Cloud.
Validating the Orbital Periods of the Coolest TESS Exoplanet Candidates
2024-11-26
Dillon Bass, Daniel Fabrycky
When an exoplanet passes in front of its host star, the resulting eclipse causes an observable decrease in stellar flux, and when multiple such transits are detected, the orbital period of the exoplanet can be determined. Over the past six years, NASA's Transiting Exoplanet Survey Satellite (TESS) has discovered thousands of potential planets by this method, mostly with short orbital periods, although some have longer reported values over one hundred days. These long orbital periods, however, are note easy to confirm due to frequent lengthy data gaps. Here we show that while the majority of these long period candidates likely have periods much shorter than reported, there are a sizable number of TESS candidates with true long periods. These candidates generally only have two reported transits, but the periods of duo-transits like this, and even candidates with three or more transits, can be confirmed if the data rules out all possible shorter period aliases. Using TESS data, we confirm long orbital periods for nine candidate planets, and present five others that are likely long period. Due to their long periods, these planets will have relatively cool equilibrium temperatures, and may be more likely to host exomoons or rings. We present these TOIs, along with a variety of small corrections to other TESS orbital periods and three planet candidates with possible transit timing variations, with the goal of refining the TESS data set and enabling future research with respect to cool transiting planets.
Machine Learning and Multi-source Remote Sensing in Forest Carbon Stock Estimation: A Review
2024-11-26
Autumn Nguyen, Sulagna Saha
Quantifying forest carbon is crucial for informing decisions and policies that will protect the planet. Machine learning (ML) and remote sensing (RS) techniques have been used to do this task more effectively, yet there lacks a systematic review on the most recent ML methods and RS combinations, especially with the consideration of forest characteristics. This study systematically analyzed 25 papers meeting strict inclusion criteria from over 80 related studies, identifying 28 ML methods and key combinations of RS data. Random Forest had the most frequent appearance (88\% of studies), while Extreme Gradient Boosting showed superior performance in 75\% of the studies in which it was compared with other methods. Sentinel-1 emerged as the most utilized remote sensing source, with multi-sensor approaches (e.g., Sentinel-1, Sentinel-2, and LiDAR) proving especially effective. Our findings provide grounds for recommending best practices in integrating machine learning and remote sensing for accurate and scalable forest carbon stock estimation.
Constraining primordial non-Gaussianity with DESI 2024 LRG and QSO samples
2024-11-26
E. Chaussidon, C. Yèche, A. de Mattia, C. Payerne, P. McDonald, A. J. Ross, S. Ahlen, D. Bianchi, D. Brooks, E. Burtin, T. Claybaugh, A. de la Macorra, P. Doel, S. Ferraro, A. Font-Ribera, J. E. Forero-Romero, E. Gaztañaga, H. Gil-Marín, S. Gontcho A Gontcho, G. Gutierrez, J. Guy, K. Honscheid, C. Howlett, D. Huterer, R. Kehoe, D. Kirkby, T. Kisner, A. Kremin, L. Le Guillou, M. E. Levi, M. Manera, A. Meisner, R. Miquel, J. Moustakas, J. A. Newman, G. Niz, N. Palanque-Delabrouille, W. J. Percival, F. Prada, I. Pérez-Ràfols, C. Ravoux, G. Rossi, E. Sanchez, D. Schlegel, M. Schubnell, H. Seo, D. Sprayberry, G. Tarlé, M. Vargas-Magaña, B. A. Weaver, C. Zhao, H. Zou
We analyse the large-scale clustering of the Luminous Red Galaxy (LRG) and Quasar (QSO) sample from the first data release (DR1) of the Dark Energy Spectroscopic Instrument (DESI). In particular, we constrain the primordial non-Gaussianity (PNG) parameter $f_{\rm NL}^{\rm loc}$ via the large-scale scale-dependent bias in the power spectrum using $1,631,716$ LRGs ($0.6 < z < 1.1$) and $1,189,129$ QSOs ($0.8 < z < 3.1$). This new measurement takes advantage of the enormous statistical power at large scales of DESI DR1 data, surpassing the latest data release (DR16) of the extended Baryon Oscillation Spectroscopic Survey (eBOSS). For the first time in this kind of analysis, we use a blinding procedure to mitigate the risk of confirmation bias in our results. We improve the model of the radial integral constraint proposing an innovative correction of the window function. We also carefully test the mitigation of the dependence of the target selection on the photometry qualities by incorporating an angular integral constraint contribution to the window function, and validate our methodology with the blinded data. Finally, combining the two samples, we measure $f_{\rm NL}^{\rm loc} = {-3.6}_{-9.1}^{+9.0}$ at $68\%$ confidence, where we assume the universality relation for the LRG sample and a recent merger model for the QSO sample about the response of bias to primordial non-Gaussianity. Adopting the universality relation for the PNG bias in the QSO analysis leads to $f_{\rm NL}^{\rm loc} = 3.5_{-7.4}^{+10.7}$ at $68\%$ confidence. This measurement is the most precise determination of primordial non-Gaussianity using large-scale structure to date, surpassing the latest result from eBOSS by a factor of $2.3$.
In software, a vulnerability is a defect in a program that attackers might utilize to acquire unauthorized access, alter system functions, and acquire information. These vulnerabilities arise from programming faults, design flaws, incorrect setups, and a lack of security protective measures. To mitigate these vulnerabilities, regular software upgrades, code reviews, safe development techniques, and the use of security tools to find and fix problems have been important. Several ways have been delivered in recent studies to address difficulties related to software vulnerabilities. However, previous approaches have significant limitations, notably in feature embedding and precisely recognizing specific vulnerabilities. To overcome these drawbacks, we present CodeGraphNet, an experimental method that combines GraphCodeBERT and Graph Convolutional Network (GCN) approaches, where, CodeGraphNet reveals data in a high-dimensional vector space, with comparable or related properties grouped closer together. This method captures intricate relationships between features, providing for more exact identification and separation of vulnerabilities. Using this feature embedding approach, we employed four machine learning models, applying both independent testing and 10-fold cross-validation. The DeepTree model, which is a hybrid of a Decision Tree and a Neural Network, outperforms state-of-the-art approaches. In additional validation, we evaluated our model using feature embeddings from LSA, GloVe, FastText, CodeBERT and GraphCodeBERT, and found that the CodeGraphNet method presented improved vulnerability identification with 98% of accuracy. Our model was tested on a real-time dataset to determine its capacity to handle real-world data and to focus on defect localization, which might influence future studies.
A Distractor-Aware Memory for Visual Object Tracking with SAM2
2024-11-26
Jovana Videnovic, Alan Lukezic, Matej Kristan
Memory-based trackers are video object segmentation methods that form the target model by concatenating recently tracked frames into a memory buffer and localize the target by attending the current image to the buffered frames. While already achieving top performance on many benchmarks, it was the recent release of SAM2 that placed memory-based trackers into focus of the visual object tracking community. Nevertheless, modern trackers still struggle in the presence of distractors. We argue that a more sophisticated memory model is required, and propose a new distractor-aware memory model for SAM2 and an introspection-based update strategy that jointly addresses the segmentation accuracy as well as tracking robustness. The resulting tracker is denoted as SAM2.1++. We also propose a new distractor-distilled DiDi dataset to study the distractor problem better. SAM2.1++ outperforms SAM2.1 and related SAM memory extensions on seven benchmarks and sets a solid new state-of-the-art on six of them.
A geometric approach to estimate background in astronomical images
2024-11-26
Pushpak Pandey, Kanak Saha
Estimating the true background in an astronomical image is fundamental to detecting faint sources. In a typical low-photon count astronomical image, such as in the far and near-ultraviolet wavelength range, conventional methods relying on the 3-sigma clipping and median or mode estimation often fail to capture the true background level accurately. As a consequence, differentiating true sources from noise peaks remains a challenging task. Additionally, in such images, effectively identifying and excluding faint sources during the background estimation process remains crucial, as undetected faint sources could contaminate the background. This results in overestimating the true background and obscuring the detection of very faint sources. To tackle this problem, we introduce a geometric approach based on the method of steepest descent to identify local minima in an astronomical image. The proposed algorithm based on the minima statistics effectively reduces the confusion between sources and background in the image; thereby ensuring a better background estimation and enhancing the reliability of faint source detection. Our algorithm performs well compared to conventional methods in estimating the background even in crowded field images. In low-photon count, less crowded images, our algorithm recovers the background within 10\%, while traditional methods drastically underestimate it by a few orders of magnitude. In crowded fields, the conventional methods overestimates the background by $\sim 200\%$ whereas our algorithm recovers the true background within $\sim 14\%$. We provide a simple prescription to create a background map using our algorithm and discuss its application in large astronomical surveys.
2024-11-26
Antoine Quiriny, Václav Kučera, Jonathan Lambrechts, Nicolas Moës, Jean-François Remacle
In this paper, we propose a new approach -- the Tempered Finite Element Method (TFEM) -- that extends the Finite Element Method (FEM) to classes of meshes that include zero-measure or nearly degenerate elements for which standard FEM approaches do not allow convergence. First, we review why the maximum angle condition [2] is not necessary for FEM convergence and what are the real limitations in terms of meshes. Next, we propose a simple modification of the classical FEM for elliptic problems that provably allows convergence for a wider class of meshes including bands of caps that cause locking of the solution in standard FEM formulations. The proposed method is trivial to implement in an existing FEM code and can be theoretically analyzed. We prove that in the case of exactly zero-measure elements it corresponds to mortaring. We show numerically and theoretically that what we propose is functional and sound. The remainder of the paper is devoted to extensions of the TFEM method to linear elasticity, mortaring of non-conforming meshes, high-order elements, and advection.
Chemically Self-Consistent Modeling of the Globular Cluster NGC 2808 and its Effects on the Inferred Helium Abundance of Multiple Stellar Populations
2024-11-26
Emily M. Boudreaux, Brian C. Chaboyer, Amanda Ash, Renata Edaes Hoh, Gregory Feiden
The helium abundances in the multiple populations that are now known to comprise all closely studied Milky Way globular clusters are often inferred by fitting isochrones generated from stellar evolutionary models to globular cluster photometry. It is therefore important to build stellar models that are chemically self-consistent in terms of their structure, atmosphere, and opacity. In this work we present the first chemically self-consistent stellar models of the Milky Way globular cluster NGC 2808 using MARCS model atmospheres, OPLIB high-temperature radiative opacities, and AESOPUS low-temperature radiative opacities. These stellar models were fit to the NGC 2808 photometry using Fidanka , a new software tool that was developed to optimally fit cluster photometry to isochrones and for population synthesis. Fidanka can determine, in a relatively unbiased way, the ideal number of distinct populations that exist within a dataset and then fit isochrones to each population. We achieve this outcome through a combination of Bayesian Gaussian Mixture Modeling and a novel number density estimation algorithm. Using Fidanka and F275W-F814W photometry from the Hubble UV Globular Cluster Survey we find that the helium abundance of the second generation of stars in NGC 2808 is higher than the first generation by $15\pm3\%$. This is in agreement with previous studies of NGC 2808. This work, along with previous work by Dotter et al. (2015) focused on NGC 6752, demonstrates that chemically self-consistent models of globular clusters do not significantly alter inferred helium abundances, and are therefore unlikely to be worth the significant additional time investment.
Visual Question Answering (VQA) is a challenge task that combines natural language processing and computer vision techniques and gradually becomes a benchmark test task in multimodal large language models (MLLMs). The goal of our survey is to provide an overview of the development of VQA and a detailed description of the latest models with high timeliness. This survey gives an up-to-date synthesis of natural language understanding of images and text, as well as the knowledge reasoning module based on image-question information on the core VQA tasks. In addition, we elaborate on recent advances in extracting and fusing modal information with vision-language pretraining models and multimodal large language models in VQA. We also exhaustively review the progress of knowledge reasoning in VQA by detailing the extraction of internal knowledge and the introduction of external knowledge. Finally, we present the datasets of VQA and different evaluation metrics and discuss possible directions for future work.
Radio sources of the survey on the declination of the pulsar in Crab Nebula
2024-11-26
A. A. Kudryashova, N. N. Bursov, S. A. Trushkin
The results of the analysis of 205 brightest sources ( $S>15$ mJy), which were found in the sky survey at the declination of the pulsar in the Crab Nebula, are presented. The survey was conducted at a frequency of 4.7~GHz using a three-beam radiometer complex installed in the focus of the Western Sector of the RATAN-600 radio telescope in 2018-2019. Based on the measurements and data collected in the database of astrophysical catalogs CATS built radio spectra of objects. For a quarter of all detected sources, data at a frequency higher than 4~GHz were obtained for the first time, and for the rest, they were appended. The variability of radiation sources on the scales of the year, from days to months, was studied. The greatest change in the radio flux was found in the blazar B2~1324+22. The search for daily variability was carried out for 26 the brightest sources with an average value of $S_{4.7} \sim 250$ mJy. All sources are identified with objects from optical and infrared catalogs. Radio luminosity is calculated for 112 objects with a known redshift.
The BINGO/ABDUS Project: Forecast for cosmological parameter from a mock Fast Radio Bursts survey
2024-11-26
Xue Zhang, Yu Sang, Gabriel A. Hoerning, Filipe B. Abdalla, Elcio Abdalla, Amilcar Queiroz, Andre A. Costa, Ricardo G. Landim, Chang Feng, Bin Wang, Marcelo V. dos Santos, Thyrso Villela, Carlos A. Wuensche, Jiajun Zhang, Edmar Gurjao, Alessandro Marins, Alexandre Serres, Linfeng Xiao
There are hosts of surveys that will provide excellent data to search for and locate Fast Radio Bursts (FRBs) at cosmological distances. The BINGO project is one such surveys, and this collaboration has already estimated a FRB detection rate that the project will yield with the main telescope helped by a set of outrigger stations. This paper aims to simulate and estimate the potential of FRBs in constraining our current cosmological model. We present a forecast of the future constraints that the BINGO FRB detections and localizations will have when added to other current cosmological datasets. We quantify the dispersion measure (DM) as a function of redshift ($z$) for the BINGO FRB mock sample. Furthermore, we use current datasets (Supernovae, Baryonic Acoustic Oscillations, and Cosmic Microwave Background data) prior to assessing the efficacy of constraining dark energy models using Monte Carlo methods. Our results show that spatially located BINGO FRB dataset will provide promising constraints on the population of host galaxies intrinsic DM and be able to measure the nuisance parameters present within a FRB cosmological analysis. Still, they will also provide alternative estimates on other parameters such as the Hubble constant and the dark energy equation of state. In particular, we should see that BINGO FRB data can put constraints on the degenerate $w-H_0$ plane, which the CMB is incapable of measuring, allowing FRBs to be a viable alternative to BAO to constrain the dark energy equation of state. We conclude that FRBs remain a promising future probe for cosmology and that the FRBs detected by the BINGO project will contribute significantly to our knowledge of the current cosmological model.
Rewiring Techniques to Mitigate Oversquashing and Oversmoothing in GNNs: A Survey
2024-11-26
Hugo Attali, Davide Buscaldi, Nathalie Pernelle
Graph Neural Networks (GNNs) are powerful tools for learning from graph-structured data, but their effectiveness is often constrained by two critical challenges: oversquashing, where the excessive compression of information from distant nodes results in significant information loss, and oversmoothing, where repeated message-passing iterations homogenize node representations, obscuring meaningful distinctions. These issues, intrinsically linked to the underlying graph structure, hinder information flow and constrain the expressiveness of GNNs. In this survey, we examine graph rewiring techniques, a class of methods designed to address these structural bottlenecks by modifying graph topology to enhance information diffusion. We provide a comprehensive review of state-of-the-art rewiring approaches, delving into their theoretical underpinnings, practical implementations, and performance trade-offs.
Isomer triplets in odd-odd transitional rare earth nuclei: unique features, orbital systematics and characterization
2024-11-26
N. Susshma, S. Deepa, K. Vijay Sai, R. Gowrishankar
The existence of low-lying long-lived isomers, predominantly in odd-odd nuclei of the light rare-earth mass region, is investigated through an extensive survey of available nuclear data. The characteristics of these isomeric states and their systematics has revealed intriguing and unusual properties, including the identification of isomer triplets, a phenomenon specific to odd-odd deformed nuclei close to the transition region. This exclusive feature was observed in the following odd-odd nuclei, namely, 152Pm, 152Eu, 154Tb, 156Tb, 156Ho, 158Ho, 160Ho, 162Lu and 166Lu. We present a detailed overview of these isomer triplets by exploring the systematics of single-quasiparticle proton and neutron orbitals near the Fermi surface relevant in this mass region, to elucidate the factors responsible for their formation. The low-lying level structures of 156Tb and 154Tb, were constructed using the well-tested Two Quasiparticle Rotor Model to resolve the ambiguities in the spin, parity, energy and orbital configuration of these isomeric states. These results were extended to study the systematics of low-lying isomer triplets in the other five light rare-earth nuclei of interest. Our review and analysis of 1qp proton and neutron orbital systematics in the neighboring odd-mass isotopes and isotones highlights the crucial role of high-spin intruder neutron orbital in the formation of these isomer triplets.
The Extractive-Abstractive Spectrum: Uncovering Verifiability Trade-offs in LLM Generations
2024-11-26
Theodora Worledge, Tatsunori Hashimoto, Carlos Guestrin
Across all fields of academic study, experts cite their sources when sharing information. While large language models (LLMs) excel at synthesizing information, they do not provide reliable citation to sources, making it difficult to trace and verify the origins of the information they present. In contrast, search engines make sources readily accessible to users and place the burden of synthesizing information on the user. Through a survey, we find that users prefer search engines over LLMs for high-stakes queries, where concerns regarding information provenance outweigh the perceived utility of LLM responses. To examine the interplay between verifiability and utility of information-sharing tools, we introduce the extractive-abstractive spectrum, in which search engines and LLMs are extreme endpoints encapsulating multiple unexplored intermediate operating points. Search engines are extractive because they respond to queries with snippets of sources with links (citations) to the original webpages. LLMs are abstractive because they address queries with answers that synthesize and logically transform relevant information from training and in-context sources without reliable citation. We define five operating points that span the extractive-abstractive spectrum and conduct human evaluations on seven systems across four diverse query distributions that reflect real-world QA settings: web search, language simplification, multi-step reasoning, and medical advice. As outputs become more abstractive, we find that perceived utility improves by as much as 200%, while the proportion of properly cited sentences decreases by as much as 50% and users take up to 3 times as long to verify cited information. Our findings recommend distinct operating points for domain-specific LLM systems and our failure analysis informs approaches to high-utility LLM systems that empower users to verify information.
Towards Robust Cross-Domain Recommendation with Joint Identifiability of User Preference
2024-11-26
Jing Du, Zesheng Ye, Bin Guo, Zhiwen Yu, Jia Wu, Jian Yang, Michael Sheng, Lina Yao
Recent cross-domain recommendation (CDR) studies assume that disentangled domain-shared and domain-specific user representations can mitigate domain gaps and facilitate effective knowledge transfer. However, achieving perfect disentanglement is challenging in practice, because user behaviors in CDR are highly complex, and the true underlying user preferences cannot be fully captured through observed user-item interactions alone. Given this impracticability, we instead propose to model {\it joint identifiability} that establishes unique correspondence of user representations across domains, ensuring consistent preference modeling even when user behaviors exhibit shifts in different domains. To achieve this, we introduce a hierarchical user preference modeling framework that organizes user representations by the neural network encoder's depth, allowing separate treatment of shallow and deeper subspaces. In the shallow subspace, our framework models the interest centroids for each user within each domain, probabilistically determining the users' interest belongings and selectively aligning these centroids across domains to ensure fine-grained consistency in domain-irrelevant features. For deeper subspace representations, we enforce joint identifiability by decomposing it into a shared cross-domain stable component and domain-variant components, linked by a bijective transformation for unique correspondence. Empirical studies on real-world CDR tasks with varying domain correlations demonstrate that our method consistently surpasses state-of-the-art, even with weakly correlated tasks, highlighting the importance of joint identifiability in achieving robust CDR.
Different Bias Under Different Criteria: Assessing Bias in LLMs with a Fact-Based Approach
2024-11-26
Changgeon Ko, Jisu Shin, Hoyun Song, Jeongyeon Seo, Jong C. Park
Large language models (LLMs) often reflect real-world biases, leading to efforts to mitigate these effects and make the models unbiased. Achieving this goal requires defining clear criteria for an unbiased state, with any deviation from these criteria considered biased. Some studies define an unbiased state as equal treatment across diverse demographic groups, aiming for balanced outputs from LLMs. However, differing perspectives on equality and the importance of pluralism make it challenging to establish a universal standard. Alternatively, other approaches propose using fact-based criteria for more consistent and objective evaluations, though these methods have not yet been fully applied to LLM bias assessments. Thus, there is a need for a metric with objective criteria that offers a distinct perspective from equality-based approaches. Motivated by this need, we introduce a novel metric to assess bias using fact-based criteria and real-world statistics. In this paper, we conducted a human survey demonstrating that humans tend to perceive LLM outputs more positively when they align closely with real-world demographic distributions. Evaluating various LLMs with our proposed metric reveals that model bias varies depending on the criteria used, highlighting the need for multi-perspective assessment.
Mass Transfer Physics in Binary Stars and Applications in Gravitational Wave Sources
2024-11-26
Hongwei Ge, Zhanwen Han
The stability criteria of rapid mass transfer and common-envelope evolution are fundamental in binary star evolution. They determine the mass, mass ratio, and orbital distribution of many important systems, such as X-ray binaries, type Ia supernovae, and merging gravitational-wave sources. In the limit of extremely rapid mass transfer, the response of a donor star in an interacting binary becomes asymptotically one of adiabatic expansion. We built the adiabatic mass-loss model and systematically surveyed the thresholds for dynamical timescale mass transfer over the entire span of possible donor star evolutionary states. Many studies indicate that new mass transfer stability thresholds play an essential role in the formation and properties of double compact object populations and the progenitors of SNe Ia and detectable GW sources. For example, our studies show that the mass transfer in the red giant and the asymptotic giant branch stars and the massive stars can be more stable than previously believed. Consequently, detailed binary population synthesis studies, using updated unstable mass transfer criteria, predicate the non-conservative stable mass transfer may dominate the formation channel of double stellar-mass black holes and can explain the population of the large mass ratio double stellar-mass black holes. Using our updated mass transfer thresholds, binary population thesis studies by Li et al. show that Ge et al.'s results support the observational double white dwarfs merger rate distribution per Galaxy and the space density of double white dwarfs in the Galaxy.
2024-11-26
Ray P. Norris, Bärbel S. Koribalski, Catherine L. Hale, Matt J. Jarvis, Peter J. Macgregor, A. Russell Taylor
We present the discovery of a new Odd Radio Circle (ORC J0219--0505) in 1.2~GHz radio continuum data from the MIGHTEE survey taken with the MeerKAT telescope. The radio-bright host is a massive elliptical galaxy, which shows extended stellar structure, possibly tidal tails or shells, suggesting recent interactions or mergers. The radio ring has a diameter of 35", corresponding to 114~kpc at the host galaxy redshift of $z_{\rm spec} = 0.196$. This MIGHTEE ORC is a factor 3--5 smaller than previous ORCs with central elliptical galaxies. The discovery of this MIGHTEE ORC in a deep but relatively small-area radio survey implies that more ORCs will be found in deeper surveys. While the small numbers currently available are insufficient to estimate the flux density distribution, this is consistent with the simplest hypothesis that ORCs have a flux density distribution similar to that of the general population of extragalactic radio sources.
The recent success of denoising diffusion models has significantly advanced text-to-image generation. While these large-scale pretrained models show excellent performance in general image synthesis, downstream objectives often require fine-tuning to meet specific criteria such as aesthetics or human preference. Reward gradient-based strategies are promising in this context, yet existing methods are limited to single-reward tasks, restricting their applicability in real-world scenarios that demand adapting to multiple objectives introduced incrementally over time. In this paper, we first define this more realistic and unexplored problem, termed Reward Incremental Learning (RIL), where models are desired to adapt to multiple downstream objectives incrementally. Additionally, while the models adapt to the ever-emerging new objectives, we observe a unique form of catastrophic forgetting in diffusion model fine-tuning, affecting both metric-wise and visual structure-wise image quality. To address this catastrophic forgetting challenge, we propose Reward Incremental Distillation (RID), a method that mitigates forgetting with minimal computational overhead, enabling stable performance across sequential reward tasks. The experimental results demonstrate the efficacy of RID in achieving consistent, high-quality generation in RIL scenarios. The source code of our work will be publicly available upon acceptance.
We review 2d CFT in the bootstrap approach, and sketch the known exactly solvable CFTs with no extended chiral symmetry: Liouville theory, (generalized) minimal models, limits thereof, and loop CFTs, including the $O(n)$, Potts and $PSU(n)$ CFTs. Exact solvability relies on local conformal symmetry, and on the existence of degenerate fields. We show how these assumptions constrain the spectrum and correlation functions. We discuss how crossing symmetry equations can be solved analytically and/or numerically, leading to analytic expressions for structure constants in terms of the double Gamma function. In the case of loop CFTs, we sketch the corresponding statistical models, and derive the relation between statistical and CFT variables. We review the resulting combinatorial description of correlation functions, and discuss what remains to be done for solving the CFTs.
MiceBoneChallenge: Micro-CT public dataset and six solutions for automatic growth plate detection in micro-CT mice bone scans
2024-11-26
Nikolay Burlutskiy, Marija Kekic, Jordi de la Torre, Philipp Plewa, Mehdi Boroumand, Julia Jurkowska, Borjan Venovski, Maria Chiara Biagi, Yeman Brhane Hagos, Roksana Malinowska-Traczyk, Yibo Wang, Jacek Zalewski, Paula Sawczuk, Karlo Pintarić, Fariba Yousefi, Leif Hultin
Detecting and quantifying bone changes in micro-CT scans of rodents is a common task in preclinical drug development studies. However, this task is manual, time-consuming and subject to inter- and intra-observer variability. In 2024, Anonymous Company organized an internal challenge to develop models for automatic bone quantification. We prepared and annotated a high-quality dataset of 3D $\mu$CT bone scans from $83$ mice. The challenge attracted over $80$ AI scientists from around the globe who formed $23$ teams. The participants were tasked with developing a solution to identify the plane where the bone growth happens, which is essential for fully automatic segmentation of trabecular bone. As a result, six computer vision solutions were developed that can accurately identify the location of the growth plate plane. The solutions achieved the mean absolute error of $1.91\pm0.87$ planes from the ground truth on the test set, an accuracy level acceptable for practical use by a radiologist. The annotated 3D scans dataset along with the six solutions and source code, is being made public, providing researchers with opportunities to develop and benchmark their own approaches. The code, trained models, and the data will be shared.
Critically assessing atavism, an evolution-centered and deterministic hypothesis on cancer
2024-11-26
Bertrand Daignan-Fornier, Thomas Pradeu
Cancer is most commonly viewed as resulting from somatic mutations enhancing proliferation and invasion. Some hypotheses further propose that these new capacities reveal a breakdown of multicellularity allowing cancer cells to escape proliferation and cooperation control mechanisms that were implemented during evolution of multicellularity. Here we critically review one such hypothesis, named ``atavism,'' which puts forward the idea that cancer results from the re-expression of normally repressed genes forming a program, or toolbox, inherited from unicellular or simple multicellular ancestors. This hypothesis places cancer in an interesting evolutionary perspective that has not been widely explored and deserves attention. Thinking about cancer within an evolutionary framework, especially the major transitions to multicellularity, offers particularly promising perspectives. It is therefore of the utmost important to analyze why one approach that tries to achieve this aim, the atavism hypothesis, has not so far emerged as a major theory on cancer. We outline the features of the atavism hypothesis that, would benefit from clarification and, if possible, unification.
Pre-Release Experimentation in Indie Game Development: An Interview Survey
2024-11-26
Johan Linåker, Elizabeth Bjarnason, Fabian Fagerholm
[Background] The game industry faces fierce competition and games are developed on short deadlines and tight budgets. Continuously testing and experimenting with new ideas and features is essential in validating and guiding development toward market viability and success. Such continuous experimentation (CE) requires user data, which is often limited in early development stages. This challenge is further exacerbated for independent (indie) game companies with limited resources. [Aim] We wanted to gain insights into CE practices in pre-release indie game development. [Method] We performed an exploratory interview survey with 10 indie game developers from different companies and synthesised findings through an iterative coding process. [Results] We present a CE framework for game development that highlights key parts to consider when planning and implementing an experiment and note that pre-release experimentation is centred on qualitative data. Time and resource constraints impose limits on the type and extent of experimentation and playtesting that indie companies can perform, e.g. due to limited access to participants, biases and representativeness of the target audience. [Conclusions] Our results outline challenges and practices for conducting experiments with limited user data in early stages of indie game development, and may be of value also for larger game companies, and for software intensive organisations in other industries.
Exploring the Impact of Anthropomorphism in Role-Playing AI Chatbots on Media Dependency: A Case Study of Xuanhe AI
2024-11-26
Qiufang Yu, Xingyu Lan
Powered by large language models, the conversational capabilities of AI have seen significant improvements. In this context, a series of role-playing AI chatbots have emerged, exhibiting a strong tendency toward anthropomorphism, such as conversing like humans, possessing personalities, and fulfilling social and companionship functions. Informed by media dependency theory in communication studies, this work hypothesizes that a higher level of anthropomorphism of the role-playing chatbots will increase users' media dependency (i.e., people will depend on media that meets their needs and goals). Specifically, we conducted a user study on a Chinese role-playing chatbot platform, Xuanhe AI, selecting four representative chatbots as research targets. We invited 149 users to interact with these chatbots over a period. A questionnaire survey revealed a significant positive correlation between the degree of anthropomorphism in role-playing chatbots and users' media dependency, with user satisfaction mediating this relationship. Next, based on the quantitative results, we conducted semi-structured interviews with ten users to further understand the factors that deterred them from depending on anthropomorphic chatbots. In conclusion, this work has provided empirical insights for the design of role-playing AI chatbots and deepened the understanding of how users engage with conversational AI over a longer period.
The widespread dissemination of hate speech, harassment, harmful and sexual content, and violence across websites and media platforms presents substantial challenges and provokes widespread concern among different sectors of society. Governments, educators, and parents are often at odds with media platforms about how to regulate, control, and limit the spread of such content. Technologies for detecting and censoring the media contents are a key solution to addressing these challenges. Techniques from natural language processing and computer vision have been used widely to automatically identify and filter out sensitive content such as offensive languages, violence, nudity, and addiction in both text, images, and videos, enabling platforms to enforce content policies at scale. However, existing methods still have limitations in achieving high detection accuracy with fewer false positives and false negatives. Therefore, more sophisticated algorithms for understanding the context of both text and image may open rooms for improvement in content censorship to build a more efficient censorship system. In this paper, we evaluate existing LLM-based content moderation solutions such as OpenAI moderation model and Llama-Guard3 and study their capabilities to detect sensitive contents. Additionally, we explore recent LLMs such as GPT, Gemini, and Llama in identifying inappropriate contents across media outlets. Various textual and visual datasets like X tweets, Amazon reviews, news articles, human photos, cartoons, sketches, and violence videos have been utilized for evaluation and comparison. The results demonstrate that LLMs outperform traditional techniques by achieving higher accuracy and lower false positive and false negative rates. This highlights the potential to integrate LLMs into websites, social media platforms, and video-sharing services for regulatory and content moderation purposes.
Processing of GASKAP-HI pilot survey data using a commercial supercomputer
2024-11-26
Ian P. Kemp, Nickolas M. Pingel, Rowan Worth, Justin Wake, Daniel A. Mitchell, Stuart D. Midgely, Steven J. Tingay, James Dempsey, Helga Dénes, John M. Dickey, Steven J. Gibson, Kate E. Jameson, Callum Lynn, Yik Ki Ma, Antoine Marchal, Naomi M. McClure-Griffiths, Snežana Stanimirović, Jacco Th. van Loon
Modern radio telescopes generate large amounts of data, with the next generation Very Large Array (ngVLA) and the Square Kilometre Array (SKA) expected to feed up to 292 GB of visibilities per second to the science data processor (SDP). However, the continued exponential growth in the power of the world's largest supercomputers suggests that for the foreseeable future there will be sufficient capacity available to provide for astronomers' needs in processing 'science ready' products from the new generation of telescopes, with commercial platforms becoming an option for overflow capacity. The purpose of the current work is to trial the use of commercial high performance computing (HPC) for a large scale processing task in astronomy, in this case processing data from the GASKAP-HI pilot surveys. We delineate a four-step process which can be followed by other researchers wishing to port an existing workflow from a public facility to a commercial provider. We used the process to provide reference images for an ongoing upgrade to ASKAPSoft (the ASKAP SDP software), and to provide science images for the GASKAP collaboration, using the joint deconvolution capability of WSClean. We document the approach to optimising the pipeline to minimise cost and elapsed time at the commercial provider, and give a resource estimate for processing future full survey data. Finally we document advantages, disadvantages, and lessons learned from the project, which will aid other researchers aiming to use commercial supercomputing for radio astronomy imaging. We found the key advantage to be immediate access and high availability, and the main disadvantage to be the need for improved HPC knowledge to take best advantage of the facility.
The Spatial Distribution of Globular Cluster Systems in Early Type Galaxies: Estimation Procedure and Catalog of Properties for Globular Cluster Systems Observed with Deep Imaging Surveys
2024-11-26
Sungsoon Lim, Eric W. Peng, Patrick Côté, Laura Ferrarese, Joel C. Roediger, Chengze Liu, Chelsea Spengler, Elisabeth Sola, Pierre-Alain Duc, Laura V. Sales, John P. Blakeslee, Jean-Charles Cuillandre, Patrick R. Durrell, Eric Emsellem, Stephen D. J. Gwyn, Ariane Lançon, Francine R. Marleau, J. Christopher Mihos, Oliver Müller, Thomas H. Puzia, Rubén Sánchez-Janssen
We present an analysis of the spatial distribution of globular cluster (GC) systems of 118 nearby early-type galaxies in the Next Generation Virgo Cluster Survey (NGVS) and Mass Assembly of early-Type GaLAxies with their fine Structures (MATLAS) survey programs, which both used MegaCam on the Canada-France-Hawaii Telescope. We describe the procedure used to select GC candidates and fit the spatial distributions of GCs to a two-dimensional S\'ersic function, which provides effective radii (half number radii) and S\'ersic indices, and estimate background contamination by adding a constant term to the S'ersic function. In cases where a neighboring galaxy affects the estimation of the GC spatial distribution in the target galaxy, we fit two 2D S\'ersic functions, simultaneously. We also investigate the color distributions of GCs in our sample by using Gaussian Mixture Modeling. For GC systems with bimodal color distributions, we divide the GCs into blue and red subgroups and fit their respective spatial distributions with S\'ersic functions. Finally, we measure the total number of GCs based on our fitted S\'ersic function, and calculate the GC specific frequency.
Redefining Crowdsourced Test Report Prioritization: An Innovative Approach with Large Language Model
2024-11-26
Yuchen Ling, Shengcheng Yu, Chunrong Fang, Guobin Pan, Jun Wang, Jia Liu
Context: Crowdsourced testing has gained popularity in software testing, especially for mobile app testing, due to its ability to bring diversity and tackle fragmentation issues. However, the openness of crowdsourced testing presents challenges, particularly in the manual review of numerous test reports, which is time-consuming and labor-intensive. Objective: The primary goal of this research is to improve the efficiency of review processes in crowdsourced testing. Traditional approaches to test report prioritization lack a deep understanding of semantic information in textual descriptions of these reports. This paper introduces LLMPrior, a novel approach for prioritizing crowdsourced test reports using large language models (LLMs). Method: LLMPrior leverages LLMs for the analysis and clustering of crowdsourced test reports based on the types of bugs revealed in their textual descriptions. This involves using prompt engineering techniques to enhance the performance of LLMs. Following the clustering, a recurrent selection algorithm is applied to prioritize the reports. Results: Empirical experiments are conducted to evaluate the effectiveness of LLMPrior. The findings indicate that LLMPrior not only surpasses current state-of-the-art approaches in terms of performance but also proves to be more feasible, efficient, and reliable. This success is attributed to the use of prompt engineering techniques and the cluster-based prioritization strategy. Conclusion: LLMPrior represents a significant advancement in crowdsourced test report prioritization. By effectively utilizing large language models and a cluster-based strategy, it addresses the challenges in traditional prioritization approaches, offering a more efficient and reliable solution for app developers dealing with crowdsourced test reports.
This survey offers a comprehensive review of recent advancements in multimodal alignment and fusion within machine learning, spurred by the growing diversity of data types such as text, images, audio, and video. Multimodal integration enables improved model accuracy and broader applicability by leveraging complementary information across different modalities, as well as facilitating knowledge transfer in situations with limited data. We systematically categorize and analyze existing alignment and fusion techniques, drawing insights from an extensive review of more than 200 relevant papers. Furthermore, this survey addresses the challenges of multimodal data integration - including alignment issues, noise resilience, and disparities in feature representation - while focusing on applications in domains like social media analysis, medical imaging, and emotion recognition. The insights provided are intended to guide future research towards optimizing multimodal learning systems to enhance their scalability, robustness, and generalizability across various applications.
Trinity: Synchronizing Verbal, Nonverbal, and Visual Channels to Support Academic Oral Presentation Delivery
2024-11-26
Yuchen Wu, Shengxin Li, Shizhen Zhang, Xingbo Wang, Quan Li
Academic Oral Presentation (AOP) allows English-As-Foreign-Language (EFL) students to express ideas, engage in academic discourse, and present research findings. However, while previous efforts focus on training efficiency or speech assistance, EFL students often face the challenge of seamlessly integrating verbal, nonverbal, and visual elements into their presentations to avoid coming across as monotonous and unappealing. Based on a need-finding survey, a design study, and an expert interview, we introduce Trinity, a hybrid mobile-centric delivery support system that provides guidance for multichannel delivery on-the-fly. On the desktop side, Trinity facilitates script refinement and offers customizable delivery support based on large language models (LLMs). Based on the desktop configuration, Trinity App enables a remote mobile visual control, multi-level speech pace modulation, and integrated delivery prompts for synchronized delivery. A controlled between-subject user study suggests that Trinity effectively supports AOP delivery and is perceived as significantly more helpful than baselines, without excessive cognitive load.
Recent findings from photometric and spectroscopic JWST surveys have identified examples of high-redshift galaxies at $z \gtrsim 10$. These high-$z$ galaxies appear to form much earlier and exhibit greater UV luminosity than predicted by theoretical work. In this study, our goal is to reproduce the brightness of these sources by simulating high-redshift galaxies with virial masses $M_{\rm vir} = 10^{9} - 10^{10} M_{\odot}$ at $z > 10$. To achieve this, we conduct cosmological hydrodynamic zoom-in simulations, modifying baryonic sub-grid physics, and post-process our simulation results to confirm the observability of our simulated galaxies. Specifically, we enhanced star formation activity in high-redshift galaxies by either increasing the star formation efficiency up to 100\% or adopting a top-heavy initial mass function (IMF). Our simulation results indicate that both increasing star formation efficiency and adopting a top-heavy IMF play crucial roles in boosting the UV luminosity of high-redshift galaxies, potentially exceeding the limiting magnitude of JWST surveys in earlier epochs. Especially, the episodic starburst resulting from enhanced star formation efficiency may explain the high-redshift galaxies observed by JWST, as it evacuates dust from star-forming regions, making the galaxies more observable. We demonstrate this correlation between star formation activity and dust mass evolution within the simulated galaxies. Also, adopting a top-heavy IMF could enhance observability due to an overabundance of massive stars, although it may also facilitate rapid metal enrichment. Using our simulation results, we derive several observables such as effective radius, UV slope, and emission line rates, which could serve as valuable theoretical estimates for comparison with existing spectroscopic results and forthcoming data from the JWST NIRSpec and MIRI instruments.
Event-based Spiking Neural Networks for Object Detection: A Review of Datasets, Architectures, Learning Rules, and Implementation
2024-11-26
Craig Iaboni, Pramod Abichandani
Spiking Neural Networks (SNNs) represent a biologically inspired paradigm offering an energy-efficient alternative to conventional artificial neural networks (ANNs) for Computer Vision (CV) applications. This paper presents a systematic review of datasets, architectures, learning methods, implementation techniques, and evaluation methodologies used in CV-based object detection tasks using SNNs. Based on an analysis of 151 journal and conference articles, the review codifies: 1) the effectiveness of fully connected, convolutional, and recurrent architectures; 2) the performance of direct unsupervised, direct supervised, and indirect learning methods; and 3) the trade-offs in energy consumption, latency, and memory in neuromorphic hardware implementations. An open-source repository along with detailed examples of Python code and resources for building SNN models, event-based data processing, and SNN simulations are provided. Key challenges in SNN training, hardware integration, and future directions for CV applications are also identified.
Evaluating Generative AI-Enhanced Content: A Conceptual Framework Using Qualitative, Quantitative, and Mixed-Methods Approaches
2024-11-26
Saman Sarraf
Generative AI (GenAI) has revolutionized content generation, offering transformative capabilities for improving language coherence, readability, and overall quality. This manuscript explores the application of qualitative, quantitative, and mixed-methods research approaches to evaluate the performance of GenAI models in enhancing scientific writing. Using a hypothetical use case involving a collaborative medical imaging manuscript, we demonstrate how each method provides unique insights into the impact of GenAI. Qualitative methods gather in-depth feedback from expert reviewers, analyzing their responses using thematic analysis tools to capture nuanced improvements and identify limitations. Quantitative approaches employ automated metrics such as BLEU, ROUGE, and readability scores, as well as user surveys, to objectively measure improvements in coherence, fluency, and structure. Mixed-methods research integrates these strengths, combining statistical evaluations with detailed qualitative insights to provide a comprehensive assessment. These research methods enable quantifying improvement levels in GenAI-generated content, addressing critical aspects of linguistic quality and technical accuracy. They also offer a robust framework for benchmarking GenAI tools against traditional editing processes, ensuring the reliability and effectiveness of these technologies. By leveraging these methodologies, researchers can evaluate the performance boost driven by GenAI, refine its applications, and guide its responsible adoption in high-stakes domains like healthcare and scientific research. This work underscores the importance of rigorous evaluation frameworks for advancing trust and innovation in GenAI.
State Anxiety Biomarker Discovery: Electrooculography and Electrodermal Activity in Stress Monitoring
2024-11-26
Jadelynn Dao, Ruixiao Liu, Sarah Solomon, Samuel Solomon
Anxiety has become a significant health concern affecting mental and physical well-being, with state anxiety, a transient emotional response, linked to adverse cardiovascular and long-term health outcomes. This research explores the potential of non-invasive wearable technology to enhance the real-time monitoring of physiological responses associated with state anxiety. Using electrooculography (EOG) and electrodermal activity (EDA), we have reviewed novel biomarkers that reveal nuanced emotional and stress responses. Our study presents two datasets: 1) EOG signal blink identification dataset BLINKEO, containing both true blink events and motion artifacts, and 2) EOG and EDA signals dataset EMOCOLD, capturing physiological responses from a Cold Pressor Test (CPT). From analyzing blink rate variability, skin conductance peaks, and associated arousal metrics, we identified multiple new anxiety-specific biomarkers. SHapley Additive exPlanations (SHAP) were used to interpret and refine our model, enabling a robust understanding of the biomarkers that correlate strongly with state anxiety. These results suggest that a combined analysis of EOG and EDA data offers significant improvements in detecting real-time anxiety markers, underscoring the potential of wearables in personalized health monitoring and mental health intervention strategies. This work contributes to the development of context-sensitive models for anxiety assessment, promoting more effective applications of wearable technology in healthcare.
Combining Threat Intelligence with IoT Scanning to Predict Cyber Attack
2024-11-26
Jubin Abhishek Soni
While the Web has become a worldwide platform for communication, hackers and hacktivists share their ideology and communicate with members on the "Dark Web"-the reverse of the Web. Currently, the problems of information overload and difficulty to obtain a comprehensive picture of hackers and cyber-attackers hinder the effective analysis of predicting their activities on the Web. Also, there are currently more objects connected to the internet than there are people in the world and this gap will continue to grow as more and more objects gain ability to directly interface with the Internet. Many technical communities are vigorously pursuing research topics that contribute to the Internet of Things (IoT). In this paper I have proposed a novel methodology for collecting and analyzing the Dark Web information to identify websites of hackers from the Web sea, and how this information can help us in predicting IoT vulnerabilities. This methodology incorporates information collection, analysis, visualization techniques, and exploits some of the IoT devices. Through this research I want to contribute to the existing literature on cyber-security that could potentially guide in both policy-making and intelligence research.
A Practical Approach to Formal Methods: An Eclipse Integrated Development Environment (IDE) for Security Protocols
2024-11-26
Rémi Garcia, Paolo Modesti
To develop trustworthy distributed systems, verification techniques and formal methods, including lightweight and practical approaches, have been employed to certify the design or implementation of security protocols. Lightweight formal methods offer a more accessible alternative to traditional fully formalised techniques by focusing on simplified models and tool support, making them more applicable in practical settings. The technical advantages of formal verification over manual testing are increasingly recognised in the cybersecurity community. However, for practitioners, formal modelling and verification are often too complex and unfamiliar to be used routinely. In this paper, we present an Eclipse IDE for the design, verification, and implementation of security protocols and evaluate its effectiveness, including feedback from users in educational settings. It offers user-friendly assistance in the formalisation process as part of a Model-Driven Development approach. This IDE centres around the Alice & Bob (AnB) notation, the AnBx Compiler and Code Generator, the OFMC model checker, and the ProVerif cryptographic protocol verifier. For the evaluation, we identify the six most prominent limiting factors for formal method adoption, based on relevant literature in this field, and we consider the IDE's effectiveness against those criteria. Additionally, we conducted a structured survey to collect feedback from university students who have used the toolkit for their projects. The findings demonstrate that this contribution is valuable as a workflow aid and helps users grasp essential cybersecurity concepts, even for those with limited knowledge of formal methods or cryptography. Crucially, users reported that the IDE has been an important component to complete their projects and that they would use again in the future, given the opportunity.
In recent years, deepfakes (DFs) have been utilized for malicious purposes, such as individual impersonation, misinformation spreading, and artists' style imitation, raising questions about ethical and security concerns. However, existing surveys have focused on accuracy performance of passive DF detection approaches for single modalities, such as image, video or audio. This comprehensive survey explores passive approaches across multiple modalities, including image, video, audio, and multi-modal domains, and extend our discussion beyond detection accuracy, including generalization, robustness, attribution, and interpretability. Additionally, we discuss threat models for passive approaches, including potential adversarial strategies and different levels of adversary knowledge and capabilities. We also highlights current challenges in DF detection, including the lack of generalization across different generative models, the need for comprehensive trustworthiness evaluation, and the limitations of existing multi-modal approaches. Finally, we propose future research directions that address these unexplored and emerging issues in the field of passive DF detection, such as adaptive learning, dynamic benchmark, holistic trustworthiness evaluation, and multi-modal detectors for talking-face video generation.
Assessing microbiome engraftment extent following fecal microbiota transplant with q2-fmt
2024-11-26
Chloe Herman, Evan Bolyen, Anthony Simard, Liz Gehret, J. Gregory Caporaso
We present q2-fmt, a QIIME 2 plugin that provides diverse methods for assessing the extent of microbiome engraftment following fecal microbiota transplant. The methods implemented here were informed by a recent literature review on approaches for assessing FMT engraftment, and cover aspects of engraftment including Chimeric Asymmetric Community Coalescence, Donated Microbiome Indicator Features, and Temporal Stability. q2-fmt is free for all use, and detailed documentation illustrating worked examples on a real-world data set are provided in the project's documentation.
A Novel Test for MOND: Gravitational Lensing by Disc Galaxies
2024-11-26
Christopher Harvey-Hawes, Marco Galoppo
Disc galaxies represent a promising laboratory for the study of gravitational physics, including alternatives to dark matter, owing to the possibility of coupling rotation curves' dynamical data with strong gravitational lensing observations. In particular, Euclid, DES and LSST are predicted to observe hundreds of thousands of gravitational lenses. Here, we investigate disc galaxy strong gravitational lensing in the MOND framework. We employ the concept of equivalent Newtonian systems within the quasi-linear MOND formulation to make use of the standard lensing formalism. We derive the phantom dark matter distribution predicted for realistic disc galaxy models and study the impact of morphological and mass parameters on the expected lensing. We find purely MONDian effects dominate the lensing and generate non-trivial correlations between the lens parameters and the lensing cross section. Moreover, we show that the standard realisation of MOND predicts a number count of disc galaxy lenses of one order of magnitude higher than the dark matter-driven predictions, making it distinguishable from the latter in upcoming surveys. Finally, we show that disc galaxy gravitational lensing can be used to strongly constrain the interpolating function of MOND.
The MAGPI Survey: radial trends in star formation across different cosmological simulations in comparison with observations at $z \sim$ 0.3
2024-11-26
Marcie Mun, Emily Wisnioski, Katherine E. Harborne, Claudia D. P. Lagos, Lucas M. Valenzuela, Rhea-Silvia Remus, J. Trevor Mendel, Andrew J. Battisti, Sara L. Ellison, Caroline Foster, Matias Bravo, Sarah Brough, Scott M. Croom, Tianmu Gao, Kathryn Grasha, Anshu Gupta, Yifan Mai, Anilkumar Mailvaganam, Eric G. M. Muller, Gauri Sharma, Sarah M. Sweet, Edward N. Taylor, Tayyaba Zafar
We investigate the internal and external mechanisms that regulate and quench star formation (SF) in galaxies at $z \sim 0.3$ using MAGPI observations and the EAGLE, Magneticum, and IllustrisTNG cosmological simulations. Using SimSpin to generate mock observations of simulated galaxies, we match detection/resolution limits in star formation rates and stellar mass, along with MAGPI observational details including the average point spread function and pixel scale. While we find a good agreement in the slope of the global star-forming main sequence (SFMS) between MAGPI observations and all three simulations, the slope of the resolved SFMS does not agree within 1 $-$ 2$\sigma$. Furthermore, in radial SF trends, good agreement between observations and simulations exists only for galaxies far below the SFMS, where we capture evidence for inside-out quenching. The simulations overall agree with each other between $\sim1.5-4 \ R_{\rm e}$ but show varying central suppression within $R \sim 1.5 \ R_{\rm e}$ for galaxies on and below the SFMS, attributable to different AGN feedback prescriptions. All three simulations show similar dependencies of SF radial trends with environment. Central galaxies are subject to both internal and external mechanisms, showing increased SF suppression in the centre with increasing halo mass, indicating AGN feedback. Satellite galaxies display increasing suppression in the outskirts as halo mass increases, indicative of environmental processes. These results demonstrate the power of spatially resolved studies of galaxies; while global properties align, radial profiles reveal discrepancies between observations and simulations and their underlying physics.
The transition from Galactic to extragalactic cosmic rays: the high-energy end of the Galactic spectrum
2024-11-26
P. Cristofari
Understanding the transition from Galactic to extragalactic cosmic rays (CRs) is essential to make sense of the Local cosmic ray spectrum. Several models have been proposed to account for this transition in the 0.1 - 10 $\times 10^{18}$ eV range. For instance: ankle models, where the change from a steep Galactic component to a hard extragalactic spectrum occurs in the $4-10 \times 10^{18}$ eV region, dip models, where the interactions of CR protons with the CMB producing electron-positron pairs shape the ankle, or mixed composition models, in which extragalactic CRs are composed of nuclei of various types. In all these scenarios, the low-energy part of the transition involves the high-energy part of the Galactic component. Therefore, any information on the Galactic component, such as maximum energy, chemical composition, and spectrum after propagation, is crucial to understanding the Galactic-extragalactic transition. We briefly review the high-energy part of the CR spectrum expected from the best potential sources of Galactic CRs.
Scintillation Bandwidth Measurements from 23 Pulsars from the AO327 Survey
2024-11-26
Sofia Z. Sheikh, Grayce C. Brown, Jackson MacTaggart, Thomas Nguyen, William D. Fletcher, Brenda L. Jones, Emma Koller, Veronica Petrus, Katie F. Pighini, Gray Rosario, Vincent A. Smedile, Adam T. Stone, Shawn You, Maura A. McLaughlin, Jacob E. Turner, Julia S. Deneva, Michael T. Lam, Brent J. Shapiro-Albert
A pulsar's scintillation bandwidth is inversely proportional to the scattering delay, making accurate measurements of scintillation bandwidth critical to characterize unmitigated delays in efforts to measure low-frequency gravitational waves with pulsar timing arrays. In this pilot work, we searched for a subset of known pulsars within $\sim$97% of the data taken with the PUPPI instrument for the AO327 survey with the Arecibo telescope, attempting to measure the scintillation bandwidths in the dataset by fitting to the 2D autocorrelation function of their dynamic spectra. We successfully measured 38 bandwidths from 23 pulsars (six without prior literature values), finding that: almost all of the measurements are larger than the predictions from NE2001 and YMW16 (two popular galactic models); NE2001 is more consistent with our measurements than YMW16; Gaussian fits to the bandwidth are more consistent with both electron density models than Lorentzian ones; and for the 17 pulsars with prior literature values, the measurements between various sources often vary by factors of a few. The success of Gaussian fits may be due to the use of Gaussian fits to train models in previous work. The variance of literature values over time could relate to the scaling factor used to compare measurements, but also seems consistent with time-varying interstellar medium parameters. This work can be extended to the rest of AO327 to further investigate these trends, highlighting the continuing importance of large archival datasets for projects beyond their initial conception.
A Precisão da Metrologia Quântica: Limite de Cramér-Rao, Informação de Fisher e possíveis Aplicações Tecnológicas
2024-11-26
Leonardo A. M. Souza
This paper explores as didactically as possible the fundamental principles of both classical and quantum metrology, focusing on the Cram\'er-Rao Bound and how it defines the maximum precision in parameter estimation, taking into account noise and the information extracted from the data. We also conduct a detailed study of Fisher Information (both classical and quantum), showing the physical significance of this important figure of merit in metrology. We further discuss how quantum states can surpass classical limits, providing much greater precision. Examples of technological applications include the development of quantum sensors, quantum thermometers, and phase parameter estimation. Finally, we review our results on the estimation of the unknown compression parameter applied to a mode of a quantum Gaussian state.
Peptides, short chains of amino acids, interact with target proteins, making them a unique class of protein-based therapeutics for treating human diseases. Recently, deep generative models have shown great promise in peptide generation. However, several challenges remain in designing effective peptide binders. First, not all residues contribute equally to peptide-target interactions. Second, the generated peptides must adopt valid geometries due to the constraints of peptide bonds. Third, realistic tasks for peptide drug development are still lacking. To address these challenges, we introduce PepHAR, a hot-spot-driven autoregressive generative model for designing peptides targeting specific proteins. Building on the observation that certain hot spot residues have higher interaction potentials, we first use an energy-based density model to fit and sample these key residues. Next, to ensure proper peptide geometry, we autoregressively extend peptide fragments by estimating dihedral angles between residue frames. Finally, we apply an optimization process to iteratively refine fragment assembly, ensuring correct peptide structures. By combining hot spot sampling with fragment-based extension, our approach enables de novo peptide design tailored to a target protein and allows the incorporation of key hot spot residues into peptide scaffolds. Extensive experiments, including peptide design and peptide scaffold generation, demonstrate the strong potential of PepHAR in computational peptide binder design.
Reanalysis of Everett's relative-state formulation of quantum mechanics
2024-11-26
Jon Geist
Everett's "Relative State Formulation of Quantum Mechanics" (RSQM), which appeared in Reviews of Modern Physics, is based on his thesis "The Theory of the Universal Wavefunction". The most noteworthy property of these works is the claim by other authors that these works are the seminal contribution to Many Worlds theories of branching realities and the claim that practical laboratory experimental tests of RSQM are not possible. This report shows that Everett's two works describe a formulation of quantum mechanics that contradicts an overwhelming body of experimental physics.
Temporal Models for Demographic and Global Health Outcomes in Multiple Populations: Introducing the Normal-with-Optional-Shrinkage Data Model Class
2024-11-26
Leontine Alkema, Herbert Susmann, Evan Ray
Statistical models are used to produce estimates of demographic and global health indicators in populations with limited data. Such models integrate multiple data sources to produce estimates and forecasts with uncertainty based on model assumptions. Model assumptions can be divided into assumptions that describe latent trends in the indicator of interest versus assumptions on the data generating process of the observed data, conditional on the latent process value. Focusing on the latter, we introduce a class of data models that can be used to combine data from multiple sources with various reporting issues. The proposed data model accounts for sampling errors and differences in observational uncertainty based on survey characteristics. In addition, the data model employs horseshoe priors to produce estimates that are robust to outlying observations. We refer to the data model class as the normal-with-optional-shrinkage (NOS) set up. We illustrate the use of the NOS data model for the estimation of modern contraceptive use and other family planning indicators at the national level for countries globally, using survey data.
A Supervised Machine Learning Approach for Assessing Grant Peer Review Reports
2024-11-25
Gabriel Okasa, Alberto de León, Michaela Strinzel, Anne Jorstad, Katrin Milzow, Matthias Egger, Stefan Müller
Peer review in grant evaluation informs funding decisions, but the contents of peer review reports are rarely analyzed. In this work, we develop a thoroughly tested pipeline to analyze the texts of grant peer review reports using methods from applied Natural Language Processing (NLP) and machine learning. We start by developing twelve categories reflecting content of grant peer review reports that are of interest to research funders. This is followed by multiple human annotators' iterative annotation of these categories in a novel text corpus of grant peer review reports submitted to the Swiss National Science Foundation. After validating the human annotation, we use the annotated texts to fine-tune pre-trained transformer models to classify these categories at scale, while conducting several robustness and validation checks. Our results show that many categories can be reliably identified by human annotators and machine learning approaches. However, the choice of text classification approach considerably influences the classification performance. We also find a high correspondence between out-of-sample classification performance and human annotators' perceived difficulty in identifying categories. Our results and publicly available fine-tuned transformer models will allow researchers and research funders and anybody interested in peer review to examine and report on the contents of these reports in a structured manner. Ultimately, we hope our approach can contribute to ensuring the quality and trustworthiness of grant peer review.
Modeling heart flow dynamics using numerical simulations to identify the vortex ring: a practical guide
2024-11-25
Eneko Lazpita, Andrea Mares, Pedro Quintero, Jesús Garicano-Mena, Soledad Le Clainche
In this study, we present a comprehensive numerical analysis of blood flow within human left ventricle models, with particular emphasis on optimizing simulation conditions to enhance the realism and computational efficiency of heart flow dynamics. The objective is to determine the most effective mesh configurations, flow conditions, and boundary settings necessary for accurately capturing the formation and behavior of the vortex ring, a pivotal element in ventricular flow dynamics. Utilizing a computational fluid mechanics approach, we review the influence of both idealized and patient specific geometries on simulation outcomes. It is imperative to consider the necessity of dynamic wall motion and the precise calibration of inlet and outlet boundary conditions, which must be designed to mimic physiological conditions as accurately as possible. These factors are of paramount importance in achieving a balance between computational resource demands and the fidelity of the simulations, thereby providing valuable insights for future cardiovascular modeling efforts.
Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters
2024-11-25
Dietmar Jannach, Alan Said, Marko Tkalčič, Markus Zanker
In the area of recommender systems, the vast majority of research efforts is spent on developing increasingly sophisticated recommendation models, also using increasingly more computational resources. Unfortunately, most of these research efforts target a very small set of application domains, mostly e-commerce and media recommendation. Furthermore, many of these models are never evaluated with users, let alone put into practice. The scientific, economic and societal value of much of these efforts by scholars therefore remains largely unclear. To achieve a stronger positive impact resulting from these efforts, we posit that we as a research community should more often address use cases where recommender systems contribute to societal good (RS4Good). In this opinion piece, we first discuss a number of examples where the use of recommender systems for problems of societal concern has been successfully explored in the literature. We then proceed by outlining a paradigmatic shift that is needed to conduct successful RS4Good research, where the key ingredients are interdisciplinary collaborations and longitudinal evaluation approaches with humans in the loop.
Recent Trends in Linear Text Segmentation: a Survey
2024-11-25
Iacopo Ghinassi, Lin Wang, Chris Newell, Matthew Purver
Linear Text Segmentation is the task of automatically tagging text documents with topic shifts, i.e. the places in the text where the topics change. A well-established area of research in Natural Language Processing, drawing from well-understood concepts in linguistic and computational linguistic research, the field has recently seen a lot of interest as a result of the surge of text, video, and audio available on the web, which in turn require ways of summarising and categorizing the mole of content for which linear text segmentation is a fundamental step. In this survey, we provide an extensive overview of current advances in linear text segmentation, describing the state of the art in terms of resources and approaches for the task. Finally, we highlight the limitations of available resources and of the task itself, while indicating ways forward based on the most recent literature and under-explored research directions.
Chemical enrichment in LINERs from MaNGA. I. Tracing Oxygen and Nitrogen Nuclear Abundances in LINERs with Varied Ionizing Sources
2024-11-25
Borja Pérez-Díaz, Enrique Pérez-Montero, Igor A. Zinchenko, José M. Vílchez
The chemical enrichment in low-ionization nuclear emission-line regions (LINERs) is still an issue with spatial resolution spectroscopic data due to the lack of studies and the uncertainties in the nature of their ionizing source, despite being the most abundant type of active galaxies in the nearby Universe. Considering different scenarios for the ionizing source (hot old stellar populations, active galactic nuclei (AGN) or inefficient accretion disks), we analyze the implications of these assumptions to constrain the chemical content of the gas-phase interstellar medium (ISM). We used a sample of 105 galaxies from Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, whose nuclear central spaxels show LINER-like emission. For each scenario considered, we built a grid of photoionization models (4928 models for each considered ionizing source) which are later used in the open-source code HII-CHI-Mistry, allowing us to estimate chemical abundance ratios such as 12+log(O/H) or log(N/O) and constrain the ionization parameter that characterize the ionized ISM in those galaxies. We obtain that oxygen abundances in the nuclear region of LINER-like galaxies spread over a wide range 8.08 < 12+log(O/H) < 8.89, with a median solar value (in agreement with previous studies) if AGN models are considered. Nevertheless, the derived nitrogen-to-oxygen ratio is much less affected by the assumptions on the ionizing source, and point towards suprasolar values (log(N/O) = -0.69). By comparing the different analyzed scenarios, we show that if hot old stellar populations were responsible of the ionization of the ISM a complex picture (such as outflows and/or inflows scaling with galaxy chemical abundance) would be needed to explain the chemical enrichment history, whereas the assumption of AGN activity is compatible with the standard scenario found in most galaxies.
Review of the foundations of time-dependent density-functional theory (TDDFT)
2024-11-25
Jochen Schirmer
Time-dependent density-functional theory (TDDFT) is deemed to be a formally rigorous way of dealing with the time-evolution of a many-electron system at the level of electron densities rather than the underlying wavefunctions, which in turn provides an, in principle, exact density-based approach to the treatment of electron excitations in atoms and molecules. This claim has not remained unchallenged, and a detailed account of the relevant criticism is given in this paper. In view of our analysis one has to face the conclusion that there is currently no valid justification for TDDFT, and expectations of finding a remedy here are hardly justified.
Solitons in relativistic field theories are not necessarily topologically charged. In particular, non-topological solitons -- known as Q-balls -- arise naturally in nonlinear field theories endowed with attractive interactions and internal symmetries. Even without stabilizing internal symmetries, quasi-solitons known as oscillons, which are long-lived, can also exist. Both Q-balls and oscillons have significant applications in cosmology and particle physics. This review is an updated account of the intriguing properties and dynamics of these non-topological solitons and quasi-solitons, as well as their important roles in early-universe scenarios and particle physics models.
From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge
2024-11-25
Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, Kai Shu, Lu Cheng, Huan Liu
Assessment and evaluation have long been critical challenges in artificial intelligence (AI) and natural language processing (NLP). However, traditional methods, whether matching-based or embedding-based, often fall short of judging subtle attributes and delivering satisfactory results. Recent advancements in Large Language Models (LLMs) inspire the "LLM-as-a-judge" paradigm, where LLMs are leveraged to perform scoring, ranking, or selection across various tasks and applications. This paper provides a comprehensive survey of LLM-based judgment and assessment, offering an in-depth overview to advance this emerging field. We begin by giving detailed definitions from both input and output perspectives. Then we introduce a comprehensive taxonomy to explore LLM-as-a-judge from three dimensions: what to judge, how to judge and where to judge. Finally, we compile benchmarks for evaluating LLM-as-a-judge and highlight key challenges and promising directions, aiming to provide valuable insights and inspire future research in this promising research area. Paper list and more resources about LLM-as-a-judge can be found at \url{https://github.com/llm-as-a-judge/Awesome-LLM-as-a-judge} and \url{https://llm-as-a-judge.github.io}.
Anomaly Detection and RFI Classification with Unsupervised Learning in Narrowband Radio Technosignature Searches
2024-11-25
Ben Jacobson-Bell, Steve Croft, Carmen Choza, Alex Andersson, Daniel Bautista, Vishal Gajjar, Matthew Lebofsky, David H. E. MacMahon, Caleb Painter, Andrew P. V. Siemion
The search for radio technosignatures is an anomaly detection problem: candidate signals represent needles of interest in the proverbial haystack of radio-frequency interference (RFI). Current search frameworks find an enormity of false-positive signals, especially in large surveys, requiring manual follow-up to a sometimes prohibitive degree. Unsupervised learning provides an algorithmic way to winnow the most anomalous signals from the chaff, as well as group together RFI signals that bear morphological similarities. We present GLOBULAR (Grouping Low-frequency Observations By Unsupervised Learning After Reduction) clustering, a signal processing method that uses HDBSCAN to reduce the false-positive rate and isolate outlier signals for further analysis. When combined with a standard narrowband signal detection and spatial filtering pipeline, such as turboSETI, GLOBULAR clustering offers significant improvements in the false-positive rate over the standard pipeline alone, suggesting dramatic potential for the amelioration of manual follow-up requirements for future large surveys. By removing RFI signals in regions of high spectral occupancy, GLOBULAR clustering may also enable the detection of signals missed by the standard pipeline. We benchmark our method against the Choza et al. (2024) turboSETI-only search of 97 nearby galaxies at L-band, demonstrating a false-positive hit reduction rate of 93.1% and a false-positive event reduction rate of 99.3%.
The hot gas mass fraction in halos. From Milky Way-like groups to massive clusters
2024-11-25
P. Popesso, A. Biviano, I. Marini, K. Dolag, S. Vladutescu-Zopp, B. Csizi, V. Biffi, G. Lamer, A. Robothan, M. Bravo, L. Lovisari, S. Ettori, M. Angelinelli, S. Driver, V. Toptun, A. Dev, D. Mazengo, A. Merloni, J. Comparat, G. Ponti, T. Mroczkowski, E. Bulbul, S. Grandis, E. Bahar
By using eROSITA data in the eFEDS area, we provide a measure of the hot gas fraction vs. halo mass relation over the largest halo mass range, from Milky Way-sized halos to massive clusters, and to the largest radii ever probed so far in local systems. To cope with the incompleteness and selection biases of the X-ray selection, we apply the stacking technique in eROSITA data of a highly complete and tested sample of optically selected groups. The method has been extensively tested on mock observations. In massive clusters, the hot gas alone provides a baryon budget within $R_{200}$ consistent with the cosmic value. At the same time, at the group mass scale, it accounts only for 20-40% of it. The hot gas fraction vs. halo mass relation is well-fitted by a power law, with a consistent shape and a normalization varying at maximum by a factor of 2 from $r_{500}$ to $r_{200}$. Such a relation is consistent with other works in the literature that consider X-ray survey data at the same depth as eFEDS. However, it provides a lower average gas fraction in the group regime than works based on X-ray bright group samples. The comparison of the observed relation with the predictions of several hydrodynamical simulations (BAHAMAS, FLAMINGO, SIMBA, Illustris, IllustrisTNG, MillenniumTNG, and Magneticum) shows that all simulations but Magneticum and SIMBA overpredict the gas fraction, with the largest discrepancy (up to a factor of 3) in the massive group-poor cluster halo mass range. We emphasize the need for mechanisms that can effectively expel gas to larger radii in galaxy groups without excessively quenching star formation in their member galaxies. Current hydrodynamical simulations face a significant challenge in balancing their subgrid physics: none can sufficiently evacuate gas from the halo virial region without negatively impacting the properties of the resident galaxy population.
Representation Collapsing Problems in Vector Quantization
2024-11-25
Wenhao Zhao, Qiran Zou, Rushi Shah, Dianbo Liu
Vector quantization is a technique in machine learning that discretizes continuous representations into a set of discrete vectors. It is widely employed in tokenizing data representations for large language models, diffusion models, and other generative models. Despite its prevalence, the characteristics and behaviors of vector quantization in generative models remain largely underexplored. In this study, we investigate representation collapse in vector quantization - a critical degradation where codebook tokens or latent embeddings lose their discriminative power by converging to a limited subset of values. This collapse fundamentally compromises the model's ability to capture diverse data patterns. By leveraging both synthetic and real datasets, we identify the severity of each type of collapses and triggering conditions. Our analysis reveals that restricted initialization and limited encoder capacity result in tokens collapse and embeddings collapse. Building on these findings, we propose potential solutions aimed at mitigating each collapse. To the best of our knowledge, this is the first comprehensive study examining representation collapsing problems in vector quantization.
The perils of stacking optically selected groups in eROSITA data. The Magneticum perspective
2024-11-25
P. Popesso, I. Marini, K. Dolag, G. Lamer, B. Csizi, S. Vladutescu-Zopp, V. Biffi, A. Robothan, M. Bravo, E. Tempel, X. Yang, Q. Li, A. Biviano, L. Lovisari, S. Ettori, M. Angelinelli, S. Driver, V. Toptun, A. Dev, D. Mazengo, A. Merloni, T. Mroczkowski, J. Comparat, Y. Zhang, G. Ponti, E. Bulbul
Hydrodynamical simulation predictions are often compared with observational data without fully accounting for systematics and biases specific to observational techniques. Using the magnetohydrodynamical simulation Magneticum, we generate mock eROSITA eRASS:4 data, combined with GAMA-like spectroscopic surveys and optically selected galaxy catalogs from the same light-cone, to analyze hot gas properties in galaxy groups via a stacking technique. This study aims to (i) incorporate observational systematics into predictions and (ii) evaluate the reliability of stacking techniques for determining average X-ray properties of galaxy groups. Our analysis provides X-ray emission predictions from Magneticum, including contributions from AGN, X-ray binaries (XRBs), and the Intra-Group Medium (IGM) as a function of halo mass, covering Milky Way (MW)-like groups to poor clusters. We find that AGN and XRBs dominate the X-ray surface brightness profiles of low-mass halos. The reliability of stacking techniques is tested by reproducing input X-ray surface brightness and electron density profiles, accounting for completeness and contamination of prior samples, miscentering of optical group centers, uncertainties in X-ray emissivity due to gas temperature and metallicity assumption, and systematics in halo mass proxies. The halo mass proxy emerges as the primary source of systematics, affecting X-ray surface brightness and scaling relations. We show that stacked X-ray luminosity-mass relations are flatter than input relations but consistent with observations. Additionally, the retrieved hot gas fraction-mass relation aligns well with observational data. These results highlight the need to account for systematic errors when comparing stacking techniques to other methods using different prior catalogs or predictions.
Square ice Coulomb phase as a percolated vertex lattice
2024-11-25
Johann Coraux, Nicodème Rougier, Benjamin Canals, Nicolas Rougemaille
The square ice is a canonical example of a Coulomb phase in two dimensions: Its ground state is extensively degenerate and satisfies a local constraint on the spin arrangement (the so-called ice rule). In this paper, we use a loop flip algorithm to explore the properties of this ground state that we analyze not in terms of a spin texture, but rather in terms of a spatial distribution of ice-rule satisfying vertices. More specifically, we determine for various lattice sizes the average vertex populations characterizing the ice manifold, the pairwise vertex correlations, and the size distribution of vertex clusters. Comparing these results to those obtained from random, constraint-free vertex tilings, the square ice manifold is found to resemble an almost ideal vertex gas, and the cluster size distribution of ice-rule satisfying vertices is well approximated by percolation theory. Remarkably, this description remains reasonably accurate when monopoles are present in a dilute amount, allowing a direct comparison with experiments. Revising former experimental results on two artificial square ice systems, we illustrate the interest of our approach to spot the presence of a Coulomb phase from a vertex analysis.
Unusual Coulomb phase physics in the arctic square ice
2024-11-25
Johann Coraux, Nicolas Rougemaille
The square ice is a two-dimensional spin liquid hosting a Coulomb phase physics. When constrained under specific boundary conditions, the so-called domain-wall boundary conditions, a phase separation occurs that leads to the formation of a spin liquid confined within a disk surrounded by magnetically ordered regions. Here, we numerically characterize the ground-state properties of this spin liquid, coined the arctic square ice in reference to a phenomenon known in statistical mechanics. Our results reveal that both the vertex distributions and the magnetic correlations are inhomogeneous within the liquid region, and they exhibit a radial dependence. If these properties resemble those of the conventional square ice close to the center of the disk, they evolve continuously as the disk perimeter is approached. There, the spin liquid orders. As a result, pinch points, signaling the presence of algebraic spin correlations, coexist with magnetic Bragg peaks in the magnetic structure factor computed within the disk. The arctic square ice thus appears as an unconventional Coulomb phase sharing common features with a fragmented spin liquid, albeit on a charge-neutral vacuum.
Two-dimensional silicon carbide stands out among 2D materials, primarily due to its notable band gap, unlike its carbon-based counterparts. However, the binary nature and non-layered structure of bulk SiC present challenges in fabricating its 2D counterpart. Recent advancements in technology have led to the successful synthesis of atomically thin, large-scale epitaxial monolayers of hexagonal-SiC and Si9C15 , marking a significant milestone in semiconductor research. Inspired by these advancements, we have computationally designed another stable phase of 2D-SiC in the popular biphenylene network, termed SiC-biphenylene. This structure is characterized by interconnected polygons of octagons, hexagons, and tetragons arranged periodically. The dynamical and thermal stability has been confirmed through ab initio phonon dispersion and molecular dynamics simulations. The structure demonstrates a high melting point of approximately 3475 K and a direct band gap of 2.16 eV using the HSE06 functional. Upon considering many-body effects, the quasiparticle band gap widens to 2.89 eV at the G0W0 level, indicating pronounced electron correlation effects within the material. The optical spectrum obtained from solving the Bethe-Salpeter equation (G0W0+BSE) identifies the first optically active exciton peak at 2.07 eV, corresponding to a strongly bound exciton with a binding energy of 0.82 eV. Furthermore, the investigation into stable bilayer structures across various stacking configurations highlights the impact of stacking patterns on excitonic binding energies. Our investigation extends to identifying the stable bulk phase of SiC-biphenylene, revealing lower self-energy corrections compared to monolayer and bilayer structures, attributed to increased electron delocalization in bulk structures.
Cosmic Structure Formation in the Non-linear Regime: Beyond Gaussian Statistics and Standard Cosmologies
2024-11-25
Alex Gough
The cosmic large scale structure encodes the formation and evolution of a weblike network of dark matter and galaxies within the Universe. The cosmological information is wrapped up in non-Gaussian statistics requiring characterisation beyond two-point correlations. Accurate modelling of these non-Gaussian statistics and the underlying non-linear dynamics of gravitational collapse are key to extracting maximal information from ongoing and upcoming cosmological surveys. This thesis centres on questions relating to clustering statistics, dynamics, and fundamental physics: A. How can we efficiently characterise the statistics of the late time matter field? B. How can we capture the non-linear phase-space dynamics of gravitational collapse? C. How do changes to fundamental physics impact those clustering statistics and dynamics? Specifically we present four aspects addressing these questions: 1. We demonstrate the probability distribution function (PDF) of the matter density can be accurately predicted in modified gravity and dynamical dark energy models, and that it provides good complementarity to standard two-point analyses for detecting these features. 2. We demonstrate the joint PDF of densities in two cells can be used to predict the covariance of the one-point PDF in simple clustering models, providing estimates of the density dependent clustering and super-sample covariance missed in cosmological simulations. 3. We use a wave-based forward model of dark matter to demonstrate its capability to encode the full phase-space dynamics beyond a perfect fluid and determine certain universal scaling features in such models. 4. Using the wave dark matter forward model we analyse one-point statistics to complement existing analytic and numerical approaches in studying fundamentally wavelike dark matter.
Emerging new phases in correlated Mott insulator Ca2RuO4
2024-11-25
Giuseppe Cuono, Filomena Forte, Alfonso Romano, Canio Noce
The Mott insulator Ca2RuO4 is a paradigmatic example among transition metal oxides, where the interplay of charge, spin, orbital, and lattice degrees of freedom leads to competing quantum phases. In this paper, we focus on and review some key aspects, from the underlying physical framework and its basic properties, to recent theoretical efforts that aim to trigger unconventional quantum ground states, using several external parameters and stimuli. Using first-principle calculations, we demonstrate that Ca2RuO4 shows a spin splitting in the reciprocal space, and identify it as an altermagnetic candidate material. The non relativistic spin-splitting has an orbital selective nature, dictated by the local crystallographic symmetry. Next, we consider two routes that may trigger exotic quantum states. The first one corresponds to transition metal substitution of the 4d4 Ru with isovalent 3d3 ions. This substitutional doping may alter the spin-orbital correlations favoring the emergence of negative thermal expansion. The second route explores fledgling states arising in a nonequilibrium steady state under the influence of an applied electric field. We show that the electric field can directly affect the orbital density, eventually leading to strong orbital fluctuations and the suppression of orbital imbalance, which may, in turn, reduce antiferromagnetism. These aspects suggest possible practical applications, as its unique properties may open up possibilities for augmenting existing technologies, surpassing the limitations of conventional materials.
Detecting clusters and groups of galaxies populating the local Universe in large optical spectroscopic surveys
2024-11-25
I. Marini, P. Popesso, K. Dolag, M. Bravo, A. Robotham, E. Tempel, Q. Li, X. Yang, B. Csizi, P. Behroozi, V. Biffi, A. Biviano, G. Lamer, N. Malavasi, D. Mazengo, V. Toptun
Wide-field cosmological surveys provide hundreds of thousands of spectroscopically confirmed galaxy groups and clusters, valuable for tracing baryonic matter distribution. However, controlling systematics in identifying host dark matter halos and estimating their properties is crucial. We evaluate three group detection methods on a simulated dataset replicating the GAMA selection to understand systematics and selection effects. This is key for interpreting data from SDSS, GAMA, DESI, WAVES, and leveraging optical catalogues in the (X-ray) eROSITA era to quantify baryonic mass in galaxy groups. Using a lightcone from the Magneticum hydrodynamical simulation, we simulate a spectroscopic galaxy survey in the local Universe (down to $z<0.2$ and stellar mass completeness $M_{\star}\geq10^{9.8} M_{\odot}$). We assess completeness and contamination of reconstructed halo catalogues, evaluate membership accuracy, and analyse the halo mass recovery rate of group finders. All three group finders achieve high completeness ($>80\%$) at group and cluster scales, confirming optical selection's suitability for dense regions. Contamination at low masses ($M_{200}<10^{13} M_{\odot}$) arises from interlopers and fragmentation. Membership is at least 70\% accurate above the group mass scale, but inaccuracies bias halo mass estimates using galaxy velocity dispersion. Alternative proxies, like total stellar luminosity or mass, yield more accurate halo masses. The cumulative luminosity function of galaxy members matches predictions, showing the group finders' accuracy in identifying galaxy populations. These results confirm the reliability and completeness of spectroscopic catalogues produced by state-of-the-art group finders. This supports studies requiring large spectroscopic samples of galaxy groups and clusters, as well as investigations into galaxy evolution across diverse environments.
Persistence and bimodality of large-scale turbulent structures across a Rayleigh-Taylor layer: Impact on transport and physical modelling through two-field-conditional correlations
2024-11-25
R. Watteaux, J. A. Redford, A. Llor
The distribution functions of field fluctuations of the turbulent mixing layer produced by a Rayleigh-Taylor instability (RTI) have long been hypothesized to involve bimodal effects. The present work reviews existing quantitative and qualitative evidence in support this conjecture, provides an associated theoretical framework, and measures the corresponding relevant statistical quantities on a simulation of a turbulent RTI at low Atwood number. The bimodal behaviour of fluctuations, readily observable close to the edges of the mixing zone, is less obvious within the mixing zone where indirect evidence comes from different sources, here gathered, discussed, and expanded: energy structure of buoyancy-drag equation, visual eduction from simulated RTI, two-fluid conditional analysis of energy balance, bulk non-dimensional turbulent numbers (Stokes, Knudsen, and Reynolds)... In order to carry out a bimodal analysis of the statistical quantities, two complementary indicator functions are here defined, the `light, upward moving' and `heavy downward moving' fluid zones. An adapted reconstruction is introduced here, based on the thresholding of a space- and time-filtered field (here the vertical velocity). The prescription for the two-structure-field segmentation was applied to a direct numerical simulation of an RTI and the corresponding structure-conditioned averages of the main quantities were obtained (concentrations, momentum, turbulent energies...). Notable features for turbulence understanding and modelling are then observed. The measured conditional averages provide the first know reference data for validation and calibration of so-called `two-structure' RANS turbulence models such as Youngs' (2015 Int. J. Heat Fluid Fl. 56, 233--250 and refs therein) and 2SFK (2003 Laser Part.\ Beams 21 (3), 311--315).
Optimal Boundary Regularity for Uniformly Degenerate Elliptic Equations
2024-11-25
Qing Han, Jiongduo Xie
In this survey paper, we study the optimal regularity of solutions to uniformly degenerate elliptic equations in bounded domains and establish the H\"older continuity of solutions and their derivatives up to the boundary.
A Survey of Blockchain-Based Privacy Applications: An Analysis of Consent Management and Self-Sovereign Identity Approaches
2024-11-25
Rodrigo Dutra Garcia, Gowri Ramachandran, Kealan Dunnett, Raja Jurdak, Caetano Ranieri, Bhaskar Krishnamachari, Jo Ueyama
Modern distributed applications in healthcare, supply chain, and the Internet of Things handle a large amount of data in a diverse application setting with multiple stakeholders. Such applications leverage advanced artificial intelligence (AI) and machine learning algorithms to automate business processes. The proliferation of modern AI technologies increases the data demand. However, real-world networks often include private and sensitive information of businesses, users, and other organizations. Emerging data-protection regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) introduce policies around collecting, storing, and managing digital data. While Blockchain technology offers transparency, auditability, and immutability for multi-stakeholder applications, it lacks inherent support for privacy. Typically, privacy support is added to a blockchain-based application by incorporating cryptographic schemes, consent mechanisms, and self-sovereign identity. This article surveys the literature on blockchain-based privacy-preserving systems and identifies the tools for protecting privacy. Besides, consent mechanisms and identity management in the context of blockchain-based systems are also analyzed. The article concludes by highlighting the list of open challenges and further research opportunities.
Adapter-based Approaches to Knowledge-enhanced Language Models -- A Survey
2024-11-25
Alexander Fichtl, Juraj Vladika, Georg Groh
Knowledge-enhanced language models (KELMs) have emerged as promising tools to bridge the gap between large-scale language models and domain-specific knowledge. KELMs can achieve higher factual accuracy and mitigate hallucinations by leveraging knowledge graphs (KGs). They are frequently combined with adapter modules to reduce the computational load and risk of catastrophic forgetting. In this paper, we conduct a systematic literature review (SLR) on adapter-based approaches to KELMs. We provide a structured overview of existing methodologies in the field through quantitative and qualitative analysis and explore the strengths and potential shortcomings of individual approaches. We show that general knowledge and domain-specific approaches have been frequently explored along with various adapter architectures and downstream tasks. We particularly focused on the popular biomedical domain, where we provided an insightful performance comparison of existing KELMs. We outline the main trends and propose promising future directions.
Two-gap superconductivity in the noncentrosymmetric La$_3$Se$_4$ compound
2024-11-25
F. Košuth, N. Potomová, Z. Pribulová, J. Kačmarčík, M. Naskar, D. S. Inosov, S. Ash, A. K. Ganguli, J. Šoltýs, V. Cambel, P. Szabó, P. Samuely
Point-contact Andreev reflection spectroscopy at low temperatures and high magnetic fields has been performed on a noncentrosymmetric La$_3$Se$_4$ superconductor with a critical temperature $T_c$ = 8 K. Two superconducting energy gaps $\Delta_1$ and $\Delta_2$ with $2\Delta_{1}/k_{B} T_{c}$ ~ 5.8 and $2\Delta_{2}/k_{B} T_{c}$ ~ 2.3, are directly observed in some of the spectra. The temperature and magnetic field effects help to resolve a two-gap structure even on the most frequent spectra where at low temperatures only a single gap is apparent, reflected in a pair of maxima around the zero bias. Two-gap superconductivity consistently with the point contact Andreev reflection spectroscopy is also supported by the heat capacity and the Hall probe magnetization measurements.
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation
2024-11-25
M. M. A. Valiuddin, R. J. G. van Sloun, C. G. A. Viviers, P. H. N. de With, F. van der Sommen
Advancements in image segmentation play an integral role within the greater scope of Deep Learning-based computer vision. Furthermore, their widespread applicability in critical real-world tasks has given rise to challenges related to the reliability of such algorithms. Hence, uncertainty quantification has been extensively studied within this context, enabling expression of model ignorance (epistemic uncertainty) or data ambiguity (aleatoric uncertainty) to prevent uninformed decision making. Due to the rapid adoption of Convolutional Neural Network (CNN)-based segmentation models in high-stake applications, a substantial body of research has been published on this very topic, causing its swift expansion into a distinct field. This work provides a comprehensive overview of probabilistic segmentation by discussing fundamental concepts in uncertainty that govern advancements in the field as well as the application to various tasks. We identify that quantifying aleatoric and epistemic uncertainty approximates Bayesian inference w.r.t. to either latent variables or model parameters, respectively. Moreover, literature on both uncertainties trace back to four key applications; (1) to quantify statistical inconsistencies in the annotation process due ambiguous images, (2) correlating prediction error with uncertainty, (3) expanding the model hypothesis space for better generalization, and (4) active learning. Then, a discussion follows that includes an overview of utilized datasets for each of the applications and comparison of the available methods. We also highlight challenges related to architectures, uncertainty-based active learning, standardization and benchmarking, and recommendations for future work such as methods based on single forward passes and models that appropriately leverage volumetric data.
4FGL J1544.2$-$2554: a new spider pulsar candidate
2024-11-25
A. V. Karpova, S. V. Zharikov, D. A. Zyuzin, A. Yu. Kirichenko, Yu. A. Shibanov, I. F. Márquez
Context. Spider pulsars are millisecond pulsars in tight binary systems, in which a low-mass companion star is heated and ablated by the pulsar wind. Their observations allow one to study stellar evolution with formation of millisecond pulsars and physics of superdense matter in neutron stars. However, spiders are rare due to difficulties of their discovery using typical radio search techniques. The Fermi $\gamma$-ray source 4FGL J1544.2$-$2554 was recently proposed as a pulsar candidate, and its likely X-ray and optical counterparts with the galactic coordinates $l\approx344.\!\!^\circ76$, $b\approx22.\!\!^\circ59$ and the magnitude $G\approx20.6$ were found using the eROSITA and Gaia surveys. Aims. Our goals are to study whether the source is a new spider pulsar and to estimate its fundamental parameters. Methods. We performed the first optical time-series multi-band photometry of the object. We used the Lomb-Scargle periodogram to search for its brightness periodicity and fitted its light curves with a model of direct heating of the binary companion by the pulsar wind. Results. The source shows a strong brightness variability with a period of $\approx$ 2.724 h and an amplitude of $\gtrsim$ 2.5 mag, and its light curves have a single broad peak per period. These features are typical for spider pulsars. The curves are well fitted by the direct heating model, resulting in an orbit inclination of the presumed spider system of $\approx 83^\circ$, a companion mass of $\approx 0.1$ M$_\odot$, its ''day-side'' and ''night-side'' temperatures of $\approx 7200$ K and $\approx 3000$ K, a Roche-lobe filling factor of $\approx 0.65$ and a distance of $\approx 2.1$ kpc. Conclusions. Our findings suggest that 4FGL J1544.2$-$2554 is a spider pulsar. This encourages searches for the pulsar millisecond pulsations in the radio and $\gamma$-rays to confirm its nature.
We give a review of brackets and interior products in bosonic string theory, in different representations, used in formulation of a theory and derived in a transformation of related mathematical structures. We consider the C-bracket, defined in double string theory, in a context of Poisson algebra of symmetry generator, and show its connection to prior brackets trough anomaly reduction, and its characteristics related to Courant algebroid definitions.
Lane detection plays an important role in autonomous driving perception systems. As deep learning algorithms gain popularity, monocular lane detection methods based on deep learning have demonstrated superior performance and emerged as a key research direction in autonomous driving perception. The core design of these algorithmic frameworks can be summarized as follows: (1) Task paradigm, focusing on lane instance-level discrimination; (2) Lane modeling, representing lanes as a set of learnable parameters in the neural network; (3) Global context supplementation, enhancing the detection of obscure lanes; (4) Perspective effect elimination, providing 3D lanes usable for downstream applications. From these perspectives, this paper presents a comprehensive overview of existing methods, encompassing both the increasingly mature 2D lane detection approaches and the developing 3D lane detection works. For a relatively fair comparison, in addition to comparing the performance of mainstream methods on different benchmarks, their inference speed is also investigated under a unified setting. Moreover, we present some extended works on lane detection, including multi-task perception, video lane detection, online high-definition map construction, and lane topology reasoning, to offer readers a comprehensive roadmap for the evolution of lane detection. Finally, we point out some potential future research directions in this field. We exhaustively collect the papers and codes of existing works at https://github.com/Core9724/Awesome-Lane-Detection and will keep tracing the research.
In lung radiotherapy, the primary objective is to optimize treatment outcomes by minimizing exposure to healthy tissues while delivering the prescribed dose to the target volume. The challenge lies in accounting for lung tissue motion due to breathing, which impacts precise treatment alignment. To address this, the paper proposes a prospective approach that relies solely on pre-treatment information, such as planning CT scans and derived data like vector fields from deformable image registration. This data is compared to analogous patient data to tailor treatment strategies, i.e., to be able to review treatment parameters and success for similar patients. To allow for such a comparison, an embedding and clustering strategy of prospective patient data is needed. Therefore, the main focus of this study lies on reducing the dimensionality of deformable registration-based vector fields by employing a voxel-wise spherical coordinate transformation and a low-dimensional 2D oriented histogram representation. Afterwards, a fully unsupervised UMAP embedding of the encoded vector fields (i.e., patient-specific motion information) becomes applicable. The functionality of the proposed method is demonstrated with 71 in-house acquired 4D CT data sets and 33 external 4D CT data sets. A comprehensive analysis of the patient clusters is conducted, focusing on the similarity of breathing patterns of clustered patients. The proposed general approach of reducing the dimensionality of registration vector fields by encoding the inherent information into oriented histograms is, however, applicable to other tasks.
A Survey of Cameron-Liebler Sets and Low Degree Boolean Functions in Grassmann Graphs
2024-11-25
Ferdinand Ihringer
We survey results for Cameron-Liebler sets and low degree Boolean functions for Hamming graphs, Johnson graphs and Grassmann graphs from the point of view of association schemes. This survey covers selected results in finite geometry, Boolean function analysis, design theory, coding theory, and cryptography.
2024-11-25
Estela Suarez, Hendryk Bockelmann, Norbert Eicker, Jan Eitzinger, Salem El Sayed, Thomas Fieseler, Martin Frank, Peter Frech, Pay Giesselmann, Daniel Hackenberg, Georg Hager, Andreas Herten, Thomas Ilsche, Bastian Koller, Erwin Laure, Cristina Manzano, Sebastian Oeste, Michael Ott, Klaus Reuter, Ralf Schneider, Kay Thust, Benedikt von St. Vieth
High-Performance Computing (HPC) systems are among the most energy-intensive scientific facilities, with electric power consumption reaching and often exceeding 20 megawatts per installation. Unlike other major scientific infrastructures such as particle accelerators or high-intensity light sources, which are few around the world, the number and size of supercomputers are continuously increasing. Even if every new system generation is more energy efficient than the previous one, the overall growth in size of the HPC infrastructure, driven by a rising demand for computational capacity across all scientific disciplines, and especially by artificial intelligence workloads (AI), rapidly drives up the energy demand. This challenge is particularly significant for HPC centers in Germany, where high electricity costs, stringent national energy policies, and a strong commitment to environmental sustainability are key factors. This paper describes various state-of-the-art strategies and innovations employed to enhance the energy efficiency of HPC systems within the national context. Case studies from leading German HPC facilities illustrate the implementation of novel heterogeneous hardware architectures, advanced monitoring infrastructures, high-temperature cooling solutions, energy-aware scheduling, and dynamic power management, among other optimizations. By reviewing best practices and ongoing research, this paper aims to share valuable insight with the global HPC community, motivating the pursuit of more sustainable and energy-efficient HPC operations.
This study proposes a versatile framework for optimizing psychomotor learning through human motion analysis. Utilizing a wearable IMU sensor system, the motion trajectories of a given psychomotor task are acquired and then linked to points in a performance space using a predefined set of quality metrics specific to the psychomotor skill. This enables the identification of a benchmark cluster in the performance space, allowing correspondences to be established between the performance clusters and sets of trajectories in the motion space. As a result, common or specific deviations in the performance space can be identified, enabling remedial actions in the motion space to optimize performance. A thorough validation of the proposed framework is done in this paper using a Table Tennis forehand stroke as a case study. The resulting quantitative and visual representation of performance empowers individuals to optimize their skills and achieve peak performance.
When humans read a specific text, they often visualize the corresponding images, and we hope that computers can do the same. Text-to-image synthesis (T2I), which focuses on generating high-quality images from textual descriptions, has become a significant aspect of Artificial Intelligence Generated Content (AIGC) and a transformative direction in artificial intelligence research. Foundation models play a crucial role in T2I. In this survey, we review over 440 recent works on T2I. We start by briefly introducing how GANs, autoregressive models, and diffusion models have been used for image generation. Building on this foundation, we discuss the development of these models for T2I, focusing on their generative capabilities and diversity when conditioned on text. We also explore cutting-edge research on various aspects of T2I, including performance, controllability, personalized generation, safety concerns, and consistency in content and spatial relationships. Furthermore, we summarize the datasets and evaluation metrics commonly used in T2I research. Finally, we discuss the potential applications of T2I within AIGC, along with the challenges and future research opportunities in this field.
Stop Playing the Guessing Game! Target-free User Simulation for Evaluating Conversational Recommender Systems
2024-11-25
Sunghwan Kim, Tongyoung Kim, Kwangwook Seo, Jinyoung Yeo, Dongha Lee
Recent approaches in Conversational Recommender Systems (CRSs) have tried to simulate real-world users engaging in conversations with CRSs to create more realistic testing environments that reflect the complexity of human-agent dialogue. Despite the significant advancements, reliably evaluating the capability of CRSs to elicit user preferences still faces a significant challenge. Existing evaluation metrics often rely on target-biased user simulators that assume users have predefined preferences, leading to interactions that devolve into simplistic guessing game. These simulators typically guide the CRS toward specific target items based on fixed attributes, limiting the dynamic exploration of user preferences and struggling to capture the evolving nature of real-user interactions. Additionally, current evaluation metrics are predominantly focused on single-turn recall of target items, neglecting the intermediate processes of preference elicitation. To address this, we introduce PEPPER, a novel CRS evaluation protocol with target-free user simulators constructed from real-user interaction histories and reviews. PEPPER enables realistic user-CRS dialogues without falling into simplistic guessing games, allowing users to gradually discover their preferences through enriched interactions, thereby providing a more accurate and reliable assessment of the CRS's ability to elicit personal preferences. Furthermore, PEPPER presents detailed measures for comprehensively evaluating the preference elicitation capabilities of CRSs, encompassing both quantitative and qualitative measures that capture four distinct aspects of the preference elicitation process. Through extensive experiments, we demonstrate the validity of PEPPER as a simulation environment and conduct a thorough analysis of how effectively existing CRSs perform in preference elicitation and recommendation.
In diagnosing mental diseases from electroencephalography (EEG) data, neural network models such as Transformers have been employed to capture temporal dynamics. Additionally, it is crucial to learn the spatial relationships between EEG sensors, for which Graph Neural Networks (GNNs) are commonly used. However, fine-tuning large-scale complex neural network models simultaneously to capture both temporal and spatial features increases computational costs due to the more significant number of trainable parameters. It causes the limited availability of EEG datasets for downstream tasks, making it challenging to fine-tune large models effectively. We propose EEG-GraphAdapter (EGA), a parameter-efficient fine-tuning (PEFT) approach to address these challenges. EGA is integrated into pre-trained temporal backbone models as a GNN-based module and fine-tuned itself alone while keeping the backbone model parameters frozen. This enables the acquisition of spatial representations of EEG signals for downstream tasks, significantly reducing computational overhead and data requirements. Experimental evaluations on healthcare-related downstream tasks of Major Depressive Disorder and Abnormality Detection demonstrate that our EGA improves performance by up to 16.1% in the F1-score compared with the backbone BENDR model.
Beyond Task Vectors: Selective Task Arithmetic Based on Importance Metrics
2024-11-25
Tian Bowen, Lai Songning, Wu Jiemin, Shuai Zhihao, Ge Shiming, Yue Yutao
Pretrained models have revolutionized deep learning by enabling significant performance improvements across a wide range of tasks, leveraging large-scale, pre-learned knowledge representations. However, deploying these models in real-world multi-task learning (MTL) scenarios poses substantial challenges, primarily due to high computational costs and inefficiencies in inference. Traditional approaches such as pruning, quantization, and knowledge distillation have been explored to mitigate these issues, but they often fall short in fully addressing the complexities of multi-task environments. This paper introduces \textbf{\underline{S}}elective \textbf{\underline{T}}ask \textbf{\underline{A}}rithmetic \underline{\textbf{(STA)}}, a training-free framework designed to enhance multi-task performance through task-specific parameter fusion. STA addresses three key challenges: (i) \textbf{Parameter importance diversity: } Recognizing that different tasks relie on distinct parameters, STA employs a loss-sensitive parameter importance metric derived from a first-order Taylor expansion to accurately measure the importance of parameters for each task. (ii) \textbf{Over-reliance on hyperparameter tuning: }By enhancing the sparsity of task vectors through parameter importance metrics, STA reduces the need for extensive hyperparameter tuning, thereby improving the generalization and robustness of the model. (iii) \textbf{Neglect of other abilities in task arithmetic: } Previous works have largely overlooked the potential for more precise task forgetting. STA leverages its parameter importance metric to achieve more controlled and effective task forgetting, minimizing the impact of noisy elements that can degrade model performance. Experimental results demonstrate that STA achieves superior multi-task performance across benchmarks and excellent performance in task forgetting.
An Optically Led Search for Kilonovae to z$\sim$0.3 with the Kilonova and Transients Program (KNTraP)
2024-11-25
Natasha Van Bemmel, Jielai Zhang, Jeff Cooke, Armin Rest, Anais Möller, Igor Andreoni, Katie Auchettl, Dougal Dobie, Bruce Gendre, Simon Goode, James Freeburn, David O. Jones, Charles D. Kilpatrick, Amy Lien, Arne Rau, Lee Spitler, Mark Suhr, Fransisco Valdes
Compact binary mergers detectable in gravitational waves can be accompanied by a kilonova, an electromagnetic transient powered by radioactive decay of newly synthesised r-process elements. A few kilonova candidates have been observed during short gamma-ray burst follow-up, and one found associated with a gravitational wave detection, GW170817. However, robust kilonova candidates are yet to be found in un-triggered, wide-field optical surveys, that is, a search not requiring an initial gravitational wave or gamma-ray burst trigger. Here we present the first observing run for the Kilonova and Transients Program (KNTraP) using the Dark Energy Camera. The first KNTraP run ran for 11 nights, covering 31 fields at a nightly cadence in two filters. The program can detect transients beyond the LIGO/Virgo/KAGRA horizon, be agnostic to the merger orientation, avoid the Sun and/or Galactic plane, and produces high cadence multi-wavelength light curves. The data were processed nightly in real-time for rapid identification of transient candidates, allowing for follow-up of interesting candidates before they faded away. Three fast-rising candidates were identified in real-time, however none had the characteristics of the kilonova AT2017gfo associated with GW170817 or with the expected evolution for kilonovae from our fade-rate models. After the run, the data were reprocessed, then subjected to stringent filtering and model fitting to search for kilonovae offline. Multiple KNTraP runs (3+) are expected to detect kilonovae via this optical-only search method. No kilonovae were detected in this first KNTraP run using our selection criteria, constraining the KN rate to $R < 1.8\times10^{5}$ Gpc$^{-3}$ yr$^{-1}$.
The application of Virtual Reality Environments (VRE) has been gaining momentum as a relatively new tool to assist with mitigating various difficulties including abstractness of concepts, lack of user engagement, perception of disconnection from other users. A VRE may offer both synchronous and asynchronous experiences, in addition to an immersive environment which promotes users' engagement. Past research has shown that, in general, VRE do improve the experiences they try to enhance in many aspects of human activity. Terms like immersiveness and 3D representation of real life objects and environments are, as it appears, the two most obvious positive effects of Virtual Reality (VR) applications. However, despite these benefits it does not come without challenges. The main three concepts/challenges are the spatial design, the collaboration interaction between its members and the VRE, and the audio and video fidelity. Each of the three includes a number of other components that should be addressed for the total experience to be fine-tuned. These include mutual embodiment and shared perspectives, teleportation, gestural interaction, symmetric and asymmetric collaboration, physical and virtual co-location, inventory, and time and spatial synchronization. This paper comprises a survey of the literature, that identifies and explains the features introduced and the challenges involved with the VREs, and furthermore provides various interesting future research directions.
In recent years, the growth of data across various sectors, including healthcare, security, finance, and education, has created significant opportunities for analysis and informed decision-making. However, these datasets often contain sensitive and personal information, which raises serious privacy concerns. Protecting individual privacy is crucial, yet many existing machine learning and data publishing algorithms struggle with high-dimensional data, facing challenges related to computational efficiency and privacy preservation. To address these challenges, we introduce an effective data publishing algorithm \emph{DP-CDA}. Our proposed algorithm generates synthetic datasets by randomly mixing data in a class-specific manner, and inducing carefully-tuned randomness to ensure formal privacy guarantees. Our comprehensive privacy accounting shows that DP-CDA provides a stronger privacy guarantee compared to existing methods, allowing for better utility while maintaining strict level of privacy. To evaluate the effectiveness of DP-CDA, we examine the accuracy of predictive models trained on the synthetic data, which serves as a measure of dataset utility. Importantly, we identify an optimal order of mixing that balances privacy guarantee with predictive accuracy. Our results indicate that synthetic datasets produced using the DP-CDA can achieve superior utility compared to those generated by traditional data publishing algorithms, even when subject to the same privacy requirements.
Objectives: The vast and complex nature of human genomic sequencing data presents challenges for effective analysis. This review aims to investigate the application of Natural Language Processing (NLP) techniques, particularly Large Language Models (LLMs) and transformer architectures, in deciphering genomic codes, focusing on tokenization, transformer models, and regulatory annotation prediction. The goal of this review is to assess data and model accessibility in the most recent literature, gaining a better understanding of the existing capabilities and constraints of these tools in processing genomic sequencing data. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, our scoping review was conducted across PubMed, Medline, Scopus, Web of Science, Embase, and ACM Digital Library. Studies were included if they focused on NLP methodologies applied to genomic sequencing data analysis, without restrictions on publication date or article type. Results: A total of 26 studies published between 2021 and April 2024 were selected for review. The review highlights that tokenization and transformer models enhance the processing and understanding of genomic data, with applications in predicting regulatory annotations like transcription-factor binding sites and chromatin accessibility. Discussion: The application of NLP and LLMs to genomic sequencing data interpretation is a promising field that can help streamline the processing of large-scale genomic data while also providing a better understanding of its complex structures. It has the potential to drive advancements in personalized medicine by offering more efficient and scalable solutions for genomic analysis. Further research is also needed to discuss and overcome current limitations, enhancing model transparency and applicability.
SAGEval: The frontiers of Satisfactory Agent based NLG Evaluation for reference-free open-ended text
2024-11-25
Reshmi Ghosh, Tianyi Yao, Lizzy Chen, Sadid Hasan, Tianwei Chen, Dario Bernal, Huitian Jiao, H M Sajjad Hossain
Large Language Model (LLM) integrations into applications like Microsoft365 suite and Google Workspace for creating/processing documents, emails, presentations, etc. has led to considerable enhancements in productivity and time savings. But as these integrations become more more complex, it is paramount to ensure that the quality of output from the LLM-integrated applications are relevant and appropriate for use. Identifying the need to develop robust evaluation approaches for natural language generation, wherein references/ground labels doesn't exist or isn't amply available, this paper introduces a novel framework called "SAGEval" which utilizes a critiquing Agent to provide feedback on scores generated by LLM evaluators. We show that the critiquing Agent is able to rectify scores from LLM evaluators, in absence of references/ground-truth labels, thereby reducing the need for labeled data even for complex NLG evaluation scenarios, like the generation of JSON-structured forms/surveys with responses in different styles like multiple choice, likert ratings, single choice questions, etc.
The brain versus AI: World-model-based versatile circuit computation underlying diverse functions in the neocortex and cerebellum
2024-11-25
Shogo Ohmae, Keiko Ohmae
AI's significant recent advances using general-purpose circuit computations offer a potential window into how the neocortex and cerebellum of the brain are able to achieve a diverse range of functions across sensory, cognitive, and motor domains, despite their uniform circuit structures. However, comparing the brain and AI is challenging unless clear similarities exist, and past reviews have been limited to comparison of brain-inspired vision AI and the visual neocortex. Here, to enable comparisons across diverse functional domains, we subdivide circuit computation into three elements -- circuit structure, input/outputs, and the learning algorithm -- and evaluate the similarities for each element. With this novel approach, we identify wide-ranging similarities and convergent evolution in the brain and AI, providing new insights into key concepts in neuroscience. Furthermore, inspired by processing mechanisms of AI, we propose a new theory that integrates established neuroscience theories, particularly the theories of internal models and the mirror neuron system. Both the neocortex and cerebellum predict future world events from past information and learn from prediction errors, thereby acquiring models of the world. These models enable three core processes: (1) Prediction -- generating future information, (2) Understanding -- interpreting the external world via compressed and abstracted sensory information, and (3) Generation -- repurposing the future-information generation mechanism to produce other types of outputs. The universal application of these processes underlies the ability of the neocortex and cerebellum to accomplish diverse functions with uniform circuits. Our systematic approach, insights, and theory promise groundbreaking advances in understanding the brain.
Energy landscape analysis based on the Ising model: Tutorial review
2024-11-25
Naoki Masuda, Saiful Islam, Si Thu Aung, Takamitsu Watanabe
We review a class of energy landscape analysis method that uses the Ising model and takes multivariate time series data as input. The method allows one to capture dynamics of the data as trajectories of a ball from one basin to a different basin to yet another, constrained on the energy landscape specified by the estimated Ising model. While this energy landscape analysis has mostly been applied to functional magnetic resonance imaging (fMRI) data from the brain for historical reasons, there are emerging applications outside fMRI data and neuroscience. To inform such applications in various research fields, this review paper provides a detailed tutorial on each step of the analysis, terminologies, concepts underlying the method, and validation, as well as recent developments of extended and related methods.
A Review of Design Concerns in Superconducting Quantum Circuits
2024-11-25
Eli M. Levenson-Falk, Sadman Ahmed Shanto
In this short review we describe the process of designing a superconducting circuit device for quantum information applications. We discuss the factors that must be considered to implement a desired effective Hamiltonian on a device. We describe the translation between a device's physical layout, the circuit graph, and the effective Hamiltonian. We go over the process of electromagnetic simulation of a device layout to predict its behavior. We also discuss concerns such as connectivity, crosstalk suppression, and radiation shielding, and how they affect both on-chip design and enclosure structures. This paper provides an overview of the challenges in superconducting quantum circuit design and acts as a starter document for researchers working on any of these challenges.
Strengthening Power System Resilience to Extreme Weather Events Through Grid Enhancing Technologies
2024-11-25
Joseph Nyangon
Climate change significantly increases risks to power systems, exacerbating issues such as aging infrastructure, evolving regulations, cybersecurity threats, and fluctuating demand. This paper focuses on the utilization of Grid Enhancing Technologies (GETs) to strengthen power system resilience in the face of extreme weather events. GETs are pivotal in optimizing energy distribution, enabling predictive maintenance, ensuring reliable electricity supply, facilitating renewable energy integration, and automating responses to power instabilities and outages. Drawing insights from resilience theory, the paper reviews recent grid resilience literature, highlighting increasing vulnerabilities due to severe weather events. It demonstrates how GETs are crucial in optimizing smart grid operations, thereby not only mitigating climate-related impacts but also promoting industrial transformation. Keywords: Climate change, power systems, grid enhancing technologies (GETs), power system resilience, extreme weather
Contrastive Deep Learning Reveals Age Biomarkers in Histopathological Skin Biopsies
2024-11-25
Kaustubh Chakradeo, Pernille Nielsen, Lise Mette Rahbek Gjerdrum, Gry Sahl Hansen, David A Duchêne, Laust H Mortensen, Majken K Jensen, Samir Bhatt
As global life expectancy increases, so does the burden of chronic diseases, yet individuals exhibit considerable variability in the rate at which they age. Identifying biomarkers that distinguish fast from slow ageing is crucial for understanding the biology of ageing, enabling early disease detection, and improving prevention strategies. Using contrastive deep learning, we show that skin biopsy images alone are sufficient to determine an individual's age. We then use visual features in histopathology slides of the skin biopsies to construct a novel biomarker of ageing. By linking with comprehensive health registers in Denmark, we demonstrate that visual features in histopathology slides of skin biopsies predict mortality and the prevalence of chronic age-related diseases. Our work highlights how routinely collected health data can provide additional value when used together with deep learning, by creating a new biomarker for ageing which can be actively used to determine mortality over time.
Probing the limitations of multimodal language models for chemistry and materials research
2024-11-25
Nawaf Alampara, Mara Schilling-Wilhelmi, Martiño Ríos-García, Indrajeet Mandal, Pranav Khetarpal, Hargun Singh Grover, N. M. Anoop Krishnan, Kevin Maik Jablonka
Recent advancements in artificial intelligence have sparked interest in scientific assistants that could support researchers across the full spectrum of scientific workflows, from literature review to experimental design and data analysis. A key capability for such systems is the ability to process and reason about scientific information in both visual and textual forms - from interpreting spectroscopic data to understanding laboratory setups. Here, we introduce MaCBench, a comprehensive benchmark for evaluating how vision-language models handle real-world chemistry and materials science tasks across three core aspects: data extraction, experimental understanding, and results interpretation. Through a systematic evaluation of leading models, we find that while these systems show promising capabilities in basic perception tasks - achieving near-perfect performance in equipment identification and standardized data extraction - they exhibit fundamental limitations in spatial reasoning, cross-modal information synthesis, and multi-step logical inference. Our insights have important implications beyond chemistry and materials science, suggesting that developing reliable multimodal AI scientific assistants may require advances in curating suitable training data and approaches to training those models.
Super sample covariance and the volume scaling of galaxy survey covariance matrices
2024-11-25
Greg Schreiner, Alex Krolewski, Shahab Joudaki, Will J. Percival
Super sample covariance (SSC) is important when estimating covariance matrices using a set of mock catalogues for galaxy surveys. If the underlying cosmological simulations do not include the variation in background parameters appropriate for the simulation sizes, then the scatter between mocks will be missing the SSC component. The coupling between large and small modes due to non-linear structure growth makes this pernicious on small scales. We compare different methods for generating ensembles of mocks with SSC built in to the covariance, and contrast against methods where the SSC component is computed and added to the covariance separately. We find that several perturbative expansions, developed to derive background fluctuations, give similar results. We then consider scaling covariance matrices calculated for simulations of different volumes to improve the accuracy of covariance matrix estimation for a given amount of computational time. On large scales, we find that the primary limitation is from the discrete number of modes contributing to the measured power spectrum, and we propose a new method for correcting this effect. Correct implementation of SSC and the effect of discrete mode numbers allows covariance matrices created from mocks to be scaled between volumes, potentially leading to a significant saving on computational resources when producing covariance matrices. We argue that a sub-percent match is difficult to achieve because of the effects of modes on scales between the box sizes, which cannot be easily included. Even so, a 3% match is achievable on scales of interest for current surveys scaling the simulation volume by 512x, costing a small fraction of the computational time of running full-sized simulations. This is comparable to the agreement between analytic and mock-based covariance estimates to be used with DESI Y1 results.
Electromagnetic waves in anisotropic media (e.g., weakly absorbing biaxial dielectric crystals) have a singular profile of the form $\propto (\mathbf{n} \mathbf{r}) \exp(i q \mathbf{n} \mathbf{r})$ along special directions, they are also known as Voigt waves. Therefore, for such directions, the wave polarization depend on the coordinate. A similar singular form is possible for surface waves, but the conditions for its appearance are less strict. The boundary between two, generally different, uniaxial media is considered. Conditions have been determined under which a surface polariton can propagate along the boundary and has a field distribution of singular form in both media. The existence condition of a bisingular surface polariton strongly depends on the angle between the optic axes of the media. It is shown that in simple cases (one the media is almost isotropic, or two identical uniaxial media) a bisingular polariton exists only for a single angle between the optic axes. In general, for two arbitrary media there can be up to five such angles.
A Gigaparsec-Scale Hydrodynamic Volume Reconstructed with Deep Learning
2024-11-25
Cooper Jacobus, Solene Chabanier, Peter Harrington, JD Emberson, Zarija Lukić, Salman Habib
The next generation of cosmological spectroscopic sky surveys will probe the distribution of matter across several Gigaparsecs (Gpc) or many billion light-years. In order to leverage the rich data in these new maps to gain a better understanding of the physics that shapes the large-scale structure of the cosmos, observed matter distributions must be compared to simulated mock skies. Small mock skies can be produced using precise, physics-driven hydrodynamical simulations. However, the need to capture small, kpc-scale density fluctuations in the intergalactic medium (IGM) places tight restrictions on the necessary minimum resolution of these simulations. Even on the most powerful supercomputers, it is impossible to run simulations of such high resolution in volumes comparable to what will be probed by future surveys, due to the vast quantity of data needed to store such a simulation in computer memory. However, it is possible to represent the essential features of these high-resolution simulations using orders of magnitude less memory. We present a hybrid approach that employs a physics-driven hydrodynamical simulation at a much lower-than-necessary resolution, followed by a data-driven, deep-learning Enhancement. This hybrid approach allows us to produce hydrodynamic mock skies that accurately capture small, kpc-scale features in the IGM but which span hundreds of Megaparsecs. We have produced such a volume which is roughly one Gigaparsec in diameter and examine its relevant large-scale statistical features, emphasizing certain properties that could not be captured by previous smaller simulations. We present this hydrodynamic volume as well as a companion n-body dark matter simulation and halo catalog which we are making publically available to the community for use in calibrating data pipelines for upcoming survey analyses.
The Economics of Climate Adaptation: An Assessment
2024-11-25
Anna Josephson, Rodrigo Guerra Su, Greg Collins, Katharine Jacobs
The cost of the impacts of climate change have already proven to be larger than previously believed. Understanding the costs and benefits of adapting to the changing climate is necessary to make targeted and appropriate investment decisions. In this paper, we use a narrative review to synthesize the current literature on the economic case for climate adaptation, with the objective of assessing the value (economic and otherwise) of climate change adaptation, as well as the strength of the methods and evidence that have been used to date. We find that skepticism is warranted about many of the estimates about costs and benefits of climate adaptation and their underlying assumptions, due to a range of complexities associated with (1) uncertainty in distinguishing the economic impacts of climate change from seasonal variability; (2) difficulties in non-market valuation; (3) lack of consistent data collection over time at multiple scales; and (4) distributional inequities in access to proactive adaptation and recovery funding. While useful for broad stroke advocacy purposes, these estimates fall short of the refinement and rigor needed to inform investment decision-making, particularly at micro and local scales. Most estimates rely on cost benefit analysis and do not effectively address these issues. An emergent and promising literature tackles alternative estimation strategies and attempts to address some of them, including the complexities of uncertainty and non-market valuation.
Information and Complexity Analysis of Spatial Data
2024-11-25
Jose M. Angulo, Francisco J. Esquivel, Ana E. Madrid, Francisco J. Alonso
Information Theory provides a fundamental basis for analysis, and for a variety of subsequent methodological approaches, in relation to uncertainty quantification. The transversal character of concepts and derived results justifies its omnipresence in scientific research, in almost every area of knowledge, particularly in Physics, Communications, Geosciences, Life Sciences, etc. Information-theoretic aspects underlie modern developments on complexity and risk. A proper use and exploitation of structural characteristics inherent to spatial data motivates, according to the purpose, special considerations in this context. In this paper, some of the most relevant approaches introduced, in particular recent contributions and directions, regarding the informational analysis of spatial data and related aspects concerning complexity analysis, are reviewed under a conceptually connective evolutionary perspective. The discussion involves the cases of spatial data from magnitude measurements and spatial point patterns, with the latter possibly being of a multifractal nature.
Rediscovering the Milky Way with orbit superposition approach and APOGEE data II. Chrono-chemo-kinematics of the disc
2024-11-25
Sergey Khoperskov, Matthias Steinmetz, Misha Haywood, Glenn van de Ven, Davor Krajnovic, Bridget Ratcliffe, Ivan Minchev, Paola Di Matteo, Nikolay Kacharov, Léa Marques, Marica Valentini, Roelof S. de Jong
The stellar disc is the dominant luminous component of the Milky Way (MW). Although our understanding of its structure is rapidly expanding due to advances in large-scale stellar surveys, our picture of the MW disc remains substantially obscured by selection functions and incomplete spatial coverage of observational data. In this work, we present the comprehensive chrono-chemo-kinematic structure of the MW disc, recovered using a novel orbit superposition approach combined with data from APOGEE DR 17. We detect periodic azimuthal metallicity variations within 6-8 kpc with an amplitude of 0.05-0.1 dex peaking along the bar major axis. The radial metallicity profile of the MW also varies with azimuth, displaying a pattern typical among other disc galaxies: a decline outside the solar radius and an almost flat profile in the inner region, attributed to the presence of old, metal-poor high-{\alpha} populations, which comprise about 40% of the total stellar mass. The geometrically defined thick disc and the high-{\alpha} populations have comparable masses, with differences in their stellar population content, which we quantify using the reconstructed 3D MW structure. The well-known [{\alpha}/Fe]-bimodality in the MW disc, once weighted by stellar mass, is less pronounced at a given metallicity for the whole galaxy but distinctly visible in a narrow range of galactic radii (5-9 kpc), explaining its relative lack of prominence in external galaxies and galaxy formation simulations. Analysing a more evident double age-abundance sequence, we construct a scenario for the MW disc formation, advocating for an inner/outer disc dichotomy genetically linked to the MW's evolutionary stages. In this picture, the extended solar vicinity is a transition zone that shares chemical properties of both the inner (old age-metallicity sequence) and outer discs (young age-metallicity sequence).
Non-thermal emission in galaxy groups at extremely low frequency: the case of A1213
2024-11-25
T. Pasini, V. H. Mahatma, M. Brienza, K. Kolokythas, D. Eckert, F. de Gasperin, R. J. van Weeren, F. Gastaldello, D. Hoang, R. Santra
Galaxy clusters and groups are the last link in the chain of hierarchical structure formation. Their environments can be significantly affected by outbursts from AGN, especially in groups where the medium density is lower and the gravitational potential shallower. The interaction between AGN and group weather can therefore greatly impact their evolution. We investigate the non-thermal radio emission in Abell 1213, a galaxy group which is part of a larger sample of ~50 systems (X-GAP) recently granted XMM-Newton observations. We exploit proprietary LOFAR 54 MHz and uGMRT 380 MHz observations, complementing them with 144 MHz LOFAR survey and XMM-Newton archival data. A1213 hosts a bright AGN associated with one of the central members, 4C 29.41, which was previously optically identified as a dumb-bell galaxy. Observations at 144 MHz at a resolution of 0.3'' allow us to resolve the central radio galaxy. From this source, a ~500 kpc-long tail extends North-East. Our analysis suggests that the tail likely originated from a past outburst of 4C 29.41, and its current state might be the result of the interaction with the surrounding environment. The plateau of the spectral index distribution in the Easternmost part of the tail suggests mild particle re-acceleration, that could have re-energised seed electrons from the past activity of the AGN. While we observe a spatial and physical correlation of the extended, central emission with the thermal plasma, which might hint at a mini-halo, current evidence cannot conclusively prove this. A1213 is only the first group, among the X-GAP sample, that we are able to investigate through low-frequency radio observations. Its complex environment once again demonstrates the significant impact that the interplay between thermal and non-thermal processes can have on galaxy groups.
OrCAS: Origins, Compositions, and Atmospheres of Sub-neptunes. I. Survey Definition
2024-11-25
Ian J. M. Crossfield, Alex S. Polanski, Paul Robertson, Joseph Akana Murphy, Emma V. Turtelboom, Rafael Luque, Thomas Beatty, Tansu Daylan, Howard Isaacson, Jonathan Brande, Laura Kreidberg, Natalie M. Batalha, Daniel Huber, Maleah Rhem, Courtney Dressing, Stephen R. Kane, Malik Bossett, Anna Gagnebin, Maxwell A. Kroft, Pranav H. Premnath, Claire J. Rogers, Karen A. Collins, David W. Latham, Cristilyn N. Watkins, David R. Ciardi, Steve B. Howell, Arjun B. Savel, Perry Berlind, Michael L. Calkins, Gilbert A. Esquerdo, Jessica Mink, Catherine A. Clark, Michael B. Lund, Rachel A. Matson, Mark E. Everett, Joshua E. Schlieder, Elisabeth C. Matthews, Steven Giacalone, Thomas Barclay, Roberto Zambelli, Peter Plavchan, Taylor Ellingson, Michael Bowen, Gregor Srdoc, Kim K. McLeod, Richard P. Schwarz, Khalid Barkaoui, Jacob Kamler, Felipe Murgas, Enric Palle, Norio Narita, Akihiko Fukui, Howard M. Relles, Allyson Bieryla, Eric Girardin, Bob Massey, Chris Stockdale, Pablo Lewin, Riccardo Papini, Pere Guerra, Dennis M. Conti, Selcuk Yalcinkaya, Ozgur Basturk, Ghachoui Mourad
Sub-Neptunes - volatile-rich exoplanets smaller than Neptune - are intrinsically the most common type of planet known. However, the formation and nature of these objects, as well as the distinctions between sub-classes (if any), remain unclear. Two powerful tools to tease out the secrets of these worlds are measurements of (i) atmospheric composition and structure revealed by transit and/or eclipse spectroscopy, and (ii) mass, radius, and density revealed by transit photometry and Doppler spectroscopy. Here we present OrCAS, a survey to better elucidate the origins, compositions, and atmospheres of sub-Neptunes. This radial velocity survey uses a repeatable, quantifiable metric to select targets suitable for subsequent transmission spectroscopy and address key science themes about the atmospheric & internal compositions and architectures of these systems. Our survey targets 26 systems with transiting sub-Neptune planet candidates, with the overarching goal of increasing the sample of such planets suitable for subsequent atmospheric characterization. This paper lays out our survey's science goals, defines our target prioritization metric, and performs light-curve fits and statistical validation using existing TESS photometry and ground-based follow-up observations. Our survey serves to continue expanding the sample of small exoplanets with well-measured properties orbiting nearby bright stars, ensuring fruitful studies of these systems for many years to come.
Simulation of single diffraction dissociation in resonance region at LHC energies
2024-11-25
O. S. Potiienko, D. V. Zhuravel, D. M. Riabov, N. O. Chudak
A comprehensive review of the single diffraction dissociation duality-based model at low missing masses have been presented. The distinguishing feature of the model is the nonlinear Regge proton trajectory used to account for the resonances contributions to the cross-sections. It helps classify and understand the spectrum of excited states of proton and their decays, providing insights into the internal structure and dynamics of particles. The behavior of the differential cross-section in the resonance region at small missing masses $M_x$ is investiaged. The model parameters are refined in the light of new experimental data.
Android malware detection based on machine learning (ML) and deep learning (DL) models is widely used for mobile device security. Such models offer benefits in terms of detection accuracy and efficiency, but it is often difficult to understand how such learning models make decisions. As a result, these popular malware detection strategies are generally treated as black boxes, which can result in a lack of trust in the decisions made, as well as making adversarial attacks more difficult to detect. The field of eXplainable Artificial Intelligence (XAI) attempts to shed light on such black box models. In this paper, we apply XAI techniques to ML and DL models that have been trained on a challenging Android malware classification problem. Specifically, the classic ML models considered are Support Vector Machines (SVM), Random Forest, and $k$-Nearest Neighbors ($k$-NN), while the DL models we consider are Multi-Layer Perceptrons (MLP) and Convolutional Neural Networks (CNN). The state-of-the-art XAI techniques that we apply to these trained models are Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive exPlanations (SHAP), PDP plots, ELI5, and Class Activation Mapping (CAM). We obtain global and local explanation results, and we discuss the utility of XAI techniques in this problem domain. We also provide a literature review of XAI work related to Android malware.
2024-11-25
Manoel Horta Ribeiro, Robert West, Ryan Lewis, Sanjay Kairam
Effective content moderation in online communities is often a delicate balance between maintaining content quality and fostering user participation. In this paper, we introduce post guidance, a novel approach to community moderation that proactively guides users' contributions using rules that trigger interventions as users draft a post to be submitted. For instance, rules can surface messages to users, prevent post submissions, or flag posted content for review. This uniquely community-specific, proactive, and user-centric approach can increase adherence to rules without imposing additional burdens on moderators. We evaluate a version of Post Guidance implemented on Reddit, which enables the creation of rules based on both post content and account characteristics, via a large randomized experiment, capturing activity from 97,616 posters in 33 subreddits over 63 days. We find that Post Guidance (1) increased the number of ``successful posts'' (posts not removed after 72 hours), (2) decreased moderators' workload in terms of manually-reviewed reports, (3) increased contribution quality, as measured by community engagement, and (4) had no impact on posters' own subsequent activity, within communities adopting the feature. Post Guidance on Reddit was similarly effective for community veterans and newcomers, with greater benefits in communities that used the feature more extensively. Our findings indicate that post guidance represents a transformative approach to content moderation, embodying a paradigm that can be easily adapted to other platforms to improve online communities across the Web.
Blockchain Meets LLMs: A Living Survey on Bidirectional Integration
2024-11-25
Jianghao Gong, Peiqi Yan, Yue Zhang, Hongli An, Logan Liu
In the domain of large language models, considerable advancements have been attained in multimodal large language models and explainability research, propelled by the continuous technological progress and innovation. Nonetheless, security and privacy concerns continue to pose as prominent challenges in this field. The emergence of blockchain technology, marked by its decentralized nature, tamper-proof attributes, distributed storage functionality, and traceability, has provided novel approaches for resolving these issues. Both of these technologies independently hold vast potential for development; yet, their combination uncovers substantial cross-disciplinary opportunities and growth prospects. The current research tendencies are increasingly concentrating on the integration of blockchain with large language models, with the aim of compensating for their respective limitations through this fusion and promoting further technological evolution. In this study, we evaluate the advantages and developmental constraints of the two technologies, and explore the possibility and development potential of their combination. This paper primarily investigates the technical convergence in two directions: Firstly, the application of large language models to blockchain, where we identify six major development directions and explore solutions to the shortcomings of blockchain technology and their application scenarios; Secondly, the application of blockchain technology to large language models, leveraging the characteristics of blockchain to remedy the deficiencies of large language models and exploring its application potential in multiple fields.
Euclid: Searches for strong gravitational lenses using convolutional neural nets in Early Release Observations of the Perseus field
2024-11-25
R. Pearce-Casey, B. C. Nagam, J. Wilde, V. Busillo, L. Ulivi, I. T. Andika, A. Manjón-García, L. Leuzzi, P. Matavulj, S. Serjeant, M. Walmsley, J. A. Acevedo Barroso, C. M. O'Riordan, B. Clément, C. Tortora, T. E. Collett, F. Courbin, R. Gavazzi, R. B. Metcalf, R. Cabanac, H. M. Courtois, J. Crook-Mansour, L. Delchambre, G. Despali, L. R. Ecker, A. Franco, P. Holloway, K. Jahnke, G. Mahler, L. Marchetti, A. Melo, M. Meneghetti, O. Müller, A. A. Nucita, J. Pearson, K. Rojas, C. Scarlata, S. Schuldt, D. Sluse, S. H. Suyu, M. Vaccari, S. Vegetti, A. Verma, G. Vernardos, M. Bolzonella, M. Kluge, T. Saifollahi, M. Schirmer, C. Stone, A. Paulino-Afonso, L. Bazzanini, N. B. Hogg, L. V. E. Koopmans, S. Kruk, F. Mannucci, J. M. Bromley, A. Díaz-Sánchez, H. J. Dickinson, D. M. Powell, H. Bouy, R. Laureijs, B. Altieri, A. Amara, S. Andreon, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, A. Caillat, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, A. M. Di Giorgio, J. Dinis, F. Dubath, X. Dupac, S. Dusini, M. Farina, S. Farrens, F. Faustini, S. Ferriol, M. Frailis, E. Franceschi, S. Galeotta, K. George, W. Gillard, B. Gillis, C. Giocoli, P. Gómez-Alvarez, A. Grazian, F. Grupp, S. V. H. Haugan, W. Holmes, I. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, M. Kilbinger, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, E. Merlin, G. Meylan, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, P. Schneider, T. Schrabback, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, J. Skottfelt, L. Stanco, J. Steinwagner, P. Tallada-Crespí, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, E. A. Valentijn, L. Valenziano, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, G. Zamorani, E. Zucca, C. Burigana, M. Calabrese, A. Mora, M. Pöntinen, V. Scottez, M. Viel, B. Margalef-Bentabol
The Euclid Wide Survey (EWS) is predicted to find approximately 170 000 galaxy-galaxy strong lenses from its lifetime observation of 14 000 deg^2 of the sky. Detecting this many lenses by visual inspection with professional astronomers and citizen scientists alone is infeasible. Machine learning algorithms, particularly convolutional neural networks (CNNs), have been used as an automated method of detecting strong lenses, and have proven fruitful in finding galaxy-galaxy strong lens candidates. We identify the major challenge to be the automatic detection of galaxy-galaxy strong lenses while simultaneously maintaining a low false positive rate. One aim of this research is to have a quantified starting point on the achieved purity and completeness with our current version of CNN-based detection pipelines for the VIS images of EWS. We select all sources with VIS IE < 23 mag from the Euclid Early Release Observation imaging of the Perseus field. We apply a range of CNN architectures to detect strong lenses in these cutouts. All our networks perform extremely well on simulated data sets and their respective validation sets. However, when applied to real Euclid imaging, the highest lens purity is just 11%. Among all our networks, the false positives are typically identifiable by human volunteers as, for example, spiral galaxies, multiple sources, and artefacts, implying that improvements are still possible, perhaps via a second, more interpretable lens selection filtering stage. There is currently no alternative to human classification of CNN-selected lens candidates. Given the expected 10^5 lensing systems in Euclid, this implies 10^6 objects for human classification, which while very large is not in principle intractable and not without precedent.
Langlands Duality and Invariant Differential Operators
2024-11-25
V. K. Dobrev
Langlands duality is one of the most influential topics in mathematical research. It has many different appearances and influential subtopics. Yet there is a topic that until now seems unrelated to the Langlands program. That is the topic of invariant differential operators. That is strange since both items are deeply rooted in Harish-Chandra's representation theory of semisimple Lie groups. In this paper we start building the bridge between the two programs.
Equivalence between the zero distributions of the Riemann zeta function and a two-dimensional Ising model with randomly distributed competing interactions
2024-11-25
Zhidong Zhang
In this work, we prove the equivalence between the zero distributions of the Riemann zeta function {\zeta}(s) and a two-dimensional (2D) Ising model with a mixture of ferromagnetic and randomly distributed competing interactions. At first, we review briefly the characteristics of the Riemann hypothesis and its connections to physics, in particular, to statistical physics. Second, we build a 2D Ising model, M_(FI+SGI)^2D, in which interactions between the nearest neighboring spins are ferromagnetic along one crystallographic direction while competing ferromagnetic/antiferromagnetic interactions are randomly distributed along another direction. Third, we prove that all energy eigenvalues of this 2D Ising model M_(FI+SGI)^2D are real and randomly distributed as the M\"obius function {\mu}(n), the Dirichlet L(s,\c{hi}_k ) function as well as the Riemann zeta function {\zeta}(s). Fourth, we prove that the eigenvectors of the 2D Ising model M_(FI+SGI)^2D are constructed by the eigenvectors of the 1D Ising model with phases related to the Riemann zeta function {\zeta}(s), via the relation {\omega}({\gamma}_2j) between the angle {\omega} and the energy eigenvalues {\gamma}_2j, which form the Hilbert-P\'olya space. Fifth, we prove that all the zeros of the partition function of the 2D Ising model M_(FI+SGI)^2D lie on an unit circle in a complex temperature plane (i.e. Fisher zeros), which can be mapped to the zero distribution of the Dirichlet L(s,\c{hi}_k ) function and also the Riemann zeta function {\zeta}(s) in the critical line. In a conclusion, we have proven the closure of the nontrivial zero distribution of the L(s,\c{hi}_k ) function (including the Riemann zeta function {\zeta}(s)).
Parameter Efficient Instruction Tuning: An Empirical Study
2024-11-25
Pengfei He
Instruction tuning has become an important step for finetuning pretrained language models to better follow human instructions and generalize on various tasks. Nowadays, pretrained language models become increasingly larger, and full parameter finetuning is overwhelmingly costly. Therefore, Parameter Efficient Finetuning (PEFT) has arisen as a cost-effective practice for instruction tuning because of significantly smaller computational, memory, and storage cost compared to full finetuning. Despite their widespread adaptations, the vast hyperparameter spaces, the number of PEFT methods, the different focus of instruction tuning capabilities make disentangling the impact of each aspect difficult. This study systematically investigates several representative PEFT methods, surveying the effect of hyperparameter choices including training hyperparameters and PEFT-specific hyperparameters, how different models sizes and the number of instruction tasks affect the performance, in-task-distribution memorization and open instruction following capability. Our empirical study shows that only LoRA and adapter can get close to full finetuning with ideal training settings. The ideal training setting includes an appropriate learning rate, largest LoRA rank or adapter size allowed and diverse training tasks. On the other hand, LoRA and adapter suffer from training instability if such an ideal training condition is not met. Additionally, LoRA requires a greater number of tasks for effective unseen task generalization, exhibit slower learning speed. Moreover, LoRA has weaker task-level memorization. Lastly, LoRA and adapter fall short in complex reasoning, coding and long-form generation compared to finetuning in open instruction tuning settings but it shows stronger capabilities compared to adapter.
Establishing Design Routines for Efficient Control of Automated Robots
2024-11-24
Hariharan Ragothaman, Harihar M, SK Guhananthan
With continual advancements in technology, efforts to develop robots simulating human behavior have intensified. Cognitive robotics, combined with artificial intelligence (AI), has proven effective in surveying and research analysis. However, despite progress, human intervention remains necessary, and incorporating AI into robotic systems continues to pose challenges. This paper explores methodologies to integrate AI into robotic designs, aiming to enhance human-robot interactions. Several approaches are proposed to improve robotic performance, including routines for efficient control in varied environments and the incorporation of digital image processing for enhanced line-of-sight capabilities. A key contribution of this work is testing robotic systems in real-time environments to assess efficiency relative to existing models. Additionally, the paper introduces a robotic system with universal control capabilities, suitable for industrial applications, developed and programmed on the Arduino platform. Features such as GPS control for safe operations and progressive memory algorithms for efficient memory management are presented, offering advancements in both industrial and research applications.
The FAST Galactic Plane Pulsar Snapshot survey: VI. The discovery of 473 new pulsars
2024-11-24
J. L. Han, D. J. Zhou, C. Wang, W. Q. Su, Yi Yan, W. C. Jing, Z. L. Yang, P. F. Wang, T. Wang, J. Xu, N. N. Cai, J. H. Sun, Q. L. Yang, R. X. Xu, H. G. Wang, X. P. You
The Five-hundred-meter Aperture Spherical radio Telescope (FAST) is the most sensitive telescope at the L-band (1.0-1.5GHz) and has been used to carry out the FAST Galactic Plane Pulsar Snapshot (GPPS) survey in the last 5 years. Up to now, the survey has covered one-fourth of the planned areas within $10^{\circ}$ from the Galactic plane visible by the FAST, and discovered 751 pulsars. After the first publishing of the discovery of 201 pulsars and one rotating radio transient (RRAT) in 2021 and 76 RRATs in 2023, here we report the discovery of 473 new pulsars from the FAST GPPS survey, including 137 new millisecond field pulsars and 30 new RRATs. We find that 34 millisecond pulsars discovered by the GPPS survey which can be timed with a precision better than 3~$\mu$s by using FAST 15-minute observations and can be used for the pulsar timing arrays. The GPPS survey has discovered 8 pulsars with periods greater than 10~s including one with 29.77~s. The profiles of integrated profiles of pulsars and individual pulses of RRATs are presented. During the FAST GPPS survey, we also detected previously known pulsars and updated parameters for 45 pulsars. In addition, we discover 2 fast radio bursts plus one probable case with high dispersion measures indicating their extragalactic origin.
Thermal lattice QCD results from the FASTSUM collaboration
2024-11-24
Chris Allton, Gert Aarts, M. Naeem Anwar, Ryan Bignell, Timothy J. Burns, Sergio Chaves García-Mascaraque, Simon Hands, Benjamin Jäger, Seyong Kim, Maria Paola Lombardo, Benjamin Page, Sínead Ryan, Jon-Ivar Skullerud, Antonio Smecca, Thomas Spriggs
The FASTSUM Collaboration has developed a comprehensive research programme in thermal lattice QCD using 2+1 flavour ensembles. We review our recent hadron spectrum analyses of open charm mesons and charm baryons at non-zero temperature. We also detail our determination of the interquark potential in the bottomonium system using NRQCD quarks. All of our work uses anisotropic lattices where the temporal lattice spacing is considerably finer than the spatial one allowing better resolution of temporal correlation functions.
In domain adaptation, there are two popular paradigms: Unsupervised Domain Adaptation (UDA), which aligns distributions using source data, and Source-Free Domain Adaptation (SFDA), which leverages pre-trained source models without accessing source data. Evaluating the superiority of UDA versus SFDA is an open and timely question with significant implications for deploying adaptive algorithms in practical applications. In this study, we demonstrate through predictive coding theory and extensive experiments on multiple benchmark datasets that SFDA generally outperforms UDA in real-world scenarios. Specifically, SFDA offers advantages in time efficiency, storage requirements, targeted learning objectives, reduced risk of negative transfer, and increased robustness against overfitting. Notably, SFDA is particularly effective in mitigating negative transfer when there are substantial distribution discrepancies between source and target domains. Additionally, we introduce a novel data-model fusion scenario, where data sharing among stakeholders varies (e.g., some provide raw data while others provide only models), and reveal that traditional UDA and SFDA methods do not fully exploit their potential in this context. To address this limitation and capitalize on the strengths of SFDA, we propose a novel weight estimation method that effectively integrates available source data into multi-SFDA (MSFDA) approaches, thereby enhancing model performance within this scenario. This work provides a thorough analysis of UDA versus SFDA and advances a practical approach to model adaptation across diverse real-world environments.
We study the differences in physical properties of quasar-host galaxies using an optically selected sample of radio loud (RL) and radio quiet (RQ) quasars (in the redshift range 0.15 < z < 1.9) which we have further cross-matched with the VLA-FIRST survey catalog. The sources in our sample have broad Hbeta and MgII emission lines (1000 km/s < FWHM < 15000 km/s) with a subsample of high broad line quasars (FWHM > 15000 km/s). We construct the broadband spectral energy distribution (SED) of our broad line quasars using multi-wavelength archival data and targeted observations with the AstroSat telescope. We use the state-of-the-art SED modeling code CIGALE v2022.0 to model the SEDs and determine the best-fit physical parameters of the quasar host galaxies namely their star-formation rate (SFR), main-sequence stellar mass, luminosity absorbed by dust, e-folding time and stellar population age. We find that the emission from the host galaxy of our sources is between 20%-35% of the total luminosity, as they are mostly dominated by the central quasars. Using the best-fit estimates, we reconstruct the optical spectra of our quasars which show remarkable agreement in reproducing the observed SDSS spectra of the same sources. We plot the main-sequence relation for our quasars and note that they are significantly away from the main sequence of star-forming galaxies. Further, the main sequence relation shows a bimodality for our RL quasars indicating populations segregated by Eddington ratios. We conclude that RL quasars in our sample with lower Eddington ratios tend to have substantially lower star-formation rates for similar stellar mass. Our analyses, thus, provide a completely independent route in studying the host galaxies of quasars and addressing the radio dichotomy problem from the host galaxy perspective.
A review of geometric modeling methods in microstructure design and manufacturing
2024-11-24
Qiang Zou, Guoyue Luo
Microstructures, characterized by intricate structures at the microscopic scale, hold the promise of important disruptions in the field of mechanical engineering due to the superior mechanical properties they offer. One fundamental technique of microstructure design and manufacturing is geometric modeling, which generates the 3D computer models required to run high-level procedures such as simulation, optimization, and process planning. There is, however, a lack of comprehensive discussions on this body of knowledge. The goal of this paper is to compile existing microstructure modeling methods and clarify the challenges, progress, and limitations of current research. It also concludes with future research directions that may improve and/or complement current methods, such as compressive and generative microstructure representations. By doing so, the paper sheds light on what has already been made possible for microstructure modeling, what developments can be expected in the near future, and which topics remain problematic.
Regular distribution of star formation regions along the spiral arms and rings of disk galaxies
2024-11-24
A. S. Gusev, E. V. Shimanovskaya
Last years studies have shown that the spatial regularity in the distribution of young stellar population along the spiral arms and rings of galaxies, previously considered to be rare, is a fairly common phenomenon. Spatial regularity has been found in the spiral arms and rings of galaxies of various morphology, from lenticular to extremely late-type spiral. The characteristic regularity scale is equal to 350-500 pc or a multiple thereof in all studied galaxies. Theoretical models predict a scale of instability of the stellar-gas disk on the order of a few kpc, which is several times larger than observed, although the most recent magneto-hydrodynamic simulations predict the formation of regular chains of star formation regions in spiral arms on a scale of 500-700 pc for the Milky Way-like galaxies. Modern high-quality surveys, such as PHANGS-MUSE, provide the necessary observational data (surface densities and velocity dispersions of gas and stellar population) to directly calculate the regularity scales in galaxies with high spatial resolution and wide field of view, which is a promising direction for research in this field.
A review on Machine Learning based User-Centric Multimedia Streaming Techniques
2024-11-24
Monalisa Ghosh, Chetna Singhal
The multimedia content and streaming are a major means of information exchange in the modern era and there is an increasing demand for such services. This coupled with the advancement of future wireless networks B5G/6G and the proliferation of intelligent handheld mobile devices, has facilitated the availability of multimedia content to heterogeneous mobile users. Apart from the conventional video, the 360$^o$ videos have gained popularity with the emerging virtual reality applications. All formats of videos (conventional and 360$^o$) undergo processing, compression, and transmission across dynamic wireless channels with restricted bandwidth to facilitate the streaming services. This causes video impairments, leading to quality degradation and poses challenges in delivering good Quality-of-Experience (QoE) to the viewers. The QoE is a prominent subjective quality measure to assess multimedia services. This requires end-to-end QoE evaluation. Efficient multimedia streaming techniques can improve the service quality while dealing with dynamic network and end-user challenges. A paradigm shift in user-centric multimedia services is envisioned with a focus on Machine Learning (ML) based QoE modeling and streaming strategies. This survey paper presents a comprehensive overview of the overall and continuous, time varying QoE modeling for the purpose of QoE management in multimedia services. It also examines the recent research on intelligent and adaptive multimedia streaming strategies, with a special emphasis on ML based techniques for video (conventional and 360$^o$) streaming. This paper discusses the overall and continuous QoE modeling to optimize the end-user viewing experience, efficient video streaming with a focus on user-centric strategies, associated datasets for modeling and streaming, along with existing shortcoming and open challenges.
Comparative Analysis of Diffusion Generative Models in Computational Pathology
2024-11-24
Denisha Thakkar, Vincent Quoc-Huy Trinh, Sonal Varma, Samira Ebrahimi Kahou, Hassan Rivaz, Mahdi S. Hosseini
Diffusion Generative Models (DGM) have rapidly surfaced as emerging topics in the field of computer vision, garnering significant interest across a wide array of deep learning applications. Despite their high computational demand, these models are extensively utilized for their superior sample quality and robust mode coverage. While research in diffusion generative models is advancing, exploration within the domain of computational pathology and its large-scale datasets has been comparatively gradual. Bridging the gap between the high-quality generation capabilities of Diffusion Generative Models and the intricate nature of pathology data, this paper presents an in-depth comparative analysis of diffusion methods applied to a pathology dataset. Our analysis extends to datasets with varying Fields of View (FOV), revealing that DGMs are highly effective in producing high-quality synthetic data. An ablative study is also conducted, followed by a detailed discussion on the impact of various methods on the synthesized histopathology images. One striking observation from our experiments is how the adjustment of image size during data generation can simulate varying fields of view. These findings underscore the potential of DGMs to enhance the quality and diversity of synthetic pathology data, especially when used with real data, ultimately increasing accuracy of deep learning models in histopathology. Code is available from https://github.com/AtlasAnalyticsLab/Diffusion4Path
An investigation into the performances of the Current state-of-the-art Naive Bayes, Non-Bayesian and Deep Learning Based Classifier for Phishing Detection: A Survey
2024-11-24
Tosin Ige, Christopher Kiekintveld, Aritran Piplai, Amy Waggler, Olukunle Kolade, Bolanle Hafiz Matti
Phishing is one of the most effective ways in which cybercriminals get sensitive details such as credentials for online banking, digital wallets, state secrets, and many more from potential victims. They do this by spamming users with malicious URLs with the sole purpose of tricking them into divulging sensitive information which is later used for various cybercrimes. In this research, we did a comprehensive review of current state-of-the-art machine learning and deep learning phishing detection techniques to expose their vulnerabilities and future research direction. For better analysis and observation, we split machine learning techniques into Bayesian, non-Bayesian, and deep learning. We reviewed the most recent advances in Bayesian and non-Bayesian-based classifiers before exploiting their corresponding weaknesses to indicate future research direction. While exploiting weaknesses in both Bayesian and non-Bayesian classifiers, we also compared each performance with a deep learning classifier. For a proper review of deep learning-based classifiers, we looked at Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and Long Short Term Memory Networks (LSTMs). We did an empirical analysis to evaluate the performance of each classifier along with many of the proposed state-of-the-art anti-phishing techniques to identify future research directions, we also made a series of proposals on how the performance of the under-performing algorithm can improved in addition to a two-stage prediction model
Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond
2024-11-24
Takaaki Fujita
To better handle real-world uncertainty, concepts such as fuzzy sets, neutrosophic sets, rough sets, and soft sets have been introduced. For example, neutrosophic sets, which simultaneously represent truth, indeterminacy, and falsehood, have proven to be valuable tools for modeling uncertainty in complex systems. These set concepts are increasingly studied in graphized forms, and generalized graph concepts now encompass well-known structures such as hypergraphs and superhypergraphs. Furthermore, hyperconcepts and superhyperconcepts are being actively researched in areas beyond graph theory. Combinatorics, uncertain sets (including fuzzy sets, neutrosophic sets, rough sets, soft sets, and plithogenic sets), uncertain graphs, and hyper and superhyper concepts are active areas of research with significant mathematical and practical implications. Recognizing their importance, this paper explores new graph and set concepts, as well as hyper and superhyper concepts, as detailed in the "Results" section of "The Structure of the Paper." Additionally, this work aims to consolidate recent findings, providing a survey-like resource to inform and engage readers. For instance, we extend several graph concepts by introducing Neutrosophic Oversets, Neutrosophic Undersets, Neutrosophic Offsets, and the Nonstandard Real Set. This paper defines a variety of concepts with the goal of inspiring new ideas and serving as a valuable resource for researchers in their academic pursuits.
Advances in understanding vacuum break dynamics in liquid helium-cooled tubes for accelerator beamline applications
2024-11-23
Yinghe Qi, Wei Guo
Understanding air propagation and condensation following a catastrophic vacuum break in particle accelerator beamlines cooled by liquid helium is essential for ensuring operational safety. This review summarizes experimental and theoretical work conducted in our cryogenics lab to address this issue. Systematic measurements were performed to study nitrogen gas propagation in uniform copper tubes cooled by both normal liquid helium (He I) and superfluid helium (He II). These experiments revealed a nearly exponential deceleration of the gas front, with stronger deceleration observed in He II-cooled tubes. To interpret these results, a one-dimensional (1D) theoretical model was developed, incorporating gas dynamics, heat transfer, and condensation mechanisms. The model successfully reproduced key experimental observations in the uniform tube system. However, recent experiments involving a bulky copper cavity designed to mimic the geometry of a superconducting radio-frequency (SRF) cavity revealed strong anisotropic flow patterns of nitrogen gas within the cavity, highlighting limitations in extrapolating results from simplified tube geometries to real accelerator beamlines. To address these complexities, we outline plans for systematic studies using tubes with multiple bulky cavities and the development of a two-dimensional (2D) model to simulate gas dynamics in these more intricate configurations. These efforts aim to provide a comprehensive understanding of vacuum breaks in particle accelerators and improve predictive capabilities for their operational safety.
Enabling Efficient Serverless Inference Serving for LLM (Large Language Model) in the Cloud
2024-11-23
Himel Ghosh
This review report discusses the cold start latency in serverless inference and existing solutions. It particularly reviews the ServerlessLLM method, a system designed to address the cold start problem in serverless inference for large language models. Traditional serverless approaches struggle with high latency due to the size of LLM checkpoints and the overhead of initializing GPU resources. ServerlessLLM introduces a multitier checkpoint loading system, leveraging underutilized GPU memory and storage to reduce startup times by 6--8x compared to existing methods. It also proposes live inference migration and a startup-time-optimized model scheduler, ensuring efficient resource allocation and minimizing delays. This system significantly improves performance and scalability in serverless environments for LLM workloads. Besides ServerlessLLM, several other methods from recent research literature, including Rainbowcake, are reviewed in this paper. Further discussions explore how FaaS providers tackle cold starts and the possible future scopes.
For over a century canonical correlations, variables, and related concepts have been studied across various fields, with contributions dating back to Jordan [1875] and Hotelling [1936]. This text surveys the evolution of canonical correlation analysis, a fundamental statistical tool, beginning with its foundational theorems and progressing to recent developments and open research problems. Along the way we introduce and review methods, notions, and fundamental concepts from linear algebra, random matrix theory, and high-dimensional statistics, placing particular emphasis on rigorous mathematical treatment. The survey is intended for technically proficient graduate students and other researchers with an interest in this area. The content is organized into five chapters, supplemented by six sets of exercises found in Chapter 6. These exercises introduce additional material, reinforce key concepts, and serve to bridge ideas across chapters. We recommend the following sequence: first, solve Problem Set 0, then proceed with Chapter 1, solve Problem Set 1, and so on through the text.
New radial velocities for 40 nearby dwarf galaxies
2024-11-23
Igor D. Karachentsev, Maxim I. Chazov, Serafim S. Kaisin
The 6-meter BTA telescope has been used to determine radial velocities for 40 galaxies, recently identified in the DESI Legacy Imaging Surveys as nearby objects. Half of them have kinematic distances within 11 Mpc being new probable companions to the bright Local Volume galaxies: NGC628, Maffei2, NGC2787, M81, NGC4605 and NGC4631. Six relatively isolated objects with heliocentric velocities in the range of $[-150, +70]$km s$^{-1}$, together with the blue compact dwarf NGC 6789, form a diffuse association of dwarf galaxies located in the near part of the Local Void.
Vision-language tracking (VLT) extends traditional single object tracking by incorporating textual information, providing semantic guidance to enhance tracking performance under challenging conditions like fast motion and deformations. However, current VLT trackers often underperform compared to single-modality methods on multiple benchmarks, with semantic information sometimes becoming a "distraction." To address this, we propose VLTVerse, the first fine-grained evaluation framework for VLT trackers that comprehensively considers multiple challenge factors and diverse semantic information, hoping to reveal the role of language in VLT. Our contributions include: (1) VLTVerse introduces 10 sequence-level challenge labels and 6 types of multi-granularity semantic information, creating a flexible and multi-dimensional evaluation space for VLT; (2) leveraging 60 subspaces formed by combinations of challenge factors and semantic types, we conduct systematic fine-grained evaluations of three mainstream SOTA VLT trackers, uncovering their performance bottlenecks across complex scenarios and offering a novel perspective on VLT evaluation; (3) through decoupled analysis of experimental results, we examine the impact of various semantic types on specific challenge factors in relation to different algorithms, providing essential guidance for enhancing VLT across data, evaluation, and algorithmic dimensions. The VLTVerse, toolkit, and results will be available at \url{http://metaverse.aitestunion.com}.
Accurate and consistent evaluation is crucial for decision-making across numerous fields, yet it remains a challenging task due to inherent subjectivity, variability, and scale. Large Language Models (LLMs) have achieved remarkable success across diverse domains, leading to the emergence of "LLM-as-a-Judge," where LLMs are employed as evaluators for complex tasks. With their ability to process diverse data types and provide scalable, cost-effective, and consistent assessments, LLMs present a compelling alternative to traditional expert-driven evaluations. However, ensuring the reliability of LLM-as-a-Judge systems remains a significant challenge that requires careful design and standardization. This paper provides a comprehensive survey of LLM-as-a-Judge, addressing the core question: How can reliable LLM-as-a-Judge systems be built? We explore strategies to enhance reliability, including improving consistency, mitigating biases, and adapting to diverse assessment scenarios. Additionally, we propose methodologies for evaluating the reliability of LLM-as-a-Judge systems, supported by a novel benchmark designed for this purpose. To advance the development and real-world deployment of LLM-as-a-Judge systems, we also discussed practical applications, challenges, and future directions. This survey serves as a foundational reference for researchers and practitioners in this rapidly evolving field.
Medillustrator: Improving Retrospective Learning in Physicians' Continuous Medical Education via Multimodal Diagnostic Data Alignment and Representation
2024-11-23
Yuansong Xu, Jiahe Dong, Yijie Fan, Yuheng Shao, Chang Jiang, Lixia Jin, Yuanwu Cao, Quan Li
Continuous Medical Education (CME) plays a vital role in physicians' ongoing professional development. Beyond immediate diagnoses, physicians utilize multimodal diagnostic data for retrospective learning, engaging in self-directed analysis and collaborative discussions with peers. However, learning from such data effectively poses challenges for novice physicians, including screening and identifying valuable research cases, achieving fine-grained alignment and representation of multimodal data at the semantic level, and conducting comprehensive contextual analysis aided by reference data. To tackle these challenges, we introduce Medillustrator, a visual analytics system crafted to facilitate novice physicians' retrospective learning. Our structured approach enables novice physicians to explore and review research cases at an overview level and analyze specific cases with consistent alignment of multimodal and reference data. Furthermore, physicians can record and review analyzed results to facilitate further retrospection. The efficacy of Medillustrator in enhancing physicians' retrospective learning processes is demonstrated through a comprehensive case study and a controlled in-lab between-subject user study.
Exploring Viewing Modalities in Cinematic Virtual Reality: A Systematic Review and Meta-Analysis of Challenges in Evaluating User Experience
2024-11-23
Yawen Zhang, Han Zhou, Zhoumingju Jiang, Zilu Tang, Tao Luo, Qinyuan Lei
Cinematic Virtual Reality (CVR) is a narrative-driven VR experience that uses head-mounted displays with a 360-degree field of view. Previous research has explored different viewing modalities to enhance viewers' CVR experience. This study conducted a systematic review and meta-analysis focusing on how different viewing modalities, including intervened rotation, avatar assistance, guidance cues, and perspective shifting, influence the CVR experience. The study has screened 3444 papers (between 01/01/2013 and 17/06/2023) and selected 45 for systematic review, 13 of which also for meta-analysis. We conducted separate random-effects meta-analysis and applied Robust Variance Estimation to examine CVR viewing modalities and user experience outcomes. Evidence from experiments was synthesized as differences between standardized mean differences (SMDs) of user experience of control group ("Swivel-Chair" CVR) and experiment groups. To our surprise, we found inconsistencies in the effect sizes across different studies, even with the same viewing modalities. Moreover, in these studies, terms such as "presence," "immersion," and "narrative engagement" were often used interchangeably. Their irregular use of questionnaires, overreliance on self-developed questionnaires, and incomplete data reporting may have led to unrigorous evaluations of CVR experiences. This study contributes to Human-Computer Interaction (HCI) research by identifying gaps in CVR research, emphasizing the need for standardization of terminologies and methodologies to enhance the reliability and comparability of future CVR research.
Navigating Fog Federation: Classifying Current Research and Identifying Challenges
2024-11-23
Dhairya Patel, Shaifali P. Malukani
Fog computing has gained significant attention for its potential to enhance resource management and service delivery by bringing computation closer to the network edge.While numerous surveys have explored various aspects of fog computing, there is a distinct gap in the literature when it comes to fog federation, a crucial extension that enables collaboration and resource sharing across multiple fog environments, enhancing scalability, service availability, and resource optimization.This paper provides a comprehensive survey of the existing work on fog federation, classifying the contributions from its inception to the present.We analyze the various approaches, architectures, and methodologies proposed for fog federation and identify the primary challenges addressed in this field.In addition, we explore the simulation tools and platforms utilized in evaluating fog federation systems.Our survey uniquely contributes to the literature by addressing the specific topic of fog federation, offering insights into the current state of the art and highlighting open research gaps and future directions.
Radio Halo Detection in MWA Data using Deep Neural Networks and Generative Data Augmentation
2024-11-23
Ashutosh K. Mishra, Emma Tolley, Shreyam Parth Krishna, Jean-Paul Kneib
Detecting diffuse radio emission, such as from halos, in galaxy clusters is crucial for understanding large-scale structure formation in the universe. Traditional methods, which rely on X-ray and Sunyaev-Zeldovich (SZ) cluster pre-selection, introduce biases that limit our understanding of the full population of diffuse radio sources. In this work, we provide a possible resolution for this astrophysical tension by developing a machine learning (ML) framework capable of unbiased detection of diffuse emission, using a limited real dataset like those from the Murchison Widefield Array (MWA). We generate for the first time radio halo images using Wasserstein Generative Adversarial Networks (WGANs) and Denoising Diffusion Probabilistic Models (DDPMs), and apply them to train a neural network classifier independent of pre-selection methods. The halo images generated by DDPMs are of higher quality than those produced by WGANs. The diffusion-supported classifier with a multi-head attention block achieved the best average validation accuracy of 95.93% over 10 runs, using 36 clusters for training and 10 for testing, without further hyperparameter tuning. Using our classifier, we rediscovered 9/12 halos (75% detection rate) from the MeerKAT Galaxy Cluster Legacy Survey (MGCLS) Catalogue, and 5/8 halos (63% detection rate) from the Planck Sunyaev-Zeldovich Catalogue 2 (PSZ2) within the GaLactic and Extragalactic All-sky MWA (GLEAM) survey. In addition, we identify 11 potential new halos, minihalos, or candidates in the COSMOS field using XMM-chandra-detected clusters in GLEAM data. This work demonstrates the potential of ML for unbiased detection of diffuse emission and provides labeled datasets for further study.
The Detection of H$_2$O Maser Emission from mid-IR Red Galaxies
2024-11-23
C. Y. Kuo, C. Y. Tai, A. Constantin, J. A. Braatz, H. H. Chung, B. Y. Chen, D. W. Pesce, C. M. V. Impellizzeri, F. Gao, Y. Y. Chang
We report the detection of H$_2$O maser emission in 4 out of 77 (5.2%) mid-IR red galaxies that meet the color criteria of $W1-W2 > 0.5$ and $W1-W4 > 7$ and are classified as Type-2 AGNs based on optical, near-IR, and mid-IR spectral energy distribution (SED) fitting. Here, $W1$, $W2$, and $W4$ represent the IR magnitudes at 3.4, 4.6, and 22 micron, respectively, as measured by the Wide-field Infrared Survey Explorer. Three of the four newly identified maser galaxies are classified as either Seyfert 2 or LINER systems, but none are disk maser systems. Our analysis indicates that AGN identifications based solely on SED fitting are unreliable, resulting in an unexpectedly low detection rate. By restricting our sample to optically classified Type 2 AGNs that satisfy the mid-IR color criteria, we achieve a maser detection rate of ~13-18%, aligning with previous predictions for mid-IR red sources. These selection criteria are the most effective to date for facilitating new maser detections, particularly in light of the recent identification of additional Type 2 AGNs identified from ongoing galaxy and AGN surveys.
Interactive Visual Assessment for Text-to-Image Generation Models
2024-11-23
Xiaoyue Mi, Fan Tang, Juan Cao, Qiang Sheng, Ziyao Huang, Peng Li, Yang Liu, Tong-Yee Lee
Visual generation models have achieved remarkable progress in computer graphics applications but still face significant challenges in real-world deployment. Current assessment approaches for visual generation tasks typically follow an isolated three-phase framework: test input collection, model output generation, and user assessment. These fashions suffer from fixed coverage, evolving difficulty, and data leakage risks, limiting their effectiveness in comprehensively evaluating increasingly complex generation models. To address these limitations, we propose DyEval, an LLM-powered dynamic interactive visual assessment framework that facilitates collaborative evaluation between humans and generative models for text-to-image systems. DyEval features an intuitive visual interface that enables users to interactively explore and analyze model behaviors, while adaptively generating hierarchical, fine-grained, and diverse textual inputs to continuously probe the capability boundaries of the models based on their feedback. Additionally, to provide interpretable analysis for users to further improve tested models, we develop a contextual reflection module that mines failure triggers of test inputs and reflects model potential failure patterns supporting in-depth analysis using the logical reasoning ability of LLM. Qualitative and quantitative experiments demonstrate that DyEval can effectively help users identify max up to 2.56 times generation failures than conventional methods, and uncover complex and rare failure patterns, such as issues with pronoun generation and specific cultural context generation. Our framework provides valuable insights for improving generative models and has broad implications for advancing the reliability and capabilities of visual generation systems across various domains.
SILVERRUSH. XIV. Lya Luminosity Functions and Angular Correlation Functions from ~20,000 Lya Emitters at z~2.2-7.3 from upto 24 ${\rm deg}^2$ HSC-SSP and CHORUS Surveys: Linking the Post-Reionization Epoch to the Heart of Reionization
We present the luminosity functions (LFs) and angular correlation functions (ACFs) derived from 18,960 Ly$\alpha$ emitters (LAEs) at $z=2.2-7.3$ over a wide survey area of $\lesssim24 {\rm deg^2}$ that are identified in the narrowband data of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) and the Cosmic HydrOgen Reionization Unveiled with Subaru (CHORUS) surveys. Confirming the large sample with the 241 spectroscopically identified LAEs, we determine Ly$\alpha$ LFs and ACFs in the brighter luminosity range down to $0.5L_{\star}$, and confirm that our measurements are consistent with previous studies but offer significantly reduced statistical uncertainties. The improved precision of our ACFs allows us to clearly detect one-halo terms at some redshifts, and provides large-scale bias measurements that indicate hosting halo masses of $\sim 10^{11} M_\odot$ over $z\simeq 2-7$. By comparing our Ly$\alpha$ LF (ACF) measurements with reionization models, we estimate the neutral hydrogen fractions in the intergalactic medium to be $x_{\rm \HI}<0.05$ (=${0.06}^{+0.12}_{-0.03}$), $0.15^{+0.10}_{-0.08}$ (${0.21}^{+0.19}_{-0.14}$), $0.18^{+0.14}_{-0.12}$, and $0.75^{+0.09}_{-0.13}$ at $z=5.7$, $6.6$, $7.0$, and $7.3$, respectively. Our findings suggest that the neutral hydrogen fraction remains relatively low, $x_{\rm \HI} \lesssim 0.2$, at $z=5-7$, but increases sharply at $z > 7$, reaching $x_{\rm \HI} \sim 0.9$ by $z \simeq 8-9$, as indicated by recent JWST studies. The combination of our results from LAE observations with recent JWST observations suggests that the major epoch of reionization occurred around $z \sim 7-8$, likely driven by the emergence of massive sources emitting significant ionizing photons.
Stochastic Volterra Equations for Local Times of Spectrally Positive Levy Processes with Gaussian Components
2024-11-23
Wei Xu
Following our previous work [68], this paper continues to investigate the evolution dynamics of local times of spectrally positive Levy processes with Gaussian components in the spatial direction. We first prove that conditioned on the finiteness of the first time at which the local time at zero exceeds a given value, local times at positive line are equal in law to the unique solution of a stochastic Volterra equation driven by a Gaussian white noise and two Poisson random measures with convolution kernel given in terms of the scale function. The existence and uniqueness of strong solution of the stochastic Volterra equation are also proved when the Levy measure has finite first moment. We then provide several equivalent representations and propose a flow in the local times by establishing a comparison principle for local times of Levy processes with various drifts or stopped when local times at zero exceed different given values. Armed with the stochastic Volterra equation, we also provide some properties of local times, including the uniform moment estimates, $(1/2-\varepsilon)$-Holder continuity and maximal inequality. By using the method of duality, we establish an exponential-affine representation of the Laplace functional in terms of the unique non-negative solution of a nonlinear Volterra equation associated with the Laplace exponent of the Levy process. This gives us another perspective on the evolution dynamics of local times in the spatial direction.
An upgraded GMRT and MeerKAT study of radio relics in the low mass merging cluster PSZ2 G200.95-28.16
2024-11-23
Arpan Pal, Ruta Kale, Qian H. S. Wang, Daniel R. Wik
Diffuse radio sources known as radio relics are direct tracers of shocks in the outskirts of merging galaxy clusters. PSZ2 G200.95-28.16, a low-mass merging cluster($\textrm{M}_{500} = (2.7 \pm 0.2) \times 10^{14}~\mathrm{M}_{\odot}$) features a prominent radio relic, first identified by Kale et al. 2017. We name this relic as the Seahorse. The MeerKAT Galaxy Cluster Legacy Survey has confirmed two additional radio relics, R2 and R3 in this cluster. We present new observations of this cluster with the Upgraded GMRT at 400 and 650 MHz paired with the Chandra X-ray data. The largest linear sizes for the three relics are~1.53 Mpc, 1.12~kpc, and 340~kpc. All three radio relics are polarized at 1283~MHz. Assuming the diffusive shock acceleration model, the spectral indices of the relics imply shock Mach Numbers of $3.1 \pm 0.8$ and $2.8 \pm 0.9$ for the Seahorse and R2, respectively. The Chandra X-ray surface brightness map shows two prominent subclusters, but the relics are not perpendicular to the likely merger axis as typically observed; no shocks are detected at the locations of the relics. We discuss the possible merger scenarios in light of the low mass of the cluster and the radio and X-ray properties of the relics. The relic R2 follows the correlation known in the radio relic power and cluster mass plane, but the Seahorse and R3 relics are outliers. We have also discovered a radio ring in our 650~MHz uGMRT image that could be an Odd radio circle candidate.
3D Gaussian Splatting (3DGS) allows flexible adjustments to scene representation, enabling continuous optimization of scene quality during dense visual simultaneous localization and mapping (SLAM) in static environments. However, 3DGS faces challenges in handling environmental disturbances from dynamic objects with irregular movement, leading to degradation in both camera tracking accuracy and map reconstruction quality. To address this challenge, we develop an RGB-D dense SLAM which is called Gaussian Splatting SLAM in Dynamic Environments (Gassidy). This approach calculates Gaussians to generate rendering loss flows for each environmental component based on a designed photometric-geometric loss function. To distinguish and filter environmental disturbances, we iteratively analyze rendering loss flows to detect features characterized by changes in loss values between dynamic objects and static components. This process ensures a clean environment for accurate scene reconstruction. Compared to state-of-the-art SLAM methods, experimental results on open datasets show that Gassidy improves camera tracking precision by up to 97.9% and enhances map quality by up to 6%.
Project TAIPAN: Results from a Novel Gravity Gradiometer Field Test
2024-11-23
Alexey V. Veryaskin, Howard C. Golden, Khyl J. McMahon, Neil M. Provins, Frank J. van Kann, Thomas J. Meyer
Project TAIPAN has been carried out jointly by Trinity Research Lab and the Frequency and Quantum Metrology Research Group located at the School of Physics, Mathematics and Computing of the University of Western Australia (UWA). Lockheed Martin Corporation (USA) has also been a partner in this joint collaboration providing financial backing to the project and other support including advanced modelling, assessment of laboratory tests and data analysis. The project aim was to develop a miniaturised gravity gradiometer to measure horizontal mixed gradient components of the Earth gravity in a small, lightweight package that can be deployed in a fixed 4D mode, in a borehole, or on moving exploration platforms including ground-based, airborne and submersible. The gradiometer design has evolved through a few prototypes combining the design of its sensing element with ultra low noise microwave and capacitive read out. The most recent prototype of the gradiometer using novel ultra sensitive capacitive pick off metrology has been trialled in the harsh environment of Outback Western Australia over a known gravity anomaly displaying steep gradients. Despite adverse weather conditions, results of the trial indicate that the gradiometer operated as expected, closely replicating the gravity gradient profile extrapolated from a regional gravity survey.
The Indian Institutes of Technology (IITs) are vital to India's research ecosystem, advancing technology and engineering for industrial and societal benefits. This study reviews the research performance of top IITs-Bombay, Delhi, Madras, Kharagpur, and Kanpur based on Scopus-indexed publications (1952-2024). Research output has grown exponentially, supported by increased funding and collaborations. IIT-Kanpur excels in research impact, while IIT-Bombay and IIT-Madras are highly productive but show slightly lower per-paper impact. Internationally, IITs collaborate robustly with the USA, Germany, and the UK, alongside Asian nations like Japan and South Korea, with IIT-Madras leading inter-IIT partnerships. Research priorities align with SDG 3 (Health), SDG 7 (Clean Energy), and SDG 11 (Sustainable Cities). Despite strengths in fields like energy, fluid dynamics, and materials science, challenges persist, including limited collaboration with newer IITs and gaps in emerging fields. Strengthening specialization and partnerships is crucial for addressing global challenges and advancing sustainable development.
Recent observations have revealed a super-virial temperature gas phase at log(T/K) $\sim7$ in the Milky Way, challenging existing galaxy-formation models. This hot gas phase was discovered toward extragalactic absorption sightlines and blank-sky emission fields, both at high galactic latitudes. The location of this hot component is unknown; is it in the extended circumgalactic medium (CGM) or in the interstellar medium (ISM) instead? We analyzed X-ray spectra from Chandra's High-Energy Transmission Grating (HETG) observations of 27 Galactic X-ray binaries (XRBs) to investigate whether the hot gas component is present in the ISM. We searched for absorption lines of SXVI K$\alpha$, SiXIV K$\alpha$, and NeX K$\alpha$, which are the tell-tale signatures of the hot gas and which have been detected toward extragalactic sightlines. Of the 27 targets, these lines were detected in the spectra of only 7, with two sources displaying broad line features likely intrinsic to the XRB systems. Additionally, most of the detected lines are time-variable, reinforcing their likely association with the XRBs. Our results suggest that the super-virial temperature gas is not a widespread component of the ISM but may instead be located in extraplanar regions or the extended CGM, in line with some recent simulation results.
Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sources
2024-11-23
Siddhant Dutta, Iago Leal de Freitas, Pedro Maciel Xavier, Claudio Miceli de Farias, David Esteban Bernal Neira
Federated Learning (FL) is a decentralized machine learning approach that has gained attention for its potential to enable collaborative model training across clients while protecting data privacy, making it an attractive solution for the chemical industry. This work aims to provide the chemical engineering community with an accessible introduction to the discipline. Supported by a hands-on tutorial and a comprehensive collection of examples, it explores the application of FL in tasks such as manufacturing optimization, multimodal data integration, and drug discovery while addressing the unique challenges of protecting proprietary information and managing distributed datasets. The tutorial was built using key frameworks such as $\texttt{Flower}$ and $\texttt{TensorFlow Federated}$ and was designed to provide chemical engineers with the right tools to adopt FL in their specific needs. We compare the performance of FL against centralized learning across three different datasets relevant to chemical engineering applications, demonstrating that FL will often maintain or improve classification performance, particularly for complex and heterogeneous data. We conclude with an outlook on the open challenges in federated learning to be tackled and current approaches designed to remediate and improve this framework.
About Time: Advances, Challenges, and Outlooks of Action Understanding
2024-11-22
Alexandros Stergiou, Ronald Poppe
We have witnessed impressive advances in video action understanding. Increased dataset sizes, variability, and computation availability have enabled leaps in performance and task diversification. Current systems can provide coarse- and fine-grained descriptions of video scenes, extract segments corresponding to queries, synthesize unobserved parts of videos, and predict context. This survey comprehensively reviews advances in uni- and multi-modal action understanding across a range of tasks. We focus on prevalent challenges, overview widely adopted datasets, and survey seminal works with an emphasis on recent advances. We broadly distinguish between three temporal scopes: (1) recognition tasks of actions observed in full, (2) prediction tasks for ongoing partially observed actions, and (3) forecasting tasks for subsequent unobserved action. This division allows us to identify specific action modeling and video representation challenges. Finally, we outline future directions to address current shortcomings.
2024-11-22
Christopher A. J. Duncan, Michael L. Brown
We show, using the pseudo-$C_\ell$ technique, how to estimate cosmic shear and galaxy-galaxy lensing power spectra that are insensitive to the effects of multiple sources of lensing bias including source-lens clustering, magnification bias and obscuration effects. All of these effects are of significant concern for ongoing and near-future Stage-IV cosmic shear surveys. Their common attribute is that they all introduce a cosmological dependence into the selection of the galaxy shear sample. Here, we show how a simple adaptation of the pseudo-$C_\ell$ method can help to suppress these biases to negligible levels in a model-independent way. Our approach is based on making pixelised maps of the shear field and then using a uniform weighting of those shear maps when extracting power spectra. To produce unbiased measurements, the weighting scheme must be independent of the cosmological signal, which makes the commonly-used inverse-variance weighting scheme unsuitable for cosmic shear measurements. We demonstrate this explicitly. A frequently-cited motivation for using inverse-variance weights is to minimize the errors on the resultant power spectra. We find that, for a Stage-IV-like survey configuration, this motivation is not compelling: the precision of power spectra recovered from uniform-weighted maps is only very slightly degraded compared to those recovered from an inverse-variance analysis, and we predict no degradation in cosmological parameter constraints. We suggest that other 2-point statistics, such as real-space correlation functions, can be rendered equally robust to these lensing biases by applying those estimators to pixelised shear maps using a uniform weighting scheme.
In multi-agent systems, the agent behavior is highly influenced by its utility function, as these utilities shape both individual goals as well as interactions with the other agents. Inverse Reinforcement Learning (IRL) is a well-established approach to inferring the utility function by observing an expert behavior within a given environment. In this paper, we extend the IRL framework to the multi-agent setting, assuming to observe agents who are following Nash Equilibrium (NE) policies. We theoretically investigate the set of utilities that explain the behavior of NE experts. Specifically, we provide an explicit characterization of the feasible reward set and analyze how errors in estimating the transition dynamics and expert behavior impact the recovered rewards. Building on these findings, we provide the first sample complexity analysis for the multi-agent IRL problem. Finally, we provide a numerical evaluation of our theoretical results.
The EE-Classifier: A classification method for functional data based on extremality indexes
2024-11-22
Catalina Lesmes, Francisco Zuluaga, Henry Laniado, Andres Gomez, Andrea Carvajal
Functional data analysis has gained significant attention due to its wide applicability. This research explores the extension of statistical analysis methods for functional data, with a primary focus on supervised classification techniques. It provides a review on the existing depth-based methods used in functional data samples. Building on this foundation, it introduces an extremality-based approach, which takes the modified epigraph and hypograph indexes properties as classification techniques. To demonstrate the effectiveness of the classifier, it is applied to both real-world and synthetic data sets. The results show its efficacy in accurately classifying functional data. Additionally, the classifier is used to analyze the fluctuations in the S\&P 500 stock value. This research contributes to the field of functional data analysis by introducing a new extremality-based classifier. The successful application to various data sets shows its potential for supervised classification tasks and provides valuable insights into financial data analysis.
Twice Fourier transformable measures and diffraction theory
2024-11-22
Hans G. Feichtinger, Christoph Richard, Christoph Schumacher, Nicolae Strungaru
Mathematical diffraction theory has been developed since about 1995. Hof's initial approach relied on tempered distributions in euclidean space. Nowadays often the Fourier theory by Argabright and Gil de Lamadrid is used, which applies to appropriate measures on locally compact abelian groups. We review diffraction theory using Wiener amalgams as test function spaces. For translation bounded measures, this unifies and simplifies the former two approaches. We treat weighted versions of Meyer's model sets as examples.
Optimization Strategies for Parallel Computation of Skylines
2024-11-22
Paolo Ciaccia, Davide Martinenghi
Skyline queries are one of the most widely adopted tools for Multi-Criteria Analysis, with applications covering diverse domains, including, e.g., Database Systems, Data Mining, and Decision Making. Skylines indeed offer a useful overview of the most suitable alternatives in a dataset, while discarding all the options that are dominated by (i.e., worse than) others. The intrinsically quadratic complexity associated with skyline computation has pushed researchers to identify strategies for parallelizing the task, particularly by partitioning the dataset at hand. In this paper, after reviewing the main partitioning approaches available in the relevant literature, we propose two orthogonal optimization strategies for reducing the computational overhead, and compare them experimentally in a multi-core environment equipped with PySpark.
Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing
2024-11-22
Miriam Alber, Christoph Hönes, Patrick Baier
One of the most promising use-cases for machine learning in industrial manufacturing is the early detection of defective products using a quality control system. Such a system can save costs and reduces human errors due to the monotonous nature of visual inspections. Today, a rich body of research exists which employs machine learning methods to identify rare defective products in unbalanced visual quality control datasets. These methods typically rely on two components: A visual backbone to capture the features of the input image and an anomaly detection algorithm that decides if these features are within an expected distribution. With the rise of transformer architecture as visual backbones of choice, there exists now a great variety of different combinations of these two components, ranging all along the trade-off between detection quality and inference time. Facing this variety, practitioners in the field often have to spend a considerable amount of time on researching the right combination for their use-case at hand. Our contribution is to help practitioners with this choice by reviewing and evaluating current vision transformer models together with anomaly detection methods. For this, we chose SotA models of both disciplines, combined them and evaluated them towards the goal of having small, fast and efficient anomaly detection models suitable for industrial manufacturing. We evaluated the results of our experiments on the well-known MVTecAD and BTAD datasets. Moreover, we give guidelines for choosing a suitable model architecture for a quality control system in practice, considering given use-case and hardware constraints.
Window convolution of the galaxy clustering bispectrum
2024-11-22
Mike Shengbo Wang, Florian Beutler, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, A. de la Macorra, P. Doel, A. Font-Ribera, E. Gaztañaga, G. Gutierrez, K. Honscheid, C. Howlett, D. Kirkby, A. Lambert, M. Landriau, R. Miquel, G. Niz, F. Prada, I. Pérez-Ràfols, G. Rossi, E. Sanchez, D. Schlegel, M. Schubnell, D. Sprayberry, G. Tarlé, B. A. Weaver
In galaxy survey analysis, the observed clustering statistics do not directly match theoretical predictions but rather have been processed by a window function that arises from the survey geometry including the sky footprint, redshift-dependent background number density and systematic weights. While window convolution of the power spectrum is well studied, for the bispectrum with a larger number of degrees of freedom, it poses a significant numerical and computational challenge. In this work, we consider the effect of the survey window in the tripolar spherical harmonic decomposition of the bispectrum and lay down a formal procedure for their convolution via a series expansion of configuration-space three-point correlation functions, which was first proposed by Sugiyama et al. (2019). We then provide a linear algebra formulation of the full window convolution, where an unwindowed bispectrum model vector can be directly premultiplied by a window matrix specific to each survey geometry. To validate the pipeline, we focus on the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) luminous red galaxy (LRG) sample in the South Galactic Cap (SGC) in the redshift bin $0.4 \leqslant z \leqslant 0.6$. We first perform convergence checks on the measurement of the window function from discrete random catalogues, and then investigate the convergence of the window convolution series expansion truncated at a finite of number of terms as well as the performance of the window matrix. This work highlights the differences in window convolution between the power spectrum and bispectrum, and provides a streamlined pipeline for the latter for current surveys such as DESI and the Euclid mission.
Completeness of Energy Eigenfunctions for the Reflectionless Potential in Quantum Mechanics
2024-11-22
F. Erman, O. T. Turgut
There are few exactly solvable potentials in quantum mechanics for which the completeness relation of the energy eigenstates can be explicitly verified. In this article, we give an elementary proof that the set of bound (discrete) states together with the scattering (continuum) states of the reflectionless potential form a complete set. We also review a direct and elegant derivation of the energy eigenstates with proper normalization by introducing an analog of the creation and annihilation operators of the harmonic oscillator problem. We further show that, in the case of a single bound state, the corresponding wave function can be found from the knowledge of continuum eigenstates of the system. Finally, completeness is shown by using the even/odd parity eigenstates of the Hamiltonian, which provides another explicit demonstration of a fundamental property of quantum mechanical Hamiltonians.
MIGHTEE-HI: The star-forming properties of HI selected galaxies
2024-11-22
Madalina N. Tudorache, M. J. Jarvis, A. A. Ponomareva, I. Heywood, N. Maddox, B. S. Frank, M. Baes, R. Dave, S. L. Jung, M. Maksymowicz-Maciata, H. Pan, K. Spekkens
The interplay between atomic gas, the star-formation history of a galaxy and its environment are intrinsically linked, and we need to decouple these dependencies to understand their role in galaxy formation and evolution. In this paper, we analyse the star formation histories (SFHs) of 187 galaxies from the MIGHTEE-HI Survey Early Science Release data, focusing on the relationships between HI properties and star formation. A strong correlation emerges between a galaxy's HI-to-stellar mass ratio and the time of formation, alongside an inverse correlation between stellar mass and time of formation, regardless of the inferred SFH. Additionally, galaxies with lower stellar masses and higher HI-to-stellar mass ratios exhibit longer gas depletion times compared to more massive galaxies, which appear to have depleted their gas and formed stars more efficiently. This suggests that smaller, gas-rich galaxies have higher depletion times due to shallower potential wells and less efficient star formation. Furthermore, we explore the connection between spin-filament alignment and HI content. We find no significant correlation between peak star formation activity and proximity to filaments. However, we do find that the two galaxies in our sample within 1 Mpc of a filament have very low gas-depletion timescales and have their spin axis misaligned with the filament, suggestive of a link between the galaxy properties and proximity to a filament.
Task-aware robotic grasping is a challenging problem that requires the integration of semantic understanding and geometric reasoning. Traditional grasp planning approaches focus on stable or feasible grasps, often disregarding the specific tasks the robot needs to accomplish. This paper proposes a novel framework that leverages Large Language Models (LLMs) and Quality Diversity (QD) algorithms to enable zero-shot task-conditioned grasp selection. The framework segments objects into meaningful subparts and labels each subpart semantically, creating structured representations that can be used to prompt an LLM. By coupling semantic and geometric representations of an object's structure, the LLM's knowledge about tasks and which parts to grasp can be applied in the physical world. The QD-generated grasp archive provides a diverse set of grasps, allowing us to select the most suitable grasp based on the task. We evaluate the proposed method on a subset of the YCB dataset, where a Franka Emika robot is assigned to perform various actions based on object-specific task requirements. We created a ground truth by conducting a survey with six participants to determine the best grasp region for each task-object combination according to human intuition. The model was evaluated on 12 different objects across 4--7 object-specific tasks, achieving a weighted intersection over union (IoU) of 76.4% when compared to the survey data.
LoRePIE: $\ell_0$ Regularised Extended Ptychographical Iterative Engine for Low-dose and Fast Electron Ptychography
2024-11-22
Amirafshar Moshtaghpour, Abner Velazco-Torrejon, Alex W. Robinson, Nigel D. Browning, Angus I. Kirkland
The extended Ptychographical Iterative Engine (ePIE) is a widely used phase retrieval algorithm for Electron Ptychography from 4-dimensional (4-D) Scanning Transmission Electron Microscopy (4-D STEM) measurements acquired with a focused or defocused electron probe. However, ePIE relies on redundancy in the data and hence requires adjacent illuminated areas to overlap. In this paper, we propose a regularised variant of ePIE that is more robust to low overlap ratios. We examine the performance of the proposed algorithm on an experimental 4-D STEM data of double layered Rotavirus particles acquired in a full scan with 85% overlap. By artificial down-sampling of the probe positions, we have created synthetic 4-D STEM datasets with different overlap ratios and use these to show that a high quality reconstruction of Rotavirus particles can be obtained from data with an overlap as low as 56%.
A Reproducibility and Generalizability Study of Large Language Models for Query Generation
2024-11-22
Moritz Staudinger, Wojciech Kusa, Florina Piroi, Aldo Lipani, Allan Hanbury
Systematic literature reviews (SLRs) are a cornerstone of academic research, yet they are often labour-intensive and time-consuming due to the detailed literature curation process. The advent of generative AI and large language models (LLMs) promises to revolutionize this process by assisting researchers in several tedious tasks, one of them being the generation of effective Boolean queries that will select the publications to consider including in a review. This paper presents an extensive study of Boolean query generation using LLMs for systematic reviews, reproducing and extending the work of Wang et al. and Alaniz et al. Our study investigates the replicability and reliability of results achieved using ChatGPT and compares its performance with open-source alternatives like Mistral and Zephyr to provide a more comprehensive analysis of LLMs for query generation. Therefore, we implemented a pipeline, which automatically creates a Boolean query for a given review topic by using a previously defined LLM, retrieves all documents for this query from the PubMed database and then evaluates the results. With this pipeline we first assess whether the results obtained using ChatGPT for query generation are reproducible and consistent. We then generalize our results by analyzing and evaluating open-source models and evaluating their efficacy in generating Boolean queries. Finally, we conduct a failure analysis to identify and discuss the limitations and shortcomings of using LLMs for Boolean query generation. This examination helps to understand the gaps and potential areas for improvement in the application of LLMs to information retrieval tasks. Our findings highlight the strengths, limitations, and potential of LLMs in the domain of information retrieval and literature review automation.
Radio and gamma-ray timing of TRAPUM L-band Fermi pulsar survey discoveries
2024-11-22
M. Burgay, L. Nieder, C. J. Clark, P. C. C. Freire, S. Buchner, T. Thongmeearkom, J. D. Turner, E. Carli, I. Cognard, J. M. Grießmeier, R. Karuppusamy, M. C. i Bernadich, A. Possenti, V. Venkatraman Krishnan, R. P. Breton, E. D. Barr, B. W. Stappers, M. Kramer, L. Levin, S. M. Ransom, P. V. Padmanabh
This paper presents the results of a joint radio and gamma-ray timing campaign on the nine millisecond pulsars (MSPs) discovered as part of the L-band targeted survey of Fermi-LAT sources performed in the context of the Transients and Pulsars with MeerKAT (TRAPUM) Large Survey Project. Out of these pulsars, eight are members of binary systems; of these eight, two exhibit extended eclipses of the radio emission. Using an initial radio timing solution, pulsations were found in the gamma rays for six of the targets. For these sources, a joint timing analysis of radio times of arrival and gamma-ray photons was performed, using a newly developed code that optimises the parameters through a Markov chain Monte Carlo (MCMC) technique. This approach has allowed us to precisely measure both the short- and long-term timing parameters. This study includes a proper motion measurement for four pulsars, which a gamma ray-only analysis would not have been sensitive to, despite the 15-year span of Fermi data.
Physical and Software Based Fault Injection Attacks Against TEEs in Mobile Devices: A Systemisation of Knowledge
2024-11-22
Aaron Joy, Ben Soh, Zhi Zhang, Sri Parameswaran, Darshana Jayasinghe
Trusted Execution Environments (TEEs) are critical components of modern secure computing, providing isolated zones in processors to safeguard sensitive data and execute secure operations. Despite their importance, TEEs are increasingly vulnerable to fault injection (FI) attacks, including both physical methods, such as Electromagnetic Fault Injection (EMFI), and software-based techniques. This survey examines these FI methodologies, exploring their ability to disrupt TEE operations and expose vulnerabilities in devices ranging from smartphones and IoT systems to cloud platforms. The study highlights the evolution and effectiveness of non-invasive techniques, such as EMFI, which induce faults through electromagnetic disturbances without physical modifications to hardware, making them harder to detect and mitigate. Real-world case studies illustrate the significant risks posed by these attacks, including unauthorised access, privilege escalation, and data corruption. In addition, the survey identifies gaps in existing TEE security architectures and emphasises the need for enhanced countermeasures, such as dynamic anomaly detection and updated threat models. The findings underline the importance of interdisciplinary collaboration to address these vulnerabilities, involving researchers, manufacturers, and policymakers. This survey provides actionable insights and recommendations to guide the development of more robust TEE architectures in mobile devices, fortify FI resilience, and shape global security standards. By advancing TEE security, this research aims to protect critical digital infrastructure and maintain trust in secure computing systems worldwide.
Unusual intra-burst variations of polarization states in FRB 20210912A and FRB 20230708A : Effects of plasma birefringence?
2024-11-22
Apurba Bera, Clancy W. James, Mark M. McKinnon, Ronald D. Ekers, Tyson Dial, Adam T. Deller, Keith W. Bannister, Marcin Glowacki, Ryan M. Shannon
Fast radio bursts (FRBs) are highly energetic events of short-duration intense radio emission, the origin of which remains elusive till date. Polarization of the FRB signals carry information about the emission source as well as the magneto-ionic media the signal passes through before reaching terrestrial radio telescopes. Currently known FRBs show a diverse range of polarization, sometimes with complex features, making it challenging to describe them in a unified model. FRB 20230708A and FRB 20210912A are two bright and highly polarized FRBs detected in the Commensal Real-time ASKAP Fast Transients (CRAFT) survey with the Australian Square Kilometre Array Pathfinder (ASKAP) that exhibit time-dependent conversion between linear and circular polarization as well as intra-burst (apparent) variation of Faraday rotation measure. We investigate the intra-burst temporal evolution of the polarization state of radio emission in these two events using the Poincar\'e sphere representation and find that the trajectories of the polarization state are well described by great circles on the Poincar\'e sphere. These polarization features may be signatures of a transition between two partially coherent orthogonal polarization modes or propagation through a birefringent medium. We find that the observed variations of the polarization states of these two FRBs are qualitatively consistent a magnetospheric origin of the bursts and the effects of propagation through a birefringent medium with linearly polarized modes in the outer magnetosphere or near-wind region of a neutron star.
Cosmological Analysis with Calibrated Neural Quantile Estimation and Approximate Simulators
2024-11-22
He Jia
A major challenge in extracting information from current and upcoming surveys of cosmological Large-Scale Structure (LSS) is the limited availability of computationally expensive high-fidelity simulations. We introduce Neural Quantile Estimation (NQE), a new Simulation-Based Inference (SBI) method that leverages a large number of approximate simulations for training and a small number of high-fidelity simulations for calibration. This approach guarantees an unbiased posterior and achieves near-optimal constraining power when the approximate simulations are reasonably accurate. As a proof of concept, we demonstrate that cosmological parameters can be inferred at field level from projected 2-dim dark matter density maps up to $k_{\rm max}\sim1.5\,h$/Mpc at $z=0$ by training on $\sim10^4$ Particle-Mesh (PM) simulations with transfer function correction and calibrating with $\sim10^2$ Particle-Particle (PP) simulations. The calibrated posteriors closely match those obtained by directly training on $\sim10^4$ expensive PP simulations, but at a fraction of the computational cost. Our method offers a practical and scalable framework for SBI of cosmological LSS, enabling precise inference across vast volumes and down to small scales.
AI Tailoring: Evaluating Influence of Image Features on Fashion Product Popularity
2024-11-22
Xiaomin Li, Junyi Sha
Identifying key product features that influence consumer preferences is essential in the fashion industry. In this study, we introduce a robust methodology to ascertain the most impactful features in fashion product images, utilizing past market sales data. First, we propose the metric called "influence score" to quantitatively assess the importance of product features. Then we develop a forecasting model, the Fashion Demand Predictor (FDP), which integrates Transformer-based models and Random Forest to predict market popularity based on product images. We employ image-editing diffusion models to modify these images and perform an ablation study, which validates the impact of the highest and lowest-scoring features on the model's popularity predictions. Additionally, we further validate these results through surveys that gather human rankings of preferences, confirming the accuracy of the FDP model's predictions and the efficacy of our method in identifying influential features. Notably, products enhanced with "good" features show marked improvements in predicted popularity over their modified counterparts. Our approach develops a fully automated and systematic framework for fashion image analysis that provides valuable guidance for downstream tasks such as fashion product design and marketing strategy development.
We utilize a hybrid approach that integrates the traditional cross-correlation function (CCF) and machine learning to detect spectroscopic multi-systems, specifically focusing on double-line spectroscopic binary (SB2). Based on the ninth data release (DR9) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), which includes a medium-resolution survey (MRS) containing 29,920,588 spectra, we identify 27,164 double-line and 3124 triple-line spectra, corresponding to 7096 SB2 candidates and 1903 triple-line spectroscopic binary (SB3) candidates, respectively, representing about 1% of the selection dataset from LAMOST-MRS DR9. Notably, 70.1% of the SB2 candidates and 89.6% of the SB3 candidates are newly identified. Compared to using only the traditional CCF technique, our method significantly improves the efficiency of detecting SB2, saves time on visual inspections by a factor of four.
Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction Methods
2024-11-22
Shuming Liang, Yu Ding, Zhidong Li, Bin Liang, Siqi Zhang, Yang Wang, Fang Chen
This paper explores the ability of Graph Neural Networks (GNNs) in learning various forms of information for link prediction, alongside a brief review of existing link prediction methods. Our analysis reveals that GNNs cannot effectively learn structural information related to the number of common neighbors between two nodes, primarily due to the nature of set-based pooling of the neighborhood aggregation scheme. Also, our extensive experiments indicate that trainable node embeddings can improve the performance of GNN-based link prediction models. Importantly, we observe that the denser the graph, the greater such the improvement. We attribute this to the characteristics of node embeddings, where the link state of each link sample could be encoded into the embeddings of nodes that are involved in the neighborhood aggregation of the two nodes in that link sample. In denser graphs, every node could have more opportunities to attend the neighborhood aggregation of other nodes and encode states of more link samples to its embedding, thus learning better node embeddings for link prediction. Lastly, we demonstrate that the insights gained from our research carry important implications in identifying the limitations of existing link prediction methods, which could guide the future development of more robust algorithms.
Quantum Advantage via Solving Multivariate Quadratics
2024-11-22
Pierre Briaud, Riddhi Ghosal, Aayush Jain, Paul Lou, Amit Sahai
In this work, we propose a new way to (non-interactively, verifiably) demonstrate Quantum Advantage by solving the average-case $\mathsf{NP}$ search problem of finding a solution to a system of (underdetermined) multivariate quadratic equations over the finite field $\mathbb{F}_2$ drawn from a specified distribution. In particular, we design a distribution of degree-2 polynomials $\{p_i(x_1,\ldots,x_n)\}_{i\in [m]}$ for $m
MAGAZ3NE: Evidence for Galactic Conformity in $z\gtrsim3$ Protoclusters
2024-11-22
Ian McConachie, Gillian Wilson, Ben Forrest, Z. Cemile Marsan, Adam Muzzin, M. C. Cooper, Marianna Annunziatella, Danilo Marchesini, Percy Gomez, Wenjun Chang, Stephanie M. Urbano Stawinski, Michael McDonald, Tracy Webb, Allison Noble, Brian C. Lemaux, Ekta A. Shah, Priti Staab, Lori M. Lubin, Roy R. Gal
We examine the quiescent fractions of massive galaxies in six $z\gtrsim3$ spectroscopically-confirmed protoclusters in the COSMOS field, one of which is newly confirmed and presented here. We report the spectroscopic confirmation of MAGAZ3NE~J100143+023021 at $z=3.122^{+0.007}_{-0.004}$ by the Massive Ancient Galaxies At $z>3$ NEar-infrared (MAGAZ3NE) survey. MAGAZ3NE~J100143+023021 contains a total of 79 protocluster members (28 spectroscopic and 51 photometric). Three spectroscopically-confirmed members are star-forming ultra-massive galaxies ($\log(M_{\star}/{\rm M}_\odot)>11$; UMGs), the most massive of which has $\log(M_{\star}/{\rm M}_\odot)=11.15^{+0.05}_{-0.06}$. Combining Keck/MOSFIRE spectroscopy and the COSMOS2020 photometric catalog, we use a weighted Gaussian kernel density estimator to map the protocluster and measure its total mass $2.25^{+1.55}_{-0.65}\times10^{14}~{\rm M}_{\odot}$ in the dense ``core'' region. For each of the six COSMOS protoclusters, we compare the quiescent fraction to the status of the central UMG as star-forming or quiescent. We observe that galaxies in these protoclusters appear to obey galactic conformity: elevated quiescent fractions are found in protoclusters with $UVJ$ quiescent UMGs and low quiescent fractions are found in protoclusters containing $UVJ$ star-forming UMGs. This correlation of star-formation/quiescence in UMGs and the massive galaxies nearby in these protoclusters is the first evidence for the existence of galactic conformity at $z>3$. Despite disagreements over mechanisms behind conformity at low redshifts, its presence at these early cosmic times would provide strong constraints on the physics proposed to drive galactic conformity.
Improved Background Estimation for Gas Plume Identification in Hyperspectral Images
2024-11-22
Scout Jarman, Zigfried Hampel-Arias, Adra Carr, Kevin R. Moon
Longwave infrared (LWIR) hyperspectral imaging can be used for many tasks in remote sensing, including detecting and identifying effluent gases by LWIR sensors on airborne platforms. Once a potential plume has been detected, it needs to be identified to determine exactly what gas or gases are present in the plume. During identification, the background underneath the plume needs to be estimated and removed to reveal the spectral characteristics of the gas of interest. Current standard practice is to use ``global" background estimation, where the average of all non-plume pixels is used to estimate the background for each pixel in the plume. However, if this global background estimate does not model the true background under the plume well, then the resulting signal can be difficult to identify correctly. The importance of proper background estimation increases when dealing with weak signals, large libraries of gases of interest, and with uncommon or heterogeneous backgrounds. In this paper, we propose two methods of background estimation, in addition to three existing methods, and compare each against global background estimation to determine which perform best at estimating the true background radiance under a plume, and for increasing identification confidence using a neural network classification model. We compare the different methods using 640 simulated plumes. We find that PCA is best at estimating the true background under a plume, with a median of 18,000 times less MSE compared to global background estimation. Our proposed K-Nearest Segments algorithm improves median neural network identification confidence by 53.2%.
Differential systems in Sobolev spaces with generic inhomogeneous boundary conditions
2024-11-22
Vladimir Mikhailets, Olena Atlasiuk
The paper contains a review of results on linear systems of ordinary differential equations of an arbitrary order on a finite interval with the most general inhomogeneous boundary conditions in Sobolev spaces. The character of the solvability of such problems is investigated, their Fredholm properties are established, and their indexes and the dimensions of their kernels and co-kernels are found. In addition, necessary and sufficient conditions of continuity in the parameter of the solutions of the introduced classes of boundary-value problems in Sobolev spaces of an arbitrary order are obtained.
Analysis of the Internal Radial Gradient of Chemical Abundances in Spiral Galaxies from CALIFA
2024-11-22
A. F. S. Cardoso, O. Cavichia, M. Mollá, L. Sánchez-Menguiano
The study of chemical evolution is of paramount importance for understanding the galaxies evolution. Models and observations propose an inside-out mechanism in the formation of spiral galaxy disks implying a negative radial gradient of elemental abundances when represented in logarithmic scale. However, observed chemical abundance gradients, in some instances, deviate from a single linear negative straight line, revealing inner drops or outer flattenings, particularly in more massive galaxies. This study analyzes oxygen abundance gradients in spiral galaxies based on observations from the Calar Alto Legacy Integral Field Area (CALIFA) survey. Our focus is specifically on examining oxygen abundance gradient profiles, as obtained with data from HII regions, with a special emphasis on the inner radial gradient. We employ an automated fitting procedure to establish correlations between the physical properties of galaxies and bulges and the presence of these inner drops, seeking for potential explanations for these gradient variations. We find that the different criteria used in the literature to distinguish HII regions from other ionization sources in the galaxy, such as Active Galactic Nuclei, significantly impact the results, potentially altering abundance gradient profiles and uncovering galaxies with distinct inner drops. Additionally, we analyze the abundance radial gradients to investigate the impact of diffuse ionized gas (DIG) decontamination on oxygen abundances over the featuring inner drops. We observe that DIG, concentrated mainly in the central regions of galaxies, can modify oxygen abundance gradient profiles if left unaddressed.
MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs
2024-11-22
Chaoyou Fu, Yi-Fan Zhang, Shukang Yin, Bo Li, Xinyu Fang, Sirui Zhao, Haodong Duan, Xing Sun, Ziwei Liu, Liang Wang, Caifeng Shan, Ran He
As a prominent direction of Artificial General Intelligence (AGI), Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia. Building upon pre-trained LLMs, this family of models further develops multimodal perception and reasoning capabilities that are impressive, such as writing code given a flow chart or creating stories based on an image. In the development process, evaluation is critical since it provides intuitive feedback and guidance on improving models. Distinct from the traditional train-eval-test paradigm that only favors a single task like image classification, the versatility of MLLMs has spurred the rise of various new benchmarks and evaluation methods. In this paper, we aim to present a comprehensive survey of MLLM evaluation, discussing four key aspects: 1) the summarised benchmarks types divided by the evaluation capabilities, including foundation capabilities, model self-analysis, and extented applications; 2) the typical process of benchmark counstruction, consisting of data collection, annotation, and precautions; 3) the systematic evaluation manner composed of judge, metric, and toolkit; 4) the outlook for the next benchmark. This work aims to offer researchers an easy grasp of how to effectively evaluate MLLMs according to different needs and to inspire better evaluation methods, thereby driving the progress of MLLM research.
Sycophancy in Large Language Models: Causes and Mitigations
2024-11-22
Lars Malmqvist
Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing tasks. However, their tendency to exhibit sycophantic behavior - excessively agreeing with or flattering users - poses significant risks to their reliability and ethical deployment. This paper provides a technical survey of sycophancy in LLMs, analyzing its causes, impacts, and potential mitigation strategies. We review recent work on measuring and quantifying sycophantic tendencies, examine the relationship between sycophancy and other challenges like hallucination and bias, and evaluate promising techniques for reducing sycophancy while maintaining model performance. Key approaches explored include improved training data, novel fine-tuning methods, post-deployment control mechanisms, and decoding strategies. We also discuss the broader implications of sycophancy for AI alignment and propose directions for future research. Our analysis suggests that mitigating sycophancy is crucial for developing more robust, reliable, and ethically-aligned language models.
The constraining power of the Marked Power Spectrum: an analytical study
2024-11-21
Marco Marinucci, Gabriel Jung, Michele Liguori, Andrea Ravenni, Francesco Spezzati, Adam Andrews, Marco Baldi, William R. Coulton, Dionysios Karagiannis, Francisco Villaescusa-Navarro, Benjamin Wandlet
The marked power spectrum - a two-point correlation function of a transformed density field - has emerged as a promising tool for extracting cosmological information from the large-scale structure of the Universe. In this work, we present the first comprehensive analytical study of the marked power spectrum's sensitivity to primordial non-Gaussianity (PNG) of the non-local type. We extend previous effective field theory frameworks to incorporate PNG, developing a complete theoretical model that we validate against the Quijote simulation suite. Through a systematic Fisher analysis, we compare the constraining power of the marked power spectrum against traditional approaches combining the power spectrum and bispectrum (P+B). We explore different choices of mark parameters to evaluate their impact on parameter constraints, particularly focusing on equilateral and orthogonal PNG as well as neutrino masses. Our analysis shows that while marking up underdense regions yields optimal constraints in the low shot-noise regime, the marked power spectrum's performance for discrete tracers with BOSS-like number densities does not surpass that of P+B analysis at mildly non-linear scales ($k \lesssim 0.25 \,h/\text{Mpc}$). However, the marked approach offers several practical advantages, including simpler estimation procedures and potentially more manageable systematic effects. Our theoretical framework reveals how the marked power spectrum incorporates higher-order correlation information through terms resembling tree-level bispectra and power spectrum convolutions. This work establishes a robust foundation for applying marked statistics to future large-volume surveys.
Machine learning framework to predict the performance of lipid nanoparticles for nucleic acid delivery
2024-11-21
Gaurav Kumar, Arezoo M. Ardekani
Lipid nanoparticles (LNPs) are highly effective carriers for gene therapies, including mRNA and siRNA delivery, due to their ability to transport nucleic acids across biological membranes, low cytotoxicity, improved pharmacokinetics, and scalability. A typical approach to formulate LNPs is to establish a quantitative structure-activity relationship (QSAR) between their compositions and in vitro/in vivo activities which allows for the prediction of activity based on molecular structure. However, developing QSAR for LNPs can be challenging due to the complexity of multi-component formulations, interactions with biological membranes, and stability in physiological environments. To address these challenges, we developed a machine learning framework to predict the activity and cell viability of LNPs for nucleic acid delivery. We curated data from 6,398 LNP formulations in the literature, applied nine featurization techniques to extract chemical information, and trained five machine learning models for binary and multiclass classification. Our binary models achieved over 90% accuracy, while the multiclass models reached over 95% accuracy. Our results demonstrated that molecular descriptors, particularly when used with random forest and gradient boosting models, provided the most accurate predictions. Our findings also emphasized the need for large training datasets and comprehensive LNP composition details, such as constituent structures, molar ratios, nucleic acid types, and dosages, to enhance predictive performance.
Adaptive Anomaly Detection for Identifying Attacks in Cyber-Physical Systems: A Systematic Literature Review
2024-11-21
Pablo Moriano, Steven C. Hespeler, Mingyan Li, Maria Mahbub
Modern cyberattacks in cyber-physical systems (CPS) rapidly evolve and cannot be deterred effectively with most current methods which focused on characterizing past threats. Adaptive anomaly detection (AAD) is among the most promising techniques to detect evolving cyberattacks focused on fast data processing and model adaptation. AAD has been researched in the literature extensively; however, to the best of our knowledge, our work is the first systematic literature review (SLR) on the current research within this field. We present a comprehensive SLR, gathering 397 relevant papers and systematically analyzing 65 of them (47 research and 18 survey papers) on AAD in CPS studies from 2013 to 2023 (November). We introduce a novel taxonomy considering attack types, CPS application, learning paradigm, data management, and algorithms. Our analysis indicates, among other findings, that reviewed works focused on a single aspect of adaptation (either data processing or model adaptation) but rarely in both at the same time. We aim to help researchers to advance the state of the art and help practitioners to become familiar with recent progress in this field. We identify the limitations of the state of the art and provide recommendations for future research directions.
Neuro-Symbolic Query Optimization in Knowledge Graphs
2024-11-21
Maribel Acosta, Chang Qin, Tim Schwabe
This chapter delves into the emerging field of neuro-symbolic query optimization for knowledge graphs (KGs), presenting a comprehensive exploration of how neural and symbolic techniques can be integrated to enhance query processing. Traditional query optimizers in knowledge graphs rely heavily on symbolic methods, utilizing dataset summaries, statistics, and cost models to select efficient execution plans. However, these approaches often suffer from misestimations and inaccuracies, particularly when dealing with complex queries or large-scale datasets. Recent advancements have introduced neural models, which capture non-linear aspects of query optimization, offering promising alternatives to purely symbolic methods. In this chapter, we introduce neuro-symbolic query optimizers, a novel approach that combines the strengths of symbolic reasoning with the adaptability of neural computation. We discuss the architecture of these hybrid systems, highlighting the interplay between neural and symbolic components to improve the optimizer's ability to navigate the search space and produce efficient execution plans. Additionally, the chapter reviews existing neural components tailored for optimizing queries over knowledge graphs and examines the limitations and challenges in deploying neuro-symbolic query optimizers in real-world environments.
A study of the star clusters' population in the giant molecular cloud G174+2.5
2024-11-21
T. A. Permyakova, G. Carraro, A. F. Seleznev, A. M. Sobolev, D. A. Ladeyschikov, M. S. Kirsanova
We study the structure, interstellar absorption, color-magnitude diagrams, kinematics, and dynamical state of embedded star clusters in the star-forming region associated with the giant molecular cloud G174+2.5. Our investigation is based on photometric data from the UKIDSS Galactic Plane Survey catalog and astrometric data from the Gaia DR3 catalogs. First, we recover all the known embedded clusters and candidate clusters in the region using surface density maps. Then, for the detected clusters, we determine their general parameters: the center positions, radii, number of stars, and reddening. To evaluate the reddening, we use both the NICEST algorithm and the Q-method. Both methods produce consistent extinction maps in the regions of the four studied clusters. However, the Q-method yields a much smaller color scatter in the CMD. For four clusters in particular (S235~North-West, S235~A-B-C, S235~Central, and S235~East1+East2), we were able to compute individual membership probabilities, the cluster distances, the cluster masses, and their average proper motions. By building on these results, we have studied the clusters' kinematics and dynamics. Moreover, we estimate the mass of the gas component and the star formation efficiency (SFE) in the regions of these four clusters. Finally, we provide an estimate of the total energy of the stellar and gas components in the area of these four clusters to determine whether the clusters are bound (here we consider a `cluster' as the system `stars + gas'). The gravitational bound strongly depends on the region for which we estimate the gas mass. If we consider the mass of the entire cloud, all these four clusters turn out to be bound.
The goal of the present study is to analyze an application of deep learning models in order to augment the diagnostic performance of oral squamous cell carcinoma (OSCC) with a longitudinal cohort study using the Histopathological Imaging Database for oral cancer analysis. The dataset consisted of 5192 images (2435 Normal and 2511 OSCC), which were allocated between training, testing, and validation sets with an estimated ratio repartition of about 52% for the OSCC group, and still, our performance measure was validated on a combination set that contains almost equal number of sample in this use case as entire database have been divided into half using stratified splitting technique based again near binary proportion but total distribution was around even. We selected four deep-learning architectures for evaluation in the present study: ResNet101, DenseNet121, VGG16, and EfficientnetB3. EfficientNetB3 was found to be the best, with an accuracy of 98.33% and F1 score (0.9844), and it took remarkably less computing power in comparison with other models. The subsequent one was DenseNet121, with 90.24% accuracy and an F1 score of 90.45%. Moreover, we employed the Local Interpretable Model-agnostic Explanations (LIME) method to clarify why EfficientNetB3 made certain decisions with its predictions to improve the explainability and trustworthiness of results. This work provides evidence for the possible superior diagnosis in OSCC activated from the EfficientNetB3 model with the explanation of AI techniques such as LIME and paves an important groundwork to build on towards clinical usage.
Chebyshev polynomials in the complex plane and on the real line
2024-11-21
Olof Rubin
We present a survey of key developments in the study of Chebyshev polynomials, first introduced by P. L. Chebyshev and later significantly expanded upon by G. Faber to the complex setting. Our primary focus is on their defining property: being the polynomial with a specified leading coefficient that minimizes the supremum norm on a given set.
Autonomous System Safety Properties with Multi-Machine Hybrid Event-B
2024-11-21
Richard Banach
Event-B is a well known methodology for the verified design and development of systems that can be characterised as discrete transition systems. Hybrid Event-B is a conservative extension that interleaves the discrete transitions of Event-B (assumed to be temporally isolated) with episodes of continuously varying state change. While a single Hybrid Event-B machine is sufficient for applications with a single locus of control, it will not do for autonomous systems, which have several loci of control by default. Multi-machine Hybrid Event-B is designed to allow the specification of systems with several loci of control. The formalism is succinctly surveyed, pointing out the subtle semantic issues involved. The multi-machine formalism is then used to specify a relatively simple incident response system, involving a controller, two drones and three responders, working in a partly coordinated and partly independent fashion to manage a putative hazardous scenario.
The Human Visual System (HVS), with its intricate sophistication, is capable of achieving ultra-compact information compression for visual signals. This remarkable ability is coupled with high generalization capability and energy efficiency. By contrast, the state-of-the-art Versatile Video Coding (VVC) standard achieves a compression ratio of around 1,000 times for raw visual data. This notable disparity motivates the research community to draw inspiration to effectively handle the immense volume of visual data in a green way. Therefore, this paper provides a survey of how visual data can be efficiently represented for green multimedia, in particular when the ultimate task is knowledge extraction instead of visual signal reconstruction. We introduce recent research efforts that promote green, sustainable, and efficient multimedia in this field. Moreover, we discuss how the deep understanding of the HVS can benefit the research community, and envision the development of future green multimedia technologies.
GASP: Efficient Black-Box Generation of Adversarial Suffixes for Jailbreaking LLMs
2024-11-21
Advik Raj Basani, Xiao Zhang
Large Language Models (LLMs) have shown impressive proficiency across a range of natural language processing tasks yet remain vulnerable to adversarial prompts, known as jailbreak attacks, carefully designed to elicit harmful responses from LLMs. Traditional methods rely on manual heuristics, which suffer from limited generalizability. While being automatic, optimization-based attacks often produce unnatural jailbreak prompts that are easy to detect by safety filters or require high computational overhead due to discrete token optimization. Witnessing the limitations of existing jailbreak methods, we introduce Generative Adversarial Suffix Prompter (GASP), a novel framework that combines human-readable prompt generation with Latent Bayesian Optimization (LBO) to improve adversarial suffix creation in a fully black-box setting. GASP leverages LBO to craft adversarial suffixes by efficiently exploring continuous embedding spaces, gradually optimizing the model to improve attack efficacy while balancing prompt coherence through a targeted iterative refinement procedure. Our experiments show that GASP can generate natural jailbreak prompts, significantly improving attack success rates, reducing training times, and accelerating inference speed, thus making it an efficient and scalable solution for red-teaming LLMs.
Uncertainty-Aware Regression for Socio-Economic Estimation via Multi-View Remote Sensing
2024-11-21
Fan Yang, Sahoko Ishida, Mengyan Zhang, Daniel Jenson, Swapnil Mishra, Jhonathan Navott, Seth Flaxman
Remote sensing imagery offers rich spectral data across extensive areas for Earth observation. Many attempts have been made to leverage these data with transfer learning to develop scalable alternatives for estimating socio-economic conditions, reducing reliance on expensive survey-collected data. However, much of this research has primarily focused on daytime satellite imagery due to the limitation that most pre-trained models are trained on 3-band RGB images. Consequently, modeling techniques for spectral bands beyond the visible spectrum have not been thoroughly investigated. Additionally, quantifying uncertainty in remote sensing regression has been less explored, yet it is essential for more informed targeting and iterative collection of ground truth survey data. In this paper, we introduce a novel framework that leverages generic foundational vision models to process remote sensing imagery using combinations of three spectral bands to exploit multi-spectral data. We also employ methods such as heteroscedastic regression and Bayesian modeling to generate uncertainty estimates for the predictions. Experimental results demonstrate that our method outperforms existing models that use RGB or multi-spectral models with unstructured band usage. Moreover, our framework helps identify uncertain predictions, guiding future ground truth data acquisition.
Prandtl Equations and Related Boundary Layer Equations
2024-11-21
Yuming Qin, Xiaolei Dong, Xiuqing Wang
This book aims to present some recent results on Prandtl equations and MHD boundary layer equations. This book is essentially divided into two parts. Chapter 1 as the first part systematically surveys the results till 2020 on Prandtl equations and MHD boundary layer equations. Chapter 2 to 6 are the main part of the book, which presents the local and the global well-posedness of solutions to the Prandtl equations and MHD boundary layer equations. In detail, Chapter 2 is concerned with global well-posedness of solutions to the 2D Prandtl-Hartmann equations in an analytic framework. Chapter 3 investigates the local existence of solutions to the 2D Prandtl equations in a weighted Sobolev space. Chapter 4 studies the local well-posedness of solutions to the 2D mixed Prandtl equations in a Sobolev space without monotonicity and lower bound. Chapter 5 is concerned with global existence of solutions to the 2D magnetic Prandtl equations in the Prandtl-Hartmann regime. Chapter 6 proves the local existence of solutions to the 3D Prandtl equations with a special structure. Mathematicians and physicists who are interested in fluid dynamics will find this book helpful.
2024-11-21
Marta Casanellas, Luis Sierra, Piotr Zwiernik
Tensors are ubiquitous in statistics and data analysis. The central object that links data science to tensor theory and algebra is that of a model with latent variables. We provide an overview of tensor theory, with a particular emphasis on its applications in algebraic statistics. This high-level treatment is supported by numerous examples to illustrate key concepts. Additionally, an extensive literature review is included to guide readers toward more detailed studies on the subject.
The probability current is a vital quantity in the Fokker-Planck description of stochastic processes. It characterizes nonequilibrium stationary states, appears in linear response calculation, and has been related to the entropy production and the heat flux. We recover and review the probability current in the Onsager-Machlup functional approach to Markov processes. We derive a self contained expression for the stationary probability current and the non-equilibrium fluctuation-dissipation theorem using field theoretical methods. The derived formulas are explicitly evaluated in the Ornstein-Uhlenbeck process of a harmonically bound particle in shear flow as exemplary analytic expressions. Our work closes a gap since it removes a missing link, i.e.~the probability current, in the supposed equivalence of the Fokker-Planck and the path-integral approach.
Does the square-root price impact law belong to the strict universal scalings?: quantitative support by a complete survey of the Tokyo stock exchange market
2024-11-21
Yuki Sato, Kiyoshi Kanazawa
Universal power laws have been scrutinised in physics and beyond, and a long-standing debate exists in econophysics regarding the strict universality of the nonlinear price impact, commonly referred to as the square-root law (SRL). The SRL posits that the average price impact $I$ follows a power law with respect to transaction volume $Q$, such that $I(Q) \propto Q^{\delta}$ with $\delta \approx 1/2$. Some researchers argue that the exponent $\delta$ should be system-specific, without universality. Conversely, others contend that $\delta$ should be exactly $1/2$ for all stocks across all countries, implying universality. However, resolving this debate requires high-precision measurements of $\delta$ with errors of around $0.1$ across hundreds of stocks, which has been extremely challenging due to the scarcity of large microscopic datasets -- those that enable tracking the trading behaviour of all individual accounts. Here we conclusively support the universality hypothesis of the SRL by a complete survey of all trading accounts for all liquid stocks on the Tokyo Stock Exchange (TSE) over eight years. Using this comprehensive microscopic dataset, we show that the exponent $\delta$ is equal to $1/2$ within statistical errors at both the individual stock level and the individual trader level. Additionally, we rejected two prominent models supporting the nonuniversality hypothesis: the Gabaix-Gopikrishnan-Plerou-Stanley and the Farmer-Gerig-Lillo-Waelbroeck models. Our work provides exceptionally high-precision evidence for the universality hypothesis in social science and could prove useful in evaluating the price impact by large investors -- an important topic even among practitioners.
The concept of neurodiversity, encompassing conditions such as Autism Spectrum Disorder (ASD), Attention-Deficit/Hyperactivity Disorder (ADHD), dyslexia, and dyspraxia, challenges traditional views of these neurodevelopmental variations as disorders and instead frames them as natural cognitive differences that contribute to unique ways of thinking and problem-solving. Within the software development industry, known for its emphasis on innovation, there is growing recognition of the value neurodivergent individuals bring to technical teams. Despite this, research on the contributions of neurodivergent individuals in Software Engineering (SE) remains limited. This interdisciplinary Socio-Technical Grounded Theory study addresses this gap by exploring the experiences of neurodivergent software engineers with ASD and ADHD, examining the cognitive and emotional challenges they face in software teams. Based on interviews and a survey with 25 neurodivergent and 5 neurotypical individuals, our theory describes how neurodivergent cognitive dysfunctions affect SE performance, and how the individuals' individual journey and various accommodations can regulate this effect. We conclude our paper with a list of inclusive Agile practices, allowing organizations to better support neurodivergent employees and fully leverage their capabilities.
A Systematic Literature Review on Technology Acceptance Research on Augmented Reality in the Field of Training and Education
2024-11-21
Stefan Graser, Stephan Böhm
Augmented Reality (AR) is an emerging technology that ranks among the top innovations in interactive media. With the emergence of new technologies, the question about the factors influencing user acceptance arises. Many research models on the user acceptance of technologies were developed and extended to answer this question in the last decades. This research paper provides an overview of the current state in the scientific literature on user acceptance factors of AR in training and education. We conducted a systematic literature review, identifying 45 scientific papers on technology acceptance of augmented reality. Twenty-two papers refer more specifically to the field of training and education. Overall, 33 different technology acceptance models and 34 acceptance variables were identified. Based on the results, there is a great potential for further research.
Independent Optical Frequency Combs Powered 546 km Field Test of Twin-Field Quantum Key Distribution
2024-11-21
Lai Zhou, Jinping Lin, Chengfang Ge, Yuanbin Fan, Zhiliang Yuan, Hao Dong, Yang Liu, Di Ma, Jiu-Peng Chen, Cong Jiang, Xiang-Bin Wang, Li-Xing You, Qiang Zhang, Jian-Wei Pan
Owing to its repeater-like rate-loss scaling, twin-field quantum key distribution (TF-QKD) has repeatedly exhibited in laboratory its superiority for secure communication over record fiber lengths. Field trials pose a new set of challenges however, which must be addressed before the technology's roll-out into real-world. Here, we verify in field the viability of using independent optical frequency combs -- installed at sites separated by a straight-line distance of 300~km -- to achieve a versatile TF-QKD setup that has no need for optical frequency dissemination and thus enables an open and network-friendly fiber configuration. Over 546 and 603 km symmetric links, we record a finite-size secure key rate (SKR) of 0.53~bit/s and an asymptotic SKR of 0.12 bit/s, respectively. Of practical importance, the setup is demonstrated to support 44~km fiber asymmetry in the 452 km link. Our work marks an important step towards incorporation of long-haul fiber links into large quantum networks.
Ground-state phase transitions in spin-1 Bose-Einstein condensates with spin-orbit coupling
2024-11-21
Xin-Feng Zhang, Yuan-Fen Liu, Huan-Bo Luo, Bin Liu, Fu-Quan Dou, Yongyao Li, Boris A. Malomed
We investigate phase transitions of the ground state (GS) of spin-1 Bose-Einstein condensates under the combined action of the spin-orbit coupling (SOC) and gradient magnetic field. Introducing appropariate raising and lowering operators, we exactly solve the linear system. Analyzing the obtained energy spectrum, we conclude that simultaneous variation of the magnetic-field gradient and SOC strength leads to the transition of excited states into the GS. As a result, any excited state can transition to the GS, at appropriate values of the system's parameters. The nonlinear system is solved numerically, showing that the GS phase transition, similar to the one in the linear system, still exists under the action of the repulsive nonlinearity. In the case of weak attraction, a mixed state appears near the GS transition point, while the GS transitions into an edge state under the action of strong attractive interaction.
Dressing the Imagination: A Dataset for AI-Powered Translation of Text into Fashion Outfits and A Novel KAN Adapter for Enhanced Feature Adaptation
2024-11-21
Gayatri Deshmukh, Somsubhra De, Chirag Sehgal, Jishu Sen Gupta, Sparsh Mittal
Specialized datasets that capture the fashion industry's rich language and styling elements can boost progress in AI-driven fashion design. We present FLORA (Fashion Language Outfit Representation for Apparel Generation), the first comprehensive dataset containing 4,330 curated pairs of fashion outfits and corresponding textual descriptions. Each description utilizes industry-specific terminology and jargon commonly used by professional fashion designers, providing precise and detailed insights into the outfits. Hence, the dataset captures the delicate features and subtle stylistic elements necessary to create high-fidelity fashion designs. We demonstrate that fine-tuning generative models on the FLORA dataset significantly enhances their capability to generate accurate and stylistically rich images from textual descriptions of fashion sketches. FLORA will catalyze the creation of advanced AI models capable of comprehending and producing subtle, stylistically rich fashion designs. It will also help fashion designers and end-users to bring their ideas to life. As a second orthogonal contribution, we introduce KAN Adapters, which leverage Kolmogorov-Arnold Networks (KAN) as adaptive modules. They serve as replacements for traditional MLP-based LoRA adapters. With learnable spline-based activations, KAN Adapters excel in modeling complex, non-linear relationships, achieving superior fidelity, faster convergence and semantic alignment. Extensive experiments and ablation studies on our proposed FLORA dataset validate the superiority of KAN Adapters over LoRA adapters. To foster further research and collaboration, we will open-source both the FLORA and our implementation code.
A Hierarchical Poisson Generator for Universal Graphs under Limited Resources
2024-11-21
Xiaorui Qi, Yanlong Wen, Xiaojie Yuan
Graph generation is one of the most challenging tasks in recent years, and its core is to learn the ground truth distribution hiding in the training data. However, training data may not be available due to security concerns or unaffordable costs, which severely blows the learning models, especially the deep generative models. The dilemma leads us to rethink non-learned generation methods based on graph invariant features. Based on the observation of scale-free property, we propose a hierarchical Poisson graph generation algorithm. Specifically, we design a two-stage generation strategy. In the first stage, we sample multiple anchor nodes according to the Poisson distribution to further guide the formation of substructures, splitting the initial node set into multiple ones. Next, we progressively generate edges by sampling nodes through a degree mixing distribution, adjusting the tolerance towards exotic structures via two thresholds. We provide theoretical guarantees for hierarchical generation and verify the effectiveness of our method under 12 datasets of three categories. Experimental results show that our method fits the ground truth distribution better than various generation strategies and other distribution observations.
Weak synchronization in heterogeneous multi-agent systems
2024-11-21
Anton A. Stoorvogel, Ali Saberi, Zhenwei Liu
In this paper, we propose a new framework for synchronization of heterogeneous multi agent system which we refer to as weak synchronization. This new framework of synchronization is based on achieving the network stability in the absence of any information on communication network including the connectivity. Here by network stability, we mean that in the basic setup of a multi-agent system, we require that the signals exchanged over the network converge to zero. As such if the network happens to have a directed spanning tree then we obtain classical synchronization. Moreover, we design protocols which achieve weak synchronization for any network without making any kind of assumptions on communication network. If the network happens to have a directed spanning tree, then we obtain classical synchronization. However, if this is not the case then we describe in detail in this paper what kind of synchronization properties are preserved in the system and the output of the different agents can behave.
A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles
2024-11-21
Junae Kim, Amardeep Kaur
In autonomous driving, the combination of AI and vehicular technology offers great potential. However, this amalgamation comes with vulnerabilities to adversarial attacks. This survey focuses on the intersection of Adversarial Machine Learning (AML) and autonomous systems, with a specific focus on LiDAR-based systems. We comprehensively explore the threat landscape, encompassing cyber-attacks on sensors and adversarial perturbations. Additionally, we investigate defensive strategies employed in countering these threats. This paper endeavors to present a concise overview of the challenges and advances in securing autonomous driving systems against adversarial threats, emphasizing the need for robust defenses to ensure safety and security.
Which Channel, Low-rank or Full-rank, more needs RIS?
2024-11-21
Feng Shu, Maolin Li, Ke Yang, Bin Deng
RIS, as an efficient tool to improve receive signal-to-noise ratio, extend coverage and create more spatial diversity, is viewed as a most promising technique for the future wireless networks like 6G. As you know, IRS is very suitable for a special wireless scenario with wireless link between BS and users being completely blocked. In this paper, we extend its applications to a general scenario, i.e., rank-deficient-channel, particularly some extremely low-rank ones such as no link, and line-of-sight (LoS). Actually, there are several potential important low-rank applications of like satellite, UAV communications, marine, and deep-space communications. In such a situation, it is found that RIS may make a dramatic DoF enhancement over no RIS. By using a distributed RIS placement, the DoF of channels from BS to users may be even boosted from a low-rank like 0/1 to full-rank. This will achieve an extremely rate improvement via multiple spatial streams transmission per user. In this paper, we present a complete review of make a in-depth discussion on DoF effect of RIS.
The advent of Large Language Models (LLMs) has enabled the development of LLM agents capable of autonomously achieving under-specified goals and continuously evolving through post-deployment improvement, sometimes without requiring code or model updates. Conventional approaches, such as pre-defined test cases and code/model redevelopment pipelines, are inadequate for addressing the unique challenges of LLM agent development, particularly in terms of quality and risk control. This paper introduces an evaluation-driven design approach, inspired by test-driven development, to address these challenges. Through a multivocal literature review (MLR), we synthesize existing LLM evaluation methods and propose a novel process model and reference architecture specifically designed for LLM agents. The proposed approach integrates online and offline evaluations to support adaptive runtime adjustments and systematic offline redevelopment, improving runtime pipelines, artifacts, system architecture, and LLMs by continuously incorporating evaluation results, including fine-grained feedback from human and AI evaluators.
Roadmap on Advances in Visual and Physiological Optics
2024-11-21
Jesús E. Gómez-Correa, Brian Vohnsen, Barbara K. Pierścionek, Sabino Chávez-Cerda, Sabine Kling, Jos J. Rozema, Raymond A. Applegate, Giuliano Scarcelli, J. Bradley Randleman, Alexander V. Goncharov, Amy Fitzpatrick, Jessica I. W. Morgan, Austin Roorda, David A. Atchison, Juan P. Trevino, Alejandra Consejo, Charlie Börjeson, Linda Lundström, Seung Pil Bang, Geunyoung Yoon, Karol Karnowski, Bartlomiej J. Kaluzny, Ireneusz Grulkowski, Sergio Barbero, Pablo Artal, Juan Tabernero, Pete Kollbaum, Stéphanie C. Thébault
The field of visual and physiological optics is undergoing continuous significant advancements, driven by a deeper understanding of the human visual system and the development of cutting-edge optical technologies. This Roadmap, authored by leading experts, delves into critical areas such as corneal biomechanical properties, keratoconus, and advancements in corneal imaging and elastography. It explores the intricate structure-function relationship within the eye lens, offering new perspectives through lens models and ray tracing techniques. The document also covers advancements in retinal imaging, highlighting the current state and future directions, and the role of adaptive optics in evaluating retinal structure and function in both healthy and diseased eyes. Furthermore, it addresses the modelling of ocular surfaces using different mathematical functions and examines the factors affecting peripheral image quality in the human eye, emphasizing the importance of these aspects in visual performance. Additional topics include schematic and functional models of the human eye, the impact of optical and chromatic aberrations, and the design of contact, and intraocular lenses. Finally, the Roadmap addresses the intersection of neurosciences with vision health, presenting a comprehensive overview of current research and future trends aimed at improving visual health and optical performance. Ultimately, this Roadmap aims to serve as a valuable resource for ophthalmologists, optometrists, vision scientists, and engineers dedicated to advancing the field of visual and physiological optics.
He awa whiria: the tidal streams of interstellar objects
2024-11-21
John C. Forbes, Michele T. Bannister, Chris Lintott, Angus Forrest, Simon Portegies Zwart, Rosemary C. Dorsey, Leah Albrow, Matthew J. Hopkins
Upcoming surveys are likely to discover a new sample of interstellar objects (ISOs) within the Solar System, but questions remain about the origin and distribution of this population within the Galaxy. ISOs are ejected from their host systems with a range of velocities, spreading out into tidal streams - analogous to the stellar streams routinely observed from the disruption of star clusters and dwarf galaxies. We create a simulation of ISO streams orbiting in the Galaxy, deriving a simple model for their density distribution over time. We then construct a population model to predict the properties of the streams in which the Sun is currently embedded. We find that the number of streams encountered by the Sun is quite large, ~ 10^6 or more. However, the wide range of stream properties means that for reasonable future samples of ISOs observed in the Solar System, we may see ISOs from the same star ("siblings"), and we are likely to see ISOs from the same star cluster ("cousins"). We also find that ISOs are typically not traceable to their parent star, though this may be possible for ISO siblings. Any ISOs observed with a common origin will come from younger, dynamically colder streams.
Privacy-Preserving Video Anomaly Detection: A Survey
2024-11-21
Jing Liu, Yang Liu, Xiaoguang Zhu
Video Anomaly Detection (VAD) aims to automatically analyze spatiotemporal patterns in surveillance videos collected from open spaces to detect anomalous events that may cause harm without physical contact. However, vision-based surveillance systems such as closed-circuit television often capture personally identifiable information. The lack of transparency and interpretability in video transmission and usage raises public concerns about privacy and ethics, limiting the real-world application of VAD. Recently, researchers have focused on privacy concerns in VAD by conducting systematic studies from various perspectives including data, features, and systems, making Privacy-Preserving Video Anomaly Detection (P2VAD) a hotspot in the AI community. However, current research in P2VAD is fragmented, and prior reviews have mostly focused on methods using RGB sequences, overlooking privacy leakage and appearance bias considerations. To address this gap, this article systematically reviews the progress of P2VAD for the first time, defining its scope and providing an intuitive taxonomy. We outline the basic assumptions, learning frameworks, and optimization objectives of various approaches, analyzing their strengths, weaknesses, and potential correlations. Additionally, we provide open access to research resources such as benchmark datasets and available code. Finally, we discuss key challenges and future opportunities from the perspectives of AI development and P2VAD deployment, aiming to guide future work in the field.
Galaxy Clustering with LSST: Effects of Number Count Bias from Blending
2024-11-21
Benjamin Levine, Javier Sánchez, Chihway Chang, Anja von der Linden, Eboni Collins, Eric Gawiser, Katarzyna Krzyżańska, Boris Leistedt, The LSST Dark Energy Science Collaboration
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will survey the southern sky to create the largest galaxy catalog to date, and its statistical power demands an improved understanding of systematic effects such as source overlaps, also known as blending. In this work we study how blending introduces a bias in the number counts of galaxies (instead of the flux and colors), and how it propagates into galaxy clustering statistics. We use the $300\,$deg$^2$ DC2 image simulation and its resulting galaxy catalog (LSST Dark Energy Science Collaboration et al. 2021) to carry out this study. We find that, for a LSST Year 1 (Y1)-like cosmological analyses, the number count bias due to blending leads to small but statistically significant differences in mean redshift measurements when comparing an observed sample to an unblended calibration sample. In the two-point correlation function, blending causes differences greater than 3$\sigma$ on scales below approximately $10'$, but large scales are unaffected. We fit $\Omega_{\rm m}$ and linear galaxy bias in a Bayesian cosmological analysis and find that the recovered parameters from this limited area sample, with the LSST Y1 scale cuts, are largely unaffected by blending. Our main results hold when considering photometric redshift and a LSST Year 5 (Y5)-like sample.
Hot and cool: Characterising the companions of red supergiant stars in binary systems
2024-11-21
L. R. Patrick, I. Negueruela
In this article we study the nature of the recently identified populations of hot companions to red supergiant stars (RSGs). To this end, we compile the literature on the most well characterised systems with the aim of better understanding the hot companions identified with ultra-violet photometry and confirmed with Hubble Space Telescope spectra in the Local Group. We identify 9 systems with current masses greater than around 8 solar masses that have constraints on their orbital periods, which are in the range 3 to 75a. Antares (Alpha Sco) is the obvious outlier in this distribution, having an estimated orbital period of around 2000 a. Mass-ratio (q=M2/MRSG) estimates are available only for 5 of the compiled systems and range between 0.16 < q < 1. Ongoing efforts to identify and characterise hot companions to RSGs in the SMC have revealed 88 hot companions that have observational constraints free of contamination from the RSG component. We present a summary of a recently conducted HST UV spectroscopic survey that aims to characterise a subset of these companions. These companions show a flat-q distribution in the range 0.3 < q < 1.
Experimental optimal discrimination of $N$ states of a qubit with fixed rates of inconclusive outcomes
2024-11-21
L. F. Melo, M. A. Solís-Prosser, O. Jiménez, A. Delgado, L. Neves
In a general optimized measurement scheme for discriminating between nonorthogonal quantum states, the error rate is minimized under the constraint of a fixed rate of inconclusive outcomes (FRIO). This so-called optimal FRIO measurement encompasses the standard and well known minimum-error and optimal unambiguous (or maximum-confidence) discrimination strategies as particular cases. Here, we experimentally demonstrate the optimal FRIO discrimination between $N=2,3,5,$ and $7$ equally likely symmetric states of a qubit encoded in photonic path modes. Our implementation consists of applying a probabilistic quantum map which increases the distinguishability between the inputs in a controlled way, followed by a minimum-error measurement on the successfully transformed outputs. The results obtained corroborate this two-step approach and, in our experimental scheme, it can be straightforwardly extended to higher dimensions. The optimized measurement demonstrated here will be useful for quantum communication scenarios where the error rate and the inconclusive rate must be kept below the levels provided by the respective standard strategies.
Dwarf Galaxies with Radio-excess AGNs in the VLA Sky Survey
2024-11-21
John-Michael Eberhard, Amy E. Reines, Hansung B. Gim, Jeremy Darling, Jenny E. Greene
We present a systematic search for radio active galactic nuclei (AGNs) in dwarf galaxies using recent observations taken by the Very Large Array Sky Survey (VLASS). To select these objects, we first establish a criterion to identify radio-excess AGNs using the infrared-radio correlation (IRRC) parameter, $q$, that describes the tight relation between radio and IR emission in star forming (SF) galaxies. We find a $2\sigma$ threshold of $q < 1.94$ to select radio-excess AGNs, which is derived from a sample of $\sim 7,000$ galaxies across the full mass range in the NASA-Sloan Atlas (NSA) that have radio and IR detections from VLASS and the Wide-Field Infrared Survey Explorer, respectively. We create catalogs of radio-excess AGNs and SF galaxies and make these available to the community. Applying our criterion to dwarf galaxies with stellar masses $M_\star \lesssim 3 \times 10^9 M_\odot$ and redshifts $z \le 0.15$, and carefully removing interlopers, we find 10 radio-excess AGNs with radio-optical positional offsets between $\sim$ 0 and 2.3 arcseconds (0 - 2.7 kpc). Based on statistical arguments and emission line diagnostics, we expect the majority of these radio-excess AGNs to be associated with the dwarf host galaxies rather than background AGNs. Five of the objects have evidence for hosting AGNs at other wavelengths, and 5 objects are identified as AGNs in dwarf galaxies for the first time. We also identify 8 variable radio sources in dwarf galaxies by comparing the VLASS epoch 1 and epoch 2 observations to FIRST detections presented in arXiv:1909.04670.
Time evolution of o-H$_2$D$^+$, N$_2$D$^+$, and N$_2$H$^+$ during the high-mass star formation process
2024-11-21
G. Sabatini, S. Bovino, E. Redaelli, F. Wyrowski, J. S. Urquhart, A. Giannetti, J. Brand, K. M. Menten
Deuterium fractionation is a well-established evolutionary tracer in low-mass star formation, but its applicability to the high-mass regime remains an open question. The abundances and ratios of deuterated species have often been proposed as reliable evolutionary indicators for different stages of the high-mass star formation. We investigate the role of N$_2$H$^+$ and key deuterated molecules as tracers of the different stages of the high-mass star formation, and test whether their abundance ratios can serve as reliable evolutionary indicators. We conducted APEX observations of o-H$_2$D$^+$ (1$_{10}$-1$_{11}$), N$_2$H$^+$ (4-3), and N$_2$d$^+$ (3-2) in 40 high-mass clumps at different evolutionary stages, selected from the ATLASGAL survey. Molecular column densities ($N$) and abundances ($X$), were derived through spectral line modelling, both under local thermodynamic equilibrium (LTE) and non-LTE conditions. The $N$(o-H$_2$D$^+$) show the smallest deviation from LTE results when derived under non-LTE assumptions. In contrast, N$_2$D$^+$ shows the largest discrepancy between the $N$ derived from LTE and non-LTE. In all the cases discussed, we found that $X$(o-H$_2$D$^+$) decreases more significantly with time than in the case of $X$(N$_2$D$^+$); whereas $X$(N$_2$H$^+$) increases slightly. Therefore, the validity of the recently proposed $X$(o-H$_2$D$^+$)/$X$(N$_2$D$^+$) ratio as a reliable evolutionary indicator was not observed for this sample. While the deuteration fraction derived from N$_2$D$^+$ and N$_2$H$^+$ clearly decreases with clump evolution, the interpretation of this trend is complex, given the different distribution of the two tracers. Our results suggest that a careful consideration of the observational biases and beam-dilution effects are crucial for an accurate interpretation of the evolution of the deuteration process during the high-mass star formation process.
Anomaly detection (AD) is a machine learning task that identifies anomalies by learning patterns from normal training data. In many real-world scenarios, anomalies vary in severity, from minor anomalies with little risk to severe abnormalities requiring immediate attention. However, existing models primarily operate in a binary setting, and the anomaly scores they produce are usually based on the deviation of data points from normal data, which may not accurately reflect practical severity. In this paper, we address this gap by making three key contributions. First, we propose a novel setting, Multilevel AD (MAD), in which the anomaly score represents the severity of anomalies in real-world applications, and we highlight its diverse applications across various domains. Second, we introduce a novel benchmark, MAD-Bench, that evaluates models not only on their ability to detect anomalies, but also on how effectively their anomaly scores reflect severity. This benchmark incorporates multiple types of baselines and real-world applications involving severity. Finally, we conduct a comprehensive performance analysis on MAD-Bench. We evaluate models on their ability to assign severity-aligned scores, investigate the correspondence between their performance on binary and multilevel detection, and study their robustness. This analysis offers key insights into improving AD models for practical severity alignment. The code framework and datasets used for the benchmark will be made publicly available.
AKRA 2.0: Accurate Kappa Reconstruction Algorithm for masked shear catalog
2024-11-21
Yuan Shi, Pengjie Zhang, Furen Deng, Shuren Zhou, Hongbo Cai, Ji Yao, Zeyang Sun
Cosmic shear surveys serve as a powerful tool for mapping the underlying matter density field, including non-visible dark matter. A key challenge in cosmic shear surveys is the accurate reconstruction of lensing convergence ($\kappa$) maps from shear catalogs impacted by survey boundaries and masks, which seminal Kaiser-Squires (KS) method are not designed to handle. To overcome these limitations, we previously proposed the Accurate Kappa Reconstruction Algorithm (AKRA), a prior-free maximum likelihood map-making method. Initially designed for flat sky scenarios with periodic boundary conditions, AKRA has proven successful in recovering high-precision $\kappa$ maps from masked shear catalogs. In this work, we upgrade AKRA to AKRA 2.0 by integrating the tools designed for spherical geometry. This upgrade employs spin-weighted spherical harmonic transforms to reconstruct the convergence field over the full sky. To optimize computational efficiency, we implement a scale-splitting strategy that segregates the analysis into two parts: large-scale analysis on the sphere (referred to as AKRA-sphere) and small-scale analysis on the flat sky (referred to as AKRA-flat); the results from both analyses are then combined to produce final reconstructed $\kappa$ map. We tested AKRA 2.0 using simulated shear catalogs with various masks, demonstrating that the reconstructed $\kappa$ map by AKRA 2.0 maintains high accuracy. For the reconstructed $\kappa$ map in unmasked regions, the reconstructed convergence power spectrum $C_\kappa^{\rm{rec}}$ and the correlation coefficient with the true $\kappa$ map $r_\ell$ achieve accuracies of $(1-C_\ell^{\rm{rec}}/C_\ell^{\rm{true}}) \lesssim 1\%$ and $(1-r_\ell) \lesssim 1\%$, respectively. Our algorithm is capable of straightforwardly handling further issues such as inhomogeneous shape measurement noise, which we will address in subsequent analysis.
Understanding World or Predicting Future? A Comprehensive Survey of World Models
2024-11-21
Jingtao Ding, Yunke Zhang, Yu Shang, Yuheng Zhang, Zefang Zong, Jie Feng, Yuan Yuan, Hongyuan Su, Nian Li, Nicholas Sukiennik, Fengli Xu, Yong Li
The concept of world models has garnered significant attention due to advancements in multimodal large language models such as GPT-4 and video generation models such as Sora, which are central to the pursuit of artificial general intelligence. This survey offers a comprehensive review of the literature on world models. Generally, world models are regarded as tools for either understanding the present state of the world or predicting its future dynamics. This review presents a systematic categorization of world models, emphasizing two primary functions: (1) constructing internal representations to understand the mechanisms of the world, and (2) predicting future states to simulate and guide decision-making. Initially, we examine the current progress in these two categories. We then explore the application of world models in key domains, including autonomous driving, robotics, and social simulacra, with a focus on how each domain utilizes these aspects. Finally, we outline key challenges and provide insights into potential future research directions.
Adaptive Intelligence: leveraging insights from adaptive behavior in animals to build flexible AI systems
2024-11-21
Mackenzie Weygandt Mathis
Biological intelligence is inherently adaptive -- animals continually adjust their actions based on environmental feedback. However, creating adaptive artificial intelligence (AI) remains a major challenge. The next frontier is to go beyond traditional AI to develop "adaptive intelligence," defined here as harnessing insights from biological intelligence to build agents that can learn online, generalize, and rapidly adapt to changes in their environment. Recent advances in neuroscience offer inspiration through studies that increasingly focus on how animals naturally learn and adapt their world models. In this Perspective, I will review the behavioral and neural foundations of adaptive biological intelligence, the parallel progress in AI, and explore brain-inspired approaches for building more adaptive algorithms.
A Survey of H I and O VI Absorption Lines in the Outskirts of $z\lesssim0.3$ Galaxy Clusters
2024-11-20
Priscilla Holguin Luna, Joseph N. Burchett, Daisuke Nagai, Todd M. Tripp, Nicolas Tejos, J. Xavier Prochaska
The intracluster medium (ICM) in the far outskirts (r $>$ 2-3 R$_{200}$) of galaxy clusters interfaces with the intergalactic medium (IGM) and is theorized to comprise diffuse, multiphase gas. This medium may hold vital clues to clusters' thermodynamic evolution and far-reaching impacts on infalling, future cluster galaxies. The diffuse outskirts of clusters are well-suited for quasar absorption line observations, capable of detecting gas to extremely low column densities. We analyze 18 QSO spectra observed with the Cosmic Origins Spectrograph aboard the Hubble Space Telescope whose lines of sight trace the gaseous environments of 26 galaxy clusters from within R$_{200}$ to 6 R$_{200}$ in projection. We measure the dN/dz and covering fraction of H I and O VI associated with the foreground clusters as a function of normalized impact parameter. We find the dN/dz for H I is consistent with the IGM field value for all impact parameter bins, with an intriguing slight elevation between 2 and 3 R$_{200}$. The dN/dz for O VI is also consistent with the field value (within 3$\sigma$) for all impact parameter bins, with potential elevations in dN/dz both within 1-2 R$_{200}$ and beyond 4 R$_{200}$ at $>2\sigma$. We propose physical scenarios that may give rise to these tentative excesses, such as a buildup of neutral gas at the outer accretion shock front and a signature of the warm-hot IGM. We do not find a systematic excess of potentially associated galaxies near the sightlines where O VI is detected; thus, the detected O VI does not have a clear circumgalactic origin.
BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games
2024-11-20
Davide Paglieri, Bartłomiej Cupiał, Samuel Coward, Ulyana Piterbarg, Maciej Wolczyk, Akbir Khan, Eduardo Pignatelli, Łukasz Kuciński, Lerrel Pinto, Rob Fergus, Jakob Nicolaus Foerster, Jack Parker-Holder, Tim Rocktäschel
Large Language Models (LLMs) and Vision Language Models (VLMs) possess extensive knowledge and exhibit promising reasoning abilities; however, they still struggle to perform well in complex, dynamic environments. Real-world tasks require handling intricate interactions, advanced spatial reasoning, long-term planning, and continuous exploration of new strategies-areas in which we lack effective methodologies for comprehensively evaluating these capabilities. To address this gap, we introduce BALROG, a novel benchmark designed to assess the agentic capabilities of LLMs and VLMs through a diverse set of challenging games. Our benchmark incorporates a range of existing reinforcement learning environments with varying levels of difficulty, including tasks that are solvable by non-expert humans in seconds to extremely challenging ones that may take years to master (e.g., the NetHack Learning Environment). We devise fine-grained metrics to measure performance and conduct an extensive evaluation of several popular open-source and closed-source LLMs and VLMs. Our findings indicate that while current models achieve partial success in the easier games, they struggle significantly with more challenging tasks. Notably, we observe severe deficiencies in vision-based decision-making, as models perform worse when visual representations of the environments are provided. We release BALROG as an open and user-friendly benchmark to facilitate future research and development in the agentic community.
The social networks of people experiencing homelessness are an understudied but vital aspect of their lives, offering access to information, support, and safety. In 2023, the U.S. Department of Housing and Urban Development reported 653,100 people experiencing homelessness on any given night -- a 23% rise since 2022, though likely an undercount. This paper examines a unique three-year dataset (2022-2024) of survey responses from over 3,000 unhoused individuals in King County, WA, collected via network-based sampling methods to estimate the unsheltered population. Our study analyzes the networks of the unsheltered population, focusing on acquaintance, close friendship, kinship, and peer referral networks. Findings reveal a decline in social connectivity over time. The average number of acquaintances dropped from 80 in 2023 to 40 in 2024. Close friendship levels remained stable at 2.5, but given the growth in the homeless population, this suggests decreased network connectivity. Kinship networks expanded, indicating that more family members of unhoused individuals are also experiencing homelessness. These trends suggest increasing social disconnection, possibly driven by displacement and a rise in newly homeless individuals. The growing isolation may reduce opportunities for information sharing and mutual support. However, the increased reliance on family networks highlights the shifting dynamics of social support within this community. This research underscores the need for policies fostering social connections and community building, such as reducing displacement and providing spaces for congregation, to counter the growing anomie among unhoused populations.
Utilizing Large Language Models to Synthesize Product Desirability Datasets
2024-11-20
John D. Hastings, Sherri Weitl-Harms, Joseph Doty, Zachary J. Myers, Warren Thompson
This research explores the application of large language models (LLMs) to generate synthetic datasets for Product Desirability Toolkit (PDT) testing, a key component in evaluating user sentiment and product experience. Utilizing gpt-4o-mini, a cost-effective alternative to larger commercial LLMs, three methods, Word+Review, Review+Word, and Supply-Word, were each used to synthesize 1000 product reviews. The generated datasets were assessed for sentiment alignment, textual diversity, and data generation cost. Results demonstrated high sentiment alignment across all methods, with Pearson correlations ranging from 0.93 to 0.97. Supply-Word exhibited the highest diversity and coverage of PDT terms, although with increased generation costs. Despite minor biases toward positive sentiments, in situations with limited test data, LLM-generated synthetic data offers significant advantages, including scalability, cost savings, and flexibility in dataset production.
Besides extremely high throughput, Wi-Fi 7 is also aimed at providing users a more deterministic behavior, characterized by shorter average latency and smaller jitters. A key mechanism to achieve this is multi-link operation, which brings simultaneous multi-band communication to client stations as well. In this paper, traffic steering policies are briefly reviewed and grouped into general classes, each one with its advantages and limitations. A basic mechanism for supporting dynamic steering is then described, which is simple enough to allow implementation in real Wi-Fi chipsets but highly flexible at the same time. Its operation can be driven by the host on a per-packet basis, and this permits to optimize spectrum usage depending on the requirements of applications and the traffic pattern they generate.
The Dean-Kawasaki equation and stochastic density functional theory
2024-11-20
Pierre Illien
The Dean-Kawasaki (DK) equation, which is at the basis of stochastic density functional theory (SDFT), was proposed in the mid-nineties to describe the evolution of the density of interacting Brownian particles, which can represent a large number of systems such as colloidal suspensions, supercooled liquids, polymer melts, biological molecules, active or chemotactic particles, or ions in solution. This theoretical framework, which can be summarized as a mathematical reformulation of the coupled overdamped Langevin equations that govern the dynamics of the particles, has attracted a significant amount of attention during the past thirty years. In this review, I present the context in which this framework was introduced, and I recall the main assumptions and calculation techniques that are employed to derive the DK equation. Then, in the broader context of statistical mechanics, I show how SDFT is connected to other theories, such fluctuating hydrodynamics, macroscopic fluctuation theory, or mode-coupling theory. The mathematical questions that are raised by the DK equation are presented in a non-specialist language. In the last parts of the review, I show how the original result was extended in several directions, I present the different strategies and approximations that have been employed to solve the DK equation, both analytically and numerically. I finally list the different situations where SDFT was employed to describe the fluctuations of Brownian suspensions, from the physics of active matter to the description of charged particles and electrolytes.
Reinforcement learning (RL) is one of the active fields in machine learning, demonstrating remarkable potential in tackling real-world challenges. Despite its promising prospects, this methodology has encountered with issues and challenges, hindering it from achieving the best performance. In particular, these approaches lack decent performance when navigating environments and solving tasks with large observation space, often resulting in sample-inefficiency and prolonged learning times. This issue, commonly referred to as the curse of dimensionality, complicates decision-making for RL agents, necessitating a careful balance between attention and decision-making. RL agents, when augmented with human or large language models' (LLMs) feedback, may exhibit resilience and adaptability, leading to enhanced performance and accelerated learning. Such feedback, conveyed through various modalities or granularities including natural language, serves as a guide for RL agents, aiding them in discerning relevant environmental cues and optimizing decision-making processes. In this survey paper, we mainly focus on problems of two-folds: firstly, we focus on humans or an LLMs assistance, investigating the ways in which these entities may collaborate with the RL agent in order to foster optimal behavior and expedite learning; secondly, we delve into the research papers dedicated to addressing the intricacies of environments characterized by large observation space.
Issues with Input-Space Representation in Nonlinear Data-Based Dissipativity Estimation
2024-11-20
Ethan LoCicero, Alex Penne, Leila Bridgeman
In data-based control, dissipativity can be a powerful tool for attaining stability guarantees for nonlinear systems if that dissipativity can be inferred from data. This work provides a tutorial on several existing methods for data-based dissipativity estimation of nonlinear systems. The interplay between the underlying assumptions of these methods and their sample complexity is investigated. It is shown that methods based on delta-covering result in an intractable trade-off between sample complexity and robustness. A new method is proposed to quantify the robustness of machine learning-based dissipativity estimation. It is shown that this method achieves a more tractable trade-off between robustness and sample complexity. Several numerical case studies demonstrate the results.
The evolutionary state of the red giant star L$_2$ Puppis
2024-11-20
S. Uttenthaler
Context: L$_2$ Puppis (L$_2$ Pup) is a nearby red giant star and an important object in late-type star research because it has a dust disc and potentially a companion. Aims: L$_2$ Pup is often called the second-closest Asymptotic Giant Branch (AGB) star to the sun, second only to R Doradus. However, whether the star is indeed on the AGB or the Red Giant Branch (RGB) is questionable. We review its evolutionary state. Methods: We analyse high-resolution optical archive spectra to search for absorption lines of the third dredge-up indicator technetium (Tc) in L$_2$~Pup. We also compare the star to a sample of well-known AGB stars in terms of luminosity and pulsation properties and place it in a Gaia-2MASS diagram. Results: L$_2$ Pup is found to be Tc-poor. Thus, it is not undergoing third dredge-up events. The star is fainter than the RGB tip and fainter than all Tc-rich stars in the comparison sample. L$_2$ Pup pulsates in the fundamental mode, similar to Mira variables, but its pulsation properties do not allow us to distinguish between the RGB and AGB stages. Conclusions: In conclusion, L$_2$ Pup could be an RGB or early AGB star, but it is more likely to be an RGB than an AGB star. Our findings are important for a better understanding of the L$_2$ Pup system and its past and future evolution
'Weather' in the LSST Camera: Investigating Patterns in Differenced Flat Images
2024-11-20
John Banovetz, Yousuke Utsumi, Joshua Meyers, Maya Beleznay, Andrew Rasmussen, Aaron Roodman
During electro-optical testing of the camera for the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time, a unique low-signal pattern was found in differenced pairs of flat images used to create photon transfer curves, with peak-to-peak variations of a factor of 10^-3. A turbulent pattern of this amplitude was apparent in many differenced flat-fielded images. The pattern changes from image to image and shares similarities with atmospheric 'weather' turbulence patterns. We applied several strategies to determine the source of the turbulent pattern and found that it is representative of the mixing of the air and index of refraction variations caused by the internal camera purge system displacing air, which we are sensitive to due to our flat field project setup. Characterizing this changing environment with 2-D correlation functions of the 'weather' patterns provides evidence that the images reflect the changes in the camera environment due to the internal camera purge system. Simulations of the full optical system using the galsim and batoid codes show that the weather pattern affects the dispersion of the camera point-spread function at only the one part in 10^-4 level
Recitation tasks revamped? Evaluation of smartphone experiment tasks in introductory mechanics
2024-11-20
Simon Zacharias Lahme, Dominik Dorsel, Heidrun Heinke, Pascal Klein, Andreas Müller, Christoph Stampfer, Sebastian Staacks
This study presents an approach to integrate innovative forms of recitation tasks into first-year introductory mechanics, with a primary focus on smartphone-based experimental tasks and additional programming tasks for comparison. Smartphones enable inexpensive physics experiments with digitized first-hand data collection outside lab settings. Such student experiments can enhance homework assignments, breaking down barriers between lectures, recitation groups, and labs, and thereby linking theoretical and experimental aspects of undergraduate physics education. To explore this potential, we implemented and evaluated a sample set of nine smartphone-based experimental tasks and three programming tasks as weekly exercises in a first-year physics course at RWTH Aachen University. Through twelve short surveys involving up to 188 participants, we investigated students' perceptions of learning with the new tasks, focusing on factors such as goal clarity, difficulty, or feasibility at home. In two additional surveys with 108 and 78 participants, students compared the new experimental and programming tasks to each other and to standard recitation tasks based on affective variables. Our findings indicate that the smartphone-based experimental tasks were generally well-suited to the students, which tended to outperform the programming tasks in terms of perceptions of learning with the tasks and affective responses. Overall, students responded positively to the new experimental tasks, with perceptions comparable to, or only partly below those of established standard recitation tasks. Given that most of the experimental tasks were newly implemented "on-the-fly" within a running course, while the standard recitation tasks have been refined over years, these results are encouraging. They suggest that smartphone-based experimental tasks can be successfully integrated into teaching.
Upgrade of the Diagnostic Neutral Beam Injector for the RFX-mod2 experiment
2024-11-20
Marco Barbisan, Marco Boldrin, Luca Cinnirella, Bruno Laterza, Alberto Maistrello, Lionello Marrelli, Federico Molon, Simone Peruzzo, Cesare Taliercio, Marco Valisa, Enrico Zampiva
Diagnostic Neutral Beam Injectors (DNBI), through the combined use of Charge Exchange Recombination Spectroscopy (CHERS) and Motional Stark effect diagnostics (MSE), are a well-known tool to access important information about magnetically confined plasmas, such as radial profiles of ion temperature, ion flow, impurity content and intensity and direction of the magnetic field. For this purpose, a DNBI was installed and operated in the RFX-mod experiment, which was designed to confine plasma mainly through the Reversed Field Pinch configuration. The DNBI, designed and built by the Budker Institute of Plasma Physics, was based on a source of positive hydrogen ions, accelerated to 50 keV and for an equivalent neutral beam current of about 5 A at the source. The beam could be modulated and the maximum overall duration was 50 ms. With the upgrade of RFX-mod to the present RFX-mod2 machine, the DNBI is being renovated to solve several plant faults and improve the overall reliability of the system. The 50 kV power supply is being improved, as well as the power supplies in the high voltage deck and its insulation transformer. The control system, originally based on CAMAC technology, was redesigned to be fully replaced. This contribution reviews the technical criticalities emerged in the DNBI check-up and the new solutions adopted to make the DNBI operative and more reliable.
Explainable Finite-Memory Policies for Partially Observable Markov Decision Processes
2024-11-20
Muqsit Azeem, Debraj Chakraborty, Sudeep Kanav, Jan Kretinsky
Partially Observable Markov Decision Processes (POMDPs) are a fundamental framework for decision-making under uncertainty and partial observability. Since in general optimal policies may require infinite memory, they are hard to implement and often render most problems undecidable. Consequently, finite-memory policies are mostly considered instead. However, the algorithms for computing them are typically very complex, and so are the resulting policies. Facing the need for their explainability, we provide a representation of such policies, both (i) in an interpretable formalism and (ii) typically of smaller size, together yielding higher explainability. To that end, we combine models of Mealy machines and decision trees; the latter describing simple, stationary parts of the policies and the former describing how to switch among them. We design a translation for policies of the finite-state-controller (FSC) form from standard literature and show how our method smoothly generalizes to other variants of finite-memory policies. Further, we identify specific properties of recently used "attractor-based" policies, which allow us to construct yet simpler and smaller representations. Finally, we illustrate the higher explainability in a few case studies.
This short paper is concerned with the use of spherical t-designs as optimal designs for the spherical harmonic regression model in three dimensions over a range of specified criteria. The nature of the designs is explored and their availability and suitability is reviewed.
Impact of Jupiter's heating and self-shadowing on the Jovian circumplanetary disk structure
2024-11-20
Antoine Schneeberger, Olivier Mousis
Deciphering the structure of the circumplanetary disk that surrounded Jupiter at the end of its formation is key to understanding how the Galilean moons formed. Three-dimensional hydrodynamic simulations have shown that this disk was optically thick and significantly heated to very high temperatures due to the intense radiation emitted by the hot, young planet. Analyzing the impact of Jupiter's radiative heating and shadowing on the structure of the circumplanetary disk can provide valuable insights into the conditions that shaped the formation of the Galilean moons. To assess the impact of Jupiter's radiative heating and shadowing, we have developed a two-dimensional quasi-stationary circumplanetary disk model and used a grey atmosphere radiative transfer method to determine the thermal structure of the disk. We find that the circumplanetary disk self-shadowing has a significant effect, with a temperature drop of approximately 100 K in the shadowed zone compared to the surrounding areas. This shadowed zone, located around 10 Jupiter radii, can act as a cold trap for volatile species such as NH$_3$, CO$_2$ and H$_2$S. The existence of these shadows in Jupiter's circumplanetary disk may have influenced the composition of the building blocks of the Galilean moons, potentially shaping their formation and characteristics. Our study suggests that the thermal structure of Jupiter's circumplanetary disk, particularly the presence of cold traps due to self-shadowing, may have played a crucial role in the formation and composition of the Galilean moons.
2024-11-20
Konstantin Haubner, Federico Lelli, Enrico Di Teodoro, Francis Duey, Stacy McGaugh, James Schombert, Kelley M. Hess, the Apertif Team
The Surface Photometry and Accurate Rotation Curves (SPARC) database has provided the community with mass models for 175 nearby galaxies, allowing different research teams to test different dark matter models, galaxy evolution models, and modified gravity theories. Extensive tests, however, are hampered by the somewhat heterogeneous nature of the HI rotation curves and the limited sample size of SPARC. To overcome these limitations, we are working on BIG-SPARC, a new database that consists of about 4000 galaxies with HI datacubes from public telescope archives (APERTIF, ASKAP, ATCA, GMRT, MeerKAT, VLA, and WSRT) and near infrared photometry from WISE. For these galaxies, we will provide homogeneously derived HI rotation curves, surface brightness profiles, and mass models. BIG-SPARC is expected to increase the size of its predecessor by a factor of more than 20. This is a necessary step to prepare for the additional order of magnitude increase in sample size expected from ongoing and future HI surveys with the Square Kilometre Array (SKA) and its pathfinders
2024-11-20
Hiep Nguyen, Haiyang Tang, Matthew Alger, Antoine Marchal, Eric G. M. Muller, Cheng Soon Ong, N. M. McClure-Griffiths
We introduce TPCNet, a neural network predictor that combines Convolutional and Transformer architectures with Positional encodings, for neutral atomic hydrogen (HI) spectral analysis. Trained on synthetic datasets, our models predict cold neutral gas fraction ($f_\text{CNM}$) and HI opacity correction factor ($R_\text{HI}$) from emission spectra based on the learned relationships between the desired output parameters and observables (optically-thin column density and peak brightness). As a follow-up to Murray et al. (2020)'s shallow Convolutional Neural Network (CNN), we construct deep CNN models and compare them to TPCNet models. TPCNet outperforms deep CNNs, achieving a 10% average increase in testing accuracy, algorithmic (training) stability, and convergence speed. Our findings highlight the robustness of the proposed model with sinusoidal positional encoding applied directly to the spectral input, addressing perturbations in training dataset shuffling and convolutional network weight initializations. Higher spectral resolutions with increased spectral channels offer advantages, albeit with increased training time. Diverse synthetic datasets enhance model performance and generalization, as demonstrated by producing $f_\text{CNM}$ and $R_\text{HI}$ values consistent with evaluation ground truths. Applications of TPCNet to observed emission data reveal strong agreement between the predictions and Gaussian decomposition-based estimates (from emission and absorption surveys), emphasizing its potential in HI spectral analysis.
A machine learning approach to estimate mid-infrared fluxes from WISE data
2024-11-20
Nuria Fonseca-Bonilla, Luis Cerdán, Alberto Noriega-Crespo, Amaya Moro-Martín
While WISE is the largest, best quality infrared all-sky survey to date, a smaller coverage mission, Spitzer, was designed to have better sensitivity and spatial resolution at similar wavelengths. Confusion and contamination in WISE data result in discrepancies between them. We present a novel approach to work with WISE measurements with the goal of maintaining both its high coverage and vast amount of data while taking full advantage of the higher sensitivity and spatial resolution of Spitzer. We have applied machine learning (ML) techniques to a complete WISE data sample of open cluster members, using a training set of paired data from high-quality Spitzer Enhanced Imaging Products (SEIP), MIPS and IRAC, and allWISE catalogs, W1 (3.4 {\mu}m) to W4 (22 {\mu}m) bands. We have tested several ML regression models with the aim of predicting mid-infrared fluxes at MIPS1 (24 {\mu}m) and IRAC4 (8 {\mu}m) bands from WISE fluxes and quality flags. In addition, to improve the prediction quality, we have implemented feature selection techniques to remove irrelevant WISE variables. We have notably enhanced WISE detection capabilities, mostly at lowest magnitudes, which previously showed the largest discrepancies with Spitzer. In our particular case, extremely randomized trees was found to be the best algorithm to predict mid-infrared fluxes from WISE variables. We have tested our results in the SED of members of IC 348. We show discrepancies in the measurements of Spitzer and WISE and demonstrate the good concordance of our predicted fluxes with the real ones. ML is a fast and powerful tool that can be used to find hidden relationships between datasets, as the ones that exist between WISE and Spitzer fluxes. We believe this approach could be employed for other samples from the allWISE catalog with SEIP positional counterparts, and in other astrophysical studies with analogous discrepancies.
ZnO-based Semiconductors and Structures for Transistors, Optoelectronic Devices and Sustainable Electronics
2024-11-20
Darragh Buckley, Alex Lonergan, Colm O'Dwyer
Metal oxide thin films are of great interest in scientific advancement, particularly semiconductor thin films in transistors and in a wide range of optoelectronic applications. Many metal oxide thin films attract interest for their electronic bandgap, charge carrier mobility, optical opacity, luminescence, low cost, relative abundance and environmentally-friendly production. Additionally, these properties are often tuneable via particle size, film density, surface morphology, film deposition, growth method, hetero-interface engineering or ion-doping. Zinc oxide as a n-type semiconducting metal oxide is material of great interest owing to its intrinsically wide direct bandgap, high electron mobility, relatively high exciton binding energy, high optical transparency, demonstrated metal-ion doping optoelectronic effects, a range of different particle morphologies and deposition methods, photoluminescence ability, low cost and a variety of existing green synthesis methods. Here, these aspects of zinc oxide and some related oxides are reviewed, focusing on how the unique properties of this metal oxide make it suitable for a range of different applications from thin film transistors, high mobility oxide interfaces, transparent conductive oxides, photoanodes photodetectors, chemical sensors, photocatalysts, superlattice electronics and more. The properties and deposition methods and their impact on functionality will be discussed alongside their role in sustainable optoelectronics for future devices.
This brief review is dedicated to the memory of Maxim V. Polyakov and his pioneering contributions to pentaquark physics. We focus on his seminal 1997 work with Diakonov and Petrov that predicted the $\Theta^+$ pentaquark, a breakthrough that initiated an intense period of research in hadron physics. The field faced a significant setback when the CLAS Collaboration at Jefferson Lab reported null results in 2006, leading to a dramatic decline in light pentaquark research. Nevertheless, Maxim maintained his scientific conviction, supported by continued positive signals from DIANA and LEPS collaborations. Through recent experimental findings on the $\Theta^+$ and the nucleon-like resonance $N^*(1685)$, we examine how Polyakov's theoretical insights, particularly the prediction of a narrow width ($\Gamma \approx 0.5$-$1.0$ MeV), remain relevant to our understanding of the $\Theta^+$ light pentaquark.
Characterizing the Hard and Soft Nanoparticle-Protein Corona with Multilayer Adsorption
2024-11-20
Oriol Vilanova, Alberto Martinez-Serra, Marco P Monopoli, Giancarlo Franzese
Nanoparticles (NPs) in contact with biological fluid adsorb biomolecules into a corona. This corona comprises proteins that strongly bind to the NP (hard corona) and loosely bound proteins (soft corona) that dynamically exchange with the surrounding solution. While the kinetics of hard corona formation is relatively well understood, thanks to experiments and robust simulation models, the experimental characterization and simulation of the soft corona present a more significant challenge. Here, we review the current state of the art in soft corona characterization and introduce a novel open-source computational model to simulate its dynamic behavior, for which we provide the documentation. We focus on the case of transferrin (Tf) interacting with polystyrene NPs as an illustrative example, demonstrating how this model captures the complexities of the soft corona and offers deeper insights into its structure and behavior. We show that the soft corona is dominated by a glassy evolution that we relate to crowding effects. This work advances our understanding of the soft corona, bridging experimental limitations with improved simulation techniques.
An iterative method to deblend AGN-Host contributions for Integral Field spectroscopic observations
2024-11-20
Héctor Ibarra-Medel, Castalia Alenka Negrete, Ivan Lacerna, Héctor Manuel Hernández-Toledo, Edgar Cortes-Suárez, Sebastián Francisco Sánchez
We present a new iterative deblending method to separate the host galaxy (HG) and their Active Galactic Nuclei (AGN) emission with the use of Integral Field spectroscopic (IFS) data. The method decomposes the resolved HG emission from the unresolved AGN emission by modelling the two-dimensional surface brightness (SB) profile of the point-spread function (PSF) and the two-dimensional SB HG continuum simultaneously per each monochromatic slide. Our method does not require any prior information about the observed SB profile or a detailed fitting of the PSF, making it ideal for the automatic analysis of large galaxy samples. In this work, we test the quality of our method, its advantages, and its disadvantages. We test our method by using a set of IFS mock data cubes to quantify the reliability of our deblending process and further compare our method with the {\sc QDeblend3D} analysis tool. Furthermore, we applied our method to three data cubes selected from the MaNGA survey according to the dominance of either its HG or its AGN. We show that our deblending method is capable of disengaging the bright, nonresolved AGN emission from the HG continuum and its narrow emission lines. However, the decoupling depends on how well the IFS spatially resolves the PSF, and on the relative flux intensity of the HG-AGN. Therefore, the method is ideal for disentangling the bright-flux contribution from AGN-dominated spectra.
Ageing and Quenching: Influence of Galaxy Environment and Nuclear Activity in Transition Stage
2024-11-20
Pius Privatus, Umananda Dev Goswami
This study aims to investigate whether the environment and the nuclear activity of a particular galaxy influence the ageing and quenching at the transition stage of the galaxy evolution using the volume-limited sample constructed from the twelve release of the Sloan Digital Sky Survey. To this end, the galaxies were classified into isolated and non-isolated environments and then each subsample was further classified according to their nuclear activity using the WHAN diagnostic diagram, and ageing diagram to obtain ageing and quenching galaxies. The ageing and quenching galaxies at the transition stage were selected for the rest of the analysis. Using the star formation rate and the $u-r$ colour-stellar mass diagrams, the study revealed a significant change of $0.03$ dex in slope and $0.30$ dex in intercept for ageing galaxies and an insignificant change of $0.02$ dex in slope and $0.12$ dex in intercept of the star formation main sequence between isolated and non-isolated quenching galaxies. Further, a more significant change in the number of ageing galaxies above, within and below the main sequence and the green valley was observed. On the other hand, an insignificant change in the number of quenching galaxies above, within and below the main sequence and the green valley was observed. The study concludes that ageing depends on the environment and the dependence is influenced by the nuclear activity of a particular galaxy while quenching does not depend on the environment and this independence is not influenced by the nuclear activity.
Extremum and Nash Equilibrium Seeking with Delays and PDEs: Designs & Applications
2024-11-20
Tiago Roux Oliveira, Miroslav Krstić, Tamer Başar
The development of extremum seeking (ES) has progressed, over the past hundred years, from static maps, to finite-dimensional dynamic systems, to networks of static and dynamic agents. Extensions from ODE dynamics to maps and agents that incorporate delays or even partial differential equations (PDEs) is the next natural step in that progression through ascending research challenges. This paper reviews results on algorithm design and theory of ES for such infinite-dimensional systems. Both hyperbolic and parabolic dynamics are presented: delays or transport equations, heat-dominated equation, wave equations, and reaction-advection-diffusion equations. Nash equilibrium seeking (NES) methods are introduced for noncooperative game scenarios of the model-free kind and then specialized to single-agent optimization. Even heterogeneous PDE games, such as a duopoly with one parabolic and one hyperbolic agent, are considered. Several engineering applications are touched upon for illustration, including flow-traffic control for urban mobility, oil-drilling systems, deep-sea cable-actuated source seeking, additive manufacturing modeled by the Stefan PDE, biological reactors, light-source seeking with flexible-beam structures, and neuromuscular electrical stimulation.
AIDBench: A benchmark for evaluating the authorship identification capability of large language models
2024-11-20
Zichen Wen, Dadi Guo, Huishuai Zhang
As large language models (LLMs) rapidly advance and integrate into daily life, the privacy risks they pose are attracting increasing attention. We focus on a specific privacy risk where LLMs may help identify the authorship of anonymous texts, which challenges the effectiveness of anonymity in real-world systems such as anonymous peer review systems. To investigate these risks, we present AIDBench, a new benchmark that incorporates several author identification datasets, including emails, blogs, reviews, articles, and research papers. AIDBench utilizes two evaluation methods: one-to-one authorship identification, which determines whether two texts are from the same author; and one-to-many authorship identification, which, given a query text and a list of candidate texts, identifies the candidate most likely written by the same author as the query text. We also introduce a Retrieval-Augmented Generation (RAG)-based method to enhance the large-scale authorship identification capabilities of LLMs, particularly when input lengths exceed the models' context windows, thereby establishing a new baseline for authorship identification using LLMs. Our experiments with AIDBench demonstrate that LLMs can correctly guess authorship at rates well above random chance, revealing new privacy risks posed by these powerful models. The source code and data will be made publicly available after acceptance.
The Composite Spectral Energy Distribution of Quasars is Surprisingly Universal Since Cosmic Noon
2024-11-20
Zhen-Yi Cai
Leveraging the photometric data of the Sloan Digital Sky Survey and the Galaxy Evolution Explorer (GALEX), we construct mean/median spectral energy distributions (SEDs) for unique bright quasars in redshift bins of 0.2 and up to $z \simeq 3$, after taking the GALEX non-detection into account. Further correcting for the absorption of the intergalactic medium, these mean/median quasar SEDs constitute a surprisingly redshift-independent mean/median composite SED from the rest-frame optical down to $\simeq 500~{\rm \mathring A}$ for quasars with bolometric luminosity brighter than $10^{45.5}~{\rm erg s^{-1}}$. Moreover, the mean/median composite quasar SED is plausibly also independent of black hole mass and Eddington ratio, and suggests similar properties of dust and gas in the quasar host galaxies since cosmic noon. Both the mean and median composite SEDs are nicely consistent with previous mean composite quasar spectra at wavelengths beyond $\simeq 1000~{\rm \mathring A}$, but at shorter wavelengths, are redder, indicating, on average, less ionizing radiation than previously expected. Through comparing the model-predicted to the observed composite quasar SEDs, we favor a simply truncated disk model, rather than a standard thin disk model, for the quasar central engine, though we request more sophisticated disk models. Future deep ultraviolet facilities, such as the China Space Station Telescope and the Ultraviolet Explorer, would prompt revolutions in many aspects, including the quasar central engine, production of the broad emission lines in quasars, and cosmic reionization.
Preserving curvature lower bounds when Ricci flowing non-smooth initial data
2024-11-20
Miles Simon
In this paper we survey some results on Ricci flowing non-smooth initial data. Among other things, we give a non-exhaustive list of various weak initial data which can be evolved with the Ricci flow. We also survey results which show that various curvature lower bounds will, possibly up to a constant, be preserved, if we start with such possibly non-smooth initial data. Some proofs/proof sketches are given in certain cases. A list of some open problems related to these areas is given in the last section of the paper.
A Systematic Review of Empirical Research on Graphing Numerical Data in K-12 STEM Education
2024-11-20
Verena Ruf, Dominik Thues, Sarah Malone, Stefan Kuechemann, Sebastian Becker-Genschow, Markus Vogel, Roland Bruenken, Jochen Kuhn
Graphs are essential representations in the professions and education concerning the science, technology, engineering, and mathematics (STEM) disciplines. Beyond their academic relevance, graphs find extensive utility in everyday scenarios, ranging from news media to educational materials. This underscores the importance of people's being able to understand graphs. However, the ability to understand graphs is connected to the ability to create graphs. Therefore, in school education, particularly in STEM subjects, not only the understanding but also the skill of constructing graphs from numerical data is emphasized. Although constructing graphs is a skill that most people do not require in their everyday lives and professions, it is a well-established student activity that has been empirically studied several times. Therefore, since a synthesis of the research findings on this topic has not yet been conducted, a summary of the studies investigating graphing via various viewpoints and differing methods could be a valuable contribution. To provide an overview of the empirical literature on this important topic, our systematic review identifies how the construction of convention-based graphical representations of numerical data, referred to as graphing, has been studied in previous research, how effective graphing is, and which types of difficulties are encountered by students. Based on these aspects, we defined inclusion criteria that led to 50 peer-reviewed empirical studies on graphing in K-12 STEM education found in SCOPUS, ERIC, and PsychInfo. Graphing instruction seemed to be beneficial for student learning, not only improving graph construction but also graph interpretation skills. However, the students experienced various difficulties during graphing, both during graph construction and the interpretation and usage of data.
Investor Sentiment in Asset Pricing Models: A Review of Empirical Evidence
2024-11-20
Szymon Lis
This study conducted a comprehensive review of 71 papers published between 2000 and 2021 that employed various measures of investor sentiment to model returns. The analysis indicates that higher complexity of sentiment measures and models improves the coefficient of determination. However, there was insufficient evidence to support that models incorporating more complex sentiment measures have better predictive power than those employing simpler proxies. Additionally, the significance of sentiment varies based on the asset and time period being analyzed, suggesting that the consensus relying on the BW index as a sentiment measure may be subject to change.
Closer Look at Efficient Inference Methods: A Survey of Speculative Decoding
2024-11-20
Hyun Ryu, Eric Kim
Efficient inference in large language models (LLMs) has become a critical focus as their scale and complexity grow. Traditional autoregressive decoding, while effective, suffers from computational inefficiencies due to its sequential token generation process. Speculative decoding addresses this bottleneck by introducing a two-stage framework: drafting and verification. A smaller, efficient model generates a preliminary draft, which is then refined by a larger, more sophisticated model. This paper provides a comprehensive survey of speculative decoding methods, categorizing them into draft-centric and model-centric approaches. We discuss key ideas associated with each method, highlighting their potential for scaling LLM inference. This survey aims to guide future research in optimizing speculative decoding and its integration into real-world LLM applications.
Motivated by the vast gap between photometric and spectroscopic data volumes, there is great potential in using 5D kinematic information to identify and study substructures of the Milky Way. We identify substructures in the Galactic halo using 46,575 RR Lyrae stars (RRLs) from Gaia DR3 with the photometric metallicities and distances newly estimated by Li et al. (2023). Assuming a Gaussian prior distribution of radial velocity, we calculate the orbital distribution characterized by the integrals of motion for each RRL based on its 3D positions, proper motions and corresponding errors, and then apply the friends-of-friends algorithm to identify groups moving along similar orbits. We have identified several known substructures, including Sagittarius (Sgr) Stream, Hercules-Aquila Cloud (HAC), Virgo Overdensity (VOD), Gaia-Enceladus-Sausage (GES), Orphan-Chenab stream, Cetus-Palca, Helmi Streams, Sequoia, Wukong and Large Magellanic Cloud (LMC) leading arm, along with 18 unknown groups. Our findings indicate that HAC and VOD have kinematic and chemical properties remarkably similar to GES, with most HAC and VOD members exhibiting eccentricity as high as GES, suggesting that they may share a common origin with GES. The ability to identify the low mass and spatially dispersed substructures further demonstrates the potential of our method, which breaks the limit of spectroscopic survey and is competent to probe the substructures in the whole Galaxy. Finally, we have also identified 18 unknown groups with good spatial clustering and proper motion consistency, suggesting more excavation of Milky Way substructures in the future with only 5D data.
The present contribution is in the field of quantum modelling of macroscopic phenomena. The focus is on one enigmatic aspect of quantum physics, namely the Einstein-Podolsky-Rosen paradox and entanglement. After a review of the state of the art concerning macroscopic quantum effects and quantum interaction, this contribution proposes a link between embryology and acupuncture in the framework of macroscopic intricate states induced by quantum mechanics.
AMaze: An intuitive benchmark generator for fast prototyping of generalizable agents
2024-11-20
Kevin Godin-Dubois, Karine Miras, Anna V. Kononova
Traditional approaches to training agents have generally involved a single, deterministic environment of minimal complexity to solve various tasks such as robot locomotion or computer vision. However, agents trained in static environments lack generalization capabilities, limiting their potential in broader scenarios. Thus, recent benchmarks frequently rely on multiple environments, for instance, by providing stochastic noise, simple permutations, or altogether different settings. In practice, such collections result mainly from costly human-designed processes or the liberal use of random number generators. In this work, we introduce AMaze, a novel benchmark generator in which embodied agents must navigate a maze by interpreting visual signs of arbitrary complexities and deceptiveness. This generator promotes human interaction through the easy generation of feature-specific mazes and an intuitive understanding of the resulting agents' strategies. As a proof-of-concept, we demonstrate the capabilities of the generator in a simple, fully discrete case with limited deceptiveness. Agents were trained under three different regimes (one-shot, scaffolding, interactive), and the results showed that the latter two cases outperform direct training in terms of generalization capabilities. Indeed, depending on the combination of generalization metric, training regime, and algorithm, the median gain ranged from 50% to 100% and maximal performance was achieved through interactive training, thereby demonstrating the benefits of a controllable human-in-the-loop benchmark generator.
In recent years, terrestrial laser scanning technology has been widely used to collect tree point cloud data, aiding in measurements of diameter at breast height, biomass, and other forestry survey data. Since a single scan from terrestrial laser systems captures data from only one angle, multiple scans must be registered and fused to obtain complete tree point cloud data. This paper proposes a marker-free automatic registration method for single-tree point clouds based on similar tetrahedras. First, two point clouds from two scans of the same tree are used to generate tree skeletons, and key point sets are constructed from these skeletons. Tetrahedra are then filtered and matched according to similarity principles, with the vertices of these two matched tetrahedras selected as matching point pairs, thus completing the coarse registration of the point clouds from the two scans. Subsequently, the ICP method is applied to the coarse-registered leaf point clouds to obtain fine registration parameters, completing the precise registration of the two tree point clouds. Experiments were conducted using terrestrial laser scanning data from eight trees, each from different species and with varying shapes. The proposed method was evaluated using RMSE and Hausdorff distance, compared against the traditional ICP and NDT methods. The experimental results demonstrate that the proposed method significantly outperforms both ICP and NDT in registration accuracy, achieving speeds up to 593 times and 113 times faster than ICP and NDT, respectively. In summary, the proposed method shows good robustness in single-tree point cloud registration, with significant advantages in accuracy and speed compared to traditional ICP and NDT methods, indicating excellent application prospects in practical registration scenarios.
Spatio-Temporal Scene Graphs (STSGs) provide a concise and expressive representation of dynamic scenes by modelling objects and their evolving relationships over time. However, real-world visual relationships often exhibit a long-tailed distribution, causing existing methods for tasks like Video Scene Graph Generation (VidSGG) and Scene Graph Anticipation (SGA) to produce biased scene graphs. To this end, we propose ImparTail, a novel training framework that leverages curriculum learning and loss masking to mitigate bias in the generation and anticipation of spatio-temporal scene graphs. Our approach gradually decreases the dominance of the head relationship classes during training and focuses more on tail classes, leading to more balanced training. Furthermore, we introduce two new tasks, Robust Spatio-Temporal Scene Graph Generation and Robust Scene Graph Anticipation, designed to evaluate the robustness of STSG models against distribution shifts. Extensive experiments on the Action Genome dataset demonstrate that our framework significantly enhances the unbiased performance and robustness of STSG models compared to existing methods.
EWOCS-III: JWST observations of the supermassive star cluster Westerlund 1
2024-11-20
M. G. Guarcello, V. Almendros-Abad, J. B. Lovell, K. Monsch, K. Muzic, J. R. Martiinez-Galarza, J. J. Drake, K. Anastasopoulou, M. Andersen, C. Argiroffi, A. Bayo, R. Bonito, D. Capela, F. Damiani, M. Gennaro, A. Ginsburg, E. K. Grebel, J. L. Hora, E. Moraux, F. Najarro, I. Negueruela, L. Prisinzano, N. D. Richardson, B. Ritchie, M. Robberto, T. Rom, E. Sabbi, S. Sciortino, G. Umana, A. Winter, N. J. Wright, P. Zeidler
The typically large distances, extinction, and crowding of Galactic supermassive star clusters have so far hampered the identification of their very low mass members, required to extend our understanding of star and planet formation, and early stellar evolution, to starburst. This situation has now evolved thanks to the James Webb Space Telescope (JWST), and its unmatched resolution and sensitivity in the infrared. In this paper, the third of the series of the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS), we present JWST/NIRCam and JWST/MIRI observations of the supermassive star cluster Westerlund 1. These observations are specifically designed to unveil the cluster members down to the BD mass regime, and to allow us to select and study the protoplanetary disks and to study the mutual feedback between the cluster members and the surrounding environment. Westerlund 1 was observed as part of JWST GO-1905 for 23.6 hours. The data have been reduced using the JWST calibration pipeline, together with specific tools necessary to remove artifacts. Source identification and photometry were performed with DOLPHOT. The MIRI images show a plethora of different features. Diffuse nebular emission is observed around the cluster, which is typically composed of myriads of droplet-like features pointing toward the cluster center or the group of massive stars surrounding the WR star W72/A. A long pillar is also observed in the NW. The MIRI images also show resolved shells and outflows surrounding the M-type RSG W20, W26, W75, and W237, the sgB[e] star W9 and the YHG W4. The color-magnitude diagrams built using the NIRCam photometry show a clear cluster sequence, which is marked in its upper part by the 1828 NIRCam stars with X-ray counterparts. NIRCam observations using the F115W filter have reached the 23.8 mag limit with 50\% completeness (roughly corresponding to a 0.06 Msol brown dwarf).
"It was 80% me, 20% AI": Seeking Authenticity in Co-Writing with Large Language Models
2024-11-20
Angel Hsing-Chi Hwang, Q. Vera Liao, Su Lin Blodgett, Alexandra Olteanu, Adam Trischler
Given the rising proliferation and diversity of AI writing assistance tools, especially those powered by large language models (LLMs), both writers and readers may have concerns about the impact of these tools on the authenticity of writing work. We examine whether and how writers want to preserve their authentic voice when co-writing with AI tools and whether personalization of AI writing support could help achieve this goal. We conducted semi-structured interviews with 19 professional writers, during which they co-wrote with both personalized and non-personalized AI writing-support tools. We supplemented writers' perspectives with opinions from 30 avid readers about the written work co-produced with AI collected through an online survey. Our findings illuminate conceptions of authenticity in human-AI co-creation, which focus more on the process and experience of constructing creators' authentic selves. While writers reacted positively to personalized AI writing tools, they believed the form of personalization needs to target writers' growth and go beyond the phase of text production. Overall, readers' responses showed less concern about human-AI co-writing. Readers could not distinguish AI-assisted work, personalized or not, from writers' solo-written work and showed positive attitudes toward writers experimenting with new technology for creative writing.
Enhancing Transportation Cyber-Physical Systems Security: A Shift to Post-Quantum Cryptography
2024-11-20
Abdullah Al Mamun, Akid Abrar, Mizanur Rahman, M Sabbir Salek, Mashrur Chowdhury
The rise of quantum computing threatens traditional cryptographic algorithms that secure Transportation Cyber-Physical Systems (TCPS). Shor's algorithm poses a significant threat to RSA and ECC, while Grover's algorithm reduces the security of symmetric encryption schemes, such as AES. The objective of this paper is to underscore the urgency of transitioning to post-quantum cryptography (PQC) to mitigate these risks in TCPS by analyzing the vulnerabilities of traditional cryptographic schemes and the applicability of standardized PQC schemes in TCPS. We analyzed vulnerabilities in traditional cryptography against quantum attacks and reviewed the applicability of NIST-standardized PQC schemes, including CRYSTALS-Kyber, CRYSTALS-Dilithium, and SPHINCS+, in TCPS. We conducted a case study to analyze the vulnerabilities of a TCPS application from the Architecture Reference for Cooperative and Intelligent Transportation (ARC-IT) service package, i.e., Electronic Toll Collection, leveraging the Microsoft Threat Modeling tool. This case study highlights the cryptographic vulnerabilities of a TCPS application and presents how PQC can effectively counter these threats. Additionally, we evaluated CRYSTALS-Kyber's performance across wired and wireless TCPS data communication scenarios. While CRYSTALS-Kyber proves effective in securing TCPS applications over high-bandwidth, low-latency Ethernet networks, our analysis highlights challenges in meeting the stringent latency requirements of safety-critical wireless applications within TCPS. Future research should focus on developing lightweight PQC solutions and hybrid schemes that integrate traditional and PQC algorithms, to enhance compatibility, scalability, and real-time performance, ensuring robust protection against emerging quantum threats in TCPS.
Revisiting the activity-rotation relation for evolved stars
2024-11-20
Henggeng Han, Song Wang, Xue Li, Chuanjie Zheng, Jifeng Liu
The magnetic dynamo mechanism of giant stars remains an open question, which can be explored by investigating their activity-rotation relations with multiple proxies. By using the data from the LAMOST and \emph{GALEX} surveys, we carried out a comprehensive study of activity-rotation relations of evolved stars based on \cahk lines, $\rm{H\alpha}$ lines and near ultraviolet (NUV) emissions. Our results show that evolved stars and dwarfs obey a similar power-law in the unsaturated region of the activity-rotation relation, indicating a common dynamo mechanism in both giant and dwarfs. There is no clear difference in the activity levels between red giant branch stars and red clump stars, nor between single giants and those in binaries. Additionally, our results show that the NUV activity levels of giants are comparable to those of G- and K-type dwarfs and are higher than those of M dwarfs.
FAST-Splat: Fast, Ambiguity-Free Semantics Transfer in Gaussian Splatting
2024-11-20
Ola Shorinwa, Jiankai Sun, Mac Schwager
We present FAST-Splat for fast, ambiguity-free semantic Gaussian Splatting, which seeks to address the main limitations of existing semantic Gaussian Splatting methods, namely: slow training and rendering speeds; high memory usage; and ambiguous semantic object localization. In deriving FAST-Splat , we formulate open-vocabulary semantic Gaussian Splatting as the problem of extending closed-set semantic distillation to the open-set (open-vocabulary) setting, enabling FAST-Splat to provide precise semantic object localization results, even when prompted with ambiguous user-provided natural-language queries. Further, by exploiting the explicit form of the Gaussian Splatting scene representation to the fullest extent, FAST-Splat retains the remarkable training and rendering speeds of Gaussian Splatting. Specifically, while existing semantic Gaussian Splatting methods distill semantics into a separate neural field or utilize neural models for dimensionality reduction, FAST-Splat directly augments each Gaussian with specific semantic codes, preserving the training, rendering, and memory-usage advantages of Gaussian Splatting over neural field methods. These Gaussian-specific semantic codes, together with a hash-table, enable semantic similarity to be measured with open-vocabulary user prompts and further enable FAST-Splat to respond with unambiguous semantic object labels and 3D masks, unlike prior methods. In experiments, we demonstrate that FAST-Splat is 4x to 6x faster to train with a 13x faster data pre-processing step, achieves between 18x to 75x faster rendering speeds, and requires about 3x smaller GPU memory, compared to the best-competing semantic Gaussian Splatting methods. Further, FAST-Splat achieves relatively similar or better semantic segmentation performance compared to existing methods. After the review period, we will provide links to the project website and the codebase.
The Grüneisen parameter applied to critical phenomena and experimental investigations of correlated phenomena in molecular conductors
2024-11-20
Lucas Squillante
In this Ph.D. Thesis, a systematic review is performed on the derivation and generalization of the Gr\"uneisen parameter followed by its unprecedented applications to several distinct scenarios, such as magnetic model systems, zero-field quantum phase transitions, the maximization of caloric effects close to any critical-end point based on entropy arguments, the here-proposed adiabatic magnetization of a paramagnetic salt, as well as for Cosmology in the frame of the universe expansion. Since this Ph.D. Thesis is a symbiosis between theoretical and experimental results, an experimental investigation of correlated phenomena was carried out for molecular conductors of the (TMTTF)$_2$X family, where TMTTF is the base molecule tetramethyltetrathiafulvalene and X a monovalent counter-anion such as PF$_6$, SbF$_6$, or AsF$_6$. Such strongly correlated electron systems are considered suitable ones for the exploration of Mott insulating phase, charge-ordering, spin-Peierls, and superconductivity. In particular, the investigation of a possible multiferroic character in these salts was performed via quasi-static (low-frequency) dielectric constant $\varepsilon'$ measurements as a function of temperature where a maximum in $\varepsilon'$ as a function of temperature was observed at the corresponding charge-ordering temperature for both hydrogenated and 97.5% deuterated (TMTTF)$_2$SbF$_6$ salts. Furthermore, Raman measurements were performed on the 97.5% deuterated (TMTTF)$_2$PF$_6$, showing a possible magneto-optical effect on the $\nu_4(a_g)$ vibrational mode of the TMTTF molecule. Yet, fluorescence measurements demonstrated that the fully-hydrogenated (TMTTF)$_2$AsF$_6$ presents an expressive fluorescence background, which is roughly five orders of magnitude lower than that for the 97.5% deuterated variant of (TMTTF)$_2$PF$_6$.
Federated Continual Learning for Edge-AI: A Comprehensive Survey
2024-11-20
Zi Wang, Fei Wu, Feng Yu, Yurui Zhou, Jia Hu, Geyong Min
Edge-AI, the convergence of edge computing and artificial intelligence (AI), has become a promising paradigm that enables the deployment of advanced AI models at the network edge, close to users. In Edge-AI, federated continual learning (FCL) has emerged as an imperative framework, which fuses knowledge from different clients while preserving data privacy and retaining knowledge from previous tasks as it learns new ones. By so doing, FCL aims to ensure stable and reliable performance of learning models in dynamic and distributed environments. In this survey, we thoroughly review the state-of-the-art research and present the first comprehensive survey of FCL for Edge-AI. We categorize FCL methods based on three task characteristics: federated class continual learning, federated domain continual learning, and federated task continual learning. For each category, an in-depth investigation and review of the representative methods are provided, covering background, challenges, problem formalisation, solutions, and limitations. Besides, existing real-world applications empowered by FCL are reviewed, indicating the current progress and potential of FCL in diverse application domains. Furthermore, we discuss and highlight several prospective research directions of FCL such as algorithm-hardware co-design for FCL and FCL with foundation models, which could provide insights into the future development and practical deployment of FCL in the era of Edge-AI.
Assessing Gender Bias in LLMs: Comparing LLM Outputs with Human Perceptions and Official Statistics
2024-11-20
Tetiana Bas
This study investigates gender bias in large language models (LLMs) by comparing their gender perception to that of human respondents, U.S. Bureau of Labor Statistics data, and a 50% no-bias benchmark. We created a new evaluation set using occupational data and role-specific sentences. Unlike common benchmarks included in LLM training data, our set is newly developed, preventing data leakage and test set contamination. Five LLMs were tested to predict the gender for each role using single-word answers. We used Kullback-Leibler (KL) divergence to compare model outputs with human perceptions, statistical data, and the 50% neutrality benchmark. All LLMs showed significant deviation from gender neutrality and aligned more with statistical data, still reflecting inherent biases.
A Systematic Literature Review on a Decade of Industrial TLA+ Practice
2024-11-20
Roman Bögli, Leandro Lerena, Christos Tsigkanos, Timo Kehrer
TLA+ is a formal specification language used for designing, modeling, documenting, and verifying systems through model checking. Despite significant interest from the research community, knowledge about usage of the TLA+ ecosystem in practice remains scarce. Industry reports suggest that software engineers could benefit from insights, innovations, and solutions to the practical challenges of TLA+. This paper explores this development by conducting a systematic literature review of TLA+'s industrial usage over the past decade. We analyze the trend in industrial application, characterize its use, examine whether its promised benefits resonate with practitioners, and identify challenges that may hinder further adoption.
The Decline and Fall of ROME. V. A Preliminary Search for Star-Disrupted Planet Interactions and Coronal Activity at 5 GHz Among White Dwarfs within 25 pc
2024-11-20
Matthew Route
The recent discovery of planetesimals orbiting white dwarfs has renewed interest in the final chapters of the evolution of planetary systems. Although observational and theoretical studies have examined the dynamical evolution of these systems, studies of their magnetic star-planet interactions, as powered by unipolar induction, have thus far only been assessed theoretically. This fifth paper of the ROME (Radio Observations of Magnetized Exoplanets) series presents the results of a targeted mini-survey of nine white dwarfs within 25 pc without known stellar mass companions in search of radio emissions generated by magnetic interactions between white dwarfs and their planetary remnants. This $\sim$5 GHz Arecibo radio telescope survey achieved mJy-level sensitivity over $<$1 s integration times. Although no exoplanet-induced stellar radio flares were detected, this is the first survey to search for magnetic star-planet interactions between white dwarfs and planetary companions, cores, or disrupted planetesimals. It is also the most extensive and sensitive radio survey for intrinsic coronal emissions from apparently isolated white dwarfs. The study of radio emissions from white dwarf systems may present a new means to detect and measure DC and DQ magnetic fields, search for white dwarf coronae, characterize the density and spatial distribution of white dwarf magnetospheric plasma, characterize the dynamical and electrical properties of planetary cores, and offer new constraints on the modeling of double degenerate merger events.
Elephant in the Room: Dissecting and Reflecting on the Evolution of Online Social Network Research
2024-11-20
Luca Pajola, Saskia Laura Schröer, Pier Paolo Tricomi, Mauro Conti, Giovanni Apruzzese
Billions of individuals engage with Online Social Networks (OSN) daily. The owners of OSN try to meet the demands of their end-users while complying with business necessities. Such necessities may, however, lead to the adoption of restrictive data access policies that hinder research activities from "external" scientists -- who may, in turn, resort to other means (e.g., rely on static datasets) for their studies. Given the abundance of literature on OSN, we -- as academics -- should take a step back and reflect on what we have done so far, after having written thousands of papers on OSN. This is the first paper that provides a holistic outlook to the entire body of research that focused on OSN -- since the seminal work by Acquisti and Gross (2006). First, we search through over 1 million peer-reviewed publications, and derive 13,842 papers that focus on OSN: we organize the metadata of these works in the Minerva-OSN dataset, the first of its kind -- which we publicly release. Next, by analyzing Minerva-OSN, we provide factual evidence elucidating trends and aspects that deserve to be brought to light, such as the predominant focus on Twitter or the difficulty in obtaining OSN data. Finally, as a constructive step to guide future research, we carry out an expert survey (n=50) with established scientists in this field, and coalesce suggestions to improve the status quo such as an increased involvement of OSN owners. Our findings should inspire a reflection to "rescue" research on OSN. Doing so would improve the overall OSN ecosystem, benefiting both their owners and end-users and, hence, our society.
In face of climate change and increasing urbanization, the predictive mosquito-borne diseases (MBD) transmission models require constant updates. Thus, is urgent to comprehend the driving forces of this non stationary behavior, observed through spatial and incidence expansion. We observed that temperature is a critical driver in predictive models for MBD transmission, also being consistently used in multiple reviewed papers with considerable incidence predictive capacity. Rainfall, however, have more subtle importance as moderate precipitation creates breeding sites for mosquitoes, but excessive rainfall can reduce larvae populations. We highlight the frequent use of mechanistic models, particularly those that integrate temperature-dependent biological parameters of disease transmission in incidence proxies as the Vectorial Capacity (VC) and temperature-based basic reproduction number $R_0(t)$, for example. These models show the importance of climate variables, but the socio-demographic factors are often not considered. This gap is a significant opportunity for future research to incorporate socio-demographic data into long-term predictive models for more comprehensive and reliable forecasts. With this survey, we outline the most promising paths to be followed by long-term MBD transmission research and highlighting the potential facing challenges. Thus, we offer a valuable foundation for enhancing disease forecasting models and supporting more effective public health interventions, specially in the long term.
Detecting electromagnetic counterparts to LIGO/Virgo/KAGRA gravitational wave events with DECam: Neutron Star Mergers
2024-11-20
Keerthi Kunnumkai, Antonella Palmese, Amanda M Farah, Mattia Bulla, Tim Dietrich, Peter T H Pang, Shreya Anand, Igor Andreoni, Tomas Cabrera, Brendan O Connor
With GW170817 being the only multimessenger gravitational wave (GW) event with an associated kilonova (KN) detected so far, there exists a pressing need for realistic estimation of the GW localization uncertainties and rates, as well as optimization of available telescope time to enable the detection of new KNe. For this purpose, we simulate GW events assuming a data-driven, GW-motivated distribution of binary parameters for the LIGO/Virgo/KAGRA (LVK) fourth and fifth observing runs (O4 and O5). For our particular case of estimating KN detection rates in the O4 and O5 runs, we map the binary neutron star (BNS) and neutron star-black hole (NSBH) properties to the optical light curves arising from r-process nucleosynthesis in the ejecta. We use the simulated population of KNe to generate follow-up observing plans, with the primary goal of optimizing detection with the Gravitational Wave Multi-Messenger Astronomy DECam Survey (GW-MMADS). We explore KN detectability as a function of mass, distance, and spin of the binaries. Out of the mergers that produce a KN in our simulations, we expect detectable KNe for DECam-like instruments at a per-year rate of: $1-16$ ($0-1$) for BNS (NSBH) in O4, and $16-165$ ($1-12$) for BNS (NSBH) in O5, using a fiducial exposure time and conditional on the uncertainty on the equation of state (EOS) and volumetric rates of the mergers. Taking into account observability constraints, our scheduler covers the location of the KN $\sim 30-38\%$ of the times for our fiducial EOS. We provide the depths needed to detect a significant fraction of our simulated mergers for the astronomical community to use in their follow-up campaigns.
We present radial profiles of luminosity-weighted age, $age_L$, and $\Delta \Sigma_{SFR}$ for various populations of high- and low- mass central and satellite galaxies in the TNG100 cosmological simulation. Using these profiles, we investigate the impact of intrinsic and environmental factors on the radial distribution of star formation. For both central galaxies and satellites, we investigate the effects of black hole mass, cumulative AGN feedback energy, morphology, halo mass, and local galaxy overdensity on the profiles. In addition, we investigate the dependence of radial profiles of the satellite galaxies as a function of the redshifts at which they joined their hosts, as well as the net change in star-forming gas mass since the satellites joined their host. We find that high-mass ($M_*>10^{10.5} M_{\odot}$) central and satellite galaxies show evidence of inside-out quenching driven by AGN feedback. Effects from environmental processes only become apparent in averaged profiles at extreme halo masses and local overdensities. We find that the dominant quenching process for low-mass galaxies ($M_*<10^{10} M_{\odot}$) is environmental, generally occurring at low halo mass and high local galaxy overdensity for low-mass central galaxies and at high host halo masses for low-mass satellite galaxies. Overall, we find that environmental processes generally drive quenching from the outside-in.
Accounting carbon emissions from electricity generation: a review and comparison of emission factor-based methods
2024-11-20
Marina Bertolini, Pierdomenico Duttilo, Francesco Lisi
Accurate estimation of greenhouse gas (GHG) is essential to meet carbon neutrality targets, particularly through the calculation of direct CO2 emissions from electricity generation. This work reviews and compares emission factor-based methods for accounting direct carbon emissions from electricity generation. The emission factor approach is commonly worldwide used. Empirical comparisons are based on emission factors computed using data from the Italian electricity market. The analyses reveal significant differences in the CO2 estimates according to different methods. This, in turn, highlights the need to select an appropriate method for reliable emissions, which could support effective regulatory compliance and informed policy-making. As concerns, in particular, the market zones of the Italian electricity market, the results underscore the importance of tailoring emission factors to accurately capture regional fuel variations.
CHANCES, The Chilean Cluster Galaxy Evolution Survey: selection and initial characterization of clusters and superclusters
2024-11-20
Cristóbal Sifón, Alexis Finoguenov, Christopher P. Haines, Yara Jaffé, B. M. Amrutha, Ricardo Demarco, E. V. R. Lima, Ciria Lima-Dias, Hugo Méndez-Hernández, Paola Merluzzi, Antonela Monachesi, Gabriel S. M. Teixeira, Nicolas Tejos, Pablo Araya-Araya, Maria Argudo-Fernández, Raúl Baier-Soto, Lawrence E. Bilton, C. R. Bom, Juan Pablo Calderón, Letizia P. Cassarà, Johan Comparat, H. M. Courtois, Giuseppe D'Ago, Alexandra Dupuy, Alexander Fritz, Rodrigo F. Haack, Fabio R. Herpich, E. Ibar, Ulrike Kuchner, Amanda R. Lopes, Sebastian Lopez, Elismar Lösch, Sean McGee, C. Mendes de Oliveira, Lorenzo Morelli, Alessia Moretti, Diego Pallero, Franco Piraino-Cerda, Emanuela Pompei, U. Rescigno, Rory Smith, Analía V. Smith Castelli, Laerte Sodré Jr, Elmo Tempel
CHANCES, the CHileAN Cluster galaxy Evolution Survey, will study the evolution of galaxies in and around ${\sim}$150 massive galaxy clusters, from the local universe out to z=0.45. CHANCES will use the new 4MOST Spectroscopic Survey Facility on the VISTA 4m telescope to obtain spectra for ${\sim}$500,000 galaxies with magnitudes $r_\mathrm{AB} < 20.5$, providing comprehensive spectroscopic coverage of each cluster out to $5r_{200}$. Its wide and deep scope will trace massive and dwarf galaxies from the surrounding filaments and groups to the cores of galaxy clusters, enabling the study of galaxy pre-processing and the role of the evolving environment on galaxy evolution. In this paper we present and characterize the sample of clusters and superclusters to be targeted by CHANCES. We used literature catalogues based on X-ray emission and Sunyaev-Zel'dovich effect to define the cluster sample in a homogeneous way, with attention to cluster mass and redshift, as well as the availability of ancillary data. We calibrated literature mass estimates from various surveys against each other and provide an initial mass estimate for each cluster, which we used to define the radial extent of the 4MOST coverage. We also present an initial assessment of the structure surrounding these clusters based on the redMaPPer red-sequence algorithm as a preview of some of the science CHANCES will enable.
Accurate Shear Estimation with Fourth-Order Moments
2024-11-20
Andy Park, Xiangchong Li, Rachel Mandelbaum
As imaging surveys progress in exploring the large-scale structure of the Universe through the use of weak gravitational lensing, achieving subpercent accuracy in estimating shape distortions caused by lensing, or shear, is imperative for precision cosmology. In this paper, we extend the \texttt{FPFS} shear estimator using fourth-order shapelet moments and combine it with the original second-order shear estimator to reduce galaxy shape noise. We calibrate this novel shear estimator analytically to a subpercent level accuracy using the \texttt{AnaCal} framework. This higher-order shear estimator is tested with realistic image simulations, and after analytical correction for the detection/selection bias and noise bias, the multiplicative shear bias $|m|$ is below $3\times10^{-3}$ ($99.7\%$ confidence interval) for both isolated and blended galaxies. Once combined with the second-order \texttt{FPFS} shear estimator, the shape noise is reduced by $\sim35\%$ for isolated galaxies in simulations with HSC and LSST observational conditions. However, for blended galaxies, the effective number density does not significantly improve with the combination of the two estimators. Based on these results, we recommend exploration of how this framework can further reduce the systematic uncertainties in shear due to PSF leakage and modelling error, and potentially provide improved precision in shear inference in high-resolution space-based images.
WEAVE First Light Observations: Origin and Dynamics of the Shock Front in Stephan's Quintet
2024-11-20
M. I. Arnaudova, S. Das, D. J. B. Smith, M. J. Hardcastle, N. Hatch, S. C. Trager, R. J. Smith, A. B. Drake, J. C. McGarry, S. Shenoy, J. P. Stott, J. H. Knapen, K. M. Hess, K. J. Duncan, A. Gloudemans, P. N. Best, R. García-Benito, R. Kondapally, M. Balcells, G. S. Couto, D. C. Abrams, D. Aguado, J. A. L. Aguerri, R. Barrena, C. R. Benn, T. Bensby, S. R. Berlanas, D. Bettoni, D. Cano-Infantes, R. Carrera, P. J. Concepción, G. B. Dalton, G. D'Ago, K. Dee, L. Domínguez-Palmero, J. E. Drew, E. L. Escott, C. Fariña, M. Fossati, M. Fumagalli, E. Gafton, F. J. Gribbin, S. Hughes, A. Iovino, S. Jin, I. J. Lewis, M. Longhetti, J. Méndez-Abreu, A. Mercurio, A. Molaeinezhad, E. Molinari, M. Monguió, D. N. A. Murphy, S. Picó, M. M. Pieri, A. W. Ridings, M. Romero-Gómez, E. Schallig, T. W. Shimwell, R. Skvarĉ, R. Stuik, A. Vallenari, J. M. van der Hulst, N. A. Walton, C. C. Worley
We present a detailed study of the large-scale shock front in Stephan's Quintet, a byproduct of past and ongoing interactions. Using integral-field spectroscopy from the new William Herschel Telescope Enhanced Area Velocity Explorer (WEAVE), recent 144 MHz observations from the LOFAR Two-metre Sky Survey (LoTSS), and archival data from the Very Large Array and James Webb Space Telescope (JWST), we obtain new measurements of key shock properties and determine its impact on the system. Harnessing the WEAVE large integral field unit's (LIFU) field of view (90 $\times$ 78 arcsec$^{2}$), spectral resolution ($R\sim2500$) and continuous wavelength coverage across the optical band, we perform robust emission line modeling and dynamically locate the shock within the multi-phase intergalactic medium (IGM) with higher precision than previously possible. The shocking of the cold gas phase is hypersonic, and comparisons with shock models show that it can readily account for the observed emission line ratios. In contrast, we demonstrate that the shock is relatively weak in the hot plasma visible in X-rays (with Mach number of $\mathcal{M} \sim 2 - 4$), making it inefficient at producing the relativistic particles needed to explain the observed synchrotron emission. Instead, we propose that it has led to an adiabatic compression of the medium, which has increased the radio luminosity ten-fold. Comparison of the Balmer line-derived extinction map with the molecular gas and hot dust observed with JWST suggests that pre-existing dust may have survived the collision, allowing the condensation of H$_{2}$ - a key channel for dissipating the shock energy.
A Glimpse of the New Redshift Frontier Through Abell S1063
2024-11-20
Vasily Kokorev, Hakim Atek, John Chisholm, Ryan Endsley, Iryna Chemerynska, Julian B. Muñoz, Lukas J. Furtak, Richard Pan, Danielle Berg, Seiji Fujimoto, Pascal A. Oesch, Andrea Weibel, Angela Adamo, Jeremy Blaizot, Rychard Bouwens, Miroslava Dessauges-Zavadsky, Gourav Khullar, Damien Korber, Ilias Goovaerts, Michelle Jecmen, Ivo Labbé, Floriane Leclercq, Rui Marques-Chaves, Charlotte Mason, Kristen B. W. McQuinn, Rohan Naidu, Priyamvada Natarajan, Erica Nelson, Joki Rosdahl, Alberto Saldana-Lopez, Daniel Schaerer, Maxime Trebitsch, Marta Volonteri, Adi Zitrin
We report the discovery of five galaxy candidates at redshifts between $15.9
Sparse Input View Synthesis: 3D Representations and Reliable Priors
2024-11-20
Nagabhushan Somraj
Novel view synthesis refers to the problem of synthesizing novel viewpoints of a scene given the images from a few viewpoints. This is a fundamental problem in computer vision and graphics, and enables a vast variety of applications such as meta-verse, free-view watching of events, video gaming, video stabilization and video compression. Recent 3D representations such as radiance fields and multi-plane images significantly improve the quality of images rendered from novel viewpoints. However, these models require a dense sampling of input views for high quality renders. Their performance goes down significantly when only a few input views are available. In this thesis, we focus on the sparse input novel view synthesis problem for both static and dynamic scenes. In the first part of this work, we mainly focus on sparse input novel view synthesis of static scenes using neural radiance fields (NeRF). We study the design of reliable and dense priors to better regularize the NeRF in such situations. In particular, we propose a prior on the visibility of the pixels in a pair of input views. We show that this visibility prior, which is related to the relative depth of objects, is dense and more reliable than existing priors on absolute depth. We compute the visibility prior using plane sweep volumes without the need to train a neural network on large datasets. We evaluate our approach on multiple datasets and show that our model outperforms existing approaches for sparse input novel view synthesis. In the second part, we aim to further improve the regularization by learning a scene-specific prior that does not suffer from generalization issues. We achieve this by learning the prior on the given scene alone without pre-training on large datasets. In particular, we design augmented NeRFs to obtain better depth supervision in certain regions of the scene for the main NeRF. Further, we extend this framework to also apply to newer and faster radiance field models such as TensoRF and ZipNeRF. Through extensive experiments on multiple datasets, we show the superiority of our approach in sparse input novel view synthesis. The design of sparse input fast dynamic radiance fields is severely constrained by the lack of suitable representations and reliable priors for motion. We address the first challenge by designing an explicit motion model based on factorized volumes that is compact and optimizes quickly. We also introduce reliable sparse flow priors to constrain the motion field, since we find that the popularly employed dense optical flow priors are unreliable. We show the benefits of our motion representation and reliable priors on multiple datasets. In the final part of this thesis, we study the application of view synthesis for frame rate upsampling in video gaming. Specifically, we consider the problem of temporal view synthesis, where the goal is to predict the future frames given the past frames and the camera motion. The key challenge here is in predicting the future motion of the objects by estimating their past motion and extrapolating it. We explore the use of multi-plane image representations and scene depth to reliably estimate the object motion, particularly in the occluded regions. We design a new database to effectively evaluate our approach for temporal view synthesis of dynamic scenes and show that we achieve state-of-the-art performance.
From Statistical Methods to Pre-Trained Models; A Survey on Automatic Speech Recognition for Resource Scarce Urdu Language
2024-11-20
Muhammad Sharif, Zeeshan Abbas, Jiangyan Yi, Chenglin Liu
Automatic Speech Recognition (ASR) technology has witnessed significant advancements in recent years, revolutionizing human-computer interactions. While major languages have benefited from these developments, lesser-resourced languages like Urdu face unique challenges. This paper provides an extensive exploration of the dynamic landscape of ASR research, focusing particularly on the resource-constrained Urdu language, which is widely spoken across South Asian nations. It outlines current research trends, technological advancements, and potential directions for future studies in Urdu ASR, aiming to pave the way for forthcoming researchers interested in this domain. By leveraging contemporary technologies, analyzing existing datasets, and evaluating effective algorithms and tools, the paper seeks to shed light on the unique challenges and opportunities associated with Urdu language processing and its integration into the broader field of speech research.
2024-11-20
Jing Yi Wang, Nicholas Sukiennik, Tong Li, Weikang Su, Qianyue Hao, Jingbo Xu, Zihan Huang, Fengli Xu, Yong Li
The rapid evolution of large language models (LLMs) and their capacity to simulate human cognition and behavior has given rise to LLM-based frameworks and tools that are evaluated and applied based on their ability to perform tasks traditionally performed by humans, namely those involving cognition, decision-making, and social interaction. This survey provides a comprehensive examination of such human-centric LLM capabilities, focusing on their performance in both individual tasks (where an LLM acts as a stand-in for a single human) and collective tasks (where multiple LLMs coordinate to mimic group dynamics). We first evaluate LLM competencies across key areas including reasoning, perception, and social cognition, comparing their abilities to human-like skills. Then, we explore real-world applications of LLMs in human-centric domains such as behavioral science, political science, and sociology, assessing their effectiveness in replicating human behaviors and interactions. Finally, we identify challenges and future research directions, such as improving LLM adaptability, emotional intelligence, and cultural sensitivity, while addressing inherent biases and enhancing frameworks for human-AI collaboration. This survey aims to provide a foundational understanding of LLMs from a human-centric perspective, offering insights into their current capabilities and potential for future development.
Quenching of Galaxies at Cosmic Noon: Understanding the Effect of Environment
2024-11-19
Akriti Singh, Lucia Guaita, Pascale Hibon, Boris Häussler, Kyoung-Soo Lee, Vandana Ramakrishnan, Ankit Kumar, Nelson Padilla, Nicole M. Firestone, Hyunmi Song, Maria Celeste Artale, Ho Seong Hwang, Paulina Troncoso Iribarren, Caryl Gronwall, Eric Gawiser, Julie Nantais, Francisco Valdes, Changbom Park, Yujin Yang
The aim of this study is to identify quiescent galaxies in the 2-deg$^2$ COSMOS field at $z \sim 3.1$ and analyze their environment. Using data from the ODIN survey and COSMOS2020 catalog, we identify 24 massive quiescent galaxies (MQGs) with stellar masses $\geq 10^{10.6}$ and derive their star formation histories and quenching timescales using SED fitting with BAGPIPES. Voronoi-based density maps trace local and large-scale environments using Lyman-$\alpha$ Emitters and photometric galaxies. Results indicate uniformly short quenching timescales ($<$500 Myr) independent of environmental density, suggesting rapid internal mechanisms such as AGN feedback dominate over environmental factors. MQGs do not correlate with protoclusters or filaments, although some are near gas-rich filaments but show no rejuvenation. These findings suggest quenching at high redshift is driven primarily by internal processes rather than environmental interactions.
Enhancing Multi-Class Disease Classification: Neoplasms, Cardiovascular, Nervous System, and Digestive Disorders Using Advanced LLMs
2024-11-19
Ahmed Akib Jawad Karim, Muhammad Zawad Mahmud, Samiha Islam, Aznur Azam
In this research, we explored the improvement in terms of multi-class disease classification via pre-trained language models over Medical-Abstracts-TC-Corpus that spans five medical conditions. We excluded non-cancer conditions and examined four specific diseases. We assessed four LLMs, BioBERT, XLNet, and BERT, as well as a novel base model (Last-BERT). BioBERT, which was pre-trained on medical data, demonstrated superior performance in medical text classification (97% accuracy). Surprisingly, XLNet followed closely (96% accuracy), demonstrating its generalizability across domains even though it was not pre-trained on medical data. LastBERT, a custom model based on the lighter version of BERT, also proved competitive with 87.10% accuracy (just under BERT's 89.33%). Our findings confirm the importance of specialized models such as BioBERT and also support impressions around more general solutions like XLNet and well-tuned transformer architectures with fewer parameters (in this case, LastBERT) in medical domain tasks.
Large non-Gaussianities corresponding to first-order phase transitions during inflation
2024-11-19
Haipeng An, Qi Chen, Yuhang Li, Yuan Yin
In this study, we explore the back reaction of phase transitions in the spectator sector on the inflaton field during slow-roll inflation. Due to the significant excursion of the inflaton field, these phase transitions are likely to occur and can induce substantial non-Gaussian correlations in the curvature perturbation. Our results suggest that these correlations could be detectable by future observations of the cosmic microwave background radiation and large-scale structure surveys. Furthermore, we demonstrate that in certain parameter spaces, a scaling non-Gaussian signal can be produced, offering deeper insights into both the inflaton and spectator sectors. Additionally, phase transitions during inflation can generate gravitational wave signals with distinctive signatures, potentially explaining observations made by pulsar timing array experiments. The associated non-Gaussian correlations provide collateral evidence for these phase transitions.
IMUVIE: Pickup Timeline Action Localization via Motion Movies
2024-11-19
John Clapham, Kenneth Koltermann, Yanfu Zhang, Yuming Sun, Evie N Burnet, Gang Zhou
Falls among seniors due to difficulties with tasks such as picking up objects pose significant health and safety risks, impacting quality of life and independence. Reliable, accessible assessment tools are critical for early intervention but often require costly clinic-based equipment and trained personnel, limiting their use in daily life. Existing wearable-based pickup measurement solutions address some needs but face limitations in generalizability. We present IMUVIE, a wearable system that uses motion movies and a machine-learning model to automatically detect and measure pickup events, providing a practical solution for frequent monitoring. IMUVIE's design principles-data normalization, occlusion handling, and streamlined visuals-enhance model performance and are adaptable to tasks beyond pickup classification. In rigorous leave one subject out cross validation evaluations, IMUVIE achieves exceptional window level localization accuracy of 91-92% for pickup action classification on 256,291 motion movie frame candidates while maintaining an event level recall of 97% when evaluated on 129 pickup events. IMUVIE has strong generalization and performs well on unseen subjects. In an interview survey, IMUVIE demonstrated strong user interest and trust, with ease of use identified as the most critical factor for adoption. IMUVIE offers a practical, at-home solution for fall risk assessment, facilitating early detection of movement deterioration, and supporting safer, independent living for seniors.
A Survey of General Relativistic Magnetohydrodynamic Models for Black Hole Accretion Systems
2024-11-19
Vedant Dhruv, Ben Prather, George Wong, Charles F. Gammie
General Relativistic Magnetohydrodynamics (GRMHD) simulations are an indispensable tool in studying accretion onto compact objects. The Event Horizon Telescope (EHT) frequently uses libraries of ideal GRMHD simulations to interpret polarimetric, event-horizon-scale observations of supermassive black holes at the centers of galaxies. In this work, we present a library of ten non-radiative, ideal GRMHD simulations that were utilized by the EHT Collaboration in their analysis of Sagittarius A*. The parameter survey explores both low (SANE) and high (MAD) magnetization states across five black hole spins $a_{*}=-15/16,-1/2,0,+1/2,+15/16$ where each simulation was run out to $30,000\hspace{0.1cm}\mathrm{GM/c}^{3}$. We find the angular momentum and energy flux in SANE simulations closely matches the thin-disk value, with minor deviations in prograde models due to fluid forces. This leads to spin equilibrium around $a_{*}\sim0.94$, consistent with previous studies. We study the flow of conserved quantities in our simulations and find mass, angular momentum, and energy transport in SANE accretion flows to be primarily inward and fluid-dominated. MAD models produce powerful jets with outflow efficiency $>1$ for $a_{*}=+0.94$, leading to black hole spin-down in prograde cases. We observe outward directed energy and angular momentum fluxes on the horizon, as expected for the Blandford-Znajek mechanism. MAD accretion flows are sub-Keplerian and exhibit greater variability than their SANE counterpart. They are also hotter than SANE disks within $r\lesssim 10\hspace{0.1cm}\mathrm{GM/c}^{2}$. This study is accompanied by a public release of simulation data at \url{http://thz.astro.illinois.edu/}.
The precise reconstruction of 3D objects from a single RGB image in complex scenes presents a critical challenge in virtual reality, autonomous driving, and robotics. Existing neural implicit 3D representation methods face significant difficulties in balancing the extraction of global and local features, particularly in diverse and complex environments, leading to insufficient reconstruction precision and quality. We propose M3D, a novel single-view 3D reconstruction framework, to tackle these challenges. This framework adopts a dual-stream feature extraction strategy based on Selective State Spaces to effectively balance the extraction of global and local features, thereby improving scene comprehension and representation precision. Additionally, a parallel branch extracts depth information, effectively integrating visual and geometric features to enhance reconstruction quality and preserve intricate details. Experimental results indicate that the fusion of multi-scale features with depth information via the dual-branch feature extraction significantly boosts geometric consistency and fidelity, achieving state-of-the-art reconstruction performance.
A Generalized Flux-Corrected Transport Algorithm I: A Finite-Difference Formulation
2024-11-19
William J Rider, Dennis R Liles
This paper presents a generalized flux-corrected transport (FCT) algorithm, which is shown to be total variation diminishing under some conditions. The new algorithm has improved properties from the standpoint of use and analysis. Results show that the new FCT algorithm performs better than the older FCT algorithms and is comparable with other modern methods. This reformulation will also allow the FCT to be used effectively with exact or approximate Riemann solvers and as an implicit algorithm. This paper was originally submitted to the Journal of Computational Physics in 1990. It got lost in review. One reviewer loved the paper and suggested it be published immediately (he also died while it was in review). Another reviewer savaged the paper being from the FCT camp. The journal also went through several changes in management. Ultimately I declined to continue pursuing the paper as I had one infant child at the time and another on the way in 1995. Now 30 years on I am going to put this online.
Light-Matter Interactions in Photosynthetic Protein Attached to solids and Nanostructures
2024-11-19
Itai Carmeli, Chanoch Carmeli
The interaction of light with photosynthetic proteins is an extremely efficient process and has been thoroughly investigated. However, exploring light-matter interactions in hybrid nano-solid-photosynthetic proteins is a relatively new and existing field of research. The properties of these hybrid materials significantly influence the energy levels, non-radiative energy transfer, absorption, and fluorescence of the photosynthetic proteins upon interaction with light. There is special interest in levering these light-matter interactions for applications such as photo-sensing and converting light energy to electricity. The development of efficient devices requires the formation of a junction for oriented attachment, facilitating efficient energy and electronic transfer between the solids and the proteins. This review will outline the major advancements in solid-state photosynthetic protein devices, elucidate the underlying mechanism, and assess electron transfer efficiency. Furthermore, it will explore and analyze the effect of plasmons on the enhancement of absorption, fluorescence, and photocurrent in hybrid devices.
The gravitational potential decay rate (DR) is caused by the cosmic acceleration of the universe, providing a direct probe into the existence of dark energy (DE). We present measurements of DR and explore its implications for DE models using the Data Release 9 galaxy catalog of DESI imaging surveys and the Planck cosmic microwave background maps. Our analysis includes six redshift bins within the range of $0.2\le z<1.4$ and achieves a total significance of 3.1$\sigma$, extending the DR measurements to a much higher redshift comparing to Dong et al. (2022), which focused on $0.2\le z<0.8$. Other improvements involve addressing potential systematics in the DR-related measurements of correlation functions, including imaging systematics and magnification bias. We explore the constraining power of DR both the $w$CDM model and the $w_0w_a$CDM model. We find that, the addition of DR can significantly improves DE constraints, over Sloan Digital Sky Survey baryon acoustic oscillation (BAO) data alone or PantheonPlus supernovae (SNe) compilation alone, although it shows only a modest improvement for DESI BAO. In the $w$CDM model, all three probes-DR, DESI BAO and SNe-favor $w=-1$. For the $w_0w_a$CDM, while DESI BAO prefers $w_0>-1$ and $w_a<0$, SNe Ia and DR data constrain $w_0=-0.94^{+0.11}_{-0.13}$ and $w_a=-0.22^{+0.57}_{-0.97}$. Namely SNe Ia and DR data has no preference on dynamical dark energy over $\Lambda$.
CHANG-ES XXXV: Cosmic Ray Transport and Magnetic Field Structure of NGC 3556 at 3 GHz
2024-11-19
Jianghui Xu, Yang Yang, Jiang-Tao Li, Guilin Liu, Judith Irwin, Ralf-Jürgen Dettmar, Michael Stein, Theresa Wiegert, Q. Daniel Wang, Jayanne English
Radio halos of edge-on galaxies are crucial for investigating cosmic ray propagation and magnetic field structures in galactic environments. We present VLA C-configuration S-band (2--4 GHz) observations of the spiral galaxy NGC 3556, a target from the Continuum Halos in Nearby Galaxies - an EVLA Survey (CHANG-ES). We estimate the thermal contribution to the radio emission from a combination of the H$\alpha$ and mid-IR data, and employ Rotation Measure Synthesis to reveal the magnetic field structures. In our data, NGC 3556 exhibits a box-like radio halo extending nearly 7 kpc from the galactic plane. The scale height of the total S-band intensity in the halo is $1.68\pm 0.29$ kpc, while that of the non-thermal intensity is $1.93\pm 0.28$ kpc. Fitting the data to a 1-D cosmic-ray transport model, we find advection to describe the cosmic-ray propagation within the halo better than diffusion, with advection speeds of $245 \pm 15$ km s$^{-1}$ and $205 \pm 25$ km s$^{-1}$ above and below the disk, respectively. The magnetic field is detected patchily across the galaxy, displaying a toroidal configuration in the rotation measure map. The mean equipartition magnetic field strength is approximately $8.3\ \mu$G in the disk and $4.5\ \mu$G in the halo. In addition, a bubble-like structure extends nearly 3~kpc into the southern halo, aligned with the polarized intensity and H$\alpha$ image, suggestive of superwinds generated by recent star formation feedback in the nuclear region.
Germany's Tax Revenue and its Total Administrative Cost
2024-11-19
Christopher Mantzaris, Ajda Fošner
Tax administrative cost reduction is an economically and socially desirable goal for public policy. This article proposes total administrative cost as percentage of total tax revenue as a vivid measurand, also useful for cross-jurisdiction comparisons. Statistical data, surveys and a novel approach demonstrate: Germany's 2021 tax administrative costs likely exceeded 20% of total tax revenue, indicating need for improvement of Germany's taxation system - and for the many jurisdictions with similar tax regimes. In addition, this article outlines possible reasons for and implications of the seemingly high tax administrative burden as well as solutions.
Predicting Customer Satisfaction by Replicating the Survey Response Distribution
2024-11-19
Etienne Manderscheid, Matthias Lee
For many call centers, customer satisfaction (CSAT) is a key performance indicator (KPI). However, only a fraction of customers take the CSAT survey after the call, leading to a biased and inaccurate average CSAT value, and missed opportunities for coaching, follow-up, and rectification. Therefore, call centers can benefit from a model predicting customer satisfaction on calls where the customer did not complete the survey. Given that CSAT is a closely monitored KPI, it is critical to minimize any bias in the average predicted CSAT (pCSAT). In this paper, we introduce a method such that predicted CSAT (pCSAT) scores accurately replicate the distribution of survey CSAT responses for every call center with sufficient data in a live production environment. The method can be applied to many multiclass classification problems to improve the class balance and minimize its changes upon model updates.
Electromagnetic Response of the Electron Gas and the Thermal Casimir Pressure Anomaly
2024-11-19
Carsten Henkel
A review of the nonlocal electromagnetic response functions of the degenerate electron gas, computed within standard perturbation theory, is given. These expressions due to Lindhard, Klimontovich and Silin are used to re-analyze the classical Casimir interaction between two thick conducting plates (zero'th term of Matsubara series). Up to small corrections that we discuss, the results of the conventional Drude model are confirmed. The difference between longitudinal and transverse permittivities (or polarization tensors) yields the Landau (orbital) diamagnetism of the electron gas.
Multilayer occupancy grid for obstacle avoidance in an autonomous ground vehicle using RGB-D camera
2024-11-19
Jhair S. Gallego, Ricardo E. Ramirez
This work describes the process of integrating a depth camera into the navigation system of a self-driving ground vehicle (SDV) and the implementation of a multilayer costmap that enhances the vehicle's obstacle identification process by expanding its two-dimensional field of view, based on 2D LIDAR, to a three-dimensional perception system using an RGB-D camera. This approach lays the foundation for a robust vision-based navigation and obstacle detection system. A theoretical review is presented and implementation results are discussed for future work.
Fifty Years After the Discovery of the First Stellar-Mass Black Hole: A Review of Cyg X-1
2024-11-19
Jiachen Jiang
Around 50 years ago, the famous bet between Stephen Hawking and Kip Thorne on whether Cyg X-1 hosts a stellar-mass black hole became a well-known story in the history of black hole science. Today, Cyg X-1 is widely recognised as hosting a stellar-mass black hole with a mass of approximately 20 solar masses. With the advancement of X-ray telescopes, Cyg X-1 has become a prime laboratory for studies in stellar evolution, accretion physics, and high-energy plasma physics. In this review, we explore the latest results from X-ray observations of Cyg X-1, focusing on its implications for black hole spin, its role in stellar evolution, the geometry of the innermost accretion regions, and the plasma physics insights derived from its X-ray emissions. This review primarily focuses on Cyg X-1; however, the underlying physics applies to other black hole X-ray binaries and, to some extent, to AGNs.
Recent advances in space sailing missions and technology: review of the 6th International Symposium on Space Sailing (ISSS 2023)
2024-11-19
Elena Ancona, Roman Ya. Kezerashvili
The 6th International Symposium on Space Sailing (ISSS 2023) took place on June 5-9, 2023 at the New York City College of Technology, the City University of New York. Since its inauguration in Herrsching (Germany, 2007), the ISSS has been held in New York (USA, 2010), Glasgow (UK, 2013), Kyoto (Japan, 2017) and Aachen (Germany, 2019). During the five-day symposium, participants from 14 countries gathered to discuss recent advances in space sailing, investigating new concepts and designs, describing innovative hardware and enabling technologies, strategies for dynamics and control, and providing updates on testing results for systems under development and future mission applications. As part of the 18 sessions, almost 50 oral presentations were held and, subsequently, 17 papers were submitted for review and publication. This paper aims to give an overview of all the cutting-edge technologies, detailed analysis and promising results shared with the scientific community as part of the event. Following the noteworthy deployment of the world's first solar sail IKAROS in 2010, missions like NanoSail-D2 (2011) and LightSail-2 (2019) have showcased the potential of solar sailing technology through successful demonstrations. Besides highlighting advancements in present and future programs, the symposium was an opportunity to reflect on objectives, design and test results from research centers and universities, as well as illustrate applications for interstellar travel, evaluate degrading performance and suggest alternative solutions for known limitations. The following Symposium is scheduled for early summer 2025 and will be hosted by TU Delft.
Distinguishing super-Nyquist frequencies via their temporal variation in $γ$ Doradus stars from continuous photometry
2024-11-19
Xuan Wang, Weikai Zong, Xiao-Yu Ma, Stéphane Charpinet, Tao Wu, Haotian Wang
As mixed with real pulsations, the reflection of super-Nyquist frequencies (SNFs) pose a threat to asteroseismic properties. Although SNFs have been studied in several pulsating stars, a systematic survey remains scarcely explored. Here we propose a method to identify SNFs from Kepler and TESS photometry by characterizing their periodic frequency modulations using a sliding Fourier transform. After analyzing long cadence photometry in the Kepler legacy, we have identified 304 SNFs in 56 stars from 45607 frequencies in $\sim600$ $\gamma$ Doradus stars, corresponding to a fraction of approximately $0.67\%$ and $9.2\%$, respectively. Most SNFs are detected in the frequency range of pressure mode over 120 $\mu$Hz and the fraction of SNF detection increases as frequency up to $\sim7\%$. We barely found two potential SNFs mixed with gravity modes in two $\gamma$ Doradus stars. These findings indicate that SNFs have a negligible impact on the global seismic properties, such as those derived from period spacing in $\gamma$ Doradus stars. However, we stress that SNFs must be carefully and systematically examined by this method in other pulsating stars, particularly $\delta$ Scuti and hot B subdwarf stars, to establish a solid foundation for precise asteroseismolgy of various types of pulsators.
Quantum Thermodynamics in Spin Systems: A Review of Cycles and Applications
2024-11-19
Vinicius Gomes de Paula, Wanisson S. Santana, Clebson Cruz, Mario Reis
Quantum thermodynamics is a powerful theoretical tool for assessing the suitability of quantum materials as platforms for novel technologies. In particular, the modeling of quantum cycles allows us to investigate the heat changes and work extraction at the nanoscale, where quantum effects dominate over classical ones. In this Review, we cover the mathematical formulation used to model the quantum thermodynamic behavior of small-scale systems, building up the quantum analog versions of thermodynamic processes and reversible cycles. We discuss theoretical results obtained after applying this approach to model Heisenberg-like spin systems, which are toy models for metal complex systems. In addition, we discuss recent experimental advances in this class of materials that have been achieved using the quantum thermodynamic approach, paving the way for the development of quantum devices. Finally, we point out perspectives to guide future efforts in using quantum thermodynamics as a reliable and effective approach to establish metal complex systems as quantum materials for energy storage/harvesting purposes.
Empirical Privacy Evaluations of Generative and Predictive Machine Learning Models -- A review and challenges for practice
2024-11-19
Flavio Hafner, Chang Sun
Synthetic data generators, when trained using privacy-preserving techniques like differential privacy, promise to produce synthetic data with formal privacy guarantees, facilitating the sharing of sensitive data. However, it is crucial to empirically assess the privacy risks associated with the generated synthetic data before deploying generative technologies. This paper outlines the key concepts and assumptions underlying empirical privacy evaluation in machine learning-based generative and predictive models. Then, this paper explores the practical challenges for privacy evaluations of generative models for use cases with millions of training records, such as data from statistical agencies and healthcare providers. Our findings indicate that methods designed to verify the correct operation of the training algorithm are effective for large datasets, but they often assume an adversary that is unrealistic in many scenarios. Based on the findings, we highlight a crucial trade-off between the computational feasibility of the evaluation and the level of realism of the assumed threat model. Finally, we conclude with ideas and suggestions for future research.
Thermalization of a Closed Sachdev-Ye-Kitaev System in the Thermodynamic Limit
2024-11-19
Santiago Salazar Jaramillo, Rishabh Jha, Stefan Kehrein
The question of thermalization of a closed quantum systems is of central interest in non-equilibrium quantum many-body physics. Here we present one such study analyzing the dynamics in a closed coupled Majorana SYK system. We have a large-$q$ SYK model prepared initially at equilibrium quenched by introducing a random hopping term, thus leading to non-equilibrium dynamics. We find that the final stationary state reaches thermal equilibrium with respect to the Green's functions and energy. Accordingly, the final state is characterized by calculating its final temperature and the thermalization rate. We provide a detailed review of analytical methods and derive the required Kadanoff-Baym equations which are then solved using the algorithm developed in this work. Our results display rich thermalization dynamics in a closed quantum system in the thermodynamic limit.
Classification of the trajectories of uncharged particles in the Schwarzschild-Melvin metric
2024-11-19
Ivan Bizyaev
This paper investigates the trajectories of neutral particles in the Schwarzschild-Melvin spacetime. After reduction by cyclic coordinates this problem reduces to investigating a two-degree-of-freedom Hamiltonian system that has no additional integral. A classification of regions of possible motion of a particle is performed according to the values of the momentum and energy integrals. Bifurcations of periodic solutions of the reduced system are analyzed using a Poincare map.
Controlled Partial Gravity Platform for Milligravity in Drop Tower Experiments
2024-11-19
Kolja Joeris, Matthias Keulen, Jonathan E. Kollmer
We detail a platform for partial g environment and an experiment for simulated impacts on asteroid surfaces based on it. The partial g environment is created by a two stage approach: First, create microgravity using the ZARM drop tower. Second, convert microgravity to partial gravity by steady acceleration of experiment volume on linear drive inside microgravity environment. The experiment we conducted on this platform simulates low-velocity impacts into a simulated asteroid surface. To recreate the asteroid environment, in addition to the partial gravity, a vacuum chamber is used. We explain requirements, setup and operation of partial gravity platform and experiment and discuss its performance. Finally, we are open for requests for external experiments which might benefit from our platform with $9.3\,$s of controlled partial gravity down to the mm/s$^2$ range with low g-jitter.
Non-IID data in Federated Learning: A Systematic Review with Taxonomy, Metrics, Methods, Frameworks and Future Directions
2024-11-19
Daniel M. Jimenez G., David Solans, Mikko Heikkila, Andrea Vitaletti, Nicolas Kourtellis, Aris Anagnostopoulos, Ioannis Chatzigiannakis
Recent advances in machine learning have highlighted Federated Learning (FL) as a promising approach that enables multiple distributed users (so-called clients) to collectively train ML models without sharing their private data. While this privacy-preserving method shows potential, it struggles when data across clients is not independent and identically distributed (non-IID) data. The latter remains an unsolved challenge that can result in poorer model performance and slower training times. Despite the significance of non-IID data in FL, there is a lack of consensus among researchers about its classification and quantification. This systematic review aims to fill that gap by providing a detailed taxonomy for non-IID data, partition protocols, and metrics to quantify data heterogeneity. Additionally, we describe popular solutions to address non-IID data and standardized frameworks employed in FL with heterogeneous data. Based on our state-of-the-art review, we present key lessons learned and suggest promising future research directions.
2024-11-19
Juan B. Roldán, Enrique Miranda, David Maldonado, Alexey N. Mikhaylov, Nikolay V. Agudov, Alexander A. Dubkov, Maria N. Koryazhkina, Mireia B. González, Marco A. Villena, Samuel Poblador, Mercedes Saludes-Tapia, Rodrigo Picos, Francisco Jiménez-Molinos, Stavros G. Stavrinides, Emili Salvador, Francisco J. Alonso, Francesca Campabadal, Bernardo Spagnolo, Mario Lanza, Leon O. Chua
Resistive memories are outstanding electron devices that have displayed a large potential in a plethora of applications such as nonvolatile data storage, neuromorphic computing, hardware cryptography, etc. Their fabrication control and performance have been notably improved in the last few years to cope with the requirements of massive industrial production. However, the most important hurdle to progress in their development is the so-called cycle-to-cycle variability, which is inherently rooted in the resistive switching mechanism behind the operational principle of these devices. In order to achieve the whole picture, variability must be assessed from different viewpoints going from the experimental characterization to the adequation of modeling and simulation techniques. Herein, special emphasis is put on the modeling part because the accurate representation of the phenomenon is critical for circuit designers. In this respect, a number of approaches are used to the date: stochastic, behavioral, mesoscopic..., each of them covering particular aspects of the electron and ion transport mechanisms occurring within the switching material. These subjects are dealt with in this review, with the aim of presenting the most recent advancements in the treatment of variability in resistive memories.
Transition-path sampling for Run-and-Tumble particles
2024-11-19
Thomas Kiechl, Thomas Franosch, Michele Caraglio
We elaborate and validate a generalization of the renowned transition-path-sampling algorithm for a paradigmatic model of active particles, namely the Run-and-Tumble particles. Notwithstanding the non-equilibrium character of these particles, we show how the consequent lack of the microscopical reversibility property, which is usually required by transition-path sampling, can be circumvented by identifying reasonable backward dynamics with a well-defined path-probability density. Our method is then applied to characterize the structure and kinetics of rare transition pathways undergone by Run-and-Tumble particles having to cross a potential barrier in order to find a target.
Design of Dual-Band Plasmonic Absorber for Biomedical Sensing and Environmental Monitoring
2024-11-19
Ayon Sarker, Sajid Muhaimin Choudhury
This study introduces a dual-band plasmonic absorber designed for simultaneous sensing applications in the near-infrared (NIR) and mid-infrared (MIR) regions. The absorber, composed of silver nanostructures on a metal plate with a dielectric spacer, exhibits a combination of localized and gap surface plasmon resonances, resulting in two distinct absorption peaks in theoretical analysis based on the FDTD method. Numerical simulations also validate the sensor's high refractive index sensitivity, enabling the detection of biomolecules, proteins, viruses, and various solutes in aqueous solutions. The absorber demonstrates significant resonance shifts, making it a promising candidate for environmental monitoring, medical diagnostics, and chemical sensing.
Enhancing Blind Source Separation with Dissociative Principal Component Analysis
2024-11-19
Muhammad Usman Khalid
Sparse principal component analysis (sPCA) enhances the interpretability of principal components (PCs) by imposing sparsity constraints on loading vectors (LVs). However, when used as a precursor to independent component analysis (ICA) for blind source separation (BSS), sPCA may underperform due to its focus on simplicity, potentially disregarding some statistical information essential for effective ICA. To overcome this limitation, a sophisticated approach is proposed that preserves the interpretability advantages of sPCA while significantly enhancing its source extraction capabilities. This consists of two tailored algorithms, dissociative PCA (DPCA1 and DPCA2), which employ adaptive and firm thresholding alongside gradient and coordinate descent approaches to optimize the proposed model dynamically. These algorithms integrate left and right singular vectors from singular value decomposition (SVD) through dissociation matrices (DMs) that replace traditional singular values, thus capturing latent interdependencies effectively to model complex source relationships. This leads to refined PCs and LVs that more accurately represent the underlying data structure. The proposed approach avoids focusing on individual eigenvectors, instead, it collaboratively combines multiple eigenvectors to disentangle interdependencies within each SVD variate. The superior performance of the proposed DPCA algorithms is demonstrated across four varied imaging applications including functional magnetic resonance imaging (fMRI) source retrieval, foreground-background separation, image reconstruction, and image inpainting. They outperformed traditional methods such as PCA+ICA, PPCA+ICA, SPCA+ICA, PMD, and GPower.
This paper presents, for the first time, the soft planar vertical take-off and landing (Soft-PVTOL) aircraft. This concept captures the soft aerial vehicle's fundamental dynamics with a minimum number of states and inputs but retains the main features to consider when designing control laws. Unlike conventional PVTOL and multi-rotors, where altering position inevitably impacts orientation due to their underactuated design, the Soft-PVTOL offers the unique advantage of separating these dynamics, opening doors to unparalleled maneuverability and precision. We demonstrate that the Soft-PVTOL can be modeled using the Euler-Lagrange equations by assuming a constant curvature model in the aerial robot's arms. Such a mathematical model is presented in detail and can be extended to several constant curvature segments in each Soft-PVTOL arm. Moreover, we design a passivity-based control law that exploits the flexibility of the robot's arms. We solve the tracking control problem, proving that the error equilibrium globally exponentially converges to zero. The controller is tested in numerical simulations, demonstrating robust performance and ensuring the efficacy of the closed-loop system.
Enhancing UX Research Activities Using GenAI -- Potential Applications and Challenges
2024-11-19
Stefan Graser, Anastasia Snimshchikova, Martin Schrepp, Stephan Böhm
User Experience (UX) Research covers various methods for gathering the users' subjective impressions of a product. For this, practitioners face different activities and tasks related to the research process. This includes processing a large amount of data based on qualitative and quantitative data. However, this can be very laborious in practice. Thus, the application of GenAI can support UX research activities. This paper provides a practical perspective on this topic. Based on previous studies, we present different use cases indicating the potential of GenAI in UX research. Moreover, we provide insights into an exploratory study using GenAI along an entire UX research process. Results show that Large Language Models (LLMs) are useful for various tasks. Thus, the research activities can be carried out more efficiently. However, the researcher should always review results to ensure quality. In summary, we want to express the potential of GenAI enhancing UX research
CUE-M: Contextual Understanding and Enhanced Search with Multimodal Large Language Model
2024-11-19
Dongyoung Go, Taesun Whang, Chanhee Lee, Hwayeon Kim, Sunghoon Park, Seunghwan Ji, Dongchan Kim, Young-Bum Kim
The integration of Retrieval-Augmented Generation (RAG) with Multimodal Large Language Models (MLLMs) has expanded the scope of multimodal query resolution. However, current systems struggle with intent understanding, information retrieval, and safety filtering, limiting their effectiveness. This paper introduces Contextual Understanding and Enhanced Search with MLLM (CUE-M), a novel multimodal search pipeline that addresses these challenges through a multi-stage framework comprising image context enrichment, intent refinement, contextual query generation, external API integration, and relevance-based filtering. CUE-M incorporates a robust safety framework combining image-based, text-based, and multimodal classifiers, dynamically adapting to instance- and category-specific risks. Evaluations on a multimodal Q&A dataset and a public safety benchmark demonstrate that CUE-M outperforms baselines in accuracy, knowledge integration, and safety, advancing the capabilities of multimodal retrieval systems.
Compositeness of hadrons, nuclei, and atomic systems
2024-11-19
Tomona Kinugawa, Tetsuo Hyodo
Recent observations of exotic hadrons have been stimulating the theoretical investigation of the internal structure of hadrons. While all hadrons are eventually composed of quarks and gluons by the strong interaction, clustering phenomena of quarks can generate hadronic molecules -- weakly bound systems of hadrons -- which are expected to emerge near two-hadron thresholds. However, it should be noted that a pure hadronic molecule is not realized, as the strong interaction induces mixing with other possible configurations. The compositeness of hadrons has been developed as a promising concept to quantitatively characterize the fraction of the hadronic molecular component. Here we summarize the modern understanding of the compositeness to study the internal structure of hadrons, and review the applications of the compositeness to various quantum systems in different energy scales, such as nuclei and atomic systems in addition to hadrons.
DT-RaDaR: Digital Twin Assisted Robot Navigation using Differential Ray-Tracing
2024-11-19
Sunday Amatare, Gaurav Singh, Raul Shakya, Aavash Kharel, Ahmed Alkhateeb, Debashri Roy
Autonomous system navigation is a well-researched and evolving field. Recent advancements in improving robot navigation have sparked increased interest among researchers and practitioners, especially in the use of sensing data. However, this heightened focus has also raised significant privacy concerns, particularly for robots that rely on cameras and LiDAR for navigation. Our innovative concept of Radio Frequency (RF) map generation through ray-tracing (RT) within digital twin environments effectively addresses these concerns. In this paper, we propose DT-RaDaR, a robust privacy-preserving, deep reinforcement learning-based framework for robot navigation that leverages RF ray-tracing in both static and dynamic indoor scenarios as well as in smart cities. We introduce a streamlined framework for generating RF digital twins using open-source tools like Blender and NVIDIA's Sionna RT. This approach allows for high-fidelity replication of real-world environments and RF propagation models, optimized for service robot navigation. Several experimental validations and results demonstrate the feasibility of the proposed framework in indoor environments and smart cities, positioning our work as a significant advancement toward the practical implementation of robot navigation using ray-tracing-generated data.
This paper explores the rapidly evolving ecosystem of publicly available AI models, and their potential implications on the security and safety landscape. As AI models become increasingly prevalent, understanding their potential risks and vulnerabilities is crucial. We review the current security and safety scenarios while highlighting challenges such as tracking issues, remediation, and the apparent absence of AI model lifecycle and ownership processes. Comprehensive strategies to enhance security and safety for both model developers and end-users are proposed. This paper aims to provide some of the foundational pieces for more standardized security, safety, and transparency in the development and operation of AI models and the larger open ecosystems and communities forming around them.
A Review on Generative AI Models for Synthetic Medical Text, Time Series, and Longitudinal Data
2024-11-19
Mohammad Loni, Fatemeh Poursalim, Mehdi Asadi, Arash Gharehbaghi
This paper presents the results of a novel scoping review on the practical models for generating three different types of synthetic health records (SHRs): medical text, time series, and longitudinal data. The innovative aspects of the review, which incorporate study objectives, data modality, and research methodology of the reviewed studies, uncover the importance and the scope of the topic for the digital medicine context. In total, 52 publications met the eligibility criteria for generating medical time series (22), longitudinal data (17), and medical text (13). Privacy preservation was found to be the main research objective of the studied papers, along with class imbalance, data scarcity, and data imputation as the other objectives. The adversarial network-based, probabilistic, and large language models exhibited superiority for generating synthetic longitudinal data, time series, and medical texts, respectively. Finding a reliable performance measure to quantify SHR re-identification risk is the major research gap of the topic.
Low-resource Machine Translation: what for? who for? An observational study on a dedicated Tetun language translation service
2024-11-19
Raphael Merx, Hanna Suominen, Adérito José Guterres Correia, Trevor Cohn, Ekaterina Vylomova
The impact of machine translation (MT) on low-resource languages remains poorly understood. In particular, observational studies of actual usage patterns are scarce. Such studies could provide valuable insights into user needs and behaviours, complementing survey-based methods. Here we present an observational analysis of real-world MT usage for Tetun, the lingua franca of Timor-Leste, using server logs from a widely-used MT service with over $70,000$ monthly active users. Our analysis of $100,000$ translation requests reveals patterns that challenge assumptions based on existing corpora. We find that users, many of them students on mobile devices, typically translate short texts into Tetun across diverse domains including science, healthcare, and daily life. This contrasts sharply with available Tetun corpora, which are dominated by news articles covering government and social issues. Our results suggest that MT systems for languages like Tetun should prioritise translating into the low-resource language, handling brief inputs effectively, and covering a wide range of domains relevant to educational contexts. More broadly, this study demonstrates how observational analysis can inform low-resource language technology development, by grounding research in practical community needs.
Performance of Large Language Models in Technical MRI Question Answering: A Comparative Study
2024-11-19
Alan B McMillan
Background: Advances in artificial intelligence, particularly large language models (LLMs), have the potential to enhance technical expertise in magnetic resonance imaging (MRI), regardless of operator skill or geographic location. Methods: We assessed the accuracy of several LLMs in answering 570 technical MRI questions derived from a standardized review book. The questions spanned nine MRI topics, including Basic Principles, Image Production, and Safety. Closed-source models (e.g., OpenAI's o1 Preview, GPT-4o, GPT-4 Turbo, and Claude 3.5 Haiku) and open-source models (e.g., Phi 3.5 Mini, Llama 3.1, smolLM2) were tested. Models were queried using standardized prompts via the LangChain framework, and responses were graded against correct answers using an automated scoring protocol. Accuracy, defined as the proportion of correct answers, was the primary outcome. Results: The closed-source o1 Preview model achieved the highest accuracy (94%), exceeding the random-guess baseline (26.5%). GPT-4o and o1 Mini scored 88%, and GPT-4 Turbo and Claude 3.5 Haiku each scored 84%. Among open-source models, Phi 3.5 Mini performed well, achieving 78% accuracy, comparable to several closed-source models. Accuracy was highest in Basic Principles and Instrumentation categories but lower in Image Weighting and Contrast, History, and Artifacts and Corrections. Conclusions: LLMs exhibit high accuracy in addressing technical MRI questions, suggesting their potential to standardize and enhance MRI practice. These models may improve image quality and consistency across varied clinical environments. Further studies are needed to refine LLMs for clinical use and integrate them into MRI workflows.
2024-11-19
Paula Pereira, Jácome Cunha, João P. Fernandes
Data collection is pervasively bound to our digital lifestyle. A recent study by the IDC reports that the growth of the data created and replicated in 2020 was even higher than in the previous years due to pandemic-related confinements to an astonishing global amount of 64.2 zettabytes of data. While not all the produced data is meant to be analyzed, there are numerous companies whose services/products rely heavily on data analysis. That is to say that mining the produced data has already revealed great value for businesses in different sectors. But to be able to fully realize this value, companies need to be able to hire professionals that are capable of gleaning insights and extracting value from the available data. We hypothesize that people nowadays conducting data-science-related tasks in practice may not have adequate training or formation. So in order to be able to fully support them in being productive in their duties, e.g. by building appropriate tools that increase their productivity, we first need to characterize the current generation of data scientists. To contribute towards this characterization, we conducted a public survey to fully understand who is doing data science, how they work, what are the skills they hold and lack, and which tools they use and need.
Halo Spin Dependence on Environment for HI-bearing galaxies
2024-11-19
Zichen Hua, Yu Rong, Huijie Hu
Leveraging the semi-analytic method, we compute halo spins for a substantial sample of HI-bearing galaxies observed in the Arecibo Legacy Fast Alfa Survey. Our statistical analysis reveals a correlation between halo spin and environment, although the trend is subtle. On average, galaxies exhibit a decreasing halo spin tendency in denser environments. This observation contrasts with previous results from $N$-body simulations in the Lambda cold dark matter framework. The discrepancy may be attributed to environmental gas stripping, leading to an underestimation of halo spins in galaxies in denser environments, or to baryonic processes that significantly alter the original dark matter halo spins, deviating from previous $N$-body simulation findings.
Moderate Influence of Halo Spin on Stellar Mass Distributions in Dwarf and Massive Galaxies
2024-11-19
Yu Rong, Zichen Hua, Huijie Hu
We estimate halo spins for HI-rich galaxies in the Arecibo Legacy Fast Alfa Survey using a semi-analytic approach, examining the relationship between halo spin and stellar surface density. Our findings reveal an inverse correlation in both low- and high-mass galaxy samples, with stellar surface density decreasing as halo spin increases. This trend highlights the pivotal role of halo spin in galaxy evolution and suggests a universal formation scenario: high-spin halos, accompanied by high-spin accreted gas, retain angular momentum, preventing gas from efficiently condensing in the galactic center and thus suppressing star formation. Consequently, weak feedback redistributes gas to the halo outskirts without significant expulsion. The shallower central gravitational potential in high-spin halos promotes outward stellar migration, leading to more extended stellar distributions and lower stellar surface densities.
A Survey of Medical Vision-and-Language Applications and Their Techniques
2024-11-19
Qi Chen, Ruoshan Zhao, Sinuo Wang, Vu Minh Hieu Phan, Anton van den Hengel, Johan Verjans, Zhibin Liao, Minh-Son To, Yong Xia, Jian Chen, Yutong Xie, Qi Wu
Medical vision-and-language models (MVLMs) have attracted substantial interest due to their capability to offer a natural language interface for interpreting complex medical data. Their applications are versatile and have the potential to improve diagnostic accuracy and decision-making for individual patients while also contributing to enhanced public health monitoring, disease surveillance, and policy-making through more efficient analysis of large data sets. MVLMS integrate natural language processing with medical images to enable a more comprehensive and contextual understanding of medical images alongside their corresponding textual information. Unlike general vision-and-language models trained on diverse, non-specialized datasets, MVLMs are purpose-built for the medical domain, automatically extracting and interpreting critical information from medical images and textual reports to support clinical decision-making. Popular clinical applications of MVLMs include automated medical report generation, medical visual question answering, medical multimodal segmentation, diagnosis and prognosis and medical image-text retrieval. Here, we provide a comprehensive overview of MVLMs and the various medical tasks to which they have been applied. We conduct a detailed analysis of various vision-and-language model architectures, focusing on their distinct strategies for cross-modal integration/exploitation of medical visual and textual features. We also examine the datasets used for these tasks and compare the performance of different models based on standardized evaluation metrics. Furthermore, we highlight potential challenges and summarize future research trends and directions. The full collection of papers and codes is available at: https://github.com/YtongXie/Medical-Vision-and-Language-Tasks-and-Methodologies-A-Survey.
From Centralized RAN to Open RAN: A Survey on the Evolution of Distributed Antenna Systems
2024-11-19
Mahmoud A. Hasabelnaby, Mohanad Obeed, Mohammed Saif, Anas Chaaban, M. J. Hossain
Next-generation mobile networks require evolved radio access network (RAN) architectures to meet the demands of high capacity, massive connectivity, reduced costs, and energy efficiency, and to realize communication with ultra-low latency and ultra-high reliability. {Meeting such} requirements for both mobile users and vertical industries in the next decade {requires novel solutions. One of the potential solutions that attracted significant research attention in the past 15 years} is to redesign the radio access network (RAN). In this survey, we present a comprehensive survey on distributed antenna system (DAS) architectures that address these challenges and improve network performance. We cover the transition from traditional decentralized RAN to DAS, including cloud radio-access networks (C-RAN), fog radio-access networks (F-RAN), virtualized radio-access networks (V-RAN), cell-free massive multiple-input multiple-output (CF-mMIMO), and {the most recent advances manifested in} open radio-access network (O-RAN). In the process, we discuss the benefits and limitations of these architectures, including the impact of limited-capacity fronthaul links, various cooperative uplink and downlink coding strategies, cross-layer optimization, and techniques to optimize the performance of DAS. Moreover, we review key enabling technologies for next-generation RAN systems, such as multi-access edge computing, network function virtualization, software-defined networking, and network slicing; in addition to some crucial radio access technologies, such as millimeter wave, massive multi-input multi-output, device-to-device communication, and massive machine-type communication. Last but not least, we discuss the major research challenges in DAS and identify several possible directions for future research.
Modelling carbon chain and complex organic molecules in the DR21(OH) clump
2024-11-19
P. Freeman, S. Bottinelli, R. Plume, E. Caux, B. Mookerjea
Star-forming regions host a large and evolving suite of molecular species. Molecular transition lines, particularly of complex molecules, can reveal the physical and dynamical environment of star formation. We aim to study the large-scale structure and environment of high-mass star formation through single-dish observations of CH$_3$CCH, CH$_3$OH, and H$_2$CO. We have conducted a wide-band spectral survey with the IRAM 30-m telescope and the 100-m GBT towards the high-mass star-forming region DR21(OH)/N44. We use a multi-component local thermodynamic equilibrium model to determine the large-scale physical environment near DR21(OH) and the surrounding dense clumps. We follow up with a radiative transfer code for CH$_3$OH to look at non-LTE behaviour. We then use a gas-grain chemical model to understand the formation routes of these molecules in their observed environments. We disentangle multiple components of DR21(OH) in each of the three molecules. We find a warm and cold component each towards the dusty condensations MM1 and MM2, and a fifth broad, outflow component. We also reveal warm and cold components towards other dense clumps in our maps: N40, N36, N41, N38, and N48. We find thermal mechanisms are adequate to produce the observed abundances of H$_2$CO and CH$_3$CCH while non-thermal mechanisms are needed to produce CH$_3$OH. Through a combination of wide-band mapping observations, LTE and non-LTE model analysis, and chemical modelling, we disentangle the different velocity and temperature components within our clump-scale beam, a scale that links a star-forming core to its parent cloud. We find numerous warm, 20-80 K components corresponding to known cores and outflows in the region. We determine the production routes of these species to be dominated by grain chemistry.
Towards Fairness in AI for Melanoma Detection: Systemic Review and Recommendations
2024-11-19
Laura N Montoya, Jennafer Shae Roberts, Belen Sanchez Hidalgo
Early and accurate melanoma detection is crucial for improving patient outcomes. Recent advancements in artificial intelligence AI have shown promise in this area, but the technologys effectiveness across diverse skin tones remains a critical challenge. This study conducts a systematic review and preliminary analysis of AI based melanoma detection research published between 2013 and 2024, focusing on deep learning methodologies, datasets, and skin tone representation. Our findings indicate that while AI can enhance melanoma detection, there is a significant bias towards lighter skin tones. To address this, we propose including skin hue in addition to skin tone as represented by the LOreal Color Chart Map for a more comprehensive skin tone assessment technique. This research highlights the need for diverse datasets and robust evaluation metrics to develop AI models that are equitable and effective for all patients. By adopting best practices outlined in a PRISMA Equity framework tailored for healthcare and melanoma detection, we can work towards reducing disparities in melanoma outcomes.
Frustrated Spin Systems: History of the Emergence of a Modern Physics
2024-11-19
Hung T. Diep
In 1977, G\'erard Toulouse has proposed a new concept termed as "frustration" in spin systems. Using this definition, several frustrated models have been created and studied, among them we can mention the Villain's model, the fully frustrated simple cubic lattice, the antiferromagnetic triangular lattice. The former models are systems with mixed ferromagnetic and antiferromagnetic bonds, while in the latter containing only an antiferromagnetic interaction, the frustration is caused by the lattice geometry. These frustrated spin systems have novel properties that we will review in this paper. One of the striking aspects is the fact that well-established methods such as the renormalization group fail to deal with the nature of the phase transition in frustrated systems. Investigations of properties of frustrated spin systems have been intensive since the 80's. I myself got involved in several investigations of frustrated spin systems soon after my PhD. I have learned a lot from numerous discussions with G\'erard Toulouse. Until today, I am still working on frustrated systems such as skyrmions. In this review, I trace back a number of my works over the years on frustrated spin systems going from exactly solved 2D Ising frustrated models, to XY and Heisenberg 2D and 3D frustrated lattices. At the end I present my latest results on skyrmions resulting from the frustration caused by the competition between the exchange interaction and the Dzyaloshinskii-Moriya interaction under an applied magnetic field. A quantum spin-wave theory using the Green's function method is shown and discussed.
The d-electron contribution to the stopping power of transition metals
2024-11-19
J. P. Peralta, A. M. P. Mendez, D. M. Mitnik, C. C. Montanari
We present a new non-perturbative model to describe the stopping power by ionization of the $d$-electrons of transition metals. These metals are characterized by the filling of the d-subshell and the promotion of part of the electrons to the conduction band. The contribution of d-electrons at low-impact energies has been noted experimentally in the past as a break of the linear dependence of the stopping power with the ion velocity. In this contribution, we describe the response of these electrons considering the atomic "inhomogeneous" momentum distribution. We focus on the transition metals of Groups 10 and 11 in the periodic table: Ni, Pd, Pt, Cu, Ag, and Au. Results describe the low energy-stopping power, with good agreement with the experimental data and available TDDFT results. By combining the present non-perturbative model for the $d$-subshell contribution with other approaches for the valence electrons and for the inner shells, we provide a coherent theoretical method capable of describing the stopping power of these transition metals from the very low to the high energy region.
The GAPS Programme at TNG. LXV. Precise density measurement of TOI-1430 b, a young planet with an evaporating atmosphere
2024-11-19
D. Nardiello, J. M. Akana Murphy, R. Spinelli, M. Baratella, S. Desidera, V. Nascimbeni, L. Malavolta, K. Biazzo, A. Maggio, D. Locci, S. Benatti, N. M. Batalha, V. D'Orazi, L. Borsato, G. Piotto, R. J. Oelkers, M. Mallonn, A. Sozzetti, L. R. Bedin, G. Mantovan, T. Zingales, L. Affer, A. Bignamini, A. S. Bonomo, L. Cabona, K. A. Collins, M. Damasso, S. Filomeno, A. Ghedina, A. Harutyunyan, A. F. Lanza, L. Mancini, M. Rainer, G. Scandariato, R. P. Schwarz, R. Sefako, G. Srdoc
Small-sized exoplanets in tight orbits around young stars (10-1000 Myr) give us the opportunity to investigate the mechanisms that led to their formation, the evolution of their physical and orbital properties and, especially, of their atmospheres. Thanks to the all-sky survey carried out by TESS, many of these exoplanets have been discovered and have subsequently been characterized with dedicated follow-up observations. In the context of a collaboration among the GAPS, TKS and CPS teams, we measured with a high level of precision the mass and the radius of TOI-1430 b, a young (~700 Myr) exoplanet with an escaping He atmosphere orbiting the K-dwarf star HD 235088 (TOI-1430). By adopting appropriate stellar parameters, which were measured in this work, we were able to simultaneously model the signals due to strong stellar activity and the transiting planet TOI-1430 b in both photometric and spectroscopic series. This allowed us to measure the density of the planet with high precision, and reconstruct the evolution of its atmosphere. TOI-1430 is an active K-dwarf star born 700+/-150 Myr ago and rotates in ~12 d. It hosts a mini-Neptune whose orbital period is Pb=7.434133+/-0.000004 d. Thanks to long-term monitoring of this target performed with TESS, HARPS-N, HIRES, and APF, we estimated a radius Rb=1.98+/-0.07 $R_{\oplus}$, a mass Mb=4.2+/-0.8 $M_{\oplus}$, and thus a planetary density $\rho$b=0.5+/-0.1 $\rho_{\oplus}$. TOI-1430 b is hence a low-density mini-Neptune with an extended atmosphere, at the edge of the radius gap. Because this planet is known to have an evaporating atmosphere of He, we reconstructed its atmospheric history. Our analysis supports the scenario in which, shortly after its birth, TOI-1430 b may have been super-puffy, with a radius 5x-13x and a mass 1.5x-2x that of today; in ~200 Myr from now, TOI-1430 b should lose its envelope, showing its Earth-size core.
Super-Size Me: The Big Multi-AGN Catalog (The Big MAC), Data Release 1: The Source Catalog
2024-11-19
Ryan W. Pfeifle, Kimberly A. Weaver, Nathan J. Secrest, Barry Rothberg, David R. Patton
Galaxy mergers represent the most transformative and dramatic avenue for galaxy and supermassive black hole (SMBH) evolution. Multi-active galactic nuclei (multi-AGNs) are expected to ignite, grow, and evolve alongside the host galaxies, and these represent different evolutionary stages of the SMBHs over the merger sequence. However, no comprehensive census exists of observed multi-AGN systems. Here we present The Big Multi-AGN Catalog (The Big MAC), the first literature-complete catalog of all known (confirmed and candidate) multi-AGN systems, which includes dual AGNs (separations $\sim0.03-110$ kpc), binary AGNs (gravitationally bound, $\lesssim30$ pc), recoiling AGNs, and N-tuple AGNs (involving three or more AGNs), gleaned from hundreds of literature articles spanning the years 1970-2020. The Big MAC is the first archive to assemble all multi-AGN systems and candidates across all selection methods, redshifts, and galaxy mass ratios, and this catalog offers a solid foundation for archival and targeted multiwavelength follow-up investigations. In this work, we provide an overview of the creation of the multi-AGN literature library and the catalog itself, present definitions for different multi-AGN classes (including new definitions for dual AGNs derived from galaxy pairs in Illustris-TNG100), describe the general properties of the catalog as a function of redshift space and separation, and we provide a thorough examination of selection and confirmation method usage within the literature. We also discuss best practices for the multi-AGN literature, and we emphasize that a diverse, multiwavelength array of selection approaches is crucial for a complete understanding of multi-AGNs and - by extension - answering long-standing, open questions regarding the importance of AGNs and galaxy mergers.
Chikungunya virus (CHIKV) is one of the most relevant arboviruses affecting public health today. It belongs to the Togaviridae family and alphavirus genus, causing an arthritogenic disease known as Chikungunya fever (CHIKF). This multifaceted disease is distinguished from other arbovirus infections by intense arthralgia, which can persist for months or even years in some individuals. The virus has re-emerged as a global health threat in recent decades, originating in Africa and spreading across Asia and America, leading to widespread outbreaks affecting millions. Despite more than 50 years of research on CHIKV pathogenesis, no drugs or vaccines are available. Current management focuses on supportive care to alleviate symptoms and improve patient's quality of life. The ongoing threat posed by CHIKV highlights the need to understand its pathogenesis better. This review provides a comprehensive overview of CHIKV, focusing on host factors, vector-related factors, and complex viral genetic interactions. By exploring these intricate connections, we aim to offer insights that may lead to more effective strategies for preventing and managing this re-emerging global health threat.
User Experience Evaluation of Augmented Reality: A Systematic Literature Review
2024-11-19
Stefan Graser, Felix Kirschenlohr, Stephan Böhm
Due to technological development, Augmented Reality (AR) can be applied in different domains. However, innovative technologies refer to new interaction paradigms, thus creating a new experience for the user. This so-called User Experience (UX) is essential for developing and designing interactive products. Moreover, UX must be measured to get insights into the user's perception and, thus, to improve innovative technologies. We conducted a Systematic Literature Review (SLR) to provide an overview of the current research concerning UX evaluation of AR. In particular, we aim to identify (1) research referring to UX evaluation of AR and (2) articles containing AR-specific UX models or frameworks concerning the theoretical foundation. The SLR is a five-step approach including five scopes. From a total of 498 records based on eight search terms referring to two databases, 30 relevant articles were identified and further analyzed. Results show that most approaches concerning UX evaluation of AR are quantitative. In summary, five UX models/frameworks were identified. Concerning the UX evaluation results of AR in Training and Education, the UX was consistently positive. Negative aspects refer to errors and deficiencies concerning the AR system and its functionality. No specific metric for UX evaluation of AR in the field of Training and Education exists. Only three AR-specific standardized UX questionnaires could be found. However, the questionnaires do not refer to the field of Training and Education. Thus, there is a lack of research in the field of UX evaluation of AR in Training and Education.
Lattice QCD and the Neutron Electric Dipole Moment
2024-11-19
Keh-Fei Liu
The recent lattice QCD calculations of the neutron and proton electric dipole moments (EDMs) and the CP-violating $\pi {\rm NN}$ coupling constant due to the $\theta$ term are reviewed. Progress towards nucleon EDM calculations, including the Weinberg three-gluon operator, and the quark chromoelectric dipole moment operator and their renormalization, is also discussed.
The JWST EXCELS survey: tracing the chemical enrichment pathways of high-redshift star-forming galaxies with O, Ar and Ne abundances
2024-11-18
T. M. Stanton, F. Cullen, A. C. Carnall, D. Scholte, K. Z. Arellano-Córdova, D. J. McLeod, R. Begley, C. T. Donnan, J. S. Dunlop, M. L. Hamadouche, R. J. McLure, A. E. Shapley, C. Bondestam, S. Stevenson
We present an analysis of nine star-forming galaxies with $\langle z \rangle = 3.95$ from the JWST EXCELS survey for which we obtain robust chemical abundance estimates for the $\alpha$-elements O, Ne and Ar. The $\alpha$-elements are primarily produced via core-collapse supernovae (CCSNe) which should result in $\alpha$-element abundance ratios that do not vary significantly across cosmic time. However, Type Ia supernovae (SNe Ia) models predict an excess production of Ar relative to O and Ne. The Ar/O abundance ratio can therefore be used as a tracer of the relative enrichment of CCSNe and SNe Ia in galaxies. Our sample approximately doubles the number of sources with measurements of ${\rm Ar/O}$ at $z > 2$, and we find that our sample exhibits sub-solar Ar/O ratios on average, with $\rm{Ar/O} = 0.62 \pm 0.10 \, (\rm{Ar/O})_{\odot}$. In contrast, the average Ne/O abundance is fully consistent with the solar ratio, with $\rm{Ne/O} = 1.07 \pm 0.12 \, (\rm{Ne/O})_{\odot}$. Our results support a scenario in which Ar has not had time to build up in the interstellar medium of young high-redshift galaxies, which are dominated by CCSNe enrichment. We show that these abundance estimates are in good agreement with recent Milky Way chemical evolution models, and with Ar/O trends observed for planetary nebulae in the Andromeda galaxy. These results highlight the potential for using multiple element abundance ratios to constrain the chemical enrichment pathways of early galaxies with JWST.
Investigating the galaxy-halo connection of DESI Emission-Line Galaxies with SHAMe-SF
2024-11-18
Sara Ortega-Martinez, Sergio Contreras, Raul E. Angulo, Jonas Chaves-Montero
The Dark Energy Spectroscopic Instrument (DESI) survey is mapping the large-scale distribution of millions of Emission Line Galaxies (ELGs) over vast cosmic volumes to measure the growth history of the Universe. However, compared to Luminous Red Galaxies (LRGs), very little is known about the connection of ELGs with the underlying matter field. In this paper, we employ a novel theoretical model, SHAMe-SF, to infer the connection between ELGs and their host dark matter subhaloes. SHAMe-SF is a version of subhalo abundance matching that incorporates prescriptions for multiple processes, including star formation, tidal stripping, environmental correlations, and quenching. We analyse the public measurements of the projected and redshift-space ELGs correlation functions at $z=1.0$ and $z=1.3$ from DESI One Percent data release, which we fit over a broad range of scales $r \in [0.1, 30]/h^{-1}$Mpc to within the statistical uncertainties of the data. We also validate the inference pipeline using two mock DESI ELG catalogues built from hydrodynamical (TNG300) and semi-analytical galaxy formation models (\texttt{L-Galaxies}). SHAMe-SF is able to reproduce the clustering of DESI-ELGs and the mock DESI samples within statistical uncertainties. We infer that DESI ELGs typically reside in haloes of $\sim 10^{11.8}h^{-1}$M$_{\odot}$ when they are central, and $\sim 10^{12.5}h^{-1}$M$_{\odot}$ when they are a satellite, which occurs in $\sim$30 \% of the cases. In addition, compared to the distribution of dark matter within halos, satellite ELGs preferentially reside both in the outskirts and inside haloes, and have a net infall velocity towards the centre. Finally, our results show evidence of assembly bias and conformity.
Leveraging Global IoT Networks for a Distributed Radio Telescope: Calibration Methods and Performance Analysis
2024-11-18
Junming Diao
This paper introduces an innovative approach to radio astronomy by utilizing the global network of Internet of Things (IoT) devices to form a distributed radio telescope. Leveraging existing IoT infrastructure with minimal modifications, the proposed system employs widely dispersed devices to simultaneously capture both astronomical and communication signals. Digital beamforming techniques are applied to align the astronomical signals, effectively minimizing interference from communication sources. Calibration is achieved using multiple distributed satellites transmitting known signals, enabling precise channel estimation and phase correction via GPS localization. We analyze two calibration methods, Phase Alignment Calibration (PAC) and Eigenvalue-Based Calibration (EVC), and demonstrate that EVC outperforms PAC in environments with significant variation in node performance. Compared to the Green Bank Telescope (GBT), the IoT-based telescope enhances antenna gain by three orders of magnitude and increases survey speed by eight orders of magnitude, owing to the vast number of nodes and expansive field of view (FoV). These findings demonstrate the feasibility and significant advantages of the IoT-enabled telescope, paving the way for cost-effective, high-speed, and widely accessible astronomical observations.
Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods
2024-11-18
Egor Kovalev, Georgii Bychkov, Khaled Abud, Aleksandr Gushchin, Anna Chistyakova, Sergey Lavrushkin, Dmitriy Vatolin, Anastasia Antsiferova
Adversarial robustness of neural networks is an increasingly important area of research, combining studies on computer vision models, large language models (LLMs), and others. With the release of JPEG AI - the first standard for end-to-end neural image compression (NIC) methods - the question of its robustness has become critically significant. JPEG AI is among the first international, real-world applications of neural-network-based models to be embedded in consumer devices. However, research on NIC robustness has been limited to open-source codecs and a narrow range of attacks. This paper proposes a new methodology for measuring NIC robustness to adversarial attacks. We present the first large-scale evaluation of JPEG AI's robustness, comparing it with other NIC models. Our evaluation results and code are publicly available online (link is hidden for a blind review).
AQUILA: A Laboratory Facility for the Irradiation of Astrochemical Ice Analogues by keV Ions
2024-11-18
R. Rácz, S. T. S. Kovács, G. Lakatos, K. K. Rahul, D. V. Mifsud, P. Herczku, B. Sulik, Z. Juhász, Z. Perduk, S. Ioppolo, N. J. Mason, T. A. Field, S. Biri, R. W. McCullough
The detection of various molecular species, including complex organic molecules relevant to biochemical and geochemical processes, in astronomical settings such as the interstellar medium or the outer Solar System has led to the increased need for a better understanding of the chemistry occurring in these cold regions of space. In this context, the chemistry of ices prepared and processed at cryogenic temperatures has proven to be of particular interest due to the fact that many interstellar molecules are believed to originate within the icy mantles adsorbed on nano- and micro-scale dust particles. The chemistry leading to the formation of such molecules may be initiated by ionising radiation in the form of galactic cosmic rays or stellar winds, and thus there has been an increased interest in commissioning experimental set-ups capable of simulating and better characterising this solid-phase radiation astrochemistry. In this article, we describe a new facility called AQUILA (Atomki-Queen's University Ice Laboratory for Astrochemistry) which has been purposefully designed to study the chemical evolution of ices analogous to those that may be found in the dense interstellar medium or the outer Solar System as a result of their exposure to keV ion beams. The results of some ion irradiation studies of CH3OH ice at 20 K are discussed to exemplify the experimental capabilities of the AQUILA as well as to highlight its complementary nature to another laboratory astrochemistry set-up at our institute.
An introduction to relativistic spin hydrodynamics
2024-11-18
Xu-Guang Huang
Spin polarization and spin transport are common phenomena in many quantum systems. Relativistic spin hydrodynamics provides an effective low-energy framework to describe these processes in quantum many-body systems. The fundamental symmetry underlying relativistic spin hydrodynamics is angular momentum conservation, which naturally leads to inter-conversion between spin and orbital angular momenta. This inter-conversion is a key feature of relativistic spin hydrodynamics, closely related to entropy production and introducing ambiguity in the construction of constitutive relations. In this article, we present a pedagogical introduction to relativistic spin hydrodynamics. We demonstrate how to derive the constitutive relations by applying local thermodynamic laws and explore several distinctive aspects of spin hydrodynamics. These include the pseudo-gauge ambiguity, the behavior of the system in the presence of strong vorticity, and the challenges of modeling the freeze-out of spin in heavy-ion collisions. We also outline some future prospects for spin hydrodynamics.
Bridge the Cosmological Tensions with Thawing Gravity
2024-11-18
Gen Ye
It is found that a non-minimally coupled scalar tensor theory, Thawing Gravity (TG), can explain multiple tensions plaguing the standard cosmological model $\Lambda$CDM while fitting better to observations than the latter. Using the standard Bayes model comparison method, TG has moderate evidence over $\Lambda$CDM with a Bayes factor $\ln B=+1.5$ in the baseline analysis including CMB, BAO and SNIa. In the baseline+$H_0$ analysis which further takes into account the Cepheids calibration of the SNIa distance ladder from SH0ES, TG has very strong evidence over $\Lambda$CDM with $\ln B=+11.8$. In particular, TG yields $H_0=71.78\pm0.86 \ {\rm km/s/Mpc}$ and $S_8=0.793\pm0.012$, consistent with both local $H_0$ measurement and the large scale structure surveys.
Hamiltonian Monte Carlo vs. event-chain Monte Carlo: an appraisal of sampling strategies beyond the diffusive regime
2024-11-18
Werner Krauth
We discuss Hamiltonian Monte Carlo (HMC) and event-chain Monte Carlo (ECMC) for the one-dimensional chain of particles with harmonic interactions and benchmark them against local reversible Metropolis algorithms. While HMC achieves considerable speedup with respect to local reversible Monte Carlo algorithms, its autocorrelation functions of global observables such as the structure factor have slower scaling with system size than for ECMC, a lifted non-reversible Markov chain. This can be traced to the dependence of ECMC on a parameter of the harmonic energy, the equilibrium distance, which drops out when energy differences or gradients are evaluated. We review the recent literature and provide pseudocodes and Python programs. We finally discuss related models and generalizations beyond one-dimensional particle systems.
2024-11-18
Alessio Notari, Michele Redi, Andrea Tesi
We critically review the evidence for time-varying dark energy from recent Baryon Acoustic Oscillations (BAO) and Supernova (SN) observations. First, we show that such evidence is present at the 3$\sigma$ level, even without the new BAO data from the dark energy Spectroscopic Instrument (DESI), by instead using BAO data from the dark energy Survey (DES), combined with the DES5Y supernovae and Planck CMB data. Next, we examine the role of the DES5Y supernova dataset, showing that the preference for time-varying dark energy is driven by the low redshift supernovae common to both the DES5Y and Pantheon+ compilations. We find that combining Pantheon+ and DES5Y supernovae by removing the common supernovae leads to two different results, depending on whether they are removed from the DES5Y or the Pantheon+ catalog, leading to stronger or weaker exclusion of $\Lambda$CDM, at the (3.8$\sigma$) and (2.5$\sigma$) level, respectively. These common supernovae have smaller error bars in DES5Y compared to Pantheon+, and, as recently pointed out, there is an offset in magnitude in DES5Y between supernovae at ($z > 0.1$), where almost all the measurements taken during the full five years of DES are, and the low-redshift ones ($z < 0.1$), where all the historical set of nearby supernovae lies. We show that marginalizing over such an offset in DES5Y would lead to significantly weaker evidence for evolving dark energy.
The ethical landscape of robot-assisted surgery. A systematic review
2024-11-18
Joschka Haltaufderheide, Stefanie Pfisterer-Heise, Dawid Pieper, Robert Ranisch
Background: Robot-assisted surgery has been widely adopted in recent years. However, compared to other health technologies operating in close proximity to patients in a vulnerable state, ethical issues of robot-assisted surgery have received less attention. Against the background of increasing automation that are expected to raise new ethical issues, this systematic review aims to map the state of the ethical debate in this field. Methods: A protocol was registered in the international prospective register of systematic reviews (PROSPERO CRD42023397951). Medline via PubMed, EMBASE, CINHAL, Philosophers' Index, IEEE Xplorer, Web of Science (Core Collection), Scopus and Google Scholar were searched in January 2023. Screening, extraction, and analysis were conducted independently by two authors. A qualitative narrative synthesis was performed. Results: Out of 1,723 records, 66 records were included in the final dataset. Seven major strands of the ethical debate emerged during analysis. These include questions of harms and benefits, responsibility and control, professional-patient relationship, ethical issues in surgical training and learning, justice, translational questions, and economic considerations. Discussion: The identified themes testify to a broad range of different and differing ethical issues requiring careful deliberation and integration into the surgical ethos. Looking forward, we argue that a different perspective in addressing robotic surgical devices might be helpful to consider upcoming challenges of automation.
Deep learning-based medical image segmentation helps assist diagnosis and accelerate the treatment process while the model training usually requires large-scale dense annotation datasets. Weakly semi-supervised medical image segmentation is an essential application because it only requires a small amount of scribbles and a large number of unlabeled data to train the model, which greatly reduces the clinician's effort to fully annotate images. To handle the inadequate supervisory information challenge in weakly semi-supervised segmentation (WSSS), a SuperPixel-Propagated Pseudo-label (SP${}^3$) learning method is proposed, using the structural information contained in superpixel for supplemental information. Specifically, the annotation of scribbles is propagated to superpixels and thus obtains a dense annotation for supervised training. Since the quality of pseudo-labels is limited by the low-quality annotation, the beneficial superpixels selected by dynamic thresholding are used to refine pseudo-labels. Furthermore, aiming to alleviate the negative impact of noise in pseudo-label, superpixel-level uncertainty is incorporated to guide the pseudo-label supervision for stable learning. Our method achieves state-of-the-art performance on both tumor and organ segmentation datasets under the WSSS setting, using only 3\% of the annotation workload compared to fully supervised methods and attaining approximately 80\% Dice score. Additionally, our method outperforms eight weakly and semi-supervised methods under both weakly supervised and semi-supervised settings. Results of extensive experiments validate the effectiveness and annotation efficiency of our weakly semi-supervised segmentation, which can assist clinicians in achieving automated segmentation for organs or tumors quickly and ultimately benefit patients.
Chapter 7 Review of Data-Driven Generative AI Models for Knowledge Extraction from Scientific Literature in Healthcare
2024-11-18
Leon Kopitar, Primoz Kocbek, Lucija Gosak, Gregor Stiglic
This review examines the development of abstractive NLP-based text summarization approaches and compares them to existing techniques for extractive summarization. A brief history of text summarization from the 1950s to the introduction of pre-trained language models such as Bidirectional Encoder Representations from Transformer (BERT) and Generative Pre-training Transformers (GPT) are presented. In total, 60 studies were identified in PubMed and Web of Science, of which 29 were excluded and 24 were read and evaluated for eligibility, resulting in the use of seven studies for further analysis. This chapter also includes a section with examples including an example of a comparison between GPT-3 and state-of-the-art GPT-4 solutions in scientific text summarisation. Natural language processing has not yet reached its full potential in the generation of brief textual summaries. As there are acknowledged concerns that must be addressed, we can expect gradual introduction of such models in practise.
Federated Named Entity Recognition (FNER) boosts model training within each local client by aggregating the model updates of decentralized local clients, without sharing their private data. However, existing FNER methods assume fixed entity types and local clients in advance, leading to their ineffectiveness in practical applications. In a more realistic scenario, local clients receive new entity types continuously, while new local clients collecting novel data may irregularly join the global FNER training. This challenging setup, referred to here as Federated Incremental NER, renders the global model suffering from heterogeneous forgetting of old entity types from both intra-client and inter-client perspectives. To overcome these challenges, we propose a Local-Global Forgetting Defense (LGFD) model. Specifically, to address intra-client forgetting, we develop a structural knowledge distillation loss to retain the latent space's feature structure and a pseudo-label-guided inter-type contrastive loss to enhance discriminative capability over different entity types, effectively preserving previously learned knowledge within local clients. To tackle inter-client forgetting, we propose a task switching monitor that can automatically identify new entity types under privacy protection and store the latest old global model for knowledge distillation and pseudo-labeling. Experiments demonstrate significant improvement of our LGFD model over comparison methods.
2024-11-18
Lars Reichwein, Zheng Gong, Chuan Zheng, Liangliang Ji, Alexander Pukhov, Markus Büscher
Spin-polarized particle beams are of interest for applications like deep-inelastic scattering, e.g. to gain further understanding of the proton's nuclear structure. With the advent of high-intensity laser facilities, laser-plasma-based accelerators offer a promising alternative to common radiofrequency-based accelerators, as they can shorten the required acceleration length significantly. However, in the scope of spin-polarized particles, they bring unique challenges. This paper reviews the developments in the field of spin-polarized particles on the basis of the interaction of laser pulses and high-energy particle beams with plasma. The relevant scaling laws for spin-dependent effects in laser-plasma interaction, as well as acceleration schemes for polarized leptons, ions and gamma quanta are discussed.
2024-11-18
Leo Cazenille, Maxime Toquebiau, Nicolas Lobato-Dauzier, Alessia Loi, Loona Macabre, Nathanael Aubert-Kato, Anthony Genot, Nicolas Bredeche
This paper investigates the role of communication in improving coordination within robot swarms, focusing on a paradigm where learning and execution occur simultaneously in a decentralized manner. We highlight the role communication can play in addressing the credit assignment problem (individual contribution to the overall performance), and how it can be influenced by it. We propose a taxonomy of existing and future works on communication, focusing on information selection and physical abstraction as principal axes for classification: from low-level lossless compression with raw signal extraction and processing to high-level lossy compression with structured communication models. The paper reviews current research from evolutionary robotics, multi-agent (deep) reinforcement learning, language models, and biophysics models to outline the challenges and opportunities of communication in a collective of robots that continuously learn from one another through local message exchanges, illustrating a form of social learning.
2024-11-18
Muqsit Azeem, Jan Kretinsky, Maximilian Weininger
Algorithmic analysis of Markov decision processes (MDP) and stochastic games (SG) in practice relies on value-iteration (VI) algorithms. Since the basic version of VI does not provide guarantees on the precision of the result, variants of VI have been proposed that offer such guarantees. In particular, sound value iteration (SVI) not only provides precise lower and upper bounds on the result, but also converges faster in the presence of probabilistic cycles. Unfortunately, it is neither applicable to SG, nor to MDP with end components. In this paper, we extend SVI and cover both cases. The technical challenge consists mainly in proper treatment of end components, which require different handling than in the literature. Moreover, we provide several optimizations of SVI. Finally, we also evaluate our prototype implementation experimentally to confirm its advantages on systems with probabilistic cycles.
In this article, we review recent machine learning methods used in challenging particle identification of heavy-boosted particles at high-energy colliders. Our primary focus is on attention-based Transformer networks. We report the performance of state-of-the-art deep learning networks and further improvement coming from the modification of networks based on physics insights. Additionally, we discuss interpretable methods to understand network decision-making, which are crucial when employing highly complex and deep networks.
Theoretical and Experimental Study on Heat Transfer Characteristics of Water Heat Pipe
2024-11-18
Ziyi Wang, Huang Zhang, Shanfang Huang
Heat pipe is an efficient heat transfer element based on two-phase natural circulation, which has advantages of simple structure, strong heat transfer ability, and good isothermal performance. Heat pipes are widely used in heat transfer and other fields, and especially have important applications in nuclear engineering. One of its most important characteristics is the heat transfer limit. In this work, heat transfer limits are first reviewed, and the detailed calculation equations are presented. Then, a Matlab code to calculate heat transfer limits as well as the thermal conductance are provided. Second, an experimental setup for testing the heat transfer characteristics of heat pipes was developed, which could be used to measure the thermal conductance as well as the heat transfer limits. The calculated results show that, for water heat pipes, the capillary limit mainly affects heat transfer in low temperature conditions, while in high temperature conditions, boiling limit dominates. In addition, the experiment results show that the thermal conductance of the measured heat pipe is 7267 W/(m^2*K), which agrees with the calculation result.
Asymptotic properties of bridging transitions in sinusoidally-shaped slits
2024-11-18
Alexandr Malijevský, Martin Pospíšil
We study bridging transitions that emerge between two sinusoidally-shaped walls of amplitude $A$, wavenumber $k$, and mean separation $L$. The focus is on weakly corrugated walls to examine the properties of bridging transitions in the limit when the walls become flat. The reduction of walls roughness can be achieved in two ways which we show differ qualitatively: a) By decreasing $k$, (i.e., by increasing the system wavelength), which induces a continuous phenomenon associated with the growth of bridging films concentrated near the system necks, the thickness of with the thickness of these films diverging as $\sim k^{-2/3}$ in the limit of $k\to0$. Simultaneously, the location of the transition approaches that of capillary condensation in an infinite planar slit of an appropriate width as $\sim k^{2/3}$; b) in contrast, the limit of vanishing walls roughness by reducing $A$ cannot be considered in this context, as there exists a minimal value $A_{\rm min}(k,L)$ of the amplitude below which bridging transition does not occur. On the other hand, for amplitudes $A>A_{\rm min}(k,L)$, the bridging transition always precedes global condensation in the system. These predictions, including the scaling property $A_{\rm min}\propto kL^2$, are verified numerically using density functional theory.
Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering
2024-11-18
Xinyan Guan, Yanjiang Liu, Xinyu Lu, Boxi Cao, Ben He, Xianpei Han, Le Sun, Jie Lou, Bowen Yu, Yaojie Lu, Hongyu Lin
The evolution of machine learning has increasingly prioritized the development of powerful models and more scalable supervision signals. However, the emergence of foundation models presents significant challenges in providing effective supervision signals necessary for further enhancing their capabilities. Consequently, there is an urgent need to explore novel supervision signals and technical approaches. In this paper, we propose verifier engineering, a novel post-training paradigm specifically designed for the era of foundation models. The core of verifier engineering involves leveraging a suite of automated verifiers to perform verification tasks and deliver meaningful feedback to foundation models. We systematically categorize the verifier engineering process into three essential stages: search, verify, and feedback, and provide a comprehensive review of state-of-the-art research developments within each stage. We believe that verifier engineering constitutes a fundamental pathway toward achieving Artificial General Intelligence.
SoK: On the Role and Future of AIGC Watermarking in the Era of Gen-AI
2024-11-18
Kui Ren, Ziqi Yang, Li Lu, Jian Liu, Yiming Li, Jie Wan, Xiaodi Zhao, Xianheng Feng, Shuo Shao
The rapid advancement of AI technology, particularly in generating AI-generated content (AIGC), has transformed numerous fields, e.g., art video generation, but also brings new risks, including the misuse of AI for misinformation and intellectual property theft. To address these concerns, AIGC watermarks offer an effective solution to mitigate malicious activities. However, existing watermarking surveys focus more on traditional watermarks, overlooking AIGC-specific challenges. In this work, we propose a systematic investigation into AIGC watermarking and provide the first formal definition of AIGC watermarking. Different from previous surveys, we provide a taxonomy based on the core properties of the watermark which are summarized through comprehensive literature from various AIGC modalities. Derived from the properties, we discuss the functionality and security threats of AIGC watermarking. In the end, we thoroughly investigate the AIGC governance of different countries and practitioners. We believe this taxonomy better aligns with the practical demands for watermarking in the era of GenAI, thus providing a clearer summary of existing work and uncovering potential future research directions for the community.
2024-11-18
Ayush Moharana, K. G. Helminiak, T. Pawar, G. Pawar
Compact hierarchical triples (CHTs) are triple stars where the tertiary is in an orbit of a period less than 1000 d. They were thought to be rare but we are discovering more of these systems recently, thanks to space-based missions like TESS, Kepler, and GAIA. In this work, we use orbital parameters obtained from these missions to constrain the formation process of CHTs. We also use spectroscopic and systemic parameters from our work, and the literature to understand the effects of metallicity and dynamics on the formation processes.
Robust Markov Decision Processes: A Place Where AI and Formal Methods Meet
2024-11-18
Marnix Suilen, Thom Badings, Eline M. Bovy, David Parker, Nils Jansen
Markov decision processes (MDPs) are a standard model for sequential decision-making problems and are widely used across many scientific areas, including formal methods and artificial intelligence (AI). MDPs do, however, come with the restrictive assumption that the transition probabilities need to be precisely known. Robust MDPs (RMDPs) overcome this assumption by instead defining the transition probabilities to belong to some uncertainty set. We present a gentle survey on RMDPs, providing a tutorial covering their fundamentals. In particular, we discuss RMDP semantics and how to solve them by extending standard MDP methods such as value iteration and policy iteration. We also discuss how RMDPs relate to other models and how they are used in several contexts, including reinforcement learning and abstraction techniques. We conclude with some challenges for future work on RMDPs.
Strong Correlation between Galactic HI-to-stellar Mass Ratio And Halo Spin Explored by HI-rich Galaxies
2024-11-18
Shihong Liu, Yu Rong, Zichen Hua, Huijie Hu
Using a semi-analytic approach, we estimate halo spins for a large sample of HI-rich galaxies from the Arecibo Legacy Fast Alfa Survey and examine the correlation between HI mass fractions and halo spins. Our analysis reveals a strong correlation between halo spin and the HI-to-stellar mass ratio in both low-mass and massive galaxy samples. This finding suggests a universal formation scenario: higher halo spin reduces angular momentum loss and gas condensation, leading to lower star formation rates and weaker feedback, which in turn helps retain gas within dark matter halos.
Halo Spin Depends on The Distance to Large-scale Filament
2024-11-18
Wenxiao Xue, Yu Rong, Zichen Hua
We employ a semi-analytical methodology to estimate the dark matter halo spin of HI gas-rich galaxies in the Arecibo Legacy Fast Alfa Survey and investigate the relationship between halo spin and the proximity of galaxies to large-scale filaments. We exclude galaxies with low HI signal-to-noise ratios, those potentially influenced by velocity dispersions, and those affiliated with galaxy clusters/groups. Additionally, we apply a mass-weighting technique to ensure consistent mass distribution across galaxy samples at varying distances from filaments. Our analysis reveals, for the first time, a subtle yet statistically significant correlation between halo spin and filament distance in observational data, indicating higher spins closer to filaments. This suggests that the tidal forces exerted by filaments may impact the spin of dark matter halos.
Review on vortex dynamics in the left ventricle as an early diagnosis marker for heart diseases and its treatment outcomes
2024-11-18
Mahesh S. Nagargoje, Eneko Lazpita, Jesús Garicano-Mena, Soledad Le Clainche
The heart is the central part of the cardiovascular network. Its role is to pump blood to various body organs. Many cardiovascular diseases occur due to an abnormal functioning of the heart. A diseased heart leads to severe complications and in some cases death of an individual. The medical community believes that early diagnosis and treatment of heart diseases can be controlled by referring to numerical simulations of image-based heart models. Computational Fluid Dynamics (CFD) is a commonly used tool for patient-specific simulations in the cardiac flows, and it can be equipped to allow a better understanding of flow patterns. In this paper, we review the progress of CFD tools to understand the flow patterns in healthy and dilated cardiomyopathic (DCM) left ventricles (LV). The formation of an asymmetric vortex in a healthy LV shows an efficient way of blood transport. The vortex pattern changes before any change in the geometry of LV is noticeable. This flow change can be used as a marker of DCM progression. We can conclude that understanding vortex dynamics in LV using various vortex indexes coupled with data-driven approaches can be used as an early diagnosis tool and improvement in DCM treatment.
Causal Effect of Group Diversity on Redundancy and Coverage in Peer-Reviewing
2024-11-18
Navita Goyal, Ivan Stelmakh, Nihar Shah, Hal Daumé III
A large host of scientific journals and conferences solicit peer reviews from multiple reviewers for the same submission, aiming to gather a broader range of perspectives and mitigate individual biases. In this work, we reflect on the role of diversity in the slate of reviewers assigned to evaluate a submitted paper as a factor in diversifying perspectives and improving the utility of the peer-review process. We propose two measures for assessing review utility: review coverage -- reviews should cover most contents of the paper -- and review redundancy -- reviews should add information not already present in other reviews. We hypothesize that reviews from diverse reviewers will exhibit high coverage and low redundancy. We conduct a causal study of different measures of reviewer diversity on review coverage and redundancy using observational data from a peer-reviewed conference with approximately 5,000 submitted papers. Our study reveals disparate effects of different diversity measures on review coverage and redundancy. Our study finds that assigning a group of reviewers that are topically diverse, have different seniority levels, or have distinct publication networks leads to broader coverage of the paper or review criteria, but we find no evidence of an increase in coverage for reviewer slates with reviewers from diverse organizations or geographical locations. Reviewers from different organizations, seniority levels, topics, or publications networks (all except geographical diversity) lead to a decrease in redundancy in reviews. Furthermore, publication network-based diversity alone also helps bring in varying perspectives (that is, low redundancy), even within specific review criteria. Our study adopts a group decision-making perspective for reviewer assignments in peer review and suggests dimensions of diversity that can help guide the reviewer assignment process.
Implicit Regularization for Multi-label Feature Selection
2024-11-18
Dou El Kefel Mansouri, Khalid Benabdeslem, Seif-Eddine Benkabou
In this paper, we address the problem of feature selection in the context of multi-label learning, by using a new estimator based on implicit regularization and label embedding. Unlike the sparse feature selection methods that use a penalized estimator with explicit regularization terms such as $l_{2,1}$-norm, MCP or SCAD, we propose a simple alternative method via Hadamard product parameterization. In order to guide the feature selection process, a latent semantic of multi-label information method is adopted, as a label embedding. Experimental results on some known benchmark datasets suggest that the proposed estimator suffers much less from extra bias, and may lead to benign overfitting.
Deep Learning-based Code Reviews: A Paradigm Shift or a Double-Edged Sword?
2024-11-18
Rosalia Tufano, Alberto Martin-Lopez, Ahmad Tayeb, Ozren Dabić, Sonia Haiduc, Gabriele Bavota
Several techniques have been proposed to automate code review. Early support consisted in recommending the most suited reviewer for a given change or in prioritizing the review tasks. With the advent of deep learning in software engineering, the level of automation has been pushed to new heights, with approaches able to provide feedback on source code in natural language as a human reviewer would do. Also, recent work documented open source projects adopting Large Language Models (LLMs) as co-reviewers. Although the research in this field is very active, little is known about the actual impact of including automatically generated code reviews in the code review process. While there are many aspects worth investigating, in this work we focus on three of them: (i) review quality, i.e., the reviewer's ability to identify issues in the code; (ii) review cost, i.e., the time spent reviewing the code; and (iii) reviewer's confidence, i.e., how confident is the reviewer about the provided feedback. We run a controlled experiment with 29 experts who reviewed different programs with/without the support of an automatically generated code review. During the experiment we monitored the reviewers' activities, for over 50 hours of recorded code reviews. We show that reviewers consider valid most of the issues automatically identified by the LLM and that the availability of an automated review as a starting point strongly influences their behavior: Reviewers tend to focus on the code locations indicated by the LLM rather than searching for additional issues in other parts of the code. The reviewers who started from an automated review identified a higher number of low-severity issues while, however, not identifying more high-severity issues as compared to a completely manual process. Finally, the automated support did not result in saved time and did not increase the reviewers' confidence.
Thickness-dependent Topological Phases and Flat Bands in Rhombohedral Multilayer Graphene
2024-11-18
H. B. Xiao, C. Chen, X. Sui, S. H. Zhang, M. Z. Sun, H. Gao, Q. Jiang, Q. Li, L. X. Yang, M. Ye, F. Y. Zhu, M. X. Wang, J. P. Liu, Z. B. Zhang, Z. J. Wang, Y. L. Chen, K. H. Liu, Z. K. Liu
Rhombohedral multilayer graphene has emerged as an extraordinary platform for investigating exotic quantum states, such as superconductivity and fractional quantum anomalous Hall effects, mainly due to the existence of topological surface flatbands. Despite extensive research efforts, a systematic spectroscopic investigation on the evolution of its electronic structure from thin layers to bulk remains elusive. Using state-of-the-art angle-resolved photoemission spectroscopy with submicron spatial resolution, we directly probe and trace the thickness evolution of the topological electronic structures of rhombohedral multilayer graphene. As the layer number increases, the gapped subbands transform into the 3D Dirac nodes that spirals in the momentum space; while the flatbands are constantly observed around Fermi level, and eventually evolve into the topological drumhead surface states. This unique thickness-dependent topological phase transition can be well captured by the 3D generalization of 1D Su-Schrieffer-Heeger chain in thin layers, to the topological Dirac nodal spiral semimetal in the bulk limit. Our findings establish a solid foundation for exploring the exotic quantum phases with nontrivial topology and correlation effects in rhombohedral multilayer graphene.
Oracle character recognition-an analysis of ancient Chinese inscriptions found on oracle bones-has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods of oracle character recognition have relied heavily on manual interpretation by experts, which is not only labor-intensive but also limits broader accessibility to the general public. With recent breakthroughs in pattern recognition and deep learning, there is a growing movement towards the automation of oracle character recognition (OrCR), showing considerable promise in tackling the challenges inherent to these ancient scripts. However, a comprehensive understanding of OrCR still remains elusive. Therefore, this paper presents a systematic and structured survey of the current landscape of OrCR research. We commence by identifying and analyzing the key challenges of OrCR. Then, we provide an overview of the primary benchmark datasets and digital resources available for OrCR. A review of contemporary research methodologies follows, in which their respective efficacies, limitations, and applicability to the complex nature of oracle characters are critically highlighted and examined. Additionally, our review extends to ancillary tasks associated with OrCR across diverse disciplines, providing a broad-spectrum analysis of its applications. We conclude with a forward-looking perspective, proposing potential avenues for future investigations that could yield significant advancements in the field.
Recently, an increasing number of laws have governed the useability of users' privacy. For example, Article 17 of the General Data Protection Regulation (GDPR), the right to be forgotten, requires machine learning applications to remove a portion of data from a dataset and retrain it if the user makes such a request. Furthermore, from the security perspective, training data for machine learning models, i.e., data that may contain user privacy, should be effectively protected, including appropriate erasure. Therefore, researchers propose various privacy-preserving methods to deal with such issues as machine unlearning. This paper provides an in-depth review of the security and privacy concerns in machine learning models. First, we present how machine learning can use users' private data in daily life and the role that the GDPR plays in this problem. Then, we introduce the concept of machine unlearning by describing the security threats in machine learning models and how to protect users' privacy from being violated using machine learning platforms. As the core content of the paper, we introduce and analyze current machine unlearning approaches and several representative research results and discuss them in the context of the data lineage. Furthermore, we also discuss the future research challenges in this field.
This is a pedagogical review of the possible connection between the stochastic quantization in physics and the diffusion models in machine learning. For machine-learning applications, the denoising diffusion model has been established as a successful technique, which is formulated in terms of the stochastic differential equation (SDE). In this review, we focus on an SDE approach used in the score-based generative modeling. Interestingly, the evolution of the probability distribution is equivalently described by a particular class of SDEs, and in a particular limit, the stochastic noises can be eliminated. Then, we turn to a similar mathematical formulation in quantum physics, that is, the stochastic quantization. We make a brief overview on the stochastic quantization using a simple toy model of the one-dimensional integration. The analogy between the diffusion model and the stochastic quantization is clearly seen in this concrete example. Finally, we discuss how the sign problem arises in the toy model with complex parameters. The origin of the difficulty is understood based on the Lefschetz thimble analysis. We point out that the SDE is not invariant under the variable change which induces a kernel and a special choice of the kernel guided by the Lefschetz thimble analysis can reduce the sign problem.
Reducing Label Dependency for Underwater Scene Understanding: A Survey of Datasets, Techniques and Applications
2024-11-18
Scarlett Raine, Frederic Maire, Niko Suenderhauf, Tobias Fischer
Underwater surveys provide long-term data for informing management strategies, monitoring coral reef health, and estimating blue carbon stocks. Advances in broad-scale survey methods, such as robotic underwater vehicles, have increased the range of marine surveys but generate large volumes of imagery requiring analysis. Computer vision methods such as semantic segmentation aid automated image analysis, but typically rely on fully supervised training with extensive labelled data. While ground truth label masks for tasks like street scene segmentation can be quickly and affordably generated by non-experts through crowdsourcing services like Amazon Mechanical Turk, ecology presents greater challenges. The complexity of underwater images, coupled with the specialist expertise needed to accurately identify species at the pixel level, makes this process costly, time-consuming, and heavily dependent on domain experts. In recent years, some works have performed automated analysis of underwater imagery, and a smaller number of studies have focused on weakly supervised approaches which aim to reduce the expert-provided labelled data required. This survey focuses on approaches which reduce dependency on human expert input, while reviewing the prior and related approaches to position these works in the wider field of underwater perception. Further, we offer an overview of coastal ecosystems and the challenges of underwater imagery. We provide background on weakly and self-supervised deep learning and integrate these elements into a taxonomy that centres on the intersection of underwater monitoring, computer vision, and deep learning, while motivating approaches for weakly supervised deep learning with reduced dependency on domain expert data annotations. Lastly, the survey examines available datasets and platforms, and identifies gaps, barriers, and opportunities for automating underwater surveys.
Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimation
2024-11-18
Hechuan Wen, Tong Chen, Guanhua Ye, Li Kheng Chai, Shazia Sadiq, Hongzhi Yin
Causal effect estimation (CEE) provides a crucial tool for predicting the unobserved counterfactual outcome for an entity. As CEE relaxes the requirement for ``perfect'' counterfactual samples (e.g., patients with identical attributes and only differ in treatments received) that are impractical to obtain and can instead operate on observational data, it is usually used in high-stake domains like medical treatment effect prediction. Nevertheless, in those high-stake domains, gathering a decently sized, fully labelled observational dataset remains challenging due to hurdles associated with costs, ethics, expertise and time needed, etc., of which medical treatment surveys are a typical example. Consequently, if the training dataset is small in scale, low generalization risks can hardly be achieved on any CEE algorithms. Unlike existing CEE methods that assume the constant availability of a dataset with abundant samples, in this paper, we study a more realistic CEE setting where the labelled data samples are scarce at the beginning, while more can be gradually acquired over the course of training -- assuredly under a limited budget considering their expensive nature. Then, the problem naturally comes down to actively selecting the best possible samples to be labelled, e.g., identifying the next subset of patients to conduct the treatment survey. However, acquiring quality data for reducing the CEE risk under limited labelling budgets remains under-explored until now. To fill the gap, we theoretically analyse the generalization risk from an intriguing perspective of progressively shrinking its upper bound, and develop a principled label acquisition pipeline exclusively for CEE tasks. With our analysis, we propose the Model Agnostic Causal Active Learning (MACAL) algorithm for batch-wise label acquisition, which aims to reduce both the CEE model's uncertainty and the post-acquisition ...
The Blue Horizontal-Branch Stars From the LAMOST Survey: Atmospheric Parameters
2024-11-18
Jie Ju, Bo Zhang, Wenyuan Cui, ZhenYan Huo, Chao Liu, Yang Huang, JianRong Shi
Blue horizontal-branch (BHB) stars are crucial for studying the structure of the Galactic halo. Accurate atmospheric parameters of BHB stars are essential for investigating the formation and evolution of the Galaxy. In this work, a data-driven technique named stellar label machine (SLAM) is used to estimate the atmospheric parameters of Large Sky Area Multi-Object Fiber Spectroscopic Telescope low-resolution spectra (LAMOST-LRS) for BHB stars with a set of A-type theoretical spectra as the training dataset. We add color indexes ($(BP-G), (G-RP), (BP-RP), (J-H)$) during the training process to constrain the stellar temperature further. Finally, we derive the atmospheric parameters ($T_\mathrm{eff}$, log\, $g$, [Fe/H]) for 5,355 BHB stars. Compared to existing literature results, our results are more robust, after taking the color index into account, the resulted precisoin of $T_\mathrm{eff}$, log\, $g$ is significantly improved, especially for the spectrum with low signal-to-noise ratio (S/N). Based on the duplicate observations with a S/N difference $< 20\%$, the random errors are around 30\,K, 0.1~dex, and 0.12~dex for $T_\mathrm{eff}$, log\,$g$, [Fe/H], respectively. The stellar labels provided by SLAM are also compared to those from the high-resolution spectra in literature. The standard deviation between the predicted star labels and the published values from the high-resolution spectra is adopted as \sout{to} the statistical uncertainty of our results. They are $\sigma$($T_\mathrm{eff}$) = 76\,K, $\sigma$(log\,$g$) = 0.04~dex, and $\sigma$([Fe/H]) = 0.09~dex, respectively.
ALMA Lensing Cluster Survey: Dust mass measurements as a function of redshift, stellar-mass and star formation rate, from z=1 to z=5
2024-11-18
Jean-Baptiste Jolly, Kirsten Knudsen, Nicolas Laporte, Andrea Guerrero, Seiji Fujimoto, Kotaro Kohno, Vasily Kokorev, Claudia del P. Lagos, Thiébaut-Antoine Schirmer, Franz Bauer, Miroslava Dessauge-Zavadsky, Daniel Espada, Bunyo Hatsukade, Anton M. Koekemoer, Johan Richard, Fengwu Sun, John F. Wu
Understanding the dust content of galaxies, its evolution with redshift and its relationship to stars and star formation is fundamental for our understanding of galaxy evolution. Using the ALMA Lensing Cluster Survey (ALCS) wide-area band-6 continuum dataset ($\sim\,$110 arcmin$^2$ across 33 lensing clusters), we aimed at constraining the dust mass evolution with redshift, stellar mass and star formation rate (SFR). After binning sources according to redshift, SFR and stellar mass -- extracted from an HST-IRAC catalog -- we performed a set of continuum stacking analyses in the image domain using \textsc{LineStacker} on sources between $z=1$ and $z=5$, further improving the depth of our data. The large field of view provided by the ALCS allows us to reach a final sample of $\sim4000$ galaxies with known coordinates and SED-derived physical parameters. We stack sources with SFR between $10^{-3}$ and $10^{3}$ M$_\odot$ per year, and stellar mass between $10^{8}$ and $10^{12}$ M$_\odot$, splitting them in different stellar mass and SFR bins. Through stacking we retrieve the continuum 1.2\,mm flux, a known dust mass tracer, allowing us to derive the dust mass evolution with redshift and its relation with SFR and stellar mass. We observe clear continuum detections in the majority of the subsamples. From the non detections we derive 3-$\sigma$ upper limits. We observe a steady decline in the average dust mass with redshift. Moreover, sources with higher stellar mass or SFR have higher dust mass on average, allowing us to derive scaling relations. Our results are mostly in good agreement with models at $z\sim1$-3, but indicate typically lower dust-mass than predicted at higher redshift.
Miscentering of Optical Galaxy Clusters Based on Sunyaev-Zeldovich Counterparts
2024-11-18
Jupiter Ding, Roohi Dalal, Tomomi Sunayama, Michael A. Strauss, Masamune Oguri, Nobuhiro Okabe, Matt Hilton, Rogério Monteiro-Oliveira, Cristóbal Sifón, Suzanne T. Staggs
The "miscentering effect," i.e., the offset between a galaxy cluster's optically-defined center and the center of its gravitational potential, is a significant systematic effect on brightest cluster galaxy (BCG) studies and cluster lensing analyses. We perform a cross-match between the optical cluster catalog from the Hyper Suprime-Cam (HSC) Survey S19A Data Release and the Sunyaev-Zeldovich cluster catalog from Data Release 5 of the Atacama Cosmology Telescope (ACT). We obtain a sample of 186 clusters in common in the redshift range $0.1 \leq z \leq 1.4$ over an area of 469 deg$^2$. By modeling the distribution of centering offsets in this fiducial sample, we find a miscentered fraction (corresponding to clusters offset by more than 330 kpc) of ~25%, a value consistent with previous miscentering studies. We examine the image of each miscentered cluster in our sample and identify one of several reasons to explain the miscentering. Some clusters show significant miscentering for astrophysical reasons, i.e., ongoing cluster mergers. Others are miscentered due to non-astrophysical, systematic effects in the HSC data or the cluster-finding algorithm. After removing all clusters with clear, non-astrophysical causes of miscentering from the sample, we find a considerably smaller miscentered fraction, ~10%. We show that the gravitational lensing signal within 1 Mpc of miscentered clusters is considerably smaller than that of well-centered clusters, and we suggest that the ACT SZ centers are a better estimate of the true cluster potential centroid.
From lab to landscape-scale experiments for the morphodynamics of sand dunes
2024-11-18
P. Claudin, S. Courrech du Pont, C. Narteau
We review the main processes that drive the morphodynamics of dunes, i.e. their growth in height, migration and elongation, and emphasise the contribution of experiments to the understanding of these mechanisms. The main control parameters are the sediment flux $Q$ and the saturation length $L_{\rm sat}$ associated with the spatial relaxation of the flux towards the transport capacity. The other relevant quantities are essentially dimensionless: fluid response to a bed perturbation, dune geometry (orientation, aspect ratio), transport ratios under multi-directional wind regimes. We argue that laboratory experiments dealing with sedimentary bedforms in water flows are good analogues to study the morphodynamics of aeolian dunes at reduced length and time scales, as $L_{\rm sat}$ and $L_{\rm sat}^2/Q$ are expected to be smaller for subaqueous bedload. Besides, dune shape and dynamics are mainly governed by flow and boundary conditions, independent of the transport mode. We discuss different experimental set-ups and results, especially concerning dune pattern orientation and dune interaction. Under natural wind regimes in terrestrial deserts, we show the potential of field experiments in which the control of initial and boundary conditions allows for the quantification of all the relevant mechanisms involved in dune growth. We emphasise the general agreement between observations, measurements and theoretical predictions, which indicates a robust comprehension of the underlying processes. This understanding can serve as a foundation for further investigations, including the interpretation of dune landscapes and the resolution of inverse problems.
Easy-plane ferromagnetic ordering and crystal-field ground state in the Kondo lattice CeCuSi
2024-11-18
Hanshang Jin, Owen Moulding, James C. Fettinger, Yingzheng Gao, Peter Klavins, Marie-Aude Méasson, Valentin Taufour
We report the successful growth of CeCuSi single crystals using a metallic flux method and the physical properties using structural, magnetic, electrical transport, optical, and heat capacity measurements. CeCuSi crystallizes in a hexagonal-bar shape, and single crystal x-ray diffraction confirms the ZrBeSi-type structure (space group $P6_{3}/mmc$). CeCuSi orders ferromagnetically below $T_\textrm{C}=15.5$ K with easy magnetization direction within the basal plane. The Ce$^{3+}$ ions are situated within a triangular lattice with a point group of $D_{3d}$. We perform a detailed crystalline electric field (CEF) analysis of the anisotropic magnetic susceptibility, the Schottky anomaly in heat capacity, and the Raman-active excitations. The results indicate a ground state doublet with magnetic moment primarily in the basal plane, and a ferromagnetic interaction along both directions. The exponential behavior in resistivity and in heat capacity below $T_\textrm{C}$ can also be well explained by the ferromagnetic magnon model. We found that CeCuSi does not exhibit the CEF hard axis ordering observed in many ferromagnetic Kondo lattice (FM-KL) compounds. Our CEF analysis suggests that the exchange interactions along both axes are ferromagnetic, potentially explaining the absence of hard-axis ordering.
Clouds and Hazes in the Atmospheres of Triton and Pluto
2024-11-18
Peter Gao, Kazumasa Ohno
Clouds and hazes are abundant in the thin and cold atmospheres of Triton and Pluto, where they are thought to be produced by interactions between atmospheric gases and ultraviolet photons from the Sun and those scattered by the local interstellar medium. These interactions lead to a rich network of chemical reactions that produces higher order hydrocarbons and nitriles that condense out to form ice clouds, and ultimately complex haze particles that rain down onto the surface that impact the atmospheric thermal structure, gas chemistry, and surface evolution. In this chapter, we will review the observational evidence for clouds and hazes in the atmospheres of Triton and Pluto and theoretical interpretations thereof, and the emerging set of experiments aiming to produce Triton and Pluto clouds and hazes in the lab to learn about them in detail.
DESI 2024 VII: Cosmological Constraints from the Full-Shape Modeling of Clustering Measurements
2024-11-18
DESI Collaboration, A. G. Adame, J. Aguilar, S. Ahlen, S. Alam, D. M. Alexander, C. Allende Prieto, M. Alvarez, O. Alves, A. Anand, U. Andrade, E. Armengaud, S. Avila, A. Aviles, H. Awan, B. Bahr-Kalus, S. Bailey, C. Baltay, A. Bault, J. Behera, S. BenZvi, F. Beutler, D. Bianchi, C. Blake, R. Blum, M. Bonici, S. Brieden, A. Brodzeller, D. Brooks, E. Buckley-Geer, E. Burtin, R. Calderon, R. Canning, A. Carnero Rosell, R. Cereskaite, J. L. Cervantes-Cota, S. Chabanier, E. Chaussidon, J. Chaves-Montero, D. Chebat, S. Chen, X. Chen, T. Claybaugh, S. Cole, A. Cuceu, T. M. Davis, K. Dawson, A. de la Macorra, A. de Mattia, N. Deiosso, A. Dey, B. Dey, Z. Ding, P. Doel, J. Edelstein, S. Eftekharzadeh, D. J. Eisenstein, W. Elbers, A. Elliott, P. Fagrelius, K. Fanning, S. Ferraro, J. Ereza, N. Findlay, B. Flaugher, A. Font-Ribera, D. Forero-Sánchez, J. E. Forero-Romero, C. S. Frenk, C. Garcia-Quintero, L. H. Garrison, E. Gaztañaga, H. Gil-Marín, S. Gontcho A Gontcho, A. X. Gonzalez-Morales, V. Gonzalez-Perez, C. Gordon, D. Green, D. Gruen, R. Gsponer, G. Gutierrez, J. Guy, B. Hadzhiyska, C. Hahn, M. M. S Hanif, H. K. Herrera-Alcantar, K. Honscheid, C. Howlett, D. Huterer, V. Iršič, M. Ishak, R. Joyce, S. Juneau, N. G. Karaçaylı, R. Kehoe, S. Kent, D. Kirkby, H. Kong, S. E. Koposov, A. Kremin, A. Krolewski, O. Lahav, Y. Lai, T. -W. Lan, M. Landriau, D. Lang, J. Lasker, J. M. Le Goff, L. Le Guillou, A. Leauthaud, M. E. Levi, T. S. Li, K. Lodha, C. Magneville, M. Manera, D. Margala, P. Martini, W. Matthewson, M. Maus, P. McDonald, L. Medina-Varela, A. Meisner, J. Mena-Fernández, R. Miquel, J. Moon, S. Moore, J. Moustakas, N. Mudur, E. Mueller, A. Muñoz-Gutiérrez, A. D. Myers, S. Nadathur, L. Napolitano, R. Neveux, J. A. Newman, N. M. Nguyen, J. Nie, G. Niz, H. E. Noriega, N. Padmanabhan, E. Paillas, N. Palanque-Delabrouille, J. Pan, S. Penmetsa, W. J. Percival, M. M. Pieri, M. Pinon, C. Poppett, A. Porredon, F. Prada, A. Pérez-Fernández, I. Pérez-Ràfols, D. Rabinowitz, A. Raichoor, C. Ramírez-Pérez, S. Ramirez-Solano, M. Rashkovetskyi, C. Ravoux, M. Rezaie, J. Rich, A. Rocher, C. Rockosi, N. A. Roe, A. Rosado-Marin, A. J. Ross, G. Rossi, R. Ruggeri, V. Ruhlmann-Kleider, L. Samushia, E. Sanchez, C. Saulder, E. F. Schlafly, D. Schlegel, M. Schubnell, H. Seo, A. Shafieloo, R. Sharples, J. Silber, A. Slosar, A. Smith, D. Sprayberry, T. Tan, G. Tarlé, P. Taylor, S. Trusov, R. Vaisakh, D. Valcin, F. Valdes, G. Valogiannis, M. Vargas-Magaña, L. Verde, M. Walther, B. Wang, M. S. Wang, B. A. Weaver, N. Weaverdyck, R. H. Wechsler, D. H. Weinberg, M. White, M. J. Wilson, L. Yi, J. Yu, Y. Yu, S. Yuan, C. Yèche, E. A. Zaborowski, P. Zarrouk, H. Zhang, C. Zhao, R. Zhao, R. Zhou, T. Zhuang, H. Zou
We present cosmological results from the measurement of clustering of galaxy, quasar and Lyman-$\alpha$ forest tracers from the first year of observations with the Dark Energy Spectroscopic Instrument (DESI Data Release 1). We adopt the full-shape (FS) modeling of the power spectrum, including the effects of redshift-space distortions, in an analysis which has been validated in a series of supporting papers. In the flat $\Lambda$CDM cosmological model, DESI (FS+BAO), combined with a baryon density prior from Big Bang Nucleosynthesis and a weak prior on the scalar spectral index, determines matter density to $\Omega_\mathrm{m}=0.2962\pm 0.0095$, and the amplitude of mass fluctuations to $\sigma_8=0.842\pm 0.034$. The addition of the cosmic microwave background (CMB) data tightens these constraints to $\Omega_\mathrm{m}=0.3056\pm 0.0049$ and $\sigma_8=0.8121\pm 0.0053$, while further addition of the the joint clustering and lensing analysis from the Dark Energy Survey Year-3 (DESY3) data leads to a 0.4% determination of the Hubble constant, $H_0 = (68.40\pm 0.27)\,{\rm km\,s^{-1}\,Mpc^{-1}}$. In models with a time-varying dark energy equation of state, combinations of DESI (FS+BAO) with CMB and type Ia supernovae continue to show the preference, previously found in the DESI DR1 BAO analysis, for $w_0>-1$ and $w_a<0$ with similar levels of significance. DESI data, in combination with the CMB, impose the upper limits on the sum of the neutrino masses of $\sum m_\nu < 0.071\,{\rm eV}$ at 95% confidence. DESI data alone measure the modified-gravity parameter that controls the clustering of massive particles, $\mu_0=0.11^{+0.45}_{-0.54}$, while the combination of DESI with the CMB and the clustering and lensing analysis from DESY3 constrains both modified-gravity parameters, giving $\mu_0 = 0.04\pm 0.22$ and $\Sigma_0 = 0.044\pm 0.047$, in agreement with general relativity. [Abridged.]
DESI 2024 V: Full-Shape Galaxy Clustering from Galaxies and Quasars
2024-11-18
DESI Collaboration, A. G. Adame, J. Aguilar, S. Ahlen, S. Alam, D. M. Alexander, M. Alvarez, O. Alves, A. Anand, U. Andrade, E. Armengaud, S. Avila, A. Aviles, H. Awan, S. Bailey, C. Baltay, A. Bault, J. Behera, S. BenZvi, F. Beutler, D. Bianchi, C. Blake, R. Blum, S. Brieden, A. Brodzeller, D. Brooks, E. Buckley-Geer, E. Burtin, R. Calderon, R. Canning, A. Carnero Rosell, R. Cereskaite, J. L. Cervantes-Cota, S. Chabanier, E. Chaussidon, J. Chaves-Montero, S. Chen, X. Chen, T. Claybaugh, S. Cole, A. Cuceu, T. M. Davis, K. Dawson, A. de la Macorra, A. de Mattia, N. Deiosso, A. Dey, B. Dey, Z. Ding, P. Doel, J. Edelstein, S. Eftekharzadeh, D. J. Eisenstein, A. Elliott, P. Fagrelius, K. Fanning, S. Ferraro, J. Ereza, N. Findlay, B. Flaugher, A. Font-Ribera, D. Forero-Sánchez, J. E. Forero-Romero, C. Garcia-Quintero, L. H. Garrison, E. Gaztañaga, H. Gil-Marín, S. Gontcho A Gontcho, A. X. Gonzalez-Morales, V. Gonzalez-Perez, C. Gordon, D. Green, D. Gruen, R. Gsponer, G. Gutierrez, J. Guy, B. Hadzhiyska, C. Hahn, M. M. S Hanif, H. K. Herrera-Alcantar, K. Honscheid, C. Howlett, D. Huterer, V. Iršič, M. Ishak, S. Juneau, N. G. Karaçaylı, R. Kehoe, S. Kent, D. Kirkby, H. Kong, S. E. Koposov, A. Kremin, A. Krolewski, Y. Lai, T. -W. Lan, M. Landriau, D. Lang, J. Lasker, J. M. Le Goff, L. Le Guillou, A. Leauthaud, M. E. Levi, T. S. Li, K. Lodha, C. Magneville, M. Manera, D. Margala, P. Martini, M. Maus, P. McDonald, L. Medina-Varela, A. Meisner, J. Mena-Fernández, R. Miquel, J. Moon, S. Moore, J. Moustakas, E. Mueller, A. Muñoz-Gutiérrez, A. D. Myers, S. Nadathur, L. Napolitano, R. Neveux, J. A. Newman, N. M. Nguyen, J. Nie, G. Niz, H. E. Noriega, N. Padmanabhan, E. Paillas, N. Palanque-Delabrouille, J. Pan, S. Penmetsa, W. J. Percival, M. M. Pieri, M. Pinon, C. Poppett, A. Porredon, F. Prada, A. Pérez-Fernández, I. Pérez-Ràfols, D. Rabinowitz, A. Raichoor, C. Ramírez-Pérez, S. Ramirez-Solano, M. Rashkovetskyi, C. Ravoux, M. Rezaie, J. Rich, A. Rocher, C. Rockosi, F. Rodríguez-Martínez, N. A. Roe, A. Rosado-Marin, A. J. Ross, G. Rossi, R. Ruggeri, V. Ruhlmann-Kleider, L. Samushia, E. Sanchez, C. Saulder, E. F. Schlafly, D. Schlegel, M. Schubnell, H. Seo, R. Sharples, J. Silber, A. Slosar, A. Smith, D. Sprayberry, T. Tan, G. Tarlé, S. Trusov, R. Vaisakh, D. Valcin, F. Valdes, M. Vargas-Magaña, L. Verde, M. Walther, B. Wang, M. S. Wang, B. A. Weaver, N. Weaverdyck, R. H. Wechsler, D. H. Weinberg, M. White, M. J. Wilson, J. Yu, Y. Yu, S. Yuan, C. Yèche, E. A. Zaborowski, P. Zarrouk, H. Zhang, C. Zhao, R. Zhao, R. Zhou, H. Zou
We present the measurements and cosmological implications of the galaxy two-point clustering using over 4.7 million unique galaxy and quasar redshifts in the range $0.1
Exploring the Nature of Little Red Dots: Constraints on AGN and Stellar Contributions from PRIMER MIRI Imaging
2024-11-18
Gene C. K. Leung, Steven L. Finkelstein, Pablo G. Pérez-González, Alexa M. Morales, Anthony J. Taylor, Guillermo Barro, Dale D. Kocevski, Hollis B. Akins, Adam C. Carnall, Óscar A. Chávez Ortiz, Nikko J. Cleri, Fergus Cullen, Callum T. Donnan, James S. Dunlop, Richard S. Ellis, Norman A. Grogin, Michaela Hirschmann, Anton M. Koekemoer, Vasily Kokorev, Ray A. Lucas, Derek J. McLeod, Casey Papovich, L. Y. Aaron Yung
JWST has revealed a large population of compact, red galaxies at $z>4$ known as Little Red Dots (LRDs). We analyze the spectral energy distributions (SEDs) of 95 LRDs from the JWST PRIMER survey with complete photometric coverage from $1-18\ \mu$m using NIRCam and MIRI imaging, representing the most extensive SED analysis on a large LRD sample with long-wavelength MIRI data. We examine SED models in which either galaxy or active galactic nucleus (AGN) emission dominates the rest-frame UV or optical continuum, extracting physical properties to explore each scenario's implications. In the galaxy-only model, we find massive, dusty stellar populations alongside unobscured, low-mass components, hinting at inhomogeneous obscuration. The AGN-only model indicates dusty, luminous AGNs with low hot dust fractions compared to typical quasars. A hybrid AGN and galaxy model suggests low-mass, unobscured galaxies in the UV, with stellar mass estimates spanning $\sim$2 dex across the different models, underscoring the need for caution in interpreting LRD stellar masses. With MIRI photometry, the galaxy-only model produces stellar masses within cosmological limits, but extremely high stellar mass densities are inferred. The hybrid model infers highly overmassive black holes exceeding those in recently reported high-redshift AGNs, hinting at a partial AGN contribution to the rest-optical continuum or widespread super-Eddington accretion. Our findings highlight the extreme conditions required for both AGN or galaxy dominated scenarios in LRDs, supporting a mixed contribution to the red continuum, or novel scenarios to explain the observed emission.
Disc asymmetry characterisation in JWST-observed galaxies at 1 < z < 4
2024-11-18
Ananya Ganapathy, Michael S. Petersen, Rashid Yaaqib, Carrie Filion
We present a novel technique using Fourier series and Laguerre polynomials to represent morphological features of disc galaxies. To demonstrate the utility of this technique, we study the evolution of asymmetry in a sample of disc galaxies drawn from the Extended Groth Strip and imaged by the JWST Cosmic Evolution Early Release Science Survey as well as archival HST observations. We measure disc asymmetry as the amplitude of the of the m = 1 Fourier harmonic for galaxies within redshift ranges of 1 < z < 4. We show that when viewed in shorter rest frame wavelengths, disc galaxies have a higher asymmetry as the flux is dominated by star forming regions. We find generally low asymmetry at rest frame infrared wavelengths, where our metric tracks asymmetry in morphological features such as bars and spiral arms. We show that higher mass galaxies have lower asymmetry and vice versa. Higher asymmetry in lower mass galaxies comes from lower mass galaxies (typically) having higher star formation rates. We measure the relation between disc galaxy asymmetry and redshift and find no conclusive relationship between them. We demonstrate the utility of the Fourier-Laguerre technique for recovering physically informative asymmetry measurements as compared to rotational asymmetry measurements. We also release the software pipeline and quantitative analysis for each galaxy.
The Gravitational Wave Bias Parameter from Angular Power Spectra: Bridging Between Galaxies and Binary Black Holes
2024-11-18
Amir Dehghani, J. Leo Kim, Dorsa Sadat Hosseini, Alex Krolewski, Suvodip Mukherjee, Ghazal Geshnizjani
This study presents the modeling of the gravitational wave (GW) bias parameter by bridging a connection between simulated GW sources and galaxies in low redshift galaxy surveys 2MPZ and WISExSCOS (WISC). We study this connection by creating a mock GW catalog, populating galaxy surveys with binary black holes (BBHs) for different scenarios of the GW occupation fraction (or selection function) as a function of the galaxy stellar mass. We probe the observable consequences of this connection by exploring the spatial clustering of the GW sources in terms of the GW bias parameter. We consider a phenomenological broken power law model for the selection function, with a potential turnover $M_{K}$ at high stellar mass ($10^{11}$ $M_{\odot}$ in the fiducial model) where the star formation efficiency begins to drop. We vary the parameters of the selection function and find that generically the GW bias increases as $M_{K}$ increases (and gets suppressed as $M_{K}$ decreases). The change in the GW bias parameter shows a maximum change of about $30\%$ for different scenarios explored in this work in comparison to the galaxy bias. Future measurements of the GW bias can help constrain $M_{K}$ and the slopes of the selection function and thus offer insights into the underlying astrophysical processes.
MUSEQuBES: Connecting HI absorption with Ly$α$ emitters at $z \approx 3.3$
2024-11-18
Eshita Banerjee, Sowgat Muzahid, Joop Schaye, Jérémy Blaizot, Nicolas Bouché, Sebastiano Cantalupo, Sean D. Johnson, Jorryt Matthee, Anne Verhamme
We present a comprehensive analysis of HI absorption around 96 lya emitters (LAEs) at $z\approx3.3$ (median lya luminosity $\approx10^{42}$ erg.s$^{-1}$). These LAEs were identified within 8 MUSE fields, each $1'\times1'$ on the sky and centered on a bright background quasar, as part of the MUSEQuBES survey. Using Voigt profile fitting for all HI absorbers detected within $\pm500$ km.$s^{-1}$ of these LAEs, we compiled a catalog of 800 HI absorption components. Our analysis shows that HI absorption is enhanced near the LAEs compared to the IGM. However, no trend is found between the column densities of HI absorbers and their impact parameters from the LAEs (spanning $\approx54$ to 260 pkpc). Additionally, all galaxies associated with Lyman-limit systems have impact parameters $>50$ pkpc from the quasar sightlines, suggesting that true absorber-hosts may be too faint to detect. The LAEs show an overall HI covering fraction (fc(HI)) of $\approx88\%$ for a threshold logN(HI)$=15$. Notably, at the same threshold, the pairs/group LAEs exhibit a $100\%$ HI covering fraction out to $\approx 250$ pkpc. In contrast, isolated LAEs consistently show a lower fc(HI) of $\approx80\%$. This environmental influence on fc(HI) is also evident up to $\approx 300$ km.$s^{-1}$ in differential bins of line-of-sight velocity. We find an anti-correlation between fc(HI) and the rest-frame lya-emission equivalent width (ew). Based on the lya-shell model, this could imply that gas-rich galaxies tend to reside in gas-rich environments or that the higher EW LAEs are more efficient at ionizing their surrounding medium.
Protoclusters are high-$z$ overdense regions that will evolve into clusters of galaxies by $z=0$, making them ideal for studying galaxy evolution expected to be accelerated by environmental effects. However, it has been challenging to identify protoclusters beyond $z=3$ only by photometry due to large redshift uncertainties, hindering statistical study. To tackle the issue, we develop a new deep-learning-based protocluster detection model, PCFNet, which considers a protocluster as a point cloud. To detect protoclusters at $z\sim4$ using only optical broad-band photometry, we train and evaluate PCFNet with mock $g$-dropout galaxies based on the N-body simulation with the semi-analytic model. We use the sky distribution, $i$-band magnitude, $(g-i)$ color, and the redshift probability density function surrounding a target galaxy on the sky. PCFNet achieves to detect five times more protocluster member candidates while maintaining high purity (recall $=7.5\pm0.2$%, precision $=44\pm1$%) than conventional methods. Moreover, PCFNet is able to detect more progenitors ($M_\mathrm{halo}^{z=0}=10^{14-14.5}\,M_\odot$) that are less massive than supermassive clusters like the Coma cluster. We apply PCFNet to the observational photometric dataset of the HSC-SSP Deep/UltraDeep layer ($\sim17\mathrm{\,deg^2}$) and detect $121$ protocluster candidates at $z\sim4$. We find the rest-UV luminosities of our protocluster member candidates are brighter than those of field galaxies, which is consistent with previous studies. Additionally, the quenching of satellite galaxies depends on both the core galaxy's halo mass at $z\sim4$ and accumulated mass until $z=0$ in the simulation. PCFNet is very flexible and can find protoclusters at other redshifts or in future extensive surveys by Euclid, LSST, and Roman.
Introducing Milabench: Benchmarking Accelerators for AI
2024-11-18
Pierre Delaunay, Xavier Bouthillier, Olivier Breuleux, Satya Ortiz-Gagné, Olexa Bilaniuk, Fabrice Normandin, Arnaud Bergeron, Bruno Carrez, Guillaume Alain, Soline Blanc, Frédéric Osterrath, Joseph Viviano, Roger Creus-Castanyer Darshan Patil, Rabiul Awal, Le Zhang
AI workloads, particularly those driven by deep learning, are introducing novel usage patterns to high-performance computing (HPC) systems that are not comprehensively captured by standard HPC benchmarks. As one of the largest academic research centers dedicated to deep learning, Mila identified the need to develop a custom benchmarking suite to address the diverse requirements of its community, which consists of over 1,000 researchers. This report introduces Milabench, the resulting benchmarking suite. Its design was informed by an extensive literature review encompassing 867 papers, as well as surveys conducted with Mila researchers. This rigorous process led to the selection of 26 primary benchmarks tailored for procurement evaluations, alongside 16 optional benchmarks for in-depth analysis. We detail the design methodology, the structure of the benchmarking suite, and provide performance evaluations using GPUs from NVIDIA, AMD, and Intel. The Milabench suite is open source and can be accessed at github.com/mila-iqia/milabench.
LLMs are increasingly fine-tuned using RLHF datasets to align them with human preferences and values. However, very limited research has investigated which specific human values are operationalized through these datasets. In this paper, we introduce Value Imprint, a framework for auditing and classifying the human values embedded within RLHF datasets. To investigate the viability of this framework, we conducted three case study experiments by auditing the Anthropic/hh-rlhf, OpenAI WebGPT Comparisons, and Alpaca GPT-4-LLM datasets to examine the human values embedded within them. Our analysis involved a two-phase process. During the first phase, we developed a taxonomy of human values through an integrated review of prior works from philosophy, axiology, and ethics. Then, we applied this taxonomy to annotate 6,501 RLHF preferences. During the second phase, we employed the labels generated from the annotation as ground truth data for training a transformer-based machine learning model to audit and classify the three RLHF datasets. Through this approach, we discovered that information-utility values, including Wisdom/Knowledge and Information Seeking, were the most dominant human values within all three RLHF datasets. In contrast, prosocial and democratic values, including Well-being, Justice, and Human/Animal Rights, were the least represented human values. These findings have significant implications for developing language models that align with societal values and norms. We contribute our datasets to support further research in this area.
Feynman's Relativistic Cut-Off for Quantum Electrodynamics
2024-11-18
Hari Chapagain
Feynman's modification to electrodynamics and its application to the calculation of self-energy of a free spin-$\frac{1}{2}$ particle, appearing in his 1948 Physical Review paper, is shown to be applicable for the self-energy calculation of a free spin-0 particle as well. Feynman's modification to electrodynamics is shown to be equivalent to a Hamiltonian approach developed by Podolsky.
Exploring the Potential Role of Generative AI in the TRAPD Procedure for Survey Translation
2024-11-18
Erica Ann Metheney, Lauren Yehle
This paper explores and assesses in what ways generative AI can assist in translating survey instruments. Writing effective survey questions is a challenging and complex task, made even more difficult for surveys that will be translated and deployed in multiple linguistic and cultural settings. Translation errors can be detrimental, with known errors rendering data unusable for its intended purpose and undetected errors leading to incorrect conclusions. A growing number of institutions face this problem as surveys deployed by private and academic organizations globalize, and the success of their current efforts depends heavily on researchers' and translators' expertise and the amount of time each party has to contribute to the task. Thus, multilinguistic and multicultural surveys produced by teams with limited expertise, budgets, or time are at significant risk for translation-based errors in their data. We implement a zero-shot prompt experiment using ChatGPT to explore generative AI's ability to identify features of questions that might be difficult to translate to a linguistic audience other than the source language. We find that ChatGPT can provide meaningful feedback on translation issues, including common source survey language, inconsistent conceptualization, sensitivity and formality issues, and nonexistent concepts. In addition, we provide detailed information on the practicality of the approach, including accessing the necessary software, associated costs, and computational run times. Lastly, based on our findings, we propose avenues for future research that integrate AI into survey translation practices.
The widespread adoption of deep learning across various industries has introduced substantial challenges, particularly in terms of model explainability and security. The inherent complexity of deep learning models, while contributing to their effectiveness, also renders them susceptible to adversarial attacks. Among these, backdoor attacks are especially concerning, as they involve surreptitiously embedding specific triggers within training data, causing the model to exhibit aberrant behavior when presented with input containing the triggers. Such attacks often exploit vulnerabilities in outsourced processes, compromising model integrity without affecting performance on clean (trigger-free) input data. In this paper, we present a comprehensive review of existing mitigation strategies designed to counter backdoor attacks in image recognition. We provide an in-depth analysis of the theoretical foundations, practical efficacy, and limitations of these approaches. In addition, we conduct an extensive benchmarking of sixteen state-of-the-art approaches against eight distinct backdoor attacks, utilizing three datasets, four model architectures, and three poisoning ratios. Our results, derived from 122,236 individual experiments, indicate that while many approaches provide some level of protection, their performance can vary considerably. Furthermore, when compared to two seminal approaches, most newer approaches do not demonstrate substantial improvements in overall performance or consistency across diverse settings. Drawing from these findings, we propose potential directions for developing more effective and generalizable defensive mechanisms in the future.
Early Adoption of Generative Artificial Intelligence in Computing Education: Emergent Student Use Cases and Perspectives in 2023
2024-11-17
C. Estelle Smith, Kylee Shiekh, Hayden Cooreman, Sharfi Rahman, Yifei Zhu, Md Kamrul Siam, Michael Ivanitskiy, Ahmed M. Ahmed, Michael Hallinan, Alexander Grisak, Gabe Fierro
Because of the rapid development and increasing public availability of Generative Artificial Intelligence (GenAI) models and tools, educational institutions and educators must immediately reckon with the impact of students using GenAI. There is limited prior research on computing students' use and perceptions of GenAI. In anticipation of future advances and evolutions of GenAI, we capture a snapshot of student attitudes towards and uses of yet emerging GenAI, in a period of time before university policies had reacted to these technologies. We surveyed all computer science majors in a small engineering-focused R1 university in order to: (1) capture a baseline assessment of how GenAI has been immediately adopted by aspiring computer scientists; (2) describe computing students' GenAI-related needs and concerns for their education and careers; and (3) discuss GenAI influences on CS pedagogy, curriculum, culture, and policy. We present an exploratory qualitative analysis of this data and discuss the impact of our findings on the emerging conversation around GenAI and education.
Leveraging genomic deep learning models for non-coding variant effect prediction
2024-11-17
Pooja Kathail, Ayesha Bajwa, Nilah M. Ioannidis
The majority of genetic variants identified in genome-wide association studies of complex traits are non-coding, and characterizing their function remains an important challenge in human genetics. Genomic deep learning models have emerged as a promising approach to enable in silico prediction of variant effects. These include supervised sequence-to-activity models, which predict genome-wide chromatin states or gene expression levels directly from DNA sequence, and self-supervised genomic language models. Here, we review progress in leveraging these models for non-coding variant effect prediction. We describe practical considerations for making such predictions and categorize the types of ground truth data that have been used to evaluate deep learning-based variant effect predictions, providing insight into the settings in which current models are most useful. We also discuss downstream applications of such models to understanding disease-relevant non-coding variants. Our review highlights key considerations for practitioners and opportunities for future improvements in model development and evaluation.
Visual question answering (VQA) refers to the problem where, given an image and a natural language question about the image, a correct natural language answer has to be generated. A VQA model has to demonstrate both the visual understanding of the image and the semantic understanding of the question, demonstrating reasoning capability. Since the inception of this field, a plethora of VQA datasets and models have been published. In this article, we meticulously analyze the current state of VQA datasets and models, while cleanly dividing them into distinct categories and then summarizing the methodologies and characteristics of each category. We divide VQA datasets into four categories: (1) available datasets that contain a rich collection of authentic images, (2) synthetic datasets that contain only synthetic images produced through artificial means, (3) diagnostic datasets that are specially designed to test model performance in a particular area, e.g., understanding the scene text, and (4) KB (Knowledge-Based) datasets that are designed to measure a model's ability to utilize outside knowledge. Concurrently, we explore six main paradigms of VQA models: fusion, where we discuss different methods of fusing information between visual and textual modalities; attention, the technique of using information from one modality to filter information from another; external knowledge base, where we discuss different models utilizing outside information; composition or reasoning, where we analyze techniques to answer advanced questions that require complex reasoning steps; explanation, which is the process of generating visual and textual descriptions to verify sound reasoning; and graph models, which encode and manipulate relationships through nodes in a graph. We also discuss some miscellaneous topics, such as scene text understanding, counting, and bias reduction.
From 2D Document Interactions into Immersive Information Experience: An Example-Based Design by Augmenting Content, Spatializing Placement, Enriching Long-Term Interactions, and Simplifying Content Creations
2024-11-17
Chen Chen
Documents serve as a crucial and indispensable medium for everyday workplace tasks. However, understanding, interacting and creating such documents on today's planar interfaces without any intelligent support are challenging due to our natural cognitive constraints on remembering, processing, understanding and interacting with these information. My doctorate research investigates how to bring 2D document interactions into immersive information experience using multiple of today's emergent technologies. With the examples of four specific types of documents -- medical scans, instruction document, self-report diary survey, and reference images for visual artists -- my research demonstrates how to transform such of today's 2D document interactions into an immersive information experience, by augmenting content with virtual reality, spatializing document placements with mixed reality, enriching long-term and continuous interactions with voice assistants, and simplify document creation workflow with generative AI.
Thermal Activation Signatures of the Anderson Insulator and the Wigner Solid forming near $ν=1$
2024-11-17
S. A. Myers, Haoyun Huang, Waseem Hussain, L. N. Pfeiffer, K. W. West, G. A. Csáthy
When interactions overcome disorder, integer quantum Hall plateaus support topological phases with different bulk insulators. In the center of the $\nu=1$ plateau the bulk is an Anderson-type insulator, while in the flanks of the plateau the bulk is the integer quantum Hall Wigner solid. We find that the activation energy along the $\nu=1$ plateau exhibits a very dramatic non-monotonic dependence on the magnetic field, a dependence that is strongly correlated with the stability regions of the two phases. Furthermore, the activation energy has an unexpected minimum at the boundary between the Anderson insulator and the Wigner solid. Our findings constrain the theory of the integer quantum Hall Wigner solid, determine its thermodynamic properties, and reveal novel behavior at the boundary between the Anderson insulator and the Wigner solid.
Greenhouse warming potential of a suite of gas species on early Mars evaluated using a radiative-convective climate model
2024-11-17
Jason Jorge, Robin Wordsworth, Danica Adams
Abundant geomorphological and geochemical evidence of liquid water on the surface of early Mars during the late Noachian and early Hesperian periods needs to be reconciled with a fainter young Sun. While a dense CO2 atmosphere and related warming mechanisms are potential solutions to the early Mars climate problem, further investigation is warranted. Here, we complete a comprehensive survey of the warming potential of all known greenhouse gases and perform detailed calculations for 15 different minor gas species under early Martian conditions. We find that of these 15 species, H2O2, HNO3, NH3, SO2, and C2H4 cause significant greenhouse warming at concentrations of ~0.1 ppmv or greater. However, the most highly effective greenhouse gas species also tend to be more condensable, soluble and vulnerable to photolytic destruction. To provide a reference for future atmospheric evolution and photochemical studies, we have made our warming potential database freely available online.
This paper provides a comprehensive survey of the latest research on multilingual large language models (MLLMs). MLLMs not only are able to understand and generate language across linguistic boundaries, but also represent an important advancement in artificial intelligence. We first discuss the architecture and pre-training objectives of MLLMs, highlighting the key components and methodologies that contribute to their multilingual capabilities. We then discuss the construction of multilingual pre-training and alignment datasets, underscoring the importance of data quality and diversity in enhancing MLLM performance. An important focus of this survey is on the evaluation of MLLMs. We present a detailed taxonomy and roadmap covering the assessment of MLLMs' cross-lingual knowledge, reasoning, alignment with human values, safety, interpretability and specialized applications. Specifically, we extensively discuss multilingual evaluation benchmarks and datasets, and explore the use of LLMs themselves as multilingual evaluators. To enhance MLLMs from black to white boxes, we also address the interpretability of multilingual capabilities, cross-lingual transfer and language bias within these models. Finally, we provide a comprehensive review of real-world applications of MLLMs across diverse domains, including biology, medicine, computer science, mathematics and law. We showcase how these models have driven innovation and improvements in these specialized fields while also highlighting the challenges and opportunities in deploying MLLMs within diverse language communities and application scenarios. We listed the paper related in this survey and publicly available at https://github.com/tjunlp-lab/Awesome-Multilingual-LLMs-Papers.
Energy conditions are attempts to summarise the properties of realistic descriptions of matter via constraints on the energy-momentum tensor. This is, for example, useful when one wants to understand the types of spacetime geometry that can be realised in general relativity. However, it is currently unknown what (if any) energy condition a realistic quantum theory interacting gravitationally would obey. In this thesis, we provide support for the conformally invariant averaged null energy condition (CANEC), a particular proposal towards a more generally valid energy condition. We motivate our interest in the CANEC by reviewing the role of the null energy condition (NEC) in classical gravity, summarising a general proof of the averaged NEC (ANEC) for quantum field theories in flat spacetime, and going over a holographic proof of the CANEC in a class of curved spacetimes. The context in which the CANEC applies is then extended with two proofs: a holographic proof in two types of spacetime to which the existing proof does not apply, and a field theoretical proof for conformal field theories in conformally flat spacetimes. Finally, we consider light-ray operators in Minkowski spacetime. The connection between these operators and the CANEC is that the CANEC on a lightcone in Minkowski spacetime constrains a light-ray operator for an even number of spacetime dimensions $d$. Via an explicit computation, we show that many light-ray operators are positive-semidefinite in a class of non-minimally coupled but otherwise free real scalar field theories. One of the operators for which this holds is the CANEC-operator on the lightcone for even $d$.
The fundamental idea of the excitonic insulator (EI) driven by electron-hole correlations in narrow-gap semiconductors or semimetals was originally proposed in the 1960s, and only theoretical studies had been advanced for a long time. However, the rise of new candidate materials and recent developments in measurement techniques have enabled us to discuss the possibilities of EI states in real materials experimentally. In this article, we review recent progress in the research of EIs. We start with an introduction to the theoretical background of the EI and the mechanism of the order formation including its relation to the physics of the Bardeen-Cooper-Schrieffer (BCS) - Bose-Einstein condensation (BEC) crossover. We also review the EI states studied in the context of strongly correlated electron systems. Then, we introduce the candidate materials for the EI and the issues raised by recent experiments. For example, the phase transitions in several candidate materials are accompanied by lattice distortions, and the contributions from electron-lattice coupling hinder the identification of the excitonic entity. We also review the profiles of the collective modes to discuss the dynamical signatures of the EI.
Accuracy of Stellar Mass-to-light Ratios of Nearby Galaxies in the Near-Infrared
2024-11-17
Taehyun Kim, Minjin Kim, Luis C. Ho, Yang A. Li, Woong-Seob Jeong, Dohyeong Kim, Yongjung Kim, Bomee Lee, Dongseob Lee, Jeong Hwan Lee, Jeonghyun Pyo, Hyunjin Shim, Suyeon Son, Hyunmi Song, Yujin Yang
Future satellite missions are expected to perform all-sky surveys, thus providing the entire sky near-infrared spectral data and consequently opening a new window to investigate the evolution of galaxies. Specifically, the infrared spectral data facilitate the precise estimation of stellar masses of numerous low-redshift galaxies. We utilize the synthetic spectral energy distribution (SED) of 2853 nearby galaxies drawn from the DustPedia (435) and Stripe 82 regions (2418). The stellar mass-to-light ratio ($M_*/L$) estimation accuracy over a wavelength range of $0.75-5.0$ $\mu$m is computed through the SED fitting of the multi-wavelength photometric dataset, which has not yet been intensively explored in previous studies. We find that the scatter in $M_*/L$ is significantly larger in the shorter and longer wavelength regimes due to the effect of the young stellar population and the dust contribution, respectively. While the scatter in $M_*/L$ approaches its minimum ($\sim0.10$ dex) at $\sim1.6$ $\mu$m, it remains sensitive to the adopted star formation history model. Furthermore, $M_*/L$ demonstrates weak and strong correlations with the stellar mass and the specific star formation rate (SFR), respectively. Upon adequately correcting the dependence of $M_*/L$ on the specific SFR, the scatter in the $M_*/L$ further reduces to $0.02$ dex at $\sim1.6$ $\mu$m. This indicates that the stellar mass can be estimated with an accuracy of $\sim0.02$ dex with a prior knowledge of SFR, which can be estimated using the infrared spectra obtained with future survey missions.
Random walk models in the life sciences: including births, deaths and local interactions
2024-11-17
Michael J. Plank, Matthew J. Simpson, Ruth E. Baker
Random walks and related spatial stochastic models have been used in a range of application areas including animal and plant ecology, infectious disease epidemiology, developmental biology, wound healing, and oncology. Classical random walk models assume that all individuals in a population behave independently, ignoring local physical and biological interactions. This assumption simplifies the mathematical description of the population considerably, enabling continuum-limit descriptions to be derived and used in model analysis and fitting. However, interactions between individuals can have a crucial impact on population-level behaviour. In recent decades, research has increasingly been directed towards models that include interactions, including physical crowding effects and local biological processes such as adhesion, competition, dispersal, predation and adaptive directional bias. In this article, we review the progress that has been made with models of interacting individuals. We aim to provide an overview that is accessible to researchers in application areas, as well as to specialist modellers. We focus particularly on derivation of asymptotically exact or approximate continuum-limit descriptions and simplified deterministic models of mean-field behaviour and resulting spatial patterns. We provide worked examples and illustrative results of selected models. We conclude with a discussion of current areas of focus and future challenges.
While traditional program evaluations typically rely on surveys to measure outcomes, certain economic outcomes such as living standards or environmental quality may be infeasible or costly to collect. As a result, recent empirical work estimates treatment effects using remotely sensed variables (RSVs), such mobile phone activity or satellite images, instead of ground-truth outcome measurements. Common practice predicts the economic outcome from the RSV, using an auxiliary sample of labeled RSVs, and then uses such predictions as the outcome in the experiment. We prove that this approach leads to biased estimates of treatment effects when the RSV is a post-outcome variable. We nonparametrically identify the treatment effect, using an assumption that reflects the logic of recent empirical research: the conditional distribution of the RSV remains stable across both samples, given the outcome and treatment. Our results do not require researchers to know or consistently estimate the relationship between the RSV, outcome, and treatment, which is typically mis-specified with unstructured data. We form a representation of the RSV for downstream causal inference by predicting the outcome and predicting the treatment, with better predictions leading to more precise causal estimates. We re-evaluate the efficacy of a large-scale public program in India, showing that the program's measured effects on local consumption and poverty can be replicated using satellite
Generalist Virtual Agents: A Survey on Autonomous Agents Across Digital Platforms
2024-11-17
Minghe Gao, Wendong Bu, Bingchen Miao, Yang Wu, Yunfei Li, Juncheng Li, Siliang Tang, Qi Wu, Yueting Zhuang, Meng Wang
In this paper, we introduce the Generalist Virtual Agent (GVA), an autonomous entity engineered to function across diverse digital platforms and environments, assisting users by executing a variety of tasks. This survey delves into the evolution of GVAs, tracing their progress from early intelligent assistants to contemporary implementations that incorporate large-scale models. We explore both the philosophical underpinnings and practical foundations of GVAs, addressing their developmental challenges and the methodologies currently employed in their design and operation. By presenting a detailed taxonomy of GVA environments, tasks, and capabilities, this paper aims to bridge the theoretical and practical aspects of GVAs, concluding those that operate in environments closely mirroring the real world are more likely to demonstrate human-like intelligence. We discuss potential future directions for GVA research, highlighting the necessity for realistic evaluation metrics and the enhancement of long-sequence decision-making capabilities to advance the field toward more systematic or embodied applications. This work not only synthesizes the existing body of literature but also proposes frameworks for future investigations, contributing significantly to the ongoing development of intelligent systems.
AIGS: Generating Science from AI-Powered Automated Falsification
2024-11-17
Zijun Liu, Kaiming Liu, Yiqi Zhu, Xuanyu Lei, Zonghan Yang, Zhenhe Zhang, Peng Li, Yang Liu
Rapid development of artificial intelligence has drastically accelerated the development of scientific discovery. Trained with large-scale observation data, deep neural networks extract the underlying patterns in an end-to-end manner and assist human researchers with highly-precised predictions in unseen scenarios. The recent rise of Large Language Models (LLMs) and the empowered autonomous agents enable scientists to gain help through interaction in different stages of their research, including but not limited to literature review, research ideation, idea implementation, and academic writing. However, AI researchers instantiated by foundation model empowered agents with full-process autonomy are still in their infancy. In this paper, we study $\textbf{AI-Generated Science}$ (AIGS), where agents independently and autonomously complete the entire research process and discover scientific laws. By revisiting the definition of scientific research, we argue that $\textit{falsification}$ is the essence of both human research process and the design of an AIGS system. Through the lens of falsification, prior systems attempting towards AI-Generated Science either lack the part in their design, or rely heavily on existing verification engines that narrow the use in specialized domains. In this work, we propose Baby-AIGS as a baby-step demonstration of a full-process AIGS system, which is a multi-agent system with agents in roles representing key research process. By introducing FalsificationAgent, which identify and then verify possible scientific discoveries, we empower the system with explicit falsification. Experiments on three tasks preliminarily show that Baby-AIGS could produce meaningful scientific discoveries, though not on par with experienced human researchers. Finally, we discuss on the limitations of current Baby-AIGS, actionable insights, and related ethical issues in detail.
Two-dimensional (2D) carrier systems exhibit various significant physical phenomena for electronics and spintronics, where one of the most promising traits is efficient spin-to-charge conversion stemming from their Rashba-type spin-orbit interaction. Meanwhile, a nuisance in quests of promising materials for spintronics application is that vast majority of the investigated platforms consists of rare and/or toxic elements, such as Pt and Te, which hinders progress of spin conversion physics in view of element strategy and green technology. Here, we show the emergence of giant spin-orbit torque driven by 2D hole gas at the surface of hydrogen-terminated diamond, where the constituent substances are ubiquitous elements, carbon and hydrogen. The index of its spin torque efficiency at room temperature is several times greater than that of rare metal, Pt, the benchmark system/element for spin-to-charge conversion. Our finding opens a new pathway for more sustainable spintronics and spin-orbitronics applications, with efficient spin-orbit torque employing ubiquitous non-toxic elements.
Bias in Large Language Models: Origin, Evaluation, and Mitigation
2024-11-16
Yufei Guo, Muzhe Guo, Juntao Su, Zhou Yang, Mengqiu Zhu, Hongfei Li, Mengyang Qiu, Shuo Shuo Liu
Large Language Models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This comprehensive review examines the landscape of bias in LLMs, from its origins to current mitigation strategies. We categorize biases as intrinsic and extrinsic, analyzing their manifestations in various NLP tasks. The review critically assesses a range of bias evaluation methods, including data-level, model-level, and output-level approaches, providing researchers with a robust toolkit for bias detection. We further explore mitigation strategies, categorizing them into pre-model, intra-model, and post-model techniques, highlighting their effectiveness and limitations. Ethical and legal implications of biased LLMs are discussed, emphasizing potential harms in real-world applications such as healthcare and criminal justice. By synthesizing current knowledge on bias in LLMs, this review contributes to the ongoing effort to develop fair and responsible AI systems. Our work serves as a comprehensive resource for researchers and practitioners working towards understanding, evaluating, and mitigating bias in LLMs, fostering the development of more equitable AI technologies.
The Odd-Parity Part of the Observed Galaxy Trispectrum
2024-11-16
Pritha Paul, Chris Clarkson, Roy Maartens
Recently the galaxy matter density 4-point correlation function has been looked at to investigate parity violation in large scale structure surveys. The 4-point correlation function is the lowest order statistic which is sensitive to parity violation, since a tetrahedron is the simplest shape that cannot be superimposed on its mirror image by a rotation. If the parity violation is intrinsic in nature, this could give us a window into inflationary physics. However, we need to exhaust all other contaminations before we consider them to be intrinsic. Even though the standard Newtonian redshift-space distortions are parity symmetric, the full relativistic picture is not. Therefore, we expect a parity-odd trispectrum when observing in redshift space. We calculate the trispectrum with the leading-order relativistic effects and investigate in detail the parameter space of the trispectrum and the effects of these relativistic corrections for different parameter values and configurations. We also look at different surveys and how the evolution and magnification biases can be affected by different parameter choices.
Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Coding
2024-11-16
Trevor Stalnaker, Nathan Wintersgill, Oscar Chaparro, Laura A. Heymann, Massimiliano Di Penta, Daniel M German, Denys Poshyvanyk
Generative AI (GenAI) tools have already started to transform software development practices. Despite their utility in tasks such as writing code, the use of these tools raises important legal questions and potential risks, particularly those associated with copyright law. In the midst of this uncertainty, this paper presents a study jointly conducted by software engineering and legal researchers that surveyed 574 GitHub developers who use GenAI tools for development activities. The survey and follow-up interviews probed the developers' opinions on emerging legal issues as well as their perception of copyrightability, ownership of generated code, and related considerations. We also investigate potential developer misconceptions, the impact of GenAI on developers' work, and developers' awareness of licensing/copyright risks. Qualitative and quantitative analysis showed that developers' opinions on copyright issues vary broadly and that many developers are aware of the nuances these legal questions involve. We provide: (1) a survey of 574 developers on the licensing and copyright aspects of GenAI for coding, (2) a snapshot of practitioners' views at a time when GenAI and perceptions of it are rapidly evolving, and (3) an analysis of developers' views, yielding insights and recommendations that can inform future regulatory decisions in this evolving field.
Finding Permutiples of a Known Base and Multiplier
2024-11-16
Benjamin V. Holt
Natural numbers which are nontrivial multiples of some permutation of their base-$b$ digit representations are called permutiples. Specific cases include numbers which are multiples of cyclic permutations (cyclic numbers) and reversals of their digits (palintiples). Previous efforts have produced methods which construct new examples of permutiples with the same set of digits as a known example. Using simple graph-theoretical and finite-state machine constructions, we advance previous work by describing two methods for finding permutiples of a known base and multiplier with no need for known examples or prior knowledge of digits.
DEAL: Decoupled Classifier with Adaptive Linear Modulation for Group Robust Early Diagnosis of MCI to AD Conversion
2024-11-16
Donggyu Lee, Juhyeon Park, Taesup Moon
While deep learning-based Alzheimer's disease (AD) diagnosis has recently made significant advancements, particularly in predicting the conversion of mild cognitive impairment (MCI) to AD based on MRI images, there remains a critical gap in research regarding the group robustness of the diagnosis. Although numerous studies pointed out that deep learning-based classifiers may exhibit poor performance in certain groups by relying on unimportant attributes, this issue has been largely overlooked in the early diagnosis of MCI to AD conversion. In this paper, we present the first comprehensive investigation of the group robustness in the early diagnosis of MCI to AD conversion using MRI images, focusing on disparities in accuracy between groups, specifically sMCI and pMCI individuals divided by age. Our experiments reveal that standard classifiers consistently underperform for certain groups across different architectures, highlighting the need for more tailored approaches. To address this, we propose a novel method, dubbed DEAL (DEcoupled classifier with Adaptive Linear modulation), comprising two key components: (1) a linear modulation of features from the penultimate layer, incorporating easily obtainable age and cognitive indicative tabular features, and (2) a decoupled classifier that provides more tailored decision boundaries for each group, further improving performance. Through extensive experiments and evaluations across different architectures, we demonstrate the efficacy of DEAL in improving the group robustness of the MCI to AD conversion prediction.
The crab-waist collision scheme has been the baseline choice for SuperKEKB and future circular $e^+e^-$ colliders. Achieved through properly phased sextupoles, the crab-waist transform is essential in suppressing beam-beam resonances, thereby enabling high luminosity in these colliders. In this paper, we explore potential sources of imperfections that may compromise the effectiveness of the crab-waist transform. We begin by reviewing the theoretical framework of the ideal crab-waist scheme and the associated weak-strong beam-beam resonances. Following this, we analyze how machine imperfections could amplify these resonances, thereby impacting collider performance. Finally, we briefly address the connections between theoretical models, simulations, and beam experiments, with a particular focus on the use of weak-strong beam experiments to identify and diagnose potential imperfections in machine settings.
Existence of $ε$-Nash Equilibria in Nonzero-Sum Borel Stochastic Games and Equilibria of Quantized Models
2024-11-16
Naci Saldi, Gurdal Arslan, Serdar Yuksel
Establishing the existence of exact or near Markov or stationary perfect Nash equilibria in nonzero-sum Markov games over Borel spaces remains a challenging problem, with few positive results to date. In this paper, we establish the existence of approximate Markov and stationary Nash equilibria for nonzero-sum stochastic games over Borel spaces, assuming only mild regularity conditions on the model. Our approach involves analyzing a quantized version of the game, for which we provide an explicit construction under both finite-horizon and discounted cost criteria. This work has significant implications for emerging applications such as multi-agent learning. Our results apply to both compact and non-compact state spaces. For the compact state space case, we first approximate the standard Borel model with a finite state-action model. Using the existence of Markov and stationary perfect Nash equilibria for these finite models under finite-horizon and discounted cost criteria, we demonstrate that these joint policies constitute approximate Markov and stationary perfect equilibria under mild continuity conditions on the one-stage costs and transition probabilities. For the non-compact state space case, we achieve similar results by first approximating the model with a compact-state model. Compared with previous results in the literature, which we comprehensively review, we provide more general and complementary conditions, along with explicit approximation models whose equilibria are $\epsilon$-equilibria for the original model.
Temperature dependence of charge transport in the half-filled 1D Hubbard model
2024-11-16
J. M. P. Carmelo, P. D. Sacramento
The use of hydrodynamic transport theory seems to indicate that the charge diffusion constant D of the one-dimensional (1D) half-filled Hubbard model, whose Drude weight vanishes, diverges for temperature T>0, which would imply anomalous superdiffusive charge transport. Here the leading term of that constant is derived for low finite temperatures much smaller than the the Mott-Hubbard gap. It only diverges in the temperature infinite limit, being finite and decreasing upon increasing T within the low-temperature regime. Our exact results both provide valuable physical information ona complex quantum problem and bring about the interesting unsolved issue of how charge transport evolves from normal diffusive for low temperatures to anomalous superdiffusive in the infinite temperature limit.
Advances in optical microscopy scanning have significantly contributed to computational pathology (CPath) by converting traditional histopathological slides into whole slide images (WSIs). This development enables comprehensive digital reviews by pathologists and accelerates AI-driven diagnostic support for WSI analysis. Recent advances in foundational pathology models have increased the need for benchmarking tasks. The Camelyon series is one of the most widely used open-source datasets in computational pathology. However, the quality, accessibility, and clinical relevance of the labels have not been comprehensively evaluated. In this study, we reprocessed 1,399 WSIs and labels from the Camelyon-16 and Camelyon-17 datasets, removing low-quality slides, correcting erroneous labels, and providing expert pixel annotations for tumor regions in the previously unreleased test set. Based on the sizes of re-annotated tumor regions, we upgraded the binary cancer screening task to a four-class task: negative, micro-metastasis, macro-metastasis, and Isolated Tumor Cells (ITC). We reevaluated pre-trained pathology feature extractors and multiple instance learning (MIL) methods using the cleaned dataset, providing a benchmark that advances AI development in histopathology.
Symmetry-protected Landau-Zener-Stückelberg-Majorana interference and non-adiabatic topological transport of edge states
2024-11-16
Shi Hu, Shihao Li, Meiqing Hu, Zhoutao Lei
We systematically investigate Landau-Zener-St\"uckelberg-Majorana (LZSM) interference under chiral-mirror-like symmetry and propose its application to non-adiabatic topological transport of edge states. Protected by this symmetry, complete destructive interference emerges and can be characterized through occupation probability. This symmetry-protected LZSM interference enables state transitions to be achieved within remarkably short time scales. To demonstrate our mechanism, we provide two distinctive two-level systems as examples and survey them in detail. By tuning evolution speed or increasing holding time, the complete destructive interferences are observed. Furthermore, we make use of this mechanism for topological edge states of Su-Schrieffer-Heeger (SSH) chain by taking them as an isolated two-level system. Through carefully designed time sequences, we construct symmetry-protected LZSM interference of topological edge states, enabling non-adiabatic topological transport. Our work unveils an alternative way to study quantum control, quantum state transfer, and quantum communication via non-adiabatic topological transport.
Machine Learning algorithms are extensively used in an increasing number of systems, applications, technologies, and products, both in industry and in society as a whole. They enable computing devices to learn from previous experience and therefore improve their performance in a certain context or environment. In this way, many useful possibilities have been made accessible. However, dealing with an increasing amount of data poses difficulties for classical devices. Quantum systems may offer a way forward, possibly enabling to scale up machine learning calculations in certain contexts. On the other hand, quantum systems themselves are also hard to scale up, due to decoherence and the fragility of quantum superpositions. In the short and mid term, it has been evidenced that a quantum paradigm that combines evolution under large analog blocks with discrete quantum gates, may be fruitful to achieve new knowledge of classical and quantum systems with no need of having a fault-tolerant quantum computer. In this Perspective, we review some recent works that employ this digital-analog quantum paradigm to carry out efficient machine learning calculations with current quantum devices.
DGS-SLAM: Gaussian Splatting SLAM in Dynamic Environment
2024-11-16
Mangyu Kong, Jaewon Lee, Seongwon Lee, Euntai Kim
We introduce Dynamic Gaussian Splatting SLAM (DGS-SLAM), the first dynamic SLAM framework built on the foundation of Gaussian Splatting. While recent advancements in dense SLAM have leveraged Gaussian Splatting to enhance scene representation, most approaches assume a static environment, making them vulnerable to photometric and geometric inconsistencies caused by dynamic objects. To address these challenges, we integrate Gaussian Splatting SLAM with a robust filtering process to handle dynamic objects throughout the entire pipeline, including Gaussian insertion and keyframe selection. Within this framework, to further improve the accuracy of dynamic object removal, we introduce a robust mask generation method that enforces photometric consistency across keyframes, reducing noise from inaccurate segmentation and artifacts such as shadows. Additionally, we propose the loop-aware window selection mechanism, which utilizes unique keyframe IDs of 3D Gaussians to detect loops between the current and past frames, facilitating joint optimization of the current camera poses and the Gaussian map. DGS-SLAM achieves state-of-the-art performance in both camera tracking and novel view synthesis on various dynamic SLAM benchmarks, proving its effectiveness in handling real-world dynamic scenes.
The nanosized boron vacancy ($V_\mathrm{B}^-$) defect spot in hexagonal boron nitride ($h$-BN) is promising for a local magnetic field quantum sensor. One of its advantages is that a helium-ion microscope can make a spot at any location in an $h$-BN flake with nanometer accuracy. In this study, we investigate the properties of the created nanosized $V_\mathrm{B}^-$ defect spots by systematically varying three conditions: the helium-ion dose, the thickness of the $h$-BN flakes, and the substrate on which the $h$-BN flakes are attached. The physical background of the results obtained is successfully interpreted using Monte Carlo calculations. From the findings obtained here, a guideline for their optimal creation conditions is obtained to maximize its performance as a quantum sensor concerning sensitivity and localization.
Distribution of Europium in The Milky Way Disk; Its Connection to Planetary Habitability and The Source of The R-Process
2024-11-16
Evan M. Carrasco, Matthew Shetrone, Francis Nimmo, Enrico Ramirez-Ruiz, Joel Primack, Natalie M. Batalha
The energy provided in the radioactive decay of thorium (Th) and uranium (U) isotopes, embedded in planetary mantles, sustains geodynamics important for surface habitability such as the generation of a planetary magnetic dynamo. In order to better understand the thermal evolution of nearby exoplanets, stellar photospheric abundances can be used to infer the material composition of orbiting planets. Here we constrain the intrinsic dispersion of the r-process element europium (Eu) (measured in relative abundance [Eu/H]) as a proxy for Th and U in local F, G, and K type dwarf stars. Adopting stellar-chemical data from two high quality spectroscopic surveys, we have determined a small intrinsic scatter of 0.025 dex in [Eu/H] within the disk. We further investigate the stellar anti-correlation in [Eu/$\alpha$] vs [$\alpha$/H] at late metallicities to probe in what regimes planetary radiogenic heating may lead to periods of extended dynamo collapse. We find that only near-solar metallicity stars in the disk have Eu inventories supportive of a persistent dynamo in attendant planets, supporting the notion of a ``metallicity Goldilocks zone'' in the galactic disk. The observed anti-correlation further provides novel evidence regarding the nature of r-processes injection by substantiating $\alpha$ element production is decoupled from Eu injection. This suggests either a metallicity-dependent r-process in massive core-collapse supernovae, or that neutron-star merger events dominate r-process production in the recent universe.
With the development of digital imaging in medical microscopy, artificial intelligent-based analysis of pathological whole slide images (WSIs) provides a powerful tool for cancer diagnosis. Limited by the expensive cost of pixel-level annotation, current research primarily focuses on representation learning with slide-level labels, showing success in various downstream tasks. However, given the diversity of lesion types and the complex relationships between each other, these techniques still deserve further exploration in addressing advanced pathology tasks. To this end, we introduce the concept of hierarchical pathological image classification and propose a representation learning called PathTree. PathTree considers the multi-classification of diseases as a binary tree structure. Each category is represented as a professional pathological text description, which messages information with a tree-like encoder. The interactive text features are then used to guide the aggregation of hierarchical multiple representations. PathTree uses slide-text similarity to obtain probability scores and introduces two extra tree specific losses to further constrain the association between texts and slides. Through extensive experiments on three challenging hierarchical classification datasets: in-house cryosectioned lung tissue lesion identification, public prostate cancer grade assessment, and public breast cancer subtyping, our proposed PathTree is consistently competitive compared to the state-of-the-art methods and provides a new perspective on the deep learning-assisted solution for more complex WSI classification.
Direct correlation between the near-proton-emission threshold resonance in $^{11}$B and the branching ratio of beta-delayed proton emission from $^{11}$Be
2024-11-16
Le-Anh Nguyen, Minh-Loc Bui
Background: Beta-delayed proton emission from neutron halo nuclei $^{11}$Be represents a rare decay process. The existence of the narrow resonance near the proton-emission threshold in $^{11}$B explains its unexpectedly high probability. However, the accurate value of the branching ratio remains challenging to determine. Purpose: We aim to provide a microscopic potential model to determine the branching ratio for beta-delayed proton emission from $^{11}$Be. We focus on quantifying the influence of the narrow resonance near the proton emission threshold on the result of the branching ratio. Method: We employ the Skyrme Hartree-Fock calculation within the potential model to obtain the branching ratio. We derive the single-particle potentials for the halo neutron and the emitting proton from the Skyrme Hartree-Fock calculation with minimal adjustment. As the resonance position is tightly linked to the potential depth, we can demonstrate quantitatively how variations in its location impact the outcome. Result: Slight variations in the resonance position significantly impact the branching ratio, with the upper limit reaching the order of $10^{-5}$. Conclusion: Experimental determination of the resonance energy, particularly whether it lies below $200$ keV, is crucial for determining the value of the branching ratio.
HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization
2024-11-16
Huaqin Zhao, Jiaxi Li, Yi Pan, Shizhe Liang, Xiaofeng Yang, Wei Liu, Xiang Li, Fei Dou, Tianming Liu, Jin Lu
Fine-tuning large language models (LLMs) poses significant memory challenges, as the back-propagation process demands extensive resources, especially with growing model sizes. Recent work, MeZO, addresses this issue using a zeroth-order (ZO) optimization method, which reduces memory consumption by matching the usage to the inference phase. However, MeZO experiences slow convergence due to varying curvatures across model parameters. To overcome this limitation, we introduce HELENE, a novel scalable and memory-efficient optimizer that integrates annealed A-GNB gradients with a diagonal Hessian estimation and layer-wise clipping, serving as a second-order pre-conditioner. This combination allows for faster and more stable convergence. Our theoretical analysis demonstrates that HELENE improves convergence rates, particularly for models with heterogeneous layer dimensions, by reducing the dependency on the total parameter space dimension. Instead, the method scales with the largest layer dimension, making it highly suitable for modern LLM architectures. Experimental results on RoBERTa-large and OPT-1.3B across multiple tasks show that HELENE achieves up to a 20x speedup compared to MeZO, with average accuracy improvements of 1.5%. Furthermore, HELENE remains compatible with both full parameter tuning and parameter-efficient fine-tuning (PEFT), outperforming several state-of-the-art optimizers. The codes will be released after reviewing.
Large Language Models (LLMs) excel in diverse tasks such as text generation, data analysis, and software development, making them indispensable across domains like education, business, and creative industries. However, the rapid proliferation of LLMs (with over 560 companies developing or deploying them as of 2024) has raised concerns about their originality and trustworthiness. A notable issue, termed identity confusion, has emerged, where LLMs misrepresent their origins or identities. This study systematically examines identity confusion through three research questions: (1) How prevalent is identity confusion among LLMs? (2) Does it arise from model reuse, plagiarism, or hallucination? (3) What are the security and trust-related impacts of identity confusion? To address these, we developed an automated tool combining documentation analysis, self-identity recognition testing, and output similarity comparisons--established methods for LLM fingerprinting--and conducted a structured survey via Credamo to assess its impact on user trust. Our analysis of 27 LLMs revealed that 25.93% exhibit identity confusion. Output similarity analysis confirmed that these issues stem from hallucinations rather than replication or reuse. Survey results further highlighted that identity confusion significantly erodes trust, particularly in critical tasks like education and professional use, with declines exceeding those caused by logical errors or inconsistencies. Users attributed these failures to design flaws, incorrect training data, and perceived plagiarism, underscoring the systemic risks posed by identity confusion to LLM reliability and trustworthiness.
Modeling the Differential Rate for Signal Interactions in Coincidence with Noise Fluctuations or Large Rate Backgrounds
2024-11-16
Xinran Li, Matt Pyle, Bernard Sadoulet
The characteristic energy of a relic dark matter interaction with a detector scales strongly with the putative dark matter mass. Consequently, experimental search sensitivity at the lightest masses will always come from interactions whose size is similar to noise fluctuations and low energy backgrounds in the detector. In this paper, we correctly calculate the net change in measured differential rate due to signal interactions that overlap in time with noise and backgrounds, accounting for both periods of time when the signal is coincident with noise/backgrounds and for the decreased amount of time in which only noise/backgrounds occur. Previous experimental searches have not accounted for this second fundamental effect, and thus either vastly overestimate their experimental search sensitivity (very bad) or use ad hoc conservative cuts which can underestimate experimental sensitivity (not ideal). We find that the detector response to dark matter can be trivially and conservatively understood as long as the true probability of dark matter pileup is small. We also show that introducing random events in the continuous raw data stream (a form of ``salting") provides a correct and practical implementation that correctly accounts for the decreased live time available for noise fluctuations and background events out of coincidence with a true dark matter signal.
Stellar Halos of Bright Central Galaxies: A View from the FEGA Semi-Analytic Model of Galaxy Formation and VEGAS Survey
2024-11-16
Emanuele Contini, Marilena Spavone, Rossella Ragusa, Enrichetta Iodice, Sukyoung K. Yi
We present theoretical predictions and extrapolations from observed data of the stellar halos surrounding central group/cluster galaxies and the transition radius between them and the intracluster or diffuse light. Leveraging the state-of-the-art semi-analytic model of galaxy formation, {\small FEGA} (\citealt{contini2024c}), applied to two dark matter-only cosmological simulations, we derive both the stellar halo mass and its radius. Using theoretical assumptions about the diffuse light distribution and halo concentration, we extrapolate the same information for observed data from the {\small VEGAS} survey (\citealt{capaccioli2015,iodice2021}). Our model, supported by observational data and independent simulation results, predicts an increasing transition radius with halo mass, a constant stellar halo-to-intracluster light ratio, and a stable stellar halo mass fraction with increasing halo mass. Specifically, we find that the transition radius between the stellar halo and the diffuse light ranges from 20 to 250 kpc, from Milky Way-like halos to large clusters, while the stellar halo mass comprises only a small fraction, between 7\% and 18\%, of the total stellar mass within the virial radius. These results support the idea that the stellar halo can be viewed as a transition region between the stars bound to the galaxy and those belonging to the intracluster light, consistent with recent observations and theoretical predictions.
Pedagogical Design Considerations for Mobile Augmented Reality Serious Games (MARSGs): A Literature Review
2024-11-16
Cassidy R. Nelson, Joseph L. Gabbard
As technology advances, conceptualizations of effective strategies for teaching and learning shift. Due in part to their facilitation of unique affordances for learning, mobile devices, augmented reality, and games are all becoming more prominent elements in learning environments. In this work, we examine mobile augmented reality serious games (MARSGs) as the intersection of these technology-based experiences and to what effect their combination can yield even greater learning outcomes. We present a PRISMA review of 23 papers (from 610) spanning the entire literature timeline from 2002 to 2023. Among these works, there is wide variability in the realized application of game elements and pedagogical theories underpinning the game experience. For an educational tool to be effective, it must be designed to facilitate learning while anchored by pedagogical theory. Given that most MARSG developers are not pedagogical experts, this review further provides design considerations regarding which game elements might proffer the best of three major pedagogical theories for modern learning (cognitive constructivism, social constructivism, and behaviorism) based on existing applications. We will also briefly touch on radical constructivism and the instructional elements embedded within MARSGs. Lastly, this work offers a synthesis of current MARSG findings and extended future directions for MARSG development.
False Discovery Control in Multiple Testing: A Brief Overview of Theories and Methodologies
2024-11-16
Jianliang He, Bowen Gang, Luella Fu
As the volume and complexity of data continue to expand across various scientific disciplines, the need for robust methods to account for the multiplicity of comparisons has grown widespread. A popular measure of type 1 error rate in multiple testing literature is the false discovery rate (FDR). The FDR provides a powerful and practical approach to large-scale multiple testing and has been successfully used in a wide range of applications. The concept of FDR has gained wide acceptance in the statistical community and various methods has been proposed to control the FDR. In this work, we review the latest developments in FDR control methodologies. We also develop a conceptual framework to better describe this vast literature; understand its intuition and key ideas; and provide guidance for the researcher interested in both the application and development of the methodology.
Stability frontiers in the AM$_6$X$_6$ kagome metals; The LnNb$_6$Sn$_6$ (Ln:Ce-Lu,Y) family and density-wave transition in LuNb$_6$Sn$_6$
2024-11-16
Brenden R. Ortiz, William R. Meier, Ganesh Pokharel, Juan Chamorro, Fazhi Yang, Shirin Mozaffari, Alex Thaler, Steven J. Gomez Alvarado, Heda Zhang, David S. Parker, German D. Samolyuk, Joseph A. M. Paddison, Jiaqiang Yan, Feng Ye, Suchismita Sarker, Stephen D. Wilson, Hu Miao, David Mandrus, Michael A. McGuire
The kagome motif is a versatile platform for condensed matter physics, hosting rich interactions between magnetic, electronic, and structural degrees of freedom. In recent years, the discovery of a charge density wave (CDW) in the AV$_3$Sb$_5$ superconductors and structurally-derived bond density waves in FeGe and ScV$_6$Sn$_6$ have stoked the search for new kagome platforms broadly exhibiting density wave (DW) transitions. In this work, we evaluate the known AM$_6$X$_6$ chemistries and construct a stability diagram that summarizes the structural relationships between the $\approx$125 member family. Subsequently we introduce our discovery of the broader LnNb$_6$Sn$_6$ (Ln:Ce-Nd,Sm,Gd-Tm,Lu,Y) family of kagome metals and an analogous DW transition in LuNb$_6$Sn$_6$. Our X-ray scattering measurements clearly indicate a (1/3, 1/3, 1/3) ordering wave vector ($\sqrt{3}\times\sqrt{3}\times3$ superlattice) and diffuse scattering on half-integer $L$-planes. Our analysis of the structural data supports the ``rattling mode'' DW model proposed for ScV$_6$Sn$_6$ and paints a detailed picture of the steric interactions between the rare-earth filler element and the host Nb-Sn kagome scaffolding. We also provide a broad survey of the magnetic properties within the HfFe$_6$Ge$_6$-type LnNb$_6$Sn$_6$ members, revealing a number of complex antiferromagnetic and metamagnetic transitions throughout the family. This work integrates our new LnNb$_6$Sn$_6$ series of compounds into the broader AM$_6$X$_6$ family, providing new material platforms and forging a new route forward at the frontier of kagome metal research.
A Novel Approach to Eliminating Hallucinations in Large Language Model-Assisted Causal Discovery
2024-11-16
Grace Sng, Yanming Zhang, Klaus Mueller
The increasing use of large language models (LLMs) in causal discovery as a substitute for human domain experts highlights the need for optimal model selection. This paper presents the first hallucination survey of popular LLMs for causal discovery. We show that hallucinations exist when using LLMs in causal discovery so the choice of LLM is important. We propose using Retrieval Augmented Generation (RAG) to reduce hallucinations when quality data is available. Additionally, we introduce a novel method employing multiple LLMs with an arbiter in a debate to audit edges in causal graphs, achieving a comparable reduction in hallucinations to RAG.
SN 2023adsy -- a normal Type Ia Supernova at z=2.9, discovered by JWST
2024-11-15
Jozsef Vinko, Eniko Regos
SN 2023adsy, a Type Ia supernova discovered by JWST at z = 2.9, was found to be a peculiar event, being extremely red and faint, but showing very similar rest-frame light curve decline rate to the majority of low-redshift SNe Ia. In this paper we show that the red color and faint peak magnitude could be explained by significant reddening/extinction due to dust in the host galaxy. If host galaxy extinction is accounted for, the parameters of the best-fit light curve templates in the SALT3-NIR model are compatible with a slowly declining, but still normal SN Ia. Comparison of the inferred luminosity distance with the prediction of the LambdaCDM cosmology (assuming H0 = 70 km/s/Mpc and OmegaM = 0.315) on the Hubble-diagram suggests no significant evolution of the SN Ia peak luminosity at z > 2 redshifts. It is also shown that the discovery of a single SN Ia between 2 < z < 3 within the area of the JADES survey during 1 year is consistent with the current estimates for the SN Ia rates at such redshifts.
Deciphering SCN2A: A comprehensive review of rodent models of Scn2a dysfunction
2024-11-15
Katelin E. J Scott, Maria F. Hermosillo Arrieta, Aislinn J. Williams
SCN2A encodes the alpha subunit of the voltage-gated sodium channel Nav1.2, which is involved in action potential initiation and backpropagation in glutamatergic neurons. Mutations in the gene lead to SCN2A-related disorders, which are highly heterogeneous and manifest in a variety of diagnoses, such as self-limited familial and non-familial infantile epilepsy (SeLFNIE, previously benign familial infantile seizures or BFNIS), epileptic encephalopathies (EE), infantile spasms, ataxia, autism spectrum disorder (ASD), intellectual disability (ID), and schizophrenia. Researchers have attempted to elucidate the complexity of the SCN2A gene and Nav1.2 channel function through the use and development of non-human mammalian models. These models have been invaluable to uncovering the molecular underpinnings of why changes in SCN2A lead to such a heterogenous group of disorders. This review aims to evaluate and compare the published rodent models to consolidate findings, identify limitations, and highlight future research directions.
Exploring the Future Metaverse: Research Models for User Experience, Business Readiness, and National Competitiveness
2024-11-15
Amir Reza Asadi, Shiva Ghasemi
This systematic literature review paper explores perspectives on the ideal metaverse from user experience, business, and national levels, considering both academic and industry viewpoints. The study examines the metaverse as a sociotechnical imaginary, enabled collectively by virtual reality (VR), augmented reality (AR), and mixed reality (MR) technologies. Through a systematic literature review, n=144 records were included and by employing grounded theory for analysis of data, we developed three research models, which can guide researchers in examining the metaverse as a sociotechnical future of information technology. Designers can apply the metaverse user experience maturity model to develop more user-friendly services, while business strategists can use the metaverse business readiness model to assess their firms' current state and prepare for transformation. Additionally, policymakers and policy analysts can utilize the metaverse national competitiveness model to track their countries' competitiveness during this paradigm shift. The synthesis of the results also led to the development of practical assessment tools derived from these models that can guide researchers
How to Build a Quantum Supercomputer: Scaling Challenges and Opportunities
2024-11-15
Masoud Mohseni, Artur Scherer, K. Grace Johnson, Oded Wertheim, Matthew Otten, Navid Anjum Aadit, Kirk M. Bresniker, Kerem Y. Camsari, Barbara Chapman, Soumitra Chatterjee, Gebremedhin A. Dagnew, Aniello Esposito, Farah Fahim, Marco Fiorentino, Abdullah Khalid, Xiangzhou Kong, Bohdan Kulchytskyy, Ruoyu Li, P. Aaron Lott, Igor L. Markov, Robert F. McDermott, Giacomo Pedretti, Archit Gajjar, Allyson Silva, John Sorebo, Panagiotis Spentzouris, Ziv Steiner, Boyan Torosov, Davide Venturelli, Robert J. Visser, Zak Webb, Xin Zhan, Yonatan Cohen, Pooya Ronagh, Alan Ho, Raymond G. Beausoleil, John M. Martinis
In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits and proof-of-principle error-correction on a single logical qubit. Nevertheless, despite significant progress and excitement, the path toward a full-stack scalable technology is largely unknown. There are significant outstanding quantum hardware, fabrication, software architecture, and algorithmic challenges that are either unresolved or overlooked. These issues could seriously undermine the arrival of utility-scale quantum computers for the foreseeable future. Here, we provide a comprehensive review of these scaling challenges. We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits, employing system engineering approaches, and performing distributed quantum computation within heterogeneous high-performance computing infrastructures. These opportunities for research and development could unlock certain promising applications, in particular, efficient quantum simulation/learning of quantum data generated by natural or engineered quantum systems. To estimate the true cost of such promises, we provide a detailed resource and sensitivity analysis for classically hard quantum chemistry calculations on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. Furthermore, we argue that, to tackle industry-scale classical optimization and machine learning problems in a cost-effective manner, distributed quantum-assisted probabilistic computing with custom-designed accelerators should be considered as a complementary path toward scalability.
We report on the dynamics of a soliton propagating on the surface of a fluid in a 4-m-long canal with a random or periodic bottom topography. Using a full space-and-time resolved wavefield measurement, we evidence, for the first time experimentally, how the soliton is affected by the disorder, in the context of Anderson localization, and how localization depends on nonlinearity. For weak soliton amplitudes, the localization length is found in quantitative agreement with a linear shallow-water theory. For higher amplitudes, this spatial attenuation of the soliton amplitude is found to be enhanced. Behind the leading soliton slowed down by the topography, different experimentally unreported dynamics occur: Fission into backward and forward nondispersive pulses for the periodic case, and scattering into dispersive waves for the random case. Our findings open doors to potential applications regarding ocean coastal protection against large-amplitude waves.
Event Causality Identification (ECI) has become a crucial task in Natural Language Processing (NLP), aimed at automatically extracting causalities from textual data. In this survey, we systematically address the foundational principles, technical frameworks, and challenges of ECI, offering a comprehensive taxonomy to categorize and clarify current research methodologies, as well as a quantitative assessment of existing models. We first establish a conceptual framework for ECI, outlining key definitions, problem formulations, and evaluation standards. Our taxonomy classifies ECI methods according to the two primary tasks of sentence-level (SECI) and document-level (DECI) event causality identification. For SECI, we examine feature pattern-based matching, deep semantic encoding, causal knowledge pre-training and prompt-based fine-tuning, and external knowledge enhancement methods. For DECI, we highlight approaches focused on event graph reasoning and prompt-based techniques to address the complexity of cross-sentence causal inference. Additionally, we analyze the strengths, limitations, and open challenges of each approach. We further conduct an extensive quantitative evaluation of various ECI methods on two benchmark datasets. Finally, we explore future research directions, highlighting promising pathways to overcome current limitations and broaden ECI applications.
A Survey of Cassini Images of Spokes in Saturn's Rings: Unusual Spoke Types and Seasonal Trends
2024-11-15
S. R. Callos, M. M. Hedman, D. P. Hamilton
Spokes are localized clouds of fine particles that appear over the outer part of Saturn's B ring. Over the course of the Cassini Mission, the Imaging Science Subsystem (ISS) obtained over 20,000 images of the outer B ring, providing the most comprehensive data set for quantifying spoke properties currently available. Consistent with prior work, we find that spokes typically appear as dark features when the lit side of the rings are viewed at low phase angles, and as bright features when the rings are viewed at high phase angles or the dark side of the rings are observed. However, we also find examples of spokes on the dark side of the rings that transition between being brighter and darker than the background ring as they move around the planet. Most interestingly, we also identify spokes that appear to be darker than the background ring near their center and brighter than the background ring near their edges. These "mixed spokes" indicate that the particle size distribution can vary spatially within a spoke. In addition, we document seasonal variations in the overall spoke activity over the course of the Cassini mission using statistics derived from lit-side imaging sequences. These statistics demonstrate that while spokes can be detected over a wide range of solar elevation angles, spoke activity increases dramatically when the Sun is within 10 degrees of the ring plane.
Generalized Conditional Functional Principal Component Analysis
2024-11-15
Yu Lu, Xinkai Zhou, Erjia Cui, Dustin Rogers, Ciprian M. Crainiceanu, Julia Wrobel, Andrew Leroux
We propose generalized conditional functional principal components analysis (GC-FPCA) for the joint modeling of the fixed and random effects of non-Gaussian functional outcomes. The method scales up to very large functional data sets by estimating the principal components of the covariance matrix on the linear predictor scale conditional on the fixed effects. This is achieved by combining three modeling innovations: (1) fit local generalized linear mixed models (GLMMs) conditional on covariates in windows along the functional domain; (2) conduct a functional principal component analysis (FPCA) on the person-specific functional effects obtained by assembling the estimated random effects from the local GLMMs; and (3) fit a joint functional mixed effects model conditional on covariates and the estimated principal components from the previous step. GC-FPCA was motivated by modeling the minute-level active/inactive profiles over the day ($1{,}440$ 0/1 measurements per person) for $8{,}700$ study participants in the National Health and Nutrition Examination Survey (NHANES) 2011-2014. We show that state-of-the-art approaches cannot handle data of this size and complexity, while GC-FPCA can.
Selected topics from the theory of intersections of balls
2024-11-15
Károly Bezdek, Zsolt Lángi, Márton Naszódi
In this survey, we discuss volumetric and combinatorial results concerning (mostly finite) intersections or unions of balls (mostly of equal radii) in the $d$-dimensional real vector space, mostly equipped with the Euclidean norm. Our first topic is the Kneser-Poulsen Conjecture, according to which if a finite number of unit balls are rearranged so that the pairwise distances of the centers increase, then the volume of the union (resp., intersection) increases (resp., decreases). Next, we discuss Blaschke-Santal\'o type, and isoperimetric inequalities for convex sets in Euclidean $d$-space obtained as intersections of (possibly infinitely many) unit balls, which we call spindle convex sets. We present some results on spindle convex sets in the plane, with special attention paid to their approximation by the spindle convex hull of a finite subset. A ball-polyhedron is a convex body obtained as the intersection of finitely many unit balls in Euclidean $d$-space. We consider the combinatorial structure of their faces, and volumetric properties of ball polyhedra obtained from choosing the centers of the balls randomly.
Unveiling Topological Structures in Text: A Comprehensive Survey of Topological Data Analysis Applications in NLP
2024-11-15
Adaku Uchendu, Thai Le
The surge of data available on the internet has led to the adoption of various computational methods to analyze and extract valuable insights from this wealth of information. Among these, the field of Machine Learning (ML) has thrived by leveraging data to extract meaningful insights. However, ML techniques face notable challenges when dealing with real-world data, often due to issues of imbalance, noise, insufficient labeling, and high dimensionality. To address these limitations, some researchers advocate for the adoption of Topological Data Analysis (TDA), a statistical approach that discerningly captures the intrinsic shape of data despite noise. Despite its potential, TDA has not gained as much traction within the Natural Language Processing (NLP) domain compared to structurally distinct areas like computer vision. Nevertheless, a dedicated community of researchers has been exploring the application of TDA in NLP, yielding 85 papers we comprehensively survey in this paper. Our findings categorize these efforts into theoretical and nontheoretical approaches. Theoretical approaches aim to explain linguistic phenomena from a topological viewpoint, while non-theoretical approaches merge TDA with ML features, utilizing diverse numerical representation techniques. We conclude by exploring the challenges and unresolved questions that persist in this niche field. Resources and a list of papers on this topic can be found at: https://github.com/AdaUchendu/AwesomeTDA4NLP.
Moving Forward: A Review of Autonomous Driving Software and Hardware Systems
2024-11-15
Xu Wang, Mohammad Ali Maleki, Muhammad Waqar Azhar, Pedro Trancoso
With their potential to significantly reduce traffic accidents, enhance road safety, optimize traffic flow, and decrease congestion, autonomous driving systems are a major focus of research and development in recent years. Beyond these immediate benefits, they offer long-term advantages in promoting sustainable transportation by reducing emissions and fuel consumption. Achieving a high level of autonomy across diverse conditions requires a comprehensive understanding of the environment. This is accomplished by processing data from sensors such as cameras, radars, and LiDARs through a software stack that relies heavily on machine learning algorithms. These ML models demand significant computational resources and involve large-scale data movement, presenting challenges for hardware to execute them efficiently and at high speed. In this survey, we first outline and highlight the key components of self-driving systems, covering input sensors, commonly used datasets, simulation platforms, and the software architecture. We then explore the underlying hardware platforms that support the execution of these software systems. By presenting a comprehensive view of autonomous driving systems and their increasing demands, particularly for higher levels of autonomy, we analyze the performance and efficiency of scaled-up off-the-shelf GPU/CPU-based systems, emphasizing the challenges within the computational components. Through examples showcasing the diverse computational and memory requirements in the software stack, we demonstrate how more specialized hardware and processing closer to memory can enable more efficient execution with lower latency. Finally, based on current trends and future demands, we conclude by speculating what a future hardware platform for autonomous driving might look like.
The Link Between Large Scientific Collaboration and Productivity. Rethinking How to Estimate the Monetary Value of Publications
2024-11-15
Francesco Giffoni, Emanuela Sirtori, Louis Colnot
This paper addresses how to assign a monetary value to scientific publications, particularly in the case of multi-author papers arising from large-scale research collaborations. Contemporary science increasingly relies on extensive and varied collaborations to tackle global challenges in fields such as life sciences, climate science, energy, high-energy physics, astronomy, and many others. We argue that existing literature fails to address the collaborative nature of research by overlooking the relationship between coauthorship and scientists productivity. Using the Marginal Cost of Production (MCP) approach, we first highlight the methodological limitations of ignoring this relationship, then propose a generalised MCP model to value co-authorship. As a case study, we examine High-Energy Physics (HEP) collaborations at the Large Hadron Collider (LHC) at CERN, analysing approximately half a million scientific outputs by over 50,000 authors from 1990 to 2021. Our findings indicate that collaborative adjustments yield monetary valuations for subsets of highly collaborative papers up to 3 orders of magnitude higher than previous estimates, with elevated values correlating with high research quality. This study contributes to the literature on research output evaluation, addressing debates in science policy around assessing research performance and impact. Our methodology is applicable to authorship valuation both within academia and in large-scale scientific collaborations, fitting diverse research impact assessment frameworks or as self-standing procedure. Additionally, we discuss the conditions under which this method may complement survey-based approaches.
We investigate the phase transitions in a one-dimensional system with colored noise. Previous studies indicated that the phase diagram of this system included extended and disorder-induced localized phases. However, by studying the properties of wave functions, we find that this phase diagram can be further refined, revealing the existence of a bound phase for the large potential amplitude $W$ and noise control parameter $\alpha$. In the bound phase, the wave function cannot extend throughout the entire chain, tails decay faster than exponentially and its distribution expands as the system size increases. By adjusting the potential amplitude to induce a transition from the extended phase to the bound phase, we find that bound states coexist with extended states in the spectrum. In contrast, when the system transitions from the Anderson localized phase to the bound phase, we do not observe the obvious coexistence of Anderson localized and bound states. Finally, by performing the time evolution, we find that the dynamic transition point of the bound phase is inconsistent with the static one for large $\alpha$.
Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review
2024-11-15
Hossein Hassani, Roozbeh Razavi-Far, Mehrdad Saif, Liang Lin
Reinforcement learning (RL) is a sub-domain of machine learning, mainly concerned with solving sequential decision-making problems by a learning agent that interacts with the decision environment to improve its behavior through the reward it receives from the environment. This learning paradigm is, however, well-known for being time-consuming due to the necessity of collecting a large amount of data, making RL suffer from sample inefficiency and difficult generalization. Furthermore, the construction of an explicit reward function that accounts for the trade-off between multiple desiderata of a decision problem is often a laborious task. These challenges have been recently addressed utilizing transfer and inverse reinforcement learning (T-IRL). In this regard, this paper is devoted to a comprehensive review of realizing the sample efficiency and generalization of RL algorithms through T-IRL. Following a brief introduction to RL, the fundamental T-IRL methods are presented and the most recent advancements in each research field have been extensively reviewed. Our findings denote that a majority of recent research works have dealt with the aforementioned challenges by utilizing human-in-the-loop and sim-to-real strategies for the efficient transfer of knowledge from source domains to the target domain under the transfer learning scheme. Under the IRL structure, training schemes that require a low number of experience transitions and extension of such frameworks to multi-agent and multi-intention problems have been the priority of researchers in recent years.
Topological geometric frustration in a cube-surface artificial spin ice
2024-11-15
Zixiong Yuan, Wen-Cheng Yue, Peiyuan Huang, Yang-Yang Lyu, Sining Dong, Ying Dong, Huabing Wang, Peiheng Wu, Yong-Lei Wang
Artificial spin ices provide a controlled platform for investigating diverse physical phenomena, such as geometric frustration, magnetic monopoles, and phase transitions, via deliberate design. Here, we introduce a novel approach by developing artificial spin ice on the surfaces of a three-dimensional cube, which leads to emergent geometric frustration mediated by topologically protected domain walls, distinct from its flat counterparts. These domain walls connect vertices at the corners of cube that acting as intrinsic topological defects. Utilizing Monte Carlo simulations, we observe robust, topologically protected correlations among the intrinsic topological defects, regardless of their spatial separation. Our findings demonstrate that three-dimensional surfaces can unveil emergent properties absent in flat architectures.
Super $L_\infty$-algebras unify extended super-symmetry with rational classifying spaces for higher flux densities: The super-invariant super-fluxes which control super $p$-branes and their supergravity target super-spaces are, together with their (non-linear) Bianchi identities, neatly encoded in (non-abelian) super-$L_\infty$ cocycles. These are the rational shadows of flux-quantization laws (in ordinary cohomology, K-theory, Cohomotopy, iterated K-theory, etc). We first review, in streamlined form while filling some previous gaps, double-dimensional reduction/oxidation and 10D superspace T-duality along higher-dimensional super-tori. We do so tangent super-space wise, by viewing it as an instance of adjunctions (dualities) between super-$L_\infty$-extensions and -cyclifications, applied to the avatar super-flux densities of 10D supergravity. In particular, this yields a derivation, at the rational level, of the traditional laws of "topological T-duality" from the super-$L_\infty$ structure of type II superspace. At this level, we also discuss a higher categorical analog of T-duality involving M-branes. Then, by considering super-space T-duality along all 1+9 spacetime dimensions while retaining the 11th dimension as in F-theory, we find the M-algebra appearing as the complete brane-charge extension of the fully T-doubled/correspondence super-spacetime. On this backdrop, we recognize the "decomposed" M-theory 3-form on the "hidden M-algebra" as an M-theoretic lift of the Poincar\'e super 2-form that controls superspace T-duality as the integral kernel of the super Fourier-Mukai transform. This provides the super-space structure of an M-theory lift of the doubled/correspondence space geometry, which controls T-duality.
Trajectory effects on charge exchange and energy loss in collisions of H$^+$, He$^{2+}$, Li$^{3+}$, and Be$^{4+}$ ions with atomic hydrogen
2024-11-15
Miranda Nichols, D. Hanstorp, R. Cabrera-Trujillo
The charge exchange and energy loss processes provide insights into fundamental processes across physical, chemical, and engineered systems. While this field has been thoroughly investigated, a clear study on trajectory effects is lacking, particularly in the context of inelastic processes at low energies. In this work, we address this gap by solving the time-dependent Schr\"odinger equation for the electron in a numerical lattice with a coupled electron-nuclear dynamics approach as well as a straight-line trajectory approximation for the nuclei to asses trajectory effects on charge exchange and energy loss. The collision dynamics are studied using bare ion projectiles with charge $Z=$1-4 incident on atomic hydrogen in an energy range of 0.1 to 900 keV/u. We find that the charge exchange process is trajectory-independent within the energy range considered, showing excellent agreement with available experimental and theoretical data. However, projectile energy loss exhibits strong trajectory dependence, with the straight-line approximation overestimating energy loss at low collision energies due to the forced linear path. Our results for electronic stopping cross sections with electron-nuclear coupled trajectories are consistent with the available experimental data at high collision energies. At low energies, nuclear energy loss becomes prominent, driven by polarization effects induced by the ion charge on the hydrogen target. Overall, our work highlights the importance of nuclear trajectory considerations in collision dynamics and offers a foundation for further investigations of more complex systems.
Satellite monitoring of long period ocean-induced magnetic field variations
2024-11-15
C. C. Finlay, J. Velímský, C. Kloss, R. M. Blangsbøll
Satellite magnetic field observations have the potential to provide valuable information on dynamics, heat content and salinity throughout the ocean. Here we present the expected spatio-temporal characteristics of the ocean-induced magnetic field at satellite altitude on periods of months to decades. We compare these to the characteristics of other sources of Earth's magnetic field, and discuss whether it is feasible for the ocean-induced magnetic field to be retrieved and routinely monitored from space. We focus on large length scales (spherical harmonic degrees up to 30) and periods from one month up to five years. To characterize the expected ocean signal we make use of advanced numerical simulations taking high resolution oceanographic inputs and solve the magnetic induction equation in 3D including galvanic coupling and self induction effects. We find the time-varying ocean-induced signal dominates over the primary source of the internal field, the core dynamo, at high spherical harmonic degree with the cross-over taking place at degree 15 to 20 depending on the considered period. The ionospheric and magnetospheric fields (including their Earth induced counterparts) have most power on periods shorter than one month and are expected to be mostly zonal in magnetic coordinates at satellite altitude. Based on these findings we discuss future prospects for isolating and monitoring long period ocean induced magnetic field variations using data collected by present and upcoming magnetic survey satellites.
CART: Compositional Auto-Regressive Transformer for Image Generation
2024-11-15
Siddharth Roheda
In recent years, image synthesis has achieved remarkable advancements, enabling diverse applications in content creation, virtual reality, and beyond. We introduce a novel approach to image generation using Auto-Regressive (AR) modeling, which leverages a next-detail prediction strategy for enhanced fidelity and scalability. While AR models have achieved transformative success in language modeling, replicating this success in vision tasks has presented unique challenges due to the inherent spatial dependencies in images. Our proposed method addresses these challenges by iteratively adding finer details to an image compositionally, constructing it as a hierarchical combination of base and detail image factors. This strategy is shown to be more effective than the conventional next-token prediction and even surpasses the state-of-the-art next-scale prediction approaches. A key advantage of this method is its scalability to higher resolutions without requiring full model retraining, making it a versatile solution for high-resolution image generation.
In this paper, we review legal testing methods based on Large Language Models (LLMs), using the OPENAI o1 model as a case study to evaluate the performance of large models in applying legal provisions. We compare current state-of-the-art LLMs, including open-source, closed-source, and legal-specific models trained specifically for the legal domain. Systematic tests are conducted on English and Chinese legal cases, and the results are analyzed in depth. Through systematic testing of legal cases from common law systems and China, this paper explores the strengths and weaknesses of LLMs in understanding and applying legal texts, reasoning through legal issues, and predicting judgments. The experimental results highlight both the potential and limitations of LLMs in legal applications, particularly in terms of challenges related to the interpretation of legal language and the accuracy of legal reasoning. Finally, the paper provides a comprehensive analysis of the advantages and disadvantages of various types of models, offering valuable insights and references for the future application of AI in the legal field.
Prompting and Fine-tuning Large Language Models for Automated Code Review Comment Generation
2024-11-15
Md. Asif Haider, Ayesha Binte Mostofa, Sk. Sabit Bin Mosaddek, Anindya Iqbal, Toufique Ahmed
Generating accurate code review comments remains a significant challenge due to the inherently diverse and non-unique nature of the task output. Large language models pretrained on both programming and natural language data tend to perform well in code-oriented tasks. However, large-scale pretraining is not always feasible due to its environmental impact and project-specific generalizability issues. In this work, first we fine-tune open-source Large language models (LLM) in parameter-efficient, quantized low-rank (QLoRA) fashion on consumer-grade hardware to improve review comment generation. Recent studies demonstrate the efficacy of augmenting semantic metadata information into prompts to boost performance in other code-related tasks. To explore this in code review activities, we also prompt proprietary, closed-source LLMs augmenting the input code patch with function call graphs and code summaries. Both of our strategies improve the review comment generation performance, with function call graph augmented few-shot prompting on the GPT-3.5 model surpassing the pretrained baseline by around 90% BLEU-4 score on the CodeReviewer dataset. Moreover, few-shot prompted Gemini-1.0 Pro, QLoRA fine-tuned Code Llama and Llama 3.1 models achieve competitive results (ranging from 25% to 83% performance improvement) on this task. An additional human evaluation study further validates our experimental findings, reflecting real-world developers' perceptions of LLM-generated code review comments based on relevant qualitative metrics.
2024-11-15
Joon Sung Park, Carolyn Q. Zou, Aaron Shaw, Benjamin Mako Hill, Carrie Cai, Meredith Ringel Morris, Robb Willer, Percy Liang, Michael S. Bernstein
The promise of human behavioral simulation--general-purpose computational agents that replicate human behavior across domains--could enable broad applications in policymaking and social science. We present a novel agent architecture that simulates the attitudes and behaviors of 1,052 real individuals--applying large language models to qualitative interviews about their lives, then measuring how well these agents replicate the attitudes and behaviors of the individuals that they represent. The generative agents replicate participants' responses on the General Social Survey 85% as accurately as participants replicate their own answers two weeks later, and perform comparably in predicting personality traits and outcomes in experimental replications. Our architecture reduces accuracy biases across racial and ideological groups compared to agents given demographic descriptions. This work provides a foundation for new tools that can help investigate individual and collective behavior.
That Chip Has Sailed: A Critique of Unfounded Skepticism Around AI for Chip Design
2024-11-15
Anna Goldie, Azalia Mirhoseini, Jeff Dean
In 2020, we introduced a deep reinforcement learning method capable of generating superhuman chip layouts, which we then published in Nature and open-sourced on GitHub. AlphaChip has inspired an explosion of work on AI for chip design, and has been deployed in state-of-the-art chips across Alphabet and extended by external chipmakers. Even so, a non-peer-reviewed invited paper at ISPD 2023 questioned its performance claims, despite failing to run our method as described in Nature. For example, it did not pre-train the RL method (removing its ability to learn from prior experience), used substantially fewer compute resources (20x fewer RL experience collectors and half as many GPUs), did not train to convergence (standard practice in machine learning), and evaluated on test cases that are not representative of modern chips. Recently, Igor Markov published a meta-analysis of three papers: our peer-reviewed Nature paper, the non-peer-reviewed ISPD paper, and Markov's own unpublished paper (though he does not disclose that he co-authored it). Although AlphaChip has already achieved widespread adoption and impact, we publish this response to ensure that no one is wrongly discouraged from innovating in this impactful area.
Hollywood's misrepresentation of death: A comparison of overall and by-gender mortality causes in film and the real world
2024-11-15
Calla Beauregard, Christopher M. Danforth, Peter Sheridan Dodds
The common phrase 'representation matters' asserts that media has a measurable and important impact on civic society's perception of self and others. The representation of health in media, in particular, may reflect and perpetuate a society's disease burden. Here, for the top 10 major causes of death in the United States, we examine how cinematic representation of overall and by-gender mortality diverges from reality. Using crowd-sourced data on film deaths from Cinemorgue Wiki, we employ natural language processing (NLP) techniques to analyze shifts in representation of deaths in movies versus the 2021 National Vital Statistic Survey (NVSS) top ten mortality causes. Overall, movies strongly overrepresent suicide and, to a lesser degree, accidents. In terms of gender, movies overrepresent men and underrepresent women for nearly every major mortality cause, including heart disease and cerebrovascular disease. The two exceptions for which women are overrepresented are suicide and accidents. We discuss the implications of under- and over-representing causes of death overall and by gender, as well as areas of future research.
Model Inversion Attacks: A Survey of Approaches and Countermeasures
2024-11-15
Zhanke Zhou, Jianing Zhu, Fengfei Yu, Xuan Li, Xiong Peng, Tongliang Liu, Bo Han
The success of deep neural networks has driven numerous research studies and applications from Euclidean to non-Euclidean data. However, there are increasing concerns about privacy leakage, as these networks rely on processing private data. Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training by abusing access to a well-trained model. The effectiveness of MIAs has been demonstrated in various domains, including images, texts, and graphs. These attacks highlight the vulnerability of neural networks and raise awareness about the risk of privacy leakage within the research community. Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs across different domains. This survey aims to summarize up-to-date MIA methods in both attacks and defenses, highlighting their contributions and limitations, underlying modeling principles, optimization challenges, and future directions. We hope this survey bridges the gap in the literature and facilitates future research in this critical area. Besides, we are maintaining a repository to keep track of relevant research at https://github.com/AndrewZhou924/Awesome-model-inversion-attack.
Understanding Graph Databases: A Comprehensive Tutorial and Survey
2024-11-15
Sydney Anuyah, Victor Bolade, Oluwatosin Agbaakin
This tutorial serves as a comprehensive guide for understanding graph databases, focusing on the fundamentals of graph theory while showcasing practical applications across various fields. It starts by introducing foundational concepts and delves into the structure of graphs through nodes and edges, covering different types such as undirected, directed, weighted, and unweighted graphs. Key graph properties, terminologies, and essential algorithms for network analysis are outlined, including Dijkstras shortest path algorithm and methods for calculating node centrality and graph connectivity. The tutorial highlights the advantages of graph databases over traditional relational databases, particularly in efficiently managing complex, interconnected data. It examines leading graph database systems such as Neo4j, Amazon Neptune, and ArangoDB, emphasizing their unique features for handling large datasets. Practical instructions on graph operations using NetworkX and Neo4j are provided, covering node and edge creation, attribute assignment, and advanced queries with Cypher. Additionally, the tutorial explores common graph visualization techniques using tools like Plotly and Neo4j Bloom, which enhance the interpretation and usability of graph data. It also delves into community detection algorithms, including the Louvain method, which facilitates clustering in large networks. Finally, the paper concludes with recommendations for researchers interested in exploring the vast potential of graph technologies.
Distributed ledger systems, such as blockchains, rely on consensus protocols that constantly commit messages in an agreed order for processing. In practice, message ordering within these systems is often reward-driven. This raises concerns about fairness, particularly in decentralized finance applications, where nodes can exploit transaction orders to maximize rewards (Maximal Extractable Value, MEV). This paper provides a structured review of consensus protocols that order messages with different approaches, especially focusing on the ones that promote order fairness, using methods including First-In-First-Out (FIFO), random, and blind ordering. We review the challenges and trade-offs of deriving fair message ordering in a Byzantine fault-tolerant setting, and summarize the key steps for making a fair message ordering consensus protocol. We introduce a design guideline, with which we propose a performance optimization to the state-of-the-art FIFO ordering protocol Themis. This work establishes a unified framework for accessing and enhancing fairness in distributed ledger systems.
Instruction-Guided Editing Controls for Images and Multimedia: A Survey in LLM era
2024-11-15
Thanh Tam Nguyen, Zhao Ren, Trinh Pham, Phi Le Nguyen, Hongzhi Yin, Quoc Viet Hung Nguyen
The rapid advancement of large language models (LLMs) and multimodal learning has transformed digital content creation and manipulation. Traditional visual editing tools require significant expertise, limiting accessibility. Recent strides in instruction-based editing have enabled intuitive interaction with visual content, using natural language as a bridge between user intent and complex editing operations. This survey provides an overview of these techniques, focusing on how LLMs and multimodal models empower users to achieve precise visual modifications without deep technical knowledge. By synthesizing over 100 publications, we explore methods from generative adversarial networks to diffusion models, examining multimodal integration for fine-grained content control. We discuss practical applications across domains such as fashion, 3D scene manipulation, and video synthesis, highlighting increased accessibility and alignment with human intuition. Our survey compares existing literature, emphasizing LLM-empowered editing, and identifies key challenges to stimulate further research. We aim to democratize powerful visual editing across various industries, from entertainment to education. Interested readers are encouraged to access our repository at https://github.com/tamlhp/awesome-instruction-editing.
Refined and Segmented Price Sentiment Indices from Survey Comments
2024-11-15
Masahiro Suzuki, Hiroki Sakaji
We aim to enhance a price sentiment index and to more precisely understand price trends from the perspective of not only consumers but also businesses. We extract comments related to prices from the Economy Watchers Survey conducted by the Cabinet Office of Japan and classify price trends using a large language model (LLM). We classify whether the survey sample reflects the perspective of consumers or businesses, and whether the comments pertain to goods or services by utilizing information on the fields of comments and the industries of respondents included in the Economy Watchers Survey. From these classified price-related comments, we construct price sentiment indices not only for a general purpose but also for more specific objectives by combining perspectives on consumers and prices, as well as goods and services. It becomes possible to achieve a more accurate classification of price directions by employing a LLM for classification. Furthermore, integrating the outputs of multiple LLMs suggests the potential for the better performance of the classification. The use of more accurately classified comments allows for the construction of an index with a higher correlation to existing indices than previous studies. We demonstrate that the correlation of the price index for consumers, which has a larger sample size, is further enhanced by selecting comments for aggregation based on the industry of the survey respondents.
Fluid-solid interfacial free energy (IFE) is a fundamental parameter influencing wetting behaviors, which play a crucial role across a broad range of industrial applications. Obtaining reliable data for fluid-solid IFE remains challenging with experimental and semi-empirical methods, and the applicability of first-principle theoretical methods is constrained by a lack of accessible computational tools. In recent years, a variety of molecular simulation methods have been developed for determining the fluid-solid IFE. This review provides a comprehensive summary and critical evaluation of these techniques. The developments, fundamental principles, and implementations of various simulation methods are presented from mechanical routes, such as the contact angle approach, the technique using Bakker's equation, and the Wilhelmy simulation method, as well as thermodynamic routes, including the cleaving wall method, the Frenkel-Ladd technique, and the test-volume/area methods. These approaches can be applied to compute various fluid-solid interfacial properties, including IFE, relative IFE, surface stress, and superficial tension, although these properties are often used without differentiation in the literature. Additionally, selected applications of these methods are reviewed to provide insight into the behavior of fluid-solid interfacial energies in diverse systems. We also illustrate two interpretations of the fluid-solid IFE based on the theory of Navascues and Berry and Bakker's equation. It is shown that the simulation methods developed from these two interpretations are identical. This review advocates for the broader adoption of molecular simulation methods in estimating fluid-solid IFE, which is essential for advancing our understanding of wetting behaviors in various chemical systems.
To ensure secure and reliable communication in wireless systems, authenticating the identities of numerous nodes is imperative. Traditional cryptography-based authentication methods suffer from issues such as low compatibility, reliability, and high complexity. Physical-Layer Authentication (PLA) is emerging as a promising complement due to its exploitation of unique properties in wireless environments. Recently, Machine Learning (ML)-based PLA has gained attention for its intelligence, adaptability, universality, and scalability compared to non-ML approaches. However, a comprehensive overview of state-of-the-art ML-based PLA and its foundational aspects is lacking. This paper presents a comprehensive survey of characteristics and technologies that can be used in the ML-based PLA. We categorize existing ML-based PLA schemes into two main types: multi-device identification and attack detection schemes. In deep learning-based multi-device identification schemes, Deep Neural Networks are employed to train models, avoiding complex processing and expert feature transformation. Deep learning-based multi-device identification schemes are further subdivided, with schemes based on Convolutional Neural Networks being extensively researched. In ML-based attack detection schemes, receivers utilize intelligent ML techniques to set detection thresholds automatically, eliminating the need for manual calculation or knowledge of channel models. ML-based attack detection schemes are categorized into three sub-types: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Additionally, we summarize open-source datasets used for PLA, encompassing Radio Frequency fingerprints and channel fingerprints. Finally, this paper outlines future research directions to guide researchers in related fields.
The HI Mass Function of the Local Universe: Combining Measurements from HIPASS, ALFALFA and FASHI
2024-11-15
Wenlin Ma, Hong Guo, Haojie Xu, Michael G. Jones, Chuan-Peng Zhang, Ming Zhu, Jing Wang, Jie Wang, Peng Jiang
We present the first HI mass function (HIMF) measurement for the recent FAST All Sky HI (FASHI) survey and the most complete measurements of HIMF in the local universe so far by combining the HI catalogues from HI Parkes All Sky Survey (HIPASS), Arecibo Legacy Fast ALFA (ALFALFA) and FASHI surveys at redshift 0 < z < 0.05, covering 76% of the entire sky. We adopt the same methods to estimate distances, calculate sample completeness, and determine the HIMF for all three surveys. The best-fitting Schechter function for the total HIMF has a low-mass slope parameter alpha = -1.30 and a knee mass log(Ms) = 9.86 and a normalization phi_s = 0.00658. This gives the cosmic HI abundance omega_HI= 0.000454. We find that a double Schechter function with the same slope alpha better describes our HIMF, and the two different knee masses are log(Ms1) = 9.96 and log(Ms2) = 9.65. We verify that the measured HIMF is marginally affected by the choice of distance estimates. The effect of cosmic variance is significantly suppressed by combining the three surveys and it provides a unique opportunity to obtain an unbiased estimate of the HIMF in the local universe.
Our review of the lattice chiral fermion delves into some critical areas of lattice field theory. By abandoning Hermiticity, the non-Hermitian formulation circumvents the Nielsen-Ninomiya theorem while maintaining chiral symmetry, a novel approach. Comparing the Wilson and overlap fermions gives insight into how lattice formulations handle chiral symmetry. The Wilson fermion explicitly breaks chiral symmetry to eliminate doublers. In contrast, the overlap fermion restores a modified form of chiral symmetry using the Ginsparg-Wilson relation. We investigate how the (1+1)D Wilson fermion relates to the (1+1)D overlap fermion in the Hamiltonian formulation. This connection could provide a clearer physical understanding of how chiral symmetry manifests at the lattice level. Depending on Hermiticity for efficiency, Monte Carlo methods face unique challenges in a non-Hermitian setting. We investigate how to correctly apply this method to non-Hermitian lattice fermions, which is essential for practical simulations. Finally, the review of topological charge is crucial, as topological features in lattice formulations are strongly connected to chiral symmetry, anomalies, and the index theorem.
Face De-identification: State-of-the-art Methods and Comparative Studies
2024-11-15
Jingyi Cao, Xiangyi Chen, Bo Liu, Ming Ding, Rong Xie, Li Song, Zhu Li, Wenjun Zhang
The widespread use of image acquisition technologies, along with advances in facial recognition, has raised serious privacy concerns. Face de-identification usually refers to the process of concealing or replacing personal identifiers, which is regarded as an effective means to protect the privacy of facial images. A significant number of methods for face de-identification have been proposed in recent years. In this survey, we provide a comprehensive review of state-of-the-art face de-identification methods, categorized into three levels: pixel-level, representation-level, and semantic-level techniques. We systematically evaluate these methods based on two key criteria, the effectiveness of privacy protection and preservation of image utility, highlighting their advantages and limitations. Our analysis includes qualitative and quantitative comparisons of the main algorithms, demonstrating that deep learning-based approaches, particularly those using Generative Adversarial Networks (GANs) and diffusion models, have achieved significant advancements in balancing privacy and utility. Experimental results reveal that while recent methods demonstrate strong privacy protection, trade-offs remain in visual fidelity and computational complexity. This survey not only summarizes the current landscape but also identifies key challenges and future research directions in face de-identification.
SymbolFit: Automatic Parametric Modeling with Symbolic Regression
2024-11-15
Ho Fung Tsoi, Dylan Rankin, Cecile Caillol, Miles Cranmer, Sridhara Dasu, Javier Duarte, Philip Harris, Elliot Lipeles, Vladimir Loncar
We introduce SymbolFit, a framework that automates parametric modeling by using symbolic regression to perform a machine-search for functions that fit the data, while simultaneously providing uncertainty estimates in a single run. Traditionally, constructing a parametric model to accurately describe binned data has been a manual and iterative process, requiring an adequate functional form to be determined before the fit can be performed. The main challenge arises when the appropriate functional forms cannot be derived from first principles, especially when there is no underlying true closed-form function for the distribution. In this work, we address this problem by utilizing symbolic regression, a machine learning technique that explores a vast space of candidate functions without needing a predefined functional form, treating the functional form itself as a trainable parameter. Our approach is demonstrated in data analysis applications in high-energy physics experiments at the CERN Large Hadron Collider (LHC). We demonstrate its effectiveness and efficiency using five real proton-proton collision datasets from new physics searches at the LHC, namely the background modeling in resonance searches for high-mass dijet, trijet, paired-dijet, diphoton, and dimuon events. We also validate the framework using several toy datasets with one and more variables.
Feature Importance of Climate Vulnerability Indicators with Gradient Boosting across Five Global Cities
2024-11-15
Lidia Cano Pecharroman, Melissa O. Tier, Elke U. Weber
Efforts are needed to identify and measure both communities' exposure to climate hazards and the social vulnerabilities that interact with these hazards, but the science of validating hazard vulnerability indicators is still in its infancy. Progress is needed to improve: 1) the selection of variables that are used as proxies to represent hazard vulnerability; 2) the applicability and scale for which these indicators are intended, including their transnational applicability. We administered an international urban survey in Buenos Aires, Argentina; Johannesburg, South Africa; London, United Kingdom; New York City, United States; and Seoul, South Korea in order to collect data on exposure to various types of extreme weather events, socioeconomic characteristics commonly used as proxies for vulnerability (i.e., income, education level, gender, and age), and additional characteristics not often included in existing composite indices (i.e., queer identity, disability identity, non-dominant primary language, and self-perceptions of both discrimination and vulnerability to flood risk). We then use feature importance analysis with gradient-boosted decision trees to measure the importance that these variables have in predicting exposure to various types of extreme weather events. Our results show that non-traditional variables were more relevant to self-reported exposure to extreme weather events than traditionally employed variables such as income or age. Furthermore, differences in variable relevance across different types of hazards and across urban contexts suggest that vulnerability indicators need to be fit to context and should not be used in a one-size-fits-all fashion.
KAT to KANs: A Review of Kolmogorov-Arnold Networks and the Neural Leap Forward
2024-11-15
Divesh Basina, Joseph Raj Vishal, Aarya Choudhary, Bharatesh Chakravarthi
The curse of dimensionality poses a significant challenge to modern multilayer perceptron-based architectures, often causing performance stagnation and scalability issues. Addressing this limitation typically requires vast amounts of data. In contrast, Kolmogorov-Arnold Networks have gained attention in the machine learning community for their bold claim of being unaffected by the curse of dimensionality. This paper explores the Kolmogorov-Arnold representation theorem and the mathematical principles underlying Kolmogorov-Arnold Networks, which enable their scalability and high performance in high-dimensional spaces. We begin with an introduction to foundational concepts necessary to understand Kolmogorov-Arnold Networks, including interpolation methods and Basis-splines, which form their mathematical backbone. This is followed by an overview of perceptron architectures and the Universal approximation theorem, a key principle guiding modern machine learning. This is followed by an overview of the Kolmogorov-Arnold representation theorem, including its mathematical formulation and implications for overcoming dimensionality challenges. Next, we review the architecture and error-scaling properties of Kolmogorov-Arnold Networks, demonstrating how these networks achieve true freedom from the curse of dimensionality. Finally, we discuss the practical viability of Kolmogorov-Arnold Networks, highlighting scenarios where their unique capabilities position them to excel in real-world applications. This review aims to offer insights into Kolmogorov-Arnold Networks' potential to redefine scalability and performance in high-dimensional learning tasks.
Contextualizing Security and Privacy of Software-Defined Vehicles: State of the Art and Industry Perspectives
2024-11-15
Marco De Vincenzi, Mert D. Pesé, Chiara Bodei, Ilaria Matteucci, Richard R. Brooks, Monowar Hasan, Andrea Saracino, Mohammad Hamad, Sebastian Steinhorst
The growing reliance on software in vehicles has given rise to the concept of Software-Defined Vehicles (SDVs), fundamentally reshaping the vehicles and the automotive industry. This survey explores the cybersecurity and privacy challenges posed by SDVs, which increasingly integrate features like Over-the-Air (OTA) updates and Vehicle-to-Everything (V2X) communication. While these advancements enhance vehicle capabilities and flexibility, they also come with a flip side: increased exposure to security risks including API vulnerabilities, third-party software risks, and supply-chain threats. The transition to SDVs also raises significant privacy concerns, with vehicles collecting vast amounts of sensitive data, such as location and driver behavior, that could be exploited using inference attacks. This work aims to provide a detailed overview of security threats, mitigation strategies, and privacy risks in SDVs, primarily through a literature review, enriched with insights from a targeted questionnaire with industry experts. Key topics include defining SDVs, comparing them to Connected Vehicles (CVs) and Autonomous Vehicles (AVs), discussing the security challenges associated with OTA updates and the impact of SDV features on data privacy. Our findings highlight the need for robust security frameworks, standardized communication protocols, and privacy-preserving techniques to address the issues of SDVs. This work ultimately emphasizes the importance of a multi-layered defense strategy,integrating both in-vehicle and cloud-based security solutions, to safeguard future SDVs and increase user trust.
Drag reduction during the side-by-side motion of a pair of intruders in a granular medium
2024-11-15
D. D. Carvalho, Y. Bertho, A. Seguin, E. M. Franklin, B. Darbois Texier
When several intruders move in a granular medium, coupling effects are observed, the motion of one intruder affecting that of others. In this paper, we investigate experimentally how the drag forces acting on a pair of spherical intruders moving amid grains at constant velocity vary with the transverse separation between them and their depth. When intruders are sufficiently far apart, they do not influence each other, and the average drag felt by each of them matches that of a single intruder. However, for small distances between intruders and at a given depth, the average drag per intruder decreases, highlighting a collaborative effect that facilitates motion. This collaboration effect is amplified when the depth of the intruders increases. We propose a model for the drag reduction of a pair of intruders based on the breakup of contact chains, caused by the perturbation generated by the neighbor intruder. Our findings provide new insights into the interaction effects on the motion of solids in sand, such as those observed in animal locomotion, root growth, and soil survey.
2024-11-15
Ana-Catalina Plesa, Julia Maia, Solmaz Adeli, Tina Rückriemen-Bez
Rocky planets in our Solar System, namely Mercury, Venus, Earth, Mars, and the Moon, which is generally added to this group due to its geological complexity, possess a solid surface and share a common structure divided into major layers, namely a silicate crust, a silicate mantle, and an iron-rich core. However, while all terrestrial planets share a common structure, the thickness of their interior layers, their bulk chemical composition, and surface expressions of geological processes are often unique to each of them. In this chapter we provide an overview of the surfaces and interiors of rocky planets in the Solar System. We list some of the major discoveries in planetary exploration and discuss how they have helped to answer fundamental questions about planetary evolution while at the same time opening new avenues. For each of the major planetary layers, i.e., the surface, the crust and lithosphere, the mantle, and the core, we review key geological and geophysical processes that have shaped the planets that we observe today. Understanding the similarities and differences between the terrestrial planets in the Solar System will teach us about the diversity of evolutionary paths a planet could follow, helping us to better understand our own home, the Earth.
EELG1002: A Record-Breaking [OIII]+H$β$ EW $\sim 3700$Å~Galaxy at $z \sim 0.8$ -- Analog of Early Galaxies?
2024-11-15
Ali Ahmad Khostovan, Jeyhan S. Kartaltepe, Malte Brinch, Caitlin Casey, Andreas Faisst, Santosh Harish, Ghassem Gozaliasl, Masato Onodera, Kiyoto Yabe
We present a detailed analysis of EELG1002: a $z = 0.8275$ EELG identified within archival Gemini/GMOS spectroscopy as part of the COSMOS Spectroscopic Archive. Combining GMOS spectra and available multi-wavelength photometry, we find EELG1002 is a low-mass ($10^{7 - 8}$ M$_\odot$), compact ($\sim 530$ pc), and bursty star-forming galaxy with mass doubling timescales of $\sim 5 - 15$ Myr. EELG1002 has record-breaking rest-frame [OIII]+H$\beta$ EW of $\sim 2800 - 3700$\AA~which is $\sim 16 - 35 \times$ higher than typical $z \sim 0.8$ [OIII] emitters with similar stellar mass and even higher than typical $z > 5$ galaxies. We find no clear evidence of an AGN suggesting the emission lines are star formation driven. EELG1002 is chemically unevolved (direct $T_e$; $12+\log_{10} (\textrm{O/H}) \sim 7.5$ consistent with $z > 5$ galaxies at fixed stellar mass) and may be undergoing a first intense, bursty star formation phase analogous to conditions expected of galaxies in the early Universe. We find evidence for a highly energetic ISM ([OIII]/[OII] $\sim 11$) and hard ionizing radiation field (elevated [NeIII]/[OII] at fixed [OIII]/[OII]). Coupled with its compact, metal-poor, and actively star-forming nature, EELG1002 is found to efficiently produce ionizing photons with $\xi_{ion} \sim 10^{25.70 - 25.75}$ erg$^{-1}$ Hz and may have $\sim 10 - 20\%$ LyC escape fraction suggesting such sources may be important reionization-era analogs. We find dynamical mass of $\sim 10^9$ M$_\odot$ suggesting copious amounts of gas to support intense star-formation activity as also suggested by analogs identified in Illustris-TNG. EELG1002 may be an ideal low-$z$ laboratory of galaxies in the early Universe and demonstrates how archival datasets can support high-$z$ science and next-generation surveys planned with \textit{Euclid} and \textit{Roman}.
Star-Formation in Neutral Hydrogen Gas Reservoirs at Cosmic Noon
2024-11-15
Dharmender, Ravi Joshi, Michele Fumagalli, Pasquier Noterdaeme, Hum Chand, Luis C. Ho
We aim to constrain the average star formation associated with neutral hydrogen gas reservoirs at cosmic noon. Using a unprecedented sample of 1716 high column density Damped Ly-$\alpha$ absorbers (DLAs) from the Sloan Digital Sky Survey with log($N$(HI) / cm$^{-2}$) $\ge$21, we generated the average Ly-$\alpha$ emission spectrum associated to DLAs, free from emission from the background quasar. We measured Ly$\alpha$ emission at $> 5.8\sigma$ level with luminosity $8.95\pm 1.54 \times \rm 10^{40}\ \text{erg}\ \text{s}^{-1}$ (corresponding to about 0.02 L$^{\star}$ at $z \sim$ 2-3) in systems with average log($N$(HI)/ $cm^{-2}$) $\approx$21.2 and at median redshift of $z \sim$ 2.64. The peak of the Ly$\alpha$ emission is apparently redshifted by $\sim$300 km s$^{-1}$ relative to the absorption redshift, which is seemingly due to suppression of blue Ly-$\alpha$ photons by radiative transfer through expanding gas. We infer that DLAs form stars with an average rate of (0.08 $\pm$ 0.01)/$\text{f}_\text{esc}\ \rm \text{M}_{\odot}\ \text{yr}^{-1}$, i.e, $\approx (0.54\pm 0.09)\rm \text{M}_{\odot}\ \text{yr}^{-1}$ for a typical escape fraction, $\text{f}_{\text{esc}} =0.15$, of Lyman-$\alpha$ emitting galaxies. DLA galaxies follows the star formation main sequence of star-forming galaxies at high redshift, suggesting that the DLA population is dominated by the lower mass end of Lyman-$\alpha$ emitting galaxies.
DECaPS and SMA discovery of a highly-inclined class I YSO with an outflow: IRAS 08235-4316
2024-11-15
Joshua Bennett Lovell, Kristina Monsch, Garrett K. Keating, David J. Wilner, Gordian Edenhofer, Mark Gurwell, Ramprasad Rao
We present images of IRAS~08235--4316 with the Dark Energy Camera Plane Survey (DECaPS; spanning 0.398--1.034$\,\mu$m, at ${\sim}1''$ resolution) and the Submillimeter Array (SMA; at 1.38\,mm/217\,GHz, at ${\sim}1.9''\times1.2''$ resolution), a YSO located in the Vela constellation near to the Puppis boundary, detected in a systematic search for new large/extended emission sources. The DECaPS data show an asymmetric bi--polar morphology with a large angular extent of ${\sim}7.1''$ separated by a dark lane, characteristic of highly--inclined protoplanetary disks and less-evolved YSOs with outflows. The SMA data show an extended continuum structure along the optical dark lane with a smaller angular extent of ${\sim}4.6''$. The detected $^{12}$CO J=2--1 emission tentatively shows a velocity gradient along the position angle of the dark lane/millimeter continuum, that may trace rotating gas. Additional $^{12}$CO emission is present which could trace infalling/outflowing gas, and/or a nearby gas cloud. We estimate a distance to IRAS~08235--4316 of at least ${\sim}191\,$pc. Supported by additional SED modelling, we infer IRAS~08235--4316 to be a newly discovered class~I YSO with an outflow, host to an embedded protoplanetary disk, with a large millimeter radius of ${\sim}440$\,au and dust mass ${\gtrsim}11\,M_\oplus$.
Towards cosmological inference on unlabeled out-of-distribution HI observational data
2024-11-15
Sambatra Andrianomena, Sultan Hassan
We present an approach that can be utilized in order to account for the covariate shift between two datasets of the same observable with different distributions, so as to improve the generalizability of a neural network model trained on in-distribution samples (IDs) when inferring cosmology at the field level on out-of-distribution samples (OODs) of {\it unknown labels}. We make use of HI maps from the two simulation suites in CAMELS, IllustrisTNG and SIMBA. We consider two different techniques, namely adversarial approach and optimal transport, to adapt a target network whose initial weights are those of a source network pre-trained on a labeled dataset. Results show that after adaptation, salient features that are extracted by source and target encoders are well aligned in the embedding space, indicating that the target encoder has learned the representations of the target domain via the adversarial training and optimal transport. Furthermore, in all scenarios considered in our analyses, the target encoder, which does not have access to any labels ($\Omega_{\rm m}$) during adaptation phase, is able to retrieve the underlying $\Omega_{\rm m}$ from out-of-distribution maps to a great accuracy of $R^{2}$ score $\ge$ 0.9, comparable to the performance of the source encoder trained in a supervised learning setup. We further test the viability of the techniques when only a few out-of-distribution instances are available and find that the target encoder still reasonably recovers the matter density. Our approach is critical in extracting information from upcoming large scale surveys.
X-ray reverberation modelling of the continuum, optical/UV time-lags in quasars
2024-11-14
D. A. Langis, I. E. Papadakis, E. Kammoun, C. Panagiotou, M. Dovčiak
Context: Extensive, multi-wavelength monitoring campaigns of nearby and higher redshift active galactic nuclei (AGN) have shown that the UV/optical variations are well correlated with time delays which increase with increasing wavelength. Such behaviour is expected in the context of the X-ray thermal reverberation of the accretion disc in AGN. Aims: Our main objective is to use time-lag measurements of luminous AGN and fit them with sophisticated X-ray reverberation time-lags models. In this way we can investigate whether X-ray reverberation can indeed explain the observed continuum time lags, and whether time-lag measurements can be used to measure physical parameters such as the X-ray corona height and the spin of the black hole (BH) in these systems. Methods: We use archival time-lag measurements for quasars from different surveys, and we compute their rest frame, mean time-lags spectrum. We fit the data with analytical X-ray reverberation models, using $\chi^2$ statistics, and fitting for both maximal and non spinning BHs, for various colour correction values and X-ray corona heights. Results: We found that X-ray reverberation can explain very well the observed time lags, assuming the measured BH mass, accretion rate and X-ray luminosity of the quasars in the sample. The model agrees well with the data both for non-rotating and maximally rotating BHs, as long as the corona height is larger than $\sim 40$ gravitational radii. This is in agreement with previous results which showed that X-ray reverberation can also explain the disc radius in micro-lensed quasars, for the same corona heights. The corona height we measure depends on the model assumption of a perfectly flat disc. More realistic disc models may result in lower heights for the X-ray corona.
2024-11-14
Xiaohuan Xia, Mathieu Ouellet, Shubhankar P. Patankar, Diana I. Tamir, Dani S. Bassett
Modern science is formally structured around scholarly publication, where scientific knowledge is canonized through citation. Precisely how citations are given and accrued can provide information about the value of discovery, the history of scientific ideas, the structure of fields, and the space or scope of inquiry. Yet parsing this information has been challenging because citations are not simply present or absent; rather, they differ in purpose, function, and sentiment. In this paper, we investigate how critical and favorable sentiments are distributed across citations, and demonstrate that citation sentiment tracks sociocultural norms across scales of collaboration, discipline, and country. At the smallest scale of individuals, we find that researchers cite scholars they have collaborated with more favorably (and less critically) than scholars they have not collaborated with. Outside collaborative relationships, higher h-index scholars cite lower h-index scholars more critically. At the mesoscale of disciplines, we find that wetlab disciplines tend to be less critical than drylab disciplines, and disciplines that engage in more synthesis through publishing more review articles tend to be less critical. At the largest scale of countries, we find that greater individualism (and lesser acceptance of the unequal distribution of power) is associated with more critical sentiment. Collectively, our results demonstrate how sociocultural factors can explain variations in sentiment in scientific communication. As such, our study contributes to the broader understanding of how human factors influence the practice of science, and underscore the importance of considering the larger sociocultural contexts in which science progresses.
Crossing bridges between percolation models and Bienaymé-Galton-Watson trees
2024-11-14
Airam Blancas, María Clara Fittipaldi, Saraí Hernández-Torres
In this survey, we explore the connections between two areas of probability: percolation theory and population genetic models. Our first goal is to highlight a construction on Galton-Watson trees, which has been described in two different ways: Bernoulli bond percolation and neutral mutations. Next, we introduce a novel connection between the Divide-and-Color percolation model and a particular multi-type Galton-Watson tree. We provide a gentle introduction to these topics while presenting an overview of the results that connect them.
Effect of viewing angle in Gamma-ray Burst properties
2024-11-14
Sreelakshmi P Chakyar, Sarath Prabhavu J, Lekshmi Resmi
The empirical classification of Gamma-Ray Bursts (GRBs) is based on their distribution in the plane of burst duration and spectral hardness. Two distinct distributions, long-soft and short-hard bursts, are observed in this plane, forming the basis for the long and short classification scheme. Traditionally, this scheme was mapped to two different GRB progenitor classes. However, several recent bursts have challenged this mapping. This work investigates how an observer's viewing angle relative to the jet axis influences the duration-hardness plane. We simulate single-pulse GRBs using an optically and geometrically thin homogeneous top-hat jet model. Bursts are simulated with an isotropic viewing angle distribution, and we calculate the pulse duration and spectral hardness corresponding to \textit{FERMI} Gamma-Ray Burst Monitor (GBM) energy bands. The viewing angle significantly impacts spectral hardness for our assumed broken power-law spectra, while its effect on duration is less pronounced. Our analysis indicates that soft and low-luminous bursts are likely off-axis events. It is possible that some of the fast X-ray transients and X-ray rich GRBs observed by the Einstein Probe and the Space Variable Objects Monitor (SVOM) missions originate from off-axis jets.
VST-SMASH: the VST Survey of Mass Assembly and Structural Hierarchy
2024-11-14
Crescenzo Tortora, Rossella Ragusa, Massimiliano Gatto, Marilena Spavone, Leslie Hunt, Vincenzo Ripepi, Massimo Dall'Ora, Abdurro'uf, Francesca Annibali, Maarten Baes, Francesco Michel Concetto Belfiore, Nicola Bellucco, Micol Bolzonella, Michele Cantiello, Paola Dimauro, Mathias Kluge, Federico Lelli, Nicola R. Napolitano, Achille Nucita, Mario Radovich, Roberto Scaramella, Eva Schinnerer, Vincenzo Testa, Aiswarya Unni
The VLT Survey Telescope Survey of Mass Assembly and Structural Hierarchy (VST-SMASH) aims to detect tidal features and remnants around very nearby galaxies, a unique and essential diagnostic of the hierarchical nature of galaxy formation. Leveraging optimal sky conditions at ESO's Paranal Observatory, combined with the VST's multi-band optical filters, VST-SMASH aims to be the definitive survey of stellar streams and tidal remnants in the Local Volume, targeting a low surface-brightness limit of $\mu \sim$ 30 mag arcsec$^{-2}$ in the g and r bands, and $\mu \sim$ 28 mag arcsec$^{-2}$ in the i band, in a volume-limited sample of local galaxies within 11 Mpc and the Euclid footprint.
Expert Study on Interpretable Machine Learning Models with Missing Data
2024-11-14
Lena Stempfle, Arthur James, Julie Josse, Tobias Gauss, Fredrik D. Johansson
Inherently interpretable machine learning (IML) models provide valuable insights for clinical decision-making but face challenges when features have missing values. Classical solutions like imputation or excluding incomplete records are often unsuitable in applications where values are missing at test time. In this work, we conducted a survey with 71 clinicians from 29 trauma centers across France, including 20 complete responses to study the interaction between medical professionals and IML applied to data with missing values. This provided valuable insights into how missing data is interpreted in clinical machine learning. We used the prediction of hemorrhagic shock as a concrete example to gauge the willingness and readiness of the participants to adopt IML models from three classes of methods. Our findings show that, while clinicians value interpretability and are familiar with common IML methods, classical imputation techniques often misalign with their intuition, and that models that natively handle missing values are preferred. These results emphasize the need to integrate clinical intuition into future IML models for better human-computer interaction.
Software Performance Engineering for Foundation Model-Powered Software (FMware)
2024-11-14
Haoxiang Zhang, Shi Chang, Arthur Leung, Kishanthan Thangarajah, Boyuan Chen, Hanan Lutfiyya, Ahmed E. Hassan
The rise of Foundation Models (FMs) like Large Language Models (LLMs) is revolutionizing software development. Despite the impressive prototypes, transforming FMware into production-ready products demands complex engineering across various domains. A critical but overlooked aspect is performance engineering, which aims at ensuring FMware meets performance goals such as throughput and latency to avoid user dissatisfaction and financial loss. Often, performance considerations are an afterthought, leading to costly optimization efforts post-deployment. FMware's high computational resource demands highlight the need for efficient hardware use. Continuous performance engineering is essential to prevent degradation. This paper highlights the significance of Software Performance Engineering (SPE) in FMware, identifying four key challenges: cognitive architecture design, communication protocols, tuning and optimization, and deployment. These challenges are based on literature surveys and experiences from developing an in-house FMware system. We discuss problems, current practices, and innovative paths for the software engineering community.
The Redshift-Space Momentum Power Spectrum III: measuring the growth rate from the SDSSv survey using auto- and cross- power spectrum of the galaxy density and momentum fields
2024-11-14
Fei Qin, Cullan Howlett, David Parkinson
The large-scale structure of the Universe and its evolution over time contains an abundance of cosmological information. One way to unlock this is by measuring the density and momentum power spectrum from the positions and peculiar velocities of galaxies, and fitting the cosmological parameters from these power spectrum. In this paper, we will explore the cross power spectrum between the density and momentum fields of galaxies. We derive the estimator of the density-momentum cross power spectrum multipoles. The growth rate of the large-scale-structure, $f\sigma_8$ is measured from fitting the combined density monopole, momentum monopole and cross dipole power spectrum. The estimators and models of power spectrum as well as our fitting method have been tested using mock catalogues, and we find that they perform well in recovering the fiducial values of the cosmological parameters of the simulations, and we also find that the errors of the parameters can be largely reduced by including the cross-power spectrum in the fit. We measure the auto-density, auto-momentum and cross power spectrum using the Sloan Digital Sky Survey Data Release 14 peculiar velocity catalogue. The fit result of the growth rate $f\sigma_8$ is $f\sigma_8=0.413^{+0.050}_{-0.058}$ at effective redshift $z_{\mathrm{eff}}=0.073$, and our measurement is consistent with the prediction of the $\Lambda$ Cold Dark Matter cosmological model assuming General Relativity.
A Practical Guide to Fine-tuning Language Models with Limited Data
2024-11-14
Márton Szép, Daniel Rueckert, Rüdiger von Eisenhart-Rothe, Florian Hinterwimmer
Employing pre-trained Large Language Models (LLMs) has become the de facto standard in Natural Language Processing (NLP) despite their extensive data requirements. Motivated by the recent surge in research focused on training LLMs with limited data, particularly in low-resource domains and languages, this paper surveys recent transfer learning approaches to optimize model performance in downstream tasks where data is scarce. We first address initial and continued pre-training strategies to better leverage prior knowledge in unseen domains and languages. We then examine how to maximize the utility of limited data during fine-tuning and few-shot learning. The final section takes a task-specific perspective, reviewing models and methods suited for different levels of data scarcity. Our goal is to provide practitioners with practical guidelines for overcoming the challenges posed by constrained data while also highlighting promising directions for future research.
With the continuous development of large language models (LLMs), transformer-based models have made groundbreaking advances in numerous natural language processing (NLP) tasks, leading to the emergence of a series of agents that use LLMs as their control hub. While LLMs have achieved success in various tasks, they face numerous security and privacy threats, which become even more severe in the agent scenarios. To enhance the reliability of LLM-based applications, a range of research has emerged to assess and mitigate these risks from different perspectives. To help researchers gain a comprehensive understanding of various risks, this survey collects and analyzes the different threats faced by these agents. To address the challenges posed by previous taxonomies in handling cross-module and cross-stage threats, we propose a novel taxonomy framework based on the sources and impacts. Additionally, we identify six key features of LLM-based agents, based on which we summarize the current research progress and analyze their limitations. Subsequently, we select four representative agents as case studies to analyze the risks they may face in practical use. Finally, based on the aforementioned analyses, we propose future research directions from the perspectives of data, methodology, and policy, respectively.
GAN-Based Architecture for Low-dose Computed Tomography Imaging Denoising
2024-11-14
Yunuo Wang, Ningning Yang, Jialin Li
Generative Adversarial Networks (GANs) have surfaced as a revolutionary element within the domain of low-dose computed tomography (LDCT) imaging, providing an advanced resolution to the enduring issue of reconciling radiation exposure with image quality. This comprehensive review synthesizes the rapid advancements in GAN-based LDCT denoising techniques, examining the evolution from foundational architectures to state-of-the-art models incorporating advanced features such as anatomical priors, perceptual loss functions, and innovative regularization strategies. We critically analyze various GAN architectures, including conditional GANs (cGANs), CycleGANs, and Super-Resolution GANs (SRGANs), elucidating their unique strengths and limitations in the context of LDCT denoising. The evaluation provides both qualitative and quantitative results related to the improvements in performance in benchmark and clinical datasets with metrics such as PSNR, SSIM, and LPIPS. After highlighting the positive results, we discuss some of the challenges preventing a wider clinical use, including the interpretability of the images generated by GANs, synthetic artifacts, and the need for clinically relevant metrics. The review concludes by highlighting the essential significance of GAN-based methodologies in the progression of precision medicine via tailored LDCT denoising models, underlining the transformative possibilities presented by artificial intelligence within contemporary radiological practice.
The Use of Readability Metrics in Legal Text: A Systematic Literature Review
2024-11-14
Yu Han, Aaron Ceross, Jeroen H. M. Bergmann
Understanding the text in legal documents can be challenging due to their complex structure and the inclusion of domain-specific jargon. Laws and regulations are often crafted in such a manner that engagement with them requires formal training, potentially leading to vastly different interpretations of the same texts. Linguistic complexity is an important contributor to the difficulties experienced by readers. Simplifying texts could enhance comprehension across a broader audience, not just among trained professionals. Various metrics have been developed to measure document readability. Therefore, we adopted a systematic review approach to examine the linguistic and readability metrics currently employed for legal and regulatory texts. A total of 3566 initial papers were screened, with 34 relevant studies found and further assessed. Our primary objective was to identify which current metrics were applied for evaluating readability within the legal field. Sixteen different metrics were identified, with the Flesch-Kincaid Grade Level being the most frequently used method. The majority of studies (73.5%) were found in the domain of "informed consent forms". From the analysis, it is clear that not all legal domains are well represented in terms of readability metrics and that there is a further need to develop more consensus on which metrics should be applied for legal documents.
Charge-Neutral Electronic Excitations in Quantum Insulators
2024-11-14
Sanfeng Wu, Leslie M. Schoop, Inti Sodemann, Roderich Moessner, Robert J. Cava, N. P. Ong
Experiments on quantum materials have uncovered many interesting quantum phases ranging from superconductivity to a variety of topological quantum matter including the recently observed fractional quantum anomalous Hall insulators. The findings have come in parallel with the development of approaches to probe the rich excitations inherent in such systems. In contrast to observing electrically charged excitations, the detection of charge-neutral electronic excitations in condensed matter remains difficult, though they are essential to understanding a large class of strongly correlated phases. Low-energy neutral excitations are especially important in characterizing unconventional phases featuring electron fractionalization, such as quantum spin liquids, spin ices, and insulators with neutral Fermi surfaces. In this perspective, we discuss searches for neutral fermionic, bosonic, or anyonic excitations in unconventional insulators, highlighting theoretical and experimental progress in probing excitonic insulators, new quantum spin liquid candidates and emergent correlated insulators based on two-dimensional layered crystals and moir\'e materials. We outline the promises and challenges in probing and utilizing quantum insulators, and discuss exciting new opportunities for future advancements offered by ideas rooted in next-generation quantum materials, devices, and experimental schemes.
This review presents recent developments in the study of inhomogeneous XX spin chains, highlighting results on perfect state transfer, out-of-equilibrium stationary dynamics in open systems, and entanglement and correlations in ground states. We discuss the conditions on couplings that enable perfect state transfer, examine how heat currents scale when the chains are coupled to thermal baths, explore the role of tridiagonal matrices in approximating the entanglement Hamiltonian and investigate bulk and boundary entanglement negativity and correlation decay. These findings underscore some of the distinctive physical behavior of inhomogeneous spin chains and their potential applications in quantum information and thermal transport.
Electrospray Propulsion Time-of-Flight Secondary Ion Mass Spectrometry Diagnostic
2024-11-14
Giuliana Caramella Hofheins, Zach Ulibarri, Elaine M. Petro
The design and capability of a novel time-of-flight secondary ion mass spectrometry electrospray propulsion diagnostic is presented to investigate secondary species emission from surface impingement of high-velocity, energetic molecular ion plumes. Designed on the basis of traditional Secondary Ion Mass Spectrometry (SIMS) principles, this diagnostic provides information on the relative intensity and chemical composition of secondary species given electrospray operational parameters like incident angle, primary ion energy, and target surface composition. The system consists of an externally-wetted tungsten ion source operating with room temperature ionic liquid propellant, a target with a secondary species extraction mesh, and a time-of-flight mass spectrometer featuring an electrostatic deflection gate and a multichannel plate detector. Results show that energetic primary plume impacts with metallic surfaces induce molecular secondary ion emission in both positive and negative polarities. Likely sources of these secondary ions are considered - including hydrocarbon contamination of the target surface and charged fragments of the molecular primary ionic liquid ions. For electrospray propulsion, these secondary species contribute not only to lifetime limiting processes intrinsic to thruster operation like impingement and thus degradation of electrodes and emitters, but also contribute to facility effects corrupting ground-based testing and thruster flight qualification.
The Perceptions of Software Engineers Concerning the Utilization of Bots in the OSS Development Process: An Exploratory Survey
2024-11-14
Danyellias Vaz de Lima Manso, Valdemar Vicente Graciano Neto, Mohamad Kassab
Software bots, extensively adopted by Open Source Software (OSS) projects, support developers across several activities, from automating predefined tasks to generating code that aids software engineers. However, with the growing prominence of bots, questions have emerged regarding the extension to which they truly assist or hinder software engineers in their routine tasks. To address this, an exploratory survey was conducted with 37 software engineers to gather insights into their views on the use of bots within the software development process. The findings suggest that bots are present across multiple phases of the software development lifecycle, providing daily support to professionals by enhancing productivity and facilitating task automation. Respondents stated that current bots are not sufficiently intelligent and raised new challenges and enhancements to aid bot designers in developing additional functionalities and integrations.
A search for HI absorption in distant star-forming galaxies with ASKAP-FLASH -- I. Selection and analysis of the radio sample
2024-11-14
Sophie L. Eden, Elaine M. Sadler, Kevin A. Pimbblet, Elizabeth K. Mahony, Hyein Yoon
We present and discuss two catalogues of UV-selected (NUV $< 22.8$ mag) galaxies that lie within a 200 deg$^2$ area of sky covered by the ASKAP FLASH survey and have an impact parameter of less than 20 arcsec to a FLASH radio continuum source. These catalogues are designed to enable a future search for 21 cm HI absorption in and around star-forming galaxies at redshift $0.480 percent) star-forming and resemble the overall WiggleZ population. The ($g - i$) colour evolution with redshift is consistent with a history of active star-formation, but the radio-associated objects are typically redder and have colours similar to high-excitation radio galaxies. The redshift distribution of the two catalogues matches the overall distribution for WiggleZ galaxies, despite their otherwise rare radio properties. These catalogues can be expanded in future as new radio data become available, and a forthcoming paper will present the HI absorption results.
Role of the Coulomb Potential in Compton Scattering
2024-11-14
N. Melzer, M. Kircher, A. Pier, L. Kaiser, J. Kruse, N. Anders, J. Stindl, L. Sommerlad, D. McGinnis, M. Schmidt, L. Nowak, A. Kügler, I. Dwojak, J. Drnec, F. Trinter, M. S. Schöffler, L. Ph. Schmidt, N. M. Novikovskiy, Ph. V. Demekhin, T. Jahnke, R. Dörner
We report a fully differential study of ionization of the Ne L-shell by Compton scattering of 20 keV photons. We find two physical mechanisms which modify the Compton-electron emission. Firstly, we observe scattering of the Compton electrons at their parent nucleus. Secondly, we find a distinct maximum in the electron momentum distribution close-to-zero momentum which we attribute to a focusing of the electrons by the Coulomb potential.
The 21 cm emission from neutral hydrogen surveys holds great potential as a valuable method for exploring the large-scale structure of the Universe. In this paper, we forecast for the cross-correlation between the Thermal Sunyaev-Zel'dovich (SZ) fluctuations as probed by the Planck satellite, and fluctuations in the HI brightness temperature as probed by the ground-based Five-hundred-meter Aperture Spherical Telescope (FAST), to trace the connection between galaxy clusters and the HI large-scale structure. Assuming that the measurement is limited by instrumental noise rather than by foreground, we estimate the potential detectability of the cross-correlation signal and their improvement in the measurement of the HI cosmic density, the hydrostatic mass bias parameter, and the universal pressure profile (UPP) parameters. We obtain a constraint on the cosmic neutral hydrogen density parameter significantly to $\sigma(\Omega_{\rm HI}) = 1.0 \times 10^{-6}$. We also find that the average halo masses contributing to the ${{\rm HI}-y}$ cross-power spectrum in the one-halo regime is $\sim 1.5\times 10^{14} M_{\odot}$. Our results also show that the HI-SZ cross-correlation has great potential to probe the distribution of neutral hydrogen (HI) within halos at low redshift.
The discovery of advanced materials is the cornerstone of human technological development and progress. The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of freedom such as lattice, charge, spin, symmetry, and topology. This poses significant challenges for the inverse design methods of materials. Humans have long explored new materials through a large number of experiments and proposed corresponding theoretical systems to predict new material properties and structures. With the improvement of computational power, researchers have gradually developed various electronic structure calculation methods, particularly such as the one based density functional theory, as well as high-throughput computational methods. Recently, the rapid development of artificial intelligence technology in the field of computer science has enabled the effective characterization of the implicit association between material properties and structures, thus opening up an efficient paradigm for the inverse design of functional materials. A significant progress has been made in inverse design of materials based on generative and discriminative models, attracting widespread attention from researchers. Considering this rapid technological progress, in this survey, we look back on the latest advancements in AI-driven inverse design of materials by introducing the background, key findings, and mainstream technological development routes. In addition, we summarize the remaining issues for future directions. This survey provides the latest overview of AI-driven inverse design of materials, which can serve as a useful resource for researchers.
Rapid identification of lensed type Ia supernovae with color-magnitude selection
2024-11-14
Prajakta Mane, Anupreeta More, Surhud More
Strongly lensed type Ia supernovae (SNe Ia) provide a unique cosmological probe to address the Hubble tension problem in cosmology. In addition to the sensitivity of the time delays to the value of the Hubble constant, the transient and standard candle nature of SNe Ia also enable valuable joint constraints on the model of the lens and the cosmological parameters. The upcoming Legacy Survey of Space and Time (LSST) with the Vera C. Rubin Observatory is expected to increase the number of observed SNe Ia by an order of magnitude in ten years of its lifetime. However, finding such systems in the LSST data is a challenge. In this work, we revisit the color-magnitude (CM) diagram used previously as a means to identify lensed SNe Ia and extend the work further as follows. We simulate LSST-like photometric data ($rizy$~bands) of lensed SNe Ia and analyze it in the CM parameter space. We find that a subset of lensed SNe Ia are redder compared to unlensed SNe Ia at a given magnitude, both in the rising and falling phases of their light curves and for SNe up to $z=3$. We propose a modified selection criterion based on these new results. We show that the contamination coming from the unlensed core-collapse (CC) SNe is negligible, whereas a small fraction of lensed CC SNe types Ib and Ic may get selected by this criterion as potential lensed SNe. Finally, we demonstrate that our criterion works well on a wide sample of observed unlensed SNe Ia, a handful of known multiply-imaged lensed SNe systems, and a representative sample of observed super-luminous supernovae.
Strong Metric Subregularity of the optimality mapping and second-order sufficient optimality conditions in extremal problems with constraints
2024-11-14
Nikolai P. Osmolovskii, Vladimir M. Veliov
This is a review paper, summarizing without proofs recent results by the authors on the property of strong metric subregularity (SMSR) in optimization. It presents sufficient conditions for SMSR of the optimality mapping associated with a set of necessary optimality conditions in three types of constrained optimization problems: mathematical programming, calculus of variations, and optimal control. The conditions are based on second-order sufficient optimality conditions in the corresponding optimization problems and guarantee small changes in the optimal solution and Lagrange multipliers for small changes in the data.
A survey of probabilistic generative frameworks for molecular simulations
2024-11-14
Richard John, Lukas Herron, Pratyush Tiwary
Generative artificial intelligence is now a widely used tool in molecular science. Despite the popularity of probabilistic generative models, numerical experiments benchmarking their performance on molecular data are lacking. In this work, we introduce and explain several classes of generative models, broadly sorted into two categories: flow-based models and diffusion models. We select three representative models: Neural Spline Flows, Conditional Flow Matching, and Denoising Diffusion Probabilistic Models, and examine their accuracy, computational cost, and generation speed across datasets with tunable dimensionality, complexity, and modal asymmetry. Our findings are varied, with no one framework being the best for all purposes. In a nutshell, (i) Neural Spline Flows do best at capturing mode asymmetry present in low-dimensional data, (ii) Conditional Flow Matching outperforms other models for high-dimensional data with low complexity, and (iii) Denoising Diffusion Probabilistic Models appears the best for low-dimensional data with high complexity. Our datasets include a Gaussian mixture model and the dihedral torsion angle distribution of the Aib\textsubscript{9} peptide, generated via a molecular dynamics simulation. We hope our taxonomy of probabilistic generative frameworks and numerical results may guide model selection for a wide range of molecular tasks.
Survey on second submodules of modules over commutative rings
2024-11-14
Faranak Farshadifar
Let R be a commutative ring with identity. The concept of second submodule of an R-module (as a dual notion of prime submodules) was introduced and studied by S.Yassemi in 2001. This notion has obtained a great attention by many authors and now there is a considerable amount of research concerning this class of modules. The main purpose of this paper is to collect these results and provide a useful source for those who are interested in research in this field.
Upper bounds on diagonal Ramsey numbers [after Campos, Griffiths, Morris, and Sahasrabudhe]
2024-11-14
Yuval Wigderson
Ramsey's theorem states that if $N$ is sufficiently large, then no matter how one colors the edges among $N$ vertices with two colors, there are always $k$ vertices spanning edges in only one color. Given this theorem, it is natural to ask ``how large is sufficiently large?'' Ramsey's original proof showed that $N=k!$ is sufficient, and five years later Erd\H{o}s and Szekeres improved this bound to $N=4^k$. And then progress stalled for almost 90 years. In this survey, I present the history of the problem and discuss some of the ideas used in the recent breakthrough of Campos--Griffiths--Morris--Sahasrabudhe, who proved that $N=3.993^k$ is sufficient.
Socio-Economic Consequences of Generative AI: A Review of Methodological Approaches
2024-11-14
Carlos J. Costa, Joao Tiago Aparicio, Manuela Aparicio
The widespread adoption of generative artificial intelligence (AI) has fundamentally transformed technological landscapes and societal structures in recent years. Our objective is to identify the primary methodologies that may be used to help predict the economic and social impacts of generative AI adoption. Through a comprehensive literature review, we uncover a range of methodologies poised to assess the multifaceted impacts of this technological revolution. We explore Agent-Based Simulation (ABS), Econometric Models, Input-Output Analysis, Reinforcement Learning (RL) for Decision-Making Agents, Surveys and Interviews, Scenario Analysis, Policy Analysis, and the Delphi Method. Our findings have allowed us to identify these approaches' main strengths and weaknesses and their adequacy in coping with uncertainty, robustness, and resource requirements.
Hearing the Robot's Mind: Sonification for Explicit Feedback in Human-Robot Interaction
2024-11-14
Simone Arreghini, Antonio Paolillo, Gabriele Abbate, Alessandro Giusti
Social robots are required not only to understand human intentions but also to effectively communicate their intentions or own internal states to users. This study explores the use of sonification to provide explicit auditory feedback, enhancing mutual understanding in HRI. We introduce a novel sonification approach that conveys the robot's internal state, linked to its perception of nearby individuals and their interaction intentions. The approach is evaluated through a two-fold user study: an online video-based survey with $26$ participants and live experiments with $10$ participants. Results indicate that while sonification improves the robot's expressivity and communication effectiveness, the design of the auditory feedback needs refinement to enhance user experience. Participants found the auditory cues useful but described the sounds as uninteresting and unpleasant. These findings underscore the importance of carefully designed auditory feedback in developing more effective and engaging HRI systems.
DTELS: Towards Dynamic Granularity of Timeline Summarization
2024-11-14
Chenlong Zhang, Tong Zhou, Pengfei Cao, Zhuoran Jin, Yubo Chen, Kang Liu, Jun Zhao
The rapid proliferation of online news has posed significant challenges in tracking the continuous development of news topics. Traditional timeline summarization constructs a chronological summary of the events but often lacks the flexibility to meet the diverse granularity needs. To overcome this limitation, we introduce a new paradigm, Dynamic-granularity TimELine Summarization, (DTELS), which aims to construct adaptive timelines based on user instructions or requirements. This paper establishes a comprehensive benchmark for DTLES that includes: (1) an evaluation framework grounded in journalistic standards to assess the timeline quality across four dimensions: Informativeness, Granular Consistency, Factuality, and Coherence; (2) a large-scale, multi-source dataset with multiple granularity timeline annotations based on a consensus process to facilitate authority; (3) extensive experiments and analysis with two proposed solutions based on Large Language Models (LLMs) and existing state-of-the-art TLS methods. The experimental results demonstrate the effectiveness of LLM-based solutions. However, even the most advanced LLMs struggle to consistently generate timelines that are both informative and granularly consistent, highlighting the challenges of the DTELS task.
Enhancing generalization in high energy physics using white-box adversarial attacks
2024-11-14
Franck Rothen, Samuel Klein, Matthew Leigh, Tobias Golling
Machine learning is becoming increasingly popular in the context of particle physics. Supervised learning, which uses labeled Monte Carlo (MC) simulations, remains one of the most widely used methods for discriminating signals beyond the Standard Model. However, this paper suggests that supervised models may depend excessively on artifacts and approximations from Monte Carlo simulations, potentially limiting their ability to generalize well to real data. This study aims to enhance the generalization properties of supervised models by reducing the sharpness of local minima. It reviews the application of four distinct white-box adversarial attacks in the context of classifying Higgs boson decay signals. The attacks are divided into weight space attacks, and feature space attacks. To study and quantify the sharpness of different local minima this paper presents two analysis methods: gradient ascent and reduced Hessian eigenvalue analysis. The results show that white-box adversarial attacks significantly improve generalization performance, albeit with increased computational complexity.
Jailbreak Attacks and Defenses against Multimodal Generative Models: A Survey
2024-11-14
Xuannan Liu, Xing Cui, Peipei Li, Zekun Li, Huaibo Huang, Shuhan Xia, Miaoxuan Zhang, Yueying Zou, Ran He
The rapid evolution of multimodal foundation models has led to significant advancements in cross-modal understanding and generation across diverse modalities, including text, images, audio, and video. However, these models remain susceptible to jailbreak attacks, which can bypass built-in safety mechanisms and induce the production of potentially harmful content. Consequently, understanding the methods of jailbreak attacks and existing defense mechanisms is essential to ensure the safe deployment of multimodal generative models in real-world scenarios, particularly in security-sensitive applications. To provide comprehensive insight into this topic, this survey reviews jailbreak and defense in multimodal generative models. First, given the generalized lifecycle of multimodal jailbreak, we systematically explore attacks and corresponding defense strategies across four levels: input, encoder, generator, and output. Based on this analysis, we present a detailed taxonomy of attack methods, defense mechanisms, and evaluation frameworks specific to multimodal generative models. Additionally, we cover a wide range of input-output configurations, including modalities such as Any-to-Text, Any-to-Vision, and Any-to-Any within generative systems. Finally, we highlight current research challenges and propose potential directions for future research.The open-source repository corresponding to this work can be found at https://github.com/liuxuannan/Awesome-Multimodal-Jailbreak.
Application of Optical Tweezers in the Study of Emulsions for Multiple Applications
2024-11-14
Qifei Ma, Huaizhou Jin, Xiaoxiao Shang, Tamas Pardy, Ott Scheler, Simona Bartkova, Dan Cojoc, Denis Garoli, Shangzhong Jin
Emulsions are ubiquitous in everyday life and find applications in various industries. Optical tweezers (OTs) have emerged as the preferred method for studying emulsion dynamics. In this review, we first introduce the theory of optical trapping and emulsion stability. We then survey applications in the manipulation of emulsions, stability mechanism, the processes of aggregation and coalescence, and important responsive and switchable behaviors. And we overview the instrumentation framework of various OT setups, and evaluate their complexity and cost with a view towards the democratization of this technology. Following this, we delve into basic experimentation methods, the challenges associated with using OTs in emulsion applications. Additionally, we present a promising research outlook, including studies on stability mechanism of emulsions stabilized by compound or mixed emulsifiers or rigid or soft particles, as well as dynamic processes of responsive or functional emulsions.
Incorporating external knowledge into dialogue generation has been proven to benefit the performance of an open-domain Dialogue System (DS), such as generating informative or stylized responses, controlling conversation topics. In this article, we study the open-domain DS that uses unstructured text as external knowledge sources (\textbf{U}nstructured \textbf{T}ext \textbf{E}nhanced \textbf{D}ialogue \textbf{S}ystem, \textbf{UTEDS}). The existence of unstructured text entails distinctions between UTEDS and traditional data-driven DS and we aim to analyze these differences. We first give the definition of the UTEDS related concepts, then summarize the recently released datasets and models. We categorize UTEDS into Retrieval and Generative models and introduce them from the perspective of model components. The retrieval models consist of Fusion, Matching, and Ranking modules, while the generative models comprise Dialogue and Knowledge Encoding, Knowledge Selection, and Response Generation modules. We further summarize the evaluation methods utilized in UTEDS and analyze the current models' performance. At last, we discuss the future development trends of UTEDS, hoping to inspire new research in this field.
Impact of Large-Scale Anisotropies on Galaxy Clustering and Cosmological Constraints
2024-11-14
Prabhakar Tiwari
We critically assess the impact of significant dipole and large-scale anisotropies on galaxy clustering signals, with a focus on radio continuum surveys. Our study reveals that these anisotropies -- resulting from intrinsic cosmological effects and/or observational systematics -- profoundly influence the two-point correlation function (2PCF) and angular power spectrum ($C_\ell$). Notably, large-scale anisotropies can obscure or simulate non-Gaussianity signals, complicating the extraction of precise cosmological information. The results emphasize that it is crucial to address systematics and rigorously mask the dipole and its surrounding multipoles to obtain accurate cosmological constraints. This approach is essential for extracting cosmological results from clustering signals, particularly for future surveys such as SKA, DESI, and LSST, to ensure the precision and reliability of cosmological analyses.
Whenever graphs admit equitable partitions, their quotient graphs highlight the structure evidenced by the partition. It is therefore very natural to ask what can be said about two graphs that have the same quotient according to certain equitable partitions. This question has been connected to the theory of fractional isomorphisms and covers of graphs in well-known results that we briefly survey in this paper. We then depart to develop theory of what happens when the two graphs have the same symmetrized quotient, proving a structural result connecting this with the existence of certain doubly stochastic matrices. We apply this theorem to derive a new characterization of when two graphs have the same combinatorial quotient, and we also study graphs with weighted vertices and the related concept of pseudo-equitable partitions. Our results connect to known old and recent results, and are naturally applicable to study quantum walks.
The rapid development of generative AI technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we propose a model-centric framework for democratizing generative AI deployment on mobile and edge networks. First, we comprehensively review key compact model strategies, such as quantization, model pruning, and knowledge distillation, and present key performance metrics to optimize generative AI for mobile deployment. Next, we provide a focused review of mobile and edge networks, emphasizing the specific challenges and requirements of these environments. We further conduct a case study demonstrating the effectiveness of these strategies by deploying LLMs on real mobile edge devices. Experimental results highlight the practicality of democratized LLMs, with significant improvements in generalization accuracy, hallucination rate, accessibility, and resource consumption. Finally, we discuss potential research directions to further advance the deployment of generative AI in resource-constrained environments.
2024-11-14
Charles Roques-Carmes, Kai Wang, Yuanmu Yang, Arka Majumdar, Zin Lin
Metasurfaces -- ultrathin structures composed of subwavelength optical elements -- have revolutionized light manipulation by enabling precise control over electromagnetic waves' amplitude, phase, polarization, and spectral properties. Concurrently, computational imaging leverages algorithms to reconstruct images from optically processed signals, overcoming limitations of traditional imaging systems. This review explores the synergistic integration of metaoptics and computational imaging, "computational metaoptics," which combines the physical wavefront shaping ability of metasurfaces with advanced computational algorithms to enhance imaging performance beyond conventional limits. We discuss how computational metaoptics addresses the inherent limitations of single-layer metasurfaces in achieving multifunctionality without compromising efficiency. By treating metasurfaces as physical preconditioners and co-designing them with reconstruction algorithms through end-to-end (inverse) design, it is possible to jointly optimize the optical hardware and computational software. This holistic approach allows for the automatic discovery of optimal metasurface designs and reconstruction methods that significantly improve imaging capabilities. Advanced applications enabled by computational metaoptics are highlighted, including phase imaging and quantum state measurement, which benefit from the metasurfaces' ability to manipulate complex light fields and the computational algorithms' capacity to reconstruct high-dimensional information. We also examine performance evaluation challenges, emphasizing the need for new metrics that account for the combined optical and computational nature of these systems. Finally, we identify new frontiers in computational metaoptics which point toward a future where computational metaoptics may play a central role in advancing imaging science and technology.
2024-11-14
Yuri Alexeev, Marwa H. Farag, Taylor L. Patti, Mark E. Wolf, Natalia Ares, Alán Aspuru-Guzik, Simon C. Benjamin, Zhenyu Cai, Zohim Chandani, Federico Fedele, Nicholas Harrigan, Jin-Sung Kim, Elica Kyoseva, Justin G. Lietz, Tom Lubowe, Alexander McCaskey, Roger G. Melko, Kouhei Nakaji, Alberto Peruzzo, Sam Stanwyck, Norm M. Tubman, Hanrui Wang, Timothy Costa
Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas. Its significance also extends to technical challenges within science and engineering, including the nascent field of quantum computing (QC). The counterintuitive nature and high-dimensional mathematics of QC make it a prime candidate for AI's data-driven learning capabilities, and in fact, many of QC's biggest scaling challenges may ultimately rest on developments in AI. However, bringing leading techniques from AI to QC requires drawing on disparate expertise from arguably two of the most advanced and esoteric areas of computer science. Here we aim to encourage this cross-pollination by reviewing how state-of-the-art AI techniques are already advancing challenges across the hardware and software stack needed to develop useful QC - from device design to applications. We then close by examining its future opportunities and obstacles in this space.
Detection of an orthogonal alignment between parsec scale AGN jets and their host galaxies
2024-11-14
D. Fernández Gil, J. A. Hodgson, B. L'Huillier, J. Asorey, C. Saulder, K. Finner, M. J. Jee, D. Parkinson, F. Combes
The relationship between galaxies and their supermassive black holes (SMBHs) is an area of active research. One way to investigate this is to compare parsec-scale jets formed by SMBHs with the projected shape of their kiloparsec-scale host galaxies. We analyse Very Long Baseline Interferometry (VLBI) images of Active Galactic Nuclei (AGN) and optical images of their host galaxies. We compare the inner-jet position angle in VLBI-detected radio sources with the optical shapes of galaxies as measured by several large optical surveys. In total 6273 galaxy-AGN pairs were found. We carefully account for the systematics of the cross-matched sources and find that Dark Energy Spectroscopic Instrument Legacy Imaging Surveys data (DESI LS) is significantly less affected by them. Using DESI LS, with which 5853 galaxy-AGN pairs were cross-matched, we find a weak but significant alignment signal (with a p-value $\leqslant$ 0.01) between the parsec-scale AGN jet and the kpc projected minor axis of the optical host galaxy in sources with measured spectroscopic redshifts. Our results show that the observed source properties are connected over 3 orders of magnitude in scale. This points towards an intimate connection between the SMBH, their host galaxies and their subsequent evolution.
KMT-2021-BLG-0284, KMT-2022-BLG-2480, and KMT-2024-BLG-0412: Three microlensing events involving two lens masses and two source stars
2024-11-14
Cheongho Han, Andrzej Udalski, Ian A. Bond, Chung-Uk Lee, Andrew Gould, Michael D. Albrow, Sun-Ju Chung, Kyu-Ha Hwang, Youn Kil Jung, Yoon-Hyun Ryu, Yossi Shvartzvald, In-Gu Shin, Jennifer C. Yee, Hongjing Yang, Weicheng Zang, Sang-Mok Cha, Doeon Kim, Dong-Jin Kim, Seung-Lee Kim, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge, Przemek Mróz, Michał K. Szymański, Jan Skowron, Radosław Poleski, Igor Soszyński, Paweł Pietrukowicz, Szymon Kozłowski, Krzysztof A. Rybicki, Patryk Iwanek, Krzysztof Ulaczyk, Marcin Wrona, Mariusz Gromadzki, Mateusz J. Mróz, Fumio Abe, Richard Barry, David P. Bennett, Aparna Bhattacharya, Hirosame Fujii, Akihiko Fukui, Ryusei Hamada, Yuki Hirao, Stela Ishitani Silva, Yoshitaka Itow, Rintaro Kirikawa, Naoki Koshimoto, Yutaka Matsubara, Shota Miyazaki, Yasushi Muraki, Greg Olmschenk, Clément Ranc, Nicholas J. Rattenbury, Yuki Satoh, Takahiro Sumi, Daisuke Suzuki, Mio Tomoyoshi, Paul J. Tristram, Aikaterini Vandorou, Hibiki Yama, Kansuke Yamashita
We carried out a project involving the systematic analysis of microlensing data from the Korea Microlensing Telescope Network survey. The aim of this project is to identify lensing events with complex anomaly features that are difficult to explain using standard binary-lens or binary-source models. Our investigation reveals that the light curves of microlensing events KMT-2021-BLG-0284, KMT-2022-BLG-2480, and KMT-2024-BLG-0412 display highly complex patterns with three or more anomaly features. These features cannot be adequately explained by a binary-lens (2L1S) model alone. However, the 2L1S model can effectively describe certain segments of the light curve. By incorporating an additional source into the modeling, we identified a comprehensive model that accounts for all the observed anomaly features. Bayesian analysis, based on constraints provided by lensing observables, indicates that the lenses of KMT-2021-BLG-0284 and KMT-2024-BLG-0412 are binary systems composed of M dwarfs. For KMT-2022-BLG-2480, the primary lens is an early K-type main-sequence star with an M dwarf companion. The lenses of KMT-2021-BLG-0284 and KMT-2024-BLG-0412 are likely located in the bulge, whereas the lens of KMT-2022-BLG-2480 is more likely situated in the disk. In all events, the binary stars of the sources have similar magnitudes due to a detection bias favoring binary source events with a relatively bright secondary source star, which increases detection efficiency.
$^{11}$B states above the $α$-decay threshold studied via $^{10}$B$(d,p){}^{11}$B
2024-11-14
A. N. Kuchera, G. Ryan, G. Selby, D. Snider, S. Anderson, S. Almaraz-Calderon, L. T. Baby, B. A. Brown, K. Hanselman, E. Lopez-Saavedra, K. T. Macon, G. W. McCann, K. W. Kemper, M. Spieker, I. Wiedenhöver
The resonance region of $^{11}$B covering excitation energies from 8.4 MeV to 13.6 MeV was investigated with the $(d,p)$ reaction performed on an enriched $^{10}$B target at the Florida State University Super-Enge Split-Pole Spectrograph of the John D. Fox Superconducting Linear Accelerator Laboratory. Complementary measurements were performed with a target enriched in $^{11}$B to identify possible $^{12}$B contaminants in the $(d,p)$ reaction. Four strongly populated $^{11}$B states were observed above the $\alpha$-decay threshold. Angular distributions were measured and compared to DWBA calculations to extract angular momentum transfers and $^{10}\mathrm{B}\left(3^+\right)+n$ spectroscopic factors. The recently observed and heavily discussed resonance at 11.4 MeV in $^{11}$B was not observed in this work. This result is consistent with the interpretation that it is predominantly a $^{10}\mathrm{Be}\left(0^+\right)+p$ resonance with a possible additional $^{7}\mathrm{Li}+\alpha$ contribution. The predicted $^{10}\mathrm{B}\left(3^+\right)+n$ resonance at 11.6 MeV, analogous to the 11.4-MeV proton resonance, was not observed either. Upper limits for the $^{10}\mathrm{B}\left(3^+\right)+n$ spectroscopic factors of the 11.4-MeV and 11.6-MeV states were determined. In addition, supporting configuration interaction shell model calculations with the effective WBP interaction are presented.
Technological Novelties of Ground-Based Very High Energy Gamma-Ray Astrophysics with the Imaging Atmospheric Cherenkov Telescopes
2024-11-14
Razmik Mirzoyan
Over the last three decades, the ground-based technique of imaging atmospheric Cherenkov telescopes has established itself as a powerful scientific discipline. About 250 very high gamma-ray sources of both galactic and extragalactic origin have been discovered, largely thanks to this technique. The study of these sources provides clues to many fundamental questions in astrophysics, astroparticle physics, cosmic ray physics and cosmology. The current generation of telescopes in operation offers solid performance. Further improvements in this technique led to the next generation large-scale instrument known as the Cherenkov Telescope Array (CTA). In its final configuration, the sensitivity of CTA will be several times higher than that of the current best instruments VERITAS, H.E.S.S. and MAGIC. This article is devoted to presenting the technological developments that have shaped this technique and led to its current success.
Radiative corrections to one- and two-meson tau decays for precise new physics tests
2024-11-14
Pablo Roig
We review the radiative corrections to the $\tau \to P (P) \nu_\tau [\gamma]$ decays and their implications for several SM tests: lepton universality, CKM unitarity and non-standard interactions.
Research evaluation with ChatGPT: Is it age, country, length, or field biased?
2024-11-14
Mike Thelwall, Zeyneb Kurt
Some research now suggests that ChatGPT can estimate the quality of journal articles from their titles and abstracts. This has created the possibility to use ChatGPT quality scores, perhaps alongside citation-based formulae, to support peer review for research evaluation. Nevertheless, ChatGPT's internal processes are effectively opaque, despite it writing a report to support its scores, and its biases are unknown. This article investigates whether publication date and field are biasing factors. Based on submitting a monodisciplinary journal-balanced set of 117,650 articles from 26 fields published in the years 2003, 2008, 2013, 2018 and 2023 to ChatGPT 4o-mini, the results show that average scores increased over time, and this was not due to author nationality or title and abstract length changes. The results also varied substantially between fields, and first author countries. In addition, articles with longer abstracts tended to receive higher scores, but plausibly due to such articles tending to be better rather than due to ChatGPT analysing more text. Thus, for the most accurate research quality evaluation results from ChatGPT, it is important to normalise ChatGPT scores for field and year and check for anomalies caused by sets of articles with short abstracts.
Evaluating the Predictive Capacity of ChatGPT for Academic Peer Review Outcomes Across Multiple Platforms
2024-11-14
Mike Thelwall, Abdullah Yaghi
While previous studies have demonstrated that Large Language Models (LLMs) can predict peer review outcomes to some extent, this paper builds on that by introducing two new contexts and employing a more robust method - averaging multiple ChatGPT scores. The findings that averaging 30 ChatGPT predictions, based on reviewer guidelines and using only the submitted titles and abstracts, failed to predict peer review outcomes for F1000Research (Spearman's rho=0.00). However, it produced mostly weak positive correlations with the quality dimensions of SciPost Physics (rho=0.25 for validity, rho=0.25 for originality, rho=0.20 for significance, and rho = 0.08 for clarity) and a moderate positive correlation for papers from the International Conference on Learning Representations (ICLR) (rho=0.38). Including the full text of articles significantly increased the correlation for ICLR (rho=0.46) and slightly improved it for F1000Research (rho=0.09), while it had variable effects on the four quality dimension correlations for SciPost LaTeX files. The use of chain-of-thought system prompts slightly increased the correlation for F1000Research (rho=0.10), marginally reduced it for ICLR (rho=0.37), and further decreased it for SciPost Physics (rho=0.16 for validity, rho=0.18 for originality, rho=0.18 for significance, and rho=0.05 for clarity). Overall, the results suggest that in some contexts, ChatGPT can produce weak pre-publication quality assessments. However, the effectiveness of these assessments and the optimal strategies for employing them vary considerably across different platforms, journals, and conferences. Additionally, the most suitable inputs for ChatGPT appear to differ depending on the platform.
Adversarial Attacks Using Differentiable Rendering: A Survey
2024-11-14
Matthew Hull, Chao Zhang, Zsolt Kira, Duen Horng Chau
Differentiable rendering methods have emerged as a promising means for generating photo-realistic and physically plausible adversarial attacks by manipulating 3D objects and scenes that can deceive deep neural networks (DNNs). Recently, differentiable rendering capabilities have evolved significantly into a diverse landscape of libraries, such as Mitsuba, PyTorch3D, and methods like Neural Radiance Fields and 3D Gaussian Splatting for solving inverse rendering problems that share conceptually similar properties commonly used to attack DNNs, such as back-propagation and optimization. However, the adversarial machine learning research community has not yet fully explored or understood such capabilities for generating attacks. Some key reasons are that researchers often have different attack goals, such as misclassification or misdetection, and use different tasks to accomplish these goals by manipulating different representation in a scene, such as the mesh or texture of an object. This survey adopts a task-oriented unifying framework that systematically summarizes common tasks, such as manipulating textures, altering illumination, and modifying 3D meshes to exploit vulnerabilities in DNNs. Our framework enables easy comparison of existing works, reveals research gaps and spotlights exciting future research directions in this rapidly evolving field. Through focusing on how these tasks enable attacks on various DNNs such as image classification, facial recognition, object detection, optical flow and depth estimation, our survey helps researchers and practitioners better understand the vulnerabilities of computer vision systems against photorealistic adversarial attacks that could threaten real-world applications.
Caught in the Act: Observations of the Double-mode RR Lyrae V338 Boo During the Disappearance of a Pulsation Mode
2024-11-14
Kenneth Carrell, Ronald Wilhelm, Andrew Tom, Horace Smith, Adam Popowicz, Gary Hug, Stephen M. Brincat, Fabio Salvaggio, Keith Nakonechny, Darrell Lee, Teofilo Arranz Heras, Tony Vale, Davide Mortari, Andre Steenkamp, Ralph Rogge, Jacek Checinski
New results on the behavior of the double-mode RR Lyrae V338 Boo are presented. The Transiting Exoplanet Survey Satellite (TESS) observed this star again in 2022, and an observing campaign of the American Association of Variable Star Observers (AAVSO) was completed after the TESS observations as a follow-up. We find that the first overtone pulsation mode in this star completely disappears during the TESS observing window. This mode reappears at the end of the TESS observations, and the AAVSO observing campaign shows that in the months that followed, the first overtone mode was not only present, but was the dominant mode of pulsation. This star, and potentially others like it, could hold the key to finally solving the mystery of the Blazhko effect in RR Lyrae.
Gas thermodynamics meets galaxy kinematics: Joint mass measurements for eROSITA galaxy clusters
2024-11-14
Pengfei Li, Ang Liu, Matthias Kluge, Johan Comparat, Yong Tian, Mariana P. Júlio, Marcel S. Pawlowski, Jeremy Sanders, Esra Bulbul, Axel Schwope, Vittorio Ghirardini, Xiaoyuan Zhang, Y. Emre Bahar, Miriam E. Ramos-Ceja, Fabian Balzer, Christian Garrel
The mass of galaxy clusters is a critical quantity for probing cluster cosmology and testing theories of gravity, but its measurement could be biased given assumptions are inevitable. In this paper, we employ and compare two mass proxies for galaxy clusters: thermodynamics of the intracluster medium and kinematics of member galaxies. We select 22 galaxy clusters from the cluster catalog in the first SRG/eROSITA All-Sky Survey (eRASS1) that have sufficient optical and near-infrared observations. We generate multi-band images in the energy range of (0.3, 7) keV for each cluster, and derive their temperature profiles, gas mass profiles and hydrostatic mass profiles using a parametric approach that does not assume dark matter halo models. With spectroscopically confirmed member galaxies collected from multiple surveys, we numerically solve the spherical Jeans equation for their dynamical mass profiles. Our results quantify the correlation between dynamical mass and line-of-sight velocity dispersion with an rms scatter of 0.14 dex. We find the two mass proxies lead to roughly the same total mass, with no observed systematic bias. As such, the $\sigma_8$ tension is not specific to hydrostatic mass or weak lensing shears, but also appears with galaxy kinematics. We also compare our hydrostatic masses with the latest weak lensing masses inferred with scaling relations. The comparison shows the weak lensing mass is significantly higher than our hydrostatic mass by $\sim$110%. This might explain the significantly larger value of $\sigma_8$ from the latest measurement using eRASS1 clusters than almost all previous estimates in the literature. Finally, we test the radial acceleration relation (RAR) established in disk galaxies. We confirm the missing baryon problem in the inner region of galaxy clusters using three independent mass proxies for the first time.
The SDSS-V Local Volume Mapper (LVM): Data Analysis Pipeline
2024-11-14
Sebastian F. Sanchez, Alfredo Mejía-Narvaez, Oleg V. Egorov, Kathryn Kreckel, Niv Drory, Guillermo A. Blanc, J. Eduardo Méndez-Delgado, Jorge K. Barrera-Ballesteros, Hector Ibarra, Dmitry Bizyaev, Pablo García, Aida Wofford, Alejandra Z. Lugo-Aranda
We introduce the Data Analysis Pipeline (DAP) for the Sloan Digital Sky Survey V (SDSS-V) Local Volume Mapper (LVM) project, referred to as the LVM-DAP. We outline our methods for recovering both stellar and emission line components from the optical integral field spectroscopy, highlighting the developments and changes implemented to address specific challenges of the data set. The observations from the LVM project are unique because they cover a wide range of physical resolutions, from approximately 0.05 pc to 100 pc, depending on the distance to the targets. This, along with the varying number of stars sampled in each aperture (ranging from zero, just one of a few, to thousands), presents challenges in using previous spectral synthesis methods and interpreting the spectral fits. We provide a detailed explanation of how we model the stellar content and separate it from the ionized gas emission lines. To assess the accuracy of our results, we compare them with both idealized and more realistic simulations, highlighting the limitations of our methods. We find that the DAP robustly correct for stellar continuum features and recover emission line parameters (e.g. flux, equivalent width, systemtic velocity and velocity dispersion) with a precision and accuracy that fulfill the requirements of the primary goal of the analysis. In addition, the recovered stellar parameters are reliable for single stars, the recovery of integrated populations is less precise. We conclude with a description of the data products we provide, instructions for downloading and using our software, and a showcase illustrating the quality of the data and the analysis on a deep exposure taken on the Huygens region at the center of the Orion Nebula.
Spatio-Temporal Jump Model for Urban Thermal Comfort Monitoring
2024-11-14
Federico P. Cortese, Antonio Pievatolo
Thermal comfort is essential for well-being in urban spaces, especially as cities face increasing heat from urbanization and climate change. Existing thermal comfort models usually overlook temporal dynamics alongside spatial dependencies. We address this problem by introducing a spatio-temporal jump model that clusters data with persistence across both spatial and temporal dimensions. This framework enhances interpretability, minimizes abrupt state changes, and easily handles missing data. We validate our approach through extensive simulations, demonstrating its accuracy in recovering the true underlying partition. When applied to hourly environmental data gathered from a set of weather stations located across the city of Singapore, our proposal identifies meaningful thermal comfort regimes, demonstrating its effectiveness in dynamic urban settings and suitability for real-world monitoring. The comparison of these regimes with feedback on thermal preference indicates the potential of an unsupervised approach to avoid extensive surveys.
2024-11-14
Ayush Agarwal, Raghavendra Ramachandra, Sushma Venkatesh, S. R. Mahadeva Prasanna
In the domain of Extended Reality (XR), particularly Virtual Reality (VR), extensive research has been devoted to harnessing this transformative technology in various real-world applications. However, a critical challenge that must be addressed before unleashing the full potential of XR in practical scenarios is to ensure robust security and safeguard user privacy. This paper presents a systematic survey of the utility of biometric characteristics applied in the XR environment. To this end, we present a comprehensive overview of the different types of biometric modalities used for authentication and representation of users in a virtual environment. We discuss different biometric vulnerability gateways in general XR systems for the first time in the literature along with taxonomy. A comprehensive discussion on generating and authenticating biometric-based photorealistic avatars in XR environments is presented with a stringent taxonomy. We also discuss the availability of different datasets that are widely employed in evaluating biometric authentication in XR environments together with performance evaluation metrics. Finally, we discuss the open challenges and potential future work that need to be addressed in the field of biometrics in XR.
Utilising quantum computing technology to enhance artificial intelligence systems is expected to improve training and inference times, increase robustness against noise and adversarial attacks, and reduce the number of parameters without compromising accuracy. However, moving beyond proof-of-concept or simulations to develop practical applications of these systems while ensuring high software quality faces significant challenges due to the limitations of quantum hardware and the underdeveloped knowledge base in software engineering for such systems. In this work, we have conducted a systematic mapping study to identify the challenges and solutions associated with the software architecture of quantum-enhanced artificial intelligence systems. Our review uncovered several architectural patterns that describe how quantum components can be integrated into inference engines, as well as middleware patterns that facilitate communication between classical and quantum components. These insights have been compiled into a catalog of architectural patterns. Each pattern realises a trade-off between efficiency, scalability, trainability, simplicity, portability and deployability, and other software quality attributes.
Artificial Intelligence (AI) and infectious diseases prediction have recently experienced a common development and advancement. Machine learning (ML) apparition, along with deep learning (DL) emergence, extended many approaches against diseases apparition and their spread. And despite their outstanding results in predicting infectious diseases, conflicts appeared regarding the types of data used and how they can be studied, analyzed, and exploited using various emerging methods. This has led to some ongoing discussions in the field. This research aims not only to provide an overview of what has been accomplished, but also to highlight the difficulties related to the types of data used, and the learning methods applied for each research objective. It categorizes these contributions into three areas: predictions using Public Health Data to prevent the spread of a transmissible disease within a region; predictions using Patients' Medical Data to detect whether a person is infected by a transmissible disease; and predictions using both Public and patient medical data to estimate the extent of disease spread in a population. The paper also critically assesses the potential of AI and outlines its limitations in infectious disease management.
Quantum cryptography beyond key distribution: theory and experiment
2024-11-13
Mathieu Bozzio, Claude Crépeau, Petros Wallden, Philip Walther
Due to its fundamental principles, quantum theory holds the promise to enhance the security of modern cryptography, from message encryption to anonymous communication, digital signatures, online banking, leader election, one-time passwords and delegated computation. While quantum key distribution (QKD) has already enabled secure key exchange over hundreds of kilometers, a myriad of other quantum-cryptographic primitives are being developed to secure future applications against quantum adversaries. This article surveys the theoretical and experimental developments in quantum cryptography beyond QKD over the past decades, along with advances in secure quantum computation. It provides an intuitive classification of the main quantum primitives and their security levels, summarizes their possibilities and limits, and discusses their implementation with current photonic technology.
Basaltic mini-moon: Characterizing 2024 PT5 with the 10.4 m Gran Telescopio Canarias and the Two-meter Twin Telescope
2024-11-13
R. de la Fuente Marcos, J. de León, M. Serra-Ricart, C. de la Fuente Marcos, M. R. Alarcon, J. Licandro, S. Geier, A. Tejero, A. Perez Romero, F. Perez-Toledo, A. Cabrera-Lavers
Context. Small bodies in Earth-like orbits, the Arjunas, are good targets for scientific exploration and mining studies as they enable low-cost missions. The subset of such objects that experience recurrent temporarily captured flyby or orbiter events, also called mini-moon episodes, are among the best ranked in terms of accessibility. Only a handful of objects are known to have engaged in such a dynamical behavior. Finding and characterizing more of them may help to expand scientific and commercial research activities in space over the next few decades. Asteroid 2024 PT5 was found recently and belongs to this group of interesting objects. Aims. Here, we investigate the orbital context of 2024 PT5, and its spectral and rotational properties. Methods. We studied the short-term orbital evolution of 2024 PT5 using direct N-body simulations. We identified its spectral class from the visible reflectance spectrum and used photometric observations to derive its rotational properties. Observational data were obtained with the OSIRIS camera spectrograph at the 10.4 m Gran Telescopio Canarias and the Two-meter Twin Telescope. Results. Asteroid 2024 PT5 experiences recurrent co-orbital engagements and mini-moon events of the temporarily captured flyby type. Its visible spectrum is consistent with that of an Sv-type asteroid or perhaps lunar ejecta. Its rotational period could be less than or close to 1 h. Conclusions. The discovery of 2024 PT5 confirms that temporarily captured flybys are relatively frequent and involve objects larger than a few meters, suitable as accessible targets for scientific research activities and commercial mining ventures in space.
ALMA Survey of Orion Planck Galactic Cold Clumps (ALMASOP): Nested Morphological and Kinematic Structures of Outflows Revealed in SiO and CO Emission
2024-11-13
Chun-Fan Liu, Hsien Shang, Doug Johnstone, Tsung-Han Ai, Tsz Ming Lee, Ruben Krasnopolsky, Naomi Hirano, Somnath Dutta, Shih-Ying Hsu, Jesús Alejandro López-Vázquez, Sheng-Yuan Liu, Tie Liu, Ken'ichi Tatematsu, Qizhou Zhang, Mark G. Rawlings, David Eden, Zhiyuan Ren, Patricio Sanhueza, Woojin Kwon, Chang Won Lee, Yi-Jehng Kuan, Somdeb Bandopadhyay, Miikka S. Väisälä, Chin-Fei Lee, Indrani Das
The Atacama Large Millimeter/submillimeter Array Survey of Orion Planck Galactic Cold Clumps (ALMASOP) reveals complex nested morphological and kinematic features of molecular outflows through the CO (J = 2 - 1) and SiO (J = 5 - 4) emission. We characterize the jet and outflow kinematics of the ALMASOP sample in four representative sources (HOPS 10, 315, 358, and G203.21-11.20W2) through channel maps and position-velocity diagrams (PVDs) parallel and transverse to the outflow axes. The combined CO and SiO emission exhibits the coexistence of the conventional extremely-high-velocity (EHV) jets and shell-like low-velocity (LV) cavity walls and new features. More complex, nested bubble-like and filamentary structures in the images and channel maps, triangle-shaped regions near the base of the parallel PVDs, and regions composed of rhombus/oval shapes in the transverse PVDs, are also evident. Such features find natural explanations within the bubble structure of the unified model of jet, wind, and ambient medium. The reverse shock cavity is revealed on the PVD base regions, and other features naturally arise within the dynamic postshock region of magnetic interaction. The finer nested shells observed within the compressed wind region reveal previously unnoticed shocked emission between the jet and the conventional large cavity walls. These pseudopulse-produced filamentary features connect to the jet-like knotty blobs, creating an impression of episodicity in mass ejection. SiO emission is enhanced downstream of the reverse shock boundary, with jet-like excitation conditions. Combined, these observed features reveal the extended structures induced by the magnetic interplay between a jet-bearing magnetized wide-angle wind and its ambient magnetized surrounding medium.
Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence
2024-11-13
Berfin Simsek, Amire Bendjeddou, Daniel Hsu
This work focuses on the gradient flow dynamics of a neural network model that uses correlation loss to approximate a multi-index function on high-dimensional standard Gaussian data. Specifically, the multi-index function we consider is a sum of neurons $f^*(x) \!=\! \sum_{j=1}^k \! \sigma^*(v_j^T x)$ where $v_1, \dots, v_k$ are unit vectors, and $\sigma^*$ lacks the first and second Hermite polynomials in its Hermite expansion. It is known that, for the single-index case ($k\!=\!1$), overcoming the search phase requires polynomial time complexity. We first generalize this result to multi-index functions characterized by vectors in arbitrary directions. After the search phase, it is not clear whether the network neurons converge to the index vectors, or get stuck at a sub-optimal solution. When the index vectors are orthogonal, we give a complete characterization of the fixed points and prove that neurons converge to the nearest index vectors. Therefore, using $n \! \asymp \! k \log k$ neurons ensures finding the full set of index vectors with gradient flow with high probability over random initialization. When $ v_i^T v_j \!=\! \beta \! \geq \! 0$ for all $i \neq j$, we prove the existence of a sharp threshold $\beta_c \!=\! c/(c+k)$ at which the fixed point that computes the average of the index vectors transitions from a saddle point to a minimum. Numerical simulations show that using a correlation loss and a mild overparameterization suffices to learn all of the index vectors when they are nearly orthogonal, however, the correlation loss fails when the dot product between the index vectors exceeds a certain threshold.
SoK: Towards a Common Understanding of Cryptographic Agility
2024-11-13
Christian Näther, Daniel Herzinger, Jan-Philipp Steghöfer, Stefan-Lukas Gazdag, Eduard Hirsch, Daniel Loebenberger
Cryptographic agility is gaining attention due to its crucial role in maintaining cryptographic security in a rapidly evolving technological landscape. However, despite its increasing importance, the term cryptographic agility remains vaguely defined and there is no clear consensus on its exact meaning. This lack of clarity poses a challenge since the need for agility becomes more urgent as new cryptographic vulnerabilities and advanced computing threats emerge, emphasizing the need for a systematic approach to clarify and refine the notion on cryptographic agility. In this paper, we systematize the concept of cryptographic agility by providing three research contributions. First, we review current definitions across academic and gray literature, identifying six distinct categories to differentiate every aspect within the definitions. Second, we synthesize these insights to establish a comprehensive, canonical definition of cryptographic agility. Third, we explore the relationship between cryptographic agility and the related concepts cryptographic versatility and interoperability. In our discussion, we examine the relevance of cryptographic agility, highlight its trade-offs with complexity, assess its individual applicability, and illustrate its various contexts by offering an additional application-specific definition. Our work provides a new perspective on cryptographic agility and related concepts, based on systematical research to clarify and enhance its future use.
Causal-DRF: Conditional Kernel Treatment Effect Estimation using Distributional Random Forest
2024-11-13
Jeffrey Näf, Herbert Susmann
The conditional average treatment effect (CATE) is a commonly targeted statistical parameter for measuring the mean effect of a treatment conditional on covariates. However, the CATE will fail to capture effects of treatments beyond differences in conditional expectations. Inspired by causal forests for CATE estimation, we develop a forest-based method to estimate the conditional kernel treatment effect (CKTE), based on the recently introduced Distributional Random Forest (DRF) algorithm. Adapting the splitting criterion of DRF, we show how one forest fit can be used to obtain a consistent and asymptotically normal estimator of the CKTE, as well as an approximation of its sampling distribution. This allows to study the difference in distribution between control and treatment group and thus yields a more comprehensive understanding of the treatment effect. In particular, this enables the construction of a conditional kernel-based test for distributional effects with provably valid type-I error. We show the effectiveness of the proposed estimator in simulations and apply it to the 1991 Survey of Income and Program Participation (SIPP) pension data to study the effect of 401(k) eligibility on wealth.
Confidence intervals for adaptive trial designs II: Case study and practical guidance
2024-11-13
David S. Robertson, Thomas Burnett, Babak Choodari-Oskooei, Munya Dimairo, Michael Grayling, Philip Pallmann, Thomas Jaki
In adaptive clinical trials, the conventional confidence interval (CI) for a treatment effect is prone to undesirable properties such as undercoverage and potential inconsistency with the final hypothesis testing decision. Accordingly, as is stated in recent regulatory guidance on adaptive designs, there is the need for caution in the interpretation of CIs constructed during and after an adaptive clinical trial. However, it may be unclear which of the available CIs in the literature are preferable. This paper is the second in a two-part series that explores CIs for adaptive trials. Part I provided a methodological review of approaches to construct CIs for adaptive designs. In this paper (part II), we present an extended case study based around a two-stage group sequential trial, including a comprehensive simulation study of the proposed CIs for this setting. This facilitates an expanded description of considerations around what makes for an effective CI procedure following an adaptive trial. We show that the CIs can have notably different properties. Finally, we propose a set of guidelines for researchers around the choice of CIs and the reporting of CIs following an adaptive design.
The impact of large-scale galaxy clustering on the variance of the Hellings-Downs correlation: numerical results
2024-11-13
Nastassia Grimm, Martin Pijnenburg, Giulia Cusin, Camille Bonvin
Pulsar timing array experiments have recently found evidence for a stochastic gravitational wave (GW) background, which induces correlations among pulsar timing residuals described by the Hellings and Downs (HD) curve. Standard calculations of the HD correlation and its variance assume an isotropic background. However, for a background of astrophysical origin, we expect a higher GW spectral density in directions with higher galaxy number densities. In a companion paper, we have developed a theoretical formalism to account for the anisotropies arising from large-scale galaxy clustering, leading to a new contribution to the variance of the HD correlation. In this subsequent work, we provide numerical results for this novel effect. We consider a GW background resulting from mergers of supermassive black hole binaries, and relate the merger number density to the overdensity of galaxies. We find that anisotropies due to large-scale galaxy clustering lead to a standard deviation of the HD correlation at most at percent level, remaining well below the standard contributions to the HD variance. Hence, this kind of anisotropies in the GW source distribution does not represent a substantial contamination to the correlations of timing residuals in present and future PTA surveys. Suitable statistical methods to extract the galaxy clustering signal from PTA data will be investigated in the future.
Recommender systems and reinforcement learning for building control and occupant interaction: A text-mining driven review of scientific literature
2024-11-13
Wenhao Zhang, Matias Quintana, Clayton Miller
The indoor environment greatly affects health and well-being; enhancing health and reducing energy use in these settings is a key research focus. With advancing Information and Communication Technology (ICT), recommendation systems and reinforcement learning have emerged as promising methods to induce behavioral changes that improve indoor environments and building energy efficiency. This study employs text-mining and Natural Language Processing (NLP) to examine these approaches in building control and occupant interaction. Analyzing approximately 27,000 articles from the ScienceDirect database, we found extensive use of recommendation systems and reinforcement learning for space optimization, location recommendations, and personalized control suggestions. Despite broad applications, their use in optimizing indoor environments and energy efficiency is limited. Traditional recommendation algorithms are commonly used, but optimizing indoor conditions and energy efficiency often requires advanced machine learning techniques like reinforcement and deep learning. This review highlights the potential for expanding recommender systems and reinforcement learning applications in buildings and indoor environments. Areas for innovation include predictive maintenance, building-related product recommendations, and optimizing environments for specific needs like sleep and productivity enhancements based on user feedback.
Quantitative imaging of the fresh/saltwater interface with airborne electromagnetics: examining different sources of uncertainty
2024-11-13
Wouter Deleersnyder, David Dudal, Thomas Hermans
Knowing the distribution between fresh and saline groundwater is imperative for sustainable and integrated management of water resources in coastal areas. The airborne electromagnetic (AEM) method is increasingly used for hydrogeological mapping over large areas via bulk electrical resistivity. However, accurately and reliably mapping the fresh/saltwater interface (FSI) requires accurate knowledge about the transition zone. The objective is to quantify the uncertainty in using AEM data to inform on the depth of the FSI. The study mimics a dual-moment time-domain SkyTEM sounding recorded in the Belgian coastal plain based on borehole data. It quantifies uncertainty using a differential evolution adaptive Metropolis algorithm to sample the posterior distribution. The results indicate the importance of reliable altitude, pitch and roll logging. Gathering prior knowledge about the transition zone, for example, through borehole logs, significantly improves the estimation of the FSI. The Resolve frequency-domain system, especially in context with very shallow to shallow FSIs, is more suitable for salinity mapping than the time-domain SkyTEM used in the field survey. The depth of the FSI may be defined via various threshold values. The uncertainty of three different thresholds is studied. The FSI based on the middle of the transition zone is the most reliable, while the FSI based on the 1500 mg/L total dissolved solids threshold is the least robust.
2024-11-13
Marco Baldovin, Giacomo Gradenigo, Angelo Vulpiani, Nino Zanghì
Although not as wide, and popular, as that of quantum mechanics, the investigation of fundamental aspects of statistical mechanics constitutes an important research field in the building of modern physics. Besides the interest for itself, both for physicists and philosophers, and the obvious pedagogical motivations, there is a further, compelling reason for a thorough understanding of the subject. The fast development of models and methods at the edge of the established domain of the field requires indeed a deep reflection on the essential aspects of the theory, which are at the basis of its success. These elements should never be disregarded when trying to expand the domain of statistical mechanics to systems with novel, little known features. It is thus important to (re)consider in a careful way the main ingredients involved in the foundations of statistical mechanics. Among those, a primary role is covered by the dynamical aspects (e.g. presence of chaos), the emergence of collective features for large systems, and the use of probability in the building of a consistent statistical description of physical systems. With this goal in mind, in the present review we aim at providing a consistent picture of the state of the art of the subject, both in the classical and in the quantum realm. In particular, we will highlight the similarities of the key technical and conceptual steps with emphasis on the relevance of the many degrees of freedom, to justify the use of statistical ensembles in the two domains.
MVKTrans: Multi-View Knowledge Transfer for Robust Multiomics Classification
2024-11-13
Shan Cong, Zhiling Sang, Hongwei Liu, Haoran Luo, Xin Wang, Hong Liang, Jie Hao, Xiaohui Yao
The distinct characteristics of multiomics data, including complex interactions within and across biological layers and disease heterogeneity (e.g., heterogeneity in etiology and clinical symptoms), drive us to develop novel designs to address unique challenges in multiomics prediction. In this paper, we propose the multi-view knowledge transfer learning (MVKTrans) framework, which transfers intra- and inter-omics knowledge in an adaptive manner by reviewing data heterogeneity and suppressing bias transfer, thereby enhancing classification performance. Specifically, we design a graph contrastive module that is trained on unlabeled data to effectively learn and transfer the underlying intra-omics patterns to the supervised task. This unsupervised pretraining promotes learning general and unbiased representations for each modality, regardless of the downstream tasks. In light of the varying discriminative capacities of modalities across different diseases and/or samples, we introduce an adaptive and bi-directional cross-omics distillation module. This module automatically identifies richer modalities and facilitates dynamic knowledge transfer from more informative to less informative omics, thereby enabling a more robust and generalized integration. Extensive experiments on four real biomedical datasets demonstrate the superior performance and robustness of MVKTrans compared to the state-of-the-art. Code and data are available at https://github.com/Yaolab-fantastic/MVKTrans.
Traditional phase I dose finding cancer clinical trial designs aim to determine the maximum tolerated dose (MTD) of the investigational cytotoxic agent based on a single toxicity outcome, assuming a monotone dose-response relationship. However, this assumption might not always hold for newly emerging therapies such as immuno-oncology therapies and molecularly targeted therapies, making conventional dose finding trial designs based on toxicity no longer appropriate. To tackle this issue, numerous early phase dose finding clinical trial designs have been developed to identify the optimal biological dose (OBD), which takes both toxicity and efficacy outcomes into account. In this article, we review the current model-assisted dose finding designs, BOIN-ET, BOIN12, UBI, TEPI-2, PRINTE, STEIN, and uTPI to identify the OBD and compare their operating characteristics. Extensive simulation studies and a case study using a CAR T-cell therapy phase I trial have been conducted to compare the performance of the aforementioned designs under different possible dose-response relationship scenarios. The simulation results demonstrate that the performance of different designs varies depending on the particular dose-response relationship and the specific metric considered. Based on our simulation results and practical considerations, STEIN, PRINTE, and BOIN12 outperform the other designs from different perspectives.
Investigating Changing-Look Active Galactic Nuclei with Long-term Optical and X-Ray Observations
2024-11-13
A. Jana, C. Ricci, M. J. Temple, H. -K. Chang, E. Shablovinskaya, B. Trakhtenbrot, Y. Diaz, D. Ilic, P. Nandi, M. Koss
Changing-look active galactic nuclei (CLAGNs) show the appearance and disappearance of broad emission lines in their UV/optical spectra on timescales of months to decades. Here, we investigate how CL transitions depend on several AGN parameters such as accretion rate, obscuration properties and black hole mass. We study a sample of 20 nearby optically-identified CLAGNs from the BAT AGN Spectroscopic Survey (BASS), using quasi-simultaneous optical and X-ray observations taken in the last $\sim 40$ years. We find that for all CLAGNs, the transition is accompanied by a change in Eddington ratio. The CL transitions are not associated with changes in the obscuration properties of the AGN. CLAGNs are found to have a median Eddington ratio lower than the AGNs in the BASS sample in which CL transitions were not detected. The median of the transition Eddington ratio (Eddington ratio at which AGN changes its state) is found to be $\sim 0.01$ for type 1 $\leftrightarrow$ 1.8/1.9/2 transition, which is consistent with the hard $\leftrightarrow$ soft state transition in black hole X-ray binaries. Most CL events are constrained to occur within 3-4 years, which is considerably shorter than the expected viscous timescale in AGN accretion disk. The transitions of the optical CLAGNs studied here are likely associated to state changes in the accretion flow, possibly driven by disk-instability.
Autoregressive models have demonstrated great performance in natural language processing (NLP) with impressive scalability, adaptability and generalizability. Inspired by their notable success in NLP field, autoregressive models have been intensively investigated recently for computer vision, which perform next-token predictions by representing visual data as visual tokens and enables autoregressive modelling for a wide range of vision tasks, ranging from visual generation and visual understanding to the very recent multimodal generation that unifies visual generation and understanding with a single autoregressive model. This paper provides a systematic review of vision autoregressive models, including the development of a taxonomy of existing methods and highlighting their major contributions, strengths, and limitations, covering various vision tasks such as image generation, video generation, image editing, motion generation, medical image analysis, 3D generation, robotic manipulation, unified multimodal generation, etc. Besides, we investigate and analyze the latest advancements in autoregressive models, including thorough benchmarking and discussion of existing methods across various evaluation datasets. Finally, we outline key challenges and promising directions for future research, offering a roadmap to guide further advancements in vision autoregressive models.
Many fluid-dynamical systems met in nature are quasi-two-dimensional: they are constrained to evolve in approximately two dimensions with little or no variation along the third direction. This has a drastic effect in the flow evolution because the properties of three dimensional turbulence are fundamentally different from those of two dimensional turbulence. In three-dimensions energy is transferred on average towards small scales, while in two dimensions energy is transferred towards large scales. Quasi-two-dimensional flows thus stand in a crossroad, with two-dimensional motions attempting to self-organize and generate large scales while three dimensional perturbations cause disorder, disrupting any large scale organization. Where is energy transferred in such systems? It has been realized recently that in fact the two behaviors can coexist with a simultaneous transfer of energy both to large and to small scales. How the cascade properties change as the variations along the third direction are suppressed has lead to discovery of different regimes or phases of turbulence of unexpected richness in behavior. Here, recent discoveries on such systems are reviewed. It is described how the transition from three-dimensional to two-dimensional flows takes place, the different phases of turbulence met and the nature of the transitions from one phase to the other. Finally, the implications these new discoveries have on different physical systems are discussed.
2024-11-13
Martijn S. S. L. Oei, Martin J. Hardcastle, Roland Timmerman, Aivin R. D. J. G. I. B. Gast, Andrea Botteon, Antonio C. Rodriguez, Daniel Stern, Gabriela Calistro Rivera, Reinout J. van Weeren, Huub J. A. Röttgering, Huib T. Intema, Francesco de Gasperin, S. G. Djorgovski
Jets launched by supermassive black holes transport relativistic leptons, magnetic fields, and atomic nuclei from the centres of galaxies to their outskirts and beyond. These outflows embody the most energetic pathway by which galaxies respond to their Cosmic Web environment. Studying black hole feedback is an astrophysical frontier, providing insights on star formation, galaxy cluster stability, and the origin of cosmic rays, magnetism, and heavy elements throughout the Universe. This feedback's cosmological importance is ultimately bounded by the reach of black hole jets, and could be sweeping if jets travel far at early epochs. Here we present the joint LOFAR-uGMRT-Keck discovery of a black hole jet pair extending over $7$ megaparsecs -- the largest galaxy-made structure ever found. The outflow, seen $7.5$ gigayears into the past, spans two-thirds of a typical cosmic void radius, thus penetrating voids at ${\sim}95\%$ probability. This system demonstrates that jets can avoid destruction by magnetohydrodynamical instabilities over cosmological distances, even at epochs when the Universe was 15 to 7 times denser than it is today. Whereas previous record-breaking outflows were powered by radiatively inefficient active galactic nuclei, this outflow is powered by a radiatively efficient active galactic nucleus, a type common at early epochs. If, as implied, a population of early void-penetrating outflows existed, then black hole jets could have overwritten the fields from primordial magnetogenesis. This outflow shows that energy transport from supermassive black holes operates on scales of the Cosmic Web and raises the possibility that cosmic rays and magnetism in the intergalactic medium have a non-local, cross-void origin.
Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent
2024-11-13
Leonidas Askianakis
Grasping by a robot in unstructured environments is deemed a critical challenge because of the requirement for effective adaptation to a wide variation in object geometries, material properties, and other environmental factors. In this paper, we propose a novel framework for robotic grasping based on the idea of compressing high-dimensional target and gripper features in a common latent space using a set of autoencoders. Our approach simplifies grasping by using three autoencoders dedicated to the target, the gripper, and a third one that fuses their latent representations. This allows the RL agent to achieve higher learning rates at the initial stages of exploration of a new environment, as well as at non-zero shot grasp attempts. The agent explores the latent space of the third autoencoder for better quality grasp without explicit reconstruction of objects. By implementing the PoWER algorithm into the RL training process, updates on the agent's policy will be made through the perturbation in the reward-weighted latent space. The successful exploration efficiently constrains both position and pose integrity for feasible executions of grasps. We evaluate our system on a diverse set of objects, demonstrating the high success rate in grasping with minimum computational overhead. We found that approach enhances the adaptation of the RL agent by more than 35 \% in simulation experiments.
Probing the Cosmological Principle with weak lensing shear
2024-11-13
James Adam, Roy Maartens, Julien Larena, Chris Clarkson
The Cosmological Principle is a cornerstone of the standard model of cosmology and shapes how we view the Universe and our place within it. It is imperative, then, to devise multiple observational tests which can identify and quantify possible violations of this foundational principle. One possible method of probing large-scale anisotropies involves the use of weak gravitational lensing. We revisit this approach in order to analyse the imprint of late-time anisotropic expansion on cosmic shear. We show that the cross-correlation of shear $E$- and $B$-modes on large scales can be used to constrain the magnitude (and possibly direction) of anisotropic expansion. We estimate the signal to noise for multipoles $10\lesssim \ell\lesssim 100$ that is achievable by a Euclid-like survey. Our findings suggest that such a survey could detect the $E$-$B$ signal for reasonable values of the late-time anisotropy parameter.
Blandford-Znajek power as a strong-gravity signature
2024-11-13
F. Camilloni
The Blandford-Znajek mechanism is an electromagnetic manifestation of the Penrose process that currently constitutes the best theoretical candidate to explain the launching of relativistic jets by black holes. In this talk we offer a modern review about the Blandford-Znajek mechanism and the analytic construction of black hole magnetospheres. Higher order perturbative corrections are crucial in order to produce results that are complementary to numerical simulations when the black hole is in the high-spin regime, and can potentially predict new features about the non-perturbative structure of the Blandford-Znajek theory. Moreover, we show by means of an explicit example that these perturbative corrections depend in a non-degenerate manner on the underlying theory of gravity considered, enabling one to use the BZ power emitted as a strong-gravity signature to test General Relativity against alternative theories of gravity on future horizon-scale observations.
Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve complex problems. The barriers include a lack of proper conceptual foundations, familiarity with graph applications in SCM, and real-world benchmark datasets for GNN-based supply chain research. To address this, we discuss and connect supply chains with graph structures for effective GNN application, providing detailed formulations, examples, mathematical definitions, and task guidelines. Additionally, we present a multi-perspective real-world benchmark dataset from a leading FMCG company in Bangladesh, focusing on supply chain planning. We discuss various supply chain tasks using GNNs and benchmark several state-of-the-art models on homogeneous and heterogeneous graphs across six supply chain analytics tasks. Our analysis shows that GNN-based models consistently outperform statistical Machine Learning and other Deep Learning models by around 10-30% in regression, 10-30% in classification and detection tasks, and 15-40% in anomaly detection tasks on designated metrics. With this work, we lay the groundwork for solving supply chain problems using GNNs, supported by conceptual discussions, methodological insights, and a comprehensive dataset.
Nearly 70 years since the neutrino was discovered, and 25 years since discovery of neutrino oscillations established its non-zero mass, the absolute neutrino-mass scale remains unknown. Due to its unique characteristics, determining this neutrino property requires new measurement techniques to be developed. Currently, there are four measurement approaches: using cosmological models, inference from time-of-arrival from supernovae, through observation of neutrinoless double beta decay, and the kinematics of weak decay processes. I will review the theoretical basis underlying neutrino mass measurement and present key experiments in this field. I will highlight the current best upper limits, how neutrino mass experiments are complementary to other neutrino property searches, and summarize the challenges that lie ahead of the neutrino mass community.
2024-11-13
Chalisa Veesommai Sillberg, Jose Siqueira De Cerqueira, Pekka Sillberg, Kai-Kristian Kemell, Pekka Abrahamsson
The EU AI Act was created to ensure ethical and safe Artificial Intelligence (AI) development and deployment across the EU. This study aims to identify key challenges and strategies for helping enterprises focus on resources effectively. To achieve this aim, we conducted a Multivocal Literature Review (MLR) to explore the sentiments of both the industry and the academia. From 130 articles, 56 met the criteria. Our key findings are three-fold. First, liability. Second, discrimination. Third, tool adequacy. Additionally, some negative sentiments were expressed by industry and academia regarding regulatory interpretations, specific requirements, and transparency issues. Next, our findings are three essential themes for enterprises. First, risk-based regulatory compliance. Second, ethical frameworks and principles in technology development. Third, policies and systems for regulatory risk management. These results identify the key challenges and strategies and provide less commonly discussed themes, enabling enterprises to align with the requirements and minimize their distance from the EU market.
Detection and classification of radio sources with deep learning
2024-11-13
S. Riggi, T. Cecconello, U. Becciani, F. Vitello
In this paper we present three different applications, based on deep learning methodologies, that we are developing to support the scientific analysis conducted within the ASKAP-EMU and MeerKAT radio surveys. One employs instance segmentation frameworks to detect compact and extended radio sources and imaging artefacts from radio continuum images. Another application uses gradient boosting decision trees and convolutional neural networks to classify compact sources into different astronomical classes using combined radio and infrared multi-band images. Finally, we discuss how self-supervised learning can be used to obtain valuable radio data representations for source detection, and classification studies.
Confidence intervals for adaptive trial designs I: A methodological review
2024-11-13
David S. Robertson, Thomas Burnett, Babak Choodari-Oskooei, Munya Dimairo, Michael Grayling, Philip Pallmann, Thomas Jaki
Regulatory guidance notes the need for caution in the interpretation of confidence intervals (CIs) constructed during and after an adaptive clinical trial. Conventional CIs of the treatment effects are prone to undercoverage (as well as other undesirable properties) in many adaptive designs, because they do not take into account the potential and realised trial adaptations. This paper is the first in a two-part series that explores CIs for adaptive trials. It provides a comprehensive review of the methods to construct CIs for adaptive designs, while the second paper illustrates how to implement these in practice and proposes a set of guidelines for trial statisticians. We describe several classes of techniques for constructing CIs for adaptive clinical trials, before providing a systematic literature review of available methods, classified by the type of adaptive design. As part of this, we assess, through a proposed traffic light system, which of several desirable features of CIs (such as achieving nominal coverage and consistency with the hypothesis test decision) each of these methods holds.
This comprehensive review delves deeply into the various methodologies applied to facial expression recognition (FER) through the lens of graph representation learning (GRL). Initially, we introduce the task of FER and the concepts of graph representation and GRL. Afterward, we discuss some of the most prevalent and valuable databases for this task. We explore promising approaches for graph representation in FER, including graph diffusion, spatio-temporal graphs, and multi-stream architectures. Finally, we identify future research opportunities and provide concluding remarks.
High-Time-Cadence Spectroscopy and Photometry of Stellar Flares on M-dwarf YZ Canis Minoris with Seimei Telescope and TESS. I. Discovery of Rapid and Short-Duration Prominence Eruptions
M-dwarfs show frequent flares and associated coronal mass ejections (CMEs) may significantly impact close-in habitable planets. M-dwarf flares sometimes show red/blue asymmetries in the H$\alpha$ line profile, suggesting prominence eruptions as an early stage of CMEs. However, their high-time-cadence observations are limited. We conducted spectroscopic monitoring observations of the active M-dwarf YZ Canis Minoris with $\sim$1 minute time cadence using the Seimei telescope, simultaneously with the optical photometric observations by Transiting Exoplanet Survey Satellite. We detected 27 H$\alpha$ flares with H$\alpha$ energies ranging from 1.7 $\times$ 10$^{29}$ to 3.8 $\times$ 10$^{32}$ erg and durations from 8 to 319 minutes. Among them, we identified 3 blue asymmetry and 5 red asymmetry events based on criteria using the Bayesian Information Criterion. The maximum velocity of the blue- and red-shifted components ranges from 250 to 450 km s$^{-1}$ and 190 to 400 km s$^{-1}$, respectively. The duration and time evolution show variety, and in particular, we discovered rapid, short-duration blue/red asymmetry events with the duration of 6--8 minutes. Among the 8 blue/red asymmetry events, two blue and one red asymmetry events are interpreted as prominence eruptions because of their fast velocity and time evolution. Based on this interpretation, the lower limit of occurrence frequency of prominence eruptions can be estimated to be $\sim$1.1 events per day. Our discovery of short-duration events suggests that previous studies with low time cadence may have missed these events, potentially leading to an underestimation of the occurrence frequency of prominence eruptions/CMEs.
2024-11-13
Armand Leclerc, Guillaume Laibe, Nicolas Perez
Inertial waves in convective regions of stars exhibit topological properties linked to a Chern number of 1. The first of these is a unique, unidirectional, prograde oscillation mode within the cavity, which propagates at arbitrarily low frequencies for moderate azimuthal wavenumbers. The second one are phase singularities around which the phase winds in Fourier space, with winding numbers of $\pm 1$ depending on the hemisphere. Phase winding is a collective effect over waves propagating in all directions that is strongly robust to noise. This suggests a topology-based method for wave detection in noisy observational data.
DecentPeeR: A Self-Incentivised & Inclusive Decentralized Peer Review System
2024-11-13
Johannes Gruendler, Darya Melnyk, Arash Pourdamghani, Stefan Schmid
Peer review, as a widely used practice to ensure the quality and integrity of publications, lacks a well-defined and common mechanism to self-incentivize virtuous behavior across all the conferences and journals. This is because information about reviewer efforts and author feedback typically remains local to a single venue, while the same group of authors and reviewers participate in the publication process across many venues. Previous attempts to incentivize the reviewing process assume that the quality of reviews and papers authored correlate for the same person, or they assume that the reviewers can receive physical rewards for their work. In this paper, we aim to keep track of reviewing and authoring efforts by users (who review and author) across different venues while ensuring self-incentivization. We show that our system, DecentPeeR, incentivizes reviewers to behave according to the rules, i.e., it has a unique Nash equilibrium in which virtuous behavior is rewarded.
$\Theta^+$ is a putative light pentaquark state of positive parity with minimal quark content $(uudd\bar{s})$. It naturally emerges in chiral models for baryons, but experimental evidence is uncertain. We review the theoretical foundations of chiral models and their phenomenological applications to exotic states. In particular, we discuss in detail the pentaquark widths with special emphasis on the cancellations occurring in the decay operator. We also discuss some experiments, mainly those whose positive evidence of ${\mit\Theta}^+$ persists to this day. This review is dedicated to Dmitry Diakonov, Victor Petrov, and Maxim Polyakov and their contribution to the ${\mit\Theta}^+$ story.
Everything You Wanted to Know About Consumer Light Management in Smart Energy
2024-11-13
Prajnyajit Mohanty, Umesh C. Pati, Kamalakanta Mahapatra, Saraju P. Mohanty
Consumer lighting plays a significant role in the development of smart cities and smart villages. With the advancement of (IoT) technology, smart lighting solutions have become more prevalent in residential areas as well. These solutions provide consumers with increased energy efficiency, added convenience, and improved security. On the other hand, the growing number of IoT devices has become a global concern due to the carbon footprint and carbon emissions associated with these devices. The overuse of batteries increases maintenance and cost to IoT devices and simultaneously possesses adverse environmental effects, ultimately exacerbating the pace of climate change. Therefore, in tandom with the principles of Industry 4.0, it has become crucial for manufacturing and research industries to prioritize sustainable measures adhering to smart energy as a prevention to the negative impacts. Consequently, it has undoubtedly garnered global interest from scientists, researchers, and industrialists to integrate state-of-the-art technologies in order to solve the current issues in consumer light management systems making it a complete sustainable, and smart solution for consumer lighting application. This manuscript provides a thorough investigation of various methods as well as techniques to design a state-of-the-art IoT-enabled consumer light management system. It critically reviews the existing works done in consumer light management systems, emphasizing the significant limitations and the need for sustainability. The top-down approach of developing sustainable computing frameworks for IoT-enabled consumer light management has been reviewed based on the multidisciplinary technologies involved and state-of-the-art works in the respective domains. Lastly, this article concludes by highlighting possible avenues for future research.
How Transit Countries Become Refugee Destinations: Insights from Central and Eastern Europe
2024-11-13
Liliana Harding, Ciprian Panzaru
This study explores how refugees' destination preferences evolve during transit, with a focus on Central and Eastern Europe, particularly Romania. Using a mixed-methods approach, we analyse data from the International Organization for Migration's (IOM) Flow Monitoring Surveys and complement it with qualitative insights from focus group discussions with refugees. The quantitative analysis reveals that refugees' preferences for destination countries often change during transit, influenced by factors such as safety concerns, asylum conditions, education, and the presence of relatives at the destination. Our results support the application of bounded rationality and human capital theory, showing that while economic opportunities are important, safety becomes the dominant concern during transit. The qualitative analysis adds depth to these findings, highlighting the role of political instability, social networks, and economic hardships as initial migration drivers. Additionally, the study reveals how refugees reassess their destination choices based on their experiences in transit countries, with Romania emerging as a viable settlement destination due to its relative stability and access to asylum procedures. This research contributes to migration studies by challenging the traditional view of transit countries and offering new insights into the fluid nature of refugee decision-making.
Data augmentation is a powerful technique to mitigate data scarcity. However, owing to fundamental differences in wireless data structures, traditional data augmentation techniques may not be suitable for wireless data. Fortunately, Generative Artificial Intelligence (GenAI) can be an effective alternative to wireless data augmentation due to its excellent data generation capability. This article systemically explores the potential and effectiveness of GenAI-driven data augmentation in wireless networks. We first briefly review data augmentation techniques, discuss their limitations in wireless networks, and introduce generative data augmentation, including reviewing GenAI models and their applications in data augmentation. We then explore the application prospects of GenAI-driven data augmentation in wireless networks from the physical, network, and application layers, which provides a GenAI-driven data augmentation architecture for each application. Subsequently, we propose a general generative diffusion model-based data augmentation framework for Wi-Fi gesture recognition, which uses transformer-based diffusion models to generate high-quality channel state information data. Furthermore, we develop residual neural network models for Wi-Fi gesture recognition to evaluate the role of augmented data and conduct a case study based on a real dataset. Simulation results demonstrate the effectiveness of the proposed framework. Finally, we discuss research directions for generative data augmentation.
Data-Driven Discovery of a New Ginzburg-Landau Reduced-Order Model for Vortex Shedding
2024-11-13
Joseph J. Williams, Zachary G. Nicolaou, J. Nathan Kutz, Steven L. Brunton
Vortex shedding is an important physical phenomenon observed across many spatial and temporal scales in fluids. Previous experimental and theoretical studies have established a hierarchy of local and global reduced-order models for vortex shedding based on the Stuart-Landau and Ginzburg-Landau equations. In this work, we employ data-driven methods to discover a new Landau variable for the complex Ginzburg-Landau equation (CGLE) for periodic two-dimensional vortex shedding past a cylinder. We first coarse grain vorticity field data from direct numerical simulations of the fluid by integrating over the vertical spatial dimension, and then time-delay embed this new variable to obtain phase information necessary for CGLE analysis. Finally, we use the sparse identification of nonlinear dynamics (SINDy) to learn an interpretable system of coupled real partial differential equations that balance model complexity and prediction error. We develop local homogeneous models in key regions of the wake, as well as global heterogeneous models that are parameterized by the streamwise coordinate across the entire wake. After computing the normal-form parameters from the discovered equations, we classify the different models and determine behavior and stability in the different regions of the wake, demonstrating the validity of these models across a range of spatiotemporal subdomains. Different dynamics are found to dominate at different stations within the wake. In addition to introducing a novel Landau variable for vortex shedding, this work may also help characterize other vortex shedding systems and inform the design of control schemes.
The Systems Engineering Approach in Times of Large Language Models
2024-11-13
Christian Cabrera, Viviana Bastidas, Jennifer Schooling, Neil D. Lawrence
Using Large Language Models (LLMs) to address critical societal problems requires adopting this novel technology into socio-technical systems. However, the complexity of such systems and the nature of LLMs challenge such a vision. It is unlikely that the solution to such challenges will come from the Artificial Intelligence (AI) community itself. Instead, the Systems Engineering approach is better equipped to facilitate the adoption of LLMs by prioritising the problems and their context before any other aspects. This paper introduces the challenges LLMs generate and surveys systems research efforts for engineering AI-based systems. We reveal how the systems engineering principles have supported addressing similar issues to the ones LLMs pose and discuss our findings to provide future directions for adopting LLMs.
Asymptotics, orthogonality relations and duality for the $q$ and $q^{-1}$-symmetric polynomials in the $q$-Askey scheme
2024-11-13
Howard S. Cohl, Roberto S. Costas-Santos, Xiang-Sheng Wang
In this survey we summarize the current state of known orthogonality relations for the $q$ and $q^{-1}$-symmetric and dual subfamilies of the Askey--Wilson polynomials in the $q$-Askey scheme. These polynomials are the continuous dual $q$ and $q^{-1}$-Hahn polynomials, the $q$ and $q^{-1}$-Al-Salam--Chihara polynomials, the continuous big $q$ and $q^{-1}$-Hermite polynomials and the continuous $q$ and $q^{-1}$-Hermite polynomials and their dual counterparts which are connected with the big $q$-Jacobi polynomials, the little $q$-Jacobi polynomials and the $q$ and $q^{-1}$-Bessel polynomials. The $q^{-1}$-symmetric polynomials in the $q$-Askey scheme satisfy an indeterminate moment problem, satisfying an infinite number of orthogonality relations for these polynomials. Among the infinite number of orthogonality relations for the $q^{-1}$-symmetric families, we attempt to summarize those currently known. These fall into several classes, including continuous orthogonality relations and infinite discrete (including bilateral) orthogonality relations. Using symmetric limits, we derive a new infinite discrete orthogonality relation for the continuous big $q^{-1}$-Hermite polynomials. Using duality relations, we explore orthogonality relations for and from the dual families associated with the $q$ and $q^{-1}$-symmetric subfamilies of the Askey--Wilson polynomials. In order to give a complete description of the convergence properties for these polynomials, we provide the large degree asymptotics using the Darboux method for these polynomials. In order to apply the Darboux method, we derive a generating function with two free parameters for the $q^{-1}$-Al-Salam--Chihara polynomials which has natural limits to the lower $q^{-1}$-symmetric families.
The ALMA-CRISTAL Survey: Complex kinematics of the galaxies at the end of the Reionization Era
2024-11-13
K. Telikova, J. González-López, M. Aravena, A. Posses, V. Villanueva, M. Baeza-Garay, G. C. Jones, M. Solimano, L. Lee, R. J. Assef, I. De Looze, T. Diaz Santos, A. Ferrara, R. Ikeda, R. Herrera-Camus, H. Übler, I. Lamperti, I. Mitsuhashi, M. Relano, M. Perna, K. Tadaki
The history of gas assembly in early galaxies is reflected in their complex kinematics. While a considerable fraction of galaxies at z~5 are consistent with rotating disks, current studies indicate that the dominant galaxy assembly mechanism corresponds to mergers. Despite the important progress, the dynamical classification of galaxies at these epochs is still limited by observations' resolution. We present a detailed morphological and kinematic analysis of the far-infrared bright main sequence galaxy HZ10 at z=5.65, making use of new high-resolution ($\lesssim0.3$") [CII] 158$\mu$m ALMA and rest-frame optical JWST/NIRSpec observations. These observations reveal a previously unresolved complex morphology and kinematics of the HZ10. We confirm that HZ10 is not a single galaxy but consists of at least three components in close projected separation along the east-to-west direction. We find a [CII] bright central component (C), separated by 1.5 and 4 kpc from the east (E) and west (W) components, respectively. Our [CII] observations resolve the HZ10-C component resulting in a velocity gradient, produced by either rotation or a close-in merger. We test the rotating disk possibility using DysmalPy kinematic modeling and propose three dynamical scenarios for the HZ10 system: (i) a double merger, in which the companion galaxy HZ10-W merges with the disturbed clumpy rotation disk formed by the HZ10-C and E components; (ii) a triple merger, where the companion galaxies, HZ10-W and HZ10-E, merge with the rotation disk HZ10-C; and (iii) a quadruple merger, in which the companion galaxies HZ10-W and HZ10-E merge with the close double merger HZ10-C. Comparing [CII] with JWST/NIRSpec data, we find that [CII] emission closely resembles the broad [OIII] 5007{\AA} emission. The latter reflects the interacting nature of the system and suggests that ionized and neutral gas phases in HZ10 are well mixed.
Quantum Nanophotonics with Energetic Particles:X-rays and Free Electrons
2024-11-13
Xihang Shi, Wen Wei Lee, Aviv Karnieli, Leon Merten Lohse, Alexey Gorlach, Lee Wei Wesley Wong, Tim Saldit, Shanhui Fan, Ido Kaminer, Liang Jie Wong
Rapid progress in precision nanofabrication and atomic design over the past 50 years has ushered in a succession of transformative eras for molding the generation and flow of light. The use of nanoscale and atomic features to design light sources and optical elements-encapsulated by the term nanophotonics-has led to new fundamental science and innovative technologies across the entire electromagnetic spectrum, with substantial emphasis on the microwave to visible regimes. In this review, we pay special attention to the impact and potential of nanophotonics in a relatively exotic yet technologically disruptive regime: high-energy particles such as X-ray photons and free electrons-where nanostructures and atomic design open the doors to unprecedented technologies in quantum science and versatile X-ray sources and optics. As the practical generation of X-rays is intrinsically linked to the existence of energetic free or quasi-free-electrons, our review will also capture related phenomena and technologies that combine free electrons with nanophotonics, including free-electron-driven nanophotonics at other photon energies. In particular, we delve into the demonstration and study of quantum recoil in the X-ray regime, the study of nanomaterial design and free-electron wave shaping as means to enhance and control X-ray radiation, examine the free-electron generation enabled by nanophotonics, and analyze the high-harmonic generation by quasi-free electrons. We also discuss applications of quantum nanophotonics for X-rays and free electrons, including nanostructure waveguides for X-rays, photon pair enhanced X-ray imaging, mirrors, and lenses for X-rays, among others.
$S^5$: New insights from deep spectroscopic observations of the tidal tails of the globular clusters NGC 1261 and NGC 1904
2024-11-13
Petra Awad, Ting S. Li, Denis Erkal, Reynier F. Peletier, Kerstin Bunte, Sergey E. Koposov, Andrew Li, Eduardo Balbinot, Rory Smith, Marco Canducci, Peter Tino, Alexandra M. Senkevich, Lara R. Cullinane, Gary S. Da Costa, Alexander P. Ji, Kyler Kuehn, Geraint F. Lewis, Andrew B. Pace, Daniel B. Zucker, Joss Bland-Hawthorn, Guilherme Limberg, Sarah L. Martell, Madeleine McKenzie, Yong Yang, Sam A. Usman
As globular clusters (GCs) orbit the Milky Way, their stars are tidally stripped forming tidal tails that follow the orbit of the clusters around the Galaxy. The morphology of these tails is complex and shows correlations with the phase of the orbit and the orbital angular velocity, especially for GCs on eccentric orbits. Here, we focus on two GCs, NGC 1261 and NGC 1904, that have potentially been accreted alongside Gaia-Enceladus and that have shown signatures of having, in addition of tidal tails, structures formed by distributions of extra-tidal stars that are misaligned with the general direction of the clusters' respective orbits. To provide an explanation for the formation of these structures, we make use of spectroscopic measurements from the Southern Stellar Stream Spectroscopic Survey ($S^5$) as well as proper motion measurements from Gaia's third data release (DR3), and apply a Bayesian mixture modeling approach to isolate high-probability member stars. We recover extra-tidal features similar to those found in Shipp et al. (2018) surrounding each cluster. We conduct N-body simulations and compare the expected distribution and variation in the dynamical parameters along the orbit with those of our potential member sample. Furthermore, we use Dark Energy Camera (DECam) photometry to inspect the distribution of the member stars in the color-magnitude diagram (CMD). We find that the potential members agree reasonably with the N-body simulations and that the majority of them follow a simple stellar population-like distribution in the CMD which is characteristic of GCs. In the case of NGC 1904, we clearly detect the tidal debris escaping the inner and outer Lagrange points which are expected to be prominent when at or close to the apocenter of its orbit. Our analysis allows for further exploration of other GCs in the Milky Way that exhibit similar extra-tidal features.
2024-11-13
Maya H. Debski, Gregory R. Zeimann, Gary J. Hill, Donald P. Schneider, Leah Morabito, Gavin Dalton, Matt J. Jarvis, Erin Mentuch Cooper, Robin Ciardullo, Eric Gawiser, Nika Jurlin
We combine the power of blind integral field spectroscopy from the Hobby-Eberly Telescope (HET) Dark Energy Experiment (HETDEX) with sources detected by the Low Frequency Array (LOFAR) to construct the HETDEX-LOFAR Spectroscopic Redshift Catalog. Starting from the first data release of the LOFAR Two-metre Sky Survey (LoTSS), including a value-added catalog with photometric redshifts, we extracted 28,705 HETDEX spectra. Using an automatic classifying algorithm, we assigned each object a star, galaxy, or quasar label along with a velocity/redshift, with supplemental classifications coming from the continuum and emission line catalogs of the internal, fourth data release from HETDEX (HDR4). We measured 9,087 new redshifts; in combination with the value-added catalog, our final spectroscopic redshift sample is 9,710 sources. This new catalog contains the highest substantial fraction of LOFAR galaxies with spectroscopic redshift information; it improves archival spectroscopic redshifts, and facilitates research to determine the [O II] emission properties of radio galaxies from $0.0 < z < 0.5$, and the Ly$\alpha$ emission characteristics of both radio galaxies and quasars from $1.9 < z < 3.5$. Additionally, by combining the unique properties of LOFAR and HETDEX, we are able to measure star formation rates (SFR) and stellar masses. Using the Visible Integral-field Replicable Unit Spectrograph (VIRUS), we measure the emission lines of [O III], [Ne III], and [O II] and evaluate line-ratio diagnostics to determine whether the emission from these galaxies is dominated by AGN or star formation and fit a new SFR-L$_{150MHz}$ relationship.
Exploring nuclear physics through the fundamental constituents of the strong force -- quarks and gluons -- is a formidable challenge. While numerical calculations using lattice quantum chromodynamics offer the most promising approach for this pursuit, practical implementation is arduous, especially due to the uncontrollable growth of quark-combinatorics, the so-called Wick-contraction problem of nuclei. We present here two novel methods providing a state-of-the-art solution to this problem. In the first, we exploit randomized algorithms inspired from computational number theory to detect and eliminate redundancies that arise in Wick contraction computations. Our second method explores facilities for automation of tensor computations -- in terms of efficient utilization of specialized hardware, algorithmic optimizations, as well as ease of programming and the potential for automatic code generation -- that are offered by new programming models inspired by applications in machine learning (e.g., TensorFlow). We demonstrate the efficacy of our methods by computing two-point correlation functions for Deuteron, Helium-3, Helium-4 and Lithium-7, achieving at least an order of magnitude improvement over existing algorithms with efficient implementation on GPU-accelerators. Additionally, we discover an intriguing characteristic shared by all the nuclei we study: specific spin-color combinations dominate the correlation functions, hinting at a potential connection to an as-yet-unidentified symmetry in nuclei. Moreover finding them beforehand can reduce the computing time further and substantially. Our results, with the efficiency that we achieved, suggest the possibility of extending the applicability of our methods for calculating properties of light nuclei, potentially up to A ~12 and beyond.
NGTS-33b: A Young Super-Jupiter Hosted by a Fast Rotating Massive Hot Star
2024-11-13
Douglas R. Alves, James S. Jenkins, Jose I. Vines, Matthew P. Battley, Monika Lendl, François Bouchy, Louise D. Nielsen, Samuel Gill, Maximiliano Moyano, D. R. Anderson, Matthew R. Burleigh, Sarah L. Casewell, Michael R. Goad, Faith Hawthorn, Alicia Kendall, James McCormac, Ares Osborn, Alexis M. S. Smith, Stephane Udry, Peter J. Wheatley, Suman Saha, Lena Parc, Arianna Nigioni, Ioannis Apergis, Gavin Ramsay
In the last few decades planet search surveys have been focusing on solar type stars, and only recently the high-mass regimes. This is mostly due to challenges arising from the lack of instrumental precision, and more importantly, the inherent active nature of fast rotating massive stars. Here we report NGTS-33b (TOI-6442b), a super-Jupiter planet with mass, radius and orbital period of 3.6 $\pm$ 0.3 M$_{\rm jup}$, 1.64 $\pm$ 0.07 R$_{\rm jup}$ and $2.827972 \pm 0.000001$ days, respectively. The host is a fast rotating ($0.6654 \pm 0.0006$ day) and hot (T$_{\rm eff}$ = 7437 $\pm$ 72 K) A9V type star, with a mass and radius of 1.60 $\pm$ 0.11 M$_{\odot}$ and 1.47 $\pm$ 0.06 R$_{\odot}$, respectively. Planet structure and Gyrochronology models shows that NGTS-33 is also very young with age limits of 10-50 Myr. In addition, membership analysis points towards the star being part of the Vela OB2 association, which has an age of $\sim$ 20-35 Myr, thus providing further evidences about the young nature of NGTS-33. Its low bulk density of 0.19$\pm$0.03 g cm$^{-3}$ is 13$\%$ smaller than expected when compared to transiting hot Jupiters with similar masses. Such cannot be solely explained by its age, where an up to 15$\%$ inflated atmosphere is expected from planet structure models. Finally, we found that its emission spectroscopy metric is similar to JWST community targets, making the planet an interesting target for atmospheric follow-up. Therefore, NGTS-33b's discovery will not only add to the scarce population of young, massive and hot Jupiters, but will also help place further strong constraints on current formation and evolution models for such planetary systems.
2024-11-13
L. M. Stapper, M. R. Hogerheijde, E. F. van Dishoeck, A. S. Booth, S. L. Grant, S. E. van Terwisga
Disks around intermediate mass stars called Herbig disks are the formation sites of giant exoplanets. Obtaining a complete inventory of these disks will therefore give insights into giant planet formation. However, until now no complete disk survey has been done on Herbig disks in a single star-forming region. Orion is the only nearby region with a significant number of Herbig disks (N=35) to carry out such a survey. Using new NOEMA observations of 25 Herbig disks, in combination with ALMA archival data of 10 Herbig disks, results in a complete sample of all know Herbig disks in Orion. Using uv-plane analysis for the NOEMA observed disks, and literature values of the ALMA observed disks, we obtain the dust masses of all Herbig disks and obtain a cumulative dust mass distribution. Additionally, six disks with new CO isotopologues detections are presented, one of which is detected in C17O. We calculate the external ultraviolet (UV) irradiance on each disk and compare the dust mass to it. We find a median disk dust mass of 11.7 M_\oplus for the Herbig disks. Comparing the Herbig disks in Orion to previous surveys for mainly T Tauri disks in Orion, we find that while ~50% of the Herbig disks have a mass higher than 10 M_\oplus, this is at most 25% for the T~Tauri disks. This difference is especially striking when considering that the Herbig disks are around a factor of two older than the T Tauri disks. Comparing to the Herbig disks observed with ALMA from a previous study, no significant difference is found between the distributions. We find a steeper (slope of -7.6) relationship between the dust mass and external UV irradation compared to that of the T~Tauri disks (slope of -1.3). This work shows the importance of complete samples, giving rise to the need of a complete survey of the Herbig disk population.
Generalized Cauchy-Riemann equations and relevant PDE
2024-11-13
V. Gutlyanskii, V. Ryazanov, A. Salimov, R. Salimov
Here we give a survey of consequences from the theory of the Beltrami equation in the complex plane $\mathbb C$ to generalized Cauchy-Riemann equation in the real plane $\mathbb R^2$ and clarify the relationships of the latter to the $A-$harmonic equation ${\rm div} A(z)\,{\rm grad}\, u(z) = 0$, $z=x+iy$, with matrix valued coefficients $A$ that is one of the main equations of the potential theory, namely, of the hydro\-mechanics (fluid mechanics) in anisotropic and inhomogeneous media. The survey includes various types of results as theorems on existence, representation and regularity of its solutions, in particular, for the main boundary value problems of Hilbert, Dirichlet, Neumann, Poincare and Riemann.
Predicting household socioeconomic position in Mozambique using satellite and household imagery
2024-11-13
Carles Milà, Teodimiro Matsena, Edgar Jamisse, Jovito Nunes, Quique Bassat, Paula Petrone, Elisa Sicuri, Charfudin Sacoor, Cathryn Tonne
Many studies have predicted SocioEconomic Position (SEP) for aggregated spatial units such as villages using satellite data, but SEP prediction at the household level and other sources of imagery have not been yet explored. We assembled a dataset of 975 households in a semi-rural district in southern Mozambique, consisting of self-reported asset, expenditure, and income SEP data, as well as multimodal imagery including satellite images and a ground-based photograph survey of 11 household elements. We fine-tuned a convolutional neural network to extract feature vectors from the images, which we then used in regression analyzes to model household SEP using different sets of image types. The best prediction performance was found when modeling asset-based SEP using random forest models with all image types, while the performance for expenditure- and income-based SEP was lower. Using SHAP, we observed clear differences between the images with the largest positive and negative effects, as well as identified the most relevant household elements in the predictions. Finally, we fitted an additional reduced model using only the identified relevant household elements, which had an only slightly lower performance compared to models using all images. Our results show how ground-based household photographs allow to zoom in from an area-level to an individual household prediction while minimizing the data collection effort by using explainable machine learning. The developed workflow can be potentially integrated into routine household surveys, where the collected household imagery could be used for other purposes, such as refined asset characterization and environmental exposure assessment.
iFlow: An Interactive Max-Flow/Min-Cut Algorithms Visualizer
2024-11-13
Muyang Ye, Tianrui Xia, Tianxin Zu, Qian Wang, David Kempe
The Max-Flow/Min-Cut problem is a fundamental tool in graph theory, with applications in many domains, including data mining, image segmentation, transportation planning, and many types of assignment problems, in addition to being an essential building block for many other algorithms. The Ford-Fulkerson Algorithm for Max-Flow/Min-Cut and its variants are therefore commonly taught in undergraduate and beginning graduate algorithms classes. However, these algorithms -- and in particular the so-called residual graphs they utilize -- often pose significant challenges for students. To help students achieve a deeper understanding, we developed iFlow, an interactive visualization tool for the Ford-Fulkerson Algorithm and its variants. iFlow lets users design or import flow networks, and execute the algorithm by hand. In particular, the user can select an augmentation path and amount, and then update the residual graph. The user is given detailed feedback on mistakes, and can also have iFlow auto-complete each step, to use it as a demonstration tool while still in the initial learning stages. iFlow has been made publicly available and open-sourced. We deployed iFlow in an undergraduate algorithms class, and collected students' self-reported learning benefits via an optional survey. All respondents considered the tool at least somewhat useful and engaging, with most rating it either as useful/engaging or very useful/engaging. Students also generally reported a significant increase in understanding of the algorithm.
Simulating continuum-based redshift measurement in the \textit{Roman's} High Latitude Spectroscopy Survey
2024-11-12
Zhiyuan Guo, Bhavin Joshi, Chris. W. Walter, M. A. Troxel
We investigate the capability of the \textit{Nancy Grace Roman Space Telescope's (Roman)} Wide-Field Instrument (WFI) G150 slitless grism to detect red, quiescent galaxies based on the current reference survey. We simulate dispersed images for \textit{Roman} reference High-Latitude Spectroscopic Survey (HLSS) and analyze two-dimensional spectroscopic data using the grism Redshift and Line Analysis (\verb|Grizli|) software. This study focus on assessing \textit{Roman} grism's capability for continuum-level redshift measurement for a redshift range of $0.5 \leq z \leq 2.5$. The redshift recovery is assessed by setting three requirements of: $\sigma_z = \frac{\left|z-z_{\mathrm{true}}\right|}{1+z}\leq0.01$, signal-to-noise ratio (S/N) $\geq 5$ and the presence of a single dominant peak in redshift likelihood function. We find that, for quiescent galxaies, the reference HLSS can reach a redshift recovery completeness of $\geq50\%$ for F158 magnitude brighter than 20.2 mag. We also explore how different survey parameters, such as exposure time and the number of exposures, influence the accuracy and completeness of redshift recovery, providing insights that could optimize future survey strategies and enhance the scientific yield of the \textit{Roman} in cosmological research.
The Multi-Scale Multi-Phase Circumgalactic Medium: Observed and Simulated
2024-11-12
Celine Peroux, Dylan Nelson
These are exciting times for studies of galaxy formation and the growth of structures. New observatories and advanced simulations are revolutionising our understanding of the cycling of matter into, through, and out of galaxies. This chapter first describes why baryons are essential for galaxy evolution, providing a key test of Lambda-Cold Dark Matter cosmological model. In particular, we describe a basic framework to convert measurements of the gas properties observed in absorption spectra into global estimates of the condensed (stars and cold gas) matter mass densities. We then review our current understanding of the cycling of baryons from global to galactic scales, in the so-called circumgalactic medium. The final sections are dedicated to future prospects, identifying new techniques and up-coming facilities as well as key open questions. This chapter is complemented with a series of hands-on exercises which provide a practical guide to using publicly available hydrodynamical cosmological simulations. Beyond providing a direct connection between new observations and advanced simulations, these exercises give the reader the necessary tools to make use of these theoretical models to address their own science questions. Ultimately, our increasingly accurate description of the circumgalactic medium reveals its crucial role in transforming the pristine early Universe into the rich and diverse Universe of the present day.
The Nature of Optical Afterglows Without Gamma-ray Bursts: Identification of AT2023lcr and Multiwavelength Modeling
2024-11-12
Maggie L. Li, Anna Y. Q. Ho, Geoffrey Ryan, Daniel A. Perley, Gavin P. Lamb, A. J. Nayana, Igor Andreoni, G. C. Anupama, Eric C. Bellm, Edo Berger, Joshua S. Bloom, Eric Burns, Ilaria Caiazzo, Poonam Chandra, Michael W. Coughlin, Kareem El-Badry, Matthew J. Graham, Mansi Kasliwal, Garrett K. Keating, S. R. Kulkarni, Harsh Kumar, Frank J. Masci, Richard A. Perley, Josiah Purdum, Ramprasad Rao, Antonio C. Rodriguez, Ben Rusholme, Nikhil Sarin, Jesper Sollerman, Gokul P. Srinivasaragavan, Vishwajeet Swain, Zachary Vanderbosch
In the past few years, the improved sensitivity and cadence of wide-field optical surveys have enabled the discovery of several afterglows without associated detected gamma-ray bursts (GRBs). We present the identification, observations, and multiwavelength modeling of a recent such afterglow (AT2023lcr), and model three literature events (AT2020blt, AT2021any, and AT2021lfa) in a consistent fashion. For each event, we consider the following possibilities as to why a GRB was not observed: 1) the jet was off-axis; 2) the jet had a low initial Lorentz factor; and 3) the afterglow was the result of an on-axis classical GRB (on-axis jet with physical parameters typical of the GRB population), but the emission was undetected by gamma-ray satellites. We estimate all physical parameters using afterglowpy and Markov Chain Monte Carlo methods from emcee. We find that AT2023lcr, AT2020blt, and AT2021any are consistent with on-axis classical GRBs, and AT2021lfa is consistent with both on-axis low Lorentz factor ($\Gamma_0 \approx 5 - 13$) and off-axis ($\theta_\text{obs}=2\theta_\text{jet}$) high Lorentz factor ($\Gamma_0 \approx 100$) jets.
MUltiplexed Survey Telescope: Perspectives for Large-Scale Structure Cosmology in the Era of Stage-V Spectroscopic Survey
2024-11-12
Cheng Zhao, Song Huang, Mengfan He, Paulo Montero-Camacho, Yu Liu, Pablo Renard, Yunyi Tang, Aurelien Verdier, Wenshuo Xu, Xiaorui Yang, Jiaxi Yu, Yao Zhang, Siyi Zhao, Xingchen Zhou, Shengyu He, Jean-Paul Kneib, Jiayi Li, Zhuoyang Li, Wen-Ting Wang, Zhong-Zhi Xianyu, Yidian Zhang, Rafaela Gsponer, Xiao-Dong Li, Antoine Rocher, Siwei Zou, Ting Tan, Zhiqi Huang, Zhuoxiao Wang, Pei Li, Maxime Rombach, Chenxing Dong, Daniel Forero-Sanchez, Huanyuan Shan, Tao Wang, Yin Li, Zhongxu Zhai, Yuting Wang, Gong-Bo Zhao, Yong Shi, Shude Mao, Lei Huang, Liquan Guo, Zheng Cai
The MUltiplexed Survey Telescope (MUST) is a 6.5-meter telescope under development. Dedicated to highly-multiplexed, wide-field spectroscopic surveys, MUST observes over 20,000 targets simultaneously using 6.2-mm pitch positioning robots within a ~5 deg2 field of view. MUST aims to carry out the first Stage-V spectroscopic survey in the 2030s to map the 3D Universe with over 100 million galaxies and quasars, spanning from the nearby Universe to redshift z~5.5, corresponding to around 1 billion years after the Big Bang. To cover this extensive redshift range, we present an initial conceptual target selection algorithm for different types of galaxies, from local bright galaxies, luminous red galaxies, and emission line galaxies to high-redshift (2 < z < 5.5) Lyman-break galaxies. Using Fisher forecasts, we demonstrate that MUST can address fundamental questions in cosmology, including the nature of dark energy, test of gravity theories, and investigations into primordial physics. This is the first paper in the series of science white papers for MUST, with subsequent developments focusing on additional scientific cases such as galaxy and quasar evolution, Milky Way physics, and dynamic phenomena in the time-domain Universe.
Commissioning An All-Sky Infrared Camera Array for Detection Of Airborne Objects
2024-11-12
Laura Dominé, Ankit Biswas, Richard Cloete, Alex Delacroix, Andriy Fedorenko, Lucas Jacaruso, Ezra Kelderman, Eric Keto, Sarah Little, Abraham Loeb, Eric Masson, Mike Prior, Forrest Schultz, Matthew Szenher, Wes Watters, Abby White
To date there is little publicly available scientific data on Unidentified Aerial Phenomena (UAP) whose properties and kinematics purportedly reside outside the performance envelope of known phenomena. To address this deficiency, the Galileo Project is designing, building, and commissioning a multi-modal ground-based observatory to continuously monitor the sky and conduct a rigorous long-term aerial census of all aerial phenomena, including natural and human-made. One of the key instruments is an all-sky infrared camera array using eight uncooled long-wave infrared FLIR Boson 640 cameras. Their calibration includes a novel extrinsic calibration method using airplane positions from Automatic Dependent Surveillance-Broadcast (ADS-B) data. We establish a first baseline for the system performance over five months of field operation, using a real-world dataset derived from ADS-B data, synthetic 3-D trajectories, and a hand-labelled real-world dataset. We report acceptance rates (e.g. viewable airplanes that are recorded) and detection efficiencies (e.g. recorded airplanes which are successfully detected) for a variety of weather conditions, range and aircraft size. We reconstruct $\sim$500,000 trajectories of aerial objects from this commissioning period. A toy outlier search focused on large sinuosity of the 2-D reconstructed trajectories flags about 16% of trajectories as outliers. After manual review, 144 trajectories remain ambiguous: they are likely mundane objects but cannot be elucidated at this stage of development without distance and kinematics estimation or other sensor modalities. Our observed count of ambiguous outliers combined with systematic uncertainties yields an upper limit of 18,271 outliers count for the five-month interval at a 95% confidence level. This likelihood-based method to evaluate significance is applicable to all of our future outlier searches.
Nonlocal interaction and collective excitation in deuteron breakup on ${}^{24}$Mg nucleus
2024-11-12
A. Deltuva, D. Jurčiukonis
Deuteron breakup in collision with a ${}^{24}\mathrm{Mg}$ nucleus is studied using rigorous three-body scattering equations, extended to include also the excitation of the nucleus. Predictions based on local and nonlocal nucleon-nucleus optical potentials with rotational quadrupole deformation enabling the excitation of the ${}^{24}\mathrm{Mg}(2^+)$ state are compared. The nonlocality effect is less pronounced than in the deuteron inelastic scattering ${}^{24}\mathrm{Mg}(d,d')$ at the same energies, and manifests itself quite differently for semi-inclusive differential cross sections of elastic and inelastic breakup.
The scarcity of comprehensive datasets in the traffic light detection and recognition domain and the poor performance of state-of-the-art models under hostile weather conditions present significant challenges. To address these issues, this paper proposes a novel approach by merging two widely used datasets, LISA and S2TLD. The merged dataset is further processed to tackle class imbalance, a common problem in this domain. This merged dataset becomes our source domain. Synthetic rain and fog are added to the dataset to create our target domain. We employ Fourier Domain Adaptation (FDA) to create a final dataset with a minimized domain gap between the two datasets, helping the model trained on this final dataset adapt to rainy and foggy weather conditions. Additionally, we explore Semi-Supervised Learning (SSL) techniques to leverage the available data more effectively. Experimental results demonstrate that models trained on FDA-augmented images outperform those trained without FDA across confidence-dependent and independent metrics, like mAP50, mAP50-95, Precision, and Recall. The best-performing model, YOLOv8, achieved a Precision increase of 5.1860%, Recall increase of 14.8009%, mAP50 increase of 9.5074%, and mAP50-95 increase of 19.5035%. On average, percentage increases of 7.6892% in Precision, 19.9069% in Recall, 15.8506% in mAP50, and 23.8099% in mAP50-95 were observed across all models, highlighting the effectiveness of FDA in mitigating the impact of adverse weather conditions on model performance. These improvements pave the way for real-world applications where reliable performance in challenging environmental conditions is critical.
2024-11-12
Constantin Ulrich, Tassilo Wald, Emily Tempus, Maximilian Rokuss, Paul F. Jaeger, Klaus Maier-Hein
Current interactive segmentation approaches, inspired by the success of META's Segment Anything model, have achieved notable advancements, however, they come with substantial limitations that hinder their practical application in real clinical scenarios. These include unrealistic human interaction requirements, such as slice-by-slice operations for 2D models on 3D data, a lack of iterative refinement, and insufficient evaluation experiments. These shortcomings prevent accurate assessment of model performance and lead to inconsistent outcomes across studies. IntRaBench overcomes these challenges by offering a comprehensive and reproducible framework for evaluating interactive segmentation methods in realistic, clinically relevant scenarios. It includes diverse datasets, target structures, and segmentation models, and provides a flexible codebase that allows seamless integration of new models and prompting strategies. Additionally, we introduce advanced techniques to minimize clinician interaction, ensuring fair comparisons between 2D and 3D models. By open-sourcing IntRaBench, we invite the research community to integrate their models and prompting techniques, ensuring continuous and transparent evaluation of interactive segmentation models in 3D medical imaging.
Broad-Line Region Characterization in Dozens of Active Galactic Nuclei Using Small-Aperture Telescopes
2024-11-12
Catalina Sobrino Figaredo, Doron Chelouche, Martin Haas, Michael Ramolla, Shai Kaspi, Swayamtrupta Panda, Martin W. Ochmann, Shay Zucker, Rolf Chini, Malte A. Probst, Wolfram Kollatschny, Miguel Murphy
We present the results of a nearly decade-long photometric reverberation mapping (PRM) survey of the H$\alpha$ emission line in nearby ($0.01\lesssim z \lesssim0.05$) Seyfert-Galaxies using small ($15\,\mathrm{cm}-40\,\mathrm{cm}$) telescopes. Broad-band filters were used to trace the continuum emission, while narrow-band filters tracked the H$\alpha$-line signal. We introduce a new PRM formalism to determine the time delay between continuum and line emission using combinations of auto- and cross-correlation functions. We obtain robust delays for 33/80 objects, allowing us to estimate the broad-line region (BLR) size. Additionally, we measure multi-epoch delays for 6 objects whose scatter per source is smaller than the scatter in the BLR size-luminosity relation. Our study enhances the existing H$\alpha$ size-luminosity relation by adding high-quality results for 31 objects, whose nuclear luminosities were estimated using the flux-variation gradient method, resulting in a scatter of 0.26dex within our sample. The scatter reduces to 0.17dex when the 6 lowest luminosity sources are discarded, which is comparable to that found for the H$\beta$ line. Single-epoch spectra enable us to estimate black hole masses using the H$\alpha$ line and derive mass accretion rates from the iron-blend feature adjacent to H$\beta$. A similar trend, as previously reported for the H$\beta$ line, is implied whereby highly accreting objects tend to lie below the size-luminosity relation of the general population. Our work demonstrates the effectiveness of small telescopes in conducting high-fidelity PRM campaigns of prominent emission lines in bright active galactic nuclei.
Ethical Concern Identification in NLP: A Corpus of ACL Anthology Ethics Statements
2024-11-12
Antonia Karamolegkou, Sandrine Schiller Hansen, Ariadni Christopoulou, Filippos Stamatiou, Anne Lauscher, Anders Søgaard
What ethical concerns, if any, do LLM researchers have? We introduce EthiCon, a corpus of 1,580 ethical concern statements extracted from scientific papers published in the ACL Anthology. We extract ethical concern keywords from the statements and show promising results in automating the concern identification process. Through a survey, we compare the ethical concerns of the corpus to the concerns listed by the general public and professionals in the field. Finally, we compare our retrieved ethical concerns with existing taxonomies pointing to gaps and future research directions.
Librations and obliquity of the largest moons of Uranus
2024-11-12
Rose-Marie Baland, Valerio Filice, Sébastien Le Maistre, Antony Trinh, Marie Yseboodt, Tim Van Hoolst
Following the discovery of several ocean worlds in the solar system, and the selection of Uranus as the highest priority objective by the Planetary Science and Astrobiology Decadal Survey 2023-2032, the five largest moons of Uranus (Miranda, Ariel, Umbriel, Titania and Oberon) have been receiving renewed attention as they may also harbor a subsurface ocean. We assess how rotation measurements could help confirm the internal differentiation of the bodies and detect internal oceans if any. Because of the time-varying gravitational torque of Uranus on the flattened shape of its synchronous satellites, the latter librate with respect to their mean rotation and precess with a non zero obliquity. For a range of interior models with a rocky core surrounded by a hydrosphere, either solid or divided into an outer ice shell with a liquid ocean underneath, we compute their diurnal libration amplitude and obliquity.
Interaction Asymmetry: A General Principle for Learning Composable Abstractions
2024-11-12
Jack Brady, Julius von Kügelgen, Sébastien Lachapelle, Simon Buchholz, Thomas Kipf, Wieland Brendel
Learning disentangled representations of concepts and re-composing them in unseen ways is crucial for generalizing to out-of-domain situations. However, the underlying properties of concepts that enable such disentanglement and compositional generalization remain poorly understood. In this work, we propose the principle of interaction asymmetry which states: "Parts of the same concept have more complex interactions than parts of different concepts". We formalize this via block diagonality conditions on the $(n+1)$th order derivatives of the generator mapping concepts to observed data, where different orders of "complexity" correspond to different $n$. Using this formalism, we prove that interaction asymmetry enables both disentanglement and compositional generalization. Our results unify recent theoretical results for learning concepts of objects, which we show are recovered as special cases with $n\!=\!0$ or $1$. We provide results for up to $n\!=\!2$, thus extending these prior works to more flexible generator functions, and conjecture that the same proof strategies generalize to larger $n$. Practically, our theory suggests that, to disentangle concepts, an autoencoder should penalize its latent capacity and the interactions between concepts during decoding. We propose an implementation of these criteria using a flexible Transformer-based VAE, with a novel regularizer on the attention weights of the decoder. On synthetic image datasets consisting of objects, we provide evidence that this model can achieve comparable object disentanglement to existing models that use more explicit object-centric priors.
Likelihood as a Performance Gauge for Retrieval-Augmented Generation
2024-11-12
Tianyu Liu, Jirui Qi, Paul He, Arianna Bisazza, Mrinmaya Sachan, Ryan Cotterell
Recent work finds that retrieval-augmented generation with large language models is prone to be influenced by the order of retrieved documents in the context. However, the lack of in-depth analysis limits the use of this phenomenon for prompt engineering in practice. In this study, we posit that likelihoods serve as an effective gauge for language model performance. Through experiments on two question-answering datasets with a variety of state-of-the-art language models, we reveal correlations between answer accuracy and the likelihood of the question at both the corpus level and the instance level. In addition, we find that question likelihood can also indicate the position of the task-relevant information in the context. Based on these findings, we propose two methods that use question likelihood as a gauge for selecting and constructing prompts that lead to better performance. We demonstrate their effectiveness with experiments. In addition, our likelihood-based methods are efficient, as they only need to compute the likelihood of the input, requiring much fewer language model passes than heuristic prompt engineering methods that require generating responses. Our analysis deepens our understanding of how input prompts affect model performance and provides a promising direction for efficient prompt optimization.
Offline Reinforcement Learning (RL) has emerged as a powerful alternative to imitation learning for behavior modeling in various domains, particularly in complex navigation tasks. An existing challenge with Offline RL is the signal-to-noise ratio, i.e. how to mitigate incorrect policy updates due to errors in value estimates. Towards this, multiple works have demonstrated the advantage of hierarchical offline RL methods, which decouples high-level path planning from low-level path following. In this work, we present a novel hierarchical transformer-based approach leveraging a learned quantizer of the space. This quantization enables the training of a simpler zone-conditioned low-level policy and simplifies planning, which is reduced to discrete autoregressive prediction. Among other benefits, zone-level reasoning in planning enables explicit trajectory stitching rather than implicit stitching based on noisy value function estimates. By combining this transformer-based planner with recent advancements in offline RL, our proposed approach achieves state-of-the-art results in complex long-distance navigation environments.
Optimizing Traffic Signal Control using High-Dimensional State Representation and Efficient Deep Reinforcement Learning
2024-11-12
Lawrence Francis, Blessed Guda, Ahmed Biyabani
In reinforcement learning-based (RL-based) traffic signal control (TSC), decisions on the signal timing are made based on the available information on vehicles at a road intersection. This forms the state representation for the RL environment which can either be high-dimensional containing several variables or a low-dimensional vector. Current studies suggest that using high dimensional state representations does not lead to improved performance on TSC. However, we argue, with experimental results, that the use of high dimensional state representations can, in fact, lead to improved TSC performance with improvements up to 17.9% of the average waiting time. This high-dimensional representation is obtainable using the cost-effective vehicle-to-infrastructure (V2I) communication, encouraging its adoption for TSC. Additionally, given the large size of the state, we identified the need to have computational efficient models and explored model compression via pruning.
Detecting ultralight axions from multifrequency observations of neutral hydrogen intensity mapping
2024-11-12
Qianshuo Liu, Chang Feng, Filipe B. Abdalla
We investigate the effects of ultralight axions (ULAs) on the differential brightness temperature fluctuations of neutral hydrogen at the post-reionization stage. Unique structure suppression features under the influence of ULAs are studied from angular correlations of the neutral hydrogen signals observed at different frequencies. Moreover, ULAs can behave like dark energy or dark matter at different mass regimes, implying that the fraction of the ULA energy density would be correlated with the sum of neutrino masses and the parameters of the dark energy equation of state for dark matter and dark energy like ULAs, respectively. We have explored the parameter space for the ULA physics and possible degeneracies among dark energy, neutrinos, and ULAs using the theoretical angular correlation functions expected from future intensity mapping surveys.
This review is devoted to the problem of Coulomb (dyon)-oscillator duality in non-relativistic quantum mechanics, which is based on the so-called non-bijective quadratic transformations, i.e. Levi-Civita transformation, Kustaanheimo--Stiefel (KS-transformation) and Hurwitz transformation.
SoliDiffy: AST Differencing for Solidity Smart Contracts
2024-11-12
Mojtaba Eshghie, Viktor Åryd, Martin Monperrus, Cyrille Artho
Smart contracts, primarily written in Solidity, are integral to blockchain software applications, yet precise analysis and maintenance are hindered by the limitations of existing differencing tools. We introduce SoliDiffy, a novel Abstract Syntax Tree (AST) differencing tool specifically designed for Solidity. SoliDiffy enables fine-grained analysis by generating accurate and concise edit scripts of smart contracts, making it ideal for downstream tasks such as vulnerability detection, automated code repair, and code reviews. Our comprehensive evaluation on a large dataset of real-world Solidity contracts demonstrates that SoliDiffy delivers shorter and more precise edit scripts compared to state-of-the-art tools, while performing consistently in complex contract modifications. SoliDiffy is made publicly available at https://github.com/mojtaba-eshghie/SoliDiffy.
Symmetrized pseudofunction algebras from $L^p$-representations and amenability of locally compact groups
2024-11-12
Emilie Mai Elkiær
We show via an application of techniques from complex interpolation theory how the $L^p$-pseudofunction algebras of a locally compact group $G$ can be understood as sitting between $L^1(G)$ and $C^*(G)$. Motivated by this, we collect and review various characterizations of group amenability connected to the $p$-pseudofunction algebra of Herz and generalize these to the symmetrized setting. Along the way, we describe the Banach space dual of the symmetrized pseudofuntion algebras on $G$ associated with representations on reflexive Banach spaces.
dpvis: A Visual and Interactive Learning Tool for Dynamic Programming
2024-11-12
David H. Lee, Aditya Prasad, Ramiro Deo-Campo Vuong, Tianyu Wang, Eric Han, David Kempe
Dynamic programming (DP) is a fundamental and powerful algorithmic paradigm taught in most undergraduate (and many graduate) algorithms classes. DP problems are challenging for many computer science students because they require identifying unique problem structures and a refined understanding of recursion. In this paper, we present dpvis, a Python library that helps students understand DP through a frame-by-frame animation of dynamic programs. dpvis can easily generate animations of dynamic programs with as little as two lines of modifications compared to a standard Python implementation. For each frame, dpvis highlight the cells that have been read from and written to during an iteration. Moreover, dpvis allows users to test their understanding by prompting them with questions about the next operation performed by the algorithm. We deployed dpvis as a learning tool in an undergraduate algorithms class, and report on the results of a survey. The survey results suggest that dpvis is especially helpful for visualizing the recursive structure of DP. Although some students struggled with the installation of the tool (which has been simplified since the reported deployment), essentially all other students found the tool to be useful for understanding dynamic programs. dpvis is available at https://github.com/itsdawei/dpvis.
New Emerged Security and Privacy of Pre-trained Model: a Survey and Outlook
2024-11-12
Meng Yang, Tianqing Zhu, Chi Liu, WanLei Zhou, Shui Yu, Philip S. Yu
Thanks to the explosive growth of data and the development of computational resources, it is possible to build pre-trained models that can achieve outstanding performance on various tasks, such as neural language processing, computer vision, and more. Despite their powerful capabilities, pre-trained models have also sparked attention to the emerging security challenges associated with their real-world applications. Security and privacy issues, such as leaking privacy information and generating harmful responses, have seriously undermined users' confidence in these powerful models. Concerns are growing as model performance improves dramatically. Researchers are eager to explore the unique security and privacy issues that have emerged, their distinguishing factors, and how to defend against them. However, the current literature lacks a clear taxonomy of emerging attacks and defenses for pre-trained models, which hinders a high-level and comprehensive understanding of these questions. To fill the gap, we conduct a systematical survey on the security risks of pre-trained models, proposing a taxonomy of attack and defense methods based on the accessibility of pre-trained models' input and weights in various security test scenarios. This taxonomy categorizes attacks and defenses into No-Change, Input-Change, and Model-Change approaches. With the taxonomy analysis, we capture the unique security and privacy issues of pre-trained models, categorizing and summarizing existing security issues based on their characteristics. In addition, we offer a timely and comprehensive review of each category's strengths and limitations. Our survey concludes by highlighting potential new research opportunities in the security and privacy of pre-trained models.
2024-11-12
Zifan Zeng, Chongzhe Zhang, Feng Liu, Joseph Sifakis, Qunli Zhang, Shiming Liu, Peng Wang
With the proliferation of the Large Language Model (LLM), the concept of World Models (WM) has recently attracted a great deal of attention in the AI research community, especially in the context of AI agents. It is arguably evolving into an essential foundation for building AI agent systems. A WM is intended to help the agent predict the future evolution of environmental states or help the agent fill in missing information so that it can plan its actions and behave safely. The safety property of WM plays a key role in their effective use in critical applications. In this work, we review and analyze the impacts of the current state-of-the-art in WM technology from the point of view of trustworthiness and safety based on a comprehensive survey and the fields of application envisaged. We provide an in-depth analysis of state-of-the-art WMs and derive technical research challenges and their impact in order to call on the research community to collaborate on improving the safety and trustworthiness of WM.
Generative AI in Self-Directed Learning: A Scoping Review
2024-11-12
Jasper Roe, Mike Perkins
This scoping review examines the current body of knowledge at the intersection of Generative Artificial Intelligence (GenAI) and Self-Directed Learning (SDL). By synthesising the findings from 18 studies published from 2020 to 2024 and following the PRISMA-SCR guidelines for scoping reviews, we developed four key themes. This includes GenAI as a Potential Enhancement for SDL, The Educator as a GenAI Guide, Personalisation of Learning, and Approaching with Caution. Our findings suggest that GenAI tools, including ChatGPT and other Large Language Models (LLMs) show promise in potentially supporting SDL through on-demand, personalised assistance. At the same time, the literature emphasises that educators are as important and central to the learning process as ever before, although their role may continue to shift as technologies develop. Our review reveals that there are still significant gaps in understanding the long-term impacts of GenAI on SDL outcomes, and there is a further need for longitudinal empirical studies that explore not only text-based chatbots but also emerging multimodal applications.
Fine-grained classification of whole slide images (WSIs) is essential in precision oncology, enabling precise cancer diagnosis and personalized treatment strategies. The core of this task involves distinguishing subtle morphological variations within the same broad category of gigapixel-resolution images, which presents a significant challenge. While the multi-instance learning (MIL) paradigm alleviates the computational burden of WSIs, existing MIL methods often overlook hierarchical label correlations, treating fine-grained classification as a flat multi-class classification task. To overcome these limitations, we introduce a novel hierarchical multi-instance learning (HMIL) framework. By facilitating on the hierarchical alignment of inherent relationships between different hierarchy of labels at instance and bag level, our approach provides a more structured and informative learning process. Specifically, HMIL incorporates a class-wise attention mechanism that aligns hierarchical information at both the instance and bag levels. Furthermore, we introduce supervised contrastive learning to enhance the discriminative capability for fine-grained classification and a curriculum-based dynamic weighting module to adaptively balance the hierarchical feature during training. Extensive experiments on our large-scale cytology cervical cancer (CCC) dataset and two public histology datasets, BRACS and PANDA, demonstrate the state-of-the-art class-wise and overall performance of our HMIL framework. Our source code is available at https://github.com/ChengJin-git/HMIL.
Advancing Sustainability via Recommender Systems: A Survey
2024-11-12
Xin Zhou, Lei Zhang, Honglei Zhang, Yixin Zhang, Xiaoxiong Zhang, Jie Zhang, Zhiqi Shen
Human behavioral patterns and consumption paradigms have emerged as pivotal determinants in environmental degradation and climate change, with quotidian decisions pertaining to transportation, energy utilization, and resource consumption collectively precipitating substantial ecological impacts. Recommender systems, which generate personalized suggestions based on user preferences and historical interaction data, exert considerable influence on individual behavioral trajectories. However, conventional recommender systems predominantly optimize for user engagement and economic metrics, inadvertently neglecting the environmental and societal ramifications of their recommendations, potentially catalyzing over-consumption and reinforcing unsustainable behavioral patterns. Given their instrumental role in shaping user decisions, there exists an imperative need for sustainable recommender systems that incorporate sustainability principles to foster eco-conscious and socially responsible choices. This comprehensive survey addresses this critical research gap by presenting a systematic analysis of sustainable recommender systems. As these systems can simultaneously advance multiple sustainability objectives--including resource conservation, sustainable consumer behavior, and social impact enhancement--examining their implementations across distinct application domains provides a more rigorous analytical framework. Through a methodological analysis of domain-specific implementations encompassing transportation, food, buildings, and auxiliary sectors, we can better elucidate how these systems holistically advance sustainability objectives while addressing sector-specific constraints and opportunities. Moreover, we delineate future research directions for evolving recommender systems beyond sustainability advocacy toward fostering environmental resilience and social consciousness in society.
Understanding Audiovisual Deepfake Detection: Techniques, Challenges, Human Factors and Perceptual Insights
2024-11-12
Ammarah Hashmi, Sahibzada Adil Shahzad, Chia-Wen Lin, Yu Tsao, Hsin-Min Wang
Deep Learning has been successfully applied in diverse fields, and its impact on deepfake detection is no exception. Deepfakes are fake yet realistic synthetic content that can be used deceitfully for political impersonation, phishing, slandering, or spreading misinformation. Despite extensive research on unimodal deepfake detection, identifying complex deepfakes through joint analysis of audio and visual streams remains relatively unexplored. To fill this gap, this survey first provides an overview of audiovisual deepfake generation techniques, applications, and their consequences, and then provides a comprehensive review of state-of-the-art methods that combine audio and visual modalities to enhance detection accuracy, summarizing and critically analyzing their strengths and limitations. Furthermore, we discuss existing open source datasets for a deeper understanding, which can contribute to the research community and provide necessary information to beginners who want to analyze deep learning-based audiovisual methods for video forensics. By bridging the gap between unimodal and multimodal approaches, this paper aims to improve the effectiveness of deepfake detection strategies and guide future research in cybersecurity and media integrity.
Maritime Search and Rescue Missions with Aerial Images: A Survey
2024-11-12
Juan P. Martinez-Esteso, Francisco J. Castellanos, Jorge Calvo-Zaragoza, Antonio Javier Gallego
The speed of response by search and rescue teams at sea is of vital importance, as survival may depend on it. Recent technological advancements have led to the development of more efficient systems for locating individuals involved in a maritime incident, such as the use of Unmanned Aerial Vehicles (UAVs) equipped with cameras and other integrated sensors. Over the past decade, several researchers have contributed to the development of automatic systems capable of detecting people using aerial images, particularly by leveraging the advantages of deep learning. In this article, we provide a comprehensive review of the existing literature on this topic. We analyze the methods proposed to date, including both traditional techniques and more advanced approaches based on machine learning and neural networks. Additionally, we take into account the use of synthetic data to cover a wider range of scenarios without the need to deploy a team to collect data, which is one of the major obstacles for these systems. Overall, this paper situates the reader in the field of detecting people at sea using aerial images by quickly identifying the most suitable methodology for each scenario, as well as providing an in-depth discussion and direction for future trends.
A Performance Analysis of BFT Consensus for Blockchains
2024-11-12
J. D. Chan, Y. C. Tay, Brian R. Z. Yen
Distributed ledgers are common in the industry. Some of them can use blockchains as their underlying infrastructure. A blockchain requires participants to agree on its contents. This can be achieved via a consensus protocol, and several BFT (Byzantine Fault Tolerant) protocols have been proposed for this purpose. How do these protocols differ in performance? And how is this difference affected by the communication network? Moreover, such a protocol would need a timer to ensure progress, but how should the timer be set? This paper presents an analytical model to address these and related issues in the case of crash faults. Specifically, it focuses on two consensus protocols (Istanbul BFT and HotStuff) and two network topologies (Folded-Clos and Dragonfly). The model provides closed-form expressions for analyzing how the timer value and number of participants, faults and switches affect the consensus time. The formulas and analyses are validated with simulations. The conclusion offers some tips for analytical modeling of such protocols.
As a prominent subfield of Artificial Intelligence Generated Content (AIGC), video generation has achieved notable advancements in recent years. The introduction of Sora-alike models represents a pivotal breakthrough in video generation technologies, significantly enhancing the quality of synthesized videos. Particularly in the realm of biomedicine, video generation technology has shown immense potential such as medical concept explanation, disease simulation, and biomedical data augmentation. In this article, we thoroughly examine the latest developments in video generation models and explore their applications, challenges, and future opportunities in the biomedical sector. We have conducted an extensive review and compiled a comprehensive list of datasets from various sources to facilitate the development and evaluation of video generative models in biomedicine. Given the rapid progress in this field, we have also created a github repository to regularly update the advances of biomedical video generation at: https://github.com/Lee728243228/Biomedical-Video-Generation
A Theoretical Review of Area Production Rates as Test Statistics for Detecting Nonequilibrium Dynamics in Ornstein-Uhlenbeck Processes
2024-11-12
Alexander Strang
A stochastic process is at thermodynamic equilibrium if it obeys time-reversal symmetry; forward and reverse time are statistically indistinguishable at steady state. Non-equilibrium processes break time-reversal symmetry by maintaining circulating probability currents. In physical processes, these currents require a continual use and exchange of energy. Accordingly, signatures of non-equilibrium behavior are important markers of energy use in biophysical systems. In this article we consider a particular signature of nonequilibrium behavior: area production rates. These are, the average rate at which a stochastic process traces out signed area in its projections onto coordinate planes. Area production is an example of a linear observable: a path integral over an observed trajectory against a linear vector field. We provide a summary review of area production rates in Ornstein-Uhlenbeck (OU) processes. Then, we show that, given an OU process, a weighted Frobenius norm of the area production rate matrix is the optimal test statistic for detecting nonequilibrium behavior in the sense that its coefficient of variation decays faster in the length of time observed than the coefficient of variation of any other linear observable. We conclude by showing that this test statistic estimates the entropy production rate of the process.
Measures of luminous and dark matter in galaxies across time
2024-11-12
Jonathan Freundlich, Gauri Sharma, Sabine Thater, Mousumi Das, Benoit Famaey, Katherine Freese, Marie Korsaga, Julien Lavalle, Chung Pei Ma, Moses Mogotsi, Cristina Popescu, Francesca Rizzo, Laura V. Sales, Miguel A. Sanchez-Conde, Glenn van de Ven, Hongsheng Zhao, Alice Zocchi
Dark matter is one of the pillars of the current standard model of structure formation: it is assumed to constitute most of the matter in the Universe. However, it can so far only be probed indirectly through its gravitational effects, and its nature remains elusive. In this focus meeting, we discussed different methods used to estimate galaxies' visible and dark matter masses in the nearby and distant Universe. We reviewed successes of the standard model relying on cold dark matter, confronted observations with simulations, and highlighted inconsistencies between the two. We discussed how robust mass measurements can help plan, perform, and refine particle dark matter searches. We further exchanged about alternatives to cold dark matter, such as warm, self-interacting, and fuzzy dark matter, as well as modified gravity. Finally, we discussed prospects and strategies that could be implemented to reveal the nature of this crucial component of the Universe.
Code Language Models (CLMs) have achieved tremendous progress in source code understanding and generation, leading to a significant increase in research interests focused on applying CLMs to real-world software engineering tasks in recent years. However, in realistic scenarios, CLMs are exposed to potential malicious adversaries, bringing risks to the confidentiality, integrity, and availability of CLM systems. Despite these risks, a comprehensive analysis of the security vulnerabilities of CLMs in the extremely adversarial environment has been lacking. To close this research gap, we categorize existing attack techniques into three types based on the CIA triad: poisoning attacks (integrity \& availability infringement), evasion attacks (integrity infringement), and privacy attacks (confidentiality infringement). We have collected so far the most comprehensive (79) papers related to adversarial machine learning for CLM from the research fields of artificial intelligence, computer security, and software engineering. Our analysis covers each type of risk, examining threat model categorization, attack techniques, and countermeasures, while also introducing novel perspectives on eXplainable AI (XAI) and exploring the interconnections between different risks. Finally, we identify current challenges and future research opportunities. This study aims to provide a comprehensive roadmap for both researchers and practitioners and pave the way towards more reliable CLMs for practical applications.
Bug fixing and code generation have been core research topics in software development for many years. The recent explosive growth in Large Language Models has completely transformed these spaces, putting in reach incredibly powerful tools for both. In this survey, 27 recent papers have been reviewed and split into two groups: one dedicated to Automated Program Repair (APR) and LLM integration and the other to code generation using LLMs. The first group consists of new methods for bug detection and repair, which include locating semantic errors, security vulnerabilities, and runtime failure bugs. The place of LLMs in reducing manual debugging efforts is emphasized in this work by APR toward context-aware fixes, with innovations that boost accuracy and efficiency in automatic debugging. The second group dwells on code generation, providing an overview of both general-purpose LLMs fine-tuned for programming and task-specific models. It also presents methods to improve code generation, such as identifier-aware training, fine-tuning at the instruction level, and incorporating semantic code structures. This survey work contrasts the methodologies in APR and code generation to identify trends such as using LLMs, feedback loops to enable iterative code improvement and open-source models. It also discusses the challenges of achieving functional correctness and security and outlines future directions for research in LLM-based software development.
A Composite Hydrogel of Porous Gold Nanorods and Gelatin: Nanoscale Structure and Rheo-Mechanical Properties
2024-11-12
Irfan Khan, Snigdharani Panda, Sugam Kumar, Sunita Srivastava
Incorporating nanomaterials into hydrogels allows for the creation of versatile materials with properties that can be precisely tailored by manipulating their nanoscale structures, leading to a wide range of bulk properties. Investigating the structural and property characteristics of composite hydrogels is crucial in tailoring their performance for specific applications. This study focuses on investigating the correlation between the structural arrangement and properties of a composite hydrogel of thermoresponsive polymer, gelatin, and light-responsive antimicrobial porous gold nanorods, $PAuNR$. The rheo-mechanical properties of the composite hydrogels are correlated with their nanoscale structural characteristics, investigated using small-angle neutron scattering ($SANS$). Analysis of $SANS$ data reveals a decrease in the fractal dimension of $PAuNRs$ incorporated hydrogel matrix, as compared to pure gelatin. Incorporating $PAuNRs$ results in formation of softer composite hydrogel as evident from decrease in viscoelastic moduli, critical yield strain, denaturation temperature and swelling ratio. Our results demonstrates that the structural modulation at the nanoscale can be precisely controlled through adjusting $PAuNRs$ concentration and temperature providing an fabrication mechanism for hydrogels with desired elastic properties. The reduced elasticity of the composite hydrogel and light sensitive/antimicrobial property of the $PAuNRs$ makes this system suitable for specific biomedical applications, such as tissue engineering, device fabrication and stimuli based controlled drug delivery devices respectively.
A halo model approach for mock catalogs of time-variable strong gravitational lenses
2024-11-12
Katsuya T. Abe, Masamune Oguri, Simon Birrer, Narayan Khadka, Philip J. Marshall, Cameron Lemon, Anupreeta More, the LSST Dark Energy Science Collaboration
Time delays in both galaxy- and cluster-scale strong gravitational lenses have recently attracted a lot of attention in the context of the Hubble tension. Future wide-field cadenced surveys, such as the LSST, are anticipated to discover strong lenses across various scales. We generate mock catalogs of strongly lensed QSOs and SNe on galaxy-, group-, and cluster-scales based on a halo model that incorporates dark matter halos, galaxies, and subhalos. For the upcoming LSST survey, we predict that approximately 3500 lensed QSOs and 200 lensed SNe with resolved multiple images will be discovered. Among these, about 80 lensed QSOs and 10 lensed SNe will have maximum image separations larger than 10 arcsec, which roughly correspond to cluster-scale strong lensing. We find that adopting the Chabrier stellar IMF instead of the fiducial Salpeter IMF reduces the predicted number of strong lenses approximately by half, while the distributions of lens and source redshifts and image separations are not significantly changed. In addition to mock catalogs of multiple-image lens systems, we create mock catalogs of highly magnified systems, including both multiple-image and single-image systems. We find that such highly magnified systems are typically produced by massive galaxies, but non-negligible fraction of them are located in the outskirt of galaxy groups and clusters. Furthermore, we compare subsamples of our mock catalogs with lensed QSO samples constructed from the SDSS and Gaia to find that our mock catalogs with the fiducial Salpeter IMF reproduce the observation quite well. In contrast, our mock catalogs with the Chabrier IMF predict a significantly smaller number of lensed QSOs compared with observations, which adds evidence that the stellar IMF of massive galaxies is Salpeter-like. Our python code SL-Hammocks as well as the mock catalogs are made available online. (abridged)
Two-Layer Attention Optimization for Bimanual Coordination
2024-11-12
Justin Ting, Jing Shuang Li
Bimanual tasks performed by human agents present unique optimal control considerations compared to cyberphysical agents. These considerations include minimizing attention, distributing attention across two isolated hands, and coordinating the two hands to reach a broader goal. In this work, we propose a two-layer controller that captures these considerations. The upper layer solves an attention distribution problem, while the two lower layer controllers (one per hand) tracks a trajectory using the solution given by the upper layer. We introduce a formulation of the attention controller where attention is a vector that is bound within a hyperbolic feasible region, which is determined by specifications of the task the lower layer controllers. This two-layer controller is used to optimize a single-player game of pong, where the agent must rally the ball between two paddles for as long as possible. We find that adding an attention layer on top of the lower controllers allows the agent to coordinate the left and right hands, which minimizes attention and control effort over the course of the rallying task.
Machines and Mathematical Mutations: Using GNNs to Characterize Quiver Mutation Classes
2024-11-12
Jesse He, Helen Jenne, Herman Chau, Davis Brown, Mark Raugas, Sara Billey, Henry Kvinge
Machine learning is becoming an increasingly valuable tool in mathematics, enabling one to identify subtle patterns across collections of examples so vast that they would be impossible for a single researcher to feasibly review and analyze. In this work, we use graph neural networks to investigate quiver mutation -- an operation that transforms one quiver (or directed multigraph) into another -- which is central to the theory of cluster algebras with deep connections to geometry, topology, and physics. In the study of cluster algebras, the question of mutation equivalence is of fundamental concern: given two quivers, can one efficiently determine if one quiver can be transformed into the other through a sequence of mutations? Currently, this question has only been resolved in specific cases. In this paper, we use graph neural networks and AI explainability techniques to discover mutation equivalence criteria for the previously unknown case of quivers of type $\tilde{D}_n$. Along the way, we also show that even without explicit training to do so, our model captures structure within its hidden representation that allows us to reconstruct known criteria from type $D_n$, adding to the growing evidence that modern machine learning models are capable of learning abstract and general rules from mathematical data.
MSEG-VCUQ: Multimodal SEGmentation with Enhanced Vision Foundation Models, Convolutional Neural Networks, and Uncertainty Quantification for High-Speed Video Phase Detection Data
Purpose: High-speed video (HSV) phase detection (PD) segmentation is vital in nuclear reactors, chemical processing, and electronics cooling for detecting vapor, liquid, and microlayer phases. Traditional segmentation models face pixel-level accuracy and generalization issues in multimodal data. MSEG-VCUQ introduces VideoSAM, a hybrid framework leveraging convolutional neural networks (CNNs) and transformer-based vision models to enhance segmentation accuracy and generalizability across complex multimodal PD tasks. Methods: VideoSAM combines U-Net CNN and the Segment Anything Model (SAM) for advanced feature extraction and segmentation across diverse HSV PD modalities, spanning fluids like water, FC-72, nitrogen, and argon under varied heat flux conditions. The framework also incorporates uncertainty quantification (UQ) to assess pixel-based discretization errors, delivering reliable metrics such as contact line density and dry area fraction under experimental conditions. Results: VideoSAM outperforms SAM and modality-specific CNN models in segmentation accuracy, excelling in environments with complex phase boundaries, overlapping bubbles, and dynamic liquid-vapor interactions. Its hybrid architecture supports cross-dataset generalization, adapting effectively to varying modalities. The UQ module provides accurate error estimates, enhancing the reliability of segmentation outputs for advanced HSV PD research. Conclusion: MSEG-VCUQ, via VideoSAM, offers a robust solution for HSV PD segmentation, addressing previous limitations with advanced deep learning and UQ techniques. The open-source datasets and tools introduced enable scalable, precise, and adaptable segmentation for multimodal PD datasets, supporting advancements in HSV analysis and autonomous experimentation. The codes and data used for this paper are publicly available at https://github.com/chikap421/mseg_vcuq
Size Growth on Short Timescales of Star-Forming Galaxies: Insights from Size Variation with Rest-Frame Wavelength with JADES
2024-11-12
Cheng Jia, Enci Wang, Huiyuan Wang, Hui Li, Yao Yao, Jie Song, Hongxin Zhang, Yu Rong, Yangyao Chen, Haoran Yu, Zeyu Chen, Haixin Li, Chengyu Ma, Xu Kong
We investigate size variation with rest-frame wavelength for star-forming galaxies based on the second JWST Advanced Deep Extragalactic Survey data release. Star-forming galaxies are typically smaller at longer wavelength from UV-to-NIR at $z<3.5$, especially for more massive galaxies, indicating the inside-out assembly with in-situ star formation if ignoring dust attenuation. The size variation with wavelength shows strong dependence on stellar mass, and shows little or no dependence on redshift, specific star formation rate and galaxy environment. This suggests that the size growth of star-forming galaxies is a self-regulated process primarily governed by stellar mass. We model size as a function of both mass and redshift simultaneously, obtaining $R_{\rm e} \propto M_*^{0.23} (1+z)^{-1.04}$ at a wavelength of 0.45 ${\mu \mathrm{m}}$, and $R_{\rm e} \propto M_*^{0.20} (1+z)^{-1.08}$ at 1.0 ${\mu \mathrm{m}}$. Based on this size evolution and the star formation main sequence from the literature, we obtain the locus of typical size growth for individual galaxies of different masses on the mass-size plane. The moving trend of galaxies on the mass-size plane, which indicates the slopes of their locus, strongly correlates with the size ratio between 0.45 ${\mu \mathrm{m}}$ and 1.0 ${\mu \mathrm{m}}$, supporting the idea that the size variation with wavelength provides important information on size growth of galaxies on short timescales.
Constraints on local primordial non-Gaussianity with 3d Velocity Reconstruction from the Kinetic Sunyaev-Zeldovich Effect
2024-11-12
Alex Laguë, Mathew S. Madhavacheril, Kendrick M. Smith, Simone Ferraro, Emmanuel Schaan
The cosmic velocity field is an unbiased probe of the total matter distribution but is challenging to measure directly at intermediate and high redshifts. The large-scale velocity field imprints a signal in the cosmic microwave background (CMB) through the kinetic Sunyaev-Zeldovich (kSZ) effect. We perform the first 3d reconstruction of the large-scale velocity field from the kSZ effect by applying a quadratic estimator to CMB temperature maps and the 3d positions of galaxies. We do so by combining CMB data from the fifth data release of the Atacama Cosmology Telescope (in combination with Planck) and a spectroscopic galaxy sample from the Sloan Digital Sky Survey. We then measure the galaxy-velocity cross-power spectrum and detect the presence of the kSZ signal at a signal-to-noise ratio of 7.2$\sigma$. Using this galaxy-velocity cross-correlation alone, we constrain the amplitude of local primordial non-Gaussianity finding $f_{\rm NL}=-90^{+210}_{-350}$. This pathfinder measurement sets the stage for joint galaxy-CMB kSZ constraints to significantly enhance the $f_{\rm NL}$ information obtained from galaxy surveys through sample variance cancellation.
Hand-face interactions play a key role in many everyday tasks, providing insights into user habits, behaviors, intentions, and expressions. However, existing wearable sensing systems often struggle to track these interactions in daily settings due to their reliance on multiple sensors or privacy-sensitive, vision-based approaches. To address these challenges, we propose WristSonic, a wrist-worn active acoustic sensing system that uses speakers and microphones to capture ultrasonic reflections from hand, arm, and face movements, enabling fine-grained detection of hand-face interactions with minimal intrusion. By transmitting and analyzing ultrasonic waves, WristSonic distinguishes a wide range of gestures, such as tapping the temple, brushing teeth, and nodding, using a Transformer-based neural network architecture. This approach achieves robust recognition of 21 distinct actions with a single, low-power, privacy-conscious wearable. Through two user studies with 15 participants in controlled and semi-in-the-wild settings, WristSonic demonstrates high efficacy, achieving macro F1-scores of 93.08% and 82.65%, respectively.
2024-11-12
Anouar Moustaj, Lumen Eek, Malte Rontgen, Cristiane Morais Smith
Latent symmetries, which materialize after performing isospectral reductions, have recently been shown to be instrumental in revealing novel topological phases in one-dimensional systems, among many other applications. In this work, we explore how to construct a family of seemingly complicated two-dimensional models that result in energy-dependent Haldane models upon performing an isospectral reduction. In these models, we find energy-dependent latent Semenoff masses without introducing a staggered on-site potential. In addition, energy-dependent latent Haldane masses also emerge in decorated lattices with nearest-neighbor complex hoppings. Using the Haldane model's properties, we then predict the location of the topological gaps in the aforementioned family of models and construct phase diagrams to determine where the topological phases lie in parameter space. This idea yielded, for instance, useful insights in the case of a modified version of $\alpha$-graphyne and hexagonal plaquettes with additional decorations, where the gap-closing energies can be calculated using the ISR to predict topological phase transitions.
High-Statistics Measurement of the Cosmic-Ray Electron Spectrum with H.E.S.S
2024-11-12
F. Aharonian, F. Ait Benkhali, J. Aschersleben, H. Ashkar, M. Backes, V. Barbosa Martins, R. Batzofin, Y. Becherini, D. Berge, K. Bernlöhr, B. Bi, M. Böttcher, C. Boisson, J. Bolmont, M. de Bony de Lavergne, J. Borowska, M. Bouyahiaoui, R. Brose, A. Brown, F. Brun, B. Bruno, T. Bulik, C. Burger-Scheidlin, T. Bylund, S. Casanova, J. Celic, M. Cerruti, T. Chand, S. Chandra, A. Chen, J. Chibueze, O. Chibueze, T. Collins, G. Cotter, J. Damascene Mbarubucyeye, J. Devin, J. Djuvsland, A. Dmytriiev, K. Egberts, S. Einecke, J. -P. Ernenwein, S. Fegan, K. Feijen, G. Fontaine, S. Funk, S. Gabici, Y. A. Gallant, J. F. Glicenstein, J. Glombitza, G. Grolleron, B. Heß, W. Hofmann, T. L. Holch, M. Holler, D. Horns, Zhiqiu Huang, M. Jamrozy, F. Jankowsky, V. Joshi, I. Jung-Richardt, E. Kasai, K. Katarzynski, D. Kerszberg, R. Khatoon, B. Khelifi, W. Kluzniak, Nu. Komin, K. Kosack, D. Kostunin, A. Kundu, R. G. Lang, S. Le Stum, F. Leitl, A. Lemiere, M. Lemoine-Goumard, J. -P. Lenain, F. Leuschner, A. Luashvili, J. Mackey, D. Malyshev, D. Malyshev, V. Marandon, P. Marinos, G. Marti-Devesa, R. Marx, M. Meyer, A. Mitchell, R. Moderski, M. O. Moghadam, L. Mohrmann, A. Montanari, E. Moulin, M. de Naurois, J. Niemiec, S. Ohm, L. Olivera-Nieto, E. de Ona Wilhelmi, M. Ostrowski, S. Panny, M. Panter, D. Parsons, U. Pensec, G. Peron, G. Pühlhofer, M. Punch, A. Quirrenbach, S. Ravikularaman, M. Regeard, A. Reimer, O. Reimer, I. Reis, H. Ren, B. Reville, F. Rieger, G. Rowell, B. Rudak, E. Ruiz-Velasco, V. Sahakian, H. Salzmann, A. Santangelo, M. Sasaki, J. Schäfer, F. Schüssler, H. M. Schutte, J. N. S. Shapopi, A. Sharma, H. Sol, S. Spencer, L. Stawarz, S. Steinmassl, C. Steppa, H. Suzuki, T. Takahashi, T. Tanaka, A. M. Taylor, R. Terrier, M. Tsirou, C. van Eldik, M. Vecchi, C. Venter, J. Vink, T. Wach, S. J. Wagner, A. Wierzcholska, M. Zacharias, A. A. Zdziarski, A. Zech, N. Zywucka
Owing to their rapid cooling rate and hence loss-limited propagation distance, cosmic-ray electrons and positrons (CRe) at very high energies probe local cosmic-ray accelerators and provide constraints on exotic production mechanisms such as annihilation of dark matter particles. We present a high-statistics measurement of the spectrum of CRe candidate events from 0.3 to 40 TeV with the High Energy Stereoscopic System (H.E.S.S.), covering two orders of magnitude in energy and reaching a proton rejection power of better than $10^{4}$. The measured spectrum is well described by a broken power law, with a break around 1 TeV, where the spectral index increases from $\Gamma_1 = 3.25$ $\pm$ 0.02 (stat) $\pm$ 0.2 (sys) to $\Gamma_2 = 4.49$ $\pm$ 0.04 (stat) $\pm$ 0.2 (sys). Apart from the break, the spectrum is featureless. The absence of distinct signatures at multi-TeV energies imposes constraints on the presence of nearby CRe accelerators and the local CRe propagation mechanisms.
Current Progress of Digital Twin Construction Using Medical Imaging
2024-11-12
Feng Zhao, Yizhou Wu, Mingzhe Hu, Chih-Wei Chang, Ruirui Liu, Richard Qiu, Xiaofeng Yang
Medical imaging has played a pivotal role in advancing and refining digital twin technology, allowing for the development of highly personalized virtual models that represent human anatomy and physiological functions. A key component in constructing these digital twins is the integration of high-resolution imaging data, such as MRI, CT, PET, and ultrasound, with sophisticated computational models. Advances in medical imaging significantly enhance real-time simulation, predictive modeling, and early disease diagnosis, individualized treatment planning, ultimately boosting precision and personalized care. Although challenges persist, such as the complexity of anatomical modeling, integrating various imaging modalities, and high computational demands, recent progress in imaging and machine learning has greatly improved the precision and clinical applicability of digital twins. This review investigates the role of medical imaging in developing digital twins across organ systems. Key findings demonstrate that improvements in medical imaging have enhanced the diagnostic and therapeutic potential of digital twins beyond traditional methods, particularly in imaging accuracy, treatment effectiveness, and patient outcomes. The review also examines the technical barriers that currently limit further development of digital twin technology, despite advances in medical imaging, and outlines future research avenues aimed at overcoming these challenges to unlock the full potential of this technology in precision medicine.
Searching for GEMS: Two Super-Jupiters around M-dwarfs -- Signatures of Instability or Accretion?
2024-11-12
Andrew Hotnisky, Shubham Kanodia, Jessica Libby-Roberts, Suvrath Mahadevan, Caleb I. Canas, Arvind F. Gupta, Te Han, Henry A. Kobulnicky, Alexander Larsen, Paul Robertson, Michael Rodruck, Gudmundur Stefansson, William D. Cochran, Megan Delamer, Scott A. Diddams, Rachel B. Fernandes, Samuel Halverson, Leslie Hebb, Andrea S. J. Lin, Andrew Monson, Joe P. Ninan, Arpita Roy, Christian Schwab
We present the discovery of TOI-6303b and TOI-6330b, two massive transiting super-Jupiters orbiting a M0 and a M2 star respectively, as part of the Searching for GEMS survey. These were detected by TESS and then confirmed via ground-based photometry and radial velocity observations with the Habitable-zone Planet Finder (HPF). TOI-6303b has a mass of 7.84 +/- 0.31 MJ, a radius of 1.03 +/- 0.06 RJ , and an orbital period of 9.485 days. TOI-6330b has a mass of 10.00 +/- 0.31 MJ , a radius of 0.97 +/- 0.03 RJ , and an orbital period of 6.850 days. We put these planets in context of super-Jupiters around M-dwarfs discovered from radial-velocity surveys, as well as recent discoveries from astrometry. These planets have masses that can be attributed to two dominant planet formation mechanisms - gravitational instability and core-accretion. Their masses necessitate massive protoplanetary disks that should either be gravitationally unstable, i.e. forming through gravitational instability, or be amongst some of the most massive protoplanetary disks to form objects through core-accretion. We also discuss the eccentricity distribution of these objects, as a potential indicator of their formation and evolutionary mechanisms.
The VANDELS Survey: Star formation and quenching in two over-densities at 3 < z < 4
2024-11-12
M. Espinoza Ortiz, L. Guaita, R. Demarco, A. Calabró, L. Pentericci, M. Castellano, M. Celeste Artale, N. P. Hathi, Anton M. Koekemoer, F. Mannucci, P. Hibon, D. J. McLeod, A. Gargiulo, E. Pompei
Context: Understanding galaxy evolution in dense environments, particularly proto-clusters, is crucial for studying mechanisms driving star formation and quenching. Aims: This study examines how two proto-cluster over-densities at 3 < z < 4 impact star formation rate (SFR), stellar mass, and morphology, focusing on quenched galaxies. Methods: We identified proto-cluster over-densities in the Chandra Deep Field South (CDFS) and Ultra Deep Survey (UDS) regions of the VANDELS survey. Using spectral energy distribution analysis, Bayesian methods (BEAGLE and BAGPIPES) helped derive best-fit parameters and U-V and V-J rest-frame colours (UVJ), classifying galaxies as quenched or star-forming based on UVJ diagrams and specific star formation rates (sSFR). TNG300 simulations aided interpretation. Results: Two of 13 proto-cluster over-densities host quenched galaxies with red U-V colours, low sSFR, and properties like massive passive galaxies. These quenched members are redder, older, more massive, and more compact. The highest-density peaks at z=3.55 and z=3.43 have dark matter halo masses consistent with proto-clusters and host AGNs, with five and three AGNs, respectively. Compared to field galaxies, these quenched members are in denser environments. TNG300 simulations suggest proto-clusters with quenched galaxies at high redshift evolve to contain more passive galaxies by z=1. Conclusions: The over-densities host massive quenched galaxies and AGNs in their densest peaks. Simulations reveal that sSFR for passive galaxies in proto-clusters was high at z=6, with median mass growth rates of 96% from z=6 to z=3. Conditions for mass assembly likely involve galaxy interactions and high gas accretion in dense environments. Black hole growth and AGN feedback appear to drive quenching at z=3, aligning with the properties of quenched galaxies observed in our study.
Assessing the growth of structure over cosmic time with CMB lensing
2024-11-12
Mathew S. Madhavacheril
The standard $\Lambda$CDM cosmological model informed by cosmic microwave background (CMB) anisotropies makes a precise prediction for the growth of matter density fluctuations over cosmic time on linear scales. A variety of cosmological observables offer independent and complementary ways of testing this prediction, but results have been mixed, with many constraints on the amplitude of structure $S_8$ being 2-3$\sigma$ lower than the expectation from Planck primary CMB anisotropies. It is currently unclear whether these discrepancies are due to observational systematics, non-linearities and baryonic effects or new physics. We review how gravitational lensing of the CMB has and will continue to provide insights into this problem, including through tomographic cross-correlations with galaxy surveys over cosmic time.
The ALPINE-ALMA [CII] Survey: Modelling ALMA and JWST lines to constrain the interstellar medium of $z\sim 5$ galaxies
2024-11-12
E. Veraldi, L. Vallini, F. Pozzi, F. Esposito, M. Bethermin, M. Boquien, A. Faisst, M. Ginolfi, R. Gobat, C. Gruppioni, N. Hathi, E. Ibar, J. Molina, F. Rizzo, M. Romano, G. Zamorani
In this work, we devise a model for estimating UV and optical line emission (i.e., CIII] $1909$A, H$\beta$, [OIII] $5007$A, H$\alpha$, [NII] $6583$A) tracing HII regions in the interstellar medium (ISM) of galaxies at $z\sim4-6$ from the ALMA Large Programme ALPINE. The aim is to investigate the impact of binary stars in the stellar population along with an abrupt quenching in the Star Formation History (SFH) on line emission. This is crucial for understanding the ISM's properties in early galaxies and identifying new star formation tracers in high-$z$ galaxies. The model simulates HII+Photodissociation Region (PDR) complexes through radiative transfer in 1D slabs, characterized by gas density ($n$), ionisation parameter ($U$), and metallicity ($Z$). It considers: (a) heating from star formation (SF), simulated with Starburst99 and BPASS to quantify binary stars impact; (b) constant, exponentially declining, and quenched SFH scenarios. For each galaxy, we select theoretical ratios from CLOUDY models between [CII] line emission, tracing PDRs, and nebular lines from HII regions, using these to derive expected optical/UV lines from observed [CII]. We find binary stars strongly impact line emission post-quenching, keeping UV photon flux higher for longer, maintaining free electron temperature and ionised column density in HII regions up to 5 Myr after quenching. We constrain ISM properties of our subsample, finding a low ionisation parameter $\log U{\approx}-3.8\pm 0.2$ and moderate/high densities $\log(n/\rm cm^{-3}){\approx}2.9\pm 0.6$. Finally, we derive UV/optical line luminosities-SFR relations for different burstiness parameters ($k_s$). In the fiducial BPASS model, relations show negligible SFH dependence but depend on $k_s$, while in the SB99 case, dependence is on SFH. We propose their use for characterising the burstiness of high-$z$ galaxies.
Coupled-mode theory (CMT) is a powerful tool for simulating near-harmonic systems. In telecommunications, variations of the theory have been used extensively to study waveguides, both analytically and through numerical modelling. Analogous mathematical techniques to the CMT are also widely used in quantum mechanics. The purpose of this work is to collect different formulations of the CMT and their underlying connections to quantum mechanical techniques, and to showcase their utility in modelling slowly varying waveguides including directional couplers and photonic lanterns. My choice of example waveguides is motivated by the astronomical applications of such devices in starlight nulling, wavefront sensing, and high-resolution spectroscopy. I first provide a brief review of the standard form of the CMT, applicable for waveguides with fixed eigenmodes. Next, I show that the CMT also applies for slowly varying waveguides, and demonstrate the close relation between the CMT and several well-known approximation methods from quantum mechanics, as well as concepts like geometric phase. Finally, I present a verification of my analysis, in the form of the numerical package cbeam.
Spatially Resolved Galaxy-Dust Modeling with Coupled Data-Driven Priors
2024-11-12
Jared Siegel, Peter Melchior
A notorious problem in astronomy is the recovery of the true shape and spectral energy distribution (SED) of a galaxy despite attenuation by interstellar dust embedded in the same galaxy. This problem has been solved for a few hundred nearby galaxies with exquisite data coverage, but these techniques are not scalable to the billions of galaxies soon to be observed by large wide-field surveys like LSST, Euclid, and Roman. We present a method for jointly modeling the spatially resolved stellar and dust properties of galaxies from multi-band images. To capture the diverse geometries of galaxies, we consider non-parametric morphologies, stabilized by two neural networks that act as data-driven priors: the first informs our inference of the galaxy's underlying morphology, the second constrains the galaxy's dust morphology conditioned on our current estimate of the galaxy morphology. We demonstrate with realistic simulations that we can recover galaxy host and dust properties over a wide range of attenuation levels and geometries. We successfully apply our joint galaxy-dust model to three local galaxies observed by SDSS. In addition to improving estimates of unattenuated galaxy SEDs, our inferred dust maps will facilitate the study of dust production, transport, and destruction.
We report a novel pilot project to characterise intra-night optical variability (INOV) of an extremely rare type of quasar, which has recently been caught in the act of transiting from a radio-quiet to radio-loud state, on a decadal time scale. Such rare transitions may signify a recurrence, or conceivably the first switch-on of jet activity in optically luminous quasars. The newly formed jet could well be jittery and unsteady, both in power and direction. The optically brightest among such radio-state transition candidates, the quasar J0950+5128 ($z = 0.2142$), was monitored by us with dense sampling in the R-band, during 2020-21 in 6 sessions, each lasting $>$ 4 hours. This is the first attempt to characterise the INOV properties associated with this recently discovered, extremely rarely observed phenomenon of quasar radio-state transition. The non-detection of INOV in any of the 6 sessions, down to the 1-2% level, amounts to a lack of evidence for a blazar-like optical activity, $\sim$ 2 years after its transition to radio-loud state was found. The only INOV feature detected in J0950+5128 during our observational campaign was a $\sim$ 0.15-mag spike lasting < 6 minutes, seen at 13.97 UT on 18-March-2021. We also report the available optical light curves of this quasar from the Zwicky Transient Facility (ZTF) survey, which indicate that it had experienced a phase of INOV activity around the time its transition to the radio-loud state was detected, however that phase did not sustain until the launch of our INOV campaign $\sim$ 2 years later.
Asymmetry in the distribution of HSC galaxy spin directions: comment on arXiv:2410.18884v1
2024-11-12
Lior Shamir
In the past decade, an asymmetry in the large-scale distribution of galaxy spin directions has been observed in data from all relevant digital sky surveys, all showing a higher number of galaxies rotating in the opposite direction relative to the Milky Way as observed from Earth. Additionally, JWST deep fields have shown that the asymmetry is clear and obvious, and can be sensed even by the naked human eye. These experiments were performed using two separate statistical methods: standard binomial distribution and simple $\chi^2$ statistics. Stiskalek \& Desmond (2024) suggested that the asymmetry in the distribution of galaxy spin directions is due to the use of binomial or $\chi^2$ statistics. Instead, they developed a new complex ad-hoc statistical method that shows random distribution in galaxy spin directions, and specifically in data from HSC. Source code for the method was also made available. The primary downside of the new method is that it is not able to identify asymmetry in the distribution of galaxy spin directions. Even when the new method is provided with synthetic data with extreme and obvious asymmetry, it still reports a null-hypothesis Universe with random distribution. That shows empirically that the method cannot sense asymmetry in the distribution of the directions of rotation of galaxies. While this further concludes that the distribution of galaxy spin direction as observed from Earth is not symmetric, it is not necessarily an indication of an anomaly in the large-scale structure. The excessive number of galaxies that rotate in the opposite direction relative to the Milky Way can also be driven by the internal structure of galaxies and the physics of galaxy rotation. The phenomenon can be related to other puzzling anomalies such the Ho tension. Data are publicly available, and no code is needed to reproduce the results since only conventional statistics is used.
2024-11-11
Tom Sander, Pierre Fernandez, Alain Durmus, Teddy Furon, Matthijs Douze
Image watermarking methods are not tailored to handle small watermarked areas. This restricts applications in real-world scenarios where parts of the image may come from different sources or have been edited. We introduce a deep-learning model for localized image watermarking, dubbed the Watermark Anything Model (WAM). The WAM embedder imperceptibly modifies the input image, while the extractor segments the received image into watermarked and non-watermarked areas and recovers one or several hidden messages from the areas found to be watermarked. The models are jointly trained at low resolution and without perceptual constraints, then post-trained for imperceptibility and multiple watermarks. Experiments show that WAM is competitive with state-of-the art methods in terms of imperceptibility and robustness, especially against inpainting and splicing, even on high-resolution images. Moreover, it offers new capabilities: WAM can locate watermarked areas in spliced images and extract distinct 32-bit messages with less than 1 bit error from multiple small regions - no larger than 10% of the image surface - even for small $256\times 256$ images.
Evidence of zero-field Wigner solids in ultra-thin films of cadmium arsenide
2024-11-11
Simon Munyan, Sina Ahadi, Binghao Guo, Arman Rashidi, Susanne Stemmer
The quantum Wigner crystal is a many-body state where Coulombic repulsion quenches the kinetic energy of electrons, causing them to crystallize into a lattice. Experimental realization of a quantum Wigner crystal at zero magnetic field has been a long-sought goal. Here, we report on the experimental evidence of a Wigner solid in ultra-thin films of cadmium arsenide (Cd3As2) at zero magnetic field. We show that a finite bias depins the domains and produces an unusually sharp threshold current-voltage behavior. Hysteresis and voltage fluctuations point to domain motion across the pinning potential and disappear at finite temperature as thermal fluctuations overcome the potential. The application of a small magnetic field destroys the Wigner solid, pointing to an unconventional origin. We use Landau level spectroscopy to show that the formation of the Wigner solid is closely connected to a topological transition as the film thickness is reduced.
Transverse orbital angular momentum of amplitude-perturbed fields
2024-11-11
S. W. Hancock, N. Tripathi, M. S. Le, A. Goffin, H. M. Milchberg
We review and clarify several recent theoretical approaches to transverse orbital angular momentum (tOAM). We then apply our tOAM theory [Phys. Rev. Lett. 127, 193901 (2021)] to calculate the spatiotemporal torque applied to a light pulse by a pure amplitude perturbation. The theoretical results and associated simulations are in excellent agreement with experiments measuring the change in tOAM per photon, delta_Ly. The crucial factor in determining delta_Ly is the spatiotemporal distribution of tOAM density in the pulse. We show that even Gaussian pulses with zero total tOAM can have net tOAM induced by an amplitude perturbation stationary in the lab frame.
Tasks, Time, and Tools: Quantifying Online Sensemaking Efforts Through a Survey-based Study
2024-11-11
Andrew Kuznetsov, Michael Xieyang Liu, Aniket Kittur
Aiming to help people conduct online research tasks, much research has gone into tools for searching for, collecting, organizing, and synthesizing online information. However, outside of the lab, in-the-wild sensemaking sessions (with data on tasks, users, their tools and challenges) can ground us in the reality of such efforts and the state of tool support. We use a survey-based approach with aided recall focused on segmenting and contextualizing individual exploratory browsing sessions to conduct a mixed method analysis of everyday sensemaking sessions in the traditional desktop browser setting while preserving user privacy. We report data from our survey (n=111) collected in September, 2022, and use these results to update and deepen the rich literature on information seeking behavior and exploratory search, contributing new empirical insights into the time spent per week and distribution of that time across tasks, and the lack of externalization and tool-use despite widespread desire for support.
Transient Upstream Mesoscale Structures: Drivers of Solar-Quiet Space Weather
2024-11-11
Primož Kajdič, Xóchitl Blanco-Cano, Lucile Turc, Martin Archer, Savvas Raptis, Terry Z. Liu, Yann Pfau-Kempf, Adrian T. LaMoury, Yufei Hao, Philippe C. Escoubet, Nojan Omidi, David G. Sibeck, Boyi Wang, Hui Zhang, Yu Lin
In recent years, it has become increasingly clear that space weather disturbances can be triggered by transient upstream mesoscale structures (TUMS), independently of the occurrence of large-scale solar wind (SW) structures, such as interplanetary coronal mass ejections and stream interaction regions. Different types of magnetospheric pulsations, transient perturbations of the geomagnetic field and auroral structures are often observed during times when SW monitors indicate quiet conditions, and have been found to be associated to TUMS. In this mini-review we describe the space weather phenomena that have been associated with four of the largest-scale and the most energetic TUMS, namely hot flow anomalies, foreshock bubbles, travelling foreshocks and foreshock compressional boundaries. The space weather phenomena associated with TUMS tend to be more localized and less intense compared to geomagnetic storms. However, the quiet time space weather may occur more often since, especially during solar minima, quiet SW periods prevail over the perturbed times.
Benchmarking LLMs' Judgments with No Gold Standard
2024-11-11
Shengwei Xu, Yuxuan Lu, Grant Schoenebeck, Yuqing Kong
We introduce the GEM (Generative Estimator for Mutual Information), an evaluation metric for assessing language generation by Large Language Models (LLMs), particularly in generating informative judgments, without the need for a gold standard reference. GEM broadens the scenarios where we can benchmark LLM generation performance-from traditional ones, like machine translation and summarization, where gold standard references are readily available, to subjective tasks without clear gold standards, such as academic peer review. GEM uses a generative model to estimate mutual information between candidate and reference responses, without requiring the reference to be a gold standard. In experiments on a human-annotated dataset, GEM demonstrates competitive correlations with human scores compared to the state-of-the-art GPT-4o Examiner, and outperforms all other baselines. Additionally, GEM is more robust against strategic manipulations, such as rephrasing or elongation, which can artificially inflate scores under a GPT-4o Examiner. We also present GRE-bench (Generating Review Evaluation Benchmark) which evaluates LLMs based on how well they can generate high-quality peer reviews for academic research papers. Because GRE-bench is based upon GEM, it inherits its robustness properties. Additionally, GRE-bench circumvents data contamination problems (or data leakage) by using the continuous influx of new open-access research papers and peer reviews each year. We show GRE-bench results of various popular LLMs on their peer review capabilities using the ICLR2023 dataset.
TinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems
2024-11-11
Jacob Huckelberry, Yuke Zhang, Allison Sansone, James Mickens, Peter A. Beerel, Vijay Janapa Reddi
Tiny Machine Learning (TinyML) systems, which enable machine learning inference on highly resource-constrained devices, are transforming edge computing but encounter unique security challenges. These devices, restricted by RAM and CPU capabilities two to three orders of magnitude smaller than conventional systems, make traditional software and hardware security solutions impractical. The physical accessibility of these devices exacerbates their susceptibility to side-channel attacks and information leakage. Additionally, TinyML models pose security risks, with weights potentially encoding sensitive data and query interfaces that can be exploited. This paper offers the first thorough survey of TinyML security threats. We present a device taxonomy that differentiates between IoT, EdgeML, and TinyML, highlighting vulnerabilities unique to TinyML. We list various attack vectors, assess their threat levels using the Common Vulnerability Scoring System, and evaluate both existing and possible defenses. Our analysis identifies where traditional security measures are adequate and where solutions tailored to TinyML are essential. Our results underscore the pressing need for specialized security solutions in TinyML to ensure robust and secure edge computing applications. We aim to inform the research community and inspire innovative approaches to protecting this rapidly evolving and critical field.
Impact of LLM-based Review Comment Generation in Practice: A Mixed Open-/Closed-source User Study
2024-11-11
Doriane Olewicki, Leuson Da Silva, Suhaib Mujahid, Arezou Amini, Benjamin Mah, Marco Castelluccio, Sarra Habchi, Foutse Khomh, Bram Adams
We conduct a large-scale empirical user study in a live setup to evaluate the acceptance of LLM-generated comments and their impact on the review process. This user study was performed in two organizations, Mozilla (which has its codebase available as open source) and Ubisoft (fully closed-source). Inside their usual review environment, participants were given access to RevMate, an LLM-based assistive tool suggesting generated review comments using an off-the-shelf LLM with Retrieval Augmented Generation to provide extra code and review context, combined with LLM-as-a-Judge, to auto-evaluate the generated comments and discard irrelevant cases. Based on more than 587 patch reviews provided by RevMate, we observed that 8.1% and 7.2%, respectively, of LLM-generated comments were accepted by reviewers in each organization, while 14.6% and 20.5% other comments were still marked as valuable as review or development tips. Refactoring-related comments are more likely to be accepted than Functional comments (18.2% and 18.6% compared to 4.8% and 5.2%). The extra time spent by reviewers to inspect generated comments or edit accepted ones (36/119), yielding an overall median of 43s per patch, is reasonable. The accepted generated comments are as likely to yield future revisions of the revised patch as human-written comments (74% vs 73% at chunk-level).
Revealing hidden structures in the Zone of Avoidance -- a blind MeerKAT HI Survey of the Vela Supercluster
2024-11-11
Sambatriniaina H. A. Rajohnson, Renée C. Kraan-Korteweg, Bradley S. Frank, Hao Chen, Lister Staveley-Smith, Paolo Serra, Nadia Steyn, Sushma Kurapati, D. J. Pisano, Sharmila Goedhart
We conducted the MeerKAT Vela Supercluster survey, named Vela$-$HI, to bridge the gap between the Vela SARAO MeerKAT Galactic Plane Survey (Vela$-$SMGPS, $-2^{\circ} \leq b \leq 1^{\circ}$), and optical and near-infrared spectroscopic observations of the Vela Supercluster (hereafter Vela$-$OPT/NIR) at $|b| \gtrsim 7^{\circ}$. Covering coordinates from $263^{\circ} \leq \ell \leq 284^{\circ}$ and $1^{\circ} \leq b \leq 6.2^{\circ}$ above, and $-6.7^{\circ} \leq b \leq -2^{\circ}$ below the Galactic Plane (GP), we sampled 667 fields spread across an area of ${\sim} \rm 242 ~deg^2$. With a beam size of ${\sim} 38'' \times 31''$, Vela$-$HI achieved a sensitivity of $\langle \rm rms \rangle = 0.74$ mJy beam$^{-1}$ at 44.3 km s$^{-1}$ velocity resolution over ${\sim}$67 hours of observations. We cataloged 719 galaxies, with only 211 (29%) previously documented in the literature, primarily through the HIZOA, 2MASX, and WISE databases. Among these known galaxies, only 66 had optical spectroscopic redshift information. We found marginal differences of less than one channel resolution for all galaxies in common between HIZOA and Vela$-$SMGPS, and a mean difference of $70 \pm 15$ km s$^{-1}$ between optical and HI velocities. Combining data from Vela$-$SMGPS, Vela$-$HI, and Vela$-$OPT/NIR confirmed the connection of the Hydra/Antlia filament across the GP and revealed a previously unknown diagonal wall at a heliocentric velocity range of $6500-8000$ km s$^{-1}$. Vela$-$HI reinforces the connection between the first wall at $18500-20000$ km s$^{-1}$ and the inner ZOA. The second wall seems to traverse the GP at $270^{\circ} \leq \ell \leq 279^{\circ}$, where it appears that both walls intersect, jointly covering the velocity range $18500-21500$ km s$^{-1}$.
UAV survey coverage path planning of complex regions containing exclusion zones
2024-11-11
Shadman Tajwar Shahid, Shah Md. Ahasan Siddique, Md. Mahidul Alam
This article addresses the challenge of UAV survey coverage path planning for areas that are complex concave polygons, containing exclusion zones or obstacles. While standard drone path planners typically generate coverage paths for simple convex polygons, this study proposes a method to manage more intricate regions, including boundary splits, merges, and interior holes. To achieve this, polygonal decomposition techniques are used to partition the target area into convex sub-regions. The sub-polygons are then merged using a depth-first search algorithm, followed by the generation of continuous Boustrophedon paths based on connected components. Polygonal offset by the straight skeleton method was used to ensure a constant safe distance from the exclusion zones. This approach allows UAV path planning in environments with complex geometric constraints.
UWB Narrowband Interference Survey and Design Considerations
2024-11-11
Brian Nelson, Hussein Moradi, Behrouz Farhang-Boroujeny
A study of interference caused by incumbent radios to UWB devices is presented. Through an extensive set of measured spectral activities in the low-band IEEE802.15.4 UWB operating channels, we explore the outage probabilities of a UWB system when it is equipped with an intelligent detector for combating interferers and compare them against those of a naive system that has no interference avoidance capability. Our results reveal that a UWB system with an effective interference avoidance capability may lead to a few orders of magnitude improvement in its outage probabilities. The measured outage probabilities are confirmed through measuring the frame error rate of a simulated filter-bank UWB transceiver system when impacted by the interferences obtained through our experiments.
Estimating abilities with an Elo-informed growth model
2024-11-11
Karl Sigfrid, Ellinor Fackle-Fornius, Frank Miller
An intelligent tutoring system (ITS) aims to provide instructions and exercises tailored to the ability of a student. To do this, the ITS needs to estimate the ability based on student input. Rather than including frequent full-scale tests to update our ability estimate, we want to base estimates on the outcomes of practice exercises that are part of the learning process. A challenge with this approach is that the ability changes as the student learns, which makes traditional item response theory (IRT) models inappropriate. Most IRT models estimate an ability based on a test result, and assume that the ability is constant throughout a test. We review some existing methods for measuring abilities that change throughout the measurement period, and propose a new method which we call the Elo-informed growth model. This method assumes that the abilities for a group of respondents who are all in the same stage of the learning process follow a distribution that can be estimated. The method does not assume a particular shape of the growth curve. It performs better than the standard Elo algorithm when the measured outcomes are far apart in time, or when the ability change is rapid.
2024-11-11
Bruno Sicardy, Felipe Braga-Ribas, Marc W. Buie, José Luis Ortiz, Françoise Roques
Stellar occultations provide a powerful tool to explore objects of the outer solar system. The Gaia mission now provides milli-arcsec accuracy on the predictions of these events and makes possible observations that were previously unthinkable. Occultations return kilometric accuracies on the three-dimensional shape of bodies irrespective of their geocentric distances, with the potential of detecting topographic features along the limb. From the shape, accurate values of albedo can be derived, and if the mass is known, the bulk density is pinned down, thus constraining the internal structure and equilibrium state of the object. Occultations are also extremely sensitive to tenuous atmospheres, down to the nanobar level. They allowed the monitoring of Pluto's and Triton's atmospheres in the last three decades, constraining their seasonal evolution. They may unveil in the near future atmospheres around other remote bodies of the solar system. Since 2013, occultations have led to the surprising discovery of ring systems around the Centaur object Chariklo, the dwarf planet Haumea and the large trans-Neptunian object Quaoar, while revealing dense material around the Centaur Chiron. This suggests that rings are probably much more common features than previously thought. Meanwhile, they have raised new dynamical questions concerning the confining effect of resonances forced by irregular objects on ring particles. Serendipitous occultations by km-sized trans-Neptunian or Oort objects has the potential to provide the size distribution of a population that suffered few collisions until now, thus constraining the history of primordial planetesimals in the 1-100 km range.
The study of word maps on groups has been of deep interest in recent years. This survey focuses on the case of power maps on groups; $viz.$ the map $x\mapsto x^M$ for a group $G$, and an integer $M\geq 2$. Here, we accumulate various results on the subject and pose some questions.
MusE GAs FLOw and Wind (MEGAFLOW) XII. Rationale and design of a MgII survey of the cool circum-galactic medium with MUSE and UVES: The MEGAFLOW Survey
2024-11-11
N. F. Bouché, M. Wendt, J. Zabl, M. Cherrey, I. Schroetter, I. Langan, S. Muzahid, J. Schaye, B. Epinat, L. Wisotzki, T. Contini, J. Richard, R. Bacon, P. M. Weilbacher
We present the design, rationale, properties and catalogs of the MusE Gas FLOw and Wind survey (MEGAFLOW), a survey of the cool gaseous halos of $z\sim1$ galaxies using low-ionization MgII absorption systems. The survey consists of 22 quasar fields selected from the Sloan Digital Sky Survey (SDSS) having multiple ($\geq3$) strong MgII absorption lines over the redshift range $0.31$, i.e. more than 60 galaxies per arcmin$^{2}$. They were identified using a dual detection algorithm based on both continuum and emission line objects. The achieved [OII] 50\%\ completeness is 3.7$\times 10^{-18}$ erg/s/cm$^2$ (corresponding to SFR$>0.01$ M$_\odot$ yr$^{-1}$ at $z=1$) using realistic mock [OII] emitters and the 50\%\ completeness is $m_{F775W}\approx26$ AB magnitudes for continuum sources. We find that (i) the fraction of [OII] emitting galaxies which have no continuum is $\sim15$\%; (ii) the success rate in identifying at least one galaxy within 500 km/s and 100 kpc is $\approx90$\%\ for MgII absorptions with $W_r^{2796}\gtrsim0.5$~\AA; (iii) the mean number of galaxies per MgII absorption is $2.9\pm1.6$ within the MUSE field-of-view; (iv) of the 80 MgII systems at $0.310^9$ M$_\odot$, and star-formation rates $>1$ M$_\odot$ yr$^{-1}$.
WALLABY Pilot Survey: Gas-Rich Galaxy Scaling Relations from Marginally-Resolved Kinematic Models
2024-11-11
N. Deg, N. Arora, K. Spekkens, R. Halloran, B. Catinella, M. G. Jones, H. Courtois, K. Glazebrook, A. Bosma, L. Cortese, H. Dénes, A. Elagali, B. -Q. For, P. Kamphuis, B. S. Koribalski, K. Lee-Waddell, P. E. Mancera Piña, J. Mould, J. Rhee, L. Shao, L. Staveley-Smith, J. Wang, T. Westmeier, O. I. Wong
We present the first set of galaxy scaling relations derived from kinematic models of the Widefield ASKAP L-band Legacy All-sky Blind surveY (WALLABY) pilot phase observations. Combining the results of the first and second pilot data releases, there are 236 available kinematic models. We develop a framework for robustly measuring HI disk structural properties from these kinematic models; applicable to the full WALLABY survey. Utilizing this framework, we obtained the HI size, a measure of the rotational velocity, and angular momentum for 148 galaxies. These comprise the largest sample of galaxy properties from an untargetted, uniformly observed and modelled HI survey to date. We study the neutral atomic Hydrogen (HI) size-mass, size-velocity, mass-velocity, and angular momentum-mass scaling relations. We calculate the slope, intercept, and scatter for these scaling relations and find that they are similar to those obtained from other HI surveys. We also obtain stellar masses for 92 of the 148 robustly measured galaxies using multiband photometry through the Dark Energy Sky Instrument Legacy Imaging Survey Data Release-10 images. We use a subset of 61 of these galaxies that have consistent optical and kinematic inclinations to examine the stellar and baryonic Tully Fisher relations, the gas fraction-disk stability and gas fraction-baryonic mass relations. These measurements and relations demonstrate the unprecedented resource that WALLABY will represent for resolved galaxy scaling relations in HI.
Enhancing Accessibility in Special Libraries: A Study on AI-Powered Assistive Technologies for Patrons with Disabilities
2024-11-11
Snehasish Paul Shivali Chauhan
This study seeks to identify the potential role of AI-driven assistive technologies in enhancing access to libraries for persons with varying degrees of challenges. Traditional libraries pose a problem to many users with vision and mobility, among other conditions related to physical and infirmities. This mixed-methods research approach will examine ways in which AI-powered assistive tools and applications associated with text-to-speech, navigation systems, and personalized assistants are revolutionizing library services through a literature review, survey methods, interviews, and case studies. Our findings suggest that these technologies greatly increase the autonomy and participation of people with physical disabilities, providing personalized support and access to a wide range of resources. From this, some key findings have been deduced from the research, showing a strong impact on user experience and efficiency in services, while at the same time bringing out important considerations related to privacy and ethical implementation. This study highlights the central role of AI in making library settings more inclusive, thereby allowing equal access to knowledge and participation in the community. Such insight thus serves professionals working in libraries, policymakers, and technology developers for innovations to occur uninterruptedly, with future research directions proposed that would refine such technologies, especially toward the special needs of diverse populations. By adopting AI, libraries could uphold their mission of providing equal access to knowledge through full and equal participation of all persons, regardless of any type of physical ability, in the learning and community activities carried out by the library. This study paves the way for future innovations in creating more accessible and inclusive library spaces.
Optical orthogonal codes from a combinatorial perspective
2024-11-11
Sophie Huczynska, Siaw-Lynn Ng
Optical orthogonal codes (OOCs) are sets of $(0,1)$-sequences with good auto- and cross-correlation properties. They were originally introduced for use in multi-access communication, particularly in the setting of optical CDMA communications systems. They can also be formulated in terms of families of subsets of $\mathbb{Z}_v$, where the correlation properties can be expressed in terms of conditions on the internal and external differences within and between the subsets. With this link there have been many studies on their combinatorial properties. However, in most of these studies it is assumed that the auto- and cross-correlation values are equal; in particular, many constructions focus on the case where both correlation values are $1$. This is not a requirement of the original communications application. In this paper, we "decouple" the two correlation values and consider the situation with correlation values greater than $1$. We consider the bounds on each of the correlation values, and the structural implications of meeting these separately, as well as associated links with other combinatorial objects. We survey definitions, properties and constructions, establish some new connections and concepts, and discuss open questions.
Toward Gravitational Lensing in Modified Theories of Gravity
2024-11-11
Ali Tizfahm, Saeed Fakhry, Javad T. Firouzjaee, Antonino Del Popolo
In this study, we investigate gravitational lensing within modified gravity frameworks, focusing on the Hu-Sawicki $f(R)$ and normal branch Dvali-Gabadadze-Porrati (nDGP) models, and we compare these results with those obtained from general relativity (GR). Our results reveal that both modified gravity models consistently enhance key lensing parameters relative to GR, including the Einstein radius, lensing optical depth, and time delays. Notably, we find that the Hu-Sawicki $f(R)$ and nDGP models yield significantly larger Einstein radii and higher lensing probabilities, especially at greater redshifts, indicating an increased likelihood of lensing events under modified gravity. Our analysis of time delays further shows that the broader mass distributions in these frameworks lead to pronounced differences in high-mass lens systems, providing potential observational markers of modified gravity. Additionally, we observe amplified magnification factors in wave optics regimes, highlighting the potential for gravitational wave (GW) lensing to differentiate modified gravity effects from GR predictions. Through these findings, we propose modified gravity theories as compelling alternatives to GR in explaining cosmic phenomena, with promising implications for future high-precision gravitational lensing surveys.
A Systematic Search for Candidate Supermassive Black Hole Binaries Using Periodic Mid-Infrared Light Curves of Active Galactic Nuclei
2024-11-11
Di Luo, Ning Jiang, Xin Liu
Periodic variability in active galactic nuclei (AGNs) is a promising method for studying sub-parsec supermassive black hole binaries (SMBHBs), which are a challenging detection target. While extensive searches have been made in the optical, X-ray and gamma-ray bands, systematic infrared (IR) studies remain limited. Using data from the Wide-field Infrared Survey Explorer (WISE), which provides unique decade-long mid-IR light curves with a six-month cadence, we have conducted the first systematic search for SMBHB candidates based on IR periodicity. Analyzing a parent sample of 48,932 objects selected from about half a million AGNs, we have identified 28 candidate periodic AGNs with periods ranging from 1,268 to 2,437 days (in the observer frame) by fitting their WISE light curves with sinusoidal functions. However, our mock simulation of the parent sample indicates that stochastic variability can actually produce a similar number of periodic sources, underscoring the difficulty in robustly identifying real periodic signals with WISE light curves, given their current sampling. Notably, we found no overlap between our sample and optical periodic sources, which can be explained by a distinct preference for certain periods due to selection bias. By combining archived data from different surveys, we have identified SDSS J140336.43+174136.1 as a candidate exhibiting periodic behavior in both optical and IR bands, a phenomenon that warrants further validation through observational tests. Our results highlight the potential of IR time-domain surveys, including future missions such as the Nancy Grace-Roman Space Telescope, for identifying periodic AGNs, but complementary tests are still needed to determine their physical origins such as SMBHBs.
In this paper, we propose a novel LLM-Neo framework that efficiently transfers knowledge from a large language model (LLM) teacher to a compact student. Initially, we revisit the knowledge distillation (KD) and low-rank adaption (LoRA), and argue that they share the same paradigm. Inspired by this observation, we explore the strategy that combines LoRA and KD to enhance the efficiency of knowledge transfer. We first summarize some guidelines for this design and further develop the LLM-Neo. Experimental results on compressing Llama 2 and Llama 3 show that LLM-Neo outperforms various baselines. Further analysis demonstrates the robustness of the proposed LLM-Neo on variants of LoRA. The trained models have been available at \href{https://huggingface.co/collections/yang31210999/llm-neo-66e3c882f5579b829ff57eba}{this repository}.
2024-11-11
Alexander Rogiers, Sander Noels, Maarten Buyl, Tijl De Bie
The rapid rise of Large Language Models (LLMs) has created new disruptive possibilities for persuasive communication, by enabling fully-automated personalized and interactive content generation at an unprecedented scale. In this paper, we survey the research field of LLM-based persuasion that has emerged as a result. We begin by exploring the different modes in which LLM Systems are used to influence human attitudes and behaviors. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. We identify key factors influencing their effectiveness, such as the manner of personalization and whether the content is labelled as AI-generated. We also summarize the experimental designs that have been used to evaluate progress. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks, including the spread of misinformation, the magnification of biases, and the invasion of privacy. These risks underscore the urgent need for ethical guidelines and updated regulatory frameworks to avoid the widespread deployment of irresponsible and harmful LLM Systems.
Close-binary central stars of planetary nebulae offer a unique tool with which to study the critical and yet poorly understood common-envelope phase of binary stellar evolution. Furthermore, as the nebula itself is thought to comprise the ionised remnant of the ejected common envelope, such planetary nebulae can be used to directly probe the mass, morphology and dynamics of the ejecta. In this review, I summarise our current understanding of the importance of binarity in the formation of planetary nebulae as well as what they may be able to tell us about the common-envelope phase - including the possible relationships with other post-common-envelope phenomena like stellar mergers, novae and type Ia supernovae.
A spatially-resolved spectral analysis of giant radio galaxies with MeerKAT
2024-11-11
K. K. L. Charlton, J. Delhaize, K. Thorat, I. Heywood, M. J. Jarvis, M. J. Hardcastle, Fangxia An, I. Delvecchio, C. L. Hale, I. H. Whittam, M. Brüggen, L. Marchetti, L. Morabito, Z. Randriamanakoto, S. V. White, A. R. Taylor
In this study we report spatially resolved, wideband spectral properties of three giant radio galaxies (GRGs) in the COSMOS field: MGTC J095959.63+024608.6 , MGTC J100016.84+015133.0 and MGTC J100022.85+031520.4. One such galaxy MGTC J100022.85+031520.4 is reported here for the first time with a projected linear size of 1.29 Mpc at a redshift of 0.1034. Unlike the other two, it is associated with a brightest cluster galaxy (BCG), making it one of the few GRGs known to inhabit cluster environments. We examine the spectral age distributions of the three GRGs using new MeerKAT UHF-band (544-1088 MHz) observations, and $L$-band (900-1670 MHz) data from the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) survey. We test two different models of spectral ageing, the Jaffe-Perola and Tribble models, using the Broadband Radio Astronomy Tools (\textsc{brats}) software which we find agree well with each other. We estimate the Tribble spectral age for MGTC J095959.63+024608.6 as 68 Myr, MGTC J100016.84+015133.0 as 47 Myr and MGTC J100022.85+031520.4 as 67 Myr. We find significant disagreements between these spectral age estimates and the estimates of the dynamical ages of these GRGs, modelled in cluster and group environments. Our results highlight the need for additional processes which are not accounted for in either the dynamic age or spectral age estimations.
Structuring the Processing Frameworks for Data Stream Evaluation and Application
2024-11-11
Joanna Komorniczak, Paweł Ksieniewicz, Paweł Zyblewski
The following work addresses the problem of frameworks for data stream processing that can be used to evaluate the solutions in an environment that resembles real-world applications. The definition of structured frameworks stems from a need to reliably evaluate the data stream classification methods, considering the constraints of delayed and limited label access. The current experimental evaluation often boundlessly exploits the assumption of their complete and immediate access to monitor the recognition quality and to adapt the methods to the changing concepts. The problem is leveraged by reviewing currently described methods and techniques for data stream processing and verifying their outcomes in simulated environment. The effect of the work is a proposed taxonomy of data stream processing frameworks, showing the linkage between drift detection and classification methods considering a natural phenomenon of label delay.
Implementation of controlled-NOT quantum gate by nonlinear coupled electro-nano-optomechanical oscillators
2024-11-11
R. Alinaghipour, H. Mohammadi
Feasibility study is done for the possibility of universal set of quantum gate implementation based on phononic state via 4th order Duffing nonlinearity in an optomechanical system. The optomechanical system consists of N doubly clamped coupled nanobeam array drived by local static and radio frequency electrical potentials, coupled to a single-mode high finesse optical cavity. The results show that ideal CNOT gate can be implemented only under non-resonance dynamics when the dissipation processes are negligible.
Dwarf Galaxies in the MATLAS Survey: The satellite system of NGC474 under scrutiny with MUSE
2024-11-11
Oliver Müller, Francine R. Marleau, Nick Heesters, Pierre-Alain Duc, Marcel S. Pawlowski, Mélina Poulain, Rebecca Habas, Elisabeth Sola, Mathias Urbano, Rory Smith, Patrick Durrell, Eric Emsellem, Rubén Sánchez-Janssen, Sungsoon Lim, Sanjaya Paudel
A recent study of the distribution of dwarf galaxies in the MATLAS sample in galaxy groups revealed an excess of flattened satellite structures, reminiscent of the co-rotating planes of dwarf galaxies discovered in the local Universe. If confirmed, this lends credence to the plane-of-satellite problem and further challenges the standard model of hierarchical structure formation. However, with only photometric data and no confirmation of the satellite membership, the study could not address the plane-of-satellite problem in full detail. Here we present spectroscopic follow-up observations of one of the most promising planes-of-satellites candidates in the MATLAS survey, the satellite system of NGC 474. Employing MUSE at the VLT and full spectrum fitting, we studied 13 dwarf galaxy candidates and confirmed nine to be members of the field around NGC 474. Measuring the stellar populations of all observed galaxies, we find that the MATLAS dwarfs have lower metallicities than the Local Group dwarfs at given luminosity. Two dwarf galaxies may form a pair of satellites based on their close projection and common velocity. Within the virial radius, we do not find a significant plane-of-satellites, however, there is a sub-population of six dwarf galaxies which seem to be anti-correlated in phase-space. Due to the low number of dwarf galaxies, this signal may arise by chance. With over 2000 dwarf galaxy candidates found in the MATLAS survey, this remains an intriguing data set to study the plane-of-satellites problem in a statistical fashion once more follow-up observations have been conducted.
Edge supercurrent in Josephson junctions based on topological materials
2024-11-11
Junjie Qi, Chui-Zhen Chen, Juntao Song, Jie Liu, Ke He, Qing-Feng Sun, X. C. Xie
The interplay between novel topological states and superconductivity has garnered substantial interest due to its potential for topological quantum computing. The Josephson effect serves as a useful probe for edge superconductivity in these hybrid topological materials. In Josephson junctions based on topological materials, supercurrents exhibit unique quantum interference patterns, including the conventional Fraunhofer oscillations, the $\Phi_0$-periodic oscillation, and the $2\Phi_0$-periodic oscillation in response to the external magnetic field ($\Phi_0 = h/2e$ is the flux quantum, $h$ the Planck constant, and $e$ the electron charge). These interference patterns stem from varied Andreev reflection mechanisms and the associated current density profiles. This review seeks to comprehensively examine the theoretical and experimental advancements in understanding the quantum interference patterns of edge supercurrents in Josephson junctions based on quantum spin Hall, quantum Hall, and quantum anomalous Hall systems.
Detection and Characterisation of Giant Planets with Gaia Astrometry
2024-11-11
A. L. Wallace, A. R. Casey, A. G. A. Brown, A. Castro-Ginard
Astrometric observations with Gaia are expected to play a valuable role in future exoplanet surveys. With current data from Gaia's third data release (DR3), we are sensitive to periods from less than 1 year to more than 4 years but, unlike radial velocity are not as restricted by the orbital inclination of a potential planet. The presence and potential properties of a companion affect the primary's renormalised unit weight error (RUWE) making this a valuable quantity in the search for exoplanets. Using this value and the fitted astrometric tracks from Gaia, we use Bayesian inference to constrain the mass and orbital parameters of companions in known systems. Combining this with radial velocity measurements, we show it is possible to independently measure mass and inclination and suggest HD 66141 b is a possible brown dwarf with maximum mass 23.9$^{+7.2}_{-6.4}$ M$_{\mathrm{J}}$. We show how this method may be applied to directly imaged planets in the future, using $\beta$-Pictoris c as an example but note that the host star is bright and active, making it difficult to draw clear conclusions. We show how the next Gaia data release, which will include epoch astrometry, will allow us to accurately constrain orbital parameters from astrometric data alone, revolutionising future searches for exoplanets. Combining predicted observational limits on planet mass with theoretical distributions, we estimate the probability that a star with a given RUWE will host a detectable planet, which will be highly valuable in planning future surveys.
HomoMatcher: Dense Feature Matching Results with Semi-Dense Efficiency by Homography Estimation
2024-11-11
Xiaolong Wang, Lei Yu, Yingying Zhang, Jiangwei Lao, Lixiang Ru, Liheng Zhong, Jingdong Chen, Yu Zhang, Ming Yang
Feature matching between image pairs is a fundamental problem in computer vision that drives many applications, such as SLAM. Recently, semi-dense matching approaches have achieved substantial performance enhancements and established a widely-accepted coarse-to-fine paradigm. However, the majority of existing methods focus on improving coarse feature representation rather than the fine-matching module. Prior fine-matching techniques, which rely on point-to-patch matching probability expectation or direct regression, often lack precision and do not guarantee the continuity of feature points across sequential images. To address this limitation, this paper concentrates on enhancing the fine-matching module in the semi-dense matching framework. We employ a lightweight and efficient homography estimation network to generate the perspective mapping between patches obtained from coarse matching. This patch-to-patch approach achieves the overall alignment of two patches, resulting in a higher sub-pixel accuracy by incorporating additional constraints. By leveraging the homography estimation between patches, we can achieve a dense matching result with low computational cost. Extensive experiments demonstrate that our method achieves higher accuracy compared to previous semi-dense matchers. Meanwhile, our dense matching results exhibit similar end-point-error accuracy compared to previous dense matchers while maintaining semi-dense efficiency.
Asteroseismology has become an indispensable method for measuring stellar ages and radii, while binary systems remain the most prevalent tool for determining stellar masses. The synergy of the two, namely pulsating stars in binary systems, offer even more than the sum of their parts. The sometimes-overwhelming number of pulsation models to be examined for asteroseismic modelling can be tightly constrained when dynamical masses for both components are available. Binaries offer twice the opportunity to measure an asteroseismic age, which is then applicable to both components of a system, often providing ages for stars that are otherwise very difficult to determine. Some stellar physics, such as the strength of internal mixing or of core overshoot, is so difficult to infer that only in binary systems do sufficient constraints exist to advance our asteroseismic models. The pulsations themselves can also be used to infer dynamical masses through pulsation timing, while eclipsing binaries provide opportunities to test asteroseismic scaling relations. In these proceedings of an invited talk I selectively review the synergies afforded by pulsations in binaries, consisting of a variety of main-sequence primary spectral types, to inferences made from the oscillations of red giants, even when their companions are undetectable. I also describe how the field is changing as we become more data rich through each generation of large photometric survey.
Expansion microscopy reveals neural circuit organization in genetic animal models
2024-11-11
Shakila Behzadi, Jacquelin Ho, Zainab Tanvir, Gal Haspel, Limor Freifeld, Kristen E. Severi
Expansion Microscopy is a super-resolution technique in which physically enlarging samples in an isotropic manner increases inter-molecular distances such that nano-scale structures can be resolved using light microscopy. This is particularly useful in neuroscience as many important structures are smaller than the diffraction limit. Since its invention in 2015, a variety of Expansion Microscopy protocols have been generated and applied to advance knowledge in many prominent organisms in neuroscience, including zebrafish, mice, Drosophila, and C. elegans. Here we review the last decade of Expansion Microscopy-enabled advances with a focus on neuroscience.
Revealing Pronounced Electron-Hole Fermi Pockets in the Charge Density Wave Semimetal LaTe3
2024-11-11
T. Nakamura, Y. Fujisawa, B. R. M. Smith, N. Tomoda, T. J. Hasiweder, Y. Okada
Rare earth tri-tellurides (RTe3) are van der Waals (vdW) coupled semimetals ideal for exploring exotic electronic phases. LaTe3 is especially important for understanding the fundamental Fermiology of the RTe3 family because it is non-magnetic and has a simpler charge density wave structure. In this study, we used spectroscopic-imaging scanning tunneling microscopy to measure the Landau levels of LaTe3 with high energy resolution at 300 mK. These measurements were taken under varying magnetic fields up to 15 T, with fine intervals of 0.02 - 0.03 T. Our results reveal a pair of pronounced electron-hole Fermi pockets of similar sizes and evidence of electron-boson coupling in both pockets. Given the strong charge susceptibility typical of low-dimensional conductors, the interactions and instabilities driven by the electron-hole Fermi pockets could be a basis for searching unexplored quantum phases in other antiferromagnetic RTe3 compounds.
Uncovering hidden Fermi surface instabilities through visualizing unconventional quasiparticle interference in CeTe3
2024-11-11
B. R. M. Smith, Y. Fujisawa, P. Wu, T. Nakamura, N. Tomoda, S. Kuniyoshi, D. Ueta, R. Kobayashi, R. Okuma, K. Arai, K. Kuroda, C-H. Hsu, G. Chang, C-Y. Huang, H. Lin, Z-Y. Wang, Y. Okada
The charge density wave (CDW) state is a widespread phenomenon in low-dimensional metals/semimetals. The spectral weight of the associated folded bands (shadow bands) can be an intriguing trigger leading to additional Fermi surface instability and unexplored phase transitions. The rare earth tri-telluride CeTe3 exhibits a single CDW stabilized below ~400 K and antiferromagnetism below ~3 K. The distinct periodicities between the Te-square net, the CeTe block layer, and the CDW give rise to rich shadow band formations. In this work, we reveal the predominant scattering between the original and shadow bands at 4 K, with the scattering within the original bands being relatively suppressed at Fermi energy. This unconventional quasi-particle scattering collectively underscores the vital role of the shadow bands' spectral weight and the hidden matrix element effect, which are crucial for controlling electronic properties in this system. Furthermore, our finding points to the existence of rich and unexplored Fermi surface instabilities, which potentially play a role in controlling the nature of long-range antiferromagnetism at lower temperatures in the presence of finite charge-spin interaction.
The Local Volume Database: a library of the observed properties of nearby dwarf galaxies and star clusters
2024-11-11
Andrew B. Pace
I present the Local Volume Database (LVDB), a catalog of the observed properties of dwarf galaxies and star clusters in the Local Group and Local Volume. The LVDB includes positional, structural, kinematic, chemical, and dynamical parameters for dwarf galaxies and star clusters. I discuss the motivation, structure, construction, and future expansion plans of the LVDB. I highlight catalogs on faint and compact ambiguous Milky Way systems, new Milky Way globular clusters and candidates, and globular clusters in nearby dwarf galaxies. The LVDB is complete for known dwarf galaxies within $\sim 3 ~{\rm Mpc}$ and current efforts are underway to expand the database to resolved star systems in the Local Volume. I present publicly available examples and use cases of the LVDB focused on the census and population-level properties of the Local Group and discuss some theoretical avenues. The next decade will be an exciting era for near-field cosmology with many upcoming surveys and facilities, such as the Legacy Survey of Space and Time at the Vera C. Rubin Observatory, the Euclid mission, and the Nancy Grace Roman Space Telescope, that will both discover new dwarf galaxies and star clusters in the Local Volume and characterize known dwarf galaxies and star clusters in more detail than ever before. The LVDB will be continually updated and is built to support and enable future dwarf galaxy and star cluster research in this data-rich era. The LVDB catalogs and package are publicly available as a GitHub repository, $\texttt{local_volume_database}$, and community use and contributions via GitHub are encouraged.
Cauchy problems for Einstein equations in three-dimensional spacetimes
2024-11-11
Piotr T. Chruściel, Wan Cong, Théophile Quéau, Raphaela Wutte
We analyze existence and properties of solutions of two-dimensional general relativistic initial data sets with a negative cosmological constant, both on spacelike and characteristic surfaces. A new family of such vacuum, spacelike data parameterised by poles at the conformal boundary at infinity is constructed. We review the notions of global Hamiltonian charges, emphasising the difficulties arising in this dimension, both in a spacelike and characteristic setting. One or two, depending upon the topology, lower bounds for energy in terms of angular momentum, linear momentum, and center of mass are established.
Review and Validation of Stochastic Ground Motion Models: which one does it better?
2024-11-11
Maijia Su, Mayssa Dabaghi, Marco Broccardo
Stochastic ground motion models (GMMs) are gaining popularity and momentum among engineers to perform time-history analysis of structures and infrastructures. This paper aims to review and validate hierarchical stochastic GMMs, with a focus on identifying their ''optimal'' configuration. We introduce the word ''hierarchical'' as its formulation contains two steps:(1) selecting a modulated filtered white noise model (MFWNM) to replicate a target record and (2) constructing a joint probability density function (PDF) for the parameters of the selected MFWNM, accounting for the record-to-record variability. In the first step, we review the development of MFWNMs and explore the ''optimal'' modeling of time-varying spectral content. Specifically, we investigate different frequency filters (single- and multi-mode) and various trends (constant, linear, and non-parametric) to describe the filters' time-varying properties. In the second step, the joint PDF is decomposed into a product of marginal distributions and a correlation structure, represented by copula models. We explore two copula models: the Gaus-sian copula and the R-vine copula. The hierarchical GMMs are evaluated by comparing specific statistical metrics, calculated from 1,001 real strong motions, with those derived from their corresponding synthetic dataset. Based on the selected validation metrics, we conclude that (1) Hierarchical stochastic GMMs can generate ground motions with high statistical compatibility to the real datasets, in terms of four key intensity measures and linear- and nonlinear-response spectra; (2) A parsimonious 11-parameter MFWNM, incorporating either the Gaussian copula or the R-vine copula, offers sufficient and similar accuracy.
Beyond Keywords: A Context-based Hybrid Approach to Mining Ethical Concern-related App Reviews
2024-11-11
Aakash Sorathiya, Gouri Ginde
With the increasing proliferation of mobile applications in our everyday experiences, the concerns surrounding ethics have surged significantly. Users generally communicate their feedback, report issues, and suggest new functionalities in application (app) reviews, frequently emphasizing safety, privacy, and accountability concerns. Incorporating these reviews is essential to developing successful products. However, app reviews related to ethical concerns generally use domain-specific language and are expressed using a more varied vocabulary. Thus making automated ethical concern-related app review extraction a challenging and time-consuming effort. This study proposes a novel Natural Language Processing (NLP) based approach that combines Natural Language Inference (NLI), which provides a deep comprehension of language nuances, and a decoder-only (LLaMA-like) Large Language Model (LLM) to extract ethical concern-related app reviews at scale. Utilizing 43,647 app reviews from the mental health domain, the proposed methodology 1) Evaluates four NLI models to extract potential privacy reviews and compares the results of domain-specific privacy hypotheses with generic privacy hypotheses; 2) Evaluates four LLMs for classifying app reviews to privacy concerns; and 3) Uses the best NLI and LLM models further to extract new privacy reviews from the dataset. Results show that the DeBERTa-v3-base-mnli-fever-anli NLI model with domain-specific hypotheses yields the best performance, and Llama3.1-8B-Instruct LLM performs best in the classification of app reviews. Then, using NLI+LLM, an additional 1,008 new privacy-related reviews were extracted that were not identified through the keyword-based approach in previous research, thus demonstrating the effectiveness of the proposed approach.
The Mega-MUSCLES Treasury Survey: X-ray to infrared Spectral Energy Distributions of a representative sample of M dwarfs
2024-11-11
David J. Wilson, Cynthia S. Froning, Girish M. Duvvuri, Allison Youngblood, Kevin France, Alexander Brown, Zachory Berta-Thompson, P. Christian Schneider, Andrea P. Buccino, Jeffrey Linsky, R. O. Parke Loyd, Yamila Miguel, Elisabeth Newton, J. Sebastian Pineda, Seth Redfield, Aki Roberge, Sarah Rugheimer, Mariela C. Vieytes
We present 5-1x10^7 Angstrom spectral energy distributions (SEDs) for twelve M dwarf stars covering spectral types M0-M8. Our SEDs are provided for community use as a sequel to the Measurements of the Ultraviolet Spectral Characteristics of Low-mass Exoplanetary Systems (MUSCLES) survey. The twelve stars include eight known exoplanet hosts and four stars chosen to fill out key parameter space in spectral type and rotation period. The SEDs are constructed from Hubble Space Telescope ultraviolet spectroscopy and XMM Newton, Chandra and/or Swift X-ray observations and completed with various model data, including Lyman alpha reconstructions, PHOENIX optical models, APEC coronal models and Differential Emission Measure models in the currently-unobservable Extreme Ultraviolet. We provide a complete overview of the Mega-MUSCLES program, including a description of the observations, models, and SED construction. The SEDs are available as MAST High-Level Science Products and we describe the various data products here. We also present ensemble measurements from our sample that are of particular relevance to exoplanet science, including the high-energy fluxes in the habitable zone and the FUV/NUV ratio. Combined with MUSCLES, Mega-MUSCLES provides SEDs covering a wide range of M\,dwarf spectral types and ages such that suitable proxies for any M dwarf planet host of interest may be found in our sample. However, we find that ultraviolet and X-ray fluxes can vary even between stars with similar parameters, such that observations of each exoplanet host star will remain the gold standard for interpreting exoplanet atmosphere observations.
Chemical Fingerprints of M Dwarfs: High-Resolution Spectroscopy on 31 M Dwarfs with SPIRou
2024-11-11
Farbod Jahandar, René Doyon, Étienne Artigau, Neil J. Cook, Charles Cadieux, Jean-François Donati, Nicolas B. Cowan, Ryan Cloutier, Stefan Pelletier, Alan Alves-Brito, Jorge H. C. Martins, Hsien Shang, Andrés Carmona
We extend the methodology introduced by Jahandar et al. (2024) to determine the effective temperature and chemical abundances of 31 slowly-rotating solar neighborhood M dwarfs (M1-M5) using high-resolution spectra from CFHT/SPIRou. This group includes 10 M dwarfs in binary systems with FGK primaries of known metallicity from optical measurements. By testing our $T_{\rm eff}$ method on various synthetic models, we find a consistent inherent synthetic uncertainty of $\sim$10 K at a signal-to-noise ratio greater than 100. Additionally, we find that our results align with interferometric measurements, showing a consistent residual of $-$29 $\pm$ 31 K. Taking the inherent uncertainties into account, we infer the $T_{\rm eff}$ values of our targets and find an excellent agreement with previous optical and NIR studies. Our high-resolution chemical analysis examines hundreds of absorption lines using $\chi^2$ minimization using PHOENIX-ACES stellar atmosphere models. We present elemental abundances for up to 10 different elements, including refractory elements such as Si, Mg, and Fe, which are important for modelling the interior structure of exoplanets. In binary systems, we find an average [Fe/H] of $-$0.15 $\pm$ 0.08 for M dwarfs, marginally lower than the reported metallicity of $-$0.06 $\pm$ 0.18 for the FGK primaries from Mann et al. (2013a). We also observe slightly sub-solar chemistry for various elements in our non-binary M dwarfs, most notably for O, C, and K abundances. In particular, we find an average metallicity of $-$0.11 $\pm$ 0.16 lower but still consistent with the typical solar metallicity of FGK stars (e.g. [Fe/H] = 0.04 $\pm$ 0.20 from Brewer et al. 2016). This study highlights significant discrepancies in various major M dwarf surveys likely related to differences in the methodologies employed.
ChatGPT Inaccuracy Mitigation during Technical Report Understanding: Are We There Yet?
2024-11-11
Salma Begum Tamanna, Gias Uddin, Song Wang, Lan Xia, Longyu Zhang
Hallucinations, the tendency to produce irrelevant/incorrect responses, are prevalent concerns in generative AI-based tools like ChatGPT. Although hallucinations in ChatGPT are studied for textual responses, it is unknown how ChatGPT hallucinates for technical texts that contain both textual and technical terms. We surveyed 47 software engineers and produced a benchmark of 412 Q&A pairs from the bug reports of two OSS projects. We find that a RAG-based ChatGPT (i.e., ChatGPT tuned with the benchmark issue reports) is 36.4% correct when producing answers to the questions, due to two reasons 1) limitations to understand complex technical contents in code snippets like stack traces, and 2) limitations to integrate contexts denoted in the technical terms and texts. We present CHIME (ChatGPT Inaccuracy Mitigation Engine) whose underlying principle is that if we can preprocess the technical reports better and guide the query validation process in ChatGPT, we can address the observed limitations. CHIME uses context-free grammar (CFG) to parse stack traces in technical reports. CHIME then verifies and fixes ChatGPT responses by applying metamorphic testing and query transformation. In our benchmark, CHIME shows 30.3% more correction over ChatGPT responses. In a user study, we find that the improved responses with CHIME are considered more useful than those generated from ChatGPT without CHIME.
Variational autoencoder (VAE) is one of the most common techniques in the field of medical image generation, where this architecture has shown advanced researchers in recent years and has developed into various architectures. VAE has advantages including improving datasets by adding samples in smaller datasets and in datasets with imbalanced classes, and this is how data augmentation works. This paper provides a comprehensive review of studies on VAE in medical imaging, with a special focus on their ability to create synthetic images close to real data so that they can be used for data augmentation. This study reviews important architectures and methods used to develop VAEs for medical images and provides a comparison with other generative models such as GANs on issues such as image quality, and low diversity of generated samples. We discuss recent developments and applications in several medical fields highlighting the ability of VAEs to improve segmentation and classification accuracy.
Wetting, Algebraic Curves and Conformal Invariance
2024-11-11
Andrew O. Parry, C. Rascón
Recent studies of wetting in a two-component square-gradient model of interfaces in a fluid mixture, showing three-phase bulk coexistence, have revealed some highly surprising features. Numerical results show that the density profile paths, which form a tricuspid shape in the density plane, have curious geometric properties, while conjectures for the analytical form of the surface tensions imply that nonwetting may persist up to the critical end points, contrary to the usual expectation of critical point wetting. Here, we solve the model exactly and show that the profile paths are conformally invariant quartic algebraic curves that change genus at the wetting transition. Being harmonic, the profile paths can be represented by an analytic function in the complex plane which then conformally maps the paths onto straight lines. Using this, we derive the conjectured form of the surface tensions and explain the geometrical properties of the tricuspid and its relation to the Neumann triangle for the contact angles. The exact solution confirms that critical point wetting is absent in this square-gradient model.
Artificial Intelligence-Informed Handheld Breast Ultrasound for Screening: A Systematic Review of Diagnostic Test Accuracy
2024-11-11
Arianna Bunnell, Dustin Valdez, Fredrik Strand, Yannik Glaser, Peter Sadowski, John A. Shepherd
Background. Breast cancer screening programs using mammography have led to significant mortality reduction in high-income countries. However, many low- and middle-income countries lack resources for mammographic screening. Handheld breast ultrasound (BUS) is a low-cost alternative but requires substantial training. Artificial intelligence (AI) enabled BUS may aid in both the detection (perception) and classification (interpretation) of breast cancer. Materials and Methods. This review (CRD42023493053) is reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) and SWiM (Synthesis Without Meta-analysis) guidelines. PubMed and Google Scholar were searched from January 1, 2016 to December 12, 2023. A meta-analysis was not attempted. Studies are grouped according to their AI task type, application time, and AI task. Study quality is assessed using the QUality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Results. Of 763 candidate studies, 314 total full texts were reviewed. 34 studies are included. The AI tasks of included studies are as follows: 1 frame selection, 6 detection, 11 segmentation, and 16 classification. In total, 5.7 million BUS images from over 185,000 patients were used for AI training or validation. A single study included a prospective testing set. 79% of studies were at high or unclear risk of bias. Conclusion. There has been encouraging development of AI for BUS. Despite studies demonstrating high performance across all identified tasks, the evidence supporting AI-enhanced BUS generally lacks robustness. High-quality model validation will be key to realizing the potential for AI-enhanced BUS in increasing access to screening in resource-limited environments.
Harnessing Smartphone Sensors for Enhanced Road Safety: A Comprehensive Dataset and Review
2024-11-11
Amith Khandakar, David G. Michelson, Mansura Naznine, Abdus Salam, Md. Nahiduzzaman, Khaled M. Khan, Ponnuthurai Nagaratnam Suganthan, Mohamed Arselene Ayari, Hamid Menouar, Julfikar Haider
Severe collisions can result from aggressive driving and poor road conditions, emphasizing the need for effective monitoring to ensure safety. Smartphones, with their array of built-in sensors, offer a practical and affordable solution for road-sensing. However, the lack of reliable, standardized datasets has hindered progress in assessing road conditions and driving patterns. This study addresses this gap by introducing a comprehensive dataset derived from smartphone sensors, which surpasses existing datasets by incorporating a diverse range of sensors including accelerometer, gyroscope, magnetometer, GPS, gravity, orientation, and uncalibrated sensors. These sensors capture extensive parameters such as acceleration force, gravitation, rotation rate, magnetic field strength, and vehicle speed, providing a detailed understanding of road conditions and driving behaviors. The dataset is designed to enhance road safety, infrastructure maintenance, traffic management, and urban planning. By making this dataset available to the community, the study aims to foster collaboration, inspire further research, and facilitate the development of innovative solutions in intelligent transportation systems.
Quantum Metrology for Gravitational Wave Astronomy
2024-11-11
Roman Schnabel, Nergis Mavalvala, David E. McClelland, Ping Koy Lam
Einstein's General Theory of Relativity predicts that accelerating mass distributions produce gravitational radiation, analogous to electromagnetic radiation from accelerating charges. These gravitational waves have not been directly detected to date, but are expected to open a new window to the Universe in the near future. Suitable telescopes are kilometre-scale laser interferometers measuring the distance between quasi free-falling mirrors. Recent advances in quantum metrology may now provide the required sensitivity boost. So-called squeezed light is able to quantum entangle the high-power laser fields in the interferometer arms, and could play a key role in the realization of gravitational wave astronomy.
2024-11-11
William Roster, Mara Salvato, Sven Krippendorf, Aman Saxena, Raphael Shirley, Johannes Buchner, Julien Wolf, Tom Dwelly, Franz E. Bauer, James Aird, Claudio Ricci, Roberto J. Assef, Scott F. Anderson, Xin Liu, Andrea Merloni, Jochen Weller, Kirpal Nandra
Computing photo-z for AGN is challenging, primarily due to the interplay of relative emissions associated with the SMBH and its host galaxy. SED fitting methods, effective in pencil-beam surveys, face limitations in all-sky surveys with fewer bands available, lacking the ability to capture the AGN contribution to the SED accurately. This limitation affects the many 10s of millions of AGN clearly singled out and identified by SRG/eROSITA. Our goal is to significantly enhance photometric redshift performance for AGN in all-sky surveys while avoiding the need to merge multiple data sets. Instead, we employ readily available data products from the 10th Data Release of the Imaging Legacy Survey for DESI, covering > 20,000 deg$^{2}$ with deep images and catalog-based photometry in the grizW1-W4 bands. We introduce PICZL, a machine-learning algorithm leveraging an ensemble of CNNs. Utilizing a cross-channel approach, the algorithm integrates distinct SED features from images with those obtained from catalog-level data. Full probability distributions are achieved via the integration of Gaussian mixture models. On a validation sample of 8098 AGN, PICZL achieves a variance $\sigma_{\textrm{NMAD}}$ of 4.5% with an outlier fraction $\eta$ of 5.6%, outperforming previous attempts to compute accurate photo-z for AGN using ML. We highlight that the model's performance depends on many variables, predominantly the depth of the data. A thorough evaluation of these dependencies is presented in the paper. Our streamlined methodology maintains consistent performance across the entire survey area when accounting for differing data quality. The same approach can be adopted for future deep photometric surveys such as LSST and Euclid, showcasing its potential for wide-scale realisation. With this paper, we release updated photo-z (including errors) for the XMM-SERVS W-CDF-S, ELAIS-S1 and LSS fields.
The impact of the cosmological constant on past and future star formation
2024-11-11
Daniele Sorini, John A. Peacock, Lucas Lombriser
We present an extended analytic model for cosmic star formation, with the aim of investigating the impact of cosmological parameters on the star formation history within the $\Lambda$CDM paradigm. Constructing an ensemble of flat $\Lambda$CDM models where the cosmological constant varies between $\Lambda = 0$ and $10^5$ times the observed value, $\Lambda_{\rm obs}$, we find that the fraction of cosmic baryons that are converted into stars over the entire history of the universe peaks at $\sim$27% for $0.01 \lesssim \Lambda/\Lambda_{\rm obs} \lesssim 1$. We explain, from first principles, that the decline of this asymptotic star-formation efficiency for lower and higher values of $\Lambda$ is driven respectively by the astrophysics of star formation, and by the suppression of cosmic structure formation. However, the asymptotic efficiency declines slowly as $\Lambda$ increases, falling below 5% only for $\Lambda>100 \, \Lambda_{\rm obs}$. Making the minimal assumption that the probability of generating observers is proportional to this efficiency, and following Weinberg in adopting a flat prior on $\Lambda$, the median posterior value of $\Lambda$ is $539 \, \Lambda_{\rm obs}$. Furthermore, the probability of observing $\Lambda\leq \Lambda_{\rm obs}$ is only $0.5\%$. Although this work has not considered recollapsing models with $\Lambda<0$, the indication is thus that $\Lambda_{\rm obs}$ appears to be unreasonably small compared to the predictions of the simplest multiverse ensemble. This poses a challenge for anthropic reasoning as a viable explanation for cosmic coincidences and the apparent fine-tuning of the universe: either the approach is invalid, or more parameters than $\Lambda$ alone must vary within the ensemble.
Spectroscopic Confirmation of a Massive Protocluster with Two Substructures at $z \simeq 3.1$
2024-11-11
Michael J. Nicandro Rosenthal, Amy J. Barger, Lennox L. Cowie, Logan H. Jones, Stephen J. McKay, Anthony J. Taylor
We present the results of a Keck and NOEMA spectroscopic survey of 507 galaxies, where we confirm the presence of two massive overdensities at $z = 3.090 - 3.110$ and $z = 3.133 - 3.155$ in the neighborhood of the GOODS-N, each with over a dozen spectroscopically confirmed members. We find that both of these have galaxy overdensities of NIR-detected galaxies of $\delta_{\rm gal, obs} = 6 - 9$ within corrected volumes of $(6 - 7) \times 10^3~{\rm cMpc}^3$. We estimate the properties of the $z = 0$ descendants of these overdensities using a spherical collapse model and find that both should virialize by $z \simeq 0.5 - 0.8$, with total masses of $M_{\rm tot} \simeq (6 - 7) \times 10^{14}~{\rm M}_\odot$. The same spherical collapse calculations, as well as a clustering-of-clusters statistical analysis, suggest a >80% likelihood that the two overdensities will collapse into a single cluster with $M_{\rm tot} = (1.0 - 1.5) \times 10^{15}~{\rm M}_\odot$ by $z \sim 0.1-0.4$. The $z = 3.14$ substructure contains a core of four bright dusty star-forming galaxies with $\Sigma {\rm SFR} = 2700 \pm 700~{\rm M}_\odot~{\rm yr}^{-1}$ in a volume of only 280 ${\rm cMpc}^3$.
Type IIn Supernovae. I. Uniform Light Curve Characterization and a Bimodality in the Radiated Energy Distribution
2024-11-11
Daichi Hiramatsu, Edo Berger, Sebastian Gomez, Peter K. Blanchard, Harsh Kumar, Wasundara Athukoralalage
We present the largest uniform study to date of Type IIn supernovae (SNe IIn), focusing in this first paper on the multi-band optical light curves of $487$ SNe IIn. The sample, constructed from multiple surveys, extends to $z \approx 0.8$, with the majority of events at $z \lesssim 0.3$. We construct uniform multi-band and bolometric light curves using Gaussian process regression, and determine key observed properties in the rest-frame (e.g., peak luminosity, timescales, radiated energy). We find that SNe IIn span broad ranges in peak luminosity ($\sim 10^{42}-10^{44}$ erg s$^{-1}$) and timescales ($\sim 20-300$ days above 50% of peak luminosity), but the sample divides into two clear groups in the luminosity-timescale phase-space around the median peak luminosity ($\approx 10^{43}$ erg s$^{-1}$): faint-fast and luminous-slow groups. This leads to a strong bimodality in the radiated energy distribution, with peaks at $\sim 10^{49}$ and $\sim 2\times10^{50}$ erg, with the latter events having a characteristic timescale of $\sim 100$ days, and the former appearing to bifurcate into two branches with timescales of $\sim 40$ and $\sim 70$ days. Therefore, SNe IIn exhibit at least two dominant groupings, and perhaps three, which are likely reflective of different progenitor and/or circumstellar medium formation pathways. We do not find any obvious transition in SN IIn properties at the arbitrary cut-off of $\approx -20$ mag used for the designation "Type II Superluminous Supernovae", and we argue that this classification should be abandoned. The absence of SNe IIn with timescales of $\lesssim 14$ days defines the region occupied by fast transients with evidence for interaction with hydrogen-poor circumstellar medium.
Opportunities and Challenges of Solid-State Quantum Nonlinear Optics
2024-11-10
Abhinav Kala, David Sharp, Minho Choi, Arnab Manna, Prathmesh Deshmukh, Vijin Kizhake Veetil, Vinod Menon, Matthew Pelton, Edo Waks, Arka Majumdar
Nonlinear interactions between single quantum particles are at the heart of any quantum information system, including analog quantum simulation and fault-tolerant quantum computing. This remains a particularly difficult problem for photonic qubits, as photons do not interact with each other. While engineering light-matter interaction can effectively create photon-photon interaction, the required photon number to observe any nonlinearity is very high, where any quantum mechanical signature disappears. However, with emerging low-dimensional materials, and engineered photonic resonators, the photon number can be potentially reduced to reach the quantum nonlinear optical regime. In this review paper, we discuss different mechanisms exploited in solid-state platforms to attain quantum nonlinear optics. We review emerging materials and optical resonator architecture with different dimensionalities. We also present new research directions and open problems in this field.
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning
2024-11-10
Caleb J. S. Barr, Olivia Erdelyi, Paul D. Docherty, Randolph C. Grace
Recent regulatory proposals for artificial intelligence emphasize fairness requirements for machine learning models. However, precisely defining the appropriate measure of fairness is challenging due to philosophical, cultural and political contexts. Biases can infiltrate machine learning models in complex ways depending on the model's context, rendering a single common metric of fairness insufficient. This ambiguity highlights the need for criteria to guide the selection of context-aware measures, an issue of increasing importance given the proliferation of ever tighter regulatory requirements. To address this, we developed a flowchart to guide the selection of contextually appropriate fairness measures. Twelve criteria were used to formulate the flowchart. This included consideration of model assessment criteria, model selection criteria, and data bias. We also review fairness literature in the context of machine learning and link it to core regulatory instruments to assist policymakers, AI developers, researchers, and other stakeholders in appropriately addressing fairness concerns and complying with relevant regulatory requirements.
Deep drilling in the time domain with DECam II: characterizing the light curves of candidates in the extragalactic fields
2024-11-10
Melissa L. Graham, Midori Rollins, Robert A. Knop, Suhail Dhawan, Gloria Fonseca Alvarez, Christopher A. Phillips, Guy Nir, Emily Ramey, Peter E. Nugent
In this second paper on the DECam deep drilling field (DDF) program we release 2,020 optical gri-band light curves for transients and variables in the extragalactic COSMOS and ELAIS fields based on time series observations with a 3-day cadence from semester 2021A through 2023A. In order to demonstrate the wide variety of time domain events detected by the program and encourage others to use the data set, we characterize the sample by presenting a brief analysis of the light curve parameters such as time span, amplitude, and peak brightness. We also present preliminary light curve categorizations, and identify potential stellar variables, active galactic nuclei, tidal disruption events, supernovae (such as Type Ia, Type IIP, superluminous, and gravitationally lensed supernovae), and fast transients. Where relevant, the number of identified transients is compared to the predictions of the original proposal. We also discuss the challenges of analyzing DDF data in the context of the upcoming Vera C. Rubin Observatory and its Legacy Survey of Space and Time, which will include DDFs. Images from the DECam DDF program are available without proprietary period and the light curves presented in this work are publicly available for analysis.
Machine Learning and data mining techniques (i.e. supervised and unsupervised techniques) are used across domains to detect user safety violations. Examples include classifiers used to detect whether an email is spam or a web-page is requesting bank login information. However, existing ML/DM classifiers are limited in their ability to understand natural languages w.r.t the context and nuances. The aforementioned challenges are overcome with the arrival of Gen-AI techniques, along with their inherent ability w.r.t translation between languages, fine-tuning between various tasks and domains. In this manuscript, we provide a comprehensive overview of the various work done while using Gen-AI techniques w.r.t user safety. In particular, we first provide the various domains (e.g. phishing, malware, content moderation, counterfeit, physical safety) across which Gen-AI techniques have been applied. Next, we provide how Gen-AI techniques can be used in conjunction with various data modalities i.e. text, images, videos, audio, executable binaries to detect violations of user-safety. Further, also provide an overview of how Gen-AI techniques can be used in an adversarial setting. We believe that this work represents the first summarization of Gen-AI techniques for user-safety.
Analysis of spatially clustered survival data with unobserved covariates using SBART
2024-11-10
Durbadal Ghosh, Debajyoti Sinha, Antonio R. Linero, George Rust
Usual parametric and semi-parametric regression methods are inappropriate and inadequate for large clustered survival studies when the appropriate functional forms of the covariates and their interactions in hazard functions are unknown, and random cluster effects and cluster-level covariates are spatially correlated. We present a general nonparametric method for such studies under the Bayesian ensemble learning paradigm called Soft Bayesian Additive Regression Trees. Our methodological and computational challenges include large number of clusters, variable cluster sizes, and proper statistical augmentation of the unobservable cluster-level covariate using a data registry different from the main survival study. We use an innovative 3-step approach based on latent variables to address our computational challenges. We illustrate our method and its advantages over existing methods by assessing the impacts of intervention in some county-level and patient-level covariates to mitigate existing racial disparity in breast cancer survival in 67 Florida counties (clusters) using two different data resources. Florida Cancer Registry (FCR) is used to obtain clustered survival data with patient-level covariates, and the Behavioral Risk Factor Surveillance Survey (BRFSS) is used to obtain further data information on an unobservable county-level covariate of Screening Mammography Utilization (SMU).
Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing Contact-Rich Plans?
2024-11-10
Yuki Shirai, Tong Zhao, H. J. Terry Suh, Huaijiang Zhu, Xinpei Ni, Jiuguang Wang, Max Simchowitz, Tao Pang
Designing planners and controllers for contact-rich manipulation is extremely challenging as contact violates the smoothness conditions that many gradient-based controller synthesis tools assume. Contact smoothing approximates a non-smooth system with a smooth one, allowing one to use these synthesis tools more effectively. However, applying classical control synthesis methods to smoothed contact dynamics remains relatively under-explored. This paper analyzes the efficacy of linear controller synthesis using differential simulators based on contact smoothing. We introduce natural baselines for leveraging contact smoothing to compute (a) open-loop plans robust to uncertain conditions and/or dynamics, and (b) feedback gains to stabilize around open-loop plans. Using robotic bimanual whole-body manipulation as a testbed, we perform extensive empirical experiments on over 300 trajectories and analyze why LQR seems insufficient for stabilizing contact-rich plans. The video summarizing this paper and hardware experiments is found here: https://youtu.be/HLaKi6qbwQg?si=_zCAmBBD6rGSitm9.
Multiphysics continuum mechanics models to advance glaucoma research: State-of-the-art and future perspectives
2024-11-10
Daniel Sebastia-Saez, Jinyuan Luo, Mengqi Qin, Tao Chen, Cynthia Yu-Wai-Man
This review provides a broad vision on the use of mechanistic mathematical models based on continuum mechanics to tackle current challenges in glaucoma research. At present, the advent of Artificial Intelligence and data-based models have resulted in significant progress in drug candidate selection, target identification and drug delivery. Mechanistic models (not based on data gathered beforehand, but on fundamental physical principles), and specifically those based on continuum mechanics, have the potential to contribute to a better understanding of glaucoma, too. So far, these models have expanded our understanding of ocular fluid dynamics, including descriptions of fluid flow profiles within the anterior chamber of the eye under glaucomatous conditions. With the emergence of models with complex multiphysics capabilities (not only on fluid dynamics, but also diffusion, heat transfer and fluid-structure interaction), there are now even greater possibilities for the application of mechanistic models to help with current challenges in glaucoma, such as the design of drug-releasing implants, novel glaucoma drainage devices, minimally invasive glaucoma surgery procedures, and laser peripheral iridotomy.
String dualities and modular symmetries in supergravity: a review
2024-11-10
Niccolò Cribiori, Dieter Lust
We review the remarkable interplay between modular symmetries and supergravity, which has led to major advances in both physics and mathematics in recent decades. Our focus will be on four-dimensional models with $\mathcal{N}=1$ and $\mathcal{N}=2$ local supersymmetry. We will look at the early articles on the topic, but also touch on recent developments. These results testify to how supergravity, when supplemented with the appropriate assumptions, can be turned into a unique window into quantum gravity.
Chiral quantum optics: recent developments, and future directions
2024-11-10
D. G. Suárez-Forero, M. Jalali Mehrabad, C. Vega, A. González-Tudela, M. Hafezi
Chiral quantum optics is a growing field of research where light-matter interactions become asymmetrically dependent on momentum and spin, offering novel control over photonic and electronic degrees of freedom. Recently, the platforms for investigating chiral light-matter interactions have expanded from laser-cooled atoms and quantum dots to various solid-state systems, such as microcavity polaritons and two-dimensional layered materials, integrated into photonic structures like waveguides, cavities, and ring resonators. In this perspective, we begin by establishing the foundation for understanding and engineering these chiral light-matter regimes. We review the cutting-edge platforms that have enabled their successful realization in recent years, focusing on solid-state platforms, and discuss the most relevant experimental challenges to fully harness their potential. Finally, we explore the vast opportunities these chiral light-matter interfaces present, particularly their ability to reveal exotic quantum many-body phenomena, such as chiral many-body superradiance and fractional quantum Hall physics.
CO(1--0) imaging reveals 10-kiloparsec molecular gas reservoirs around star-forming galaxies at high redshift
2024-11-10
Matus Rybak, J. T. Jansen, M. Frias Castillo, J. A. Hodge, P. P. van der Werf, I. Smail, G. Calistro Rivera, S. Chapman, C. -C. Chen, E. da Cunha, H. Dannerbauer, E. F. Jiménez-Andrade, C. Lagos, C. -L. Liao, E. J. Murphy, D. Scott, A. M. Swinbank, F. Walter
Massive, intensely star-forming galaxies at high redshift require a supply of molecular gas from their gas reservoirs, replenished by infall from the surrounding circumgalactic medium, to sustain their immense star-formation rates. However, our knowledge of the extent and morphology of their cold-gas reservoirs is still in its infancy. We present the results of stacking 80 hours of JVLA observations of CO(1--0) emission -- which traces the cold molecular gas -- in 19 $z=2.0-4.5$ dusty, star-forming galaxies from the AS2VLA survey. The visibility-plane stack reveals extended emission with a half-light radius of $3.8\pm0.5$~kpc, 2--3$\times$ more extended than the dust-obscured star formation and $1.4\pm0.2\times$ more extended than the stellar emission. Similarly, stacking the [CI](1--0) observations for a subsample of our galaxies yields sizes consistent with CO(1--0). The CO(1--0) size is comparable to the [CII] halos detected around high-redshift star-forming galaxies.The bulk (up to 80\%) of molecular gas resides outside the star-forming region; only a small part of their molecular gas reservoir directly contributes to their current star formation. Photon-dissociation region modelling indicates that the extended CO(1--0) emission arises from clumpy, dense clouds rather than smooth, diffuse gas.
Self-Training Meets Consistency: Improving LLMs' Reasoning With Consistency-Driven Rationale Evaluation
2024-11-10
Jaehyeok Lee, Keisuke Sakaguchi, JinYeong Bak
Self-training approach for large language models (LLMs) improves reasoning abilities by training the models on their self-generated rationales. Previous approaches have labeled rationales that produce correct answers for a given question as appropriate for training. However, a single measure risks misjudging rationale quality, leading the models to learn flawed reasoning patterns. To address this issue, we propose CREST (Consistency-driven Rationale Evaluation for Self-Training), a self-training framework that further evaluates each rationale through follow-up questions and leverages this evaluation to guide its training. Specifically, we introduce two methods: (1) filtering out rationales that frequently result in incorrect answers on follow-up questions and (2) preference learning based on mixed preferences from rationale evaluation results of both original and follow-up questions. Experiments on three question-answering datasets using open LLMs show that CREST not only improves the logical robustness and correctness of rationales but also improves reasoning abilities compared to previous self-training approaches.
BayesNAM: Leveraging Inconsistency for Reliable Explanations
2024-11-10
Hoki Kim, Jinseong Park, Yujin Choi, Seungyun Lee, Jaewook Lee
Neural additive model (NAM) is a recently proposed explainable artificial intelligence (XAI) method that utilizes neural network-based architectures. Given the advantages of neural networks, NAMs provide intuitive explanations for their predictions with high model performance. In this paper, we analyze a critical yet overlooked phenomenon: NAMs often produce inconsistent explanations, even when using the same architecture and dataset. Traditionally, such inconsistencies have been viewed as issues to be resolved. However, we argue instead that these inconsistencies can provide valuable explanations within the given data model. Through a simple theoretical framework, we demonstrate that these inconsistencies are not mere artifacts but emerge naturally in datasets with multiple important features. To effectively leverage this information, we introduce a novel framework, Bayesian Neural Additive Model (BayesNAM), which integrates Bayesian neural networks and feature dropout, with theoretical proof demonstrating that feature dropout effectively captures model inconsistencies. Our experiments demonstrate that BayesNAM effectively reveals potential problems such as insufficient data or structural limitations of the model, providing more reliable explanations and potential remedies.
Simulating Solar Neighborhood Brown Dwarfs I: The Luminosity Function Above and Below the Galactic Plane
2024-11-10
Easton J. Honaker, John E. Gizis
Brown dwarfs form the key, yet poorly understood, link between stellar and planetary astrophysics. These objects offer unique tests of Galactic structure, but observational limitations have inhibited the large-scale analysis of these objects to date. Major upcoming sky surveys will reveal unprecedented numbers of brown dwarfs, among even greater numbers of stellar objects, enabling the statistical study of brown dwarfs. To extract the comparatively rare brown dwarfs from these massive datasets, we must understand how they will look in upcoming surveys. In this work, we construct a synthetic population of brown dwarfs in the Solar Neighborhood to explore their evolutionary properties using Gaia-derived star formation histories alongside observational mass, metallicity, and age relationships. We apply the cloudless Sonora Bobcat, hybrid SM08, and gravity-dependent hybrid Sonora Diamondback evolutionary models to the sample. We present the simulated luminosity function and its evolution with distance from the Galactic Plane. Our simulation shows that brown dwarf population statistics are a function of height above/below the Galactic Plane and sample different age distributions. Interpreting the local sample requires combining evolutionary models, the initial mass function, the star formation history, and kinematic heating. Our models are a guide to how well height-dependent samples can test these scenarios. Sub-populations of brown dwarfs farther from the Plane are older and occupy a different region of brown dwarf parameter space than younger sub-populations closer to the Galactic Plane. Therefore, fully exploring population statistics both near and far from the Plane is critical to prepare for upcoming surveys.
VLA 22 GHz Imaging of Massive Star Formation in Local Wolf-Rayet Galaxies
2024-11-09
Nicholas G. Ferraro, Jean L. Turner, Sara C. Beck, Edwin Alexani, Runa Indrei, Bethany M. Welch, Tunhui Xie
We present 22 GHz imaging of regions of massive star formation within the Local Wolf-Rayet Galaxy Sample (LWRGS), a NSF's Karl G. Jansky Very Large Array (VLA) survey of 30 local galaxies showing spectral features of Wolf-Rayet (WR) stars. These spectral features are present in galaxies with young super star clusters (SSCs), and are an indicator of large concentrations of massive stars. We present a catalog of 92 individually-identified regions of likely free-free emission associated with potential young SSCs located in these WR galaxies. The free-free fluxes from these maps allow extinction-free estimates of the Lyman continuum rates, masses, and luminosities of the emission regions. 39 of these regions meet the minimum Lyman continuum rate to contain at least once SSC, and 29 of these regions could contain individual SSCs massive enough to test specific theories on star formation and feedback inhibition in SSCs, requiring follow-up observations at higher spatial resolution. The resulting catalog provides sources for future molecular line and infrared studies into the properties of super star cluster formation.
TinyML NLP Approach for Semantic Wireless Sentiment Classification
2024-11-09
Ahmed Y. Radwan, Mohammad Shehab, Mohamed-Slim Alouini
Natural Language Processing (NLP) operations, such as semantic sentiment analysis and text synthesis, may often impair users' privacy and demand significant on device computational resources. Centralized learning (CL) on the edge offers an alternative energy-efficient approach, yet requires the collection of raw information, which affects the user's privacy. While Federated learning (FL) preserves privacy, it requires high computational energy on board tiny user devices. We introduce split learning (SL) as an energy-efficient alternative, privacy-preserving tiny machine learning (TinyML) scheme and compare it to FL and CL in the presence of Rayleigh fading and additive noise. Our results show that SL reduces processing power and CO2 emissions while maintaining high accuracy, whereas FL offers a balanced compromise between efficiency and privacy. Hence, this study provides insights into deploying energy-efficient, privacy-preserving NLP models on edge devices.
A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-Language Tasks
2024-11-09
Chia Xin Liang, Pu Tian, Caitlyn Heqi Yin, Yao Yua, Wei An-Hou, Li Ming, Tianyang Wang, Ziqian Bi, Ming Liu
This survey and application guide to multimodal large language models(MLLMs) explores the rapidly developing field of MLLMs, examining their architectures, applications, and impact on AI and Generative Models. Starting with foundational concepts, we delve into how MLLMs integrate various data types, including text, images, video and audio, to enable complex AI systems for cross-modal understanding and generation. It covers essential topics such as training methods, architectural components, and practical applications in various fields, from visual storytelling to enhanced accessibility. Through detailed case studies and technical analysis, the text examines prominent MLLM implementations while addressing key challenges in scalability, robustness, and cross-modal learning. Concluding with a discussion of ethical considerations, responsible AI development, and future directions, this authoritative resource provides both theoretical frameworks and practical insights. It offers a balanced perspective on the opportunities and challenges in the development and deployment of MLLMs, and is highly valuable for researchers, practitioners, and students interested in the intersection of natural language processing and computer vision.
Two scholarly publishing cultures? Open access drives a divergence in European academic publishing practices
2024-11-09
Leon Kopitar, Nejc Plohl, Mojca Tancer Verboten, Gregor Štiglic, Roger Watson, Dean Korošak
The current system of scholarly publishing is often criticized for being slow, expensive, and not transparent. The rise of open access publishing as part of open science tenets, promoting transparency and collaboration, together with calls for research assesment reforms are the results of these criticisms. The emergence of new open access publishers presents a unique opportunity to empirically test how universities and countries respond to shifts in the academic publishing landscape. These new actors challenge traditional publishing models, offering faster review times and broader accessibility, which could influence strategic publishing decisions. Our findings reveal a clear division in European publishing practices, with countries clustering into two groups distinguished by the ratio of publications in new open access journals with accelerated review times versus legacy journals. This divide underscores a broader shift in academic culture, highlighting new open access publishing venues as a strategic factor influencing national and institutional publishing practices, with significant implications for research accessibility and collaboration across Europe.
Hilbert series for quark flavor invariants and CP violation
2024-11-09
Eduardo Lourenço Fabio de Lima
This work delves into the study of flavor invariants and, in special, invariants capable of detecting CP (Charge-Parity) violation. Through the mathematical tool of the Hilbert series, we systematically enumerate and explore flavor invariants that are unchanged under weak basis transformations. After reviewing the Hilbert series and the flavor invariants of the SM quark sector, we apply the tool of Hilbert series to the SM extended by a singlet vector-like quark (VLQ) of down-type. The introduction of these hypothetical particles leads to a simple extension of the SM that can be motivated by many problems, including the need for new sources of CP violation to explain the observed matter-antimatter asymmetry in the universe. We were successful in calculating the Hilbert series for the VLQ extension in the mass basis of the VLQ, where the spurion transformations are simpler. Based on the Hilbert series, we build and enumerate the basic flavor invariants with which all invariants can be constructed. For a generic basis, where the spurion transformations involve a larger group, we could only get the first few terms of the Hilbert series.
Grasping Object: Challenges and Innovations in Robotics and Virtual Reality
2024-11-09
Mingzhao Zhou, Nadine Aburumman
In real life, grasping is one of the fundamental and effective forms of interaction when manipulating objects. This holds true in the physical and virtual world; however, unlike the physical world, virtual reality (VR) is grasped in a complex formulation that includes graphics, physics, and perception. In virtual reality, the user's immersion level depends on realistic haptic feedback and high-quality graphics, which are computationally demanding and hard to achieve in real-time. Current solutions fail to produce plausible visuals and haptic feedback when simulation grasping in VR with a variety of targeted object dynamics. In this paper, we review the existing techniques for grasping in VR and robotics and indicate the main challenges that grasping faces in the domains. We aim to explore and understand the complexity of hand-grasping objects with different dynamics and inspire various ideas to improve and come up with potential solutions suitable for virtual reality applications.
Solid-liquid interfacial free energy from computer simulations: Challenges and recent advances
2024-11-09
Nicodemo Di Pasquale, Jesus Algaba, Pablo Montero de Hijes, Ignacio Sanchez-Burgos, Andres R. Tejedor, Stephen R. Yeandel, Felipe J. Blas, Ruslan L. Davidchack, Jorge R. Espinosa, Colin L. Freeman, John H. Harding, Brian B. Laird, Eduardo Sanz, Carlos Vega, Lorenzo Rovigatti
The theory of interfacial properties in liquid-liquid or liquid-vapour systems is nearly 200 years old. The advent of computational tools has greatly advanced the field, mainly through the use of Molecular Dynamics simulations. Despite the successes and advances in the theory of interfacial phenomena for liquid-liquid systems, the study of solid-liquid interfaces remains a challenge both theoretically and experimentally. The main reason why the treatment of solid-liquid systems has fallen behind that of liquid-liquid systems is that there are complications that arise whenever an interface involving solid systems is considered involving both theory of the solid-liquid interface and the calculations using MD simulations. An example of the former is that, contrary to the liquid-liquid case, the interfacial properties of solids depend on the lattice orientation. The main complications in these calculations arise from the fact that for solids the ``mechanical route'' cannot be used. To overcome this problem, several numerical approaches were proposed. The main purpose of this review is to provide an overview of these different methodologies and to discuss their strengths and weaknesses. We classify these methodologies into two main groups: direct and indirect methods. Direct methods are those that can calculate directly the properties of interfaces, while in indirect approaches the properties of the interface are not the primary result of the simulations. We also included a discussion on the origin of the difficulties in considering solid interfaces from a thermodynamic point of view. In the second part of the review, we discuss two key related topics: nucleation theory and curved interfaces. They both represent an important problem in the study of interfaces and in the context of solid-liquid ones for which the research is still extremely active.
Artificial Intelligence for Collective Intelligence: A National-Scale Research Strategy
2024-11-09
Seth Bullock, Nirav Ajmeri, Mike Batty, Michaela Black, John Cartlidge, Robert Challen, Cangxiong Chen, Jing Chen, Joan Condell, Leon Danon, Adam Dennett, Alison Heppenstall, Paul Marshall, Phil Morgan, Aisling O'Kane, Laura G. E. Smith, Theresa Smith, Hywel T. P. Williams
Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or trans-national scale. Pressing challenges in healthcare, finance, infrastructure and sustainability, for instance, might all be productively addressed by leveraging and amplifying AI for national-scale collective intelligence. The development and deployment of this kind of AI faces distinctive challenges, both technical and socio-technical. Here, a research strategy for mobilising inter-disciplinary research to address these challenges is detailed and some of the key issues that must be faced are outlined.
Large Language Models and Artificial Intelligence Generated Content Technologies Meet Communication Networks
2024-11-09
Jie Guo, Meiting Wang, Hang Yin, Bin Song, Yuhao Chi, Fei Richard Yu, Chau Yuen
Artificial intelligence generated content (AIGC) technologies, with a predominance of large language models (LLMs), have demonstrated remarkable performance improvements in various applications, which have attracted great interests from both academia and industry. Although some noteworthy advancements have been made in this area, a comprehensive exploration of the intricate relationship between AIGC and communication networks remains relatively limited. To address this issue, this paper conducts an exhaustive survey from dual standpoints: firstly, it scrutinizes the integration of LLMs and AIGC technologies within the domain of communication networks; secondly, it investigates how the communication networks can further bolster the capabilities of LLMs and AIGC. Additionally, this research explores the promising applications along with the challenges encountered during the incorporation of these AI technologies into communication networks. Through these detailed analyses, our work aims to deepen the understanding of how LLMs and AIGC can synergize with and enhance the development of advanced intelligent communication networks, contributing to a more profound comprehension of next-generation intelligent communication networks.
Towards an Efficient Synthetic Image Data Pipeline for Training Vision-Based Robot Systems
2024-11-09
Peter Gavriel, Adam Norton, Kenneth Kimble, Megan Zimmerman
Training data is an essential resource for creating capable and robust vision systems which are integral to the proper function of many robotic systems. Synthesized training data has been shown in recent years to be a viable alternative to manually collecting and labelling data. In order to meet the rising popularity of synthetic image training data we propose a framework for defining synthetic image data pipelines. Additionally we survey the literature to identify the most promising candidates for components of the proposed pipeline. We propose that defining such a pipeline will be beneficial in reducing development cycles and coordinating future research.
From References to Insights: Collaborative Knowledge Minigraph Agents for Automating Scholarly Literature Review
2024-11-09
Zhi Zhang, Yan Liu, Sheng-hua Zhong, Gong Chen, Yu Yang, Jiannong Cao
Literature reviews play a crucial role in scientific research for understanding the current state of research, identifying gaps, and guiding future studies on specific topics. However, the process of conducting a comprehensive literature review is yet time-consuming. This paper proposes a novel framework, collaborative knowledge minigraph agents (CKMAs), to automate scholarly literature reviews. A novel prompt-based algorithm, the knowledge minigraph construction agent (KMCA), is designed to identify relationships between information pieces from academic literature and automatically constructs knowledge minigraphs. By leveraging the capabilities of large language models on constructed knowledge minigraphs, the multiple path summarization agent (MPSA) efficiently organizes information pieces and relationships from different viewpoints to generate literature review paragraphs. We evaluate CKMAs on three benchmark datasets. Experimental results demonstrate that the proposed techniques generate informative, complete, consistent, and insightful summaries for different research problems, promoting the use of LLMs in more professional fields.
The COBREX archival survey: improved constraints on the occurrence rate of wide-orbit substellar companions. I. A uniform re-analysis of 400 stars from the GPIES survey
2024-11-09
V. Squicciarini, J. Mazoyer, A. -M. Lagrange, A. Chomez, P. Delorme, O. Flasseur, F. Kiefer
Direct imaging (DI) campaigns are uniquely suited to probing the outer regions around young stars and looking for giant exoplanet and brown dwarf companions, hence providing key complementary information to radial velocity (RV) and transit searches for the purpose of demographic studies. However, the critical 5-20 au region, where most giant planets are thought to form, remains poorly explored, lying in-between RV and DI capabilities. Significant gains in detection performances can be attained at no instrumental cost by means of advanced post-processing techniques. In the context of the COBREX project, we have assembled the largest collection of archival DI observations to date in order to undertake a large and uniform re-analysis. In particular, this paper details the re-analysis of 400 stars from the GPIES survey operated at GPI@Gemini South. Following the pre-reduction of raw frames, GPI data cubes were processed by means of the PACO algorithm. Candidates were identified and vetted based on multi-epoch proper motion analysis -- whenever possible -- and by means of a suitable color-magnitude diagram. The conversion of detection limits into detectability maps allowed for an estimate of unbiased occurrence frequencies of giant planets and brown dwarfs. Deeper detection limits were derived compared to the literature, with up to a twofold gain in minimum detectable mass compared to previous GPI-based publications. Although no new substellar companion was confirmed, we identified two interesting planet candidates awaiting follow-up observations. We derive an occurrence rate of $1.7_{-0.7}^{+0.9}\%$ for $5$~\mjup$ < m < 13$~\mjup planets in $10~\text{au}< a < 100~\text{au}$, that raises to $2.2_{-0.8}^{+1.0}\%$ when including substellar objects up to 80 \mjup.(abridged)
A Critical Analysis of Foundations, Challenges and Directions for Zero Trust Security in Cloud Environments
2024-11-09
Ganiyu Oladimeji
This review discusses the theoretical frameworks and application prospects of Zero Trust Security (ZTS) in cloud computing context. This is because, as organisations move more of their applications and data to the cloud, the old borders-based security model that many implemented are inadequate, therefore a model that has a trust no one, verify everything approach is required. This paper analyzes the core principles of ZTS, including micro-segmentation, least privileged access, and continuous monitoring, while critically examining four major controversies: scalability issues, Economics, Integration issues with existing systems, and Compliance to legal requirements. In this paper, having reviewed the existing literature in the field and various implementation cases, the main barriers to implementing zero trust security were outlined, including the dimensions of decreased performance in large-scale production and the need for major upfront investments that can be difficult for small companies to meet effectively. This research shows that there is no clear correlation between security effectiveness and operational efficiency: while organisations experience up to 40% decrease of security incidents after implementation, they note first negative impacts on performance. This study also shows that to support ZTS there is a need to address the context as the economics and operations of ZTS differ in strengths depending on the size of the organizations and the infrastructures. Some of these are: performance enhancement and optimizations, economic optimization, architectural blend, and privacy-preserving technologies. This review enriches the existing literature on cloud security by presenting both the theoretical framework of ZTS and the observed issues, and provides suggestions useful for future research and practice in the construction of the cloud security architecture.
Detecting Reference Errors in Scientific Literature with Large Language Models
2024-11-09
Tianmai M. Zhang, Neil F. Abernethy
Reference errors, such as citation and quotation errors, are common in scientific papers. Such errors can result in the propagation of inaccurate information, but are difficult and time-consuming to detect, posing a significant challenge to scientific publishing. To support automatic detection of reference errors, this work evaluated the ability of large language models in OpenAI's GPT family to detect quotation errors. Specifically, we prepared an expert-annotated, general-domain dataset of statement-reference pairs from journal articles. Large language models were evaluated in different settings with varying amounts of reference information provided by retrieval augmentation. Our results showed that large language models are able to detect erroneous citations with limited context and without fine-tuning. This study contributes to the growing literature that seeks to utilize artificial intelligence to assist in the writing, reviewing, and publishing of scientific papers. Potential avenues for further improvements in this task are also discussed.
We investigate granular flows over an inclined rigid base, which is vibrated externally in a direction normal to itself, through discrete element simulations. We vary the base inclination angle theta, vibration frequency f, and amplitude A to study changes in the granular flow profile and the mass flow rate Q. We find that the flow velocity profiles for the vibrated bases are nonlinear, unlike their fixed base counterparts. Our study reveals that Q may be maintained nearly constant in flows over vibrated bases utilizing appropriate combinations of theta, A, and f. At the same time, by vibrating the base at a fixed inclination, we may increase the mass flow rate by as much as 30 times the value found in flows over a stationary base. Finally, we show that Q depends upon a nondimensional number S obtained by taking the ratio of vibrational and gravitational energies.
This paper presents a tutorial and review of SRAM-based Compute-in-Memory (CIM) circuits, with a focus on both Digital CIM (DCIM) and Analog CIM (ACIM) implementations. We explore the fundamental concepts, architectures, and operational principles of CIM technology. The review compares DCIM and ACIM approaches, examining their respective advantages and challenges. DCIM offers high computational precision and process scaling benefits, while ACIM provides superior power and area efficiency, particularly for medium-precision applications. We analyze various ACIM implementations, including current-based, time-based, and charge-based approaches, with a detailed look at charge-based ACIMs. The paper also discusses emerging hybrid CIM architectures that combine DCIM and ACIM to leverage the strengths of both approaches.
This systematic review explores the theoretical foundations, evolution, applications, and future potential of Kolmogorov-Arnold Networks (KAN), a neural network model inspired by the Kolmogorov-Arnold representation theorem. KANs distinguish themselves from traditional neural networks by using learnable, spline-parameterized functions instead of fixed activation functions, allowing for flexible and interpretable representations of high-dimensional functions. This review details KAN's architectural strengths, including adaptive edge-based activation functions that improve parameter efficiency and scalability in applications such as time series forecasting, computational biomedicine, and graph learning. Key advancements, including Temporal-KAN, FastKAN, and Partial Differential Equation (PDE) KAN, illustrate KAN's growing applicability in dynamic environments, enhancing interpretability, computational efficiency, and adaptability for complex function approximation tasks. Additionally, this paper discusses KAN's integration with other architectures, such as convolutional, recurrent, and transformer-based models, showcasing its versatility in complementing established neural networks for tasks requiring hybrid approaches. Despite its strengths, KAN faces computational challenges in high-dimensional and noisy data settings, motivating ongoing research into optimization strategies, regularization techniques, and hybrid models. This paper highlights KAN's role in modern neural architectures and outlines future directions to improve its computational efficiency, interpretability, and scalability in data-intensive applications.
2024-11-09
Haibo Sun, Naoki Otani, Hannah Kim, Dan Zhang, Nikita Bhutani
Conversational Recommender Systems (CRS) engage users in interactive dialogues to gather preferences and provide personalized recommendations. Traditionally, CRS rely on pre-defined attributes or expensive, domain-specific annotated datasets to guide conversations, which limits flexibility and adaptability across domains. In this work, we introduce SnipRec, a novel CRS that enhances dialogues and recommendations by extracting diverse expressions and preferences from user-generated content (UGC) like customer reviews. Using large language models, SnipRec maps user responses and UGC to concise snippets, which are used to generate clarification questions and retrieve relevant items. Our approach eliminates the need for domain-specific training, making it adaptable to new domains and effective without prior knowledge of user preferences. Extensive experiments on the Yelp dataset demonstrate the effectiveness of snippet-based representations against document and sentence-based representations. Additionally, SnipRec is able to improve Hits@10 by 0.25 over the course of five conversational turns, underscoring the efficiency of SnipRec in capturing user preferences through multi-turn conversations.
Non-linear Cosmological Perturbations for Coupled Dark Energy
2024-11-09
Bilal Tüdes, Luca Amendola
We estimate the one-loop perturbation kernels for a minimal modified gravity model in which dark energy is coupled to dark mater via a constant coupling. We derive the time-dependent kernels via analytical and numerical solutions and provide accurate fitting functions. These kernels can be directly employed to test for modified gravity in forthcoming large-scale surveys.
Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learning
2024-11-09
Chenqing Hua
Graphs serve as fundamental descriptors for systems composed of interacting elements, capturing a wide array of data types, from molecular interactions to social networks and knowledge graphs. In this paper, we present an exhaustive review of the latest advancements in graph representation learning and Graph Neural Networks (GNNs). GNNs, tailored to handle graph-structured data, excel in deriving insights and predictions from intricate relational information, making them invaluable for tasks involving such data. Graph representation learning, a pivotal approach in analyzing graph-structured data, facilitates numerous downstream tasks and applications across machine learning, data mining, biomedicine, and healthcare. Our work delves into the capabilities of GNNs, examining their foundational designs and their application in addressing real-world challenges. We introduce a GNN equipped with an advanced high-order pooling function, adept at capturing complex node interactions within graph-structured data. This pooling function significantly enhances the GNN's efficacy in both node- and graph-level tasks. Additionally, we propose a molecular graph generative model with a GNN as its core framework. This GNN backbone is proficient in learning invariant and equivariant molecular characteristics. Employing these features, the molecular graph generative model is capable of simultaneously learning and generating molecular graphs with atom-bond structures and precise atom positions. Our models undergo thorough experimental evaluations and comparisons with established methods, showcasing their superior performance in addressing diverse real-world challenges with various datasets.
2024-11-09
Jiayao Zhang, Yuran Bi, Mengye Cheng, Jinfei Liu, Kui Ren, Qiheng Sun, Yihang Wu, Yang Cao, Raul Castro Fernandez, Haifeng Xu, Ruoxi Jia, Yongchan Kwon, Jian Pei, Jiachen T. Wang, Haocheng Xia, Li Xiong, Xiaohui Yu, James Zou
Data is the new oil of the 21st century. The growing trend of trading data for greater welfare has led to the emergence of data markets. A data market is any mechanism whereby the exchange of data products including datasets and data derivatives takes place as a result of data buyers and data sellers being in contact with one another, either directly or through mediating agents. It serves as a coordinating mechanism by which several functions, including the pricing and the distribution of data as the most important ones, interact to make the value of data fully exploited and enhanced. In this article, we present a comprehensive survey of this important and emerging direction from the aspects of data search, data productization, data transaction, data pricing, revenue allocation as well as privacy, security, and trust issues. We also investigate the government policies and industry status of data markets across different countries and different domains. Finally, we identify the unresolved challenges and discuss possible future directions for the development of data markets.
Curriculum Learning for Few-Shot Domain Adaptation in CT-based Airway Tree Segmentation
2024-11-08
Maxime Jacovella, Ali Keshavarzi, Elsa Angelini
Despite advances with deep learning (DL), automated airway segmentation from chest CT scans continues to face challenges in segmentation quality and generalization across cohorts. To address these, we propose integrating Curriculum Learning (CL) into airway segmentation networks, distributing the training set into batches according to ad-hoc complexity scores derived from CT scans and corresponding ground-truth tree features. We specifically investigate few-shot domain adaptation, targeting scenarios where manual annotation of a full fine-tuning dataset is prohibitively expensive. Results are reported on two large open-cohorts (ATM22 and AIIB23) with high performance using CL for full training (Source domain) and few-shot fine-tuning (Target domain), but with also some insights on potential detrimental effects if using a classic Bootstrapping scoring function or if not using proper scan sequencing.
Observation of Critical Scaling in Spin Glasses below Tc using the Thermoremanent Magnetization
2024-11-08
G. G. Kenning, M. Brandt, R. Brake, M. Hepler, D. Tennant
Time-dependent Thermoremanent Magnetization (TRM) studies have been instrumental in probing energy dynamics within the spin glass phase. In this paper, we will review the evolution of the TRM experiment over the last half century and discuss some aspects related to how it has been employed in the understanding of spin glasses. We will also report on recent experiments using high resolution DC SQUID magnetometry to probe the TRM at temperatures less than but near to the transition temperature Tc. These experiments have been performed as a function of waiting time, temperature, and five different magnetic fields. We find that as the transition temperature is approached from below, the characteristic time scale of the TRM is suppressed up to several orders of magnitude in time. In the highest temperature region, we find that the waiting time effect goes away, and a waiting time independent crossover line is reached. We also find that increasing the magnetic field, further suppresses the crossover line. Using a first principles energy argument across the crossover line, we derive an equation that is an excellent fit to the crossover lines for all magnetic fields probed. The data show strong evidence for an H = 0 Oe phase transition.
Foundations for the psychological safety of human and autonomous vehicles interaction
2024-11-08
Yandika Sirgabsou, Benjamin Hardin, François Leblanc, Efi Raili, Pericle Salvini, David Jackson, Marina Jirotka, Lars Kunze
This paper addresses the critical issue of psychological safety in the design and operation of autonomous vehicles, which are increasingly integrated with artificial intelligence technologies. While traditional safety standards focus primarily on physical safety, this paper emphasizes the psychological implications that arise from human interactions with autonomous vehicles, highlighting the importance of trust and perceived risk as significant factors influencing user acceptance. Through a review of existing safety techniques, the paper defines psychological safety in the context of autonomous vehicles, proposes a risk model to identify and assess psychological risks, and adopts a system-theoretic analysis method. The paper illustrates the potential psychological hazards using a scenario involving a family's experience with an autonomous vehicle, aiming to systematically evaluate situations that could lead to psychological harm. By establishing a framework that incorporates psychological safety alongside physical safety, the paper contributes to the broader discourse on the safe deployment of autonomous vehicle and aims to guide future developments in user-cantered design and regulatory practices.
Schmidt decomposition is a powerful tool in quantum information. While Schmidt decomposition is universal for bipartite states, its not for multipartite states. In this article, we review properties of bipartite Schmidt decompositions and study which of them extend to multipartite states. In particular, Schmidt number (the number of non-zero terms in Schmidt decomposition) define an equivalence class using separable unitary transforms. We show that it is NP-complete to partition a multipartite state that attains the highest Schmidt number. In addition, we observe that purifications of a density matrix of a composite system preserves Schmidt decomposability.
Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification
2024-11-08
Antonio De Santis, Riccardo Campi, Matteo Bianchi, Marco Brambilla
Convolutional Neural Networks (CNNs) have seen significant performance improvements in recent years. However, due to their size and complexity, they function as black-boxes, leading to transparency concerns. State-of-the-art saliency methods generate local explanations that highlight the area in the input image where a class is identified but cannot explain how a concept of interest contributes to the prediction, which is essential for bias mitigation. On the other hand, concept-based methods, such as TCAV (Testing with Concept Activation Vectors), provide insights into how sensitive is the network to a concept, but cannot compute its attribution in a specific prediction nor show its location within the input image. This paper introduces a novel post-hoc explainability framework, Visual-TCAV, which aims to bridge the gap between these methods by providing both local and global explanations for CNN-based image classification. Visual-TCAV uses Concept Activation Vectors (CAVs) to generate saliency maps that show where concepts are recognized by the network. Moreover, it can estimate the attribution of these concepts to the output of any class using a generalization of Integrated Gradients. This framework is evaluated on popular CNN architectures, with its validity further confirmed via experiments where ground truth for explanations is known, and a comparison with TCAV. Our code will be made available soon.
Mobility-as-a-Service (MaaS) integrates different transport modalities and can support more personalisation of travellers' journey planning based on their individual preferences, behaviours and wishes. To fully achieve the potential of MaaS, a range of AI (including machine learning and data mining) algorithms are needed to learn personal requirements and needs, to optimise journey planning of each traveller and all travellers as a whole, to help transport service operators and relevant governmental bodies to operate and plan their services, and to detect and prevent cyber attacks from various threat actors including dishonest and malicious travellers and transport operators. The increasing use of different AI and data processing algorithms in both centralised and distributed settings opens the MaaS ecosystem up to diverse cyber and privacy attacks at both the AI algorithm level and the connectivity surfaces. In this paper, we present the first comprehensive review on the coupling between AI-driven MaaS design and the diverse cyber security challenges related to cyber attacks and countermeasures. In particular, we focus on how current and emerging AI-facilitated privacy risks (profiling, inference, and third-party threats) and adversarial AI attacks (evasion, extraction, and gamification) may impact the MaaS ecosystem. These risks often combine novel attacks (e.g., inverse learning) with traditional attack vectors (e.g., man-in-the-middle attacks), exacerbating the risks for the wider participation actors and the emergence of new business models.
Information system for analysis of nanostructure morphology
2024-11-08
Ludia T. Khusainova, Konstantin S. Kolegov
The primary focus of this work is the automating of morphology analysis for colloidal structures, achieved through the development of the ISANM information system. The ISANM system integrates various analysis methods and is designed prioritize usability, catering primarily to users rather than developers. This paper outlines the scope of the subject area, reviews selected methods for morphology analysis, and presents the scripts developed for implementing these methods in Python. The system core is implemented in C#, utilizing the .NET Framework and the MS SQL Server, and is developed within the Microsoft Visual Studio 2019 environment on Windows 10 using the client-server architecture. The system has been deployed on a VDS server using the Ubuntu 20.04 distribution. MS SQL Server is used as the database management system. The system can be accessed at https://isanm.space. An ORCID account is required for authorization. Geometric analysis of colloidal structures is significant in applications such as photonic crystals for optoelectronics and microelectronics, functional coatings in materials science, and biosensors for medical and environmental applications.
There is a persistent $\sim 5 \sigma$ tension between the value of the Hubble constant, as derived from either the local distance ladder or the cosmic microwave background, signaling either unaccounted for systematics in the measurements or `new physics'. Determining the Hubble constant using Type Ia supernovae requires non-trivial and accurate corrections for dust extinction. To circumvent this obstacle, we here determine the Hubble constant from blue, and hence presumably unextinguished, supernovae, only. For two different compilations of Type Ia supernova data and lightcurve fitting methods we find that the derived Hubble constant is consistently lower by $\sim$ 3 km s$^{-1}$ Mpc$^{-1}$ ($\sim 70$ km s$^{-1}$ Mpc$^{-1}$), and within 1 $\sigma$ of the Cosmic Microwave Background measurement, when using only blue supernovae as opposed to using all supernovae. Although the number of blue calibrating Type Ia supernovae is small, this indicates potential systematic effects in dust corrections in standard supernova cosmology. Upcoming major transient surveys will discover numerous unextinguished SNe~Ia, and thus be able to increase precision of the Hubble constant measured from blue SNe~Ia, heralding a promising path toward resolving the Hubble constant tension.
Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Survey
2024-11-08
Zhihong Liu, Xin Xu, Peng Qiao, Dongsheng Li
Deep reinforcement learning has led to dramatic breakthroughs in the field of artificial intelligence for the past few years. As the amount of rollout experience data and the size of neural networks for deep reinforcement learning have grown continuously, handling the training process and reducing the time consumption using parallel and distributed computing is becoming an urgent and essential desire. In this paper, we perform a broad and thorough investigation on training acceleration methodologies for deep reinforcement learning based on parallel and distributed computing, providing a comprehensive survey in this field with state-of-the-art methods and pointers to core references. In particular, a taxonomy of literature is provided, along with a discussion of emerging topics and open issues. This incorporates learning system architectures, simulation parallelism, computing parallelism, distributed synchronization mechanisms, and deep evolutionary reinforcement learning. Further, we compare 16 current open-source libraries and platforms with criteria of facilitating rapid development. Finally, we extrapolate future directions that deserve further research.
A Two-Step Concept-Based Approach for Enhanced Interpretability and Trust in Skin Lesion Diagnosis
2024-11-08
Cristiano Patrício, Luís F. Teixeira, João C. Neves
The main challenges hindering the adoption of deep learning-based systems in clinical settings are the scarcity of annotated data and the lack of interpretability and trust in these systems. Concept Bottleneck Models (CBMs) offer inherent interpretability by constraining the final disease prediction on a set of human-understandable concepts. However, this inherent interpretability comes at the cost of greater annotation burden. Additionally, adding new concepts requires retraining the entire system. In this work, we introduce a novel two-step methodology that addresses both of these challenges. By simulating the two stages of a CBM, we utilize a pretrained Vision Language Model (VLM) to automatically predict clinical concepts, and a Large Language Model (LLM) to generate disease diagnoses based on the predicted concepts. We validate our approach on three skin lesion datasets, demonstrating that it outperforms traditional CBMs and state-of-the-art explainable methods, all without requiring any training and utilizing only a few annotated examples. The code is available at https://github.com/CristianoPatricio/2-step-concept-based-skin-diagnosis.
Machine learning-driven Anomaly Detection and Forecasting for Euclid Space Telescope Operations
2024-11-08
Pablo Gómez, Roland D. Vavrek, Guillermo Buenadicha, John Hoar, Sandor Kruk, Jan Reerink
State-of-the-art space science missions increasingly rely on automation due to spacecraft complexity and the costs of human oversight. The high volume of data, including scientific and telemetry data, makes manual inspection challenging. Machine learning offers significant potential to meet these demands. The Euclid space telescope, in its survey phase since February 2024, exemplifies this shift. Euclid's success depends on accurate monitoring and interpretation of housekeeping telemetry and science-derived data. Thousands of telemetry parameters, monitored as time series, may or may not impact the quality of scientific data. These parameters have complex interdependencies, often due to physical relationships (e.g., proximity of temperature sensors). Optimising science operations requires careful anomaly detection and identification of hidden parameter states. Moreover, understanding the interactions between known anomalies and physical quantities is crucial yet complex, as related parameters may display anomalies with varied timing and intensity. We address these challenges by analysing temperature anomalies in Euclid's telemetry from February to August 2024, focusing on eleven temperature parameters and 35 covariates. We use a predictive XGBoost model to forecast temperatures based on historical values, detecting anomalies as deviations from predictions. A second XGBoost model predicts anomalies from covariates, capturing their relationships to temperature anomalies. We identify the top three anomalies per parameter and analyse their interactions with covariates using SHAP (Shapley Additive Explanations), enabling rapid, automated analysis of complex parameter relationships. Our method demonstrates how machine learning can enhance telemetry monitoring, offering scalable solutions for other missions with similar data challenges.
An Evidence-Based Curriculum Initiative for Hardware Reverse Engineering Education
2024-11-08
René Walendy, Markus Weber, Steffen Becker, Christof Paar, Nikol Rummel
The increasing importance of supply chain security for digital devices -- from consumer electronics to critical infrastructure -- has created a high demand for skilled cybersecurity experts. These experts use Hardware Reverse Engineering (HRE) as a crucial technique to ensure trust in digital semiconductors. Recently, the US and EU have provided substantial funding to educate this cybersecurity-ready semiconductor workforce, but success depends on the widespread availability of academic training programs. In this paper, we investigate the current state of education in hardware security and HRE to identify efficient approaches for establishing effective HRE training programs. Through a systematic literature review, we uncover 13 relevant courses, including eight with accompanying academic publications. We identify common topics, threat models, key pedagogical features, and course evaluation methods. We find that most hardware security courses do not prioritize HRE, making HRE training scarce. While the predominant course structure of lectures paired with hands-on projects appears to be largely effective, we observe a lack of standardized evaluation methods and limited reliability of student self-assessment surveys. Our results suggest several possible improvements to HRE education and offer recommendations for developing new training courses. We advocate for the integration of HRE education into curriculum guidelines to meet the growing societal and industry demand for HRE experts.
Mitigating Consequences of Prestige in Citations of Publications
2024-11-08
Michael Balzer, Adhen Benlahlou
For many public research organizations, funding creation of science and maximizing scientific output is of central interest. Typically, when evaluating scientific production for funding, citations are utilized as a proxy, although these are severely influenced by factors beyond scientific impact. This study aims to mitigate the consequences of the Matthew effect in citations, where prominent authors and prestigious journals receive more citations regardless of the scientific content of the publications. To this end, the study presents an approach to predicting citations of papers based solely on observable characteristics available at the submission stage of a double-blind peer-review process. Combining classical linear models, generalized linear models and utilizing large-scale data sets on biomedical papers based on the PubMed database, the results demonstrate that it is possible to make fairly accurate predictions of citations using only observable characteristics of papers excluding information on authors and journals, thereby mitigating the Matthew effect. Thus, the outcomes have important implications for the field of scientometrics, providing a more objective method for citation prediction by relying on pre-publication variables that are immune to manipulation by authors and journals, thereby enhancing the objectivity of the evaluation process. Our approach is thus important for government agencies responsible for funding the creation of high-quality scientific content rather than perpetuating prestige.
Large language models (LLMs) have excelled in numerous benchmarks, advancing AI applications in both linguistic and non-linguistic tasks. However, this has primarily benefited well-resourced languages, leaving less-resourced ones (LRLs) at a disadvantage. In this paper, we highlight the current state of the NLP field in the specific LRL: kyrgyz tili. Human evaluation, including annotated datasets created by native speakers, remains an irreplaceable component of reliable NLP performance, especially for LRLs where automatic evaluations can fall short. In recent assessments of the resources for Turkic languages, Kyrgyz is labeled with the status 'Scraping By', a severely under-resourced language spoken by millions. This is concerning given the growing importance of the language, not only in Kyrgyzstan but also among diaspora communities where it holds no official status. We review prior efforts in the field, noting that many of the publicly available resources have only recently been developed, with few exceptions beyond dictionaries (the processed data used for the analysis is presented at https://kyrgyznlp.github.io/). While recent papers have made some headway, much more remains to be done. Despite interest and support from both business and government sectors in the Kyrgyz Republic, the situation for Kyrgyz language resources remains challenging. We stress the importance of community-driven efforts to build these resources, ensuring the future advancement sustainability. We then share our view of the most pressing challenges in Kyrgyz NLP. Finally, we propose a roadmap for future development in terms of research topics and language resources.
Divided Differences and Multivariate Holomorphic Calculus
2024-11-08
Luiz Hartmann, Matthias Lesch
We review the multivariate holomorphic functional calculus for tuples in a commutative Banach algebra and establish a simple "na\"ive" extension to commuting tuples in a general Banach algebra. The approach is na\"ive in the sense that the na\"ively defined joint spectrum maybe too big. The advantage of the approach is that the functional calculus then is given by a simple concrete formula from which all its continuity properties can easily be derived. We apply this framework to multivariate functions arising as divided differences of a univariate function. This provides a rich set of examples to which our na\"ive calculus applies. Foremost, we offer a natural and straightforward proof of the Connes-Moscovici Rearrangement Lemma in the context of the multivariate holomorphic functional calculus. Secondly, we show that the Daletski-Krein type noncommutative Taylor expansion is a natural consequence of our calculus. Also Magnus' Theorem which gives a nonlinear differential equation for the $\log$ of the solutions to a linear matrix ODE follows naturally and easily from our calculus. Finally, we collect various combinatorial related formulas.
Cell Balancing Paradigms: Advanced Types, Algorithms, and Optimization Frameworks
2024-11-08
Anupama R Itagi, Rakhee Kallimani, Krishna Pai, Sridhar Iyer, Onel L. A. López, Sushant Mutagekar
The operation efficiency of the electric transportation, energy storage, and grids mainly depends on the fundamental characteristics of the employed batteries. Fundamental variables like voltage, current, temperature, and estimated parameters, like the State of Charge (SoC) of the battery pack, influence the functionality of the system. This motivates the implementation of a Battery Management System (BMS), critical for managing and maintaining the health, safety, and performance of a battery pack. This is ensured by measuring parameters like temperature, cell voltage, and pack current. It also involves monitoring insulation levels and fire hazards, while assessing the prevailing useful life of the batteries and estimating the SoC and State of Health (SoH). Additionally, the system manages and controls key activities like cell balancing and charge/discharge processes. Thus functioning of the battery can be optimised, by guaranteeing the vital parameters to be well within the prescribed range. This article discusses the several cell balancing schemes, and focuses on the intricacies of cell balancing algorithms and optimisation methods for cell balancing. We begin surveying recent cell balancing algorithms and then provide selection guidelines taking into account their advantages, disadvantages, and applications. Finally, we discuss various optimization algorithms and outline the essential parameters involved in the cell balancing process.
Solving the mystery of extreme light variability in the massive eccentric system MACHO 80.7443.1718
2024-11-08
Piotr A. Kołaczek-Szymański, Piotr Łojko, Andrzej Pigulski, Tomasz Różański, Dawid Moździerski
The evolution of massive stars is heavily influenced by their binarity, and the massive eccentric binary system MACHO 80.7443.1718 (ExtEV) serves as a prime example. This study explores whether the light variability of ExtEV, observed near the periastron during its 32.8-day orbit, can be explained by a wind-wind collision (WWC) model and reviews other potential explanations. Using broadband photometry, TESS data, ground-based $UBV$ time-series photometry, and high-resolution spectroscopy, we analysed the system's parameters. We ruled out the presence of a Keplerian disk and periodic Roche-lobe overflow. Our analysis suggests the primary component has a radius of about $30\,{\rm R}_\odot$, luminosity of $\sim6.6\times10^5\,{\rm L}_\odot$, and mass between $25$ and $45\,{\rm M}_\odot$, with a high wind mass-loss rate of $4.5\times10^{-5}\,{\rm M}_\odot\,{\rm yr}^{-1}$, likely enhanced by tidal interactions, rotation, and tidally excited oscillations. We successfully modelled ExtEV's light curve, identifying atmospheric eclipse and light scattering in the WWC cone as key contributors. The system's mass-loss rate exceeds theoretical predictions, indicating that ExtEV is in a rare evolutionary phase, offering insights into enhanced mass loss in massive binary systems.
Improving the detection of technical debt in Java source code with an enriched dataset
2024-11-08
Nam Le Hai, Anh M. T. Bui, Phuong T. Nguyen, Davide Di Ruscio, Rick Kazman
Technical debt (TD) is a term used to describe the additional work and costs that emerge when developers have opted for a quick and easy solution to a problem, rather than a more effective and well-designed, but time-consuming approach. Self-Admitted Technical Debts (SATDs) are a specific type of technical debts that developers intentionally document and acknowledge, typically via textual comments. While these self-admitted comments are a useful tool for identifying technical debts, most of the existing approaches focus on capturing crucial tokens associated with various categories of TD, neglecting the rich information embedded within the source code itself. Recent research has focused on detecting SATDs by analyzing comments embedded in source code, and there has been little work dealing with technical debts contained in the source code. To fill such a gap, in this study, through the analysis of comments and their associated source code from 974 Java projects hosted in the Stack corpus, we curated the first ever dataset of TD identified by code comments, coupled with its associated source code. Through an empirical evaluation, we found out that the comments of the resulting dataset help enhance the prediction performance of state-of-the-art SATD detection models. More importantly, including the classified source code significantly improves the accuracy in predicting various types of technical debt. In this respect, our work is two-fold: (i) We believe that our dataset will catalyze future work in the domain, inspiring various research issues related to the recognition of technical debt; (ii) The proposed classifiers may serve as baselines for other studies on the detection of TD by means of the curated dataset.
Migration of celestial bodies in the Solar system and in several exoplanetary systems
2024-11-08
S. I. Ipatov
A review of the results on the migration of celestial bodies in the Solar System and in some exoplanetary systems is presented. Some problems of planet accumulation and migration of planetesimals, small bodies and dust in the forming and present Solar System are considered. It has been noted that the outer layers of the Earth and Venus could have accumulated similar planetesimals from different areas of the feeding zone of the terrestrial planets.The formation of the embryos of the Earth and the Moon from a common rarefied condensation with subsequent growth of the main mass of the embryo of the Moon near the Earth is also discussed. The influence of changes in the semimajor axis of Jupiter's orbit on the formation of the asteroid belt is discussed, as well as the influence of planetesimals from the feeding zone of the giant planets on the formation of bodies beyond the orbit of Neptune. The migration of bodies to the terrestrial planets from different distances from the Sun is considered. It is noted that bodies from the feeding zone of the giant planets and from the outer asteroid belt could deliver to the Earth a quantity of water comparable to the mass of water in the Earth's oceans. The migration of bodies ejected from the Earth is considered. The probabilities of collisions of dust particles with the Earth are usually an order of magnitude greater than the probabilities of collisions of their parent bodies with the Earth. The migration of planetesimals is considered in exoplanetary systems Proxima Centauri and TRAPPIST-1. The amount of water delivered to the inner planet Proxima Centauri b, may have been more than the amount delivered to the Earth. The outer layers of neighboring planets in the TRAPPIST-1 system may contain similar material if there were many planetesimals near their orbits during the late stages of planetary accumulation.
Wireless networking is encouraged by the constant enhancement of sensors' ability and wireless communication. To provide service quality support for multimedia viz. audio and video streams, the IEEE 802.11e MAC (Media Access Control) improves basic 802.11 MAC. IEEE 802.11 standard series such as IEEE 802.11a, b, g, n, p, and ac have been promoted and specified in the current communications and connection development. Each standard has functionality that matches the kind of applications for which the standard is intended. IEEE 802.11ac has better performance with fewer interferences and achieves gigabits per second capacity transfer rates. This paper discusses the comparative examination of the IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n, IEEE 802.11p, and IEEE 802.11ac standards which increase accuracy and performance pertaining to the IEEE 802.11 standard. In this paper, we investigate the design requirements for numerous simultaneous peer-to-peer connections. Further, this study offers a systematic review and analysis of the MAC layer in WMN (Wireless Mesh Network) and also highlights their open research issues and challenges. Finally, this paper discusses various potential directions for future research in this area with an emphasis on their strengths and limitations.
We survey quadratic Hessian equations: definition, background, rigidity of entire solutions, regularity of viscosity solutions, a priori Hessian estimates, and open problems.
Scientific literature is typically dense, requiring significant background knowledge and deep comprehension for effective engagement. We introduce SciDQA, a new dataset for reading comprehension that challenges LLMs for a deep understanding of scientific articles, consisting of 2,937 QA pairs. Unlike other scientific QA datasets, SciDQA sources questions from peer reviews by domain experts and answers by paper authors, ensuring a thorough examination of the literature. We enhance the dataset's quality through a process that carefully filters out lower quality questions, decontextualizes the content, tracks the source document across different versions, and incorporates a bibliography for multi-document question-answering. Questions in SciDQA necessitate reasoning across figures, tables, equations, appendices, and supplementary materials, and require multi-document reasoning. We evaluate several open-source and proprietary LLMs across various configurations to explore their capabilities in generating relevant and factual responses. Our comprehensive evaluation, based on metrics for surface-level similarity and LLM judgements, highlights notable performance discrepancies. SciDQA represents a rigorously curated, naturally derived scientific QA dataset, designed to facilitate research on complex scientific text understanding.
A Taxonomy of AgentOps for Enabling Observability of Foundation Model based Agents
2024-11-08
Liming Dong, Qinghua Lu, Liming Zhu
The ever-improving quality of LLMs has fueled the growth of a diverse range of downstream tasks, leading to an increased demand for AI automation and a burgeoning interest in developing foundation model (FM)-based autonomous agents. As AI agent systems tackle more complex tasks and evolve, they involve a wider range of stakeholders, including agent users, agentic system developers and deployers, and AI model developers. These systems also integrate multiple components such as AI agent workflows, RAG pipelines, prompt management, agent capabilities, and observability features. In this case, obtaining reliable outputs and answers from these agents remains challenging, necessitating a dependable execution process and end-to-end observability solutions. To build reliable AI agents and LLM applications, it is essential to shift towards designing AgentOps platforms that ensure observability and traceability across the entire development-to-production life-cycle. To this end, we conducted a rapid review and identified relevant AgentOps tools from the agentic ecosystem. Based on this review, we provide an overview of the essential features of AgentOps and propose a comprehensive overview of observability data/traceable artifacts across the agent production life-cycle. Our findings provide a systematic overview of the current AgentOps landscape, emphasizing the critical role of observability/traceability in enhancing the reliability of autonomous agent systems.
Quantization of foundational models (FMs) is significantly more challenging than traditional DNNs due to the emergence of large magnitude features called outliers. Existing outlier-aware algorithm/architecture co-design techniques either use mixed-precision, retaining outliers at high precision but compromise hardware efficiency, or quantize inliers and outliers at the same precision, improving hardware efficiency at the cost of accuracy. To address this mutual exclusivity, in this paper, we propose MicroScopiQ, a novel co-design technique that leverages pruning to complement outlier-aware quantization. MicroScopiQ retains outliers at higher precision while pruning a certain fraction of least important weights to distribute the additional outlier bits; ensuring high accuracy, aligned memory and hardware efficiency. We design a high-throughput, low overhead accelerator architecture composed of simple multi-precision INT processing elements and a novel network-on-chip called ReCoN that efficiently abstracts the complexity of supporting high-precision outliers. Additionally, unlike existing alternatives, MicroScopiQ does not assume any locality of outlier weights, enabling applicability to a broad range of FMs. Extensive experiments across various quantization settings show that MicroScopiQ achieves SoTA quantization performance while simultaneously improving inference performance by 3x and reducing energy by 2x over existing alternatives.
KMT-2024-BLG-1044L: A sub-Uranus microlensing planet around a host at the star-brown dwarf mass boundary
2024-11-08
Cheongho Han, Yoon-Hyun Ryu, Chung-Uk Lee, Andrew Gould, Michael D. Albrow, Sun-Ju Chung, Kyu-Ha Hwang, Youn Kil Jung, Yossi Shvartzvald, In-Gu Shin, Jennifer C. Yee, Hongjing Yang, Weicheng Zang, Doeon Kim, Dong-Jin Kim, Byeong-Gon Park, Richard W. Pogge
We analysed microlensing data to uncover the nature of the anomaly that appeared near the peak of the short-timescale microlensing event KMT-2024-BLG-1044. Despite the anomaly's brief duration of less than a day, it was densely observed through high-cadence monitoring conducted by the KMTNet survey. Detailed modelling of the light curve confirmed the planetary origin of the anomaly and revealed two possible solutions, due to an inner--outer degeneracy. The two solutions provide different measured planet parameters: $(s, q)_{\rm inner} = [1.0883 \pm 0.0027, (3.125 \pm 0.248)\times 10^{-4}]$ for the inner solutions and $(s, q)_{\rm outer} = [1.0327 \pm 0.0054, (3.350 \pm 0.316)\times 10^{-4}]$ for the outer solutions. Using Bayesian analysis with constraints provided by the short event timescale ($t_{\rm E} \sim 9.1$~day) and the small angular Einstein radius ($\theta_{\rm E}\sim 0.16$~mas for the inner solution and $\sim 0.10$~mas for the outer solutio), we determined that the lens is a planetary system consisting of a host near the boundary between a star and a brown dwarf and a planet with a mass lower than that of Uranus. The discovery of the planetary system highlights the crucial role of the microlensing technique in detecting planets that orbit substellar brown dwarfs or very low-mass stars.
Developing a Safety Management System for the Autonomous Vehicle Industry
2024-11-08
David Wichner, Jeffrey Wishart, Jason Sergent, Sunder Swaminathan
Safety Management Systems (SMSs) have been used in many safety-critical industries and are now being developed and deployed in the automated driving system (ADS)-equipped vehicle (AV) sector. Industries with decades of SMS deployment have established frameworks tailored to their specific context. Several frameworks for an AV industry SMS have been proposed or are currently under development. These frameworks borrow heavily from the aviation industry although the AV and aviation industries differ in many significant ways. In this context, there is a need to review the approach to develop an SMS that is tailored to the AV industry, building on generalized lessons learned from other safety-sensitive industries. A harmonized AV-industry SMS framework would establish a single set of SMS practices to address management of broad safety risks in an integrated manner and advance the establishment of a more mature regulatory framework. This paper outlines a proposed SMS framework for the AV industry based on robust taxonomy development and validation criteria and provides rationale for such an approach. Keywords: Safety Management System (SMS), Automated Driving System (ADS), ADS-Equipped Vehicle, Autonomous Vehicles (AV)
SCExAO/CHARIS Spectroscopic Characterization of Cloudy L/T Transition Companion Brown Dwarf HIP 93398 B
2024-11-08
Briley Lewis, Yiting Li, Aidan Gibbs, Michael P. Fitzgerald, Timothy Brandt, Daniella Bardalez Gagliuffi, Qier An, Minghan Chen, Rachel Bowens-Rubin, Maissa Salama, Julien Lozi, Rebecca Jensen-Clem, Ben Mazin
Brown dwarfs with measured dynamical masses and spectra from direct imaging are benchmarks that anchor substellar atmosphere cooling and evolution models. We present Subaru SCExAO/CHARIS infrared spectroscopy of HIP 93398 B, a brown dwarf companion recently discovered by Li et al. 2023 as part of an informed survey using the Hipparcos-Gaia Catalog of Accelerations. This object was previously classified as a T6 dwarf based on its luminosity, with its independently-derived age and dynamical mass in tension with existing models of brown dwarf evolution. Spectral typing via empirical standard spectra, temperatures derived by fitting substellar atmosphere models, and J-H, J-K and H-L' colors all suggest that this object has a substantially higher temperature and luminosity, consistent with classification as a late-L dwarf near the L/T transition (T = 1200$^{+140}_{-119}$ K) with moderate to thick clouds possibly present in its atmosphere. When compared with the latest generation of evolution models that account for clouds with our revised luminosity and temperature for the object, the tension between the model-independent mass/age and model predictions is resolved.
2024-11-08
Kushan Mitra, Dan Zhang, Sajjadur Rahman, Estevam Hruschka
Large Language Models (LLMs) have shown impressive capability in language generation and understanding, but their tendency to hallucinate and produce factually incorrect information remains a key limitation. To verify LLM-generated contents and claims from other sources, traditional verification approaches often rely on holistic models that assign a single factuality label to complex claims, potentially obscuring nuanced errors. In this paper, we advocate for a shift toward fine-grained verification, where complex claims are broken down into smaller sub-claims for individual verification, allowing for more precise identification of inaccuracies, improved transparency, and reduced ambiguity in evidence retrieval. However, generating sub-claims poses challenges, such as maintaining context and ensuring semantic equivalence with respect to the original claim. We introduce FactLens, a benchmark for evaluating fine-grained fact verification, with metrics and automated evaluators of sub-claim quality. The benchmark data is manually curated to ensure high-quality ground truth. Our results show alignment between automated FactLens evaluators and human judgments, and we discuss the impact of sub-claim characteristics on the overall verification performance.
A method based on Generative Adversarial Networks for disentangling physical and chemical properties of stars in astronomical spectra
2024-11-08
Raúl Santoveña, Carlos Dafonte, Minia Manteiga
Data compression techniques focused on information preservation have become essential in the modern era of big data. In this work, an encoder-decoder architecture has been designed, where adversarial training, a modification of the traditional autoencoder, is used in the context of astrophysical spectral analysis. The goal of this proposal is to obtain an intermediate representation of the astronomical stellar spectra, in which the contribution to the flux of a star due to the most influential physical properties (its surface temperature and gravity) disappears and the variance reflects only the effect of the chemical composition over the spectrum. A scheme of deep learning is used with the aim of unraveling in the latent space the desired parameters of the rest of the information contained in the data. This work proposes a version of adversarial training that makes use of a discriminator per parameter to be disentangled, thus avoiding the exponential combination that occurs in the use of a single discriminator, as a result of the discretization of the values to be untangled. To test the effectiveness of the method, synthetic astronomical data are used from the APOGEE and Gaia surveys. In conjunction with the work presented, we also provide a disentangling framework (GANDALF) available to the community, which allows the replication, visualization, and extension of the method to domains of any nature.
Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts for Colonic Pathology Diagnostics
2024-11-08
Cassandre Notton, Vasudev Sharma, Vincent Quoc-Huy Trinh, Lina Chen, Minqi Xu, Sonal Varma, Mahdi S. Hosseini
Colorectal cancer (CRC) is one of the few cancers that have an established dysplasia-carcinoma sequence that benefits from screening. Everyone over 50 years of age in Canada is eligible for CRC screening. About 20\% of those people will undergo a biopsy for a pre-neoplastic polyp and, in many cases, multiple polyps. As such, these polyp biopsies make up the bulk of a pathologist's workload. Developing an efficient computational model to help screen these polyp biopsies can improve the pathologist's workflow and help guide their attention to critical areas on the slide. DL models face significant challenges in computational pathology (CPath) because of the gigapixel image size of whole-slide images and the scarcity of detailed annotated datasets. It is, therefore, crucial to leverage self-supervised learning (SSL) methods to alleviate the burden and cost of data annotation. However, current research lacks methods to apply SSL frameworks to analyze pathology data effectively. This paper aims to propose an optimized Barlow Twins framework for colorectal polyps screening. We adapt its hyperparameters, augmentation strategy and encoder to the specificity of the pathology data to enhance performance. Additionally, we investigate the best Field of View (FoV) for colorectal polyps screening and propose a new benchmark dataset for CRC screening, made of four types of colorectal polyps and normal tissue, by performing downstream tasking on MHIST and NCT-CRC-7K datasets. Furthermore, we show that the SSL representations are more meaningful and qualitative than the supervised ones and that Barlow Twins benefits from the Swin Transformer when applied to pathology data. Codes are avaialble from https://github.com/AtlasAnalyticsLab/PathBT.
Harnessing coherence generation for precision single- and two-qubit quantum thermometry
2024-11-08
Youssef Aiache, Abderrahim El Allati, Khadija El Anouz
Quantum probes, such as single- and two-qubit probes, can accurately measure the temperature of a bosonic bath. The current investigation assesses the precision of temperature estimate using quantum Fisher information and the accompanying quantum signal-to-noise ratio. Employing an ancilla as a mediator between the probe and the bath improves thermometric sensitivity by transmitting temperature information into the probe qubit's coherences. In addition, we analyze two interacting qubits that were initially entangled or separated as quantum probes for various environmental configurations. Our findings show that increased precision is gained when the probe approaches its steady state, which is determined by the coupling between the two qubits. Furthermore, we can obtain high efficiency temperature estimation for any low temperature by changing the interaction between the two qubits.
GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise
2024-11-08
Moseli Mots'oehli, kyungim Baek
Active learning aims to train accurate classifiers while minimizing labeling costs by strategically selecting informative samples for annotation. This study focuses on image classification tasks, comparing AL methods on CIFAR10, CIFAR100, Food101, and the Chest X-ray datasets under varying label noise rates. We investigate the impact of model architecture by comparing Convolutional Neural Networks (CNNs) and Vision Transformer (ViT)-based models. Additionally, we propose a novel deep active learning algorithm, GCI-ViTAL, designed to be robust to label noise. GCI-ViTAL utilizes prediction entropy and the Frobenius norm of last-layer attention vectors compared to class-centric clean set attention vectors. Our method identifies samples that are both uncertain and semantically divergent from typical images in their assigned class. This allows GCI-ViTAL to select informative data points even in the presence of label noise while flagging potentially mislabeled candidates. Label smoothing is applied to train a model that is not overly confident about potentially noisy labels. We evaluate GCI-ViTAL under varying levels of symmetric label noise and compare it to five other AL strategies. Our results demonstrate that using ViTs leads to significant performance improvements over CNNs across all AL strategies, particularly in noisy label settings. We also find that using the semantic information of images as label grounding helps in training a more robust model under label noise. Notably, we do not perform extensive hyperparameter tuning, providing an out-of-the-box comparison that addresses the common challenge practitioners face in selecting models and active learning strategies without an exhaustive literature review on training and fine-tuning vision models on real-world application data.
First principles approaches and concepts for electrochemical systems
2024-11-08
Mira Todorova, Stefan Wippermann, Jörg Neugebauer
Ab initio techniques have revolutionized the way how theory can help practitioners to explore critical mechanisms and devise new strategies in discovering and designing materials. Yet, their application to electrochemical systems is still limited. A well known example how novel concepts can boost our ability to perform such studies is the introduction of temperature control into ab initio simulations. The analogous technique to model electrochemical systems -- potential control -- is just emerging. In this review, we critically discuss state-of-the-art approaches to describe electrified interfaces between a solid electrode and a liquid electrolyte in realistic environments. By exchanging energy, electronic charge and ions with their environment, electrochemical interfaces are thermodynamically open systems. In addition, on the time and length scales relevant for chemical reactions, large fluctuations of the electrostatic potential and field occur. We systematically discuss the key challenges in including these features in realistic ab initio simulations, as well as the available techniques and approaches to overcome them, in order to facilitate the development and use of these novel techniques to the wider community. These methodological developments provide researchers with a new level of realism to explore fundamental electrochemical mechanisms and reactions from first principles.
2024-11-08
Ismaël Ahlouche Lahlali, Josh A. O'Connor
In these lectures we review two approaches to constructing particle actions from coset spaces of symmetry groups: non-linear realisations and coadjoint orbits. At the level of particle actions, we observe that they coincide. We also provide an introduction to symplectic geometry and we sketch the theory of coadjoint orbits for the Poincar\'e group.
Cross-correlating the EMU Pilot Survey 1 with CMB lensing: Constraints on cosmology and galaxy bias with harmonic-space power spectra
2024-11-08
K. Tanidis, J. Asorey, C. S. Saraf, C. L. Hale, B. Bahr-Kalus, D. Parkinson, S. Camera, R. P. Norris, A. M. Hopkins, M. Bilicki, N. Gupta
We measured the harmonic-space power spectrum of galaxy clustering auto-correlation from the Evolutionary Map of the Universe Pilot Survey 1 data (EMU PS1) and its cross-correlation with the lensing convergence map of cosmic microwave background (CMB) from Planck Public Release 4 at the linear scale range from $\ell=2$ to 500. We applied two flux density cuts at $0.18$ and $0.4$mJy on the radio galaxies observed at 944MHz and considered two source detection algorithms. We found the auto-correlation measurements from the two algorithms at the 0.18mJy cut to deviate for $\ell\gtrsim250$ due to the different criteria assumed on the source detection and decided to ignore data above this scale. We report a cross-correlation detection of EMU PS1 with CMB lensing at $\sim$5.5$\sigma$, irrespective of flux density cut. In our theoretical modelling we considered two redshift distribution simulation models that yield consistent results, a linear and a non-linear matter power spectrum, and two linear galaxy bias models. That is a constant redshift-independent galaxy bias $b(z)=b_g$ and a constant amplitude galaxy bias $b(z)=b_g/D(z)$. By fixing a cosmology model and considering a non-linear matter power spectrum, we measured a constant galaxy bias at $0.18$mJy ($0.4$mJy) with $b_g=2.32^{+0.41}_{-0.33}$ ($2.18^{+0.17}_{-0.25}$) and a constant amplitude bias with $b_g=1.72^{+0.31}_{-0.21}$ ($1.78^{+0.22}_{-0.15}$). When $\sigma_8$ is a free parameter for the same models at $0.18$mJy ($0.4$mJy) with the constant model we found $\sigma_8=0.68^{+0.16}_{-0.14}$ ($0.82\pm0.10$), while with the constant amplitude model we measured $\sigma_8=0.61^{+0.18}_{-0.20}$ ($0.78^{+0.11}_{-0.09}$), respectively. Our results agree at $1\sigma$ with the measurements from Planck CMB and the weak lensing surveys and also show the potential of cosmology studies with future radio continuum survey data.
Autoregressive modeling has been a huge success in the field of natural language processing (NLP). Recently, autoregressive models have emerged as a significant area of focus in computer vision, where they excel in producing high-quality visual content. Autoregressive models in NLP typically operate on subword tokens. However, the representation strategy in computer vision can vary in different levels, \textit{i.e.}, pixel-level, token-level, or scale-level, reflecting the diverse and hierarchical nature of visual data compared to the sequential structure of language. This survey comprehensively examines the literature on autoregressive models applied to vision. To improve readability for researchers from diverse research backgrounds, we start with preliminary sequence representation and modeling in vision. Next, we divide the fundamental frameworks of visual autoregressive models into three general sub-categories, including pixel-based, token-based, and scale-based models based on the strategy of representation. We then explore the interconnections between autoregressive models and other generative models. Furthermore, we present a multi-faceted categorization of autoregressive models in computer vision, including image generation, video generation, 3D generation, and multi-modal generation. We also elaborate on their applications in diverse domains, including emerging domains such as embodied AI and 3D medical AI, with about 250 related references. Finally, we highlight the current challenges to autoregressive models in vision with suggestions about potential research directions. We have also set up a Github repository to organize the papers included in this survey at: \url{https://github.com/ChaofanTao/Autoregressive-Models-in-Vision-Survey}.
2024-11-07
C. L. Hale, I. Heywood, M. J. Jarvis, I. H. Whittam, P. N. Best, Fangxia An, R. A. A. Bowler, I. Harrison, A. Matthews, D. J. B. Smith, A. R. Taylor, M. Vaccari
The MeerKAT International GHz Tiered Extragalactic Exploration Survey (MIGHTEE) is one of the large survey projects using the MeerKAT telescope, covering four fields that have a wealth of ancillary data available. We present Data Release 1 of the MIGHTEE continuum survey, releasing total intensity images and catalogues over $\sim$20 deg$^2$, across three fields at $\sim$1.2-1.3 GHz. This includes 4.2 deg$^2$ over the Cosmic Evolution Survey (COSMOS) field, 14.4 deg$^2$ over the XMM Large-Scale Structure (XMM-LSS) field and deeper imaging over 1.5 deg$^2$ of the Extended Chandra Deep Field South (CDFS). We release images at both a lower resolution (7-9 arcsec) and higher resolution ($\sim 5$ arcsec). These images have central rms sensitivities of $\sim$1.3$-$2.7 $\mu $Jy beam$^{-1}$ ($\sim$1.2$-$3.6 $\mu $Jy beam$^{-1}$) in the lower (higher) resolution images respectively. We also release catalogues comprised of $\sim$144~000 ($\sim$114 000) sources using the lower (higher) resolution images. We compare the astrometry and flux-density calibration with the Early Science data in the COSMOS and XMM-LSS fields and previous radio observations in the CDFS field, finding broad agreement. Furthermore, we extend the source counts at the $\sim$10 $\mu$Jy level to these larger areas ($\sim 20$ deg$^2$) and, using the areal coverage of MIGHTEE we measure the sample variance for differing areas of sky. We find a typical sample variance of 10-20 per cent for 0.3 and 0.5 sq. deg. sub-regions at $S_{1.4} \leq 200$ $\mu $Jy, which increases at brighter flux densities, given the lower source density and expected higher galaxy bias for these sources.
Evidence for the $^{15}\text{Be}$ ground state from $^{12}$Be$+3$n events
2024-11-07
A. N. Kuchera, R. Shahid, J. Zhao, A. Edmondson, P. A. DeYoung, N. Frank, J. McDonaugh, O. Peterson-Veatch, W. F. Rogers, T. Redpath, M. Thoennessen
Background: $^{15}$Be is an unbound nuclide that has been observed to decay by one-neutron emission. Shell model calculations predict two low-lying states in its energy spectrum, however, only a single resonance has been observed from coincident measurements of $^{14}$Be+n. It has been suggested that the yet unobserved state may decay sequentially through the first excited state in $^{14}$Be followed by a two-neutron emission to $^{12}$Be. Purpose: The ground state of $^{15}$Be has yet to be confirmed. A search for this predicted $^{15}$Be state by reconstructing $^{12}$Be$+3$n events allows a possible determination of its ground state properties. Methods: A neutron-pickup reaction was performed with a $^{14}$Be beam on a CD$_2$ target to populate unbound $^{15}$Be states. Decay energies were reconstructed using invariant mass spectroscopy by detecting $^{12}$Be daughter nuclei in coincidence with up to three neutrons. Results: Evidence for at least one resonance in $^{15}$Be is presented based on the reconstruction of $^{12}$Be$+3$n events. Through comparison with simulations, the energy of the strongest resonance in the analyzed reaction and decay channel is determined to be $E_{^{12}Be+3n}=330(20)$ keV. Conclusions: The inclusion of a new $^{15}$Be state among the $^{12}$Be+$3$n events lower in relative decay energy than the previous $^{14}$Be+n observations provides the best fit to the data. Because this suggested new state would be lower in energy than the previously observed state, it is a candidate for the ground state of $^{15}$Be.
We study $\varepsilon$-stability in continuous logic. We first consider stability in a model, where we obtain a definability of types result with a better approximation than that in the literature. We also prove forking symmetry for $\varepsilon$-stability and briefly discuss finitely satisfiable types. We then do a short survey of $\varepsilon$-stability in a theory. Finally, we consider the map that takes each formula to its "degree" of stability in a given theory and show that it is a seminorm. All of this is done in the context of a first-order formalism that allows predicates to take values in arbitrary compact metric spaces.
Dark energy reconstructions combining BAO data with galaxy clusters and intermediate redshift catalogs
2024-11-07
Orlando Luongo, Marco Muccino
Cosmological parameters and dark energy (DE) behavior are generally constrained assuming \textit{a priori} models. We work out a model-independent reconstruction to bound the key cosmological quantities and the DE evolution. Through the model-independent \textit{B\'ezier interpolation} method, we reconstruct the Hubble rate from the observational Hubble data and derive analytic expressions for the distances of galaxy clusters, type Ia supernovae, and uncorrelated baryonic acoustic oscillation (BAO) data. In view of the discrepancy between Sloan Digital Sky Survey (SDSS) and Dark Energy Spectroscopic Instrument (DESI) BAO data, they are kept separate in two distinct analyses. Correlated BAO data are employed to break the baryonic--dark matter degeneracy. All these interpolations enable us to single out and reconstruct the DE behavior with the redshift $z$ in a totally model-independent way. In both analyses, with SDSS-BAO or DESI-BAO data sets, the constraints agree at $1$--$\sigma$ confidence level (CL) with the flat $\Lambda$CDM model. The Hubble constant tension appears solved in favor of the Planck satellite value. The reconstructed DE behavior exhibits deviations at small $z$ ($>1$--$\sigma$ CL), but agrees ($<1$--$\sigma$ CL) with the cosmological constant paradigm at larger $z$. Our method hints for a slowly evolving DE, consistent with a cosmological constant at early times.
Effective field theories for collective excitations of atomic nuclei
2024-11-07
E. A. Coello Pérez, T. Papenbrock
Collective modes emerge as the relevant degrees of freedom that govern low-energy excitations of atomic nuclei. These modes - rotations, pairing rotations, and vibrations - are separated in energy from non-collective excitations, making it possible to describe them in the framework of effective field theory. Rotations and pairing rotations are the remnants of Nambu-Goldstone modes from the emergent breaking of rotational symmetry and phase symmetries in finite deformed and finite superfluid nuclei, respectively. The symmetry breaking severely constrains the structure of low-energy Lagrangians and thereby clarifies what is essential and simplifies the description. The approach via effective field theories exposes the essence of nuclear collective excitations and is defined with a breakdown scale in mind. This permits one to make systematic improvements and to estimate and quantify uncertainties. Effective field theories of collective excitations have been used to compute spectra, transition rates, and other matrix elements of interest. In particular, predictions of the nuclear matrix element for neutrinoless double beta decay then come with quantified uncertainties. This review summarizes these results and also compares the approach via effective field theories to well-known models and ab initio computations.
Recent advances in foundation models, particularly Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs), facilitate intelligent agents being capable of performing complex tasks. By leveraging the ability of (M)LLMs to process and interpret Graphical User Interfaces (GUIs), these agents can autonomously execute user instructions by simulating human-like interactions such as clicking and typing. This survey consolidates recent research on (M)LLM-based GUI agents, highlighting key innovations in data, frameworks, and applications. We begin by discussing representative datasets and benchmarks. Next, we summarize a unified framework that captures the essential components used in prior research, accompanied by a taxonomy. Additionally, we explore commercial applications of (M)LLM-based GUI agents. Drawing from existing work, we identify several key challenges and propose future research directions. We hope this paper will inspire further developments in the field of (M)LLM-based GUI agents.
Astrophysical parameters associated to 'hot' stars in Gaia DR3
2024-11-07
Yves Frémat
Hot stars, of spectral types O-, B-, and A-, represent a small fraction of the stars observed by the Gaia satellite. Their properties and the specifications of the on-board instruments make their identification challenging. In the Gaia DR3, 12, 104,577 targets have been assigned a temperature greater than 7500 K. It represents about 2.5 percent of the stars having an effective temperature value in the Gaia DR3 astrophysical parameters table. We review the results obtained by the Apsis modules, focusing on the effective temperature, surface gravity, interstellar extinction, and V sin i parameters.
A multi-purpose automatic editing system based on lecture semantics for remote education
2024-11-07
Panwen Hu, Rui Huang
Remote teaching has become popular recently due to its convenience and safety, especially under extreme circumstances like a pandemic. However, online students usually have a poor experience since the information acquired from the views provided by the broadcast platforms is limited. One potential solution is to show more camera views simultaneously, but it is technically challenging and distracting for the viewers. Therefore, an automatic multi-camera directing/editing system, which aims at selecting the most concerned view at each time instance to guide the attention of online students, is in urgent demand. However, existing systems mostly make simple assumptions and focus on tracking the position of the speaker instead of the real lecture semantics, and therefore have limited capacities to deliver optimal information flow. To this end, this paper proposes an automatic multi-purpose editing system based on the lecture semantics, which can both direct the multiple video streams for real-time broadcasting and edit the optimal video offline for review purposes. Our system directs the views by semantically analyzing the class events while following the professional directing rules, mimicking a human director to capture the regions of interest from the viewpoint of the onsite students. We conduct both qualitative and quantitative analyses to verify the effectiveness of the proposed system and its components.
Plasticity Loss in Deep Reinforcement Learning: A Survey
2024-11-07
Timo Klein, Lukas Miklautz, Kevin Sidak, Claudia Plant, Sebastian Tschiatschek
Akin to neuroplasticity in human brains, the plasticity of deep neural networks enables their quick adaption to new data. This makes plasticity particularly crucial for deep Reinforcement Learning (RL) agents: Once plasticity is lost, an agent's performance will inevitably plateau because it cannot improve its policy to account for changes in the data distribution, which are a necessary consequence of its learning process. Thus, developing well-performing and sample-efficient agents hinges on their ability to remain plastic during training. Furthermore, the loss of plasticity can be connected to many other issues plaguing deep RL, such as training instabilities, scaling failures, overestimation bias, and insufficient exploration. With this survey, we aim to provide an overview of the emerging research on plasticity loss for academics and practitioners of deep reinforcement learning. First, we propose a unified definition of plasticity loss based on recent works, relate it to definitions from the literature, and discuss metrics for measuring plasticity loss. Then, we categorize and discuss numerous possible causes of plasticity loss before reviewing currently employed mitigation strategies. Our taxonomy is the first systematic overview of the current state of the field. Lastly, we discuss prevalent issues within the literature, such as a necessity for broader evaluation, and provide recommendations for future research, like gaining a better understanding of an agent's neural activity and behavior.
Rubin ToO 2024: Envisioning the Vera C. Rubin Observatory LSST Target of Opportunity program
2024-11-07
Igor Andreoni, Raffaella Margutti, John Banovetz, Sarah Greenstreet, Claire-Alice Hebert, Tim Lister, Antonella Palmese, Silvia Piranomonte, S. J. Smartt, Graham P. Smith, Robert Stein, Tomas Ahumada, Shreya Anand, Katie Auchettl, Michele T. Bannister, Eric C. Bellm, Joshua S. Bloom, Bryce T. Bolin, Clecio R. Bom, Daniel Brethauer, Melissa J. Brucker, David A. H. Buckley, Poonam Chandra, Ryan Chornock, Eric Christensen, Jeff Cooke, Alessandra Corsi, Michael W. Coughlin, Bolivia Cuevas-Otahola, D'Ammando Filippo, Biwei Dai, S. Dhawan, Alexei V. Filippenko, Ryan J. Foley, Anna Franckowiak, Andreja Gomboc, Benjamin P. Gompertz, Leanne P. Guy, Nandini Hazra, Christopher Hernandez, Griffin Hosseinzadeh, Maryam Hussaini, Dina Ibrahimzade, Luca Izzo, R. Lynne Jones, Yijung Kang, Mansi M. Kasliwal, Matthew Knight, Keerthi Kunnumkai, Gavin P Lamb, Natalie LeBaron, Cassandra Lejoly, Andrew J. Levan, Sean MacBride, Franco Mallia, Alex I. Malz, Adam A. Miller, J. C. Mora, Gautham Narayan, Nayana A. J., Matt Nicholl, Tiffany Nichols, S. R. Oates, Akshay Panayada, Fabio Ragosta, Tiago Ribeiro, Dan Ryczanowski, Nikhil Sarin, Megan E. Schwamb, Huei Sears, Darryl Z. Seligman, Ritwik Sharma, Manisha Shrestha, Simran, Michael C. Stroh, Giacomo Terreran, Aishwarya Linesh Thakur, Aum Trivedi, J. Anthony Tyson, Yousuke Utsumi, Aprajita Verma, V. Ashley Villar, Kathryn Volk, Meet J. Vyas, Amanda R. Wasserman, J. Craig Wheeler, Peter Yoachim, Angela Zegarelli, Federica Bianco
The Legacy Survey of Space and Time (LSST) at Vera C. Rubin Observatory is planned to begin in the Fall of 2025. The LSST survey cadence has been designed via a community-driven process regulated by the Survey Cadence Optimization Committee (SCOC), which recommended up to 3% of the observing time to carry out Target of Opportunity (ToO) observations. Experts from the scientific community, Rubin Observatory personnel, and members of the SCOC were brought together to deliver a recommendation for the implementation of the ToO program during a workshop held in March 2024. Four main science cases were identified: gravitational wave multi-messenger astronomy, high energy neutrinos, Galactic supernovae, and small potentially hazardous asteroids possible impactors. Additional science cases were identified and briefly addressed in the documents, including lensed or poorly localized gamma-ray bursts and twilight discoveries. Trigger prioritization, automated response, and detailed strategies were discussed for each science case. This document represents the outcome of the Rubin ToO 2024 workshop, with additional contributions from members of the Rubin Science Collaborations. The implementation of the selection criteria and strategies presented in this document has been endorsed in the SCOC Phase 3 Recommendations document (PSTN-056). Although the ToO program is still to be finalized, this document serves as a baseline plan for ToO observations with the Rubin Observatory.
Conversations and Deliberations: Non-Standard Cosmological Epochs and Expansion Histories
2024-11-07
Brian Batell, Keith R. Dienes, Brooks Thomas, Scott Watson, Rouzbeh Allahverdi, Mustafa Amin, Kimberly K. Boddy, M. Sten Delos, Adrienne L. Erickcek, Akshay Ghalsasi, John T. Giblin Jr., James Halverson, Fei Huang, Andrew J. Long, Lauren Pearce, Barmak Shams Es Haghi, Jessie Shelton, Gary Shiu, Kuver Sinha, Tristan L. Smith
This document summarizes the discussions which took place during the PITT-PACC Workshop entitled "Non-Standard Cosmological Epochs and Expansion Histories," held in Pittsburgh, Pennsylvania, Sept. 5-7, 2024. Much like the non-standard cosmological epochs that were the subject of these discussions, the format of this workshop was also non-standard. Rather than consisting of a series of talks from participants, with each person presenting their own work, this workshop was instead organized around free-form discussion blocks, with each centered on a different overall theme and guided by a different set of Discussion Leaders. This document is not intended to serve as a comprehensive review of these topics, but rather as an informal record of the discussions that took place during the workshop, in the hope that the content and free-flowing spirit of these discussions may inspire new ideas and research directions.
Mining the Minoria: Unknown, Under-represented, and Under-performing Minority Groups
2024-11-07
Mohsen Dehghankar, Abolfazl Asudeh
Due to a variety of reasons, such as privacy, data in the wild often misses the grouping information required for identifying minorities. On the other hand, it is known that machine learning models are only as good as the data they are trained on and, hence, may underperform for the under-represented minority groups. The missing grouping information presents a dilemma for responsible data scientists who find themselves in an unknown-unknown situation, where not only do they not have access to the grouping attributes but do not also know what groups to consider. This paper is an attempt to address this dilemma. Specifically, we propose a minority mining problem, where we find vectors in the attribute space that reveal potential groups that are under-represented and under-performing. Technically speaking, we propose a geometric transformation of data into a dual space and use notions such as the arrangement of hyperplanes to design an efficient algorithm for the problem in lower dimensions. Generalizing our solution to the higher dimensions is cursed by dimensionality. Therefore, we propose a solution based on smart exploration of the search space for such cases. We conduct comprehensive experiments using real-world and synthetic datasets alongside the theoretical analysis. Our experiment results demonstrate the effectiveness of our proposed solutions in mining the unknown, under-represented, and under-performing minorities.
Fast Generation of Weak Lensing Maps with Analytical Point Transformation Functions
2024-11-07
Kunhao Zhong, Gary Bernstein, Supranta S. Boruah, Bhuvnesh Jain, Sanjit Kobla
Nonlinear cosmological fields like galaxy density and lensing convergence can be approximately related to Gaussian fields via analytic point transforms. The lognormal transform (LN) has been widely used and is a simple example of a function that relates nonlinear fields to Gaussian fields. We consider more accurate General Point-Transformed Gaussian (GPTG) functions for such a mapping and apply them to convergence maps. We show that we can create maps that preserve the LN's ability to exactly match any desired power spectrum but go beyond LN by significantly improving the accuracy of the probability distribution function (PDF). With the aid of symbolic regression, we find a remarkably accurate GPTG function for convergence maps: its higher-order moments, scattering wavelet transform, Minkowski functionals, and peak counts match those of N-body simulations to the statistical uncertainty expected from tomographic lensing maps of the Rubin LSST 10 years survey. Our five-parameter function performs 2 to 5$\times$ better than the lognormal. We restrict our study to scales above about 7 arcmin; baryonic feedback alters the mass distribution on smaller scales. We demonstrate that the GPTG can robustly emulate variations in cosmological parameters due to the simplicity of the analytic transform. This opens up several possible applications, such as field-level inference, rapid covariance estimation, and other uses based on the generation of arbitrarily many maps with laptop-level computation capability.
On the Extent, Correlates, and Consequences of Reporting Bias in Survey Wages
2024-11-07
Marco Caliendo, Katrin Huber, Ingo E. Isphording, Jakob Wegmann
Surveys are an indispensable source of data for applied economic research; however, their reliance on self-reported information can introduce bias, especially if core variables such as personal income are misreported. To assess the extent and impact of this misreporting bias, we compare self-reported wages from the German Socio-Economic Panel (SOEP) with administrative wages from social security records (IEB) for the same individuals. Using a novel and unique data linkage (SOEP-ADIAB), we identify a modest but economically significant reporting bias, with SOEP respondents underreporting their administrative wages by about 7.3%. This misreporting varies systematically with individual, household, and especially job and firm characteristics. In replicating common empirical analyses in which wages serve as either dependent or independent variables, we find that misreporting is consequential for some, but not all estimated relationships. It turns out to be inconsequential for examining the returns to education, but relevant for analyzing the gender wage gap. In addition we find that misreporting bias can significantly affect the results when wage is used as the independent variable. Specifically, estimates of the wage-satisfaction relationship are substantially overestimated when based on survey data, although this bias is mitigated when focusing on interpersonal changes. Our findings underscore that survey-based measures of individual wages can significantly bias commonly estimated empirical relationships. They also demonstrate the enormous research potential of linked administrative-survey data.
Differential Privacy Overview and Fundamental Techniques
2024-11-07
Ferdinando Fioretto, Pascal Van Hentenryck, Juba Ziani
This chapter is meant to be part of the book "Differential Privacy in Artificial Intelligence: From Theory to Practice" and provides an introduction to Differential Privacy. It starts by illustrating various attempts to protect data privacy, emphasizing where and why they failed, and providing the key desiderata of a robust privacy definition. It then defines the key actors, tasks, and scopes that make up the domain of privacy-preserving data analysis. Following that, it formalizes the definition of Differential Privacy and its inherent properties, including composition, post-processing immunity, and group privacy. The chapter also reviews the basic techniques and mechanisms commonly used to implement Differential Privacy in its pure and approximate forms.
We survey some ideas from the subject of Random Algebraic Geometry, a field that introduces a probabilistic perspective on classical topics in real algebraic geometry. This offers a modern approach to classical problems, such as Hilbert's Sixteenth Problem.
The Multiple Dimensions of Spuriousness in Machine Learning
2024-11-07
Samuel J. Bell, Skyler Wang
Learning correlations from data forms the foundation of today's machine learning (ML) and artificial intelligence (AI) research. While such an approach enables the automatic discovery of patterned relationships within big data corpora, it is susceptible to failure modes when unintended correlations are captured. This vulnerability has expanded interest in interrogating spuriousness, often critiqued as an impediment to model performance, fairness, and robustness. In this article, we trace deviations from the conventional definition of statistical spuriousness-which denotes a non-causal observation arising from either coincidence or confounding variables-to articulate how ML researchers make sense of spuriousness in practice. Drawing on a broad survey of ML literature, we conceptualize the "multiple dimensions of spuriousness," encompassing: relevance ("Models should only use correlations that are relevant to the task."), generalizability ("Models should only use correlations that generalize to unseen data"), human-likeness ("Models should only use correlations that a human would use to perform the same task"), and harmfulness ("Models should only use correlations that are not harmful"). These dimensions demonstrate that ML spuriousness goes beyond the causal/non-causal dichotomy and that the disparate interpretative paths researchers choose could meaningfully influence the trajectory of ML development. By underscoring how a fundamental problem in ML is contingently negotiated in research contexts, we contribute to ongoing debates about responsible practices in AI development.
CaPo: Cooperative Plan Optimization for Efficient Embodied Multi-Agent Cooperation
2024-11-07
Jie Liu, Pan Zhou, Yingjun Du, Ah-Hwee Tan, Cees G. M. Snoek, Jan-Jakob Sonke, Efstratios Gavves
In this work, we address the cooperation problem among large language model (LLM) based embodied agents, where agents must cooperate to achieve a common goal. Previous methods often execute actions extemporaneously and incoherently, without long-term strategic and cooperative planning, leading to redundant steps, failures, and even serious repercussions in complex tasks like search-and-rescue missions where discussion and cooperative plan are crucial. To solve this issue, we propose Cooperative Plan Optimization (CaPo) to enhance the cooperation efficiency of LLM-based embodied agents. Inspired by human cooperation schemes, CaPo improves cooperation efficiency with two phases: 1) meta-plan generation, and 2) progress-adaptive meta-plan and execution. In the first phase, all agents analyze the task, discuss, and cooperatively create a meta-plan that decomposes the task into subtasks with detailed steps, ensuring a long-term strategic and coherent plan for efficient coordination. In the second phase, agents execute tasks according to the meta-plan and dynamically adjust it based on their latest progress (e.g., discovering a target object) through multi-turn discussions. This progress-based adaptation eliminates redundant actions, improving the overall cooperation efficiency of agents. Experimental results on the ThreeDworld Multi-Agent Transport and Communicative Watch-And-Help tasks demonstrate that CaPo achieves much higher task completion rate and efficiency compared with state-of-the-arts.
Accurate hypocenter determination in the Nankai Trough subduction zone is essential for hazard assessment and advancing our understanding of seismic activity in the region. A handy hypocenter determination tool incorporating a realistic 3D velocity structure, accessible to the scientific community, is beneficial. In this study, we developed HypoNet Nankai, a rapid hypocenter determination tool based on a physics-informed neural network (PINN) emulator (surrogate model) for travel time calculations. This tool leverages a PINN trained to predict P-wave travel times between arbitrary underground sources and surface receivers with a realistic 3D P-wave velocity structure model of the Nankai Trough subduction zone that incorporates marine seismic survey data. The PINN embeds physical laws, namely, the Eikonal equation, directly into the loss function of training and circumvents the need for labeled training data. To address the training challenges posed by small-scale features in the velocity model, we employed a simple domain decomposition approach and Fourier feature embedding. Once trained, the PINN immediately infers the P-wave travel time, enabling rapid hypocenter determination. The data size required to store NNs for travel time calculations is significantly smaller than those of conventional travel-time tables. HypoNet Nankai provides high flexibility for addition of new observation points. We verified HypoNet Nankai by comparing its performance with a widely used grid-based numerical method for forward travel time calculations and synthetic hypocenter determination. In both tests, HypoNet Nankai provided results consistent with those for the conventional method. HypoNet Nankai offers a rapid, accurate, and easy-to-use hypocenter determination method for the Nankai Trough subduction zone, with greater data efficiency and extendibility compared to conventional approaches.
Exploring Charged Higgs at the Future Circular Collider (FCC): A Review of Two-Higgs-Doublet Models
2024-11-07
Ijaz Ahmed, Basit Ali, M. S. Amjad, M. Jamil, Saba Shafaq, Usman Ahmad
This paper reports on the theoretical investigation of charged Higgs bosons and their coupling to fermions within the Two Higgs Doublet Model (THDM). The study focuses on the discovery potential of charged Higgs bosons predicted in Types III and IV at the future Circular Hadron-Hadron Collider (FCC-hh) with a center-of-mass energy of (\sqrt{s} = 100) TeV. By analyzing their decays, couplings to fermions, branching ratios, and production cross-section via (pp \rightarrow tH^-), we investigate the signatures of charged Higgs bosons, including kinematical distributions based on the background processes of (b\bar{b}) quarks.
Unveiling VVV/WISE Mira variables on the far side of the Galactic disk: Distances, kinematics and a new extinction law
2024-11-07
Rogelio Albarracín, M. Zoccali, J. Olivares Carvajal, Á. Rojas-Arriagada, J. H. Minniti, M. Catelan, M. De Leo, F. Gran, R. Contreras Ramos, Á. Valenzuela Navarro, C. Salvo-Guajardo
The structure and kinematics of the Milky Way disk are largely inferred from the solar vicinity. To gain a comprehensive understanding, it is essential to find reliable tracers in less-explored regions like the bulge and the far side of the disk. Mira variables, which are well-studied and bright standard candles, offer an excellent opportunity to trace intermediate and old populations in these complex regions. We aim to isolate a clean sample of Miras in the Vista Variables in the V\'ia L\'actea survey using Gaussian process algorithms. This sample will be used to study intermediate and old age populations in the Galactic bulge and far disk. Near- and mid-infrared time-series photometry were processed using Gaussian Process algorithms to identify Mira variables and model their light curves. We calibrated selection criteria with a visually inspected sample to create a high-purity sample of Miras, integrating multi-band photometry and kinematic data from proper motions. We present a catalog of 3602 Mira variables. By analyzing photometry, we classify them by O-rich or C-rich surface chemistry and derive selective-to-total extinction ratios of $A_{K_{s}}/E(J - K_{s}) = 0.471 \pm 0.01$ and $A_{K_{s}}/E(H - K_{s}) = 1.320 \pm 0.020$. Using the Mira period-age relation, we find evidence supporting the inside-out formation of the Milky Way disk. The distribution of proper motions and distances aligns with the Galactic rotation curve and disk kinematics. We extend the rotation curve up to R$_{\rm GC} \sim 17 \ \rm{kpc}$ and find no strong evidence of the nuclear stellar disk in our Mira sample. This study constitutes the largest catalog of variable stars on the far side of the Galactic disk to date.
Tibyan Corpus: Balanced and Comprehensive Error Coverage Corpus Using ChatGPT for Arabic Grammatical Error Correction
2024-11-07
Ahlam Alrehili, Areej Alhothali
Natural language processing (NLP) utilizes text data augmentation to overcome sample size constraints. Increasing the sample size is a natural and widely used strategy for alleviating these challenges. In this study, we chose Arabic to increase the sample size and correct grammatical errors. Arabic is considered one of the languages with limited resources for grammatical error correction (GEC). Furthermore, QALB-14 and QALB-15 are the only datasets used in most Arabic grammatical error correction research, with approximately 20,500 parallel examples, which is considered low compared with other languages. Therefore, this study aims to develop an Arabic corpus called "Tibyan" for grammatical error correction using ChatGPT. ChatGPT is used as a data augmenter tool based on a pair of Arabic sentences containing grammatical errors matched with a sentence free of errors extracted from Arabic books, called guide sentences. Multiple steps were involved in establishing our corpus, including the collection and pre-processing of a pair of Arabic texts from various sources, such as books and open-access corpora. We then used ChatGPT to generate a parallel corpus based on the text collected previously, as a guide for generating sentences with multiple types of errors. By engaging linguistic experts to review and validate the automatically generated sentences, we ensured that they were correct and error-free. The corpus was validated and refined iteratively based on feedback provided by linguistic experts to improve its accuracy. Finally, we used the Arabic Error Type Annotation tool (ARETA) to analyze the types of errors in the Tibyan corpus. Our corpus contained 49 of errors, including seven types: orthography, morphology, syntax, semantics, punctuation, merge, and split. The Tibyan corpus contains approximately 600 K tokens.
On the Inherent Robustness of One-Stage Object Detection against Out-of-Distribution Data
2024-11-07
Aitor Martinez-Seras, Javier Del Ser, Alain Andres, Pablo Garcia-Bringas
Robustness is a fundamental aspect for developing safe and trustworthy models, particularly when they are deployed in the open world. In this work we analyze the inherent capability of one-stage object detectors to robustly operate in the presence of out-of-distribution (OoD) data. Specifically, we propose a novel detection algorithm for detecting unknown objects in image data, which leverages the features extracted by the model from each sample. Differently from other recent approaches in the literature, our proposal does not require retraining the object detector, thereby allowing for the use of pretrained models. Our proposed OoD detector exploits the application of supervised dimensionality reduction techniques to mitigate the effects of the curse of dimensionality on the features extracted by the model. Furthermore, it utilizes high-resolution feature maps to identify potential unknown objects in an unsupervised fashion. Our experiments analyze the Pareto trade-off between the performance detecting known and unknown objects resulting from different algorithmic configurations and inference confidence thresholds. We also compare the performance of our proposed algorithm to that of logits-based post-hoc OoD methods, as well as possible fusion strategies. Finally, we discuss on the competitiveness of all tested methods against state-of-the-art OoD approaches for object detection models over the recently published Unknown Object Detection benchmark. The obtained results verify that the performance of avant-garde post-hoc OoD detectors can be further improved when combined with our proposed algorithm.
Automatic summarization has consistently attracted attention, due to its versatility and wide application in various downstream tasks. Despite its popularity, we find that annotation efforts have largely been disjointed, and have lacked common terminology. Consequently, it is challenging to discover existing resources or identify coherent research directions. To address this, we survey a large body of work spanning 133 datasets in over 100 languages, creating a novel ontology covering sample properties, collection methods and distribution. With this ontology we make key observations, including the lack in accessible high-quality datasets for low-resource languages, and the field's over-reliance on the news domain and on automatically collected distant supervision. Finally, we make available a web interface that allows users to interact and explore our ontology and dataset collection, as well as a template for a summarization data card, which can be used to streamline future research into a more coherent body of work.
Confirming the Evolution of the Dust Mass Function in Galaxies over the past 5 Billion Years
2024-11-07
R A Beeston, H L Gomez, L Dunne, S Maddox, S A Eales, M W L Smith
The amount of evolution in the dust content of galaxies over the past five billion years of cosmic history is contested in the literature. Here we present a far-infrared census of dust based on a sample of 29,241 galaxies with redshifts ranging from 0 < z < 0.5 using data from the Herschel Astrophysical Terahertz Survey (H-ATLAS). We use the spectral energy distribution fitting tool MAGPHYS and a stacking analysis to investigate the evolution of dust mass and temperature of far-infrared-selected galaxies as a function of both luminosity and redshift. At low redshifts, we find that the mass-weighted and luminosity-weighted dust temperatures from the stacking analysis both exhibit a trend for brighter galaxies to have warmer dust. In higher redshift bins, we see some evolution in both mass-weighted and luminosity-weighted dust temperatures with redshift, but the effect is strongest for luminosity-weighted temperature. The measure of dust content in galaxies at z<0.1 (the Dust Mass Function) has a different shape to that derived using optically-selected galaxies from the same region of sky. We revise the local dust mass density (z<0.1) to $\rho_{\rm d} =(1.37\pm0.08)\times 10^5 {\rm\,M_{\odot}\,Mpc^{-3}}\,h_{70}^{-1}$; corresponding to an overall fraction of baryons (by mass) stored in dust of $f_{\rm mb} {(\rm dust)} = (2.22\pm 0.13) \times 10^{-5}$. We confirm evolution in both the luminosity density and dust mass density over the past few billion years ($\rho_{\rm d} \propto (1+z)^{2.6 \pm 0.6}$), with a flatter evolution than observed in previous FIR-selected studies. We attribute the evolution in $\rho_L$ and $\rho_m$ to an evolution in the dust mass.
2024-11-07
Lukas Ebner, Berndt Müller, Andreas Schäfer, Leonhard Schmotzer, Clemens Seidl, Xiaojun Yao
We review recent and present new results on thermalization of nonabelian gauge theory obtained by exact numerical simulation of the real-time dynamics of two-dimensional SU(2) lattice gauge theory. We discuss: (1) tests confirming the Eigenstate Thermalization Hypothesis; (2) the entanglement entropy of sublattices, including the Page curve, the transition from area to volume scaling with increasing energy of the eigenstate and its time evolution that shows thermalization of localized regions to be a two-step process; (3) the absence of quantum many-body scars for $j_{\rm max}>1/2$; (4) the spectral form factor, which exhibits the expected slope-ramp-plateau structure for late times; (5) the entanglement Hamiltonian for SU(2), which has properties in accordance with the Bisognano-Wichmann theorem; and (6) a measure for non-stabilizerness or "magic" that is found to reach its maximum during thermalization. We conclude that the thermalization of nonabelian gauge theories is a promising process to establish quantum advantage.
Fast, Accurate and Perturbative Forward Modeling of Galaxy Clustering Part II: Redshift Space
2024-11-07
Julia Stadler, Fabian Schmidt, Martin Reinecke, Matteo Esposito
Forward modeling the galaxy density within the Effective Field Theory of Large Scale Structure (EFT of LSS) enables field-level analyses that are robust to theoretical uncertainties. At the same time, they can maximize the constraining power from galaxy clustering on the scales amenable to perturbation theory. In order to apply the method to galaxy surveys, the forward model must account for the full observational complexity of the data. In this context, a major challenge is the inclusion of redshift space distortions (RSDs) from the peculiar motion of galaxies. Here, we present improvements in the efficiency and accuracy of the RSD modeling in the perturbative LEFTfield forward model. We perform a detailed quantification of the perturbative and numerical error for the prediction of momentum, velocity and the redshift-space matter density. Further, we test the recovery of cosmological parameters at the field level, namely the growth rate $f$, from simulated halos in redshift space. For a rigorous test and to scan through a wide range of analysis choices, we fix the linear (initial) density field to the known ground truth but marginalize over all unknown bias coefficients and noise amplitudes. With a third-order model for gravity and bias, our results yield $<1\,\%$ statistical and $<1.5\,\%$ systematic error. The computational cost of the redshift-space forward model is only $\sim 1.5$ times of the rest frame equivalent, enabling future field-level inference that simultaneously targets cosmological parameters and the initial matter distribution.
We introduce a manifold analysis technique for neural network representations. Normalized Space Alignment (NSA) compares pairwise distances between two point clouds derived from the same source and having the same size, while potentially possessing differing dimensionalities. NSA can act as both an analytical tool and a differentiable loss function, providing a robust means of comparing and aligning representations across different layers and models. It satisfies the criteria necessary for both a similarity metric and a neural network loss function. We showcase NSA's versatility by illustrating its utility as a representation space analysis metric, a structure-preserving loss function, and a robustness analysis tool. NSA is not only computationally efficient but it can also approximate the global structural discrepancy during mini-batching, facilitating its use in a wide variety of neural network training paradigms.
A Comprehensive Review of Multimodal XR Applications, Risks, and Ethical Challenges in the Metaverse
2024-11-07
Panagiotis Kourtesis
This scoping review examines the broad applications, risks, and ethical challenges associated with Extended Reality (XR) technologies, including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), within the context of Metaverse. XR is revolutionizing fields such as immersive learning in education, medical and professional training, neuropsychological assessment, therapeutic interventions, arts, entertainment, retail, e-commerce, remote work, sports, architecture, urban planning, and cultural heritage preservation. The integration of multimodal technologies such as haptics, eye-tracking, face- and body-tracking, and brain-computer interfaces, enhances user engagement and interactivity, playing a key role in shaping the immersive experiences in the Metaverse. However, XR's expansion raises serious concerns, including data privacy risks, cybersecurity vulnerabilities, cybersickness, addiction, dissociation, harassment, bullying, and misinformation. These psychological, social, and security challenges are further complicated by intense advertising, manipulation of public opinion, and social inequality, which could disproportionately affect vulnerable individuals and social groups. This review emphasizes the urgent need for robust ethical frameworks and regulatory guidelines to address these risks while promoting equitable access, privacy, autonomy, and mental well-being. As XR technologies increasingly integrate with artificial intelligence, responsible governance is essential to ensure the safe and beneficial development of the Metaverse and the broader application of XR in enhancing human development.
2024-11-07
Chang-Hao Chen, Luis C. Ho, Ruancun Li, Ming-Yang Zhuang
We investigate the host galaxy properties of eight little red dots (LRDs) selected from the JWST UNCOVER survey, applying a new technique ({\tt\string GalfitS}) to simultaneously fit the morphology and spectral energy distribution using multi-band NIRCam images covering $\sim 1-4\,\mu {\rm m}$. We detect the host galaxy in only one LRD, MSAID38108 at $z = 4.96$, which has a stellar mass of $\log (M_*/M_{\odot}) = 8.66^{+0.24}_{-0.23}$, an effective radius $R_e=0.66^{+0.08}_{-0.05}$ kpc, and a S\'ersic index $n=0.71^{+0.07}_{-0.08}$. No host emission centered on the central point source is found in the other seven LRDs. We derive stringent upper limits for the stellar mass of a hypothetical host galaxy by conducting realistic mock simulations that place high-redshift galaxy images under the LRDs. Based on the black hole masses estimated from the broad H$\alpha$ emission line, the derived stellar mass limits are at least a factor of 10 lower than expected from the $z \approx 0$ scaling relation between black hole mass and host galaxy stellar mass. Intriguingly, four of the LRDs (50\% of the sample) show extended, off-centered emission, which is particularly prominent in the bluer bands. The asymmetric emission of two sources can be modeled as stellar emission, but the nature of the other two is unclear.
The OASES Project: Exploring the Outer Solar System through Stellar Occultation with Amateur-Class Telescopes
2024-11-07
Ko Arimatsu
The exploration of kilometre-sized trans-Neptunian objects (TNOs) is one of the ultimate goals in the search for the origin and evolution of the Solar System. However, such exploration is challenging because these small bodies are too faint to be directly detected. One potential avenue for detecting and investigating small and faint TNOs is the monitoring of stellar occultation events. This paper reviews the concept and methodology of monitoring observations of stellar occultations by small, unidentified TNOs, focusing on our observational programme, Organized Autotelescopes for Serendipitous Event Survey (OASES). OASES aims to detect and investigate stellar occultations by unidentified TNOs using multiple amateur-sized telescopes equipped with commercial Complementary Metal-Oxide-Semiconductor (CMOS) cameras. Through the monitoring observations conducted so far, OASES has found one possible occultation event by a kilometre-sized TNO. The paper also discusses future developments of the OASES project and deliberates on the potential of movie observations in expanding the frontiers of outer Solar System research. This article is part of the themed issue "Major Advances in Planetary Sciences thanks to Stellar Occultations".
Political Hegemony, Imitation Isomorphism, and Project Familiarity: Instrumental Variables to Understand Funding Impact on Scholar Performance
2024-11-07
Yang Ding, Yi Bu
This paper contributes a new idea for exploring research funding effects on scholar performance. By collecting details of 9,501 research grants received by principal investigators from universities in the U.S. social sciences from 2000 to 2019 and data on their publications and citations in the Microsoft Academic Graph and Web of Science bibliographic collections, we build a novel dataset of grants and article counts, citations, and journal CiteScore. Based on this dataset, we first introduce three instrumental variables (IVs) suitable for isolating endogeneity issues in the study of competing grant effects, namely scholars' political hegemony in academia, imitation isomorphic behavior among scholars, and project familiarity. Then, this study explains the research funding effects by combining the three IVs with a two-stage least square (2SLS) model and by considering gaps from individual academics, universities, and research fields. Also, we provide validity and robustness tests of these three IVs and research funding effects. We find that our IVs serve the function of exogenizing and isolating endogeneity in capturing the research funding effect. Furthermore, empirical findings show that receiving research funding increases a scholar's research output and impact, but does not result in the scholar's research being published in more prestigious academic journals.
Programmable photonic unitary circuits for light computing
2024-11-07
Kyuho Kim, Kunwoo Park, Hyungchul Park, Sunkyu Yu, Namkyoo Park, Xianji Piao
Unitarity serves as a fundamental concept for characterizing linear and conservative wave phenomena in both classical and quantum systems. Developing platforms that perform unitary operations on light waves in a uni-versal and programmable manner enables the emulation of complex light-matter interactions and the execution of general-purpose functionalities for wave manipulations, photonic computing, and quantum circuits. Recent-ly, numerous approaches to implementing programmable photonic unitary circuits have been proposed and demonstrated, each employing different design strategies that distinctly impact overall device performance. Here, we review foundational design principles and recent achievements in the implementation of programma-ble photonic unitary circuits, with a particular focus on integrated photonic platforms. We classify the design strategies based on the dimensionality of nontrivial unit operations in their building blocks: lower-dimensional unitary units, such as SU(2) operations, and higher-dimensional ones, such as Fourier transforms. In each cate-gory, recent efforts to leverage alternative physical axes, such as the temporal and frequency domains, to ad-dress scalability challenges are also reviewed. We discuss the underlying concepts, design procedures, and trade-offs of each design strategy, especially in relation to light-based computing.
Enhancing performance while reducing costs is the fundamental design philosophy of integrated circuits (ICs). With advancements in packaging technology, interposer-based chiplet architecture has emerged as a promising solution. Chiplet integration, often referred to as 2.5D IC, offers significant benefits, including cost-effectiveness, reusability, and improved performance. However, realizing these advantages heavily relies on effective electronic design automation (EDA) processes. EDA plays a crucial role in optimizing architecture design, partitioning, combination, physical design, reliability analysis, etc. Currently, optimizing the automation methodologies for chiplet architecture is a popular focus; therefore, we propose a survey to summarize current methods and discuss future directions. This paper will review the research literature on design automation methods for chiplet-based architectures, highlighting current challenges and exploring opportunities in 2.5D IC from an EDA perspective. We expect this survey will provide valuable insights for the future development of EDA tools for chiplet-based integrated architectures.
Approximate Frank-Wolfe Algorithm over Graph-structured Support Set
2024-11-07
Yijian Pan
In this project, we reviewed a paper that deals graph-structured convex optimization (GSCO) problem with the approximate Frank-Wolfe (FW) algorithm. We analyzed and implemented the original algorithm and introduced some extensions based on that. Then we conducted experiments to compare the results and concluded that our backtracking line-search method effectively reduced the number of iterations, while our new DMO method (Top-g+ optimal visiting) did not make satisfying enough improvements.
SuperQ-GRASP: Superquadrics-based Grasp Pose Estimation on Larger Objects for Mobile-Manipulation
2024-11-07
Xun Tu, Karthik Desingh
Grasp planning and estimation have been a longstanding research problem in robotics, with two main approaches to find graspable poses on the objects: 1) geometric approach, which relies on 3D models of objects and the gripper to estimate valid grasp poses, and 2) data-driven, learning-based approach, with models trained to identify grasp poses from raw sensor observations. The latter assumes comprehensive geometric coverage during the training phase. However, the data-driven approach is typically biased toward tabletop scenarios and struggle to generalize to out-of-distribution scenarios with larger objects (e.g. chair). Additionally, raw sensor data (e.g. RGB-D data) from a single view of these larger objects is often incomplete and necessitates additional observations. In this paper, we take a geometric approach, leveraging advancements in object modeling (e.g. NeRF) to build an implicit model by taking RGB images from views around the target object. This model enables the extraction of explicit mesh model while also capturing the visual appearance from novel viewpoints that is useful for perception tasks like object detection and pose estimation. We further decompose the NeRF-reconstructed 3D mesh into superquadrics (SQs) -- parametric geometric primitives, each mapped to a set of precomputed grasp poses, allowing grasp composition on the target object based on these primitives. Our proposed pipeline overcomes the problems: a) noisy depth and incomplete view of the object, with a modeling step, and b) generalization to objects of any size. For more qualitative results, refer to the supplementary video and webpage https://bit.ly/3ZrOanU
Many nations are promoting the green transition in the energy sector to attain neutral carbon emissions by 2050. Smart Grid 2.0 (SG2) is expected to explore data-driven analytics and enhance communication technologies to improve the efficiency and sustainability of distributed renewable energy systems. These features are beyond smart metering and electric surplus distribution in conventional smart grids. Given the high dependence on communication networks to connect distributed microgrids in SG2, potential cascading failures of connectivity can cause disruption to data synchronization to the remote control systems. This paper reviews security threats and defense tactics for three stakeholders: power grid operators, communication network providers, and consumers. Through the survey, we found that SG2's stakeholders are particularly vulnerable to substation attacks/vandalism, malware/ransomware threats, blockchain vulnerabilities and supply chain breakdowns. Furthermore, incorporating artificial intelligence (AI) into autonomous energy management in distributed energy resources of SG2 creates new challenges. Accordingly, adversarial samples and false data injection on electricity reading and measurement sensors at power plants can fool AI-powered control functions and cause messy error-checking operations in energy storage, wrong energy estimation in electric vehicle charging, and even fraudulent transactions in peer-to-peer energy trading models. Scalable blockchain-based models, physical unclonable function, interoperable security protocols, and trustworthy AI models designed for managing distributed microgrids in SG2 are typical promising protection models for future research.
We propose a new approach to promote safety in classification tasks with established concepts. Our approach -- called a conceptual safeguard -- acts as a verification layer for models that predict a target outcome by first predicting the presence of intermediate concepts. Given this architecture, a safeguard ensures that a model meets a minimal level of accuracy by abstaining from uncertain predictions. In contrast to a standard selective classifier, a safeguard provides an avenue to improve coverage by allowing a human to confirm the presence of uncertain concepts on instances on which it abstains. We develop methods to build safeguards that maximize coverage without compromising safety, namely techniques to propagate the uncertainty in concept predictions and to flag salient concepts for human review. We benchmark our approach on a collection of real-world and synthetic datasets, showing that it can improve performance and coverage in deep learning tasks.
Large language models (LLMs) have shown remarkable performance across various tasks, yet their ability to handle long-context reading remains challenging. This study explores the effectiveness of leveraging high-quality academic peer review data for fine-tuning LLMs to enhance their long-context capabilities. We compare the Direct Preference Optimization (DPO) method with the Supervised Fine-Tuning (SFT) method, demonstrating DPO's superiority and data efficiency. Our experiments show that the fine-tuned model achieves a 4.04-point improvement over phi-3 and a 2.6\% increase on the Qasper benchmark using only 2000 samples. Despite facing limitations in data scale and processing costs, this study underscores the potential of DPO and high-quality data in advancing LLM performance. Additionally, the zero-shot benchmark results indicate that aggregated high-quality human reviews are overwhelmingly preferred over LLM-generated responses, even for the most capable models like GPT-4o. This suggests that high-quality human reviews are extremely rich in information, reasoning, and long-context retrieval, capabilities that even the most advanced models have not fully captured. These findings highlight the high utility of leveraging human reviews to further advance the field.
Content moderation, the process of reviewing and monitoring the safety of generated content, is important for development of welcoming online platforms and responsible large language models. Content moderation contains various tasks, each with its unique requirements tailored to specific scenarios. Therefore, it is crucial to develop a model that can be easily adapted to novel or customized content moderation tasks accurately without extensive model tuning. This paper presents STAND-GUARD, a Small Task-Adaptive coNtent moDeration model. The basic motivation is: by performing instruct tuning on various content moderation tasks, we can unleash the power of small language models (SLMs) on unseen (out-of-distribution) content moderation tasks. We also carefully study the effects of training tasks and model size on the efficacy of cross-task fine-tuning mechanism. Experiments demonstrate STAND-Guard is comparable to GPT-3.5-Turbo across over 40 public datasets, as well as proprietary datasets derived from real-world business scenarios. Remarkably, STAND-Guard achieved nearly equivalent results to GPT-4-Turbo on unseen English binary classification tasks
Strong Scatterings Invalidate Proposed Models of Enhanced TDE Rates in Post-Starburst Galaxies
2024-11-07
Odelia Teboul, Hagai Perets
Stars wandering too close to supermassive black holes (SMBHs) can be ripped apart by the tidal forces of the black hole. Recent optical surveys have revealed that E+A galaxies are overrepresented by a factor $\sim $ 30, while green galaxies are overrepresented in both optical and infrared surveys. Different stellar models have been proposed to explain this Tidal Disruption Event (TDE) preference: ultra-steep stellar densities in the nuclear cluster, radial velocity anisotropies, and top-heavy Initial Mass Function (IMF). Here we explore these hypotheses in the framework of our revised loss cone theory that accounts for both weak and strong scattering, i.e., a scattering strong enough to eject a star from the nuclear cluster. We find that, when accounting for weak and strong scatterings, both ultra-steep densities and radial velocity anisotropies fail to explain the post-starburst preference of TDEs except when considering a high anisotropy factor together with a high SMBH mass and a shallow density profile of stellar mass black holes $\gamma_{\rm bh} =7/4$. Our findings hold when combining either model with top-heavy IMFs. Hence, new models to explain the post-starburst preference of TDEs are needed.
Constrain Spatial Curvature and Dark Energy with Strong Lenses and Complementary Probes: a Forecast for Next-Generation Surveys
2024-11-07
Yang Hu, Suhail Dhawan
Inferring spatial curvature of the Universe with high-fidelity is a longstanding interest in cosmology. However, the strong degeneracy between dark energy equation-of-state parameter $w$ and curvature density parameter $\Omega_{\rm K}$ has always been a hurdle for precision measurements of curvature from late-universe probes. With the imminent commissioning of Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), we demonstrate for the first time, using simulations of stage-IV surveys, the crucial role of time-delay distances from strong gravitational lenses in breaking this degeneracy. Our findings suggest that in non-flat $ow$CDM model, while strong lensing data alone only yield a $\Omega_{\rm K}$ constraint at $\sim O(10^{-1})$ level, the integration with SNe Ia and BAO data breaks the $w$-$\Omega_{\rm K}$ degeneracy and refines the $\Omega_{\rm K}$ constraint to $\sim O(10^{-2})$. This surpasses the constraints typically derived from SNe Ia Hubble diagrams and BAO data and is comparable to the constraints obtained from \textit{Planck} Primary CMB data. Additionally, we present a non-parametric approach using Gaussian Process to avoid parameter-dependency of the expansion history $H(z)$ and achieve similar $O(10^{-2})$ level constraint on $\Omega_{\rm K}$. This study demonstrates the significant potential of strong gravitational lenses and Stage-IV surveys like LSST to achieve high-fidelity, independent constraints on $\Omega_{\rm K}$, contributing to our understanding of the Universe's geometry and the dynamics of dark energy.
Dynamically generated spin-interactions and nutational spin inertia in normal metal-ferromagnet heterostructures
2024-11-07
Christian Svingen Johnsen, Asle Sudbø
We consider the spin dynamics of a normal metal-ferromagnet heterostructure, with emphasis on spin-nutation terms arising from a dynamical Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction. We find that the spin-nutation term is anisotropic in spin space due to the broken time-reversal symmetry of the ferromagnet. This contrasts with what one obtains in the paramagnetic state, where the nutation term is isotropic in spin space. We compute the effects this has on the magnetization dynamics derived from a Landau-Lifshitz-Gilbert equation. In particular, due to broken time-reversal symmetry, we predict a third ferromagnetic resonance due to nutational spin dynamics. This resonance frequency is tunable by applying an external magnetic field. We propose this as a strong indicator for the existence of nutation in spin systems.
2024-11-07
Csaba Balazs, Torsten Bringmann, Felix Kahlhoefer, Martin White
Dark matter is a fundamental constituent of the universe, which is needed to explain a wide variety of astrophysical and cosmological observations. Although the existence of dark matter was first postulated nearly a century ago and its abundance is precisely measured, approximately five times larger than that of ordinary matter, its underlying identity remains a mystery. A leading hypothesis is that it is composed of new elementary particles, which are predicted to exist in many extensions of the Standard Model of particle physics. In this article we review the basic evidence for dark matter and the role it plays in cosmology and astrophysics, and discuss experimental searches and potential candidates. Rather than targeting researchers in the field, we aim to provide an accessible and concise summary of the most important ideas and results, which can serve as a first entry point for advanced undergraduate students of physics or astronomy.
A large amount of information in today's world is now stored in knowledge bases. Named Entity Recognition (NER) is a process of extracting, disambiguation, and linking an entity from raw text to insightful and structured knowledge bases. More concretely, it is identifying and classifying entities in the text that are crucial for Information Extraction, Semantic Annotation, Question Answering, Ontology Population, and so on. The process of NER has evolved in the last three decades since it first appeared in 1996. In this survey, we study the evolution of techniques employed for NER and compare the results, starting from supervised to the developing unsupervised learning methods.
Seeing is Deceiving: Exploitation of Visual Pathways in Multi-Modal Language Models
2024-11-07
Pete Janowczyk, Linda Laurier, Ave Giulietta, Arlo Octavia, Meade Cleti
Multi-Modal Language Models (MLLMs) have transformed artificial intelligence by combining visual and text data, making applications like image captioning, visual question answering, and multi-modal content creation possible. This ability to understand and work with complex information has made MLLMs useful in areas such as healthcare, autonomous systems, and digital content. However, integrating multiple types of data also creates security risks. Attackers can manipulate either the visual or text inputs, or both, to make the model produce unintended or even harmful responses. This paper reviews how visual inputs in MLLMs can be exploited by various attack strategies. We break down these attacks into categories: simple visual tweaks and cross-modal manipulations, as well as advanced strategies like VLATTACK, HADES, and Collaborative Multimodal Adversarial Attack (Co-Attack). These attacks can mislead even the most robust models while looking nearly identical to the original visuals, making them hard to detect. We also discuss the broader security risks, including threats to privacy and safety in important applications. To counter these risks, we review current defense methods like the SmoothVLM framework, pixel-wise randomization, and MirrorCheck, looking at their strengths and limitations. We also discuss new methods to make MLLMs more secure, including adaptive defenses, better evaluation tools, and security approaches that protect both visual and text data. By bringing together recent developments and identifying key areas for improvement, this review aims to support the creation of more secure and reliable multi-modal AI systems for real-world use.
With the growing applications of Deep Learning (DL), especially recent spectacular achievements of Large Language Models (LLMs) such as ChatGPT and LLaMA, the commercial significance of these remarkable models has soared. However, acquiring well-trained models is costly and resource-intensive. It requires a considerable high-quality dataset, substantial investment in dedicated architecture design, expensive computational resources, and efforts to develop technical expertise. Consequently, safeguarding the Intellectual Property (IP) of well-trained models is attracting increasing attention. In contrast to existing surveys overwhelmingly focusing on model IPP mainly, this survey not only encompasses the protection on model level intelligence but also valuable dataset intelligence. Firstly, according to the requirements for effective IPP design, this work systematically summarizes the general and scheme-specific performance evaluation metrics. Secondly, from proactive IP infringement prevention and reactive IP ownership verification perspectives, it comprehensively investigates and analyzes the existing IPP methods for both dataset and model intelligence. Additionally, from the standpoint of training settings, it delves into the unique challenges that distributed settings pose to IPP compared to centralized settings. Furthermore, this work examines various attacks faced by deep IPP techniques. Finally, we outline prospects for promising future directions that may act as a guide for innovative research.
Trends, Challenges, and Future Directions in Deep Learning for Glaucoma: A Systematic Review
2024-11-07
Mahtab Faraji, Homa Rashidisabet, George R. Nahass, RV Paul Chan, Thasarat S Vajaranant, Darvin Yi
Here, we examine the latest advances in glaucoma detection through Deep Learning (DL) algorithms using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This study focuses on three aspects of DL-based glaucoma detection frameworks: input data modalities, processing strategies, and model architectures and applications. Moreover, we analyze trends in employing each aspect since the onset of DL in this field. Finally, we address current challenges and suggest future research directions.
Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review
2024-11-07
Luis M. Lopez-Ramos, Florian Leiser, Aditya Rastogi, Steven Hicks, Inga Strümke, Vince I. Madai, Tobias Budig, Ali Sunyaev, Adam Hilbert
The joint implementation of Federated learning (FL) and Explainable artificial intelligence (XAI) will allow training models from distributed data and explaining their inner workings while preserving important aspects of privacy. Towards establishing the benefits and tensions associated with their interplay, this scoping review maps those publications that jointly deal with FL and XAI, focusing on publications where an interplay between FL and model interpretability or post-hoc explanations was found. In total, 37 studies met our criteria, with more papers focusing on explanation methods (mainly feature relevance) than on interpretability (mainly algorithmic transparency). Most works used simulated horizontal FL setups involving 10 or fewer data centers. Only one study explicitly and quantitatively analyzed the influence of FL on model explanations, revealing a significant research gap. Aggregation of interpretability metrics across FL nodes created generalized global insights at the expense of node-specific patterns being diluted. 8 papers addressed the benefits of incorporating explanation methods as a component of the FL algorithm. Studies using established FL libraries or following reporting guidelines are a minority. More quantitative research and structured, transparent practices are needed to fully understand their mutual impact and under which conditions it happens.
An Adaptive Online Smoother with Closed-Form Solutions and Information-Theoretic Lag Selection for Conditional Gaussian Nonlinear Systems
2024-11-07
Marios Andreou, Nan Chen, Yingda Li
Data assimilation (DA) combines partial observations with a dynamical model to improve state estimation. Filter-based DA uses only past and present data and is the prerequisite for real-time forecasts. Smoother-based DA exploits both past and future observations. It aims to fill in missing data, provide more accurate estimations, and develop high-quality datasets. However, the standard smoothing procedure requires using all historical state estimations, which is storage-demanding, especially for high-dimensional systems. This paper develops an adaptive-lag online smoother for a large class of complex dynamical systems with strong nonlinear and non-Gaussian features, which has important applications to many real-world problems. The adaptive lag allows the DA to utilize only observations within a nearby window, significantly reducing computational storage. Online lag adjustment is essential for tackling turbulent systems, where temporal autocorrelation varies significantly over time due to intermittency, extreme events, and nonlinearity. Based on the uncertainty reduction in the estimated state, an information criterion is developed to systematically determine the adaptive lag. Notably, the mathematical structure of these systems facilitates the use of closed analytic formulae to calculate the online smoother and the adaptive lag, avoiding empirical tunings as in ensemble-based DA methods. The adaptive online smoother is applied to studying three important scientific problems. First, it helps detect online causal relationships between state variables. Second, its advantage of computational storage is illustrated via Lagrangian DA, a high-dimensional nonlinear problem. Finally, the adaptive smoother advances online parameter estimation with partial observations, emphasizing the role of the observed extreme events in accelerating convergence.
Boosting the Efficiency of Metaheuristics Through Opposition-Based Learning in Optimum Locating of Control Systems in Tall Buildings
2024-11-07
Salar Farahmand-Tabar, Sina Shirgir
Opposition-based learning (OBL) is an effective approach to improve the performance of metaheuristic optimization algorithms, which are commonly used for solving complex engineering problems. This chapter provides a comprehensive review of the literature on the use of opposition strategies in metaheuristic optimization algorithms, discussing the benefits and limitations of this approach. An overview of the opposition strategy concept, its various implementations, and its impact on the performance of metaheuristic algorithms are presented. Furthermore, case studies on the application of opposition strategies in engineering problems are provided, including the optimum locating of control systems in tall building. A shear frame with Magnetorheological (MR) fluid damper is considered as a case study. The results demonstrate that the incorporation of opposition strategies in metaheuristic algorithms significantly enhances the quality and speed of the optimization process. This chapter aims to provide a clear understanding of the opposition strategy in metaheuristic optimization algorithms and its engineering applications, with the ultimate goal of facilitating its adoption in real-world engineering problems.
Exploring the Feasibility of Affordable Sonar Technology: Object Detection in Underwater Environments Using the Ping 360
2024-11-07
Md Junayed Hasan, Somasundar Kannan, Ali Rohan, Mohd Asif Shah
This study explores the potential of the Ping 360 sonar device, primarily used for navigation, in detecting complex underwater obstacles. The key motivation behind this research is the device's affordability and open-source nature, offering a cost-effective alternative to more expensive imaging sonar systems. The investigation focuses on understanding the behaviour of the Ping 360 in controlled environments and assessing its suitability for object detection, particularly in scenarios where human operators are unavailable for inspecting offshore structures in shallow waters. Through a series of carefully designed experiments, we examined the effects of surface reflections and object shadows in shallow underwater environments. Additionally, we developed a manually annotated sonar image dataset to train a U-Net segmentation model. Our findings indicate that while the Ping 360 sonar demonstrates potential in simpler settings, its performance is limited in more cluttered or reflective environments unless extensive data pre-processing and annotation are applied. To our knowledge, this is the first study to evaluate the Ping 360's capabilities for complex object detection. By investigating the feasibility of low-cost sonar devices, this research provides valuable insights into their limitations and potential for future AI-based interpretation, marking a unique contribution to the field.
Effects of doping on polar order in SrTiO$_{3}$ from first-principles modeling
2024-11-07
Alex Hallett, John W. Harter
SrTiO$_{3}$ is an incipient ferroelectric and an exceptionally dilute superconductor with a dome-like dependence on carrier concentration. Stabilization of a polar phase through chemical substitution or strain significantly enhances the superconducting critical temperature, suggesting a possible connection between the polar instability and unconventional Cooper pairing. To investigate the effects of doping on the polar order in SrTiO$_{3}$, we develop a simplified free energy model which includes only the degrees of freedom necessary to capture the relevant physics of a doped, biaxially compressively strained system. We simulate the polar and antiferrodistortive thermal phase transitions using Monte Carlo methods for different doping levels and comment on the doping dependence of the transition temperatures and the formation of polar nanodomains. In addition, the temperature-dependent phonon spectral function is calculated using Langevin simulations to investigate the lattice dynamics of the doped system. We also examine the effects of doping on the electronic structure within the polar phase, including the density of states and band splitting. Finally, we compute the polarization dependence of the Rashba parameter and the doping dependence of the Midgal ratio, and place our results in the broader context of proposed pairing mechanisms.
Quantum metrology with a continuous-variable system
2024-11-06
Matteo Fadel, Noah Roux, Manuel Gessner
As one of the main pillars of quantum technologies, quantum metrology aims to improve measurement precision using techniques from quantum information. The two main strategies to achieve this are the preparation of nonclassical states and the design of optimized measurement observables. We discuss precision limits and optimal strategies in quantum metrology and sensing with a single mode of quantum continuous variables. We focus on the practically most relevant cases of estimating displacements and rotations and provide the sensitivities of the most important classes of states that includes Gaussian states and superpositions of Fock states or coherent states. Fundamental precision limits that are obtained from the quantum Fisher information are compared to the precision of a simple moment-based estimation strategy based on the data obtained from possibly sub-optimal measurement observables, including homodyne, photon number, parity and higher moments. Finally, we summarize some of the main experimental achievements and present emerging platforms for continuous-variable sensing. These results are of particular interest for experiments with quantum light, trapped ions, mechanical oscillators, and microwave resonators.
Theoretical Diagnostics for Narrow Line Regions of Active Galactic Nuclei
2024-11-06
Peixin Zhu, Lisa J. Kewley, Ralph Sutherland
Gas metallicity, ionization parameter, and gas pressure can affect the observed ratios of specific strong emission lines within galaxies. While the theoretical strong lines diagnostics for gas metallicity, ionization parameters, and gas pressure in star-forming regions are well-established, theoretical diagnostics for active galactic nuclei (AGNs) narrow line regions are still lacking. In Zhu et al. (2023), we presented a new AGN model that provides the best predictions for observations spanning the UV, optical, and infrared wavelengths. This paper presents a suite of theoretical diagnostics for the gas metallicity, ionization parameter, gas pressure, and the peak energy in AGN ionizing radiation field $E_{peak}$ for AGN narrow-line regions spanning the UV and optical wavelengths. We investigate the model dependency on the ionization parameter, gas pressure, $E_{peak}$, and the nitrogen scaling relation and make recommendations on metallicity diagnostics that are most robust against these parameters. We test our new AGN metallicity diagnostics using optical galaxy spectra from Sloan Digital Sky Survey DR16. These tests show that the metallicities measured from different diagnostics in this paper are consistent within $\sim0.3$ dex. We compare consistent HII and AGN diagnostics and demonstrate that HII and AGN diagnostics should not be used interchangeably. With a wide wavelength coverage, we anticipate that these AGN diagnostics will enable new metallicity studies of galaxies dominated by AGN.
A Collaborative Content Moderation Framework for Toxicity Detection based on Conformalized Estimates of Annotation Disagreement
2024-11-06
Guillermo Villate-Castillo, Javier Del Ser, Borja Sanz
Content moderation typically combines the efforts of human moderators and machine learning models. However, these systems often rely on data where significant disagreement occurs during moderation, reflecting the subjective nature of toxicity perception. Rather than dismissing this disagreement as noise, we interpret it as a valuable signal that highlights the inherent ambiguity of the content,an insight missed when only the majority label is considered. In this work, we introduce a novel content moderation framework that emphasizes the importance of capturing annotation disagreement. Our approach uses multitask learning, where toxicity classification serves as the primary task and annotation disagreement is addressed as an auxiliary task. Additionally, we leverage uncertainty estimation techniques, specifically Conformal Prediction, to account for both the ambiguity in comment annotations and the model's inherent uncertainty in predicting toxicity and disagreement.The framework also allows moderators to adjust thresholds for annotation disagreement, offering flexibility in determining when ambiguity should trigger a review. We demonstrate that our joint approach enhances model performance, calibration, and uncertainty estimation, while offering greater parameter efficiency and improving the review process in comparison to single-task methods.
Cosmology from weak lensing, galaxy clustering, CMB lensing and tSZ: II. Optimizing Roman survey design for CMB cross-correlation science
2024-11-06
Tim Eifler, Xiao Fang, Elisabeth Krause, Christopher M. Hirata, Jiachuan Xu, Karim Benabed, Simone Ferraro, Vivian Miranda, Pranjal R. S., Emma Ayçoberry, Yohan Dubois
We explore synergies between the Nancy Grace Roman Space Telescope High Latitude Wide Area Survey (HLWAS) and CMB experiments, specifically Simons Observatory (SO) and CMB-Stage4 (S4). Our simulated analyses include weak lensing, photometric galaxy clustering, CMB lensing, thermal SZ, and cross-correlations between these probes. While we assume the nominal 16,500 square degree area for SO and S4, we consider multiple survey designs for Roman that overlap with Rubin Observatory's Legacy Survey of Space and Time (LSST): the 2000 square degree reference survey using four photometric bands, and two shallower single-band surveys that cover 10,000 and 18,000 square degree, respectively. We find a ~2x increase in the dark energy figure of merit when including CMB-S4 data for all Roman survey designs. We further find a strong increase in constraining power for the Roman wide survey scenario cases, despite the reduction in galaxy number density, and the increased systematic uncertainties assumed due to the single band coverage. Even when tripling the already worse systematic uncertainties in the Roman wide scenarios, which reduces the 10,000 square degree FoM from 269 to 178, we find that the larger survey area is still significantly preferred over the reference survey (FoM 64). We conclude that for the specific analysis choices and metrics of this paper, a Roman wide survey is unlikely to be systematics-limited (in the sense that one saturates the improvement that can be obtained by increasing survey area). We outline several specific implementations of a two-tier Roman survey (1000 square degree with 4 bands, and a second wide tier in one band) that can further mitigate the risk of systematics for Roman wide concepts.
This paper introduces a definition of ideological polarization of an electorate around a particular central point. This definition does not assume a particular location or boundaries of the center. Using US survey data, the paper shows how this approach can be used to establish whether polarization is occurring, and find the position around which it is happening. I then show the ideological polarization as defined here is related to other phenomena, such as affective polarization and increased salience of divisive issues.
Distinguishing Coupled Dark Energy Models with Neural Networks
2024-11-06
L. W. K. Goh, I. Ocampo, S. Nesseris, V. Pettorino
We investigate whether Neural Networks (NN) can accurately differentiate between growth-rate data of the Large Scale Structure (LSS) of the Universe, simulated via two models: a cosmological constant and cold dark matter ({\Lambda}CDM) model and a tomographic Coupled Dark Energy (CDE) model. We build an NN classifier and test its accuracy in distinguishing between cosmological models. For our dataset, we generate f{\sigma_8}(z) growth-rate observables simulating a realistic Stage IV galaxy survey-like setup, for both {\Lambda}CDM and a tomographic CDE model, for various values of the model parameters. We then optimise and train our NN with Optuna, aiming to avoid overfitting and maximising the accuracy of the trained model. We conduct our analysis for both a binary classification, comparing between {\Lambda}CDM and a CDE model where only one tomographic coupling bin is activated, and a multiclass classification scenario where all the models are combined. For the case of binary classification, we find that our NN can confidently (with > 86% accuracy) detect non-zero values of the tomographic coupling regardless of the redshift range at which coupling is activated, and at a 100% confidence level, detect the {\Lambda}CDM model. For the multiclass classification task, we find that the NN performs adequately well at distinguishing between {\Lambda}CDM, a CDE model with low redshift coupling, and a model with high redshift coupling, with 99%, 79% and 84% accuracy respectively. By leveraging the power of machine learning, our pipeline can be a useful tool to analyse growth-rate data and maximise the potential of current surveys to probe for deviations from General Relativity.
Reproducible Hybrid Time-Travel Retrieval in Evolving Corpora
2024-11-06
Moritz Staudinger, Florina Piroi, Andreas Rauber
There are settings in which reproducibility of ranked lists is desirable, such as when extracting a subset of an evolving document corpus for downstream research tasks or in domains such as patent retrieval or in medical systematic reviews, with high reproducibility expectations. However, as global term statistics change when documents change or are added to a corpus, queries using typical ranked retrieval models are not even reproducible for the parts of the document corpus that have not changed. Thus, Boolean retrieval frequently remains the mechanism of choice in such settings. We present a hybrid retrieval system combining Lucene for fast retrieval with a column-store-based retrieval system maintaining a versioned and time-stamped index. The latter component allows re-execution of previously posed queries resulting in the same ranked list and further allows for time-travel queries over evolving collection, as web archives, while maintaining the original ranking. Thus, retrieval results in evolving document collections are fully reproducible even when document collections and thus term statistics change.
Binary stars are prevalent yet challenging to detect. We present a novel approach using convolutional neural networks (CNNs) to identify binary stars from low-resolution spectra obtained by the LAMOST survey. The CNN is trained on a dataset that distinguishes binaries from single main sequence stars based on their positions on the Hertzsprung-Russell diagram. The network achieves high accuracy with an area under the receiver operating characteristic curve of 0.949 on the test set. Its performance is further validated against known eclipsing binaries (97% detection rate) and binary stars identified by radial velocity variations (92% detection rate). Applying the trained CNN to a sample of one million main sequence stars from LAMOST DR10 and Gaia DR3 yields a catalog of 468,634 binary stars. This catalog includes 115 binary stars located beyond 10 kpc from the Sun and 128 cross-matched with known exoplanet hosts from the NASA Exoplanet Archive. This new catalog provides a valuable resource for future research on the properties, formation, and evolution of binary systems, particularly for statistically characterizing large populations.
Filaments stand as pivotal structures within the cosmic web. However, direct detection of the cold gas content of the filaments remains challenging due to its inherent low brightness temperature. With the TNG hydrodynamical simulations, we demonstrate the effectiveness of isolating faint filament HI signal from the FAST HI intensity mapping (IM) survey through pairwise stacking of galaxies, which yields an average HI filament signal amplitude of $\sim 0.28\ {\mu{\rm K}}$ at $z\simeq 0.1$. However, our simulations reveal a non-negligible contribution from HI-rich galaxies within or near the filaments. Particularly, the faint galaxies dominantly contribute to the extra filament HI signal. Our simulation also shows that the measurement uncertainty is produced by both thermal noise and background variation caused by brightness leakage from surrounding random galaxies. Given a fixed total observation time, a wide-field HI IM survey, which includes a large number of galaxy pairs, can simultaneously reduce thermal noise to below the filament signal level and minimize background variation to a negligible level. Through the end-to-end simulation, this work demonstrates the critical role of the galaxy pairwise stacking method in future filament HI detection, outlining a road map for filament HI detection in the next-generation HI IM surveys.
2024-11-06
Quyet V. Do, Junze Li, Tung-Duong Vuong, Zhaowei Wang, Yangqiu Song, Xiaojuan Ma
Commonsense datasets have been well developed in Natural Language Processing, mainly through crowdsource human annotation. However, there are debates on the genuineness of commonsense reasoning benchmarks. In specific, a significant portion of instances in some commonsense benchmarks do not concern commonsense knowledge. That problem would undermine the measurement of the true commonsense reasoning ability of evaluated models. It is also suggested that the problem originated from a blurry concept of commonsense knowledge, as distinguished from other types of knowledge. To demystify all of the above claims, in this study, we survey existing definitions of commonsense knowledge, ground into the three frameworks for defining concepts, and consolidate them into a multi-framework unified definition of commonsense knowledge (so-called consolidated definition). We then use the consolidated definition for annotations and experiments on the CommonsenseQA and CommonsenseQA 2.0 datasets to examine the above claims. Our study shows that there exists a large portion of non-commonsense-knowledge instances in the two datasets, and a large performance gap on these two subsets where Large Language Models (LLMs) perform worse on commonsense-knowledge instances.
Continuous-Time State Estimation Methods in Robotics: A Survey
2024-11-06
William Talbot, Julian Nubert, Turcan Tuna, Cesar Cadena, Frederike Dümbgen, Jesus Tordesillas, Timothy D. Barfoot, Marco Hutter
Accurate, efficient, and robust state estimation is more important than ever in robotics as the variety of platforms and complexity of tasks continue to grow. Historically, discrete-time filters and smoothers have been the dominant approach, in which the estimated variables are states at discrete sample times. The paradigm of continuous-time state estimation proposes an alternative strategy by estimating variables that express the state as a continuous function of time, which can be evaluated at any query time. Not only can this benefit downstream tasks such as planning and control, but it also significantly increases estimator performance and flexibility, as well as reduces sensor preprocessing and interfacing complexity. Despite this, continuous-time methods remain underutilized, potentially because they are less well-known within robotics. To remedy this, this work presents a unifying formulation of these methods and the most exhaustive literature review to date, systematically categorizing prior work by methodology, application, state variables, historical context, and theoretical contribution to the field. By surveying splines and Gaussian processes together and contextualizing works from other research domains, this work identifies and analyzes open problems in continuous-time state estimation and suggests new research directions.
Building the radio sky template are crucial for detecting the 21 cm emission line signal from the Epoch of Reionization (EoR), as well as for other cosmological research endeavors. Utilizing data from the LOFAR Two-meter Sky Survey (LoTSS) at 150 MHz, we recalibrated the luminosity function for various types of radio sources, including High Excitation Radio Galaxies (HERGs), Low Excitation Radio Galaxies (LERGs), Radio-Quiet Active Galactic Nuclei (RQ-AGNs), and Star-Forming Galaxies (SFGs). We subsequently updated the Tiered Radio Extragalactic Continuum Simulation (T-RECS) code to generate refined mock radio source catalogues. The simulated source counts from this work align more closely with observed data at redshifts greater than $z>4$. Additionally, the differential source counts in total intensity within the flux density range of $0.1-1~\mathrm{mJy}$ closely mirror actual observations. Due to our model incorporating a lower number of faint sources compared to T-RECS, it predicts a reduced power spectrum for point sources, suggesting a potential advantage in studies in low frequency band.
Act in Collusion: A Persistent Distributed Multi-Target Backdoor in Federated Learning
2024-11-06
Tao Liu, Wu Yang, Chen Xu, Jiguang Lv, Huanran Wang, Yuhang Zhang, Shuchun Xu, Dapeng Man
Federated learning, a novel paradigm designed to protect data privacy, is vulnerable to backdoor attacks due to its distributed nature. Current research often designs attacks based on a single attacker with a single backdoor, overlooking more realistic and complex threats in federated learning. We propose a more practical threat model for federated learning: the distributed multi-target backdoor. In this model, multiple attackers control different clients, embedding various triggers and targeting different classes, collaboratively implanting backdoors into the global model via central aggregation. Empirical validation shows that existing methods struggle to maintain the effectiveness of multiple backdoors in the global model. Our key insight is that similar backdoor triggers cause parameter conflicts and injecting new backdoors disrupts gradient directions, significantly weakening some backdoors performance. To solve this, we propose a Distributed Multi-Target Backdoor Attack (DMBA), ensuring efficiency and persistence of backdoors from different malicious clients. To avoid parameter conflicts, we design a multi-channel dispersed frequency trigger strategy to maximize trigger differences. To mitigate gradient interference, we introduce backdoor replay in local training to neutralize conflicting gradients. Extensive validation shows that 30 rounds after the attack, Attack Success Rates of three different backdoors from various clients remain above 93%. The code will be made publicly available after the review period.
Evaluation data contamination in LLMs: how do we measure it and (when) does it matter?
2024-11-06
Aaditya K. Singh, Muhammed Yusuf Kocyigit, Andrew Poulton, David Esiobu, Maria Lomeli, Gergely Szilvasy, Dieuwke Hupkes
Hampering the interpretation of benchmark scores, evaluation data contamination has become a growing concern in the evaluation of LLMs, and an active area of research studies its effects. While evaluation data contamination is easily understood intuitively, it is surprisingly difficult to define precisely which samples should be considered contaminated and, consequently, how it impacts benchmark scores. We propose that these questions should be addressed together and that contamination metrics can be assessed based on whether models benefit from the examples they mark contaminated. We propose a novel analysis method called ConTAM, and show with a large scale survey of existing and novel n-gram based contamination metrics across 13 benchmarks and 7 models from 2 different families that ConTAM can be used to better understand evaluation data contamination and its effects. We find that contamination may have a much larger effect than reported in recent LLM releases and benefits models differently at different scales. We also find that considering only the longest contaminated substring provides a better signal than considering a union of all contaminated substrings, and that doing model and benchmark specific threshold analysis greatly increases the specificity of the results. Lastly, we investigate the impact of hyperparameter choices, finding that, among other things, both using larger values of n and disregarding matches that are infrequent in the pre-training data lead to many false negatives. With ConTAM, we provide a method to empirically ground evaluation data contamination metrics in downstream effects. With our exploration, we shed light on how evaluation data contamination can impact LLMs and provide insight into the considerations important when doing contamination analysis. We end our paper by discussing these in more detail and providing concrete suggestions for future work.
Probing the heating of the neutral atomic interstellar medium in the Dwarf Galaxy Survey through infrared cooling lines
2024-11-06
Maxime Varese, Vianney Lebouteiller, Lise Ramambason, Frédéric Galliano, Chris T. Richardson, Suzanne C. Madden
Star formation in galaxies is regulated by dynamical and thermal processes. The photoelectric effect on small dust grains usually dominates the heating of the star-forming neutral atomic gas reservoir in metal-rich galaxies, while the lower dust-to-gas mass ratio and the higher luminosity of X-ray sources in metal-poor galaxies suggest that other heating mechanisms may be at play. We calculate the relative contributions of the photoelectric effect, photoionization by UV and X-ray photons, and ionization by cosmic rays to the total heating in a sample of 37 nearby galaxies reaching down to 3% the Milky Way metallicity. We use the statistical code MULTIGRIS together with a grid of Cloudy models propagating radiation from stellar clusters and X-ray sources to the ionized and neutral gas, each galaxy being described as a statistical distribution of many 1D components. Infrared cooling lines from the interstellar medium (ISM) are used as constraints to evaluate the most likely distributions and parameters. We show that the photoelectric effect heating dominates in high-metallicity galaxies (>1/18 the Milky Way value) while cosmic rays and especially photoionization from X-rays become predominant in low-metallicity galaxies. Our models predict reasonably well the X-ray source fluxes in the 0.3-8 keV band using indirect ISM tracers, illustrating that the adopted strategy makes it possible to recover the global intrinsic radiation field properties when X-ray observations are unavailable, for instance in early universe galaxies. Finally, we show that the photoelectric effect heating efficiency on PAHs may be recovered through the [CII]+[OI] / PAH observational proxy only if the other heating mechanisms are accounted for (abridged).
Retentive Neural Quantum States: Efficient Ansätze for Ab Initio Quantum Chemistry
2024-11-06
Oliver Knitter, Dan Zhao, James Stokes, Martin Ganahl, Stefan Leichenauer, Shravan Veerapaneni
Neural-network quantum states (NQS) has emerged as a powerful application of quantum-inspired deep learning for variational Monte Carlo methods, offering a competitive alternative to existing techniques for identifying ground states of quantum problems. A significant advancement toward improving the practical scalability of NQS has been the incorporation of autoregressive models, most recently transformers, as variational ansatze. Transformers learn sequence information with greater expressiveness than recurrent models, but at the cost of increased time complexity with respect to sequence length. We explore the use of the retentive network (RetNet), a recurrent alternative to transformers, as an ansatz for solving electronic ground state problems in $\textit{ab initio}$ quantum chemistry. Unlike transformers, RetNets overcome this time complexity bottleneck by processing data in parallel during training, and recurrently during inference. We give a simple computational cost estimate of the RetNet and directly compare it with similar estimates for transformers, establishing a clear threshold ratio of problem-to-model size past which the RetNet's time complexity outperforms that of the transformer. Though this efficiency can comes at the expense of decreased expressiveness relative to the transformer, we overcome this gap through training strategies that leverage the autoregressive structure of the model -- namely, variational neural annealing. Our findings support the RetNet as a means of improving the time complexity of NQS without sacrificing accuracy. We provide further evidence that the ablative improvements of neural annealing extend beyond the RetNet architecture, suggesting it would serve as an effective general training strategy for autoregressive NQS.
Biomechanics-Aware Trajectory Optimization for Navigation during Robotic Physiotherapy
2024-11-06
Italo Belli, J. Micah Prendergast, Ajay Seth, Luka Peternel
Robotic devices hold promise for aiding patients in orthopedic rehabilitation. However, current robotic-assisted physiotherapy methods struggle including biomechanical metrics in their control algorithms, crucial for safe and effective therapy. This paper introduces BATON, a Biomechanics-Aware Trajectory Optimization approach to robotic Navigation of human musculoskeletal loads. The method integrates a high-fidelity musculoskeletal model of the human shoulder into real-time control of robot-patient interaction during rotator cuff tendon rehabilitation. We extract skeletal dynamics and tendon loading information from an OpenSim shoulder model to solve an optimal control problem, generating strain-minimizing trajectories. Trajectories were realized on a healthy subject by an impedance-controlled robot while estimating the state of the subject's shoulder. Target poses were prescribed to design personalized rehabilitation across a wide range of shoulder motion avoiding high-strain areas. BATON was designed with real-time capabilities, enabling continuous trajectory replanning to address unforeseen variations in tendon strain, such as those from changing muscle activation of the subject.
A GMRT 610 MHz radio survey of the North Ecliptic Pole (NEP, ADF-N) / Euclid Deep Field North
2024-11-06
Glenn J. White, L. Barrufet, S. Serjeant, C. P. Pearson, C. Sedgwick, S. Pal, T. W. Shimwell, S. K. Sirothia, P. Chiu, N. Oi, T. Takagi, H. Shim, H. Matsuhara, D. Patra, M. Malkan, H. K. Kim, T. Nakagawa, K. Malek, D. Burgarella, T. Ishigaki
This paper presents a 610 MHz radio survey covering 1.94 square degrees around the North Ecliptic Pole (NEP), which includes parts of the AKARI (ADF-N) and Euclid, Deep Fields North. The median 5-sigma sensitivity is 28 microJy beam per beam, reaching as low as 19 microJy per beam, with a synthesised beam of 3.6 x 4.1 arcsec. The catalogue contains 1675 radio components, with 339 grouped into multi-component sources and 284 isolated components likely part of double radio sources. Imaging, cataloguing, and source identification are presented, along with preliminary scientific results. From a non-statistical sub-set of 169 objects with multi-wavelength AKARI and other detections, luminous infrared galaxies (LIRGs) represent 66 percent of the sample, ultra-luminous infrared galaxies (ULIRGs) 4 percent, and sources with L_IR < 1011 L_sun 30 percent. In total, 56 percent of sources show some AGN presence, though only seven are AGN-dominated. ULIRGs require three times higher AGN contribution to produce high-quality SED fits compared to lower luminosity galaxies, and AGN presence increases with AGN fraction. The PAH mass fraction is insignificant, although ULIRGs have about half the PAH strength of lower IR-luminosity galaxies. Higher luminosity galaxies show gas and stellar masses an order of magnitude larger, suggesting higher star formation rates. For LIRGs, AGN presence increases with redshift, indicating that part of the total luminosity could be contributed by AGN activity rather than star formation. Simple cross-matching revealed 13 ROSAT QSOs, 45 X-ray sources, and 61 sub-mm galaxies coincident with GMRT radio sources.
Components of star formation in NGC 253 : Non-negative Matrix Factorization Analysis with the ALCHEMI integrated intensity images
2024-11-06
Ryo Kishikawa, Nanase Harada, Toshiki Saito, Susanne Aalto, Laura Colzi, Mark Gorski, Christian Henkel, Jeffrey G. Mangum, Sergio Martín, Sebastian Muller, Yuri Nishimura, Víctor M. Rivilla, Kazushi Sakamoto, Paul van der Werf, Serena Viti
It is essential to examine the physical or chemical properties of molecular gas in starburst galaxies to reveal the underlying mechanisms characterizing starbursts. We used non-negative matrix factorization (NMF) to extract individual molecular or physical components involved in the star formation process in NGC\,253. We used images of 148 transitions from 44 different species of the ALMA large program ALCHEMI. Additionally, we included the continuum images at ALMA Bands 3 and 7 from the same dataset. For the five NMF components (NF1--5), we obtained that their distributions correspond to various basic phenomena related to star formation: i) low-density gas extended through the galactic central molecular zone (NF2), ii) shocks (NF3), iii) starburst regions (NF4), and iv) young star-forming regions (NF5). The other component (NF1) is related to excitation; three components obtained by NMF (NF3, 1, and 5) show a strong dependence upon the upper state energies of transitions, and represent low-, intermediate-, and high-excitation, respectively. We also compared our results using principal component analysis (PCA) previously applied to the same dataset. Molecular components extracted from NMF are similar to the ones obtained from PCA. However, NMF is better at extracting components associated with a single physical component, while a single component in PCA usually contains information on multiple physical components. This is especially true for features with weak intensities like emission from outflows. Our results suggest that NMF can be one of promising methods interpreting molecular line survey data, especially in the upcoming era of wide-band receivers.
FedRISE: Rating Induced Sign Election of Gradients for Byzantine Tolerant Federated Aggregation
2024-11-06
Joseph Geo Benjamin, Mothilal Asokan, Mohammad Yaqub, Karthik Nandakumar
One of the most common defense strategies against model poisoning in federated learning is to employ a robust aggregator mechanism that makes the training more resilient. Many of the existing Byzantine robust aggregators provide theoretical guarantees and are empirically effective against certain categories of attacks. However, we observe that certain high-strength attacks can subvert the aggregator and collapse the training. In addition, most aggregators require identifying tolerant settings to converge. Impact of attacks becomes more pronounced when the number of Byzantines is near-majority, and becomes harder to evade if the attacker is omniscient with access to data, honest updates and aggregation methods. Motivated by these observations, we develop a robust aggregator called FedRISE for cross-silo FL that is consistent and less susceptible to poisoning updates by an omniscient attacker. The proposed method explicitly determines the optimal direction of each gradient through a sign-voting strategy that uses variance-reduced sparse gradients. We argue that vote weighting based on the cosine similarity of raw gradients is misleading, and we introduce a sign-based gradient valuation function that ignores the gradient magnitude. We compare our method against 8 robust aggregators under 6 poisoning attacks on 3 datasets and architectures. Our results show that existing robust aggregators collapse for at least some attacks under severe settings, while FedRISE demonstrates better robustness because of a stringent gradient inclusion formulation.
Finite-time thermodynamics: A journey beginning with optimizing heat engines
2024-11-06
Yu-Han Ma, Xiu-Hua Zhao
In this paper, we summarize the historical development of finite-time thermodynamics and review the current state of research over the past two decades in this field, focusing on fundamental constraints of finite-time thermodynamic cycles, optimal control and optimization of thermodynamic processes, the operation of unconventional heat engines, and experimental progress.
2024-11-06
Chiranjib Mukhopadhyay, Victor Montenegro, Abolfazl Bayat
Quantum sensors are now universally acknowledged as one of the most promising near-term quantum technologies. The traditional formulation of quantum sensing introduces a concrete bound on ultimate precision through the so-called local sensing framework, in which a significant knowledge of prior information about the unknown parameter value is implicitly assumed. Moreover, the framework provides a systematic approach for optimizing the sensing protocol. In contrast, the paradigm of global sensing aims to find a precision bound for parameter estimation in the absence of such prior information. In recent years, vigorous research has been pursued to describe the contours of global quantum estimation. Here, we review some of these emerging developments. These developments are both in the realm of finding ultimate precision bounds with respect to appropriate figures of merit in the global sensing paradigm, as well as in the search for algorithms that achieve these bounds. We categorize these developments into two largely mutually exclusive camps; one employing Bayesian updating and the other seeking to generalize the frequentist picture of local sensing towards the global paradigm. In the first approach, in order to achieve the best performance, one has to optimize the measurement settings adaptively. In the second approach, the measurement setting is fixed, however the challenge is to identify this fixed measurement optimally.
ALMA Spectral Survey of An eruptive Young star, V883 Ori (ASSAY): II. Freshly Sublimated Complex Organic Molecules (COMs) in the Keplerian Disk
2024-11-06
Jae-Hong Jeong, Jeong-Eun Lee, Seonjae Lee, Giseon Baek, Ji-Hyun Kang, Seokho Lee, Chul-Hwan Kim, Hyeong-Sik Yun, Yuri Aikawa, Gregory J. Herczeg, Doug Johnstone, Lucas Cieza
We present an investigation of Complex Organic Molecules (COMs) in the spatially resolved Keplerian disk around V883 Ori, an eruptive young star, based on a spectral survey carried out with ALMA in Band 6 (220.7$-$274.9 GHz). We identified about 3,700 molecular emission lines and discovered 23 COMs in the disk. We estimated the column densities of COMs detected through the iterative LTE line fitting method. According to our analyses, using only optically thin lines is critical to deriving the reliable column densities of COMs. Therefore, covering a large frequency range is important for the studies of COMs. The most distinct phenomenon found from the spectra of the V883 Ori disk is that nitrogen-bearing COMs other than CH$_{3}$CN are missing, whereas various oxygen-bearing COMs, except for the CH$_2$OH-bearing molecules, are detected. The missing CH$_2$OH-bearing COMs may indicate the warm water-ice dominant environment for forming COMs. We compared our results with various objects in different evolutionary stages, from Class 0 hot corinos to a Solar System comet 67P/Churyumov-Gerasimenko, to examine the effect of evolution on the COM compositions. In general, the COMs abundances relative to methanol in V883 Ori are higher than in the hot corinos and hot cores, while they are comparable to the cometary values. This may indicate the planet-forming material chemically evolves in the disk midplane after being accreted from the envelope. In addition, as found in the comet 67P/Churyumov-Gerasimenko, nitrogen might also be trapped as ammonium salt within the dust grains in the V883 Ori disk.
Beyond The Rainbow: High Performance Deep Reinforcement Learning On A Desktop PC
2024-11-06
Tyler Clark, Mark Towers, Christine Evers, Jonathon Hare
Rainbow Deep Q-Network (DQN) demonstrated combining multiple independent enhancements could significantly boost a reinforcement learning (RL) agent's performance. In this paper, we present "Beyond The Rainbow" (BTR), a novel algorithm that integrates six improvements from across the RL literature to Rainbow DQN, establishing a new state-of-the-art for RL using a desktop PC, with a human-normalized interquartile mean (IQM) of 7.4 on atari-60. Beyond Atari, we demonstrate BTR's capability to handle complex 3D games, successfully training agents to play Super Mario Galaxy, Mario Kart, and Mortal Kombat with minimal algorithmic changes. Designing BTR with computational efficiency in mind, agents can be trained using a desktop PC on 200 million Atari frames within 12 hours. Additionally, we conduct detailed ablation studies of each component, analzying the performance and impact using numerous measures.
Recent technological advances in healthcare have led to unprecedented growth in patient data quantity and diversity. While artificial intelligence (AI) models have shown promising results in analyzing individual data modalities, there is increasing recognition that models integrating multiple complementary data sources, so-called multimodal AI, could enhance clinical decision-making. This scoping review examines the landscape of deep learning-based multimodal AI applications across the medical domain, analyzing 432 papers published between 2018 and 2024. We provide an extensive overview of multimodal AI development across different medical disciplines, examining various architectural approaches, fusion strategies, and common application areas. Our analysis reveals that multimodal AI models consistently outperform their unimodal counterparts, with an average improvement of 6.2 percentage points in AUC. However, several challenges persist, including cross-departmental coordination, heterogeneous data characteristics, and incomplete datasets. We critically assess the technical and practical challenges in developing multimodal AI systems and discuss potential strategies for their clinical implementation, including a brief overview of commercially available multimodal AI models for clinical decision-making. Additionally, we identify key factors driving multimodal AI development and propose recommendations to accelerate the field's maturation. This review provides researchers and clinicians with a thorough understanding of the current state, challenges, and future directions of multimodal AI in medicine.
Towards pandemic preparedness: ability to estimate high-resolution social contact patterns from longitudinal surveys
2024-11-06
Shozen Dan, Joshua Tegegne, Yu Chen, Zhi Ling, Veronika K. Jaeger, André Karch, Swapnil Mishra, Oliver Ratmann
Social contact surveys are an important tool to assess infection risks within populations, and the effect of non-pharmaceutical interventions on social behaviour during disease outbreaks, epidemics, and pandemics. Numerous longitudinal social contact surveys were conducted during the COVID-19 era, however data analysis is plagued by reporting fatigue, a phenomenon whereby the average number of social contacts reported declines with the number of repeat participations and as participants' engagement decreases over time. Using data from the German COVIMOD Study between April 2020 to December 2021, we demonstrate that reporting fatigue varied considerably by sociodemographic factors and was consistently strongest among parents reporting children contacts (parental proxy reporting), students, middle-aged individuals, those in full-time employment and those self-employed. We find further that, when using data from first-time participants as gold standard, statistical models incorporating a simple logistic function to control for reporting fatigue were associated with substantially improved estimation accuracy relative to models with no reporting fatigue adjustments, and that no cap on the number of repeat participations was required. These results indicate that existing longitudinal contact survey data can be meaningfully interpreted under an easy-to-implement statistical approach adressing reporting fatigue confounding, and that longitudinal designs including repeat participants are a viable option for future social contact survey designs.
With the development of artificial intelligence, its contribution to science is evolving from simulating a complex problem to automating entire research processes and producing novel discoveries. Achieving this advancement requires both specialized general models grounded in real-world scientific data and iterative, exploratory frameworks that mirror human scientific methodologies. In this paper, we present PROTEUS, a fully automated system for scientific discovery from raw proteomics data. PROTEUS uses large language models (LLMs) to perform hierarchical planning, execute specialized bioinformatics tools, and iteratively refine analysis workflows to generate high-quality scientific hypotheses. The system takes proteomics datasets as input and produces a comprehensive set of research objectives, analysis results, and novel biological hypotheses without human intervention. We evaluated PROTEUS on 12 proteomics datasets collected from various biological samples (e.g. immune cells, tumors) and different sample types (single-cell and bulk), generating 191 scientific hypotheses. These were assessed using both automatic LLM-based scoring on 5 metrics and detailed reviews from human experts. Results demonstrate that PROTEUS consistently produces reliable, logically coherent results that align well with existing literature while also proposing novel, evaluable hypotheses. The system's flexible architecture facilitates seamless integration of diverse analysis tools and adaptation to different proteomics data types. By automating complex proteomics analysis workflows and hypothesis generation, PROTEUS has the potential to considerably accelerate the pace of scientific discovery in proteomics research, enabling researchers to efficiently explore large-scale datasets and uncover biological insights.
Potential Energy Landscape as a Framework for Developing Innovative Materials
2024-11-06
Nadezhda A. Andreeva, Vitaly V. Chaban
In the contemporary era of rapid advancements in materials science, the development of new compounds and materials is proceeding at an accelerated pace. The concept of the potential energy landscape (PEL) plays a pivotal role in supporting the meticulous engineering of novel structures. This review article examines the historical evolution of the PEL concept and its diverse applications in materials design. A comprehensive overview of the major methods employed to sample the PEL is presented, accompanied by a critical discussion highlighting relevant modern endeavors. Specific applications of the PEL in rationalizing the design of molecules and materials for energy storage, electrolytic solutions, and greenhouse gas capture are exemplified. This review serves as an up-to-date guide for exploring and analyzing the PEL, facilitating a deeper understanding of its significance in materials science.
Tuning One Dimensional Plasmonic Gap at Nanometer Scale for Advanced SERS Detection
2024-11-06
Mahsa Haddadi Moghaddam, Sobhagyam Sharma, Daehwan Park, Dai Sik Kim
The hotspots, which are typically found in nanogaps between metal structures, are critical for the enhancement of the electromagnetic field. Surface-enhanced Raman scattering (SERS), a technique known for its exceptional sensitivity and molecular detection capability, relies on the creation of these hotspots within nanostructures, where localized surface plasmon resonance (LSPR) amplifies Raman signals. However, creating adjustable nanogaps on a large scale remains challenging, particularly for applications involving biomacromolecules of various sizes. The development of tunable plasmonic nanostructures on flexible substrates represents a significant advance in the creation and precise control of these hotspots. Our work introduces tunable nanogaps on flexible substrates, utilizing thermally responsive materials to allow real-time control of gap width for different molecule sizes. Through advanced nanofabrication techniques, we have achieved uniform, tunable nanogaps over large areas wafer scale, enabling dynamic modulation of SERS signals. This approach resulted in an enhancement factor of over 10^7, sufficient for single-molecule detection, with a detection limit as low as 10^-12 M. Our thermally tunable nanogaps provide a powerful tool for precise detection of molecules and offer significant advantages for a wide range of sensing and analytical applications
Multi-Modal Intelligent Channel Modeling: A New Modeling Paradigm via Synesthesia of Machines
2024-11-06
Lu Bai, Ziwei Huang, Mingran Sun, Xiang Cheng, Lizhen Cui
In the future sixth-generation (6G) era, to support accurate localization sensing and efficient communication link establishment for intelligent agents, a comprehensive understanding of the surrounding environment and proper channel modeling are indispensable. The existing method, which solely exploits radio frequency (RF) communication information, is difficult to accomplish accurate channel modeling. Fortunately, multi-modal devices are deployed on intelligent agents to obtain environmental features, which could further assist in channel modeling. Currently, some research efforts have been devoted to utilizing multi-modal information to facilitate channel modeling, while still lack a comprehensive review. To fill this gap, we embark on an initial endeavor with the goal of reviewing multi-modal intelligent channel modeling (MMICM) via Synesthesia of Machines (SoM). Compared to channel modeling approaches that solely utilize RF communication information, the utilization of multi-modal information can provide a more in-depth understanding of the propagation environment around the transceiver, thus facilitating more accurate channel modeling. First, this paper introduces existing channel modeling approaches from the perspective of the channel modeling evolution. Then, we have elaborated and investigated recent advances in the topic of capturing typical channel characteristics and features, i.e., channel non-stationarity and consistency, by characterizing the mathematical, spatial, coupling, and mapping relationships. In addition, applications that can be supported by MMICM are summarized and analyzed. To corroborate the superiority of MMICM via SoM, we give the simulation result and analysis. Finally, some open issues and potential directions for the MMICM are outlined from the perspectives of measurements, modeling, and applications.
The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Models
2024-11-06
Anaelia Ovalle, Krunoslav Lehman Pavasovic, Louis Martin, Luke Zettlemoyer, Eric Michael Smith, Adina Williams, Levent Sagun
Natural-language assistants are designed to provide users with helpful responses while avoiding harmful outputs, largely achieved through alignment to human preferences. Yet there is limited understanding of whether alignment techniques may inadvertently perpetuate or even amplify harmful biases inherited from their pre-aligned base models. This issue is compounded by the choice of bias evaluation benchmarks in popular preference-finetuned models, which predominantly focus on dominant social categories, such as binary gender, thereby limiting insights into biases affecting underrepresented groups. Towards addressing this gap, we center transgender, nonbinary, and other gender-diverse identities to investigate how alignment procedures interact with pre-existing gender-diverse bias in LLMs. Our key contributions include: 1) a comprehensive survey of bias evaluation modalities across leading preference-finetuned LLMs, highlighting critical gaps in gender-diverse representation, 2) systematic evaluation of gender-diverse biases across 12 models spanning Direct Preference Optimization (DPO) stages, uncovering harms popular bias benchmarks fail to detect, and 3) a flexible framework for measuring harmful biases in implicit reward signals applicable to other social contexts. Our findings reveal that DPO-aligned models are particularly sensitive to supervised finetuning (SFT), and can amplify two forms of real-world gender-diverse harms from their base models: stigmatization and gender non-affirmative language. We conclude with recommendations tailored to DPO and broader alignment practices, advocating for the adoption of community-informed bias evaluation frameworks to more effectively identify and address underrepresented harms in LLMs.
Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey
2024-11-06
Amer Essakine, Yanqi Cheng, Chun-Wun Cheng, Lipei Zhang, Zhongying Deng, Lei Zhu, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
Implicit Neural Representations (INRs) have emerged as a paradigm in knowledge representation, offering exceptional flexibility and performance across a diverse range of applications. INRs leverage multilayer perceptrons (MLPs) to model data as continuous implicit functions, providing critical advantages such as resolution independence, memory efficiency, and generalisation beyond discretised data structures. Their ability to solve complex inverse problems makes them particularly effective for tasks including audio reconstruction, image representation, 3D object reconstruction, and high-dimensional data synthesis. This survey provides a comprehensive review of state-of-the-art INR methods, introducing a clear taxonomy that categorises them into four key areas: activation functions, position encoding, combined strategies, and network structure optimisation. We rigorously analyse their critical properties, such as full differentiability, smoothness, compactness, and adaptability to varying resolutions while also examining their strengths and limitations in addressing locality biases and capturing fine details. Our experimental comparison offers new insights into the trade-offs between different approaches, showcasing the capabilities and challenges of the latest INR techniques across various tasks. In addition to identifying areas where current methods excel, we highlight key limitations and potential avenues for improvement, such as developing more expressive activation functions, enhancing positional encoding mechanisms, and improving scalability for complex, high-dimensional data. This survey serves as a roadmap for researchers, offering practical guidance for future exploration in the field of INRs. We aim to foster new methodologies by outlining promising research directions for INRs and applications.
Machine learning algorithms have achieved remarkable success across various disciplines, use cases and applications, under the prevailing assumption that training and test samples are drawn from the same distribution. Consequently, these algorithms struggle and become brittle even when samples in the test distribution start to deviate from the ones observed during training. Domain adaptation and domain generalization have been studied extensively as approaches to address distribution shifts across test and train domains, but each has its limitations. Test-time adaptation, a recently emerging learning paradigm, combines the benefits of domain adaptation and domain generalization by training models only on source data and adapting them to target data during test-time inference. In this survey, we provide a comprehensive and systematic review on test-time adaptation, covering more than 400 recent papers. We structure our review by categorizing existing methods into five distinct categories based on what component of the method is adjusted for test-time adaptation: the model, the inference, the normalization, the sample, or the prompt, providing detailed analysis of each. We further discuss the various preparation and adaptation settings for methods within these categories, offering deeper insights into the effective deployment for the evaluation of distribution shifts and their real-world application in understanding images, video and 3D, as well as modalities beyond vision. We close the survey with an outlook on emerging research opportunities for test-time adaptation.
PMN J1310-5552: A Gamma-ray Emitting Blazar Candidate Harboring A Cosmic Monster
2024-11-06
Vaidehi S. Paliya
Relativistic jets manifest some of the most intriguing activities in the nuclear regions of active galaxies. Identifying the most powerful relativistic jets permits us to probe the most luminous accretion systems and, in turn, the most massive black holes. This paper reports the identification of one such object, PMN J1310$-$5552 ($z=1.56$), a blazar candidate of uncertain type detected with the Fermi Large Area Telescope (LAT) and Swift Burst Alert Telescope. The detection of broad emission lines in its optical spectra taken with the X-Shooter and Goodman spectrographs classifies it to be a flat-spectrum radio quasar. The analysis of the Goodman optical spectrum has revealed PMN J1310$-$5552 harbors a massive black hole (log scale $M_{\rm BH}=9.90\pm0.07$, in $M_{\odot}$) and luminous accretion disk (log scale $L_{\rm disk}=46.86\pm0.03$, in erg s$^{-1}$). The fitting of the observed big blue bump with the standard accretion disk model resulted in the log scale $M_{\rm BH}=9.81^{+0.19}_{-0.20}$ (in $M_{\odot}$) and $L_{\rm disk}=46.86^{+0.09}_{-0.09}$ (in erg s$^{-1}$), respectively. These parameters suggest PMN J1310$-$5552 hosts one of the most massive black holes and the most luminous accretion disks among the blazar population. The physical properties of this enigmatic blazar were studied by modeling the broadband spectral energy distribution considering the data from NuSTAR, Swift, Fermi-LAT, and archival observations. Overall, PMN J1310$-$5552 is a powerful `MeV' blazar with physical parameters similar to other members of this unique class of blazars. These results provide glimpses of monsters lurking among the unknown high-energy emitters and demonstrate the importance of ongoing wide-field sky surveys to discover them.
How do practitioners gain confidence in assurance cases?
2024-11-06
Simon Diemert, Caleb Shortt, Jens H. Weber
CONTEXT: Assurance Cases (ACs) are prepared to argue that the system's desired quality attributes (e.g., safety or security) are satisfied. While there is strong adoption of ACs, practitioners are often left asking an important question: are we confident that the claims made by the case are true? While many confidence assessment methods (CAMs) exist, little is known about the use of these methods in practice OBJECTIVE: Develop an understanding of the current state of practice for AC confidence assessment: what methods are used in practice and what barriers exist for their use? METHOD: Structured interviews were performed with practitioners with experience contributing to real-world ACs. Open-coding was performed on transcripts. A description of the current state of AC practice and future considerations for researchers was synthesized from the results. RESULTS: A total of n = 19 practitioners were interviewed. The most common CAMs were (peer-)review of ACs, dialectic reasoning ("defeaters"), and comparing against checklists. Participants preferred qualitative methods and expressed concerns about quantitative CAMs. Barriers to using CAMs included additional work, inadequate guidance, subjectivity and interpretation of results, and trustworthiness of methods. CONCLUSION: While many CAMs are described in the literature there is a gap between the proposed methods and needs of practitioners. Researchers working in this area should consider the need to: connect CAMs to established practices, use CAMs to communicate with interest holders, crystallize the details of CAM application, curate accessible guidance, and confirm that methods are trustworthy.
Requirements Engineering for Older Adult Digital Health Software: A Systematic Literature Review
2024-11-06
Yuqing Xiao, John Grundy, Anuradha Madugalla
Growth of the older adult population has led to an increasing interest in technology-supported aged care. However, the area has some challenges such as a lack of caregivers and limitations in understanding the emotional, social, physical, and mental well-being needs of seniors. Furthermore, there is a gap in the understanding between developers and ageing people of their requirements. Digital health can be important in supporting older adults wellbeing, emotional requirements, and social needs. Requirements Engineering (RE) is a major software engineering field, which can help to identify, elicit and prioritize the requirements of stakeholders and ensure that the systems meet standards for performance, reliability, and usability. We carried out a systematic review of the literature on RE for older adult digital health software. This was necessary to show the representatives of the current stage of understanding the needs of older adults in aged care digital health. Using established guidelines outlined by the Kitchenham method, the PRISMA and the PICO guideline, we developed a protocol, followed by the systematic exploration of eight databases. This resulted in 69 primary studies of high relevance, which were subsequently subjected to data extraction, synthesis, and reporting. We highlight key RE processes in digital health software for ageing people. It explored the utilization of technology for older user well-being and care, and the evaluations of such solutions. The review also identified key limitations found in existing primary studies that inspire future research opportunities. The results indicate that requirement gathering and understanding have a significant variation between different studies. The differences are in the quality, depth, and techniques adopted for requirement gathering and these differences are largely due to uneven adoption of RE methods.
PyroGuardian: An IoT-Enabled System for Health and Location Monitoring in High-Risk Firefighting Environments
2024-11-06
Berkay Kaplan, Buhe Li
First responders risk their lives to reduce property damage and prevent injuries during disasters. Among first responders, firefighters work with fires in residential properties, forests, or other locations where fire occurs. We built the PyroGuardian system that uses wearable modules to transmit unit information over Long Range (LoRa) to an Android tablet. The tablet runs our application, PyroPortal, to assign each firefighter's stats, such as body temperature, heart rate, and GPS location. PyroPortal displays this information on unit dashboards, and markers on Google Maps represent the firefighter's location and the direction they are facing. These dashboards can help the incident commander (IC) make more informed decisions on mission control operations and remove specific units whose health stats, such as oximeter and pulse, passed certain thresholds. PyroGuardian completes all these tasks at an affordable cost and in an impressive maximum range between the units and IC. In addition, PyroGuardian has various application scenarios, such as law enforcement and military operations, besides firefighting. We also conducted a sample mission inside a burning building while real firefighters watched. After the demonstration, they completed a survey on system usability and PyroGuardian's potential to meet their requirements.
Quantum imaginarity and quantum nonlocality capture the essence of quantumness of a physical system from different aspects. We establish a connection between the imaginarity and quantum nonlocality in any two-qubit states. Based on the imaginarity $l_1$-norm and relative entropy, we first derive complementary relations among the quantum imaginarities with respect to any set of mutually unbiased bases for arbitrary qubit states. Based on these complementary relations, we introduce the nonlocal advantage of quantum imaginarity (NAQI). We show that any two-qubit state with NAQI is quantum steerable, but not the vice-versa. In addition, we investigate the exclusion property of NAQI in three-qubit pure states.
Triple-$α$ reaction rates below $T_9=3$ by a non-adiabatic three-body model
2024-11-06
M. Katsuma
The triple-$\alpha$ reaction from ternary continuum states at off-resonant energies, $\alpha+\alpha+\alpha \rightarrow ^{12}$C, still remains in unsolved problems. This direct process is estimated with a non-adiabatic Faddeev hyper-spherical harmonics and $R$-matrix expansion method. After reviewing this model, the resultant photo-disintegration of $^{12}$C($2^+_1 \rightarrow 0^+$) is shown to be in 10$^{-15}$--10$^{-3}$ pico-barn order for $0.15 \le E \le 0.35$ MeV. This is far below the values predicted by the recent adiabatic calculations. In spite of the large difference, the derived reaction rates are illustrated to be concordant with the current evaluated rates for $0.08 \le T_9 \le 3$ including helium burning temperatures. The difference below $E = 0.20$ MeV can be seen in the rates for $T_9 \le 0.07$. In comparison with the calculations, the triple-$\alpha$ reaction rates are found to be reduced by about 10$^{-4}$ at $T_9 = 0.05$, because of an accurate description for $^8$Be break-up. Uncertainties of the rates are also estimated by examining sensitivity to 3$\alpha$ potentials. With introducing three-body $S$-factors and a resonant term, the present rates are expressed in an analytic form, and they are provided in a tabular form for astrophysical applications. To update the evaluated rates, non-resonant sequential process between $\alpha+^8$Be may be eliminated by hand.
2024-11-06
Thomas Sandholm, Sarah Dong, Sayandev Mukherjee, John Feland, Bernardo A. Huberman
We present a novel AI-based ideation assistant and evaluate it in a user study with a group of innovators. The key contribution of our work is twofold: we propose a method of idea exploration in a constrained domain by means of LLM-supported semantic navigation of problem and solution spaces, and employ novel automated data input filtering to improve generations. We found that semantic exploration is preferred to the traditional prompt-output interactions, measured both in explicit survey rankings, and in terms of innovation assistant engagement, where 2.1x more generations were performed using semantic exploration. We also show that filtering input data with metrics such as relevancy, coherence and human alignment leads to improved generations in the same metrics as well as enhanced quality of experience among innovators.
This paper summarises the intersection of Web3 and AI technologies, synergies between these technologies, and gaps that we suggest exist in the conception of the possible integrations of these technologies. The summary is informed by a comprehensive literature review of current academic and industry papers, analyst reports, and Ethereum research community blogposts. We focus our contribution on the perceived gaps and detail some novel approaches that would benefit the blockchain/Web3 ecosystem. We believe that the overview presented in this paper will help guide researchers interested in the intersection of Web3 and AI technologies.
Variational Analysis of a Nonconvex and Nonsmooth Optimization Problem: An Introduction
2024-11-06
Johannes O. Royset
Variational analysis provides the theoretical foundations and practical tools for constructing optimization algorithms without being restricted to smooth or convex problems. We survey the central concepts in the context of a concrete but broadly applicable problem class from composite optimization in finite dimensions. While prioritizing accessibility over mathematical details, we introduce subgradients of arbitrary functions and the resulting optimality conditions, describe approximations and the need for going pointwise and uniform convergence, and summarize proximal methods. We derive dual problems from parametrization of the actual problem and the resulting relaxations. The paper ends with an introduction to second-order theory and its role in stability analysis of optimization problems.
Influential Factors in Increasing an Amazon products Sales Rank
2024-11-06
Ben Chen, Rohit Mokashi, Mamata Khadka, Robert Reyes, Huthaifa I. Ashqar
Amazon is the world number one online retailer and has nearly every product a person could need along with a treasure trove of product reviews to help consumers make educated purchases. Companies want to find a way to increase their sales in a very crowded market, and using this data is key. A very good indicator of how a product is selling is its sales rank; which is calculated based on all-time sales of a product where recent sales are weighted more than older sales. Using the data from the Amazon products and reviews we determined that the most influential factors in determining the sales rank of a product were the number of products Amazon showed that other customers also bought, the number of products Amazon showed that customers also viewed, and the price of the product. These results were consistent for the Digital Music category, the Office Products category, and the subcategory Holsters under Cell Phones and Accessories.
Analysis of Droughts and Their Intensities in California from 2000 to 2020
2024-11-06
Ujjwal, Shikha C. Patel, Bansari K. Shah, Nicholas Ogbonna, Huthaifa I Ashqar
Drought has been perceived as a persistent threat globally and the complex mechanism of various factors contributing to its emergence makes it more troublesome to understand. Droughts and their severity trends have been a point of concern in the USA as well, since the economic impact of droughts has been substantial, especially in parts that contribute majorly to US agriculture. California is the biggest agricultural contributor to the United States with its share amounting up to 12% approximately for all of US agricultural produce. Although, according to a 20-year average, California ranks fifth on the list of the highest average percentage of drought-hit regions. Therefore, drought analysis and drought prediction are of crucial importance for California in order to mitigate the associated risks. However, the design of a consistent drought prediction model based on the dynamic relationship of the drought index remains a challenging task. In the present study, we trained a Voting Ensemble classifier utilizing a soft voting system and three different Random Forest models, to predict the presence of drought and also its intensity. In this paper, initially, we have discussed the trends of droughts and their intensities in various California counties reviewed the correlation of meteorological indicators with drought intensities and used these meteorological indicators for drought prediction so as to evaluate their effectiveness as well as significance.
Unfair Alignment: Examining Safety Alignment Across Vision Encoder Layers in Vision-Language Models
2024-11-06
Saketh Bachu, Erfan Shayegani, Trishna Chakraborty, Rohit Lal, Arindam Dutta, Chengyu Song, Yue Dong, Nael Abu-Ghazaleh, Amit K. Roy-Chowdhury
Vision-language models (VLMs) have improved significantly in multi-modal tasks, but their more complex architecture makes their safety alignment more challenging than the alignment of large language models (LLMs). In this paper, we reveal an unfair distribution of safety across the layers of VLM's vision encoder, with earlier and middle layers being disproportionately vulnerable to malicious inputs compared to the more robust final layers. This 'cross-layer' vulnerability stems from the model's inability to generalize its safety training from the default architectural settings used during training to unseen or out-of-distribution scenarios, leaving certain layers exposed. We conduct a comprehensive analysis by projecting activations from various intermediate layers and demonstrate that these layers are more likely to generate harmful outputs when exposed to malicious inputs. Our experiments with LLaVA-1.5 and Llama 3.2 show discrepancies in attack success rates and toxicity scores across layers, indicating that current safety alignment strategies focused on a single default layer are insufficient.
We conduct a scoping review of existing approaches for synthetic EHR data generation, and benchmark major methods with proposed open-source software to offer recommendations for practitioners. We search three academic databases for our scoping review. Methods are benchmarked on open-source EHR datasets, MIMIC-III/IV. Seven existing methods covering major categories and two baseline methods are implemented and compared. Evaluation metrics concern data fidelity, downstream utility, privacy protection, and computational cost. 42 studies are identified and classified into five categories. Seven open-source methods covering all categories are selected, trained on MIMIC-III, and evaluated on MIMIC-III or MIMIC-IV for transportability considerations. Among them, GAN-based methods demonstrate competitive performance in fidelity and utility on MIMIC-III; rule-based methods excel in privacy protection. Similar findings are observed on MIMIC-IV, except that GAN-based methods further outperform the baseline methods in preserving fidelity. A Python package, ``SynthEHRella'', is provided to integrate various choices of approaches and evaluation metrics, enabling more streamlined exploration and evaluation of multiple methods. We found that method choice is governed by the relative importance of the evaluation metrics in downstream use cases. We provide a decision tree to guide the choice among the benchmarked methods. Based on the decision tree, GAN-based methods excel when distributional shifts exist between the training and testing populations. Otherwise, CorGAN and MedGAN are most suitable for association modeling and predictive modeling, respectively. Future research should prioritize enhancing fidelity of the synthetic data while controlling privacy exposure, and comprehensive benchmarking of longitudinal or conditional generation methods.
Novel Non-Prehensile Rolling Problem: Modelling and Balance Control of Pendulum-Driven Reconfigurable Disks Motion with Magnetic Coupling in Simulation
2024-11-06
Ollie Wiltshire, Seyed Amir Tafrishi
This paper presents a novel type of mobile rolling robot designed as a modular platform for non-prehensile manipulation, highlighting the associated control challenges in achieving balancing control of the robotic system. The developed rolling disk modules incorporate an innovative internally actuated magnetic-pendulum coupling mechanism, which introduces a compelling control problem due to the frictional and sliding interactions, as well as the magnetic effects between each module. In this paper, we derive the nonlinear dynamics of the robot using the Euler-Lagrange formulation. Then, through simulation, the motion behavior of the system is studied and analyzed, providing critical insights for future investigations into control methods for complex non-prehensile motion between robotic modules. Also, we study the balancing of this new platform and introduce a new motion pattern of lifting. This research aims to enhance the understanding and implementation of modular self-reconfigurable robots in various scenarios for future applications.
2024-11-06
Y. Aoki, T. Blum, S. Collins, L. Del Debbio, M. Della Morte, P. Dimopoulos, X. Feng, M. Golterman, Steven Gottlieb, R. Gupta, G. Herdoiza, P. Hernandez, A. Jüttner, T. Kaneko, E. Lunghi, S. Meinel, C. Monahan, A. Nicholson, T. Onogi, P. Petreczky, A. Portelli, A. Ramos, S. R. Sharpe, J. N. Simone, S. Sint, R. Sommer, N. Tantalo, R. Van de Water, A. Vaquero, U. Wenger, H. Wittig
We review lattice results related to pion, kaon, $D$-meson, $B$-meson, and nucleon physics with the aim of making them easily accessible to the nuclear and particle physics communities. More specifically, we report on the determination of the light-quark masses, the form factor $f_+(0)$ arising in the semileptonic $K \to \pi$ transition at zero momentum transfer, as well as the decay-constant ratio $f_K/f_\pi$ and its consequences for the CKM matrix elements $V_{us}$ and $V_{ud}$. We review the determination of the $B_K$ parameter of neutral kaon mixing as well as the additional four $B$ parameters that arise in theories of physics beyond the Standard Model. For the heavy-quark sector, we provide results for $m_c$ and $m_b$ as well as those for the decay constants, form factors, and mixing parameters of charmed and bottom mesons and baryons. These are the heavy-quark quantities most relevant for the determination of CKM matrix elements and the global CKM unitarity-triangle fit. We review the status of lattice determinations of the strong coupling constant $\alpha_s$. We review the determinations of nucleon charges from the matrix elements of both isovector and flavour-diagonal axial, scalar and tensor local quark bilinears, and momentum fraction, helicity moment and the transversity moment from one-link quark bilinears. We also review determinations of scale-setting quantities. Finally, in this review we have added a new section on the general definition of the low-energy limit of the Standard Model.
MonoRollBot: 3-DOF Spherical Robot with Underactuated Single Compliant Actuator Design
2024-11-06
Zhiwei Liu, Seyed Amir Tafrishi
Spherical rolling robots have garnered significant attention in the field of mobile robotics for applications such as inspection and space exploration. Designing underactuated rolling robots poses challenges in achieving multi-directional propulsion with high degrees of freedom while utilizing a limited number of actuators. This paper presents the MonoRollBot, a novel 3-degree-of-freedom (DOF) spherical robot that utilizes an underactuated mechanism driven by only a single spring-motor system. Unlike conventional spherical robots, MonoRollBot employs a minimalist actuation approach, relying on only one motor and a passive spring to control its locomotion. The robot achieves 3-DOF motion through an innovative coupling of spring dynamics and motor control. In this work, we detail the design of the MonoRollBot and evaluate its motion capabilities through design studies. We also do studies on its locomotion behaviours based on changes in rotating mass and stiffness properties.
The FENIKS Survey: Stellar-Halo Mass Relationship of Central and Satellite Galaxies in UDS and COSMOS at 0.2 < z < 4.5
2024-11-06
Kumail Zaidi, David A. Wake, Danilo Marchesini, Kartheik Iyer, Adam Muzzin, Casey Papovich, Jacqueline Antwi-Danso, Karl Glazebrook, Ivo Labbé
We present a comprehensive analysis of the observed Stellar-to-Halo mass relationship (SHMR) spanning redshifts from 0.2 to 4.5. This was enabled through galaxy clustering and abundance measurements from two large (effective area ~ 1.61 deg^2) and homogeneously prepared photometric catalogs - UltraVISTA ultra-deep stripes DR3 (COSMOS) and FENIKS v1 (UDS). To translate these measurements into the SHMR, we introduce a novel halo occupation distribution (HOD) fitting approach (``smooth-$z$'') whereby HOD parameters between neighboring z-bins are connected via physically motivated continuity (smoothing) priors. As a result, the high constraining power at z <~ 2, due to a much wider dynamical range in stellar mass (~ 3 dex), helps constrain the SHMR at z >~ 2, where that range shrinks down to <~ 1 dex. We find that the halo mass is tightly coupled to star formation: the halo mass with peak integrated star-forming efficiency (SFE), M_h^peak remains constant within ~ 10^12.2 - 10^12.4 Msolar throughout the redshifts probed. Furthermore, we show that if we had relied on COSMOS alone (as opposed to COSMOS+UDS), as has been done by many preceding studies, M_h^peak would be systematically lower by up to ~0.15 dex at z < 1.5, highlighting the importance of mitigating cosmic variance. Finally, for the first time, we show how the SFE evolves with redshift as halos grow in mass along their progenitor merger trees, instead of at fixed halo masses.
In many causal learning problems, variables of interest are often not all measured over the same observations, but are instead distributed across multiple datasets with overlapping variables. Tillman et al. (2008) presented the first algorithm for enumerating the minimal equivalence class of ground-truth DAGs consistent with all input graphs by exploiting local independence relations, called ION. In this paper, this problem is formulated as a more computationally efficient answer set programming (ASP) problem, which we call ION-C, and solved with the ASP system clingo. The ION-C algorithm was run on random synthetic graphs with varying sizes, densities, and degrees of overlap between subgraphs, with overlap having the largest impact on runtime, number of solution graphs, and agreement within the output set. To validate ION-C on real-world data, we ran the algorithm on overlapping graphs learned from data from two successive iterations of the European Social Survey (ESS), using a procedure for conducting joint independence tests to prevent inconsistencies in the input.
Differentially Private Finite Population Estimation via Survey Weight Regularization
2024-11-06
Jeremy Seeman, Yajuan Si, Jerome P Reiter
In general, it is challenging to release differentially private versions of survey-weighted statistics with low error for acceptable privacy loss. This is because weighted statistics from complex sample survey data can be more sensitive to individual survey response and weight values than unweighted statistics, resulting in differentially private mechanisms that can add substantial noise to the unbiased estimate of the finite population quantity. On the other hand, simply disregarding the survey weights adds noise to a biased estimator, which also can result in an inaccurate estimate. Thus, the problem of releasing an accurate survey-weighted estimate essentially involves a trade-off among bias, precision, and privacy. We leverage this trade-off to develop a differentially private method for estimating finite population quantities. The key step is to privately estimate a hyperparameter that determines how much to regularize or shrink survey weights as a function of privacy loss. We illustrate the differentially private finite population estimation using the Panel Study of Income Dynamics. We show that optimal strategies for releasing DP survey-weighted mean income estimates require orders-of-magnitude less noise than naively using the original survey weights without modification.
On the Work of Cartan and Münzner on Isoparametric Hypersurfaces
2024-11-06
Thomas E. Cecil
A hypersurface $M^n$ in a real space form ${\bf R}^{n+1}$, $S^{n+1}$, or $H^{n+1}$ is isoparametric if it has constant principal curvatures. This paper is a survey of the fundamental work of Cartan and M\"{u}nzner on the theory of isoparametric hypersurfaces in real space forms, in particular, spheres. This work is contained in four papers of Cartan published during the period 1938--1940, and two papers of M\"{u}nzner that were published in preprint form in the early 1970's, and as journal articles in 1980--1981. These papers of Cartan and M\"{u}nzner have been the foundation of the extensive field of isoparametric hypersurfaces, and they have all been recently translated into English by the author. The paper concludes with a brief survey of the recently completed classification of isoparametric hypersurfaces in spheres.
Quantifying the Contamination From Nearby Stellar Companions in Gaia DR3 Photometry
2024-11-06
Kendall Sullivan, Adam L. Kraus, Travis A. Berger, Daniel Huber
Identifying and removing binary stars from stellar samples is a crucial but complicated task. Regardless of how carefully a sample is selected, some binaries will remain and complicate interpretation of results, especially via flux contamination of survey photometry. One such sample is the data from the Gaia spacecraft, which is collecting photometry and astrometry of more than $10^{9}$ stars. To quantify the impact of binaries on Gaia photometry, we assembled a sample of known binary stars observed with adaptive optics and with accurately measured parameters, which we used to predict Gaia photometry for each stellar component. We compared the predicted photometry to the actual Gaia photometry for each system, and found that the contamination of Gaia photometry because of multiplicity decreases non-linearly from near-complete contamination ($\rho \leq 0''.15$) to no contamination (binary projected separation, or $\rho > 0''.3$). We provide an analytic relation to analytically correct photometric bias in a sample of Gaia stars using the binary separation. This correction is necessary because the Gaia PSF photometry extraction does not fully remove the secondary star flux for binaries with separations with $\rho \lesssim 0''.3$. We also evaluated the utility of various Gaia quality-of-fit metrics for identifying binary stars and found that RUWE remains the best indicator for unresolved binaries, but multi-peak image fraction probes a separation regime not currently accessible to RUWE.
Astronomaly Protege: Discovery Through Human-Machine Collaboration
2024-11-06
Michelle Lochner, Lawrence Rudnick
Modern telescopes generate catalogs of millions of objects with the potential for new scientific discoveries, but this is beyond what can be examined visually. Here we introduce Astronomaly: Protege, an extension of the general purpose machine learning-based active anomaly detection framework Astronomaly. Protege is designed to provide well-selected recommendations for visual inspection, based on a small amount of optimized human labeling. The resulting sample contains rare or unusual sources which are simultaneously as diverse as the human trainer chooses, and of scientific interest to them. We train Protege on images from the MeerKAT Galaxy Cluster Legacy Survey, leveraging the self-supervised deep learning algorithm Bootstrap Your Own Latent to find a low-dimensional representation of the radio galaxy cutouts. By operating in this feature space, Protege is able to recommend interesting sources with completely different morphologies in image space to those it has been trained on. This provides important advantages over similarity searches, which can only find more examples of known sources, or blind anomaly detection, which selects unusual, but not necessarily scientifically interesting sources. Using an evaluation subset, we show that, with minimal training, Protege provides excellent recommendations and find that it is even able to recommend sources that the authors missed. We briefly highlight some of Protege's top recommendations, which include X- and circular-shaped sources, filamentary structures and one-sided structures. These results illustrate the power of an optimized human-machine collaboration such as Protege to make unexpected discoveries in samples beyond human-accessible scales.
MAUVE: An Ultraviolet Astrophysics Probe Mission Concept
2024-11-06
Mayura Balakrishnan, Rory Bowens, Fernando Cruz Aguirre, Kaeli Hughes, Rahul Jayaraman, Emily Kuhn, Emma Louden, Dana R. Louie, Keith McBride, Casey McGrath, Jacob Payne, Tyler Presser, Joshua S. Reding, Emily Rickman, Rachel Scrandis, Teresa Symons, Lindsey Wiser, Keith Jahoda, Tiffany Kataria, Alfred Nash, Team X
We present the mission concept "Mission to Analyze the UltraViolet universE" (MAUVE), a wide-field spectrometer and imager conceived during the inaugural NASA Astrophysics Mission Design School. MAUVE responds to the 2023 Announcement of Opportunity for Probe-class missions, with a budget cap of \$1 billion, and would hypothetically launch in 2031. However, the formulation of MAUVE was an educational exercise and the mission is not being developed further. The Principal Investigator-led science of MAUVE aligns with the priorities outlined in the 2020 Astrophysics Decadal Survey, enabling new characterizations of exoplanet atmospheres, the early-time light curves of some of the universe's most explosive transients, and the poorly-understood extragalactic background light. Because the Principal Investigator science occupies 30% of the observing time available during the mission's 5 yr lifespan, we provide an observing plan that would allow for 70% of the observing time to be used for General Observer programs, with community-solicited proposals. The onboard detector (THISTLE) claims significant heritage from the Space Telescope Imaging Spectrograph on Hubble, but extends its wavelength range down to the extreme UV. We note that MAUVE would be the first satellite in decades with the ability to access this regime of the electromagnetic spectrum. MAUVE has a field of view of 900" x 900" a photometric sensitivity extending to $m_{UV}\leq 24$, and a resolving power of $R\sim1000$. This paper provides full science and mission traceability matrices for this concept, and also outlines cost and scheduling timelines aimed at enabling a within-budget mission and an on-time launch.
Parametrization of GPDs from t-channel string exchange in AdS spaces
2024-11-06
Kiminad A. Mamo, Ismail Zahed
We introduce a string-based parametrization for nucleon quark and gluon generalized parton distributions (GPDs) that is valid for all skewness. Our approach leverages conformal moments, representing them as the sum of spin-j nucleon A-form factor and skewness-dependent spin-j nucleon D-form factor, derived from t-channel string exchange in AdS spaces consistent with Lorentz invariance and unitarity. This model-independent framework, satisfying the polynomiality condition due to Lorentz invariance, uses Mellin moments from empirical data to estimate these form factors. With just five Regge slope parameters, our method accurately produces various nucleon quark GPD types and symmetric nucleon gluon GPDs through pertinent Mellin-Barnes integrals. Our isovector nucleon quark GPD is in agreement with existing lattice data, promising to improve the empirical extraction and global analysis of nucleon GPDs in exclusive processes, by avoiding the deconvolution problem at any skewness, for the first time.
Urban Flood Mapping Using Satellite Synthetic Aperture Radar Data: A Review of Characteristics, Approaches and Datasets
2024-11-06
Jie Zhao, Ming Li, Yu Li, Patrick Matgen, Marco Chini
Understanding the extent of urban flooding is crucial for assessing building damage, casualties and economic losses. Synthetic Aperture Radar (SAR) technology offers significant advantages for mapping flooded urban areas due to its ability to collect data regardless weather and solar illumination conditions. However, the wide range of existing methods makes it difficult to choose the best approach for a specific situation and to identify future research directions. Therefore, this study provides a comprehensive review of current research on urban flood mapping using SAR data, summarizing key characteristics of floodwater in SAR images and outlining various approaches from scientific articles. Additionally, we provide a brief overview of the advantages and disadvantages of each method category, along with guidance on selecting the most suitable approach for different scenarios. This study focuses on the challenges and advancements in SAR-based urban flood mapping. It specifically addresses the limitations of spatial and temporal resolution in SAR data and discusses the essential pre-processing steps. Moreover, the article explores the potential benefits of Polarimetric SAR (PolSAR) techniques and uncertainty analysis for future research. Furthermore, it highlights a lack of open-access SAR datasets for urban flood mapping, hindering development in advanced deep learning-based methods. Besides, we evaluated the Technology Readiness Levels (TRLs) of urban flood mapping techniques to identify challenges and future research areas. Finally, the study explores the practical applications of SAR-based urban flood mapping in both the private and public sectors and provides a comprehensive overview of the benefits and potential impact of these methods.
From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models
2024-11-06
Charles Zhang, Benji Peng, Xintian Sun, Qian Niu, Junyu Liu, Keyu Chen, Ming Li, Pohsun Feng, Ziqian Bi, Ming Liu, Yichao Zhang, Cheng Fei, Caitlyn Heqi Yin, Lawrence KQ Yan, Tianyang Wang
Word embeddings and language models have transformed natural language processing (NLP) by facilitating the representation of linguistic elements in continuous vector spaces. This review visits foundational concepts such as the distributional hypothesis and contextual similarity, tracing the evolution from sparse representations like one-hot encoding to dense embeddings including Word2Vec, GloVe, and fastText. We examine both static and contextualized embeddings, underscoring advancements in models such as ELMo, BERT, and GPT and their adaptations for cross-lingual and personalized applications. The discussion extends to sentence and document embeddings, covering aggregation methods and generative topic models, along with the application of embeddings in multimodal domains, including vision, robotics, and cognitive science. Advanced topics such as model compression, interpretability, numerical encoding, and bias mitigation are analyzed, addressing both technical challenges and ethical implications. Additionally, we identify future research directions, emphasizing the need for scalable training techniques, enhanced interpretability, and robust grounding in non-textual modalities. By synthesizing current methodologies and emerging trends, this survey offers researchers and practitioners an in-depth resource to push the boundaries of embedding-based language models.
Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies
2024-11-06
Abrar Morshed, Abdulla Al Shihab, Md Abrar Jahin, Md Jaber Al Nahian, Md Murad Hossain Sarker, Md Sharjis Ibne Wadud, Mohammad Istiaq Uddin, Muntequa Imtiaz Siraji, Nafisa Anjum, Sumiya Rajjab Shristy, Tanvin Rahman, Mahmuda Khatun, Md Rubel Dewan, Mosaddeq Hossain, Razia Sultana, Ripel Chakma, Sonet Barua Emon, Towhidul Islam, Mohammad Arafat Hussain
The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field.
Are Deep Learning Methods Suitable for Downscaling Global Climate Projections? Review and Intercomparison of Existing Models
2024-11-06
Jose González-Abad, José Manuel Gutiérrez
Deep Learning (DL) has shown promise for downscaling global climate change projections under different approaches, including Perfect Prognosis (PP) and Regional Climate Model (RCM) emulation. Unlike emulators, PP downscaling models are trained on observational data, so it remains an open question whether they can plausibly extrapolate unseen conditions and changes in future emissions scenarios. Here we focus on this problem as the main drawback for the operationalization of these methods and present the results of 1) a literature review to identify state-of-the-art DL models for PP downscaling and 2) an intercomparison experiment to evaluate the performance of these models and to assess their extrapolation capability using a common experimental framework, taking into account the sensitivity of results to different training replicas. We focus on minimum and maximum temperatures and precipitation over Spain, a region with a range of climatic conditions with different influential regional processes. We conclude with a discussion of the findings, limitations of existing methods, and prospects for future development.
Production and propagation of secondary antideuteron in the Galaxy
2024-11-05
Luis Fernando Galicia Cruztitla, Diego Mauricio Gomez Coral
This work reviews the current state of the antideuteron ($\bar{d}$) production cross-sections in cosmic ray interactions and its uncertainties, considering the coalescence model and measurements in accelerator experiments. These cross-sections have been included in a simulation of cosmic rays propagation in the Galaxy using GALPROP v.57, with updated parameters of the diffusive reacceleration model. An estimation of the expected antideuteron flux at Earth is presented.
Examining Human-AI Collaboration for Co-Writing Constructive Comments Online
2024-11-05
Farhana Shahid, Maximilian Dittgen, Mor Naaman, Aditya Vashistha
This paper examines how large language models (LLMs) can help people write constructive comments in online debates on divisive social issues and whether the notions of constructiveness vary across cultures. Through controlled experiments with 600 participants from India and the US, who reviewed and wrote constructive comments on online threads on Islamophobia and homophobia, we found potential misalignment in how LLMs and humans perceive constructiveness in online comments. While the LLM was more likely to view dialectical comments as more constructive, participants favored comments that emphasized logic and facts more than the LLM did. Despite these differences, participants rated LLM-generated and human-AI co-written comments as significantly more constructive than those written independently by humans. Our analysis also revealed that LLM-generated and human-AI co-written comments exhibited more linguistic features associated with constructiveness compared to human-written comments on divisive topics. When participants used LLMs to refine their comments, the resulting comments were longer, more polite, positive, less toxic, and more readable, with added argumentative features that retained the original intent but occasionally lost nuances. Based on these findings, we discuss ethical and design considerations in using LLMs to facilitate constructive discourse online.
Using Assurance Cases to Guide Verification and Validation of Research Software
2024-11-05
W. Spencer Smith, Jingyi Lin
Research software engineers can use Assurance Cases (ACs) to guide Verification and Validation (VnV) efforts. An AC is a structured argument that a property like correctness holds. We illustrate how ACs can guide VnV activities via a case study of software for automatically extracting the 3D segmentation of the aorta from medical images of the chest. The AC argument suggests that the following evidence is required: comparison to a pseudo-oracle; traceability between requirements, design, code and tests; review of all artifacts by a domain expert with proper credentials; documentation of input assumptions; and a warning that only qualified people should use the software. The case study highlights that code is not the only artifact of interest for building confidence and that making an explicit distinction between software and user responsibilities is useful.
Traditional approaches to motion modeling for skid-steer robots struggle with capturing nonlinear tire-terrain dynamics, especially during high-speed maneuvers. In this paper, we tackle such nonlinearities by enhancing a dynamic unicycle model with Gaussian Process (GP) regression outputs. This enables us to develop an adaptive, uncertainty-informed navigation formulation. We solve the resultant stochastic optimal control problem using a chance-constrained Model Predictive Path Integral (MPPI) control method. This approach formulates both obstacle avoidance and path-following as chance constraints, accounting for residual uncertainties from the GP to ensure safety and reliability in control. Leveraging GPU acceleration, we efficiently manage the non-convex nature of the problem, ensuring real-time performance. Our approach unifies path-following and obstacle avoidance across different terrains, unlike prior works which typically focus on one or the other. We compare our GP-MPPI method against unicycle and data-driven kinematic models within the MPPI framework. In simulations, our approach shows superior tracking accuracy and obstacle avoidance. We further validate our approach through hardware experiments on a skid-steer robot platform, demonstrating its effectiveness in high-speed navigation. The GPU implementation of the proposed method and supplementary video footage are available at https: //stochasticmppi.github.io.
While multi-agent systems have been shown to significantly enhance the performance of Large Language Models (LLMs) across various tasks and applications, the dense interaction between scaling agents potentially hampers their efficiency and diversity. To address these challenges, we draw inspiration from the sparse mixture-of-agents (SMoE) and propose a sparse mixture-of-agents (SMoA) framework to improve the efficiency and diversity of multi-agent LLMs. Unlike completely connected structures, SMoA introduces novel Response Selection and Early Stopping mechanisms to sparsify information flows among individual LLM agents, striking a balance between performance and efficiency. Additionally, inspired by the expert diversity principle in SMoE frameworks for workload balance between experts, we assign distinct role descriptions to each LLM agent, fostering diverse and divergent thinking. Extensive experiments on reasoning, alignment, and fairness benchmarks demonstrate that SMoA achieves performance comparable to traditional mixture-of-agents approaches but with significantly lower computational costs. Further analysis reveals that SMoA is more stable, has a greater capacity to scale, and offers considerable potential through hyper-parameter optimization. Code and data will be available at: https://github.com/David-Li0406/SMoA.
Data-driven model validation for neutrino-nucleus cross section measurements
2024-11-05
MicroBooNE collaboration, P. Abratenko, O. Alterkait, D. Andrade Aldana, L. Arellano, J. Asaadi, A. Ashkenazi, S. Balasubramanian, B. Baller, A. Barnard, G. Barr, D. Barrow, J. Barrow, V. Basque, J. Bateman, O. Benevides Rodrigues, S. Berkman, A. Bhanderi, A. Bhat, M. Bhattacharya, M. Bishai, A. Blake, B. Bogart, T. Bolton, M. B. Brunetti, L. Camilleri, Y. Cao, D. Caratelli, F. Cavanna, G. Cerati, A. Chappell, Y. Chen, J. M. Conrad, M. Convery, L. Cooper-Troendle, J. I. Crespo-Anadon, R. Cross, M. Del Tutto, S. R. Dennis, P. Detje, R. Diurba, Z. Djurcic, K. Duffy, S. Dytman, B. Eberly, P. Englezos, A. Ereditato, J. J. Evans, C. Fang, B. T. Fleming, W. Foreman, D. Franco, A. P. Furmanski, F. Gao, D. Garcia-Gamez, S. Gardiner, G. Ge, S. Gollapinni, E. Gramellini, P. Green, H. Greenlee, L. Gu, W. Gu, R. Guenette, P. Guzowski, L. Hagaman, M. D. Handley, O. Hen, C. Hilgenberg, G. A. Horton-Smith, Z. Imani, B. Irwin, M. S. Ismail, C. James, X. Ji, J. H. Jo, R. A. Johnson, Y. J. Jwa, D. Kalra, G. Karagiorgi, W. Ketchum, M. Kirby, T. Kobilarcik, N. Lane, J. -Y. Li, Y. Li, K. Lin, B. R. Littlejohn, L. Liu, W. C. Louis, X. Luo, T. Mahmud, C. Mariani, D. Marsden, J. Marshall, N. Martinez, D. A. Martinez Caicedo, S. Martynenko, A. Mastbaum, I. Mawby, N. McConkey, V. Meddage, L. Mellet, J. Mendez, J. Micallef, K. Miller, K. Mistry, T. Mohayai, A. Mogan, M. Mooney, A. F. Moor, C. D. Moore, L. Mora Lepin, M. M. Moudgalya, S. Mulleria Babu, D. Naples, A. Navrer-Agasson, N. Nayak, M. Nebot-Guinot, C. Nguyen, J. Nowak, N. Oza, O. Palamara, N. Pallat, V. Paolone, A. Papadopoulou, V. Papavassiliou, H. Parkinson, S. F. Pate, N. Patel, Z. Pavlovic, E. Piasetzky, K. Pletcher, I. Pophale, X. Qian, J. L. Raaf, V. Radeka, A. Rafique, M. Reggiani-Guzzo, L. Ren, L. Rochester, J. Rodriguez Rondon, M. Rosenberg, M. Ross-Lonergan, I. Safa, D. W. Schmitz, A. Schukraft, W. Seligman, M. H. Shaevitz, R. Sharankova, J. Shi, E. L. Snider, M. Soderberg, S. Soldner-Rembold, J. Spitz, M. Stancari, J. St. John, T. Strauss, A. M. Szelc, N. Taniuchi, K. Terao, C. Thorpe, D. Torbunov, D. Totani, M. Toups, A. Trettin, Y. -T. Tsai, J. Tyler, M. A. Uchida, T. Usher, B. Viren, J. Wang, M. Weber, H. Wei, A. J. White, S. Wolbers, T. Wongjirad, M. Wospakrik, K. Wresilo, W. Wu, E. Yandel, T. Yang, L. E. Yates, H. W. Yu, G. P. Zeller, J. Zennamo, C. Zhang
Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation parameters and search for physics beyond the Standard Model. We review the difficulties associated with modeling neutrino-nucleus interactions that lead to a dependence on event generators in oscillation analyses and cross section measurements alike. We then describe data-driven model validation techniques intended to address this model dependence. The method relies on utilizing various goodness-of-fit tests and the correlations between different observables and channels to probe the model for defects in the phase space relevant for the desired analysis. These techniques shed light on relevant mis-modeling, allowing it to be detected before it begins to bias the cross section results. We compare more commonly used model validation methods which directly validate the model against alternative ones to these data-driven techniques and show their efficacy with fake data studies. These studies demonstrate that employing data-driven model validation in cross section measurements represents a reliable strategy to produce robust results that will stimulate the desired improvements to interaction modeling.
Asymptotic stability equals exponential stability -- while you twist your eyes
2024-11-05
Wouter Jongeneel
Suppose that two vector fields on a smooth manifold render some equilibrium point globally asymptotically stable (GAS). We show that there exists a homotopy between the corresponding semiflows such that this point remains GAS along this homotopy.
2024-11-05
Boris Khesin, Gerard Misiołek, Klas Modin
The study of diffeomorphism groups and their applications to problems in analysis and geometry has a long history. In geometric hydrodynamics, pioneered by V.~Arnold in the 1960s, one considers an ideal fluid flow as the geodesic motion on the infinite-dimensional group of volume-preserving diffeomorphisms of the fluid domain with respect to the metric defined by the kinetic energy. Similar considerations on the space of densities lead to a geometric description of optimal mass transport and the Kantorovich-Wasserstein metric. Likewise, information geometry associated with the Fisher-Rao metric and the Hellinger distance has an equally beautiful infinite-dimensional geometric description and can be regarded as a higher-order Sobolev analogue of optimal transportation. In this work we review various metrics on diffeomorphism groups relevant to this approach and introduce appropriate topology, smooth structures and dynamics on the corresponding infinite-dimensional manifolds. Our main goal is to demonstrate how, alongside topological hydrodynamics, Hamiltonian dynamics and optimal mass transport, information geometry with its elaborate toolbox has become yet another exciting field for applications of geometric analysis on diffeomorphism groups.
Tuning into spatial frequency space: Satellite and space debris detection in the ZTF alert stream
2024-11-05
J. P. Carvajal, F. E. Bauer, I. Reyes-Jainaga, F. Förster, A. M. Muñoz Arancibia, M. Catelan, P. Sánchez-Sáez, C. Ricci, A. Bayo
A significant challenge in the study of transient astrophysical phenomena is the identification of bogus events, with human-made Earth-orbiting satellites and debris remain a key contaminant. Existing pipelines effectively identify satellite trails but can miss more complex signatures, such as collections of dots known as satellite glints. In the Rubin Observatory era, the scale of the operations will increase tenfold with respect to its precursor, the Zwicky Transient Facility (ZTF), requiring crucial improvements in classification purity, data compression, pipeline speed and more. We explore the use of the 2D Fast Fourier Transform (FFT) on difference images as a tool to improve satellite detection algorithms. Adopting the single-stamp classification model from the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker as a baseline, we adapt its architecture to receive a cutout of the FFT of the difference image, in addition to the three (science, reference, difference) ZTF image cutouts (hereafter stamps). We study different stamp sizes and resolutions for these four channels, aiming to assess the benefit of including the FFT image, especially in scenarios with data compression and processing speed requirements (e.g., for surveys like the Legacy Survey of Space and Time). The inclusion of the FFT improved satellite detection accuracy, with the most notable increase observed in the model with the smallest field of view (16''), where accuracy rose from 66.9% to 79.7% (a statistically significant improvement of ~13% with a 95% confidence interval of 7.8% to 17.8%). This result demonstrates the effectiveness of FFT in compressing relevant information and extracting features that characterize satellite signatures in larger difference images. We show how FFTs can be leveraged to cull satellite and space debris signatures from alert streams.
How the StarDICE photometric calibration of standard stars can improve cosmological constraints?
2024-11-05
T. Souverin, J. Neveu, M. Betoule, S. Bongard, P. E. Blanc, J. Cohen Tanugi, S. Dagoret-Campagne, F. Feinstein, M. Ferrari, F. Hazenberg, C. Juramy, L. Le Guillou, A. Le Van Suu, M. Moniez, E. Nuss, B. Plez, N. Regnault, E. Sepulveda, K. Sommer
The number of type Ia supernova (SNe Ia) observations will grow significantly within the next decade, mainly thanks to the Legacy Survey of Space and Time (LSST) undertaken by the Vera Rubin Observatory in Chile. With this improvement, statistical uncertainties will decrease, and flux calibration will become the main uncertainty for the characterization of dark energy. Currently, the astronomical flux scale is anchored on the numerical models of white dwarf atmospheres from the CALSPEC catalog, and every error on the model can induce a bias over cosmological parameters inference. The StarDICE experiment proposes a new calibration reference that only relies on observations from the optical watt defined by the NIST towards the magnitude of standard stars. It is currently operating at l'Observatoire de Haute-Provence and has been collecting data since the beginning of 2023. To overcome the photometric calibration uncertainty and reach a sub-percent precision, the instrument throughput has been calibrated with a Collimated Beam Projector. It will be monitored on-site with a LED-based artificial star source calibrated with NIST photodiodes. In this proceeding, we will first illustrate how an error in the photometric calibration can impact the SNe Ia distance moduli and thus bias the measurement of cosmological parameters. Then we will present the StarDICE experiment and how we can recalibrate the CALSPEC catalog at the millimagnitude level on the NIST scale with photometric analysis.
DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models
2024-11-05
Ying Zhou, Xinyao Wang, Yulei Niu, Yaojie Shen, Lexin Tang, Fan Chen, Ben He, Le Sun, Longyin Wen
Recent advancements in large language models (LLMs) have significantly enhanced their knowledge and generative capabilities, leading to a surge of interest in leveraging LLMs for high-quality data synthesis. However, synthetic data generation via prompting LLMs remains challenging due to LLMs' limited understanding of target data distributions and the complexity of prompt engineering, especially for structured formatted data. To address these issues, we introduce DiffLM, a controllable data synthesis framework based on variational autoencoder (VAE), which further (1) leverages diffusion models to reserve more information of original distribution and format structure in the learned latent distribution and (2) decouples the learning of target distribution knowledge from the LLM's generative objectives via a plug-and-play latent feature injection module. As we observed significant discrepancies between the VAE's latent representations and the real data distribution, the latent diffusion module is introduced into our framework to learn a fully expressive latent distribution. Evaluations on seven real-world datasets with structured formatted data (i.e., Tabular, Code and Tool data) demonstrate that DiffLM generates high-quality data, with performance on downstream tasks surpassing that of real data by 2-7 percent in certain cases. The data and code will be publicly available upon completion of internal review.
Earth Observation (EO) data analysis has been significantly revolutionized by deep learning (DL), with applications typically limited to grid-like data structures. Graph Neural Networks (GNNs) emerge as an important innovation, propelling DL into the non-Euclidean domain. Naturally, GNNs can effectively tackle the challenges posed by diverse modalities, multiple sensors, and the heterogeneous nature of EO data. To introduce GNNs in the related domains, our review begins by offering fundamental knowledge on GNNs. Then, we summarize the generic problems in EO, to which GNNs can offer potential solutions. Following this, we explore a broad spectrum of GNNs' applications to scientific problems in Earth systems, covering areas such as weather and climate analysis, disaster management, air quality monitoring, agriculture, land cover classification, hydrological process modeling, and urban modeling. The rationale behind adopting GNNs in these fields is explained, alongside methodologies for organizing graphs and designing favorable architectures for various tasks. Furthermore, we highlight methodological challenges of implementing GNNs in these domains and possible solutions that could guide future research. While acknowledging that GNNs are not a universal solution, we conclude the paper by comparing them with other popular architectures like transformers and analyzing their potential synergies.
A Machine Learning Approach for the Efficient Estimation of Ground-Level Air Temperature in Urban Areas
2024-11-05
Iñigo Delgado-Enales, Joshua Lizundia-Loiola, Patricia Molina-Costa, Javier Del Ser
The increasingly populated cities of the 21st Century face the challenge of being sustainable and resilient spaces for their inhabitants. However, climate change, among other problems, makes these objectives difficult to achieve. The Urban Heat Island (UHI) phenomenon that occurs in cities, increasing their thermal stress, is one of the stumbling blocks to achieve a more sustainable city. The ability to estimate temperatures with a high degree of accuracy allows for the identification of the highest priority areas in cities where urban improvements need to be made to reduce thermal discomfort. In this work we explore the usefulness of image-to-image deep neural networks (DNNs) for correlating spatial and meteorological variables of a urban area with street-level air temperature. The air temperature at street-level is estimated both spatially and temporally for a specific use case, and compared with existing, well-established numerical models. Based on the obtained results, deep neural networks are confirmed to be faster and less computationally expensive alternative for ground-level air temperature compared to numerical models.
A survey on boundary regularity for the fractional p-Laplacian and its applications
2024-11-05
Antonio Iannizzotto
We survey some recent regularity results for fractional p-Laplacian elliptic equations, especially focusing on pure and weighted boundary H\"older continuity of the solutions of related Dirichlet problems. Then, we present some applications of such results to general nonlinear elliptic equations of fractional order, treated via either variational or topological methods.
Understanding the scaling relation between baryonic observables and dark matter halo properties is crucial not only for studying galaxy formation and evolution, but also for deriving accurate cosmological constraints from galaxy surveys. In this paper, we constrain the stellar-to-halo mass relation of galaxy groups identified by the Galaxy and Mass Assembly survey, using weak lensing signals measured by the Kilo-Degree Survey. We compared our measured scaling relation with predictions from the FLAMINGO hydrodynamical simulations and the L-Galaxies semi-analytical model. We find general agreement between our measurements and simulation predictions for haloes with masses ${\gtrsim}10^{13.5}\ h_{70}^{-1}{\rm M}_{\odot}$, but observe slight discrepancies with the FLAMINGO simulations at lower halo masses. We explored improvements to the current halo model framework by incorporating simulation-informed scatter in the group stellar mass distribution as a function of halo mass. We find that including a simulation-informed scatter model tightens the constraints on scaling relations, despite the current data statistics being insufficient to directly constrain the variable scatter. We also tested the robustness of our results against different statistical models of miscentring effects from selected central galaxies. We find that accounting for miscentring is essential, but our current measurements do not distinguish among different miscentring models.
The Macquart relation and time-delay cosmography are now two promising ways to fast radio burst (FRB) cosmology. In this work, we propose a joint method that combines strongly lensed and unlensed FRBs for improving cosmological parameter estimation by using simulated FRB data from the future sensitive coherent all-sky monitor survey, which is expected to detect a large number of FRBs including galaxy-galaxy strongly lensed events. We find that using a detectable sample of 100,000 localized FRBs including $40$ lensed events can simultaneously constrain the Hubble constant and the equation of state of dark energy, with high precision of $\varepsilon(H_0)=0.4\%$ and $\varepsilon(w)=4.5\%$ in the simplest dynamical dark energy model. The joint analysis of unlensed and lensed FRBs significantly improves the constraint on $H_0$, which could be more effective than combining either the unlensed FRBs with future gravitational wave (GW) standard sirens or the lensed FRBs with CMB. Furthermore, combining the full FRB sample with the CMB+BAO+SNe data yields $\sigma(H_0)=0.29~{\rm km~s^{-1}~Mpc^{-1}}$, $\sigma(w_0)=0.046$, and $\sigma(w_a)=0.15$ in the two-parameter dynamical dark energy model, which outperform the results from the CMB+BAO+SNe+GW data. This reinforces the cosmological implications of a multi-wavelength observational strategy in optical and radio bands. We conclude that the future FRB observations will shed light on the nature of dark energy and also the Hubble tension if enough events with long-duration lensing are incorporated.
2024-11-05
Carlo Giunti, Konstantin Kouzakov, Yu-Feng Li, Alexander Studenikin
Neutrinos are neutral in the Standard Model, but they have tiny charge radii generated by radiative corrections. In theories Beyond the Standard Model, neutrinos can also have magnetic and electric moments and small electric charges (millicharges). We review the general theory of neutrino electromagnetic form factors, which reduce, for ultrarelativistic neutrinos and small momentum transfers, to the neutrino charges, effective charge radii, and effective magnetic moments. We discuss the phenomenology of these electromagnetic neutrino properties and we review the existing experimental bounds. We briefly review also the electromagnetic processes of astrophysical neutrinos and the neutrino magnetic moment portal in the presence of sterile neutrinos.
A self-correcting quantum memory is a type of quantum error correcting code that can correct errors passively through cooling. A major open question in the field is whether self-correcting quantum memories can exist in 3D. In this work, we propose two candidate constructions for 3D self-correcting quantum memories. The first construction is an extension of Haah's code, which retains translation invariance. The second construction is based on fractals with greater flexibility in its design. Additionally, we review existing 3D quantum codes and suggest that they are not self-correcting.
Sum Rate Maximization in the Constant Envelope MIMO Downlink with the RZF Precoder
2024-11-05
Ferhad Askerbeyli, Wen Xu, Josef A. Nossek
Feeding power amplifiers (PAs) with constant envelope (CE) signals is an effective way to reduce the power consumption in massive multiple-input-multiple-output (MIMO) systems. The nonlinear distortion caused by CE signaling must be mitigated by means of signal processing to improve the achievable sum rates. To this purpose, many linear and nonlinear precoding techniques have been developed for the CE MIMO downlink. The vast majority of these CE precoding techniques do not include a power allocation scheme, which is indispensable to achieve adequate performances in the downlink with channel gain imbalances between users. In this paper, we present two algorithms to produce a power allocation scheme for regularized zero-forcing (RZF) precoding in CE MIMO downlink. Both techniques are based on transforming the CE quantized MIMO downlink to an approximately equivalent system of parallel single-input-single-output (SISO) channels. The first technique is proven to solve the sum rate maximization problem in the approximate system optimally, whereas the second technique obtains the local maximum with lower complexity. We also extend another state-of-the-art quantization aware sum rate maximization algorithm with linear precoding to the CE downlink. Numerical results illustrate significant gains for the performance of the RZF precoder when the CE quantization is taken into account in a power allocation. Another key numerical result is that the proposed RZF techniques achieve almost the identical performance so that the one with lower computational complexity is chosen as the main method. Results also show that the proposed RZF precoding schemes perform at least as good as the state-of-the-art method with an advantage that the main RZF method has significantly lower computational complexity than the state-of-the-art.
We present a review of the Semi-Symmetric Metric Gravity (SSMG) theory, representing a geometric extension of standard general relativity, based on a connection introduced by Friedmann and Schouten in 1924. The semi-symmetric connection is a connection that generalizes the Levi-Civita one, by allowing for the presence of a simple form of the torsion, described in terms of a torsion vector. The Einstein field equations are postulated to have the same form as in standard general relativity, thus relating the Einstein tensor constructed with the help of the semi-symmetric connection, with the energy-momentum tensor. The inclusion of the torsion contributions in the field equations has intriguing cosmological implications, particularly during the late-time evolution of the Universe. Presumably, these effects also dominate under high-energy conditions, and thus SSMG could potentially address unresolved issues in General Relativity and cosmology, such as the initial singularity, inflation, or the $^7$Li problem of the Big-Bang Nucleosynthesis. The explicit presence of torsion in the field equations leads to the non-conservation of the energy-momentum tensor, which can be interpreted within the irreversible thermodynamics of open systems, as describing particle creation processes. We also review in detail the cosmological applications of the theory, and investigate the statistical tests for several models, by constraining the model parameters via comparison with several observational datasets.
Alternate Learning and Compression Approaching R(D)
2024-11-05
Ram Zamir, Kenneth Rose
The inherent trade-off in on-line learning is between exploration and exploitation. A good balance between these two (conflicting) goals can achieve a better long-term performance. Can we define an optimal balance? We propose to study this question through a backward-adaptive lossy compression system, which exhibits a "natural" trade-off between exploration and exploitation.
2024-11-05
Daniel de Vassimon Manela, Linying Yang, Robin J. Evans
Ensuring robust model performance across diverse real-world scenarios requires addressing both transportability across domains with covariate shifts and extrapolation beyond observed data ranges. However, there is no formal procedure for statistically evaluating generalizability in machine learning algorithms, particularly in causal inference. Existing methods often rely on arbitrary metrics like AUC or MSE and focus predominantly on toy datasets, providing limited insights into real-world applicability. To address this gap, we propose a systematic and quantitative framework for evaluating model generalizability under covariate distribution shifts, specifically within causal inference settings. Our approach leverages the frugal parameterization, allowing for flexible simulations from fully and semi-synthetic benchmarks, offering comprehensive evaluations for both mean and distributional regression methods. By basing simulations on real data, our method ensures more realistic evaluations, which is often missing in current work relying on simplified datasets. Furthermore, using simulations and statistical testing, our framework is robust and avoids over-reliance on conventional metrics. Grounded in real-world data, it provides realistic insights into model performance, bridging the gap between synthetic evaluations and practical applications.
2024-11-05
David S. Pereira, João Ferraz, Francisco S. N. Lobo, José P. Mimoso
This review delves into the pivotal primordial stage of the universe, a period that holds the key to understanding its current state. To fully grasp this epoch, it is essential to consider three fundamental domains of physics: gravity, particle physics, and thermodynamics. The thermal history of the universe recreates the extreme high-energy conditions that are critical for exploring the unification of the fundamental forces, making it a natural laboratory for high-energy physics. This thermal history also offers valuable insights into how the laws of thermodynamics have governed the evolution of the universe's constituents, shaping them into the forms we observe today. Focusing on the Standard Cosmological Model (SCM) and the Standard Model of Particles (SM), this paper provides an in-depth analysis of thermodynamics in the primordial universe. The structure of the study includes an introduction to the SCM and its strong ties to thermodynamic principles. It then explores equilibrium thermodynamics in the context of the expanding universe, followed by a detailed analysis of out-of-equilibrium phenomena that were pivotal in shaping key events during the early stages of the universe's evolution.
Constitutive Models for Active Skeletal Muscle: Review, Comparison, and Application in a Novel Continuum Shoulder Model
2024-11-05
Laura Engelhardt, Renate Sachse, Rainer Burgkart, Wolfgang A. Wall
The shoulder joint is one of the functionally and anatomically most sophisticated articular systems in the human body. Both complex movement patterns and the stabilization of the highly mobile joint rely on intricate three-dimensional interactions among various components. Continuum-based finite element models can capture such complexity, and are thus particularly relevant in shoulder biomechanics. Considering their role as active joint stabilizers and force generators, skeletal muscles require special attention regarding their constitutive description. In this contribution, we propose a constitutive description to model active skeletal muscle within complex musculoskeletal systems, focusing on a novel continuum shoulder model. We thoroughly review existing material models before analyzing three selected ones in detail: an active-stress, an active-strain, and a generalized active-strain approach. To establish a basis for comparison, we identify the material parameters based on experimental stress-strain data obtained under various active and passive loading conditions. We discuss the concepts to incorporate active contractile behavior from a mathematical and physiological perspective, address analytical and numerical challenges arising from the mathematical formulations, and analyze the included biophysical principles of force generation in terms of physiological correctness and relevance for human shoulder modeling. Based on these insights, we present an improved constitutive model combining the studied models' most promising and relevant features. Using the example of a fusiform muscle, we investigate force generation, deformation, and kinematics during active isometric and free contractions. Eventually, we demonstrate the applicability of the suggested material model in a novel continuum mechanical model of the human shoulder.
Artificial intelligence (AI) has transformed various fields, significantly impacting our daily lives. A major factor in AI success is high-quality data. In this paper, we present a comprehensive review of the evolution of data quality (DQ) awareness from traditional data management systems to modern data-driven AI systems, which are integral to data science. We synthesize the existing literature, highlighting the quality challenges and techniques that have evolved from traditional data management to data science including big data and ML fields. As data science systems support a wide range of activities, our focus in this paper lies specifically in the analytics aspect driven by machine learning. We use the cause-effect connection between the quality challenges of ML and those of big data to allow a more thorough understanding of emerging DQ challenges and the related quality awareness techniques in data science systems. To the best of our knowledge, our paper is the first to provide a review of DQ awareness spanning traditional and emergent data science systems. We hope that readers will find this journey through the evolution of data quality awareness insightful and valuable.
Accelerating Task Generalisation with Multi-Level Hierarchical Options
2024-11-05
Thomas P Cannon, Özgür Simsek
Creating reinforcement learning agents that generalise effectively to new tasks is a key challenge in AI research. This paper introduces Fracture Cluster Options (FraCOs), a multi-level hierarchical reinforcement learning method that achieves state-of-the-art performance on difficult generalisation tasks. FraCOs identifies patterns in agent behaviour and forms options based on the expected future usefulness of those patterns, enabling rapid adaptation to new tasks. In tabular settings, FraCOs demonstrates effective transfer and improves performance as it grows in hierarchical depth. We evaluate FraCOs against state-of-the-art deep reinforcement learning algorithms in several complex procedurally generated environments. Our results show that FraCOs achieves higher in-distribution and out-of-distribution performance than competitors.
[Vision Paper] PRObot: Enhancing Patient-Reported Outcome Measures for Diabetic Retinopathy using Chatbots and Generative AI
2024-11-05
Maren Pielka, Tobias Schneider, Jan Terheyden, Rafet Sifa
We present an outline of the first large language model (LLM) based chatbot application in the context of patient-reported outcome measures (PROMs) for diabetic retinopathy. By utilizing the capabilities of current LLMs, we enable patients to provide feedback about their quality of life and treatment progress via an interactive application. The proposed framework offers significant advantages over the current approach, which encompasses only qualitative collection of survey data or a static survey with limited answer options. Using the PROBot LLM-PROM application, patients will be asked tailored questions about their individual challenges, and can give more detailed feedback on the progress of their treatment. Based on this input, we will use machine learning to infer conventional PROM scores, which can be used by clinicians to evaluate the treatment status. The goal of the application is to improve adherence to the healthcare system and treatments, and thus ultimately reduce cases of subsequent vision impairment. The approach needs to be further validated using a survey and a clinical study.
A resolved, multi-wavelength study of gas-rich dwarf galaxies in the Fornax cluster using MUSE, MeerKAT, and ALMA
2024-11-05
Nikki Zabel, Alessandro Loni, Marc Sarzi, Paolo Serra, Arjun Chawla, Timothy A. Davis, Dane Kleiner, S. Ilani Loubser, Reynier Peletier
We combine new and archival MUSE observations with data from the MeerKAT Fornax Survey and the ALMA Fornax Cluster Survey to study the ionised, atomic, and molecular gas in six gas-rich dwarf galaxies in the Fornax cluster in detail. We compare the distributions and velocity fields of the three gas phases with each other, with MUSE white-light images, and with the stellar velocity fields. Additionally, we derive the resolved molecular Kennicutt-Schmidt relation for each object, and compare these with existing relations for field galaxies and for the Fornax and Virgo clusters. Finally, we explore global measurements such as gas deficiencies and star formation rates to paint as complete a picture of their evolutionary state as possible. We find that all six gas-rich dwarf galaxies have very disturbed ISM, with all three gas phases being irregular both in terms of spatial distribution and velocity field. Most objects lie well below the Kennicutt-Schmidt relations from the literature. Furthermore, they are quite deficient in HI (with def_HI between ~1 and ~2 dex), and moderately deficient in H2 (with def_H2 between ~0 and ~1), suggesting that, while both cold gas phases are affected simultaneously, HI is removed in significant quantities before H2. We suggest that these dwarfs are on their first infall into the cluster, and are in the process of transitioning from star-forming to passive. A combination of tidal interactions, mergers/pre-processing, and ram pressure stripping is likely responsible for these transformations.
Exploring the Interplay Between Video Generation and World Models in Autonomous Driving: A Survey
2024-11-05
Ao Fu, Yi Zhou, Tao Zhou, Yi Yang, Bojun Gao, Qun Li, Guobin Wu, Ling Shao
World models and video generation are pivotal technologies in the domain of autonomous driving, each playing a critical role in enhancing the robustness and reliability of autonomous systems. World models, which simulate the dynamics of real-world environments, and video generation models, which produce realistic video sequences, are increasingly being integrated to improve situational awareness and decision-making capabilities in autonomous vehicles. This paper investigates the relationship between these two technologies, focusing on how their structural parallels, particularly in diffusion-based models, contribute to more accurate and coherent simulations of driving scenarios. We examine leading works such as JEPA, Genie, and Sora, which exemplify different approaches to world model design, thereby highlighting the lack of a universally accepted definition of world models. These diverse interpretations underscore the field's evolving understanding of how world models can be optimized for various autonomous driving tasks. Furthermore, this paper discusses the key evaluation metrics employed in this domain, such as Chamfer distance for 3D scene reconstruction and Fr\'echet Inception Distance (FID) for assessing the quality of generated video content. By analyzing the interplay between video generation and world models, this survey identifies critical challenges and future research directions, emphasizing the potential of these technologies to jointly advance the performance of autonomous driving systems. The findings presented in this paper aim to provide a comprehensive understanding of how the integration of video generation and world models can drive innovation in the development of safer and more reliable autonomous vehicles.
Analyzing Poverty through Intra-Annual Time-Series: A Wavelet Transform Approach
2024-11-05
Mohammad Kakooei, Klaudia Solska, Adel Daoud
Reducing global poverty is a key objective of the Sustainable Development Goals (SDGs). Achieving this requires high-frequency, granular data to capture neighborhood-level changes, particularly in data scarce regions such as low- and middle-income countries. To fill in the data gaps, recent computer vision methods combining machine learning (ML) with earth observation (EO) data to improve poverty estimation. However, while much progress have been made, they often omit intra-annual variations, which are crucial for estimating poverty in agriculturally dependent countries. We explored the impact of integrating intra-annual NDVI information with annual multi-spectral data on model accuracy. To evaluate our method, we created a simulated dataset using Landsat imagery and nighttime light data to evaluate EO-ML methods that use intra-annual EO data. Additionally, we evaluated our method against the Demographic and Health Survey (DHS) dataset across Africa. Our results indicate that integrating specific NDVI-derived features with multi-spectral data provides valuable insights for poverty analysis, emphasizing the importance of retaining intra-annual information.
Transfer of solitons and half-vortex solitons via adiabatic passage
2024-11-05
Chenhui Wang, Yongping Zhang, V. V. Konotop
We show that transfer of matter-wave solitons and half-vortex solitons in a spin-orbit coupled Bose-Einstein condensate between two (or more) arbitrarily chosen sites of an optical lattice can be implemented using the adiabatic passage. The underlying linear Hamiltonian has a flat band in its spectrum, so that even sufficiently weak inter-atomic interactions can sustain well-localized Wannier solitons which are involved in the transfer process. The adiabatic passage is assisted by properly chosen spatial and temporal modulations of the Rabi frequency. Within the framework of a few-mode approximation, the mechanism is enabled by a dark state created by coupling the initial and target low-energy solitons with a high-energy extended Bloch state, like in the conventional stimulated Raman adiabatic passage used for the coherent control of quantum states. In real space, however, the atomic transfer between initial and target states is sustained by the current carried by the extended Bloch state which remains populated during the whole process. The full description of the transfer is provided by the Gross-Pitaevskii equation. Protocols for the adiabatic passage are described for one- and two-dimensional optical lattices, as well as for splitting and subsequent transfer of an initial wavepacket simultaneously to two different target locations.
Enhancing EmoBot: An In-Depth Analysis of User Satisfaction and Faults in an Emotion-Aware Chatbot
2024-11-05
Taseen Mubassira, Mehedi Hasan, A. B. M. Alim Al Iislam
The research community has traditionally shown a keen interest in emotion modeling, with a notable emphasis on the detection aspect. In contrast, the exploration of emotion generation has received less attention.This study delves into an existing state-of-the-art emotional chatbot, EmoBot, designed for generating emotions in general-purpose conversations. This research involves a comprehensive examination, including a survey to evaluate EmoBot's proficiency in key dimensions like usability, accuracy, and overall user satisfaction, with a specific focus on fault tolerance. By closely examining the chatbot's operations, we identified some noteworthy shortcomings in the existing model. We propose some solutions designed to address and overcome the identified issues.
In recent years, advanced artificial intelligence technologies, such as ChatGPT, have significantly impacted various fields, including education and research. Developed by OpenAI, ChatGPT is a powerful language model that presents numerous opportunities for students and educators. It offers personalized feedback, enhances accessibility, enables interactive conversations, assists with lesson preparation and evaluation, and introduces new methods for teaching complex subjects. However, ChatGPT also poses challenges to traditional education and research systems. These challenges include the risk of cheating on online exams, the generation of human-like text that may compromise academic integrity, a potential decline in critical thinking skills, and difficulties in assessing the reliability of information generated by AI. This study examines both the opportunities and challenges ChatGPT brings to education from the perspectives of students and educators. Specifically, it explores the role of ChatGPT in helping students develop their subjective skills. To demonstrate its effectiveness, we conducted several subjective experiments using ChatGPT, such as generating solutions from subjective problem descriptions. Additionally, surveys were conducted with students and teachers to gather insights into how ChatGPT supports subjective learning and teaching. The results and analysis of these surveys are presented to highlight the impact of ChatGPT in this context.
The Evolution of RWKV: Advancements in Efficient Language Modeling
2024-11-05
Akul Datta
This paper reviews the development of the Receptance Weighted Key Value (RWKV) architecture, emphasizing its advancements in efficient language modeling. RWKV combines the training efficiency of Transformers with the inference efficiency of RNNs through a novel linear attention mechanism. We examine its core innovations, adaptations across various domains, and performance advantages over traditional models. The paper also discusses challenges and future directions for RWKV as a versatile architecture in deep learning.
Safety alignment of Large Language Models (LLMs) has recently become a critical objective of model developers. In response, a growing body of work has been investigating how safety alignment can be bypassed through various jailbreaking methods, such as adversarial attacks. However, these jailbreak methods can be rather costly or involve a non-trivial amount of creativity and effort, introducing the assumption that malicious users are high-resource or sophisticated. In this paper, we study how simple random augmentations to the input prompt affect safety alignment effectiveness in state-of-the-art LLMs, such as Llama 3 and Qwen 2. We perform an in-depth evaluation of 17 different models and investigate the intersection of safety under random augmentations with multiple dimensions: augmentation type, model size, quantization, fine-tuning-based defenses, and decoding strategies (e.g., sampling temperature). We show that low-resource and unsophisticated attackers, i.e. $\textit{stochastic monkeys}$, can significantly improve their chances of bypassing alignment with just 25 random augmentations per prompt.
A Convex Relaxation Approach to Generalization Analysis for Parallel Positively Homogeneous Networks
2024-11-05
Uday Kiran Reddy Tadipatri, Benjamin D. Haeffele, Joshua Agterberg, René Vidal
We propose a general framework for deriving generalization bounds for parallel positively homogeneous neural networks--a class of neural networks whose input-output map decomposes as the sum of positively homogeneous maps. Examples of such networks include matrix factorization and sensing, single-layer multi-head attention mechanisms, tensor factorization, deep linear and ReLU networks, and more. Our general framework is based on linking the non-convex empirical risk minimization (ERM) problem to a closely related convex optimization problem over prediction functions, which provides a global, achievable lower-bound to the ERM problem. We exploit this convex lower-bound to perform generalization analysis in the convex space while controlling the discrepancy between the convex model and its non-convex counterpart. We apply our general framework to a wide variety of models ranging from low-rank matrix sensing, to structured matrix sensing, two-layer linear networks, two-layer ReLU networks, and single-layer multi-head attention mechanisms, achieving generalization bounds with a sample complexity that scales almost linearly with the network width.
2024-11-05
Pedro R. A. S. Bassi, Qilong Wu, Wenxuan Li, Sergio Decherchi, Andrea Cavalli, Alan Yuille, Zongwei Zhou
As medical datasets rapidly expand, creating detailed annotations of different body structures becomes increasingly expensive and time-consuming. We consider that requesting radiologists to create detailed annotations is unnecessarily burdensome and that pre-existing AI models can largely automate this process. Following the spirit don't use a sledgehammer on a nut, we find that, rather than creating annotations from scratch, radiologists only have to review and edit errors if the Best-AI Labels have mistakes. To obtain the Best-AI Labels among multiple AI Labels, we developed an automatic tool, called Label Critic, that can assess label quality through tireless pairwise comparisons. Extensive experiments demonstrate that, when incorporated with our developed Image-Prompt pairs, pre-existing Large Vision-Language Models (LVLM), trained on natural images and texts, achieve 96.5% accuracy when choosing the best label in a pair-wise comparison, without extra fine-tuning. By transforming the manual annotation task (30-60 min/scan) into an automatic comparison task (15 sec/scan), we effectively reduce the manual efforts required from radiologists by an order of magnitude. When the Best-AI Labels are sufficiently accurate (81% depending on body structures), they will be directly adopted as the gold-standard annotations for the dataset, with lower-quality AI Labels automatically discarded. Label Critic can also check the label quality of a single AI Label with 71.8% accuracy when no alternatives are available for comparison, prompting radiologists to review and edit if the estimated quality is low (19% depending on body structures).
With the advent of next-generation surveys, constraints on cosmological parameters are anticipated to become more stringent, particularly for the total neutrino mass. This study forecasts these constraints utilizing galaxy clusters from the Chinese Space Station Telescope (CSST). Employing Fisher matrix analysis, we derive the constraint $\sigma(M_\nu)$ from cluster number counts, cluster power spectrum, and their combination. The investigation ranges from the standard cosmological model with neutrinos $\nu\Lambda$CDM to the inclusion of dynamic dark energy in the $\nu w_0 w_a$CDM model, revealing a minor impact of dark energy on neutrino constraints. We examine the largest source of systematic uncertainty arising from the mass-observable relation and find that, with perfect knowledge of the scaling relation parameters, CSST clusters have the potential to enhance precision, tightening constraints to $0.034$ eV. We also study the effects of the maximum redshift $z_{max}$ and other uncertainties, including redshift, halo mass function, and bias. Furthermore, we emphasize the significance of accounting for the growth-induced scale-dependent bias (GISDB) effect, which is expected to have an impact of 1.5 to 2.2 times on the final constraint.
Modeling and Design of Compact, Permanent-Magnet Transport Systems for Highly Divergent, Broad Energy Spread Laser-Driven Proton Beams
2024-11-05
J. T. De Chant, K. Nakamura, Q. Ji, L. Obst-Huebl, S. Barber, A. M. Snijders, C. G. R. Geddes, J. van Tilborg, A. J. Gonsalves, C. B. Schroeder, E. Esarey
Laser-driven (LD) ion acceleration has been explored in a newly constructed short focal length beamline at the BELLA petawatt facility (interaction point 2, iP2). For applications utilizing such LD ion beams, a beam transport system is required, which for reasons of compactness be ideally contained within 3 m. While they are generated from a micron-scale source, large divergence and energy spread of LD ion beams present a unique challenge to transporting them compared to beams from conventional accelerators. This study gives an overview of proposed compact transport designs using permanent magnets satisfying different requirements depending on the application for the iP2 proton beamline such as radiation biology, material science, and high energy density science. These designs are optimized for different parameters such as energy spread and peak proton density according to the application's need. The various designs consist solely of permanent magnet elements, which can provide high magnetic field gradients on a small footprint. While the field strengths are fixed, we have shown that the beam size is able to be tuned effectively by varying the placement of the magnets. The performance of each design was evaluated based on high order particle tracking simulations of typical LD proton beams. We also examine the ability of certain configurations to tune and select beam energies, critical for specific applications. A more detailed investigation was carried out for a design to deliver 10 MeV LD accelerated ions for radiation biology applications. With these transport system designs, the iP2 beamline is ready to house various application experiments.
Transformers have had tremendous impact for several sequence related tasks, largely due to their ability to retrieve from any part of the sequence via softmax based dot-product attention. This mechanism plays a crucial role in Transformer's performance. We analyze the gradients backpropagated through the softmax operation in the attention mechanism and observe that these gradients can often be small. This poor gradient signal backpropagation can lead to inefficient learning of parameters preceeding the attention operations. To this end, we introduce a new attention mechanism called LASER, which we analytically show to admit a larger gradient signal. We show that LASER Attention can be implemented by making small modifications to existing attention implementations. We conduct experiments on autoregressive large language models (LLMs) with upto 2.2 billion parameters where we show upto 3.38% and an average of ~1% improvement over standard attention on downstream evaluations. Using LASER gives the following relative improvements in generalization performance across a variety of tasks (vision, text and speech): 4.67% accuracy in Vision Transformer (ViT) on Imagenet, 2.25% error rate in Conformer on the Librispeech speech-to-text and 0.93% fraction of incorrect predictions in BERT with 2.2 billion parameters.
Assessing the impact of uniform rotation on the structure of neutron stars
2024-11-05
Marc Salinas, Jorge Piekarewicz
Driven by recent laboratory experiments and astronomical observations, significant advances have deepened our understanding of neutron-star physics. NICER's Pulse Profile Modeling has refined our knowledge of neutron star masses and radii, while gravitational-wave detections have revealed key insights into the structure of neutron stars. Particularly relevant is the extraction of the tidal deformability by the LIGO-Virgo collaboration and the most recent determination of stellar radii by NICER, both suggesting a relatively soft equation of state (EOS) at intermediate densities. Additionally, measurements from the PREX collaboration and from pulsar timing suggest instead that the EOS is stiff in the vicinity of saturation density and at the highest densities accessible to date. But how stiff can the EOS be at these very high densities? Recent events featuring compact objects near the "lower mass gap" have raised questions about the existence of very massive neutron stars. Motivated by this finding and in light of new refinements to theoretical models, we explore the possibility that these massive objects may indeed be rapidly rotating neutron stars. We explore how rotation affects both the maximum neutron star mass and their associated radii, and discuss the implications they may have on the equation of state.
Chance-Constrained Convex MPC for Robust Quadruped Locomotion Under Parametric and Additive Uncertainties
2024-11-05
Ananya Trivedi, Sarvesh Prajapati, Mark Zolotas, Michael Everett, Taskin Padir
Recent advances in quadrupedal locomotion have focused on improving stability and performance across diverse environments. However, existing methods often lack adequate safety analysis and struggle to adapt to varying payloads and complex terrains, typically requiring extensive tuning. To overcome these challenges, we propose a Chance-Constrained Model Predictive Control (CCMPC) framework that explicitly models payload and terrain variability as distributions of parametric and additive disturbances within the single rigid body dynamics (SRBD) model. Our approach ensures safe and consistent performance under uncertain dynamics by expressing the model friction cone constraints, which define the feasible set of ground reaction forces, as chance constraints. Moreover, we solve the resulting stochastic control problem using a computationally efficient quadratic programming formulation. Extensive Monte Carlo simulations of quadrupedal locomotion across varying payloads and complex terrains demonstrate that CCMPC significantly outperforms two competitive benchmarks: Linear MPC (LMPC) and MPC with hand-tuned safety margins to maintain stability, reduce foot slippage, and track the center of mass. Hardware experiments on the Unitree Go1 robot show successful locomotion across various indoor and outdoor terrains with unknown loads exceeding 50% of the robot body weight, despite no additional parameter tuning. A video of the results and accompanying code can be found at: https://cc-mpc.github.io/.
Sustainability and Carbon Emissions of Future Accelerators
2024-11-05
Kenneth Bloom, Véronique Boisvert
Future accelerators and experiments for energy-frontier particle physics will be built and operated during a period in which society must also address the climate change emergency by significantly reducing emissions of carbon dioxide. The carbon intensity of many particle-physics activities is potentially significant, such that as a community particle physicists could create substantially more emissions than average citizens, which is currently more than budgeted to limit the increase in average global temperatures. We estimate the carbon impacts of potential future accelerators, detectors, computing, and travel, and find that while emissions from civil construction dominate by far, some other activities make noticeable contributions. We discuss potential mitigation strategies, and research and development activities that can be pursued to make particle physics more sustainable.
Digital Twin for Autonomous Surface Vessels: Enabler for Safe Maritime Navigation
2024-11-05
Daniel Menges, Adil Rasheed
Autonomous surface vessels (ASVs) are becoming increasingly significant in enhancing the safety and sustainability of maritime operations. To ensure the reliability of modern control algorithms utilized in these vessels, digital twins (DTs) provide a robust framework for conducting safe and effective simulations within a virtual environment. Digital twins are generally classified on a scale from 0 to 5, with each level representing a progression in complexity and functionality: Level 0 (Standalone) employs offline modeling techniques; Level 1 (Descriptive) integrates sensors and online modeling to enhance situational awareness; Level 2 (Diagnostic) focuses on condition monitoring and cybersecurity; Level 3 (Predictive) incorporates predictive analytics; Level 4 (Prescriptive) embeds decision-support systems; and Level 5 (Autonomous) enables advanced functionalities such as collision avoidance and path following. These digital representations not only provide insights into the vessel's current state and operational efficiency but also predict future scenarios and assess life endurance. By continuously updating with real-time sensor data, the digital twin effectively corrects modeling errors and enhances decision-making processes. Since DTs are key enablers for complex autonomous systems, this paper introduces a comprehensive methodology for establishing a digital twin framework specifically tailored for ASVs. Through a detailed literature survey, we explore existing state-of-the-art enablers across the defined levels, offering valuable recommendations for future research and development in this rapidly evolving field.
2024-11-05
Matthew S. Clement, Andre Izidoro, Sean N. Raymond, Rogerio Deienno
Our understanding of the process of terrestrial planet formation has grown markedly over the past 20 years, yet key questions remain. This review begins by first addressing the critical, earliest stage of dust coagulation and concentration. While classic studies revealed how objects that grow to $\sim$meter sizes are rapidly removed from protoplanetary disks via orbital decay (seemingly precluding growth to larger sizes), this chapter addresses how this is resolved in contemporary, streaming instability models that favor rapid planetesimal formation via gravitational collapse of solids in over-dense regions. Once formed, planetesimals grow into Mars-Earth-sized planetary embryos by a combination of pebble- and planetesimal accretion within the lifetime of the nebular disk. After the disk dissipates, these embryos typically experience a series of late giant impacts en route to attaining their final architectures. This review also highlights three different inner Solar System formation models that can match a number of empirical constraints, and also reviews ways that one or more might be ruled out in favor of another in the near future. These include (1) the Grand Tack, (2) the Early Instability and (3) Planet Formation from Rings. Additionally, this chapter discusses formation models for the closest known analogs to our own terrestrial planets: super-Earths and terrestrial exoplanets in systems also hosting gas giants. Finally, this review lays out a chain of events that may explain why the Solar System looks different than more than 99% of exoplanet systems.
AI Horizon Scanning -- White Paper p3395, IEEE-SA. Part III: Technology Watch: a selection of key developments, emerging technologies, and industry trends in Artificial Intelligence
2024-11-05
George Tambouratzis, Marina Cortês, Andrew R. Liddle
Generative Artificial Intelligence (AI) technologies are in a phase of unprecedented rapid development following the landmark release of Chat-GPT, which brought the phenomenon to wide public attention. As the deployment of AI products rises geometrically, considerable attention is being given to the threats and opportunities that AI technologies offer, and to the need for regulatory and standards initiatives to ensure that use of the technology aligns with societal needs and generates broad benefits while mitigating risks and threats. This manuscript is the third of a series of White Papers informing the development of IEEE-SA's p3995 {\it `Standard for the Implementation of Safeguards, Controls, and Preventive Techniques for Artificial Intelligence Models'} \cite{P3395}, Chair Marina Cort\^{e}s. This part focuses on assessing calmly and objectively, as far as is possible, the current state of Artificial Intelligence (AI) technology development and identifying predominant trends, prospects, and ensuing risks. It necessarily forms a snapshot of the current instant of a rapidly-evolving landscape, with new products and innovations emerging continuously. While our main focus is on software and hardware developments and their corporate context, we also briefly review progress on robotics within the AI context and describe some implications of the substantial and growing AI energy demand.
The GECKOS Survey: Identifying kinematic sub-structures in edge-on galaxies
2024-11-05
A. Fraser-McKelvie, J. van de Sande, D. A. Gadotti, E. Emsellem, T. Brown, D. B. Fisher, M. Martig, M. Bureau, O. Gerhard, A. J. Battisti, J. Bland-Hawthorn, B. Catinella, F. Combes, L. Cortese, S. M. Croom, T. A. Davis, J. Falcón-Barroso, F. Fragkoudi, K. C. Freeman, M. R. Hayden, R. McDermid, B. Mazzilli Ciraulo, J. T. Mendel, F. Pinna, A. Poci, T. H. Rutherford, C. de Sá-Freitas, L. A. Silva-Lima, L. M. Valenzuela, G. van de Ven, Z. Wang, A. B. Watts
The vertical evolution of galactic discs is governed by the sub-structures within them. We examine the diversity of kinematic sub-structure present in the first 12 galaxies observed from the GECKOS survey, a VLT/MUSE large programme providing a systematic study of 35 edge-on, Milky Way-mass disc galaxies. Employing the nGIST analysis pipeline, we derive the mean line-of-sight stellar velocity ($V_{\star}$), velocity dispersion ($\sigma_{\star}$), skew ($h_{3}$), and kurtosis ($h_{4}$) for the sample, and examine 2D maps and 1D line profiles. Visually, the majority of this sample (8/12) are found to possess boxy-peanut bulges and host the corresponding kinematic structure predicted for stellar bars viewed in projection. Four galaxies exhibit strong evidence for the presence of nuclear discs, including central $h_{3}$-$V_{\star}$ anti-correlations, `croissant'-shaped central depressions in $\sigma_{\star}$ maps, strong gradients in $h_{3}$, and positive $h_{4}$ plateaus over the expected nuclear disc extent. The strength of the $h_{3}$ feature corresponds to the size of the nuclear disc, measured from the $h_{3}$ turnover radius. We can explain the features within the kinematic maps of all sample galaxies via disc structure(s) alone. We do not find any need to invoke the existence of dispersion-dominated bulges. Obtaining the specialised data products for this paper and the broader GECKOS survey required significant development of existing integral field spectroscopic (IFS) analysis tools. Therefore, we also present the nGIST pipeline: a modern, sophisticated, and easy-to-use pipeline for the analysis of galaxy IFS data. We conclude that the variety of kinematic sub-structures seen in GECKOS galaxies requires a contemporary view of galaxy morphology, expanding on the traditional view of galaxy structure, and uniting the kinematic complexity observed in the Milky Way with the extragalactic.
Usefulness of LLMs as an Author Checklist Assistant for Scientific Papers: NeurIPS'24 Experiment
2024-11-05
Alexander Goldberg, Ihsan Ullah, Thanh Gia Hieu Khuong, Benedictus Kent Rachmat, Zhen Xu, Isabelle Guyon, Nihar B. Shah
Large language models (LLMs) represent a promising, but controversial, tool in aiding scientific peer review. This study evaluates the usefulness of LLMs in a conference setting as a tool for vetting paper submissions against submission standards. We conduct an experiment at the 2024 Neural Information Processing Systems (NeurIPS) conference, where 234 papers were voluntarily submitted to an "LLM-based Checklist Assistant." This assistant validates whether papers adhere to the author checklist used by NeurIPS, which includes questions to ensure compliance with research and manuscript preparation standards. Evaluation of the assistant by NeurIPS paper authors suggests that the LLM-based assistant was generally helpful in verifying checklist completion. In post-usage surveys, over 70% of authors found the assistant useful, and 70% indicate that they would revise their papers or checklist responses based on its feedback. While causal attribution to the assistant is not definitive, qualitative evidence suggests that the LLM contributed to improving some submissions. Survey responses and analysis of re-submissions indicate that authors made substantive revisions to their submissions in response to specific feedback from the LLM. The experiment also highlights common issues with LLMs: inaccuracy (20/52) and excessive strictness (14/52) were the most frequent issues flagged by authors. We also conduct experiments to understand potential gaming of the system, which reveal that the assistant could be manipulated to enhance scores through fabricated justifications, highlighting potential vulnerabilities of automated review tools.
Detection of Thermal Emission at Millimeter Wavelengths from Low-Earth Orbit Satellites
2024-11-05
A. Foster, A. Chokshi, A. J. Anderson, B. Ansarinejad, M. Archipley, L. Balkenhol, K. Benabed, A. N. Bender, D. R. Barron, B. A. Benson, F. Bianchini, L. E. Bleem, F. R. Bouchet, L. Bryant, E. Camphuis, J. E. Carlstrom, C. L. Chang, P. Chaubal, P. M. Chichura, T. -L. Chou, A. Coerver, T. M. Crawford, C. Daley, T. de Haan, K. R. Dibert, M. A. Dobbs, A. Doussot, D. Dutcher, W. Everett, C. Feng, K. R. Ferguson, K. Fichman, S. Galli, A. E. Gambrel, R. W. Gardner, F. Ge, N. Goeckner-Wald, R. Gualtieri, F. Guidi, S. Guns, N. W. Halverson, E. Hivon, G. P. Holder, W. L. Holzapfel, J. C. Hood, A. Hryciuk, N. Huang, F. Kéruzoré, A. R. Khalife, L. Knox, M. Korman, K. Kornoelje, C. -L. Kuo, K. Levy, A. E. Lowitz, C. Lu, A. Maniyar, E. S. Martsen, F. Menanteau, M. Millea, J. Montgomery, Y. Nakato, T. Natoli, G. I. Noble, Y. Omori, Z. Pan, P. Paschos, K. A. Phadke, A. W. Pollak, K. Prabhu, W. Quan, S. Raghunathan, M. Rahimi, A. Rahlin, C. L. Reichardt, M. Rouble, J. E. Ruhl, E. Schiappucci, J. A. Sobrin, A. A. Stark, J. Stephen, C. Tandoi, B. Thorne, C. Trendafilova, C. Umilta, J. D. Vieira, A. Vitrier, Y. Wan, N. Whitehorn, W. L. K. Wu, M. R. Young, J. A. Zebrowski
The detection of satellite thermal emission at millimeter wavelengths is presented using data from the 3rd-Generation receiver on the South Pole Telescope (SPT-3G). This represents the first reported detection of thermal emission from artificial satellites at millimeter wavelengths. Satellite thermal emission is shown to be detectable at high signal-to-noise on timescales as short as a few tens of milliseconds. An algorithm for downloading orbital information and tracking known satellites given observer constraints and time-ordered observatory pointing is described. Consequences for cosmological surveys and short-duration transient searches are discussed, revealing that the integrated thermal emission from all large satellites does not contribute significantly to the SPT-3G survey intensity map. Measured satellite positions are found to be discrepant from their two-line element (TLE) derived ephemerides up to several arcminutes which may present a difficulty in cross-checking or masking satellites from short-duration transient searches.
Energy Price Modelling: A Comparative Evaluation of four Generations of Forecasting Methods
2024-11-05
Alexandru-Victor Andrei, Georg Velev, Filip-Mihai Toma, Daniel Traian Pele, Stefan Lessmann
Energy is a critical driver of modern economic systems. Accurate energy price forecasting plays an important role in supporting decision-making at various levels, from operational purchasing decisions at individual business organizations to policy-making. A significant body of literature has looked into energy price forecasting, investigating a wide range of methods to improve accuracy and inform these critical decisions. Given the evolving landscape of forecasting techniques, the literature lacks a thorough empirical comparison that systematically contrasts these methods. This paper provides an in-depth review of the evolution of forecasting modeling frameworks, from well-established econometric models to machine learning methods, early sequence learners such LSTMs, and more recent advancements in deep learning with transformer networks, which represent the cutting edge in forecasting. We offer a detailed review of the related literature and categorize forecasting methodologies into four model families. We also explore emerging concepts like pre-training and transfer learning, which have transformed the analysis of unstructured data and hold significant promise for time series forecasting. We address a gap in the literature by performing a comprehensive empirical analysis on these four family models, using data from the EU energy markets, we conduct a large-scale empirical study, which contrasts the forecasting accuracy of different approaches, focusing especially on alternative propositions for time series transformers.
An updated catalogue of 310 Galactic supernova remnants and their statistical properties
2024-11-05
D. A. Green
A revised catalogue of 310 Galactic supernova remnants (SNRs) is presented, along with some statistics of their properties. 21 SNRs have been added to the catalogue since the previous published version from 2019, and 5 entries have been removed, as they have been identified as HII regions. Also discussed are some basics statistics of the remnants in the catalogue, the selection effects that apply to the identification of Galactic SNRs and their consequences.
Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey
2024-11-05
Zhongling Huang, Xidan Zhang, Zuqian Tang, Feng Xu, Mihai Datcu, Junwei Han
SAR images possess unique attributes that present challenges for both human observers and vision AI models to interpret, owing to their electromagnetic characteristics. The interpretation of SAR images encounters various hurdles, with one of the primary obstacles being the data itself, which includes issues related to both the quantity and quality of the data. The challenges can be addressed using generative AI technologies. Generative AI, often known as GenAI, is a very advanced and powerful technology in the field of artificial intelligence that has gained significant attention. The advancement has created possibilities for the creation of texts, photorealistic pictures, videos, and material in various modalities. This paper aims to comprehensively investigate the intersection of GenAI and SAR. First, we illustrate the common data generation-based applications in SAR field and compare them with computer vision tasks, analyzing the similarity, difference, and general challenges of them. Then, an overview of the latest GenAI models is systematically reviewed, including various basic models and their variations targeting the general challenges. Additionally, the corresponding applications in SAR domain are also included. Specifically, we propose to summarize the physical model based simulation approaches for SAR, and analyze the hybrid modeling methods that combine the GenAI and interpretable models. The evaluation methods that have been or could be applied to SAR, are also explored. Finally, the potential challenges and future prospects are discussed. To our best knowledge, this survey is the first exhaustive examination of the interdiscipline of SAR and GenAI, encompassing a wide range of topics, including deep neural networks, physical models, computer vision, and SAR images. The resources of this survey are open-source at \url{https://github.com/XAI4SAR/GenAIxSAR}.
Improving Scientific Hypothesis Generation with Knowledge Grounded Large Language Models
2024-11-04
Guangzhi Xiong, Eric Xie, Amir Hassan Shariatmadari, Sikun Guo, Stefan Bekiranov, Aidong Zhang
Large language models (LLMs) have demonstrated remarkable capabilities in various scientific domains, from natural language processing to complex problem-solving tasks. Their ability to understand and generate human-like text has opened up new possibilities for advancing scientific research, enabling tasks such as data analysis, literature review, and even experimental design. One of the most promising applications of LLMs in this context is hypothesis generation, where they can identify novel research directions by analyzing existing knowledge. However, despite their potential, LLMs are prone to generating ``hallucinations'', outputs that are plausible-sounding but factually incorrect. Such a problem presents significant challenges in scientific fields that demand rigorous accuracy and verifiability, potentially leading to erroneous or misleading conclusions. To overcome these challenges, we propose KG-CoI (Knowledge Grounded Chain of Ideas), a novel system that enhances LLM hypothesis generation by integrating external, structured knowledge from knowledge graphs (KGs). KG-CoI guides LLMs through a structured reasoning process, organizing their output as a chain of ideas (CoI), and includes a KG-supported module for the detection of hallucinations. With experiments on our newly constructed hypothesis generation dataset, we demonstrate that KG-CoI not only improves the accuracy of LLM-generated hypotheses but also reduces the hallucination in their reasoning chains, highlighting its effectiveness in advancing real-world scientific research.
IR and UV limits of CDT and their relations to FRG
2024-11-04
Jan Ambjorn, Jakub Gizbert-Studnicki, Andrzej Goerlich, Daniel Nemeth
Causal Dynamical Triangulations (CDT) is a lattice theory of quantum gravity. It is shown how to identify the IR and the UV limits of this lattice theory with similar limits studied using the continuum, functional renormalization group (FRG) approach. The main technical tool in this study will be the so-called two-point function. It will allow us to identify a correlation length not directly related to the propagation of physical degrees of freedom.
Recent advancements in large language models, including GPT-4 and its variants, and Generative AI-assisted coding tools like GitHub Copilot, ChatGPT, and Tabnine, have significantly transformed software development. This paper analyzes how these innovations impact productivity and software test development metrics. These tools enable developers to generate complete software programs with minimal human intervention before deployment. However, thorough review and testing by developers are still crucial. Utilizing the Test Pyramid concept, which categorizes tests into unit, integration, and end-to-end tests, we evaluate three popular AI coding assistants by generating and comparing unit tests for opensource modules. Our findings show that AI-generated tests are of equivalent quality to original tests, highlighting differences in usage and results among the tools. This research enhances the understanding and capabilities of AI-assistant tools in automated testing.
2024-11-04
Shukai Liu, Linzheng Chai, Jian Yang, Jiajun Shi, He Zhu, Liran Wang, Ke Jin, Wei Zhang, Hualei Zhu, Shuyue Guo, Tao Sun, Jiaheng Liu, Yunlong Duan, Yu Hao, Liqun Yang, Guanglin Niu, Ge Zhang, Zhoujun Li
Code large language models (LLMs) have made significant progress in code debugging by directly generating the correct code based on the buggy code snippet. Programming benchmarks, typically consisting of buggy code snippet and their associated test cases, are used to assess the debugging capabilities of LLMs. However, many existing benchmarks primarily focus on Python and are often limited in terms of language diversity (e.g., DebugBench and DebugEval). To advance the field of multilingual debugging with LLMs, we propose the first massively multilingual debugging benchmark, which includes 3.6K test samples of 18 programming languages and covers the automated program repair (APR) task, the code review (CR) task, and the bug identification (BI) task. Further, we introduce the debugging instruction corpora MDEVAL-INSTRUCT by injecting bugs into the correct multilingual queries and solutions (xDebugGen). Further, a multilingual debugger xDebugCoder trained on MDEVAL-INSTRUCT as a strong baseline specifically to handle the bugs of a wide range of programming languages (e.g. "Missing Mut" in language Rust and "Misused Macro Definition" in language C). Our extensive experiments on MDEVAL reveal a notable performance gap between open-source models and closed-source LLMs (e.g., GPT and Claude series), highlighting huge room for improvement in multilingual code debugging scenarios.
Class imbalance and label noise are pervasive in large-scale datasets, yet much of machine learning research assumes well-labeled, balanced data, which rarely reflects real world conditions. Existing approaches typically address either label noise or class imbalance in isolation, leading to suboptimal results when both issues coexist. In this work, we propose Conformal-in-the-Loop (CitL), a novel training framework that addresses both challenges with a conformal prediction-based approach. CitL evaluates sample uncertainty to adjust weights and prune unreliable examples, enhancing model resilience and accuracy with minimal computational cost. Our extensive experiments include a detailed analysis showing how CitL effectively emphasizes impactful data in noisy, imbalanced datasets. Our results show that CitL consistently boosts model performance, achieving up to a 6.1% increase in classification accuracy and a 5.0 mIoU improvement in segmentation. Our code is publicly available: CitL.
Refining the nonlinear modelling of primordial oscillatory features
2024-11-04
Mario Ballardini, Nicola Barbieri
Primordial oscillatory features in the power spectrum of curvature perturbations are sensitive probes of the dynamics of the early Universe and can provide insights beyond the standard inflationary scenario. While these features have been the focus of extensive studies using cosmic microwave background anisotropy data, large-scale structure surveys now provide the opportunity to probe their effects at smaller scales with higher precision. In this paper, we present a complete description of the nonlinear model for primordial oscillatory features in the context of time-sliced perturbation theory extending the results already presented in the literature. We derive analytical expressions including novel contributions such as the mixed term between primordial oscillations and baryon acoustic oscillations, and we also calculate the corrections arising from the specific envelope of the oscillatory pattern, corresponding to a scale-dependent amplitude. These results are compared with N-body simulations using the COLA method and show consistent behaviour across different scales. Although the corrections are found to be small, they represent an important step towards fully characterising the nonlinear imprints of primordial features on the matter power spectrum. Our results offer new predictions for future cosmological surveys that seek to detect these subtle signatures in the matter distribution.
La Serena School for Data Science and the Spanish Virtual Observatory Schools: Initiatives Based on Hands on Experience
2024-11-04
A. Bayo, V. Mesa, G. Damke, M. Cerda, M. J. Graham, D. Norman, F. Forster, C. Ibarlucea, N. Monsalves
The worlds of Data Science (including big and/or federated data, machine learning, etc) and Astrophysics started merging almost two decades ago. For instance, around 2005, international initiatives such as the Virtual Observatory framework rose to standardize the way we publish and transfer data, enabling new tools such as VOSA (SED Virtual Observatory Analyzer) to come to existence and remain relevant today. More recently, new facilities like the Vera Rubin Observatory, serve as motivation to develop efficient and extremely fast (very often deep learning based) methodologies in order to fully exploit the informational content of the vast Legacy Survey of Space and Time (LSST) dataset. However, fundamental changes in the way we explore and analyze data cannot permeate in the "astrophysical sociology and idiosyncrasy" without adequate training. In this talk, I will focus on one specific initiative that has been extremely successful and is based on "learning by doing": the La Serena School for Data Science. I will also briefly touch on a different successful approach: a series of schools organized by the Spanish Virtual Observatory. The common denominator among the two kinds of schools is to present the students with real scientific problems that benefit from the concepts / methodologies taught. On the other hand, the demographics targeted by both initiatives vary significantly and can represent examples of two "flavours" to be followed by others.
Compressible Navier--Stokes--Coriolis system in critical Besov spaces
2024-11-04
Mikihiro Fujii, Keiichi Watanabe
We consider the three-dimensional compressible Navier--Stokes system with the Coriolis force and prove the long-time existence of a unique strong solution. More precisely, we show that for any $0
A Survey on AI-driven Energy Optimisation in Terrestrial Next Generation Radio Access Networks
2024-11-04
Kishan Sthankiya, Nagham Saeed, Greg McSorley, Mona Jaber, Richard G. Clegg
This survey uncovers the tension between AI techniques designed for energy saving in mobile networks and the energy demands those same techniques create. We compare modeling approaches that estimate power usage cost of current commercial terrestrial next-generation radio access network deployments. We then categorize emerging methods for reducing power usage by domain: time, frequency, power, and spatial. Next, we conduct a timely review of studies that attempt to estimate the power usage of the AI techniques themselves. We identify several gaps in the literature. Notably, real-world data for the power consumption is difficult to source due to commercial sensitivity. Comparing methods to reduce energy consumption is beyond challenging because of the diversity of system models and metrics. Crucially, the energy cost of AI techniques is often overlooked, though some studies provide estimates of algorithmic complexity or run-time. We find that extracting even rough estimates of the operational energy cost of AI models and data processing pipelines is complex. Overall, we find the current literature hinders a meaningful comparison between the energy savings from AI techniques and their associated energy costs. Finally, we discuss future research opportunities to uncover the utility of AI for energy saving.
Learning Multiple Initial Solutions to Optimization Problems
2024-11-04
Elad Sharony, Heng Yang, Tong Che, Marco Pavone, Shie Mannor, Peter Karkus
Sequentially solving similar optimization problems under strict runtime constraints is essential for many applications, such as robot control, autonomous driving, and portfolio management. The performance of local optimization methods in these settings is sensitive to the initial solution: poor initialization can lead to slow convergence or suboptimal solutions. To address this challenge, we propose learning to predict \emph{multiple} diverse initial solutions given parameters that define the problem instance. We introduce two strategies for utilizing multiple initial solutions: (i) a single-optimizer approach, where the most promising initial solution is chosen using a selection function, and (ii) a multiple-optimizers approach, where several optimizers, potentially run in parallel, are each initialized with a different solution, with the best solution chosen afterward. We validate our method on three optimal control benchmark tasks: cart-pole, reacher, and autonomous driving, using different optimizers: DDP, MPPI, and iLQR. We find significant and consistent improvement with our method across all evaluation settings and demonstrate that it efficiently scales with the number of initial solutions required. The code is available at $\href{https://github.com/EladSharony/miso}{\tt{https://github.com/EladSharony/miso}}$.
This paper presents an educational tool designed to enhance cryptography education for K-12 students, utilizing Kolb's Experiential Learning (EL) model and engaging visual components. Our tool incorporates the four stages of EL -- Concrete Experience, Reflective Observation, Abstract Conceptualization, and Active Experimentation -- to teach key cryptographic concepts, including hashing, symmetric cryptography, and asymmetric cryptography. The learning experience is enriched with real-world simulations, customized AI-based conversation agents, video demonstrations, interactive scenarios, and a simplified Python coding terminal focused on cryptography. Targeted at beginners in cybersecurity, the tool encourages independent learning with minimal instructor involvement. An evaluation with 51 middle and high school students showed positive feedback from 93% of participants, who found the simulations, visualizations, AI reflections, scenarios, and coding capabilities engaging and conducive to learning. Comprehension surveys indicated a high understanding of cryptography concepts: hashing (middle school: 89%, high school: 92%), symmetric cryptography (middle school: 93%, high school: 97%), and asymmetric cryptography (middle school: 91%, high school: 94%).
Deep Learning on 3D Semantic Segmentation: A Detailed Review
2024-11-04
Thodoris Betsas, Andreas Georgopoulos, Anastasios Doulamis, Pierre Grussenmeyer
In this paper an exhaustive review and comprehensive analysis of recent and former deep learning methods in 3D Semantic Segmentation (3DSS) is presented. In the related literature, the taxonomy scheme used for the classification of the 3DSS deep learning methods is ambiguous. Based on the taxonomy schemes of 9 existing review papers, a new taxonomy scheme of the 3DSS deep learning methods is proposed, aiming to standardize it and improve the comparability and clarity across related studies. Furthermore, an extensive overview of the available 3DSS indoor and outdoor datasets is provided along with their links. The core part of the review is the detailed presentation of recent and former 3DSS deep learning methods and their classification using the proposed taxonomy scheme along with their GitHub repositories. Additionally, a brief but informative analysis of the evaluation metrics and loss functions used in 3DSS is included. Finally, a fruitful discussion of the examined 3DSS methods and datasets, is presented to foster new research directions and applications in the field of 3DSS. Supplementary, to this review a GitHub repository is provided (https://github.com/thobet/Deep-Learning-on-3D-Semantic-Segmentation-a- Detailed-Review) including a quick classification of over 400 3DSS methods, using the proposed taxonomy scheme.
I review the twistor theory construction of stationary and axisymmetric, Lorentzian signature solutions of the Einstein vacuum equations and the related toric Ricci-flat metrics of Riemannian signature, \cite{W,MW,F,FW}. The construction arises from the Ward construction \cite{W2} of anti-self-dual Yang-Mills fields as holomorphic vector bundles on twistor space, with the observation of Witten \cite{LW} that the Einstein equations for these metrics include the anti-self-dual Yang-Mills equations. The principal datum for a solution is the holomorphic patching matrix $P$ for a holomorphic vector bundle on a reduced twistor space, and $P$ is typically simpler than the corresponding metric to write down. I give a catalogue of examples, building on earlier collections \cite{F,AG}, and consider the inverse problem: how far does the rod structure of such a metric, together with its asymptotics, determine $P$?
The evolution of volumetric video: A survey of smart transcoding and compression approaches
2024-11-04
Preetish Kakkar, Hariharan Ragothaman
Volumetric video, the capture and display of three-dimensional (3D) imagery, has emerged as a revolutionary technology poised to transform the media landscape, enabling immersive experiences that transcend the limitations of traditional 2D video. One of the key challenges in this domain is the efficient delivery of these high-bandwidth, data-intensive volumetric video streams, which requires innovative transcoding and compression techniques. This research paper explores the state-of-the-art in volumetric video compression and delivery, with a focus on the potential of AI-driven solutions to address the unique challenges posed by this emerging medium.
Fast Fixes and Faulty Drivers: An Empirical Analysis of Regression Bug Fixing Times in the Linux Kernel
2024-11-04
Jukka Ruohonen, Adam Alami
Regression bugs refer to situations in which something that worked previously no longer works currently. Such bugs have been pronounced in the Linux kernel. The paper focuses on regression bug tracking in the kernel by considering the time required to fix regression bugs. The dataset examined is based on the regzbot automation framework for tracking regressions in the Linux kernel. According to the results, (i) regression bug fixing times have been faster than previously reported; between 2021 and 2024, on average, it has taken less than a month to fix regression bugs. It is further evident that (ii) device drivers constitute the most prone subsystem for regression bugs, and also the fixing times vary across the kernel's subsystems. Although (iii) most commits fixing regression bugs have been reviewed, tested, or both, the kernel's code reviewing and manual testing practices do not explain the fixing times. Likewise, (iv) there is only a weak signal that code churn might contribute to explaining the fixing times statistically. Finally, (v) some subsystems exhibit strong effects for explaining the bug fixing times statistically, although overall statistical performance is modest but not atypical to the research domain. With these empirical results, the paper contributes to the efforts to better understand software regressions and their tracking in the Linux kernel.
Affordances and Design Principles of The Political Left and Right
2024-11-04
Felix Anand Epp, Jesse Haapoja, Matti Nelimarkka
Like any form of technology, social media services embed values. To examine how societal values may be present in these systems, we focus on exploring political ideology as a value system. We organised four co-design workshops with political representatives from five major parties in Finland to investigate what values they would incorporate into social media services. The participants were divided into one right-leaning group, two left-leaning groups, and one mixed group. This approach allows us to examine how their political ideologies, i.e., value systems, influenced the design of social media. We analysed produced artefacts (early-stage paper mockups) to identify different features and affordances for each group and then contrasted the ideological compositions. Our results revealed a clear distinction between groups: right-leaning groups favoured market-based visibility, while left-leaning groups rejected such design principles in favour of open profile work. Additionally, we found tentative differences in design outcomes along the liberal-conservative dimension. These findings underscore the importance of acknowledging existing political value systems in the design of social computing systems. They also highlight the need for further research to map out political ideologies in technology design.
Semi-empirical calibration of the oxygen abundance for LINER galaxies based on SDSS-IV MaNGA -- The case for strong and weak AGN
2024-11-04
C. B. Oliveira, O. L. Dors, I. A. Zinchenko, M. V. Cardaci, G. F. Hägele, I. N. Morais, P. C. Santos, G. C. Almeida
In this paper, we present a semi-empirical calibration between the oxygen abundance and the $N2$ emission-line ratio for Low Ionization Nuclear Emission Regions (LINERs). This relation was derived by comparing the optical spectroscopic data of 118 nuclear spaxels classified as LINERs using three different BPT diagrams from the Mapping Nearby Galaxies survey (MaNGA) and sub-classified as weak (wAGN, 84 objects) and strong (sAGN, 34 objects) AGN (active galactic nucleus) from the WHAN diagnostic diagram and photoionization model results obtained with the {\sc cloudy} code assuming gas accretion into a black hole (representing an AGN). We found that our wAGN LINERs exhibit an oxygen abundance in the range of $8.50 \lesssim \mathrm{12+\log(O/H)} \lesssim 8.90 $, with an average value of $\mathrm{12+\log(O/H)}=8.68$, while our sAGN LINERs exhibit an oxygen abundance in the range of $8.51 \lesssim \: \mathrm{12+\log(O/H)} \: \lesssim \: 8.81 $, with an average value of $\mathrm{12+\log(O/H)}=8.65$. Our abundance estimations are in good agreement with those derived for another two different samples one of them with 463 Seyfert 2 objects and the other with 43 LINERs galaxies ionized by post-AGB stars, showing that the assumptions of our models are likely suitable for wAGN and sAGN LINERs. A relation between the equivalent width of the observed H$\alpha$ emission-line and the estimated ionization parameter provided by models was obtained. Our results also suggest that LINERs does not show a clear correlation between oxygen abundances and the stellar mass of the hosting galaxies.
GDP nowcasting with large-scale inter-industry payment data in real time -- A network approach
2024-11-04
Anastasia Mantziou, Kerstin Hotte, Mihai Cucuringu, Gesine Reinert
Real-time economic information is essential for policy-making but difficult to obtain. We introduce a granular nowcasting method for macro- and industry-level GDP using a network approach and data on real-time monthly inter-industry payments in the UK. To this purpose we devise a model which we call an extended generalised network autoregressive (GNAR-ex) model, tailored for networks with time-varying edge weights and nodal time series, that exploits the notion of neighbouring nodes and neighbouring edges. The performance of the model is illustrated on a range of synthetic data experiments. We implement the GNAR-ex model on the payments network including time series information of GDP and payment amounts. To obtain robustness against statistical revisions, we optimise the model over 9 quarterly releases of GDP data from the UK Office for National Statistics. Our GNAR-ex model can outperform baseline autoregressive benchmark models, leading to a reduced forecasting error. This work helps to obtain timely GDP estimates at the aggregate and industry level derived from alternative data sources compared to existing, mostly survey-based, methods. Thus, this paper contributes both, a novel model for networks with nodal time series and time-varying edge weights, and the first network-based approach for GDP nowcasting based on payments data.
Towards the design of model-based means and methods to characterize and diagnose teachers' digital maturity
2024-11-04
Christine Michel, Laëtitia Pierrot
This article examines how models of teacher digital maturity can be combined to produce a unified version that can be used to design diagnostic tools and methods. 11 models applicable to the field of compulsory education were identified through a literature review. The models and how their constituent dimensions contribute to the determination of maturity levels were analyzed. The summary highlights the diversity of the dimensions used and the fact that digital maturity is only partially taken into account. What's more, most of these models focus on the most recent maturity levels associated with innovative or pioneering teachers. The models tend to exclude teachers who are not digital users or who have a low level of digital use, but who are present in the French context. In the final part of the article, a proposal for a unified model of teachers' digital maturity, MUME, which addresses these two issues, is described, together with the preliminary results of a study aimed at designing a diagnostic method.
Obscured star clusters in the Inner Milky Way. How many massive young clusters are still awaiting detection?
2024-11-04
Akash Gupta, Valentin D. Ivanov, Thomas Preibisch, Dante Minniti
Aims. Our goal is twofold. First, to detect new clusters we apply the newest methods for the detection of clustering with the best available wide-field sky surveys in the mid-infrared because they are the least affected by extinction. Second, we address the question of cluster detection's completeness, for now limiting it to the most massive star clusters. Methods. This search is based on the mid-infrared Galactic Legacy Infrared Mid Plane Survey Extraordinaire (GLIMPSE), to minimize the effect of dust extinction. The search Ordering Points To Identify the Clustering Structure (OPTICS) clustering algorithm is applied to identify clusters, after excluding the bluest, presumably foreground sources, to improve the cluster-to-field contrast. The success rate for cluster identification is estimated with a semi-empirical simulation that adds clusters, based on the real objects, to the point source catalog, to be recovered later with the same search algorithm that was used in the search for new cluster candidates. As a first step, this is limited to the most massive star clusters with a total mass of 104 $M_\odot$. Results. Our automated search, combined with inspection of the color-magnitude diagrams and images yielded 659 cluster candidates; 106 of these appear to have been previously identified, suggesting that a large hidden population of star clusters still exists in the inner Milky Way. However, the search for the simulated supermassive clusters achieves a recovery rate of 70 to 95%, depending on the distance and extinction toward them. Conclusions. The new candidates, if confirmed, indicate that the Milky Way still harbors a sizeable population of still unknown clusters. However, they must be objects of modest richness, because our simulation indicates that there is no substantial hidden population of supermassive clusters in the central region of our Galaxy.
Low Mass Contact Binaries: Orbital Stability at Extreme Low Mass Ratios
2024-11-04
Surjit S. Wadhwa, Natalia R. Landin, Bojan Arbutina, Nicholas F. H. Tothill, Ain Y. De Horta, Miroslav D. Filipovic, Jelena Petrovic, Gojko Djurasevic
With the ever-increasing number of light curve solutions of contact binary systems increasing number of potential bright red nova progenitors are being reported. There remains, however, only one confirmed event. In the present study we undertake a comprehensive review of orbital stability of contact binary systems considering the effects of the stellar internal composition (metallicity) and age on the evolution of the gyration radius and its effect on the instability mass ratio of contact binaries. We find that both metallicity and age have an independent effect on orbital stability with metal poor and older systems being more stable. The combined effects of age and metallicity are quite profound such that for most systems with primaries of solar mass or greater which are halfway or more through the main sequence lifespans have instability mass ratio at levels where the secondary component would be below the hydrogen fusion mass limit. We find that from the currently available solutions we cannot confidently assign any system as unstable. Although we identify 8 potential red nova progenitors all have methodological or astrophysical concerns which lower our confidence in designating any of them as potential merger candidates.
Shortcut learning refers to the phenomenon where models employ simple, non-robust decision rules in practical tasks, which hinders their generalization and robustness. With the rapid development of large language models (LLMs) in recent years, an increasing number of studies have shown the impact of shortcut learning on LLMs. This paper provides a novel perspective to review relevant research on shortcut learning in In-Context Learning (ICL). It conducts a detailed exploration of the types of shortcuts in ICL tasks, their causes, available benchmarks, and strategies for mitigating shortcuts. Based on corresponding observations, it summarizes the unresolved issues in existing research and attempts to outline the future research landscape of shortcut learning.
Mobile agents are essential for automating tasks in complex and dynamic mobile environments. As foundation models evolve, the demands for agents that can adapt in real-time and process multimodal data have grown. This survey provides a comprehensive review of mobile agent technologies, focusing on recent advancements that enhance real-time adaptability and multimodal interaction. Recent evaluation benchmarks have been developed better to capture the static and interactive environments of mobile tasks, offering more accurate assessments of agents' performance. We then categorize these advancements into two main approaches: prompt-based methods, which utilize large language models (LLMs) for instruction-based task execution, and training-based methods, which fine-tune multimodal models for mobile-specific applications. Additionally, we explore complementary technologies that augment agent performance. By discussing key challenges and outlining future research directions, this survey offers valuable insights for advancing mobile agent technologies. A comprehensive resource list is available at https://github.com/aialt/awesome-mobile-agents
2024-11-04
Mario De Lucia, Paolo Dal Bo, Eugenia Di Giorgi, Tommaso Lari, Claudio Puglia, Federico Paolucci
Transition Edge Sensors (TESs) are amongst the most sensitive cryogenic detectors and can be easily optimized for the detection of massive particles or photons ranging from X-rays all the way down to millimetre radiation. Furthermore, TESs exhibit unmatched energy resolution while being easily frequency domain multiplexed in arrays of several hundred pixels. Such great performance, along with rather simple and sturdy readout and amplification chains make TESs extremely compelling for applications in many fields of scientific endeavour. While the first part of this article is an in-depth discussion on the working principles of Transition Edge Sensors, the remainder of this review article focuses on the applications of Transition Edge Sensors in advanced scientific instrumentation serving as an accessible and thorough list of possible starting points for more comprehensive literature research.
On the non-special divisors in algebraic function fields defined over $\mathbb{F}_q$
2024-11-04
S Ballet, M Koutchoukali
In the theory of algebraic function fields and their applications to the information theory, the Riemann-Roch theorem plays a fundamental role. But its use, delicate in general, is efficient and practical for applications especially in the case of non-special divisors. So, in this paper, we give a survey of the known results concerning the non-special divisors in the algebraic function fields defined over finite fields, enriched with some new results about the existence of such divisors in curves of defect k. In particular, we have chosen to be self-contained by giving the full proofs of each result, the original proofs or shorter alternative proofs.
The JCMT BISTRO Survey: The Magnetic Fields of the IC 348 Star-forming Region
2024-11-04
Youngwoo Choi, Woojin Kwon, Kate Pattle, Doris Arzoumanian, Tyler L. Bourke, Thiem Hoang, Jihye Hwang, Patrick M. Koch, Sarah Sadavoy, Pierre Bastien, Ray Furuya, Shih-Ping Lai, Keping Qiu, Derek Ward-Thompson, David Berry, Do-Young Byun, Huei-Ru Vivien Chen, Wen Ping Chen, Mike Chen, Zhiwei Chen, Tao-Chung Ching, Jungyeon Cho, Minho Choi, Yunhee Choi, Simon Coudé, Antonio Chrysostomou, Eun Jung Chung, Sophia Dai, Victor Debattista, James Di Francesco, Pham Ngoc Diep, Yasuo Doi, Hao-Yuan Duan, Yan Duan, Chakali Eswaraiah, Lapo Fanciullo, Jason Fiege, Laura M. Fissel, Erica Franzmann, Per Friberg, Rachel Friesen, Gary Fuller, Tim Gledhill, Sarah Graves, Jane Greaves, Matt Griffin, Qilao Gu, Ilseung Han, Tetsuo Hasegawa, Martin Houde, Charles L. H. Hull, Tsuyoshi Inoue, Shu-ichiro Inutsuka, Kazunari Iwasaki, Il-Gyo Jeong, Doug Johnstone, Janik Karoly, Vera Könyves, Ji-hyun Kang, Kevin Lacaille, Chi-Yan Law, Chang Won Lee, Hyeseung Lee, Chin-Fei Lee, Jeong-Eun Lee, Sang-Sung Lee, Dalei Li, Di Li, Guangxing Li, Hua-bai Li, Sheng-Jun Lin, Hong-Li Liu, Tie Liu, Sheng-Yuan Liu, Junhao Liu, Steven Longmore, Xing Lu, A-Ran Lyo, Steve Mairs, Masafumi Matsumura, Brenda Matthews, Gerald Moriarty-Schieven, Tetsuya Nagata, Fumitaka Nakamura, Hiroyuki Nakanishi, Nguyen Bich Ngoc, Nagayoshi Ohashi, Takashi Onaka, Geumsook Park, Harriet Parsons, Nicolas Peretto, Felix Priestley, Tae-Soo Pyo, Lei Qian, Ramprasad Rao, Jonathan Rawlings, Mark Rawlings, Brendan Retter, John Richer, Andrew Rigby, Hiro Saito, Giorgio Savini, Masumichi Seta, Ekta Sharma, Yoshito Shimajiri, Hiroko Shinnaga, Archana Soam, Miju Kang, Akimasa Kataoka, Koji Kawabata, Francisca Kemper, Jongsoo Kim, Shinyoung Kim, Gwanjeong Kim, Kyoung Hee Kim, Mi-Ryang Kim, Kee-Tae Kim, Hyosung Kim, Florian Kirchschlager, Jason Kirk, Masato I. N. Kobayashi, Takayoshi Kusune, Jungmi Kwon, Motohide Tamura, Ya-Wen Tang, Xindi Tang, Kohji Tomisaka, Yusuke Tsukamoto, Serena Viti, Hongchi Wang, Jia-Wei Wang, Jintai Wu, Jinjin Xie, Meng-Zhe Yang, Hsi-Wei Yen, Hyunju Yoo, Jinghua Yuan, Hyeong-Sik Yun, Tetsuya Zenko, Guoyin Zhang, Yapeng Zhang, Chuan-Peng Zhang, Jianjun Zhou, Lei Zhu, Ilse de Looze, Philippe André, C. Darren Dowell, David Eden, Stewart Eyres, Sam Falle, Valentin J. M. Le Gouellec, Frédérick Poidevin, Sven van Loo
We present 850 $\mu$m polarization observations of the IC 348 star-forming region in the Perseus molecular cloud as part of the B-fields In STar-forming Region Observation (BISTRO) survey. We study the magnetic properties of two cores (HH 211 MMS and IC 348 MMS) and a filamentary structure of IC 348. We find that the overall field tends to be more perpendicular than parallel to the filamentary structure of the region. The polarization fraction decreases with intensity, and we estimate the trend by power-law and the mean of the Rice distribution fittings. The power indices for the cores are much smaller than 1, indicative of possible grain growth to micron size in the cores. We also measure the magnetic field strengths of the two cores and the filamentary area separately by applying the Davis-Chandrasekhar-Fermi method and its alternative version for compressed medium. The estimated mass-to-flux ratios are 0.45-2.20 and 0.63-2.76 for HH 211 MMS and IC 348 MMS, respectively, while the ratios for the filament is 0.33-1.50. This result may suggest that the transition from subcritical to supercritical conditions occurs at the core scale ($\sim$ 0.05 pc) in the region. In addition, we study the energy balance of the cores and find that the relative strength of turbulence to the magnetic field tends to be stronger for IC 348 MMS than HH 211 MMS. The result could potentially explain the different configurations inside the two cores: a single protostellar system in HH 211 MMS and multiple protostars in IC 348 MMS.
The first identification of Lyman $α$ Changing-look Quasars at high-redshift in DESI
2024-11-04
Wei-Jian Guo, Zhiwei Pan, Małgorzata Siudek, Jessica Nicole Aguilar, Steven Ahlen, Davide Bianchi, David Brooks, Todd Claybaugh, Kyle Dawson, Axel de la Macorra, Peter Doel, Kevin Fanning, Jaime E. Forero-Romero, Enrique Gaztañaga, Satya Gontcho A Gontcho, Klaus Honscheid, Robert Kehoe, Theodore Kisner, Andrew Lambert, Martin Landriau, Laurent Le Guillou, Marc Manera, Aaron Meisner, John Moustakas, Andrea Muñoz-Gutiérrez, Adam Myers, Jundan Nie, Nathalie Palanque-Delabrouille, Claire Poppett, Francisco Prada, Mehdi Rezaie, Graziano Rossi, Eusebio Sanchez, Michael Schubnelll, Hee-Jong Seo, Joseph Harry Silber, David Sprayberry, Gregory Tarlé, Benjamin Alan Weaver, Zhimin Zhou, Hu Zou
We present two cases of Ly$\alpha$ changing-look (CL) quasars (J1306 and J1512) along with two additional candidates (J1511 and J1602), all discovered serendipitously at $z >2$ through the Dark Energy Spectroscopic Instrument (DESI) and the Sloan Digital Sky Survey (SDSS). It is the first time to capture CL events in Ly$\alpha$ at high redshift, which is crucial for understanding underlying mechanisms driving the CL phenomenon and the evolution of high-redshift quasars and galaxies. The variability of all four sources is confirmed by the significant change of amplitude in the $r$ band ($|r_{\rm DESI}-r_{\rm SDSS}| >0.5 \ \rm mag$). We find that the accretion rate in the dim state for these CL objects corresponds to a relatively low value ($\mathscr{\dot M} \approx 2\times10^{-3}$), which suggests that the inner region of the accretion disk might be in transition between the Advection Dominated Accretion Flow ($\mathscr{\dot M}<10^{-3}\sim 10^{-2}$) and the canonical accretion disk (optically thick, geometrically thin). However, unlike in C {\sc iv} CL quasars in which broad Ly$\alpha$ remained, the broad C {\sc iv} may still persist after a CL event occurs in Ly$\alpha$, making the physical origin of the CL and ionization mechanism event more puzzling and interesting.
Bankruptcy prediction is an important research area that heavily relies on data science. It aims to help investors, managers, and regulators better understand the operational status of corporations and predict potential financial risks in advance. To improve prediction, researchers and practitioners have begun to utilize a variety of different types of data, ranging from traditional financial indicators to unstructured data, to aid in the construction and optimization of bankruptcy forecasting models. Over time, not only instrumentalized data improved, but also instrumentalized methodology for data structuring, cleaning, and analysis. With the aid of advanced analytical techniques that deploy machine learning and deep learning algorithms, bankruptcy assessment became more accurate over time. However, due to the sensitivity of financial data, the scarcity of valid public datasets remains a key bottleneck for the rapid modeling and evaluation of machine learning algorithms for targeted tasks. This study therefore introduces a taxonomy of datasets for bankruptcy research, and summarizes their characteristics. This paper also proposes a set of metrics to measure the quality and the informativeness of public datasets The taxonomy, coupled with the informativeness measure, thus aims at providing valuable insights to better assist researchers and practitioners in developing potential applications for various aspects of credit assessment and decision making by pointing at appropriate datasets for their studies.
Euclid: The $r_{\rm b}$-$M_\ast$ relation as a function of redshift. I. The $5 \times 10^9 M_\odot$ black hole in NGC 1272
2024-11-04
R. Saglia, K. Mehrgan, S. de Nicola, J. Thomas, M. Kluge, R. Bender, D. Delley, P. Erwin, M. Fabricius, B. Neureiter, S. Andreon, C. Baccigalupi, M. Baldi, S. Bardelli, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, A. Caillat, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, H. Degaudenzi, G. De Lucia, J. Dinis, X. Dupac, S. Dusini, M. Farina, S. Farrens, F. Faustini, S. Ferriol, N. Fourmanoit, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, B. Gillis, C. Giocoli, A. Grazian, F. Grupp, L. Guzzo, S. V. H. Haugan, J. Hoar, W. Holmes, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, E. Keihänen, S. Kermiche, A. Kiessling, M. Kilbinger, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, E. Medinaceli, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, M. Moresco, L. Moscardini, E. Munari, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, M. Schirmer, P. Schneider, T. Schrabback, A. Secroun, M. Seiffert, S. Serrano, C. Sirignano, G. Sirri, J. Skottfelt, L. Stanco, J. Steinwagner, P. Tallada-Crespí, D. Tavagnacco, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, T. Vassallo, G. Verdoes Kleijn, Y. Wang, J. Weller, G. Zamorani, E. Zucca, C. Burigana, V. Scottez, L. Ferrarese, E. Lusso, D. Scott
Core ellipticals, massive early-type galaxies have an almost constant inner surface brightness profile. The size of the core region correlates with the mass of the finally merged black hole. Here we report the first Euclid-based dynamical mass determination of a supermassive black hole. We study the centre of NGC 1272, the second most luminous elliptical galaxy in the Perseus cluster, combining the Euclid VIS photometry coming from the Early Release Observations of the Perseus cluster with VIRUS spectroscopic observations at the Hobby-Eberly Telescope. The core of NGC 1272 is detected on the Euclid VIS image. Its size is $1.29\pm 0.07''$ or 0.45 kpc, determined by fitting PSF-convolved core-S\'ersic and Nuker-law functions. The two-dimensional stellar kinematics of the galaxy is measured from the VIRUS spectra by deriving optimally regularized non-parametric line-of-sight velocity distributions. Dynamical models of the galaxy are constructed using our axisymmetric and triaxial Schwarzschild codes. We measure a black hole mass of $(5\pm3) \times 10^9 M_\odot$, in line with the expectation from the $M_{\rm BH}$-$r_{\rm b}$ correlation, but eight times larger than predicted by the $M_{\rm BH}$-$\sigma$ correlation (at $1.8\sigma$ significance). The core size, rather than the velocity dispersion, allows one to select galaxies harboring the most massive black holes. The spatial resolution, wide area coverage, and depth of the \Euclid (Wide and Deep) surveys allow us to find cores of passive galaxies larger than 2 kpc up to redshift 1.
Efficient Active Imitation Learning with Random Network Distillation
2024-11-04
Emilien Biré, Anthony Kobanda, Ludovic Denoyer, Rémy Portelas
Developing agents for complex and underspecified tasks, where no clear objective exists, remains challenging but offers many opportunities. This is especially true in video games, where simulated players (bots) need to play realistically, and there is no clear reward to evaluate them. While imitation learning has shown promise in such domains, these methods often fail when agents encounter out-of-distribution scenarios during deployment. Expanding the training dataset is a common solution, but it becomes impractical or costly when relying on human demonstrations. This article addresses active imitation learning, aiming to trigger expert intervention only when necessary, reducing the need for constant expert input along training. We introduce Random Network Distillation DAgger (RND-DAgger), a new active imitation learning method that limits expert querying by using a learned state-based out-of-distribution measure to trigger interventions. This approach avoids frequent expert-agent action comparisons, thus making the expert intervene only when it is useful. We evaluate RND-DAgger against traditional imitation learning and other active approaches in 3D video games (racing and third-person navigation) and in a robotic locomotion task and show that RND-DAgger surpasses previous methods by reducing expert queries. https://sites.google.com/view/rnd-dagger
Integrated population models (IPMs) combine multiple ecological data types such as capture-mark-recapture histories, reproduction surveys, and population counts into a single statistical framework. In such models, each data type is generated by a probabilistic submodel, and an assumption of independence between the different data types is usually made. The fact that the same biological individuals can contribute to multiple data types has been perceived as affecting their independence, and several studies have even investigated IPM robustness in this scenario. However, what matters from a statistical perspective is probabilistic independence: the joint probability of observing all data is equal to the product of the likelihoods of the various datasets. Contrary to a widespread perception, probabilistic non-independence does not automatically result from collecting data on the same physical individuals. Conversely, while there can be good reasons for non-independence of IPM submodels arising from sharing of individuals between data types, these relations do not seem to be included in IPMs whose robustness is being investigated. Furthermore, conditional rather than true independence is sometimes assumed. In this conceptual paper, I survey the various independence concepts used in IPMs, try to make sense of them by getting back to first principles in toy models, and show that it is possible to obtain probabilistic independence (or near-independence) despite two or three data types collected on the same set of biological individuals. I then revisit recommendations pertaining to component data collection and IPM robustness checks, and provide some suggestions to bridge the current gap between individual-level IPMs and their population-level approximations using composite likelihoods.
A sky survey of ultraviolet sources observed through AstroSat's UVIT: A point source catalog
2024-11-04
Swagat Bordoloi, P. Shalima, Rupjyoti Gogoi, Jayant Murthy
The Ultra Violet Imaging Telescope (UVIT) onboard India's first dedicated multiwavelength satellite \textit{AstroSat} observed a significant fraction of the sky in the ultraviolet with a spatial resolution of 1.4\arcsec. We present a catalog of the point sources observed by UVIT in the far ultraviolet (FUV; 1300-1800 \AA) and near ultraviolet (NUV; 2000-3000 \AA). We carried out astrometry and photometry of 428 field pointings in the FUV and 54 field pointings in the NUV band, observed in 5 filter bands in each channel respectively, covering an area of about 63 square degrees. The final catalog contains about 102,773 sources. The limiting magnitude(AB) of the F148W band filter, that has the largest number of detections is $\sim21.3$. For the NUV channel, we find the limiting magnitude at around $\sim23$. We describe the final catalog and present the results of the statistical analysis.
Thinking Forward and Backward: Effective Backward Planning with Large Language Models
2024-11-04
Allen Z. Ren, Brian Ichter, Anirudha Majumdar
Large language models (LLMs) have exhibited remarkable reasoning and planning capabilities. Most prior work in this area has used LLMs to reason through steps from an initial to a goal state or criterion, thereby effectively reasoning in a forward direction. Nonetheless, many planning problems exhibit an inherent asymmetry such that planning backward from the goal is significantly easier -- for example, if there are bottlenecks close to the goal. We take inspiration from this observation and demonstrate that this bias holds for LLM planning as well: planning performance in one direction correlates with the planning complexity of the problem in that direction. However, our experiments also reveal systematic biases which lead to poor planning in the backward direction. With this knowledge, we propose a backward planning algorithm for LLMs that first flips the problem and then plans forward in the flipped problem. This helps avoid the backward bias, generate more diverse candidate plans, and exploit asymmetries between the forward and backward directions in planning problems -- we find that combining planning in both directions with self-verification improves the overall planning success rates by 4-24% in three planning domains.
Feature attributions attempt to highlight what inputs drive predictive power. Good attributions or explanations are thus those that produce inputs that retain this predictive power; accordingly, evaluations of explanations score their quality of prediction. However, evaluations produce scores better than what appears possible from the values in the explanation for a class of explanations, called encoding explanations. Probing for encoding remains a challenge because there is no general characterization of what gives the extra predictive power. We develop a definition of encoding that identifies this extra predictive power via conditional dependence and show that the definition fits existing examples of encoding. This definition implies, in contrast to encoding explanations, that non-encoding explanations contain all the informative inputs used to produce the explanation, giving them a "what you see is what you get" property, which makes them transparent and simple to use. Next, we prove that existing scores (ROAR, FRESH, EVAL-X) do not rank non-encoding explanations above encoding ones, and develop STRIPE-X which ranks them correctly. After empirically demonstrating the theoretical insights, we use STRIPE-X to uncover encoding in LLM-generated explanations for predicting the sentiment in movie reviews.
Two lectures on Yang-Lee edge singularity and analytic structure of QCD equation of state
2024-11-04
Vladimir V. Skokov
These lecture notes, prepared for the 2024 XQCD PhD, provide an introduction to the analytic structure of an equation of state near a second-order phase transition and its most prominent landmark: the Yang-Lee edge singularity. In addition to discussing general properties, the notes review recent theoretical progress in locating the QCD critical point by tracking the trajectory of the Yang-Lee edge singularity.
Fair and Welfare-Efficient Constrained Multi-matchings under Uncertainty
2024-11-04
Elita Lobo, Justin Payan, Cyrus Cousins, Yair Zick
We study fair allocation of constrained resources, where a market designer optimizes overall welfare while maintaining group fairness. In many large-scale settings, utilities are not known in advance, but are instead observed after realizing the allocation. We therefore estimate agent utilities using machine learning. Optimizing over estimates requires trading-off between mean utilities and their predictive variances. We discuss these trade-offs under two paradigms for preference modeling -- in the stochastic optimization regime, the market designer has access to a probability distribution over utilities, and in the robust optimization regime they have access to an uncertainty set containing the true utilities with high probability. We discuss utilitarian and egalitarian welfare objectives, and we explore how to optimize for them under stochastic and robust paradigms. We demonstrate the efficacy of our approaches on three publicly available conference reviewer assignment datasets. The approaches presented enable scalable constrained resource allocation under uncertainty for many combinations of objectives and preference models.
A Scoping Review of Functional Near-Infrared Spectroscopy (fNIRS) Applications in Game-Based Learning Environments
2024-11-04
Shayla Sharmin, Roghayeh Leila Barmaki
This scoping review analyzes the use of Functional Near-Infrared Spectroscopy (fNIRS) in game-based learning (GBL) settings, providing a thorough examination of contemporary trends and approaches.Employing the PRISMA framework, an initial collection of 956 articles was methodically screened, resulting in 18 research papers that satisfied the inclusion criteria. Each chosen study was assessed based on many criteria, including measurable outcomes, equipment characteristics, and study design. The review categorizes fNIRS-based GBL research into two primary types: cognitive response studies, which analyze how the brain function during tasks and comparative studies, which evaluate finding across different study materials or methods based on neural activities. The analysis includes learning platforms, gaming devices, and various fNIRS devices that has been used. Additionally, study designs and data collection methodologies were reviewed to evaluate their impact on research results. A comprehensive analysis outlines the specifications of fNIRS devices used in diverse studies, including yearly publication trends categorized by learning type, gaming equipment, fNIRS study classification, and outcome measures such as learning improvements and cerebral pattern analysis. Furthermore, the study design and analysis techniques are detailed alongside the number of studies in each category, emphasizing methodological trends and analytical strategies.
We present a method to detect departures from business-justified workflows among support agents. Our goal is to assist auditors in identifying agent actions that cannot be explained by the activity within their surrounding context, where normal activity patterns are established from historical data. We apply our method to help audit millions of actions of over three thousand support agents. We collect logs from the tools used by support agents and construct a bipartite graph of Actions and Entities representing all the actions of the agents, as well as background information about entities. From this graph, we sample subgraphs rooted on security-significant actions taken by the agents. Each subgraph captures the relevant context of the root action in terms of other actions, entities and their relationships. We then prioritize the rooted-subgraphs for auditor review using feed-forward and graph neural networks, as well as nearest neighbors techniques. To alleviate the issue of scarce labeling data, we use contrastive learning and domain-specific data augmentations. Expert auditors label the top ranked subgraphs as ``worth auditing" or ``not worth auditing" based on the company's business policies. This system finds subgraphs that are worth auditing with high enough precision to be used in production.
High-resolution seismic reflections are essential for imaging and monitoring applications. In seismic land surveys using sources and receivers at the surface, surface waves often dominate, masking the reflections. In this study, we demonstrate the efficacy of a two-step procedure to suppress surface waves in an active-source reflection seismic dataset. First, we apply seismic interferometry (SI) by cross-correlation, turning receivers into virtual sources to estimate the dominant surface waves. Then, we perform adaptive subtraction to minimise the difference between the surface waves in the original data and the result of SI. We propose a new approach where the initial suppression results are used for further iterations, followed by adaptive subtraction. This technique aims to enhance the efficacy of data-driven surface-wave suppression through an iterative process. We use a 2D seismic reflection dataset from Scheemda, situated in the Groningen province of the Netherlands, to illustrate the technique's efficiency. A comparison between the data after recursive interferometric surface-wave suppression and the original data across time and frequency-wavenumber domains shows significant suppression of the surface waves, enhancing visualization of the reflections for following subsurface imaging and monitoring studies.
Context-Informed Machine Translation of Manga using Multimodal Large Language Models
2024-11-04
Philip Lippmann, Konrad Skublicki, Joshua Tanner, Shonosuke Ishiwatari, Jie Yang
Due to the significant time and effort required for handcrafting translations, most manga never leave the domestic Japanese market. Automatic manga translation is a promising potential solution. However, it is a budding and underdeveloped field and presents complexities even greater than those found in standard translation due to the need to effectively incorporate visual elements into the translation process to resolve ambiguities. In this work, we investigate to what extent multimodal large language models (LLMs) can provide effective manga translation, thereby assisting manga authors and publishers in reaching wider audiences. Specifically, we propose a methodology that leverages the vision component of multimodal LLMs to improve translation quality and evaluate the impact of translation unit size, context length, and propose a token efficient approach for manga translation. Moreover, we introduce a new evaluation dataset -- the first parallel Japanese-Polish manga translation dataset -- as part of a benchmark to be used in future research. Finally, we contribute an open-source software suite, enabling others to benchmark LLMs for manga translation. Our findings demonstrate that our proposed methods achieve state-of-the-art results for Japanese-English translation and set a new standard for Japanese-Polish.
Effects of survey design features on response rates: a meta-analytical approach using the example of crime surveys
2024-11-04
Jonas Klingwort, Vera Toepoel
When conducting a survey, many choices regarding survey design features have to be made. These choices affect the response rate of a survey. This paper analyzes the individual effects of these survey design features on the response rate. For this purpose, data from a systematic review of crime surveys conducted in Germany between 2001--2021 were used. First, a meta-analysis of proportions is used to estimate the summary response rate. Second, a meta-regression was fitted, modeling the relationship between the observed response rates and survey-design features, such as the study year, target population, coverage area, data collection mode, and institute. The developed model informs about the influence of certain survey design features and can predict the expected response rate when (re-) designing a survey. This study highlights that a thoughtful survey design and professional survey administration can result in high response rates.
2024-11-04
Daniel Kuhn, Soroosh Shafiee, Wolfram Wiesemann
Distributionally robust optimization (DRO) studies decision problems under uncertainty where the probability distribution governing the uncertain problem parameters is itself uncertain. A key component of any DRO model is its ambiguity set, that is, a family of probability distributions consistent with any available structural or statistical information. DRO seeks decisions that perform best under the worst distribution in the ambiguity set. This worst case criterion is supported by findings in psychology and neuroscience, which indicate that many decision-makers have a low tolerance for distributional ambiguity. DRO is rooted in statistics, operations research and control theory, and recent research has uncovered its deep connections to regularization techniques and adversarial training in machine learning. This survey presents the key findings of the field in a unified and self-contained manner.
Estimating journey time for two-point vehicle re-identification survey with limited observable scope using 2-dimensional truncated distributions
2024-11-04
Diyi Liu, Yangsong Gu, Lee D. Han
In transportation, Weigh-in motion (WIM) stations, Electronic Toll Collection (ETC) systems, Closed-circuit Television (CCTV) are widely deployed to collect data at different locations. Vehicle re-identification, by matching the same vehicle at different locations, is helpful in understanding the long-distance journey patterns. In this paper, the potential hazards of ignoring the survivorship bias effects are firstly identified and analyzed using a truncated distribution over a 2-dimensional time-time domain. Given journey time modeled as Exponential or Weibull distribution, Maximum Likelihood Estimation (MLE), Fisher Information (F.I.) and Bootstrap methods are formulated to estimate the parameter of interest and their confidence intervals. Besides formulating journey time distributions, an automated framework querying the observable time-time scope are proposed. For complex distributions (e.g, three parameter Weibull), distributions are modeled in PyTorch to automatically find first and second derivatives and estimated results. Three experiments are designed to demonstrate the effectiveness of the proposed method. In conclusion, the paper describes a very unique aspects in understanding and analyzing traffic status. Although the survivorship bias effects are not recognized and long-ignored, by accurately describing travel time over time-time domain, the proposed approach have potentials in travel time reliability analysis, understanding logistics systems, modeling/predicting product lifespans, etc.
The Second Case of a Major Merger Triggering a Starburst in a Green Pea Galaxy
2024-11-04
S. Purkayastha, N. Kanekar, S. Kumari, J. Rhoads, S. Malhotra, J. Pharo, T. Ghosh
We have used the Karl G. Jansky Very Large Array (VLA) to map H{\sc i} 21\,cm emission from the Green Pea galaxy GP~J1148+2546 at $z\approx0.0451$, only the second measurement of the H{\sc i} spatial distribution of a Green Pea. The VLA H{\sc i} 21\,cm image, the DECaLS optical image, and Sloan Digital Sky Survey spectroscopy show that GP~J1148+2546 has two neighbours, the nearer of which is only $\approx 17.5$~kpc away, and that the H{\sc i} 21\,cm emission extends in an inverted ``C'' shape around the Green Pea and its companions, with the highest H{\sc i} column density between the two neighbouring galaxies. The starburst in GP~J1148+2546 is likely to have been triggered by the ongoing merger with its neighbours, although the velocity field and velocity dispersion images do not show clear merger signatures at the Green Pea location. The H{\sc i} mass of the Green Pea and its immediate surroundings is $(3.58 \pm 0.37) \times 10^9 \, M_\odot$, a factor of $\approx 7.4$ lower than the total H{\sc i} mass of the system of three interacting galaxies, while the H{\sc i} depletion timescale of GP~J1148+2546 is $\approx 0.69$~Gyr, much shorter than that of typical galaxies at $z \approx 0$. We detect damped Ly$\alpha$ absorption and Ly$\alpha$ emission from the Green Pea in a Hubble Space Telescope Cosmic Origins Spectrograph spectrum, obtaining a high H{\sc i} column density, $\approx 2.0 \times 10^{21}$~cm$^{-2}$, and a low Ly$\alpha$ escape fraction, $\approx 0.8$\%, consistent with the relatively low value ($\approx 5.4$) of the ratio O32~$\equiv$~[O{\sc iii}]$\lambda 5007 + \lambda 4959$/[O{\sc ii}]$\lambda$3727,3729.
Quantum Linear System Solvers: A Survey of Algorithms and Applications
2024-11-04
Mauro E. S. Morales, Lirandë Pira, Philipp Schleich, Kelvin Koor, Pedro C. S. Costa, Dong An, Lin Lin, Patrick Rebentrost, Dominic W. Berry
Solving linear systems of equations plays a fundamental role in numerous computational problems from different fields of science. The widespread use of numerical methods to solve these systems motivates investigating the feasibility of solving linear systems problems using quantum computers. In this work, we provide a survey of the main advances in quantum linear systems algorithms, together with some applications. We summarize and analyze the main ideas behind some of the algorithms for the quantum linear systems problem in the literature. The analysis begins by examining the Harrow-Hassidim-Lloyd (HHL) solver. We note its limitations and reliance on computationally expensive quantum methods, then highlight subsequent research efforts which aimed to address these limitations and optimize runtime efficiency and precision via various paradigms. We focus in particular on the post-HHL enhancements which have paved the way towards optimal lower bounds with respect to error tolerance and condition number. By doing so, we propose a taxonomy that categorizes these studies. Furthermore, by contextualizing these developments within the broader landscape of quantum computing, we explore the foundational work that have inspired and informed their development, as well as subsequent refinements. Finally, we discuss the potential applications of these algorithms in differential equations, quantum machine learning, and many-body physics.
Hubble Space Telescope survey of Magellanic Cloud star clusters. Binaries among the split main sequences of NGC 1818, NGC 1850, and NGC 2164
2024-11-04
F. Muratore, A. P. Milone, F. D'Antona, E. J. Nastasio, G. Cordoni, M. V. Legnardi, C. He, T. Ziliotto, E. Dondoglio, M. Bernizzoni, M. Tailo, E. Bortolan, F. Dell'Agli, L. Deng, E. P. Lagioia, C. Li, A. F. Marino, P. Ventura
Nearly all star clusters younger than ~600 Myr exhibit extended main sequence turn offs and split main sequences (MSs) in their color-magnitude diagrams. Works based on both photometry and spectroscopy have firmly demonstrated that the red MS is composed of fast-rotating stars, whereas blue MS stars are slow rotators. Nevertheless, the mechanism responsible for the formation of stellar populations with varying rotation rates remains a topic of debate. Potential mechanisms proposed for the split MS include binary interactions, early evolution of pre-main sequence stars, and the merging of binary systems, but a general consensus has yet to be reached. These formation scenarios predict different fractions of binaries among blue- and red-MS stars. Therefore, studying the binary populations can provide valuable constraints that may help clarify the origins of the split MSs. We use high-precision photometry from the Hubble Space Telescope (HST) to study the binaries of three young Magellanic star clusters exhibiting split MS, namely NGC 1818, NGC 1850, and NGC 2164. By analyzing the photometry in the F225W, F275W, F336W, and F814W filters for observed binaries and comparing it to a large sample of simulated binaries, we determine the fractions of binaries within the red and the blue MS. We find that the fractions of binaries among the blue MS are higher than those of red-MS stars by a factor of ~1.5, 4.6, and ~1.9 for NGC 1818, NGC 1850, and NGC 2164, respectively. We discuss these results in the context of the formation scenarios of the split MS.
This review article provides the basics and discusses some important applications of thermal field theory, namely the combination of statistical mechanics and relativistic quantum field theory. In a first part the fundamentals are covered: the density matrix, the corresponding averages and the treatment of fields of various spin in a medium. A second part is dedicated to the computation of thermal Green's function for scalars, vectors and fermions with path-integral methods. These functions play a crucial role in thermal field theory, as explained here. A more applicative part of the review is dedicated to the production of particles in a medium and to phase transitions in field theory, including the process of vacuum decay in a general theory featuring a first-order phase transition. To understand this review, the reader should only have a good knowledge of non-statistical quantum field theory.
The empirical laws of galaxy dynamics: from gas kinematics to weak lensing
2024-11-04
Federico Lelli, Tobias Mistele, Stacy S. McGaugh, James M. Schombert, Pengfei Li
Galaxies obey a set of strict dynamical laws, which imply a close coupling between the visible matter (stars and gas) and the observed dynamics (set by dark matter in the standard cosmological context). Here we review recent results from weak gravitational lensing, which allows studying the empirical laws of galaxy dynamics out to exceedingly large radii in both late-type galaxies (LTGs) and early-type galaxies (ETGs). We focus on three laws: (1) the circular velocity curves of both LTGs and ETGs remain indefinitely flat out to several hundreds of kpc; (2) the same baryonic Tully-Fisher relation is followed by LTGs and ETGs; (3) the same radial acceleration relation (RAR) is followed by LTGs and ETGs. Combining galaxy data with Solar System data, the RAR covers about 16 orders of magnitude in the Newtonian baryonic acceleration. Remarkably, these empirical facts were predicted a priori by MOND.
Stars have been recognized as optimal laboratories to probe axion properties. In the last decades there have been significant advances in this field due to a better modelling of stellar systems and accurate observational data. In this work we review the current status of constraints on axions from stellar physics. We focus in particular on the Sun, globular cluster stars, white dwarfs and (proto)-neutron stars.
Model-independent cosmology with joint observations of gravitational waves and $γ$-ray bursts
2024-11-04
Andrea Cozzumbo, Ulyana Dupletsa, Rodrigo Calderón, Riccardo Murgia, Gor Oganesyan, Marica Branchesi
Multi-messenger (MM) observations of binary neutron star (BNS) mergers provide a promising approach to trace the distance-redshift relation, crucial for understanding the expansion history of the Universe and, consequently, testing the nature of Dark Energy (DE). While the gravitational wave (GW) signal offers a direct measure of the distance to the source, high-energy observatories can detect the electromagnetic counterpart and drive the optical follow-up providing the redshift of the host galaxy. In this work, we exploit up-to-date catalogs of $\gamma$-ray bursts (GRBs) supposedly coming from BNS mergers observed by the Fermi $\gamma$-ray Space Telescope and the Neil Gehrels Swift Observatory, to construct a large set of mock MM data. We explore how combinations of current and future generations of GW observatories operating under various underlying cosmological models would be able to detect GW signals from these GRBs. We achieve the reconstruction of the GW parameters by means of a novel prior-informed Fisher matrix approach. We then use these mock data to perform an agnostic reconstruction of the DE phenomenology, thanks to a machine learning method based on forward modeling and Gaussian Processes (GP). Our study highlights the paramount importance of observatories capable of detecting GRBs and identifying their redshift. In the best-case scenario, the GP constraints are 1.5 times more precise than those produced by classical parametrizations of the DE evolution. We show that, in combination with forthcoming cosmological surveys, fewer than 40 GW-GRB detections will enable unprecedented precision on $H_\mathrm{0}$ and $\Omega_\mathrm{m}$, and accurately reconstruct the DE density evolution.
Time series anomaly detection (TSAD) is becoming increasingly vital due to the rapid growth of time series data across various sectors. Anomalies in web service data, for example, can signal critical incidents such as system failures or server malfunctions, necessitating timely detection and response. However, most existing TSAD methodologies rely heavily on manual feature engineering or require extensive labeled training data, while also offering limited interpretability. To address these challenges, we introduce a pioneering framework called the Time Series Anomaly Multimodal Analyzer (TAMA), which leverages the power of Large Multimodal Models (LMMs) to enhance both the detection and interpretation of anomalies in time series data. By converting time series into visual formats that LMMs can efficiently process, TAMA leverages few-shot in-context learning capabilities to reduce dependence on extensive labeled datasets. Our methodology is validated through rigorous experimentation on multiple real-world datasets, where TAMA consistently outperforms state-of-the-art methods in TSAD tasks. Additionally, TAMA provides rich, natural language-based semantic analysis, offering deeper insights into the nature of detected anomalies. Furthermore, we contribute one of the first open-source datasets that includes anomaly detection labels, anomaly type labels, and contextual description, facilitating broader exploration and advancement within this critical field. Ultimately, TAMA not only excels in anomaly detection but also provides a comprehensive approach for understanding the underlying causes of anomalies, pushing TSAD forward through innovative methodologies and insights.
Tabular Data Synthesis with Differential Privacy: A Survey
2024-11-04
Mengmeng Yang, Chi-Hung Chi, Kwok-Yan Lam, Jie Feng, Taolin Guo, Wei Ni
Data sharing is a prerequisite for collaborative innovation, enabling organizations to leverage diverse datasets for deeper insights. In real-world applications like FinTech and Smart Manufacturing, transactional data, often in tabular form, are generated and analyzed for insight generation. However, such datasets typically contain sensitive personal/business information, raising privacy concerns and regulatory risks. Data synthesis tackles this by generating artificial datasets that preserve the statistical characteristics of real data, removing direct links to individuals. However, attackers can still infer sensitive information using background knowledge. Differential privacy offers a solution by providing provable and quantifiable privacy protection. Consequently, differentially private data synthesis has emerged as a promising approach to privacy-aware data sharing. This paper provides a comprehensive overview of existing differentially private tabular data synthesis methods, highlighting the unique challenges of each generation model for generating tabular data under differential privacy constraints. We classify the methods into statistical and deep learning-based approaches based on their generation models, discussing them in both centralized and distributed environments. We evaluate and compare those methods within each category, highlighting their strengths and weaknesses in terms of utility, privacy, and computational complexity. Additionally, we present and discuss various evaluation methods for assessing the quality of the synthesized data, identify research gaps in the field and directions for future research.
A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness
2024-11-04
Fali Wang, Zhiwei Zhang, Xianren Zhang, Zongyu Wu, Tzuhao Mo, Qiuhao Lu, Wanjing Wang, Rui Li, Junjie Xu, Xianfeng Tang, Qi He, Yao Ma, Ming Huang, Suhang Wang
Large language models (LLM) have demonstrated emergent abilities in text generation, question answering, and reasoning, facilitating various tasks and domains. Despite their proficiency in various tasks, LLMs like LaPM 540B and Llama-3.1 405B face limitations due to large parameter sizes and computational demands, often requiring cloud API use which raises privacy concerns, limits real-time applications on edge devices, and increases fine-tuning costs. Additionally, LLMs often underperform in specialized domains such as healthcare and law due to insufficient domain-specific knowledge, necessitating specialized models. Therefore, Small Language Models (SLMs) are increasingly favored for their low inference latency, cost-effectiveness, efficient development, and easy customization and adaptability. These models are particularly well-suited for resource-limited environments and domain knowledge acquisition, addressing LLMs' challenges and proving ideal for applications that require localized data handling for privacy, minimal inference latency for efficiency, and domain knowledge acquisition through lightweight fine-tuning. The rising demand for SLMs has spurred extensive research and development. However, a comprehensive survey investigating issues related to the definition, acquisition, application, enhancement, and reliability of SLM remains lacking, prompting us to conduct a detailed survey on these topics. The definition of SLMs varies widely, thus to standardize, we propose defining SLMs by their capability to perform specialized tasks and suitability for resource-constrained settings, setting boundaries based on the minimal size for emergent abilities and the maximum size sustainable under resource constraints. For other aspects, we provide a taxonomy of relevant models/methods and develop general frameworks for each category to enhance and utilize SLMs effectively.
In this study, we introduce a novel method, called MamT$^4$, which is used for simultaneous analysis of four mammography images. A decision is made based on one image of a breast, with attention also devoted to three additional images: another view of the same breast and two images of the other breast. This approach enables the algorithm to closely replicate the practice of a radiologist who reviews the entire set of mammograms for a patient. Furthermore, this paper emphasizes the preprocessing of images, specifically proposing a cropping model (U-Net based on ResNet-34) to help the method remove image artifacts and focus on the breast region. To the best of our knowledge, this study is the first to achieve a ROC-AUC of 84.0 $\pm$ 1.7 and an F1 score of 56.0 $\pm$ 1.3 on an independent test dataset of Vietnam digital mammography (VinDr-Mammo), which is preprocessed with the cropping model.
Can education correct appearance discrimination in the labor market?
2024-11-03
Hambur Wang
This study explores the impact of appearance discrimination in the labor market and whether education can mitigate this issue. A statistical analysis of approximately 1.058 million job advertisements in China from 2008 to 2010 found that about 7.7% and 2.6% of companies had explicit requirements regarding candidates' appearance and height, particularly in positions with lower educational requirements. Literature review indicates that attractive job seekers typically enjoy higher employment opportunities and wages, while unattractive individuals face significant income penalties. Regression analysis of 1,260 participants reveals a significant positive correlation between attractiveness scores and wages, especially in low-education groups. Conversely, in high-education groups, the influence of appearance on income is not significant. The study suggests that enhancing education levels can effectively alleviate income declines associated with appearance, providing policy recommendations to reduce appearance discrimination in the labor market.
Investigating the Impact of Interpersonal Challenges on Feeling Welcome in OSS
2024-11-03
Bianca Trinkenreich, Zixuan Feng, Rudrajit Choudhuri, Marco Gerosa, Anita Sarma, Igor Steinmacher
The sustainability of open source software (OSS) projects hinges on contributor retention. Interpersonal challenges can inhibit a feeling of welcomeness among contributors, particularly from underrepresented groups, which impacts their decision to continue with the project. How much this impact is, varies among individuals, underlining the importance of a thorough understanding of their effects. Here, we investigate the effects of interpersonal challenges on the sense of welcomeness among diverse populations within OSS, through the diversity lenses of gender, race, and (dis)ability. We analyzed the large-scale Linux Foundation Diversity and Inclusion survey (n = 706) to model a theoretical framework linking interpersonal challenges with the sense of welcomeness through Structural Equation Models Partial Least Squares (PLS-SEM). We then examine the model to identify the impact of these challenges on different demographics through Multi-Group Analysis (MGA). Finally, we conducted a regression analysis to investigate how differently people from different demographics experience different types of interpersonal challenges. Our findings confirm the negative association between interpersonal challenges and the feeling of welcomeness in OSS, with this relationship being more pronounced among gender minorities and people with disabilities. We found that different challenges have unique impacts on how people feel welcomed, with variations across gender, race, and disability groups. We also provide evidence that people from gender minorities and with disabilities are more likely to experience interpersonal challenges than their counterparts, especially when we analyze stalking, sexual harassment, and doxxing. Our insights benefit OSS communities, informing potential strategies to improve the landscape of interpersonal relationships, ultimately fostering more inclusive and welcoming communities.
We survey 25 years of research on decidability issues for Petri nets. We collect results on the decidability of important properties, equivalence notions, and temporal logics.
N. G. de Bruijn (1958) studied the asymptotic expansion of iterates of sin$(x)$ with $0 < x \leq \pi/2$. Bencherif & Robin (1994) generalized this result to increasing analytic functions $f(x)$ with an attractive fixed point at 0 and $x > 0$ suitably small. Mavecha & Laohakosol (2013) formulated an algorithm for explicitly deriving required parameters. We review their method, testing it initally on the logistic function $\ell(x)$, a certain radical function $r(x)$, and later on several transcendental functions. Along the way, we show how $\ell(x)$ and $r(x)$ are kindred functions; the same is also true for sin$(x)$ and arcsinh$(x)$.
The Role of Interfaces and Charge for Chemical Reactivity in Microdroplets
2024-11-03
R. Allen LaCour, Joseph P. Heindel, Ruoqi Zhao, Teresa Head-Gordon
A wide variety of reactions are reported to be dramatically accelerated in aqueous microdroplets, making them a promising platform for environmentally clean chemical synthesis. However to fully utilize the microdroplets for accelerating chemical reactions requires a fundamental understanding of how microdroplet chemistry differs from that of a homogeneous phase. Here we provide our perspective on recent progress to this end both experimentally and theoretically. We begin by reviewing the many ways in which microdroplets can be prepared, creating water/hydrophobic interfaces which have been frequently implicated in microdroplet reactivity due to preferential surface adsorption of solutes, persistent electric fields, and their acidity or basicity. These features of the interface interplay with specific mechanisms proposed for microdroplet reactivity, including partial solvation and possible gas phase channels. We especially highlight the role of droplet charge, which appears key to understanding how certain reactions, like the formation of hydrogen peroxide and reduced transition metal complexes, are thermodynamically possible in microdroplets. Lastly, we emphasize opportunities for theoretical advances in the microdroplet field generally, and to suggest experiments which would greatly enhance our understanding of this fascinating and emerging subject.
Trustworthy Federated Learning: Privacy, Security, and Beyond
2024-11-03
Chunlu Chen, Ji Liu, Haowen Tan, Xingjian Li, Kevin I-Kai Wang, Peng Li, Kouichi Sakurai, Dejing Dou
While recent years have witnessed the advancement in big data and Artificial Intelligence (AI), it is of much importance to safeguard data privacy and security. As an innovative approach, Federated Learning (FL) addresses these concerns by facilitating collaborative model training across distributed data sources without transferring raw data. However, the challenges of robust security and privacy across decentralized networks catch significant attention in dealing with the distributed data in FL. In this paper, we conduct an extensive survey of the security and privacy issues prevalent in FL, underscoring the vulnerability of communication links and the potential for cyber threats. We delve into various defensive strategies to mitigate these risks, explore the applications of FL across different sectors, and propose research directions. We identify the intricate security challenges that arise within the FL frameworks, aiming to contribute to the development of secure and efficient FL systems.
Donald Trumps in the Virtual Polls: Simulating and Predicting Public Opinions in Surveys Using Large Language Models
2024-11-03
Shapeng Jiang, Lijia Wei, Chen Zhang
In recent years, large language models (LLMs) have attracted attention due to their ability to generate human-like text. As surveys and opinion polls remain key tools for gauging public attitudes, there is increasing interest in assessing whether LLMs can accurately replicate human responses. This study examines the potential of LLMs, specifically ChatGPT-4o, to replicate human responses in large-scale surveys and to predict election outcomes based on demographic data. Employing data from the World Values Survey (WVS) and the American National Election Studies (ANES), we assess the LLM's performance in two key tasks: simulating human responses and forecasting U.S. election results. In simulations, the LLM was tasked with generating synthetic responses for various socio-cultural and trust-related questions, demonstrating notable alignment with human response patterns across U.S.-China samples, though with some limitations on value-sensitive topics. In prediction tasks, the LLM was used to simulate voting behavior in past U.S. elections and predict the 2024 election outcome. Our findings show that the LLM replicates cultural differences effectively, exhibits in-sample predictive validity, and provides plausible out-of-sample forecasts, suggesting potential as a cost-effective supplement for survey-based research.
An Integer-N Frequency Synthesizer for Flexible On-Chip Clock Generation
2024-11-03
Soumyajit Mandal, Piotr Maj, Grzegorz W. Deptuch
A low-power integer-N frequency synthesizer for flexible on-chip clock generation has been designed in 65 nm CMOS technology. The circuit can be programmed to generate two independent low-jitter clocks between 30 MHz and 3 GHz that are locked a 10-50 MHz reference input. The design uses a phase-locked loop (PLL) with a dual-tuned LC voltage-controlled oscillator (VCO), programmable feedback divider, and dual output dividers. The total power consumption from 1.2 V and 0.8 V supplies is 4.0 mW. Experimental results confirm the functionality of the proposed synthesizer over a wide range of output frequencies.
A new sample of super-slowly rotating Ap (ssrAp) stars from the Zwicky Transient Facility survey
2024-11-03
S. Hümmerich, K. Bernhard, E. Paunzen
The magnetic chemically peculiar Ap stars exhibit an extreme spread of rotational velocities, the reason of which is not well understood. Ap stars with rotational periods of 50 days or longer are know as super-slowly rotating Ap (ssrAp) stars. Photometrically variable Ap stars are commonly termed alpha2 Canum Venaticorum (ACV) variables. Our study aims at enlarging the sample of known ssrAp stars using data from the Zwicky Transient Facility (ZTF) survey to enable more robust and significant statistical studies of these objects. Using selection criteria based on the known characteristics of ACV variables, candidate stars were gleaned from the ZTF catalogues of periodic and suspected variable stars and from ZTF raw data. ssrAp stars were identified from this list via their characteristic photometric properties, Delta a photometry, and spectral classification. The final sample consists of 70 new ssrAp stars, which mostly exhibit rotational periods between 50 and 200 days. The object with the longest period has a rotational period of 2551.7 days. We present astrophysical parameters and a Hertzsprung-Russell diagram for the complete sample of known ssrAp stars. With very few exceptions, the ssrAp stars are grouped in the middle of the main sequence with ages in excess of 150 Myr. ZTF J021309.72+582827.7 was identified as a possible binary star harbouring an Ap star and a cool component, possibly shrouded in dust. With our study, we enlarge the sample of known ssrAp stars by about 150%, paving the way for more in-depth statistical studies.
A New Limit on the Graviton Mass from the Convergence Scale of the CMB Dipole
2024-11-03
Abraham Loeb
The clustering dipole in the 2MASS galaxy survey converges on a scale of ~400Mpc to the local peculiar velocity inferred from the Cosmic-Microwave-Background dipole. I show that this limits the graviton mass in Yukawa theories of gravity to less than 5x10^{-32}eV. The new limit is 2.5x10^8 times tighter than the latest constraint from gravitational waves detected by the LIGO-Virgo-KAGRA collaboration.
Upstroke wing clapping in bats and bat-inspired robots improves both lift generation and power economy
2024-11-03
Xiaozhou Fan, Alberto Bortoni, Siyang Hao, Sharon Swartz, Kenneth Breuer
Wing articulation is critical for efficient flight of bird- and bat-sized animals. Inspired by the flight of $\textit{Cynopterus brachyotis}$, the lesser short-nosed fruit bat, we built a two-degree-of-freedom flapping wing platform with variable wing folding capability. In late upstroke, the wings "clap" and produce an air jet that significantly increases lift production, with a positive peak matched to that produced in downstroke. Though ventral clapping has been observed in avian flight, potential aerodynamic benefit of this behavior has yet to be rigorously assessed. We used multiple approaches -- quasi-steady modeling, direct force/power measurement, and PIV experiments in a wind tunnel -- to understand critical aspects of lift/power variation in relation to wing folding magnitude over Strouhal numbers between $St = 0.2 - 0.4$. While lift increases monotonically with folding amplitude in that range, power economy (ratio of lift/power) is more nuanced. At $St = 0.2 - 0.3$, it increase with wing folding amplitude monotonically. At $St = 0.3 - 0.4$, it features two maxima -- one at medium folding amplitude ($\sim 30^\circ$), and the other at maximum folding. These findings illuminate two strategies available to flapping wing animals and robots -- symmetry-breaking lift augmentation and appendage-based jet propulsion.
Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision
2024-11-03
Xiangzhong Luo, Di Liu, Hao Kong, Shuo Huai, Hui Chen, Guochu Xiong, Weichen Liu
Deep neural networks (DNNs) have recently achieved impressive success across a wide range of real-world vision and language processing tasks, spanning from image classification to many other downstream vision tasks, such as object detection, tracking, and segmentation. However, previous well-established DNNs, despite being able to maintain superior accuracy, have also been evolving to be deeper and wider and thus inevitably necessitate prohibitive computational resources for both training and inference. This trend further enlarges the computational gap between computation-intensive DNNs and resource-constrained embedded computing systems, making it challenging to deploy powerful DNNs upon real-world embedded computing systems towards ubiquitous embedded intelligence. To alleviate the above computational gap and enable ubiquitous embedded intelligence, we, in this survey, focus on discussing recent efficient deep learning infrastructures for embedded computing systems, spanning from training to inference, from manual to automated, from convolutional neural networks to transformers, from transformers to vision transformers, from vision models to large language models, from software to hardware, and from algorithms to applications. Specifically, we discuss recent efficient deep learning infrastructures for embedded computing systems from the lens of (1) efficient manual network design for embedded computing systems, (2) efficient automated network design for embedded computing systems, (3) efficient network compression for embedded computing systems, (4) efficient on-device learning for embedded computing systems, (5) efficient large language models for embedded computing systems, (6) efficient deep learning software and hardware for embedded computing systems, and (7) efficient intelligent applications for embedded computing systems.
AURA: Amplifying Understanding, Resilience, and Awareness for Responsible AI Content Work
2024-11-03
Alice Qian Zhang, Judith Amores, Mary L. Gray, Mary Czerwinski, Jina Suh
Behind the scenes of maintaining the safety of technology products from harmful and illegal digital content lies unrecognized human labor. The recent rise in the use of generative AI technologies and the accelerating demands to meet responsible AI (RAI) aims necessitates an increased focus on the labor behind such efforts in the age of AI. This study investigates the nature and challenges of content work that supports RAI efforts, or "RAI content work," that span content moderation, data labeling, and red teaming -- through the lived experiences of content workers. We conduct a formative survey and semi-structured interview studies to develop a conceptualization of RAI content work and a subsequent framework of recommendations for providing holistic support for content workers. We validate our recommendations through a series of workshops with content workers and derive considerations for and examples of implementing such recommendations. We discuss how our framework may guide future innovation to support the well-being and professional development of the RAI content workforce.
Recent advancements in computational chemistry have leveraged the power of trans-former-based language models, such as MoLFormer, pre-trained using a vast amount of simplified molecular-input line-entry system (SMILES) sequences, to understand and predict molecular properties and activities, a critical step in fields like drug discovery and materials science. To further improve performance, researchers have introduced graph neural networks with graph-based molecular representations, such as GEM, incorporating the topology, geometry, 2D or even 3D structures of molecules into pre-training. While most of molecular graphs in existing studies were automatically converted from SMILES sequences, it is to assume that transformer-based language models might be able to implicitly learn structure-aware representations from SMILES sequences. In this paper, we propose \ours{} -- a SMILES-based \underline{\em M}olecular \underline{\em L}anguage \underline{\em M}odel, which randomly masking SMILES subsequences corresponding to specific molecular \underline{\em F}unctional \underline{\em G}roups to incorporate structure information of atoms during the pre-training phase. This technique aims to compel the model to better infer molecular structures and properties, thus enhancing its predictive capabilities. Extensive experimental evaluations across 11 benchmark classification and regression tasks in the chemical domain demonstrate the robustness and superiority of \ours{}. Our findings reveal that \ours{} outperforms existing pre-training models, either based on SMILES or graphs, in 9 out of the 11 downstream tasks, ranking as a close second in the remaining ones.
An Exploration of Higher Education Course Evaluation by Large Language Models
2024-11-03
Bo Yuan, Jiazi Hu
Course evaluation is a critical component in higher education pedagogy. It not only serves to identify limitations in existing course designs and provide a basis for curricular innovation, but also to offer quantitative insights for university administrative decision-making. Traditional evaluation methods, primarily comprising student surveys, instructor self-assessments, and expert reviews, often encounter challenges, including inherent subjectivity, feedback delays, inefficiencies, and limitations in addressing innovative teaching approaches. Recent advancements in large language models (LLMs) within artificial intelligence (AI) present promising new avenues for enhancing course evaluation processes. This study explores the application of LLMs in automated course evaluation from multiple perspectives and conducts rigorous experiments across 100 courses at a major university in China. The findings indicate that: (1) LLMs can be an effective tool for course evaluation; (2) their effectiveness is contingent upon appropriate fine-tuning and prompt engineering; and (3) LLM-generated evaluation results demonstrate a notable level of rationality and interpretability.
This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR). It examines the evolution from traditional CPR methods to innovative ML-driven approaches, highlighting the impact of predictive modeling, AI-enhanced devices, and real-time data analysis in improving resuscitation outcomes. The paper provides a comprehensive overview, classification, and critical analysis of current applications, challenges, and future directions in this emerging field.
High-performance automated abstract screening with large language model ensembles
2024-11-03
Rohan Sanghera, Arun James Thirunavukarasu, Marc El Khoury, Jessica O'Logbon, Yuqing Chen, Archie Watt, Mustafa Mahmood, Hamid Butt, George Nishimura, Andrew Soltan
Large language models (LLMs) excel in tasks requiring processing and interpretation of input text. Abstract screening is a labour-intensive component of systematic review involving repetitive application of inclusion and exclusion criteria on a large volume of studies identified by a literature search. Here, LLMs (GPT-3.5 Turbo, GPT-4 Turbo, GPT-4o, Llama 3 70B, Gemini 1.5 Pro, and Claude Sonnet 3.5) were trialled on systematic reviews in a full issue of the Cochrane Library to evaluate their accuracy in zero-shot binary classification for abstract screening. Trials over a subset of 800 records identified optimal prompting strategies and demonstrated superior performance of LLMs to human researchers in terms of sensitivity (LLMmax = 1.000, humanmax = 0.775), precision (LLMmax = 0.927, humanmax = 0.911), and balanced accuracy (LLMmax = 0.904, humanmax = 0.865). The best performing LLM-prompt combinations were trialled across every replicated search result (n = 119,691), and exhibited consistent sensitivity (range 0.756-1.000) but diminished precision (range 0.004-0.096). 66 LLM-human and LLM-LLM ensembles exhibited perfect sensitivity with a maximal precision of 0.458, with less observed performance drop in larger trials. Significant variation in performance was observed between reviews, highlighting the importance of domain-specific validation before deployment. LLMs may reduce the human labour cost of systematic review with maintained or improved accuracy and sensitivity. Systematic review is the foundation of evidence-based medicine, and LLMs can contribute to increasing the efficiency and quality of this mode of research.
Exploring the MBTI distribution among Chinese undergraduate physics students: the influence of family income on career trajectories
2024-11-02
Songyang Bai, Weitian Chen, Zihan Gao
This study investigated the distribution of MBTI personality types among physics undergraduates at Zhejiang University and analyzed their career aspirations, family income and mental health status. A comprehensive survey of 68 questions, including 52 multiple-choice questions and 16 short-answer questions, assessed various personality traits and their correlation with academic intent and emotional state. The results showed that INTJ and INTP personality types showed the most significant tendency to pursue academic research. They usually come from middle and above-class backgrounds. Notably, these two personality types accounted for about 41\% of the total surveyed population, while NT types combined accounted for about 58\%. This indicates that although NT personality is more suitable for academic research, students with introverted tendencies are more suitable. Research has further shown that people with ISTJ personalities often exhibit unexpectedly strong academic interests. In addition, individuals who aspire to enter academia generally maintain a stable state of mind, as evidenced by their consistent work efficiency in the face of personal challenges. Interestingly, although some aspiring academics come from financially stable families, most have encountered financial constraints. These results contribute to a deeper understanding of how personality traits and socioeconomic factors influence the academic career paths of physics students.
New Cold Subdwarf Discoveries from Backyard Worlds and a Metallicity Classification System for T Subdwarfs
2024-11-02
Adam J. Burgasser, Adam C. Schneider, Aaron M. Meisner, Dan Caselden, Chih-Chun Hsu, Roman Gerasimov, Christian Aganze, Emma Softich, Preethi Karpoor, Christopher A. Theissen, Hunter Brooks, Thomas P. Bickle, Jonathan Gagné, Étienne Artigau, Michaël Marsset, Austin Rothermich, Jacqueline K. Faherty, J. Davy Kirkpatrick, Marc J. Kuchner, Nikolaj Stevnbak Andersen, Paul Beaulieu, Guillaume Colin, Jean Marc Gantier, Leopold Gramaize, Les Hamlet, Ken Hinckley, Martin Kabatnik, Frank Kiwy, David W. Martin, Diego H. Massat, William Pendrill, Arttu Sainio, Jörg Schümann, Melina Thévenot, Jim Walla, Zbigniew Wędracki, the Backyard Worlds, :, Planet 9 Collaboration
We report the results of a spectroscopic survey of candidate T subdwarfs identified by the Backyard Worlds: Planet 9 program. Near-infrared spectra of 31 sources with red $J-W2$ colors and large $J$-band reduced proper motions show varying signatures of subsolar metallicity, including strong collision-induced H$_2$ absorption, obscured methane and water features, and weak K I absorption. These metallicity signatures are supported by spectral model fits and 3D velocities, indicating thick disk and halo population membership for several sources. We identify three new metal-poor T subdwarfs ([M/H] $\lesssim$ $-$0.5), CWISE J062316.19+071505.6, WISEA J152443.14$-$262001.8, and CWISE J211250.11-052925.2; and 19 new "mild" subdwarfs with modest metal deficiency ([M/H] $\lesssim$ $-$0.25). We also identify three metal-rich brown dwarfs with thick disk kinematics. We provide kinematic evidence that the extreme L subdwarf 2MASS J053253.46+824646.5 and the mild T subdwarf CWISE J113010.07+313944.7 may be part of the Thamnos population, while the T subdwarf CWISE J155349.96+693355.2 may be part of the Helmi stream. We define a metallicity classification system for T dwarfs that adds mild subdwarfs (d/sdT), subdwarfs (sdT), and extreme subdwarfs (esdT) to the existing dwarf sequence. We also define a metallicity spectral index that correlates with metallicities inferred from spectral model fits and iron abundances from stellar primaries of benchmark T dwarf companions. This expansion of the T dwarf classification system supports investigations of ancient, metal-poor brown dwarfs now being uncovered in deep imaging and spectroscopic surveys.
Artificial Intelligence Driven Course Generation: A Case Study Using ChatGPT
2024-11-02
Djaber Rouabhia
This study explores Artificial Intelligence use, specifically ChatGPT, in creating educational content. The study aims to elaborate on using ChatGPT to create course materials. The main objective is to assess the efficiency, quality, and impact of AI-driven course generation, and to create a Multimedia Databases course as a case study. The study highlights the potential of AI to revolutionize educational content creation, making it more accessible, personalized, and efficient. The course content was generated in less than one day through iterative methods, using prompts for translation, content expansion, practical examples, assignments, supplementary materials, and LaTeX formatting. Each part was verified immediately after generation to ensure accuracy. Post-generation analysis with Detectia and Turnitin showed similarity rates of 8.7% and 13%, indicating high originality. Experts and university committees reviewed and approved the course, with English university teachers praising its language quality. ChatGPT also created a well-structured and diversified exam for the module. Key findings reveal significant time efficiency, comprehensive content coverage, and high flexibility. The study underscores AI's transformative potential in education, addressing challenges related to data privacy, technology dependence, content accuracy, and algorithmic biases. The conclusions emphasize the need for collaboration between educators, policymakers, and technology developers to harness AI's benefits in education fully.
Transformer-Based Astronomical Time Series Model with Uncertainty Estimation for Detecting Misclassified Instances
2024-11-02
Martina Cádiz-Leyton, Guillermo Cabrera-Vives, Pavlos Protopapas, Daniel Moreno-Cartagena, Cristobal Donoso-Oliva
In this work, we present a framework for estimating and evaluating uncertainty in deep-attention-based classifiers for light curves for variable stars. We implemented three techniques, Deep Ensembles (DEs), Monte Carlo Dropout (MCD) and Hierarchical Stochastic Attention (HSA) and evaluated models trained on three astronomical surveys. Our results demonstrate that MCD and HSA offers a competitive and computationally less expensive alternative to DE, allowing the training of transformers with the ability to estimate uncertainties for large-scale light curve datasets. We conclude that the quality of the uncertainty estimation is evaluated using the ROC AUC metric.
Condensation and the Volatility Trend of the Earth
2024-11-02
Katharina Lodders, Bruce Fegley, Klaus Mezger, Denton Ebel
This article describes condensation of the elements and use of condensation temperatures (Tcond) to interpret the volatility trend of the Earth. Major points are: (1) a listing of updated 50% Tcond for all natural elements and Pu at 1e-2 to 1e-8 bar pressure for solar composition matter. (2) The Tcond are mainly controlled by the Gibbs energy of condensation reactions and also by the Gibbs energy of ideal mixing if elements (compounds) condense in a solution. The Gibbs energy change of non-ideal solution (activity coefficients not equal to 1) is a secondary effect. (3) The theoretically correct relationship between Tcond and fraction condensed is derived from mass balance and chemical thermodynamic considerations. (4) The maximum amount of element condensed per 1/T, is at the inflection point in the logistic (sigmoid) curve for an element, which is also at (or close to) the 50% Tcond. (5) Plots of normalized elemental abundances versus 50% Tcond (volatility trends) are qualitative indicators of elemental fractionations due to volatility. (6) Volatility trend plots for average elemental abundances in CM, CO, CV, CR, H, L, LL, EH, EL chondrites show different trends for moderately and highly volatile elements, which may be linear, curved, a step function, or plateau. Comparison of three abundance sets for CM and CV chondrites shows trends depend on which elements are plotted, which data sources are used, and which temperature range is considered. (7) Proposed mechanisms for volatile element depletion in carbonaceous chondrites and the Earth are reviewed. (8) Possible implications of volatile element abundances in the bulk silicate Earth are discussed.
Can Humans Oversee Agents to Prevent Privacy Leakage? A Study on Privacy Awareness, Preferences, and Trust in Language Model Agents
2024-11-02
Zhiping Zhang, Bingcan Guo, Tianshi Li
Language model (LM) agents that act on users' behalf for personal tasks can boost productivity, but are also susceptible to unintended privacy leakage risks. We present the first study on people's capacity to oversee the privacy implications of the LM agents. By conducting a task-based survey (N=300), we investigate how people react to and assess the response generated by LM agents for asynchronous interpersonal communication tasks, compared with a response they wrote. We found that people may favor the agent response with more privacy leakage over the response they drafted or consider both good, leading to an increased harmful disclosure from 15.7% to 55.0%. We further uncovered distinct patterns of privacy behaviors, attitudes, and preferences, and the nuanced interactions between privacy considerations and other factors. Our findings shed light on designing agentic systems that enable privacy-preserving interactions and achieve bidirectional alignment on privacy preferences to help users calibrate trust.
Quark jet evolution: from classical to quantum simulation
2024-11-02
Meijian Li
Quark jet provides one of the best ways to probe the matter produced in ultrarelativistic high-energy collisions, from cold nuclear matter to the hot quark-gluon plasma. In this proceeding paper, we review a series of works on the development of nonperturbative computational framework of in-medium quark jet evolution, from classical to quantum simulation. The application of the time-dependent Basis Light-front Quantization (tBLFQ), a nonperturbative computational approach based on light-front Hamiltonian formalism, to in-medium jet evolution enables a fully quantum treatment to the jet state on the amplitude level. Based on the tBLFQ framework, with applying novel quantum technologies, we have constructed a digital quantum circuit that tracks the evolution of a multi-particle jet probe within a stochastic color background field. With the obtained simulation results, we extracted the medium induced modification in terms of jet momentum broadening and gluon production. These studies provide a baseline for future works of in-medium jet evolution using quantum computers.
Multilevel Monte Carlo methods for simulating forward-backward stochastic differential equations using neural networks
2024-11-02
Oliver Sheridan-Methven
We introduce forward-backward stochastic differential equations, highlighting the connection between solutions of these and solutions of partial differential equations, related by the Feynman-Kac theorem. We review the technique of approximating solutions to high dimensional partial differential equations using neural networks, and similarly approximate solutions of stochastic differential equations using multilevel Monte Carlo. Connecting the multilevel Monte Carlo method with the neural network framework using the setup established by E et al. and Raissi, we replicate many of their empirical results, and provide novel numerical analyses to produce strong error bounds for the specific framework of Raissi. Our results bound the overall strong error in terms of the maximum of the discretisation error and the neural network's approximation error. Our analyses are pivotal for applications of multilevel Monte Carlo, for which we propose suitable frameworks to exploit the variance structures of the multilevel estimators we elucidate. Also, focusing on the loss function advocated by Raissi, we expose the limitations of this, highlighting and quantifying its bias and variance. Lastly, we propose various avenues of further research which we anticipate should offer significant insight and speed improvements.
A High-Resolution Far-Infrared Survey to Probe Black Hole-Galaxy Co-Evolution
2024-11-02
Matteo Bonato, David Leisawitz, Gianfranco De Zotti, Laura Sommovigo, Irene Shivaei, C. Megan Urry, Duncan Farrah, Locke Spencer, Berke V. Ricketti, Hannah Rana, Susanne Aalto, David B. Sanders, Lee G. Mundy
Far-infrared (FIR) surveys are critical to probing the co-evolution of black holes and galaxies, since of order half the light from accreting black holes and active star formation is emitted in the rest-frame infrared over $0.5\lesssim z \lesssim 10$. For deep fields with areas of 1 deg$^2$ or less, like the legacy surveys GOODS, COSMOS, and CANDELS, source crowding means that sub-arcsecond resolution is essential. In this paper we show with a simulation of the FIR sky that measurements made with a small telescope (2 m) at low angular resolution yield biased results, and we demonstrate the scientific value of a space mission that would offer sub-arcsecond resolution. We envisage a facility that would provide high-resolution imaging and spectroscopy over the wavelength range $25-400\,\mu$m, and we present predictions for an extragalactic survey covering $0.5\,\hbox{deg}^2$. Such a survey is expected to detect tens of thousands of star-forming galaxies and thousands of Active Galactic Nuclei (AGN), in multiple FIR lines (e.g. [CII], [OI], [CI]) and continuum. At the longest wavelengths (200-400$\,\mu$m), it would probe beyond the reionization epoch, up to $z\sim 7$-8. A combination of spectral resolution, line sensitivity, and broad spectral coverage would allow us to learn about the physical conditions (temperature, density, metallicity) characterizing the interstellar medium of galaxies over the past $\sim 12$ billion years and to investigate galaxy-AGN co-evolution.
Class-specific feature selection for classification explainability
2024-11-02
Jesus S. Aguilar-Ruiz
Feature Selection techniques aim at finding a relevant subset of features that perform equally or better than the original set of features at explaining the behavior of data. Typically, features are extracted from feature ranking or subset selection techniques, and the performance is measured by classification or regression tasks. However, while selected features may not have equal importance for the task, they do have equal importance for each class. This work first introduces a comprehensive review of the concept of class-specific, with a focus on feature selection and classification. The fundamental idea of the class-specific concept resides in the understanding that the significance of each feature can vary from one class to another. This contrasts with the traditional class-independent approach, which evaluates the importance of attributes collectively for all classes. For example, in tumor prediction scenarios, each type of tumor may be associated with a distinct subset of relevant features. These features possess significant discriminatory power, enabling the differentiation of one tumor type from others. This class-specific perspective offers a more effective approach to classification tasks by recognizing and leveraging the unique characteristics of each class. Secondly, classification schemes from one-versus-all and one-versus-each strategies are described, and a novel deep one-versus-each strategy is introduced, which offers advantages from the point of view of explainability (feature selection) and decomposability (classification). Thirdly, a novel class-specific relevance matrix is presented, from which some more sophisticated classification schemes can be derived, such as the three-layer class-specific scheme. The potential for further advancements is wide and will open new horizons for exploring novel research directions in multiclass hyperdimensional contexts.
LEARNER: Learning Granular Labels from Coarse Labels using Contrastive Learning
2024-11-02
Gautam Gare, Jana Armouti, Nikhil Madaan, Rohan Panda, Tom Fox, Laura Hutchins, Amita Krishnan, Ricardo Rodriguez, Bennett DeBoisblanc, Deva Ramanan, John Galeotti
A crucial question in active patient care is determining if a treatment is having the desired effect, especially when changes are subtle over short periods. We propose using inter-patient data to train models that can learn to detect these fine-grained changes within a single patient. Specifically, can a model trained on multi-patient scans predict subtle changes in an individual patient's scans? Recent years have seen increasing use of deep learning (DL) in predicting diseases using biomedical imaging, such as predicting COVID-19 severity using lung ultrasound (LUS) data. While extensive literature exists on successful applications of DL systems when well-annotated large-scale datasets are available, it is quite difficult to collect a large corpus of personalized datasets for an individual. In this work, we investigate the ability of recent computer vision models to learn fine-grained differences while being trained on data showing larger differences. We evaluate on an in-house LUS dataset and a public ADNI brain MRI dataset. We find that models pre-trained on clips from multiple patients can better predict fine-grained differences in scans from a single patient by employing contrastive learning.
LoRA-Contextualizing Adaptation of Large Multimodal Models for Long Document Understanding
2024-11-02
Jian Chen, Ruiyi Zhang, Yufan Zhou, Tong Yu, Franck Dernoncourt, Jiuxiang Gu, Ryan A. Rossi, Changyou Chen, Tong Sun
Large multimodal models (LMMs) have recently shown great progress in text-rich image understanding, yet they still struggle with complex, multi-page, visually-rich documents. Traditional methods using document parsers for retrieval-augmented generation suffer from performance and efficiency limitations, while directly presenting all pages to LMMs leads to inefficiencies, especially with lengthy documents. In this work, we present a novel framework named LoRA-Contextualizing Adaptation of Large multimodal models (LoCAL), which broadens the capabilities of any LMM to support long-document understanding. We demonstrate that LMMs can effectively serve as multimodal retrievers, fetching relevant pages to answer user questions based on these pages. LoCAL is implemented with two specific LMM adapters: one for evidence page retrieval and another for question answering. Empirical results show state-of-the-art performance on public benchmarks, demonstrating the effectiveness of LoCAL.
Artificial Intelligence for Microbiology and Microbiome Research
2024-11-02
Xu-Wen Wang, Tong Wang, Yang-Yu Liu
Advancements in artificial intelligence (AI) have transformed many scientific fields, with microbiology and microbiome research now experiencing significant breakthroughs through machine learning and deep learning applications. This review provides a comprehensive overview of AI-driven approaches tailored for microbiology and microbiome studies, emphasizing both technical advancements and biological insights. We begin with an introduction to foundational AI techniques, including primary machine learning paradigms and various deep learning architectures, and offer guidance on choosing between machine learning and deep learning methods based on specific research goals. The primary section on application scenarios spans diverse research areas, from taxonomic profiling, functional annotation & prediction, microbe-X interactions, microbial ecology, metabolic modeling, precision nutrition, clinical microbiology, to prevention & therapeutics. Finally, we discuss challenges unique to this field, including the balance between interpretability and complexity, the "small n, large p" problem, and the critical need for standardized benchmarking datasets to validate and compare models. Together, this review underscores AI's transformative role in microbiology and microbiome research, paving the way for innovative methodologies and applications that enhance our understanding of microbial life and its impact on our planet and our health.
Fabry-Pérot open resonant cavities for measuring the dielectric parameters of mm-wave optical materials
2024-11-01
Brodi D. Elwood, Paul K. Grimes, John Kovac, Miranda Eiben, Grant Meiners
As millimeter-wave cosmology experiments refine their optical chains, precisely characterizing their optical materials under cryogenic conditions becomes increasingly important. For instance, as the aperture sizes and bandwidths of millimeter-wave receivers increase, the design of antireflection coatings becomes progressively more constrained by an accurate measure of material optical properties in order to achieve forecasted performance. Likewise, understanding dielectric and scattering losses is relevant to photon noise modeling in presently-deploying receivers such as BICEP Array and especially to future experiments such as CMB-S4. Additionally, the design of refractive elements such as lenses necessitates an accurate measure of the refractive index. High quality factor Fabry-P\'{e}rot open resonant cavities provide an elegant means for measuring these optical properties. Employing a hemispherical resonator that is compatible with a quick-turnaround 4 Kelvin cryostat, we can measure the dielectric and scattering losses of low-loss materials at both ambient and cryogenic temperatures. We review the design, characterization, and metrological applications of quasioptical cavities commissioned for measuring the dielectric materials in the BICEP3 (95 GHz) and BICEP Array mid-frequency (150 GHz) optics. We also discuss the efforts to improve the finesse of said cavities, for better resolution of degenerate higher order modes, which can provide stronger constraints on cavity parameters and sample material thickness.
Exploratory Models of Human-AI Teams: Leveraging Human Digital Twins to Investigate Trust Development
2024-11-01
Daniel Nguyen, Myke C. Cohen, Hsien-Te Kao, Grant Engberson, Louis Penafiel, Spencer Lynch, Svitlana Volkova
As human-agent teaming (HAT) research continues to grow, computational methods for modeling HAT behaviors and measuring HAT effectiveness also continue to develop. One rising method involves the use of human digital twins (HDT) to approximate human behaviors and socio-emotional-cognitive reactions to AI-driven agent team members. In this paper, we address three research questions relating to the use of digital twins for modeling trust in HATs. First, to address the question of how we can appropriately model and operationalize HAT trust through HDT HAT experiments, we conducted causal analytics of team communication data to understand the impact of empathy, socio-cognitive, and emotional constructs on trust formation. Additionally, we reflect on the current state of the HAT trust science to discuss characteristics of HAT trust that must be replicable by a HDT such as individual differences in trust tendencies, emergent trust patterns, and appropriate measurement of these characteristics over time. Second, to address the question of how valid measures of HDT trust are for approximating human trust in HATs, we discuss the properties of HDT trust: self-report measures, interaction-based measures, and compliance type behavioral measures. Additionally, we share results of preliminary simulations comparing different LLM models for generating HDT communications and analyze their ability to replicate human-like trust dynamics. Third, to address how HAT experimental manipulations will extend to human digital twin studies, we share experimental design focusing on propensity to trust for HDTs vs. transparency and competency-based trust for AI agents.
AGISim, An Open Source Airborne Gimbal Mounted IMU Signal Simulator Considering Flight Dynamics Model
2024-11-01
Alireza Kazemi, Reza Rohani Sarvestani
In this work we present more comprehensive evaluations on our airborne Gimbal mounted inertial measurement unit (IMU) signal simulator which also considers flight dynamic model (FDM). A flexible IMU signal simulator is an enabling tool in design, development, improvement, test and verification of aided inertial navigation systems (INS). Efforts by other researchers had been concentrated on simulation of the strapdown INS (SINS) with the IMU rigidly attached to the moving body frame. However custom airborne surveying/mapping applications that need pointing and stabilizing camera or any other surveying sensor, require mounting the IMU beside the sensor on a Gimbal onboard the airframe. Hence the proposed Gimbal mounted IMU signal simulator is of interest whilst itself requires further analysis and verifications. Extended evaluation results in terms of both unit tests and functional/integration tests (using aided inertial navigation algorithms with variable/dynamic lever arms), verifies the simulator and its applicability for the mentioned tasks. We have further packaged and published our MATLAB code for the proposed simulator as an open source GitHub repository.
A lightweight Convolutional Neural Network based on U shape structure and Attention Mechanism for Anterior Mediastinum Segmentation
2024-11-01
Sina Soleimani-Fard, Won Gi Jeong, Francis Ferri Ripalda, Hasti Sasani, Younhee Choi, S Deiva, Gong Yong Jin, Seok-bum Ko
To automatically detect Anterior Mediastinum Lesions (AMLs) in the Anterior Mediastinum (AM), the primary requirement will be an automatic segmentation model specifically designed for the AM. The prevalence of AML is extremely low, making it challenging to conduct screening research similar to lung cancer screening. Retrospectively reviewing chest CT scans over a specific period to investigate the prevalence of AML requires substantial time. Therefore, developing an Artificial Intelligence (AI) model to find location of AM helps radiologist to enhance their ability to manage workloads and improve diagnostic accuracy for AMLs. In this paper, we introduce a U-shaped structure network to segment AM. Two attention mechanisms were used for maintaining long-range dependencies and localization. In order to have the potential of Multi-Head Self-Attention (MHSA) and a lightweight network, we designed a parallel MHSA named Wide-MHSA (W-MHSA). Maintaining long-range dependencies is crucial for segmentation when we upsample feature maps. Therefore, we designed a Dilated Depth-Wise Parallel Path connection (DDWPP) for this purpose. In order to design a lightweight architecture, we introduced an expanding convolution block and combine it with the proposed W-MHSA for feature extraction in the encoder part of the proposed U-shaped network. The proposed network was trained on 2775 AM cases, which obtained an average Dice Similarity Coefficient (DSC) of 87.83%, mean Intersection over Union (IoU) of 79.16%, and Sensitivity of 89.60%. Our proposed architecture exhibited superior segmentation performance compared to the most advanced segmentation networks, such as Trans Unet, Attention Unet, Res Unet, and Res Unet++.
2024-11-01
Larissa Barbosa, Sávio Freire, Rita S. P. Maciel, Manoel Mendonça, Marcos Kalinowski, Zadia Codabux, Rodrigo Spínola
[Context and Motivation] Several studies have investigated attributes of great software practitioners. However, the investigation of such attributes is still missing in Requirements Engineering (RE). The current knowledge on attributes of great software practitioners might not be easily translated to the context of RE because its activities are, usually, less technical and more human-centered than other software engineering activities. [Question/Problem] This work aims to investigate which are the attributes of great requirements engineers, the relationship between them, and strategies that can be employed to obtain these attributes. We follow a method composed of a survey with 18 practitioners and follow up interviews with 11 of them. [Principal Ideas/Results] Investigative ability in talking to stakeholders, judicious, and understand the business are the most commonly mentioned attributes amongst the set of 22 attributes identified, which were grouped into four categories. We also found 38 strategies to improve RE skills. Examples are training, talking to all stakeholders, and acquiring domain knowledge. [Contribution] The attributes, their categories, and relationships are organized into a map. The relations between attributes and strategies are represented in a Sankey diagram. Software practitioners can use our findings to improve their understanding about the role and responsibilities of requirements engineers.
Multiplex Imaging Analysis in Pathology: a Comprehensive Review on Analytical Approaches and Digital Toolkits
2024-11-01
Mohamed Omar, Giuseppe Nicolo Fanelli, Fabio Socciarelli, Varun Ullanat, Sreekar Reddy Puchala, James Wen, Alex Chowdhury, Itzel Valencia, Cristian Scatena, Luigi Marchionni, Renato Umeton, Massimo Loda
Conventional histopathology has long been essential for disease diagnosis, relying on visual inspection of tissue sections. Immunohistochemistry aids in detecting specific biomarkers but is limited by its single-marker approach, restricting its ability to capture the full tissue environment. The advent of multiplexed imaging technologies, like multiplexed immunofluorescence and spatial transcriptomics, allows for simultaneous visualization of multiple biomarkers in a single section, enhancing morphological data with molecular and spatial information. This provides a more comprehensive view of the tissue microenvironment, cellular interactions, and disease mechanisms - crucial for understanding disease progression, prognosis, and treatment response. However, the extensive data from multiplexed imaging necessitates sophisticated computational methods for preprocessing, segmentation, feature extraction, and spatial analysis. These tools are vital for managing large, multidimensional datasets, converting raw imaging data into actionable insights. By automating labor-intensive tasks and enhancing reproducibility and accuracy, computational tools are pivotal in diagnostics and research. This review explores the current landscape of multiplexed imaging in pathology, detailing workflows and key technologies like PathML, an AI-powered platform that streamlines image analysis, making complex dataset interpretation accessible for clinical and research settings.
A Wideband Chemical Survey of Massive Star-forming Regions at Subarcsecond Resolution with the Submillimeter Array
2024-11-01
Charles J. Law, Qizhou Zhang, Arielle C. Frommer, Karin I. Öberg, Roberto Galván-Madrid, Eric Keto, Hauyu Baobab Liu, Paul T. P. Ho, Andrés F. Izquierdo, L. Ilsedore Cleeves
Massive star-forming regions exhibit a rich chemistry with complex gas distributions, especially on small scales. While surveys have yielded constraints on typical gas conditions, they often have coarse spatial resolution and limited bandwidths. Thus, to establish an interpretative framework for these efforts, detailed observations that simultaneously provide high sensitivity, spatial resolution, and large bandwidths for a subset of diverse sources are needed. Here, we present wideband (32 GHz) Submillimeter Array observations of four high-mass star-forming regions (G28.20-0.05, G20.08-0.14 N, G35.58-0.03, W33 Main) at subarcsecond resolution, where we detect and spatially-resolve 100s of lines from over 60 molecules, including many complex organic molecules (COMs). The chemical richness of our sample is consistent with an evolutionary sequence from the line-rich hot cores and HC HII regions of G28.20-0.05 and G20.08-0.14 N to the more chemically-modest UC HII regions in G35.58-0.03, followed by the molecule-poor HII region W33 Main. We detect lines across a range of excitation conditions (Eu=20 to >800 K) and from numerous isotopologues, which enables robust estimates of gas properties. We derive nearly constant COM column density ratios that agree with literature values in other low- and high-mass protostellar cores, supporting the idea that COM abundances are set during the pre-stellar phase. In all regions, we identify spatial offsets among different molecular families, due to a combination of source physical structure and chemistry. In particular, we find potential evidence of carbon grain sublimation in G28.20-0.05 and identify an elemental oxygen gradient and rich sulfur-chemistry in G35.58-0.03. Overall, these results demonstrate that the SMA's wide bandwidth is a powerful tool to untangle the complex molecular gas structures associated with massive star formation.
Hierarchical Transformer for Electrocardiogram Diagnosis
2024-11-01
Xiaoya Tang, Jake Berquist, Benjamin A. Steinberg, Tolga Tasdizen
Transformers, originally prominent in NLP and computer vision, are now being adapted for ECG signal analysis. This paper introduces a novel hierarchical transformer architecture that segments the model into multiple stages by assessing the spatial size of the embeddings, thus eliminating the need for additional downsampling strategies or complex attention designs. A classification token aggregates information across feature scales, facilitating interactions between different stages of the transformer. By utilizing depth-wise convolutions in a six-layer convolutional encoder, our approach preserves the relationships between different ECG leads. Moreover, an attention gate mechanism learns associations among the leads prior to classification. This model adapts flexibly to various embedding networks and input sizes while enhancing the interpretability of transformers in ECG signal analysis.
Two-photon contribution to the $K_L\toμ^+μ^-$ decay amplitude from a $1/a\approx 1 $ GeV lattice
2024-11-01
En-Hung Chao, Ceran Hu
We present a lattice-QCD calculation of the long-distance two-photon contribution to the decay amplitude of a long-lived kaon to a pair of muons on a $24^3\times 64$ physical-pion-mass ensemble. In these proceedings, the theoretical foundation of this calculation will be reviewed and preliminary numerical results will be presented for all diagrams with the exception of those that are quark-line disconnected SU(3)-flavor-suppressed. At this stage, the intermediate $\eta$ contribution appears to be the largest source of statistical uncertainty. Our proposed strategies to reduce these statistical errors are discussed in great detail.
A Practical Guide to Statistical Techniques in Particle Physics
2024-11-01
Alejandro Segura, Angie Catalina Parra
In high-energy physics (HEP), both the exclusion and discovery of new theories depend not only on the acquisition of high-quality experimental data but also on the rigorous application of statistical methods. These methods provide probabilistic criteria (such as p-values) to compare experimental data with theoretical models, aiming to describe the data as accurately as possible. Hypothesis testing plays a central role in this process, as it enables comparisons between established theories and potential new explanations for the observed data. This report reviews key statistical methods currently employed in particle physics, using synthetic data and numerical comparisons to illustrate the concepts in a clear and accessible way. Our results highlight the practical significance of these statistical tools in enhancing the experimental sensitivity and model exclusion capabilities in HEP. All numerical results are estimated using Python and RooFit, a high-level statistical modeling package used by the ATLAS and CMS collaborations at CERN to model and report results from experimental data.
Algorithmic Transparency in Forecasting Support Systems
2024-11-01
Leif Feddersen
Most organizations adjust their statistical forecasts (e.g. on sales) manually. Forecasting Support Systems (FSS) enable the related process of automated forecast generation and manual adjustments. As the FSS user interface connects user and statistical algorithm, it is an obvious lever for facilitating beneficial adjustments whilst discouraging harmful adjustments. This paper reviews and organizes the literature on judgemental forecasting, forecast adjustments, and FSS design. I argue that algorithmic transparency may be a key factor towards better, integrative forecasting and test this assertion with three FSS designs that vary in their degrees of transparency based on time series decomposition. I find transparency to reduce the variance and amount of harmful forecast adjustments. Letting users adjust the algorithm's transparent components themselves, however, leads to widely varied and overall most detrimental adjustments. Responses indicate a risk of overwhelming users with algorithmic transparency without adequate training. Accordingly, self-reported satisfaction is highest with a non-transparent FSS.
The world faces increasing challenges related to climate change and shifting geopolitical situations. The COVID 19 pandemic clearly illustrated the need for resilience not just within civil engineering but also within social and economic systems. While significant work has focused on applying digital technologies to solve the Sustainable Development Goals (SDGs), less effort has been placed on the ability of digital technologies to enable humanity to continue to live effectively within the Planetary Boundaries. Within this paper, we perform a Systematic Literature Review using the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) methodology. Nine hundred and twenty papers were reviewed. Based on the Systematic Literature Review (SLR), we have outlined specific methods to apply digital technologies to enable humanity to live within PB. Critical capabilities provided by digital technologies, specifically the ability to create dynamic networks for enabling humanity to live within PB, are identified and illustrated in food and agriculture. The paper closes with a brief assessment of the concept's applicability to other Critical National Infrastructure.
Making Sense of Metadata Mess: Alignment & Risk Assessment for Diatom Data Use Case
2024-11-01
Kio Polson, Marina Potapova, Uttam Meena, Chad Peiper, Joshua Brown, Joshua Agar, Jane Greenberg
Biologists study Diatoms, a fundamental algae, to assess the health of aquatic systems. Diatom specimens have traditionally been preserved on analog slides, where a single slide can contain thousands of these microscopic organisms. Digitization of these collections presents both metadata challenges and opportunities. This paper reports on metadata research aimed at providing access to a digital portion of the Academy of Natural Sciences' Diatom Herbarium, Drexel University. We report results of a 3-part study covering 1) a review of relevant metadata standards and a microscopy metadata framework shared by Hammer et al., 2) a baseline metadata alignment mapping current diatom metadata properties to standard metadata types, and 3) a metadata risk analysis associated with the course of standard data curation practices. This research is part of an effort involving the transfer of these digital slides to an new system, DataFed, to support global accessible. The final section of this paper includes a conclusion and discusses next steps.
Model Predictive Contouring Control with Barrier and Lyapunov Functions for Stable Path-Following in UAV systems
2024-11-01
Bryan S. Guevara, Viviana Moya, Luis F. Recalde, David Pozo-Espin, Daniel C. Gandolfo, Juan M. Toibero
In this study, we propose a novel method that integrates Nonlinear Model Predictive Contour Control (NMPCC) with an Exponentially Stabilizing Control Lyapunov Function (ES-CLF) and Exponential Higher-Order Control Barrier Functions to achieve stable path-following and obstacle avoidance in UAV systems. This framework enables unmanned aerial vehicles (UAVs) to safely navigate around both static and dynamic obstacles while strictly adhering to desired paths. The quaternion-based formulation ensures precise orientation and attitude control, while a robust optimization solver enforces the constraints imposed by the Control Lyapunov Function (CLF) and Control Barrier Functions (CBF), ensuring reliable real-time performance. The method was validated in a Model-in-the-Loop (MiL) environment, demonstrating effective path tracking and obstacle avoidance. The results highlight the framework's ability to minimize both orthogonal and tangential errors, ensuring stability and safety in complex environments.
A Fast, Analytic Empirical Model of the Gaia Data Release 3 Astrometric Orbit Catalog Selection Function
2024-11-01
Casey Y. Lam, Kareem El-Badry, Joshua D. Simon
In June 2022, the Gaia mission released a catalog of astrometric orbital solutions for 168,065 binary systems, by far the largest such catalog to date. Unlike previous binary stars catalogs, which were heterogeneous collections of orbits from different surveys and instruments, these orbits were derived using Gaia data alone. Despite this homogeneity, the selection function is difficult to characterize because of choices made in the construction of the catalog. Understanding the catalog's selection function is required to model and interpret its contents. We use a combination of analytic and empirical prescriptions to construct a function that computes the probability that a binary with a given set of properties would have been published in the Gaia Data Release 3 astrometric orbit catalog. We also construct a binary population synthesis model based on Moe & Di Stefano (2017) to validate our characterization of the selection function, finding good agreement with the actual Gaia NSS catalog, with the exception of the orbital eccentricity distribution. The NSS catalog suggests high-eccentricity orbits are relatively uncommon at intermediate periods $100 \lesssim P_{orb} \lesssim 1000$ days. As an example application of the selection function, we estimate the Gaia DR3 detection probabilities of the star + BH binaries Gaia BH1, BH2, and BH3. We also estimate the population of Sun-like star + BH binaries in the Galaxy to be $\sim 5000$ for $100 < P_{orb} < 400$ day, $\lesssim 2,000$ for $400 < P_{orb} < 1000$ day, and $ \lesssim 20,000$ for $1000 < P_{orb} < 2000$ days.
Ensuring usability is crucial for the success of mobile apps. Usability issues can compromise user experience and negatively impact the perceived app quality. This paper presents UX-LLM, a novel tool powered by a Large Vision-Language Model that predicts usability issues in iOS apps. To evaluate the performance of UX-LLM we predicted usability issues in two open-source apps of a medium complexity and asked usability experts to assess the predictions. We also performed traditional usability testing and expert review for both apps and compared the results to those of UX-LLM. UX-LLM demonstrated precision ranging from 0.61 and 0.66 and recall between 0.35 and 0.38, indicating its ability to identify valid usability issues, yet failing to capture the majority of issues. Finally, we conducted a focus group with an app development team of a capstone project developing a transit app for visually impaired persons. The focus group expressed positive perceptions of UX-LLM as it identified unknown usability issues in their app. However, they also raised concerns about its integration into the development workflow, suggesting potential improvements. Our results show that UX-LLM cannot fully replace traditional usability evaluation methods but serves as a valuable supplement particularly for small teams with limited resources, to identify issues in less common user paths, due to its ability to inspect the source code.
Generative AI and Agency in Education: A Critical Scoping Review and Thematic Analysis
2024-11-01
Jasper Roe, Mike Perkins
This scoping review examines the relationship between Generative AI (GenAI) and agency in education, analyzing the literature available through the lens of Critical Digital Pedagogy. Following PRISMA-ScR guidelines, we collected 10 studies from academic databases focusing on both learner and teacher agency in GenAI-enabled environments. We conducted an AI-supported hybrid thematic analysis that revealed three key themes: Control in Digital Spaces, Variable Engagement and Access, and Changing Notions of Agency. The findings suggest that while GenAI may enhance learner agency through personalization and support, it also risks exacerbating educational inequalities and diminishing learner autonomy in certain contexts. This review highlights gaps in the current research on GenAI's impact on agency. These findings have implications for educational policy and practice, suggesting the need for frameworks that promote equitable access while preserving learner agency in GenAI-enhanced educational environments.
Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization
2024-11-01
Zeyuan Ma, Hongshu Guo, Yue-Jiao Gong, Jun Zhang, Kay Chen Tan
In this survey, we introduce Meta-Black-Box-Optimization (MetaBBO) as an emerging avenue within the Evolutionary Computation (EC) community, which incorporates Meta-learning approaches to assist automated algorithm design. Despite the success of MetaBBO, the current literature provides insufficient summaries of its key aspects and lacks practical guidance for implementation. To bridge this gap, we offer a comprehensive review of recent advances in MetaBBO, providing an in-depth examination of its key developments. We begin with a unified definition of the MetaBBO paradigm, followed by a systematic taxonomy of various algorithm design tasks, including algorithm selection, algorithm configuration, solution manipulation, and algorithm generation. Further, we conceptually summarize different learning methodologies behind current MetaBBO works, including reinforcement learning, supervised learning, neuroevolution, and in-context learning with Large Language Models. A comprehensive evaluation of the latest representative MetaBBO methods is then carried out, alongside an experimental analysis of their optimization performance, computational efficiency, and generalization ability. Based on the evaluation results, we meticulously identify a set of core designs that enhance the generalization and learning effectiveness of MetaBBO. Finally, we outline the vision for the field by providing insight into the latest trends and potential future directions. Relevant literature will be continuously collected and updated at https://github.com/GMC-DRL/Awesome-MetaBBO.
On Deep Learning for Geometric and Semantic Scene Understanding Using On-Vehicle 3D LiDAR
2024-11-01
Li Li
3D LiDAR point cloud data is crucial for scene perception in computer vision, robotics, and autonomous driving. Geometric and semantic scene understanding, involving 3D point clouds, is essential for advancing autonomous driving technologies. However, significant challenges remain, particularly in improving the overall accuracy (e.g., segmentation accuracy, depth estimation accuracy, etc.) and efficiency of these systems. To address the challenge in terms of accuracy related to LiDAR-based tasks, we present DurLAR, the first high-fidelity 128-channel 3D LiDAR dataset featuring panoramic ambient (near infrared) and reflectivity imagery. To improve efficiency in 3D segmentation while ensuring the accuracy, we propose a novel pipeline that employs a smaller architecture, requiring fewer ground-truth annotations while achieving superior segmentation accuracy compared to contemporary approaches. To improve the segmentation accuracy, we introduce Range-Aware Pointwise Distance Distribution (RAPiD) features and the associated RAPiD-Seg architecture. All contributions have been accepted by peer-reviewed conferences, underscoring the advancements in both accuracy and efficiency in 3D LiDAR applications for autonomous driving. Full abstract: https://etheses.dur.ac.uk/15738/.
AAD-LLM: Adaptive Anomaly Detection Using Large Language Models
2024-11-01
Alicia Russell-Gilbert, Alexander Sommers, Andrew Thompson, Logan Cummins, Sudip Mittal, Shahram Rahimi, Maria Seale, Joseph Jaboure, Thomas Arnold, Joshua Church
For data-constrained, complex and dynamic industrial environments, there is a critical need for transferable and multimodal methodologies to enhance anomaly detection and therefore, prevent costs associated with system failures. Typically, traditional PdM approaches are not transferable or multimodal. This work examines the use of Large Language Models (LLMs) for anomaly detection in complex and dynamic manufacturing systems. The research aims to improve the transferability of anomaly detection models by leveraging Large Language Models (LLMs) and seeks to validate the enhanced effectiveness of the proposed approach in data-sparse industrial applications. The research also seeks to enable more collaborative decision-making between the model and plant operators by allowing for the enriching of input series data with semantics. Additionally, the research aims to address the issue of concept drift in dynamic industrial settings by integrating an adaptability mechanism. The literature review examines the latest developments in LLM time series tasks alongside associated adaptive anomaly detection methods to establish a robust theoretical framework for the proposed architecture. This paper presents a novel model framework (AAD-LLM) that doesn't require any training or finetuning on the dataset it is applied to and is multimodal. Results suggest that anomaly detection can be converted into a "language" task to deliver effective, context-aware detection in data-constrained industrial applications. This work, therefore, contributes significantly to advancements in anomaly detection methodologies.
Segmentation algorithms and modeling of recurrent bursting events in neuronal and glial time series
2024-11-01
Lou Zonca, Elena Dossi, Nathalie Rouach, D. Holcman
Long-time series of neuronal recordings are resulting from the activity of connected neuronal networks. Yet how neuronal properties can be extracted remains empirical. We review here the data analysis based on network models to recover physiological parameters from electrophysiological and calcium recordings in neurons and astrocytes. After, we present the recording techniques and activation events, such as burst and interburst and Up and Down states. We then describe time-serie segmentation methods developed to detect and to segment these events. To interpret the statistics extracted from time series, we present computational models of neuronal populations based on synaptic short-term plasticity and After hyperpolarization. We discuss how these models are calibrated so that they can reproduce the statistics observed in the experimental time series. They serve to extract specific parameters by comparing numerical and experimental statistical moment or entire distributions. Finally, we discus cases where calibrated models are used to predict the selective impact of some parameters on the circuit behavior, properties that would otherwise be difficult to dissect experimentally.
Sulfur-bearing molecules in a sample of early star-forming cores
2024-11-01
N. C. Martinez, S. Paron, M. E. Ortega, A. Petriella, A. Álamo, M. Brook, C. Carballo, T. Heberling
The sulfur content in dense molecular regions of the interstellar medium is highly depleted in comparison to diffuse clouds. The reason of this phenomenon is unclear, thus it is necessary to carry out observational studies of sulfur-bearing species towards dense regions, mainly at early evolutive stages to uncover the early sulfur chemistry. Using data from the Atacama Large Millimeter Array (ALMA) data archive, we investigated a sample of 37 dense cores embedded in the most massive infrared-quiet molecular clumps from the ATLASGAL survey. Lines of 34SO, SO2, NS, SO, SO+, and H2CS were analyzed and column densities of each molecular species were obtained. From the continuum emission, and two CH3OH lines, the 37 cores were characterized in density and temperature, and the corresponding H2 column densities were derived. The abundances of such sulfur-bearing species were derived and studied. We find that the abundances of the analyzed species increase with the growth of the gas temperature, suggesting that the chemistry involved in the formation of each of the analyzed molecule may have a similar dependence with Tk in the range 20 to 100 K. We find that the comparisons among abundances are, in general, highly correlated. Given that such correlation decreases in more evolved sources, we suggest that the sulfur-bearing species here analyzed should have a similar chemical origin. From the measured line widths we point out that molecules with oxygen content (34SO, SO2, SO, and SO+) may be associated with warmer and more turbulent gas than the other ones. H2CS and NS are associated with more quiescent gas, probably in the external envelopes of the cores. This work gives quantitative information about abundances that could be useful in chemical models pointing to explain the sulfur chemistry in the interstellar medium.
Initial Mass Functions of Young Stellar Clusters from the Gemini Spectroscopic Survey of Nearby Galaxies I. Young Massive Clusters in the Antennae galaxies
2024-11-01
Jae-Rim Koo, Hyun-Jeong Kim, Beomdu Lim
The stellar initial mass function (IMF) is a key parameter to understand the star formation process and the integrated properties of stellar populations in remote galaxies. We present a spectroscopic study of young massive clusters (YMCs) in the starburst galaxies NGC 4038/39. The integrated spectra of seven YMCs obtained with GMOS-S attached to the 8.2-m Gemini South telescope reveal the spectral features associated with stellar ages and the underlying IMFs. We constrain the ages of the YMCs using the absorption lines and strong emission bands from Wolf-Rayet stars. The internal reddening is also estimated from the strength of the Na I D absorption lines. Based on these constraints, the observed spectra are matched with the synthetic spectra generated from a simple stellar population model. Several parameters of the clusters including age, reddening, cluster mass, and the underlying IMF are derived from the spectral matching. The ages of the YMCs range from 2.5 to 6.5 Myr, and these clusters contain stellar masses ranging from 1.6 X 10^5 M_sun to 7.9 X 10^7 M_sun. The underlying IMFs appear to differ from the universal form of the Salpeter/Kroupa IMF. Interestingly, massive clusters tend to have the bottom-heavy IMFs, although the masses of some clusters are overestimated due to the crowding effect. Based on this, our results suggest that the universal form of the IMF is not always valid when analyzing integrated light from unresolved stellar systems. However, further study with a larger sample size is required to reach a definite conclusion.
Zero-shot Generalization in Inventory Management: Train, then Estimate and Decide
2024-11-01
Tarkan Temizöz, Christina Imdahl, Remco Dijkman, Douniel Lamghari-Idrissi, Willem van Jaarsveld
Deploying deep reinforcement learning (DRL) in real-world inventory management presents challenges, including dynamic environments and uncertain problem parameters, e.g. demand and lead time distributions. These challenges highlight a research gap, suggesting a need for a unifying framework to model and solve sequential decision-making under parameter uncertainty. We address this by exploring an underexplored area of DRL for inventory management: training generally capable agents (GCAs) under zero-shot generalization (ZSG). Here, GCAs are advanced DRL policies designed to handle a broad range of sampled problem instances with diverse inventory challenges. ZSG refers to the ability to successfully apply learned policies to unseen instances with unknown parameters without retraining. We propose a unifying Super-Markov Decision Process formulation and the Train, then Estimate and Decide (TED) framework to train and deploy a GCA tailored to inventory management applications. The TED framework consists of three phases: training a GCA on varied problem instances, continuously estimating problem parameters during deployment, and making decisions based on these estimates. Applied to periodic review inventory problems with lost sales, cyclic demand patterns, and stochastic lead times, our trained agent, the Generally Capable Lost Sales Network (GC-LSN) consistently outperforms well-known traditional policies when problem parameters are known. Moreover, under conditions where demand and/or lead time distributions are initially unknown and must be estimated, we benchmark against online learning methods that provide worst-case performance guarantees. Our GC-LSN policy, paired with the Kaplan-Meier estimator, is demonstrated to complement these methods by providing superior empirical performance.
On the Formation of the Double Neutron Star Binary PSR J1846-0513
2024-11-01
Long Jiang, Kun Xu, Shuai Zha, Yun-Lang Guo, Jian-Ping Yuan, Xiang-Li Qian, Wen-Cong Chen, Na Wang
The double neutron star PSR J1846-0513 is discovered by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) in Commensal Radio Astronomy FAST Survey. The pulsar is revealed to be harbored in an eccentric orbit with $e=0.208$ and orbital period of 0.613 days. The total mass of the system is constrained to be $2.6287(35)\rm{M}_{\odot}$, with a mass upper limit of $1.3455{\rm~M}_{\odot}$ for the pulsar and a mass lower limit of $1.2845{\rm~M}_{\odot}$ for the companion star. To reproduce its evolution history, we perform a 1D model for the formation of PSR J1846-0513 whose progenitor is assumed to be neutron star - helium (He) star system via MESA code. Since the large eccentricity is widely believed to originate from an asymmetric supernova explosion, we also investigate the dynamical effects of the supernova explosion. Our simulated results show that the progenitor of PSR J1846-0513 could be a binary system consisting of a He star of $3.3-4.0{\rm~M}_\odot$ and a neutron star in a circular orbit with an initial period of $\sim0.5$ days.
2024-11-01
E. da Hora, C. dos Santos, Fabiano C. Simas
We consider an extended model with two real scalar fields, $\phi(x,t)$ and $\chi(x,t)$. The first sector is controlled by the sine-Gordon superpotential, while the second field is submitted to the $\chi^4$ one. The fields mutually interact via a nontrivial coupling function $f(\chi)$ that also changes the kinematics of $\phi$. We briefly review the implementation of the Bogomol'nyi-Prasad-Sommerfield (BPS) prescription. We then solve the resulting BPS equations for two different interactions $f$. The first one leads to a single kink-kink configuration, while the second one gives rise to a inhomogeneous sine-Gordon kink lattice. We study the linear stability of these new solutions, focusing on their translational modes. We also explore how the strength of the mutual interaction affects the BPS profiles. In particular, we show that a homogeneous lattice with identical kinks is attained in the regime of extremely strong interactions.
IoT Architectures for Indoor Radon Management: A Prospective Analysis
2024-11-01
Oscar Blanco-Novoa, Paulo Barros, Paula Fraga-Lamas, Sergio Ivan Lopes, Tiago M. Fernandez-Carames
The demand for real-time Indoor Air Quality (IAQ) management has increased recently, since low-cost and modern sensors such as Particulate Matter (PM), Volatile Organic Compounds (VOCs), Carbon Monoxide (CO), Carbon Dioxide (CO2), Radon (Rn), among others, have been put forward with considerable accuracy. Although these low-cost sensors cannot be considered measurement instruments, they are very useful for a vast number of application domains, such as home automation, smart building management, IAQ management, risk exposure assessment, to name a few. This paper presents a literature review and a prospective analysis and discussion regarding Internet of Things (IoT) technologies adopted to deal with scenarios that present known indoor Radon gas problems. Specifically, the main requirements for developing IoT-enabled radon management solutions are reviewed. Thus, a traditional IoT architecture is described, its main components are analyzed and some of the most recent academic solutions are reviewed. Finally, novel approaches for deploying IoT radon management architectures are presented together with the most relevant open challenges. In this way, this article presents a holistic review of the past, present, and future of indoor radon management in order to provide guidelines for future designers and developers.
Human-inspired Perspectives: A Survey on AI Long-term Memory
2024-11-01
Zihong He, Weizhe Lin, Hao Zheng, Fan Zhang, Matt Jones, Laurence Aitchison, Xuhai Xu, Miao Liu, Per Ola Kristensson, Junxiao Shen
With the rapid advancement of AI systems, their abilities to store, retrieve, and utilize information over the long term - referred to as long-term memory - have become increasingly significant. These capabilities are crucial for enhancing the performance of AI systems across a wide range of tasks. However, there is currently no comprehensive survey that systematically investigates AI's long-term memory capabilities, formulates a theoretical framework, and inspires the development of next-generation AI long-term memory systems. This paper begins by systematically introducing the mechanisms of human long-term memory, then explores AI long-term memory mechanisms, establishing a mapping between the two. Based on the mapping relationships identified, we extend the current cognitive architectures and propose the Cognitive Architecture of Self-Adaptive Long-term Memory (SALM). SALM provides a theoretical framework for the practice of AI long-term memory and holds potential for guiding the creation of next-generation long-term memory driven AI systems. Finally, we delve into the future directions and application prospects of AI long-term memory.
Advancing Mathematical Epidemiology and Chemical Reaction Network Theory via Synergies Between Them
2024-11-01
Florin Avram, Rim Adenane, Mircea Neagu
Our paper reviews some key concepts in chemical reaction network theory and mathematical epidemiology, and examines their intersection, with three goals. The first is to make the case that mathematical epidemiology (ME), and also related sciences like population dynamics, virology, ecology, etc., could benefit by adopting the universal language of essentially non-negative kinetic systems as developed by chemical reaction network (CRN) researchers. In this direction, our investigation of the relations between CRN and ME lead us to propose for the first time a definition of ME models, stated in Open Problem 1. Our second goal is to inform researchers outside ME of the convenient next generation matrix (NGM) approach for studying the stability of boundary points, which do not seem suficiently well known. Last but not least, we want to help students and researchers who know nothing about either ME or CRN to learn them quickly, by offering them a Mathematica package "BootCamp", located at https://github.com/adhalanay/epidemiology_crns, including illustrating notebooks (and certain sections below will contain associated suggested notebooks; however, readers with experience may safely skip the bootcamp). We hope that the files indicated in the titles of various sections will be helpful, though of course improvement is always possible, and we ask the help of the readers for that.
Cityscape-Adverse: Benchmarking Robustness of Semantic Segmentation with Realistic Scene Modifications via Diffusion-Based Image Editing
2024-11-01
Naufal Suryanto, Andro Aprila Adiputra, Ahmada Yusril Kadiptya, Thi-Thu-Huong Le, Derry Pratama, Yongsu Kim, Howon Kim
Recent advancements in generative AI, particularly diffusion-based image editing, have enabled the transformation of images into highly realistic scenes using only text instructions. This technology offers significant potential for generating diverse synthetic datasets to evaluate model robustness. In this paper, we introduce Cityscape-Adverse, a benchmark that employs diffusion-based image editing to simulate eight adverse conditions, including variations in weather, lighting, and seasons, while preserving the original semantic labels. We evaluate the reliability of diffusion-based models in generating realistic scene modifications and assess the performance of state-of-the-art CNN and Transformer-based semantic segmentation models under these challenging conditions. Additionally, we analyze which modifications have the greatest impact on model performance and explore how training on synthetic datasets can improve robustness in real-world adverse scenarios. Our results demonstrate that all tested models, particularly CNN-based architectures, experienced significant performance degradation under extreme conditions, while Transformer-based models exhibited greater resilience. We verify that models trained on Cityscape-Adverse show significantly enhanced resilience when applied to unseen domains. Code and datasets will be released at https://github.com/naufalso/cityscape-adverse.
Inference-to-complete: A High-performance and Programmable Data-plane Co-processor for Neural-network-driven Traffic Analysis
2024-11-01
Dong Wen, Zhongpei Liu, Tong Yang, Tao Li, Tianyun Li, Chenglong Li, Jie Li, Zhigang Sun
Neural-networks-driven intelligent data-plane (NN-driven IDP) is becoming an emerging topic for excellent accuracy and high performance. Meanwhile we argue that NN-driven IDP should satisfy three design goals: the flexibility to support various NNs models, the low-latency-high-throughput inference performance, and the data-plane-unawareness harming no performance and functionality. Unfortunately, existing work either over-modify NNs for IDP, or insert inline pipelined accelerators into the data-plane, failing to meet the flexibility and unawareness goals. In this paper, we propose Kaleidoscope, a flexible and high-performance co-processor located at the bypass of the data-plane. To address the challenge of meeting three design goals, three key techniques are presented. The programmable run-to-completion accelerators are developed for flexible inference. To further improve performance, we design a scalable inference engine which completes low-latency and low-cost inference for the mouse flows, and perform complex NNs with high-accuracy for the elephant flows. Finally, raw-bytes-based NNs are introduced, which help to achieve unawareness. We prototype Kaleidoscope on both FPGA and ASIC library. In evaluation on six NNs models, Kaleidoscope reaches 256-352 ns inference latency and 100 Gbps throughput with negligible influence on the data-plane. The on-board tested NNs perform state-of-the-art accuracy among other NN-driven IDP, exhibiting the the significant impact of flexibility on enhancing traffic analysis accuracy.
Orbital Edelstein effect of electronic itinerant orbital motion at edges
2024-11-01
Jongjun M. Lee, Min Ju Park, Hyun-Woo Lee
In the study of orbital angular momentum (OAM), the focus has been predominantly on the intra-atomic contribution. However, recent research has begun to shift towards exploring the inter-atomic contribution to OAM dynamics. In this paper, we investigate the orbital Edelstein effect (OEE) arising from the inter-atomic OAM at the edges. We explore the OAM texture within edge states and unveil the OAM accumulation at the edges using several lattice models based on the $s$ orbital. By comparing slabs with differently shaped edges, we not only clarify the role of electron wiggling motion in shaping OAM texture but also highlight the absence of bulk-boundary correspondence in the accumulation process. The topological insulator and higher-order topological insulator models further confirm these findings and provide evidence for the relationship between the higher-order topology and the OEE. Our study advances the comprehension of orbital physics and extends its scope to higher-order topological insulators.
Cryptocurrencies have gained popularity due to their transparency, security, and accessibility compared to traditional financial systems, with Bitcoin, introduced in 2009, leading the market. Bitcoin's security relies on blockchain technology - a decentralized ledger consisting of a consensus and an incentive mechanism. The consensus mechanism, Proof of Work (PoW), requires miners to solve difficult cryptographic puzzles to add new blocks, while the incentive mechanism rewards them with newly minted bitcoins. However, as Bitcoin's acceptance grows, it faces increasing threats from attacks targeting these mechanisms, such as selfish mining, double-spending, and block withholding. These attacks compromise security, efficiency, and reward distribution. Recent research shows that these attacks can be combined with each other or with either malicious strategies, such as network-layer attacks, or non-malicious strategies, like honest mining. These combinations lead to more sophisticated attacks, increasing the attacker's success rates and profitability. Therefore, understanding and evaluating these attacks is essential for developing effective countermeasures and ensuring long-term security. This paper begins by examining individual attacks executed in isolation and their profitability. It then explores how combining these attacks with each other or with other malicious and non-malicious strategies can enhance their overall effectiveness and profitability. The analysis further explores how the deployment of attacks such as selfish mining and block withholding by multiple competing mining pools against each other impacts their economic returns. Lastly, a set of design guidelines is provided, outlining areas future work should focus on to prevent or mitigate the identified threats.
A Survey on Bundle Recommendation: Methods, Applications, and Challenges
2024-11-01
Meng Sun, Lin Li, Ming Li, Xiaohui Tao, Dong Zhang, Peipei Wang, Jimmy Xiangji Huang
In recent years, bundle recommendation systems have gained significant attention in both academia and industry due to their ability to enhance user experience and increase sales by recommending a set of items as a bundle rather than individual items. This survey provides a comprehensive review on bundle recommendation, beginning by a taxonomy for exploring product bundling. We classify it into two categories based on bundling strategy from various application domains, i.e., discriminative and generative bundle recommendation. Then we formulate the corresponding tasks of the two categories and systematically review their methods: 1) representation learning from bundle and item levels and interaction modeling for discriminative bundle recommendation; 2) representation learning from item level and bundle generation for generative bundle recommendation. Subsequently, we survey the resources of bundle recommendation including datasets and evaluation metrics, and conduct reproducibility experiments on mainstream models. Lastly, we discuss the main challenges and highlight the promising future directions in the field of bundle recommendation, aiming to serve as a useful resource for researchers and practitioners. Our code and datasets are publicly available at https://github.com/WUT-IDEA/bundle-recommendation-survey.
The large-scale imaging survey will produce massive photometric data in multi-bands for billions of galaxies. Defining strategies to quickly and efficiently extract useful physical information from this data is mandatory. Among the stellar population parameters for galaxies, their stellar masses and star formation rates (SFRs) are the most fundamental. We develop a novel tool, \textit{Multi-Layer Perceptron for Predicting Galaxy Parameters} (MLP-GaP), that uses a machine-learning (ML) algorithm to accurately and efficiently derive the stellar masses and SFRs from multi-band catalogs. We first adopt a mock dataset generated by the \textit{Code Investigating GALaxy Emission} (CIGALE) for training and testing datasets. Subsequently, we used a multi-layer perceptron model to build MLP-GaP and effectively trained it with the training dataset. The results of the test performed on the mock dataset show that MLP-GaP can accurately predict the reference values. Besides MLP-GaP has a significantly faster processing speed than CIGALE. To demonstrate the science-readiness of the MLP-GaP, we also apply it to a real data sample and compare the stellar masses and SFRs with CIGALE. Overall, the predicted values of MLP-GaP show a very good consistency with the estimated values derived from SED fitting. Therefore, the capability of MLP-GaP to rapidly and accurately predict stellar masses and SFRs makes it particularly well-suited for analyzing huge amounts of galaxies in the era of large sky surveys.
Forecasting Mortality in the Middle-Aged and Older Population of England: A 1D-CNN Approach
2024-11-01
Marjan Qazvini
Convolutional Neural Networks (CNNs) are proven to be effective when data are homogeneous such as images, or when there is a relationship between consecutive data such as time series data. Although CNNs are not famous for tabular data, we show that we can use them in longitudinal data, where individuals' information is recorded over a period and therefore there is a relationship between them. This study considers the English Longitudinal Study of Ageing (ELSA) survey, conducted every two years. We use one-dimensional convolutional neural networks (1D-CNNs) to forecast mortality using socio-demographics, diseases, mobility impairment, Activities of Daily Living (ADLs), Instrumental Activities of Daily Living (IADLs), and lifestyle factors. As our dataset is highly imbalanced, we try different over and undersampling methods and find that over-representing the small class improves the results. We also try our model with different activation functions. Our results show that swish nonlinearity outperforms other functions.
Due to GPT's impressive generative capabilities, its applications in games are expanding rapidly. To offer researchers a comprehensive understanding of the current applications and identify both emerging trends and unexplored areas, this paper introduces an updated scoping review of 131 articles, 76 of which were published in 2024, to explore GPT's potential for games. By coding and synthesizing the papers, we identify five prominent applications of GPT in current game research: procedural content generation, mixed-initiative game design, mixed-initiative gameplay, playing games, and game user research. Drawing on insights from these application areas and emerging research, we propose future studies should focus on expanding the technical boundaries of the GPT models and exploring the complex interaction dynamics between them and users. This review aims to illustrate the state of the art in innovative GPT applications in games, offering a foundation to enrich game development and enhance player experiences through cutting-edge AI innovations.
Generating accurate radiology reports from medical images is a clinically important but challenging task. While current Vision Language Models (VLMs) show promise, they are prone to generating hallucinations, potentially compromising patient care. We introduce RadFlag, a black-box method to enhance the accuracy of radiology report generation. Our method uses a sampling-based flagging technique to find hallucinatory generations that should be removed. We first sample multiple reports at varying temperatures and then use a Large Language Model (LLM) to identify claims that are not consistently supported across samples, indicating that the model has low confidence in those claims. Using a calibrated threshold, we flag a fraction of these claims as likely hallucinations, which should undergo extra review or be automatically rejected. Our method achieves high precision when identifying both individual hallucinatory sentences and reports that contain hallucinations. As an easy-to-use, black-box system that only requires access to a model's temperature parameter, RadFlag is compatible with a wide range of radiology report generation models and has the potential to broadly improve the quality of automated radiology reporting.
Analysis of ELSA COVID-19 Substudy response rate using machine learning algorithms
2024-11-01
Marjan Qazvini
National Statistical Organisations every year spend time and money to collect information through surveys. Some of these surveys include follow-up studies, and usually, some participants due to factors such as death, immigration, change of employment, health, etc, do not participate in future surveys. In this study, we focus on the English Longitudinal Study of Ageing (ELSA) COVID-19 Substudy, which was carried out during the COVID-19 pandemic in two waves. In this substudy, some participants from wave 1 did not participate in wave 2. Our purpose is to predict non-responses using Machine Learning (ML) algorithms such as K-nearest neighbours (KNN), random forest (RF), AdaBoost, logistic regression, neural networks (NN), and support vector classifier (SVC). We find that RF outperforms other models in terms of balanced accuracy, KNN in terms of precision and test accuracy, and logistics regressions in terms of the area under the receiver operating characteristic curve (ROC), i.e. AUC.
Alive and Strongly Kicking: Stable X-ray Quasi-Periodic Eruptions from eRO-QPE2 over 3.5 Years
2024-11-01
Dheeraj Pasham, Shubham Kejriwal, Eric Coughlin, Vojtěch Witzany, Alvin J. K. Chua, Michal Zajaček, Thomas Wevers, Yukta Ajay
Quasi-periodic eruptions (QPEs) are recurring bursts of soft X-rays from the nuclei of galaxies. Their physical origin is currently a subject of debate, with models typically invoking an orbiter around a massive black hole or disk instabilities. Here we present and analyze the temporal and spectral evolution of the QPE source eRO-QPE2 over 3.5 years. We find that eRO-QPE2 1) is remarkably stable over the entire 3.5-year temporal baseline in its eruption peak luminosity, eruption temperature, quiescent temperature, and quiescent luminosity, 2) has a stable mean eruption recurrence time of 2.35 hours, with marginal ($\sim$2$\sigma$) evidence for a $0.1$ hour reduction over the 3.5 yr period, and 3) has a long-short variation in its recurrence time in August 2020, but this pattern is absent from all subsequent observations. The stability of its peak eruption luminosity and that of the quiescent state are notably dissimilar from three previously tracked QPEs (GSN069, eRO-QPE1, eRO-QPE3), which show declines in eruption and quiescent flux over comparable temporal baselines. This stability is even more pronounced in eRO-QPE2 due to its 2.4 hour average recurrence time compared to GSN-069's 9 hour, eRO-QPE1's 16 hour, and eRO-QPE3's 20 hour recurrence times, i.e., this system has undergone 4-8 times more cycles than these other systems over the 3.5 years of observations. We discuss the implications of these observations within the context of some proposed extreme mass ratio inspiral (EMRI) models.
The Sensitivity of Bayesian Kernel Machine Regression (BKMR) to Data Distribution: A Comprehensive Simulation Analysis
2024-11-01
Kazi Tanvir Hasan, Gabriel Odom, Zoran Bursac, Boubakari Ibrahimou
Bayesian Kernel Machine Regression (BKMR) has emerged as a powerful tool to detect negative health effects from exposure to complex multi-pollutant mixtures. However, its performance is degraded when data deviate from normality. In this comprehensive simulation analysis, we show that BKMR's power and test size vary under different distributions and covariance matrix structures. Our results demonstrate specifically that BKMR's robustness is influenced by the response's coefficient of variation (CV), resulting in reduced accuracy to detect true effects when data are skewed. Test sizes become uncontrolled (> 0.05) as CV values increase, leading to inflated false detection rates. However, we find that BKMR effectively utilizes off-diagonal covariance information corresponding to predictor interdependencies, increasing statistical power and accuracy. To achieve reliable and accurate results, we advocate for scrutiny of data skewness and covariance before applying BKMR, particularly when used to predict cognitive decline from blood/urine heavy metal concentrations in environmental health contexts.
Compact dust-obscured star-formation and the origin of the galaxy bimodality
2024-11-01
Maxime Tarrasse, Carlos Gómez-Guijarro, David Elbaz, Benjamin Magnelli, Mark Dickinson, Aurélien Henry, Maximilien Franco, Yipeng Lyu, Jean-Baptiste Billand, Rachana Bhatawdekar, Yingjie Cheng, Adriano Fontana, Steven L. Finkelstein, Giovanni Gandolfi, Nimish Hathi, Michaela Hirschmann, Benne W. Holwerda, Anton M. Koekemoer, Ray A. Lucas, Lise-Marie Seillé, Stephen Wilkins, L. Y. Aaron Yung
During the last decade, studies about highly attenuated and massive red star-forming galaxies (RedSFGs) at $z \sim 4$ have suggested that they could constitute a crucial population for unraveling the mechanisms driving the transition from vigorous star formation to quiescence at high redshifts. Since such a transition seems to be linked to a morphological transformation, studying the morphological properties of these RedSFGs is essential to our understanding of galaxy evolution. To this end, we are using JWST/NIRCam images from the CEERS survey to assemble a mass-complete sample of 188 massive galaxies at $z=3-4$, for which we perform resolved-SED fit. After classifying galaxies into typical blue SFGs (BlueSFGs), RedSFGs and quiescent galaxies (QGs), we compare the morphologies of each population in terms of stellar mass density, SFR density, sSFR, dust-attenuation and mass-weighted age. We find that RedSFGs and QGs present similar stellar surface density profiles and that RedSFGs manifest a dust attenuation concentration significantly higher than that of BlueSFGs at all masses. This indicates that to become quiescent, a BlueSFG must transit through a major compaction phase once it has become sufficiently massive. At the same time, we find RedSFGs and QGs to account for more than $50\%$ of galaxies with ${\rm log}(M_\ast/M_\odot)> 10.4$ at this redshift. This transition mass corresponds to the "critical mass" delineating the bimodality between BlueSFGs and QGs in the local Universe. We then conclude that there is a bimodality between extended BlueSFGs and compact, highly attenuated RedSFGs that have undergone a major gas compaction phase enabling the latter to build a massive bulb in situ. There is evidence that this early-stage separation is at the origin of the local bimodality between BlueSFGs and QGs, which we refer to as a "primeval bimodality".
Dust mass in protoplanetary disks with porous dust opacities
2024-11-01
Yao Liu, Hélène Roussel, Hendrik Linz, Min Fang, Sebastian Wolf, Florian Kirchschlager, Thomas Henning, Haifeng Yang, Fujun Du, Mario Flock, Hongchi Wang
ALMA surveys have suggested that protoplanetary disks are not massive enough to form the known exoplanet population, under the assumption that the millimeter continuum emission is optically thin. In this work, we investigate how the mass determination is influenced when the porosity of dust grains is considered in radiative transfer models. The results show that disks with porous dust opacities yield similar dust temperature, but systematically lower millimeter fluxes compared to disks incorporating compact dust grains. Moreover, we recalibrate the relation between dust temperature and stellar luminosity for a wide range of stellar parameters, and calculate the dust masses of a large sample of disks using the traditionally analytic approach. The median dust mass from our calculation is about 6 times higher than the literature result, and this is mostly driven by the different opacities of porous and compact grains. A comparison of the cumulative distribution function between disk dust masses and exoplanet masses show that the median exoplanet mass is about 2 times lower than the median dust mass, if grains are porous, and there are no exoplanetary systems with masses higher than the most massive disks. Our analysis suggests that adopting porous dust opacities may alleviate the mass budget problem for planet formation. As an example illustrating the combined effects of optical depth and porous dust opacities on the mass estimation, we conduct new IRAM/NIKA-2 observations toward the IRAS 04370+2559 disk and perform a detailed radiative transfer modeling of the spectral energy distribution. The best-fit dust mass is roughly 100 times higher than the value from the traditionally analytic calculation. Future spatially resolved observations at various wavelengths are required to better constrain the dust mass.
Generative modelling for mass-mapping with fast uncertainty quantification
2024-10-31
Jessica J. Whitney, Tobías I. Liaudat, Matthew A. Price, Matthijs Mars, Jason D. McEwen
Understanding the nature of dark matter in the Universe is an important goal of modern cosmology. A key method for probing this distribution is via weak gravitational lensing mass-mapping - a challenging ill-posed inverse problem where one infers the convergence field from observed shear measurements. Upcoming stage IV surveys, such as those made by the Vera C. Rubin Observatory and Euclid satellite, will provide a greater quantity and precision of data for lensing analyses, necessitating high-fidelity mass-mapping methods that are computationally efficient and that also provide uncertainties for integration into downstream cosmological analyses. In this work we introduce MMGAN, a novel mass-mapping method based on a regularised conditional generative adversarial network (GAN) framework, which generates approximate posterior samples of the convergence field given shear data. We adopt Wasserstein GANs to improve training stability and apply regularisation techniques to overcome mode collapse, issues that otherwise are particularly acute for conditional GANs. We train and validate our model on a mock COSMOS-style dataset before applying it to true COSMOS survey data. Our approach significantly outperforms the Kaiser-Squires technique and achieves similar reconstruction fidelity as alternative state-of-the-art deep learning approaches. Notably, while alternative approaches for generating samples from a learned posterior are slow (e.g. requiring $\sim$10 GPU minutes per posterior sample), MMGAN can produce a high-quality convergence sample in less than a second.
StarDICE III: Characterization of the photometric instrument with a Collimated Beam Projector
2024-10-31
Thierry Souverin, Jérémy Neveu, Marc Betoule, Sébastien Bongard, Christopher W. Stubbs, Elana Urbach, Sasha Brownsberger, Pierre Éric Blanc, Johann Cohen Tanugi, Sylvie Dagoret-Campagne, Fabrice Feinstein, Delphine Hardin, Claire Juramy, Laurent Le Guillou, Auguste Le Van Suu, Marc Moniez, Bertrand Plez, Nicolas Regnault, Eduardo Sepulveda, Kélian Sommer, the LSST Dark Energy Science Collaboration
The measurement of type Ia supernovae magnitudes provides cosmological distances, which can be used to constrain dark energy parameters. Large photometric surveys require a substantial improvement in the calibration precision of their photometry to reduce systematic uncertainties in cosmological constraints. The StarDICE experiment is designed to establish accurate broadband flux references for these surveys, aiming for sub-percent precision in magnitude measurements. This requires a precise measurement of the filter bandpasses of both the StarDICE and survey instruments with sub-nanometer accuracy. To that end, we have developed the Collimated Beam Projector (CBP), an optical device capable of calibrating the throughput of an astronomical telescope and of its filters. The CBP is built from a tunable laser source and a reversed telescope to emit a parallel monochromatic light beam that is continuously monitored in flux and wavelength. The CBP output light flux is measured using a large area photodiode, previously calibrated relative to a NIST photodiode. We derive the StarDICE telescope throughput and filter transmissions from the CBP measurements, anchoring it to the absolute calibration provided by the NIST. After analyzing the systematic uncertainties, we achieved sub-nanometer accuracy in determining filter central wavelengths, measured each filter transmission with a precision of 0.5% per 1nm bin, and detected out-of-band leakages at 0.01%. Furthermore, we have synthesized the equivalent transmission for full pupil illumination from four sample positions in the StarDICE telescope mirror, with an accuracy of approximately 0.2nm for central wavelengths and 7mmag for broadband fluxes. We demonstrated our ability to characterize a telescope throughput down to the mmag, and paved the way for future developments, such as a portable CBP version for in-situ transmission monitoring.
Spectral features and variable circular polarisation in the radio emission from the pre-cataclysmic variable QS Vir
2024-10-31
M. E. Ridder, A. K. Hughes, C. O. Heinke, G. R. Sivakoff, R. D. Sydora
QS Vir is a low-accretion rate cataclysmic variable (CV), or pre-CV, as the M dwarf companion is just filling its Roche lobe. We recently identified radio emission from QS Vir in the Very Large Array Sky Survey, at a flux of ~1 mJy. The origin of radio emission from CVs is not fully understood, with evidence for synchrotron emission from jets and other coherent plasma emission processes, such as electron cyclotron maser emission (ECME) or plasma radiation. Our aim is to constrain the radio emission mechanism for QS Vir, through spectroscopic, polarisation, and time variability measurements, all while checking for correlated X-ray variations. We took 3 epochs of new observations with the VLA in S, C, and X bands, with full Stokes polarisation information, complemented by near-simultaneous Swift/XRT X-ray data. Radio spectra are extracted to search for emission features characteristic of coherent plasma emission processes (e.g. high circular polarisation and narrow-band emission). We fit the X-ray spectra with absorbed power-laws, finding no strong X-ray variability. QS Vir showed a nearly flat radio spectrum, with fluxes of 0.4-0.6 mJy in all bands. Swift/XRT showed L_X ~ 5x10^29 erg/s in all observations. We identified strong, variable circular polarisation, ranging from 33+/-3% in S band in the last observation, to <11% in the middle observation in all bands. Linear polarisation was not detected, with upper limits of at most 15%. Intriguingly, the S-band spectra show circularly polarised spectral bumps (width ~0.5 GHz) that rise and decay within <5 minutes. We suggest that the radio emission from QS Vir consists of two components: a relatively constant, low-polarisation flat-spectrum component and a band-limited, rapidly variable, and strongly circularly polarised component. This latter coherent component may be associated with ECME or plasma radiation.
Inconsistencies in Simple Thermal Model Results for Near-Earth Asteroids between Infrared Telescope Facility SpeX and NEOWISE Data
2024-10-31
Samuel A. Myers, Ellen S. Howell, Christopher Magri, Ronald J. Vervack Jr., Yanga R. Fernández, Mary L. Hinkle, Sean E. Marshall
Understanding the properties of near-Earth asteroids (NEAs) is key for many aspects of planetary science, particularly planetary defense. Our current knowledge of NEA sizes and regolith properties is heavily dependent on simple thermal models. These models are often used to analyze data from missions such as NEOWISE because they are well suited to deal with large volumes of data. However, simple model results based on NEOWISE data may be inconsistent with results based on other types of observation in some cases. In this work, we seek to better understand these potential inconsistencies, as well as the situations for which they are most prevalent. We do this by comparing simple model results based on Infrared Telescope Facility SpeX data to similar results based on NEOWISE data. This is carried out for six NEAs that represent a range of spectral types, shapes, and rotation states. We find that models based on SpeX and NEOWISE data for these six objects are inconsistent in some cases, even though the SpeX results are consistent with other methods and observations. We find that objects observed at fainter magnitudes and objects with more primitive compositions are more likely to produce inconsistent fits. These results highlight the importance of better understanding the limitations of simple models as applied to large survey data sets like NEOWISE. This is particularly true as we move into an era where our understanding of the NEA population will be dominated by future large surveys such as NEO Surveyor.
First Light and Reionisation Epoch Simulations (FLARES) XVII: Learning the galaxy-halo connection at high redshifts
2024-10-31
Maxwell G. A. Maltz, Peter A. Thomas, Christoper C. Lovell, William J. Roper, Aswin P. Vijayan, Dimitrios Irodotou, Shihong Liao, Louise T. C. Seeyave, Stephen M. Wilkins
Understanding the galaxy-halo relationship is not only key for elucidating the interplay between baryonic and dark matter, it is essential for creating large mock galaxy catalogues from N-body simulations. High-resolution hydrodynamical simulations are limited to small volumes by their large computational demands, hindering their use for comparisons with wide-field observational surveys. We overcome this limitation by using the First Light and Reionisation Epoch Simulations (FLARES), a suite of high-resolution (M_gas = 1.8 x 10^6 M_Sun) zoom simulations drawn from a large, (3.2 cGpc)^3 box. We use an extremely randomised trees machine learning approach to model the relationship between galaxies and their subhaloes in a wide range of environments. This allows us to build mock catalogues with dynamic ranges that surpass those obtainable through periodic simulations. The low cost of the zoom simulations facilitates multiple runs of the same regions, differing only in the random number seed of the subgrid models; changing this seed introduces a butterfly effect, leading to random differences in the properties of matching galaxies. This randomness cannot be learnt by a deterministic machine learning model, but by sampling the noise and adding it post-facto to our predictions, we are able to recover the distributions of the galaxy properties we predict (stellar mass, star formation rate, metallicity, and size) remarkably well. We also explore the resolution-dependence of our models' performances and find minimal depreciation down to particle resolutions of order M_DM ~ 10^8 M_Sun, enabling the future application of our models to large dark matter-only boxes.
Asymptotic completeness for short-range N-body systems revisited
2024-10-31
Erik Skibsted
We review Yafaev's approach to asymptotic completeness for systems of particles mutually interacting with short-range potentials. The theory is based on computation of commutators with time-independent (mostly bounded) observables yielding a sufficient supply of Kato smoothness bounds.
Simulation-based inference of the 2D ex-situ stellar mass fraction distribution of galaxies using variational autoencoders
2024-10-31
Eirini Angeloudi, Marc Huertas-Company, Jesús Falcón-Barroso, Regina Sarmiento, Daniel Walo-Martín, Annalisa Pillepich, Jesús Vega Ferrero
Galaxies grow through star formation (in-situ) and accretion (ex-situ) of other galaxies. Reconstructing the relative contribution of these two growth channels is crucial for constraining the processes of galaxy formation in a cosmological context. In this on-going work, we utilize a conditional variational autoencoder along with a normalizing flow - trained on a state-of-the-art cosmological simulation - in an attempt to infer the posterior distribution of the 2D ex-situ stellar mass distribution of galaxies solely from observable two-dimensional maps of their stellar mass, kinematics, age and metallicity. Such maps are typically obtained from large Integral Field Unit Surveys such as MaNGA. We find that the average posterior provides an estimate of the resolved accretion histories of galaxies with a mean ~10% error per pixel. We show that the use of a normalizing flow to conditionally sample the latent space results in a smaller reconstruction error. Due to the probabilistic nature of our architecture, the uncertainty of our predictions can also be quantified. To our knowledge, this is the first attempt to infer the 2D ex-situ fraction maps from observable maps.
A Martingale-Free Introduction to Conditional Gaussian Nonlinear Systems
2024-10-31
Marios Andreou, Nan Chen
The Conditional Gaussian Nonlinear System (CGNS) is a broad class of nonlinear stochastic dynamical systems. Given the trajectories for a subset of state variables, the remaining follow a Gaussian distribution. Despite the conditionally linear structure, the CGNS exhibits strong nonlinearity, thus capturing many non-Gaussian characteristics observed in nature through its joint and marginal distributions. Desirably, it enjoys closed analytic formulae for the time evolution of its conditional Gaussian statistics, which facilitate the study of data assimilation and other related topics. In this paper, we develop a martingale-free approach to improve the understanding of CGNSs. This methodology provides a tractable approach to proving the time evolution of the conditional statistics by deriving results through time discretization schemes, with the continuous-time regime obtained via a formal limiting process as the discretization time-step vanishes. This discretized approach further allows for developing analytic formulae for optimal posterior sampling of unobserved state variables with correlated noise. These tools are particularly valuable for studying extreme events and intermittency and apply to high-dimensional systems. Moreover, the approach improves the understanding of different sampling methods in characterizing uncertainty. The effectiveness of the framework is demonstrated through a physics-constrained, triad-interaction climate model with cubic nonlinearity and state-dependent cross-interacting noise.
Are CMB derived cosmological parameters affected by foregrounds associated to nearby galaxies?
2024-10-31
Facundo Toscano, Frode K. Hansen, Diego Garcia Lambas, Heliana Luparello, Pablo Fosalba, Enrique Gaztañaga
We perform cosmological parameters estimation on Planck Cosmic Microwave Background (CMB) maps masking the recently discovered foreground related to nearby spiral galaxies. In addition, we also analyse the association between these foreground regions and recent claims of cosmological causal horizons in localized CMB parameter estimates. Our analysis shows consistent cosmological parameter values regardless of the masking approach, though reduced sky areas introduce larger uncertainties. By modelling the new extragalactic foreground, we identify a resemblance with local parameter variation maps with a statistical significance at the 3 sigma level, suggesting that a simplified foreground model partially accounts, (40-50)% correlation with 15% uncertainty, for the observed causal horizons. These findings add new evidence to the existence of the new foreground associated with large spiral galaxies and show that estimates of cosmological parameters on smaller patches on the sky can be largely affected by these foregrounds, but that the parameters taken over the full sky are unaltered.
Cross-correlation of Luminous Red Galaxies with ML-selected AGN in HSC-SSP II: AGN classification and clustering with DESI spectroscopy
2024-10-31
Rodrigo Córdova Rosado, Andy D. Goulding, Jenny E. Greene, Nickolas Kokron, Michael A. Strauss, ChangHoon Hahn, Grayson C. Petter, Ryan C. Hickox
An unresolved question in studies of active galactic nuclei (AGN) is whether their different classes probe different evolutionary stages of black hole--host galaxy interaction. We present the projected two-point cross-correlation function between a sample of Dark Energy Spectroscopic Instrument (DESI)-matched AGN selected from Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) optical + Wide-field Infrared Survey Explorer ($WISE$) mid-IR photometry, and DESI-designated luminous red galaxies, for $z\in 0.5-1.0$. The total overlap area is 43.4 deg$^2$, including $\sim27,000$ spectroscopic LRGs in our redshift range. We visually classified 1,991 matched HSC-DESI objects in our redshift range, spectroscopically confirming that 1,517 ($76\%$) of them are AGN. Of these 1,517 objects, $73\%$ are broad-line AGN, $27\%$ are obscured AGN. We infer that the parent HSC+$WISE$ AGN catalog has a number density of at least $\sim 240$ deg$^{-2}$, confirming it is one of the most complete optical/infrared AGN catalog to date. We investigate the AGN clustering as a function of the spectroscopic classification and infer the halo mass for each sample. The inferred average mass of the halos $\langle M_h\rangle$ that host unobscured broad-line AGN ($M_h \approx 10^{13.4}h^{-1}M_\odot$) is $\sim 5.5\times$ larger than the halos that host obscured AGN ($M_h \approx 10^{12.6}\, h^{-1}M_\odot$), at $2.8\sigma$ significance, in the same sense as our prior work based on photometric redshifts. This suggests that we may relax our concerns about systematic shifts in the inferred redshift distribution producing this halo mass difference. While we do not yet find statistically significant spectroscopic evidence that unobscured AGN reside in more massive halos than their obscured counterparts, further analyses are necessary to distinguish if more complex evolutionary histories are needed to model these AGN populations.
An Overview of Solar Orbiter Observations of Interplanetary Shocks in Solar Cycle 25
2024-10-31
D. Trotta, A. Dimmock, H. Hietala, X. Blanco-Cano, T. S. Horbury, R. Vainio, N. Dresing, I. C. Jebaraj, F. Espinosa, R. Gomez-Herrero, J. Rodriguez-Pacheco, Y. Kartavykh, D. Lario, J. Gieseler, M. Janvier, M. Maksimovic, N. Talebpour Sheshvan, C. J. Owen, E. K. J. Kilpua, R. Wimmer-Schweingruber
Interplanetary shocks are fundamental constituents of the heliosphere, where they form as a result of solar activity. We use previously unavailable measurements of interplanetary shocks in the inner heliosphere provided by Solar Orbiter, and present a survey of the first 100 shocks observed in situ at different heliocentric distances during the rising phase of solar cycle 25. The fundamental shock parameters (shock normals, shock normal angles, shock speeds, compression ratios, Mach numbers) have been estimated and studied as a function of heliocentric distance, revealing a rich scenario of configurations. Comparison with large surveys of shocks at 1~au show that shocks in the quasi-parallel regime and with high speed are more commonly observed in the inner heliosphere. The wave environment of the shocks has also been addressed, with about 50\% of the events exhibiting clear shock-induced upstream fluctuations. We characterize energetic particle responses to the passage of IP shocks at different energies, often revealing complex features arising from the interaction between IP shocks and pre-existing fluctuations, including solar wind structures being processed upon shock crossing. Finally, we give details and guidance on the access use of the present survey, available on the EU-project ``solar energetic particle analysis platform for the inner heliosphere'' (SERPENTINE) website. The algorithm used to identify shocks in large datasets, now publicly available, is also described.
Mutually Unbiased Bases in Composite Dimensions -- A Review
2024-10-31
Daniel McNulty, Stefan Weigert
Maximal sets of mutually unbiased bases are useful throughout quantum physics, both in a foundational context and for applications. To date, it remains unknown if complete sets of mutually unbiased bases exist in Hilbert spaces of dimensions different from a prime power, i.e. in composite dimensions such as six or ten. Fourteen mathematically equivalent formulations of the existence problem are presented. We comprehensively summarise analytic, computer-aided and numerical results relevant to the case of composite dimensions. Known modifications of the existence problem are reviewed and potential solution strategies are outlined.
2024-10-31
Joyce A. Guzik, Brian Kloppenborg, Jason Jackiewicz
Deneb, the prototype alpha Cygni variable, is a blue-white supergiant that shows irregular variability with quasi-period around 12 days in brightness and radial velocity. Abt et al. (2023) found that larger amplitude 12-day variations appear to resume abruptly and at an arbitrary phase and damp out after several cycles, with an interval of around 70 days between these resumptions. Here we make use of an 8.6-year photometric data set for Deneb from the Solar Mass Ejection Imager (SMEI) to better characterize this behavior. We find that the interval between pulsation resumptions is not exact, with the most common intervals between 100 and 120 days. Sometimes one or more intervals are skipped. We also examine AAVSO and Transiting Exoplanet Survey Satellite (TESS) light curves for alpha Cyg variables Rigel, Saiph, and Alnilam in Orion, Aludra in Canis Major, and 6 Cas to compare with the behavior of alpha Cyg. Except for 6 Cas, the time series are too short, or the observations too infrequent to draw any conclusions about similarities between the behavior of these stars and alpha Cyg. We also summarize results of evolution and pulsation modeling for Deneb and alpha Cyg variables from the literature. The alpha Cyg variables may not be a homogenous group with a common mechanism for their variability. It has not been determined whether they are on the first crossing of the Hertzsprung-Russell diagram toward the red supergiant phase or are on their second crossing after having been red supergiants. Future plans include examining BRITE Constellation data for Deneb, processing SMEI data for other bright alpha Cyg variables, and comparing 6 Cas light curves from AAVSO and TESS data taken concurrently.
Representative Social Choice: From Learning Theory to AI Alignment
2024-10-31
Tianyi Qiu
Social choice theory is the study of preference aggregation across a population, used both in mechanism design for human agents and in the democratic alignment of language models. In this study, we propose the representative social choice framework for the modeling of democratic representation in collective decisions, where the number of issues and individuals are too large for mechanisms to consider all preferences directly. These scenarios are widespread in real-world decision-making processes, such as jury trials, indirect elections, legislation processes, corporate governance, and, more recently, language model alignment. In representative social choice, the population is represented by a finite sample of individual-issue pairs based on which social choice decisions are made. We show that many of the deepest questions in representative social choice can be naturally formulated as statistical learning problems, and prove the generalization properties of social choice mechanisms using the theory of machine learning. We further formulate axioms for representative social choice, and prove Arrow-like impossibility theorems with new combinatorial tools of analysis. Our framework introduces the representative approach to social choice, opening up research directions at the intersection of social choice, learning theory, and AI alignment.
Deep Learning Frameworks for Cognitive Radio Networks: Review and Open Research Challenges
2024-10-31
Senthil Kumar Jagatheesaperumal, Ijaz Ahmad, Marko Höyhtyä, Suleman Khan, Andrei Gurtov
Deep learning has been proven to be a powerful tool for addressing the most significant issues in cognitive radio networks, such as spectrum sensing, spectrum sharing, resource allocation, and security attacks. The utilization of deep learning techniques in cognitive radio networks can significantly enhance the network's capability to adapt to changing environments and improve the overall system's efficiency and reliability. As the demand for higher data rates and connectivity increases, B5G/6G wireless networks are expected to enable new services and applications significantly. Therefore, the significance of deep learning in addressing cognitive radio network challenges cannot be overstated. This review article provides valuable insights into potential solutions that can serve as a foundation for the development of future B5G/6G services. By leveraging the power of deep learning, cognitive radio networks can pave the way for the next generation of wireless networks capable of meeting the ever-increasing demands for higher data rates, improved reliability, and security.
2024-10-31
Joyce A. Guzik, Helmut A. Abt, Jason Jackiewicz, Brian Kloppenborg
Deneb (alpha Cygni) is a bright (V magnitude 1.25) blue-white supergiant (spectral type A2 Ia) which shows variability in both radial velocity and photometric measurements. H. Abt reviewed radial velocity measurements by Paddock (1935) using the Lick observatory 36-inch telescope spectrograph during 1927-1935. Abt noticed resumptions of pulsations with a dominant quasi-period of around 12 days that occur at intervals of around 70 days, and damp out after a few cycles. These resumptions appear to happen at arbitrary phase. Perhaps another event like this was captured in a shorter series of radial velocity measurements by Abt in 1956. We examined subsequent radial velocity and photometric data available in the literature, along with photometric measurements by the TESS spacecraft and V-magnitude observations by AAVSO observers. We find some evidence for periodic resumptions of larger-amplitude pulsations in these data. However, longer contiguous data sets combined with more frequent sampling are needed to confirm this periodicity in resumption of pulsations, and to help answer many more questions about Deneb and the alpha Cygni variables.
Collective dynamics in spiking neural networks: A systematic review
2024-10-31
Afifurrahman, Mohd Hafiz Mohd
This study aims to review recent research on the collective behaviour of excitatory and inhibitory (E-I) spiking neural networks. The research methodology used is Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) procedures, comprising three primary stages: an initial search for literature in the SCOPUS database, a screening process based on specific inclusion and exclusion criteria, and a review of the selected literatures. Out of 491 documents from 2014 to 2024, 6 research papers are qualified for review. Four distinct dynamical states have been identified: synchrony, irregular behaviour, stationary state, and oscillatory dynamics. Our review findings suggest that the collective dynamics of E-I spiking neurons stem from the interaction of intrinsic neuronal characteristics, balance mechanisms, and the type of external stimuli. Additionally, the widespread use of Quadratic Integrate-and-Fire (QIF) neurons in the literature highlights its significance as a robust candidate for exploring collective behaviours in large-scale neuronal networks.
2024-10-31
Julius T. Gohsrich, Ayan Banerjee, Flore K. Kunst
The non-Hermitian (NH) skin effect is a truly NH feature, which manifests itself as an accumulation of states, known as skin states, on the boundaries of a system. In this perspective, we discuss several aspects of the NH skin effect focusing on the most interesting facets of this phenomenon. Non-normality and non-reciprocity are reviewed as necessary requirements to see the NH skin effect. We further discuss the NH skin effect as a topological effect that can be seen as a manifestation of a truly NH bulk-boundary correspondence, and show how topological boundary states can be distinguished from skin states. As most theoretical work has focused on studying the NH skin effect in the one-dimensional single-particle picture, recent developments of studying this effect in higher dimensions as well as in the many-body case are also highlighted. Lastly, experimental realizations and applications of the NH skin effect are reviewed.
Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding
2024-10-31
Jinlong He, Pengfei Li, Gang Liu, Shenjun Zhong
Multimodal Large Language Models (MLLMs) inherit the superior text understanding capabilities of LLMs and extend these capabilities to multimodal scenarios. These models achieve excellent results in the general domain of multimodal tasks. However, in the medical domain, the substantial training costs and the requirement for extensive medical data pose challenges to the development of medical MLLMs. Furthermore, due to the free-text form of answers, tasks such as visual grounding that need to produce output in a prescribed form become difficult for MLLMs. So far, there have been no medical MLLMs works in medical visual grounding area. For the medical vision grounding task, which involves identifying locations in medical images based on short text descriptions, we propose Parameter-efficient Fine-tuning medical multimodal large language models for Medcial Visual Grounding (PFMVG). To validate the performance of the model, we evaluate it on a public benchmark dataset for medical visual grounding, where it achieves competitive results, and significantly outperforming GPT-4v. Our code will be open sourced after peer review.
Mass scaling is widely used in finite element models of structural dynamics for increasing the critical time step of explicit time integration methods. While the field has been flourishing over the years, it still lacks a strong theoretical basis and mostly relies on numerical experiments as the only means of assessment. This contribution thoroughly reviews existing methods and connects them to established linear algebra results to derive rigorous eigenvalue bounds and condition number estimates. Our results cover some of the most successful mass scaling techniques, unraveling for the first time well-known numerical observations.
Promoting Reliable Knowledge about Climate Change: A Systematic Review of Effective Measures to Resist Manipulation on Social Media
2024-10-31
Aliaksandr Herasimenka, Xianlingchen Wang, Ralph Schroeder
We present a systematic review of peer-reviewed research into ways to mitigate manipulative information about climate change on social media. Such information may include disinformation, harmful influence campaigns, or the unintentional spread of misleading information. We find that commonly recommended approaches to addressing manipulation about climate change include corrective information sharing and education campaigns targeting media literacy. However, most relevant research fails to test the approaches and interventions it proposes. We locate research gaps that include the lack of attention to large commercial and political entities involved in generating and disseminating manipulation, video- and image-focused platforms, and computational methods to collect and analyze data. Evidence drawn from many studies demonstrates an emerging consensus about policies required to promote reliable knowledge about climate change and resist manipulation.
Electric, Magnetic and Quadrupole Form Factors and Charge Radii of Vector Mesons: From Light to Heavy Sector in a Contact Interaction
2024-10-31
R. J. Hernández-Pinto, L. X. Gutiérrez-Guerrero, M. A. Bedolla, A. Bashir
We present a detailed survey of electric, magnetic and quadrupole form factors of light and heavy spin-1 vector mesons. It complements our analogous analysis of the electromagnetic form factors of pseudoscalar and scalar mesons reported earlier. Our formalism is based upon the Schwinger-Dyson equations treatment of a vector $\times$ vector contact interaction and the Bethe-Salpeter equation description of relativistic two-body bound states. We compute the form factors, associated moments and charge radii, comparing these quantities to earlier theoretical studies and experimental results if and when possible. We also investigate the quark-mass dependence of the charge radii and find the anticipated hierarchy such that it decreases with increasing dressed quark masses. In addition, our analysis shows that the magnetic moment is independent of the mass of the light and heavy mesons. Our results agree with most measurements reported earlier, finding a negative quadrupole moment, implying the charge distribution is oblate.
The ability to read, interpret, and critique data visualizations has mainly been assessed using data visualization tasks like value retrieval. Although evidence on different facets of Visual Data Literacy (VDL) is well established in visualization research and includes numeracy, graph familiarity, or aesthetic elements, they have not been sufficiently considered in ability assessments. Here, VDL is considered a multidimensional ability whose facets can be partially self-assessed. We introduce an assessment in which VDL is deconstructed as a process of understanding, in reference to frameworks from the learning sciences. MAVIL, Multidimensional Assessment of Visual Data Literacy, is composed of six ability dimensions: General Impression/Abstract Thinking, Graph Elements/Familiarity, Aesthetic Perception, Visualization Criticism, Data Reading Tasks and Numeracy/Topic Knowledge. MAVIL was designed for general audiences and implemented in a survey (n=438), representative of Austria's age groups (18-74 years) and gender split. The survey mirrors the population's VDL and shows the perception of two climate data visualizations, a line and bar chart. We found that $48\%$ of respondents make mistakes with the simple charts, while $5\%$ believe that they cannot summarize the visualization content. About a quarter have deficits in comprehending simple data units, and $19-20\%$ are unfamiliar with each displayed chart type.
Carbon Neutral Greenhouse: Economic Model Predictive Control Framework for Education
2024-10-31
Marek Wadinger, Rastislav Fáber, Erika Pavlovičová, Radoslav Paulen
This paper presents a comprehensive framework aimed at enhancing education in modeling, optimal control, and nonlinear Model Predictive Control~(MPC) through a practical greenhouse climate control model. The framework includes a detailed mathematical model of lettuce growth and greenhouse, which are influenced by real-time external weather conditions obtained via an application programming interface~(API). Using this data, the MPC-based approach dynamically adjusts greenhouse conditions, optimizing plant growth and energy consumption and minimizing the social cost of CO\textsubscript{2}. The presented results demonstrate the effectiveness of this approach in balancing energy use with crop yield and reducing CO\textsubscript{2} emissions, contributing to economic efficiency and environmental sustainability. Besides optimizing lettuce production, the framework also provides a valuable resource for making control systems education more engaging and effective. The main aim is to provide students with a hands-on platform to understand the principles of modeling, the complexity of MPC and the trade-offs between profitability and sustainability in agricultural systems. This framework provides students with hands-on experience, helping them to understand the control theory better, connecting it to the practical implementation, and developing their problem-solving skills. The framework can be accessed at \url{ecompc4greenhouse.streamlit.app}.
The decline of bee and wind-based pollination systems in greenhouses due to controlled environments and limited access has boost the importance of finding alternative pollination methods. Robotic based pollination systems have emerged as a promising solution, ensuring adequate crop yield even in challenging pollination scenarios. This paper presents a comprehensive review of the current robotic-based pollinators employed in greenhouses. The review categorizes pollinator technologies into major categories such as air-jet, water-jet, linear actuator, ultrasonic wave, and air-liquid spray, each suitable for specific crop pollination requirements. However, these technologies are often tailored to particular crops, limiting their versatility. The advancement of science and technology has led to the integration of automated pollination technology, encompassing information technology, automatic perception, detection, control, and operation. This integration not only reduces labor costs but also fosters the ongoing progress of modern agriculture by refining technology, enhancing automation, and promoting intelligence in agricultural practices. Finally, the challenges encountered in design of pollinator are addressed, and a forward-looking perspective is taken towards future developments, aiming to contribute to the sustainable advancement of this technology.
Training on inclusivity and cultural diversity in the ALICE Collaboration
2024-10-31
Sami Räsänen
Large experimental Collaborations at the LHC (ALICE, ATLAS, CMS, and LHCb) bring together over 13,000 people from hundreds of institutes over the world. There are many (working) cultures inside these international Collaborations. It is important to acknowledge that cultural differences exist and manifest in our workspaces in various ways such as communication style, attitude and expectations, and ways to provide feedback on work, just to name a few examples. Various studies over the world have raised concerns on mental wellbeing of academics, especially PhD students. A survey on mental wellbeing was conducted among all major LHC Collaborations in 2023. The survey attracted a total of 404 replies ranging from students to experienced researchers. Although the number of responses is not small, it corresponds to only a few per cent of the whole LHC community, and hence we cannot be sure of how representative the sample is. However, this limited sample contained worrisome findings on wellbeing among LHC researchers. While moderate stress can enhance working performance and help in keeping deadlines, stress should remain in manageable level and one should take care of adequate recovery. However, there are some stress factors related to environment and ways we work that cause unnecessary load. In these proceedings, we will raise a few results on the LHC mental wellbeing survey and discuss trainings on inclusivity and cultural diversity in ALICE. These trainings aim to create a welcoming and positive work environment by strengthening the sense of community and commitment to the Collaboration. These, in turn, can have a positive impact both on wellbeing and productivity.
Physics of collective transport and traffic phenomena in biology: progress in 20 years
2024-10-31
Debashish Chowdhury, Andreas Schadschneider, Katsuhiro Nishinari
Enormous progress have been made in the last 20 years since the publication of our review \cite{csk05polrev} in this journal on transport and traffic phenomena in biology. In this brief article we present a glimpse of the major advances during this period. First, we present similarities and differences between collective intracellular transport of a single micron-size cargo by multiple molecular motors and that of a cargo particle by a team of ants on the basis of the common principle of load-sharing. Second, we sketch several models all of which are biologically motivated extensions of the Asymmetric Simple Exclusion Process (ASEP); some of these models represent the traffic of molecular machines, like RNA polymerase (RNAP) and ribosome, that catalyze template-directed polymerization of RNA and proteins, respectively, whereas few other models capture the key features of the traffic of ants on trails. More specifically, using the ASEP-based models we demonstrate the effects of traffic of RNAPs and ribosomes on random and `programmed' errors in gene expression as well as on some other subcellular processes. We recall a puzzling empirical result on the single-lane traffic of predatory ants {\it Leptogenys processionalis} as well as recent attempts to account for this puzzle. We also mention some surprising effects of lane-changing rules observed in a ASEP-based model for 3-lane traffic of army ants. Finally, we explain the conceptual similarities between the pheromone-mediated indirect communication, called stigmergy, between ants on a trail and the floor-field-mediated interaction between humans in a pedestrian traffic. For the floor-field model of human pedestrian traffic we present a major theoretical result that is relevant from the perspective of all types of traffic phenomena.
2024-10-31
Antonio Rago, Kristijonas Čyras, Jack Mumford, Oana Cocarascu
This chapter provides an overview of research works that present approaches with some degree of cross-fertilisation between Computational Argumentation and Machine Learning. Our review of the literature identified two broad themes representing the purpose of the interaction between these two areas: argumentation for machine learning and machine learning for argumentation. Across these two themes, we systematically evaluate the spectrum of works across various dimensions, including the type of learning and the form of argumentation framework used. Further, we identify three types of interaction between these two areas: synergistic approaches, where the Argumentation and Machine Learning components are tightly integrated; segmented approaches, where the two are interleaved such that the outputs of one are the inputs of the other; and approximated approaches, where one component shadows the other at a chosen level of detail. We draw conclusions about the suitability of certain forms of Argumentation for supporting certain types of Machine Learning, and vice versa, with clear patterns emerging from the review. Whilst the reviewed works provide inspiration for successfully combining the two fields of research, we also identify and discuss limitations and challenges that ought to be addressed in order to ensure that they remain a fruitful pairing as AI advances.
Features characterizing safe aerial-aquatic robots
2024-10-31
Andrea Giordano, Luca Romanello, Diego Perez Gonzalez, Mirko Kovac, Sophie F. Armanini
This paper underscores the importance of environmental monitoring, and specifically of freshwater ecosystems, which play a critical role in sustaining life and global economy. Despite their importance, insufficient data availability prevents a comprehensive understanding of these ecosystems, thereby impeding informed decision-making concerning their preservation. Aerial-aquatic robots are identified as effective tools for freshwater sensing, offering rapid deployment and avoiding the need of using ships and manned teams. To advance the field of aerial aquatic robots, this paper conducts a comprehensive review of air-water transitions focusing on the water entry strategy of existing prototypes. This analysis also highlights the safety risks associated with each transition and proposes a set of design requirements relating to robots' tasks, mission objectives, and safety measures. To further explore the proposed design requirements, we present a novel robot with VTOL capability, enabling seamless air water transitions.
Ferroelectric terpolymer films with enhanced cooling efficiency: An integrated approach considering electrocaloric response and dielectric losses
2024-10-31
Nouh Zeggai, Martino LoBue, Morgan Almanza
In response to the growing demand for more efficient and compact refrigeration and energy conversion devices, electrocaloric poly(vinylidene fluoride-trifluoroethylene-chlorofluoroethylene) is among the most promising active substances. However, despite its high electrocaloric response, the maximum efficiency achievable over a cooling cycle is hampered by losses. To overcome this major limitation, losses have been reduced by using an electrothermal poling treatment as well as by controlling the surface roughness. The upper bound of the efficiency computed over a thermodynamic cycle mimicking the working conditions of an actual cooling device is increased from 1% to 10% of the Carnot efficiency. This represents a major improvement in enhancing ferroelectric materials for advanced energy applications.
Adversarial attacks, which manipulate input data to undermine model availability and integrity, pose significant security threats during machine learning inference. With the advent of Large Vision-Language Models (LVLMs), new attack vectors, such as cognitive bias, prompt injection, and jailbreak techniques, have emerged. Understanding these attacks is crucial for developing more robust systems and demystifying the inner workings of neural networks. However, existing reviews often focus on attack classifications and lack comprehensive, in-depth analysis. The research community currently needs: 1) unified insights into adversariality, transferability, and generalization; 2) detailed evaluations of existing methods; 3) motivation-driven attack categorizations; and 4) an integrated perspective on both traditional and LVLM attacks. This article addresses these gaps by offering a thorough summary of traditional and LVLM adversarial attacks, emphasizing their connections and distinctions, and providing actionable insights for future research.
Radiation forces and torques in optics and acoustics
2024-10-31
Ivan Toftul, Sebastian Golat, Francisco J. Rodríguez-Fortuño, Franco Nori, Yuri Kivshar, Konstantin Y. Bliokh
Mechanical action of various kinds of waves have been known for several centuries. The first tide of scientific interest in wave-induced forces and torques emerged at the end of the 19th / beginning of the 20th centuries, with the development of wave theories and the concepts of wave momentum and angular momentum. A second tide appeared in the past several decades, connected to technological breakthroughs: the creation of lasers and the controlled generation of structured wavefields. This resulted in several important discoveries: optical trapping and manipulation of small particles, from atomic to micro sizes, as well as acoustic and acoustofluidic manipulation and sorting of larger particles, including biological cells and samples. Here we provide a unifying review of optical and acoustic forces and torques on various particles, addressing both their theoretical fundamentals and the main applications. Our approach employs the universal connection between the local energy, momentum, and spin densities in the wave fields and the principal forces and torques on small Rayleigh particles. Moreover, we describe the most important cases of nontrivial forces and complex particles: lateral and pulling forces, chiral and anisotropic particles, etc. We also describe the main experimental achievements and applications related to optical and acoustic forces and torques in structured wave fields. Our goal is to illuminate the common fundamental origin and close interconnections between the mechanical actions of optical and acoustic fields, in order to facilitate their profound understanding and the further development of optomechanical and acoustomechanical applications.
In the digital age, the exposure of sensitive information poses a significant threat to security. Leveraging the ubiquitous nature of code-sharing platforms like GitHub and BitBucket, developers often accidentally disclose credentials and API keys, granting unauthorized access to critical systems. Despite the availability of tools for detecting such breaches in source code, detecting secret breaches in software issue reports remains largely unexplored. This paper presents a novel technique for secret breach detection in software issue reports using a combination of language models and state-of-the-art regular expressions. We highlight the challenges posed by noise, such as log files, URLs, commit IDs, stack traces, and dummy passwords, which complicate the detection process. By employing relevant pre-processing techniques and leveraging the capabilities of advanced language models, we aim to mitigate potential breaches effectively. Drawing insights from existing research on secret detection tools and methodologies, we propose an approach combining the strengths of state-of-the-art regexes with the contextual understanding of language models. Our method aims to reduce false positives and improve the accuracy of secret breach detection in software issue reports. We have curated a benchmark dataset of 25000 instances with only 437 true positives. Although the data is highly skewed, our model performs well with a 0.6347 F1-score, whereas state-of-the-art regular expression hardly manages to get a 0.0341 F1-Score with a poor precision score. We have also developed a secret breach mitigator tool for GitHub, which will warn the user if there is any secret in the posted issue report. By addressing this critical gap in contemporary research, our work aims at enhancing the overall security posture of software development practices.
A model for the emission line galaxy luminosity function and flux ratios at high-redshifts
2024-10-31
Aadarsh Pathak, J. Stuart B. Wyithe, Ralph S. Sutherland, L. J Kewley
We present $[OIII]/H_{\rm \beta}$ emision line flux ratio predictions for galaxies at $z \sim 7-9$ using the MAPPINGS V v5.2.0 photoionization modelling code combined with an analytic galaxy formation model. Properties such as pressure and ionization parameter that determine emission line properties are thought to evolve towards high redshift. In order to determine the range of expected interstellar conditions we extend previous modelling of the Star Formation Rate Density (SFRD) function to calculate the metallicity and ionization parameter, and incorporate the potential impact of turbulence on the density of the ISM. To validate our emission line predictions we calculate the [OIII] line luminosity and its dependence on UV luminosity, as well as the flux ratio $[OIII]/H_{\rm \beta}$ and its variation with the line luminosity, finding that both reproduce recent JWST observations from the FRESCO survey. We also use our model to predict the number counts of emission line galaxies across a range of redshift as well as the dependence of $[OIII]/H_{\rm \beta}$ on ionization parameter and metallicity. Finally, we show that the dependence of flux ratio on luminosity may provide a diagnostic of turbulent motion in galactic discs.
Bayesian Hierarchical Model for Synthesizing Registry and Survey Data on Female Breast Cancer Prevalence
2024-10-31
Qiao Wang, Chester Lee Schmaltz, Jeannette Jackson-Thompson, Dongchu Sun, Zhuoqiong He, Zhongheng Cai, Hwanhee Hong
In public health, it is critical for policymakers to assess the relationship between the disease prevalence and associated risk factors or clinical characteristics, facilitating effective resources allocation. However, for diseases like female breast cancer (FBC), reliable prevalence data at specific geographical levels, such as the county-level, are limited because the gold standard data typically come from long-term cancer registries, which do not necessarily collect needed risk factors. In addition, it remains unclear whether fitting each model separately or jointly results in better estimation. In this paper, we identify two data sources to produce reliable county-level prevalence estimates in Missouri, USA: the population-based Missouri Cancer Registry (MCR) and the survey-based Missouri County-Level Study (CLS). We propose a two-stage Bayesian model to synthesize these sources, accounting for their differences in the methodological design, case definitions, and collected information. The first stage involves estimating the county-level FBC prevalence using the raking method for CLS data and the counting method for MCR data, calibrating the differences in the methodological design and case definition. The second stage includes synthesizing two sources with different sets of covariates using a Bayesian generalized linear mixed model with Zeller-Siow prior for the coefficients. Our data analyses demonstrate that using both data sources have better results than at least one data source, and including a data source membership matters when there exist systematic differences in these sources. Finally, we translate results into policy making and discuss methodological differences for data synthesis of registry and survey data.
The Microlensing Event Rate and Optical Depth from MOA-II 9 year Survey toward the Galactic Bulge
2024-10-31
Kansuke Nunota, Takahiro Sumi, Naoki Koshimoto, Nicholas J. Rattenbury, Fumio Abe, Richard Barry, David P. Bennett, Aparna Bhattacharya, Akihiko Fukui, Ryusei Hamada, Shunya Hamada, Naoto Hamasaki, Yuki Hirao, Stela Ishitani Silva, Yoshitaka Itow, Yutaka Matsubara, Shota Miyazaki, Yasushi Muraki, Tsutsumi Nagai, Greg Olmschenk, Clement Ranc, Yuki K. Satoh, Daisuke Suzuki, Paul J. Tristram, Aikaterini Vandorou, Hibiki Yama
We present measurements of the microlensing optical depth and event rate toward the Galactic bulge using the dataset from the 2006--2014 MOA-II survey, which covers 22 bulge fields spanning ~42 deg^2 between -5 deg < l < 10 deg and -7 deg < b < -1 deg. In the central region with |l|<5 deg, we estimate an optical depth of {\tau} = [1.75+-0.04]*10^-6exp[(0.34+-0.02)(3 deg-|b|)] and an event rate of {\Gamma} = [16.08+-0.28]*10^-6exp[(0.44+-0.02)(3 deg-|b|)] star^-1 year^-1 using a sample consisting of 3525 microlensing events, with Einstein radius crossing times of tE < 760 days and source star magnitude of IsWe confirm our results are consistent with the latest measurements from OGLE-IV 8 year dataset (Mr\'oz et al. 2019). We find our result is inconsistent with a prediction based on Galactic models, especially in the central region with |b|<3 deg. These results can be used to improve the Galactic bulge model, and more central regions can be further elucidated by future microlensing experiments, such as The PRime-focus Infrared Microlensing Experiment (PRIME) and Nancy Grace Roman Space Telescope.
2024-10-31
Ricardo Falcon-Perez, Ruohan Gao, Gregor Mueckl, Sebastia V. Amengual Gari, Ishwarya Ananthabhotla
The task of Novel View Acoustic Synthesis (NVAS) - generating Room Impulse Responses (RIRs) for unseen source and receiver positions in a scene - has recently gained traction, especially given its relevance to Augmented Reality (AR) and Virtual Reality (VR) development. However, many of these efforts suffer from similar limitations: they infer RIRs in the time domain, which prove challenging to optimize; they focus on scenes with simple, single-room geometries; they infer only single-channel, directionally-independent acoustic characteristics; and they require inputs, such as 3D geometry meshes with material properties, that may be impractical to obtain for on-device applications. On the other hand, research suggests that sample-wise accuracy of RIRs is not required for perceptual plausibility in AR and VR. Standard acoustic parameters like Clarity Index (C50) or Reverberation Time (T60) have been shown to capably describe pertinent characteristics of the RIRs, especially late reverberation. To address these gaps, this paper introduces a new task centered on estimating spatially distributed acoustic parameters that can be then used to condition a simple reverberator for arbitrary source and receiver positions. The approach is modelled as an image-to-image translation task, which translates 2D floormaps of a scene into 2D heatmaps of acoustic parameters. We introduce a new, large-scale dataset of 1000 scenes consisting of complex, multi-room apartment conditions, and show that our method outperforms statistical baselines significantly. Moreover, we show that the method also works for directionally-dependent (i.e. beamformed) parameter prediction. Finally, the proposed method operates on very limited information, requiring only a broad outline of the scene and a single RIR at inference time.
A Systematic Review of NeurIPS Dataset Management Practices
2024-10-31
Yiwei Wu, Leah Ajmani, Shayne Longpre, Hanlin Li
As new machine learning methods demand larger training datasets, researchers and developers face significant challenges in dataset management. Although ethics reviews, documentation, and checklists have been established, it remains uncertain whether consistent dataset management practices exist across the community. This lack of a comprehensive overview hinders our ability to diagnose and address fundamental tensions and ethical issues related to managing large datasets. We present a systematic review of datasets published at the NeurIPS Datasets and Benchmarks track, focusing on four key aspects: provenance, distribution, ethical disclosure, and licensing. Our findings reveal that dataset provenance is often unclear due to ambiguous filtering and curation processes. Additionally, a variety of sites are used for dataset hosting, but only a few offer structured metadata and version control. These inconsistencies underscore the urgent need for standardized data infrastructures for the publication and management of datasets.
Learning Optimal Interaction Weights in Multi-Agents Systems
2024-10-31
Sara Honarvar, Yancy Diaz-Mercado
This paper presents a spatio-temporal inverse optimal control framework for understanding interactions in multi-agent systems (MAS). We employ a graph representation approach and model the dynamics of interactions between agents as state-dependent edge weights in a consensus algorithm, incorporating both spatial and temporal dynamics. Our method learns these edge weights from trajectory observations, such as provided by expert demonstrations, which allows us to capture the complexity of nonlinear, distributed interaction behaviors. We derive necessary and sufficient conditions for the optimality of these interaction weights, explaining how the network topology affects MAS coordination. The proposed method is demonstrated on a multi-agent formation control problem, where we show its effectiveness in recovering the interaction weights and coordination patterns from sample trajectory data.
Evolutionary accumulation modelling in AMR: machine learning to infer and predict evolutionary dynamics of multi-drug resistance
2024-10-31
Jessica Renz, Kazeem A. Dauda, Olav N. L. Aga, Ramon Diaz-Uriarte, Iren H. Löhr, Bjørn Blomberg, Iain G. Johnston
Can we understand and predict the evolutionary pathways by which bacteria acquire multi-drug resistance (MDR)? These questions have substantial potential impact in basic biology and in applied approaches to address the global health challenge of antimicrobial resistance (AMR). Here, we review how a class of machine learning approaches called evolutionary accumulation modelling (EvAM) may help reveal these dynamics using genetic and/or phenotypic AMR datasets, without requiring longitudinal sampling. These approaches are well-established in cancer progression and evolutionary biology, but currently less used in AMR research. We discuss how EvAM can learn the evolutionary pathways by which drug resistances and AMR features are acquired as pathogens evolve, predict next evolutionary steps, identify influences between AMR features, and explore differences in MDR evolution between regions, demographics, and more. We demonstrate a case study on MDR evolution in Mycobacterium tuberculosis and discuss the strengths and weaknesses of these approaches, providing links to some approaches for implementation.
2024-10-31
Marek Kirejczyk, Maciej Kalka, Leonid Logvinov
This paper presents a comprehensive analysis of storage proofs in the Ethereum ecosystem, examining their role in addressing historical and cross-chain state access challenges. We systematically review existing approaches to historical state verification, comparing Merkle Mountain Range (MMR) and Merkle-Patricia trie (MPT) architectures. An analysis involves their respective performance characteristics within zero-knowledge contexts, where performance challenges related to Keccak-256 are explored. The paper also examines the cross-chain verification, particularly focusing on the interactions between Ethereum and Layer 2 networks. Through careful analysis of storage proof patterns across different network configurations, we identify and formalize three architectures for cross-chain verification. By organizing this complex technical landscape, this analysis provides a structured framework for understanding storage proof implementations in the Ethereum ecosystem, offering insights into their practical applications and limitations.
2024-10-31
Carlo Bellavita, Vassilis Daskalogiannis, Georgios Stylogiannis
This article aims to explore the most recent developments in the study of the Hilbert matrix, acting as an operator on spaces of analytic functions and sequence spaces. We present the latest advances in this area, aiming to provide a concise overview for researchers interested in delving into the captivating theory of operator matrices.
What Makes An Expert? Reviewing How ML Researchers Define "Expert"
2024-10-31
Mark Díaz, Angela DR Smith
Human experts are often engaged in the development of machine learning systems to collect and validate data, consult on algorithm development, and evaluate system performance. At the same time, who counts as an 'expert' and what constitutes 'expertise' is not always explicitly defined. In this work, we review 112 academic publications that explicitly reference 'expert' and 'expertise' and that describe the development of machine learning (ML) systems to survey how expertise is characterized and the role experts play. We find that expertise is often undefined and forms of knowledge outside of formal education and professional certification are rarely sought, which has implications for the kinds of knowledge that are recognized and legitimized in ML development. Moreover, we find that expert knowledge tends to be utilized in ways focused on mining textbook knowledge, such as through data annotation. We discuss the ways experts are engaged in ML development in relation to deskilling, the social construction of expertise, and implications for responsible AI development. We point to a need for reflection and specificity in justifications of domain expert engagement, both as a matter of documentation and reproducibility, as well as a matter of broadening the range of recognized expertise.
SeafloorAI: A Large-scale Vision-Language Dataset for Seafloor Geological Survey
2024-10-31
Kien X. Nguyen, Fengchun Qiao, Arthur Trembanis, Xi Peng
A major obstacle to the advancements of machine learning models in marine science, particularly in sonar imagery analysis, is the scarcity of AI-ready datasets. While there have been efforts to make AI-ready sonar image dataset publicly available, they suffer from limitations in terms of environment setting and scale. To bridge this gap, we introduce SeafloorAI, the first extensive AI-ready datasets for seafloor mapping across 5 geological layers that is curated in collaboration with marine scientists. We further extend the dataset to SeafloorGenAI by incorporating the language component in order to facilitate the development of both vision- and language-capable machine learning models for sonar imagery. The dataset consists of 62 geo-distributed data surveys spanning 17,300 square kilometers, with 696K sonar images, 827K annotated segmentation masks, 696K detailed language descriptions and approximately 7M question-answer pairs. By making our data processing source code publicly available, we aim to engage the marine science community to enrich the data pool and inspire the machine learning community to develop more robust models. This collaborative approach will enhance the capabilities and applications of our datasets within both fields.
DESIVAST: A Catalog of Low-Redshift Voids using Data from the DESI DR1 Bright Galaxy Survey
2024-10-31
Hernan Rincon, Segev BenZvi, Kelly Douglass, Dahlia Veyrat, Jessica Nicole Aguilar, Steven Ahlen, Davide Bianchi, David Brooks, Todd Claybaugh, Shaun Cole, Axel de la Macorra, Peter Doel, Andreu Font-Ribera, Jaime E. Forero-Romero, Enrique Gaztañaga, Satya Gontcho A Gontcho, Gaston Gutierrez, Klaus Honscheid, Cullan Howlett, Stephanie Juneau, Robert Kehoe, Sergey Koposov, Andrew Lambert, Martin Landriau, Laurent Le Guillou, Aaron Meisner, Ramon Miquel, John Moustakas, Gustavo Niz, Will Percival, Francisco Prada, Ignasi Pérez-Ràfols, Graziano Rossi, Eusebio Sanchez, Michael Schubnell, Hee-Jong Seo, David Sprayberry, Gregory Tarlé, Benjamin Alan Weaver, Hu Zou
We present three separate void catalogs created using a volume-limited sample of the DESI Year 1 Bright Galaxy Survey. We use the algorithms VoidFinder and V2 to construct void catalogs out to a redshift of z=0.24. We obtain 1,461 interior voids with VoidFinder, 420 with V2 using REVOLVER pruning, and 295 with V2 using VIDE pruning. Comparing our catalog with an overlapping SDSS void catalog, we find generally consistent void properties but significant differences in the void volume overlap, which we attribute to differences in the galaxy selection and survey masks. These catalogs are suitable for studying the variation in galaxy properties with cosmic environment and for cosmological studies.
Enhancing Brain Source Reconstruction through Physics-Informed 3D Neural Networks
2024-10-31
Marco Morik, Ali Hashemi, Klaus-Robert Müller, Stefan Haufe, Shinichi Nakajima
Reconstructing brain sources is a fundamental challenge in neuroscience, crucial for understanding brain function and dysfunction. Electroencephalography (EEG) signals have a high temporal resolution. However, identifying the correct spatial location of brain sources from these signals remains difficult due to the ill-posed structure of the problem. Traditional methods predominantly rely on manually crafted priors, missing the flexibility of data-driven learning, while recent deep learning approaches focus on end-to-end learning, typically using the physical information of the forward model only for generating training data. We propose the novel hybrid method 3D-PIUNet for EEG source localization that effectively integrates the strengths of traditional and deep learning techniques. 3D-PIUNet starts from an initial physics-informed estimate by using the pseudo inverse to map from measurements to source space. Secondly, by viewing the brain as a 3D volume, we use a 3D convolutional U-Net to capture spatial dependencies and refine the solution according to the learned data prior. Training the model relies on simulated pseudo-realistic brain source data, covering different source distributions. Trained on this data, our model significantly improves spatial accuracy, demonstrating superior performance over both traditional and end-to-end data-driven methods. Additionally, we validate our findings with real EEG data from a visual task, where 3D-PIUNet successfully identifies the visual cortex and reconstructs the expected temporal behavior, thereby showcasing its practical applicability.
Learning Low-Dimensional Strain Models of Soft Robots by Looking at the Evolution of Their Shape with Application to Model-Based Control
2024-10-31
Ricardo Valadas, Maximilian Stölzle, Jingyue Liu, Cosimo Della Santina
Obtaining dynamic models of continuum soft robots is central to the analysis and control of soft robots, and researchers have devoted much attention to the challenge of proposing both data-driven and first-principle solutions. Both avenues have, however, shown their limitations; the former lacks structure and performs poorly outside training data, while the latter requires significant simplifications and extensive expert knowledge to be used in practice. This paper introduces a streamlined method for learning low-dimensional, physics-based models that are both accurate and easy to interpret. We start with an algorithm that uses image data (i.e., shape evolutions) to determine the minimal necessary segments for describing a soft robot's movement. Following this, we apply a dynamic regression and strain sparsification algorithm to identify relevant strains and define the model's dynamics. We validate our approach through simulations with various planar soft manipulators, comparing its performance against other learning strategies, showing that our models are both computationally efficient and 25x more accurate on out-of-training distribution inputs. Finally, we demonstrate that thanks to the capability of the method of generating physically compatible models, the learned models can be straightforwardly combined with model-based control policies.
High-velocity outflows persist up to 1 Gyr after a starburst in recently-quenched galaxies at z > 1
2024-10-31
Elizabeth Taylor, David Maltby, Omar Almaini, Michael Merrifield, Vivienne Wild, Kate Rowlands, Jimi Harrold
High-velocity outflows are ubiquitous in star-forming galaxies at cosmic noon, but are not as common in passive galaxies at the same epoch. Using optical spectra of galaxies selected from the UKIDSS Ultra Deep Survey (UDS) at z > 1, we perform a stacking analysis to investigate the transition in outflow properties along a quenching time sequence. To do this, we use MgII (2800 A) absorption profiles to investigate outflow properties as a function of time since the last major burst of star formation (tburst). We find evidence for high-velocity outflows in the star-forming progenitor population (vout ~ 1400 $\pm$ 210 km/s), for recently quenched galaxies with tburst < 0.6 Gyr (vout ~ 990 $\pm$ 250 km/s), and for older quenched galaxies with 0.6 < tburst < 1 Gyr (vout ~ 1400 $\pm$ 220 km/s). The oldest galaxies (tburst > 1 Gyr) show no evidence for significant outflows. Our samples show no signs of AGN in optical observations, suggesting that any AGN in these galaxies have very short duty cycles, and were 'off' when observed. The presence of significant outflows in the older quenched galaxies (tburst > 0.6 Gyr) is difficult to explain with starburst activity, however, and may indicate energy input from episodic AGN activity as the starburst fades.
Improving the Global SMEFT Picture with Bounds on Neutrino NSI
2024-10-31
Pilar Coloma, Enrique Fernández-Martínez, Jacobo López-Pavón, Xabier Marcano, Daniel Naredo-Tuero, Salvador Urrea
We analyze how neutrino oscillation and coherent elastic neutrino-nucleus scattering data impact the global SMEFT fit. We first review the mapping between the SMEFT parameters and the so-called NSI framework, commonly considered in the neutrino literature. We also present a detailed discussion of how the measurements for the normalization of neutrino fluxes and cross sections, that will also be affected by the new physics, indirectly impact the measured oscillation probabilities. We then analyze two well-motivated simplified scenarios. Firstly, we study a lepton flavour conserving case, usually assumed in global SMEFT analyses, showing the complementarity of neutrino oscillation and CE$\nu$NS experiments with other low-energy observables. We find that the inclusion of neutrino data allows to constrain previously unbounded SMEFT operators involving the tau flavour and confirm the improvement of the constraint on a combination of Wilson coefficients previously identified. Moreover, we find that neutrino oscillation constraints on NSI are improved when embedded in the global SMEFT framework. Secondly, we study a lepton flavour violating scenario and find that neutrino data also improves over previously derived global constraints thanks to its sensitivity to new combinations of Wilson coefficients.
Tripling the Census of Dwarf AGN Candidates Using DESI Early Data
2024-10-31
Ragadeepika Pucha, S. Juneau, Arjun Dey, M. Siudek, M. Mezcua, J. Moustakas, S. BenZvi, K. Hainline, R. Hviding, Yao-Yuan Mao, D. M. Alexander, R. Alfarsy, C. Circosta, Wei-Jian Guo, V. Manwadkar, P. Martini, B. A. Weaver, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, R. Canning, T. Claybaugh, K. Dawson, A. de la Macorra, Biprateep Dey, P. Doel, A. Font-Ribera, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, R. Kehoe, S. E. Koposov, A. Lambert, M. Landriau, L. Le Guillou, A. Meisner, R. Miquel, F. Prada, G. Rossi, E. Sanchez, D. Schlegel, M. Schubnell, H. Seo, D. Sprayberry, G. Tarlé, H. Zou
Using early data from the Dark Energy Spectroscopic Instrument (DESI) survey, we search for AGN signatures in 410,757 line-emitting galaxies. By employing the BPT emission-line ratio diagnostic diagram, we identify AGN in 75,928/296,261 ($\approx$25.6%) high-mass ($\log (M_{\star}/\rm M_{\odot}) >$ 9.5) and 2,444/114,496 ($\approx$2.1%) dwarf ($\log (M_{\star}/\rm M_{\odot}) \leq$ 9.5) galaxies. Of these AGN candidates, 4,181 sources exhibit a broad H$\alpha$ component, allowing us to estimate their BH masses via virial techniques. This study more than triples the census of dwarf AGN as well as that of intermediate-mass black hole (IMBH; $M_{\rm BH} \le 10^6~\rm M_{\odot}$) candidates, spanning a broad discovery space in stellar mass (7 $< \log (M_{\star}/\rm M_{\odot}) <$ 12) and redshift (0.001 $< \rm z <$ 0.45). The observed AGN fraction in dwarf galaxies ($\approx$2.1%) is nearly four times higher than prior estimates, primarily due to DESI's smaller fiber size, which enables the detection of lower luminosity dwarf AGN candidates. We also extend the $M_{\rm BH}$ - $M_{\star}$ scaling relation down to $\log (M_{\star}/\rm M_{\odot}) \approx$ 8.5 and $\log (M_{\rm BH}/M_{\odot}) \approx$ 4.4, with our results aligning well with previous low-redshift studies. The large statistical sample of dwarf AGN candidates from current and future DESI releases will be invaluable for enhancing our understanding of galaxy evolution at the low-mass end of the galaxy mass function.
2024-10-31
Maximilian Ruhdorfer, Ennio Salvioni, Andrea Wulzer
We survey the opportunities offered by the detection of the forward muons that accompany the creation of neutral effective vector bosons at a muon collider, in different kinematic regimes. Vectors with relatively low energy produce the Higgs boson and the extended muon angular coverage enables studies of the Higgs properties, such as the measurement of the inclusive production cross section and of the branching ratio to invisible final states. New heavy particles could be produced by vectors of higher energy, through Higgs portal interactions. If the new particles are invisible, the detection of the forward muons is essential in order to search for this scenario. The angular correlations of the forward muons are sensitive to the quantum interference between the vector boson helicity amplitudes and can be exploited for the characterisation of vector boson scattering and fusion processes. This is illustrated by analysing the CP properties of the Higgs coupling to the Z boson. Our findings provide a physics case and a set of benchmarks for the design of a dedicated forward muon detector.
Differing Manifestations of Spatial Curvature in Cosmological FRW Models
2024-10-31
Meir Shimon, Yoel Rephaeli
We find statistical evidence for a mismatch between the (global) spatial curvature parameter $K$ in the geodesic equation for incoming photons, and the corresponding parameter in the Friedmann equation that determines the time evolution of the background spacetime and its perturbations. The mismatch hereafter referred to as `curvature-slip' is especially evident when the SH0ES prior on the current expansion rate is assumed. This result is based on joint analyses of cosmic microwave background (CMB) observations with the PLANCK satellite (P18), first year results of the Dark Energy Survey (DES), Baryonic Oscillation (BAO) data, and - at a lower level of significance - also on Pantheon SNIa (SN) catalog. For example, the betting odds against the Null Hypothesis are greater than $10^7$:1, 1400:1 and 1000:1 when P18+SH0ES, P18+DES+SH0ES, and P18+BAO+SH0ES, respectively, are considered. Datasets involving SNIa weaken this curvature slip considerably. Notably, even when the SH0ES prior is not imposed the betting odds for the rejection of the Null Hypothesis are 70:1 and 160:1 in cases where P18+DES and P18+BAO are considered. When the SH0ES prior is imposed, global fit of the modified model (that allows for a nonvanishing `curvature slip') strongly outperforms that of $\Lambda$CDM as is manifested by significant Deviance Information Criterion (DIC) gains, ranging between 7 and 23, depending on the dataset combination considered. Even in comparison to K$\Lambda$CDM the proposed model results in significant, albeit smaller, DIC gains when SN data are excluded. Our finding could possibly be interpreted as an inherent inconsistency between the (idealized) maximally symmetric nature of the FRW metric, and the dynamical evolution of the GR-based homogeneous and isotropic $\Lambda$CDM model (abridged)
What the %PCSA? Addressing Diversity in Lower-Limb Musculoskeletal Models: Age- and Sex-related Differences in PCSA and Muscle Mass
2024-10-31
R. Maarleveld, H. E. J. Veeger, F. C. T. van der Helm, J. Son, R. L. Lieber, E. van der Kruk
Musculoskeletal (MSK) models offer a non-invasive way to understand biomechanical loads on joints and tendons, which are difficult to measure directly. Variations in muscle strength, especially relative differences between muscles, significantly impact model outcomes. Typically, scaled generic MSK models use maximum isometric forces that are not adjusted for different demographics, raising concerns about their accuracy. This review provides an overview on experimentally derived strength parameters, including physiological cross-sectional area (PCSA), muscle mass (Mm), and relative muscle mass (%Mm), which is the relative distribution of muscle mass across the leg. We analysed differences by age and sex, and compared open-source lower limb MSK model parameters with experimental data from 57 studies. Our dataset, with records dating back to 1884, shows that uniformly increasing all maximum isometric forces in MSK models does not capture key muscle ratio differences due to age and sex. Males have a higher proportion of muscle mass in the rectus femoris and semimembranosus muscles, while females have a greater relative muscle mass in the pelvic (gluteus maximus and medius) and ankle muscles (tibialis anterior, tibialis posterior, and extensor digitorum longus). Older adults have a higher relative muscle mass in the gluteus medius, while younger individuals show more in the gastrocnemius. Current MSK models do not accurately represent muscle mass distribution for specific age or sex groups, and none of them accurately reflect female muscle mass distribution. Further research is needed to explore musculotendon age- and sex differences.
VecCity: A Taxonomy-guided Library for Map Entity Representation Learning
2024-10-31
Wentao Zhang, Jingyuan Wang, Yifan Yang, Leong Hou U
Electronic maps consist of diverse entities, such as points of interest (POIs), road networks, and land parcels, playing a vital role in applications like ITS and LBS. Map entity representation learning (MapRL) generates versatile and reusable data representations, providing essential tools for efficiently managing and utilizing map entity data. Despite the progress in MapRL, two key challenges constrain further development. First, existing research is fragmented, with models classified by the type of map entity, limiting the reusability of techniques across different tasks. Second, the lack of unified benchmarks makes systematic evaluation and comparison of models difficult. To address these challenges, we propose a novel taxonomy for MapRL that organizes models based on functional module-such as encoders, pre-training tasks, and downstream tasks-rather than by entity type. Building on this taxonomy, we present a taxonomy-driven library, VecCity, which offers easy-to-use interfaces for encoding, pre-training, fine-tuning, and evaluation. The library integrates datasets from nine cities and reproduces 21 mainstream MapRL models, establishing the first standardized benchmarks for the field. VecCity also allows users to modify and extend models through modular components, facilitating seamless experimentation. Our comprehensive experiments cover multiple types of map entities and evaluate 21 VecCity pre-built models across various downstream tasks. Experimental results demonstrate the effectiveness of VecCity in streamlining model development and provide insights into the impact of various components on performance. By promoting modular design and reusability, VecCity offers a unified framework to advance research and innovation in MapRL. The code is available at https://github.com/Bigscity-VecCity/VecCity.
Soft polymers are ubiquitous materials found in nature and as engineering materials with properties varying from rate-independent to significantly rate-dependent depending on the crosslinking mechanisms. Current fracture toughness measures such as energy release rate are non-unique for rate-dependent soft materials for varying loading profiles and specimen geometries. Works on modeling fracture in rate-dependent soft polymers are limited to crack tip stress field analyses or crack tip driving force approaches for specific pre-cracked geometries. We have developed a model to predict damage initiation and growth in soft polymers based on a generalized multi-mechanism gradient damage framework. We propose and show that a critical value of stress work $\mathit{W}_{cr}$ can uniquely quantify the total energy dissipation per unit referential volume associated with the complete failure of a material point under a loading mode. $\mathit{W}_{cr}$ can be evaluated using homogeneous deformation experiments and without a constitutive model. $\mathit{W}_{cr}$ is demonstrated to be approximately constant with strain rate for two rate-dependent soft polymers and different loading modes for an elastomer. We propose the energetic contribution to $\mathit{W}_{cr}$ as a suitable damage initiation criterion. The proposed initiation criterion in the damage model enabled successful predictions of fracture in an important rate-stiffening soft polymer Polyborosiloxane in a variety of experiments involving different specimen geometries and loading conditions. The model also provides a consistent energy density estimate for fracture-associated microstructural processes in Polyborosiloxane. The broader applicability of the fracture model is shown by its ability to predict fracture in an elastomer (EPDM) and another viscous soft polymer (EPS25 vitrimer).
Towards Monte Carlo based Full Spectrum Modeling of Airborne Gamma-Ray Spectrometry Systems
2024-10-31
David Breitenmoser
This monograph presents advancements in Airborne Gamma-Ray Spectrometry (AGRS), a critical tool for emergency response to radiological incidents such as severe nuclear accidents or nuclear weapon detonations. Current AGRS calibration and data evaluation methods struggle to accurately quantify many radioactive materials expected in radiological emergencies, limiting the risk assessment and, hence, the effectiveness of emergency response actions. To address these limitations, this work introduces a full spectrum numerical modeling approach that features three key innovations: high-fidelity Monte Carlo simulations that combine an advanced scintillation physics model with detailed geometric representations of the aircraft and detector system; a surrogate model that replicates the Monte Carlo simulations with significantly reduced computation time; and a data evaluation methodology that leverages the surrogate model within a Bayesian inversion framework, enabling the quantification of arbitrarily complex gamma-ray fields. The methodology presented here, rigorously validated through laboratory and field measurements, achieves not only a significant improvement in accuracy and sensitivity over traditional methods but also substantially expands the operational capabilities of AGRS systems for both emergency response scenarios and geophysical surveys. These innovations lay the groundwork for establishing a new global standard for AGRS, ultimately supporting better-informed protective actions and reducing health risks during radiological emergencies.
$100K or 100 Days: Trade-offs when Pre-Training with Academic Resources
2024-10-30
Apoorv Khandelwal, Tian Yun, Nihal V. Nayak, Jack Merullo, Stephen H. Bach, Chen Sun, Ellie Pavlick
Pre-training is notoriously compute-intensive and academic researchers are notoriously under-resourced. It is, therefore, commonly assumed that academics can't pre-train models. In this paper, we seek to clarify this assumption. We first survey academic researchers to learn about their available compute and then empirically measure the time to replicate models on such resources. We introduce a benchmark to measure the time to pre-train models on given GPUs and also identify ideal settings for maximizing training speed. We run our benchmark on a range of models and academic GPUs, spending 2,000 GPU-hours on our experiments. Our results reveal a brighter picture for academic pre-training: for example, although Pythia-1B was originally trained on 64 GPUs for 3 days, we find it is also possible to replicate this model (with the same hyper-parameters) in 3x fewer GPU-days: i.e. on 4 GPUs in 18 days. We conclude with a cost-benefit analysis to help clarify the trade-offs between price and pre-training time. We believe our benchmark will help academic researchers conduct experiments that require training larger models on more data. We fully release our codebase at: https://github.com/apoorvkh/academic-pretraining.
Burstiness in low stellar-mass Ha emitters at z~2 and z~4-6 from JWST medium band photometry in GOODS-S
2024-10-30
R. Navarro-Carrera, P. Rinaldi, K. I. Caputi, E. Iani, V. Kokorev, J. Kerutt, R. Cooper
We analyze a sample of 4500 photometrically-selected Ha emitter galaxies at redshifts z~2 and z~4-6 selected from James Webb Space Telescope (JWST) Near-Infrared Camera (NIRCam) medium-band images in the Great Observatories Origins Deep Survey South (GOODS-S). The bulk (80%) of the galaxies in our sample have stellar masses lower than 10^8 Msun, with a median stellar mass of ~10^7.3 Msun. We derive Ha rest-frame equivalent widths ( EW0(Ha) ), line fluxes, and star formation rates using a robust photometric excess technique tested against spectroscopic measurements, being sensitive to EW0(Ha) > 75 A. Both EW0(Ha) and sSFR(Ha) anti-correlate with stellar mass, and at fixed stellar mass, show a steep increasing trend with redshift sSFR(Ha) ~ (1+z)^2.55. By comparing the Ha and rest-frame UV-derived SFRs, we probe the star formation histories (SFHs) of our galaxies in the past 100 Myr. The fraction of low-mass galaxies (M < 10^8 Msun) with signs of bursty star formation from their SFR(Ha)/SFR(UV) is ~ 50 %. It quickly drops to ~ 25 % for M > 10^8 Msun. This is consistent with the results from sSFR(Ha), showing 80% and 17%, respectively. SFR(Ha)/SFR(UV) is a stricter criterion than those based on the galaxy sSFR(Ha), as it only identifies the strongest starbursts, the ones at the initial phases of a bursty star-formation episode.
2024-10-30
Merel L. R. van 't Hoff, Jennifer B. Bergner
Knowledge of the composition of material that will form planets is crucial to understand planetary diversity and the occurrence of potentially habitable planets. Ultimately, it is the chemistry in circumstellar disks that determines the global make up of planetary systems, as the dust in these disks grows into giant planet cores and rocky planets, the gas becomes incorporated in giant planet atmospheres, and the ices can be delivered to rocky planets by comets and meteorites. With the advent of ALMA a decade ago and the recent launch of JWST, the composition of the disk gas and ice can now be studied in great detail. This review will provide an overview of our current knowledge of the disk chemical structure, focusing on the six elements essential to life on Earth: carbon (C), hydrogen (H), nitrogen (N), oxygen (O), phosphorus (P) and sulfur (S).
Relative volume of comparable pairs under semigroup majorization
2024-10-30
Fabio Deelan Cunden, Jakub Czartowski, Giovanni Gramegna, A. de Oliveira Junior
Any semigroup $\mathcal{S}$ of stochastic matrices induces a semigroup majorization relation $\prec^{\mathcal{S}}$ on the set $\Delta_{n-1}$ of probability $n$-vectors. Pick $X,Y$ at random in $\Delta_{n-1}$: what is the probability that $X$ and $Y$ are comparable under $\prec^{\mathcal{S}}$? We review recent asymptotic ($n\to\infty$) results and conjectures in the case of \emph{majorization} relation (when $\mathcal{S}$ is the set of bistochastic matrices), discuss natural generalisations, and prove new exact finite-$n$ results in the case of \emph{UT-majorization} relation, i.e.,\ when $\mathcal{S}$ is the set of upper-triangular stochastic matrices.
Shedding a Light on the Kinetics of the Carboxysulfitic Scenario
2024-10-30
S. B. White, P. B. Rimmer, Z. Liu
One way in which we can attempt to relate chemical pathways to geochemical environments is by studying the kinetics of a given sequence of reactions and identifying the conditions under which this chemistry is the most productive. Many prebiotic reactions rely on a source of fixed carbon, therefore chemical pathways that suggest prebiotically plausible ways of fixing carbon are of significant interest. One such pathway is the carboxysulfitic reaction network which uses solvated electrons, produced as a result of electron photodetachment from sulfite, to reduce carbon. In this work we explore carboxysulfitic chemistry at three different pH values: 6, 9, and 12. We utilise a new light source, that matches the broadband spectral shape of the young Sun, to irradiate a mixture of bicarbonate and sulfite. We determine the rate equation for the production of formate from these compounds and find the order to be 0.71 $\pm$ 0.12 with respect to bicarbonate and -0.60 $\pm$ 0.10 with respect to sulfite. Following this, we determine rate constants for the production of formate considering two different mechanisms. We find this chemistry to be feasible at all three of the pH values tested, with the magnitude of the rate constants being highly dependent on the assumed mechanism. We suggest that these results may have implications for Mars Sample Return owing to Jezero Crater having had lakes similar to those in which we propose carboxysulfitic chemistry to have been the most productive. Due to Mars' relatively unaltered surface, we propose that Mars Sample Return missions could look for preserved tracers of this chemistry, shedding light on Mars' past conditions and its potential for having hosted life.
We investigate how low-quality AI advisors, lacking quality disclosures, can help spread text-based lies while seeming to help people detect lies. Participants in our experiment discern truth from lies by evaluating transcripts from a game show that mimicked deceptive social media exchanges on topics with objective truths. We find that when relying on low-quality advisors without disclosures, participants' truth-detection rates fall below their own abilities, which recovered once the AI's true effectiveness was revealed. Conversely, high-quality advisor enhances truth detection, regardless of disclosure. We discover that participants' expectations about AI capabilities contribute to their undue reliance on opaque, low-quality advisors.
KiDS-Legacy: angular galaxy clustering from deep surveys with complex selection effects
2024-10-30
Ziang Yan, Angus H. Wright, Nora Elisa Chisari, Christos Georgiou, Shahab Joudaki, Arthur Loureiro, Robert Reischke, Marika Asgari, Maciej Bilicki, Andrej Dvornik, Catherine Heymans, Hendrik Hildebrandt, Priyanka Jalan, Benjamin Joachimi, Giorgio Francesco Lesci, Shun-Sheng Li, Laila Linke, Constance Mahony, Lauro Moscardini, Nicola R. Napolitano, Benjamin Stoelzner, Maximilian Von Wietersheim-Kramsta, Mijin Yoon
Photometric galaxy surveys, despite their limited resolution along the line of sight, encode rich information about the large-scale structure (LSS) of the Universe thanks to the large number density and extensive depth of the data. However, the complicated selection effects in wide and deep surveys will potentially cause significant bias in the angular two-point correlation function (2PCF) measured from those surveys. In this paper, we measure the 2PCF from the newly published KiDS-Legacy sample. Given an $r$-band $5\sigma$ magnitude limit of $24.8$ and survey footprint of $1347$ deg$^2$, it achieves an excellent combination of sky coverage and depth for such a measurement. We find that complex selection effects, primarily induced by varying seeing, introduce over-estimation of the 2PCF by approximately an order of magnitude. To correct for such effects, we apply a machine learning-based method to recover an ``organised random'' (OR) that presents the same selection pattern as the galaxy sample. The basic idea is to find the selection-induced clustering of galaxies using a combination of self-organising maps (SOM) and hierarchical clustering (HC). This unsupervised machine learning method is able to recover complicated selection effects without specifying their functional forms. We validate this ``SOM+HC'' method on mock deep galaxy samples with realistic systematics and selections derived from the KiDS-Legacy catalogue. Using mock data, we demonstrate that the OR delivers unbiased 2PCF cosmological parameter constraints, removing the $27\sigma$ offset in the galaxy bias parameter that is recovered when adopting uniform randoms. Blinded measurements on the real KiDS-Legacy data show that the corrected 2PCF is robust to the SOM+HC configuration near the optimal setup suggested by the mock tests. Our software is open-source for future usage.
Revisiting MAE pre-training for 3D medical image segmentation
2024-10-30
Tassilo Wald, Constantin Ulrich, Stanislav Lukyanenko, Andrei Goncharov, Alberto Paderno, Leander Maerkisch, Paul F. Jäger, Klaus Maier-Hein
Self-Supervised Learning (SSL) presents an exciting opportunity to unlock the potential of vast, untapped clinical datasets, for various downstream applications that suffer from the scarcity of labeled data. While SSL has revolutionized fields like natural language processing and computer vision, their adoption in 3D medical image computing has been limited by three key pitfalls: Small pre-training dataset sizes, architectures inadequate for 3D medical image analysis, and insufficient evaluation practices. We address these issues by i) leveraging a large-scale dataset of 44k 3D brain MRI volumes and ii) using a Residual Encoder U-Net architecture within the state-of-the-art nnU-Net framework. iii) A robust development framework, incorporating 5 development and 8 testing brain MRI segmentation datasets, allowed performance-driven design decisions to optimize the simple concept of Masked Auto Encoders (MAEs) for 3D CNNs. The resulting model not only surpasses previous SSL methods but also outperforms the strong nnU-Net baseline by an average of approximately 3 Dice points. Furthermore, our model demonstrates exceptional stability, achieving the highest average rank of 2 out of 7 methods, compared to the second-best method's mean rank of 3.
No evidence for a metallicity-dependent enhancement of distant giant companions to close-in small planets in the California Legacy Survey
2024-10-30
Judah Van Zandt, Erik Petigura
Understanding the relationship between close-in small planets (CS) and distant giants (DG) is central to understanding the formation of planetary systems like our own. Most studies of this connection have found evidence for a positive correlation, though significant statistical and systematic uncertainties remain due to differences in sample size, target selection bias, and even the definitions of `close-in small' and `distant giant' planets. Recently, Bryan & Lee (2024) conducted a study of 184 stars hosting super-Earths ($M \sin i = 1-20 \, M_{E}$ or $R = 1-4 \, R_{E}$) to determine the effect of stellar metallicity on the prevalence of distant giant companions ($M \sin i = 0.5-20 \, M_{Jup}$, $a = 1-10$ AU). They found that such giants are twice as common in the presence of inner planets, but \textit{only} in metal-rich systems: P(DG|CS, [Fe/H]>0) = $28^{+4.9}_{-4.6}\%$ vs. P(DG|[Fe/H]>0) = $14.3 ^{+2.0}_{-1.8}\%$. Further, they found that this correlation disappears for metal-poor stars: P(DG|CS, [Fe/H]$\leq$0) = $4.5^{+2.6}_{-1.9}\%$ vs. P(DG|[Fe/H]$\leq$0) = $5.0^{+1.6}_{-1.3}\%$. We conducted an analogous study to test whether this effect was present in the California Legacy Survey sample. Using the same planet definitions, we did not find evidence for an enhancement in metal-rich systems: P(DG|CS, [Fe/H]>0) = $14^{+12}_{-8}\%$ vs. P(DG|[Fe/H]>0) = $16.4^{+2.5}_{-2.4}\%$. We also found a $2\sigma$ tension between our conditional rate and a 2x enhancement. The discrepancy between our findings and theirs may arise from a variety of sources, underscoring the need for large exoplanet surveys to overcome both small number statistics and sample inhomogeneities.
LLMs Integration in Software Engineering Team Projects: Roles, Impact, and a Pedagogical Design Space for AI Tools in Computing Education
2024-10-30
Ahmed Kharrufa, Sami Alghamdi, Abeer Aziz, Christopher Bull
This work takes a pedagogical lens to explore the implications of generative AI (GenAI) models and tools, such as ChatGPT and GitHub Copilot, in a semester-long 2nd-year undergraduate Software Engineering Team Project. Qualitative findings from survey (39 students) and interviews (eight students) provide insights into the students' views on the impact of GenAI use on their coding experience, learning, and self-efficacy. Our results address a particular gap in understanding the role and implications of GenAI on teamwork, team-efficacy, and team dynamics. The analysis of the learning aspects is distinguished by the application of learning and pedagogy informed lenses to discuss the data. We propose a preliminary design space for GenAI-based programming learning tools highlighting the importance of considering the roles that GenAI can play during the learning process, the varying support-ability patterns that can be applied to each role, and the importance of supporting transparency in GenAI for team members and students in addition to educators.
Strongly quasiconvex functions: what we know (so far)
2024-10-30
Sorin-Mihai Grad, Felipe Lara, Raúl T. Marcavillaca
Introduced by Polyak in 1966, the class of strongly quasiconvex functions includes some interesting nonconvex members, like the square root of the Euclidean norm or ratios with a nonnegative strongly convex numerator and a concave and positive denominator. This survey collects the vast majority of the results involving strongly quasiconvex functions available in the literature at the moment, presenting, in particular, algorithms for minimizing such functions, and suggests some directions where additional investigations would be welcome.
Exploring the Potential of Multi-modal Sensing Framework for Forest Ecology
2024-10-30
Luca Romanello, Tian Lan, Mirko Kovac, Sophie F. Armanini, Basaran Bahadir Kocer
Forests offer essential resources and services to humanity, yet preserving and restoring them presents challenges, particularly due to the limited availability of actionable data, especially in hard-to-reach areas like forest canopies. Accessibility continues to pose a challenge for biologists collecting data in forest environments, often requiring them to invest significant time and energy in climbing trees to place sensors. This operation not only consumes resources but also exposes them to danger. Efforts in robotics have been directed towards accessing the tree canopy using robots. A swarm of drones has showcased autonomous navigation through the canopy, maneuvering with agility and evading tree collisions, all aimed at mapping the area and collecting data. However, relying solely on free-flying drones has proven insufficient for data collection. Flying drones within the canopy generates loud noise, disturbing animals and potentially corrupting the data. Additionally, commercial drones often have limited autonomy for dexterous tasks where aerial physical interaction could be required, further complicating data acquisition efforts. Aerial deployed sensor placement methods such as bio-gliders and sensor shooting have proven effective for data collection within the lower canopy. However, these methods face challenges related to retrieving the data and sensors, often necessitating human intervention.
Camber-changing flapping hydrofoils for efficient and environmental-safe water propulsion system
2024-10-30
Luca Romanello, Leonard Hohaus, David-Marian Schmitt, Mirko Kovac, Sophie F. Armanini
This research introduces a novel hydrofoil-based propulsion framework for unmanned aquatic robots, inspired by the undulating locomotion observed in select aquatic species. The proposed system incorporates a camber-modulating mechanism to enhance hydrofoil propulsive force generation and eventually efficiency. Through dynamic simulations, we validate the effectiveness of the camber-adjusting hydrofoil compared to a symmetric counterpart. The results demonstrate a significant improvement in horizontal thrust, emphasizing the potential of the cambering approach to enhance propulsive performance. Additionally, a prototype flipper design is presented, featuring individual control of heave and pitch motions, as well as a camber-adjustment mechanism. The integrated system not only provides efficient water-based propulsion but also offers the capacity for generating vertical forces during take-off maneuvers for seaplanes. The design is tailored to harness wave energy, contributing to the exploration of alternative energy resources. This work advances the understanding of bionic oscillatory principles for aquatic robots and provides a foundation for future developments in environmentally safe and agile underwater exploration.
Rediscussion of eclipsing binaries. Paper XXII. The B-type system MU Cassiopeiae
2024-10-30
John Southworth
MU Cas is a detached eclipsing binary containing two B5 V stars in an orbit of period 9.653 d and eccentricity 0.192, which has been observed in seven sectors using the Transiting Exoplanet Survey Satellite (TESS). We use these new light curves together with published spectroscopic results to measure the physical properties of the component stars, finding masses of 4.67 +/- 0.09 Msun and 4.59 +/- 0.08 Msun, and radii of 4.12 +/- 0.04 Rsun and 3.65 +/- 0.05 Rsun. These values agree with previous results save for a change in which of the two stars is designated the primary component. The measured distance to the system, 1814 +/- 37 pc, is 1.8$\sigma$ shorter than the distance from the Gaia DR3 parallax. A detailed spectroscopic analysis of the system is needed to obtain improved temperature and radial velocity measurements for the component stars; a precise spectroscopic light ratio is also required for better measurement of the stellar radii. MU Cas matches the predictions of theoretical stellar evolutionary models for a solar chemical composition and an age of 87 +/- 5 Myr. No evidence for pulsations was found in the light curves.
Rediscussion of eclipsing binaries. Paper XXI. The totally-eclipsing B-type system IQ Persei
2024-10-30
John Southworth
IQ Per is a totally-eclipsing binary system containing a B8 V star and an A6 V star in an orbit of period 1.744 d with eccentricity and apsidal motion. We use new light curves from the Transiting Exoplanet Survey Satellite (TESS) and published spectroscopy from Lacy & Frueh (1985) to measure the physical properties of the component stars, finding masses of 3.516 +/- 0.050 Msun and 1.738 +/- 0.023 Msun, and radii of 2.476 +/- 0.015 Rsun and 1.503 +/- 0.016 Rsun. Our fit to the light curve is imperfect, with a small sinusoidal trend in the residuals versus orbital phase and a slight mismatch in the depth of secondary eclipse, but the total eclipses mean the system is still well-characterised. The distance to the system from its masses, temperatures, apparent magnitudes and bolometric corrections is in agreement with the parallax distance from Gaia DR3. Theoretical models cannot adequately match the measured properties of the system, and new spectroscopy to confirm the temperatures and determine the chemical compositions of the stars would be useful. A Fourier analysis of the residuals of the best fit to the light curve shows many peaks at multiples of the orbital frequency, and one significant peak at 1.33 c/d which is not. This pulsation and the properties of the primary component are consistent with it being a slowly-pulsating B star.
PDSR: Efficient UAV Deployment for Swift and Accurate Post-Disaster Search and Rescue
2024-10-30
Alaa Awad Abdellatif, Ali Elmancy, Amr Mohamed, Ahmed Massoud, Wadha Lebda, Khalid K. Naji
This paper introduces a comprehensive framework for Post-Disaster Search and Rescue (PDSR), aiming to optimize search and rescue operations leveraging Unmanned Aerial Vehicles (UAVs). The primary goal is to improve the precision and availability of sensing capabilities, particularly in various catastrophic scenarios. Central to this concept is the rapid deployment of UAV swarms equipped with diverse sensing, communication, and intelligence capabilities, functioning as an integrated system that incorporates multiple technologies and approaches for efficient detection of individuals buried beneath rubble or debris following a disaster. Within this framework, we propose architectural solution and address associated challenges to ensure optimal performance in real-world disaster scenarios. The proposed framework aims to achieve complete coverage of damaged areas significantly faster than traditional methods using a multi-tier swarm architecture. Furthermore, integrating multi-modal sensing data with machine learning for data fusion could enhance detection accuracy, ensuring precise identification of survivors.
There have been works discussing the adoption of a human rights framework for responsible AI, emphasizing various rights such as the right to contribute to scientific advancements. Yet, to the best of our knowledge, this is the first attempt to take this framework with special focus on computer vision and documenting human rights violations in its community. This work summarizes such incidents accompanied with evidence from the lens of a female African Muslim Hijabi researcher. While previous works resorted to qualitative surveys that gather opinions from various researchers in the field, this work argues that a single documented violation is sufficient to warrant attention regardless of the stature of this researcher. Incidents documented in this work include silence on Genocides that are occurring while promoting the governments contributing to it, a broken reviewing system and corruption in the faculty support systems. This work discusses that demonizing individuals for discrimination based on gender, ethnicity, creed or reprisal has been a successful tool for exclusion with documented evidence from a single case. We argue that human rights are guaranteed for every single individual even the ones that might be labelled as devils in the community for whichever reasons to dismantle such a tool from its roots.
An Efficient Representation of Whole-body Model Predictive Control for Online Compliant Dual-arm Mobile Manipulation
2024-10-30
Wenqian Du, Ran Long, João Moura, Jiayi Wang, Saeid Samadi, Sethu Vijayakumar
Dual-arm mobile manipulators can transport and manipulate large-size objects with simple end-effectors. To interact with dynamic environments with strict safety and compliance requirements, achieving whole-body motion planning online while meeting various hard constraints for such highly redundant mobile manipulators poses a significant challenge. We tackle this challenge by presenting an efficient representation of whole-body motion trajectories within our bilevel model-based predictive control (MPC) framework. We utilize B\'ezier-curve parameterization to represent the optimized collision-free trajectories of two collaborating end-effectors in the first MPC, facilitating fast long-horizon object-oriented motion planning in SE(3) while considering approximated feasibility constraints. This approach is further applied to parameterize whole-body trajectories in the second MPC for whole-body motion generation with predictive admittance control in a relatively short horizon while satisfying whole-body hard constraints. This representation enables two MPCs with continuous properties, thereby avoiding inaccurate model-state transition and dense decision-variable settings in existing MPCs using the discretization method. It strengthens the online execution of the bilevel MPC framework in high-dimensional space and facilitates the generation of consistent commands for our hybrid position/velocity-controlled robot. The simulation comparisons and real-world experiments demonstrate the efficiency and robustness of this approach in various scenarios for static and dynamic obstacle avoidance, and compliant interaction control with the manipulated object and external disturbances.
Non-rigid point cloud registration is a critical challenge in 3D scene understanding, particularly in surgical navigation. Although existing methods achieve excellent performance when trained on large-scale, high-quality datasets, these datasets are prohibitively expensive to collect and annotate, e.g., organ data in authentic medical scenarios. With insufficient training samples and data noise, existing methods degrade significantly since non-rigid patterns are more flexible and complicated than rigid ones, and the distributions across samples are more distinct, leading to higher difficulty in representation learning with few data. In this work, we aim to deal with this challenging few-shot non-rigid point cloud registration problem. Based on the observation that complex non-rigid transformation patterns can be decomposed into rigid and small non-rigid transformations, we propose a novel and effective framework, UniRiT. UniRiT adopts a two-step registration strategy that first aligns the centroids of the source and target point clouds and then refines the registration with non-rigid transformations, thereby significantly reducing the problem complexity. To validate the performance of UniRiT on real-world datasets, we introduce a new dataset, MedMatch3D, which consists of real human organs and exhibits high variability in sample distribution. We further establish a new challenging benchmark for few-shot non-rigid registration. Extensive empirical results demonstrate that UniRiT achieves state-of-the-art performance on MedMatch3D, improving the existing best approach by 94.22%.
A Graph-Based Model for Vehicle-Centric Data Sharing Ecosystem
2024-10-30
Haiyue Yuan, Ali Raza, Nikolay Matyunin, Jibesh Patra, Shujun Li
The development of technologies has prompted a paradigm shift in the automotive industry, with an increasing focus on connected services and autonomous driving capabilities. This transformation allows vehicles to collect and share vast amounts of vehicle-specific and personal data. While these technological advancements offer enhanced user experiences, they also raise privacy concerns. To understand the ecosystem of data collection and sharing in modern vehicles, we adopted the ontology 101 methodology to incorporate information extracted from different sources, including analysis of privacy policies using GPT-4, a small-scale systematic literature review, and an existing ontology, to develop a high-level conceptual graph-based model, aiming to get insights into how modern vehicles handle data exchange among different parties. This serves as a foundational model with the flexibility and scalability to further expand for modelling and analysing data sharing practices across diverse contexts. Two realistic examples were developed to demonstrate the usefulness and effectiveness of discovering insights into privacy regarding vehicle-related data sharing. We also recommend several future research directions, such as exploring advanced ontology languages for reasoning tasks, supporting topological analysis for discovering data privacy risks/concerns, and developing useful tools for comparative analysis, to strengthen the understanding of the vehicle-centric data sharing ecosystem.
In our urbanised societies, the management and regulation of traffic and pedestrian flows is of considerable interest for public safety, economic development, and the conservation of the environment. However, modelling and controlling the collective dynamics of vehicles and pedestrians raises several challenges. Not only are the individual entities self-propelled and hard to describe, but their complex nonlinear physical and social interactions makes the multi-agent problem of crowd and traffic flow even more involved. In this chapter, we purport to review the suitability and limitations of classical modelling approaches through four examples of collective behaviour: stop-and-go waves in traffic flow, lane formation, long-term avoidance behaviour, and load balancing in pedestrian dynamics. While stop-and-go dynamics and lane formation can both be addressed by basic reactive models (at least to some extent), the latter two require anticipation and/or coordination at the level of the group. The results highlight the limitations of classical force-based models, but also the need for long-term anticipation mechanisms and multiscale modelling approaches. In response, we review new developments and modelling concepts.
Radiative corrections and Monte Carlo tools for low-energy hadronic cross sections in $e^+ e^-$ collisions
2024-10-30
Riccardo Aliberti, Paolo Beltrame, Ettore Budassi, Carlo M. Carloni Calame, Gilberto Colangelo, Lorenzo Cotrozzi, Achim Denig, Anna Driutti, Tim Engel, Lois Flower, Andrea Gurgone, Martin Hoferichter, Fedor Ignatov, Sophie Kollatzsch, Bastian Kubis, Andrzej Kupść, Fabian Lange, Alberto Lusiani, Stefan E. Müller, Jérémy Paltrinieri, Pau Petit Rosàs, Fulvio Piccinini, Alan Price, Lorenzo Punzi, Marco Rocco, Olga Shekhovtsova, Andrzej Siódmok, Adrian Signer, Giovanni Stagnitto, Peter Stoffer, Thomas Teubner, William J. Torres Bobadilla, Francesco P. Ucci, Yannick Ulrich, Graziano Venanzoni
We present the results of Phase I of an ongoing review of Monte Carlo tools relevant for low-energy hadronic cross sections. This includes a detailed comparison of Monte Carlo codes for electron-positron scattering into a muon pair, pion pair, and electron pair, for scan and radiative-return experiments. After discussing the various approaches that are used and effects that are included, we show differential cross sections obtained with AfkQed, BabaYaga@NLO, KKMC, MCGPJ, McMule, Phokhara, and Sherpa, for scenarios that are inspired by experiments providing input for the dispersive evaluation of the hadronic vacuum polarisation.
Machine learning based stellar classification with highly sparse photometry data
2024-10-30
Sean Enis Cody, Sebastian Scher, Iain McDonald, Albert Zijlstra, Emma Alexander, Nick L. J. Cox
Identifying stars belonging to different classes is vital in order to build up statistical samples of different phases and pathways of stellar evolution. In the era of surveys covering billions of stars, an automated method of identifying these classes becomes necessary. Many classes of stars are identified based on their emitted spectra. In this paper, we use a combination of the multi-class multi-label Machine Learning (ML) method XGBoost and the PySSED spectral-energy-distribution fitting algorithm to classify stars into nine different classes, based on their photometric data. The classifier is trained on subsets of the SIMBAD database. Particular challenges are the very high sparsity (large fraction of missing values) of the underlying data as well as the high class imbalance. We discuss the different variables available, such as photometric measurements on the one hand, and indirect predictors such as Galactic position on the other hand. We show the difference in performance when excluding certain variables, and discuss in which contexts which of the variables should be used. Finally, we show that increasing the number of samples of a particular type of star significantly increases the performance of the model for that particular type, while having little to no impact on other types. The accuracy of the main classifier is ~0.7 with a macro F1 score of 0.61. While the current accuracy of the classifier is not high enough to be reliably used in stellar classification, this work is an initial proof of feasibility for using ML to classify stars based on photometry.
Hyperparameters are configuration variables controlling the behavior of machine learning algorithms. They are ubiquitous in machine learning and artificial intelligence and the choice of their values determine the effectiveness of systems based on these technologies. Manual hyperparameter search is often unsatisfactory and becomes unfeasible when the number of hyperparameters is large. Automating the search is an important step towards automating machine learning, freeing researchers and practitioners alike from the burden of finding a good set of hyperparameters by trial and error. In this survey, we present a unified treatment of hyperparameter optimization, providing the reader with examples and insights into the state-of-the-art. We cover the main families of techniques to automate hyperparameter search, often referred to as hyperparameter optimization or tuning, including random and quasi-random search, bandit-, model- and gradient- based approaches. We further discuss extensions, including online, constrained, and multi-objective formulations, touch upon connections with other fields such as meta-learning and neural architecture search, and conclude with open questions and future research directions.
Dataset of polarimetric images of mechanically generated water surface waves coupled with surface elevation records by wave gauges linear array
2024-10-30
Noam Ginio, Michael Lindenbaum, Barak Fishbain, Dan Liberzon
Effective spatio-temporal measurements of water surface elevation (water waves) in laboratory experiments are essential for scientific and engineering research. Existing techniques are often cumbersome, computationally heavy and generally suffer from limited wavenumber/frequency response. To address these challenges a novel method was developed, using polarization filter equipped camera as the main sensor and Machine Learning (ML) algorithms for data processing [1,2]. The developed method training and evaluation was based on in-house made supervised dataset. Here we present this supervised dataset of polarimetric images of the water surface coupled with the water surface elevation measurements made by a linear array of resistance-type wave gauges (WG). The water waves were mechanically generated in a laboratory waves basin, and the polarimetric images were captured under an artificial light source. Meticulous camera and WGs calibration and instruments synchronization supported high spatio-temporal resolution. The data set covers several wavefield conditions, from simple monochromatic wave trains of various steepness, to irregular wavefield of JONSWAP prescribed spectral shape and several wave breaking scenarios. The dataset contains measurements repeated in several camera positions relative to the wave field propagation direction.
Less is More: DocString Compression in Code Generation
2024-10-30
Guang Yang, Yu Zhou, Wei Cheng, Xiangyu Zhang, Xiang Chen, Terry Yue Zhuo, Ke Liu, Xin Zhou, David Lo, Taolue Chen
The widespread use of Large Language Models (LLMs) in software engineering has intensified the need for improved model and resource efficiency. In particular, for neural code generation, LLMs are used to translate function/method signature and DocString to executable code. DocStrings which capture user re quirements for the code and used as the prompt for LLMs, often contains redundant information. Recent advancements in prompt compression have shown promising results in Natural Language Processing (NLP), but their applicability to code generation remains uncertain. Our empirical study show that the state-of-the-art prompt compression methods achieve only about 10% reduction, as further reductions would cause significant performance degradation. In our study, we propose a novel compression method, ShortenDoc, dedicated to DocString compression for code generation. Our extensive experiments on six code generation datasets, five open-source LLMs (1B to 10B parameters), and one closed-source LLM GPT-4o confirm that ShortenDoc achieves 25-40% compression while preserving the quality of generated code, outperforming other baseline methods at similar compression levels. The benefit of this research is to improve efficiency and reduce the cost while maintaining the quality of the generated code, especially when calling third-party APIs, and is able to reduce the token processing cost by 25-40%.
On a conjecture of Tokushige for cross-$t$-intersecting families
2024-10-30
Huajun Zhang, Biao Wu
Two families of sets $\mathcal{A}$ and $\mathcal{B}$ are called cross-$t$-intersecting if $|A\cap B|\ge t$ for all $A\in \mathcal{A}$, $B\in \mathcal{B}$. An active problem in extremal set theory is to determine the maximum product of sizes of cross-$t$-intersecting families. This incorporates the classical Erd\H{o}s--Ko--Rado (EKR) problem. In the present paper, we prove that if $\mathcal{A}$ and $\mathcal{B}$ are cross-$t$-intersecting families of $\binom {[n]}k$ with $k\ge t\ge 3$ and $n\ge (t+1)(k-t+1)$, then $|\mathcal{A}||\mathcal{B}|\le {\binom{n-t}{k-t}}^2$; moreover, if $n>(t+1)(k-t+1)$, then equality holds if and only if $\mathcal{A}=\mathcal{B}$ is a maximum $t$-intersecting subfamily of $\binom{[n]}{k}$. This confirms a conjecture of Tokushige for $t\ge 3$.
We propose novel numerical schemes based on the Boris method in curved spacetime, incorporating both hadronic and radiative interactions for the first time. Once the proton has lost significant energy due to radiative and hadronic losses, and its gyroradius has decreased below typical scales on which the electromagnetic field varies, we apply a guiding center approximation (GCA). We fundamentally simulate collision processes either with a Monte-Carlo method or, where applicable, as a continuous energy loss, contingent on the local optical depth. To test our algorithm for the first time combining the effects of electromagnetic, gravitational, and radiation fields including hadronic interactions, we simulate highly relativistic protons traveling through various electromagnetic fields and proton backgrounds. We provide unit tests in various spatially dependent electromagnetic and gravitational fields and background photon and proton distributions, comparing the trajectory against analytic results. We propose that our method can be used to analyze hadronic interactions in black hole accretion disks, jets, and coronae to study the neutrino abundance from active galactic nuclei.
Toward Designing Accessible and Meaningful Software for Cancer Survivors
2024-10-30
Kyrie Zhixuan Zhou, Royta Iftakher, Sean P. Mullen, Rachel F. Adler, Devorah Kletenik
Cancer survivors experience a wide range of impairments arising from cancer or its treatment, such as chemo brain, visual impairments, and physical impairments. These impairments degrade their quality of life and potentially make software use more challenging for them. However, there has been limited research on designing accessible software for cancer survivors. To bridge this research gap, we conducted a formative study including a survey (n=46), semi-structured interviews (n=20), and a diary study (n=10) with cancer survivors. Our results revealed a wide range of impairments experienced by cancer survivors, including chemo brain, neuropathy, and visual impairments. Cancer survivors heavily relied on software for socialization, health purposes, and cancer advocacy, but their impairments made software use more challenging for them. Based on the results, we offer a set of accessibility guidelines that software designers can utilize when creating applications for cancer survivors. Further, we suggest design features for inclusion, such as health resources, socialization tools, and games, tailored to the needs of cancer survivors. This research aims to spotlight cancer survivors' software accessibility challenges and software needs and invite more research in this important yet under-investigated domain.
Discovering Changing-Look AGN in the 6dF Galaxy Survey using ATLAS Light curves
2024-10-30
Neelesh Amrutha, Christian Wolf, Christopher A. Onken, Wei Jeat Hon, Samuel Lai, John L. Tonry, Rachel Webster
Changing-Look Active Galactic Nuclei (CLAGN) are characterised by extreme variations in line emission over short timescales, mostly affecting broad H$\beta$ lines. While a few hundred CLAGN are known, a complete sample of turn-on CLAGN is still elusive. Here, we present a search for turn-on CLAGN in a complete sample of galaxies, using archival spectra and recent light curves. We obtained light curves from the Asteroid Terrestrial Impact Last Alert System (ATLAS) for 16,232 emission line galaxies, including both star-forming and active galaxies, at $z<0.1$ with spectra from the Six-degree Field Galaxy Survey (6dFGS). We first establish typical variability behaviour for different AGN types, as recorded between 2001 and 2009, and then select outliers from the bulk behaviour as CLAGN candidates. We obtain new spectra for the candidates and identify 12 new turn-on CLAGN (appearing broad H$\beta$ line) and 19 new turn-off CLAGN (disappearing broad H$\beta$ line). We may have missed AGN that changed and reverted their state over the 15$-$20 years since 6dFGS spectra were taken, and thus our CLAGN rates of 1.7$\%$ for turn-on and 9.6$\%$ for turn-off are lower limits. The turn-on rate is naturally much lower as the type 1.9/2 sample is dominated by obscured AGN due to orientation, which are not expected to change. However, the number of turn-on (27) and turn-off (24) CLAGN we find are similar, suggesting that our parent AGN sample is reasonably complete in our search volume at $z<0.1$.
Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers
2024-10-30
Lam Nguyen Tung, Steven Cho, Xiaoning Du, Neelofar Neelofar, Valerio Terragni, Stefano Ruberto, Aldeida Aleti
Machine learning (ML) for text classification has been widely used in various domains, such as toxicity detection, chatbot consulting, and review analysis. These applications can significantly impact ethics, economics, and human behavior, raising serious concerns about trusting ML decisions. Several studies indicate that traditional metrics, such as model confidence and accuracy, are insufficient to build human trust in ML models. These models often learn spurious correlations during training and predict based on them during inference. In the real world, where such correlations are absent, their performance can deteriorate significantly. To avoid this, a common practice is to test whether predictions are reasonable. Along with this, a challenge known as the trustworthiness oracle problem has been introduced. Due to the lack of automated trustworthiness oracles, the assessment requires manual validation of the decision process disclosed by explanation methods, which is time-consuming and not scalable. We propose TOKI, the first automated trustworthiness oracle generation method for text classifiers, which automatically checks whether the prediction-contributing words are related to the predicted class using explanation methods and word embeddings. To demonstrate its practical usefulness, we introduce a novel adversarial attack method targeting trustworthiness issues identified by TOKI. We compare TOKI with a naive baseline based solely on model confidence using human-created ground truths of 6,000 predictions. We also compare TOKI-guided adversarial attack method with A2T, a SOTA adversarial attack method. Results show that relying on prediction uncertainty cannot distinguish between trustworthy and untrustworthy predictions, TOKI achieves 142% higher accuracy than the naive baseline, and TOKI-guided adversarial attack method is more effective with fewer perturbations than A2T.
We report the magnetic properties of CaCo$_2$TeO$_6$ as a Kitaev candidate. CaCo$_2$TeO$_6$ was synthesized through a topochemical process, wherein all Na$^+$ ions in Na$_2$Co$_2$TeO$_6$ were replaced with half the amount of Ca$^{2+}$ ions. This substitution brings the CoO$_6$ octahedra closer to an approximate cubic symmetry. CaCo$_2$TeO$_6$ exhibits antiferromagnetic ordering at $T_N \sim 13$ K, which is lower than $\sim$ 27 K observed for Na$_2$Co$_2$TeO$_6$. Notably, its magnetic order is suppressed in a relatively low magnetic field of approximately 4 T, indicating that non-Kitaev interactions can be restrained by reducing trigonal distortion. Our findings highlight the potential of CaCo$_2$TeO$_6$ as a viable platform for exploring Kitaev quantum spin liquids and pave the way for a deeper understanding of the fundamental mechanisms in Kitaev physics.
Research has shown the effectiveness of reasoning (e.g., Chain-of-Thought), planning (e.g., SelfAsk), and retrieval augmented generation strategies to improve the performance of Large Language Models (LLMs) on various tasks, such as question answering. However, using a single fixed strategy to answer different kinds of questions is suboptimal in performance and inefficient in terms of generated output tokens and performed retrievals. In our work, we propose a novel technique DyPlan, to induce a dynamic strategy selection process in LLMs, to improve performance and reduce costs in question-answering. DyPlan incorporates an initial decision step to select the most suitable strategy conditioned on the input question and guides the LLM's response generation accordingly. We extend DyPlan to DyPlan-verify, adding an internal verification and correction process to further enrich the generated answer. Experiments on three prominent multi-hop question answering (MHQA) datasets reveal how DyPlan can improve model performance by 7-13% while reducing the cost by 11-32% relative to the best baseline model.
Collage: Decomposable Rapid Prototyping for Information Extraction on Scientific PDFs
2024-10-30
Sireesh Gururaja, Yueheng Zhang, Guannan Tang, Tianhao Zhang, Kevin Murphy, Yu-Tsen Yi, Junwon Seo, Anthony Rollett, Emma Strubell
Recent years in NLP have seen the continued development of domain-specific information extraction tools for scientific documents, alongside the release of increasingly multimodal pretrained transformer models. While the opportunity for scientists outside of NLP to evaluate and apply such systems to their own domains has never been clearer, these models are difficult to compare: they accept different input formats, are often black-box and give little insight into processing failures, and rarely handle PDF documents, the most common format of scientific publication. In this work, we present Collage, a tool designed for rapid prototyping, visualization, and evaluation of different information extraction models on scientific PDFs. Collage allows the use and evaluation of any HuggingFace token classifier, several LLMs, and multiple other task-specific models out of the box, and provides extensible software interfaces to accelerate experimentation with new models. Further, we enable both developers and users of NLP-based tools to inspect, debug, and better understand modeling pipelines by providing granular views of intermediate states of processing. We demonstrate our system in the context of information extraction to assist with literature review in materials science.
Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems
2024-10-30
Rokas Gipiškis, Ayrton San Joaquin, Ze Shen Chin, Adrian Regenfuß, Ariel Gil, Koen Holtman
There is an urgent need to identify both short and long-term risks from newly emerging types of Artificial Intelligence (AI), as well as available risk management measures. In response, and to support global efforts in regulating AI and writing safety standards, we compile an extensive catalog of risk sources and risk management measures for general-purpose AI (GPAI) systems, complete with descriptions and supporting examples where relevant. This work involves identifying technical, operational, and societal risks across model development, training, and deployment stages, as well as surveying established and experimental methods for managing these risks. To the best of our knowledge, this paper is the first of its kind to provide extensive documentation of both GPAI risk sources and risk management measures that are descriptive, self-contained and neutral with respect to any existing regulatory framework. This work intends to help AI providers, standards experts, researchers, policymakers, and regulators in identifying and mitigating systemic risks from GPAI systems. For this reason, the catalog is released under a public domain license for ease of direct use by stakeholders in AI governance and standards.
2024-10-30
Nassim Bozorgnia, Joseph Bramante, James M. Cline, David Curtin, David McKeen, David E. Morrissey, Adam Ritz, Simon Viel, Aaron C. Vincent, Yue Zhang
Astrophysical observations suggest that most of the matter in the cosmos consists of a new form that has not been observed on Earth. The nature and origin of this mysterious dark matter are among the most pressing questions in fundamental science. In this review we summarize the current state of dark matter research from two perspectives. First, we provide an overview of the leading theoretical proposals for dark matter. And second, we describe how these proposals have driven a broad and diverse global search program for dark matter involving direct laboratory searches and astrophysical observations. This review is based on a Green Paper on dark matter prepared as part of the 2020 Astroparticle Community Planning initiative undertaken by the Canadian Subatomic Physics community but has been significantly updated to reflect recent advances.
Venire: A Machine Learning-Guided Panel Review System for Community Content Moderation
2024-10-30
Vinay Koshy, Frederick Choi, Yi-Shyuan Chiang, Hari Sundaram, Eshwar Chandrasekharan, Karrie Karahalios
Research into community content moderation often assumes that moderation teams govern with a single, unified voice. However, recent work has found that moderators disagree with one another at modest, but concerning rates. The problem is not the root disagreements themselves. Subjectivity in moderation is unavoidable, and there are clear benefits to including diverse perspectives within a moderation team. Instead, the crux of the issue is that, due to resource constraints, moderation decisions end up being made by individual decision-makers. The result is decision-making that is inconsistent, which is frustrating for community members. To address this, we develop Venire, an ML-backed system for panel review on Reddit. Venire uses a machine learning model trained on log data to identify the cases where moderators are most likely to disagree. Venire fast-tracks these cases for multi-person review. Ideally, Venire allows moderators to surface and resolve disagreements that would have otherwise gone unnoticed. We conduct three studies through which we design and evaluate Venire: a set of formative interviews with moderators, technical evaluations on two datasets, and a think-aloud study in which moderators used Venire to make decisions on real moderation cases. Quantitatively, we demonstrate that Venire is able to improve decision consistency and surface latent disagreements. Qualitatively, we find that Venire helps moderators resolve difficult moderation cases more confidently. Venire represents a novel paradigm for human-AI content moderation, and shifts the conversation from replacing human decision-making to supporting it.
Brain Network Dynamics and Multiscale Modelling of Neurodegenerative Disorders: A Review
2024-10-30
Hina Shaheen, Roderick Melnik
It is essential to understand the complex structure of the human brain to develop new treatment approaches for neurodegenerative disorders (NDDs). This review paper comprehensively discusses the challenges associated with modelling the complex brain networks and dynamic processes involved in NDDs, particularly Alzheimer's disease (AD), Parkinson's disease (PD), and cortical spreading depression (CSD). We investigate how the brain's biological processes and associated multiphysics interact and how this influences the structure and functionality of the brain. We review the literature on brain network models and dynamic processes, highlighting the need for sophisticated mathematical and statistical modelling techniques. Specifically, we go through large-scale brain network models relevant to AD and PD, highlighting the pathological mechanisms and potential therapeutic strategies investigated in the literature. Additionally, we investigate the propagation of CSD in the brain and its implications for neurological disorders. Furthermore, we discuss how data-driven approaches and artificial neural networks refine and validate models related to NDDs. Overall, this review underscores the significance of coupled multiscale models in deciphering disease mechanisms, offering potential avenues for therapeutic development and advancing our understanding of pathological brain dynamics.
The Transformative Impact of AI and Deep Learning in Business: A Literature Review
2024-10-30
Fabio S. Dias, Grace A. Lauretta
This paper aims to review the radical role of AI and deep learning in various functional areas of the business, such as marketing, finance, operations, human resources and customer service. Thus, based on the overview of the latest research and practices focusing on AI technologies in different industries, the possibilities of improving organizational efficiency by personalized AI for making decisions based on big data and personalizing clients' interactions with organizations are presented and discussed. Several operational issues, ethical concerns, and regulatory concerns have also been discussed in the review of the literature. Moreover, it covers material applications in the healthcare sector, the retail and manufacturing industry, agriculture and farming, and finance before considering possible future developments and themes for further investigation. Drawing from this revolutionary ethnographic review, organizations aiming to implement strategic and responsible optimization benefit from detailed guides.
2024-10-30
Marc Subirana-Granés, Jill Hoffman, Haoyu Zhang, Christina Akirtava, Sutanu Nandi, Kevin Fotso, Milton Pividori
Understanding the genetic basis of complex traits is a longstanding challenge in the field of genomics. Genome-wide association studies (GWAS) have identified thousands of variant-trait associations, but most of these variants are located in non-coding regions, making the link to biological function elusive. While traditional approaches, such as transcriptome-wide association studies (TWAS), have advanced our understanding by linking genetic variants to gene expression, they often overlook gene-gene interactions. Here, we review current approaches to integrate different molecular data, leveraging machine learning methods to identify gene modules based on co-expression and functional relationships. These integrative approaches, like PhenoPLIER, combine TWAS and drug-induced transcriptional profiles to effectively capture biologically meaningful gene networks. This integration provides a context-specific understanding of disease processes while highlighting both core and peripheral genes. These insights pave the way for novel therapeutic targets and enhance the interpretability of genetic studies in personalized medicine.
The SOFIA Massive (SOMA) Star Formation Q-band follow-up I. Carbon-chain chemistry of intermediate-mass protostars
2024-10-30
Kotomi Taniguchi, Prasanta Gorai, Jonathan C. Tan, Miguel Gomez-Garrido, Ruben Fedriani, Yao-Lun Yang, T. K. Sridharan, Kei Tanaka, Masao Saito, Yichen Zhang, Lawrence Morgan, Giuliana Cosentino, Chi-Yan Law
Evidence for similar chemical characteristics around low- and high-mass protostars has been found: in particular, a variety of carbon-chain species and complex organic molecules (COMs) are formed around them. On the other hand, the chemical compositions around intermediate-mass (IM; $2 M_{\odot} < m_* <8 M_{\odot}$) protostars have not been studied with large samples. In particular, it is unclear the extent to which carbon-chain species are formed around them. We aim to obtain the chemical compositions, particularly focusing on carbon-chain species, towards a sample of IM protostars. We have conducted Q-band (31.5-50 GHz) line survey observations towards eleven mainly intermediate-mass protostars with the Yebes 40 m radio telescope. The target protostars were selected from a sub-sample of the source list of the SOFIA Massive (SOMA) Star Formation project. Nine carbon-chain species (HC$_3$N, HC$_5$N, C$_3$H, C$_4$H, $linear-$H$_2$CCC, $cyclic-$C$_3$H$_2$, CCS, C$_3$S, and CH$_3$CCH), three COMs (CH$_3$OH, CH$_3$CHO, and CH$_3$CN), H$_2$CCO, HNCO, and four simple sulfur (S)-bearing species ($^{13}$CS, C$^{34}$S, HCS$^+$, H$_2$CS) have been detected. The rotational temperatures of HC$_5$N are derived to be $\sim20-30$ K in three IM protostars and they are very similar compared to those around low- and high-mass protostars. These results indicate that carbon-chain molecules are formed in lukewarm ($\sim20-30$ K) gas around the IM protostars by the Warm Carbon-Chain Chemistry (WCCC) process. Carbon-chain formation occurs ubiquitously in the warm gas around protostars across a wide range of stellar masses. Carbon-chain molecules and COMs coexist around most of the target IM protostars, which is similar to the situation in low- and high-mass protostars. The chemical characteristics around protostars are common in the low-, intermediate- and high-mass regimes.
Gravitational Polarizability of Schwarzschild Black Holes
2024-10-30
Gabriel Vidal, Gabriel M. Dantas, Riccardo Sturani, Gabriel Menezes
The linear response of a Schwarzschild black hole to an external quadrupolar perturbation is studied in analogy to a mechanical electrodynamical system, with the goal to describe the gravitational polarizability. Its causality properties imply dispersion relations that relate fluctuation and dissipative properties. We review and combine results obtained via the Regge-Wheeler equation on one side and a perturbative, worldline effective field theory description on the other, obtaining a consistent description of the dispersion relations for the gravitational polarizability of a Schwarzschild black hole. We find that the classical part of the 2-point correlation function of the black hole multipole depends on the boundary conditions of the space-time the black hole is immersed in, which is relevant for the dispersion relations considered.
The VST ATLAS Quasar Survey III: Halo mass function via quasar clustering and quasar-CMB lensing cross-clustering
2024-10-30
Alice M. Eltvedt, T. Shanks, N. Metcalfe, B. Ansarinejad, L. F. Barrientos, D. N. A. Murphy, D. M. Alexander
We exploit the VST ATLAS quasar/QSO catalogue to perform three measurements of the quasar halo mass profile. First, we make a new estimate of the angular auto-correlation function of $\approx230,000$ ATLAS quasars with $z_{photo}\lesssim 2.5$ and $17
NGDEEP: The Star Formation and Ionization Properties of Galaxies at $1.7 < z < 3.4$
2024-10-30
Lu Shen, Casey Papovich, Jasleen Matharu, Nor Pirzkal, Weida Hu, Danielle A. Berg, Micaela B. Bagley, Bren E. Backhaus, Nikko J. Cleri, Mark Dickinson, Steven L. Finkelstein, Nimish P. Hathi, Marc Huertas-Company, Taylor A. Hutchison, Mauro Giavalisco, Norman A. Grogin, Anne E. Jaskot, Intae Jung, Jeyhan S. Kartaltepe, Anton M. Koekemoer, Jennifer M. Lotz, Pablo G. Pérez-González, Barry Rothberg, Raymond C. Simons, Brittany N. Vanderhoof, L. Y. Aaron Yung
We use JWST/NIRISS slitless spectroscopy from the Next Generation Deep Extragalactic Exploratory Public (NGDEEP) Survey to investigate the physical condition of star-forming galaxies at $1.7 < z < 3.4$. At these redshifts, the deep NGDEEP NIRISS slitless spectroscopy covers the [O II]$\lambda\lambda$3726,3729, [O III]$\lambda\lambda$4959,5007, H$\beta$ and H$\alpha$ emission features for galaxies with stellar masses $\log(\mathrm{M_\ast/M_\odot}) \gtrsim 7$, nearly a factor of a hundred lower than previous studies. We focus on the [O III]/[O II] (O$_{32}$) ratio which is primarily sensitive to the ionization state and with a secondary dependence on the gas-phase metallicity of the interstellar medium. We find significant ($\gtrsim5\sigma$) correlations between the O$_{32}$ ratio and galaxy properties as O$_{32}$ increases with decreasing stellar mass, decreasing star formation rate (SFR), increasing specific SFR (sSFR$\equiv \mathrm{SFR}/M_*$), and increasing equivalent width (EW) of H$\beta$ and H$\alpha$. These trends suggest a tight connection between the ionization parameter and these galaxy properties. Galaxies at $z\sim2-3$ exhibit a higher O$_{32}$ than local normal galaxies with the same stellar masses and SFRs, indicating that they have a higher ionization parameter and lower metallicity than local normal galaxies. In addition, we observe an evolutionary trend in the O$_{32}$ -- EW(H$\beta$) relation from $z\sim0$ and $z\gtrsim5$, such that higher redshift galaxies have higher EW(H$\beta$) and higher O$_{32}$ at fixed EW. We argue that both the enhanced recent star formation activity and the higher star formation surface density may contribute to the increase in O$_{32}$ and the ionization parameter.
The SRG/eROSITA diffuse soft X-ray background. I. The local hot bubble in the western Galactic hemisphere
2024-10-30
Michael C. H. Yeung, Gabriele Ponti, Michael J. Freyberg, Konrad Dennerl, Teng Liu, Nicola Locatelli, Martin G. F. Mayer, Jeremy S. Sanders, Manami Sasaki, Andy Strong, Yi Zhang, Xueying Zheng, Efrain Gatuzz
The SRG/eROSITA All-Sky Surveys (eRASSs) combine the advantages of complete sky coverage and the energy resolution provided by the charge couple device and offer the most holistic and detailed view of the diffuse soft X-ray background (SXRB) to date. The first eRASS (eRASS1) was completed at solar minimum, when solar wind charge exchange emission was minimal, providing the clearest view of the SXRB. We aim to extract spatial and spectral information from each constituent of the SXRB in the western Galactic hemisphere, focusing on the local hot bubble (LHB). We extracted and analysed eRASS1 spectra from almost all directions in the western Galactic hemisphere by dividing the sky into equal signal-to-noise bins. We fitted all bins with fixed spectral templates of known background constituents. We find the temperature of the LHB exhibits a north-south dichotomy at high latitudes ($|b|>30^{\circ}$), with the south being hotter, with a mean temperature at $kT=121.8\pm0.6\,$eV and the north at $kT=100.8\pm0.5\,$eV. At low latitudes, the LHB temperature increases towards the Galactic plane, especially towards the inner Galaxy. The LHB emission measure (${\rm EM_{LHB}}$) enhances approximately towards the Galactic poles. The ${\rm EM_{LHB}}$ map shows clear anti-correlation with the local dust column density. In particular, we found tunnels of dust cavities filled with hot plasma, potentially forming a wider network of hot interstellar medium. We also constructed a three-dimensional LHB model from ${\rm EM_{LHB}}$, assuming constant density. The average thermal pressure of the LHB is $P_{\rm thermal}/k=10100^{+1200}_{-1500}\,{\rm cm^{-3}\,K}$, a lower value than typical supernova remnants and wind-blown bubbles. This could be an indication of the LHB being open towards high Galactic latitudes.
The VST ATLAS Quasar Survey II: Halo mass profiles of galaxies, LRGs and galaxy clusters via quasar and CMB lensing
2024-10-30
Alice M. Eltvedt, T. Shanks, 1 N. Metcalfe, B. Ansarinejad, L. F. Barrientos, D. N. A. Murph, D. M. Alexander
We cross-correlate a low-contamination subset of the VST ATLAS g < 22.5 quasar catalogue with g < 21.5 galaxy clusters, r < 21 galaxies and r < 19.5 Luminous Red Galaxies (LRGs) to probe their halo mass profiles via quasar magnification bias caused by weak lensing. In the case of galaxy clusters we find that at small scales their mass profiles are well fitted by Navarro, Frenk and White (NFW) models with masses within the expected range. For the galaxies, we find consistency with previous SDSS-based results for the galaxy-quasar cross-correlation and the galaxy auto-correlation functions. Disagreement as to whether the cross-correlation results are in tension with $\Lambda$CDM appears due to different assumptions as to whether galaxies trace mass. We conclude that halo occupation distribution (HOD) models fit the galaxy - quasar lensing results better than models where galaxies trace the mass. We further test the cluster and galaxy HOD models in the 2-halo range using the Planck Cosmic Microwave Background (CMB) lensing map, finding that the cross-correlation with both the poorest clusters and the galaxies may be marginally over-predicted by the above HOD models. Finally, we measure the magnification bias of LRGs using both quasar and CMB lensing and find that the observed quasar lensing amplitude may be $\approx 2\times$ too high and, on larger scales, the CMB lensing amplitude may be too low to be explained by a standard LRG HOD model.
Unsupervised Object Discovery: A Comprehensive Survey and Unified Taxonomy
2024-10-30
José-Fabian Villa-Vásquez, Marco Pedersoli
Unsupervised object discovery is commonly interpreted as the task of localizing and/or categorizing objects in visual data without the need for labeled examples. While current object recognition methods have proven highly effective for practical applications, the ongoing demand for annotated data in real-world scenarios drives research into unsupervised approaches. Furthermore, existing literature in object discovery is both extensive and diverse, posing a significant challenge for researchers that aim to navigate and synthesize this knowledge. Motivated by the evidenced interest in this avenue of research, and the lack of comprehensive studies that could facilitate a holistic understanding of unsupervised object discovery, this survey conducts an in-depth exploration of the existing approaches and systematically categorizes this compendium based on the tasks addressed and the families of techniques employed. Additionally, we present an overview of common datasets and metrics, highlighting the challenges of comparing methods due to varying evaluation protocols. This work intends to provide practitioners with an insightful perspective on the domain, with the hope of inspiring new ideas and fostering a deeper understanding of object discovery approaches.
eDOC: Explainable Decoding Out-of-domain Cell Types with Evidential Learning
2024-10-30
Chaochen Wu, Meiyun Zuo, Lei Xie
Single-cell RNA-seq (scRNA-seq) technology is a powerful tool for unraveling the complexity of biological systems. One of essential and fundamental tasks in scRNA-seq data analysis is Cell Type Annotation (CTA). In spite of tremendous efforts in developing machine learning methods for this problem, several challenges remains. They include identifying Out-of-Domain (OOD) cell types, quantifying the uncertainty of unseen cell type annotations, and determining interpretable cell type-specific gene drivers for an OOD case. OOD cell types are often associated with therapeutic responses and disease origins, making them critical for precision medicine and early disease diagnosis. Additionally, scRNA-seq data contains tens thousands of gene expressions. Pinpointing gene drivers underlying CTA can provide deep insight into gene regulatory mechanisms and serve as disease biomarkers. In this study, we develop a new method, eDOC, to address aforementioned challenges. eDOC leverages a transformer architecture with evidential learning to annotate In-Domain (IND) and OOD cell types as well as to highlight genes that contribute both IND cells and OOD cells in a single cell resolution. Rigorous experiments demonstrate that eDOC significantly improves the efficiency and effectiveness of OOD cell type and gene driver identification compared to other state-of-the-art methods. Our findings suggest that eDOC may provide new insights into single-cell biology.
Advancing Crime Linkage Analysis with Machine Learning: A Comprehensive Review and Framework for Data-Driven Approaches
2024-10-30
Vinicius Lima, Umit Karabiyik
Crime linkage is the process of analyzing criminal behavior data to determine whether a pair or group of crime cases are connected or belong to a series of offenses. This domain has been extensively studied by researchers in sociology, psychology, and statistics. More recently, it has drawn interest from computer scientists, especially with advances in artificial intelligence. Despite this, the literature indicates that work in this latter discipline is still in its early stages. This study aims to understand the challenges faced by machine learning approaches in crime linkage and to support foundational knowledge for future data-driven methods. To achieve this goal, we conducted a comprehensive survey of the main literature on the topic and developed a general framework for crime linkage processes, thoroughly describing each step. Our goal was to unify insights from diverse fields into a shared terminology to enhance the research landscape for those intrigued by this subject.
Larger models yield better results? Streamlined severity classification of ADHD-related concerns using BERT-based knowledge distillation
2024-10-30
Ahmed Akib Jawad Karim, Kazi Hafiz Md. Asad, Md. Golam Rabiul Alam
This work focuses on the efficiency of the knowledge distillation approach in generating a lightweight yet powerful BERT based model for natural language processing applications. After the model creation, we applied the resulting model, LastBERT, to a real-world task classifying severity levels of Attention Deficit Hyperactivity Disorder (ADHD)-related concerns from social media text data. Referring to LastBERT, a customized student BERT model, we significantly lowered model parameters from 110 million BERT base to 29 million, resulting in a model approximately 73.64% smaller. On the GLUE benchmark, comprising paraphrase identification, sentiment analysis, and text classification, the student model maintained strong performance across many tasks despite this reduction. The model was also used on a real-world ADHD dataset with an accuracy and F1 score of 85%. When compared to DistilBERT (66M) and ClinicalBERT (110M), LastBERT demonstrated comparable performance, with DistilBERT slightly outperforming it at 87%, and ClinicalBERT achieving 86% across the same metrics. These findings highlight the LastBERT model's capacity to classify degrees of ADHD severity properly, so it offers a useful tool for mental health professionals to assess and comprehend material produced by users on social networking platforms. The study emphasizes the possibilities of knowledge distillation to produce effective models fit for use in resource-limited conditions, hence advancing NLP and mental health diagnosis. Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. Especially using readily available computational tools like Google Colab. This study shows the accessibility and usefulness of advanced NLP methods in pragmatic world applications.
Survey of Cultural Awareness in Language Models: Text and Beyond
2024-10-30
Siddhesh Pawar, Junyeong Park, Jiho Jin, Arnav Arora, Junho Myung, Srishti Yadav, Faiz Ghifari Haznitrama, Inhwa Song, Alice Oh, Isabelle Augenstein
Large-scale deployment of large language models (LLMs) in various applications, such as chatbots and virtual assistants, requires LLMs to be culturally sensitive to the user to ensure inclusivity. Culture has been widely studied in psychology and anthropology, and there has been a recent surge in research on making LLMs more culturally inclusive in LLMs that goes beyond multilinguality and builds on findings from psychology and anthropology. In this paper, we survey efforts towards incorporating cultural awareness into text-based and multimodal LLMs. We start by defining cultural awareness in LLMs, taking the definitions of culture from anthropology and psychology as a point of departure. We then examine methodologies adopted for creating cross-cultural datasets, strategies for cultural inclusion in downstream tasks, and methodologies that have been used for benchmarking cultural awareness in LLMs. Further, we discuss the ethical implications of cultural alignment, the role of Human-Computer Interaction in driving cultural inclusion in LLMs, and the role of cultural alignment in driving social science research. We finally provide pointers to future research based on our findings about gaps in the literature.
Deep Learning for 3D Point Cloud Enhancement: A Survey
2024-10-30
Siwen Quan, Junhao Yu, Ziming Nie, Muze Wang, Sijia Feng, Pei An, Jiaqi Yang
Point cloud data now are popular data representations in a number of three-dimensional (3D) vision research realms. However, due to the limited performance of sensors and sensing noise, the raw data usually suffer from sparsity, noise, and incompleteness. This poses great challenges to down-stream point cloud processing tasks. In recent years, deep-learning-based point cloud enhancement methods, which aim to achieve dense, clean, and complete point clouds from low-quality raw point clouds using deep neural networks, are gaining tremendous research attention. This paper, for the first time to our knowledge, presents a comprehensive survey for deep-learning-based point cloud enhancement methods. It covers three main perspectives for point cloud enhancement, i.e., (1) denoising to achieve clean data; (2) completion to recover unseen data; (3) upsampling to obtain dense data. Our survey presents a new taxonomy for recent state-of-the-art methods and systematic experimental results on standard benchmarks. In addition, we share our insightful observations, thoughts, and inspiring future research directions for point cloud enhancement with deep learning.
Exotic Quantum Phenomena in Twisted Bilayer Graphene
2024-10-30
Giovanna Feraco, Wissem Boubaker, Petra Rudolf, Antonija Grubišić-Čabo
Bilayer graphene twisted at the angle of about 1.1{\deg} better known as magic angle, exhibits ultra-flat moir\'e superlattice bands that are a source of highly-tunable, exotic quantum phenomena. Such phenomena, like superconductivity, correlated Mott-like insulating states and orbital ferromagnetism are driven by strong-correlation physics that defies classical theories. The inadequacy of such classical models and the lack of theoretical understanding of the recently observed exotic phenomena calls for revisiting the theory behind the material system and associating it with the observed behaviour. This article reviews the physics behind twisted bilayer graphene, focusing primarily on moir\'e physics and the importance of electronic (flat) band structure. In addition, this paper provides a brief overview of the emerging phenomena of correlated insulating states, superconductivity and orbital ferromagnetism. Finally, the most recent developments in controlling the interaction-driven states and tuning the electronic interactions are presented.
Misconceptions, Pragmatism, and Value Tensions: Evaluating Students' Understanding and Perception of Generative AI for Education
2024-10-29
Aditya Johri, Ashish Hingle, Johannes Schleiss
In this research paper we examine undergraduate students' use of and perceptions of generative AI (GenAI). Students are early adopters of the technology, utilizing it in atypical ways and forming a range of perceptions and aspirations about it. To understand where and how students are using these tools and how they view them, we present findings from an open-ended survey response study with undergraduate students pursuing information technology degrees. Students were asked to describe 1) their understanding of GenAI; 2) their use of GenAI; 3) their opinions on the benefits, downsides, and ethical issues pertaining to its use in education; and 4) how they envision GenAI could ideally help them with their education. Findings show that students' definitions of GenAI differed substantially and included many misconceptions - some highlight it as a technique, an application, or a tool, while others described it as a type of AI. There was a wide variation in the use of GenAI by students, with two common uses being writing and coding. They identified the ability of GenAI to summarize information and its potential to personalize learning as an advantage. Students identified two primary ethical concerns with using GenAI: plagiarism and dependency, which means that students do not learn independently. They also cautioned that responses from GenAI applications are often untrustworthy and need verification. Overall, they appreciated that they could do things quickly with GenAI but were cautious as using the technology was not necessarily in their best long-term as it interfered with the learning process. In terms of aspirations for GenAI, students expressed both practical advantages and idealistic and improbable visions. They said it could serve as a tutor or coach and allow them to understand the material better. We discuss the implications of the findings for student learning and instruction.
2024-10-29
J. McCullough, A. Amon, E. Legnani, D. Gruen, A. Roodman, O. Friedrich, N. MacCrann, M. R. Becker, J. Myles, S. Dodelson, S. Samuroff, J. Blazek, J. Prat, K. Honscheid, A. Pieres, A. Ferté, A. Alarcon, A. Drlica-Wagner, A. Choi, A. Navarro-Alsina, A. Campos, A. A. Plazas Malagón, A. Porredon, A. Farahi, A. J. Ross, A. Carnero Rosell, B. Yin, B. Flaugher, B. Yanny, C. Sánchez, C. Chang, C. Davis, C. To, C. Doux, D. Brooks, D. J. James, D. Sanchez Cid, D. L. Hollowood, D. Huterer, E. S. Rykoff, E. Gaztanaga, E. M. Huff, E. Suchyta, E. Sheldon, E. Sanchez, F. Tarsitano, F. Andrade-Oliveira, F. J. Castander, G. M. Bernstein, G. Gutierrez, G. Giannini, G. Tarle, H. T. Diehl, H. Huang, I. Harrison, I. Sevilla-Noarbe, I. Tutusaus, I. Ferrero, J. Elvin-Poole, J. L. Marshall, J. Muir, J. Weller, J. Zuntz, J. Carretero, J. DeRose, J. Frieman, J. Cordero, J. De Vicente, J. García-Bellido, J. Mena-Fernández, K. Eckert, A. K. Romer, K. Bechtol, K. Herner, K. Kuehn, L. F. Secco, L. N. da Costa, M. Paterno, 21 M. Soares-Santos, M. Gatti, M. Raveri, M. Yamamoto, M. Smith, M. Carrasco Kind, M. A. Troxel, M. Aguena, M. Jarvis, M. E. C. Swanson, N. Weaverdyck, O. Lahav, P. Doel, P. Wiseman, R. Miquel, R. A. Gruendl, R. Cawthon, S. Allam, S. R. Hinton, S. L. Bridle, S. Bocquet, S. Desai, S. Pandey, S. Everett, S. Lee, T. Shin, A. Palmese, C. Conselice, D. L. Burke, E. Buckley-Geer, M. Lima, M. Vincenzi, M. E. S. Pereira, M. Crocce, M. Schubnell, N. Jeffrey, O. Alves, V. Vikram, Y. Zhang, DES Collaboration
Modeling the intrinsic alignment (IA) of galaxies poses a challenge to weak lensing analyses. The Dark Energy Survey is expected to be less impacted by IA when limited to blue, star-forming galaxies. The cosmological parameter constraints from this blue cosmic shear sample are stable to IA model choice, unlike passive galaxies in the full DES Y3 sample, the goodness-of-fit is improved and the $\Omega_{m}$ and $S_8$ better agree with the cosmic microwave background. Mitigating IA with sample selection, instead of flexible model choices, can reduce uncertainty in $S_8$ by a factor of 1.5.
Bolometric Corrections for FU Ori Object Accretion Disk Luminosities
2024-10-29
Adolfo S. Carvalho, Lynne A. Hillenbrand
The accretion luminosity of an FU Ori disk is a fundamental system parameter, but a challenging one to estimate for all but the most well-studied systems. FU Ori objects are dynamically evolving accretion disks, especially close in time to the outburst epoch. They have a complex multi-temperature disk structure that results in distinctly shaped, broad SEDs. Detailed spectroscopic analysis is required for simultaneous constraint on relevant physical parameters such as the central stellar mass, inner disk radius, disk inclination, and disk accretion rate. However, outbursting systems that are deeply embedded and/or distant may be limited to only photometric measurement, and over only a narrow range of wavelengths. The bolometric corrections necessary to estimate accretion luminosities are not straightforward, and in particular can not be adopted from existing literature on isotropically radiating stars. We present bolometric corrections specific to astrophysical accretion disks for a variety of filters in ongoing and upcoming all-sky surveys.
The realm of Aurora. Density distribution of metal-poor giants in the heart of the Galaxy
2024-10-29
Evgeny P. Kurbatov, Vasily Belokurov, Sergey Koposov, Andrey Kravtsov, Elliot Y. Davies, Anthony G. A. Brown, Tristan Cantat-Gaudin, Alfred Castro-Ginard, Andrew R. Casey, Ronald Drimmel, Morgan Fouesneau, Shourya Khanna, Hans-Walter Rix, Alex Wallace
The innermost portions of the Milky Way's stellar halo have avoided scrutiny until recently. The lack of wide-area survey data, made it difficult to reconstruct an uninterrupted view of the density distribution of the metal-poor stars inside the Solar radius. In this study, we utilize red giant branch (RGB) stars from Gaia, with metallicities estimated using spectro-photometry from Gaia Data Release 3. Accounting for Gaia's selection function, we examine the spatial distribution of metal-poor ([M/H]<-1.3) RGB stars, from the Galactic centre (r~1 kpc) out to beyond the Solar radius (r~18 kpc). Our best-fitting single-component cored power-law model shows a vertical flattening of ~0.5 and a slope -3.4, consistent with previous studies. Motivated by the mounting evidence for two distinct stellar populations in the inner halo, we additionally test a range of two-component models. One of the components models the tidal debris from the Gaia Sausage/Enceladus merger, while the other captures the Aurora population -- stars that predate the Galactic disk formation. Our best-fit two-component model suggests that both populations contribute equally around the Solar radius, but Aurora dominates the inner halo with a steeper power-law index of -4.5, in agreement with the nitrogen-rich star distribution measured by Horta et al. (2021).
Disjoint zero-sum subsets in Abelian groups and its application -- survey
2024-10-29
Sylwia Cichacz
We provide a summary of research on disjoint zero-sum subsets in finite Abelian groups, which is a branch of additive group theory and combinatorial number theory. An orthomorphism of a group $\Gamma$ is defined as a bijection $\varphi$ $\Gamma$ such that the mapping $g \mapsto g^{-1}\varphi(g)$ is also bijective. In 1981, Friedlander, Gordon, and Tannenbaum conjectured that when $\Gamma$ is Abelian, for any $k \geq 2$ dividing $|\Gamma| -1$, there exists an orthomorphism of $\Gamma$ fixing the identity and permuting the remaining elements as products of disjoint $k$-cycles. Using the idea of disjoint-zero sum subset we provide a solution of this conjecture for $k=3$ and $|\Gamma|\cong 4\pmod{24}$. We also present some applications of zero-sum sets in graph labeling.
Semi-supervised Spectral Classification of DESI White Dwarfs by Dimensionality Reduction
2024-10-29
Xander Byrne, Amy Bonsor, Laura K. Rogers, Christopher J. Manser
As a new generation of large-sky spectroscopic surveys comes online, the enormous data volume poses unprecedented challenges in classifying spectra. Modern unsupervised techniques have the power to group spectra based on their dominant features, circumventing the complete reliance on training data suffered by supervised methods. We outline the use of dimensionality reduction to generate a 2D map of the structure of an intermediate-resolution spectroscopic dataset. This technique efficiently separates white dwarfs of different spectral classes in the Dark Energy Spectroscopic Instrument's Early Data Release (DESI EDR), identifying spectral features that had been missed even by visual classification. By focusing the method on particular spectral regions, we identify white dwarfs with helium features at 90 per cent recall, and cataclysmic variables at 100 per cent recall, illustrating rapid selection of low-contamination samples from spectroscopic surveys. We also demonstrate the use of dimensionality reduction in a supervised manner, outlining a procedure to classify any white dwarf spectrum in comparison with those in the DESI EDR. With upcoming surveys promising tens of millions of spectra, our work highlights the potential for semi-supervised techniques as an efficient means of classification and dataset visualisation.
Towards Unifying Understanding and Generation in the Era of Vision Foundation Models: A Survey from the Autoregression Perspective
2024-10-29
Shenghao Xie, Wenqiang Zu, Mingyang Zhao, Duo Su, Shilong Liu, Ruohua Shi, Guoqi Li, Shanghang Zhang, Lei Ma
Autoregression in large language models (LLMs) has shown impressive scalability by unifying all language tasks into the next token prediction paradigm. Recently, there is a growing interest in extending this success to vision foundation models. In this survey, we review the recent advances and discuss future directions for autoregressive vision foundation models. First, we present the trend for next generation of vision foundation models, i.e., unifying both understanding and generation in vision tasks. We then analyze the limitations of existing vision foundation models, and present a formal definition of autoregression with its advantages. Later, we categorize autoregressive vision foundation models from their vision tokenizers and autoregression backbones. Finally, we discuss several promising research challenges and directions. To the best of our knowledge, this is the first survey to comprehensively summarize autoregressive vision foundation models under the trend of unifying understanding and generation. A collection of related resources is available at https://github.com/EmmaSRH/ARVFM.
Insulating moiré homobilayers lack a threefold symmetric second harmonic generation
2024-10-29
Luis Enrique Rosas-Hernandez, Jose Luis Cabellos, Angiolo Huamán, Bernardo Mendoza, Salvador Barraza-Lopez
Atoms within moir\'e bilayers relax in-plane to minimize elastic energy [e.g., Cazeaux et al., J. Elast. 154, 443 (2023)]; such relaxation brings their space group symmetries down to P1. Here, the ab initio second harmonic generation (SHG) of twisted and atomistically optimized hBN bilayers was determined at four twist angles ($\theta=38.21^{\circ}$, $60.00^{\circ}$, $73.17^{\circ}$, and $98.21^{\circ}$) and for three displacements $\boldsymbol{\tau}$ measured away from the ground state $AA^{\prime}$ configuration. All moir\'e bilayers have a P1 space symmetry after structural optimization. This situation is quite different to monolayers with hexagonal lattices, which retain a three-fold symmetry. We point out that the actual symmetries of the SHG reported for hBN bilayers on two experimental works do not coincide with the sixfold symmetric theoretical profiles they provide [either $\sin^2(3\phi)$ or $\cos^2(3\phi)$], and show that the intrinsic low structural symmetry of (atomically optimized) hBN bilayer moir\'es can in fact be read out from experimental SHG intensity profiles--which are tunable by $\theta$ and by the frequency of light $\omega$: The SHG is most definitely not sixfold-symmetric because moir\'es do not retain a three-fold symmetry. Furthermore, an extrinsic twofold symmetry of the SHG emission is realized by tilting the pump by an angle $\alpha$ away from the 2D material's normal, regardless of $\theta$ and $\omega$. The design of in-plane and ultrathin sources of SHG with low symmetry could be useful for the eventual creation of entanglement sources from 2D materials.
Natural Language Processing for Analyzing Electronic Health Records and Clinical Notes in Cancer Research: A Review
2024-10-29
Muhammad Bilal, Ameer Hamza, Nadia Malik
Objective: This review aims to analyze the application of natural language processing (NLP) techniques in cancer research using electronic health records (EHRs) and clinical notes. This review addresses gaps in the existing literature by providing a broader perspective than previous studies focused on specific cancer types or applications. Methods: A comprehensive literature search was conducted using the Scopus database, identifying 94 relevant studies published between 2019 and 2024. Data extraction included study characteristics, cancer types, NLP methodologies, dataset information, performance metrics, challenges, and future directions. Studies were categorized based on cancer types and NLP applications. Results: The results showed a growing trend in NLP applications for cancer research, with breast, lung, and colorectal cancers being the most studied. Information extraction and text classification emerged as predominant NLP tasks. A shift from rule-based to advanced machine learning techniques, particularly transformer-based models, was observed. The Dataset sizes used in existing studies varied widely. Key challenges included the limited generalizability of proposed solutions and the need for improved integration into clinical workflows. Conclusion: NLP techniques show significant potential in analyzing EHRs and clinical notes for cancer research. However, future work should focus on improving model generalizability, enhancing robustness in handling complex clinical language, and expanding applications to understudied cancer types. Integration of NLP tools into clinical practice and addressing ethical considerations remain crucial for utilizing the full potential of NLP in enhancing cancer diagnosis, treatment, and patient outcomes.
Analyzing Multimodal Interaction Strategies for LLM-Assisted Manipulation of 3D Scenes
2024-10-29
Junlong Chen, Jens Grubert, Per Ola Kristensson
As more applications of large language models (LLMs) for 3D content for immersive environments emerge, it is crucial to study user behaviour to identify interaction patterns and potential barriers to guide the future design of immersive content creation and editing systems which involve LLMs. In an empirical user study with 12 participants, we combine quantitative usage data with post-experience questionnaire feedback to reveal common interaction patterns and key barriers in LLM-assisted 3D scene editing systems. We identify opportunities for improving natural language interfaces in 3D design tools and propose design recommendations for future LLM-integrated 3D content creation systems. Through an empirical study, we demonstrate that LLM-assisted interactive systems can be used productively in immersive environments.
Planet-star interactions with precise transit timing. IV. Probing the regime of dynamical tides for GK host stars
2024-10-29
G. Maciejewski, J. Golonka, M. Fernandez, J. Ohlert, V. Casanova, D. Perez Medialdea
Statistical studies show that stars of GK spectral types, with masses below 1.1 Sun mass, are depleted in hot Jupiters. This finding is evidence of tidal orbital decay during the main-sequence lifetime. Theoretical considerations show that in some configurations, the tidal energy dissipation can be boosted by non-linear effects in dynamical tides, which are wave-like responses to tidal forcing. To probe the regime of these dynamical tides in GK stars, we searched for orbital period shortening for 6 selected hot Jupiters in systems with 0.8-1 Sun mass host stars: HATS-18, HIP 65A, TrES-3, WASP-19, WASP-43, and WASP-173A. For the hot Jupiters of our sample, we analysed transit timing data sets based on mid-transit points homogeneously determined from observations performed with the Transiting Exoplanet Survey Satellite and high-quality data available in the literature. For the TrES-3 system, we also used new transit light curves we acquired with ground-based telescopes. The mid-transit times were searched for shortening of orbital periods through statistical testing of quadratic transit ephemerides. Theoretical predictions on the dissipation rate for dynamical tides were calculated under the regimes of internal gravity waves (IGWs) undergoing wave breaking (WB) in stellar centres and weak non-linear (WNL) wave-wave interactions in radiative layers. Stellar parameters of the host stars, such as mass and age, which were used in those computations, were homogeneously redetermined using evolutionary models with the Bayesian inference. We found that transit times follow the refined linear ephemerides for all ultra-hot Jupiters of our sample. Non-detection of orbital decay allowed us to place lower constraints on the tidal dissipation rates in those planet-star systems. In three systems, HATS-18, WASP-19, and WASP-43, we reject a scenario with total dissipation of IGWs. We conclude that...
2024-10-29
Jinli Chen, Chaohan Cui, Ben Lawrie, Yongzhou Xue, Saikat Guha, Matt Eichenfield, Huan Zhao, Xiaodong Yan
Solid-state single-photon emitters (SPEs) are attracting significant attention as fundamental components in quantum computing, communication, and sensing. Low-dimensional materials-based SPEs (LD-SPEs) have drawn particular interest due to their high photon extraction efficiency, ease of integration with photonic circuits, and strong coupling with external fields. The accessible surfaces of LD materials allow for deterministic control over quantum light emission, while enhanced quantum confinement and light-matter interactions improve photon emissive properties. This review examines recent progress in LDSPEs across four key materials: zero-dimensional (0D) semiconductor quantum dots, one-dimensional (1D) nanotubes, two-dimensional (2D) materials, including hexagonal boron nitride (hBN) and transition metal dichalcogenides (TMDCs). We explore their structural and photophysical properties, along with techniques such as spectral tuning and cavity coupling that enhance SPE performance. Finally, we address future challenges and suggest strategies for optimizing LD-SPEs for practical quantum applications.
Modeling the Cosmic Dispersion Measure in the D < 120 Mpc Local Universe
2024-10-29
Yuxin Huang, Khee-Gan Lee, Noam I. Libeskind, Sunil Simha, Aurélien Valade, J. Xavier Prochaska
The Local Universe (D < 120 Mpc) has been intensely studied for decades, with highly complete galaxy redshift surveys now publicly available. These data have driven density reconstructions of the underlying matter density field, as well as constrained simulations that aim to reproduce the observed structures. In this paper, we introduce a dispersion measure (DM) model that makes use of this detailed knowledge of our Local Universe within D < 120 Mpc. The model comprises three key components: (i) the DM from the Milky Way halo and the intra-group medium (up to 3.4 Mpc), derived from the HESTIA simulations, a series of constrained hydrodynamic simulations designed to reproduce our Local Group; (ii) the DM contribution from the large-scale intergalactic medium beyond the Local Group (3.4 Mpc < D < 120 Mpc), calculated using the HAMLET reconstructed matter density field; and (iii) the individual DM contributions from Local Universe galaxy halos and clusters based on data from the 2MASS Galaxy Group Catalog and the NASA/IPAC Extragalactic Database. This comprehensive model will be made available as a Python package. As the most realistic model to date for DM in the local volume, it promises to improve the constraints of DM contributions from the Intergalactic Medium and Circumgalactic Medium of FRBs, thereby enhancing the accuracy of cosmic baryon distribution calculations based on DM analysis of FRBs.
HRPVT: High-Resolution Pyramid Vision Transformer for medium and small-scale human pose estimation
2024-10-29
Zhoujie Xu
Human pose estimation on medium and small scales has long been a significant challenge in this field. Most existing methods focus on restoring high-resolution feature maps by stacking multiple costly deconvolutional layers or by continuously aggregating semantic information from low-resolution feature maps while maintaining high-resolution ones, which can lead to information redundancy. Additionally, due to quantization errors, heatmap-based methods have certain disadvantages in accurately locating keypoints of medium and small-scale human figures. In this paper, we propose HRPVT, which utilizes PVT v2 as the backbone to model long-range dependencies. Building on this, we introduce the High-Resolution Pyramid Module (HRPM), designed to generate higher quality high-resolution representations by incorporating the intrinsic inductive biases of Convolutional Neural Networks (CNNs) into the high-resolution feature maps. The integration of HRPM enhances the performance of pure transformer-based models for human pose estimation at medium and small scales. Furthermore, we replace the heatmap-based method with SimCC approach, which eliminates the need for costly upsampling layers, thereby allowing us to allocate more computational resources to HRPM. To accommodate models with varying parameter scales, we have developed two insertion strategies of HRPM, each designed to enhancing the model's ability to perceive medium and small-scale human poses from two distinct perspectives.
Mapping the Neuro-Symbolic AI Landscape by Architectures: A Handbook on Augmenting Deep Learning Through Symbolic Reasoning
2024-10-29
Jonathan Feldstein, Paulius Dilkas, Vaishak Belle, Efthymia Tsamoura
Integrating symbolic techniques with statistical ones is a long-standing problem in artificial intelligence. The motivation is that the strengths of either area match the weaknesses of the other, and $\unicode{x2013}$ by combining the two $\unicode{x2013}$ the weaknesses of either method can be limited. Neuro-symbolic AI focuses on this integration where the statistical methods are in particular neural networks. In recent years, there has been significant progress in this research field, where neuro-symbolic systems outperformed logical or neural models alone. Yet, neuro-symbolic AI is, comparatively speaking, still in its infancy and has not been widely adopted by machine learning practitioners. In this survey, we present the first mapping of neuro-symbolic techniques into families of frameworks based on their architectures, with several benefits: Firstly, it allows us to link different strengths of frameworks to their respective architectures. Secondly, it allows us to illustrate how engineers can augment their neural networks while treating the symbolic methods as black-boxes. Thirdly, it allows us to map most of the field so that future researchers can identify closely related frameworks.
Simulation is crucial for validating algorithmic strategies in real-world scenarios. While LLM-based social simulation shows promise as a mainstream tool, simulating complex scenarios like psychological counseling remains challenging. We present ECAs (short for Embodied Conversational Agents), a framework for simulating psychological counseling clients' embodied memory, integrating embodied cognition and counseling theories. We formulate six design goals based on a comprehensive review of psychological counseling theories. Using LLMs, we expand real counseling case data into a nuanced embodied cognitive memory space and generate dialogues based on high-frequency counseling questions. We validate our framework using the D4 dataset, with evaluations by licensed counselors. Results show our approach significantly outperforms baselines in simulation authenticity and necessity. To demonstrate scalability, we created a public ECAs dataset through batch simulations. This research provides valuable insights for future social simulation studies in psychological counseling and Embodied Counseling Agents research.
GaiaUnlimited: The old stellar disc of the Milky Way as traced by the Red Clump
2024-10-29
Shourya Khanna, Jie Yu, Ronald Drimmel, Eloisa Poggio, Tristan Cantat-Gaudin, Alfred Castro-Ginard, Evgeny Kurbatov, Vasily Belokurov, Anthony Brown, Morgan Fouesneau, Andrew Casey, Hans-Walter Rix
We present an exploration of the Milky Way's structural parameters using an all-sky sample of RC giants to map the stellar density from the inner to the outer parts of the Galactic disc. These evolved giants are considered to be standard candles due to their low intrinsic variance in their absolute luminosities, allowing us to estimate their distances with reasonable confidence. We exploit all-sky photometry from the AllWISE mid-infrared survey and the Gaia survey, along with astrometry from Gaia Data Release 3 and recent 3D extinction maps, to develop a probabilistic scheme in order to select with high confidence \rc{}-like stars. Our curated catalogue contains about 10 million sources, for which we estimate photometric distances based on the WISE $W1$ photometry. We then derive the selection function for our sample, which is the combined selection function of sources with both \gaia{} and \allwise{} photometry. Using the distances and accounting for the full selection function of our observables, we are able to fit a two-disc, multi-parameter model to constrain the scale height (\hz{}), scale-length (\rd{}), flaring, and the relative mass ratios of the two disc components. We illustrate and verify our methodology using mock catalogues of \rc{} stars. We find that the \rc{} population is best described by a flared thin disc with scale length \rd{}=$3.56\pm0.32$ kpc and scale height at the Sun of \hzsun{}=$0.17\pm0.01$ kpc, and a shorter and thicker disc with \rd{}=$2.59\pm0.11$ kpc, \hzsun{}=$0.45\pm0.11$ kpc, with no flare. The thicker disc constitutes 64\% of the \rc{} stellar mass beyond 3 kpc, while the thin disk shows evidence of being warped beyond 9 kpc from the Galactic center. The residuals between the predicted number density of RC stars from our axisymmetric model and the measured counts show possible evidence of a two-armed spiral perturbation in the disc of the Milky Way.
With the rapid development of robotics swarm technology, there are more tasks that require the swarm to pass through complicate environments safely and efficiently. Virtual tube technology is a novel way to achieve this goal. Virtual tubes are free spaces connecting two places that provide safety boundaries and direction of motion for swarm robotics. How to determine the design quality of a virtual tube is a fundamental problem. For such a purpose, this paper presents a degree of flowability (DOF) for two-dimensional virtual tubes according to a minimum energy principle. After that, methods to calculate DOF are proposed with a feasibility analysis. Simulations of swarm robotics in different kinds of two-dimensional virtual tubes are performed to demonstrate the effectiveness of the proposed method of calculating DOF.
Uncertainty, characterised by randomness and stochasticity, is ubiquitous in applications of evolutionary game theory across various fields, including biology, economics and social sciences. The uncertainty may arise from various sources such as fluctuating environments, behavioural errors or incomplete information. Incorporating uncertainty into evolutionary models is essential for enhancing their relevance to real-world problems. In this perspective article, we survey the relevant literature on evolutionary dynamics with random payoff matrices, with an emphasis on continuous models. We also pose challenging open problems for future research in this important area.
The growing penetration of IoT devices in power grids despite its benefits, raises cyber security concerns. In particular, load-altering attacks (LAAs) targetting high-wattage IoT-controllable load devices pose serious risks to grid stability and disrupt electricity markets. This paper provides a comprehensive review of LAAs, highlighting the threat model, analyzing its impact on transmission and distribution networks, and the electricity market dynamics. We also review the detection and localization schemes for LAAs that employ either model-based or data-driven approaches, with some hybrid methods combining the strengths of both. Additionally, mitigation techniques are examined, focusing on both preventive measures, designed to thwart attack execution, and reactive methods, which aim to optimize responses to ongoing attacks. We look into the application of each study and highlight potential streams for future research in this field.
Understanding Code Understandability Improvements in Code Reviews
2024-10-29
Delano Oliveira, Reydne Santos, Benedito de Oliveira, Martin Monperrus, Fernando Castor, Fernanda Madeiral
Motivation: Code understandability is crucial in software development, as developers spend 58% to 70% of their time reading source code. Improving it can improve productivity and reduce maintenance costs. Problem: Experimental studies often identify factors influencing code understandability in controlled settings but overlook real-world influences like project culture, guidelines, and developers' backgrounds. Ignoring these factors may yield results with limited external validity. Objective: This study investigates how developers enhance code understandability through code review comments, assuming that code reviewers are specialists in code quality. Method and Results: We analyzed 2,401 code review comments from Java open-source projects on GitHub, finding that over 42% focus on improving code understandability. We further examined 385 comments specifically related to this aspect and identified eight categories of concerns, such as inadequate documentation and poor identifiers. Notably, 83.9% of suggestions for improvement were accepted and integrated, with fewer than 1% later reverted. We identified various types of patches that enhance understandability, from simple changes like removing unused code to context-dependent improvements such as optimizing method calls. Additionally, we evaluated four well-known linters for their ability to flag these issues, finding they cover less than 30%, although many could be easily added as new rules. Implications: Our findings encourage the development of tools to enhance code understandability, as accepted changes can serve as reliable training data for specialized machine-learning models. Our dataset supports this training and can inform the development of evidence-based code style guides. Data Availability: Our data is publicly available at https://codeupcrc.github.io.
A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Anomaly Detection
2024-10-29
Yuxuan Lin, Yang Chang, Xuan Tong, Jiawen Yu, Antonio Liotta, Guofan Huang, Wei Song, Deyu Zeng, Zongze Wu, Yan Wang, Wenqiang Zhang
In the advancement of industrial informatization, Unsupervised Industrial Anomaly Detection (UIAD) technology effectively overcomes the scarcity of abnormal samples and significantly enhances the automation and reliability of smart manufacturing. While RGB, 3D, and multimodal anomaly detection have demonstrated comprehensive and robust capabilities within the industrial informatization sector, existing reviews on industrial anomaly detection have not sufficiently classified and discussed methods in 3D and multimodal settings. We focus on 3D UIAD and multimodal UIAD, providing a comprehensive summary of unsupervised industrial anomaly detection in three modal settings. Firstly, we compare our surveys with recent works, introducing commonly used datasets, evaluation metrics, and the definitions of anomaly detection problems. Secondly, we summarize five research paradigms in RGB, 3D and multimodal UIAD and three emerging industrial manufacturing optimization directions in RGB UIAD, and review three multimodal feature fusion strategies in multimodal settings. Finally, we outline the primary challenges currently faced by UIAD in three modal settings, and offer insights into future development directions, aiming to provide researchers with a thorough reference and offer new perspectives for the advancement of industrial informatization. Corresponding resources are available at https://github.com/Sunny5250/Awesome-Multi-Setting-UIAD.
Impact of cosmology dependence of baryonic feedback in weak lensing
2024-10-29
Pranjal R. S., Elisabeth Krause, Klaus Dolag, Karim Benabed, Tim Eifler, Emma Ayçoberry, Yohan Dubois
Robust modeling of non-linear scales is critical for accurate cosmological inference in Stage IV surveys. For weak lensing analyses in particular, a key challenge arises from the incomplete understanding of how non-gravitational processes, such as supernovae and active galactic nuclei - collectively known as baryonic feedback - affect the matter distribution. Several existing methods for modeling baryonic feedback treat it independently from the underlying cosmology, an assumption which has been found to be inaccurate by hydrodynamical simulations. In this work, we examine the impact of this coupling between baryonic feedback and cosmology on parameter inference at LSST Y1 precision. We build mock 3$\times$2pt data vectors using the Magneticum suite of hydrodynamical simulations, which span a wide range of cosmologies while keeping subgrid parameters fixed. We perform simulated likelihood analyses for two baryon mitigation techniques: (i) the Principal Component Analysis (PCA) method which identifies eigenmodes for capturing the effect baryonic feedback on the data vector and (ii) HMCode2020 (Mead et al. 2021) which analytically models the modification in the matter distribution using a halo model approach. Our results show that the PCA method is robust to the coupling between cosmology and baryonic feedback, whereas, when using HMCode2020 there can be up to $0.5\sigma$ bias in $\Omega_\text{m}$-$S_8$. For HMCode2020, the bias also correlates with the input cosmology while for PCA we find no such correlation.
Characterization and classification of $γ$-ray bursts from blazars
2024-10-29
Matteo Cerruti
Blazars are the most common sources of $\gamma$-ray photons in the extra-galactic sky. Their $\gamma$-ray light-curves are characterized by bright flaring episodes, similarly to what is observed at longer wavelengths. These Gamma-Ray Bursts from Blazars (GRBBLs) have been extensively studied individually, but never in terms of a population. The goal of this work is to provide a global characterization of GRBBLs, to investigate the parameter space of the population, and ultimately to classify GRBBLs. Their global properties could give insights on the physical mechanisms responsible for the $\gamma$-ray radiation and on the origin of the observed variability. I analyze a sample of publicly available Fermi-LAT light-curves, utilizing only blazars with certain redshift measurements. The redshift-corrected light-curves are then automatically scanned to identify GRBBLs. A simple flare profile, with exponential rise and decay, is then fitted to all events. The fit parameters, together with global properties from the LAT catalog, are then used as input for unsupervised machine learning classification. The analysis shows that the GRBBL population is remarkably homogeneous. When using only the properties of the integral light-curves, the classifier converges into a single population. When adding information on the evolution of the photon index, the classifier splits the population into achromatic (the large majority) and chromatic (the outliers) GRBBLs. As by product of this study, I identify a correlation between the rising/decay time-scales of the GRBBLs and their peak luminosity.
Capivara: A Spectral-based Segmentation Method for IFU Data Cubes
2024-10-29
Rafael S. de Souza, Luis G. Dahmer-Hahn, Shiyin Shen, Ana L. Chies-Santos, Mi Chen, P. T. Rahna, Renhao Ye, Behzad Tahmasebzade
We present capivara, a fast and scalable multi-decomposition package designed to study astrophysical properties within distinct structural components of galaxies. Our spectro-decomposition code for analyzing integral field unit (IFU) data enables a more holistic approach, moving beyond conventional radial gradients and the bulge-plus-disk dichotomy. It facilitates comprehensive comparisons of integrated stellar ages and metallicities across various galactic structures. Our classification method naturally identifies outliers and organizes the different pixels based on their dominant spectral features. The algorithm leverages the scalability and GPU acceleration of Torch, outputting both a one-dimensional spectrum and a full data cube for each galaxy component, without relying on Voronoi binning. We demonstrate the capabilities of our approach using a sample of galaxies from the MaNGA survey, processing the resulting data cubes with the starlight spectral fitting code to derive both stellar population and ionized gas properties of the galaxy components. Our method effectively groups regions with similar spectral properties in both the continuum and emission lines. By aggregating the spectra of these regions, we enhance the signal-to-noise ratio of our analysis while significantly speeding up computations by reducing the number of spectra processed simultaneously. capivara will be freely available on GitHub.
Spatio-temporal Transformers for Action Unit Classification with Event Cameras
2024-10-29
Luca Cultrera, Federico Becattini, Lorenzo Berlincioni, Claudio Ferrari, Alberto Del Bimbo
Face analysis has been studied from different angles to infer emotion, poses, shapes, and landmarks. Traditionally RGB cameras are used, yet for fine-grained tasks standard sensors might not be up to the task due to their latency, making it impossible to record and detect micro-movements that carry a highly informative signal, which is necessary for inferring the true emotions of a subject. Event cameras have been increasingly gaining interest as a possible solution to this and similar high-frame rate tasks. We propose a novel spatiotemporal Vision Transformer model that uses Shifted Patch Tokenization (SPT) and Locality Self-Attention (LSA) to enhance the accuracy of Action Unit classification from event streams. We also address the lack of labeled event data in the literature, which can be considered one of the main causes of an existing gap between the maturity of RGB and neuromorphic vision models. Gathering data is harder in the event domain since it cannot be crawled from the web and labeling frames should take into account event aggregation rates and the fact that static parts might not be visible in certain frames. To this end, we present FACEMORPHIC, a temporally synchronized multimodal face dataset composed of RGB videos and event streams. The dataset is annotated at a video level with facial Action Units and contains streams collected with various possible applications, ranging from 3D shape estimation to lip-reading. We then show how temporal synchronization can allow effective neuromorphic face analysis without the need to manually annotate videos: we instead leverage cross-modal supervision bridging the domain gap by representing face shapes in a 3D space. Our proposed model outperforms baseline methods by effectively capturing spatial and temporal information, crucial for recognizing subtle facial micro-expressions.
DiffusionVel: Multi-Information Integrated Velocity Inversion Using Generative Diffusion Models
2024-10-29
Hao Zhang, Yuanyuan Li, Jianping Huang
Full waveform inversion (FWI) is capable of reconstructing subsurface properties with high resolution from seismic data. However, conventional FWI faces challenges such as cycle-skipping and high computational costs. Recently, deep learning method has emerged as a promising solution for efficient velocity estimation. We develop DiffusionVel, a data-driven technique based on the state-of-the-art generative diffusion models (GDMs) with integration of multiple information including seismic data, background velocity, geological knowledge, and well logs. We use two separate conditional GDMs, namely the seismic-data GDM and the well-log GDM, and an unconditional GDM, i.e., the geology-oriented GDM, to adapt the generated velocity model to the constraints of seismic data, well logs, and prior geological knowledge, respectively. Besides, the background velocity can be incorporated into the generated velocity model with a low-pass filter. The generation of these GDM are then combined together with a weighted summation in the sampling process. We can flexibly control the constraints from each information by adjusting the weighting factors. We make a comprehensive comparison between the proposed DiffusionVel and three previously-developed methods including conventional FWI, InversionNet, and VelocityGAN by using the OpenFWI datasets and the Hess VTI model example. The test results demonstrate that the proposed DiffusionVel method predicts the velocity model reasonably by integrating multiple information effectively.
Building Castles in the Cloud: Architecting Resilient and Scalable Infrastructure
2024-10-29
Naresh Kumar Gundla
In the contemporary world of dynamic digital solutions and services, the significance of effective and stable cloud solutions cannot be overestimated. The cloud adaptation is becoming more popular due to mobile advantages, including flexibility, cheaper costs and scalability. However, creating a fail-proof architecture that can accommodate scale-up and enable high data availability and security is not an easy task. In this paper, a discussion will be made regarding significant measures required in designing contexts inside the cloud environment. It explores the need for replicate servers, fault tolerance, disaster backup and load balancing for high availability. Further, the paper also discusses the optimum strategy for designing cloud infrastructures such as microservices, containerization, and serverless. Based on the literature review, we analyze various approaches that are used to improve cloud reliability and elasticity. The paper also provides a best practice guide for designing a cloud infrastructure for these requirements concerning cases. The results and discussion section outlines the improvement in business continuity and operational efficiency when using the proposed architecture. This paper concludes with recommendations for future studies and the successful application of the elaborated matters.
The evolution of two-point correlation function of galaxies with a twin-peak initial power spectrum
2024-10-29
Yang Zhang, Bichu Li
The evolution equation of two-point correlation function $\xi$ of galaxies can analytically describe the large scale structure of the galaxy distribution, and the solution depends also upon the initial condition. The primeval spectrum of the baryon acoustic oscillations (BAO) contains multi peaks that survived the Silk damping, and, as a relevant portion, two peaks of the primeval BAO spectrum fall into the range of current galaxy surveys. Incorporating this portion, we use a twin-peak initial power spectrum of the galaxies, and obtain the evolution solution from a redshift $z=8$ to $z=0$ in the Gaussian approximation. The outcome $\xi(r)$ at $z=0.6$ still exhibits the 100 Mpc periodic bumps as observed by the WiggleZ survey, a feature largely determined by the Jeans length $\lambda_J$ in the equation. In particular, due to the superposition of the twin peaks in the initial condition, $\xi(r)$ shows a shallow trough at $\sim 70 h^{-1}$Mpc and a deep trough at $\sim 140 h^{-1}$Mpc, agreeing with the observational data, much better than our previous work that used a simple one-peak initial spectrum.
Topological electronic structure and electronic nematicity in candidate kagome superconductors, ATi$_{3}$Bi$_{5}$ (A = Rb, Cs)
2024-10-29
Yong Hu, Congcong Le, Xianxin Wu, Ming Shi
The newly discovered family of titanium-based kagome metals, ATi$_{3}$Bi$_{5}$ (where A can be Rb or Cs), has been found to exhibit non-trivial band topology and fascinating electronic instabilities, including electronic nematicity and potential bulk superconductivity. Distinct from their vanadium-based counterparts (AV$_{3}$Sb$_{5}$), which display a charge density wave (CDW) phase that already breaks rotational symmetry, ATi$_{3}$Bi$_{5}$ shows no evidence of CDW, providing a unique platform to study nematicity in its pure form and its interplay with other correlated quantum phenomena, such as superconductivity. In this review, we highlight recent progress in both experimental and theoretical research on ATi$_{3}$Bi$_{5}$ and discuss the unresolved questions and challenges in this burgeoning field.
Knowledge-Guided Prompt Learning for Request Quality Assurance in Public Code Review
2024-10-29
Lin Li, Xinchun Yu, Xinyu Chen, Peng Liang
Public Code Review (PCR) is an assistant to the internal code review of the development team, in the form of a public Software Question Answering (SQA) community, to help developers access high-quality and efficient review services. Current methods on PCR mainly focus on the reviewer's perspective, including finding a capable reviewer, predicting comment quality, and recommending/generating review comments. However, it is not well studied that how to satisfy the review necessity requests posted by developers which can increase their visibility, which in turn acts as a prerequisite for better review responses. To this end, we propose a Knowledge-guided Prompt learning for Public Code Review (KP-PCR) to achieve developer-based code review request quality assurance (i.e., predicting request necessity and recommending tags subtask). Specifically, we reformulate the two subtasks via 1) text prompt tuning which converts both of them into a Masked Language Model (MLM) by constructing prompt templates using hard prompt; 2) knowledge and code prefix tuning which introduces external knowledge by soft prompt, and uses data flow diagrams to characterize code snippets. Finally, both of the request necessity prediction and tag recommendation subtasks output predicted results through an answer engineering module. In addition, we further analysis the time complexity of our KP-PCR that has lightweight prefix based the operation of introducing knowledge. Experimental results on the PCR dataset for the period 2011-2023 demonstrate that our KP-PCR outperforms baselines by 8.3%-28.8% in the request necessity prediction and by 0.1%-29.5% in the tag recommendation. The code implementation is released at https://github.com/WUT-IDEA/KP-PCR.
Out-of-distribution (OOD) detection is a critical issue for the stable and reliable operation of systems using a deep neural network (DNN). Although many OOD detection methods have been proposed, it remains unclear how the differences between in-distribution (ID) and OOD samples are generated by each processing step inside DNNs. We experimentally clarify this issue by investigating the layer dependence of feature representations from multiple perspectives. We find that intrinsic low dimensionalization of DNNs is essential for understanding how OOD samples become more distinct from ID samples as features propagate to deeper layers. Based on these observations, we provide a simple picture that consistently explains various properties of OOD samples. Specifically, low-dimensional weights eliminate most information from OOD samples, resulting in misclassifications due to excessive attention to dataset bias. In addition, we demonstrate the utility of dimensionality by proposing a dimensionality-aware OOD detection method based on alignment of features and weights, which consistently achieves high performance for various datasets with lower computational cost.
Boundary transfer matrices arising from quantum symmetric pairs
2024-10-29
Andrea Appel, Bart Vlaar
We introduce a universal framework for boundary transfer matrices, inspired by Sklyanin's two-row transfer matrix approach for quantum integrable systems with boundary conditions. The main examples arise from quantum symmetric pairs of finite and affine type. As a special case we recover a construction by Kolb in finite type. We review recent work on universal solutions to the reflection equation and highlight several open problems in this field.
Continuous-Time Line-of-Sight Constrained Trajectory Planning for 6-Degree of Freedom Systems
2024-10-29
Christopher R. Hayner, John M. Carson III, Behçet Açıkmeşe, Karen Leung
Perception algorithms are ubiquitous in modern autonomy stacks, providing necessary environmental information to operate in the real world. Many of these algorithms depend on the visibility of keypoints, which must remain within the robot's line-of-sight (LoS), for reliable operation. This paper tackles the challenge of maintaining LoS on such keypoints during robot movement. We propose a novel method that addresses these issues by ensuring applicability to various sensor footprints, adaptability to arbitrary nonlinear dynamics, and constant enforcement of LoS throughout the robot's path. Through our experiments, we show that the proposed approach achieves significantly reduced LoS violation and runtime compared to existing state-of-the-art methods in several representative and challenging scenarios.
DRUID: Source Detection and Deblending in Astronomical Images with Persistent Homology
2024-10-29
R. A. Shaw, S. Fotopoulou, M. Birkinshaw, N. Maddox, H. Stewart
Source detection is a vital part of any astronomical survey analysis pipeline. In addition, a versatile source finder that can recover and handle sources of all morphological types is becoming more important as surveys get bigger and achieve a higher resolution than ever before. Here we present Detector of astRonomical soUrces in optIcal and raDio images (DRUID), a source finder that utilises persistent homology to detect and deblend sources. This method enables us to effectively and uniquely segment structures within morphologically complex sources and deal with high source density images. We test DRUID on the complex morphologies of 3CR radio loud active galactic nuclei, where we demonstrate its ability to usefully segment the main structures in the sources. We also demonstrate the level of structure DRUID segments within well resolved galaxies in the optical. Finally, we present two source catalogues on the LoTSS Deep field observation of the Lockman Hole and an example tile from the KiDS r-band survey. We conclude that DRUID's method of utilising persistent homology provides a new way to detect and deblend highly nested sources.
Anticipating Future with Large Language Model for Simultaneous Machine Translation
2024-10-29
Siqi Ouyang, Oleksii Hrinchuk, Zhehuai Chen, Vitaly Lavrukhin, Jagadeesh Balam, Lei Li, Boris Ginsburg
Simultaneous machine translation (SMT) takes streaming input utterances and incrementally produces target text. Existing SMT methods only use the partial utterance that has already arrived at the input and the generated hypothesis. Motivated by human interpreters' technique to forecast future words before hearing them, we propose $\textbf{T}$ranslation by $\textbf{A}$nticipating $\textbf{F}$uture (TAF), a method to improve translation quality while retraining low latency. Its core idea is to use a large language model (LLM) to predict future source words and opportunistically translate without introducing too much risk. We evaluate our TAF and multiple baselines of SMT on four language directions. Experiments show that TAF achieves the best translation quality-latency trade-off and outperforms the baselines by up to 5 BLEU points at the same latency (three words).
We introduce Visual Premise Proving (VPP), a novel task tailored to refine the process of chart question answering by deconstructing it into a series of logical premises. Each of these premises represents an essential step in comprehending a chart's content and deriving logical conclusions, thereby providing a granular look at a model's reasoning abilities. This approach represents a departure from conventional accuracy-based evaluation methods, emphasizing the model's ability to sequentially validate each premise and ideally mimic human analytical processes. A model adept at reasoning is expected to demonstrate proficiency in both data retrieval and the structural understanding of charts, suggesting a synergy between these competencies. However, in our zero-shot study using the sophisticated MATCHA model on a scientific chart question answering dataset, an intriguing pattern emerged. The model showcased superior performance in chart reasoning (27\%) over chart structure (19\%) and data retrieval (14\%). This performance gap suggests that models might more readily generalize reasoning capabilities across datasets, benefiting from consistent mathematical and linguistic semantics, even when challenged by changes in the visual domain that complicate structure comprehension and data retrieval. Furthermore, the efficacy of using accuracy of binary QA for evaluating chart reasoning comes into question if models can deduce correct answers without parsing chart data or structure. VPP highlights the importance of integrating reasoning with visual comprehension to enhance model performance in chart analysis, pushing for a balanced approach in evaluating visual data interpretation capabilities.
Data curation is a field with origins in librarianship and archives, whose scholarship and thinking on data issues go back centuries, if not millennia. The field of machine learning is increasingly observing the importance of data curation to the advancement of both applications and fundamental understanding of machine learning models - evidenced not least by the creation of the Datasets and Benchmarks track itself. This work provides an analysis of dataset development practices at NeurIPS through the lens of data curation. We present an evaluation framework for dataset documentation, consisting of a rubric and toolkit developed through a literature review of data curation principles. We use the framework to assess the strengths and weaknesses in current dataset development practices of 60 datasets published in the NeurIPS Datasets and Benchmarks track from 2021-2023. We summarize key findings and trends. Results indicate greater need for documentation about environmental footprint, ethical considerations, and data management. We suggest targeted strategies and resources to improve documentation in these areas and provide recommendations for the NeurIPS peer-review process that prioritize rigorous data curation in ML. Finally, we provide results in the format of a dataset that showcases aspects of recommended data curation practices. Our rubric and results are of interest for improving data curation practices broadly in the field of ML as well as to data curation and science and technology studies scholars studying practices in ML. Our aim is to support continued improvement in interdisciplinary research on dataset practices, ultimately improving the reusability and reproducibility of new datasets and benchmarks, enabling standardized and informed human oversight, and strengthening the foundation of rigorous and responsible ML research.
Measuring redshift and galaxy properties via a multi-task neural net with probabilistic outputs: An application to simulated MOONS spectra
2024-10-29
Michele Ginolfi, Filippo Mannucci, Francesco Belfiore, Alessandro Marconi, Nicholas Boardman, Lucia Pozzetti, Micol Bolzonella, Enrico Di Teodoro, Giovanni Cresci, Vivienne Wild, Myriam Rodrigues, Roberto Maiolino, Michele Cirasuolo, Ernesto Oliva
The era of large-scale astronomical surveys demands innovative approaches for rapid and accurate analysis of extensive spectral data, and a promising direction to address this challenge is offered by artificial intelligence (AI). Here we introduce a new pipeline, M-TOPnet (Multi-Task network Outputting Probabilities), which employs a convolutional neural network (CNN) with residual learning to simultaneously derive redshift and other key physical properties of galaxies from their spectra. Our tool efficiently encodes spectral information into a latent space, employing distinct downstream branches for each physical quantity, thereby benefiting from multi-task learning. Notably, our method handles the redshift output as a probability distribution, allowing for a more refined and robust estimation of this critical parameter. We demonstrate preliminary results using simulated data from the MOONS instrument, which will be soon operating at the ESO/VLT. We highlight the effectiveness of our tool in accurately predicting redshift, stellar mass, and star-formation rate for galaxies at z>~1-3, even for faint sources (m_H >~ 24) where traditional methods often struggle. Through analysis of the output probability distributions, we demonstrate that our pipeline enables robust quality screening of the results, achieving accuracy rates of up to 99% in redshift determination (defined as predictions within |Delta_z| < 0.01 relative to the true redshift) with 8 h exposure spectra, while automatically identifying potentially problematic cases. Our AI pipeline thus emerges as a powerful solution for the upcoming challenges in observational astronomy, combining precision, interpretability, and efficiency, all aspects which are crucial for analysing the massive datasets expected from next-generation instruments.
Direct-method metallicity gradients derived from spectral stacking with SDSS-IV MaNGA
2024-10-29
Amir H. Khoram, Francesco Belfiore
Chemical abundances are key tracers of the cycle of baryons driving the evolution of galaxies. Most measurements of interstellar medium (ISM) abundance and metallicity gradients in galaxies are based, however, on model-dependent strong-line methods. Direct chemical abundances can be obtained via the detection of weak auroral lines, but such lines are too faint to be detected across large spectroscopic surveys of the local Universe. In this work we overcome this limitation and obtain metallicity gradients from direct method abundances by stacking spectra from the MaNGA integral field spectroscopy survey. In particular we stack 4140 star-forming galaxies across the star formation rate-stellar mass (SFR-M$_\star$) plane and across six radial bins. We calculate electron temperatures for [OII], [SII], [NII], [SIII] and [OIII] across the majority of stacks. We find that the T[OII] $\sim$ T[SII] $\sim$ T[OII], as expected since these ions all trace the low-ionization zone of nebulae. The [OIII] temperatures become substantially larger than those of other ions at high metallicity, indicating potentially unaccounted for spectral contamination or additional physics. In light of this uncertainty we base our abundance calculation on the temperatures of [SIII] and the low-ionization ions. We recover a mass-metallicity relation (MZR) similar to that obtained with different empirical calibrations. We do not find evidence, however, for a secondary dependence on SFR using direct metallicities. Finally, we derive metallicity gradients that becomes steeper with stellar mass for $\log(M_\star/M_\odot) < 10.5$. At higher masses, the gradients flatten again, confirming with auroral line determinations the trends previously defined with strong-line calibrators.
Structural Decomposition of Merger-Free Galaxies Hosting Luminous AGNs
2024-10-29
Matthew J. Fahey, Izzy. L. Garland, Brooke. D. Simmons, William C. Keel, Jesse Shanahan, Alison Coil, Eilat Glikman, Chris J. Lintott, Karen L. Masters, Ed Moran, Rebecca J. Smethurst, Tobias Géron, Matthew R. Thorne
Active galactic nucleus (AGN) growth in disk-dominated, merger-free galaxies is poorly understood, largely due to the difficulty in disentangling the AGN emission from that of the host galaxy. By carefully separating this emission, we examine the differences between AGNs in galaxies hosting a (possibly) merger-grown, classical bulge, and AGNs in secularly grown, truly bulgeless disk galaxies. We use GALFIT to obtain robust, accurate morphologies of 100 disk-dominated galaxies imaged with the Hubble Space Telescope. Adopting an inclusive definition of classical bulges, we detect a classical bulge component in $53.3 \pm 0.5$ per cent of the galaxies. These bulges were not visible in Sloan Digital Sky Survey photometry, however these galaxies are still unambiguously disk-dominated, with an average bulge-to-total luminosity ratio of $0.1 \pm 0.1$. We find some correlation between bulge mass and black hole mass for disk-dominated galaxies, though this correlation is significantly weaker in comparison to the relation for bulge-dominated or elliptical galaxies. Furthermore, a significant fraction ($\gtrsim 90$ per cent) of our black holes are overly massive when compared to the relationship for elliptical galaxies. We find a weak correlation between total stellar mass and black hole mass for the disk-dominated galaxies, hinting that the stochasticity of black hole-galaxy co-evolution may be higher disk-dominated than bulge-dominated systems.
WALLABY Pilot Survey: Star Formation Enhancement and Suppression in Gas-rich Galaxy Pairs
2024-10-29
Qifeng Huang, Jing Wang, Xuchen Lin, Se-Heon Oh, Xinkai Chen, Barbara Catinella, Nathan Deg, Helga Dénes, Bi-Qing For, Baerbel Koribalski, Karen Lee-Waddell, Jonghwan Rhee, Austin Shen, Li Shao, Kristine Spekkens, Lister Staveley-Smith, Tobias Westmeier, O. Ivy Wong, Albert Bosma
Galaxy interactions can significantly affect the star formation in galaxies, but it remains a challenge to achieve a consensus on the star formation rate (SFR) enhancement in galaxy pairs. Here, we investigate the SFR enhancement of gas-rich galaxy pairs detected by the Widefield ASKAP L-band Legacy All-sky Blind surveY (WALLABY). We construct a sample of 278 paired galaxies spanning a stellar mass ($M_\ast$) range from $10^{7.6}$ to $10^{11.2}M_\odot$. We obtain individual masses of atomic hydrogen (HI) for these paired galaxies, using a novel deblending algorithm for HI data cubes. Quantifying the interaction stages and strengths with parameters motivated by first principles, we find that at fixed stellar and HI mass, the alteration in SFR of galaxy pairs starts when their dark matter halos encounter. For galaxies with stellar mass lower than $10^9M_\odot$, their SFRs show tentative suppression of 1.4 sigma after the halo encounter, and then become enhanced when their HI disks overlap, regardless of mass ratios. In contrast, the SFRs of galaxies with $M_\ast > 10^9M_\odot$ increase monotonically toward smaller projected distances and radial velocity offsets. When a close companion is present, a pronounced SFR enhancement is found for the most HI-poor high-mass galaxies in our sample. Collecting the observational evidence, we provide a coherent picture of the evolution of galaxy pairs, and discuss how the tidal effects and hydrodynamic processes shape the SFR enhancement. Our results provide a coherent picture of gas-rich galaxy interactions and impose constraints on the underlying physical processes.
The recent tension in the value of the cosmological parameter $S_8 \equiv \sigma_8(\Omega_M/0.3)^{1/2}$, which represents the amplitude of the matter density fluctuations of the universe, has not been resolved. In this work, we present constraints on $S_8$ with the X-ray angular power spectra of clusters and groups measured with the half-sky map from the eROSITA All Sky Survey data release 1 (eRASS1). Thanks to the extensive sky coverage of eRASS1, its power spectrum achieves unprecedented precision compared to previous measurements. Using a well-calibrated, physical halo gas model that includes astrophysics of feedback and non-thermal pressure support, we obtain $S_8 = 0.80^{+0.02}_{-0.01}$ with 1$\sigma$ uncertainty that is competitive against other cosmological probes. Our derived $S_8$ value is smaller than the primary CMB measurements from Planck, but still consistent to within $1\sigma$. We also obtain constraints on the astrophysics of feedback, non-thermal pressure, equation of state in cluster cores, and gas boundaries in clusters and groups. We discuss how additional X-ray observations, and cosmological surveys in microwave and optical, will further improve the cosmological constraints with the angular power spectrum. Our work demonstrates that the angular power spectrum of clusters and groups is a promising probe of both cosmology and astrophysics.
Numerous studies have assessed the proficiency of AI systems, particularly large language models (LLMs), in facilitating everyday tasks such as email writing, question answering, and creative content generation. However, researchers face unique challenges and opportunities in leveraging LLMs for their own work, such as brainstorming research ideas, designing experiments, and writing or reviewing papers. In this study, we introduce AAAR-1.0, a benchmark dataset designed to evaluate LLM performance in three fundamental, expertise-intensive research tasks: (i) EquationInference, assessing the correctness of equations based on the contextual information in paper submissions; (ii) ExperimentDesign, designing experiments to validate research ideas and solutions; (iii) PaperWeakness, identifying weaknesses in paper submissions; and (iv) REVIEWCRITIQUE, identifying each segment in human reviews is deficient or not. AAAR-1.0 differs from prior benchmarks in two key ways: first, it is explicitly research-oriented, with tasks requiring deep domain expertise; second, it is researcher-oriented, mirroring the primary activities that researchers engage in on a daily basis. An evaluation of both open-source and proprietary LLMs reveals their potential as well as limitations in conducting sophisticated research tasks. We will keep iterating AAAR-1.0 to new versions.
Activity-based models in transport are crucial for providing a comprehensive and realistic understanding of individuals' activity-travel patterns. Traditionally, travel surveys have been used to develop these models, but they are often costly and have small sample sizes. Mobile phone application data, one example of emerging data sources, offers an alternative with wider population coverage over extended periods for developing activity-based models. However, the challenges of using these data include sampling biases in the population coverage and individual-level data sparsity due to intermittent and irregular data collection. To synthesise activity-travel plans, we propose a novel model that combines mobile phone application data with travel survey data, addressing their limitations. Our generative model simulates multiple average weekday activity schedules for over 263,000 individuals living in Sweden, approximately 2.6% of Sweden's population. We also introduce a temporal-score approach to improve home and work location identification approaches. We assess the model's performance against an existing large-scale agent-based model of Sweden (SySMo) and a dummy model using only mobile application data. The generated activity-travel plans are comparable to the SySMo model's output and significantly surpass the dummy model's results, suggesting the proposed model's capability to generate reasonable activity-travel schedules. The proposed model is adaptable to other regions with similar travel surveys and emerging data sources, like call detail records, advancing the use of these data for activity-based models in a cost-effective, easily updated manner.
A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification
2024-10-29
Flavio Corradini, Marco Gori, Carlo Lucheroni, Marco Piangerelli, Martina Zannotti
In recent years, spatio-temporal graph neural networks (GNNs) have attracted considerable interest in the field of time series analysis, due to their ability to capture dependencies among variables and across time points. The objective of the presented systematic literature review is hence to provide a comprehensive overview of the various modeling approaches and application domains of GNNs for time series classification and forecasting. A database search was conducted, and over 150 journal papers were selected for a detailed examination of the current state-of-the-art in the field. This examination is intended to offer to the reader a comprehensive collection of proposed models, links to related source code, available datasets, benchmark models, and fitting results. All this information is hoped to assist researchers in future studies. To the best of our knowledge, this is the first systematic literature review presenting a detailed comparison of the results of current spatio-temporal GNN models in different domains. In addition, in its final part this review discusses current limitations and challenges in the application of spatio-temporal GNNs, such as comparability, reproducibility, explainability, poor information capacity, and scalability.
Constraining cosmology with thermal Sunyaev-Zel'dovich maps: Minkowski functionals, peaks, minima, and moments
2024-10-28
Alina Sabyr, J. Colin Hill, Zoltan Haiman
The thermal Sunyaev-Zel'dovich effect (tSZ) is a sensitive probe of cosmology, as it traces the abundance of galaxy clusters and groups in the late-time Universe. Upcoming cosmic microwave background experiments such as the Simons Observatory (SO) and CMB-S4 will provide low-noise and high-resolution component-separated tSZ maps covering a large sky fraction. The tSZ signal is highly non-Gaussian; therefore, higher-order statistics are needed to optimally extract information from these maps. In this work, we study the cosmological constraining power of several tSZ statistics -- Minkowski functionals (MFs), peaks, minima, and moments -- that have yielded promising results in capturing non-Gaussian information from other cosmological data. Using a large suite of halo-model-based tSZ simulations with varying $\Omega_{c}$ and $\sigma_{8}$ (154 cosmologies and over $800, 000$ maps, each $10.5\times10.5$ deg$^{2}$), we show that by combining these observables, we can achieve $\approx 29\times$ tighter constraints compared to using the tSZ power spectrum alone in an idealized noiseless case, with the MFs dominating the constraints. We show that much of the MF constraining power arises from halos below the detection threshold of cluster surveys, suggesting promising synergies with cluster-count analyses. Finally, we demonstrate that these statistics have the potential to deliver tight constraints even in the presence of noise. For example, using post-component-separation tSZ noise expected for SO, we obtain $\approx1.6\times$ and $\approx1.8\times$ tighter constraints than the power spectrum with MFs and all statistics combined, respectively. We show that the constraints from MFs approach the noiseless case for white-noise levels $\lesssim 1 \,\, \mu$K-arcmin.
We use a suite of the most recent cosmological observations to test models of dynamical dark energy motivated by quantum gravity. Specifically, we focus on hilltop quintessence scenarios, able to satisfy theoretical constraints from quantum gravity. We discuss their realisation based on axions, their supersymmetric partners, and Higgs-like string constructions. We also examine a specific parameterisation for dynamical dark energy suitable for hilltop quintessence. We then perform an analysis based on Markov Chain Monte-Carlo to assess their predictions against CMB, galaxy surveys, and supernova data. We show to what extent current data can distinguish amongst different hilltop set-ups, providing model parameter constraints that are complementary to and synergetic with theoretical bounds from quantum gravity conjectures, as well as model comparisons across the main dark energy candidates in the literature. However, all these constraints are sensitive to priors based on theoretical assumptions about viable regions of parameter space. Consequently, we discuss theoretical challenges in refining these priors, with the aim of maximizing the informative power of current and forthcoming cosmological datasets for testing dark energy scenarios in quantum gravity.
Three observations on the Colin de Verdière spectral graph parameter
2024-10-28
Vojtěch Kaluža, Vadym Koval
In this small note, we collect several observations pertaining to the famous spectral graph parameter $\mu$ introduced in 1990 by Y. Colin de Verdi\`ere. This parameter is defined as the maximum corank among certain matrices akin to weighted Laplacians; we call them CdV matrices. First, we answer negatively a question mentioned in passing in the influential 1996 survey on $\mu$ by van der Holst, Lov\'asz, and Schrijver concerning the Perron--Frobenious eigenvector of CdV matrices. Second, by definition, CdV matrices posses certain transversality property. In some cases, this property is known to be satisfied automatically. We add one such case to the list. Third, Y. Colin de Verdi\`ere conjectured an upper bound on $\mu(G)$ for graphs embeddable into a fixed closed surface. Following a recent computer-verified counterexample to a continuous version of the conjecture by Fortier Bourque, Gruda-Mediavilla, Petri, and Pineault [arXiv:2312.03504], we also check using computer that the analogous example shows the failure of the conjectured upper bound on $\mu(G)$ for graphs embeddable into 10-torus as well as to several other larger surfaces.
Boosting HI-Galaxy Cross-Clustering Signal through Higher-Order Cross-Correlations
2024-10-28
Eishica Chand, Arka Banerjee, Simon Foreman, Francisco Villaescusa-Navarro
After reionization, neutral hydrogen (HI) traces the large-scale structure (LSS) of the Universe, enabling HI intensity mapping (IM) to capture the LSS in 3D and constrain key cosmological parameters. We present a new framework utilizing higher-order cross-correlations to study HI clustering around galaxies, tested using real-space data from the IllustrisTNG300 simulation. This approach computes the joint distributions of $k$-nearest neighbor ($k$NN) optical galaxies and the HI brightness temperature field smoothed at relevant scales (the $k$NN-field framework), providing sensitivity to all higher-order cross-correlations, unlike two-point statistics. To simulate HI data from actual surveys, we add random thermal noise and apply a simple foreground cleaning model, filtering out Fourier modes of the brightness temperature field with $k_\parallel < k_{\rm min,\parallel}$. Under current levels of thermal noise and foreground cleaning, typical of a Canadian Hydrogen Intensity Mapping Experiment (CHIME)-like survey, the HI-galaxy cross-correlation signal in our simulations, using the $k$NN-field framework, is detectable at $>30\sigma$ across $r = [3,12] \, h^{-1}$Mpc. In contrast, the detectability of the standard two-point correlation function (2PCF) over the same scales depends strongly on the foreground filter: a sharp $k_\parallel$ filter can spuriously boost detection to $8\sigma$ due to position-space ringing, whereas a less sharp filter yields no detection. Nonetheless, we conclude that $k$NN-field cross-correlations are robustly detectable across a broad range of foreground filtering and thermal noise conditions, suggesting their potential for enhanced constraining power over 2PCFs.
Large language models (LLM) have sparked significant impact with regard to both intelligence and productivity. In recent years, a great surge has been witnessed in the introduction of both commercial and open-source LLMs. Many businesses have adopted the LLMs into their applications to solve their own domain-specific tasks. However, integrating LLMs into specific business scenarios requires more than just utilizing the models themselves. Instead, it is a systematic process that involves substantial components, which are collectively referred to as the LLM supply chain. The LLM supply chain inherently carries risks. Therefore, it is essential to understand the types of components that may be introduced into the supply chain and the associated risks, enabling different stakeholders to implement effective mitigation measures. While some literature discusses risks associated with LLMs, there is currently no paper that clearly outlines the LLM supply chain from the perspective of both providing and consuming its components. As LLMs have become essential infrastructure in the new era, we believe that a thorough review of the LLM supply chain, along with its inherent risks and mitigation strategies, would be valuable for industry practitioners to avoid potential damages and losses, and enlightening for academic researchers to rethink existing approaches and explore new avenues of research. Our paper provides a comprehensive overview of the LLM supply chain, detailing the stakeholders, composing artifacts, and the supplying types. We developed taxonomies of risk types, risky actions, and mitigations related to various supply chain stakeholders and components. In summary, our work explores the technical and operational aspects of the LLM supply chain, offering valuable insights for researchers and engineers in the evolving LLM landscape.
Towards Human-centered Design of Explainable Artificial Intelligence (XAI): A Survey of Empirical Studies
2024-10-28
Shuai Ma
With the advances of AI research, AI has been increasingly adopted in numerous domains, ranging from low-stakes daily tasks such as movie recommendations to high-stakes tasks such as medicine, and criminal justice decision-making. Explainability is becoming an essential requirement for people to understand, trust and adopt AI applications. Despite a vast collection of explainable AI (XAI) algorithms produced by the AI research community, successful examples of XAI are still relatively scarce in real-world AI applications. This can be due to the gap between what the XAI is designed for and how the XAI is actually perceived by end-users. As explainability is an inherently human-centered property, in recent years, the XAI field is starting to embrace human-centered approaches and increasingly realizing the importance of empirical studies of XAI design by involving human subjects. To move a step towards a systematic review of empirical study for human-centered XAI design, in this survey, we first brief the technical landscape of commonly used XAI algorithms in existing empirical studies. Then we analyze the diverse stakeholders and needs-finding approaches. Next, we provide an overview of the design space explored in the current human-centered XAI design. Further, we summarize the evaluation metrics based on evaluation goals. Afterward, we analyze the common findings and pitfalls derived from existing studies. For each chapter, we provide a summary of current challenges and research opportunities. Finally, we conclude the survey with a framework for human-centered XAI design with empirical studies.
Privacy-Preserving for Images in Satellite Communications: A Comprehensive Review of Chaos-Based Encryption
2024-10-28
Farrukh Bin Rashid, Windhya Rankothge, Somayeh Sadeghi, Hesamodin Mohammadian, Ali Ghorbani
In an era where global connectivity has become critical, satellite communication is essential for businesses, governments, and individuals. Widely used services with satellite communication such as climate change monitoring, military surveillance and real-time event broadcasting, involve data in the form of images rather text. Therefore, securing image transmission in satellite communication using efficient and effective encryption approaches, has gained a significant attention from academia as well as the industry. In this paper, we specifically focus on chaos based image encryption as one of the key privacy-preserving techniques for satellite communication. While there are several privacy enhancing techniques for protecting image data but chaos based encryption has distinct advantages such as high flexibility, high security, less computational overheads, less computing power and ease of implementation. First, we present a solid background about satellite communication and image encryption in satellite communication, covering theoretical aspects of chaotic systems and their practical usage for image encryption. Next we present a comprehensive literature review on all state-of-the-art studies specifically for chaos based satellite image encryption, with a detailed analysis of the evaluation process, including evaluation parameters and conditions. Finally, we discuss about existing challenges and open research problems for chaos based satellite image encryption.
Document parsing is essential for converting unstructured and semi-structured documents-such as contracts, academic papers, and invoices-into structured, machine-readable data. Document parsing extract reliable structured data from unstructured inputs, providing huge convenience for numerous applications. Especially with recent achievements in Large Language Models, document parsing plays an indispensable role in both knowledge base construction and training data generation. This survey presents a comprehensive review of the current state of document parsing, covering key methodologies, from modular pipeline systems to end-to-end models driven by large vision-language models. Core components such as layout detection, content extraction (including text, tables, and mathematical expressions), and multi-modal data integration are examined in detail. Additionally, this paper discusses the challenges faced by modular document parsing systems and vision-language models in handling complex layouts, integrating multiple modules, and recognizing high-density text. It emphasizes the importance of developing larger and more diverse datasets and outlines future research directions.
2024-10-28
Arnab Sharma, N'Dah Jean Kouagou, Axel-Cyrille Ngonga Ngomo
In recent years, knowledge graphs have gained interest and witnessed widespread applications in various domains, such as information retrieval, question-answering, recommendation systems, amongst others. Large-scale knowledge graphs to this end have demonstrated their utility in effectively representing structured knowledge. To further facilitate the application of machine learning techniques, knowledge graph embedding (KGE) models have been developed. Such models can transform entities and relationships within knowledge graphs into vectors. However, these embedding models often face challenges related to noise, missing information, distribution shift, adversarial attacks, etc. This can lead to sub-optimal embeddings and incorrect inferences, thereby negatively impacting downstream applications. While the existing literature has focused so far on adversarial attacks on KGE models, the challenges related to the other critical aspects remain unexplored. In this paper, we, first of all, give a unified definition of resilience, encompassing several factors such as generalisation, performance consistency, distribution adaption, and robustness. After formalizing these concepts for machine learning in general, we define them in the context of knowledge graphs. To find the gap in the existing works on resilience in the context of knowledge graphs, we perform a systematic survey, taking into account all these aspects mentioned previously. Our survey results show that most of the existing works focus on a specific aspect of resilience, namely robustness. After categorizing such works based on their respective aspects of resilience, we discuss the challenges and future research directions.
Towards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessments
2024-10-28
Marharyta Domnich, Julius Valja, Rasmus Moorits Veski, Giacomo Magnifico, Kadi Tulver, Eduard Barbu, Raul Vicente
As machine learning models evolve, maintaining transparency demands more human-centric explainable AI techniques. Counterfactual explanations, with roots in human reasoning, identify the minimal input changes needed to obtain a given output and, hence, are crucial for supporting decision-making. Despite their importance, the evaluation of these explanations often lacks grounding in user studies and remains fragmented, with existing metrics not fully capturing human perspectives. To address this challenge, we developed a diverse set of 30 counterfactual scenarios and collected ratings across 8 evaluation metrics from 206 respondents. Subsequently, we fine-tuned different Large Language Models (LLMs) to predict average or individual human judgment across these metrics. Our methodology allowed LLMs to achieve an accuracy of up to 63% in zero-shot evaluations and 85% (over a 3-classes prediction) with fine-tuning across all metrics. The fine-tuned models predicting human ratings offer better comparability and scalability in evaluating different counterfactual explanation frameworks.
Current State-of-the-Art of Bias Detection and Mitigation in Machine Translation for African and European Languages: a Review
2024-10-28
Catherine Ikae, Mascha Kurpicz-Briki
Studying bias detection and mitigation methods in natural language processing and the particular case of machine translation is highly relevant, as societal stereotypes might be reflected or reinforced by these systems. In this paper, we analyze the state-of-the-art with a particular focus on European and African languages. We show how the majority of the work in this field concentrates on few languages, and that there is potential for future research to cover also the less investigated languages to contribute to more diversity in the research field.
Large Language Model-assisted Speech and Pointing Benefits Multiple 3D Object Selection in Virtual Reality
2024-10-28
Junlong Chen, Jens Grubert, Per Ola Kristensson
Selection of occluded objects is a challenging problem in virtual reality, even more so if multiple objects are involved. With the advent of new artificial intelligence technologies, we explore the possibility of leveraging large language models to assist multi-object selection tasks in virtual reality via a multimodal speech and raycast interaction technique. We validate the findings in a comparative user study (n=24), where participants selected target objects in a virtual reality scene with different levels of scene perplexity. The performance metrics and user experience metrics are compared against a mini-map based occluded object selection technique that serves as the baseline. Results indicate that the introduced technique, AssistVR, outperforms the baseline technique when there are multiple target objects. Contrary to the common belief for speech interfaces, AssistVR was able to outperform the baseline even when the target objects were difficult to reference verbally. This work demonstrates the viability and interaction potential of an intelligent multimodal interactive system powered by large laguage models. Based on the results, we discuss the implications for design of future intelligent multimodal interactive systems in immersive environments.
2024-10-28
T. Majumder, M. V. Pruzhinskaya, E. E. O. Ishida, K. L. Malanchev, T. A. Semenikhin
The advent of large astronomical surveys has made available large and complex data sets. However, the process of discovery and interpretation of each potentially new astronomical source is, many times, still handcrafted. In this context, machine learning algorithms have emerged as a powerful tool to mine large data sets and lower the burden on the domain expert. Active learning strategies are specially good in this task. In this report, we used the PineForest algorithm to search for superluminous supernova (SLSN) candidates in the Zwicky Transient Facility. We showcase how the use of previously confirmed sources can provide important information to boost the convergence of the active learning algorithm. Starting from a data set of $\sim$14 million objects, and using 8 previously confirmed SLSN light curves as priors, we scrutinized 120 candidates and found 8 SLSN candidates, 2 of which have not been reported before (AT 2018moa and AT 2018mob). These results demonstrate how existing spectroscopic samples can be used to improve the efficiency of active learning strategies in searching for rare astronomical sources.
Textual descriptions in cyber threat intelligence (CTI) reports, such as security articles and news, are rich sources of knowledge about cyber threats, crucial for organizations to stay informed about the rapidly evolving threat landscape. However, current CTI extraction methods lack flexibility and generalizability, often resulting in inaccurate and incomplete knowledge extraction. Syntax parsing relies on fixed rules and dictionaries, while model fine-tuning requires large annotated datasets, making both paradigms challenging to adapt to new threats and ontologies. To bridge the gap, we propose CTINexus, a novel framework leveraging optimized in-context learning (ICL) of large language models (LLMs) for data-efficient CTI knowledge extraction and high-quality cybersecurity knowledge graph (CSKG) construction. Unlike existing methods, CTINexus requires neither extensive data nor parameter tuning and can adapt to various ontologies with minimal annotated examples. This is achieved through (1) a carefully designed automatic prompt construction strategy with optimal demonstration retrieval for extracting a wide range of cybersecurity entities and relations; (2) a hierarchical entity alignment technique that canonicalizes the extracted knowledge and removes redundancy; (3) an ICL-enhanced long-distance relation prediction technique to further complete the CKSG with missing links. Our extensive evaluations using 150 real-world CTI reports collected from 10 platforms demonstrate that CTINexus significantly outperforms existing methods in constructing accurate and complete CSKGs, highlighting its potential to transform CTI analysis with an efficient and adaptable solution for the dynamic threat landscape.
A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networks
2024-10-28
Pau Ferrer-Cid, Jose M. Barcelo-Ordinas, Jorge Garcia-Vidal
The development of Internet of Things (IoT) technologies has led to the widespread adoption of monitoring networks for a wide variety of applications, such as smart cities, environmental monitoring, and precision agriculture. A major research focus in recent years has been the development of graph-based techniques to improve the quality of data from sensor networks, a key aspect for the use of sensed data in decision-making processes, digital twins, and other applications. Emphasis has been placed on the development of machine learning and signal processing techniques over graphs, taking advantage of the benefits provided by the use of structured data through a graph topology. Many technologies such as the graph signal processing (GSP) or the successful graph neural networks (GNNs) have been used for data quality enhancement tasks. In this survey, we focus on graph-based models for data quality control in monitoring sensor networks. Furthermore, we delve into the technical details that are commonly leveraged for providing powerful graph-based solutions for data quality tasks in sensor networks, including missing value imputation, outlier detection, or virtual sensing. To conclude, we have identified future trends and challenges such as graph-based models for digital twins or model transferability and generalization.
Bethe/Gauge correspodence: a short review on an aspect of the integrability nature of supersymmetric gauge theories
2024-10-28
Rui-Dong Zhu
In this article, we provide a short review (written in Chinese) on the Bethe/Gauge correspondence. We first explain the basic idea in an explicit example of the correspondence between XXX spin chains and 2d $\mathcal{N}=(2,2)$ gauge theories. The connection between 4d and 2d will then be explored by comparing the instanton and vortex partition functions. We conclude this article by briefly mentioning the similarity in the integrability structure of 4d gauge theories and 2d ones from an algebraic aspect, and a potential relation between two different integrable systems.
Tidal forces in close binary systems have diverse impacts on magnetic activity. The synchronicity characteristic of close systems counteracts magnetic braking, thereby sustaining rapid rotation-a key factor in increased levels of magnetic activity. Tidal effects can also work against the slowing down of rotation during stellar evolution, when the star inflates into a red giant. A notable manifestation of the effect of binarity on activity in such systems is the appearance of active longitudes, which are thought to arise from the excitation of non-axisymmetric dynamo modes. Through some recent examples, the dynamo operation in RS CVn and BY Dra type systems is briefly reviewed in terms of spot cycles, active longitudes, flare activity, and differential rotation.
The irrelevant composite operator $T\bar{T}$, constructed from components of the stress-energy tensor, exhibits unique properties in two-dimensional quantum field theories and represents a distinctive form of integrable deformation. Significant progress has been made in understanding the bulk aspects of the theory, including its interpretation in terms of coordinate transformations and its connection to topological gravity models. However, the behavior of $T\bar{T}$-deformed theories in the presence of boundaries and defects remains largely unexplored. In this note, we review analytical results obtained through various techniques. Specifically, we study the $T\bar{T}$-deformed exact g-function within the framework of the Thermodynamic Bethe Ansatz and show that the results coincide with those obtained by solving the corresponding Burgers-type flow equation. Finally, we highlight some potentially significant open problems.
In this minireview article, we examine the inconsistent results of thermal parameters derived from various models in high-energy collisions. Through a comprehensive literature review and based on the average transverse momentum or the root-mean-square transverse momentum, we propose model-independent parameters to address these inconsistencies. The relevant parameters include: the initial temperature, the effective temperature, the kinetic freeze-out temperature, and the average transverse velocity. Our findings indicate that these four parameters are larger in central collisions, within central rapidity regions, at higher energies, and in larger collision systems. As collision energy increases, excitation functions for all four parameters rise rapidly (slowly) within ranges below (above) approximately 7.7 GeV. At higher energies (>39) GeV, fluctuations occur in trends for these excitation functions, with only slight changes observed in their growth rates. Additionally, this work reveals a mass-dependent multi-temperature scenario pertaining to both initial states and kinetic freeze-out processes.
Code Collaborate: Dissecting Team Dynamics in First-Semester Programming Students
2024-10-28
Santiago Berrezueta-Guzman, Patrick Bassner, Stefan Wagner, Stephan Krusche
Understanding collaboration patterns in introductory programming courses is essential, as teamwork is a critical skill in computer science. In professional environments, software development relies on effective teamwork, navigating diverse perspectives, and contributing to shared goals. This paper offers a comprehensive analysis of the factors influencing team efficiency and project success, providing actionable insights to enhance the effectiveness of collaborative programming education. By analyzing version control data, survey responses, and performance metrics, the study highlights the collaboration trends that emerge as first-semester students develop a 2D game project. Results indicate that students often slightly overestimate their contributions, with more engaged individuals more likely to acknowledge mistakes. Team performance shows no significant variation based on nationality or gender composition, though teams that disbanded frequently consisted of lone wolves, highlighting collaboration challenges and the need for strengthened teamwork skills. Presentations closely reflected individual project contributions, with active students excelling in evaluative questioning and performing better on the final exam. Additionally, the complete absence of plagiarism underscores the effectiveness of proactive academic integrity measures, reinforcing honest collaboration in educational settings.
Co-produced decentralised surveys as a trustworthy vector to put employees' well-being at the core of companies' performance
2024-10-28
Adèle Bréart De Boisanger, Wendy Sims-Schouten, Francois Sicard
Assessing employees' well-being has become central to fostering an environment where employees can thrive and contribute to companies' adaptability and competitiveness in the market. Traditional methods for assessing well-being often face significant challenges, with a major issue being the lack of trust and confidence employees may have in these processes. Employees may hesitate to provide honest feedback due to concerns not only about data integrity and confidentiality, but also about power imbalances among stakeholders. In this context, blockchain-based decentralised surveys, leveraging the immutability, transparency, and pseudo-anonymity of blockchain technology, offer significant improvements in aligning responsive actions with employees' feedback securely and transparently. Nevertheless, their implementation raises complex issues regarding the balance between trust and confidence. While blockchain can function as a confidence machine for data processing and management, it does not inherently address the equally important cultural element of trust. To effectively integrate blockchain technology into well-being assessments, decentralised well-being surveys must be supported by cultural practices that build and sustain trust. Drawing on blockchain technology management and relational cultural theory, we explain how trust-building can be achieved through the co-production of decentralised well-being surveys, which helps address power imbalances between the implementation team and stakeholders. Our goal is to provide a dual cultural-technological framework along with conceptual clarity on how the technological implementation of confidence can connect with the cultural development of trust, ensuring that blockchain-based decentralised well-being surveys are not only secure and reliable but also perceived as trustworthy vector to improve workplace conditions.
Approximate Message Passing (AMP), originally developed to address high-dimensional linear inverse problems, has found widespread applications in signal processing and statistical inference. Among its notable variants, Vector Approximate Message Passing (VAMP), Generalized Approximate Survey Propagation (GASP), and Vector Approximate Survey Propagation (VASP) have demonstrated effectiveness even when the assumed generative models differ from the true models. However, many fundamental questions regarding model mismatch remain unanswered. For instance, it is still unclear what level of model mismatch is required for the postulated posterior estimate (PPE) to exhibit a replica symmetry breaking (RSB) structure in the extremum conditions of its free energy, and what order of RSB is necessary. In this paper, we introduce a novel approximate message passing algorithm that incorporates K-step RSB (KRSB) and naturally reduces to VAMP and VASP with specific parameter selections. We refer to this as the K-step VASP (KVASP) algorithm. Simulations show that KVASP significantly outperforms VAMP and GASP in estimation accuracy, particularly when the assumed prior has discrete support and the measurement matrix is non-i.i.d.. Additionally, the state evolution (SE) of KVASP, derived heuristically, accurately tracks the per-iteration mean squared error (MSE). A comparison between the SE and the free energy under the KRSB ansatz reveals that the fixed-point equations of SE align with the saddle-point equations of the free energy. This suggests that, once the KRSB ansatz holds and the SE fixed point is reached, KVASP can accurately compute the PPE in the large system limit (LSL).
Open questions for strongly interacting Fermi gases with zero-range interactions
2024-10-28
Yvan Castin
We review some unresolved theoretical issues in three-dimensional two-component Fermi gases, drawing on recent experiments on cold atoms in immaterial traps close to a magnetic Feshbach resonance. We distinguish successively (i) the open questions arising in the few-body problem with Wigner-Bethe-Peierls contact interactions - essentially the stability of the gas with respect to the Efimov effect and the calculation of the cluster (or virial) coefficients, (ii) those arising in the effective low-energy theory of Landau and Khalatnikov quantum hydrodynamics - essentially the damping of phonon modes and the coherence time of the condensate of pairs, and finally (iii) questions requiring a complete, microscopic solution of the many-body problem, such as the properties of the acoustic excitation branch (Goldstone) of the condensate of pairs, or its collective excitation branch (Higgs) in the broken-pair continuum.
Deep Insights into Automated Optimization with Large Language Models and Evolutionary Algorithms
2024-10-28
He Yu, Jing Liu
Designing optimization approaches, whether heuristic or meta-heuristic, usually demands extensive manual intervention and has difficulty generalizing across diverse problem domains. The combination of Large Language Models (LLMs) and Evolutionary Algorithms (EAs) offers a promising new approach to overcome these limitations and make optimization more automated. In this setup, LLMs act as dynamic agents that can generate, refine, and interpret optimization strategies, while EAs efficiently explore complex solution spaces through evolutionary operators. Since this synergy enables a more efficient and creative search process, we first conduct an extensive review of recent research on the application of LLMs in optimization. We focus on LLMs' dual functionality as solution generators and algorithm designers. Then, we summarize the common and valuable designs in existing work and propose a novel LLM-EA paradigm for automated optimization. Furthermore, centered on this paradigm, we conduct an in-depth analysis of innovative methods for three key components: individual representation, variation operators, and fitness evaluation. We address challenges related to heuristic generation and solution exploration, especially from the LLM prompts' perspective. Our systematic review and thorough analysis of the paradigm can assist researchers in better understanding the current research and promoting the development of combining LLMs with EAs for automated optimization.
Reduction-based Pseudo-label Generation for Instance-dependent Partial Label Learning
2024-10-28
Congyu Qiao, Ning Xu, Yihao Hu, Xin Geng
Instance-dependent Partial Label Learning (ID-PLL) aims to learn a multi-class predictive model given training instances annotated with candidate labels related to features, among which correct labels are hidden fixed but unknown. The previous works involve leveraging the identification capability of the training model itself to iteratively refine supervision information. However, these methods overlook a critical aspect of ID-PLL: the training model is prone to overfitting on incorrect candidate labels, thereby providing poor supervision information and creating a bottleneck in training. In this paper, we propose to leverage reduction-based pseudo-labels to alleviate the influence of incorrect candidate labels and train our predictive model to overcome this bottleneck. Specifically, reduction-based pseudo-labels are generated by performing weighted aggregation on the outputs of a multi-branch auxiliary model, with each branch trained in a label subspace that excludes certain labels. This approach ensures that each branch explicitly avoids the disturbance of the excluded labels, allowing the pseudo-labels provided for instances troubled by these excluded labels to benefit from the unaffected branches. Theoretically, we demonstrate that reduction-based pseudo-labels exhibit greater consistency with the Bayes optimal classifier compared to pseudo-labels directly generated from the predictive model.
Collective excitations in the hot QCD medium and the propagation of Heavy Quarks
2024-10-28
Mohammad Yousuf Jamal, Bedangadas Mohanty
This review explores the current understanding of collective excitations and the dynamics of heavy quark propagation in the quark-gluon plasma (QGP) formed in relativistic heavy-ion collisions. We focus on three core aspects: the theoretical modelling of the QGP, including momentum anisotropy, medium-induced collisions, finite chemical potential, and non-ideal interactions; the collective behaviours within the plasma; and the interaction dynamics of heavy quarks as they traverse the medium. Along with the polarization energy loss mechanisms, we also review the possibility of energy gain due to thermal field fluctuations. Lastly, we discuss how these theoretical insights can be tested through experiments and outline possible directions for future research.
Hydrogen in its metallic form is the most common material in our solar system, found under the extreme pressure and temperature conditions found in giant planets. Such conditions are inaccessible to experiment and consequently, theoretical work has typically led experiment. Many remarkable properties are proposed for metallic hydrogen, which is expected to exist in solid form down to low temperatures. Relevant here is that solid metallic hydrogen is not expected to have a close-packed structure, even at extreme pressures. The predicted crystal structure has a packing fraction of only 0.44 compared with 0.74 for fcc. Alloying with other metallic elements can form compounds with atomic hydrogen and much reduced metallization pressure. Here, we investigate the possible solid solubility of a representative range of elements (Be, B, Mg, S, Fe, La) in metallic atomic hydrogen near 500GPa. Zero point energy is highly significant in all cases. The metallic elements Be, Mg, Fe, and La have a well-defined clathrate-style first neighbour shell at 300K, while the non-metallic B and S have more open arrangements. All the elements considered are soluble, in sites substituting for more than one hydrogen atom. The solid solubility of such a wide range of ambient bonding types: simple metals, covalent materials, transition metals, lanthanides, which gives a strong indication that most elements will dissolve in solid metallic hydrogen.
Janus transition metal-dichalcogenide (JTMD) materials have attracted a great deal of attention due to their remarkable physical properties arising from the two-dimensional geometry and the breakdown of the out-of-plane symmetry. Using first-principles density functional theory, we investigated the phase stability, strain-enhanced phase stability, and superconductivity of Janus WSeH and WSH. In addition, we investigated the contribution of the phonon linewidths from the phonon energy spectrum responsible for the superconductivity, and the electron-phonon coupling as a function of phonon wave vectors and modes. Previous work has examined hexagonal 2H and tetragonal 1T structures, but we found that neither is a ground state structure. The metastable 2H phase of WSeH is dynamically stable with Tc around 11.60K, similar to WSH. Compressive biaxial strain - the 2D equivalent of pressure - can stabilize the 1T structures of WSeH and WSH with Tc around 9.23K and 10.52K, respectively.
Combining Domain-Specific Models and LLMs for Automated Disease Phenotyping from Survey Data
2024-10-28
Gal Beeri, Benoit Chamot, Elena Latchem, Shruthi Venkatesh, Sarah Whalan, Van Zyl Kruger, David Martino
This exploratory pilot study investigated the potential of combining a domain-specific model, BERN2, with large language models (LLMs) to enhance automated disease phenotyping from research survey data. Motivated by the need for efficient and accurate methods to harmonize the growing volume of survey data with standardized disease ontologies, we employed BERN2, a biomedical named entity recognition and normalization model, to extract disease information from the ORIGINS birth cohort survey data. After rigorously evaluating BERN2's performance against a manually curated ground truth dataset, we integrated various LLMs using prompt engineering, Retrieval-Augmented Generation (RAG), and Instructional Fine-Tuning (IFT) to refine the model's outputs. BERN2 demonstrated high performance in extracting and normalizing disease mentions, and the integration of LLMs, particularly with Few Shot Inference and RAG orchestration, further improved accuracy. This approach, especially when incorporating structured examples, logical reasoning prompts, and detailed context, offers a promising avenue for developing tools to enable efficient cohort profiling and data harmonization across large, heterogeneous research datasets.
Photon-mediated dipole-dipole interactions as a resource for quantum science and technology in cold atoms
2024-10-28
H. H. Jen
Photon-mediated dipole-dipole interactions arise from atom-light interactions, which are universal and prevalent in a wide range of open quantum systems. This pairwise and long-range spin-exchange interaction results from multiple light scattering among the atoms. A recent surge of interests and progresses in both experiments and theories promises this core mechanism of collective interactions as a resource to study quantum science and to envision next-generation applications in quantum technology. Here we summarize recent developments in both theories and experiments, where we introduce several central theoretical approaches and focus on cooperative light scattering from small sample of free-space atoms, an atom-waveguide coupled interface that hosts the waveguide QED, and topological quantum optical platforms. The aim of this review is to manifest the essential and distinct features of collective dynamics induced by resonant dipole-dipole interactions and to reveal unprecedented opportunities in enhancing the performance or offering new applications in light manipulations, quantum metrology, quantum computations, and light harvesting innovations.
The James Webb Space Telescope (JWST) has uncovered an abundant population of compact, extremely red, and X-ray weak objects at $z\gtrsim4$, knows as ``Little Red Dots" (LRDs). These objects exhibit spectral energy distributions that resemble both active galactic nuclei (AGN) and stellar population templates. However, whether dominated by AGN activity or compact star formation, the high redshifts and masses/luminosities of LRDs, coupled with their significant abundance, present potential challenges to the standard $\Lambda$CDM model. In this work, we proposes a novel cosmic interpretation of this anomaly, suggesting that these LRDs are likely massive galaxies seeded by supermassive primordial black holes (SMPBHs) came into being in the very early universe. We analyze 434 known LRDs from the 0.54 ${\rm deg}^2$ COSMOS-Web survey and test the hypothesis that they originated from SMPBHs assuming sub-Eddington accretion. According to our result, SMPBHs actually could lead to the existence of more LRDs, even at higher redshifts ($z>8$).
Terahertz Spin-Light Coupling in Proximitized Dirac Materials
2024-10-28
Konstantin S. Denisov, Igor V. Rozhansky, Sergio O. Valenzuela, Igor Žutić
The two-dimensional (2D) materials are highly susceptible to the influence of their neighbors, thereby enabling the design by proximity phenomena. We reveal a remarkable terahertz (THz) spin-light interaction in 2D Dirac materials that arises from magnetic and spin-orbital proximity effects. The dynamical realization of the spin-charge conversion, the electric dipole spin resonance (EDSR), of Dirac electrons displays distinctive THz features, upon emerging spin-pseudospin proximity terms in the Hamiltonian. To capture the effect of fast pseudospin dynamics on the electron spin, we develop a mean-field theory and complement it with a quantum-mechanical treatment. As a specific example, we investigate the THz response of a single graphene layer proximitized by a magnetic substrate. Our analysis demonstrates a strong enhancement and anomalous polarization structure of the THz-light absorption which can enable THz detection and efficient generation and control of spins in spin-based quantum devices. The identified coupled spin-pseudospin dynamics is not limited to EDSR and may influence a broad range of optical, transport, and ultrafast phenomena.
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
2024-10-28
Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka, Benjamin Nachman, Dalila Salamani, David Shih, Anna Zaborowska, Oz Amram, Kerstin Borras, Matthew R. Buckley, Erik Buhmann, Thorsten Buss, Renato Paulo Da Costa Cardoso, Anthony L. Caterini, Nadezda Chernyavskaya, Federico A. G. Corchia, Jesse C. Cresswell, Sascha Diefenbacher, Etienne Dreyer, Vijay Ekambaram, Engin Eren, Florian Ernst, Luigi Favaro, Matteo Franchini, Frank Gaede, Eilam Gross, Shih-Chieh Hsu, Kristina Jaruskova, Benno Käch, Jayant Kalagnanam, Raghav Kansal, Taewoo Kim, Dmitrii Kobylianskii, Anatolii Korol, William Korcari, Dirk Krücker, Katja Krüger, Marco Letizia, Shu Li, Qibin Liu, Xiulong Liu, Gabriel Loaiza-Ganem, Thandikire Madula, Peter McKeown, Isabell-A. Melzer-Pellmann, Vinicius Mikuni, Nam Nguyen, Ayodele Ore, Sofia Palacios Schweitzer, Ian Pang, Kevin Pedro, Tilman Plehn, Witold Pokorski, Huilin Qu, Piyush Raikwar, John A. Raine, Humberto Reyes-Gonzalez, Lorenzo Rinaldi, Brendan Leigh Ross, Moritz A. W. Scham, Simon Schnake, Chase Shimmin, Eli Shlizerman, Nathalie Soybelman, Mudhakar Srivatsa, Kalliopi Tsolaki, Sofia Vallecorsa, Kyongmin Yeo, Rui Zhang
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including Variational AutoEncoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, Diffusion models, and models based on Conditional Flow Matching. We compare all submissions in terms of quality of generated calorimeter showers, as well as shower generation time and model size. To assess the quality we use a broad range of different metrics including differences in 1-dimensional histograms of observables, KPD/FPD scores, AUCs of binary classifiers, and the log-posterior of a multiclass classifier. The results of the CaloChallenge provide the most complete and comprehensive survey of cutting-edge approaches to calorimeter fast simulation to date. In addition, our work provides a uniquely detailed perspective on the important problem of how to evaluate generative models. As such, the results presented here should be applicable for other domains that use generative AI and require fast and faithful generation of samples in a large phase space.
Chatbot Companionship: A Mixed-Methods Study of Companion Chatbot Usage Patterns and Their Relationship to Loneliness in Active Users
2024-10-28
Auren R. Liu, Pat Pataranutaporn, Sherry Turkle, Pattie Maes
As artificial intelligence becomes increasingly sophisticated, companion chatbots have been proposed as a potential solution to the growing epidemic of loneliness. However, the impact of these AI companions on users' psychological well-being and social behaviors remains poorly understood. This study presents a large-scale survey (n = 404) of regular users of companion chatbots, investigating the relationship between chatbot usage and their experience of loneliness. We find a small but significant direct correlation between session length with chatbots and loneliness, and we develop a model of this relationship through multiple regression analysis, finding social attraction and neuroticism as moderators. We find seven clusters of users, including socially fulfilled dependent users to lonely dependent users. Our work contributes to the ongoing dialogue about the role of AI in social and emotional support, offering insights for what kind of human-AI connections might lead to emotional well-being and complement rather than replace human connections.
Multiple-beam Interference Spectroscopy: Instrument Analysis and Spectrum Reconstruction
2024-10-28
Mohamad Jouni, Daniele Picone, Mauro Dalla Mura
Hyperspectral imaging systems based on multiple-beam interference (MBI), such as Fabry-Perot interferometry, are attracting interest due to their compact design, high throughput, and fine resolution. Unlike dispersive devices, which measure spectra directly, the desired spectra in interferometric systems are reconstructed from measured interferograms. Although the response of MBI devices is modeled by the Airy function, existing reconstruction techniques are often limited to Fourier-transform spectroscopy, which is tailored for two-beam interference (TBI). These methods impose limitations for MBI and are susceptible to non-idealities like irregular sampling and noise, highlighting the need for an in-depth numerical framework. To fill this gap, we propose a rigorous taxonomy of the TBI and MBI instrument description and propose a unified Bayesian formulation which both embeds the description of existing literature works and adds some of the real-world non-idealities of the acquisition process. Under this framework, we provide a comprehensive review of spectroscopy forward and inverse models. In the forward model, we propose a thorough analysis of the discretization of the continuous model and the ill-posedness of the problem. In the inverse model, we extend the range of existing solutions for spectrum reconstruction, framing them as an optimization problem. Specifically, we provide a progressive comparative analysis of reconstruction methods from more specific to more general scenarios, up to employing the proposed Bayesian framework with prior knowledge, such as sparsity constraints. Experiments on simulated and real data demonstrate the framework's flexibility and noise robustness. The code is available at https://github.com/mhmdjouni/inverspyctrometry.
A Generative Model Based Honeypot for Industrial OPC UA Communication
2024-10-28
Olaf Sassnick, Georg Schäfer, Thomas Rosenstatter, Stefan Huber
Industrial Operational Technology (OT) systems are increasingly targeted by cyber-attacks due to their integration with Information Technology (IT) systems in the Industry 4.0 era. Besides intrusion detection systems, honeypots can effectively detect these attacks. However, creating realistic honeypots for brownfield systems is particularly challenging. This paper introduces a generative model-based honeypot designed to mimic industrial OPC UA communication. Utilizing a Long ShortTerm Memory (LSTM) network, the honeypot learns the characteristics of a highly dynamic mechatronic system from recorded state space trajectories. Our contributions are twofold: first, we present a proof-of concept for a honeypot based on generative machine-learning models, and second, we publish a dataset for a cyclic industrial process. The results demonstrate that a generative model-based honeypot can feasibly replicate a cyclic industrial process via OPC UA communication. In the short-term, the generative model indicates a stable and plausible trajectory generation, while deviations occur over extended periods. The proposed honeypot implementation operates efficiently on constrained hardware, requiring low computational resources. Future work will focus on improving model accuracy, interaction capabilities, and extending the dataset for broader applications.
We prove that for any presentably symmetric monoidal $\infty$-category $\mathcal{V}$, the $\infty$-category $\mathbf{Mod}_\mathcal{V}(\mathbf{Pr}^{\mathrm{L}})^{\mathrm{dbl}}$ of dualizable presentable $\mathcal{V}$-modules and internal left adjoints between them is itself presentable. Along the way, we survey formal properties of these dualizable $\mathcal V$-modules. We pay close attention to the case of the $\infty$-category of spectra, where we survey the foundational properties of ``compact morphisms''.
We survey the theory of locally rigid and rigid symmetric monoidal $\infty$-categories over an arbitrary base $\mathcal{V}\in\mathrm{CAlg}(\mathbf{Pr}^\mathrm{L})$. Along the way, we introduce and study ``$\mathcal{V}$-atomic morphisms'', which are analogues of compact morphisms over an arbitrary base $\mathcal{V}$.
2024-10-28
Joe Benton, Misha Wagner, Eric Christiansen, Cem Anil, Ethan Perez, Jai Srivastav, Esin Durmus, Deep Ganguli, Shauna Kravec, Buck Shlegeris, Jared Kaplan, Holden Karnofsky, Evan Hubinger, Roger Grosse, Samuel R. Bowman, David Duvenaud
Sufficiently capable models could subvert human oversight and decision-making in important contexts. For example, in the context of AI development, models could covertly sabotage efforts to evaluate their own dangerous capabilities, to monitor their behavior, or to make decisions about their deployment. We refer to this family of abilities as sabotage capabilities. We develop a set of related threat models and evaluations. These evaluations are designed to provide evidence that a given model, operating under a given set of mitigations, could not successfully sabotage a frontier model developer or other large organization's activities in any of these ways. We demonstrate these evaluations on Anthropic's Claude 3 Opus and Claude 3.5 Sonnet models. Our results suggest that for these models, minimal mitigations are currently sufficient to address sabotage risks, but that more realistic evaluations and stronger mitigations seem likely to be necessary soon as capabilities improve. We also survey related evaluations we tried and abandoned. Finally, we discuss the advantages of mitigation-aware capability evaluations, and of simulating large-scale deployments using small-scale statistics.
We summarise the physics of first-order phase transitions in the early universe, and the possible ways in which they might come about. We then focus on gravitational waves, emphasising general qualitative features of stochastic backgrounds produced by early universe phase transitions and the cosmology of their present-day appearance. Finally, we conclude by discussing some of the ways in which a stochastic background might be detected.
Pattern formation in coiling of falling viscous threads: Revisiting the geometric model
2024-10-28
Will Sze, Eusebius J. Doedel, Ida Karimfazli, Behrooz Yousefzadeh
The "Fluid Mechanic Sewing Machine" creates periodic patterns through the coiling nature of a viscous fluid falling onto a moving surface. At relatively moderate heights, the reported patterns are translating coiling, alternating loops, W pattern, and meander. A simplified theoretical model based on the geometry and local bending of the contact point can predict these patterns. We experimentally explore the patterns in this region by collecting new data to compare with the model. Our review of the model's bifurcation diagram reveals additional patterns beyond the ones reported, although current experiments have not shown their existence. The W pattern, previously omitted in a regime diagram because of its small region, is now shown explicitly. We report on the consistent appearance of a period-doubled version of the W pattern, as well as rare appearances of resonant patterns, both reported for the first time. Comparing the theoretical model to experimental data, we find that the predicted phase diagram and the meander variation deviate from observations. These deviations hint at an unaccounted dynamics that merits further study.
Nonlinear photoconductivity in pump-probe spectroscopy. I. Optical coefficients
2024-10-28
Leya Lopez, Derek G. Sahota, J. Steven Dodge
We analyze the optical pump-probe reflection and transmission coefficients when the photoinduced response depends nonlinearly on the incident pump intensity. Under these conditions, we expect the photoconductivity depth profile to change shape as a function of the incident fluence, unlike the case when the photoinduced response is linear in the incident intensity. We consider common optical nonlinearities, including photoconductivity saturation and two-photon absorption, and we derive analytic expressions for the photoconductivity depth profile when one or more is present. We review the theory of the electromagnetic transmission and reflection coefficients in a stratified medium, and we derive general expressions for these coefficients for a medium with an arbitrary photoconductivity depth profile. For several photoconductivity profiles of importance in pump-probe spectroscopy, we show that the wave equation can be transformed into one of three standard differential equations$\unicode{x2014}$the Bessel equation, the hypergeometric equation, and the Heun equation$\unicode{x2014}$with analytic solutions in terms of their associated special functions. From these solutions, we derive exact analytic expressions for the optical coefficients in terms of the photoconductivity at the optical interface, and we discuss their limiting forms in various physical limits. Our results provide a systematic guide for analyzing pump-probe measurements over a wide range of pump intensities, and establishes a framework for constraining the systematic uncertainty associated with nonlinear photoconductivity profile distortion.
RoBIn: A Transformer-Based Model For Risk Of Bias Inference With Machine Reading Comprehension
2024-10-28
Abel Corrêa Dias, Viviane Pereira Moreira, João Luiz Dihl Comba
Objective: Scientific publications play a crucial role in uncovering insights, testing novel drugs, and shaping healthcare policies. Accessing the quality of publications requires evaluating their Risk of Bias (RoB), a process typically conducted by human reviewers. In this study, we introduce a new dataset for machine reading comprehension and RoB assessment and present RoBIn (Risk of Bias Inference), an innovative model crafted to automate such evaluation. The model employs a dual-task approach, extracting evidence from a given context and assessing the RoB based on the gathered evidence. Methods: We use data from the Cochrane Database of Systematic Reviews (CDSR) as ground truth to label open-access clinical trial publications from PubMed. This process enabled us to develop training and test datasets specifically for machine reading comprehension and RoB inference. Additionally, we created extractive (RoBInExt) and generative (RoBInGen) Transformer-based approaches to extract relevant evidence and classify the RoB effectively. Results: RoBIn is evaluated across various settings and benchmarked against state-of-the-art methods for RoB inference, including large language models in multiple scenarios. In most cases, the best-performing RoBIn variant surpasses traditional machine learning and LLM-based approaches, achieving an ROC AUC of 0.83. Conclusion: Based on the evidence extracted from clinical trial reports, RoBIn performs a binary classification to decide whether the trial is at a low RoB or a high/unclear RoB. We found that both RoBInGen and RoBInExt are robust and have the best results in many settings.
PEPSI's non-detection of escaping hydrogen and metal lines adds to the enigma of WASP-12 b
2024-10-28
Anusha Pai Asnodkar, Ji Wang, Madelyn Broome, Chenliang Huang, Marshall C. Johnson, Ilya Ilyin, Klaus G. Strassmeier, Adam Jensen
WASP-12 b is an ultra-hot Jupiter (UHJ) of special interest for atmospheric studies since it is on an inspiraling orbit in an extreme environment of intense radiation and circumstellar gas. Previously claimed detections of active mass loss from this planet are controversial across the literature. To address this controversy, we obtain two new transit observations of WASP-12 b with the optical high-resolution PEPSI spectrograph on the Large Binocular Telescope. Contrary to previous work, we do not observe planetary H$\alpha$ absorption and rule out the amplitude of previously reported detections. Our non-detection may be limited by the sensitivity of our data or could indicate weaker mass loss than suggested by previous studies. We conduct injection-recovery experiments to place constraints on the radial extent of WASP-12 b's escaping atmosphere as probed by Balmer lines, but find that our data do not have the sensitivity to probe down to the planet's Roche Lobe. Using physically motivated models of atmospheric escape, we explore upper limit constraints on the planet's mass-loss rate and deem the data quality in the wavelength regime of Balmer lines insufficient to determine a physically meaningful constraint. We also conduct a spectral survey of other optical absorbers to trace atmospheric circulation but detect no additional absorption. We conclude that previous claims of H$\alpha$ absorption from the atmosphere of WASP-12 b should be reevaluated. Given the anticipated line strength of Balmer/optical features, observing the atmosphere of this faint target will require stacking more observations even with the largest telescope facilities available.
Can Large Language Models Act as Symbolic Reasoners?
2024-10-28
Rob Sullivan, Nelly Elsayed
The performance of Large language models (LLMs) across a broad range of domains has been impressive but have been critiqued as not being able to reason about their process and conclusions derived. This is to explain the conclusions draw, and also for determining a plan or strategy for their approach. This paper explores the current research in investigating symbolic reasoning and LLMs, and whether an LLM can inherently provide some form of reasoning or whether supporting components are necessary, and, if there is evidence for a reasoning capability, is this evident in a specific domain or is this a general capability? In addition, this paper aims to identify the current research gaps and future trends of LLM explainability, presenting a review of the literature, identifying current research into this topic and suggests areas for future work.
A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions
2024-10-28
René Manassé Galekwa, Jean Marie Tshimula, Etienne Gael Tajeuna, Kyamakya Kyandoghere
The sports betting industry has experienced rapid growth, driven largely by technological advancements and the proliferation of online platforms. Machine learning (ML) has played a pivotal role in the transformation of this sector by enabling more accurate predictions, dynamic odds-setting, and enhanced risk management for both bookmakers and bettors. This systematic review explores various ML techniques, including support vector machines, random forests, and neural networks, as applied in different sports such as soccer, basketball, tennis, and cricket. These models utilize historical data, in-game statistics, and real-time information to optimize betting strategies and identify value bets, ultimately improving profitability. For bookmakers, ML facilitates dynamic odds adjustment and effective risk management, while bettors leverage data-driven insights to exploit market inefficiencies. This review also underscores the role of ML in fraud detection, where anomaly detection models are used to identify suspicious betting patterns. Despite these advancements, challenges such as data quality, real-time decision-making, and the inherent unpredictability of sports outcomes remain. Ethical concerns related to transparency and fairness are also of significant importance. Future research should focus on developing adaptive models that integrate multimodal data and manage risk in a manner akin to financial portfolios. This review provides a comprehensive examination of the current applications of ML in sports betting, and highlights both the potential and the limitations of these technologies.
A revisited Correction to the Halo Mass Function for local-type Primordial non-Gaussianity
2024-10-28
Luca Fiorino, Sofia Contarini, Federico Marulli, Ariel G. Sanchez, Marco Baldi, Andrea Fiorilli, Lauro Moscardini
We investigate the effect of primordial non-Gaussianities on halo number counts using N-body simulations with different values of $f_{\rm NL}^{\rm loc}$. We show how current theoretical models fail to adequately describe the non-Gaussian mass function of halos identified with different overdensity thresholds, $\Delta_{\rm b}$. We explain how these discrepancies are related to a variation in the density profile of dark matter halos, finding that the internal steepness (i.e. the compactness) of halos depends on the value of $f_{\rm NL}^{\rm loc}$. We then parametrize these deviations in halo number counts with a factor $\kappa(\Delta_{\rm b})$ that modifies the linear density threshold for collapse according to the halo identification threshold used, defined with respect to the Universe background density. We rely on a second-degree polynomial to describe $\kappa$ and employ a Bayesian analysis to determine the coefficients of this polynomial. In addition, we verify the independence of the latter on the sign and absolute value of $f_{\rm NL}^{\rm loc}$. Finally, we show how this re-parametrization prevents the extraction of biased constraints on $f_{\rm NL}^{\rm loc}$, correcting for large systematic errors especially in the case of halos identified with high density thresholds. This improvement is crucial in the perspective of deriving cosmological constraints with the non-Gaussian mass function from real data, as different mass definitions can be employed depending on the properties of the survey.
Complete mathematical theory of the jamming transition
2024-10-28
Alessio Zaccone
The jamming transition of frictionless athermal particles is a paradigm to understand the mechanics of amorphous materials at the atomic scale. Concepts related to the jamming transition and the mechanical response of jammed packings have cross-fertilized into other areas such as atomistic descriptions of the elasticity and plasticity of glasses. In this perspective article, the microscopic mathematical theory of the jamming transition is reviewed from first-principles. The starting point of the derivation is a microscopically-reversible particle-bath Hamiltonian from which the governing equation of motion for the grains under an external deformation is derived. From this equation of motion, microscopic expressions are obtained for both the shear modulus and the viscosity as a function of the distance from the jamming transition (respectively, above and below the transition). Regarding the vanishing of the shear modulus at the unjamming transition, this theory, as originally demonstrated in [Zaccone \& Scossa-Romano, Phys. Rev. B 83, 184205 (2011)], is currently the only quantitative microscopic theory in parameter-free agreement with numerical simulations of [O'Hern et al. Phys. Rev. E 68, 011306 (2003)] for jammed packings. The divergence of the viscosity upon approaching the jamming transition from below is derived here, for the first time, from the same microscopic Hamiltonian. The quantitative microscopic prediction of the diverging viscosity is shown to be in fair agreement with numerical results of sheared 2D soft disks from [Olsson \& Teitel, Phys. Rev. Lett. 99, 178001 (2007)].
LIMFAST. III. Timing Cosmic Reionization with the 21 cm and Near-Infrared Backgrounds
2024-10-28
Guochao Sun, Adam Lidz, Tzu-Ching Chang, Jordan Mirocha, Steven R. Furlanetto
The timeline of cosmic reionization remains uncertain despite sustained efforts to study how the ionizing output of early galaxies shaped the intergalactic medium (IGM). Using the semi-numerical code LIMFAST, we investigate the prospects for timing the reionization process by cross-correlating the 21 cm signal with the cosmic near-infrared background (NIRB) contributed by galaxies at $z>5$. Tracing opposite phases of the IGM on large scales during reionization, the two signals together serve as a powerful probe for the reionization history. However, because long-wavelength, line-of-sight Fourier modes -- the only modes probed by NIRB fluctuations -- are contaminated by 21 cm foregrounds and thus inevitably lost to foreground cleaning or avoidance, a direct cross-correlation of the two signals vanishes. We show that this problem can be circumvented by squaring the foreground-filtered 21 cm signal and cross-correlating the squared field with the NIRB. This statistic is related to the 21 cm--21 cm--NIRB cross-bispectrum and encodes valuable information regarding the reionization timeline. Particularly, the 21 cm$^2$ and NIRB signals are positively correlated during the early phases of reionization and negatively correlated at later stages. We demonstrate that this behavior is generic across several different reionization models and compare our simulated results with perturbative calculations. We show that this cross-correlation can be detected at high significance by forthcoming 21 cm and NIRB surveys such as SKA and SPHEREx. Our methodology is more broadly applicable to cross-correlations between line intensity mapping data and 2D tracers of the large-scale structure, including photometric galaxy surveys and CMB lensing mass maps, among others.
CO isotopologue-derived molecular gas conditions and CO-to-H$_2$ conversion factors in M51
2024-10-28
Jakob den Brok, María J. Jiménez-Donaire, Adam Leroy, Eva Schinnerer, Frank Bigiel, Jérôme Pety, Glen Petitpas, Antonio Usero, Yu-Hsuan Teng, Pedro Humire, Eric W. Koch, Erik Rosolowsky, Karin Sandstrom, Daizhong Liu, Qizhou Zhang, Sophia Stuber, Mélanie Chevance, Daniel A. Dale, Cosima Eibensteiner, Ina Galić, Simon C. O. Glover, Hsi-An Pan, Miguel Querejeta, Rowan J. Smith, Thomas G. Williams, David J. Wilner, Valencia Zhang
Over the past decade, several millimeter interferometer programs have mapped the nearby star-forming galaxy M51 at a spatial resolution of ${\le}170$ pc. This study combines observations from three major programs: the PdBI Arcsecond Whirlpool Survey (PAWS), the SMA M51 large program (SMA-PAWS), and the Surveying the Whirlpool at Arcseconds with NOEMA (SWAN). The dataset includes the (1-0) and (2-1) rotational transitions of $^{12}$CO, $^{13}$CO, and C$^{18}$O isotopologues. The observations cover the $r{<}\rm 3\,kpc$ region including center and part of the disk, thereby ensuring strong detections of the weaker $^{13}$CO and C$^{18}$O lines. All observations are convolved in this analysis to an angular resolution of 4$''$, corresponding to a physical scale of ${\sim}$170 pc. We investigate empirical line ratio relations and quantitatively evaluate molecular gas conditions such as temperature, density, and the CO-to-H$_2$ conversion factor ($\alpha_{\rm CO}$). We employ two approaches to study the molecular gas conditions: (i) assuming local thermal equilibrium (LTE) to analytically determine the CO column density and $\alpha_{\rm CO}$, and (ii) using non-LTE modeling with RADEX to fit physical conditions to observed CO isotopologue intensities. We find that the $\alpha_{\rm CO}$ values {in the center and along the inner spiral arm} are $\sim$0.5 dex (LTE) and ${\sim}$0.1 dex (non-LTE) below the Milky Way inner disk value. The average non-LTE $\alpha_{\rm CO}$ is $2.4{\pm}0.5$ M$_\odot$ pc$^{-2}$ (K km s$^{-1}$)$^{-1}$. While both methods show dispersion due to underlying assumptions, the scatter is larger for LTE-derived values. This study underscores the necessity for robust CO line modeling to accurately constrain the molecular ISM's physical and chemical conditions in nearby galaxies.
A Survey of Dynamical and Gravitational Lensing Tests in Scale Invariance: The Fall of Dark Matter?
2024-10-28
Andre Maeder, Frederic Courbin
We first briefly review the adventure of scale invariance in physics, from Galileo Galilei, Weyl, Einstein, and Feynman to the revival by Dirac (1973) and Canuto et al. (1977). We then gather concrete observational evidence that scale-invariant effects are present and measurable in astronomical objects spanning a vast range of masses (0.5 M$_{\odot} <$ M $< 10^{14}$ M$_{\odot}$) and an equally impressive range of spatial scales (0.01 pc $<$ r $<$ 1 Gpc). Scale invariance accounts for the observed excess in velocity in galaxy clusters with respect to the visible mass, the relatively flat/small slope of rotation curves in local galaxies, the observed steep rotation curves of high-redshift galaxies, and the excess of velocity in wide binary stars with separations above 3000 kau found in Gaia DR3. Last but not least, we investigate the effect of scale invariance on gravitational lensing. We show that scale invariance does not affect the geodesics of light rays as they pass in the vicinity of a massive galaxy. However, scale-invariant effects do change the inferred mass-to-light ratio of lens galaxies as compared to GR. As a result, the discrepancies seen in GR between the total lensing mass of galaxies and their stellar mass from photometry may be accounted for. This holds true both for lenses at high redshift like JWST-ER1 and at low redshift like in the SLACS sample. Of note is that none of the above observational tests require dark matter or any adjustable parameter to tweak the theory at any given mass or spatial scale.
easyCHEM: A Python package for calculating chemical equilibrium abundances in exoplanet atmospheres
2024-10-28
Elise Lei, Paul Mollière
For modeling the spectra of exoplanets one must know their atmospheric composition. This is necessary because the abundance of molecules, atoms, ions and condensates is needed to construct the total cross-section for the interaction between electro-magnetic radiation and matter. In addition, when solving for the temperature structure of an atmosphere the so-called adiabatic temperature gradient must be known, which describes the pressure-temperature dependence in convectively unstable regions well. Depending on the planetary properties, the composition and adiabatic gradients may be well described by equilibrium chemistry, which means that chemical reactions occur faster than any other relevant processes in the atmosphere, such as mixing. What is more, the equilibrium assumption often serves as a useful starting point for non-equilibrium calculations. Efficient and easy-to-use codes for determining equilibrium abundances are therefore needed. Here we report on our easyCHEM Python package that calculates atmospheric compositions and adiabatic temperature gradients in chemical equilibrium for any user-specified elemental composition.
Molecular Hydrogen in the Extremely Metal-Poor, Star-Forming Galaxy Leo P
2024-10-28
O. Grace Telford, Karin M. Sandstrom, Kristen B. W. McQuinn, Simon C. O. Glover, Elizabeth J. Tarantino, Alberto D. Bolatto, Ryan J. Rickards Vaught
The James Webb Space Telescope (JWST) has revealed unexpectedly rapid galaxy assembly in the early universe, in tension with models of star and galaxy formation. In the gas conditions typical of early galaxies, particularly their low abundances of heavy elements (metals) and dust, the star-formation process is poorly understood. Some models predict that stars form in atomic gas at low metallicity, in contrast to forming in molecular gas as observed in higher-metallicity galaxies. To understand the very high star-formation rates at early epochs, it is necessary to determine whether molecular gas formation represents a bottleneck to star formation, or if it is plentiful even at extremely low metallicity. Despite repeated searches, star-forming molecular gas has not yet been observed in any galaxy below 7% of the Solar metallicity, leaving the question of how stars form at lower metallicities unresolved. Here, we report the detection of rotationally excited emission from molecular hydrogen in the star-forming region of the nearby, 3% Solar metallicity galaxy Leo P with the MIRI-MRS instrument onboard JWST. These observations place a lower limit on the molecular gas content of Leo P and, combined with our upper limit on carbon monoxide emission from a deep search of this galaxy, demonstrate that MIRI-MRS is sensitive to much smaller molecular gas masses at extremely low metallicity compared to the traditional observational tracer. This discovery pushes the maximum metallicity at which purely atomic gas may fuel star formation a factor of two lower, providing crucial empirical guidance for models of star formation in the early universe.
An Indication of Gas Inflow in Clumpy Star-Forming Galaxies near $z\sim1$: Lower Gas-Phase Metallicities in Clumpy Galaxies Compared to Non-Clumpy Galaxies
2024-10-28
Visal Sok, Adam Muzzin, Pascale Jablonka, Vivian Yun Yan Tan, Z. Cemile Marsan, Danilo Marchesini, Gillian Wilson, Leo Y. Alcorn
Despite the ubiquity of clumpy star-forming galaxies at high-redshift, the origin of clumps are still largely unconstrained due to the limited observations that can validate the mechanisms for clump formation. We postulate that if clumps form due to the accretion of metal-poor gas that leads to violent disk instability, clumpy galaxies should have lower gas-phase metallicities compared to non-clumpy galaxies. In this work, we obtain the near-infrared spectrum for 42 clumpy and non-clumpy star-forming galaxies of similar masses, SFRs, and colors at $z\approx0.7$ using the Gemini Near-Infrared Spectrograph (GNIRS) and infer their gas-phase metallicity from the {\nii} and {\halpha} line ratio. We find that clumpy galaxies have lower metallicities compared to non-clumpy galaxies, with an offset in the weighted average metallicity of $0.07\pm0.02$ dex. We also find an offset of $0.06\pm0.02$ dex between clumpy and non-clumpy galaxies in a comparable sample of 23 star-forming galaxies at $z\approx1.5$ using existing data from the FMOS-COSMOS survey. Similarly, lower {\nii}/{\halpha} ratio are typically found in galaxies that have more of their $\mathrm{UV_{rest}}$ luminosity originating from clumps, suggesting that \enquote{clumpier} galaxies are more metal poor. We also derive the intrinsic velocity dispersion and line-of-sight rotational velocity for galaxies from the GNIRS sample. The majority of galaxies have $\sigma_0/v_c \approx 0.2$, with no significant difference between clumpy and non-clumpy galaxies. Our result indicates that clump formation may be related to the inflow of metal-poor gas; however, the process that forms them does not necessarily require significant, long-term kinematic instability in the disk.
Domain Adaptation with a Single Vision-Language Embedding
2024-10-28
Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc, Patrick Pérez, Raoul de Charette
Domain adaptation has been extensively investigated in computer vision but still requires access to target data at the training time, which might be difficult to obtain in some uncommon conditions. In this paper, we present a new framework for domain adaptation relying on a single Vision-Language (VL) latent embedding instead of full target data. First, leveraging a contrastive language-image pre-training model (CLIP), we propose prompt/photo-driven instance normalization (PIN). PIN is a feature augmentation method that mines multiple visual styles using a single target VL latent embedding, by optimizing affine transformations of low-level source features. The VL embedding can come from a language prompt describing the target domain, a partially optimized language prompt, or a single unlabeled target image. Second, we show that these mined styles (i.e., augmentations) can be used for zero-shot (i.e., target-free) and one-shot unsupervised domain adaptation. Experiments on semantic segmentation demonstrate the effectiveness of the proposed method, which outperforms relevant baselines in the zero-shot and one-shot settings.
A Survey on Automatic Credibility Assessment of Textual Credibility Signals in the Era of Large Language Models
2024-10-28
Ivan Srba, Olesya Razuvayevskaya, João A. Leite, Robert Moro, Ipek Baris Schlicht, Sara Tonelli, Francisco Moreno García, Santiago Barrio Lottmann, Denis Teyssou, Valentin Porcellini, Carolina Scarton, Kalina Bontcheva, Maria Bielikova
In the current era of social media and generative AI, an ability to automatically assess the credibility of online social media content is of tremendous importance. Credibility assessment is fundamentally based on aggregating credibility signals, which refer to small units of information, such as content factuality, bias, or a presence of persuasion techniques, into an overall credibility score. Credibility signals provide a more granular, more easily explainable and widely utilizable information in contrast to currently predominant fake news detection, which utilizes various (mostly latent) features. A growing body of research on automatic credibility assessment and detection of credibility signals can be characterized as highly fragmented and lacking mutual interconnections. This issue is even more prominent due to a lack of an up-to-date overview of research works on automatic credibility assessment. In this survey, we provide such systematic and comprehensive literature review of 175 research papers while focusing on textual credibility signals and Natural Language Processing (NLP), which undergoes a significant advancement due to Large Language Models (LLMs). While positioning the NLP research into the context of other multidisciplinary research works, we tackle with approaches for credibility assessment as well as with 9 categories of credibility signals (we provide a thorough analysis for 3 of them, namely: 1) factuality, subjectivity and bias, 2) persuasion techniques and logical fallacies, and 3) claims and veracity). Following the description of the existing methods, datasets and tools, we identify future challenges and opportunities, while paying a specific attention to recent rapid development of generative AI.
"We do use it, but not how hearing people think": How the Deaf and Hard of Hearing Community Uses Large Language Model Tools
2024-10-28
Shuxu Huffman, Si Chen, Kelly Avery Mack, Haotian Su, Qi Wang, Raja Kushalnagar
Generative AI tools, particularly those utilizing large language models (LLMs), have become increasingly prevalent in both professional and personal contexts, offering powerful capabilities for text generation and communication support. While these tools are widely used to enhance productivity and accessibility, there has been limited exploration of how Deaf and Hard of Hearing (DHH) individuals engage with text-based generative AI tools, as well as the challenges they may encounter. This paper presents a mixed-method survey study investigating how the DHH community uses Text AI tools, such as ChatGPT, to reduce communication barriers, bridge Deaf and hearing cultures, and improve access to information. Through a survey of 80 DHH participants and separate interviews with 11 other participants, we found that while these tools provide significant benefits, including enhanced communication and mental health support, they also introduce barriers, such as a lack of American Sign Language (ASL) support and understanding of Deaf cultural nuances. Our findings highlight unique usage patterns within the DHH community and underscore the need for inclusive design improvements. We conclude by offering practical recommendations to enhance the accessibility of Text AI for the DHH community and suggest directions for future research in AI and accessibility.
2024-10-28
Lawrence K. Q. Yan, Ming Li, Yichao Zhang, Caitlyn Heqi Yin, Cheng Fei, Benji Peng, Ziqian Bi, Pohsun Feng, Keyu Chen, Junyu Liu, Qian Niu
With the increasing application of large language models (LLMs) in the medical domain, evaluating these models' performance using benchmark datasets has become crucial. This paper presents a comprehensive survey of various benchmark datasets employed in medical LLM tasks. These datasets span multiple modalities including text, image, and multimodal benchmarks, focusing on different aspects of medical knowledge such as electronic health records (EHRs), doctor-patient dialogues, medical question-answering, and medical image captioning. The survey categorizes the datasets by modality, discussing their significance, data structure, and impact on the development of LLMs for clinical tasks such as diagnosis, report generation, and predictive decision support. Key benchmarks include MIMIC-III, MIMIC-IV, BioASQ, PubMedQA, and CheXpert, which have facilitated advancements in tasks like medical report generation, clinical summarization, and synthetic data generation. The paper summarizes the challenges and opportunities in leveraging these benchmarks for advancing multimodal medical intelligence, emphasizing the need for datasets with a greater degree of language diversity, structured omics data, and innovative approaches to synthesis. This work also provides a foundation for future research in the application of LLMs in medicine, contributing to the evolving field of medical artificial intelligence.
Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach
2024-10-28
Firas Bayram, Bestoun S. Ahmed
Artificial intelligence (AI), and especially its sub-field of Machine Learning (ML), are impacting the daily lives of everyone with their ubiquitous applications. In recent years, AI researchers and practitioners have introduced principles and guidelines to build systems that make reliable and trustworthy decisions. From a practical perspective, conventional ML systems process historical data to extract the features that are consequently used to train ML models that perform the desired task. However, in practice, a fundamental challenge arises when the system needs to be operationalized and deployed to evolve and operate in real-life environments continuously. To address this challenge, Machine Learning Operations (MLOps) have emerged as a potential recipe for standardizing ML solutions in deployment. Although MLOps demonstrated great success in streamlining ML processes, thoroughly defining the specifications of robust MLOps approaches remains of great interest to researchers and practitioners. In this paper, we provide a comprehensive overview of the trustworthiness property of MLOps systems. Specifically, we highlight technical practices to achieve robust MLOps systems. In addition, we survey the existing research approaches that address the robustness aspects of ML systems in production. We also review the tools and software available to build MLOps systems and summarize their support to handle the robustness aspects. Finally, we present the open challenges and propose possible future directions and opportunities within this emerging field. The aim of this paper is to provide researchers and practitioners working on practical AI applications with a comprehensive view to adopt robust ML solutions in production environments.
Error Bounds for Deep Learning-based Uncertainty Propagation in SDEs
2024-10-28
Chun-Wei Kong, Luca Laurenti, Jay McMahon, Morteza Lahijanian
Stochastic differential equations are commonly used to describe the evolution of stochastic processes. The uncertainty of such processes is best represented by the probability density function (PDF), whose evolution is governed by the Fokker-Planck partial differential equation (FP-PDE). However, it is generally infeasible to solve the FP-PDE in closed form. In this work, we show that physics-informed neural networks (PINNs) can be trained to approximate the solution PDF using existing methods. The main contribution is the analysis of the approximation error: we develop a theory to construct an arbitrary tight error bound with PINNs. In addition, we derive a practical error bound that can be efficiently constructed with existing training methods. Finally, we explain that this error-bound theory generalizes to approximate solutions of other linear PDEs. Several numerical experiments are conducted to demonstrate and validate the proposed methods.
Survey of User Interface Design and Interaction Techniques in Generative AI Applications
2024-10-28
Reuben Luera, Ryan A. Rossi, Alexa Siu, Franck Dernoncourt, Tong Yu, Sungchul Kim, Ruiyi Zhang, Xiang Chen, Hanieh Salehy, Jian Zhao, Samyadeep Basu, Puneet Mathur, Nedim Lipka
The applications of generative AI have become extremely impressive, and the interplay between users and AI is even more so. Current human-AI interaction literature has taken a broad look at how humans interact with generative AI, but it lacks specificity regarding the user interface designs and patterns used to create these applications. Therefore, we present a survey that comprehensively presents taxonomies of how a human interacts with AI and the user interaction patterns designed to meet the needs of a variety of relevant use cases. We focus primarily on user-guided interactions, surveying interactions that are initiated by the user and do not include any implicit signals given by the user. With this survey, we aim to create a compendium of different user-interaction patterns that can be used as a reference for designers and developers alike. In doing so, we also strive to lower the entry barrier for those attempting to learn more about the design of generative AI applications.
Exocentric To Egocentric Transfer For Action Recognition: A Short Survey
2024-10-27
Anirudh Thatipelli, Shao-Yuan Lo, Amit K. Roy-Chowdhury
Egocentric vision captures the scene from the point of view of the camera wearer while exocentric vision captures the overall scene context. Jointly modeling ego and exo views is crucial to developing next-generation AI agents. The community has regained interest in the field of egocentric vision. While the third-person view and first-person have been thoroughly investigated, very few works aim to study both synchronously. Exocentric videos contain many relevant signals that are transferrable to egocentric videos. In this paper, we provide a broad overview of works combining egocentric and exocentric visions.
A quick guide to ordinary state-dependent delay differential equations
2024-10-27
Bernhard Aigner, Marcus Waurick
We review $H^{1}$-well-posedness for initial value problems of ordinary differential equations with state-dependent right-hand side. We streamline known approaches to infer existence and uniqueness of solutions for small times given a Lipschitz-continuous prehistory. The paramount feature is a reduction of the differential equation to a fixed point problem that admits a unique solution appealing to the contraction mapping principle. The use of exponentially weighted Sobolev spaces in this endeavor proves to be as powerful as for ordinary differential equations without delay. Our result includes a blow-up criterium for global existence of solutions. The discussion of well-posedness is concluded by new results covering continuous dependence on initial prehistories and on the right-hand sides.
Zero-Trust Network Access (ZTNA) marks a significant shift in network security by adopting a "never trust, always verify" approach. This work provides an in-depth analysis of ZTNA, offering a comprehensive framework for understanding its principles, architectures, and applications. We discuss its role in securing modern, complex network environments, which include cloud platforms, Internet of Things (IoT) devices, and hybrid enterprise networks. Our objective is to create a key resource for researchers and practitioners by reviewing critical methodologies, analyzing current implementations, and highlighting open challenges and research directions.
A Framework for Real-Time Volcano-Seismic Event Recognition Based on Multi-Station Seismograms and Semantic Segmentation Models
2024-10-27
Camilo Espinosa-Curilem, Millaray Curilem, Daniel Basualto
In volcano monitoring, effective recognition of seismic events is essential for understanding volcanic activity and raising timely warning alerts. Traditional methods rely on manual analysis, which can be subjective and labor-intensive. Furthermore, current automatic approaches often tackle detection and classification separately, mostly rely on single station information and generally require tailored preprocessing and representations to perform predictions. These limitations often hinder their application to real-time monitoring and utilization across different volcano conditions. This study introduces a novel approach that utilizes Semantic Segmentation models to automate seismic event recognition by applying a straight forward transformation of multi-channel 1D signals into 2D representations, enabling their use as images. Our framework employs a data-driven, end-to-end design that integrates multi-station seismic data with minimal preprocessing, performing both detection and classification simultaneously for five seismic event classes. We evaluated four state-of-the-art segmentation models (UNet, UNet++, DeepLabV3+ and SwinUNet) on approximately 25.000 seismic events recorded at four different Chilean volcanoes: Nevados del Chill\'an Volcanic Complex, Laguna del Maule, Villarrica and Puyehue-Cord\'on Caulle. Among these models, the UNet architecture was identified as the most effective model, achieving mean F1 and Intersection over Union (IoU) scores of up to 0.91 and 0.88, respectively, and demonstrating superior noise robustness and model flexibility to unseen volcano datasets.
Galaxy catalogs from the SAGE Semi-Analytic Model calibrated on THE THREE HUNDRED hydrodynamical simulations: A method to push the limits toward lower mass galaxies in dark matter only clusters simulations
2024-10-27
Jonathan S. Gómez, Gustavo Yepes, A. Jiménez Muñoz, Weiguang Cui
The new generation of upcoming deep photometric and spectroscopic surveys will allow us to measure the astrophysical properties of faint galaxies in massive clusters. This would demand to produce simulations of galaxy clusters with better mass resolution than the ones available today if we want to make comparisons between the upcoming observations and predictions of cosmological models. But producing full-physics hydrodynamical simulations of the most massive clusters is not an easy task. This would involve billions of computational elements to reliably resolve low mass galaxies similar to those measured in observations. On the other hand, dark matter only simulations of cluster size halos can be done with much larger mass resolution but at the cost of having to apply a model that populate galaxies within each of the subhalos in these simulations. In this paper we present the results of a new set of dark matter only simulations with different mass resolutions within the THE THREE HUNDRED project. We have generated catalogs of galaxies with stellar and luminosity properties by applying the SAGE Semi-Analytical Model of galaxy formation. To obtain the catalogs consistent with the results from hydrodynamical simulations, the internal physical parameters of SAGE were calibrated with the Particle Swarm Optimization method using a subset of full-physics runs with the same mass resolution than the dark matter only ones.
A Comprehensive Survey on Green Blockchain: Developing the Next Generation of Energy Efficient and Sustainable Blockchain Systems
2024-10-27
Tiago M. Fernández-Caramés, Paula Fraga-Lamas
Although Blockchain has been successfully used in many different fields and applications, it has been traditionally regarded as an energy-intensive technology, essentially due to the past use of inefficient consensus algorithms that prioritized security over sustainability. However, in the last years, thanks to the significant progress made on key blockchain components, their energy consumption can be decreased noticeably. To achieve this objective, this article analyzes the main components of blockchains and explores strategies to reduce their energy consumption. In this way, this article delves into each component of a blockchain system, including consensus mechanisms, network architecture, data storage and validation, smart contract execution, mining and block creation, and outlines specific strategies to decrease their energy consumption. For such a purpose, consensus mechanisms are compared, recommendations for reducing network communications energy consumption are provided, techniques for data storage and validation are suggested and diverse optimizations are proposed both for software and hardware components. Moreover, the main challenges and limitations of reducing power consumption in blockchain systems are analyzed. As a consequence, this article provides a guideline for the future researchers and developers who aim to develop the next generation of Green Blockchain solutions.
Asynchronous Perception Machine For Efficient Test-Time-Training
2024-10-27
Rajat Modi, Yogesh Singh Rawat
In this work, we propose Asynchronous Perception Machine (APM), a computationally-efficient architecture for test-time-training (TTT). APM can process patches of an image one at a time in any order \textit{asymmetrically,} and \textit{still encode} semantic-awareness in the net. We demonstrate APM's ability to recognize out-of-distribution images \textit{without} dataset-specific pre-training, augmentation or any-pretext task. APM offers competitive performance over existing TTT approaches. To perform TTT, APM just distills test sample's representation \textit{once}. APM possesses a unique property: it can learn using just this single representation and starts predicting semantically-aware features. APM demostrates potential applications beyond test-time-training: APM can scale up to a dataset of 2D images and yield semantic-clusterings in a single forward pass. APM also provides first empirical evidence towards validating GLOM's insight, i.e. input percept is a field. Therefore, APM helps us converge towards an implementation which can do \textit{both} interpolation and perception on a \textit{shared}-connectionist hardware. Our code is publicly available at this link: https://github.com/rajatmodi62/apm.
Understanding Communication Preferences of Information Workers in Engagement with Text-Based Conversational Agents
2024-10-27
Ananya Bhattacharjee, Jina Suh, Mahsa Ershadi, Shamsi T. Iqbal, Andrew D. Wilson, Javier Hernandez
Communication traits in text-based human-AI conversations play pivotal roles in shaping user experiences and perceptions of systems. With the advancement of large language models (LLMs), it is now feasible to analyze these traits at a more granular level. In this study, we explore the preferences of information workers regarding chatbot communication traits across seven applications. Participants were invited to participate in an interactive survey, which featured adjustable sliders, allowing them to adjust and express their preferences for five key communication traits: formality, personification, empathy, sociability, and humor. Our findings reveal distinct communication preferences across different applications; for instance, there was a preference for relatively high empathy in wellbeing contexts and relatively low personification in coding. Similarities in preferences were also noted between applications such as chatbots for customer service and scheduling. These insights offer crucial design guidelines for future chatbots, emphasizing the need for nuanced trait adjustments for each application.
A Rapidly Accreting Active Galactic Nucleus Hidden in a Dust-Obscured Galaxy at $z \sim 0.8$
2024-10-27
Nathan Cristello, Fan Zou, William N. Brandt, Zhibo Yu, Fabio Vito, Shifu Zhu
Dust-obscured galaxies (DOGs) containing central supermassive black holes (SMBHs) that are rapidly accreting (i.e., having high Eddington ratios, $\lambda_\mathrm{Edd}$) may represent a key phase closest to the peak of both the black-hole and galaxy growth in the coevolution framework for SMBHs and galaxies. In this work, we present a 68 ks XMM-Newton observation of the high-$\lambda_\mathrm{Edd}$ DOG J1324+4501 at \mbox{$z \sim 0.8$}, which was initially observed by Chandra. We analyze the XMM-Newton spectra jointly with archival Chandra spectra. In performing a detailed \mbox{X-ray} spectral analysis, we find that the source is intrinsically \mbox{X-ray} luminous with $\log (L_\mathrm{X}$/erg s$^{-1}) = 44.71^{+0.08}_{-0.12}$ and heavily obscured with $\log (N_\mathrm{H}/\mathrm{cm}^{-2}) = 23.43^{+0.09}_{-0.13}$. We further utilize UV-to-IR archival photometry to measure and fit the source's spectral energy distribution (SED) to estimate its host-galaxy properties. We present a supplementary comparison sample of 21 \mbox{X-ray} luminous DOGs from the XMM-SERVS survey with sufficient ($> 200$) $0.5-10$ keV counts to perform a similarly detailed X-ray spectral analysis. Of the X-ray luminous DOGs in our sample, we find that J1324+4501 is the most remarkable, possessing one of the highest \mbox{X-ray} luminosities, column densities, and star-formation rates. We demonstrate that J1324+4501 is in an extreme evolutionary stage where SMBH accretion and galaxy growth are at their peaks.
The Local Ultraviolet to Infrared Treasury I. Survey Overview of the Broadband Imaging
2024-10-27
Karoline M. Gilbert, Yumi Choi, Martha L. Boyer, Benjamin F. Williams, Daniel R. Weisz, Eric F. Bell, Julianne J. Dalcanton, Kristen B. W. McQuinn, Evan D. Skillman, Guglielmo Costa, Morgan Fouesneau, Léo Girardi, Steven R. Goldman, Karl D. Gordon, Puragra Guhathakurta, Maude Gull, Lea Hagen, Ky Huynh, Christina W. Lindberg, Paola Marigo, Claire E. Murray, Giada Pastorelli, Petia Yanchulova Merica-Jones
The Local Ultraviolet to Infrared Treasury (LUVIT) is a Hubble Space Telescope program that combines newly acquired data in the near ultraviolet (NUV), optical, and near infrared (NIR) with archival optical and NIR imaging to produce multiband panchromatic resolved stellar catalogs for 23 pointings in 22 low-mass, star-forming galaxies ranging in distance from the outskirts of the Local Group to ~3.8 Mpc. We describe the survey design, detail the LUVIT broadband filter observations and the archival datasets included in the LUVIT reductions, and summarize the simultaneous multiband data reduction steps. The spatial distributions and color-magnitude diagrams (CMDs) from the resulting stellar catalogs are presented for each target, from the NUV to the NIR. We demonstrate in which regions of the CMDs stars with NUV and optical, optical and NIR, and NUV through NIR detections reside. For each target, we use the results from artificial star tests to measure representative completeness, bias, and total photometric uncertainty as a function of magnitude in each broadband filter. We also assess which LUVIT targets have significant spatial variation in the fraction of stars recovered at a given magnitude. The panchromatic LUVIT stellar catalogs will provide a rich legacy dataset for a host of resolved stellar population studies.
Images of coral reefs provide invaluable information, which is essentially critical for surveying and monitoring the coral reef ecosystems. Robust and precise identification of coral reef regions within surveying imagery is paramount for assessing coral coverage, spatial distribution, and other statistical analyses. However, existing coral reef analytical approaches mainly focus on sparse points sampled from the whole imagery, which are highly subject to the sampling density and cannot accurately express the coral ambulance. Meanwhile, the analysis is both time-consuming and labor-intensive, and it is also limited to coral biologists. In this work, we propose CoralSCOP-LAT, an automatic and semi-automatic coral reef labeling and analysis tool, specially designed to segment coral reef regions (dense pixel masks) in coral reef images, significantly promoting analysis proficiency and accuracy. CoralSCOP-LAT leverages the advanced coral reef foundation model to accurately delineate coral regions, supporting dense coral reef analysis and reducing the dependency on manual annotation. The proposed CoralSCOP-LAT surpasses the existing tools by a large margin from analysis efficiency, accuracy, and flexibility. We perform comprehensive evaluations from various perspectives and the comparison demonstrates that CoralSCOP-LAT not only accelerates the coral reef analysis but also improves accuracy in coral segmentation and analysis. Our CoralSCOP-LAT, as the first dense coral reef analysis tool in the market, facilitates repeated large-scale coral reef monitoring analysis, contributing to more informed conservation efforts and sustainable management of coral reef ecosystems. Our tool will be available at https://coralscop.hkustvgd.com/.
A Deconfounding Framework for Human Behavior Prediction: Enhancing Robotic Systems in Dynamic Environments
2024-10-27
Wentao Gao, Cheng Zhou
Accurate prediction of human behavior is crucial for effective human-robot interaction (HRI) systems, especially in dynamic environments where real-time decisions are essential. This paper addresses the challenge of forecasting future human behavior using multivariate time series data from wearable sensors, which capture various aspects of human movement. The presence of hidden confounding factors in this data often leads to biased predictions, limiting the reliability of traditional models. To overcome this, we propose a robust predictive model that integrates deconfounding techniques with advanced time series prediction methods, enhancing the model's ability to isolate true causal relationships and improve prediction accuracy. Evaluation on real-world datasets demonstrates that our approach significantly outperforms traditional methods, providing a more reliable foundation for responsive and adaptive HRI systems.
2024-10-27
Vincent Hue, Thibault Cavalié, James A. Sinclair, Xi Zhang, Bilal Benmahi, Pablo Rodríguez-Ovalle, Rohini S. Giles, Tom S. Stallard, Rosie E. Johnson, Michel Dobrijevic, Thierry Fouchet, Thomas K. Greathouse, Denis C. Grodent, Ricardo Hueso, Olivier Mousis, Conor A. Nixon
Observations of the Jovian upper atmosphere at high latitudes in the UV, IR and mm/sub-mm all indicate that the chemical distributions and thermal structure are broadly influenced by auroral particle precipitations. Mid-IR and UV observations have shown that several light hydrocarbons (up to 6 carbon atoms) have altered abundances near Jupiter's main auroral ovals. Ion-neutral reactions influence the hydrocarbon chemistry, with light hydrocarbons produced in the upper stratosphere, and heavier hydrocarbons as well as aerosols produced in the lower stratosphere. One consequence of the magnetosphere-ionosphere coupling is the existence of ionospheric jets that propagate into the neutral middle stratosphere, likely acting as a dynamical barrier to the aurora-produced species. As the ionospheric jets and the background atmosphere do not co-rotate at the same rate, this creates a complex system where chemistry and dynamics are intertwined. The ion-neutral reactions produce species with a spatial distribution following the SIII longitude system in the upper stratosphere. As these species sediment down to the lower stratosphere, and because of the progressive dynamical decoupling between the ionospheric flows and the background atmosphere, the spatial distribution of the auroral-related species progressively follows a zonal distribution with increasing pressures that ultimately produces a system of polar and subpolar hazes that extends down to the bottom of the stratosphere. This paper reviews the most recent work addressing different aspects of this environment.
The significance of open data in higher education stems from the changing tendencies towards open science, and open research in higher education encourages new ways of making scientific inquiry more transparent, collaborative and accessible. This study focuses on the critical role of open data stewards in this transition, essential for managing and disseminating research data effectively in universities, while it also highlights the increasing demand for structured training and professional policies for data stewards in academic settings. Building upon this context, the paper investigates the essential skills and competences required for effective data stewardship in higher education institutions by elaborating on a critical literature review, coupled with practical engagement in open data stewardship at universities, provided insights into the roles and responsibilities of data stewards. In response to these identified needs, the paper proposes a structured training framework and comprehensive curriculum for data stewardship, a direct response to the gaps identified in the literature. It addresses five key competence categories for open data stewards, aligning them with current trends and essential skills and knowledge in the field. By advocating for a structured approach to data stewardship education, this work sets the foundation for improved data management in universities and serves as a critical step towards professionalizing the role of data stewards in higher education. The emphasis on the role of open data stewards is expected to advance data accessibility and sharing practices, fostering increased transparency, collaboration, and innovation in academic research. This approach contributes to the evolution of universities into open ecosystems, where there is free flow of data for global education and research advancement.
2024-10-27
Chunlei Zhang, Mun Kim, Yi-Hui Zhang, Yi-Pu Wang, Deepanshu Trivedi, Alex Krasnok, Jianbo Wang, Dustin Isleifson, Roy Roshko, Can-Ming Hu
Achieving oscillations with small dimensions, high power, high coherence, and low phase noise has been a long-standing goal in wave physics, driving innovations across classical electromagnetic theory and quantum physics. Key applications include electronic oscillators, lasers, and spin-torque oscillations. In recent decades, physicists have increasingly focused on harnessing passive oscillatory modes to manipulate these oscillations, leading to the development of diverse gain-loss coupled systems, including photon-photon, exciton-photon, photon-magnon, magnon-phonon, and magnon-magnon couplings. This review provides a comprehensive overview of these systems, exploring their fundamental physical structures, key experimental observations, and theoretical insights. By synthesizing insights from these studies, we propose future research directions to further advance the understanding and application of gain-loss coupled systems for quantum science and quantum technologies. (The field of gain-loss coupled systems is vast. The authors welcome suggestions and feedback from the community to continuously improve this review article until it is published).
Branch-and-bound algorithm for efficient reliability analysis of general coherent systems
2024-10-27
Ji-Eun Byun, Hyeuk Ryu, Daniel Straub
Branch and bound algorithms have been developed for reliability analysis of coherent systems. They exhibit a set of advantages; in particular, they can find a computationally efficient representation of a system failure or survival event, which can be re-used when the input probability distributions change over time or when new data is available. However, existing branch-and-bound algorithms can handle only a limited set of system performance functions, mostly network connectivity and maximum flow. Furthermore, they run redundant analyses on component vector states whose system state can be inferred from previous analysis results. This study addresses these limitations by proposing branch and bound for reliability analysis of general coherent systems} (BRC) algorithm: an algorithm that automatically finds minimal representations of failure/survival events of general coherent systems. Computational efficiency is attained by dynamically inferring importance of component events from hitherto obtained results. We demonstrate advantages of the BRC method as a real-time risk management tool by application to the Eastern Massachusetts highway benchmark network.
Social media platforms like Twitter, Facebook, and Instagram have facilitated the spread of misinformation, necessitating automated detection systems. This systematic review evaluates 36 studies that apply machine learning (ML) and deep learning (DL) models to detect fake news, spam, and fake accounts on social media. Using the Prediction model Risk Of Bias ASsessment Tool (PROBAST), the review identified key biases across the ML lifecycle: selection bias due to non-representative sampling, inadequate handling of class imbalance, insufficient linguistic preprocessing (e.g., negations), and inconsistent hyperparameter tuning. Although models such as Support Vector Machines (SVM), Random Forests, and Long Short-Term Memory (LSTM) networks showed strong potential, over-reliance on accuracy as an evaluation metric in imbalanced data settings was a common flaw. The review highlights the need for improved data preprocessing (e.g., resampling techniques), consistent hyperparameter tuning, and the use of appropriate metrics like precision, recall, F1 score, and AUROC. Addressing these limitations can lead to more reliable and generalizable ML/DL models for detecting deceptive content, ultimately contributing to the reduction of misinformation on social media.
Classification under strategic adversary manipulation using pessimistic bilevel optimisation
2024-10-26
David Benfield, Stefano Coniglio, Martin Kunc, Phan Tu Vuong, Alain Zemkoho
Adversarial machine learning concerns situations in which learners face attacks from active adversaries. Such scenarios arise in applications such as spam email filtering, malware detection and fake-image generation, where security methods must be actively updated to keep up with the ever improving generation of malicious data.We model these interactions between the learner and the adversary as a game and formulate the problem as a pessimistic bilevel optimisation problem with the learner taking the role of the leader. The adversary, modelled as a stochastic data generator, takes the role of the follower, generating data in response to the classifier. While existing models rely on the assumption that the adversary will choose the least costly solution leading to a convex lower-level problem with a unique solution, we present a novel model and solution method which do not make such assumptions. We compare these to the existing approach and see significant improvements in performance suggesting that relaxing these assumptions leads to a more realistic model.
Proactive Fraud Defense: Machine Learning's Evolving Role in Protecting Against Online Fraud
2024-10-26
Md Kamrul Hasan Chy
As online fraud becomes more sophisticated and pervasive, traditional fraud detection methods are struggling to keep pace with the evolving tactics employed by fraudsters. This paper explores the transformative role of machine learning in addressing these challenges by offering more advanced, scalable, and adaptable solutions for fraud detection and prevention. By analyzing key models such as Random Forest, Neural Networks, and Gradient Boosting, this paper highlights the strengths of machine learning in processing vast datasets, identifying intricate fraud patterns, and providing real-time predictions that enable a proactive approach to fraud prevention. Unlike rule-based systems that react after fraud has occurred, machine learning models continuously learn from new data, adapting to emerging fraud schemes and reducing false positives, which ultimately minimizes financial losses. This research emphasizes the potential of machine learning to revolutionize fraud detection frameworks by making them more dynamic, efficient, and capable of handling the growing complexity of fraud across various industries. Future developments in machine learning, including deep learning and hybrid models, are expected to further enhance the predictive accuracy and applicability of these systems, ensuring that organizations remain resilient in the face of new and emerging fraud tactics.
This survey offers a comprehensive overview of Large Language Models (LLMs) designed for Arabic language and its dialects. It covers key architectures, including encoder-only, decoder-only, and encoder-decoder models, along with the datasets used for pre-training, spanning Classical Arabic, Modern Standard Arabic, and Dialectal Arabic. The study also explores monolingual, bilingual, and multilingual LLMs, analyzing their architectures and performance across downstream tasks, such as sentiment analysis, named entity recognition, and question answering. Furthermore, it assesses the openness of Arabic LLMs based on factors, such as source code availability, training data, model weights, and documentation. The survey highlights the need for more diverse dialectal datasets and attributes the importance of openness for research reproducibility and transparency. It concludes by identifying key challenges and opportunities for future research and stressing the need for more inclusive and representative models.
Photon-Counting CT in Cancer Radiotherapy: Technological Advances and Clinical Benefits
2024-10-26
Keyur D. Shah, Jun Zhou, Justin Roper, Anees Dhabaan, Hania Al-Hallaq, Amir Pourmorteza, Xiaofeng Yang
Photon-counting computed tomography (PCCT) marks a significant advancement over conventional energy-integrating detector (EID) CT systems. This review highlights PCCT's superior spatial and contrast resolution, reduced radiation dose, and multi-energy imaging capabilities, which address key challenges in radiotherapy, such as accurate tumor delineation, precise dose calculation, and treatment response monitoring. PCCT's improved anatomical clarity enhances tumor targeting while minimizing damage to surrounding healthy tissues. Additionally, metal artifact reduction (MAR) and quantitative imaging capabilities optimize workflows, enabling adaptive radiotherapy and radiomics-driven personalized treatment. Emerging clinical applications in brachytherapy and radiopharmaceutical therapy (RPT) show promising outcomes, although challenges like high costs and limited software integration remain. With advancements in artificial intelligence (AI) and dedicated radiotherapy packages, PCCT is poised to transform precision, safety, and efficacy in cancer radiotherapy, marking it as a pivotal technology for future clinical practice.
2024-10-26
Muhammad Zubair Irshad, Mauro Comi, Yen-Chen Lin, Nick Heppert, Abhinav Valada, Rares Ambrus, Zsolt Kira, Jonathan Tremblay
Neural Fields have emerged as a transformative approach for 3D scene representation in computer vision and robotics, enabling accurate inference of geometry, 3D semantics, and dynamics from posed 2D data. Leveraging differentiable rendering, Neural Fields encompass both continuous implicit and explicit neural representations enabling high-fidelity 3D reconstruction, integration of multi-modal sensor data, and generation of novel viewpoints. This survey explores their applications in robotics, emphasizing their potential to enhance perception, planning, and control. Their compactness, memory efficiency, and differentiability, along with seamless integration with foundation and generative models, make them ideal for real-time applications, improving robot adaptability and decision-making. This paper provides a thorough review of Neural Fields in robotics, categorizing applications across various domains and evaluating their strengths and limitations, based on over 200 papers. First, we present four key Neural Fields frameworks: Occupancy Networks, Signed Distance Fields, Neural Radiance Fields, and Gaussian Splatting. Second, we detail Neural Fields' applications in five major robotics domains: pose estimation, manipulation, navigation, physics, and autonomous driving, highlighting key works and discussing takeaways and open challenges. Finally, we outline the current limitations of Neural Fields in robotics and propose promising directions for future research. Project page: https://robonerf.github.io
Improving Galaxy Cluster Selection with the Outskirt Stellar Mass of Galaxies
2024-10-26
Matthew Kwiecien, Tesla Jeltema, Alexie Leauthaud, Song Huang, Eli Rykoff, Sven Heydenreich, Johannes Lange, Spencer Everett, Conghao Zhou, Paige Kelly, Yuanyuan Zhang, Tae-Hyeon Shin, Jesse Golden-Marx, J. L. Marshall, M. Aguena, S. S. Allam, S. Bocquet, D. Brooks, A. Carnero Rosell, J. Carretero, L. N. da Costa, M. E. S. Pereira, T. M. Davis, J. De Vicente, P. Doel, I. Ferrero, B. Flaugher, J. Frieman, J. García-Bellido, M. Gatti, E. Gaztanaga, G. Giannini, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, S. Lee, R. Miquel, A. Pieres, A. A. Plazas Malagón, A. K. Romer, S. Samuroff, E. Sanchez, B. Santiago, I. Sevilla-Noarbe, M. Smith, E. Suchyta, M. E. C. Swanson, G. Tarle, D. L. Tucker, V. Vikram, N. Weaverdyck, P. Wiseman
The number density and redshift evolution of optically selected galaxy clusters offer an independent measurement of the amplitude of matter fluctuations, $S_8$. However, recent results have shown that clusters chosen by the redMaPPer algorithm show richness-dependent biases that affect the weak lensing signals and number densities of clusters, increasing uncertainty in the cluster mass calibration and reducing their constraining power. In this work, we evaluate an alternative cluster proxy, outskirt stellar mass, $M_{\textrm{out}}$, defined as the total stellar mass within a $[50,100]$ kpc envelope centered on a massive galaxy. This proxy exhibits scatter comparable to redMaPPer richness, $\lambda$, but is less likely to be subject to projection effects. We compare the Dark Energy Survey Year 3 redMaPPer cluster catalog with a $M_{\textrm{out}}$ selected cluster sample from the Hyper-Suprime Camera survey. We use weak lensing measurements to quantify and compare the scatter of $M_{\textrm{out}}$ and $\lambda$ with halo mass. Our results show $M_{\textrm{out}}$ has a scatter consistent with $\lambda$, with a similar halo mass dependence, and that both proxies contain unique information about the underlying halo mass. We find $\lambda$-selected samples introduce features into the measured $\Delta \Sigma$ signal that are not well fit by a log-normal scatter only model, absent in $M_{\textrm{out}}$ selected samples. Our findings suggest that $M_{\textrm{out}}$ offers an alternative for cluster selection with more easily calibrated selection biases, at least at the generally lower richnesses probed here. Combining both proxies may yield a mass proxy with a lower scatter and more tractable selection biases, enabling the use of lower mass clusters in cosmology. Finally, we find the scatter and slope in the $\lambda-M_{\textrm{out}}$ scaling relation to be $0.49 \pm 0.02$ and $0.38 \pm 0.09$.
Generative AI in Health Economics and Outcomes Research: A Taxonomy of Key Definitions and Emerging Applications, an ISPOR Working Group Report
2024-10-26
Rachael Fleurence, Xiaoyan Wang, Jiang Bian, Mitchell K. Higashi, Turgay Ayer, Hua Xu, Dalia Dawoud, Jagpreet Chhatwal
Objective: This article offers a taxonomy of generative artificial intelligence (AI) for health economics and outcomes research (HEOR), explores its emerging applications, and outlines methods to enhance the accuracy and reliability of AI-generated outputs. Methods: The review defines foundational generative AI concepts and highlights current HEOR applications, including systematic literature reviews, health economic modeling, real-world evidence generation, and dossier development. Approaches such as prompt engineering (zero-shot, few-shot, chain-of-thought, persona pattern prompting), retrieval-augmented generation, model fine-tuning, and the use of domain-specific models are introduced to improve AI accuracy and reliability. Results: Generative AI shows significant potential in HEOR, enhancing efficiency, productivity, and offering novel solutions to complex challenges. Foundation models are promising in automating complex tasks, though challenges remain in scientific reliability, bias, interpretability, and workflow integration. The article discusses strategies to improve the accuracy of these AI tools. Conclusion: Generative AI could transform HEOR by increasing efficiency and accuracy across various applications. However, its full potential can only be realized by building HEOR expertise and addressing the limitations of current AI technologies. As AI evolves, ongoing research and innovation will shape its future role in the field.
In recent years, Large Language Models (LLMs) have become fundamental to a broad spectrum of artificial intelligence applications. As the use of LLMs expands, precisely estimating the uncertainty in their predictions has become crucial. Current methods often struggle to accurately identify, measure, and address the true uncertainty, with many focusing primarily on estimating model confidence. This discrepancy is largely due to an incomplete understanding of where, when, and how uncertainties are injected into models. This paper introduces a comprehensive framework specifically designed to identify and understand the types and sources of uncertainty, aligned with the unique characteristics of LLMs. Our framework enhances the understanding of the diverse landscape of uncertainties by systematically categorizing and defining each type, establishing a solid foundation for developing targeted methods that can precisely quantify these uncertainties. We also provide a detailed introduction to key related concepts and examine the limitations of current methods in mission-critical and safety-sensitive applications. The paper concludes with a perspective on future directions aimed at enhancing the reliability and practical adoption of these methods in real-world scenarios.
LIGHTS. The extended point spread functions of the Large Binocular Cameras at the LBT
2024-10-26
Nafise Sedighi, Zahra Sharbaf, Ignacio Trujillo, Sepideh Eskandarlou, Giulia Golini, Raúl Infante-Sainz, Samane Raji, Dennis Zaritsky, Pedram Ashofteh Ardakani, Nushkia Chamba, Zahra Hosseini Shahisavandi, Richard Donnerstein, Mauro D'Onofrio, Garreth Martin, Mireia Montes, Javier Román
With the arrival of the next generation of ultra-deep optical imaging surveys reaching $\mu_V$$\sim$30 mag/arcsec$^2$ (3$\sigma$; 10"$\times$10"), the removal of scattered light due to the point spread function (PSF) effect remains a critical step for the scientific exploitation of the low surface brightness information contained in these data. Because virtually all pixels in the ground-based images are affected by an unwanted screen of light with a brightness greater than $\mu_V$$\sim$29 mag/arcsec$^2$, the characterization of the extended PSF (R$>$5 arcmin) is mandatory. We describe the procedure used to construct the extended PSFs of the LIGHTS survey in the g- and r-band images taken with the Large Binocular Cameras (LBCs) of the Large Binocular Telescope (LBT). We produce PSFs with a radial extension of 7.5 arcmins. These are later extended to 30 arcmins following an empirically motivated power-law extrapolation of their behaviour in their outermost regions. As an example of the application of our methodology, we subtract the scattered light around the galaxy NGC3198. The result of this subtraction clearly shows the outermost parts of the galaxy's disc, which have been obscured by the influence of nearby bright stars. Following a reproducible science philosophy, we make all the PSF models and the scripts used to do the analysis of this paper publicly available.
Magnet-superconductor hybrid quantum systems: a materials platform for topological superconductivity
2024-10-26
Roberto Lo Conte, Jens Wiebe, Stephan Rachel, Dirk K. Morr, Roland Wiesendanger
Magnet-superconductor hybrid (MSH) systems have recently emerged as one of the most significant developments in condensed matter physics. This has generated, in the last decade, a steadily rising interest in the understanding of their unique properties. They have been proposed as one of the most promising platforms for the establishment of topological superconductivity, which holds high potential for application in future quantum information technologies. Scanning tunneling microscopy (STM) and spectroscopy (STS) plays a crucial role in the race to unveil the fundamental origin of the unique properties of MSH systems, with the aim to discover new hybrid quantum materials capable of hosting topologically non-trivial unconventional superconducting phases. In particular, the combination of STM studies with tight-binding model calculations have represented, so far, the most successful approach to unveil and explain the emergent electronic properties of MSHs. The scope of this review is to offer a broad perspective on the field of MSHs from an atomic-level investigation point-of-view. The focus is on discussing the link between the magnetic ground state hosted by the hybrid system and the corresponding emergent superconducting phase. This is done for MSHs with both one-dimensional (atomic chains) and two-dimensional (atomic lattices and thin films) magnetic systems proximitized to conventional s-wave superconductors. We present a systematic categorization of the experimentally investigated systems with respect to defined experimentally accessible criteria to verify or falsify the presence of topological superconductivity and Majorana edge modes. Given the vast number of publications on the topic, we limit ourselves to discuss works which are most relevant to the search for topological superconductivity.
2024-10-26
Yingjun Du, Gaowen Liu, Yuzhang Shang, Yuguang Yao, Ramana Kompella, Cees G. M. Snoek
Foundation models enable prompt-based classifiers for zero-shot and few-shot learning. Nonetheless, the conventional method of employing fixed prompts suffers from distributional shifts that negatively impact generalizability to unseen samples. This paper introduces prompt diffusion, which uses a diffusion model to gradually refine the prompts to obtain a customized prompt for each sample. Specifically, we first optimize a collection of prompts to obtain over-fitted prompts per sample. Then, we propose a prompt diffusion model within the prompt space, enabling the training of a generative transition process from a random prompt to its overfitted prompt. As we cannot access the label of a test image during inference, our model gradually generates customized prompts solely from random prompts using our trained, prompt diffusion. Our prompt diffusion is generic, flexible, and modality-agnostic, making it a simple plug-and-play module seamlessly embedded into existing prompt learning methods for textual, visual, or multi-modal prompt learning. Our diffusion model uses a fast ODE-based sampling strategy to optimize test sample prompts in just five steps, offering a good trade-off between performance improvement and computational efficiency. For all prompt learning methods tested, adding prompt diffusion yields more robust results for base-to-new generalization, cross-dataset generalization, and domain generalization in classification tasks tested over 15 diverse datasets.
Causal Abstraction in Model Interpretability: A Compact Survey
2024-10-26
Yihao Zhang
The pursuit of interpretable artificial intelligence has led to significant advancements in the development of methods that aim to explain the decision-making processes of complex models, such as deep learning systems. Among these methods, causal abstraction stands out as a theoretical framework that provides a principled approach to understanding and explaining the causal mechanisms underlying model behavior. This survey paper delves into the realm of causal abstraction, examining its theoretical foundations, practical applications, and implications for the field of model interpretability.
Simulations on the collision between debris stream and outer dusty torus: a possible channel for forming fast-rise and long-delayed radio outburst in tidal disruption events
The geometrically thick dusty torus structure is believed to exist in the nuclear region of galaxies (especially in active galactic nuclei, AGNs). The debris stream from a tidal disruption event (TDE) will possibly collide with the dusty torus and produce a transient flare. We perform three-dimensional hydrodynamic simulations to model the dynamical evolution of the interaction between unbound debris and dusty torus. During the continuous interaction, the shocked material will be spilled out from the interaction region and form an outflow. We calculate the temporal evolution of synchrotron emission by assuming that the shock accelerates a fraction of electrons in the outflow into a non-thermal distribution. We find that radio emission from the debris-torus collision generates a steep-rise and slow-decline radio light curve due to the sharp edge and dense gas of dusty torus, where the radio outburst delays the main optical/X-ray outburst by several years or even several tens of years. We apply our model to a TDE that happened in a narrow-line Seyfert I (PS16dtm), where both the radio spectrum and the light curve can be roughly reproduced. Future high-sensitivity, wide-field-of-view radio surveys have the opportunity to detect more such radio flares.
Competing few-body correlations in ultracold Fermi polarons
2024-10-26
Ruijin Liu, Xiaoling Cui
Polaron, a typical quasi-particle that describes a single impurity dressed with surrounding environment, serves as an ideal platform for bridging few- and many-body physics. In particular, different few-body correlations can compete with each other and lead to many intriguing phenomena. In this work, we review the recent progresses made in understanding few-body correlation effects in attractive Fermi polarons of ultracold gases. By adopting a unified variational ansatz that incorporates different few-body correlations in a single framework, we will discuss their competing effects in Fermi polarons when the impurity and majority fermions have the same or different masses. For the equal-mass case, we review the nature of polaron-molecule transition that is driven by two-body correlations, and especially highlight the finite momentum character and huge degeneracy of molecule states. For the mass-imbalanced case, we focus on the smooth crossover between polaron and various dressed clusters that originate from high-order correlations. These competing few-body correlations reviewed in Fermi polarons suggest a variety of exotic new phases in the corresponding many-body system of Fermi-Fermi mixtures.
COSMIC: A Galaxy Cluster Finding Algorithm Using Machine Learning
2024-10-26
Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia
Building a comprehensive catalog of galaxy clusters is a fundamental task for the studies on the structure formation and galaxy evolution. In this paper, we present COSMIC (Cluster Optical Search using Machine Intelligence in Catalogs), an algorithm utilizing machine learning techniques to efficiently detect galaxy clusters. COSMIC involves two steps, including the identification of the brightest cluster galaxies and the estimation of the cluster richness. We train our models on the galaxy data from the Sloan Digital Sky Survey and WHL galaxy cluster catalog. Validated to a test data in the region of northern Galactic cap, COSMIC algorithm demonstrates a high completeness when cross-matching with previous cluster catalogs. Richness comparison with previous optical and X-ray measurements also demonstrated a tight correlation. Our methodology showcases robust performance in galaxy cluster detection and holds promising prospects for applications in upcoming large-scale surveys.
A Time-Dependent Ginzburg-Landau Framework for Sample-Specific Simulation of Superconductors for SRF Applications
2024-10-26
Aiden V. Harbick, Mark K. Transtrum
Modern superconducting radio frequency (SRF) applications require precise control of a wide range of material properties, from microscopic material parameters to mesoscopic/macroscopic surface structures. Mesoscopic simulation of superconductors has proven itself to be a powerful tool in SRF development, connecting microscopic properties to the mesoscopic structures of the material. We outline a Time-Dependent Ginzburg-Landau (TDGL) framework which combines both experimental measurements and theoretical estimations of the properties of realistic sample materials into spatially varying TDGL parameters. We also derive a way to estimate SRF quality factors from TDGL solutions. We demonstrate an application of this framework by modeling Sn-deficient islands in Nb$_3$Sn samples. We calculate the dissipation due to interactions of the superconducting order parameter and electromagnetic fields with these defects and estimate the impact to SRF cavity quality factor.
Neural encoding of artificial neural networks (ANNs) links their computational representations to brain responses, offering insights into how the brain processes information. Current studies mostly use linear encoding models for clarity, even though brain responses are often nonlinear. This has sparked interest in developing nonlinear encoding models that are still interpretable. To address this problem, we propose LinBridge, a learnable and flexible framework based on Jacobian analysis for interpreting nonlinear encoding models. LinBridge posits that the nonlinear mapping between ANN representations and neural responses can be factorized into a linear inherent component that approximates the complex nonlinear relationship, and a mapping bias that captures sample-selective nonlinearity. The Jacobian matrix, which reflects output change rates relative to input, enables the analysis of sample-selective mapping in nonlinear models. LinBridge employs a self-supervised learning strategy to extract both the linear inherent component and nonlinear mapping biases from the Jacobian matrices of the test set, allowing it to adapt effectively to various nonlinear encoding models. We validate the LinBridge framework in the scenario of neural visual encoding, using computational visual representations from CLIP-ViT to predict brain activity recorded via functional magnetic resonance imaging (fMRI). Our experimental results demonstrate that: 1) the linear inherent component extracted by LinBridge accurately reflects the complex mappings of nonlinear neural encoding models; 2) the sample-selective mapping bias elucidates the variability of nonlinearity across different levels of the visual processing hierarchy. This study presents a novel tool for interpreting nonlinear neural encoding models and offers fresh evidence about hierarchical nonlinearity distribution in the visual cortex.
Studying Morphological Variation: Exploring the Shape Space in Evolutionary Anthropology
2024-10-26
Shira Faigenbaum-Golovin, Ingrid Daubechies
We present results of a long-term team collaboration of mathematicians and biologists. We focus on building a mathematical framework for the shape space constituted by a collection of homologous bones or teeth from many species. The biological application is to quantitative morphological understanding of the evolutionary history of primates in particular, and mammals more generally. Similar to the practice of biologists, we leverage the power of the whole collection for results that are more robust than can be obtained by only pairwise comparisons, using tools from differential geometry and machine learning. This paper concentrates on the mathematical framework. We review methods for comparing anatomical surfaces, discuss the problem of registration and alignment, and address the computation of different distances. Next, we cover broader questions related to cross-dataset landmark selection, shape segmentation, and shape classification analysis. This paper summarizes the work of many team members other than the authors; in this paper that unites (for the first time) all their results in one joint context, space restrictions prevent a full description of the mathematical details, which are thoroughly covered in the original articles. Although our application is to the study of anatomical surfaces, we believe our approach has much wider applicability.
Training the Untrainable: Introducing Inductive Bias via Representational Alignment
2024-10-26
Vighnesh Subramaniam, David Mayo, Colin Conwell, Tomaso Poggio, Boris Katz, Brian Cheung, Andrei Barbu
We demonstrate that architectures which traditionally are considered to be ill-suited for a task can be trained using inductive biases from another architecture. Networks are considered untrainable when they overfit, underfit, or converge to poor results even when tuning their hyperparameters. For example, plain fully connected networks overfit on object recognition while deep convolutional networks without residual connections underfit. The traditional answer is to change the architecture to impose some inductive bias, although what that bias is remains unknown. We introduce guidance, where a guide network guides a target network using a neural distance function. The target is optimized to perform well and to match its internal representations, layer-by-layer, to those of the guide; the guide is unchanged. If the guide is trained, this transfers over part of the architectural prior and knowledge of the guide to the target. If the guide is untrained, this transfers over only part of the architectural prior of the guide. In this manner, we can investigate what kinds of priors different architectures place on untrainable networks such as fully connected networks. We demonstrate that this method overcomes the immediate overfitting of fully connected networks on vision tasks, makes plain CNNs competitive to ResNets, closes much of the gap between plain vanilla RNNs and Transformers, and can even help Transformers learn tasks which RNNs can perform more easily. We also discover evidence that better initializations of fully connected networks likely exist to avoid overfitting. Our method provides a mathematical tool to investigate priors and architectures, and in the long term, may demystify the dark art of architecture creation, even perhaps turning architectures into a continuous optimizable parameter of the network.
Cross-Survey Image Transformation: Enhancing SDSS and DECaLS Images to Near-HSC Quality for Advanced Astronomical Analysis
2024-10-26
Zhijian Luo, Shaohua Zhang, Jianzhen Chen, Zhu Chen, Liping Fu, Hubing Xiao, Wei Du, Chenggang Shu
This study focuses on transforming galaxy images between astronomical surveys, specifically enhancing images from the Sloan Digital Sky Survey (SDSS) and the Dark Energy Camera Legacy Survey (DECaLS) to achieve quality comparable to the Hyper Suprime-Cam survey (HSC). We proposed a hybrid model called Pix2WGAN, which integrates the pix2pix framework with the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) to convert low-quality observational images into high-quality counterparts. Our model successfully transformed DECaLS images into pseudo-HSC images, yielding impressive results and significantly enhancing the identification of complex structures, such as galaxy spiral arms and tidal tails, which may have been overlooked in the original DECaLS images. Moreover, Pix2WGAN effectively addresses issues like artifacts, noise, and blurriness in both source and target images. In addition to the basic Pix2WGAN model, we further developed an advanced architecture called Cascaded Pix2WGAN, which incorporates a multi-stage training mechanism designed to bridge the quality gap between SDSS and HSC images, demonstrating similarly promising outcomes. We systematically assessed the similarity between the model-generated pseudo-HSC images and actual HSC images using various metrics, including Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM), along with perceptual metrics such as Learned Perceptual Image Patch Similarity (LPIPS) and Fr\'echet Inception Distance (FID). The results indicate that images transformed by our model outperform both the original SDSS and DECaLS images across nearly all evaluation metrics. Our research is expected to provide significant technical support for astronomical data analysis, cross-survey image integration, and high-precision astrometry.
Weak Lensing of the Lyman-alpha Forest from Simulated DESI Data
2024-10-26
Patrick Shaw, Rupert A. C. Croft, R. Benton Metcalf
The apparent angular positions of quasars are deflected on the sky by the gravitational field sourced by foreground matter. This weak lensing effect is measurable through the distortions it introduces in the lensed quasar spectra. Discrepancies in the statistics of the Lyman-$\alpha$ forest spectral absorption features can be used to reconstruct the foreground lensing potential. We extend the study of this method of Lyman-$\alpha$ forest weak gravitational lensing to lower angular forest spectrum source densities than previous work. We evaluate the performance of the Lyman-$\alpha$ lensing estimator of Metcalf et al. (2020) on mock data based on the angular forest source density ($50$ per square degree) and volume ($\sim$700,000 spectra total) of the DESI survey. We simulate the foreground galaxy distribution and lensing potentials with redshift evolution approximated by N-body simulation and simulate Gaussian-random Lyman-$\alpha$ forests to produce mock data for the entire DESI footprint. By correlating the foreground galaxy distribution with the potential reconstructed by the estimator, we find that a weak lensing detection with signal to noise of $\sim4$ will be possible with the full DESI data. We show that spectral surveys with low density and high volume are promising candidates for forest weak lensing in addition to the high resolution data that have been considered in previous work. We present forecasts for future spectral surveys and show that with larger datasets a detection with signal to noise $>10$ will be possible.
Cosmology with voids from the Nancy Grace Roman Space Telescope
2024-10-25
Giovanni Verza, Giulia Degni, Alice Pisani, Nico Hamaus, Elena Massara, Andrew Benson, Stéphanie Escoffier, Yun Wang, Zhongxu Zhai, Olivier Doré
We provide an accurate forecast of the expected constraining power from the main void statistics -- the void size function and the void-galaxy cross-correlation function -- to be measured by the Roman reference High Latitude Spectroscopic Survey from the Nancy Grace Roman Space Telescope. Relying on a realistic galaxy mock lightcone, covering 2000 square degrees, we find more than $8\times 10^4 $ voids and explore their constraining power in the framework of three different cosmological models: $\Lambda$CDM, $w$CDM, and $w_0 w_{\rm a}$CDM. This work confirms the strong complementarity of different void statistics and showcases the constraining power to be expected from Roman voids thanks to the combination of its high tracer density and large observed volume.
Integrating LoRaWAN with Mobile Ad-hoc Networks for Enhanced Campus Communication
2024-10-25
Ramakant Kumar
The integration of Long Range Wide Area Network (LoRaWAN) with Mobile Ad-hoc Networks (MANETs) presents a promising solution for enhancing communication networks within campus environments. This paper explores the unique advantages of combining these two technologies, including scalability, energy efficiency, flexibility, and support for diverse applications. LoRaWAN low power consumption and extended range capabilities address the challenges of traditional communication methods, enabling reliable data transmission across various campus scenarios, such as emergency alerts, event coordination, and real-time monitoring. We also identify key challenges faced in this integrated architecture, including signal interference, data packet collisions, and energy management. By providing a comprehensive survey of existing techniques and solutions categorized by the network protocol stack layers, this study aims to inform future research and development efforts in creating robust, energy efficient communication systems tailored for modern educational institutions. Ultimately, the findings highlight the potential of LoRaWAN MANET architectures to transform campus communication into a more reliable, adaptable, and cost effective framework.
Estimating the Masses of Supercluster-Scale Filaments from Redshift Dispersions
2024-10-25
Mary Crone Odekon, Trevor W. Viscardi, Jake Rabinowitz, Brandon Young
We present a strategy for estimating the mass per unit length along supercluster-scale filaments that are oriented across the sky, based on mock redshift surveys of 264 filaments from the Millennium simulation. In our fiducial scenario, we place each simulated filament at a distance of 300 Mpc, perpendicular to the line of sight, and calculate the redshift dispersion using galaxies with magnitudes r<19.5. Some regions are dynamically complicated in ways that interfere with finding a simple relationship between dispersion sigma and linear mass density mu. However, by examining individual overlapping segments along the filaments, we find a relationship that allows us to successfully predict log(mu) from log(sigma) with a scatter of about +/- 0.20 dex, for ~ 70% of the regions along filaments. This relationship is robust to changes in the distance to the filament if the physical segment length and the absolute magnitude for galaxy selection are held constant. The relationship between redshift dispersion and mass is similar to that obtained for a simple analytical model where filaments are dynamically relaxed, and we examine the possibility that the galaxies are indeed relaxed within the gravitational potential of the filament. We find that this is not the case; galaxy dynamics are strongly effected by infall to the filament and by orbits within groups and clusters
Amoeba: An AGN Model of Optical Emissions Beyond steady-state Accretion discs
2024-10-25
Henry Best, Matthew O'Dowd, Joshua Fagin, James H. H. Chan, Bridget Ierace
Active Galactic Nuclei (AGN) are objects located in the heart of galaxies which emit powerful and complex radiation across the electromagnetic spectrum. Understanding AGN has become a topic of interest due to their importance in galactic evolution and their ability to act as a probe to the distant Universe. Within the next few years, wide-field surveys such as the Legacy Survey of Space and Time (LSST) at the Rubin Vera Observatory are expected to increase the number of known AGN to $\mathcal{O} (10^{7})$ and the number of strongly lensed AGN to $\mathcal{O} (10^{4})$. In this paper we introduce \texttt{Amoeba}: an AGN Model of Optical Emission Beyond steady-state Accretion discs. The goal of \texttt{Amoeba} is to provide a modular and flexible modelling environment for AGN, in which all components can interact with each other. Through this work we describe the framework for major AGN components to vary self-consistently and keep flux distributions to connect these components to spatial dependent processes. We model properties beyond traditional single-component models, such as the reverberation of the corona's bending power law power spectrum through the accretion disc and broad line region (BLR). We simulate obscuration by the dusty torus and differential magnification of the disc and BLR due to microlensing. These features are joined together to create some of the most realistic light curve simulations to date. \texttt{Amoeba} takes a step forward in AGN modelling by joining the accretion disc, BLR, torus, intrinsic signal, and microlensing into a coherent model.
Modave Lecture notes: Introduction to Exceptional Field Theory
2024-10-25
Colin Sterckx
These notes are based on lectures given at the XIX Modave School on Mathematical Physics and present an introduction to Exceptional Field Theory. We cover the standard Kaluza-Klein reductions on tori, with applications to supergravity. We review the supergravity action of type IIA/IIB and eleven-dimensional supergravities. We motivate the appearance of the hidden symmetry groups $\mathrm{E}_{d(d)}$ through the lens of string dualities. We explore the construction and properties of $\mathrm{E}_{7(7)}$-ExFT and review other $\mathrm{E}_{d(d)}$-ExFT for $3\leq d\leq 8$, as well as double field theories. These notes conclude with some applications of ExFT to consistent truncations.
Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina
2024-10-25
Yuan Gao, Dokyun Lee, Gordon Burtch, Sina Fazelpour
Recent studies suggest large language models (LLMs) can exhibit human-like reasoning, aligning with human behavior in economic experiments, surveys, and political discourse. This has led many to propose that LLMs can be used as surrogates for humans in social science research. However, LLMs differ fundamentally from humans, relying on probabilistic patterns, absent the embodied experiences or survival objectives that shape human cognition. We assess the reasoning depth of LLMs using the 11-20 money request game. Almost all advanced approaches fail to replicate human behavior distributions across many models, except in one case involving fine-tuning using a substantial amount of human behavior data. Causes of failure are diverse, relating to input language, roles, and safeguarding. These results caution against using LLMs to study human behaviors or as human surrogates.
Institutional Review Boards as Soft Governance Mechanisms of R&D: Governing the R&D of AI-based Medical Products
2024-10-25
Antoni Lorente
Risk-based approaches to governance bear an ambiguous stance regarding the Research and Development stages of AI, for they the possibility of explicit risks before they are posed by a given finalised product. In this context, Institutional Review Boards (IRBs) stand as unique governance mechanisms, capable of addressing the step from general research to concrete product development. However, IRBs face several challenges in governing AI-based medical products, including: (a) achieving consistency, (b) being exhaustive, (c) ensuring process transparency, and (d) reducing the existing capacity and knowledge asymmetry between different stakeholders. This article explores four governance levers that can be used to effect change, four governance entry-points throughout a product's lifecycle, and five different behaviours that IRBs should try to advance to ensure the effective governance of the R&D stages of AI-based medical projects. In doing so, IRBs can seize the unique opportunity they present to bring principles into practice, increase research quality, reduce governance costs, and bridge the knowledge gap between stakeholders.
Let $F$ and $G$ be generalized H\'enon maps. We show that the Kato surfaces associated to the germs of $F$ and $G$ near infinity are biholomorphic if and only if $F$ and $G$ are conjugate in $\text{Aut}(\mathbb{C}^2)$. This answers a question raised by Favre in X\'enelkis de H\'enon's survey. We also show that two generalized H\'enon maps are conjugate near infinity, if and only if they are affinely conjugate.
AgentForge: A Flexible Low-Code Platform for Reinforcement Learning Agent Design
2024-10-25
Francisco Erivaldo Fernandes Junior, Antti Oulasvirta
Developing a reinforcement learning (RL) agent often involves identifying effective values for a large number of parameters, covering the policy, reward function, environment, and the agent's internal architecture, such as parameters controlling how the peripheral vision and memory modules work. Critically, since these parameters are interrelated in complex ways, optimizing them can be viewed as a black box optimization problem, which is especially challenging for non-experts. Although existing optimization-as-a-service platforms (e.g., Vizier, Optuna) can handle such problems, they are impractical for RL systems, as users must manually map each parameter to different components, making the process cumbersome and error-prone. They also require deep understanding of the optimization process, limiting their application outside ML experts and restricting access for fields like cognitive science, which models human decision-making. To tackle these challenges, we present AgentForge, a flexible low-code framework to optimize any parameter set across an RL system. AgentForge allows the user to perform individual or joint optimization of parameter sets. An optimization problem can be defined in a few lines of code and handed to any of the interfaced optimizers. We evaluated its performance in a challenging vision-based RL problem. AgentForge enables practitioners to develop RL agents without requiring extensive coding or deep expertise in optimization.
The tunnel magnetoresistance (TMR) effect is one of the representative phenomena in spintronics. Ferromagnets, which have a net spin polarization, have been utilized for the TMR effect. Recently, by contrast, the TMR effect with antiferromagnets, which do not possess a macroscopic spin polarization, has been proposed, and also been observed in experiments. In this topical review, we discuss recent developments in the TMR effect, particularly focusing on the TMR effect with antiferromagnets. First, we review how the TMR effect can occur in antiferromagnetic tunnel junctions. The Julliere model, which has been conventionally utilized to grasp the TMR effect with ferromagnets, breaks down for the antiferromagnetic TMR effect. Instead, we see that the momentum dependent spin splitting explains the antiferromagnetic TMR effect. After that, we revisit the TMR effect from viewpoint of the local density of states (LDOS). We particularly focus on the LDOS inside the barrier, and show that the product of the LDOS will qualitatively capture the TMR effect not only in the ferromagnetic tunnel junctions but also in the ferrimagnetic and antiferromagnetic tunnel junctions. This method is expected to work usefully for designing magnetic tunnel junctions.
TRADE: Transfer of Distributions between External Conditions with Normalizing Flows
2024-10-25
Stefan Wahl, Armand Rousselot, Felix Draxler, Ullrich Köthe
Modeling distributions that depend on external control parameters is a common scenario in diverse applications like molecular simulations, where system properties like temperature affect molecular configurations. Despite the relevance of these applications, existing solutions are unsatisfactory as they require severely restricted model architectures or rely on backward training, which is prone to unstable training. We introduce TRADE, which overcomes these limitations by formulating the learning process as a boundary value problem. By initially training the model for a specific condition using either i.i.d. samples or backward KL training, we establish a boundary distribution. We then propagate this information across other conditions using the gradient of the unnormalized density with respect to the external parameter. This formulation, akin to the principles of physics-informed neural networks, allows us to efficiently learn parameter-dependent distributions without restrictive assumptions. Experimentally, we demonstrate that TRADE achieves excellent results in a wide range of applications, ranging from Bayesian inference and molecular simulations to physical lattice models.
The unabridged satellite luminosity function of Milky Way-like galaxies in $Λ$CDM: the contribution of "orphan" satellites
2024-10-25
Isabel Santos-Santos, Carlos Frenk, Julio Navarro, Shaun Cole, John Helly
We study the abundance, radial distribution, and orbits of luminous satellites in simulations of Milky Way-mass dark halos in the $\Lambda$CDM cosmology. We follow the evolution of a halo from the Aquarius project and the formation of satellites with the GALFORM semi-analytic model of galaxy formation, in which gas cools radiatively into halos before reionization and in halos that exceed a redshift-dependent ``critical'' virial mass after that. Subhalos are prone to disruption in the tidal field of the main halo, with the number of surviving self-bound subhalos increasing with resolution. Even in the highest resolution simulation (Aq-L1, with particle mass $m_{\rm p}\sim10^3\, M_\odot$), a substantial number of subhalos are disrupted but their galaxies may survive as ``orphans''. Whether or not a satellite becomes an orphan depends primarily on its time of infall. When orphans are included, the simulations yield a converged satellite stellar mass function across different resolution levels. The total number of luminous satellites is sensitive to the assumed redshift of reionization, but the shape of the satellite stellar mass function is robust, peaking at the stellar mass ($\sim 10^3\, M_\odot$) of a halo just above the critical threshold. Most orphans are found in the central regions of the main halo and make up roughly half of all satellites in Aq-L1. When orphans are taken into account there is no need to populate subhalos below the critical mass with satellites to fit the radial distribution of Milky Way satellites, as had been argued in recent work. Our model predicts that orphans dominate the ultra-faint population and that many more satellites with small apocentric radii should be detected in upcoming deep wide-field surveys.
Learned Reference-based Diffusion Sampling for multi-modal distributions
2024-10-25
Maxence Noble, Louis Grenioux, Marylou Gabrié, Alain Oliviero Durmus
Over the past few years, several approaches utilizing score-based diffusion have been proposed to sample from probability distributions, that is without having access to exact samples and relying solely on evaluations of unnormalized densities. The resulting samplers approximate the time-reversal of a noising diffusion process, bridging the target distribution to an easy-to-sample base distribution. In practice, the performance of these methods heavily depends on key hyperparameters that require ground truth samples to be accurately tuned. Our work aims to highlight and address this fundamental issue, focusing in particular on multi-modal distributions, which pose significant challenges for existing sampling methods. Building on existing approaches, we introduce Learned Reference-based Diffusion Sampler (LRDS), a methodology specifically designed to leverage prior knowledge on the location of the target modes in order to bypass the obstacle of hyperparameter tuning. LRDS proceeds in two steps by (i) learning a reference diffusion model on samples located in high-density space regions and tailored for multimodality, and (ii) using this reference model to foster the training of a diffusion-based sampler. We experimentally demonstrate that LRDS best exploits prior knowledge on the target distribution compared to competing algorithms on a variety of challenging distributions.
The LAMOST Spectroscopic Survey of Supergiants in M31 and M33
2024-10-25
Hao Wu, Yang Huang, Huawei Zhang, Haibo Yuan, Zhiying Huo, Cheng Liu
We present systematic identifications of supergiants of M31/M33 based on massive LAMOST spectroscopic survey. Radial velocities of nearly 5000 photometrically selected M31/M33 supergiant candidates have been properly derived from the qualified spectra released in LAMOST DR10. By comparing their radial velocities with those predicted from the rotation curves of M31, as well as utilizing {\it Gaia} astrometric measurements to exclude foreground contaminations, 199 supergiant members in M31, including 168 `Rank1' and 31 `Rank2', have been successfully identified. This sample contains 62 blue supergiants (BSGs, all `Rank1'), 134 yellow supergiants (YSGs, 103 `Rank1' and 31 `Rank2') and 3 red supergiants (RSGs, all `Rank1'). For M33, we identify 84 supergiant members (56 `Rank1' and 28 `Rank2'), which includes 28 BSGs (all `Rank1'), 53 YSGs (25 `Rank1' and 28 `Rank2') and 3 RSGs (all `Rank1'). So far, this is one of the largest supergiant sample of M31/M33 with full optical wavelength coverage (3700 \textless $\lambda$ \textless 9100 \AA). This sample is valuable for understanding the star formation and stellar evolution under different environments.
2024-10-25
Wanja Timm Schulze, Sebastian Schwalbe, Kai Trepte, Stefanie Gräfe
In current electronic structure research endeavors such as warm dense matter or machine learning applications, efficient development necessitates non-monolithic software, providing an extendable and flexible interface. The open-source idea offers the advantage of having a source code base that can be reviewed and modified by the community. However, practical implementations can often diverge significantly from their theoretical counterpart. Leveraging the efforts of recent theoretical formulations and the features of Python, we try to mitigate these problems. We present eminus, an education- and development-friendly electronic structure package designed for convenient and customizable workflows, yet built with intelligible and modular implementations.
Hot (non-equilibrium) electron relaxation: A review of the ultra-fast phenomena in metals and superconductors (PART I)
2024-10-25
Navinder Singh
The famous Two-Temperature Model (TTM) used extensively in the investigations of energy relaxation in photo-excited systems originated in the seminal work of M. I. Kaganov, I. M. Lifshitz, and L. V. Tanatarov (KLT) in 1957. The idea that with an ultra short laser pulse a temporal (transient) state of electrons in a metal can be created in which electrons after absorbing energy from the laser pulse heat up and their temperature becomes substantially greater than that of lattice was originated in the work of S. I. Anisimov, B. L. Kapeliovich, and T. L. Perel'man in 1974. The heated electron sub-system ("hot" electrons) loses its energy to phonon sub-system via electron-phonon scattering (relaxation) and thermodynamic equilibrium re-establishes over a time scale of a few pico-seconds (psecs) in metals. This field saw great developments in the 1980s and 1990s with the advent of femto-second (fsec) pump-probe spectroscopy. And from 2000 onwards focus shifted from non-equilibrium phenomena in simple metals to that in more complex systems including strongly correlated systems such as high Tc cuprate superconductors. In 1987, P. B. Allen re-visits the calculations of KLT and re-writes the electron-phonon heat transfer coefficient $\alpha$ in terms of a very important parameter in the theory of superconductivity. Year 2000 onwards, field saw the development of models that go beyond the original TTM. Very recently, field enters into atto-second domain. In this article, author attempts a rigorous and concise account of the entire development of this very fascinating area of research including a summary of the current research in this field.
Large Language Models (LLMs) and Text-To-Image (T2I) models have demonstrated the ability to generate compelling text and visual stories. However, their outputs are predominantly aligned with the sensibilities of the Global North, often resulting in an outsider's gaze on other cultures. As a result, non-Western communities have to put extra effort into generating culturally specific stories. To address this challenge, we developed a visual storytelling pipeline called KAHANI that generates culturally grounded visual stories for non-Western cultures. Our pipeline leverages off-the-shelf models GPT-4 Turbo and Stable Diffusion XL (SDXL). By using Chain of Thought (CoT) and T2I prompting techniques, we capture the cultural context from user's prompt and generate vivid descriptions of the characters and scene compositions. To evaluate the effectiveness of KAHANI, we conducted a comparative user study with ChatGPT-4 (with DALL-E3) in which participants from different regions of India compared the cultural relevance of stories generated by the two tools. Results from the qualitative and quantitative analysis performed on the user study showed that KAHANI was able to capture and incorporate more Culturally Specific Items (CSIs) compared to ChatGPT-4. In terms of both its cultural competence and visual story generation quality, our pipeline outperformed ChatGPT-4 in 27 out of the 36 comparisons.
Motion Planning for Robotics: A Review for Sampling-based Planners
2024-10-25
Liding Zhang, Kuanqi Cai, Zewei Sun, Zhenshan Bing, Chaoqun Wang, Luis Figueredo, Sami Haddadin, Alois Knoll
Recent advancements in robotics have transformed industries such as manufacturing, logistics, surgery, and planetary exploration. A key challenge is developing efficient motion planning algorithms that allow robots to navigate complex environments while avoiding collisions and optimizing metrics like path length, sweep area, execution time, and energy consumption. Among the available algorithms, sampling-based methods have gained the most traction in both research and industry due to their ability to handle complex environments, explore free space, and offer probabilistic completeness along with other formal guarantees. Despite their widespread application, significant challenges still remain. To advance future planning algorithms, it is essential to review the current state-of-the-art solutions and their limitations. In this context, this work aims to shed light on these challenges and assess the development and applicability of sampling-based methods. Furthermore, we aim to provide an in-depth analysis of the design and evaluation of ten of the most popular planners across various scenarios. Our findings highlight the strides made in sampling-based methods while underscoring persistent challenges. This work offers an overview of the important ongoing research in robotic motion planning.
Photometric Redshift Estimation for CSST Survey with LSTM Neural Networks
2024-10-25
Zhijian Luo, Yicheng Li, Junhao Lu, Zhu Chen, Liping Fu, Shaohua Zhang, Hubing Xiao, Wei Du, Yan Gong, Chenggang Shu, Wenwen Ma, Xianmin Meng, Xingchen Zhou, Zuhui Fan
Accurate estimation of photometric redshifts (photo-$z$s) is crucial for cosmological surveys. Various methods have been developed for this purpose, such as template fitting methods and machine learning techniques, each with its own applications, advantages, and limitations. In this study, we propose a new approach that utilizes a deep learning model based on Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) to predict photo-$z$. Unlike many existing machine learning models, our method requires only flux measurements from different observed filters as input. The model can automatically learn the complex relationships between the flux data across different wavelengths, eliminating the need for manually extracted or derived input features, thereby providing precise photo-$z$ estimates. The effectiveness of our proposed model is evaluated using simulated data from the Chinese Space Station Telescope (CSST) sourced from the Hubble Space Telescope Advanced Camera for Surveys (HST-ACS) and the COSMOS catalog, considering anticipated instrument effects of the future CSST. Results from experiments demonstrate that our LSTM model, compared to commonly used template fitting and machine learning approaches, requires minimal input parameters and achieves high precision in photo-$z$ estimation. For instance, when trained on the same dataset and provided only with photometric fluxes as input features, the proposed LSTM model yields one-third of the outliers $f_{out}$ observed with a Multi-Layer Perceptron Neural Network (MLP) model, while the normalized median absolute deviation $\rm \sigma_{NMAD}$ is only two-thirds that of the MLP model. This study presents a novel approach to accurately estimate photo-$z$s of galaxies using photometric data from large-scale survey projects.
We explore the joint weak lensing and galaxy clustering analysis from the photometric survey operated by the China Space Station Telescope (CSST), and study the strength of the cosmological constraints. We employ a high-resolution JiuTian-1G simulation to construct a partial-sky light cone to $z=3$ covering 100 deg$^2$, and obtain the CSST galaxy mock samples based on an improved semi-analytical model. We perform a multi-lens-plane algorithm to generate corresponding synthetic weak lensing maps and catalogs. Then we generate the mock data based on these catalogs considering the instrumental and observational effects of the CSST, and use the Markov Chain Monte Carlo (MCMC) method to perform the constraints. The covariance matrix includes non-Gaussian contributions and super-sample covariance terms, and the systematics from intrinsic alignments, galaxy bias, photometric redshift uncertainties, shear calibration, and non-linear effects are considered in the analysis. We find that, for the joint analysis of the CSST weak lensing and galaxy clustering surveys, the cosmological parameters can be constrained to a few percent or even less than one percent level. This indicates the CSST photometric survey is powerful for exploring the Universe.
Unified Cross-Modal Image Synthesis with Hierarchical Mixture of Product-of-Experts
2024-10-25
Reuben Dorent, Nazim Haouchine, Alexandra Golby, Sarah Frisken, Tina Kapur, William Wells
We propose a deep mixture of multimodal hierarchical variational auto-encoders called MMHVAE that synthesizes missing images from observed images in different modalities. MMHVAE's design focuses on tackling four challenges: (i) creating a complex latent representation of multimodal data to generate high-resolution images; (ii) encouraging the variational distributions to estimate the missing information needed for cross-modal image synthesis; (iii) learning to fuse multimodal information in the context of missing data; (iv) leveraging dataset-level information to handle incomplete data sets at training time. Extensive experiments are performed on the challenging problem of pre-operative brain multi-parametric magnetic resonance and intra-operative ultrasound imaging.
Assessing the Association between the Globular Cluster NGC 4147 and the Sagittarius Dwarf Galaxy
2024-10-25
YingHua Zhang, Jundan Nie, Hao Tian, Chao Liu
The potential association of the globular cluster (GC) NGC 4147 with the Sagittarius (Sgr) dwarf spheroidal galaxy has been proposed due to their comparable locations and radial velocities. However, there are still debates about this connection. In this study, we use data from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys to assess their association. We redetermine thefundamental parameters of NGC 4147 and find that the cluster is 11.0 Gyr old, has a metallicity of Z=0.0006, and is located 18.5 kpc from the Sun. We utilize the matched filter algorithm to identify extratidal structures in the surrounding sky of NGC 4147. The multiarmed tidal structures we find align more closely with the result of internal two-body relaxation processes within the cluster itself. The orientations of the dispersed tidal structures, the orbital direction of the cluster, and the mean orbital direction of Sgr do not show any apparent connection to each other. It seems to challenge the hypothesis of a common origin between the cluster and Sgr. To further investigate the association, we study the kinematics of NGC 4147 with the newly determined fundamental parameters. We find that the orbit, orbital energy, and angular momentum of NGC 4147 are not compatible with those of Sgr or its streams. This suggests that the cluster is not dynamically associated with Sgr. The morphology and dynamics of NGC 4147 are more consistent with it being a GC that formed with other origin rather than being accreted from the Sgr dwarf galaxy.
Extreme values of the mass distribution associated with $d$-quasi-copulas via linear programming
2024-10-25
Matej Belšak, Matjaž Omladič, Martin Vuk, Aljaž Zalar
The recent survey published in Fuzzy Sets and Systems nicknamed ``Hitchhiker's Guide'' has raised the rating of quasi-copula problems in the dependence modeling community in spite of the lack of statistical interpretation of quasi-copulas. Some of the open problems listed there were solved, and some conjectured one way or the other. This paper concentrates on the Open Problem 5 of this list concerning bounds on the volume of a $d$--variate quasi-copula. We disprove a recent conjecture published in the same journal on the lower bound of this volume. We also give evidence that the problem is much more difficult than suspected and provide hints about its final solution.
Stopping Set Analysis for Concatenated Polar Code Architectures
2024-10-25
Ziyuan Zhu, Paul H. Siegel
This paper investigates properties of concatenated polar codes and their potential applications. We start with reviewing previous work on stopping set analysis for conventional polar codes, which we extend in this paper to concatenated architectures. Specifically, we present a stopping set analysis for the factor graph of concatenated polar codes, deriving an upper bound on the size of the minimum stopping set. To achieve this bound, we propose new bounds on the size of the minimum stopping set for conventional polar code factor graphs. The tightness of these proposed bounds is investigated empirically and analytically. We show that, in some special cases, the exact value of the minimum stopping set can be determined with a time complexity of $O(N)$, where $N$ is the codeword length. The stopping set analysis motivates a novel construction method for concatenated polar codes. This method is used to design outer polar codes for two previously proposed concatenated polar code architectures: augmented polar codes and local-global polar codes. Simulation results demonstrate the advantage of the proposed codes over previously proposed constructions based on density evolution (DE).
A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation
2024-10-25
Kexin Zhang, Shuhan Liu, Song Wang, Weili Shi, Chen Chen, Pan Li, Sheng Li, Jundong Li, Kaize Ding
Distribution shifts on graphs -- the discrepancies in data distribution between training and employing a graph machine learning model -- are ubiquitous and often unavoidable in real-world scenarios. These shifts may severely deteriorate model performance, posing significant challenges for reliable graph machine learning. Consequently, there has been a surge in research on graph machine learning under distribution shifts, aiming to train models to achieve satisfactory performance on out-of-distribution (OOD) test data. In our survey, we provide an up-to-date and forward-looking review of deep graph learning under distribution shifts. Specifically, we cover three primary scenarios: graph OOD generalization, training-time graph OOD adaptation, and test-time graph OOD adaptation. We begin by formally formulating the problems and discussing various types of distribution shifts that can affect graph learning, such as covariate shifts and concept shifts. To provide a better understanding of the literature, we systematically categorize the existing models based on our proposed taxonomy and investigate the adopted techniques behind. We also summarize commonly used datasets in this research area to facilitate further investigation. Finally, we point out promising research directions and the corresponding challenges to encourage further study in this vital domain. Additionally, we provide a continuously updated reading list at https://github.com/kaize0409/Awesome-Graph-OOD.
Spatioformer: A Geo-encoded Transformer for Large-Scale Plant Species Richness Prediction
2024-10-25
Yiqing Guo, Karel Mokany, Shaun R. Levick, Jinyan Yang, Peyman Moghadam
Earth observation data have shown promise in predicting species richness of vascular plants ($\alpha$-diversity), but extending this approach to large spatial scales is challenging because geographically distant regions may exhibit different compositions of plant species ($\beta$-diversity), resulting in a location-dependent relationship between richness and spectral measurements. In order to handle such geolocation dependency, we propose Spatioformer, where a novel geolocation encoder is coupled with the transformer model to encode geolocation context into remote sensing imagery. The Spatioformer model compares favourably to state-of-the-art models in richness predictions on a large-scale ground-truth richness dataset (HAVPlot) that consists of 68,170 in-situ richness samples covering diverse landscapes across Australia. The results demonstrate that geolocational information is advantageous in predicting species richness from satellite observations over large spatial scales. With Spatioformer, plant species richness maps over Australia are compiled from Landsat archive for the years from 2015 to 2023. The richness maps produced in this study reveal the spatiotemporal dynamics of plant species richness in Australia, providing supporting evidence to inform effective planning and policy development for plant diversity conservation. Regions of high richness prediction uncertainties are identified, highlighting the need for future in-situ surveys to be conducted in these areas to enhance the prediction accuracy.
Presolar Grains As Probes of Supernova Nucleosynthesis
2024-10-25
Nan Liu, Maria Lugaro, Jan Leitner, Bradley S. Meyer, Maria Schönbächler
We provide an overview of the isotopic signatures of presolar supernova grains, specifically focusing on 44Ti-containing grains with robustly inferred supernova origins and their implications for nucleosynthesis and mixing mechanisms in supernovae. Recent technique advancements have enabled the differentiation between radiogenic (from 44Ti decay) and nonradiogenic 44Ca excesses in presolar grains, made possible by enhanced spatial resolution of Ca-Ti isotope analyses with the Cameca NanoSIMS (Nano-scale Secondary Ion Mass Spectrometer) instrument. Within the context of presolar supernova grain data, we discuss (i) the production of 44Ti in supernovae and the impact of interstellar medium heterogeneities on the galactic chemical evolution of 44Ca/40Ca, (ii) the nucleosynthesis processes of neutron bursts and explosive H-burning in Type II supernovae, and (iii) challenges in identifying the progenitor supernovae for 54Cr-rich presolar nanospinel grains. Drawing on constraints and insights derived from presolar supernova grain data, we also provide an overview of our current understanding of the roles played by various supernova types - including Type II, Type Ia, and electron capture supernovae - in accounting for the diverse array of nucleosynthetic isotopic variations identified in bulk meteorites and meteoritic components. We briefly overview the potential mechanisms that have been proposed to explain these nucleosynthetic variations by describing the transport and distribution of presolar dust carriers in the protoplanetary disk. We highlight existing controversies in the interpretation of presolar grain data and meteoritic nucleosynthetic isotopic variations, while also outlining potential directions for future research.
2024-10-25
Chien Van Nguyen, Xuan Shen, Ryan Aponte, Yu Xia, Samyadeep Basu, Zhengmian Hu, Jian Chen, Mihir Parmar, Sasidhar Kunapuli, Joe Barrow, Junda Wu, Ashish Singh, Yu Wang, Jiuxiang Gu, Franck Dernoncourt, Nesreen K. Ahmed, Nedim Lipka, Ruiyi Zhang, Xiang Chen, Tong Yu, Sungchul Kim, Hanieh Deilamsalehy, Namyong Park, Mike Rimer, Zhehao Zhang, Huanrui Yang, Ryan A. Rossi, Thien Huu Nguyen
Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources, making them ideal for various settings including on-device, mobile, edge devices, among many others. In this article, we present a comprehensive survey on SLMs, focusing on their architectures, training techniques, and model compression techniques. We propose a novel taxonomy for categorizing the methods used to optimize SLMs, including model compression, pruning, and quantization techniques. We summarize the benchmark datasets that are useful for benchmarking SLMs along with the evaluation metrics commonly used. Additionally, we highlight key open challenges that remain to be addressed. Our survey aims to serve as a valuable resource for researchers and practitioners interested in developing and deploying small yet efficient language models.
2024-10-25
Shakhrul Iman Siam, Hyunho Ahn, Li Liu, Samiul Alam, Hui Shen, Zhichao Cao, Ness Shroff, Bhaskar Krishnamachari, Mani Srivastava, Mi Zhang
The integration of the Internet of Things (IoT) and modern Artificial Intelligence (AI) has given rise to a new paradigm known as the Artificial Intelligence of Things (AIoT). In this survey, we provide a systematic and comprehensive review of AIoT research. We examine AIoT literature related to sensing, computing, and networking & communication, which form the three key components of AIoT. In addition to advancements in these areas, we review domain-specific AIoT systems that are designed for various important application domains. We have also created an accompanying GitHub repository, where we compile the papers included in this survey: https://github.com/AIoT-MLSys-Lab/AIoT-Survey. This repository will be actively maintained and updated with new research as it becomes available. As both IoT and AI become increasingly critical to our society, we believe AIoT is emerging as an essential research field at the intersection of IoT and modern AI. We hope this survey will serve as a valuable resource for those engaged in AIoT research and act as a catalyst for future explorations to bridge gaps and drive advancements in this exciting field.
Estimation of Ru-97 Half-Life Using the Most Frequent Value Method and Bootstrapping Techniques
2024-10-25
Victor V. Golovko
A new and robust statistics was applied to previous measurements of the 97Ru half-life. This process incorporates the most frequent value (MFV) technique along with hybrid parametric bootstrap (HPB) method to deliver a more precise estimate of the 97Ru half-life. The derived value is T1/2,MFV(HPB) = 2.8385+0.0022-0.0075 days. This estimate corresponds to a 68.27% confidence interval ranging from 2.8310 to 2.8407 days, and a 95.45% confidence interval ranging from 2.8036 to 2.8485 days, calculated using the percentile method. This level of uncertainty is significantly lower-over 30 times-than the uncertainty in the previously recognized half-life value found in nuclear data sheets. Employing an alternate approach to minimization could further cut down the statistical uncertainty by 44% for the 97Ru half-life. In particular, the HPB method accounts for uncertainties in small datasets when determining the confidence interval. When the HPB method, in combination with the MFV approach, was used to review a four-element dataset of the specific activity of 39Ar based on underground data, the result was SA_MFV(HPB) = 0.966 +0.027 -0.020 Bq/kg_atmAr. This value results in a 68.27% confidence interval of 0.946 to 0.993, along with a 95.45% confidence interval of 0.921 to 1.029, also determined using the percentile method.
The Galaxy Zoo Catalogs for the Galaxy And Mass Assembly (GAMA) Survey
2024-10-25
Benne W. Holwerda, Clayton Robertson, Kyle Cook, Kevin A. Pimbblet, Sarah Casura, Anne E. Sansom, Divya Patel, Trevor Butrum, David H. W. Glass, Lee Kelvin, Ivan K. Baldry, Roberto De Propris, Steven Bamford, Karen Masters, Maria Stone, Tim Hardin, Mike Walmsley, Jochen Liske, S M Rafee Adnan
Galaxy Zoo is an online project to classify morphological features in extra-galactic imaging surveys with public voting. In this paper, we compare the classifications made for two different surveys, the Dark Energy Spectroscopic Instrument (DESI) imaging survey and a part of the Kilo-Degree Survey (KiDS), in the equatorial fields of the Galaxy And Mass Assembly (GAMA) survey. Our aim is to cross-validate and compare the classifications based on different imaging quality and depth. We find that generally the voting agrees globally but with substantial scatter i.e. substantial differences for individual galaxies. There is a notable higher voting fraction in favor of ``smooth'' galaxies in the DESI+\rev{{\sc zoobot}} classifications, most likely due to the difference between imaging depth. DESI imaging is shallower and slightly lower resolution than KiDS and the Galaxy Zoo images do not reveal details such as disk features \rev{and thus are missed in the {\sc zoobot} training sample}. \rev{We check against expert visual classifications and find good agreement with KiDS-based Galaxy Zoo voting.} We reproduce the results from Porter-Temple+ (2022), on the dependence of stellar mass, star-formation, and specific star-formation on the number of spiral arms. This shows that once corrected for redshift, the DESI Galaxy Zoo and KiDS Galaxy Zoo classifications agree well on population properties. The zoobot cross-validation increases confidence in its ability to compliment Galaxy Zoo classifications and its ability for transfer learning across surveys.
Testing the effects of an unobservable factor: Do marriage prospects affect college major choice?
2024-10-25
Hayri Alper Arslan, Brantly Callaway, Tong Li
Motivated by studying the effects of marriage prospects on students' college major choices, this paper develops a new econometric test for analyzing the effects of an unobservable factor in a setting where this factor potentially influences both agents' decisions and a binary outcome variable. Our test is built upon a flexible copula-based estimation procedure and leverages the ordered nature of latent utilities of the polychotomous choice model. Using the proposed method, we demonstrate that marriage prospects significantly influence the college major choices of college graduates participating in the National Longitudinal Study of Youth (97) Survey. Furthermore, we validate the robustness of our findings with alternative tests that use stated marriage expectation measures from our data, thereby demonstrating the applicability and validity of our testing procedure in real-life scenarios.
The scrap-based electric arc furnace process is expected to capture a significant share of the steel market in the future due to its potential for reducing environmental impacts through steel recycling. However, managing impurities, particularly phosphorus, remains a challenge. This study aims to develop a machine learning model to estimate the steel phosphorus content at the end of the process based on input parameters. Data were collected over two years from a steel plant, focusing on the chemical composition and weight of the scrap, the volume of oxygen injected, and process duration. After preprocessing the data, several machine learning models were evaluated, with the artificial neural network (ANN) emerging as the most effective. The best ANN model included four hidden layers. The model was trained for 500 epochs with a batch size of 50. The best model achieves a mean square error (MSE) of 0.000016, a root-mean-square error (RMSE) of 0.0049998, a coefficient of determination (R2) of 99.96%, and a correlation coefficient (r) of 99.98%. Notably, the model achieved a 100% hit rate for predicting phosphorus content within +-0.001 wt% (+-10 ppm). These results demonstrate that the optimized ANN model offers accurate predictions for the steel final phosphorus content.
Collaborative Inference over Wireless Channels with Feature Differential Privacy
2024-10-25
Mohamed Seif, Yuqi Nie, Andrea J. Goldsmith, H. Vincent Poor
Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications, particularly for sensing and computer vision. This approach typically involves a three-stage process: a) data acquisition through sensing, b) feature extraction, and c) feature encoding for transmission. However, transmitting the extracted features poses a significant privacy risk, as sensitive personal data can be exposed during the process. To address this challenge, we propose a novel privacy-preserving collaborative inference mechanism, wherein each edge device in the network secures the privacy of extracted features before transmitting them to a central server for inference. Our approach is designed to achieve two primary objectives: 1) reducing communication overhead and 2) ensuring strict privacy guarantees during feature transmission, while maintaining effective inference performance. Additionally, we introduce an over-the-air pooling scheme specifically designed for classification tasks, which provides formal guarantees on the privacy of transmitted features and establishes a lower bound on classification accuracy.
Scylla IV: Intrinsic Stellar Properties and Line-of-Sight Dust Extinction Measurements Towards 1.5 Million Stars in the SMC and LMC
2024-10-25
Christina W. Lindberg, Claire E. Murray, Petia Yanchulova Merica-Jones, Caroline Bot, Clare Burhenne, Yumi Choi, Christopher J. R. Clark, Roger E. Cohen, Karoline M. Gilbert, Steven R. Goldman, Karl D. Gordon, Alec S. Hirschauer, Kristen B. W. McQuinn, Julia C. Roman-Duval, Karin M. Sandstrom, Elizabeth Tarantino, Benjamin F. Williams
By analyzing the spectral energy distributions (SEDs) of resolved stars in nearby galaxies, we can constrain their stellar properties and line-of-sight dust extinction. From the Scylla survey, we obtain ultraviolet to near-infrared photometry from Wide Field Camera 3 onboard the {\it Hubble Space Telescope} for more than 1.5 million stars in the SMC and LMC. We use the Bayesian Extinction and Stellar Tool (BEAST) to analyze the multi-band SEDs of these sources and characterize their initial masses, ages, metallicities, distances, and line-of-sight extinction properties (e.g.~$A_V$, $R_V$). We apply quality cuts and perform validation simulations to construct a catalog of over 550,000 stars with high-reliability SED fits, which we use to analyze the stellar content and extinction properties of the SMC and LMC. We detect stars with masses as low as 0.6 $M_{\odot}$. Observed stellar age distributions show a jump in stars around 6 Gyrs ago, which is in agreement with other star-formation histories. Extinctions ($A_V$) in both galaxies follow a log-normal distribution. We compare $A_V$ with ancillary gas and dust tracers like $HI$, $H_\alpha$, and far infrared (FIR) dust emission and find positive correlations on a field-by-field basis. We convert observed $A_V$ to predicted dust surface densities using the Draine et. al. (2014) model and find $A_V$-based dust surface densities are a factor of $\sim$2.5 lower than observed FIR-based dust surface densities, a correction factor similar to other studies.
Weak-lensing Mass Reconstruction of Galaxy Clusters with a Convolutional Neural Network -- II: Application to Next-Generation Wide-Field Surveys
2024-10-25
Sangjun Cha, M. James Jee, Sungwook E. Hong, Sangnam Park, Dongsu Bak, Taehwan kim
Traditional weak-lensing mass reconstruction techniques suffer from various artifacts, including noise amplification and the mass-sheet degeneracy. In Hong et al. (2021), we demonstrated that many of these pitfalls of traditional mass reconstruction can be mitigated using a deep learning approach based on a convolutional neural network (CNN). In this paper, we present our improvements and report on the detailed performance of our CNN algorithm applied to next-generation wide-field observations. Assuming the field of view ($3\deg.5 \times 3\deg.5$) and depth (27 mag at $5\sigma$) of the Vera C. Rubin Observatory, we generated training datasets of mock shear catalogs with a source density of 33 arcmin$^{-2}$ from cosmological simulation ray-tracing data. We find that the current CNN method provides high-fidelity reconstructions consistent with the true convergence field, restoring both small and large-scale structures. In addition, the cluster detection utilizing our CNN reconstruction achieves $\sim75$% completeness down to $\sim 10^{14}M_{\odot}$. We anticipate that this CNN-based mass reconstruction will be a powerful tool in the Rubin era, enabling fast and robust wide-field mass reconstructions on a routine basis.
A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection
2024-10-25
Muath Alsuhaibani, Ali Pourramezan Fard, Jian Sun, Farida Far Poor, Peter S. Pressman, Mohammad H. Mahoor
This review paper explores recent advances in deep learning approaches for non-invasive cognitive impairment detection. We examine various non-invasive indicators of cognitive decline, including speech and language, facial, and motoric mobility. The paper provides an overview of relevant datasets, feature-extracting techniques, and deep-learning architectures applied to this domain. We have analyzed the performance of different methods across modalities and observed that speech and language-based methods generally achieved the highest detection performance. Studies combining acoustic and linguistic features tended to outperform those using a single modality. Facial analysis methods showed promise for visual modalities but were less extensively studied. Most papers focused on binary classification (impaired vs. non-impaired), with fewer addressing multi-class or regression tasks. Transfer learning and pre-trained language models emerged as popular and effective techniques, especially for linguistic analysis. Despite significant progress, several challenges remain, including data standardization and accessibility, model explainability, longitudinal analysis limitations, and clinical adaptation. Lastly, we propose future research directions, such as investigating language-agnostic speech analysis methods, developing multi-modal diagnostic systems, and addressing ethical considerations in AI-assisted healthcare. By synthesizing current trends and identifying key obstacles, this review aims to guide further development of deep learning-based cognitive impairment detection systems to improve early diagnosis and ultimately patient outcomes.
2024-10-25
Naga VS Raviteja Chappa, Page Daniel Dobbs, Bhiksha Raj, Khoa Luu
The proliferation of tobacco-related content on social media platforms poses significant challenges for public health monitoring and intervention. This paper introduces a novel multi-modal deep learning framework named Flow-Attention Adaptive Semantic Hierarchical Fusion (FLAASH) designed to analyze tobacco-related video content comprehensively. FLAASH addresses the complexities of integrating visual and textual information in short-form videos by leveraging a hierarchical fusion mechanism inspired by flow network theory. Our approach incorporates three key innovations, including a flow-attention mechanism that captures nuanced interactions between visual and textual modalities, an adaptive weighting scheme that balances the contribution of different hierarchical levels, and a gating mechanism that selectively emphasizes relevant features. This multi-faceted approach enables FLAASH to effectively process and analyze diverse tobacco-related content, from product showcases to usage scenarios. We evaluate FLAASH on the Multimodal Tobacco Content Analysis Dataset (MTCAD), a large-scale collection of tobacco-related videos from popular social media platforms. Our results demonstrate significant improvements over existing methods, outperforming state-of-the-art approaches in classification accuracy, F1 score, and temporal consistency. The proposed method also shows strong generalization capabilities when tested on standard video question-answering datasets, surpassing current models. This work contributes to the intersection of public health and artificial intelligence, offering an effective tool for analyzing tobacco promotion in digital media.
MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding
2024-10-25
Fengbin Zhu, Ziyang Liu, Xiang Yao Ng, Haohui Wu, Wenjie Wang, Fuli Feng, Chao Wang, Huanbo Luan, Tat Seng Chua
Large Vision-Language Models (LVLMs) have achieved remarkable performance in many vision-language tasks, yet their capabilities in fine-grained visual understanding remain insufficiently evaluated. Existing benchmarks either contain limited fine-grained evaluation samples that are mixed with other data, or are confined to object-level assessments in natural images. To holistically assess LVLMs' fine-grained visual understanding capabilities, we propose using document images with multi-granularity and multi-modal information to supplement natural images. In this light, we construct MMDocBench, a benchmark with various OCR-free document understanding tasks for the evaluation of fine-grained visual perception and reasoning abilities. MMDocBench defines 15 main tasks with 4,338 QA pairs and 11,353 supporting regions, covering various document images such as research papers, receipts, financial reports, Wikipedia tables, charts, and infographics. Based on MMDocBench, we conduct extensive experiments using 13 open-source and 3 proprietary advanced LVLMs, assessing their strengths and weaknesses across different tasks and document image types. The benchmark, task instructions, and evaluation code will be made publicly available.
Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges
2024-10-25
Farid Ariai, Gianluca Demartini
Natural Language Processing is revolutionizing the way legal professionals and laypersons operate in the legal field. The considerable potential for Natural Language Processing in the legal sector, especially in developing computational tools for various legal processes, has captured the interest of researchers for years. This survey follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, reviewing 148 studies, with a final selection of 127 after manual filtering. It explores foundational concepts related to Natural Language Processing in the legal domain, illustrating the unique aspects and challenges of processing legal texts, such as extensive document length, complex language, and limited open legal datasets. We provide an overview of Natural Language Processing tasks specific to legal text, such as Legal Document Summarization, legal Named Entity Recognition, Legal Question Answering, Legal Text Classification, and Legal Judgment Prediction. In the section on legal Language Models, we analyze both developed Language Models and approaches for adapting general Language Models to the legal domain. Additionally, we identify 15 Open Research Challenges, including bias in Artificial Intelligence applications, the need for more robust and interpretable models, and improving explainability to handle the complexities of legal language and reasoning.
What are the opinions of American registered voters about election fraud and types of election fraud as we head into the final stages of the 2024 Presidential election? In this paper we use data from an online national survey of 2,211 U.S. registered voters interviewed between June 26 - July 3, 2024. Respondents were asked how common they thought that ten different types of election fraud might be in the U.S. In our analysis, we show that substantial proportions of U.S. registered voters believe that these types of election fraud are common. Our multivariate analysis shows that partisanship correlates strongly with endorsement of types of election fraud, with Republicans consistently more likely to state that types of election fraud are common, even when we control for a wide variety of other factors. We also find that conspiratorial thinking is strongly correlated with belief in the occurrence of types of election fraud, even when we control for partisanship. Our results reported in this paper provide important data regarding how American registered voters perceive the prevalence of types of election fraud, just months before the 2024 Presidential election.
ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models
2024-10-25
Benjamin Newman, Yoonjoo Lee, Aakanksha Naik, Pao Siangliulue, Raymond Fok, Juho Kim, Daniel S. Weld, Joseph Chee Chang, Kyle Lo
When conducting literature reviews, scientists often create literature review tables - tables whose rows are publications and whose columns constitute a schema, a set of aspects used to compare and contrast the papers. Can we automatically generate these tables using language models (LMs)? In this work, we introduce a framework that leverages LMs to perform this task by decomposing it into separate schema and value generation steps. To enable experimentation, we address two main challenges: First, we overcome a lack of high-quality datasets to benchmark table generation by curating and releasing arxivDIGESTables, a new dataset of 2,228 literature review tables extracted from ArXiv papers that synthesize a total of 7,542 research papers. Second, to support scalable evaluation of model generations against human-authored reference tables, we develop DecontextEval, an automatic evaluation method that aligns elements of tables with the same underlying aspects despite differing surface forms. Given these tools, we evaluate LMs' abilities to reconstruct reference tables, finding this task benefits from additional context to ground the generation (e.g. table captions, in-text references). Finally, through a human evaluation study we find that even when LMs fail to fully reconstruct a reference table, their generated novel aspects can still be useful.
Deep Insights into Cognitive Decline: A Survey of Leveraging Non-Intrusive Modalities with Deep Learning Techniques
2024-10-24
David Ortiz-Perez, Manuel Benavent-Lledo, Jose Garcia-Rodriguez, David Tomás, M. Flores Vizcaya-Moreno
Cognitive decline is a natural part of aging, often resulting in reduced cognitive abilities. In some cases, however, this decline is more pronounced, typically due to disorders such as Alzheimer's disease. Early detection of anomalous cognitive decline is crucial, as it can facilitate timely professional intervention. While medical data can help in this detection, it often involves invasive procedures. An alternative approach is to employ non-intrusive techniques such as speech or handwriting analysis, which do not necessarily affect daily activities. This survey reviews the most relevant methodologies that use deep learning techniques to automate the cognitive decline estimation task, including audio, text, and visual processing. We discuss the key features and advantages of each modality and methodology, including state-of-the-art approaches like Transformer architecture and foundation models. In addition, we present works that integrate different modalities to develop multimodal models. We also highlight the most significant datasets and the quantitative results from studies using these resources. From this review, several conclusions emerge. In most cases, the textual modality achieves the best results and is the most relevant for detecting cognitive decline. Moreover, combining various approaches from individual modalities into a multimodal model consistently enhances performance across nearly all scenarios.
Large language models (LLMs) have demonstrated great performance across various benchmarks, showing potential as general-purpose task solvers. However, as LLMs are typically trained on vast amounts of data, a significant concern in their evaluation is data contamination, where overlap between training data and evaluation datasets inflates performance assessments. While multiple approaches have been developed to identify data contamination, these approaches rely on specific assumptions that may not hold universally across different settings. To bridge this gap, we systematically review 47 papers on data contamination detection, categorize the underlying assumptions, and assess whether they have been rigorously validated. We identify and analyze eight categories of assumptions and test three of them as case studies. Our analysis reveals that when classifying instances used for pretraining LLMs, detection approaches based on these three assumptions perform close to random guessing, suggesting that current LLMs learn data distributions rather than memorizing individual instances. Overall, this work underscores the importance of approaches clearly stating their underlying assumptions and testing their validity across various scenarios.
Context is Key: A Benchmark for Forecasting with Essential Textual Information
2024-10-24
Andrew Robert Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin
Forecasting is a critical task in decision making across various domains. While numerical data provides a foundation, it often lacks crucial context necessary for accurate predictions. Human forecasters frequently rely on additional information, such as background knowledge or constraints, which can be efficiently communicated through natural language. However, the ability of existing forecasting models to effectively integrate this textual information remains an open question. To address this, we introduce "Context is Key" (CiK), a time series forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context, requiring models to integrate both modalities. We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters, and propose a simple yet effective LLM prompting method that outperforms all other tested methods on our benchmark. Our experiments highlight the importance of incorporating contextual information, demonstrate surprising performance when using LLM-based forecasting models, and also reveal some of their critical shortcomings. By presenting this benchmark, we aim to advance multimodal forecasting, promoting models that are both accurate and accessible to decision-makers with varied technical expertise. The benchmark can be visualized at https://servicenow.github.io/context-is-key-forecasting/v0/ .
Building a Planet Atmosphere: Fundamental Physics and Chemistry
2024-10-24
Emily Rauscher
This chapter provides an overview of the basic concepts foundational to atmospheric physics and chemistry. We discuss the retention of atmospheres against thermal evaporation and the global energy balance of planets. We present simple derivations of the vertical profile of an atmosphere, which may be shaped by convective and radiative transport. We then briefly touch upon the three-dimensional atmospheric structure, as shaped by circulation patterns. We describe how the abundances of chemical species in the atmosphere are determined, starting with the assumption of chemical equilibrium and then expanding to various disequilibrium effects. We introduce the particles that can be important components of atmospheres (clouds and hazes) and sketch out some of their complexity. Finally, we review some of the differences between atmospheres of terrestrial and gaseous worlds.
Large Language Models (LLMs) exhibit strong contextual understanding and remarkable multi-task performance. Therefore, researchers have been seeking to integrate LLMs in the broad sense of Spoken Language Understanding (SLU) field. Different from the traditional method of cascading LLMs to process text generated by Automatic Speech Recognition(ASR), new efforts have focused on designing architectures centered around Audio Feature Extraction - Multimodal Information Fusion - LLM Inference(Speech LLMs). This approach enables richer audio feature extraction while simultaneously facilitating end-to-end fusion of audio and text modalities, thereby achieving deeper understanding and reasoning from audio data. This paper elucidates the development of Speech LLMs, offering an in-depth analysis of system architectures and training strategies. Through extensive research and a series of targeted experiments, the paper assesses Speech LLMs' advancements in Rich Audio Transcription and its potential for Cross-task Integration within the SLU field. Additionally, it indicates key challenges uncovered through experimentation, such as the Dormancy of LLMs under certain conditions. The paper further delves into the training strategies for Speech LLMs, proposing potential solutions based on these findings, and offering valuable insights and references for future research in this domain, as well as LLM applications in multimodal contexts.
Finite-Time Processes In Quantum Thermodynamics: The Limits Of Irreversibility
2024-10-24
Alberto Rolandi
The emergence of irreversibility in physical processes, despite the fundamentally reversible nature of quantum mechanics, remains an open question in physics. This thesis explores the intricate relationship between quantum mechanics and thermodynamics, with a particular focus on minimizing entropy production in finite-time processes. By employing tools from quantum information theory and geometric thermodynamics, we tackle the challenge of deriving irreversible thermodynamic behavior from the reversible microscopic framework of quantum mechanics. We begin with a comprehensive review of the laws of thermodynamics, setting the stage for the subsequent analyses. We introduce novel developments in quantum thermodynamics through a generalized framework for geometric thermodynamics, which enables the derivation of finite-time corrections beyond the Markovian regime. Building on this foundation, we extend Landauer's principle by incorporating a finite-time correction that highlights the necessity of strong coupling for optimal information erasure processes. This result underscores the emergence of Planckian time as a fundamental speed limit to thermalization. Additionally, we explore how collective effects can be harnessed to reduce energy dissipation in thermodynamic operations, revealing that classical correlations between systems can significantly mitigate dissipation, though this may pose new questions regarding the third law of thermodynamics. Finally, we optimize thermodynamic processes in mesoscopic systems, including quantum dot engines and information engines. These findings not only enhance our understanding of the fundamental limits of irreversibility but also open new avenues for research. Future works will focus on fully exploiting collective effects, aligning these with the third law of thermodynamics, and understanding the thermodynamic consistency of master equations.
Seismic exploration is currently the most mature approach for studying subsurface structures, yet the presence of noise greatly restricts its imaging accuracy. Previous methods still face significant challenges: traditional computational methods are often computationally complex and their effectiveness is hard to guarantee; deep learning approaches rely heavily on datasets, and the complexity of network training makes them difficult to apply in practical field scenarios. In this paper, we proposed a method that has only 2464 learnable parameters, and its parameter constraints rely on priors rather than requiring training data. The three priors we proposed can effectively attenuate random noise while significantly reducing signal leakage, ensuring that the separated noise remains as independent as possible from the processed data. We validated our method on National Petroleum Reserve-Alaska Survey, and the results indicate that our proposed approach effectively enhances noise elimination and seismic data resolution.
Using Large Language Models (LLMs) to produce robot programs from natural language has allowed for robot systems that can complete a higher diversity of tasks. However, LLM-generated programs may be faulty, either due to ambiguity in instructions, misinterpretation of the desired task, or missing information about the world state. As these programs run, the state of the world changes and they gather new information. When a failure occurs, it is important that they recover from the current world state and avoid repeating steps that they they previously completed successfully. We propose RoboRepair, a system which traces the execution of a program up until error, and then runs an LLM-produced recovery program that minimizes repeated actions. To evaluate the efficacy of our system, we create a benchmark consisting of eleven tasks with various error conditions that require the generation of a recovery program. We compare the efficiency of the recovery program to a plan built with an oracle that has foreknowledge of future errors.
Sarcasm is a rhetorical device that is used to convey the opposite of the literal meaning of an utterance. Sarcasm is widely used on social media and other forms of computer-mediated communication motivating the use of computational models to identify it automatically. While the clear majority of approaches to sarcasm detection have been carried out on text only, sarcasm detection often requires additional information present in tonality, facial expression, and contextual images. This has led to the introduction of multimodal models, opening the possibility to detect sarcasm in multiple modalities such as audio, images, text, and video. In this paper, we present the first comprehensive survey on multimodal sarcasm detection - henceforth MSD - to date. We survey papers published between 2018 and 2023 on the topic, and discuss the models and datasets used for this task. We also present future research directions in MSD.
Exploring the Universe with SNAD: Anomaly Detection in Astronomy
2024-10-24
Alina A. Volnova, Patrick D. Aleo, Anastasia Lavrukhina, Etienne Russeil, Timofey Semenikhin, Emmanuel Gangler, Emille E. O. Ishida, Matwey V. Kornilov, Vladimir Korolev, Konstantin Malanchev, Maria V. Pruzhinskaya, Sreevarsha Sreejith
SNAD is an international project with a primary focus on detecting astronomical anomalies within large-scale surveys, using active learning and other machine learning algorithms. The work carried out by SNAD not only contributes to the discovery and classification of various astronomical phenomena but also enhances our understanding and implementation of machine learning techniques within the field of astrophysics. This paper provides a review of the SNAD project and summarizes the advancements and achievements made by the team over several years.
Learning Collusion in Episodic, Inventory-Constrained Markets
2024-10-24
Paul Friedrich, Barna Pásztor, Giorgia Ramponi
Pricing algorithms have demonstrated the capability to learn tacit collusion that is largely unaddressed by current regulations. Their increasing use in markets, including oligopolistic industries with a history of collusion, calls for closer examination by competition authorities. In this paper, we extend the study of tacit collusion in learning algorithms from basic pricing games to more complex markets characterized by perishable goods with fixed supply and sell-by dates, such as airline tickets, perishables, and hotel rooms. We formalize collusion within this framework and introduce a metric based on price levels under both the competitive (Nash) equilibrium and collusive (monopolistic) optimum. Since no analytical expressions for these price levels exist, we propose an efficient computational approach to derive them. Through experiments, we demonstrate that deep reinforcement learning agents can learn to collude in this more complex domain. Additionally, we analyze the underlying mechanisms and structures of the collusive strategies these agents adopt.
The Cat and Mouse Game: The Ongoing Arms Race Between Diffusion Models and Detection Methods
2024-10-24
Linda Laurier, Ave Giulietta, Arlo Octavia, Meade Cleti
The emergence of diffusion models has transformed synthetic media generation, offering unmatched realism and control over content creation. These advancements have driven innovation across fields such as art, design, and scientific visualization. However, they also introduce significant ethical and societal challenges, particularly through the creation of hyper-realistic images that can facilitate deepfakes, misinformation, and unauthorized reproduction of copyrighted material. In response, the need for effective detection mechanisms has become increasingly urgent. This review examines the evolving adversarial relationship between diffusion model development and the advancement of detection methods. We present a thorough analysis of contemporary detection strategies, including frequency and spatial domain techniques, deep learning-based approaches, and hybrid models that combine multiple methodologies. We also highlight the importance of diverse datasets and standardized evaluation metrics in improving detection accuracy and generalizability. Our discussion explores the practical applications of these detection systems in copyright protection, misinformation prevention, and forensic analysis, while also addressing the ethical implications of synthetic media. Finally, we identify key research gaps and propose future directions to enhance the robustness and adaptability of detection methods in line with the rapid advancements of diffusion models. This review emphasizes the necessity of a comprehensive approach to mitigating the risks associated with AI-generated content in an increasingly digital world.
Demystifying Large Language Models for Medicine: A Primer
2024-10-24
Qiao Jin, Nicholas Wan, Robert Leaman, Shubo Tian, Zhizheng Wang, Yifan Yang, Zifeng Wang, Guangzhi Xiong, Po-Ting Lai, Qingqing Zhu, Benjamin Hou, Maame Sarfo-Gyamfi, Gongbo Zhang, Aidan Gilson, Balu Bhasuran, Zhe He, Aidong Zhang, Jimeng Sun, Chunhua Weng, Ronald M. Summers, Qingyu Chen, Yifan Peng, Zhiyong Lu
Large language models (LLMs) represent a transformative class of AI tools capable of revolutionizing various aspects of healthcare by generating human-like responses across diverse contexts and adapting to novel tasks following human instructions. Their potential application spans a broad range of medical tasks, such as clinical documentation, matching patients to clinical trials, and answering medical questions. In this primer paper, we propose an actionable guideline to help healthcare professionals more efficiently utilize LLMs in their work, along with a set of best practices. This approach consists of several main phases, including formulating the task, choosing LLMs, prompt engineering, fine-tuning, and deployment. We start with the discussion of critical considerations in identifying healthcare tasks that align with the core capabilities of LLMs and selecting models based on the selected task and data, performance requirements, and model interface. We then review the strategies, such as prompt engineering and fine-tuning, to adapt standard LLMs to specialized medical tasks. Deployment considerations, including regulatory compliance, ethical guidelines, and continuous monitoring for fairness and bias, are also discussed. By providing a structured step-by-step methodology, this tutorial aims to equip healthcare professionals with the tools necessary to effectively integrate LLMs into clinical practice, ensuring that these powerful technologies are applied in a safe, reliable, and impactful manner.
A diffusion MRI model for random walks confined on cylindrical surfaces: Towards non-invasive quantification of myelin sheath radius
2024-10-24
Erick J Canales-Rodríguez, Chantal M. W. Tax, Elda Fischi-Gomez, Derek K. Jones, Jean-Philippe Thiran, Jonathan Rafael-Patiño
Quantifying the myelin sheath radius of myelinated axons in vivo is important for understanding, diagnosing, and monitoring various neurological disorders. Despite advancements in diffusion MRI (dMRI) microstructure techniques, models specifically designed to estimate myelin sheath radii remain unavailable. In this proof-of-concept theoretical study, we present two novel dMRI models that characterize the signal from water diffusion confined to cylindrical surfaces, approximating myelin water diffusion. We derive their spherical mean signals, which conveniently eliminate fiber orientation and dispersion effects. These models are further extended to account for multiple concentric cylinders, mimicking the layered structure of myelin. Additionally, we introduce a method to convert histological distributions of axonal inner radii from the literature into myelin sheath radius distributions and derive analytical expressions to estimate the effective myelin sheath radius expected from these distributions. Monte Carlo (MC) simulations conducted in cylindrical and spiral geometries validate the models, demonstrating agreement with analytical predictions across various diffusion regimes and significant correlations between the effective radii estimated from the histological distributions and the effective radius obtained by fitting the resulting dMRI signal to a single-cylinder model. These models may be integrated into existing multi-compartment dMRI techniques, opening the door to non-invasive, in vivo assessments of myelin sheath radii in MRI scanners equipped with strong diffusion gradients that enable measurements with short echo times. Further work is required to validate the technique with real dMRI data.
Mergers of compact objects with cores of massive stars: evolution, r-process and multi-messenger signatures
2024-10-24
Aldana Grichener
The study of massive binary systems has steadily progressed over the past decades, with increasing focus on their evolution, interactions and mergers, driven by improvements in computational modelling and observational techniques. In particular, when a binary system involves a massive giant and a neutron star (NS) or a black hole (BH) that go through common envelope evolution (CEE), it might results in the merger of the compact object with the core of its giant companion, giving rise to various high energy astrophysical phenomena. We review the different evolutionary channels that lead to compact objects-core mergers, key physical processes with emphasis on the role of accretion physics, feasibility of r-process nucleosynthesis, expected observable electromagnetic, neutrinos and gravitational-waves (GWs) signatures, as well as potential correlation with detected core collapse supernovae (CCSNe), luminous fast blue optical transients (LFBOTs) and low luminosity long gamma-ray bursts (LGRBs). After presenting our current understanding of these mergers, we conclude discussing prospects for future advancements.
We present the discovery of an extended and faint tail observed in the isolated environment associated with galaxy NGC 3785. This study additionally provides observational evidence supporting the formation of ultra-diffuse galaxies at the end of the tail. We utilized the Gnuastro software to detect and analyse the low surface brightness structures in the optical $g$, and $r$ bands using data from the Dark Energy Camera Legacy Survey. We created a detection map to identify the faint tail, and measured its length using cubic spline fitting. Additionally, we found 84 star-forming clumps along the tail and performed photometric analysis on the tail portion after applying a significance threshold on the signal-to-noise ratio. We have measured the projected length of the tail, which is $\sim$390 kpc. We propose that this tail arises from the interaction of the NGC 3785 with a gas-rich galaxy, which ends up as ultra-diffuse galaxy at the end of the tail.
Solving complex chart Q&A tasks requires advanced visual reasoning abilities in multimodal large language models (MLLMs). Recent studies highlight that these abilities consist of two main parts: recognizing key information from visual inputs and conducting reasoning over it. Thus, a promising approach to enhance MLLMs is to construct relevant training data focusing on the two aspects. However, collecting and annotating complex charts and questions is costly and time-consuming, and ensuring the quality of annotated answers remains a challenge. In this paper, we propose Code-as-Intermediary Translation (CIT), a cost-effective, efficient and easily scalable data synthesis method for distilling visual reasoning abilities from LLMs to MLLMs. The code serves as an intermediary that translates visual chart representations into textual representations, enabling LLMs to understand cross-modal information. Specifically, we employ text-based synthesizing techniques to construct chart-plotting code and produce ReachQA, a dataset containing 3k reasoning-intensive charts and 20k Q&A pairs to enhance both recognition and reasoning abilities. Experiments show that when fine-tuned with our data, models not only perform well on chart-related benchmarks, but also demonstrate improved multimodal reasoning abilities on general mathematical benchmarks like MathVista. The code and dataset are publicly available at https://github.com/hewei2001/ReachQA.
A New Definition of Demand Response in the Distributed Energy Resource Era
2024-10-24
Johanna L. Mathieu, Gregor Verbič, Thomas Morstyn, Mads Almassalkhi, Kyri Baker, Julio Braslavsky, Kenneth Bruninx, Yury Dvorkin, Gregory S. Ledva, Nariman Mahdavi, Hrvoje Pandžić, Alessandra Parisio, Vedran Perić
Demand response is a concept that has been around since the very first electric power systems. However, we have seen an explosion of research on demand response and demand-side technologies in the past 30 years, coinciding with the shift towards liberalized/deregulated electricity markets and efforts to decarbonize the power sector. Now we are also seeing a shift towards more distributed/decentralized electric systems; we have entered the era of "distributed energy resources," which require new grid management, operational, and control strategies. Given this paradigm shift, we argue that the concept of demand response needs to be revisited, and more carefully/consistently defined to enable us to better utilize this massive resource for economic, technical, environmental, and societal aims. In this paper, we survey existing demand response definitions, highlight their shortcomings, propose a new definition, and describe how this new definition enables us to more effectively harness the value of demand response in modern power systems. We conclude with a demand response research agenda informed by a discussion of demand response barriers and enablers.
Breaking Down the Barriers: Investigating Non-Expert User Experiences in Robotic Teleoperation in UK and Japan
2024-10-24
Florent P Audonnet, Andrew Hamilton, Yakiyasu Domae, Ixchel G Ramirez-Alpizar, Gerardo Aragon-Camarasa
Robots are being created each year with the goal of integrating them into our daily lives. As such, there is an interest in research in evaluating the trust of humans toward robots. In addition, teleoperating robotic arms can be challenging for non-experts. In order to reduce the strain put on the user, we created TELESIM, a modular and plug-and-play framework that enables direct teleoperation of any robotic arm using a digital twin as the interface between users and the robotic system. However, analysis of the strain put on the user and its ability to trust robots was omitted. This paper addresses these omissions by presenting the additional results of our user survey of 37 participants carried out in UK. In addition, we present the results of an additional user survey, under similar conditions performed in Japan, with the goal of addressing the limitations of our previous approach, by interfacing a VR controller with a UR5e. Our experimental results show that the UR5e has a higher number of towers built. Additionally, the UR5e gives the least amount of cognitive stress, while the combination of Senseglove and UR3 gives the user the highest physical strain and causes the user to feel more frustrated. Finally, Japanese seems more trusting towards robots than British.
We introduce GrammaMT, a grammatically-aware prompting approach for machine translation that uses Interlinear Glossed Text (IGT), a common form of linguistic description providing morphological and lexical annotations for source sentences. GrammaMT proposes three prompting strategies: gloss-shot, chain-gloss and model-gloss. All are training-free, requiring only a few examples that involve minimal effort to collect, and making them well-suited for low-resource setups. Experiments show that GrammaMT enhances translation performance on open-source instruction-tuned LLMs for various low- to high-resource languages across three benchmarks: (1) the largest IGT corpus, (2) the challenging 2023 SIGMORPHON Shared Task data over endangered languages, and (3) even in an out-of-domain setting with FLORES. Moreover, ablation studies reveal that leveraging gloss resources could substantially boost MT performance (by over 17 BLEU points) if LLMs accurately generate or access input sentence glosses.
In this paper, we present a comprehensive survey on the pervasive issue of medical misinformation in social networks from the perspective of information technology. The survey aims at providing a systematic review of related research and helping researchers and practitioners navigate through this fast-changing field. Specifically, we first present manual and automatic approaches for fact-checking. We then explore fake news detection methods, using content, propagation features, or source features, as well as mitigation approaches for countering the spread of misinformation. We also provide a detailed list of several datasets on health misinformation and of publicly available tools. We conclude the survey with a discussion on the open challenges and future research directions in the battle against health misinformation.
On Evidence for Elastic Interactions in the Dark Sector
2024-10-24
Jose Beltrán Jiménez, Dario Bettoni, David Figueruelo, Florencia A. Teppa Pannia
The increasing quality of cosmological data has revealed some tensions that could be signalling the necessity of incorporating new physics into our cosmological model. One particularly intriguing possibility is the existence of elastic interactions between dark matter and dark energy. Not only do these interactions provide a natural mechanism to relief cosmological tensions, but there is also compelling observational evidence for them, to the extent that a detection could even be claimed. We review the potential of these scenarios in relation to the cosmological tensions and discuss distinctive signatures that can be probed with future data, thus providing a smoking gun for these interactions.
The top-quark mass is a fundamental parameter of the Standard Model, as it plays a crucial role in the electroweak precision tests, stability of the vacuum and inflation. I review the method and the main results contained in a recent ATLAS analysis which measures the top mass by using the invariant mass of the leptons coming from W and B-hadron decays. The extracted top mass turns out to be the most precise single measurement by ATLAS.
On Classification and Geometric Characterizations of Ensembled $2\times2$ Pseudo Hermitian and PT-Symmetric Matrices
2024-10-24
Stalin Abraham, Ameeya A. Bhagwat
Non-Hermitian matrices $H\in M_2(\mathbb{C})$ satisfying the relation $ H^{\dag}G = GH $, for invertible and singular Hermitian matrices $G$ have been studied. The matrices $H$ corresponding to invertible $G$ are known in the literature as G-pseudo Hermitian matrices. We label the matrices corresponding to the singular $G_s$ as $G_s$-pseudo Hermitian. We have proved that all $ 2\times 2$ $G$-pseudo Hermitian matrices are PT-symmetric. For a given $G$ ($G_s$), all $G$ ($G_s$)-pseudo-Hermitian $H\in M_2(\mathbb{C})$ are found to be expressed as a linear variety. It is further found that for any two Hermitian $G_i,G_j\in M_2(\mathbb{C})$ such that $G_i\neq \lambda G_j$, there always exists exactly one trace less $H\in M_2(\mathbb{C})$ (up to real scaling) which is pseudo-Hermitian with respect to both these $G$ matrices. The set of all $G$- and $G_s$- pseudo-Hermitian matrices has been divided into seven distinct ensembles of matrices and the set of all PT-symmetric matrices in $M_2(\mathbb{C})$ is partitioned into four cells, denoted by $S_1,S_2,S_3$ and $S_4$. The ensembles of trace-less G-pseudo Hermitian matrices are shown to be written as a linear combination of three basis elements from these cells. When $\mathrm{Tr}(G) = 0$, one basis element is from $S_1$ and the other two are from $S_2$. On the other hand, when $\mathrm{Tr}(G)\neq0$, one basis element is from $S_1$ and the other two are from $S_4$. The determinant of such ensembles of trace-less matrices are shown to be quadrics, which could be hyperboloid of two sheets, hyperboloid of one sheet, ellipsoid or quadric cone for invertible $G$, whereas it is two parallel planes or a plane for singular $G_s$. Finally, the set of all the matrices $G\in M_2(\mathbb{C})$, satisfying $H^{\dagger}G = GH$, given a specific $H\in M_2(\mathbb{C})$, are shown to be describable in terms of quadratic variety.
Recent advances in large language models have demonstrated promising capabilities in following simple instructions through instruction tuning. However, real-world tasks often involve complex, multi-step instructions that remain challenging for current NLP systems. Despite growing interest in this area, there lacks a comprehensive survey that systematically analyzes the landscape of complex instruction understanding and processing. Through a systematic review of the literature, we analyze available resources, representation schemes, and downstream tasks related to instructional text. Our study examines 177 papers, identifying trends, challenges, and opportunities in this emerging field. We provide AI/NLP researchers with essential background knowledge and a unified view of various approaches to complex instruction understanding, bridging gaps between different research directions and highlighting future research opportunities.
The baryon acoustic oscillation (BAO) reconstruction plays a crucial role in cosmological analysis for spectroscopic galaxy surveys because it can make the density field effectively more linear and more Gaussian. The combination of the power spectra before and after the BAO reconstruction helps break degeneracies among parameters, then improve the constraints on cosmological parameters. It is therefore important to estimate the covariance matrix between pre- and post-reconstructed power spectra. In this work, we use perturbation theory to estimate the covariance matrix of the related power spectra multipoles, and check the accuracy of the derived covariance model using a large suite of dark matter halo catalogs at $z=0.5$. We find that the diagonal part of the auto covariance is well described by the Gaussian prediction, while the cross covariance deviates from the Gaussian prediction quickly when $k > 0.1\,h\,\mathrm{Mpc}^{-1}$. Additionally, we find the non-Gaussian effect in the non-diagonal part of the cross covariance is comparable to, or even stronger than the pre-reconstruction covariance. By adding the non-Gaussian contribution, we obtain good agreement between analytical and numerical covariance matrices in the non-diagonal part up to $k \simeq 0.15\,h\,\mathrm{Mpc}^{-1}$. The agreement in the diagonal part is also improved, but still under-predicts the correlation in the cross covariance block.
Crystalline electric field excitations and their nonlinear splitting under magnetic fields in YbOCl
2024-10-24
Yanzhen Cai, Wei Ren
Recently reported van der Waals layered honeycomb rare-earth chalcohalides REChX (RE = rare earth, Ch = chalcogen, and X = halogen) are considered to be promising Kitaev spin liquid (KSL) candidates. The high-quality single crystals of YbOCl, a representative member of the family with an effective spin of 1/2, are available now. The crystalline electric field (CEF) excitations in a rare-earth spin system are fundamentally important for understanding both finite-temperature and ground-state magnetism, but remain unexplored in YbOCl so far. In this paper, we conduct a comprehensive Raman scattering study to unambiguously identify the CEF excitations in YbOCl and determine the CEF parameters and wave functions. Our Raman experiments further reveal the anomalous nonlinear CEF splitting under magnetic fields. We have grown single crystals of YbOCl, the nonmagnetic LuOCl, and the diluted magnetic Lu_{0.86}Yb_{0.14}OCl to make a completely comparative investigation. Polarized Raman spectra on the samples at 1.8 K allow us to clearly assign all the Raman-active phonon modes and explicitly identify the CEF excitations in YbOCl. The CEF excitations are further examined using temperature-dependent Raman measurements and careful symmetry analysis based on Raman tensors related to CEF excitations. By applying the CEF Hamiltonian to the experimentally determined CEF excitations, we extract the CEF parameters and eventually determine the CEF wave functions. The study experimentally pins down the CEF excitations in the Kitaev compound YbOCl and sets a foundation for understanding its finite-temperature magnetism and exploring the possible nontrivial spin ground state.
The development of large language models (LLMs) has significantly expanded model sizes, resulting in substantial GPU memory requirements during inference. The key and value storage of the attention map in the KV (key-value) cache accounts for more than 80\% of this memory consumption. Nowadays, most existing KV cache compression methods focus on intra-layer compression within a single Transformer layer but few works consider layer-wise compression. In this paper, we propose a plug-and-play method called \textit{KVSharer}, which shares the KV cache between layers to achieve layer-wise compression. Rather than intuitively sharing based on higher similarity, we discover a counterintuitive phenomenon: sharing dissimilar KV caches better preserves the model performance. Experiments show that \textit{KVSharer} can reduce KV cache computation by 30\%, thereby lowering memory consumption without significantly impacting model performance and it can also achieve at least 1.3 times generation acceleration. Additionally, we verify that \textit{KVSharer} is compatible with existing intra-layer KV cache compression methods, and combining both can further save memory.
Potemkin village of Russian science: The case of JINR
2024-10-24
Tetiana Berger-Hrynova
This document reviews international cooperation of the Joint Institute of Nuclear Research (JINR). It shows that of the original 11 founding members of the JINR, only one -- Mongolia -- is still fully active within the organization. Out of 16 existing members four have their participation suspended. The review of the Topical Plan for JINR Research and International Cooperation 2024 shows that the number of 990 institutes collaborating with JINR is overestimated by at least 10% and probably closer to 25%. Countries with the highest number of participating institutes listed for 2024 are Russia, USA and Italy with 203, 77 and 49 institutes, respectively. JINR collaboration with impostor institutes on the occupied territories of Ukraine is presented as well as JINR publications which associate the occupied Ukrainian territories to Russia. JINR cooperation with organizations in Russia, which are under sanctions for their support of Russia's invasion of Ukraine and contribution to the war effort, is also discussed.
Multi-View Stereo plays a pivotal role in civil engineering by facilitating 3D modeling, precise engineering surveying, quantitative analysis, as well as monitoring and maintenance. It serves as a valuable tool, offering high-precision and real-time spatial information crucial for various engineering projects. However, Multi-View Stereo algorithms encounter challenges in reconstructing weakly-textured regions within large-scale building scenes. In these areas, the stereo matching of pixels often fails, leading to inaccurate depth estimations. Based on the Segment Anything Model and RANSAC algorithm, we propose an algorithm that accurately segments weakly-textured regions and constructs their plane priors. These plane priors, combined with triangulation priors, form a reliable prior candidate set. Additionally, we introduce a novel global information aggregation cost function. This function selects optimal plane prior information based on global information in the prior candidate set, constrained by geometric consistency during the depth estimation update process. Experimental results on both the ETH3D benchmark dataset, aerial dataset, building dataset and real scenarios substantiate the superior performance of our method in producing 3D building models compared to other state-of-the-art methods. In summary, our work aims to enhance the completeness and density of 3D building reconstruction, carrying implications for broader applications in urban planning and virtual reality.
Joint Modeling of Quasar Variability and Accretion Disk Reprocessing using Latent Stochastic Differential Equation
2024-10-24
Joshua Fagin, James Hung-Hsu Chan, Henry Best, Matthew O'Dowd
Quasars are bright active galactic nuclei powered by the accretion of matter around supermassive black holes at the center of galaxies. Their stochastic brightness variability depends on the physical properties of the accretion disk and black hole. The upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to observe tens of millions of quasars, so there is a need for efficient techniques like machine learning that can handle the large volume of data. Quasar variability is believed to be driven by an X-ray corona, which is reprocessed by the accretion disk and emitted as UV/optical variability. We are the first to introduce an auto-differentiable simulation of the accretion disk and reprocessing. We use the simulation as a direct component of our neural network to jointly model the driving variability and reprocessing to fit simulated LSST 10-year quasar light curves. The driving variability is reconstructed using a latent stochastic differential equation, a physically motivated, generative deep learning method that can model continuous-time stochastic dynamics. By embedding these physical processes into our network, we achieve a model that is more robust and interpretable. We also use transformers to scale our model to tens of millions of parameters. We demonstrate how our model outperforms a Gaussian process regression baseline and can infer accretion disk parameters and time delays between wavebands, even for out-of-distribution driving signals. Our approach provides a powerful and scalable framework that can be adapted to solve other inverse problems in multivariate time series with irregular sampling.
Proteins adopt multiple structural conformations to perform their diverse biological functions, and understanding these conformations is crucial for advancing drug discovery. Traditional physics-based simulation methods often struggle with sampling equilibrium conformations and are computationally expensive. Recently, deep generative models have shown promise in generating protein conformations as a more efficient alternative. However, these methods predominantly rely on the diffusion process within a 3D geometric space, which typically centers around the vicinity of metastable states and is often inefficient in terms of runtime. In this paper, we introduce Structure Language Modeling (SLM) as a novel framework for efficient protein conformation generation. Specifically, the protein structures are first encoded into a compact latent space using a discrete variational auto-encoder, followed by conditional language modeling that effectively captures sequence-specific conformation distributions. This enables a more efficient and interpretable exploration of diverse ensemble modes compared to existing methods. Based on this general framework, we instantiate SLM with various popular LM architectures as well as proposing the ESMDiff, a novel BERT-like structure language model fine-tuned from ESM3 with masked diffusion. We verify our approach in various scenarios, including the equilibrium dynamics of BPTI, conformational change pairs, and intrinsically disordered proteins. SLM provides a highly efficient solution, offering a 20-100x speedup than existing methods in generating diverse conformations, shedding light on promising avenues for future research.
Radio spectral shape of quasars can provide insight into the ages of quasars. We have compiled data for 1804 quasars with $z\lesssim1$ from the Sloan Digital Sky Survey (SDSS). Additionally, these quasars were also mapped by the Low-Frequency Array at 144 MHz and the Very Large Array Sky Survey at 3000 MHz. The radio spectral index, designated as $\alpha^{\rm 144}_{\rm 3000}$ (with $S(\nu)\propto\nu^\alpha$), is analyzed between 144 MHz and 3000 MHz as a proxy for the ages of quasars. We measure the [O III] $\lambda$5007 emission line in the SDSS spectra. A strong correlation was found between the equivalent width of the core component of the [O III] $\lambda$5007 emission line and $\alpha^{\rm 144}_{\rm 3000}$. This relationship suggests that the core component of the [O III] $\lambda$5007 emission line could potentially serve as a surrogate for the evolutionary stage of a quasar. The quasars at an early stage of evolutions tend to show weaker [O III] $\lambda$5007 emission, while older quasars exhibit stronger [O III] $\lambda$5007 emission.
Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey
2024-10-24
Xinyu Wang, Wenbo Zhang, Sarah Rajtmajer
In today's global digital landscape, misinformation transcends linguistic boundaries, posing a significant challenge for moderation systems. While significant advances have been made in misinformation detection, the focus remains largely on monolingual high-resource contexts, with low-resource languages often overlooked. This survey aims to bridge that gap by providing a comprehensive overview of the current research on low-resource language misinformation detection in both monolingual and multilingual settings. We review the existing datasets, methodologies, and tools used in these domains, identifying key challenges related to: data resources, model development, cultural and linguistic context, real-world applications, and research efforts. We also examine emerging approaches, such as language-agnostic models and multi-modal techniques, while emphasizing the need for improved data collection practices, interdisciplinary collaboration, and stronger incentives for socially responsible AI research. Our findings underscore the need for robust, inclusive systems capable of addressing misinformation across diverse linguistic and cultural contexts.
Interpretable Bilingual Multimodal Large Language Model for Diverse Biomedical Tasks
2024-10-24
Lehan Wang, Haonan Wang, Honglong Yang, Jiaji Mao, Zehong Yang, Jun Shen, Xiaomeng Li
Several medical Multimodal Large Languange Models (MLLMs) have been developed to address tasks involving visual images with textual instructions across various medical modalities, achieving impressive results. Most current medical generalist models are region-agnostic, treating the entire image as a holistic representation. However, they struggle to identify which specific regions they are focusing on when generating a sentence. To mimic the behavior of doctors, who typically begin by reviewing the entire image before concentrating on specific regions for a thorough evaluation, we aim to enhance the capability of medical MLLMs in understanding anatomical regions within entire medical scans. To achieve it, we first formulate Region-Centric tasks and construct a large-scale dataset, MedRegInstruct, to incorporate regional information into training. Combining our collected dataset with other medical multimodal corpora for training, we propose a Region-Aware medical MLLM, MedRegA, which is the first bilingual generalist medical AI system to simultaneously handle image-level and region-level medical vision-language tasks across a broad range of modalities. Our MedRegA not only enables three region-centric tasks, but also achieves the best performance for visual question answering, report generation and medical image classification over 8 modalities, showcasing significant versatility. Experiments demonstrate that our model can not only accomplish powerful performance across various medical vision-language tasks in bilingual settings, but also recognize and detect structures in multimodal medical scans, boosting the interpretability and user interactivity of medical MLLMs. Our project page is https://medrega.github.io.
Identifying Factors Contributing to Bad Days for Software Developers: A Mixed Methods Study
2024-10-24
Ike Obi, Jenna Butler, Sankeerti Haniyur, Brian Hassan, Margaret-Anne Storey, Brendan Murphy
Software development is a dynamic activity that requires engineers to work effectively with tools, processes, and collaborative teams. As a result, the presence of friction can significantly hinder productivity, increase frustration, and contribute to low morale among developers. By contrast, higher satisfaction levels are positively correlated with higher levels of perceived productivity. Hence, understanding the factors that cause bad experiences for developers is critical for fostering a positive and productive engineering environment. In this research, we employed a mixed-method approach, including interviews, surveys, diary studies, and analysis of developer telemetry data to uncover and triangulate common factors that cause "bad days" for developers. The interviews involved 22 developers across different levels and roles. The survey captured the perception of 214 developers about factors that cause them to have "bad days," their frequency, and their impact on job satisfaction. The daily diary study engaged 79 developers for 30 days to document factors that caused "bad days" in the moment. We examined the telemetry signals of 131 consenting participants to validate the impact of bad developer experience using system data. Findings from our research revealed factors that cause "bad days" for developers and significantly impact their work and well-being. We discuss the implications of these findings and suggest future work.
Assessing Alcohol Use Disorder: Insights from Lifestyle, Background, and Family History with Machine Learning Techniques
2024-10-24
Chenlan Wang, Gaojian Huang, Yue Luo
This study explored how lifestyle, personal background, and family history contribute to the risk of developing Alcohol Use Disorder (AUD). Survey data from the All of Us Program was utilized to extract information on AUD status, lifestyle, personal background, and family history for 6,016 participants. Key determinants of AUD were identified using decision trees including annual income, recreational drug use, length of residence, sex/gender, marital status, education level, and family history of AUD. Data visualization and Chi-Square Tests of Independence were then used to assess associations between identified factors and AUD. Afterwards, machine learning techniques including decision trees, random forests, and Naive Bayes were applied to predict an individual's likelihood of developing AUD. Random forests were found to achieve the highest accuracy (82%), compared to Decision Trees and Naive Bayes. Findings from this study can offer insights that help parents, healthcare professionals, and educators develop strategies to reduce AUD risk, enabling early intervention and targeted prevention efforts.
This review explores the most recent advancements in transformer-based data fusion techniques in agricultural remote sensing (RS), with a particular focus on soil analysis. Utilizing a systematic, data-driven approach, we demonstrate that transformers have significantly outperformed conventional deep learning and machine learning methods since 2022, achieving prediction performance between 92% and 97%. The review is specifically focused on soil analysis, due to the importance of soil condition in optimizing crop productivity and ensuring sustainable farming practices. Transformer-based models have shown remarkable capabilities in handling complex multivariate soil data, improving the accuracy of soil moisture prediction, soil element analysis, and other soil-related applications. This systematic review primarily focuses on 1) analysing research trends and patterns in the literature, both chronologically and technically, and 2) conducting a comparative analysis of data fusion approaches, considering factors such as data types, techniques, and RS applications. Finally, we propose a roadmap for implementing data fusion methods in agricultural RS.
ODIN: Strong Clustering of Protoclusters at Cosmic Noon
2024-10-24
Vandana Ramakrishnan, Kyoung-Soo Lee, Nicole Firestone, Eric Gawiser, Maria Celeste Artale, Caryl Gronwall, Lucia Guaita, Sang Hyeok Im, Woong-Seob Jeong, Seongjae Kim, Ankit Kumar, Jaehyun Lee, Byeongha Moon, Nelson Padilla, Changbom Park, Hyunmi Song, Paulina Troncoso, Yujin Yang
The One-hundred-deg$^2$ DECam Imaging in Narrowbands (ODIN) survey is carrying out a systematic search for protoclusters during Cosmic Noon, using Ly$\alpha$-emitting galaxies (LAEs) as tracers. Once completed, ODIN aims to identify hundreds of protoclusters at redshifts of 2.4, 3.1, and 4.5 across seven extragalactic fields, covering a total area of up to 91~deg$^2$. In this work, we report strong clustering of high-redshift protoclusters through the protocluster-LAE cross-correlation function measurements of 150 protocluster candidates at $z~=~2.4$ and 3.1, identified in two ODIN fields with a total area of 13.9 deg$^2$. At $z~=~2.4$ and 3.1, respectively, the inferred protocluster biases are $6.6^{+1.3}_{-1.1}$ and $6.1^{+1.3}_{-1.1}$, corresponding to mean halo masses of $\log \langle M /M_\odot\rangle = 13.53^{+0.21}_{-0.24}$ and $12.96^{+0.28}_{-0.33}$. By the present day, these protoclusters are expected to evolve into virialized galaxy clusters with a mean mass of $\sim$ $10^{14.5}~M_\odot$. By comparing the observed number density of protoclusters to that of halos with the measured clustering strength, we find that our sample is highly complete. Finally, the similar descendant masses derived for our samples at $z=2.4$ and 3.1 assuming that the halo number density remains constant suggest that they represent similar structures observed at different cosmic epochs. As a consequence, any observed differences between the two samples can be understood as redshift evolution. The ODIN protocluster samples will thus provide valuable insights into the cosmic evolution of cluster galaxies.
Dear Diary: A randomized controlled trial of Generative AI coding tools in the workplace
2024-10-24
Jenna Butler, Jina Suh, Sankeerti Haniyur, Constance Hadley
Generative AI coding tools are relatively new, and their impact on developers extends beyond traditional coding metrics, influencing beliefs about work and developers' roles in the workplace. This study aims to illuminate developers' preexisting beliefs about generative AI tools, their self perceptions, and how regular use of these tools may alter these beliefs. Using a mixed methods approach, including surveys, a randomized controlled trial, and a three week diary study, we explored the real world application of generative AI tools within a large multinational software company. Our findings reveal that the introduction and sustained use of generative AI coding tools significantly increases developers' perceptions of these tools as both useful and enjoyable. However, developers' views on the trustworthiness of AI generated code remained unchanged. We also discovered unexpected uses of these tools, such as replacing web searches and fostering creative ideation. Additionally, 84 percent of participants reported positive changes in their daily work practices, and 66 percent noted shifts in their feelings about their work, ranging from increased enthusiasm to heightened awareness of the need to stay current with technological advances. This research provides both qualitative and quantitative insights into the evolving role of generative AI in software development and offers practical recommendations for maximizing the benefits of this emerging technology, particularly in balancing the productivity gains from AI-generated code with the need for increased scrutiny and critical evaluation of its outputs.
Review of wavelet-based unsupervised texture segmentation, advantage of adaptive wavelets
2024-10-24
Yuan Huang, Valentin De Bortoli, Fugen Zhou, Jerome Gilles
Wavelet-based segmentation approaches are widely used for texture segmentation purposes because of their ability to characterize different textures. In this paper, we assess the influence of the chosen wavelet and propose to use the recently introduced empirical wavelets. We show that the adaptability of the empirical wavelet permits to reach better results than classic wavelets. In order to focus only on the textural information, we also propose to perform a cartoon + texture decomposition step before applying the segmentation algorithm. The proposed method is tested on six classic benchmarks, based on several popular texture images.
Network analysis is increasingly important across various fields, including the fragrance industry, where perfumes are represented as nodes and shared user preferences as edges in perfume networks. Community detection can uncover clusters of similar perfumes, providing insights into consumer preferences, enhancing recommendation systems, and informing targeted marketing strategies. This study aims to apply community detection techniques to group perfumes favored by users into relevant clusters for better recommendations. We constructed a bipartite network from user reviews on the Persian retail platform "Atrafshan," with nodes representing users and perfumes, and edges formed by positive comments. This network was transformed into a Perfume Co-Preference Network, connecting perfumes liked by the same users. By applying community detection algorithms, we identified clusters based on shared preferences, enhancing our understanding of user sentiment in the fragrance market. To improve sentiment analysis, we integrated emojis and a user voting system for greater accuracy. Emojis, aligned with their Persian counterparts, captured the emotional tone of reviews, while user ratings for scent, longevity, and sillage refined sentiment classification. Edge weights were adjusted by combining adjacency values with user ratings in a 60:40 ratio, reflecting both connection strength and user preferences. These enhancements led to improved modularity of detected communities, resulting in more accurate perfume groupings. This research pioneers the use of community detection in perfume networks, offering new insights into consumer preferences. Our advancements in sentiment analysis and edge weight refinement provide actionable insights for optimizing product recommendations and marketing strategies in the fragrance industry.
Rewrite it in Rust: A Computational Physics Case Study
2024-10-24
Willow Veytsman, Shuang Zhai, Chen Ding, Adam B. Sefkow
Surveys of computational science show that many scientists use languages like C and C++ in order to write code for scientific computing, especially in scenarios where performance is a key factor. In this paper, we seek to evaluate the use of Rust in such a scenario, through implementations of a physics simulation in both C++ and Rust. We also create a parallel version of our Rust code, in order to further explore performance as well as parallel code complexity. Measuring performance as program runtime, we find that Rust can offer better performance than C++, with some test cases showing as much as a 5.6$\times$ performance increase, and that parallel code in Rust can further improve performance while being easy to write safely. Finally, we provide some preliminary profiling to better understand the difference between the way our implementations perform.
Ground-based gravitational-wave detectors like the Advanced LIGO, Advanced Virgo, and KAGRA experiments now regularly witness gravitational waves from compact binary mergers: the relativistic collisions of neutron stars and/or stellar-mass black holes. With hundreds of such events observed to date, gravitational-wave observations are enabling increasingly precise surveys of the demographics of merging compact binaries, including the distributions of their masses, rotation rates, and positions throughout the Universe. This article will provide an overview of our observational knowledge of the compact binary population, as it stands today. I will discuss, in turn, observations of binary black holes, binary neutron stars, and neutron star-black hole mergers, describing what is currently known (or not yet known) about these different gravitational-wave sources. I will highlight emerging classes of binaries that do not fall cleanly into any of these existing categories. And I will conclude by reviewing the methodology by which population analyses of gravitational-wave sources are performed.
Hidden in Plain Sight: Searching for Dark Companions to Bright Stars with the Large Binocular Telescope and SHARK-VIS
2024-10-24
D. M. Rowan, Todd A. Thompson, C. S. Kochanek, G. Li Causi, J. Roth, P. Vaccari, F. Pedichini, R. Piazzesi, S. Antoniucci, V. Testa, M. C. Johnson, J. Crass, J. R. Crepp, A. Bechter, E. B. Bechter, B. L. Sands, R. J. Harris
We report the results from a pilot study to search for black holes and other dark companions in binary systems using direct imaging with SHARK-VIS and the iLocater pathfinder "Lili" on the Large Binocular Telescope. Starting from known single-lined spectroscopic binaries, we select systems with high mass functions that could host dark companions and whose spectroscopic orbits indicate a projected orbital separation $\geq 30$ mas. For this first exploration, we selected four systems (HD 137909, HD 104438, HD 117044, and HD 176695). In each case, we identify a luminous companion and measure the flux ratio and angular separation. However, two of the systems (HD 104438 and HD 176695) are not consistent with simple binary systems and are most likely hierarchical triples. The observed companions rule out a massive compact object for HD 137909, HD 117044, and HD 176695. HD 104438 requires further study because the identified star cannot be responsible for the RV orbit and is likely a dwarf tertiary companion. The SHARK-VIS observation was taken near pericenter, and a second image near apocenter is needed to discriminate between a closely separated luminous secondary and a compact object. We show how the combination of RVs and direct imaging can be used to constrain the orbital inclination and companion mass, and discuss the potential of high resolution direct imaging surveys to identify and confirm non-interacting compact object candidates.
The Atacama Cosmology Telescope: A measurement of galaxy cluster temperatures through relativistic corrections to the thermal Sunyaev-Zeldovich effect
2024-10-24
William R. Coulton, Adriaan J. Duivenvoorden, Zachary Atkins, Nicholas Battaglia, Elia Stefano Battistelli, J Richard Bond, Hongbo Cai, Erminia Calabrese, Steve K. Choi, Kevin T. Crowley, Mark J. Devlin, Jo Dunkley, Simone Ferraro, Yilun Guan, Carlos Hervías-Caimapo, J. Colin Hill, Matt Hilton, Adam D. Hincks, Arthur Kosowsky, Mathew S. Madhavacheril, Joshiwa van Marrewijk, Fiona McCarthy, Kavilan Moodley, Tony Mroczkowski, Michael D. Niemack, Lyman A. Page, Bruce Partridge, Emmanuel Schaan, Neelima Sehgal, Blake Sherwin, Cristóbal Sifón, David N. Spergel, Suzanne T. Staggs, Eve M. Vavagiakis, Edward J. Wollack
The high electron temperature in galaxy clusters ($>1\,$keV or $>10^7\,$K) leads to corrections at the level of a few percent in their thermal Sunyaev-Zeldovich effect signatures. Both the size and frequency dependence of these corrections, which are known as relativistic temperature corrections, depend upon the temperature of the objects. In this work we exploit this effect to measure the average temperature of a stack of Compton-$y$ selected clusters. Specifically, we apply the "spectroscopic method" and search for the temperature that best fits the clusters' signal measured at frequencies from 30 to 545 GHz by the Atacama Cosmology Telescope and Planck satellite. We measure the average temperature of clusters detected in the ACT maps to be $8.5\pm 2.4\,$keV, with an additional systematic error of comparable amplitude dominated by passband uncertainty. Upcoming surveys, such as the Simons Observatory and CMB-S4, have the potential to dramatically improve upon these measurements and thereby enable precision studies of cluster temperatures with millimeter observations. The key challenge for future observations will be mitigating instrumental systematic effects, which already limit this analysis.
Perspectives on the Physics of Late-Type Stars from Beyond Low Earth Orbit, the Moon and Mars
2024-10-24
Savita Mathur, Ângela R. G. Santos
With the new discoveries enabled thanks to the recent space missions, stellar physics is going through a revolution. However, these discoveries opened the door to many new questions that require more observations. The European Space Agency's Human and Robotic Exploration programme provides an excellent opportunity to push forward the limits of our knowledge and better understand stellar structure and dynamics evolution. Long-term observations, Ultra-Violet observations, and a stellar imager are a few highlights of proposed missions for late-type stars that will enhance the already planned space missions.
The Roberts linkage is recognized for enabling long-period pendulum motion in a compact format. Utilizing this characteristic, we are developing a three-point Roberts linkage for vibration isolation systems, with an eye towards its potential contribution to the development of next-generation interferometric gravitational wave antennas. In this article, we derived the equations to determine the essential parameters when using this linkage as a vibration isolation system, namely the equivalent pendulum length and the relationship between translational motion of the center of mass and rigid body rotation, from size parameters. Additionally, we analyzed the behavior in response to various errors.
The growing capabilities of AI in generating video content have brought forward significant challenges in effectively evaluating these videos. Unlike static images or text, video content involves complex spatial and temporal dynamics which may require a more comprehensive and systematic evaluation of its contents in aspects like video presentation quality, semantic information delivery, alignment with human intentions, and the virtual-reality consistency with our physical world. This survey identifies the emerging field of AI-Generated Video Evaluation (AIGVE), highlighting the importance of assessing how well AI-generated videos align with human perception and meet specific instructions. We provide a structured analysis of existing methodologies that could be potentially used to evaluate AI-generated videos. By outlining the strengths and gaps in current approaches, we advocate for the development of more robust and nuanced evaluation frameworks that can handle the complexities of video content, which include not only the conventional metric-based evaluations, but also the current human-involved evaluations, and the future model-centered evaluations. This survey aims to establish a foundational knowledge base for both researchers from academia and practitioners from the industry, facilitating the future advancement of evaluation methods for AI-generated video content.
Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies
2024-10-24
Luping Wang, Sheng Chen, Linnan Jiang, Shu Pan, Runze Cai, Sen Yang, Fei Yang
The large models, as predicted by scaling raw forecasts, have made groundbreaking progress in many fields, particularly in natural language generation tasks, where they have approached or even surpassed human levels. However, the unprecedented scale of their parameters brings significant computational and storage costs. These large models require substantial computational resources and GPU memory to operate. When adapting large models to specific downstream tasks, their massive parameter scale poses a significant challenge in fine-tuning on hardware platforms with limited computational power and GPU memory. To address this issue, Parameter-Efficient Fine-Tuning (PEFT) offers a practical solution by efficiently adjusting the parameters of large pre-trained models to suit various downstream tasks. Specifically, PEFT adjusts the parameters of pre-trained large models to adapt to specific tasks or domains, minimizing the introduction of additional parameters and the computational resources required. This review mainly introduces the preliminary knowledge of PEFT, the core ideas and principles of various PEFT algorithms, the applications of PEFT, and potential future research directions. By reading this review, we believe that interested parties can quickly grasp the PEFT methodology, thereby accelerating its development and innovation.
The electrocardiogram (ECG) is ubiquitous across various healthcare domains, such as cardiac arrhythmia detection and sleep monitoring, making ECG analysis critically essential. Traditional deep learning models for ECG are task-specific, with a narrow scope of functionality and limited generalization capabilities. Recently, foundation models (FMs), also known as large pre-training models, have fundamentally reshaped the scheme of model design and representation learning, enhancing the performance across a variety of downstream tasks. This success has drawn interest in the exploration of FMs to address ECG-based medical challenges concurrently. This survey provides a timely, comprehensive and up-to-date overview of FMs for large-scale ECG-FMs. First, we offer a brief background introduction to FMs. Then, we discuss the model architectures, pre-training methods, and adaptation approaches of ECG-FMs from a methodology perspective. Despite the promising opportunities of ECG-FMs, we also outline the challenges and potential future directions. Overall, this survey aims to provide researchers and practitioners with insights into the research of ECG-FMs on theoretical underpinnings, domain-specific applications, and avenues for future exploration.
Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey
2024-10-24
Kun Shi, Shibo He, Zhenyu Shi, Anjun Chen, Zehui Xiong, Jiming Chen, Jun Luo
Multi-modal fusion is imperative to the implementation of reliable object detection and tracking in complex environments. Exploiting the synergy of heterogeneous modal information endows perception systems the ability to achieve more comprehensive, robust, and accurate performance. As a nucleus concern in wireless-vision collaboration, radar-camera fusion has prompted prospective research directions owing to its extensive applicability, complementarity, and compatibility. Nonetheless, there still lacks a systematic survey specifically focusing on deep fusion of radar and camera for object detection and tracking. To fill this void, we embark on an endeavor to comprehensively review radar-camera fusion in a holistic way. First, we elaborate on the fundamental principles, methodologies, and applications of radar-camera fusion perception. Next, we delve into the key techniques concerning sensor calibration, modal representation, data alignment, and fusion operation. Furthermore, we provide a detailed taxonomy covering the research topics related to object detection and tracking in the context of radar and camera technologies.Finally, we discuss the emerging perspectives in the field of radar-camera fusion perception and highlight the potential areas for future research.
CO-CAVITY project: Molecular gas and star formation in void galaxies
2024-10-23
M. I. Rodríguez, U. Lisenfeld, S. Duarte Puertas, D. Espada, J. Domínguez-Gómez, M. Sánchez-Portal, A. Bongiovanni, M. Alcázar-Laynez, M. Argudo-Fernández, B. Bidaran, S. B. De Daniloff, J. Falcón-Barroso, E. Florido, R. García-Benito, A. Jimenez, K. Kreckel, R. F. Peletier, I. Pérez, T. Ruiz-Lara, L. Sánchez-Menguiano, G. Torres-Ríos, P. Villalba-González, S. Verley, A. Zurita
Cosmic voids, distinguished by their low-density environment, provide a unique opportunity to explore the interplay between the cosmic environment and the processes of galaxy formation and evolution. Data on the molecular gas has been scarce so far. In this paper, we continue previous research done in the CO-CAVITY pilot project to study the molecular gas content and properties in void galaxies to search for possible differences compared to galaxies that inhabit denser structures. We observed at the IRAM 30 m telescope the CO(1-0) and CO(2-1) emission of 106 void galaxies selected from the CAVITY survey. Together with data from the literature, we obtained a sample of 200 void galaxies with CO data. We conducted a comprehensive comparison of the specific star formation rate (sSFR = SFR/M$_*$), the molecular gas fraction (MH$_2$/M$_*$), and the star formation efficiency (SFE = SFR/MH$_2$) between the void galaxies and a comparison sample of galaxies in filaments and walls, selected from the xCOLD GASS survey. We found no statistically significant difference between void galaxies and the comparison sample in the molecular gas fraction as a function of stellar mass for galaxies on the star-forming main sequence (SFMS). However, for void galaxies, the SFE was found to be constant across all stellar mass bins, while there is a decreasing trend with M$_*$ for the comparison sample. Finally, we found some indications for a smaller dynamical range in the molecular gas fraction as a function of distance to the SFMS in void galaxies. Overall, our analysis finds that the molecular gas properties of void galaxies are not very different from denser environments. The physical origin of the most significant difference that we found - a constant SFE as a function of stellar mass in void galaxies - is unclear and requires further investigation and higher-resolution data.
2024-10-23
Xue Zheng, Tian Xie, Xuwei Tan, Aylin Yener, Xueru Zhang, Ali Payani, Myungjin Lee
Performative prediction (PP) is a framework that captures distribution shifts that occur during the training of machine learning models due to their deployment. As the trained model is used, its generated data could cause the model to evolve, leading to deviations from the original data distribution. The impact of such model-induced distribution shifts in the federated learning (FL) setup remains unexplored despite being increasingly likely to transpire in real-life use cases. Although Jin et al. (2024) recently extended PP to FL in a straightforward manner, the resulting model only converges to a performative stable point, which may be far from optimal. The methods in Izzo et al. (2021); Miller et al. (2021) can find a performative optimal point in centralized settings, but they require the performative risk to be convex and the training data to be noiseless, assumptions often violated in realistic FL systems. This paper overcomes all of these shortcomings and proposes Performative robust optimal Federated Learning (ProFL), an algorithm that finds performative optimal points in FL from noisy and contaminated data. We present the convergence analysis under the Polyak-Lojasiewicz condition, which applies to non-convex objectives. Extensive experiments on multiple datasets validate our proposed algorithms' efficiency.
2024-10-23
Robert D Kavanagh, Harish K Vedantham, Kovi Rose, Sanne Bloot
At the sub-stellar boundary, signatures of magnetic fields begin to manifest at radio wavelengths, analogous to the auroral emission of the magnetised solar system planets. This emission provides a singular avenue for measuring magnetic fields at planetary scales in extrasolar systems. So far, exoplanets have eluded detection at radio wavelengths. However, ultracool dwarfs (UCDs), their higher mass counterparts, have been detected for over two decades in the radio. Given their similar characteristics to massive exoplanets, UCDs are ideal targets to bridge our understanding of magnetic field generation from stars to planets. In this work, we develop a new tomographic technique for inverting both the viewing angle and large-scale magnetic field structure of UCDs from observations of coherent radio bursts. We apply our methodology to the nearby T8 dwarf WISE J062309.94-045624.6 (J0623) which was recently detected at radio wavelengths, and show that it is likely viewed pole-on. We also find that J0623's rotation and magnetic axes are misaligned significantly, reminiscent of Uranus and Neptune, and show that it may be undergoing a magnetic cycle with a period exceeding 6 months in duration. These findings demonstrate that our method is a robust new tool for studying magnetic fields on planetary-mass objects. With the advent of next-generation low-frequency radio facilities, the methods presented here could facilitate the characterisation of exoplanetary magnetospheres for the first time.
Introduction to Fusion Ignition Principles: Zeroth Order Factors of Fusion Energy Research
2024-10-23
Huasheng Xie
The physical goal of fusion energy research is to confine fusion fuel in a certain way so that the energy released from fusion exceeds the energy consumed to sustain the fusion process, thereby achieving economically viable energy production. Based on fundamental physics, this book focuses on the physics of the core region in fusion energy reactors, discussing the relevant limiting factors and parameter ranges. By examining the zeroth-order quantity system of the fundamental principles of fusion ignition, we review the current progress and challenges in fusion energy research. These challenges encompass various aspects such as physics, engineering, materials, and economics, providing better insights for the future development of fusion as an energy source. [This English version is primarily a translation generated using ChatGPT based on the original Chinese book, and can be cited as "Huasheng Xie, Introduction to Fusion Ignition Principles, USTC press, Hefei, 2023." For any inaccuracies or ambiguities, please refer to the original Chinese version for clarification.]
Local Primordial Non-Gaussian Bias at the Field Level
2024-10-23
James M. Sullivan, Shi-Fan Chen
Local primordial non-Gaussianity (LPNG) couples long-wavelength cosmological fluctuations to the short-wavelength behavior of galaxies. This coupling is encoded in bias parameters including $b_{\phi}$ and $b_{\delta\phi}$ at linear and quadratic order in the large-scale biasing framework. We perform the first field-level measurement of $b_{\phi}$ and $b_{\delta\phi}$ using Lagrangian bias and non-linear displacements from N-body simulations. We compare our field level measurements with universality predictions and separate universe results, finding qualitative consistency, but disagreement in detail. We also quantify the information on $f_{\mathrm{NL}}^{(\mathrm{loc})}$ available in the field given various assumptions on knowledge of $b_{\phi}$ at fixed initial conditions. We find that it is not possible to precisely constrain $f_{\mathrm{NL}}^{(\mathrm{loc})}$ when marginalizing over $b_{\phi} f_{\mathrm{NL}}^{(\mathrm{loc})}$ even at the field level, observing a 2-3X degradation in constraints between a linear and quadratic biasing model on perturbative field-level mocks, suggesting that a $b_{\phi}$ prior is necessary to meaningfully constrain $f_{\mathrm{NL}}^{(\mathrm{loc})}$ at the field level even in this idealized scenario. For simulated dark matter halos, the pure $f_{\mathrm{NL}}^{(\mathrm{loc})}$ constraints from both linear and quadratic field-level models appear biased when marginalizing over bias parameters including $b_{\phi}$ and $b_{\delta\phi}$ due largely to the $f_{\mathrm{NL}}^{(\mathrm{loc})} - b_\phi$ degeneracy. Our results are an important consistency test of the large-scale bias framework for LPNG and highlight the importance of physically motivated priors on LPNG bias parameters for future surveys.
JWST/NIRSpec Reveals the Nested Morphology of Disk Winds from Young Stars
2024-10-23
Ilaria Pascucci, Tracy L. Beck, Sylvie Cabrit, Naman S. Bajaj, Suzan Edwards, Fabien Louvet, Joan Najita, Bennett N. Skinner, Uma Gorti, Colette Salyk, Sean D. Brittain, Sebastiaan Krijt, James Muzerolle Page, Maxime Ruaud, Kamber Schwarz, Dmitry Semenov, Gaspard Duchene, Marion Villenave
Radially extended disk winds could be the key to unlocking how protoplanetary disks accrete and how planets form and migrate. A distinctive characteristic is their nested morphology of velocity and chemistry. Here we report JWST/NIRSpec spectro-imaging of four young stars with edge-on disks in the Taurus star-forming region that demonstrate the ubiquity of this structure. In each source, a fast collimated jet traced by [Fe II] is nested inside a hollow cavity within wider lower-velocity H2 and, in one case, also CO ro-vibrational (v=1-0) emission. Furthermore, in one of our sources, ALMA CO(2-1) emission, paired with our NIRSpec images, reveals the nested wind structure extends further outward. This nested wind morphology strongly supports theoretical predictions for wind-driven accretion and underscores the need for theoretical work to assess the role of winds in the formation and evolution of planetary systems
Radio sensitivity to a new population of millisecond pulsars in the Sagittarius Dwarf Spheroidal Galaxy
2024-10-23
Lucia Gebauer-Werner, Oscar Macias, Christoph Weniger
Observations with the Fermi Gamma-Ray Space Telescope reveal an excess of extended gamma-ray emission likely caused by an undiscovered population of millisecond pulsars (MSPs) in the core of the Sagittarius dwarf spheroidal galaxy (Sgr dSph). However, additional evidence, such as multi-wavelength searches, is necessary to confirm this theory. A significant discovery could be made if radio pulsations from individual MSPs in the Sgr dSph are detected. In this study, we investigate the possibility of detecting MSPs in the Sgr dSph with present and upcoming radio surveys using a phenomenological model based on the observed luminosity function of MSPs in the Milky Way's globular clusters. Our findings suggest that the Square Kilometer Array (SKA) is the most sensitive instrument for detecting these objects. We demonstrate that to observe one MSP with MeerKAT, we would need to perform a pointing observation of the core region of the Sgr dSph for about two hours. In this same observation time, SKA can identify $9^{+5}_{-3}$ MSPs in the entire system. Based on the distance of the Sgr dSph galaxy and our dispersion measure distance estimate, we find it possible to differentiate between MSPs belonging to the Sgr dSph and those of the Galactic disk and bulge. Furthermore, the MSPs hypothesis for the Sgr dSph gamma-ray excess could be confirmed at the 99.7\% confidence level by detecting at least six MSPs in a two-hour SKA observation of the Sgr dSph.
Retrieving snow depth distribution by downscaling ERA5 Reanalysis with ICESat-2 laser altimetry
2024-10-23
Zhihao Liu, Simon Filhol, Désirée Treichler
Estimating the variability of seasonal snow cover, in particular snow depth in remote areas, poses significant challenges due to limited spatial and temporal data availability. This study uses snow depth measurements from the ICESat-2 satellite laser altimeter, which are sparse in both space and time, and incorporates them with climate reanalysis data into a downscaling-calibration scheme to produce monthly gridded snow depth maps at microscale (10 m). Snow surface elevation measurements from ICESat-2 along profiles are compared to a digital elevation model to determine snow depth at each point. To efficiently turn sparse measurements into snow depth maps, a regression model is fitted to establish a relationship between the retrieved snow depth and the corresponding ERA5 Land snow depth. This relationship, referred to as subgrid variability, is then applied to downscale the monthly ERA5 Land snow depth data. The method can provide timeseries of monthly snow depth maps for the entire ERA5 time range (since 1950). The validation of downscaled snow depth data was performed at an intermediate scale (100 m x 500 m) using datasets from airborne laser scanning (ALS) in the Hardangervidda region of southern Norway. Results show that snow depth prediction achieved R2 values ranging from 0.74 to 0.88 (post-calibration). The method relies on globally available data and is applicable to other snow regions above the treeline. Though requiring area-specific calibration, our approach has the potential to provide snow depth maps in areas where no such data exist and can be used to extrapolate existing snow surveys in time and over larger areas. With this, it can offer valuable input data for hydrological, ecological or permafrost modeling tasks.
Anderson localization of elementary excitations in disordered binary Bose mixtures: Effects of the Lee-Huang-Yang quantum and thermal corrections
2024-10-23
Zohra Mehri, Abdelaali Boudjemaa
We investigate analytically and numerically the Anderson localization of quasiparticles in binary Bose mixtures in the presence of the Lee-Huang-Yang quantum and thermal corrections subjected to correlated disordered potentials. We calculate the density profiles, the Bogoliubov quasiparticles modes, and the localization length in both the mixture and droplet phases. We show that for one-dimensional speckle potentials, the peculiar interplay of disorder and the Lee-Huang-Yang fluctuations may enhance localization in one component while inducing delocalization in the other. Our results reveal also that thermal fluctuations lead to the emergence of two and multiple localization maxima. In the droplet state, our findings uncover that the Anderson localization of quasiparticles is weak in the flat-top region since its excitation modes are restricted to those below the particle-emission threshold.
Dynamic surface-enhanced Raman spectroscopy (SERS) is nowadays one of the most interesting applications of SERS, in particular for single molecule studies. In fact, it enables the study of real-time processes at the molecular level. This review summarizes the latest developments in dynamic SERS techniques and their applications, focusing on new instrumentation, data analysis methods, temporal resolution and sensitivity improvements, and novel substrates. We highlight the progress and applications of single-molecule dynamic SERS in monitoring chemical reactions, catalysis, biomolecular interactions, conformational dynamics, and real-time sensing and detection. We aim to provide a comprehensive review on its advancements, applications as well as its current challenges and development frontiers.
Kleinian singularities: some geometry, combinatorics and representation theory
2024-10-23
Lukas Bertsch, Ádám Gyenge, Balázs Szendrői
We review the relationship between discrete groups of symmetries of Euclidean three-space, constructions in algebraic geometry around Kleinian singularities including versions of Hilbert and Quot schemes, and their relationship to finite-dimensional and affine Lie algebras via the McKay correspondence. We focus on combinatorial aspects, such as the enumeration of certain types of partition-like objects, reviewing in particular a recently developed root-of-unity-substitution calculus. While the most complete results are in type A, we also develop aspects of the theory in type D, and end with some questions about the exceptional type E cases.
Is the GPU Half-Empty or Half-Full? Practical Scheduling Techniques for LLMs
2024-10-23
Ferdi Kossmann, Bruce Fontaine, Daya Khudia, Michael Cafarella, Samuel Madden
Serving systems for Large Language Models (LLMs) improve throughput by processing several requests concurrently. However, multiplexing hardware resources between concurrent requests involves non-trivial scheduling decisions. Practical serving systems typically implement these decisions at two levels: First, a load balancer routes requests to different servers which each hold a replica of the LLM. Then, on each server, an engine-level scheduler decides when to run a request, or when to queue or preempt it. Improved scheduling policies may benefit a wide range of LLM deployments and can often be implemented as "drop-in replacements" to a system's current policy. In this work, we survey scheduling techniques from the literature and from practical serving systems. We find that schedulers from the literature often achieve good performance but introduce significant complexity. In contrast, schedulers in practical deployments often leave easy performance gains on the table but are easy to implement, deploy and configure. This finding motivates us to introduce two new scheduling techniques, which are both easy to implement, and outperform current techniques on production workload traces.
Anomalous conductance steps in 3D TI HgTe-based quantum point contacts
2024-10-23
Elisabeth Richter, Michael Barth, Dmitriy A. Kozlov, Angelika Knothe, Nikolay N. Mikhailov, Juliane Steidl, Cosimo Gorini, Stefan Hartl, Wolfgang Himmler, Klaus Richter, Dieter Weiss
We explore electrical transport through a point contact in strained HgTe, a three-dimensional topological insulator. In the absence of a magnetic field $B$, there is no quantization. However, under higher magnetic fields, we observe distinct non-integer conductance steps. Based on numerical tight-binding calculations and a phenomenological Landauer-B\"uttiker approach, we attribute these atypical, non-integer quantized plateaus to significant scattering effects at the point contact.
YOLOv11: An Overview of the Key Architectural Enhancements
2024-10-23
Rahima Khanam, Muhammad Hussain
This study presents an architectural analysis of YOLOv11, the latest iteration in the YOLO (You Only Look Once) series of object detection models. We examine the models architectural innovations, including the introduction of the C3k2 (Cross Stage Partial with kernel size 2) block, SPPF (Spatial Pyramid Pooling - Fast), and C2PSA (Convolutional block with Parallel Spatial Attention) components, which contribute in improving the models performance in several ways such as enhanced feature extraction. The paper explores YOLOv11's expanded capabilities across various computer vision tasks, including object detection, instance segmentation, pose estimation, and oriented object detection (OBB). We review the model's performance improvements in terms of mean Average Precision (mAP) and computational efficiency compared to its predecessors, with a focus on the trade-off between parameter count and accuracy. Additionally, the study discusses YOLOv11's versatility across different model sizes, from nano to extra-large, catering to diverse application needs from edge devices to high-performance computing environments. Our research provides insights into YOLOv11's position within the broader landscape of object detection and its potential impact on real-time computer vision applications.
Beware of Calibration Data for Pruning Large Language Models
2024-10-23
Yixin Ji, Yang Xiang, Juntao Li, Qingrong Xia, Ping Li, Xinyu Duan, Zhefeng Wang, Min Zhang
As large language models (LLMs) are widely applied across various fields, model compression has become increasingly crucial for reducing costs and improving inference efficiency. Post-training pruning is a promising method that does not require resource-intensive iterative training and only needs a small amount of calibration data to assess the importance of parameters. Previous research has primarily focused on designing advanced pruning methods, while different calibration data's impact on pruning performance still lacks systematical exploration. We fill this blank and surprisingly observe that the effects of calibration data even value more than designing advanced pruning strategies, especially for high sparsity. Our preliminary exploration also discloses that using calibration data similar to the training data can yield better performance. As pre-training data is usually inaccessible for advanced LLMs, we further provide a self-generating calibration data synthesis strategy to construct feasible calibration data. We conduct experiments on the recent strong open-source LLMs (e.g., DCLM, and LLaMA-3), and the results show that the proposed method outperforms commonly used calibration data and can effectively enhance strong pruning methods (e.g., Wanda, OWL).
ProGeny II: the impact of libraries and model configurations on inferred galaxy properties in SED fitting
2024-10-23
Sabine Bellstedt, Aaron S. G. Robotham
We use a volume-complete sample of ~8,000 galaxies from the GAMA survey to characterise the impact of stellar population libraries (SPLs) and model configurations on the resulting inferred galaxy properties from Spectral Energy Distribution (SED) fitting. We compare a fiducial SPL from ProGeny (a new tool that can generate SPLs quickly and flexibly) against five other commonly used SPLs using the SED-fitting code ProSpect. The impact of selecting each SPL is compared to the consequence of changing the model implementation in the SED fitting process, including the implementation of metallicity evolution versus a fixed or constant metallicity, and a functional parametric star formation history (SFH) versus a stepwise parametric (or "non-parametric") SFH. Furthermore, we use ProGeny to assess the impact of sub-SPL choices, including isochrone selection, atmosphere selection, and IMF selection. Through a comparison of derived stellar masses, star formation rates, metallicities, ages, and the inferred cosmic star formation history (CSFH), we rank the impact of varying choices. Overall the assumption of a solar metallicity creates the greatest biases, with a substantial impact also caused by the choice of a specific SPL. To recover a CSFH most consistent with observations, we advocate for the use of the fiducial implementation with a skewed Normal functional form for the SFH, and an evolving metallicity. While all current SPLs currently underestimate the peak in the CSFH, ProGeny and FSPS are the closest to recovering the observed CSFH.
The MAGPI Survey: Insights into the Lyman-alpha line widths and the size of ionized bubbles at the edge of cosmic reionization
2024-10-23
T. Mukherjee, T. Zafar, T. Nanayakkara, A. Gupta, S. Gurung-Lopez, A. Battisti, E. Wisnioski, C. Foster, J. T. Mendel, K. E. Harborne, C. D. P. Lagos, T. Kodama, S. M. Croom, S. Thater, J. Webb, S. Barsanti, S. M. Sweet, J. Prathap, L. M. Valenzuela, A. Mailvaganam, J. L. Carrillo Martinez
We present spectroscopic properties of 22 Lyman-alpha emitters(LAEs) at z=5.5-6.6 with Lyman-alpha(Lya) luminosity log($L_{Lya}$[$ergs^{-1}$])=42.4-43.5, obtained using VLT/MUSE as part of the Middle Ages Galaxy Properties with Integral Field Spectroscopy(MAGPI) survey. Additionally, we incorporate broad-band photometric data from the Subaru Hyper Suprime-Cam(HSC) for 17 LAEs in our sample. The HSC-y band magnitudes show that our LAEs are UV-bright, with rest-frame absolute UV magnitudes -19.7 < $M_{UV}$ < -23.3. We find that the Lya line width increases with luminosity, and this trend becomes more prominent at z > 6 where Lya lines become significantly broadened (> 260 $kms^{-1}$) at luminosities log($L_{Lya}$[$ergs^{-1}$]) > 43. This broadening is consistent with previous studies, suggesting that these sources are located inside larger ionized bubbles. We observe a slightly elevated ionizing photon production efficiency estimated for LAEs at z > 6, indicating that younger galaxies could be producing more ionizing photons per UV luminosity. A tentative anti-correlation between ionizing photon production efficiency and Lya rest-frame equivalent width is noticed, which could indicate a time delay between production and escape of ionizing photon primarily due to supernovae activity. Furthermore, we find a positive correlation between bubble radius and Lya line width, which again suggests that large ionized bubbles are created around these LAEs, allowing them to self-shield from the scattering effects of the intergalactic medium (IGM). We also detect two closely separated LAEs at z=6.046 (projected spatial separation is 15.92 kpc). The size of their respective bubbles suggests that they likely sit inside a common large ionized region. Such a closely-separated LAE pair increases the size of ionized bubble, potentially allowing a boosted transmission of Lya through neutral IGM. (Abridged)
Since the discovery of superconductivity in the Fe(Te,Se) system, it has been a general consensus that the end member of FeTe is not superconducting. Nonetheless, in recent years, there have been reports of superconducting FeTe films, but the origin of their superconductivity remains mysterious. Here, we provide the first comprehensive review of all the reported FeTe films regarding the relationship between their superconductivity and neighboring layers. Based on this review, we show that telluride neighboring layers are the key to superconducting FeTe films. Then, with additional new studies, we show that stoichiometric Te content, which can be readily achieved in FeTe films with the assistance of neighboring telluride layers, might be crucial to stabilizing the superconductivity in this system. This work provides insights into the underlying mechanism behind superconductivity in FeTe films and sheds light on the critical role of neighboring layers and stoichiometry control toward manipulating topological superconductivity in FeTe heterostructures.
Deep Generative Models for 3D Medical Image Synthesis
2024-10-23
Paul Friedrich, Yannik Frisch, Philippe C. Cattin
Deep generative modeling has emerged as a powerful tool for synthesizing realistic medical images, driving advances in medical image analysis, disease diagnosis, and treatment planning. This chapter explores various deep generative models for 3D medical image synthesis, with a focus on Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Models (DDMs). We discuss the fundamental principles, recent advances, as well as strengths and weaknesses of these models and examine their applications in clinically relevant problems, including unconditional and conditional generation tasks like image-to-image translation and image reconstruction. We additionally review commonly used evaluation metrics for assessing image fidelity, diversity, utility, and privacy and provide an overview of current challenges in the field.
Multiscale approach for modeling magnetization properties of inhomogeneous ultrathin magnetic layers
2024-10-23
Julien Mordret, Jean-Christophe Le Breton, Gabriel Delhaye, Bruno Lépine, Philippe Schieffer, Sylvain Tricot
We report on spin atomistic calculations used to model static and dynamic magnetic properties of inhomogeneous ultrathin iron films. Active magnetic layers in next-generation spintronic devices are becoming so thin that they exhibit some variable degree of roughness at the low-scale making them magnetically inhomogeneous. We propose a multiscale approach to progressively shift from a rough atomic-scale system to an ensemble of macrospins. By studying nanoscale islands of atoms in contact with each other, we demonstrate that ultrathin rough layers can be described by a set of macrospins coupled by a Heisenberg-like exchange interaction driven by the existence and shape of nanoconstrictions linking the islands. We show that the magnetization dynamics at 0 K is strongly impacted by this surface morphology since the resonant frequency of a typical ultrathin iron layer can drop by up to an order of magnitude due to inhomogeneities. Additionally, we used Monte Carlo simulations to determine the ferromagnetic-paramagnetic and spin reorientation transition temperatures for various morphology parameters and we show how nanoconstrictions and shapes of the atomic clusters can modify these transition temperatures. Our results demonstrate the possibility to account for the morphology of ultrathin structures with significant roughness. We believe that our approach makes it possible to model complete devices as close as possible to experimental reality.
Integrating Large Language Models for UAV Control in Simulated Environments: A Modular Interaction Approach
2024-10-23
Abhishek Phadke, Alihan Hadimlioglu, Tianxing Chu, Chandra N Sekharan
The intersection of LLMs (Large Language Models) and UAV (Unoccupied Aerial Vehicles) technology represents a promising field of research with the potential to enhance UAV capabilities significantly. This study explores the application of LLMs in UAV control, focusing on the opportunities for integrating advanced natural language processing into autonomous aerial systems. By enabling UAVs to interpret and respond to natural language commands, LLMs simplify the UAV control and usage, making them accessible to a broader user base and facilitating more intuitive human-machine interactions. The paper discusses several key areas where LLMs can impact UAV technology, including autonomous decision-making, dynamic mission planning, enhanced situational awareness, and improved safety protocols. Through a comprehensive review of current developments and potential future directions, this study aims to highlight how LLMs can transform UAV operations, making them more adaptable, responsive, and efficient in complex environments. A template development framework for integrating LLMs in UAV control is also described. Proof of Concept results that integrate existing LLM models and popular robotic simulation platforms are demonstrated. The findings suggest that while there are substantial technical and ethical challenges to address, integrating LLMs into UAV control holds promising implications for advancing autonomous aerial systems.
Flexible Approach for Statistical Disclosure Control in Geospatial Data
2024-10-23
Jon Olav Skøien, Nicolas Lampach, Helena Ramos, Rudolf Seljak, Renate Koeble, Linda See, Marijn van der Velde
We develop a flexible approach by combining the Quadtree-based method with suppression to maximize the utility of the grid data and simultaneously to reduce the risk of disclosing private information from individual units. To protect data confidentiality, we produce a high resolution grid from geo-reference data with a minimum size of 1 km nested in grids with increasingly larger resolution on the basis of statistical disclosure control methods (i.e threshold and concentration rule). While our implementation overcomes certain weaknesses of Quadtree-based method by accounting for irregularly distributed and relatively isolated marginal units, it also allows creating joint aggregation of several variables. The method is illustrated by relying on synthetic data of the Danish agricultural census 2020 for a set of key agricultural indicators, such as the number of agricultural holdings, the utilized agricultural area and the number of organic farms. We demonstrate the need to assess the reliability of indicators when using a sub-sample of synthetic data followed by an example that presents the same approach for generating a ratio (i.e., the share of organic farming). The methodology is provided as the open-source \textit{R}-package \textit{MRG} that is adaptable to use with other geo-referenced survey data underlying confidentiality or other privacy restrictions.
Predicting company growth is crucial for strategic adjustment, operational decision-making, risk assessment, and loan eligibility reviews. Traditional models for company growth often focus too much on theory, overlooking practical forecasting, or they rely solely on time series forecasting techniques, ignoring interpretability and the inherent mechanisms of company growth. In this paper, we propose a machine learning-based prediction framework that incorporates an econophysics model for company growth. Our model captures both the intrinsic growth mechanisms of companies led by scaling laws and the fluctuations influenced by random factors and individual decisions, demonstrating superior predictive performance compared with methods that use time series techniques alone. Its advantages are more pronounced in long-range prediction tasks. By explicitly modeling the baseline growth and volatility components, our model is more interpretable.
The rare-earth chalcogenide $ARECh_{2}$ family ($A =$ alkali metal or monovalent ions, $RE =$ rare earth, $Ch =$ chalcogen) has emerged as a paradigmatic platform for studying frustrated magnetism on a triangular lattice. The family members exhibit a variety of ground states, from quantum spin liquid to exotic ordered phases, providing fascinating insight into quantum magnetism. Their simple crystal structure and chemical tunability enable systematic exploration of competing interactions in quantum magnets. Recent neutron scattering and thermodynamic studies have revealed rich phase diagrams and unusual excitations, refining theoretical models of frustrated systems. This review provides a succinct introduction to $ARECh_{2}$ research. It summarizes key findings on crystal structures, single-ion physics, magnetic Hamiltonians, ground states, and low-energy excitations. By highlighting current developments and open questions, we aim to catalyze further exploration and deeper physical understanding on this frontier of quantum magnetism.
The disk reverberation mapping of the lensed quasar Q0957+561
2024-10-23
Marcin Marculewicz, Mouyuan Sun, Zhixiang Zhang, Tuan Yi
The measurement of continuum time lags in lensed quasars can effectively probe the accretion physics of quasars. This is because microlensing observations of lensed quasars can provide constraints on the half-light radii of quasar accretion disks. By combining the microlensing results with time lag measurements, one can, for the first time, estimate the propagation velocity of the physical process that drives inter-band time lags and cross-correlations among disk emission (i.e. in UV/optical bands). In this study, we perform the disk reverberation mapping study for the well-studied lensed quasar, Q0957+561. The cross-correlation between the Zwicky Transient Facility (ZTF) $g$ and $r$ bands was measured; the $g$ variations lead the $r$ ones by $6.4\pm 2.6$ days in the rest frame. In combination with the half-light radius from the existing literature, we find that the propagation velocity of the variability mechanism should be $1.7^{+1.5}_{-0.7}$ times the speed of light. We discuss the possible outcomes of this result. Similar studies can be applied to other lensed quasars by utilizing the Legacy Survey of Space and Time (LSST) observations.
Responsible Multilingual Large Language Models: A Survey of Development, Applications, and Societal Impact
2024-10-23
Junhua Liu, Bin Fu
Multilingual Large Language Models (MLLMs) represent a pivotal advancement in democratizing artificial intelligence across linguistic boundaries. While theoretical foundations are well-established, practical implementation guidelines remain scattered. This work bridges this gap by providing a comprehensive end-to-end framework for developing and deploying MLLMs in production environments. We make three distinctive contributions: First, we present an actionable pipeline from data pre-processing through deployment, integrating insights from academic research and industrial applications. Second, using Llama2 as a case study, we provide detailed optimization strategies for enhancing multilingual capabilities, including curriculum learning approaches for balancing high-resource and low-resource languages, tokenization strategies, and effective sampling methods. Third, we offer an interdisciplinary analysis that considers technical, linguistic, and cultural perspectives in MLLM development. Our findings reveal critical challenges in supporting linguistic diversity, with 88.38% of world languages categorized as low-resource, affecting over a billion speakers. We examine practical solutions through real-world applications in customer service, search engines, and machine translation. By synthesizing theoretical frameworks with production-ready implementation strategies, this survey provides essential guidance for practitioners and researchers working to develop more inclusive and effective multilingual AI systems.
Online reviews significantly impact consumers' decision-making process and firms' economic outcomes and are widely seen as crucial to the success of online markets. Firms, therefore, have a strong incentive to manipulate ratings using fake reviews. This presents a problem that academic researchers have tried to solve over two decades and on which platforms expend a large amount of resources. Nevertheless, the prevalence of fake reviews is arguably higher than ever. To combat this, we collect a dataset of reviews for thousands of Amazon products and develop a general and highly accurate method for detecting fake reviews. A unique difference between previous datasets and ours is that we directly observe which sellers buy fake reviews. Thus, while prior research has trained models using lab-generated reviews or proxies for fake reviews, we are able to train a model using actual fake reviews. We show that products that buy fake reviews are highly clustered in the product-reviewer network. Therefore, features constructed from this network are highly predictive of which products buy fake reviews. We show that our network-based approach is also successful at detecting fake reviews even without ground truth data, as unsupervised clustering methods can accurately identify fake review buyers by identifying clusters of products that are closely connected in the network. While text or metadata can be manipulated to evade detection, network-based features are more costly to manipulate because these features result directly from the inherent limitations of buying reviews from online review marketplaces, making our detection approach more robust to manipulation.
An Ontology-Enabled Approach For User-Centered and Knowledge-Enabled Explanations of AI Systems
2024-10-23
Shruthi Chari
Explainable Artificial Intelligence (AI) focuses on helping humans understand the working of AI systems or their decisions and has been a cornerstone of AI for decades. Recent research in explainability has focused on explaining the workings of AI models or model explainability. There have also been several position statements and review papers detailing the needs of end-users for user-centered explainability but fewer implementations. Hence, this thesis seeks to bridge some gaps between model and user-centered explainability. We create an explanation ontology (EO) to represent literature-derived explanation types via their supporting components. We implement a knowledge-augmented question-answering (QA) pipeline to support contextual explanations in a clinical setting. Finally, we are implementing a system to combine explanations from different AI methods and data modalities. Within the EO, we can represent fifteen different explanation types, and we have tested these representations in six exemplar use cases. We find that knowledge augmentations improve the performance of base large language models in the contextualized QA, and the performance is variable across disease groups. In the same setting, clinicians also indicated that they prefer to see actionability as one of the main foci in explanations. In our explanations combination method, we plan to use similarity metrics to determine the similarity of explanations in a chronic disease detection setting. Overall, through this thesis, we design methods that can support knowledge-enabled explanations across different use cases, accounting for the methods in today's AI era that can generate the supporting components of these explanations and domain knowledge sources that can enhance them.
Uniformly Elliptic Equations on Domains with Capacity Density Conditions: Existence of Hölder Continuous Solutions and Homogenization Results
2024-10-23
Takanobu Hara
This is a progress report on study of uniformly elliptic Poisson-type equations on domains with capacity density conditions (CDC domains). We give a brief summary of known facts of CDC domains, including Hardy's inequality, and review a previous work of existence of globally H\"{o}lder continuous solutions. Additionally, we apply the result to homogenization problems of $\epsilon$-periodic coefficients and present a convergence rate estimate of $L^{\infty}$ norms.
Unified Microphone Conversion: Many-to-Many Device Mapping via Feature-wise Linear Modulation
2024-10-23
Myeonghoon Ryu, Hongseok Oh, Suji Lee, Han Park
In this study, we introduce Unified Microphone Conversion, a unified generative framework to enhance the resilience of sound event classification systems against device variability. Building on the limitations of previous works, we condition the generator network with frequency response information to achieve many-to-many device mapping. This approach overcomes the inherent limitation of CycleGAN, requiring separate models for each device pair. Our framework leverages the strengths of CycleGAN for unpaired training to simulate device characteristics in audio recordings and significantly extends its scalability by integrating frequency response related information via Feature-wise Linear Modulation. The experiment results show that our method outperforms the state-of-the-art method by 2.6% and reducing variability by 0.8% in macro-average F1 score.
Self-Supervised Learning for Time Series: A Review & Critique of FITS
2024-10-23
Andreas Løvendahl Eefsen, Nicholas Erup Larsen, Oliver Glozmann Bork Hansen, Thor Højhus Avenstrup
Accurate time series forecasting is a highly valuable endeavour with applications across many industries. Despite recent deep learning advancements, increased model complexity, and larger model sizes, many state-of-the-art models often perform worse or on par with simpler models. One of those cases is a recently proposed model, FITS, claiming competitive performance with significantly reduced parameter counts. By training a one-layer neural network in the complex frequency domain, we are able to replicate these results. Our experiments on a wide range of real-world datasets further reveal that FITS especially excels at capturing periodic and seasonal patterns, but struggles with trending, non-periodic, or random-resembling behavior. With our two novel hybrid approaches, where we attempt to remedy the weaknesses of FITS by combining it with DLinear, we achieve the best results of any known open-source model on multivariate regression and promising results in multiple/linear regression on price datasets, on top of vastly improving upon what FITS achieves as a standalone model.
1-2-3-Go! Policy Synthesis for Parameterized Markov Decision Processes via Decision-Tree Learning and Generalization
2024-10-23
Muqsit Azeem, Debraj Chakraborty, Sudeep Kanav, Jan Kretinsky, Mohammadsadegh Mohagheghi, Stefanie Mohr, Maximilian Weininger
Despite the advances in probabilistic model checking, the scalability of the verification methods remains limited. In particular, the state space often becomes extremely large when instantiating parameterized Markov decision processes (MDPs) even with moderate values. Synthesizing policies for such \emph{huge} MDPs is beyond the reach of available tools. We propose a learning-based approach to obtain a reasonable policy for such huge MDPs. The idea is to generalize optimal policies obtained by model-checking small instances to larger ones using decision-tree learning. Consequently, our method bypasses the need for explicit state-space exploration of large models, providing a practical solution to the state-space explosion problem. We demonstrate the efficacy of our approach by performing extensive experimentation on the relevant models from the quantitative verification benchmark set. The experimental results indicate that our policies perform well, even when the size of the model is orders of magnitude beyond the reach of state-of-the-art analysis tools.
2024-10-23
Fernando Abalos, Oscar Reula, David Hilditch
Systems of PDEs comprised of a combination of constraints and evolution equations are ubiquitous in physics. For both theoretical and practical reasons, such as numerical integration, it is desirable to have a systematic understanding of the well-posedness of the Cauchy problem for these systems. Presently we review the use of hyperbolic reductions, in which the evolution equations are singled out for consideration. We then examine in greater detail the extensions, in which constraints are evolved as auxiliary variables alongside the original variables. Assuming a particular structure of the original system, we give sufficient conditions for strong-hyperbolicity of an extension. This theory is then applied to the examples of electromagnetism and a toy for magnetohydrodynamics.
Improving measurement error and representativeness in nonprobability surveys
2024-10-23
Aditi Sen, Partha Lahiri
Nonprobability surveys suffer from representativeness issues due to their unknown selection mechanism. Recent research on nonprobability surveys has primarily focused on reducing such selection bias. But bias due to measurement error is also present, as pointed out by Kennedy, Mercer and Lau (2024) using a benchmarking study in the case of commercial online nonprobability surveys in the United States. Before this study, measurement error bias in nonprobability surveys has mostly been overlooked and statistical methods have been devised for reducing only the selection bias, under the assumption of accuracy of survey responses. Motivated by this case study, our research focuses on combining two key areas in nonprobability sampling research: representativeness and measurement error, specifically aiming to mitigate bias from both sampling and measurement errors. In the context of finite population mean estimation, we propose a new composite estimator that integrates both probability and nonprobability surveys, promising improved results compared to benchmark values from large government surveys. Its performance in comparison to an existing composite estimator is analyzed in terms of mean squared error, analytically and empirically. In the context of the aforementioned case study, we further investigate when the proposed composite estimator outperforms estimator from probability surveys alone.
A new look at the extragalactic Very High Energy sky: searching for TeV-emitting candidates among the X-ray bright, non-Fermi detected blazar population
2024-10-23
Stefano Marchesi, Antonio Iuliano, Elisa Prandini, Paolo Da Vela, Michele Doro, Serena Loporchio, Davide Miceli, Chiara Righi, Roberta Zanin, Ettore Bronzini, Cristian Vignali
We present the results of a multi-wavelength study of blazars selected from the 5th ROMABZCAT catalog. We selected from this sample a subsample of 2435 objects having at least one counterpart in one of the three main archival X-ray catalogs, which is, the fourth release of the XMM-Newton Survey Science Catalogue, the second release of the Chandra Source Catalog, and the second Swift X-ray Point Source catalog of detections by Swift-XRT, or in the recently released eROSITA-DE Data Release 1 catalog. We first searched for different multi-wavelength trends between sources with a Gamma-ray counterpart in the Fermi-LAT 14-year Source Catalog (4FGL-DR4) and sources lacking one. We find that the non-4FGL sources are on average fainter both in the X-rays and in the radio with respect to the 4FGL-detected ones, but the two samples have similar X-ray-to-radio flux ratios, as well as synchrotron peak frequencies. We then focused on the 1007 non-Gamma-ray detected population, to determine if there is a sample of X-ray sources that could be TeV emitters. We find that a large number of sources, mostly BL Lacs or BL Lacs with host-galaxy contribution to the spectral energy distribution, have large synchrotron peak frequency and X-ray to radio flux ratio, two properties that characterize the vast majority of known TeV emitters. With respect to these known TeV emitters, our targets have X-ray fluxes ~1 order of magnitude fainter. We then computed the 0.2-12 keV and 20 GeV - 300 TeV fluxes for the known 5BZCAT TeV emitters, and determined the existence of a direct correlation between X-ray and TeV fluxes in the BL Lacs population. We used this trend to estimate the VHE flux of our targets, and found a promising sample of sources for follow-up observations with current or future, more sensitive, Cherenkov telescopes, first and foremost the Cherenkov Telescope Array Observatory.
Screw Geometry Meets Bandits: Incremental Acquisition of Demonstrations to Generate Manipulation Plans
2024-10-23
Dibyendu Das, Aditya Patankar, Nilanjan Chakraborty, C. R. Ramakrishnan, I. V. Ramakrishnan
In this paper, we study the problem of methodically obtaining a sufficient set of kinesthetic demonstrations, one at a time, such that a robot can be confident of its ability to perform a complex manipulation task in a given region of its workspace. Although Learning from Demonstrations has been an active area of research, the problems of checking whether a set of demonstrations is sufficient, and systematically seeking additional demonstrations have remained open. We present a novel approach to address these open problems using (i) a screw geometric representation to generate manipulation plans from demonstrations, which makes the sufficiency of a set of demonstrations measurable; (ii) a sampling strategy based on PAC-learning from multi-armed bandit optimization to evaluate the robot's ability to generate manipulation plans in a subregion of its task space; and (iii) a heuristic to seek additional demonstration from areas of weakness. Thus, we present an approach for the robot to incrementally and actively ask for new demonstration examples until the robot can assess with high confidence that it can perform the task successfully. We present experimental results on two example manipulation tasks, namely, pouring and scooping, to illustrate our approach. A short video on the method: https://youtu.be/R-qICICdEos
Electromagnetic follow-up of gravitational waves: review and lessons learned
2024-10-23
Matt Nicholl, Igor Andreoni
The detection of gravitational waves (GWs) has provided a new tool to study the Universe, with the scientific return enriched when combined with established probes: electromagnetic (EM) radiation and energetic particles. Since the groundbreaking detection in 2017 of merging neutron stars producing GW emission, a gamma-ray burst and an optical 'kilonova', the field has grown rapidly. At present, no additional neutron star mergers have been jointly detected in GW and EM radiation, but with upgrades in EM and GW facilities now is a chance to take stock of almost a decade of observations. We discuss the motivations for following up GW sources and the basic challenges of searching large areas for a rapidly-evolving EM signal. We examine how the kilonova counterpart to GW170817 was discovered and the association confirmed, and outline some of the key physics enabled by this discovery. We then review the status of EM searches since 2017, highlighting areas where more information (in GW alerts or catalogs) can improve efficiency, and discuss what we have learned about kilonovae despite the lack of further multi-messenger detections. We discuss upcoming facilities and the many lessons learned, considering also how these could inform searches for lensed mergers.
Nonrelativistic superfluids in cosmology from a relativistic approach: Revisiting two formulations of superfluidity
2024-10-23
Aline Favero, Heliudson Bernardo
Two formulations of superfluidity are reviewed: Landau's phenomenological two-fluid model and a relativistic effective field theory description. We demonstrate how the two-fluid formalism can be recovered from the nonrelativistic limit of the relativistic effective theory at finite temperatures. We show how self-gravitating, nonrelativistic superfluids are obtained from the Newtonian limit of the relativistic approach on curved spaces. The concepts are presented in an accessible manner for readers who may not be deeply familiar with superfluidity from a condensed matter perspective.
COBIPULSE: A Systematic Search for Compact Binary Millisecond Pulsars
2024-10-23
Marco Turchetta, Manuel Linares, Karri Koljonen, Jorge Casares, Paulo A. Miles-Páez, Pablo Rodríguez-Gil, Tariq Shahbaz, Jordan A. Simpson
We report here the results obtained from a systematic optical photometric survey aimed at finding new compact binary millisecond pulsars (also known as "spiders"): the COmpact BInary PULsar SEarch (COBIPULSE). We acquired multi-band optical images over one year around $33$ unidentified Fermi-LAT sources, selected as pulsar candidates based on their curved GeV spectra and steady $\gamma$-ray emission. We present the discovery of four optical variables coinciding with the Fermi sources 3FGL J0737.2$-$3233, 3FGL J2117.6$+$3725 (two systems in this field) and 3FGL J2221.6$+$6507, which we propose as new candidate spider systems. Indeed, they all show optical flux modulation consistent with orbital periods of $0.3548(5) \ \mathrm{d}$, $0.25328(6) \ \mathrm{d}$, $0.441961(2) \ \mathrm{d}$, and $0.165(4) \ \mathrm{d}$, respectively, with amplitudes $\gtrsim 0.3 \ \mathrm{mag}$ and colors compatible with companion star temperatures of $5000$--$6000 \ \mathrm{K}$. These properties are consistent with the "redback" sub-class of spider pulsars. If confirmed as a millisecond pulsar, 3FGL J0737.2$-$3233 will be the closest known spider to Earth ($D=659_{-20}^{+16} \ \mathrm{pc}$, from Gaia-DR3 parallax). We searched and did not find any X-ray sources matching our four candidates, placing $3\sigma$ upper limits of $\sim10^{31}$--$10^{32} \ \mathrm{erg} \ \mathrm{s}^{-1}$ ($0.3$--$10 \ \mathrm{keV}$) on their soft X-ray luminosities. We also present and discuss other multi-wavelength information on our spider candidates, from infrared to X-rays.
Baryon-free $S_8$ tension with Stage IV cosmic shear surveys
2024-10-23
Ottavia Truttero, Joe Zuntz, Alkistis Pourtsidou, Naomi Robertson
Accurately modelling matter power spectrum effects at small scales, such as baryonic feedback, is essential to avoid significant bias in the estimation of cosmological parameters with cosmic shear. However, Stage IV surveys like LSST will be so precise that significant information can still be extracted from large scales alone. In this work, we simulate LSST Y1-like mock data and perform a cosmic shear analysis, considering different models of baryonic feedback. To focus on large scales, we apply physically motivated scale cuts which account for the redshift dependence of the multipoles in the tomographic bin. Our main focus is to study the changes in the constraining power of $S_8$ and $\Omega_m$ parameters and assess possible effects on the tension with Planck measurements. We find that the $S_8$ tension is clearly detectable at $k_{\rm eff}^{\rm max}=0.20\,h\rm Mpc^{-1}$ in the analysis where we imposed a DES-sized tension, and at $k_{\rm eff}^{\rm max}=0.10\,h\rm Mpc^{-1}$ with a KiDS-sized tension, regardless of whether an incorrect model for baryons is assumed. However, to achieve these results, LSST will need high precision measurement of the redshift distributions, with photo-$z$ biases of the order of $10^{-3}$. Without this, the ability to constrain cosmological parameters independently of baryonic feedback - particularly regarding the $S_8$ tension - will be compromised.
Dust-Obscured Galaxies in the XMM-SERVS Fields: Selection, Multiwavelength Characterization, and Physical Nature
2024-10-23
Zhibo Yu, W. N. Brandt, Fan Zou, Ziyuan Zhu, Franz E. Bauer, Nathan Cristello, Bin Luo, Qingling Ni, Fabio Vito, Yongquan Xue
Dust-obscured galaxies (DOGs) are enshrouded by dust, and many are believed to host accreting supermassive black holes (SMBHs), which makes them unique objects for probing the coevolution of galaxies and SMBHs. We select and characterize DOGs in the $13\,deg^2$ XMM-Spitzer Extragalactic Representative Volume Survey (XMM-SERVS), leveraging the superb multiwavelength data from X-rays to radio. We select 3738 DOGs at $z\approx1.6-2.1$ in XMM-SERVS, while maintaining good data quality without introducing significant bias. This represents the largest DOG sample with thorough multiwavelength source characterization. Spectral energy distribution (SED) modeling shows DOGs are a heterogeneous population consisting of both normal galaxies and active galactic nuclei (AGNs). Our DOGs are massive ($\log M_\bigstar/M_\odot\approx10.7-11.3$), 174 are detected in X-rays, and they are generally radio-quiet systems. X-ray detected DOGs are luminous and are moderately to heavily obscured in X-rays. Stacking analyses for the X-ray undetected DOGs show highly significant average detections. Critically, we compare DOGs with matched galaxy populations. DOGs have similar AGN fractions compared with typical galaxy populations. X-ray detected DOGs have higher $M_\bigstar$ and higher X-ray obscuration, but they are not more star-forming than typical X-ray AGNs. The results potentially challenge the relevance of the merger-driven galaxy-SMBH coevolution framework for X-ray detected DOGs.
JWST/MIRI detection of a carbon-rich chemistry in a solar nebula analog
2024-10-23
Maria Jose Colmenares, Edwin Bergin, Colette Salyk, Klaus M. Pontopiddan, Nicole Arulanantham, Jenny Calahan, Andrea Banzatti, Sean Andrews, Geoffrey A. Blake, Fred Ciesla, Joel Green, Feng Long, Michiel Lambrechts, Joan Najita, Ilaria Pascucci, Paola Pinilla, Sebastiaan Krijt, Leon Trapman, the JDISCS Collaboration
It has been proposed, and confirmed by multiple observations, that disks around low mass stars display a molecule-rich emission and carbon-rich disk chemistry as compared to their hotter, more massive solar counterparts. In this work, we present JWST Disk Infrared Spectral Chemistry Survey (JDISCS) MIRI-MRS observations of the solar-mass star DoAr 33, a low-accretion rate T Tauri star showing an exceptional carbon-rich inner disk. We report detections of H$_2$O, OH, and CO$_2$, as well as the more complex hydrocarbons, C$_2$H$_2$ and C$_4$H$_2$. Through the use of thermochemical models, we explore different spatial distributions of carbon and oxygen across the inner disk and compare the column densities and temperatures obtained from LTE slab model retrievals. We find a best match to the observed column densities with models that have carbon enrichment, and the retrieved emitting temperature and area of C$_2$H$_2$ with models that have C/O $=$ 2$-$4 inside the 500 K carbon-rich dust sublimation line. This suggests that the origin of the carbon-rich chemistry is likely due to the sublimation of carbon rich grains near the soot line. This would be consistent with the presence of dust processing as indicated by the detection of crystalline silicates. We propose that this long-lived hydrocarbon rich chemistry observed around a solar-mass star is a consequence of the unusually low M-star-like accretion rate of the central star, which lengthens the radial mixing timescale of the inner disk allowing the chemistry powered by carbon grain destruction to linger.
CHEX-MATE: Dynamical masses for a sample of 101 Planck Sunyaev-Zeldovich-selected galaxy clusters
2024-10-23
Mauro Sereno, Sophie Maurogordato, Alberto Cappi, Rafael Barrena, Christophe Benoist, Christopher P. Haines, Mario Radovich, Mario Nonino, Stefano Ettori, Antonio Ferragamo, Raphael Gavazzi, Sophie Huot, Lorenzo Pizzuti, Gabriel W. Pratt, Alina Streblyanska, Stefano Zarattini, Gianluca Castignani, Dominique Eckert, Fabio Gastaldello, Scott T. Kay, Lorenzo Lovisari, Ben J. Maughan, Etienne Pointecouteau, Elena Rasia, Mariachiara Rossetti, Jack Sayers
The Cluster HEritage project with XMM-Newton - Mass Assembly and Thermodynamics at the Endpoint of structure formation (CHEX-MATE) is a programme to study a minimally biased sample of 118 galaxy clusters detected by Planck through the Sunyaev-Zeldovich effect. Accurate and precise mass measurements are required to exploit CHEX-MATE as an astrophysical laboratory and a calibration sample for cosmological probes in the era of large surveys. We measured masses based on the galaxy dynamics, which are highly complementary to weak-lensing or X-ray estimates. We analysed the sample with a uniform pipeline that is stable both for poorly sampled or rich clusters - using spectroscopic redshifts from public (NED, SDSS, and DESI) or private archives - and dedicated observational programmes. We modelled the halo mass density and the anisotropy profile. Membership is confirmed with a cleaning procedure in phase space. We derived masses from measured velocity dispersions under the assumed model. We measured dynamical masses for 101 CHEX-MATE clusters with at least ten confirmed members within the virial radius r_200c. Estimated redshifts and velocity dispersions agree with literature values when available. Validation with weak-lensing masses shows agreement within 8+-16(stat.)+-5(sys.)%, and confirms dynamical masses as an unbiased proxy. Comparison with {\it Planck} masses shows them to be biased low by 34+-3(stat.)+-5(sys.)%. A follow-up spectroscopic campaign is underway to cover the full CHEX-MATE sample.
Permutation Entropy for the Characterization of the Attractive Hamiltonian Mean-Field Model
2024-10-23
Melissa Fuentealba, Danilo M. Rivera, Roberto E. Navarro
The Hamiltonian Mean-Field (HMF) model is a long-range interaction model that exhibits quasi-stationary states associated with a phase transition. Its quasi-stationary states with a lifetime diverging with the number of particles in the system. These states are characterized by homogeneous or non-homogeneous structures in phase-space. There exists a phase-transition between these states that have been traditionally characterized by the their mean magnetization. However, the magnetization also exhibits fluctuations in time around its mean value, that can be an indicator of the kind of quasi-stationary state. Thus, we want to characterize the quasi-stationary states of the HMF model through the time-series of the magnetization and its fluctuations through a measure of information, i.e. the permutation entropy and the complexity-entropy plane. Permutation entropy is a measure for characterizing chaotic time series, especially in the presence of dynamic and observational noise, as it is computationally and conceptually simple. For non-homogeneous states, the permutation entropy shows that the HMF model tends towards order, while the magnetizacion fluctuations reveal reduced structures in time. On the contrary, homogeneous states tend to disorder and the structures of the magnetization fluctuations increase as the initial magnetization is larger. In all the study cases of this thesis, the HMF model is characterized by low entropy values but the highest possible complexity value. Thus, the HMF model can be described as a chaotic, deterministic and intermitent system. This aligns with previous studies of the model in the phase space. The results demonstrate that the HMF model can be understood and interpreted from the fluctuations of magnetization using permutation entropy and the complexity-entropy plane.
2024-10-22
Ge-Peng Ji, Jingyi Liu, Peng Xu, Nick Barnes, Fahad Shahbaz Khan, Salman Khan, Deng-Ping Fan
Colonoscopy is currently one of the most sensitive screening methods for colorectal cancer. This study investigates the frontiers of intelligent colonoscopy techniques and their prospective implications for multimodal medical applications. With this goal, we begin by assessing the current data-centric and model-centric landscapes through four tasks for colonoscopic scene perception, including classification, detection, segmentation, and vision-language understanding. This assessment enables us to identify domain-specific challenges and reveals that multimodal research in colonoscopy remains open for further exploration. To embrace the coming multimodal era, we establish three foundational initiatives: a large-scale multimodal instruction tuning dataset ColonINST, a colonoscopy-designed multimodal language model ColonGPT, and a multimodal benchmark. To facilitate ongoing monitoring of this rapidly evolving field, we provide a public website for the latest updates: https://github.com/ai4colonoscopy/IntelliScope.
Dhoroni: Exploring Bengali Climate Change and Environmental Views with a Multi-Perspective News Dataset and Natural Language Processing
2024-10-22
Azmine Toushik Wasi, Wahid Faisal, Taj Ahmad, Abdur Rahman, Mst Rafia Islam
Climate change poses critical challenges globally, disproportionately affecting low-income countries that often lack resources and linguistic representation on the international stage. Despite Bangladesh's status as one of the most vulnerable nations to climate impacts, research gaps persist in Bengali-language studies related to climate change and NLP. To address this disparity, we introduce Dhoroni, a novel Bengali (Bangla) climate change and environmental news dataset, comprising a 2300 annotated Bangla news articles, offering multiple perspectives such as political influence, scientific/statistical data, authenticity, stance detection, and stakeholder involvement. Furthermore, we present an in-depth exploratory analysis of Dhoroni and introduce BanglaBERT-Dhoroni family, a novel baseline model family for climate and environmental opinion detection in Bangla, fine-tuned on our dataset. This research contributes significantly to enhancing accessibility and analysis of climate discourse in Bengali (Bangla), addressing crucial communication and research gaps in climate-impacted regions like Bangladesh with 180 million people.
2024-10-22
David Ohlin, Anders Rantzer, Emma Tegling
This work considers infinite-horizon optimal control of positive linear systems applied to the case of network routing problems. We demonstrate the equivalence between Stochastic Shortest Path (SSP) problems and optimal control of a certain class of linear systems. This is used to construct a heuristic search framework for this class of linear systems inspired by existing methods for SSP. We propose a heuristics-based algorithm for finding local solutions to the analyzed class of optimal control problems with positive state and linear dynamics. More fundamentally, the results allow for analysis of the conditions for explicit solutions to the Bellman equation utilized by heuristic search methods.
2024-10-22
Mete Ismayilzada, Debjit Paul, Antoine Bosselut, Lonneke van der Plas
Creativity is the ability to produce novel, useful, and surprising ideas, and has been widely studied as a crucial aspect of human cognition. Machine creativity on the other hand has been a long-standing challenge. With the rise of advanced generative AI, there has been renewed interest and debate regarding AI's creative capabilities. Therefore, it is imperative to revisit the state of creativity in AI and identify key progresses and remaining challenges. In this work, we survey leading works studying the creative capabilities of AI systems, focusing on creative problem-solving, linguistic, artistic, and scientific creativity. Our review suggests that while the latest AI models are largely capable of producing linguistically and artistically creative outputs such as poems, images, and musical pieces, they struggle with tasks that require creative problem-solving, abstract thinking and compositionality and their generations suffer from a lack of diversity, originality, long-range incoherence and hallucinations. We also discuss key questions concerning copyright and authorship issues with generative models. Furthermore, we highlight the need for a comprehensive evaluation of creativity that is process-driven and considers several dimensions of creativity. Finally, we propose future research directions to improve the creativity of AI outputs, drawing inspiration from cognitive science and psychology.
Purpose: Bangladesh's legal system struggles with major challenges like delays, complexity, high costs, and millions of unresolved cases, which deter many from pursuing legal action due to lack of knowledge or financial constraints. This research seeks to develop a specialized Large Language Model (LLM) to assist in the Bangladeshi legal system. Methods: We created UKIL-DB-EN, an English corpus of Bangladeshi legal documents, by collecting and scraping data on various legal acts. We fine-tuned the GPT-2 model on this dataset to develop GPT2-UKIL-EN, an LLM focused on providing legal assistance in English. Results: The model was rigorously evaluated using semantic assessments, including case studies supported by expert opinions. The evaluation provided promising results, demonstrating the potential for the model to assist in legal matters within Bangladesh. Conclusion: Our work represents the first structured effort toward building an AI-based legal assistant for Bangladesh. While the results are encouraging, further refinements are necessary to improve the model's accuracy, credibility, and safety. This is a significant step toward creating a legal AI capable of serving the needs of a population of 180 million.
Knowledge Editing (KE) algorithms alter models' internal weights to perform targeted updates to incorrect, outdated, or otherwise unwanted factual associations. In order to better define the possibilities and limitations of these approaches, recent work has shown that applying KE can adversely affect models' factual recall accuracy and diminish their general reasoning abilities. While these studies give broad insights into the potential harms of KE algorithms, e.g., via performance evaluations on benchmarks, we argue little is understood as to why such destructive failures occur. Is it possible KE methods distort representations of concepts beyond the targeted fact, hence hampering abilities at broad? If so, what is the extent of this distortion? To take a step towards addressing such questions, we define a novel synthetic task wherein a Transformer is trained from scratch to internalize a ``structured'' knowledge graph. The structure enforces relationships between entities of the graph, such that editing a factual association has "trickling effects" on other entities in the graph (e.g., altering X's parent is Y to Z affects who X's siblings' parent is). Through evaluations of edited models and analysis of extracted representations, we show that KE inadvertently affects representations of entities beyond the targeted one, distorting relevant structures that allow a model to infer unseen knowledge about an entity. We call this phenomenon representation shattering and demonstrate that it results in degradation of factual recall and reasoning performance more broadly. To corroborate our findings in a more naturalistic setup, we perform preliminary experiments with a pretrained GPT-2-XL model and reproduce the representation shattering effect therein as well. Overall, our work yields a precise mechanistic hypothesis to explain why KE has adverse effects on model capabilities.
Exploring Unobscured QSOs in the Southern Hemisphere with KS4
2024-10-22
Yongjung Kim, Minjin Kim, Myungshin Im, Seo-Won Chang, Mankeun Jeong, Woowon Byun, Joonho Kim, Dohyeong Kim, Hyunjin Shim, Hyunmi Song
We present a catalog of unobscured QSO candidates in the southern hemisphere from the early interim data of the KMTNet Synoptic Survey of Southern Sky (KS4). The KS4 data covers $\sim2500\,{\rm deg}^{2}$ sky area, reaching 5$\sigma$ detection limits of $\sim$22.1-22.7 AB mag in the $BVRI$ bands. Combining this with available infrared photometric data from the surveys covering the southern sky, we select the unobscured QSO candidates based on their colors and spectral energy distributions (SEDs) fitting results. The final catalog contains 72,964 unobscured QSO candidates, of which only 0.4% are previously identified as QSOs based on spectroscopic observations. Our selection method achieves an 87% recovery rate for spectroscopically confirmed bright QSOs at $z<2$ within the KS4 survey area. In addition, the number count of our candidates is comparable to that of spectroscopically confirmed QSOs from the Sloan Digital Sky Survey in the northern sky. These demonstrate that our approach is effective in searching for unobscured QSOs in the southern sky. Future spectro-photometric surveys covering the southern sky will enable us to discern their true nature and enhance our understanding of QSO populations in the southern hemisphere.
Impact of 3D LiDAR Resolution in Graph-based SLAM Approaches: A Comparative Study
2024-10-22
J. Jorge, T. Barros, C. Premebida, M. Aleksandrov, D. Goehring, U. J. Nunes
Simultaneous Localization and Mapping (SLAM) is a key component of autonomous systems operating in environments that require a consistent map for reliable localization. SLAM has been a widely studied topic for decades with most of the solutions being camera or LiDAR based. Early LiDAR-based approaches primarily relied on 2D data, whereas more recent frameworks use 3D data. In this work, we survey recent 3D LiDAR-based Graph-SLAM methods in urban environments, aiming to compare their strengths, weaknesses, and limitations. Additionally, we evaluate their robustness regarding the LiDAR resolution namely 64 $vs$ 128 channels. Regarding SLAM methods, we evaluate SC-LeGO-LOAM, SC-LIO-SAM, Cartographer, and HDL-Graph on real-world urban environments using the KITTI odometry dataset (a LiDAR with 64-channels only) and a new dataset (AUTONOMOS-LABS). The latter dataset, collected using instrumented vehicles driving in Berlin suburban area, comprises both 64 and 128 LiDARs. The experimental results are reported in terms of quantitative `metrics' and complemented by qualitative maps.
2024-10-22
Mary C. Tedeschi, Paola Ricaurte, Sridevi Ayloo, Joseph Corneli, Charles Jeffrey Danoff, Sergio Belich
At EuroPLoP 2024 Mary Tedeschi led the "AI Future Envisioning with PLACARD" focus group in Germany. Three conference attendees joined in the room while Sridevi, Paola, and Charles co-facilitated remotely via a web conference. The participants were introduced to a Futures Studies technique with the goal of capturing envisionments of Artificial Intelligence (AI) going forward. To set an atmosphere a technology focused card game was used to make the session more interactive. To close everyone co-created a Project Action Review to recap of the event to capture learnings that has been summarized in this paper. The Focus Group was structured based on lessons learned over six earlier iterations.
Empowering the Grid: Decentralized Autonomous Control for Effective Utilization and Resilience
2024-10-22
Sai Pushpak Nandanoori, Alok Kumar Bharati, Subhrajit Sinha, Soumya Kundu, Veronica Adetola, Kevin Schneider
With the emergence of low-inertia microgrids powered by inverter-based generation, there remains a concern about the operational resilience of these systems. Grid-forming inverters (GFMs), enabled by various device-level (primary) and system-level (secondary) control methods, are poised to play a significant role in achieving certain operational objectives, such as the effective utilization of clean energy resources while maintaining stability. However, despite the recent advances in GFMs, there is a lack of suitable controls that can ascertain resilience-constrained operations, like maintaining critical operational safety limits during transients under various cyber-physical disruptions. In this work, we develop decentralized autonomous controllers (DACs) that enforce resilience-constrained operation via local, minimally invasive adjustments (e.g., changes in set-points) while co-existing within the hierarchy of existing (primary and secondary) controls. The DACs work autonomously by sensing only local GFM measurements and act only when operational resilience constraints are violated. The proposed DAC scheme is computationally efficient (only algebraic computations), which enables fast, real-time execution and demonstrates the efficacy of the proposed control framework on GridLAB-D-HELICS-based control-grid co-simulations on the IEEE 123-node networked microgrid. Finally, we show how the developed DACs empower the grid by utilizing the available resources entirely to ensure resilience (maintain frequency safe limits).
Understanding Transfer Learning via Mean-field Analysis
2024-10-22
Gholamali Aminian, Łukasz Szpruch, Samuel N. Cohen
We propose a novel framework for exploring generalization errors of transfer learning through the lens of differential calculus on the space of probability measures. In particular, we consider two main transfer learning scenarios, $\alpha$-ERM and fine-tuning with the KL-regularized empirical risk minimization and establish generic conditions under which the generalization error and the population risk convergence rates for these scenarios are studied. Based on our theoretical results, we show the benefits of transfer learning with a one-hidden-layer neural network in the mean-field regime under some suitable integrability and regularity assumptions on the loss and activation functions.
Automated neuroradiological support systems for multiple cerebrovascular disease markers -- A systematic review and meta-analysis
2024-10-22
Jesse Phitidis, Alison Q. O'Neil, William N. Whiteley, Beatrice Alex, Joanna M. Wardlaw, Miguel O. Bernabeu, Maria Valdés Hernández
Cerebrovascular diseases (CVD) can lead to stroke and dementia. Stroke is the second leading cause of death world wide and dementia incidence is increasing by the year. There are several markers of CVD that are visible on brain imaging, including: white matter hyperintensities (WMH), acute and chronic ischaemic stroke lesions (ISL), lacunes, enlarged perivascular spaces (PVS), acute and chronic haemorrhagic lesions, and cerebral microbleeds (CMB). Brain atrophy also occurs in CVD. These markers are important for patient management and intervention, since they indicate elevated risk of future stroke and dementia. We systematically reviewed automated systems designed to support radiologists reporting on these CVD imaging findings. We considered commercially available software and research publications which identify at least two CVD markers. In total, we included 29 commercial products and 13 research publications. Two distinct types of commercial support system were available: those which identify acute stroke lesions (haemorrhagic and ischaemic) from computed tomography (CT) scans, mainly for the purpose of patient triage; and those which measure WMH and atrophy regionally and longitudinally. In research, WMH and ISL were the markers most frequently analysed together, from magnetic resonance imaging (MRI) scans; lacunes and PVS were each targeted only twice and CMB only once. For stroke, commercially available systems largely support the emergency setting, whilst research systems consider also follow-up and routine scans. The systems to quantify WMH and atrophy are focused on neurodegenerative disease support, where these CVD markers are also of significance. There are currently no openly validated systems, commercially, or in research, performing a comprehensive joint analysis of all CVD markers (WMH, ISL, lacunes, PVS, haemorrhagic lesions, CMB, and atrophy).
Thermal evolution of dark matter in the early universe from a symplectic glueball model
2024-10-22
Mattia Bruno, Niccolò Forzano, Marco Panero, Antonio Smecca
The hypothesis that dark matter could be a bound state of a strongly coupled non-Abelian gauge theory is theoretically appealing and has a variety of interesting phenomenological implications. In particular, an interpretation of dark matter as the lightest glueball state in the spectrum of a dark Yang-Mills theory, possibly coupled to the visible sector only through gravitational interactions, has been discussed quite extensively in the literature, but most of previous work has been focused on dark SU(N) gauge theories. In this article, we consider an alternative model, based on a symplectic gauge group, which has a first-order confinement/deconfinement phase transition at a finite critical temperature. We first determine the equation of state of this theory, focusing on temperatures close to the transition, and evaluating the associated latent heat. Then we discuss the evolution of this dark-matter model in the early universe, commenting on the mechanisms by which it could indirectly interact with the visible sector, on the spectrum of gravitational waves it could produce, and on the relic abundances it would lead to. Our discussion includes an extensive review of relevant literature, a number of comments on similarities and differences between our model and dark SU(N) gauge theories, as well as some possible future extensions of the present study.
Dynamic Massive Star Formation: Radio Flux Variability in UCHII Regions
2024-10-22
A. Y. Yang, M. A. Thompson, J. S. Urquhart, A. Brunthaler, K. M. Menten, Y. Gong, Chao-Wei Tsai, A. L. Patel, D. Li, W. D. Cotton
Context: Theoretical models of early accretion during the formation process of massive stars have predicted that HII regions exhibit radio variability on timescales of decades. However, large-scale searches for such temporal variations with sufficient sensitivity have not yet been carried out. Aims: We aim to identify HII regions with variable radio wavelength fluxes and to investigate the properties of the identified objects, especially those with the highest level of variability. Methods: We compared the peak flux densities of 86 ultracompact HII (UCHII) regions measured by the GLOSTAR and CORNISH surveys and identified variables that show flux variations higher than 30% over ~8 yr timespan between these surveys. Results: We found a sample of 38 variable UCHII regions, which is the largest sample identified to date. The overall occurrence of variability is 44$\pm$5%, suggesting that variation in UCHII regions is significantly more common than prediction. The variable UCHII regions are found to be younger than non-variable UCHII regions, all of them meeting the size criterion of hypercompact (HC) HII regions. We studied the 7 UCHII regions (the ``Top7'') that show the highest variability with variations > 100%. The Top7 variable UCHII regions are optically thick at 4--8 GHz and compact, suggesting they are in a very early evolutionary stage of HCHII or UCHII regions. There is a significant correlation between variability and the spectral index of the radio emission. No dependence is observed between the variations and the properties of the sources' natal clumps traced by submillimeter continuum emission from dust, although variable HII regions are found in clumps at an earlier evolutionary stage.
The FLAMINGO project: Baryon effects on the matter power spectrum
2024-10-22
Matthieu Schaller, Joop Schaye, Roi Kugel, Jeger C. Broxterman, Marcel P. van Daalen
The effect of baryon physics associated with galaxy formation onto the large-scale matter distribution of the Universe is a key uncertainty in the theoretical modelling required for the interpretation of Stage IV cosmology surveys. We use the FLAMINGO suite of simulations to study the baryon response due to galaxy formation of the total matter power spectrum. We find that it is only well converged for simulation volumes in excess of $(200~Mpc)^3$. We report results for simulations of varying feedback intensity, which either match the X-ray inferred gas fractions in clusters and the z=0 stellar mass function, or shifted versions of the data, as well as for different implementations of AGN feedback. We package our results in the form of a Gaussian process emulator which can rapidly reproduce all the simulations' predictions to better than 1% up to the comoving wavenumber $k = 10~h/Mpc$ and up to z=2 for all the feedback models present in the FLAMINGO suite. We find that the response becomes stronger, the range of scales affected increases, and the position of the minimum of the response moves to smaller scales as the redshift decreases. We find that lower gas fractions in groups and clusters lead to a stronger response and that the use of collimated jets instead of thermally driven winds for AGN feedback enhances the effect. Lowering the stellar masses at fixed cluster gas fractions also increases the magnitude of the response. We find only a small (1% at $k<10~h/Mpc$) dependence of our results on the background cosmology, but a wider range of cosmology variations will be needed to confirm this result. The response we obtain for our strongest feedback models is compatible with some of the recent analyses combining weak lensing with external data. Such a response is, however, in strong tension with the X-ray inferred gas fractions in clusters used to calibrate the FLAMINGO model.
A Survey on Deep Learning-based Gaze Direction Regression: Searching for the State-of-the-art
2024-10-22
Franko Šikić, Donik Vršnak, Sven Lončarić
In this paper, we present a survey of deep learning-based methods for the regression of gaze direction vector from head and eye images. We describe in detail numerous published methods with a focus on the input data, architecture of the model, and loss function used to supervise the model. Additionally, we present a list of datasets that can be used to train and evaluate gaze direction regression methods. Furthermore, we noticed that the results reported in the literature are often not comparable one to another due to differences in the validation or even test subsets used. To address this problem, we re-evaluated several methods on the commonly used in-the-wild Gaze360 dataset using the same validation setup. The experimental results show that the latest methods, although claiming state-of-the-art results, significantly underperform compared with some older methods. Finally, we show that the temporal models outperform the static models under static test conditions.
The fusion of speech and language in the era of large language models has garnered significant attention. Discrete speech token is often utilized in text-to-speech tasks for speech compression and portability, which is convenient for joint training with text and have good compression efficiency. However, we found that the discrete speech tokenizer still suffers from information loss. Therefore, we propose a simple yet effective continuous speech tokenizer and a text-to-speech model based on continuous speech tokens. Our results show that the speech language model based on the continuous speech tokenizer has better continuity and higher estimated Mean Opinion Scores (MoS). This enhancement is attributed to better information preservation rate of the continuous speech tokenizer across both low and high frequencies in the frequency domain.
FlowTracer: A Tool for Uncovering Network Path Usage Imbalance in AI Training Clusters
2024-10-22
Hasibul Jamil, Abdul Alim, Laurent Schares, Pavlos Maniotis, Liran Schour, Ali Sydney, Abdullah Kayi, Tevfik Kosar, Bengi Karacali
The increasing complexity of AI workloads, especially distributed Large Language Model (LLM) training, places significant strain on the networking infrastructure of parallel data centers and supercomputing systems. While Equal-Cost Multi- Path (ECMP) routing distributes traffic over parallel paths, hash collisions often lead to imbalanced network resource utilization and performance bottlenecks. This paper presents FlowTracer, a tool designed to analyze network path utilization and evaluate different routing strategies. FlowTracer aids in debugging network inefficiencies by providing detailed visibility into traffic distribution and helping to identify the root causes of performance degradation, such as issues caused by hash collisions. By offering flow-level insights, FlowTracer enables system operators to optimize routing, reduce congestion, and improve the performance of distributed AI workloads. We use a RoCEv2-enabled cluster with a leaf-spine network and 16 400-Gbps nodes to demonstrate how FlowTracer can be used to compare the flow imbalances of ECMP routing against a statically configured network. The example showcases a 30% reduction in imbalance, as measured by a new metric we introduce.
Proleptic Temporal Ensemble for Improving the Speed of Robot Tasks Generated by Imitation Learning
2024-10-22
Hyeonjun Park, Daegyu Lim, Seungyeon Kim, Sumin Park
Imitation learning, which enables robots to learn behaviors from demonstrations by non-experts, has emerged as a promising solution for generating robot motions in such environments. The imitation learning based robot motion generation method, however, has the drawback of being limited by the demonstrators task execution speed. This paper presents a novel temporal ensemble approach applied to imitation learning algorithms, allowing for execution of future actions. The proposed method leverages existing demonstration data and pretrained policies, offering the advantages of requiring no additional computation and being easy to implement. The algorithms performance was validated through real world experiments involving robotic block color sorting, demonstrating up to 3x increase in task execution speed while maintaining a high success rate compared to the action chunking with transformer method. This study highlights the potential for significantly improving the performance of imitation learning-based policies, which were previously limited by the demonstrator's speed. It is expected to contribute substantially to future advancements in autonomous object manipulation technologies aimed at enhancing productivity.
The swift advancement of unmanned aerial vehicle (UAV) technologies necessitates new standards for developing human-drone interaction (HDI) interfaces. Most interfaces for HDI, especially first-person view (FPV) goggles, limit the operator's ability to obtain information from the environment. This paper presents a novel interface, FlightAR, that integrates augmented reality (AR) overlays of UAV first-person view (FPV) and bottom camera feeds with head-mounted display (HMD) to enhance the pilot's situational awareness. Using FlightAR, the system provides pilots not only with a video stream from several UAV cameras simultaneously, but also the ability to observe their surroundings in real time. User evaluation with NASA-TLX and UEQ surveys showed low physical demand ($\mu=1.8$, $SD = 0.8$) and good performance ($\mu=3.4$, $SD = 0.8$), proving better user assessments in comparison with baseline FPV goggles. Participants also rated the system highly for stimulation ($\mu=2.35$, $SD = 0.9$), novelty ($\mu=2.1$, $SD = 0.9$) and attractiveness ($\mu=1.97$, $SD = 1$), indicating positive user experiences. These results demonstrate the potential of the system to improve UAV piloting experience through enhanced situational awareness and intuitive control. The code is available here: https://github.com/Sautenich/FlightAR
Simplified vine copula models: state of science and affairs
2024-10-22
Thomas Nagler
Vine copula models have become highly popular practical tools for modeling multivariate dependencies. To maintain tractability, a commonly employed simplifying assumption is that conditional copulas remain unchanged by the conditioning variables. This assumption has sparked a somewhat polarizing debate within the copula community. The fact that much of this dispute occurs outside the public record has placed the field in an unfortunate position, impeding scientific progress. In this article, I will review what we know about the flexibility and limitations of simplified vine copula models, explore the broader implications, and offer my own, hopefully reconciling, perspective on the issue.
Here we provide a short review on the so-called Fradkin-Vasiliev formalism for the construction of higher spin cubic interactions. Initially it was formulated for the massless fields only, but later on it was extended to the arbitrary collections of massive and massless fields.
Beyond Retrieval: Generating Narratives in Conversational Recommender Systems
2024-10-22
Krishna Sayana, Raghavendra Vasudeva, Yuri Vasilevski, Kun Su, Liam Hebert, Hubert Pham, Ambarish Jash, Sukhdeep Sodhi
The recent advances in Large Language Model's generation and reasoning capabilities present an opportunity to develop truly conversational recommendation systems. However, effectively integrating recommender system knowledge into LLMs for natural language generation which is tailored towards recommendation tasks remains a challenge. This paper addresses this challenge by making two key contributions. First, we introduce a new dataset (REGEN) for natural language generation tasks in conversational recommendations. REGEN (Reviews Enhanced with GEnerative Narratives) extends the Amazon Product Reviews dataset with rich user narratives, including personalized explanations of product preferences, product endorsements for recommended items, and summaries of user purchase history. REGEN is made publicly available to facilitate further research. Furthermore, we establish benchmarks using well-known generative metrics, and perform an automated evaluation of the new dataset using a rater LLM. Second, the paper introduces a fusion architecture (CF model with an LLM) which serves as a baseline for REGEN. And to the best of our knowledge, represents the first attempt to analyze the capabilities of LLMs in understanding recommender signals and generating rich narratives. We demonstrate that LLMs can effectively learn from simple fusion architectures utilizing interaction-based CF embeddings, and this can be further enhanced using the metadata and personalization data associated with items. Our experiments show that combining CF and content embeddings leads to improvements of 4-12% in key language metrics compared to using either type of embedding individually. We also provide an analysis to interpret how CF and content embeddings contribute to this new generative task.
Bacterial Pathogenicity Regulation by RNA-binding Antiterminators
2024-10-22
Diane Soussan, Ali Tahrioui, R R de la Haba, Adrien Forge, Sylvie Chevalier, Olivier Lesouhaitier, Cécile Muller
Antiterminators are essential components of bacterial transcriptional regulation, allowing the control of gene expression in response to fluctuating environmental conditions. RNA-binding antiterminators are particularly important regulatory proteins that play a significant role in preventing transcription termination by binding to specific RNA sequences. These RNA-binding antiterminators have been extensively studied for their roles in regulating various metabolic pathways. However, their role in modulating the physiology of pathogens requires further investigations. This review focuses on these RNA-binding proteins in both Gram-positive and Gram-negative bacteria, particularly on their structures, mechanism of action, and target genes. Additionally, the involvement of the antitermination mechanisms in bacterial pathogenicity will be discussed. This knowledge is crucial for understanding the regulatory mechanisms that govern bacterial pathogenicity, opening up exciting prospects for future research, and potentially new alternative strategies to fight against infectious diseases.
Magnetic Preference Optimization: Achieving Last-iterate Convergence for Language Models Alignment
2024-10-22
Mingzhi Wang, Chengdong Ma, Qizhi Chen, Linjian Meng, Yang Han, Jiancong Xiao, Zhaowei Zhang, Jing Huo, Weijie J. Su, Yaodong Yang
Self-play methods have demonstrated remarkable success in enhancing model capabilities across various domains. In the context of Reinforcement Learning from Human Feedback (RLHF), self-play not only boosts Large Language Model (LLM) performance but also overcomes the limitations of traditional Bradley-Terry (BT) model assumptions by finding the Nash equilibrium (NE) of a preference-based, two-player constant-sum game. However, existing methods either guarantee only average-iterate convergence, incurring high storage and inference costs, or converge to the NE of a regularized game, failing to accurately reflect true human preferences. In this paper, we introduce Magnetic Preference Optimization (MPO), a novel approach capable of achieving last-iterate convergence to the NE of the original game, effectively overcoming the limitations of existing methods. Building upon Magnetic Mirror Descent (MMD), MPO attains a linear convergence rate, making it particularly suitable for fine-tuning LLMs. To ensure our algorithm is both theoretically sound and practically viable, we present a simple yet effective implementation that adapts the theoretical insights to the RLHF setting. Empirical results demonstrate that MPO can significantly enhance the performance of LLMs, highlighting the potential of self-play methods in alignment.
Resonances leave prominent signatures in atomic and molecular ionization triggered by the absorption of single or multiple photons. These signatures reveal various aspects of the ionization process, characterizing both the initial and final states of the target. Resonant spectral features are typically associated with sharp variations in the photoionization phase, providing an opportunity for laser-assisted interferometric techniques to measure this phase and convert it into a photoemission time delay. This time delay offers a precise characterization of the timing of the photoemission process. In this review, a unified approach to resonant photoionization is presented by examining the analytic properties of ionization amplitude in the complex photoelectron energy plane. This approach establishes a connection between the resonant photoemission time delay and the corresponding photoionization cross-section. Numerical illustrations of this method include: (i) giant or shape resonances, where the photoelectron is spatially confined within a potential barrier, (ii) Fano resonances, where bound states are embedded in the continuum, (iii) Cooper minima (anti-resonances) arising from kinematic nodes in the dipole transition matrix elements, and (iv) confinement resonances in atoms encapsulated within a fullerene cage. The second part of this review focuses on two-photon resonant ionization processes, where the photon energies can be tuned to a resonance in either the intermediate or final state of the atomic target. Our examples include one- or two-electron discrete excitations both below and above the ionization threshold. These resonant states are probed using laser-assisted interferometric techniques. Additionally, we employ laser-assisted photoemission to measure the lifetimes of several atomic autoionizing states.
SERN: Simulation-Enhanced Realistic Navigation for Multi-Agent Robotic Systems in Contested Environments
2024-10-22
Jumman Hossain, Emon Dey, Snehalraj Chugh, Masud Ahmed, MS Anwar, Abu-Zaher Faridee, Jason Hoppes, Theron Trout, Anjon Basak, Rafidh Chowdhury, Rishabh Mistry, Hyun Kim, Jade Freeman, Niranjan Suri, Adrienne Raglin, Carl Busart, Timothy Gregory, Anuradha Ravi, Nirmalya Roy
The increasing deployment of autonomous systems in complex environments necessitates efficient communication and task completion among multiple agents. This paper presents SERN (Simulation-Enhanced Realistic Navigation), a novel framework integrating virtual and physical environments for real-time collaborative decision-making in multi-robot systems. SERN addresses key challenges in asset deployment and coordination through a bi-directional communication framework using the AuroraXR ROS Bridge. Our approach advances the SOTA through accurate real-world representation in virtual environments using Unity high-fidelity simulator; synchronization of physical and virtual robot movements; efficient ROS data distribution between remote locations; and integration of SOTA semantic segmentation for enhanced environmental perception. Our evaluations show a 15% to 24% improvement in latency and up to a 15% increase in processing efficiency compared to traditional ROS setups. Real-world and virtual simulation experiments with multiple robots demonstrate synchronization accuracy, achieving less than 5 cm positional error and under 2-degree rotational error. These results highlight SERN's potential to enhance situational awareness and multi-agent coordination in diverse, contested environments.
Causal reasoning (CR) is a crucial aspect of intelligence, essential for problem-solving, decision-making, and understanding the world. While large language models (LLMs) can generate rationales for their outputs, their ability to reliably perform causal reasoning remains uncertain, often falling short in tasks requiring a deep understanding of causality. In this survey, we provide a comprehensive review of research aimed at enhancing LLMs for causal reasoning. We categorize existing methods based on the role of LLMs: either as reasoning engines or as helpers providing knowledge or data to traditional CR methods, followed by a detailed discussion of the methodologies in each category. We then evaluate the performance of LLMs on various causal reasoning tasks, providing key findings and in-depth analysis. Finally, we provide insights from current studies and highlight promising directions for future research. We aim for this work to serve as a comprehensive resource, fostering further advancements in causal reasoning with LLMs. Resources are available at https://github.com/chendl02/Awesome-LLM-causal-reasoning.
Autonomous navigation in unstructured outdoor environments is inherently challenging due to the presence of asymmetric traversal costs, such as varying energy expenditures for uphill versus downhill movement. Traditional reinforcement learning methods often assume symmetric costs, which can lead to suboptimal navigation paths and increased safety risks in real-world scenarios. In this paper, we introduce QuasiNav, a novel reinforcement learning framework that integrates quasimetric embeddings to explicitly model asymmetric costs and guide efficient, safe navigation. QuasiNav formulates the navigation problem as a constrained Markov decision process (CMDP) and employs quasimetric embeddings to capture directionally dependent costs, allowing for a more accurate representation of the terrain. This approach is combined with adaptive constraint tightening within a constrained policy optimization framework to dynamically enforce safety constraints during learning. We validate QuasiNav across three challenging navigation scenarios-undulating terrains, asymmetric hill traversal, and directionally dependent terrain traversal-demonstrating its effectiveness in both simulated and real-world environments. Experimental results show that QuasiNav significantly outperforms conventional methods, achieving higher success rates, improved energy efficiency, and better adherence to safety constraints.
Accurate diagnosis of ophthalmic diseases relies heavily on the interpretation of multimodal ophthalmic images, a process often time-consuming and expertise-dependent. Visual Question Answering (VQA) presents a potential interdisciplinary solution by merging computer vision and natural language processing to comprehend and respond to queries about medical images. This review article explores the recent advancements and future prospects of VQA in ophthalmology from both theoretical and practical perspectives, aiming to provide eye care professionals with a deeper understanding and tools for leveraging the underlying models. Additionally, we discuss the promising trend of large language models (LLM) in enhancing various components of the VQA framework to adapt to multimodal ophthalmic tasks. Despite the promising outlook, ophthalmic VQA still faces several challenges, including the scarcity of annotated multimodal image datasets, the necessity of comprehensive and unified evaluation methods, and the obstacles to achieving effective real-world applications. This article highlights these challenges and clarifies future directions for advancing ophthalmic VQA with LLMs. The development of LLM-based ophthalmic VQA systems calls for collaborative efforts between medical professionals and AI experts to overcome existing obstacles and advance the diagnosis and care of eye diseases.
Radial Profiles of $Σ_{\ast}$, $Σ_{\rm SFR}$, Gas Metallicity and Their Correlations Across the Galactic Mass-Size Plane
2024-10-22
Lin Lin, Shiyin Shen, Hassen M. Yesuf, Ye-Wei Mao, Lei Hao
We analyzed the global and resolved properties of approximately 1,240 nearby star-forming galaxies from the MaNGA survey, comparing compact and extended galaxies -- those with smaller and larger radii ($R_{\rm e}$), respectively -- at a fixed stellar mass ($M_{\ast}$). Compact galaxies typically exhibit lower HI gas fractions, higher dust extinction, higher metallicity, greater mass concentration, and lower angular momentum on a global scale. Radial profiles of stellar mass surface density ($\Sigma_{\ast}$) and star formation rate surface density ($\Sigma_{\rm SFR}$), as functions of the effective radius ($R/$$R_{\rm e}$), reveal that compact galaxies display steeper gradients and higher values, resulting in elevated specific star formation rates (sSFR) in their inner regions compared to their outskirts. At a given $\Sigma_{\ast}$, compact galaxies have higher sSFR than extended galaxies, particularly in low-mass galaxies (log($M_{\ast}$/$M_{\odot}$)$\,\leq\,$10$^{10}$). Additionally, their metallicity profiles differ significantly: extended galaxies have steeper metallicity gradients, while compact galaxies exhibit flatter slopes and higher metallicity at a given $R/$$R_{\rm e}$. After accounting for the dependence of metallicity on $M_{\ast}$ and $\Sigma_{\ast}$, no further correlation with SFR is observed. The combination of higher sSFR and potentially higher star formation efficiency in compact galaxies suggests that their central gas is being rapidly consumed, leading to older stellar populations, as indicated by D$_{n}$(4000) and EW(H$\delta_A$), and resulting in faster central growth. Our results reveal that radial SFR profiles cannot be fully determined by $M_{\ast}$ and $\Sigma_{\ast}$ alone; other factors, such as galaxy size or angular momentum, must be considered to fully understand the observed trends.
Chatting with Bots: AI, Speech Acts, and the Edge of Assertion
2024-10-22
Iwan Williams, Tim Bayne
This paper addresses the question of whether large language model-powered chatbots are capable of assertion. According to what we call the Thesis of Chatbot Assertion (TCA), chatbots are the kinds of things that can assert, and at least some of the output produced by current-generation chatbots qualifies as assertion. We provide some motivation for TCA, arguing that it ought to be taken seriously and not simply dismissed. We also review recent objections to TCA, arguing that these objections are weighty. We thus confront the following dilemma: how can we do justice to both the considerations for and against TCA? We consider two influential responses to this dilemma - the first appeals to the notion of proxy-assertion; the second appeals to fictionalism - and argue that neither is satisfactory. Instead, reflecting on the ontogenesis of assertion, we argue that we need to make space for a category of proto-assertion. We then apply the category of proto-assertion to chatbots, arguing that treating chatbots as proto-assertors provides a satisfactory resolution to the dilemma of chatbot assertion.
The spatial-temporal evolution of coronal plasma parameters of the solar outer atmosphere at global scales, derived from solar full-disk imaging spectroscopic observation in the extreme-ultraviolet band, is critical for understanding and forecasting solar eruptions. We propose a multi-slits extreme ultraviolet imaging spectrograph for global coronal diagnostics with high cadence and present the preliminary instrument designs for the wavelength range from 18.3 to 19.8 nm. The instrument takes a comprehensive approach to obtain global coronal spatial and spectral information, improve the detected cadence and avoid overlapping. We first describe the relationship between optical properties and structural parameters, especially the relationship between the overlapping and the number of slits, and give a general multi-slits extreme-ultraviolet imaging spectrograph design process. Themultilayer structure is optimized to enhance the effective areas in the observation band. Five distantly-separated slits are set to divide the entire solar field of view, which increase the cadence for raster scanning the solar disk by 5 times relative to a single slit. The spectral resolving power of the optical system with an aperture diameter of 150 mm are optimized to be greater than 1461. The spatial resolution along the slits direction and the scanning direction are about 4.4''and 6.86'', respectively. The Al/Mo/B4C multilayer structure is optimized and the peak effective area is about 1.60 cm2 at 19.3 nm with a full width at half maximum of about 1.3 nm. The cadence to finish full-disk raster scan is about 5 minutes. Finally, the instrument performance is evaluated by an end-to-end calculation of the system photon budget and a simulation of the observational image and spectra. Our investigation shows that this approach is promising for global coronal plasma diagnostics.
Doping dependence of the nonlinear Hall resistivity in electron-doped Pr$_{2-x}$Ce$_{x}$CuO$_{4 \pm δ}$
2024-10-22
M. Dion, S. Ghotb, G. Hardy, P. Fournier
We report on a systematic study of the field dependence of the Hall resistivity $\rho _{yx} (B)$ as a function of doping in thin films of electron-doped superconducting cuprate Pr$_{2-x}$Ce$_{x}$CuO$_{4 \pm \delta}$ (PCCO). Across the studied doping range from $x = 0.125$ to $x = 0.20$, we observe a nonlinear dependence of $\rho_{yx}$ with $B$. The leading $B^{3}$ nonlinear term is negative, increases with decreasing temperature and peaks around optimal doping ($x = 0.15$). The observed nonlinear contribution is consistent with the presence of two different types of free carriers (electron-like and hole-like) even for doping with only an apparent hole Fermi surface as observed by angle-resolved photoemission spectroscopy. Based on an analysis using the two-carrier model, this negative nonlinear contribution to $\rho _{yx} (B)$ implies that the density of the charge carriers behaving like electrons is larger than the density of those behaving like holes for all the doping values explored. Combined with the Hall coefficient reaching $R_{H} = 0$ at specific temperatures for selected doping levels, we also conclude that the mobility of hole-like carriers is larger than that of electron-like carriers for doping around $x^{*} \approx 0.165$.
Generative AI for Overall Mission Effectiveness at the Habitable Worlds Observatory
2024-10-22
Megan Shabram, Ryan McClelland, John Wu, Hamsa Shwetha Venkataram, Heidi Segars, Bruce Dean, Christine Ye, Aquib Moin, Megan Ansdell, Mark Moussa, Umaa Rebbapragada, Hamed Valizadegan, Dominick Perini, Glenn Ko, Victoria Da Poian, Sam Gharib-Nezhad, Giuseppe Cataldo
Here we present several use cases for using Generative AI (Gen AI) to improve systems engineering and cognitive knowledge management related to the future of astronomy from a culmination of working meetings and presentations as part of the Gen AI Task Group for the NASA Habitable Worlds Observatory (HWO) Science and Technology Architecture Review Team (START) AI/ML Working Group. Collectively, our group mission statement is "Where is the Human-in-the-loop as Gen AI systems become more powerful and autonomous?" with an emphasis on the ethical applications of Gen AI, guided by using these systems to remove drudgery from human work while simultaneously increasing opportunities for humans to experience more collective creativity and innovation. The HWO mission stands to benefit dramatically from generative models for different data types including text, time series/spectra, and image data. These cover a wide range of applications in science and engineering for HWO, including: mission development acceleration, data analysis and interpretation, enhancing imaging capabilities, anomaly detection, predictive modeling and simulation, data augmentation for machine learning, instrument calibration and optimization, public engagement and education, and assisting in mission planning. As an example, through sensitivity analysis of simulated exoplanet population science data sets of various generative model complexity, we can reverse engineer the measurement uncertainty requirements for HWO instruments to produce data that can constrain population models and thus inform HWO design requirements. This approach to HWO design is one example of a strategy that can ensure that HWO remains AI-ready. Through presenting herein a combination of visionary ideas balanced with grounded validated use case examples, we aim to support the development of a long-term strategy to keep HWO AI-ready as it moves forward.
Remote Sensing (RS) is a crucial technology for observing, monitoring, and interpreting our planet, with broad applications across geoscience, economics, humanitarian fields, etc. While artificial intelligence (AI), particularly deep learning, has achieved significant advances in RS, unique challenges persist in developing more intelligent RS systems, including the complexity of Earth's environments, diverse sensor modalities, distinctive feature patterns, varying spatial and spectral resolutions, and temporal dynamics. Meanwhile, recent breakthroughs in large Foundation Models (FMs) have expanded AI's potential across many domains due to their exceptional generalizability and zero-shot transfer capabilities. However, their success has largely been confined to natural data like images and video, with degraded performance and even failures for RS data of various non-optical modalities. This has inspired growing interest in developing Remote Sensing Foundation Models (RSFMs) to address the complex demands of Earth Observation (EO) tasks, spanning the surface, atmosphere, and oceans. This survey systematically reviews the emerging field of RSFMs. It begins with an outline of their motivation and background, followed by an introduction of their foundational concepts. It then categorizes and reviews existing RSFM studies including their datasets and technical contributions across Visual Foundation Models (VFMs), Visual-Language Models (VLMs), Large Language Models (LLMs), and beyond. In addition, we benchmark these models against publicly available datasets, discuss existing challenges, and propose future research directions in this rapidly evolving field.
Applied-Field Magnetoplasmadynamic Thrusters for Deep Space Exploration
2024-10-22
Matthew Han, Hannah Rana
Recent advancements in the development of Applied-Field Magnetoplasmadynamic thrusters (AF-MPDTs) present themselves to be an increasingly promising propulsion technology for deep space exploration missions. Various entities, ranging from state-sponsored institutions to privately-owned startups, have developed AF-MPDTs across a wide range of power levels. Current developments in superconducting technologies, namely High-Temperature Superconducting (HTS) coils such as REBCO, have enabled research into the integration of HTS coils into the applied-field module to generate MPD thrust. Developments in space cryocoolers have opened the doors for HTS use within a spaceflight design of an AF-MPDT, where the applied-field module is at 40 K. A TRL of 4-5 has been reached by some AF-MPDT prototypes; venturing beyond this will require higher cooling power space cryocoolers to be developed in parallel and appropriately integrated into the thruster. Moreover, bespoke thermal control is required to maintain the thruster's extreme temperature gradient where the cryocooled HTS are in close proximity to the thruster cathode. More effective space power supply units with higher power generation is a further limitation to reaching TRL 9. This review examines the underlying principles behind AF-MPDT propulsion and the recent global developments in AF-MPDT technology, with an in-depth analysis and critical discussion on the spaceflight components necessary to permit AF-MPDTs to become a widely-adopted spaceflight-ready propulsion technology.
Generation of non-classical and entangled light states using intense laser-matter interactions
2024-10-22
Th. Lamprou, P. Stammer, J. Rivera-Dean, N. Tsatrafyllis, M. F. Ciappina, M. Lewenstein, P. Tzallas
Non-classical and entangled light states are of fundamental interest in quantum mechanics and they are a powerful tool for the emergence of new quantum technologies. The development of methods that can lead to the generation of such light states is therefore of high importance. Recently, we have demonstrated that intense laser-matter interactions can serve towards this direction. Specifically, we have shown how the use of fully quantized approaches in intense laser-matter interactions and the process of high harmonic generation, can lead to the generation high photon-number non-classical (optical Schr\"{o}dinger's "cat" or squeezed) and entangled states from the far-infrared (IR) to the extreme-ultraviolet (XUV). Here, after a brief introduction on the fundamentals, we summarize the operation principles of these approaches and we discuss the future directions of non-classical light engineering using strong laser fields, and the potential applications in ultrafast and quantum information science. Our findings open the way to a novel quantum nonlinear spectroscopy method, based on the interplay between the quantum properties of light with that of quantum matter.
Role of inclusiveness of learning environment in predicting students' outcomes in courses in which women are not underrepresented
2024-10-22
Sonja Cwik, Chandralekha Singh
Student beliefs in introductory physics courses can influence their course outcomes and retention in STEM disciplines and future career aspirations. This study used survey data from 501 students in the first of two-semester algebra-based introductory physics courses primarily taken by bioscience majors, in which women make up approximately 65% of the class. We investigated how the learning environment including perceived recognition, peer interaction, and sense of belonging correlate with students' physics outcomes, including their physics self-efficacy, interest, and identity. We found that in general, women had lower physics beliefs than men and the learning environment plays a major role in explaining student outcomes. We also found that perceived recognition played an important role in predicting students' physics identity and students' sense of belonging played an important role in predicting students' physics self-efficacy in the first algebra-based introductory physics course investigated. These findings can be useful to contemplate strategies to create an equitable and inclusive learning environment to help all students to excel in these physics courses.
AI-focused HPC Data Centers Can Provide More Power Grid Flexibility and at Lower Cost
2024-10-22
Yihong Zhou, Angel Paredes, Chaimaa Essayeh, Thomas Morstyn
The recent growth of Artificial Intelligence (AI), particularly large language models, requires energy-demanding high-performance computing (HPC) data centers, which poses a significant burden on power system capacity. Scheduling data center computing jobs to manage power demand can alleviate network stress with minimal infrastructure investment and contribute to fast time-scale power system balancing. This study, for the first time, comprehensively analyzes the capability and cost of grid flexibility provision by GPU-heavy AI-focused HPC data centers, along with a comparison with CPU-heavy general-purpose HPC data centers traditionally used for scientific computing. Using real-world data from 7 AI-focused HPC data centers, 7 general-purpose HPC data centers, and 3 cloud platforms, we find that AI-focused HPC data centers can offer greater flexibility at 50% lower cost for a range of power system services. By comparing the cost to flexibility market prices, we illustrate the financial profitability of flexibility provision for AI-focused HPC data centers.
2024-10-22
Abdoul Jalil Djiberou Mahamadou, Artem A. Trotsyuk
Efforts to mitigate bias and enhance fairness in the artificial intelligence (AI) community have predominantly focused on technical solutions. While numerous reviews have addressed bias in AI, this review uniquely focuses on the practical limitations of technical solutions in healthcare settings, providing a structured analysis across five key dimensions affecting their real-world implementation: who defines bias and fairness; which mitigation strategy to use and prioritize among dozens that are inconsistent and incompatible; when in the AI development stages the solutions are most effective; for which populations; and the context in which the solutions are designed. We illustrate each limitation with empirical studies focusing on healthcare and biomedical applications. Moreover, we discuss how value-sensitive AI, a framework derived from technology design, can engage stakeholders and ensure that their values are embodied in bias and fairness mitigation solutions. Finally, we discuss areas that require further investigation and provide practical recommendations to address the limitations covered in the study.
Uncovering RL Integration in SSL Loss: Objective-Specific Implications for Data-Efficient RL
2024-10-22
Ömer Veysel Çağatan, Barış Akgün
In this study, we investigate the effect of SSL objective modifications within the SPR framework, focusing on specific adjustments such as terminal state masking and prioritized replay weighting, which were not explicitly addressed in the original design. While these modifications are specific to RL, they are not universally applicable across all RL algorithms. Therefore, we aim to assess their impact on performance and explore other SSL objectives that do not accommodate these adjustments like Barlow Twins and VICReg. We evaluate six SPR variants on the Atari 100k benchmark, including versions both with and without these modifications. Additionally, we test the performance of these objectives on the DeepMind Control Suite, where such modifications are absent. Our findings reveal that incorporating specific SSL modifications within SPR significantly enhances performance, and this influence extends to subsequent frameworks like SR-SPR and BBF, highlighting the critical importance of SSL objective selection and related adaptations in achieving data efficiency in self-predictive reinforcement learning.
The HUSTLE Program: The UV to Near-Infrared HST WFC3/UVIS G280 Transmission Spectrum of WASP-127b
2024-10-22
V. A. Boehm, N. K. Lewis, C. E. Fairman, S. E. Moran, C. Gascón, H. R. Wakeford, M. K. Alam, L. Alderson, J. Barstow, N. E. Batalha, D. Grant, M. López-Morales, R. J. MacDonald, M. S. Marley, K. Ohno
Ultraviolet wavelengths offer unique insights into aerosols in exoplanetary atmospheres. However, only a handful of exoplanets have been observed in the ultraviolet to date. Here, we present the ultraviolet-visible transmission spectrum of the inflated hot Jupiter WASP-127b. We observed one transit of WASP-127b with WFC3/UVIS G280 as part of the Hubble Ultraviolet-optical Survey of Transiting Legacy Exoplanets (HUSTLE), obtaining a transmission spectrum from 200-800 nm. Our reductions yielded a broad-band transit depth precision of 91 ppm and a median precision of 240 ppm across 59 spectral channels. Our observations are suggestive of a high-altitude cloud layer with forward modeling showing they are composed of sub-micron particles and retrievals indicating a high opacity patchy cloud. While our UVIS/G280 data only offers weak evidence for Na, adding archival HST WFC3/IR and STIS observations raises the overall Na detection significance to 4.1-sigma. Our work demonstrates the capabilities of HST WFC3/UVIS G280 observations to probe the aerosols and atmospheric composition of transiting hot Jupiters with comparable precision to HST STIS.
[CII] line intensity mapping the epoch of reionization with the Prime-Cam on FYST II. CO foreground masking based on an external catalog
2024-10-22
C. Karoumpis, B. Magnelli, E. Romano-Díaz, K. Garcia, A. Dev, J. Clarke, T. -M. Wang, T. Badescu, D. Riechers, F. Bertoldi
Context. The Fred Young Submillimeter Telescope (FYST) line intensity mapping (LIM) survey will measure the power spectrum (PS) of the singly ionized carbon 158 $\rm \mu$m fine-structure line, [CII], to trace the appearance of the first galaxies that emerged during and right after the epoch of reionization (EoR, $6
Blast: a Web Application for Characterizing the Host Galaxies of Astrophysical Transients
2024-10-22
D. O. Jones, P. McGill, T. A. Manning, A. Gagliano, B. Wang, D. A. Coulter, R. J. Foley, G. Narayan, V. A. Villar, L. Braff, A. W. Engel, D. Farias, Z. Lai, K. Loertscher, J. Kutcka, S. Thorp, J. Vazquez
Characterizing the host galaxies of astrophysical transients is important to many areas of astrophysics, including constraining the progenitor systems of core-collapse supernovae, correcting Type Ia supernova distances, and probabilistically classifying transients without photometric or spectroscopic data. Given the increasing transient discovery rate in the coming years, there is substantial utility in providing public, transparent, reproducible, and automatic characterization for large samples of transient host galaxies. Here we present Blast, a web application that ingests live streams of transient alerts, matches transients to their host galaxies, and performs photometry on coincident archival imaging data of the host galaxy. The photometry is then used to infer both global host-galaxy properties and galaxy properties within 2 kpc of the transient location by using the Prospector Bayesian inference framework, with an acceleration in evaluation speed achieved via simulation-based inference. Blast provides host-galaxy properties to users via a web browser or an application program interface. The software can be extended to support alternative photometric or SED-fitting algorithms, and can be scaled via an asynchronous worker queue across multiple compute nodes to handle the processing of large volumes of transient alerts for upcoming transient surveys. Blast has been ingesting newly discovered transients from the Transient Name Server since mid-2024, and has currently measured SED parameters for more than 6000 transients. The service is publicly available at https://blast.scimma.org/.
3-D CMZ III: Constraining the 3-D structure of the Central Molecular Zone via molecular line emission and absorption
2024-10-22
Daniel L. Walker, Cara Battersby, Dani Lipman, Mattia C. Sormani, Adam Ginsburg, Simon C. O. Glover, Jonathan D. Henshaw, Steven N. Longmore, Ralf S. Klessen, Katharina Immer, Danya Alboslani, John Bally, Ashley Barnes, H Perry Hatchfield, Elisabeth A. C. Mills, Rowan Smith, Robin G. Tress, Qizhou Zhang
The Milky Way's Central Molecular Zone (CMZ) is the largest concentration of dense molecular gas in the Galaxy, the structure of which is shaped by the complex interplay between Galactic-scale dynamics and extreme physical conditions. Understanding the 3-D geometry of this gas is crucial as it determines the locations of star formation and subsequent feedback. We present a catalogue of clouds in the CMZ using Herschel data. Using archival data from the APEX and MOPRA CMZ surveys, we measure averaged kinematic properties of the clouds at 1mm and 3mm. We use archival ATCA data of the H$_{2}$CO (1$_{1,0}$ - 1$_{1,1}$) 4.8 GHz line to search for absorption towards the clouds, and 4.85 GHz GBT C-band data to measure the radio continuum emission. We measure the absorption against the continuum to provide new constraints for the line-of-sight positions of the clouds relative to the Galactic centre, and find a highly asymmetric distribution, with most clouds residing in front of the Galactic centre. The results are compared with different orbital models, and we introduce a revised toy model of a vertically-oscillating closed elliptical orbit. We find that most models describe the PPV structure of the gas reasonably well, but find significant inconsistencies in all cases regarding the near vs. far placement of individual clouds. Our results highlight that the CMZ is likely more complex than can be captured by these simple geometric models, along with the need for new data to provide further constraints on the true 3-D structure of the CMZ.
2024-10-22
J. Polo, W. J. Chetcuti, T. Haug, A. Minguzzi, K. Wright, L. Amico
Persistent currents flowing in spatially closed tracks define one of the most iconic concepts in mesoscopic physics. They have been studied in solid-state platforms such as superfluids, superconductors and metals. Cold atoms trapped in magneto-optical toroidal circuits and driven by suitable artificial gauge fields allow us to study persistent currents with unprecedented control and flexibility of the system's physical conditions. Here, we review persistent currents of ultracold matter. Capitalizing on the remarkable progress in driving different atomic species to quantum degeneracy, persistent currents of single or multicomponent bosons/fermions, and their mixtures can be addressed within the present experimental know-how. This way, fundamental concepts of quantum science and many-body physics, like macroscopic quantum coherence, solitons, vortex dynamics, fermionic pairing and BEC-BCS crossover can be studied from a novel perspective. Finally, we discuss how persistent currents can form the basis of new technological applications like matter-wave gyroscopes and interferometers.
Raising the Stakes: Performance Pressure Improves AI-Assisted Decision Making
2024-10-21
Nikita Haduong, Noah A. Smith
AI systems are used in many domains to assist with decision making, and although the potential for AI systems to assist with decision making is much discussed, human-AI collaboration often underperforms. Investigation into why the performance potential is not realized has revealed many factors, including (mis)trust in the AI system and mental models of AI capabilities on subjective tasks. Performance pressure is known to influence human decision making behavior, yet how it interacts with human-AI decision making is understudied. In this work, we show the effects of performance pressure on AI advice reliance when laypeople (Amazon Mechanical Turk crowdworkers) complete a common AI-assisted task (fake review detection) and thus have inherently low performance pressure. We manipulate performance pressure by leveraging people's loss aversion towards potential monetary gains when completing a task. We find that when the stakes are high, people use AI advice more appropriately than when stakes are lower, regardless of the presence of an AI explanation. Furthermore, when the AI system gives incorrect advice, people correctly discount the poor advice more often when the stakes are higher than when they are lower. We conclude by discussing the implications of how performance pressure influences AI-assisted decision making and encourage future research to incorporate performance pressure analysis.
Probing the structure and dynamics of the actinides from U through Lr using Regge-pole analysis
2024-10-21
Zineb Felfli, Zoe Landers, Alfred Z. Msezane
The structure and dynamics of the actinide atoms from U to Lr is probed through the electron elastic scattering total cross sections (TCSs) calculated using Regge pole analysis. The crucial Regge trajectories probe electron attachment at the fundamental level near threshold leading to stable ground, metastable and excited negative-ion formation as resonances as well as Ramsauer-Townsend (R-T) minima and shape resonances (SRs). A polarization-induced metastable TCS with a deep R-T minimum near threshold first appears in the Pu TCSs, marking the first break in periodicity in the actinides while in the Cf TCSs the R-T minimum manifests as a SR very close to threshold, demonstrating the second break. These results are consistent with observations. Moreover, the highest excited states TCSs of U and Pu exhibit the strongest fullerene molecular behavior among the actinides, implying their multi-functionalization, including nanocatalysis through their doubly-charged anions. Electron affinities are also extracted from the TCSs.
Projections of standardised energy indices in future climate scenarios
2024-10-21
Edgar Dolores-Tesillos, Noelia Otero, Sam Allen
Renewable energy is becoming an increasingly important component of energy systems. However, renewable energy production is heavily dependent on the prevailing weather conditions, which are changing as a result of climate change. It is therefore necessary to build energy systems that are robust to energy shortages caused by weather-dependent changes to energy demand and renewable energy production. To design such systems, we must monitor how changes in the climate are expected to influence future energy production and demand; this is important for policymakers to decide when, where, and by how much renewable energy installed capacities should be increased, for example. In this paper, we study the behaviour of standardised energy indices in future European climate projections, and use this to monitor how characteristics of energy production droughts in Europe are expected to change in the future. We use these results to make suggestions regarding how the energy mix should be adapted in the future to decrease the risk of energy production droughts.
Large language models enabled multiagent ensemble method for efficient EHR data labeling
2024-10-21
Jingwei Huang, Kuroush Nezafati, Ismael Villanueva-Miranda, Zifan Gu, Ann Marie Navar, Tingyi Wanyan, Qin Zhou, Bo Yao, Ruichen Rong, Xiaowei Zhan, Guanghua Xiao, Eric D. Peterson, Donghan M. Yang, Yang Xie
This study introduces a novel multiagent ensemble method powered by LLMs to address a key challenge in ML - data labeling, particularly in large-scale EHR datasets. Manual labeling of such datasets requires domain expertise and is labor-intensive, time-consuming, expensive, and error-prone. To overcome this bottleneck, we developed an ensemble LLMs method and demonstrated its effectiveness in two real-world tasks: (1) labeling a large-scale unlabeled ECG dataset in MIMIC-IV; (2) identifying social determinants of health (SDOH) from the clinical notes of EHR. Trading off benefits and cost, we selected a pool of diverse open source LLMs with satisfactory performance. We treat each LLM's prediction as a vote and apply a mechanism of majority voting with minimal winning threshold for ensemble. We implemented an ensemble LLMs application for EHR data labeling tasks. By using the ensemble LLMs and natural language processing, we labeled MIMIC-IV ECG dataset of 623,566 ECG reports with an estimated accuracy of 98.2%. We applied the ensemble LLMs method to identify SDOH from social history sections of 1,405 EHR clinical notes, also achieving competitive performance. Our experiments show that the ensemble LLMs can outperform individual LLM even the best commercial one, and the method reduces hallucination errors. From the research, we found that (1) the ensemble LLMs method significantly reduces the time and effort required for labeling large-scale EHR data, automating the process with high accuracy and quality; (2) the method generalizes well to other text data labeling tasks, as shown by its application to SDOH identification; (3) the ensemble of a group of diverse LLMs can outperform or match the performance of the best individual LLM; and (4) the ensemble method substantially reduces hallucination errors. This approach provides a scalable and efficient solution to data-labeling challenges.
2024-10-21
Álvaro Perales-Eceiza, Toby Cubitt, Mile Gu, David Pérez-García, Michael M. Wolf
The study of undecidability in problems arising from physics has experienced a renewed interest, mainly in connection with quantum information problems. The goal of this review is to survey this recent development. After a historical introduction, we first explain the necessary results about undecidability in mathematics and computer science. Then we briefly review the first results about undecidability in physics which emerged mostly in the 80s and early 90s. Finally we focus on the most recent contributions, which we divide in two main categories: many body systems and quantum information problems.
SPHERE: Scaling Personalized Feedback in Programming Classrooms with Structured Review of LLM Outputs
2024-10-21
Xiaohang Tang, Sam Wong, Marcus Huynh, Zicheng He, Yalong Yang, Yan Chen
Effective personalized feedback is crucial for learning programming. However, providing personalized, real-time feedback in large programming classrooms poses significant challenges for instructors. This paper introduces SPHERE, an interactive system that leverages Large Language Models (LLMs) and structured LLM output review to scale personalized feedback for in-class coding activities. SPHERE employs two key components: an Issue Recommendation Component that identifies critical patterns in students' code and discussion, and a Feedback Review Component that uses a ``strategy-detail-verify'' approach for efficient feedback creation and verification. An in-lab, between-subject study demonstrates SPHERE's effectiveness in improving feedback quality and the overall feedback review process compared to a baseline system using off-the-shelf LLM outputs. This work contributes a novel approach to scaling personalized feedback in programming education, addressing the challenges of real-time response, issue prioritization, and large-scale personalization.
The domain of human resources (HR) includes a broad spectrum of tasks related to natural language processing (NLP) techniques. Recent breakthroughs in NLP have generated significant interest in its industrial applications in this domain and potentially alleviate challenges such as the difficulty of resource acquisition and the complexity of problems. At the same time, the HR domain can also present unique challenges that drive state-of-the-art in NLP research. To support this, we provide NLP researchers and practitioners with an overview of key HR tasks from an NLP perspective, illustrating how specific sub-tasks (e.g., skill extraction) contribute to broader objectives (e.g., job matching). Through this survey, we identify opportunities in NLP for HR and suggest directions for future exploration.
Despite the recent discoveries of planets orbiting stars at all evolutionary stages, the evolution of planetary systems remains poorly understood. Studying planetary systems around red giant branch stars can reveal how main sequence planetary systems can change and evolve into white dwarf systems over time. Decades of radial velocity and transit surveys have yielded the detection of hundreds of planets and planet candidates orbiting evolved stars. These planetary systems have provided important insights into understanding how planetary atmospheres and orbits can be disrupted by stellar evolution, potentially being restructured at late stages, and how planets can be eventually engulfed by their stars, possibly reborn as white dwarf planetary systems. Evolved star targets will reveal planet occurrence at the largest distances and most varied environments across the Galaxy.
A multiwavelength overview of the giant spiral UGC 2885
2024-10-21
Matheus C. Carvalho, Bavithra Naguleswaran, Pauline Barmby, Mark Gorski, Sabine Köenig, Benne Holwerda, Jason E. Young
UGC 2885 (z = 0.01935) is one of the largest and most massive galaxies in the local Universe, yet its undisturbed spiral structure is unexpected for such an object and unpredicted in cosmological simulations. Understanding the detailed properties of extreme systems such as UGC 2885 can provide insight on the limits of scaling relations and physical processes driving galaxy evolution. Our goal is to understand whether UGC 2885 has followed a similar evolutionary path to other high-mass galaxies by examining its place on the fundamental metallicity relation and the star-forming main sequence. We present new observations of UGC 2885 with the CFHT and IRAM 30-m telescopes. These novel data are used to respectively calculate metallicity and molecular hydrogen mass values. We estimate stellar mass (M*) and star formation rate (SFR) based on mid-infrared observations with the Wide-field Infrared Survey Explorer. We find global metallicities Z = 9.28, 9.08 and 8.74 at the 25 kpc ellipsoid from N2O2, R23 and O3N2 indices, respectively. This puts UGC 2885 at the high end of the galaxy metallicity distribution. The molecular hydrogen mass is calculated as M(H2)=(1.89+/-0.24)e11 Msun, the SFR as 1.63+/-0.72 Msun/yr and the stellar mass as (4.83 +/- 1.52)e11 Msun, which gives a star formation efficiency (SFE = SFR/M(H2)) of (8.67+/-4.20)e12/yr. This indicates that UGC 2885 has an extremely high molecular gas content when compared to known samples of star forming galaxies (~100 times more) and a relatively low SFR for its current gas content. We conclude that UGC 2885 has gone through cycles of star formation periods, which increased its stellar mass and metallicity to its current state. The mechanisms that are fueling the current molecular gas reservoir and keeping the galaxy from producing stars remain uncertain. We discuss the possibility that a molecular bar is quenching star forming activity.
STAR: A Simple Training-free Approach for Recommendations using Large Language Models
2024-10-21
Dong-Ho Lee, Adam Kraft, Long Jin, Nikhil Mehta, Taibai Xu, Lichan Hong, Ed H. Chi, Xinyang Yi
Recent progress in large language models (LLMs) offers promising new approaches for recommendation system (RecSys) tasks. While the current state-of-the-art methods rely on fine-tuning LLMs to achieve optimal results, this process is costly and introduces significant engineering complexities. Conversely, methods that bypass fine-tuning and use LLMs directly are less resource-intensive but often fail to fully capture both semantic and collaborative information, resulting in sub-optimal performance compared to their fine-tuned counterparts. In this paper, we propose a Simple Training-free Approach for Recommendation (STAR), a framework that utilizes LLMs and can be applied to various recommendation tasks without the need for fine-tuning. Our approach involves a retrieval stage that uses semantic embeddings from LLMs combined with collaborative user information to retrieve candidate items. We then apply an LLM for pairwise ranking to enhance next-item prediction. Experimental results on the Amazon Review dataset show competitive performance for next item prediction, even with our retrieval stage alone. Our full method achieves Hits@10 performance of +23.8% on Beauty, +37.5% on Toys and Games, and -1.8% on Sports and Outdoors relative to the best supervised models. This framework offers an effective alternative to traditional supervised models, highlighting the potential of LLMs in recommendation systems without extensive training or custom architectures.
Susu Box or Piggy Bank: Assessing Cultural Commonsense Knowledge between Ghana and the U.S
2024-10-21
Christabel Acquaye, Haozhe An, Rachel Rudinger
Recent work has highlighted the culturally-contingent nature of commonsense knowledge. We introduce AMAMMER${\epsilon}$, a test set of 525 multiple-choice questions designed to evaluate the commonsense knowledge of English LLMs, relative to the cultural contexts of Ghana and the United States. To create AMAMMER${\epsilon}$, we select a set of multiple-choice questions (MCQs) from existing commonsense datasets and rewrite them in a multi-stage process involving surveys of Ghanaian and U.S. participants. In three rounds of surveys, participants from both pools are solicited to (1) write correct and incorrect answer choices, (2) rate individual answer choices on a 5-point Likert scale, and (3) select the best answer choice from the newly-constructed MCQ items, in a final validation step. By engaging participants at multiple stages, our procedure ensures that participant perspectives are incorporated both in the creation and validation of test items, resulting in high levels of agreement within each pool. We evaluate several off-the-shelf English LLMs on AMAMMER${\epsilon}$. Uniformly, models prefer answers choices that align with the preferences of U.S. annotators over Ghanaian annotators. Additionally, when test items specify a cultural context (Ghana or the U.S.), models exhibit some ability to adapt, but performance is consistently better in U.S. contexts than Ghanaian. As large resources are devoted to the advancement of English LLMs, our findings underscore the need for culturally adaptable models and evaluations to meet the needs of diverse English-speaking populations around the world.
Information retrieval from Euclidean path integral
2024-10-21
Dong-han Yeom
In this article, we review the information loss paradox in the spirit of the Euclidean path integral approach. First, we argue that there is a long debate about the information loss paradox, and the non-perturbative quantum gravitational wave function must include the clue to the paradox. The Euclidean path integral approach provides the best way to describe the wave function. From this wave function, we can notice that there are not only semi-classical but also non-perturbative contributions, which are highly suppressed but preserved information. Information retrieval will be sufficiently explained if such non-perturbative contributions must be dominated by the late time. We will show that there is sufficient evidence that this scenario can be realized in generic circumstances. Finally, we compare this scenario with alternative approaches. Also, we comment on some unresolved issues that need to be clarified.
A study of sixty-one massive over-contact binary candidates from the OGLE survey of the Magellanic Clouds
2024-10-21
Athira Menon, Michal Pawlak, Daniel J. Lennon, Koushik Sen, Norbert Langer
Massive contact binaries are fundamental test-beds to study properties of close binaries en route to stellar mergers. Progress in their understanding has been largely impeded due to a dearth in their observed counterparts. We accumulate 61 over-contact binary candidates from the OGLE survey of the Magellanic Clouds and form the largest known sample of such systems. We mine the OGLE-IV catalogue for B-type and earlier systems which match our synthetic light curves, and thereafter examine their properties in comparison with our binary populations. Simultaneously, we also investigate the degeneracy in the light curves between over-contact and near-contact binaries and attempt to distinguish these populations. We construct new synthetic populations of such systems from detailed $\texttt{MESA}$ models with initial parameters of M$_\textrm{total,i}=14-80\,\textrm{M}_{\odot}$ and P$_\textrm{i}=0.6-23\,$days, and compute analogous $\texttt{PHOEBE}$ light curves. We then extract OGLE systems that match our synthetic light curves and compare their numbers, and distributions in periods, absolute V-band magnitudes and colours (V-I) with our population predictions. We expect massive over-contact binaries to have smooth equal-depth light curves due to their equal-mass ($q\approx1$) and consequently, equal-temperature components. We mine 47 and 14 predominantly B-type over-contact OGLE binaries from the Large and Small Magellanic Clouds respectively, with periods of $\approx0.6-1\,$d, along with a few wider O-type systems. This sample is consistent with our theoretically expected numbers and colour-magnitude and period distributions. Additionally, we find that binaries nearing contact have periods as large as $4.5\,$d and mass ratios of $q\approx0.4-0.95$, some of which may masquerade as over-contact binaries and thereby lead to potential observational misclassifications.
In response to global warming and energy shortages, there has been a significant shift towards integrating renewable energy sources, energy storage systems, and electric vehicles. Deploying electric vehicles within smart grids offers a promising solution to reduce carbon emissions. However, managing the charging and discharging processes of them as distributed power supplies present significant challenges. Additionally, the intermittent nature of renewable energy, uncertainties in electric vehicle-related parameters, fluctuating energy prices, and varying loads make maintaining stable power system operations more complex. Effective management systems for electric vehicle battery charging are crucial to coordinating these processes and ensuring a secure, efficient, and reliable power system. Reinforcement learning, enhanced by deep learning, has gained substantial interest for its model-free approach and real-time optimization, effectively managing electric vehicle charging by maximizing cumulative rewards. This review synthesizes existing literature on reinforcement learning-based frameworks, objectives, and architectures for electric vehicle charging coordination strategies in power systems, classifying methods into centralized and decentralized categories. Additionally, the article offers suggestions for future research directions to further enhance reinforcement learning-based electric vehicle charging optimization.
Integrating Reinforcement Learning with Foundation Models for Autonomous Robotics: Methods and Perspectives
2024-10-21
Angelo Moroncelli, Vishal Soni, Asad Ali Shahid, Marco Maccarini, Marco Forgione, Dario Piga, Blerina Spahiu, Loris Roveda
Foundation models (FMs), large deep learning models pre-trained on vast, unlabeled datasets, exhibit powerful capabilities in understanding complex patterns and generating sophisticated outputs. However, they often struggle to adapt to specific tasks. Reinforcement learning (RL), which allows agents to learn through interaction and feedback, offers a compelling solution. Integrating RL with FMs enables these models to achieve desired outcomes and excel at particular tasks. Additionally, RL can be enhanced by leveraging the reasoning and generalization capabilities of FMs. This synergy is revolutionizing various fields, including robotics. FMs, rich in knowledge and generalization, provide robots with valuable information, while RL facilitates learning and adaptation through real-world interactions. This survey paper comprehensively explores this exciting intersection, examining how these paradigms can be integrated to advance robotic intelligence. We analyze the use of foundation models as action planners, the development of robotics-specific foundation models, and the mutual benefits of combining FMs with RL. Furthermore, we present a taxonomy of integration approaches, including large language models, vision-language models, diffusion models, and transformer-based RL models. We also explore how RL can utilize world representations learned from FMs to enhance robotic task execution. Our survey aims to synthesize current research and highlight key challenges in robotic reasoning and control, particularly in the context of integrating FMs and RL--two rapidly evolving technologies. By doing so, we seek to spark future research and emphasize critical areas that require further investigation to enhance robotics. We provide an updated collection of papers based on our taxonomy, accessible on our open-source project website at: https://github.com/clmoro/Robotics-RL-FMs-Integration.
UVCANDELS: Catalogs of photometric redshifts and galaxy physical properties
2024-10-21
Vihang Mehta, Marc Rafelski, Ben Sunnquist, Harry I. Teplitz, Claudia Scarlata, Xin Wang, Adriano Fontana, Nimish P. Hathi, Kartheik G. Iyer, Anahita Alavi, James Colbert, Norman Grogin, Anton Koekemoer, Kalina V. Nedkova, Matthew Hayes, Laura Prichard, Brian Siana, Brent M. Smith, Rogier Windhorst, Teresa Ashcraft, Micaela Bagley, Ivano Baronchelli, Guillermo Barro, Alex Blanche, Adam Broussard, Timothy Carleton, Nima Chartab, Alex Codoreanu, Seth Cohen, Christopher Conselice, Y. Sophia Dai, Behnam Darvish, Romeel Dave, Laura DeGroot, Duilia De Mello, Mark Dickinson, Najmeh Emami, Henry Ferguson, Leonardo Ferreira, Keely Finkelstein, Steven Finkelstein, Jonathan P. Gardner, Eric Gawiser, Timothy Gburek, Mauro Giavalisco, Andrea Grazian, Caryl Gronwall, Yicheng Guo, Pablo Arrabal Haro, Shoubaneh Hemmati, Justin Howell, Rolf A. Jansen, Zhiyuan Ji, Sugata Kaviraj, Keunho J. Kim, Peter Kurczynski, Ilin Lazar, Ray A. Lucas, John MacKenty, Kameswara Bharadwaj Mantha, Alec Martin, Garreth Martin, Tyler McCabe, Bahram Mobasher, Alexa M. Morales, Robert O'Connell, Charlotte Olsen, Lillian Otteson, Swara Ravindranath, Caleb Redshaw, Michael Rutkowski, Brant Robertson, Zahra Sattari, Emmaris Soto, Lei Sun, Sina Taamoli, Eros Vanzella, L. Y. Aaron Yung, Bonnabelle Zabelle
The UltraViolet imaging of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey Fields (UVCANDELS) program provides deep HST F275W and F435W imaging over four CANDELS fields (GOODS-N, GOODS-S, COSMOS, and EGS). We combine this newly acquired UV imaging with existing HST imaging from CANDELS as well as existing ancillary data to obtain robust photometric redshifts and reliable estimates for galaxy physical properties for over 150,000 galaxies in the $\sim$430 arcmin$^2$ UVCANDELS area. Here, we leverage the power of the new UV photometry to not only improve the photometric redshift measurements in these fields, but also constrain the full redshift probability distribution combining multiple redshift fitting tools. Furthermore, using the full UV-to-IR photometric dataset, we measure the galaxy physical properties by fitting templates from population synthesis models with two different parameterizations (flexible and fixed-form) of the star-formation histories (SFHs). Compared to the flexible SFH parametrization, we find that the fixed-form SFHs systematically underestimate the galaxy stellar masses, both at the low- ($\lesssim10^9 M_\odot$) and high- ($\gtrsim10^{10} M_\odot$) mass end, by as much as $\sim0.5$ dex. This underestimation is primarily due the limited ability of fixed-form SFH parameterization to simultaneously capture the chaotic nature of star-formation in these galaxies.
LLM-based Optimization of Compound AI Systems: A Survey
2024-10-21
Matthieu Lin, Jenny Sheng, Andrew Zhao, Shenzhi Wang, Yang Yue, Yiran Wu, Huan Liu, Jun Liu, Gao Huang, Yong-Jin Liu
In a compound AI system, components such as an LLM call, a retriever, a code interpreter, or tools are interconnected. The system's behavior is primarily driven by parameters such as instructions or tool definitions. Recent advancements enable end-to-end optimization of these parameters using an LLM. Notably, leveraging an LLM as an optimizer is particularly efficient because it avoids gradient computation and can generate complex code and instructions. This paper presents a survey of the principles and emerging trends in LLM-based optimization of compound AI systems. It covers archetypes of compound AI systems, approaches to LLM-based end-to-end optimization, and insights into future directions and broader impacts. Importantly, this survey uses concepts from program analysis to provide a unified view of how an LLM optimizer is prompted to optimize a compound AI system. The exhaustive list of paper is provided at https://github.com/linyuhongg/LLM-based-Optimization-of-Compound-AI-Systems.
2024-10-21
Haoyan Xu, Kay Liu, Zhengtao Yao, Philip S. Yu, Kaize Ding, Yue Zhao
How can we train graph-based models to recognize unseen classes while keeping labeling costs low? Graph open-set learning (GOL) and out-of-distribution (OOD) detection aim to address this challenge by training models that can accurately classify known, in-distribution (ID) classes while identifying and handling previously unseen classes during inference. It is critical for high-stakes, real-world applications where models frequently encounter unexpected data, including finance, security, and healthcare. However, current GOL methods assume access to many labeled ID samples, which is unrealistic for large-scale graphs due to high annotation costs. In this paper, we propose LEGO-Learn (Label-Efficient Graph Open-set Learning), a novel framework that tackles open-set node classification on graphs within a given label budget by selecting the most informative ID nodes. LEGO-Learn employs a GNN-based filter to identify and exclude potential OOD nodes and then select highly informative ID nodes for labeling using the K-Medoids algorithm. To prevent the filter from discarding valuable ID examples, we introduce a classifier that differentiates between the C known ID classes and an additional class representing OOD nodes (hence, a C+1 classifier). This classifier uses a weighted cross-entropy loss to balance the removal of OOD nodes while retaining informative ID nodes. Experimental results on four real-world datasets demonstrate that LEGO-Learn significantly outperforms leading methods, with up to a 6.62% improvement in ID classification accuracy and a 7.49% increase in AUROC for OOD detection.
Modelling the density and mass of the Milky Way's proto-galaxy components with $APOGEE$-$Gaia$
2024-10-21
Danny Horta, Ricardo P. Schiavon
Unravelling galaxy formation theory requires understanding galaxies both at high and low redshifts. A possible way to connect both realms is by studying the oldest stars in the Milky Way (i.e., the proto-Galaxy). We use the $APOGEE$-$Gaia$ surveys to perform a purely chemical dissection of Milky Way (MW) stellar populations, and identify samples of stars likely belonging to proto-Galactic fragments. The metallicity dependence of the distribution of old MW stars in the [Mg/Mn]-[Al/Fe] enables the distinction of at least two populations in terms of their star formation histories: a rapidly evolved population likely associated with the main progenitor system of the proto-MW; and populations characterised by less efficient, slower, star formation. In the Solar neighbourhood these populations are dominated by the $Gaia-Enceladus/Sausage$ accretion debris. In the inner Galaxy, they are largely associated with the $Heracles$ structure. We model the density of chemically defined proto-Galaxy populations, finding that they are well represented by a Plummer model with a scale radius of $a$ ~3.5 kpc, and an oblate ellipsoid with flattening parameters [$p$ ~0.8; $q$ ~0.6]; this finding indicates that the MW plausibly hosts a low-mass, metal-poor, bulge component. We integrate this density for $chemically$ $unevolved$ stars between $-2$ < [Fe/H] < $-0.5$ to obtain a minimum stellar mass for the proto-Galaxy of $M_{*}$ ($r$ < 10 kpc) = $9.1\pm0.2\times10^{8}$ M$_{\odot}$. Our results suggest the proto-Milky Way is at least comprised of two significant fragments: the main $in$ $situ$ progenitor and the $Heracles$ structure.
Filament Accretion and Fragmentation in the Perseus Molecular Cloud
2024-10-21
Michael Chun-Yuan Chen, James Di Francesco, Rachel K. Friesen, Jaime E. Pineda, Paola Caselli, Adam Ginsburg, Helen Kirk, Anna Punanova, the GAS Collaboration
Observations suggest that filaments in molecular clouds can grow by mass accretion while forming cores via fragmentation. Here we present one of the first large sample studies of filament accretion using velocity gradient measurements of star-forming filaments on the $\sim 0.05$ pc scale with NH$_3$ observations of the Perseus Molecular Cloud, primarily obtained as a part of the GBT Ammonia Survey (GAS). In this study, we find significant correlations between velocity gradient, velocity dispersion, mass per unit length, and the number of cores per unit length of the Perseus filaments. Our results suggest a scenario in which filaments not only grow through mass accretion but also form new cores continuously in the process well into the thermally supercritical regime. Such behavior is contrary to that expected from isolated filament models but consistent with how filaments form within a more realistic cloud environment, suggesting that the cloud environment plays a crucial role in shaping core formation and evolution in filaments. Furthermore, even though velocity gradients within filaments are not oriented randomly, we find no correlation between velocity gradient orientation and the filament properties we analyzed. This result suggests that gravity is unlikely the dominant mechanism imposing order on the $\sim 0.05$ pc scale for dense star-forming gas.
Machine-learning the gap between real and simulated nebulae: A domain-adaptation approach to classify ionised nebulae in nearby galaxies
2024-10-21
Francesco Belfiore, Michele Ginolfi, Guillermo Blanc, Mederic Boquien, Melanie Chevance, Enrico Congiu, Simon C. O. Glover, Brent Groves, Ralf S. Klessen, Eduardo Méndez-Delgado, Thomas G. Williams
Classifying ionised nebulae in nearby galaxies is crucial to studying stellar feedback mechanisms and understanding the physical conditions of the interstellar medium. This classification task is generally performed by comparing observed line ratios with photoionisation simulations of different types of nebulae (HII regions, planetary nebulae, and supernova remnants). However, due to simplifying assumptions, such simulations are generally unable to fully reproduce the line ratios in observed nebulae. This discrepancy limits the performance of the classical machine-learning approach, where a model is trained on the simulated data and then used to classify real nebulae. In this study, we use a domain-adversarial neural network (DANN) to bridge the gap between photoionisation models (source domain) and observed ionised nebulae from the PHANGS-MUSE survey (target domain). The DANN is an example of a domain-adaptation algorithm, whose goal is to maximise the performance of a model trained on labelled data in the source domain on an unlabelled target domain by extracting domain-invariant features. Our results indicate a significant improvement in classification performance in the target domain when employing the DANN framework compared to a classical neural network (NN) classifier. Additionally, we investigate the impact of adding noise to the source dataset, finding that noise injection acts as a form of regularisation, further enhancing the performances of both the NN and DANN models on the observational data. The combined use of domain adaptation and noise injection improves the classification accuracy in the target domain by 24%. This study highlights the potential of domain adaptation methods in tackling the domain-shift challenge when using theoretical models to train machine-learning pipelines in astronomy.
A Novel high-z submm Galaxy Efficient Line Survey in ALMA bands 3 through 8 -- An ANGELS Pilot
2024-10-21
T. J. L. C. Bakx, A. Amvrosiadis, G. J. Bendo, H. S. B. Algera, S. Serjeant, L. Bonavera, E. Borsato, X. Chen, P. Cox, J. González-Nuevo, M. Hagimoto, K. C. Harrington, R. J. Ivison, P. Kamieneski, L. Marchetti, D. A. Riechers, T. Tsukui, P. P. van der Werf, C. Yang, J. A. Zavala, P. Andreani, S. Berta, A. R. Cooray, G. De Zotti, S. Eales, R. Ikeda, K. K. Knudsen, I. Mitsuhashi, M. Negrello, R. Neri, A. Omont, D. Scott, Y. Tamura, P. Temi, S. A. Urquhart
We use the Atacama Large sub/Millimetre Array (ALMA) to efficiently observe spectral lines across Bands 3, 4, 5, 6, 7, and 8 at high-resolution (0.5" - 0.1") for 16 bright southern Herschel sources at $1.5 < z < 4.2$. With only six and a half hours of observations, we reveal 66 spectral lines in 17 galaxies. These observations detect emission from CO (3-2) to CO(18-17), as well as atomic ([CI](1-0), (2-1), [OI] 145 $\mu$m and [NII] 205 $\mu$m) lines. Additional molecular lines are seen in emission (${\rm H_2O}$ and ${\rm H_2O^+}$) and absorption (OH$^+$ and CH$^+$). The morphologies based on dust continuum ranges from extended sources to strong lensed galaxies with magnifications between 2 and 30. CO line transitions indicate a diverse set of excitation conditions with a fraction of the sources ($\sim 35$%) showcasing dense, warm gas. The resolved gas to star-formation surface densities vary strongly per source, and suggest that the observed diversity of dusty star-forming galaxies could be a combination of lensed, compact dusty starbursts and extended, potentially-merging galaxies. The predicted gas depletion timescales are consistent with 100 Myr to 1 Gyr, but require efficient fueling from the extended gas reservoirs onto the more central starbursts, in line with the Doppler-shifted absorption lines that indicate inflowing gas for two out of six sources. This pilot paper explores a successful new method of observing spectral lines in large samples of galaxies, supports future studies of larger samples, and finds that the efficiency of this new observational method will be further improved with the planned ALMA Wideband Sensitivity Upgrade.
Galaxy Size and Mass Build-up in the First 2 Gyrs of Cosmic History from Multi-Wavelength JWST NIRCam Imaging
2024-10-21
Natalie Allen, Pascal A. Oesch, Sune Toft, Jasleen Matharu, Conor J. R. McPartland, Andrea Weibel, Gabe Brammer, Rebecca A. A. Bowler, Kei Ito, Rashmi Gottumukkala, Francesca Rizzo, Francesco Valentino, Rohan G. Varadaraj, John R. Weaver, Katherine E. Whitaker
The evolution of galaxy sizes in different wavelengths provides unique insights on galaxy build-up across cosmic epochs. Such measurements can now finally be done at $z>3$ thanks to the exquisite spatial resolution and multi-wavelength capability of the JWST. With the public data from the CEERS, PRIMER-UDS, and PRIMER-COSMOS surveys, we measure the sizes of $\sim 3500$ star-forming galaxies at $3 \leqslant z<9$, in 7 NIRCam bands using the multi-wavelength model fitting code GalfitM. The size-mass relation is measured in four redshift bins, across all NIRCam bands. We find that, the slope and intrinsic scatter of the rest-optical size-mass relation are constant across this redshift range and consistent with previous HST-based studies at low-z. When comparing the relations across different wavelengths, the average rest-optical and rest-UV relations are consistent with each other up to $z=6$, but the intrinsic scatter is largest in rest-UV wavelengths compared to rest-optical and redder bands. This behaviour is independent of redshift and we speculate that it is driven by bursty star-formation in $z>4$ galaxies. Additionally, for $3\leqslant z<4$ star-forming galaxies at $\rm M_* > 10^{10} M_{\odot}$, we find smaller rest-$\rm 1\rm\,\mu m$ sizes in comparison to rest-optical (and rest-UV) sizes, suggestive of colour gradients. When comparing to simulations, we find agreement over $\rm M_* \approx 10^{9} - 10^{10} M_{\odot}$ but beyond this mass, the observed size-mass relation is significantly steeper. Our results show the power of JWST/NIRCam to provide new constraints on galaxy formation models.
On priors and scale cuts in EFT-based full-shape analyses
2024-10-21
Anton Chudaykin, Mikhail M. Ivanov, Takahiro Nishimichi
Parameter estimation from galaxy survey data from the full-shape method depends on scale cuts and priors on EFT parameters. The effects of priors, including the so-called "prior volume" phenomenon have been originally studied in Ivanov et al. (2019) and subsequent works. In this note, we repeat and extend these tests and also apply them to the priors used by D'Amico et al. (2019). We point out that in addition to the "prior volume" effect there is a much more dangerous effect that is largely overlooked: a systematic bias on cosmological parameters due to overoptimistic scale cuts. Unlike the "prior volume" effect, this is a genuine systematic bias due to two-loop corrections that does not vanish with better priors or with larger data volumes. Our study is based on the high fidelity BOSS-like PT Challenge simulation data which offer many advantages over analyses based on synthetic data generated with fitting pipelines. We show that the analysis choices of D'Amico et al.~(2019), especially their scale cuts, significantly bias parameter recovery, overestimating $\sigma_8$ by over $5\%$ (equivalent to $1\sigma$). The bias on measured EFT parameters is even more significant. In contrast, the analysis choices associated with the CLASS-PT code lead to a negligible ($\lesssim 1\%$) bias in the recovery of cosmological parameters based on their best-fit values.
The period-luminosity relation of long-period variables in the Large Magellanic Cloud observed with ATLAS
2024-10-21
Daniel Hey, John Tonry, Benjamin Shappee, Daniel Huber
Period-luminosity relations of long period variables (LPVs) are a powerful tool to map the distances of stars in our galaxy, and are typically calibrated using stars in the Large Magellanic Cloud (LMC). Recent results demonstrated that these relations show a strong dependence on the amplitude of the variability, which can be used to greatly improve distance estimates. However, one of the only highly sampled catalogs of such variables in the LMC is based on OGLE photometry, which does not provide all-sky coverage. Here, we provide the first measurement of the period-luminosity relation of long-period variables in the LMC using photometry from the Asteroid Terrestrial-impact Last Alert System (ATLAS). We derive conversions between ugriz, Gaia, and ATLAS c and o passbands with a precision of approximately 0.02 mag, which enable the measurement of reliable amplitudes with ATLAS for crowded fields. We successfully reproduce the known PL sequences A through E, and show evidence for sequence F using the ratios of amplitudes observed in both ATLAS pass-bands. Our work demonstrates that the ATLAS survey can recover variability in evolved red giants and lays the foundation for an all-sky distance map of the Milky Way using long-period variables.
Cosmic Shimmering: the Gravitational Wave Signal of Time-Resolved Cosmic Shear Observations
2024-10-21
Giorgio Mentasti, Carlo R. Contaldi
We introduce a novel approach for detecting gravitational waves through their influence on the shape of resolved astronomical objects. This method, complementary to pulsar timing arrays and astrometric techniques, explores the time-dependent distortions caused by gravitational waves on the shapes of celestial bodies, such as galaxies or any resolved extended object. By developing a formalism based on that adopted in the analysis of weak lensing effects, we derive the response functions for gravitational wave-induced distortions and compute their angular correlation functions. Our results highlight the sensitivity of these distortions to the lowest frequencies of the gravitational wave spectrum and demonstrate how they produce distinct angular correlation signatures, including null and polarisation-sensitive correlations. These findings pave the way for future high-resolution surveys to exploit this novel observable, potentially offering new insights into the stochastic gravitational wave background and cosmological models.
Large Language Models in Computer Science Education: A Systematic Literature Review
2024-10-21
Nishat Raihan, Mohammed Latif Siddiq, Joanna C. S. Santos, Marcos Zampieri
Large language models (LLMs) are becoming increasingly better at a wide range of Natural Language Processing tasks (NLP), such as text generation and understanding. Recently, these models have extended their capabilities to coding tasks, bridging the gap between natural languages (NL) and programming languages (PL). Foundational models such as the Generative Pre-trained Transformer (GPT) and LLaMA series have set strong baseline performances in various NL and PL tasks. Additionally, several models have been fine-tuned specifically for code generation, showing significant improvements in code-related applications. Both foundational and fine-tuned models are increasingly used in education, helping students write, debug, and understand code. We present a comprehensive systematic literature review to examine the impact of LLMs in computer science and computer engineering education. We analyze their effectiveness in enhancing the learning experience, supporting personalized education, and aiding educators in curriculum development. We address five research questions to uncover insights into how LLMs contribute to educational outcomes, identify challenges, and suggest directions for future research.
Serendipitous detection of an intense X-ray flare in the weak-line T Tauri star KM Ori with SRG/eROSITA
2024-10-21
Savithri H. Ezhikode, Hema Anilkumar, R. Arun, Blesson Mathew, V. Jithesh, Suman Bhattacharyya, Sneha Nedhath, T. B. Cysil, S. Muneer, Sreeja S. Kartha, Pramod Kumar S
Weak-line T Tauri stars (WTTS) exhibit X-ray flares, likely resulting from magnetic reconnection that heats the stellar plasma to very high temperatures. These flares are difficult to identify through targeted observations. Here, we report the serendipitous detection of the brightest X-ray flaring state of KM Ori in the eROSITA DR1 survey. Observations from SRG/eROSITA, Chandra X-ray Observatory, and XMM-Newton are analysed to assess the X-ray properties of KM Ori, thereby establishing its flaring state at the eROSITA epoch. The long-term (1999-2020) X-ray light curve generated for the Chandra observations confirmed that eROSITA captured the source at its highest X-ray flaring state recorded to date. Multi-instrument observations support the X-ray flaring state of the source, with time-averaged X-ray luminosity ($L_{0.2-5keV}$) reaching $\sim 1.9\times10^{32}{erg~s^{-1}}$ at the eROSITA epoch, marking it the brightest and possibly the longest flare observed to date. Such intense X-ray flares have been detected only in a few WTTS. The X-ray spectral analysis unveils the presence of multiple thermal plasma components at all epochs. The notably high luminosity ($L_{0.5-8~keV}\sim10^{32}{erg~s}^{-1}$), energy ($E_{0.5-8~keV}\sim10^{37}$erg), and the elevated emission measures of the thermal components in the eROSITA epoch indicate a superflare/megaflare state of KM Ori. Additionally, the H$\alpha$ line equivalent width of $\sim$-5\AA from our optical spectral analysis, combined with the lack of infrared excess in the spectral energy distribution, were used to re-confirm the WTTS (thin disk/disk-less) classification of the source. The long-duration flare of KM Ori observed by eROSITA indicates the possibility of a slow-rise top-flat flare. The detection demonstrates the potential of eROSITA to uncover such rare, transient events, thereby providing new insights into the X-ray activity of WTTS.
LLaVA-KD: A Framework of Distilling Multimodal Large Language Models
2024-10-21
Yuxuan Cai, Jiangning Zhang, Haoyang He, Xinwei He, Ao Tong, Zhenye Gan, Chengjie Wang, Xiang Bai
The success of Large Language Models (LLM) has led researchers to explore Multimodal Large Language Models (MLLM) for unified visual and linguistic understanding. However, the increasing model size and computational complexity of MLLM limit their use in resource-constrained environments. Small-scale MLLM (s-MLLM) aims to retain the capabilities of the large-scale model (l-MLLM) while reducing computational demands, but resulting in a significant decline in performance. To address the aforementioned issues, we propose a novel LLaVA-KD framework to transfer knowledge from l-MLLM to s-MLLM. Specifically, we introduce Multimodal Distillation (MDist) to minimize the divergence between the visual-textual output distributions of l-MLLM and s-MLLM, and Relation Distillation (RDist) to transfer l-MLLM's ability to model correlations between visual features. Additionally, we propose a three-stage training scheme to fully exploit the potential of s-MLLM: 1) Distilled Pre-Training to align visual-textual representations, 2) Supervised Fine-Tuning to equip the model with multimodal understanding, and 3) Distilled Fine-Tuning to further transfer l-MLLM capabilities. Our approach significantly improves performance without altering the small model's architecture. Extensive experiments and ablation studies validate the effectiveness of each proposed component. Code will be available at https://github.com/Fantasyele/LLaVA-KD.
The global rise in depression necessitates innovative detection methods for early intervention. Social media provides a unique opportunity to identify depression through user-generated posts. This systematic review evaluates machine learning (ML) models for depression detection on social media, focusing on biases and methodological challenges throughout the ML lifecycle. A search of PubMed, IEEE Xplore, and Google Scholar identified 47 relevant studies published after 2010. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was utilized to assess methodological quality and risk of bias. Significant biases impacting model reliability and generalizability were found. There is a predominant reliance on Twitter (63.8%) and English-language content (over 90%), with most studies focusing on users from the United States and Europe. Non-probability sampling methods (approximately 80%) limit representativeness. Only 23% of studies explicitly addressed linguistic nuances like negations, crucial for accurate sentiment analysis. Inconsistent hyperparameter tuning was observed, with only 27.7% properly tuning models. About 17% did not adequately partition data into training, validation, and test sets, risking overfitting. While 74.5% used appropriate evaluation metrics for imbalanced data, others relied on accuracy without addressing class imbalance, potentially skewing results. Reporting transparency varied, often lacking critical methodological details. These findings highlight the need to diversify data sources, standardize preprocessing protocols, ensure consistent model development practices, address class imbalance, and enhance reporting transparency. By overcoming these challenges, future research can develop more robust and generalizable ML models for depression detection on social media, contributing to improved mental health outcomes globally.
This systematic review examines the synergistic and individual influences of resistance exercise, dietary protein supplementation, and sleep/recovery on muscle protein synthesis (MPS). Electronic databases such as Scopus, Google Scholar, and Web of Science were extensively used. Studies were selected based on relevance to the criteria and were ensured to be directly applicable to the objectives. Research indicates that a protein dose of 20 to 25 grams maximally stimulates MPS post-resistance training. It is observed that physically frail individuals aged 76 to 92 and middle-aged adults aged 62 to 74 have lower mixed muscle protein synthetic rates than individuals aged 20 to 32. High-whey protein and leucine-enriched supplements enhance MPS more efficiently than standard dairy products in older adults engaged in resistance programs. Similarly, protein intake before sleep boosts overnight MPS rates, which helps prevent muscle loss associated with sleep debt, exercise-induced damage, and muscle-wasting conditions like sarcopenia and cachexia. Resistance exercise is a functional intervention to achieve muscular adaptation and improve function. Future research should focus on variables such as fluctuating fitness levels, age groups, genetics, and lifestyle factors to generate more accurate and beneficial results.
An automated occultation network for gravitational mapping of the trans-neptunian solar system
2024-10-21
Daniel C. H. Gomes, Gary M. Bernstein
We explore the potential of an array of O(100) small fixed telescopes, aligned along a meridian and automated to measure millions of occultations of Gaia stars by minor planets, to constrain gravitational signatures from a "Planet X" mass in the outer solar system. The accuracy of center-of-mass tracking for the occulters is limited by photon noise, uncertainties in asteroid shapes, and Gaia's astrometry of the occulted stars. Using both parametric calculations and survey simulations, we assess the total information obtainable from occultation measurements of main-belt asteroids (MBAs), Jovian Trojans and trans-Neptunian objects (TNOs). We find that MBAs are the optimal target population due to their higher occultation rates and abundance of objects above LSST detection thresholds. A 10-year survey of occultations by MBAs and Trojans using an array of 200 40 cm telescopes at 5 km separation would achieve $5\sigma$ sensitivity to the gravitational tidal field of a $5M_\oplus$ Planet X at 800 AU for $>90\%$ of potential sky locations. This configuration corresponds to an initial cost of $\approx $15 million. While the occultation survey's sensitivity to tidal forces improves rapidly with increasing number of telescopes, sensitivity to a Planet X becomes limited by degeneracy with the uncertain masses of large moonless TNOs. The 200-telescope survey would additionally detect $\approx $1800 TNO occultations, providing detailed shape, size, and albedo information. The survey would also measure the Yarkovski effect on many individual MBAs, measure the masses of many asteroids involved in mutual gravitational deflections, and enable better searches for primordial black holes and departures from General Relativity.
Muscle coactivation primes the nervous system for fast and task-dependent feedback control
2024-10-21
Philipp Maurus, Daniel P. Armstrong, Stephen H. Scott, Tyler Cluff
Humans and other animals coactivate agonist and antagonist muscles in many motor actions. Increases in muscle coactivation are thought to leverage viscoelastic properties of skeletal muscles to provide resistance against limb motion. However, coactivation also emerges in scenarios where it seems paradoxical because the goal is not to resist limb motion but instead to rapidly mobilize the limb(s) or body to launch or correct movements. Here, we present a new perspective on muscle coactivation: to prime the nervous system for fast, task-dependent responses to sensory stimuli. We review distributed neural control mechanisms that may allow the healthy nervous system to leverage muscle coactivation to produce fast and flexible responses to sensory feedback.
Deep Multimodal Representation Learning for Stellar Spectra
2024-10-21
Tobias Buck, Christian Schwarz
Recently, contrastive learning (CL), a technique most prominently used in natural language and computer vision, has been used to train informative representation spaces for galaxy spectra and images in a self-supervised manner. Following this idea, we implement CL for stars in the Milky Way, for which recent astronomical surveys have produced a huge amount of heterogeneous data. Specifically, we investigate Gaia XP coefficients and RVS spectra. Thus, the methods presented in this work lay the foundation for aggregating the knowledge implicitly contained in the multimodal data to enable downstream tasks like cross-modal generation or fused stellar parameter estimation. We find that CL results in a highly structured representation space that exhibits explicit physical meaning. Evaluating Using this representation space to perform cross-modal generation and stellar label regression results in excellent performance with high-quality generated samples as well as accurate and precise label predictions.
On-Device LLMs for SMEs: Challenges and Opportunities
2024-10-21
Jeremy Stephen Gabriel Yee, Pai Chet Ng, Zhengkui Wang, Ian McLoughlin, Aik Beng Ng, Simon See
This paper presents a systematic review of the infrastructure requirements for deploying Large Language Models (LLMs) on-device within the context of small and medium-sized enterprises (SMEs), focusing on both hardware and software perspectives. From the hardware viewpoint, we discuss the utilization of processing units like GPUs and TPUs, efficient memory and storage solutions, and strategies for effective deployment, addressing the challenges of limited computational resources typical in SME settings. From the software perspective, we explore framework compatibility, operating system optimization, and the use of specialized libraries tailored for resource-constrained environments. The review is structured to first identify the unique challenges faced by SMEs in deploying LLMs on-device, followed by an exploration of the opportunities that both hardware innovations and software adaptations offer to overcome these obstacles. Such a structured review provides practical insights, contributing significantly to the community by enhancing the technological resilience of SMEs in integrating LLMs.
Continuous Speech Synthesis using per-token Latent Diffusion
2024-10-21
Arnon Turetzky, Nimrod Shabtay, Slava Shechtman, Hagai Aronowitz, David Haws, Ron Hoory, Avihu Dekel
The success of autoregressive transformer models with discrete tokens has inspired quantization-based approaches for continuous modalities, though these often limit reconstruction quality. We therefore introduce SALAD, a per-token latent diffusion model for zero-shot text-to-speech, that operates on continuous representations. SALAD builds upon the recently proposed expressive diffusion head for image generation, and extends it to generate variable-length outputs. Our approach utilizes semantic tokens for providing contextual information and determining the stopping condition. We suggest three continuous variants for our method, extending popular discrete speech synthesis techniques. Additionally, we implement discrete baselines for each variant and conduct a comparative analysis of discrete versus continuous speech modeling techniques. Our results demonstrate that both continuous and discrete approaches are highly competent, and that SALAD achieves a superior intelligibility score while obtaining speech quality and speaker similarity on par with the ground-truth audio.
Domain-Adaptive Neural Posterior Estimation for Strong Gravitational Lens Analysis
2024-10-21
Paxson Swierc, Marcos Tamargo-Arizmendi, Aleksandra Ćiprijanović, Brian D. Nord
Modeling strong gravitational lenses is prohibitively expensive for modern and next-generation cosmic survey data. Neural posterior estimation (NPE), a simulation-based inference (SBI) approach, has been studied as an avenue for efficient analysis of strong lensing data. However, NPE has not been demonstrated to perform well on out-of-domain target data -- e.g., when trained on simulated data and then applied to real, observational data. In this work, we perform the first study of the efficacy of NPE in combination with unsupervised domain adaptation (UDA). The source domain is noiseless, and the target domain has noise mimicking modern cosmology surveys. We find that combining UDA and NPE improves the accuracy of the inference by 1-2 orders of magnitude and significantly improves the posterior coverage over an NPE model without UDA. We anticipate that this combination of approaches will help enable future applications of NPE models to real observational data.
On the spatial distribution of the Large-Scale structure: An Unsupervised search for Parity Violation
2024-10-21
Samuel Hewson, Will J. Handley, Christopher G. Lester
We use machine learning methods to search for parity violations in the Large-Scale Structure (LSS) of the Universe, motivated by recent claims of chirality detection using the 4-Point Correlation Function (4PCF), which would suggest new physics during the epoch of inflation. This work seeks to reproduce these claims using methods originating from high energy collider analyses. Our machine learning methods optimise some underlying parity odd function of the data, and use it to evaluate the parity odd fraction. We demonstrate the effectiveness and suitability of these methods and then apply them to the Baryon Oscillation Spectroscopic Survey (BOSS) catalogue. No parity violation is detected at any significance.
2024-10-21
Charles Lovering, Michael Krumdick, Viet Dac Lai, Nilesh Kumar, Varshini Reddy, Rik Koncel-Kedziorski, Chris Tanner
Some information is factual (e.g., "Paris is in France"), whereas other information is probabilistic (e.g., "the coin flip will be a [Heads/Tails]."). We believe that good Language Models (LMs) should understand and reflect this nuance. Our work investigates this by testing if LMs' output probabilities are calibrated to their textual contexts. We define model "calibration" as the degree to which the output probabilities of candidate tokens are aligned with the relative likelihood that should be inferred from the given context. For example, if the context concerns two equally likely options (e.g., heads or tails for a fair coin), the output probabilities should reflect this. Likewise, context that concerns non-uniformly likely events (e.g., rolling a six with a die) should also be appropriately captured with proportionate output probabilities. We find that even in simple settings the best LMs (1) are poorly calibrated, and (2) have systematic biases (e.g., preferred colors and sensitivities to word orderings). For example, gpt-4o-mini often picks the first of two options presented in the prompt regardless of the options' implied likelihood, whereas Llama-3.1-8B picks the second. Our other consistent finding is mode-collapse: Instruction-tuned models often over-allocate probability mass on a single option. These systematic biases introduce non-intuitive model behavior, making models harder for users to understand.
This paper presents our system description and error analysis of our entry for NLLP 2024 shared task on Legal Natural Language Inference (L-NLI) \citep{hagag2024legallenssharedtask2024}. The task required classifying these relationships as entailed, contradicted, or neutral, indicating any association between the review and the complaint. Our system emerged as the winning submission, significantly outperforming other entries with a substantial margin and demonstrating the effectiveness of our approach in legal text analysis. We provide a detailed analysis of the strengths and limitations of each model and approach tested, along with a thorough error analysis and suggestions for future improvements. This paper aims to contribute to the growing field of legal NLP by offering insights into advanced techniques for natural language inference in legal contexts, making it accessible to both experts and newcomers in the field.
PROMPTHEUS: A Human-Centered Pipeline to Streamline SLRs with LLMs
2024-10-21
João Pedro Fernandes Torres, Catherine Mulligan, Joaquim Jorge, Catarina Moreira
The growing volume of academic publications poses significant challenges for researchers conducting timely and accurate Systematic Literature Reviews, particularly in fast-evolving fields like artificial intelligence. This growth of academic literature also makes it increasingly difficult for lay people to access scientific knowledge effectively, meaning academic literature is often misrepresented in the popular press and, more broadly, in society. Traditional SLR methods are labor-intensive and error-prone, and they struggle to keep up with the rapid pace of new research. To address these issues, we developed \textit{PROMPTHEUS}: an AI-driven pipeline solution that automates the SLR process using Large Language Models. We aimed to enhance efficiency by reducing the manual workload while maintaining the precision and coherence required for comprehensive literature synthesis. PROMPTHEUS automates key stages of the SLR process, including systematic search, data extraction, topic modeling using BERTopic, and summarization with transformer models. Evaluations conducted across five research domains demonstrate that PROMPTHEUS reduces review time, achieves high precision, and provides coherent topic organization, offering a scalable and effective solution for conducting literature reviews in an increasingly crowded research landscape. In addition, such tools may reduce the increasing mistrust in science by making summarization more accessible to laypeople. The code for this project can be found on the GitHub repository at https://github.com/joaopftorres/PROMPTHEUS.git
The diagnosis of glaucoma plays a critical role in the management and treatment of this vision-threatening disease. Glaucoma is a group of eye diseases that cause blindness by damaging the optic nerve at the back of the eye. Often called "silent thief of sight", it exhibits no symptoms during the early stages. Therefore, early detection is crucial to prevent vision loss. With the rise of Artificial Intelligence (AI), particularly Deep Learning (DL) techniques, Computer-Aided Diagnosis (CADx) systems have emerged as promising tools to assist clinicians in accurately diagnosing glaucoma early. This paper aims to provide a comprehensive overview of AI techniques utilized in CADx systems for glaucoma diagnosis. Through a detailed analysis of current literature, we identify key gaps and challenges in these systems, emphasizing the need for improved safety, reliability, interpretability, and explainability. By identifying research gaps, we aim to advance the field of CADx systems especially for the early diagnosis of glaucoma, in order to prevent any potential loss of vision.
Machine learning methods to study disordered proteins
2024-10-21
Sören von Bülow, Giulio Tesei, Kresten Lindorff-Larsen
Recent years have seen tremendous developments in the use of machine learning models to link amino acid sequence, structure and function of folded proteins. These methods are, however, rarely applicable to the wide range of proteins and sequences that comprise intrinsically disordered regions. We here review developments in the study of disordered proteins that exploit or are used to train machine learning models. These include methods for generating conformational ensembles and designing new sequences, and for linking sequences to biophysical properties and biological functions. We highlight how these developments are built on a tight integration between experiment, theory and simulations, and account for evolutionary constraints, which operate on sequences of disordered regions differently than on those of folded domains.
Conceptual and practical approaches for investigating irreversible processes
2024-10-21
Dario Lucente, Marco Baldovin, Fabio Cecconi, Massimo Cencini, Niccolò Cocciaglia, Andrea Puglisi, Massimiliano Viale, Angelo Vulpiani
Current research in statistical mechanics mostly concerns the investigation of out-of-equilibrium, irreversible processes, which are ubiquitous in nature and still far from being theoretically understood. Even the precise characterization of irreversibility is the object of an open debate: while in the context of Hamiltonian systems the one-century-old proposal by M. Smoluchowski looks still valid (a process appears irreversible when the initial state has a recurrence time that is long compared to the time of observation [1]), in dissipative systems, particularly in the case of stochastic processes, the problem is more involved, and quantifying the "degree of irreversibility" is a pragmatic need. The most employed strategies rely on the estimation of entropy production: this quantity, although mathematically well-defined, is often difficult to compute, especially when analyzing experimental data. Moreover, being a global observable, entropy production fails to capture specific aspects of irreversibility in extended systems, such as the role of different currents and their spatial development. This review aims to address various conceptual and technical challenges encountered in the analysis of irreversibility, including the role of the coarse-graining procedure and the treatment of data in the absence of complete information. The discussion will be mostly based on simple models, analytically treatable, and supplemented by examples of complex, more realistic non-equilibrium systems.
Simulation-based inference of single-molecule experiments
2024-10-21
Lars Dingeldein, Pilar Cossio, Roberto Covino
Single-molecule experiments are a unique tool to characterize the structural dynamics of biomolecules. However, reconstructing molecular details from noisy single-molecule data is challenging. Simulation-based inference (SBI) integrates statistical inference, physics-based simulators, and machine learning and is emerging as a powerful framework for analysing complex experimental data. Recent advances in deep learning have accelerated the development of new SBI methods, enabling the application of Bayesian inference to an ever-increasing number of scientific problems. Here, we review the nascent application of SBI to the analysis of single-molecule experiments. We introduce parametric Bayesian inference and discuss its limitations. We then overview emerging deep-learning-based SBI methods to perform Bayesian inference for complex models encoded in computer simulators. We illustrate the first applications of SBI to single-molecule force-spectroscopy and cryo-electron microscopy experiments. SBI allows us to leverage powerful computer algorithms modeling complex biomolecular phenomena to connect scientific models and experiments in a principled way.
Cryogenic Control and Readout Integrated Circuits for Solid-State Quantum Computing
2024-10-21
Lingxiao Lei, Heng Huang, Pingxing Chen, Mingtang Deng
In the pursuit of quantum computing, solid-state quantum systems, particularly superconducting ones, have made remarkable advancements over the past two decades. However, achieving fault-tolerant quantum computing for next-generation applications necessitates the integration of several million qubits, which presents significant challenges in terms of interconnection complexity and latency that are currently unsolvable with state-of-the-art room-temperature control and readout electronics. Recently, cryogenic integrated circuits (ICs), including CMOS radio-frequency ICs and rapid-single-flux-quantum-logic ICs, have emerged as potential alternatives to room-temperature electronics. Unlike their room-temperature counterparts, these ICs are deployed within cryostats to enhance scalability by reducing the number and length of transmission lines. Additionally, operating at cryogenic temperatures can suppress electronic noise and improve qubit control fidelity. However, for CMOS ICs specifically, circuit design uncertainties arise due to a lack of reliable models for cryogenic field effect transistors as well as issues related to severe fickle noises and power dissipation at cryogenic temperatures. This paper provides a comprehensive review of recent research on both types of cryogenic control and readout ICs but primarily focuses on the more mature CMOS technology. The discussion encompasses principles underlying control and readout techniques employed in cryogenic CMOS ICs along with their architectural designs; characterization and modeling approaches for field effect transistors under cryogenic conditions; as well as fundamental concepts pertaining to rapid single flux quantum circuits.
A measure of departure from symmetry via the Fisher-Rao distance for contingency tables
2024-10-21
Wataru Urasaki, Go Kawamitsu, Tomoyuki Nakagawa, Kouji Tahata
A measure of asymmetry is a quantification method that allows for the comparison of categorical evaluations before and after treatment effects or among different target populations, irrespective of sample size. We focus on square contingency tables that summarize survey results between two time points or cohorts, represented by the same categorical variables. We propose a measure to evaluate the degree of departure from a symmetry model using cosine similarity. This proposal is based on the Fisher-Rao distance, allowing asymmetry to be interpreted as a geodesic distance between two distributions. Various measures of asymmetry have been proposed, but visualizing the relationship of these quantification methods on a two-dimensional plane demonstrates that the proposed measure provides the geometrically simplest and most natural quantification. Moreover, the visualized figure indicates that the proposed method for measuring departures from symmetry is less affected by very few cells with extreme asymmetry. A simulation study shows that for square contingency tables with an underlying asymmetry model, our method can directly extract and quantify only the asymmetric structure of the model, and can more sensitively detect departures from symmetry than divergence-type measures.
We review some representative results for first-passage problems involving so-called mortal or evanescent walkers, i.e., walkers with a finite lifetime. The mortality constraint plays a key role in the modeling of many real scenarios, as it filters out long Brownian trajectories, thereby drastically modifying space exploration properties. Among such scenarios, we consider here different first-passage problems, including one or many searchers, resetting, anomalous diffusion, evolving domains, and the narrow escape problem. In spite of the different physics, the mathematical treatment draws strongly on the formalism for standard (i.e., immortal) walkers.
The acceleration of software development and delivery requires rigorous continuous testing and deployment of software systems, which are being deployed in increasingly diverse, complex, and dynamic environments. In recent years, the popularization of DevOps and integrated software forges like GitLab and GitHub has largely democratized Continuous Integration (CI) practices for a growing number of software. However, this trend intersects significantly with global energy consumption concerns and the growing demand for frugality in the Information and Communication Technology (ICT) sector. CI pipelines typically run in data centers which contribute significantly to the environmental footprint of ICT, yet there is little information available regarding their environmental impact. This article aims to bridge this gap by conducting the first large-scale analysis of the energy footprint of CI pipelines implemented with GitHub Actions and to provide a first overview of the energy impact of CI. We collect, instrument, and reproduce 838 workflows from 396 Java repositories hosted on GitHub to measure their energy consumption. We observe that the average unitary energy cost of a pipeline is relatively low, at 10 Wh. However, due to repeated invocations of these pipelines in real settings, the aggregated energy consumption cost per project is high, averaging 22 kWh. When evaluating CO2 emissions based on regional Wh-to-CO2 estimates, we observe that the average aggregated CO2 emissions are significant, averaging 10.5 kg. To put this into perspective, this is akin to the emissions produced by driving approximately 100 kilometers in a typical European car (110 gCO2/km). In light of our results, we advocate that developers should have the means to better anticipate and reflect on the environmental consequences of their CI choices when implementing DevOps practices.
New stellar age estimates using SPInS based on Gaia DR3 photometry and LAMOST DR8 abundances
2024-10-21
L. Casamiquela, D. R. Reese, Y. Lebreton, M. Haywood, P. Di Matteo, F. Anders, R. Jash, D. Katz, V. Cerqui, T. Boin, G. Kordopatis
Reliable stellar age estimates are fundamental for testing several problems in modern astrophysics, in particular since they set the time scales of Galactic dynamical and chemical evolution. In this study, we determine ages using only Gaia DR3 photometry and parallaxes, in combination with interstellar extinction maps, spectroscopic metallicities and $\alpha$ abundances from the latest data release (DR8) of the LAMOST survey. In contrast with previous age estimates, we do not use spectroscopic effective temperatures or surface gravities, thus relying on the excellent precision and accuracy of the Gaia photometry. We use a new version of the publicly available SPInS code with improved features, including the on-the-fly computation of the autocorrelation time and the automatic convergence evaluation. We determine reliable age estimates for 35,096 and 243,768 sub-giant and main-sequence turn-off stars in the LAMOST DR8 low- and medium-resolution surveys with typical uncertainties smaller than 10%. In addition, we successfully test our method on more than 4,000 stars of 14 well-studied open and globular star clusters covering a wide range of ages, confirming the reliability of our age and uncertainty estimates.
Generalizing Motion Planners with Mixture of Experts for Autonomous Driving
2024-10-21
Qiao Sun, Huimin Wang, Jiahao Zhan, Fan Nie, Xin Wen, Leimeng Xu, Kun Zhan, Peng Jia, Xianpeng Lang, Hang Zhao
Large real-world driving datasets have sparked significant research into various aspects of data-driven motion planners for autonomous driving. These include data augmentation, model architecture, reward design, training strategies, and planner pipelines. These planners promise better generalizations on complicated and few-shot cases than previous methods. However, experiment results show that many of these approaches produce limited generalization abilities in planning performance due to overly complex designs or training paradigms. In this paper, we review and benchmark previous methods focusing on generalizations. The experimental results indicate that as models are appropriately scaled, many design elements become redundant. We introduce StateTransformer-2 (STR2), a scalable, decoder-only motion planner that uses a Vision Transformer (ViT) encoder and a mixture-of-experts (MoE) causal Transformer architecture. The MoE backbone addresses modality collapse and reward balancing by expert routing during training. Extensive experiments on the NuPlan dataset show that our method generalizes better than previous approaches across different test sets and closed-loop simulations. Furthermore, we assess its scalability on billions of real-world urban driving scenarios, demonstrating consistent accuracy improvements as both data and model size grow.
Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark
2024-10-21
Thomas George, Pierre Nodet, Alexis Bondu, Vincent Lemaire
Mislabeled examples are ubiquitous in real-world machine learning datasets, advocating the development of techniques for automatic detection. We show that most mislabeled detection methods can be viewed as probing trained machine learning models using a few core principles. We formalize a modular framework that encompasses these methods, parameterized by only 4 building blocks, as well as a Python library that demonstrates that these principles can actually be implemented. The focus is on classifier-agnostic concepts, with an emphasis on adapting methods developed for deep learning models to non-deep classifiers for tabular data. We benchmark existing methods on (artificial) Completely At Random (NCAR) as well as (realistic) Not At Random (NNAR) labeling noise from a variety of tasks with imperfect labeling rules. This benchmark provides new insights as well as limitations of existing methods in this setup.
Rechargeable lithium metal batteries (LMBs) with an ultrahigh theoretical energy density have attracted more and more attentions for their crucial applications of portable electronic devices, electric vehicles, and smart grids. However, the implementation of LMBs in practice is still facing numerous challenges, such as low Coulombic e ciency, poor cycling performance, and complicated interfacial reactions. First-principles calculations have become a powerful technique in lithium battery research eld, in terms of modeling the structures and properties of speci c electrode materials, understanding the charge/discharge mechanisms at the atomic scale, and delivering rational design strategies for electrode materials as well as electrolytes. In this review, theoretical studies on sulfur cathodes, oxygen cathodes, lithium metal anodes, and solid-state electrolytes (SSEs) of LMBs are summarized. A brief introduction of simulation methods is o ered at rst. The next two chapters mainly focus on issues concerning cathodes of LMBs. Then the theoretical researches on the Li metal anode and SSEs are particularly reviewed. The current challenges and potential research directions in each field of LMBs are prospected from a theoretical viewpoint.
2024-10-21
Terne Sasha Thorn Jakobsen, Andreas Bjerre-Nielsen, Robert Böhm
Crowdsourced annotations of data play a substantial role in the development of Artificial Intelligence (AI). It is broadly recognised that annotations of text data can contain annotator bias, where systematic disagreement in annotations can be traced back to differences in the annotators' backgrounds. Being unaware of such annotator bias can lead to representational bias against minority group perspectives and therefore several methods have been proposed for recognising bias or preserving perspectives. These methods typically require either a substantial number of annotators or annotations per data instance. In this study, we propose a simple method for handling bias in annotations without requirements on the number of annotators or instances. Instead, we ask annotators about their beliefs of other annotators' judgements of an instance, under the hypothesis that these beliefs may provide more representative and less biased labels than judgements. The method was examined in two controlled, survey-based experiments involving Democrats and Republicans (n=1,590) asked to judge statements as arguments and then report beliefs about others' judgements. The results indicate that bias, defined as systematic differences between the two groups of annotators, is consistently reduced when asking for beliefs instead of judgements. Our proposed method therefore has the potential to reduce the risk of annotator bias, thereby improving the generalisability of AI systems and preventing harm to unrepresented socio-demographic groups, and we highlight the need for further studies of this potential in other tasks and downstream applications.
We survey theoretical, algorithmic, and computational results at the intersection of distance geometry problems and mathematical programming, both with and without adjacencies as part of the input. While mathematical programming methods can solve large-scale distance geometry problems with adjacencies, they are severely challenged in the absence thereof.
Procedural Content Generation in Games: A Survey with Insights on Emerging LLM Integration
2024-10-21
Mahdi Farrokhi Maleki, Richard Zhao
Procedural Content Generation (PCG) is defined as the automatic creation of game content using algorithms. PCG has a long history in both the game industry and the academic world. It can increase player engagement and ease the work of game designers. While recent advances in deep learning approaches in PCG have enabled researchers and practitioners to create more sophisticated content, it is the arrival of Large Language Models (LLMs) that truly disrupted the trajectory of PCG advancement. This survey explores the differences between various algorithms used for PCG, including search-based methods, machine learning-based methods, other frequently used methods (e.g., noise functions), and the newcomer, LLMs. We also provide a detailed discussion on combined methods. Furthermore, we compare these methods based on the type of content they generate and the publication dates of their respective papers. Finally, we identify gaps in the existing academic work and suggest possible directions for future research.
Higher-order topology in twisted multilayer systems: a review
2024-10-21
Chunbo Hua, Dong-Hui Xu
In recent years, there has been a surge of interest in higher-order topological phases (HOTPs) across various disciplines within the field of physics. These unique phases are characterized by their ability to harbor topological protected boundary states at lower-dimensional boundaries, a distinguishing feature that sets them apart from conventional topological phases and is attributed to the higher-order bulk-boundary correspondence. Two-dimensional (2D) twisted systems offer an optimal platform for investigating HOTPs, owing to their strong controllability and experimental feasibility. Here, we provide a comprehensive overview of the latest research advancements on HOTPs in 2D twisted multilayer systems. We will mainly review the HOTPs in electronic, magnonic, acoustic, photonic and mechanical twisted systems, and finally provide a perspective of this topic.
Language models for code (CodeLMs) have emerged as powerful tools for code-related tasks, outperforming traditional methods and standard machine learning approaches. However, these models are susceptible to security vulnerabilities, drawing increasing research attention from domains such as software engineering, artificial intelligence, and cybersecurity. Despite the growing body of research focused on the security of CodeLMs, a comprehensive survey in this area remains absent. To address this gap, we systematically review 67 relevant papers, organizing them based on attack and defense strategies. Furthermore, we provide an overview of commonly used language models, datasets, and evaluation metrics, and highlight open-source tools and promising directions for future research in securing CodeLMs.
Patrol Security Game: Defending Against Adversary with Freedom in Attack Timing, Location, and Duration
2024-10-21
Hao-Tsung Yang, Ting-Kai Weng, Ting-Yu Chang, Kin Sum Liu, Shan Lin, Jie Gao, Shih-Yu Tsai
We explored the Patrol Security Game (PSG), a robotic patrolling problem modeled as an extensive-form Stackelberg game, where the attacker determines the timing, location, and duration of their attack. Our objective is to devise a patrolling schedule with an infinite time horizon that minimizes the attacker's payoff. We demonstrated that PSG can be transformed into a combinatorial minimax problem with a closed-form objective function. By constraining the defender's strategy to a time-homogeneous first-order Markov chain (i.e., the patroller's next move depends solely on their current location), we proved that the optimal solution in cases of zero penalty involves either minimizing the expected hitting time or return time, depending on the attacker model, and that these solutions can be computed efficiently. Additionally, we observed that increasing the randomness in the patrol schedule reduces the attacker's expected payoff in high-penalty cases. However, the minimax problem becomes non-convex in other scenarios. To address this, we formulated a bi-criteria optimization problem incorporating two objectives: expected maximum reward and entropy. We proposed three graph-based algorithms and one deep reinforcement learning model, designed to efficiently balance the trade-off between these two objectives. Notably, the third algorithm can identify the optimal deterministic patrol schedule, though its runtime grows exponentially with the number of patrol spots. Experimental results validate the effectiveness and scalability of our solutions, demonstrating that our approaches outperform state-of-the-art baselines on both synthetic and real-world crime datasets.
With the rapid advancement of large language models (LLMs), aligning policy models with human preferences has become increasingly critical. Direct Preference Optimization (DPO) has emerged as a promising approach for alignment, acting as an RL-free alternative to Reinforcement Learning from Human Feedback (RLHF). Despite DPO's various advancements and inherent limitations, an in-depth review of these aspects is currently lacking in the literature. In this work, we present a comprehensive review of the challenges and opportunities in DPO, covering theoretical analyses, variants, relevant preference datasets, and applications. Specifically, we categorize recent studies on DPO based on key research questions to provide a thorough understanding of DPO's current landscape. Additionally, we propose several future research directions to offer insights on model alignment for the research community.
As a cornerstone of modern information access, search engines have become indispensable in everyday life. With the rapid advancements in AI and natural language processing (NLP) technologies, particularly large language models (LLMs), search engines have evolved to support more intuitive and intelligent interactions between users and systems. Conversational search, an emerging paradigm for next-generation search engines, leverages natural language dialogue to facilitate complex and precise information retrieval, thus attracting significant attention. Unlike traditional keyword-based search engines, conversational search systems enhance user experience by supporting intricate queries, maintaining context over multi-turn interactions, and providing robust information integration and processing capabilities. Key components such as query reformulation, search clarification, conversational retrieval, and response generation work in unison to enable these sophisticated interactions. In this survey, we explore the recent advancements and potential future directions in conversational search, examining the critical modules that constitute a conversational search system. We highlight the integration of LLMs in enhancing these systems and discuss the challenges and opportunities that lie ahead in this dynamic field. Additionally, we provide insights into real-world applications and robust evaluations of current conversational search systems, aiming to guide future research and development in conversational search.
The Unified Object Detection (UOD) task aims to achieve object detection of all merged categories through training on multiple datasets, and is of great significance in comprehensive object detection scenarios. In this paper, we conduct a thorough analysis of the cross datasets missing annotations issue, and propose an Online Pseudo-Label Unified Object Detection scheme. Our method uses a periodically updated teacher model to generate pseudo-labels for the unlabelled objects in each sub-dataset. This periodical update strategy could better ensure that the accuracy of the teacher model reaches the local maxima and maximized the quality of pseudo-labels. In addition, we survey the influence of overlapped region proposals on the accuracy of box regression. We propose a category specific box regression and a pseudo-label RPN head to improve the recall rate of the Region Proposal Network (PRN). Our experimental results on common used benchmarks (\eg COCO, Object365 and OpenImages) indicates that our online pseudo-label UOD method achieves higher accuracy than existing SOTA methods.
A Comprehensive Investigation of Environmental Influences on Galaxies in Group Environments
2024-10-21
W. Van Kempen, M. E. Cluver, T. H. Jarrett, D. J. Croton, T. S. Lambert, V. A. Kilborn, E. N. Taylor, C. Magoulas, H. F. M. Yao
Environment has long been known to impact the evolution of galaxies, but disentangling its effects from mass evolution requires careful analysis of statistically significant samples. By implementing advanced visualisation methods to test group-finding algorithms, we utilise a mass-complete sample of galaxies to z < 0.1, comprising spectroscopic redshifts from prominent surveys such as the 2dFGRS and GAMA. Our group-finding methods identify 1,413 galaxy groups made up of 8,990 galaxies, corresponding to 36% of galaxies associated with group environments. We also search for close pairs, with separations of $r_{sep}$ < 50 h$^{-1}$ kpc and $v_{sep}$ < 500 km/s, and classify them into major ($M_{sec}/M_{prim} \leq$ 0.25) and minor ($M_{sec}/M_{prim}$ > 0.25) pairs. To examine the impact of environmental factors, we employ bespoke WISE photometry to derive a star-forming main sequence relation that shows star-formation (SF) within galaxies is pre-processed as a function of group membership. Our analysis reveals that SF in galaxies is pre-processed as a function of group membership. We observe an increase in the fraction of quiescent galaxies relative to the field as group membership rises, quantified using the environmental quenching efficiency metric ($\epsilon_{env}$). Within the star-forming population, we detect pre-processing with the relative difference in specific SF rates (${\Delta} sSFR$), showing a net decrease in SF as group membership increases, particularly at larger stellar masses. Our sample of close pairs at low stellar masses shows enhanced SF efficiencies compared to the field, while at larger masses, deficiencies are evident. Our results indicate that the small-scale environments of galaxies influence SF properties, demonstrating that galaxies do not evolve in isolation over cosmic time but are shaped by complex interactions between internal dynamics and external influences.
Disease Outbreak Detection and Forecasting: A Review of Methods and Data Sources
2024-10-21
Ghazaleh Babanejaddehaki, Aijun An, Manos Papagelis
Infectious diseases occur when pathogens from other individuals or animals infect a person, resulting in harm to both individuals and society as a whole. The outbreak of such diseases can pose a significant threat to human health. However, early detection and tracking of these outbreaks have the potential to reduce the mortality impact. To address these threats, public health authorities have endeavored to establish comprehensive mechanisms for collecting disease data. Many countries have implemented infectious disease surveillance systems, with the detection of epidemics being a primary objective. The clinical healthcare system, local/state health agencies, federal agencies, academic/professional groups, and collaborating governmental entities all play pivotal roles within this system. Moreover, nowadays, search engines and social media platforms can serve as valuable tools for monitoring disease trends. The Internet and social media have become significant platforms where users share information about their preferences and relationships. This real-time information can be harnessed to gauge the influence of ideas and societal opinions, making it highly useful across various domains and research areas, such as marketing campaigns, financial predictions, and public health, among others. This article provides a review of the existing standard methods developed by researchers for detecting outbreaks using time series data. These methods leverage various data sources, including conventional data sources and social media data or Internet data sources. The review particularly concentrates on works published within the timeframe of 2015 to 2022.
Who Puts the "Social" in "Social Computing"?: Using A Neurodiversity Framing to Review Social Computing Research
2024-10-20
Philip Baillargeon, Jina Yoon, Amy Zhang
Human-Computer Interaction (HCI) and Computer Supported Collaborative Work (CSCW) have a longstanding tradition of interrogating the values that underlie systems in order to create novel and accessible experiences. In this work, we use a neurodiversity framing to examine how people with ways of thinking, speaking, and being that differ from normative assumptions are perceived by researchers seeking to study and design social computing systems for neurodivergent people. From a critical analysis of 84 publications systematically gathered across a decade of social computing research, we determine that research into social computing with neurodiverse participants is largely medicalized, adheres to historical stereotypes of neurodivergent children and their families, and is insensitive to the wide spectrum of neurodivergent people that are potential users of social technologies. When social computing systems designed for neurodivergent people rely upon a conception of disability that restricts expression for the sake of preserving existing norms surrounding social experience, the result is often simplistic and restrictive systems that prevent users from "being social" in a way that feels natural and enjoyable. We argue that a neurodiversity perspective informed by critical disability theory allows us to engage with alternative forms of sociality as meaningful and desirable rather than a deficit to be compensated for. We conclude by identifying opportunities for researchers to collaborate with neurodivergent users and their communities, including the creation of spectrum-conscious social systems and the embedding of double empathy into systems for more equitable design.
Nanothermodynamics: There's plenty of room on the inside
2024-10-20
Ralph V. Chamberlin, Stuart M. Lindsay
Nanothermodynamics provides the theoretical foundation for understanding stable distributions of statistically independent subsystems inside larger systems. In this review it is emphasized that adapting ideas from nanothermodynamics to simplistic models improves agreement with the measured properties of many materials. Examples include non-classical critical scaling near ferromagnetic transitions, thermal and dynamic behavior near liquid-glass transitions, and the 1/f-like noise in metal films and qubits. A key feature in several models is to allow separate time steps for distinct conservation laws: one type of step conserves energy and the other conserves momentum (e.g. dipole alignment). This "orthogonal dynamics" explains how the relaxation of a single parameter can exhibit multiple responses such as primary, secondary, and microscopic peaks in the dielectric loss of supercooled liquids, and the crossover in thermal fluctuations from Johnson-Nyquist (white) noise at high frequencies to 1/f-like noise at low frequencies. Nanothermodynamics also provides new insight into three basic questions. First, it gives a novel solution to Gibbs' paradox for the entropy of the semi-classical ideal gas. Second, it yields the stable equilibrium of Ising's original model for finite-sized chains of interacting binary degrees of freedom ("spins"). Third, it confronts Loschmidt's paradox for the arrow of time, showing that an intrinsically irreversible step is required for maximum entropy and the second law of thermodynamics, not only in the thermodynamic limit but also in systems as small as N=2 particles
The current work is a qualitative study that aims to explore the implementation of Twisted and Coiled Artificial Muscles (TCAMs) for actuating and replicating the bending motion of an octopus-like soft robot arm underwater. Additionally, it investigates the impact of hydrostatic and dynamic forces from steady-state fluid flow on the arm's motion. The artificial muscles are lightweight and low-cost actuators that generate a high power-to-weight ratio, producing tensile force up to 12,600 times their own weight, which is close to the functionality of biological muscles. The "extended" Cosserat theory of rods is employed to formulate a quasi-static continuum model of arm motion, where the arm's cross-section is not only capable of rigid rotation but also deforms within its plane. This planar deformation of the arm cross-section aligns with the biological behavior of the octopus arm, where the stiffness of the hydrostat is directly induced by the incompressibility of the tissues. In line with the main goal, a constitutive model is derived for the material of the octopus arm to capture its characteristic behavior.
Event-based Sensor Fusion and Application on Odometry: A Survey
2024-10-20
Jiaqiang Zhang, Xianjia Yu, Ha Sier, Haizhou Zhang, Tomi Westerlund
Event cameras, inspired by biological vision, are asynchronous sensors that detect changes in brightness, offering notable advantages in environments characterized by high-speed motion, low lighting, or wide dynamic range. These distinctive properties render event cameras particularly effective for sensor fusion in robotics and computer vision, especially in enhancing traditional visual or LiDAR-inertial odometry. Conventional frame-based cameras suffer from limitations such as motion blur and drift, which can be mitigated by the continuous, low-latency data provided by event cameras. Similarly, LiDAR-based odometry encounters challenges related to the loss of geometric information in environments such as corridors. To address these limitations, unlike the existing event camera-related surveys, this paper presents a comprehensive overview of recent advancements in event-based sensor fusion for odometry applications particularly, investigating fusion strategies that incorporate frame-based cameras, inertial measurement units (IMUs), and LiDAR. The survey critically assesses the contributions of these fusion methods to improving odometry performance in complex environments, while highlighting key applications, and discussing the strengths, limitations, and unresolved challenges. Additionally, it offers insights into potential future research directions to advance event-based sensor fusion for next-generation odometry applications.
Nonlinearity, Fractals, Fourier decay -- Harmonic analysis of equilibrium states for hyperbolic dynamical systems
2024-10-20
Gaétan Leclerc
This is my (reviewed) PhD manuscript. It contains 6 Chapters, which contains mostly already published work, except for Chapter 5 which is new. Chapter 1 introduce basic notions on fractal geometry: the Fourier dimension, the thermodynamical formalism and additive combinatorics. Chapter 2 is a generalized version of arXiv:2211.08088. Chapter 3 is a slightly upgraded version of arXiv:2112.00701. Chapter 4 is a generalized version of arXiv:2301.10623. Chapter 5 and Chapter 4 together contains the first proof of the positivity of the Fourier dimension for basic sets of nonlinear, area-preserving, smooth Axiom A diffeomorphisms on surfaces. This is possible by adapting some ideas that can be found in Tsujii-Zhang's work arXiv:2006.04293. Some future work still needs to be done to prove that the nonlinearity conditions are generic in this setting: this should become a proper research article in the future. Chapter 6 is a slighty upgraded version of arXiv:2307.10755.
Large language models (LLMs) are employed to simulate human-like responses in social surveys, yet it remains unclear if they develop biases like social desirability response (SDR) bias. To investigate this, GPT-4 was assigned personas from four societies, using data from the 2022 Gallup World Poll. These synthetic samples were then prompted with or without a commitment statement intended to induce SDR. The results were mixed. While the commitment statement increased SDR index scores, suggesting SDR bias, it reduced civic engagement scores, indicating an opposite trend. Additional findings revealed demographic associations with SDR scores and showed that the commitment statement had limited impact on GPT-4's predictive performance. The study underscores potential avenues for using LLMs to investigate biases in both humans and LLMs themselves.
A spinodal decomposition model for the large-scale structure of the universe
2024-10-20
Nitish Yadav
The tremendous efforts placed in understanding the structure and galaxy formation in the universe have led to major improvements in our models for the universe. Particular the computational simulations that solve its non-linear growth have painted a clearer picture of the processes behind the enormous scale and shape of the structures observed experimentally. Inspired by porous structure of polymer membranes prepared using phase-inversion, we have taken a fresh-approach to understand how the universe got its present structure. We present the results of a finite-element method based simulation to predict large-scale structure formation in the universe. The novel method applies the Cahn-Hilliard model of spinodal decomposition of a binary mixture to predict the dynamics of matter and dark-energy distribution in the universe. The results closely match the various surveys of matter distribution in the universe and can prove to be an important step towards decreasing the computational resources required for future simulations.
Ethical AI in Retail: Consumer Privacy and Fairness
2024-10-20
Anthonette Adanyin
The adoption of artificial intelligence (AI) in retail has significantly transformed the industry, enabling more personalized services and efficient operations. However, the rapid implementation of AI technologies raises ethical concerns, particularly regarding consumer privacy and fairness. This study aims to analyze the ethical challenges of AI applications in retail, explore ways retailers can implement AI technologies ethically while remaining competitive, and provide recommendations on ethical AI practices. A descriptive survey design was used to collect data from 300 respondents across major e-commerce platforms. Data were analyzed using descriptive statistics, including percentages and mean scores. Findings shows a high level of concerns among consumers regarding the amount of personal data collected by AI-driven retail applications, with many expressing a lack of trust in how their data is managed. Also, fairness is another major issue, as a majority believe AI systems do not treat consumers equally, raising concerns about algorithmic bias. It was also found that AI can enhance business competitiveness and efficiency without compromising ethical principles, such as data privacy and fairness. Data privacy and transparency were highlighted as critical areas where retailers need to focus their efforts, indicating a strong demand for stricter data protection protocols and ongoing scrutiny of AI systems. The study concludes that retailers must prioritize transparency, fairness, and data protection when deploying AI systems. The study recommends ensuring transparency in AI processes, conducting regular audits to address biases, incorporating consumer feedback in AI development, and emphasizing consumer data privacy.
A Survey of Hallucination in Large Visual Language Models
2024-10-20
Wei Lan, Wenyi Chen, Qingfeng Chen, Shirui Pan, Huiyu Zhou, Yi Pan
The Large Visual Language Models (LVLMs) enhances user interaction and enriches user experience by integrating visual modality on the basis of the Large Language Models (LLMs). It has demonstrated their powerful information processing and generation capabilities. However, the existence of hallucinations has limited the potential and practical effectiveness of LVLM in various fields. Although lots of work has been devoted to the issue of hallucination mitigation and correction, there are few reviews to summary this issue. In this survey, we first introduce the background of LVLMs and hallucinations. Then, the structure of LVLMs and main causes of hallucination generation are introduced. Further, we summary recent works on hallucination correction and mitigation. In addition, the available hallucination evaluation benchmarks for LVLMs are presented from judgmental and generative perspectives. Finally, we suggest some future research directions to enhance the dependability and utility of LVLMs.
The ALMA-QUARKS Survey: Fibers' role in star formation unveiled in an intermediate-mass protocluster region of the Vela D cloud
2024-10-20
Dongting Yang, HongLi Liu, Tie Liu, Anandmayee Tej, Xunchuan Liu, Jinhua He, Guido Garay, Amelia Stutz, Lei Zhu, Sheng-Li Qin, Fengwei Xu, Pak-Shing Li, Mika Juvela, Pablo Garcia, Paul F. Goldsmith, Siju Zhang, Xindi Tang, Patricio Sanhueza, Shanghuo Li, Chang Won Lee, Swagat Ranjan Das, Wenyu Jiao, Xiaofeng Mai, Prasanta Gorai, Yichen Zhang, Zhiyuan Ren, L. Viktor Toth, Jihye Hwang, Leonardo Bronfman, Kenichi Tatematsu, Lokesh Dewangan, James O. Chibueze, Suinan Zhang, Gang Wu, Jinjin Xie
In this paper, we present a detailed analysis of the IRS 17 filament within the intermediate-mass protocluster IRAS 08448-4343 (of $\sim\,10^3\,\rm L_{\odot}$), using ALMA data from the ATOMS 3-mm and QUARKS 1.3-mm surveys. The IRS 17 filament, which spans $\sim$54000 au ($0.26\,\rm pc$) in length and $\sim$4000 au ($0.02\,\rm pc$) in width, exhibits a complex, multi-component velocity field, and harbours hierarchical substructures. These substructures include three bundles of seven velocity-coherent fibers, and 29 dense ($n\sim 10^8\,\rm cm^{-3}$) condensations. The fibers have a median length of $\sim 4500\,\rm au$ and a median width of $\sim 1400\,\rm au$. Among these fibers, four are identified as ``fertile", each hosting at least three dense condensations, which are regarded as the ``seeds" of star formation. While the detected cores are randomly spaced within the IRS\,17 filament based on the 3-mm dust continuum image, periodic spacing ($\sim1600\,\rm au$) of condensations is observed in the fertile fibers according to the 1.3-mm dust map, consistent with the predictions of linear isothermal cylinder fragmentation models. These findings underscore the crucial role of fibers in star formation and suggest a hierarchical fragmentation process that extends from the filament to the fibers, and ultimately, to the smallest-scale condensations.
Inter-Cation Charge Transfer Mediated Antiferromagnetism in Co$_{1+x}$Ir$_{2-x}$S$_4$
2024-10-20
Liang-Wen Ji, Si-Qi Wu, Bai-Zhuo Li, Wu-Zhang Yang, Shi-Jie Song, Yi Liu, Jing Li, Zhi Ren, Guang-Han Cao
The antiferromagnetism in transition metal compounds is mostly mediated by the bridging anions through a so-called superexchange mechanism. However, in materials like normal spinels $AB_2X_4$ with local moments only at the $A$ site, such an anion-mediated superexchange needs to be modified. Here we report a new spinel compound Co$_{1+x}$Ir$_{2-x}$S$_4$ ($x$ = 0.3). The physical property measurements strongly suggest an antiferromagnetic-like transition at 292 K in the Co($A$) diamond sublattice. The first-principle calculations reveal that the nearest-neighbor Co($A$) spins align antiferromagnetically with an ordered magnetic moment of 1.67 $\mu_\mathrm{B}$, smaller than the expected $S = 3/2$ for Co$^{2+}$. In the antiferromagnetic state, there exists an inter-cation charge-transfer gap between the non-bonding Ir-$t_\mathrm{2g}$ orbitals at the valence band maximum and the Co-S antibonding molecular orbitals at the conduction band minimum. The small charge transfer energy significantly enhances the virtual hopping between these two states, facilitating a robust long-range superexchange interaction between two neighboring CoS$_4$ complexes, which accounts for the high N\'{e}el temperature in Co$_{1+x}$Ir$_{2-x}$S$_4$. This inter-cation charge transfer mediated magnetic interaction expands the traditional superexchange theory, which could be applicable in complex magnetic materials with multiple cations.
As large language models (LLMs) continue to evolve, understanding and quantifying the uncertainty in their predictions is critical for enhancing application credibility. However, the existing literature relevant to LLM uncertainty estimation often relies on heuristic approaches, lacking systematic classification of the methods. In this survey, we clarify the definitions of uncertainty and confidence, highlighting their distinctions and implications for model predictions. On this basis, we integrate theoretical perspectives, including Bayesian inference, information theory, and ensemble strategies, to categorize various classes of uncertainty estimation methods derived from heuristic approaches. Additionally, we address challenges that arise when applying these methods to LLMs. We also explore techniques for incorporating uncertainty into diverse applications, including out-of-distribution detection, data annotation, and question clarification. Our review provides insights into uncertainty estimation from both definitional and theoretical angles, contributing to a comprehensive understanding of this critical aspect in LLMs. We aim to inspire the development of more reliable and effective uncertainty estimation approaches for LLMs in real-world scenarios.
Recent breakthroughs in artificial intelligence have driven a paradigm shift, where large language models (LLMs) with billions or trillions of parameters are trained on vast datasets, achieving unprecedented success across a series of language tasks. However, despite these successes, LLMs still rely on probabilistic modeling, which often captures spurious correlations rooted in linguistic patterns and social stereotypes, rather than the true causal relationships between entities and events. This limitation renders LLMs vulnerable to issues such as demographic biases, social stereotypes, and LLM hallucinations. These challenges highlight the urgent need to integrate causality into LLMs, moving beyond correlation-driven paradigms to build more reliable and ethically aligned AI systems. While many existing surveys and studies focus on utilizing prompt engineering to activate LLMs for causal knowledge or developing benchmarks to assess their causal reasoning abilities, most of these efforts rely on human intervention to activate pre-trained models. How to embed causality into the training process of LLMs and build more general and intelligent models remains unexplored. Recent research highlights that LLMs function as causal parrots, capable of reciting causal knowledge without truly understanding or applying it. These prompt-based methods are still limited to human interventional improvements. This survey aims to address this gap by exploring how causality can enhance LLMs at every stage of their lifecycle-from token embedding learning and foundation model training to fine-tuning, alignment, inference, and evaluation-paving the way for more interpretable, reliable, and causally-informed models. Additionally, we further outline six promising future directions to advance LLM development, enhance their causal reasoning capabilities, and address the current limitations these models face.
The Massive and Distant Clusters of WISE Survey 2: Second Data Release
2024-10-20
Khunanon Thongkham, Anthony H. Gonzalez, Mark Brodwin, Ariane Trudeau, Peter Eisenhardt, S. A. Stanford, Emily Moravec, Thomas Connor, Daniel Stern, Ryan Spivey, Karolina Garcia
We present the second data release of the Massive and Distant Clusters of WISE Survey 2 (MaDCoWS2). We expand from the equatorial first data release to most of the Dark Energy Camera Legacy Survey area, covering a total area of 6498 deg^2. The catalog consists of 133,036 S/N $\geq5$ galaxy cluster candidates at $0.1\leq z \leq2$, including 6790 candidates at z > 1.5. We train a convolutional neural network (CNN) to identify spurious detections, and include CNN-based cluster probabilities in the final catalog. We also compare the MaDCoWS2 sample with literature catalogs in the same area. The larger sample provides robust results that are consistent with our first data release. At S/N $\geq5$, we rediscover 59-91% of clusters in existing catalogs that lie in the unmasked area of MC2. The median positional offsets are under 250 kpc, and the standard deviation of the redshifts is 0.031(1+z). We fit a redshift-dependent power law to the relation between MaDCoWS2 S/N and observables from existing catalogs. Over the redshift ranges where the surveys overlap with MaDCoWS2, the lowest scatter is found between S/N and observables from optical/infrared surveys. We also assess the performance of our method using a mock light cone measuring purity and completeness as a function of cluster mass. The purity is above 90%, and we estimate the 50% completeness threshold at a virial mass of log(M/M$_\odot$)$\approx14.3$. The completeness estimate is uncertain due to the small number of massive halos in the light cone, but consistent with the recovery fraction found by comparing to other cluster catalogs.
A novel approach to cosmological non-linearities as an effective fluid
2024-10-20
Leonardo Giani, Rodrigo Von Marttens, Ryan Camilleri
We propose a two parameters extension of the flat $\Lambda$CDM model to capture the impact of matter inhomogeneities on the background evolution of the Universe. Non virialized but non-linearly evolving overdense and underdense regions, whose abundance is quantified using the Press-Schechter formalism, are collectively described by two effective perfect fluids $\rho_{\rm{c}},\rho_{\rm{v}}$ with non vanishing equation of state parameters $w_{\rm{c,v}}\neq 0$. These fluids are coupled to the pressureless dust, akin to an interacting DM-DE scenario. The resulting phenomenology is very rich, and could potentially address a number of inconsistencies of the standard model, including a simultaneous resolution of the Hubble and $\sigma_8$ tensions. To assess the viability of the model, we set initial conditions compatible to the Planck 2018 best fit $\Lambda$CDM cosmology and fit its additional parameters using SN~Ia observations from DESY5 and a sample of uncorrelated $f\sigma_8$ measurements. Our findings show that backreaction effects from the cosmic web could restore the concordance between early and late Universe cosmological probes.
Web3 aims to enhance user control over data and assets, but this vision is challenged by non-transparent, scam-prone applications and vulnerable smart contracts. While code audits are one solution to this problem, the lack of smart contracts source code on many blockchain platforms, such as Sui, hinders the ease of auditing. A promising approach to this issue is the use of a decompiler to reverse-engineer smart contract bytecode. However, existing decompilers for Sui produce code that is difficult to understand and cannot be directly recompiled. To address this, we developed the Move AI Decompiler (MAD), a Large Language Model (LLM)-powered web application that decompiles smart contract bytecodes on Sui into logically correct, human-readable, and re-compilable source code. Our evaluation shows that MAD produces logically correct code that successfully passes original unit tests and achieves a 66.7% recompilation success rate on real-world smart contracts. Additionally, in a user study involving 12 developers, MAD significantly reduced the auditing workload compared to using traditional decompilers. Participants found MAD's outputs comparable to the original source code, simplifying the process of smart contract logic comprehension and auditing. Despite some limitations, such as occasional hallucinations and compile errors, MAD still provides significant improvements over traditional decompilers. MAD has practical implications for blockchain smart contract transparency, auditing, and education. It empowers users to review and audit non-open-source smart contracts, fostering trust and accountability. Additionally, MAD's approach could potentially extend to other smart contract languages, like Solidity, promoting transparency across various blockchains.
Asymmetry in galaxy spin directions: a fully reproducible experiment using HSC data
2024-10-20
Lior Shamir
The asymmetry in the large-scale distribution of the directions towards spiral galaxies rotate has been observed by multiple telescopes, all show a consistent asymmetry in the distribution of galaxy spin directions as observed from Earth. Here, galaxies with redshift from HSC DR3 are annotated by their direction of rotation, and their distribution is analyzed. The results show that galaxies that rotate in the opposite direction relative to the Milky Way as observed from Earth are significantly more prevalent compared to galaxies that rotate in the same direction relative to the Milky Way. The asymmetry also forms a dipole axis that becomes stronger when the redshift gets higher. These results are aligned with observations from virtually all premier digital sky surveys, as well as space telescopes such as HST and JWST. That shows that the distribution of galaxy spin directions as observed from Earth is not symmetric, and has a possible link to the rotational velocity of the Milky Way. The experiment is provided with data, code, and a full protocol that allows to easily reproduce the results in a transparent manner. That practice is used to overcome the ``reproducibility crisis" in science.
Interpolation techniques for reconstructing Galactic Faraday rotation
2024-10-20
Affan Khadir, Ayush Pandhi, Sebastian Hutschenreuter, Bryan Gaensler, Shannon Vanderwoude, Jennifer West, Shane O'Sullivan
The line-of-sight structure of the Galactic magnetic field (GMF) can be studied using Faraday rotation measure (RM) grids. We analyze how the choice of interpolation kernel can affect the accuracy and reliability of reconstructed RM maps. We test the following kernels: inverse distance weighting (IDW), natural neighbour interpolation (NNI), inverse multiquadric interpolation (IM), thin-plate spline interpolation (TPS), and a Bayesian rotation measure sky (BRMS); all techniques were tested on two simulated Galactic foreground RMs (one assuming the GMF has patchy structures and the other assuming it has filamentary structures) using magnetohydrodynamic simulations. Both foregrounds were sampled to form RM grids with densities of $\sim$40 sources deg$^{-2}$ and area $\sim$144 deg$^2$. The techniques were tested on data sets with different noise levels and Gaussian random extragalactic RM contributions. The data set that most closely emulates expected data from current surveys, such as the POlarization Sky Survey of the Universe's Magnetism (POSSUM), had extragalactic contributions and a noise standard deviation of $\sim 1.5$ rad m$^{-2}$. For this data set, the accuracy of the techniques for the patchy structures from best to worst was: BRMS, NNI, TPS, IDW and IM; while in the filamentary simulate foreground it was: BRMS, NNI, TPS, and IDW. IDW is the most computationally expensive technique, while TPS and IM are the least expensive. BRMS and NNI have the same, intermediate computational cost. This analysis lays the groundwork for Galactic RM studies with large radio polarization sky surveys, such as POSSUM.
Jailbreaking and Mitigation of Vulnerabilities in Large Language Models
2024-10-20
Benji Peng, Ziqian Bi, Qian Niu, Ming Liu, Pohsun Feng, Tianyang Wang, Lawrence K. Q. Yan, Yizhu Wen, Yichao Zhang, Caitlyn Heqi Yin
Large Language Models (LLMs) have transformed artificial intelligence by advancing natural language understanding and generation, enabling applications across fields beyond healthcare, software engineering, and conversational systems. Despite these advancements in the past few years, LLMs have shown considerable vulnerabilities, particularly to prompt injection and jailbreaking attacks. This review analyzes the state of research on these vulnerabilities and presents available defense strategies. We roughly categorize attack approaches into prompt-based, model-based, multimodal, and multilingual, covering techniques such as adversarial prompting, backdoor injections, and cross-modality exploits. We also review various defense mechanisms, including prompt filtering, transformation, alignment techniques, multi-agent defenses, and self-regulation, evaluating their strengths and shortcomings. We also discuss key metrics and benchmarks used to assess LLM safety and robustness, noting challenges like the quantification of attack success in interactive contexts and biases in existing datasets. Identifying current research gaps, we suggest future directions for resilient alignment strategies, advanced defenses against evolving attacks, automation of jailbreak detection, and consideration of ethical and societal impacts. This review emphasizes the need for continued research and cooperation within the AI community to enhance LLM security and ensure their safe deployment.
Bias Amplification: Language Models as Increasingly Biased Media
2024-10-19
Ze Wang, Zekun Wu, Jeremy Zhang, Navya Jain, Xin Guan, Adriano Koshiyama
As Large Language Models (LLMs) become increasingly integrated into various facets of society, a significant portion of online text consequently become synthetic. This raises concerns about bias amplification, a phenomenon where models trained on synthetic data amplify the pre-existing biases over successive training iterations. Previous literature seldom discusses bias amplification as an independent issue from model collapse. In this work, we address the gap in understanding the bias amplification of LLMs with four main contributions. Firstly, we propose a theoretical framework, defining the necessary and sufficient conditions for its occurrence, and emphasizing that it occurs independently of model collapse. Using statistical simulations with weighted maximum likelihood estimation, we demonstrate the framework and show how bias amplification arises without the sampling and functional form issues that typically drive model collapse. Secondly, we conduct experiments with GPT-2 to empirically demonstrate bias amplification, specifically examining open-ended generational political bias with a benchmark we developed. We observe that GPT-2 exhibits a right-leaning bias in sentence continuation tasks and that the bias progressively increases with iterative fine-tuning on synthetic data generated by previous iterations. Thirdly, we explore three potential mitigation strategies: Overfitting, Preservation, and Accumulation. We find that both Preservation and Accumulation effectively mitigate bias amplification and model collapse. Finally, using novel mechanistic interpretation techniques, we demonstrate that in the GPT-2 experiments, bias amplification and model collapse are driven by distinct sets of neurons, which aligns with our theoretical framework.
Despite the popularity of multi-agent reinforcement learning (RL) in simulated and two-player applications, its success in messy real-world applications has been limited. A key challenge lies in its generalizability across problem variations, a common necessity for many real-world problems. Contextual reinforcement learning (CRL) formalizes learning policies that generalize across problem variations. However, the lack of standardized benchmarks for multi-agent CRL has hindered progress in the field. Such benchmarks are desired to be based on real-world applications to naturally capture the many open challenges of real-world problems that affect generalization. To bridge this gap, we propose IntersectionZoo, a comprehensive benchmark suite for multi-agent CRL through the real-world application of cooperative eco-driving in urban road networks. The task of cooperative eco-driving is to control a fleet of vehicles to reduce fleet-level vehicular emissions. By grounding IntersectionZoo in a real-world application, we naturally capture real-world problem characteristics, such as partial observability and multiple competing objectives. IntersectionZoo is built on data-informed simulations of 16,334 signalized intersections derived from 10 major US cities, modeled in an open-source industry-grade microscopic traffic simulator. By modeling factors affecting vehicular exhaust emissions (e.g., temperature, road conditions, travel demand), IntersectionZoo provides one million data-driven traffic scenarios. Using these traffic scenarios, we benchmark popular multi-agent RL and human-like driving algorithms and demonstrate that the popular multi-agent RL algorithms struggle to generalize in CRL settings.
An Electoral Approach to Diversify LLM-based Multi-Agent Collective Decision-Making
2024-10-19
Xiutian Zhao, Ke Wang, Wei Peng
Modern large language models (LLMs) have exhibited cooperative synergy on complex task-solving, and collective decision-making (CDM) is a pivotal component in LLM-based multi-agent collaboration frameworks. Our survey on 52 recent such systems uncovers a severe lack of diversity, with a heavy reliance on dictatorial and plurality voting for CDM. Through the lens of social choice theory, we scrutinize widely-adopted CDM methods and identify their limitations. To enrich current landscape of LLM-based CDM, we present GEDI, an electoral CDM module that incorporates various ordinal preferential voting mechanisms. Our empirical case study across three benchmarks shows that the integration of certain CDM methods can markedly improve the reasoning capabilities and robustness of some leading LLMs, all without requiring intricate system designs. Additionally, we find that some CDM mechanisms generate positive synergies even with as few as three agents. The voting-based methods also demonstrate robustness against single points of failure, as well as diversity in terms of hit-rate@k and subject-wise impacts.
2024-10-19
Rafael Vazquez, Jean Auriol, Federico Bribiesca-Argomedo, Miroslav Krstic
Systems modeled by partial differential equations (PDEs) are at least as ubiquitous as systems that are by nature finite-dimensional and modeled by ordinary differential equations (ODEs). And yet, systematic and readily usable methodologies, for such a significant portion of real systems, have been historically scarce. Around the year 2000, the backstepping approach to PDE control began to offer not only a less abstract alternative to PDE control techniques replicating optimal and spectrum assignment techniques of the 1960s, but also enabled the methodologies of adaptive and nonlinear control, matured in the 1980s and 1990s, to be extended from ODEs to PDEs, allowing feedback synthesis for physical and engineering systems that are uncertain, nonlinear, and infinite-dimensional. The PDE backstepping literature has grown in its nearly a quarter century of development to many hundreds of papers and nearly a dozen books. This survey aims to facilitate the entry, for a new researcher, into this thriving area of overwhelming size and topical diversity. Designs of controllers and observers, for parabolic, hyperbolic, and other classes of PDEs, in one and more dimensions (in box and spherical geometries), with nonlinear, adaptive, sampled-data, and event-triggered extensions, are covered in the survey. The lifeblood of control are technology and physics. The survey places a particular emphasis on applications that have motivated the development of the theory and which have benefited from the theory and designs: applications involving flows, flexible structures, materials, thermal and chemically reacting dynamics, energy (from oil drilling to batteries and magnetic confinement fusions), and vehicles.
A survey of neural-network-based methods utilising comparable data for finding translation equivalents
2024-10-19
Michaela Denisová, Pavel Rychlý
The importance of inducing bilingual dictionary components in many natural language processing (NLP) applications is indisputable. However, the dictionary compilation process requires extensive work and combines two disciplines, NLP and lexicography, while the former often omits the latter. In this paper, we present the most common approaches from NLP that endeavour to automatically induce one of the essential dictionary components, translation equivalents and focus on the neural-network-based methods using comparable data. We analyse them from a lexicographic perspective since their viewpoints are crucial for improving the described methods. Moreover, we identify the methods that integrate these viewpoints and can be further exploited in various applications that require them. This survey encourages a connection between the NLP and lexicography fields as the NLP field can benefit from lexicographic insights, and it serves as a helping and inspiring material for further research in the context of neural-network-based methods utilising comparable data.
PS1-11aop: Probing the Mass Loss History of a Luminous Interacting Supernova Prior to its Final Eruption with Multi-wavelength Observations
2024-10-19
Adaeze L. Ibik, Maria R. Drout, Raffaela Margutti, David Matthews, V. Ashley Villar, Edo Berger, Ryan Chornock, Kate D. Alexander, Tarraneh Eftekhari, Tanmoy Laskar, Ragnhild Lunnan, Ryan J. Foley, David Jones, Dan Milisavljevic, Armin Rest, Daniel Scolnic, Peter K. G. Williams
Luminous interacting supernovae are a class of stellar explosions whose progenitors underwent vigorous mass loss in the years prior to core-collapse. While the mechanism by which this material is ejected is still debated, obtaining the full density profile of the circumstellar medium (CSM) could reveal more about this process. Here, we present an extensive multi-wavelength study of PS1-11aop, a luminous and slowly declining Type IIn SN discovered by the PanSTARRS Medium Deep Survey. PS1-11aop had a peak r-band magnitude of $-$20.5\,mag, a total radiated energy $>$ 8$\times$10$^{50}$\,erg, and it exploded near the center of a star-forming galaxy with super-solar metallicity. We obtained multiple detections at the location of PS1-11aop in the radio and X-ray bands between 4 and 10\,years post-explosion, and if due to the SN, it is one of the most luminous radio supernovae identified to date. Taken together, the multiwavelength properties of PS1-11aop are consistent with a CSM density profile with multiple zones. The early optical emission is consistent with the supernova blastwave interacting with a dense and confined CSM shell which contains multiple solar masses of material that was likely ejected in the final $<$10-100 years prior to the explosion,($\sim$0.05$-$1.0 M$_{\odot}$yr$^{-1}$ at radii of $\lesssim$10$^{16}$\,cm). The radio observations, on the other hand, are consistent with a sparser environment ($\lesssim$2$\times 10^{-3}$ M$_{\odot}$yr$^{-1}$ at radii of $\sim$0.5-1$\times$10$^{17}$\,cm) -- thus probing the history of the progenitor star prior to its final mass loss episode.
The celebrated Dvoretzky theorem asserts that every $N$-dimensional convex body admits central sections of dimension $d = \Omega(\log N)$, which is nearly spherical. For many instances of convex bodies, typically unit balls with respect to some norm, much better lower bounds on $d$ have been obtained, with most research focusing on such lower bounds and on the degree of approximation of the section by a $k$-dimensional Euclidean ball. In this note we focus on another parameter, namely the radius of the approximating ball. We focus on the case of the unit ball of the space $\ell_1^N$ (the so-called cross-polytope), which is relevant to various questions of interest in theoretical computer science. We will also survey other instances where similar questions for other normed spaces (most often $\ell_p$-spaces or their non-commutative analogues) were found relevant to problems in various areas of mathematics and its applications, and state some open problems. Finally, in view of the computer science ramifications, we will comment on the algorithmic aspects of finding nearly spherical sections.
A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends
2024-10-19
Junjun Jiang, Zengyuan Zuo, Gang Wu, Kui Jiang, Xianming Liu
Image restoration (IR) refers to the process of improving visual quality of images while removing degradation, such as noise, blur, weather effects, and so on. Traditional IR methods typically target specific types of degradation, which limits their effectiveness in real-world scenarios with complex distortions. In response to this challenge, the all-in-one image restoration (AiOIR) paradigm has emerged, offering a unified framework that adeptly addresses multiple degradation types. These innovative models enhance both convenience and versatility by adaptively learning degradation-specific features while simultaneously leveraging shared knowledge across diverse corruptions. In this review, we delve into the AiOIR methodologies, emphasizing their architecture innovations and learning paradigm and offering a systematic review of prevalent approaches. We systematically categorize prevalent approaches and critically assess the challenges these models encounter, proposing future research directions to advance this dynamic field. Our paper begins with an introduction to the foundational concepts of AiOIR models, followed by a categorization of cutting-edge designs based on factors such as prior knowledge and generalization capability. Next, we highlight key advancements in AiOIR, aiming to inspire further inquiry and innovation within the community. To facilitate a robust evaluation of existing methods, we collate and summarize commonly used datasets, implementation details, and evaluation metrics. Additionally, we present an objective comparison of open-sourced methods, providing valuable insights for researchers and practitioners alike. This paper stands as the first comprehensive and insightful review of AiOIR. A related repository is available at https://github.com/Harbinzzy/All-in-One-Image-Restoration-Survey.
Degrees of Freedom of New General Relativity: Type 2, Type 3, Type 5, and Type 8
2024-10-19
Kyosuke Tomonari, Daniel Blixt
We investigate the degrees of freedom of new general relativity. This theory is a three-parameter theory and is classified into nine irreducible types according to the rotation symmetry of $SO(3)$ on each leaf of ADM-foliation. In this work, we focus on unveiling the degrees of freedom of the physically interesting types of NGR: Type 2, Type 3, Type 5, and Type 8, which contain the gravitational propagating degrees of freedom. First, we revisit the theory based on the gauge approach to gravity and reformulate the Lagrangian of the theory. Second, we review the irreducible decomposition of the theory while focusing on the Hamiltonian and the primary constraints in each type. Third, we perform the Dirac-Bergmann analysis to unveil the degrees of freedom of the theory in the case of Type 2, Type 3, Type 5, and Type 8. We find a novel new behavior of constraints in Type 8, which is classified as second-class but not to determine any Lagrange multipliers and to provide the gauge invariance of the theory under the satisfaction of a specific condition of the multipliers. The degrees of freedom of Type 2, Type 3, and Type 5 are unveiled as six, five, and seven, respectively. The degrees of freedom of Type 8 is either four under a specific condition to the Lagrange multipliers or six in the generic case. Finally, we conclude this work with several future perspectives.
Testing the Efficacy of Hyperparameter Optimization Algorithms in Short-Term Load Forecasting
2024-10-19
Tugrul Cabir Hakyemez, Omer Adar
Accurate forecasting of electrical demand is essential for maintaining a stable and reliable power grid, optimizing the allocation of energy resources, and promoting efficient energy consumption practices. This study investigates the effectiveness of five hyperparameter optimization (HPO) algorithms -- Random Search, Covariance Matrix Adaptation Evolution Strategy (CMA--ES), Bayesian Optimization, Partial Swarm Optimization (PSO), and Nevergrad Optimizer (NGOpt) across univariate and multivariate Short-Term Load Forecasting (STLF) tasks. Using the Panama Electricity dataset (n=48,049), we evaluate HPO algorithms' performances on a surrogate forecasting algorithm, XGBoost, in terms of accuracy (i.e., MAPE, $R^2$) and runtime. Performance plots visualize these metrics across varying sample sizes from 1,000 to 20,000, and Kruskal--Wallis tests assess the statistical significance of the performance differences. Results reveal significant runtime advantages for HPO algorithms over Random Search. In univariate models, Bayesian optimization exhibited the lowest accuracy among the tested methods. This study provides valuable insights for optimizing XGBoost in the STLF context and identifies areas for future research.
Adanonymizer: Interactively Navigating and Balancing the Duality of Privacy and Output Performance in Human-LLM Interaction
2024-10-19
Shuning Zhang, Xin Yi, Haobin Xing, Lyumanshan Ye, Yongquan Hu, Hewu Li
Current Large Language Models (LLMs) cannot support users to precisely balance privacy protection and output performance during individual consultations. We introduce Adanonymizer, an anonymization plug-in that allows users to control this balance by navigating a trade-off curve. A survey (N=221) revealed a privacy paradox, where users frequently disclosed sensitive information despite acknowledging privacy risks. The study further demonstrated that privacy risks were not significantly correlated with model output performance, highlighting the potential to navigate this trade-off. Adanonymizer normalizes privacy and utility ratings by type and automates the pseudonymization of sensitive terms based on user preferences, significantly reducing user effort. Its 2D color palette interface visualizes the privacy-utility trade-off, allowing users to adjust the balance by manipulating a point. An evaluation (N=36) compared Adanonymizer with ablation methods and differential privacy techniques, where Adanonymizer significantly reduced modification time, achieved better perceived model performance and overall user preference.
2024-10-19
Mengnan Zhao, Lihe Zhang, Jingwen Ye, Huchuan Lu, Baocai Yin, Xinchao Wang
Adversarial training (AT) refers to integrating adversarial examples -- inputs altered with imperceptible perturbations that can significantly impact model predictions -- into the training process. Recent studies have demonstrated the effectiveness of AT in improving the robustness of deep neural networks against diverse adversarial attacks. However, a comprehensive overview of these developments is still missing. This survey addresses this gap by reviewing a broad range of recent and representative studies. Specifically, we first describe the implementation procedures and practical applications of AT, followed by a comprehensive review of AT techniques from three perspectives: data enhancement, network design, and training configurations. Lastly, we discuss common challenges in AT and propose several promising directions for future research.
Revisiting the Velocity Dispersion-Size Relation in Molecular Cloud Structures
2024-10-19
Haoran Feng, Zhiwei Chen, Zhibo Jiang, Yuehui Ma, Yang Yang, Shuling Yu, Dongqing Ge, Wei Zhou, Fujun Du, Chen Wang, Shiyu Zhang, Yang Su, Ji Yang
Structures in molecular ISM are observed to follow a power-law relation between the velocity dispersion and spatial size, known as Larson's first relation, which is often attributed to the turbulent nature of molecular ISM and imprints the dynamics of molecular cloud structures. Using the ${}^{13}\mathrm{CO}~(J=1-0)$ data from the Milky Way Imaging Scroll Painting survey, we built a sample with 360 structures having relatively accurate distances obtained from either the reddened background stars with Gaia parallaxes or associated maser parallaxes, spanning from $0.4$ to $\sim 15~\mathrm{kpc}$. Using this sample and about 0.3 million pixels, we analyzed the correlations between velocity dispersion, surface/column density, and spatial scales. Our structure-wise results show power-law indices smaller than 0.5 in both the $\sigma_v$-$R_{\mathrm{eff}}$ and $\sigma_v$-$R_{\mathrm{eff}} \cdot \Sigma$ relations. In the pixel-wise results, the $\sigma_v^{\mathrm{pix}}$ is statistically scaling with the beam physical size ($R_{\mathrm{s}} \equiv \Theta D/2$) in form of $\sigma_v^{\mathrm{pix}} \propto R_{\mathrm{s}}^{0.43 \pm 0.03}$. Meanwhile, $\sigma_v^{\mathrm{pix}}$ in the inner Galaxy is statistically larger than the outer side. We also analyzed correlations between $\sigma_v^{\mathrm{pix}}$ and the $\mathrm{H_2}$ column density $N(\mathrm{H_2})$, finding that $\sigma_v^{\mathrm{pix}}$ stops increasing with $N(\mathrm{H_2})$ after $\gtrsim 10^{22}~{\mathrm{cm^{-2}}}$. The structures with and without high-column-density ($> 10^{22}~\mathrm{cm^{-2}}$) pixels show different $\sigma_v^{\mathrm{pix}} \propto N(\mathrm{H_2})^{\xi}$ relations, where the mean (std) $\xi$ values are $0.38~(0.14)$ and $0.62~(0.27)$, respectively.
In the rapidly evolving field of conversational AI, Ontology Expansion (OnExp) is crucial for enhancing the adaptability and robustness of conversational agents. Traditional models rely on static, predefined ontologies, limiting their ability to handle new and unforeseen user needs. This survey paper provides a comprehensive review of the state-of-the-art techniques in OnExp for conversational understanding. It categorizes the existing literature into three main areas: (1) New Intent Discovery, (2) New Slot-Value Discovery, and (3) Joint OnExp. By examining the methodologies, benchmarks, and challenges associated with these areas, we highlight several emerging frontiers in OnExp to improve agent performance in real-world scenarios and discuss their corresponding challenges. This survey aspires to be a foundational reference for researchers and practitioners, promoting further exploration and innovation in this crucial domain.
Wave (from) Polarized Light Learning (WPLL) method: high resolution spatio-temporal measurements of water surface waves in laboratory setups
2024-10-19
Noam Ginio, Michael Lindenbaum, Barak Fishbain, Dan Liberzon
Effective spatio-temporal measurements of water surface elevation (water waves) in laboratory experiments are crucial for scientific and engineering research. Existing techniques are often cumbersome, computationally heavy and generally suffer from limitations in wavenumber/frequency response. To address these challenges, we propose Wave (from) Polarized Light Learning (WPLL), a learning based remote sensing method for laboratory implementation, capable of inferring surface elevation and slope maps in high resolution. The method uses the polarization properties of light reflected from the water surface. The WPLL uses a deep neural network (DNN) model that approximates the water surface slopes from the polarized light intensities. Once trained on simple monochromatic wave trains, the WPLL is capable of producing high-resolution and accurate 2D reconstruction of the water surface slopes and elevation in a variety of irregular wave fields. The method's robustness is demonstrated by showcasing its high wavenumber/frequency response, its ability to reconstruct wave fields propagating at arbitrary angles relative to the camera optical axis, and its computational efficiency. This developed methodology is an accurate and cost-effective near-real time remote sensing tool for laboratory water surface waves measurements, setting the path for upscaling to open sea application for research, monitoring, and short-time forecasting.
Graph foundation models (GFMs) have recently gained significant attention. However, the unique data processing and evaluation setups employed by different studies hinder a deeper understanding of their progress. Additionally, current research tends to focus on specific subsets of graph learning tasks, such as structural tasks, node-level tasks, or classification tasks. As a result, they often incorporate specialized modules tailored to particular task types, losing their applicability to other graph learning tasks and contradicting the original intent of foundation models to be universal. Therefore, to enhance consistency, coverage, and diversity across domains, tasks, and research interests within the graph learning community in the evaluation of GFMs, we propose GFMBench-a systematic and comprehensive benchmark comprising 26 datasets. Moreover, we introduce LangGFM, a novel GFM that relies entirely on large language models. By revisiting and exploring the effective graph textualization principles, as well as repurposing successful techniques from graph augmentation and graph self-supervised learning within the language space, LangGFM achieves performance on par with or exceeding the state of the art across GFMBench, which can offer us new perspectives, experiences, and baselines to drive forward the evolution of GFMs.
Unlike biomarkers in biofluids, airborne biomarkers are dilute and difficult to trace. Detecting diverse airborne biomarkers with sufficient sensitivity typically relies on bulky and expensive equipment like mass spectrometers that remain inaccessible to the general population. Here, we introduce Airborne Biomarker Localization Engine (ABLE), a simple, affordable, and portable platform that can detect both volatile, non-volatile, molecular, and particulate biomarkers in about 15 minutes. ABLE significantly improves gas detection limits by converting dilute gases into droplets by water condensation, producing concentrated aqueous samples that are easy to be tested. Fundamental studies of multiphase condensation revealed unexpected stability in condensate-trapped biomarkers, making ABLE a reliable, accessible, and high-performance system for open-air-based biosensing applications such as non-contact infant healthcare, pathogen detection in public space, and food safety.
Are AI Detectors Good Enough? A Survey on Quality of Datasets With Machine-Generated Texts
2024-10-18
German Gritsai, Anastasia Voznyuk, Andrey Grabovoy, Yury Chekhovich
The rapid development of autoregressive Large Language Models (LLMs) has significantly improved the quality of generated texts, necessitating reliable machine-generated text detectors. A huge number of detectors and collections with AI fragments have emerged, and several detection methods even showed recognition quality up to 99.9% according to the target metrics in such collections. However, the quality of such detectors tends to drop dramatically in the wild, posing a question: Are detectors actually highly trustworthy or do their high benchmark scores come from the poor quality of evaluation datasets? In this paper, we emphasise the need for robust and qualitative methods for evaluating generated data to be secure against bias and low generalising ability of future model. We present a systematic review of datasets from competitions dedicated to AI-generated content detection and propose methods for evaluating the quality of datasets containing AI-generated fragments. In addition, we discuss the possibility of using high-quality generated data to achieve two goals: improving the training of detection models and improving the training datasets themselves. Our contribution aims to facilitate a better understanding of the dynamics between human and machine text, which will ultimately support the integrity of information in an increasingly automated world.
A diverse, overlooked population of Type Ia supernovae exhibiting mid-infrared signatures of delayed circumstellar interaction
2024-10-18
Geoffrey Mo, Kishalay De, Eli Wiston, Nayana A. J., Raffaella Margutti, Danielle Frostig, Jesper Sollerman, Yashvi Sharma, Takashi J. Moriya, Kevin B. Burdge, Jacob Jencson, Viraj R. Karambelkar, Nathan P. Lourie
Type Ia supernovae arise from the thermonuclear explosions of white dwarfs in multiple star systems. A rare sub-class of SNe Ia exhibit signatures of interaction with circumstellar material (CSM), allowing for direct constraints on companion material. While most known events show evidence for dense nearby CSM identified via peak-light spectroscopy (as SNe Ia-CSM), targeted late-time searches have revealed a handful of cases exhibiting delayed CSM interaction with detached shells. Here, we present the first all-sky search for late CSM interaction in SNe Ia using a new image-subtraction pipeline for mid-infrared data from the NEOWISE space telescope. Analyzing a sample of $\approx 8500$ SNe Ia, we report evidence for late-time mid-infrared brightening in six previously overlooked events spanning sub-types SNe Iax, normal SNe Ia, SNe Ia-91T and super-Chandra SNe Ia. Our systematic search doubles the known sample, and suggests that $\gtrsim 0.1$% of SNe Ia exhibit mid-infrared signatures of delayed CSM interaction. The mid-infrared light curves ubiquitously indicate the presence of multiple (or extended) detached CSM shells located at $\gtrsim 10^{16}-10^{17}$ cm, containing $10^{-4}-10^{-2}$ $M_\odot$ of dust, with some sources showing evidence for new dust formation, likely within the cold, dense shell of the ejecta. We do not detect interaction signatures in spectroscopic and radio follow-up; however, the limits are largely consistent with previously confirmed events given the sensitivity and observation phase. Our results highlight that CSM interaction is more prevalent than previously estimated from optical and ultraviolet searches, and that mid-infrared synoptic surveys provide a unique window into this phenomenon.
syren-new: Precise formulae for the linear and nonlinear matter power spectra with massive neutrinos and dynamical dark energy
2024-10-18
Ce Sui, Deaglan J. Bartlett, Shivam Pandey, Harry Desmond, Pedro G. Ferreira, Benjamin D. Wandelt
Current and future large scale structure surveys aim to constrain the neutrino mass and the equation of state of dark energy. We aim to construct accurate and interpretable symbolic approximations to the linear and nonlinear matter power spectra as a function of cosmological parameters in extended $\Lambda$CDM models which contain massive neutrinos and non-constant equations of state for dark energy. This constitutes an extension of the syren-halofit emulators to incorporate these two effects, which we call syren-new (SYmbolic-Regression-ENhanced power spectrum emulator with NEutrinos and $W_0-w_a$). We also obtain a simple approximation to the derived parameter $\sigma_8$ as a function of the cosmological parameters for these models. Our results for the linear power spectrum are designed to emulate CLASS, whereas for the nonlinear case we aim to match the results of EuclidEmulator2. We compare our results to existing emulators and $N$-body simulations. Our analytic emulators for $\sigma_8$, the linear and nonlinear power spectra achieve root mean squared errors of 0.1%, 0.3% and 1.3%, respectively, across a wide range of cosmological parameters, redshifts and wavenumbers. We verify that emulator-related discrepancies are subdominant compared to observational errors and other modelling uncertainties when computing shear power spectra for LSST-like surveys. Our expressions have similar accuracy to existing (numerical) emulators, but are at least an order of magnitude faster, both on a CPU and GPU. Our work greatly improves the accuracy, speed and range of applicability of current symbolic approximations to the linear and nonlinear matter power spectra. We provide publicly available code for all symbolic approximations found.
DRACO-DehazeNet: An Efficient Image Dehazing Network Combining Detail Recovery and a Novel Contrastive Learning Paradigm
2024-10-18
Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus, Daniel Puiu Poenar, Vu Duong
Image dehazing is crucial for clarifying images obscured by haze or fog, but current learning-based approaches is dependent on large volumes of training data and hence consumed significant computational power. Additionally, their performance is often inadequate under non-uniform or heavy haze. To address these challenges, we developed the Detail Recovery And Contrastive DehazeNet, which facilitates efficient and effective dehazing via a dense dilated inverted residual block and an attention-based detail recovery network that tailors enhancements to specific dehazed scene contexts. A major innovation is its ability to train effectively with limited data, achieved through a novel quadruplet loss-based contrastive dehazing paradigm. This approach distinctly separates hazy and clear image features while also distinguish lower-quality and higher-quality dehazed images obtained from each sub-modules of our network, thereby refining the dehazing process to a larger extent. Extensive tests on a variety of benchmarked haze datasets demonstrated the superiority of our approach. The code repository for this work will be available soon.
RAG-ConfusionQA: A Benchmark for Evaluating LLMs on Confusing Questions
2024-10-18
Zhiyuan Peng, Jinming Nian, Alexandre Evfimievski, Yi Fang
Conversational AI agents use Retrieval Augmented Generation (RAG) to provide verifiable document-grounded responses to user inquiries. However, many natural questions do not have good answers: about 25\% contain false assumptions~\cite{Yu2023:CREPE}, and over 50\% are ambiguous~\cite{Min2020:AmbigQA}. RAG agents need high-quality data to improve their responses to confusing questions. This paper presents a novel synthetic data generation method to efficiently create a diverse set of context-grounded confusing questions from a given document corpus. We conduct an empirical comparative evaluation of several large language models as RAG agents to measure the accuracy of confusion detection and appropriate response generation. We contribute a benchmark dataset to the public domain.
The concept of diversity is widely used in various applications: from image or molecule generation to recommender systems. Thus, being able to properly measure diversity is important. This paper addresses the problem of quantifying diversity for a set of objects. First, we make a systematic review of existing diversity measures and explore their undesirable behavior in some cases. Based on this review, we formulate three desirable properties (axioms) of a reliable diversity measure: monotonicity, uniqueness, and continuity. We show that none of the existing measures has all three properties and thus these measures are not suitable for quantifying diversity. Then, we construct two examples of measures that have all the desirable properties, thus proving that the list of axioms is not self-contradicting. Unfortunately, the constructed examples are too computationally complex for practical use, thus we pose an open problem of constructing a diversity measure that has all the listed properties and can be computed in practice.
High-Precision Galaxy Clustering Predictions from Small-Volume Hydrodynamical Simulations via Control Variates
2024-10-18
Alexandra Doytcheva, Filomela V. Gerou, Johannes U. Lange
Cosmological simulations of galaxy formation are an invaluable tool for understanding galaxy formation and its impact on cosmological parameter inference from large-scale structure. However, their high computational cost is a significant obstacle for running simulations that probe cosmological volumes comparable to those analyzed by contemporary large-scale structure experiments. In this work, we explore the possibility of obtaining high-precision galaxy clustering predictions from small-volume hydrodynamical simulations such as MilleniumTNG and FLAMINGO via control variates. In this approach, the hydrodynamical full-physics simulation is paired with a matched low-resolution gravity-only simulation. By learning the galaxy-halo connection from the hydrodynamical simulation and applying it to the gravity-only counterpart, one obtains a galaxy population that closely mimics the one in the more expensive simulation. One can then construct an estimator of galaxy clustering that combines the clustering amplitudes in the small-volume hydrodynamical and gravity-only simulations with clustering amplitudes in a large-volume gravity-only simulation. Depending on the galaxy sample, clustering statistic, and scale, this galaxy clustering estimator can have an effective volume of up to around $100$ times the volume of the original hydrodynamical simulation in the non-linear regime. With this approach, we can construct galaxy clustering predictions from existing simulations that are precise enough for mock analyses of next-generation large-scale structure surveys such as the Dark Energy Spectroscopic Instrument and the Legacy Survey of Space and Time.
Cryptocurrencies, as decentralized digital assets, have experienced rapid growth and adoption, with over 23,000 cryptocurrencies and a market capitalization nearing \$1.1 trillion (about \$3,400 per person in the US) as of 2023. This dynamic market presents significant opportunities and risks, highlighting the need for accurate price prediction models to manage volatility. This chapter comprehensively reviews machine learning (ML) techniques applied to cryptocurrency price prediction from 2014 to 2024. We explore various ML algorithms, including linear models, tree-based approaches, and advanced deep learning architectures such as transformers and large language models. Additionally, we examine the role of sentiment analysis in capturing market sentiment from textual data like social media posts and news articles to anticipate price fluctuations. With expertise in optimizing complex systems and processes, industrial engineers are pivotal in enhancing these models. They contribute by applying principles of process optimization, efficiency, and risk mitigation to improve computational performance and data management. This chapter highlights the evolving landscape of cryptocurrency price prediction, the integration of emerging technologies, and the significant role of industrial engineers in refining predictive models. By addressing current limitations and exploring future research directions, this chapter aims to advance the development of more accurate and robust prediction systems, supporting better-informed investment decisions and more stable market behavior.
Geometric Proof of the Irrationality of Square-Roots for Select Integers
2024-10-18
Zongyun Chen, Steven J. Miller, Chenghan Wu
This paper presents geometric proofs for the irrationality of square roots of select integers, extending classical approaches. Building on known geometric methods for proving the irrationality of sqrt(2), the authors explore whether similar techniques can be applied to other non-square integers. They begin by reviewing well-known results, such as Euclid's proof for the irrationality of sqrt(2), and discuss subsequent geometric extensions for sqrt(3), sqrt(5), and sqrt(6). The authors then introduce new geometric constructions, particularly using hexagons, to prove the irrationality of sqrt(6). Furthermore, the paper investigates the limitations and challenges of extending these geometric methods to triangular numbers. Through detailed geometric reasoning, the authors successfully generalize the approach to several square-free numbers and identify cases where the method breaks down. The paper concludes by inviting further exploration of geometric irrationality proofs for other integers, proposing potential avenues for future work.
Planet formation encompasses processes that span a remarkable 40 magnitudes in mass, ranging from collisions between micron-sized grains inherit from the ISM to accretion of gas by giant planets. The planet formation process takes place in the interior of dusty disks, which offer us only limited observational constraints. Historically, the two main paradigms describing planet formation are the disk instability and core accretion models. In the former giant planets condense directly form a disk massive enough to fragments under its self-gravity. In contrast, the core accretion model follows a bottom-up approach driven by solids. This model includes the direct growth phase, where pebble and possibly planetesimal sized bodies form by surface-driven processes. Alternatively, planetesimal bodies can emerge from instabilities in the solid population, leading to the formation of clumps that ultimately collapse. The later growth phases of the core accretion model involve the creation of a protoplanet out of the planetesimal population, which then accrete smaller bodies|planetesimals or pebbles|before ultimately accreting freely the gas of the disk. In this Chapter I will review these formation mechanisms, aiming, where possible, to build an intuitive understanding from elementary physical principles.
2024-10-18
Felix Koulischer, Johannes Deleu, Gabriel Raya, Thomas Demeester, Luca Ambrogioni
Negative Prompting (NP) is widely utilized in diffusion models, particularly in text-to-image applications, to prevent the generation of undesired features. In this paper, we show that conventional NP is limited by the assumption of a constant guidance scale, which may lead to highly suboptimal results, or even complete failure, due to the non-stationarity and state-dependence of the reverse process. Based on this analysis, we derive a principled technique called Dynamic Negative Guidance, which relies on a near-optimal time and state dependent modulation of the guidance without requiring additional training. Unlike NP, negative guidance requires estimating the posterior class probability during the denoising process, which is achieved with limited additional computational overhead by tracking the discrete Markov Chain during the generative process. We evaluate the performance of DNG class-removal on MNIST and CIFAR10, where we show that DNG leads to higher safety, preservation of class balance and image quality when compared with baseline methods. Furthermore, we show that it is possible to use DNG with Stable Diffusion to obtain more accurate and less invasive guidance than NP.
Optimizing over FP/EDF Execution Times: Known Results and Open Problems
2024-10-18
Enrico Bini
In many use cases the execution time of tasks is unknown and can be chosen by the designer to increase or decrease the application features depending on the availability of processing capacity. If the application has real-time constraints, such as deadlines, then the necessary and sufficient schedulability test must allow the execution times to be left unspecified. By doing so, the designer can then perform optimization of the execution times by picking the schedulable values that minimize any given cost. In this paper, we review existing results on the formulation of both the Fixed Priority and Earliest Deadline First exact schedulability constraints. The reviewed formulations are expressed by a combination of linear constraints, which enables then optimization routines.
This essay reviews a modern understanding of a quantum measurement. Rather than reducing the picture to the observer's experience with quantum system, we try to put it in the context of a broader physical picture. We also attempt to distinguish some basic components and stages of a generic measurement. In this way, we want to understand which aspects of quantum measurements are important to mainstream interpretations of quantum mechanics.
Exploring jets: substructure and flavour tagging in CMS and ATLAS
2024-10-18
Andrea Malara
The identification and characterization of jets are crucial tasks for effectively probing fundamental particle interactions. The ATLAS and CMS experiments have developed cutting-edge techniques to improve jet identification and calibration, employing innovative approaches including advanced neural network architectures, attention-based mechanisms, and adversarial training. These proceedings provide a comprehensive review of the state-of-the-art methods employed by both collaborations, highlighting their similarities, unique strengths, and limitations through a comparative analysis.
Optimizing importance weighting in the presence of sub-population shifts
2024-10-18
Floris Holstege, Bram Wouters, Noud van Giersbergen, Cees Diks
A distribution shift between the training and test data can severely harm performance of machine learning models. Importance weighting addresses this issue by assigning different weights to data points during training. We argue that existing heuristics for determining the weights are suboptimal, as they neglect the increase of the variance of the estimated model due to the finite sample size of the training data. We interpret the optimal weights in terms of a bias-variance trade-off, and propose a bi-level optimization procedure in which the weights and model parameters are optimized simultaneously. We apply this optimization to existing importance weighting techniques for last-layer retraining of deep neural networks in the presence of sub-population shifts and show empirically that optimizing weights significantly improves generalization performance.
Continuous models combining slacks-based measures of efficiency and super-efficiency
2024-10-18
Vicente J. Bolos, Rafael Benitez, Vicente Coll-Serrano
In the framework of data envelopment analysis (DEA), Tone (2001) introduced the slacks-based measure (SBM) of efficiency, which is a nonradial model that incorporates all the slacks of the evaluated decision-making units (DMUs) into their efficiency scores, unlike classical radial efficiency models. Next, Tone (2002) developed the SBM super-efficiency model in order to differentiate and rank efficient DMUs, whose SBM efficiency scores are always $1$. However, as pointed out by Chen (2013), some interpretation problems arise when the so-called super-efficiency projections are weakly efficient, leading to an overestimation of the SBM super-efficiency score. Moreover, this overestimation is closely related to discontinuity issues when implementing SBM super-efficiency in conjunction with SBM efficiency. Chen (2013) and Chen et al. (2019) treated these problems, but they did not arrive to a fully satisfactory solution. In this paper, we review these papers and propose a new complementary score, called composite SBM, that actually fixes the discontinuity problems by counteracting the overestimation of the SBM super-efficiency score. Moreover, we extend the composite SBM model to different orientations and variable returns to scale, and propose additive versions. Finally, we give examples and state some open problems.
We propose a bound preserving Point-Average-Moment PolynomiAl-interpreted (PAMPA) method by blending the third-order construction and first-order construction. The originality of the present construction is that it does not need any explicit reconstruction within each element, and therefore the construction is very flexible. The construction uses a classical blending approach between a first order bound preserving scheme and a high order scheme that is not bound preserving. We show its efficiency on many problems, ranging from scalar to system cases (Euler equations). In both cases, we provide optimal values of the blending parameter. In the system case, we use the recent geometric quasi-linearisation of [Wu and Shu, SIAM Review, 65 (2023), pp. 1031--1073].
First results from the JWST Early Release Science Program Q3D: The Fast Outflow in a Red Quasar at z=0.44
2024-10-18
Weizhe Liu, Sylvain Veilleux, Swetha Sankar, David S. N. Rupke, Nadia L. Zakamska, Dominika Wylezalek, Andrey Vayner, Caroline Bertemes, Yu-Ching Chen, Yuzo Ishikawa, Jenny E. Greene, Timothy Heckman, Guilin Liu, Hsiao-Wen Chen, Dieter Lutz, Sean D. Johnson, Nicole P. H. Nesvadba, Patrick Ogle, Nadiia Diachenko, Andy D. Goulding, Kevin N. Hainline, Fred Hamann, Hui Xian Grace Lim, Nora Lützgendorf, Vincenzo Mainieri, Ryan McCrory, Grey Murphree, Eckhard Sturm, Lillian Whitesell
Quasar feedback may play a key role in the evolution of massive galaxies. The dust-reddened quasar, F2M110648.35$+$480712 at $z = 0.4352$ is one of the few cases at its redshift that exhibits powerful quasar feedback through bipolar outflows. Our new observation with the integral field unit mode of Near-infrared Spectrograph onboard JWST opens a new window to examine this spectacular outflow through Pa$\alpha$ emission line with $\sim$3$\times$ better spatial resolution than previous work. The morphology and kinematics of the Pa$\alpha$ nebula confirm the existence of a bipolar outflow extending on a scale of $\sim$17$\times$14 kpc and with a velocity reaching $\sim$1100 km s$^{-1}$. The higher spatial resolution of our new observation leads to more reliable measurements of outflow kinematics. Considering only the spatially resolved outflow and assuming an electron density of 100 cm$^{-2}$, the mass, momentum and kinetic energy outflow rates are $\sim$50-210 M$_{\odot}$ yr$^{-1}$, $\sim$0.3-1.7$\times$10$^{36}$ dynes ($\sim$14-78\% of the quasar photon momentum flux) and $\sim$0.16-1.27$\times$10$^{44}$ erg s$^{-1}$ ($\sim$0.02-0.20\% of the quasar bolometric luminosity), respectively. The local instantaneous outflow rates generally decrease radially. We infer that the quasar is powerful enough to drive the outflow, while stellar processes cannot be overlooked as a contributing energy source. The mass outflow rate is $\sim$0.4-1.5 times the star formation rate, and the ratio of kinetic energy outflow rate to the quasar bolometric luminosity is comparable to the minimum value required for negative quasar feedback in simulations. This outflow may help regulate the star formation activity within the system to some extent.
Takin-ADA: Emotion Controllable Audio-Driven Animation with Canonical and Landmark Loss Optimization
2024-10-18
Bin Lin, Yanzhen Yu, Jianhao Ye, Ruitao Lv, Yuguang Yang, Ruoye Xie, Pan Yu, Hongbin Zhou
Existing audio-driven facial animation methods face critical challenges, including expression leakage, ineffective subtle expression transfer, and imprecise audio-driven synchronization. We discovered that these issues stem from limitations in motion representation and the lack of fine-grained control over facial expressions. To address these problems, we present Takin-ADA, a novel two-stage approach for real-time audio-driven portrait animation. In the first stage, we introduce a specialized loss function that enhances subtle expression transfer while reducing unwanted expression leakage. The second stage utilizes an advanced audio processing technique to improve lip-sync accuracy. Our method not only generates precise lip movements but also allows flexible control over facial expressions and head motions. Takin-ADA achieves high-resolution (512x512) facial animations at up to 42 FPS on an RTX 4090 GPU, outperforming existing commercial solutions. Extensive experiments demonstrate that our model significantly surpasses previous methods in video quality, facial dynamics realism, and natural head movements, setting a new benchmark in the field of audio-driven facial animation.
Good Parenting is all you need -- Multi-agentic LLM Hallucination Mitigation
2024-10-18
Ted Kwartler, Matthew Berman, Alan Aqrawi
This study explores the ability of Large Language Model (LLM) agents to detect and correct hallucinations in AI-generated content. A primary agent was tasked with creating a blog about a fictional Danish artist named Flipfloppidy, which was then reviewed by another agent for factual inaccuracies. Most LLMs hallucinated the existence of this artist. Across 4,900 test runs involving various combinations of primary and reviewing agents, advanced AI models such as Llama3-70b and GPT-4 variants demonstrated near-perfect accuracy in identifying hallucinations and successfully revised outputs in 85% to 100% of cases following feedback. These findings underscore the potential of advanced AI models to significantly enhance the accuracy and reliability of generated content, providing a promising approach to improving AI workflow orchestration.
Parallel Writing of Nested Data in Columnar Formats
2024-10-18
Jonas Hahnfeld, Jakob Blomer, Thorsten Kollegger
High Energy Physics (HEP) experiments, for example at the Large Hadron Collider (LHC) at CERN, store data at exabyte scale in sets of files. They use a binary columnar data format by the ROOT framework, that also transparently compresses the data. In this format, cells are not necessarily atomic but they may contain nested collections of variable size. The fact that row and block sizes are not known upfront makes it challenging to implement efficient parallel writing. In particular, the data cannot be organized in a regular grid where it is possible to precompute indices and offsets for independent writing. In this paper, we propose a scalable approach to efficient multithreaded writing of nested data in columnar format into a single file. Our approach removes the bottleneck of a single writer while staying fully compatible with the compressed, columnar, variably row-sized data representation. We discuss our design choices and the implementation of scalable parallel writing for ROOT's RNTuple format. An evaluation of our approach shows perfect scalability only limited by storage bandwidth for a synthetic benchmark. Finally we evaluate the benefits for a real-world application of dataset skimming.
Digital Humanities in the TIME-US Project: Richness and Contribution of Interdisciplinary Methods for Labour History
2024-10-18
Marie Puren
In 2015, the Annales journal, traditionally open to interdisciplinary approaches in history, referred to 'the current historiographical moment [as] call [ing] for an experimentation of approaches'. 1 Although this observation did not exclusively refer to the new possibilities offered by the technological advancements of the time -particularly in the field of artificial intelligence 2 -it was nonetheless motivated by these rapid and numerous changes, which also affect the historiographical landscape. A year earlier, St\'ephane Lamass\'e and Philippe Rygiel spoke of the 'new frontiers of the historian', frontiers opened a few years earlier by the realisation of the unprecedented impact of new technologies on historical practices, leading to a 'mutation des conditions de production et de diffusion des connaissances historiques, voire de la nature de celles-ci' ('transformation of the conditions of production and dissemination of historical knowledge, and even the nature of this knowledge'). 3 It was in this fertile ground, conducive to the cross-fertilisation of approaches, that the TIME-US project was born in 2016. TIME-US is directly the result of this awareness and reflects the transformations induced by major technological advancements, disrupting not only our daily practices but also our historical practices. 1 Annales 2015, 216. 2 For example, convolutional neural networks, which have revolutionised the field of artificial intelligence, began to gain popularity just before the 2010s. 3 Translated by the author. Lamass\'e and Rygiel 2014. To quantify women's work in the past, labour historians cannot rely on the classic sources of their discipline, which allow to produce large statistical data series, systematically treatable in the form of databases. What to do when such data are not available? Should the task simply be abandoned? As Maria {\AA}gren points out, the invisibility of women's participation in the labour market does not mean non-existence 8 ; there must therefore be traces of it. To quantify women's economic activity, Sara Horrell and Jane Humphries, for example, turned to household budgets from 59 different sources (from Parliamentary Papers to autobiographical texts), which had never before been systematically used to identify women's work patterns and their contribution to family income. 9 In her study A Bitter Living: Women, Markets, and Social Capital in Early Modern Germany published in 2003, Sheilagh Ogilvie used information contained in court records to identify activities carried out by women and the time spent on these activities. Court records were not intended to record such information; yet, in their testimonies, witnesses often described in detail the activities they were engaged in while a crime was unfolding before their eyes. Sheilagh Ogilvie thus identified nearly 3000 such observations. 10 These works have opened two main avenues for the TIME-US project. First, making already digitised sources accessible in homogeneous corpora. 11 Following the example of previous research, TIME-US mobilised varied sources containing traces of professional activities carried out by women in France during the period studied: these include both printed (posters and petitions, working-class newspapers, and contemporary surveys on workers) and handwritten sources (labour court decisions, police reports, company archives, personal archives, surveys, petitions). 12 One of the project's objectives was to gather and 8 {\AA}gren 2018a, 144. 9 Horrell and Humphries 1995.
HD 28185 Revisited: An Outer Planet, Instead of a Brown Dwarf, On a Saturn-like Orbit
2024-10-18
Alexander Venner, Qier An, Chelsea X. Huang, Timothy D. Brandt, Robert A. Wittenmyer, Andrew Vanderburg
As exoplanet surveys reach ever-higher sensitivities and durations, planets analogous to the solar system giant planets are increasingly within reach. HD 28185 is a Sun-like star known to host a $m\sin i=6 M_J$ planet on an Earth-like orbit; more recently, a brown dwarf with a more distant orbit has been claimed. In this work we present a comprehensive reanalysis of the HD 28185 system, based on 22 years of radial velocity observations and precision Hipparcos-Gaia astrometry. We confirm the previous characterisation of HD 28185 b as a temperate giant planet, with its $385.92^{+0.06}_{-0.07}$ day orbital period giving it an Earth-like incident flux. In contrast, we substantially revise the parameters of HD 28185 c; with a new mass of $m=6.0\pm0.6 M_J$ we reclassify this companion as a super-jovian planet. HD 28185 c has an orbital period of $24.9^{+1.3}_{-1.1}$ years, a semi-major axis of $8.50^{+0.29}_{-0.26}$ AU, and a modest eccentricity of $0.15\pm0.04$, resulting in one of the most Saturn-like orbits of any known exoplanet. HD 28185 c lies at the current intersection of detection limits for RVs and direct imaging, and highlights how the discovery of giant planets at $\approx$10 AU separations is becoming increasingly routine.
In industrial control systems, the generation and verification of Programmable Logic Controller (PLC) code are critical for ensuring operational efficiency and safety. While Large Language Models (LLMs) have made strides in automated code generation, they often fall short in providing correctness guarantees and specialized support for PLC programming. To address these challenges, this paper introduces Agents4PLC, a novel framework that not only automates PLC code generation but also includes code-level verification through an LLM-based multi-agent system. We first establish a comprehensive benchmark for verifiable PLC code generation area, transitioning from natural language requirements to human-written-verified formal specifications and reference PLC code. We further enhance our `agents' specifically for industrial control systems by incorporating Retrieval-Augmented Generation (RAG), advanced prompt engineering techniques, and Chain-of-Thought strategies. Evaluation against the benchmark demonstrates that Agents4PLC significantly outperforms previous methods, achieving superior results across a series of increasingly rigorous metrics. This research not only addresses the critical challenges in PLC programming but also highlights the potential of our framework to generate verifiable code applicable to real-world industrial applications.
Speciesism in Natural Language Processing Research
2024-10-18
Masashi Takeshita, Rafal Rzepka
Natural Language Processing (NLP) research on AI Safety and social bias in AI has focused on safety for humans and social bias against human minorities. However, some AI ethicists have argued that the moral significance of nonhuman animals has been ignored in AI research. Therefore, the purpose of this study is to investigate whether there is speciesism, i.e., discrimination against nonhuman animals, in NLP research. First, we explain why nonhuman animals are relevant in NLP research. Next, we survey the findings of existing research on speciesism in NLP researchers, data, and models and further investigate this problem in this study. The findings of this study suggest that speciesism exists within researchers, data, and models, respectively. Specifically, our survey and experiments show that (a) among NLP researchers, even those who study social bias in AI, do not recognize speciesism or speciesist bias; (b) among NLP data, speciesist bias is inherent in the data annotated in the datasets used to evaluate NLP models; (c) OpenAI GPTs, recent NLP models, exhibit speciesist bias by default. Finally, we discuss how we can reduce speciesism in NLP research.
An automated method to detect and characterise semi-resolved star clusters
2024-10-18
Amy E. Miller, Zachary Slepian, Elizabeth A. Lada, Richard de Grijs, Maria-Rosa L. Cioni, Mark R. Krumholz, Amir E. Bazkiaei, Valentin D. Ivanov, Joana M. Oliveira, Vincenzo Ripepi, Jacco Th. van Loon
We present a novel method for automatically detecting and characterising semi-resolved star clusters: clusters where the observational point-spread function (PSF) is smaller than the cluster's radius, but larger than the separations between individual stars. We apply our method to a 1.77 deg$^2$ field located in the Large Magellanic Cloud (LMC) using the VISTA survey of the Magellanic Clouds (VMC), which surveyed the LMC in the $YJK_\text{s}$ bands. Our approach first models the position-dependent PSF to detect and remove point sources from deep $K_\text{s}$ images; this leaves behind extended objects such as star clusters and background galaxies. We then analyse the isophotes of these extended objects to characterise their properties, perform integrated photometry, and finally remove any spurious objects this procedure identifies. We demonstrate our approach in practice on a deep VMC $K_\text{s}$ tile that contains the most active star-forming regions in the LMC: 30 Doradus, N158, N159, and N160. We select this tile because it is the most challenging for automated techniques due both to crowding and nebular emission. We detect 682 candidate star clusters, with an estimated contamination rate of 13% from background galaxies and chance blends of physically unrelated stars. We compare our candidates to publicly available James Webb Space Telescope data and find that at least 80% of our detections appear to be star clusters.
Decentralized Finance: Impact on Financial Services and required DeFi Literacy in 2034
2024-10-18
Daniel Liebau
This study explores the potential future impact of Decentralized Finance (DeFi) on the financial services industry and the competencies financial professionals will require in a decade from now, in 2034. Using a survey of 109 experts from industry, academia, and regulatory bodies, the research highlights growing confidence in DeFi's potential to reshape critical areas such as risk management and operations. Over 40% of respondents anticipate high levels of DeFi adoption by 2034. However, the study identifies critical issues that need to be addressed, particularly in the areas of data management & privacy, and security. These concerns, alongside regulatory challenges, underscore the need for financial institutions to prepare carefully. The findings also suggest that strategic competencies, sector-specific domain expertise, and technological skills will become increasingly vital. The insights offered are valuable for regulators, policymakers, and industry professionals, emphasizing the need for continuous upskilling to remain competitive in an evolving financial services landscape.
Towards Faithful Natural Language Explanations: A Study Using Activation Patching in Large Language Models
2024-10-18
Wei Jie Yeo, Ranjan Satapthy, Erik Cambria
Large Language Models (LLMs) are capable of generating persuasive Natural Language Explanations (NLEs) to justify their answers. However, the faithfulness of these explanations should not be readily trusted at face value. Recent studies have proposed various methods to measure the faithfulness of NLEs, typically by inserting perturbations at the explanation or feature level. We argue that these approaches are neither comprehensive nor correctly designed according to the established definition of faithfulness. Moreover, we highlight the risks of grounding faithfulness findings on out-of-distribution samples. In this work, we leverage a causal mediation technique called activation patching, to measure the faithfulness of an explanation towards supporting the explained answer. Our proposed metric, Causal Faithfulness quantifies the consistency of causal attributions between explanations and the corresponding model outputs as the indicator of faithfulness. We experimented across models varying from 2B to 27B parameters and found that models that underwent alignment tuning tend to produce more faithful and plausible explanations. We find that Causal Faithfulness is a promising improvement over existing faithfulness tests by taking into account the model's internal computations and avoiding out of distribution concerns that could otherwise undermine the validity of faithfulness assessments. We release the code in \url{https://github.com/wj210/Causal-Faithfulness}
On Finding Black Holes in Photometric Microlensing Surveys
2024-10-18
Zofia Kaczmarek, Peter McGill, Scott E. Perkins, William A. Dawson, Macy Huston, Ming-Feng Ho, Natasha S. Abrams, Jessica R. Lu
There are expected to be millions of isolated black holes in the Galaxy resulting from the death of massive stars. Measuring the abundance and properties of this remnant population would shed light on the end stages of stellar evolution and the evolution paths of black hole systems. Detecting isolated black holes is currently only possible via gravitational microlensing which has so far yielded one definitive detection. The difficulty in finding microlensing black holes lies in having to choose a small subset of events based on characteristics of their lightcurves to allocate expensive and scarce follow-up resources to confirm the identity of the lens. Current methods either rely on simple cuts in parameter space without using the full distribution information or are only effective on a small subsets of events. In this paper we present a new lens classification method. The classifier takes in posterior constraints on lightcurve parameters and combines them with a Galactic simulation to estimate the lens class probability. This method is flexible and can be used with any set of microlensing lightcurve parameters making it applicable to large samples of events. We make this classification framework available via the popclass python package. We apply the classifier to $\sim10,000$ microlensing events from the OGLE survey and find $23$ high-probability black hole candidates. Our classifier also suggests that the only known isolated black hole is an observational outlier according to current Galactic models and allocation of astrometric follow-up on this event was a high-risk strategy.
Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction
2024-10-18
Devlina Chakravarty, Myeongsang Lee, Lauren L. Porter
In recent years, advances in artificial intelligence (AI) have transformed structural biology, particularly protein structure prediction. Though AI-based methods, such as AlphaFold (AF), often predict single conformations of proteins with high accuracy and confidence, predictions of alternative folds are often inaccurate, low-confidence, or simply not predicted at all. Here, we review three blind spots that alternative conformations reveal about AF-based protein structure prediction. First, proteins that assume conformations distinct from their training-set homologs can be mispredicted. Second, AF overrelies on its training set to predict alternative conformations. Third, degeneracies in pairwise representations can lead to high-confidence predictions inconsistent with experiment. These weaknesses suggest approaches to predict alternative folds more reliably.
Circumplanetary Disks are Rare around Planets at Large Orbital Radii: A Parameter Survey of Flow Morphology around Giant Planets
2024-10-18
Sabina Sagynbayeva, Rixin Li, Aleksandra Kuznetsova, Zhaohuan Zhu, Yan-Fei Jiang, Philip J. Armitage
We investigate how the formation and structure of circumplanetary disks (CPDs) varies with planet mass and protoplanetary disk aspect ratio. Using static mesh refinement and a near-isothermal equation of state, we perform a small parameter survey of hydrodynamic simulations with parameters appropriate for disk-embedded protoplanets at moderate to large orbital radii. We find that CPD formation occurs along a continuum, with ``diskiness'' increasing smoothly with planetary mass and decreasing disk aspect ratio. As expected from disk hydrostatic equilibrium arguments, the transition from envelope-dominated to disk-dominated structures is determined to first order by the ratio of the planetary Hill sphere radius to the disk scale height, but planets need to be significantly super-thermal to host classical rotationally supported CPDs. The circularization radius of inflowing gas (as a fraction of the Hill sphere radius) shows an approximately quadratic power-law scaling with the ratio of planetary mass to the thermal mass. Compared to more physically complete radiation hydrodynamic simulations, our runs almost maximize the possibility for classical CPD formation, and hence define a plausible necessary condition for CPDs. The low abundance of detected CPDs in disks where planetary companions are inferred from substructure data may be due to a combination of the large scale height of the protoplanetary disk, and a low frequency of sufficiently massive protoplanets. Unless their CPDs cool below the local protoplanetary disk temperature, most of the wide-orbit giant planet population will be embedded in quasi-spherical envelopes that are hard to detect. Disks, and satellite systems, are more likely to form around smaller orbital separation planets.
Energy-Preserving Coupling of Explicit Particle-In-Cell with Monte Carlo Collisions
2024-10-18
Jean-Luc Vay, Justin Ray Angus, Olga Shapoval, Remi Lehe, David Grote, Axel Huebl
The Particle-In-Cell (PIC) and Monte Carlo Collisions (MCC) methods are workhorses of many numerical simulations of physical systems. Recently, it was pointed out that, while the two methods can be exactly - or nearly - energy-conserving independently, combining the two is leading to anomalous numerical heating. This paper reviews the standard explicit PIC-MCC algorithm, elucidates the origins of the anomalous numerical heating and explains how to couple the two methods such that the anomalous numerical heating is avoided.
SMILES: Discovery of Higher Ionizing Photon Production Efficiency in Overdense Regions
2024-10-18
Yongda Zhu, Stacey Alberts, Jianwei Lyu, Jane Morrison, George H. Rieke, Yang Sun, Jakob M. Helton, Zhiyuan Ji, Rachana Bhatawdekar, Nina Bonaventura, Andrew J. Bunker, Xiaojing Lin, Marcia J. Rieke, Pierluigi Rinaldi, Irene Shivaei, Christopher N. A. Willmer, Junyu Zhang
The topology of reionization and the environments where galaxies efficiently produce ionizing photons are key open questions. For the first time, we investigate the correlation between ionizing photon production efficiency, $\xi_{\rm ion}$, and galaxy overdensity, $\log(1+\delta)$. We analyze the ionizing properties of 93 galaxies between $0.7 < z < 6.9$ using JWST NIRSpec medium-resolution spectra from the Systematic Mid-infrared Instrument (MIRI) Legacy Extragalactic Survey (SMILES) program. Among these, 67 galaxies have H$\alpha$ coverage, spanning $0.7 < z < 3.7$. The galaxy overdensity, $\log(1+\delta)$, is measured using the JADES photometric catalog, which covers the SMILES footprint. For the subset with H$\alpha$ coverage, we find that $\log\xi_{\rm ion}$ is positively correlated with $\log(1+\delta)$, with a slope of $0.94_{-0.46}^{+0.46}$. Additionally, the mean $\xi_{\rm ion}$ for galaxies in overdense regions ($\log(1+\delta) > 0.1$) is 2.43 times that of galaxies in lower density regions ($\log(1+\delta) < 0.1$). This strong correlation is found to be independent of redshift evolution. Furthermore, our results confirm the robust correlations between $\xi_{\rm ion}$ and the rest-frame equivalent widths of the [O III] or H$\alpha$ emission lines. Our results suggest that galaxies in high-density regions are efficient producers of ionizing photons.
The disappearance of a massive star marking the birth of a black hole in M31
2024-10-18
Kishalay De, Morgan MacLeod, Jacob E. Jencson, Elizabeth Lovegrove, Andrea Antoni, Erin Kara, Mansi M. Kasliwal, Ryan M. Lau, Abraham Loeb, Megan Masterson, Aaron M. Meisner, Christos Panagiotou, Eliot Quataert, Robert Simcoe
Stellar mass black holes are formed from the terminal collapse of massive stars if the ensuing neutrino shock is unable to eject the stellar envelope. Direct observations of black hole formation remain inconclusive. We report observations of M31-2014-DS1, a massive, hydrogen-depleted supergiant in the Andromeda galaxy identified via a mid-infrared brightening in 2014. Its total luminosity remained nearly constant for the subsequent thousand days, before fading dramatically over the next thousand days by $\gtrsim 10\times$ and $\gtrsim 10^4\times$ in total and visible light, respectively. Together with the lack of a detected optical outburst, the observations are explained by the fallback of the stellar envelope into a newly formed black hole, moderated by the injection of a $\sim 10^{48}$ erg shock. Unifying these observations with a candidate in NGC 6946, we present a concordant picture for the birth of stellar mass black holes from stripped massive stars.
Following the Tidal Trail: A History of Modeling the Magellanic Stream
2024-10-18
Scott Lucchini
The formation of the Magellanic Stream has puzzled astronomers for decades. In this review, we outline the history of our understanding of the Magellanic System highlighting key observations that have revolutionized thinking of its evolution. We also walk through the major models and theoretical advances that have led to our current paradigm - (1) the LMC and SMC have just had their first pericentric passage around the Milky Way, having approached recently as a bound pair; (2) the LMC and SMC have had several tidal interactions in which material has been stripped out into the Trailing Stream and Leading Arm; (3) the LMC hosted an ionized gas circumgalactic medium which envelops the Clouds and the neutral Stream today, providing the bulk of the associated mass; and (4) the MW's circumgalactic gas provides strong ram pressure and hydrodynamic forces to shape the morphology of the Magellanic System including the formation of a bow shock due to the LMC's supersonic approach.
Ultralight dark photon dark matter features distinctive cosmological and astrophysical signatures and is also supported by a burgeoning direct-detection program searching for its kinetic mixing with the ordinary photon over a wide mass range. Dark photons, however, cannot necessarily constitute the dark matter in all of this parameter space. In minimal models where the dark photon mass arises from a dark Higgs mechanism, early Universe dynamics can easily breach the regime of validity of the low-energy effective theory for a massive vector field. In the process, the dark sector can collapse into a cosmic string network, precluding dark photons as viable dark matter. We establish the general conditions under which dark photon production avoids significant backreaction on the dark Higgs and identify regions of parameter space that naturally circumvent these constraints. After surveying implications for known dark photon production mechanisms, we propose novel models that set well-motivated experimental targets across much of the accessible parameter space. We also discuss complementary cosmological and astrophysical signatures that can probe the dark sector physics responsible for dark photon production.
Reconstructing a complete object from its parts is a fundamental problem in many scientific domains. The purpose of this article is to provide a systematic survey on this topic. The reassembly problem requires understanding the attributes of individual pieces and establishing matches between different pieces. Many approaches also model priors of the underlying complete object. Existing approaches are tightly connected problems of shape segmentation, shape matching, and learning shape priors. We provide existing algorithms in this context and emphasize their similarities and differences to general-purpose approaches. We also survey the trends from early non-deep learning approaches to more recent deep learning approaches. In addition to algorithms, this survey will also describe existing datasets, open-source software packages, and applications. To the best of our knowledge, this is the first comprehensive survey on this topic in computer graphics.
Medical AI for Early Detection of Lung Cancer: A Survey
2024-10-18
Guohui Cai, Ying Cai, Zeyu Zhang, Yuanzhouhan Cao, Lin Wu, Daji Ergu, Zhinbin Liao, Yang Zhao
Lung cancer remains one of the leading causes of morbidity and mortality worldwide, making early diagnosis critical for improving therapeutic outcomes and patient prognosis. Computer-aided diagnosis (CAD) systems, which analyze CT images, have proven effective in detecting and classifying pulmonary nodules, significantly enhancing the detection rate of early-stage lung cancer. Although traditional machine learning algorithms have been valuable, they exhibit limitations in handling complex sample data. The recent emergence of deep learning has revolutionized medical image analysis, driving substantial advancements in this field. This review focuses on recent progress in deep learning for pulmonary nodule detection, segmentation, and classification. Traditional machine learning methods, such as SVM and KNN, have shown limitations, paving the way for advanced approaches like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Generative Adversarial Networks (GAN). The integration of ensemble models and novel techniques is also discussed, emphasizing the latest developments in lung cancer diagnosis. Deep learning algorithms, combined with various analytical techniques, have markedly improved the accuracy and efficiency of pulmonary nodule analysis, surpassing traditional methods, particularly in nodule classification. Although challenges remain, continuous technological advancements are expected to further strengthen the role of deep learning in medical diagnostics, especially for early lung cancer detection and diagnosis. A comprehensive list of lung cancer detection models reviewed in this work is available at https://github.com/CaiGuoHui123/Awesome-Lung-Cancer-Detection
Enhancing Precision of Signal Correction in PVES Experiments: The Impact of Bayesian Analysis on the Results of the QWeak and MOLLER Experiments
2024-10-18
Elham Gorgannejad, Wouter Deconinck, David S. Armstrong
The precise measurement of parity-violating asymmetries in parity-violating electron scattering experiments is a powerful tool for probing new physics beyond the Standard Model. Achieving the expected precision requires both experimental and post-processing signal corrections. This includes using auxiliary detectors to distinguish the main signal from background signals and implementing post-measurement corrections, such as the Bayesian statistics method, to address uncontrolled factors during the experiments. Asymmetry values in the scattering of electrons off proton targets in QWeak and P2 and off electron targets in MOLLER are influenced by detector array configurations, beam polarization angles, and beam spin variations. The Bayesian framework refines full probabilistic models to account for all necessary factors, thereby extracting asymmetry values and the underlying physics under specified conditions. For the QWeak experiment, a reanalysis of the inelastic asymmetry measurement using the Bayesian method has yielded a closer fit to measured asymmetries, with uncertainties reduced by 40\% compared to the Monte Carlo minimization method. This approach was successfully applied to simulated data for the MOLLER experiment and is predicted to be similarly effective in P2.
Machine Learning Aided Modeling of Granular Materials: A Review
2024-10-18
Mengqi Wang, Krishna Kumar, Y. T. Feng, Tongming Qu, Min Wang
Artificial intelligence (AI) has become a buzz word since Google's AlphaGo beat a world champion in 2017. In the past five years, machine learning as a subset of the broader category of AI has obtained considerable attention in the research community of granular materials. This work offers a detailed review of the recent advances in machine learning-aided studies of granular materials from the particle-particle interaction at the grain level to the macroscopic simulations of granular flow. This work will start with the application of machine learning in the microscopic particle-particle interaction and associated contact models. Then, different neural networks for learning the constitutive behaviour of granular materials will be reviewed and compared. Finally, the macroscopic simulations of practical engineering or boundary value problems based on the combination of neural networks and numerical methods are discussed. We hope readers will have a clear idea of the development of machine learning-aided modelling of granular materials via this comprehensive review work.
Can MLLMs Understand the Deep Implication Behind Chinese Images?
2024-10-17
Chenhao Zhang, Xi Feng, Yuelin Bai, Xinrun Du, Jinchang Hou, Kaixin Deng, Guangzeng Han, Qinrui Li, Bingli Wang, Jiaheng Liu, Xingwei Qu, Yifei Zhang, Qixuan Zhao, Yiming Liang, Ziqiang Liu, Feiteng Fang, Min Yang, Wenhao Huang, Chenghua Lin, Ge Zhang, Shiwen Ni
As the capabilities of Multimodal Large Language Models (MLLMs) continue to improve, the need for higher-order capability evaluation of MLLMs is increasing. However, there is a lack of work evaluating MLLM for higher-order perception and understanding of Chinese visual content. To fill the gap, we introduce the **C**hinese **I**mage **I**mplication understanding **Bench**mark, **CII-Bench**, which aims to assess the higher-order perception and understanding capabilities of MLLMs for Chinese images. CII-Bench stands out in several ways compared to existing benchmarks. Firstly, to ensure the authenticity of the Chinese context, images in CII-Bench are sourced from the Chinese Internet and manually reviewed, with corresponding answers also manually crafted. Additionally, CII-Bench incorporates images that represent Chinese traditional culture, such as famous Chinese traditional paintings, which can deeply reflect the model's understanding of Chinese traditional culture. Through extensive experiments on CII-Bench across multiple MLLMs, we have made significant findings. Initially, a substantial gap is observed between the performance of MLLMs and humans on CII-Bench. The highest accuracy of MLLMs attains 64.4%, where as human accuracy averages 78.2%, peaking at an impressive 81.0%. Subsequently, MLLMs perform worse on Chinese traditional culture images, suggesting limitations in their ability to understand high-level semantics and lack a deep knowledge base of Chinese traditional culture. Finally, it is observed that most models exhibit enhanced accuracy when image emotion hints are incorporated into the prompts. We believe that CII-Bench will enable MLLMs to gain a better understanding of Chinese semantics and Chinese-specific images, advancing the journey towards expert artificial general intelligence (AGI). Our project is publicly available at https://cii-bench.github.io/.
MEMS Attitude Heading Reference Systems are widely employed to determine a system's attitude, but sensor measurement biases limit their accuracy. This paper introduces a novel factor graph-based method called MAgnetometer and GYroscope Calibration (MAGYC). MAGYC leverages three-axis angular rate measurements from an angular rate gyroscope to enhance calibration for batch and online applications. Our approach imposes less restrictive conditions for instrument movements required for calibration, eliminates the need for knowledge of the local magnetic field or instrument attitude, and facilitates integration into factor graph algorithms within Smoothing and Mapping frameworks. We evaluate the proposed methods through numerical simulations and in-field experimental assessments using a sensor installed on an underwater vehicle. Ultimately, our proposed methods reduced the underwater vehicle's heading error standard deviation from 6.21 to 0.57 degrees for a standard seafloor mapping survey.
Whitehead doubles provide a plethora of examples of knots that are topologically slice but not smoothly slice. We discuss the problem of the Whitehead double of the Figure 8 knot and survey commonly used techniques to obstructing sliceness. Additionally, we improve bounds in general for the non-orientable 4 genus of $t$-twisted Whitehead doubles and provide genus 1 non-orientable cobordisms to cable knots.
HI line observations of 290 evolved stars made with the Nancay Radio Telescope -- I. Data
2024-10-17
E. Gerard, W. van Driel, L. D. Matthews, T. Le Bertre, J. -M. Martin, N. Q. Rieu
We present a compendium of HI 21-cm line observations of circumstellar envelopes (CSEs) of 290 evolved stars, mostly (~84%) on the asymptotic giant branch (AGB), made with the 100m-class, single-dish Nancay Radio Telescope. The observational and data reduction procedures were optimised for separating genuine CSE HI emission from surrounding Galactic line features. For most targets (254) the results have not been previously published. Clear detections were made of 34 objects, for 33 of which the total HI flux and the size of the CSE could be determined. Possible detections were made of 21 objects, and upper limits could be determined for 95 undetected targets, while for 140 objects confusion from Galactic HI emission along the line-of-sight precluded meaningful upper limits. The collective results of this survey can provide guidance on detectability of circumstellar HI gas for future mapping and imaging studies.
Atomic structure analysis of crystalline materials is a paramount endeavor in both chemical and material sciences. This sophisticated technique necessitates not only a solid foundation in crystallography but also a profound comprehension of the intricacies of the accompanying software, posing a significant challenge in meeting the rigorous daily demands. For the first time, we confront this challenge head-on by harnessing the power of deep learning for ultra-precise structural analysis at the full-atom level. To validate the performance of the model, named CrystalX, we employed a vast dataset comprising over 50,000 X-ray diffraction measurements derived from authentic experiments, demonstrating performance that is commensurate with human experts and adept at deciphering intricate geometric patterns. Remarkably, CrystalX revealed that even peer-reviewed publications can harbor errors that are stealthy to human scrutiny, yet CrystalX adeptly rectifies them. This deep learning model revolutionizes the time frame for crystal structure analysis, slashing it down to seconds. It has already been successfully applied in the structure analysis of newly discovered compounds in the latest research without human intervention. Overall, CrystalX marks the beginning of a new era in automating routine structural analysis within self-driving laboratories.
Recent advancements in building domain-specific large language models (LLMs) have shown remarkable success, especially in tasks requiring reasoning abilities like logical inference over complex relationships and multi-step problem solving. However, creating a powerful all-in-one LLM remains challenging due to the need for proprietary data and vast computational resources. As a resource-friendly alternative, we explore the potential of merging multiple expert models into a single LLM. Existing studies on model merging mainly focus on generalist LLMs instead of domain experts, or the LLMs under the same architecture and size. In this work, we propose an unconstrained model merging framework that accommodates both homogeneous and heterogeneous model architectures with a focus on reasoning tasks. A fine-grained layer-wise weight merging strategy is designed for homogeneous models merging, while heterogeneous model merging is built upon the probabilistic distribution knowledge derived from instruction-response fine-tuning data. Across 7 benchmarks and 9 reasoning-optimized LLMs, we reveal key findings that combinatorial reasoning emerges from merging which surpasses simple additive effects. We propose that unconstrained model merging could serve as a foundation for decentralized LLMs, marking a notable progression from the existing centralized LLM framework. This evolution could enhance wider participation and stimulate additional advancement in the field of artificial intelligence, effectively addressing the constraints posed by centralized models.
Complete minimal surfaces with Cantor ends in minimally convex domains
2024-10-17
Antonio Alarcon
We survey the recent history of the conformal Calabi-Yau problem consisting in determining the complex structures admitted by complete bounded minimal surfaces in $\mathbb{R}^3$. Moreover, we prove that for any minimally convex domain $\Omega$ in $\mathbb{R}^3$ and any compact Riemann surface $R$ there is a Cantor set $C$ in $R$ whose complement $R\setminus C$ is the complex structure of a complete proper minimal surface in $\Omega$.
Assessing the capabilities and limitations of large language models (LLMs) has garnered significant interest, yet the evaluation of multiple models in real-world scenarios remains rare. Multilingual evaluation often relies on translated benchmarks, which typically do not capture linguistic and cultural nuances present in the source language. This study provides an extensive assessment of 24 LLMs on real world data collected from Indian patients interacting with a medical chatbot in Indian English and 4 other Indic languages. We employ a uniform Retrieval Augmented Generation framework to generate responses, which are evaluated using both automated techniques and human evaluators on four specific metrics relevant to our application. We find that models vary significantly in their performance and that instruction tuned Indic models do not always perform well on Indic language queries. Further, we empirically show that factual correctness is generally lower for responses to Indic queries compared to English queries. Finally, our qualitative work shows that code-mixed and culturally relevant queries in our dataset pose challenges to evaluated models.
Red and blue language: Word choices in the Trump & Harris 2024 presidential debate
2024-10-17
Philipp Wicke, Marianna M. Bolognesi
Political debates are a peculiar type of political discourse, in which candidates directly confront one another, addressing not only the the moderator's questions, but also their opponent's statements, as well as the concerns of voters from both parties and undecided voters. Therefore, language is adjusted to meet specific expectations and achieve persuasion. We analyse how the language of Trump and Harris during the debate (September 10th 2024) differs in relation to the following semantic and pragmatic features, for which we formulated targeted hypotheses: framing values and ideology, appealing to emotion, using words with different degrees of concreteness and specificity, addressing others through singular or plural pronouns. Our findings include: differences in the use of figurative frames (Harris often framing issues around recovery and empowerment, Trump often focused on crisis and decline); similar use of emotional language, with Trump showing a slight higher tendency toward negativity and toward less subjective language compared to Harris; no significant difference in the specificity of candidates' responses; similar use of abstract language, with Trump showing more variability than Harris, depending on the subject discussed; differences in addressing the opponent, with Trump not mentioning Harris by name, while Harris referring to Trump frequently; different uses of pronouns, with Harris using both singular and plural pronouns equally, while Trump using more singular pronouns. The results are discussed in relation to previous literature on Red and Blue language, which refers to distinct linguistic patterns associated with conservative (Red) and liberal (Blue) political ideologies.
Phenotype structuring in collective cell migration:a tutorial of mathematical models and methods
2024-10-17
Tommaso Lorenzi, Kevin J Painter, Chiara Villa
Populations are heterogeneous, deviating in numerous ways. Phenotypic diversity refers to the range of traits or characteristics across a population, where for cells this could be the levels of signalling, movement and growth activity, etc. Clearly, the phenotypic distribution -- and how this changes over time and space -- could be a major determinant of population-level dynamics. For instance, across a cancerous population, variations in movement, growth, and ability to evade death may determine its growth trajectory and response to therapy. In this review, we discuss how classical partial differential equation (PDE) approaches for modelling cellular systems and collective cell migration can be extended to include phenotypic structuring. The resulting non-local models -- which we refer to as phenotype-structured partial integro-differential equations (PS-PIDEs) -- form a sophisticated class of models with rich dynamics. We set the scene through a brief history of structured population modelling, and then review the extension of several classic movement models -- including the Fisher-KPP and Keller-Segel equations -- into a PS-PIDE form. We proceed with a tutorial-style section on derivation, analysis, and simulation techniques. First, we show a method to formally derive these models from underlying agent-based models. Second, we recount travelling waves in PDE models of spatial spread dynamics and concentration phenomena in non-local PDE models of evolutionary dynamics, and combine the two to deduce phenotypic structuring across travelling waves in PS-PIDE models. Third, we discuss numerical methods to simulate PS-PIDEs, illustrating with a simple scheme based on the method of lines and noting the finer points of consideration. We conclude with a discussion of future modelling and mathematical challenges.
lightcurver: A Python Pipeline for Precise Photometry of Multiple-Epoch Wide-Field Images
2024-10-17
Frédéric Dux
lightcurver is a photometric pipeline for time series astronomical imaging data, designed for the semi-automatic extraction of precise light curves from small, blended targets. Such targets include, but are not limited to, lensed quasars, supernovae, or Cepheids in crowded fields. lightcurver leverages STARRED (Michalewicz et al., 2023; Millon et al., 2024) to generate state-of-the-art empirical point spread function (PSF) models for each image. It then determines the relative zeropoints between epochs by combining the PSF-photometry fluxes of several stars in the field of view. Subsequently, STARRED is used again to simultaneously model the calibrated pixels of the region of interest across all epochs. This process yields light curves of the point sources and a high-resolution image model of the region of interest, cumulating the signal from all epochs. lightcurver aims to be maintainable, fast, and incremental in its processing approach. As such, it can enable the daily photometric analysis of a large number of blended targets in the context of the upcoming Rubin Observatory Legacy Survey of Space and Time.
Narrative-driven recommenders aim to provide personalized suggestions for user requests expressed in free-form text such as "I want to watch a thriller with a mind-bending story, like Shutter Island." Although large language models (LLMs) have been shown to excel in processing general natural language queries, their effectiveness for handling such recommendation requests remains relatively unexplored. To close this gap, we compare the performance of 38 open- and closed-source LLMs of various sizes, such as LLama 3.2 and GPT-4o, in a movie recommendation setting. For this, we utilize a gold-standard, crowdworker-annotated dataset of posts from reddit's movie suggestion community and employ various prompting strategies, including zero-shot, identity, and few-shot prompting. Our findings demonstrate the ability of LLMs to generate contextually relevant movie recommendations, significantly outperforming other state-of-the-art approaches, such as doc2vec. While we find that closed-source and large-parameterized models generally perform best, medium-sized open-source models remain competitive, being only slightly outperformed by their more computationally expensive counterparts. Furthermore, we observe no significant differences across prompting strategies for most models, underscoring the effectiveness of simple approaches such as zero-shot prompting for narrative-driven recommendations. Overall, this work offers valuable insights for recommender system researchers as well as practitioners aiming to integrate LLMs into real-world recommendation tools.
Connection between Non-Axisymmetric Structures and Neutral Gas Distribution in Disk Galaxies
2024-10-17
Ze-Zhong Liang, Jing Wang, Hua Gao, Luis C. Ho, E. Athanassoula
Non-axisymmetric structures, such as bars and spiral arms, are known to concentrate molecular gas and star formation in galaxy centers, actively building up the pseudo-bulges. However, a direct link between the neutral (i.e., molecular and atomic) gas distribution and the exerted torque forces over a broader radial range of galactic disks still remains to be explored. In the present work, we investigate this link by carefully evaluating the torque force field using the $3.6\, \mathrm{\mu m}$ images for 17 The H I Nearby Galaxy Survey (THINGS) galaxies, and measuring neutral gas distribution on resolved atomic and molecular line maps. We find that galaxies with stronger torque forces show a more concentrated neutral gas distribution over the disk-scale, defined as half the isophotal radius at $25.5\, \mathrm{mag\, arcsec^{-2}}$. The correlation holds regardless of whether the neutral gas fraction, or the effective stellar mass surface density is controlled for. In addition, $\mathrm{kpc}$-scale neutral gas over-densities tend to be located close to the local maxima of torque forces. Most of these correlations involving the torque forces are comparatively stronger than those using the traditional Fourier amplitudes to quantify the non-axisymmetric structures. These results are consistent with the scenario that non-axisymmetric structures exert torque forces, and trigger dissipative processes to transport gas inward, not only to build the pseudo-bulges, but also fuel the inner disk growth. In this regard, non-axisymmetric structures inducing stronger torque forces appear to be more efficient in these processes.
Undecidability of the spectral gap in rotationally symmetric Hamiltonians
2024-10-17
Laura Castilla-Castellano, Angelo Lucia
The problem of determining the existence of a spectral gap in a lattice quantum spin system was previously shown to be undecidable for one [J. Bausch et al., "Undecidability of the spectral gap in one dimension", Physical Review X 10 (2020)] or more dimensions [T. S. Cubitt et al., "Undecidability of the spectral gap", Nature 528 (2015)]. In these works, families of nearest-neighbor interactions are constructed whose spectral gap depends on the outcome of a Turing machine Halting problem, therefore making it impossible for an algorithm to predict its existence. While these models are translationally invariant, they are not invariant under the other symmetries of the lattice, a property which is commonly found in physically relevant cases, posing the question of whether the spectral gap is still an undecidable problem for Hamiltonians with stronger symmetry constraints. We give a positive answer to this question, in the case of models with 4-body (plaquette) interactions on the square lattice satisfying rotation, but not reflection, symmetry: rotational symmetry is not enough to make the problem decidable.
Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?
2024-10-17
Che Liu, Zhongwei Wan, Haozhe Wang, Yinda Chen, Talha Qaiser, Chen Jin, Fariba Yousefi, Nikolay Burlutskiy, Rossella Arcucci
Medical Vision-Language Pre-training (MedVLP) has made significant progress in enabling zero-shot tasks for medical image understanding. However, training MedVLP models typically requires large-scale datasets with paired, high-quality image-text data, which are scarce in the medical domain. Recent advancements in Large Language Models (LLMs) and diffusion models have made it possible to generate large-scale synthetic image-text pairs. This raises the question: *Can MedVLP succeed using purely synthetic data?* To address this, we use off-the-shelf generative models to create synthetic radiology reports and paired Chest X-ray (CXR) images, and propose an automated pipeline to build a diverse, high-quality synthetic dataset, enabling a rigorous study that isolates model and training settings, focusing entirely from the data perspective. Our results show that MedVLP models trained *exclusively on synthetic data* outperform those trained on real data by **3.8%** in averaged AUC on zero-shot classification. Moreover, using a combination of synthetic and real data leads to a further improvement of **9.07%**. Additionally, MedVLP models trained on synthetic or mixed data consistently outperform those trained on real data in zero-shot grounding, as well as in fine-tuned classification and segmentation tasks. Our analysis suggests MedVLP trained on well-designed synthetic data can outperform models trained on real datasets, which may be limited by low-quality samples and long-tailed distributions.
Progenitor diversity in the accreted stellar halos of Milky Way-like galaxies
2024-10-17
Sy-Yun Pu, Andrew P. Cooper, Robert J. J. Grand, Facundo A. Gómez, Antonela Monachesi
Ongoing large stellar spectroscopic surveys of the Milky Way seek to reconstruct the major events in the assembly history of the Galaxy. Chemical and kinematic observations can be used to separate the contributions of different progenitor galaxies to the present-day stellar halo. Here we compute the number of progenitors that contribute to the accreted stellar halos of simulated Milky Way-like galaxies as a function of radius (the radial diversity) in three suites of models: Bullock & Johnston, Aquarius and Auriga. We show that there are significant differences between the predictions of these three models, beyond the halo-to-halo scatter expected in $\Lambda$CDM. Predictions of diversity from numerical simulations are sensitive to model-dependent assumptions regarding the efficiency of star formation in dwarf galaxies. We compare, at face value, to current constraints on the radial diversity of the Milky Way's accreted halo. These constraints imply that the halo of our Galaxy is dominated by $\sim2$ progenitors in the range $8-45\,\mathrm{kpc}$, in contrast to averages of $7$ progenitors in the Bullock & Johnston models, $3.5$ in Aquarius and $4.2$ in Auriga over the same region. We additionally find that the models with radial diversity most similar to that of the Milky Way are predominantly those with ongoing merger events. The Milky Way therefore appears unusual in having an accreted stellar halo dominated by a small number of progenitors accreted at very early times.
The S$^4$G-WISE View of Global Star Formation in the Nearby Universe
2024-10-17
M. E. Cluver, T. H. Jarrett, D. A. Dale, J. -D. T. Smith, M. J. I. Brown, W. van Kempen, E. Lengerer, R. Incoll, C. Davey, R. Holloway, J. Cameron, K. Sheth
In this work we present source-tailored WISE mid-infrared photometry (at 3.4$\mu$m, 4.6$\mu$m, 12$\mu$m, and 23$\mu$m) of 2812 galaxies in the extended Spitzer Survey of Stellar Structure in Galaxies (S$^4$G) sample, and characterise the mid-infrared colors and dust properties of this legacy nearby galaxy data set. Informed by the relative emission between W3 (12$\mu$ m) and W4 (23$\mu$ m), we re-derive star formation rate (SFR) scaling relations calibrated to L$_{\rm TIR}$, which results in improved agreement between the two tracers. By inverse-variance weighting the W3 and W4-derived SFRs, we generate a combined mid-infrared SFR that is a broadly robust measure of star formation activity in dusty, star-forming galaxies in the nearby Universe. In addition, we investigate the use of a W3-derived dust density metric, $\Sigma_{\rm 12\mu m}$ (L$_\odot$/kpc$^2$), to estimate the SFR deficit of low mass, low dust galaxies. This is achieved by combining WISE with existing GALEX ultraviolet (UV) photometry, which we further use to explore the relationship between dust and UV emission as a function of morphology. Finally, we use our derived SFR prescriptions to examine the location of galaxies in the log SFR - log M$_\textrm{stellar}$ plane, as a function of morphological type, which underscores the complexity of dust-derived properties seen in galaxies of progressively earlier type.
RAMPA: Robotic Augmented Reality for Machine Programming and Automation
2024-10-17
Fatih Dogangun, Serdar Bahar, Yigit Yildirim, Bora Toprak Temir, Emre Ugur, Mustafa Doga Dogan
As robotics continue to enter various sectors beyond traditional industrial applications, the need for intuitive robot training and interaction systems becomes increasingly more important. This paper introduces Robotic Augmented Reality for Machine Programming (RAMPA), a system that utilizes the capabilities of state-of-the-art and commercially available AR headsets, e.g., Meta Quest 3, to facilitate the application of Programming from Demonstration (PfD) approaches on industrial robotic arms, such as Universal Robots UR10. Our approach enables in-situ data recording, visualization, and fine-tuning of skill demonstrations directly within the user's physical environment. RAMPA addresses critical challenges of PfD, such as safety concerns, programming barriers, and the inefficiency of collecting demonstrations on the actual hardware. The performance of our system is evaluated against the traditional method of kinesthetic control in teaching three different robotic manipulation tasks and analyzed with quantitative metrics, measuring task performance and completion time, trajectory smoothness, system usability, user experience, and task load using standardized surveys. Our findings indicate a substantial advancement in how robotic tasks are taught and refined, promising improvements in operational safety, efficiency, and user engagement in robotic programming.
2024-10-17
Marina Ruiz-García, Miguel Querejeta, Santiago García-Burillo, Eric Emsellem, Sharon E. Meidt, Mattia C. Sormani, Eva Schinnerer, Thomas G. Williams, Zein Bazzi, Dario Colombo, Damian R. Gleis, Oleg Y. Gnedin, Ralf S. Klessen, Adam K. Leroy, Patricia Sánchez-Blázquez, Sophia K. Stuber
Bars are remarkable stellar structures that can transport gas toward centers and drive the secular evolution of galaxies. In this context, it is important to locate dynamical resonances associated with bars. For this study, we used ${Spitzer}$ near-infrared images as a proxy for the stellar gravitational potential and the ALMA CO(J=2-1) gas distribution from the PHANGS survey to determine the position of the main dynamical resonances associated with the bars in the PHANGS sample of 74 nearby star-forming galaxies. We used the gravitational torque method to estimate the location of the bar corotation radius ($R_{\rm CR}$), where stars and gas rotate at the same angular velocity as the bar. Of the 46 barred galaxies in PHANGS, we have successfully determined the corotation (CR) for 38 of them. The mean ratio of the $R_{\rm CR}$ to the bar radius ($R_{\rm bar}$) is $\mathcal{R} = R_{\rm CR}/R_{\rm bar} = 1.12$, with a standard deviation of $0.39$. This is consistent with the average value expected from theory and suggests that bars are predominantly fast. We also compared our results with other bar CR measurements from the literature, which employ different methods, and find good agreement ($\rho = 0.64$). Finally, using rotation curves, we have estimated other relevant resonances such as the inner Lindblad resonance (ILR) and the outer Lindblad resonance (OLR), which are often associated with rings. This work provides a useful catalog of resonances for a large sample of nearby galaxies and emphasizes the clear connection between bar dynamics and morphology.
The Standardized Uptake Value (SUV) is a critical metric in positron emission tomography (PET) imaging, used to assess metabolic activity. However, calculating SUV from DICOM files presents challenges due to vendor-specific DICOM attributes and variations in the encoding of radiotracer accumulation times. This technical note introduces a robust, vendor-specific SUV calculation strategy that addresses inconsistencies in current methodologies. We also integrate this strategy into an open-source software solution, Z-Rad, capable of converting raw PET DICOM data into body-weight normalized SUV NIfTI files. Our SUV calculation strategy was developed by reviewing DICOM conformance statements from GE, Philips, and Siemens. Validation was conducted using real-world PET datasets, and the proposed strategy was compared to existing software solutions. Our SUV calculation approach demonstrated improved accuracy, particularly in resolving time-related discrepancies in the studied data. Our analysis also identified inconsistencies in the SUV calculation methods used by popular commercial and open-source software solutions, which do not fully account for vendor-specific DICOM attributes and PET image acquisition times. These limitations resulted in errors in SUV estimation reaching 33\% when comparing our strategy to studied software. The proposed vendor-specific SUV calculation strategy significantly enhances accuracy in PET imaging by addressing key inconsistencies caused by variations in DICOM attributes and image acquisition times across different vendors. This method effectively reduces SUV calculation errors and has been integrated into an open-source software, Z-Rad.
Technologies based on the use of hydrogen are promising for future energy requirements in a more sustainable world. Consequently, modelling fuel cells is crucial, for instance, to optimize their control to achieve excellent performance, to test new materials and configurations on a limited budget, or to consider their degradation for improved lifespan. To develop such models, a comprehensive study is required, encompassing both well-established and the latest governing laws on matter transport and voltage polarisation for Proton Exchange Membrane Fuel Cells (PEMFCs). Recent articles often rely on outdated or inappropriate equations, lacking clear explanations regarding their background. Indeed, inconsistent understanding of theoretical and experimental choices or model requirements hinders comprehension and contributes to the misuse of these equations. Additionally, specific researches are needed to construct more accurate models. This study aims to offer a comprehensive understanding of the current state-of-the-art in PEMFC modeling. It clarifies the corresponding governing equations, their usage conditions, and assumptions, thus serving as a foundation for future developments. The presented laws and equations are applicable in most multi-dimensional, dynamic, and two-phase PEMFC models.
In this paper, we investigate the phase transitions and critical behavior of a nonlinear magnetically charged rotating AdS black hole, with a particular emphasis on the influence of a quintessence field. Our comprehensive thermodynamic analysis explores the impact of thermal fluctuations on the black hole's properties. We observe that for larger black holes, the corrected entropy remains positive, similar to the uncorrected case, which highlights the significant role of thermal fluctuations in modifying the entropy of smaller black holes. Through a meticulous analysis of various thermodynamic properties, we derive explicit analytical expressions for the critical points. Our findings demonstrate that the quintessence field significantly affects phase transitions, resulting in distinct critical phenomena. Notably, the black hole's phase transitions exhibit striking similarities to those observed in van der Waals fluids, offering deeper insights into the complex thermodynamic behavior of these systems across different scales.
Les Houches lecture notes on moduli spaces of Riemann surfaces
2024-10-17
Alessandro Giacchetto, Danilo Lewański
In these lecture notes, we provide an introduction to the moduli space of Riemann surfaces, a fundamental concept in the theories of 2D quantum gravity, topological string theory, and matrix models. We begin by reviewing some basic results concerning the recursive boundary structure of the moduli space and the associated cohomology theory. We then present Witten's celebrated conjecture and its generalisation, framing it as a recursive computation of cohomological field theory correlators via topological recursion. We conclude with a discussion of JT gravity in relation to hyperbolic geometry and topological strings. These lecture notes accompanied a series of lectures at the Les Houches school "Quantum Geometry (Mathematical Methods for Gravity, Gauge Theories and Non-Perturbative Physics)" in Summer 2024.
The Latent Road to Atoms: Backmapping Coarse-grained Protein Structures with Latent Diffusion
2024-10-17
Xu Han, Yuancheng Sun, Kai Chen, Kang Liu, Qiwei Ye
Coarse-grained(CG) molecular dynamics simulations offer computational efficiency for exploring protein conformational ensembles and thermodynamic properties. Though coarse representations enable large-scale simulations across extended temporal and spatial ranges, the sacrifice of atomic-level details limits their utility in tasks such as ligand docking and protein-protein interaction prediction. Backmapping, the process of reconstructing all-atom structures from coarse-grained representations, is crucial for recovering these fine details. While recent machine learning methods have made strides in protein structure generation, challenges persist in reconstructing diverse atomistic conformations that maintain geometric accuracy and chemical validity. In this paper, we present Latent Diffusion Backmapping (LDB), a novel approach leveraging denoising diffusion within latent space to address these challenges. By combining discrete latent encoding with diffusion, LDB bypasses the need for equivariant and internal coordinate manipulation, significantly simplifying the training and sampling processes as well as facilitating better and wider exploration in configuration space. We evaluate LDB's state-of-the-art performance on three distinct protein datasets, demonstrating its ability to efficiently reconstruct structures with high structural accuracy and chemical validity. Moreover, LDB shows exceptional versatility in capturing diverse protein ensembles, highlighting its capability to explore intricate conformational spaces. Our results position LDB as a powerful and scalable approach for backmapping, effectively bridging the gap between CG simulations and atomic-level analyses in computational biology.
Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendation
2024-10-17
Ryotaro Shimizu, Takashi Wada, Yu Wang, Johannes Kruse, Sean O'Brien, Sai HtaungKham, Linxin Song, Yuya Yoshikawa, Yuki Saito, Fugee Tsung, Masayuki Goto, Julian McAuley
Recent research on explainable recommendation generally frames the task as a standard text generation problem, and evaluates models simply based on the textual similarity between the predicted and ground-truth explanations. However, this approach fails to consider one crucial aspect of the systems: whether their outputs accurately reflect the users' (post-purchase) sentiments, i.e., whether and why they would like and/or dislike the recommended items. To shed light on this issue, we introduce new datasets and evaluation methods that focus on the users' sentiments. Specifically, we construct the datasets by explicitly extracting users' positive and negative opinions from their post-purchase reviews using an LLM, and propose to evaluate systems based on whether the generated explanations 1) align well with the users' sentiments, and 2) accurately identify both positive and negative opinions of users on the target items. We benchmark several recent models on our datasets and demonstrate that achieving strong performance on existing metrics does not ensure that the generated explanations align well with the users' sentiments. Lastly, we find that existing models can provide more sentiment-aware explanations when the users' (predicted) ratings for the target items are directly fed into the models as input. We will release our code and datasets upon acceptance.
From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning
2024-10-17
Juan Diego Toscano, Vivek Oommen, Alan John Varghese, Zongren Zou, Nazanin Ahmadi Daryakenari, Chenxi Wu, George Em Karniadakis
Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Over the past few years, significant advancements have been made in the training and optimization of PINNs, covering aspects such as network architectures, adaptive refinement, domain decomposition, and the use of adaptive weights and activation functions. A notable recent development is the Physics-Informed Kolmogorov-Arnold Networks (PIKANS), which leverage a representation model originally proposed by Kolmogorov in 1957, offering a promising alternative to traditional PINNs. In this review, we provide a comprehensive overview of the latest advancements in PINNs, focusing on improvements in network design, feature expansion, optimization techniques, uncertainty quantification, and theoretical insights. We also survey key applications across a range of fields, including biomedicine, fluid and solid mechanics, geophysics, dynamical systems, heat transfer, chemical engineering, and beyond. Finally, we review computational frameworks and software tools developed by both academia and industry to support PINN research and applications.
Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation
2024-10-17
Sreyan Ghosh, Mohammad Sadegh Rasooli, Michael Levit, Peidong Wang, Jian Xue, Dinesh Manocha, Jinyu Li
Generative Error Correction (GEC) has emerged as a powerful post-processing method to enhance the performance of Automatic Speech Recognition (ASR) systems. However, we show that GEC models struggle to generalize beyond the specific types of errors encountered during training, limiting their ability to correct new, unseen errors at test time, particularly in out-of-domain (OOD) scenarios. This phenomenon amplifies with named entities (NEs), where, in addition to insufficient contextual information or knowledge about the NEs, novel NEs keep emerging. To address these issues, we propose DARAG (Data- and Retrieval-Augmented Generative Error Correction), a novel approach designed to improve GEC for ASR in in-domain (ID) and OOD scenarios. We augment the GEC training dataset with synthetic data generated by prompting LLMs and text-to-speech models, thereby simulating additional errors from which the model can learn. For OOD scenarios, we simulate test-time errors from new domains similarly and in an unsupervised fashion. Additionally, to better handle named entities, we introduce retrieval-augmented correction by augmenting the input with entities retrieved from a database. Our approach is simple, scalable, and both domain- and language-agnostic. We experiment on multiple datasets and settings, showing that DARAG outperforms all our baselines, achieving 8\% -- 30\% relative WER improvements in ID and 10\% -- 33\% improvements in OOD settings.
Evaluating Self-Generated Documents for Enhancing Retrieval-Augmented Generation with Large Language Models
2024-10-17
Jiatao Li, Xinyu Hu, Xunjian Yin, Xiaojun Wan
In retrieval-augmented generation systems, the integration of self-generated documents (SGDs) alongside retrieved content has emerged as a promising strategy for enhancing the performance of large language model. However, previous research primarily focuses on optimizing the use of SGDs, with the inherent properties of SGDs remaining underexplored. Therefore, this paper conducts a comprehensive analysis of different types of SGDs and experiments on various knowledge-intensive tasks. We develop a taxonomy of SGDs grounded in Systemic Functional Linguistics (SFL) to compare the influence of different SGD categories. Our findings offer key insights into what kinds of SGDs most effectively contribute to improving LLM's performance. The results and further fusion methods based on SGD categories also provide practical guidelines for taking better advantage of SGDs to achieve significant advancements in knowledge-driven QA tasks with RAG.
Retrieving gene functional networks from knowledge databases presents a challenge due to the mismatch between disease networks and subtype-specific variations. Current solutions, including statistical and deep learning methods, often fail to effectively integrate gene interaction knowledge from databases or explicitly learn subtype-specific interactions. To address this mismatch, we propose GeSubNet, which learns a unified representation capable of predicting gene interactions while distinguishing between different disease subtypes. Graphs generated by such representations can be considered subtype-specific networks. GeSubNet is a multi-step representation learning framework with three modules: First, a deep generative model learns distinct disease subtypes from patient gene expression profiles. Second, a graph neural network captures representations of prior gene networks from knowledge databases, ensuring accurate physical gene interactions. Finally, we integrate these two representations using an inference loss that leverages graph generation capabilities, conditioned on the patient separation loss, to refine subtype-specific information in the learned representation. GeSubNet consistently outperforms traditional methods, with average improvements of 30.6%, 21.0%, 20.1%, and 56.6% across four graph evaluation metrics, averaged over four cancer datasets. Particularly, we conduct a biological simulation experiment to assess how the behavior of selected genes from over 11,000 candidates affects subtypes or patient distributions. The results show that the generated network has the potential to identify subtype-specific genes with an 83% likelihood of impacting patient distribution shifts. The GeSubNet resource is available: https://anonymous.4open.science/r/GeSubNet/
Mapping Bias in Vision Language Models: Signposts, Pitfalls, and the Road Ahead
2024-10-17
Kuleen Sasse, Shan Chen, Jackson Pond, Danielle Bitterman, John Osborne
As Vision Language Models (VLMs) gain widespread use, their fairness remains under-explored. In this paper, we analyze demographic biases across five models and six datasets. We find that portrait datasets like UTKFace and CelebA are the best tools for bias detection, finding gaps in performance and fairness between LLaVa and CLIP models. However, scene based datasets like PATA, VLStereoSet fail to be useful benchmarks for bias due to their construction. As for pronoun based datasets like VisoGender, we receive mixed signals as only some subsets of the data are useful in providing insights. To alleviate this problem, we introduce a more difficult version of VisoGender to serve as a more rigorous evaluation. Based on these results, we call for more effective and carefully designed datasets to ensure VLMs are both fair and reliable.
Let Students Take the Wheel: Introducing Post-Quantum Cryptography with Active Learning
2024-10-17
Ainaz Jamshidi, Khushdeep Kaur, Aryya Gangopadhyay, Lei Zhang
Quantum computing presents a double-edged sword: while it has the potential to revolutionize fields such as artificial intelligence, optimization, healthcare, and so on, it simultaneously poses a threat to current cryptographic systems, such as public-key encryption. To address this threat, post-quantum cryptography (PQC) has been identified as the solution to secure existing software systems, promoting a national initiative to prepare the next generation with the necessary knowledge and skills. However, PQC is an emerging interdisciplinary topic, presenting significant challenges for educators and learners. This research proposes a novel active learning approach and assesses the best practices for teaching PQC to undergraduate and graduate students in the discipline of information systems. Our contributions are two-fold. First, we compare two instructional methods: 1) traditional faculty-led lectures and 2) student-led seminars, both integrated with active learning techniques such as hands-on coding exercises and Kahoot games. The effectiveness of these methods is evaluated through student assessments and surveys. Second, we have published our lecture video, slides, and findings so that other researchers and educators can reuse the courseware and materials to develop their own PQC learning modules. We employ statistical analysis (e.g., t-test and chi-square test) to compare the learning outcomes and students' feedback between the two learning methods in each course. Our findings suggest that student-led seminars significantly enhance learning outcomes, particularly for graduate students, where a notable improvement in comprehension and engagement is observed. Moving forward, we aim to scale these modules to diverse educational contexts and explore additional active learning and experiential learning strategies for teaching complex concepts of quantum information science.
2024-10-17
William Agnew, Julia Barnett, Annie Chu, Rachel Hong, Michael Feffer, Robin Netzorg, Harry H. Jiang, Ezra Awumey, Sauvik Das
Generative audio models are rapidly advancing in both capabilities and public utilization -- several powerful generative audio models have readily available open weights, and some tech companies have released high quality generative audio products. Yet, while prior work has enumerated many ethical issues stemming from the data on which generative visual and textual models have been trained, we have little understanding of similar issues with generative audio datasets, including those related to bias, toxicity, and intellectual property. To bridge this gap, we conducted a literature review of hundreds of audio datasets and selected seven of the most prominent to audit in more detail. We found that these datasets are biased against women, contain toxic stereotypes about marginalized communities, and contain significant amounts of copyrighted work. To enable artists to see if they are in popular audio datasets and facilitate exploration of the contents of these datasets, we developed a web tool audio datasets exploration tool at https://audio-audit.vercel.app.
Deep Learning-based Software Engineering: Progress, Challenges, and Opportunities
2024-10-17
Xiangping Chen, Xing Hu, Yuan Huang, He Jiang, Weixing Ji, Yanjie Jiang, Yanyan Jiang, Bo Liu, Hui Liu, Xiaochen Li, Xiaoli Lian, Guozhu Meng, Xin Peng, Hailong Sun, Lin Shi, Bo Wang, Chong Wang, Jiayi Wang, Tiantian Wang, Jifeng Xuan, Xin Xia, Yibiao Yang, Yixin Yang, Li Zhang, Yuming Zhou, Lu Zhang
Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many papers have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However, although several surveys have provided overall pictures of the application of deep learning techniques in software engineering, they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this paper, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out the through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically.
Computational Social Choice: Parameterized Complexity and Challenges
2024-10-17
Jiehua Chena, Christian Hatschka, Sofia Simola
We survey two key problems-Multi-Winner Determination and Hedonic Games in Computational Social Choice, with a special focus on their parameterized complexity, and propose some research challenges in the field.
Student Reflections on Self-Initiated GenAI Use in HCI Education
2024-10-17
Hauke Sandhaus, Quiquan Gu, Maria Teresa Parreira, Wendy Ju
Generative Artificial Intelligence's (GenAI) impact on Human-Computer Interaction (HCI) education and technology design is pervasive but poorly understood. This study examines how graduate students in an applied HCI course utilized GenAI tools across various stages of interactive device design. Although the course policy neither explicitly encouraged nor prohibited using GenAI, students independently integrated these tools into their work. Through conducting 12 post-class group interviews, we reveal the dual nature of GenAI: while it stimulates creativity and accelerates design iterations, it also raises concerns about shallow learning and over-reliance. Our findings indicate that GenAI's benefits are most pronounced in the Execution phase of the design process, particularly for rapid prototyping and ideation. In contrast, its use in the Discover phase and design reflection may compromise depth. This study underscores the complex role of GenAI in HCI education and offers recommendations for curriculum improvements to better prepare future designers for effectively integrating GenAI into their creative processes.
2024-10-17
Bolin Lai, Sam Toyer, Tushar Nagarajan, Rohit Girdhar, Shengxin Zha, James M. Rehg, Kris Kitani, Kristen Grauman, Ruta Desai, Miao Liu
Predicting future human behavior is an increasingly popular topic in computer vision, driven by the interest in applications such as autonomous vehicles, digital assistants and human-robot interactions. The literature on behavior prediction spans various tasks, including action anticipation, activity forecasting, intent prediction, goal prediction, and so on. Our survey aims to tie together this fragmented literature, covering recent technical innovations as well as the development of new large-scale datasets for model training and evaluation. We also summarize the widely-used metrics for different tasks and provide a comprehensive performance comparison of existing approaches on eleven action anticipation datasets. This survey serves as not only a reference for contemporary methodologies in action anticipation, but also a guideline for future research direction of this evolving landscape.
Solutions for Sustainable and Resilient Communication Infrastructure in Disaster Relief and Management Scenarios
2024-10-17
Bilal Karaman, Ilhan Basturk, Sezai Taskin, Engin Zeydan, Ferdi Kara, Esra Aycan Beyazit, Miguel Camelo, Emil Björnson, Halim Yanikomeroglu
As natural disasters become more frequent and severe, ensuring a resilient communications infrastructure is of paramount importance for effective disaster response and recovery. This disaster-resilient infrastructure should also respond to sustainability goals by providing an energy-efficient and economically feasible network that is accessible to everyone. This paper provides a comprehensive exploration of the technological solutions and strategies necessary to build and maintain resilient communications networks that can withstand and quickly recover from disaster scenarios. The paper starts with a survey of existing literature and related reviews to establish a solid foundation, followed by an overview of the global landscape of disaster communications and power supply management. We then introduce the key enablers of communications and energy resource technologies to support communications infrastructure, examining emerging trends that improve the resilience of these systems. Pre-disaster planning is emphasized as a critical phase where proactive communication and energy supply strategies can significantly mitigate the impact of disasters. We explore the essential technologies for disaster response, focusing on real-time communications and energy solutions that support rapid deployment and coordination in times of crisis. The paper presents post-disaster communication and energy management planning for effective rescue and evacuation operations. The main findings derived from the comprehensive survey are also summarized for each disaster phase. This is followed by an analysis of existing vendor products and services as well as standardization efforts and ongoing projects that contribute to the development of resilient infrastructures. A detailed case study of the Turkiye earthquakes is presented to illustrate the practical application of these technologies and strategies.
M-theory geometric engineering for rank-0 3d $\mathcal{N}=2$ theories
2024-10-17
Andrea Sangiovanni, Roberto Valandro
M-theory geometric engineering on non-compact Calabi-Yau fourfolds (CY4) produces 3d theories with 4 supercharges. Carefully establishing a dictionary between the geometry of the CY4 and the QFT in the transverse directions remains, to a large extent, an unresolved challenge, complicated by subtleties arising from M5-brane instanton corrections. Such difficulties can be circumvented in the restricted and yet controlled setting offered by CY4 with terminal singularities, as they do not admit crepant resolutions with compact exceptional divisors. After a general review of their properties and partial classifications, we focus on a subclass of terminal CY4 constructed as deformed Du Val singularities, that admit crepant resolutions with at most exceptional 2-cycles. We extract the corresponding 3d $\mathcal{N}=2$ supersymmetric theory descendant in an unambiguous fashion, as the absence of compact 4-cycles leaves no room for a choice of background $G_4$ flux. These turn out to be theories of chiral multiplets with no gauge group and at most abelian flavor factors: we argue that they serve as the simplest building blocks to substantiate a rigorous CY4/3d QFT geometric engineering mapping.
A search for self-lensing binaries with TESS and constraints on their occurrence rate
2024-10-17
Natsuko Yamaguchi, Kareem El-Badry
Five self-lensing binaries (SLBs) have been discovered with Kepler light curves. They contain white dwarfs (WDs) in AU-scale orbits that gravitationally lens solar-type companions. Forming SLBs likely requires common envelope evolution when the WD progenitor is an AGB star and has a weakly bound envelope. No SLBs have yet been discovered with data from the Transiting Exoplanet Survey Satellite (TESS), which observes far more stars than Kepler did. Identifying self-lensing in TESS data is made challenging by the fact that TESS only observes most stars for $\sim$25 days at a time, so only a single lensing event will be observed for typical SLBs. TESS's smaller aperture also makes it sensitive only to SLBs a factor of $\sim$100 brighter than those to which Kepler is sensitive. We demonstrate that TESS has nevertheless likely already observed $\sim$4 times more detectable SLBs than Kepler. We describe a search for non-repeating self-lensing signals in TESS light curves and present preliminary candidates for which spectroscopic follow-up is ongoing. We calculate the sensitivity of our search with injection and recovery tests on TESS and Kepler light curves. Based on the 5 SLBs discovered with Kepler light curves, we estimate that $(1.1 \pm 0.6)\%$ of solar-type stars are orbited by WDs with periods of 100-1000 d. This implies a space density of AU-scale WD + main sequence (MS) binaries a factor of 20-100 larger than that of astrometrically-identified WD + MS binaries with orbits in Gaia DR3. We conclude that the Gaia sample is still quite incomplete, mainly because WD + MS binaries can only be unambiguously identified as such for high mass ratios.
This paper proposes an AI-based scheme for islanding detection in active distribution networks. By reviewing existing studies, it is clear that there are several gaps in the field to ensure reliable islanding detection, including (i) model complexity and stability concerns, (ii) limited accuracy under noisy conditions, and (iii) limited applicability to systems with different types of resources. Accordingly, this paper proposes a WaveNet classifier reinforced by a denoising U-Net model to address these shortcomings. The proposed scheme has a simple structure due to the use of 1D convolutional layers and incorporates residual connections that significantly enhance the model's generalization. Additionally, the proposed scheme is robust against noisy conditions by incorporating a denoising U-Net model. Furthermore, the model is sufficiently fast using a sliding window time series of 10 milliseconds for detection. Utilizing positive/negative/zero sequence components of voltages, superimposed waveforms, and the rate of change of frequency provides the necessary features to precisely detect the islanding condition. In order to assess the effectiveness of the suggested scheme, over 3k islanding/non-islanding cases were tested, considering different load active/reactive powers values, load switching transients, capacitor bank switching, fault conditions in the main grid, different load quality factors, signal-to-noise ratio levels, and both types of conventional and inverter-based sources.
Mathematical modelling to inform outbreak response vaccination
2024-10-17
Manjari Shankar, Anna-Maria Hartner, Callum R. K. Arnold, Ezra Gayawan, Hyolim Kang, Jong-Hoon Kim, Gemma Nedjati Gilani, Anne Cori, Han Fu, Mark Jit, Rudzani Muloiwa, Allison Portnoy, Caroline Trotter, Katy A. M. Gaythorpe
Mathematical models are established tools to assist in outbreak response. They help characterise complex patterns in disease spread, simulate control options to assist public health authorities in decision-making, and longer-term operational and financial planning. In the context of vaccine-preventable diseases (VPDs), vaccines are one of the most-cost effective outbreak response interventions, with the potential to avert significant morbidity and mortality through timely delivery. Models can contribute to the design of vaccine response by investigating the importance of timeliness, identifying high-risk areas, prioritising the use of limited vaccine supply, highlighting surveillance gaps and reporting, and determining the short- and long-term benefits. In this review, we examine how models have been used to inform vaccine response for 10 VPDs, and provide additional insights into the challenges of outbreak response modelling, such as data gaps, key vaccine-specific considerations, and communication between modellers and stakeholders. We illustrate that while models are key to policy-oriented outbreak vaccine response, they can only be as good as the surveillance data that inform them.
The rise of AI-driven manipulation poses significant risks to societal trust and democratic processes. Yet, studying these effects in real-world settings at scale is ethically and logistically impractical, highlighting a need for simulation tools that can model these dynamics in controlled settings to enable experimentation with possible defenses. We present a simulation environment designed to address this. We elaborate upon the Concordia framework that simulates offline, `real life' activity by adding online interactions to the simulation through social media with the integration of a Mastodon server. We improve simulation efficiency and information flow, and add a set of measurement tools, particularly longitudinal surveys. We demonstrate the simulator with a tailored example in which we track agents' political positions and show how partisan manipulation of agents can affect election results.
SouLLMate: An Application Enhancing Diverse Mental Health Support with Adaptive LLMs, Prompt Engineering, and RAG Techniques
2024-10-17
Qiming Guo, Jinwen Tang, Wenbo Sun, Haoteng Tang, Yi Shang, Wenlu Wang
Mental health issues significantly impact individuals' daily lives, yet many do not receive the help they need even with available online resources. This study aims to provide diverse, accessible, stigma-free, personalized, and real-time mental health support through cutting-edge AI technologies. It makes the following contributions: (1) Conducting an extensive survey of recent mental health support methods to identify prevalent functionalities and unmet needs. (2) Introducing SouLLMate, an adaptive LLM-driven system that integrates LLM technologies, Chain, Retrieval-Augmented Generation (RAG), prompt engineering, and domain knowledge. This system offers advanced features such as Risk Detection and Proactive Guidance Dialogue, and utilizes RAG for personalized profile uploads and Conversational Information Extraction. (3) Developing novel evaluation approaches for preliminary assessments and risk detection via professionally annotated interview data and real-life suicide tendency data. (4) Proposing the Key Indicator Summarization (KIS), Proactive Questioning Strategy (PQS), and Stacked Multi-Model Reasoning (SMMR) methods to enhance model performance and usability through context-sensitive response adjustments, semantic coherence evaluations, and enhanced accuracy of long-context reasoning in language models. This study contributes to advancing mental health support technologies, potentially improving the accessibility and effectiveness of mental health care globally.
The Nature of a Recently Discovered Wolf-Rayet Binary: Archetype of Stripping?
2024-10-16
Philip Massey, Kathryn F. Neugent, Nidia I. Morrell, Desmond John Hillier, Laura R. Penny
LMCe055-1 was recently discovered in a survey for WRs in the Large Magellanic Cloud, and classified as a WN4/O4, a lower excitation version of the WN3/O3 class discovered as part of the same survey. Its absolute magnitude precluded it from being a WN4+O4 binary. OGLE photometry show shallow primary and secondary eclipses with a 2.2 day period. The spectral characteristics and short period pointed to a possible origin due to binary stripping. Such stripped WR binaries should be common but have proven elusive to identify conclusively. In order to establish its nature, we obtained HST UV and Magellan optical spectra, along with imaging. Our work shows that the WR emission and He II absorption arise in one star, and the He I absorption in another. The He I contributor is the primary of the 2.2-day system and exhibits ~300 km/sed radial velocity variations on that time scale. However, the WR star shows 30-40 km/sed radial velocity variations, with a likely 35-day period and a highly eccentric orbit. Possibly LMCe055-1 is a physical triple, but that would require the 2.2-day pair to have been captured by the WR star. A more likely explanation is that the WR star has an unseen companion in a 35-day orbit and that the 2.2-day pair is in a longer period orbit about the two. Such examples of multiple systems are well known among massive stars, such as HD 5980. Regardless, we argue that it is highly unlikely that the WR component of the LMCe055-1 system resulted from stripping.
WorldMedQA-V: a multilingual, multimodal medical examination dataset for multimodal language models evaluation
2024-10-16
João Matos, Shan Chen, Siena Placino, Yingya Li, Juan Carlos Climent Pardo, Daphna Idan, Takeshi Tohyama, David Restrepo, Luis F. Nakayama, Jose M. M. Pascual-Leone, Guergana Savova, Hugo Aerts, Leo A. Celi, A. Ian Wong, Danielle S. Bitterman, Jack Gallifant
Multimodal/vision language models (VLMs) are increasingly being deployed in healthcare settings worldwide, necessitating robust benchmarks to ensure their safety, efficacy, and fairness. Multiple-choice question and answer (QA) datasets derived from national medical examinations have long served as valuable evaluation tools, but existing datasets are largely text-only and available in a limited subset of languages and countries. To address these challenges, we present WorldMedQA-V, an updated multilingual, multimodal benchmarking dataset designed to evaluate VLMs in healthcare. WorldMedQA-V includes 568 labeled multiple-choice QAs paired with 568 medical images from four countries (Brazil, Israel, Japan, and Spain), covering original languages and validated English translations by native clinicians, respectively. Baseline performance for common open- and closed-source models are provided in the local language and English translations, and with and without images provided to the model. The WorldMedQA-V benchmark aims to better match AI systems to the diverse healthcare environments in which they are deployed, fostering more equitable, effective, and representative applications.
A Gamma-ray Stacking Survey of Fermi-LAT Undetected Globular Clusters
2024-10-16
Owen K. Henry, Timothy A. D. Paglione, Yuzhe Song, Joshua Tan, David Zurek, Vanessa Pinto
We present evidence for $\gamma$-ray emission from a stacked population of 39 high-latitude globular clusters (GCs) not detected in the Fermi Point Source Catalog, likely attributable to populations of millisecond pulsars within them. In this work, we use 13 years of data collected by the Large Area Telescope aboard the Fermi Gamma-Ray Space Telescope to search for a cumulative signal from undetected GCs and compared them to control fields (CFs), selected to match the celestial distribution of the target clusters so as to distinguish the $\gamma$-ray signal from background emission. The joint likelihood distribution of the GCs has a significant separation ($\sim4\sigma$) from that of the CFs. We also investigate correlations between detected cluster luminosities and other cluster properties such as distance, the number of millisecond pulsars associated with each cluster, and stellar encounter rate but find no significant relationships.
Do They Understand What They Are Using? -- Assessing Perception and Usage of Biometrics
2024-10-16
Lukas Mecke, Alia Saad, Sarah Prange, Uwe Gruenefeld, Stefan Schneegass, Florian Alt
In this paper we assess how well users know biometric authentication methods, how they perceive them, and if they have misconceptions about them. We present the results of an online survey that we conducted in two rounds (2019, N=57; and 2023, N=47) to understand the impact of the increasing availability of biometrics on their use and perception. The survey covered participants' general understanding of physiological and behavioral biometrics and their perceived usability and security. While most participants were able to name examples and stated that they use biometrics in their daily lives, they still had difficulties explaining the concepts behind them. We shed light on participants' misconceptions, their coping strategies with authentication failures and potential attacks, as well as their perception of the usability and security of biometrics in general. As such, our results can support the design of both further studies to gain deeper insights and future biometric interfaces to foster the informed use of biometrics.
Simulation of Quantum Computers: Review and Acceleration Opportunities
2024-10-16
Alessio Cicero, Mohammad Ali Maleki, Muhammad Waqar Azhar, Anton Frisk Kockum, Pedro Trancoso
Quantum computing has the potential to revolutionize multiple fields by solving complex problems that can not be solved in reasonable time with current classical computers. Nevertheless, the development of quantum computers is still in its early stages and the available systems have still very limited resources. As such, currently, the most practical way to develop and test quantum algorithms is to use classical simulators of quantum computers. In addition, the development of new quantum computers and their components also depends on simulations. Given the characteristics of a quantum computer, their simulation is a very demanding application in terms of both computation and memory. As such, simulations do not scale well in current classical systems. Thus different optimization and approximation techniques need to be applied at different levels. This review provides an overview of the components of a quantum computer, the levels at which these components and the whole quantum computer can be simulated, and an in-depth analysis of different state-of-the-art acceleration approaches. Besides the optimizations that can be performed at the algorithmic level, this review presents the most promising hardware-aware optimizations and future directions that can be explored for improving the performance and scalability of the simulations.
From Measurement Instruments to Data: Leveraging Theory-Driven Synthetic Training Data for Classifying Social Constructs
2024-10-16
Lukas Birkenmaier, Matthias Roth, Indira Sen
Computational text classification is a challenging task, especially for multi-dimensional social constructs. Recently, there has been increasing discussion that synthetic training data could enhance classification by offering examples of how these constructs are represented in texts. In this paper, we systematically examine the potential of theory-driven synthetic training data for improving the measurement of social constructs. In particular, we explore how researchers can transfer established knowledge from measurement instruments in the social sciences, such as survey scales or annotation codebooks, into theory-driven generation of synthetic data. Using two studies on measuring sexism and political topics, we assess the added value of synthetic training data for fine-tuning text classification models. Although the results of the sexism study were less promising, our findings demonstrate that synthetic data can be highly effective in reducing the need for labeled data in political topic classification. With only a minimal drop in performance, synthetic data allows for substituting large amounts of labeled data. Furthermore, theory-driven synthetic data performed markedly better than data generated without conceptual information in mind.
Icarus revisited: An Ancient, Metal-poor Accreted Stellar Stream in the Disk of the Milky Way
2024-10-16
Paola Re Fiorentin, Alessandro Spagna, Mario G. Lattanzi, Michele Cignoni, Sara Vitali
The search for accreted satellites in the Galactic disk is a challenging task, to which Gaia plays a crucial role in synergy with ground-based spectroscopic surveys. In 2021, Re Fiorentin et al. discovered five substructures with disk kinematics including Icarus. To gain more insight into the origin of Icarus as a remnant of a dwarf galaxy rather than a signature of secular processes of disk formation, we complement astrometric Gaia DR3 data with spectroscopy from APOGEE DR17 and GALAH DR3, and explore the chemo-dynamical distributions within 3 kpc of the Sun. We select 622 stars in the accreted/unevolved regions of [Mg/Mn]-[Al/Fe] and [Mg/Fe]-[Fe/H], where we identify 81 and 376 stars with $-2<{\rm[Fe/H]}<-0.7$ belonging to Icarus and Gaia-Sausage-Enceladus (GSE), respectively. The revised properties of Icarus are: $\langle V+V_{\rm LSR}\rangle\simeq171~\rm{km~s}^{-1}$, $\sigma_{V}\simeq37\rm{km~s}^{-1}$, $\langle e\rangle\simeq0.36$, $\langle{\rm[Fe/H]}\rangle\simeq-1.35$, $\langle{\rm[Mg/Fe]}\rangle\simeq+0.27$, $\langle{\rm[Al/Fe]}\rangle\simeq-0.13$, and $\langle{\rm[Mn/Fe]}\rangle\simeq-0.39$. From the CMD of its members, Icarus appears older than 12 Gyr. Such age and dynamical properties are reminiscent of the metal-weak thick disk. However, detailed chemical analysis in the diagnostic spaces [Ni/Fe]-[(C+N)/O], [Y/Eu]-[Fe/H], [Eu/Mg]-[Fe/H], [Ba/Y]-[Fe/H], and [Ba/Mg]-[Mg/H] evidences that Icarus and GSE occupy the accreted region, well separated from the bulk of in situ disk stars. Updated comparisons with N-body simulations confirm that Icarus' stars are consistent with the debris of a dwarf galaxy with a stellar mass of $\sim~10^9~M_\odot$ accreted onto a primordial disk on an initial prograde low-inclination orbit.
On "Inconsistencies of metalens performance and comparison with conventional diffractive optics"
2024-10-16
Amir Arbabi, Andrei Faraon
It was recently claimed1 that reported focusing efficiency values of high numerical aperture metalenses are inconsistent with a theoretical bound, and their measurement results are incorrectly interpreted. We review the article and conclude that these claims are not well supported.
Exploring Plural Perspectives in Self-Tracking Technologies: Trust and Reflection in Self Tracking Practices
2024-10-16
Sujay Shalawadi, Rosa van Koningsbruggen, Rikke Hagensby Jensen
Contemporary self-tracking technologies (STTs), such as smartwatches and smartphone apps, allow people to become self-aware through the datafication of their everyday lives. However, concerns are emerging over the global north/Western portrayal of the self in the envisionment of STTs. Given the call to diversify participant samples in HCI knowledge building, we see it timely in understanding the influence of ubiquitous STTs in global south societies. We conduct a between-group analysis of 156 and 121 participants from Global North and South through two iterative surveys, respectively. We uncover significant differences in perceived trust with their STTs and reflection practices between the groups. We provide an empirical understanding on advocating for inclusive design strategies that recognize diverse interpretations of STTs and highlight the need to prioritize local values and flexibility in tracking to foster deeper reflection across cultures. Lastly, we discuss our findings in relation to the existing literature and highlight design recommendations for future research.
Large Language Model (LLM)-driven interactive systems currently show potential promise in healthcare domains. Despite their remarkable capabilities, LLMs typically lack personalized recommendations and diagnosis analysis in sophisticated medical applications, causing hallucinations and performance bottlenecks. To address these challenges, this paper proposes MedAide, an LLM-based omni medical multi-agent collaboration framework for specialized healthcare services. Specifically, MedAide first performs query rewriting through retrieval-augmented generation to accomplish accurate medical intent understanding. Immediately, we devise a contextual encoder to obtain intent prototype embeddings, which are used to recognize fine-grained intents by similarity matching. According to the intent relevance, the activated agents collaborate effectively to provide integrated decision analysis. Extensive experiments are conducted on four medical benchmarks with composite intents. Experimental results from automated metrics and expert doctor evaluations show that MedAide outperforms current LLMs and improves their medical proficiency and strategic reasoning.
Mitigating baryon feedback bias in cosmic shear through a theoretical error covariance in the matter power spectrum
2024-10-16
Alessandro Maraio, Alex Hall, Andy Taylor
Forthcoming cosmic shear surveys will make precise measurements of the matter density field down to very small scales, scales which are dominated by baryon feedback. The modelling of baryon feedback is crucial to ensure unbiased cosmological parameter constraints; the most efficient approach is to use analytic models, but these are limited by how well they can capture the physics of baryon feedback. We investigate the fitting and residual errors of various baryon feedback models to a suite of hydrodynamic simulations, and propagate these to cosmological parameter constraints for cosmic shear. We present an alternative formalism to binary scale-cuts through the use of a theoretical error covariance, which is a well-motivated alternative using errors in the power spectrum modelling itself. We depart from previous works by modelling baryonic feedback errors directly in the matter power spectrum, which is the natural basis to do so and thus preserves information in the lensing kernels. When including angular multipoles up to $\ell_{\mathrm{max}} = 5000$, and assuming Euclid-like survey properties, we find that even multi-parameter models of baryon feedback can introduce significant levels of bias. In contrast, our theoretical error reduces the bias in $\Omega_{\mathrm{m}}$ and $S_{8}$ to acceptable levels, with only a modest increase in parameter variances. The theoretical error approach bypasses the need to directly determine the per-bin $\ell_{\mathrm{max}}$ values, as it naturally suppresses the biassing small-scale information. We also present a detailed study of how flexible HMCode-2020, a widely-used non-linear and baryonic feedback model, is at fitting a range of hydrodynamical simulations.
The properties, impact, and fate of hot stars cannot be understood without considering their winds. Revealed to be an almost ubiquitous phenomenon in the regime of massive stars, the winds of hot stars arise from a complex physical mechanism that still provides a major challenge for our understanding of massive stars. Different flavours of hot stars vary significantly in their winds with current evolution models still having problems to connect the zoo of observed phenomena. Moreover, the driving of hot star winds is inherently connected to the opacities arising from spectral line transitions, making the properties and strength of the winds strongly dependent on metallicity and changing them over cosmic time. In these proceedings, the current status in our understanding of hot star winds is briefly reviewed and recent progress in our perception of hot star winds and the consequences for the evolution of massive stars are presented. A particular emphasis is given on current efforts with hydrodynamically-consistent atmosphere models towards a better description of radiatively-driven mass loss of Wolf-Rayet stars with remaining hydrogen envelopes.
Learning to Predict Usage Options of Product Reviews with LLM-Generated Labels
2024-10-16
Leo Kohlenberg, Leonard Horns, Frederic Sadrieh, Nils Kiele, Matthis Clausen, Konstantin Ketterer, Avetis Navasardyan, Tamara Czinczoll, Gerard de Melo, Ralf Herbrich
Annotating large datasets can be challenging. However, crowd-sourcing is often expensive and can lack quality, especially for non-trivial tasks. We propose a method of using LLMs as few-shot learners for annotating data in a complex natural language task where we learn a standalone model to predict usage options for products from customer reviews. We also propose a new evaluation metric for this scenario, HAMS4, that can be used to compare a set of strings with multiple reference sets. Learning a custom model offers individual control over energy efficiency and privacy measures compared to using the LLM directly for the sequence-to-sequence task. We compare this data annotation approach with other traditional methods and demonstrate how LLMs can enable considerable cost savings. We find that the quality of the resulting data exceeds the level attained by third-party vendor services and that GPT-4-generated labels even reach the level of domain experts. We make the code and generated labels publicly available.
Challenges, Methods, Data -- a Survey of Machine Learning in Water Distribution Networks
2024-10-16
Valerie Vaquet, Fabian Hinder, André Artelt, Inaam Ashraf, Janine Strotherm, Jonas Vaquet, Johannes Brinkrolf, Barbara Hammer
Research on methods for planning and controlling water distribution networks gains increasing relevance as the availability of drinking water will decrease as a consequence of climate change. So far, the majority of approaches is based on hydraulics and engineering expertise. However, with the increasing availability of sensors, machine learning techniques constitute a promising tool. This work presents the main tasks in water distribution networks, discusses how they relate to machine learning and analyses how the particularities of the domain pose challenges to and can be leveraged by machine learning approaches. Besides, it provides a technical toolkit by presenting evaluation benchmarks and a structured survey of the exemplary task of leakage detection and localization.
Prompt Compression for Large Language Models: A Survey
2024-10-16
Zongqian Li, Yinhong Liu, Yixuan Su, Nigel Collier
Leveraging large language models (LLMs) for complex natural language tasks typically requires long-form prompts to convey detailed requirements and information, which results in increased memory usage and inference costs. To mitigate these challenges, multiple efficient methods have been proposed, with prompt compression gaining significant research interest. This survey provides an overview of prompt compression techniques, categorized into hard prompt methods and soft prompt methods. First, the technical approaches of these methods are compared, followed by an exploration of various ways to understand their mechanisms, including the perspectives of attention optimization, Parameter-Efficient Fine-Tuning (PEFT), modality integration, and new synthetic language. We also examine the downstream adaptations of various prompt compression techniques. Finally, the limitations of current prompt compression methods are analyzed, and several future directions are outlined, such as optimizing the compression encoder, combining hard and soft prompts methods, and leveraging insights from multimodality.
Superconducting Quantum Simulation for Many-Body Physics beyond Equilibrium
2024-10-16
Yunyan Yao, Liang Xiang
Quantum computing is an exciting field that uses quantum principles, such as quantum superposition and entanglement, to tackle complex computational problems. Superconducting quantum circuits, based on Josephson junctions, is one of the most promising physical realizations to achieve the long-term goal of building fault-tolerant quantum computers. The past decade has witnessed the rapid development of this field, where many intermediate-scale multi-qubit experiments emergedtosimulatenonequilibriumquantummany-bodydynamicsthatarechallenging for classical computers. Here, we review the basic concepts of superconducting quantum simulation and their recent experimental progress in exploring exotic nonequilibrium quantum phenomena emerging in strongly interacting many-body systems, e.g., many-body localization, quantum many body scars, and discrete time crystals. We further discuss the prospects of quantum simulation experiments to truly solve open problems in nonequilibrium many-body systems.
Privacy by Design: Bringing User Awareness to Privacy Risks in Internet of Things
2024-10-16
Usama Younus, Rie Kamikubo
This paper aims to cover and summarize the field of IoT and related privacy concerns through the lens of privacy by design. With the ever-increasing incorporation of technology within our daily lives and an ever-growing active research into smart devices and technologies, privacy concerns are inevitable. We intend to briefly cover the broad topic of privacy in the IoT space, the inherent challenges and risks in such systems, and a few recent techniques that intend to resolve these issues on the subdomain level and a system scale level. We then proceed to approach this situation through design thinking and privacy-by-design, given that most of the prior efforts are based on resolving privacy concerns on technical grounds with system-level design. We participated in a co-design workshop for the privacy of a content creation platform and used those findings to deploy a survey-based mechanism to tackle some key concern areas for user groups and formulate design principles for privacy that promote transparent, user-centered, and awareness-provoking privacy design.
Highly anisotropic Drude-weight-reduction and enhanced linear-dichroism in van der Waals Weyl semimetal Td-MoTe2 with coherent interlayer electronic transport
2024-10-16
Bo Su, Weikang Wu, Jianzhou Zhao, Xiutong Deng, Wenhui Li, Shengyuan A. Yang, Youguo Shi, Qiang Li, Jianlin Luo, Genda Gu, Zhi-Guo Chen
Weyl semimetal (WSM) states can be achieved by breaking spatial-inversion symmetry or time reversal symmetry. However, the anisotropy of the energy reduction contributing to the emergence of WSM states has seldom been investigated by experiments. A van der Waals metal MoTe2 exhibits a type-II WSM phase below the monoclinic-to-orthorhombic-phase-transition temperature Tc ~ 250 K. Here, we report a combined linearly-polarized optical-spectroscopy and electrical-transport study of MoTe2 at different temperatures. The Drude components in the a-axis, b-axis and c-axis optical conductivity spectra, together with the metallic out-of-plane and in-plane electrical resistivities, indicate the coherent inter-layer and in-plane charge transports. Moreover, the Drude weight in {\sigma}1a({\omega}), rather than the Drude weights in {\sigma}1b({\omega}) and {\sigma}1c({\omega}), decreases dramatically below Tc, which exhibits a highly anisotropic decrease in its Drude weight and thus suggests a strongly anisotropic reduction of the electronic kinetic energy in the WSM phase. Furthermore, below Tc, due to the in-plane anisotropic spectral-weight transfer from Drude component to high-energy region, the in-plane inter-band-absorption anisotropy increases remarkably around 770 meV, and has the largest value (~ 0.68) of normalized linear dichroism among the reported type-II WSMs. Our work sheds light on seeking new WSMs and developing novel photonic devices based on WSMs.
2024-10-16
Nir Shlezinger, Guy Revach, Anubhab Ghosh, Saikat Chatterjee, Shuo Tang, Tales Imbiriba, Jindrich Dunik, Ondrej Straka, Pau Closas, Yonina C. Eldar
The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on mathematical representations in the form of simple state-space (SS) models, which may be crude and inaccurate descriptions of the underlying dynamics. Emerging data-centric artificial intelligence (AI) techniques tackle these tasks using deep neural networks (DNNs), which are model-agnostic. Recent developments illustrate the possibility of fusing DNNs with classic Kalman-type filtering, obtaining systems that learn to track in partially known dynamics. This article provides a tutorial-style overview of design approaches for incorporating AI in aiding KF-type algorithms. We review both generic and dedicated DNN architectures suitable for state estimation, and provide a systematic presentation of techniques for fusing AI tools with KFs and for leveraging partial SS modeling and data, categorizing design approaches into task-oriented and SS model-oriented. The usefulness of each approach in preserving the individual strengths of model-based KFs and data-driven DNNs is investigated in a qualitative and quantitative study, whose code is publicly available, illustrating the gains of hybrid model-based/data-driven designs. We also discuss existing challenges and future research directions that arise from fusing AI and Kalman-type algorithms.
Beyond Oversmoothing: Evaluating DDPM and MSE for Scalable Speech Synthesis in ASR
2024-10-16
Christoph Minixhofer, Ondrej Klejch, Peter Bell
Synthetically generated speech has rapidly approached human levels of naturalness. However, the paradox remains that ASR systems, when trained on TTS output that is judged as natural by humans, continue to perform badly on real speech. In this work, we explore whether this phenomenon is due to the oversmoothing behaviour of models commonly used in TTS, with a particular focus on the behaviour of TTS-for-ASR as the amount of TTS training data is scaled up. We systematically compare Denoising Diffusion Probabilistic Models (DDPM) to Mean Squared Error (MSE) based models for TTS, when used for ASR model training. We test the scalability of the two approaches, varying both the number hours, and the number of different speakers. We find that for a given model size, DDPM can make better use of more data, and a more diverse set of speakers, than MSE models. We achieve the best reported ratio between real and synthetic speech WER to date (1.46), but also find that a large gap remains.
Kagome superconductors AV3Sb5 (A = K, Rb, and Cs) have attracted considerable attention due to their intriguing combination of unique electron correlations and nontrivial band topology. The interplay of these fundamental aspects gives rise to a diverse array of exotic electronic phenomena, including superconductivity and charge density wave (CDW) states. In this review, we present recent advancements in the study of the electronic band structure of AV3Sb5 using angle-resolved photoemission spectroscopy (ARPES), including the identification of the multiple van Hove singularities near the Fermi level and their close relationship with the CDW transition, spectroscopic features related to CDW-induced symmetry breakings, as well as direct observations of nodeless superconducting gaps and moderate electron-phonon couplings through ultrahigh-resolution ARPES, providing critical insights into the origins of CDW order and electron pairing symmetry. By synthesizing these key ARPES findings, this review aims to deepen our understanding of kagome-related physics.
The GALAH Survey: Stellar parameters and abundances for 800,000 Gaia RVS spectra using GALAH DR4 and The Cannon
2024-10-16
Pradosh Barun Das, Daniel B. Zucker, Gayandhi M. De Silva, Nicholas W. Borsato, Aldo Mura-Guzmán, Sven Buder, Melissa Ness, Thomas Nordlander, Andrew R. Casey, Sarah L. Martell, Joss Bland-Hawthorn, Richard de Grijs, Ken C. Freeman, Janez Kos, Dennis Stello, Geraint F. Lewis, Michael R. Hayden, Sanjib Sharma
Analysing stellar parameters and abundances from nearly one million Gaia DR3 Radial Velocity Spectrometer (RVS) spectra poses challenges due to the limited spectral coverage (restricted to the infrared Ca II triplet) and variable signal-to-noise ratios of the data. To address this, we use The Cannon, a data-driven method, to transfer stellar parameters and abundances from the GALAH Data Release 4 (DR4; R ~ 28,000) catalogue to the lower resolution Gaia DR3 RVS spectra (R ~ 11,500). Our model, trained on 14,484 common targets, predicts parameters such as Teff, log g, and [Fe/H], along with several other elements across approximately 800,000 Gaia RVS spectra. We utilise stars from open and globular clusters present in the Gaia RVS catalogue to validate our predicted mean [Fe/H] with high precision (~0.02-0.10 dex). Additionally, we recover the bimodal distribution of [Ti/Fe] versus [Fe/H], reflecting the high and low alpha-components of Milky Way disk stars, demonstrating The Cannon's capability for accurate stellar abundance determination from medium-resolution Gaia RVS spectra. The methodologies and resultant catalogue presented in this work highlight the remarkable potential of the RVS dataset, which by the end of the Gaia mission will comprise spectra of over 200 million stars.
An Automatic and Cost-Efficient Peer-Review Framework for Language Generation Evaluation
2024-10-16
Junjie Chen, Weihang Su, Zhumin Chu, Haitao Li, Qinyao Ai, Yiqun Liu, Min Zhang, Shaoping Ma
With the rapid development of large language models (LLMs), how to efficiently evaluate them has become an important research question. Existing evaluation methods often suffer from high costs, limited test formats, the need of human references, and systematic evaluation biases. To address these limitations, our study introduces the Auto-PRE, an automatic LLM evaluation framework based on peer review. In contrast to previous studies that rely on human annotations, Auto-PRE selects evaluator LLMs automatically based on their inherent traits including consistency, self-confidence, and pertinence. We conduct extensive experiments on three tasks: summary generation, non-factoid question-answering, and dialogue generation. Experimental results indicate our Auto-PRE achieves state-of-the-art performance at a lower cost. Moreover, our study highlights the impact of prompt strategies and evaluation formats on evaluation performance, offering guidance for method optimization in the future.
2024-10-16
Siting Zhu, Guangming Wang, Dezhi Kong, Hesheng Wang
Dense 3D representations of the environment have been a long-term goal in the robotics field. While previous Neural Radiance Fields (NeRF) representation have been prevalent for its implicit, coordinate-based model, the recent emergence of 3D Gaussian Splatting (3DGS) has demonstrated remarkable potential in its explicit radiance field representation. By leveraging 3D Gaussian primitives for explicit scene representation and enabling differentiable rendering, 3DGS has shown significant advantages over other radiance fields in real-time rendering and photo-realistic performance, which is beneficial for robotic applications. In this survey, we provide a comprehensive understanding of 3DGS in the field of robotics. We divide our discussion of the related works into two main categories: the application of 3DGS and the advancements in 3DGS techniques. In the application section, we explore how 3DGS has been utilized in various robotics tasks from scene understanding and interaction perspectives. The advance of 3DGS section focuses on the improvements of 3DGS own properties in its adaptability and efficiency, aiming to enhance its performance in robotics. We then summarize the most commonly used datasets and evaluation metrics in robotics. Finally, we identify the challenges and limitations of current 3DGS methods and discuss the future development of 3DGS in robotics.
Relativistic accretors are cosmic objects that pull matter from their surroundings at speeds almost equal to the light's speed. Because of the tremendous gravitational force from the accretors and the angular momentum of infalling material, which often result in discs of gas and dust that are heated to extremely high temperatures. We encounter strong radiation throughout the electromagnetic spectrum, including intense X-rays. The X-ray view provides a unique window into the behavior of accretors. In this review, we discuss different accretors, particularly binaries, and their origin, involved mechanisms, and properties, including energy spectra and the variability of X-rays from the accretors. This X-ray perspective gives a unique insight into the evolution and connections of these systems with their environment. Future research in this area is necessary to fully understand the process underlying X-ray emission from relativistic accretors.
Accurate and Data-Efficient Toxicity Prediction when Annotators Disagree
2024-10-16
Harbani Jaggi, Kashyap Murali, Eve Fleisig, Erdem Bıyık
When annotators disagree, predicting the labels given by individual annotators can capture nuances overlooked by traditional label aggregation. We introduce three approaches to predicting individual annotator ratings on the toxicity of text by incorporating individual annotator-specific information: a neural collaborative filtering (NCF) approach, an in-context learning (ICL) approach, and an intermediate embedding-based architecture. We also study the utility of demographic information for rating prediction. NCF showed limited utility; however, integrating annotator history, demographics, and survey information permits both the embedding-based architecture and ICL to substantially improve prediction accuracy, with the embedding-based architecture outperforming the other methods. We also find that, if demographics are predicted from survey information, using these imputed demographics as features performs comparably to using true demographic data. This suggests that demographics may not provide substantial information for modeling ratings beyond what is captured in survey responses. Our findings raise considerations about the relative utility of different types of annotator information and provide new approaches for modeling annotators in subjective NLP tasks.
Recent studies show that LLMs, particularly open-source models, struggle to follow complex instructions with multiple constraints. Despite the importance, methods to improve LLMs' adherence to such constraints remain unexplored, and current research focuses on evaluating this ability rather than developing solutions. While a few studies enhance constraint adherence through model tuning, this approach is computationally expensive and heavily reliant on training data quality. An alternative is to leverage LLMs' self-correction capabilities, allowing them to adjust responses to better meet specified constraints. However, this self-correction ability of LLMs is limited by the feedback quality, as LLMs cannot autonomously generate reliable feedback or detect errors. Moreover, the self-refinement process heavily depends on few-shot examples that illustrate how to modify responses to meet constraints. As constraints in complex instructions are diverse and vary widely, manually crafting few-shot examples for each constraint type can be labor-intensive and sub-optimal. To deal with these two challenges, we propose the Divide-Verify-Refine (DVR) framework with three steps: (1) Divide complex instructions into single constraints and prepare appropriate tools; (2) Verify: To address the feedback quality problem, these tools will rigorously verify responses and provide reliable feedback; (3) Refine: To address the constraint diversity challenge, we design a refinement repository that collects successful refinement processes and uses them as few-shot demonstrations for future cases, allowing LLMs to learn from the past experience during inference. Additionally, we develop a new dataset of complex instructions, each containing 1-6 constraints. Experiments show that the framework significantly improves performance, doubling LLama3.1-8B's constraint adherence on instructions with 6 constraints.
We present deep Subaru/FOCAS spectra for two extreme emission line galaxies (EELGs) at $z\sim 1$ with strong {\sc[Oiii]}$\lambda$5007 emission lines, exhibiting equivalent widths (EWs) of $2905^{+946}_{-578}$ \AA\ and $2000^{+188}_{-159}$ \AA, comparable to those of EELGs at high redshifts that are now routinely identified with JWST spectroscopy. Adding a similarly large {\sc [Oiii]} EW ($2508^{+1487}_{-689}$ \AA) EELG found at $z\sim 2$ in the JWST CEERS survey to our sample, we explore for the physical origins of the large {\sc [Oiii]} EWs of these three galaxies with the Subaru spectra and various public data including JWST/NIRSpec, NIRCam, and MIRI data. While there are no clear signatures of AGN identified by the optical line diagnostics, we find that two out of two galaxies covered by the MIRI data show strong near-infrared excess in the spectral energy distributions (SEDs) indicating obscured AGN. Because none of the three galaxies show clear broad H$\beta$ lines, the upper limits on the flux ratios of broad-H$\beta$ to {\sc [Oiii]} lines are small, $\lesssim 0.15$ that are comparable with Seyfert $1.8-2.0$ galaxies. We conduct \texttt{Cloudy} modeling with the stellar and AGN incident spectra, allowing a wide range of parameters including metallicities and ionization parameters. We find that the large {\sc [Oiii]} EWs are not self-consistently reproduced by the spectra of stars or unobscured AGN, but obscured AGN that efficiently produces O$^{++}$ ionizing photons with weak nuclear and stellar continua that are consistent with the SED shapes.
2024-10-16
Kostas E. Bekris, Joe Doerr, Patrick Meng, Sumanth Tangirala
This paper reviews the large spectrum of methods for generating robot motion proposed over the 50 years of robotics research culminating in recent developments. It crosses the boundaries of methodologies, typically not surveyed together, from those that operate over explicit models to those that learn implicit ones. The paper discusses the current state-of-the-art as well as properties of varying methodologies, highlighting opportunities for integration.
Facing Identity: The Formation and Performance of Identity via Face-Based Artificial Intelligence Technologies
2024-10-16
Wells Lucas Santo
How is identity constructed and performed in the digital via face-based artificial intelligence technologies? While questions of identity on the textual Internet have been thoroughly explored, the Internet has progressed to a multimedia form that not only centers the visual, but specifically the face. At the same time, a wealth of scholarship has and continues to center the topics of surveillance and control through facial recognition technologies (FRTs), which have extended the logics of the racist pseudoscience of physiognomy. Much less work has been devoted to understanding how such face-based artificial intelligence technologies have influenced the formation and performance of identity. This literature review considers how such technologies interact with faciality, which entails the construction of what a face may represent or signify, along axes of identity such as race, gender, and sexuality. In grappling with recent advances in AI such as image generation and deepfakes, I propose that we are now in an era of "post-facial" technologies that build off our existing culture of facility while eschewing the analog face, complicating our relationship with identity vis-a-vis the face. Drawing from previous frameworks of identity play in the digital, as well as trans practices that have historically played with or transgressed the boundaries of identity classification, we can develop concepts adequate for analyzing digital faciality and identity given the current landscape of post-facial artificial intelligence technologies that allow users to interface with the digital in an entirely novel manner. To ground this framework of transgression, I conclude by proposing an interview study with VTubers -- online streamers who perform using motion-captured avatars instead of their real-life faces -- to gain qualitative insight on how these sociotechnical experiences.
When researchers pay to publish: Results from a survey on APCs in four countries
2024-10-16
Osvaldo Gallardo, Matías Milia, André Luiz Appel, Grip-APC Team, François van Schalkwyk
This paper provides an empirical overview of the impact and practices of paying Article Processing Charges (APCs) by four nationally categorized groups of researchers in Argentina, Brazil, Mexico, and South Africa. The data was collected from 13,577 researchers through an online questionnaire. The analysis compares the practice of publishing in journals that charge APCs across different dimensions, including country, discipline, gender, and age of the researchers. The paper also focuses on the maximum amount APC paid and the methods and strategies researchers use to cover APC payments, such as waivers, research project funds, payment by coauthors, and the option to publish in closed access, where possible. Different tendencies were identified among the different disciplines and the national systems examined. Findings show that Argentine researchers apply for waivers most frequently and often use personal funds or international coauthors for APCs, with younger researchers less involved in APC payments. In contrast, Brazil, South Africa, and Mexico have more older researchers, yet younger researchers still publish more in APC journals. South African researchers lead in APC publications, likely due to better funding access and read and publish agreements. This study lays the groundwork for further analysis of gender asymmetries, funding access, and views on the commercial Open Access model of scientific dissemination.
Intrinsic grain boundary mobility tensor from three-dimensional interface random walk
2024-10-16
Xinyuan Song, Chuang Deng
In recent years, studies have demonstrated that the grain boundary (GB) migration is a three-dimensional (3D) process, characterized by a 3D mobility tensor. In this study, we develop a 3D interface random walk theory to extract the GB mobility and shear coupling tensors based on the random walk of the GB position. Using this approach, we mathematically prove the symmetry of the GB mobility tensor in the case of overdamped GB migration. The theory and its conclusions align with molecular dynamics simulation results, and the computed shear coupling aligns closely with predictions from disconnection analysis. Additionally, we refined the fast adapted random walk (FAIRWalk) method, enabling efficient extraction of the GB mobility tensor from fewer simulations while maintaining high accuracy. Building on this advancement, we conducted an extensive survey of the mobility, shear coupling, and activation energy for the migration of 388 coincidence-site lattice Ni GBs in the Olmsted database. Several intriguing phenomena were observed, including temperature-induced sudden emergence, disappearance, or inversion of shear coupling; GBs with "zero" normal mobility but high shear mobility; and a non-linear relationship between activation energy and mobility. These findings challenge the traditional understanding of GB migration and warrant further investigation.
Parametric Graph Representations in the Era of Foundation Models: A Survey and Position
2024-10-16
Dongqi Fu, Liri Fang, Zihao Li, Hanghang Tong, Vetle I. Torvik, Jingrui He
Graphs have been widely used in the past decades of big data and AI to model comprehensive relational data. When analyzing a graph's statistical properties, graph laws serve as essential tools for parameterizing its structure. Identifying meaningful graph laws can significantly enhance the effectiveness of various applications, such as graph generation and link prediction. Facing the large-scale foundation model developments nowadays, the study of graph laws reveals new research potential, e.g., providing multi-modal information for graph neural representation learning and breaking the domain inconsistency of different graph data. In this survey, we first review the previous study of graph laws from multiple perspectives, i.e., macroscope and microscope of graphs, low-order and high-order graphs, static and dynamic graphs, different observation spaces, and newly proposed graph parameters. After we review various real-world applications benefiting from the guidance of graph laws, we conclude the paper with current challenges and future research directions.
Exploring Quantum Materials with Resonant Inelastic X-Ray Scattering
2024-10-16
M. Mitrano, S. Johnston, Young-June Kim, M. P. M. Dean
Understanding quantum materials -- solids in which quantum-mechanical interactions among constituent electrons yield a great variety of novel emergent phenomena -- is a forefront challenge in modern condensed matter physics. This goal has driven the invention and refinement of several experimental methods, which can spectroscopically determine the elementary excitations and correlation functions that determine material properties. This Perspectives article focuses on the future experimental and theoretical trends of resonant inelastic x-ray scattering (RIXS), which is a remarkably versatile and rapidly growing technique for probing different charge, lattice, spin, and orbital excitations in quantum materials. We provide a forward-looking introduction to RIXS and outline how this technique is poised to deepen our insight into the nature of quantum materials and their emergent electronic phenomena.
ERAS: Evaluating the Robustness of Chinese NLP Models to Morphological Garden Path Errors
2024-10-16
Qinchan Li, Sophie Hao
In languages without orthographic word boundaries, NLP models perform word segmentation, either as an explicit preprocessing step or as an implicit step in an end-to-end computation. This paper shows that Chinese NLP models are vulnerable to morphological garden path errors: errors caused by a failure to resolve local word segmentation ambiguities using sentence-level morphosyntactic context. We propose a benchmark, ERAS, that tests a model's vulnerability to morphological garden path errors by comparing its behavior on sentences with and without local segmentation ambiguities. Using ERAS, we show that word segmentation models make garden path errors on locally ambiguous sentences, but do not make equivalent errors on unambiguous sentences. We further show that sentiment analysis models with character-level tokenization make implicit garden path errors, even without an explicit word segmentation step in the pipeline. Our results indicate that models' segmentation of Chinese text often fails to account for morphosyntactic context.
LFOSum: Summarizing Long-form Opinions with Large Language Models
2024-10-16
Mir Tafseer Nayeem, Davood Rafiei
Online reviews play a pivotal role in influencing consumer decisions across various domains, from purchasing products to selecting hotels or restaurants. However, the sheer volume of reviews -- often containing repetitive or irrelevant content -- leads to information overload, making it challenging for users to extract meaningful insights. Traditional opinion summarization models face challenges in handling long inputs and large volumes of reviews, while newer Large Language Model (LLM) approaches often fail to generate accurate and faithful summaries. To address those challenges, this paper introduces (1) a new dataset of long-form user reviews, each entity comprising over a thousand reviews, (2) two training-free LLM-based summarization approaches that scale to long inputs, and (3) automatic evaluation metrics. Our dataset of user reviews is paired with in-depth and unbiased critical summaries by domain experts, serving as a reference for evaluation. Additionally, our novel reference-free evaluation metrics provide a more granular, context-sensitive assessment of summary faithfulness. We benchmark several open-source and closed-source LLMs using our methods. Our evaluation reveals that LLMs still face challenges in balancing sentiment and format adherence in long-form summaries, though open-source models can narrow the gap when relevant information is retrieved in a focused manner.
Design and Feasibility of a Community Motorcycle Ambulance System in the Philippines
2024-10-16
Aaron Rodriguez, Aidan Chen, Ryan Rodriguez
This study investigates the potential for motorcycle ambulance (motorlance) deployment in Metro Manila and Iloilo City to improve emergency medical care in high-traffic, underserved regions of the Philippines. VSee, a humanitarian technology company, has organized numerous free clinics in the Philippines and identified a critical need for improved emergency services. Motorlances offer a fast, affordable alternative to traditional ambulances, particularly in congested urban settings and remote rural locations. Pilot programs in Malawi, Thailand, and Iran have demonstrated significant improvements in response times and cost-efficiency with motorlance systems. This study presents a framework for motorlance operation and identifies three potential pilot locations: Mandaluyong, Smokey Mountain, and Iloilo City. Site visits, driver interviews, and user surveys indicate public trust in the motorlance concept and positive reception to potential motorlance deployment. Cost analysis verifies the financial feasibility of motorlance systems. Future work will focus on implementing a physical pilot in Mandaluyong, with the aim of expanding service to similar regions contingent on the Mandaluyong pilot's success.
Splitting methods with complex coefficients for linear and nonlinear evolution equations
2024-10-16
Sergio Blanes, Fernando Casas, Cesareo Gonzalez, Mechthild Thalhammer
This contribution is dedicated to the exploration of exponential operator splitting methods for the time integration of evolution equations. It entails the review of previous achievements as well as the depiction of novel results. The standard class of splitting methods involving real coefficients is contrasted with an alternative approach that relies on the incorporation of complex coefficients. In view of long-term computations for linear evolution equations, it is expedient to distinguish symmetric, symmetric-conjugate, and alternating-conjugate schemes. The scope of applications comprises high-order reaction-diffusion equations and complex Ginzburg-Landau equations, which are of relevance in the theories of patterns and superconductivity. Time-dependent Gross-Pitaevskii equations and their parabolic counterparts, which model the dynamics of Bose-Einstein condensates and arise in ground state computations, are formally included as special cases. Numerical experiments confirm the validity of theoretical stability conditions and global error bounds as well as the benefits of higher-order complex splitting methods in comparison with standard schemes.
Applying Astronomical Solutions and Milankovi{ć} Forcing in the Earth Sciences
2024-10-16
Richard E. Zeebe, Ilja J. Kocken
Astronomical solutions provide calculated orbital and rotational parameters of solar system bodies based on the dynamics and physics of the solar system. Application of astronomical solutions in the Earth sciences has revolutionized our understanding in at least two areas of active research. (i) The Astronomical (or Milankovic) forcing of climate on time scales > ~10 kyr and (ii) the dating of geologic archives. The latter has permitted the development of the astronomical time scale, widely used today to reconstruct highly accurate geological dates and chronologies. The tasks of computing vs. applying astronomical solutions are usually performed by investigators from different backgrounds, which has led to confusion and recent inaccurate results on the side of the applications. Here we review astronomical solutions and Milankovic forcing in the Earth sciences, primarily aiming at clarifying the astronomical basis, applicability, and limitations of the solutions. We provide a summary of current up-to-date and outdated astronomical solutions and their valid time span. We discuss the fundamental limits imposed by dynamical solar system chaos on astronomical calculations and geological/astrochronological applications. We illustrate basic features of chaotic behavior using a simple mechanical system, i.e., the driven pendulum. Regarding so-called astronomical "metronomes", we point out that the current evidence does not support the notion of generally stable and prominent metronomes for universal use in astrochronology and cyclostratigraphy. We also describe amplitude and frequency modulation of astronomical forcing signals and the relation to their expression in cyclostratigraphic sequences. Furthermore, the various quantities and terminology associated with Earth's axial precession are discussed in detail. Finally, we provide some suggestions regarding practical considerations.
Revisiting the hydrodynamic modulation of short surface waves by longer waves
2024-10-16
Milan Curcic
Hydrodynamic modulation of short ocean surface waves by longer ambient waves is a well-known ocean surface process that affects remote sensing, the interpretation of in situ wave measurements, and numerical wave forecasting. In this paper, we revisit the linear wave theory and derive higher-order steady solutions for the change of short-wave wavenumber, action density, and gravitational acceleration due to the presence of longer waves. We validate the analytical solutions with numerical simulations of the full wave crest and action conservation equations. The nonlinear analytical solutions of short-wave wavenumber, amplitude, and steepness modulation significantly deviate from the linear analytical solutions of Longuet-Higgins & Stewart (1960), and are similar to the nonlinear numerical solutions by Longuet-Higgins (1987) and Zhang & Melville (1990). The short-wave steepness modulation is attributed to be 5/8 due to the wavenumber, 1/4 due to the wave action, and 1/8 due to the effective gravity. We further examine the result of Peureux et al. (2021) who found through numerical simulations that the short-wave modulation grows unsteadily with each long-wave passage. We show that this unsteady growth only occurs only for homogeneous initial conditions as a special case and does not generally occur, for example in more realistic long-wave groups. The proposed steady solutions are a good approximation of the fully nonlinear numerical solutions in long-wave steepness up to ~0.2. Except for a subset of initial conditions, the solutions to the fully nonlinear crest-action conservation equations are mostly steady in the reference frame of the long waves.
modOpt: A modular development environment and library for optimization algorithms
2024-10-16
Anugrah Jo Joshy, John T. Hwang
Recent advances in computing hardware and modeling software have given rise to new applications for numerical optimization. These new applications occasionally uncover bottlenecks in existing optimization algorithms and necessitate further specialization of the algorithms. However, such specialization requires expert knowledge of the underlying mathematical theory and the software implementation of existing algorithms. To address this challenge, we present modOpt, an open-source software framework that facilitates the construction of optimization algorithms from modules. The modular environment provided by modOpt enables developers to tailor an existing algorithm for a new application by only altering the relevant modules. modOpt is designed as a platform to support students and beginner developers in quickly learning and developing their own algorithms. With that aim, the entirety of the framework is written in Python, and it is well-documented, well-tested, and hosted open-source on GitHub. Several additional features are embedded into the framework to assist both beginner and advanced developers. In addition to providing stock modules, the framework also includes fully transparent implementations of pedagogical optimization algorithms in Python. To facilitate testing and benchmarking of new algorithms, the framework features built-in visualization and recording capabilities, interfaces to modeling frameworks such as OpenMDAO and CSDL, interfaces to general-purpose optimization algorithms such as SNOPT and SLSQP, an interface to the CUTEst test problem set, etc. In this paper, we present the underlying software architecture of modOpt, review its various features, discuss several educational and performance-oriented algorithms within modOpt, and present numerical studies illustrating its unique benefits.
Best-of-N decoding methods instruct large language models (LLMs) to generate multiple solutions, score each using a scoring function, and select the highest scored as the final answer to mathematical reasoning problems. However, this repeated independent process often leads to the same mistakes, making the selected solution still incorrect. We propose a novel prompting method named Stepwise Correction (StepCo) that helps LLMs identify and revise incorrect steps in their generated reasoning paths. It iterates verification and revision phases that employ a process-supervised verifier. The verify-then-revise process not only improves answer correctness but also reduces token consumption with fewer paths needed to generate. With StepCo, a series of LLMs demonstrate exceptional performance. Notably, using GPT-4o as the backend LLM, StepCo achieves an average accuracy of 94.1 across eight datasets, significantly outperforming the state-of-the-art Best-of-N method by +2.4, while reducing token consumption by 77.8%.
SoK: On Finding Common Ground in Loss Landscapes Using Deep Model Merging Techniques
2024-10-16
Arham Khan, Todd Nief, Nathaniel Hudson, Mansi Sakarvadia, Daniel Grzenda, Aswathy Ajith, Jordan Pettyjohn, Kyle Chard, Ian Foster
Understanding neural networks is crucial to creating reliable and trustworthy deep learning models. Most contemporary research in interpretability analyzes just one model at a time via causal intervention or activation analysis. Yet despite successes, these methods leave significant gaps in our understanding of the training behaviors of neural networks, how their inner representations emerge, and how we can predictably associate model components with task-specific behaviors. Seeking new insights from work in related fields, here we survey literature in the field of model merging, a field that aims to combine the abilities of various neural networks by merging their parameters and identifying task-specific model components in the process. We analyze the model merging literature through the lens of loss landscape geometry, an approach that enables us to connect observations from empirical studies on interpretability, security, model merging, and loss landscape analysis to phenomena that govern neural network training and the emergence of their inner representations. To systematize knowledge in this area, we present a novel taxonomy of model merging techniques organized by their core algorithmic principles. Additionally, we distill repeated empirical observations from the literature in these fields into characterizations of four major aspects of loss landscape geometry: mode convexity, determinism, directedness, and connectivity. We argue that by improving our understanding of the principles underlying model merging and loss landscape geometry, this work contributes to the goal of ensuring secure and trustworthy machine learning in practice.
A transiting planet invites us to measure its size, mass, orbital parameters, atmospheric composition, and other characteristics. But the invitation can only be accepted if the host star is bright enough for precise measurements of its flux and spectrum. NASA's Transiting Exoplanet Survey Satellite (TESS) is dedicated to finding such favorable systems. Operating from a 13.7-day elliptical orbit around the Earth, TESS uses four 10.5 cm telescopes to capture optical images of a 24 x 96 degree field of view. By shifting the field of view every 27 days, TESS can survey most of the sky every few years. In its first six years, TESS has identified approximately 7,000 planet candidates, with several hundred confirmed as planets. Mass measurements of these planets allow astronomers to differentiate between rocky "super-Earths" and gas-rich or volatile-rich "mini-Neptunes," while observations with the James Webb Space Telescope are revealing the secrets of their atmospheres. TESS has discovered planets orbiting a wide range of stars, including young stars, low-mass stars, binary stars, and even a white dwarf star. Beyond planet detection, TESS probes the optical variability of stars and a diverse array of other astronomical objects, including asteroids, comets, supernovae, and active galactic nuclei.
Globular cluster ages and their relation to high-redshift stellar cluster formation times from different globular cluster models
2024-10-16
Lucas M. Valenzuela, Duncan A. Forbes, Rhea-Silvia Remus
The formation details of globular clusters (GCs) are still poorly understood due to their old ages and the lack of detailed observations of their formation. A large variety of models for the formation and evolution of GCs have been created to improve our understanding of their origins, based on GC properties observed at z=0. We present the first side-by-side comparison of six current GC formation models with respect to their predictions for the GC ages and formation redshifts in Milky Way (MW)-like galaxies. We find that all the models are capable of forming most of the surviving GCs at more than 10 Gyr ago, in general agreement with the observation that most GCs are old. However, the measured MW GC ages are still systematically older than those predicted in the galaxies of four of the models. Investigating the variation of modelled GC age distributions for general MW-mass galaxies, we find that some of the models predict that a significant fraction of MW-mass galaxies would entirely lack a GC population older than 10 Gyr, whereas others predict that all MW-mass galaxies have a significant fraction of old GCs. This will have to be further tested in upcoming surveys, as systems without old GCs in that mass range are currently not known. Finally, we show that the models predict different formation redshifts for the oldest surviving GCs, highlighting that models currently disagree about whether the recently observed young star clusters at high redshifts could be the progenitors of today's GCs.
The success of Large Language Models (LLMs) is inherently linked to the availability of vast, diverse, and high-quality data for training and evaluation. However, the growth rate of high-quality data is significantly outpaced by the expansion of training datasets, leading to a looming data exhaustion crisis. This underscores the urgent need to enhance data efficiency and explore new data sources. In this context, synthetic data has emerged as a promising solution. Currently, data generation primarily consists of two major approaches: data augmentation and synthesis. This paper comprehensively reviews and summarizes data generation techniques throughout the lifecycle of LLMs, including data preparation, pre-training, fine-tuning, instruction-tuning, preference alignment, and applications. Furthermore, We discuss the current constraints faced by these methods and investigate potential pathways for future development and research. Our aspiration is to equip researchers with a clear understanding of these methodologies, enabling them to swiftly identify appropriate data generation strategies in the construction of LLMs, while providing valuable insights for future exploration.
Automatic question generation is a critical task that involves evaluating question quality by considering factors such as engagement, pedagogical value, and the ability to stimulate critical thinking. These aspects require human-like understanding and judgment, which automated systems currently lack. However, human evaluations are costly and impractical for large-scale samples of generated questions. Therefore, we propose a novel system, MIRROR (Multi-LLM Iterative Review and Response for Optimized Rating), which leverages large language models (LLMs) to automate the evaluation process for questions generated by automated question generation systems. We experimented with several state-of-the-art LLMs, such as GPT-4, Gemini, and Llama2-70b. We observed that the scores of human evaluation metrics, namely relevance, appropriateness, novelty, complexity, and grammaticality, improved when using the feedback-based approach called MIRROR, tending to be closer to the human baseline scores. Furthermore, we observed that Pearson's correlation coefficient between GPT-4 and human experts improved when using our proposed feedback-based approach, MIRROR, compared to direct prompting for evaluation. Error analysis shows that our proposed approach, MIRROR, significantly helps to improve relevance and appropriateness.
The JWST Emission Line Survey (JELS): An untargeted search for H$α$ emission line galaxies at $z > 6$ and their physical properties
2024-10-15
C. A. Pirie, P. N. Best, K. J. Duncan, D. J. McLeod, R. K. Cochrane, M. Clausen, J. S. Dunlop, S. R. Flury, J. E. Geach, C. L. Hale, E. Ibar, R. Kondapally, Zefeng Li, J. Matthee, R. J. McLure, L. Ossa-Fuentes, A. L. Patrick, Ian Smail, D. Sobral, H. M. O. Stephenson, J. P. Stott, A. M. Swinbank
We present the first results of the JWST Emission Line Survey (JELS). Utilising the first NIRCam narrow-band imaging at 4.7$\mu$m, over 63 arcmin$^{2}$ in the PRIMER/COSMOS field, we have identified 609 emission line galaxy candidates. From these, we robustly selected 35 H$\alpha$ star-forming galaxies at $z \sim 6.1$, with H$\alpha$ star-formation rates ($\rm{SFR_{H\alpha}}$) of $\sim0.9-15\ \rm{M_{\odot} yr^{-1}}$. Combining our unique H$\alpha$ sample with the exquisite panchromatic data in the field, we explore their physical properties and star-formation histories, and compare these to a broad-band selected sample at $z\sim 6$ to offer vital new insight into the nature of high-redshift galaxies. UV-continuum slopes ($\beta$) are considerably redder for our H$\alpha$ sample ($\langle\beta\rangle\sim-1.92$) compared to the broad-band sample ($\langle\beta\rangle\sim-2.35$). This is not due to dust attenuation as our H$\alpha$ sample is relatively dust-poor (median $A_V=0.23$); instead, we argue that the reddened slopes could be due to nebular continuum. We compared $\rm{SFR_{H\alpha}}$ and the UV-continuum-derived $\rm{SFR_{UV}}$ to SED-fitted measurements averaged over canonical timescales of 10 and 100 Myr ($\rm{SFR_{10}}$ and $\rm{SFR_{100}}$). We find an increase in recent SFR for our sample of H$\alpha$ emitters, particularly at lower stellar masses ($<10^9 \rm{M_{\odot}}$). We also find that $\rm{SFR_{H\alpha}}$ strongly traces SFR averaged over 10 Myr timescales, whereas the UV-continuum over-predicts SFR on 100 Myr timescales at low stellar masses. These results point to our H$\alpha$ sample undergoing `bursty' star formation. Our F356W $z \sim 6$ sample show a larger scatter in $\rm{SFR_{10}/SFR_{100}}$ across all stellar masses, highlighting how narrow-band photometric selections of H$\alpha$ emitters are key to quantifying the burstiness of star-formation activity.
Large language models (LLMs) combined with tool learning have gained impressive results in real-world applications. During tool learning, LLMs may call multiple tools in nested orders, where the latter tool call may take the former response as its input parameters. However, current research on the nested tool learning capabilities is still under-explored, since the existing benchmarks lack of relevant data instances. To address this problem, we introduce NesTools to bridge the current gap in comprehensive nested tool learning evaluations. NesTools comprises a novel automatic data generation method to construct large-scale nested tool calls with different nesting structures. With manual review and refinement, the dataset is in high quality and closely aligned with real-world scenarios. Therefore, NesTools can serve as a new benchmark to evaluate the nested tool learning abilities of LLMs. We conduct extensive experiments on 22 LLMs, and provide in-depth analyses with NesTools, which shows that current LLMs still suffer from the complex nested tool learning task.
As one of the most popular and sought-after generative models in the recent years, diffusion models have sparked the interests of many researchers and steadily shown excellent advantage in various generative tasks such as image synthesis, video generation, molecule design, 3D scene rendering and multimodal generation, relying on their dense theoretical principles and reliable application practices. The remarkable success of these recent efforts on diffusion models comes largely from progressive design principles and efficient architecture, training, inference, and deployment methodologies. However, there has not been a comprehensive and in-depth review to summarize these principles and practices to help the rapid understanding and application of diffusion models. In this survey, we provide a new efficiency-oriented perspective on these existing efforts, which mainly focuses on the profound principles and efficient practices in architecture designs, model training, fast inference and reliable deployment, to guide further theoretical research, algorithm migration and model application for new scenarios in a reader-friendly way. \url{https://github.com/ponyzym/Efficient-DMs-Survey}
We present the all-sky catalogue of absolute positions and estimates of correlated flux density of 21,942 compact radio sources determined from processing interferometric visibility data of virtually all VLBI observing sessions at 2 to 23 GHz from 72 programs suitable for absolute astrometry collected for 30 years. We used a novel technique of generation of a dataset of fused observables that allowed us to incorporate all available data in our analysis. The catalogue is the most complete and most precise to date. It forms the foundation and reference for positional astronomy, space geodesy, space navigation, population analysis of active galactic nuclei (AGNs), and provides calibrators for phase-referencing for differential astrometry and VLBI astrophysical observations. Its accuracy was evaluated through a detailed accounting systematic errors, rigorous decimation tests, comparison of different datasets, and comparison with other catalogues. The catalogue samples preferentially the AGNs with a strong contemporary parsec-scale synchrotron emission. Its milliarcsecond level positional accuracy allows association of these AGNs with detections in a wide range of the electromagnetic spectrum from low-frequency radio to gamma-rays and high energy neutrinos. We describe the innovative data processing and calibration technique in full detail, report the in depth analysis of random and systematic positional errors, and provide the list of associations with large surveys at different wavelengths.
Analyzing (In)Abilities of SAEs via Formal Languages
2024-10-15
Abhinav Menon, Manish Shrivastava, David Krueger, Ekdeep Singh Lubana
Autoencoders have been used for finding interpretable and disentangled features underlying neural network representations in both image and text domains. While the efficacy and pitfalls of such methods are well-studied in vision, there is a lack of corresponding results, both qualitative and quantitative, for the text domain. We aim to address this gap by training sparse autoencoders (SAEs) on a synthetic testbed of formal languages. Specifically, we train SAEs on the hidden representations of models trained on formal languages (Dyck-2, Expr, and English PCFG) under a wide variety of hyperparameter settings, finding interpretable latents often emerge in the features learned by our SAEs. However, similar to vision, we find performance turns out to be highly sensitive to inductive biases of the training pipeline. Moreover, we show latents correlating to certain features of the input do not always induce a causal impact on model's computation. We thus argue that causality has to become a central target in SAE training: learning of causal features should be incentivized from the ground-up. Motivated by this, we propose and perform preliminary investigations for an approach that promotes learning of causally relevant features in our formal language setting.
From Feynman diagrams to the amplituhedron: a gentle review
2024-10-15
Shounak De, Dmitrii Pavlov, Marcus Spradlin, Anastasia Volovich
In these notes we review, for a mathematical audience, the computation of (tree-level) scattering amplitudes in Yang-Mills theory in detail. In particular we demonstrate explicitly how the same formulas for six-particle NMHV helicity amplitudes are obtained from summing Feynman diagrams and from computing the canonical form of the $n=6, k=1, m=4$ amplituhedron.
2024-10-15
Giacomo May, Emanuele Dalsasso, Benjamin Kellenberger, Devis Tuia
Automated wildlife surveys based on drone imagery and object detection technology are a powerful and increasingly popular tool in conservation biology. Most detectors require training images with annotated bounding boxes, which are tedious, expensive, and not always unambiguous to create. To reduce the annotation load associated with this practice, we develop POLO, a multi-class object detection model that can be trained entirely on point labels. POLO is based on simple, yet effective modifications to the YOLOv8 architecture, including alterations to the prediction process, training losses, and post-processing. We test POLO on drone recordings of waterfowl containing up to multiple thousands of individual birds in one image and compare it to a regular YOLOv8. Our experiments show that at the same annotation cost, POLO achieves improved accuracy in counting animals in aerial imagery.
A study on applications of various Energy Generation in pure Electric Vehicles: progress towards sustainability
2024-10-15
Dibakar Das, Biplab Satpati, Md Arif, Gourab Das
The present work is an attempt to understand and review existing methods of energy generation in electric vehicles in the modern day context. Previous works in the field have proposed various mechanisms of energy generation that are very well adaptable to commercial scale uses and can be used as alternative power sourcing for electric vehicles having nil or very low environmental impact. The paper discusses strategies such as photovoltaic cell systems, regenerative braking, fuel cell, thermoelectric generators and micro wind-turbines with adequate propositions to select them on the basis of their suitability. The document also includes important formulas that can be used for individual modeling and designing. The paper emphasises on introducing the mechanisms that can be introduced as assistive mechanisms or secondary sources so that the range and other parameters are not compromised.
Adaptive Coordinators and Prompts on Heterogeneous Graphs for Cross-Domain Recommendations
2024-10-15
Hengyu Zhang, Chunxu Shen, Xiangguo Sun, Jie Tan, Yu Rong, Chengzhi Piao, Hong Cheng, Lingling Yi
In the online digital world, users frequently engage with diverse items across multiple domains (e.g., e-commerce platforms, streaming services, and social media networks), forming complex heterogeneous interaction graphs. Leveraging this multi-domain information can undoubtedly enhance the performance of recommendation systems by providing more comprehensive user insights and alleviating data sparsity in individual domains. However, integrating multi-domain knowledge for the cross-domain recommendation is very hard due to inherent disparities in user behavior and item characteristics and the risk of negative transfer, where irrelevant or conflicting information from the source domains adversely impacts the target domain's performance. To address these challenges, we offer HAGO, a novel framework with $\textbf{H}$eterogeneous $\textbf{A}$daptive $\textbf{G}$raph co$\textbf{O}$rdinators, which dynamically integrate multi-domain graphs into a cohesive structure by adaptively adjusting the connections between coordinators and multi-domain graph nodes, thereby enhancing beneficial inter-domain interactions while mitigating negative transfer effects. Additionally, we develop a universal multi-domain graph pre-training strategy alongside HAGO to collaboratively learn high-quality node representations across domains. To effectively transfer the learned multi-domain knowledge to the target domain, we design an effective graph prompting method, which incorporates pre-trained embeddings with learnable prompts for the recommendation task. Our framework is compatible with various graph-based models and pre-training techniques, demonstrating broad applicability and effectiveness. Further experimental results show that our solutions outperform state-of-the-art methods in multi-domain recommendation scenarios and highlight their potential for real-world applications.
AI Rules? Characterizing Reddit Community Policies Towards AI-Generated Content
2024-10-15
Travis Lloyd, Jennah Gosciak, Tung Nguyen, Mor Naaman
How are Reddit communities responding to AI-generated content? We explored this question through a large-scale analysis of subreddit community rules and their change over time. We collected the metadata and community rules for over 300,000 public subreddits and measured the prevalence of rules governing AI. We labeled subreddits and AI rules according to existing taxonomies from the HCI literature and a new taxonomy we developed specific to AI rules. While rules about AI are still relatively uncommon, the number of subreddits with these rules almost doubled over the course of a year. AI rules are also more common in larger subreddits and communities focused on art or celebrity topics, and less common in those focused on social support. These rules often focus on AI images and evoke, as justification, concerns about quality and authenticity. Overall, our findings illustrate the emergence of varied concerns about AI, in different community contexts.
Scylla II. The Spatially Resolved Star Formation History of the Large Magellanic Cloud Reveals an Inverted Radial Age Gradient
2024-10-15
Roger E. Cohen, Kristen B. W. McQuinn, Claire E. Murray, Benjamin F. Williams, Yumi Choi, Christina W. Lindberg, Clare Burhenne, Karl D. Gordon, Petia Yanchulova Merica-Jones, Karoline M. Gilbert, Martha L. Boyer, Steven Goldman, Andrew E. Dolphin, O. Grace Telford
The proximity of the Magellanic Clouds provides the opportunity to study interacting dwarf galaxies near a massive host, and spatial trends in their stellar population properties in particular, with a unique level of detail. The Scylla pure parallel program has obtained deep (80% complete to >1 mag below the ancient main sequence turnoff), homogeneous two-filter Hubble Space Telescope (HST) imaging sampling the inner star-forming disk of the Large Magellanic Cloud (LMC), the perfect complement to shallower, contiguous ground-based surveys. We harness this imaging together with extant archival data and fit lifetime star formation histories (SFHs) to resolved color-magnitude diagrams (CMDs) of 111 individual fields, using three different stellar evolutionary libraries. We validate per-field recovered distances and extinctions as well as the combined global LMC age-metallicity relation and SFH against independent estimates. We find that the present-day radial age gradient reverses from an inside-out gradient in the inner disk to an outside-in gradient beyond $\sim$2 disk scalelengths, supported by ground-based measurements. The gradients become relatively flatter at earlier lookback times, while the location of the inversion remains constant over an order of magnitude in lookback time, from $\sim$1$-$10 Gyr. This suggests at least one mechanism that predates the recent intense LMC-SMC interaction. We compare observed radial age trends to other late-type galaxies at fixed stellar mass and discuss similarities and differences in the context of potential drivers, implying strong radial migration in the LMC.
Scylla I: A pure-parallel, multi-wavelength imaging survey of the ULLYSES fields in the LMC and SMC
2024-10-15
Claire E. Murray, Christina W. Lindberg, Petia Yanchulova Merica-Jones, Benjamin F. Williams, Roger E. Cohen, Karl D. Gordon, Kristen B. W. McQuinn, Yumi Choi, Clare Burhenne, Karin M. Sandstrom, Caroline Bot, L. Clifton Johnson, Steven R. Goldman, Christopher J. R. Clark, Julia C. Roman-Duval, Karoline M. Gilbert, J. E. G. Peek, Alec S. Hirschauer, Martha L. Boyer, Andrew E. Dolphin
Scylla is a deep Hubble Space Telescope survey of the stellar populations, interstellar medium and star formation in the LMC and SMC. As a pure-parallel complement to the Ultraviolet Legacy Library of Young Stars as Essential Standards (ULLYSES) survey, Scylla obtained 342 orbits of ultraviolet (UV) through near-infrared (IR) imaging of the LMC and SMC with Wide Field Camera 3. In this paper, we describe the science objectives, observing strategy, data reduction procedure, and initial results from our photometric analysis of 96 observed fields. Although our observations were constrained by ULYSSES primary exposures, we imaged all fields in at least two filters (F475W and F814W), and 64% of fields in at least three and as many as seven WFC3 filters spanning the UV to IR. Overall, we reach average 50% completeness of $m_{\rm F225W}=26.0$, $m_{\rm F275W}=26.2$, $m_{\rm F336W}=26.9$, $m_{\rm F475W}=27.8$, $m_{\rm F814W}=25.5$, $m_{\rm F110W}=24.7$, and $m_{\rm F160W}=24.0$ Vega magnitudes in our photometric catalogs, which is faintward of the ancient main sequence turnoff in all filters. The primary science goals of Scylla include characterizing the structure and properties of dust in the MCs, as well as their spatially-resolved star formation and chemical enrichment histories. Our images and photometric catalogs, which represent the widest-area coverage of MCs with HST photometry to date, are available as a high-level science product at the Barbara A. Mikulski Archive for Space Telescopes.
A Survey of Low-shot Vision-Language Model Adaptation via Representer Theorem
2024-10-15
Kun Ding, Ying Wang, Gaofeng Meng, Shiming Xiang
The advent of pre-trained vision-language foundation models has revolutionized the field of zero/few-shot (i.e., low-shot) image recognition. The key challenge to address under the condition of limited training data is how to fine-tune pre-trained vision-language models in a parameter-efficient manner. Previously, numerous approaches tackling this challenge have been proposed. Meantime, a few survey papers are also published to summarize these works. However, there still lacks a unified computational framework to integrate existing methods together, identify their nature and support in-depth comparison. As such, this survey paper first proposes a unified computational framework from the perspective of Representer Theorem and then derives many of the existing methods by specializing this framework. Thereafter, a comparative analysis is conducted to uncover the differences and relationships between existing methods. Based on the analyses, some possible variants to improve the existing works are presented. As a demonstration, we extend existing methods by modeling inter-class correlation between representers in reproducing kernel Hilbert space (RKHS), which is implemented by exploiting the closed-form solution of kernel ridge regression. Extensive experiments on 11 datasets are conducted to validate the effectiveness of this method. Toward the end of this paper, we discuss the limitations and provide further research directions.
Population synthesis and astrophysical inference for high-$z$ JWST galaxies
2024-10-15
Trey Driskell, Ethan O. Nadler, Andrew Benson, Vera Gluscevic
Observations of the high-$z$ Universe from JWST have revealed a new population of bright, early galaxies. A robust statistical interpretation of this data requires fast forward models that account for uncertainties in galaxy evolution and incorporate observational systematic effects. We present a probabilistic framework for population synthesis of high-$z$ galaxies and inference of their properties. Our framework is based on the semi-analytic galaxy-formation model Galacticus. To infer the astrophysical parameters governing high-$z$ galaxy evolution, we analyze JWST data from the CEERS and NGDEEP surveys and calculate the likelihood of observing individual objects in apparent magnitude--redshift space, for $z\geq8.5$ galaxy candidates. We include observational selection effects due to limited survey volume and depth, as well as photometric redshift uncertainties. We recover the posterior probability distribution for parameters describing star formation and outflow rates. We place an upper limit on the star formation timescale of $500~\mathrm{Myr}$ at a disk velocity of $50~\mathrm{km\ s}^{-1}$, and we infer a characteristic velocity at which the outflow mass-loading factor is $\sim 1$ of $150^{+280}_{-60}~\mathrm{km\ s}^{-1}$, both at $95\%$ confidence. Marginalizing over our astrophysical model, we find that galaxies in CEERS and NGDEEP data occupy halos with virial masses $10^{10\pm 0.5}~M_{\mathrm{\odot}}$ at $8.5\leq z\leq 12$, at $95\%$ confidence. The star formation timescale preferred by our fit is relatively short compared to typical values at lower redshifts, consistent with previous findings. The modeling and analysis framework presented here can enable systematic tests of high-$z$ galaxies' dust content, initial mass functions, and star formation burstiness in the future.
Exploiting the high-resolution NIKA2 data to study the intracluster medium and dynamical state of ACT-CL J0240.0+0116
2024-10-15
A. Paliwal, M. De Petris, A. Ferragamo, R. Adam, P. Ade, H. Ajeddig, P. André, E. Artis, H. Aussel, I. Bartalucci, A. Beelen, A. Benoît, S. Berta, L. Bing, O. Bourrion, M. Calvo, A. Catalano, F. De Luca, F. -X. Désert, S. Doyle, E. F. C. Driessen, G. Ejlali, A. Gomez, J. Goupy, C. Hanser, S. Katsioli, F. Kéruzoré, C. Kramer, B. Ladjelate, G. Lagache, S. Leclercq, J. -F. Lestrade, J. F. Macías-Pérez, S. C. Madden, A. Maury, P. Mauskopf, F. Mayet, J. -B. Melin, A. Monfardini, A. Moyer-Anin, M. Muñoz-Echeverría, L. Perotto, G. Pisano, E. Pointecouteau, N. Ponthieu, G. W. Pratt, V. Revéret, A. J. Rigby, A. Ritacco, C. Romero, H. Roussel, F. Ruppin, K. Schuster, A. Sievers, C. Tucker, R. Wicker, R. Zylka
Having a detailed knowledge of the intracluster medium (ICM) to infer the exact cluster physics such as the cluster dynamical state is crucial for cluster-based cosmological studies. This knowledge limits the accuracy and precision of mass estimation, a key parameter for such studies. In this paper, we conduct an in-depth analysis of cluster ACT-CL J0240.0+0116 using a multi-wavelength approach, with a primary focus on high angular resolution Sunyaev-Zeldovich (SZ) thermal component observations obtained under the NIKA2 Sunyaev-Zeldovich Large Programme (LPSZ). We create composite images using NIKA2, X-ray, and optical galaxy number density maps. The results reveal distinct signs of disturbance within the cluster with the distributions of gas and member galaxies that do not overlap. We also find suggestions of an inflow of matter onto the cluster from the southwestern direction. Ultimately, we classify the cluster as disturbed, using morphological indicators derived from its SZ, X-ray, and optical image. The cluster SZ signal is also contaminated by a strong central point source. We adopt different approaches to handling this contaminant and find the estimates of our pressure and hydrostatic mass profiles robust to the point source mitigation model. The cluster hydrostatic mass is estimated at $4.25^{+0.50}_{-0.45\, } \times 10^{14} \,\mathrm{M}_{\odot}$ for the case where the point source was masked. These values are consistent with the mass estimated using only X-ray data and with those from previous SZ studies of the Atacama cosmology telescope (ACT) survey, with improved precision on the mass estimate. Our findings strongly suggest that ACT-CL J0240.0+0116 is a disturbed cluster system, and the detailed observations and derived values serve as a compelling case study for the capabilities of the LPSZ in mapping the cluster ICM with high precision.
Hot subdwarf (SD) stars are the stripped cores of red giant stars in transition to the white dwarf sequence. The B-type subdwarfs (sdB) are powered by helium fusion in the core, more evolved ones (sdO) by shell burning. Low mass SDs may evolve through this stage without any support by nuclear fusion. Because the loss of the giants' envelopes is likely caused by mass transfer in binaries, hot SDs are test beds for close-binary evolution through stable and unstable Roche lobe overflow, common envelope formation and ejection as well as mergers. Many classes of hot SDs can be identified according to surface composition, binarity, magnetism, pulsation characteristics and population membership, including members of globular clusters. Observed binaries show a wide spread of orbital periods from 20 minutes to more than 1,000 days with white dwarf or main sequence companions. The closest systems qualify as type Ia supernova progenitors and LISA detectable gravitational wave sources. High-precision light curves from Kepler and TESS combined with radial velocity curves are used to derive masses, while asteroseismology adds information on the internal structure, slow rotation, and synchronization. Gaia's parallax measurements now allow us to place the stars in the Hertzsprung-Russell diagram and derive stellar parameters by combining them with multi-band photometry. The stellar radius can be determined to high precision this way. Newton's law can then be used to derive masses if accurate surface gravities are available. Large-scale spectroscopic surveys will provide atmospheric parameters for large samples of stars, allowing the mass distributions for the diverse subtypes to be established. These are crucial for testing binary synthesis models and constraining poorly known parameters such as the common envelope efficiency as well as the critical threshold mass-ratio for mass transfer stability.
Evidence of equilibrium dynamics in human social networks evolving in time
2024-10-15
Miguel A. González-Casado, Andreia Sofia Teixeira, Angel Sánchez
The dynamics of personal relationships remain largely unexplored due to the inherent difficulties of the longitudinal data collection process. In this paper, we analyse a dataset tracking the temporal evolution of a network of personal relationships among 900 people over the course of four years. We search for evidence that the network is in equilibrium, meaning that all macroscopic properties remain constant, fluctuating around stable values, while the internal microscopic dynamics are active. We find that the probabilities governing the network dynamics are stationary over time and that the degree distributions, as well as edge and triangle abundances match the theoretical equilibrium distributions expected under these dynamics. Furthermore, we verify that the system satisfies the detailed balance condition, with only minor point deviations, confirming that it is indeed in equilibrium. Remarkably, this equilibrium persists despite a high turnover in network composition, suggesting that it is an inherent characteristic of human social interactions rather than a trait of the individuals themselves. We argue that this equilibrium may be a general feature of human social networks arising from the competition between different dynamical mechanisms and also from the cognitive and material resources management of individuals. From a practical perspective, the fact that networks are in equilibrium could simplify data collection processes, validate the use of cross-sectional data-based methods like Exponential Random Graph Models, and inform the design of interventions. Our findings advance the understanding of collective human behaviour predictability and our ability to describe it using simple mathematical models.
Spin induction from scattering of two spinning black holes in dense clusters
2024-10-15
Jorge L. Rodríguez-Monteverde, Santiago Jaraba, Juan García-Bellido
In this paper, we study with numerical relativity the spin induction effect within close hyperbolic encounters for equal-mass black holes. We review the initially nonspinning case and explore the cases of initially aligned, anti-aligned and orthogonal spins with respect to the orbital angular momentum $\vec L$. We find that, for a given initial effective spin, the black hole with a smaller initial spin acquires a greater spin-up than the other black hole after the close hyperbolic encounter. We study three different scenarios regarding initial effective spin ($\chi_{\rm eff}=-0.1, \ 0.0, \ 0.1$), using three different scattering angles in order to obtain maximally spin-inducing scenarios. We also find that the final effective spins with respect to the initial spins are well-fitted by a parabola. When considering spins orthogonal to $\vec L$, we observe that the black hole spins precess, and that the induced spin in the $z$-direction depends quadratically on the value of the initial spins. These new phenomena suggest that dense black hole clusters are very interesting dynamical systems where black hole spins may acquire a non trivial distribution, both for aligned and isotropic spins.
Remarks on the Study of Electronic Properties and Photoionization Process in Rotating 2D Quantum Rings
2024-10-15
Carlos Magno O. Pereira, Frankbelson dos S. Azevedo, Edilberto O. Silva
Electronic and optical properties of a mesoscopic heterostructure of a two-dimensional quantum ring composed of Gallium Arsenide (GaAs) semiconductors are investigated. Using the confinement potential proposed by Tan-Inkson to describe the system under analysis, we conducted a numerical study of the photoionization cross section for a 2D quantum ring with and without rotation effects. The interior of the quantum ring is traversed by an AB flux. Our research aims to investigate how this mesoscopic structure's electronic and optical properties respond to variations in the following parameters: average radius, AB flux, angular velocity, and incident photon energy. Under these conditions, we establish that optical transitions occur from the ground state to the next excited state in the conduction sub-band, following a specific selection rule. One of the fundamental objectives of this study is to analyze how these rules can influence the general properties of two-dimensional quantum rings. To clarify the influence of rotation on the photoionization process within the system, we offer findings that illuminate the effects of the pertinent physical parameters within the described model. We emphasize that, although this is a review, it provides critical commentary, analysis, and new perspectives on existing research. Some results presented in this paper can be compared with those in the literature; however, new physical parameters and quantum ring configurations are used.
Functional Data Analysis on Wearable Sensor Data: A Systematic Review
2024-10-15
Nihan Acar-Denizli, Pedro Delicado
Wearable devices and sensors have recently become a popular way to collect data, especially in the health sciences. The use of sensors allows patients to be monitored over a period of time with a high observation frequency. Due to the continuous-on-time structure of the data, novel statistical methods are recommended for the analysis of sensor data. One of the popular approaches in the analysis of wearable sensor data is functional data analysis. The main objective of this paper is to review functional data analysis methods applied to wearable device data according to the type of sensor. In addition, we introduce several freely available software packages and open databases of wearable device data to facilitate access to sensor data in different fields.
Large Language Models (LLMs) have recently demonstrated remarkable performance in general tasks across various fields. However, their effectiveness within specific domains such as drug development remains challenges. To solve these challenges, we introduce \textbf{Y-Mol}, forming a well-established LLM paradigm for the flow of drug development. Y-Mol is a multiscale biomedical knowledge-guided LLM designed to accomplish tasks across lead compound discovery, pre-clinic, and clinic prediction. By integrating millions of multiscale biomedical knowledge and using LLaMA2 as the base LLM, Y-Mol augments the reasoning capability in the biomedical domain by learning from a corpus of publications, knowledge graphs, and expert-designed synthetic data. The capability is further enriched with three types of drug-oriented instructions: description-based prompts from processed publications, semantic-based prompts for extracting associations from knowledge graphs, and template-based prompts for understanding expert knowledge from biomedical tools. Besides, Y-Mol offers a set of LLM paradigms that can autonomously execute the downstream tasks across the entire process of drug development, including virtual screening, drug design, pharmacological properties prediction, and drug-related interaction prediction. Our extensive evaluations of various biomedical sources demonstrate that Y-Mol significantly outperforms general-purpose LLMs in discovering lead compounds, predicting molecular properties, and identifying drug interaction events.
Grassmann time-evolving matrix product operators: An efficient numerical approach for fermionic path integral simulations
2024-10-15
Xiansong Xu, Chu Guo, Ruofan Chen
Developing numerical exact solvers for open quantum systems is a challenging task due to the non-perturbative and non-Markovian nature when coupling to structured environments. The Feynman-Vernon influence functional approach is a powerful analytical tool to study the dynamics of open quantum systems. Numerical treatments of the influence functional including the quasi-adiabatic propagator technique and the tensor-network-based time-evolving matrix product operator method, have proven to be efficient in studying open quantum systems with bosonic environments. However, the numerical implementation of the fermionic path integral suffers from the Grassmann algebra involved. In this work, we present a detailed introduction of the Grassmann time-evolving matrix product operator method for fermionic open quantum systems. In particular, we introduce the concepts of Grassmann tensor, signed matrix product operator, and Grassmann matrix product state to handle the Grassmann path integral. Using the single-orbital Anderson impurity model as an example, we review the numerical benchmarks for structured fermionic environments for real-time nonequilibrium dynamics, real-time and imaginary-time equilibration dynamics, and its application as an impurity solver. These benchmarks show that our method is a robust and promising numerical approach to study strong coupling physics and non-Markovian dynamics. It can also serve as an alternative impurity solver to study strongly-correlated quantum matter with dynamical mean-field theory.
Moments of random multiplicative functions, III: A short review
2024-10-15
Adam J. Harper
We give a short review of recent progress on determining the order of magnitude of moments $\mathbb{E}|\sum_{n \leq x} f(n)|^{2q}$ of random multiplicative functions, and of closely related issues. We hope this can serve as a concise introduction to some of the ideas involved, for those who may not have too much background in the area.
Holder regularity for fully nonlinear nonlocal equations
2024-10-15
Juan Pablo Cabeza
In this survey we prove H\"older regularity results for viscosity solutions of fully nonlinear nonlocal uniformly elliptic second order differential equations with local gradient terms. This extends the nonlocal counterpart of the work of G. Barles, E. Chasseigne and C. Imbert in JEMS, 2011, to fully nonlinear extremal nonlocal operators.
Survey on Neighbor Discovery and Beam Alignment in mmWave-Enabled UAV Swarm Networks
2024-10-15
Muhammad Morshed Alam, Sangman Moh
Millimeter wave (mmWave)-enabled unmanned aerial vehicle (UAV) swarm networks (UAVSNs) can utilize a large spectrum of resources to provide low latency and high data transmission rate. Additionally, owing to the short wavelength, UAVs equipped with large antenna arrays can form secure narrow directive beam to establish communication with less interference. However, due to the high UAV mobility, limited beam coverage, beam misalignment, and high path loss, it is very challenging to adopt the mmWave communication in UAVSNs. In this article, we present a comprehensive survey on neighbor discovery and beam alignment techniques for directional communication in mmWave-enabled UAVSNs. The existing techniques are reviewed and compared with each other. We also discuss key open issues and challenges with potential research direction.
Non-perturbative suppression of Chiral Effects in hot QGP for $Λ/\barΛ$-hyperons spin polarization in heavy-ion collisions
2024-10-15
Ruslan A. Abramchuk
We review the calculation of suppression of Chiral Separational and Chiral Vortical Effects for strange quarks (which allegedly yield spin polarization of $\Lambda/\bar\Lambda$-hyperons in peripheral Heavy-Ion Collisions) by the non-perturbative interactions in hot deconfined QCD with the Field Correlator Method. The parameter range in the temperature-baryon density plane is expected to cover LHC-ALICE and RHIC-STAR data.
On Championing Foundation Models: From Explainability to Interpretability
2024-10-15
Shi Fu, Yuzhu Chen, Yingjie Wang, Dacheng Tao
Understanding the inner mechanisms of black-box foundation models (FMs) is essential yet challenging in artificial intelligence and its applications. Over the last decade, the long-running focus has been on their explainability, leading to the development of post-hoc explainable methods to rationalize the specific decisions already made by black-box FMs. However, these explainable methods have certain limitations in terms of faithfulness, detail capture and resource requirement. Consequently, in response to these issues, a new class of interpretable methods should be considered to unveil the underlying mechanisms in an accurate, comprehensive, heuristic and resource-light way. This survey aims to review interpretable methods that comply with the aforementioned principles and have been successfully applied to FMs. These methods are deeply rooted in machine learning theory, covering the analysis of generalization performance, expressive capability, and dynamic behavior. They provide a thorough interpretation of the entire workflow of FMs, ranging from the inference capability and training dynamics to their ethical implications. Ultimately, drawing upon these interpretations, this review identifies the next frontier research directions for FMs.
Effect modification and non-collapsibility leads to conflicting treatment decisions: a review of marginal and conditional estimands and recommendations for decision-making
2024-10-15
David M. Phillippo, Antonio Remiro-Azócar, Anna Heath, Gianluca Baio, Sofia Dias, A. E. Ades, Nicky J. Welton
Effect modification occurs when a covariate alters the relative effectiveness of treatment compared to control. It is widely understood that, when effect modification is present, treatment recommendations may vary by population and by subgroups within the population. Population-adjustment methods are increasingly used to adjust for differences in effect modifiers between study populations and to produce population-adjusted estimates in a relevant target population for decision-making. It is also widely understood that marginal and conditional estimands for non-collapsible effect measures, such as odds ratios or hazard ratios, do not in general coincide even without effect modification. However, the consequences of both non-collapsibility and effect modification together are little-discussed in the literature. In this paper, we set out the definitions of conditional and marginal estimands, illustrate their properties when effect modification is present, and discuss the implications for decision-making. In particular, we show that effect modification can result in conflicting treatment rankings between conditional and marginal estimates. This is because conditional and marginal estimands correspond to different decision questions that are no longer aligned when effect modification is present. For time-to-event outcomes, the presence of covariates implies that marginal hazard ratios are time-varying, and effect modification can cause marginal hazard curves to cross. We conclude with practical recommendations for decision-making in the presence of effect modification, based on pragmatic comparisons of both conditional and marginal estimates in the decision target population. Currently, multilevel network meta-regression is the only population-adjustment method capable of producing both conditional and marginal estimates, in any decision target population.
Search for young stellar objects within 4XMM-DR13 using CatBoost and SPE
2024-10-15
Xiangyao Ma, Yanxia Zhang, Jingyi Zhang, Changhua Li, Zihan Kang, Ji Li
Classifying and summarizing large data sets from different sky survey projects is essential for various subsequent scientific research. By combining data from 4XMM-DR13, SDSS DR18, and CatWISE, we formed an XMM-WISE-SDSS sample that included information in the X-ray, optical, and infrared bands. By cross-matching this sample with datasets from known spectral classifications from SDSS and LAMOST, we obtained a training dataset containing stars, galaxies, quasars, and Young Stellar Objects (YSOs). Two machine learning methods, CatBoost and Self-Paced Ensemble (SPE), were used to train and construct machine learning models through training sets to classify the XMM-WISE-SDSS sample. Notably, the SPE classifier showed excellent performance in YSO classification, identifying 1102 YSO candidates from 160,545 sources, including 258 known YSOs. Then we further verify whether these candidates are YSOs by the spectra in LAMOST and the identification in the SIMBAD and VizieR dabtabases. Finally there are 412 unidentified YSO candidates. The discovery of these new YSOs is an important addition to existing YSO samples and will deepen our understanding of star formation and evolution. Moreover we provided a classification catalog for the whole XMM-WISE-SDSS sample.
This article reviews and develops an epistemological tradition in philosophy of science, called convergentism, which holds that inference methods should be assessed in terms of their abilities to converge to the truth. This tradition is compared with three competing ones: (1) explanationism, which holds that theory choice should be guided by a theory's overall balance of explanatory virtues, such as simplicity and fit with data; (2) instrumentalism, according to which scientific inference should be driven by the goal of obtaining useful models, rather than true theories; (3) Bayesianism, which features a shift of focus from all-or-nothing beliefs to degrees of belief.
Capturing Perception to Poverty using Conjoint Analysis & Partial Profile Choice Experiment
2024-10-15
Anushka De, Diganta Mukherjee
The objective of this study is applying a utility based analysis to a comparatively efficient design experiment which can capture people's perception towards the various components of a commodity. Here we studied the multi-dimensional poverty index and the relative importance of its components and their two-factor interaction effects. We also discussed how to model a choice based conjoint data for determining the utility of the components and their interactions. Empirical results from survey data shows the nature of coefficients, in terms of utility derived by the individuals, their statistical significance and validity in the present framework. There has been some discrepancies in the results between the bootstrap model and the original model, which can be understood by surveying more people, and ensuring comparative homogeneity in the data.
Survey and Evaluation of Converging Architecture in LLMs based on Footsteps of Operations
2024-10-15
Seongho Kim, Jihyun Moon, Juntaek Oh, Insu Choi, Joon-Sung Yang
The advent of the Attention mechanism and Transformer architecture enables contextually natural text generation and compresses the burden of processing entire source information into singular vectors. Based on these two main ideas, model sizes gradually increases to accommodate more precise and comprehensive information, leading to the current state-of-the-art LLMs being very large, with parameters around 70 billion. As the model sizes are growing, the demand for substantial storage and computational capacity increases. This leads to the development of high-bandwidth memory and accelerators, as well as a variety of model architectures designed to meet these requirements. We note that LLM architectures have increasingly converged. This paper analyzes how these converged architectures perform in terms of layer configurations, operational mechanisms, and model sizes, considering various hyperparameter settings. In this paper, we conduct a concise survey of the history of LLMs by tracing the evolution of their operational improvements. Furthermore, we summarize the performance trends of LLMs under various hyperparameter settings using the RTX 6000, which features the state-of-the-art Ada Lovelace architecture. We conclude that even the same model can exhibit different behaviors depending on the hyperparameters or whether it is deployed in server or edge environments.
Model-based control is a crucial component of robotic navigation. However, it often struggles with entrapment in local minima due to its inherent nature as a finite, myopic optimization procedure. Previous studies have addressed this issue but sacrificed either solution quality due to their reactive nature or computational efficiency in generating explicit paths for proactive guidance. To this end, we propose a motion planning method that proactively avoids local minima without any guidance from global paths. The key idea is repulsive potential augmentation, integrating high-level directional information into the Model Predictive Path Integral control as a single repulsive term through an artificial potential field. We evaluate our method through theoretical analysis and simulations in environments with obstacles that induce local minima. Results show that our method guarantees the avoidance of local minima and outperforms existing methods in terms of global optimality without decreasing computational efficiency.
Augmentation-Driven Metric for Balancing Preservation and Modification in Text-Guided Image Editing
2024-10-15
Yoonjeon Kim, Soohyun Ryu, Yeonsung Jung, Hyunkoo Lee, Joowon Kim, June Yong Yang, Jaeryong Hwang, Eunho Yang
The development of vision-language and generative models has significantly advanced text-guided image editing, which seeks \textit{preservation} of core elements in the source image while implementing \textit{modifications} based on the target text. However, in the absence of evaluation metrics specifically tailored for text-guided image editing, existing metrics are limited in balancing the consideration of preservation and modification. Especially, our analysis reveals that CLIPScore, the most commonly used metric, tends to favor modification and ignore core attributes to be preserved, resulting in inaccurate evaluations. To address this problem, we propose \texttt{AugCLIP}, \black{which balances preservation and modification by estimating the representation of an ideal edited image that aligns with the target text with minimum alteration on the source image. We augment detailed textual descriptions on the source image and the target text using a multi-modal large language model, to model a hyperplane that separates CLIP space into source or target. The representation of the ideal edited image is an orthogonal projection of the source image into the hyperplane, which encapsulates the relative importance of each attribute considering the interdependent relationships.} Our extensive experiments on five benchmark datasets, encompassing a diverse range of editing scenarios, demonstrate that \texttt{AugCLIP} aligns remarkably well with human evaluation standards compared to existing metrics. The code for evaluation will be open-sourced to contribute to the community.
Graph-structured data is prevalent in the real world. Recently, due to the powerful emergent capabilities, Large Language Models (LLMs) have shown promising performance in modeling graphs. The key to effectively applying LLMs on graphs is converting graph data into a format LLMs can comprehend. Graph-to-token approaches are popular in enabling LLMs to process graph information. They transform graphs into sequences of tokens and align them with text tokens through instruction tuning, where self-supervised instruction tuning helps LLMs acquire general knowledge about graphs, and supervised fine-tuning specializes LLMs for the downstream tasks on graphs. Despite their initial success, we find that existing methods have a misalignment between self-supervised tasks and supervised downstream tasks, resulting in negative transfer from self-supervised fine-tuning to downstream tasks. To address these issues, we propose Graph Alignment Large Language Models (GALLM) to benefit from aligned task templates. In the self-supervised tuning stage, we introduce a novel text matching task using templates aligned with downstream tasks. In the task-specific tuning stage, we propose two category prompt methods that learn supervision information from additional explanation with further aligned templates. Experimental evaluations on four datasets demonstrate substantial improvements in supervised learning, multi-dataset generalizability, and particularly in zero-shot capability, highlighting the model's potential as a graph foundation model.
Origin of granular axial segregation bands in a rotating tumbler: An interface-mixing driven Rayleigh-Taylor instability
2024-10-15
Umberto d'Ortona, Richard Lueptow, Nathalie Thomas
The origin of large and small particle axial bands in long rotating tumblers is a long-standing question. Using DEM simulations, we show that this axial segregation is due to a Rayleigh-Taylor instability which is characterized by the fact that the density of a granular medium increases with mixing and decreases with segregation. For initially mixed particles, segregation and collisional diffusion in the flowing layer balance and lead to a three-layer system, with a layer of large particles over a layer of small particles, and, interposed between these layers, a layer of more densely packed mixed particles. The higher density mixed particle layer over the lower density small particle layer induces a Rayleigh-Taylor instability, evident as waviness in the interface between the layers. The waviness destabilizes into ascending plumes of small particles and descending plumes of mixed particles with large particles enriched near the surface, which become evident as small and large particle bands visible at the free surface. Rolls driven by segregation at the tilted interface between plumes maintain the pattern of frozen plumes. Published by the American Physical Society 2024
Smartphone users often regret aspects of their phone use, but pinpointing specific ways in which the design of an interface contributes to regrettable use can be challenging due to the complexity of app features and user intentions. We conducted a one-week study with 17 Android users, using a novel method where we passively collected screenshots every five seconds, which were analyzed via a multimodal large language model to extract fine-grained activity. Paired with experience sampling, surveys, and interviews, we found that regret varies based on user intention, with non-intentional and social media use being especially regrettable. Regret also varies by social media activity; participants were most likely to regret viewing comments and algorithmically recommended content. Additionally, participants frequently deviated to browsing social media when their intention was direct communication, which slightly increased their regret. Our findings provide guidance to designers and policy-makers seeking to improve users' experience and autonomy.
DIAR: Diffusion-model-guided Implicit Q-learning with Adaptive Revaluation
2024-10-15
Jaehyun Park, Yunho Kim, Sejin Kim, Byung-Jun Lee, Sundong Kim
We propose a novel offline reinforcement learning (offline RL) approach, introducing the Diffusion-model-guided Implicit Q-learning with Adaptive Revaluation (DIAR) framework. We address two key challenges in offline RL: out-of-distribution samples and long-horizon problems. We leverage diffusion models to learn state-action sequence distributions and incorporate value functions for more balanced and adaptive decision-making. DIAR introduces an Adaptive Revaluation mechanism that dynamically adjusts decision lengths by comparing current and future state values, enabling flexible long-term decision-making. Furthermore, we address Q-value overestimation by combining Q-network learning with a value function guided by a diffusion model. The diffusion model generates diverse latent trajectories, enhancing policy robustness and generalization. As demonstrated in tasks like Maze2D, AntMaze, and Kitchen, DIAR consistently outperforms state-of-the-art algorithms in long-horizon, sparse-reward environments.
Diffusion-Based Offline RL for Improved Decision-Making in Augmented ARC Task
2024-10-15
Yunho Kim, Jaehyun Park, Heejun Kim, Sejin Kim, Byung-Jun Lee, Sundong Kim
Effective long-term strategies enable AI systems to navigate complex environments by making sequential decisions over extended horizons. Similarly, reinforcement learning (RL) agents optimize decisions across sequences to maximize rewards, even without immediate feedback. To verify that Latent Diffusion-Constrained Q-learning (LDCQ), a prominent diffusion-based offline RL method, demonstrates strong reasoning abilities in multi-step decision-making, we aimed to evaluate its performance on the Abstraction and Reasoning Corpus (ARC). However, applying offline RL methodologies to enhance strategic reasoning in AI for solving tasks in ARC is challenging due to the lack of sufficient experience data in the ARC training set. To address this limitation, we introduce an augmented offline RL dataset for ARC, called Synthesized Offline Learning Data for Abstraction and Reasoning (SOLAR), along with the SOLAR-Generator, which generates diverse trajectory data based on predefined rules. SOLAR enables the application of offline RL methods by offering sufficient experience data. We synthesized SOLAR for a simple task and used it to train an agent with the LDCQ method. Our experiments demonstrate the effectiveness of the offline RL approach on a simple ARC task, showing the agent's ability to make multi-step sequential decisions and correctly identify answer states. These results highlight the potential of the offline RL approach to enhance AI's strategic reasoning capabilities.
2024-10-15
Yiming Li, Yi Wang, Wenqian Wang, Dan Lin, Bingbing Li, Kim-Hui Yap
Exploring new knowledge is a fundamental human ability that can be mirrored in the development of deep neural networks, especially in the field of object detection. Open world object detection (OWOD) is an emerging area of research that adapts this principle to explore new knowledge. It focuses on recognizing and learning from objects absent from initial training sets, thereby incrementally expanding its knowledge base when new class labels are introduced. This survey paper offers a thorough review of the OWOD domain, covering essential aspects, including problem definitions, benchmark datasets, source codes, evaluation metrics, and a comparative study of existing methods. Additionally, we investigate related areas like open set recognition (OSR) and incremental learning (IL), underlining their relevance to OWOD. Finally, the paper concludes by addressing the limitations and challenges faced by current OWOD algorithms and proposes directions for future research. To our knowledge, this is the first comprehensive survey of the emerging OWOD field with over one hundred references, marking a significant step forward for object detection technology. A comprehensive source code and benchmarks are archived and concluded at https://github.com/ArminLee/OWOD Review.
Enhancing Assamese NLP Capabilities: Introducing a Centralized Dataset Repository
2024-10-15
S. Tamang, D. J. Bora
This paper introduces a centralized, open-source dataset repository designed to advance NLP and NMT for Assamese, a low-resource language. The repository, available at GitHub, supports various tasks like sentiment analysis, named entity recognition, and machine translation by providing both pre-training and fine-tuning corpora. We review existing datasets, highlighting the need for standardized resources in Assamese NLP, and discuss potential applications in AI-driven research, such as LLMs, OCR, and chatbots. While promising, challenges like data scarcity and linguistic diversity remain. The repository aims to foster collaboration and innovation, promoting Assamese language research in the digital age.
Calabi-Yau metrics through Grassmannian learning and Donaldson's algorithm
2024-10-15
Carl Henrik Ek, Oisin Kim, Challenger Mishra
Motivated by recent progress in the problem of numerical K\"ahler metrics, we survey machine learning techniques in this area, discussing both advantages and drawbacks. We then revisit the algebraic ansatz pioneered by Donaldson. Inspired by his work, we present a novel approach to obtaining Ricci-flat approximations to K\"ahler metrics, applying machine learning within a `principled' framework. In particular, we use gradient descent on the Grassmannian manifold to identify an efficient subspace of sections for calculation of the metric. We combine this approach with both Donaldson's algorithm and learning on the $h$-matrix itself (the latter method being equivalent to gradient descent on the fibre bundle of Hermitian metrics on the tautological bundle over the Grassmannian). We implement our methods on the Dwork family of threefolds, commenting on the behaviour at different points in moduli space. In particular, we observe the emergence of nontrivial local minima as the moduli parameter is increased.
"Is Hate Lost in Translation?": Evaluation of Multilingual LGBTQIA+ Hate Speech Detection
2024-10-15
Fai Leui Chan, Duke Nguyen, Aditya Joshi
This paper explores the challenges of detecting LGBTQIA+ hate speech of large language models across multiple languages, including English, Italian, Chinese and (code-switched) English-Tamil, examining the impact of machine translation and whether the nuances of hate speech are preserved across translation. We examine the hate speech detection ability of zero-shot and fine-tuned GPT. Our findings indicate that: (1) English has the highest performance and the code-switching scenario of English-Tamil being the lowest, (2) fine-tuning improves performance consistently across languages whilst translation yields mixed results. Through simple experimentation with original text and machine-translated text for hate speech detection along with a qualitative error analysis, this paper sheds light on the socio-cultural nuances and complexities of languages that may not be captured by automatic translation.
Sampling Strategies for Creation of a Benchmark for Dialectal Sentiment Classification
2024-10-15
Dipankar Srirag, Jordan Painter, Aditya Joshi, Diptesh Kanojia
This paper investigates data sampling strategies to create a benchmark for dialectal sentiment classification of Google Places reviews written in English. Based on location-based filtering, we collect a self-supervised dataset of reviews in Australian (Australian English), Indian (Indian English), and British (British English) English with self-supervised sentiment labels (1-star to 5-star). We employ sampling techniques based on label semantics, review length, and sentiment proportion and report performances on three fine-tuned BERT-based models. Our multi-dialect evaluation provides pointers to challenging scenarios for inner-circle (Australian English and British English) as well as non-native dialects (Indian English) of English, highlighting the need for more diverse benchmarks.
Experimental Road to a Charming Family of Tetraquarks ... and Beyond
2024-10-15
Feng Zhu, Gerry Bauer, Kai Yi
Discovery of the X(3872) meson in 2003 ignited intense interest in exotic (neither $q\bar{q}$ nor $qqq$) hadrons, but a $c\bar{c}$ interpretation of this state was difficult to exclude. An unequivocal exotic was discovered in the $Z_c(3900)^+$ meson -- a charged charmonium-like state. A variety of models of exotic structure have been advanced but consensus is elusive. The grand lesson from heavy quarkonia was that heavy quarks bring clarity. Thus, the recently reported triplet of all-charm tetraquark candidates -- $X(6600)$, $X(6900)$, and $X(7100)$ -- decaying to $J/\psi\,J/\psi$ is a great boon, promising important insights. We review some history of exotics, chronicle the road to prospective all-charm tetraquarks, discuss in some detail the divergent modeling of $J/\psi\,J/\psi$ structures, and offer some inferences about them. These states form a Regge trajectory and appear to be a family of radial excitations. A reported, but unexplained, threshold excess could hint at a fourth family member. We close with a brief look at a step beyond: all-bottom tetraquarks.
Distance and stellar parameter estimations of solar-like stars from the LAMOST spectroscopic survey
2024-10-15
Yue-Yue Shen, A-Li Luo
Context. The Gaia mission has opened up a new era for the precise astrometry of stars, thus revolutionizing our understanding of the Milky Way. However, beyond a few kiloparseconds from the Sun, parallax measurements become less reliable, and even within 2 kpc, there still exist stars with large uncertainties. Aims. Our aim was to determine the distance and stellar parameters of 521,424 solar-like stars from LAMOST DR9; these stars lacked precise distance measurements (uncertainties higher than 20\% or even without any distance estimations) when checked with Gaia. Methods. We proposed a convolutional neural network (CNN) model to predict the absolute magnitudes, colors, and stellar parameters (T_eff, logg, and [FeH]) directly from low-resolution spectra. For spectra with signal-to-noise ratios at g band (S/N_g) greater than 10, the model achieves a precision of 85 K for T_eff, 0.07 dex for logg, 0.06 dex for [Fe/H], 0.25 mag for M_g, and 0.03 mag for bp-rp. The estimated distances have a median fractional error of 4% with a standard deviation of 8%. Results: We applied the trained CNN model to 521,424 solar-like stars to derive the distance and stellar parameters. Compared with other distance estimation studies and spectroscopic surveys, the results show good consistency. Additionally, we investigated the metallicity gradients of the Milky Way from a subsample, and find a radial gradient ranging from -0.05 < Delta{[Fe/H]}/Delta{R} < 0.0 dex/kpc and a vertical gradient ranging from -0.26 < Delta{[Fe/H]}/Delta{Z} < -0.07dex/kpc Conclusions. We conclude that our method is effective in estimating distances and stellar parameters for solar-like stars with limited astrometric data. Our measurements are reliable for Galactic structure studies and hopefully will be useful for exoplanet researches.
Multi-wavelength constraints on the local black hole occupation fraction
2024-10-15
Colin J. Burke, Priyamvada Natarajan, Vivienne F. Baldassare, Marla Geha
The fraction of dwarf galaxies hosting central, intermediate-mass black holes (IMBHs) at low redshifts is an important observational probe of black hole seeding at high redshift. Detections of nuclear accretion signatures in dwarf galaxies provides strong evidence for the presence of these IMBHs. We develop a Bayesian model to infer the black hole occupation fraction assuming a broken power law Eddington ratio distribution function. Our approach accounts for non-detections, incompleteness, and contamination from star-forming-related emission. We apply this model to galaxies with X-ray data from the Chandra Source Catalog at distances $<50$ Mpc, radio data from the VLA Sky Survey at $< 50$ Mpc, and optical variability data from the Palomar Transient Factory at $z<0.055$. We find a black hole occupation fraction of at least $90$ percent at stellar masses of $M_{\star}=10^8~M_{\odot}$ and at least $39$ percent at $M_{\star} = 10^7~M_{\odot}$ (95\% confidence intervals). We show the resulting black hole mass function. These constraints on the IMBH population have implications for the Laser Interferometer Space Antenna (LISA) mission and for cosmological models of black hole seeding and growth. We also constrain the extremely low luminosity end ($L_{\rm{bol}}\lesssim10^{40}$ erg s$^{-1}$) of the AGN luminosity functions at $z=0$. Our AGN luminosity functions are broadly consistent with an extrapolation of the shallow slope of the AGN luminosity functions from previous work.
We prove a conjecture of Petrunin and Tuschmann on the non-existence of asymptotically flat 4-manifolds asymptotic to the half plane. We also survey recent progress and questions concerning gravitational instantons, which serve as our motivation for studying this question.
Differentiable Programming for Computational Plasma Physics
2024-10-15
Nick McGreivy
Differentiable programming allows for derivatives of functions implemented via computer code to be calculated automatically. These derivatives are calculated using automatic differentiation (AD). This thesis explores two applications of differentiable programming to computational plasma physics. First, we consider how differentiable programming can be used to simplify and improve stellarator optimization. We introduce a stellarator coil design code (FOCUSADD) that uses gradient-based optimization to produce stellarator coils with finite build. Because we use reverse mode AD, which can compute gradients of scalar functions with the same computational complexity as the function, FOCUSADD is simple, flexible, and efficient. We then discuss two additional applications of AD in stellarator optimization. Second, we explore how machine learning (ML) can be used to improve or replace the numerical methods used to solve partial differential equations (PDEs), focusing on time-dependent PDEs in fluid mechanics relevant to plasma physics. Differentiable programming allows neural networks and other techniques from ML to be embedded within numerical methods. This is a promising, but relatively new, research area. We focus on two basic questions. First, can we design ML-based PDE solvers that have the same guarantees of conservation, stability, and positivity that standard numerical methods do? The answer is yes; we introduce error-correcting algorithms that preserve invariants of time-dependent PDEs. Second, which types of ML-based solvers work best at solving PDEs? We perform a systematic review of the scientific literature on solving PDEs with ML. Unfortunately we discover two issues, weak baselines and reporting biases, that affect the interpretation reproducibility of a significant majority of published research. We conclude that using ML to solve PDEs is not as promising as we initially believed.
A Problem-Based Learning Approach to Teaching Design in CS1
2024-10-15
Christopher William Schankula, Habib Ghaffari Hadigheh, Spencer Smith, Christopher Kumar Anand
Design skills are increasingly recognized as a core competency for software professionals. Unfortunately, these skills are difficult to teach because design requires freedom and open-ended thinking, but new designers require a structured process to keep them from being overwhelmed by possibilities. We scaffolded this by creating worksheets for every Design Thinking step, and embedding them in a PowerPoint deck on which students can collaborate. We present our experience teaching a team design project course to 200 first-year-university students, taking them from user interviews to functional prototypes. To challenge and support every student in a class where high school programming experience ranged from zero hours to three computer science courses, we gave teams the option of developing single-user or multi-user (distributed) web applications, using two Event-Driven Programming frameworks. We identified common failure modes from previous years, and developed the scaffolded approach and problem definition to avoid them. The techniques developed include using a "game matrix" for structured brainstorming and developing projects that require students to empathize with users very different from themselves. We present quantitative and qualitative evidence from surveys and focus groups that show how these strategies impacted learning, and the extent to which students' awareness of the strategies led to the development of metacognitive abilities.
The DAWES review: Gravitational-wave burst astrophysics
2024-10-15
Jade Powell, Paul Lasky
Over a hundred gravitational-wave signals have now been detected from the mergers of black holes and neutron stars, but other sources of gravitational waves have not yet been discovered. Some of the most violent explosive events in the Universe are predicted to emit bursts of gravitational waves, and may result in the next big multi-messenger discovery. Gravitational-wave burst signals often have an unknown waveform shape, and unknown gravitational-wave energy, due to unknown or very complicated progenitor astrophysics. Potential sources of gravitational-wave bursts include core-collapse supernovae, cosmic strings, fast radio bursts, eccentric binary systems, and gravitational-wave memory. In this review, we discuss the astrophysical properties of the main predicted sources of gravitational-wave bursts, and the known features of their gravitational-wave emission. We summarise their future detection prospects, and discuss the challenges of searching for gravitational-wave burst signals and interpreting the astrophysics of the source.
Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Products
2024-10-15
Nadia Nahar, Christian Kästner, Jenna Butler, Chris Parnin, Thomas Zimmermann, Christian Bird
Large Language Models (LLMs) are increasingly embedded into software products across diverse industries, enhancing user experiences, but at the same time introducing numerous challenges for developers. Unique characteristics of LLMs force developers, who are accustomed to traditional software development and evaluation, out of their comfort zones as the LLM components shatter standard assumptions about software systems. This study explores the emerging solutions that software developers are adopting to navigate the encountered challenges. Leveraging a mixed-method research, including 26 interviews and a survey with 332 responses, the study identifies 19 emerging solutions regarding quality assurance that practitioners across several product teams at Microsoft are exploring. The findings provide valuable insights that can guide the development and evaluation of LLM-based products more broadly in the face of these challenges.
2024-10-15
Shriyank Somvanshi, Subasish Das, Syed Aaqib Javed, Gian Antariksa, Ahmed Hossain
Tabular data, widely used in industries like healthcare, finance, and transportation, presents unique challenges for deep learning due to its heterogeneous nature and lack of spatial structure. This survey reviews the evolution of deep learning models for tabular data, from early fully connected networks (FCNs) to advanced architectures like TabNet, SAINT, TabTranSELU, and MambaNet. These models incorporate attention mechanisms, feature embeddings, and hybrid architectures to address tabular data complexities. TabNet uses sequential attention for instance-wise feature selection, improving interpretability, while SAINT combines self-attention and intersample attention to capture complex interactions across features and data points, both advancing scalability and reducing computational overhead. Hybrid architectures such as TabTransformer and FT-Transformer integrate attention mechanisms with multi-layer perceptrons (MLPs) to handle categorical and numerical data, with FT-Transformer adapting transformers for tabular datasets. Research continues to balance performance and efficiency for large datasets. Graph-based models like GNN4TDL and GANDALF combine neural networks with decision trees or graph structures, enhancing feature representation and mitigating overfitting in small datasets through advanced regularization techniques. Diffusion-based models like the Tabular Denoising Diffusion Probabilistic Model (TabDDPM) generate synthetic data to address data scarcity, improving model robustness. Similarly, models like TabPFN and Ptab leverage pre-trained language models, incorporating transfer learning and self-supervised techniques into tabular tasks. This survey highlights key advancements and outlines future research directions on scalability, generalization, and interpretability in diverse tabular data applications.
On Classification with Large Language Models in Cultural Analytics
2024-10-15
David Bamman, Kent K. Chang, Li Lucy, Naitian Zhou
In this work, we survey the way in which classification is used as a sensemaking practice in cultural analytics, and assess where large language models can fit into this landscape. We identify ten tasks supported by publicly available datasets on which we empirically assess the performance of LLMs compared to traditional supervised methods, and explore the ways in which LLMs can be employed for sensemaking goals beyond mere accuracy. We find that prompt-based LLMs are competitive with traditional supervised models for established tasks, but perform less well on de novo tasks. In addition, LLMs can assist sensemaking by acting as an intermediary input to formal theory testing.
Ultra Long Period Cepheids as standard candles from Gaia to Rubin-LSST
2024-10-15
I. Musella, S. Leccia, R. Molinaro, M. Marconi, F. Cusano, M. Di Criscienzo, G. Fiorentino, V. Braga, V. Ripepi, G. De Somma, M. Gatto, E. Luongo, T. Sicignano
An analysis of the Ultra Long Period Cepheids (ULPs) properties could significantly contribute to understanding the Hubble constant tension, e.g. the current discrepancy between determinations based on local distance indicators and those relying on cosmic microwave background measurements. These highly luminous variables are observable beyond 100 Mpc, so if they were confirmed to behave as standard candles, they would allow us a direct measurement of cosmological distances without any secondary distance indicator, thus reducing potential systematic errors in the calibration of the cosmic distance scale. This paper presents an analysis of the largest known sample of 73 ULPs, including 15 objects in nearby galaxies, with new accurate and homogeneous photometry obtained by Gaia DR3, and a new object, in our Galaxy, identified as Long Period Variable in Gaia DR3, but recently reclassified as ULP. The obtained results suggest that, by improving photometric accuracy, the ULP Period-Wesenheit relation shows a smaller dispersion than that obtained in literature and is in better agreement with the Classical Cepheid (CC) one, supporting the hypothesis that ULPs are the extension of the CCs at higher period, mass and luminosity. However, to reach this aim, it is necessary to enrich the sample with high-quality data. The Rubin-LSST survey offers the possibility to achieve this thanks to its photometric characteristics and time extension. In particular, we will explore the capabilities of the Rubin-LSST survey to recover ULP theoretical light-curves by using a new tool called PulsationStarRecovery, built by our group for this type of analysis.
2024-10-15
Hyejun Jeong, Shiqing Ma, Amir Houmansadr
Bias in machine learning models has been a chronic problem, especially as these models influence decision-making in human society. In generative AI, such as Large Language Models, the impact of bias is even more profound compared to the classification models. LLMs produce realistic and human-like content that users may unconsciously trust, which could perpetuate harmful stereotypes to the uncontrolled public. It becomes particularly concerning when utilized in journalism or education. While prior studies have explored and quantified bias in individual AI models, no work has yet compared bias similarity across different LLMs. To fill this gap, we take a comprehensive look at ten open- and closed-source LLMs from four model families, assessing the extent of biases through output distribution. Using two datasets-one containing 4k questions and another with one million questions for each of the four bias dimensions -- we measure functional similarity to understand how biases manifest across models. Our findings reveal that 1) fine-tuning does not significantly alter output distributions, which would limit its ability to mitigate bias, 2) LLMs within the same family tree do not produce similar output distributions, implying that addressing bias in one model could have limited implications for others in the same family, and 3) there is a possible risk of training data information leakage, raising concerns about privacy and data security. Our analysis provides insight into LLM behavior and highlights potential risks in real-world deployment.
The versatility of the Drinfeld double of a finite group
2024-10-15
Giovanna Carnovale, Nicolas Ciccoli, Elena Collacciani
In this survey we review different instances in which the Drinfeld double of a finite group and its representations play a role, touching upon some of Tom Koornwinder's research interests: harmonic analysis, Lie algebras, quantum groups, non-commutative geometry, and Verlinde formula.
Model independent bounds on heavy sterile neutrinos from the angular distribution of $\mathbf{B\to D^*\ellν}$ decays
2024-10-15
Florian U. Bernlochner, Marco Fedele, Tim Kretz, Ulrich Nierste, Markus T. Prim
In this paper we study the bounds that can be inferred on New Physics couplings to heavy sterile neutrinos $N$ from the recent measurements performed by the Belle collaboration of the angular analysis of $B\to D^*\ell\bar\nu_\ell$ decays, with $\ell=e,\mu$. Indeed, a sterile neutrino $N$ may lead to competing $B\to D^*\ell\bar N$ decays and Belle might have measured an incoherent sum of these two independent channels. After reviewing the theoretical formalism required to describe this phenomenon in full generality, we first perform a bump hunt in the $M_{\rm miss}^2$ Belle distribution to search for evidences of an additional massive neutrino. We found in such a way a small hint at $M_{\rm miss}^2 \sim (350\ {\rm MeV})^2$. However, the Belle angular analysis is sensitive to $N$ masses up to $\mathcal{O}$(50 MeV), preventing us to further inspect this hint. Nevertheless, we study the potential impact of this additional channel in the allowed mass range on the measured angular distributions and extract model-independent bounds on the new-physics couplings which could mediate such an interaction. In particular, in the mass window here inspected, we obtain the most stringent bounds for vector and left-handed scalar operators to date.
Deep vectorised operators for pulsatile hemodynamics estimation in coronary arteries from a steady-state prior
2024-10-15
Julian Suk, Guido Nannini, Patryk Rygiel, Christoph Brune, Gianluca Pontone, Alberto Redaelli, Jelmer M. Wolterink
Cardiovascular hemodynamic fields provide valuable medical decision markers for coronary artery disease. Computational fluid dynamics (CFD) is the gold standard for accurate, non-invasive evaluation of these quantities in vivo. In this work, we propose a time-efficient surrogate model, powered by machine learning, for the estimation of pulsatile hemodynamics based on steady-state priors. We introduce deep vectorised operators, a modelling framework for discretisation independent learning on infinite-dimensional function spaces. The underlying neural architecture is a neural field conditioned on hemodynamic boundary conditions. Importantly, we show how relaxing the requirement of point-wise action to permutation-equivariance leads to a family of models that can be parametrised by message passing and self-attention layers. We evaluate our approach on a dataset of 74 stenotic coronary arteries extracted from coronary computed tomography angiography (CCTA) with patient-specific pulsatile CFD simulations as ground truth. We show that our model produces accurate estimates of the pulsatile velocity and pressure while being agnostic to re-sampling of the source domain (discretisation independence). This shows that deep vectorised operators are a powerful modelling tool for cardiovascular hemodynamics estimation in coronary arteries and beyond.
Recent theoretical developments in quarkonia production in relativistic heavy ion collisions
2024-10-15
Jiaxing Zhao
Quarkonium is a golden probe of the heavy ion collisions, which has attracted much attention from both experimental and theoretical sides. In this paper, I will review the recent developments on quarkonium thermal properties and its real-time evolution in heavy ion collisions from the theoretical side.
Explainable AI Methods for Multi-Omics Analysis: A Survey
2024-10-15
Ahmad Hussein, Mukesh Prasad, Ali Braytee
Advancements in high-throughput technologies have led to a shift from traditional hypothesis-driven methodologies to data-driven approaches. Multi-omics refers to the integrative analysis of data derived from multiple 'omes', such as genomics, proteomics, transcriptomics, metabolomics, and microbiomics. This approach enables a comprehensive understanding of biological systems by capturing different layers of biological information. Deep learning methods are increasingly utilized to integrate multi-omics data, offering insights into molecular interactions and enhancing research into complex diseases. However, these models, with their numerous interconnected layers and nonlinear relationships, often function as black boxes, lacking transparency in decision-making processes. To overcome this challenge, explainable artificial intelligence (xAI) methods are crucial for creating transparent models that allow clinicians to interpret and work with complex data more effectively. This review explores how xAI can improve the interpretability of deep learning models in multi-omics research, highlighting its potential to provide clinicians with clear insights, thereby facilitating the effective application of such models in clinical settings.
The Atacama Cosmology Telescope DR6 and DESI: Structure growth measurements from the cross-correlation of DESI Legacy Imaging galaxies and CMB lensing from ACT DR6 and Planck PR4
2024-10-14
Frank J. Qu, Qianjun Hang, Gerrit Farren, Boris Bolliet, Jessica Nicole Aguilar, Steven Ahlen, Shadab Alam, David Brooks, Yan-Chuan Cai, Erminia Calabrese, Todd Claybaugh, Axel de la Macorra, Mark J. Devlin, Peter Doel, Carmen Embil-Villagra, Simone Ferraro, Andreu Font-Ribera, Jaime E. Forero-Romero, Enrique Gaztañaga, Vera Gluscevic, Satya Gontcho A Gontcho, Gaston Gutierrez, Cullan Howlett, Robert Kehoe, Joshua Kim, Anthony Kremin, Andrew Lambert, Martin Landriau, Laurent Le Guillou, Michael Levi, Thibaut Louis, Aaron Meisner, Ramon Miquel, John Moustakas, Jeffrey A. Newman, Gustavo Niz, John Peacock, Will Percival, Claire Poppett, Francisco Prada, Ignasi Pérez-Ràfols, Graziano Rossi, Eusebio Sanchez, David Schlegel, Neelima Sehgal, Shabbir Shaikh, Blake Sherwin, Cristóbal Sifón, Michael Schubnell, David Sprayberry, Gregory Tarlé, Benjamin Alan Weaver, Edward J. Wollack, Hu Zou
We measure the growth of cosmic density fluctuations on large scales and across the redshift range $0.3
Context-Parametric Inversion: Why Instruction Finetuning May Not Actually Improve Context Reliance
2024-10-14
Sachin Goyal, Christina Baek, J. Zico Kolter, Aditi Raghunathan
Large language models are instruction-finetuned to enhance their ability to follow user instructions and process the input context. However, even state-of-the-art models often struggle to follow the instruction, especially when the input context is not aligned with the model's parametric knowledge. This manifests as various failures, such as hallucinations where the responses are outdated, biased or contain unverified facts. In this work, we try to understand the underlying reason for this poor context reliance, especially after instruction tuning. We observe an intriguing phenomenon: during instruction tuning, the context reliance initially increases as expected, but then gradually decreases as instruction finetuning progresses. We call this phenomenon context-parametric inversion and observe it across multiple general purpose instruction tuning datasets like TULU, Alpaca and Ultrachat, as well as model families such as Llama, Mistral and Pythia. In a simple theoretical setup, we isolate why context-parametric inversion occurs along the gradient descent trajectory of instruction finetuning. We tie this phenomena to examples in the instruction finetuning data mixture where the input context provides information that is already present in the model's parametric knowledge. Our analysis suggests natural mitigation strategies that provide some limited gains, while also validating our theoretical insights. We hope that our work serves as a starting point in addressing this failure mode in a staple part of LLM training.
Biases in galaxy spectral analysis from strong lensing differential magnification effect and correction methods
2024-10-14
Wenshuo Xu, Dandan Xu, Xinzhong Er, Junqiang Ge
Strong gravitational lensing has significantly advanced the study of high-redshift galaxies, particularly those from the very early universe. However, the spectral analysis of lensed galaxies is inevitably affected by the differential magnification effect, which introduces biases into the derived properties. In this work, we investigate such biases in mock lensing systems generated with data from MaNGA survey and IllustrisTNG simulations. We study the changes of several spectral properties (stellar metallicity, age, flux of H$\alpha$ emission line and flux ratios of optical emission lines) before and after being lensed. Our result shows that all of these properties are significantly biased after being lensed and can be either over- or under-estimated, except for the consistently increasing H$\alpha$ flux. We also investigate the variations of biases with different lensing configurations and find that it always exists when part of the source galaxy falls into the strong lensing regime. Considering the prevalence of such biases, we examine two correction methods to recover the intrinsic source properties, with average magnification factor ($\bar{\mu}$) and full ray-tracing, respectively. Our findings reveal that both methods can reduce the overestimation of H$\alpha$ flux while the value corrected with $\bar{\mu}$ shows a larger discrepancy. For stellar population properties and emission line ratios, the $\bar{\mu}$ method fails to correct the biases, whereas the full ray-tracing method works effectively. The application of these two methods to a statistical sample of mock systems further shows their strong dependence on the accuracy of lens modeling. Our study indicates that spatially resolved spectroscopic observations of lensed images and precise lens modeling are crucial for the spectral analysis of strongly lensed high-redshift galaxies.
Generative AI and Its Impact on Personalized Intelligent Tutoring Systems
2024-10-14
Subhankar Maity, Aniket Deroy
Generative Artificial Intelligence (AI) is revolutionizing educational technology by enabling highly personalized and adaptive learning environments within Intelligent Tutoring Systems (ITS). This report delves into the integration of Generative AI, particularly large language models (LLMs) like GPT-4, into ITS to enhance personalized education through dynamic content generation, real-time feedback, and adaptive learning pathways. We explore key applications such as automated question generation, customized feedback mechanisms, and interactive dialogue systems that respond to individual learner needs. The report also addresses significant challenges, including ensuring pedagogical accuracy, mitigating inherent biases in AI models, and maintaining learner engagement. Future directions highlight the potential advancements in multimodal AI integration, emotional intelligence in tutoring systems, and the ethical implications of AI-driven education. By synthesizing current research and practical implementations, this report underscores the transformative potential of Generative AI in creating more effective, equitable, and engaging educational experiences.
DR-MPC: Deep Residual Model Predictive Control for Real-world Social Navigation
2024-10-14
James R. Han, Hugues Thomas, Jian Zhang, Nicholas Rhinehart, Timothy D. Barfoot
How can a robot safely navigate around people exhibiting complex motion patterns? Reinforcement Learning (RL) or Deep RL (DRL) in simulation holds some promise, although much prior work relies on simulators that fail to precisely capture the nuances of real human motion. To address this gap, we propose Deep Residual Model Predictive Control (DR-MPC), a method to enable robots to quickly and safely perform DRL from real-world crowd navigation data. By blending MPC with model-free DRL, DR-MPC overcomes the traditional DRL challenges of large data requirements and unsafe initial behavior. DR-MPC is initialized with MPC-based path tracking, and gradually learns to interact more effectively with humans. To further accelerate learning, a safety component estimates when the robot encounters out-of-distribution states and guides it away from likely collisions. In simulation, we show that DR-MPC substantially outperforms prior work, including traditional DRL and residual DRL models. Real-world experiments show our approach successfully enables a robot to navigate a variety of crowded situations with few errors using less than 4 hours of training data.
Functional Flexibility in Generative AI Interfaces: Text Editing with LLMs through Conversations, Toolbars, and Prompts
2024-10-14
Florian Lehmann, Daniel Buschek
Prompting-based user interfaces (UIs) shift the task of defining and accessing relevant functions from developers to users. However, how UIs shape this flexibility has not yet been investigated explicitly. We explored interaction with Large Language Models (LLMs) over four years, before and after the rise of general-purpose LLMs: (1) Our survey (N=121) elicited how users envision to delegate writing tasks to AI. This informed a conversational UI design. (2) A user study (N=10) revealed that people regressed to using short command-like prompts. (3) When providing these directly as shortcuts in a toolbar UI, in addition to prompting, users in our second study (N=12) dynamically switched between specified and flexible AI functions. We discuss functional flexibility as a new theoretical construct and thinking tool. Our work highlights the value of moving beyond conversational UIs, by considering how different UIs shape users' access to the functional space of generative AI models.
Field-level cosmological model selection: field-level simulation-based inference for Stage IV cosmic shear can distinguish dynamical dark energy
2024-10-14
A. Spurio Mancini, K. Lin, J. D. McEwen
We present a framework that for the first time allows Bayesian model comparison to be performed for field-level inference of cosmological models. We achieve this by taking a simulation-based inference (SBI) approach using neural likelihood estimation, which we couple with the learned harmonic mean estimator in order to compute the Bayesian evidence for model comparison. We apply our framework to mock Stage IV cosmic shear observations to assess its effectiveness at distinguishing between various models of dark energy. If the recent DESI results that provided exciting hints of dynamical dark energy were indeed the true underlying model, our analysis shows Stage IV cosmic shear surveys could definitively detect dynamical dark energy. We also perform traditional power spectrum likelihood-based inference for comparison, which we find is not able to distinguish between dark energy models, highlighting the enhanced constraining power for model comparison of our field-level SBI approach.
Testing interacting dark energy with Stage IV cosmic shear surveys through differentiable neural emulators
2024-10-14
K. Carrion, A. Spurio Mancini, D. Piras, J. C. Hidalgo
We employ a novel framework for accelerated cosmological inference, based on neural emulators and gradient-based sampling methods, to forecast constraints on dark energy models from Stage IV cosmic shear surveys. We focus on dark scattering (DS), an interacting dark energy model with pure momentum exchange in the dark sector, and train COSMOPOWER emulators to accurately and efficiently model the DS non-linear matter power spectrum produced by the halo model reaction framework, including the effects of baryon feedback and massive neutrinos. We embed the emulators within a fully-differentiable pipeline for gradient-based cosmological inference for which the batch likelihood call is up to $O(10^5)$ times faster than with traditional approaches, producing parameter constraints from simulated Stage IV cosmic shear data running on a single graphics processing unit (GPU). We also perform model comparison on the output chains from the inference process, employing the learnt harmonic mean estimator implemented in the software HARMONIC. We investigate degeneracies between dark energy and systematics parameters and assess the impact of scale cuts on the final constraints. Assuming a DS model for the mock data vector, we find that a Stage IV survey cosmic shear analysis can constrain the DS amplitude parameter $A_{\mathrm{ds}}$ with an uncertainty roughly an order of magnitude smaller than current constraints from Stage III surveys, even after marginalising over baryonic feedback, intrinsic alignments and redshift distribution uncertainties. These results show great promise for constraining DS with Stage IV data; furthermore, our methodology can be straightforwardly extended to a wide range of dark energy and modified gravity models.
SOFIA/HAWC+ Far-Infrared Polarimetric Large-Area CMZ Exploration Survey. IV. Relative Magnetic Field Orientation Throughout the CMZ
2024-10-14
Dylan M. Paré, David T. Chuss, Kaitlyn Karpovich, Natalie O. Butterfield, Jeffrey Inara Iulliano, Xing Pan, Edward J. Wollack, Qizhou Zhang, Mark R. Morris, Matthilda Nilsson, Roy J. Zhao
The nature of the magnetic field structure throughout the Galactic Center (GC) has long been of interest. The recent Far-InfraREd Polarimetric Large-Area CMZ Exploration (FIREPLACE) Survey reveals preliminary connections between the seemingly distinct vertical and horizontal magnetic field distributions previously observed in the GC. We use the statistical techniques of the Histogram of Relative Orientation (HRO) and the Projected Rayleigh Statistic (PRS) to assess whether the CMZ magnetic field preferentially aligns with the column density gradient derived from CMZ molecular clouds or the morphology of the non-thermal emission of the GC NTF population. We find that there is a range of magnetic field orientations throughout the population of CMZ molecular clouds, ranging from parallel to perpendicular orientation. This range of orientations contrasts with what is observed in Galactic Disk star-forming regions. We also compare the magnetic field orientation from dust polarimetry with individual prominent NTFs, finding a preferred perpendicular relative orientation. This perpendicular orientation indicates that the vertical field component found in the FIREPLACE observations is not spatially confined to the NTFs, providing evidence for a more pervasive vertical field in the GC. We estimate an upper limit on the magnetic field strength for this vertical field of 4 mG. A field close to this upper limit would indicate that the NTFs are likely not local enhancements of a weaker background field and that the locations of the NTFs depend on proximity to sites of cosmic ray production.
Neural networks that overcome classic challenges through practice
2024-10-14
Kazuki Irie, Brenden M. Lake
Since the earliest proposals for neural network models of the mind and brain, critics have pointed out key weaknesses in these models compared to human cognitive abilities. Here we review recent work that has used metalearning to help overcome some of these challenges. We characterize their successes as addressing an important developmental problem: they provide machines with an incentive to improve X (where X represents the desired capability) and opportunities to practice it, through explicit optimization for X; unlike conventional approaches that hope for achieving X through generalization from related but different objectives. We review applications of this principle to four classic challenges: systematicity, catastrophic forgetting, few-shot learning and multi-step reasoning; we also discuss related aspects of human development in natural environments.
Development of a 3D virtual world tool for sustainable energy education
2024-10-14
Marta Guerra-Mota, Dimosthenis Minas, Michalis Xenos, Maria Manuel Sa
The UNESCO (2022) points out that the gap between the existing awareness of a person or a community, and the actual habits of everyday life, is attributed to: low levels of understanding the environmental issues at stake; low levels of knowledge regarding energy and climate issues; and lack of attention to social, emotional or behavioral learning. In this context, as environmental and energetic concerns grow rapidly in our daily lives, RAISE - Raising environmental knowledge & awareness, an ERAMUS+ project with partners from three countries (Greece, Portugal, and Italy), was born. The project's first and main objective is to increase the environmental knowledge and awareness (EK&A) of schoolchildren. To achieve this goal, a survey was first carried out to ascertain the students' knowledge and, based on these results, scenarios and educational material were drawn up to build a pedagogical tool - a 3D Virtual World Learning Environment (3D VWLE). This paper presents the desktop research results and the main features of the game that are intended to match the identified educational needs. The 3D VWLE puts students in situations where they can acquire transferable skills. The project demonstrated that the simulation effect of 3D VWLE on skills provides an excellent online learning environment for students to work on and improve their abilities. In other words, virtual worlds open the door to a new way of learning and teaching.
Following the wealth of new results enabled by multimessenger observations of the binary neutron star (BNS) merger GW170817, the next goal is increasing the number of detections of electromagnetic (EM) counterparts to gravitational wave (GW) events. Here, we study the detectability of the prompt emission and afterglows produced by the relativistic jets launched by BNS mergers that will be detected by LIGO-Virgo-KAGRA (LVK) during their fifth observing run (O5), and by next generation (XG) GW detectors (Einstein Telescope and Cosmic Explorer). We quantify the impact of various BNS merger and jet afterglow parameters on the likelihood of detection for a wide range of telescopes, focusing on the impact of the observer's viewing angle $\theta_\textrm{v}$ and the jet's core half-opening angle $\theta_\textrm{c}$. We find that during the LVK O5 run, the James Webb Space Telescope (JWST) is expected to reach the largest number of afterglow detections, up to $32\%$ of BNS mergers with $27\%$ detectable on timescales $>10$ d. Overall the detection of $20-30\%$ of events is possible across the EM spectrum with the available instruments, including the Chandra X-ray Observatory, the Nancy Grace Roman Space Telescope, the Square Kilometer Array (SKA), and the Very Large Array (VLA). In the XG era, afterglows for $20-25\%$ of events will be detectable with AXIS, Athena, Lynx, UVEX, JWST and the next generation VLA (ngVLA), reaching beyond redshift $z \gtrsim 1$. We also find that the majority of detected afterglows are expected to accompany prompt gamma-ray emission detectable by the Neil Gehrels Swift Observatory, the Space-based multi-band astronomical Variable Objects Monitor (SVOM), and the Transient High-Energy Sky and Early Universe Surveyor (THESEUS), with at least $\sim 6\%$ of afterglows being orphan.
Some singular spacetimes and their possible alternatives
2024-10-14
Andrew DeBenedictis
This is a brief review article of a seminar given at the International Conference on Gravitation, Astrophysics and Cosmology 2024 (ICGAC-2024), Mathura, India. We begin with a historical survey of some singular solutions in the theory of gravitation, as well as a very brief discussion of how black holes could physically form. Some possible scenarios which could perhaps eliminate these singularities are then reviewed and discussed. Due to the vastness of the field the coverage is not exhaustive, but instead the concentration is on a small subset of topics such as possible quantum gravity effects, non-commutative geometry, and gravastars. A simple singularity theorem is also presented in the appendix. Although parts of the manuscript assume some familiarity with relativistic gravitation or differential geometry, the aim is for the broad picture to be accessible to non-specialists of other physical sciences and mathematics.
The training data for LLMs embeds societal values, increasing their familiarity with the language's culture. Our analysis found that 44% of the variance in the ability of GPT-4o to reflect the societal values of a country, as measured by the World Values Survey, correlates with the availability of digital resources in that language. Notably, the error rate was more than five times higher for the languages of the lowest resource compared to the languages of the highest resource. For GPT-4-turbo, this correlation rose to 72%, suggesting efforts to improve the familiarity with the non-English language beyond the web-scraped data. Our study developed one of the largest and most robust datasets in this topic area with 21 country-language pairs, each of which contain 94 survey questions verified by native speakers. Our results highlight the link between LLM performance and digital data availability in target languages. Weaker performance in low-resource languages, especially prominent in the Global South, may worsen digital divides. We discuss strategies proposed to address this, including developing multilingual LLMs from the ground up and enhancing fine-tuning on diverse linguistic datasets, as seen in African language initiatives.
Kinetic models for optimization: a unified mathematical framework for metaheuristics
2024-10-14
Giacomo Borghi, Michael Herty, Lorenzo Pareschi
Metaheuristic algorithms, widely used for solving complex non-convex and non-differentiable optimization problems, often lack a solid mathematical foundation. In this review, we explore how concepts and methods from kinetic theory can offer a potential unifying framework for a variety of metaheuristic optimization methods. By applying principles from collisional and non-collisional kinetic theory, we outline how particle-based algorithms like Simulated Annealing, Genetic Algorithms, Particle Swarm Optimization, and Ensemble Kalman Filter may be described through a common statistical perspective. This approach not only provides a path to deeper theoretical insights and connects different methods under suitable asymptotic scalings, but also enables the derivation of novel algorithms using alternative numerical solvers. While not exhaustive, our review highlights how kinetic models can enhance the mathematical understanding of existing optimization algorithms and inspire new computational strategies.
2024-10-14
Konstantinos Kellaris, George Papadakis, Miguel Alfonso Mendez, Marinos Manolesos
Flatback airfoils, characterized by a blunt trailing edge, are used at the root of large wind turbine blades. A low-drag pocket has recently been identified in the flow past these airfoils at high angles of attack, potentially offering opportunities for enhanced energy extraction. This study uses three-dimensional Detached Eddy Simulations (DES) combined with statistical and data-driven modal analysis techniques to explore the aerodynamics and coherent structures of a flatback airfoil in these conditions. Two angles of attack - one inside $\left(12^{\circ}\right)$ and one outside $\left(0^{\circ}\right)$ of the low-drag pocket - are examined more thoroughly. The spanwise correlation length of secondary instability is analyzed in terms of autocorrelation of the $\Gamma_{1}$ vortex identification criterion, while coherent structures were extracted via the multiscale Proper Orthogonal Decomposition (mPOD). The results show increased base pressure, BL thickness, vortex formation length, and more organized wake structures inside the low-drag regime. While the primary instability (B\'enard-von K\'arm\'an vortex street) dominates in both cases, the secondary instability is distinguishable only for the $12^{\circ}$ case and is identified as a Mode S$^{\prime}$ instability.
Recently, research on Text-Attributed Graphs (TAGs) has gained significant attention due to the prevalence of free-text node features in real-world applications and the advancements in Large Language Models (LLMs) that bolster TAG methodologies. However, current TAG approaches face two primary challenges: (i) Heavy reliance on label information and (ii) Limited cross-domain zero/few-shot transferability. These issues constrain the scaling of both data and model size, owing to high labor costs and scaling laws, complicating the development of graph foundation models with strong transferability. In this work, we propose the GraphCLIP framework to address these challenges by learning graph foundation models with strong cross-domain zero/few-shot transferability through a self-supervised contrastive graph-summary pretraining method. Specifically, we generate and curate large-scale graph-summary pair data with the assistance of LLMs, and introduce a novel graph-summary pretraining method, combined with invariant learning, to enhance graph foundation models with strong cross-domain zero-shot transferability. For few-shot learning, we propose a novel graph prompt tuning technique aligned with our pretraining objective to mitigate catastrophic forgetting and minimize learning costs. Extensive experiments show the superiority of GraphCLIP in both zero-shot and few-shot settings, while evaluations across various downstream tasks confirm the versatility of GraphCLIP. Our code is available at: https://github.com/ZhuYun97/GraphCLIP
2024-10-14
Daohan Su, Xunkai Li, Zhenjun Li, Yinping Liao, Rong-Hua Li, Guoren Wang
Recently, graph neural network (GNN) has emerged as a powerful representation learning tool for graph-structured data. However, most approaches are tailored for undirected graphs, neglecting the abundant information embedded in the edges of directed graphs (digraphs). In fact, digraphs are widely applied in the real world (e.g., social networks and recommendations) and are also confirmed to offer a new perspective for addressing topological heterophily challenges (i.e., connected nodes have complex patterns of feature distribution or labels). Despite recent significant advancements in DiGNNs, existing spatial- and spectral-based methods have inherent limitations due to the complex learning mechanisms and reliance on high-quality topology, leading to low efficiency and unstable performance. To address these issues, we propose Directed Random Walk (DiRW), which can be viewed as a plug-and-play strategy or an innovative neural architecture that provides a guidance or new learning paradigm for most spatial-based methods or digraphs. Specifically, DiRW incorporates a direction-aware path sampler optimized from the perspectives of walk probability, length, and number in a weight-free manner by considering node profiles and topological structure. Building upon this, DiRW utilizes a node-wise learnable path aggregator for generalized messages obtained by our proposed adaptive walkers to represent the current node. Extensive experiments on 9 datasets demonstrate that DiRW: (1) enhances most spatial-based methods as a plug-and-play strategy; (2) achieves SOTA performance as a new digraph learning paradigm.
Negative piezoelectricity in quasi-two/one-dimensional ferroelectrics
2024-10-14
Ning Ding, Shuai Dong
In recent years, the investigation of low-dimensional ferroelectrics has attracted great attention for their promising applications in nano devices. Piezoelectricity is one of the most core properties of ferroelectric materials, which plays the essential role in micro-electromechanical systems. Very recently, the anomalous negative piezoelectricity has been predicted/discovered in many quasi-two-dimensional layered ferroelectric materials. In this Topical Review, we will briefly introduce on the negative piezoelectricity in quasi-two/one-dimensional ferroelectrics, including its fundamental concept, typical materials, theoretical predictions, as well as experimental phenomena. The underlying physical mechanisms for negative piezoelectricity are divergent and varying from case by case, which can be categorized into four types. First, the soft van der Waals layer is responsible for the volume shrinking upon pressure while the electric dipoles is from non van der Waals layer. Second, the noncollinearity of local dipoles creates a ferrielectricity, which leads to orthogonal ferroelectric and antiferroelectric axes. Third, the electric dipoles come from interlayer/interchain couplings, which can be enhanced during the volume shrinking. Fourth, the special buckling structure contributes to local dipoles, which can be enhanced upon pressure. In real materials, more than one mechanism may work together. Finally, the future directions of negative piezoelectricity and their potential applications are outlooked.
Back-of-the-Book Index Automation for Arabic Documents
2024-10-14
Nawal Haidar, Fadi A. Zaraket
Back-of-the-book indexes are crucial for book readability. Their manual creation is laborious and error prone. In this paper, we consider automating back-of-the-book index extraction for Arabic books to help simplify both the creation and review tasks. Given a back-of-the-book index, we aim to check and identify the accurate occurrences of index terms relative to the associated pages. To achieve this, we first define a pool of candidates for each term by extracting all possible noun phrases from paragraphs appearing on the relevant index pages. These noun phrases, identified through part-of-speech analysis, are stored in a vector database for efficient retrieval. We use several metrics, including exact matches, lexical similarity, and semantic similarity, to determine the most appropriate occurrence. The candidate with the highest score based on these metrics is chosen as the occurrence of the term. We fine-tuned a heuristic method, that considers the above metrics and that achieves an F1-score of .966 (precision=.966, recall=.966). These excellent results open the door for future work related to automation of back-of-the-book index generation and checking.
Trust or Bust: Ensuring Trustworthiness in Autonomous Weapon Systems
2024-10-14
Kasper Cools, Clara Maathuis
The integration of Autonomous Weapon Systems (AWS) into military operations presents both significant opportunities and challenges. This paper explores the multifaceted nature of trust in AWS, emphasising the necessity of establishing reliable and transparent systems to mitigate risks associated with bias, operational failures, and accountability. Despite advancements in Artificial Intelligence (AI), the trustworthiness of these systems, especially in high-stakes military applications, remains a critical issue. Through a systematic review of existing literature, this research identifies gaps in the understanding of trust dynamics during the development and deployment phases of AWS. It advocates for a collaborative approach that includes technologists, ethicists, and military strategists to address these ongoing challenges. The findings underscore the importance of Human-Machine teaming and enhancing system intelligibility to ensure accountability and adherence to International Humanitarian Law. Ultimately, this paper aims to contribute to the ongoing discourse on the ethical implications of AWS and the imperative for trustworthy AI in defense contexts.
This paper presents a review and perspective on quantum gases in bubble traps. We emphasize how the idea of realizing shell shaped condensates emerged and was enabled by the invention of the radiofrequency adiabatic potential technique. We review the many subsequent theoretical works that address the new physics emerging for a condensate trapped on a closed surface. We present the current status of the experiments, the challenges ahead and highlight how a different approach using an immiscible mixture of two condensates enabled the first observation of a shell-shaped degenerate gas. Finally we list a few open questions that we believe provide interesting research directions.
A Survey on Performance, Current and Future Usage of Vehicle-To-Everything Communication Standards
2024-10-14
Falk Dettinger, Matthias Weiß, Daniel Dittler, Johannes Stümpfle, Maurice Artelt, Michael Weyrich
Wireless communication between road users is essential for environmental perception, reasoning, and mission planning to enable fully autonomous vehicles, and thus improve road safety and transport efficiency. To enable collaborative driving, the concept of vehicle-to-Everything (V2X) has long been introduced to the industry. Within the last two decades, several communication standards have been developed based on IEEE 802.11p and cellular standards, namely Dedicated Short-Range Communication (DSRC), Intelligent Transportation System G5 (ITS-G5), and Cellular- and New Radio- Vehicle-to-Everything (C-V2X and NR-V2X). However, while there exists a high quantity of available publications concerning V2X and the analysis of the different standards, only few surveys exist that summarize these results. Furthermore, to our knowledge, no survey that provides an analysis about possible future trends and challenges for the global implementation of V2Xexists. Thus, this contribution provides a detailed survey on Vehicle-to-Everything communication standards, their performance, current and future applications, and associated challenges. Based on our research, we have identified several research gaps and provide a picture about the possible future of the Vehicle-to-Everything communication domain.
Inferring the dust emission at submillimeter and millimeter wavelengths using neural networks
2024-10-14
D. Paradis, C. Mény, A. Noriega-Crespo, K. Demyk, I. Ristorcelli, N. Ysard
The Planck mission provided all-sky dust emission maps in the submm to mm range at an angular resolution of 5'. In addition, some specific sources can be observed at long wavelengths and higher resolution using ground-based telescopes. These observations are limited to small scales and require extensive data processing before they become available for scientific analysis. They also suffer from extended emission filtering. At present, we are still unable to fully understand the emissivity variations observed in different astrophysical environments at long wavelengths. It is therefore challenging to estimate any dust emission in the submm-mm at a better resolution than the 5' from Planck. In this analysis, based on supervised deep learning algorithms, we produced dust emission predictions in the two Planck bands centered at 850 mic and 1.38 mm, at the Herschel resolution (37''). Herschel data of Galactic environments, ranging from 160 to 500 mic and smoothed to 5', were used to train the neural network. Then, using Herschel data only, the model was applied to predict dust emission maps at 37''. The neural network is capable of reproducing dust emission maps of various Galactic environments. Remarkably, it also performs well for nearby extragalactic environments. This could indicate that large dust grains have similar properties in both our Galaxy and nearby galaxies, or at least that their spectral behaviors are comparable in Galactic and extragalactic environments. We provide dust emission prediction maps at 850 mic and 1.38 mm at the 37'' of several surveys: Hi-GAL, Gould Belt, Cold Cores, HERITAGE, Helga, HerM33es, KINGFISH, and VNGS. The ratio of these two wavelength brightness bands reveals a derived emissivity spectral index statistically close to 1 for all the surveys, which favors the hypothesis of a flattened dust emission spectrum for wavelengths larger than 850 mic.
The formula for the completion time of project networks
2024-10-14
Manuel Castejón-Limas, Gabriel Medina Martínez, Virginia Riego del Castillo, Laura Fernández-Robles
This paper formulates the completion time $\tau$ of a project network as $ \tau =\|\mathbf{R} \mathbf{t} \|_\infty $ where the rows of $\mathbf{R}$ are simple paths of the network and $\mathbf{t}$ is a column vector representing the duration of the activities. Considering this product as a linear transformation leads to interesting findings on the topological relevance of both paths and activities using singular value decomposition. The notion of spectral networks is introduced to condense the fundamental structure of the project network. A definition of project stress is introduced to establish a comparison index between two alternatives in terms of slack. Additionally, the Moore-Penrose inverse of $\mathbf{R}$ is presented to find the configuration of the durations of the activities resulting in a given simple path duration vector. Then, the systematic mapping review process carried out to assess our claims' novelty is reported. Finally, we reflect on the notion of relevance for paths and activities and the relationship of the incidence matrix with the proposed approach.
Graph autoencoders (GAEs) are self-supervised learning models that can learn meaningful representations of graph-structured data by reconstructing the input graph from a low-dimensional latent space. Over the past few years, GAEs have gained significant attention in academia and industry. In particular, the recent advent of GAEs with masked autoencoding schemes marks a significant advancement in graph self-supervised learning research. While numerous GAEs have been proposed, the underlying mechanisms of GAEs are not well understood, and a comprehensive benchmark for GAEs is still lacking. In this work, we bridge the gap between GAEs and contrastive learning by establishing conceptual and methodological connections. We revisit the GAEs studied in previous works and demonstrate how contrastive learning principles can be applied to GAEs. Motivated by these insights, we introduce lrGAE (left-right GAE), a general and powerful GAE framework that leverages contrastive learning principles to learn meaningful representations. Our proposed lrGAE not only facilitates a deeper understanding of GAEs but also sets a new benchmark for GAEs across diverse graph-based learning tasks. The source code for lrGAE, including the baselines and all the code for reproducing the results, is publicly available at https://github.com/EdisonLeeeee/lrGAE.
Detecting anomalies such as incorrect combinations of objects or deviations in their positions is a challenging problem in industrial anomaly detection. Traditional methods mainly focus on local features of normal images, such as scratches and dirt, making detecting anomalies in the relationships between features difficult. Masked image modeling(MIM) is a self-supervised learning technique that predicts the feature representation of masked regions in an image. To reconstruct the masked regions, it is necessary to understand how the image is composed, allowing the learning of relationships between features within the image. We propose a novel approach that leverages the characteristics of MIM to detect logical anomalies effectively. To address blurriness in the reconstructed image, we replace pixel prediction with predicting the probability distribution of discrete latent variables of the masked regions using a tokenizer. We evaluated the proposed method on the MVTecLOCO dataset, achieving an average AUC of 0.867, surpassing traditional reconstruction-based and distillation-based methods.
The Stellar Abundances and Galactic Evolution Survey (SAGES) III -- The g/r/i-band Data Release
2024-10-14
Chun Li, Zhou Fan, Gang Zhao, Wei Wang, Jie Zheng, Kefeng Tan, Jingkun Zhao, Yang Huang, Haibo Yuan, Kai Xiao, Yuqin Chen, Haining Li, Yujuan Liu, Nan Song, Ali Esamdin, Hu-Biao Niu, Jin-Zhong Liu, Guo-Jie Feng
The Stellar Abundances and Galactic Evolution Survey (SAGES) is a multi-band survey that covers the northern sky area of ~12000 deg2. Nanshan One-meter Wide-field Telescope (NOWT) of Xinjiang Astronomical Observatory (XAO) carried out observations on g/r/i bands. We present here the survey strategy, data processing, catalog construction, and database schema. The observations of NOWT started in 2016 August and was completed in 2018 January, total 17827 frames were obtained and ~4600 deg2 sky areas were covered. In this paper, we released the catalog of the data in the g/r/i bands observed with NOWT. In total, there are 109,197,578 items of the source records. The catalog is the supplement for the SDSS for the bright end, and the combination of our catalog and these catalogs could be helpful for source selections for other surveys and the Milky Way sciences, e.g., white dwarf candidates and stellar flares.
The Second Law of Thermodynamics, Life and Earth's Planetary Machinery Revisited
2024-10-14
Axel Kleidon
Life is a planetary feature that depends on its environment, but it has also strongly shaped the physical conditions on Earth, having created conditions highly suitable for a productive biosphere. Clearly, the second law of thermodynamics must apply to these dynamics as well, but how? What insights can we gain by placing life and its effects on planetary functioning in the context of the second law? In Kleidon (2010), I described a thermodynamic Earth system perspective by placing the functioning of the Earth system in terms of the second law. The Earth system is represented by a planetary hierarchy of energy transformations that are driven predominantly by incoming solar radiation, these transformations are constrained by the second law, but they are also modified by the feedbacks from various dissipative activities. It was then hypothesised that life evolves its dissipative activity to the limits imposed by this hierarchy and evolves feedbacks aimed at pushing these limits to higher levels of dissipative activity. Here I provide an update of this perspective. I first review applications to climate and global climate change to demonstrate its success in predicting magnitudes of physical processes, particularly regarding temperatures, heat redistribution and hydrological cycling. I then focus on the limits to dissipative activity of the biosphere. It would seem that the limitations by thermodynamics act indirectly by imposing limitations associated with transport and material exchange. I substantiate this interpretation and discuss the broader implications for habitability, the emergence and evolution of life, and the contemporary biosphere.
Effi-Code: Unleashing Code Efficiency in Language Models
2024-10-14
Dong Huang, Guangtao Zeng, Jianbo Dai, Meng Luo, Han Weng, Yuhao Qing, Heming Cui, Zhijiang Guo, Jie M. Zhang
As the use of large language models (LLMs) for code generation becomes more prevalent in software development, it is critical to enhance both the efficiency and correctness of the generated code. Existing methods and models primarily focus on the correctness of LLM-generated code, ignoring efficiency. In this work, we present Effi-Code, an approach to enhancing code generation in LLMs that can improve both efficiency and correctness. We introduce a Self-Optimization process based on Overhead Profiling that leverages open-source LLMs to generate a high-quality dataset of correct and efficient code samples. This dataset is then used to fine-tune various LLMs. Our method involves the iterative refinement of generated code, guided by runtime performance metrics and correctness checks. Extensive experiments demonstrate that models fine-tuned on the Effi-Code show significant improvements in both code correctness and efficiency across task types. For example, the pass@1 of DeepSeek-Coder-6.7B-Instruct generated code increases from \textbf{43.3\%} to \textbf{76.8\%}, and the average execution time for the same correct tasks decreases by \textbf{30.5\%}. Effi-Code offers a scalable and generalizable approach to improving code generation in AI systems, with potential applications in software development, algorithm design, and computational problem-solving. The source code of Effi-Code was released in \url{https://github.com/huangd1999/Effi-Code}.
Experimental progress in Eu(Al,Ga)$_4$ topological antiferromagnets
2024-10-14
Tian Shang, Yang Xu, Shang Gao, Run Yang, Toni Shiroka, Ming Shi
The non-trivial magnetic and electronic phases occurring in topological magnets are often entangled, thus leading to a variety of exotic physical properties. Recently, the BaAl$_4$-type compounds have been extensively investigated to elucidate the topological features appearing in their real- and momentum spaces. In particular, the topological Hall effect and the spin textures, typical of the centrosymmetric Eu(Al,Ga)$_4$ family, have stimulated extensive experimental and theoretical research. In this topical review, we discuss the latest findings regarding the Eu(Al,Ga)$_4$ topological antiferromagnets and related materials, arising from a vast array of experimental techniques. We show that Eu(Al,Ga)$_4$ represents a suitable platform to explore the interplay between lattice-, charge-, and spin degrees of freedom, and associated emergent phenomena. Finally, we address some key questions open to future investigation.
Radio Morphology of Gamma-ray Sources: Double-Lobed Radio Sources
2024-10-14
Vaidehi S. Paliya, D. J. Saikia, Alberto Domínguez, C. S. Stalin
The extragalactic gamma-ray sky is dominated by relativistic jets aligned to the observer's line of sight, i.e., blazars. A few of their misaligned counterparts, e.g., radio galaxies, are also detected with the Fermi-Large Area Telescope (LAT) albeit in a small number ($\sim$50), indicating the crucial role played by the jet viewing angle in detecting gamma-ray emission from jets. These gamma-ray emitting misaligned active galactic nuclei (AGN) provide us with a unique opportunity to understand the high-energy emission production mechanisms from a different viewpoint than the more common blazars. With this goal in mind, we have systematically studied the radio morphology of gamma-ray emitting sources present in the fourth data release of the fourth catalog of Fermi-LAT detected gamma-ray sources to identify misaligned AGN. By utilizing the high-resolution and sensitive MHz and GHz frequency observations delivered by the Very Large Array Sky Survey, Low-Frequency Array Two-metre Sky Survey, Faint Images of the Radio Sky at Twenty-Centimeters, and Rapid ASKAP Continuum Survey, here we present a catalog of 149 gamma-ray detected misaligned AGN, thus $\sim$tripling the number of known objects of this class. Our sample includes a variety of radio morphologies, e.g., edge-darkened and edge-brightened, hybrids, wide-angle-tailed, bent jets, and giants. Since the gamma-ray emission is thought to be highly sensitive to the jet viewing angle, such an enlarged sample of gamma-ray detected misaligned radio sources will permit us to explore the origin of high-energy emission in relativistic jets and radio lobes and study AGN unification, in general.
Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations
2024-10-14
Julius Aka, Johannes Brunnemann, Jörg Eiden, Arne Speerforck, Lars Mikelsons
Variational Autoencoders (VAEs) are a powerful framework for learning compact latent representations, while NeuralODEs excel in learning transient system dynamics. This work combines the strengths of both to create fast surrogate models with adjustable complexity. By leveraging the VAE's dimensionality reduction using a non-hierarchical prior, our method adaptively assigns stochastic noise, naturally complementing known NeuralODE training enhancements and enabling probabilistic time series modeling. We show that standard Latent ODEs struggle with dimensionality reduction in systems with time-varying inputs. Our approach mitigates this by continuously propagating variational parameters through time, establishing fixed information channels in latent space. This results in a flexible and robust method that can learn different system complexities, e.g. deep neural networks or linear matrices. Hereby, it enables efficient approximation of the Koopman operator without the need for predefining its dimensionality. As our method balances dimensionality reduction and reconstruction accuracy, we call it Balanced Neural ODE (B-NODE). We demonstrate the effectiveness of this method on academic test cases and apply it to a real-world example of a thermal power plant.
ASW$^2$DF: Census of the obscured star formation in a galaxy cluster in formation at $z=2.2$
2024-10-14
Y. H. Zhang, H. Dannerbauer, J. M. Pérez-Martínez, Y. Koyama, X. Z. Zheng, C. D'Eugenio, B. H. C. Emonts, R. Calvi, Z. Chen, K. Daikuhara, C. De Breuck, S. Jin, T. Kodama, M. D. Lehnert, A. Naufal, R. Shimakawa
We report the results of the deep and wide Atacama Large Millimeter/submillimeter Array (ALMA) 1.2 mm mapping of the Spiderweb protocluster at $z=2.16$. The observations were divided into six contiguous fields covering a survey area of 19.3\,arcmin$^2$. With $\sim$13h on-source time, the final maps in the six fields reach the 1$\sigma$ rms noise in a range of $40.3-57.1 \mu$Jy at a spatial resolution of $0.5-0.9$ arcsec. By using different source extraction codes and careful visual inspection, we detect 47 ALMA sources at a significance higher than 4$\sigma$. We construct the differential and cumulative number counts down to $\sim0.2$ mJy after the correction for purity and completeness obtained from Monte Carlo simulations. The ALMA 1.2 mm number counts of dusty star-forming galaxies (DSFGs) in the Spiderweb protocluster are overall two times that of general fields, some fields/regions showing even higher overdensities (more than a factor of 3). This is consistent with the results from previous studies over a larger scale using single-dish instruments. Comparison of the spatial distributions between different populations indicates that our ALMA sources are likely drawn from the same distribution as CO(1-0) emitters from the COALAS large program, but distinct from that of H$\alpha$ emitters. The cosmic SFR density of the ALMA sources is consistent with previous results (e.g. LABOCA 870 $\mu$m observations) after accounting for the difference in volume. We show that molecular gas masses estimates from dust measurements are not consistent with the ones derived from CO(1-0) and thus have to be taken with caution. The multiplicity fraction of single-dish DSFGs is higher than that of the field. Moreover, two extreme concentrations of ALMA sources are found on the outskirts of the Spiderweb protocluster, with an excess of more than 12 times that of general fields.
From x*y=k to Uniswap Hooks; A Comparative Review of Decentralized Exchanges (DEX)
2024-10-14
Mohammad Ali Asef, Seyed Mojtaba Hosseini Bamakan
Decentralized exchanges (DEXs) are pivotal applications in the Decentralized finance (DeFi) landscape, aiming to facilitate trustless cryptocurrency trading by relying on smart contracts and blockchain networks. The developments in the DEXs sector began with the implementation of an automated market maker (AMM) system using a simple math formula by Uniswap V1 in 2018. Absorbing significant funding and the attention of web3 enthusiasts, DEXs have seen numerous advancements in their evolution. A notable recent advancement is the introduction of hooks in Uniswap v4, which allows users to take advantage of a wide range of plugin-like features with liquidity pools. This paper provides a comprehensive classification and comparative analyses of prominent DEX protocols, namely Uniswap, Curve, and Balancer, in addition to investigating other protocols' noteworthy aspects. The evaluation framework encompasses mechanisms, components, mathematical formulations, and the performance of liquidity pools. The goals are to elucidate the strengths and limitations of different AMM models, highlight emerging concepts in DEX development, outline current challenges, and differentiate optimal models for specific applications. The results and comparative insights can be a reference for web3 developers, blockchain researchers, traders, and regulatory parties.
Supply Chain Optimization Strategies: An Empirical Study on Fresh Product Delivery Routes
2024-10-14
Yu Yuan, Xiaoke Xie, Yulei Xie
In recent years, with rising consumer demand, fresh products have gained increasing attention, leading to rapid growth in the fresh food market. However, due to their perishable nature and sensitivity to storage conditions, fresh products are vulnerable to damage during transportation. Improper handling, excessive transit times, and physical impacts can result in significant losses. As a result, enhancing the efficiency of fresh product distribution while maintaining quality has become critical to the further development of the fresh food industry. Using Y chain supermarket as a case study, this paper investigates the logistics of fresh product distribution, identifying current challenges and inefficiencies. Through literature review, expert interviews, and comparative analysis, the study offers strategic recommendations for optimizing fresh product delivery routes to improve distribution efficiency and product quality.
Signage-Aware Exploration in Open World using Venue Maps
2024-10-14
Chang Chen, Liang Lu, Lei Yang, Yinqiang Zhang, Yizhou Chen, Ruixing Jia, Jia Pan
Current exploration methods struggle to search for shops in unknown open-world environments due to a lack of prior knowledge and text recognition capabilities. Venue maps offer valuable information that can aid exploration planning by correlating scene signage with map data. However, the arbitrary shapes and styles of the text on signage, along with multi-view inconsistencies, pose significant challenges for accurate recognition by robots. Additionally, the discrepancies between real-world environments and venue maps hinder the incorporation of text information into planners. This paper introduces a novel signage-aware exploration system to address these challenges, enabling the robot to utilize venue maps effectively. We propose a signage understanding method that accurately detects and recognizes the text on signage using a diffusion-based text instance retrieval method combined with a 2D-to-3D semantic fusion strategy. Furthermore, we design a venue map-guided exploration-exploitation planner that balances exploration in unknown regions using a directional heuristic derived from venue maps with exploitation to get close and adjust orientation for better recognition. Experiments in large-scale shopping malls demonstrate our method's superior signage recognition accuracy and coverage efficiency, outperforming state-of-the-art scene text spotting methods and traditional exploration methods.
A First Look at Package-to-Group Mechanism: An Empirical Study of the Linux Distributions
2024-10-14
Dongming Jin, Nianyu Li, Kai Yang, Minghui Zhou, Zhi Jin
Reusing third-party software packages is a common practice in software development. As the scale and complexity of open-source software (OSS) projects continue to grow (e.g., Linux distributions), the number of reused third-party packages has significantly increased. Therefore, maintaining effective package management is critical for developing and evolving OSS projects. To achieve this, a package-to-group mechanism (P2G) is employed to enable unified installation, uninstallation, and updates of multiple packages at once. To better understand this mechanism, this paper takes Linux distributions as a case study and presents an empirical study focusing on its application trends, evolutionary patterns, group quality, and developer tendencies. By analyzing 11,746 groups and 193,548 packages from 89 versions of 5 popular Linux distributions and conducting questionnaire surveys with Linux practitioners and researchers, we derive several key insights. Our findings show that P2G is increasingly being adopted, particularly in popular Linux distributions. P2G follows six evolutionary patterns (\eg splitting and merging groups). Interestingly, packages no longer managed through P2G are more likely to remain in Linux distributions rather than being directly removed. To assess the effectiveness of P2G, we propose a metric called {\sc GValue} to evaluate the quality of groups and identify issues such as inadequate group descriptions and insufficient group sizes. We also summarize five types of packages that tend to adopt P2G, including graphical desktops, networks, etc. To the best of our knowledge, this is the first study focusing on the P2G mechanisms. We expect our study can assist in the efficient management of packages and reduce the burden on practitioners in rapidly growing Linux distributions and other open-source software projects.
Lens Modeling of STRIDES Strongly Lensed Quasars using Neural Posterior Estimation
2024-10-14
Sydney Erickson, Sebastian Wagner-Carena, Phil Marshall, Martin Millon, Simon Birrer, Aaron Roodman, Thomas Schmidt, Tommaso Treu, Stefan Schuldt, Anowar Shajib, Padma Venkatraman, The LSST Dark Energy Science Collaboration
Strongly lensed quasars can be used to constrain cosmological parameters through time-delay cosmography. Models of the lens masses are a necessary component of this analysis. To enable time-delay cosmography from a sample of $\mathcal{O}(10^3)$ lenses, which will soon become available from surveys like the Rubin Observatory's Legacy Survey of Space and Time (LSST) and the Euclid Wide Survey, we require fast and standardizable modeling techniques. To address this need, we apply neural posterior estimation (NPE) for modeling galaxy-scale strongly lensed quasars from the Strong Lensing Insights into the Dark Energy Survey (STRIDES) sample. NPE brings two advantages: speed and the ability to implicitly marginalize over nuisance parameters. We extend this method by employing sequential NPE to increase precision of mass model posteriors. We then fold individual lens models into a hierarchical Bayesian inference to recover the population distribution of lens mass parameters, accounting for out-of-distribution shift. After verifying our method using simulated analogs of the STRIDES lens sample, we apply our method to 14 Hubble Space Telescope single-filter observations. We find the population mean of the power-law elliptical mass distribution slope, $\gamma_{\text{lens}}$, to be $\mathcal{M}_{\gamma_{\text{lens}}}=2.13 \pm 0.06$. Our result represents the first population-level constraint for these systems. This population-level inference from fully automated modeling is an important stepping stone towards cosmological inference with large samples of strongly lensed quasars.
Can We Predict Performance of Large Models across Vision-Language Tasks?
2024-10-14
Qinyu Zhao, Ming Xu, Kartik Gupta, Akshay Asthana, Liang Zheng, Stephen Gould
Evaluating large vision-language models (LVLMs) is very expensive, due to the high computational costs and the wide variety of tasks. The good news is that if we already have some observed performance scores, we may be able to infer unknown ones. In this study, we propose a new framework for predicting unknown performance scores based on observed ones from other LVLMs or tasks. We first formulate the performance prediction as a matrix completion task. Specifically, we construct a sparse performance matrix $\boldsymbol{R}$, where each entry $R_{mn}$ represents the performance score of the $m$-th model on the $n$-th dataset. By applying probabilistic matrix factorization (PMF) with Markov chain Monte Carlo (MCMC), we can complete the performance matrix, that is, predict unknown scores. Additionally, we estimate the uncertainty of performance prediction based on MCMC. Practitioners can evaluate their models on untested tasks with higher uncertainty first, quickly reducing errors in performance prediction. We further introduce several improvements to enhance PMF for scenarios with sparse observed performance scores. In experiments, we systematically evaluate 108 LVLMs on 176 datasets from 36 benchmarks, constructing training and testing sets for validating our framework. Our experiments demonstrate the accuracy of PMF in predicting unknown scores, the reliability of uncertainty estimates in ordering evaluations, and the effectiveness of our enhancements for handling sparse data.
The inter-band lags among the optical broad-band continua of active galactic nuclei (AGNs) have been intensively explored over the past decade. However, the nature of the lags remains under debate. Here utilizing two distinct scenarios for AGN variability, i.e., the thermal fluctuation of accretion disk and the reprocessing of both the accretion disk and clouds in the broad line region, we show that, owing to the random nature of AGN variability, the inter-band lags of an individual AGN would vary from one campaign with a finite baseline to another. Specifically, the thermal fluctuation scenario implies larger variations in the lags than the reprocessing scenario. Moreover, the former predicts a positive correlation between the lag and variation amplitude, while the latter does not result in such a correlation. For both scenarios, averaging the lags of an individual AGN measured with repeated and non-overlapping campaigns would give rise to a stable lag, which is larger for a longer baseline and gets saturation for a sufficiently long baseline. However, obtaining the stable lag for an individual AGN is very time-consuming. Alternatively, it can be equivalently inferred by averaging the lags of a sample of AGNs with similar physical properties, thus can be properly compared with predictions of AGN models. In addition, discussed are several new observational tests suggested by our simulations as well as the role of the deep high-cadence surveys of the Wide Field Survey Telescope in enriching our knowledge of the lags.
Data Modeling for Connected Data -- A systematic literature review
2024-10-14
Veronica Santos
A data model specifies how real-world entities and their relationships are represented and operated. In the NoSQL world data modeling usually begins from identifying application queries and designing the data model to efficiently answer them so each database is designed to meet requirements of just one or more applications. But this practice causes a strong coupling between the data model and application queries and promotes data silos. Newly developed applications that manipulate connected data, usually stored in NoSQL Graph Databases, suffer from this type of problem, which is a challenge for data integration projects in Big Data scenarios. This systematic literature review (SLR) was carried out to identify the known approaches for data modeling of connected data. The main contribution of this SLR is an analysis of sixteen works, from 2013 to 2020, in terms of three dimensions: type of contribution, bibliometrics, and data modeling characteristics. Through this analysis, it was possible to identify that reverse engineering of connected data is still a research opportunity since few and incomplete works were found.
LLM Unlearning via Loss Adjustment with Only Forget Data
2024-10-14
Yaxuan Wang, Jiaheng Wei, Chris Yuhao Liu, Jinlong Pang, Quan Liu, Ankit Parag Shah, Yujia Bao, Yang Liu, Wei Wei
Unlearning in Large Language Models (LLMs) is essential for ensuring ethical and responsible AI use, especially in addressing privacy leak, bias, safety, and evolving regulations. Existing approaches to LLM unlearning often rely on retain data or a reference LLM, yet they struggle to adequately balance unlearning performance with overall model utility. This challenge arises because leveraging explicit retain data or implicit knowledge of retain data from a reference LLM to fine-tune the model tends to blur the boundaries between the forgotten and retain data, as different queries often elicit similar responses. In this work, we propose eliminating the need to retain data or the reference LLM for response calibration in LLM unlearning. Recognizing that directly applying gradient ascent on the forget data often leads to optimization instability and poor performance, our method guides the LLM on what not to respond to, and importantly, how to respond, based on the forget data. Hence, we introduce Forget data only Loss AjustmenT (FLAT), a "flat" loss adjustment approach which addresses these issues by maximizing f-divergence between the available template answer and the forget answer only w.r.t. the forget data. The variational form of the defined f-divergence theoretically provides a way of loss adjustment by assigning different importance weights for the learning w.r.t. template responses and the forgetting of responses subject to unlearning. Empirical results demonstrate that our approach not only achieves superior unlearning performance compared to existing methods but also minimizes the impact on the model's retained capabilities, ensuring high utility across diverse tasks, including copyrighted content unlearning on Harry Potter dataset and MUSE Benchmark, and entity unlearning on the TOFU dataset.
Sulfur in the Giant Planets, their Moons, and Extrasolar Gas Giant Planets
2024-10-14
Katharina Lodders, Bruce Fegley
We review sulfur chemistry of the gas giant planets and their moons where sulfur compounds are observed. The major S-bearing gas in the upper atmospheres of the giant planets is H2S and is removed from their observable atmospheres by condensation into cloud layers (NH4SH on all four planets and additionally H2S ice on Uranus and Neptune). Any remaining H2S at higher altitudes is destroyed photochemically. Among the moons Io is the world dominated by sulfur. We summarize the sulfur cycle on Io and how pyrovolcanism is spreading sulfur across the Jovian system. Implantation of sulfur into icy surfaces of the other Galilean moons via magnetospheric transfer and radiolysis are major processes affecting the sulfur chemistry on their icy surfaces. On the icy worlds, we are literally looking at the top of the icebergs. Subsurface liquid salty bodies reveal themselves through cryovolcanism on Europa, Ganymede, and Enceladus, where salt deposits are indicated. Subsurface oceans are suspected on several other moons. We summarize the sulfur cycle for the icy Galilean moons. The occurrence of sulfates can be explained by salt exchange reactions of radiolytically produced H2SO4 with brine salts (carbonates and halides), or from a subsurface ocean that has become acidified by uptake of H2SO4 leaked from ice. In the primordial oceans of the moons that accreted with high ice rock ratios, sulfur is expected as sulfide and bisulfide anions and H2S in aqueous solution. Cosmochemical constraints suggest that pyrrhotite, tochilinite and green rusts could be important sulfide bearing compounds found with hydrous silicates such as serpentine, and magnetite on the sea floors. In N-C-rich worlds such as Titan, sulfides such as NH4SH and possibly thiazyl compounds could be important, and sulfates are unstable. Nothing is known about the sulfur chemistry on the Uranian and Neptunian moons.
Deformation and breakup of a ferrofluid compound droplet migrating in a microchannel under a magnetic field: A phase-field-based multiple-relaxation time lattice Boltzmann study
2024-10-14
Parham Poureslami, Mohammad Majidi, Javad Ranjbar Kermani, Mohamad Ali Bijarchi
Though ubiquitous in many engineering applications, including drug delivery, the compound droplet hydrodynamics in confined geometries have been barely surveyed. For the first time, this study thoroughly investigates the hydrodynamics of a ferrofluid compound droplet (FCD) during its migration in a microchannel under the presence of a pressure-driven flow and a uniform external magnetic field (UEMF) to manipulate its morphology and retard its breakup. Finite difference and phase-field multiple-relaxation time lattice Boltzmann approaches are coupled to determine the magnetic field and ternary flow system, respectively. Firstly, the influence of the magnetic Bond number (Bo) on the FCD morphology is explored depending on whether the core or shell is ferrofluid when the UEMF is applied along {\alpha}=0{\deg} and {\alpha}=90{\deg} relative to the fluid flow. It is ascertained that imposing the UEMF at {\alpha}=0{\deg} when the shell is ferrofluid can postpone the breakup. Intriguingly, when the core is ferrofluid, strengthening the UEMF enlarges the shell deformation. Afterward, the effects of the Capillary number (Ca), density ratio, viscosity ratio, radius ratio, and surface tension coefficients are scrutinized on the FCD deformation and breakup. The results indicate that augmenting the core-to-shell viscosity and density ratios accelerates the breakup process. Additionally, surface tension between the core and shell suppresses the core deformation. Moreover, increasing the Ca number intensifies the viscous drag force exerted on the shell, flattening its rear side, which causes a triangular-like configuration. Ultimately, by varying Bo and Ca numbers, five distinguished regimes are observed, whose regime map is established.
Large language models (LLMs) have the potential to enhance K-12 STEM education by improving both teaching and learning processes. While previous studies have shown promising results, there is still a lack of comprehensive understanding regarding how LLMs are effectively applied, specifically through prompt engineering-the process of designing prompts to generate desired outputs. To address this gap, our study investigates empirical research published between 2021 and 2024 that explores the use of LLMs combined with prompt engineering in K-12 STEM education. Following the PRISMA protocol, we screened 2,654 papers and selected 30 studies for analysis. Our review identifies the prompting strategies employed, the types of LLMs used, methods of evaluating effectiveness, and limitations in prior work. Results indicate that while simple and zero-shot prompting are commonly used, more advanced techniques like few-shot and chain-of-thought prompting have demonstrated positive outcomes for various educational tasks. GPT-series models are predominantly used, but smaller and fine-tuned models (e.g., Blender 7B) paired with effective prompt engineering outperform prompting larger models (e.g., GPT-3) in specific contexts. Evaluation methods vary significantly, with limited empirical validation in real-world settings.
2024-10-14
Anna Wirth-Singh, Aurelia M. Brook, Rose Johnson, Johannes E. Fröch, Arka Majumdar
The field of meta-optics has grown to include metalenses spanning the ultraviolet to terahertz regimes. Imaging is a key application of meta-optics, with recent works demonstrating meta-optical imaging with advanced functionalities including wide field of view, broadband operation, and polarization sensitivity. In this review, we focus on meta-optical imaging for thermal wavelengths. Thermal meta-optics are less well-studied than those in the visible range but have vast potential applications spanning defense, health, and geological sensing. We first introduce these applications and their specific challenges. With compact form-factor and multi-functional capabilities, we suggest that meta-optics are particularly well-suited to meet the needs of imaging in the mid- and long-wave infrared. Then, we review published experimental demonstrations of thermal imaging via meta-optics. These meta-optics vary in complexity from simple hyperboloid metalenses to complex systems composed of engineered meta-atoms and multiple layers of optics. Finally, we suggest some areas where thermal meta-optics may be useful, and we identify some emerging approaches to solve lingering challenges of meta-optical imaging.
2024-10-14
Adam Smith, Maria Mylova, Philippe Brax, Carsten van de Bruck, C. P. Burgess, Anne-Christine Davis
In scalar-tensor theories it is the two-derivative sigma-model interactions that like to compete at low energies with the two-derivative interactions of General Relativity (GR) $\unicode{x2014}$ at least once the dangerous zero-derivative terms of the scalar potential are suppressed (such as by a shift symmetry). But nontrivial two-derivative interactions require at least two scalars to exist and so never arise in the single-scalar models most commonly explored. Axio-dilaton models provide a well-motivated minimal class of models for which these self-interactions can be explored. We review this class of models and investigate whether these minimal two fields can suffice to describe both Dark Matter and Dark Energy. We find that they can $\unicode{x2014}$ the axion is the Dark Matter and the dilaton is the Dark Energy $\unicode{x2014}$ and that they robustly predict several new phenomena for the CMB and structure formation that can be sought in observations. These include specific types of Dark Energy evolution and small space- and time-dependent changes to particle masses post-recombination that alter the Integrated Sachs-Wolfe effect, cause small changes to structure growth and more.
Analysis of biasing from noise from the Nancy Grace Roman Space Telescope: implications for weak lensing
2024-10-14
Katherine Laliotis, Emily Macbeth, Christopher M. Hirata, Kaili Cao, Masaya Yamamoto, Michael Troxel
The Nancy Grace Roman Space Telescope, set to launch in 2026, will bring unprecedented precision to measurements of weak gravitational lensing. Because weak lensing is an inherently small signal, it is imperative to minimize systematic errors in measurements as completely as possible; this will ensure that the lensing measurements can be used to their full potential when extracting cosmological information. In this paper, we use laboratory tests of the Roman detectors, simulations of the Roman High Latitude Survey observations, and the proposed Roman image combination pipeline to investigate the magnitude of detector read noise biasing on weak lensing measurements from Roman. First, we combine lab-measured detector noise fields with simulated observations and propagate these images through the Roman image combination pipeline, IMCOM. We characterize the specific signatures of the noise fields in the resultant images and find that noise contributes to the combined images most strongly at scales relevant to physical characteristics of the detector including PSF shape, chip boundaries, and roll angles. We then measure shapes of simulated stars and galaxies and determine the magnitude of noise-induced shear bias on these measurements. We find that star shape correlations satisfy the system noise requirements as defined by the Roman Science Requirements Document. However, for galaxies fainter than $m_{\rm AB}\simeq24$, correction for noise correlations will be needed in order to ensure confidence in shape measurements in any observation band.
Review of honeycomb-based Kitaev materials with zigzag magnetic ordering
2024-10-14
V. Ovidiu Garlea, Colin L. Sarkis
The search for a Kitaev quantum spin liquid in crystalline magnetic materials has fueled intense interest in the two-dimensional honeycomb systems. Many promising candidate Kitaev systems are characterized by a long range ordered magnetic structure with an antiferromagnetic zigzag-type order, where the static moments form alternating ferromagnetic chains. Recent experiments on high-quality single crystals uncovered the existence of intriguing multi-$\mathbf{k}$ magnetic structures, which evolved from zigzag structures. Those discoveries have sparked new theoretical developments and amplified interest in these materials. We present an overview of the honeycomb materials known to display this type of magnetic structure and provide detailed crystallographic information for the possible single and multi-$\mathbf{k}$ variants.
Predicting Chess Puzzle Difficulty with Transformers
2024-10-14
Szymon Miłosz, Paweł Kapusta
This study addresses the challenge of quantifying chess puzzle difficulty - a complex task that combines elements of game theory and human cognition and underscores its critical role in effective chess training. We present GlickFormer, a novel transformer-based architecture that predicts chess puzzle difficulty by approximating the Glicko-2 rating system. Unlike conventional chess engines that optimize for game outcomes, GlickFormer models human perception of tactical patterns and problem-solving complexity. The proposed model utilizes a modified ChessFormer backbone for spatial feature extraction and incorporates temporal information via factorized transformer techniques. This approach enables the capture of both spatial chess piece arrangements and move sequences, effectively modeling spatio-temporal relationships relevant to difficulty assessment. Experimental evaluation was conducted on a dataset of over 4 million chess puzzles. Results demonstrate GlickFormer's superior performance compared to the state-of-the-art ChessFormer baseline across multiple metrics. The algorithm's performance has also been recognized through its competitive results in the IEEE BigData 2024 Cup: Predicting Chess Puzzle Difficulty competition. The insights gained from this study have implications for personalized chess training and broader applications in educational technology and cognitive modeling.
An HST Transmission Spectrum of the Closest M-Dwarf Transiting Rocky Planet LTT 1445Ab
2024-10-14
Katherine A. Bennett, David K. Sing, Kevin B. Stevenson, Hannah R. Wakeford, Zafar Rustamkulov, Natalie H. Allen, Joshua D. Lothringer, Ryan J. MacDonald, Nathan J. Mayne, Guangwei Fu
Which rocky exoplanets have atmospheres, and which do not? This presumably simply question is the first that must be answered in the pathway toward understanding the prevalence of nearby habitable planets. A mere 6.9 pc from Earth, the LTT 1445A system is the closest transiting M-dwarf system, and its largest known planet, at $\rm 1.31\; R_{\oplus}$ and 424 K, is one of the best rocky targets in which to search for an atmosphere. We use HST/WFC3 transmission spectroscopy with the G280 and G141 grisms to study the spectrum of LTT 1445Ab between $\rm 0.2-1.65\;\mu m$. In doing so, we uncover a flare on the neighboring star LTT 1445C, and report one of the first simultaneous near-UV/optical spectra of an M dwarf flare. The planet spectrum is consistent with a flat line, though the infrared portion displays potential features that could be explained by known opacity sources such as HCN. Some atmospheric retrievals weakly favor ($\sim2\sigma$) an atmosphere over a flat line, but it remains challenging to discern between stellar contamination, an atmosphere, and a featureless spectrum at this time. We do, however, confidently rule out $\leq100\times$ solar metallicity atmospheres. Although stellar contamination retrievals cannot fit the infrared features well, the overall spectrum is consistent with stellar contamination from hot spots, cold spots, or both. Based on the UV/optical data, we place limits on the extent of stellar variability expected in the near-infrared ($30-40$ ppm), which will be critical for upcoming JWST observations of this highly optimal target.
Hot Rocks Survey I : A shallow eclipse for LHS 1478 b
2024-10-14
Prune C. August, Lars A. Buchhave, Hannah Diamond-Lowe, João M. Mendonça, Amélie Gressier, Alexander D. Rathcke, Natalie H. Allen, Mark Fortune, Kathryn D. Jones, Erik A. Meier-Valdés, Brice-Olivier Demory, Nestor Espinoza, Chloe E. Fisher, Neale P. Gibson, Kevin Heng, Jens Hoeijmakers, Matthew J. Hooton, Daniel Kitzmann, Bibiana Prinoth
M dwarf systems offer a unique opportunity to study terrestrial exoplanetary atmospheres due to their smaller size and cooler temperatures. However, due to the extreme conditions these host stars impose, it is unclear whether their small, close-in rocky planets are able to retain any atmosphere at all. The Hot Rocks Survey aims to answer this question by targeting nine different M dwarf rocky planets spanning a range of planetary and stellar properties. LHS 1478 b orbits an M3-type star, has an equilibrium temperature of Teq = 585 K and experiences an instellation 21 times greater than that of Earth. We observe two secondary eclipses using photometric imaging at 15 um using the Mid-Infrared Instrument on the James Webb Space Telescope (JWST MIRI) to measure thermal emission from the dayside of the planet. We then compare these values to different atmospheric scenarios to evaluate potential heat transport and CO2 absorption signatures. We find a secondary eclipse depth of 146 +/- 56 ppm based on the first observation, while the second observation results in a non-detection due to significantly larger unexplained systematics. Based on the first observation alone, we can reject the null hypothesis of the dark (zero Bond albedo) no atmosphere bare rock model with a confidence level of 3.4 sigma. For an airless body with a Bond albedo of A=0.2, the significance decreases to 2.9 sigma. The secondary eclipse depth is consistent with the majority of atmospheric scenarios we considered, which all involve atmospheres which include different concentrations of CO2, and surface pressures from 0.1 to 10 bar. However, we stress that the two observations from our program do not yield consistent results, and more observations are needed to verify our findings. The Hot Rocks Survey serves as a relevant primer for future endeavors such as the Director's Discretionary Time (DDT) Rocky Worlds program.
Comparing Step Counting Algorithms for High-Resolution Wrist Accelerometry Data in NHANES 2011-2014
2024-10-14
Lily Koffman, Ciprian Crainiceanu, John Muschelli III
Purpose: To quantify the relative performance of step counting algorithms in studies that collect free-living high-resolution wrist accelerometry data and to highlight the implications of using these algorithms in translational research. Methods: Five step counting algorithms (four open source and one proprietary) were applied to the publicly available, free-living, high-resolution wrist accelerometry data collected by the National Health and Nutrition Examination Survey (NHANES) in 2011-2014. The mean daily total step counts were compared in terms of correlation, predictive performance, and estimated hazard ratios of mortality. Results: The estimated number of steps were highly correlated (median=0.91, range 0.77 to 0.98), had high and comparable predictive performance of mortality (median concordance=0.72, range 0.70 to 0.73). The distributions of the number of steps in the population varied widely (mean step counts range from 2,453 to 12,169). Hazard ratios of mortality associated with a 500-step increase per day varied among step counting algorithms between HR=0.88 and 0.96, corresponding to a 300% difference in mortality risk reduction ([1-0.88]/[1-0.96]=3). Conclusion: Different step counting algorithms provide correlated step estimates and have similar predictive performance that is better than traditional predictors of mortality. However, they provide widely different distributions of step counts and estimated reductions in mortality risk for a 500-step increase.
TESS Giants Transiting Giants. VII. A Hot Saturn Orbiting an Oscillating Red Giant Star
2024-10-14
Nicholas Saunders, Samuel K. Grunblatt, Daniel Huber, J. M. Joel Ong, Kevin C. Schlaufman, Daniel Hey, Yaguang Li, R. P. Butler, Jeffrey D. Crane, Steve Shectman, Johanna K. Teske, Samuel N. Quinn, Samuel W. Yee, Rafael Brahm, Trifon Trifonov, Andrés Jordán, Thomas Henning, David K. Sing, Meredith MacGregor, Emma Page, David Rapetti, Ben Falk, Alan M. Levine, Chelsea X. Huang, Michael B. Lund, George R. Ricker, S. Seager, Joshua N. Winn, Jon M. Jenkins
We present the discovery of TOI-7041 b (TIC 201175570 b), a hot Saturn transiting a red giant star with measurable stellar oscillations. We observe solar-like oscillations in TOI-7041 with a frequency of maximum power of $\nu_{\rm max} = 218.50\pm2.23$ $\mu$Hz and a large frequency separation of $\Delta\nu = 16.5282\pm0.0186$ $\mu$Hz. Our asteroseismic analysis indicates that TOI-7041 has a radius of $4.10 \pm 0.06$(stat) $\pm$ 0.05(sys) $R_\odot$, making it one of the largest stars around which a transiting planet has been discovered with the Transiting Exoplanet Survey Satellite (TESS), and the mission's first oscillating red giant with a transiting planet. TOI-7041 b has an orbital period of $9.691 \pm 0.006$ days and a low eccentricity of $e = 0.04 \pm 0.04$. We measure a planet radius of $1.02 \pm 0.03$ $R_J$ with photometry from TESS, and a planet mass of $0.36 \pm 0.16$ $M_J$ ($114 \pm 51$ $M_\oplus$) with ground-based radial velocity measurements. TOI-7041 b appears less inflated than similar systems receiving equivalent incident flux, and its circular orbit indicates that it is not undergoing tidal heating due to circularization. The asteroseismic analysis of the host star provides some of the tightest constraints on stellar properties for a TESS planet host and enables precise characterization of the hot Saturn. This system joins a small number of TESS-discovered exoplanets orbiting stars that exhibit clear stellar oscillations and indicates that extended TESS observations of evolved stars will similarly provide a path to improved exoplanet characterization.
The evolution of [OIII]$+\rm{H}β$ equivalent width from $\mathbf{z\simeq3-8}$: implications for the production and escape of ionizing photons during reionization
2024-10-14
R. Begley, R. J. McLure, F. Cullen, D. J. McLeod, J. S. Dunlop, A. C. Carnall, T. M. Stanton, A. E. Shapley, R. Cochrane, C. T. Donnan, R. S. Ellis, A. Fontana, N. A. Grogin, A. M. Koekemoer
Accurately quantifying the ionizing photon production efficiency ($\xi_\rm{ion}$) of $z>6$ star-forming galaxies (SFGs) is necessary to understand their contribution to reionization. We investigate the ionizing properties of N=289 SFGs selected at $z=6.9-7.6$ from the JWST Cycle-1 imaging programmes; PRIMER and JADES. We use BAGPIPES to consistently infer the equivalent widths of their [OIII]+$\rm{H\beta}$ emission lines ($W_\lambda$) and their physical properties. To supplement our high-redshift galaxies, we measure $W_\lambda$ photometrically for a sample of N=253 $z=3.2-3.6$ SFGs selected from the VANDELS spectroscopic survey. Comparing these samples, we find a strong apparent redshift evolution in their median $W_\lambda$, increasing from $W_\lambda =310\pm25\,\r{A}$ in VANDELS to $W_\lambda =630\pm30\,\r{A}$ in our JWST-based sample. In the JWST sample at $z>7$, we find that $W_\lambda$ correlates with both stellar mass and UV luminosity, with high-mass, $M_{ UV}-$faint galaxies producing systematically weaker emission lines. Moreover, we discover a departure from the standard log-normal shape of the $W_\lambda$ distribution, characterised by a more pronounced tail at lower $W_\lambda$, consistent with increasingly bursty star formation. Using $W_\lambda$ as a proxy for $\xi_\rm{ion}$, and UV spectral slope as a proxy for LyC escape fraction ($f_\rm{esc}$), we find a minority of galaxies with high $\xi_\rm{ion}$ and $f_\rm{esc}$ (e.g., $\rm{log(\xi_{ion}/erg^{-1}Hz})\sim25.6$ and $f_\rm{esc}\sim0.15$). However, we find that the LyC photon budget at $z>7$ is dominated by galaxies with more moderate output, close to the sample median of $\rm{log(\xi_{ion}/erg^{-1}Hz})\sim25.3$ and $f_\rm{esc}\sim0.05$. This is consistent with estimates for the number of LyC photons required to power reionization at $z>7$, with no evidence for over or under-production.
Hic Sunt Dracones: Uncovering Dynamical Perturbers Within the Habitable Zone
2024-10-14
Stephen R. Kane, Jennifer A. Burt
The continuing exploration of neighboring planetary systems is providing deeper insights into the relative prevalence of various system architectures, particularly with respect to the solar system. However, a full assessment of the dynamical feasibility of possible terrestrial planets within the Habitable Zones (HZ) of nearby stars requires detailed knowledge of the masses and orbital solutions of any known planets within these systems. Moreover, the presence of as yet undetected planets in or near the HZ will be crucial for providing a robust target list for future direct imaging surveys. In this work, we quantify the distribution of uncertainties on planetary masses and semi-major axes for 1062 confirmed planets, finding median uncertainties of 11.1% and 2.2%, respectively. We show the dependence of these uncertainties on stellar mass and orbital period, and discuss the effects of these uncertainties on dynamical analyses and the locations of mean motion resonance. We also calculate the expected radial velocity (RV) semi-amplitude for a Neptune-mass planet in the middle of the HZ for each of the proposed Habitable Worlds Observatory target stars. We find that for more than half of these stars, the RV semi-amplitude is less than 1.5 m/s, rendering them unlikely to be detected in archival RV data sets and highlighting the need for further observations to understand the dynamical viability of the HZ for these systems. We provide specific recommendations regarding stellar characterization and RV survey strategies that work toward the detection of presently unseen perturbers within the HZ.
Seven wonders of Cosmic Dawn: JWST confirms a high abundance of galaxies and AGNs at z $\simeq$ 9-11 in the GLASS field
2024-10-14
L. Napolitano, M. Castellano, L. Pentericci, P. Arrabal Haro, A. Fontana, T. Treu, P. Bergamini, A. Calabro, S. Mascia, T. Morishita, G. Roberts-Borsani, P. Santini, E. Vanzella, B. Vulcani, D. Zakharova, T. Bakx, M. Dickinson, C. Grillo, N. Leethochawalit, M. Llerena, E. Merlin, D. Paris, S. Rojas-Ruiz, P. Rosati, X. Wang, I. Yoon, J. Zavala
We present JWST/NIRSpec PRISM follow-up of candidate galaxies at z=9-11 selected from deep JWST/NIRCam photometry in GLASS-JWST Early Release Science data. We spectroscopically confirm six sources with secure redshifts at z = 9.52-10.43, each showing multiple emission lines. An additional object is likely at z = 10.66, based on its Lya-break and a single emission feature, while one source is a lower redshift interloper. The sample includes the first JWST-detected candidate at z=10, GHZ1/GLASS-z10, which we confirm at z = 9.875, and the X-ray detected AGN GHZ9 confirmed at z = 10.145. Three objects in our sample, including GHZ9, have EW(CIII])>20A and occupy a region compatible with AGN emission in the EW(CIII]) vs CIV/CIII] diagram. The spectroscopic sample confirms a high abundance of galaxies at z > 9. We measure a number density of z=10 galaxies in the GLASS-JWST ERS field that is a factor of >3 higher than other JWST-based estimates at demagnified rest-frame magnitudes of -21 < Muv < -19. We find that the positions of these galaxies in redshift and angular space are not consistent with all of them being part of a unique progenitor of present-day galaxy clusters. The high density of objects in the GLASS region can be explained either by clustering on large scales or by a superposition of different forming structures of which we observe only the brightest members. By considering all the spectroscopic z=10 sources in the Abell-2744 field, we identify two potential galaxy proto-clusters centered around GHZ9 and JD1, with relative separations between their members of 1-2 pMpc. The potential AGN nature of three of the sources in our sample lends support to a scenario in which the high abundance of bright sources determined by JWST surveys at cosmic dawn may be affected by AGN contribution to their UV luminosity.
How accurate are transient spectral classification tools? -- A study using 4,646 SEDMachine spectra
2024-10-14
Young-Lo Kim, Isobel Hook, Andrew Milligan, Lluís Galbany, Jesper Sollerman, Umut Burgaz, Georgios Dimitriadis, Christoffer Fremling, Joel Johansson, Tomás E. Müller-Bravo, James D. Neill, Jakob Nordin, Peter Nugent, Yu-Jing Qi, Philippe Rosnet, Yashvi Sharma
Accurate classification of transients obtained from spectroscopic data are important to understand their nature and discover new classes of astronomical objects. For supernovae (SNe), SNID, NGSF (a Python version of SuperFit), and DASH are widely used in the community. Each tool provides its own metric to help determine classification, such as rlap of SNID, chi2/dof of NGSF, and Probability of DASH. However, we do not know how accurate these tools are, and they have not been tested with a large homogeneous dataset. Thus, in this work, we study the accuracy of these spectral classification tools using 4,646 SEDMachine spectra, which have accurate classifications obtained from the Zwicky Transient Facility Bright Transient Survey (BTS). Comparing our classifications with those from BTS, we have tested the classification accuracy in various ways. We find that NGSF has the best performance (overall Accuracy 87.6% when samples are split into SNe Ia and Non-Ia types), while SNID and DASH have similar performance with overall Accuracy of 79.3% and 76.2%, respectively. Specifically for SNe Ia, SNID can accurately classify them when rlap > 15 without contamination from other types, such as Ibc, II, SLSN, and other objects that are not SNe (Purity > 98%). For other types, determining their classification is often uncertain. We conclude that it is difficult to obtain an accurate classification from these tools alone. This results in additional human visual inspection effort being required in order to confirm the classification. To reduce this human visual inspection and to support the classification process for future large-scale surveys, this work provides supporting information, such as the accuracy of each tool as a function of its metric.
Recent high-cadence transient surveys have discovered rapid transients whose light curve timescales are shorter than those of typical supernovae. In this paper, we present a systematic search for rapid transients at medium-high redshifts among 3381 supernova candidates obtained from the Subaru HSC-SSP transient survey. We developed a machine learning classifier to classify the supernova candidates into four types (Type Ia, Ibc, II supernovae, and rapid transients) based on the features derived from the light curves. By applying this classifier to the 3381 supernova candidates and by further applying the quality cut, we selected 14 rapid transient samples. They are located at a wide range of redshifts ($0.34 \leq z \leq 1.85$) and show a wide range of the peak absolute magnitude ($-17 \geq M \geq -22$). The event rate of the rapid transients is estimated to be $\sim 6\times10^3~\rm{events~yr^{-1}~Gpc^{-3}}$ at $z \sim 0.74$, which corresponds to about $2$ $\%$ of the event rate of normal core-collapse supernovae at the similar redshift. Based on the luminosity and color evolution, we selected two candidates of Type Ibn supernovae at $z\sim0.75$. The event rate of Type Ibn SN candidates is more than 1 $\%$ of Type Ib SN rate at the same redshift, suggesting that this fraction of massive stars at this redshift range eruptively ejects their He-rich envelope just before the explosions. Also, two objects at $z=1.37$ and 1.85 show high luminosities comparable to superluminous supernovae. Their event rate is about 10-25 $\%$ of superluminous supernovae at $z\sim 2$.
Cosmological and Astrophysical Parameter Inference from Stacked Galaxy Cluster Profiles Using CAMELS-zoomGZ
2024-10-14
Elena Hernández-Martínez, Shy Genel, Francisco Villaescusa-Navarro, Ulrich P. Steinwandel, Max E. Lee, Erwin T. Lau, David N. Spergel
We present a study on the inference of cosmological and astrophysical parameters using stacked galaxy cluster profiles. Utilizing the CAMELS-zoomGZ simulations, we explore how various cluster properties--such as X-ray surface brightness, gas density, temperature, metallicity, and Compton-y profiles--can be used to predict parameters within the 28-dimensional parameter space of the IllustrisTNG model. Through neural networks, we achieve a high correlation coefficient of 0.97 or above for all cosmological parameters, including $\Omega_{\rm m}$, $H_0$, and $\sigma_8$, and over 0.90 for the remaining astrophysical parameters, showcasing the effectiveness of these profiles for parameter inference. We investigate the impact of different radial cuts, with bins ranging from $0.1R_{200c}$ to $0.7R_{200c}$, to simulate current observational constraints. Additionally, we perform a noise sensitivity analysis, adding up to 40\% Gaussian noise (corresponding to signal-to-noise ratios as low as 2.5), revealing that key parameters such as $\Omega_{\rm m}$, $H_0$, and the IMF slope remain robust even under extreme noise conditions. We also compare the performance of full radial profiles against integrated quantities, finding that profiles generally lead to more accurate parameter inferences. Our results demonstrate that stacked galaxy cluster profiles contain crucial information on both astrophysical processes within groups and clusters and the underlying cosmology of the universe. This underscores their significance for interpreting the complex data expected from next-generation surveys and reveals, for the first time, their potential as a powerful tool for parameter inference.
Spectroscopically resolved partial phase curve of the rapid heating and cooling of the highly-eccentric Hot Jupiter HAT-P-2b with WFC3
2024-10-14
Bob Jacobs, Jean-Michel Désert, Nikole Lewis, Ryan C. Challener, L. C. Mayorga, Zoë de Beurs, Vivien Parmentier, Kevin B. Stevenson, Julien de Wit, Saugata Barat, Jonathan Fortney, Tiffany Kataria, Michael Line
The extreme environments of transiting close-in exoplanets in highly-eccentric orbits are ideal for testing exo-climate physics. Spectroscopically resolved phase curves not only allow for the characterization of their thermal response to irradiation changes but also unveil phase-dependent atmospheric chemistry and dynamics. We observed a partial phase curve of the highly-eccentric close-in giant planet HAT-P-2b ($e=0.51,M=9M_{\rm{Jup}}$) with the Wide Field Camera 3 aboard the Hubble Space Telescope. Using these data, we updated the planet's orbital parameters and radius, and retrieved high-frequency pulsations consistent with the planet-induced pulsations reported in Spitzer data. We found that the peak in planetary flux occurred at $6.7\pm0.6$ hr after periastron, with a heating and cooling timescales of $9.0^{+3.5}_{-2.1}$ hr, and $3.6^{+0.7}_{-0.6}$ hr, respectively. We compare the light-curve to various 1-dimensional and 3-dimensional forward models, varying the planet's chemical composition. The strong contrast in flux increase and decrease timescales before and after periapse indicates an opacity term that emerges during the planet's heating phase, potentially due to more H$^{-}$ than expected from chemical equilibrium models. The phase-resolved spectra are largely featureless, that we interpret as indicative an inhomogeneous dayside. However, we identified an anomalously high flux in the spectroscopic bin coinciding with the hydrogen Paschen $\beta$ line and that is likely connected to the planet's orbit. We interpret this as due to shock heating of the upper atmosphere given the short timescale involved, or evidence for other star-planet interactions.
The Deepfake technology has raised serious concerns regarding privacy breaches and trust issues. To tackle these challenges, Deepfake detection technology has emerged. Current methods over-rely on the global feature space, which contains redundant information independent of the artifacts. As a result, existing Deepfake detection techniques suffer performance degradation when encountering unknown datasets. To reduce information redundancy, the current methods use disentanglement techniques to roughly separate the fake faces into artifacts and content information. However, these methods lack a solid disentanglement foundation and cannot guarantee the reliability of their disentangling process. To address these issues, a Deepfake detection method based on progressive disentangling and purifying blended identities is innovatively proposed in this paper. Based on the artifact generation mechanism, the coarse- and fine-grained strategies are combined to ensure the reliability of the disentanglement method. Our method aims to more accurately capture and separate artifact features in fake faces. Specifically, we first perform the coarse-grained disentangling on fake faces to obtain a pair of blended identities that require no additional annotation to distinguish between source face and target face. Then, the artifact features from each identity are separated to achieve fine-grained disentanglement. To obtain pure identity information and artifacts, an Identity-Artifact Correlation Compression module (IACC) is designed based on the information bottleneck theory, effectively reducing the potential correlation between identity information and artifacts. Additionally, an Identity-Artifact Separation Contrast Loss is designed to enhance the independence of artifact features post-disentangling. Finally, the classifier only focuses on pure artifact features to achieve a generalized Deepfake detector.
On Debiasing Text Embeddings Through Context Injection
2024-10-14
Thomas Uriot
Current advances in Natural Language Processing (NLP) have made it increasingly feasible to build applications leveraging textual data. Generally, the core of these applications rely on having a good semantic representation of text into vectors, via embedding models. However, it has been shown that these embeddings capture and perpetuate biases already present in text. While a few techniques have been proposed to debias embeddings, they do not take advantage of the recent advances in context understanding of modern embedding models. In this paper, we fill this gap by conducting a review of 19 embedding models by quantifying their biases and how well they respond to context injection as a mean of debiasing. We show that higher performing models are more prone to capturing biases, but are also better at incorporating context. Surprisingly, we find that while models can easily embed affirmative semantics, they fail at embedding neutral semantics. Finally, in a retrieval task, we show that biases in embeddings can lead to non-desirable outcomes. We use our new-found insights to design a simple algorithm for top $k$ retrieval, where $k$ is dynamically selected. We show that our algorithm is able to retrieve all relevant gendered and neutral chunks.
The Internet of Things (IoT) is one of the fastest-growing computing industries. By the end of 2027, more than 29 billion devices are expected to be connected. These smart devices can communicate with each other with and without human intervention. This rapid growth has led to the emergence of new types of malware. However, traditional malware detection methods, such as signature-based and heuristic-based techniques, are becoming increasingly ineffective against these new types of malware. Therefore, it has become indispensable to find practical solutions for detecting IoT malware. Machine Learning (ML) and Deep Learning (DL) approaches have proven effective in dealing with these new IoT malware variants, exhibiting high detection rates. In this paper, we bridge the gap in research between the IoT malware analysis and the wide adoption of deep learning in tackling the problems in this domain. As such, we provide a comprehensive review on deep learning based malware analysis across various categories of the IoT domain (i.e. Extended Internet of Things (XIoT)), including Industrial IoT (IIoT), Internet of Medical Things (IoMT), Internet of Vehicles (IoV), and Internet of Battlefield Things (IoBT).
Enhancing Peer Review in Astronomy: A Machine Learning and Optimization Approach to Reviewer Assignments for ALMA
2024-10-13
John M. Carpenter, Andrea Corvillón, Nihar B. Shah
The increasing volume of papers and proposals undergoing peer review emphasizes the pressing need for greater automation to effectively manage the growing scale. In this study, we present the deployment and evaluation of machine learning and optimization techniques for assigning proposals to reviewers that was developed for the Atacama Large Millimeter/submillimeter Array (ALMA) during the Cycle 10 Call for Proposals issued in 2023. By utilizing topic modeling algorithms, we identify the proposal topics and assess reviewers' expertise based on their historical ALMA proposal submissions. We then apply an adapted version of the assignment optimization algorithm from PeerReview4All (Stelmakh et al. 2021a) to maximize the alignment between proposal topics and reviewer expertise. Our evaluation shows a significant improvement in matching reviewer expertise: the median similarity score between the proposal topic and reviewer expertise increased by 51 percentage points compared to the previous cycle, and the percentage of reviewers reporting expertise in their assigned proposals rose by 20 percentage points. Furthermore, the assignment process proved highly effective in that no proposals required reassignment due to significant mismatches, resulting in a savings of 3 to 5 days of manual effort.
SoK: A Security Architect's View of Printed Circuit Board Attacks
2024-10-13
Jacob Harrison, Nathan Jessurun, Mark Tehranipoor
Many recent papers have proposed novel electrical measurements or physical inspection technologies for defending printed circuit boards (PCBs) and printed circuit board assemblies (PCBAs) against tampering. As motivation, these papers frequently cite Bloomberg News' "The Big Hack", video game modchips, and "interdiction attacks" on IT equipment. We find this trend concerning for two reasons. First, implementation errors and security architecture are rarely discussed in recent PCBA security research, even though they were the root causes of these commonly-cited attacks and most other attacks that have occurred or been proposed by researchers. This suggests that the attacks may be poorly understood. Second, if we assume that novel countermeasures and validation methodologies are tailored to these oft-cited attacks, then significant recent work has focused on attacks that can already be mitigated instead of on open problems. We write this SoK to address these concerns. We explain which tampering threats can be mitigated by PCBA security architecture. Then, we enumerate assumptions that security architecture depends on. We compare and contrast assurances achieved by security architecture vs. by recently-proposed electrical or inspection-based tamper detection. Finally, we review over fifty PCBA attacks to show how most can be prevented by proper architecture and careful implementation.
Summarizing customer feedback to provide actionable insights for products/services at scale is an important problem for businesses across industries. Lately, the review volumes are increasing across regions and languages, therefore the challenge of aggregating and understanding customer sentiment across multiple languages becomes increasingly vital. In this paper, we propose a novel framework involving a two-step paradigm \textit{Extract-then-Summarise}, namely MARS to revolutionise traditions and address the domain agnostic aspect-level multilingual review summarisation. Extensive automatic and human evaluation shows that our approach brings substantial improvements over abstractive baselines and efficiency to real-time systems.
Responsible AI in the Global Context: Maturity Model and Survey
2024-10-13
Anka Reuel, Patrick Connolly, Kiana Jafari Meimandi, Shekhar Tewari, Jakub Wiatrak, Dikshita Venkatesh, Mykel Kochenderfer
Responsible AI (RAI) has emerged as a major focus across industry, policymaking, and academia, aiming to mitigate the risks and maximize the benefits of AI, both on an organizational and societal level. This study explores the global state of RAI through one of the most extensive surveys to date on the topic, surveying 1000 organizations across 20 industries and 19 geographical regions. We define a conceptual RAI maturity model for organizations to map how well they implement organizational and operational RAI measures. Based on this model, the survey assesses the adoption of system-level measures to mitigate identified risks related to, for example, discrimination, reliability, or privacy, and also covers key organizational processes pertaining to governance, risk management, and monitoring and control. The study highlights the expanding AI risk landscape, emphasizing the need for comprehensive risk mitigation strategies. The findings also reveal significant strides towards RAI maturity, but we also identify gaps in RAI implementation that could lead to increased (public) risks from AI systems. This research offers a structured approach to assess and improve RAI practices globally and underscores the critical need for bridging the gap between RAI planning and execution to ensure AI advancement aligns with human welfare and societal benefits.
Decentralized finance (DeFi) has revolutionized the financial landscape, with protocols like Uniswap offering innovative automated market-making mechanisms. This article explores the development of a backtesting framework specifically tailored for concentrated liquidity market makers (CLMM). The focus is on leveraging the liquidity distribution approximated using a parametric model, to estimate the rewards within liquidity pools. The article details the design, implementation, and insights derived from this novel approach to backtesting within the context of Uniswap V3. The developed backtester was successfully utilized to assess reward levels across several pools using historical data from 2023 (pools Uniswap v3 for pairs of altcoins, stablecoins and USDC/ETH with different fee levels). Moreover, the error in modeling the level of rewards for the period under review for each pool was less than 1\%. This demonstrated the effectiveness of the backtester in quantifying liquidity pool rewards and its potential in estimating LP's revenues as part of the pool rewards, as focus of our next research. The backtester serves as a tool to simulate trading strategies and liquidity provision scenarios, providing a quantitative assessment of potential returns for liquidity providers (LP). By incorporating statistical tools to mirror CLMM pool liquidity dynamics, this framework can be further leveraged for strategy enhancement and risk evaluation for LPs operating within decentralized exchanges.
Optimizing Waste Management with Advanced Object Detection for Garbage Classification
2024-10-13
Everest Z. Kuang, Kushal Raj Bhandari, Jianxi Gao
Garbage production and littering are persistent global issues that pose significant environmental challenges. Despite large-scale efforts to manage waste through collection and sorting, existing approaches remain inefficient, leading to inadequate recycling and disposal. Therefore, developing advanced AI-based systems is less labor intensive approach for addressing the growing waste problem more effectively. These models can be applied to sorting systems or possibly waste collection robots that may produced in the future. AI models have grown significantly at identifying objects through object detection. This paper reviews the implementation of AI models for classifying trash through object detection, specifically focusing on using YOLO V5 for training and testing. The study demonstrates how YOLO V5 can effectively identify various types of waste, including plastic, paper, glass, metal, cardboard, and biodegradables.
On the propagation of singularities of constant curvature, convex hypersurfaces
2024-10-13
Graham Smith
We describe the structure of the singular sets of constant curvature, convex hypersurfaces in hyperbolic space for general convex curvature functions. We apply this result to the study of the ideal Plateau problem in hyperbolic space for such curvature functions.
2024-10-13
Cynthia Jayne Amol, Everlyn Asiko Chimoto, Rose Delilah Gesicho, Antony M. Gitau, Naome A. Etori, Caringtone Kinyanjui, Steven Ndung'u, Lawrence Moruye, Samson Otieno Ooko, Kavengi Kitonga, Brian Muhia, Catherine Gitau, Antony Ndolo, Lilian D. A. Wanzare, Albert Njoroge Kahira, Ronald Tombe
Kenya, known for its linguistic diversity, faces unique challenges and promising opportunities in advancing Natural Language Processing (NLP) technologies, particularly for its underrepresented indigenous languages. This survey provides a detailed assessment of the current state of NLP in Kenya, emphasizing ongoing efforts in dataset creation, machine translation, sentiment analysis, and speech recognition for local dialects such as Kiswahili, Dholuo, Kikuyu, and Luhya. Despite these advancements, the development of NLP in Kenya remains constrained by limited resources and tools, resulting in the underrepresentation of most indigenous languages in digital spaces. This paper uncovers significant gaps by critically evaluating the available datasets and existing NLP models, most notably the need for large-scale language models and the insufficient digital representation of Indigenous languages. We also analyze key NLP applications: machine translation, information retrieval, and sentiment analysis-examining how they are tailored to address local linguistic needs. Furthermore, the paper explores the governance, policies, and regulations shaping the future of AI and NLP in Kenya and proposes a strategic roadmap to guide future research and development efforts. Our goal is to provide a foundation for accelerating the growth of NLP technologies that meet Kenya's diverse linguistic demands.
2024-10-13
Luigi Del Debbio, Alessandro Lupo, Marco Panero, Nazario Tantalo
The analytical continuation of correlation functions from imaginary to real time is a crucial step in lattice gauge theories, and it challenges our ability to derive non-perturbative predictions from lattice simulations. We review aspects of this "inverse problem", which has driven both theoretical and algorithmic advancements in recent years, opening new promising avenues for non-perturbative studies of the Standard Model and beyond. The focus of this proceeding will be on variations of Backus-Gilbert and Bayesian methods.
Artificial Intelligence in the Legal Field: Law Students Perspective
2024-10-13
Daniela Andreeva, Guergana Savova
The Artificial Intelligence field, or AI, experienced a renaissance in the last few years across various fields such as law, medicine, and finance. While there are studies outlining the landscape of AI in the legal field as well as surveys of the current AI efforts of law firms, to our knowledge there has not been an investigation of the intersection of law students and AI. Such research is critical to help ensure current law students are positioned to fully exploit this technology as they embark on their legal careers but to also assist existing legal firms to better leverage their AI skillset both operationally and in helping to formulate future legal frameworks for regulating this technology across industries. The study presented in this paper addresses this gap. Through a survey conducted from July 22 to Aug 19, 2024, the study covers the law students background, AI usage, AI applications in the legal field, AI regulations and open-ended comments to share opinions. The results from this study show the uniqueness of law students as a distinct cohort. The results differ from the ones of established law firms especially in AI engagement - established legal professionals are more engaged than law students. Somewhat surprising, the law firm participants show higher enthusiasm about AI than this student cohort. Collaborations with Computer Science departments would further enhance the AI knowledge and experience of law students in AI technologies such as prompt engineering (zero and few shot), chain-of-thought prompting, and language model hallucination management. As future work, we would like to expand the study to include more variables and a larger cohort more evenly distributed across locales. In addition, it would be insightful to repeat the study with the current cohort in one year to track how the students viewpoints evolve.
The environment as a driver of galaxy evolution with the miniJPAS survey
2024-10-13
Julio Esteban Rodríguez Martín
This thesis aims at discerning the effect of environment on galaxy evolution and on the properties of galaxies, using the data from the miniJPAS survey, a 1~deg$^2$ survey that uses the same photometric filter system as the incoming J-PAS survey, which is already in its scientific verification phase. This system is composed of 56 narrow-band filters that provide an spectral resolution comparable to low-resolution spectroscopy. We study the effect of environment using two approaches. First, we study the spatially resolved galaxies from miniJPAS. Secondly, we studied the galaxy population in the most massive galaxy cluster detected in the miniJPAS footprint, that is, the cluster mJPC2470-1771. We have developed a tool that automatises all the required processes for the analysis of the data. After probing the accuracy and reliability of the photmetry obtained with our tool, we apply it to a miniJPAS sample of spatially resolved galaxies, divided into four sub-samples according to their spectral-type (red/quiescent and blue/star forming), and environment (field or galaxy group). We find that redder, denser regions are usually older, more metal rich, and show lower values of the intensity of the star formation rate and the specific star formation rate than bluer and less dense regions. Regarding the mJPC2470-1771 cluster, our results point that more massive, redder and older galaxies typically populate the inner regions of the cluster. As a conclusion, our results suggest that galaxies in clusters are formed at a similar epoch, but have experienced different star formation histories. We conclude that the environment plays a role on galaxy evolution, but it is mainly reflected through the galaxy populations found in high density environments, such as galaxy clusters and groups, where the fraction of red, quiescent galaxies is larger in comparison to the field.
GRRIS: a real-time intra-site observation scheduling scheme for distributed survey telescope arrays
2024-10-13
Yajie Zhang, Ce Yu, Chao Sun, Yi Hu, Zhaohui Shang, Jizeng Wei, Xu Yang
The distributed telescope array offers promise for conducting large-sky-area, high-frequency time domain surveys. Multiple telescopes can be deployed at each observation site, so intra-site observation task scheduling is crucial for enhancing observation efficiency and quality. Efficient use of observable time and rapid response to special situations are critical to maximize scientific discovery in time domain surveys. Besides, the competing scientific priorities, time-varying observation conditions, and capabilities of observation equipment, lead to a vast search space of the scheduling. So with the increasing number of telescopes and observation fields, balancing computational time with solution quality in observation scheduling poses a significant challenge. Informed by the seminal contributions of earlier studies on a multilevel scheduling model and global scheduler for time domain telescope array, this study is devoted to further exploring the site scheduler. Formulating the observation scheduling of multiple telescopes at the site as a cooperative decision-making problem, this paper proposes GRRIS, a real-time intra-site observation scheduling scheme for telescope array using graph and reinforcement learning. It employs a graph neural network to learn node features that can embed the spatial structure of the observation scheduling. An algorithm based on multi-agent reinforcement learning is designed to efficiently learn the optimum allocation policy of telescope agents to field nodes. Through numerical simulations with real-world scenarios, GRRIS can achieve up to a 22% solution improvement over the most competitive scheme. It offers better scalability and sub-second decision speed, meeting the needs of observation scheduling control for future distributed telescope arrays.
AuthFace: Towards Authentic Blind Face Restoration with Face-oriented Generative Diffusion Prior
2024-10-13
Guoqiang Liang, Qingnan Fan, Bingtao Fu, Jinwei Chen, Hong Gu, Lin Wang
Blind face restoration (BFR) is a fundamental and challenging problem in computer vision. To faithfully restore high-quality (HQ) photos from poor-quality ones, recent research endeavors predominantly rely on facial image priors from the powerful pretrained text-to-image (T2I) diffusion models. However, such priors often lead to the incorrect generation of non-facial features and insufficient facial details, thus rendering them less practical for real-world applications. In this paper, we propose a novel framework, namely AuthFace that achieves highly authentic face restoration results by exploring a face-oriented generative diffusion prior. To learn such a prior, we first collect a dataset of 1.5K high-quality images, with resolutions exceeding 8K, captured by professional photographers. Based on the dataset, we then introduce a novel face-oriented restoration-tuning pipeline that fine-tunes a pretrained T2I model. Identifying key criteria of quality-first and photography-guided annotation, we involve the retouching and reviewing process under the guidance of photographers for high-quality images that show rich facial features. The photography-guided annotation system fully explores the potential of these high-quality photographic images. In this way, the potent natural image priors from pretrained T2I diffusion models can be subtly harnessed, specifically enhancing their capability in facial detail restoration. Moreover, to minimize artifacts in critical facial areas, such as eyes and mouth, we propose a time-aware latent facial feature loss to learn the authentic face restoration process. Extensive experiments on the synthetic and real-world BFR datasets demonstrate the superiority of our approach.
Invariants of almost embeddings of graphs in the plane
2024-10-13
E. Alkin, E. Bordacheva, A. Miroshnikov, A. Skopenkov
In this survey we motivate studies of the invariants from the title. A graph drawing in the plane is called an almost embedding if the images of any two non-adjacent simplices (i.e. vertices or edges) are disjoint. We introduce integer invariants of almost embeddings: winding number, cyclic and triodic Wu numbers. We prove some relations between the invariants. We demonstrate connection of these relations to homology of the deleted product of a graph. We construct almost embeddings realizing some values of these invariants. This paper is accessible to mathematicians not specialized in the area (and to students). All the necessary definitions are recalled. We present some ideas of algebraic and geometric topology in a language accessible to non-topologists. However elementary, this paper is motivated by frontline of research; there are some conjectures and an open problem.
This paper is an overview of studying the solar features in a complex network approach. First, we introduce the structural features of complex networks and important network parameters. Applying the detrended fluctuation and rescaled range analysis and nodes degree power-law distributions confirmed the non-randomness of the solar features complex networks. Using the HEALPix pixelization and considering all parts of the solar surface under the same conditions, as well as applying centrality parameters (the nodes with the highest connectivity, closeness, betweenness, and Pagerank) showed that the active areas on the solar surface were correctly identified and were consistent with observations. A review of the complex structure of the solar proton flux and active regions also showed that in these networks, the average clustering coefficient and Page-rank parameters are suitable criteria to use in event prediction methods. The complex network of sunspots has also shown that sunspots and sunspot groups are formed through complex nonlinear dynamics.
The integrity of the peer-review process is vital for maintaining scientific rigor and trust within the academic community. With the steady increase in the usage of large language models (LLMs) like ChatGPT in academic writing, there is a growing concern that AI-generated texts could compromise scientific publishing, including peer-reviews. Previous works have focused on generic AI-generated text detection or have presented an approach for estimating the fraction of peer-reviews that can be AI-generated. Our focus here is to solve a real-world problem by assisting the editor or chair in determining whether a review is written by ChatGPT or not. To address this, we introduce the Term Frequency (TF) model, which posits that AI often repeats tokens, and the Review Regeneration (RR) model, which is based on the idea that ChatGPT generates similar outputs upon re-prompting. We stress test these detectors against token attack and paraphrasing. Finally, we propose an effective defensive strategy to reduce the effect of paraphrasing on our models. Our findings suggest both our proposed methods perform better than the other AI text detectors. Our RR model is more robust, although our TF model performs better than the RR model without any attacks. We make our code, dataset, and model public.
On the Stellar Disk Vertical Scale Height of Edge-on Galaxies from S$^{4}$G
2024-10-13
Notahiana Ranaivoharimina, Toky H. Randriamampandry, Jing Wang, Karín Menéndez-Delmestre, Thiago S. Gonçalves
Disk galaxies viewed as thin planar structures resulting from the conservation of angular momentum of an initially rotating pre-galactic cloud allow merely a first-order model of galaxy formation. Still, the presence of vertically extended structures has allowed us to gather a deeper understanding of the richness in astrophysical processes (e.g., minor mergers, secular evolution) that ultimately result in the observed diversity in disk galaxies and their vertical extensions. We measure the stellar disk scale height of 46 edge-on spiral galaxies from the Spitzer Survey of Stellar Structure in Galaxies (S$^{4}$G) project. This paper aims to investigate the radial variation of the stellar disk vertical scale height and the existence of the so-called thick disk component in our sample. The measurements were done using one-, two-, and three-dimensional profile fitting techniques using simple models. We found that two-thirds of our sample shows the presence of a thick disk, suggesting that these galaxies have been accreting gaseous material from their surroundings. We found an average thick-to-thin disk scale height ratio of 2.65, which agrees with previous studies. Our findings also support the disk flaring model, which suggests that the vertical scale height increases with radius. We further found good correlations: between the scale height $h_{z}$ and the scale length and between $h_z$ and the optical de Vaucouleurs radius $R_{25}$.
Negative density-dependent dispersal of the mountain pine beetle in Alberta
2024-10-13
Evan C. Johnson, Mark Lewis
Understanding the mountain pine beetle's dispersal patterns is critical for evaluating its threat to Canada's boreal forests. It is generally believed that higher beetle densities lead to increased long-distance dispersal due to aggregation pheromones becoming repellent at high densities, causing beetles to seek areas with less competition. However, using helicopter surveys of infested trees, along with statistical models, we find no evidence supporting a positive relationship. Instead, we observe a weak negative association between population density and dispersal at all spatial scales. A possible explanation is that at low population densities, beetles cannot successfully attack healthy trees and must travel farther to find weakened hosts. Even so, the influence of beetle density on dispersal is minor compared to the spatiotemporal variation in the overall (density-independent) scale of dispersal, as revealed by our models. This variation accounts for the MPB's erratic range expansion across western Alberta, which varied from 20 km to 220 km annually.
In this work, we explore the mechanism of in-context learning (ICL) on out-of-distribution (OOD) tasks that were not encountered during training. To achieve this, we conduct synthetic experiments where the objective is to learn OOD mathematical functions through ICL using a GPT-2 model. We reveal that Transformers may struggle to learn OOD task functions through ICL. Specifically, ICL performance resembles implementing a function within the pretraining hypothesis space and optimizing it with gradient descent based on the in-context examples. Additionally, we investigate ICL's well-documented ability to learn unseen abstract labels in context. We demonstrate that such ability only manifests in the scenarios without distributional shifts and, therefore, may not serve as evidence of new-task-learning ability. Furthermore, we assess ICL's performance on OOD tasks when the model is pretrained on multiple tasks. Both empirical and theoretical analyses demonstrate the existence of the \textbf{low-test-error preference} of ICL, where it tends to implement the pretraining function that yields low test error in the testing context. We validate this through numerical experiments. This new theoretical result, combined with our empirical findings, elucidates the mechanism of ICL in addressing OOD tasks.
Technical Design Review of Duke Robotics Club's Oogway: An AUV for RoboSub 2024
2024-10-13
Will Denton, Michael Bryant, Lilly Chiavetta, Vedarsh Shah, Rico Zhu, Philip Xue, Vincent Chen, Maxwell Lin, Hung Le, Austin Camacho, Raul Galvez, Nathan Yang, Nathanael Ren, Tyler Rose, Mathew Chu, Amir Ergashev, Saagar Arya, Kaelyn Pieter, Ethan Horowitz, Maanav Allampallam, Patrick Zheng, Mia Kaarls, June Wood
The Duke Robotics Club is proud to present our robot for the 2024 RoboSub Competition: Oogway. Now in its second year, Oogway has been dramatically upgraded in both its capabilities and reliability. Oogway was built on the principle of independent, well-integrated, and reliable subsystems. Individual components and subsystems were tested and designed separately. Oogway's most advanced capabilities are a result of the tight integration between these subsystems. Such examples include a re-envisioned controls system, an entirely new electrical stack, advanced sonar integration, additional cameras and system monitoring, a new marker dropper, and a watertight capsule mechanism. These additions enabled Oogway to prequalify for Robosub 2024.
LoRD: Adapting Differentiable Driving Policies to Distribution Shifts
2024-10-13
Christopher Diehl, Peter Karkus, Sushant Veer, Marco Pavone, Torsten Bertram
Distribution shifts between operational domains can severely affect the performance of learned models in self-driving vehicles (SDVs). While this is a well-established problem, prior work has mostly explored naive solutions such as fine-tuning, focusing on the motion prediction task. In this work, we explore novel adaptation strategies for differentiable autonomy stacks consisting of prediction, planning, and control, perform evaluation in closed-loop, and investigate the often-overlooked issue of catastrophic forgetting. Specifically, we introduce two simple yet effective techniques: a low-rank residual decoder (LoRD) and multi-task fine-tuning. Through experiments across three models conducted on two real-world autonomous driving datasets (nuPlan, exiD), we demonstrate the effectiveness of our methods and highlight a significant performance gap between open-loop and closed-loop evaluation in prior approaches. Our approach improves forgetting by up to 23.33% and the closed-loop OOD driving score by 8.83% in comparison to standard fine-tuning.
Three Decades of Formal Methods in Business Process Compliance: A Systematic Literature Review
2024-10-13
Hugo A. López, Thomas T. Hildebrandt
Digitalization efforts often face a key challenge: business processes must not only be efficient in achieving their goals but also adhere to legal regulations. Business process compliance refers to aligning processes with these regulations. Numerous frameworks have been developed to address this, with the earliest dating back to 1981. This study focuses on rigorous frameworks using formal methods to verify or ensure compliance. We conducted a systematic literature review (SLR) on process compliance frameworks based on formal models. Our goal was to assess the current state of research on process model compliance and identify gaps and opportunities for future work. Starting with 5018 candidate studies from 1981 to the establishment of GDPR, we selected 46 primary studies. These frameworks were categorized by their phases, the languages used for processes and compliance, and their reasoning techniques. We also examined their practical applicability, the case studies they were tested on, the types of users involved, and the skills needed for compliance. Also, we assessed the maturity of each framework. Our findings reveal strong consensus around verification techniques as central to process compliance, though there is less agreement on the earlier and later phases of compliance. Model checking is the dominant technique, but the compliance and process languages have evolved. Most frameworks are still conceptual with prototype implementations, often failing to account for compliance professionals like legal experts or law changes. In conclusion, there is a need for comprehensive empirical studies to better understand the anatomy and maturity of regulatory compliance frameworks, and for robust evaluation methods to benchmark these frameworks. This review offers valuable insights for researchers and practitioners in process compliance.
In this report, we propose the implementation of national registries for frontier AI models as a foundational tool for AI governance. We explore the rationale, design, and implementation of such registries, drawing on comparisons with registries in analogous industries to make recommendations for a registry that is efficient, unintrusive, and which will bring AI governance closer to parity with the governmental insight into other high-impact industries. We explore key information that should be collected, including model architecture, model size, compute and data used during training, and we survey the viability and utility of evaluations developed specifically for AI. Our proposal is designed to provide governmental insight and enhance AI safety while fostering innovation and minimizing the regulatory burden on developers. By providing a framework that respects intellectual property concerns and safeguards sensitive information, this registry approach supports responsible AI development without impeding progress. We propose that timely and accurate registration should be encouraged primarily through injunctive action, by requiring third parties to use only registered models, and secondarily through direct financial penalties for non-compliance. By providing a comprehensive framework for AI model registries, we aim to support policymakers in developing foundational governance structures to monitor and mitigate risks associated with advanced AI systems.
Strong decay widths of S- and P-wave singly-, doubly- and triply-heavy charm and bottom baryons
2024-10-12
Emmanuel Ortiz-Pacheco, Roelof Bijker
We present a study of two-body decay widths of heavy baryons into another heavy baryon and a light pseudoscalar meson in the framework of the non-relativistic quark model in combination with the elementary emission model for the strong couplings. The present study includes the decays of 1S- and 1P-wave baryons with one, two or three heavy quarks, either charm (c) or bottom (b). The relative partial widths are given by the appropriate flavor SU(3) isoscalar factors. The flavor, spin and orbital contributions are discussed explicitly as well as selection rules for forbidden decays. The total widths are compared with the available experimental data as well as with other theoretical studies. The calculated widths of singly-heavy baryons are found to be in reasonable agreement with the observed widths. The decay widths of doubly-heavy baryons are suppressed with respect to those of the singly-heavy baryons by the ratios of the quark masses appearing in the orbital contributions.
A pedagogical tour of the Fourier transform with applications to NMR and IR spectroscopy
2024-10-12
Anthony J. Dominic III, Nicholas L. Cipolla, William C. Pfalzgraff, Jeffrey A. Jankowski, Rebecca J. Rapf, Andrés Montoya-Castillo
The Fourier Transform (FT) is a fundamental tool that permeates modern science and technology. While chemistry undergraduates encounter the FT as early as second year, their courses often only mention it in passing because computers frequently perform it automatically behind the scenes. Although this automation enables students to focus on `the chemistry', students miss out on an opportunity to understand and use one of the most powerful tools in the scientific arsenal capable of revealing how time-dependent signals encode chemical structure. Although many educational resources introduce chemists to the FT, they often require familiarity with sophisticated mathematical and computational concepts. Here, we present a series of three self-contained, Python-based laboratory activities for undergraduates to understand the FT and apply it to analyze audio signals, an infrared (IR) spectroscopy interferogram, and a nuclear magnetic resonance (NMR) free induction decay (FID). In these activities, students observe how the FT reveals and quantifies the contribution of each frequency present in a temporal signal and how decay timescales dictate signal broadening. Our activities empower students with the tools to transform their own temporal datasets (e.g., FID) to a frequency spectrum. To ensure accessibility of the activities and lower the barrier to implementation, we utilize Google Colab's open-source, cloud-based platform to run Jupyter notebooks. We also offer a pre-laboratory activity that introduces students to the basics of Python and the Colab platform, and reviews the math and programming skills needed to complete the lab activities. These lab activities help students build a qualitative, quantitative, and practical understanding of the FT.
The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models
2024-10-12
Subhankar Maity, Aniket Deroy
In recent years, large language models (LLMs) and generative AI have revolutionized natural language processing (NLP), offering unprecedented capabilities in education. This chapter explores the transformative potential of LLMs in automated question generation and answer assessment. It begins by examining the mechanisms behind LLMs, emphasizing their ability to comprehend and generate human-like text. The chapter then discusses methodologies for creating diverse, contextually relevant questions, enhancing learning through tailored, adaptive strategies. Key prompting techniques, such as zero-shot and chain-of-thought prompting, are evaluated for their effectiveness in generating high-quality questions, including open-ended and multiple-choice formats in various languages. Advanced NLP methods like fine-tuning and prompt-tuning are explored for their role in generating task-specific questions, despite associated costs. The chapter also covers the human evaluation of generated questions, highlighting quality variations across different methods and areas for improvement. Furthermore, it delves into automated answer assessment, demonstrating how LLMs can accurately evaluate responses, provide constructive feedback, and identify nuanced understanding or misconceptions. Examples illustrate both successful assessments and areas needing improvement. The discussion underscores the potential of LLMs to replace costly, time-consuming human assessments when appropriately guided, showcasing their advanced understanding and reasoning capabilities in streamlining educational processes.
MIRAGE: Evaluating and Explaining Inductive Reasoning Process in Language Models
2024-10-12
Jiachun Li, Pengfei Cao, Zhuoran Jin, Yubo Chen, Kang Liu, Jun Zhao
Inductive reasoning is an essential capability for large language models (LLMs) to achieve higher intelligence, which requires the model to generalize rules from observed facts and then apply them to unseen examples. We present {\scshape Mirage}, a synthetic dataset that addresses the limitations of previous work, specifically the lack of comprehensive evaluation and flexible test data. In it, we evaluate LLMs' capabilities in both the inductive and deductive stages, allowing for flexible variation in input distribution, task scenario, and task difficulty to analyze the factors influencing LLMs' inductive reasoning. Based on these multi-faceted evaluations, we demonstrate that the LLM is a poor rule-based reasoner. In many cases, when conducting inductive reasoning, they do not rely on a correct rule to answer the unseen case. From the perspectives of different prompting methods, observation numbers, and task forms, models tend to consistently conduct correct deduction without correct inductive rules. Besides, we find that LLMs are good neighbor-based reasoners. In the inductive reasoning process, the model tends to focus on observed facts that are close to the current test example in feature space. By leveraging these similar examples, the model maintains strong inductive capabilities within a localized region, significantly improving its deductive performance.
2024-10-12
Ilya A. Kondratyev, Sergey G. Moiseenko, Gennady S. Bisnovatyi-Kogan
High velocity neutron stars, observed as rapidly moving radio-pulsars, are believed to gain high linear velocities -- kicks -- in aspherical supernova explosions. The mechanism of the kick formation is probably connected with anisotropic neutrino flash, and/or anisotropic matter ejection. In this paper, we investigate a neutron star kick origin in a magnetorotational (MR) supernova explosion model. The simulations have been done for a series of core collapse supernova models with initial equatorially asymmetric magnetic fields. We have realized 2D magnetohydrodynamic simulations, considering the protoneutron star kick and explosion properties in three different asymmetric magnetic field configurations. The simulations show, that in the MR supernova model protoneutron star kicks are formed with velocities up to ~500 km/s, due to asymmetric matter ejection in jets. It may explain the observed kick velocities of some neutron stars, formed in the MR supernovae explosions.
We present measurements of hyperfine structure (HFS) of the $nP_J$ Rydberg states for large principal quantum number $n$ range ($n=41-55$) employing the microwave spectroscopy in an ultra-cold cesium Rydberg ensemble. A microwave field with 30-$\mu$s duration couples the $ nS \to nP $ transition, yielding a narrow linewidth spectroscopy that approaches the Fourier limit, which allows us to resolve the hyperfine structure of $ nP_J $ states. By analyzing the hyperfine splittings of $nP_J$ states, we determine the magnetic-dipole HFS coupling constant $\bar{A}_{HFS,P_{1/2}}=3.760(26) ~$GHz for $P_{1/2}$ state, $\bar{A}_{HFS,P_{3/2}}= 0.718(27)~$GHz, and $ \bar{B}_{HFS,P_{3/2}}= -0.084(102)~$GHz for $P_{3/2}$ state, respectively. Systematic uncertainties caused by stray electromagnetic field, microwave field power and Rydberg interaction are analyzed. This measurement is significant for the investigation of Rydberg electrometry and quantum simulation with dipole interaction involving $nP_J$ state.
Follow-up timing of 12 pulsars discovered in Commensal Radio Astronomy FAST Survey
2024-10-12
D. Zhao, J. P. Yuan, N. Wang, D. Li, P. Wang, M. Y. Xue, W. W. Zhu, C. C. Miao, W. M. Yan, J. B. Wang, J. M. Yao, Q. D. Wu, S. Q. Wang, S. N. Sun, F. F. Kou, Y. T. Chen, S. J. Dang, Y. Feng, Z. J. Liu, X. L. Miao, L. Q. Meng, M. Yuan, C. H. Niu, J. R. Niu, L. Qian, S. Wang, X. Y. Xie, Y. F. Xiao, Y. L. Yue, S. P. You, X. H. Yu, R. S. Zhao, R. Yuen, X. Zhou, L. Zhang, M. Xie, Y. X. Li, C. J. Wang, Z. K. Luo, Z. Y. Gan, Z. Y. Sun, M. m. Chi, C. J. Wang
We present phase-connected timing ephemerides, polarization pulse profiles and Faraday rotation measurements of 12 pulsars discovered by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) in the Commensal Radio Astronomy FAST Survey (CRAFTS). The observational data for each pulsar span at least one year. Among them, PSR J1840+2843 shows subpulse drifting, and five pulsars are detected to exhibit pulse nulling phenomena. PSR J0640$-$0139 and PSR J2031$-$1254 are isolated MSPs with stable spin-down rates ($\dot{P}$) of $4.8981(6) \times $10$^{-20}$\,s\,s$^{-1}$ and $6.01(2) \times $10$^{-21}$\,s\,s$^{-1}$, respectively. Additionally, one pulsar (PSR J1602$-$0611) is in a neutron star - white dwarf binary system with 18.23-d orbit and a companion of $\leq$ 0.65M$_{\odot}$. PSR J1602$-$0611 has a spin period, companion mass, and orbital eccentricity that are consistent with the theoretical expectations for MSP - Helium white dwarf (He - WD) systems. Therefore, we believe it might be an MSP-He WD binary system. The locations of PSRs J1751$-$0542 and J1840+2843 on the $P-\dot{P}$ diagram are beyond the traditional death line. This indicates that FAST has discovered some low $\dot{E}$ pulsars, contributing new samples for testing pulsar radiation theories. We estimated the distances of these 12 pulsars based on NE2001 and YMW16 electron density models, and our work enhances the dataset for investigating the electron density model of the Galaxy.
The SRG/eROSITA All-Sky Survey : Constraints on the structure growth from cluster number counts
2024-10-12
E. Artis, E. Bulbul, S. Grandis, V. Ghirardini, N. Clerc, R. Seppi, J. Comparat, M. Cataneo, A. von der Linden, Y. E. Bahar, F. Balzer, I. Chiu, D. Gruen, F. Kleinebreil, M. Kluge, S. Krippendorf, X. Li, A. Liu, N. Malavasi, A. Merloni, H. Miyatake, S. Miyazaki, K. Nandra, N. Okabe, F. Pacaud, P. Predehl, M. E. Ramos-Ceja, T. H. Reiprich, J. S. Sanders, T. Schrabback, S. Zelmer, X. Zhang
Beyond testing the current cosmological paradigm, cluster number counts can also be utilized to investigate the discrepancies currently affecting current cosmological measurements. In particular, cosmological studies based on cosmic shear and other large-scale structure probes routinely find a value of the amplitude of the fluctuations in the universe S8 smaller than the one inferred from the primary cosmic microwave background. In this work, we investigate this tension by measuring structure evolution across cosmic time as probed by the number counts of the massive halos with the first SRG/eROSITA All-Sky Survey cluster catalog in the Western Galactic Hemisphere complemented with the overlapping Dark Energy Survey Year-3, KiloDegree Survey, and Hyper Suprime-Cam data for weak lensing mass calibration, by implementing two different parameterizations and a model-agnostic method. In the first model, we measure the cosmic linear growth index as {\gamma} = 1.19 \pm 0.21, in tension with the standard value of {\gamma} = 0.55, but in good statistical agreement with other large-scale structures probes. The second model is a phenomenological scenario in which we rescale the linear matter power spectrum at low redshift to investigate a potential reduction of structure formation, providing similar results. Finally, in a third strategy, we consider a standard {\Lambda}CDM cosmology, but we separate the cluster catalog into five redshift bins, measuring the cosmological parameters in each and inferring the evolution of the structure formation, finding hints of a reduction. Interestingly, the S8 value inferred from eRASS1 cluster number counts, when we add a degree of freedom to the matter power spectrum, recovers the value inferred by cosmic shear studies.
With the rapid development of urban underground rail vehicles,subway positioning, which plays a fundamental role in the traffic navigation and collision avoidance systems, has become a research hot-spot these years. Most current subway positioning methods rely on localization beacons densely pre-installed alongside the railway tracks, requiring massive costs for infrastructure and maintenance, while commonly lacking flexibility and anti-interference ability. In this paper, we propose a low-cost and real-time visual-assisted self-localization framework to address the robust and convenient positioning problem for subways. Firstly, we perform aerial view rail sleeper detection based on the fast and efficient YOLOv8n network. The detection results are then used to achieve real-time correction of mileage values combined with geometric positioning information, obtaining precise subway locations. Front camera Videos for subway driving scenes along a 6.9 km route are collected and annotated from the simulator for validation of the proposed method. Experimental results show that our aerial view sleeper detection algorithm can efficiently detect sleeper positions with F1-score of 0.929 at 1111 fps, and that the proposed positioning framework achieves a mean percentage error of 0.1\%, demonstrating its continuous and high-precision self-localization capability.
Automatic Speech Recognition with BERT and CTC Transformers: A Review
2024-10-12
Noussaiba Djeffal, Hamza Kheddar, Djamel Addou, Ahmed Cherif Mazari, Yassine Himeur
This review paper provides a comprehensive analysis of recent advances in automatic speech recognition (ASR) with bidirectional encoder representations from transformers BERT and connectionist temporal classification (CTC) transformers. The paper first introduces the fundamental concepts of ASR and discusses the challenges associated with it. It then explains the architecture of BERT and CTC transformers and their potential applications in ASR. The paper reviews several studies that have used these models for speech recognition tasks and discusses the results obtained. Additionally, the paper highlights the limitations of these models and outlines potential areas for further research. All in all, this review provides valuable insights for researchers and practitioners who are interested in ASR with BERT and CTC transformers.
Impurities and polarons in bosonic quantum gases: a review on recent progress
2024-10-12
F. Grusdt, N. Mostaan, E. Demler, Luis A. Peña Ardila
This review describes the field of Bose polarons, arising when mobile impurities are immersed into a bosonic quantum gas. The latter can be realized by a Bose-Einstein condensate (BEC) of ultracold atoms, or of exciton polaritons in a semiconductor, which has led to a series of experimental observations of Bose polarons near inter-species Feshbach resonances that we survey. Following an introduction to the topic, with references to its historic roots and a presentation of the Bose polaron Hamiltonian, we summarize state-of-the-art experiments. Next we provide a detailed discussion of polaron models, starting from the ubiquitous Fr\"ohlich Hamiltonian that applies at weak couplings. We proceed by a survey of concurrent theoretical methods used for solving strongly interacting Bose polaron problems. The subsequent sections are devoted to the large bodies of work investigating strong coupling Bose polarons, including detailed comparisons with radio-frequency (RF) spectra obtained in ultracold atom experiments; to investigations of universal few-body and Efimov states associated with a Feshbach resonance in atomic mixtures; to studies of quantum dynamics and polarons out of equilibrium; Bose polarons in low-dimensional; induced interactions among polarons and bipolaron formation; and to Bose polarons at non-zero temperatures. We end our review by detailed discussions of closely related experimental setups and systems, including ionic impurities, systems with strong light-matter interactions, and variations and extensions of the Bose polaron concepts e.g. to baths with topological order or strong interactions relevant for correlated electrons. Finally, an outlook is presented, highlighting possible future research directions and open questions in the field as a whole.
Text Classification using Graph Convolutional Networks: A Comprehensive Survey
2024-10-12
Syed Mustafa Haider Rizvi, Ramsha Imran, Arif Mahmood
Text classification is a quintessential and practical problem in natural language processing with applications in diverse domains such as sentiment analysis, fake news detection, medical diagnosis, and document classification. A sizable body of recent works exists where researchers have studied and tackled text classification from different angles with varying degrees of success. Graph convolution network (GCN)-based approaches have gained a lot of traction in this domain over the last decade with many implementations achieving state-of-the-art performance in more recent literature and thus, warranting the need for an updated survey. This work aims to summarize and categorize various GCN-based Text Classification approaches with regard to the architecture and mode of supervision. It identifies their strengths and limitations and compares their performance on various benchmark datasets. We also discuss future research directions and the challenges that exist in this domain.
Direct alignment algorithms (DAAs), such as direct preference optimization (DPO), have become popular alternatives for Reinforcement Learning from Human Feedback (RLHF) due to their simplicity, efficiency, and stability. However, the preferences used in DAAs are usually collected before the alignment training begins and remain unchanged (off-policy). This can lead to two problems where the policy model (1) picks up on spurious correlations in the dataset (as opposed to learning the intended alignment expressed in the human preference labels), and (2) overfits to feedback on off-policy trajectories that have less likelihood of being generated by an updated policy model. To address these issues, we introduce Self-Reviewing and Alignment (SeRA), a cost-efficient and effective method that can be readily combined with existing DAAs. SeRA comprises of two components: (1) sample selection using implicit reward margins, which helps alleviate over-fitting to some undesired features, and (2) preference bootstrapping using implicit rewards to augment preference data with updated policy models in a cost-efficient manner. Extensive experimentation, including some on instruction-following tasks, demonstrate the effectiveness and generality of SeRA in training LLMs on offline preference datasets with DAAs.
2024-10-12
Tiago da Silva, Eliezer de Souza da Silva, Diego Mesquita
Generative Flow Networks (GFlowNets) are amortized inference models designed to sample from unnormalized distributions over composable objects, with applications in generative modeling for tasks in fields such as causal discovery, NLP, and drug discovery. Traditionally, the training procedure for GFlowNets seeks to minimize the expected log-squared difference between a proposal (forward policy) and a target (backward policy) distribution, which enforces certain flow-matching conditions. While this training procedure is closely related to variational inference (VI), directly attempting standard Kullback-Leibler (KL) divergence minimization can lead to proven biased and potentially high-variance estimators. Therefore, we first review four divergence measures, namely, Renyi-$\alpha$'s, Tsallis-$\alpha$'s, reverse and forward KL's, and design statistically efficient estimators for their stochastic gradients in the context of training GFlowNets. Then, we verify that properly minimizing these divergences yields a provably correct and empirically effective training scheme, often leading to significantly faster convergence than previously proposed optimization. To achieve this, we design control variates based on the REINFORCE leave-one-out and score-matching estimators to reduce the variance of the learning objectives' gradients. Our work contributes by narrowing the gap between GFlowNets training and generalized variational approximations, paving the way for algorithmic ideas informed by the divergence minimization viewpoint.
Spatio-Temporal Performance of 2D Local Inertial Hydrodynamic Models for Urban Drainage and Dam-Break Applications
2024-10-12
Marcus N. Gomes Jr., Maria A. R. A. Castro, Luis M. R. Castillo, Mateo H. Sánchez, Marcio H. Giacomoni, Rodrigo C. D. de Paiva, Paul D. Bates
Accurate flood modeling is crucial for effective analysis and forecasting. Full momentum hydrodynamic models often require extensive computational time, sometimes exceeding the forecast horizon. In contrast, low-complexity models, like local-inertial approximations, provide accurate results in subcritical flows but may have limited skillfulness in supercritical conditions. This paper explores two main aspects: (i) the impact of urban infrastructure on 2D hydrodynamic modeling without detailed sewer and drainage data, and (ii) the accuracy of 2D local-inertial modeling using three numerical schemes (original formulation, s-centered, and s-upwind) in a dam-break scenario on complex, flat terrain. The HydroPol2D model is benchmarked against HEC-RAS 2D full momentum solver. We present one numerical case study and three real-world scenarios in S\~ao Paulo, Brazil: a detention pond with a $1$ in $100$-year inflow, a highly urbanized catchment with a $1$ in $50$-year hyetograph, and a dam-break scenario threatening a coastal city of nearly 200,000 residents. Results show that the model accurately simulates internal boundary conditions, achieving peak errors under 5\% compared to HEC-RAS 2D. However, neglecting urban infrastructure can lead to a 17.5\% difference in peak discharges at the outlet and significant mismatches in hydrographs, with computational times nearly doubling. The dam-break scenario demonstrates good predictive performance for maximum flood depths (CSI = $0.95$ for the original model, $0.92$ for s-centered, and $0.89$ for s-upwind), though the model's lack of convective inertia results in faster flood wave propagation than the full momentum solver. Notably, HydroPol2D is 23 times faster than HEC-RAS 2D, making it well-suited for simulating dam collapses in forecasting systems and capable of modeling urban drainage infrastructure such as orifices, weirs, and pumps.
Towards an Incident Management Framework in Proprietary Software Ecosystems
2024-10-12
L. A. Costa, A. Fontão, R. P. Santos, A. Serebrenik
In the evolving landscape of Software Engineering, the paradigm of software ecosystems has emerged, giving rise to proprietary software ecosystems (PSECO), with their central organizations known as keystones. PSECO is characterized by the contribution of various technologies produced as private and protected by intellectual property and confidentiality agreements, centered on common technological platforms. Sustaining these PSECO technological platforms is vital, as any incident can have substantial repercussions. This work introduces a framework for incident management to support the organizations' management teams in the PSECO context, called IM Framework. The IM Framework was developed in close collaboration with practitioners across a large international organization. We grounded the IM Framework based on the results of a rapid review study that retrieved 293 studies, of which 23 were selected after applying review procedures. This framework comprises five core categories: organizational goals, practices, success factors, associated benefits, and prevalent barriers. The IM Framework offers practical guidance for the PSECO management team, focusing on real-world applications to enhance reliability and resilience in a complex and dynamic software environment. Our study also promises to fill the gap in incident management governance by supporting the PSECO organization's management team and maintaining robust technological platforms amidst evolving business demands and market pressures.
Thermal Comfort in Sight: Thermal Affordance and its Visual Assessment for Sustainable Streetscape Design
2024-10-12
Sijie Yang, Adrian Chong, Pengyuan Liu, Filip Biljecki
In response to climate change and urban heat island effects, enhancing human thermal comfort in cities is crucial for sustainable urban development. Traditional methods for investigating the urban thermal environment and corresponding human thermal comfort level are often resource intensive, inefficient, and limited in scope. To address these challenges, we (1) introduce the concept of thermal affordance, which represents the inherent capacity of a streetscape to influence human thermal comfort based on its visual and physical features; and (2) a method to evaluate it (visual assessment of thermal affordance -- VATA), which combines street view imagery (SVI), online and in-filed surveys, and statistical learning algorithms. VATA extracts five categories of image features from SVI data and establishes 19 visual-perceptual indicators for streetscape visual assessment. Using a multi-task neural network and elastic net regression, we model their chained relationship to predict and comprehend thermal affordance for Singapore. VATA predictions are validated with field-investigated OTC data, providing a cost-effective and scalable method to assess the thermal comfort potential of urban streetscape. This framework can inform streetscape design to support sustainable, livable, and resilient urban environments.
Software Engineering and Foundation Models: Insights from Industry Blogs Using a Jury of Foundation Models
2024-10-11
Hao Li, Cor-Paul Bezemer, Ahmed E. Hassan
Foundation models (FMs) such as large language models (LLMs) have significantly impacted many fields, including software engineering (SE). The interaction between SE and FMs has led to the integration of FMs into SE practices (FM4SE) and the application of SE methodologies to FMs (SE4FM). While several literature surveys exist on academic contributions to these trends, we are the first to provide a practitioner's view. We analyze 155 FM4SE and 997 SE4FM blog posts from leading technology companies, leveraging an FM-powered surveying approach to systematically label and summarize the discussed activities and tasks. We observed that while code generation is the most prominent FM4SE task, FMs are leveraged for many other SE activities such as code understanding, summarization, and API recommendation. The majority of blog posts on SE4FM are about model deployment & operation, and system architecture & orchestration. Although the emphasis is on cloud deployments, there is a growing interest in compressing FMs and deploying them on smaller devices such as edge or mobile devices. We outline eight future research directions inspired by our gained insights, aiming to bridge the gap between academic findings and real-world applications. Our study not only enriches the body of knowledge on practical applications of FM4SE and SE4FM but also demonstrates the utility of FMs as a powerful and efficient approach in conducting literature surveys within technical and grey literature domains. Our dataset, results, code and used prompts can be found in our online replication package at https://github.com/SAILResearch/fmse-blogs.
The JWST Emission Line Survey (JELS): Extending rest-optical narrow-band emission line selection into the Epoch of Reionization
2024-10-11
K. J. Duncan, D. J. McLeod, P. N. Best, C. A. Pirie, M. Clausen, R. K. Cochrane, J. S. Dunlop, S. R. Flury, J. E. Geach, C. L. Hale, E. Ibar, R. Kondapally, Zefeng Li, J. Matthee, R. J. McLure, Luis Ossa-Fuentes, A. L. Patrick, Ian Smail, D. Sobral, H. M. O. Stephenson, J. P. Stott, A. M. Swinbank
We present the JWST Emission Line Survey (JELS), a Cycle 1 JWST imaging programme exploiting the wavelength coverage and sensitivity of NIRCam to extend narrow-band rest-optical emission line selection into the epoch of reionization (EoR) for the first time, and to enable unique studies of the resolved ionised gas morphology in individual galaxies across cosmic history. The primary JELS observations comprise $\sim4.7\mu$m narrow-band imaging over $\sim63$ arcmin$^{2}$ designed to enable selection of H$\alpha$ emitters at $z\sim6.1$, as well as the selection of a host of novel emission-line samples including [OIII] at $z\sim8.3$ and Pa $\alpha/\beta$ at $z\sim1.5/2.8$. For the prime F466N and F470N narrow-band observations, the emission-line sensitivities achieved are up to $\sim2\times$ more sensitive than current slitless spectroscopy surveys (5$\sigma$ limits of 1.1-1.6$\times10^{-18}\text{erg s}^{-1}\text{cm}^{-2}$), corresponding to unobscured H$\alpha$ star-formation rates (SFRs) of 1-1.6 $\text{M}_{\odot}\,\text{yr}^{-1}$ at $z\sim6.1$ and extending emission-line selections in the EoR to fainter populations. Simultaneously, JELS also obtained F200W broadband and F212N narrow-band imaging (H$\alpha$ at $z\sim2.23$) that probes SFRs $\gtrsim5\times$ fainter than previous ground-based narrow-band studies ($\sim0.2 \text{M}_{\odot}\text{yr}^{-1}$), offering an unprecedented resolved view of star formation at cosmic noon. In this paper we describe the detailed JELS survey design, key data processing steps specific to the survey observations, and demonstrate the exceptional data quality and imaging sensitivity achieved. We then summarise the key scientific goals of JELS and present some early science results, including examples of spectroscopically confirmed H$\alpha$ and [OIII] emitters discovered by JELS that illustrate the novel parameter space probed.
Hierarchical Universal Value Function Approximators
2024-10-11
Rushiv Arora
There have been key advancements to building universal approximators for multi-goal collections of reinforcement learning value functions -- key elements in estimating long-term returns of states in a parameterized manner. We extend this to hierarchical reinforcement learning, using the options framework, by introducing hierarchical universal value function approximators (H-UVFAs). This allows us to leverage the added benefits of scaling, planning, and generalization expected in temporal abstraction settings. We develop supervised and reinforcement learning methods for learning embeddings of the states, goals, options, and actions in the two hierarchical value functions: $Q(s, g, o; \theta)$ and $Q(s, g, o, a; \theta)$. Finally we demonstrate generalization of the HUVFAs and show they outperform corresponding UVFAs.
Realizing Linear Synaptic Plasticity in Electric Double Layer-Gated Transistors for Improved Predictive Accuracy and Efficiency in Neuromorphic Computing
Neuromorphic computing offers a low-power, parallel alternative to traditional von Neumann architectures by addressing the sequential data processing bottlenecks. Electric double layer-gated transistors (EDLTs) resemble biological synapses with their ionic response and offer low power operations, making them suitable for neuromorphic applications. A critical consideration for artificial neural networks (ANNs) is achieving linear and symmetric plasticity (weight updates) during training, as this directly affects accuracy and efficiency. This study uses finite element modeling to explore EDLTs as artificial synapses in ANNs and investigates the underlying mechanisms behind the nonlinear plasticity observed experimentally in previous studies. By solving modified Poisson-Nernst-Planck (mPNP) equations, we examined ion dynamics within an EDL capacitor & their effects on plasticity, revealing that the rates of EDL formation and dissipation are concentration-dependent. Fixed-magnitude pulse inputs result in decreased formation & increased dissipation rates, leading to nonlinear weight updates and limits the number of accessible states and operating range of devices. To address this, we developed a predictive linear ionic weight update solver (LIWUS) in Python to predict voltage pulse inputs that achieve linear plasticity. We then evaluated an ANN with linear and nonlinear weight updates on the MNIST classification task. The LIWUS-provided linear weight updates required 19% fewer epochs in training and validation than the network with nonlinear weight updates to reach optimal performance. It achieved a 97.6% recognition accuracy, 1.5-4.2% higher than with nonlinear updates and a low standard deviation of 0.02%. The network model is amenable to future spiking neural network applications and the performance improvements with linear weight update is expected to increase for complex networks.
Mitigation of nonlinear galaxy bias with a theoretical-error likelihood
2024-10-11
Abdias Aires, Nickolas Kokron, Rogerio Rosenfeld, Felipe Andrade-Oliveira, Vivian Miranda
Stage-IV galaxy surveys will measure correlations at small cosmological scales with high signal-to-noise ratio. One of the main challenges of extracting information from small scales is devising accurate models, as well as characterizing the theoretical uncertainties associated with any given model. In this work, we explore the mitigation of theoretical uncertainty due to nonlinear galaxy bias in the context of photometric 2x2-point analyses. We consider linear galaxy bias as the fiducial model and derive the contribution to the covariance matrix induced by neglected higher-order bias. We construct a covariance matrix for the theoretical error in galaxy clustering and galaxy-galaxy lensing using simulation-based relations that connect higher-order parameters to linear bias. To test this mitigation model, we apply the modified likelihood to 2x2-point analyses based on two sets of mock data vectors: (1) simulated data vectors, constructed from those same relations between bias parameters, and (2) data vectors based on the AbacusSummit simulation suite. We then compare the performance of the theoretical-error approach to the commonly employed scale cuts methodology. We find most theoretical-error configurations yield results equivalent to the scale cuts in terms of precision and accuracy, in some cases producing significantly stronger bounds on cosmological parameters. These results are independent of the maximum scale k_max in the analysis with theoretical error. The scenarios where linear bias supplemented by theoretical error is unable to recover unbiased cosmology are connected to inadequate modeling of the gg-gk covariance of theoretical error. In view of its removing the ambiguity in the choice of k_max, as well as the possibility of attaining higher precision than the usual scale cuts, we consider this method to be promising for analyses of LSS in upcoming photometric galaxy surveys.
Wikimedia data for AI: a review of Wikimedia datasets for NLP tasks and AI-assisted editing
2024-10-11
Isaac Johnson, Lucie-Aimée Kaffee, Miriam Redi
Wikimedia content is used extensively by the AI community and within the language modeling community in particular. In this paper, we provide a review of the different ways in which Wikimedia data is curated to use in NLP tasks across pre-training, post-training, and model evaluations. We point to opportunities for greater use of Wikimedia content but also identify ways in which the language modeling community could better center the needs of Wikimedia editors. In particular, we call for incorporating additional sources of Wikimedia data, a greater focus on benchmarks for LLMs that encode Wikimedia principles, and greater multilingualism in Wikimedia-derived datasets.
Audio Description Generation in the Era of LLMs and VLMs: A Review of Transferable Generative AI Technologies
2024-10-11
Yingqiang Gao, Lukas Fischer, Alexa Lintner, Sarah Ebling
Audio descriptions (ADs) function as acoustic commentaries designed to assist blind persons and persons with visual impairments in accessing digital media content on television and in movies, among other settings. As an accessibility service typically provided by trained AD professionals, the generation of ADs demands significant human effort, making the process both time-consuming and costly. Recent advancements in natural language processing (NLP) and computer vision (CV), particularly in large language models (LLMs) and vision-language models (VLMs), have allowed for getting a step closer to automatic AD generation. This paper reviews the technologies pertinent to AD generation in the era of LLMs and VLMs: we discuss how state-of-the-art NLP and CV technologies can be applied to generate ADs and identify essential research directions for the future.
MeerKAT Observations of Herschel Protocluster Candidates
2024-10-11
Y. Ding, D. L. Clements, L. L. Leeuw, I. Heywood, H. Dannerbauer, A. Parmar, P. Legodi, R. J. Ivison, R. Blake, C. M. Gutiérrez, A. Carnero, W. Sutherland
High-redshift protoclusters consisting of dusty starbursts are thought to play an important role in galaxy evolution. Their dusty nature makes them bright in the FIR/submm but difficult to find in optical/NIR surveys. Radio observations are an excellent way to study these dusty starbursts, as dust is transparent in the radio and there is a tight correlation between the FIR and radio emission of a galaxy. Here, we present MeerKAT 1.28 GHz radio imaging of 3 Herschel candidate protoclusters, with a synthesised beam size of ~$7.5'' \times 6.6''$ and a central thermal noise down to $4.35~\mu$Jy/beam. Our source counts are consistent with other radio counts with no evidence of overdensities. Around 95% of the Herschel sources have 1.28 GHz IDs. Using the Herschel 250 micron primary beam size as the searching radius, we find 54.2% Herschel sources have multiple 1.28 GHz IDs. Our average FIR-radio correlation coefficient $q_{250\mu m}$ is $2.33\pm 0.26$. Adding $q_{250\mu m}$ as a new constraint, the probability of finding chance-aligned sources is reduced by a factor of ~6, but with the risk of discarding true identifications of radio-loud/quiet sources. With accurate MeerKAT positions, we cross-match our Herschel sources to optical/NIR data followed by photometric redshift estimations. By removing $z<1$ sources, the density contrasts of two of the candidate protoclusters increase, suggestive of them being real protoclusters at $z>1$. There is also potentially a $0.9
Block-based programming environments like Scratch are widely used in introductory programming courses. They facilitate learning pivotal programming concepts by eliminating syntactical errors, but logical errors that break the desired program behaviour are nevertheless possible. Finding such errors requires testing, i.e., running the program and checking its behaviour. In many programming environments, this step can be automated by providing executable tests as code; in Scratch, testing can only be done manually by invoking events through user input and observing the rendered stage. While this is arguably sufficient for learners, the lack of automated testing may be inhibitive for teachers wishing to provide feedback on their students' solutions. In order to address this issue, we introduce a new category of blocks in Scratch that enables the creation of automated tests. With these blocks, students and teachers alike can create tests and receive feedback directly within the Scratch environment using familiar block-based programming logic. To facilitate the creation and to enable batch processing sets of student solutions, we extend the Scratch user interface with an accompanying test interface. We evaluated this testing framework with 28 teachers who created tests for a popular Scratch game and subsequently used these tests to assess and provide feedback on student implementations. An overall accuracy of 0.93 of teachers' tests compared to manually evaluating the functionality of 21 student solutions demonstrates that teachers are able to create and effectively use tests. A subsequent survey confirms that teachers consider the block-based test approach useful.
Exploring the properties of the obscured hyperluminous quasar COS-87259 at z=6.853
2024-10-11
Charalambia Varnava, Andreas Efstathiou, Duncan Farrah
In this paper we explore the properties of the z=6.853 obscured hyperluminous quasar COS-87259, discovered in the Cosmological Evolution Survey (COSMOS) field, with our recently developed Bayesian spectral energy distribution (SED) fitting code SMART (Spectral energy distributions Markov chain Analysis with Radiative Transfer models). SMART fits SEDs exclusively with multicomponent radiative transfer models that constitute four different types of pre-computed libraries for the active galactic nucleus (AGN) torus, the starburst and the spheroidal or disc host. We explore two smooth radiative transfer models for the AGN torus and two two-phase models, in order to put constraints on the AGN fraction of the galaxy, the black hole mass and its star formation rate (SFR). We find that either of the smooth tapered disc or the two-phase flared disc models provide a good fit to the SED of COS-87259. The best-fitting models predict an AGN fraction in the range 86-92 per cent, a bolometric AGN luminosity of 5.8-10.3 x 10^13 Lo, a black hole mass of 1.8-3.2 x 10^9 Mo (assuming the quasar is accreting at the Eddington limit) and an SFR in the range 1985-2001 Mo/yr. The predicted space density of such objects in the redshift range 4-7 is about 20 times more than that of co-eval unobscured quasars.
In inverse problems, distribution-free uncertainty quantification (UQ) aims to obtain error bars with coverage guarantees that are independent of any prior assumptions about the data distribution. In the context of mass mapping, uncertainties could lead to errors that affects our understanding of the underlying mass distribution, or could propagate to cosmological parameter estimation, thereby impacting the precision and reliability of cosmological models. Current surveys, such as Euclid or Rubin, will provide new weak lensing datasets of very high quality. Accurately quantifying uncertainties in mass maps is therefore critical to perform reliable cosmological parameter inference. In this paper, we extend the conformalized quantile regression (CQR) algorithm, initially proposed for scalar regression, to inverse problems. We compare our approach with another distribution-free approach based on risk-controlling prediction sets (RCPS). Both methods are based on a calibration dataset, and offer finite-sample coverage guarantees that are independent of the data distribution. Furthermore, they are applicable to any mass mapping method, including blackbox predictors. In our experiments, we apply UQ on three mass-mapping method: the Kaiser-Squires inversion, iterative Wiener filtering, and the MCALens algorithm. Our experiments reveal that RCPS tends to produce overconservative confidence bounds with small calibration sets, whereas CQR is designed to avoid this issue. Although the expected miscoverage rate is guaranteed to stay below a user-prescribed threshold regardless of the mass mapping method, selecting an appropriate reconstruction algorithm remains crucial for obtaining accurate estimates, especially around peak-like structures, which are particularly important for inferring cosmological parameters. Additionally, the choice of mass mapping method influences the size of the error bars.
Retrieval-Augmented Generation (RAG) mitigates issues of the factual errors and hallucinated outputs generated by Large Language Models (LLMs) in open-domain question-answering tasks (OpenQA) via introducing external knowledge. For complex QA, however, existing RAG methods use LLMs to actively predict retrieval timing and directly use the retrieved information for generation, regardless of whether the retrieval timing accurately reflects the actual information needs, or sufficiently considers prior retrieved knowledge, which may result in insufficient information gathering and interaction, yielding low-quality answers. To address these, we propose a generic RAG approach called Adaptive Note-Enhanced RAG (Adaptive-Note) for complex QA tasks, which includes the iterative information collector, adaptive memory reviewer, and task-oriented generator, while following a new Retriever-and-Memory paradigm. Specifically, Adaptive-Note introduces an overarching view of knowledge growth, iteratively gathering new information in the form of notes and updating them into the existing optimal knowledge structure, enhancing high-quality knowledge interactions. In addition, we employ an adaptive, note-based stop-exploration strategy to decide "what to retrieve and when to stop" to encourage sufficient knowledge exploration. We conduct extensive experiments on five complex QA datasets, and the results demonstrate the superiority and effectiveness of our method and its components. The code and data are at https://github.com/thunlp/Adaptive-Note.
On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook
2024-10-11
Ana-Maria Bucur, Andreea-Codrina Moldovan, Krutika Parvatikar, Marcos Zampieri, Ashiqur R. KhudaBukhsh, Liviu P. Dinu
Computational approaches to predicting mental health conditions in social media have been substantially explored in the past years. Multiple surveys have been published on this topic, providing the community with comprehensive accounts of the research in this area. Among all mental health conditions, depression is the most widely studied due to its worldwide prevalence. The COVID-19 global pandemic, starting in early 2020, has had a great impact on mental health worldwide. Harsh measures employed by governments to slow the spread of the virus (e.g., lockdowns) and the subsequent economic downturn experienced in many countries have significantly impacted people's lives and mental health. Studies have shown a substantial increase of above 50% in the rate of depression in the population. In this context, we present a survey on natural language processing (NLP) approaches to modeling depression in social media, providing the reader with a post-COVID-19 outlook. This survey contributes to the understanding of the impacts of the pandemic on modeling depression in social media. We outline how state-of-the-art approaches and new datasets have been used in the context of the COVID-19 pandemic. Finally, we also discuss ethical issues in collecting and processing mental health data, considering fairness, accountability, and ethics.
Hydrodynamic interactions between a sedimenting squirmer and a planar wall
2024-10-11
Henry Shum, Devenayagam Palaniappan, Yuan-Nan Young
The hydrodynamic interactions between a sedimenting microswimmer and a solid wall have ubiquitous biological and technological applications. A plethora of gravity-induced swimming dynamics near a planar no-slip wall provides a platform for designing artificial microswimmers that can generate directed propulsion through their translation-rotation coupling near a wall. In this work we provide exact solutions for a squirmer (a model swimmer of spherical shape with a prescribed slip velocity) facing either towards or away from a planar wall perpendicular to gravity. These exact solutions are used to validate a numerical code based on the boundary integral method with an adaptive mesh for distances from the wall down to 0.1% of the squirmer radius. This boundary integral code is then used to investigate the rich gravity-induced dynamics near a wall, mapping out the detailed bifurcation structures of the swimming dynamics in terms of orientation and distance to the wall. Simulation results show that a squirmer may transverse along the wall, move to a fixed point at a given height with a fixed orientation in a monotonic way or in an oscillatory fashion, or oscillate in a limit cycle in the presence of wall repulsion.
JWST/NIRSpec spectroscopy of intermediate-mass quiescent galaxies at $z \sim 3-4$
2024-10-11
Riku A. Sato, Akio K. Inoue, Yuichi Harikane, Rhythm Shimakawa, Yuma Sugahara, Yoichi Tamura, Takuya Hashimoto, Kei Ito, Satoshi Yamanaka, Ken Mawatari, Yoshinobu Fudamoto, Yi W. Ren
We present the analysis of three intermediate-mass quiescent galaxies (QGs) with stellar masses of $\sim10^{10}M_{\rm \odot}$ at redshifts $z\sim 3 - 4$ using NIRSpec low-resolution spectroscopy. Utilising the SED fitting code BAGPIPES, we confirm these target galaxies are consistent with quiescent population, with their specific star formation rates (sSFR) falling below 2-dex the star-forming main sequence at the same redshifts. Additionally, we identify these QGs to be less massive than those discovered in previous works, particularly prior to the JWST era. Two of our target galaxies exhibit the potentially-blended H${\alpha}$+[NII] emission line within their spectra with $S/N>5$. We discuss whether this feature comes from an Active Galactic Nucleus (AGN) or star formation although future high-resolution spectroscopy is required to reach a conclusion. One of the target galaxies is covered by JWST/NIRCam imaging of the PRIMER survey. Using the 2D profile fitting code Galfit, we examine its morphology, revealing a disc-like profile with a S\'{e}rsic index of $n=1.1 \pm 0.1$. On the size-mass relation, we find a potential distinction between less-massive ($\log_{10}{(M_*/M_\odot)}<10.3$) and massive ($\log_{10}{(M_*/M_\odot)}>10.3$) QGs in their evolutionary pathways. The derived quenching timescales for our targets are less than 1 Gyr. This may result from these galaxies being quenched by AGN feedback, supporting the AGN scenario of the emission line features.
Room temperature ionic liquids show great promise as electrolytes in various technological applications, such as energy storage or electrotunable lubrication. These applications are particularly intriguing due to the specific behavior of ionic liquids in nanoconfinement. While previous research has been focused on optimizing the required characteristics through the selection of electrolyte properties, the contribution of confining material properties in these systems has been largely overlooked. In this Review, we provide constructive analysis of recent developments related to the description of surface material properties impact on the ionic liquid behavior in the confinement and propose potential ways for further investigations in this direction. Although the presented advances reveal the importance of surface material properties in the application processes with confined ionic liquids, there are still a lot of issues that should be thoroughly investigated in future. We believe that this review will significantly contribute to the development of new approaches with material properties consideration for confined ionic liquid research.
Particle fluctuations in systems with Bose-Einstein condensate
2024-10-11
V. I. Yukalov
Particle fluctuations in systems, exhibiting Bose-Einstein condensation, are reviewed in order to clarify the basic points that attract high interest and often confront misunderstanding. It is explained that the so-called ``grand canonical catastrophe", claiming the occurrence of catastrophic particle fluctuations in the condensed phase, treated by grand canonical ensemble, does not exist. What exists is the incorrect use of the grand canonical ensemble, where gauge symmetry is not broken, while the correct description of the condensed phase necessarily requires gauge symmetry breaking. The ideal Bose gas has no catastrophic condensate fluctuations, and moreover there are no condensate fluctuations at all, as soon as gauge symmetry is broken. However it does have anomalous fluctuations of uncondensed particles, which implies its instability. For interacting particles, there are no condensate fluctuations, as soon as gauge symmetry is broken, and anomalous fluctuations of uncondensed particles, when correctly calculated, do not appear. Particle fluctuations in the systems of trapped atoms are discussed. Canonical ensemble and grand canonical ensemble with broken gauge symmetry are equivalent with respect to the number of particle scaling.
2024-10-11
Ludovic Huguet, Quentin Kriaa, Thierry Alboussière, Michael Le Bars
The ubiquitous phenomena of crystallization and melting occur in various geophysical contexts across many spatial and temporal scales. In particular, they take place in the iron core of terrestrial planets and moons, profoundly influencing their dynamics and magnetic field generation. Crystallization and melting entail intricate multiphase flows, buoyancy effects, and out-of-equilibrium thermodynamics, posing challenges for theoretical modeling and numerical simulations. Besides, due to the inaccessible nature of the planetary deep interior, our understanding relies on indirect data from seismology, mineral physics, geochemistry, and magnetism. Consequently, phase-change-driven flows in planetary cores constitute a compelling yet challenging area of research. This paper provides an overview of the role of laboratory fluid dynamics experiments in elucidating the solid-liquid phase change phenomena occurring thousands of kilometers beneath our feet and within other planetary depths, along with their dynamic repercussions. Drawing parallel with metallurgy, it navigates through all scales of phase change dynamics, from microscopic processes (nucleation and crystal growth) to macroscopic consequences (solid-liquid segregation and large-scale flows). The review delves into the two primary planetary solidification regimes, top-down and bottom-up, and elucidates the formation of mushy and/or slurry layers in the various relevant configurations. Additionally, it outlines remaining challenges, including insights from ongoing space missions poised to unveil the diverse planetary regimes.
SpikeBottleNet: Energy Efficient Spike Neural Network Partitioning for Feature Compression in Device-Edge Co-Inference Systems
2024-10-11
Maruf Hassan, Steven Davy
The advent of intelligent mobile applications highlights the crucial demand for deploying powerful deep learning models on resource-constrained mobile devices. An effective solution in this context is the device-edge co-inference framework, which partitions a deep neural network between a mobile device and a nearby edge server. This approach requires balancing on-device computations and communication costs, often achieved through compressed intermediate feature transmission. Conventional deep neural network architectures require continuous data processing, leading to substantial energy consumption by edge devices. This motivates exploring binary, event-driven activations enabled by spiking neural networks (SNNs), known for their extremely energy efficiency. In this research, we propose a novel architecture named SpikeBottleNet, a significant improvement to the existing architecture by integrating SNNs. A key aspect of our investigation is the development of an intermediate feature compression technique specifically designed for SNNs. This technique leverages a split computing approach for SNNs to partition complex architectures, such as Spike ResNet50. By incorporating the power of SNNs within device-edge co-inference systems, experimental results demonstrate that our SpikeBottleNet achieves a significant bit compression ratio of up to 256x in the final convolutional layer while maintaining high classification accuracy with only a 2.5% reduction. Moreover, compared to the baseline BottleNet++ architecture, our framework reduces the transmitted feature size at earlier splitting points by 75%. Furthermore, in terms of the energy efficiency of edge devices, our methodology surpasses the baseline by a factor of up to 98, demonstrating significant enhancements in both efficiency and performance.
GPS Observables in Newtonian Spacetime or Why We Do Not Need 'Physical' Coordinate Systems
2024-10-11
Álvaro Mozota Frauca
Some authors have defended the claim that one needs to be able to define 'physical coordinate systems' and 'observables' in order to make sense of general relativity. Moreover, in Rovelli (Physical Review D, 65(4), 044017 2002), Rovelli proposes a way of implementing these ideas by making use of a system of satellites that allows defining a set of 'physical coordinates', the GPS coordinates. In this article I oppose these views in four ways. First, I defend an alternative way of understanding general relativity which implies that we have a perfectly fine interpretation of the models of the theory even in the absence of 'physical coordinate systems'. Second, I analyze and challenge the motivations behind the 'observable' view. Third, I analyze Rovelli's proposal and I conclude that it does not allow extracting any physical information from our models that wasn't available before. Fourth, I draw an analogy between general relativistic spacetimes and Newtonian spacetimes, which allows me to argue that as 'physical observables' are not needed in Newtonian spacetime, then neither are they in general relativity. In this sense, I conclude that the 'observable' view of general relativity is unmotivated.
Chiral, Topological, and Knotted Colloids in Liquid Crystals
2024-10-11
Ye Yuan, Ivan I. Smalyukh
The geometric shape, symmetry, and topology of colloidal particles often allow for controlling colloidal phase behavior and physical properties of these soft matter systems. In liquid crystalline dispersions, colloidal particles with low symmetry and nontrivial topology of surface confine-ment are of particular interest, including surfaces shaped as handlebodies, spirals, knots, mul-ti-component links, and so on. These types of colloidal surfaces induce topologically nontrivial three-dimensional director field configurations and topological defects. Director switching by electric fields, laser tweezing of defects, and local photo-thermal melting of the liquid crystal host medium promote transformations among many stable and metastable particle-induced director configurations that can be revealed by means of direct label-free three-dimensional nonlinear op-tical imaging. The interplay between topologies of colloidal surfaces, director fields, and defects is found to show a number of unexpected features, such as knotting and linking of line defects, often uniquely arising from the nonpolar nature of the nematic director field. This review article high-lights fascinating examples of new physical behavior arising from the interplay of nematic mo-lecular order and both chiral symmetry and topology of colloidal inclusions within the nematic host. Furthermore, the article concludes with a brief discussion of how these findings may lay the groundwork for new types of topology-dictated self-assembly in soft condensed matter leading to novel mesostructured composite materials, as well as for experimental insights into the pure-math aspects of low-dimensional topology.
In-ice Askaryan Emission from Air Showers: Implications for Radio Neutrino Detectors
2024-10-11
Alan Coleman, Christian Glaser, Ryan Rice-Smith, Steven Barwick, Dave Besson
One of the most promising techniques for detecting ultra-high energy neutrinos involves the use of radio antennas to observe the 10-1000 MHz radiation generated by the showers that neutrinos induce in large volumes of ice. The expected neutrino detection rates of one neutrino or less per detector station per 10 years make the characterization of backgrounds a priority. The largest natural background comes from ultra-high energy cosmic rays which are orders of magnitude more abundant than neutrinos. Particularly crucial is the understanding of geometries in which substantial energy of the cosmic-ray-induced air shower is deposited in the ice giving rise to a compact in-ice shower close to the ice surface. We calculated the radio emission of air-shower cores using the novel CORSIKA 8 code and found it to be similar to the predictions for neutrino-induced showers. For the first time, we calculated the detection rates for O(100m) deep antennas yielding 10-100 detections per year and detector station, which makes this a useful calibration source as these downward-going signals can be differentiated from neutrino-induced showers based on the signal arrival direction. However, the presence of reflection layers in the ice confuses the arrival directions, which makes this a potentially important background. We review the existing information on reflecting layers in the South Pole glacier and, for the first time, quantify the corresponding rate of reflected air-shower signals for the proposed IceCube-Gen2 radio array and discuss mitigation strategies. The reflectivity of the layers is the dominant uncertainty resulting in rate predictions of much less than one detection to several detections per year for IceCube-Gen2 if not mitigated.
Derivation of macroscopic epidemic models from multi-agent systems
2024-10-11
Mattia Zanella
We present a systematic review of some basic results on the derivation of classical epidemiological models from simple agent-based dynamics. The evolution of large populations is coupled with the dynamics of the contact distribution, providing insights into how individual behaviors impact macroscopic epidemiological trends. The resulting set of equations incorporates local characteristics of the operator governing the emergence of a family of contact distributions. To validate the proposed approach, we provide several numerical results based on asymptotic preserving methods, demonstrating their effectiveness in capturing the multi-scale nature of the problem and ensuring robust performance across different regimes.
MergePrint: Robust Fingerprinting against Merging Large Language Models
2024-10-11
Shojiro Yamabe, Tsubasa Takahashi, Futa Waseda, Koki Wataoka
As the cost of training large language models (LLMs) rises, protecting their intellectual property has become increasingly critical. Model merging, which integrates multiple expert models into a single model capable of performing multiple tasks, presents a growing risk of unauthorized and malicious usage. While fingerprinting techniques have been studied for asserting model ownership, existing methods have primarily focused on fine-tuning, leaving model merging underexplored. To address this gap, we propose a novel fingerprinting method MergePrint that embeds robust fingerprints designed to preserve ownership claims even after model merging. By optimizing against a pseudo-merged model, which simulates post-merged model weights, MergePrint generates fingerprints that remain detectable after merging. Additionally, we optimize the fingerprint inputs to minimize performance degradation, enabling verification through specific outputs from targeted inputs. This approach provides a practical fingerprinting strategy for asserting ownership in cases of misappropriation through model merging.
The cosmic distance duality relation in light of the time-delayed strong gravitational lensing
2024-10-11
Li Tang, Hai-Nan Lin, Ying Wu
The cosmic distance duality relation (DDR), which links the angular diameter distance and the luminosity distance, is a cornerstone in modern cosmology. Any deviation from DDR may indicate new physics beyond the standard cosmological model. In this paper, we use four high-precision time-delayed strong gravitational lensing (SGL) systems provided by the H0LiCOW to test the validity of DDR. To this end, we directly compare the angular diameter distances from these SGL systems and the luminosity distances from the latest Pantheon+ compilation of SNe Ia. In order to reduce the statistical errors arising from redshift matching, the Gaussian process method is applied to reconstruct the distance-redshift relation from the Pantheon+ dataset. We parameterize the possible violation of DDR in three different models. It is found that all results confirm the validity of DDR at 1$\sigma$ confidence level. Additionally, Monte Carlo simulations based on the future LSST survey indicate that the precision of DDR could reach $10^{-2}$ level with 100 SGL systems.
This work tackles the challenge of efficiently selecting high-performance pre-trained vision backbones for specific target tasks. Although exhaustive search within a finite set of backbones can solve this problem, it becomes impractical for large datasets and backbone pools. To address this, we introduce Vision Backbone Efficient Selection (VIBES), which aims to quickly find well-suited backbones, potentially trading off optimality for efficiency. We propose several simple yet effective heuristics to address VIBES and evaluate them across four diverse computer vision datasets. Our results show that these approaches can identify backbones that outperform those selected from generic benchmarks, even within a limited search budget of one hour on a single GPU. We reckon VIBES marks a paradigm shift from benchmarks to task-specific optimization.
Design of Secure, Privacy-focused, and Accessible E-Payment Applications for Older Adults
2024-10-11
Sanchari Das
E-payments are essential for transactional convenience in today's digital economy and are becoming increasingly important for older adults, emphasizing the need for enhanced security, privacy, and usability. To address this, we conducted a survey-based study with 400 older adults aged 65 and above to evaluate a high-fidelity prototype of an e-payment mobile application, which included features such as multi-factor authentication (MFA) and QR code-based recipient addition. Based on our findings, we developed a tailored \b{eta} version of the application to meet the specific needs of this demographic. Notably, approximately 91% of participants preferred traditional knowledge-based and single-mode authentication compared to expert-recommended MFA. We concluded by providing recommendations aimed at developing inclusive e-payment solutions that address the security, privacy, and usability requirements of older adults.
Decomposing Infrared Luminosity Functions into Star-Forming and AGN Components using CIGALE
2024-10-11
Daniel J. Lyon, Michael J. Cowley, Oliver Pye, Andrew M. Hopkins
This study presents a comprehensive analysis of the infrared (IR) luminosity functions (LF) of star-forming (SF) galaxies and active galactic nuclei (AGN) using data from the ZFOURGE survey. We employ CIGALE to decompose the spectral energy distribution (SED) of galaxies into SF and AGN components to investigate the co-evolution of these processes at higher redshifts and fainter luminosities. Our CIGALE-derived SF and AGN LFs are generally consistent with previous studies, with an enhancement at the faint end of the AGN LFs. We attribute this to CIGALE's capability to recover low-luminosity AGN more accurately, which may be underrepresented in other works. As anticipated, the CIGALE SF LFs are best fit with a Schechter function, whereas the AGN LFs align more closely with a Saunders function. We find evidence for a significant evolutionary epoch for AGN activity at $z \approx 1$, comparable to the peak of cosmic star formation at $z \approx 2$, which we also recover well. Based on our results, the gas supply in the early universe favoured the formation of brighter star-forming galaxies until $z=2$, below which the gas for SF becomes increasingly exhausted. Conversely, AGN activity peaked earlier and declined more slowly until $z \approx 1$, suggesting a possible feedback scenario in which $2.5-3$ Gyrs offset the evolution of SF and AGN activity.
Broad-line Active Galactic Nuclei in the 6dF Galaxy Survey
2024-10-11
Wei Jeat Hon, Rachel Webster, Christian Wolf
The Six-degree Field Galaxy Survey (6dFGS) is a spectroscopic redshift survey of the Southern hemisphere completed in 2006. While it provides 136,304 spectra of mostly low-redshift galaxies, a large and reliable catalogue of Active Galactic Nuclei (AGN) that are selected based on spectral signatures is still lacking. In this work, we present an extensive list of verified broad emission-line AGN in the 6dFGS sample. We visually confirm the AGN nature of all spectra, and disentangle fibre cross-talk to remove bogus AGN. The final catalogue contains 2,515 unique broad-line AGN with a median redshift of 0.207, of which 891 are identified for the first time. A flux-limited subsample contains 665 AGN to a K-band magnitude of 13. This new sample adds to the list of known low-luminosity AGN in the Southern hemisphere and thus provides a basis for investigations of low-redshift AGN with the upcoming Vera C. Rubin Observatory.
Compact [OIII] emission-line regions ("Green Seeds") in $\mathrm{Hα}$ emitters at Cosmic Noon from JWST Observations
2024-10-11
Nuo Chen, Kentaro Motohara, Lee Spitler, Matthew A. Malkan
We present a rest-frame optical, spatially resolved analysis of more than 100 $\mathrm{H\alpha}$ emitters (HAEs) at $z\sim2.2$ in the ZFOURGE-CDFS field using NIRCam imaging from the JWST Advanced Deep Extragalactic Survey (JADES). The ultra-deep, high-resolution data gives us maps of the resolved emission line regions of HAEs with stellar mass ranging from $10^{8}\,M_{\odot}$ to $10^{10}\,M_{\odot}$. An [OIII] emission-line map of each HAE is created from the flux excess in the F150W filter, leading to the discovery of a population of kiloparsec-scale compact emission line regions (``Green Seeds") with high equivalent widths ($\mathrm{EW}$). We obtain a sample of 128 Green Seeds from 68 HAEs with rest-frame $\mathrm{EW_{[OIII]}}>200\r{A}$. Moreover, 17 of them have extremely large $\mathrm{EW_{[OIII]}}>1000\r{A}$, suggesting the possible Lyman continuum (LyC) leakage from these emission line regions. Embedded within the host galaxy, many Green Seeds correspond to UV star-forming clumps and H{\sc ii} regions, indicating elevated starburst activity in them, with specific star formation rates (sSFR) several times higher than that of the host galaxy. Based on theoretical frameworks, Green Seeds are expected to be formed through gravitational disk instability and/or galaxy mergers. Considering the stellar masses of Green Seeds, we speculate that high-mass Green Seeds may migrate toward the galactic center to build the central bulge, while low-mass Green Seeds are easily disrupted and short-lived. Besides, we propose that some Green Seeds could be the progenitors of globular clusters or ultracompact dwarf galaxies observed in the local universe.
A Systematic Review of Edge Case Detection in Automated Driving: Methods, Challenges and Future Directions
2024-10-11
Saeed Rahmani, Sabine Rieder, Erwin de Gelder, Marcel Sonntag, Jorge Lorente Mallada, Sytze Kalisvaart, Vahid Hashemi, Simeon C. Calvert
The rapid development of automated vehicles (AVs) promises to revolutionize transportation by enhancing safety and efficiency. However, ensuring their reliability in diverse real-world conditions remains a significant challenge, particularly due to rare and unexpected situations known as edge cases. Although numerous approaches exist for detecting edge cases, there is a notable lack of a comprehensive survey that systematically reviews these techniques. This paper fills this gap by presenting a practical, hierarchical review and systematic classification of edge case detection and assessment methodologies. Our classification is structured on two levels: first, categorizing detection approaches according to AV modules, including perception-related and trajectory-related edge cases; and second, based on underlying methodologies and theories guiding these techniques. We extend this taxonomy by introducing a new class called "knowledge-driven" approaches, which is largely overlooked in the literature. Additionally, we review the techniques and metrics for the evaluation of edge case detection methods and identified edge cases. To our knowledge, this is the first survey to comprehensively cover edge case detection methods across all AV subsystems, discuss knowledge-driven edge cases, and explore evaluation techniques for detection methods. This structured and multi-faceted analysis aims to facilitate targeted research and modular testing of AVs. Moreover, by identifying the strengths and weaknesses of various approaches and discussing the challenges and future directions, this survey intends to assist AV developers, researchers, and policymakers in enhancing the safety and reliability of automated driving (AD) systems through effective edge case detection.
Deeper Insights into Deep Graph Convolutional Networks: Stability and Generalization
2024-10-11
Guangrui Yang, Ming Li, Han Feng, Xiaosheng Zhuang
Graph convolutional networks (GCNs) have emerged as powerful models for graph learning tasks, exhibiting promising performance in various domains. While their empirical success is evident, there is a growing need to understand their essential ability from a theoretical perspective. Existing theoretical research has primarily focused on the analysis of single-layer GCNs, while a comprehensive theoretical exploration of the stability and generalization of deep GCNs remains limited. In this paper, we bridge this gap by delving into the stability and generalization properties of deep GCNs, aiming to provide valuable insights by characterizing rigorously the associated upper bounds. Our theoretical results reveal that the stability and generalization of deep GCNs are influenced by certain key factors, such as the maximum absolute eigenvalue of the graph filter operators and the depth of the network. Our theoretical studies contribute to a deeper understanding of the stability and generalization properties of deep GCNs, potentially paving the way for developing more reliable and well-performing models.
A scientific review on advances in statistical methods for crossover design
2024-10-11
Salil Koner
A comprehensive review of the literature on crossover design is needed to highlight its evolution, applications, and methodological advancements across various fields. Given its widespread use in clinical trials and other research domains, understanding this design's challenges, assumptions, and innovations is essential for optimizing its implementation and ensuring accurate, unbiased results. This article extensively reviews the history and statistical inference methods for crossover designs. A primary focus is given to the AB-BA design as it is the most widely used design in literature. Extension from two periods to higher-order designs is discussed, and a general inference procedure for continuous response is studied. Analysis of multivariate and categorical responses is also reviewed in this context. A bunch of open problems in this area are shortlisted.
2024-10-11
Hugo Lefeuvre, Nathan Dautenhahn, David Chisnall, Pierre Olivier
Decomposing large systems into smaller components with limited privileges has long been recognized as an effective means to minimize the impact of exploits. Despite historical roots, demonstrated benefits, and a plethora of research efforts in academia and industry, the compartmentalization of software is still not a mainstream practice. This paper investigates why, and how this status quo can be improved. Noting that existing approaches are fraught with inconsistencies in terminology and analytical methods, we propose a unified model for the systematic analysis, comparison, and directing of compartmentalization approaches. We use this model to review 211 research efforts and analyze 61 mainstream compartmentalized systems, confronting them to understand the limitations of both research and production works. Among others, our findings reveal that mainstream efforts largely rely on manual methods, custom abstractions, and legacy mechanisms, poles apart from recent research. We conclude with recommendations: compartmentalization should be solved holistically; progress is needed towards simplifying the definition of compartmentalization policies; towards better challenging our threat models in the light of confused deputies and hardware limitations; as well as towards bridging the gaps we pinpoint between research and mainstream needs. This paper not only maps the historical and current landscape of compartmentalization, but also sets forth a framework to foster their evolution and adoption.
A Comprehensive Review: Impacts of Extreme Temperatures due to Climate Change on Power Grid Infrastructure and Operation
2024-10-11
Kishan Prudhvi Guddanti, Alok Kumar Bharati, Sameer Nekkalapu, Joseph McWheter, Scott Morris
The power grid is experiencing a multi-fold transformation while the global climate evolves with record-breaking extreme temperatures during heat domes, polar vortexes, and severe ice. Over the decades, these extreme temperature events have increased in frequency, duration, and intensity. The power grid infrastructure is geographically spread over thousands of square miles with millions of small and large components, and the impact of extreme temperature operations on the grid infrastructure needs to be researched further. This paper reviews academic literature, standards, industry articles, and federal reports to identify the impacts of heat domes, polar vortexes, and icing on all the T\&D grid equipment, including substations (assets owned and operated by the utilities and independent system operators). This paper classifies the equipment into primary and auxiliary equipment and determines its vulnerability to extreme temperatures for a deeper analysis of a more critical and vulnerable set of grid equipment. For each equipment under consideration, its fundamental role in the system, the impact of extreme temperatures on its operation, available monitoring, and mitigation of these impacts are discussed. The paper develops insights on standards readiness and identifies gaps concerning extreme temperature definitions. The paper also develops summary tables to identify the critical failure modes for each type of equipment, failure influence diagrams, and cascading influence diagrams to highlight and aid in translating the equipment vulnerability information into power grid contingency definitions that need to be considered in grid planning and operations.
The First High Frequency Technosignature Search Survey with the Sardinia Radio Telescope
2024-10-11
Lorenzo Manunza, Alice Vendrame, Luca Pizzuto, Monica Mulas, Karen I. Perez, Vishal Gajjar, Andrea Melis, Maura Pilia, Delphine Perrodin, Giambattista Aresu, Marta Burgay, Alessandro Cabras, Giuseppe Carboni, Silvia Casu, Tiziana Coiana, Alessandro Corongiu, Steve Croft, Elise Egron, Owen A. Johnson, Adelaide Ladu, Matt Lebofsky, Francesca Loi, David MacMahon, Emilio Molinari, Matteo Murgia, Alberto Pellizzoni, Tonino Pisanu, Antonio Poddighe, Erika Rea, Andrew Siemion, Paolo Soletta, Matteo Trudu, Valentina Vacca
The quest for radio signals from technologically-advanced extraterrestrial intelligence has traditionally concentrated on the vicinity of 1.4 GHz. In this paper, we extend the search to unprecedented territories, detailing our extensive observations at 6 GHz and initiating the first-ever survey at 18 GHz with the Sardinia Radio Telescope (SRT). Our strategy entailed rigorous observation sessions, totaling 36 hours, directed towards the Galactic Center and 72 selected TESS targets-making this the most comprehensive high-frequency technosignature search to date. Our narrowband signal search found no definitive evidence of drifting signals that could suggest an extraterrestrial origin from the surveyed regions. Nevertheless, our efforts have enabled us to set new constraints on the presence of radio emissions from approximately $5\times 10^{5}$ stars, establishing an isotropic radiated power limit of $1.8\times 10^{19} W$. We also provide a comparative analysis of the 'hits' recorded across both frequencies to highlight the significance of pursuing technosignature searches at higher frequencies, where the spectral landscape is less congested and more conducive to detection.
Learning from knockout reactions using a dispersive optical model
2024-10-11
M. C. Atkinson, W. H. Dickhoff
We present the empirical dispersive optical model (DOM) as applied to direct nuclear reactions. The DOM links both scattering and bound-state experimental data through a dispersion relation which allows for fully-consistent, data-informed predictions for nuclei where such data exists. In particular, we review investigations of the electron-induced proton knockout reaction from both $^{40}$Ca and $^{48}$Ca in a distorted-wave impulse approximation (DWIA) utilizing the DOM for a fully-consistent description. Viewing these reactions through the lens of the DOM allows us to connect the documented quenching of spectroscopic factors with increased high-momentum proton content in neutron-rich nuclei. A similar DOM-DWIA description of the proton-induced knockout from $^{40}$Ca, however, does not currently fit in the consistent story of its electron-induced counterpart. With the main difference in the proton-induced case being the use of an effective proton-proton interaction, we suggest that a more sophisticated in-medium interaction would lead to consistent results.
Unveiling the quasar main sequence: illuminating the complexity of active galactic nuclei and their evolution
2024-10-11
Swayamtrupta Panda
The Eigenvector 1 schema, or the main sequence of quasars, was introduced as an analogous scheme to the HR diagram that would allow us to understand the more complex, extended sources - active galactic nuclei (AGNs) that harbor accreting supermassive black holes. The study has spanned more than three decades and has advanced our knowledge of the diversity of Type-1 AGNs from both observational and theoretical aspects. The quasar main sequence, in its simplest form, is the plane between the FWHM of the broad H$\beta$ emission line and the strength of the optical FeII emission to the H$\beta$. While the former allows the estimation of the black hole mass, the latter enables direct measurement of the metal content and traces the accretion rate of the AGN. Together, they allow us to track the evolution of AGN in terms of the activity of the central nuclei, its effect on the line-emitting regions surrounding the AGN, and their diversity making them suitable distance indicators to study the expansion of our Universe. This mini-review aims to provide (i) a brief history leading up to the present day in the study of the quasar main sequence, (ii) introduce us to the many possibilities to study AGNs with the main sequence as a guiding tool, and (iii) highlight some recent, exciting lines of researches at the frontier of this ever-growing field.
Improving semantic understanding in speech language models via brain-tuning
2024-10-11
Omer Moussa, Dietrich Klakow, Mariya Toneva
Speech language models align with human brain responses to natural language to an impressive degree. However, current models rely heavily on low-level speech features, indicating they lack brain-relevant semantics which limits their utility as model organisms of semantic processing in the brain. In this work, we address this limitation by inducing brain-relevant bias directly into the models via fine-tuning with fMRI recordings of people listening to natural stories, a process we name brain-tuning. After testing it on 3 different pretrained model families, we show that brain-tuning not only improves overall alignment with new brain recordings in semantic language regions, but also reduces the reliance on low-level speech features for this alignment. Excitingly, we further show that brain-tuning leads to 1) consistent improvements in performance on a range of downstream tasks and 2) a representational space with increased semantic preference. Our results provide converging evidence, for the first time, that incorporating brain signals into the training of language models improves the models' semantic understanding.
Continual Learning with Neuromorphic Computing: Theories, Methods, and Applications
2024-10-11
Mishal Fatima Minhas, Rachmad Vidya Wicaksana Putra, Falah Awwad, Osman Hasan, Muhammad Shafique
To adapt to real-world dynamics, intelligent systems need to assimilate new knowledge without catastrophic forgetting, where learning new tasks leads to a degradation in performance on old tasks. To address this, continual learning concept is proposed for enabling autonomous systems to acquire new knowledge and dynamically adapt to changing environments. Specifically, energy-efficient continual learning is needed to ensure the functionality of autonomous systems under tight compute and memory resource budgets (i.e., so-called autonomous embedded systems). Neuromorphic computing, with brain-inspired Spiking Neural Networks (SNNs), offers inherent advantages for enabling low-power/energy continual learning in autonomous embedded systems. In this paper, we comprehensively discuss the foundations and methods for enabling continual learning in neural networks, then analyze the state-of-the-art works considering SNNs. Afterward, comparative analyses of existing methods are conducted while considering crucial design factors, such as network complexity, memory, latency, and power/energy efficiency. We also explore the practical applications that can benefit from SNN-based continual learning and open challenges in real-world scenarios. In this manner, our survey provides valuable insights into the recent advancements of SNN-based continual learning for real-world application use-cases.
2024-10-11
Noorbakhsh Amiri Golilarz, Elias Hossain, Abdoljalil Addeh, Keyan Alexander Rahimi
In this paper, we discuss learning algorithms and their importance in different types of applications which includes training to identify important patterns and features in a straightforward, easy-to-understand manner. We will review the main concepts of artificial intelligence (AI), machine learning (ML), deep learning (DL), and hybrid models. Some important subsets of Machine Learning algorithms such as supervised, unsupervised, and reinforcement learning are also discussed in this paper. These techniques can be used for some important tasks like prediction, classification, and segmentation. Convolutional Neural Networks (CNNs) are used for image and video processing and many more applications. We dive into the architecture of CNNs and how to integrate CNNs with ML algorithms to build hybrid models. This paper explores the vulnerability of learning algorithms to noise, leading to misclassification. We further discuss the integration of learning algorithms with Large Language Models (LLM) to generate coherent responses applicable to many domains such as healthcare, marketing, and finance by learning important patterns from large volumes of data. Furthermore, we discuss the next generation of learning algorithms and how we may have an unified Adaptive and Dynamic Network to perform important tasks. Overall, this article provides brief overview of learning algorithms, exploring their current state, applications and future direction.
Transition from adiabatic inspiral to plunge for eccentric binaries
2024-10-11
Devin R. Becker, Scott A. Hughes
Black hole binaries with small mass ratios will be critical targets for the forthcoming Laser Interferometer Space Antenna (LISA) mission. They also serve as useful tools for understanding the properties of binaries at general mass ratios. In its early stages, such a binary's gravitational-wave-driven inspiral can be modeled as the smaller body flowing through a sequence of geodesic orbits of the larger black hole's spacetime. Its motion through this sequence is determined by the rate at which backreaction changes an orbit's integrals of motion $E$, $L_z$, and $Q$. Key to the motion being close to a geodesic at any moment is the idea that the effect of backreaction is small compared to a ``restoring force'' arising from the potential which governs geodesic motion. This restoring force holds the small body on a geodesic trajectory as the backreaction causes that geodesic to slowly evolve. As the inspiraling body approaches the last stable orbit (LSO), the restoring force becomes weaker and the backreaction becomes stronger. Once the small body evolves past the LSO, its trajectory converges to a plunging geodesic. This work aims to smoothly connect these two disparate regimes: the slowly evolving adiabatic inspiral and the final plunge. Past work has focused on this transition to plunge for circular systems. Here, we study the transition for binaries with eccentricity. A well-defined eccentric transition will make it possible to develop small-mass-ratio binary waveform models that terminate in a physically reasonable way, rather than abruptly terminating as an inspiral-only model ends. A model that can explore the parameter space of eccentricity may also be useful for understanding the final cycles of eccentric binaries at less extreme mass ratios, such as those likely to be observed by ground-based detectors.
2024-10-11
Rodrigo de la Cruz, Gustavo Niz, Vid Iršič, Corentin Ravoux, César Ramírez-Pérez, Hiram K. Herrera-Alcantar
We present the first robust measurement of the one-dimensional Lyman alpha (Ly$\alpha$) forest bispectrum using the complete extended Baryon Oscillation Spectroscopic Survey (eBOSS) quasar sample, corresponding to the sixteenth data release (DR16) of the Sloan Digital Sky Survey (SDSS). The measurement employs an FFT estimator over 12 redshift bins, ranging from $z=2.2$ to $z=4.4$, and extends to scales of $0.02 ~ (\mathrm{km/s})^{-1}$. The sample consists of 122,066 quasar spectra, although only the first six redshift bins contain sufficient data to extract a physical bispectrum. To validate and correct the bispectrum measurement, we use synthetic datasets generated from lognormal and 2LPT mocks. Additionally, we detect clear evidence of correlations between Si$_\mathrm{III}$ absorption lines and the Ly$\alpha$ forest within the bispectrum signal, which we describe with an extension of the model used for the analogue of 1d power spectrum signal. In this context, the pipeline developed for this study addresses the impact of instrumental and methodological systematics and is ready for application to larger spectroscopic datasets, such as those from the first year of DESI observations. Finally, A simple perturbation theory model provides a reasonable explanation of the eBOSS bispectrum, suggesting that higher-order one-dimensional statistics in the Ly$\alpha$ forest can complement cosmological model inference based on the power spectrum in future analyses.
Exploring the interaction between the MW and LMC with a large sample of blue horizontal branch stars from the DESI survey
2024-10-11
Amanda Byström, Sergey E. Koposov, Sophia Lilleengen, Ting S. Li, Eric Bell, Leandro Beraldo e Silva, Andreia Carrillo, Vedant Chandra, Oleg Y. Gnedin, Jiwon Jesse Han, Gustavo E. Medina, Joan Najita, Alexander H. Riley, Guillaume Thomas, Monica Valluri, Jessica N. Aguilar, Steven Ahlen, Carlos Allende Prieto, David Brooks, Todd Claybaugh, Shaun Cole, Kyle Dawson, Axel de la Macorra, Andreu Font-Ribera, Jaime E. Forero-Romero, Enrique Gaztañaga, Satya Gontcho A Gontcho, Anthony Kremin, Andrew Lambert, Martin Landriau, Laurent Le Guillou, Michael E. Levi, Aaron Meisner, Ramon Miquel, John Moustakas, Francisco Prada, Ignasi Pérez-Ràfols, Graziano Rossi, Eusebio Sanchez, David Schlegel, Michael Schubnell, David Sprayberry, Gregory Tarlé, Benjamin A. Weaver, Hu Zou
The Large Magellanic Cloud (LMC) is a Milky Way (MW) satellite that is massive enough to gravitationally attract the MW disc and inner halo, causing significant motion of the inner MW with respect to the outer halo. In this work, we probe this interaction by constructing a sample of 9,866 blue horizontal branch (BHB) stars with radial velocities from the DESI spectroscopic survey out to 120 kpc from the Galactic centre. This is the largest spectroscopic set of BHB stars in the literature to date, and it contains four times more stars with Galactocentric distances beyond 50 kpc than previous BHB catalogues. Using the DESI BHB sample combined with SDSS BHBs, we measure the bulk radial velocity of stars in the outer halo and observe that the velocity in the Southern Galactic hemisphere is different by 3.7$\sigma$ from the North. Modelling the projected velocity field shows that its dipole component is directed at a point 22 degrees away from the LMC along its orbit, which we interpret as the travel direction of the inner MW. The velocity field includes a monopole term that is -24 km/s, which we refer to as compression velocity. This velocity is significantly larger than predicted by the current models of the MW and LMC interaction. This work uses DESI data from its first two years of observations, but we expect that with upcoming DESI data releases, the sample of BHB stars will increase and our ability to measure the MW-LMC interaction will improve significantly.
2024-10-11
Alexander H. Riley, Nora Shipp, Christine M. Simpson, Rebekka Bieri, Azadeh Fattahi, Shaun T. Brown, Kyle A. Oman, Francesca Fragkoudi, Facundo A. Gómez, Robert J. J. Grand, Federico Marinacci
In a hierarchically formed Universe, galaxies accrete smaller systems that tidally disrupt as they evolve in the host's potential. We present a complete catalogue of disrupting galaxies accreted onto Milky Way-mass haloes from the Auriga suite of cosmological magnetohydrodynamic zoom-in simulations. We classify accretion events as intact satellites, stellar streams, or phase-mixed systems based on automated criteria calibrated to a visually classified sample, and match accretions to their counterparts in haloes re-simulated at higher resolution. Most satellites with a bound progenitor at the present day have lost substantial amounts of stellar mass -- 67 per cent have $f_\text{bound} < 0.97$ (our threshold to no longer be considered intact), while 53 per cent satisfy a more stringent $f_\text{bound} < 0.8$. Streams typically outnumber intact systems, contribute a smaller fraction of overall accreted stars, and are substantial contributors at intermediate distances from the host centre ($\sim$0.1 to $\sim$0.7$R_\text{200m}$, or $\sim$35 to $\sim$250 kpc for the Milky Way). We also identify accretion events that disrupt to form streams around massive intact satellites instead of the main host. Streams are more likely than intact or phase-mixed systems to have experienced preprocessing, suggesting this mechanism is important for setting disruption rates around Milky Way-mass haloes. All of these results are preserved across different simulation resolutions, though we do find some hints that satellites disrupt more readily at lower resolution. The Auriga haloes suggest that disrupting satellites surrounding Milky Way-mass galaxies are the norm and that a wealth of tidal features waits to be uncovered in upcoming surveys.
A Candidate High-Velocity Exoplanet System in the Galactic Bulge
2024-10-11
Sean K. Terry, Jean-Philippe Beaulieu, David P. Bennett, Aparna Bhattacharya, Jon Hulberg, Macy J. Huston, Naoki Koshimoto, Joshua W. Blackman, Ian A. Bond, Andrew A. Cole, Jessica R. Lu, Clément Ranc, Natalia E. Rektsini, Aikaterini Vandorou
We present an analysis of adaptive optics (AO) images from the Keck-I telescope of the microlensing event MOA-2011-BLG-262. The original discovery paper by Bennett et al. 2014 reports two distinct possibilities for the lens system; a nearby gas giant lens with an exomoon companion or a very low mass star with a planetary companion in the galactic bulge. The $\sim$10 year baseline between the microlensing event and the Keck follow-up observations allows us to detect the faint candidate lens host (star) at $K = 22.3$ mag and confirm the distant lens system interpretation. The combination of the host star brightness and light curve parameters yields host star and planet masses of $M_{\rm host} = 0.19 \pm 0.03M_{\odot}$ and $m_p = 28.92 \pm 4.75M_{\oplus}$ at a distance of $D_L = 7.49 \pm 0.91\,$kpc. We perform a multi-epoch cross reference to \textit{Gaia} DR3 and measure a transverse velocity for the candidate lens system of $v_L = 541.31 \pm 65.75$ km s$^{-1}$. We conclude this event consists of the highest velocity exoplanet system detected to date, and also the lowest mass microlensing host star with a confirmed mass measurement. The high-velocity nature of the lens system can be definitively confirmed with an additional epoch of high-resolution imaging at any time now. The methods outlined in this work demonstrate that the \textit{Roman} Galactic Exoplanet Survey (RGES) will be able to securely measure low-mass host stars in the bulge.
Auriga Streams II: orbital properties of tidally disrupting satellites of Milky Way-mass galaxies
2024-10-11
Nora Shipp, Alexander H. Riley, Christine M. Simpson, Rebekka Bieri, Lina Necib, Arpit Arora, Francesca Fragkoudi, Facundo A. Gómez, Robert J. J. Grand, Federico Marinacci
Galaxies like the Milky Way are surrounded by complex populations of satellites at all stages of tidal disruption. In this paper, we present a dynamical study of the disrupting satellite galaxies in the Auriga simulations that are orbiting 28 distinct Milky Way-mass hosts across three resolutions. We find that the satellite galaxy populations are highly disrupted. The majority of satellites that remain fully intact at present day were accreted recently without experiencing more than one pericentre ($n_{\rm peri} \lesssim 1$) and have large apocentres ($r_{\rm apo} \gtrsim 200$ kpc) and pericentres ($r_{\rm peri} \gtrsim 50$ kpc). The remaining satellites have experienced significant tidal disruption and, given full knowledge of the system, would be classified as stellar streams. We find stellar streams in Auriga across the range of pericentres and apocentres of the known Milky Way dwarf galaxy streams and, interestingly, overlapping significantly with the Milky Way intact satellite population. We find no significant change in satellite orbital distributions across resolution. However, we do see substantial halo-to-halo variance of $(r_\text{peri}, r_\text{apo})$ distributions across host galaxies, as well as a dependence of satellite orbits on host halo mass - systems disrupt at larger pericentres and apocentres in more massive hosts. Our results suggest that either cosmological simulations (including, but not limited to, Auriga) are disrupting satellites far too readily, or that the Milky Way's satellites are more disrupted than current imaging surveys have revealed. Future observing facilities and careful mock observations of these systems will be key to revealing the nature of this apparent discrepancy.
Interactive segmentation has an important role in facilitating the annotation process of future LiDAR datasets. Existing approaches sequentially segment individual objects at each LiDAR scan, repeating the process throughout the entire sequence, which is redundant and ineffective. In this work, we propose interactive 4D segmentation, a new paradigm that allows segmenting multiple objects on multiple LiDAR scans simultaneously, and Interactive4D, the first interactive 4D segmentation model that segments multiple objects on superimposed consecutive LiDAR scans in a single iteration by utilizing the sequential nature of LiDAR data. While performing interactive segmentation, our model leverages the entire space-time volume, leading to more efficient segmentation. Operating on the 4D volume, it directly provides consistent instance IDs over time and also simplifies tracking annotations. Moreover, we show that click simulations are crucial for successful model training on LiDAR point clouds. To this end, we design a click simulation strategy that is better suited for the characteristics of LiDAR data. To demonstrate its accuracy and effectiveness, we evaluate Interactive4D on multiple LiDAR datasets, where Interactive4D achieves a new state-of-the-art by a large margin. Upon acceptance, we will publicly release the code and models at https://vision.rwth-aachen.de/Interactive4D.
Using Lagrangian descriptors to calculate the Maslov index of periodic orbits
2024-10-10
J. Montes, F. J. Arranz, F. Borondo
The Maslov index of a periodic orbit is an important piece in the semiclassical quantization of non-integrable systems, while almost all existing techniques that lead to a rigorous calculation of this index are elaborate and mathematically demanding. In this paper, we describe a straightforward technique, for systems with two degrees of freedom, based on the Lagrangian descriptors. Our method is illustrated by applying it to the two-dimensional coupled quartic oscillator.
DESI forecast for Dark Matter-Neutrino interactions using EFTofLSS
2024-10-10
Markus R. Mosbech, Santiago Casas, Julien Lesgourgues, Dennis Linde, Azadeh Moradinezhad Dizgah, Christian Radermacher, Jannik Truong
We apply the Effective Field Theory of Large Scale Structure (EFTofLSS) to non-standard models of dark matter with suppressed small-scale structure imprinted by early-time physics, here exemplified by interacting dark matter (IDM) coupled to standard model neutrinos, and cross-check that the EFTofLSS has no trouble replicating the real-space halo-halo power spectrum from N-body simulations. We perform forecasts for a DESI ELG-like experiment using the redshift-space power spectrum and find that, under very conservative priors on these parameters, the EFTofLSS is not expected to yield strong constraints on dark matter interactions. However, with a better understanding of the evolution of counterterms and stochastic terms with redshift, realistic IDM models could in principle be detected using the full-shape power spectrum analysis of such a spectroscopic galaxy survey.
A Review on Hydrogen Production Technologies and Its Future Demand
2024-10-10
Mohammad Abubakr, Suhaib Shahid, Iram Arman
Hydrogen production is a vital process in the quest for decarbonization and a sustainable future. This conversation explores the various technologies used in hydrogen production, such as steam methane reforming, electrolysis, and biomass gasification. These methods have different applications and efficiencies, but all contribute to the production of hydrogen gas. The future of hydrogen production looks promising, as it offers a clean and versatile energy source that can be used in various sectors, including transportation, industry, and power generation. The increasing demand for hydrogen in these sectors, coupled with the global push for decarbonization, highlights the importance of advancing hydrogen production technologies and infrastructure. This paper focuses broadly on different methods of hydrogen production like steam methane reforming, electrolysis, and biomass gasification. Steam methane reforming is currently the most common method, accounting for about 95% of global hydrogen production. Electrolysis is another method that uses electricity to split water into hydrogen and oxygen. Biomass gasification involves converting organic materials into hydrogen gas through a thermochemical process. The renewable energy sources considered are water and biomass and the methods considered are Electrolysis (grid), thermolysis, thermochemical water splitting, photoelectrochemical water splitting, and gasification. Electrolysis of water to produce hydrogen accounts for about 5% of the total hydrogen production. Statistically, global hydrogen production reached about USD 155.35 billion in 2022, with the majority of it being used in the petroleum refining and ammonia production industries. As for applications, hydrogen can be used as a fuel for fuel cell vehicles, as a feedstock for chemical processes, and as a storage medium for renewable energy.
BOWIE-ALIGN: JWST reveals hints of planetesimal accretion and complex sulphur chemistry in the atmosphere of the misaligned hot Jupiter WASP-15b
2024-10-10
James Kirk, Eva-Maria Ahrer, Alastair B. Claringbold, Maria Zamyatina, Chloe Fisher, Mason McCormack, Vatsal Panwar, Diana Powell, Jake Taylor, Daniel P. Thorngren, Duncan A. Christie, Emma Esparza-Borges, Shang-Min Tsai, Lili Alderson, Richard A. Booth, Charlotte Fairman, Mercedes López-Morales, N. J. Mayne, Annabella Meech, Paul Molliere, James E. Owen, Anna B. T. Penzlin, Denis E. Sergeev, Daniel Valentine, Hannah R. Wakeford, Peter J. Wheatley
We present a transmission spectrum of the misaligned hot Jupiter WASP-15b from 2.8--5.2 microns observed with JWST's NIRSpec/G395H grating. Our high signal to noise data, which has negligible red noise, reveals significant absorption by H$_2$O ($4.2\sigma$) and CO$_2$ ($8.9\sigma$). From independent data reduction and atmospheric retrieval approaches, we infer that WASP-15b's atmospheric metallicity is super-solar ($\gtrsim 15\times$ solar) and its C/O is consistent with solar, that together imply planetesimal accretion. Our GCM simulations for WASP-15b suggest that the C/O we measure at the limb is likely representative of the entire photosphere due to the mostly uniform spatial distribution of H$_2$O, CO$_2$ and CO. We additionally see evidence for absorption by SO$_2$ and absorption at 4.9$\mu$m, for which the current leading candidate is OCS, albeit with several caveats. If confirmed, this would be the first detection of OCS in an exoplanet atmosphere and point towards complex photochemistry of sulphur-bearing species in the upper atmosphere. These are the first observations from the BOWIE-ALIGN survey which is using JWST's NIRSpec/G395H instrument to compare the atmospheric compositions of aligned/low-obliquity and misaligned/high-obliquity hot Jupiters around F stars above the Kraft break. The goal of our survey is to determine whether the atmospheric composition differs across two populations of planets that have likely undergone different migration histories (disc versus disc-free) as evidenced by their obliquities (aligned versus misaligned).
Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving, yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods. We present Optima, a novel framework that addresses these issues by significantly enhancing both communication efficiency and task effectiveness in LLM-based MAS through LLM training. Optima employs an iterative generate, rank, select, and train paradigm with a reward function balancing task performance, token efficiency, and communication readability. We explore various RL algorithms, including Supervised Fine-Tuning, Direct Preference Optimization, and their hybrid approaches, providing insights into their effectiveness-efficiency trade-offs. We integrate Monte Carlo Tree Search-inspired techniques for DPO data generation, treating conversation turns as tree nodes to explore diverse interaction paths. Evaluated on common multi-agent tasks, including information-asymmetric question answering and complex reasoning, Optima shows consistent and substantial improvements over single-agent baselines and vanilla MAS based on Llama 3 8B, achieving up to 2.8x performance gain with less than 10\% tokens on tasks requiring heavy information exchange. Moreover, Optima's efficiency gains open new possibilities for leveraging inference-compute more effectively, leading to improved inference-time scaling laws. By addressing fundamental challenges in LLM-based MAS, Optima shows the potential towards scalable, efficient, and effective MAS (https://chenweize1998.github.io/optima-project-page).
Extending the Asteroseismic Calibration of the Stellar Rossby Number
2024-10-10
Travis S. Metcalfe, Enrico Corsaro, Alfio Bonanno, Orlagh L. Creevey, Jennifer L. van Saders
The stellar Rossby number (Ro) is a dimensionless quantity that is used in the description of fluid flows. It characterizes the relative importance of Coriolis forces on convective motions, which is central to understanding magnetic stellar evolution. Here we present an expanded sample of Kepler asteroseismic targets to help calibrate the relation between Ro and Gaia color, and we extend the relation to redder colors using observations of the mean activity levels and rotation periods for a sample of brighter stars from the Mount Wilson survey. Our quadratic fit to the combined sample is nearly linear between 0.55 < G_BP-G_RP < 1.2, and can be used to estimate Ro for stars with spectral types between F5 and K3. The strong deviation from linearity in the original calibration may reflect an observational bias against the detection of solar-like oscillations at higher activity levels for the coolest stars.
High-redshift LBG selection from broadband and wide photometric surveys using a Random Forest algorithm
2024-10-10
C. Payerne, W. d'Assignies Doumerg, C. Yèche, V. Ruhlmann-Kleider, A. Raichoor, D. Lang, J. N. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, S. Cole, A. de la Macorra, B. Dey, P. Doel, A. Font-Ribera, J. E. Forero-Romero, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, S. Juneau, A. Lambert, M. Landriau, L. Le Guillou, M. E. Levi, C. Magneville, M. Manera, A. Meisner, R. Miquel, J. Moustakas, J. A. Newman, N. Palanque-Delabrouille, W. Percival, F. Prada, I. Pérez-Ràfols, G. Rossi, E. Sanchez, D. Schlegel, M. Schubnell, D. Sprayberry, G. Tarlé, B. A. Weaver, H. Zou
In this paper, we investigate the possibility of selecting high-redshift Lyman-Break Galaxies (LBG) using current and future broadband wide photometric surveys, such as UNIONS or the Vera C. Rubin LSST, using a Random Forest algorithm. This work is conducted in the context of future large-scale structure spectroscopic surveys like DESI-II, the next phase of the Dark Energy Spectroscopic Instrument (DESI), which will start around 2029. We use deep imaging data from HSC and CLAUDS on the COSMOS and XMM-LSS fields. To predict the selection performance of LBGs with image quality similar to UNIONS, we degrade the $u, g, r, i$ and $z$ bands to UNIONS depth. The Random Forest algorithm is trained with the $u,g,r,i$ and $z$ bands to classify LBGs in the $2.5 < z < 3.5$ range. We find that fixing a target density budget of $1,100$ deg$^{-2}$, the Random Forest approach gives a density of $z>2$ targets of $873$ deg$^{-2}$, and a density of $493$ deg$^{-2}$ of confirmed LBGs after spectroscopic confirmation with DESI. This UNIONS-like selection was tested in a dedicated spectroscopic observation campaign of 1,000 targets with DESI on the COSMOS field, providing a safe spectroscopic sample with a mean redshift of 3. This sample is used to derive forecasts for DESI-II, assuming a sky coverage of 5,000 deg$^2$. We predict uncertainties on Alcock-Paczynski parameters $\alpha_\perp$ and $\alpha_{\parallel}$ to be 0.7$\%$ and 1$\%$ for $2.6
Timely NextG Communications with Decoy Assistance against Deep Learning-based Jamming
2024-10-10
Maice Costa, Yalin E. Sagduyu
We consider the transfer of time-sensitive information in next-generation (NextG) communication systems in the presence of a deep learning based eavesdropper capable of jamming detected transmissions, subject to an average power budget. A decoy-based anti-jamming strategy is presented to confuse a jammer, causing it to waste power when disrupting decoy messages instead of real messages. We investigate the effectiveness of the anti-jamming strategy to guarantee timeliness of NextG communications in addition to reliability objectives, analyzing the Age of Information subject to jamming and channel effects. We assess the effect of power control, which determines the success of a transmission but also affects the accuracy of the adversary's detection, making it more likely for the jammer to successfully identify and jam the communication. The results demonstrate the feasibility of mitigating eavesdropping and jamming attacks in NextG communications with information freshness objectives using a decoy to guarantee timely information transfer.
Star formation rate density as a function of galaxy mass at z < 0.2 with MUSE and GAMA surveys
2024-10-10
G. G. Murrell, I. K. Baldry
The star formation rate density (SFRD) is an important tool in galaxy evolution that allows us to identify at which cosmic time galaxies are more efficient at forming stars. For low-mass star-forming galaxies, the SFRD as a function of stellar mass can be straightforwardly related to the galaxy stellar mass function (GSMF). Given the uncertainty of the GSMF at the low-mass end, due to the challenges in observing dwarf galaxies, deriving the SFRD with respect to mass may be crucial to understand galaxy formation. Measurement of SFRD is more complete than number density in a cosmological volume because galaxies with higher SFR are easier to detect and characterize. In this work, the SFRD is derived using two different samples, one using the MUSE WIDE and MUSE Hubble Ultra Deep Field IFU spectroscopic surveys, and another using the GAMA spectroscopic survey. The first sample comprised a total of 27 star-forming galaxies at z $<$ 0.2 (H$\alpha$ selected), whereas the second contained 7579 galaxies at z $<$ 0.06 ($r$-band selected). The star formation rates are derived from measurements of the H$\alpha$ emission line fluxes for the first sample, and using MagPhys SED fitting for the second one. The results show the behaviour of the SFRD to the lowest stellar masses of 10$^{5.5}$ $M_{\odot}$, consistent with a constant slope (in log SFRD versus log stellar mass) and thus no turn-over in the GSMF. The results also suggest that the volume considered for the MUSE WIDE and HUDF sample is underdense.
2024-10-10
John Moore, Bruce Gendre, David Coward, Fiona Panther, Eloise Moore
The Zadko Observatory located approximately 70 kilometres north of Perth in the Yeal nature reserve within the Shire of Gingin, Western Australia, initially housed the 1.0 metre f/4 fast-slew Zadko Telescope which was commissioned in June 2008. Since the Zadko telescope has been in operation it has proven its worth by detecting numerous Gamma Ray Burst afterglows, two of these being the most distant 'optical transients' imaged by an Australian telescope. The Zadko telescope also contributed to the discovery of colliding neutron stars in 2017 capturing the imagination of the public. Another important use for the Zadko Telescope is the tracking and mapping of Space Debris which consist of all man-made objects, including their fragments or parts, other than active space vehicles larger than 10 microns and orbiting the Earth in outer space. The Zadko telescope forms part of the ARC Centre of Excellence for Gravitational Wave Discovery (OzGrav). With a view to supporting ongoing scientific instrument upgrades and observatory maintenance it has proven critical to attract additional funding and seek out collaboration with international partners. In this article, we will focus on the administrative and technical details of the Observatory, focusing on the sustainability of the Observatory and its ongoing potential for future growth into an internationally recognized Space Surveillance Hub. We will review the evolution of the Observatory, from its early, single instrument state, to its current multi-telescope and multi-instrument capabilities. We will finish by outlining the future of the Observatory and surrounds.
Smoothed pseudo-population bootstrap methods with applications to finite population quantiles
2024-10-10
Vanessa McNealis, Christian Léger
This paper introduces smoothed pseudo-population bootstrap methods for the purposes of variance estimation and the construction of confidence intervals for finite population quantiles. In an i.i.d. context, it has been shown that resampling from a smoothed estimate of the distribution function instead of the usual empirical distribution function can improve the convergence rate of the bootstrap variance estimator of a sample quantile. We extend the smoothed bootstrap to the survey sampling framework by implementing it in pseudo-population bootstrap methods for high entropy, single-stage survey designs, such as simple random sampling without replacement and Poisson sampling. Given a kernel function and a bandwidth, it consists of smoothing the pseudo-population from which bootstrap samples are drawn using the original sampling design. Given that the implementation of the proposed algorithms requires the specification of the bandwidth, we develop a plug-in selection method along with a grid search selection method based on a bootstrap estimate of the mean squared error. Simulation results suggest a gain in efficiency associated with the smoothed approach as compared to the standard pseudo-population bootstrap for estimating the variance of a quantile estimator together with mixed results regarding confidence interval coverage.
Neuroplastic Expansion in Deep Reinforcement Learning
2024-10-10
Jiashun Liu, Johan Obando-Ceron, Aaron Courville, Ling Pan
The loss of plasticity in learning agents, analogous to the solidification of neural pathways in biological brains, significantly impedes learning and adaptation in reinforcement learning due to its non-stationary nature. To address this fundamental challenge, we propose a novel approach, Neuroplastic Expansion (NE), inspired by cortical expansion in cognitive science. NE maintains learnability and adaptability throughout the entire training process by dynamically growing the network from a smaller initial size to its full dimension. Our method is designed with three key components: (1) elastic neuron generation based on potential gradients, (2) dormant neuron pruning to optimize network expressivity, and (3) neuron consolidation via experience review to strike a balance in the plasticity-stability dilemma. Extensive experiments demonstrate that NE effectively mitigates plasticity loss and outperforms state-of-the-art methods across various tasks in MuJoCo and DeepMind Control Suite environments. NE enables more adaptive learning in complex, dynamic environments, which represents a crucial step towards transitioning deep reinforcement learning from static, one-time training paradigms to more flexible, continually adapting models.
COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act
2024-10-10
Philipp Guldimann, Alexander Spiridonov, Robin Staab, Nikola Jovanović, Mark Vero, Velko Vechev, Anna Gueorguieva, Mislav Balunović, Nikola Konstantinov, Pavol Bielik, Petar Tsankov, Martin Vechev
The EU's Artificial Intelligence Act (AI Act) is a significant step towards responsible AI development, but lacks clear technical interpretation, making it difficult to assess models' compliance. This work presents COMPL-AI, a comprehensive framework consisting of (i) the first technical interpretation of the EU AI Act, translating its broad regulatory requirements into measurable technical requirements, with the focus on large language models (LLMs), and (ii) an open-source Act-centered benchmarking suite, based on thorough surveying and implementation of state-of-the-art LLM benchmarks. By evaluating 12 prominent LLMs in the context of COMPL-AI, we reveal shortcomings in existing models and benchmarks, particularly in areas like robustness, safety, diversity, and fairness. This work highlights the need for a shift in focus towards these aspects, encouraging balanced development of LLMs and more comprehensive regulation-aligned benchmarks. Simultaneously, COMPL-AI for the first time demonstrates the possibilities and difficulties of bringing the Act's obligations to a more concrete, technical level. As such, our work can serve as a useful first step towards having actionable recommendations for model providers, and contributes to ongoing efforts of the EU to enable application of the Act, such as the drafting of the GPAI Code of Practice.
Constraints on compact objects from the Dark Energy Survey five-year supernova sample
2024-10-10
Paul Shah, Tamara M. Davis, Maria Vincenzi, Patrick Armstrong, Dillon Brout, Ryan Camilleri, Lluis Galbany, Juan Garcia-Bellido, Mandeep S. S. Gill, Ofer Lahav, Jason Lee, Chris Lidman, Anais Moeller, Masao Sako, Bruno O. Sanchez, Mark Sullivan, Lorne Whiteway, Phillip Wiseman, S. Allam, M. Aguena, S. Bocquet, D. Brooks, D. L. Burke, A. Carnero Rosell, L. N. da Costa, M. E. S. Pereira, S. Desai, S. Dodelson, P. Doel, I. Ferrero, B. Flaugher, J. Frieman, E. Gaztanaga, D. Gruen, R. A. Gruendl, G. Gutierrez, K. Herner, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, K. Kuehn, S. Lee, J. L. Marshall, J. Mena-Fernandez, R. Miquel, J. Myles, A. Palmese, A. Pieres, A. A. Plazas Malagon, A. Roodman, S. Samuroff, E. Sanchez, I. Sevilla-Noarbe, M. Smith, E. Suchyta, M. E. C. Swanson, G. Tarle, C. To, V. Vikram
Gravitational lensing magnification of Type Ia supernovae (SNe Ia) allows information to be obtained about the distribution of matter on small scales. In this paper, we derive limits on the fraction $\alpha$ of the total matter density in compact objects (which comprise stars, stellar remnants, small stellar groupings and primordial black holes) of mass $M > 0.03 M_{\odot}$ over cosmological distances. Using 1,532 SNe Ia from the Dark Energy Survey Year 5 sample (DES-SN5YR) combined with a Bayesian prior for the absolute magnitude $M$, we obtain $\alpha < 0.12$ at the 95\% confidence level after marginalisation over cosmological parameters, lensing due to large-scale structure, and intrinsic non-Gaussianity. Similar results are obtained using priors from the cosmic microwave background, baryon acoustic oscillations and galaxy weak lensing, indicating our results do not depend on the background cosmology. We argue our constraints are likely to be conservative (in the sense of the values we quote being higher than the truth), but discuss scenarios in which they could be weakened by systematics of the order of $\Delta \alpha \sim 0.04$
2024-10-10
Gert Heckman, Tom Koornwinder, Eric Opdam
This is a slightly edited translation of a paper in Dutch which appeared in Nieuw Archief voor Wiskunde (5) 25 (2024), No.2, 87-90 on the occasion of I.G. Macdonald's death in 2023, and aimed at a very broad mathematical audience. First we review some of Macdonald's most important older results. Then we focus on the period 1985-1995 when Macdonald often visited the Netherlands and there was much interaction between his work, notably the Macdonald polynomials, and the work by the authors. We end with some glimpses about Macdonald as a person.
A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning
2024-10-10
Jingbo Zhang, Qiong Wu, Pingyi Fan, Qiang Fan
Federated Edge Learning (FEL), an emerging distributed Machine Learning (ML) paradigm, enables model training in a distributed environment while ensuring user privacy by using physical separation for each user data. However, with the development of complex application scenarios such as the Internet of Things (IoT) and Smart Earth, the conventional resource allocation schemes can no longer effectively support these growing computational and communication demands. Therefore, joint resource optimization may be the key solution to the scaling problem. This paper simultaneously addresses the multifaceted challenges of computation and communication, with the growing multiple resource demands. We systematically review the joint allocation strategies for different resources (computation, data, communication, and network topology) in FEL, and summarize the advantages in improving system efficiency, reducing latency, enhancing resource utilization and enhancing robustness. In addition, we present the potential ability of joint optimization to enhance privacy preservation by reducing communication requirements, indirectly. This work not only provides theoretical support for resource management in federated learning (FL) systems, but also provides ideas for potential optimal deployment in multiple real-world scenarios. By thoroughly discussing the current challenges and future research directions, it also provides some important insights into multi-resource optimization in complex application environments.
Unsupervised Data Validation Methods for Efficient Model Training
2024-10-10
Yurii Paniv
This paper investigates the challenges and potential solutions for improving machine learning systems for low-resource languages. State-of-the-art models in natural language processing (NLP), text-to-speech (TTS), speech-to-text (STT), and vision-language models (VLM) rely heavily on large datasets, which are often unavailable for low-resource languages. This research explores key areas such as defining "quality data," developing methods for generating appropriate data and enhancing accessibility to model training. A comprehensive review of current methodologies, including data augmentation, multilingual transfer learning, synthetic data generation, and data selection techniques, highlights both advancements and limitations. Several open research questions are identified, providing a framework for future studies aimed at optimizing data utilization, reducing the required data quantity, and maintaining high-quality model performance. By addressing these challenges, the paper aims to make advanced machine learning models more accessible for low-resource languages, enhancing their utility and impact across various sectors.
HeGraphAdapter: Tuning Multi-Modal Vision-Language Models with Heterogeneous Graph Adapter
2024-10-10
Yumiao Zhao, Bo Jiang, Xiao Wang, Qin Xu, Jin Tang
Adapter-based tuning methods have shown significant potential in transferring knowledge from pre-trained Vision-Language Models to the downstream tasks. However, after reviewing existing adapters, we find they generally fail to fully explore the interactions between different modalities in constructing task-specific knowledge. Also, existing works usually only focus on similarity matching between positive text prompts, making it challenging to distinguish the classes with high similar visual contents. To address these issues, in this paper, we propose a novel Heterogeneous Graph Adapter to achieve tuning VLMs for the downstream tasks. To be specific, we first construct a unified heterogeneous graph mode, which contains i) visual nodes, positive text nodes and negative text nodes, and ii) several types of edge connections to comprehensively model the intra-modality, inter-modality and inter-class structure knowledge together. Next, we employ a specific Heterogeneous Graph Neural Network to excavate multi-modality structure knowledge for adapting both visual and textual features for the downstream tasks. Finally, after HeGraphAdapter, we construct both text-based and visual-based classifiers simultaneously to comprehensively enhance the performance of the CLIP model. Experimental results on 11 benchmark datasets demonstrate the effectiveness and benefits of the proposed HeGraphAdapter.
Exploring Foundation Models in Remote Sensing Image Change Detection: A Comprehensive Survey
2024-10-10
Zihan Yu, Tianxiao Li, Yuxin Zhu, Rongze Pan
Change detection, as an important and widely applied technique in the field of remote sensing, aims to analyze changes in surface areas over time and has broad applications in areas such as environmental monitoring, urban development, and land use analysis.In recent years, deep learning, especially the development of foundation models, has provided more powerful solutions for feature extraction and data fusion, effectively addressing these complexities. This paper systematically reviews the latest advancements in the field of change detection, with a focus on the application of foundation models in remote sensing tasks.
Asymptotic Safety and the Cosmic Coincidence Problem
2024-10-10
Vasilios Zarikas
Asymptotic Safety (AS) Paradigm is an interesting set of ideas and methods towards a meaningful quantization of Gravity. A brief review of phenomenological consequences in the context of AS regarding cosmology with emphasis on dark energy is given. Furthermore, recent studies that suggest a natural explanation of the recent cosmic acceleration and its coincidence using large-scale structure and AS framework will be analyzed. Finally, the present work extends the analysis of a recent new set of modified Einstein equations inspired from AS program.
Simulating images of radio galaxies with diffusion models
2024-10-10
Tobias Vičánek Martínez, Nicolás Barón Pérez, Marcus Brüggen
With increasing amounts of data in astronomy, automated analysis methods have become crucial. Synthetic data is required for developing and testing such methods. Current simulations often suffer from insufficient detail or inaccurate representation of source type occurrences. To overcome those deficiencies, we implement a deep generative model trained on observations to generate realistic radio galaxy images with control over flux and source morphology. We use a Diffusion Model, trained with continuous time steps to reduce sampling time without quality impairments. Two models are trained on two different datasets, respectively. One is a selection of images from the second data release of the LOFAR Two-Metre Sky Survey (LoTSS). The respective model is conditioned on peak flux values to preserve signal intensity information after re-scaling image pixel values. The other is a smaller set with images from the VLA survey of Faint Images of the Radio Sky at Twenty-Centimeters (FIRST), where every image is provided with a morphological class label that the corresponding model is conditioned on. Conditioned sampling is realized with classifier-free diffusion guidance. We evaluate the quality of generated images by comparing distributions of different quantities over the real and generated data, including results from the standard source-finding algorithms. Class-conditioning is evaluated by training a classifier and comparing its performance on the real and generated data. We are able to generate realistic images of high quality using 25 sampling steps, which is unprecedented in the field of radio astronomy. Generated images are visually indistinguishable from the training data. Distributions of different image metrics are replicated. The classifier performs equally well for real and generated images, indicating accurate sampling control over morphological source properties.
Orbital Hall Effect Accompanying Quantum Hall Effect: Landau Levels Cause Orbital Polarized Edge Currents
2024-10-10
Börge Göbel, Ingrid Mertig
The quantum Hall effect emerges when two-dimensional samples are subjected to strong magnetic fields at low temperatures: Topologically protected edge states cause a quantized Hall conductivity in multiples of $e^2/h$. Here we show that the quantum Hall effect is accompanied by an orbital Hall effect. Our quantum mechanical calculations fit well the semiclassical interpretation in terms of "skipping orbits". The chiral edge states of a quantum Hall system are orbital polarized akin to a hypothetical orbital version of the quantum anomalous Hall effect in magnetic systems. The orbital Hall resistivity scales quadratically with the magnetic field making it the dominant effect at high fields.
Observations of pre- and proto-brown dwarfs in nearby clouds: paving the way to further constraining theories of brown dwarf formation
2024-10-10
Aina Palau, Nuria Huelamo, David Barrado, Michael M. Dunham, Chang Won Lee
Brown Dwarfs (BDs) are crucial objects in our understanding of both star and planet formation. However, there is still an unconcluded debate about which is the dominant formation mechanism of these objects. For this, it is mandatory to study BDs in their earliest evolutionary stages (what we call pre- and proto-BDs), comparable to the `pre-stellar' and `Class 0/I' stages well characterized for the formation of low-mass stars. In this review, the recent efforts aimed at searching, identifying and characterising pre- and proto-BD candidates in nearby star-forming regions are presented, and revised requirements for an object to be a promising proto-BD or pre-BD candidate are provided, based on a new, unexplored so far, relation between the internal luminosity and the accreted mass. By applying these requirements, a list of 67 promising proto-BD candidates is presented, along with a compilation of possible pre-BDs from the literature. Updated correlations of protostellar properties such as mass infall rate or outflow momentum rate with bolometric luminosity are provided down to the low-mass BD regime, where no significant deviations are apparent. Furthermore, the number of proto-BD candidates in different clouds of the Solar Neighborhood seem to follow the known relations of number of protostars with cloud properties. In addition, proto(star-to-BD) ratios for the different clouds are also explored, unveiling a particular underproduction of low-mass proto-BD candidates in Ophiuchus compared to Lupus and Taurus. Possible explanations for this behavior are discussed, including heating of the Ophiuchus cloud by the nearby OB stars. The overall results of this work tend to favor a star-like process for BD formation down to the planetary boundary, of about 10 Mjup, below which other mechanisms might be at work.
2024-10-10
Elias Trommer, Bernd Waschneck, Akash Kumar
In order to vary the arithmetic resource consumption of neural network applications at runtime, this work proposes the flexible reuse of approximate multipliers for neural network layer computations. We introduce a search algorithm that chooses an appropriate subset of approximate multipliers of a user-defined size from a larger search space and enables retraining to maximize task performance. Unlike previous work, our approach can output more than a single, static assignment of approximate multiplier instances to layers. These different operating points allow a system to gradually adapt its Quality of Service (QoS) to changing environmental conditions by increasing or decreasing its accuracy and resource consumption. QoS-Nets achieves this by reassigning the selected approximate multiplier instances to layers at runtime. To combine multiple operating points with the use of retraining, we propose a fine-tuning scheme that shares the majority of parameters between operating points, with only a small amount of additional parameters required per operating point. In our evaluation on MobileNetV2, QoS-Nets is used to select four approximate multiplier instances for three different operating points. These operating points result in power savings for multiplications between 15.3% and 42.8% at a Top-5 accuracy loss between 0.3 and 2.33 percentage points. Through our fine-tuning scheme, all three operating points only increase the model's parameter count by only 2.75%.
Theoretical expectations for high-mass X-ray binaries, supernova remnants, and their evolutionary paths: An overview talk for LIAC41: the eventful life of stellar multiples
2024-10-10
Ilya Mandel
In this invited talk at the 41st Li\`ege International Astrophysical Colloquium on "The eventful life of massive star multiples", I reviewed some aspects of our current understanding of neutron stars and black holes as end products of stellar evolution as well as the evolutionary paths leading to the formation of high-mass X-ray binaries.
Hexagonal boron nitride (hBN) has emerged as a compelling platform for both classical and quantum technologies. In particular, the past decade has witnessed a surge of novel ideas and developments, which may be overwhelming for newcomers to the field. This review provides an overview of the fundamental concepts and key applications of hBN, including quantum sensing, quantum key distribution, quantum computing, and quantum memory. Additionally, we highlight critical experimental and theoretical advances that have expanded the capabilities of hBN, in a cohesive and accessible manner. The objective is to equip readers with a comprehensive understanding of the diverse applications of hBN, and provide insights into ongoing research efforts.
Research on autonomous driving in unstructured outdoor environments is less advanced than in structured urban settings due to challenges like environmental diversities and scene complexity. These environments-such as rural areas and rugged terrains-pose unique obstacles that are not common in structured urban areas. Despite these difficulties, autonomous driving in unstructured outdoor environments is crucial for applications in agriculture, mining, and military operations. Our survey reviews over 250 papers for autonomous driving in unstructured outdoor environments, covering offline mapping, pose estimation, environmental perception, path planning, end-to-end autonomous driving, datasets, and relevant challenges. We also discuss emerging trends and future research directions. This review aims to consolidate knowledge and encourage further research for autonomous driving in unstructured environments. To support ongoing work, we maintain an active repository with up-to-date literature and open-source projects at: https://github.com/chaytonmin/Survey-Autonomous-Driving-in-Unstructured-Environments.
Recent advancements in UAV technology have spurred interest in developing multi-UAV aerial surveying systems for use in confined environments where GNSS signals are blocked or jammed. This paper focuses airborne magnetic surveying scenarios. To obtain clean magnetic measurements reflecting the Earth's magnetic field, the magnetic sensor must be isolated from other electronic devices, creating a significant localization challenge. We propose a visual cooperative localization solution. The solution incorporates a visual processing module and an improved manifold-based sensor fusion algorithm, delivering reliable and accurate positioning information. Real flight experiments validate the approach, demonstrating single-axis centimeter-level accuracy and decimeter-level overall 3D positioning accuracy.
Morphology is the starting point for understanding galaxies. Elmegreen et al. classified spiral galaxies into flocculent, multiple-arm, and grand-design galaxies based on the regularity of their spiral arm structure. With the release of a vast number of clear spiral galaxy images from the Sloan Digital Sky Survey, we conducted a morphological classification of 5093 blue spiral galaxies. A statistical analysis of this sample shows that the fractions of flocculent, multiple-arm, and grand-design galaxies are 38 $\pm$ 1%, 59 $\pm$ 1%, and 3 $\pm$ 1%, respectively. Redshift has no obvious influence on this classification. However, as the bulge size becomes larger, the fraction of multiple-arm galaxies increases, while that of flocculent galaxies decreases. In addition, we performed a statistical analysis of 3958 galaxies with a clear spiral arm structure, finding 82% of these galaxies have two arms in their inner regions. We also found that the majority (74%) of the barred spiral galaxies exhibit the characteristics of two inner spiral arms and multiple outer spiral arms, and there is no barred spiral galaxy in this work with four continuous spiral arms from the inner to the outer regions. These results highlight that the spiral arm structure of the Milky Way, according to the current mainstream view of a four-arm galaxy with continuous arms extending from the inner to outer regions, is quite unique. However, our findings align with the spiral morphology of the Milky Way proposed by Xu et al., in which case our Galaxy can be considered typical.
As large language models (LLMs) are rapidly advancing and achieving near-human capabilities, aligning them with human values is becoming more urgent. In scenarios where LLMs outperform humans, we face a weak-to-strong alignment problem where we need to effectively align strong student LLMs through weak supervision generated by weak teachers. Existing alignment methods mainly focus on strong-to-weak alignment and self-alignment settings, and it is impractical to adapt them to the much harder weak-to-strong alignment setting. To fill this gap, we propose a multi-agent contrastive preference optimization (MACPO) framework. MACPO facilitates weak teachers and strong students to learn from each other by iteratively reinforcing unfamiliar positive behaviors while penalizing familiar negative ones. To get this, we devise a mutual positive behavior augmentation strategy to encourage weak teachers and strong students to learn from each other's positive behavior and further provide higher quality positive behavior for the next iteration. Additionally, we propose a hard negative behavior construction strategy to induce weak teachers and strong students to generate familiar negative behavior by fine-tuning on negative behavioral data. Experimental results on the HH-RLHF and PKU-SafeRLHF datasets, evaluated using both automatic metrics and human judgments, demonstrate that MACPO simultaneously improves the alignment performance of strong students and weak teachers. Moreover, as the number of weak teachers increases, MACPO achieves better weak-to-strong alignment performance through more iteration optimization rounds.
Double-lined Spectroscopic Binaries from the LAMOST Low-Resolution Survey
2024-10-10
Junhui Liu, Bo Zhang, Jianfeng Wu, Yuan-Sen Ting
We report on a data-driven spectral model that we have developed for the identification of double-lined spectroscopic binary stars (SB2s) in the LAMOST low-resolution survey (R$\sim$1800). Employing simultaneous fitting with both single-star and binary-star models, we detected over 4800 SB2 candidates, where both components are detectably contributing to the spectrum, from an initial pool of 2.6 million objects. Tests show that our model favors FGK-type main-sequence binaries with high mass ratio ($q\geq$ 0.7) and large radial velocity separation ($\Delta \rm RV \geq$ 100~km$\,$s$^{-1}$). Almost all of these candidates are positioned above the main sequence in the color-magnitude diagram, indicating their binary nature. Additionally, we utilized various observational data, including spectroscopy, photometry, parallax, and extinction, to determine multiple physical parameters such as the effective temperature, age, metallicity, radial velocity, mass, mass ratio, stellar radius, along with their associated uncertainties for these SB2 candidates. For the 44 candidates with seven or more observational epochs, we provide complete orbital solutions. We make available catalogs containing various stellar parameters for identified SB2 systems.
From student to working professional: A graduate survey
2024-10-10
Jacqueline Whalley, Asanthika Imbulpitiya, Tony Clear, Harley Ogier
This paper reports on the results of a 2023 survey that explores the Work Integrated Learning (WiL) experiences of thirty recent Computer Science (CS) graduates. The graduates had all completed their undergraduate bachelors degree within the last five years and were currently employed in a CS industry role. The survey asked about the graduates' perceptions within a continuum of WiL experiences from final year capstone projects to professional development in their first industry-based role. Most respondents had taken a capstone course involving a team project. Only two respondents had participated in an internship program. Our results indicate that graduates value their capstone experiences and believe that they provide transferable skills including teamwork, managing client relations, exposure to technologies and methods, and time management. When entering their first industry role less than fifty percent of graduates were allocated a mentor. Overwhelmingly, these graduates noted the importance of those mentors in their transition from student to working professional. Very few of the surveyed graduates were provided with ongoing professional development opportunities. Those who did noted significant gains including growth of leadership skills and accelerated career progression. Our survey highlights a gap and an opportunity for tertiary institutions to work with industry to provide graduate onboarding and novice/early-career professional development opportunities.
Recent Progress on Multiferroic Hexagonal Rare-Earth Ferrites (h-RFeO3, R = Y,Dy-Lu)
2024-10-10
Xin Li, Yu Yun, Xiaoshan Xu
Multiferroic hexagonal rare-earth ferrites (h-RFeO3, R=Sc, Y, and rare earth), in which the improper ferroelectricity and canted antiferromagnetism coexist, have been advocated as promising candidates to pursue the room-temperature multiferroics, because of strong spin-spin interaction. The strong interactions between the ferroic orders and the structural distortions are appealing for high-density, energy-efficient electronic devices. Over the past decade, remarkable advances in atomic-scale synthesis, characterization, and material modeling enable the significant progresses in the understanding and manipulation of ferroic orders and their couplings in h-RFeO3 thin films. These results reveal a physical picture of rich ferroelectric and magnetic phenomena interconnected by a set of structural distortions and spin-lattice couplings, which provides guidance for the control of ferroic orders down to the nano scale and the discovery of novel physical phenomena. This review focus on state-of-the-art studies in complex phenomena related to the ferroelectricity and magnetism as well as the magnetoelectric couplings in multiferroic h-RFeO3, based on mostly the recent experimental efforts, aiming to stimulate fresh ideas in this field.
I-Max: Maximize the Resolution Potential of Pre-trained Rectified Flow Transformers with Projected Flow
2024-10-10
Ruoyi Du, Dongyang Liu, Le Zhuo, Qin Qi, Hongsheng Li, Zhanyu Ma, Peng Gao
Rectified Flow Transformers (RFTs) offer superior training and inference efficiency, making them likely the most viable direction for scaling up diffusion models. However, progress in generation resolution has been relatively slow due to data quality and training costs. Tuning-free resolution extrapolation presents an alternative, but current methods often reduce generative stability, limiting practical application. In this paper, we review existing resolution extrapolation methods and introduce the I-Max framework to maximize the resolution potential of Text-to-Image RFTs. I-Max features: (i) a novel Projected Flow strategy for stable extrapolation and (ii) an advanced inference toolkit for generalizing model knowledge to higher resolutions. Experiments with Lumina-Next-2K and Flux.1-dev demonstrate I-Max's ability to enhance stability in resolution extrapolation and show that it can bring image detail emergence and artifact correction, confirming the practical value of tuning-free resolution extrapolation.
We propose a strategy to continuously tune the amplitude of acoustic waves based on the interference among two mode-conversion paths in passive acoustic structures. The interference phenomenon is attributed to two conjugate acoustic geometric phases obtained with two mode-conversion processes in hybrid-type geometric-phase meta-atom (HGPM) pair. Notably, 100% modulation depth of the wave amplitude is achievable by simply varying the local orientation angle of meta-atom. We utilize the acoustic structure made of two cylindrical resonators to construct deep-subwavelength secondary source with designated initial phase delay, and HGPM supporting desired mode-conversion functionality is accordingly fabricated with four secondary sources. Both theory and experiment consistently verify the continuous amplitude modulation function of HGPM pair, which showcases a general scheme for reconfigurable amplitude-type acoustic meta-devices, i.e., those that require grayscale amplitude modulation for acoustic field engineering.
Large Language Models (LLMs) have fast become an essential tools to many conversational chatbots due to their ability to provide coherent answers for varied queries. Datasets used to train these LLMs are often a mix of generic and synthetic samples, thus lacking the verification needed to provide correct and verifiable answers for T.V. News. We collect and share a large collection of QA pairs extracted from transcripts of news recordings from various news-channels across the United States. Resultant QA pairs are then used to fine-tune an off-the-shelf LLM model. Our model surpasses base models of similar size on several open LLM benchmarks. We further integrate and propose a RAG method to improve contextualization of our answers and also point it to a verifiable news recording.
CSGDN: Contrastive Signed Graph Diffusion Network for Predicting Crop Gene-phenotype Associations
2024-10-10
Yiru Pan, Xingyu Ji, Jiaqi You, Lu Li, Zhenping Liu, Xianlong Zhang, Zeyu Zhang, Maojun Wang
Positive and negative association prediction between gene and phenotype helps to illustrate the underlying mechanism of complex traits in organisms. The transcription and regulation activity of specific genes will be adjusted accordingly in different cell types, developmental stages, and physiological states. There are the following two problems in obtaining the positive/negative associations between gene and trait: 1) High-throughput DNA/RNA sequencing and phenotyping are expensive and time-consuming due to the need to process large sample sizes; 2) experiments introduce both random and systematic errors, and, meanwhile, calculations or predictions using software or models may produce noise. To address these two issues, we propose a Contrastive Signed Graph Diffusion Network, CSGDN, to learn robust node representations with fewer training samples to achieve higher link prediction accuracy. CSGDN employs a signed graph diffusion method to uncover the underlying regulatory associations between genes and phenotypes. Then, stochastic perturbation strategies are used to create two views for both original and diffusive graphs. Lastly, a multi-view contrastive learning paradigm loss is designed to unify the node presentations learned from the two views to resist interference and reduce noise. We conduct experiments to validate the performance of CSGDN on three crop datasets: Gossypium hirsutum, Brassica napus, and Triticum turgidum. The results demonstrate that the proposed model outperforms state-of-the-art methods by up to 9.28% AUC for link sign prediction in G. hirsutum dataset.
Autonomous Robotic System with Optical Coherence Tomography Guidance for Vascular Anastomosis
2024-10-10
Jesse Haworth, Rishi Biswas, Justin Opfermann, Michael Kam, Yaning Wang, Desire Pantalone, Francis X. Creighton, Robin Yang, Jin U. Kang, Axel Krieger
Vascular anastomosis, the surgical connection of blood vessels, is essential in procedures such as organ transplants and reconstructive surgeries. The precision required limits accessibility due to the extensive training needed, with manual suturing leading to variable outcomes and revision rates up to 7.9%. Existing robotic systems, while promising, are either fully teleoperated or lack the capabilities necessary for autonomous vascular anastomosis. We present the Micro Smart Tissue Autonomous Robot (micro-STAR), an autonomous robotic system designed to perform vascular anastomosis on small-diameter vessels. The micro-STAR system integrates a novel suturing tool equipped with Optical Coherence Tomography (OCT) fiber-optic sensor and a microcamera, enabling real-time tissue detection and classification. Our system autonomously places sutures and manipulates tissue with minimal human intervention. In an ex vivo study, micro-STAR achieved outcomes competitive with experienced surgeons in terms of leak pressure, lumen reduction, and suture placement variation, completing 90% of sutures without human intervention. This represents the first instance of a robotic system autonomously performing vascular anastomosis on real tissue, offering significant potential for improving surgical precision and expanding access to high-quality care.
There are (other) ways to negate in propositional team semantics
2024-10-10
Fan Yang
The languages of logics based on team semantics typically only allow atomic negation or restricted negation. In this paper, we explore propositional team-based logics with full (intuitionistic) negation. We demonstrate that including full intutionistic negation does not complicate the axiomatization of propositional team-based logics with the downward closure property. We also review known expressive completeness results for these logics, highlighting how relevant complemented properties are expressed in propositional dependence logic without directly using negation. Building on these insights, we also prove a new result: propositional logic extended with both dependence and inclusion atoms is expressively complete.
Galaxy populations in protoclusters at cosmic noon
2024-10-10
Moira Andrews, M. Celeste Artale, Ankit Kumar, Kyoung-Soo Lee, Tess Florek, Kaustub Anand, Candela Cerdosino, Robin Ciardullo, Nicole Firestone, Eric Gawiser, Caryl Gronwall, Lucia Guaita, Sungryong Hong, Ho Seong Hwang, Jaehyun Lee, Seong-Kook Lee, Nelson Padilla, Jaehong Park, Roxana Popescu, Vandana Ramakrishnan, Hyunmi Song, Felipe Vivanco, Mark Vogelsberger
We investigate the physical properties and redshift evolution of simulated galaxies residing in protoclusters at cosmic noon, to understand the influence of the environment on galaxy formation. This work is to build clear expectations for the ongoing ODIN survey, devoted to mapping large-scale structures at z=2.4, 3.1, and 4.5 using Ly$\alpha$-emitting galaxies (LAEs) as tracers. From the IllustrisTNG simulations, we define subregions centered on the most massive clusters ranked by total stellar mass at z=0 and study the properties of galaxies within, including LAEs. To model the LAE population, we take a semi-analytical approach that assigns Ly$\alpha$ luminosity and equivalent width based on the UV luminosities to galaxies in a probabilistic manner. We investigate stellar mass, star formation rate, major mergers, and specific star formation rate of the population of star-forming galaxies and LAEs in the field and protocluster environment and trace their evolution. We find that the overall shape of the UV luminosity function (LF) in simulated protocluster environments is characterized by a shallower faint-end slope and an excess on the bright end, signaling different formation histories for galaxies therein. The difference is milder for the Ly$\alpha$ LF. While protocluster galaxies follow the same SFR-$M_{\odot}$ scaling relation as average field galaxies, a larger fraction appears to have experienced major mergers in the last 200 Myr and as a result shows enhanced star formation at a ~60% level, leading to a flatter distribution in both SFR and $M_{\odot}$ relative to galaxies in the average field. We find that protocluster galaxies, including LAEs, begin to quench much earlier (z~0.8-1.6) than field galaxies (z~0.5-0.9); our result is in agreement with recent observational results and highlights the importance of large-scale environment on the overall formation history of galaxies.
Investigating Developers' Preferences for Learning and Issue Resolution Resources in the ChatGPT Era
2024-10-10
Ahmad Tayeb, Mohammad D. Alahmadi, Elham Tajik, Sonia Haiduc
The landscape of software developer learning resources has continuously evolved, with recent trends favoring engaging formats like video tutorials. The emergence of Large Language Models (LLMs) like ChatGPT presents a new learning paradigm. While existing research explores the potential of LLMs in software development and education, their impact on developers' learning and solution-seeking behavior remains unexplored. To address this gap, we conducted a survey targeting software developers and computer science students, gathering 341 responses, of which 268 were completed and analyzed. This study investigates how AI chatbots like ChatGPT have influenced developers' learning preferences when acquiring new skills, exploring technologies, and resolving programming issues. Through quantitative and qualitative analysis, we explore whether AI tools supplement or replace traditional learning resources such as video tutorials, written tutorials, and Q&A forums. Our findings reveal a nuanced view: while video tutorials continue to be highly preferred for their comprehensive coverage, a significant number of respondents view AI chatbots as potential replacements for written tutorials, underscoring a shift towards more interactive and personalized learning experiences. Additionally, AI chatbots are increasingly considered valuable supplements to video tutorials, indicating their growing role in the developers' learning resources. These insights offer valuable directions for educators and the software development community by shedding light on the evolving preferences toward learning resources in the era of ChatGPT.
The emerging field of AI-generated art has witnessed the rise of prompt marketplaces, where creators can purchase, sell, or share prompts for generating unique artworks. These marketplaces often assert ownership over prompts, claiming them as intellectual property. This paper investigates whether concealed prompts sold on prompt marketplaces can be considered as secure intellectual property, given that humans and AI tools may be able to approximately infer the prompts based on publicly advertised sample images accompanying each prompt on sale. Specifically, our survey aims to assess (i) how accurately can humans infer the original prompt solely by examining an AI-generated image, with the goal of generating images similar to the original image, and (ii) the possibility of improving upon individual human and AI prompt inferences by crafting human-AI combined prompts with the help of a large language model. Although previous research has explored the use of AI and machine learning to infer (and also protect against) prompt inference, we are the first to include humans in the loop. Our findings indicate that while humans and human-AI collaborations can infer prompts and generate similar images with high accuracy, they are not as successful as using the original prompt.
Intellectual Property Blockchain Odyssey: Navigating Challenges and Seizing Opportunities
2024-10-10
Rabia Bajwa, Farah Tasnur Meem
This paper investigates the evolving relationship between protecting Intellectual Property Rights (IPRs) and blockchain technology. We conducted a comprehensive literature review, supplemented by case study analyses and research paper reviews, to understand the scope and implications of blockchain about intellectual property rights. Our study demonstrates how applying blockchain technology for IPR could revolutionize transparency, security, and operational efficiency. It also identifies the primary challenges and openings in this area. We provide an extensive framework for integrating blockchain technology with intellectual property rights and other technical components (some of which already exist or are resolved by blockchain; some might need attention), drawing on current research and best practices. This framework has the potential to give a new perspective in a structured manner for the intellectual property landscape by providing 360-degree coverage across different layers of operation.
Physics and Deep Learning in Computational Wave Imaging
2024-10-10
Youzuo Lin, Shihang Feng, James Theiler, Yinpeng Chen, Umberto Villa, Jing Rao, John Greenhall, Cristian Pantea, Mark A. Anastasio, Brendt Wohlberg
Computational wave imaging (CWI) extracts hidden structure and physical properties of a volume of material by analyzing wave signals that traverse that volume. Applications include seismic exploration of the Earth's subsurface, acoustic imaging and non-destructive testing in material science, and ultrasound computed tomography in medicine. Current approaches for solving CWI problems can be divided into two categories: those rooted in traditional physics, and those based on deep learning. Physics-based methods stand out for their ability to provide high-resolution and quantitatively accurate estimates of acoustic properties within the medium. However, they can be computationally intensive and are susceptible to ill-posedness and nonconvexity typical of CWI problems. Machine learning-based computational methods have recently emerged, offering a different perspective to address these challenges. Diverse scientific communities have independently pursued the integration of deep learning in CWI. This review delves into how contemporary scientific machine-learning (ML) techniques, and deep neural networks in particular, have been harnessed to tackle CWI problems. We present a structured framework that consolidates existing research spanning multiple domains, including computational imaging, wave physics, and data science. This study concludes with important lessons learned from existing ML-based methods and identifies technical hurdles and emerging trends through a systematic analysis of the extensive literature on this topic.
The language of sound search: Examining User Queries in Audio Search Engines
2024-10-10
Benno Weck, Frederic Font
This study examines textual, user-written search queries within the context of sound search engines, encompassing various applications such as foley, sound effects, and general audio retrieval. Current research inadequately addresses real-world user needs and behaviours in designing text-based audio retrieval systems. To bridge this gap, we analysed search queries from two sources: a custom survey and Freesound website query logs. The survey was designed to collect queries for an unrestricted, hypothetical sound search engine, resulting in a dataset that captures user intentions without the constraints of existing systems. This dataset is also made available for sharing with the research community. In contrast, the Freesound query logs encompass approximately 9 million search requests, providing a comprehensive view of real-world usage patterns. Our findings indicate that survey queries are generally longer than Freesound queries, suggesting users prefer detailed queries when not limited by system constraints. Both datasets predominantly feature keyword-based queries, with few survey participants using full sentences. Key factors influencing survey queries include the primary sound source, intended usage, perceived location, and the number of sound sources. These insights are crucial for developing user-centred, effective text-based audio retrieval systems, enhancing our understanding of user behaviour in sound search contexts.
Verstraete, Dehaene, and De Moor (2003) showed that SLOCC invariants provide entanglement monotones. We observe that many highly entangled or useful four-qubit states that appear in prior literature are stationary points of such entanglement measures. This motivates the search for more stationary points. We use the notion of critical points (in the sense of the Kempf-Ness theorem) together with Vinberg theory to reduce the complexity of the problem significantly. We solve the corresponding systems utilizing modern numerical nonlinear algebra methods and reduce the solutions by natural symmetries. This method produces an extended list of four-qubit stationary points, which includes all the critical states in the survey by Enriquez et al (2016).
In recent times, a considerable number of research studies have been carried out to address the issue of Missing Value Imputation (MVI). MVI aims to provide a primary solution for datasets that have one or more missing attribute values. The advancements in Artificial Intelligence (AI) drive the development of new and improved machine learning (ML) algorithms and methods. The advancements in ML have opened up significant opportunities for effectively imputing these missing values. The main objective of this article is to conduct a comprehensive and rigorous review, as well as analysis, of the state-of-the-art ML applications in MVI methods. This analysis seeks to enhance researchers' understanding of the subject and facilitate the development of robust and impactful interventions in data preprocessing for Data Analytics. The review is performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique. More than 100 articles published between 2014 and 2023 are critically reviewed, considering the methods and findings. Furthermore, the latest literature is examined to scrutinize the trends in MVI methods and their evaluation. The accomplishments and limitations of the existing literature are discussed in detail. The survey concludes by identifying the current gaps in research and providing suggestions for future research directions and emerging trends in related fields of interest.
COSMOS-Web: stellar mass assembly in relation to dark matter halos across $0.2
2024-10-10
M. Shuntov, O. Ilbert, S. Toft, R. C. Arango-Toro, H. B. Akins, C. M. Casey, M. Franco, S. Harish, J. S. Kartaltepe, A. M. Koekemoer, H. J. McCracken, L. Paquereau, C. Laigle, M. Bethermin, Y. Dubois, N. E. Drakos, A. Faisst, G. Gozaliasl, S. Gillman, C. C. Hayward, M. Hirschmann, M. Huertas-Company, C. K. Jespersen, S. Jin, V. Kokorev, E. Lambrides, D. Le Borgne, D. Liu, G. Magdis, R. Massey, C. J. R. McPartland, W. Mercier, J. E. McCleary, J. McKinney, P. A. Oesch, J. D. Rhodes, R. M. Rich, B. E. Robertson, D. Sanders, M. Trebitsch, L. Tresse, F. Valentino, A. P. Vijayan, J. R. Weaver, A. Weibel, S. M. Wilkins
We study the stellar mass function (SMF) and the co-evolution with dark matter halos via abundance matching in the largest redshift range to date $0.25$, we find increased abundances of massive (log$\, M_{\star}/M_{\odot}>10.5$) implying integrated star formation efficiencies (SFE) $\epsilon_{\star}\equiv M_{\star}\, f_{\rm b}^{-1} M_{\rm halo}^{-1} \gtrsim 0.5$. We find a flattening of the SMF at the high-mass end that is better described by a double power law at $z>5.5$. At $z \lesssim 5.5$ it transitions to a Schechter law which coincides with the emergence of the first massive quiescent galaxies in the Universe. We trace the cosmic stellar mass density (SMD) and infer the star formation rate density (SFRD), which at $z>7.5$ agrees remarkably with recent \JWST{} UV luminosity function-derived estimates. However, at $z \lesssim 3.5$, we find significant tension ($\sim 0.3$ dex) with the cosmic star formation (SF) history from instantaneous SF measures, the causes of which remain poorly understood. We infer the stellar-to-halo mass relation (SHMR) and the SFE from abundance matching out to $z=12$, finding a non-monotonic evolution. The SFE has the characteristic strong dependence with mass in the range of $0.02 - 0.2$, and mildly decreases at the low mass end out to $z\sim3.5$. At $z\sim3.5$ the SFE increases sharply from $\sim 0.1$ to approach high SFE of $0.8-1$ by $z\sim 10$ for log$(M_{\rm h}/M_{\odot})\approx11.5$, albeit with large uncertainties. Finally, we use the SHMR to track the SFE and stellar mass growth throughout the halo history and find that they do not grow at the same rate -- from the earliest times up until $z\sim3.5$ the halo growth rate outpaces galaxy assembly, but at $z>3.5$ halo growth stagnates and accumulated gas reservoirs keep the SF going and galaxies outpace halos.
Light scalar quarkonia from Laplace sum rule at NLO
2024-10-10
R. M. Albuquerque, S. Narison, D. Rabetiarivony
We review our results on light scalar quarkonia ($\bar{q}q$ and four-quark states) from (inverse) QCD Laplace sum rules (LSR) and their ratios ${\cal R}$ within stability criteria and including higher order perturbative (PT) corrections up to the (estimated) ${\mathcal O}(\alpha_{s}^{5})$. As the Operator Product Expansion (OPE) usually converges for $D\leqslant 6-8$, we evaluated the QCD spectral functions at Lowest Order (LO) of PT QCD and up to the $D=6$ dimension vaccum condensates. We request that the optimal results obey the constraint: Pole (Resonance) contribution to the spectral integral is larger than the QCD continuum one which excludes an on-shell mass around $(500-600)$MeV obtained for values of the QCD continuum threshold $t_c\leqslant(1\sim 1.5)$ GeV$^2$. Our results for the different assignments of the scalar mesons are compiled in Tables 1 to 3
CAVITY: Calar Alto Void Integral-field Treasury surveY. I. First public data release
2024-10-10
Rubén García-Benito, Andoni Jiménez, Laura Sánchez-Menguiano, Tomás Ruiz-Lara, Salvador Duarte Puertas, Jesús Domínguez-Gómez, Bahar Bidaran, Gloria Torres-Ríos, María Argudo-Fernández, Daniel Espada, Isabel Pérez, Simon Verley, Ana M. Conrado, Estrella Florido, Mónica I. Rodríguez, Almudena Zurita, Manuel Alcázar-Laynez, Simon B. De Daniloff, Ute Lisenfeld, Rien van de Weygaert, Hélène M. Courtois, Jesús Falcón-Barroso, Anna Ferré-Mateu, Lluís Galbany, Rosa M. González Delgado, Ignacio del Moral-Castro, Reynier F. Peletier, Javier Román, Sebastián F. Sánchez, Pablo M. Sánchez-Alarcón, Patricia Sánchez-Blázquez, Pedro Villalba-González, Marco Azzaro, Martín Blazek, Alba Fernández, Julia Gallego, Samuel Góngora, Ana Guijarro, Enrique de Guindos, Israel Hermelo, Ricardo Hernández, Enrique de Juan, Ignacio Vico
The Calar Alto Void Integral-field Treasury surveY (CAVITY) is a legacy project aimed at characterising the population of galaxies inhabiting voids, which are the most under-dense regions of the cosmic web, located in the Local Universe. This paper describes the first public data release (DR1) of CAVITY, comprising science-grade optical data cubes for the initial 100 out of a total of $\sim$300 galaxies in the Local Universe ($0.005 < z < 0.050$). These data were acquired using the integral-field spectrograph PMAS/PPak mounted on the 3.5m telescope at the Calar Alto observatory. The DR1 galaxy sample encompasses diverse characteristics in the color-magnitude space, morphological type, stellar mass, and gas ionisation conditions, providing a rich resource for addressing key questions in galaxy evolution through spatially resolved spectroscopy. The galaxies in this study were observed with the low-resolution V500 set-up, spanning the wavelength range 3745-7500 \AA, with a spectral resolution of 6.0 \AA (FWHM). Here, we describe the data reduction and characteristics and data structure of the CAVITY datasets essential for their scientific utilisation, highlighting such concerns as vignetting effects, as well as the identification of bad pixels and management of spatially correlated noise. We also provide instructions for accessing the CAVITY datasets and associated ancillary data through the project's dedicated database.
2024-10-10
Jason T. Wright, Macy Huston, Aidan Groenendaal, Lennon Nichol, Nick Tusay
In this third installment of SETI in 20xx, we very briefly and subjectively review developments in SETI in 2022. Our primary focus is 80 papers and books published or made public in 2022, which we sort into six broad categories: results from actual searches, new search methods and instrumentation, target and frequency selection, the development of technosignatures, theory of ETIs, and social aspects of SETI.
Artificial intelligence techniques in inherited retinal diseases: A review
2024-10-10
Han Trinh, Jordan Vice, Jason Charng, Zahra Tajbakhsh, Khyber Alam, Fred K. Chen, Ajmal Mian
Inherited retinal diseases (IRDs) are a diverse group of genetic disorders that lead to progressive vision loss and are a major cause of blindness in working-age adults. The complexity and heterogeneity of IRDs pose significant challenges in diagnosis, prognosis, and management. Recent advancements in artificial intelligence (AI) offer promising solutions to these challenges. However, the rapid development of AI techniques and their varied applications have led to fragmented knowledge in this field. This review consolidates existing studies, identifies gaps, and provides an overview of AI's potential in diagnosing and managing IRDs. It aims to structure pathways for advancing clinical applications by exploring AI techniques like machine learning and deep learning, particularly in disease detection, progression prediction, and personalized treatment planning. Special focus is placed on the effectiveness of convolutional neural networks in these areas. Additionally, the integration of explainable AI is discussed, emphasizing its importance in clinical settings to improve transparency and trust in AI-based systems. The review addresses the need to bridge existing gaps in focused studies on AI's role in IRDs, offering a structured analysis of current AI techniques and outlining future research directions. It concludes with an overview of the challenges and opportunities in deploying AI for IRDs, highlighting the need for interdisciplinary collaboration and the continuous development of robust, interpretable AI models to advance clinical applications.
An Approach for Auto Generation of Labeling Functions for Software Engineering Chatbots
2024-10-09
Ebube Alor, Ahmad Abdellatif, SayedHassan Khatoonabadi, Emad Shihab
Software engineering (SE) chatbots are increasingly gaining attention for their role in enhancing development processes. At the core of chatbots are the Natural Language Understanding platforms (NLUs), which enable them to comprehend and respond to user queries. Before deploying NLUs, there is a need to train them with labeled data. However, acquiring such labeled data for SE chatbots is challenging due to the scarcity of high-quality datasets. This challenge arises because training SE chatbots requires specialized vocabulary and phrases not found in typical language datasets. Consequently, chatbot developers often resort to manually annotating user queries to gather the data necessary for training effective chatbots, a process that is both time-consuming and resource-intensive. Previous studies propose approaches to support chatbot practitioners in annotating users' posed queries. However, these approaches require human intervention to generate rules, called labeling functions (LFs), that identify and categorize user queries based on specific patterns in the data. To address this issue, we propose an approach to automatically generate LFs by extracting patterns from labeled user queries. We evaluate the effectiveness of our approach by applying it to the queries of four diverse SE datasets (namely AskGit, MSA, Ask Ubuntu, and Stack Overflow) and measure the performance improvement gained from training the NLU on the queries labeled by the generated LFs. We find that the generated LFs effectively label data with AUC scores of up to 85.3%, and NLU's performance improvement of up to 27.2% across the studied datasets. Furthermore, our results show that the number of LFs used to generate LFs affects the labeling performance. We believe that our approach can save time and resources in labeling users' queries, allowing practitioners to focus on core chatbot functionalities.
Towards Realistic UAV Vision-Language Navigation: Platform, Benchmark, and Methodology
2024-10-09
Xiangyu Wang, Donglin Yang, Ziqin Wang, Hohin Kwan, Jinyu Chen, Wenjun Wu, Hongsheng Li, Yue Liao, Si Liu
Developing agents capable of navigating to a target location based on language instructions and visual information, known as vision-language navigation (VLN), has attracted widespread interest. Most research has focused on ground-based agents, while UAV-based VLN remains relatively underexplored. Recent efforts in UAV vision-language navigation predominantly adopt ground-based VLN settings, relying on predefined discrete action spaces and neglecting the inherent disparities in agent movement dynamics and the complexity of navigation tasks between ground and aerial environments. To address these disparities and challenges, we propose solutions from three perspectives: platform, benchmark, and methodology. To enable realistic UAV trajectory simulation in VLN tasks, we propose the OpenUAV platform, which features diverse environments, realistic flight control, and extensive algorithmic support. We further construct a target-oriented VLN dataset consisting of approximately 12k trajectories on this platform, serving as the first dataset specifically designed for realistic UAV VLN tasks. To tackle the challenges posed by complex aerial environments, we propose an assistant-guided UAV object search benchmark called UAV-Need-Help, which provides varying levels of guidance information to help UAVs better accomplish realistic VLN tasks. We also propose a UAV navigation LLM that, given multi-view images, task descriptions, and assistant instructions, leverages the multimodal understanding capabilities of the MLLM to jointly process visual and textual information, and performs hierarchical trajectory generation. The evaluation results of our method significantly outperform the baseline models, while there remains a considerable gap between our results and those achieved by human operators, underscoring the challenge presented by the UAV-Need-Help task.
MOOSE-Chem: Large Language Models for Rediscovering Unseen Chemistry Scientific Hypotheses
2024-10-09
Zonglin Yang, Wanhao Liu, Ben Gao, Tong Xie, Yuqiang Li, Wanli Ouyang, Soujanya Poria, Erik Cambria, Dongzhan Zhou
Scientific discovery contributes largely to human society's prosperity, and recent progress shows that LLMs could potentially catalyze this process. However, it is still unclear whether LLMs can discover novel and valid hypotheses in chemistry. In this work, we investigate this central research question: Can LLMs automatically discover novel and valid chemistry research hypotheses given only a chemistry research background (consisting of a research question and/or a background survey), without limitation on the domain of the research question? After extensive discussions with chemistry experts, we propose an assumption that a majority of chemistry hypotheses can be resulted from a research background and several inspirations. With this key insight, we break the central question into three smaller fundamental questions. In brief, they are: (1) given a background question, whether LLMs can retrieve good inspirations; (2) with background and inspirations, whether LLMs can lead to hypothesis; and (3) whether LLMs can identify good hypotheses to rank them higher. To investigate these questions, we construct a benchmark consisting of 51 chemistry papers published in Nature, Science, or a similar level in 2024 (all papers are only available online since 2024). Every paper is divided by chemistry PhD students into three components: background, inspirations, and hypothesis. The goal is to rediscover the hypothesis, given only the background and a large randomly selected chemistry literature corpus consisting the ground truth inspiration papers, with LLMs trained with data up to 2023. We also develop an LLM-based multi-agent framework that leverages the assumption, consisting of three stages reflecting the three smaller questions. The proposed method can rediscover many hypotheses with very high similarity with the ground truth ones, covering the main innovations.
A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Research
2024-10-09
Seongjin Choi, Zhixiong Jin, Seung Woo Ham, Jiwon Kim, Lijun Sun
Deep Generative Models (DGMs) have rapidly advanced in recent years, becoming essential tools in various fields due to their ability to learn complex data distributions and generate synthetic data. Their importance in transportation research is increasingly recognized, particularly for applications like traffic data generation, prediction, and feature extraction. This paper offers a comprehensive introduction and tutorial on DGMs, with a focus on their applications in transportation. It begins with an overview of generative models, followed by detailed explanations of fundamental models, a systematic review of the literature, and practical tutorial code to aid implementation. The paper also discusses current challenges and opportunities, highlighting how these models can be effectively utilized and further developed in transportation research. This paper serves as a valuable reference, guiding researchers and practitioners from foundational knowledge to advanced applications of DGMs in transportation research.
Dark sector interactions in light of weak lensing data
2024-10-09
M. Benetti, P. T. Z. Seidel, C. Pigozzo, I. P. R. Baranov, S. Carneiro, J. C. Fabris
The current observational tensions in the standard cosmological model have reinforced the research on dynamical dark energy, in particular on models with non-gravitational interaction between the dark components. Late-time observables like type Ia supernovas (SNe Ia) and large-scale structures (LSS) point to an energy flux from dark energy to dark matter, while the anisotropy spectrum of the cosmic microwave background (CMB) points to a small flux from dark matter to dark energy, fully consistent with no interaction at all. As background and visible matter tests are insensitive to the suppression/enhancement in the dark matter power spectrum, which is a characteristic of interacting models, while the CMB spectrum is strongly affected by it, this could be the origin of those results. In order to confirm it and at the same time to rule out the role of possible systematics between early and late-time observations, the use of a low redshift observable sensitive to the gravitational potential generated by dark matter is crucial. In the present paper, we investigate the observational viability of a class of interacting dark energy models, namely with energy exchange between vacuum-type and dust components, in the light of the Dark Energy Survey (DES) observations of galaxy weak lensing, in the context of a spatially-flat Friedmann-Lema\^itre-Robertson-Walker spacetime. The best fit of our analysis is compatible with null interaction, with a weak preference for an energy flux from dark matter to dark energy, confirming the CMB based constraints.
Transients by Black Hole Formation from Red Supergiants: Impact of Dense Circumstellar Matter
2024-10-09
Daichi Tsuna, Xiaoshan Huang, Jim Fuller, Anthony L. Piro
Failed supernovae (SNe), which are likely the main channel for forming stellar-mass black holes, are predicted to accompany mass ejections much weaker than typical core-collapse SNe. We conduct a grid of one-dimensional radiation hydrodynamical simulations to explore the emission of failed SNe from red supergiant progenitors, leveraging recent understanding of the weak explosion and the dense circumstellar matter (CSM) surrounding these stars. We find from these simulations and semi-analytical modeling that diffusion in the CSM prolongs the early emission powered by shock breakout/cooling. The early emission has peak luminosities of $\sim 10^7$-$10^8~L_\odot$ in optical and UV, and durations of days to weeks. The presence of dense CSM aids detection of the early bright peak from these events via near-future wide-field surveys such as Rubin Observatory, ULTRASAT and UVEX.
The first detailed photometric and spectroscopic analysis of the G-type eclipsing binary KM UMa is presented, which indicates that the system is a short-period detached eclipsing binary. The radial velocity curves were calculated using the cross-correlation function method based on Large Sky Area Multi-Object Fiber Spectroscopic Telescope, Sloan Digital Sky Survey, and our observations, which determined the mass ratio as $q=0.45\ (\pm0.04)$. Based on the light curves from the Transiting Exoplanet Survey Satellite, other survey data, and our multiband observations, the positive and negative O'Connell effects have been detected evolving gradually and alternately over the last 20 yr, which can be explained by the presence of spots on the primary component. A superflare event was detected in the SuperWASP data on 2007 February 28, further indicating that KM UMa is a very active system. We calculated its energy to be $5\times10^{34}$ erg by assuming it occurred on the primary star. Utilizing hundreds of medium-resolution spectra and one low-resolution spectrum, the equivalent width variations of the $H_{\alpha}$ line were calculated, indicating the presence of a 5.21 ($\pm0.67$) yr magnetic activity cycle. The orbital period variations were analyzed using the O-C method, detecting a long-term decrease superimposed with a periodic variation. The amplitude of the cyclic variation is $0.01124\ (\pm0.00004)$ day, with a period of $33.66\ (\pm 0.0012)$ yr, which exceeds the 5.21 yr activity cycle, suggesting that this is more likely attributable to the light travel time effect of a third body. Simultaneously, a visual companion has been detected based on the Gaia astrometric data, indicating that KM UMa is actually in a 2+1+1 hierarchical quadruple system.
Radio signal propagation in 5G systems equipped with RISs (PL: Propagacja sygnału radiowego w systemach 5G wyposażonych w matryce IPR)
2024-10-09
Adam Samorzewski, Adrian Kliks
In this paper, the characteristics of radio signal propagation within the boundaries of the city of Poznan (Poland) are analyzed. The study considers the use of a Radio Access Network (RAN) of the 5th generation wireless system (5G NR - New Radio), which includes 8 base stations (BSs) utilizing Single Input Single Output (SISO) or Multiple Input Multiple Output (MIMO) antenna technology depending on the adopted configuration of network cells. Additionally, 15 reflecting arrays known as Reconfigurable Intelligent Surfaces (RISs) were placed in the studied area, and their impact on radio signal propagation at different suspension heights was taken into account.
KiDS-Legacy: Covariance validation and the unified OneCovariance framework for projected large-scale structure observables
2024-10-09
Robert Reischke, Sandra Unruh, Marika Asgari, Andrej Dvornik, Hendrik Hildebrandt, Benjamin Joachimi, Lucas Porth, Maximilian von Wietersheim-Kramsta, Jan Luca van den Busch, Benjamin Stölzner, Angus H. Wright, Ziang Yan, Maciej Bilicki, Pierre Burger, Joachim Harnois-Deraps, Christos Georgiou, Catherine Heymans, Priyanka Jalan, Shahab Joudaki, Konrad Kuijken, Shun-Sheng Li, Laila Linke, Constance Mahony, Davide Sciotti, Tilman Tröster
We introduce OneCovariance, an open-source software designed to accurately compute covariance matrices for an arbitrary set of two-point summary statistics across a variety of large-scale structure tracers. Utilising the halo model, we estimate the statistical properties of matter and biased tracer fields, incorporating all Gaussian, non-Gaussian, and super-sample covariance terms. The flexible configuration permits user-specific parameters, such as the complexity of survey geometry, the halo occupation distribution employed to define each galaxy sample, or the form of the real-space and/or Fourier space statistics to be analysed. We illustrate the capabilities of OneCovariance within the context of a cosmic shear analysis of the final data release of the Kilo-Degree Survey (KiDS-Legacy). Upon comparing our estimated covariance with measurements from mock data and calculations from independent software, we ascertain that OneCovariance achieves accuracy at the per cent level. When assessing the impact of ignoring complex survey geometry in the cosmic shear covariance computation, we discover misestimations at approximately the $10\%$ level for cosmic variance terms. Nonetheless, these discrepancies do not significantly affect the KiDS-Legacy recovery of cosmological parameters. We derive the cross-covariance between real-space correlation functions, bandpowers, and COSEBIs, facilitating future consistency tests among these three cosmic shear statistics. Additionally, we calculate the covariance matrix of photometric-spectroscopic galaxy clustering measurements, validating Jackknife covariance estimates for calibrating KiDS-Legacy redshift distributions. The OneCovariance can be found on github Hub together with a comprehensive documentation and examples.
How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users
2024-10-09
Alex Berke, Enrico Bacis, Badih Ghazi, Pritish Kamath, Ravi Kumar, Robin Lassonde, Pasin Manurangsi, Umar Syed
Browser fingerprinting can be used to identify and track users across the Web, even without cookies, by collecting attributes from users' devices to create unique "fingerprints". This technique and resulting privacy risks have been studied for over a decade. Yet further research is limited because prior studies used data not publicly available. Additionally, data in prior studies lacked user demographics. Here we provide a first-of-its-kind dataset to enable further research. It includes browser attributes with users' demographics and survey responses, collected with informed consent from 8,400 US study participants. We use this dataset to demonstrate how fingerprinting risks differ across demographic groups. For example, we find lower income users are more at risk, and find that as users' age increases, they are both more likely to be concerned about fingerprinting and at real risk of fingerprinting. Furthermore, we demonstrate an overlooked risk: user demographics, such as gender, age, income level and race, can be inferred from browser attributes commonly used for fingerprinting, and we identify which browser attributes most contribute to this risk. Our data collection process also conducted an experiment to study what impacts users' likelihood to share browser data for open research, in order to inform future data collection efforts, with responses from 12,461 total participants. Female participants were significantly less likely to share their browser data, as were participants who were shown the browser data we asked to collect. Overall, we show the important role of user demographics in the ongoing work that intends to assess fingerprinting risks and improve user privacy, with findings to inform future privacy enhancing browser developments. The dataset and data collection tool we provide can be used to further study research questions not addressed in this work.
2024-10-09
Madhurima Deb, Isabel Beckenbach, Matteo Petrera, Dariush Ehsani, Marcel Fuhrmann, Yun Hao, Olaf Teschke, Moritz Schubotz
Mathematical research thrives on the effective dissemination and discovery of knowledge. zbMATH Open has emerged as a pivotal platform in this landscape, offering a comprehensive repository of mathematical literature. Beyond indexing and abstracting, it serves as a unified quality-assured infrastructure for finding, evaluating, and connecting mathematical information that advances mathematical research as well as interdisciplinary exploration. zbMATH Open enables scientific quality control by post-publication reviews and promotes connections between researchers, institutions, and research outputs. This paper represents the functionalities of the most significant features of this open-access service, highlighting its role in shaping the future of mathematical information retrieval.
2024-10-09
Nils Hüsken, Elisabetta Spadaro Norella, Ivan Polyakov
Exotic hadrons are a new class of hadronic states whose properties do not allow them to be classified as conventional quark-antiquark mesons or three quark baryons. Finding new and understanding established exotic states is the most important topic in today's hadron spectroscopy and a promising avenue to advance our knowledge on Quantum Chromodynamics in the non-perturbative regime. While several high-quality reviews on the topic exist, they are all at an advanced level. The present article aims to address new-comers to the field with a simple introduction to exotic hadrons with an emphasis on the experimental studies.
Differentiable Modeling of Planet and Substellar Atmosphere: High-Resolution Emission, Transmission, and Reflection Spectroscopy with ExoJAX2
2024-10-09
Hajime Kawahara, Yui Kawashima, Shotaro Tada, Hiroyuki Tako Ishikawa, Ko Hosokawa, Yui Kasagi, Takayuki Kotani, Kento Masuda, Stevanus Nuguroho, Motohide Tamura, Hibiki Yama, Daniel Kitzmann, Nicolas Minesi, Brett M. Morris
Modeling based on differentiable programming holds great promise for astronomy, as it can employ techniques such as Hamiltonian Monte Carlo, gradient-based optimization, and other machine learning techniques. This new programming paradigm has motivated us to develop the first auto-differentiable spectrum model of exoplanets and brown dwarfs, ExoJAX (Kawahara et al. 2022). ExoJAX is designed to directly calculate cross-sections as functions of temperature and pressure, rather than interpolating tabulated data, to minimize errors in high-dispersion spectra modeling. However, its application was primarily proof-of-concept and limited to narrowband high-dispersion emission spectroscopy. In this paper, we have enhanced the differentiable opacity calculation using a new fast and memory-efficient algorithm, and have developed differentiable radiative transfer schemes, including emission, transmission, and reflection spectroscopy. These enhancements significantly expand the range of applications, as demonstrated through actual atmospheric retrievals: high-dispersion emission spectra of the brown dwarf GL229 B, medium-dispersion transmission spectra of the hot Saturn WASP-39 b from JWST, and high-dispersion reflection spectra of Jupiter. We obtained a C/O ratio for GL229 B consistent with its host star, constrained WASP-39 b's radial velocity from molecular fine structures at original resolution ($R \sim 3,000$), and estimated Jupiter's metallicity consistent with previous studies.
Strong Nongravitational Accelerations and the Potential for Misidentification of Near-Earth Objects
2024-10-09
Aster G. Taylor, Darryl Z. Seligman, Matthew J. Holman, Peter Veres, Davide Farnocchia, Nikole Lewis, Marco Micheli, Jason T. Wright
Nongravitational accelerations in the absence of observed activity have recently been identified on NEOs, opening the question of the prevalence of anisotropic mass-loss in the near-Earth environment. Motivated by the necessity of nongravitational accelerations to identify 2010 VL$_{65}$ and 2021 UA$_{12}$ as a single object, we investigate the problem of linking separate apparitions in the presence of nongravitational perturbations. We find that nongravitational accelerations on the order of $10^{-9}$ au/d$^2$ can lead to a change in plane-of-sky positions of $\sim10^3$ arcsec between apparitions. Moreover, we inject synthetic tracklets of hypothetical nongravitationally-accelerating NEOs into the Minor Planet Center orbit identification algorithms. We find that at large nongravitational accelerations ($|A_i|\geq10^{-8}$ au/d$^2$) these algorithms fail to link a significant fraction of these tracklets. We further show that if orbits can be determined for both apparitions, the tracklets will be linked regardless of nongravitational accelerations, although they may be linked to multiple objects. In order to aid in the identification and linkage of nongravitationally accelerating objects, we propose and test a new methodology to search for unlinked pairs. When applied to the current census of NEOs, we recover the previously identified case but identify no new linkages. We conclude that current linking algorithms are generally robust to nongravitational accelerations, but objects with large nongravitational accelerations may potentially be missed. While current algorithms are well-positioned for the anticipated increase in the census population from future survey missions, it may be possible to find objects with large nongravitational accelerations hidden in isolated tracklet pairs.
Discovery of eclipses in the cataclysmic variable LAMOST J035913.61+405035.0
2024-10-09
V. P. Kozhevnikov
I conducted photometric observations of the cataclysmic variable LAMOST J035913.61+405035.0 and discovered previously unknown eclipses. During these observations, I recorded 14 eclipses over two groups of nights separated by 13 months. I accurately determined the orbital period of the system to be Porb=0.22834385+\-0.00000021 d. For the eclipses, I derived an ephemeris which is valid for a long time and suitable for studying changes in the orbital period. The out-of-eclipse magnitude of the star varied between 15.32(+\-0.02) and 17.25(+\-0.08) mag. As the brightness decreased, the eclipses became deeper and narrower. The average depth of eclipses was 1.35(+\-0.10) mag, and the average width at half-depth was 16.9(+\-0.7) min. I estimated the range of possible orbital inclinations to be between 72.8 degr and 76.0 degr, and the range of average absolute V-band magnitudes of the disc to be between 5.16(+\-0.15) and 5.44(+\-0.15) mag. Although based on the light curve from the ZTF survey, LAMOST J035913.61+405035.0 showed only small outbursts with amplitudes below 1.5 mag, it should be classified as a dwarf nova because the average disc brightness and mass transfer rate were below the limit of thermal instability. However, there have been no significant outbursts in the ZTF light curve over the past 1.6 yr. Instead, a gradual decrease in brightness lasting 120 d suggests that this object may occasionally become a nova-like variable of the VY Scl type.
Students' Perceptions and Use of Generative AI Tools for Programming Across Different Computing Courses
2024-10-09
Hieke Keuning, Isaac Alpizar-Chacon, Ioanna Lykourentzou, Lauren Beehler, Christian Köppe, Imke de Jong, Sergey Sosnovsky
Investigation of students' perceptions and opinions on the use of generative artificial intelligence (GenAI) in education is a topic gaining much interest. Studies addressing this are typically conducted with large heterogeneous groups, at one moment in time. However, how students perceive and use GenAI tools can potentially depend on many factors, including their background knowledge, familiarity with the tools, and the learning goals and policies of the courses they are taking. In this study we explore how students following computing courses use GenAI for programming-related tasks across different programs and courses: Bachelor and Master, in courses in which learning programming is the learning goal, courses that require programming as a means to achieve another goal, and in courses in which programming is optional, but can be useful. We are also interested in changes over time, since GenAI capabilities are changing at a fast pace, and users are adopting GenAI increasingly. We conducted three consecutive surveys (fall `23, winter `23, and spring `24) among students of all computing programs of a large European research university. We asked questions on the use in education, ethics, and job prospects, and we included specific questions on the (dis)allowed use of GenAI tools in the courses they were taking at the time. We received 264 responses, which we quantitatively and qualitatively analyzed, to find out how students have employed GenAI tools across 59 different computing courses, and whether the opinion of an average student about these tools evolves over time. Our study contributes to the emerging discussion of how to differentiate GenAI use across different courses, and how to align its use with the learning goals of a computing course.
The warm-hot intergalactic medium (WHIM) in inter-cluster filaments -- A forecast for HUBS observations based on eRASS1 superclusters
2024-10-09
Yuanyuan Zhao, Haiguang Xu, Ang Liu, Xiaoyuan Zhang, Li Ji, Jiang Chang, Dan Hu, Norbert Werner, Zhongli Zhang, Wei Cui, Xiangping Wu
Cosmological simulations indicate that nearly half of the baryons in the nearby Universe are in the warm-hot intergalactic medium (WHIM) phase, with about half of which residing in cosmic filaments. Recent observational studies using stacked survey data and deep exposures of galaxy cluster outskirts have detected soft X-ray excess associated with optically identified filaments. However, the physical characteristics of WHIM in filaments remain largely undetermined due to the lack of direct spectral diagnostics. In this work, we aim to select appropriate targets for WHIM characterization through pointing observations with the future Hot Universe Baryon Surveyor (HUBS) mission, which is designed with eV level energy resolution in the 0.1-2.0 keV band and a 1 square degree field-of-view. We built a sample of 1577 inter-cluster filaments based on the first eROSITA All-Sky Survey (eRASS1) supercluster catalog and estimated their soft X-ray emission, and used their modeled geometrical properties and oxygen line intensities to select four most appropriate candidate targets for HUBS observations. By simulating and analyzing their mock observations, we demonstrated that with 200 ks HUBS exposure for each candidate, the gas properties of individual filaments can be accurately determined, with the temperature constrained to +-0.01 keV, metallicity constrained to < +-0.03 solar, and density constrained to < +-10%. Elemental abundance of O, Ne, Mg, and Fe can be measured separately, providing unprecedented insights into the chemical history of the filament gas. We also showed that direct mapping of the WHIM distribution is promising with narrow-band imaging of the O viii line. Our work forecasts that next-generation X-ray missions such as HUBS will provide substantial improvement in our understanding of the physical status and evolution history of the diffuse WHIM gas in cosmic large-scale structure.
The Clear Sky Corridor: Insights Towards Aerosol Formation in Exoplanets Using An AI-based Survey of Exoplanet Atmospheres
2024-10-09
Reza Ashtari, Kevin B. Stevenson, David Sing, Mercedes Lopez-Morales, Munazza K. Alam, Nikolay K. Nikolov, Thomas M. Evans-Soma
Producing optimized and accurate transmission spectra of exoplanets from telescope data has traditionally been a manual and labor-intensive procedure. Here we present the results of the first attempt to improve and standardize this procedure using artificial intelligence (AI) based processing of light curves and spectroscopic data from transiting exoplanets observed with the Hubble Space Telescope's (HST) Wide Field Camera 3 (WFC3) instrument. We implement an AI-based parameter optimizer that autonomously operates the Eureka pipeline to produce homogeneous transmission spectra of publicly available HST WFC3 datasets, spanning exoplanet types from hot Jupiters to sub-Neptunes. Surveying 43 exoplanets with temperatures between 280 and 2580 Kelvin, we confirm modeled relationships between the amplitude of the water band at 1.4um in hot Jupiters and their equilibrium temperatures. We also identify a similar, novel trend in Neptune/sub-Neptune atmospheres, but shifted to cooler temperatures. Excitingly, a planet mass versus equilibrium temperature diagram reveals a "Clear Sky Corridor," where planets between 700 and 1700 Kelvin (depending on the mass) show stronger 1.4um H2O band measurements. This novel trend points to metallicity as a potentially important driver of aerosol formation. As we unveil and include these new discoveries into our understanding of aerosol formation, we enter a thrilling future for the study of exoplanet atmospheres. With HST sculpting this foundational understanding for aerosol formation in various exoplanet types, ranging from Jupiters to sub-Neptunes, we present a compelling platform for the James Webb Space Telescope (JWST) to discover similar atmospheric trends for more planets across a broader wavelength range.
2024-10-09
Jürgen Burin, Philipp Gaggl, Simon Waid, Andreas Gsponer, Thomas Bergauer
In this paper we review the models and their parameters to describe the relative permittivity, bandgap, impact ionization, mobility, charge carrier recombination/effective masses and incomplete dopand ionization of 4H silicon carbide in computer simulations. We aim to lower the entrance barrier for newcomers and provide a critical evaluation of the status quo to identify shortcomings and guide future research. The review reveals a rich set of often diverging values in literature based on a variety of calculation and measurement methods. Although research for all the selected parameters is still active, we show that sometimes old values or those determined for other kinds of silicon carbide are commonly used.
Star formation rates (SFRs) are a crucial observational tracer of galaxy formation and evolution. Spectroscopy, which is expensive, is traditionally used to estimate SFRs. This study tests the possibility of inferring SFRs of large samples of galaxies from only photometric data, using state-of-the-art machine learning and deep learning algorithms. The dataset adopted in this work is the one collected by Delli Veneri et al. (2019): it includes photometric data of more than 27 million galaxies coming from the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7). The algorithms we implemented and tested for comparing the performances include Linear Regression, Long Short-Term Memory (LSTM) networks, Support Vector Regression (SVR), Random Forest Regressor, Decision Tree Regressor, Gradient Boosting Regressor, and classical deep learning models. Our results mention that the Linear Regression model predicted an impressive accuracy of 98.97 percent as measured by the Mean Absolute Error (MAE), demonstrating that machine-learning approaches can be effective when it comes to photometric SFR estimation. Besides, the paper also reported the results for other intelligent algorithms, which predicted the SFRs, providing a detailed comparison of the performance of different machine learning algorithms in the photometric SFR estimation. This study not only shows the estimated SFR from photometric data is promising but also opens a door toward the application of machine learning and deep learning in astrophysics.
Systematic collapse of the accretion disc in AGN confirmed by UV photometry and broad line spectra
2024-10-09
Jia-Lai Kang, Chris Done, Scott Hagen, Matthew J. Temple, John D. Silverman, Junyao Li, Teng Liu
A recent study on the spectral energy distribution (SED) of AGN combined unobscured X-ray sources from the eROSITA eFEDS Survey with high quality optical imaging from Subaru's Hyper Suprime-Cam (HSC). The HSC data enabled accurate host galaxy subtraction as well as giving a uniform black hole mass estimator from the stellar mass. The resulting stacked optical/X-ray SEDs for black holes at fixed mass show a dramatic transition, where the dominating disc component in bright AGN evaporates into an X-ray hot plasma below $L/L_{\rm Edd}\sim 0.01$. The models fit to these datasets predicted the largest change in SED in the rest frame UV ($< 3000\,\r{A}$), but this waveband was not included in the original study. Here we use archival $u$-band and UV photometry to extend the SEDs into this range, and confirm the UV is indeed intrinsically faint in AGN below $L/L_{\rm Edd}\sim 0.01$ as predicted. This dramatic drop in UV photo-ionising flux is also seen from its effect on the broad emission lines. We stack the recently released SDSS DR18 optical spectra for this sample, and show that the broad H$\beta$ line disappears along with the UV bright component at $L/L_{\rm Edd}\sim 0.01$. This shows that there is a population of unobscured, X-ray bright, UV faint AGN which lack broad emission lines (true type 2 Seyferts).
The research focuses on determining the metallicity ([Fe/H]) predicted in the solar twin stars by using various regression modeling techniques which are, Random Forest, Linear Regression, Decision Tree, Support Vector, and Gradient Boosting. The data set that is taken into account here includes Stellar parameters and chemical abundances derived from a high-accuracy abundance catalog of solar twins from the GALAH survey. To overcome the missing values, intensive preprocessing techniques involving, imputation are done. Each model will subjected to training using different critical observables, which include, Mean Squared Error(MSE), Mean Absolute Error(MAE), Root Mean Squared Error(RMSE), and R-squared. Modeling is done by using, different feature sets like temperature: effective temperature(Teff), surface gravity: log g of 14-chemical-abundances namely, (([Na/Fe], [Mg/Fe], [Al/Fe], [Si/Fe], [Ca/Fe], [Sc/Fe], [Ti/Fe], [Cr/Fe], [Mn/Fe], [Ni/Fe], [Cu/Fe], [Zn/Fe], [Y/Fe], [Ba/Fe])). The target variable considered is the metallicity ([Fe/H]). The findings indicate that the Random Forest model achieved the highest accuracy, with an MSE of 0.001628 and an R-squared value of 0.9266. The results highlight the efficacy of ensemble methods in handling complex datasets with high dimensionality. Additionally, this study underscores the importance of selecting appropriate regression models for astronomical data analysis, providing a foundation for future research in predicting stellar properties with machine learning techniques.
2024-10-09
M. Cristina Diamantini, Carlo A. Trugenberger
Bose metals are metals made of Cooper pairs, which form at very low temperatures in superconducting films and Josephson junction arrays as an intermediate phase between superconductivity and superinsulation. We predicted the existence of this 2D metallic phase of bosons in the mid 90s, showing that they arise due to topological quantum effects. The observation of Bose metals in perfectly regular Josephson junction arrays fully confirms our original prediction and rules out alternative models based on disorder. Here, we review the basic mechanism leading to Bose metals. The key points are that the relevant vortices in granular superconductors are core-less, mobile XY vortices which can tunnel through the system due to quantum phase slips, that there is no charge-phase commutation relation preventing such vortices to be simultaneously out of condensate with charges, and that out-of-condensate charges and vortices are subject to topological mutual statistics interactions, a quantum effect that dominates at low temperatures. These repulsive mutual statistics interactions are sufficient to increase the energy of the Cooper pairs and lift them out of condensate. The result is a topological ground state in which charge conduction along edges and vortex movement across them organize themselves so as to generate the observed metallic saturation at low temperatures. This state is known today as a bosonic topological insulator.
Simultaneous uniformization of chord-arc curves and BMO Teichmüller space
2024-10-09
Katsuhiko Matsuzaki
By using a method of simultaneous uniformization, we parametrize the space of chord-arc curves within the product of BMO Teichm\"uller spaces. This method provides a biholomorphic correspondence between such embeddings $\gamma$ onto chord-arc curves $\Gamma$ and $\log \gamma'$ in the Banach space of BMO functions. In our previous work, through this correspondence $\Lambda$, we have clarified the arguments of Teichm\"uller space theory for chord-arc curves, solved the problem of the discontinuity of the Riemann mapping parametrization of chord-arc curves, and simplified the proof for a real-analytic diffeomorphism onto the space of reparametrizations by strongly quasisymmetric homeomorphisms. In this paper, we first review these results by renewing the arguments in an effort to minimize reliance on other results. Then, we present a new application of simultaneous uniformization to the Cauchy transform of BMO functions on a chord-arc curve. We show that the Cauchy transform can be described by the derivative of the biholomorphic map $\Lambda$ and hence depends holomorphically on the variation of chord-arc curves. Finally, we organize and demonstrate the corresponding results for the VMO Teichm\"uller space.
On the Minimal Theory of Consciousness Implicit in Active Inference
2024-10-09
Christopher J. Whyte, Andrew W. Corcoran, Jonathan Robinson, Ryan Smith, Rosalyn J. Moran, Thomas Parr, Karl J. Friston, Anil K. Seth, Jakob Hohwy
The multifaceted nature of experience poses a challenge to the study of consciousness. Traditional neuroscientific approaches often concentrate on isolated facets, such as perceptual awareness or the global state of consciousness and construct a theory around the relevant empirical paradigms and findings. Theories of consciousness are, therefore, often difficult to compare; indeed, there might be little overlap in the phenomena such theories aim to explain. Here, we take a different approach: starting with active inference, a first principles framework for modelling behaviour as (approximate) Bayesian inference, and building up to a minimal theory of consciousness, which emerges from the shared features of computational models derived under active inference. We review a body of work applying active inference models to the study of consciousness and argue that there is implicit in all these models a small set of theoretical commitments that point to a minimal (and testable) theory of consciousness.
Decentralized Clinical Trials in the Era of Real-World Evidence: A Statistical Perspective
2024-10-09
Jie Chen, Junrui Di, Nadia Daizadeh, Ying Lu, Hongwei Wang, Yuan-Li Shen, Jennifer Kirk, Frank W. Rockhold, Herbert Pang, Jing Zhao, Weili He, Andrew Potter, Hana Lee
There has been a growing trend that activities relating to clinical trials take place at locations other than traditional trial sites (hence decentralized clinical trials or DCTs), some of which are at settings of real-world clinical practice. Although there are numerous benefits of DCTs, this also brings some implications on a number of issues relating to the design, conduct, and analysis of DCTs. The Real-World Evidence Scientific Working Group of the American Statistical Association Biopharmaceutical Section has been reviewing the field of DCTs and provides in this paper considerations for decentralized trials from a statistical perspective. This paper first discusses selected critical decentralized elements that may have statistical implications on the trial and then summarizes regulatory guidance, framework, and initiatives on DCTs. More discussions are presented by focusing on the design (including construction of estimand), implementation, statistical analysis plan (including missing data handling), and reporting of safety events. Some additional considerations (e.g., ethical considerations, technology infrastructure, study oversight, data security and privacy, and regulatory compliance) are also briefly discussed. This paper is intended to provide statistical considerations for decentralized trials of medical products to support regulatory decision-making.
Use of Real-World Data and Real-World Evidence in Rare Disease Drug Development: A Statistical Perspective
2024-10-09
Jie Chen, Susan Gruber, Hana Lee, Haitao Chu, Shiowjen Lee, Haijun Tian, Yan Wang, Weili He, Thomas Jemielita, Yang Song, Roy Tamura, Lu Tian, Yihua Zhao, Yong Chen, Mark van der Laan, Lei Nie
Real-world data (RWD) and real-world evidence (RWE) have been increasingly used in medical product development and regulatory decision-making, especially for rare diseases. After outlining the challenges and possible strategies to address the challenges in rare disease drug development (see the accompanying paper), the Real-World Evidence (RWE) Scientific Working Group of the American Statistical Association Biopharmaceutical Section reviews the roles of RWD and RWE in clinical trials for drugs treating rare diseases. This paper summarizes relevant guidance documents and frameworks by selected regulatory agencies and the current practice on the use of RWD and RWE in natural history studies and the design, conduct, and analysis of rare disease clinical trials. A targeted learning roadmap for rare disease trials is described, followed by case studies on the use of RWD and RWE to support a natural history study and marketing applications in various settings.
Challenges and Possible Strategies to Address Them in Rare Disease Drug Development: A Statistical Perspective
2024-10-09
Jie Chen, Lei Nie, Shiowjen Lee, Haitao Chu, Haijun Tian, Yan Wang, Weili He, Thomas Jemielita, Susan Gruber, Yang Song, Roy Tamura, Lu Tian, Yihua Zhao, Yong Chen, Mark van der Laan, Hana Lee
Developing drugs for rare diseases presents unique challenges from a statistical perspective. These challenges may include slowly progressive diseases with unmet medical needs, poorly understood natural history, small population size, diversified phenotypes and geneotypes within a disorder, and lack of appropriate surrogate endpoints to measure clinical benefits. The Real-World Evidence (RWE) Scientific Working Group of the American Statistical Association Biopharmaceutical Section has assembled a research team to assess the landscape including challenges and possible strategies to address these challenges and the role of real-world data (RWD) and RWE in rare disease drug development. This paper first reviews the current regulations by regulatory agencies worldwide and then discusses in more details the challenges from a statistical perspective in the design, conduct, and analysis of rare disease clinical trials. After outlining an overall development pathway for rare disease drugs, corresponding strategies to address the aforementioned challenges are presented. Other considerations are also discussed for generating relevant evidence for regulatory decision-making on drugs for rare diseases. The accompanying paper discusses how RWD and RWE can be used to improve the efficiency of rare disease drug development.
The code review comment (CRC) is pivotal in the process of modern code review. It provides reviewers with the opportunity to identify potential bugs, offer constructive feedback, and suggest improvements. Clear and concise code review comments (CRCs) facilitate the communication between developers and is crucial to the correct understanding of the issues identified and proposed solutions. Despite the importance of CRCs' clarity, there is still a lack of guidelines on what constitutes a good clarity and how to evaluate it. In this paper, we conduct a comprehensive study on understanding and evaluating the clarity of CRCs. We first derive a set of attributes related to the clarity of CRCs, namely RIE attributes (i.e., Relevance, Informativeness, and Expression), as well as their corresponding evaluation criteria based on our literature review and survey with practitioners. We then investigate the clarity of CRCs in open-source projects written in nine programming languages and find that a large portion (i.e., 28.8%) of the CRCs lack the clarity in at least one of the attributes. Finally, we propose ClearCRC, an automated framework that evaluates the clarity of CRCs. Experimental results show that ClearCRC can effectively evaluate the clarity of CRCs and outperform the baselines.
2024-10-09
Xiaofan Zhou, Baiting Chen, Yu Gui, Lu Cheng
Conformal prediction (CP), a distribution-free uncertainty quantification (UQ) framework, reliably provides valid predictive inference for black-box models. CP constructs prediction sets that contain the true output with a specified probability. However, modern data science diverse modalities, along with increasing data and model complexity, challenge traditional CP methods. These developments have spurred novel approaches to address evolving scenarios. This survey reviews the foundational concepts of CP and recent advancements from a data-centric perspective, including applications to structured, unstructured, and dynamic data. We also discuss the challenges and opportunities CP faces in large-scale data and models.
The field of 3D representation has experienced significant advancements, driven by the increasing demand for high-fidelity 3D models in various applications such as computer graphics, virtual reality, and autonomous systems. This review examines the development and current state of 3D representation methods, highlighting their research trajectories, innovations, strength and weakness. Key techniques such as Voxel Grid, Point Cloud, Mesh, Signed Distance Function (SDF), Neural Radiance Field (NeRF), 3D Gaussian Splatting, Tri-Plane, and Deep Marching Tetrahedra (DMTet) are reviewed. The review also introduces essential datasets that have been pivotal in advancing the field, highlighting their characteristics and impact on research progress. Finally, we explore potential research directions that hold promise for further expanding the capabilities and applications of 3D representation methods.
Enabling Novel Mission Operations and Interactions with ROSA: The Robot Operating System Agent
2024-10-09
Rob Royce, Marcel Kaufmann, Jonathan Becktor, Sangwoo Moon, Kalind Carpenter, Kai Pak, Amanda Towler, Rohan Thakker, Shehryar Khattak
The advancement of robotic systems has revolutionized numerous industries, yet their operation often demands specialized technical knowledge, limiting accessibility for non-expert users. This paper introduces ROSA (Robot Operating System Agent), an AI-powered agent that bridges the gap between the Robot Operating System (ROS) and natural language interfaces. By leveraging state-of-the-art language models and integrating open-source frameworks, ROSA enables operators to interact with robots using natural language, translating commands into actions and interfacing with ROS through well-defined tools. ROSA's design is modular and extensible, offering seamless integration with both ROS1 and ROS2, along with safety mechanisms like parameter validation and constraint enforcement to ensure secure, reliable operations. While ROSA is originally designed for ROS, it can be extended to work with other robotics middle-wares to maximize compatibility across missions. ROSA enhances human-robot interaction by democratizing access to complex robotic systems, empowering users of all expertise levels with multi-modal capabilities such as speech integration and visual perception. Ethical considerations are thoroughly addressed, guided by foundational principles like Asimov's Three Laws of Robotics, ensuring that AI integration promotes safety, transparency, privacy, and accountability. By making robotic technology more user-friendly and accessible, ROSA not only improves operational efficiency but also sets a new standard for responsible AI use in robotics and potentially future mission operations. This paper introduces ROSA's architecture and showcases initial mock-up operations in JPL's Mars Yard, a laboratory, and a simulation using three different robots. The core ROSA library is available as open-source.
User Feedback in Continuous Software Engineering: Revealing the State-of-Practice
2024-10-09
Anastasiia Tkalich, Eriks Klotins, Tor Sporsem, Viktoria Stray, Nils Brede Moe, Astri Barbala
Context: Organizations opt for continuous delivery of incremental updates to deal with uncertainty and minimize waste. However, applying continuous engineering (CSE) practices requires a continuous feedback loop with input from customers and end-users. Challenges: It becomes increasingly challenging to apply traditional requirements elicitation and validation techniques with ever-shrinking software delivery cycles. At the same time, frequent deliveries generate an abundance of usage data and telemetry informing engineering teams of end-user behavior. The literature describing how practitioners work with user feedback in CSE, is limited. Objectives: We aim to explore the state of practice related to utilization of user feedback in CSE. Specifically, what practices are used, how, and the shortcomings of these practices. Method: We conduct a qualitative survey and report analysis from 21 interviews in 13 product development companies. We apply thematic and cross-case analysis to interpret the data. Results: Based on our earlier work we suggest a conceptual model of how user feedback is utilized in CSE. We further report the identified challenges with the continuous collection and analysis of user feedback and identify implications for practice. Conclusions: Companies use a combination of qualitative and quantitative methods to infer end-user preferences. At the same time, continuous collection, analysis, interpretation, and use of data in decisions are problematic. The challenges pertain to selecting the right metrics and analysis techniques, resource allocation, and difficulties in accessing vaguely defined user groups. Our advice to practitioners in CSE is to ensure sufficient resources and effort for interpretation of the feedback, which can be facilitated by telemetry dashboards.
Self-Supervised Learning for Real-World Object Detection: a Survey
2024-10-09
Alina Ciocarlan, Sidonie Lefebvre, Sylvie Le Hégarat-Mascle, Arnaud Woiselle
Self-Supervised Learning (SSL) has emerged as a promising approach in computer vision, enabling networks to learn meaningful representations from large unlabeled datasets. SSL methods fall into two main categories: instance discrimination and Masked Image Modeling (MIM). While instance discrimination is fundamental to SSL, it was originally designed for classification and may be less effective for object detection, particularly for small objects. In this survey, we focus on SSL methods specifically tailored for real-world object detection, with an emphasis on detecting small objects in complex environments. Unlike previous surveys, we offer a detailed comparison of SSL strategies, including object-level instance discrimination and MIM methods, and assess their effectiveness for small object detection using both CNN and ViT-based architectures. Specifically, our benchmark is performed on the widely-used COCO dataset, as well as on a specialized real-world dataset focused on vehicle detection in infrared remote sensing imagery. We also assess the impact of pre-training on custom domain-specific datasets, highlighting how certain SSL strategies are better suited for handling uncurated data. Our findings highlight that instance discrimination methods perform well with CNN-based encoders, while MIM methods are better suited for ViT-based architectures and custom dataset pre-training. This survey provides a practical guide for selecting optimal SSL strategies, taking into account factors such as backbone architecture, object size, and custom pre-training requirements. Ultimately, we show that choosing an appropriate SSL pre-training strategy, along with a suitable encoder, significantly enhances performance in real-world object detection, particularly for small object detection in frugal settings.
Circumventing Cosmic Variance via Remote Quadrupole Measurements
2024-10-09
Arsalan Adil, Reid Koutras, Emory F. Bunn
A number of important cosmological questions can be addressed only by probing perturbation modes on the largest accessible scales. One promising probe of these modes is the Kamionkowski-Loeb effect, i.e., the polarization induced in the cosmic microwave background (CMB) by Thomson scattering in galaxy clusters, which is proportional to the CMB quadrupole measured at the cluster's location and look-back time. We develop a Fisher formalism for assessing the amount of new information that can be obtained from a future remote quadrupole survey. To demonstrate the constraining power of such a survey, we apply our formalism to a model that suppresses the primordial power spectrum on large scales but is poorly constrained with existing CMB data. We find that the constraints can be improved by $4\sigma$ for a survey that measures around 100 clusters over $20\%$ of the sky with a signal-to-noise ratio of 1.0. This constraint improves to over $7\sigma$ for a low-noise survey with dense and full sky coverage. Our formalism, which is based in real space rather than harmonic space, can be used to explore a wide range of survey designs and our results paint an optimistic picture for the utility of remote quadrupole measurements to probe physics on the largest observable scales in the Universe.
Recommending and Release Planning of User-Driven Functionality Deletion for Mobile Apps
2024-10-09
Maleknaz Nayebi, Konstantin Kuznetsov, Andreas Zeller, Guenther Ruhe
Evolving software with an increasing number of features poses challenges in terms of comprehensibility and usability. Traditional software release planning has predominantly focused on orchestrating the addition of features, contributing to the growing complexity and maintenance demands of larger software systems. In mobile apps, an excess of functionality can significantly impact usability, maintainability, and resource consumption, necessitating a nuanced understanding of the applicability of the law of continuous growth to mobile apps. Previous work showed that the deletion of functionality is common and sometimes driven by user reviews. For most users, the removal of features is associated with negative sentiments, prompts changes in usage patterns, and may even result in user churn. Motivated by these preliminary results, we propose Radiation to input user reviews and recommend if any functionality should be deleted from an app's User Interface (UI). We evaluate radiation using historical data and survey developers' opinions. From the analysis of 190,062 reviews from 115 randomly selected apps, we show that Radiation can recommend functionality deletion with an average F-Score of 74% and if sufficiently many negative user reviews suggest so. We conducted a survey involving 141 software developers to gain insights into the decision-making process and the level of planning for feature deletions. Our findings indicate that 77.3% of the participants often or always plan for such deletions. This underscores the importance of incorporating feature deletion planning into the overall release decision-making process.
Application of Manifold Learning to Selection of Different Galaxy Populations and Scaling Relation Analysis
2024-10-09
Sogol Sanjaripour, Shoubaneh Hemmati, Bahram Mobasher, Gabriela Canalizo, Barry Barish, Irene Shivaei, Alison L. Coil, Nima Chartab, Marziye Jafariyazani, Naveen A. Reddy, Mojegan Azadi
The growing volume of data produced by large astronomical surveys necessitates the development of efficient analysis techniques capable of effectively managing high-dimensional datasets. This study addresses this need by demonstrating some applications of manifold learning and dimensionality reduction techniques, specifically the Self-Organizing Map (SOM), on the optical+NIR SED space of galaxies, with a focus on sample comparison, selection biases, and predictive power using a small subset. To this end, we utilize a large photometric sample from the five CANDELS fields and a subset with spectroscopic measurements from the KECK MOSDEF survey in two redshift bins at $z\sim1.5$ and $z\sim2.2$. We trained SOM with the photometric data and mapped the spectroscopic data onto it as our study case. We found that MOSDEF targets do not cover all SED shapes existing in the SOM. Our findings reveal that Active Galactic Nuclei (AGN) within the MOSDEF sample are mapped onto the more massive regions of the SOM, confirming previous studies and known selection biases towards higher-mass, less dusty galaxies. Furthermore, SOM were utilized to map measured spectroscopic features, examining the relationship between metallicity variations and galaxy mass. Our analysis confirmed that more massive galaxies exhibit lower [OIII]/H$\beta$ and [OIII]/[OII] ratios and higher H$\alpha$/H$\beta$ ratios, consistent with the known mass-metallicity relation. These findings highlight the effectiveness of SOM in analyzing and visualizing complex, multi-dimensional datasets, emphasizing their potential in data-driven astronomical studies.
Psi-GAN: A power-spectrum-informed generative adversarial network for the emulation of large-scale structure maps across cosmologies and redshifts
2024-10-09
Prabh Bhambra, Benjamin Joachimi, Ofer Lahav
Simulations of the dark matter distribution throughout the Universe are essential in order to analyse data from cosmological surveys. $N$-body simulations are computationally expensive, and many cheaper alternatives (such as lognormal random fields) fail to reproduce accurate statistics of the smaller, non-linear scales. In this work, we present Psi-GAN (Power-spectrum-informed Generative Adversarial Network), a machine learning model which takes a two-dimensional lognormal dark matter density field and transforms it into a more realistic field. We construct Psi-GAN so that it is continuously conditional, and can therefore generate realistic realisations of the dark matter density field across a range of cosmologies and redshifts of $z \in [0, 3]$. We train Psi-GAN as a generative adversarial network on $2\,000$ simulation boxes from the Quijote simulations. We use a novel critic architecture that utilises the power spectrum as the basis for discrimination between real and generated samples. Psi-GAN shows agreement with $N$-body simulations over a range of redshifts and cosmologies, consistently outperforming the lognormal approximation on all tests of non-linear structure, such as being able to reproduce both the power spectrum up to wavenumbers of $1~h~\mathrm{Mpc}^{-1}$, and the bispectra of target $N$-body simulations to within ${\sim}5$ per cent. Our improved ability to model non-linear structure should allow tighter constraints on cosmological parameters when used in techniques such as simulation-based inference.
Here There Be (Dusty) Monsters: High Redshift AGN are Dustier Than Their Hosts
2024-10-09
Madisyn Brooks, Raymond C. Simons, Jonathan R. Trump, Anthony J. Taylor, Bren Backhaus, Kelcey Davis, Véronique Buat, Nikko J. Cleri, Steven L. Finkelstein, Michaela Hirschmann, Benne W. Holwerda, Dale D. Kocevski, Anton M. Koekemoer, Ray A. Lucas, Fabio Pacucci, Lise-Marie Seillé
JWST spectroscopy has discovered a population of $z \gtrsim 3.5$ galaxies with broad Balmer emission lines, and narrow forbidden lines, that are consistent with hosting active galactic nuclei (AGN). Many of these systems, now known as ``little red dots" (LRDs), are compact and have unique colors that are very red in the optical/near-infrared and blue in the ultraviolet. The relative contribution of galaxy starlight and AGN to these systems remains uncertain, especially for the galaxies with unusual blue+red spectral energy distributions. In this work, we use Balmer decrements to measure the independent dust attenuation of the broad and narrow emission-line components of a sample of 29 broad-line AGN identified from three public JWST spectroscopy surveys: CEERS, JADES, and RUBIES. Stacking the narrow components from the spectra of 25 sources with broad H$\rm{\alpha}$ and no broad H$\rm{\beta}$ results in a median narrow H$\rm{\alpha}$/H$\rm{\beta}$ = $2.47^{+0.05}_{-0.05}$ (consistent with $A_{v} = 0$) and broad H$\rm{\alpha}$/H$\rm{\beta}$ $> 8.85$ ($A_{v} > 3.63$). The narrow and broad Balmer decrements imply little-to-no attenuation of the narrow emission lines, which are consistent with being powered by star formation and located on larger physical scales. Meanwhile, the lower limit in broad H$\rm{\alpha}$/H$\rm{\beta}$ decrement, with broad H$\rm{\beta}$ undetected in the stacked spectrum of 25 broad-H$\rm{\alpha}$ AGN, implies significant dust attenuation of the broad-line emitting region that is presumably associated with the central AGN. Our results indicate that these systems, on average, are consistent with heavily dust-attenuated AGN powering the red parts of their SED while their blue UV emission is powered by unattenuated star formation in the host galaxy.
The NuSTAR Local AGN $N_{\rm H}$ Distribution Survey (NuLANDS) I: Towards a Truly Representative Column Density Distribution in the Local Universe
2024-10-09
Peter G. Boorman, Poshak Gandhi, Johannes Buchner, Daniel Stern, Claudio Ricci, Mislav Baloković, Daniel Asmus, Fiona A. Harrison, Jiří Svoboda, Claire Greenwell, Michael Koss, David M. Alexander, Adlyka Annuar, Franz Bauer, William N. Brandt, Murray Brightman, Francesca Panessa, Chien-Ting J. Chen, Duncan Farrah, Karl Forster, Brian Grefenstette, Sebastian F. Hönig, Adam B. Hill, Elias Kammoun, George Lansbury, Lauranne Lanz, Stephanie LaMassa, Kristin Madsen, Stefano Marchesi, Matthew Middleton, Beatriz Mingo, Michael L. Parker, Ezequiel Treister, Yoshihiro Ueda, C. Megan Urry, Luca Zappacosta
Hard X-ray-selected samples of Active Galactic Nuclei (AGN) provide one of the cleanest views of supermassive black hole accretion, but are biased against objects obscured by Compton-thick gas column densities of $N_{\rm H}$ $>$ 10$^{24}$ cm$^{-2}$. To tackle this issue, we present the NuSTAR Local AGN $N_{\rm H}$ Distribution Survey (NuLANDS)$-$a legacy sample of 122 nearby ($z$ $<$ 0.044) AGN primarily selected to have warm infrared colors from IRAS between 25$-$60 $\mu$m. We show that optically classified type 1 and 2 AGN in NuLANDS are indistinguishable in terms of optical [OIII] line flux and mid-to-far infrared AGN continuum bolometric indicators, as expected from an isotropically selected AGN sample, while type 2 AGN are deficient in terms of their observed hard X-ray flux. By testing many X-ray spectroscopic models, we show the measured line-of-sight column density varies on average by $\sim$ 1.4 orders of magnitude depending on the obscurer geometry. To circumvent such issues we propagate the uncertainties per source into the parent column density distribution, finding a directly measured Compton-thick fraction of 35 $\pm$ 9%. By construction, our sample will miss sources affected by severe narrow-line reddening, and thus segregates sources dominated by small-scale nuclear obscuration from large-scale host-galaxy obscuration. This bias implies an even higher intrinsic obscured AGN fraction may be possible, although tests for additional biases arising from our infrared selection find no strong effects on the measured column-density distribution. NuLANDS thus holds potential as an optimized sample for future follow-up with current and next-generation instruments aiming to study the local AGN population in an isotropic manner.
Considerations for Photochemical Modeling of Possible Hycean Worlds
2024-10-09
Gregory J. Cooke, Nikku Madhusudhan
JWST is revolutionising the study of temperate sub-Neptunes, starting with the first detection of carbon-bearing molecules in the habitable-zone sub-Neptune K2-18 b. The retrieved abundances of CH$_4$ and CO$_2$ and non-detection of NH$_3$ and CO in K2-18 b are consistent with prior predictions of photochemical models for a Hycean world with a habitable ocean. However, recent photochemical modeling raised the prospect that the observed abundances may be explained by a mini-Neptune scenario instead. In this study, we explore these scenarios using independent photochemical modeling with K2-18 b as a case study. We find the previous results to be sensitive to a range of model assumptions, such as the photochemical cross sections, incident stellar spectrum, surface pressure, UV albedo, and metallicity, significantly affecting the resulting abundances. We explore a wide model space to investigate scenarios that are compatible with the retrieved molecular abundances for K2-18 b. Our analysis shows that the previously favoured mini-Neptune scenario is not compatible with most of the retrieved abundances, while the Hycean scenarios, both inhabited and uninhabited, provide better agreement. An uninhabited Hycean scenario explains most of the abundance constraints, except CH$_4$ which is generally underabundant but dependent on the model assumptions. The inhabited Hycean scenario is compatible with all the abundances if the observed CH$_4$ is assumed to be predominantly biogenic. Our results underscore the importance of systematic photochemical modeling and accurate interpretation of chemical abundance constraints for candidate Hycean worlds.
2024-10-09
Pavel Kroupa, Eda Gjergo, Tereza Jerabkova, Zhiqiang Yan
The initial mass function (IMF) is one of the most important functions in astrophysics because it is key to reconstructing the cosmological matter cycle, understanding the formation of super-massive black holes, and deciphering the light from high-redshift observations. The IMF's dependency on the physical conditions of the gas and its connection to the galaxy-wide IMF connects the molecular clumps to the cosmological scale. The extraction of the IMF from observational data requires a thorough understanding of stellar evolution, the time-dependent stellar multiplicity, the stellar-dynamical evolution of dense stellar populations, and the structures, star formation histories, and chemical enrichment histories of galaxies. The IMF in galaxies, referred to as the galaxy-wide IMF (gwIMF), and the IMF in individual star-forming regions (the stellar IMF) need not be the same, although the former must be related to the latter. Observational surveys inform on whether star-forming regions provide evidence for the stellar IMF being a probability density distribution function. They may also indicate star formation to optimally follow an IMF shaped by the physical conditions of the star-forming gas. Both theoretical and observational evidence suggest a relationship between the initial mass function of brown dwarfs and that of stars. Late-type stars may arise from feedback-regulated fragmentation of molecular cloud filaments, which build up embedded clusters. In contrast, early-type stars form under more violent accretion and feedback-regulated conditions near the centers of these clusters. The integration over all star-forming molecular cloud clumps and their stellar IMFs in a galaxy via the IGIMF theory yields its gwIMF which sensitively depends on the physical properties of the molecular cloud clumps and the range of their masses that depends on the SFR of the galaxy.
Autonomous Navigation and Collision Avoidance for Mobile Robots: Classification and Review
2024-10-09
Marcus Vinicius Leal de Carvalho, Roberto Simoni, Leopoldo Yoshioka
This paper introduces a novel classification for Autonomous Mobile Robots (AMRs), into three phases and five steps, focusing on autonomous collision-free navigation. Additionally, it presents the main methods and widely accepted technologies for each phase of the proposed classification. The purpose of this classification is to facilitate understanding and establish connections between the independent input variables of the system (hardware, software) and autonomous navigation. By analyzing well-established technologies in terms of sensors and methods used for autonomous navigation, this paper aims to provide a foundation of knowledge that can be applied in future projects of mobile robots.
Fundamental constants: from measurement to the universe, a window on gravitation and cosmology
2024-10-09
Jean-Philippe Uzan
Fundamental constants are a cornerstone of the physical laws. Any constant varying in space and/or time would signal a violation of local position invariance and be associated with a violation of the universality of free fall, and hence of the weak equivalence principle. It will reflect the existence of new degrees of freedom that couple to standard matter. Thus, testing for the stability of fundamental constants is of utmost importance for our understanding of gravity and for characterizing the domain of validity of General Relativity. Besides, it opens an independent window on dark matter and dark energy. As a consequence, thanks to the active development of experiments and of their accuracy, fundamental constants have become a key player in our search for physics beyond the standard model of and beyond General Relativity. This review details the roles of the fundamental constants in the laws of physics and in the construction of the International System of units. Then, the relations between constants, tests of the local position invariance and of the universality of free fall are exposed, as well as the construction of field theories that account for "varying constants". Then, the main experimental and observational constraints are described. It details the basics of each system, its dependence with respect to the primary parameters the variation of which can be constrained from observations, the known systematics effects and the most recent constraints. It also describes how these primary parameters can be related to the fundamental constants and the model-dependencies. Both time and space variation are considered. Given the huge increase of data and constraints, a general scheme to present experimental and observational results and to construct a collaborative data base that will be more efficient for the community and allow for better traceability, is proposed.
Deep Learning for Surgical Instrument Recognition and Segmentation in Robotic-Assisted Surgeries: A Systematic Review
2024-10-09
Fatimaelzahraa Ali Ahmed, Mahmoud Yousef, Mariam Ali Ahmed, Hasan Omar Ali, Anns Mahboob, Hazrat Ali, Zubair Shah, Omar Aboumarzouk, Abdulla Al Ansari, Shidin Balakrishnan
Applying deep learning (DL) for annotating surgical instruments in robot-assisted minimally invasive surgeries (MIS) represents a significant advancement in surgical technology. This systematic review examines 48 studies that and advanced DL methods and architectures. These sophisticated DL models have shown notable improvements in the precision and efficiency of detecting and segmenting surgical tools. The enhanced capabilities of these models support various clinical applications, including real-time intraoperative guidance, comprehensive postoperative evaluations, and objective assessments of surgical skills. By accurately identifying and segmenting surgical instruments in video data, DL models provide detailed feedback to surgeons, thereby improving surgical outcomes and reducing complication risks. Furthermore, the application of DL in surgical education is transformative. The review underscores the significant impact of DL on improving the accuracy of skill assessments and the overall quality of surgical training programs. However, implementing DL in surgical tool detection and segmentation faces challenges, such as the need for large, accurately annotated datasets to train these models effectively. The manual annotation process is labor-intensive and time-consuming, posing a significant bottleneck. Future research should focus on automating the detection and segmentation process and enhancing the robustness of DL models against environmental variations. Expanding the application of DL models across various surgical specialties will be essential to fully realize this technology's potential. Integrating DL with other emerging technologies, such as augmented reality (AR), also offers promising opportunities to further enhance the precision and efficacy of surgical procedures.
Galaxy evolution in the post-merger regime. II -- Post-merger quenching peaks within 500 Myr of coalescence
2024-10-08
Sara L. Ellison, Leonardo Ferreira, Vivienne Wild, Scott Wilkinson, Kate Rowlands, David R. Patton
(Abridged) Mechanisms for quenching star formation in galaxies remain hotly debated, with galaxy mergers an oft-proposed pathway. In Ellison et al. (2022) we tested this scenario by quantifying the fraction of recently and rapidly quenched post-starbursts (PSBs) in a sample of post-merger galaxies identified in the Ultraviolet Near Infrared Optical Northern Survey (UNIONS). With our recent development of the Multi-Model Merger Identifier (MUMMI) neural network ensemble (Ferreira et al. 2024a,b), we are now additionally able to predict the time since coalescence (T_PM) for the UNIONS post-merger galaxies up to T_PM = 1.8 Gyr, allowing us to further dissect the merger sequence and measure more precisely when quenching occurs. Based on a sample of 5927 z<0.3 post-mergers identified in UNIONS, we find that the post-coalescence population evolves from one dominated by star-forming (and starbursting) galaxies at 0 < T_PM < 0.16 Gyr, through to a population that is dominated by quenched galaxies by T_PM ~ 1.5 Gyr. We find a PSB excess throughout the post-merger regime, but with a clear peak at 0.16 < T_PM < 0.48 Gyr. In this post-merger time range PSBs are more common than in control galaxies by factors of 30-100, an excess that drops sharply at longer times since merger. We also quantify the fraction of PSBs that are mergers and find that the majority (75%) of classically selected E+A are identified as mergers, with a lower merger fraction (60%) amongst PCA selected PSBs. Our results demonstrate that 1) galaxy-galaxy interactions can lead to rapid post-merger quenching within 0.5 Gyr of coalescence, 2) the majority of (but not all) PSBs at low z are linked to mergers and 3) quenching pathways are diverse, with different PSB selection techniques likely identifying galaxies quenched by different physical processes with an additional dependence on stellar mass.
Galaxy evolution in the post-merger regime I -- Most merger-induced in-situ stellar mass growth happens post-coalescence
2024-10-08
Leonardo Ferreira, Sara L. Ellison, David R. Patton, Shoshannah Byrne-Mamahit, Scott Wilkinson, Robert Bickley, Christopher J. Conselice, Connor Bottrell
Galaxy mergers can enhance star formation rates throughout the merger sequence, with this effect peaking around the time of coalescence. However, owing to a lack of information about their time of coalescence, post-mergers could only previously be studied as a single, time-averaged population. We use timescale predictions of post-coalescence galaxies in the UNIONS survey, based on the Multi-Model Merger Identifier deep learning framework (\textsc{Mummi}) that predicts the time elapsed since the last merging event. For the first time, we capture a complete timeline of star formation enhancements due to galaxy mergers by combining these post-merger predictions with data from pre-coalescence galaxy pairs in SDSS. Using a sample of $564$ galaxies with $M_* \geq 10^{10} M_\odot$ at $0.005 < z < 0.3$ we demonstrate that: 1) galaxy mergers enhance star formation by, on average, up to a factor of two; 2) this enhancement peaks within 500 Myr of coalescence; 3) enhancements continue for up to 1~Gyr after coalescence; and 4) merger-induced star formation significantly contributes to galaxy mass assembly, with galaxies increasing their final stellar masses by, $10\%$ to $20\%$ per merging event, producing on average $\log(M_*/M_\odot) = {9.56_{-0.19}^{+0.13}}$ more mass than non-interacting star-forming galaxies solely due to the excess star formation.
The Gaussian approximation potential (GAP) machine-learning-inspired functional form was the first to be used for a general-purpose interatomic potential. The atomic cluster expansion (ACE), previously the subject of a KIM Review, and its multilayer neural-network extension (MACE) have joined GAP among the methods widely used for machine-learning interatomic potentials. Here I review extensions to the original GAP formalism, as well as ACE and MACE-based frameworks that maintain the good features and mitigate the limitations of the original GAP approach.
Moving Faster and Reducing Risk: Using LLMs in Release Deployment
2024-10-08
Rui Abreu, Vijayaraghavan Murali, Peter C Rigby, Chandra Maddila, Weiyan Sun, Jun Ge, Kaavya Chinniah, Audris Mockus, Megh Mehta, Nachiappan Nagappan
Release engineering has traditionally focused on continuously delivering features and bug fixes to users, but at a certain scale, it becomes impossible for a release engineering team to determine what should be released. At Meta's scale, the responsibility appropriately and necessarily falls back on the engineer writing and reviewing the code. To address this challenge, we developed models of diff risk scores (DRS) to determine how likely a diff is to cause a SEV, i.e., a severe fault that impacts end-users. Assuming that SEVs are only caused by diffs, a naive model could randomly gate X% of diffs from landing, which would automatically catch X% of SEVs on average. However, we aimed to build a model that can capture Y% of SEVs by gating X% of diffs, where Y >> X. By training the model on historical data on diffs that have caused SEVs in the past, we can predict the riskiness of an outgoing diff to cause a SEV. Diffs that are beyond a particular threshold of risk can then be gated. We have four types of gating: no gating (green), weekend gating (weekend), medium impact on end-users (yellow), and high impact on end-users (red). The input parameter for our models is the level of gating, and the outcome measure is the number of captured SEVs. Our research approaches include a logistic regression model, a BERT-based model, and generative LLMs. Our baseline regression model captures 18.7%, 27.9%, and 84.6% of SEVs while respectively gating the top 5% (weekend), 10% (yellow), and 50% (red) of risky diffs. The BERT-based model, StarBERT, only captures 0.61x, 0.85x, and 0.81x as many SEVs as the logistic regression for the weekend, yellow, and red gating zones, respectively. The generative LLMs, iCodeLlama-34B and iDiffLlama-13B, when risk-aligned, capture more SEVs than the logistic regression model in production: 1.40x, 1.52x, 1.05x, respectively.
On the Damped Pinney Equation from Noether Symmetry Principles
2024-10-08
Fernando Haas
There are several versions of the damped form of the celebrated Pinney equation, which is the natural partner of the undamped time-dependent harmonic oscillator. In this work these dissipative versions of the Pinney equation are briefly reviewed. We show that Noether's theorem for the usual time-dependent harmonic oscillator as a guiding principle for derivation of the Pinney equation also works in the damped case, selecting a Noether symmetry based damped Pinney equation. The results are extended to general nonlinear damped Ermakov systems. A certain time-rescaling always allows to remove the damping from the final equations.
Harnessing the Power of Noise: A Survey of Techniques and Applications
2024-10-08
Reyhaneh Abdolazimi, Shengmin Jin, Pramod K. Varshney, Reza Zafarani
Noise, traditionally considered a nuisance in computational systems, is reconsidered for its unexpected and counter-intuitive benefits across a wide spectrum of domains, including nonlinear information processing, signal processing, image processing, machine learning, network science, and natural language processing. Through a comprehensive review of both historical and contemporary research, this survey presents a dual perspective on noise, acknowledging its potential to both disrupt and enhance performance. Particularly, we highlight how noise-enhanced training strategies can lead to models that better generalize from noisy data, positioning noise not just as a challenge to overcome but as a strategic tool for improvement. This work calls for a shift in how we perceive noise, proposing that it can be a spark for innovation and advancement in the information era.
Work-in-Progress: Traded Control Transfer for Managing Real-Time Sensor Uncertainties in Autonomous Vehicle
2024-10-08
Md Sakib Galib Sourav, Liang Cheng
At Levels 2 and 3 of autonomous driving defined by the Society of Auto-motive Engineers, drivers must take on certain driving responsibilities, and automated driving must sometimes yield to human control. This situation can occur in real time due to uncertainties in sensor measurements caused by environmental factors like fog or smoke. To address this challenge, we propose a method to manage real-time sensor uncertainties in autonomous vehicles by monitoring sensor conflicts and dynamically adjusting control authority to maintain safe operation. However, to achieve this, we have introduced a novel metric called the Degree of Conflicts (DoC), which quantifies the conflict between real-time sensor data by measuring the differences between data from multiple sensors. Our approach aims to demonstrate the importance of selecting an appropriate DoC threshold for transferring control between the automation agent and the human driver. The results have shown that choosing the correct DoC threshold can enhance safety by promptly handing over the driving control from the automation system to the human driver in challenging conditions.
Constraints on Remnant Planetary Systems as a Function of Main-Sequence Mass with HST/COS
2024-10-08
Lou Baya Ould Rouis, J. J. Hermes, Boris T. Gänsicke, Snehalata Sahu, Detlev Koester, P. -E. Tremblay, Dimitri Veras, Jay Farihi, Tyler M. Heintz, Nicola Pietro Gentile Fusillo, Seth Redfield
As the descendants of stars with masses less than 8 M$_{\odot}$ on the main sequence, white dwarfs provide a unique way to constrain planetary occurrence around intermediate-mass stars (spectral types BAF) that are otherwise difficult to measure with radial-velocity or transit surveys. We update the analysis of more than 250 ultraviolet spectra of hot ($13{,}000$ K $< T_{\mathrm{eff}} <$ $30{,}000$ K), young (less than $800$ Myr) white dwarfs collected by the Hubble Space Telescope, which reveals that more than 40% of all white dwarfs show photospheric silicon and sometimes carbon, signpost for the presence of remnant planetary systems. However, the fraction of white dwarfs with metals significantly decreases for massive white dwarfs (M$_{\rm WD}~>$ 0.8 M$_{\odot}$), descendants of stars with masses greater than 3.5 M$_{\odot}$ on the main sequence, as just $11^{+6}_{-4}$% exhibit metal pollution. In contrast, $44\pm6$% of a subset of white dwarfs (M$\rm _{WD}~<$ 0.7 M$_{\odot}$) unbiased by the effects of radiative levitation are actively accreting planetary debris. While the population of massive white dwarfs is expected to be influenced by the outcome of binary evolution, we do not find merger remnants to broadly affect our sample. We connect our measured occurrence rates of metal pollution on massive white dwarfs to empirical constraints into planetary formation and survival around stars with masses greater than 3.5 M$_{\odot}$ on the main sequence.
Towards a GENEA Leaderboard -- an Extended, Living Benchmark for Evaluating and Advancing Conversational Motion Synthesis
2024-10-08
Rajmund Nagy, Hendric Voss, Youngwoo Yoon, Taras Kucherenko, Teodor Nikolov, Thanh Hoang-Minh, Rachel McDonnell, Stefan Kopp, Michael Neff, Gustav Eje Henter
Current evaluation practices in speech-driven gesture generation lack standardisation and focus on aspects that are easy to measure over aspects that actually matter. This leads to a situation where it is impossible to know what is the state of the art, or to know which method works better for which purpose when comparing two publications. In this position paper, we review and give details on issues with existing gesture-generation evaluation, and present a novel proposal for remedying them. Specifically, we announce an upcoming living leaderboard to benchmark progress in conversational motion synthesis. Unlike earlier gesture-generation challenges, the leaderboard will be updated with large-scale user studies of new gesture-generation systems multiple times per year, and systems on the leaderboard can be submitted to any publication venue that their authors prefer. By evolving the leaderboard evaluation data and tasks over time, the effort can keep driving progress towards the most important end goals identified by the community. We actively seek community involvement across the entire evaluation pipeline: from data and tasks for the evaluation, via tooling, to the systems evaluated. In other words, our proposal will not only make it easier for researchers to perform good evaluations, but their collective input and contributions will also help drive the future of gesture-generation research.
In this paper, we consider the problem of distributed reachable set computation for multi-agent systems (MASs) interacting over an undirected, stationary graph. A full state-feedback control input for such MASs depends no only on the current agent's state, but also of its neighbors. However, in most MAS applications, the dynamics are obscured by individual agents. This makes reachable set computation, in a fully distributed manner, a challenging problem. We utilize the ideas of polytopic reachable set approximation and generalize it to a MAS setup. We formulate the resulting sub-problems in a fully distributed manner and provide convergence guarantees for the associated computations. The proposed algorithm's convergence is proved for two cases: static MAS graphs, and time-varying graphs under certain restrictions.
Model Predictive Control is Almost Optimal for Restless Bandit
2024-10-08
Nicolas Gast, Dheeraj Narasimha
We consider the discrete time infinite horizon average reward restless markovian bandit (RMAB) problem. We propose a \emph{model predictive control} based non-stationary policy with a rolling computational horizon $\tau$. At each time-slot, this policy solves a $\tau$ horizon linear program whose first control value is kept as a control for the RMAB. Our solution requires minimal assumptions and quantifies the loss in optimality in terms of $\tau$ and the number of arms, $N$. We show that its sub-optimality gap is $O(1/\sqrt{N})$ in general, and $\exp(-\Omega(N))$ under a local-stability condition. Our proof is based on a framework from dynamic control known as \emph{dissipativity}. Our solution easy to implement and performs very well in practice when compared to the state of the art. Further, both our solution and our proof methodology can easily be generalized to more general constrained MDP settings and should thus, be of great interest to the burgeoning RMAB community.
2024-10-08
Divyat Mahajan, Mohammad Pezeshki, Ioannis Mitliagkas, Kartik Ahuja, Pascal Vincent
In this work, we tackle a challenging and extreme form of subpopulation shift, which is termed compositional shift. Under compositional shifts, some combinations of attributes are totally absent from the training distribution but present in the test distribution. We model the data with flexible additive energy distributions, where each energy term represents an attribute, and derive a simple alternative to empirical risk minimization termed compositional risk minimization (CRM). We first train an additive energy classifier to predict the multiple attributes and then adjust this classifier to tackle compositional shifts. We provide an extensive theoretical analysis of CRM, where we show that our proposal extrapolates to special affine hulls of seen attribute combinations. Empirical evaluations on benchmark datasets confirms the improved robustness of CRM compared to other methods from the literature designed to tackle various forms of subpopulation shifts.
Determining star formation rates in AGN hosts from strong optical emission lines
2024-10-08
Maitê S. Z. de Mellos, Rogemar A. Riffel, Jaderson S. Schimoia, Sandro B. Rembold, Rogério Riffel, Thaisa Storchi-Bergmann, Dominika Wylezalek, Gabriele S. Ilha, Marco Albán, Oli L. Dors, Lara Gatto, Angela C. Krabbe, Nicolas D. Mallmann, Marina Trevisan
The influence of Active Galactic Nuclei (AGN) on star formation within their host galaxies remains a topic of intense debate. One of the primary challenges in quantifying the star formation rate (SFR) within AGN hosts arises from the prevalent assumption in most methodologies, which attribute gas excitation to young stars alone. However, this assumption does not consider the contribution of the AGN to the ionization of the gas in their environment. To address this issue, we evaluate the use of strong optical emission lines to obtain the SFR surface density ($\Sigma{{\rm SFR_{AGN}}}$) in regions predominantly ionized by an AGN, using a sample of 293 AGN hosts from the MaNGA survey, with SFR measurements available through stellar population fitting. We propose calibrations involving the H$\alpha$ and [O\,{\sc iii}]$\lambda$5007 emission lines, which can be used to determine $\Sigma{{\rm SFR_{AGN}}}$, resulting in values consistent with those estimated through stellar population fitting.
Is Pontryagin's Maximum Principle all you need? Solving optimal control problems with PMP-inspired neural networks
2024-10-08
Kawisorn Kamtue, Jose M. F. Moura, Orathai Sangpetch
Calculus of Variations is the mathematics of functional optimization, i.e., when the solutions are functions over a time interval. This is particularly important when the time interval is unknown like in minimum-time control problems, so that forward in time solutions are not possible. Calculus of Variations offers a robust framework for learning optimal control and inference. How can this framework be leveraged to design neural networks to solve challenges in control and inference? We propose the Pontryagin's Maximum Principle Neural Network (PMP-net) that is tailored to estimate control and inference solutions, in accordance with the necessary conditions outlined by Pontryagin's Maximum Principle. We assess PMP-net on two classic optimal control and inference problems: optimal linear filtering and minimum-time control. Our findings indicate that PMP-net can be effectively trained in an unsupervised manner to solve these problems without the need for ground-truth data, successfully deriving the classical "Kalman filter" and "bang-bang" control solution. This establishes a new approach for addressing general, possibly yet unsolved, optimal control problems.
A correspondence between Hebbian unlearning and steady states generated by nonequilibrium dynamics
2024-10-08
Agnish Kumar Behera, Matthew Du, Uday Jagadisan, Srikanth Sastry, Madan Rao, Suriyanarayanan Vaikuntanathan
The classic paradigms for learning and memory recall focus on strengths of synaptic couplings and how these can be modulated to encode memories. In a previous paper [A. K. Behera, M. Rao, S. Sastry, and S. Vaikuntanathan, Physical Review X 13, 041043 (2023)], we demonstrated how a specific non-equilibrium modification of the dynamics of an associative memory system can lead to increase in storage capacity. In this work, using analytical theory and computational inference schemes, we show that the dynamical steady state accessed is in fact similar to those accessed after the operation of a classic unsupervised scheme for improving memory recall, Hebbian unlearning or ``dreaming". Together, our work suggests how nonequilibrium dynamics can provide an alternative route for controlling the memory encoding and recall properties of a variety of synthetic (neuromorphic) and biological systems.
Euclid: Relativistic effects in the dipole of the 2-point correlation function
2024-10-08
F. Lepori, S. Schulz, I. Tutusaus, M. -A. Breton, S. Saga, C. Viglione, J. Adamek, C. Bonvin, L. Dam, P. Fosalba, L. Amendola, S. Andreon, C. Baccigalupi, M. Baldi, S. Bardelli, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, A. Caillat, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, C. Colodro-Conde, G. Congedo, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, H. Degaudenzi, G. De Lucia, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, M. Farina, S. Farrens, S. Ferriol, M. Frailis, E. Franceschi, S. Galeotta, B. Gillis, C. Giocoli, A. Grazian, F. Grupp, S. V. H. Haugan, W. Holmes, F. Hormuth, A. Hornstrup, S. Ilić, K. Jahnke, M. Jhabvala, E. Keihänen, A. Kiessling, M. Kilbinger, B. Kubik, M. Kunz, H. Kurki-Suonio, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, E. Medinaceli, M. Melchior, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, M. Moresco, L. Moscardini, C. Neissner, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, C. Rosset, E. Rossetti, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, M. Schirmer, P. Schneider, T. Schrabback, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, P. Tallada-Crespí, I. Tereno, R. Toledo-Moreo, F. Torradeflot, L. Valenziano, T. Vassallo, Y. Wang, J. Weller, E. Zucca, C. Burigana, G. Fabbian, F. Finelli, A. Pezzotta, V. Scottez, M. Viel
Gravitational redshift and Doppler effects give rise to an antisymmetric component of the galaxy correlation function when cross-correlating two galaxy populations or two different tracers. In this paper, we assess the detectability of these effects in the Euclid spectroscopic galaxy survey. We model the impact of gravitational redshift on the observed redshift of galaxies in the Flagship mock catalogue using a Navarro-Frenk-White profile for the host haloes. We isolate these relativistic effects, largely subdominant in the standard analysis, by splitting the galaxy catalogue into two populations of faint and bright objects and estimating the dipole of their cross-correlation in four redshift bins. In the simulated catalogue, we detect the dipole signal on scales below $30\,h^{-1}{\rm Mpc}$, with detection significances of $4\,\sigma$ and $3\,\sigma$ in the two lowest redshift bins, respectively. At higher redshifts, the detection significance drops below $2\,\sigma$. Overall, we estimate the total detection significance in the Euclid spectroscopic sample to be approximately $6\,\sigma$. We find that on small scales, the major contribution to the signal comes from the nonlinear gravitational potential. Our study on the Flagship mock catalogue shows that this observable can be detected in Euclid Data Release 2 and beyond.
Pupil plane WFSs for LGS systems of giant telescopes: the case of Ingot
2024-10-08
Elisa Portaluri, Roberto Ragazzoni, Davide Greggio, Carmelo Arcidiacono, Maria Bergomi, Simone Di Filippo, Marco Dima, Jacopo Farinato, Tânia Gomes Machado, Demetrio Magrin, Kalyan K. R. Santhakumari, Valentina Viotto
The Ingot WFS belongs to a class of pupil-plane WFSs designed to address the challenges posed by Sodium Laser Guide Stars, and consists of a combination of refractive and reflective surfaces, arranged into a complex prismatic shape that extends in three dimensions. Specifically, it leverages the Scheimpflug principle to sense the full 3D volume of such elongated, time-varying sources, thus optimizing the performance of the next-generation AO-assisted giant telescopes. In this work we discuss the geometrical and optical motivations endorsing the development of this class of WFSs, showing the different configurations we propose to the AO community. We also provide a first order comparative analysis with other approaches and review the state-of-the-art of the Ingot project, including improvements made in the laboratory and future milestones.
UNCOVER: 404 Error -- Models Not Found for the Triply Imaged Little Red Dot A2744-QSO1
2024-10-08
Yilun Ma, Jenny E. Greene, David J. Setton, Marta Volonteri, Joel Leja, Bingjie Wang, Rachel Bezanson, Gabriel Brammer, Sam E. Cutler, Pratika Dayal, Pieter van Dokkum, Lukas J. Furtak, Karl Glazebrook, Andy D. Goulding, Anna de Graaff, Vasily Kokorev, Ivo Labbe, Richard Pan, Sedona H. Price, John R. Weaver, Christina C. Williams, Katherine E. Whitaker, Adi Zitrin
JWST has revealed an abundance of compact, red objects at $z\approx5-8$ dubbed "little red dots" (LRDs), whose SEDs display a faint blue UV continuum followed by a steep rise in the optical. Despite extensive study of their characteristic V-shaped SEDs, the nature of LRDs remains unknown. We present a new analysis of the NIRSpec/PRISM spectrum of A2744-QSO1, a triply imaged LRD at $z=7.04$ from the UNCOVER survey. The spectrum shows a strong Balmer break and broad Balmer emission lines, both of which are difficult to explain with models invoking exclusively AGN or stellar contributions. Our fiducial model decomposes the spectrum into a post-starburst galaxy dominating the UV-optical continuum and a reddened AGN being sub-dominant at all wavelength and contributing at $\sim20\%$ level. However, our most credible model infers a stellar mass of $M_\star\approx 4\times10^9\,\mathrm{M_\odot}$ within a radius of $r_\mathrm{e}<30\,$pc, driving its central density to the highest among observations to date. This high central density could be explained if A2744-QSO-1 is the early-forming core of a modern-day massive elliptical galaxy that later puffed up via the inside-out growth channel. The models also necessitate an unusually steep dust law to preserve the strong break strength, though this steepness may be explained by a deficit of large dust grains. It is also probable that these challenges reflect our ignorance of A2744-QSO1's true nature. Future variability and reverberation mapping studies could help disentangle the galaxy and AGN contribution to the continuum, and deeper redder observations could also unveil the dust properties in LRDs.
Limits on planetary-mass primordial black holes from the OGLE high-cadence survey of the Magellanic Clouds
2024-10-08
Przemek Mroz, Andrzej Udalski, Michal K. Szymanski, Igor Soszynski, Pawel Pietrukowicz, Szymon Kozlowski, Radoslaw Poleski, Jan Skowron, Krzysztof Ulaczyk, Mariusz Gromadzki, Krzysztof Rybicki, Patryk Iwanek, Marcin Wrona, Mateusz J. Mroz
Observations of the Galactic bulge revealed an excess of short-timescale gravitational microlensing events that are generally attributed to a large population of free-floating or wide-orbit exoplanets. However, in recent years, some authors suggested that planetary-mass primordial black holes (PBHs) comprising a substantial fraction (1-10%) of the dark matter in the Milky Way may be responsible for these events. If that was the case, a large number of short-timescale microlensing events should also be seen toward the Magellanic Clouds. Here we report the results of a high-cadence survey of the Magellanic Clouds carried out from October 2022 through May 2024 as part of the Optical Gravitational Lensing Experiment (OGLE). We observed almost 35 million source stars located in the central regions of the Large and Small Magellanic Clouds and found only one long-timescale microlensing event candidate. No short-timescale events were detected despite high sensitivity to such events. That allows us to infer the strongest available limits on the frequency of planetary-mass PBHs in dark matter. We find that PBHs and other compact objects with masses from $1.4 \times 10^{-8}\,M_{\odot}$ (half of the Moon mass) to $0.013\,M_{\odot}$ (planet/brown dwarf boundary) may comprise at most 1% of dark matter. That rules out the PBH origin hypothesis for the short-timescale events detected toward the Galactic bulge and indicates they are caused by the population of free-floating or wide-orbit planets.
The Atacama Cosmology Telescope: Large-scale velocity reconstruction with the kinematic Sunyaev--Zel'dovich effect and DESI LRGs
2024-10-08
Fiona McCarthy, Nicholas Battaglia, Rachel Bean, J. Richard Bond, Hongbo Cai, Erminia Calabrese, William R. Coulton, Mark J. Devlin, Jo Dunkley, Simone Ferraro, Vera Gluscevic, Yilun Guan, J. Colin Hill, Matthew C. Johnson, Aleksandra Kusiak, Alex Laguë, Niall MacCrann, Mathew S. Madhavacheril, Kavilan Moodley, Sigurd Naess, Frank J. Qu, Bernardita Ried Guachalla, Neelima Sehgal, Blake D. Sherwin, Cristóbal Sifón, Kendrick M. Smith, Suzanne T. Staggs, Alexander van Engelen, Eve M. Vavagiakis, Edward J. Wollack
The kinematic Sunyaev--Zel'dovich (kSZ) effect induces a non-zero density-density-temperature bispectrum, which we can use to reconstruct the large-scale velocity field from a combination of cosmic microwave background (CMB) and galaxy density measurements, in a procedure known as ``kSZ velocity reconstruction''. This method has been forecast to constrain large-scale modes with future galaxy and CMB surveys, improving their measurement beyond what is possible with the galaxy surveys alone. Such measurements will enable tighter constraints on large-scale signals such as primordial non-Gaussianity, deviations from homogeneity, and modified gravity. In this work, we demonstrate a statistically significant measurement of kSZ velocity reconstruction for the first time, by applying quadratic estimators to the combination of the ACT DR6 CMB+kSZ map and the DESI LRG galaxies (with photometric redshifts) in order to reconstruct the velocity field. We do so using a formalism appropriate for the 2-dimensional projected galaxy fields that we use, which naturally incorporates the curved-sky effects important on the largest scales. We find evidence for the signal by cross-correlating with an external estimate of the velocity field from the spectroscopic BOSS survey and rejecting the null (no-kSZ) hypothesis at $3.8\sigma$. Our work presents a first step towards the use of this observable for cosmological analyses.
Searching for Radio Transients with Inverted Spectra in Epoch 1 of VLASS and VCSS, and Identification of a Sample of Candidate Relativistic Nuclear Transients
2024-10-08
Yuyang Chen, B. M. Gaensler, Tracy Clarke, Wendy Peters, Emil Polisensky, Kovi Rose
For radio transients, an inverted spectrum (defined as $\alpha > 0$ for a power law spectrum $S_{\nu}\propto \nu^{\alpha}$) constrains physical properties, which in principle can be a useful criterion for selecting specific targets of interest in a transient search. To test and develop this concept, we have searched epoch 1 of the Very Large Array Sky Survey (VLASS; 3.0 GHz) and the VLITE Commensal Sky Survey (VCSS; 340 MHz) for radio transients with inverted spectra. We discover a sample of 22 inverted-spectra transient candidates that are not associated with cataloged active galactic nuclei (AGNs). To the best of our knowledge, none of our candidates have transient counterparts at other wavelengths, and only one has previously been reported as a radio transient. We find that our candidates evolve slowly over years and show either highly inverted spectra or peaked spectra over $\sim$1--3 GHz. Within our sample, nine candidates are matched to optical centers of galaxies and have estimated radio spectral luminosities of $L_{3.0\mathrm{GHz}}\sim10^{30}-10^{33}\,\mathrm{erg}\,\mathrm{s}^{-1}\,\mathrm{Hz}^{-1}$. Based on the observed properties, we find the most plausible transient classification for our candidates to be relativistic tidal disruption events. However, we are unable to fully rule out variable or transient AGNs with highly inverted spectra. Upon examining physical constraints, we confirm that mainly relativistic transients (on-axis or off-axis) with equipartition energy $E_{\mathrm{eq}}\gtrsim10^{49}-10^{53}\,\mathrm{erg}$ are expected from searching VLASS and VCSS based on inverted spectra. The obtainable physical constraints, however, can be weak due to degeneracy introduced by viewing angle.
Methods for robustly measuring the minimum spanning tree and other field level statistics from galaxy surveys
2024-10-08
Krishna Naidoo, Ofer Lahav
Field level statistics, such as the minimum spanning tree (MST), have been shown to be a promising tool for parameter inference in cosmology. However, applications to real galaxy surveys are challenging, due to the presence of small scale systematic effects and non-trivial survey selection functions. Since many field level statistics are 'hard-wired', the common practice is to forward model survey systematic effects to synthetic galaxy catalogues. However, this can be computationally demanding and produces results that are a product of cosmology and systematic effects, making it difficult to directly compare results from different experiments. We introduce a method for inverting survey systematic effects through a Monte Carlo subsampling technique where galaxies are assigned probabilities based on their galaxy weight and survey selection functions. Small scale systematic effects are mitigated through the addition of a point-process smoothing technique called jittering. The inversion technique removes the requirement for a computational and labour intensive forward modelling pipeline for parameter inference. We demonstrate that jittering can mask small scale theoretical uncertainties and survey systematic effects like fibre collisions and we show that Monte Carlo subsampling can remove the effects of survey selection functions. We outline how to measure field level statistics from future surveys.
A review on the classical Plateau problem is presented. Then, the state of the art about the Kirchhoff-Plateau problem is illustrated as well as some possible future directions of research.
Magnetic Fields Under Feedback: A Case Study of the Massive Star-Forming Hub G34.26+0.15
2024-10-08
Zacariyya A. Khan, Kate Pattle, Sarah F. Graves
We present 850$\mu$m polarized observations of the molecular cloud G34.26+0.15 taken using the POL-2 polarimeter mounted on the James Clerk Maxwell Telescope (JCMT). G34.26+0.15 is a hub-filament system with ongoing high-mass star-formation, containing multiple HII regions. We extend the Histogram of Relative Orientations technique with an alternative application that considers the alignment of the magnetic field to the filaments and a HII region boundary, denoted as the filament alignment factor ($\xi_{F}$) and the ellipse alignment factor ($\xi_{E}$) respectively. Using these metrics, we find that, although in general the magnetic field aligns parallel to the filamentary structure within the system in the north-west, the magnetic field structure of G34.26+0.15 has been radically reshaped by the expansion of an evolved HII region in the south-east, which itself may have triggered further high-mass star formation in the cloud. Thus, we suggest high-mass star formation is both occurring through mass accretion as per the hub-filament model from one side, and through compression of gas under stellar feedback from the other. We also use HARP observations of C$^{18}$O from the CHIMPS survey to estimate the magnetic field strength across the cloud, finding strengths of $\sim$0.5-1.4 mG.
Probing Interacting Dark Sector with the next generation of gravitational-wave detectors
2024-10-08
Matteo Califano, Ivan De Martino, Ruth Lazkoz
We have probed the capability of third-generation Gravitational Waves (GW) interferometers, such as the Einstein Telescope and Cosmic Explorer, to constrain a cosmological model with an interacting dark sector. We focused on GW events with a detected electromagnetic counterpart being the $\gamma$ or X emission of Gamma-Ray Burst, and a Kilonova emission. We assume the first one to be detected by the THESEUS satellite, while the second one to be detected by the Vera Rubin Observatory. We probed three different interaction kernels and found that the posterior estimation of the cosmological parameters is biased due to the existing degeneracies between the dark and matter sectors. We also found that introducing an external prior on the matter density parameter breaks the degeneracy, removes the bias results, and improves the accuracy on the dark sector parameters.
2024-10-08
M. Flores-Trivigno, A. P. Buccino, E. González, P. D. Colombo, C. González, M. Jaque-Arancibia, R. V. Ibáñez Bustos, C. Saffe, P. Miquelarena, J. Alacoria, A. Collado
The long-term solar magnetic activity and its cyclical behaviour, which is maintained by a dynamo mechanism, are both still challenging for the astrophysics. In particular, an atypical event occurred between 1645 and 1715 when the solar activity was remarkably decreased and the number of sunspots got extremely reduced. However, it is still unclear what happened to the solar cycle. The discovery of longer activity minima in cool stars may shed light on the nature of the complex mechanisms involved in the long-term behaviour of the solar-stellar dynamo. Our aim is to explore if the G5V solar-like star HD 4915, which showed a striking chromospheric activity pattern in a previous study performed with HIRES data, could be considered a bona fide Maunder Minimum (hereafter MM) candidate. We have analyzed over 380 spectra acquired between 2003 and 2022 using HARPS and HIRES spectrographs. We carried out a detailed search of activity signatures in HD 4915 by using the Mount Wilson and the Balmer H$_{\alpha}$ activity indexes. This task was performed by means of the GLS periodogram. The new HARPS data show that the chromospheric activity of HD 4915 is not decreasing. In fact, the rise of the activity after the broad minimum in three years gets to the level of activity before that phase, suggesting that it is not entering into a MM phase. HD 4915 shows a distinctive activity behaviour initially attributed to a possible and incipient MM phase. The additional HARPS data allow us to discard a MM in the star. Our analysis shows that the complex activity pattern of HD 4915 could be ruled by a multiple activity cycle, being a shorter cycle of 4.8-yr modulated by a potential longer one. More activity surveys with extensive records and suitable cadence are crucial for accurate identification of stars in Magnetic Grand Minima.
Supersingular elliptic curves, quaternion algebras and applications to cryptography
2024-10-08
Eyal Z. Goren, Jonathan R. Love
This paper contains a survey of supersingular isogeny graphs associated to supersingular elliptic curves and their various applications to cryptography. Within limitation of space, we attempt to address a broad audience and make this part widely accessible. For those graphs we also present three recent results and sketch their proofs. We then discuss a generalization to superspecial isogeny graphs associated to superspecial abelian varieties with real multiplication. These graphs were introduced by Charles, Goren and Lauter and so our discussion is brief. Motivated by their cryptographic applications, we prove a general theorem concerning generation of lattices over totally real fields by elements of specified norm. Throughout the paper we have attempted to clarify certain considerations that are either vaguely stated in the literature, or are folklore. We hope this paper will be useful both to a novice wishing to familiarize themselves with this very active area, and to the expert who may enjoy some vignettes and an overview of some new results.
Reinforcement learning (RL) has emerged as a pivotal technique for fine-tuning large language models (LLMs) on specific tasks. However, prevailing RL fine-tuning methods predominantly rely on PPO and its variants. Though these algorithms are effective in general RL settings, they often exhibit suboptimal performance and vulnerability to distribution collapse when applied to the fine-tuning of LLMs. In this paper, we propose CORY, extending the RL fine-tuning of LLMs to a sequential cooperative multi-agent reinforcement learning framework, to leverage the inherent coevolution and emergent capabilities of multi-agent systems. In CORY, the LLM to be fine-tuned is initially duplicated into two autonomous agents: a pioneer and an observer. The pioneer generates responses based on queries, while the observer generates responses using both the queries and the pioneer's responses. The two agents are trained together. During training, the agents exchange roles periodically, fostering cooperation and coevolution between them. Experiments evaluate CORY's performance by fine-tuning GPT-2 and Llama-2 under subjective and objective reward functions on the IMDB Review and GSM8K datasets, respectively. Results show that CORY outperforms PPO in terms of policy optimality, resistance to distribution collapse, and training robustness, thereby underscoring its potential as a superior methodology for refining LLMs in real-world applications.
Phase field crystal modelling of deformation assisted precipitation in binary alloys
2024-10-08
Alex Mamaev, Duncan Burns, Nikolas Provatas
We consider the process of precipitation in binary alloys in the presence of mechanical deformation. It is commonly observed that mechanical deformation prior to or during precipitation leads to microstructure with excess defects, which allows for enhanced precipitate nucleation and growth rates. To investigate this phenomenon, we employ a two-dimensional phase-field crystal alloy model endowed with a temperature dependent mobility, making it capable of recovering isothermal transformation (TTT) diagrams with a characteristic inflection point (nose) about a critical temperature. We examine the variation in the time-scale of precipitation and its connection to the time-scale of the applied deformation, focusing on the roles of atomic defects in the processes involved. Our results indicate that precipitation is initially delayed through application of a deformation until a critical strain is achieved, beyond which precipitation proceeds more rapidly, assisted by plastic deformation such as grain boundary buckling or dislocation nucleation. We show that the evolution of the precipitated fraction, $f(t)$, departs from classical Avrami behaviour. Specifically, $df/dt$ develops two peaks indicative of a ``plateau"-like inflection in $f(t)$, signalling the transition to defect assisted precipitate nucleation. We analyze these plateaus as a function of the deformation rate and demonstrate that the they exhibit a discontinuous bifurcation as the time-scale of applied deformation is increased. These findings are compared to and found to be consistent with experiments.
The ground calibration of the HERMES-Pathfinder payload flight models
2024-10-08
G. Dilillo, E. J. Marchesini, G. Baroni, G. Della Casa, R. Campana., Y. Evangelista, A. Guzmán, P. Hedderman, P. Bellutti, G. Bertuccio, F. Ceraudo, M. Citossi, D. Cirrincione, I. Dedolli, E. Demenev, M. Feroci, F. Ficorella, M. Fiorini, M. Gandola, M. Grassi, G. La Rosa, G. Lombardi, P. Malcovati, F. Mele, P. Nogara, A. Nuti, M. Perri, S. Pliego-Caballero, S. Pirrotta, S. Puccetti, I. Rashevskaya, F. Russo, G. Sottile, C. Tenzer, M. Trenti, S. Trevisan, A. Vacchi, G. Zampa, N. Zampa, F. Fiore
HERMES-Pathfinder is a space-borne mission based on a constellation of six nano-satellites flying in a low-Earth orbit. The 3U CubeSats, to be launched in early 2025, host miniaturized instruments with a hybrid Silicon Drift Detector/scintillator photodetector system, sensitive to both X-rays and gamma-rays. A seventh payload unit is installed onboard SpIRIT, an Australian-Italian nano-satellite developed by a consortium led by the University of Melbourne and launched in December 2023. The project aims at demonstrating the feasibility of Gamma-Ray Burst detection and localization using miniaturized instruments onboard nano-satellites. The HERMES flight model payloads were exposed to multiple well-known radioactive sources for spectroscopic calibration under controlled laboratory conditions. The analysis of the calibration data allows both to determine the detector parameters, necessary to map instrumental units to accurate energy measurements, and to assess the performance of the instruments. We report on these efforts and quantify features such as spectroscopic resolution and energy thresholds, at different temperatures and for all payloads of the constellation. Finally we review the performance of the HERMES payload as a photon counter, and discuss the strengths and the limitations of the architecture.
There have been several attempts in recent years to extend the notions of symplectic and Poisson structures in order to create a suitable geometrical framework for classical field theories, trying to achieve a success similar to the use of these concepts in Hamiltonian mechanics. These notions always have a graded character, since the multisymplectic forms are of a higher degree than two. Another line of work has been to extend the concept of Dirac structures to these new scenarios. In the present paper we review all these notions, relate them and propose and study a generalization that (under some mild regularity conditions) includes them and is of graded nature. We expect this generalization to allow us to advance in the study of classical field theories, their integrability, reduction, numerical approximations and even their quantization.
Large Language Model Enhanced Text-to-SQL Generation: A Survey
2024-10-08
Xiaohu Zhu, Qian Li, Lizhen Cui, Yongkang Liu
Text-to-SQL translates natural language queries into Structured Query Language (SQL) commands, enabling users to interact with databases using natural language. Essentially, the text-to-SQL task is a text generation task, and its development is primarily dependent on changes in language models. Especially with the rapid development of Large Language Models (LLMs), the pattern of text-to-SQL has undergone significant changes. Existing survey work mainly focuses on rule-based and neural-based approaches, but it still lacks a survey of Text-to-SQL with LLMs. In this paper, we survey the large language model enhanced text-to-SQL generations, classifying them into prompt engineering, fine-tuning, pre-trained, and Agent groups according to training strategies. We also summarize datasets and evaluation metrics comprehensively. This survey could help people better understand the pattern, research status, and challenges of LLM-based text-to-SQL generations.
This article surveys some theoretical aspects of Cellular Automata (CAs) research. In particular, we discuss on maximal length CA. An n-cell CA is a maximal length CA, if all the configurations except one form a single cycle. There is a bonding between maximal length CA and primitive polynomial. So, primitive polynomials occupy a good amount of space in this survey. The main goal of this survey is to provide a tutorial on maximal length CA theory to researchers with classical and new results on maximality. We also give a compact collection of known results with references to their proofs, and to suggest some open problems. Additionally, some new theorems and corollaries are added to bridge the gaps among several known results.
The Milky Way bar potential constrained by the kinematics of SiO maser stars in BAaDE Survey
2024-10-08
Tian-Ye Xia, Juntai Shen, Zhi Li, Huaijin Feng, Loránt O. Sjouwerman, Ylva M. Pihlström, Megan O. Lewis, Michael C. Stroh
We introduce a novel method that utilizes the longitude-velocity (l-v) envelope to constrain the Milky Way (MW) bar potential. Previous work (Habing 2016) used the l-v diagram to explain the distribution of the observed high-velocity stars. We successfully reproduce their results, but find that their method is limited to only one single type of periodic orbits. In contrast, we propose that the l-v envelope provides much more comprehensive constraints. We compare the properties of test particles in the Portai et al. (2017) MW potential model (P17) with the observed SiO maser stars from the Bulge Asymmetries and Dynamical Evolution (BAaDE) survey. We find that the l-v envelope generated by the bar potential demonstrates reasonable agreement with the observational data, albeit with slight discrepancies near the Galactic center. The inconsistencies suggest that the P17 potential yields a lower central rotation curve, a slightly larger quadrupole strength, or a possibly underestimated pattern speed. We also adopt an updated version of the P17 potential with a modified central mass component (CMC) proposed by Hunter et al. (2024) (H24). The fitting of the l-v envelope suggests that the H24 potential does not completely address the existing challenges and may hint at a possible underestimation of the central bar mass. Our study demonstrates that the l-v envelope can be used as a valuable tool for constraining the Galactic potential and provides insights into the Milky Way bar potential.
Learning and Unlearning: Bridging classification, memory and generative modeling in Recurrent Neural Networks
2024-10-08
Enrico Ventura
The human brain is a complex system that is fascinating scientists since a long time. Its remarkable capabilities include categorization of concepts, retrieval of memories and creative generation of new examples. At the same time, modern artificial neural networks are trained on large amounts of data to accomplish these same tasks with a considerable degree of precision. By contrast with biological systems, machines appear to be either significantly slow and energetically expensive to train, suggesting the need for a paradigmatic change in the way they learn. We here review a general learning prescription that allows to perform classification, memorization and generation of new examples in bio-inspired artificial neural networks. The training procedure can be split into a prior Hebbian learning phase and a subsequent anti-Hebbian one (usually referred to as Unlearning). The separation of training in two epochs allows the algorithm to go fully unsupervised while partially aligning with some modern biological theories of learning.
Mechanisms and Controversies of tACS (Transcranial Alternating Current Stimulation)
2024-10-08
Julien Modolo, Mengsen Zhang, Joan Duprez, Flavio Frohlich
The use of small amplitude electric currents delivered through the scalp, termed transcranial current stimulation (tCS, tDCS / tACS when using DC / AC currents) holds considerable promise for developing safe and effective treatments for central nervous system disorders. Initially welcomed with skepticism due to significant gaps of knowledge in terms of neurophysiology and biophysical mechanisms, tCS is maturing as a technology while its mechanisms of action are gradually being elucidated. However, there remain open questions about the mechanisms of action that warrant clarification to bring tCS to its full potential. In this review focused on tACS, we make an attempt at providing an overview of the converging experimental evidence, from results obtained in various species, regarding the mechanisms of action. We also highlight the remaining points of uncertainty regarding potential confounds, and propose possible experimentally testable solutions to address those issues. Finally, we outline how a continued focus on deepening our understanding of tACS mechanisms might provide significant insights into fundamental, long-standing questions in neuroscience.
Quantum secret sharing (QSS) plays a significant role in multiparty quantum communication and is a crucial component of future quantum multiparty computing networks. Therefore, it is highly valuable to develop a QSS protocol that offers both information-theoretic security and validation in real optical systems under a finite-key regime. In this work, we propose a three-user QSS protocol based on phase-encoding technology. By adopting symmetric procedures for the two players, our protocol resolves the security loopholes introduced by asymmetric basis choice without prior knowledge of the identity of the malicious player. Kato's concentration inequality is exploited to provide security against coherent attacks with the finite-key effect. Moreover, the practicality of our protocol has been validated under a 30-dB channel loss with a transmission distance of 5-km fiber. Our protocol achieves secure key rates ranging from 432 to 192 bps by choosing different pulse intensities and basis selection probabilities. Offering enhanced security and practicality, our protocol stands as an essential element for the realization of quantum multiparty computing networks.
Towards an Operational Responsible AI Framework for Learning Analytics in Higher Education
2024-10-08
Alba Morales Tirado, Paul Mulholland, Miriam Fernandez
Universities are increasingly adopting data-driven strategies to enhance student success, with AI applications like Learning Analytics (LA) and Predictive Learning Analytics (PLA) playing a key role in identifying at-risk students, personalising learning, supporting teachers, and guiding educational decision-making. However, concerns are rising about potential harms these systems may pose, such as algorithmic biases leading to unequal support for minority students. While many have explored the need for Responsible AI in LA, existing works often lack practical guidance for how institutions can operationalise these principles. In this paper, we propose a novel Responsible AI framework tailored specifically to LA in Higher Education (HE). We started by mapping 11 established Responsible AI frameworks, including those by leading tech companies, to the context of LA in HE. This led to the identification of seven key principles such as transparency, fairness, and accountability. We then conducted a systematic review of the literature to understand how these principles have been applied in practice. Drawing from these findings, we present a novel framework that offers practical guidance to HE institutions and is designed to evolve with community input, ensuring its relevance as LA systems continue to develop.
Multi-Session Client-Centered Treatment Outcome Evaluation in Psychotherapy
2024-10-08
Hongbin Na, Tao Shen, Shumao Yu, Ling Chen
In psychotherapy, therapeutic outcome assessment, or treatment outcome evaluation, is essential for enhancing mental health care by systematically evaluating therapeutic processes and outcomes. Existing large language model approaches often focus on therapist-centered, single-session evaluations, neglecting the client's subjective experience and longitudinal progress across multiple sessions. To address these limitations, we propose IPAEval, a client-Informed Psychological Assessment-based Evaluation framework that automates treatment outcome evaluations from the client's perspective using clinical interviews. IPAEval integrates cross-session client-contextual assessment and session-focused client-dynamics assessment to provide a comprehensive understanding of therapeutic progress. Experiments on our newly developed TheraPhase dataset demonstrate that IPAEval effectively tracks symptom severity and treatment outcomes over multiple sessions, outperforming previous single-session models and validating the benefits of items-aware reasoning mechanisms.
Three-dimensional Abelian and nonAbelian gauge Higgs theories
2024-10-08
Claudio Bonati, Andrea Pelissetto, Ettore Vicari
Gauge symmetries and Higgs mechanisms are key features of theories describing high-energy particle physics and collective phenomena in statistical and condensed-matter physics. In this review we address the collective behavior of systems of multicomponent scalar fields interacting with gauge fields, which can be already present in the underlying microscopic system or emerge only at criticality. The interplay between local gauge and global symmetries determines the phase diagram, the nature of the Higgs phases, and the nature of phase transitions between the high-temperature disordered and the low-temperature Higgs phases. However, further crucial features determine the universal properties of the critical behaviors at continuous transitions. Specifically, their nature also depends on the role played by the gauge modes at criticality: effective (Abelian or nonAbelian) gauge Higgs field theories emerge if gauge modes develop critical correlations, while more standard critical behaviors occur in the case they remain short ranged at the transition. In the latter case, gauge fields only prevent non-gauge invariant correlators from becoming critical. This review covers the recent progress made in the study of Higgs systems with Abelian and nonAbelian gauge fields. We mainly focus on three-dimensional systems, and briefly discuss two-dimensional systems, whose equilibrium statistical properties are described by a classical partition function. However, by using the quantum-to-classical mapping, the results on the critical behavior for classical systems in $D=d+1$ dimensions can be extended to quantum transitions in $d$ dimensions.
Software analytics for software engineering: A tertiary review
2024-10-08
Muhammad Laiq, Nauman bin Ali, Jürgen Börstler, Emelie Engström
Software analytics (SA) is frequently proposed as a tool to support practitioners in software engineering (SE) tasks. We have observed that several secondary studies on SA have been published. Some of these studies have overlapping aims and some have even been published in the same calendar year. This presents an opportunity to analyze the congruence or divergence of the conclusions in these studies. Such an analysis can help identify broader generalizations beyond any of the individual secondary studies. We identified five secondary studies on the use of SA for SE. These secondary studies cover primary research from 2000 to 2021. Despite the overlapping objectives and search time frames of these secondary studies, there is negligible overlap of primary studies between these secondary studies. Thus, each of them provides an isolated view, and together, they provide a fragmented view, i.e., there is no ``common picture'' of the area. Thus, we conclude that an overview of the literature identified by these secondary studies would be useful in providing a more comprehensive overview of the topic.
My view on and experience with Grothendieck's anabelian geometry
2024-10-08
Mohamed Saidi
This paper is based on the author's talk at the Grothendieck conference at Chapman university on 26-05-2022. It is not a survey of anabelian geometry but rather exposes some of the personal views and experiences of the author with the topic.
2024-10-08
Zhaochun Ren, Xiangnan He, Dawei Yin, Maarten de Rijke
Electronic commerce, or e-commerce, is the buying and selling of goods and services, or the transmitting of funds or data online. E-commerce platforms come in many kinds, with global players such as Amazon, Airbnb, Alibaba, Booking.com, eBay, JD.com and platforms targeting specific geographic regions such as Bol.com and Flipkart.com.Information retrieval has a natural role to play in e-commerce, especially in connecting people to goods and services. Information discovery in e-commerce concerns different types of search (e.g., exploratory search vs. lookup tasks), recommender systems, and natural language processing in e-commerce portals. The rise in popularity of e-commerce sites has made research on information discovery in e-commerce an increasingly active research area. This is witnessed by an increase in publications and dedicated workshops in this space. Methods for information discovery in e-commerce largely focus on improving the effectiveness of e-commerce search and recommender systems, on enriching and using knowledge graphs to support e-commerce, and on developing innovative question answering and bot-based solutions that help to connect people to goods and services. In this survey, an overview is given of the fundamental infrastructure, algorithms, and technical solutions for information discovery in e-commerce. The topics covered include user behavior and profiling, search, recommendation, and language technology in e-commerce.
Pathwise Gradient Variance Reduction with Control Variates in Variational Inference
2024-10-08
Kenyon Ng, Susan Wei
Variational inference in Bayesian deep learning often involves computing the gradient of an expectation that lacks a closed-form solution. In these cases, pathwise and score-function gradient estimators are the most common approaches. The pathwise estimator is often favoured for its substantially lower variance compared to the score-function estimator, which typically requires variance reduction techniques. However, recent research suggests that even pathwise gradient estimators could benefit from variance reduction. In this work, we review existing control-variates-based variance reduction methods for pathwise gradient estimators to assess their effectiveness. Notably, these methods often rely on integrand approximations and are applicable only to simple variational families. To address this limitation, we propose applying zero-variance control variates to pathwise gradient estimators. This approach offers the advantage of requiring minimal assumptions about the variational distribution, other than being able to sample from it.
Wolf2Pack: The AutoFusion Framework for Dynamic Parameter Fusion
2024-10-08
Bowen Tian, Songning Lai, Yutao Yue
In the rapidly evolving field of deep learning, specialized models have driven significant advancements in tasks such as computer vision and natural language processing. However, this specialization leads to a fragmented ecosystem where models lack the adaptability for broader applications. To overcome this, we introduce AutoFusion, an innovative framework that fuses distinct model parameters(with the same architecture) for multi-task learning without pre-trained checkpoints. Using an unsupervised, end-to-end approach, AutoFusion dynamically permutes model parameters at each layer, optimizing the combination through a loss-minimization process that does not require labeled data. We validate AutoFusion's effectiveness through experiments on commonly used benchmark datasets, demonstrating superior performance over established methods like Weight Interpolation, Git Re-Basin, and ZipIt. Our framework offers a scalable and flexible solution for model integration, positioning it as a powerful tool for future research and practical applications.
Multi-criteria decision making (MCDM) is necessary for choosing one from the available alternatives (or from the obtained Pareto-optimal solutions for multi-objective optimization), where the performance of each alternative is quantified against several criteria (or objectives). This paper presents a comprehensive review of the application of MCDM methods in chemical and process engineering. It systematically outlines the essential steps in MCDM, including the various normalization, weighting, and MCDM methods that are critical to the decision-making. The review draws on published papers identified through a search in the SCOPUS database, focusing on works by authors with more contributions to the field. Each selected paper is analyzed based on the MCDM, normalization and weighting methods used. Additionally, this paper introduces two readily available programs for performing MCDM calculations. In short, it provides insights into MCDM steps and methods, highlights their applications in chemical engineering, and discusses the challenges and prospects in this area.
DiffusionGuard: A Robust Defense Against Malicious Diffusion-based Image Editing
2024-10-08
June Suk Choi, Kyungmin Lee, Jongheon Jeong, Saining Xie, Jinwoo Shin, Kimin Lee
Recent advances in diffusion models have introduced a new era of text-guided image manipulation, enabling users to create realistic edited images with simple textual prompts. However, there is significant concern about the potential misuse of these methods, especially in creating misleading or harmful content. Although recent defense strategies, which introduce imperceptible adversarial noise to induce model failure, have shown promise, they remain ineffective against more sophisticated manipulations, such as editing with a mask. In this work, we propose DiffusionGuard, a robust and effective defense method against unauthorized edits by diffusion-based image editing models, even in challenging setups. Through a detailed analysis of these models, we introduce a novel objective that generates adversarial noise targeting the early stage of the diffusion process. This approach significantly improves the efficiency and effectiveness of adversarial noises. We also introduce a mask-augmentation technique to enhance robustness against various masks during test time. Finally, we introduce a comprehensive benchmark designed to evaluate the effectiveness and robustness of methods in protecting against privacy threats in realistic scenarios. Through extensive experiments, we show that our method achieves stronger protection and improved mask robustness with lower computational costs compared to the strongest baseline. Additionally, our method exhibits superior transferability and better resilience to noise removal techniques compared to all baseline methods. Our source code is publicly available at https://github.com/choi403/DiffusionGuard.
Bilayer Graphene on hexagonal Boron Nitride and the Family of quantum MetaMaterial
2024-10-08
Takuya Iwasaki, Yoshifumi Morita
We report on the fabrication and characterization of dual-gated hexagonal boron nitride (hBN)/bilayer-graphene (BLG) superlattices. Due to the moire effect, the hBN/BLG superlattice harbors an energy gap at the charge neutral point (CNP) and the satellites even without a perpendicular electric field. In BLG, moreover, the application of a perpendicular electric field tunes the energy gap, which contrasts with the single-layer graphene (SLG) and is linked to the family of rhombohedral multilayer graphene. Therefore, the hBN/BLG superlattice is accompanied with non-trivial energy-band topology and a narrow energy band with van Hove singularities. By the dual gating, systematic engineering of the energy-band structure can be performed and the carrier concentration is fine-tunable. This review is an extended version of the talk based on ref. [1], which is also a supplementary to the ref. T. Iwasaki, Y. Morita, K. Watanabe, T. Taniguchi, Phys. Rev. B106, 165134 (2022) and Phys. Rev. B109, 075409 (2024). The data show the universality and diversity in the physics of the hBN/BLG superlattices.
Synthesizing Efficient and Permissive Programmatic Runtime Shields for Neural Policies
2024-10-08
Jieke Shi, Junda He, Zhou Yang, Đorđe Žikelić, David Lo
With the increasing use of neural policies in control systems, ensuring their safety and reliability has become a critical software engineering task. One prevalent approach to ensuring the safety of neural policies is to deploy programmatic runtime shields alongside them to correct their unsafe commands. However, the programmatic runtime shields synthesized by existing methods are either computationally expensive or insufficiently permissive, resulting in high overhead and unnecessary interventions on the system. To address these challenges, we propose Aegis, a novel framework that synthesizes lightweight and permissive programmatic runtime shields for neural policies. Aegis achieves this by formulating the seeking of a runtime shield as a sketch-based program synthesis problem and proposing a novel method that leverages counterexample-guided inductive synthesis and Bayesian optimization to solve it. To evaluate Aegis and its synthesized shields, we use four representative control systems and compare Aegis with the current state-of-the-art. Our results show that the programmatic runtime shields synthesized by Aegis can correct all unsafe commands from neural policies, ensuring that the systems do not violate any desired safety properties at all times. Compared to the current state-of-the-art, Aegis's shields exhibit a 2.1$\times$ reduction in time overhead and a 4.4$\times$ reduction in memory usage, suggesting that they are much more lightweight. Moreover, Aegis's shields incur an average of 1.6$\times$ fewer interventions than other shields, showing better permissiveness.
We present a comprehensive survey on the use of annotations in information visualizations, highlighting their crucial role in improving audience understanding and engagement with visual data. Our investigation encompasses empirical studies on annotations, showcasing their impact on user engagement, interaction, comprehension, and memorability across various contexts. We also study the existing tools and techniques for creating annotations and their diverse applications, enhancing the understanding of both practical and theoretical aspects of annotations in data visualization. Additionally, we identify existing research gaps and propose potential future research directions, making our survey a valuable resource for researchers, visualization designers, and practitioners by providing a thorough understanding of the application of annotations in visualization.
Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future
2024-10-08
Long Chen, Yuzhi Huang, Junyu Dong, Qi Xu, Sam Kwong, Huimin Lu, Huchuan Lu, Chongyi Li
Underwater object detection (UOD), aiming to identify and localise the objects in underwater images or videos, presents significant challenges due to the optical distortion, water turbidity, and changing illumination in underwater scenes. In recent years, artificial intelligence (AI) based methods, especially deep learning methods, have shown promising performance in UOD. To further facilitate future advancements, we comprehensively study AI-based UOD. In this survey, we first categorise existing algorithms into traditional machine learning-based methods and deep learning-based methods, and summarise them by considering learning strategy, experimental dataset, utilised features or frameworks, and learning stage. Next, we discuss the potential challenges and suggest possible solutions and new directions. We also perform both quantitative and qualitative evaluations of mainstream algorithms across multiple benchmark datasets by considering the diverse and biased experimental setups. Finally, we introduce two off-the-shelf detection analysis tools, Diagnosis and TIDE, which well-examine the effects of object characteristics and various types of errors on detectors. These tools help identify the strengths and weaknesses of detectors, providing insigts for further improvement. The source codes, trained models, utilised datasets, detection results, and detection analysis tools are public available at \url{https://github.com/LongChenCV/UODReview}, and will be regularly updated.
A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models
2024-10-08
Cong Guo, Feng Cheng, Zhixu Du, James Kiessling, Jonathan Ku, Shiyu Li, Ziru Li, Mingyuan Ma, Tergel Molom-Ochir, Benjamin Morris, Haoxuan Shan, Jingwei Sun, Yitu Wang, Chiyue Wei, Xueying Wu, Yuhao Wu, Hao Frank Yang, Jingyang Zhang, Junyao Zhang, Qilin Zheng, Guanglei Zhou, Hai, Li, Yiran Chen
The rapid development of large language models (LLMs) has significantly transformed the field of artificial intelligence, demonstrating remarkable capabilities in natural language processing and moving towards multi-modal functionality. These models are increasingly integrated into diverse applications, impacting both research and industry. However, their development and deployment present substantial challenges, including the need for extensive computational resources, high energy consumption, and complex software optimizations. Unlike traditional deep learning systems, LLMs require unique optimization strategies for training and inference, focusing on system-level efficiency. This paper surveys hardware and software co-design approaches specifically tailored to address the unique characteristics and constraints of large language models. This survey analyzes the challenges and impacts of LLMs on hardware and algorithm research, exploring algorithm optimization, hardware design, and system-level innovations. It aims to provide a comprehensive understanding of the trade-offs and considerations in LLM-centric computing systems, guiding future advancements in AI. Finally, we summarize the existing efforts in this space and outline future directions toward realizing production-grade co-design methodologies for the next generation of large language models and AI systems.
Quantum-information methods for quantum gravity laboratory-based tests
2024-10-08
Chiara Marletto, Vlatko Vedral
Quantum theory and general relativity are about one century old. At present, they are considered the best available explanations of physical reality, and they have been so far corroborated by all experiments realised so far. Nonetheless, the quest to unify them is still ongoing, with several yet untested proposals for a theory of quantum gravity. Here we review the nascent field of information-theoretic methods applied to designing tests of quantum gravity in the laboratory. This field emerges from the fruitful extension of quantum information theory methodologies beyond the domain of applicability of quantum theory itself, to cover gravity. We shall focus mainly on the detection of gravitational entanglement between two quantum probes, comparing this method with single-probe schemes. We shall review the experimental proposal that has originated this field, as well as its variants, their applications, and discuss their potential implications for the quantum theory of gravity. We shall also highlight the role of general information-theoretic principles in illuminating the search for quantum effects in gravity.
DeepClean: Non-Linear Regression for Noise Reduction in the Virgo Detector
2024-10-08
R. Weizmann Kiendrebeogo, Muhammed Saleem, Marie Anne Bizouard, Nelson Christensen, Michael W. Coughlin, Kamiel Janssens, S. Zacharie Kam, Jean Koulidiati
Since the first detection of gravitational waves (GW) in 2015, there have been significant efforts to enhance the sensitivity of the ground-based detectors within the LIGO-Virgo-KAGRA collaboration. Despite these efforts, many gravitational wave signals remain below the detection threshold due to non-linear noise from the physical environment of the detectors, coupled with the main GW signal. \texttt{DeepClean}'s capability to handle a variety of noise sources, including linear, non-linear, and \emph{non-stationary} coupling mechanisms, is particularly important in fields like astrophysics and gravitational wave astronomy, where accurate data analysis is crucial for the detection and study of gravitational waves from astronomical events.
Multilayer network approaches to omics data integration in Digital Twins for cancer research
2024-10-08
Hugo Chenel, Malvina Marku, Tim James, Andrei Zinovyev, Vera Pancaldi
This review examines current and potential applications of DTs in healthcare, focusing on the integration of multi-omics data using multilayer network approaches in cancer research. We discuss methodologies, tools and platforms commonly used for this integration, while highlighting case studies, challenges, and research gaps. Finally, we advocate the need for incorporating diverse data types to produce more effective DTs for personalization of cancer treatments and in silico clinical trials.
An introduction to the fine structures on the $S$-spectrum
2024-10-08
Fabrizio Colombo, Antonino De Martino, Stefano Pinton, Irene Sabadini, Peter Schlosser
Holomorphic functions are fundamental in operator theory and their Cauchy formula is a crucial tool for defining functions of operators. The Fueter-Sce extension theorem (often called Fueter-Sce mapping theorem) provides a two-step procedure for extending holomorphic functions to hyperholomorphic functions. In the first step, slice hyperholomorphic functions are obtained, and their associated Cauchy formula establishes the $S$-functional calculus for noncommuting operators on the $S$-spectrum. The second step produces axially monogenic functions, which lead to the development of the monogenic functional calculus. In this review paper we discuss the second operator in the Fueter-Sce mapping theorem that takes slice hyperholomorphic to axially monogenic functions. This operator admits several factorizations which generate various function spaces and their corresponding functional calculi, thereby forming the so-called fine structures of spectral theories on the $S$-spectrum.
Fine-Tuning CLIP's Last Visual Projector: A Few-Shot Cornucopia
2024-10-07
Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc, Patrick Pérez, Raoul de Charette
We consider the problem of adapting a contrastively pretrained vision-language model like CLIP (Radford et al., 2021) for few-shot classification. The existing literature addresses this problem by learning a linear classifier of the frozen visual features, optimizing word embeddings, or learning external feature adapters. This paper introduces an alternative way for CLIP adaptation without adding 'external' parameters to optimize. We find that simply fine-tuning the last projection matrix of the vision encoder leads to strong performance compared to the existing baselines. Furthermore, we show that regularizing training with the distance between the fine-tuned and pretrained matrices adds reliability for adapting CLIP through this layer. Perhaps surprisingly, this approach, coined ProLIP, yields performances on par or better than state of the art on 11 few-shot classification benchmarks, few-shot domain generalization, cross-dataset transfer and test-time adaptation. Code will be made available at https://github.com/astra-vision/ProLIP .
Block MedCare: Advancing healthcare through blockchain integration
2024-10-07
Oliver Simonoski, Dijana Capeska Bogatinoska
In an era driven by information exchange, transparency and security hold crucial importance, particularly within the healthcare industry, where data integrity and confidentiality are paramount. This paper investigates the integration of blockchain technology in healthcare, focusing on its potential to revolutionize Electronic Health Records (EHR) management and data sharing. By leveraging Ethereum-based blockchain implementations and smart contracts, we propose a novel system that empowers patients to securely store and manage their medical data. Our research addresses critical challenges in implementing blockchain in healthcare, including scalability, user privacy, and regulatory compliance. We propose a solution that combines digital signatures, Role-Based Access Control, and a multi-layered architecture to enhance security and ensure controlled access. The system's key functions, including user registration, data append, and data retrieval, are facilitated through smart contracts, providing a secure and efficient mechanism for managing health information. To validate our approach, we developed a decentralized application (dApp) that demonstrates the practical implementation of our blockchain-based healthcare solution. The dApp incorporates user-friendly interfaces for patients, doctors, and administrators, showcasing the system's potential to streamline healthcare processes while maintaining data security and integrity. Additionally, we conducted a survey to gain insights into the perceived benefits and challenges of blockchain adoption in healthcare. The results indicate strong interest among healthcare professionals and IT experts, while also highlighting concerns about integration costs and technological complexity. Our findings...
High-resolution borehole earthquake monitoring at San Andreas Fault Observatory at Depth, Parkfield, California
2024-10-07
Ruiqing He, Bjorn Paulsson
Downhole earthquake monitoring, without the complex effects from the near surface, can record more and better seismic data than monitoring on surface. The San Andreas Fault Observatory at Depth (SAFOD) is a borehole observatory equipped with different instruments inside to study the earthquake mechanism of the San Andreas fault at Parkfield, California. During April to May in 2005, Paulsson deployed an 80-level 3-component geophone array in the SAFOD main hole, and continuously recorded seismic data for about 13 days. We located 125 local earthquakes from the borehole earthquake monitoring data using a homogeneous velocity model and compared it with 35 earthquakes' locations from surface earthquake monitoring by the United State Geological Survey (USGS) during the same monitoring time. The borehole earthquake locating is assumably more accurate in the borehole's vicinity. We also compared the result with 1,074 earthquakes' locations from the surface earthquake monitoring in the last 9 years from 2015 to 2024. The hypocenters from our nearly 2 weeks' borehole earthquake monitoring form similar structures as that from the 9 years' surface earthquake monitoring by the USGS.
Measurements of the galaxy 4-Point Correlation Function (4PCF) from theSloan Digital Sky Survey Baryon Oscillation Spectroscopic Survey (SDSS BOSS) have recently found strong statistical evidence for parity violation. If this signal is of genuine physical origin, it must stem from beyond-Standard Model physics, most likely during the very early Universe, prior to decoupling ($z$$\sim$$1,020$). Since the Baryon Acoustic Oscillation (BAO) features imprint at decoupling, they are expected in the parity-odd galaxy 4PCF, and so detecting them would be an additional piece of evidence that the signal is genuine. We demonstrate in a toy parity-violating model how the BAO imprint on the parity-odd 4PCF. We then outline how to perform a model-independent search for BAO in the odd 4PCF, desirable since, if the signal is real, we may not know for some time what model of e.g. inflation is producing it. If BAO are detected in the parity-odd sector, they can be used as a standard ruler as is already done in the 2PCF and 3PCF. We derive a simple formula relating the expected precision on the BAO scale to the overall parity-odd detection significance. Pursuing BAO in the odd 4PCF of future redshift surveys such as DESI, Euclid, Spherex, and Roman will be a valuable additional avenue to determine if parity violation in the distribution of galaxies is of genuine cosmological origin.
The metallicity dilution in local massive early-type galaxies
2024-10-07
Wu Yu-zhong, Zhang Wei
We derive a sample of 114 Baldwin-Phillips-Terlevich diagram - star formation (BPT-SF) and Wide-field infrared Survey Exploer - low star formation rate (WISE-LSFR) early-type galaxies (ETGs) by utilizing the criterion W2-W3$<2.5$ (where W2 and W3 are the wavelengths of 4.6 and 12 $\mu m$ in the WISE four bands) and cross-matching the $Galaxy~Zoo~1$ and the catalog of the Sloan Digital Sky Survey Data SDSS Release 7 MPA-JHU emission-line measurements. We find that \textbf{$\sim 28\%$} of our ETGs exhibit a metallicity that is at least 2 standard deviation (0.26 dex) below the mass-metallicity (MZ) relation of star-forming galaxies (SFGs) from the SDSS. We demonstrate that almost all of our ETGs locate below the ``main sequence'' of SFGs. We find that these ETGs with larger metallicity deviation from the MZ relation tend to have lower SFR and redder color. By exploring the dilution properties of these massive ETGs, we report that the dilution effect may be mainly attributed to the inflow of metal-poor gas from mergers/interaction or the intergalactic medium.
Density estimation with LLMs: a geometric investigation of in-context learning trajectories
2024-10-07
Toni J. B. Liu, Nicolas Boullé, Raphaël Sarfati, Christopher J. Earls
Large language models (LLMs) demonstrate remarkable emergent abilities to perform in-context learning across various tasks, including time series forecasting. This work investigates LLMs' ability to estimate probability density functions (PDFs) from data observed in-context; such density estimation (DE) is a fundamental task underlying many probabilistic modeling problems. We leverage the Intensive Principal Component Analysis (InPCA) to visualize and analyze the in-context learning dynamics of LLaMA-2 models. Our main finding is that these LLMs all follow similar learning trajectories in a low-dimensional InPCA space, which are distinct from those of traditional density estimation methods like histograms and Gaussian kernel density estimation (KDE). We interpret the LLaMA in-context DE process as a KDE with an adaptive kernel width and shape. This custom kernel model captures a significant portion of LLaMA's behavior despite having only two parameters. We further speculate on why LLaMA's kernel width and shape differs from classical algorithms, providing insights into the mechanism of in-context probabilistic reasoning in LLMs.
In the peculiar manner by which physicists reckon descent, this article is by a "child" and "grandchild" of the late Stanley Deser. We begin by sharing reminiscences of Stanley from over 40 years. Then we turn to a problem which was dear to his heart: the prospect that gravity might nonperturbatively screen its own ultraviolet divergences and those of other theories. After reviewing the original 1960 work by ADM, we describe a cosmological analogue of the problem and then begin the process of implementing it in gravity plus QED.
Provably Positivity-Preserving Constrained Transport (PPCT) Second-Order Scheme for Ideal Magnetohydrodynamics
2024-10-07
Dongwen Pang, Kailiang Wu
This paper proposes and analyzes a robust and efficient second-order positivity-preserving constrained transport (PPCT) scheme for ideal magnetohydrodynamics (MHD) on non-staggered Cartesian meshes. The PPCT scheme ensures two critical physical constraints: a globally discrete divergence-free (DDF) condition on the magnetic field and the positivity of density and pressure. The method is inspired by a novel splitting technique from [T.A. Dao, M. Nazarov and I. Tomas, J. Comput. Phys., 508:113009, 2024], which divides the MHD system into an Euler subsystem with steady magnetic fields and a magnetic subsystem with steady density and internal energy. To achieve these structure-preserving properties, the PPCT scheme combines a positivity-preserving (PP) finite volume method for the Euler subsystem with a finite difference constrained transport (CT) method for the magnetic subsystem via Strang splitting. The finite volume method employs a new PP limiter that retains second-order accuracy and enforces the positivity of density and pressure, with rigorous proof provided using the geometric quasilinearization (GQL) approach [K. Wu and C.-W. Shu, SIAM Review, 65:1031-1073, 2023]. For the magnetic subsystem, we develop an implicit finite difference CT method that conserves energy and maintains a globally DDF constraint. This nonlinear system is efficiently solved to machine precision using an iterative algorithm. Since the CT method is unconditionally energy-stable and conserves steady density and internal energy, the PPCT scheme requires only a mild CFL condition for the finite volume method to ensure stability and the PP property. While the focus is on 2D cases for clarity, the extension to 3D is discussed. Several challenging numerical experiments, including highly magnetized MHD jets with high Mach numbers, validate the PPCT scheme's accuracy, robustness, and high resolution.
Last Iterate Convergence in Monotone Mean Field Games
2024-10-07
Noboru Isobe, Kenshi Abe, Kaito Ariu
Mean Field Game (MFG) is a framework utilized to model and approximate the behavior of a large number of agents, and the computation of equilibria in MFG has been a subject of interest. Despite the proposal of methods to approximate the equilibria, algorithms where the sequence of updated policy converges to equilibrium, specifically those exhibiting last-iterate convergence, have been limited. We propose the use of a simple, proximal-point-type algorithm to compute equilibria for MFGs. Subsequently, we provide the first last-iterate convergence guarantee under the Lasry--Lions-type monotonicity condition. We further employ the Mirror Descent algorithm for the regularized MFG to efficiently approximate the update rules of the proximal point method for MFGs. We demonstrate that the algorithm can approximate with an accuracy of $\varepsilon$ after $\mathcal{O}({\log(1/\varepsilon)})$ iterations. This research offers a tractable approach for large-scale and large-population games.
Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization
2024-10-07
Rohan Reddy Mekala, Frederik Pahde, Simon Baur, Sneha Chandrashekar, Madeline Diep, Markus Wenzel, Eric L. Wisotzky, Galip Ümit Yolcu, Sebastian Lapuschkin, Jackie Ma, Peter Eisert, Mikael Lindvall, Adam Porter, Wojciech Samek
In the realm of dermatological diagnoses, where the analysis of dermatoscopic and microscopic skin lesion images is pivotal for the accurate and early detection of various medical conditions, the costs associated with creating diverse and high-quality annotated datasets have hampered the accuracy and generalizability of machine learning models. We propose an innovative unsupervised augmentation solution that harnesses Generative Adversarial Network (GAN) based models and associated techniques over their latent space to generate controlled semiautomatically-discovered semantic variations in dermatoscopic images. We created synthetic images to incorporate the semantic variations and augmented the training data with these images. With this approach, we were able to increase the performance of machine learning models and set a new benchmark amongst non-ensemble based models in skin lesion classification on the HAM10000 dataset; and used the observed analytics and generated models for detailed studies on model explainability, affirming the effectiveness of our solution.
Applications of the circle product with a right $Com$-module to the theory of commutative ring spectra
2024-10-07
Nicholas J. Kuhn
If Com is the reduced commutative operad, the category of Com-algebras in spectra is the category of nounital commutative ring spectra. The theme of this survey is that many important constructions on Com-algebras are given by taking the derived circle product with well chosen right Com-modules. Examples of constructions arising this way include the tensor product of a based space K with such an algebra I, and TQ(I), the Topological Andre-Quillen homology spectrum of I. We then show how filtrations of right Com-modules can be used to filter such constructions. A natural decreasing filtration on right Com-modules, when specialized to the Com-bimodule Com, defines the augmentation ideal tower of I, built out of the extended powers of TQ(I). A natural increasing filtration on right Com-modules, when specialized to the right Com-module used to define TQ(I), defines a filtration on TQ(I) built out of I and the spaces in the Lie cooperad. There are two versions of this in the literature -- by the author and by Behrens and Rezk -- and our setting here makes it easy to prove that these agree. Much of this applies with Com replaced by a more general reduced operad, and we make a few remarks about this.
SparsePO: Controlling Preference Alignment of LLMs via Sparse Token Masks
2024-10-07
Fenia Christopoulou, Ronald Cardenas, Gerasimos Lampouras, Haitham Bou-Ammar, Jun Wang
Preference Optimization (PO) has proven an effective step for aligning language models to human-desired behaviors. Current variants, following the offline Direct Preference Optimization objective, have focused on a strict setting where all tokens are contributing signals of KL divergence and rewards to the loss function. However, human preference is not affected by each word in a sequence equally but is often dependent on specific words or phrases, e.g. existence of toxic terms leads to non-preferred responses. Based on this observation, we argue that not all tokens should be weighted equally during PO and propose a flexible objective termed SparsePO, that aims to automatically learn to weight the KL divergence and reward corresponding to each token during PO training. We propose two different variants of weight-masks that can either be derived from the reference model itself or learned on the fly. Notably, our method induces sparsity in the learned masks, allowing the model to learn how to best weight reward and KL divergence contributions at the token level, learning an optimal level of mask sparsity. Extensive experiments on multiple domains, including sentiment control, dialogue, text summarization and text-to-code generation, illustrate that our approach assigns meaningful weights to tokens according to the target task, generates more responses with the desired preference and improves reasoning tasks by up to 2 percentage points compared to other token- and response-level PO methods.
Explanation sensitivity to the randomness of large language models: the case of journalistic text classification
2024-10-07
Jeremie Bogaert, Marie-Catherine de Marneffe, Antonin Descampe, Louis Escouflaire, Cedrick Fairon, Francois-Xavier Standaert
Large language models (LLMs) perform very well in several natural language processing tasks but raise explainability challenges. In this paper, we examine the effect of random elements in the training of LLMs on the explainability of their predictions. We do so on a task of opinionated journalistic text classification in French. Using a fine-tuned CamemBERT model and an explanation method based on relevance propagation, we find that training with different random seeds produces models with similar accuracy but variable explanations. We therefore claim that characterizing the explanations' statistical distribution is needed for the explainability of LLMs. We then explore a simpler model based on textual features which offers stable explanations but is less accurate. Hence, this simpler model corresponds to a different tradeoff between accuracy and explainability. We show that it can be improved by inserting features derived from CamemBERT's explanations. We finally discuss new research directions suggested by our results, in particular regarding the origin of the sensitivity observed in the training randomness.
ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery
2024-10-07
Ziru Chen, Shijie Chen, Yuting Ning, Qianheng Zhang, Boshi Wang, Botao Yu, Yifei Li, Zeyi Liao, Chen Wei, Zitong Lu, Vishal Dey, Mingyi Xue, Frazier N. Baker, Benjamin Burns, Daniel Adu-Ampratwum, Xuhui Huang, Xia Ning, Song Gao, Yu Su, Huan Sun
The advancements of language language models (LLMs) have piqued growing interest in developing LLM-based language agents to automate scientific discovery end-to-end, which has sparked both excitement and skepticism about the true capabilities of such agents. In this work, we argue that for an agent to fully automate scientific discovery, it must be able to complete all essential tasks in the workflow. Thus, we call for rigorous assessment of agents on individual tasks in a scientific workflow before making bold claims on end-to-end automation. To this end, we present ScienceAgentBench, a new benchmark for evaluating language agents for data-driven scientific discovery. To ensure the scientific authenticity and real-world relevance of our benchmark, we extract 102 tasks from 44 peer-reviewed publications in four disciplines and engage nine subject matter experts to validate them. We unify the target output for every task to a self-contained Python program file and employ an array of evaluation metrics to examine the generated programs, execution results, and costs. Each task goes through multiple rounds of manual validation by annotators and subject matter experts to ensure its annotation quality and scientific plausibility. We also propose two effective strategies to mitigate data contamination concerns. Using our benchmark, we evaluate five open-weight and proprietary LLMs, each with three frameworks: direct prompting, OpenHands, and self-debug. Given three attempts for each task, the best-performing agent can only solve 32.4% of the tasks independently and 34.3% with expert-provided knowledge. These results underscore the limited capacities of current language agents in generating code for data-driven discovery, let alone end-to-end automation for scientific research.
Function Gradient Approximation with Random Shallow ReLU Networks with Control Applications
2024-10-07
Andrew Lamperski, Siddharth Salapaka
Neural networks are widely used to approximate unknown functions in control. A common neural network architecture uses a single hidden layer (i.e. a shallow network), in which the input parameters are fixed in advance and only the output parameters are trained. The typical formal analysis asserts that if output parameters exist to approximate the unknown function with sufficient accuracy, then desired control performance can be achieved. A long-standing theoretical gap was that no conditions existed to guarantee that, for the fixed input parameters, required accuracy could be obtained by training the output parameters. Our recent work has partially closed this gap by demonstrating that if input parameters are chosen randomly, then for any sufficiently smooth function, with high-probability there are output parameters resulting in $O((1/m)^{1/2})$ approximation errors, where $m$ is the number of neurons. However, some applications, notably continuous-time value function approximation, require that the network approximates the both the unknown function and its gradient with sufficient accuracy. In this paper, we show that randomly generated input parameters and trained output parameters result in gradient errors of $O((\log(m)/m)^{1/2})$, and additionally, improve the constants from our prior work. We show how to apply the result to policy evaluation problems.
Nebular Dust Attenuation with the Balmer and Paschen Lines based on the MaNGA Survey
2024-10-07
Zesen Lin, Renbin Yan
Dust attenuations observed by stars and ionized gas are not necessarily the same. The lack of observational constraints on the nebular dust attenuation curve leaves a large uncertainty when correcting nebular dust attenuation with stellar continuum-based attenuation curves. Making use of the DAP catalogs of the MaNGA survey, we investigate the nebular dust attenuation of HII regions traced by the Balmer and Paschen lines. Based on a simple simulation, we find that star-forming regions on kpc scales favor the classic foreground screen dust model rather than the uniform mixture model. We propose a novel approach to fit the dust attenuation curve using the emission-line fluxes directly. For strong hydrogen recombination lines (e.g., H$\gamma$, H$\delta$, and H$\epsilon$), the slopes of the nebular attenuation curve can be well determined and are found to be in good agreement with the Fitzpatrick Milky Way extinction curve with an accuracy of $\lesssim 4\%$ in terms of the correction factor. However, severe contaminations/systematic uncertainties prevent us from obtaining reasonable values of the slopes for weak recombination lines (e.g., the high-order Balmer lines or the Paschen lines). We discuss how the choice of emission line measurement methods affects the results. Our results demonstrate the difficulty of deriving an average nebular dust attenuation curve given the current ground-based emission-line measurements.
Systematic Literature Review of Vision-Based Approaches to Outdoor Livestock Monitoring with Lessons from Wildlife Studies
2024-10-07
Stacey D. Scott, Zayn J. Abbas, Feerass Ellid, Eli-Henry Dykhne, Muhammad Muhaiminul Islam, Weam Ayad, Kristina Kacmorova, Dan Tulpan, Minglun Gong
Precision livestock farming (PLF) aims to improve the health and welfare of livestock animals and farming outcomes through the use of advanced technologies. Computer vision, combined with recent advances in machine learning and deep learning artificial intelligence approaches, offers a possible solution to the PLF ideal of 24/7 livestock monitoring that helps facilitate early detection of animal health and welfare issues. However, a significant number of livestock species are raised in large outdoor habitats that pose technological challenges for computer vision approaches. This review provides a comprehensive overview of computer vision methods and open challenges in outdoor animal monitoring. We include research from both the livestock and wildlife fields in the review because of the similarities in appearance, behaviour, and habitat for many livestock and wildlife. We focus on large terrestrial mammals, such as cattle, horses, deer, goats, sheep, koalas, giraffes, and elephants. We use an image processing pipeline to frame our discussion and highlight the current capabilities and open technical challenges at each stage of the pipeline. The review found a clear trend towards the use of deep learning approaches for animal detection, counting, and multi-species classification. We discuss in detail the applicability of current vision-based methods to PLF contexts and promising directions for future research.
Mean-field equation for phase-modulated optical parametric oscillator
2024-10-07
A. D. Sanchez, S. Chaitanya Kumar, M. Ebrahim-Zadeh
The widely established techniques for the generation of ultrashort optical pulses rely on passive mode-locking of lasers, with the output pulse duration and emission spectrum determined by the intrinsic lifetime of laser transition in the gain medium. Due to the instantaneous nature of nonlinear gain, optical parametric oscillators (OPOs) are capable of generating optical radiation in all time scales from continuous-wave (cw) to ultrashort femtosecond regime, if driven by laser pump sources in the corresponding time domain. In the ultrashort time scale, operation of OPOs conventionally relies on mode-locked pump lasers, with the concomitant disadvantages of large footprint and high cost. At the same time, the lack of gain storage mandates the use of synchronous pumping, resulting in increased complexity. In this paper, we present the concept of phase-modulated OPO driven by cw pump laser. The approach overcomes the traditional drawbacks of ultrafast OPOs, enabling femtosecond pulse generation without the need for synchronous pumping, resulting in a simplified, compact and cost-effective architecture using cw input pump lasers. We derive a mean-field equation for a degenerate $\chi^{(2)}$ OPO driven by a cw laser with intracavity electro-optic modulator (EOM), and also including dispersion compensation. The new equation predicts the formation of stable femtosecond pulses ($<$200~fs), in both normal and anomalous dispersion regimes, with a controllable repetition rate determined by the frequency of the EOM. The remarkable functionality of the proposed scheme paves the way for the development of a new class of widely tunable coherent femtosecond light sources in both bulk and integrated format based on $\chi^{(2)}$ OPOs using cw pump lasers.
Is the Future of Materials Amorphous? Challenges and Opportunities in Simulations of Amorphous Materials
2024-10-07
Ata Madanchi, Emna Azek, Karim Zongo, Laurent K. Béland, Normand Mousseau, Lena Simine
Amorphous solids form an enormous and underutilized class of materials. In order to drive the discovery of new useful amorphous materials further we need to achieve a closer convergence between computational and experimental methods. In this review, we highlight some of the important gaps between computational simulations and experiments, discuss popular state-of-the-art computational techniques such as the Activation Relaxation Technique nouveau (ARTn) and Reverse Monte Carlo (RMC), and introduce more recent advances: machine learning interatomic potentials (MLIPs) and generative machine learning for simulations of amorphous matter, e.g., the Morphological Autoregressive Protocol (MAP). Examples are drawn from the amorphous silicon and silica literature as well as from molecular glasses. Our outlook stresses the need for new computational methods to extend the time- and length- scales accessible through numerical simulations.
Finding Safety Violations of AI-Enabled Control Systems through the Lens of Synthesized Proxy Programs
2024-10-07
Jieke Shi, Zhou Yang, Junda He, Bowen Xu, Dongsun Kim, DongGyun Han, David Lo
Given the increasing adoption of modern AI-enabled control systems, ensuring their safety and reliability has become a critical task in software testing. One prevalent approach to testing control systems is falsification, which aims to find an input signal that causes the control system to violate a formal safety specification using optimization algorithms. However, applying falsification to AI-enabled control systems poses two significant challenges: (1)~it requires the system to execute numerous candidate test inputs, which can be time-consuming, particularly for systems with AI models that have many parameters, and (2)~multiple safety requirements are typically defined as a conjunctive specification, which is difficult for existing falsification approaches to comprehensively cover. This paper introduces Synthify, a falsification framework tailored for AI-enabled control systems. Our approach performs falsification in a two-phase process. At the start, Synthify synthesizes a program that implements one or a few linear controllers to serve as a proxy for the AI controller. This proxy program mimics the AI controller's functionality but is computationally more efficient. Then, Synthify employs the $\epsilon$-greedy strategy to sample a promising sub-specification from the conjunctive safety specification. It then uses a Simulated Annealing-based falsification algorithm to find violations of the sampled sub-specification for the control system. To evaluate Synthify, we compare it to PSY-TaLiRo, a state-of-the-art and industrial-strength falsification tool, on 8 publicly available control systems. On average, Synthify achieves a 83.5% higher success rate in falsification compared to PSY-TaLiRo with the same budget of falsification trials. The safety violations found by Synthify are also more diverse than those found by PSY-TaLiRo, covering 137.7% more sub-specifications.
RoWeeder: Unsupervised Weed Mapping through Crop-Row Detection
2024-10-07
Pasquale De Marinis, Gennaro Vessio, Giovanna Castellano
Precision agriculture relies heavily on effective weed management to ensure robust crop yields. This study presents RoWeeder, an innovative framework for unsupervised weed mapping that combines crop-row detection with a noise-resilient deep learning model. By leveraging crop-row information to create a pseudo-ground truth, our method trains a lightweight deep learning model capable of distinguishing between crops and weeds, even in the presence of noisy data. Evaluated on the WeedMap dataset, RoWeeder achieves an F1 score of 75.3, outperforming several baselines. Comprehensive ablation studies further validated the model's performance. By integrating RoWeeder with drone technology, farmers can conduct real-time aerial surveys, enabling precise weed management across large fields. The code is available at: \url{https://github.com/pasqualedem/RoWeeder}.
Photospheric hot spots at solar coronal loop footpoints revealed by hyperspectral imaging observations
2024-10-07
L. P. Chitta, M. van Noort, H. N. Smitha, E. R. Priest, L. H. M. Rouppe van der Voort
Poynting flux generated by random shuffling of photospheric magnetic footpoints is transferred through the upper atmosphere of the Sun where the plasma is heated to over 1 MK in the corona. High spatiotemporal resolution observations of the lower atmosphere at the base of coronal magnetic loops are crucial to better understand the nature of the footpoint dynamics and the details of magnetic processes that eventually channel energy into the corona. Here we report high spatial resolution ($\sim$0.1\arcsec) and cadence (1.33 s) hyperspectral imaging of the solar H$\alpha$ line, acquired by the Microlensed Hyperspectral Imager prototype installed at the Swedish 1-m Solar Telescope, that reveal photospheric hot spots at the base of solar coronal loops. These hot spots manifest themselves as H$\alpha$ wing enhancements, occurring on small spatial scales of $\sim$0.2\arcsec, and timescales of less than 100 s. By assuming that the H$\alpha$ wings and the continuum form under the local thermodynamic equilibrium condition, we inverted the H$\alpha$ line profiles and found that the hot spots are compatible with a temperature increase of about 1000 K above the ambient quiet-Sun temperature. The H$\alpha$ wing integrated Stokes $V/I$ maps indicate that hot spots are related to magnetic patches with field strengths comparable to or even stronger than the surrounding network elements. But they do not show the presence of parasitic polarity magnetic field that would support the interpretation that these hot spots are reconnection-driven Ellerman bombs, we interpret these features as proxies of locations where convection-driven magnetic field intensification in the photosphere can lead to energy transfer into higher layers. We suggest that such hot spots at coronal loop footpoints may be indicative of the specific locations and onset of energy flux injection into the upper atmosphere.
On Efficient Variants of Segment Anything Model: A Survey
2024-10-07
Xiaorui Sun, Jun Liu, Heng Tao Shen, Xiaofeng Zhu, Ping Hu
The Segment Anything Model (SAM) is a foundational model for image segmentation tasks, known for its strong generalization across diverse applications. However, its impressive performance comes with significant computational and resource demands, making it challenging to deploy in resource-limited environments such as mobile devices. To address this, a variety of SAM variants have been proposed to enhance efficiency without sacrificing accuracy. This survey provides the first comprehensive review of these efficient SAM variants. We begin by exploring the motivations driving this research. We then present core techniques used in SAM and model acceleration. This is followed by an in-depth analysis of various acceleration strategies, categorized by approach. Finally, we offer a unified and extensive evaluation of these methods, assessing their efficiency and accuracy on representative benchmarks, and providing a clear comparison of their overall performance.
2024-10-07
Gergely Kiss, Itay Londner, Máté Matolcsi, Gábor Somlai
The notion of weak tiling was a key ingredient in the proof of Fuglede's spectral set conjecture for convex bodies \cite{conv}, due to the fact that every spectral set tiles its complement weakly with a suitable Borel measure. In this paper we review the concept of weak tiling, and answer a question raised in \cite{weak} by giving an example of a set $T$ which tiles its complement weakly, but $T$ is neither spectral, nor a proper tile.
The article surveys inverse problems related to the twisted geodesic flows on Riemannian manifolds with boundary, focusing on the generalized ray transforms, tensor tomography, and rigidity problems. The twisted geodesic flow generalizes the usual geodesic flow, allowing to model richer dynamics.
2024-10-07
Laurent Bartholdi, Ruiwen Dong, Leon Pernak, Jan Philipp Wächter
We survey solvability of equations in wreath products of groups, and prove that the quadratic diophantine problem is solvable in wreath products of Abelian groups. We consider the related question of determining commutator width, and prove that the quadratic diophantine problem is also solvable in Baumslag's finitely presented metabelian group. This text is a short version of an extensive article by the first-named authors.
Cosmic void has been proven to be an effective cosmological probe of the large-scale structure (LSS). However, since voids are usually identified in spectroscopic galaxy surveys, they are generally limited to low number density and redshift. We propose to utilize the clustering of two-dimensional (2D) voids identified using Voronoi tessellation and watershed algorithm without any shape assumption to explore the LSS. We generate mock galaxy and void catalogs for the next-generation Stage IV photometric surveys in $z = 0.8-2.0$ from simulations, develop the 2D void identification method, and construct the theoretical model to fit the 2D watershed void and galaxy angular power spectra. We find that our method can accurately extract the cosmological information, and the constraint accuracies of some cosmological parameters from the 2D watershed void clustering are even comparable to the galaxy angular clustering case, which can be further improved by as large as $\sim30\%$ in the void and galaxy joint constraints. This indicates that the 2D void clustering is a good complement to galaxy angular clustering measurements, especially for the forthcoming Stage IV surveys that detect high-redshift universe.
From on-shell amplitude in AdS to cosmological correlators: gluons and gravitons
2024-10-07
Jiajie Mei, Yuyu Mo
We recently introduced a recursive bootstrap method for constructing $n$-point gluon and graviton Mellin-momentum amplitudes in (A)dS spacetime. The power of this approach was illustrated by our successful computation of the first five-point graviton amplitude in (A)dS. In this work, we provide further details of these calculations and start with a more in-depth review of the formalism by offering detailed insights into the motivation for defining on-shell amplitudes in AdS, along with various explicit examples. Furthermore, we demonstrate how cosmological correlators can be effortlessly obtained once the amplitude is determined.
Pseudospectra and eigenvalue asymptotics for disordered non-selfadjoint operators in the semiclassical limit
2024-10-07
Martin Vogel
The purpose of this note is to review some recent results concerning the pseudospectra and the eigenvalues asymptotics of non-selfadjoint semiclassical pseudo-differential operators subject to small random perturbations.
The divide between us: Internet access among people with and without disabilities in the post-pandemic era
2024-10-07
Edgar Pacheco, Hannah Burgess
The COVID-19 pandemic highlighted the importance of internet access across various aspects of life, from remote work and online education to healthcare services and social connections. As we transition to a post-pandemic era, a pressing need arises to update our understanding of the multifaceted nature of internet access. This study is one of the first attempts to do so. Using survey data from New Zealand adult internet users (n=960), it compares internet connection types, frequency of internet use at home, social media use, and concerns about online risk between people with and without disabilities. Results show people with disabilities have restricted fibre access and higher wireless broadband (a much slower connection type). People with disabilities use social media platforms less and are more concerned about certain online risks. The findings highlight persistent disparities in internet access for people with disabilities in the post-pandemic era. Implications of the study are discussed.
A Review of Artificial Intelligence based Biological-Tree Construction: Priorities, Methods, Applications and Trends
2024-10-07
Zelin Zang, Yongjie Xu, Chenrui Duan, Jinlin Wu, Stan Z. Li, Zhen Lei
Biological tree analysis serves as a pivotal tool in uncovering the evolutionary and differentiation relationships among organisms, genes, and cells. Its applications span diverse fields including phylogenetics, developmental biology, ecology, and medicine. Traditional tree inference methods, while foundational in early studies, face increasing limitations in processing the large-scale, complex datasets generated by modern high-throughput technologies. Recent advances in deep learning offer promising solutions, providing enhanced data processing and pattern recognition capabilities. However, challenges remain, particularly in accurately representing the inherently discrete and non-Euclidean nature of biological trees. In this review, we first outline the key biological priors fundamental to phylogenetic and differentiation tree analyses, facilitating a deeper interdisciplinary understanding between deep learning researchers and biologists. We then systematically examine the commonly used data formats and databases, serving as a comprehensive resource for model testing and development. We provide a critical analysis of traditional tree generation methods, exploring their underlying biological assumptions, technical characteristics, and limitations. Current developments in deep learning-based tree generation are reviewed, highlighting both recent advancements and existing challenges. Furthermore, we discuss the diverse applications of biological trees across various biological domains. Finally, we propose potential future directions and trends in leveraging deep learning for biological tree research, aiming to guide further exploration and innovation in this field.
Towards Ultra-Low-Power Neuromorphic Speech Enhancement with Spiking-FullSubNet
2024-10-07
Xiang Hao, Chenxiang Ma, Qu Yang, Jibin Wu, Kay Chen Tan
Speech enhancement is critical for improving speech intelligibility and quality in various audio devices. In recent years, deep learning-based methods have significantly improved speech enhancement performance, but they often come with a high computational cost, which is prohibitive for a large number of edge devices, such as headsets and hearing aids. This work proposes an ultra-low-power speech enhancement system based on the brain-inspired spiking neural network (SNN) called Spiking-FullSubNet. Spiking-FullSubNet follows a full-band and sub-band fusioned approach to effectively capture both global and local spectral information. To enhance the efficiency of computationally expensive sub-band modeling, we introduce a frequency partitioning method inspired by the sensitivity profile of the human peripheral auditory system. Furthermore, we introduce a novel spiking neuron model that can dynamically control the input information integration and forgetting, enhancing the multi-scale temporal processing capability of SNN, which is critical for speech denoising. Experiments conducted on the recent Intel Neuromorphic Deep Noise Suppression (N-DNS) Challenge dataset show that the Spiking-FullSubNet surpasses state-of-the-art methods by large margins in terms of both speech quality and energy efficiency metrics. Notably, our system won the championship of the Intel N-DNS Challenge (Algorithmic Track), opening up a myriad of opportunities for ultra-low-power speech enhancement at the edge. Our source code and model checkpoints are publicly available at https://github.com/haoxiangsnr/spiking-fullsubnet.
In recent years, 3D vision has become a crucial field within computer vision, powering a wide range of applications such as autonomous driving, robotics, augmented reality (AR), and medical imaging. This field relies on the accurate perception, understanding, and reconstruction of 3D scenes from 2D data sources like images and videos. Diffusion models, originally designed for 2D generative tasks, offer the potential for more flexible, probabilistic approaches that can better capture the variability and uncertainty present in real-world 3D data. However, traditional methods often struggle with efficiency and scalability. In this paper, we review the state-of-the-art approaches that leverage diffusion models for 3D visual tasks, including but not limited to 3D object generation, shape completion, point cloud reconstruction, and scene understanding. We provide an in-depth discussion of the underlying mathematical principles of diffusion models, outlining their forward and reverse processes, as well as the various architectural advancements that enable these models to work with 3D datasets. We also discuss the key challenges in applying diffusion models to 3D vision, such as handling occlusions and varying point densities, and the computational demands of high-dimensional data. Finally, we discuss potential solutions, including improving computational efficiency, enhancing multimodal fusion, and exploring the use of large-scale pretraining for better generalization across 3D tasks. This paper serves as a foundation for future exploration and development in this rapidly evolving field.
Guidance of the Center of Pressure Using Haptic Presentation
2024-10-07
Yohei Kawasaki, Yuta Sugiura
Accurately instructing posture and the position of the body's center of gravity is challenging. In this study, we propose a system that utilizes haptic feedback to induce the Center of Pressure (CoP) movement. The Wii Balance Board is employed to sense the CoP, and vibration motors are used for haptic feedback. To provide a comparison, inductions were also performed using visual and auditory feedback, and the time required for induction was measured. Additionally, after the experiments, a questionnaire survey was conducted.
Numerous recent works target to extend effective context length for language models and various methods, tasks and benchmarks exist to measure model's effective memorization length. However, through thorough investigations, we find limitations for currently existing evaluations on model's memorization capability. We provide an extensive survey for limitations in this work and propose a new method called forgetting curve to measure the memorization capability of long-context models. We show that forgetting curve has the advantage of being robust to the tested corpus and the experimental settings, of not relying on prompts and can be applied to any model size. We apply our forgetting curve to a large variety of models involving both transformer and RNN/SSM based architectures. Our measurement provides empirical evidence for the effectiveness of transformer extension techniques while raises questions for the effective length of RNN/SSM based models. We also examine the difference between our measurement and existing benchmarks as well as popular metrics for various models. Our code and results can be found at https://github.com/1azybug/ForgettingCurve.
Domains as Objectives: Domain-Uncertainty-Aware Policy Optimization through Explicit Multi-Domain Convex Coverage Set Learning
2024-10-07
Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, Takamitsu Matsubara
The problem of uncertainty is a feature of real world robotics problems and any control framework must contend with it in order to succeed in real applications tasks. Reinforcement Learning is no different, and epistemic uncertainty arising from model uncertainty or misspecification is a challenge well captured by the sim-to-real gap. A simple solution to this issue is domain randomization (DR), which unfortunately can result in conservative agents. As a remedy to this conservativeness, the use of universal policies that take additional information about the randomized domain has risen as an alternative solution, along with recurrent neural network-based controllers. Uncertainty-aware universal policies present a particularly compelling solution able to account for system identification uncertainties during deployment. In this paper, we reveal that the challenge of efficiently optimizing uncertainty-aware policies can be fundamentally reframed as solving the convex coverage set (CCS) problem within a multi-objective reinforcement learning (MORL) context. By introducing a novel Markov decision process (MDP) framework where each domain's performance is treated as an independent objective, we unify the training of uncertainty-aware policies with MORL approaches. This connection enables the application of MORL algorithms for domain randomization (DR), allowing for more efficient policy optimization. To illustrate this, we focus on the linear utility function, which aligns with the expectation in DR formulations, and propose a series of algorithms adapted from the MORL literature to solve the CCS, demonstrating their ability to enhance the performance of uncertainty-aware policies.
Doppler bias: impact of peculiar velocities on color selection and the large scale structure of galaxy surveys
2024-10-07
Batia Friedman-Shaw, Alex Krolewski, Matteo Foglieni, Niayesh Afshordi
Light cone selection effects on cosmic observables must be precisely accounted for in the next generation of surveys, including the Dark Energy Spectroscopic Instrument (DESI) survey. This will allow us to correctly model the data and extract subtle shifts from general-relativistic effects. We examine the effects of peculiar velocities on color selection in spectroscopic galaxy surveys, with a focus on their implications for the galaxy clustering dipole $P_1(k)$. Using DESI Emission Line Galaxy (ELG) targets, we show that peculiar velocities can shift spectral emission features into or out of filter bands, modifying galaxy colors and thereby changing galaxy selection. This phenomenon mimics the effect of evolution bias, and we refer to it as the Doppler bias, $b_D$. The Doppler bias is of comparable size to the evolution bias at $0.8 < z < 1$, where it is largest. This enhances the ELG-LRG (Luminous Red Galaxy) cross-correlation dipole by 15-30%. This could be detectable at the $\sim$4$\sigma$ level for the full DESI survey. Additionally, we found that our $b_D$ estimate is impacted by the incompleteness of the parent ELG sample. Therefore, this work highlights the essential need for careful consideration of spectral-dependent biases caused by peculiar velocities during the selection phase of galaxy surveys, to enable unbiased analyses.
Neural Fourier Modelling: A Highly Compact Approach to Time-Series Analysis
2024-10-07
Minjung Kim, Yusuke Hioka, Michael Witbrock
Neural time-series analysis has traditionally focused on modeling data in the time domain, often with some approaches incorporating equivalent Fourier domain representations as auxiliary spectral features. In this work, we shift the main focus to frequency representations, modeling time-series data fully and directly in the Fourier domain. We introduce Neural Fourier Modelling (NFM), a compact yet powerful solution for time-series analysis. NFM is grounded in two key properties of the Fourier transform (FT): (i) the ability to model finite-length time series as functions in the Fourier domain, treating them as continuous-time elements in function space, and (ii) the capacity for data manipulation (such as resampling and timespan extension) within the Fourier domain. We reinterpret Fourier-domain data manipulation as frequency extrapolation and interpolation, incorporating this as a core learning mechanism in NFM, applicable across various tasks. To support flexible frequency extension with spectral priors and effective modulation of frequency representations, we propose two learning modules: Learnable Frequency Tokens (LFT) and Implicit Neural Fourier Filters (INFF). These modules enable compact and expressive modeling in the Fourier domain. Extensive experiments demonstrate that NFM achieves state-of-the-art performance on a wide range of tasks (forecasting, anomaly detection, and classification), including challenging time-series scenarios with previously unseen sampling rates at test time. Moreover, NFM is highly compact, requiring fewer than 40K parameters in each task, with time-series lengths ranging from 100 to 16K.
On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning
2024-10-07
Yongyi Su, Yushu Li, Nanqing Liu, Kui Jia, Xulei Yang, Chuan-Sheng Foo, Xun Xu
Test-time adaptation (TTA) updates the model weights during the inference stage using testing data to enhance generalization. However, this practice exposes TTA to adversarial risks. Existing studies have shown that when TTA is updated with crafted adversarial test samples, also known as test-time poisoned data, the performance on benign samples can deteriorate. Nonetheless, the perceived adversarial risk may be overstated if the poisoned data is generated under overly strong assumptions. In this work, we first review realistic assumptions for test-time data poisoning, including white-box versus grey-box attacks, access to benign data, attack budget, and more. We then propose an effective and realistic attack method that better produces poisoned samples without access to benign samples, and derive an effective in-distribution attack objective. We also design two TTA-aware attack objectives. Our benchmarks of existing attack methods reveal that the TTA methods are more robust than previously believed. In addition, we analyze effective defense strategies to help develop adversarially robust TTA methods.
Deciphering Refactoring Branch Dynamics in Modern Code Review: An Empirical Study on Qt
2024-10-07
Eman Abdullah AlOmar
Context: Modern code review is a widely employed technique in both industrial and open-source projects, serving to enhance software quality, share knowledge, and ensure compliance with coding standards and guidelines. While code review is extensively studied for its general challenges, best practices, outcomes, and socio-technical aspects, little attention has been paid to how refactoring is reviewed and what developers prioritize when reviewing refactored code in the Refactor branch. Objective: The goal is to understand the review process for refactoring changes in the Refactor branch and to identify what developers care about when reviewing code in this branch. Method: In this study, we present a quantitative and qualitative examination to understand the main criteria developers use to decide whether to accept or reject refactored code submissions and identify the challenges inherent in this process. Results: Analyzing 2,154 refactoring and non-refactoring reviews across Qt open-source projects, we find that reviews involving refactoring from the Refactor branch take significantly less time to resolve in terms of code review efforts. Additionally, documentation of developer intent is notably sparse within the Refactor branch compared to other branches. Furthermore, through thematic analysis of a substantial sample of refactoring code review discussions, we construct a comprehensive taxonomy consisting of 12 refactoring review criteria.
Democratizing Strategic Planning in Master-Planned Communities
2024-10-07
Christopher K. Allsup, Irene S. Gabashvili
This paper introduces a strategic planning tool for master-planned communities designed specifically to quantify residents' subjective preferences about large investments in amenities and infrastructure projects. Drawing on data obtained from brief online surveys, the tool ranks alternative plans by considering the aggregate anticipated utilization of each proposed amenity and cost sensitivity to it (or risk sensitivity for infrastructure plans). In addition, the tool estimates the percentage of households that favor the preferred plan and predicts whether residents would actually be willing to fund the project. The mathematical underpinnings of the tool are borrowed from utility theory, incorporating exponential functions to model diminishing marginal returns on quality, cost, and risk mitigation.
2024-10-07
Cécile Armana, Sheng-Yang Kevin Ho, Mihran Papikian
In the early 1970s, Andrew Ogg made several conjectures about the rational torsion points of elliptic curves over $\mathbb{Q}$ and the Jacobians of modular curves. These conjectures were proved shortly after by Barry Mazur as a consequence of his fundamental study of the arithmetic properties of modular curves and Hecke algebras. In this paper, we review the function field analogues of Ogg's conjectures, their current status, and the methods that have been applied to prove some of these conjectures. The methods are based on the ideas of Mazur and Ogg, but there are interesting differences and technical complications that arise in the function field setting, as well as intriguing possible new directions for generalizations.
Testing Credibility of Public and Private Surveys through the Lens of Regression
2024-10-07
Debabrota Basu, Sourav Chakraborty, Debarshi Chanda, Buddha Dev Das, Arijit Ghosh, Arnab Ray
Testing whether a sample survey is a credible representation of the population is an important question to ensure the validity of any downstream research. While this problem, in general, does not have an efficient solution, one might take a task-based approach and aim to understand whether a certain data analysis tool, like linear regression, would yield similar answers both on the population and the sample survey. In this paper, we design an algorithm to test the credibility of a sample survey in terms of linear regression. In other words, we design an algorithm that can certify if a sample survey is good enough to guarantee the correctness of data analysis done using linear regression tools. Nowadays, one is naturally concerned about data privacy in surveys. Thus, we further test the credibility of surveys published in a differentially private manner. Specifically, we focus on Local Differential Privacy (LDP), which is a standard technique to ensure privacy in surveys where the survey participants might not trust the aggregator. We extend our algorithm to work even when the data analysis has been done using surveys with LDP. In the process, we also propose an algorithm that learns with high probability the guarantees a linear regression model on a survey published with LDP. Our algorithm also serves as a mechanism to learn linear regression models from data corrupted with noise coming from any subexponential distribution. We prove that it achieves the optimal estimation error bound for $\ell_1$ linear regression, which might be of broader interest. We prove the theoretical correctness of our algorithms while trying to reduce the sample complexity for both public and private surveys. We also numerically demonstrate the performance of our algorithms on real and synthetic datasets.
Simulating image coaddition with the Nancy Grace Roman Space Telescope: III. Software improvements and new linear algebra strategies
2024-10-07
Kaili Cao, Christopher M. Hirata, Katherine Laliotis, Masaya Yamamoto, Emily Macbeth, M. A. Troxel
The Nancy Grace Roman Space Telescope will implement a devoted weak gravitational lensing program with its High Latitude Wide Area Survey. For cosmological purposes, a critical step in Roman image processing is to combine dithered undersampled images into unified oversampled images and thus enable high-precision shape measurements. IMCOM is an image coaddition algorithm which offers control over point spread functions in output images. This paper presents the refactored IMCOM software, featuring full object-oriented programming, improved data structures, and alternative linear algebra strategies for determining coaddition weights. Combining these improvements and other acceleration measures, to produce almost equivalent coadded images, the consumption of core-hours has been reduced by about an order of magnitude. We then re-coadd a $16 \times 16 \,{\rm arcmin}^2$ region of our previous image simulations with three linear algebra kernels in four bands, and compare the results in terms of IMCOM optimization goals, properties of coadded noise frames, and measurements of simulated stars. The Cholesky kernel is efficient and relatively accurate, yet its irregular windows for input pixels slightly bias coaddition results. The iterative kernel avoids this issue by tailoring input pixel selection for each output pixel; it yields better noise control, but can be limited by random errors due to finite tolerance. The empirical kernel coadds images using an empirical relation based on geometry; it is inaccurate, but being much faster, it provides a valid option for "quick look" purposes. We fine-tune IMCOM hyperparameters in a companion paper.
2024-10-07
Akihiko Hirota, Jin Koda, Fumi Egusa, Tsuyoshi Sawada, Kazushi Sakamoto, Mark Heyer, Amanda M Lee, Fumiya Maeda, Samuel Boissier, Daniela Calzetti, Bruce G. Elmegreen, Nanase Harada, Luis C. Ho, Masato I. N. Kobayashi, Nario Kuno, Barry F. Madore, Sergio Martín, Jennifer Donovan Meyer, Kazuyuki Muraoka, Yoshimasa Watanabe
We present a catalog of clouds identified from the $^{12}$CO (1--0) data of M83, which was observed using Atacama Large Millimeter/submillimeter Array (ALMA) with a spatial resolution of $\sim$46 pc and a mass sensitivity of $\sim$10$^4$ $M_{\odot}$ (3 $\sigma$). The almost full-disk coverage and high sensitivity of the data allowed us to sample 5724 molecular clouds with a median mass of $\sim1.9$ $\times$ $10^5$ $M_{\odot}$, which is comparable to the most frequently sampled mass of Giant Molecular Clouds by surveys in the Milky Way. About 60 percent of the total CO luminosity in M83's disk arises from clouds more massive than 10$^6$ $M_{\odot}$. Such massive clouds comprise 16 percent of the total clouds in number and tend to concentrate toward the arm, bar, and center, while smaller clouds are more prevalent in inter-arm regions. Most $>10^6$ $M_{\odot}$ clouds have peak brightness temperatures $T_{\mathrm{peak}}$ above 2 K with the current resolution. Comparing the observed cloud properties with the scaling relations determined by Solomon et al. 1987 (S87), $T_{\mathrm{peak}}$$>2$ K clouds follow the relations, but $T_{\mathrm{peak}}$$<2$ K clouds, which are dominant in number, deviate significantly. Without considering the effect of beam dilution, the deviations would suggest modestly high virial parameters and low surface mass densities for the entire cloud samples, which are similar to values found for the Milky Way clouds by Rice et al. (2016) and Miville-Desch{\^e}nes et al. (2017). However, once beam dilution is taken into account, the observed $\alpha_{\mathrm{vir}}$ and $\Sigma$ for a majority of the clouds (mostly $T_{\mathrm{peak}}$ $<2$ K) can be potentially explained with intrinsic $\Sigma$ of $\sim$100 $M_{\mathrm{\odot}}\ \mathrm{pc}^{-2}$ and $\alpha_{\mathrm{vir}}$ of $\sim$1, which are similar to the clouds of S87.
Constructing and Masking Preference Profile with LLMs for Filtering Discomforting Recommendation
2024-10-07
Jiahao Liu, YiYang Shao, Peng Zhang, Dongsheng Li, Hansu Gu, Chao Chen, Longzhi Du, Tun Lu, Ning Gu
Personalized algorithms can inadvertently expose users to discomforting recommendations, potentially triggering negative consequences. The subjectivity of discomfort and the black-box nature of these algorithms make it challenging to effectively identify and filter such content. To address this, we first conducted a formative study to understand users' practices and expectations regarding discomforting recommendation filtering. Then, we designed a Large Language Model (LLM)-based tool named DiscomfortFilter, which constructs an editable preference profile for a user and helps the user express filtering needs through conversation to mask discomforting preferences within the profile. Based on the edited profile, DiscomfortFilter facilitates the discomforting recommendations filtering in a plug-and-play manner, maintaining flexibility and transparency. The constructed preference profile improves LLM reasoning and simplifies user alignment, enabling a 3.8B open-source LLM to rival top commercial models in an offline proxy task. A one-week user study with 24 participants demonstrated the effectiveness of DiscomfortFilter, while also highlighting its potential impact on platform recommendation outcomes. We conclude by discussing the ongoing challenges, highlighting its relevance to broader research, assessing stakeholder impact, and outlining future research directions.
Stellar halos tracing the assembly of Ultra-Faint Dwarf galaxies
2024-10-07
Lapo Querci, Andrea Pallottini, Lorenzo Branca, Stefania Salvadori
[Abridged]Ultra-faint dwarfs (UFDs) are expected to be the relics of the earliest galaxies forming in the Universe. Observations show the presence of a stellar halo around them, which can give precious insights into the evolution of UFDs. This work investigates how merger properties impact the formation of stellar halos around UFDs, focusing on Tucana II, the most promising UFD assembled through mergers. We develop N-body simulations of isolated mergers between two UFDs with $1M_\odot$ stellar resolution. We build a suite of simulations by varying: i) the merger mass ratio, $M_1/M_2$, the specific ii) kinetic energy, $k$, and iii) angular momentum, $l$, iv) the dark-to-stellar mass ratio, $M_{DM}/M_*$, of the progenitors and iv) their stellar size, $R_{1/2}$. We use a neural network to explore the parameter space, emulating the properties of the "post-merger" UFD by quantifying the half-mass radius ($R_*$) and the fraction of stars at radii $>5R_*$ ($f_5$). Our principal component analysis clearly shows that $f_5$ ($R_*$) is primarily determined by $M_1/M_2$ ($R_{1/2}$), with $R_{1/2}$ ($M_1/M_2$) playing a secondary role. Both $f_5$ and $R_*$ show almost no dependence on $k$, $l$, and $M_{DM}/M_*$ in the explored range. Using our emulator, we find that to form the stellar halo observed in Tucana II, i.e. $f_5=10\pm5\%$ and $R_*=120\pm30$pc, we need $M_1/M_2=8_{-3}^{+4}$ and $R_{1/2}=97^{+25}_{-18}$pc. Such findings are corroborated by the consistency ($\chi^2=0.5-2$) between the stellar density profile observed and those of simulations having $M_1/M_2$ and $R_{1/2}$ close to the emulator's predictions. Ongoing and planned spectroscopic surveys will greatly increase the statistics of observed stars and thus stellar halos in UFDs. By interpreting such observations with our model, we will provide new insights into the assembly history of UFDs and thus on the early galaxy formation process.
A MUSE Source-Blind Survey for Emission from the Circumgalactic Medium
2024-10-07
Huanian Zhang, Dennis Zaritsky
The recent detection of optical emission lines from the circumgalactic medium (CGM) in combined, large samples of low-redshift, normal galaxy spectra hints at the potential to map the cool ($\sim$ 10$^4$ K) CGM in individual, representative galaxies. Using archival data from a forefront instrument (MUSE) on the VLT, we present a source-blind, wide-redshift-range ($z \sim 0-5)$ narrow-band imaging survey for CGM emission. Our detected, resolved emission line sources are cataloged and include a 30 kpc wide H$\alpha$ source likely tracing the CGM of a low-mass galaxy (stellar mass $\sim$ $10^{8.78\pm0.42}$ M$_\odot$) at $z=0.1723$, a 60 kpc wide Ly $\alpha$ structure associated with a galaxy at $z=3.9076$, and a 130 kpc ($\sim r_{\rm vir}$) wide [O II] feature revealing an interaction between a galaxy pair at $z=1.2480$. The H$\alpha$ velocity field for the low-mass galaxy suggests that the CGM is more chaotic or turbulent than the galaxy disk, while that for the interacting galaxies shows large-scale ($\sim 50$ kpc) coherent motions.
Equilibrium dynamical models in the inner region of the Large Magellanic Cloud based on Gaia DR3 kinematics
2024-10-07
Nikolay Kacharov, Behzad Tahmasebzadeh, Maria-Rosa L. Cioni, Glenn van de Ven, Ling Zhu, Sergey Khoperskov
We use Gaia DR3 to explore how well equilibrium dynamical models based on the Jeans equations and the Schwarzschild orbit superposition method are able to describe LMC's 5-dimensional phase-space distribution and line-of-sight (LOS) velocity distribution, respectively. In the latter model we incorporate a triaxial bar component and derive LMC's bar pattern speed. We fit Jeans dynamical models to all Gaia DR3 stars with proper motion and LOS velocity measurements found in the VMC VISTA survey of the LMC using a discrete maximum likelihood approach. These models are very efficient at discriminating genuine LMC member stars from Milky Way foreground stars and background galaxies. They constrain the shape, orientation, and enclosed mass of the galaxy under the assumption for axisymmetry. We use the Jeans model results as a stepping stone to more complex 2-component Schwarzschild models, which include an axisymmetric disc and a co-centric triaxial bar, which we fit to the LMC Gaia DR3 LOS velocity field, using a chi^2 minimisation approach. The Jeans models describe well the rotation and velocity dispersion of the LMC disc and we find an inclination angle 25.5 deg, line of nodes orientation 124 deg, and an intrinsic thickness of the disc 0.23 (minor to major axis ratio). However, bound to axisymmetry, these models fail to properly describe the kinematics in the central region of the galaxy, dominated by the bar. We use the derived disc orientation and the Gaia DR3 density image of the LMC to obtain the intrinsic shape of the bar. Using these two components as an input to our Schwarzschild models, we perform orbit integration and weighting in a rotating reference frame fixed to the bar, deriving an independent measurement of the LMC bar pattern speed 11+/-4 km/s/kpc. Both the Jeans and Schwarzschild models predict the same enclosed mass distribution within a radius of 6.2 kpc of ~1.4e10 Msun.
A history of galaxy migrations over the Stellar Mass - SFR plane from the COSMOS-Web survey
2024-10-07
R. C. Arango-Toro, O. Ilbert, L. Ciesla, M. Shuntov, G. Aufort, W. Mercier, C. Laigle, M. Franco, M. Bethermin, D. Le Borgne, Y. Dubois, H. J. McCracken, L. Paquereau, M. Huertas-Company, J. Kartaltepe, C. M. Casey, H. Akins, N. Allen, I. Andika, M. Brinch, N. E. Drakos, A. Faisst, G. Gozaliasl, S. Harish, A. Kaminsky, A. Koekemoer, V. Kokorev, D. Liu, G. Magdis, C. L. Martin, T. Moutard, J. Rhodes, R. M. Rich, B. Robertson, D. B. Sanders, K. Sheth, M. Talia, S. Toft, L. Tresse, F. Valentino, A. Vijayan, J. Weaver
The stellar mass-star formation rate (M$_\star$ - SFR) plane is essential for distinguishing galaxy populations, but how galaxies move within this plane over cosmic time remains unclear. This study aims to describe galaxy migrations in the M$_\star$ - SFR plane by reconstructing star formation histories (SFHs) for a sample of galaxies out to redshift $z < 4$. This provides insights into the physical processes driving star formation. We use data from the COSMOS field, selecting 299131 galaxies at $z < 4$ with COSMOS-Web NIRCam data (m$_\mathrm{F444W} < 27$) over 0.54 deg$^2$. Using the SED modeling code CIGALE with non-parametric SFHs, we derive physical properties and migration vectors for these galaxies. These vectors describe the direction and velocity of evolutionary paths across the M$_\star$ - SFR plane. To assess the accuracy of these vectors, we compare them to results from the Horizon-AGN simulation. Galaxies within the main sequence show low migration amplitudes and dispersed movement directions, indicating oscillation within the main sequence. Most progenitors were already on the main sequence a billion years earlier. Starburst galaxies assembled half their mass in the last 350 Myr and originated from the main sequence. Passive galaxies show uniformly declining SFHs and include massive galaxies already in the passive region at $z = 3.5-4$, with increasing density over time. Using reconstructed SFHs up to $z < 4$, we propose a coherent picture of how galaxies migrate over cosmic time in the M$_\star$ - SFR plane, highlighting the connection between major phases in the star-formation history.
Quantifying the impact of AGN feedback on the large-scale matter distribution using two- and three-point statistics
2024-10-07
Bipradeep Saha, Sownak Bose
Feedback from active galactic nuclei (AGN) plays a critical role in shaping the matter distribution on scales comparable to and larger than individual galaxies. Upcoming surveys such as $\textit{Euclid}$ and LSST aim to precisely quantify the matter distribution on cosmological scales, making a detailed understanding of AGN feedback effects essential. Hydrodynamical simulations provide an informative framework for studying these effects, in particular by allowing us to vary the parameters that determine the strength of these feedback processes and, consequently, to predict their corresponding impact on the large-scale matter distribution. We use the EAGLE simulations to explore how changes in subgrid viscosity and AGN heating temperature affect the matter distribution, quantified via 2- and 3-point correlation functions, as well as higher order cumulants of the matter distribution. We find that varying viscosity has a small impact ($\approx 10\%$) on scales larger than $1 h^{-1}$ Mpc, while changes to the AGN heating temperature lead to substantial differences, with up to $70\%$ variation in gas clustering on small scales ($\lesssim 1 h^{-1}$ Mpc). By examining the suppression of the power spectrum as a function of time, we identify the redshift range $z = 1.5 - 1$ as a key epoch where AGN feedback begins to dominate in these simulations. The 3-point function provides complementary insight to the more familiar 2-point statistics, and shows more pronounced variations between models on the scale of individual haloes. On the other hand, we find that effects on even larger scales are largely comparable.
Towards a Categorical Foundation of Deep Learning: A Survey
2024-10-07
Francesco Riccardo Crescenzi
The unprecedented pace of machine learning research has lead to incredible advances, but also poses hard challenges. At present, the field lacks strong theoretical underpinnings, and many important achievements stem from ad hoc design choices which are hard to justify in principle and whose effectiveness often goes unexplained. Research debt is increasing and many papers are found not to be reproducible. This thesis is a survey that covers some recent work attempting to study machine learning categorically. Category theory is a branch of abstract mathematics that has found successful applications in many fields, both inside and outside mathematics. Acting as a lingua franca of mathematics and science, category theory might be able to give a unifying structure to the field of machine learning. This could solve some of the aforementioned problems. In this work, we mainly focus on the application of category theory to deep learning. Namely, we discuss the use of categorical optics to model gradient-based learning, the use of categorical algebras and integral transforms to link classical computer science to neural networks, the use of functors to link different layers of abstraction and preserve structure, and, finally, the use of string diagrams to provide detailed representations of neural network architectures.
Recent Advances of Multimodal Continual Learning: A Comprehensive Survey
2024-10-07
Dianzhi Yu, Xinni Zhang, Yankai Chen, Aiwei Liu, Yifei Zhang, Philip S. Yu, Irwin King
Continual learning (CL) aims to empower machine learning models to learn continually from new data, while building upon previously acquired knowledge without forgetting. As machine learning models have evolved from small to large pre-trained architectures, and from supporting unimodal to multimodal data, multimodal continual learning (MMCL) methods have recently emerged. The primary challenge of MMCL is that it goes beyond a simple stacking of unimodal CL methods, as such straightforward approaches often yield unsatisfactory performance. In this work, we present the first comprehensive survey on MMCL. We provide essential background knowledge and MMCL settings, as well as a structured taxonomy of MMCL methods. We categorize existing MMCL methods into four categories, i.e., regularization-based, architecture-based, replay-based, and prompt-based methods, explaining their methodologies and highlighting their key innovations. Additionally, to prompt further research in this field, we summarize open MMCL datasets and benchmarks, and discuss several promising future directions for investigation and development. We have also created a GitHub repository for indexing relevant MMCL papers and open resources available at https://github.com/LucyDYu/Awesome-Multimodal-Continual-Learning.
2024-10-07
Tatjana Wingarz, Anne Lauscher, Janick Edinger, Dominik Kaaser, Stefan Schulte, Mathias Fischer
Advanced AI applications have become increasingly available to a broad audience, e.g., as centrally managed large language models (LLMs). Such centralization is both a risk and a performance bottleneck - Edge AI promises to be a solution to these problems. However, its decentralized approach raises additional challenges regarding security and safety. In this paper, we argue that both of these aspects are critical for Edge AI, and even more so, their integration. Concretely, we survey security and safety threats, summarize existing countermeasures, and collect open challenges as a call for more research in this area.
Measuring the ISM Content of Nearby, Luminous, Type 1 and Type 2 QSOs through CO and [C II]
2024-10-06
Yuanze Luo, A. O. Petric, R. M. J. Janssen, D. Fadda, N. Flagey, A. Omont, A. M. Jacob, K. Rowlands, K. Alatalo, N. Billot, T. Heckman, B. Husemann, D. Kakkad, M. Lacy, J. Marshall, R. Minchin, R. Minsley, N. Nesvadba, J. A. Otter, P. Patil, T. Urrutia
We present observations of CO(1--0) and CO(2--1) lines from the Institut de radioastronomie millim\'etrique (IRAM) 30m telescope toward 20 nearby, optically luminous type 2 quasars (QSO2s) and observations of [C II] 158$\mu$m line from the Stratospheric Observatory For Infrared Astronomy (SOFIA) for 5 QSO2s in the CO sample and 5 type 1 quasars (QSO1s). In the traditional evolutionary scenario explaining different types of QSOs, obscured QSO2s emerge from gas-rich mergers observed as luminous infrared galaxies (LIRGs) and then turn into unobscured QSO1s as the black holes clear out the obscuring material in a blow-out phase. We test the validity of this theoretical prediction by comparing the gas fractions and star formation efficiencies among LIRGs and QSOs. We find that CO luminosity, CO-derived gas masses and gas fractions in QSO1s are consistent with those estimated for QSO2s, while LIRGs exhibit a closer resemblance to QSO2s in terms of CO-derived gas masses and gas fractions, and [C II] luminosity. However, comparisons between [C II] luminosity and star formation tracers such as the CO and infrared luminosity imply additional sources of [C II] emission in QSO1s likely tracing neutral atomic or ionized gas. All three types of galaxies have statistically indistinguishable distributions of star formation efficiency. Our results are consistent with part of the evolutionary scenario where nearby QSO2s could emerge from LIRGs, but they are unlikely to be the precursors of nearby QSO1s.
Unitary Closed Timelike Curves can Solve all of NP
2024-10-06
Omri Shmueli
Born in the intersection between quantum mechanics and general relativity, indefinite causal structure is the idea that in the continuum of time, some sets of events do not have an inherent causal order between them. Process matrices, introduced by Oreshkov, Costa and Brukner (Nature Communications, 2012), define quantum information processing with indefinite causal structure -- a generalization of the operations allowed in standard quantum information processing, and to date, are the most studied such generalization. Araujo et al. (Physical Review A, 2017) defined the computational complexity of process matrices, and showed that polynomial-time process matrix computation is equivalent to standard polynomial-time quantum computation with access to a weakening of post-selection Closed Timelike Curves (CTCs), that are restricted to be $\textit{linear}$. Araujo et al. accordingly defined the complexity class for efficient process matrix computation as $\mathbf{BQP}_{\ell CTC}$ (which trivially contains $\mathbf{BQP}$), and posed the open question of whether $\mathbf{BQP}_{\ell CTC}$ contains computational tasks that are outside $\mathbf{BQP}$. In this work we solve this open question under a widely believed hardness assumption, by showing that $\mathbf{NP} \subseteq \mathbf{BQP}_{\ell CTC}$. Our solution is captured by an even more restricted subset of process matrices that are purifiable (Araujo et al., Quantum, 2017), which (1) is conjectured more likely to be physical than arbitrary process matrices, and (2) is equivalent to polynomial-time quantum computation with access to $\textit{unitary}$ (which are in particular linear) post-selection CTCs. Conceptually, our work shows that the previously held belief, that non-linearity is what enables CTCs to solve $\mathbf{NP}$, is false, and raises the importance of understanding whether purifiable process matrices are physical or not.
ProtocoLLM: Automatic Evaluation Framework of LLMs on Domain-Specific Scientific Protocol Formulation Tasks
2024-10-06
Seungjun Yi, Jaeyoung Lim, Juyong Yoon
Automated generation of scientific protocols executable by robots can significantly accelerate scientific research processes. Large Language Models (LLMs) excel at Scientific Protocol Formulation Tasks (SPFT), but the evaluation of their capabilities rely on human evaluation. Here, we propose a flexible, automatic framework to evaluate LLM's capability on SPFT: ProtocoLLM. This framework prompts the target model and GPT-4 to extract pseudocode from biology protocols using only predefined lab actions and evaluates the output of target model using LLAM-EVAL, the pseudocode generated by GPT-4 serving as a baseline and Llama-3 acting as the evaluator. Our adaptable prompt-based evaluation method, LLAM-EVAL, offers significant flexibility in terms of evaluation model, material, criteria, and is free of cost. We evaluate GPT variations, Llama, Mixtral, Gemma, Cohere, and Gemini. Overall, we find that GPT and Cohere is a powerful scientific protocol formulators. We also introduce BIOPROT 2.0, a dataset with biology protocols and corresponding pseudocodes, which can aid LLMs in formulation and evaluation of SPFT. Our work is extensible to assess LLMs on SPFT across various domains and other fields that require protocol generation for specific goals.
The atmospheres within our Solar System can be categorized into four distinct climate regimes: "terrestrial", "Jovian", "condensable", and "exosphere". Beyond the three terrestrial planets (excluding Mercury) and the four giant planets, collisional atmospheres are also found on smaller celestial bodies such as Jupiter's moon Io, Saturn's moon Titan, Neptune's moon Triton, and Pluto. This article reviews the key characteristics of these atmospheres and the underlying physical and chemical processes that govern them. I focus on their thermal structures, chemical constituents, wind patterns, and the origins and losses of the atmospheres, and highlight the critical roles of surface ices and liquids, atmospheric hazes, and the space environments of their host planets in shaping these atmospheres. I dedicated this article to Prof. Zuo Xiao (1936-2024) at Peking University.
Observation of quantum entanglement in top quark pairs at the ATLAS experiment
2024-10-06
Baptiste Ravina
The ATLAS Collaboration has recently reported the first observation of quantum entanglement in top quark pair production at the Large Hadron Collider (LHC). A brief review of the experimental status of top quark pair spin correlations is given, before highlighting the expected differences between classical and quantum correlations. A criterion for the detection of spin entanglement in top quark pairs has been proposed; the experimental strategy to implement it and observe the effect is discussed, and the positive results of the ATLAS measurement are presented.
Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval
2024-10-06
Pengcheng Jiang, Cao Xiao, Minhao Jiang, Parminder Bhatia, Taha Kass-Hout, Jimeng Sun, Jiawei Han
Large language models (LLMs) have demonstrated significant potential in clinical decision support. Yet LLMs still suffer from hallucinations and lack fine-grained contextual medical knowledge, limiting their high-stake healthcare applications such as clinical diagnosis. Traditional retrieval-augmented generation (RAG) methods attempt to address these limitations but frequently retrieve sparse or irrelevant information, undermining prediction accuracy. We introduce KARE, a novel framework that integrates knowledge graph (KG) community-level retrieval with LLM reasoning to enhance healthcare predictions. KARE constructs a comprehensive multi-source KG by integrating biomedical databases, clinical literature, and LLM-generated insights, and organizes it using hierarchical graph community detection and summarization for precise and contextually relevant information retrieval. Our key innovations include: (1) a dense medical knowledge structuring approach enabling accurate retrieval of relevant information; (2) a dynamic knowledge retrieval mechanism that enriches patient contexts with focused, multi-faceted medical insights; and (3) a reasoning-enhanced prediction framework that leverages these enriched contexts to produce both accurate and interpretable clinical predictions. Extensive experiments demonstrate that KARE outperforms leading models by up to 10.8-15.0% on MIMIC-III and 12.6-12.7% on MIMIC-IV for mortality and readmission predictions. In addition to its impressive prediction accuracy, our framework leverages the reasoning capabilities of LLMs, enhancing the trustworthiness of clinical predictions.
Correlations between azimuthal anisotropy and mean transverse momentum in pp, pPb, and peripheral PbPb collisions
2024-10-06
CMS Collaboration
Correlations between azimuthal anisotropy and mean transverse momentum of charged particles in proton-proton (pp), proton-lead (pPb), and peripheral lead-lead (PbPb) collisions are presented as a function of charged particle multiplicity. The pp, pPb and PbPb collision data were collected using the CMS detector at the LHC with a center-of-mass energy per nucleon pair of 13, 8.16 and 5.02 TeV, respectively. The two- and four-particle cumulants for the second- and third-order Fourier anisotropy harmonics are correlated with the mean transverse momentum of charged particles on an event-by-event basis. In pp and pPb systems, the observed correlation coefficients based on two-particle cumulants are found to change from negative to positive values as the charged particle multiplicity decreases. The sign changes disappear when the correlated particles are required to be further apart in pseudorapidity. Additionally, no sign changes in correlation coefficients are observed when employing four-particle cumulants. Models incorporating initial-state gluon saturation and final-state hydrodynamic evolutions are compared to pPb data and the predicted sign changes are not observed.
Admissibility Over Winning: A New Approach to Reactive Synthesis in Robotics
2024-10-06
Karan Muvvala, Morteza Lahijanian
Reactive synthesis is a framework for modeling and automatically synthesizing strategies in robotics, typically through computing a \emph{winning} strategy in a 2-player game between the robot and the environment. Winning strategies, however, do not always exist, even in some simple cases. In such situations, it is still desirable for the robot to attempt its task rather than "giving up". In this work, we explore the notion of admissibility to define strategies beyond winning, tailored specifically for robotic systems. We introduce an ordering of admissible strategies and define \emph{admissibly rational strategies}, which aim to be winning and cooperative when possible, and non-violating and hopeful when necessary. We present an efficient synthesis algorithm and demonstrate that admissibly rational strategies produce desirable behaviors through case studies.
2024-10-06
Sebastian Pineda Arango, Maciej Janowski, Lennart Purucker, Arber Zela, Frank Hutter, Josif Grabocka
Ensemble methods are known for enhancing the accuracy and robustness of machine learning models by combining multiple base learners. However, standard approaches like greedy or random ensembles often fall short, as they assume a constant weight across samples for the ensemble members. This can limit expressiveness and hinder performance when aggregating the ensemble predictions. In this study, we explore employing neural networks as ensemble methods, emphasizing the significance of dynamic ensembling to leverage diverse model predictions adaptively. Motivated by the risk of learning low-diversity ensembles, we propose regularizing the model by randomly dropping base model predictions during the training. We demonstrate this approach lower bounds the diversity within the ensemble, reducing overfitting and improving generalization capabilities. Our experiments showcase that the dynamic neural ensemblers yield competitive results compared to strong baselines in computer vision, natural language processing, and tabular data.
A review on asymptotic stability of solitary waves in nonlinear dispersive problems in dimension one
2024-10-06
Pierre Germain
We review asymptotic stability of solitary waves for nonlinear dispersive equations set on the line. Our focus is threefold: first, the nonlinear Schrodinger equation; second, the notion of full asymptotic stability (which states that perturbations of a solitary wave decompose globally into a solitary wave and a decaying solution); and third, spectral methods. Besides this focus, we summarize the state of the art in a broader context, including nonlinear Klein-Gordon equations, the notion of local asymptotic stability, and virial methods.
Leveraging Large Language Models for Suicide Detection on Social Media with Limited Labels
2024-10-06
Vy Nguyen, Chau Pham
The increasing frequency of suicidal thoughts highlights the importance of early detection and intervention. Social media platforms, where users often share personal experiences and seek help, could be utilized to identify individuals at risk. However, the large volume of daily posts makes manual review impractical. This paper explores the use of Large Language Models (LLMs) to automatically detect suicidal content in text-based social media posts. We propose a novel method for generating pseudo-labels for unlabeled data by prompting LLMs, along with traditional classification fine-tuning techniques to enhance label accuracy. To create a strong suicide detection model, we develop an ensemble approach involving prompting with Qwen2-72B-Instruct, and using fine-tuned models such as Llama3-8B, Llama3.1-8B, and Gemma2-9B. We evaluate our approach on the dataset of the Suicide Ideation Detection on Social Media Challenge, a track of the IEEE Big Data 2024 Big Data Cup. Additionally, we conduct a comprehensive analysis to assess the impact of different models and fine-tuning strategies on detection performance. Experimental results show that the ensemble model significantly improves the detection accuracy, by 5% points compared with the individual models. It achieves a weight F1 score of 0.770 on the public test set, and 0.731 on the private test set, providing a promising solution for identifying suicidal content in social media. Our analysis shows that the choice of LLMs affects the prompting performance, with larger models providing better accuracy. Our code and checkpoints are publicly available at https://github.com/khanhvynguyen/Suicide_Detection_LLMs.
In-context Learning (ICL) is an emerging few-shot learning paradigm on Language Models (LMs) with inner mechanisms un-explored. There are already existing works describing the inner processing of ICL, while they struggle to capture all the inference phenomena in large language models. Therefore, this paper proposes a comprehensive circuit to model the inference dynamics and try to explain the observed phenomena of ICL. In detail, we divide ICL inference into 3 major operations: (1) Summarize: LMs encode every input text (demonstrations and queries) into linear representation in the hidden states with sufficient information to solve ICL tasks. (2) Semantics Merge: LMs merge the encoded representations of demonstrations with their corresponding label tokens to produce joint representations of labels and demonstrations. (3) Feature Retrieval and Copy: LMs search the joint representations similar to the query representation on a task subspace, and copy the searched representations into the query. Then, language model heads capture these copied label representations to a certain extent and decode them into predicted labels. The proposed inference circuit successfully captured many phenomena observed during the ICL process, making it a comprehensive and practical explanation of the ICL inference process. Moreover, ablation analysis by disabling the proposed steps seriously damages the ICL performance, suggesting the proposed inference circuit is a dominating mechanism. Additionally, we confirm and list some bypass mechanisms that solve ICL tasks in parallel with the proposed circuit.
Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective
2024-10-06
Jinhao Li, Jiaming Xu, Shan Huang, Yonghua Chen, Wen Li, Jun Liu, Yaoxiu Lian, Jiayi Pan, Li Ding, Hao Zhou, Guohao Dai
Large Language Models (LLMs) have demonstrated remarkable capabilities across various fields, from natural language understanding to text generation. Compared to non-generative LLMs like BERT and DeBERTa, generative LLMs like GPT series and Llama series are currently the main focus due to their superior algorithmic performance. The advancements in generative LLMs are closely intertwined with the development of hardware capabilities. Various hardware platforms exhibit distinct hardware characteristics, which can help improve LLM inference performance. Therefore, this paper comprehensively surveys efficient generative LLM inference on different hardware platforms. First, we provide an overview of the algorithm architecture of mainstream generative LLMs and delve into the inference process. Then, we summarize different optimization methods for different platforms such as CPU, GPU, FPGA, ASIC, and PIM/NDP, and provide inference results for generative LLMs. Furthermore, we perform a qualitative and quantitative comparison of inference performance with batch sizes 1 and 8 on different hardware platforms by considering hardware power consumption, absolute inference speed (tokens/s), and energy efficiency (tokens/J). We compare the performance of the same optimization methods across different hardware platforms, the performance across different hardware platforms, and the performance of different methods on the same hardware platform. This provides a systematic and comprehensive summary of existing inference acceleration work by integrating software optimization methods and hardware platforms, which can point to the future trends and potential developments of generative LLMs and hardware technology for edge-side scenarios.
The rapid expansion of video content across a variety of industries, including social media, education, entertainment, and surveillance, has made video summarization an essential field of study. The current work is a survey that explores the various approaches and methods created for video summarizing, emphasizing both abstractive and extractive strategies. The process of extractive summarization involves the identification of key frames or segments from the source video, utilizing methods such as shot boundary recognition, and clustering. On the other hand, abstractive summarization creates new content by getting the essential content from the video, using machine learning models like deep neural networks and natural language processing, reinforcement learning, attention mechanisms, generative adversarial networks, and multi-modal learning. We also include approaches that incorporate the two methodologies, along with discussing the uses and difficulties encountered in real-world implementations. The paper also covers the datasets used to benchmark these techniques. This review attempts to provide a state-of-the-art thorough knowledge of the current state and future directions of video summarization research.
Approximate Maximum Likelihood Inference for Acoustic Spatial Capture-Recapture with Unknown Identities, Using Monte Carlo Expectation Maximization
2024-10-06
Yuheng Wang, Juan Ye, Weiye Li, David L. Borchers
Acoustic spatial capture-recapture (ASCR) surveys with an array of synchronized acoustic detectors can be an effective way of estimating animal density or call density. However, constructing the capture histories required for ASCR analysis is challenging, as recognizing which detections at different detectors are of which calls is not a trivial task. Because calls from different distances take different times to arrive at detectors, the order in which calls are detected is not necessarily the same as the order in which they are made, and without knowing which detections are of the same call, we do not know how many different calls are detected. We propose a Monte Carlo expectation-maximization (MCEM) estimation method to resolve this unknown call identity problem. To implement the MCEM method in this context, we sample the latent variables from a complete-data likelihood model in the expectation step and use a semi-complete-data likelihood or conditional likelihood in the maximization step. We use a parametric bootstrap to obtain confidence intervals. When we apply our method to a survey of moss frogs, it gives an estimate within 15% of the estimate obtained using data with call capture histories constructed by experts, and unlike this latter estimate, our confidence interval incorporates the uncertainty about call identities. Simulations show it to have a low bias (6%) and coverage probabilities close to the nominal 95% value.
Recent refinements on a semiclassical approach are reviewed, aiming at describing the inhomogeneous local gap parameter in the presence of non-trivial spatial geometries and at taking into account at the same time pairing fluctuations beyond mean field. The method is applied to describe the Josephson effect over the wide range of physical conditions related to recent experiments on this topic performed with ultra-cold Fermi gases.
2024-10-06
Shakila Mahjabin Tonni, Pedro Faustini, Mark Dras
Adversarial examples pose a significant challenge to deep neural networks (DNNs) across both image and text domains, with the intent to degrade model performance through meticulously altered inputs. Adversarial texts, however, are distinct from adversarial images due to their requirement for semantic similarity and the discrete nature of the textual contents. This study delves into the concept of human suspiciousness, a quality distinct from the traditional focus on imperceptibility found in image-based adversarial examples. Unlike images, where adversarial changes are meant to be indistinguishable to the human eye, textual adversarial content must often remain undetected or non-suspicious to human readers, even when the text's purpose is to deceive NLP systems or bypass filters. In this research, we expand the study of human suspiciousness by analyzing how individuals perceive adversarial texts. We gather and publish a novel dataset of Likert-scale human evaluations on the suspiciousness of adversarial sentences, crafted by four widely used adversarial attack methods and assess their correlation with the human ability to detect machine-generated alterations. Additionally, we develop a regression-based model to quantify suspiciousness and establish a baseline for future research in reducing the suspiciousness in adversarial text generation. We also demonstrate how the regressor-generated suspicious scores can be incorporated into adversarial generation methods to produce texts that are less likely to be perceived as computer-generated. We make our human suspiciousness annotated data and our code available.
Enabling Clinical Use of Linear Energy Transfer in Proton Therapy for Head and Neck Cancer -- A Review of Implications for Treatment Planning and Adverse Events Study
2024-10-06
Jingyuan Chen, Yunze Yang, Hongying Feng, Chenbin Liu, Lian Zhang, Jason M. Holmes, Zhengliang Liu, Haibo Lin, Tianming Liu, Charles B. Simone II, Nancy Y. Lee, Steven E. Frank, Daniel J. Ma, Samir H. Patel, Wei Liu
Proton therapy offers significant advantages due to its unique physical and biological properties, particularly the Bragg peak, enabling precise dose delivery to tumors while sparing healthy tissues. However, the clinical implementation is challenged by the oversimplification of the relative biological effectiveness (RBE) as a fixed value of 1.1, which does not account for the complex interplay between dose, linear energy transfer (LET), and biological endpoints. Lack of heterogeneity control or the understanding of the complex interplay may result in unexpected adverse events and suboptimal patient outcomes. On the other hand, expanding our knowledge of variable tumor RBE and LET optimization may provide a better management strategy for radioresistant tumors. This review examines recent advancements in LET calculation methods, including analytical models and Monte Carlo simulations. The integration of LET into plan evaluation is assessed to enhance plan quality control. LET-guided robust optimization demonstrates promise in minimizing high-LET exposure to organs at risk, thereby reducing the risk of adverse events. Dosimetric seed spot analysis is discussed to show its importance in revealing the true LET-related effect upon the adverse event initialization by finding the lesion origins and eliminating the confounding factors from the biological processes. Dose-LET volume histograms (DLVH) are discussed as effective tools for correlating physical dose and LET with clinical outcomes, enabling the derivation of clinically relevant dose-LET volume constraints without reliance on uncertain RBE models. Based on DLVH, the dose-LET volume constraints (DLVC)-guided robust optimization is introduced to upgrade conventional dose-volume constraints-based robust optimization, which optimizes the joint distribution of dose and LET simultaneously.
Enhancing Android Malware Detection: The Influence of ChatGPT on Decision-centric Task
2024-10-06
Yao Li, Sen Fang, Tao Zhang, Haipeng Cai
With the rise of large language models, such as ChatGPT, non-decisional models have been applied to various tasks. Moreover, ChatGPT has drawn attention to the traditional decision-centric task of Android malware detection. Despite effective detection methods proposed by scholars, they face low interpretability issues. Specifically, while these methods excel in classifying applications as benign or malicious and can detect malicious behavior, they often fail to provide detailed explanations for the decisions they make. This challenge raises concerns about the reliability of existing detection schemes and questions their true ability to understand complex data. In this study, we investigate the influence of the non-decisional model, ChatGPT, on the traditional decision-centric task of Android malware detection. We choose three state-of-the-art solutions, Drebin, XMAL, and MaMaDroid, conduct a series of experiments on publicly available datasets, and carry out a comprehensive comparison and analysis. Our findings indicate that these decision-driven solutions primarily rely on statistical patterns within datasets to make decisions, rather than genuinely understanding the underlying data. In contrast, ChatGPT, as a non-decisional model, excels in providing comprehensive analysis reports, substantially enhancing interpretability. Furthermore, we conduct surveys among experienced developers. The result highlights developers' preference for ChatGPT, as it offers in-depth insights and enhances efficiency and understanding of challenges. Meanwhile, these studies and analyses offer profound insights, presenting developers with a novel perspective on Android malware detection--enhancing the reliability of detection results from a non-decisional perspective.
The small bodies in the Kuiper Belt region of the distant Solar System are leftovers from planet formation. Their orbital distribution today tells us about how giant planets migrated, while their surface properties, shapes, and sizes tell us about formation processes and collision rates. Probing these intrinsic properties requires a careful understanding of the observational biases that are a part of any telescopic survey that discovers small bodies. While many of the details of giant planet migration are now understood due to careful comparison between de-biased discoveries in the Kuiper Belt and computational simulations, some discoveries have orbits that are still not easy to explain. Upcoming surveys such as the planned survey on Vera Rubin Observatory will help us to leverage the Kuiper Belt and refine our knowledge about the formation and dynamical history of our own Solar System.
AI Assistants for Incident Lifecycle in a Microservice Environment: A Systematic Literature Review
2024-10-06
Dahlia Ziqi Zhou, Marios Fokaefs
Incidents in microservice environments can be costly and challenging to recover from due to their complexity and distributed nature. Recent advancements in artificial intelligence (AI) offer promising solutions for improving incident management. This paper systematically reviews primary studies on AI assistants designed to support different phases of the incident lifecycle. It highlights successful applications of AI, identifies gaps in current research, and suggests future opportunities for enhancing incident management through AI. By examining these studies, the paper aims to provide insights into the effectiveness of AI tools and their potential to address ongoing challenges in incident recovery.
We respond to the "Comment on "Reconsidering the nonlinear emergent inductance: time-varying Joule heating and its impact on the AC electrical response"", submitted by Yokouchi et al. (arXiv:2407.15682), which refers to our preprint by Furuta et al (arXiv:2407.00309). In our preprint, we argued that the nonlinear impedance reported for materials with noncollinear magnetic textures, Gd$_3$Ru$_4$Al$_{12}$, YMn$_6$Sn$_6$, and FeSn$_2$, can be attributed to the considerable contribution of Joule-heating-induced AC electrical response, rather than the emergent electric field (EEF) due to the current-induced-dynamics of magnetic textures. In the comment by Yokouchi et al., the authors presented new data and concluded that the nonlinear impedance previously reported for Gd$_3$Ru$_4$Al$_{12}$ and YMn$_6$Sn$_6$ was not due to Joule heating but to the EEF. After reviewing their data and arguments, we found that (i) they misunderstood the applicability of the Joule heating model discussed in our preprint (arXiv:2407.00309), (ii) the data they presented in the comment are affected by extrinsic superconductivity caused during the microfabrication of specimens using focused ion beam (FIB) methods, and (iii) they misinterpreted their new data regarding the magnetic-field-angle dependence of the nonlinear impedance. Thus, we maintain that the nonlinear impedance reported in the literature and the comment includes a considerable impact from Joule heating. Furthermore, we demonstrate that for Gd$_3$Ru$_4$Al$_{12}$, the cross-sectional area dependence of the nonlinear impedance refutes the EEF scenario, and that the sign change of nonlinear resistance observed by Yokouchi \textit{et al.}~is not allowed from the perspective of stability associated with the current-driven EEF.
Crystal Ball: A Simple Model for Phase Transitions on a Classical Spherical Lattice
2024-10-05
Aidan Bachmann, Pierre-Alexandre Gourdain, Eric G. Blackman
When compressed, certain lattices undergo phase transitions that may allow nuclei to gain significant kinetic energy. To explore the dynamics of this phenomenon, we develop a framework to study Coulomb coupled N-body systems constrained to a parametric surface, focusing specifically on the case of a sphere, as in the Thomson problem. We initialize $N$ total Boron nuclei as point particles on the surface of a sphere, allowing the particles to equilibrate via Coulomb scattering with a viscous damping term. To simulate a phase transition, we remove $N_{rm}$ particles, forcing the system to rearrange into a new equilibrium. We develop a scaling relation for the average peak kinetic energy attained by a single particle as a function of $N$ and $N_{rm}$. For certain values of $N$, we find an order of magnitude energy gain when increasing $N_{rm}$ from 1 to 6, indicating that it may be possible to engineer a lattice that maximizes the energy output.
Research problems on relations between Nijenhuis geometry and integrable systems
2024-10-05
Alexey V. Bolsinov, Andrey Yu. Konyaev, Vladimir S. Matveev
The paper surveys open problems and questions related to interplay between the theory of integrable systems with infinitely and finitely many degrees of freedom and Nijenhuis geometry. This text has grown out from preparatory materials for the series of research symposia and workshops on Nijenhuis geometry and integrable systems held at SMRI (Sydney) and MATRIX (Creswick) in February 2022 and at La Trobe University (Melbourne) and MATRIX in February 2024, and from the open problem sessions at these events. It includes both relatively simple questions to get familiar with the topic, as well as challenging problems that are of great importance for the field.
Robotics Process Automation is revolutionizing business operations by significantly enhancing efficiency, productivity, and operational excellence across various industries. This manuscript delivers a comprehensive review of recent advancements in RPA technologies and proposes a novel model designed to elevate RPA capabilities.
Unraveling the Nuances of AI Accountability: A Synthesis of Dimensions Across Disciplines
2024-10-05
L. H. Nguyen, S. Lins, M. Renner, A. Sunyaev
The widespread diffusion of Artificial Intelligence (AI)-based systems offers many opportunities to contribute to the well-being of individuals and the advancement of economies and societies. This diffusion is, however, closely accompanied by public scandals causing harm to individuals, markets, or society, and leading to the increasing importance of accountability. AI accountability itself faces conceptual ambiguity, with research scattered across multiple disciplines. To address these issues, we review current research across multiple disciplines and identify key dimensions of accountability in the context of AI. We reveal six themes with 13 corresponding dimensions and additional accountability facilitators that future research can utilize to specify accountability scenarios in the context of AI-based systems.
Deep Transfer Learning Based Peer Review Aggregation and Meta-review Generation for Scientific Articles
2024-10-05
Md. Tarek Hasan, Mohammad Nazmush Shamael, H. M. Mutasim Billah, Arifa Akter, Md Al Emran Hossain, Sumayra Islam, Salekul Islam, Swakkhar Shatabda
Peer review is the quality assessment of a manuscript by one or more peer experts. Papers are submitted by the authors to scientific venues, and these papers must be reviewed by peers or other authors. The meta-reviewers then gather the peer reviews, assess them, and create a meta-review and decision for each manuscript. As the number of papers submitted to these venues has grown in recent years, it becomes increasingly challenging for meta-reviewers to collect these peer evaluations on time while still maintaining the quality that is the primary goal of meta-review creation. In this paper, we address two peer review aggregation challenges a meta-reviewer faces: paper acceptance decision-making and meta-review generation. Firstly, we propose to automate the process of acceptance decision prediction by applying traditional machine learning algorithms. We use pre-trained word embedding techniques BERT to process the reviews written in natural language text. For the meta-review generation, we propose a transfer learning model based on the T5 model. Experimental results show that BERT is more effective than the other word embedding techniques, and the recommendation score is an important feature for the acceptance decision prediction. In addition, we figure out that fine-tuned T5 outperforms other inference models. Our proposed system takes peer reviews and other relevant features as input to produce a meta-review and make a judgment on whether or not the paper should be accepted. In addition, experimental results show that the acceptance decision prediction system of our task outperforms the existing models, and the meta-review generation task shows significantly improved scores compared to the existing models. For the statistical test, we utilize the Wilcoxon signed-rank test to assess whether there is a statistically significant improvement between paired observations.
Fast Object Detection with a Machine Learning Edge Device
2024-10-05
Richard C. Rodriguez, Jonah Elijah P. Bardos
This machine learning study investigates a lowcost edge device integrated with an embedded system having computer vision and resulting in an improved performance in inferencing time and precision of object detection and classification. A primary aim of this study focused on reducing inferencing time and low-power consumption and to enable an embedded device of a competition-ready autonomous humanoid robot and to support real-time object recognition, scene understanding, visual navigation, motion planning, and autonomous navigation of the robot. This study compares processors for inferencing time performance between a central processing unit (CPU), a graphical processing unit (GPU), and a tensor processing unit (TPU). CPUs, GPUs, and TPUs are all processors that can be used for machine learning tasks. Related to the aim of supporting an autonomous humanoid robot, there was an additional effort to observe whether or not there was a significant difference in using a camera having monocular vision versus stereo vision capability. TPU inference time results for this study reflect a 25% reduction in time over the GPU, and a whopping 87.5% reduction in inference time compared to the CPU. Much information in this paper is contributed to the final selection of Google's Coral brand, Edge TPU device. The Arduino Nano 33 BLE Sense Tiny ML Kit was also considered for comparison but due to initial incompatibilities and in the interest of time to complete this study, a decision was made to review the kit in a future experiment.
Transverse beam instabilities in low-emittance booster synchrotrons
2024-10-05
W. Foosang, A. Gamelin, V. Gubaidulin, R. Nagaoka
As the ring-based light source community is moving towards fourth-generation light sources, many facilities plan to upgrade their boosters in parallel to meet the more demanding beam properties for the storage ring, especially in terms of a much lower emittance. Concerns over collective effects have, therefore, risen, particularly in the transverse planes, since the vacuum chamber dimensions tend to be reduced as a way to achieve a stronger focusing force on the beam. In this article, we present numerical studies on transverse beam instabilities, both in the single- and multibunch regimes, in the SOLEIL II booster as an example of a low-emittance booster. We show that Landau damping is an efficient mechanism for suppressing both transverse single-bunch and coupled-bunch instabilities. We also prove that the damping in the longitudinal plane can diffuse to the transverse plane and limit the transverse emittance growth. Moreover, we have discovered that the beam can exhibit sawtooth instability at high energy and that broad-band impedance is one of the key factors in suppressing transverse coupled-bunch instability.
Hydrodynamic equations and near critical large deviations of active lattice gases
2024-10-05
Luke Neville
Using a path integral approach, we derive and study the hydrodynamic equations and large deviation functions for three active lattice gases. After a review of the path integral for master equations, we first look at a one dimensional model of motility induced phase separation (MIPS), re-deriving the large deviation function that was previously found through a mapping to the ABC model. After extracting the deterministic hydrodynamic equations from the large deviation function, we analyse them perturbatively near the MIPS critical point using a weakly non-linear analysis. Doing this we show that they reduce to equilibrium Model B very close to criticality, with non-equilibrium, or Active Model B+ terms emerging as we leave the critical region. The same type of weakly non-linear analysis is then applied to the full large deviation function, and we show that the near critical stationary probability distribution is given by the exponential of a $\phi^4$ free energy, as expected in ordinary equilibrium phase separation. Similar calculations are then done for the two other lattice gases, one of flocking, and another MIPS model, and in both cases we find analogous results.
TeachTune: Reviewing Pedagogical Agents Against Diverse Student Profiles with Simulated Students
2024-10-05
Hyoungwook Jin, Minju Yoo, Jeongeon Park, Yokyung Lee, Xu Wang, Juho Kim
Large language models (LLMs) can empower educators to build pedagogical conversational agents (PCAs) customized for their students. As students have different prior knowledge and motivation levels, educators must evaluate the adaptivity of their PCAs to diverse students. Existing chatbot evaluation methods (e.g., direct chat and benchmarks) are either manually intensive for multiple iterations or limited to testing only single-turn interactions. We present TeachTune, where educators can create simulated students and review PCAs by observing automated chats between PCAs and simulated students. Our technical pipeline instructs an LLM-based student to simulate prescribed knowledge levels and characteristics, helping educators explore diverse conversation patterns. Our pipeline could produce simulated students whose behaviors correlate highly to their input knowledge and motivation levels within 5% and 10% accuracy gaps. Thirty science teachers designed PCAs in a between-subjects study, and using TeachTune resulted in a lower task load and higher student profile coverage over a baseline.
Bouncing Scenario in the $f(T)$ Modified Gravity Model with Dynamical System Analysis
2024-10-05
S. Davood Sadatian, S. Mohamad Reza Hosseini
In $f(T)$ gravity, the theory modifies the gravitational action by introducing a function of the torsion scalar $T$. This approach allows for a different treatment of gravity than general relativity, particularly in cosmological contexts. Dynamical system analysis is a powerful tool for exploring the stability and behavior of cosmological solutions within this framework. The dynamical system analysis involves examining the phase space of the cosmological equations derived from the $f(T)$ model. This analysis helps identify fixed points, stability, and the evolution of the universe's scale factor. Therefore, in the following, we first review the main equations of the $f(T)$ gravity model. Then we study the dynamic analysis of the gravity model and obtain stability points. Finally, we consider the bouncing scenario in this model.
Enhance Reasoning by Learning from Mistakes: Peer-Review Knowledge Distillation from Multiple Large Language Models
2024-10-04
Zhuochun Li, Yuelyu Ji, Rui Meng, Daqing He
Large language models (LLMs) have exhibited complex reasoning abilities by generating question rationales and demonstrated exceptional performance in natural language processing (NLP) tasks. However, these reasoning capabilities generally emerge in models with tens of billions of parameters, creating significant computational challenges for real-world deployment. Recent research has concentrated on improving open-source smaller models through knowledge distillation (KD) from commercial LLMs. Nevertheless, most of these studies rely solely on the responses from one single LLM as the gold rationale for training. In this paper, we introduce a novel Mistake-Aware Peer-Review Distillation (MAPD) approach: 1) Instead of merely obtaining gold rationales from teachers, our method asks teachers to identify and explain the student's mistakes, providing customized instruction learning data. 2) We design a simulated peer-review process between teacher LLMs, which selects only the generated rationales above the acceptance threshold. This reduces the chance of teachers guessing correctly with flawed rationale, improving instructional data quality. Comprehensive experiments and analysis on mathematical, commonsense, and logical reasoning tasks demonstrate the effectiveness of our method.
In recent years, machine learning models have made strides towards human-like reasoning capabilities from several directions. In this work, we review the current state of the literature and describe the remaining steps to achieve a neural model which can perform System 2 reasoning analogous to a human. We argue that if current models are insufficient to be classed as performing reasoning, there remains very little additional progress needed to attain that goal.
BLAST: Beyond Limber Angular power Spectra Toolkit. A fast and efficient algorithm for 3x2 pt analysis
2024-10-04
Sofia Chiarenza, Marco Bonici, Will Percival, Martin White
The advent of next-generation photometric and spectroscopic surveys is approaching, bringing more data with tighter error bars. As a result, theoretical models will become more complex, incorporating additional parameters, which will increase the dimensionality of the parameter space and make posteriors more challenging to explore. Consequently, the need to improve and speed up our current analysis pipelines will grow. In this work, we focus on the 3x2 pt statistics, a summary statistic that has become increasingly popular in recent years due to its great constraining power. These statistics involve calculating angular two-point correlation functions for the auto- and cross-correlations between galaxy clustering and weak lensing. The corresponding model is determined by integrating the product of the power spectrum and two highly-oscillating Bessel functions over three dimensions, which makes the evaluation particularly challenging. Typically, this difficulty is circumvented by employing the so-called Limber approximation, which is an important source of error. We present Blast.jl, an innovative and efficient algorithm for calculating angular power spectra without employing the Limber approximation or assuming a scale-dependent growth rate, based on the use of Chebyshev polynomials. The algorithm is compared with the publicly available beyond-Limber codes, whose performances were recently tested by the LSST Dark Energy Science Collaboration in the N5K challenge. At similar accuracy, Blast.jl is $\approx 10$-$15 \times$ faster than the winning method of the challenge, also showing excellent scaling with respect to various hyper-parameters.
Electronic Health Records (EHR) are crucial for the success of digital healthcare, with a focus on putting consumers at the center of this transformation. However, the digitalization of healthcare records brings along security and privacy risks for personal data. The major concern is that different countries have varying standards for the security and privacy of medical data. This paper proposed a novel and comprehensive framework to standardize these rules globally, bringing them together on a common platform. To support this proposal, the study reviews existing literature to understand the research interest in this issue. It also examines six key laws and standards related to security and privacy, identifying twenty concepts. The proposed framework utilized K-means clustering to categorize these concepts and identify five key factors. Finally, an Ordinal Priority Approach is applied to determine the preferred implementation of these factors in the context of EHRs. The proposed study provides a descriptive then prescriptive framework for the implementation of privacy and security in the context of electronic health records. Therefore, the findings of the proposed framework are useful for professionals and policymakers in improving the security and privacy associated with EHRs.
A Novel Orbit Parameterization in Spherical Coordinates
2024-10-04
Kevin J Napier, Matthew J Holman
We present a novel orbit parameterization in spherical coordinates. This parameterization enables the mixing of varying and invariant orbital parameters, and clarifies the physics of the orbit. It also simplifies the process of placing synthetic populations at exactly specified locations on the sky, which is particularly useful for survey design and simulation studies.
Gender-Based Violence (GBV) is an increasing problem online, but existing datasets fail to capture the plurality of possible annotator perspectives or ensure the representation of affected groups. We revisit two important stages in the moderation pipeline for GBV: (1) manual data labelling; and (2) automated classification. For (1), we examine two datasets to investigate the relationship between annotator identities and attitudes and the responses they give to two GBV labelling tasks. To this end, we collect demographic and attitudinal information from crowd-sourced annotators using three validated surveys from Social Psychology. We find that higher Right Wing Authoritarianism scores are associated with a higher propensity to label text as sexist, while for Social Dominance Orientation and Neosexist Attitudes, higher scores are associated with a negative tendency to do so. For (2), we conduct classification experiments using Large Language Models and five prompting strategies, including infusing prompts with annotator information. We find: (i) annotator attitudes affect the ability of classifiers to predict their labels; (ii) including attitudinal information can boost performance when we use well-structured brief annotator descriptions; and (iii) models struggle to reflect the increased complexity and imbalanced classes of the new label sets.
2024-10-04
Alan B. H. Nguyen, Marco Bonici, Glen McGee, Will J. Percival
With the advent of the next generation of astrophysics experiments, the volume of data available to researchers will be greater than ever. As these projects will significantly drive down statistical uncertainties in measurements, it is crucial to develop novel tools to assess the ability of our models to fit these data within the specified errors. We introduce to astronomy the Leave One Out-Probability Integral Transform (LOO-PIT) technique. This first estimates the LOO posterior predictive distributions based on the model and likelihood distribution specified, then evaluates the quality of the match between the model and data by applying the PIT to each estimated distribution and data point, outputting a LOO-PIT distribution. Deviations between this output distribution and that expected can be characterised visually and with a standard Kolmogorov--Smirnov distribution test. We compare LOO-PIT and the more common $\chi^2$ test using both a simplified model and a more realistic astrophysics problem, where we consider fitting Baryon Acoustic Oscillations in galaxy survey data with contamination from emission line interlopers. LOO-PIT and $\chi^2$ tend to find different signals from the contaminants, and using these tests in conjunction increases the statistical power compared to using either test alone. We also show that LOO-PIT outperforms $\chi^2$ in certain realistic test cases.
Large conferences such as NeurIPS and AAAI serve as crossroads of various AI fields, since they attract submissions from a vast number of communities. However, in some cases, this has resulted in a poor reviewing experience for some communities, whose submissions get assigned to less qualified reviewers outside of their communities. An often-advocated solution is to break up any such large conference into smaller conferences, but this can lead to isolation of communities and harm interdisciplinary research. We tackle this challenge by introducing a notion of group fairness, called the core, which requires that every possible community (subset of researchers) to be treated in a way that prevents them from unilaterally benefiting by withdrawing from a large conference. We study a simple peer review model, prove that it always admits a reviewing assignment in the core, and design an efficient algorithm to find one such assignment. We use real data from CVPR and ICLR conferences to compare our algorithm to existing reviewing assignment algorithms on a number of metrics.
Recent advancements in diffusion models have been effective in learning data priors for solving inverse problems. They leverage diffusion sampling steps for inducing a data prior while using a measurement guidance gradient at each step to impose data consistency. For general inverse problems, approximations are needed when an unconditionally trained diffusion model is used since the measurement likelihood is intractable, leading to inaccurate posterior sampling. In other words, due to their approximations, these methods fail to preserve the generation process on the data manifold defined by the diffusion prior, leading to artifacts in applications such as image restoration. To enhance the performance and robustness of diffusion models in solving inverse problems, we propose Diffusion State-Guided Projected Gradient (DiffStateGrad), which projects the measurement gradient onto a subspace that is a low-rank approximation of an intermediate state of the diffusion process. DiffStateGrad, as a module, can be added to a wide range of diffusion-based inverse solvers to improve the preservation of the diffusion process on the prior manifold and filter out artifact-inducing components. We highlight that DiffStateGrad improves the robustness of diffusion models in terms of the choice of measurement guidance step size and noise while improving the worst-case performance. Finally, we demonstrate that DiffStateGrad improves upon the state-of-the-art on linear and nonlinear image restoration inverse problems.
Benchmarking the Fidelity and Utility of Synthetic Relational Data
2024-10-04
Valter Hudovernik, Martin Jurkovič, Erik Štrumbelj
Synthesizing relational data has started to receive more attention from researchers, practitioners, and industry. The task is more difficult than synthesizing a single table due to the added complexity of relationships between tables. For the same reason, benchmarking methods for synthesizing relational data introduces new challenges. Our work is motivated by a lack of an empirical evaluation of state-of-the-art methods and by gaps in the understanding of how such an evaluation should be done. We review related work on relational data synthesis, common benchmarking datasets, and approaches to measuring the fidelity and utility of synthetic data. We combine the best practices and a novel robust detection approach into a benchmarking tool and use it to compare six methods, including two commercial tools. While some methods are better than others, no method is able to synthesize a dataset that is indistinguishable from original data. For utility, we typically observe moderate correlation between real and synthetic data for both model predictive performance and feature importance.
HALO: A High-Precision Orbit Propagation Tool for Mission Design in the Cis-Lunar Domain
2024-10-04
Quentin Granier, Yang Yang, Andrew Dempster
With the recent implementation of the Artemis Accords, interest in the cis-lunar space is rapidly increasing, necessitating the development of more precise and accurate modeling tools. While general-purpose mission design tools are available, this study proposes an open-source mission design tool, HALO, for High-precision Analyser for Lunar Orbits. This work presents a comprehensive review of the modeling approaches, structural design, and algorithms employed, aiming at facilitating ease of use and adaptation for other research in the cis-lunar domain. Furthermore, accuracy studies of the propagator are provided for various orbits of interest within this domain, including low lunar orbits, elliptical frozen orbits, and 3-body problem orbits, such as Near Rectilinear Halo Orbits and Distant Retrograde Orbits.
JWST/NIRCam Narrowband Survey of Pa$β$ Emitters in the Spiderweb Protocluster at z=2.16
2024-10-04
Rhythm Shimakawa, J. M. Perez-Martinez, Helmut Dannerbauer, Yusei Koyama, Tadayuki Kodama, Pablo G. Perez-Gonzalez, Chiara D'Eugenio, Yuheng Zhang, Abdurrahman Naufal, Kazuki Daikuhara
We report the initial result of our Pa$\beta$ narrowband imaging on a protocluster with the JWST Near Infrared Camera (NIRCam). As NIRCam enables deep narrowband imaging of rest-frame NIR lines at $z>1$, we target one of the most studied protoclusters, the Spiderweb protocluster at $z=2.16$, in which previous studies have confirmed more than a hundred member galaxies. The NIRCam F405N narrowband filter covers in Pa$\beta$ line the protocluster redshift given by known protocluster members, allowing the search for new member candidates. The weight-corrected color-magnitude diagram obtained 57 sources showing narrowband excesses, 41 of which satisfy further color selection criteria for limiting the sample to Pa$\beta$ emitter candidates at $z\sim2.16$, and 24 of them do not have H$\alpha$ emitter counterparts. The Pa$\beta$ emitter candidates appear to follow the spatial distribution of known protocluster members; however, follow-up spectroscopic confirmation is required. Only 17 out of 58 known H$\alpha$-emitting cluster members are selected as Pa$\beta$ emitters in the current data, albeit the rest fall out of the narrowband selection owing to their small Pa$\beta$ equivalent widths. We derive Pa$\beta$ luminosity function in the Spiderweb protocluster, showing a normalization density of $\log{\phi^\ast}=-2.53\pm0.15$ at a characteristic Pa$\beta$ luminosity of $\log{L^\ast}=42.33\pm0.17$. Furthermore, we examine the possibility of detecting faint line emitters visible only in the narrow-band image, but find no promising candidates.
Does SpatioTemporal information benefit Two video summarization benchmarks?
2024-10-04
Aashutosh Ganesh, Mirela Popa, Daan Odijk, Nava Tintarev
An important aspect of summarizing videos is understanding the temporal context behind each part of the video to grasp what is and is not important. Video summarization models have in recent years modeled spatio-temporal relationships to represent this information. These models achieved state-of-the-art correlation scores on important benchmark datasets. However, what has not been reviewed is whether spatio-temporal relationships are even required to achieve state-of-the-art results. Previous work in activity recognition has found biases, by prioritizing static cues such as scenes or objects, over motion information. In this paper we inquire if similar spurious relationships might influence the task of video summarization. To do so, we analyse the role that temporal information plays on existing benchmark datasets. We first estimate a baseline with temporally invariant models to see how well such models rank on benchmark datasets (TVSum and SumMe). We then disrupt the temporal order of the videos to investigate the impact it has on existing state-of-the-art models. One of our findings is that the temporally invariant models achieve competitive correlation scores that are close to the human baselines on the TVSum dataset. We also demonstrate that existing models are not affected by temporal perturbations. Furthermore, with certain disruption strategies that shuffle fixed time segments, we can actually improve their correlation scores. With these results, we find that spatio-temporal relationship play a minor role and we raise the question whether these benchmarks adequately model the task of video summarization. Code available at: https://github.com/AashGan/TemporalPerturbSum
Discrete time scattering and Wold's decomposition in pictures
2024-10-04
Rafi Rizqy Firdaus, Serge Richard
Based on explicit computations, various concepts of discrete time scattering theory are reviewed, discussed, and illustrated. The dynamics are taking place on a discrete half-space. All operators are represented graphically. The expressions obtained for the wave operators lead to an easily visualized interpretation of Wold's decomposition, a seminal result of operator theory. This work is clearly pedagogically oriented with the aim of providing explicit formulas and pictures for usually unexplicit operators.
Beyond Film Subtitles: Is YouTube the Best Approximation of Spoken Vocabulary?
2024-10-04
Adam Nohejl, Frederikus Hudi, Eunike Andriani Kardinata, Shintaro Ozaki, Maria Angelica Riera Machin, Hongyu Sun, Justin Vasselli, Taro Watanabe
Word frequency is a key variable in psycholinguistics, useful for modeling human familiarity with words even in the era of large language models (LLMs). Frequency in film subtitles has proved to be a particularly good approximation of everyday language exposure. For many languages, however, film subtitles are not easily available, or are overwhelmingly translated from English. We demonstrate that frequencies extracted from carefully processed YouTube subtitles provide an approximation comparable to, and often better than, the best currently available resources. Moreover, they are available for languages for which a high-quality subtitle or speech corpus does not exist. We use YouTube subtitles to construct frequency norms for five diverse languages, Chinese, English, Indonesian, Japanese, and Spanish, and evaluate their correlation with lexical decision time, word familiarity, and lexical complexity. In addition to being strongly correlated with two psycholinguistic variables, a simple linear regression on the new frequencies achieves a new high score on a lexical complexity prediction task in English and Japanese, surpassing both models trained on film subtitle frequencies and the LLM GPT-4. Our code, the frequency lists, fastText word embeddings, and statistical language models are freely available at https://github.com/naist-nlp/tubelex.
Mamba in Vision: A Comprehensive Survey of Techniques and Applications
2024-10-04
Md Maklachur Rahman, Abdullah Aman Tutul, Ankur Nath, Lamyanba Laishram, Soon Ki Jung, Tracy Hammond
Mamba is emerging as a novel approach to overcome the challenges faced by Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in computer vision. While CNNs excel at extracting local features, they often struggle to capture long-range dependencies without complex architectural modifications. In contrast, ViTs effectively model global relationships but suffer from high computational costs due to the quadratic complexity of their self-attention mechanisms. Mamba addresses these limitations by leveraging Selective Structured State Space Models to effectively capture long-range dependencies with linear computational complexity. This survey analyzes the unique contributions, computational benefits, and applications of Mamba models while also identifying challenges and potential future research directions. We provide a foundational resource for advancing the understanding and growth of Mamba models in computer vision. An overview of this work is available at https://github.com/maklachur/Mamba-in-Computer-Vision.
Strategic Insights from Simulation Gaming of AI Race Dynamics
2024-10-04
Ross Gruetzemacher, Shahar Avin, James Fox, Alexander K Saeri
We present insights from "Intelligence Rising", a scenario exploration exercise about possible AI futures. Drawing on the experiences of facilitators who have overseen 43 games over a four-year period, we illuminate recurring patterns, strategies, and decision-making processes observed during gameplay. Our analysis reveals key strategic considerations about AI development trajectories in this simulated environment, including: the destabilising effects of AI races, the crucial role of international cooperation in mitigating catastrophic risks, the challenges of aligning corporate and national interests, and the potential for rapid, transformative change in AI capabilities. We highlight places where we believe the game has been effective in exposing participants to the complexities and uncertainties inherent in AI governance. Key recurring gameplay themes include the emergence of international agreements, challenges to the robustness of such agreements, the critical role of cybersecurity in AI development, and the potential for unexpected crises to dramatically alter AI trajectories. By documenting these insights, we aim to provide valuable foresight for policymakers, industry leaders, and researchers navigating the complex landscape of AI development and governance.
Diffusion models have recently been successfully applied to a wide range of robotics applications for learning complex multi-modal behaviors from data. However, prior works have mostly been confined to single-robot and small-scale environments due to the high sample complexity of learning multi-robot diffusion models. In this paper, we propose a method for generating collision-free multi-robot trajectories that conform to underlying data distributions while using only single-robot data. Our algorithm, Multi-robot Multi-model planning Diffusion (MMD), does so by combining learned diffusion models with classical search-based techniques -- generating data-driven motions under collision constraints. Scaling further, we show how to compose multiple diffusion models to plan in large environments where a single diffusion model fails to generalize well. We demonstrate the effectiveness of our approach in planning for dozens of robots in a variety of simulated scenarios motivated by logistics environments. View video demonstrations in our supplementary material, and our code at: https://github.com/yoraish/mmd.
Survey on Code Generation for Low resource and Domain Specific Programming Languages
2024-10-04
Sathvik Joel, Jie JW Wu, Fatemeh H. Fard
Large Language Models (LLMs) have shown impressive capabilities in code generation for popular programming languages. However, their performance on Low-Resource Programming Languages (LRPLs) and Domain-Specific Languages (DSLs) remains a significant challenge, affecting millions of developers-3.5 million users in Rust alone-who cannot fully utilize LLM capabilities. LRPLs and DSLs encounter unique obstacles, including data scarcity and, for DSLs, specialized syntax that is poorly represented in general-purpose datasets. Addressing these challenges is crucial, as LRPLs and DSLs enhance development efficiency in specialized domains, such as finance and science. While several surveys discuss LLMs in software engineering, none focus specifically on the challenges and opportunities associated with LRPLs and DSLs. Our survey fills this gap by systematically reviewing the current state, methodologies, and challenges in leveraging LLMs for code generation in these languages. We filtered 111 papers from over 27,000 published studies between 2020 and 2024 to evaluate the capabilities and limitations of LLMs in LRPLs and DSLs. We report the LLMs used, benchmarks, and metrics for evaluation, strategies for enhancing performance, and methods for dataset collection and curation. We identified four main evaluation techniques and several metrics for assessing code generation in LRPLs and DSLs. Our analysis categorizes improvement methods into six groups and summarizes novel architectures proposed by researchers. Despite various techniques and metrics, a standard approach and benchmark dataset for evaluating code generation in LRPLs and DSLs are lacking. This survey serves as a resource for researchers and practitioners at the intersection of LLMs, software engineering, and specialized programming languages, laying the groundwork for future advancements in code generation for LRPLs and DSLs.
Time-Domain And MultiMessenger Astrophysics Communications Science Analysis Group Report
2024-10-04
Jamie A. Kennea, Judith L. Racusin, Eric Burns, Brian W. Grefenstettte, Rebekah A. Hounsell, C. Michelle Hui, Daniel Kocevski, T. Joseph W. Lazio, Stephen Lesage, Tyler A. Pritchard, Aaron Tohuvavohu, John A. Tomsick, David Traore, Colleen A. Wilson-Hodge
The Time-Domain And MultiMessenger (TDAMM) Communications Science Analysis Group (TDAMMCommSAG) was formulated to describe the unique technical challenges of communicating rapidly to and from NASA astrophysics missions studying the most variable, transient, and extreme objects in the Universe. This report describes the study of if and how the transition from current NASA-operated space and ground relays to commercial services will adequately serve these missions. Depending on the individual mission requirements and Concept of Operations (ConOps), TDAMM missions may utilize a rapid low-rate demand access service, a low-rate continuous contact service, low-latency downlink upon demand, or a higher-latency but regular relay service. The specific implementations can vary via space relay or direct to Earth, but requires flexibility and adaptability using modern software infrastructure. The study team reviewed the current state of NASA communications services and future commercial and NASA communications services under study and in development. We explored the communications capabilities driving from the behavior of the astrophysical objects themselves.
Parton distribution functions (DFs) have long been recognised as key measures of hadron structure. Today, theoretical prediction of such DFs is becoming possible using diverse methods for continuum and lattice analyses of strong interaction (QCD) matrix elements. Recent developments include a demonstration that continuum and lattice analyses yield mutually consistent results for all pion DFs, with behaviour on the far valence domain of light-front momentum fraction that matches QCD expectations. Theory is also delivering an understanding of the distributions of proton mass and spin amongst its constituents, which varies, of course, with the resolving scale of the measuring probe. Aspects of these developments are sketched herein.
X-ray view of a massive node of the Cosmic Web at z~3 I. An exceptional overdensity of rapidly accreting SMBHs
2024-10-04
A. Travascio, S. Cantalupo, P. Tozzi, F. Vito, A. Paggi, G. Pezzulli, M. Elvis, G. Fabbiano, F. Fiore, M. Fossati, A. Fresco, M. Fumagalli, M. Galbiati, T. Lazeyras, N. Ledos, M. Pannella, A. Pensabene, G. Quadri, W. Wang
Exploring SMBH population in protoclusters offers valuable insights into how environment affects SMBH growth. However, research on AGN within these areas is still limited by the small number of protoclusters known at high redshift and by the availability of associated deep X-ray observations. To understand how different environments affect AGN triggering and growth at high redshift, we investigated the X-ray AGN population in the field of the MUSE Quasar Nebula 01 (MQN01) protocluster at z ~3.25. This field is known for hosting the largest Lya nebula in the Borisova+16 sample, and one of the largest overdensities of UV-continuum selected and sub-mm galaxies found so far at this redshift. We conducted a ultra deep Chandra X-ray survey (634 ks) observation of the MQN01 field and produced a comparative analyses of the properties of the X-ray AGNs detected in MQN01 against those observed in other selected protoclusters, such as Spiderweb and SSA22. By combining the X-ray, deep MUSE and ALMA data of the same field, we identified six X-ray AGNs within a volume of 16 cMpc^2 and \pm 1000 km/s, corresponding to an X-ray AGN overdensity of ~1000. This overdensity increases at the bright end, exceeding what was observed in the Spiderweb and SSA22 within similar volumes. The AGN fraction measured in MQN01 is significantly higher (f_AGN > 20%) than in the field and increases with stellar masses, reaching a value of 100% for log(M*/Msun) > 10.5. Lastly, we observe that the average specific accretion rate (\lambda_sBHAR) for SMBH populations in MQN01 is higher than in the field and other protoclusters, generally increasing as one moves toward the center of the overdensity. Our results, especially the large fraction of highly accreting SMBHs in the inner regions of the MQN01 overdensity, suggest that protocluster environments offer ideal physical conditions for SMBH triggering and growth.
We introduce a novel, general-purpose audio generation framework specifically designed for anomaly detection and localization. Unlike existing datasets that predominantly focus on industrial and machine-related sounds, our framework focuses a broader range of environments, particularly useful in real-world scenarios where only audio data are available, such as in video-derived or telephonic audio. To generate such data, we propose a new method inspired by the LLM-Modulo framework, which leverages large language models(LLMs) as world models to simulate such real-world scenarios. This tool is modular allowing a plug-and-play approach. It operates by first using LLMs to predict plausible real-world scenarios. An LLM further extracts the constituent sounds, the order and the way in which these should be merged to create coherent wholes. Much like the LLM-Modulo framework, we include rigorous verification of each output stage, ensuring the reliability of the generated data. The data produced using the framework serves as a benchmark for anomaly detection applications, potentially enhancing the performance of models trained on audio data, particularly in handling out-of-distribution cases. Our contributions thus fill a critical void in audio anomaly detection resources and provide a scalable tool for generating diverse, realistic audio data.
2024-10-04
Manish Jha, Jialin Qian, Michael Weber, Baozhong Yang
We use generative AI to extract managerial expectations about their economic outlook from over 120,000 corporate conference call transcripts. The overall measure, AI Economy Score, robustly predicts future economic indicators such as GDP growth, production, and employment, both in the short term and to 10 quarters. This predictive power is incremental to that of existing measures, including survey forecasts. Moreover, industry and firm-level measures provide valuable information about sector-specific and individual firm activities. Our findings suggest that managerial expectations carry unique insights about economic activities, with implications for both macroeconomic and microeconomic decision-making.
Demystifying Technology for Policymaking: Exploring the Rideshare Context and Data Initiative Opportunities to Advance Tech Policymaking Efforts
2024-10-04
Angie Zhang
In the face of rapidly advancing technologies, evidence of harms they can exacerbate, and insufficient policy to ensure accountability from tech companies, what are HCI opportunities for advancing policymaking of technology? In this paper, we explore challenges and opportunities for tech policymaking through a case study of app-based rideshare driving. We begin with background on rideshare platforms and how they operate. Next, we review literature on algorithmic management about how rideshare drivers actually experience platform features -- often to the detriment of their well-being -- and ways they respond. In light of this, researchers and advocates have called for increased worker protections, thus we turn to rideshare policy and regulation efforts in the U.S. Here, we differentiate the political strategies of platforms with those of drivers to illustrate the conflicting narratives policymakers face when trying to oversee gig work platforms. We reflect that past methods surfacing drivers' experiences may be insufficient for policymaker needs when developing oversight. To address this gap and our original inquiry -- what are HCI opportunities for advancing tech policymaking -- we briefly explore two paths forward for holding tech companies accountable in the rideshare context: (1) data transparency initiatives to enable collective auditing by workers and (2) legal frameworks for holding platforms accountable.
Safe Reference Tracking and Collision Avoidance for Taxiing Aircraft Using an MPC-CBF Framework
2024-10-04
Brooks A. Butler, Zarif Cabrera, Andy Nguyen, Philip E. Paré
In this paper, we develop a framework for the automatic taxiing of aircraft between hangar and take-off given a graph-based model of an airport. We implement a high-level path-planning algorithm that models taxiway intersections as nodes in an undirected graph, algorithmically constructs a directed graph according to the physical limitations of the aircraft, and finds the shortest valid taxi path through the directed graph using Dijkstra's algorithm. We then use this shortest path to construct a reference trajectory for the aircraft to follow that considers the turning capabilities of a given aircraft. Using high-order control barrier functions (HOCBFs), we construct safety conditions for multi-obstacle avoidance and safe reference tracking for simple 2D unicycle dynamics with acceleration control inputs. We then use these safety conditions to design an MPC-CBF framework that tracks the reference trajectory while adhering to the safety constraints. We compare the performance of our MPC-CBF controller with a PID-CBF control method via simulations.
Collaborative Safety-Critical Formation Control with Obstacle Avoidance
2024-10-04
Brooks A. Butler, Chi Ho Leung, Philip E. Paré
This work explores a collaborative method for ensuring safety in multi-agent formation control problems. We formulate a control barrier function (CBF) based safety filter control law for a generic distributed formation controller and extend our previously developed collaborative safety framework to an obstacle avoidance problem for agents with acceleration control inputs. We then incorporate multi-obstacle collision avoidance into the collaborative safety framework. This framework includes a method for computing the maximum capability of agents to satisfy their individual safety requirements. We analyze the convergence rate of our collaborative safety algorithm, and prove the linear-time convergence of cooperating agents to a jointly feasible safe action for all agents under the special case of a tree-structured communication network with a single obstacle for each agent. We illustrate the analytical results via simulation on a mass-spring kinematics-based formation controller and demonstrate the finite-time convergence of the collaborative safety algorithm in the simple proven case, the more general case of a fully-connected system with multiple static obstacles, and with dynamic obstacles.
Can Language Models Reason about Individualistic Human Values and Preferences?
2024-10-04
Liwei Jiang, Taylor Sorensen, Sydney Levine, Yejin Choi
Recent calls for pluralistic alignment emphasize that AI systems should address the diverse needs of all people. Yet, efforts in this space often require sorting people into fixed buckets of pre-specified diversity-defining dimensions (e.g., demographics, personalities, communication styles), risking smoothing out or even stereotyping the rich spectrum of individualistic variations. To achieve an authentic representation of diversity that respects individuality, we propose individualistic alignment. While individualistic alignment can take various forms, in this paper, we introduce IndieValueCatalog, a dataset transformed from the influential World Values Survey (WVS), to study language models (LMs) on the specific challenge of individualistic value reasoning. Specifically, given a sample of an individual's value-expressing statements, models are tasked with predicting their value judgments in novel cases. With IndieValueCatalog, we reveal critical limitations in frontier LMs' abilities to reason about individualistic human values with accuracies, only ranging between 55% to 65%. Moreover, our results highlight that a precise description of individualistic values cannot be approximated only via demographic information. We also identify a partiality of LMs in reasoning about global individualistic values, as measured by our proposed Value Inequity Index ({\sigma}INEQUITY). Finally, we train a series of Individualistic Value Reasoners (IndieValueReasoner) using IndieValueCatalog to enhance models' individualistic value reasoning capability, revealing new patterns and dynamics into global human values. We outline future research challenges and opportunities for advancing individualistic alignment.
A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research
2024-10-04
Teresa Salazar, Helder Araújo, Alberto Cano, Pedro Henriques Abreu
Group fairness in machine learning is a critical area of research focused on achieving equitable outcomes across different groups defined by sensitive attributes such as race or gender. Federated learning, a decentralized approach to training machine learning models across multiple devices or organizations without sharing raw data, amplifies the need for fairness due to the heterogeneous data distributions across clients, which can exacerbate biases. The intersection of federated learning and group fairness has attracted significant interest, with 47 research works specifically dedicated to addressing this issue. However, no dedicated survey has focused comprehensively on group fairness in federated learning. In this work, we present an in-depth survey on this topic, addressing the critical challenges and reviewing related works in the field. We create a novel taxonomy of these approaches based on key criteria such as data partitioning, location, and applied strategies. Additionally, we explore broader concerns related to this problem and investigate how different approaches handle the complexities of various sensitive groups and their intersections. Finally, we review the datasets and applications commonly used in current research. We conclude by highlighting key areas for future research, emphasizing the need for more methods to address the complexities of achieving group fairness in federated systems.
A Quasar-Anchored Protocluster at z=6.6 in the ASPIRE Survey: II. An Environmental Analysis of Galaxy Properties in an Overdense Structure
2024-10-04
Jaclyn B. Champagne, Feige Wang, Jinyi Yang, Xiaohui Fan, Joseph F. Hennawi, Fengwu Sun, Eduardo Bañados, Sarah E. I. Bosman, Tiago Costa, Melanie Habouzit, Xiangyu Jin, Hyunsung D. Jun, Mingyu Li, Weizhe Liu, Federica Loiacono, Alessandro Lupi, Chiara Mazzucchelli, Maria Pudoka, Sofia Rojas-Ruiz, Wei Leong Tee, Maxime Trebitsch, Haowen Zhang, Ming-Yang Zhuang, Siwei Zou
We present paper II comprising a 35 arcmin$^2$ JWST/NIRCam imaging and wide-field slitless spectroscopy mosaic centered on J0305$-$3150, a luminous quasar at $z=6.61$. The F356W grism data reveals 124 [OIII]+H$\beta$ emitters at $5.3
A Quasar-Anchored Protocluster at z=6.6 in the ASPIRE Survey: I. Properties of [OIII] Emitters in a 10 Mpc Overdensity Structure
2024-10-04
Jaclyn B. Champagne, Feige Wang, Haowen Zhang, Jinyi Yang, Xiaohui Fan, Joseph F. Hennawi, Fengwu Sun, Eduardo Bañados, Sarah E. I. Bosman, Tiago Costa, Anna-Christina Eilers, Ryan Endsley, Xiangyu Jin, Hyunsung D. Jun, Mingyu Li, Xiaojing Lin, Weizhe Liu, Federica Loiacono, Alessandro Lupi, Chiara Mazzucchelli, Maria Pudoka, Klaudia Protušovà, Sofia Rojas-Ruiz, Wei Leong Tee, Maxime Trebitsch, Bram P. Venemans, Ming-Yang Zhuang, Siwei Zou
ASPIRE (A SPectroscopic survey of bIased halos in the Reionization Era) is a quasar legacy survey primarily using JWST to target a sample of 25 $z>6$ quasars with NIRCam slitless spectroscopy and imaging. The first study in this series found evidence of a strong overdensity of galaxies around J0305-3150, a luminous quasar at $z=6.61$, within a single NIRCam pointing obtained in JWST Cycle 1. Here, we present the first results of a JWST Cycle 2 mosaic that covers 35 arcmin$^2$ with NIRCam imaging/WFSS of the same field to investigate the spatial extent of the putative protocluster. The F356W grism data targets [OIII]+H$\beta$ at $5.310$ cMpc.
Baryonic Ecosystem in Galaxies (BEINGMgII) -- II. Unveiling the Nature of Galaxies Harboring Cool Gas Reservoirs
2024-10-04
S. Das, R. Joshi, R. Chaudhary, M. Fumagalli, M. Fossati, C. Péroux, Luis C. Ho
We search for the galaxies associated with the intervening MgII absorbers over a redshift range of $0.4 \le z \le 1.0$ using imaging data from DESI Legacy Imaging Surveys and measure the redshift based on direct detection of nebular emission in the background quasar spectra from SDSS survey. We find 270 MgII absorbers associated with strong [O II] nebular emissions, at $2.5\sigma$ level. Among them, for 213 MgII absorbers, we detect an absorber host galaxy at impact parameters of $4 \le \rho \le 16$ kpc, including a galaxy pair associated with three absorbers, with best-fit galaxy SED model based on multi-passband photometric data from DESI Legacy Imaging surveys, supplemented with the infrared VISTA and unWISE imaging surveys. The detection rate of MgII absorber host with strong [O II] nebular emission in the finite SDSS fiber of 2--3 arcsec diameter increases from 0.2% to $\sim$3% with increasing equivalent width from $0.3$\r{A} to $\sim$ 3.5\r{A}, which remains near-constant across the probed redshift range. The associated MgII host galaxies exhibit a wide range of stellar mass from 7.94 $\le \rm log(M_{\star}/M_{\odot}) \le $ 11.11, with an average star-formation rate (SFR) of $ 5.81 \rm M_{\odot}\ yr^{-1}$. The MgII absorber hosts selected based on [O II] nebular emission mostly exhibit active star-forming systems including 13.4% starburst systems, but 10.2% with suppressed SFR. The near-constant absorption strength at low-impact parameters suggests a high gas covering fraction. We find that the MgII equivalent width ($W_{2796}$) positively correlates with SFR and specific-SFR, likely indicate their wind origin. The average velocity offset between the host and absorber suggests that the MgII gas is bound within the dark matter halo.
Comparative Survey of Cyber-Threat and Attack Trends and Prediction of Future Cyber-Attack Patterns
2024-10-04
Uwazie Emmanuel Chinanu, Oluyemi Amujo
This paper presents a comparative survey of cyberthreat and attack trends starting from 2010 till date Cyber security breaches are constantly on the rise with huge uncertainty and risks The trend is causing rife globally because of its consequences to national security and economy With diverse interests and motivations for various categories of threats and attacks we carried out a comparative survey and analysis of security breaches to unravel the patterns and predict what will shape future security challenges The diversity of attacks and growing state actors involvement without any sort of regulation is making cyber weapons attractive to the states States are leveraging the anonymity and attribution flaws to hit hard on perceived adversaries thereby complicating the cyber security equation
Extinction of Taurus, Orion, Perseus and California Molecular Clouds Based on the LAMOST, 2MASS, and Gaia Surveys II: The Extinction Law
2024-10-03
Zhetai Cao, Biwei Jiang, Shu Wang, Jun Li
The extinction law from ultraviolet (UV) to infrared (IR) (0.2-24 $\mu$m) is determined by relying on the blue-edge method and color excess ratios for some nearby molecular clouds, from low mass star forming region to massive star forming region. The observational data are collected from nine photometric surveys, along with stellar parameters from the APOGEE and LAMOST spectroscopic surveys. Within the uncertainties, the optical ratio of selective to total extinction ($R_{G_{BP}}$) does not vary substantially across the clouds, irrespective of the density, specifically $R_{G_{BP}} =2.302\pm0.027$, where $R_{G_{BP}} \equiv A_{G_{BP}}/E_{G_{BP},G_{RP}}$. The IR extinction law is consistent with \citet{Wang19_law}. The extinction law in the UV band is compromised by the shallow depth with $A_{V} \leq 2$ mag and is hard to describe by one parameter $R$. In addition, the extinction in the WISE/W1 band is significantly larger than in the Spitzer/IRAC1 band in the dense regions, which is attributed to the ice water absorption.
DailyDilemmas: Revealing Value Preferences of LLMs with Quandaries of Daily Life
2024-10-03
Yu Ying Chiu, Liwei Jiang, Yejin Choi
As we increasingly seek guidance from LLMs for decision-making in daily life, many of these decisions are not clear-cut and depend significantly on the personal values and ethical standards of the users. We present DailyDilemmas, a dataset of 1,360 moral dilemmas encountered in everyday life. Each dilemma includes two possible actions and with each action, the affected parties and human values invoked. Based on these dilemmas, we consolidated a set of human values across everyday topics e.g., interpersonal relationships, workplace, and environmental issues. We evaluated LLMs on these dilemmas to determine what action they will take and the values represented by these actions. Then, we analyzed these values through the lens of five popular theories inspired by sociology, psychology and philosophy. These theories are: World Value Survey, Moral Foundation Theory, Maslow's Hierarchy of Needs, Aristotle's Virtues, and Plutchik Wheel of Emotion. We find that LLMs are most aligned with the self-expression over survival values in terms of World Value Survey, care over loyalty in Moral Foundation Theory. Interestingly, we find large preferences differences in models for some core values such as truthfulness e.g., Mixtral-8x7B model tends to neglect it by 9.7% while GPT-4-turbo model tends to select it by 9.4%. We also study the recent guidance released by OpenAI (ModelSpec), and Anthropic (Constitutional AI) to understand how their released principles reflect their actual value prioritization when facing nuanced moral reasoning in daily-life settings. We find that end users cannot effectively steer such prioritization using system prompts.
CulturalBench: a Robust, Diverse and Challenging Benchmark on Measuring the (Lack of) Cultural Knowledge of LLMs
2024-10-03
Yu Ying Chiu, Liwei Jiang, Bill Yuchen Lin, Chan Young Park, Shuyue Stella Li, Sahithya Ravi, Mehar Bhatia, Maria Antoniak, Yulia Tsvetkov, Vered Shwartz, Yejin Choi
To make large language models (LLMs) more helpful across diverse cultures, it is essential to have effective cultural knowledge benchmarks to measure and track our progress. Effective benchmarks need to be robust, diverse, and challenging. We introduce CulturalBench: a set of 1,227 human-written and human-verified questions for effectively assessing LLMs' cultural knowledge, covering 45 global regions including the underrepresented ones like Bangladesh, Zimbabwe, and Peru. Questions - each verified by five independent annotators - span 17 diverse topics ranging from food preferences to greeting etiquettes. We evaluate models on two setups: CulturalBench-Easy and CulturalBench-Hard which share the same questions but asked differently. We find that LLMs are sensitive to such difference in setups (e.g., GPT-4o with 27.3% difference). Compared to human performance (92.6% accuracy), CulturalBench-Hard is more challenging for frontier LLMs with the best performing model (GPT-4o) at only 61.5% and the worst (Llama3-8b) at 21.4%. Moreover, we find that LLMs often struggle with tricky questions that have multiple correct answers (e.g., What utensils do the Chinese usually use?), revealing a tendency to converge to a single answer. Our results also indicate that OpenAI GPT-4o substantially outperform other proprietary and open source models in questions related to all but one region (Oceania). Nonetheless, all models consistently underperform on questions related to South America and the Middle East.
Magnetic evolution of active regions: formation and eruption of magnetic flux ropes
2024-10-03
P. Vemareddy
Magnetic flux ropes (FRs) are twisted structures appearing on the sun, predominantly in the magnetically concentrated regions. These structures appear as coronal features known as filaments or prominences in H$\alpha$ observations, and as sigmoids in X-ray, EUV observations. Using the continuous vector magnetic field observations from \textit{Helioseismic and Magnetic Imager} onboard \textit{Solar Dynamics Observatory}, we study the evolution of the magnetic fields in the active regions (ARs) to understand the conditions of twisted flux formation. While ARs emerge and evolve further, flux motions such as shearing and rotation are efficient mechanisms to form twisted flux ropes. Magnetic helicity quantifies the twisted magnetic fields and helicity injection through photosphere leads to its accumulation in the corona. Therefore, coronal helicity accumulation leads to twisted FR formation and its eruption. The magnetic helicity injection is seen to evolve distinctly in the regions of flux rope formation and eruption. The ARs that are associated with eruptive activity are observed with helicity injection predominantly with one sign over a period of a few days. The ARs that inject helicity with a changing sign are unlikely to form twisted FRs because coronal helicity during the period of one sign of injected helicity gets cancelled by the opposite sign of injection in the later period. As a result, the coronal field reconfigures from shared to potential structure. For a given AR, the upper limit of helicity that could cause a CME eruption is not yet understood, which can be the subject of future studies of ARs. Magnetic reconnection plays a crucial role in both the initiation and driving of FR eruptions after their formation. Data-driven simulations of the AR evolution provide more insights on the flux rope formation and its onset of eruption.
Undesirable Memorization in Large Language Models: A Survey
2024-10-03
Ali Satvaty, Suzan Verberne, Fatih Turkmen
While recent research increasingly showcases the remarkable capabilities of Large Language Models (LLMs), it's vital to confront their hidden pitfalls. Among these challenges, the issue of memorization stands out, posing significant ethical and legal risks. In this paper, we presents a Systematization of Knowledge (SoK) on the topic of memorization in LLMs. Memorization is the effect that a model tends to store and reproduce phrases or passages from the training data and has been shown to be the fundamental issue to various privacy and security attacks against LLMs. We begin by providing an overview of the literature on the memorization, exploring it across five key dimensions: intentionality, degree, retrievability, abstraction, and transparency. Next, we discuss the metrics and methods used to measure memorization, followed by an analysis of the factors that contribute to memorization phenomenon. We then examine how memorization manifests itself in specific model architectures and explore strategies for mitigating these effects. We conclude our overview by identifying potential research topics for the near future: to develop methods for balancing performance and privacy in LLMs, and the analysis of memorization in specific contexts, including conversational agents, retrieval-augmented generation, multilingual language models, and diffusion language models.
Accurate 3D object detection in real-world environments requires a huge amount of annotated data with high quality. Acquiring such data is tedious and expensive, and often needs repeated effort when a new sensor is adopted or when the detector is deployed in a new environment. We investigate a new scenario to construct 3D object detectors: learning from the predictions of a nearby unit that is equipped with an accurate detector. For example, when a self-driving car enters a new area, it may learn from other traffic participants whose detectors have been optimized for that area. This setting is label-efficient, sensor-agnostic, and communication-efficient: nearby units only need to share the predictions with the ego agent (e.g., car). Naively using the received predictions as ground-truths to train the detector for the ego car, however, leads to inferior performance. We systematically study the problem and identify viewpoint mismatches and mislocalization (due to synchronization and GPS errors) as the main causes, which unavoidably result in false positives, false negatives, and inaccurate pseudo labels. We propose a distance-based curriculum, first learning from closer units with similar viewpoints and subsequently improving the quality of other units' predictions via self-training. We further demonstrate that an effective pseudo label refinement module can be trained with a handful of annotated data, largely reducing the data quantity necessary to train an object detector. We validate our approach on the recently released real-world collaborative driving dataset, using reference cars' predictions as pseudo labels for the ego car. Extensive experiments including several scenarios (e.g., different sensors, detectors, and domains) demonstrate the effectiveness of our approach toward label-efficient learning of 3D perception from other units' predictions.
Labor Migration Modeling through Large-scale Job Query Data
2024-10-03
Zhuoning Guo, Le Zhang, Hengshu Zhu, Weijia Zhang, Hui Xiong, Hao Liu
Accurate and timely modeling of labor migration is crucial for various urban governance and commercial tasks, such as local policy-making and business site selection. However, existing studies on labor migration largely rely on limited survey data with statistical methods, which fail to deliver timely and fine-grained insights for time-varying regional trends. To this end, we propose a deep learning-based spatial-temporal labor migration analysis framework, DHG-SIL, by leveraging large-scale job query data. Specifically, we first acquire labor migration intention as a proxy of labor migration via job queries from one of the world's largest search engines. Then, a Disprepant Homophily co-preserved Graph Convolutional Network (DH-GCN) and an interpretable temporal module are respectively proposed to capture cross-city and sequential labor migration dependencies. Besides, we introduce four interpretable variables to quantify city migration properties, which are co-optimized with city representations via tailor-designed contrastive losses. Extensive experiments on three real-world datasets demonstrate the superiority of our DHG-SIL. Notably, DHG-SIL has been deployed as a core component of a cooperative partner's intelligent human resource system, and the system supported a series of city talent attraction reports.
Quantum many-body solver using artificial neural networks and its applications to strongly correlated electron systems
2024-10-03
Yusuke Nomura, Masatoshi Imada
With the evolution of numerical methods, we are now aiming at not only qualitative understanding but also quantitative prediction and design of quantum many-body phenomena. As a novel numerical approach, machine learning techniques have been introduced in 2017 to analyze quantum many-body problems. Since then, proposed various novel approaches have opened a new era, in which challenging and fundamental problems in physics can be solved by machine learning methods. Especially, quantitative and accurate estimates of material-dependent physical properties of strongly correlated matter have now become realized by combining first-principles calculations with highly accurate quantum many-body solvers developed with the help of machine learning methods. Thus developed quantitative description of electron correlations will constitute a key element of materials science in the next generation.
On the nature of the missing mass of galaxy clusters in MOND: the view from gravitational lensing
2024-10-03
Benoit Famaey, Lorenzo Pizzuti, Ippocratis D. Saltas
Modified Newtonian Dynamics (MOND) has long been known to fail in galaxy clusters, implying a residual missing mass problem for clusters in this context. Here, using mass profiles derived from strong- and weak-lensing shear, as well as magnification data, for a sample of clusters from the CLASH survey, we characterize the density profile of this residual MOND missing mass in the central Mpc of galaxy clusters. In line with results obtained in the literature from the hydrostatic equilibrium of hot gas, we find that an inner constant density core and an outer power-law slope between $-4$ and $-6$ provides a good description within $\sim 1$ Mpc. We also show that the data in the central parts of clusters can be even better represented by a `dark mass-follows-gas' profile with an exponential cut-off. Clusters with X-ray emitting gas masses $M_\text{gas} \geq 10^{14}\, {\rm M}_\odot$ display a remarkable uniformity for the missing-to-hot-gas density ratio in the inner parts, of order $\sim$10, and for the exponential cut-off radius, of order $\sim 450$ kpc. Clusters with lower observed gas mass display larger and more scattered values for both the density ratio and cut-off radius. These lensing results can in principle serve as a crucible for relativistic theories of MOND in galaxy clusters, or for any other tentative hypothesis regarding the nature of the clusters residual missing mass in the MOND context.
Agents' Room: Narrative Generation through Multi-step Collaboration
2024-10-03
Fantine Huot, Reinald Kim Amplayo, Jennimaria Palomaki, Alice Shoshana Jakobovits, Elizabeth Clark, Mirella Lapata
Writing compelling fiction is a multifaceted process combining elements such as crafting a plot, developing interesting characters, and using evocative language. While large language models (LLMs) show promise for story writing, they currently rely heavily on intricate prompting, which limits their use. We propose Agents' Room, a generation framework inspired by narrative theory, that decomposes narrative writing into subtasks tackled by specialized agents. To illustrate our method, we introduce Tell Me A Story, a high-quality dataset of complex writing prompts and human-written stories, and a novel evaluation framework designed specifically for assessing long narratives. We show that Agents' Room generates stories that are preferred by expert evaluators over those produced by baseline systems by leveraging collaboration and specialization to decompose the complex story writing task into tractable components. We provide extensive analysis with automated and human-based metrics of the generated output.
LMC Calls, Milky Way Halo Answers: Disentangling the Effects of the MW--LMC Interaction on Stellar Stream Populations
2024-10-03
Richard A. N. Brooks, Nicolás Garavito-Camargo, Kathryn V. Johnston, Adrian M. Price-Whelan, Jason L. Sanders, Sophia Lilleengen
The infall of the LMC into the Milky Way (MW) has dynamical implications throughout the MW's dark matter halo. We study the impact of this merger on the statistical properties of populations of simulated stellar streams. Specifically, we investigate the radial and on-sky angular dependence of stream perturbations caused by the direct effect of stream-LMC interactions and/or the response of the MW dark matter halo. We use a time-evolving MW--LMC simulation described by basis function expansions to simulate streams. We quantify the degree of perturbation using a set of stream property statistics including the misalignment of proper motions with the stream track. In the outer halo, direct stream--LMC interactions produce a statistically significant effect, boosting the fraction of misaligned proper motions by ~25% compared to the model with no LMC. Moreover, there is on-sky angular dependence of stream perturbations:~the highest fractions of perturbed streams coincide with the same on-sky quadrant as the present-day LMC location. In the inner halo, the MW halo dipole response primarily drives stream perturbations, but it remains uncertain whether this is a detectable signature distinct from the LMC's influence. For the fiducial MW--LMC model, we find agreement between the predicted fraction of streams with significantly misaligned proper motions, $\bar{\vartheta}>10^{\circ}$, and Dark Energy Survey data. Finally, we predict this fraction for the Vera Rubin Large Synoptic Survey Telescope (LSST) footprint. Using LSST data will improve our constraints on dark matter models and LMC properties as it is sensitive to both.
Machine Learning Approaches for Active Queue Management: A Survey, Taxonomy, and Future Directions
2024-10-03
Mohammad Parsa Toopchinezhad, Mahmood Ahmadi
Active Queue Management (AQM), a network-layer congestion control technique endorsed by the Internet Engineering Task Force (IETF), encourages routers to discard packets before the occurrence of buffer overflow. Traditional AQM techniques often employ heuristic approaches that require meticulous parameter adjustments, limiting their real-world applicability. In contrast, Machine Learning (ML) approaches offer highly adaptive, data-driven solutions custom to dynamic network conditions. Consequently, many researchers have adapted ML for AQM throughout the years, resulting in a wide variety of algorithms ranging from predicting congestion via supervised learning to discovering optimal packet-dropping policies with reinforcement learning. Despite these remarkable advancements, no previous work has compiled these methods in the form of a survey article. This paper presents the first thorough documentation and analysis of ML-based algorithms for AQM, in which the strengths and limitations of each proposed method are evaluated and compared. In addition, a novel taxonomy of ML approaches based on methodology is also established. The review is concluded by discussing unexplored research gaps and potential new directions for more robust ML-AQM methods.
A Foundation Model for the Solar Dynamics Observatory
2024-10-03
James Walsh, Daniel G. Gass, Raul Ramos Pollan, Paul J. Wright, Richard Galvez, Noah Kasmanoff, Jason Naradowsky, Anne Spalding, James Parr, Atılım Güneş Baydin
SDO-FM is a foundation model using data from NASA's Solar Dynamics Observatory (SDO) spacecraft; integrating three separate instruments to encapsulate the Sun's complex physical interactions into a multi-modal embedding space. This model can be used to streamline scientific investigations involving SDO by making the enormous datasets more computationally accessible for heliophysics research and enable investigations that require instrument fusion. We discuss four key components: an ingestion pipeline to create machine learning ready datasets, the model architecture and training approach, resultant embeddings and fine-tunable models, and finally downstream fine-tuned applications. A key component of this effort has been to include subject matter specialists at each stage of development; reviewing the scientific value and providing guidance for model architecture, dataset, and training paradigm decisions. This paper marks release of our pretrained models and embedding datasets, available to the community on Hugging Face and sdofm.org.
Determining the spatial curvature ($\Omega_k$) independent of cosmic microwave background observations plays a key role in revealing the physics of the early universe. The Hubble tension is one of the most serious issues in modern cosmology. We investigate halo catalogs identified from $N$-body simulations at $z=2$ and 3, mimicking high-redshift galaxy surveys. We measure redshift-space correlation functions of halos from the two snapshots. We detect clear features of baryon acoustic oscillations and redshift-space distortions. We find that we can obtain a few percent constraints on both the geometric distances and growth of structure at the distant universe in future surveys. By taking into account the information of the underlying matter power spectrum, we demonstrate that we can also achieve constraint on the Hubble constant $H_0$ with a few percent as well as the spatial curvature with $|\Omega_k|\lesssim 0.1$ by observing galaxies with the number density with $\bar{n}_{\rm g}\simeq 10^{-4} (~h^3{\rm ~Mpc}^{-3})$. Our analysis provides a timely forecast for the upcoming spectroscopic surveys, which target emission line galaxy or dusty star-forming galaxy samples.
Defining Knowledge: Bridging Epistemology and Large Language Models
2024-10-03
Constanza Fierro, Ruchira Dhar, Filippos Stamatiou, Nicolas Garneau, Anders Søgaard
Knowledge claims are abundant in the literature on large language models (LLMs); but can we say that GPT-4 truly "knows" the Earth is round? To address this question, we review standard definitions of knowledge in epistemology and we formalize interpretations applicable to LLMs. In doing so, we identify inconsistencies and gaps in how current NLP research conceptualizes knowledge with respect to epistemological frameworks. Additionally, we conduct a survey of 100 professional philosophers and computer scientists to compare their preferences in knowledge definitions and their views on whether LLMs can really be said to know. Finally, we suggest evaluation protocols for testing knowledge in accordance to the most relevant definitions.
Optical galaxy cluster mock catalogs with realistic projection effects: validations with the SDSS redMaPPer clusters
2024-10-03
Andy Lee, Hao-Yi Wu, Andrés N. Salcedo, Tomomi Sunayama, Matteo Costanzi, Justin Myles, Shulei Cao, Eduardo Rozo, Chun-Hao To, David H. Weinberg, Lei Yang, Conghao Zhou
Galaxy clusters identified in optical imaging surveys suffer from projection effects: physically unassociated galaxies along a cluster's line of sight can be counted as its members and boost the observed richness (the number of cluster members). To model the impact projection on cluster cosmology analyses, we apply a halo occupation distribution model to N-body simulations to simulate the red galaxies contributing to cluster members, and we use the number of galaxies in a cylinder along the line-of-sight (counts-in-cylinders) to model the impact of projection on cluster richness. We compare three projection models: uniform, quadratic, and Gaussian, and we convert between them by matching their effective cylinder volumes. We validate our mock catalogs using SDSS redMaPPer data vectors, including cluster abundance vs. richness, stacked lensing signal, spectroscopic redshift distribution of member galaxies, and richness re-measured on a redshift grid. We find the former two are insensitive to the projection model, while the latter two favor a quadratic projection model with a width of 180 Mpc/h (equivalent to the volume of a uniform model with a width of 100 Mpc/h and a Gaussian model with a width of 110 Mpc/h, or a Gaussian redshift error of 0.04). Our framework provides an efficient and flexible way to model optical cluster data vectors, paving the way for a simulation-based joint analysis for clusters, galaxies, and shear.
Visual language tracking (VLT) has emerged as a cutting-edge research area, harnessing linguistic data to enhance algorithms with multi-modal inputs and broadening the scope of traditional single object tracking (SOT) to encompass video understanding applications. Despite this, most VLT benchmarks still depend on succinct, human-annotated text descriptions for each video. These descriptions often fall short in capturing the nuances of video content dynamics and lack stylistic variety in language, constrained by their uniform level of detail and a fixed annotation frequency. As a result, algorithms tend to default to a "memorize the answer" strategy, diverging from the core objective of achieving a deeper understanding of video content. Fortunately, the emergence of large language models (LLMs) has enabled the generation of diverse text. This work utilizes LLMs to generate varied semantic annotations (in terms of text lengths and granularities) for representative SOT benchmarks, thereby establishing a novel multi-modal benchmark. Specifically, we (1) propose a new visual language tracking benchmark with diverse texts, named DTVLT, based on five prominent VLT and SOT benchmarks, including three sub-tasks: short-term tracking, long-term tracking, and global instance tracking. (2) We offer four granularity texts in our benchmark, considering the extent and density of semantic information. We expect this multi-granular generation strategy to foster a favorable environment for VLT and video understanding research. (3) We conduct comprehensive experimental analyses on DTVLT, evaluating the impact of diverse text on tracking performance and hope the identified performance bottlenecks of existing algorithms can support further research in VLT and video understanding. The proposed benchmark, experimental results and toolkit will be released gradually on http://videocube.aitestunion.com/.
Rare Occasions: Tidal Disruption Events Rarely Power the AGNs Observed in Dwarf Galaxies
2024-10-03
Joanne Tan, Guang Yang, Jonelle L. Walsh, W. N. Brandt, Bin Luo, Franz E. Bauer, Chien-Ting Chen, Mouyuan Sun, Yongquan Xue
Tidal disruption events (TDEs) could be an important growth channel for massive black holes in dwarf galaxies. Theoretical work suggests that the observed active galactic nuclei (AGNs) in dwarf galaxies are predominantly TDE-powered. To assess this claim, we perform variability analyses on the dwarf-hosted AGNs detected in the $7$ Ms Chandra Deep Field-South (CDF-S) survey, with observations spanning $\approx 16$ years. Based on the spectral energy distribution (SED) modeling with X-CIGALE, we select AGNs hosted by dwarf galaxies (stellar mass below $10^{10}\ M_\odot$). We focus on X-ray sources with full-band detections, leading to a sample of $78$ AGNs (0.122 $\leq$ $z$ $\leq$ 3.515). We fit the X-ray light curves with a canonical TDE model of $t^{-5/3}$ and a constant model. If the former outperforms the latter in fitting quality for a source, we consider the source as a potential TDE. We identify five potential TDEs, constituting a small fraction of our sample. Using true- and false-positive rates obtained from fitting models to simulated light curves, we perform Bayesian analysis to obtain the posterior of the TDE fraction for our sample. The posterior peaks close to zero ($2.56\%$), and we obtain a $2$-$\sigma$ upper limit of $9.80\%$. Therefore, our result indicates that the observed AGNs in dwarf galaxies are not predominantly powered by TDEs.
Software practitioners often face unfairness in their work, such as unequal recognition of contributions, gender bias, and unclear criteria for performance reviews. While the link between fairness and job satisfaction has been established in other fields, its relevance to software professionals remains underexplored. This study aims to examine how fairness perceptions relate to job satisfaction among software practitioners, focusing on both general trends and demographic-specific differences. We conducted an online survey of 108 software practitioners, followed by ordinal logistic regression to analyze the relationship between fairness perceptions and job satisfaction in software engineering contexts, with moderation analysis examining how this relationship varies across demographic groups. Our findings indicate that all four fairness dimensions, distributive, procedural, interpersonal, and informational, significantly affect both overall job satisfaction and satisfaction with job security. Among these, interpersonal fairness has the biggest impact, being more than twice as influential on overall job satisfaction. The relationship between fairness perceptions and job satisfaction is notably stronger for female, ethnically underrepresented, less experienced practitioners, and those with work limitations. Fairness in authorship emerged as an important factor for job satisfaction collectively, while fairness in policy implementation, high-demand situations, and working hours particularly impacted specific demographic groups. This study highlights the unique role of fairness in software engineering, offering strategies for organizations to promote fair practices and targeted approaches specific for certain demographic groups.
Quantum Machine Learning for Digital Health? A Systematic Review
2024-10-03
Riddhi S. Gupta, Carolyn E. Wood, Teyl Engstrom, Jason D. Pole, Sally Shrapnel
With the digitization of health data, the growth of electronic health and medical records lowers barriers for using algorithmic techniques for data analysis. While classical machine learning techniques for health data approach commercialization, there is not yet clear evidence whether quantum machine learning (QML) will provide any empirical advantage for digital health data processing. In this systematic literature review we assess whether QML algorithms have the potential to outperform existing classical methods in efficacy or efficiency. We include digital electronic health/medical records (EH/MRs) and data considered to be a reasonable proxy to EH/MRs. Eligible QML algorithms must be designed for quantum computing hardware, as opposed to quantum-inspired techniques. PubMed, Embase, IEEE, Scopus and arXiv yielded 4915 studies between 2015 to 10 June 2024. After screening 169 eligible studies, most studies contained widespread technical misconceptions about QML and we excluded 123 studies for insufficient rigor in analysis. Of the remaining 46 studies, only 16 studies consider realistic QML operating conditions, either by testing algorithms on quantum hardware, or using noisy quantum circuits when assessing QML algorithms. We find QML applications in digital health focus primarily on clinical decision support rather than health service delivery or public health. Nearly all QML models are linear quantum models, and therefore represent a subset of general quantum algorithms. Meanwhile, novel data-encoding strategies do not address scalability issues, except in regimes requiring restrictive assumptions about quantum hardware, rendering these protocols inefficient for the general encoding of large health datasets. By establishing the current state of evidence for QML-based health applications, we pave the way for meaningful dialogue about QML use-case discovery in digital health.
Where's Ben Nevis? A 2D optimisation benchmark with 957,174 local optima based on Great Britain terrain data
2024-10-03
Yuhang Wei, Michael Clerx, Gary R. Mirams
We present a novel optimisation benchmark based on the real landscape of Great Britain (GB). The elevation data from the UK Ordnance Survey Terrain 50 dataset is slightly modified and linearly interpolated to produce a target function that simulates the GB terrain, packaged in a new Python module nevis. We introduce a discrete approach to classifying local optima and their corresponding basins of attraction, identifying 957,174 local optima of the target function. We then develop a benchmarking framework for optimisation methods based on this target function, where we propose a Generalised Expected Running Time performance measure to enable meaningful comparisons even when algorithms do not achieve successful runs (find Ben Nevis). Hyperparameter tuning is managed using the optuna framework, and plots and animations are produced to visualise algorithm performance. Using the proposed framework, we benchmark six optimisation algorithms implemented by common Python modules. Amongst those tested, the Differential Evolution algorithm implemented by scipy is the most effective for navigating the complex GB landscape and finding the summit of Ben Nevis.
A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond
2024-10-03
Shubhi Bansal, Sreeharish A, Madhava Prasath J, Manikandan S, Sreekanth Madisetty, Mohammad Zia Ur Rehman, Chandravardhan Singh Raghaw, Gaurav Duggal, Nagendra Kumar
Mamba, a special case of the State Space Model, is gaining popularity as an alternative to template-based deep learning approaches in medical image analysis. While transformers are powerful architectures, they have drawbacks, including quadratic computational complexity and an inability to address long-range dependencies efficiently. This limitation affects the analysis of large and complex datasets in medical imaging, where there are many spatial and temporal relationships. In contrast, Mamba offers benefits that make it well-suited for medical image analysis. It has linear time complexity, which is a significant improvement over transformers. Mamba processes longer sequences without attention mechanisms, enabling faster inference and requiring less memory. Mamba also demonstrates strong performance in merging multimodal data, improving diagnosis accuracy and patient outcomes. The organization of this paper allows readers to appreciate the capabilities of Mamba in medical imaging step by step. We begin by defining core concepts of SSMs and models, including S4, S5, and S6, followed by an exploration of Mamba architectures such as pure Mamba, U-Net variants, and hybrid models with convolutional neural networks, transformers, and Graph Neural Networks. We also cover Mamba optimizations, techniques and adaptations, scanning, datasets, applications, experimental results, and conclude with its challenges and future directions in medical imaging. This review aims to demonstrate the transformative potential of Mamba in overcoming existing barriers within medical imaging while paving the way for innovative advancements in the field. A comprehensive list of Mamba architectures applied in the medical field, reviewed in this work, is available at Github.
The increasing demand for transparent and reliable models, particularly in high-stakes decision-making areas such as medical image analysis, has led to the emergence of eXplainable Artificial Intelligence (XAI). Post-hoc XAI techniques, which aim to explain black-box models after training, have been controversial in recent works concerning their fidelity to the models' predictions. In contrast, Self-eXplainable AI (S-XAI) offers a compelling alternative by incorporating explainability directly into the training process of deep learning models. This approach allows models to generate inherent explanations that are closely aligned with their internal decision-making processes. Such enhanced transparency significantly supports the trustworthiness, robustness, and accountability of AI systems in real-world medical applications. To facilitate the development of S-XAI methods for medical image analysis, this survey presents an comprehensive review across various image modalities and clinical applications. It covers more than 200 papers from three key perspectives: 1) input explainability through the integration of explainable feature engineering and knowledge graph, 2) model explainability via attention-based learning, concept-based learning, and prototype-based learning, and 3) output explainability by providing counterfactual explanation and textual explanation. Additionally, this paper outlines the desired characteristics of explainability and existing evaluation methods for assessing explanation quality. Finally, it discusses the major challenges and future research directions in developing S-XAI for medical image analysis.
One-dimensional QCD at finite density and its 't Hooft-Veneziano limit
2024-10-03
O. Borisenko, V. Chelnokov, S. Voloshyn, P. Yefanov
An exact solution of one-dimensional lattice gauge theory at finite temperature and non-zero chemical potential is reviewed for the gauge groups $G=Z(N),U(N),SU(N)$ for all values of $N$ and the number of fermion flavors $N_f$. Calculated are the partition function, free energy, the Polyakov loop expectation values, baryon density, quark condensate, meson and baryon correlation functions. Detailed analysis of the exact solutions is done for $N=2,3$ with one and two fermion flavors. In the large $N_f$ limit we uncover the Roberge-Weiss phase transition and discuss its remnants at finite $N_f$. In the case of $N_f$ degenerate flavors we also calculate 1) the large $N$ limit, 2) the large $N_f$ limit and 3) the 't Hooft-Veneziano limit of all models. The critical behavior of the models in these limits is studied and the phase structure is described in details. A comparison of all limits with $U(3)$ and $SU(3)$ QCD is also performed. In order to achieve these results we explore several representations of the partition function of one-dimensional QCD obtained and described in the text.
Computer-aided Colorization State-of-the-science: A Survey
2024-10-03
Yu Cao, Xin Duan, Xiangqiao Meng, P. Y. Mok, Ping Li, Tong-Yee Lee
This paper reviews published research in the field of computer-aided colorization technology. We argue that the colorization task originates from computer graphics, prospers by introducing computer vision, and tends to the fusion of vision and graphics, so we put forward our taxonomy and organize the whole paper chronologically. We extend the existing reconstruction-based colorization evaluation techniques, considering that aesthetic assessment of colored images should be introduced to ensure that colorization satisfies human visual-related requirements and emotions more closely. We perform the colorization aesthetic assessment on seven representative unconditional colorization models and discuss the difference between our assessment and the existing reconstruction-based metrics. Finally, this paper identifies unresolved issues and proposes fruitful areas for future research and development. Access to the project associated with this survey can be obtained at https://github.com/DanielCho-HK/Colorization.
Recent Advances in Graphene-Based Pressure Sensors: A Review
2024-10-03
Zhe Zhang, Quan Liu, Hongliang Ma, Ningfeng Ke, Jie Ding, Wendong Zhang, Xuge Fan
In recent years, pressure sensors have been widely used as crucial technology components in industrial, healthcare, consumer electronics, and automotive safety applications. With the development of intelligent technologies, there is a growing demand for pressure sensors with higher sensitivity, smaller size, and wider detection range. Graphene and its derivatives, as novel emerging materials in recent years, have received widespread attention from researchers due to their unique mechanical and electrical properties, and are considered as promising sensing materials for the high-performance pressure sensors. In general, graphene-based pressure sensors can be classified into flexible pressure sensors and gas pressure sensors. In this paper, we firstly introduce the basic properties of graphene and its derivatives and then review the research progress of both graphene-based flexible pressure sensors and graphene-based gas pressure sensors respectively, focusing on different sensing mechanisms. Finally, the application prospects of graphene-based pressure sensors as well as future challenges are discussed.
Recent Advances in Graphene-Based Humidity Sensors with the Focus of Structural Design: A Review
2024-10-03
Hongliang Ma, Jie Ding, Zhe Zhang, Qiang Gao, Quan Liu, Gaohan Wang, Wendong Zhang, Xuge Fan
The advent of the 5G era means that the concepts of robot, VR/AR, UAV, smart home, smart healthcare based on IoT (Internet of Things) have gradually entered human life. Since then, intelligent life has become the dominant direction of social development. Humidity sensors, as humidity detection tools, not only convey the comfort of human living environment, but also display great significance in the fields of meteorology, medicine, agriculture and industry. Graphene-based materials exhibit tremendous potential in humidity sensing owing to their ultra-high specific surface area and excellent electron mobility under room temperature for application in humidity sensing. This review begins with the introduction of examples of various synthesis strategies of graphene, followed by the device structure and working mechanism of graphene-based humidity sensor. In addition, several different structural design methods of graphene are summarized, demonstrating the structural design of graphene can not only optimize the performance of graphene, but also bring significant advantages in humidity sensing. Finally, key challenges hindering the further development and practical application of high-performance graphene-based humidity sensors are discussed, followed by presenting the future perspectives.
Towards a new generation of reflection models for precision measurements of accreting black holes
2024-10-03
Cosimo Bambi
Blurred reflection features are commonly observed in the X-ray spectra of accreting black holes. In the presence of high-quality data and with the correct astrophysical model, X-ray reflection spectroscopy is a powerful tool to probe the strong gravity region of black holes, study the morphology of the accreting matter, measure black hole spins, and test Einstein's theory of General Relativity in the strong field regime. In the past 10-15 years, there has been significant progress in the development of the analysis of these reflection features, thanks to both more sophisticated theoretical models and new observational data. However, the next generation of X-ray missions (e.g. eXTP, Athena, HEX-P) promises to provide unprecedented high-quality data, which will necessarily require more accurate synthetic reflection spectra than those available today. In this talk, I will review the state-of-the-art in reflection modeling and I will present current efforts to develop a new generation of reflection models with machine learning techniques.
The spatially resolved relation between dust, gas, and metal abundance with the TYPHOON survey
2024-10-03
Hye-Jin Park, Andrew J. Battisti, Emily Wisnioski, Luca Cortese, Mark Seibert, Kathryn Grasha, Barry F. Madore, Brent Groves, Jeff A. Rich, Rachael L. Beaton, Qian-Hui Chen, Marcie Mun, Naomi M. McClure-Griffiths, W. J. G. de Blok, Lisa J. Kewley
We present the spatially resolved relationship between the dust-to-gas mass ratio (DGR) and gas-phase metallicity (Zgas or 12+log(O/H)) (i.e., DGR-Zgas relation) of 11 nearby galaxies with a large metallicity range (1.5 dex of 12+log(O/H)) at (sub-)kpc scales. We used the large field-of-view (> 3') optical pseudo-Integral Field Spectroscopy data taken by the TYPHOON/PrISM survey, covering the optical size of galaxies, combining them with multi-wavelength data (far-UV to far-IR, CO, and HI 21 cm radio). A large scatter of DGR in the intermediate metallicity galaxies (8.0 < 12+log(O/H) < 8.3) is found, which is in line with dust evolution models, where grain growth begins to dominate the mechanism of dust mass accumulation. In the lowest metallicity galaxy of our sample, Sextans A (12+log(O/H) < 7.6), the star-forming regions have significantly higher DGR values (by 0.5-2 dex) than the global estimates from literature at the same metallicity but aligns with the DGR values from metal depletion method from Damped Lyman Alpha systems and high hydrogen gas density regions of Sextans A. Using dust evolution models with a Bayesian MCMC approach suggests: 1) a high SN dust yield and 2) a negligible amount of photofragmentation by UV radiation, although we note that our sample in the low-metallicity regime is limited to Sextans A. On the other hand, it is also possible that while metallicity influences DGR, gas density also plays a role, indicating an early onset of dust grain growth in the dust mass build-up process despite its low metallicity.
Multi-wavelength and Multi-messenger Counterparts of Fast Radio Bursts
2024-10-03
Bing Zhang
Fast radio bursts are brief, highly dispersed bursts detected in the radio band, originating from cosmological distances. The only such event detected in the Milky Way galaxy, FRB 20200428DD, was associated with an X-ray burst emitted by a magnetar named SGR J1935+2154, revealing the first case of a multi-wavelength counterpart of an FRB. Counterparts in other wavelengths accompanying or following FRBs, as well as the bright emission associated with the progenitor of the FRB engine, have been proposed in various FRB models, but no robust detection has been made so far. In general, FRBs as we know them are not favorite multi-messenger emitters. Nonetheless, possible neutrino and gravitational wave emission signals associated with FRBs or FRB-like events have been discussed in the literature. Here I review these suggested multi-wavelength and multi-messenger counterparts of FRBs or FRB-like events and the observational progress in searching for these signals. The topics include multi-wavelength (X-rays, $\gamma$-rays, optical) emission and neutrino emission from FRBs within the framework of the magnetar source models and possible FRB-like events associated with gravitational waves.
Can Language Models Take A Hint? Prompting for Controllable Contextualized Commonsense Inference
2024-10-03
Pedro Colon-Hernandez, Nanxi Liu, Chelsea Joe, Peter Chin, Claire Yin, Henry Lieberman, Yida Xin, Cynthia Breazeal
Generating commonsense assertions within a given story context remains a difficult task for modern language models. Previous research has addressed this problem by aligning commonsense inferences with stories and training language generation models accordingly. One of the challenges is determining which topic or entity in the story should be the focus of an inferred assertion. Prior approaches lack the ability to control specific aspects of the generated assertions. In this work, we introduce "hinting," a data augmentation technique that enhances contextualized commonsense inference. "Hinting" employs a prefix prompting strategy using both hard and soft prompts to guide the inference process. To demonstrate its effectiveness, we apply "hinting" to two contextual commonsense inference datasets: ParaCOMET and GLUCOSE, evaluating its impact on both general and context-specific inference. Furthermore, we evaluate "hinting" by incorporating synonyms and antonyms into the hints. Our results show that "hinting" does not compromise the performance of contextual commonsense inference while offering improved controllability.
The widespread adoption of smartphones and Location-Based Social Networks has led to a massive influx of spatio-temporal data, creating unparalleled opportunities for enhancing Point-of-Interest (POI) recommendation systems. These advanced POI systems are crucial for enriching user experiences, enabling personalized interactions, and optimizing decision-making processes in the digital landscape. However, existing surveys tend to focus on traditional approaches and few of them delve into cutting-edge developments, emerging architectures, as well as security considerations in POI recommendations. To address this gap, our survey stands out by offering a comprehensive, up-to-date review of POI recommendation systems, covering advancements in models, architectures, and security aspects. We systematically examine the transition from traditional models to advanced techniques such as large language models. Additionally, we explore the architectural evolution from centralized to decentralized and federated learning systems, highlighting the improvements in scalability and privacy. Furthermore, we address the increasing importance of security, examining potential vulnerabilities and privacy-preserving approaches. Our taxonomy provides a structured overview of the current state of POI recommendation, while we also identify promising directions for future research in this rapidly advancing field.
A Comprehensive Review of Propagation Models in Complex Networks: From Deterministic to Deep Learning Approaches
2024-10-03
Bin Wu, Sifu Luo, C. Steve Suh
Understanding propagation mechanisms in complex networks is essential for fields like epidemiology and multi-robot networks. This paper reviews various propagation models, from traditional deterministic frameworks to advanced data-driven and deep learning approaches. We differentiate between static and dynamic networks, noting that static models provide foundational insights, while dynamic models capture real-world temporal changes. Deterministic models like the SIR framework offer clear mathematical insights but often lack adaptability to randomness, whereas stochastic models enhance realism at the cost of interpretability. Behavior-based models focus on individual decision-making, demanding more computational resources. Data-driven approaches improve accuracy in nonlinear scenarios by adapting to evolving networks, using either traditional models or model-free machine learning techniques. We explore supervised and unsupervised learning methods, as well as reinforcement learning, which operates without predefined datasets. The application of graph neural networks (GNNs) is also discussed, highlighting their effectiveness in modeling propagation in complex networks. The paper underscores key applications and challenges associated with each model type, emphasizing the increasing importance of hybrid and machine learning-based solutions in contemporary network propagation issues.
Revealing the Unseen: Guiding Personalized Diffusion Models to Expose Training Data
2024-10-03
Xiaoyu Wu, Jiaru Zhang, Steven Wu
Diffusion Models (DMs) have evolved into advanced image generation tools, especially for few-shot fine-tuning where a pretrained DM is fine-tuned on a small set of images to capture specific styles or objects. Many people upload these personalized checkpoints online, fostering communities such as Civitai and HuggingFace. However, model owners may overlook the potential risks of data leakage by releasing their fine-tuned checkpoints. Moreover, concerns regarding copyright violations arise when unauthorized data is used during fine-tuning. In this paper, we ask: "Can training data be extracted from these fine-tuned DMs shared online?" A successful extraction would present not only data leakage threats but also offer tangible evidence of copyright infringement. To answer this, we propose FineXtract, a framework for extracting fine-tuning data. Our method approximates fine-tuning as a gradual shift in the model's learned distribution -- from the original pretrained DM toward the fine-tuning data. By extrapolating the models before and after fine-tuning, we guide the generation toward high-probability regions within the fine-tuned data distribution. We then apply a clustering algorithm to extract the most probable images from those generated using this extrapolated guidance. Experiments on DMs fine-tuned with datasets such as WikiArt, DreamBooth, and real-world checkpoints posted online validate the effectiveness of our method, extracting approximately 20% of fine-tuning data in most cases, significantly surpassing baseline performance.
Is Your Paper Being Reviewed by an LLM? Investigating AI Text Detectability in Peer Review
2024-10-03
Sungduk Yu, Man Luo, Avinash Madasu, Vasudev Lal, Phillip Howard
Peer review is a critical process for ensuring the integrity of published scientific research. Confidence in this process is predicated on the assumption that experts in the relevant domain give careful consideration to the merits of manuscripts which are submitted for publication. With the recent rapid advancements in the linguistic capabilities of large language models (LLMs), a new potential risk to the peer review process is that negligent reviewers will rely on LLMs to perform the often time consuming process of reviewing a paper. In this study, we investigate the ability of existing AI text detection algorithms to distinguish between peer reviews written by humans and different state-of-the-art LLMs. Our analysis shows that existing approaches fail to identify many GPT-4o written reviews without also producing a high number of false positive classifications. To address this deficiency, we propose a new detection approach which surpasses existing methods in the identification of GPT-4o written peer reviews at low levels of false positive classifications. Our work reveals the difficulty of accurately identifying AI-generated text at the individual review level, highlighting the urgent need for new tools and methods to detect this type of unethical application of generative AI.
Fully Automated CTC Detection, Segmentation and Classification for Multi-Channel IF Imaging
2024-10-03
Evan Schwab, Bharat Annaldas, Nisha Ramesh, Anna Lundberg, Vishal Shelke, Xinran Xu, Cole Gilbertson, Jiyun Byun, Ernest T. Lam
Liquid biopsies (eg., blood draws) offer a less invasive and non-localized alternative to tissue biopsies for monitoring the progression of metastatic breast cancer (mBCa). Immunofluoresence (IF) microscopy is a tool to image and analyze millions of blood cells in a patient sample. By detecting and genetically sequencing circulating tumor cells (CTCs) in the blood, personalized treatment plans are achievable for various cancer subtypes. However, CTCs are rare (about 1 in 2M), making manual CTC detection very difficult. In addition, clinicians rely on quantitative cellular biomarkers to manually classify CTCs. This requires prior tasks of cell detection, segmentation and feature extraction. To assist clinicians, we have developed a fully automated machine learning-based production-level pipeline to efficiently detect, segment and classify CTCs in multi-channel IF images. We achieve over 99% sensitivity and 97% specificity on 9,533 cells from 15 mBCa patients. Our pipeline has been successfully deployed on real mBCa patients, reducing a patient average of 14M detected cells to only 335 CTC candidates for manual review.
Harm Ratio: A Novel and Versatile Fairness Criterion
2024-10-03
Soroush Ebadian, Rupert Freeman, Nisarg Shah
Envy-freeness has become the cornerstone of fair division research. In settings where each individual is allocated a disjoint share of collective resources, it is a compelling fairness axiom which demands that no individual strictly prefer the allocation of another individual to their own. Unfortunately, in many real-life collective decision-making problems, the goal is to choose a (common) public outcome that is equally applicable to all individuals, and the notion of envy becomes vacuous. Consequently, this literature has avoided studying fairness criteria that focus on individuals feeling a sense of jealousy or resentment towards other individuals (rather than towards the system), missing out on a key aspect of fairness. In this work, we propose a novel fairness criterion, individual harm ratio, which is inspired by envy-freeness but applies to a broad range of collective decision-making settings. Theoretically, we identify minimal conditions under which this criterion and its groupwise extensions can be guaranteed, and study the computational complexity of related problems. Empirically, we conduct experiments with real data to show that our fairness criterion is powerful enough to differentiate between prominent decision-making algorithms for a range of tasks from voting and fair division to participatory budgeting and peer review.
2024-10-03
Yuxi Lu, Isabel L. Colman, Maryum Sayeed, Louis Amard, Sven Buder, Catherine Manea, Soichiro Hattori, Marc H. Pinsonneault, Adrian M. Price-Whelan, Megan Bedell, David Nidever, Jennifer A. Johnson, Melissa Ness, Ruth Angus, Zachary R. Claytor, Danny Horta, Aida Behmard
The existence of high-$\alpha$ stars with inferred ages < 6 Gyr has been confirmed recently with large spectroscopic and photometric surveys. However, stellar mergers or binary interactions can induce properties associated with young ages, such as high mass, rapid rotation, or high activity, even in old populations. Literature studies have confirmed that at least some of these apparently young stars are old merger products. However, none have ruled out the possibility of genuinely young high-$\alpha$ stars. Because cool GKM dwarfs spin down, rapid rotation can be used to indicate youth. In this paper, we provide strong evidence that truly young high-$\alpha$ stars exist by studying high-$\alpha$ rotators in the Kepler and K2 field with abundance measurements from GALAH and APOGEE. After excluding close binaries using radial velocity (RV) measurements from Gaia DR3 and multi-epoch RVs from APOGEE, we find a total of 70 high-$\alpha$ rapid rotators with periods ~10-30 days, 29 of which have lithium measurements from GALAH, indicating that they have not gone through past mass transfer or stellar merger events. We identify 10 young high-$\alpha$ candidates with no signs of merger-induced mixing or close companions. One clear example is a G dwarf with a measurable rotation and an age of 1.98$^{+0.12}_{-0.28}$ Gyr that is likely a single star with multiple RV measurements from APOGEE, has significant lithium detection from GALAH (A(Li) = 1.79), and has no signs of planet engulfment.
Motivated by recent developments in Hamiltonian variational principles, Hamiltonian variational integrators, and their applications such as to optimization and control, we present a new Type II variational approach for Hamiltonian systems, based on a virtual work principle that enforces the Type II boundary conditions through a combination of essential and natural boundary conditions; particularly, this approach allows us to define this variational principle intrinsically on manifolds. We first develop this variational principle on vector spaces and subsequently extend it to parallelizable manifolds, general manifolds, as well as to the infinite-dimensional setting. Furthermore, we provide a review of variational principles for Hamiltonian systems in various settings as well as their applications.
Nonasymptotic Analysis of Classical Spectrum Estimators with $L$-mixing Time-series Data
2024-10-03
Yuping Zheng, Andrew Lamperski
Spectral estimation is a fundamental problem for time series analysis, which is widely applied in economics, speech analysis, seismology, and control systems. The asymptotic convergence theory for classical, non-parametric estimators, is well-understood, but the non-asymptotic theory is still rather limited. Our recent work gave the first non-asymptotic error bounds on the well-known Bartlett and Welch methods, but under restrictive assumptions. In this paper, we derive non-asymptotic error bounds for a class of non-parametric spectral estimators, which includes the classical Bartlett and Welch methods, under the assumption that the data is an $L$-mixing stochastic process. A broad range of processes arising in time-series analysis, such as autoregressive processes and measurements of geometrically ergodic Markov chains, can be shown to be $L$-mixing. In particular, $L$-mixing processes can model a variety of nonlinear phenomena which do not satisfy the assumptions of our prior work. Our new error bounds for $L$-mixing processes match the error bounds in the restrictive settings from prior work up to logarithmic factors.
Galaxy-Galaxy Strong Lensing with U-Net (GGSL-UNet). I. Extracting 2-Dimensional Information from Multi-Band Images in Ground and Space Observations
2024-10-03
Fucheng Zhong, Ruibiao Luo, Nicola R. Napolitano, Crescenzo Tortora, Rui Li, Xincheng Zhu, Valerio Busillo, L. V. E. Koopmans, Giuseppe Longo
We present a novel deep learning method to separately extract the two-dimensional flux information of the foreground galaxy (deflector) and background system (source) of Galaxy-Galaxy Strong Lensing events using U-Net (GGSL-Unet for short). In particular, the segmentation of the source image is found to enhance the performance of the lens modeling, especially for ground-based images. By combining mock lens foreground+background components with real sky survey noise to train the GGSL-Unet, we show it can correctly model the input image noise and extract the lens signal. However, the most important result of this work is that the GGSL-UNet can accurately reconstruct real ground-based lensing systems from the Kilo Degree Survey (KiDS) in one second. We also test the GGSL-UNet on space-based (HST) lenses from BELLS GALLERY, and obtain comparable accuracy of standard lens modeling tools. Finally, we calculate the magnitudes from the reconstructed deflector and source images and use this to derive photometric redshifts (photo-z), with the photo-z of the deflector well consistent with spectroscopic ones. This first work, demonstrates the great potential of the generative network for lens finding, image denoising, source segmentation, and decomposing and modeling of strong lensing systems. For the upcoming ground- and space-based surveys, the GGSL-UNet can provide high-quality images as well as geometry and redshift information for precise lens modeling, in combination with classical MCMC modeling for best accuracy in the galaxy-galaxy strong lensing analysis.
Statistical Inference with Nonignorable Non-Probability Survey Samples
2024-10-03
Yang Liu, Meng Yuan, Pengfei Li, Changbao Wu
Statistical inference with non-probability survey samples is an emerging topic in survey sampling and official statistics and has gained increased attention from researchers and practitioners in the field. Much of the existing literature, however, assumes that the participation mechanism for non-probability samples is ignorable. In this paper, we develop a pseudo-likelihood approach to estimate participation probabilities for nonignorable non-probability samples when auxiliary information is available from an existing reference probability sample. We further construct three estimators for the finite population mean using regression-based prediction, inverse probability weighting (IPW), and augmented IPW estimators, and study their asymptotic properties. Variance estimation for the proposed methods is considered within the same framework. The efficiency of our proposed methods is demonstrated through simulation studies and a real data analysis using the ESPACOV survey on the effects of the COVID-19 pandemic in Spain.
NNetscape Navigator: Complex Demonstrations for Web Agents Without a Demonstrator
2024-10-03
Shikhar Murty, Dzmitry Bahdanau, Christopher D. Manning
We introduce NNetscape Navigator (NNetnav), a method for training web agents entirely through synthetic demonstrations. These demonstrations are collected by first interacting with a browser to generate trajectory rollouts, which are then retroactively labeled into instructions using a language model. Most work on training browser agents has relied on expensive human supervision, and the limited previous work on such interaction-first synthetic data techniques has failed to provide effective search through the exponential space of exploration. In contrast, NNetnav exploits the hierarchical structure of language instructions to make this search more tractable: complex instructions are typically decomposable into simpler subtasks, allowing NNetnav to automatically prune interaction episodes when an intermediate trajectory cannot be annotated with a meaningful sub-task. We use NNetnav demonstrations from a language model for supervised fine-tuning of a smaller language model policy, and find improvements of 6 points on WebArena and over 20 points on MiniWoB++, two popular environments for web-agents. Notably, on WebArena, we observe that language model policies can be further enhanced when fine-tuned with NNetnav demonstrations derived from the same language model. Finally, we collect and release a dataset of over 6k NNetnav demonstrations on WebArena, spanning a diverse and complex set of instructions.
Better Instruction-Following Through Minimum Bayes Risk
2024-10-03
Ian Wu, Patrick Fernandes, Amanda Bertsch, Seungone Kim, Sina Pakazad, Graham Neubig
General-purpose LLM judges capable of human-level evaluation provide not only a scalable and accurate way of evaluating instruction-following LLMs but also new avenues for supervising and improving their performance. One promising way of leveraging LLM judges for supervision is through Minimum Bayes Risk (MBR) decoding, which uses a reference-based evaluator to select a high-quality output from amongst a set of candidate outputs. In the first part of this work, we explore using MBR decoding as a method for improving the test-time performance of instruction-following LLMs. We find that MBR decoding with reference-based LLM judges substantially improves over greedy decoding, best-of-N decoding with reference-free judges and MBR decoding with lexical and embedding-based metrics on AlpacaEval and MT-Bench. These gains are consistent across LLMs with up to 70B parameters, demonstrating that smaller LLM judges can be used to supervise much larger LLMs. Then, seeking to retain the improvements from MBR decoding while mitigating additional test-time costs, we explore iterative self-training on MBR-decoded outputs. We find that self-training using Direct Preference Optimisation leads to significant performance gains, such that the self-trained models with greedy decoding generally match and sometimes exceed the performance of their base models with MBR decoding.
2024-10-03
Anna J. G. O'Grady, Takashi J. Moriya, Mathieu Renzo, Alejandro Vigna-Gómez
Interacting binary star systems play a critical role in many areas of astrophysics. One interesting example of a binary merger product are Thorne-\.Zytkow Objects (T\.ZOs), stars that look like red supergiants but contain neutron stars at their cores. T\.ZOs were theorized nearly five decades ago, and significant work has gone into understanding the physics of their formation, evolution, and stability. Several searches for T\.ZO candidates have also been carried out. Whether or not T\.ZOs could even exist or if they would be stable after formation has also been investigated. Understanding the existence and possible prevalence of T\.ZOs would have important effects on our understanding of binary evolution, stellar mergers, and inform binary population synthesis models. In this chapter, we review the formation channels, evolution and structure, final fates, and observable signatures of T\.ZOs, as well as candidates in the literature, from the inception of T\.ZO theory to recent progress in the field.
How Meteor Showers Can Guide the Search for Long Period Comets
2024-10-03
Samantha Hemmelgarn, Nicholas Moskovitz, Stuart Pilorz, Peter Jenniskens
With orbital periods longer than 200 years, most long-period comets (LPCs) remain undiscovered until they are in-bound towards perihelion. The comets that pass close to Earth's orbit are Potentially Hazardous Objects (PHOs). Those with orbital periods up to ~4000 years tend to have passed close to Earth's orbit in a previous orbit and produced a meteoroid stream dense enough to be detected at Earth as a meteor shower. In anticipation of Rubin Observatory's Legacy Survey of Space and Time (LSST), we investigate how these meteor showers can guide dedicated searches for their parent comets. Assuming search parameters informed by LSST, we calculated where the 17 known parent bodies of long-period comet meteor showers would have been discovered based on a cloud of synthetic comets generated from the shower properties as measured at Earth. We find that the synthetic comets predict the on-sky location of the parent comets at the time of their discovery. The parent comet's location on average would have been 1.51 $\pm$1.19$\deg$ from a line fit through the synthetic comet cloud. The difference between the heliocentric distance of the parent and mean heliocentric distance of synthetic comets on the line was 2.09 $\pm$1.89 au for comets with unknown absolute nuclear magnitudes and 0.96 $\pm$0.80 au for comets with known absolute nuclear magnitudes. We applied this method to the $\sigma$-Hydrids, the proposed meteor shower of Comet Nishimura, and found that it successfully matched the pre-covery location of this comet 8 months prior to Nishimura's discovery.
Bayesian Inference of Multiple Ising Models for Heterogeneous Public Opinion Survey Networks
2024-10-03
Alejandra Avalos-Pacheco, Andrea Lazzerini, Monia Lupparelli, Francesco C. Stingo
In public opinion studies, the relationships between opinions on different topics are likely to shift based on the characteristics of the respondents. Thus, understanding the complexities of public opinion requires methods that can account for the heterogeneity in responses across different groups. Multiple graphs are used to study how external factors-such as time spent online or generational differences-shape the joint dependence relationships between opinions on various topics. Specifically, we propose a class of multiple Ising models where a set of graphs across different groups are able to capture these variations and to model the heterogeneity induced in a set of binary variables by external factors. The proposed Bayesian methodology is based on a Markov Random Field prior for the multiple graph setting. Such prior enables the borrowing of strength across the different groups to encourage common edges when supported by the data. Sparse inducing spike-and-slab priors are employed on the parameters that measure graph similarities to learn which subgroups have a shared graph structure. Two Bayesian approaches are developed for the inference of multiple Ising models with a special focus on model selection: (i) a Fully Bayesian method for low-dimensional graphs based on conjugate priors specified with respect to the exact likelihood, and (ii) an Approximate Bayesian method based on a quasi-likelihood approach for high-dimensional graphs where the normalization constant required in the exact method is computationally intractable. These methods are employed for the analysis of data from two public opinion studies in US. The obtained results display a good trade-off between identifying significant edges (both shared and group-specific) and having sparse networks, all while quantifying the uncertainty of the graph structure and the graphs' similarity.
A Multi-Wavelength Characterization of the Obscuring Medium at the Center of NGC 6300
2024-10-03
D. Sengupta, N. Torres-Albà, A. Pizzetti, I. E. López, S. Marchesi, C. Vignali, L. Barchiesi, I. Cox, M. Gaspari, X. Zhao, M. Ajello, F. Esposito
Most of the super-massive black holes in the Universe accrete material in an obscured phase. While it is commonly accepted that the "dusty torus" is responsible for the nuclear obscuration, its geometrical, physical, and chemical properties are far from being properly understood. In this paper, we take advantage of the multiple X-ray observations taken between 2007 and 2020, as well as of optical to far infra-red (FIR) observations of NGC 6300, a nearby ($z=0.0037$) Seyfert 2 galaxy. The goal of this project is to study the nuclear emission and the properties of the obscuring medium, through a multi-wavelength study conducted from X-ray to IR. We perform a simultaneous X-ray spectral fitting and optical-FIR spectral energy distribution (SED) fitting to investigate the obscuring torus. For the X-ray spectral fitting, physically motivated torus models, such as borus02, UXClumpy and XClumpy are used. The SED fitting is done using XCIGALE. Through joint analysis, we constrain the physical parameters of the torus and the emission properties of the accreting supermassive black hole. Through X-ray observations taken in the last 13 years, we have not found any significant line-of-sight column density variability for this source, but observed the X-ray flux dropping $\sim40-50\%$ in 2020 with respect to previous observations. The UXClumpy model predicts the presence of an inner ring of Compton-thick gaseous medium, responsible for the reflection dominated spectra above 10 keV. Through multi-wavelength SED fitting, we measure an Eddington accretion rate $\lambda_{\rm{Edd}}\sim2\times10^{-3}$, which falls in the range of the radiatively inefficient accretion solutions.
Diffuse Scattering from Correlated Electron Systems
2024-10-03
Raymond Osborn, Damjan Pelc, Matthew Krogstad, Stephan Rosenkranz, Martin Greven
The role of inhomegeneity in determining the properties of correlated electron systems is poorly understood because of the dearth of structural probes of disorder at the nanoscale. Advances in both neutron and x-ray scattering instrumentation now allow comprehensive measurements of diffuse scattering in single crystals over large volumes of reciprocal space, enabling structural correlations to be characterized over a range of length scales from 10~{\AA} to 200~{\AA} or more. When combined with new analysis tools, such as three-dimensional difference pair-distribution functions, these advanced capabilities have produced novel insights into the interplay of structural fluctuations and electronic properties in a broad range of correlated electron materials. This review describes recent investigations that have demonstrated the importance of understanding structural inhomogeneity pertaining to phenomena as diverse as superconductivity, charge-density-wave modulations, metal-insulator transitions, and multipolar interactions.
Constraining the Low Mass End of the Stellar Halo Mass Relation with Surveys of Satellite Galaxies
2024-10-03
J. Sebastian Monzon, Frank C. van den Bosch, Kaustav Mitra
The abundance of satellite galaxies is set by the hierarchical assembly of their host halo. We leverage this to investigate the low mass end ($M_{\mathrm{H}} < 10^{11} M_{\odot}$) of the Stellar-to-Halo Mass Relation (SHMR), which is key to constraining theories of galaxy formation and cosmology. We argue that recent analyses of satellite galaxies in the Local Group environment have not adequately modelled the dominant source of scatter in satellite stellar mass functions: the variance in accretion histories for a fixed host halo mass. We present a novel inference framework that not only properly accounts for this halo-to-halo variance but also naturally identifies the amount of host halo mass mixing, which is generally unknown. Specifically, we use the semi-analytical SatGen model to construct mock satellite galaxy populations consistent with the third data release of the Satellites Around Galactic Analogs (SAGA) survey. We demonstrate that even under the most idealized circumstances, the halo-to-halo variance makes it virtually impossible to put any meaningful constraints on the scatter in the SHMR. Even a satellite galaxy survey made up 100 hosts can at best only place an upper limit of $\sim 0.5$dex on the scatter (at the 95\% confidence level). This is because the large variance in halo assembly histories dominates over the scatter in the SHMR. This problem can be overcome by increasing the sample size of the survey by an order of magnitude ($\sim 1000$ host galaxies), something that should be fairly straightforward with forthcoming spectroscopic surveys.
PHANGS-ML: the universal relation between PAH band and optical line ratios across nearby star-forming galaxies
2024-10-03
Dalya Baron, Karin Sandstrom, Jessica Sutter, Hamid Hassani, Brent Groves, Adam Leroy, Eva Schinnerer, Médéric Boquien, Matilde Brazzini, Jérémy Chastenet, Daniel Dale, Oleg Egorov, Simon Glover, Ralf Klessen, Debosmita Pathak, Erik Rosolowsky, Frank Bigiel, Mélanie Chevance, Kathryn Grasha, Annie Hughes, J. Eduardo Méndez-Delgado, Jérôme Pety, Thomas Williams, Stephen Hannon, Sumit Sarbadhicary
The structure and chemistry of the dusty interstellar medium (ISM) are shaped by complex processes that depend on the local radiation field, gas composition, and dust grain properties. Of particular importance are Polycyclic Aromatic Hydrocarbons (PAHs), which emit strong vibrational bands in the mid-infrared, and play a key role in the ISM energy balance. We recently identified global correlations between PAH band and optical line ratios across three nearby galaxies, suggesting a connection between PAH heating and gas ionization throughout the ISM. In this work, we perform a census of the PAH heating -- gas ionization connection using $\sim$700,000 independent pixels that probe scales of 40--150 pc in nineteen nearby star-forming galaxies from the PHANGS survey. We find a universal relation between $\log$PAH(11.3 \mic/7.7 \mic) and $\log$([SII]/H$\alpha$) with a slope of $\sim$0.2 and a scatter of $\sim$0.025 dex. The only exception is a group of anomalous pixels that show unusually high (11.3 \mic/7.7 \mic) PAH ratios in regions with old stellar populations and high starlight-to-dust emission ratios. Their mid-infrared spectra resemble those of elliptical galaxies. AGN hosts show modestly steeper slopes, with a $\sim$10\% increase in PAH(11.3 \mic/7.7 \mic) in the diffuse gas on kpc scales. This universal relation implies an emerging simplicity in the complex ISM, with a sequence that is driven by a single varying property: the spectral shape of the interstellar radiation field. This suggests that other properties, such as gas-phase abundances, gas ionization parameter, and grain charge distribution, are relatively uniform in all but specific cases.
The galaxy-IGM connection in THESAN: observability and information content of the galaxy-Lyman-$α$ cross-correlation at z$\geq$6
2024-10-03
Enrico Garaldi, Verena Bellscheidt
The galaxy--Lyman-$\alpha$ cross-correlation (GaLaCC) is a promising tool to study the interplay of galaxies and inter-galactic medium (IGM) in the first billion years of the Universe. Here we thoroughly characterise the impact of observational limitations on our ability to retrieve the intrinsic GaLaCC and provide new physical insights on its origin and connection to other IGM properties. This is extremely relevant to identify promising datasets, design future surveys and assess the limitations of current measurements. We find that sightline-to-sightline variations demand at least 25 independent sightlines to quantitatively recover the true signal. Once this condition is met, the intrinsic signal can be recovered even for a relatively low signal-to-noise ratio and spectral resolution. The galaxy selection method does not affect the inferred GaLaCC and lightcone effects are only relevant for redshift windows $\Delta z \gtrsim 0.5$. We discuss the implications of these findings for previous theoretical studies. We elucidate explicitly for the first time the physical origin of the GaLaCC and demonstrate that this signal is collectively sourced by the ensemble of galaxies residing in overdense regions rather than individual objects. We show that GaLaCC measured for opaque sightlines shows a larger peak at smaller scales with respect to transparent lines of sight. We connect this to the evolution of the mean free path of ionizing photons, showing that GaLaCC peak position has a very similar evolution but on smaller scales, as it probes only the core of ionised regions. Finally, we discuss which ongoing surveys can be used to measure the GaLaCC and provide an initial analysis of future developments, including using galaxies as background sources. Our results outline a bright future for the GaLaCC as a tool to unveil the galaxy-IGM interplay during the first billion years of the Universe.
IoT-LLM: Enhancing Real-World IoT Task Reasoning with Large Language Models
2024-10-03
Tuo An, Yunjiao Zhou, Han Zou, Jianfei Yang
Large Language Models (LLMs) have demonstrated remarkable capabilities across textual and visual domains but often generate outputs that violate physical laws, revealing a gap in their understanding of the physical world. Inspired by human cognition, where perception is fundamental to reasoning, we explore augmenting LLMs with enhanced perception abilities using Internet of Things (IoT) sensor data and pertinent knowledge for IoT task reasoning in the physical world. In this work, we systematically study LLMs capability to address real-world IoT tasks by augmenting their perception and knowledge base, and then propose a unified framework, IoT-LLM, to enhance such capability. In IoT-LLM, we customize three steps for LLMs: preprocessing IoT data into formats amenable to LLMs, activating their commonsense knowledge through chain-of-thought prompting and specialized role definitions, and expanding their understanding via IoT-oriented retrieval-augmented generation based on in-context learning. To evaluate the performance, We design a new benchmark with five real-world IoT tasks with different data types and reasoning difficulties and provide the benchmarking results on six open-source and close-source LLMs. Experimental results demonstrate the limitations of existing LLMs with naive textual inputs that cannot perform these tasks effectively. We show that IoT-LLM significantly enhances the performance of IoT tasks reasoning of LLM, such as GPT-4, achieving an average improvement of 65% across various tasks against previous methods. The results also showcase LLMs ability to comprehend IoT data and the physical law behind data by providing a reasoning process. Limitations of our work are claimed to inspire future research in this new era.
Accessing Generalized Parton Distributions through $2 \to 3$ exclusive processes
2024-10-03
Goran Duplančić, Saad Nabeebaccus, Kornelija Passek-K., Bernard Pire, Lech Szymanowski, Samuel Wallon
We review our results on a new class of $2 \to 3$ exclusive processes, as a probe of both chiral-even and chiral-odd quark GPDs. We consider the exclusive photoproduction of a photon-meson pair, in the kinematics where the pair has a large invariant mass, described in the collinear factorization framework. We cover the whole kinematical range from medium energies in fixed target experiments to very large energies of colliders, by considering the experimental conditions of JLab 12-GeV, COMPASS, future EIC and LHC (in ultra-peripheral collisions) cases. Our analysis covers neutral and charged rho-mesons, as well as charged pions. The case of the rho-meson, depending on its polarization, provides access to either chiral-even or chiral-odd GPDs, at leading twist. We find that the order of magnitude of the obtained cross sections are sufficiently large for a dedicated experimental analysis to be performed, especially at JLab. Furthermore, we compute the linear photon beam polarization asymmetry, which we find to be sizeable, in the case of a longitudinally polarized $\rho$-meson or of a charged pion. These predictions are obtained for both asymptotic distribution amplitude (DA) and holographic DA.
Emission-Line Ratios and Ionization Conditions of CEERS Star-Forming Galaxies with JWST/NIRSpec
2024-10-03
Ansh R. Gupta, Allison Kirkpatrick, Vital Fernandez, Pablo Arrabal Haro, Bren E. Backhaus, Nikko J. Cleri, Norman A. Grogin, Anton M. Koekemoer
Galaxy emission-line fluxes can be analyzed to determine star formation rates (SFR) and ISM ionization. Here, we investigate rest-frame optical emission lines of 71 star-forming galaxies at redshift 0.7 < z < 7 from the Cosmic Evolution Early Release Science (CEERS) survey using JWST/NIRSpec. We use H${\alpha}$ line fluxes to measure SFRs. We combine these with HST CANDELS stellar mass estimates to determine the redshift evolution of specific SFR (sSFR) and compare our sample with the star-forming galaxy main sequence. We create [O III]${\lambda}$5008/H${\beta}$ versus [Ne III]${\lambda}$3870/[O II]${\lambda}$3728 line ratio diagrams and correlate these ratios with sSFR and the distance of each galaxy from the main sequence (excess sSFR). We find a modest correlation between the line ratios and sSFR, which is consistent with previous work analyzing similar samples. However, we find a weak correlation between the line ratios and excess sSFR. Taken together, our results suggest that sSFR is the parameter that governs ionization conditions rather than SFR or a galaxy's distance from the main sequence. These measurements reveal a rich diversity of ISM conditions and physical galaxy properties throughout cosmic time.
We present the first Chandra X-ray Observatory (CXO) catalog of "pulsar X-ray filaments," or "misaligned outflows." These are linear, synchrotron radiating features powered by ultra-relativistic electrons and positrons that escape from bow shock pulsars. The filaments are misaligned with the (large) pulsar velocity, distinguishing them from the pulsar wind nebula (PWN) trail which is also often visible in CXO ACIS images. Spectral fits and morphological properties are extracted for five secure filaments and three candidates using a uniform method. We present a search of archival CXO data for linear diffuse features; the known examples are recovered and a few additional weak candidates are identified. We also report on a snapshot CXO ACIS survey of pulsars with properties similar to the filament producers, finding no new filaments, but some diffuse emission including one PWN trail. Finally, we provide an updated model for the pulsar properties required to create filaments in light of these new observations.
Deep Generative Models (DGMs), including Energy-Based Models (EBMs) and Score-based Generative Models (SGMs), have advanced high-fidelity data generation and complex continuous distribution approximation. However, their application in Markov Decision Processes (MDPs), particularly in distributional Reinforcement Learning (RL), remains underexplored, with conventional histogram-based methods dominating the field. This paper rigorously highlights that this application gap is caused by the nonlinearity of modern DGMs, which conflicts with the linearity required by the Bellman equation in MDPs. For instance, EBMs involve nonlinear operations such as exponentiating energy functions and normalizing constants. To address this, we introduce Bellman Diffusion, a novel DGM framework that maintains linearity in MDPs through gradient and scalar field modeling. With divergence-based training techniques to optimize neural network proxies and a new type of stochastic differential equation (SDE) for sampling, Bellman Diffusion is guaranteed to converge to the target distribution. Our empirical results show that Bellman Diffusion achieves accurate field estimations and is a capable image generator, converging 1.5x faster than the traditional histogram-based baseline in distributional RL tasks. This work enables the effective integration of DGMs into MDP applications, unlocking new avenues for advanced decision-making frameworks.
Towards deep learning sequence-structure co-generation for protein design
2024-10-02
Chentong Wang, Sarah Alamdari, Carles Domingo-Enrich, Ava Amini, Kevin K. Yang
Deep generative models that learn from the distribution of natural protein sequences and structures may enable the design of new proteins with valuable functions. While the majority of today's models focus on generating either sequences or structures, emerging co-generation methods promise more accurate and controllable protein design, ideally achieved by modeling both modalities simultaneously. Here we review recent advances in deep generative models for protein design, with a particular focus on sequence-structure co-generation methods. We describe the key methodological and evaluation principles underlying these methods, highlight recent advances from the literature, and discuss opportunities for continued development of sequence-structure co-generation approaches.
Reinforcement learning encounters challenges in various environments related to robustness and explainability. Traditional Q-learning algorithms cannot effectively make decisions and utilize the historical learning experience. To overcome these limitations, we propose Cognitive Belief-Driven Q-Learning (CBDQ), which integrates subjective belief modeling into the Q-learning framework, enhancing decision-making accuracy by endowing agents with human-like learning and reasoning capabilities. Drawing inspiration from cognitive science, our method maintains a subjective belief distribution over the expectation of actions, leveraging a cluster-based subjective belief model that enables agents to reason about the potential probability associated with each decision. CBDQ effectively mitigates overestimated phenomena and optimizes decision-making policies by integrating historical experiences with current contextual information, mimicking the dynamics of human decision-making. We evaluate the proposed method on discrete control benchmark tasks in various complicate environments. The results demonstrate that CBDQ exhibits stronger adaptability, robustness, and human-like characteristics in handling these environments, outperforming other baselines. We hope this work will give researchers a fresh perspective on understanding and explaining Q-learning.
Multiple-scale gas infall through gravity torques on Milky Way twins
2024-10-02
Patrícia da Silva, F. Combes
One of the main problems raised by the feeding of supermassive black holes (SMBHs) at the centres of galaxies is the huge angular momentum of the circumnuclear gas and of the gas reservoir in the galaxy disk. Because viscous torques are not efficient at kiloparsec or 100 pc scales, the angular momentum must be exchanged through gravity torques that arise from the non-axisymmetric patterns in the disks. Our goal here is to quantify the efficiency of bars and spirals in driving the gas towards the centre at different scales in galaxies. We selected a sample of nearby galaxies considered to be analogues of the Milky Way, that is, galaxies of late morphological type Sbc. Their bar strength was variable, either SB, or SAB, or SA, so that we were able to quantify the influence of the bar. The gravitational potential was computed from deprojected red images, either from Hubble Space Telescope or Legacy survey, depending on the spatial resolution and field of view considered. The torques were computed on the gas through CO emission maps from ALMA at different resolutions. H$\alpha$ maps from MUSE were used, when available. Eight out of ten galaxies are barred. The torques are found to be negative in the eight barred objects at kiloparsec scales, between corotation and the inner Lindblad resonance (ILR), with a loss of angular momentum in a few rotations. Inside the ILR, the torques are negative in only five cases, with a timescale of one to two rotations. The torques are positive for the galaxies without bars. The torques applied on the ionized gas are comparable to what is deduced from molecular gas. The bars are confirmed to be the essential pattern in the SMBH feeding at kiloparsec and 100 pc scales; higher-resolution gas maps are required to explore scales of 10 pc.
Cosmological neutral hydrogen (HI) surveys provide a promising tomographic probe of the post-reionization era and of the standard model of cosmology. Simulations of this signal are crucial for maximizing the utility of these surveys. We present a fast method for simulating the cosmological distribution of HI based on a halo model approach. Employing the approximate $\texttt{PINOCCHIO}$ code, we generate the past light cone of dark matter halos. Subsequently, the halos are populated with HI according to a HI-halo mass relation. The nature of 21 cm intensity mapping demands large-volume simulations with a high halo mass resolution. To fulfill both requirements, we simulate a past light cone for declinations between -15{\deg} and -35{\deg} in the frequency range from 700 to 800 MHz, matching HIRAX, the Hydrogen Intensity and Real-time Analysis eXperiment. We run $\texttt{PINOCCHIO}$ for a 1 h$^{-3}$Gpc$^3$ box with 6700$^3$ simulation particles. With this configuration, halos with masses as low as M$_\text{min}$ = 4.3 $\times$ 10$^{9}$M$_{\odot}$ are simulated, resulting in the recovery of more than 97% of the expected HI density. From the dark matter and HI past light cone, maps with a width of 5 MHz are created. To validate the simulations, we have implemented and present here an analytical dark matter and HI halo model in $\texttt{PyCosmo}$, a Python package tailored for theoretical cosmological predictions. We perform extensive comparisons between analytical predictions and the simulations for the mass function, mass density, power spectrum, and angular power spectrum for dark matter and HI. We find close agreement in the mass function and mass densities, with discrepancies within a few percent. For the three-dimensional power spectra and angular power spectra, we observe an agreement better than 10%.
U-shaped and Inverted-U Scaling behind Emergent Abilities of Large Language Models
2024-10-02
Tung-Yu Wu, Pei-Yu Lo
Large language models (LLMs) have been shown to exhibit emergent abilities in some downstream tasks, where performance seems to stagnate at first and then improve sharply and unpredictably with scale beyond a threshold. By dividing questions in the datasets according to difficulty level by average performance, we observe U-shaped scaling for hard questions, and inverted-U scaling followed by steady improvement for easy questions. Moreover, the emergence threshold roughly coincides with the point at which performance on easy questions reverts from inverse scaling to standard scaling. Capitalizing on the observable though opposing scaling trend on easy and hard questions, we propose a simple yet effective pipeline, called Slice-and-Sandwich, to predict both the emergence threshold and model performance beyond the threshold.
Stable Offline Value Function Learning with Bisimulation-based Representations
2024-10-02
Brahma S. Pavse, Yudong Chen, Qiaomin Xie, Josiah P. Hanna
In reinforcement learning, offline value function learning is the procedure of using an offline dataset to estimate the expected discounted return from each state when taking actions according to a fixed target policy. The stability of this procedure, i.e., whether it converges to its fixed-point, critically depends on the representations of the state-action pairs. Poorly learned representations can make value function learning unstable, or even divergent. Therefore, it is critical to stabilize value function learning by explicitly shaping the state-action representations. Recently, the class of bisimulation-based algorithms have shown promise in shaping representations for control. However, it is still unclear if this class of methods can stabilize value function learning. In this work, we investigate this question and answer it affirmatively. We introduce a bisimulation-based algorithm called kernel representations for offline policy evaluation (KROPE). KROPE uses a kernel to shape state-action representations such that state-action pairs that have similar immediate rewards and lead to similar next state-action pairs under the target policy also have similar representations. We show that KROPE: 1) learns stable representations and 2) leads to lower value error than baselines. Our analysis provides new theoretical insight into the stability properties of bisimulation-based methods and suggests that practitioners can use these methods for stable and accurate evaluation of offline reinforcement learning agents.
Avatar Appearance and Behavior of Potential Harassers Affect Users' Perceptions and Response Strategies in Social Virtual Reality (VR): A Mixed-Methods Study
2024-10-02
Xuetong Wang, Ziyan Wang, Mingmin Zhang, Kangyou Yu, Pan Hui, Mingming Fan
Sexual harassment has been recognized as a significant social issue. In recent years, the emergence of harassment in social virtual reality (VR) has become an important and urgent research topic. We employed a mixed-methods approach by conducting online surveys with VR users (N = 166) and semi-structured interviews with social VR users (N = 18) to investigate how users perceive sexual harassment in social VR, focusing on the influence of avatar appearance. Moreover, we derived users' response strategies to sexual harassment and gained insights on platform regulation. This study contributes to the research on sexual harassment in social VR by examining the moderating effect of avatar appearance on user perception of sexual harassment and uncovering the underlying reasons behind response strategies. Moreover, it presents novel prospects and challenges in platform design and regulation domains.
HARMONI at ELT: tolerance analysis and expected as-build imaging performance of the infrared spectrograph
2024-10-02
Eduard Muslimov, Edgar Castillo-Dominguez, James Kariuki, Jorge Chao-Ortiz, Matthias Tecza, Elliot Meyer, Zeynep Ozer, Fraser Clarke, Niranjan Thatte
HARMONI is the first light visible and near-IR integral field spectrograph for the ELT. It covers a large spectral range from 470nm to 2450nm with resolving powers from 3300 to 18000 and spatial sampling from 60mas to 4mas. It can operate in two Adaptive Optics modes - SCAO (including a High Contrast capability) and LTAO - or with NOAO. The project is preparing for Final Design Reviews. The integral field spectrograph is a key sub-system of HARMONI instrument, which forms the 2D spectral image and projects it onto the scientific detector. It has 40 operational modes with different platescales and gratings covering the band of 811-2450 nm with three resolution grades. In each of this configurations the as-built spectrograph wave-front error is strictly limited. We perform the sensitivity analysis for measurable and unknown errors and build the errors budget on this basis. Then we correct the values for the actual technological limits and perform a three-stage Monte-Carlo analysis combined with simulation of a few specific effect as the holographic grating wavefront error. Eventually, we show that it is possible to reach the target image quality in terms of the wavefront error and spectral resolution for the entire sub-system with practically feasible tolerances on design parameters.
The effect of gravity on bubble-particle collisions in turbulence
2024-10-02
Timothy T. K. Chan, Dominik Krug
Bubble-particle collisions in turbulence are key to the froth flotation process that is employed industrially on a vast scale to separate hydrophobic from hydrophilic materials. In our previous study (Chan et al., J. Fluid Mech., vol. 959, 2023, A6), we elucidated the collision mechanisms and critically reviewed the collision models in the no-gravity limit. In reality, gravity may play a role since ultimately separation is achieved through buoyancy induced rising of the bubbles. This effect has been included in several collision models, which have remained without a proper validation thus far due to a scarcity of available data. We therefore conduct direct numerical simulations of bubble and particles in homogeneous isotropic turbulence with various Stokes, Froude, and Reynolds numbers using the point-particle approximation. Generally, we find that turbulence enhances the collision rate compared to the pure relative settling case by increasing the collision velocity. Surprisingly, however, we observe that for certain parameters the collision rate is lower with turbulence compared to without. This is due to turbulence-induced bubble-particle spatial segregation, which is most prevalent at weak relative gravity and decreases as gravitational effects become more dominant, and reduced bubble slip velocity in turbulence. The existing bubble-particle collision models only qualitatively capture the trends in our numerical data. To improve on this, we extend the model by Dodin & Elperin (Phys. Fluids, vol. 14, no. 8, 2002, pp.2921-2924) to the bubble-particle case and found excellent quantitative agreement for small Stokes numbers when segregation is accounted for.
DLP-LoRA: Efficient Task-Specific LoRA Fusion with a Dynamic, Lightweight Plugin for Large Language Models
2024-10-02
Yuxuan Zhang, Ruizhe Li
Recent advancements in Large Language Models (LLMs) have achieved robust performance across diverse tasks, but fine-tuning these models for specific domains remains resource-intensive. Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) address this challenge by fine-tuning a small subset of parameters. However, existing methods for fusing multiple LoRAs lack dynamic fusion based on contextual inputs and often increase inference time due to token-level operations. We propose DLP-LoRA, a Dynamic Lightweight Plugin that employs a mini-MLP module with only 5M parameters to dynamically fuse multiple LoRAs at the sentence level using top-p sampling strategies. This approach reduces inference time to less than twice that of single LoRA inference by leveraging parallel computation. Evaluations across 26 tasks-including multiple-choice questions and question answering-demonstrate that DLP-LoRA achieves an average accuracy of 92.34% on multiple-choice datasets and significant improvements in BLEU and ROUGE scores on QA datasets, outperforming different LLMs backbones under composite task settings. DLP-LoRA effectively balances performance and efficiency, making it a practical solution for dynamic multi-task adaptation in LLMs. Our code is available at https://github.com/MeCuping/DLP-LoRA.
Probing Dark Relativistic Species and Their Interactions with Dark Matter through CMB and 21cm surveys
2024-10-02
Hugo Plombat, Théo Simon, Jordan Flitter, Vivian Poulin
We investigate the sensitivity of the 21cm power spectrum from cosmic dawn and the epoch of reionization to models of free-streaming dark radiation (parameterized through $N_{\rm eff}$) and interacting dark radiation-dark matter models (DM-DR). The latter models have gained attention for their potential in addressing recent cosmological tensions and structure formation challenges. We perform a Fisher matrix analysis under different assumptions regarding the astrophysical modeling, and forecast the sensitivity of HERA observations, combined with CMB data from Planck and the Simons Observatory (SO), to $N_{\rm eff}$ and DM-DR interaction modeled using the ETHOS framework assuming a constant scattering rate between the two components. Most importantly, we find that 21cm observations can improve the sensitivity to the DM-DR interaction rate by up to four order of magnitude compared to Planck and SO. Conversely, in the limit of low interaction rate (which asymptotically matches $N_{\rm eff}$), CMB data dominates the constraining power, but the inclusion of HERA data can provide a $\sim 20\%$ improvement in sensitivity over CMB data alone. Moreover, we find that HERA observations will be able to probe a region of the DM-DR interaction parameter space which is promising to explain the weak lensing amplitude `$S_8$' tension. Our results demonstrate the complementarity of 21cm and CMB data in exploring dark sector interactions.
Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process
2024-10-02
Xingyu Ji, Jiale Liu, Lu Li, Maojun Wang, Zeyu Zhang
Representation learning on text-attributed graphs (TAGs) has attracted significant interest due to its wide-ranging real-world applications, particularly through Graph Neural Networks (GNNs). Traditional GNN methods focus on encoding the structural information of graphs, often using shallow text embeddings for node or edge attributes. This limits the model to understand the rich semantic information in the data and its reasoning ability for complex downstream tasks, while also lacking interpretability. With the rise of large language models (LLMs), an increasing number of studies are combining them with GNNs for graph representation learning and downstream tasks. While these approaches effectively leverage the rich semantic information in TAGs datasets, their main drawback is that they are only partially interpretable, which limits their application in critical fields. In this paper, we propose a verbalized graph representation learning (VGRL) method which is fully interpretable. In contrast to traditional graph machine learning models, which are usually optimized within a continuous parameter space, VGRL constrains this parameter space to be text description which ensures complete interpretability throughout the entire process, making it easier for users to understand and trust the decisions of the model. We conduct several studies to empirically evaluate the effectiveness of VGRL and we believe these method can serve as a stepping stone in graph representation learning.
Geometric Signatures of Compositionality Across a Language Model's Lifetime
2024-10-02
Jin Hwa Lee, Thomas Jiralerspong, Lei Yu, Yoshua Bengio, Emily Cheng
Compositionality, the notion that the meaning of an expression is constructed from the meaning of its parts and syntactic rules, permits the infinite productivity of human language. For the first time, artificial language models (LMs) are able to match human performance in a number of compositional generalization tasks. However, much remains to be understood about the representational mechanisms underlying these abilities. We take a high-level geometric approach to this problem by relating the degree of compositionality in a dataset to the intrinsic dimensionality of its representations under an LM, a measure of feature complexity. We find not only that the degree of dataset compositionality is reflected in representations' intrinsic dimensionality, but that the relationship between compositionality and geometric complexity arises due to learned linguistic features over training. Finally, our analyses reveal a striking contrast between linear and nonlinear dimensionality, showing that they respectively encode formal and semantic aspects of linguistic composition.
2024-10-02
Xuge Fan, Chang He, Jie Ding, Qiang Gao, Hongliang Ma, Max C. Lemme, Wendong Zhang
Graphene is being increasingly used as an interesting transducer membrane in micro- and nanoelectromechanical systems (MEMS and NEMS, respectively) due to its atomical thickness, extremely high carrier mobility, high mechanical strength and piezoresistive electromechanical transductions. NEMS devices based on graphene feature increased sensitivity, reduced size, and new functionalities. In this review, we discuss the merits of graphene as a functional material for MEMS and NEMS, the related properties of graphene, the transduction mechanisms of graphene MEMS and NEMS, typical transfer methods for integrating graphene with MEMS substrates, methods for fabricating suspended graphene, and graphene patterning and electrical contact. Consequently, we provide an overview of devices based on suspended and nonsuspended graphene structures. Finally, we discuss the potential and challenges of applications of graphene in MEMS and NEMS. Owing to its unique features, graphene is a promising material for emerging MEMS, NEMS and sensor applications.
AGN -- host galaxy photometric decomposition using a fast, accurate and precise deep learning approach
2024-10-02
Berta Margalef-Bentabol, Lingyu Wang, Antonio La Marca, Vicente Rodriguez-Gomez
Identification of active galactic nuclei (AGNs) is extremely important for understanding galaxy evolution and its connection with the formation and evolution of supermassive black holes (SMBH). With the advent of deep and high angular resolution imaging surveys such as those conducted with the {\it James Webb} Space Telescope (JWST), it is now possible to identify galaxies with a central point source out to the very early Universe. In this study, we develop a fast, accurate and precise method to identify galaxies which host AGNs and recover the intrinsic AGN contribution to the observed total light ($f_{AGN}$). We trained a deep learning (DL) based method Zoobot to estimate the fractional contribution of a central point source to the total light. Our training sample comprises realistic mock JWST images of simulated galaxies from the IllustrisTNG cosmological hydrodynamical simulations. We injected different amounts of the real JWST point spread function (PSF) models to represent galaxies with different levels of $f_{AGN}$. We analyse the performance of our method and compare it with results obtained from the traditional light profile fitting tool GALFIT. We find excellent performance of our DL method in recovering the injected AGN fraction $f_{AGN}$, both in terms of precision and accuracy. The mean difference between the predicted and true injected $f_{AGN}$ is $-0.0006$ and the overall root mean square error (RMSE) is $0.027$. The relative absolute error (RAE) is $0.086$ and the outlier (defined as predictions with RAE $>20\%$) fraction is $7.8\%$. In comparison, using GALFIT on the same dataset, we achieve a mean difference of -$0.024$, RMSE of $0.09$, RAE of $0.19$ and outlier fraction of $19\%$. Our DL-based method to identify AGNs and estimate $f_{AGN}$ is extremely fast and easy to use and has a huge potential in future applications to large galaxy imaging surveys.
Terahertz harmonic generation across the Mott insulator-metal transition
2024-10-02
Gulloo Lal Prajapati, Sujay Ray, Igor Ilyakov, Alexey N. Ponomaryov, Atiqa Arshad, Thales V. A. G. de Oliveira, Gaurav Dubey, Dhanvir Singh Rana, Jan-Christoph Deinert, Philipp Werner, Sergey Kovalev
We demonstrate terahertz (THz) harmonic generation across the Mott insulator-metal transition in rare-earth nickelates (RNiO$_3$, R = rare-earth atom). The THz harmonic generation is observed in all the three different phases with distinct behaviors: the intensity of harmonics increases upon cooling in both the low-temperature antiferromagnetic (AFM) insulating and high-temperature paramagnetic (PM) metallic phases, while this trend is reversed in the intermediate PM insulating phase. Using single- and two-band Hubbard models, we find different dominant origins of THz harmonics in different phases: strong spin-charge and orbital-charge couplings in the AFM insulating phase, intraband currents from renormalized quasi-particles with frequency-dependent scattering rate in the PM metallic phase, and the reduction of the charge carrier density due to the opening of the Mott gap in the PM insulating phase. Our study offers strategies for efficient THz harmonic generation from Mott and other strongly correlated systems and insights into the fundamental physics of complex materials.
CompLex: legal systems through the lens of complexity science
2024-10-02
Pierpaolo Vivo, Daniel M. Katz, J. B. Ruhl
While "complexity science" has achieved significant successes in several interdisciplinary fields such as economics and biology, it is only a very recent observation that legal systems -- from the way legal texts are drafted and connected to the rest of the corpus, up to the level of how judges and courts reach decisions under a variety of conflicting inputs -- share several features with standard Complex Adaptive Systems. This review is meant as a gentle introduction to the use of quantitative tools and techniques of complexity science to describe, analyse, and tame the complex web of human interactions that the Law is supposed to regulate. We offer an overview of the main directions of research undertaken so far as well as an outlook for future research, and we argue that statistical physicists and complexity scientists should not ignore the opportunities offered by the cross-fertilisation between legal scholarship and complex-systems modelling.
Late-Time Supernovae Radio Re-brightening in the VAST Pilot Survey
2024-10-02
Kovi Rose, Assaf Horesh, Tara Murphy, David L. Kaplan, Itai Sfaradi, Stuart D. Ryder, Robert J. Aloisi, Dougal Dobie, Laura Driessen, Rob Fender, David A. Green, James K. Leung, Emil Lenc, Hao Qiu, David Williams-Baldwin
We present our analysis of supernovae serendipitously found to be radio-bright several years after their optical discovery. We used recent observations from the Australian SKA Pathfinder taken as part of the pilot Variables and Slow Transients and Rapid ASKAP Continuum Survey programs. We identified 29 objects by cross-matching sources from these ASKAP observations with known core-collapse supernovae below a declination of $+40^{\circ}$ and with a redshift of $z\leq0.15$. Our results focus on eight cases that show potential late-time radio emission. These supernovae exhibit significantly greater amounts of radio emission than expected from the standard model of a single shockwave propagating through a spherical circumstellar medium, with a constant density structure produced by regular stellar mass-loss. We also discuss how we can learn from future ASKAP surveys about the circumstellar environments and emission mechanisms of supernovae that undergo late-time radio re-brightening. This pilot work tested and confirmed the potential of the Variables and Slow Transients survey to discover and study late-time supernova emission.
Takin-VC: Zero-shot Voice Conversion via Jointly Hybrid Content and Memory-Augmented Context-Aware Timbre Modeling
2024-10-02
Yuguang Yang, Yu Pan, Jixun Yao, Xiang Zhang, Jianhao Ye, Hongbin Zhou, Lei Xie, Lei Ma, Jianjun Zhao
Zero-shot voice conversion (VC) aims to transform the source speaker timbre into an arbitrary unseen one without altering the original speech content.While recent advancements in zero-shot VC methods have shown remarkable progress, there still remains considerable potential for improvement in terms of improving speaker similarity and speech naturalness.In this paper, we propose Takin-VC, a novel zero-shot VC framework based on jointly hybrid content and memory-augmented context-aware timbre modeling to tackle this challenge. Specifically, an effective hybrid content encoder, guided by neural codec training, that leverages quantized features from pre-trained WavLM and HybridFormer is first presented to extract the linguistic content of the source speech. Subsequently, we introduce an advanced cross-attention-based context-aware timbre modeling approach that learns the fine-grained, semantically associated target timbre features. To further enhance both speaker similarity and real-time performance, we utilize a conditional flow matching model to reconstruct the Mel-spectrogram of the source speech. Additionally, we advocate an efficient memory-augmented module designed to generate high-quality conditional target inputs for the flow matching process, thereby improving the overall performance of the proposed system. Experimental results demonstrate that the proposed Takin-VC method surpasses state-of-the-art zero-shot VC systems, delivering superior performance in terms of both speech naturalness and speaker similarity.
A SPectroscopic survey of biased halos In the Reionization Era (ASPIRE): JWST Supports Earlier Reionization around [OIII] Emitters
2024-10-02
Xiangyu Jin, Jinyi Yang, Xiaohui Fan, Feige Wang, Koki Kakiichi, Romain A. Meyer, George D. Becker, Siwei Zou, Eduardo Bañados, Jaclyn B. Champagne, Valentina D'Odorico, Minghao Yue, Sarah E. I. Bosman, Zheng Cai, Anna-Christina Eilers, Joseph F. Hennawi, Hyunsung D. Jun, Mingyu Li, Zihao Li, Weizhe Liu, Maria Pudoka, Sindhu Satyavolu, Fengwu Sun, Wei Leong Tee, Yunjing Wu
Understanding when and how reionization happened is crucial for studying the early structure formation and the properties of first galaxies in the Universe. At $z>5.5$, the observed IGM optical depth shows a significant scatter, indicating an inhomogeneous reionization process. However, the nature of the inhomogeneous reionization remains debated. ASPIRE is a JWST Cycle 1 program that has spectroscopically identified $>400$ [OIII] emitters in 25 quasar fields at $z>6.5$. Combined with deep ground-based optical spectroscopy of ASPIRE quasars, ASPIRE program provides the current largest sample for IGM-galaxy connection studies during cosmic reionization. We present the first results of IGM effective optical depth measurements around [OIII] emitters using 14 ASPIRE quasar fields. We find the IGM transmission is tightly related with reionization-era galaxies to the extent that significant excess of Ly$\alpha$ transmission exists around [OIII] emitters. We measure the stacked IGM effective optical depth of IGM patches associated with [OIII] emitters and find they reach the same IGM effective optical depth at least dz~0.1 ahead of those IGM patches where no [OIII] emitters are detected, supporting earlier reionization around [OIII] emitters. Our results indicate an enhancement in IGM Ly$\alpha$ transmission around [OIII] emitters at scales beyond 25 $h^{-1}$ cMpc, consistent with the predicted topology of reionization from fluctuating UV background (UVB) models.
See Me and Believe Me: Causality and Intersectionality in Testimonial Injustice in Healthcare
2024-10-02
Kenya S. Andrews, Mesrob I. Ohannessian, Elena Zheleva
In medical settings, it is critical that all who are in need of care are correctly heard and understood. When this is not the case due to prejudices a listener has, the speaker is experiencing \emph{testimonial injustice}, which, building upon recent work, we quantify by the presence of several categories of unjust vocabulary in medical notes. In this paper, we use FCI, a causal discovery method, to study the degree to which certain demographic features could lead to marginalization (e.g., age, gender, and race) by way of contributing to testimonial injustice. To achieve this, we review physicians' notes for each patient, where we identify occurrences of unjust vocabulary, along with the demographic features present, and use causal discovery to build a Structural Causal Model (SCM) relating those demographic features to testimonial injustice. We analyze and discuss the resulting SCMs to show the interaction of these factors and how they influence the experience of injustice. Despite the potential presence of some confounding variables, we observe how one contributing feature can make a person more prone to experiencing another contributor of testimonial injustice. There is no single root of injustice and thus intersectionality cannot be ignored. These results call for considering more than singular or equalized attributes of who a person is when analyzing and improving their experiences of bias and injustice. This work is thus a first foray at using causal discovery to understand the nuanced experiences of patients in medical settings, and its insights could be used to guide design principles throughout healthcare, to build trust and promote better patient care.
Statistical Taylor expansion replaces the input precise variables in a conventional Taylor expansion with random variables each with known mean and deviation, to calculate the result mean and deviation. It is based on the uncorrelated uncertainty assumption: Each input variable is measured independently with fine enough statistical precision, so that their uncertainties are independent of each other. Statistical Taylor expansion reviews that the intermediate analytic expressions can no longer be regarded as independent of each other, and the result of analytic expression should be path independent. This conclusion differs fundamentally from the conventional common approach in applied mathematics to find the best execution path for a result. This paper also presents an implementation of statistical Taylor expansion called variance arithmetic, and the tests on variance arithmetic.
Markarian Multiwavelength Data Center (MMDC): A Tool for Retrieving and Modeling Multi-temporal, Multi-wavelength and Multi-messenger Data from Blazar Observations
2024-10-02
N. Sahakyan, V. Vardanyan, P. Giommi, D. Bégué, D. Israyelyan, G. Harutyunyan, M. Manvelyan, M. Khachatryan, H. Dereli-Bégué, S. Gasparyan
The Markarian Multiwavelength Data Center (MMDC) is a web-based tool designed for accessing and retrieving multiwavelength and multimessenger data from blazar observations. MMDC facilitates the construction and interactive visualization of time-resolved multi-band spectral energy distributions (SEDs) of blazars by integrating: \textit{(i)} archival data from over 80 catalogs and databases, \textit{(ii)} optical data from all-sky survey facilities such as ASAS-SN, ZTF, and Pan-STARRS, and \textit{(iii)} newly analyzed datasets in the optical/UV band from \textit{Swift}-UVOT, in the X-ray band from \textit{Swift}-XRT and NuSTAR observations, and the high-energy $\gamma$-ray band from \textit{Fermi}-LAT observations. MMDC distinguishes itself from other online platforms by the large quantity of available data. For instance, it includes data from all blazar observations by \textit{Swift} and NuSTAR, as well as the results of detailed spectral analysis in the $\gamma$-ray band during different emission states, covering the period from 2008 to 2023. Another important distinguishing feature of MMDC is its ability to enable precise, self-consistent theoretical modeling of the observed data using machine learning algorithms trained on leptonic and lepto-hadronic models, which consider the injection of particles and all relevant cooling processes. MMDC is an innovative tool which significantly enhances blazar research by providing a comprehensive framework for data accessibility, analysis, and theoretical interpretation, thereby advancing our understanding of blazar emissions and the underlying astrophysical processes.
Domain adaptation in application to gravitational lens finding
2024-10-02
Hanna Parul, Sergei Gleyzer, Pranath Reddy, Michael W. Toomey
The next decade is expected to see a tenfold increase in the number of strong gravitational lenses, driven by new wide-field imaging surveys. To discover these rare objects, efficient automated detection methods need to be developed. In this work, we assess the performance of three domain adaptation techniques -- Adversarial Discriminative Domain Adaptation (ADDA), Wasserstein Distance Guided Representation Learning (WDGRL), and Supervised Domain Adaptation (SDA) -- in enhancing lens-finding algorithms trained on simulated data when applied to observations from the Hyper Suprime-Cam Subaru Strategic Program. We find that WDGRL combined with an ENN-based encoder provides the best performance in an unsupervised setting and that supervised domain adaptation is able to enhance the model's ability to distinguish between lenses and common similar-looking false positives, such as spiral galaxies, which is crucial for future lens surveys.
The Tale of Two Telescopes: How Hubble Uniquely Complements the James Webb Space Telescope: Galaxies
2024-10-02
Rogier A. Windhorst, Jake Summers, Timothy Carleton, Seth H. Cohen, Kevin S. Croker, Rolf A. Jansen, Rosalia O'Brien, Brent M. Smith, Christopher J. Conselice, Jose M. Diego, Simon P. Driver, Brenda Frye, Haojing Yan
In this paper, we present a simple but compelling argument, focusing on galaxy science, for preserving the main imagers and operational modes of the Hubble Space Telescope (HST) for as long as is technically feasible. While star-formation started at redshifts z$\gtrsim$10$-$13, when the universe was less than 300$-$500 Myr old, the CSFH did not peak until z$\simeq$1.9, and has steadily declined since that time. Hence, at least half of all stars in the universe formed in the era where HST provides its unique rest-frame UV view of unobscured young, massive stars tracing cosmic star-formation. By rendering a subset of the 556.3 hours of available HST images in 12 filters of the Hubble Ultra Deep Field (HUDF) in an appropriate mix of colors, we illustrate the unique capabilities of HST for galaxy science emphasizing that rest-frame UV$-$optical wavelength range. We then contrast this with the 52.7 publicly available hours of JWST/NIRCam images in 8 filters of the same HUDF area from the JADES project, rendering these at the redder near-IR wavelengths to illustrate the unique capabilities of JWST to detect older stellar populations at higher redshifts, as well as very dusty stellar populations and Active Galactic Nuclei (AGN). HST uniquely probes (unobscured) young, hot, massive stars in galaxies, while JWST reveals more advanced stages of older stellar populations, as well as relatively short-lived phases where galaxies produce and shed a lot of dust from intense star-formation, and the very high redshift universe (z$\gtrsim$10$-$11) not accessible by HST. We conclude that HST and JWST are highly complementary facilities that took decades to build to ensure decades of operation. To maximize return on investment on both HST and JWST, ways will need to be found to operate HST imaging instruments in all relevant modes for as long as possible into the JWST mission.
Forecasting short-term inflation in Argentina with Random Forest Models
2024-10-02
Federico Daniel Forte
This paper examines the performance of Random Forest models in forecasting short-term monthly inflation in Argentina, based on a database of monthly indicators since 1962. It is found that these models achieve forecast accuracy that is statistically comparable to the consensus of market analysts' expectations surveyed by the Central Bank of Argentina (BCRA) and to traditional econometric models. One advantage of Random Forest models is that, as they are non-parametric, they allow for the exploration of nonlinear effects in the predictive power of certain macroeconomic variables on inflation. Among other findings, the relative importance of the exchange rate gap in forecasting inflation increases when the gap between the parallel and official exchange rates exceeds 60%. The predictive power of the exchange rate on inflation rises when the BCRA's net international reserves are negative or close to zero (specifically, below USD 2 billion). The relative importance of inflation inertia and the nominal interest rate in forecasting the following month's inflation increases when the nominal levels of inflation and/or interest rates rise.
The advancement of text generation models has granted us the capability to produce coherent and convincing text on demand. Yet, in real-life circumstances, individuals do not continuously generate text or voice their opinions. For instance, consumers pen product reviews after weighing the merits and demerits of a product, and professional analysts issue reports following significant news releases. In essence, opinion expression is typically prompted by particular reasons or signals. Despite long-standing developments in opinion mining, the appropriate timing for expressing an opinion remains largely unexplored. To address this deficit, our study introduces an innovative task - the identification of news-triggered opinion expressing timing. We ground this task in the actions of professional stock analysts and develop a novel dataset for investigation. Our approach is decision-focused, leveraging text generation models to steer the classification model, thus enhancing overall performance. Our experimental findings demonstrate that the text generated by our model contributes fresh insights from various angles, effectively aiding in identifying the optimal timing for opinion expression.
Phase-coding quantum-key-distribution system based on Sagnac-Mach-Zehnder interferometers
2024-10-02
Xiao-Tian Song, Dong Wang, Xiao-Ming Lu, Da-Jun Huang, Di Jiang, Li-Xian Li, Xi Fang, Yi-Bo Zhao, Liang-Jiang Zhou
Stability and robustness are important criteria to evaluate the performance of a quantum-key-distribution (QKD) system in real-life applications. However, the inherent birefringence effect of the fiber channel and disturbance caused by the variation of the deployment environment of the QKD system tremendously decreases its performance. To eliminate this adverse impact, we propose a polarization-insensitive phase-coding QKD system based on Sagnac-Mach-Zehnder interferometers. Verified by theoretical analysis and experimental tests, this QKD system is robust against channel polarization disturbance. The robustness and long-term stability of the QKD system is confirmed with a 10-day continuous operation over a 12.6-dB channel, which consists of a 50-km fiber spool and a polarization scrambler (2 rad/s). As a result, an average quantum bit error rate of 0.958% and a sustained secure key rate of 3.68 kbps are obtained. Moreover, the secure key rate of the QKD system for a typical channel loss of 10 dB reaches 6.89 kbps, and the achievable maximum transmission distance exceeds 125 km.
The formation histories of massive and quiescent galaxies in the 3 < z < 4.5 Universe
2024-10-02
Themiya Nanayakkara, Karl Glazebrook, Corentin Schreiber, Harry Chittenden, Gabriel Brammer, James Esdaile, Colin Jacobs, Glenn G. Kacprzak, Lalitwadee Kawinwanichakij, Lucas C. Kimmig, Ivo Labbe, Claudia Lagos, Danilo Marchesini, M. Martìnez-Marìn, Z. Cemile Marsan, Pascal A. Oesch, Casey Papovich, Rhea-Silvia Remus, Kim-Vy H. Tran
We present the formation histories of 19 massive ($\gtrsim3\times10^{10}\text{M}_\odot$) quiescent galaxy candidates at $z\sim3.0-4.5$ observed using JWST/NIRSpec. This completes the spectroscopic confirmation of the 24 $K$-selected quiescent galaxy sample from the ZFOURGE and 3DHST surveys \citep{Schreiber2018}. Utilizing Prism $1-5\mu$m spectroscopy, we confirm that all 12 sources that eluded confirmation by ground-based spectroscopy lie at $z>3$, resulting in a spectroscopically confirmed number density of $\sim1.4\times10^{-5}\text{Mpc}^{-3}$ between $z\sim3-4$. Rest-frame $U-V$ vs $V-J$ color selections show high effectiveness in identifying quiescent galaxies, with a purity of $\sim90\%$. Our analysis shows that parametric star-formation histories (SFHs) from FAST++ and binned SFHs from Prospector on average yield consistent results, revealing diverse formation and quenching times. The oldest galaxy formed $\sim6\times10^{10}\text{M}_\odot$ by $z\sim10$ and has been quiescent for over 1 Gyr at $z\sim3.2$. We detect two galaxies with ongoing star formation and six with active galactic nuclei (AGN). We demonstrate that the choice of stellar population models, stellar libraries, wavelength range, and nebular or AGN contributions does not significantly affect the derived average SFHs of the galaxies. The assumed SFH prior, however, influences the star formation rate at early times, where spectral diagnostic power is limited. Simulated $z\sim3$ quiescent galaxies from IllustrisTNG, SHARK, and Magneticum broadly match the average SFHs of the observed sample but struggle to capture the full diversity, particularly at early stages. Our results emphasize the need for mechanisms that rapidly build stellar mass and quench star formation within the first billion years of the Universe.
TESS Asteroseismic Masses and Radii of Red Giants with (and without) Planets
2024-10-02
Myles Pope, Joleen K. Carlberg, Jeff Valenti, Doug Branton
We present a study of asteroseismically derived surface gravities, masses, and radii of a sample of red giant stars both with and without confirmed planetary companions using TESS photometric light curves. These red giants were drawn from radial velocity surveys, and their reported properties in the literature rely on more traditional methods using spectroscopy and isochrone fitting. Our asteroseismically derived surface gravities achieved a precision of $\sim$0.01 dex; however, they were on average $\sim$0.1~dex smaller than the literature. The systematic larger gravities of the literature could plausibly present as a systematic overestimation of stellar masses, which would in turn lead to overestimated planetary masses of the companions. We find that the fractional discrepancies between our asteroseismically-determined parameters and those previously found are typically larger for stellar radii ($\sim$10% discrepancy) than for stellar masses ($<5$% discrepancy). However, no evidence of a systematic difference between methods was found for either fundamental parameter. Two stars, HD~100065 and HD~18742, showed significant disagreement with the literature in both mass and radii. We explore the impacts on updated stellar properties on inferred planetary properties and caution that red giant radii may be more poorly constrained than uncertainties suggest.
Zodiac: A Cardiologist-Level LLM Framework for Multi-Agent Diagnostics
2024-10-02
Yuan Zhou, Peng Zhang, Mengya Song, Alice Zheng, Yiwen Lu, Zhiheng Liu, Yong Chen, Zhaohan Xi
Large language models (LLMs) have demonstrated remarkable progress in healthcare. However, a significant gap remains regarding LLMs' professionalism in domain-specific clinical practices, limiting their application in real-world diagnostics. In this work, we introduce ZODIAC, an LLM-powered framework with cardiologist-level professionalism designed to engage LLMs in cardiological diagnostics. ZODIAC assists cardiologists by extracting clinically relevant characteristics from patient data, detecting significant arrhythmias, and generating preliminary reports for the review and refinement by cardiologists. To achieve cardiologist-level professionalism, ZODIAC is built on a multi-agent collaboration framework, enabling the processing of patient data across multiple modalities. Each LLM agent is fine-tuned using real-world patient data adjudicated by cardiologists, reinforcing the model's professionalism. ZODIAC undergoes rigorous clinical validation with independent cardiologists, evaluated across eight metrics that measure clinical effectiveness and address security concerns. Results show that ZODIAC outperforms industry-leading models, including OpenAI's GPT-4o, Meta's Llama-3.1-405B, and Google's Gemini-pro, as well as medical-specialist LLMs like Microsoft's BioGPT. ZODIAC demonstrates the transformative potential of specialized LLMs in healthcare by delivering domain-specific solutions that meet the stringent demands of medical practice. Notably, ZODIAC has been successfully integrated into electrocardiography (ECG) devices, exemplifying the growing trend of embedding LLMs into Software-as-Medical-Device (SaMD).
Review Non-convex Optimization Method for Machine Learning
2024-10-02
Greg B Fotopoulos, Paul Popovich, Nicholas Hall Papadopoulos
Non-convex optimization is a critical tool in advancing machine learning, especially for complex models like deep neural networks and support vector machines. Despite challenges such as multiple local minima and saddle points, non-convex techniques offer various pathways to reduce computational costs. These include promoting sparsity through regularization, efficiently escaping saddle points, and employing subsampling and approximation strategies like stochastic gradient descent. Additionally, non-convex methods enable model pruning and compression, which reduce the size of models while maintaining performance. By focusing on good local minima instead of exact global minima, non-convex optimization ensures competitive accuracy with faster convergence and lower computational overhead. This paper examines the key methods and applications of non-convex optimization in machine learning, exploring how it can lower computation costs while enhancing model performance. Furthermore, it outlines future research directions and challenges, including scalability and generalization, that will shape the next phase of non-convex optimization in machine learning.
Superconductivity in the parent infinite-layer nickelate NdNiO$_2$
2024-10-02
C. T. Parzyck, Y. Wu, L. Bhatt, M. Kang, Z. Arthur, T. M. Pedersen, R. Sutarto, S. Fan, J. Pelliciari, V. Bisogni, G. Herranz, A. B. Georgescu, D. G. Hawthorn, L. F. Kourkoutis, D. A. Muller, D. G. Schlom, K. M. Shen
We report evidence for superconductivity with onset temperatures up to 11 K in thin films of the infinite-layer nickelate parent compound NdNiO$_2$. A combination of oxide molecular-beam epitaxy and atomic hydrogen reduction yields samples with high crystallinity and low residual resistivities, a substantial fraction of which exhibit superconducting transitions. We survey a large series of samples with a variety of techniques, including electrical transport, scanning transmission electron microscopy, x-ray absorption spectroscopy, and resonant inelastic x-ray scattering, to investigate the possible origins of superconductivity. We propose that superconductivity could be intrinsic to the undoped infinite-layer nickelates but suppressed by disorder due to its nodal order parameter, a finding which would necessitate a reconsideration of the nickelate phase diagram. Another possible hypothesis is that the parent materials can be hole doped from randomly dispersed apical oxygen atoms, which would suggest an alternative pathway for achieving superconductivity.
Role of neutron pairing with density-gradient dependence in the semi-microscopic treatment of the inner crust of neutron stars
2024-10-02
Nicolas Chamel, John-Michael Pearson, Nikolay N. Shchechilin
Using the fourth-order extended Thomas-Fermi method with Strutinsky-integral shell and pairing corrections, we calculate the inner crust of neutron stars with the BSk31 functional, whose pairing has two terms: i) a term that is fitted to the results of microscopic calculations on homogeneous nuclear matter (accounting for both medium polarization and self-energy effects) that are more realistic than those of our earlier functionals; ii) an empirical term that is dependent on the density gradient, which permits an excellent fit to nuclear masses. Both proton and neutron pairing are taken into account, the former in the BCS theory and the latter in the local density approximation. We found that the equilibrium value of the proton number $Z$ remains 40 over the entire density range considered, whether or not neutron pairing is included. The new equation of state and the composition are very similar to those of our previously preferred functional, BSk24. However, the predicted neutron pairing fields are quite different. In particular, clusters are found to be impermeable to the neutron superfluid. The implications for the neutron superfluid dynamics are briefly discussed. Since the new pairing is more realistic, the functional BSk31 is better suited for investigating neutron superfluidity in neutron-star crusts.
Rust aims to be a safe programming language applicable to systems programming applications. In particular, its type system has strong guardrails to prevent a variety of issues, such as memory safety bugs and data races. However, these guardrails can be sidestepped via the unsafe keyword. unsafe allows certain otherwise-prohibited operations, but shifts the onus of preventing undefined behaviour from the Rust language's compile-time checks to the developer. We believe that tools have a role to play in ensuring the absence of undefined behaviour in the presence of unsafe code. Moreover, safety aside, programs would also benefit from being verified for functional correctness, ensuring that they meet their specifications. In this research proposal, we explore what it means to do Rust verification. Specifically, we explore which properties are worth verifying for Rust; what techniques exist to verify them; and which code is worth verifying. In doing so, we motivate an effort to verify safety properties of the Rust standard library, presenting the relevant challenges along with ideas to address them.
Near room temperature ferrimagnetism and half-metallicity in disordered Ca1.5La0.5MnRuO6
2024-10-02
A. G. Silva, R. B. Pontes, M. Boldrin, H. V. S. Pessoni, L. S. I. Veiga, J. R. Jesus, H. Fabrelli, A. R. C. Gonzaga, E. M. Bittar, L. Bufaiçal
The electron spin polarization on half-metallic double-perovskites is usually conditioned to the ordered rock-salt arrangement of the transition-metal ions along the lattice. In this work, we investigate a polycrystalline sample of the Ca1.5La0.5MnRuO6 compound by employing x-ray powder diffraction, high resolution transmission electron microscopy, x-ray absorption and magnetic circular dichroism at the Mn-L2,3 and Ru-M2,3 edges, magnetometry, electrical transport and first principles calculations in order to show that this is a fully disordered material exhibiting near room temperature ferrimagnetism and half-metallic conductivity, with significant intergrain tunneling magnetoresistance. These unprecedented results are compared to those of archetypical ordered double-perovskites, and discussed in terms of the Mn and Ru valences and their orbital hybridization.
The potential of LLM-generated reports in DevSecOps
2024-10-02
Nikolaos Lykousas, Vasileios Argyropoulos, Fran Casino
Alert fatigue is a common issue faced by software teams using the DevSecOps paradigm. The overwhelming number of warnings and alerts generated by security and code scanning tools, particularly in smaller teams where resources are limited, leads to desensitization and diminished responsiveness to security warnings, potentially exposing systems to vulnerabilities. This paper explores the potential of LLMs in generating actionable security reports that emphasize the financial impact and consequences of detected security issues, such as credential leaks, if they remain unaddressed. A survey conducted among developers indicates that LLM-generated reports significantly enhance the likelihood of immediate action on security issues by providing clear, comprehensive, and motivating insights. Integrating these reports into DevSecOps workflows can mitigate attention saturation and alert fatigue, ensuring that critical security warnings are addressed effectively.
We study the robustness of the Baryon Acoustic Oscillation (BAO) feature in galaxy clustering in the presence of massive neutrinos. In the standard BAO analysis pipeline a reference cosmological model is assumed to boost the BAO peak through the so-called reconstruction technique and in the modelling of the BAO feature to extract the cosmological information. State-of-the art spectroscopic BAO measurements, such as the Dark Energy Spectroscopic Instrument claim an aggregate precision of 0.52$\%$ on the BAO scale, with a systematic error of 0.1$\%$ associated to the assumption of a reference cosmology when measuring and analyzing the BAO feature. While the systematic effect induced by this arbitrary choice of fiducial cosmology has been studied for a wide range of $\Lambda$CDM-like models, it has not yet been tested for reference cosmologies with massive neutrinos with the precision afforded by next generation surveys. In this context, we employ the Quijote high-resolution dark-matter simulations with haloes above a mass of $M\sim 2\times10^{13} h^{-1}M_{\odot}$, with different values for the total sum of neutrinos masses, $\sum m_\nu[{\rm eV}] = 0,\, 0.1,\, 0,2,\, 0.4$ to study and quantify the impact of the pipeline's built-in assumption of massless neutrinos on the measurement of the BAO signal, with a special focus on the BAO reconstruction technique. We determine that any additional systematic bias introduced by the assumption of massless neutrinos is no greater than $0.1\%$ ($0.2\%$) for the isotropic (anisotropic) measurement. We expect these conclusions also hold for galaxies provided that neutrino properties do not alter the galaxy-halo connection.
Merian: A Wide-Field Imaging Survey of Dwarf Galaxies at z~0.06-0.10
2024-10-02
Shany Danieli, Erin Kado-Fong, Song Huang, Yifei Luo, Ting S Li, Lee S Kelvin, Alexie Leauthaud, Jenny E. Greene, Abby Mintz, Xiaojing Lin, Jiaxuan Li, Vivienne Baldassare, Arka Banerjee, Joy Bhattacharyya, Diana Blanco, Alyson Brooks, Zheng Cai, Xinjun Chen, Akaxia Cruz, Robel Geda, Runquan Guan, Sean Johnson, Arun Kannawadi, Stacy Y. Kim, Mingyu Li, Robert Lupton, Charlie Mace, Gustavo E. Medina, Yue Pan, Annika H. G. Peter, Justin I. Read, Rodrigo Córdova Rosado, Allen Seifert, Erik J. Wasleske, Joseph Wick
We present the Merian Survey, an optical imaging survey optimized for studying the physical properties of bright star-forming dwarf galaxies. Merian is carried out with two medium-band filters ($N708$ and $N540$, centered at $708$ and $540$ nm), custom-built for the Dark Energy Camera (DECam) on the Blanco telescope. Merian covers $\sim 750\,\mathrm{deg}^2$ of equatorial fields, overlapping with the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) wide, deep, and ultra-deep fields. When combined with the HSC-SSP imaging data ($grizy$), the new Merian DECam medium-band imaging allows for photometric redshift measurements via the detection of H$\rm\alpha$ and [OIII] line emission flux excess in the $N708$ and $N540$ filters, respectively, at $0.06
A Nonparametric Morphological Analysis of H$α$ Emission in Bright Dwarfs Using the Merian Survey
2024-10-02
Abby Mintz, Jenny E. Greene, Erin Kado-Fong, Shany Danieli, Jiaxuan Li, Yifei Luo, Alexie Leauthaud, Vivienne Baldassare, Song Huang, Annika H. G. Peter, Joy Bhattacharyya, Mingyu Li, Yue Pan
Using medium-band imaging from the newly released Merian Survey, we conduct a nonparametric morphological analysis of H$\alpha$ emission maps and stellar continua for a sample of galaxies with $8\lesssim\log (M_\star/M_\odot) < 10.3$ at $0.064
All the Little Things in Abell 2744: $>$1000 Gravitationally Lensed Dwarf Galaxies at $z=0-9$ from JWST NIRCam Grism Spectroscopy
2024-10-02
Rohan P. Naidu, Jorryt Matthee, Ivan Kramarenko, Andrea Weibel, Gabriel Brammer, Pascal A. Oesch, Peter Lechner, Lukas J. Furtak, Claudia Di Cesare, Alberto Torralba, Gauri Kotiwale, Rachel Bezanson, Rychard J. Bouwens, Vedant Chandra, Adélaïde Claeyssens, A. Lola Danhaive, Anna Frebel, Anna de Graaff, Jenny E. Greene, Kasper E. Heintz, Alexander P. Ji, Daichi Kashino, Harley Katz, Ivo Labbe, Joel Leja, Yijia Li, Michael V. Maseda, Johan Richard, Irene Shivaei, Robert A. Simcoe, David Sobral, Katherine A. Suess, Sandro Tacchella, Christina C. Williams
Dwarf galaxies hold the key to crucial frontiers of astrophysics, however, their faintness renders spectroscopy challenging. Here we present the JWST Cycle 2 survey, All the Little Things (ALT, PID 3516), which is designed to seek late-forming Pop III stars and the drivers of reionization at $z\sim6-7$. ALT has acquired the deepest NIRCam grism spectroscopy yet (7-27 hr), at JWST's most sensitive wavelengths (3-4 $\mu$m), covering the powerful lensing cluster Abell 2744. Over the same 30 arcmin$^2$, ALT's ultra-deep F070W+F090W imaging ($\sim$30 mag) enables selection of very faint sources at $z>6$. We demonstrate the success of ALT's novel ``butterfly" mosaic to solve spectral confusion and contamination, and introduce the ``Allegro" method for emission line identification. By collecting spectra for every source in the field of view, ALT has measured precise ($R\sim1600$) redshifts for 1630 sources at $z=0.2-8.5$. This includes one of the largest samples of distant dwarf galaxies: [1015, 475, 50] sources less massive than the SMC, Fornax, and Sculptor with $\log(M_{*}/M_{\odot})<$[8.5, 7.5, 6.5]. We showcase ALT's discovery space with: (i) spatially resolved spectra of lensed clumps in galaxies as faint as $M_{\rm{UV}}\sim-15$; (ii) large-scale clustering -- overdensities at $z$=[2.50, 2.58, 3.97, 4.30, 5.66, 5.77, 6.33] hosting massive galaxies with striking Balmer breaks; (iii) small-scale clustering -- a system of satellites around a Milky Way analog at $z\sim6$; (iv) spectroscopically confirmed multiple images that help constrain the lensing model underlying all science in this legacy field; (v) sensitive star-formation maps based on dust-insensitive tracers such as Pa$\alpha$; (vi) direct spectroscopic discovery of rare sources such as AGN with ionized outflows. These results provide a powerful proof of concept for how grism surveys maximize the potential of strong lensing fields.
The PANORAMIC Survey: Pure Parallel Wide Area Legacy Imaging with JWST/NIRCam
2024-10-02
Christina C. Williams, Pascal A. Oesch, Andrea Weibel, Gabriel Brammer, Aidan P. Cloonan, Katherine E. Whitaker, Laia Barrufet, Rachel Bezanson, Rebecca A. A. Bowler, Pratika Dayal, Marijn Franx, Jenny E. Greene, Anne Hutter, Zhiyuan Ji, Ivo Labbé, Sinclaire M. Manning, Michael V. Maseda, Mengyuan Xiao
We present the PANORAMIC survey, a pure parallel extragalactic imaging program with NIRCam observed during JWST Cycle 1. The survey obtained $\sim$530 sq arcmin of NIRCam imaging from 1-5$\mu$m, totaling $\sim$192 hours of science integration time. This represents the largest on-sky time investment of any Cycle 1 GO extragalactic NIRCam imaging program by nearly a factor of 2. The survey includes $\sim$432 sq arcmin of novel sky area not yet observed with JWST using at least $6$ NIRCam broad-band filters, increasing the existing area covered by similar Cycle 1 data by $\sim$60%. 70 square arcmin was also covered by a 7th filter (F410M). A fraction of PANORAMIC data ($\sim$200 sq arcmin) was obtained in or around extragalactic deep-fields, enhancing their legacy value. Pure parallel observing naturally creates a wedding cake survey with both wide and ultra-deep tiers, with 5$\sigma$ point source depths at F444W ranging from 27.8-29.4 (ABmag), and with minimized cosmic variance. The 6+ filter observing setup yields remarkably good photometric redshift performance, achieving similar median scatter and outlier fraction as CANDELS ($\sigma_{\rm NMAD}\sim0.07$; $\eta\sim0.2$), which enables a wealth of science across redshift without the need for followup or ancillary data. We overview the proposed survey, the data obtained as part of this program, and document the science-ready data products in the first data release. PANORAMIC has delivered wide-area and deep imaging with excellent photometric performance, demonstrating that pure parallel observations with JWST are a highly efficient observing mode that is key to acquiring a complete picture of galaxy evolution from rare bright galaxies to fainter, more abundant sources at all redshifts.
Lithium plays a crucial role in probing stellar physics and stellar and primordial nucleosynthesis, as well as the chemical evolution of our Galaxy. Stars are considered to be the main source of Li, still the identity of its primary stellar producer has long been a matter of debate. In light of recent theoretical and observational results, we investigate in this study the role of two candidate sources of Li enrichment in the Milky Way, namely AGB stars and, in particular, novae. We utilize a one-zone Galactic chemical evolution model to assess the viability of AGB stars and novae as stellar sources of Li. We use recent theoretical Li yields for AGB stars, while for novae we adopt observationally inferred Li yields and recently derived Delay Time Distributions (DTDs). Subsequently, we extend our analysis using a multi-zone model with radial migration to investigate spatial variations in the evolution of Li across the Milky Way disc and compare the results with observational data for field stars and open clusters. Our analysis shows that AGB stars fail by far to reproduce the meteoritic Li abundance. In contrast, novae appear as promising candidates within the adopted framework, allowing us to quantify the contribution of each Li source at Sun's formation and today. Our multi-zone model reveals the role of the differences in the DTDs of SN Ia and novae in shaping the evolution of Li in the various galactic zones. Its results are in fair agreement with the observational data for most open clusters, but small discrepancies appear in the outer disc.
Massive activations, which manifest in specific feature dimensions of hidden states, introduce a significant bias in large language models (LLMs), leading to an overemphasis on the corresponding token. In this paper, we identify that massive activations originate not from the hidden state but from the intermediate state of a feed-forward network module in an early layer. Expanding on the previous observation that massive activations occur only in specific feature dimensions, we dive deep into the weights that cause massive activations. Specifically, we define top-$k$ massive weights as the weights that contribute to the dimensions with the top-$k$ magnitudes in the intermediate state. When these massive weights are set to zero, the functionality of LLMs is entirely disrupted. However, when all weights except for massive weights are set to zero, it results in a relatively minor performance drop, even though a much larger number of weights are set to zero. This implies that during the pre-training process, learning is dominantly focused on massive weights. Building on this observation, we propose a simple plug-and-play method called MacDrop (massive weights curriculum dropout), to rely less on massive weights during parameter-efficient fine-tuning. This method applies dropout to the pre-trained massive weights, starting with a high dropout probability and gradually decreasing it as fine-tuning progresses. Through experiments, we demonstrate that MacDrop generally improves performance across zero-shot downstream tasks and generation tasks.
In this work, we present HiReview, a novel framework for hierarchical taxonomy-driven automatic literature review generation. With the exponential growth of academic documents, manual literature reviews have become increasingly labor-intensive and time-consuming, while traditional summarization models struggle to generate comprehensive document reviews effectively. Large language models (LLMs), with their powerful text processing capabilities, offer a potential solution; however, research on incorporating LLMs for automatic document generation remains limited. To address key challenges in large-scale automatic literature review generation (LRG), we propose a two-stage taxonomy-then-generation approach that combines graph-based hierarchical clustering with retrieval-augmented LLMs. First, we retrieve the most relevant sub-community within the citation network, then generate a hierarchical taxonomy tree by clustering papers based on both textual content and citation relationships. In the second stage, an LLM generates coherent and contextually accurate summaries for clusters or topics at each hierarchical level, ensuring comprehensive coverage and logical organization of the literature. Extensive experiments demonstrate that HiReview significantly outperforms state-of-the-art methods, achieving superior hierarchical organization, content relevance, and factual accuracy in automatic literature review generation tasks.
Generating Seamless Virtual Immunohistochemical Whole Slide Images with Content and Color Consistency
2024-10-01
Sitong Liu, Kechun Liu, Samuel Margolis, Wenjun Wu, Stevan R. Knezevich, David E Elder, Megan M. Eguchi, Joann G Elmore, Linda Shapiro
Immunohistochemical (IHC) stains play a vital role in a pathologist's analysis of medical images, providing crucial diagnostic information for various diseases. Virtual staining from hematoxylin and eosin (H&E)-stained whole slide images (WSIs) allows the automatic production of other useful IHC stains without the expensive physical staining process. However, current virtual WSI generation methods based on tile-wise processing often suffer from inconsistencies in content, texture, and color at tile boundaries. These inconsistencies lead to artifacts that compromise image quality and potentially hinder accurate clinical assessment and diagnoses. To address this limitation, we propose a novel consistent WSI synthesis network, CC-WSI-Net, that extends GAN models to produce seamless synthetic whole slide images. Our CC-WSI-Net integrates a content- and color-consistency supervisor, ensuring consistency across tiles and facilitating the generation of seamless synthetic WSIs while ensuring Sox10 immunohistochemistry accuracy in melanocyte detection. We validate our method through extensive image-quality analyses, objective detection assessments, and a subjective survey with pathologists. By generating high-quality synthetic WSIs, our method opens doors for advanced virtual staining techniques with broader applications in research and clinical care.
From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems
2024-10-01
Ali Mohammadjafari, Anthony S. Maida, Raju Gottumukkala
Since the onset of LLMs, translating natural language queries to structured SQL commands is assuming increasing. Unlike the previous reviews, this survey provides a comprehensive study of the evolution of LLM-based text-to-SQL systems, from early rule-based models to advanced LLM approaches, and how LLMs impacted this field. We discuss benchmarks, evaluation methods and evaluation metrics. Also, we uniquely study the role of integration of knowledge graphs for better contextual accuracy and schema linking in these systems. The current techniques fall into two categories: in-context learning of corpus and fine-tuning, which then leads to approaches such as zero-shot, few-shot learning from the end, and data augmentation. Finally, we highlight key challenges such as computational efficiency, model robustness, and data privacy with perspectives toward their development and improvements in potential areas for future of LLM-based text-to-SQL system.
$g$-vectors and $DT$-$F$-polynomials for Grassmannians
2024-10-01
Sarjick Bakshi, Bernhard Keller
We review $\mathrm{Hom}$-infinite Frobenius categorification of cluster algebras with coefficients and use it to give two applications of Jensen--King--Su's Frobenius categorification of the Grassmannian: 1) we determine the $g$-vectors of the Pl\"ucker coordinates with respect to the triangular initial seed and 2) we express the $F$-polynomials associated with the Donaldson--Thomas transformation in terms of $3$-dimensional Young diagrams thus providing a new proof for a theorem of Daping Weng.
MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages
2024-10-01
Marco Gaido, Sara Papi, Luisa Bentivogli, Alessio Brutti, Mauro Cettolo, Roberto Gretter, Marco Matassoni, Mohamed Nabih, Matteo Negri
The rise of foundation models (FMs), coupled with regulatory efforts addressing their risks and impacts, has sparked significant interest in open-source models. However, existing speech FMs (SFMs) fall short of full compliance with the open-source principles, even if claimed otherwise, as no existing SFM has model weights, code, and training data publicly available under open-source terms. In this work, we take the first step toward filling this gap by focusing on the 24 official languages of the European Union (EU). We collect suitable training data by surveying automatic speech recognition datasets and unlabeled speech corpora under open-source compliant licenses, for a total of 950k hours. Additionally, we release automatic transcripts for 441k hours of unlabeled data under the permissive CC-BY license, thereby facilitating the creation of open-source SFMs for the EU languages.
Finding radio transients with anomaly detection and active learning based on volunteer classifications
2024-10-01
Alex Andersson, Chris Lintott, Rob Fender, Michelle Lochner, Patrick Woudt, Jakob van den Eijnden, Alexander van der Horst, Assaf Horesh, Payaswini Saikia, Gregory R. Sivakoff, Lilia Tremou, Mattia Vaccari
In this work we explore the applicability of unsupervised machine learning algorithms to the task of finding radio transients. Facilities such as the Square Kilometre Array (SKA) will provide huge volumes of data in which to detect rare transients; the challenge for astronomers is how to find them. We demonstrate the effectiveness of anomaly detection algorithms using 1.3 GHz light curves from the SKA precursor MeerKAT. We make use of three sets of descriptive parameters ('feature sets') as applied to two anomaly detection techniques in the Astronomaly package and analyse our performance by comparison with citizen science labels on the same dataset. By using transients found by volunteers as our ground truth, we demonstrate that anomaly detection techniques can recall over half of the radio transients in the 10 per cent of the data with the highest anomaly scores. We find that the choice of anomaly detection algorithm makes a minimal (sub-one per cent) level difference, but that feature set choice is crucial, especially when considering available resources for human inspection and/or follow-up. Active learning, where human labels are given for just 2 per cent of the data, improves recall by up to 10 percentage points, depending on the feature-model pair. The best performing feature-model pairs result in a factor of 5 times fewer sources requiring vetting by experts. This is the first effort to apply anomaly detection techniques to finding radio transients and shows great promise for application to other datasets, and as a real-time transient detection system for upcoming large surveys.
Understanding the Human-LLM Dynamic: A Literature Survey of LLM Use in Programming Tasks
2024-10-01
Deborah Etsenake, Meiyappan Nagappan
Large Language Models (LLMs) are transforming programming practices, offering significant capabilities for code generation activities. While researchers have explored the potential of LLMs in various domains, this paper focuses on their use in programming tasks, drawing insights from user studies that assess the impact of LLMs on programming tasks. We first examined the user interaction behaviors with LLMs observed in these studies, from the types of requests made to task completion strategies. Additionally, our analysis reveals both benefits and weaknesses of LLMs showing mixed effects on the human and task. Lastly, we looked into what factors from the human, LLM or the interaction of both, affect the human's enhancement as well as the task performance. Our findings highlight the variability in human-LLM interactions due to the non-deterministic nature of both parties (humans and LLMs), underscoring the need for a deeper understanding of these interaction patterns. We conclude by providing some practical suggestions for researchers as well as programmers.
Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security
2024-10-01
Mona Esmaeili, Morteza Rahimi, Matin Khajavi, Dorsa Farahmand, Hadi Jabbari Saray
Attacks against the Internet of Things (IoT) are rising as devices, applications, and interactions become more networked and integrated. The increase in cyber-attacks that target IoT networks poses a huge vulnerability and threat to the privacy, security, functionality, and availability of critical systems, which leads to operational disruptions, financial losses, identity thefts, and data breaches. To efficiently secure IoT devices, real-time detection of intrusion systems is critical, especially those using machine learning to identify threats and mitigate risks and vulnerabilities. This paper investigates the latest research on machine learning-based intrusion detection strategies for IoT security, concentrating on real-time responsiveness, detection accuracy, and algorithm efficiency. Key studies were reviewed from all well-known academic databases, and a taxonomy was provided for the existing approaches. This review also highlights existing research gaps and outlines the limitations of current IoT security frameworks to offer practical insights for future research directions and developments.
The study of heat-to-work conversion has gained significant attention in recent years, highlighting the potential of nanoscale systems to achieve energy conversion in steady-state devices without any macroscopic moving parts. This review examines the theoretical frameworks governing the steady-state flows of quantum particles like electrons, photons, and phonons within various mesoscopic or nanoscale devices, such as thermoelectric heat engines in the context of quantum dot Aharonov-Bohm (AB) interferometric configurations. Quantum interference effects hold great promise for enhancing the thermoelectric transport properties of such quantum devices by allowing more precise control over energy levels and transport pathways. Driven quantum dot AB networks can maintain quantum coherence and provide precise experimental control. Unlike bulk systems, nanoscale systems like quantum dots reveal distinct quantum interference phenomena, including sharp features in transmission spectra and Fano resonances. This review highlights the distinction between optimization methods that produce boxcar functions and coherent control methods that result in complex interference patterns. It reveals that the effective design of thermoelectric heat engines requires careful tailoring of quantum interference and the magnetic field-induced effects to enhance performance. We emphasize how magnetic fields can change the bounds of power or efficiency. These machines with broken time-reversal symmetry provide insights into directional dependencies and asymmetries in quantum transport. We offer a thorough overview of past and current research on quantum thermoelectric heat engines using the AB effect and present a detailed review of three-terminal AB heat engines, where broken time-reversal symmetry can induce a coherent diode effect. We cover bounds on power and efficiency in systems with broken time-reversal symmetry.
Discovery and Spectroscopic Confirmation of Aquarius III: A Low-Mass Milky Way Satellite Galaxy
2024-10-01
W. Cerny, A. Chiti, M. Geha, B. Mutlu-Pakdil, A. Drlica-Wagner, C. Y. Tan, M. Adamów, A. B. Pace, J. D. Simon, D. J. Sand, A. P. Ji, T. S. Li, A. K. Vivas, E. F. Bell, J. L. Carlin, J. A. Carballo-Bello, A. Chaturvedi, Y. Choi, A. Doliva-Dolinsky, O. Y. Gnedin, G. Limberg, C. E. Martínez-Vázquez, S. Mau, G. E. Medina, M. Navabi, N. E. D. Noël, V. M. Placco, A. H. Riley, I. U. Roederer, G. S. Stringfellow, C. R. Bom, P. S. Ferguson, D. J. James, D. Martínez-Delgado, P. Massana, D. L. Nidever, J. D. Sakowska, L. Santana-Silva, N. F. Sherman, E. J. Tollerud
We present the discovery of Aquarius III, an ultra-faint Milky Way satellite galaxy identified in the second data release of the DECam Local Volume Exploration (DELVE) survey. Based on deeper follow-up imaging with DECam, we find that Aquarius III is a low-luminosity ($M_V = -2.5^{+0.3}_{-0.5}$; $L_V = 850^{+380}_{-260} \ L_{\odot}$), extended ($r_{1/2} = 41^{+9}_{-8}$ pc) stellar system located in the outer halo ($D_{\odot} = 85 \pm 4$ kpc). From medium-resolution Keck/DEIMOS spectroscopy, we identify 11 member stars and measure a mean heliocentric radial velocity of $v_{\rm sys} = -13.1^{+1.0}_{-0.9} \ \rm km \ s^{-1}$ for the system and place an upper limit of $\sigma_v < 3.5 \rm \ km \ s^{-1}$ ($\sigma_v < 1.6 \rm \ km \ s^{-1}$) on its velocity dispersion at the 95% (68%) credible level. Based on Calcium-Triplet-based metallicities of the six brightest red giant members, we find that Aquarius III is very metal-poor ([Fe/H]$ = -2.61 \pm 0.21$) with a statistically-significant metallicity spread ($\sigma_{\rm [Fe/H]} = 0.46^{+0.26}_{-0.14}$ dex). We interpret this metallicity spread as strong evidence that the system is a dwarf galaxy as opposed to a star cluster. Combining our velocity measurement with $Gaia$ proper motions, we find that Aquarius III is currently situated near its orbital pericenter in the outer halo ($r_{\rm peri} = 78 \pm 7$ kpc) and that it is plausibly on first infall onto the Milky Way. This orbital history likely precludes significant tidal disruption from the Galactic disk, notably unlike other satellites with comparably low velocity dispersion limits in the literature. Thus, if further velocity measurements confirm that its velocity dispersion is truly below $\sigma_v \lesssim 2 \rm \ km \ s^{-1}$, Aquarius III may serve as a useful laboratory for probing galaxy formation physics in low-mass halos.
A Data-driven Discovery of the Causal Connection between Galaxy and Black Hole Evolution
2024-10-01
Zehao Jin, Mario Pasquato, Benjamin L. Davis, Tristan Deleu, Yu Luo, Changhyun Cho, Pablo Lemos, Laurence Perreault-Levasseur, Yoshua Bengio, Xi Kang, Andrea Valerio Maccio, Yashar Hezaveh
Correlations between galaxies and their supermassive black holes (SMBHs) have been observed, but the causal mechanisms remained unclear. The emerging field of causal inference now enables examining these relationships using observational data. This study, using advanced causal discovery techniques and a state-of-the-art dataset, reveals a causal link between galaxy properties and SMBH masses. In elliptical galaxies, bulge properties influence SMBH growth, while in spiral galaxies, SMBHs affect host galaxy properties, potentially through feedback in gas-rich environments. For spiral galaxies, SMBHs progressively quench star formation, whereas in elliptical galaxies, quenching is complete, and the causal connection has reversed. These findings support theoretical models of active galactic nuclei feedback regulating galaxy evolution and suggest further exploration of causal links in astrophysical and cosmological scaling relations.
Galaxy clustering provides a powerful way to probe cosmology. This requires understanding of the background mean density of galaxy samples, which is estimated from the survey itself by averaging the observed galaxy number density over the angular position. The angle average includes not only the background mean density, but also the monopole fluctuation at each redshift. Here for the first time we compute the monopole fluctuations in galaxy surveys and investigate their impact on galaxy clustering. The monopole fluctuations vary as a function of redshift, and it is correlated with other fluctuations, affecting the two-point correlation function measurements. In an idealized all-sky survey, the rms fluctuation at $z=0.5$ can be as large as 7% of the two-point correlation function in amplitude at the BAO scale, and it becomes smaller than 1% at $z>2$. The monopole fluctuations are unavoidable, but they can be modeled. We discuss its relation to the integral constraint and the implications for the galaxy clustering analysis.
Euclid preparation. The impact of relativistic redshift-space distortions on two-point clustering statistics from the Euclid wide spectroscopic survey
2024-10-01
Euclid Collaboration, M. Y. Elkhashab, D. Bertacca, C. Porciani, J. Salvalaggio, N. Aghanim, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, S. Bardelli, C. Bodendorf, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, V. F. Cardone, J. Carretero, R. Casas, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, A. Da Silva, H. Degaudenzi, A. M. Di Giorgio, J. Dinis, M. Douspis, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, M. Farina, S. Farrens, S. Ferriol, P. Fosalba, M. Frailis, E. Franceschi, S. Galeotta, B. Gillis, C. Giocoli, P. Gómez-Alvarez, A. Grazian, F. Grupp, L. Guzzo, S. V. H. Haugan, W. Holmes, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, M. Kilbinger, T. Kitching, B. Kubik, K. Kuijken, M. Kümmel, M. Kunz, H. Kurki-Suonio, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, N. Martinet, F. Marulli, R. Massey, E. Medinaceli, S. Mei, Y. Mellier, M. Meneghetti, G. Meylan, M. Moresco, L. Moscardini, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, M. Schirmer, P. Schneider, T. Schrabback, M. Scodeggio, A. Secroun, E. Sefusatti, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, C. Surace, P. Tallada-Crespí, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, G. Zamorani, E. Zucca, A. Biviano, A. Boucaud, E. Bozzo, C. Burigana, M. Calabrese, D. Di Ferdinando, J. A. Escartin Vigo, R. Farinelli, F. Finelli, J. Gracia-Carpio, N. Mauri, A. Pezzotta, M. Pöntinen, V. Scottez, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, V. Allevato, S. Anselmi, A. Balaguera-Antolinez, M. Ballardini, A. Blanchard, L. Blot, H. Böhringer, S. Borgani, S. Bruton, R. Cabanac, A. Calabro, G. Canas-Herrera, A. Cappi, C. S. Carvalho, T. Castro, K. C. Chambers, A. R. Cooray, S. Davini, B. De Caro, S. de la Torre, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, H. Dole, S. Escoffier, A. G. Ferrari, P. G. Ferreira, I. Ferrero, A. Finoguenov, A. Fontana, F. Fornari, L. Gabarra, K. Ganga, J. García-Bellido, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, A. Hall, W. G. Hartley, H. Hildebrandt, J. Hjorth, A. Jimenez Muñoz, J. J. E. Kajava, V. Kansal, D. Karagiannis, C. C. Kirkpatrick, F. Lacasa, J. Le Graet, L. Legrand, A. Loureiro, G. Maggio, M. Magliocchetti, F. Mannucci, R. Maoli, C. J. A. P. Martins, S. Matthew, L. Maurin, R. B. Metcalf, M. Migliaccio, P. Monaco, C. Moretti, G. Morgante, S. Nadathur, Nicholas A. Walton, L. Patrizii, V. Popa, D. Potter, P. Reimberg, I. Risso, P. -F. Rocci, M. Sahlén, A. Schneider, M. Sereno, G. Sikkema, A. Silvestri, P. Simon, A. Spurio Mancini, K. Tanidis, C. Tao, N. Tessore, G. Testera, R. Teyssier, S. Toft, S. Tosi, A. Troja, M. Tucci, C. Valieri, J. Valiviita, D. Vergani, F. Vernizzi, G. Verza, P. Vielzeuf, C. Hernández-Monteagudo
Measurements of galaxy clustering are affected by RSD. Peculiar velocities, gravitational lensing, and other light-cone projection effects modify the observed redshifts, fluxes, and sky positions of distant light sources. We determine which of these effects leave a detectable imprint on several 2-point clustering statistics extracted from the EWSS on large scales. We generate 140 mock galaxy catalogues with the survey geometry and selection function of the EWSS and make use of the LIGER method to account for a variable number of relativistic RSD to linear order in the cosmological perturbations. We estimate different 2-point clustering statistics from the mocks and use the likelihood-ratio test to calculate the statistical significance with which the EWSS could reject the null hypothesis that certain relativistic projection effects can be neglected in the theoretical models. We find that the combined effects of lensing magnification and convergence imprint characteristic signatures on several clustering observables. Their S/N ranges between 2.5 and 6 (depending on the adopted summary statistic) for the highest-redshift galaxies in the EWSS. The corresponding feature due to the peculiar velocity of the Sun is measured with a S/N of order one or two. The $P_{\ell}(k)$ from the catalogues that include all relativistic effects reject the null hypothesis that RSD are only generated by the variation of the peculiar velocity along the line of sight with a significance of 2.9 standard deviations. As a byproduct of our study, we demonstrate that the mixing-matrix formalism to model finite-volume effects in the $P_{\ell}(k)$ can be robustly applied to surveys made of several disconnected patches. Our results indicate that relativistic RSD, the contribution from weak gravitational lensing in particular, cannot be disregarded when modelling 2-point clustering statistics extracted from the EWSS.
Massive $ν$s through the CNN lens: interpreting the field-level neutrino mass information in weak lensing
2024-10-01
Malika Golshan, Adrian E. Bayer
Modern cosmological surveys probe the Universe deep into the nonlinear regime, where massive neutrinos suppress cosmic structure. Traditional cosmological analyses, which use the 2-point correlation function to extract information, are no longer optimal in the nonlinear regime, and there is thus much interest in extracting beyond-2-point information to improve constraints on neutrino mass. Quantifying and interpreting the beyond-2-point information is thus a pressing task. We study the field-level information in weak lensing convergence maps using convolution neural networks. We find that the network performance increases as higher source redshifts and smaller scales are considered -- investigating up to a source redshift of 2.5 and $\ell_{\rm max}\simeq10^4$ -- verifying that massive neutrinos leave a distinct effect on weak lensing. However, the performance of the network significantly drops after scaling out the 2-point information from the maps, implying that most of the field-level information can be found in the 2-point correlation function alone. We quantify these findings in terms of the likelihood ratio and also use Integrated Gradient saliency maps to interpret which parts of the map the network is learning the most from, finding that the network extracts information from the most overdense and underdense regions.
Aemulus $ν$: Precision halo mass functions in w$ν$CDM cosmologies
2024-10-01
Delon Shen, Nickolas Kokron, Joseph DeRose, Jeremy Tinker, Risa H. Wechsler, Arka Banerjee, the Aemulus Collaboration
Precise and accurate predictions of the halo mass function for cluster mass scales in $w\nu{\rm CDM}$ cosmologies are crucial for extracting robust and unbiased cosmological information from upcoming galaxy cluster surveys. Here, we present a halo mass function emulator for cluster mass scales ($\gtrsim 10^{13}M_\odot /h$) up to redshift $z=2$ with comprehensive support for the parameter space of $w\nu{\rm CDM}$ cosmologies allowed by current data. Based on the Aemulus $\nu$ suite of simulations, the emulator marks a significant improvement in the precision of halo mass function predictions by incorporating both massive neutrinos and non-standard dark energy equation of state models. This allows for accurate modeling of the cosmology dependence in large-scale structure and galaxy cluster studies. We show that the emulator, designed using Gaussian Process Regression, has negligible theoretical uncertainties compared to dominant sources of error in future cluster abundance studies. Our emulator is publicly available, providing the community with a crucial tool for upcoming cosmological surveys such as LSST and Euclid.
We address the question of the asymptotic description of random tensors that are local unitary invariant, that is, invariant by conjugation by tensor products of (independent) unitary matrices. We consider both the case of an invariant random tensor with $D$ inputs and $D$ outputs, called mixed, and the case where there is a factorization between the inputs and outputs, called pure, and which includes the random tensor models that have been extensively studied in the physics literature (we provide a short review of these results). The finite size and asymptotic moments are defined in terms of the correlations of certain invariant polynomials called trace-invariants, encoded by $D$-uplets of permutations, up to relabelling equivalence. Using the unitary invariance, we define a notion of finite free cumulants associated to the expectations of these trace-invariants, through invertible finite size moment-cumulants formulas. In order to characterize the asymptotic distributions at first order, two important classes of random tensors are considered: pure random tensors that scale like a pure complex Gaussian, and mixed random tensors that scale like a Wishart tensor. In both cases, we derive a notion of free cumulants associated to the first order trace-invariants, through closed moment-cumulant formulas, which involve $D$ independent summations over non-crossing permutations. While the formulas involve the same combinatorics, the asymptotic distributions differ by the class of invariants that define the distribution at first order. Both in the pure and in the mixed case, the first order free-cumulants of a sum of two independent random tensors are shown to be additive. A preliminary discussion of the higher orders is provided. Finally, tensor freeness is then defined (at first order) as the vanishing of mixed free cumulants associated to first order trace-invariants.
Review of blockchain application with Graph Neural Networks, Graph Convolutional Networks and Convolutional Neural Networks
2024-10-01
Amy Ancelotti, Claudia Liason
This paper reviews the applications of Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs), and Convolutional Neural Networks (CNNs) in blockchain technology. As the complexity and adoption of blockchain networks continue to grow, traditional analytical methods are proving inadequate in capturing the intricate relationships and dynamic behaviors of decentralized systems. To address these limitations, deep learning models such as GNNs, GCNs, and CNNs offer robust solutions by leveraging the unique graph-based and temporal structures inherent in blockchain architectures. GNNs and GCNs, in particular, excel in modeling the relational data of blockchain nodes and transactions, making them ideal for applications such as fraud detection, transaction verification, and smart contract analysis. Meanwhile, CNNs can be adapted to analyze blockchain data when represented as structured matrices, revealing hidden temporal and spatial patterns in transaction flows. This paper explores how these models enhance the efficiency, security, and scalability of both linear blockchains and Directed Acyclic Graph (DAG)-based systems, providing a comprehensive overview of their strengths and future research directions. By integrating advanced neural network techniques, we aim to demonstrate the potential of these models in revolutionizing blockchain analytics, paving the way for more sophisticated decentralized applications and improved network performance.
Layer-dependent magnetic property in a superconducting quintuple-layer nickelate La6Ni5O12
2024-10-01
Terri Yoon, Myung Joon Han
To investigate the detailed magnetic properties of a recently discovered superconducting nickelate Nd6Ni5O12, we performed the first-principles electronic structure calculation based on density functional theory. The band dispersion, electronic charge distribution and the magnetic moment are computed with La substituted for Nd, and compared with another structural type of nickel-based superconducting material, namely, RNiO2 (R: rare-earth elements). In particular, we estimated the magnetic exchange interaction strength based on magnetic force theory. Our results show that the inter-atomic magnetic couplings are notably reduced by intrinsic hole doping from the blocking fluorite slab which validates the conventional view of regarding Nd6Ni5O12 as a doped case of its infinite-layer counterpart. At the same time, however, the interactions are markedly layer-dependent. The outer most NiO2 layer adjacent to the blocking fluorite roughly corresponds to the 20% chemical doping in the infinite-layer material whereas the inner layers have stronger couplings. The long-range nature and the out-of-plane interactions are also presented. Our results provide useful information to understand this new superconducting nickelate whose intrinsic layer structure is obviously distinctive.
Google, How Should I Vote? How Users Formulate Search Queries to Find Political Information on Search Engines
2024-10-01
Victoria Vziatysheva, Mykola Makhortykh, Maryna Sydorova, Vihang Jumle
Search engine results depend not only on the algorithms but also on how users interact with them. However, factors affecting the selection of a search query remain understudied. Using a representative survey of Swiss citizens before a round of federal popular votes, this study examines how users formulate search queries related to the retirement policies that were voted on in March 2024. Contrary to existing research, we find no direct evidence of selective exposure, or users' tendency to search for pro-attitudinal information, which we explain by the less polarizing search topics. However, we find that the sentiment of the query is partially aligned with the expected vote outcome. Our results also suggest that undecided and non-voters are more likely to search for nuanced information, such as consequences and interpretations of the policies. The perceived importance and effect of the issue, political views, and sociodemographics also affect query formulation.
The catalogue of $δ$ Sct pulsators in binary systems in 2024
2024-10-01
Alexios Liakos
The updated catalogue of $\delta$ Sct stars in binary systems as well as their statistical properties are presented. Thanks to the Kepler, K2 and TESS space missions, this version of the catalogue contains more than 1000 $\delta$ Sct pulsators in binaries. The sample is divided according to the Roche Geometry of the binary systems in order to check for any systematic differences in the pulsators' evolution due to the proximity of the companion star. Statistics, demographics, and distributions of these pulsating stars within the H-R and mass-radius diagrams are provided. We notice that the absolute parameters have been accurately determined for only approximately 10% of the whole sample. Additionally, updated correlations between pulsation and orbital periods and evolutionary status are presented. This work aims to motivate the researchers for systematic analyses of such objects in order to increase the sample of systems with well known physical properties.
Inverse Galaxy-Galaxy Lensing: Magnification, Intrinsic Alignments and Cosmology
2024-10-01
Dane N. Cross, Carles Sánchez
Current and upcoming imaging galaxy surveys are pushing galaxy samples to higher and higher redshifts. This push will be more pronounced for lens galaxies, for which we only need to measure galaxy positions, not shapes. As a result, we will increasingly often have lens galaxy samples at redshifts higher than those of source galaxies, changing the traditional configuration of galaxy-galaxy lensing (GGL). In this paper, we explore this situation, where lens galaxies are behind source galaxies, which we call inverse galaxy-galaxy lensing (IGGL). We take projected lens and source sample specifications from the Vera Rubin Observatory LSST Dark Energy Science Collaboration (DESC) to compare astrophysical and cosmological constraints between traditional GGL and IGGL. We find IGGL to behave in a different way than GGL, being especially sensitive to lensing magnification, intrinsic alignments (IA) and cosmology, but largely independent of galaxy bias (as opposed to traditional GGL). In this way, we find IGGL can provide independent and robust cosmological constraints without combination with galaxy clustering, and can also probe IA at high redshift and baryonic effects at small scales without being entwined with the effects of non-linear galaxy bias. When combined with cosmic shear, we find IGGL to improve $S_8$ constraints by 25\% compared to cosmic shear alone, while also providing tighter and more robust constraints on IA and baryons.
Personalized text-to-image diffusion models have grown popular for their ability to efficiently acquire a new concept from user-defined text descriptions and a few images. However, in the real world, a user may wish to personalize a model on multiple concepts but one at a time, with no access to the data from previous concepts due to storage/privacy concerns. When faced with this continual learning (CL) setup, most personalization methods fail to find a balance between acquiring new concepts and retaining previous ones -- a challenge that continual personalization (CP) aims to solve. Inspired by the successful CL methods that rely on class-specific information for regularization, we resort to the inherent class-conditioned density estimates, also known as diffusion classifier (DC) scores, for continual personalization of text-to-image diffusion models. Namely, we propose using DC scores for regularizing the parameter-space and function-space of text-to-image diffusion models, to achieve continual personalization. Using several diverse evaluation setups, datasets, and metrics, we show that our proposed regularization-based CP methods outperform the state-of-the-art C-LoRA, and other baselines. Finally, by operating in the replay-free CL setup and on low-rank adapters, our method incurs zero storage and parameter overhead, respectively, over the state-of-the-art.
The Sensitivity of the U.S. Presidential Election to Coordinated Voter Relocation
2024-10-01
Carlos Cardonha, David Bergman, Andre Cire, Leonardo Lozano, Tallys Yunes
U.S. presidential elections are decided by the Electoral College, established in 1789, and designed to mitigate potential risks arising from the collusion of large groups of citizens. A statewide winner-take-all popular voting system for electors is implemented in all but two states, which has led to instances where narrow victories in key states were decisive in several recent elections. Small groups of voters can significantly impact the election, for example, through voter turnout. However, another dynamic can also influence this: a surprisingly small number of dedicated voters moving short distances across state lines. The extent to which the election's outcome is sensitive to small and well-coordinated movements of people has not been investigated in detail. Using a combination of forecasting, simulation, and optimization, we show that a candidate's probability of winning can be increased by 1% through the strategic relocation of approximately 10,000 people no farther than 100 miles from their current county of residence, less than 0.006% of the eligible voting population. Moreover, an 8% probability increase can be realized by a mere 50,000 voters relocating across state lines, or 0.03% of the voting population. Given the remarkably small number of people involved and the fact that establishing electoral residence in many states takes about a month, this coordinated relocation of voters is not nearly as challenging as previously thought. As it stands, U.S. presidential elections may be vulnerable to the exploitation of the aforementioned loophole. Therefore, we anticipate our findings will have direct consequences on policymaking and campaign strategy, as well as motivate new operations research methods within the political sciences.
Discovery of interstellar 1-cyanopyrene: a four-ring polycyclic aromatic hydrocarbon in TMC-1
2024-10-01
Gabi Wenzel, Ilsa R. Cooke, P. Bryan Changala, Edwin A. Bergin, Shuo Zhang, Andrew M. Burkhardt, Alex N. Byrne, Steven B. Charnley, Martin A. Cordiner, Miya Duffy, Zachary T. P. Fried, Harshal Gupta, Martin S. Holdren, Andrew Lipnicky, Ryan A. Loomis, Hannah Toru Shay, Christopher N. Shingledecker, Mark A. Siebert, D. Archie Stewart, Reace H. J. Willis, Ci Xue, Anthony J. Remijan, Alison E. Wendlandt, Michael C. McCarthy, Brett A. McGuire
Polycyclic aromatic hydrocarbons (PAHs) are thought to be the most abundant class of molecules in space, yet their interstellar lifecycle is poorly understood due to difficulties detecting individual PAHs. Here, we present the discovery of 1-cyanopyrene, a 4-ring PAH, in the dense cloud TMC-1 using the 100-m Green Bank Telescope. We derive an abundance of ~1.52 x 10$^{12}$ cm$^{-2}$, estimating that pyrene accounts for up to 0.03-0.3% of the carbon in TMC-1 and up to 1% within comets. Our findings agree with analysis of pyrene from asteroid Ryugu, which suggests a cold, interstellar origin. The abundance indicates pyrene is an "island of stability" in interstellar PAH chemistry, and suggests that the carbon supplied to planetary systems, carried by PAHs, originates in cold clouds.
A Survey on Testing and Analysis of Quantum Software
2024-10-01
Matteo Paltenghi, Michael Pradel
Quantum computing is getting increasing interest from both academia and industry, and the quantum software landscape has been growing rapidly. The quantum software stack comprises quantum programs, implementing algorithms, and platforms like IBM Qiskit, Google Cirq, and Microsoft Q#, enabling their development. To ensure the reliability and performance of quantum software, various techniques for testing and analyzing it have been proposed, such as test generation, bug pattern detection, and circuit optimization. However, the large amount of work and the fact that work on quantum software is performed by several research communities, make it difficult to get a comprehensive overview of the existing techniques. In this work, we provide an extensive survey of the state of the art in testing and analysis of quantum software. We discuss literature from several research communities, including quantum computing, software engineering, programming languages, and formal methods. Our survey covers a wide range of topics, including expected and unexpected behavior of quantum programs, testing techniques, program analysis approaches, optimizations, and benchmarks for testing and analyzing quantum software. We create novel connections between the discussed topics and present them in an accessible way. Finally, we discuss key challenges and open problems to inspire future research.
Introduction to machine learning potentials for atomistic simulations
2024-10-01
Fabian L. Thiemann, Niamh O'Neill, Venkat Kapil, Angelos Michaelides, Christoph Schran
Machine learning potentials have revolutionised the field of atomistic simulations in recent years and are becoming a mainstay in the toolbox of computational scientists. This paper aims to provide an overview and introduction into machine learning potentials and their practical application to scientific problems. We provide a systematic guide for developing machine learning potentials, reviewing chemical descriptors, regression models, data generation and validation approaches. We begin with an emphasis on the earlier generation of models, such as high-dimensional neural network potentials (HD-NNPs) and Gaussian approximation potential (GAP), to provide historical perspective and guide the reader towards the understanding of recent developments, which are discussed in detail thereafter. Furthermore, we refer to relevant expert reviews, open-source software, and practical examples - further lowering the barrier to exploring these methods. The paper ends with selected showcase examples, highlighting the capabilities of machine learning potentials and how they can be applied to push the boundaries in atomistic simulations.
The following is an introduction to the study of higher walks, by which we mean a family of higher-dimensional extensions of Todorcevic's method of walks on the ordinals. After a brief review of this method, including, for example, definitions of the classical functions $\mathrm{Tr}$ and $\rho_2$ induced by a choice of $C$-sequence, we record a shortlist of desiderata for such extensions, along with $(n+1)$-dimensional functions $\mathrm{Tr}_n$ and $\rho_2^n$ (induced by a choice of higher-dimensional $C$-sequence) which we show to satisfy the bulk of them. Much of the interest of these higher walks functions lies in their affinity, as in the classical $n=1$ case, for the ordinals $\omega_n$ (we show, for example, that $\rho^n_2$ determines both $n$-dimensional linear orderings and $n$-coherent families on $\omega_n$, and that higher walks define nontrivial elements of the $n^{\mathrm{th}}$ cohomology groups of $\omega_n$), and in the questions that they thereby raise both about the combinatorics of the latter and about higher-dimensional infinitary combinatorics more generally; we collect the most prominent of these questions in our conclusion. These objects are also, though, of a sufficient combinatorial richness to be of interest in their own right, as we have underscored via an extended study of the first genuine novelty among them, the function $\mathrm{Tr}_2$.
An Empirical Study of Large Language Models for Type and Call Graph Analysis
2024-10-01
Ashwin Prasad Shivarpatna Venkatesh, Rose Sunil, Samkutty Sabu, Amir M. Mir, Sofia Reis, Eric Bodden
Large Language Models (LLMs) are increasingly being explored for their potential in software engineering, particularly in static analysis tasks. In this study, we investigate the potential of current LLMs to enhance call-graph analysis and type inference for Python and JavaScript programs. We empirically evaluated 24 LLMs, including OpenAI's GPT series and open-source models like LLaMA and Mistral, using existing and newly developed benchmarks. Specifically, we enhanced TypeEvalPy, a micro-benchmarking framework for type inference in Python, with auto-generation capabilities, expanding its scope from 860 to 77,268 type annotations for Python. Additionally, we introduced SWARM-CG and SWARM-JS, comprehensive benchmarking suites for evaluating call-graph construction tools across multiple programming languages. Our findings reveal a contrasting performance of LLMs in static analysis tasks. For call-graph generation in Python, traditional static analysis tools like PyCG significantly outperform LLMs. In JavaScript, the static tool TAJS underperforms due to its inability to handle modern language features, while LLMs, despite showing potential with models like mistral-large-it-2407-123b and GPT-4o, struggle with completeness and soundness in both languages for call-graph analysis. Conversely, LLMs demonstrate a clear advantage in type inference for Python, surpassing traditional tools like HeaderGen and hybrid approaches such as HiTyper. These results suggest that while LLMs hold promise in type inference, their limitations in call-graph analysis highlight the need for further research. Our study provides a foundation for integrating LLMs into static analysis workflows, offering insights into their strengths and current limitations.
A sub-Earth-mass planet orbiting Barnard's star: No evidence of transits in TESS photometry
2024-10-01
A. K. Stefanov, J. I. González Hernández, A. Suárez Mascareño, N. Nari, R. Rebolo, M. Damasso, A. Castro-González, M. -R. Zapatero Osorio, C. Allende Prieto, A. M. Silva, C. J. A. P. Martins
A sub-Earth-mass planet orbiting Barnard's star, designated as Barnard b, has been recently announced. At a similar time, the first photometric data of Barnard's star by the Transit Exoplanet Survey Satellite (TESS) was released in Sector 80. We explore the possibility of emergent transits of Barnard b in TESS photometry. The detrended 2 min light curve appears to be flat, with a flux root-mean-square of 0.411 parts per thousand. Attempts of blind and informed transit-curve model inference suggest no evidence of transiting Barnard b, or any other body. This provides a 3$\sigma$ upper bound of 87.9 degrees for the orbital inclination of Barnard b.
Revisiting the Mysterious Origin of FRB 20121102A with Machine-learning Classification
2024-10-01
Leah Ya-Ling Lin, Tetsuya Hashimoto, Tomotsugu Goto, Bjorn Jasper Raquel, Simon C. -C. Ho, Bo-Han Chen, Seong Jin Kim, Chih-Teng Ling
Fast radio bursts (FRBs) are millisecond-duration radio waves from the Universe. Even though more than 50 physical models have been proposed, the origin and physical mechanism of FRB emissions are still unknown. The classification of FRBs is one of the primary approaches to understanding their mechanisms, but previous studies classified conventionally using only a few observational parameters, such as fluence and duration, which might be incomplete. To overcome this problem, we use an unsupervised machine-learning model, the Uniform Manifold Approximation and Projection (UMAP) to handle seven parameters simultaneously, including amplitude, linear temporal drift, time duration, central frequency, bandwidth, scaled energy, and fluence. We test the method for homogeneous 977 sub-bursts of FRB 20121102A detected by the Arecibo telescope. Our machine-learning analysis identified five distinct clusters, suggesting the possible existence of multiple different physical mechanisms responsible for the observed FRBs from the FRB 20121102A source. The geometry of the emission region and the propagation effect of FRB signals could also make such distinct clusters. This research will be a benchmark for future FRB classifications when dedicated radio telescopes such as the Square Kilometer Array (SKA) or Bustling Universe Radio Survey Telescope in Taiwan (BURSTT) discover more FRBs than before.
Research Frontiers in Ambit Stochastics: In memory of Ole E. Barndorff-Nielsen
2024-10-01
Fred Espen Benth, Almut E. D. Veraart
This article surveys key aspects of ambit stochastics and remembers Ole E. Barndorff-Nielsen's important contributions to the foundation and advancement of this new research field over the last two decades. It also highlights some of the emerging trends in ambit stochastics.
Large model inference is shifting from cloud to edge due to concerns about the privacy of user interaction data. However, edge devices often struggle with limited computing power, memory, and bandwidth, requiring collaboration across multiple devices to run and speed up LLM inference. Pipeline parallelism, the mainstream solution, is inefficient for single-user scenarios, while tensor parallelism struggles with frequent communications. In this paper, we argue that tensor parallelism can be more effective than pipeline on low-resource devices, and present a compute- and memory-efficient tensor parallel inference system, named TPI-LLM, to serve 70B-scale models. TPI-LLM keeps sensitive raw data local in the users' devices and introduces a sliding window memory scheduler to dynamically manage layer weights during inference, with disk I/O latency overlapped with the computation and communication. This allows larger models to run smoothly on memory-limited devices. We analyze the communication bottleneck and find that link latency, not bandwidth, emerges as the main issue, so a star-based allreduce algorithm is implemented. Through extensive experiments on both emulated and real testbeds, TPI-LLM demonstrated over 80% less time-to-first-token and token latency compared to Accelerate, and over 90% compared to Transformers and Galaxy, while cutting the peak memory footprint of Llama 2-70B by 90%, requiring only 3.1 GB of memory for 70B-scale models.
Simultaneous Localization and Mapping (SLAM) is pivotal in robotics, with photorealistic scene reconstruction emerging as a key challenge. To address this, we introduce Computational Alignment for Real-Time Gaussian Splatting SLAM (CaRtGS), a novel method enhancing the efficiency and quality of photorealistic scene reconstruction in real-time environments. Leveraging 3D Gaussian Splatting (3DGS), CaRtGS achieves superior rendering quality and processing speed, which is crucial for scene photorealistic reconstruction. Our approach tackles computational misalignment in Gaussian Splatting SLAM (GS-SLAM) through an adaptive strategy that optimizes training, addresses long-tail optimization, and refines densification. Experiments on Replica and TUM-RGBD datasets demonstrate CaRtGS's effectiveness in achieving high-fidelity rendering with fewer Gaussian primitives. This work propels SLAM towards real-time, photorealistic dense rendering, significantly advancing photorealistic scene representation. For the benefit of the research community, we release the code on our project website: https://dapengfeng.github.io/cartgs.
Dynamic Jahn-Teller Phenomena in Heavy Transition Metal Compounds
2024-10-01
Naoya Iwahara
This paper reviews recent experimental and theoretical developments of the dynamic Jahn-Teller effect-driven phenomena in heavy transition metal spin-orbit Mott insulators. In cubic $4d/5d$ transition metal compounds, the spin, orbital, and lattice degrees of freedom can form quantum entanglement on metal sites and induce unconventional quantum phenomena. Fingerprints of orbital-lattice entanglement called the dynamic Jahn-Teller effect appear in spectroscopic data such as resonant inelastic x-ray scattering spectra. In cubic $5d^1$ compounds with the fcc structure, the dynamic Jahn-Teller states on metal sites behave cooperatively and exhibit rich ordered phases.
In recent years, quantum computing has made significant strides, particularly in light-based technology. The introduction of quantum photonic chips has ushered in an era marked by scalability, stability, and cost-effectiveness, paving the way for innovative possibilities within compact footprints. This article provides a comprehensive exploration of photonic quantum computing, covering key aspects such as encoding information in photons, the merits of photonic qubits, and essential photonic device components including light squeezers, quantum light sources, interferometers, photodetectors, and waveguides. The article also examines photonic quantum communication and internet, and its implications for secure systems, detailing implementations such as quantum key distribution and long-distance communication. Emerging trends in quantum communication and essential reconfigurable elements for advancing photonic quantum internet are discussed. The review further navigates the path towards establishing scalable and fault-tolerant photonic quantum computers, highlighting quantum computational advantages achieved using photons. Additionally, the discussion extends to programmable photonic circuits, integrated photonics and transformative applications. Lastly, the review addresses prospects, implications, and challenges in photonic quantum computing, offering valuable insights into current advancements and promising future directions in this technology.
Exploring Physics-Informed Neural Networks: From Fundamentals to Applications in Complex Systems
2024-10-01
Sai Ganga, Ziya Uddin
Physics-informed neural networks (PINNs) have emerged as a versatile and widely applicable concept across various science and engineering domains over the past decade. This article offers a comprehensive overview of the fundamentals of PINNs, tracing their evolution, modifications, and various variants. It explores the impact of different parameters on PINNs and the optimization algorithms involved. The review also delves into the theoretical advancements related to the convergence, consistency, and stability of numerical solutions using PINNs, while highlighting the current state of the art. Given their ability to address equations involving complex physics, the article discusses various applications of PINNs, with a particular focus on their utility in computational fluid dynamics problems. Additionally, it identifies current gaps in the research and outlines future directions for the continued development of PINNs.
Cosmological constraints using Minkowski functionals from the first year data of the Hyper Suprime-Cam
2024-10-01
Joaquin Armijo, Gabriela A. Marques, Camila P. Novaes, Leander Thiele, Jessica A. Cowell, Daniela Grandón, Masato Shirasaki, Jia Liu
We use Minkowski functionals to analyse weak lensing convergence maps from the first-year data release of the Subaru Hyper Suprime-Cam (HSC-Y1) survey. Minkowski functionals provide a description of the morphological properties of a field, capturing the non-Gaussian features of the Universe matter-density distribution. Using simulated catalogs that reproduce survey conditions and encode cosmological information, we emulate Minkowski functionals predictions across a range of cosmological parameters to derive the best-fit from the data. By applying multiple scales cuts, we rigorously mitigate systematic effects, including baryonic feedback and intrinsic alignments. From the analysis, combining constraints of the angular power spectrum and Minkowski functionals, we obtain $S_8 \equiv \sigma_8\sqrt{\Omega_{{\rm m}}/0.3} = {0.808}_{-0.046}^{+0.033}$ and $\Omega_{\rm m} = {0.293}_{-0.043}^{+0.157}$. These results represent a $40\%$ improvement on the $S_8$ constraints compared to using power spectrum only, and are consistent with previous non-Gaussian statistics analyses of the same dataset. Our study demonstrates the power of Minkowski functionals beyond two-point statistics for constraining and breaking the degeneracy between $\Omega_{\rm m}$ and $\sigma_8$.
Performance Improvement of IaaS Type of Cloud Computing Using Virtualization Technique
2024-10-01
Dawit Zeleke Admassu
Cloud computing has transformed the way organizations manage and scale their IT infrastructure by offering flexible, scalable, and cost-effective solutions. However, the Infrastructure as a Service (IaaS) model faces performance challenges primarily due to the limitations imposed by virtualization technology. This paper focuses on designing an effective virtualization technique for IaaS, aiming to improve infrastructure-level performance. Through a systematic literature review and a design, development, and evaluation approach, various virtualization techniques such as full virtualization, paravirtualization, and hardware-assisted virtualization are explored. The study also considers the role of hypervisors like Xen, KVM, and VMware ESXi in improving performance. The proposed solution seeks to optimize resource utilization, minimize latency, and enhance overall throughput in IaaS environments. Finally, the research discusses the potential application of this virtualization technique for public cloud computing solutions tailored for Ethiopian Small and Medium Enterprises (ESMEs) using platforms like Amazon EC2.
Evidential Deep Learning (EDL) is an emerging method for uncertainty estimation that provides reliable predictive uncertainty in a single forward pass, attracting significant attention. Grounded in subjective logic, EDL derives Dirichlet concentration parameters from neural networks to construct a Dirichlet probability density function (PDF), modeling the distribution of class probabilities. Despite its success, EDL incorporates several nonessential settings: In model construction, (1) a commonly ignored prior weight parameter is fixed to the number of classes, while its value actually impacts the balance between the proportion of evidence and its magnitude in deriving predictive scores. In model optimization, (2) the empirical risk features a variance-minimizing optimization term that biases the PDF towards a Dirac delta function, potentially exacerbating overconfidence. (3) Additionally, the structural risk typically includes a KL-divergence-minimizing regularization, whose optimization direction extends beyond the intended purpose and contradicts common sense, diminishing the information carried by the evidence magnitude. Therefore, we propose Re-EDL, a simplified yet more effective variant of EDL, by relaxing the nonessential settings and retaining the essential one, namely, the adoption of projected probability from subjective logic. Specifically, Re-EDL treats the prior weight as an adjustable hyperparameter rather than a fixed scalar, and directly optimizes the expectation of the Dirichlet PDF provided by deprecating both the variance-minimizing optimization term and the divergence regularization term. Extensive experiments and state-of-the-art performance validate the effectiveness of our method. The source code is available at https://github.com/MengyuanChen21/Re-EDL.
CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset
2024-10-01
Xiao Wang, Fuling Wang, Yuehang Li, Qingchuan Ma, Shiao Wang, Bo Jiang, Chuanfu Li, Jin Tang
X-ray image-based medical report generation (MRG) is a pivotal area in artificial intelligence which can significantly reduce diagnostic burdens and patient wait times. Despite significant progress, we believe that the task has reached a bottleneck due to the limited benchmark datasets and the existing large models' insufficient capability enhancements in this specialized domain. Specifically, the recently released CheXpert Plus dataset lacks comparative evaluation algorithms and their results, providing only the dataset itself. This situation makes the training, evaluation, and comparison of subsequent algorithms challenging. Thus, we conduct a comprehensive benchmarking of existing mainstream X-ray report generation models and large language models (LLMs), on the CheXpert Plus dataset. We believe that the proposed benchmark can provide a solid comparative basis for subsequent algorithms and serve as a guide for researchers to quickly grasp the state-of-the-art models in this field. More importantly, we propose a large model for the X-ray image report generation using a multi-stage pre-training strategy, including self-supervised autoregressive generation and Xray-report contrastive learning, and supervised fine-tuning. Extensive experimental results indicate that the autoregressive pre-training based on Mamba effectively encodes X-ray images, and the image-text contrastive pre-training further aligns the feature spaces, achieving better experimental results. Source code can be found on \url{https://github.com/Event-AHU/Medical_Image_Analysis}.
Cross Correlating the Unresolved Gamma-Ray Background with Cosmic Large-Scale Structure from DESI: Implications for Astrophysics and Dark Matter
2024-10-01
Bei Zhou, José Luis Bernal, Elena Pinetti, Hector Afonso G. Cruz, Marc Kamionkowski
The unresolved gamma-ray background (UGRB) is a diffuse gamma-ray emission arising from numerous extragalactic sources below the detection threshold and is an important component of the gamma-ray sky. Studying the UGRB is crucial for understanding high-energy astrophysical processes in the universe and for probing fundamental physics, such as the nature of dark matter. In this work, we forecast the cross-correlation between the UGRB and galaxy catalogs from the Dark Energy Spectroscopic Instrument (DESI) survey. First, we study the expected astrophysical contributions to the UGRB and their cross-correlation with DESI spectroscopic galaxies. Our calculations show that the cross-correlation signal-to-noise ratio is expected to be significant, with the highest value predicted to be 20.6 for DESI luminous red galaxies due to a higher predicted overlap in the redshift distribution with the UGRB. We consider two science cases that the UGRB-spectroscopic galaxies cross-correlation can be applied to: 1) measuring the UGRB flux as a function of redshift, achieving a precision of 10\% in some redshift bins, and 2) searching for annihilating dark matter potentially up to a mass of about 300~GeV, three times higher than the currently strongest constraints. This work underscores the importance of cross correlating the UGRB with cosmic large-scale structure tracers and highlights the multiwavelength approaches to advancing our understanding of high-energy astrophysical phenomena and fundamental physics.
A Scalable Gaussian Process Approach to Shear Mapping with MuyGPs
2024-10-01
Gregory Sallaberry, Benjamin W. Priest, Robert Armstrong, Michael D. Schneider, Amanda Muyskens, Trevor Steil, Keita Iwabuchi
Analysis of cosmic shear is an integral part of understanding structure growth across cosmic time, which in-turn provides us with information about the nature of dark energy. Conventional methods generate \emph{shear maps} from which we can infer the matter distribution in the universe. Current methods (e.g., Kaiser-Squires inversion) for generating these maps, however, are tricky to implement and can introduce bias. Recent alternatives construct a spatial process prior for the lensing potential, which allows for inference of the convergence and shear parameters given lensing shear measurements. Realizing these spatial processes, however, scales cubically in the number of observations - an unacceptable expense as near-term surveys expect billions of correlated measurements. Therefore, we present a linearly-scaling shear map construction alternative using a scalable Gaussian Process (GP) prior called MuyGPs. MuyGPs avoids cubic scaling by conditioning interpolation on only nearest-neighbors and fits hyperparameters using batched leave-one-out cross validation. We use a suite of ray-tracing results from N-body simulations to demonstrate that our method can accurately interpolate shear maps, as well as recover the two-point and higher order correlations. We also show that we can perform these operations at the scale of billions of galaxies on high performance computing platforms.
Book Review: 'Background Independence in Classical and Quantum Gravity', by James Read
2024-10-01
Sebastian De Haro
This is a review of James Read's insightful book, Background Independence in Classical and Quantum Gravity. The book introduces various notions of background-independence which it then makes precise, and uses to make verdicts about background-independence on a wide range of examples of spacetime theories in both classical and quantum gravity. This short book is, in several ways, a worthy example of how technical philosophy of physics ought to be done. I first discuss the content, then raise a number points where I disagree with the book.
Relativistic Reflection Modeling in AGN and Related Variability from PCA: A Brief Review
2024-10-01
A. Danehkar
X-ray observations of active galactic nuclei (AGNs) reveal relativistic reflections from the innermost regions of accretion disks, which contain general-relativistic footprints caused by spinning supermassive black holes (SMBH). We anticipate the spin of a SMBH to be stable over the human timeframe, so brightness changes in the high-energy corona above the SMBH should slightly alter relativistic reflection. In this brief review, we discuss the latest developments in modeling relativistic reflection, as well as the rapid small variation in relativistic emission disclosed by the principal component analysis (PCA) of X-ray variability in AGN. PCA studies of X-ray spectra from AGNs have shown that relativistically blurred reflection has negligible fluctuations over the course of observations, which could originate from rapid (intrahour) intrinsic variations in near-horizon accretion flows and photon rings. The PCA technique is an effective way to disclose relativistic reflection from X-ray observations of AGNs, simplifying the complexity of largely variable X-ray data for automated spectral analysis with machine learning algorithms.
Statistical view of orbital circularisation with 14 000 characterised TESS eclipsing binaries
2024-09-30
L. W. IJspeert, A. Tkachenko, C. Johnston, C. Aerts
Eclipsing binaries are crucial for understanding stellar physics, allowing detailed studies of stellar masses, radii, and orbital dynamics. Recent space missions like the Transiting Exoplanet Survey Satellite (TESS) have significantly expanded the catalogue of observed eclipsing binaries with uninterrupted time series photometry, providing an opportunity for large-scale ensemble studies. This study aims to analyse the statistical properties of circularisation in a large sample of intermediate-to-high mass eclipsing binaries observed by TESS. We explore the dependence of orbital circularisation on stellar properties and orbital parameters to improve our understanding of the physical processes affecting these systems. We further aim to assess the role of stellar pulsations in circularisation. We compiled a catalogue of O- to F-type stars to search for eclipsing binary signals in the TESS data. Using automated classification and data analysis methodologies, we arrive at a well-characterised sample of 14,573 eclipsing binaries. We investigate the statistical characteristics of the sample as a function of temperature, orbital period, and scaled orbital separation. The orbital circularisation was measured with statistical methods to obtain three distinct measurements of the critical period and separation in four temperature ranges. Pulsations were identified in the g- and p-mode regimes and a reduced fraction of eccentric systems was found among them. Our analysis confirmed and expanded upon previous findings that additional dissipation is needed as compared to the predictions of turbulent viscosity and non-resonant radiative damping. We speculate that pulsations may play a role in the circularisation of close binaries. Our study highlights the need for dissipative mechanisms that can produce a wide range of critical periods from a range of initial conditions.
Best Practices for Responsible Machine Learning in Credit Scoring
2024-09-30
Giovani Valdrighi, Athyrson M. Ribeiro, Jansen S. B. Pereira, Vitoria Guardieiro, Arthur Hendricks, Décio Miranda Filho, Juan David Nieto Garcia, Felipe F. Bocca, Thalita B. Veronese, Lucas Wanner, Marcos Medeiros Raimundo
The widespread use of machine learning in credit scoring has brought significant advancements in risk assessment and decision-making. However, it has also raised concerns about potential biases, discrimination, and lack of transparency in these automated systems. This tutorial paper performed a non-systematic literature review to guide best practices for developing responsible machine learning models in credit scoring, focusing on fairness, reject inference, and explainability. We discuss definitions, metrics, and techniques for mitigating biases and ensuring equitable outcomes across different groups. Additionally, we address the issue of limited data representativeness by exploring reject inference methods that incorporate information from rejected loan applications. Finally, we emphasize the importance of transparency and explainability in credit models, discussing techniques that provide insights into the decision-making process and enable individuals to understand and potentially improve their creditworthiness. By adopting these best practices, financial institutions can harness the power of machine learning while upholding ethical and responsible lending practices.
Cosmic Cartography: Bayesian reconstruction of the galaxy density informed by large-scale structure
2024-09-30
Konstantin Leyde, Tessa Baker, Wolfgang Enzi
The dark sirens method combines gravitational waves and catalogs of galaxies to constrain the cosmological expansion history, merger rates and mass distributions of compact objects, and the laws of gravity. However, the incompleteness of galaxy catalogs means faint potential host galaxies are unobserved, and must be modeled to avoid inducing a bias. The majority of dark sirens analyses to date assume that the missing galaxies are distributed uniformly across the sky, which is clearly unphysical. We introduce a new Bayesian approach to the reconstruction of galaxy catalogs, which makes full use of our knowledge of large-scale structure. Our method quantifies the uncertainties on the estimated true galaxy number count in each voxel, and is marginalized over cosmological parameters and bias parameters. Crucially, our method further assesses the (absolute) magnitude distribution of galaxies, which is not known from the galaxy catalog itself. We present the details of our method and validate our approach on a galaxy catalog associated to the Millennium Simulation. The tools developed here generate physically-informed and robust host galaxy reconstructions, enabling more informative dark sirens analyses. Stage IV galaxy surveys will display greater redshift overlap with GW observations, whilst remaining incomplete -- emphasizing the importance of our work.
Word Sense Disambiguation in Native Spanish: A Comprehensive Lexical Evaluation Resource
2024-09-30
Pablo Ortega, Jordi Luque, Luis Lamiable, Rodrigo López, Richard Benjamins
Human language, while aimed at conveying meaning, inherently carries ambiguity. It poses challenges for speech and language processing, but also serves crucial communicative functions. Efficiently solve ambiguity is both a desired and a necessary characteristic. The lexical meaning of a word in context can be determined automatically by Word Sense Disambiguation (WSD) algorithms that rely on external knowledge often limited and biased toward English. When adapting content to other languages, automated translations are frequently inaccurate and a high degree of expert human validation is necessary to ensure both accuracy and understanding. The current study addresses previous limitations by introducing a new resource for Spanish WSD. It includes a sense inventory and a lexical dataset sourced from the Diccionario de la Lengua Espa\~nola which is maintained by the Real Academia Espa\~nola. We also review current resources for Spanish and report metrics on them by a state-of-the-art system.
On Mixed Quantum/Classical Theory for Rotationally Inelastic Scattering of Identical Collision Partners
2024-09-30
Dulat Bostan, Bikramaditya Mandal, Dmitri Babikov
Mixed quantum/classical theory (MQCT) for the treatment of rotationally inelastic transitions during collisions of two identical molecules, described either as indistinguishable or distinguishable partners, is reviewed. The treatment of two molecules as indistinguishable includes symmetrization of rotational wavefunctions, introduces exchange parity, and gives state-to-state transition matrix elements different from those in the straightforward treatment of molecules as distinguishable. Moreover, the treatment of collision partners as indistinguishable is eight times faster. Numerical results, presented for $H_2 + H_2$, CO + CO and $H_2O + H_2O$ systems, indicate good agreement of MQCT calculations with full-quantum calculations from literature and show that an a posteriori correction, applied after the treatment of collision partners as distinguishable, generally produces good results that agree well with the rigorous treatment of collision partners as indistinguishable. This correction for cross section includes either multiplication by 2, or summation over physically indistinguishable processes, depending on the transition type. After this correction, the results of two treatments agree within 5% for most but may reach 10-20% for some transitions. At low collision energies dominated by scattering resonances these differences can be larger, but they tend to decrease as collision energy is increased. It is also shown that if the system is artificially forced to follow the same collision path in the indistinguishable and distinguishable treatments, then all differences between the results of two treatments disappear. This interesting finding gives new insight into the collision process and indicates that indistinguishability of identical collision partners comes into play through the collision path itself, rather than through matrix elements of inelastic transitions.
A physically-motivated template set for high-z galaxy SED fitting
2024-09-30
Judah Luberto, Steven Furlanetto, Jordan Mirocha
We introduce a new physically-motivated spectral template set for fitting the spectral energy distributions (SEDs) of high-z galaxies. We use the public galaxy formation code ARES to generate star formation histories of thirteen representative galaxies with diverse masses and generate their predicted spectra across a set of redshifts at z > 6. The model parameters are calibrated to reproduce the properties of z > 6 galaxies observed by HST. Motivated by the apparent importance of bursty star formation at high redshifts, we also include templates with recent starbursts. We use these templates with the SED-fitting code EAZY to analyze both an independent theoretical model and a public sample of JWST-observed galaxies from the JADES survey. The comparison with a semi-analytic model demonstrates that our fitting framework accurately measures the galaxy properties, even when the underlying assumptions of the model differ from ours. Our preliminary application to JWST data shows that galaxies at z > 8 are often bursty (especially at small galaxy masses), follow a star-forming main sequence similar to those at lower redshift (albeit with a higher normalization), and form stars earlier than expected in ares. Our SED-fitting framework is very fast (thanks to the efficiency of EAZY) but provides full inferred star formation histories for each source. Additionally, it enables a direct comparison to theoretical models and helps point toward improvements necessary in those models.
Self-distributive structures, braces & the Yang-Baxter equation
2024-09-30
Anastasia Doikou
The theory of the set-theoretic Yang-Baxter equation is reviewed from a purely algebraic point of view. We recall certain algebraic structures called shelves, racks and quandles. These objects satisfy a self-distributivity condition and lead to solutions of the Yang-Baxter equation. We also recall that non-involutive solutions of the braid equation are obtained from shelf and rack solutions by a suitable parametric twist, whereas all involutive set-theoretic solutions are reduced to the flip map via a parametric twist. The notion of braces is also presented as the suitable algebraic structure associated to involutive set-theoretic solutions. The quantum algebra as well as the integrability of Baxterized involutive set-theoretic solutions is also discussed. The explicit form of the Drinfel'd twist is presented allowing the derivation of general set-theoretic solutions.
Linear Projections of Teacher Embeddings for Few-Class Distillation
2024-09-30
Noel Loo, Fotis Iliopoulos, Wei Hu, Erik Vee
Knowledge Distillation (KD) has emerged as a promising approach for transferring knowledge from a larger, more complex teacher model to a smaller student model. Traditionally, KD involves training the student to mimic the teacher's output probabilities, while more advanced techniques have explored guiding the student to adopt the teacher's internal representations. Despite its widespread success, the performance of KD in binary classification and few-class problems has been less satisfactory. This is because the information about the teacher model's generalization patterns scales directly with the number of classes. Moreover, several sophisticated distillation methods may not be universally applicable or effective for data types beyond Computer Vision. Consequently, effective distillation techniques remain elusive for a range of key real-world applications, such as sentiment analysis, search query understanding, and advertisement-query relevance assessment. Taking these observations into account, we introduce a novel method for distilling knowledge from the teacher's model representations, which we term Learning Embedding Linear Projections (LELP). Inspired by recent findings about the structure of final-layer representations, LELP works by identifying informative linear subspaces in the teacher's embedding space, and splitting them into pseudo-subclasses. The student model is then trained to replicate these pseudo-classes. Our experimental evaluation on large-scale NLP benchmarks like Amazon Reviews and Sentiment140 demonstrate the LELP is consistently competitive with, and typically superior to, existing state-of-the-art distillation algorithms for binary and few-class problems, where most KD methods suffer.
Phase projection errors in rf-driven optically pumped magnetometers
2024-09-30
Zoran D. Grujić, Marija Ćurĉić, Aleksandra Kocić, Antoine Weis, Theo Scholtes
We study the phase between the oscillating (rf) excitation field and the detected (light) power modulation in scalar rf-driven optically pumped magnetometers (OPMs), in particular in the $M_x$ configuration. While the static dependence of the demodulation phase on the direction of the external static magnetic field vector can be largely overcome by aligning the oscillating rf field along the light propagation direction, we show that a dynamic (transient) phase response can appear under tilts of the magnetic field. We analytically solve the corresponding modified Bloch equation and obtain agreement with experimental observations in an $M_x$ magnetometer setup using a paraffin-coated Cs vapor cell. The results reveal fundamental limitations of $M_x$ magnetometers in terms of response time and accuracy, in particular when operated with active electronic feedback, e.g., using a phase-locked loop. Thus, the work is highly relevant in important magnetometry applications, where the direction of the quasi-static magnetic field of interest is not known \textit{a priori} and/or not constant over time, or in measurements, in which a large detection bandwidth is paramount. Such conditions are encountered, e.g., in geomagnetic surveying, in particular with mobile platforms.
A holistic exploration of the potentially recoverable redshift information of Stage IV galaxy surveys
2024-09-30
Bryan R Scott, Alex I Malz, Robert Sorba
Extragalactic science and cosmology with Stage IV galaxy surveys will rely almost exclusively on redshift measurements derived solely from photometry, which are subject to systematic and statistical uncertainties with numerous analysis choices, such as that of an estimator and prior information and no universal solution. Single-survey photometric redshift estimates ought to be improved by combining data from multiple surveys, with common wisdom asserting that optical data benefits from additional infrared coverage but not from additional ultraviolet coverage. The degree of improvement for either case is not well-characterized and attempts to quantify it necessitate assumptions of a chosen estimator and its prior information. We apply an information theoretic metric of potentially recoverable redshift information to assess the impact of multi-survey photometry combinations without assuming an estimator nor priors in the context of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) in the optical, Roman and Euclid Surveys in the infrared, and Cosmological Advanced Survey Telescope for Optical-UV Research (CASTOR) in the ultraviolet. We conclude that the addition of UV photometry can benefit redshift determination of certain galaxy populations, but that gain is tempered by their decreased chance of meeting detection criteria at higher wavelengths. We explore the spectral energy distribution (SEDs) of galaxies whose potentially recoverable redshift information is most impacted by additional photometry to provide a basis for future evaluation of which science cases may motivate which combinations. The holistic assessment approach we develop here is generic and may be applied to quantify the impact of combining photometric datasets, changing experimental design choices, including observing strategy optimization, and evaluating systematics mitigation procedures.
Navigating Threats: A Survey of Physical Adversarial Attacks on LiDAR Perception Systems in Autonomous Vehicles
2024-09-30
Amira Guesmi, Muhammad Shafique
Autonomous vehicles (AVs) rely heavily on LiDAR (Light Detection and Ranging) systems for accurate perception and navigation, providing high-resolution 3D environmental data that is crucial for object detection and classification. However, LiDAR systems are vulnerable to adversarial attacks, which pose significant challenges to the safety and robustness of AVs. This survey presents a thorough review of the current research landscape on physical adversarial attacks targeting LiDAR-based perception systems, covering both single-modality and multi-modality contexts. We categorize and analyze various attack types, including spoofing and physical adversarial object attacks, detailing their methodologies, impacts, and potential real-world implications. Through detailed case studies and analyses, we identify critical challenges and highlight gaps in existing attacks for LiDAR-based systems. Additionally, we propose future research directions to enhance the security and resilience of these systems, ultimately contributing to the safer deployment of autonomous vehicles.
Traffic Steering is a crucial technology for wireless networks, and multiple efforts have been put into developing efficient Machine Learning (ML)-enabled traffic steering schemes for Open Radio Access Networks (O-RAN). Given the swift emergence of novel ML techniques, conducting a timely survey that comprehensively examines the ML-based traffic steering schemes in O-RAN is critical. In this article, we provide such a survey along with a case study of hierarchical learning-enabled traffic steering in O-RAN. In particular, we first introduce the background of traffic steering in O-RAN and overview relevant state-of-the-art ML techniques and their applications. Then, we analyze the compatibility of the hierarchical learning framework in O-RAN and further propose a Hierarchical Deep-Q-Learning (h-DQN) framework for traffic steering. Compared to existing works, which focus on single-layer architecture with standalone agents, h-DQN decomposes the traffic steering problem into a bi-level architecture with hierarchical intelligence. The meta-controller makes long-term and high-level policies, while the controller executes instant traffic steering actions under high-level policies. Finally, the case study shows that the hierarchical learning approach can provide significant performance improvements over the baseline algorithms.
2024-09-30
Ben Armstrong, Valerie K. Chen, Alex Cuellar, Alexandra Forsey-Smerek, Julie A. Shah
Common narratives about automation often pit new technologies against workers. The introduction of advanced machine tools, industrial robots, and AI have all been met with concern that technological progress will mean fewer jobs. However, workers themselves offer a more optimistic, nuanced perspective. Drawing on a far-reaching 2024 survey of more than 9,000 workers across nine countries, this paper finds that more workers report potential benefits from new technologies like robots and AI for their safety and comfort at work, their pay, and their autonomy on the job than report potential costs. Workers with jobs that ask them to solve complex problems, workers who feel valued by their employers, and workers who are motivated to move up in their careers are all more likely to see new technologies as beneficial. In contrast to assumptions in previous research, more formal education is in some cases associated with more negative attitudes toward automation and its impact on work. In an experimental setting, the prospect of financial incentives for workers improve their perceptions of automation technologies, whereas the prospect of increased input about how new technologies are used does not have a significant effect on workers' attitudes toward automation.
Wait, but Tylenol is Acetaminophen... Investigating and Improving Language Models' Ability to Resist Requests for Misinformation
2024-09-30
Shan Chen, Mingye Gao, Kuleen Sasse, Thomas Hartvigsen, Brian Anthony, Lizhou Fan, Hugo Aerts, Jack Gallifant, Danielle Bitterman
Background: Large language models (LLMs) are trained to follow directions, but this introduces a vulnerability to blindly comply with user requests even if they generate wrong information. In medicine, this could accelerate the generation of misinformation that impacts human well-being. Objectives/Methods: We analyzed compliance to requests to generate misleading content about medications in settings where models know the request is illogical. We investigated whether in-context directions and instruction-tuning of LLMs to prioritize logical reasoning over compliance reduced misinformation risk. Results: While all frontier LLMs complied with misinformation requests, both prompt-based and parameter-based approaches can improve the detection of logic flaws in requests and prevent the dissemination of medical misinformation. Conclusion: Shifting LLMs to prioritize logic over compliance could reduce risks of exploitation for medical misinformation.
StratLearn-z: Improved photo-$z$ estimation from spectroscopic data subject to selection effects
2024-09-30
Chiara Moretti, Maximilian Autenrieth, Riccardo Serra, Roberto Trotta, David A. van Dyk, Andrei Mesinger
A precise measurement of photometric redshifts (photo-z) is key for the success of modern photometric galaxy surveys. Machine learning (ML) methods show great promise in this context, but suffer from covariate shift (CS) in training sets due to selection bias where interesting sources are underrepresented, and the corresponding ML models show poor generalisation properties. We present an application of the StratLearn method to the estimation of photo-z, validating against simulations where we enforce the presence of CS to different degrees. StratLearn is a statistically principled approach that relies on splitting the source and target datasets into strata based on estimated propensity scores (i.e. the probability for an object to be in the source set given its observed covariates). After stratification, two conditional density estimators are fit separately to each stratum, then combined via a weighted average. We benchmark our results against the GPz algorithm, quantifying the performance of the two codes with a set of metrics. Our results show that the StratLearn-z metrics are only marginally affected by the presence of CS, while GPz shows a significant degradation of performance in the photo-z prediction for fainter objects. For the strongest CS scenario, StratLearn-z yields a reduced fraction of catastrophic errors, a factor of 2 improvement for the RMSE and one order of magnitude improvement on the bias. We also assess the quality of the conditional redshift estimates with the probability integral transform (PIT). The PIT distribution obtained from StratLearn-z features fat fewer outliers and is symmetric, i.e. the predictions appear to be centered around the true redshift value, despite showing a conservative estimation of the spread of the conditional redshift distributions. Our julia implementation of the method is available at \url{https://github.com/chiaramoretti/StratLearn-z}.
Intermediate-Mass Black Holes in Green Pea Galaxies (IMBH-GP) I: a Candidate Sample from LAMOST and SDSS
2024-09-30
Ruqiu Lin, Zhen-Ya Zheng, Fang-Ting Yuan, Jun-Xian Wang, Chunyan Jiang, Ning Jiang, Lingzhi Wang, Linhua Jiang, Xiang Ji, Shuairu Zhu, Xiaodan Fu
The scaling relation of central massive black holes (MBHs) and their host galaxies is well-studied for supermassive BHs (SMBHs, $M_{\rm BH}\ \ge 10^6\, M_{\rm \odot}$). However, this relation has large uncertainties in the mass range of the intermediate-mass BHs (IMBHs, $M_{\rm BH}\ \sim10^3-10^{6}\, M_{\rm \odot}$). Since Green Pea (GP) galaxies are luminous compact dwarf galaxies, which may be likely to host less massive SMBHs or even IMBHs, we systematically search for MBHs in a large sample of 2190 GP galaxies at $z < 0.4$, selected from LAMOST and SDSS spectroscopic surveys. Here, we report a newly discovered sample of 59 MBH candidates with broad H$\alpha$ lines. This sample has a median stellar mass of $10^{8.83\pm0.11}\, M_{\rm \odot}$ and hosts MBHs with single-epoch virial masses ranging from $M_{\rm BH}\ \sim 10^{4.7}$ to $10^{8.5}\, M_{\rm \odot}$ (median $10^{5.85\pm0.64}\, M_{\rm \odot}$). Among the 59 MBH candidates, 36 have black hole masses $M_{\rm BH} \le 10^{6}\, M_{\rm \odot}$ (IMBH candidates), one of which even has $M_{\rm BH} \ \lesssim 10^{5}\, M_{\rm \odot}$. We find that the $M_{\rm BH}-M_{\rm *}$ relation of our MBH sample is consistent with the $M_{\rm BH}-M_{\rm bulge}$ relation for SMBHs, while is above the $M_{\rm BH}-M_{\rm *}$ relation for MBHs in dwarf galaxies in the same mass range. Furthermore, we show that 25 MBH candidates, including 4 IMBH candidates, have additional evidence of black hole activities, assessed through various methods such as the broad-line width, BPT diagram, mid-infrared color, X-ray luminosity, and radio emission. Our studies show that it is very promising to find IMBHs in GP galaxies, and the BH sample so obtained enables us to probe the connection between the MBHs and compact dwarf galaxies in the low-redshift Universe.
We investigate the star-forming properties of 23 Lyman Continuum (LyC) leakers at z > 3 in the Great Observatories' Deep Survey-South (GOODS-S) field based on a systematic review of LyC observations from the literature. Using data from the Hubble Space Telescope (HST) and the James Webb Space Telescope (JWST), we construct the spectral energy distributions (SEDs) for these LyC leakers, covering the spectrum from rest-frame ultraviolet to near-infrared. Through the application of a unified modeling approach, we measure the ultraviolet slope, star formation rate, and stellar mass for these LyC leakers in a consistent manner. These high-redshift LyC leakers demonstrate statistically blue UV-continuum slopes, which is consistent with their high escape fraction of LyC photons. We find that these high-redshift LyC leakers span a wide range of specific star formation rate (log(sSFR/yr) from -8.6 to -6.7). Ten of these LyC leakers are located on the star formation main sequence, instead of all being in the starburst mode. The results indicate that intense bursts of star formation are not necessarily required for the leakage of LyC photons for galaxies at z > 3.
AI generated annotations for Breast, Brain, Liver, Lungs and Prostate cancer collections in National Cancer Institute Imaging Data Commons
2024-09-30
Gowtham Krishnan Murugesan, Diana McCrumb, Rahul Soni, Jithendra Kumar, Leonard Nuernberg, Linmin Pei, Ulrike Wagner, Sutton Granger, Andrey Y. Fedorov, Stephen Moore, Jeff Van Oss
AI in Medical Imaging project aims to enhance the National Cancer Institute's (NCI) Image Data Commons (IDC) by developing nnU-Net models and providing AI-assisted segmentations for cancer radiology images. We created high-quality, AI-annotated imaging datasets for 11 IDC collections. These datasets include images from various modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), covering the lungs, breast, brain, kidneys, prostate, and liver. The nnU-Net models were trained using open-source datasets. A portion of the AI-generated annotations was reviewed and corrected by radiologists. Both the AI and radiologist annotations were encoded in compliance with the the Digital Imaging and Communications in Medicine (DICOM) standard, ensuring seamless integration into the IDC collections. All models, images, and annotations are publicly accessible, facilitating further research and development in cancer imaging. This work supports the advancement of imaging tools and algorithms by providing comprehensive and accurate annotated datasets.
Local HI Absorption towards the Magellanic Cloud foreground using ASKAP
2024-09-30
Hiep Nguyen, N. M. McClure-Griffiths, James Dempsey, John M. Dickey, Min-Young Lee, Callum Lynn, Claire E. Murray, Snežana Stanimirović, Michael P. Busch, Susan E. Clark, J. R. Dawson, Helga Dénes, Steven Gibson, Katherine Jameson, Gilles Joncas, Ian Kemp, Denis Leahy, Yik Ki Ma, Antoine Marchal, Marc-Antoine Miville-Deschênes, Nickolas M. Pingel, Amit Seta, Juan D. Soler, Jacco Th. van Loon
We present the largest Galactic neutral hydrogen HI absorption survey to date, utilizing the Australian SKA Pathfinder Telescope at an unprecedented spatial resolution of 30''. This survey, GASKAP-HI, unbiasedly targets 2,714 continuum background sources over 250 square degrees in the direction of the Magellanic Clouds, a significant increase compared to a total of 373 sources observed by previous Galactic absorption surveys across the entire Milky Way. We aim to investigate the physical properties of cold (CNM) and warm (WNM) neutral atomic gas in the Milky Way foreground, characterized by two prominent filaments at high Galactic latitudes (between $-45^{\circ}$ and $-25^{\circ}$). We detected strong HI absorption along 462 lines of sight above the 3$\sigma$ threshold, achieving an absorption detection rate of 17%. GASKAP-HI's unprecedented angular resolution allows for simultaneous absorption and emission measurements to sample almost the same gas clouds along a line of sight. A joint Gaussian decomposition is then applied to absorption-emission spectra to provide direct estimates of HI optical depths, temperatures, and column densities for the CNM and WNM components. The thermal properties of CNM components are consistent with those previously observed along a wide range of Solar neighborhood environments, indicating that cold HI properties are widely prevalent throughout the local interstellar medium. Across our region of interest, CNM accounts for ~30% of the total HI gas, with the CNM fraction increasing with column density toward the two filaments. Our analysis reveals an anti-correlation between CNM temperature and its optical depth, which implies that CNM with lower optical depth leads to a higher temperature.
Finding massive double-exponential disk galaxies with extended low surface brightness stellar disk -- an IllustrisTNG exploration
2024-09-30
Suchira Sarkar, Kanak Saha
We study massive disk galaxies (stellar mass $>$= 10$^{11}$ M$_{\odot}$ ) at $z=0$ from IllustrisTNG simulation to detect galaxies that contain two exponential stellar disks - a central high surface brightness (HSB) disk surrounded by an extended low surface brightness (LSB) envelope. This is motivated by observation of several giant LSB galaxies, reported in literature, showing such complex layered structure (e.g, Malin 1, UGC 1378, UGC 1382 etc). We use the high resolution IllustrisTNG50 data, and perform Sersic plus exponential profile modeling on the idealised, synthetic SDSS-g, r band images of the massive disk galaxies using GALFIT. We identify 7 disk galaxies (12$\%$ of the parent sample of disk galaxies) that are best represented by a central Sersic plus a central HSB disk surrounded by an extended LSB disk. The radial scale lengths of the LSB disk lie in the range of $\sim$ 9.7-31.7 kpc in agreement to that found in literature. We study the star formation properties of these simulated double-disk galaxies to understand the distribution of these from blue star-forming to red-quenched region. Some of these double-disk galaxies display a characteristic minima in their (g-r) color radial profiles. The double-disk galaxies are found to lie within $\sim$ 1.5-$\sigma$ region of the Baryonic Tully-Fisher relation from observation. Our theoretical exploration will be potentially useful in exploring the faint outskirts of massive disk galaxies in upcoming deep surveys such as LSST.
RL-GSBridge: 3D Gaussian Splatting Based Real2Sim2Real Method for Robotic Manipulation Learning
2024-09-30
Yuxuan Wu, Lei Pan, Wenhua Wu, Guangming Wang, Yanzi Miao, Hesheng Wang
Sim-to-Real refers to the process of transferring policies learned in simulation to the real world, which is crucial for achieving practical robotics applications. However, recent Sim2real methods either rely on a large amount of augmented data or large learning models, which is inefficient for specific tasks. In recent years, radiance field-based reconstruction methods, especially the emergence of 3D Gaussian Splatting, making it possible to reproduce realistic real-world scenarios. To this end, we propose a novel real-to-sim-to-real reinforcement learning framework, RL-GSBridge, which introduces a mesh-based 3D Gaussian Splatting method to realize zero-shot sim-to-real transfer for vision-based deep reinforcement learning. We improve the mesh-based 3D GS modeling method by using soft binding constraints, enhancing the rendering quality of mesh models. We then employ a GS editing approach to synchronize rendering with the physics simulator, reflecting the interactions of the physical robot more accurately. Through a series of sim-to-real robotic arm experiments, including grasping and pick-and-place tasks, we demonstrate that RL-GSBridge maintains a satisfactory success rate in real-world task completion during sim-to-real transfer. Furthermore, a series of rendering metrics and visualization results indicate that our proposed mesh-based 3D Gaussian reduces artifacts in unstructured objects, demonstrating more realistic rendering performance.
Computer-mediated therapies for stroke rehabilitation: a systematic review and meta-Analysis
2024-09-30
Stanley Mugisha. Mirko Job. Matteo Zoppi, Marco Testa, Rezia Molfino
OBJECTIVE: To evaluate the efficacy of different forms of virtual reality (VR) treatments as either immersive virtual reality (IVR) or non-immersive virtual reality (NIVR) in comparison to conventional therapy (CT) in improving physical and psychological status among stroke patients. METHODS: The literature search was conducted on seven databases. ACM Digital Library, Medline (via PubMed), Cochrane, IEEE Xplore, Web of Science, and Scopus. The effect sizes of the main outcomes were calculated using Cohen's d. Pooled results were used to present an overall estimate of the treatment effect using a random-effects model. RESULTS: A total of 22 randomized controlled trials were evaluated. 3 trials demonstrated that immersive virtual reality improved upper limb activity, function and activity of daily life in a way comparable to CT. 18 trials showed that NIVR had similar benefits to CT for upper limb activity and function, balance and mobility, activities of daily living and participation. A comparison between the different forms of VR showed that IVR may be more beneficial than NIVR for upper limb training and activities of daily life. CONCLUSIONS: This study found out that IVR therapies may be more effective than NIVR but not CT to improve upper limb activity, function, and daily life activities. However, there is no evidence of the durability of IVR treatment. More research involving studies with larger samples is needed to assess the long-term effects and promising benefits of immersive virtual reality technology.
2024-09-30
Nikolai Körber, Eduard Kromer, Andreas Siebert, Sascha Hauke, Daniel Mueller-Gritschneder, Björn Schuller
We introduce PerCo (SD), a perceptual image compression method based on Stable Diffusion v2.1, targeting the ultra-low bit range. PerCo (SD) serves as an open and competitive alternative to the state-of-the-art method PerCo, which relies on a proprietary variant of GLIDE and remains closed to the public. In this work, we review the theoretical foundations, discuss key engineering decisions in adapting PerCo to the Stable Diffusion ecosystem, and provide a comprehensive comparison, both quantitatively and qualitatively. On the MSCOCO-30k dataset, PerCo (SD) demonstrates improved perceptual characteristics at the cost of higher distortion. We partly attribute this gap to the different model capacities being used (866M vs. 1.4B). We hope our work contributes to a deeper understanding of the underlying mechanisms and paves the way for future advancements in the field. Code and trained models will be released at https://github.com/Nikolai10/PerCo.
What is the Role of Large Language Models in the Evolution of Astronomy Research?
2024-09-30
Morgan Fouesneau, Ivelina G. Momcheva, Urmila Chadayammuri, Mariia Demianenko, Antoine Dumont, Raphael E. Hviding, K. Angelique Kahle, Nadiia Pulatova, Bhavesh Rajpoot, Marten B. Scheuck, Rhys Seeburger, Dmitry Semenov, Jaime I. Villaseñor
ChatGPT and other state-of-the-art large language models (LLMs) are rapidly transforming multiple fields, offering powerful tools for a wide range of applications. These models, commonly trained on vast datasets, exhibit human-like text generation capabilities, making them useful for research tasks such as ideation, literature review, coding, drafting, and outreach. We conducted a study involving 13 astronomers at different career stages and research fields to explore LLM applications across diverse tasks over several months and to evaluate their performance in research-related activities. This work was accompanied by an anonymous survey assessing participants' experiences and attitudes towards LLMs. We provide a detailed analysis of the tasks attempted and the survey answers, along with specific output examples. Our findings highlight both the potential and limitations of LLMs in supporting research while also addressing general and research-specific ethical considerations. We conclude with a series of recommendations, emphasizing the need for researchers to complement LLMs with critical thinking and domain expertise, ensuring these tools serve as aids rather than substitutes for rigorous scientific inquiry.
Controlling sharpness, SNR and SAR for 3D FSE at 7T by end-to-end learning
2024-09-30
Peter Dawood, Martin Blaimer, Jürgen Herrler, Patrick Liebig, Simon Weinmüller, Shaihan Malik, Peter M. Jakob, Moritz Zaiss
Purpose: To non-heuristically identify dedicated variable flip angle (VFA) schemes optimized for the point-spread function (PSF) and signal-to-noise ratio (SNR) of multiple tissues in 3D FSE sequences with very long echo trains at 7T. Methods: The proposed optimization considers predefined SAR constraints and target contrast using an end-to-end learning framework. The cost function integrates components for contrast fidelity (SNR) and a penalty term to minimize image blurring (PSF) for multiple tissues. By adjusting the weights of PSF/SNR cost-function components, PSF- and SNR-optimized VFAs were derived and tested in vivo using both the open-source Pulseq standard on two volunteers as well as vendor protocols on a 7T MRI system with parallel transmit extension on three volunteers. Results: PSF-optimized VFAs resulted in significantly reduced image blurring compared to standard VFAs for T2w while maintaining contrast fidelity. Small white and gray matter structures, as well as blood vessels, are more visible with PSF-optimized VFAs. Quantitative analysis shows that the optimized VFA yields 50% less deviation from a sinc-like reference PSF than the standard VFA. The SNR-optimized VFAs yielded images with significantly improved SNR in a white and gray matter region relative to standard (81.2\pm18.4 vs. 41.2\pm11.5, respectively) as trade-off for elevated image blurring. Conclusion: This study demonstrates the potential of end-to-end learning frameworks to optimize VFA schemes in very long echo trains for 3D FSE acquisition at 7T in terms of PSF and SNR. It paves the way for fast and flexible adjustment of the trade-off between PSF and SNR for 3D FSE.
The College Melting Pot: Peers, Culture and Women's Job Search
2024-09-30
Federica Meluzzi
Gender norms are widely recognized as key determinants of persistent gender gaps in the labor market. However, our understanding of the drivers of gender norms, and their implications for preferences, remain lacking. This paper addresses this gap by examining how cultural assimilation from college peers influences women's early-career labor market decisions. For identification of causal effects, I exploit cross-cohort idiosyncratic variation in peers' geographical origins within Master's programs, combined with unique administrative and survey data covering the universe of college students in Italy. The main finding is that exposure to female classmates originating from areas with more egalitarian gender culture significantly increases women's labor supply, primarily through increased uptake of full-time jobs. A one standard deviation increase in peers' culture increases female earnings by 3.7%. The estimated peer effects are economically significant, representing more than a third of the gender earnings gap. Drawing on comprehensive data on students' job search preferences and newly collected data on their beliefs, I shed novel light on two distinct mechanisms driving peer influence: (1) shifts in preferences for non-pecuniary job attributes, and (2) social learning, particularly on the characteristics of the job offer distribution.
Divided by discipline? A systematic literature review on the quantification of online sexism and misogyny using a semi-automated approach
2024-09-30
Aditi Dutta, Susan Banducci, Chico Q. Camargo
In recent years, several computational tools have been developed to detect and identify sexism, misogyny, and gender-based hate speech, especially on online platforms. Though these tools intend to draw on knowledge from both social science and computer science, little is known about the current state of research in quantifying online sexism or misogyny. Given the growing concern over the discrimination of women in online spaces and the rise in interdisciplinary research on capturing the online manifestation of sexism and misogyny, a systematic literature review on the research practices and their measures is the need of the hour. We make three main contributions: (i) we present a semi-automated way to narrow down the search results in the different phases of selection stage in the PRISMA flowchart; (ii) we perform a systematic literature review of research papers that focus on the quantification and measurement of online gender-based hate speech, examining literature from computer science and the social sciences from 2012 to 2022; and (iii) we identify the opportunities and challenges for measuring gender-based online hate speech. Our findings from topic analysis suggest a disciplinary divide between the themes of research on sexism/misogyny. With evidence-based review, we summarise the different approaches used by the studies who have explored interdisciplinary approaches to bridge the knowledge gap. Coupled with both the existing literature on social science theories and computational modeling, we provide an analysis of the benefits and shortcomings of the methodologies used. Lastly, we discuss the challenges and opportunities for future research dedicated to measuring online sexism and misogyny.
In the shift towards human-centered manufacturing, our two-year longitudinal study investigates the real-world impact of deploying Cognitive Assistants (CAs) in factories. The CAs were designed to facilitate knowledge sharing among factory operators. Our investigation focused on smartphone-based voice assistants and LLM-powered chatbots, examining their usability and utility in a real-world factory setting. Based on the qualitative feedback we collected during the deployments of CAs at the factories, we conducted a thematic analysis to investigate the perceptions, challenges, and overall impact on workflow and knowledge sharing. Our results indicate that while CAs have the potential to significantly improve efficiency through knowledge sharing and quicker resolution of production issues, they also introduce concerns around workplace surveillance, the types of knowledge that can be shared, and shortcomings compared to human-to-human knowledge sharing. Additionally, our findings stress the importance of addressing privacy, knowledge contribution burdens, and tensions between factory operators and their managers.
Using Large Multimodal Models to Extract Knowledge Components for Knowledge Tracing from Multimedia Question Information
2024-09-30
Hyeongdon Moon, Richard Davis, Seyed Parsa Neshaei, Pierre Dillenbourg
Knowledge tracing models have enabled a range of intelligent tutoring systems to provide feedback to students. However, existing methods for knowledge tracing in learning sciences are predominantly reliant on statistical data and instructor-defined knowledge components, making it challenging to integrate AI-generated educational content with traditional established methods. We propose a method for automatically extracting knowledge components from educational content using instruction-tuned large multimodal models. We validate this approach by comprehensively evaluating it against knowledge tracing benchmarks in five domains. Our results indicate that the automatically extracted knowledge components can effectively replace human-tagged labels, offering a promising direction for enhancing intelligent tutoring systems in limited-data scenarios, achieving more explainable assessments in educational settings, and laying the groundwork for automated assessment.
The Lonely Runner Conjecture originated in Diophantine approximation will turn 60 in 2028. Even if the conjecture is still widely open, the flow of partial results, innovative tools and connections to different problems and applications has been steady on its long life. This survey attempts to give a panoramic view of the status of the problem, trying to highlight the contributions of the many papers that it has originated.
Robots' ability to follow language instructions and execute diverse 3D tasks is vital in robot learning. Traditional imitation learning-based methods perform well on seen tasks but struggle with novel, unseen ones due to variability. Recent approaches leverage large foundation models to assist in understanding novel tasks, thereby mitigating this issue. However, these methods lack a task-specific learning process, which is essential for an accurate understanding of 3D environments, often leading to execution failures. In this paper, we introduce GravMAD, a sub-goal-driven, language-conditioned action diffusion framework that combines the strengths of imitation learning and foundation models. Our approach breaks tasks into sub-goals based on language instructions, allowing auxiliary guidance during both training and inference. During training, we introduce Sub-goal Keypose Discovery to identify key sub-goals from demonstrations. Inference differs from training, as there are no demonstrations available, so we use pre-trained foundation models to bridge the gap and identify sub-goals for the current task. In both phases, GravMaps are generated from sub-goals, providing flexible 3D spatial guidance compared to fixed 3D positions. Empirical evaluations on RLBench show that GravMAD significantly outperforms state-of-the-art methods, with a 28.63% improvement on novel tasks and a 13.36% gain on tasks encountered during training. These results demonstrate GravMAD's strong multi-task learning and generalization in 3D manipulation. Video demonstrations are available at: https://gravmad.github.io.
Signal Processing for Haptic Surface Modeling: a Review
2024-09-30
Antonio Luigi Stefani, Niccolò Bisagno, Andrea Rosani, Nicola Conci, Francesco De Natale
Haptic feedback has been integrated into Virtual and Augmented Reality, complementing acoustic and visual information and contributing to an all-round immersive experience in multiple fields, spanning from the medical domain to entertainment and gaming. Haptic technologies involve complex cross-disciplinary research that encompasses sensing, data representation, interactive rendering, perception, and quality of experience. The standard processing pipeline, consists of (I) sensing physical features in the real world using a transducer, (II) modeling and storing the collected information in some digital format, (III) communicating the information, and finally, (IV) rendering the haptic information through appropriate devices, thus producing a user experience (V) perceptually close to the original physical world. Among these areas, sensing, rendering and perception have been deeply investigated and are the subject of different comprehensive surveys available in the literature. Differently, research dealing with haptic surface modeling and data representation still lacks a comprehensive dissection. In this work, we aim at providing an overview on modeling and representation of haptic surfaces from a signal processing perspective, covering the aspects that lie in between haptic information acquisition on one side and rendering and perception on the other side. We analyze, categorize, and compare research papers that address the haptic surface modeling and data representation, pointing out existing gaps and possible research directions.
We collect and discuss various results on an important family of knots and links called Turk's head knots and links $Th (p,q)$. In the mathematical literature, they also appear under different names such as rosette knots and links or weaving knots and links. Unless being the unknot or the alternating torus links $T(2,q)$, the Turk's head links $Th (p,q)$ are all known to be alternating, fibered, hyperbolic, invertible, non-split, periodic, and prime. The Turk's head links $Th (p,q)$ are also both positive and negative amphichiral if $p$ is chosen to be odd. Moreover, we highlight and present several more results, focusing on Turk's head knots $Th (3,q)$. We finally list several open problems and conjectures for Turk's head knots and links. We conclude with a short appendix on torus knots and links, which might be of independent interest.
A topology-based algorithm for the isomorphism check of 2-level Orthogonal Arrays
2024-09-30
Roberto Fontana, Marco Guerra
We introduce a construction and an algorithm, both based on Topological Data Analysis (TDA), to tackle the problem of the isomorphism check of Orthogonal Arrays (OAs). Specifically, we associate to any binary OA a persistence diagram, one of the main tools in TDA, and explore how the Wasserstein distance between persistence diagrams can be used to inform whether two designs are isomorphic.
In the last decade, EU became aware of its dependency on mining countries for mineral resources. In that context in 2022, France launched new airborne geophysical surveys in the Limousin region (northwestern French Massif Central) which is historically renown for its gold, uranium, tungsten and tin production. Thanks to these new data, it has become possible to both explore the Saint-Goussaud - Auriat zone, centered on the Puy-les-Vignes tungsten deposit and to investigate the numerous W occurrences. To do so, direct geophysical modelling using gravimetric, airborne magnetic and electromagnetic data has allowed to create a 3D regional model. It mainly illustrates locations where evolved granites deepen, possibly associated with hidden granitic cupolas. Besides, electromagnetic data have been analysed to highlight conductive anomalies. The lineament analysis has contributed to identify a conductive corridor, interpreted as a regional structure. Conductive vertical conduits, which are spatially close to mineralized occurrences and stream-sediment anomalies have also been spotted. This new method has thus allowed generating new elements which could be combined and could have implications for granite-related tungsten exploration in this region.
Towards a complete census of luminous Compton-thick Active Galactic Nuclei in the local Universe
2024-09-30
A. Akylas, I. Georgantopoulos, P. Gandhi, P. Boorman, C. L. Greenwell
X-ray surveys provide the most efficient means for the detection of Active Galactic Nuclei (AGN). However, they face difficulties in detecting the most heavily obscured Compton-thick AGN. The BAT detector on board the Gehrels/Swift mission, operating in the very hard 14-195 keV band, has provided the largest samples of Compton-thick AGN in the local Universe. However, even these flux limited samples can miss the most obscured sources among the Compton-thick AGN population. A robust way to find these local sources is to systematically study volume-limited AGN samples detected in the IR or the optical part of the spectrum. Here, we utilize a local sample (<100 Mpc) of mid-IR selected AGN, unbiased against obscuration, to determine the fraction of Compton-thick sources in the local universe. When available we acquire X-ray spectral information for the sources in our sample from previously published studies. Additionally, to maximize the X-ray spectral information for the sources in our sample, we analyse, for the first time, eleven unexplored XMM-Newton and NuSTAR observations, identifying four new Compton-thick sources. Our results reveal an increased fraction of Compton-thick AGN among the sources that have not been detected by BAT of 44 %. Overall we estimate a fraction of Compton thick sources in the local universe of 25-30% among mid-IR selected AGN. We find no evidence for evolution of the AGN Compton-thick fraction with luminosity.
The advancement of Large Language Models (LLMs) has significantly impacted various domains, including Web search, healthcare, and software development. However, as these models scale, they become more vulnerable to cybersecurity risks, particularly backdoor attacks. By exploiting the potent memorization capacity of LLMs, adversaries can easily inject backdoors into LLMs by manipulating a small portion of training data, leading to malicious behaviors in downstream applications whenever the hidden backdoor is activated by the pre-defined triggers. Moreover, emerging learning paradigms like instruction tuning and reinforcement learning from human feedback (RLHF) exacerbate these risks as they rely heavily on crowdsourced data and human feedback, which are not fully controlled. In this paper, we present a comprehensive survey of emerging backdoor threats to LLMs that appear during LLM development or inference, and cover recent advancement in both defense and detection strategies for mitigating backdoor threats to LLMs. We also outline key challenges in addressing these threats, highlighting areas for future research.
In this paper, we present an early evaluation of the OpenAI o1-mini model, analyzing its performance in gender inclusivity and bias. Our research, conducted on 700 personas 350 from GPT-4o mini and 350 from o1-mini, reveals that despite improvements in inclusivity regarding personality traits and preferences, significant gender biases remain. For instance, o1-mini rated male personas higher in competency, with a score of 8.06, compared to female personas at 7.88 and non-binary personas at 7.80. Additionally, o1-mini assigned PhD roles to 28% of male personas but only 22.4% of females and 0% of non-binary personas. Male personas were also more likely to be perceived as successful founders, at 69.4%, and CEOs, at 62.17%, compared to female personas at 67.97% and 61.11%, and non-binary personas at 65.7% and 58.37%. The analysis reveals persistent gender biases across fields like Engineering, Data, and Technology, where males dominate, reflecting traditional stereotypes. Conversely, fields like Design, Art, and Marketing show a stronger presence of females, reinforcing societal notions that associate creativity and communication with females. These findings highlight ongoing challenges in mitigating gender bias, reinforcing the need for further interventions to ensure equitable representation across all genders in AI models.
Understanding Challenges and Opportunities in Body Movement Education of People who are Blind or have Low Vision
2024-09-30
Madhuka Thisuri De Silva, Sarah Goodwin, Leona M Holloway, Matthew Butler
Actively participating in body movement such as dance, sports, and fitness activities is challenging for people who are blind or have low vision (BLV). Teachers primarily rely on verbal instructions and physical demonstrations with limited accessibility. Recent work shows that technology can support body movement education for BLV people. However, there is limited involvement with the BLV community and their teachers to understand their needs. By conducting a series of two surveys, 23 interviews and four focus groups, we gather the voices and perspectives of BLV people and their teachers. This provides a rich understanding of the challenges of body movement education. We identify ten major themes, four key design challenges, and propose potential solutions. We encourage the assistive technologies community to co-design potential solutions to these identified design challenges promoting the quality of life of BLV people and supporting the teachers in the provision of inclusive education.
Here we review scanning tunneling microscopy research on the surface determination for various types of kagome materials, including 11-type (CoSn, FeSn, FeGe), 32-type (Fe3Sn2), 13-type (Mn3Sn), 135-type (AV3Sb5, A = K, Rb, Cs), 166-type (TbMn6Sn6, YMn6Sn6 and ScV6Sn6), and 322-type (Co3Sn2S2 and Ni3In2Se2). We first demonstrate that the measured step height between different surfaces typically deviates from the expected value of +-0.4~0.8A, which is owing to the tunneling convolution effect with electronic states and becomes a serious issue for Co3Sn2S2 where the expected Sn-S interlayer distance is 0.6A. Hence, we put forward a general methodology for surface determination as atomic step geometry imaging, which is fundamental but also experimentally challenging to locate the step and to image with atomic precision. We discuss how this method can be used to resolve the surface termination puzzle in Co3Sn2S2. This method provides a natural explanation for the existence of adatoms and vacancies, and beyond using unknown impurity states, we propose and use designer layer-selective substitutional chemical markers to confirm the validity of this method. Finally, we apply this method to determine the surface of a new kagome material Ni3In2Se2, as a cousin of Co3Sn2S2, and we image the underlying kagome geometry on the determined Se surface above the kagome layer, which directly visualizes the p-d hybridization physics. We emphasize that this general method does not rely on theory, but the determined surface identity can provide guidelines for first-principles calculations with adjustable parameters on the surface-dependent local density of states and quasi-particle interference patterns.
Balancing Generalization and Specialization: Offline Metalearning for Bandwidth Estimation
2024-09-30
Aashish Gottipati, Sami Khairy, Yasaman Hosseinkashi, Gabriel Mittag, Vishak Gopal, Francis Y. Yan, Ross Cutler
User experience in real-time video applications requires continuously adjusting video encoding bitrates to match available network capacity, which hinges on accurate bandwidth estimation (BWE). However, network heterogeneity prevents a one-size-fits-all solution to BWE, motivating the demand for personalized approaches. Although personalizing BWE algorithms offers benefits such as improved adaptability to individual network conditions, it faces the challenge of data drift -- where estimators degrade over time due to evolving network environments. To address this, we introduce Ivy, a novel method for BWE that leverages offline metalearning to tackle data drift and maximize end-user Quality of Experience (QoE). Our key insight is that dynamically selecting the most suitable BWE algorithm for current network conditions allows for more effective adaption to changing environments. Ivy is trained entirely offline using Implicit Q-learning, enabling it to learn from individual network conditions without a single, live videoconferencing interaction, thereby reducing deployment complexity and making Ivy more practical for real-world personalization. We implemented our method in a popular videoconferencing application and demonstrated that Ivy can enhance QoE by 5.9% to 11.2% over individual BWE algorithms and by 6.3% to 11.4% compared to existing online meta heuristics.
2024-09-30
S. Buder, J. Kos, E. X. Wang, M. McKenzie, M. Howell, S. L. Martell, M. R. Hayden, D. B. Zucker, T. Nordlander, B. T. Montet, G. Traven, J. Bland-Hawthorn, G. M. De Silva, K. C. Freeman, G. F. Lewis, K. Lind, S. Sharma, J. D. Simpson, D. Stello, T. Zwitter, A. M. Amarsi, J. J. Armstrong, K. Banks, M. A. Beavis, K. Beeson, B. Chen, I. Ciucă, G. S. Da Costa, R. de Grijs, B. Martin, D. M. Nataf, M. K. Ness, A. D. Rains, T. Scarr, R. Vogrinčič, Z. Wang, R. A. Wittenmyer, Y. Xie, The GALAH Collaboration
The stars of the Milky Way carry the chemical history of our Galaxy in their atmospheres as they journey through its vast expanse. Like barcodes, we can extract the chemical fingerprints of stars from high-resolution spectroscopy. The fourth data release (DR4) of the Galactic Archaeology with HERMES (GALAH) Survey, based on a decade of observations, provides the chemical abundances of up to 32 elements for 917 588 stars that also have exquisite astrometric data from the $Gaia$ satellite. For the first time, these elements include life-essential nitrogen to complement carbon, and oxygen as well as more measurements of rare-earth elements critical to modern-life electronics, offering unparalleled insights into the chemical composition of the Milky Way. For this release, we use neural networks to simultaneously fit stellar parameters and abundances across the full spectrum, leveraging synthetic grids computed with Spectroscopy Made Easy. These grids account for atomic line formation in non-local thermodynamic equilibrium for 14 elements. In a two-iteration process, we first fit stellar labels for all 1 085 520 spectra, then co-add repeated observations and refine these labels using astrometric data from $Gaia$ and 2MASS photometry, improving the accuracy and precision of stellar parameters and abundances. Our validation thoroughly assesses the reliability of spectroscopic measurements and highlights key caveats for catalogue users. GALAH DR4 represents yet another milestone in Galactic archaeology, combining detailed chemical compositions from multiple nucleosynthetic channels with kinematic information and age estimates. The resulting dataset, covering nearly a million stars, opens new avenues for understanding not only the chemical and dynamical history of the Milky Way, but also the broader questions of the origin of elements and the evolution of planets, stars, and galaxies.
2024-09-30
Levi Burner, Cornelia Fermüller, Yiannis Aloimonos
Suppose you are at your desk looking at some objects on it. You don't know the precise distance from your eye to any particular object in meters. However, you can immediately reach out and touch any of them. Instead of the meter, your knowledge of distance is encoded in unknown but embodied units of action. In contrast, standard approaches in robotics assume calibration to the meter, so that separated vision and control processes can be interfaced. Consequently, robots are precisely manufactured and calibrated, resulting in expensive systems available in only a few configurations. In response, we propose Embodied Visuomotor Representation, a framework that allows distance to be measured by a robot's own actions and thus minimizes dependence on calibrated 3D sensors and physical models. Using it, we demonstrate that a robot without knowledge of its size, environmental scale, or its own strength can become capable of touching and clearing obstacles after several seconds of operation. Similarly, we demonstrate in simulation that an agent, without knowledge of its mass or strength, can jump a gap of unknown size after performing a few test oscillations. These experiments parallel bee and gerbil behavior, respectively.
We present a dataset built for machine learning applications consisting of galaxy photometry, images, spectroscopic redshifts, and structural properties. This dataset comprises 286,401 galaxy images and photometry from the Hyper-Suprime-Cam Survey PDR2 in five imaging filters ($g,r,i,z,y$) with spectroscopically confirmed redshifts as ground truth. Such a dataset is important for machine learning applications because it is uniform, consistent, and has minimal outliers but still contains a realistic range of signal-to-noise ratios. We make this dataset public to help spur development of machine learning methods for the next generation of surveys such as Euclid and LSST. The aim of GalaxiesML is to provide a robust dataset that can be used not only for astrophysics but also for machine learning, where image properties cannot be validated by the human eye and are instead governed by physical laws. We describe the challenges associated with putting together a dataset from publicly available archives, including outlier rejection, duplication, establishing ground truths, and sample selection. This is one of the largest public machine learning-ready training sets of its kind with redshifts ranging from 0.01 to 4. The redshift distribution of this sample peaks at redshift of 1.5 and falls off rapidly beyond redshift 2.5. We also include an example application of this dataset for redshift estimation, demonstrating that using images for redshift estimation produces more accurate results compared to using photometry alone. For example, the bias in redshift estimate is a factor of 10 lower when using images between redshift of 0.1 to 1.25 compared to photometry alone. Results from dataset such as this will help inform us on how to best make use of data from the next generation of galaxy surveys.
Operando Ultrafast Damage-free Diffraction-Enhanced X-ray Absorption Spectroscopy for Chemical Reactivity of Polymer in Solvents
2024-09-30
Zhengxing Peng, Antoine Lainé, Ka Chon Ng, Mutian Hua, Brett A. Helms, Miquel B. Salmeron, Cheng Wang
Chemical recycling of plastics to its constituent monomers is a promising solution to develop a sustainable circular plastic economy. An in-situ X-ray absorption spectra (XAS) characterization is an important way to understand the deconstruction process. However, radiation damage, and long acquisition time, prevent such characterization for fast chemical process. Here, we present a novel experimental technique, which we name Diffraction-Enhanced X-ray Absorption Spectroscopy (DE-XAS), where the plastic material is supported on a graphene layer covering a periodic pattern of holes in a perforated SiNx membrane. The XAS is obtained by measuring the energy-dependent intensity of the X-ray diffracted beams going through the plastic film over the holes in the SiNx membrane. Our method decreases beam damage by orders of magnitude while providing good signal/noise ratio data. We demonstrate the suppression of beam damage with samples of polymethyl methacrylate (PMMA) and the operando characterization of polymer deconstruction process in acid with polydiketoenamine (PDK). This proof-of-concept of the DE-XAS technique shows its great potential for studying fast chemical processes, picoseconds to nanoseconds, using fast CCD detectors with short readout time. We believe the DE-XAS technique offers opportunities for studies of chemical reactions, e.g., photoresist, and many others.
2024-09-30
Michael Zemcov, Richard Feder, Ryan Wills
We have regenerated Herschel-SPIRE maps covering 360 square degrees near the celestial equator. These are the largest extragalactic surveys designed to overlap with cosmic microwave background legacy fields mapped at sub-mm wavelengths. We provide documentation detailing their construction and use. The maps are available on zenodo as https://doi.org/10.5281/zenodo.13352296.
Next-generation multiple access (NGMA) serves as an umbrella term for transmission schemes distinct from conventional orthogonal methods. A key candidate of NGMA, non-orthogonal multiple access (NOMA), emerges as a solution to enhance connectivity by allowing multiple users to share time, frequency, and space concurrently. However, NOMA faces challenges in implementation, particularly in canceling inter-user interference. In this paper, we discuss the principles behind NOMA and review conventional NOMA methods. Then, to address these challenges, we present asynchronous transmission and interference-aware modulation techniques, enabling decoding without successive interference cancellation. The goal is to design constellations that dynamically adapt to interference, minimizing bit error rates (BERs) and enhancing user throughput in the presence of inter-user, inter-carrier, and inter-cell interference. The traditional link between minimizing BER and increasing spectral efficiency is explored, with deep autoencoders for end-to-end communication emerging as a potential solution to improve BERs. Interference-aware modulation can revolutionize constellation design for non-orthogonal channels. Rate-splitting multiple access (RSMA) is another promising interference management technique in multi-user systems. In addition to addressing challenges in finite-alphabet NOMA, this paper offers new insights and provides an overview of code-domain NOMA, trellis-coded NOMA, and RSMA as key NGMA candidates. We also discuss the evolution of channel coding toward low-latency communication and examine modulation and coding schemes in 5G networks. Finally, we highlight future research directions, emphasizing their importance for realizing NOMA from concept to functional technology.
2024-09-30
Denise Hung, Brian C. Lemaux, Olga Cucciati, Ben Forrest, Ekta A. Shah, Roy R. Gal, Finn Giddings, Derek Sikorski, Emmet Golden-Marx, Lori M. Lubin, Nimish Hathi, Giovanni Zamorani, Sandro Bardelli, Letizia P. Cassara, Gabriella De Lucia, Fabio Fontanot, Bianca Garilli, Lucia Guaita, Michaela Monika Hirschmann, Kyoung-Soo Lee, Andrew B. Newman, Vandana Ramakrishnan, Daniela Vergani, Lizhi Xie, Elena Zucca
The Charting Cluster Construction with VUDS and ORELSE (C3VO) survey is an ongoing imaging and spectroscopic campaign aiming to map out the growth of structure up to $z\sim5$ and was born from the combination of the VIMOS Ultra Deep Survey (VUDS) and the Observations of Redshift Evolution in Large-Scale Environments (ORELSE) survey. As we previously accomplished with the ORELSE survey, we apply our technique known as Voronoi tessellation Monte-Carlo (VMC) mapping to search for serendipitous galaxy overdensities at $2 14$. In the C3VO fields, we were able to recover many of the previously known structures in the literature as well as find hundreds of new overdensity candidates, once again demonstrating the powerful capabilities of VMC mapping when applied to wide-field optical and infrared galaxy evolution surveys at ever higher redshifts.
Records from the S-Matrix Marathon: A Timeless History of Time
2024-09-30
Mang Hei Gordon Lee, Enrico Pajer, Mathieu Giroux, Holmfridur S. Hannesdottir, Sebastian Mizera, Celina Pasiecznik
By directly probing the initial conditions of our universe, cosmological surveys offer us a unique observational handle on quantum field theory in curved spacetime with dynamical gravity and might even allow us to glean information about a full theory of quantum gravity. Here we report on recent progress to study the natural observables in the problem, namely cosmological correlators. After setting the stage, we review results from three different approaches. First, we present the in-out formalism as an interesting alternative to the well-known in-in formalism and stress some of its advantages, such as the derivation of recursion relations, correlators cutting rules and a proposal for a de Sitter scattering matrix. Second, we tackle the important open problem of constructing effective theories in curved spacetime, which generally requires an open quantum system approach. Third, we provide an executive summary of general properties of the field-theoretic wavefunction that follow from symmetries, unitarity, causality and locality. We describe how these properties can be leveraged to bootstrap all tree-level results and we discuss loop contributions. These notes are based on a series of lectures held during the S-Matrix Marathon workshop at the Institute for Advanced Study on 11-22 March 2024.
Characterizing and Efficiently Accelerating Multimodal Generation Model Inference
2024-09-30
Yejin Lee, Anna Sun, Basil Hosmer, Bilge Acun, Can Balioglu, Changhan Wang, Charles David Hernandez, Christian Puhrsch, Daniel Haziza, Driss Guessous, Francisco Massa, Jacob Kahn, Jeffrey Wan, Jeremy Reizenstein, Jiaqi Zhai, Joe Isaacson, Joel Schlosser, Juan Pino, Kaushik Ram Sadagopan, Leonid Shamis, Linjian Ma, Min-Jae Hwang, Mingda Chen, Mostafa Elhoushi, Pedro Rodriguez, Ram Pasunuru, Scott Yih, Sravya Popuri, Xing Liu, Carole-Jean Wu
Generative artificial intelligence (AI) technology is revolutionizing the computing industry. Not only its applications have broadened to various sectors but also poses new system design and optimization opportunities. The technology is capable of understanding and responding in multiple modalities. However, the advanced capability currently comes with significant system resource demands. To sustainably scale generative AI capabilities to billions of users in the world, inference must be fast and efficient. This paper pinpoints key system design and optimization opportunities by characterizing a family of emerging multi-modal generation models on real systems. Auto-regressive token generation is a critical latency performance bottleneck, typically dominated by GPU idle time. In addition to memory-intensive attention across the generative AI models, linear operations constitute significant inference latency due to the feed forward networks in Transformer-based models. We demonstrate that state-of-the-art optimization levers, spanning from applications to system software and hardware, set a 3.88x better baseline.
JWST captures a sudden stellar outburst and inner disk wall destruction
2024-09-30
Chengyan Xie, Ilaria Pascucci, Dingshan Deng, Naman S. Bajaj, Richard Alexander, Andrew Sellek, Agnes Kospal, Giulia Ballabio, Uma Gorti
We present JWST/MIRI observations of T~Cha, a highly variable ($\Delta V \sim$3-5\,mag) accreting Sun-like star surrounded by a disk with a large ($\sim 15$\,au) dust gap. We find that the JWST mid-infrared spectrum is signiticantly different from the {\it Spitzer} spectrum obtained 17 years before, where the emission at short wavelengths ($5-10 \mu m$) has decreased by $\sim 2/3$ while at longer wavelengths ($15-25 \mu m$) it increased by up to a factor of $\sim 3$. This 'seesaw' behavior is contemporary with a fairly constant higher optical emission captured by the All Sky Automated Survey. By analyzing and modelling both SEDs, we propose that JWST caught the star during an outburst that destructed the asymmetric inner disk wall responsible for the high optical variability and lower $15-25$\,micron\ emission during the {\it Spitzer} time. The dust mass lost during this outburst is estimated to be comparable ($\sim 1/5$) to the upper limit of the total micron-sized dust mass in the inner disk of T~Cha now. Monitoring this system during possible future outbursts and more observations of its quiescent state will reveal if the inner disk can be replenished or will continue to be depleted and vanish.
The distant Milky Way halo from the Southern hemisphere: Characterization of the LMC-induced dynamical-friction wake
2024-09-30
Manuel Cavieres, Julio Chanamé, Camila Navarrete, Yasna Ordenes-Briceño, Nicolás Garavito-Camargo, Gurtina Besla, Maren Hempel, Katherina Vivas, Facundo Gómez
The infall of the LMC into the Milky Way's halo impacts the distribution of stars and dark matter in our Galaxy. Mapping the observational consequences of this encounter can inform us about the properties of both galaxies, details of their interaction, and possibly even distinguish between different dark matter models. N-body simulations predict large-scale density asymmetries in the Galactic halo both in baryonic and dark matter due to the passage of the LMC, with the overdensity directly trailing its current orbit through the Southern hemisphere known as the wake. Using the VIRCAM and DECam instruments, we collected wide-field deep near-infrared and optical photometry in four fields chosen to cover the region of the sky expected to span most of the predicted density contrast of the wake. We identify more than 400 stars comprising two different tracers, near main sequence turn-off stars and red giants, that map the distant halo between $\sim 60$ - $100$ kpc, and use them to derive stellar halo densities as a function of position in the sky and Galactocentric radius. We detect (1) a break in the radial density profile of halo stars at 70 kpc that has not been seen in halo studies done from the North, and (2) a clear halo overdensity that starts also at 70 kpc and exhibits a density contrast that increases steadily when moving across the sky into the predicted current location of the LMC wake. If identifying this overdensity with the LMC wake, the peak density contrast we measure is more pronounced than in all available models of the LMC infall, which would indicate the need for a more massive LMC and/or with a different orbit than presently favored. Alternatively, contamination from unidentified substructure may be biasing our detections, so wider-area surveys with similar depth would be needed for confirmation.
The AURORA Survey: An Extraordinarily Mature, Star-forming Galaxy at $z\sim 7$
2024-09-30
Alice E. Shapley, Ryan L. Sanders, Michael W. Topping, Naveen A. Reddy, Anthony J. Pahl, Pascal A. Oesch, Danielle A. Berg, Rychard J. Bouwens, Gabriel Brammer, Adam C. Carnall, Fergus Cullen, Romeel Davé, James S. Dunlop, Richard S. Ellis, N. M. Förster Schreiber, Steven R . Furlanetto, Karl Glazebrook, Garth D. Illingworth, Tucker Jones, Mariska Kriek, Derek J. McLeod, Ross J. McLure, Desika Narayanan, Max Pettini, Daniel Schaerer, Daniel P. Stark, Charles C. Steidel, Mengtao Tang, Leonardo Clarke, Callum T. Donnan, Emily Kehoe
We present the properties of a massive, large, dusty, metal-rich, star-forming galaxy at z_spec=6.73. GOODSN-100182 was observed with JWST/NIRSpec as part of the AURORA survey, and is also covered by public multi-wavelength HST and JWST imaging. While the large mass of GOODSN-100182 (~10^10 M_sun) was indicated prior to JWST, NIRCam rest-optical imaging now reveals the presence of an extended disk (r_eff~1.5 kpc). In addition, the NIRSpec R~1000 spectrum of GOODSN-100182 includes the detection of a large suite of rest-optical nebular emission lines ranging in wavelength from [OII]3727 up to [NII]6583. The ratios of Balmer lines suggest significant dust attenuation (E(B-V)_gas=0.40+0.10/-0.09), consistent with the red rest-UV slope inferred for GOODSN-100182 (beta=-0.50+/-0.09). The star-formation rate based on dust-corrected H-alpha emission is log(SFR(H-alpha)/ M_sun/yr)=2.02+0.13/-0.14, well above the z~7 star-forming main sequence in terms of specific SFR. Strikingly, the ratio of [NII]6583/H-alpha emission suggests almost solar metallicity, as does the ratio ([OIII]5007/H-beta)/([NII]6583/H-alpha) and the detection of the faint [FeII]4360 emission feature, whereas the [OIII]5007/[OII]3727 ratio suggests roughly 50% solar metallicity. Overall, the excitation and ionization properties of GOODSN-100182 more closely resemble those of typical star-forming galaxies at z~2-3 rather than z~7. Based on public spectroscopy of the GOODS-N field, we find that GOODSN-100182 resides within a significant galaxy overdensity, and is accompanied by a spectroscopically-confirmed neighbor galaxy. GOODSN-100182 demonstrates the existence of mature, chemically-enriched galaxies within the first billion years of cosmic time, whose properties must be explained by galaxy formation models.
New Moons of Uranus and Neptune from Ultra-Deep Pencil Beam Surveys
2024-09-30
Scott Sheppard, David Tholen, Marina Brozovic, Robert Jacobson, Chadwick Trujillo, Patryk Sofia Lykawka, Mike Alexandersen
We have conducted extremely ultra-deep pencil beam observations for new satellites around both Uranus and Neptune. Tens of images on several different nights in 2021, 2022 and 2023 were obtained and shifted and added together to reach as faint as 26.9 and 27.2 magnitudes in the r-band around Uranus and Neptune, respectively. One new moon of Uranus, S/2023 U1, and two new moons of Neptune, S/2021 N1 and S/2002 N5, were found. S/2023 U1 was 26.6 mags, is about 7 km in diameter and has a distant, eccentric and inclined retrograde orbit similar to Caliban and Stephano, implying these satellites are fragments from a once larger parent satellite. S/2023 U1 almost completely overlaps Stephano in orbital phase space. S/2021 N1 was 26.9 mags, about 14 km in size and has a retrograde orbit similar to Neso and Psamathe, indicating they are a dynamical family. We find S/2021 N1 is in a Kozai-Lidov orbital resonance. S/2002 N5 was 25.9 mags, is about 23 km in size and it makes a family of distant prograde satellites with Sao and Laomedeia. All three new moons show for the first time dynamical groups of moons exist around both Uranus and Neptune. The creation of these groups likely produced dust that could be the source of red material seen on the leading hemispheres of some larger inner satellites like Titania, Oberon and Umbriel. We also detected all known outer moons of Uranus and Neptune on multiple nights. This survey mostly completes the outer satellites of Uranus to about 8 km and Neptune to about 14 km in diameter. The size distributions of satellite dynamical families around the giant planets shows a strong steepening in the power law size distribution smaller than 5 km in diameter. The satellites of a family become much more common smaller than 5 km and their size distribution is consistent with a collisional break-up of a once larger parent satellite.
Improved Empirical Backgrounds for JWST NIRISS Image/WFSS Data Reduction
2024-09-30
Raphael E. Hviding, Ivelina G. Momcheva, Leonardo Clarke
The Near Infrared Imager and Slitless Spectrograph (NIRISS) on the James Webb Space Telescope (JWST) is a versatile instrument for collecting imaging and wide-field slitless spectroscopy (WFSS) data for surveys of galaxy clusters, emission-line galaxies, stellar populations, and more. Dispersed zodiacal light imprints distinct structures on space-based near-infrared imaging and WFSS observations, necessitating careful subtraction during JWST NIRISS data reduction. As of 2024-09-24 NIRISS WFSS calibration backgrounds introduce significant spatially-dependent artifacts, up to 5% of the overall background level, which can severely affect data quality and following astronomical analysis. Notably, there are no existing backgrounds for NIRISS imaging data which also show systematic artifacts, such as the `light saber' effect. In this work, we present improved empirical JWST NIRISS imaging and WFSS backgrounds derived from all available public data in the F115W, F150W, and F200W filters. We demonstrate that our empirical backgrounds provide a more accurate representation of the background structure in NIRISS imaging and WFSS data than existing reference files, mitigating the impact of spatially-dependent artifacts. Our empirical backgrounds are publicly available and can be used to improve the quality of JWST NIRISS imaging and WFSS data reduction.
Reconstructing the Assembly of Massive Galaxies. III: Quiescent Galaxies Loose Angular Momentum as They Evolve in a Mass-dependent Fashion
2024-09-30
Zhiyuan Ji, Mauro Giavalisco
We study the evolution of stellar kinematics of a sample of 952 massive quiescent galaxies with $M_*>10^{10.5}M_\odot$ at $0.610^{11.3}M_\odot$, the relationship between $\sigma^\prime_{star}$ and $q$ becomes significantly flattened with increasing stellar age. This indicates that, as the very massive galaxy populations continue to evolve after quenching, angular momentum gradually reduces, which eventually transforms them into velocity-dispersion supported systems. We suggest that incoherent, continuous merging and accretion events onto the galaxies are the main drivers of the observed mass-dependent, posting-quenching dynamical evolution, because more massive galaxies are more likely to undergo such interactions. We are witnessing the early formation epoch of fast and slow rotators at $z \sim 0.8$, when the Universe was only half of its age nowadays.
An Investigation Into The Selection and Colors of Little Red Dots and Active Galactic Nuclei
2024-09-30
Kevin N. Hainline, Roberto Maiolino, Ignas Juodzbalis, Jan Scholtz, Hannah Ubler, Francesco D'Eugenio, Jakob M. Helton, Yang Sun, Fengwu Sun, Brant Robertson, Sandro Tacchella, Andrew J. Bunker, Stefano Carniani, Stephane Charlot, Emma Curtis-Lake, Eiichi Egami, Benjamin D. Johnson, Xiaojing Lin, Jianwei Lyu, Pablo G. Perez-Gonzalez, Pierluigi Rinaldi, Maddie S. Silcock, Christina C. Williams, Christopher N. A. Willmer, Chris Willott, Junyu Zhang, Yongda Zhu
Recently, a large number of compact sources at $z > 4$ with blue UV slopes and extremely red rest-frame optical slopes have been found in James Webb Space Telescope (JWST) extragalactic surveys. As a subsample of these sources, commonly called ``little red dots'' (LRDs), have been spectroscopically observed to host a broad-line active galactic nucleus (AGN), they have been the focus of multiple recent studies in an attempt to understand the origin of their UV and optical emission. Here, we assemble a sample of 123 LRDs from the literature along with spectroscopic and photometric JWST-identified samples of AGNs to compare their colors and spectral slopes. We find that while obscured AGNs at $z < 6$ have highly dissimilar colors to LRDs, unobscured AGNs at $z < 6$ span a wide range of colors, with only a subsample showing colors similar to LRDs. At $z > 6$, the majority of the unobscured AGNs that have been found in these samples are LRDs, but this may be related to the fact that these sources are at large bolometric luminosities. Because LRDs occupy a unique position in galaxy color space, they are more straightforward to target, and the large number of broad-line AGNs that do not have LRD colors and slopes are therefore underrepresented in many spectroscopic surveys because they are more difficult to pre-select. Current LRD selection techniques return a large and disparate population, including many sources having $2-5\mu$m colors impacted by emission line flux boosting in individual filters.
The planetary population synthesis method aims at comprehensively testing planet formation theories against observational evidence and providing theoretical sets of planets to help interpret observations and inform instrument development. Recent developments on the theoretical and observational sides are reviewed: First, observational constraints are summarized, then, the work flow of population synthesis and its two main components are presented, which are, global end-to-end models of planetary formation and evolution and probability distributions for the disk initial conditions. Next, the output of four recent population synthesis models is compared in detail and differences and similarities are discussed. The goal is to help the reader understand the assumptions that were made and how they impact the results. Furthermore, future directions of research are identified and the impact of current and future observational programs is discussed. With JWST, evidence on disk and planet compositions emerges. Planet formation models need to prepare for these near-future developments by including self-consistent magnetic wind-driven gas and dust disk evolution, planetary migration, as well as employ hybrid pebble and planetesimal accretion, which are identified as dominant modes of accretion in different mass regimes.
2024-09-30
Giannis Daras, Hyungjin Chung, Chieh-Hsin Lai, Yuki Mitsufuji, Jong Chul Ye, Peyman Milanfar, Alexandros G. Dimakis, Mauricio Delbracio
Diffusion models have become increasingly popular for generative modeling due to their ability to generate high-quality samples. This has unlocked exciting new possibilities for solving inverse problems, especially in image restoration and reconstruction, by treating diffusion models as unsupervised priors. This survey provides a comprehensive overview of methods that utilize pre-trained diffusion models to solve inverse problems without requiring further training. We introduce taxonomies to categorize these methods based on both the problems they address and the techniques they employ. We analyze the connections between different approaches, offering insights into their practical implementation and highlighting important considerations. We further discuss specific challenges and potential solutions associated with using latent diffusion models for inverse problems. This work aims to be a valuable resource for those interested in learning about the intersection of diffusion models and inverse problems.
RNA-Protein Interaction Prediction Based on Deep Learning: A Comprehensive Survey
2024-09-30
Danyu Li, Rubing Huang, Chenhui Cui, Dave Towey, Ling Zhou, Jinyu Tian, Bin Zou
The interaction between Ribonucleic Acids (RNAs) and proteins, also called RNA Protein Interaction (RPI), plays an important role in the life activities of organisms, including in various regulatory processes, such as gene splicing, gene localization, and disease pathogenesis. RPI Prediction (RPIP) predicts the interactions between RNAs and proteins, which includes looking for the existence of interactions and the binding sites of interactions, and adding RNA-protein functional annotations (such as immunity regulation, neuroprotection, etc). Due to the huge amounts of complex biological data, Deep Learning-based RPIP (DL-based RPIP) has been widely investigated, as it can extract high-dimensional features from data and make accurate predictions. Over the last decade, there have been many achievements and contributions in DL-based RPIP. Although some previous studies review DL-based RPIP, to the best of our knowledge, there is still a lack of a comprehensive survey. In this paper, we extensively survey DL-based RPIP in terms of its entire process, including: feature encoding, deep learning modeling, results evaluation, RPIP application domains, and available websites and software. We also identify some open research challenges, and discuss the potential future work for DL-based RPIP.
Seasonal Performance Evaluation of a Hybrid PV-Wind-Battery Power System for a Mars Base
2024-09-30
Abdollah Masoud Darya, Ramesh C. Bansal, Omaima Anwar Jarndal
This work investigates a hybrid photovoltaic-wind-battery power system designed to sustain a Mars base under varying seasonal and climatic conditions. The Mars Climate Database was utilized to simulate the effects of seasonal changes, diurnal cycles, and dust storms on the system's power generation. The seasonal performance was analyzed across the Martian surface and at potential habitation sites proposed in the "First Landing Site/Exploration Zone Workshop for Human Missions to the Surface of Mars (FLSW).'' Within the hybrid system, the photovoltaic arrays serve as the primary energy source, with wind turbines providing essential backup during nighttime and dust storms. A single $1\,000\,\mathrm{m}^2$ photovoltaic array, a $33.4\,\mathrm{m}$ diameter wind turbine, and a $312\,\mathrm{kWh}$ battery can support a six-person Mars base at $32.1\%$ of the Martian surface during the equinoxes and solstices, expanding to $51.7\%$ with three sets of arrays and turbines. Additionally, $24$ FLSW sites can be supported throughout the solstices and equinoxes by a single photovoltaic array, turbine, and battery, even during global dust storms. Among the $24$ sites, Hebrus Valles, Huygens Crater, and Noctis Labyrinthus had the highest energy production potential. These findings are expected to guide further research on hybrid renewable power systems for Mars exploration.
Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches
2024-09-30
Xuefeng Liu, Songhao Jiang, Xiaotian Duan, Archit Vasan, Chong Liu, Chih-chan Tien, Heng Ma, Thomas Brettin, Fangfang Xia, Ian T. Foster, Rick L. Stevens
Protein-ligand binding is the process by which a small molecule (drug or inhibitor) attaches to a target protein. The binding affinity, which refers to the strength of this interaction, is central to many important problems in bioinformatics such as drug design. An extensive amount of work has been devoted to predicting binding affinity over the past decades due to its significance. In this paper, we review all significant recent works, focusing on the methods, features, and benchmark datasets. We have observed a rising trend in the use of traditional machine learning and deep learning models for predicting binding affinity, accompanied by an increasing amount of data on proteins and small drug-like molecules. While prediction results are constantly improving, we also identify several open questions and potential directions that remain unexplored in the field. This paper could serve as an excellent starting point for machine learning researchers who wish to engage in the study of binding affinity, or for anyone with general interests in machine learning, drug discovery, and bioinformatics.
Blockchain-enhanced Integrity Verification in Educational Content Assessment Platform: A Lightweight and Cost-Efficient Approach
2024-09-29
Talgar Bayan, Richard Banach, Askar Nurbekov, Makhmud Mustafabek Galy, Adi Sabyrbayev, Zhanat Nurbekova
The growing digitization of education presents significant challenges in maintaining the integrity and trustworthiness of educational content. Traditional systems often fail to ensure data authenticity and prevent unauthorized alterations, particularly in the evaluation of teachers' professional activities, where demand for transparent and secure assessment mechanisms is increasing. In this context, Blockchain technology offers a novel solution to address these issues. This paper introduces a Blockchain-enhanced framework for the Electronic Platform for Expertise of Content (EPEC), a platform used for reviewing and assessing educational materials. Our approach integrates the Polygon network, a Layer-2 solution for Ethereum, to securely store and retrieve encrypted reviews, ensuring both privacy and accountability. By leveraging Python, Flask, and Web3.py, we interact with a Solidity-based smart contract to securely link each review to a unique identifier (UID) that connects on-chain data with real-world databases. The system, containerized using Docker, facilitates easy deployment and integration through API endpoints. Our implementation demonstrates significant cost savings, with a 98\% reduction in gas fees compared to Ethereum, making it a scalable and cost-effective solution. This research contributes to the ongoing effort to implement Blockchain in educational content verification, offering a practical and secure framework that enhances trust and transparency in the digital education landscape.
RAG (Retrieval-Augmented Generation) have recently gained significant attention for their enhanced ability to integrate external knowledge sources in open-domain question answering (QA) tasks. However, it remains unclear how these models address fairness concerns, particularly with respect to sensitive attributes such as gender, geographic location, and other demographic factors. First, as language models evolve to prioritize utility, like improving exact match accuracy, fairness may have been largely overlooked. Second, RAG methods are complex pipelines, making it hard to identify and address biases, as each component is optimized for different goals. In this paper, we aim to empirically evaluate fairness in several RAG methods. We propose a fairness evaluation framework tailored to RAG methods, using scenario-based questions and analyzing disparities across demographic attributes. The experimental results indicate that, despite recent advances in utility-driven optimization, fairness issues persist in both the retrieval and generation stages, highlighting the need for more targeted fairness interventions within RAG pipelines. We will release our dataset and code upon acceptance of the paper.
CRScore: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smells
2024-09-29
Atharva Naik, Marcus Alenius, Daniel Fried, Carolyn Rose
The task of automated code review has recently gained a lot of attention from the machine learning community. However, current review comment evaluation metrics rely on comparisons with a human-written reference for a given code change (also called a diff), even though code review is a one-to-many problem like generation and summarization with many "valid reviews" for a diff. To tackle these issues we develop a CRScore - a reference-free metric to measure dimensions of review quality like conciseness, comprehensiveness, and relevance. We design CRScore to evaluate reviews in a way that is grounded in claims and potential issues detected in the code by LLMs and static analyzers. We demonstrate that CRScore can produce valid, fine-grained scores of review quality that have the greatest alignment with human judgment (0.54 Spearman correlation) and are more sensitive than reference-based metrics. We also release a corpus of 2.6k human-annotated review quality scores for machine-generated and GitHub review comments to support the development of automated metrics.
Extensions of the blast-wave model for the description of non-central collisions are reviewed and the physics behind them is explained and illustrated. It is shown how the second-order anisotropy in expansion velocity together with the anisotropy in the shape or the density profile of the fireball determine the elliptic flow of the produced hadrons. Ambiguities and limitations of the models are discussed and different models are compared among themselves and with example data. It is concluded that model results should always be compared to $v_2(p_t)$ from several identified species in order to receive meaningful results about the freeze-out stage of the fireball and that even such a comparison may not be conclusive as the models may not be able to reproduce all relevant data.
Natural Language Generation for Visualizations: State of the Art, Challenges and Future Directions
2024-09-29
Enamul Hoque, Mohammed Saidul Islam
Natural language and visualization are two complementary modalities of human communication that play a crucial role in conveying information effectively. While visualizations help people discover trends, patterns, and anomalies in data, natural language descriptions help explain these insights. Thus, combining text with visualizations is a prevalent technique for effectively delivering the core message of the data. Given the rise of natural language generation (NLG), there is a growing interest in automatically creating natural language descriptions for visualizations, which can be used as chart captions, answering questions about charts, or telling data-driven stories. In this survey, we systematically review the state of the art on NLG for visualizations and introduce a taxonomy of the problem. The NLG tasks fall within the domain of Natural Language Interfaces (NLI) for visualization, an area that has garnered significant attention from both the research community and industry. To narrow down the scope of the survey, we primarily concentrate on the research works that focus on text generation for visualizations. To characterize the NLG problem and the design space of proposed solutions, we pose five Wh-questions, why and how NLG tasks are performed for visualizations, what the task inputs and outputs are, as well as where and when the generated texts are integrated with visualizations. We categorize the solutions used in the surveyed papers based on these "five Wh-questions." Finally, we discuss the key challenges and potential avenues for future research in this domain.
A Systematic Review of NLP for Dementia- Tasks, Datasets and Opportunities
2024-09-29
Lotem Peled-Cohen, Roi Reichart
The close link between cognitive decline and language has fostered long-standing collaboration between the NLP and medical communities in dementia research. To examine this, we reviewed over 200 papers applying NLP to dementia related efforts, drawing from medical, technological, and NLP-focused literature. We identify key research areas, including dementia detection, linguistic biomarker extraction, caregiver support, and patient assistance, showing that half of all papers focus solely on dementia detection using clinical data. However, many directions remain unexplored: artificially degraded language models, synthetic data, digital twins, and more. We highlight gaps and opportunities around trust, scientific rigor, applicability, and cross-community collaboration, and showcase the diverse datasets encountered throughout our review: recorded, written, structured, spontaneous, synthetic, clinical, social media based, and more. This review aims to inspire more creative approaches to dementia research within the medical and NLP communities.
The principal distinction in transitioning from far-field multiple-input multiple-output (MIMO) systems to near-field MIMO configurations lies in the notable augmentation of spatial degrees of freedom (DoF). This increase is not static;rather, it dynamically fluctuates in response to user mobility. A critical challenge emerges in effectively leveraging this significantly enhanced and continuously evolving spatial DoF, particularly when constrained by a limited and energy-intensive array of radio frequency (RF) chains. This article presents an exhaustive review of the current methodologies for exploring spatial DoF in MIMO systems, with a particular emphasis on the near-field context. Central to this review is the exploration of beamspace modulation, a technique that ingeniously capitalizes on the increased and dynamic spatial DoFs inherent in near-field MIMO systems. This strategic exploitation is demonstrated to yield significant enhancements in both spectral efficiency and system reliability. Furthermore, the article delves into a detailed analysis of the multifaceted challenges associated with implementing this technology. Through this comprehensive evaluation, the work provides crucial insights into ongoing efforts to navigate these challenges and suggests potential pathways for future research in this rapidly evolving field.
Obstacle-Aware Quadrupedal Locomotion With Resilient Multi-Modal Reinforcement Learning
2024-09-29
I Made Aswin Nahrendra, Byeongho Yu, Minho Oh, Dongkyu Lee, Seunghyun Lee, Hyeonwoo Lee, Hyungtae Lim, Hyun Myung
Quadrupedal robots hold promising potential for applications in navigating cluttered environments with resilience akin to their animal counterparts. However, their floating base configuration makes them vulnerable to real-world uncertainties, yielding substantial challenges in their locomotion control. Deep reinforcement learning has become one of the plausible alternatives for realizing a robust locomotion controller. However, the approaches that rely solely on proprioception sacrifice collision-free locomotion because they require front-feet contact to detect the presence of stairs to adapt the locomotion gait. Meanwhile, incorporating exteroception necessitates a precisely modeled map observed by exteroceptive sensors over a period of time. Therefore, this work proposes a novel method to fuse proprioception and exteroception featuring a resilient multi-modal reinforcement learning. The proposed method yields a controller that showcases agile locomotion performance on a quadrupedal robot over a myriad of real-world courses, including rough terrains, steep slopes, and high-rise stairs, while retaining its robustness against out-of-distribution situations.
Gravitational Wave Astrophysics with TianQin: A brief progress review
2024-09-29
Yi-Ming Hu
As a space-borne gravitational wave observatory, TianQin can observe a large variety of gravitational wave sources. The rich signals can be composed by different types of astronomical systems, like Galactic compact binaries, inspiral of stellar mass black holes, merger of massive black holes, and extreme mass ratio inspirals. The incoherent summation of these signals can also form a stochastic gravitational wave background. Using the future TianQin observation, it is possible to put stringent constraints on fundamental science, like the expansion history of our universe. In this work, we provide a brief review of these topics that contributes to the 7th International Workshop on the TianQin Science Mission.
Identifying Knowledge Editing Types in Large Language Models
2024-09-29
Xiaopeng Li, Shangwen Wang, Shezheng Song, Bin Ji, Huijun Liu, Shasha Li, Jun Ma, Jie Yu
Knowledge editing has emerged as an efficient technology for updating the knowledge of large language models (LLMs), attracting increasing attention in recent years. However, there is a lack of effective measures to prevent the malicious misuse of this technology, which could lead to harmful edits in LLMs. These malicious modifications could cause LLMs to generate toxic content, misleading users into inappropriate actions. In front of this risk, we introduce a new task, Knowledge Editing Type Identification (KETI), aimed at identifying different types of edits in LLMs, thereby providing timely alerts to users when encountering illicit edits. As part of this task, we propose KETIBench, which includes five types of harmful edits covering most popular toxic types, as well as one benign factual edit. We develop four classical classification models and three BERT-based models as baseline identifiers for both open-source and closed-source LLMs. Our experimental results, across 42 trials involving two models and three knowledge editing methods, demonstrate that all seven baseline identifiers achieve decent identification performance, highlighting the feasibility of identifying malicious edits in LLMs. Additional analyses reveal that the performance of the identifiers is independent of the reliability of the knowledge editing methods and exhibits cross-domain generalization, enabling the identification of edits from unknown sources. All data and code are available in https://github.com/xpq-tech/KETI. Warning: This paper contains examples of toxic text.
Remaining Useful Life (RUL) prediction is a critical aspect of Prognostics and Health Management (PHM), aimed at predicting the future state of a system to enable timely maintenance and prevent unexpected failures. While existing deep learning methods have shown promise, they often struggle to fully leverage the spatial information inherent in complex systems, limiting their effectiveness in RUL prediction. To address this challenge, recent research has explored the use of Graph Neural Networks (GNNs) to model spatial information for more accurate RUL prediction. This paper presents a comprehensive review of GNN techniques applied to RUL prediction, summarizing existing methods and offering guidance for future research. We first propose a novel taxonomy based on the stages of adapting GNNs to RUL prediction, systematically categorizing approaches into four key stages: graph construction, graph modeling, graph information processing, and graph readout. By organizing the field in this way, we highlight the unique challenges and considerations at each stage of the GNN pipeline. Additionally, we conduct a thorough evaluation of various state-of-the-art (SOTA) GNN methods, ensuring consistent experimental settings for fair comparisons. This rigorous analysis yields valuable insights into the strengths and weaknesses of different approaches, serving as an experimental guide for researchers and practitioners working in this area. Finally, we identify and discuss several promising research directions that could further advance the field, emphasizing the potential for GNNs to revolutionize RUL prediction and enhance the effectiveness of PHM strategies. The benchmarking codes are available in GitHub: https://github.com/Frank-Wang-oss/GNN\_RUL\_Benchmarking.
Programming on Bitcoin: A Survey of Layer 1 and Layer 2 Technologies in Bitcoin Ecosystem
2024-09-29
Guofu Liao, Taotao Wang, Qing Yang, Yihan Xia, Long Shi, Xiang Zhao, Xiaoxiao Wu, Shengli Zhang, Anthony Chan, Richard Yuen
This paper surveys innovative protocols that enhance the programming functionality of the Bitcoin blockchain, a key part of the "Bitcoin Ecosystem." Bitcoin utilizes the Unspent Transaction Output (UTXO) model and a stack-based script language for efficient peer-to-peer payments, but it faces limitations in programming capability and throughput. The 2021 Taproot upgrade introduced the Schnorr signature algorithm and P2TR transaction type, significantly improving Bitcoin's privacy and programming capabilities. This upgrade has led to the development of protocols like Ordinals, Atomicals, and BitVM, which enhance Bitcoin's programming functionality and enrich its ecosystem. We explore the technical aspects of the Taproot upgrade and examine Bitcoin Layer 1 protocols that leverage Taproot's features to program non-fungible tokens (NFTs) into transactions, including Ordinals and Atomicals, along with the fungible token standards BRC-20 and ARC-20. Additionally, we categorize certain Bitcoin ecosystem protocols as Layer 2 solutions similar to Ethereum's, analyzing their impact on Bitcoin's performance. By analyzing data from the Bitcoin blockchain, we gather metrics on block capacity, miner fees, and the growth of Taproot transactions. Our findings confirm the positive effects of these protocols on Bitcoin's mainnet, bridging gaps in the literature regarding Bitcoin's programming capabilities and ecosystem protocols and providing valuable insights for practitioners and researchers.
Upper-body musculoskeletal pain and eye strain among language professionals: a descriptive, cross-sectional study
2024-09-29
Emma Goldsmith
Language professionals spend long hours at the computer, which may have an impact on their short- and long-term physical health. In 2023, I ran a survey to investigate workstation ergonomics, eye and upper-body problems, and self-reported strategies that alleviate those problems among language professionals who work sitting or standing at a desk. Of the 791 respondents, about one third reported eye problems and over two-thirds reported upper-body aches or pains in the past 12 months, with significantly higher upper-body pain prevalence among females than males, and also among younger respondents than older ones. While the pain prevalence rate in the survey was similar to figures published in the literature, as was the sex risk factor, the association of higher pain prevalence among younger people contrasted with other studies that have found increasing age to be a risk factor for pain. In this article I share the survey results in detail and discuss possible explanations for the findings.
We attempt to measure the DIBs $\lambda$5780, $\lambda$5797 and $\lambda$6614 in over two million low-resolution spectra of cool stars from LAMOST. Based on the DIB measurements, the correlation between DIBs and extinction, the kinematics of DIBs, and the Galactic distribution of DIBs are reviewed and investigated from the perspective of statistics. A pipeline is developed to measure the DIBs $\lambda$5780, $\lambda$5797 and $\lambda$6614 in the LAMOST low-resolution spectra. We obtain the DIB measurements of spectra of late-type stars from LAMOST, and screen out 176,831, 13,473 and 110,152 high-quality (HQ) measurements of the DIBs $\lambda$5780, $\lambda$5797 and $\lambda$6614, respectively, corresponding to 142,074, 11,480 and 85,301 unique sources. Utilizing these HQ measurements, we present the Galactic maps of the DIBs $\lambda$5780 and $\lambda$6614 in the northern sky for the first time. The central wavelengths of the DIBs $\lambda$5780, $\lambda$5797 and $\lambda$6614 in air are determined to be 5780.48 $\pm$ 0.01, 5796.94 $\pm$ 0.02 and 6613.64 $\pm$ 0.01 {\AA}, respectively, based on their kinematics. The equivalent widths of these three DIBs per unit extinction are statistically fitted to be 0.565, 0.176 and 0.256 {\AA}/mag. As a part of our work, three catalogs of the HQ measurements for the DIBs $\lambda$5780, $\lambda$5797 and $\lambda$6614 are provided online. To the best of our knowledge, this is the largest number of measurements of these three DIBs to date. It is also the first time that the Galactic maps of the DIBs $\lambda$5780 and $\lambda$6614 in the northern hemisphere are presented, and the central wavelengths of the DIBs $\lambda$5780, $\lambda$5797 and $\lambda$6614 are estimated from the kinematics.
Perspectives and challenges in bolide infrasound processing and interpretation: A focused review with case studies
2024-09-29
Elizabeth A. Silber
Infrasound sensing plays a critical role in the detection and analysis of bolides, offering passive, cost-effective global monitoring capabilities. Key objectives include determining the timing, location, and yield of these events. Achieving these goals requires a robust approach to detect, analyze, and interpret rapidly moving elevated sources such as bolides (also re-entry and space debris). In light of advancements in infrasonic methodologies, there is a need for a comprehensive overview of the characteristics that distinguish bolides from other infrasound sources, and methodologies for bolide infrasound analysis. This paper provides a focused review of key considerations and presents a unified framework to enhance infrasound processing approaches specifically tailored for bolides. Three representative case studies are presented to demonstrate the practical application of infrasound processing methodologies and deriving source parameters, while exploring challenges associated with bolide-generated infrasound. These case studies underscore the effectiveness of infrasound in determining source parameters and highlight interpretative challenges, such as variations in signal period measurements across different studies. Future research should place emphasis on improving geolocation and yield accuracy. This can be achieved through rigorous and systematic analyses of large, statistically significant samples of such events. aiming to resolve interpretative inconsistencies and explore the causes for variability in signal periods and back azimuths. The topic described here is also relevant to space exploration involving planetary bodies with atmospheres, such as Venus, Mars, and Titan.
Video DataFlywheel: Resolving the Impossible Data Trinity in Video-Language Understanding
2024-09-29
Xiao Wang, Jianlong Wu, Zijia Lin, Fuzheng Zhang, Di Zhang, Liqiang Nie
Recently, video-language understanding has achieved great success through large-scale pre-training. However, data scarcity remains a prevailing challenge. This study quantitatively reveals an "impossible trinity" among data quantity, diversity, and quality in pre-training datasets. Recent efforts seek to refine large-scale, diverse ASR datasets compromised by low quality through synthetic annotations. These methods successfully leverage useful information in multimodal video content (frames, tags, ASR transcripts, etc.) to refine the original annotations. Nevertheless, they struggle to mitigate noise within synthetic annotations and lack scalability as the dataset size expands. To address these issues, we introduce the Video DataFlywheel framework, which iteratively refines video annotations with improved noise control methods. For iterative refinement, we first leverage a video-language model to generate synthetic annotations, resulting in a refined dataset. Then, we pre-train on it and fine-tune on human refinement examples for a stronger model. These processes are repeated for continuous improvement. For noise control, we present AdaTaiLr, a novel noise control method that requires weaker assumptions on noise distribution, thereby proving more effective in large datasets with theoretical guarantees. The combination of iterative refinement and AdaTaiLr can achieve better scalability in video-language understanding. Extensive experiments show that our framework outperforms existing data refinement baselines, delivering a 3% performance boost and improving dataset quality with minimal diversity loss. Furthermore, our refined dataset facilitates significant improvements in various video-language understanding tasks, including video question answering and text-video retrieval.
Magnetization Plateaus by the Field-Induced Partitioning of Spin Lattices
2024-09-29
Myung-Hwan Whangbo, Hyun-Joo Koo, Reinhard K. Kremer, Alexander N. Vasiliev
To search for a conceptual picture describing the magnetization plateau phenomenon, we surveyed the crystal structures and the spin lattices of those magnets exhibiting plateaus in their magnetization vs. magnetic field curves by probing the three questions: (a) why only certain magnets exhibit magnetization plateaus, (b) why there occur several different types of magnetization plateaus, and (c) what controls the widths of magnetization plateaus. We show that the answers to these questions lie in how the magnets under field absorb Zeeman energy hence changing their magnetic structures. The magnetic structure of a magnet insulator is commonly described in terms of its spin lattice, which requires the determination of the spin exchanges nonnegligible strengths between the magnetic ions. Our work strongly suggests that a magnet under magnetic field partitions its spin lattice into antiferromagnetic (AFM) or ferrimagnetic fragments by breaking its weak magnetic bonds. Our supposition of the field-induced partitioning of spin lattice into magnetic fragments is supported by the anisotropic magnetization plateaus of Ising magnets and by the highly anisotropic width of the 1/3-magnetization plateau in azurite. The answers to the three questions (a) - (c) emerge naturally by analyzing how these fragments are formed under magnetic field.
BuildingView: Constructing Urban Building Exteriors Databases with Street View Imagery and Multimodal Large Language Mode
2024-09-29
Zongrong Li, Yunlei Su, Chenyuan Zhu, Wufan Zhao
Urban Building Exteriors are increasingly important in urban analytics, driven by advancements in Street View Imagery and its integration with urban research. Multimodal Large Language Models (LLMs) offer powerful tools for urban annotation, enabling deeper insights into urban environments. However, challenges remain in creating accurate and detailed urban building exterior databases, identifying critical indicators for energy efficiency, environmental sustainability, and human-centric design, and systematically organizing these indicators. To address these challenges, we propose BuildingView, a novel approach that integrates high-resolution visual data from Google Street View with spatial information from OpenStreetMap via the Overpass API. This research improves the accuracy of urban building exterior data, identifies key sustainability and design indicators, and develops a framework for their extraction and categorization. Our methodology includes a systematic literature review, building and Street View sampling, and annotation using the ChatGPT-4O API. The resulting database, validated with data from New York City, Amsterdam, and Singapore, provides a comprehensive tool for urban studies, supporting informed decision-making in urban planning, architectural design, and environmental policy. The code for BuildingView is available at https://github.com/Jasper0122/BuildingView.
Effective summarisation evaluation metrics enable researchers and practitioners to compare different summarisation systems efficiently. Estimating the effectiveness of an automatic evaluation metric, termed meta-evaluation, is a critically important research question. In this position paper, we review recent meta-evaluation practices for summarisation evaluation metrics and find that (1) evaluation metrics are primarily meta-evaluated on datasets consisting of examples from news summarisation datasets, and (2) there has been a noticeable shift in research focus towards evaluating the faithfulness of generated summaries. We argue that the time is ripe to build more diverse benchmarks that enable the development of more robust evaluation metrics and analyze the generalization ability of existing evaluation metrics. In addition, we call for research focusing on user-centric quality dimensions that consider the generated summary's communicative goal and the role of summarisation in the workflow.
Adoption and Adaptation of CI/CD Practices in Very Small Software Development Entities: A Systematic Literature Review
2024-09-29
Mario Ccallo, Alex Quispe-Quispe
This study presents a systematic literature review on the adoption of Continuous Integration and Continuous Delivery (CI/CD) practices in Very Small Entities (VSEs) in software development. The research analyzes 13 selected studies to identify common CI/CD practices, characterize the specific limitations of VSEs, and explore strategies for adapting these practices to small-scale environments. The findings reveal that VSEs face significant challenges in implementing CI/CD due to resource constraints and complex tool ecosystems. However, the adoption of accessible tools like Jenkins and Docker, coupled with micro-pipeline practices and simplified frameworks such as ISO 29110, can effectively address these challenges. The study highlights the growing trend of microservices architecture adoption and the importance of tailoring CI/CD processes to VSE-specific needs. This research contributes to the understanding of how small software entities can leverage CI/CD practices to enhance their competitiveness and software quality, despite limited resources.
Survey of Security and Data Attacks on Machine Unlearning In Financial and E-Commerce
2024-09-29
Carl E. J. Brodzinski
This paper surveys the landscape of security and data attacks on machine unlearning, with a focus on financial and e-commerce applications. We discuss key privacy threats such as Membership Inference Attacks and Data Reconstruction Attacks, where adversaries attempt to infer or reconstruct data that should have been removed. In addition, we explore security attacks including Machine Unlearning Data Poisoning, Unlearning Request Attacks, and Machine Unlearning Jailbreak Attacks, which target the underlying mechanisms of unlearning to manipulate or corrupt the model. To mitigate these risks, various defense strategies are examined, including differential privacy, robust cryptographic guarantees, and Zero-Knowledge Proofs (ZKPs), offering verifiable and tamper-proof unlearning mechanisms. These approaches are essential for safeguarding data integrity and privacy in high-stakes financial and e-commerce contexts, where compromised models can lead to fraud, data leaks, and reputational damage. This survey highlights the need for continued research and innovation in secure machine unlearning, as well as the importance of developing strong defenses against evolving attack vectors.
FairPIVARA: Reducing and Assessing Biases in CLIP-Based Multimodal Models
2024-09-28
Diego A. B. Moreira, Alef Iury Ferreira, Gabriel Oliveira dos Santos, Luiz Pereira, João Medrado Gondim, Gustavo Bonil, Helena Maia, Nádia da Silva, Simone Tiemi Hashiguti, Jefersson A. dos Santos, Helio Pedrini, Sandra Avila
Despite significant advancements and pervasive use of vision-language models, a paucity of studies has addressed their ethical implications. These models typically require extensive training data, often from hastily reviewed text and image datasets, leading to highly imbalanced datasets and ethical concerns. Additionally, models initially trained in English are frequently fine-tuned for other languages, such as the CLIP model, which can be expanded with more data to enhance capabilities but can add new biases. The CAPIVARA, a CLIP-based model adapted to Portuguese, has shown strong performance in zero-shot tasks. In this paper, we evaluate four different types of discriminatory practices within visual-language models and introduce FairPIVARA, a method to reduce them by removing the most affected dimensions of feature embeddings. The application of FairPIVARA has led to a significant reduction of up to 98% in observed biases while promoting a more balanced word distribution within the model. Our model and code are available at: https://github.com/hiaac-nlp/FairPIVARA.
2024-09-28
Nicolai A. Jork, Nikolai P. Osmolovskii, Vladimir M. Veliov
This is mainly a survey on the properties of Strong Metric Regularity (SMR) and Strong Metric subRegularity (SMsR) of mappings representing first order optimality conditions (so-called optimality mappings) of optimization problems in infinite dimensional spaces. The focus is on the optimality mappings associated with optimal control problems for ODE systems or PDEs. We especially emphasize an extension of the concepts of SMR and SMsR which involves two metrics either in the domain or in the image spaces. The paper shows the relevance of this extension in optimal control.
In this paper, we employ the Density Dependent Relativistic Hadron (DDRH) field theoretical Model in a Bayesian analysis to investigate the equation of state (EOS) of dense matter featuring antikaon condensation for $K^-$ and $\bar{K}^0$ inside neutron stars. The vector coupling parameters within the kaonic sector are determined through the iso-spin counting rule and quark model. Our study integrates various constraints, including $\chi$EFT calculations, nuclear saturation properties, and astrophysical observations from pulsars PSR J0030+0451 and PSR J0740+66 and from the GW170817 event. We present posterior distributions of model parameters derived from these constraints, enabling us to explore the distributions of nuclear matter properties and neutron star (NS) characteristics such as radii, tidal deformabilities, central energy densities, and speed of sound. The antikaon potential at the 68(90)\% confidence intervals is determined to be $-129.36^{+12.53(+32.617)}_{-3.837(-5.696)}$ MeV. This aligns with several studies providing estimates within the range of $-120$ to $-150$ MeV. We find that the maximum neutron star mass is constrained to around 2M$_\odot$ due to the significant softening of the EOS caused by antikaon condensation. This softening results in a considerable decrease in the speed of sound. Although antikaon condensation for $K^-$ is not feasible inside the canonical neutron stars, it becomes feasible for higher NS masses. The condensation of both $K^-$ and $\bar{K}^0$ is probably present in the interior of neutron star with mass greater than 2M$_\odot$. We also discuss the interconnections among input variables, isoscalar and isovector aspects of the EOS, and specific NS properties in the context of antikaon condensation.
The advancements of Large Language Models (LLMs) have decentralized the responsibility for the transparency of AI usage. Specifically, LLM users are now encouraged or required to disclose the use of LLM-generated content for varied types of real-world tasks. However, an emerging phenomenon, users' secret use of LLM, raises challenges in ensuring end users adhere to the transparency requirement. Our study used mixed-methods with an exploratory survey (125 real-world secret use cases reported) and a controlled experiment among 300 users to investigate the contexts and causes behind the secret use of LLMs. We found that such secretive behavior is often triggered by certain tasks, transcending demographic and personality differences among users. Task types were found to affect users' intentions to use secretive behavior, primarily through influencing perceived external judgment regarding LLM usage. Our results yield important insights for future work on designing interventions to encourage more transparent disclosure of the use of LLMs or other AI technologies.
Advanced Clustering Techniques for Speech Signal Enhancement: A Review and Metanalysis of Fuzzy C-Means, K-Means, and Kernel Fuzzy C-Means Methods
2024-09-28
Abdulhady Abas Abdullah, Aram Mahmood Ahmed, Tarik Rashid, Hadi Veisi, Yassin Hussein Rassul, Bryar Hassan, Polla Fattah, Sabat Abdulhameed Ali, Ahmed S. Shamsaldin
Speech signal processing is a cornerstone of modern communication technologies, tasked with improving the clarity and comprehensibility of audio data in noisy environments. The primary challenge in this field is the effective separation and recognition of speech from background noise, crucial for applications ranging from voice-activated assistants to automated transcription services. The quality of speech recognition directly impacts user experience and accessibility in technology-driven communication. This review paper explores advanced clustering techniques, particularly focusing on the Kernel Fuzzy C-Means (KFCM) method, to address these challenges. Our findings indicate that KFCM, compared to traditional methods like K-Means (KM) and Fuzzy C-Means (FCM), provides superior performance in handling non-linear and non-stationary noise conditions in speech signals. The most notable outcome of this review is the adaptability of KFCM to various noisy environments, making it a robust choice for speech enhancement applications. Additionally, the paper identifies gaps in current methodologies, such as the need for more dynamic clustering algorithms that can adapt in real time to changing noise conditions without compromising speech recognition quality. Key contributions include a detailed comparative analysis of current clustering algorithms and suggestions for further integrating hybrid models that combine KFCM with neural networks to enhance speech recognition accuracy. Through this review, we advocate for a shift towards more sophisticated, adaptive clustering techniques that can significantly improve speech enhancement and pave the way for more resilient speech processing systems.
'Simulacrum of Stories': Examining Large Language Models as Qualitative Research Participants
2024-09-28
Shivani Kapania, William Agnew, Motahhare Eslami, Hoda Heidari, Sarah Fox
The recent excitement around generative models has sparked a wave of proposals suggesting the replacement of human participation and labor in research and development--e.g., through surveys, experiments, and interviews--with synthetic research data generated by large language models (LLMs). We conducted interviews with 19 qualitative researchers to understand their perspectives on this paradigm shift. Initially skeptical, researchers were surprised to see similar narratives emerge in the LLM-generated data when using the interview probe. However, over several conversational turns, they went on to identify fundamental limitations, such as how LLMs foreclose participants' consent and agency, produce responses lacking in palpability and contextual depth, and risk delegitimizing qualitative research methods. We argue that the use of LLMs as proxies for participants enacts the surrogate effect, raising ethical and epistemological concerns that extend beyond the technical limitations of current models to the core of whether LLMs fit within qualitative ways of knowing.
Constraining primordial non-Gaussianity by combining photometric galaxy and 21cm intensity mapping surveys
2024-09-28
Mponeng Kopana, Sheean Jolicoeur, Roy Maartens
The fluctuations produced during cosmic inflation may exhibit non-Gaussian characteristics that are imprinted in the large-scale structure of the Universe. This non-Gaussian imprint is an ultra-large scale signal that can be detected using the power spectrum. We focus on the local-type non-Gaussianity $f_{\rm{NL}}$ and employ a multi-tracer analysis that combines different probes in order to mitigate cosmic variance and maximize the non-Gaussian signal. In our previous paper, we showed that combining spectroscopic galaxy surveys with 21cm intensity mapping surveys in interferometer mode could lead to a $\sim 20-30\%$ improvement in the precision on this non-Gaussian signal. Here we combine the same 21 cm experiments, including also single-dish surveys, with photometric galaxy surveys. The 21 cm single-dish surveys are based on MeerKAT and SKAO and the interferometric surveys are alike to HIRAX and PUMA. We implement foreground-avoidance filters and utilize models for the 21 cm thermal noise associated with single-dish and interferometer modes. The photometric galaxy surveys are similar to the DES and LSST. Our multi-tracer Fisher forecasts show a better precision for the combination of the photometric galaxy surveys and 21 cm interferometric surveys than with the 21 cm single-dish surveys -leading to at most an improvement of $23\%$ in the former case and $16\%$ in the latter case. Furthermore, we examine the impact of varying the foreground filter parameter, redshift range and sky area on the derived constraint. We find that the $f_{\rm{NL}}$ constraint is highly sensitive to both the redshift range and sky area. The foreground filter parameter shows negligible effect.
Conditional Image Synthesis with Diffusion Models: A Survey
2024-09-28
Zheyuan Zhan, Defang Chen, Jian-Ping Mei, Zhenghe Zhao, Jiawei Chen, Chun Chen, Siwei Lyu, Can Wang
Conditional image synthesis based on user-specified requirements is a key component in creating complex visual content. In recent years, diffusion-based generative modeling has become a highly effective way for conditional image synthesis, leading to exponential growth in the literature. However, the complexity of diffusion-based modeling, the wide range of image synthesis tasks, and the diversity of conditioning mechanisms present significant challenges for researchers to keep up with rapid developments and understand the core concepts on this topic. In this survey, we categorize existing works based on how conditions are integrated into the two fundamental components of diffusion-based modeling, i.e., the denoising network and the sampling process. We specifically highlight the underlying principles, advantages, and potential challenges of various conditioning approaches in the training, re-purposing, and specialization stages to construct a desired denoising network. We also summarize six mainstream conditioning mechanisms in the essential sampling process. All discussions are centered around popular applications. Finally, we pinpoint some critical yet still open problems to be solved in the future and suggest some possible solutions. Our reviewed works are itemized at https://github.com/zju-pi/Awesome-Conditional-Diffusion-Models.
Constraining the low-energy cosmic ray flux in the center molecular zone from MeV nuclear deexcitation line observations
2024-09-28
Bing Liu, Ruizhi Yang
Low-energy cosmic rays (LECRs) dominate the ionization in dense regions of molecular clouds in which other ionizers such as UV or X-ray photons are effectively shielded. Thus it was argued that the high ionization rate at the central molecular zone (CMZ) of our Galaxy is mainly caused by LECRs. However, the required LECR flux is orders of magnitude higher than the extrapolation of GeV cosmic ray (CR) flux derived from GeV gamma-ray observations. In this paper, we considered two types of additional LECR components and found that only very soft anomalous CR components can explain such a high ionization rate. This LECR component will inevitably produce MeV nuclear deexcitation lines due to their inelastic scattering with the ambient gas. We calculated the MeV line emission and discussed the detectability of next-generation MeV instruments. We found that future MeV observations can be used to pin down the origin of the high ionization rate in the CMZ.
Evolutionarily stable strategy (ESS) is the defining concept of evolutionary game theory. It has a fairly unanimously accepted definition for the case of symmetric games which are played in a homogeneous population where all individuals are in same role. However, in asymmetric games, which are played in a population with multiple subpopulations (each of which has individuals in one particular role), situation is not as clear. Various generalizations of ESS defined for such cases differ in how they correspond to fixed points of replicator equation which models evolutionary dynamics of frequencies of strategies in the population. Moreover, some of the definitions may even be equivalent, and hence, redundant in the scheme of things. Along with reporting some new results, this paper is partly indented as a contextual mini-review of some of the most important definitions of ESS in asymmetric games. We present the definitions coherently and scrutinize them closely while establishing equivalences -- some of them hitherto unreported -- between them wherever possible. Since it is desirable that a definition of ESS should correspond to asymptotically stable fixed points of replicator dynamics, we bring forward the connections between various definitions and their dynamical stabilities. Furthermore, we find the use of principle of relative entropy to gain information-theoretic insights into the concept of ESS in asymmetric games, thereby establishing a three-fold connection between game theory, dynamical system theory, and information theory in this context. We discuss our conclusions also in the backdrop of asymmetric hypermatrix games where more than two individuals interact simultaneously in the course of getting payoffs.
Discovery of two cyano derivatives of acenaphthylene (C$_{12}$H$_8$) in TMC-1 with the QUIJOTE line survey
2024-09-28
J. Cernicharo, C. Cabezas, R. Fuentetaja, M. Agúndez, B. Tercero, J. Janeiro, M. Juanes, R. I. Kaiser, Y. Endo, A. L. Steber, D. Pérez, C. Pérez, A. Lesarri, N. Marcelino, P. de Vicente
We report the discovery in TMC-1 of two cyano derivatives of the PAH acenaphthylene (C$_{12}$H$_8$). We have found two series of lines with the QUIJOTE line survey that we assign to 1-C$_{12}$H$_7$CN and 5-C$_{12}$H$_7$CN. For the 1-isomer, we have detected and assigned 173 rotational transitions with $J$ up to 46 and $K_a$ up to 9, corresponding to 107 independent frequencies. For the 5-isomer, the identification is based on 56 individual lines, corresponding to 117 rotational transitions with $J$ up to 40 and $K_a$ up to 8. Identification of the carriers was achieved through a careful analysis of the derived rotational constants, which permit us to focus on molecules larger than naphthalene but smaller than anthracene and phenanthrene. Moreover, the derived rotational constants indicate that the species are planar; this allows us to discard derivatives of fluorene and acenaphthene, which are non-planar species. Quantum chemical calculations and subsequent chemical synthesis of these molecules, as well as the observation of their rotational transitions in the laboratory, unequivocally support our identifications. We also confirm, via a robust line-by-line detection, the previous claimed detection of 1- and 2- cyanonaphthalene, which were obtained through statistical stacking techniques. The column densities of 1- and 5-cyanoacenaphthylene are (9.5\,$\pm$\,0.9)$\,\times$\,10$^{11}$ cm$^{-2}$, while those of 1- and 2-cyanonapthalene are (5.5\,$\pm$\,0.5)$\,\times$\,10$^{11}$ cm$^{-2}$. Hence, it seems that acenaphthylene could be a factor of 1.7 more abundant than naphthalene. These results support a scenario in which PAHs grow in cold dark clouds based on fused five- and six-membered carbon rings.
Interacting systems with zero thermodynamic curvature
2024-09-28
Juan Rodrigo, Ian Vega
We review a conjecture by Ruppeiner that relates the nature of interparticle interactions to the sign of the thermodynamic curvature scalar $R$, paying special attention to the case of zero curvature. We highlight the underappreciated fact that there are two Ruppeiner metrics that are equally viable in principle, which are obtained by restricting to systems of constant volume and constant particle number, respectively. We then demonstrate the existence of thermodynamic systems with vanishing curvature scalar but nontrivial interactions. Information about interactions in these systems is obtained by carrying out an inversion procedure on the virial coefficients. Finally, we show using the virial expansion that the ideal gas is the unique physical system for which both curvature scalars vanish. This leads us to propose an extension to Ruppeiner's conjecture.
How do Practitioners Perceive Energy Consumption on Stack Overflow?
2024-09-28
Bihui Jin, Heng Li, Ying Zou
Energy consumption of software applications has emerged as a critical issue for practitioners to contemplate in their daily development processes. Previous studies have performed user surveys with a limited number of practitioners to comprehend practitioners' viewpoints on energy consumption. In this paper, we complement prior studies by conducting an empirical analysis of a meticulously curated dataset comprising 985 Stack Overflow (SO) questions concerning energy consumption. These questions reflect real-world energy-related predicaments faced by practitioners in their daily development activities. To understand practitioners' perception of energy consumption, we investigate the intentions behind these questions, their semantic topics, as well as the tag categories associated with these questions. Our empirical study results reveal that (i) the intentions that drive the questioners to initiate posts and ask questions are primarily associated with understanding a concept or how to use an API; (ii) the most prevalent topic related to energy consumption concerns computing resources; (iii) monitoring energy usage poses a challenging issue, and it takes the longest response time to receive a community response to the questions; and (iv) practitioners are apprehensive about energy consumption from different levels, i.e., hardware, operating systems, and programming languages, during the development of the applications. Our work furnishes insights into the issues related to energy consumption faced by practitioners. Our observations raise awareness among practitioners about the impact of energy consumption on developing software systems from different perspectives, such as coding efficiency and energy monitoring, and shed light on future research opportunities to assist practitioners in developing energy-efficient software systems.
A Search for $z=5$ H$α$ and H$β+$[O III] Dual-Line Emitting Galaxies in the JWST CEERS Field: Implications for the AGN Abundance
2024-09-28
Jingsong Guo, Masafusa Onoue, Kohei Inayoshi, Dale D. Kocevski, Steven L. Finkelstein, Micaela B. Bagley, Elizabeth J. McGrath
The James Webb Space Telescope (JWST) has enabled us to uncover faint galaxies and active galactic nuclei (AGNs) in the early universe. Taking advantage of the unique filter combination used in the Cosmic Evolution Early Release Science Survey (CEERS) program, we perform an extensive photometric search of galaxies emitting strong H$\beta+$[O III] and H$\alpha$ lines. The redshift range of the galaxies is limited to $5.03\leq z\leq 5.26$ by requiring photometric excesses in NIRCam's F277W and F410M images. A total of 261 H$\beta+$[O III] and H$\alpha$ dual-line emitters are found over the absolute UV magnitude $-22\lesssim M_{\mathrm{UV}}\lesssim -17$, with a mean rest-frame equivalent width of 1010 A for H$\beta+$[O III] and 1040 A for H$\alpha$. This population accounts for $\sim 40\%$ of the Lyman break galaxies at this redshift range. Intriguingly, there are 58 objects (22% of the whole sample) that exhibit compact morphology at the rest-UV or optical wavelength. With an assumption that these compact dual-line emitters are dominated by AGN, their AGN bolometric luminosities are in the range of $2\times 10^{43} \lesssim L_{\rm bol}/({\rm erg~s}^{-1})\lesssim 3\times 10^{44}$. Their number density is two orders of magnitude higher than the extrapolation from the UV-selected luminous quasars, which is in good agreement with previous JWST studies of broad-line AGNs, requiring a $\sim 10\%$ of the AGN duty cycle. Moreover, our dual-line emitter sample reaches the faint end of the H$\alpha$ and [O III] luminosity functions down to $\lesssim 10^{42}~{\rm erg~s}^{-1}$. Spectroscopic follow-up observations are planned in an approved JWST Cycle 3 program, in which we aim to confirm their nature, characterize their black hole activity, and construct their mass distribution at $10^6\lesssim M_{\rm BH}/M_\odot \lesssim 10^8$.
ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions $-$ XVII. High-mass star-formation through a large-scale collapse in IRAS 15394$-$5358
2024-09-28
Swagat R. Das, Manuel Merello, Leonardo Bronfman, Tie Liu, Guido Garay, Amelia Stutz, Diego Mardones, Jian-Wen Zhou, Patricio Sanhueza, Hong-Li Liu, Enrique Vázquez-Semadeni, Gilberto C. Gómez, Aina Palau, Anandmayee Tej, Feng-Wei Xu, Tapas Baug, Lokesh K. Dewangan, Jinhua He, Lei Zhu, Shanghuo Li1, Mika Juvela, Anindya Saha, Namitha Issac, Jihye Hwang, Hafiz Nazeer, L. Viktor Toth
Hub-filament systems are considered as natural sites for high-mass star formation. Kinematic analysis of the surroundings of hub-filaments is essential to better understand high-mass star formation within such systems. In this work, we present a detailed study of the massive Galactic protocluster IRAS 15394$-$5358, using continuum and molecular line data from the ALMA Three-millimeter Observations of Massive Star-forming Regions (ATOMS) survey. The 3~mm dust continuum map reveals the fragmentation of the massive ($\rm M=843~M_{\odot}$) clump into six cores. The core C-1A is the largest (radius = 0.04~pc), the most massive ($\rm M=157~M_{\odot}$), and lies within the dense central region, along with two smaller cores ($\rm M=7~and~3~M_{\odot}$). The fragmentation process is consistent with the thermal Jeans fragmentation mechanism and virial analysis shows that all the cores have small virial parameter values ($\rm \alpha_{vir}<<2$), suggesting that the cores are gravitationally bound. The mass vs. radius relation indicates that three cores can potentially form at least a single massive star. The integrated intensity map of $\rm H^{13}CO^{+}$ shows that the massive clump is associated with a hub-filament system, where the central hub is linked with four filaments. A sharp velocity gradient is observed towards the hub, suggesting a global collapse where the filaments are actively feeding the hub. We discuss the role of global collapse and the possible driving mechanisms for the massive star formation activity in the protocluster.
LML: Language Model Learning a Dataset for Data-Augmented Prediction
2024-09-27
Praneeth Vadlapati
This paper introduces a new approach to using Large Language Models (LLMs) for classification tasks, which are typically handled using Machine Learning (ML) models. Unlike ML models that rely heavily on data cleaning and feature engineering, this method streamlines the process using LLMs. This paper proposes a new concept called "Language Model Learning (LML)" powered by a new method called "Data-Augmented Prediction (DAP)". The classification is performed by LLMs using a method similar to humans manually exploring and understanding the data and deciding classifications using data as a reference. Training data is summarized and evaluated to determine the features that lead to the classification of each label the most. In the process of DAP, the system uses the data summary to automatically create a query, which is used to retrieve relevant rows from the dataset. A classification is generated by the LLM using data summary and relevant rows, ensuring satisfactory accuracy even with complex data. Usage of data summary and similar data in DAP ensures context-aware decision-making. The proposed method uses the words "Act as an Explainable Machine Learning Model" in the prompt to enhance the interpretability of the predictions by allowing users to review the logic behind each prediction. In some test cases, the system scored an accuracy above 90%, proving the effectiveness of the system and its potential to outperform conventional ML models in various scenarios. The code is available at https://github.com/Pro-GenAI/LML-DAP
The integration of Large Language Models (LLMs) with visual encoders has recently shown promising performance in visual understanding tasks, leveraging their inherent capability to comprehend and generate human-like text for visual reasoning. Given the diverse nature of visual data, MultiModal Large Language Models (MM-LLMs) exhibit variations in model designing and training for understanding images, short videos, and long videos. Our paper focuses on the substantial differences and unique challenges posed by long video understanding compared to static image and short video understanding. Unlike static images, short videos encompass sequential frames with both spatial and within-event temporal information, while long videos consist of multiple events with between-event and long-term temporal information. In this survey, we aim to trace and summarize the advancements of MM-LLMs from image understanding to long video understanding. We review the differences among various visual understanding tasks and highlight the challenges in long video understanding, including more fine-grained spatiotemporal details, dynamic events, and long-term dependencies. We then provide a detailed summary of the advancements in MM-LLMs in terms of model design and training methodologies for understanding long videos. Finally, we compare the performance of existing MM-LLMs on video understanding benchmarks of various lengths and discuss potential future directions for MM-LLMs in long video understanding.
Mixed Warm Dark Matter Constraints using Milky Way Satellite Galaxy Counts
2024-09-27
Chin Yi Tan, Ariane Dekker, Alex Drlica-Wagner
Warm dark matter has been strongly constrained in recent years as the sole component of dark matter. However, a less-explored alternative is that dark matter consists of a mixture of warm and cold dark matter (MWDM). In this work, we use observations of Milky Way satellite galaxies to constrain MWDM scenarios where the formation of small-scale structure is suppressed either by generic thermal relic warm dark matter or a sterile neutrino produced through the Shi-Fuller mechanism. To achieve this, we model satellite galaxies by combining numerical simulations with semi-analytical models for the subhalo population, and use a galaxy--halo connection model to match galaxies onto dark matter subhalos. By comparing the number of satellites predicted by MWDM models to the observed satellite population from the Dark Energy Survey and Pan-STARRS1, we constrain the fraction of warm dark matter, $f_{\rm WDM}$, as a function of its mass, $m_{\rm WDM}$. We exclude dark matter being composed entirely of thermal relic warm dark matter with $m_{\rm WDM} \leq 6.6 $ keV at a posterior ratio of 10:1, consistent with previous works. However, we find that warm dark matter with smaller mass is allowed when mixed with cold dark matter, and that the $f_{\rm WDM}$ constraints strengthen with decreasing $m_{\rm WDM}$ until they plateau at $f_{\rm WDM} \lesssim 0.45 $ for $m_{\rm WDM} \lesssim 1.5$ keV. Likewise, in the case of a sterile neutrino with mass of 7 keV produced through the Shi-Fuller mechanism, we exclude a fraction of $f_{\nu_s} \lesssim 0.45$, independent of mixing angle. Our results extend constraints on MWDM to a region of parameter space that has been relatively unconstrained with previous analysis.
A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs
2024-09-27
Yan Sun, Li Shen, Dacheng Tao
As a popular paradigm for juggling data privacy and collaborative training, federated learning (FL) is flourishing to distributively process the large scale of heterogeneous datasets on edged clients. Due to bandwidth limitations and security considerations, it ingeniously splits the original problem into multiple subproblems to be solved in parallel, which empowers primal dual solutions to great application values in FL. In this paper, we review the recent development of classical federated primal dual methods and point out a serious common defect of such methods in non-convex scenarios, which we say is a "dual drift" caused by dual hysteresis of those longstanding inactive clients under partial participation training. To further address this problem, we propose a novel Aligned Federated Primal Dual (A-FedPD) method, which constructs virtual dual updates to align global consensus and local dual variables for those protracted unparticipated local clients. Meanwhile, we provide a comprehensive analysis of the optimization and generalization efficiency for the A-FedPD method on smooth non-convex objectives, which confirms its high efficiency and practicality. Extensive experiments are conducted on several classical FL setups to validate the effectiveness of our proposed method.
Inference with Sequential Monte-Carlo Computation of $p$-values: Fast and Valid Approaches
2024-09-27
Ivo V. Stoepker, Rui M. Castro
Hypothesis tests calibrated by (re)sampling methods (such as permutation, rank and bootstrap tests) are useful tools for statistical analysis, at the computational cost of requiring Monte-Carlo sampling for calibration. It is common and almost universal practice to execute such tests with predetermined and large number of Monte-Carlo samples, and disregard any randomness from this sampling at the time of drawing and reporting inference. At best, this approach leads to computational inefficiency, and at worst to invalid inference. That being said, a number of approaches in the literature have been proposed to adaptively guide analysts in choosing the number of Monte-Carlo samples, by sequentially deciding when to stop collecting samples and draw inference. These works introduce varying competing notions of what constitutes "valid" inference, complicating the landscape for analysts seeking suitable methodology. Furthermore, the majority of these approaches solely guarantee a meaningful estimate of the testing outcome, not the $p$-value itself $\unicode{x2014}$ which is insufficient for many practical applications. In this paper, we survey the relevant literature, and build bridges between the scattered validity notions, highlighting some of their complementary roles. We also introduce a new practical methodology that provides an estimate of the $p$-value of the Monte-Carlo test, endowed with practically relevant validity guarantees. Moreover, our methodology is sequential, updating the $p$-value estimate after each new Monte-Carlo sample has been drawn, while retaining important validity guarantees regardless of the selected stopping time. We conclude this paper with a set of recommendations for the practitioner, both in terms of selection of methodology and manner of reporting results.
HM3: Hierarchical Multi-Objective Model Merging for Pretrained Models
2024-09-27
Yu Zhou, Xingyu Wu, Jibin Wu, Liang Feng, Kay Chen Tan
Model merging is a technique that combines multiple large pretrained models into a single model with enhanced performance and broader task adaptability. It has gained popularity in large pretrained model development due to its ability to bypass the need for original training data and further training processes. However, most existing model merging approaches focus solely on exploring the parameter space, merging models with identical architectures. Merging within the architecture space, despite its potential, remains in its early stages due to the vast search space and the challenges of layer compatibility. This paper marks a significant advance toward more flexible and comprehensive model merging techniques by modeling the architecture-space merging process as a reinforcement learning task. We train policy and value networks using offline sampling of weight vectors, which are then employed for the online optimization of merging strategies. Moreover, a multi-objective optimization paradigm is introduced to accommodate users' diverse task preferences, learning the Pareto front of optimal models to offer customized merging suggestions. Experimental results across multiple tasks, including text translation, mathematical reasoning, and code generation, validate the effectiveness and superiority of the proposed framework in model merging. The code will be made publicly available after the review process.
The application of large deep learning models in weather forecasting has led to significant advancements in the field, including higher-resolution forecasting and extended prediction periods exemplified by models such as Pangu and Fuxi. Despite these successes, previous research has largely been characterized by the neglect of extreme weather events, and the availability of datasets specifically curated for such events remains limited. Given the critical importance of accurately forecasting extreme weather, this study introduces a comprehensive dataset that incorporates high-resolution extreme weather cases derived from the High-Resolution Rapid Refresh (HRRR) data, a 3-km real-time dataset provided by NOAA. We also evaluate the current state-of-the-art deep learning models and Numerical Weather Prediction (NWP) systems on HR-Extreme, and provide a improved baseline deep learning model called HR-Heim which has superior performance on both general loss and HR-Extreme compared to others. Our results reveal that the errors of extreme weather cases are significantly larger than overall forecast error, highlighting them as an crucial source of loss in weather prediction. These findings underscore the necessity for future research to focus on improving the accuracy of extreme weather forecasts to enhance their practical utility.
An Overview and Catalogue of Dependency Challenges in Open Source Software Package Registries
2024-09-27
Tom Mens, Alexandre Decan
While open-source software has enabled significant levels of reuse to speed up software development, it has also given rise to the dreadful dependency hell that all software practitioners face on a regular basis. This article provides a catalogue of dependency-related challenges that come with relying on OSS packages or libraries. The catalogue is based on a review of the abundant scientific literature on empirical research that has been conducted to understand, quantify and overcome these challenges. Our results can be used as a starting point for junior and senior researchers as well as practitioners that would like to learn more about research advances in dealing with the challenges that come with the dependency networks of large OSS package registries.
CemiFace: Center-based Semi-hard Synthetic Face Generation for Face Recognition
2024-09-27
Zhonglin Sun, Siyang Song, Ioannis Patras, Georgios Tzimiropoulos
Privacy issue is a main concern in developing face recognition techniques. Although synthetic face images can partially mitigate potential legal risks while maintaining effective face recognition (FR) performance, FR models trained by face images synthesized by existing generative approaches frequently suffer from performance degradation problems due to the insufficient discriminative quality of these synthesized samples. In this paper, we systematically investigate what contributes to solid face recognition model training, and reveal that face images with certain degree of similarities to their identity centers show great effectiveness in the performance of trained FR models. Inspired by this, we propose a novel diffusion-based approach (namely Center-based Semi-hard Synthetic Face Generation (CemiFace)) which produces facial samples with various levels of similarity to the subject center, thus allowing to generate face datasets containing effective discriminative samples for training face recognition. Experimental results show that with a modest degree of similarity, training on the generated dataset can produce competitive performance compared to previous generation methods.
Mitigating Selection Bias with Node Pruning and Auxiliary Options
2024-09-27
Hyeong Kyu Choi, Weijie Xu, Chi Xue, Stephanie Eckman, Chandan K. Reddy
Large language models (LLMs) often show unwarranted preference for certain choice options when responding to multiple-choice questions, posing significant reliability concerns in LLM-automated systems. To mitigate this selection bias problem, previous solutions utilized debiasing methods to adjust the model's input and/or output. Our work, in contrast, investigates the model's internal representation of the selection bias. Specifically, we introduce a novel debiasing approach, Bias Node Pruning (BNP), which eliminates the linear layer parameters that contribute to the bias. Furthermore, we present Auxiliary Option Injection (AOI), a simple yet effective input modification technique for debiasing, which is compatible even with black-box LLMs. To provide a more systematic evaluation of selection bias, we review existing metrics and introduce Choice Kullback-Leibler Divergence (CKLD), which addresses the insensitivity of the commonly used metrics to label imbalance. Experiments show that our methods are robust and adaptable across various datasets when applied to three LLMs.
New Insights into Supradense Matter from Dissecting Scaled Stellar Structure Equations
2024-09-27
Bao-Jun Cai, Bao-An Li
The strong-field gravity in General Relativity (GR) realized in neutron stars (NSs) renders the Equation of State (EOS) $P(\varepsilon)$ of supradense neutron star (NS) matter to be essentially nonlinear and refines the upper bound for $\phi\equiv P/\varepsilon$ to be much smaller than the Special Relativity (SR) requirement with linear EOSs, where $P$ and $\varepsilon$ are respectively the pressure and energy density of the system considered. Specifically, a tight bound $\phi\lesssim0.374$ is obtained by anatomizing perturbatively the intrinsic structures of the scaled Tolman--Oppenheimer--Volkoff (TOV) equations without using any input nuclear EOS. New insights gained from this novel analysis provide EOS-model independent constraints on properties (e.g., density profiles of the sound speed squared $s^2=\d P/\d\varepsilon$ and trace anomaly $\Delta=1/3-\phi$) of cold supradense matter in NS cores. Using the gravity-matter duality in theories describing NSs, we investigate the impact of gravity on supradense matter EOS in NSs. In particular, we show that the NS mass $M_{\rm{NS}}$, radius $R$ and its compactness $\xi\equiv M_{\rm{NS}}/R$ scale with certain combinations of its central pressure and energy density (encapsulating its central EOS). Thus, observational data on these properties of NSs can straightforwardly constrain NS central EOSs without relying on any specific nuclear EOS-model.
Constructing Confidence Intervals for 'the' Generalization Error -- a Comprehensive Benchmark Study
2024-09-27
Hannah Schulz-Kümpel, Sebastian Fischer, Thomas Nagler, Anne-Laure Boulesteix, Bernd Bischl, Roman Hornung
When assessing the quality of prediction models in machine learning, confidence intervals (CIs) for the generalization error, which measures predictive performance, are a crucial tool. Luckily, there exist many methods for computing such CIs and new promising approaches are continuously being proposed. Typically, these methods combine various resampling procedures, most popular among them cross-validation and bootstrapping, with different variance estimation techniques. Unfortunately, however, there is currently no consensus on when any of these combinations may be most reliably employed and how they generally compare. In this work, we conduct the first large-scale study comparing CIs for the generalization error - empirically evaluating 13 different methods on a total of 18 tabular regression and classification problems, using four different inducers and a total of eight loss functions. We give an overview of the methodological foundations and inherent challenges of constructing CIs for the generalization error and provide a concise review of all 13 methods in a unified framework. Finally, the CI methods are evaluated in terms of their relative coverage frequency, width, and runtime. Based on these findings, we are able to identify a subset of methods that we would recommend. We also publish the datasets as a benchmarking suite on OpenML and our code on GitHub to serve as a basis for further studies.
Photometry and kinematics of dwarf galaxies from the Apertif HI survey
2024-09-27
Barbara Šiljeg, Elizabeth A. K. Adams, Filippo Fraternali, Kelley M. Hess, Tom A. Oosterloo, Antonino Marasco, Björn Adebahr, Helga Dénes, Danielle M. Lucero, Pavel E. Mancera Piña, Vanessa A. Moss, Anastasia A. Ponomareva, J. M. van der Hulst
Context. Understanding the dwarf galaxy population in low density environments is crucial for testing the LCDM cosmological model. The increase in diversity towards low mass galaxies is seen as an increase in the scatter of scaling relations such as the stellar mass-size and the baryonic Tully-Fisher relation (BTFR), and is also demonstrated by recent in-depth studies of an extreme subclass of dwarf galaxies of low surface brightness, but large physical sizes, called ultra-diffuse galaxies (UDGs). Aims. We select galaxies from the Apertif HI survey, and apply a constraint on their i-band absolute magnitude to exclude high mass systems. The sample consists of 24 galaxies, and span HI mass ranges of 8.6 < log ($M_{HI}/M_{Sun}$) < 9.7 and stellar mass range of 8.0 < log ($M_*/M_{Sun}$) < 9.7 (with only three galaxies having log ($M_*/M_{Sun}$) > 9). Methods. We determine the geometrical parameters of the HI and stellar discs, build kinematic models from the HI data using 3DBarolo, and extract surface brightness profiles in g-, r- and i-band from the Pan-STARRS 1 photometric survey. Results. We find that, at fixed stellar mass, our HI selected dwarfs have larger optical effective radii than isolated, optically-selected dwarfs from the literature. We find misalignments between the optical and HI morphologies for some of our sample. For most of our galaxies, we use the HI morphology to determine their kinematics, and we stress that deep optical observations are needed to trace the underlying stellar discs. Standard dwarfs in our sample follow the same BTFR of high-mass galaxies, whereas UDGs are slightly offset towards lower rotational velocities, in qualitative agreement with results from previous studies. Finally, our sample features a fraction (25%) of dwarf galaxies in pairs that is significantly larger with respect to previous estimates based on optical spectroscopic data.
It is acknowledged that the most common cause of dementia worldwide is Alzheimer's disease (AD). This condition progresses in severity from mild to severe and interferes with people's everyday routines. Early diagnosis plays a critical role in patient care and clinical trials. Convolutional neural networks (CNN) are used to create a framework for identifying specific disease features from MRI scans Classification of dementia involves approaches such as medical history review, neuropsychological tests, and magnetic resonance imaging (MRI). However, the image dataset obtained from Kaggle faces a significant issue of class imbalance, which requires equal distribution of samples from each class to address. In this article, to address this imbalance, the Synthetic Minority Oversampling Technique (SMOTE) is utilized. Furthermore, a pre-trained convolutional neural network has been applied to the DEMNET dementia network to extract key features from AD images. The proposed model achieved an impressive accuracy of 98.67%.
Mesoporous Silica Nanoparticles-based Smart Nanocarriers for Targeted Drug Delivery in Colorectal Cancer Therapy
2024-09-27
Rochelle A. Mann, Md. Emran Hossen, Alexander David McGuire Withrow, Jack Thomas Burton, Sean M Blythe, Camryn Grace Evett
Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, highlighting the urgent need for advanced therapeutic strategies. Nanoparticle-based drug delivery systems have emerged as a promising approach to improve the specificity and efficacy of anticancer treatments. This review examines three cutting-edge mesoporous silica nanoparticle (MSN)-based drug delivery to introduce novel CRC therapy, each utilizing unique functionalization strategies for targeted drug release. The first system, hyaluronidase-responsive MSN-HA/DOX, employs biotin-modified hyaluronic acid to facilitate dual-stimulus drug release in the tumor microenvironment, exhibiting enhanced in vivo tumor inhibition. The DOX/SLN-PEG-Biotin utilizes polyethylene glycol and biotin to improve drug stability and target biotin-overexpressing CRC cells, demonstrating superior anti-cancer efficacy in vitro and in vivo. Lastly, galactosylated chitosan-functionalized MSNs enable targeted delivery through asialoglycoprotein receptors, providing controlled drug release and strong therapeutic potential. Collectively, these systems highlight the advancements in nanoparticle functionalization for CRC treatment, offering a pathway to overcome the limitations of conventional chemotherapy. Further research is required to translate these innovations into clinical practice, ensuring safety and scalability.
Giant planets population around B stars from the first part of the BEAST survey
2024-09-27
P. Delorme, A. Chomez, V. Squicciarini, M. Janson, O. Flasseur, O. Schib, R. Gratton, A-M. Lagrange, M. Langlois, L. Mayer, R. Helled, S Reïffert, F. Kiefer, B. Biller, G. Chauvin, C. Fontanive, Th. Henning, M. Kenworthy, G-D. Marleau, D. Mesa, M. R. Meyer, C. Mordasini, S. C. Ringqvist, M. Samland, A. Vigan, G. Viswanath
Exoplanets form from circumstellar protoplanetary discs whose fundamental properties (notably their extent, composition, mass, temperature and lifetime) depend on the host star properties, such as their mass and luminosity. B-stars are among the most massive stars and their protoplanetary discs test extreme conditions for exoplanet formation. This paper investigates the frequency of giant planet companions around young B-stars (median age of 16 Myr) in the Scorpius-Centaurus association, the closest association containing a large population of B-stars. We systematically search for massive exoplanets with the high-contrast direct imaging instrument SPHERE using the data from the BEAST survey, that targets an homogeneous sample of young B-stars from the wide Sco-Cen association. We derive accurate detection limits in case of non-detections. We found evidence in previous papers for two substellar companions around 42 stars. The masses of these companions are straddling the ~13 Jupiter mass deuterium burning limit but their mass ratio with respect to their host star is close to that of Jupiter. We derive a frequency of such massive planetary mass companions around B stars of 11-5+7%, accounting for the survey sensitivity. The discoveries of substellar companions bcen b and mu2sco B happened after only few stars in the survey had been observed, raising the possibility that massive Jovian planets might be common around B-stars. However our statistical analysis show that the occurrence rate of such planets is similar around B-stars and around solar-type stars of similar age, while B-star companions exhibit low mass ratios and larger semi-major axis.
Excavating in the Wild: The GOOSE-Ex Dataset for Semantic Segmentation
2024-09-27
Raphael Hagmanns, Peter Mortimer, Miguel Granero, Thorsten Luettel, Janko Petereit
The successful deployment of deep learning-based techniques for autonomous systems is highly dependent on the data availability for the respective system in its deployment environment. Especially for unstructured outdoor environments, very few datasets exist for even fewer robotic platforms and scenarios. In an earlier work, we presented the German Outdoor and Offroad Dataset (GOOSE) framework along with 10000 multimodal frames from an offroad vehicle to enhance the perception capabilities in unstructured environments. In this work, we address the generalizability of the GOOSE framework. To accomplish this, we open-source the GOOSE-Ex dataset, which contains additional 5000 labeled multimodal frames from various completely different environments, recorded on a robotic excavator and a quadruped platform. We perform a comprehensive analysis of the semantic segmentation performance on different platforms and sensor modalities in unseen environments. In addition, we demonstrate how the combined datasets can be utilized for different downstream applications or competitions such as offroad navigation, object manipulation or scene completion. The dataset, its platform documentation and pre-trained state-of-the-art models for offroad perception will be made available on https://goose-dataset.de/. \
Honesty is a fundamental principle for aligning large language models (LLMs) with human values, requiring these models to recognize what they know and don't know and be able to faithfully express their knowledge. Despite promising, current LLMs still exhibit significant dishonest behaviors, such as confidently presenting wrong answers or failing to express what they know. In addition, research on the honesty of LLMs also faces challenges, including varying definitions of honesty, difficulties in distinguishing between known and unknown knowledge, and a lack of comprehensive understanding of related research. To address these issues, we provide a survey on the honesty of LLMs, covering its clarification, evaluation approaches, and strategies for improvement. Moreover, we offer insights for future research, aiming to inspire further exploration in this important area.
A POMDP-based hierarchical planning framework for manipulation under pose uncertainty
2024-09-27
Muhammad Suhail Saleem, Rishi Veerapaneni, Maxim Likhachev
Robots often face challenges in domestic environments where visual feedback is ineffective, such as retrieving objects obstructed by occlusions or finding a light switch in the dark. In these cases, utilizing contacts to localize the target object can be effective. We propose an online planning framework using binary contact signals for manipulation tasks with pose uncertainty, formulated as a Partially Observable Markov Decision Process (POMDP). Naively representing the belief as a particle set makes planning infeasible due to the large uncertainties in domestic settings, as identifying the best sequence of actions requires rolling out thousands of actions across millions of particles, taking significant compute time. To address this, we propose a hierarchical belief representation. Initially, we represent the uncertainty coarsely in a 3D volumetric space. Policies that refine uncertainty in this space are computed and executed, and once uncertainty is sufficiently reduced, the problem is translated back into the particle space for further refinement before task completion. We utilize a closed-loop planning and execution framework with a heuristic-search-based anytime solver that computes partial policies within a limited time budget. The performance of the framework is demonstrated both in real world and in simulation on the high-precision task of inserting a plug into a port using a UR10e manipulator, resolving positional uncertainties up to 50 centimeters and angular uncertainties close to $2\pi$. Experimental results highlight the framework's effectiveness, achieving a 93\% success rate in the real world and over 50\% improvement in solution quality compared to greedy baselines, significantly accelerating planning and enabling real-time solutions for complex problems.
Forthcoming cosmological imaging surveys, such as the Rubin Observatory LSST, require large-scale simulations encompassing realistic galaxy populations for a variety of scientific applications. Of particular concern is the phenomenon of intrinsic alignments (IA), whereby galaxies orient themselves towards overdensities, potentially introducing significant systematic biases in weak gravitational lensing analyses if they are not properly modeled. Due to computational constraints, simulating the intricate details of galaxy formation and evolution relevant to IA across vast volumes is impractical. As an alternative, we propose a Deep Generative Model trained on the IllustrisTNG-100 simulation to sample 3D galaxy shapes and orientations to accurately reproduce intrinsic alignments along with correlated scalar features. We model the cosmic web as a set of graphs, each graph representing a halo with nodes representing the subhalos/galaxies. The architecture consists of a SO(3) $\times$ $\mathbb{R}^n$ diffusion generative model, for galaxy orientations and $n$ scalars, implemented with E(3) equivariant Graph Neural Networks that explicitly respect the Euclidean symmetries of our Universe. The model is able to learn and predict features such as galaxy orientations that are statistically consistent with the reference simulation. Notably, our model demonstrates the ability to jointly model Euclidean-valued scalars (galaxy sizes, shapes, and colors) along with non-Euclidean valued SO(3) quantities (galaxy orientations) that are governed by highly complex galactic physics at non-linear scales.
A study on the effects of mixed explicit and implicit communications in human-virtual-agent interactions
2024-09-27
Ana Christina Almada Campos, Bruno Vilhena Adorno
Communication between humans and robots (or virtual agents) is essential for interaction and often inspired by human communication, which uses gestures, facial expressions, gaze direction, and other explicit and implicit means. This work presents an interaction experiment where humans and virtual agents interact through explicit (gestures, manual entries using mouse and keyboard, voice, sound, and information on screen) and implicit (gaze direction, location, facial expressions, and raise of eyebrows) communication to evaluate the effect of mixed explicit-implicit communication against purely explicit communication. Results obtained using Bayesian parameter estimation show that the number of errors and task execution time did not significantly change when mixed explicit and implicit communications were used, and neither the perceived efficiency of the interaction. In contrast, acceptance, sociability, and transparency of the virtual agent increased when using mixed communication modalities (88.3%, 92%, and 92.9% of the effect size posterior distribution of each variable, respectively, were above the upper limit of the region of practical equivalence). This suggests that task-related measures, such as time, number of errors, and perceived efficiency of the interaction, have not been influenced by the communication type in our particular experiment. However, the improvement of subjective measures related to the virtual agent, such as acceptance, sociability, and transparency, suggests that humans are more receptive to mixed explicit and implicit communications.
Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects
2024-09-27
Sabrine Ennaji, Fabio De Gaspari, Dorjan Hitaj, Alicia K/Bidi, Luigi V. Mancini
Machine learning has brought significant advances in cybersecurity, particularly in the area of intrusion detection systems. This improvements can be mostly attributed to the ability of machine learning algorithms to identify complex relations between features in the data and to generalize well to unseen samples. Deep neural networks in particular contributed to this progress by enabling the analysis of large amounts of training data, significantly enhancing detection performance. However, machine learning models are vulnerable to adversarial attacks: manipulations of input data designed to mislead the models into making incorrect predictions. While much attention has been given to adversarial threats in unstructured data such as text and images, their effectiveness in structured data such as network traffic has not been as thoroughly explored. This survey seeks to fill this gap by providing an critical review of machine learning-based Network Intrusion Detection Systems (NIDS) and a thorough analysis of their vulnerability to adversarial attacks. We critically review existing NIDS research, highlighting key trends, strengths, and limitations, and we identify gaps in understanding that require further exploration. We further discuss emerging challenges and offer insights for developing more robust and resilient NIDS models. In summary, this paper aims to enhance understanding of adversarial attacks and defenses in NIDS and guide future research in improving the robustness of machine learning models in cybersecurity applications.
Quantifying genuine tripartite entanglement by reshaping the state
2024-09-27
Dong-Dong Dong, Li-Juan Li, Xue-Ke Song, Liu Ye, Dong Wang
Although genuine multipartite entanglement (GME), as one quantum resource, is indispensable in quantum information processing, most of the existing measures cannot detect GME faithfully. In this paper, we present a novel GME measure, namely the minimum pairwise concurrence (MPC), by introducing pairwise entanglement, which characters the entanglement between two single-qubit subsystems of a multipartite system without tracing out the remaining qubit. The pairwise entanglement can be obtained by combining the entanglement of reduced subsystem and three-tangle. Compared with the existing measures, the MPC measure outperforms the previous ones in many aspects. Due to its fine properties, it thus is believed that the MPC could be one of good candidates in achieving potential quantum tasks and also facilitate the understanding for GME.
Towards Integrating Epistemic Uncertainty Estimation into the Radiotherapy Workflow
2024-09-27
Marvin Tom Teichmann, Manasi Datar, Lisa Kratzke, Fernando Vega, Florin C. Ghesu
The precision of contouring target structures and organs-at-risk (OAR) in radiotherapy planning is crucial for ensuring treatment efficacy and patient safety. Recent advancements in deep learning (DL) have significantly improved OAR contouring performance, yet the reliability of these models, especially in the presence of out-of-distribution (OOD) scenarios, remains a concern in clinical settings. This application study explores the integration of epistemic uncertainty estimation within the OAR contouring workflow to enable OOD detection in clinically relevant scenarios, using specifically compiled data. Furthermore, we introduce an advanced statistical method for OOD detection to enhance the methodological framework of uncertainty estimation. Our empirical evaluation demonstrates that epistemic uncertainty estimation is effective in identifying instances where model predictions are unreliable and may require an expert review. Notably, our approach achieves an AUC-ROC of 0.95 for OOD detection, with a specificity of 0.95 and a sensitivity of 0.92 for implant cases, underscoring its efficacy. This study addresses significant gaps in the current research landscape, such as the lack of ground truth for uncertainty estimation and limited empirical evaluations. Additionally, it provides a clinically relevant application of epistemic uncertainty estimation in an FDA-approved and widely used clinical solution for OAR segmentation from Varian, a Siemens Healthineers company, highlighting its practical benefits.
This paper analyses the set of scientific publications in open access, other than journals (monographs, conferences proceedings, teaching materials and grey literature), published by Spanish public universities, studying their volume, documentary typology, level of description and open access policies with the aim of measuring their degree of incorporation and compliance with the principles of Open Science. An exhaustive review of the disposed material in open access by these publishers has been carried out, which has allowed to make a diagnosis of their level of open access publishing. Grey literature is the most common documentary type followed by the monograph, in the open publication of these publishers that does not reach even 5% of the average editorial production. The results allow us to conclude that the academic publishing, and more specifically the academic books in open access, still has a very reduced presence within the editorial production of these institutions.
A Survey on Complex Tasks for Goal-Directed Interactive Agents
2024-09-27
Mareike Hartmann, Alexander Koller
Goal-directed interactive agents, which autonomously complete tasks through interactions with their environment, can assist humans in various domains of their daily lives. Recent advances in large language models (LLMs) led to a surge of new, more and more challenging tasks to evaluate such agents. To properly contextualize performance across these tasks, it is imperative to understand the different challenges they pose to agents. To this end, this survey compiles relevant tasks and environments for evaluating goal-directed interactive agents, structuring them along dimensions relevant for understanding current obstacles. An up-to-date compilation of relevant resources can be found on our project website: https://coli-saar.github.io/interactive-agents.
Handling Missingness, Failures, and Non-Convergence in Simulation Studies: A Review of Current Practices and Recommendations
2024-09-27
Samuel Pawel, František Bartoš, Björn S. Siepe, Anna Lohmann
Simulation studies are commonly used in methodological research for the empirical evaluation of data analysis methods. They generate artificial data sets under specified mechanisms and compare the performance of methods across conditions. However, simulation repetitions do not always produce valid outputs, e.g., due to non-convergence or other algorithmic failures. This phenomenon complicates the interpretation of results, especially when its occurrence differs between methods and conditions. Despite the potentially serious consequences of such "missingness", quantitative data on its prevalence and specific guidance on how to deal with it are currently limited. To this end, we reviewed 482 simulation studies published in various methodological journals and systematically assessed the prevalence and handling of missingness. We found that only 23.0% (111/482) of the reviewed simulation studies mention missingness, with even fewer reporting frequency (92/482 = 19.1%) or how it was handled (67/482 = 13.9%). We propose a classification of missingness and possible solutions. We give various recommendations, most notably to always quantify and report missingness, even if none was observed, to align missingness handling with study goals, and to share code and data for reproduction and reanalysis. Using a case study on publication bias adjustment methods, we illustrate common pitfalls and solutions.
uGMRT Survey of EXoplanets Around M-dwarfs (GS-EXAM): Radio observations of GJ 1151
2024-09-27
Mayank Narang, Manoj Puravankara, H. K. Vedantham, C. H. Ishwara Chandra, Ayanabha De, Himanshu Tyagi, Bihan Banerjee, Prasanta K. Nayak, Arun Surya, B. Shridharan, Vinod C. Pathak, Mihir Tripathi
Coherent radio emission with properties similar to planetary auroral signals has been reported from GJ 1151, a quiescent, slow-rotating mid-M star, by the LOFAR Two-metre (120-170 MHz) Sky Survey (LoTSS). The observed {LOFAR} emission is fairly bright at 0.89 mJy with 64% circular polarization, and the emission characteristics are consistent with the interaction between an Earth-sized planet with an orbital period of 1-5 days and the magnetic field of the host star. However, no short-period planet has been detected around GJ 1151. To confirm the reported radio emission caused by the putative planet around GJ 1151 and to investigate the nature of this emission, we carried out uGMRT observations of GJ 1151 at 150, 218, and 400 MHz over 33 hours across ten epochs. No emission was detected at any frequency. While at 150 MHz and 218 MHz, non-detection could be due to the low sensitivity of our observations, at 400 MHz, the rms sensitivities achieved were sufficient to detect the emission observed with LOFAR at $\sim$ 20$\sigma$ level. Our findings suggest that the radio emission is highly time-variable, likely influenced by the star-planet system's phase and the host star's magnetic field. Additional observations below 170 MHz, at more frequent epochs (as the periodicity of the emission is unknown), especially during periods of high stellar magnetic field strength, are needed to confirm the emission.
HSTFL: A Heterogeneous Federated Learning Framework for Misaligned Spatiotemporal Forecasting
2024-09-27
Shuowei Cai, Hao Liu
Spatiotemporal forecasting has emerged as an indispensable building block of diverse smart city applications, such as intelligent transportation and smart energy management. Recent advancements have uncovered that the performance of spatiotemporal forecasting can be significantly improved by integrating knowledge in geo-distributed time series data from different domains, \eg enhancing real-estate appraisal with human mobility data; joint taxi and bike demand predictions. While effective, existing approaches assume a centralized data collection and exploitation environment, overlooking the privacy and commercial interest concerns associated with data owned by different parties. In this paper, we investigate multi-party collaborative spatiotemporal forecasting without direct access to multi-source private data. However, this task is challenging due to 1) cross-domain feature heterogeneity and 2) cross-client geographical heterogeneity, where standard horizontal or vertical federated learning is inapplicable. To this end, we propose a Heterogeneous SpatioTemporal Federated Learning (HSTFL) framework to enable multiple clients to collaboratively harness geo-distributed time series data from different domains while preserving privacy. Specifically, we first devise vertical federated spatiotemporal representation learning to locally preserve spatiotemporal dependencies among individual participants and generate effective representations for heterogeneous data. Then we propose a cross-client virtual node alignment block to incorporate cross-client spatiotemporal dependencies via a multi-level knowledge fusion scheme. Extensive privacy analysis and experimental evaluations demonstrate that HSTFL not only effectively resists inference attacks but also provides a significant improvement against various baselines.
A TextGCN-Based Decoding Approach for Improving Remote Sensing Image Captioning
2024-09-27
Swadhin Das, Raksha Sharma
Remote sensing images are highly valued for their ability to address complex real-world issues such as risk management, security, and meteorology. However, manually captioning these images is challenging and requires specialized knowledge across various domains. This letter presents an approach for automatically describing (captioning) remote sensing images. We propose a novel encoder-decoder setup that deploys a Text Graph Convolutional Network (TextGCN) and multi-layer LSTMs. The embeddings generated by TextGCN enhance the decoder's understanding by capturing the semantic relationships among words at both the sentence and corpus levels. Furthermore, we advance our approach with a comparison-based beam search method to ensure fairness in the search strategy for generating the final caption. We present an extensive evaluation of our approach against various other state-of-the-art encoder-decoder frameworks. We evaluated our method across three datasets using seven metrics: BLEU-1 to BLEU-4, METEOR, ROUGE-L, and CIDEr. The results demonstrate that our approach significantly outperforms other state-of-the-art encoder-decoder methods.
A comparison of Bayesian sampling algorithms for high-dimensional particle physics and cosmology applications
2024-09-27
Joshua Albert, Csaba Balazs, Andrew Fowlie, Will Handley, Nicholas Hunt-Smith, Roberto Ruiz de Austri, Martin White
For several decades now, Bayesian inference techniques have been applied to theories of particle physics, cosmology and astrophysics to obtain the probability density functions of their free parameters. In this study, we review and compare a wide range of Markov Chain Monte Carlo (MCMC) and nested sampling techniques to determine their relative efficacy on functions that resemble those encountered most frequently in the particle astrophysics literature. Our first series of tests explores a series of high-dimensional analytic test functions that exemplify particular challenges, for example highly multimodal posteriors or posteriors with curving degeneracies. We then investigate two real physics examples, the first being a global fit of the $\Lambda$CDM model using cosmic microwave background data from the Planck experiment, and the second being a global fit of the Minimal Supersymmetric Standard Model using a wide variety of collider and astrophysics data. We show that several examples widely thought to be most easily solved using nested sampling approaches can in fact be more efficiently solved using modern MCMC algorithms, but the details of the implementation matter. Furthermore, we also provide a series of useful insights for practitioners of particle astrophysics and cosmology.
Review of Digital Asset Development with Graph Neural Network Unlearning
2024-09-27
Zara Lisbon
In the rapidly evolving landscape of digital assets, the imperative for robust data privacy and compliance with regulatory frameworks has intensified. This paper investigates the critical role of Graph Neural Networks (GNNs) in the management of digital assets and introduces innovative unlearning techniques specifically tailored to GNN architectures. We categorize unlearning strategies into two primary classes: data-driven approximation, which manipulates the graph structure to isolate and remove the influence of specific nodes, and model-driven approximation, which modifies the internal parameters and architecture of the GNN itself. By examining recent advancements in these unlearning methodologies, we highlight their applicability in various use cases, including fraud detection, risk assessment, token relationship prediction, and decentralized governance. We discuss the challenges inherent in balancing model performance with the requirements for data unlearning, particularly in the context of real-time financial applications. Furthermore, we propose a hybrid approach that combines the strengths of both unlearning strategies to enhance the efficiency and effectiveness of GNNs in digital asset ecosystems. Ultimately, this paper aims to provide a comprehensive framework for understanding and implementing GNN unlearning techniques, paving the way for secure and compliant deployment of machine learning in the digital asset domain.
I review possible signals at hadron colliders of bileptons, namely doubly-charged vectors or scalars with lepton number L=+/- 2, as predicted by a 331 model, based on a SU(3)_c x SU(3)_L x U(1)_X$ symmetry.In particular, I account for a version of the 331 model, wherein the embedding of the hypercharge is obtained with the addition of 3 exotic quarks and vector bileptons. Furthermore, a sextet of SU(3)_L, necessary to give masses to leptons, yields an extra scalar sector, including a doubly-charged Higgs, i.e. scalar bileptons. As bileptons are mostly produced in pairs at hadron colliders, their main signal is given by two same-sign lepton pairs at high invariant mass. Nevertheless, they can also decay according to non-leptonic modes, such as a TeV-scale heavy quark, charged 4/3 or 5/3, plus a Standard Model quark. I explore both leptonic and non-leptonic decays and the sensitivity to such processes of present and future hadron colliders.
SciDFM: A Large Language Model with Mixture-of-Experts for Science
2024-09-27
Liangtai Sun, Danyu Luo, Da Ma, Zihan Zhao, Baocai Chen, Zhennan Shen, Su Zhu, Lu Chen, Xin Chen, Kai Yu
Recently, there has been a significant upsurge of interest in leveraging large language models (LLMs) to assist scientific discovery. However, most LLMs only focus on general science, while they lack domain-specific knowledge, such as chemical molecules and amino acid sequences. To bridge these gaps, we introduce SciDFM, a mixture-of-experts LLM, which is trained from scratch and is able to conduct college-level scientific reasoning and understand molecules and amino acid sequences. We collect a large-scale training corpus containing numerous scientific papers and books from different disciplines as well as data from domain-specific databases. We further fine-tune the pre-trained model on lots of instruction data to improve performances on downstream benchmarks. From experiment results, we show that SciDFM achieves strong performance on general scientific benchmarks such as SciEval and SciQ, and it reaches a SOTA performance on domain-specific benchmarks among models of similar size. We further analyze the expert layers and show that the results of expert selection vary with data from different disciplines. To benefit the broader research community, we open-source SciDFM at https://huggingface.co/OpenDFM/SciDFM-MoE-A5.6B-v1.0.
Constraining cosmological parameters using the pairwise kinematic Sunyaev-Zel'dovich effect with CMB-S4 and future galaxy cluster surveys
2024-09-27
E. Schiappucci, S. Raghunathan, C. To, F. Bianchini, C. L. Reichardt, N. Battaglia, B. Hadzhiyska, S. Kim, J. B. Melin, C. Sifón, E. M. Vavagiakis
We present a forecast of the pairwise kinematic Sunyaev-Zel'dovich (kSZ) measurement that will be achievable with the future CMB-S4 experiment. CMB-S4 is the next stage for ground-based cosmic microwave background experiments, with a planned wide area survey that will observe approximately $50\%$ of the sky. We construct a simulated sample of galaxy clusters that have been optically selected in an LSST-like survey and have spectroscopic redshifts. For this cluster sample, we predict that CMB-S4 will reject the null hypothesis of zero pairwise kSZ signal at $36 \,\sigma$. We estimate the effects of systematic uncertainties such as scatter in the mass-richness scaling relation and cluster mis-centering. We find that these effects can reduce the signal-to-noise ratio of the CMB-S4 pairwise kSZ measurement by $20\%$. We explore the constraining power of the measured kSZ signal in combination with measurements of the galaxy clusters' thermal SZ emission on two extensions to the standard cosmological model. The first extension allows the dark energy equation of state $w$ to vary. We find the CMB-S4 pairwise kSZ measurement yields a modest reduction in the uncertainty on $w$ by a factor of 1.36 over the \Planck's 2018 uncertainty. The second extension tests General Relativity by varying the growth index $\gamma$. We find that CMB-S4's pairwise kSZ measurement will yield a $28\sigma$ constraint on $\gamma$, and strongly constrain alternative theories of gravity.
Architecture for Protecting Data Privacy in Decentralized Social Networks
2024-09-27
Quang Cao, Katerina Vgena, Aikaterini-Georgia Mavroeidi, Christos Kalloniatis, Xun Yi, Son Hoang Dau
Centralized social networks have experienced a transformative impact on our digital era communication, connection, and information-sharing information. However, it has also raised significant concerns regarding users' privacy and individual rights. In response to these concerns, this paper proposes a novel Decentralized Social Network employing Blockchain technology and Decentralized Storage Networks completed by Access Control Smart Contracts. The initial phase comprises a comprehensive literature review, delving into decentralized social networks, explaining the review methodology, and presenting the resulting findings. Building upon these findings and an analysis of previous research gaps, we propose a novel architecture for decentralized social networks. In conclusion, the principal results highlight the benefit of our decentralized social network to protect user privacy. Moreover, the users have all rights to their posted information following the General Data Protection Regulation (GDPR).
Esports Training, Periodization, and Tools -- a Scoping Review
2024-09-27
Andrzej Białecki, Bartłomiej Michalak, Jan Gajewski
Electronic sports (esports) and research on this emerging field are interdisciplinary in nature. By extension, it is essential to understand how to standardize and structure training with the help of existing tools developed by years of research in sports sciences and informatics. Our goal in this article was to verify if the current body of research contains substantial evidence of the training systems applied to training esports players. To verify the existing sources, we have applied a framework of scoping review to address the search from multiple scientific databases with further local processing. We conclude that the current research on esports dealt mainly with describing and modeling performance metrics spanned over multiple fragmented research areas (psychology, nutrition, informatics), and yet these building blocks were not assembled into an existing well-functioning theory of performance in esports by providing exercise regimes, and ways of periodization for esports.
A comprehensive review and new taxonomy on superpixel segmentation
2024-09-27
I. B. Barcelos, F. de C. Belém, L. de M. João, Z. K. G. do Patrocínio Jr., A. X. Falcão, S. J. F. Guimarães
Superpixel segmentation consists of partitioning images into regions composed of similar and connected pixels. Its methods have been widely used in many computer vision applications since it allows for reducing the workload, removing redundant information, and preserving regions with meaningful features. Due to the rapid progress in this area, the literature fails to catch up on more recent works among the compared ones and to categorize the methods according to all existing strategies. This work fills this gap by presenting a comprehensive review with new taxonomy for superpixel segmentation, in which methods are classified according to their processing steps and processing levels of image features. We revisit the recent and popular literature according to our taxonomy and evaluate 20 strategies based on nine criteria: connectivity, compactness, delineation, control over the number of superpixels, color homogeneity, robustness, running time, stability, and visual quality. Our experiments show the trends of each approach in pixel clustering and discuss individual trade-offs. Finally, we provide a new benchmark for superpixel assessment, available at https://github.com/IMScience-PPGINF-PucMinas/superpixel-benchmark.
Reducing Overtreatment of Indeterminate Thyroid Nodules Using a Multimodal Deep Learning Model
2024-09-27
Shreeram Athreya, Andrew Melehy, Sujit Silas Armstrong Suthahar, Vedrana Ivezić, Ashwath Radhachandran, Vivek Sant, Chace Moleta, Henry Zheng, Maitraya Patel, Rinat Masamed, Corey W. Arnold, William Speier
Objective: Molecular testing (MT) classifies cytologically indeterminate thyroid nodules as benign or malignant with high sensitivity but low positive predictive value (PPV), only using molecular profiles, ignoring ultrasound (US) imaging and biopsy. We address this limitation by applying attention multiple instance learning (AMIL) to US images. Methods: We retrospectively reviewed 333 patients with indeterminate thyroid nodules at UCLA medical center (259 benign, 74 malignant). A multi-modal deep learning AMIL model was developed, combining US images and MT to classify the nodules as benign or malignant and enhance the malignancy risk stratification of MT. Results: The final AMIL model matched MT sensitivity (0.946) while significantly improving PPV (0.477 vs 0.448 for MT alone), indicating fewer false positives while maintaining high sensitivity. Conclusion: Our approach reduces false positives compared to MT while maintaining the same ability to identify positive cases, potentially reducing unnecessary benign thyroid resections in patients with indeterminate nodules.
Pruning then Reweighting: Towards Data-Efficient Training of Diffusion Models
2024-09-27
Yize Li, Yihua Zhang, Sijia Liu, Xue Lin
Despite the remarkable generation capabilities of Diffusion Models (DMs), conducting training and inference remains computationally expensive. Previous works have been devoted to accelerating diffusion sampling, but achieving data-efficient diffusion training has often been overlooked. In this work, we investigate efficient diffusion training from the perspective of dataset pruning. Inspired by the principles of data-efficient training for generative models such as generative adversarial networks (GANs), we first extend the data selection scheme used in GANs to DM training, where data features are encoded by a surrogate model, and a score criterion is then applied to select the coreset. To further improve the generation performance, we employ a class-wise reweighting approach, which derives class weights through distributionally robust optimization (DRO) over a pre-trained reference DM. For a pixel-wise DM (DDPM) on CIFAR-10, experiments demonstrate the superiority of our methodology over existing approaches and its effectiveness in image synthesis comparable to that of the original full-data model while achieving the speed-up between 2.34 times and 8.32 times. Additionally, our method could be generalized to latent DMs (LDMs), e.g., Masked Diffusion Transformer (MDT) and Stable Diffusion (SD), and achieves competitive generation capability on ImageNet. Code is available here (https://github.com/Yeez-lee/Data-Selection-and-Reweighting-for-Diffusion-Models).
Generative deep learning for the inverse design of materials
2024-09-27
Teng Long, Yixuan Zhang, Hongbin Zhang
In addition to the forward inference of materials properties using machine learning, generative deep learning techniques applied on materials science allow the inverse design of materials, i.e., assessing the composition-processing-(micro-)structure-property relationships in a reversed way. In this review, we focus on the (micro-)structure-property mapping, i.e., crystal structure-intrinsic property and microstructure-extrinsic property, and summarize comprehensively how generative deep learning can be performed. Three key elements, i.e., the construction of latent spaces for both the crystal structures and microstructures, generative learning approaches, and property constraints, are discussed in detail. A perspective is given outlining the challenges of the existing methods in terms of computational resource consumption, data compatibility, and yield of generation.
Diversity of Parahydrogen-Induced Hyperpolarization Effects in Chemistry
2024-09-27
Andrey N. Pravdivtsev, Ben. J. Tickner, Stefan Glöggler, Jan-Bernd Hövener, Gerd Buntkowsky, Simon B. Duckett, Clifford R. Bowers, Vladimir V. Zhivonitko
Nuclear spin hyperpolarization utilizing parahydrogen (pH2) has broad applications in chemistry, biochemistry, and medicine. This review focuses on examining the less traveled roads associated with parahydrogen-induced polarization (PHIP) leading to MR signal enhancement. We begin with photo-induced PHIP, which allows the investigation of short-lived processes and photo-activated catalysis on timescales as short as microseconds. Next, we review the partially negative line (PNL) effect, in which distinctive lineshape helps to reveal information about rapid exchange with pH2 and the role of short lived catalytic species. The signal enhancement of a single proton in oneH-PHIP is discussed which challenges the underpinning concept of pairwise hydrogenation. Furthermore, we examine metal-free PHIP facilitated by novel molecular tweezers and radicaloids, thereby demonstrating alternative routes to conventional hydrogenation using metal-based catalysts. Although symmetric molecules incorporating pH2 are NMR silent, we showcase methods that reveal hyperpolarized states through post-hydrogenation reactions. Additionally, we discuss chemical exchange processes that mediate polarization transfer between pH2 and a molecular target, expanding the reach of PHIP without synthesizing specialized precursors. We conclude this review by highlighting the role PHIP has played in uncovering the H2 activation mechanisms of hydrogenases. By providing a detailed review of these diverse phenomena, we aim to broaden the understanding and hidden potential of PHIP, which keeps evolving rapidly close to 40 years after its first discovery.
Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosure
2024-09-27
Mahasweta Chakraborti, Bert Joseph Prestoza, Nicholas Vincent, Seth Frey
The rapid scaling of AI has spurred a growing emphasis on ethical considerations in both development and practice. This has led to the formulation of increasingly sophisticated model auditing and reporting requirements, as well as governance frameworks to mitigate potential risks to individuals and society. At this critical juncture, we review the practical challenges of promoting responsible AI and transparency in informal sectors like OSS that support vital infrastructure and see widespread use. We focus on how model performance evaluation may inform or inhibit probing of model limitations, biases, and other risks. Our controlled analysis of 7903 Hugging Face projects found that risk documentation is strongly associated with evaluation practices. Yet, submissions (N=789) from the platform's most popular competitive leaderboard showed less accountability among high performers. Our findings can inform AI providers and legal scholars in designing interventions and policies that preserve open-source innovation while incentivizing ethical uptake.
Our fundamental theories, i.e., the quantum theory and general relativity, are invariant under time reversal. Only when we treat system from the point of view of thermodynamics, i.e., averaging between many subsystem components, an arrow of time emerges. The relation between thermodynamic and the quantum theory has been fertile, deeply explored and still a source of new investigations. The relation between the quantum theory and gravity, while it has not yet brought an established theory of quantum gravity, has certainly sparkled in depth analysis and tentative new theories. On the other hand, the connection between gravity and thermodynamics is less investigated and more puzzling. I review a selection of results in covariant thermodynamics, such as the construction of a covariant notion of thermal equilibrium by considering tripartite systems. I discuss how such construction requires a relational take on thermodynamics, similarly of what happens in the quantum theory and in gravity
Discovery of the optical counterpart of the fast X-ray transient EP240414a
2024-09-27
S. Srivastav, T. -W. Chen, J. H. Gillanders, L. Rhodes, S. J. Smartt, M. E. Huber, A. Aryan, S. Yang, A. Beri, A. J. Cooper, M. Nicholl, K. W. Smith, H. F. Stevance, F. Carotenuto, K. C. Chambers, A. Aamer, C. R. Angus, M. D. Fulton, T. Moore, I. A. Smith, D. R. Young, T. de Boer, H. Gao, C. -C. Lin, T. Lowe, E. A. Magnier, P. Minguez, Y. -C. Pan, R. J. Wainscoat
Fast X-ray transients (FXTs) are extragalactic bursts of X-rays first identified in archival X-ray data, and now routinely discovered by the Einstein Probe in real time, which is continuously surveying the night sky in the soft ($0.5 - 4$ keV) X-ray regime. In this Letter, we report the discovery of the second optical counterpart (AT2024gsa) to an FXT (EP240414a). EP240414a is located at a projected radial separation of 27 kpc from its likely host galaxy at $z = 0.4018 \pm 0.0010$. The optical light curve of AT2024gsa displays three distinct components. The initial decay from our first observation is followed by a re-brightening episode, displaying a rapid rise in luminosity to an absolute magnitude of $M_r \sim -21$ after two rest-frame days. While the early optical luminosity and decline rate is similar to luminous fast blue optical transients, the colour temperature of AT2024gsa is distinctly red and we show that the peak flux is inconsistent with a thermal origin. The third component peaks at $M_i \sim -19$ at $\gtrsim 16$ rest-frame days post-FXT, and is compatible with an emerging supernova. We fit the $riz$-band data with a series of power laws and find that the decaying components are in agreement with gamma-ray burst afterglow models, and that the re-brightening may originate from refreshed shocks. By considering EP240414a in context with all previously reported known-redshift FXT events, we propose that Einstein Probe FXT discoveries may all result from high-redshift gamma-ray bursts, and thus are distinct from the previously discovered lower redshift, lower luminosity population of FXTs.
Value Added Catalog of physical properties of more than 1.3 million galaxies from the DESI Survey
2024-09-27
M. Siudek, R. Pucha, M. Mezcua, S. Juneau, J. Aguilar, S. Ahlen, D. Brooks, C. Circosta, T. Claybaugh, S. Cole, K. Dawson, A. de la Macorra, Arjun Dey, Biprateep Dey, P. Doel, A. Font-Ribera, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, C. Howlett, M. Ishak, R. Kehoe, D. Kirkby, T. Kisner, A. Kremin, A. Lambert, M. Landriau, L. Le Guillou, M. Manera, P. Martini, A. Meisner, R. Miquel, J. Moustakas, J. A. Newman, G. Niz, Z. Pan, W. J. Percival, C. Poppett, F. Prada, G. Rossi, A. Saintonge, E. Sanchez, D. Schlegel, D. Scholte, M. Schubnell, H. Seo, F. Speranza, D. Sprayberry, G. Tarle, B. A. Weaver, H. Zou
Aims. We present an extensive catalog of the physical properties of more than a million galaxies within the Dark Energy Spectroscopic Instrument (DESI), one of the largest spectroscopic surveys to date. Spanning over a full variety of target types, including emission line galaxies and luminous red galaxies as well as quasars, our survey encompasses an unprecedented range of spectroscopic redshifts, stretching from 0 to 6. Methods. The physical properties, such as stellar masses and star formation rates, are derived via the CIGALE spectral energy distribution (SED) fitting code accounting for the contribution coming from active galactic nuclei (AGN). Based on the modeling of the optical-mid-infrared (grz complemented by WISE photometry) SEDs, we study galaxy properties with respect to their location on the main sequence. Results. We revise the dependence of stellar mass estimates on model choices and availability of the WISE photometry. The WISE information is mandatory to minimize the misclassification of star-forming galaxies as AGN. The lack of WISE bands in SED fits leads to elevated AGN fractions for 68% of star-forming galaxies identified using emission line diagnostic diagram but does not significantly affect their stellar mass nor star formation estimates.
Inhomogeneous Dust Biases Photometric Redshifts and Stellar Masses for LSST
2024-09-27
ChangHoon Hahn, Peter Melchior
Spectral energy distribution (SED) modeling is one of the main methods to estimate galaxy properties, such as photometric redshifts, $z$, and stellar masses, $M_*$, for extragalactic imaging surveys. SEDs are currently modeled as light from a composite stellar population attenuated by a uniform foreground dust screen, despite evidence from simulations and observations that find large spatial variations in dust attenuation due to the detailed geometry of stars and gas within galaxies. In this work, we examine the impact of this simplistic dust assumption on inferred $z$ and $M_*$ for Rubin LSST. We first construct synthetic LSST-like observations ($ugrizy$ magnitudes) from the NIHAO-SKIRT catalog, which provides SEDs from high-resolution hydrodynamic simulations using 3D Monte Carlo radiative transfer. We then infer $z$ and $M_*$ from the synthetic observations using the PROVABGS Bayesian SED modeling framework. Overall, the uniform dust screen assumption biases both $z$ and $M_*$ in galaxies, consistently and significantly for galaxies with dust attenuation $A_V \gtrsim 0.5$, and likely below. The biases depend on the orientation in which the galaxies are observed. At $z=0.4$, $z$ is overestimated by $\sim$0.02 for face-on galaxies and $M_*$ is underestimated by $\sim$0.4 dex for edge-on galaxies. The bias in photo-$z$ is equivalent to the desired redshift precision level of LSST "gold sample" and will be larger at higher redshifts. Our results underscore the need for SED models with additional flexibility in the dust parameterization to mitigate significant systematic biases in cosmological analyses with LSST.
Introducing cosmosTNG: simulating galaxy formation with constrained realizations of the COSMOS field
2024-09-27
Chris Byrohl, Dylan Nelson, Benjamin Horowitz, Khee-Gan Lee, Annalisa Pillepich
We introduce the new cosmological simulation project cosmosTNG, a first-of-its-kind suite of constrained galaxy formation simulations for the universe at Cosmic Noon ($z\sim 2$). cosmosTNG simulates a $0.2$ deg$^2$ patch of the COSMOS field at $z \simeq 2.0-2.2$ using an initial density field inferred from galaxy redshift surveys and the CLAMATO Lyman-alpha forest tomography survey, reconstructed by the TARDIS algorithm. We evolve eight different realizations of this volume to capture small-scale variations. All runs use the IllustrisTNG galaxy formation model with a baryonic mass resolution of $10^6$ M$_\odot$, equal to TNG100-1. In this initial study, we demonstrate qualitative agreement between the evolved large-scale structure and the spatial distribution of observed galaxy populations in COSMOS, emphasizing the zFIRE protocluster region. We then compare the statistical properties and scaling relations of the galaxy population, covering stellar, gaseous, and supermassive black hole (SMBH) components, between cosmosTNG, observations in COSMOS, and $z \sim 2$ observational data in general. We find that galaxy quenching and environmental effects in COSMOS are modulated by its specific large-scale structure, particularly the collapsing protoclusters in the region. With respect to a random region of the universe, the abundance of high-mass galaxies is larger, and the quenched fraction of galaxies is significant lower at fixed mass. This suggests an accelerated growth of stellar mass, as reflected in a higher cosmic star formation rate density, due to the unique assembly histories of galaxies in the simulated COSMOS subvolume. The cosmosTNG suite will be a valuable tool for studying galaxy formation at cosmic noon, particularly when interpreting extragalactic observations with HST, JWST, and other large multi-wavelength survey programs of the COSMOS field.
This paper presents a novel weakly supervised semantic segmentation method for radar segmentation, where the existing LiDAR semantic segmentation models are employed to generate semantic labels, which then serve as supervision signals for training a radar semantic segmentation model. The obtained radar semantic segmentation model outperforms LiDAR-based models, providing more consistent and robust segmentation under all-weather conditions, particularly in the snow, rain and fog. To mitigate potential errors in LiDAR semantic labels, we design a dedicated refinement scheme that corrects erroneous labels based on structural features and distribution patterns. The semantic information generated by our radar segmentation model is used in two downstream tasks, achieving significant performance improvements. In large-scale radar-based localization using OpenStreetMap, it leads to localization error reduction by 20.55\% over prior methods. For the odometry task, it improves translation accuracy by 16.4\% compared to the second-best method, securing the first place in the radar odometry competition at the Radar in Robotics workshop of ICRA 2024, Japan
Formulating the Proxy Pattern-Mixture Model as a Selection Model to Assist with Sensitivity Analysis
2024-09-26
Seth Adarkwah Yiadom, Rebecca Andridge
Proxy pattern-mixture models (PPMM) have previously been proposed as a model-based framework for assessing the potential for nonignorable nonresponse in sample surveys and nonignorable selection in nonprobability samples. One defining feature of the PPMM is the single sensitivity parameter, $\phi$, that ranges from 0 to 1 and governs the degree of departure from ignorability. While this sensitivity parameter is attractive in its simplicity, it may also be of interest to describe departures from ignorability in terms of how the odds of response (or selection) depend on the outcome being measured. In this paper, we re-express the PPMM as a selection model, using the known relationship between pattern-mixture models and selection models, in order to better understand the underlying assumptions of the PPMM and the implied effect of the outcome on nonresponse. The selection model that corresponds to the PPMM is a quadratic function of the survey outcome and proxy variable, and the magnitude of the effect depends on the value of the sensitivity parameter, $\phi$ (missingness/selection mechanism), the differences in the proxy means and standard deviations for the respondent and nonrespondent populations, and the strength of the proxy, $\rho^{(1)}$. Large values of $\phi$ (beyond $0.5$) often result in unrealistic selection mechanisms, and the corresponding selection model can be used to establish more realistic bounds on $\phi$. We illustrate the results using data from the U.S. Census Household Pulse Survey.
Open-World Evaluation for Retrieving Diverse Perspectives
2024-09-26
Hung-Ting Chen, Eunsol Choi
We study retrieving a set of documents that covers various perspectives on a complex and contentious question (e.g., will ChatGPT do more harm than good?). We curate a Benchmark for Retrieval Diversity for Subjective questions (BERDS), where each example consists of a question and diverse perspectives associated with the question, sourced from survey questions and debate websites. On this data, retrievers paired with a corpus are evaluated to surface a document set that contains diverse perspectives. Our framing diverges from most retrieval tasks in that document relevancy cannot be decided by simple string matches to references. Instead, we build a language model based automatic evaluator that decides whether each retrieved document contains a perspective. This allows us to evaluate the performance of three different types of corpus (Wikipedia, web snapshot, and corpus constructed on the fly with retrieved pages from the search engine) paired with retrievers. Retrieving diverse documents remains challenging, with the outputs from existing retrievers covering all perspectives on only 33.74% of the examples. We further study the impact of query expansion and diversity-focused reranking approaches and analyze retriever sycophancy. Together, we lay the foundation for future studies in retrieval diversity handling complex queries.
DiffSSC: Semantic LiDAR Scan Completion using Denoising Diffusion Probabilistic Models
2024-09-26
Helin Cao, Sven Behnke
Perception systems play a crucial role in autonomous driving, incorporating multiple sensors and corresponding computer vision algorithms. 3D LiDAR sensors are widely used to capture sparse point clouds of the vehicle's surroundings. However, such systems struggle to perceive occluded areas and gaps in the scene due to the sparsity of these point clouds and their lack of semantics. To address these challenges, Semantic Scene Completion (SSC) jointly predicts unobserved geometry and semantics in the scene given raw LiDAR measurements, aiming for a more complete scene representation. Building on promising results of diffusion models in image generation and super-resolution tasks, we propose their extension to SSC by implementing the noising and denoising diffusion processes in the point and semantic spaces individually. To control the generation, we employ semantic LiDAR point clouds as conditional input and design local and global regularization losses to stabilize the denoising process. We evaluate our approach on autonomous driving datasets and our approach outperforms the state-of-the-art for SSC.
CARRSSPipeline: Flux Calibration and Non-linear Reprojection for SALT-RSS Multi-Object Spectroscopy over 3500-9500 Å
2024-09-26
George V. Kharchilava, Eric Gawiser, Matt Hilton, Elisabeth Turner, Nicole Firestone, Kyoung-Soo Lee
The Robert Stobie Spectrograph (RSS) on the Southern African Large Telescope (SALT) offers multi-object spectroscopy over an 8' field-of-view at resolutions up to 3000. Reduction is typically conducted using RSSMOSPipeline, which performs basic data calibrations, sky subtraction, and wavelength calibration. However, flux calibration of SALT-RSS using spectrophotometric standard star observations is difficult due to variable primary mirror illumination. We describe a novel approach where stars with Sloan Digital Sky Survey spectra are included as alignment stars on RSS slitmasks and then used to perform flux calibration of the resulting data. RSS offers multiple settings that can be pieced together to cover the entire optical range, utilizing grating angle dithers to fill chip gaps. We introduce a non-linear reprojection routine that defines an exponential wavelength array spanning 3500-9500 Angstroms with gradually decreasing resolution and then reprojects several individual settings into a single 2D spectrum for each object. Our flux calibration and non-linear reprojection routines are released as part of the Calibration And Reprojection for RSS Pipeline (CARRSSPipeline; https://github.com/GeorgeTheGeorgian/CARRSSPipeline.git ), that enables the extraction of full-optical-coverage, flux-calibrated, medium-resolution one-dimensional spectra.
Efficacy of a hybrid take home and in class summative assessment for the postsecondary physics classroom
2024-09-26
Paul Stonaha, Stephanie Douglas
Assigning course grades to students requires obtaining accurate measures of the students' understanding and knowledge of the topic. The induced stress from a traditional summative assessment is known to negatively impact student grades, confounding the connection between knowledge and test grades. Documented approaches to reduce stress during examinations can lead to a distracting testing environment (two-stage tests) or are subject to cheating (take-home tests). We have developed a hybrid take-home/in-class exam that avoids such difficulties. We present herein the responses from student surveys conducted after each exam. The results of the surveys indicate that the hybrid exam method reduces test anxiety while improving students' self-reported mastery of physics, as compared to traditional in-class summative assessments. These findings are significant at the p < 0.05 level. Lastly, we discuss the difficulties encountered in applying this test approach.
Multilingual Evaluation of Long Context Retrieval and Reasoning
2024-09-26
Ameeta Agrawal, Andy Dang, Sina Bagheri Nezhad, Rhitabrat Pokharel, Russell Scheinberg
Recent large language models (LLMs) demonstrate impressive capabilities in handling long contexts, some exhibiting near-perfect recall on synthetic retrieval tasks. However, these evaluations have mainly focused on English text and involved a single target sentence within lengthy contexts. Our work investigates how LLM performance generalizes to multilingual settings with multiple hidden target sentences. We comprehensively evaluate several long-context LLMs on retrieval and reasoning tasks across five languages: English, Vietnamese, Indonesian, Swahili, and Somali. These languages share the Latin script but belong to distinct language families and resource levels. Our analysis reveals a significant performance gap between languages. The best-performing models such as Gemini-1.5 and GPT-4o, achieve around 96% accuracy in English to around 36% in Somali with a single target sentence. However, this accuracy drops to 40% in English and 0% in Somali when dealing with three target sentences. Our findings highlight the challenges long-context LLMs face when processing longer contexts, an increase in the number of target sentences, or languages of lower resource levels.
Diffusion models have been successfully applied to robotics problems such as manipulation and vehicle path planning. In this work, we explore their application to end-to-end navigation -- including both perception and planning -- by considering the problem of jointly performing global localization and path planning in known but arbitrary 2D environments. In particular, we introduce a diffusion model which produces collision-free paths in a global reference frame given an egocentric LIDAR scan, an arbitrary map, and a desired goal position. To this end, we implement diffusion in the space of paths in SE(2), and describe how to condition the denoising process on both obstacles and sensor observations. In our evaluation, we show that the proposed conditioning techniques enable generalization to realistic maps of considerably different appearance than the training environment, demonstrate our model's ability to accurately describe ambiguous solutions, and run extensive simulation experiments showcasing our model's use as a real-time, end-to-end localization and planning stack.
Reinforcement Learning (RL) has shown its remarkable and generalizable capability in legged locomotion through sim-to-real transfer. However, while adaptive methods like domain randomization are expected to make policy more robust to diverse environments, such comprehensiveness potentially detracts from the policy's performance in any specific environment according to the No Free Lunch theorem, leading to a suboptimal solution once deployed in the real world. To address this issue, we propose a lifelong policy adaptation framework named LoopSR, which utilizes a transformer-based encoder to project real-world trajectories into a latent space, and accordingly reconstruct the real-world environments back in simulation for further improvement. Autoencoder architecture and contrastive learning methods are adopted to better extract the characteristics of real-world dynamics. The simulation parameters for continual training are derived by combining predicted parameters from the decoder with retrieved parameters from the simulation trajectory dataset. By leveraging the continual training, LoopSR achieves superior data efficiency compared with strong baselines, with only a limited amount of data to yield eminent performance in both sim-to-sim and sim-to-real experiments.
Extracting Affect Aggregates from Longitudinal Social Media Data with Temporal Adapters for Large Language Models
2024-09-26
Georg Ahnert, Max Pellert, David Garcia, Markus Strohmaier
This paper proposes temporally aligned Large Language Models (LLMs) as a tool for longitudinal analysis of social media data. We fine-tune Temporal Adapters for Llama 3 8B on full timelines from a panel of British Twitter users, and extract longitudinal aggregates of emotions and attitudes with established questionnaires. We validate our estimates against representative British survey data and find strong positive, significant correlations for several collective emotions. The obtained estimates are robust across multiple training seeds and prompt formulations, and in line with collective emotions extracted using a traditional classification model trained on labeled data. To the best of our knowledge, this is the first work to extend the analysis of affect in LLMs to a longitudinal setting through Temporal Adapters. Our work enables new approaches towards the longitudinal analysis of social media data.
2024-09-26
Tian-Rui Sun, Jin-Jun Geng, Jing-Zhi Yan, You-Dong Hu, Xue-Feng Wu, Alberto J. Castro-Tirado, Chao Yang, Yi-Ding Ping, Chen-Ran Hu, Fan Xu, Hao-Xuan Gao, Ji-An Jiang, Yan-Tian Zhu, Yongquan Xue, Ignacio Pérez-García, Si-Yu Wu, Emilio Fernández-García, María D. Caballero-García, Rubén Sánchez-Ramírez, Sergiy Guziy, Ignacio Olivares, Carlos Jesus Pérez del Pulgar, A. Castellón, Sebastián Castillo, Ding-Rong Xiong, Shashi B. Pandey, David Hiriart, Guillermo García-Segura, William H. Lee, I. M. Carrasco-García, Il H. Park, Petrus J. Meintjes, Hendrik J. van Heerden, Antonio Martín-Carrillo, Lorraine Hanlon, Bin-Bin Zhang, Alain Maury, L. Hernández-García, Maria Gritsevich, Andrea Rossi, Elisabetta Maiorano, Felice Cusano, Paolo D'Avanzo, Matteo Ferro, Andrea Melandri, Massimiliano De Pasquale, Riccardo Brivio, Min Fang, Lu-Lu Fan, Wei-Da Hu, Zhen Wan, Lei Hu, Ying-Xi Zuo, Jin-Long Tang, Xiao-Ling Zhang, Xian-Zhong Zheng, Bin Li, Wen-Tao Luo, Wei Liu, Jian Wang, Hong-Fei Zhang, Hao Liu, Jie Gao, Ming Liang, Hai-Ren Wang, Da-Zhi Yao, Jing-Quan Cheng, Wen Zhao, Zi-Gao Dai
Thanks to the rapidly increasing time-domain facilities, we are entering a golden era of research on gamma-ray bursts (GRBs). In this Letter, we report our observations of GRB 240529A with the Burst Optical Observer and Transient Exploring System, the 1.5-meter telescope at Observatorio Sierra Nevada, the 2.5-meter Wide Field Survey Telescope of China, the Large Binocular Telescope, and the Telescopio Nazionale Galileo. The prompt emission of GRB 240529A shows two comparable energetic episodes separated by a quiescence time of roughly 400 s. Combining all available data on the GRB Coordinates Network, we reveal the simultaneous apparent X-ray plateau and optical re-brightening around $10^3-10^4$ s after the burst. Rather than the energy injection from the magnetar as widely invoked for similar GRBs, the multi-wavelength emissions could be better explained as two shocks launched from the central engine separately. The optical peak time and our numerical modeling suggest that the initial bulk Lorentz factor of the later shock is roughly 50, which indicates that the later jet should be accretion-driven and have a higher mass loading than a typical one. The quiescence time between the two prompt emission episodes may be caused by the transition between different accretion states of a central magnetar or black hole, or the fall-back accretion process. A sample of similar bursts with multiple emission episodes in the prompt phase and sufficient follow-up could help to probe the underlying physics of GRB central engines.
Manufacturing, processing, applications, and advancements of Fe-based shape memory alloys
2024-09-26
Anwar Algamal, Hossein Abedi, Umesh Gandhi, Othmane Benafan, Mohammad Elahinia, Ala Qattawi
Fe-based shape memory alloys (Fe-SMAs) belong to smart metallic materials that can memorize or restore their preset shape after experiencing a substantial amount of deformation under heat, stress, or magnetic stimuli. Fe-SMAs have remarkable thermomechanical properties and have attracted significant interest because of their potential merits, such as cost-effective alloying elements, superior workability, weldability, a stable superelastic response, and low-temperature dependence of critical stress required for stress-induced martensitic transformation. Therefore, Fe-SMAs can be an intriguing and economical alternative to other SMAs. The recent advancements in fabrication methods of conventional metals and SMAs are helping the production of customized powder composition and then customized geometries by additive manufacturing (AM). The technology in these areas, i.e., fabrication techniques, experimental characterization, and theoretical formulations of Fe-SMAs for conventional and AM has been rapidly advancing and is lacking a comprehensive review. This paper provides a critical review of the recent developments in Fe-SMAs-related research. The conventional and AM-based methods of producing Fe-SMAs are discussed, and a detailed review of the current research trends on Fe-SMAs including 4-D printing of Fe-SMAs are comprehensively documented. The presented review provides a comprehensive review of experimental methods and processes used to determine the material characteristics and features of Fe-SMAs. In addition, the work provides a review of the reported computational modeling of Fe-SMAs to help design new Fe-SMA composition and geometry. Finally, different Fe-SMAs-based applications such as sensing and damping systems, tube coupling, and reinforced concrete are also discussed.
The Large Hadron Collider (LHC), the world's largest and most powerful particle accelerator, has been a pivotal tool in advancing our understanding of fundamental physics. By colliding heavy ions (such as lead ions), the LHC recreates conditions similar to those just after the Big Bang. This allows scientists to study the Quark-Gluon Plasma (QGP), a state of matter where quarks and gluons are not confined within protons and neutrons. These studies provide insights into the strong force and the early universe's behavior. In this paper, we provide a comprehensive overview of recent significant findings from A Large Ion Collider Experiment (ALICE) at LHC. The topics encompass measurements regarding to properties of the QGP, particle production, flow and correlations, dileptons, quarkonia and electromagnetic probes, heavy flavor, and jets. Additionally, we introduce future plans for detector upgrades of the ALICE experiment.
Participatory design: A systematic review and insights for future practice
2024-09-26
Peter Wacnik, Shanna Daly, Aditi Verma
Participatory Design -- an iterative, flexible design process that uses the close involvement of stakeholders, most often end users -- is growing in use across design disciplines. As an increasing number of practitioners turn to Participatory Design (PD), it has become less rigidly defined, with stakeholders engaged to varying degrees through the use of disjointed techniques. This ambiguous understanding can be counterproductive when discussing PD processes. Our findings synthesize key decisions and approaches from design peers that can support others in engaging in PD practice. We investigated how scholars report the use of Participatory Design in the field through a systematic literature review. We found that a majority of PD literature examined specific case studies of PD (53 of 88 articles), with the design of intangible systems representing the most common design context (61 of 88 articles). Stakeholders most often participated throughout multiple stages of a design process (65 of 88 articles), recruited in a variety of ways and engaged in several of the 14 specific participatory techniques identified. This systematic review provides today's practitioners synthesized learnings from past Participatory Design processes to inform and improve future use of PD, attempting to remedy inequitable design by engaging directly with stakeholders and users.
2024-09-26
Manish Jha, Jialin Qian, Michael Weber, Baozhong Yang
We create a firm-level ChatGPT investment score, based on conference calls, that measures managers' anticipated changes in capital expenditures. We validate the score with interpretable textual content and its strong correlation with CFO survey responses. The investment score predicts future capital expenditure for up to nine quarters, controlling for Tobin's $q$ and other determinants, implying the investment score provides incremental information about firms' future investment opportunities. The investment score also separately forecasts future total, intangible, and R\&D investments. Consistent with theoretical predictions, high-investment-score firms experience significant positive short-term returns upon disclosure, and negative long-run future abnormal returns. We demonstrate ChatGPT's applicability to measure other policies, such as dividends and employment.
Visualization of Age Distributions as Elements of Medical Data-Stories
2024-09-26
Sophia Dowlatabadi, Bernhard Preim, Monique Meuschke
In various fields, including medicine, age distributions are crucial. Despite widespread media coverage of health topics, there remains a need to enhance health communication. Narrative medical visualization is promising for improving information comprehension and retention. This study explores the most effective ways to present age distributions of diseases through narrative visualizations. We conducted a thorough analysis of existing visualizations, held workshops with a broad audience, and reviewed relevant literature. From this, we identified design choices focusing on comprehension, aesthetics, engagement, and memorability. We specifically tested three pictogram variants: pictograms as bars, stacked pictograms, and annotations. After evaluating 18 visualizations with 72 participants and three expert reviews, we determined that annotations were most effective for comprehension and aesthetics. However, traditional bar charts were preferred for engagement, and other variants were more memorable. The study provides a set of design recommendations based on these insights.
Software Security Analysis in 2030 and Beyond: A Research Roadmap
2024-09-26
Marcel Böhme, Eric Bodden, Tevfik Bultan, Cristian Cadar, Yang Liu, Giuseppe Scanniello
As our lives, our businesses, and indeed our world economy become increasingly reliant on the secure operation of many interconnected software systems, the software engineering research community is faced with unprecedented research challenges, but also with exciting new opportunities. In this roadmap paper, we outline our vision of Software Security Analysis for the software systems of the future. Given the recent advances in generative AI, we need new methods to evaluate and maximize the security of code co-written by machines. As our software systems become increasingly heterogeneous, we need practical approaches that work even if some functions are automatically generated, e.g., by deep neural networks. As software systems depend evermore on the software supply chain, we need tools that scale to an entire ecosystem. What kind of vulnerabilities exist in future systems and how do we detect them? When all the shallow bugs are found, how do we discover vulnerabilities hidden deeply in the system? Assuming we cannot find all security flaws, how can we nevertheless protect our system? To answer these questions, we start our research roadmap with a survey of recent advances in software security, then discuss open challenges and opportunities, and conclude with a long-term perspective for the field.
Phase glides and self-organization of atomically abrupt interfaces out of stochastic disorder in $α$-Ga$_{2}$O$_{3}$
2024-09-26
Alexander Azarov, Javier García Fernández, Junlei Zhao, Ru He, Ji-Hyeon Park, Dae-Woo Jeon, Øystein Prytz, Flyura Djurabekova, Andrej Kuznetsov
Disorder-induced ordering and unprecedentedly high radiation tolerance in $\gamma$-phase of gallium oxide is a recent spectacular discovery at the intersection of the fundamental physics and electronic applications. Importantly, by far, these data were collected with initial samples in form of the thermodynamically stable $\beta$-phase of this material. Here, we investigate these phenomena starting instead from already metastable $\alpha$-phase and explain radically new trend occurring in the system. We argue that in contrast to that in $\beta$-to-$\gamma$ disorder-induced transitions, the O sublattice in $\alpha$-phase exhibits hexagonal close-packed structure, so that to activate $\alpha$-to-$\gamma$ transformation significant structural rearrangements are required in both Ga and O sublattices. Moreover, consistently with theoretical predictions, $\alpha$-to-$\gamma$ phase transformation requires accumulation of the substantial tensile strain to initiate otherwise impossible lattice glides. Thus, we explain the experimentally observed trends in term of the combination of disorder and strain governing the process. Finally, and perhaps most amazingly, we demonstrate atomically abrupt $\alpha$/$\gamma$ interfaces paradoxically self-organized out of the stochastic disorder.
Bias Assessment and Data Drift Detection in Medical Image Analysis: A Survey
2024-09-26
Andrea Prenner, Bernhard Kainz
Machine Learning (ML) models have gained popularity in medical imaging analysis given their expert level performance in many medical domains. To enhance the trustworthiness, acceptance, and regulatory compliance of medical imaging models and to facilitate their integration into clinical settings, we review and categorise methods for ensuring ML reliability, both during development and throughout the model's lifespan. Specifically, we provide an overview of methods assessing models' inner-workings regarding bias encoding and detection of data drift for disease classification models. Additionally, to evaluate the severity in case of a significant drift, we provide an overview of the methods developed for classifier accuracy estimation in case of no access to ground truth labels. This should enable practitioners to implement methods ensuring reliable ML deployment and consistent prediction performance over time.
CASPFormer: Trajectory Prediction from BEV Images with Deformable Attention
2024-09-26
Harsh Yadav, Maximilian Schaefer, Kun Zhao, Tobias Meisen
Motion prediction is an important aspect for Autonomous Driving (AD) and Advance Driver Assistance Systems (ADAS). Current state-of-the-art motion prediction methods rely on High Definition (HD) maps for capturing the surrounding context of the ego vehicle. Such systems lack scalability in real-world deployment as HD maps are expensive to produce and update in real-time. To overcome this issue, we propose Context Aware Scene Prediction Transformer (CASPFormer), which can perform multi-modal motion prediction from rasterized Bird-Eye-View (BEV) images. Our system can be integrated with any upstream perception module that is capable of generating BEV images. Moreover, CASPFormer directly decodes vectorized trajectories without any postprocessing. Trajectories are decoded recurrently using deformable attention, as it is computationally efficient and provides the network with the ability to focus its attention on the important spatial locations of the BEV images. In addition, we also address the issue of mode collapse for generating multiple scene-consistent trajectories by incorporating learnable mode queries. We evaluate our model on the nuScenes dataset and show that it reaches state-of-the-art across multiple metrics
Advanced Classification of Hot Subdwarf Binaries Using Artificial Intelligence Techniques and Gaia DR3 data
2024-09-26
C. Viscasillas Vázquez, E. Solano, A. Ulla, M. Ambrosch, M. A. Álvarez, M. Manteiga, L. Magrini, R. Santoveña-Gómez, C. Dafonte, E. Pérez-Fernández, A. Aller, A. Drazdauskas, Š. Mikolaitis, C. Rodrigo
Hot subdwarfs are compact blue evolved objects, burning helium in their cores surrounded by a tiny hydrogen envelope. Most models agree on a common envelope binary evolution scenario in the Red Giant phase. However, the binarity rate for these objects is yet unsolved. We aim to develop a novel classification method for identifying hot subdwarf binaries within large datasets using Artificial Intelligence methods and Gaia DR3 data. The results will be compared with those obtained previously using VOSA (Virtual Observatory Sed Analyzer) on coincident samples. The methods include several machine learning techniques. We used Support Vector Machines (SVM) to classify 3084 hot subdwarf stars based on their color-magnitude properties. Of these, 2815 objects have Gaia Data Release 3 BP/RP spectra, which were classified using Self-Organizing Maps (SOM) and Convolutional Neural Networks (CNN). The findings demonstrate a high agreement level (70-90%) with VOSA's classification, indicating that machine learning methods effectively classify sources with an accuracy comparable to human inspection or non-AI techniques. SVM in a radial basis function achieves 70.97% reproducibility for binary targets using photometry. CNN reaches 84.94% for binary detection using spectroscopy. We also found that the single-binary differences are especially observable on the infrared flux in our GDR3 BP/BR spectra, at wavelengths larger than 700 nm. We found that all our methods are effective in discerning between single and binary systems and are consistent with the results previously obtained with VOSA. In global terms, considering all quality metrics, CNN is the method that provides the best accuracy. The methods are also effective for detecting peculiarities in the spectra. Further research is needed to refine our techniques and enhance automated classification reliability, especially for large-scale surveys.
We investigate higher spin AdS$_{3}$ gravity with real split forms of complex A$_{N}$ B$_{N}$, C$_{N}$ and D$_{N}$ Lie algebras. This is done by linking $SO(1,2)$ spin multiplets with splitted root systems using Tits-Satake diagrams of real forms. Unlike $SL(N,R)$, we show that the orthogonal families have two different higher spin (HS) spectrums: vectorial and spinorial. We find amongst others that the spinorial spectrum has an isolated spin j$_{\mathcal{N}}$ given by $\mathcal{N}\left(\mathcal{N}+1\right)/2$ for $SO(\mathcal{N},1+\mathcal{N})$ and $\mathcal{N} \left( \mathcal{N}-1\right) /2$ for $SO(\mathcal{N},\mathcal{N})$. We implement these results into the computation of the HS partition functions in these gravity theories and identify the individual contributions of the higher spin fields; valuable to manoeuver the HS-BTZ black hole partition function
Energy-scaling behavior of intrinsic transverse momentum parameters in Drell-Yan simulation
2024-09-26
CMS Collaboration
We present an analysis based on models of the intrinsic transverse momentum of partons in nucleons by studying the dilepton transverse momentum in Drell-Yan events. Using parameter tuning in event generators and existing data from fixed-target experiments, from the Tevatron, and from the LHC, our investigation spans three orders of magnitude in center-of-mass energy and two orders of magnitude in dilepton invariant mass. The results show an energy-scaling behavior of the intrinsic transverse momentum parameters, independent of the dilepton invariant mass at a given center-of-mass energy.
Recent advances in interpretable machine learning using structure-based protein representations
2024-09-26
Luiz Felipe Vecchietti, Minji Lee, Begench Hangeldiyev, Hyunkyu Jung, Hahnbeom Park, Tae-Kyun Kim, Meeyoung Cha, Ho Min Kim
Recent advancements in machine learning (ML) are transforming the field of structural biology. For example, AlphaFold, a groundbreaking neural network for protein structure prediction, has been widely adopted by researchers. The availability of easy-to-use interfaces and interpretable outcomes from the neural network architecture, such as the confidence scores used to color the predicted structures, have made AlphaFold accessible even to non-ML experts. In this paper, we present various methods for representing protein 3D structures from low- to high-resolution, and show how interpretable ML methods can support tasks such as predicting protein structures, protein function, and protein-protein interactions. This survey also emphasizes the significance of interpreting and visualizing ML-based inference for structure-based protein representations that enhance interpretability and knowledge discovery. Developing such interpretable approaches promises to further accelerate fields including drug development and protein design.
MIGHTEE-HI: deep spectral line observations of the COSMOS field
2024-09-26
I. Heywood, A. A. Ponomareva, N. Maddox, M. J. Jarvis, B. S. Frank, E. A. K. Adams, M. Baes, A. Bianchetti, J. D. Collier, R. P. Deane, M. Glowacki, S. L. Jung, H. Pan, S. H. A. Rajohnson, G. Rodighiero, I. Ruffa, M. G. Santos, F. Sinigaglia, M. Vaccari
The MIGHTEE survey utilises the South African MeerKAT radio telescope to observe four extragalactic deep fields, with the aim of advancing our understanding of the formation and evolution of galaxies across cosmic time. MIGHTEE's frequency coverage encompasses the $\textrm{H}\scriptstyle\mathrm{I}$ line to a redshift of z $\simeq$ 0.58, and OH megamasers to z $\simeq$ 0.9. We present the MIGHTEE-$\textrm{H}\scriptstyle\mathrm{I}$ imaging products for the COSMOS field, using a total of 94.2 h on-target and a close-packed mosaic of 15 individual pointings. The spectral imaging covers two broad, relatively interference-free regions (960-1150 and 1290-1520~MHz) within MeerKAT's L-band, with up to 26 kHz spectral resolution (5.5 km s$^{-1}$ at $z$ = 0). The median noise in the highest spectral resolution data is 74 $\mu$Jy beam$^{-1}$, corresponding to a 5$\sigma$ $\textrm{H}\scriptstyle\mathrm{I}$ mass limit of 10$^{8.5}$ M$_{\odot}$ for a 300 km s$^{-1}$ line at $z$ = 0.07. The mosaics cover $>$4 deg$^{2}$, provided at multiple angular resolution / sensitivity pairings, with an angular resolution for $\textrm{H}\scriptstyle\mathrm{I}$ at $z$ = 0 of 12$''$. We describe the spectral line processing workflow that will be the basis for future MIGHTEE-$\textrm{H}\scriptstyle\mathrm{I}$ products, and validation of, and some early results from, the spectral imaging of the COSMOS field. We find no evidence for line emission at the position of the $z$ = 0.376 \HI~line reported from the CHILES survey at a $>$94 per cent confidence level, placing a 3$\sigma$ upper limit of 8.1 $\times$ 10$^{9}$ M$_{\odot}$ on $M_{\mathrm{HI}}$ for this galaxy. A public data release accompanies this article.
Preserving logical and functional dependencies in synthetic tabular data
2024-09-26
Chaithra Umesh, Kristian Schultz, Manjunath Mahendra, Saparshi Bej, Olaf Wolkenhauer
Dependencies among attributes are a common aspect of tabular data. However, whether existing tabular data generation algorithms preserve these dependencies while generating synthetic data is yet to be explored. In addition to the existing notion of functional dependencies, we introduce the notion of logical dependencies among the attributes in this article. Moreover, we provide a measure to quantify logical dependencies among attributes in tabular data. Utilizing this measure, we compare several state-of-the-art synthetic data generation algorithms and test their capability to preserve logical and functional dependencies on several publicly available datasets. We demonstrate that currently available synthetic tabular data generation algorithms do not fully preserve functional dependencies when they generate synthetic datasets. In addition, we also showed that some tabular synthetic data generation models can preserve inter-attribute logical dependencies. Our review and comparison of the state-of-the-art reveal research needs and opportunities to develop task-specific synthetic tabular data generation models.
Stereopsis has widespread appeal in robotics as it is the predominant way by which living beings perceive depth to navigate our 3D world. Event cameras are novel bio-inspired sensors that detect per-pixel brightness changes asynchronously, with very high temporal resolution and high dynamic range, enabling machine perception in high-speed motion and broad illumination conditions. The high temporal precision also benefits stereo matching, making disparity (depth) estimation a popular research area for event cameras ever since its inception. Over the last 30 years, the field has evolved rapidly, from low-latency, low-power circuit design to current deep learning (DL) approaches driven by the computer vision community. The bibliography is vast and difficult to navigate for non-experts due its highly interdisciplinary nature. Past surveys have addressed distinct aspects of this topic, in the context of applications, or focusing only on a specific class of techniques, but have overlooked stereo datasets. This survey provides a comprehensive overview, covering both instantaneous stereo and long-term methods suitable for simultaneous localization and mapping (SLAM), along with theoretical and empirical comparisons. It is the first to extensively review DL methods as well as stereo datasets, even providing practical suggestions for creating new benchmarks to advance the field. The main advantages and challenges faced by event-based stereo depth estimation are also discussed. Despite significant progress, challenges remain in achieving optimal performance in not only accuracy but also efficiency, a cornerstone of event-based computing. We identify several gaps and propose future research directions. We hope this survey inspires future research in this area, by serving as an accessible entry point for newcomers, as well as a practical guide for seasoned researchers in the community.
Transport Layer Security (TLS) protocol is a cryptographic protocol designed to secure communication over the internet. The TLS protocol has become a fundamental in secure communication, most commonly used for securing web browsing sessions. In this work, we investigate the TLSNotary protocol, which aim to enable the Client to obtain proof of provenance for data from TLS session, while getting as much as possible from the TLS security properties. To achieve such proofs without any Server-side adjustments or permissions, the power of secure multi-party computation (MPC) together with zero knowledge proofs is used to extend the standard TLS Protocol. To make the compliacted landscape of MPC as comprehensible as possible we first introduce the cryptographic primitives required to understand the TLSNotary protocol and go through standard TLS protocol. Finally, we look at the TLSNotary protocol in detail.
Magnetised HI superbubbles in the Small Magellanic Cloud revealed by the POSSUM pilot survey
2024-09-26
Seoyoung Lyla Jung, A. Seta, J. M. Price, N. M. McClure-Griffiths, J. D. Livingston, B. M. Gaensler, Y. K. Ma, M. Tahani, C. S. Anderson, C. Federrath, C. L. Van Eck, D. Leahy, S. P. O'Sullivan, J. West, G. Heald, T. Akahori
Neutral hydrogen (HI) bubbles and shells are common in the interstellar medium (ISM). Studying their properties provides insight into the characteristics of the local ISM as well as the galaxy in which the bubbles reside. We report the detection of magnetic fields associated with superbubbles in the nearby irregular galaxy, the Small Magellanic Cloud (SMC). Using the Polarisation Sky Survey of the Universe's Magnetism (POSSUM) pilot survey, we obtain a high-density grid ($\approx 25 \,\rm sources\,deg^{-2}$) of Faraday rotation measure (RM) from polarized sources behind the SMC. This provides a sufficiently large number of RM measurements to study the magnetic properties of three of the largest HI shells previously identified in the SMC. The RM profiles as a function of distance from the shell centre show characteristic patterns at angular scales comparable to the shell size. We demonstrate that this can be explained by magneto-hydrodynamic simulation models of bubbles expanding in magnetised environments. From the observations, we estimate the line-of-sight magnetic field strength at the edges of the shells is enhanced by $\sim1\,\rm \mu G$ with respect to their centres. This is an order of magnitude larger than the field strength in the ambient medium ($\sim 0.1\,\rm \mu G$) estimated based on the expansion velocity of the shells. This paper highlights the power of densely mapped RM grids in studying the magnetic properties of galactic substructures beyond the Milky Way.
Nodal line semimetals, a class of topological quantum materials, exhibit a variety of novel phenomena due to their properties, such as bands touching on a one-dimensional line or a ring in the Brillouin zone and drumhead-like surface states. In addition, these semimetals are protected by the combined space-inversion and time-reversal ($\mathcal{PT}$) symmetry. In this study, we investigate the longitudinal DC conductivity of the Dirac nodal line semimetals for the broken $\mathcal{PT}$-symmetric system by the mass term. Here, using the quantum kinetic technique, we find the intrinsic (field-driven) and extrinsic (scattering-driven) contributions to the total DC conductivity due to interband effects. Interestingly, the resulting intrinsic conductivity is the Fermi sea contribution, while the extrinsic stems from the Fermi surface contribution. We show that at low chemical potential, the extrinsic part contributes more and dominates over the traditional Drude intraband term, while at the high chemical potential, the intrinsic conductivity contributes. Furthermore, the total DC response due to interband effects saturates at high chemical potential and its strength decreases with increasing mass value. Our findings suggest that the extrinsic contributions are rich enough to understand the overall feature of the response for the three-dimensional system.
Attitudes and perceived effectiveness among first-time online instructors during Covid-19
2024-09-26
Owen Xingjian Zhang
Online teaching has expanded access to education, offering flexibility compared to traditional face-to-face instruction. While early research has explored online teaching, it is important to understand the perspective of instructors who conducted their first online classes during the Covid-19 pandemic. This study focuses on instructors teaching online for the first time, regardless of whether they volunteered. Surveys were conducted when universities transitioned from in-person to online instruction in April 2020, with a follow-up survey after their first online teaching semester. The study investigated instructors' expectations of class success before their first online teaching experience. Using Bayesian modeling, we analyzed how these expectations varied based on instructors' characteristics (self-efficacy in online teaching, technological proficiency, and acceptance of technology) and course attributes (subject area, class size, and instructional design). Results showed that instructors' self-efficacy significantly impacted their expectations of success, while smaller class sizes were associated with lower expectations. Interestingly, factors like prior use of technology platforms and classroom design did not contribute significantly to expectations. The study offers practical recommendations to support online teaching. To improve self-efficacy, instructors should collaborate with colleagues and familiarize themselves with online platforms. Universities should provide workshops or training to enhance teaching skills. In small interactive classes, nonverbal communication should be emphasized, and institutions should establish support teams and feedback mechanisms to ensure quality and effectiveness in online education.
AsIf: Asset Interface Analysis of Industrial Automation Devices
2024-09-26
Thomas Rosenstatter, Christian Schäfer, Olaf Saßnick, Stefan Huber
As Industry 4.0 and the Industrial Internet of Things continue to advance, industrial control systems are increasingly adopting IT solutions, including communication standards and protocols. As these systems become more decentralized and interconnected, a critical need for enhanced security measures arises. Threat modeling is traditionally performed in structured brainstorming sessions involving domain and security experts. Such sessions, however, often fail to provide an exhaustive identification of assets and interfaces due to the lack of a systematic approach. This is a major issue, as it leads to poor threat modeling, resulting in insufficient mitigation strategies and, lastly, a flawed security architecture. We propose a method for the analysis of assets in industrial systems, with special focus on physical threats. Inspired by the ISO/OSI reference model, a systematic approach is introduced to help identify and classify asset interfaces. This results in an enriched system model of the asset, offering a comprehensive overview visually represented as an interface tree, thereby laying the foundation for subsequent threat modeling steps. To demonstrate the proposed method, the results of its application to a programmable logic controller (PLC) are presented. In support of this, a study involving a group of 12 security experts was conducted. Additionally, the study offers valuable insights into the experts' general perspectives and workflows on threat modeling.
Leveraging Annotator Disagreement for Text Classification
2024-09-26
Jin Xu, Mariët Theune, Daniel Braun
It is common practice in text classification to only use one majority label for model training even if a dataset has been annotated by multiple annotators. Doing so can remove valuable nuances and diverse perspectives inherent in the annotators' assessments. This paper proposes and compares three different strategies to leverage annotator disagreement for text classification: a probability-based multi-label method, an ensemble system, and instruction tuning. All three approaches are evaluated on the tasks of hate speech and abusive conversation detection, which inherently entail a high degree of subjectivity. Moreover, to evaluate the effectiveness of embracing annotation disagreements for model training, we conduct an online survey that compares the performance of the multi-label model against a baseline model, which is trained with the majority label. The results show that in hate speech detection, the multi-label method outperforms the other two approaches, while in abusive conversation detection, instruction tuning achieves the best performance. The results of the survey also show that the outputs from the multi-label models are considered a better representation of the texts than the single-label model.
Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs
2024-09-26
Deniz Gündüz, Michèle A. Wigger, Tze-Yang Tung, Ping Zhang, Yong Xiao
Semantic- and task-oriented communication has emerged as a promising approach to reducing the latency and bandwidth requirements of next-generation mobile networks by transmitting only the most relevant information needed to complete a specific task at the receiver. This is particularly advantageous for machine-oriented communication of high data rate content, such as images and videos, where the goal is rapid and accurate inference, rather than perfect signal reconstruction. While semantic- and task-oriented compression can be implemented in conventional communication systems, joint source-channel coding (JSCC) offers an alternative end-to-end approach by optimizing compression and channel coding together, or even directly mapping the source signal to the modulated waveform. Although all digital communication systems today rely on separation, thanks to its modularity, JSCC is known to achieve higher performance in finite blocklength scenarios, and to avoid cliff and the levelling-off effects in time-varying channel scenarios. This article provides an overview of the information theoretic foundations of JSCC, surveys practical JSCC designs over the decades, and discusses the reasons for their limited adoption in practical systems. We then examine the recent resurgence of JSCC, driven by the integration of deep learning techniques, particularly through DeepJSCC, highlighting its many surprising advantages in various scenarios. Finally, we discuss why it may be time to reconsider today's strictly separate architectures, and reintroduce JSCC to enable high-fidelity, low-latency communications in critical applications such as autonomous driving, drone surveillance, or wearable systems.
JoyType: A Robust Design for Multilingual Visual Text Creation
2024-09-26
Chao Li, Chen Jiang, Xiaolong Liu, Jun Zhao, Guoxin Wang
Generating images with accurately represented text, especially in non-Latin languages, poses a significant challenge for diffusion models. Existing approaches, such as the integration of hint condition diagrams via auxiliary networks (e.g., ControlNet), have made strides towards addressing this issue. However, diffusion models often fall short in tasks requiring controlled text generation, such as specifying particular fonts or producing text in small fonts. In this paper, we introduce a novel approach for multilingual visual text creation, named JoyType, designed to maintain the font style of text during the image generation process. Our methodology begins with assembling a training dataset, JoyType-1M, comprising 1 million pairs of data. Each pair includes an image, its description, and glyph instructions corresponding to the font style within the image. We then developed a text control network, Font ControlNet, tasked with extracting font style information to steer the image generation. To further enhance our model's ability to maintain font style, notably in generating small-font text, we incorporated a multi-layer OCR-aware loss into the diffusion process. This enhancement allows JoyType to direct text rendering using low-level descriptors. Our evaluations, based on both visual and accuracy metrics, demonstrate that JoyType significantly outperforms existing state-of-the-art methods. Additionally, JoyType can function as a plugin, facilitating the creation of varied image styles in conjunction with other stable diffusion models on HuggingFace and CivitAI. Our project is open-sourced on https://jdh-algo.github.io/JoyType/.
Functional Classification of Spiking Signal Data Using Artificial Intelligence Techniques: A Review
2024-09-26
Danial Sharifrazi, Nouman Javed, Javad Hassannataj Joloudari, Roohallah Alizadehsani, Prasad N. Paradkar, Ru-San Tan, U. Rajendra Acharya, Asim Bhatti
Human brain neuron activities are incredibly significant nowadays. Neuronal behavior is assessed by analyzing signal data such as electroencephalography (EEG), which can offer scientists valuable information about diseases and human-computer interaction. One of the difficulties researchers confront while evaluating these signals is the existence of large volumes of spike data. Spikes are some considerable parts of signal data that can happen as a consequence of vital biomarkers or physical issues such as electrode movements. Hence, distinguishing types of spikes is important. From this spot, the spike classification concept commences. Previously, researchers classified spikes manually. The manual classification was not precise enough as it involves extensive analysis. Consequently, Artificial Intelligence (AI) was introduced into neuroscience to assist clinicians in classifying spikes correctly. This review discusses the importance and use of AI in spike classification, focusing on the recognition of neural activity noises. The task is divided into three main components: preprocessing, classification, and evaluation. Existing methods are introduced and their importance is determined. The review also highlights the need for more efficient algorithms. The primary goal is to provide a perspective on spike classification for future research and provide a comprehensive understanding of the methodologies and issues involved. The review organizes materials in the spike classification field for future studies. In this work, numerous studies were extracted from different databases. The PRISMA-related research guidelines were then used to choose papers. Then, research studies based on spike classification using machine learning and deep learning approaches with effective preprocessing were selected.
An exploratory analysis of Community-based Question-Answering Platforms and GPT-3-driven Generative AI: Is it the end of online community-based learning?
2024-09-26
Mohammed Mehedi Hasan, Mahady Hasan, Mamun Bin Ibne Reaz, Jannat Un Nayeem Iqra
Context: The advent of Large Language Model-driven tools like ChatGPT offers software engineers an interactive alternative to community question-answering (CQA) platforms like Stack Overflow. While Stack Overflow provides benefits from the accumulated crowd-sourced knowledge, it often suffers from unpleasant comments, reactions, and long waiting times. Objective: In this study, we assess the efficacy of ChatGPT in providing solutions to software engineering questions by analyzing its performance specifically against human solutions. Method: We empirically analyze 2564 Python and JavaScript questions from StackOverflow that were asked between January 2022 and December 2022. We parse the questions and answers from Stack Overflow, then collect the answers to the same questions from ChatGPT through API, and employ four textual and four cognitive metrics to compare the answers generated by ChatGPT with the accepted answers provided by human subject matter experts to find out the potential reasons for which future knowledge seekers may prefer ChatGPT over CQA platforms. We also measure the accuracy of the answers provided by ChatGPT. We also measure user interaction on StackOverflow over the past two years using three metrics to determine how ChatGPT affects it. Results: Our analysis indicates that ChatGPT's responses are 66% shorter and share 35% more words with the questions, showing a 25% increase in positive sentiment compared to human responses. ChatGPT's answers' accuracy rate is between 71 to 75%, with a variation in response characteristics between JavaScript and Python. Additionally, our findings suggest a recent 38% decrease in comment interactions on Stack Overflow, indicating a shift in community engagement patterns. A supplementary survey with 14 Python and JavaScript professionals validated these findings.
Using Convolutional Neural Networks to Search for Strongly Lensed Quasars in KiDS DR5
2024-09-26
Zizhao He, Rui Li, Yiping Shu, Crescenzo Tortora, Xinzhong Er, Raoul Canameras, Stefan Schuldt, Nicola R. Napolitano, Bharath Chowdhary N, Qihang Chen, Nan Li, Haicheng Feng, Limeng Deng, Guoliang Li, L. V. E. Koopmans, Andrej Dvornik
Gravitationally strongly lensed quasars (SL-QSO) offer invaluable insights into cosmological and astrophysical phenomena. With the data from ongoing and next-generation surveys, thousands of SL-QSO systems can be discovered expectedly, leading to unprecedented opportunities. However, the challenge lies in identifying SL-QSO from enormous datasets with high recall and purity in an automated and efficient manner. Hence, we developed a program based on a Convolutional Neural Network (CNN) for finding SL-QSO from large-scale surveys and applied it to the Kilo-degree Survey Data Release 5 (KiDS DR5). Our approach involves three key stages: firstly, we pre-selected ten million bright objects (with $r$-band $\tt{MAG\_AUTO} < 22$), excluding stars from the dataset; secondly, we established realistic training and test sets to train and fine-tune the CNN, resulting in the identification of 4195 machine candidates, and the false positive rate (FPR) of $\sim$1/2000 and recall of 0.8125 evaluated by using the real test set containing 16 confirmed lensed quasars; thirdly, human inspections were performed for further selections, and then 272 SL-QSO candidates were eventually found in total, including 16 high-score, 118 median-score, and 138 lower-score candidates, separately. Removing the systems already confirmed or identified in other papers, we end up with 229 SL-QSO candidates, including 7 high-score, 95 median-score, and 127 lower-score candidates, and the corresponding catalog is publicly available online.
How Do Observational Astronomers Learn to Inspect Imaging Data
2024-09-26
Hugo Walsh, Christopher Fluke, Sara Webb, Lisa Wise
Astronomy is entering an unprecedented era of data collection. Upcoming large surveys will gather more data than ever before, generated at rates requiring real-time decision making. Looking ahead, it is inevitable that astronomers will need to rely more heavily on automated processes. Indeed, some instances have already arisen wherein the majority of the inspection process is automated. Visual discovery, performed traditionally by humans, is one key area where automation is now being integrated rapidly. Visual discovery comprises two aspects: (1) visual inspection, the skill associated with examining an image to identify areas or objects of interest; and (2) visual interpretation, the knowledge associated with the classification of the objects or features. Both skills and knowledge are vital for humans to perform visual discovery, however, there appears to have been limited investigation into how the skill of visual inspection in astronomy is acquired. We report on a survey of 70 professional observational astronomers, at various career stages and from different geographical regions. We found that between 63% and 73% of the astronomers surveyed had received formal and$/$or informal training in visual inspection of images, although formal training (21%) was less common than informal training (60%). Surprisingly, out of the 37% who did not recall having received training in visual inspection, 29% (20 astronomers) indicated that they provided training to others. This suggests the emergence of `expertise without precedent' where a first expert in the field provides a new way of achieving a task. These results, paired with a set of three pilot interviews, present a touchstone against which the training of future observational astronomers can be compared.
What is the social benefit of hate speech detection research? A Systematic Review
2024-09-26
Sidney Gig-Jan Wong
While NLP research into hate speech detection has grown exponentially in the last three decades, there has been minimal uptake or engagement from policy makers and non-profit organisations. We argue the absence of ethical frameworks have contributed to this rift between current practice and best practice. By adopting appropriate ethical frameworks, NLP researchers may enable the social impact potential of hate speech research. This position paper is informed by reviewing forty-eight hate speech detection systems associated with thirty-seven publications from different venues.
Simulated annealing of reduced magnetohydrodynamic systems
2024-09-26
M. Furukawa, P. J. Morrison
Theory of simulated annealing (SA), a method for equilibrium and stability analyses for Hamiltonian systems, is reviewed. The SA explained in this review is based on a double bracket formulation that derives from Hamiltonian structure. In addition to general theoretical aspects, the explicit formulation as well as numerical applications are presented. Both finite and infinite degree-of-freedom systems are treated, in particular, the heavy top, a toy model mimicking low-beta reduced magnetohydrodynamics (MHD) and low- and high-beta reduced MHD. Numerical results successfully demonstrate the usefulness of SA for equilibrium and stability analyses. At the same time, the results raise some future issues that are discussed in the paper.
A census of dwarf galaxy satellites around LMC-mass galaxy NGC 2403
2024-09-26
Jeffrey L. Carlin, David J. Sand, Burcin Mutlu-Pakdil, Denija Crnojevic, Amandine Doliva-Dolinsky, Christopher T. Garling, Annika H. G. Peter, Jean P. Brodie, Duncan A. Forbes, Jonathan R. Hargis, Aaron J. Romanowsky, Kristine Spekkens, Jay Strader, Beth Willman
We present the first comprehensive census of the satellite population around a Large Magellanic Cloud (LMC) stellar-mass galaxy, as part of the Magellanic Analog Dwarf Companions and Stellar Halos (MADCASH) survey. We have surveyed NGC 2403 (D=3.0 Mpc) with the Subaru/Hyper Suprime-Cam imager out to a projected radius of 90 kpc (with partial coverage extending out to ~110 kpc, or ~80% of the virial radius of NGC 2403), resolving stars in the uppermost ~2.5 mags of its red giant branch. By looking for stellar overdensities in the red giant branch spatial density map, we identify 149 satellite candidates, of which only the previously discovered MADCASH J074238+65201-dw is a bona fide dwarf, together with the more massive and disrupting satellite DDO 44. We carefully assess the completeness of our search via injection of artificial dwarf galaxies into the images, finding that we are reliably sensitive to candidates down to M_V ~ -7.5 mag (and somewhat sensitive to even fainter satellites). A comparison of the satellite luminosity function of NGC 2403 down to this magnitude limit to theoretical expectations shows overall good agreement. This is the first of a full sample of 11 Magellanic Cloud-mass host galaxies we will analyze, creating a statistical sample that will provide the first quantitative constraints on hierarchical models of galaxy formation around low-mass hosts.
Moving target defense (MTD) in power grids is an emerging defense technique that has gained prominence in the recent past. It aims to solve the long-standing problem of securing the power grid against stealthy attacks. The key idea behind MTD is to introduce periodic/event-triggered controlled changes to the power grid's SCADA network/physical plant, thereby invalidating the knowledge attackers use for crafting stealthy attacks. In this paper, we provide a comprehensive overview of this topic and classify the different ways in which MTD is implemented in power grids. We further introduce the guiding principles behind the design of MTD, key performance metrics, and the associated trade-offs in MTD and identify the future development of MTD for power grid security.
A survey of simplicial, relative, and chain complex homology theories for hypergraphs
2024-09-26
Ellen Gasparovic, Emilie Purvine, Radmila Sazdanovic, Bei Wang, Yusu Wang, Lori Ziegelmeier
Hypergraphs have seen widespread applications in network and data science communities in recent years. We present a survey of recent work to define topological objects from hypergraphs -- specifically simplicial, relative, and chain complexes -- that can be used to build homology theories for hypergraphs. We define and describe nine different homology theories and their relevant topological objects. We discuss some interesting properties of each method to show how the hypergraph structures are preserved or destroyed by modifying a hypergraph. Finally, we provide a series of illustrative examples by computing many of these homology theories for small hypergraphs to show the variability of the methods and build intuition.
Size-Based Spectrophotometric Analysis of the Polana-Eulalia Complex
2024-09-26
Lucas McClure, Josh Emery, Cristina Thomas, Kevin Walsh, Riley Williams
The Polana-Eulalia Complex (PEC) is an Inner Main Belt, C-complex asteroid population that may be the source of the near-Earth asteroid spacecraft mission targets (101955) Bennu and (162173) Ryugu. Here, we report a size-based investigation of the visible (VIS; 0.47 -- 0.89 um) spectrophotometric slopes of the PEC's constituent families, the New Polana and Eulalia Families. Using two releases of the Sloan Digital Sky Survey's Moving Object Catalog as well as the 3rd data release of the Gaia catalog, we present evidence of size-based slope variability within each family. We find that Eulalia family members exhibit lower average slopes than Polana family members in all catalogs' samples, particularly for objects < 9 km in diameter. We are unable to conclude that VIS slope distinguishability between the families is statistically significant, but we explore a potential cause of the bulk slope differences between the PEC families, in addition to providing commentary on size-slope trends generally.
Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing
2024-09-26
Huthaifa I. Ashqar, Ahmed Jaber, Taqwa I. Alhadidi, Mohammed Elhenawy
This study aims to comprehensively review and empirically evaluate the application of multimodal large language models (MLLMs) and Large Vision Models (VLMs) in object detection for transportation systems. In the first fold, we provide a background about the potential benefits of MLLMs in transportation applications and conduct a comprehensive review of current MLLM technologies in previous studies. We highlight their effectiveness and limitations in object detection within various transportation scenarios. The second fold involves providing an overview of the taxonomy of end-to-end object detection in transportation applications and future directions. Building on this, we proposed empirical analysis for testing MLLMs on three real-world transportation problems that include object detection tasks namely, road safety attributes extraction, safety-critical event detection, and visual reasoning of thermal images. Our findings provide a detailed assessment of MLLM performance, uncovering both strengths and areas for improvement. Finally, we discuss practical limitations and challenges of MLLMs in enhancing object detection in transportation, thereby offering a roadmap for future research and development in this critical area.
Trustworthy Text-to-Image Diffusion Models: A Timely and Focused Survey
2024-09-26
Yi Zhang, Zhen Chen, Chih-Hong Cheng, Wenjie Ruan, Xiaowei Huang, Dezong Zhao, David Flynn, Siddartha Khastgir, Xingyu Zhao
Text-to-Image (T2I) Diffusion Models (DMs) have garnered widespread attention for their impressive advancements in image generation. However, their growing popularity has raised ethical and social concerns related to key non-functional properties of trustworthiness, such as robustness, fairness, security, privacy, factuality, and explainability, similar to those in traditional deep learning (DL) tasks. Conventional approaches for studying trustworthiness in DL tasks often fall short due to the unique characteristics of T2I DMs, e.g., the multi-modal nature. Given the challenge, recent efforts have been made to develop new methods for investigating trustworthiness in T2I DMs via various means, including falsification, enhancement, verification \& validation and assessment. However, there is a notable lack of in-depth analysis concerning those non-functional properties and means. In this survey, we provide a timely and focused review of the literature on trustworthy T2I DMs, covering a concise-structured taxonomy from the perspectives of property, means, benchmarks and applications. Our review begins with an introduction to essential preliminaries of T2I DMs, and then we summarise key definitions/metrics specific to T2I tasks and analyses the means proposed in recent literature based on these definitions/metrics. Additionally, we review benchmarks and domain applications of T2I DMs. Finally, we highlight the gaps in current research, discuss the limitations of existing methods, and propose future research directions to advance the development of trustworthy T2I DMs. Furthermore, we keep up-to-date updates in this field to track the latest developments and maintain our GitHub repository at: https://github.com/wellzline/Trustworthy_T2I_DMs
"Little Red Dots" cannot reside in the same dark matter halos as comparably luminous unobscured quasars
2024-09-26
Elia Pizzati, Joseph F. Hennawi, Joop Schaye, Anna-Christina Eilers, Jiamu Huang, Jan-Torge Schindler, Feige Wang
The James Webb Space Telescope (JWST) has uncovered a new population of candidate broad-line AGN emerging in the early Universe, named ''little red dots'' (LRDs) because of their compactness and red colors at optical wavelengths. LRDs appear to be surprisingly abundant ($\approx 10^{-5}\,\mathrm{cMpc}^{-3}$) given that their inferred bolometric luminosities largely overlap with the ones of the UV-luminous quasars identified at high $z$ in wide-field spectroscopic surveys. In this work, we investigate how the population of LRDs and/or other UV-obscured AGN relates to the one of unobscured, UV-selected quasars. By comparing their number densities, we infer an extremely large and rapidly evolving obscured:unobscured ratio, ranging from $\approx20:1$ at $z\approx4$ to $\approx2300:1$ at $z\approx7$, and possibly extending out to very high ($\approx10^{47}\,\mathrm{erg}\,\mathrm{s}^{-1}$) bolometric luminosities. This large obscured:unobscured ratio is incompatible with the UV-luminous duty cycle measured for unobscured quasars at $z\approx4-6$, suggesting that LRDs are too abundant to be hosted by the same halos as unobscured quasars. This implies that either (a) the bolometric luminosities of LRDs are strongly overestimated (possibly because LRDs are dominated by stellar galaxy light) or (b) LRDs follow different scaling relations than the ones of UV-selected quasars, representing a new population of accreting SMBHs emerging in the early Universe. A direct comparison between the clustering of LRDs and the one of faint UV-selected quasars will ultimately confirm these findings, and shed light on key properties of LRDs such as their host mass distribution and duty cycle. We provide a mock analysis for the clustering of LRDs and show that it is feasible with current and upcoming JWST surveys.
AI Policy Projector: Grounding LLM Policy Design in Iterative Mapmaking
2024-09-26
Michelle S. Lam, Fred Hohman, Dominik Moritz, Jeffrey P. Bigham, Kenneth Holstein, Mary Beth Kery
Whether a large language model policy is an explicit constitution or an implicit reward model, it is challenging to assess coverage over the unbounded set of real-world situations that a policy must contend with. We introduce an AI policy design process inspired by mapmaking, which has developed tactics for visualizing and iterating on maps even when full coverage is not possible. With Policy Projector, policy designers can survey the landscape of model input-output pairs, define custom regions (e.g., "violence"), and navigate these regions with rules that can be applied to LLM outputs (e.g., if output contains "violence" and "graphic details," then rewrite without "graphic details"). Policy Projector supports interactive policy authoring using LLM classification and steering and a map visualization reflecting the policy designer's work. In an evaluation with 12 AI safety experts, our system helps policy designers to address problematic model behaviors extending beyond an existing, comprehensive harm taxonomy.
Soil organic carbon sequestration potential and policy optimization
2024-09-26
Jacob Spertus, Philip Stark, Whendee Silver, Eric Slessarev
Land management could help mitigate climate change by sequestering atmospheric carbon dioxide as soil organic carbon (SOC). The impact of a given management change on the SOC content of a given volume of soil is generally unknown, but is likely moderated by features of the land that collectively determine its sequestration potential. To maximize sequestration, management interventions should be preferentially applied to fields with the highest sequestration potential and the lowest cost of application. We present a design-based statistical framework for estimating sequestration potential, average treatment effects, and optimal management policies from a randomized experiment with baseline covariate information. We review the myriad and nested sources of uncertainty that arise in this context and formalize the problem using potential outcomes. We show that a particular regression estimator -- regressing field-level SOC on management indicators and their interactions with covariates -- can help identify effective policies. The regression estimator also gives asymptotically valid inference on average treatment effects under the randomized design -- without modeling assumptions -- and can increase precision and power compared to the difference-in-means $T$-test. We conclude by discussing the saturation hypothesis in relation to sequestration potential, other study designs including observational studies of SOC, models for policy costs, nonparametric inference, and broader policy uncertainties.
Azimuthal Variations of Stellar Populations in Barred Galaxies
2024-09-26
Justus Neumann, Daniel Thomas, Claudia Maraston, Damian R. Gleis, Chuanming Mao, Eva Schinnerer, Sophia K. Stuber
Bars are expected to impact the distribution of stellar populations both during bar formation, as they rearrange stars into new orbits, and afterwards, due to the redistribution of star-formation-fuelling gas and transfer of angular momentum. We study the impact of stellar bars on the azimuthal variation of stellar population age, metallicity and mass surface density in $\sim1\,000$ nearby barred galaxies from the SDSS-IV/MaNGA survey. Bars have higher stellar mass density ($0.113^{+0.065}_{-0.067}$ dex) and are more metal-rich ($0.028^{+0.033}_{-0.040}$ dex) than the discs at the same radii. Stellar ages show a variety of bar to inter-bar contrasts with no consistent trend. The difference in metallicity increases with total stellar mass of the galaxy and distance below the star-forming main sequence. We discuss a combination of potentially responsible processes including kinematic separation, more extended star formation histories and more efficient recycling in bars and at bar-spiral arm connections. Additionally, we observe an offset (10{\deg}-40{\deg}) of the peak metallicity to the bar major axis in star-forming bars in low-mass galaxies, and more metal-rich regions outside the ends of the bar in long bars and quenched galaxies. Furthermore, there is a subtle trend of lower metallicities on the leading side of spiral arms compared to the trailing side. Finally, we report a spiral arm surface density feature, which could point towards a dominant bar-spiral connection and pitch angle of $\alpha \sim 25${\deg}. We interpret these features in the context of bar formation and the impact of large-scale gas flows associated with their presence.
Stacking and Analyzing $z\approx 2$ MOSDEF Galaxies by Spectral Types: Implications for Dust Geometry and Galaxy Evolution
2024-09-26
Brian Lorenz, Mariska Kriek, Alice E. Shapley, Ryan L. Sanders, Alison L. Coil, Joel Leja, Bahram Mobasher, Erica Nelson, Sedona H. Price, Naveen A. Reddy, Jordan N. Runco, Katherine A. Suess, Irene Shivaei, Brian Siana, Daniel R. Weisz
We examine star-formation and dust properties for a sample of 660 galaxies at $1.37\leq z\leq 2.61$ in the MOSDEF survey by dividing them into groups with similarly-shaped spectral energy distributions (SEDs). For each group, we combine the galaxy photometry into a finely-sampled composite SED, and stack their spectra. This method enables the study of more complete galaxy samples, including galaxies with very faint emission lines. We fit these composite SEDs with Prospector to measure the stellar attenuation and SED-based star-formation rates (SFRs). We also derive emission-line properties from the spectral stacks, including Balmer decrements, dust-corrected SFRs, and metallicities. We find that stellar attenuation correlates most strongly with mass, while nebular attenuation correlates strongly with both mass and SFR. Furthermore, the excess of nebular compared to stellar attenuation correlates most strongly with SFR. The highest SFR group has 2 mag of excess nebular attenuation. Our results are consistent with a model in which star-forming regions become more dusty as galaxy mass increases. To explain the increasing excess nebular attenuation, we require a progressively larger fraction of star formation to occur in highly-obscured regions with increasing SFR. This highly-obscured star formation could occur in dusty clumps or central starbursts. Additionally, as each galaxy group represents a different evolutionary stage, we study their locations on the UVJ and SFR-mass diagrams. As mass increases, metallicity and dust attenuation increase, while sSFR decreases. However, the most massive group moves towards the quiescent region of the UVJ diagram, while showing less obscuration, potentially indicating removal of dust.
Planet Mass Function around M stars at 1-10 au: A Plethora of sub-Earth mass objects
2024-09-26
Yayaati Chachan, Eve J. Lee
Small planets ($\lesssim 1$ M$_\oplus$) at intermediate orbital distances ($\sim$1 au) represent an uncharted territory in exoplanetary science. The upcoming microlensing survey by the Nancy Grace Roman Space Telescope will be sensitive to objects as light as Ganymede and unveil the small planet population at $1-10$ au. Instrumental sensitivity to such planets is low and the number of objects we will discover is strongly dependent on the underlying planet mass function. In this work, we provide a physically motivated planet mass function by combining the efficiency of planet formation by pebble accretion with the observed disk mass function. Because the disk mass function for M dwarfs ($0.4-0.6 \, M_\odot$) is bottom heavy, the initial planet mass function is also expected to be bottom-heavy, skewing towards Ganymede and Mars mass objects, more so for heavier initial planetary seeds. We follow the subsequent dynamical evolution of planetary systems over $\sim$100 Myr varying the initial eccentricity and orbital spacing. For initial planet separations of $\geq$3 local disk scale heights, we find that Ganymede and Mars mass planets do not grow significantly by mergers. However, Earth-like planets undergo vigorous merging and turn into super-Earths, potentially creating a gap in the planet mass function at $\sim 1$ M$_{\oplus}$. Our results demonstrate that the slope of the mass function and the location of the potential gap in the mass function can probe the initial architecture of multi-planet systems. We close by discussing implications on the expected difference between bound and free-floating planet mass functions.
Evaluation of Large Language Models for Summarization Tasks in the Medical Domain: A Narrative Review
2024-09-26
Emma Croxford, Yanjun Gao, Nicholas Pellegrino, Karen K. Wong, Graham Wills, Elliot First, Frank J. Liao, Cherodeep Goswami, Brian Patterson, Majid Afshar
Large Language Models have advanced clinical Natural Language Generation, creating opportunities to manage the volume of medical text. However, the high-stakes nature of medicine requires reliable evaluation, which remains a challenge. In this narrative review, we assess the current evaluation state for clinical summarization tasks and propose future directions to address the resource constraints of expert human evaluation.
Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey
2024-09-26
Tiansheng Huang, Sihao Hu, Fatih Ilhan, Selim Furkan Tekin, Ling Liu
Recent research demonstrates that the nascent fine-tuning-as-a-service business model exposes serious safety concerns -- fine-tuning over a few harmful data uploaded by the users can compromise the safety alignment of the model. The attack, known as harmful fine-tuning, has raised a broad research interest among the community. However, as the attack is still new, \textbf{we observe from our miserable submission experience that there are general misunderstandings within the research community.} We in this paper aim to clear some common concerns for the attack setting, and formally establish the research problem. Specifically, we first present the threat model of the problem, and introduce the harmful fine-tuning attack and its variants. Then we systematically survey the existing literature on attacks/defenses/mechanical analysis of the problem. Finally, we outline future research directions that might contribute to the development of the field. Additionally, we present a list of questions of interest, which might be useful to refer to when reviewers in the peer review process question the realism of the experiment/attack/defense setting. A curated list of relevant papers is maintained and made accessible at: \url{https://github.com/git-disl/awesome_LLM-harmful-fine-tuning-papers}.
A Survey on Neural Architecture Search Based on Reinforcement Learning
2024-09-26
Wenzhu Shao
The automation of feature extraction of machine learning has been successfully realized by the explosive development of deep learning. However, the structures and hyperparameters of deep neural network architectures also make huge difference on the performance in different tasks. The process of exploring optimal structures and hyperparameters often involves a lot of tedious human intervene. As a result, a legitimate question is to ask for the automation of searching for optimal network structures and hyperparameters. The work of automation of exploring optimal hyperparameters is done by Hyperparameter Optimization. Neural Architecture Search is aimed to automatically find the best network structure given specific tasks. In this paper, we firstly introduced the overall development of Neural Architecture Search and then focus mainly on providing an overall and understandable survey about Neural Architecture Search works that are relevant with reinforcement learning, including improvements and variants based on the hope of satisfying more complex structures and resource-insufficient environment.
The Nexus of AR/VR, Large Language Models, UI/UX, and Robotics Technologies in Enhancing Learning and Social Interaction for Children: A Systematic Review
2024-09-26
Biplov Paneru, Bishwash Paneru
The combination of large language models (LLMs), augmented reality (AR), and user interface/user experience (UI/UX) design in therapies for children, especially with disorders like autism spectrum disorder (ASD), is examined in this review study. 150 publications were found by a thorough literature search throughout PubMed, ACM, IEEE Xplore, Elsevier, and Google Scholar; 42 of them were chosen for in-depth study due to their methodological rigor and relevance. Three primary areas are covered in this review: how AR can improve social and learning results; how LLMs can help with communication; and how UI/UX design affects how effective these technologies are. Results reveal that while LLMs can provide individualized learning and communication support, AR has demonstrated promise in enhancing social skills, motivation, and attention. For children with ASD, accessible and interesting interventions depend heavily on effective UI/UX design. To optimize the benefits of these technologies in ASD therapies, the study emphasizes the need for additional research to address difficulties related to customization, accessibility, and integration.
Bitcoin's global success has led to the rise of blockchain, but many systems labeled as "blockchain" deviate from its core principles, adding complexity to the ecosystem. This survey addresses the need for a comprehensive review and taxonomy to clarify the differences between blockchain and blockchain-like systems. We propose a reference model with four key layers: data, consensus, execution, and application, and introduce a new taxonomy for better classification. Through a qualitative and quantitative analysis of 44 DLT solutions and 26 consensus mechanisms, we highlight key challenges and offer research directions in the field.
A Systematisation of Knowledge: Connecting European Digital Identities with Web3
2024-09-26
Ben Biedermann, Matthew Scerri, Victoria Kozlova, Joshua Ellul
The terms self-sovereign identity (SSI) and decentralised identity are often used interchangeably, which results in increasing ambiguity when solutions are being investigated and compared. This article aims to provide a clear distinction between the two concepts in relation to the revised Regulation as Regards establishing the European Digital Identity Framework (eIDAS 2.0) by providing a systematisation of knowledge of technological developments that led up to implementation of eIDAS 2.0. Applying an inductive exploratory approach, relevant literature was selected iteratively in waves over a nine months time frame and covers literature between 2005 and 2024. The review found that the decentralised identity sector emerged adjacent to the OpenID Connect (OIDC) paradigm of Open Authentication, whereas SSI denotes the sector's shift towards blockchain-based solutions. In this study, it is shown that the interchangeable use of SSI and decentralised identity coincides with novel protocols over OIDC. While the first part of this paper distinguishes OIDC from decentralised identity, the second part addresses the incompatibility between OIDC under eIDAS 2.0 and Web3. The paper closes by suggesting further research for establishing a digital identity bridge for connecting applications on public-permissionless ledgers with data originating from eIDAS 2.0 and being presented using OIDC.
Quarks to Cosmos: Particles and Plasma in Cosmological evolution
2024-09-26
Johann Rafelski, Jeremiah Birrell, Christopher Grayson, Andrew Steinmetz, Cheng Tao Yang
We describe in the context of the particle physics (PP) standard model (SM) `PP-SM' the understanding of the primordial properties and composition of the Universe in the temperature range $130\GeV>T>20\keV$. The Universe evolution is described using FLRW cosmology. We present a global view on particle content across time and describe the different evolution eras using deceleration parameter $q$. We follow the arrow of time in the expanding and cooling Universe: After the PP-SM heavies $(t, h, W, Z)$ diminish in abundance below $T\simeq 50\GeV$, the PP-SM plasma in the Universe is governed by the strongly interacting Quark-Gluon content. Once the temperature drops below $T\simeq 150\MeV$, quarks and gluons hadronize into strongly interacting matter particles. Rapid disappearance of baryonic antimatter completes at $T_\mathrm{B}=38.2\MeV$. We study the ensuing disappearance of strangeness and mesons in general. We show that the different eras defined by particle populations are barely separated from each other with abundance of muons fading out just prior to $T=\mathcal{O}(2.5)\MeV$, the era of emergence of the free-streaming neutrinos. We discuss the two relevant fundamental constants controlling the decoupling of neutrinos. We subsequently follow the primordial Universe as it passes through the hot dense electron-positron plasma epoch. The high density of positron antimatter disappears near $T=20.3\keV$: Nuclear reactions occur in the presence of a highly mobile and relatively strongly interacting electron-positron plasma phase. We apply plasma theory methods to describe the strong screening effects between heavy dust particle (nucleons). We analyze the paramagnetic characteristics of the electron-positron plasma when exposed to an external primordial magnetic field.
Code Generation and Algorithmic Problem Solving Using Llama 3.1 405B
2024-09-26
Aniket Deroy, Subhankar Maity
Code generation by Llama 3.1 models, such as Meta's Llama 3.1 405B, represents a significant advancement in the field of artificial intelligence, particularly in natural language processing and programming automation. This paper explores the capabilities and applications of Llama-driven code generation, highlighting its ability to translate natural language prompts into executable code across multiple programming languages. Key features include contextual awareness, multi-language support, and enhanced debugging and optimization functionalities. By examining these aspects, we illustrate how Llama can serve as a versatile tool for developers of all skill levels, improving productivity and efficiency in software development. The potential implications for education, industry, and the future of coding practices are also discussed, underscoring the transformative impact of AI in programming. Experimentation shows that while Llama 3.1 405B performs well with simple algorithmic and data structure based problems, it still struggles with problems on Quantum Computing, Bioinformatics, and Artificial Intelligence.
The increasing precision of cosmology data in the modern era is calling for methods to allow the extraction of non-Gaussian information using tools beyond two-point statistics. The marked power spectrum has the potential to extract beyond two-point information in a computationally efficient way while using much of the infrastructure already available for the power-spectrum. In this work we explore the marked power spectrum from an analytical perspective. In particular, we explore a low-order polynomial for the mark that allows us to better control the theoretical uncertainties and we show that with minimal new degrees of freedom the analytical results match measurements from N-body simulations for both the matter field and biased tracers in redshift space. Finally, we show that even within the limited forms of mark that we consider, there are degeneracies that can be broken by inclusion of the marked auto-spectrum or the cross-spectrum with the unmarked field. We discuss future theoretical developments that would enable us to apply this approach to survey data.
This paper investigates the structural functional observability (SFO) and structural output controllability (SOC) of a class of systems with generically diagonalizable state matrices and explores the associated minimal sensor and actuator placement problems. The verification of SOC and the corresponding sensor and actuator placement problems, i.e., the problems of determining the minimum number of outputs and inputs required to achieve SFO and SOC, respectively, are yet open for general systems, which motivates our focus on a class of systems enabling polynomial-time solutions. In this line, we first define and characterize generically diagonalizable systems, referring to structured systems for which almost all realizations of the state matrices are diagonalizable. We then develop computationally efficient criteria for SFO and SOC within the context of generically diagonalizable systems. Our work expands the class of systems amenable to polynomial-time SOC verification. Thanks to the simplicity of the obtained criteria, we derive closed-form solutions for determining the minimal sensor placement to achieve SFO and the minimal actuator deployment to achieve SOC in such systems, along with efficient weighted maximum matching based and weighted maximum flow based algorithms. For more general systems to achieve SFO, an upper bound is given by identifying a non-decreasing property of SFO with respect to a specific class of edge additions, which is shown to be optimal under certain circumstances.
The effect of image quality on galaxy merger identification with deep learning
2024-09-25
Robert W. Bickley, Scott Wilkinson, Leonardo Ferreira, Sara L. Ellison, Connor Bottrell, Debarpita Jyoti
Studies have shown that the morphologies of galaxies are substantially transformed following coalescence after a merger, but post-mergers are notoriously difficult to identify, especially in imaging that is shallow or low-resolution. We train convolutional neural networks (CNNs) to identify simulated post-merger galaxies in a range of image qualities, modelled after five real surveys: the Sloan Digital Sky Survey (SDSS), the Dark Energy Camera Legacy Survey (DECaLS), the Canada-France Imaging Survey (CFIS), the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), and the Legacy Survey of Space and Time (LSST). Holding constant all variables other than imaging quality, we present the performance of the CNNs on reserved test set data for each image quality. The success of CNNs on a given dataset is found to be sensitive to both imaging depth and resolution. We find that post-merger recovery generally increases with depth, but that limiting 5 sigma point-source depths in excess of ~25 mag, similar to what is achieved in CFIS, are only marginally beneficial. Finally, we present the results of a cross-survey inference experiment, and find that CNNs trained on a given image quality can sometimes be applied to different imaging data to good effect. The work presented here therefore represents a useful reference for the application of CNNs for merger searches in both current and future imaging surveys.
Dynamical Dark Energy Beyond Planck? Constraints from multiple CMB probes, DESI BAO and Type-Ia Supernovae
2024-09-25
William Giarè
Baryon Acoustic Oscillation (BAO) measurements from the Dark Energy Spectroscopic Instrument (DESI) collaboration, when combined with Planck satellite Cosmic Microwave Background (CMB) data and Type Ia Supernovae, suggest a preference for Dynamical Dark Energy (DDE) at a significance level ranging from $2.5\sigma$ to $3.9\sigma$. In this work, I test whether, and to what extent, this preference is supported by CMB experiments other than Planck. I analyze the Atacama Cosmology Telescope (ACT) and South Pole Telescope (SPT) temperature, polarization, and lensing spectra at small scales, eventually combining them with Planck or WMAP 9-year observations at large angular scales. My analysis shows that ACT and WMAP data, when combined with DESI BAO and Pantheon-plus Supernovae, yield independent constraints with a precision comparable to Planck. Notably, in this case, the cosmological constant value is recovered within two standard deviations. A preference for DDE reappears when Pantheon-plus is replaced with distance moduli measurements from the Dark Energy Survey Supernova Program (DESy5). However, it remains less pronounced compared to the Planck-based results. When considering SPT data, no clear preference for DDE is found, although the parameter uncertainties are significantly larger compared to both Planck- and ACT-based constraints. Overall, CMB experiments other than Planck generally weaken the evidence for DDE. I argue that the subsets of Planck data that strengthen the shift toward DDE are the temperature and E-mode polarization anisotropy measurements at large angular scales $\ell \lesssim 30$.
With the rapid growth of the Natural Language Processing (NLP) field, a vast variety of Large Language Models (LLMs) continue to emerge for diverse NLP tasks. As an increasing number of papers are presented, researchers and developers face the challenge of information overload. Thus, it is particularly important to develop a system that can automatically extract and organise key information about LLMs from academic papers (\textbf{LLM model card}). This work is to develop such a pioneer system by using Named Entity Recognition (\textbf{NER}) and Relation Extraction (\textbf{RE}) methods that automatically extract key information about large language models from the papers, helping researchers to efficiently access information about LLMs. These features include model \textit{licence}, model \textit{name}, and model \textit{application}. With these features, we can form a model card for each paper. \textbf{Data-contribution} wise, 106 academic papers were processed by defining three dictionaries - LLMs name, licence, and application. 11,051 sentences were extracted through dictionary lookup, and the dataset was constructed through manual review of the final selection of 129 sentences that have a link between the name and the licence, and 106 sentences that have a link between the model name and the application.
Towards User-Focused Research in Training Data Attribution for Human-Centered Explainable AI
2024-09-25
Elisa Nguyen, Johannes Bertram, Evgenii Kortukov, Jean Y. Song, Seong Joon Oh
While Explainable AI (XAI) aims to make AI understandable and useful to humans, it has been criticised for relying too much on formalism and solutionism, focusing more on mathematical soundness than user needs. We propose an alternative to this bottom-up approach inspired by design thinking: the XAI research community should adopt a top-down, user-focused perspective to ensure user relevance. We illustrate this with a relatively young subfield of XAI, Training Data Attribution (TDA). With the surge in TDA research and growing competition, the field risks repeating the same patterns of solutionism. We conducted a needfinding study with a diverse group of AI practitioners to identify potential user needs related to TDA. Through interviews (N=10) and a systematic survey (N=31), we uncovered new TDA tasks that are currently largely overlooked. We invite the TDA and XAI communities to consider these novel tasks and improve the user relevance of their research outcomes.
Computational electron-phonon superconductivity: from theoretical physics to material science
2024-09-25
Shiya Chen, Feng Zheng, Zhen Zhang, Shunqing Wu, Kai-Ming Ho, Vladimir Antropov, Yang Sun
The search for room-temperature superconductors is a major challenge in modern physics. The discovery of copper-oxide superconductors in 1986 brought hope but also revealed complex mechanisms that are difficult to analyze and compute. In contrast, the traditional electron-phonon coupling (EPC) mechanism facilitated the practical realization of superconductivity in metallic hydrogen. Since 2015, the discovery of new hydrogen compounds has shown that EPC can enable room-temperature superconductivity under high pressures, driving extensive research. Advances in computational capabilities, especially exascale computing, now allow for the exploration of millions of materials. This paper reviews newly predicted superconducting systems in 2023-2024, focusing on hydrides, boron-carbon systems, and compounds with nitrogen, carbon, and pure metals. Although many computationally predicted high-Tc superconductors were not experimentally confirmed, some low-temperature superconductors were successfully synthesized. This paper provides a review of these developments and future research directions.
AI-assisted Gaze Detection for Proctoring Online Exams
2024-09-25
Yong-Siang Shih, Zach Zhao, Chenhao Niu, Bruce Iberg, James Sharpnack, Mirza Basim Baig
For high-stakes online exams, it is important to detect potential rule violations to ensure the security of the test. In this study, we investigate the task of detecting whether test takers are looking away from the screen, as such behavior could be an indication that the test taker is consulting external resources. For asynchronous proctoring, the exam videos are recorded and reviewed by the proctors. However, when the length of the exam is long, it could be tedious for proctors to watch entire exam videos to determine the exact moments when test takers look away. We present an AI-assisted gaze detection system, which allows proctors to navigate between different video frames and discover video frames where the test taker is looking in similar directions. The system enables proctors to work more effectively to identify suspicious moments in videos. An evaluation framework is proposed to evaluate the system against human-only and ML-only proctoring, and a user study is conducted to gather feedback from proctors, aiming to demonstrate the effectiveness of the system.
The fast rise of the unusual Type IIL/IIb SN 2018ivc
2024-09-25
A. Reguitti, R. Dastidar, G. Pignata, K. Maeda, T. J. Moriya, H. Kuncarayakti, Ó. Rodríguez, M. Bersten, J. P. Anderson, P. Charalampopoulos, M. Fraser, M. Gromadzki, D. R. Young, S. Benetti, Y. -Z. Cai, N. Elias-Rosa, P. Lundqvist, R. Carini, S. P. Cosentino, L. Galbany, M. Gonzalez-Bañuelos, C. P. Gutiérrez, M. Kopsacheili, J. A. Pineda G., M. Ramirez
We present an analysis of the photometric and spectroscopic dataset of the Type II supernova (SN) 2018ivc in the nearby (10 Mpc) galaxy Messier 77. Thanks to the high cadence of the CHASE survey, we observed the SN rising very rapidly by nearly three magnitudes in five hours (or 18 mag d$^{-1}$). The $r$-band light curve presents four distinct phases: the maximum light is reached in just one day, then a first, rapid linear decline precedes a short-duration plateau. Finally, a long, slower linear decline lasted for one year. Following a radio rebrightening, we detected SN 2018ivc four years after the explosion. The early spectra show a blue, nearly featureless continuum, but the spectra evolve rapidly: after about 10 days a prominent H$\alpha$ line starts to emerge, with a peculiar profile, but the spectra are heavily contaminated by emission lines from the host galaxy. He I lines, namely $\lambda\lambda$5876,7065, are also strong. On top of the former, a strong absorption from the Na I doublet is visible, indicative of a non-negligible internal reddening. From its equivalent width, we derive a lower limit on the host reddening of $A_V\simeq1.5$ mag, while from the Balmer decrement and a match of the $B-V$ colour curve of SN 2018ivc to that of the comparison objects, a host reddening of $A_V\simeq3.0$ mag is obtained. The spectra are similar to those of SNe II, but with strong He lines. Given the peculiar light curve and spectral features, we suggest SN 2018ivc could be a transitional object between the Type IIL and Type IIb SNe classes. In addition, we found signs of interaction with circumstellar medium in the light curve, making SN 2018ivc also an interacting event. Finally, we modelled the early multi-band light curves and photospheric velocity of SN 2018ivc to estimate the explosion and CSM physical parameters.
Searching for GEMS: TOI-6383Ab, a giant planet transiting an M3-dwarf star in a binary system
2024-09-25
Lia Marta Bernabò, Shubham Kanodia, Caleb I. Canas, William D. Cochran, Szilárd Csizmadia, Suvrath Mahadevan, Gudhmundur Stefánsson, Arvind F. Gupta, Andrew Monson, Henry A. Kobulnicky, Alexander K. Larsen, Ethan G. Cotter, Alexina Birkholz, Tera N. Swaby, Gregory Zeimann, Chad F. Bender, Scott A. Diddams, Jessica E. Libby-Roberts, Andrea S. J. Lin, Joe P. Ninan, Heike Rauer, Varghese Reji, Paul Robertson, Arpita Roy, Christian Schwab
We report on the discovery of a transiting giant planet around the 3500 K M3-dwarf star TOI-6383A located 172 pc from Earth. It was detected by the Transiting Exoplanet Survey Satellite (TESS) and confirmed by a combination of ground-based follow-up photometry and precise radial velocity measurements. This planet has an orbital period of $\sim$1.791 days, mass of 1.040$\pm$0.094 $M_J$ and a radius of 1d.008$^{+0.036}_{-0.033} ~R_J$, resulting in a mean bulk density of 1.26$^{+0.18}_{-0.17}$ g cm$^{-3}$. TOI-6383A has an M-dwarf companion star, TOI-6383B, which has a stellar effective temperature $T_{eff}$ $\sim$ 3100 K and a projected orbital separation of 3100 AU. TOI-6383A is a low-mass dwarf star hosting a giant planet and is an intriguing object for planetary evolution studies due to its high planet-to-star mass ratio. This discovery is part of the \textit{Searching for Giant Exoplanets around M-dwarf Stars (GEMS)} Survey, intending to provide robust and accurate estimates of the occurrence of GEMS and the statistics on their physical and orbital parameters. This paper presents an interesting addition to the small number of confirmed GEMS, particularly notable since its formation necessitates massive, ust-rich protoplanetary discs and high accretion efficiency ($>$ 10\%).
When operating in human environments, robots need to handle complex tasks while both adhering to social norms and accommodating individual preferences. For instance, based on common sense knowledge, a household robot can predict that it should avoid vacuuming during a social gathering, but it may still be uncertain whether it should vacuum before or after having guests. In such cases, integrating common-sense knowledge with human preferences, often conveyed through human explanations, is fundamental yet a challenge for existing systems. In this paper, we introduce GRACE, a novel approach addressing this while generating socially appropriate robot actions. GRACE leverages common sense knowledge from Large Language Models (LLMs), and it integrates this knowledge with human explanations through a generative network architecture. The bidirectional structure of GRACE enables robots to refine and enhance LLM predictions by utilizing human explanations and makes robots capable of generating such explanations for human-specified actions. Our experimental evaluations show that integrating human explanations boosts GRACE's performance, where it outperforms several baselines and provides sensible explanations.
Automating Traffic Model Enhancement with AI Research Agent
2024-09-25
Xusen Guo, Xinxi Yang, Mingxing Peng, Hongliang Lu, Meixin Zhu, Hai Yang
Developing efficient traffic models is essential for optimizing transportation systems, yet current approaches remain time-intensive and susceptible to human errors due to their reliance on manual processes. Traditional workflows involve exhaustive literature reviews, formula optimization, and iterative testing, leading to inefficiencies in research. In response, we introduce the Traffic Research Agent (TR-Agent), an AI-driven system designed to autonomously develop and refine traffic models through an iterative, closed-loop process. Specifically, we divide the research pipeline into four key stages: idea generation, theory formulation, theory evaluation, and iterative optimization; and construct TR-Agent with four corresponding modules: Idea Generator, Code Generator, Evaluator, and Analyzer. Working in synergy, these modules retrieve knowledge from external resources, generate novel ideas, implement and debug models, and finally assess them on the evaluation datasets. Furthermore, the system continuously refines these models based on iterative feedback, enhancing research efficiency and model performance. Experimental results demonstrate that TR-Agent achieves significant performance improvements across multiple traffic models, including the Intelligent Driver Model (IDM) for car following, the MOBIL lane-changing model, and the Lighthill-Whitham-Richards (LWR) traffic flow model. Additionally, TR-Agent provides detailed explanations for its optimizations, allowing researchers to verify and build upon its improvements easily. This flexibility makes the framework a powerful tool for researchers in transportation and beyond. To further support research and collaboration, we have open-sourced both the code and data used in our experiments, facilitating broader access and enabling continued advancements in the field.
Relative distances and peculiar velicities of 140 groups and clusters of galaxies at low redshifts: the Hubble diagram
2024-09-25
Flera G. Kopylova, Alexander I. Kopylov
To determine the relative distances and peculiar velocities of 140 groups and clusters of galaxies at low redshifts ($z$ < 0.12), we used the fundamental plane (FP) of early-type galaxies (from the Sloan Digital Sky Survey (SDSS) data). We constructed the Hubble diagram for the relative distances of galaxy groups/clusters versus their radial velocities in the cosmic microwave background (CMB) reference frame in the flat $\Lambda$ cold dark matter ($\Lambda$CDM) model ($\Omega_m=0.3$, $H_0=70$~km~s$^{-1}$ Mpc$^{-1}$). We have found that the standard logarithmic deviation for groups and clusters of galaxies on the Hubble diagram (minus peculiar velocities) is $\pm0.0173$ ($N$ = 140), which corresponds to a deviation of $70\pm2.8$~km~s$^{-1}$ Mpc$^{-1}$ in the Hubble constant. For a sample of galaxy systems ($N$ = 63), the X-ray luminosity of which is in an interval of (0.151--4)~$\times 10^{44}$~erg/s, this quantity turned out to be $70\pm2.1$~km~s$^{-1}$ Mpc$^{-1}$. The root-mean-square deviations of peculiar velocities with quadratic accounting for errors are $^{1/2}$ = $714\pm7$~km/s and $600\pm7$~km/s, respectively. For five large superclusters of galaxies from the SDSS region, the average peculiar velocity relaive to the CMB reference frame is $+240\pm250$~km/s. We detected no outflow of galaxy systems from the void (Giant Void, $\alpha \approx 13^h, \delta \approx 40^\circ, z \approx 0.107$) formed by groups and clusters of galaxies.
The Role of Language Models in Modern Healthcare: A Comprehensive Review
2024-09-25
Amna Khalid, Ayma Khalid, Umar Khalid
The application of large language models (LLMs) in healthcare has gained significant attention due to their ability to process complex medical data and provide insights for clinical decision-making. These models have demonstrated substantial capabilities in understanding and generating natural language, which is crucial for medical documentation, diagnostics, and patient interaction. This review examines the trajectory of language models from their early stages to the current state-of-the-art LLMs, highlighting their strengths in healthcare applications and discussing challenges such as data privacy, bias, and ethical considerations. The potential of LLMs to enhance healthcare delivery is explored, alongside the necessary steps to ensure their ethical and effective integration into medical practice.
Measurements and System Identification for the Characterization of Smooth Muscle Cell Dynamics
2024-09-25
Dilan Ozturk, Pepijn Saraber, Kevin Bielawski, Alessandro Giudici, Leon Schurgers, Koen Reesink, Maarten Schoukens
Biological tissue integrity is actively maintained by cells. It is essential to comprehend how cells accomplish this in order to stage tissue diseases. However, addressing the complexity of a cell's system of interrelated mechanisms poses a challenge. This necessitates a well-structured identification framework and an effective integration of measurements. Here we introduce the use of state-of-the-art frequency-domain system identification techniques combined with an indentation measurement platform to analyze the underlying mechanisms from the perspective of control system theory. The ultimate goal is to explore how mechanical and biological factors are related in induced Pluripotent Stem Cell-derived vascular smooth muscle cells. We study on the frequency-domain analysis for the investigation and characterization of cellular dynamics of smooth muscle cells from the measured data. The measurement model in this study exploits the availability of human tissue and samples, enabling fundamental investigations of vascular tissue disease. This approach using human cell lines holds significant potential to decrease the necessity for animal-based safety and efficacy studies. The focus of this review is to investigate the cellular dynamics underlying the myogenic response and to demonstrate the practicability of employing a nano-indentation measurement setup for the broadband frequency-domain characterization of induced Pluripotent Stem Cell-derived vascular smooth muscle cells.
2024-09-25
Purin Sukpanichnant, Anna Rapberger, Francesca Toni
Peer review is an essential process to determine the quality of papers submitted to scientific conferences or journals. However, it is subjective and prone to biases. Several studies have been conducted to apply techniques from NLP to support peer review, but they are based on black-box techniques and their outputs are difficult to interpret and trust. In this paper, we propose a novel pipeline to support and understand the reviewing and decision-making processes of peer review: the PeerArg system combining LLMs with methods from knowledge representation. PeerArg takes in input a set of reviews for a paper and outputs the paper acceptance prediction. We evaluate the performance of the PeerArg pipeline on three different datasets, in comparison with a novel end-2-end LLM that uses few-shot learning to predict paper acceptance given reviews. The results indicate that the end-2-end LLM is capable of predicting paper acceptance from reviews, but a variant of the PeerArg pipeline outperforms this LLM.
Scalable Ensemble Diversification for OOD Generalization and Detection
2024-09-25
Alexander Rubinstein, Luca Scimeca, Damien Teney, Seong Joon Oh
Training a diverse ensemble of models has several practical applications such as providing candidates for model selection with better out-of-distribution (OOD) generalization, and enabling the detection of OOD samples via Bayesian principles. An existing approach to diverse ensemble training encourages the models to disagree on provided OOD samples. However, the approach is computationally expensive and it requires well-separated ID and OOD examples, such that it has only been demonstrated in small-scale settings. $\textbf{Method.}$ This work presents a method for Scalable Ensemble Diversification (SED) applicable to large-scale settings (e.g. ImageNet) that does not require OOD samples. Instead, SED identifies hard training samples on the fly and encourages the ensemble members to disagree on these. To improve scaling, we show how to avoid the expensive computations in existing methods of exhaustive pairwise disagreements across models. $\textbf{Results.}$ We evaluate the benefits of diversification with experiments on ImageNet. First, for OOD generalization, we observe large benefits from the diversification in multiple settings including output-space (classical) ensembles and weight-space ensembles (model soups). Second, for OOD detection, we turn the diversity of ensemble hypotheses into a novel uncertainty score estimator that surpasses a large number of OOD detection baselines. Code is available here: https://github.com/AlexanderRubinstein/diverse-universe-public.
Spacewalker: Traversing Representation Spaces for Fast Interactive Exploration and Annotation of Unstructured Data
2024-09-25
Lukas Heine, Fabian Hörst, Jana Fragemann, Gijs Luijten, Miriam Balzer, Jan Egger, Fin Bahnsen, M. Saquib Sarfraz, Jens Kleesiek, Constantin Seibold
Unstructured data in industries such as healthcare, finance, and manufacturing presents significant challenges for efficient analysis and decision making. Detecting patterns within this data and understanding their impact is critical but complex without the right tools. Traditionally, these tasks relied on the expertise of data analysts or labor-intensive manual reviews. In response, we introduce Spacewalker, an interactive tool designed to explore and annotate data across multiple modalities. Spacewalker allows users to extract data representations and visualize them in low-dimensional spaces, enabling the detection of semantic similarities. Through extensive user studies, we assess Spacewalker's effectiveness in data annotation and integrity verification. Results show that the tool's ability to traverse latent spaces and perform multi-modal queries significantly enhances the user's capacity to quickly identify relevant data. Moreover, Spacewalker allows for annotation speed-ups far superior to conventional methods, making it a promising tool for efficiently navigating unstructured data and improving decision making processes. The code of this work is open-source and can be found at: https://github.com/code-lukas/Spacewalker
Lectures on scattering theory in partial-wave amplitudes
2024-09-25
J. A. Oller
These lectures treat scattering theory from a non-perturbative point of view. The course begins with a review of formal aspects in scattering theory, discussing the in/out states and the $S$ matrix that connects them. Unitarity relations, phase space, and the Lippmann-Schwinger equation for the $T$ matrix are discussed. The calculation of cross sections, the optical theorem and Boltzmann $H$-theorem from unitarity of the $S$ matrix are also explained. Special emphasis is given to expansions in partial-wave amplitudes of two-body scattering amplitudes, both for massive and massless particles, and to unitarity in partial-wave amplitudes. In this way, partial-wave expansions in terms of $\ell SJ$ states, and using helicity states with definite total angular momentum $J$ are discussed. Crossing symmetry is also explained, and connected to crossed channels, analyticity and crossed-channel cuts in partial-wave amplitudes. Different non-perturbative techniques and general parameterizations are developed for partial-wave amplitudes that satisfy unitarity and are consistent with the analyticity properties that they must have. Then, the $N/D$ method and additional solutions generated by adding CDD poles are covered in detail. The change to different non-physical Riemann sheets and the search for resonant poles is discussed as well. A differentiation is made between dynamically generated resonances by the degrees of freedom explicitly accounted for versus pre-existing resonances derived from short-distance dynamics. We exemplify it with the cases of the $\sigma/f_0(500)$ and the $\rho(770)$ resonances in $\pi\pi$ scattering. The reader is also introduced to final-state interactions, Watson's theorem, and general parameterizations to take them into account. It ends with an appendix dedicated to the Sugawara-Kanazawa theorem regarding the number of subtractions in dispersion relations.
World Model-based Perception for Visual Legged Locomotion
2024-09-25
Hang Lai, Jiahang Cao, Jiafeng Xu, Hongtao Wu, Yunfeng Lin, Tao Kong, Yong Yu, Weinan Zhang
Legged locomotion over various terrains is challenging and requires precise perception of the robot and its surroundings from both proprioception and vision. However, learning directly from high-dimensional visual input is often data-inefficient and intricate. To address this issue, traditional methods attempt to learn a teacher policy with access to privileged information first and then learn a student policy to imitate the teacher's behavior with visual input. Despite some progress, this imitation framework prevents the student policy from achieving optimal performance due to the information gap between inputs. Furthermore, the learning process is unnatural since animals intuitively learn to traverse different terrains based on their understanding of the world without privileged knowledge. Inspired by this natural ability, we propose a simple yet effective method, World Model-based Perception (WMP), which builds a world model of the environment and learns a policy based on the world model. We illustrate that though completely trained in simulation, the world model can make accurate predictions of real-world trajectories, thus providing informative signals for the policy controller. Extensive simulated and real-world experiments demonstrate that WMP outperforms state-of-the-art baselines in traversability and robustness. Videos and Code are available at: https://wmp-loco.github.io/.
Interface engineering of van der Waals heterostructures towards energy-efficient quantum devices operating at room temperature
2024-09-25
Manh-Ha Doan, Peter Bøggild
Quantum devices, which rely on quantum mechanical effects for their operation, may offer advantages, such as reduced dimensions, increased speed, and energy efficiency, compared to conventional devices. However, quantum phenomena are typically observed only at cryogenic temperatures, which limits their practical applications. Two-dimensional materials and their van der Waals (vdW) heterostructures provide a promising platform for high-temperature quantum devices owing to their strong Coulomb interactions and/or spin-orbit coupling. In this review, we summarise recent research on emergent quantum phenomena in vdW heterostructures based on interlayer tunnelling and the coupling of charged particles and spins, including negative differential resistance, Josephson tunnelling, exciton condensation, and topological superconductivity. The natural homojunction within vdW layered materials offers clean interfaces and perfectly aligned structures for enhanced interlayer coupling. Twisted bilayers with small angles may also give rise to novel quantum effects. In addition, we highlight several proposed structures for achieving high-temperature Majorana zero modes, which are critical elements of topological quantum computing. This review is helpful for researchers working on interface engineering of vdW heterostructures for energy-efficient quantum devices.
SDCL: Students Discrepancy-Informed Correction Learning for Semi-supervised Medical Image Segmentation
2024-09-25
Bentao Song, Qingfeng Wang
Semi-supervised medical image segmentation (SSMIS) has been demonstrated the potential to mitigate the issue of limited medical labeled data. However, confirmation and cognitive biases may affect the prevalent teacher-student based SSMIS methods due to erroneous pseudo-labels. To tackle this challenge, we improve the mean teacher approach and propose the Students Discrepancy-Informed Correction Learning (SDCL) framework that includes two students and one non-trainable teacher, which utilizes the segmentation difference between the two students to guide the self-correcting learning. The essence of SDCL is to identify the areas of segmentation discrepancy as the potential bias areas, and then encourage the model to review the correct cognition and rectify their own biases in these areas. To facilitate the bias correction learning with continuous review and rectification, two correction loss functions are employed to minimize the correct segmentation voxel distance and maximize the erroneous segmentation voxel entropy. We conducted experiments on three public medical image datasets: two 3D datasets (CT and MRI) and one 2D dataset (MRI). The results show that our SDCL surpasses the current State-of-the-Art (SOTA) methods by 2.57\%, 3.04\%, and 2.34\% in the Dice score on the Pancreas, LA, and ACDC datasets, respectively. In addition, the accuracy of our method is very close to the fully supervised method on the ACDC dataset, and even exceeds the fully supervised method on the Pancreas and LA dataset. (Code available at \url{https://github.com/pascalcpp/SDCL}).
Theory and Atomistic Simulation of Electrodeposition
2024-09-25
Shayantan Chaudhuri, Reinhard J. Maurer
Electrodeposition is a fundamental process in electrochemistry, and has applications in numerous industries, such as corrosion protection, decorative finishing, energy storage, catalysis, and electronics. While there is a long history of using electrodeposition, its application for controlled nanostructure growth is limited. The establishment of an atomic-scale understanding of the electrodeposition process and dynamics is crucial to enable the controlled fabrication of metal nanoparticles and other nanostructures. Significant advancements in molecular simulation capabilities and the electronic structure theory of electrified solid-liquid interfaces bring theory closer to realistic applications, but a gap remains between realistic applications, theoretical understanding of dynamics, and atomistic simulation. In this review we briefly summarize the current state-of-the-art computational techniques available for the simulation of electrodeposition and electrochemical growth on surfaces, and identify the remaining open challenges.
Large language models (LLMs) have achieved remarkable advancements in natural language processing, showcasing exceptional performance across various tasks. However, the expensive memory and computational requirements present significant challenges for their practical deployment. Low-bit quantization has emerged as a critical approach to mitigate these challenges by reducing the bit-width of model parameters, activations, and gradients, thus decreasing memory usage and computational demands. This paper presents a comprehensive survey of low-bit quantization methods tailored for LLMs, covering the fundamental principles, system implementations, and algorithmic strategies. An overview of basic concepts and new data formats specific to low-bit LLMs is first introduced, followed by a review of frameworks and systems that facilitate low-bit LLMs across various hardware platforms. Then, we categorize and analyze techniques and toolkits for efficient low-bit training and inference of LLMs. Finally, we conclude with a discussion of future trends and potential advancements of low-bit LLMs. Our systematic overview from basic, system, and algorithm perspectives can offer valuable insights and guidelines for future works to enhance the efficiency and applicability of LLMs through low-bit quantization.
Erase then Rectify: A Training-Free Parameter Editing Approach for Cost-Effective Graph Unlearning
2024-09-25
Zhe-Rui Yang, Jindong Han, Chang-Dong Wang, Hao Liu
Graph unlearning, which aims to eliminate the influence of specific nodes, edges, or attributes from a trained Graph Neural Network (GNN), is essential in applications where privacy, bias, or data obsolescence is a concern. However, existing graph unlearning techniques often necessitate additional training on the remaining data, leading to significant computational costs, particularly with large-scale graphs. To address these challenges, we propose a two-stage training-free approach, Erase then Rectify (ETR), designed for efficient and scalable graph unlearning while preserving the model utility. Specifically, we first build a theoretical foundation showing that masking parameters critical for unlearned samples enables effective unlearning. Building on this insight, the Erase stage strategically edits model parameters to eliminate the impact of unlearned samples and their propagated influence on intercorrelated nodes. To further ensure the GNN's utility, the Rectify stage devises a gradient approximation method to estimate the model's gradient on the remaining dataset, which is then used to enhance model performance. Overall, ETR achieves graph unlearning without additional training or full training data access, significantly reducing computational overhead and preserving data privacy. Extensive experiments on seven public datasets demonstrate the consistent superiority of ETR in model utility, unlearning efficiency, and unlearning effectiveness, establishing it as a promising solution for real-world graph unlearning challenges.
GraphLoRA: Structure-Aware Contrastive Low-Rank Adaptation for Cross-Graph Transfer Learning
2024-09-25
Zhe-Rui Yang, Jindong Han, Chang-Dong Wang, Hao Liu
Graph Neural Networks (GNNs) have demonstrated remarkable proficiency in handling a range of graph analytical tasks across various domains, such as e-commerce and social networks. Despite their versatility, GNNs face significant challenges in transferability, limiting their utility in real-world applications. Existing research in GNN transfer learning overlooks discrepancies in distribution among various graph datasets, facing challenges when transferring across different distributions. How to effectively adopt a well-trained GNN to new graphs with varying feature and structural distributions remains an under-explored problem. Taking inspiration from the success of Low-Rank Adaptation (LoRA) in adapting large language models to various domains, we propose GraphLoRA, an effective and parameter-efficient method for transferring well-trained GNNs to diverse graph domains. Specifically, we first propose a Structure-aware Maximum Mean Discrepancy (SMMD) to align divergent node feature distributions across source and target graphs. Moreover, we introduce low-rank adaptation by injecting a small trainable GNN alongside the pre-trained one, effectively bridging structural distribution gaps while mitigating the catastrophic forgetting. Additionally, a structure-aware regularization objective is proposed to enhance the adaptability of the pre-trained GNN to target graph with scarce supervision labels. Extensive experiments on six real-world datasets demonstrate the effectiveness of GraphLoRA against eleven baselines by tuning only 20% of parameters, even across disparate graph domains. The code is available at https://anonymous.4open.science/r/GraphLoRA.
The ESO SupJup Survey III: Confirmation of 13CO in YSES 1 b and Atmospheric Detection of YSES 1 c with CRIRES+
2024-09-25
Yapeng Zhang, Darío González Picos, Sam de Regt, Ignas A. G. Snellen, Siddharth Gandhi, Christian Ginski, Aurora Y. Kesseli, Rico Landman, Paul Mollière, Evert Nasedkin, Alejandro Sánchez-López, Tomas Stolker, Julie Inglis, Heather A. Knutson, Dimitri Mawet, Nicole Wallack, Jerry W. Xuan
High-resolution spectroscopic characterization of young super-Jovian planets enables precise constraints on elemental and isotopic abundances of their atmospheres. As part of the ESO SupJup Survey, we present high-resolution spectral observations of two wide-orbit super-Jupiters in YSES 1 (or TYC 8998-760-1) using the upgraded VLT/CRIRES+ (R~100,000) in K-band. We carry out free atmospheric retrieval analyses to constrain chemical and isotopic abundances, temperature structures, rotation velocities, and radial velocities. We confirm the previous detection of 13CO in YSES 1 b at a higher significance of 12.6{\sigma}, but point to a higher 12CO/13CO ratio of 88+/-13 (1{\sigma} confidence interval), consistent with the primary's isotope ratio 66+/-5. We retrieve a solar-like composition in YSES 1 b with a C/O=0.57+/-0.01, indicating a formation via gravitational instability or core accretion beyond the CO iceline. Additionally, the observations lead to detections of H2O and CO in the outer planet YSES 1 c at 7.3{\sigma} and 5.7{\sigma}, respectively. We constrain the atmospheric C/O ratio of YSES 1 c to be either solar or subsolar (C/O=0.36+/-0.15), indicating the accretion of oxygen-rich solids. The two companions have distinct vsini, 5.34+/-0.14 km/s for YSES 1 b and 11.3+/-2.1 km/s for YSES 1 c, despite their similar natal environments. This may indicate different spin axis inclinations or effective magnetic braking by the long-lived circumplanetary disk around YSES 1 b. YSES 1 represents an intriguing system for comparative studies of super-Jovian companions and linking present atmospheres to formation histories.
Claim-Guided Textual Backdoor Attack for Practical Applications
2024-09-25
Minkyoo Song, Hanna Kim, Jaehan Kim, Youngjin Jin, Seungwon Shin
Recent advances in natural language processing and the increased use of large language models have exposed new security vulnerabilities, such as backdoor attacks. Previous backdoor attacks require input manipulation after model distribution to activate the backdoor, posing limitations in real-world applicability. Addressing this gap, we introduce a novel Claim-Guided Backdoor Attack (CGBA), which eliminates the need for such manipulations by utilizing inherent textual claims as triggers. CGBA leverages claim extraction, clustering, and targeted training to trick models to misbehave on targeted claims without affecting their performance on clean data. CGBA demonstrates its effectiveness and stealthiness across various datasets and models, significantly enhancing the feasibility of practical backdoor attacks. Our code and data will be available at https://github.com/PaperCGBA/CGBA.
Supernova Remnant Candidates Discovered by the SARAO MeerKAT Galactic Plane Survey
2024-09-25
L. D. Anderson, F. Camilo, Timothy Faerber, M. Bietenholz, C. Bordiu, F. Bufano, J. O. Chibueze, W. D. Cotton, A. Ingallinera, S. Loru, A. Rigby, S. Riggi, M. A. Thompson, C. Trigilio, G. Umana, G. M. Williams
Context. Sensitive radio continuum data could remove the difference between the number of known supernova remnants (SNRs) in the Galaxy compared to that expected, but due to confusion in the Galactic plane, faint SNRs can be challenging to distinguish from brighter HII regions and filamentary radio emission. Aims. We wish to exploit new SARAO MeerKAT 1.3 GHz Galactic Plane Survey (SMGPS) radio continuum data, which covers $251\deg \le l \le 358\deg$ and $2\deg \le l \le 61\deg$ at $|b|\le 1.5\deg$, to search for SNR candidates in the Milky Way disk. Methods. We also use MIR data from the Spitzer GLIMPSE, Spitzer MIPSGAL, and WISE surveys to help identify SNR candidates. The identified SNR candidate are sources of extended radio continuum emission that lack MIR counterparts, are not known as HII regions in the WISE Catalog of Galactic HII Regions, and are not known previously as SNRs Results. We locate 237 new Galactic SNR candidates in the SMGPS data. We also identify and confirm the expected radio morphology for 201 objects listed in the literature as being SNRs and 130 previously-identified SNR candidates. The known and candidate SNRs have similar spatial distributions and angular sizes. Conclusions. The SMGPS data allowed us to identify a large population of SNR candidates that can be confirmed as true SNRs using radio polarization measurements or by deriving radio spectral indices. If the 237 candidates are confirmed as true SNRs, it would approximately double the number of known Galactic SNRs in the survey area, alleviating much of the difference between the known and expected populations.
Evaluating and Enhancing Large Language Models for Novelty Assessment in Scholarly Publications
2024-09-25
Ethan Lin, Zhiyuan Peng, Yi Fang
Recent studies have evaluated the creativity/novelty of large language models (LLMs) primarily from a semantic perspective, using benchmarks from cognitive science. However, accessing the novelty in scholarly publications is a largely unexplored area in evaluating LLMs. In this paper, we introduce a scholarly novelty benchmark (SchNovel) to evaluate LLMs' ability to assess novelty in scholarly papers. SchNovel consists of 15000 pairs of papers across six fields sampled from the arXiv dataset with publication dates spanning 2 to 10 years apart. In each pair, the more recently published paper is assumed to be more novel. Additionally, we propose RAG-Novelty, which simulates the review process taken by human reviewers by leveraging the retrieval of similar papers to assess novelty. Extensive experiments provide insights into the capabilities of different LLMs to assess novelty and demonstrate that RAG-Novelty outperforms recent baseline models.
Overview of the First Shared Task on Clinical Text Generation: RRG24 and "Discharge Me!"
2024-09-25
Justin Xu, Zhihong Chen, Andrew Johnston, Louis Blankemeier, Maya Varma, Jason Hom, William J. Collins, Ankit Modi, Robert Lloyd, Benjamin Hopkins, Curtis Langlotz, Jean-Benoit Delbrouck
Recent developments in natural language generation have tremendous implications for healthcare. For instance, state-of-the-art systems could automate the generation of sections in clinical reports to alleviate physician workload and streamline hospital documentation. To explore these applications, we present a shared task consisting of two subtasks: (1) Radiology Report Generation (RRG24) and (2) Discharge Summary Generation ("Discharge Me!"). RRG24 involves generating the 'Findings' and 'Impression' sections of radiology reports given chest X-rays. "Discharge Me!" involves generating the 'Brief Hospital Course' and 'Discharge Instructions' sections of discharge summaries for patients admitted through the emergency department. "Discharge Me!" submissions were subsequently reviewed by a team of clinicians. Both tasks emphasize the goal of reducing clinician burnout and repetitive workloads by generating documentation. We received 201 submissions from across 8 teams for RRG24, and 211 submissions from across 16 teams for "Discharge Me!".
A Review of Artificial Intelligence in Brachytherapy
2024-09-25
Jingchu Chen, Richard Qiu, Tonghe Wang, Shadab Momin, Xiaofeng Yang
Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in facilitating various aspects of brachytherapy. We analyze AI's role in making brachytherapy treatments more personalized, efficient, and effective. The applications are systematically categorized into seven categories: imaging, preplanning, treatment planning, applicator reconstruction, quality assurance, outcome prediction, and real-time monitoring. Each major category is further subdivided based on cancer type or specific tasks, with detailed summaries of models, data sizes, and results presented in corresponding tables. This review offers insights into the current advancements, challenges, and the impact of AI on treatment paradigms, encouraging further research to expand its clinical utility.
2024-09-25
Javier José Murgas Ibarra, Eirik Eik Svanes
We review recent results for heterotic moduli and the Hull--Strominger system. In particular, we discuss mathematical properties of the recently derived deformation operator $\bar D$ associated to the deformation complex of heterotic $SU(3)$ solutions. We review results on Serre duality, showing that the operator has a vanishing index, and discuss a notion of \v{C}ech cohomology and a particular instance of a Dolbeault theorem for $\bar D$. Specifically, the cohomology parametrising infinitesimal deformations is isomorphic to the first \v{C}ech cohomology of an associated cochain complex. This will be useful for future research, as it provides a more algebraic handle on the heterotic moduli problem, which is useful for understanding notions of stability, geometric invariants, and enumerative geometry for the Hull--Strominger system.
A Hierarchical Gradient Tracking Algorithm for Mitigating Subnet-Drift in Fog Learning Networks
2024-09-25
Evan Chen, Shiqiang Wang, Christopher G. Brinton
Federated learning (FL) encounters scalability challenges when implemented over fog networks that do not follow FL's conventional star topology architecture. Semi-decentralized FL (SD-FL) has proposed a solution for device-to-device (D2D) enabled networks that divides model cooperation into two stages: at the lower stage, D2D communications is employed for local model aggregations within subnetworks (subnets), while the upper stage handles device-server (DS) communications for global model aggregations. However, existing SD-FL schemes are based on gradient diversity assumptions that become performance bottlenecks as data distributions become more heterogeneous. In this work, we develop semi-decentralized gradient tracking (SD-GT), the first SD-FL methodology that removes the need for such assumptions by incorporating tracking terms into device updates for each communication layer. Our analytical characterization of SD-GT reveals upper bounds on convergence for non-convex, convex, and strongly-convex problems. We show how the bounds enable the development of an optimization algorithm that navigates the performance-efficiency trade-off by tuning subnet sampling rate and D2D rounds for each global training interval. Our subsequent numerical evaluations demonstrate that SD-GT obtains substantial improvements in trained model quality and communication cost relative to baselines in SD-FL and gradient tracking on several datasets.
Exploring the Use of ChatGPT for a Systematic Literature Review: a Design-Based Research
2024-09-25
Qian Huang, Qiyun Wang
ChatGPT has been used in several educational contexts,including learning, teaching and research. It also has potential to conduct the systematic literature review (SLR). However, there are limited empirical studies on how to use ChatGPT in conducting a SLR. Based on a SLR published,this study used ChatGPT to conduct a SLR of the same 33 papers in a design-based approach, to see what the differences are by comparing the reviews' results,and to answer: To what extent can ChatGPT conduct SLR? What strategies can human researchers utilize to structure prompts for ChatGPT that enhance the reliability and validity of a SLR? This study found that ChatGPT could conduct a SLR. It needs detailed and accurate prompts to analyze the literature. It also has limitations. Guiding principles are summarized from this study for researchers to follow when they need to conduct SLRs using ChatGPT.
Website visits can predict angler presence using machine learning
2024-09-25
Julia S. Schmid, Sean Simmons, Mark A. Lewis, Mark S. Poesch, Pouria Ramazi
Understanding and predicting recreational fishing activity is important for sustainable fisheries management. However, traditional methods of measuring fishing pressure, such as surveys, can be costly and limited in both time and spatial extent. Predictive models that relate fishing activity to environmental or economic factors typically rely on historical data, which often restricts their spatial applicability due to data scarcity. In this study, high-resolution angler-generated data from an online platform and easily accessible auxiliary data were tested to predict daily boat presence and aerial counts of boats at almost 200 lakes over five years in Ontario, Canada. Lake-information website visits alone enabled predicting daily angler boat presence with 78% accuracy. While incorporating additional environmental, socio-ecological, weather and angler-generated features into machine learning models did not remarkably improve prediction performance of boat presence, they were substantial for the prediction of boat counts. Models achieved an R2 of up to 0.77 at known lakes included in the model training, but they performed poorly for unknown lakes (R2 = 0.21). The results demonstrate the value of integrating angler-generated data from online platforms into predictive models and highlight the potential of machine learning models to enhance fisheries management.
Solar Active Regions Emergence Prediction Using Long Short-Term Memory Networks
2024-09-25
Spiridon Kasapis, Irina N. Kitiashvili, Alexander G. Kosovichev, John T. Stefan
We developed Long Short-Term Memory (LSTM) models to predict the formation of active regions (ARs) on the solar surface. Using the Doppler shift velocity, the continuum intensity, and the magnetic field observations from the Solar Dynamics Observatory (SDO) Helioseismic and Magnetic Imager (HMI), we have created time-series datasets of acoustic power and magnetic flux, which are used to train LSTM models on predicting continuum intensity, 12 hours in advance. These novel machine learning (ML) models are able to capture variations of the acoustic power density associated with upcoming magnetic flux emergence and continuum intensity decrease. Testing of the models' performance was done on data for 5 ARs, unseen from the models during training. Model 8, the best performing model trained, was able to make a successful prediction of emergence for all testing active regions in an experimental setting and three of them in an operational. The model predicted the emergence of AR11726, AR13165, and AR13179 respectively 10, 29, and 5 hours in advance, and variations of this model achieved average RMSE values of 0.11 for both active and quiet areas on the solar disc. This work sets the foundations for ML-aided prediction of solar ARs.
Spiders Based on Anxiety: How Reinforcement Learning Can Deliver Desired User Experience in Virtual Reality Personalized Arachnophobia Treatment
2024-09-25
Athar Mahmoudi-Nejad, Matthew Guzdial, Pierre Boulanger
The need to generate a spider to provoke a desired anxiety response arises in the context of personalized virtual reality exposure therapy (VRET), a treatment approach for arachnophobia. This treatment involves patients observing virtual spiders in order to become desensitized and decrease their phobia, which requires that the spiders elicit specific anxiety responses. However, VRET approaches tend to require therapists to hand-select the appropriate spider for each patient, which is a time-consuming process and takes significant technical knowledge and patient insight. While automated methods exist, they tend to employ rules-based approaches with minimal ability to adapt to specific users. To address these challenges, we present a framework for VRET utilizing procedural content generation (PCG) and reinforcement learning (RL), which automatically adapts a spider to elicit a desired anxiety response. We demonstrate the superior performance of this system compared to a more common rules-based VRET method.
6x2pt: Forecasting gains from joint weak lensing and galaxy clustering analyses with spectroscopic-photometric galaxy cross-correlations
2024-09-25
Harry Johnston, Nora Elisa Chisari, Shahab Joudaki, Robert Reischke, Benjamin Stölzner, Arthur Loureiro, Constance Mahony, Sandra Unruh, Angus H. Wright, Marika Asgari, Maciej Bilicki, Pierre Burger, Andrej Dvornik, Christos Georgiou, Benjamin Giblin, Catherine Heymans, Hendrik Hildebrandt, Benjamin Joachimi, Konrad Kuijken, Shun-Sheng Li, Laila Linke, Lucas Porth, HuanYuan Shan, Tilman Tröster, Jan Luca van den Busch, Maximilian von Wietersheim-Kramsta, Ziang Yan, Yun-Hao Zhang
We explore the enhanced self-calibration of photometric galaxy redshift distributions, $n(z)$, through the combination of up to six two-point functions. Our $\rm 3\times2pt$ configuration is comprised of photometric shear, spectroscopic galaxy clustering, and spectroscopic-photometric galaxy-galaxy lensing (GGL). We further include spectroscopic-photometric cross-clustering; photometric GGL; and photometric auto-clustering, using the photometric shear sample as density tracer. We perform simulated likelihood forecasts of the cosmological and nuisance parameter constraints for Stage-III- and Stage-IV-like surveys. For the Stage-III-like case, we employ realistic but perturbed redshift distributions, and distinguish between "coherent" shifting in one direction, versus more internal scattering and full-shape errors. For perfectly known $n(z)$, a $\rm 6\times2pt$ analysis gains $\sim40\%$ in Figure of Merit (FoM) in the $S_8\equiv\sigma_8\sqrt{\Omega_{\rm m}/0.3}$ and $\Omega_{\rm m}$ plane relative to the $\rm 3\times2pt$ analysis. If untreated, coherent and incoherent redshift errors lead to inaccurate inferences of $S_8$ and $\Omega_{\rm m}$, respectively. Employing bin-wise scalar shifts $\delta{z}_i$ in the tomographic mean redshifts reduces cosmological parameter biases, with a $\rm 6x2pt$ analysis constraining the shift parameters with $2-4$ times the precision of a photometric $\rm 3^{ph}\times2pt$ analysis. For the Stage-IV-like survey, a $\rm 6\times2pt$ analysis doubles the FoM($\sigma_8{-}\Omega_{\rm m}$) compared to any $\rm 3\times2pt$ or $\rm 3^{ph}\times2pt$ analysis, and is only $8\%$ less constraining than if the $n(z)$ were perfectly known. A Gaussian mixture model for the $n(z)$ reduces mean-redshift errors and preserves the $n(z)$ shape. It also yields the most accurate and precise cosmological constraints for any $N\rm\times2pt$ configuration given $n(z)$ biases.
Using photometry and proper motions from Pan-STARRS, DECaLS, and Gaia DR3, we detect a ~35 to 70 degree-long trailing stellar debris stream associated with the globular cluster NGC 288. We also detect a broad, ~40 to 80 degree-long leading tail that appears to be composed of at least two narrower, spatially offset, and kinematically distinct streams. Stream modeling predicts a very similar broad composite of streams and suggests that these narrower components are each made up of one or more generations of tidal tails, each formed during different orbits over the past few gigayears. The trajectory of the trailing tail is not well matched by a model stream evolved in a static Galactic potential, but is reasonably well matched by a stream modeled in a potential that incorporates a massive, infalling Large Magellanic Cloud. Tables of the most highly ranked stream star candidates are provided for ongoing and future spectroscopic surveys.
Energy-Efficient & Real-Time Computer Vision with Intelligent Skipping via Reconfigurable CMOS Image Sensors
2024-09-25
Md Abdullah-Al Kaiser, Sreetama Sarkar, Peter A. Beerel, Akhilesh R. Jaiswal, Gourav Datta
Current video-based computer vision (CV) applications typically suffer from high energy consumption due to reading and processing all pixels in a frame, regardless of their significance. While previous works have attempted to reduce this energy by skipping input patches or pixels and using feedback from the end task to guide the skipping algorithm, the skipping is not performed during the sensor read phase. As a result, these methods can not optimize the front-end sensor energy. Moreover, they may not be suitable for real-time applications due to the long latency of modern CV networks that are deployed in the back-end. To address this challenge, this paper presents a custom-designed reconfigurable CMOS image sensor (CIS) system that improves energy efficiency by selectively skipping uneventful regions or rows within a frame during the sensor's readout phase, and the subsequent analog-to-digital conversion (ADC) phase. A novel masking algorithm intelligently directs the skipping process in real-time, optimizing both the front-end sensor and back-end neural networks for applications including autonomous driving and augmented/virtual reality (AR/VR). Our system can also operate in standard mode without skipping, depending on application needs. We evaluate our hardware-algorithm co-design framework on object detection based on BDD100K and ImageNetVID, and gaze estimation based on OpenEDS, achieving up to 53% reduction in front-end sensor energy while maintaining state-of-the-art (SOTA) accuracy.
The Evolution of Emojis for Sharing Emotions: A Systematic Review of the HCI Literature
2024-09-25
Charles Chiang, Diego Gomez-Zara
With the prevalence of instant messaging and social media platforms, emojis have become important artifacts for expressing emotions and feelings in our daily lives. We ask how HCI researchers have examined the role and evolution of emojis in sharing emotions over the past 10 years. We conducted a systematic literature review of papers addressing emojis employed for emotion communication between users. After screening more than 1,000 articles, we identified 42 articles of studies analyzing ways and systems that enable users to share emotions with emojis. Two main themes described how these papers have (1) improved how users select the right emoji from an increasing emoji lexicon, and (2) employed emojis in new ways and digital materials to enhance communication. We also discovered an increasingly broad scope of functionality across appearance, medium, and affordance. We discuss and offer insights into potential opportunities and challenges emojis will bring for HCI research.
Bridging Superconductors with UN Development Goals: Perspectives and Applications
2024-09-25
Edimar A. S. Duran, Alfonso Pulgar, Rodolfo Izquierdo, Diana M. Koblischka, Anjela Koblischka-Veneva, Michael R. Koblischka, Rafael Zadorosny
Superconductors exhibit remarkable properties such as zero resistivity and diamagnetism at the boiling temperature of liquid hydrogen (20 K) and even aboven the boiling temperature of liquid nitrogen (77 K), making them promising candidates for various applications including electrical engines, energy generation, storage, and high-tech devices like single photon detectors. In this overview, we explore the correlation between ceramic superconductors and the United Nations (UN) Development Goals, emphasizing their potential impact on sustainable development. Through bibliometric analysis, we underscore the significance of ceramic superconductors in addressing global challenges outlined by the UN. Additionally, we discuss the application of supermagnets and second-generation tapes in healthcare systems, particularly in magnetic resonance imaging (MRI) devices for diagnostic imaging. Electric superconducting motors offer a clean alternative to highly polluting diesel engines in maritime transportation and superconducting wires/cables enable effective transport of energy on large scale as well as in industrial structures. Magnetic levitation technology holds promise for developing zero-emission public transportation systems, and magnetic separation with strong magnets will contribute to solve the microplastic pollution. The combination of superconductivity with the planned hydrogen economy further offers new possibilities to bring superconductivity to common applications. At the nanoscale, superconducting nanowire single photon detectors (SNSPDs) enable real-time monitoring of environmental health, exemplified by applications in plant physiology, and superconducting qubits provide the best-engineered structures for quantum computers. However it is still crordwork to works with superconductors UN develepoment goals.
Identification of Basins of Attraction in the Local Universe
2024-09-25
Aurelien Valade, Noam I. Libeskind, Daniel Pomarede, R. Brent Tully, Yehuda Hoffmann, Simon Pfeifer, Ehsan Kourkchi
Structure in the Universe is believed to have evolved out of quantum fluctuations seeded by inflation in the early Universe. These fluctuations lead to density perturbations that grow via gravitational instability into large cosmological structures. In the linear regime, the growth of structure is directly coupled to the velocity field since perturbations are amplified by attracting (and accelerating) matter. Surveys of galaxy redshifts and distances allow one to infer the underlying density and velocity fields. Here, assuming the LCDM standard model of cosmology and applying a Hamiltonian Monte-Carlo algorithm to the grouped Cosmicflows-4 (CF4) compilation of 38,000 groups of galaxies, the large scale structure of the Universe is reconstructed out to a redshift corresponding to about 30, 000 km/s. Our method provides a probabilistic assessment of the domains of gravitational potential minima: basins of attraction (BoA). Earlier Cosmicflows catalogs suggested the Milky Way Galaxy was associated with a BoA called Laniakea. Now with the newer CF4 data, there is a slight probabilistic preference for Laniakea to be part of the much larger Shapley BoA. The largest BoA recovered from the CF4 data is associated with the Sloan Great Wall with a volume within the sample of 15.5 10^6(Mpc/h)^3, which is more than twice the size of the second largest Shapley BoA.
Evaluation of Galaxy as a User-friendly Bioinformatics Tool for Enhancing Clinical Diagnostics in Genetics Laboratories
2024-09-25
Hadi Almohab, Ramzy Al-Othmany
Bioinformatics platforms have significantly changed clinical diagnostics by facilitating the analysis of genomic data, thereby advancing personalized medicine and improving patient care. This study examines the integration, usage patterns, challenges, and impact of the Galaxy platform within clinical diagnostics laboratories. We employed a convergent parallel mixed-methods design, collecting quantitative survey data and qualitative insights from structured interviews with fifteen participants across various clinical roles. The findings indicate a wide adoption of Galaxy, with participants expressing high satisfaction due to its user-friendly interface and notable improvements in workflow efficiency and diagnostic accuracy. Challenges such as data security and training needs were also identified, highlighting the platform's role in simplifying complex data analysis tasks. This study contributes to understanding the transformative potential of Galaxy in clinical practice and offers recommendations for optimizing its integration and functionality. These insights are crucial for advancing clinical diagnostics and enhancing patient outcomes.
Einstein-de Haas effect: a bridge linking mechanics, magnetism, and topology
2024-09-25
Xin Nie, Dao-Xin Yao
The Einstein-de Haas (EdH) effect is an interesting phenomenon linking mechanics and magnetism, in which changes in magnetization induce mechanical rotation. Despite being discovered more than a century ago, the EdH effect still shows crucial significance in modern science, particularly in the realms of spintronics and ultrafast magnetism. Recently, it has been predicted that the EdH effect can be realised in topological magnon systems, which undoubtedly brings even richer properties. In this perspective, we aim to review the recent progress of the EdH effect and discuss its developments in three key aspects: the microscopic mechanism of the EdH effect, the EdH effect of topological magnons, and the chiral phonon--magnon conversion. These discussions are poised to inspire further explorations in physics and promising applications in different areas.
An Hα view of galaxy build-up in the first 2 Gyr: luminosity functions at z~4-6.5 from NIRCam/grism spectroscopy
2024-09-25
Alba Covelo-Paz, Emma Giovinazzo, Pascal A. Oesch, Romain A. Meyer, Andrea Weibel, Gabriel Brammer, Yoshinobu Fudamoto, Josephine Kerutt, Jamie Lin, Jasleen Matharu, Rohan P. Naidu, Anna Velichko, Victoria Bollo, Rychard Bouwens, John Chisholm, Garth D. Illingworth, Ivan Kramarenko, Daniel Magee, Michael Maseda, Jorryt Matthee, Erica Nelson, Naveen Reddy, Daniel Schaerer, Mauro Stefanon, Mengyuan Xiao
The H{\alpha} nebular emission line is an optimal tracer for recent star formation in galaxies. With the advent of JWST, this line has recently become observable at z>3 for the first time. We present a catalog of 1013 H{\alpha} emitters at 3.73 obtained based purely on spectroscopic data, robustly tracing galaxy star formation rates (SFRs) beyond the peak of the cosmic star formation history. We compare our results with theoretical predictions from three different simulations and find good agreement at z~4-6. The UV LFs of this spectroscopically-confirmed sample are in good agreement with pre-JWST measurements obtained with photometrically-selected objects. Finally, we derive SFR functions and integrate these to compute the evolution of the cosmic star-formation rate densities across z~4-6, finding values in good agreement with recent UV estimates from Lyman-break galaxies, which imply a continuous decrease in SFR density by a factor of 3x over z~4 to z~6. Our work shows the power of NIRCam grism observations to efficiently provide new tests for early galaxy formation models based on emission line statistics.
X-ray multiple-beam (n-baem) dynamical diffraction theories, numerical methods to solve them and experimental verification by using the synchrotron X-rays
2024-09-25
Kouhei Okitsu
Behavior of X-rays diffracted in a perfect or quasi-perfect crystal can be described by the dynamical theory of X-ray diffraction. Study on the two-beam cases in which only transmitted and one reflected X-ray beams are strong has a history of one hundred years. However, the population of researchers who study on the multiple-beam cases (n-beam cases) in which more than two beams are simultaneously strong is small. The present author has derived the Takagi-Taupin (T-T) dynamical theory that can be applied to the n-beam cases, coded the computer programs to solve it and experimentally verified them by using the synchrotron X-rays. The equivalence between the Ewald-Laue (E-L) and the T-T dynamical theories described by the Fourier transform also for the n-beam cases is explicitly verified in the present paper. Further, the methods of the computer simulations and the experiments are also described. Furthermore, a hypothesis concerning the too large values of R-factor in protein crystallography is also described. This might be extremely important in protein crystallography in the future.
Optical Multilayer Thin Film Structure Inverse Design: From Optimization to Deep Learning
2024-09-25
Taigao Ma, Mingqian Ma, L. Jay Guo
Optical multilayer thin film structures have been widely used in numerous photonic domains and applications. The key component to enable these applications is the inverse design. Different from other photonic structures such as metasurface or waveguide, multilayer thin film is a one-dimensional structure, which deserves its own treatment for the design process. Optimization has always been the standard design algorithm for decades. Recent years have witnessed a rapid development of integrating different deep learning algorithms to tackle the inverse design problems. A natural question to ask is: how do these algorithms differ from each other? Why do we need to develop so many algorithms and what type of challenges do they solve? What is the state-of-the-art algorithm in this domain? Here, we review recent progress and provide a guide-tour through this research area, starting from traditional optimization to recent deep learning approaches. Challenges and future perspectives are also discussed.
When Sensitivity Bias Varies Across Subgroups: The Impact of Non-uniform Polarity in List Experiments
2024-09-25
Sophia Hatz, David Randahl
Survey researchers face the problem of sensitivity bias: since people are reluctant to reveal socially undesirable or otherwise risky traits, aggregate estimates of these traits will be biased. List experiments offer a solution by conferring respondents greater privacy. However, little is know about how list experiments fare when sensitivity bias varies across respondent subgroups. For example, a trait that is socially undesirable to one group may socially desirable in a second group, leading sensitivity bias to be negative in the first group, while it is positive in the second. Or a trait may be not sensitive in one group, leading sensitivity bias to be zero in one group and non-zero in another. We use Monte Carlo simulations to explore what happens when the polarity (sign) of sensitivity bias is non-uniform. We find that a general diagnostic test yields false positives and that commonly used estimators return biased estimates of the prevalence of the sensitive trait, coefficients of covariates, and sensitivity bias itself. The bias is worse when polarity runs in opposite directions across subgroups, and as the difference in subgroup sizes increases. Significantly, non-uniform polarity could explain why some list experiments appear to 'fail'. By defining and systematically investigating the problem of non-uniform polarity, we hope to save some studies from the file-drawer and provide some guidance for future research.
Traditional transistors based on complementary metal-oxide-semiconductor (CMOS) and metal-oxide-semiconductor field-effect transistors (MOSFETs) are facing significant limitations as device scaling reaches the limits of Moore's Law. These limitations include increased leakage currents, pronounced short-channel effects (SCEs), and quantum tunneling through the gate oxide, leading to higher power consumption and deviations from ideal behavior. Tunnel Field-Effect Transistors (TFETs) can overcome these challenges by utilizing quantum tunneling of charge carriers to switch between on and off states and achieve a subthreshold swing (SS) below 60 mV/decade. This allows for lower power consumption, continued scaling, and improved performance in low-power applications. This review focuses on the design and operation of TFETs, emphasizing the optimization of device performance through material selection and advanced simulation techniques. The discussion will specifically address the use of two-dimensional (2D) materials in TFET design and explore simulation methods ranging from multi-scale (MS) approaches to machine learning (ML)-driven optimization.
GOALS-JWST: Constraining the Emergence Timescale for Massive Star Clusters in NGC 3256
2024-09-24
Sean T. Linden, Thomas Lai, Aaron S. Evans, Lee Armus, Kirsten L. Larson, Jeffrey A. Rich, Vivian U, George C. Privon, Hanae Inami, Yiqing Song, Marina Bianchin, Thomas Bohn, Victorine A. Buiten, Maria Sanchez-Garcia, Justin Kader, Laura Lenkic, Anne M. Medling, Torsten Boeker, Tanio Diaz-Santos, Vassilis Charmandaris, Loreto Barcos-Munoz, Paul van der Werf, Sabrina Stierwalt, Susanne Aalto, Philip Appleton, Christopher C. Hayward, Justin H. Howell, Matthew A. Malkan, Joseph M. Mazzarella, Eric J. Murphy, Jason Surace
We present the results of a James Webb Space Telescope (JWST) NIRCam and NIRSpec investigation into the young massive star cluster (YMC) population of NGC 3256, the most cluster-rich luminous infrared galaxy (LIRG) in the Great Observatories All Sky LIRG Survey. We detect 3061 compact YMC candidates with a $S/N \geq 3$ at F150W, F200W, and F335M. Based on yggdrasil stellar population models, we identify 116/3061 sources with F150W - F200W $> 0.47$ and F200W - F355M $> -1.37$ colors suggesting they are young (t $\leq 5$ Myr), dusty ($A_{V} = 5 - 15$), and massive ($M_{\odot} > 10^{5}$). This increases the sample of dust-enshrouded YMCs detected in this system by an order of magnitude relative to previous HST studies. With NIRSpec IFU pointings centered on the northern and southern nucleus, we extract the Pa$\alpha$ and 3.3$\mu$m PAH equivalent widths for 8 bright and isolated YMCs. Variations in both the F200W - F335M color and 3.3$\mu$m PAH emission with the Pa$\alpha$ line strength suggest a rapid dust clearing ($< 3 - 4$ Myr) for the emerging YMCs in the nuclei of NGC 3256. Finally, with both the age and dust emission accurately measured we use yggdrasil to derive the color excess (E(B - V)) for all 8 YMCs. We demonstrate that YMCs with strong 3.3$\mu$m PAH emission (F200W - F335M $> 0$) correspond to sources with E(B - V) $> 3$, which are typically missed in UV-optical studies. This underscores the importance of deep near-infrared imaging for finding and characterizing these very young and dust-embedded sources.
NoTeeline: Supporting Real-Time Notetaking from Keypoints with Large Language Models
2024-09-24
Faria Huq, Abdus Samee, David Chuan-en Lin, Xiaodi Alice Tang, Jeffrey P. Bigham
Video has become a popular media form for information sharing and consumption. However, taking notes while watching a video requires significant time and effort. To address this, we propose a novel interactive system, NoTeeline, for taking real-time, personalized notes. NoTeeline lets users quickly jot down keypoints (micronotes), which are automatically expanded into full-fledged notes that capture the content of the user's micronotes and are consistent with the user's writing style. In a within-subjects study (N=12), we found that NoTeeline helps users create high-quality notes that capture the essence of their micronotes with a higher factual correctness (93.2%) while accurately reflecting their writing style. While using NoTeeline, participants experienced significantly reduced mental effort, captured satisfactory notes while writing 47% less text, and completed notetaking with 43.9% less time compared to a manual notetaking baseline.
We have conducted Q-band (30 GHz $-$ 50 GHz) line survey observations toward a carbon-chain emission peak in the Serpens South cluster-forming region with the extended Q-band (eQ) receiver installed on the Nobeyama 45 m radio telescope. Approximately 180 lines have been detected including tentative detection, and these lines are attributed to 52 molecules including isotopologues. It has been found that this position is rich in carbon-chain species as much as Cyanopolyyne Peak in Taurus Molecular Cloud-1 (TMC-1 CP), suggesting chemical youth. Not only carbon-chain species, but several complex organic molecules (CH$_3$OH, CH$_3$CHO, HCCCHO, CH$_3$CN, and tentatively C$_2$H$_3$CN) have also been detected, which is similar to the chemical complexity found in evolved prestellar cores. The HDCS/H$_2$CS ratio has been derived to be $11.3 \pm 0.5$ %, and this value is similar to the prestellar core L1544. The chemically young features that are similar to the less-dense starless core TMC-1 CP ($10^4$ cm$^{-3}$ $-$ $10^5$ cm$^{-3}$) and chemically evolved characters which resemble the dense prestellar core L1544 ($\sim 10^6$ cm$^{-3}$) mean that the clump including the observed position is a pre-cluster clump without any current star formation activity.
Proactive Schemes: A Survey of Adversarial Attacks for Social Good
2024-09-24
Vishal Asnani, Xi Yin, Xiaoming Liu
Adversarial attacks in computer vision exploit the vulnerabilities of machine learning models by introducing subtle perturbations to input data, often leading to incorrect predictions or classifications. These attacks have evolved in sophistication with the advent of deep learning, presenting significant challenges in critical applications, which can be harmful for society. However, there is also a rich line of research from a transformative perspective that leverages adversarial techniques for social good. Specifically, we examine the rise of proactive schemes-methods that encrypt input data using additional signals termed templates, to enhance the performance of deep learning models. By embedding these imperceptible templates into digital media, proactive schemes are applied across various applications, from simple image enhancements to complicated deep learning frameworks to aid performance, as compared to the passive schemes, which don't change the input data distribution for their framework. The survey delves into the methodologies behind these proactive schemes, the encryption and learning processes, and their application to modern computer vision and natural language processing applications. Additionally, it discusses the challenges, potential vulnerabilities, and future directions for proactive schemes, ultimately highlighting their potential to foster the responsible and secure advancement of deep learning technologies.
The accelerated expansion of the Internet of Things (IoT) has raised critical challenges associated with privacy, security, and data integrity, specifically in infrastructures such as smart cities or smart manufacturing. Blockchain technology provides immutable, scalable, and decentralized solutions to address these challenges, and integrating deep reinforcement learning (DRL) into the IoT environment offers enhanced adaptability and decision-making. This paper investigates the integration of blockchain and DRL to optimize mobile transmission and secure data exchange in IoT-assisted smart cities. Through the clustering and categorization of IoT application systems, the combination of DRL and blockchain is shown to enhance the performance of IoT networks by maintaining privacy and security. Based on the review of papers published between 2015 and 2024, we have classified the presented approaches and offered practical taxonomies, which provide researchers with critical perspectives and highlight potential areas for future exploration and research. Our investigation shows how combining blockchain's decentralized framework with DRL can address privacy and security issues, improve mobile transmission efficiency, and guarantee robust, privacy-preserving IoT systems. Additionally, we explore blockchain integration for DRL and outline the notable applications of DRL technology. By addressing the challenges of machine learning and blockchain integration, this study proposes novel perspectives for researchers and serves as a foundational exploration from an interdisciplinary standpoint.
Large Language Models(LLMs) have revolutionized various applications in natural language processing (NLP) by providing unprecedented text generation, translation, and comprehension capabilities. However, their widespread deployment has brought to light significant concerns regarding biases embedded within these models. This paper presents a comprehensive survey of biases in LLMs, aiming to provide an extensive review of the types, sources, impacts, and mitigation strategies related to these biases. We systematically categorize biases into several dimensions. Our survey synthesizes current research findings and discusses the implications of biases in real-world applications. Additionally, we critically assess existing bias mitigation techniques and propose future research directions to enhance fairness and equity in LLMs. This survey serves as a foundational resource for researchers, practitioners, and policymakers concerned with addressing and understanding biases in LLMs.
Lessons for Editors of AI Incidents from the AI Incident Database
2024-09-24
Kevin Paeth, Daniel Atherton, Nikiforos Pittaras, Heather Frase, Sean McGregor
As artificial intelligence (AI) systems become increasingly deployed across the world, they are also increasingly implicated in AI incidents - harm events to individuals and society. As a result, industry, civil society, and governments worldwide are developing best practices and regulations for monitoring and analyzing AI incidents. The AI Incident Database (AIID) is a project that catalogs AI incidents and supports further research by providing a platform to classify incidents for different operational and research-oriented goals. This study reviews the AIID's dataset of 750+ AI incidents and two independent taxonomies applied to these incidents to identify common challenges to indexing and analyzing AI incidents. We find that certain patterns of AI incidents present structural ambiguities that challenge incident databasing and explore how epistemic uncertainty in AI incident reporting is unavoidable. We therefore report mitigations to make incident processes more robust to uncertainty related to cause, extent of harm, severity, or technical details of implicated systems. With these findings, we discuss how to develop future AI incident reporting practices.
A Heavy Seed Black Hole Mass Function at High Redshift -- Prospects for LISA
2024-09-24
Joe McCaffrey, John Regan, Britton Smith, John Wise, Brian O'Shea, Michael Norman
The advent of new and near-future observatories probing the earliest epochs of the Universe has opened the opportunity to investigate the formation and growth of the first massive black holes (MBHs). Additionally, the use of high resolution cosmological simulations to investigate these high-redshift environments is needed to predict the dark matter halos in which these MBH seeds will form. We use the $\textit{Renaissance}$ simulations to analyse the formation and growth of so-called heavy seed black holes. Other past work has investigated the formation and growth of light (black hole) seeds with $\textit{Renaissance}$ and found that these black holes do not grow in the environments in which they reside. In this work we seed MBHs, in post-processing, and track accretion onto the MBHs as well as mergers with other MBHs at high-redshift. We show that the heavy seeds struggle to achieve high accretion rates with only the most massive black holes ($\gtrsim 10^5 \text{M}_\odot$) growing at close to the Eddington limit under optimistic conditions. Despite the lack of significant growth for these early MBHs, the signals from their merger events will be sufficiently strong (SNR $\sim 10^2$) to be probed by the next generation of gravitational wave observatories, such as $\textit{LISA}$. We predict that $\textit{LISA}$ will observe of the order of $10$ MBH merger events per year where the mergers occur at z $\gtrsim$ 10 or at least begin their early inspiral phase at z $\gtrsim$ 10.
Towards Representation Learning for Weighting Problems in Design-Based Causal Inference
2024-09-24
Oscar Clivio, Avi Feller, Chris Holmes
Reweighting a distribution to minimize a distance to a target distribution is a powerful and flexible strategy for estimating a wide range of causal effects, but can be challenging in practice because optimal weights typically depend on knowledge of the underlying data generating process. In this paper, we focus on design-based weights, which do not incorporate outcome information; prominent examples include prospective cohort studies, survey weighting, and the weighting portion of augmented weighting estimators. In such applications, we explore the central role of representation learning in finding desirable weights in practice. Unlike the common approach of assuming a well-specified representation, we highlight the error due to the choice of a representation and outline a general framework for finding suitable representations that minimize this error. Building on recent work that combines balancing weights and neural networks, we propose an end-to-end estimation procedure that learns a flexible representation, while retaining promising theoretical properties. We show that this approach is competitive in a range of common causal inference tasks.
2024-09-24
Karl Daningburg, A. E. Lovell, R. O'Shaughnessy
A wide variety of emulators have been developed for nuclear physics, particularly for use in quantifying and propagating parametric uncertainties to observables. Most of these methods have been used to emulate structure observables, such as energies, masses, or separation energies, or reaction observables, such as cross sections. Rarely, if ever, have event generators for theory models been emulated. Here, we describe one such novel emulator for the fission fragment decay code, $\texttt{CGMF}$, which calculates the emission of prompt neutrons and $\gamma$ rays from fission fragments. The emulator described in this work uses a combination of a noisy emission model and mixture density network to model the neutron and $\gamma$-ray multiplicities and energies. In this manuscript, we display the power of this type of emulator for not only modeling average prompt fission observables but also correlations between fission fragment initial conditions and these observables, using both neutron-induced and spontaneous fission reactions.
The current state of Advanced Persistent Threats (APT) attribution primarily relies on time-consuming manual processes. These include mapping incident artifacts onto threat attribution frameworks and employing expert reasoning to uncover the most likely responsible APT groups. This research aims to assist the threat analyst in the attribution process by presenting an attribution method named CAPTAIN (Comprehensive Advanced Persistent Threat AttrIbutioN). This novel APT attribution approach leverages the Tactics, Techniques, and Procedures (TTPs) employed by various APT groups in past attacks. CAPTAIN follows two significant development steps: baseline establishment and similarity measure for attack pattern matching. This method starts by maintaining a TTP database of APTs seen in past attacks as baseline behaviour of threat groups. The attribution process leverages the contextual information added by TTP sequences, which reflects the sequence of behaviours threat actors demonstrated during the attack on different kill-chain stages. Then, it compares the provided TTPs with established baseline to identify the most closely matching threat group. CAPTAIN introduces a novel similarity measure for APT group attack-pattern matching that calculates the similarity between TTP sequences. The proposed approach outperforms traditional similarity measures like Cosine, Euclidean, and Longest Common Subsequence (LCS) in performing attribution. Overall, CAPTAIN performs attribution with the precision of 61.36% (top-1) and 69.98% (top-2), surpassing the existing state-of-the-art attribution methods.
Strong solar flares and coronal mass ejections (CMEs) are prone to originate within and near active regions (ARs) with a high magnetic complexity. Therefore, to better understand the generation mechanism of flares and the resultant CME eruption and to gain insight into their stellar counterparts, it is crucial to reveal how solar flare-productive ARs are generated and developed. In this review, first, we summarize some general aspects of solar flares and key observational characteristics of such ARs. Then, we discuss a series of flux emergence simulations that were performed to elucidate the subsurface origins of their complexity and introduce state-of-the-art models that consider the effect of turbulent thermal convection. Future flare observations using SOLAR-C, a next-generation high-throughput extreme ultraviolet spectroscopy mission, are also discussed.
The advent of large integral field spectroscopic surveys has found that elliptical galaxies (EGs) can be classified into two classes: the fast rotators (whose kinematics are dominated by rotation) and the slow rotators (which exhibit slow or no rotation pattern). It is often suggested that while the slow rotators typically have boxy isophotal shapes, have a high $\alpha$-to-iron abundance ratio, and are quite massive, the fast rotators often exhibit the opposite properties (that is, having disky isophotes, lower $\alpha$-to-iron ratio, and of typical masses). Whether the EGs consist of two distinct populations (i.e., a dichotomy exists), remains an unsolved issue. To examine the existence of the dichotomy, we used a sample of 1,895 EGs from the SDSS-IV MaNGA survey, and measured robustly the stellar kinematics, isophotal shapes, and [Mg/Fe] ratio. We confirmed the previous finding that the bulk of the EGs are disky (65%) and fast rotators (67%), but found no evidence supporting a dichotomy, based on a principal component analysis. The different classes (boxy/disky and slow/fast rotators) of EGs occupy slightly different loci in the principal component space. This may explain the observed trends that led to the premature support of a dichotomy based on small samples of galaxies.
How can robot manipulation policies generalize to novel tasks involving unseen object types and new motions? In this paper, we provide a solution in terms of predicting motion information from web data through human video generation and conditioning a robot policy on the generated video. Instead of attempting to scale robot data collection which is expensive, we show how we can leverage video generation models trained on easily available web data, for enabling generalization. Our approach Gen2Act casts language-conditioned manipulation as zero-shot human video generation followed by execution with a single policy conditioned on the generated video. To train the policy, we use an order of magnitude less robot interaction data compared to what the video prediction model was trained on. Gen2Act doesn't require fine-tuning the video model at all and we directly use a pre-trained model for generating human videos. Our results on diverse real-world scenarios show how Gen2Act enables manipulating unseen object types and performing novel motions for tasks not present in the robot data. Videos are at https://homangab.github.io/gen2act/
Bound-preserving OEDG schemes for Aw-Rascle-Zhang traffic models on networks
2024-09-24
Wei Chen, Shumo Cui, Kailiang Wu, Tao Xiong
Physical solutions to the widely used Aw-Rascle-Zhang (ARZ) traffic model and the adapted pressure (AP) ARZ model should satisfy the positivity of density, the minimum and maximum principles with respect to the velocity $v$ and other Riemann invariants. Many numerical schemes suffer from instabilities caused by violating these bounds, and the only existing bound-preserving (BP) numerical scheme (for ARZ model) is random, only first-order accurate, and not strictly conservative. This paper introduces arbitrarily high-order provably BP DG schemes for these two models, preserving all the aforementioned bounds except the maximum principle of $v$, which has been rigorously proven to conflict with the consistency and conservation of numerical schemes. Although the maximum principle of $v$ is not directly enforced, we find that the strictly preserved maximum principle of another Riemann invariant $w$ actually enforces an alternative upper bound on $v$. At the core of this work, analyzing and rigorously proving the BP property is a particularly nontrivial task: the Lax-Friedrichs (LF) splitting property, usually expected for hyperbolic conservation laws and employed to construct BP schemes, does not hold for these two models. To overcome this challenge, we formulate a generalized version of the LF splitting property, and prove it via the geometric quasilinearization (GQL) approach [Kailiang Wu and Chi-Wang Shu, SIAM Review, 65: 1031-1073, 2023]. To suppress spurious oscillations in the DG solutions, we employ the oscillation-eliminating (OE) technique, recently proposed in [Manting Peng, Zheng Sun, and Kailiang Wu, Mathematics of Computation, in press], which is based on the solution operator of a novel damping equation. Several numerical examples are included to demonstrate the effectiveness, accuracy, and BP properties of our schemes, with applications to traffic simulations on road networks.
The high penetration of distributed energy resources (DERs) in modern smart power systems introduces unforeseen uncertainties for the electricity sector, leading to increased complexity and difficulty in the operation and control of power systems. As a cutting-edge machine learning technology, deep reinforcement learning (DRL) has been widely implemented in recent years to handle the uncertainty in power systems. However, in critical infrastructures such as power systems, safety issues always receive top priority, while DRL may not always meet the safety requirements of power system operators. The concept of safe reinforcement learning (safe RL) is emerging as a potential solution to overcome the shortcomings of conventional DRL in the operation and control of power systems. This study provides a rigorous review of the latest research efforts focused on safe RL to derive power system control policies while accounting for the unique safety requirements of power grids. Furthermore, this study highlights various safe RL algorithms applied in diverse applications within the power system sector, from single grid-connected power converters, residential smart homes, and buildings to large power distribution networks. For all methods outlined, a discussion on their bottlenecks, research challenges, and potential opportunities in the operation and control of power system applications is also presented. This review aims to support research in the area of safe RL algorithms, embracing smart power system operation with safety constraints amid high uncertainty from DERs.
Water in protoplanetary disks with JWST-MIRI: spectral excitation atlas, diagnostic diagrams for temperature and column density, and detection of disk-rotation line broadening
2024-09-24
Andrea Banzatti, Colette Salyk, Klaus M. Pontoppidan, John Carr, Ke Zhang, Nicole Arulanantham, L. Ilsedore Cleeves, Joan Najita, Karin I. Oberg, Ilaria Pascucci, Geoffrey A. Blake, Sebastiaan Krijt, Carlos E. Munoz-Romero, Edwin A. Bergin, Lucas A. Cieza, Paola Pinilla, Feng Long, Patrick Mallaney, Chengyan Xie, the JDISCS collaboration
This work aims at providing some general tools for the analysis of water spectra as observed in protoplanetary disks with JWST-MIRI. We use 25 high-quality spectra from the JDISC Survey reduced with asteroid calibrators as presented in Pontoppidan et al. (2024). First, we present a spectral atlas to illustrate the clustering of water transitions from different upper level energies ($E_u$) and identify single (un-blended) lines that provide the most reliable measurements. With the atlas, we demonstrate two important excitation effects: one related to the opacity saturation of ortho-para line pairs that overlap, and the other to the sub-thermal excitation of $v=1-1$ lines scattered across the $v=0-0$ rotational band. Second, from this larger line selection we define a list of fundamental lines spanning $E_u$ from 1500 to 6000 K to develop simple line-ratio diagrams as diagnostics of temperature components and column density. Third, we report the detection of disk-rotation Doppler broadening of molecular lines, which demonstrates the radial distribution of water emission at different $E_u$ and confirms from gas kinematics a radially-extended $\approx$ 170-190 K reservoir recently suggested from the analysis of line fluxes. We also report the detection of narrow blue-shifted absorption from an inner disk wind in ro-vibrational H$_2$O and CO lines, which may be observed in disks at inclinations $> 50$ deg. We summarize these findings and tools into a general recipe that should be beneficial to community efforts to study water in planet-forming regions.
LLM Echo Chamber: personalized and automated disinformation
2024-09-24
Tony Ma
Recent advancements have showcased the capabilities of Large Language Models like GPT4 and Llama2 in tasks such as summarization, translation, and content review. However, their widespread use raises concerns, particularly around the potential for LLMs to spread persuasive, humanlike misinformation at scale, which could significantly influence public opinion. This study examines these risks, focusing on LLMs ability to propagate misinformation as factual. To investigate this, we built the LLM Echo Chamber, a controlled digital environment simulating social media chatrooms, where misinformation often spreads. Echo chambers, where individuals only interact with like minded people, further entrench beliefs. By studying malicious bots spreading misinformation in this environment, we can better understand this phenomenon. We reviewed current LLMs, explored misinformation risks, and applied sota finetuning techniques. Using Microsoft phi2 model, finetuned with our custom dataset, we generated harmful content to create the Echo Chamber. This setup, evaluated by GPT4 for persuasiveness and harmfulness, sheds light on the ethical concerns surrounding LLMs and emphasizes the need for stronger safeguards against misinformation.
Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review
2024-09-24
Nikolas Koutsoubis, Asim Waqas, Yasin Yilmaz, Ravi P. Ramachandran, Matthew Schabath, Ghulam Rasool
Artificial Intelligence (AI) has demonstrated significant potential in automating various medical imaging tasks, which could soon become routine in clinical practice for disease diagnosis, prognosis, treatment planning, and post-treatment surveillance. However, the privacy concerns surrounding patient data present a major barrier to the widespread adoption of AI in medical imaging, as large, diverse training datasets are essential for developing accurate, generalizable, and robust Artificial intelligence models. Federated Learning (FL) offers a solution that enables organizations to train AI models collaboratively without sharing sensitive data. federated learning exchanges model training information, such as gradients, between the participating sites. Despite its promise, federated learning is still in its developmental stages and faces several challenges. Notably, sensitive information can still be inferred from the gradients shared during model training. Quantifying AI models' uncertainty is vital due to potential data distribution shifts post-deployment, which can affect model performance. Uncertainty quantification (UQ) in FL is particularly challenging due to data heterogeneity across participating sites. This review provides a comprehensive examination of FL, privacy-preserving FL (PPFL), and UQ in FL. We identify key gaps in current FL methodologies and propose future research directions to enhance data privacy and trustworthiness in medical imaging applications.
A scaffolded curriculum to foster experimental skills acquisition in an introductory physics lab course
2024-09-24
M. Alemani
This article outlines a comprehensive redesign of an introductory physics laboratory course aimed at promoting active student engagement, enhancing scientific experimental skills and the development of expert-like attitudes. The redesign process involved redefining specific and measurable learning goals and the development of tailored activities and assessment rubrics to reach and assess those goals. In order to implement course scaffolding, we made significant modifications to the previously existing course format, space, physics topics contexts, and assessment. Instructors` roles evolved from imparting knowledge to promoting student decision-making and fostering a collaborative learning environment. To guide the laboratory transformation we used the results of a research-based survey. This work offers practical insights for educators seeking to redesign laboratory courses, emphasizing authentic scientific practices and student-driven learning experiences in physics education.
Tiny Robotics Dataset and Benchmark for Continual Object Detection
2024-09-24
Francesco Pasti, Riccardo De Monte, Davide Dalle Pezze, Gian Antonio Susto, Nicola Bellotto
Detecting objects in mobile robotics is crucial for numerous applications, from autonomous navigation to inspection. However, robots are often required to perform tasks in different domains with respect to the training one and need to adapt to these changes. Tiny mobile robots, subject to size, power, and computational constraints, encounter even more difficulties in running and adapting these algorithms. Such adaptability, though, is crucial for real-world deployment, where robots must operate effectively in dynamic and unpredictable settings. In this work, we introduce a novel benchmark to evaluate the continual learning capabilities of object detection systems in tiny robotic platforms. Our contributions include: (i) Tiny Robotics Object Detection (TiROD), a comprehensive dataset collected using a small mobile robot, designed to test the adaptability of object detectors across various domains and classes; (ii) an evaluation of state-of-the-art real-time object detectors combined with different continual learning strategies on this dataset, providing detailed insights into their performance and limitations; and (iii) we publish the data and the code to replicate the results to foster continuous advancements in this field. Our benchmark results indicate key challenges that must be addressed to advance the development of robust and efficient object detection systems for tiny robotics.
Chiral effects on radiation and energy loss in quark-gluon plasma
2024-09-24
Jeremy Hansen, Kirill Tuchin
The emergent axion excitations in the quark-gluon plasma are generated by the sphaleron transitions. Their interactions with the photon and gluon fields are governed by the axion electro- and chromodynamics respectively. We discuss the effect of the emergent axion on the photon production and energy loss in the quark-gluon plasma. In particular, we review the Chiral Cherenkov effect, and the impact of the chiral anomaly on bremsstrahlung.
Determination of the strong coupling from jet measurements at CMS
2024-09-24
Patrick L. S. Connor
A vast program of determinations of the strong coupling $\alpha_\mathrm{S}$ is being undertaken by CMS. These measurements exploit several QCD-dominated processes that are sensitive to $\alpha_\mathrm{S}$, and present different theoretical and experimental challenges. A review of the current public results and perspective is given.
Non-singular cosmological scenarios in scalar-tensor theories and their stability: a review
2024-09-24
S. Mironov, V. Volkova
This article gives a concise overview of the development and current status of studies on healthy models of the early Universe without an initial singularity, namely the cosmological bounce and Genesis scenarios, constructed within a broad class of scalar-tensor theories, specifically Horndeski theories and their generalizations. The review focuses on the topics related to linear stability at the perturbation level over the non-singular background solutions: 1) the no-go theorem, valid for non-singular cosmologies within Horndeski theory, 2) the updates on possible approaches to evade the no-go theorem, 3) the role of disformal transformations relating the Horndeski subclasses with the generalized theories like DHOST, 4) the effects on stability caused by additional matter coupling and potential emergence of superluminal perturbation modes in the multi-component setting.
Machine learning approaches for automatic defect detection in photovoltaic systems
2024-09-24
Swayam Rajat Mohanty, Moin Uddin Maruf, Vaibhav Singh, Zeeshan Ahmad
Solar photovoltaic (PV) modules are prone to damage during manufacturing, installation and operation which reduces their power conversion efficiency. This diminishes their positive environmental impact over the lifecycle. Continuous monitoring of PV modules during operation via unmanned aerial vehicles is essential to ensure that defective panels are promptly replaced or repaired to maintain high power conversion efficiencies. Computer vision provides an automatic, non-destructive and cost-effective tool for monitoring defects in large-scale PV plants. We review the current landscape of deep learning-based computer vision techniques used for detecting defects in solar modules. We compare and evaluate the existing approaches at different levels, namely the type of images used, data collection and processing method, deep learning architectures employed, and model interpretability. Most approaches use convolutional neural networks together with data augmentation or generative adversarial network-based techniques. We evaluate the deep learning approaches by performing interpretability analysis on classification tasks. This analysis reveals that the model focuses on the darker regions of the image to perform the classification. We find clear gaps in the existing approaches while also laying out the groundwork for mitigating these challenges when building new models. We conclude with the relevant research gaps that need to be addressed and approaches for progress in this field: integrating geometric deep learning with existing approaches for building more robust and reliable models, leveraging physics-based neural networks that combine domain expertise of physical laws to build more domain-aware deep learning models, and incorporating interpretability as a factor for building models that can be trusted. The review points towards a clear roadmap for making this technology commercially relevant.
An X-ray flaring event and a variable soft X-ray excess in the Seyfert LCRS B040659.9-385922 as detected with eROSITA
2024-09-24
S. Krishnan, A. G. Markowitz, M. Krumpe, D. Homan, R. Brogan, S. Haemmerich, M. Gromadzki, T. Saha, M. Schramm, D. E. Reichart, H. Winkler, S. Waddell, J. Wilms, A. Rau, Z. Liu, I. Grotova
Extreme continuum variability in AGNs can indicate extreme changes in accretion flows onto supermassive black holes. We explore the multiwavelength nature of a continuum flare in the Seyfert LCRS B040659.9$-$385922. The all-sky X-ray surveys conducted by the eROSITA showed that its X-ray flux increased by a factor of roughly five over six months, and concurrent optical photometric monitoring with the ATLAS showed a simultaneous increase. We triggered a multiwavelength follow-up monitoring program (XMM, NICER; optical spectroscopy) to study the evolution of the accretion disk, broad-line region, and X-ray corona. During the campaign, X-ray and optical continuum flux subsided over roughly six months. We detected a soft X-ray excess near the flare peak and after it subsided, both exhibiting a power-law (nonthermal) behavior. We modeled the broadband optical/UV/X-ray spectral energy distribution at both the flare peak and post-flare times with the AGNSED model, incorporating thermal disk emission into the optical/UV and warm thermal Comptonization in the soft X-rays. Additionally, we find that the broad Heii $\lambda$4686 emission line fades significantly as the optical/UV/X-ray continuum fades, which could indicate a substantial flare of disk emission above 54 eV. We also observed a redshifted broad component in the H${\beta}$ emission line that is present during the high flux state of the source and disappears in subsequent observations. We witnessed a likely sudden strong increase in local accretion rate, which manifested itself via an increase in accretion disk emission and thermal Comptonization emission in the soft X-rays, followed by a decrease in accretion and Comptonized luminosity. The physical processes leading to such substantial variations are still an open question, and future continuous monitoring along with multi-wavelength studies will shed some light on it.
Unlocking Markets: A Multilingual Benchmark to Cross-Market Question Answering
2024-09-24
Yifei Yuan, Yang Deng, Anders Søgaard, Mohammad Aliannejadi
Users post numerous product-related questions on e-commerce platforms, affecting their purchase decisions. Product-related question answering (PQA) entails utilizing product-related resources to provide precise responses to users. We propose a novel task of Multilingual Cross-market Product-based Question Answering (MCPQA) and define the task as providing answers to product-related questions in a main marketplace by utilizing information from another resource-rich auxiliary marketplace in a multilingual context. We introduce a large-scale dataset comprising over 7 million questions from 17 marketplaces across 11 languages. We then perform automatic translation on the Electronics category of our dataset, naming it as McMarket. We focus on two subtasks: review-based answer generation and product-related question ranking. For each subtask, we label a subset of McMarket using an LLM and further evaluate the quality of the annotations via human assessment. We then conduct experiments to benchmark our dataset, using models ranging from traditional lexical models to LLMs in both single-market and cross-market scenarios across McMarket and the corresponding LLM subset. Results show that incorporating cross-market information significantly enhances performance in both tasks.
Robust Inference for Non-Linear Regression Models with Applications in Enzyme Kinetics
2024-09-24
Suryasis Jana, Abhik Ghosh
Despite linear regression being the most popular statistical modelling technique, in real-life we often need to deal with situations where the true relationship between the response and the covariates is nonlinear in parameters. In such cases one needs to adopt appropriate non-linear regression (NLR) analysis, having wider applications in biochemical and medical studies among many others. In this paper we propose a new improved robust estimation and testing methodologies for general NLR models based on the minimum density power divergence approach and apply our proposal to analyze the widely popular Michaelis-Menten (MM) model in enzyme kinetics. We establish the asymptotic properties of our proposed estimator and tests, along with their theoretical robustness characteristics through influence function analysis. For the particular MM model, we have further empirically justified the robustness and the efficiency of our proposed estimator and the testing procedure through extensive simulation studies and several interesting real data examples of enzyme-catalyzed (biochemical) reactions.
We give a review of three works on the construction of random models for singular stochastic partial differential equations within the theory of regularity structures.
Toward Scalable and Efficient Visual Data Transmission in 6G Networks
2024-09-24
Junhao Cai, Taegun An, Changhee Joo
6G network technology will emerge in a landscape where visual data transmissions dominate global mobile traffic and are expected to grow continuously, driven by the increasing demand for AI-based computer vision applications. This will make already challenging task of visual data transmission even more difficult. In this work, we review effective techniques for visual data transmission, such as content compression and adaptive video streaming, highlighting their advantages and limitations. Further, considering the scalability and cost issues of cloud-based and on-device AI services, we explore distributed in-network computing architecture like fog-computing as a direction of 6G networks, and investigate the necessary technical properties for the timely delivery of visual data.
Vulnerabilities that arise from poor governance in Distributed Ledger Technologies
2024-09-24
Aida Manzano Kharman, William Sanders
Current implementations of governance in Distributed Ledger Technologies leave them susceptible to a number of attacks. We survey the state of the art of Distributed Ledger Technologies (DLTs) governance protocols and work carried out to systematise good governance properties in the context of DLTs. We then select the most appropriate taxonomy of good governance properties and point to formal security notions that good governance protocols should satisfy. We point practitioners to existing solutions to deliver them, where possible. Furthermore, we outline a number of vulnerabilities that arise in the absence of good governance properties. We call on the research community and DLT research practitioners to prioritise delivering these good governance properties and continue to develop tools to do so, to avoid attacks to DLT protocols that exploit their poor governance models.
Large transformer-based models have significant potential for speech transcription and translation. Their self-attention mechanisms and parallel processing enable them to capture complex patterns and dependencies in audio sequences. However, this potential comes with challenges, as these large and computationally intensive models lead to slow inference speeds. Various optimization strategies have been proposed to improve performance, including efficient hardware utilization and algorithmic enhancements. In this paper, we introduce Whisper-Medusa, a novel approach designed to enhance processing speed with minimal impact on Word Error Rate (WER). The proposed model extends the OpenAI's Whisper architecture by predicting multiple tokens per iteration, resulting in a 50% reduction in latency. We showcase the effectiveness of Whisper-Medusa across different learning setups and datasets.
2024-09-24
Diego Candia-Rivera, Luca Faes, Fabrizio De Vico Fallani, Mario Chavez
The exploration of brain-heart interactions within various paradigms, including affective computing, human-computer interfaces, and sensorimotor evaluation, stands as a significant milestone in biomarker development and neuroscientific research. A range of techniques, spanning from molecular to behavioral approaches, has been proposed to measure these interactions. Different frameworks use signal processing techniques, from the estimation of brain responses to individual heartbeats to higher-order dynamics linking cardiac inputs to changes in brain organization. This review provides an overview to the most notable signal processing strategies currently used for measuring and modeling brain-heart interactions. It discusses their usability and highlights the main challenges that need to be addressed for future methodological developments. Current methodologies have deepened our understanding of the impact of neural disruptions on brain-heart interactions, solidifying it as a biomarker for evaluation of the physiological state of the nervous system and holding immense potential for disease stratification. The vast outlook of these methods becomes apparent specially in neurological and psychiatric disorders. As we tackle new methodological challenges, gaining a more profound understanding of how these interactions operate, we anticipate further insights into the role of peripheral neurons and the environmental input from the rest of the body in shaping brain functioning.
Supervised Fine-Tuning: An Activation Pattern Optimization Process for Attention Heads
2024-09-24
Yang Zhao, Li Du, Xiao Ding, Kai Xiong, Ting Liu, Bing Qin
Though demonstrating promising potential, LLMs' performance on complex tasks, such as advanced mathematics and complex disease diagnosis is still unsatisfactory. A key issue is the present LLMs learn in a data-driven schema, while the instruction dataset about these complex tasks is both scarce and hard to collect or construct. On the contrary, a prominent phenomenon is that LLMs can learn rather fast on those simpler tasks with adequate prior knowledge captured during pretraining stage. Thus, if the prerequisite and mechanism of such rapid generalization could be elucidated, it could be highly beneficial in enhancing the efficiency and effectiveness of the LLM's ability to learn complex tasks. Thus, in this paper, we employ a gradient-based method, to dissect the process that the SFT process adapts LLMs to downstream tasks via the perspective of attention patterns. We find that: (1) LLMs selectively activate task-specific attention heads during SFT; (2) activation patterns for complex tasks are combinations of basic task patterns; and (3) changes in a few parameters can significantly impact activation patterns after SFT on a small number of samples. Based on these insights, we conduct experiments to examine whether these conclusions could effectively enhance the efficiency and effectiveness of SFT, particularly in handling complex tasks and when instructional resources are scarce. Our research not only uncovers the underlying reasons behind LLMs' rapid learning and generalization mechanisms but also provides practical solutions for addressing data challenges in complex and specialized tasks.
Diffusion Models for Intelligent Transportation Systems: A Survey
2024-09-24
Mingxing Peng, Kehua Chen, Xusen Guo, Qiming Zhang, Hongliang Lu, Hui Zhong, Di Chen, Meixin Zhu, Hai Yang
Intelligent Transportation Systems (ITS) are vital in modern traffic management and optimization, significantly enhancing traffic efficiency and safety. Recently, diffusion models have emerged as transformative tools for addressing complex challenges within ITS. In this paper, we present a comprehensive survey of diffusion models for ITS, covering both theoretical and practical aspects. First, we introduce the theoretical foundations of diffusion models and their key variants, including conditional diffusion models and latent diffusion models, highlighting their suitability for modeling complex, multi-modal traffic data and enabling controllable generation. Second, we outline the primary challenges in ITS and the corresponding advantages of diffusion models, providing readers with a deeper understanding of the intersection between ITS and diffusion models. Third, we offer a multi-perspective investigation of current applications of diffusion models in ITS domains, including autonomous driving, traffic simulation, trajectory prediction, and traffic safety. Finally, we discuss state-of-the-art diffusion model techniques and highlight key ITS research directions that warrant further investigation. Through this structured overview, we aim to provide researchers with a comprehensive understanding of diffusion models for ITS, thereby advancing their future applications in the transportation domain.
Small Language Models: Survey, Measurements, and Insights
2024-09-24
Zhenyan Lu, Xiang Li, Dongqi Cai, Rongjie Yi, Fangming Liu, Xiwen Zhang, Nicholas D. Lane, Mengwei Xu
Small language models (SLMs), despite their widespread adoption in modern smart devices, have received significantly less academic attention compared to their large language model (LLM) counterparts, which are predominantly deployed in data centers and cloud environments. While researchers continue to improve the capabilities of LLMs in the pursuit of artificial general intelligence, SLM research aims to make machine intelligence more accessible, affordable, and efficient for everyday tasks. Focusing on transformer-based, decoder-only language models with 100M-5B parameters, we survey 59 state-of-the-art open-source SLMs, analyzing their technical innovations across three axes: architectures, training datasets, and training algorithms. In addition, we evaluate their capabilities in various domains, including commonsense reasoning, in-context learning, mathematics, and coding. To gain further insight into their on-device runtime costs, we benchmark their inference latency and memory footprints. Through in-depth analysis of our benchmarking data, we offer valuable insights to advance research in this field.
In space applications, space charge inside dielectric materials can lead to unwanted phenomena such as breakdown and electrostatic discharge. To prevent such incidents, the pulsed electroacoustic (PEA) device is employed to measure the space charge of dielectric samples. Unfortunately, most of the current PEA signal processing methods are inaccurate in studying the dielectric materials that attenuate and disperse the acoustic wave, as well as studying multi-layer or thin samples. A model under the PSpice software is developed in this work with the aim of improving the PEA signal processing. This model is an evolution of previous works, including the attenuation and dispersion of a polymer sample using the nearly local Kramers--Kronig relation. This model of the polymer sample is implemented into PSpice as a frequency dependent transmission line using Branin's method of characteristics model. A comparison of a model simulation and an experimental PEA signal obtained from a PTFE sample is presented.
AnyCar to Anywhere: Learning Universal Dynamics Model for Agile and Adaptive Mobility
2024-09-24
Wenli Xiao, Haoru Xue, Tony Tao, Dvij Kalaria, John M. Dolan, Guanya Shi
Recent works in the robot learning community have successfully introduced generalist models capable of controlling various robot embodiments across a wide range of tasks, such as navigation and locomotion. However, achieving agile control, which pushes the limits of robotic performance, still relies on specialist models that require extensive parameter tuning. To leverage generalist-model adaptability and flexibility while achieving specialist-level agility, we propose AnyCar, a transformer-based generalist dynamics model designed for agile control of various wheeled robots. To collect training data, we unify multiple simulators and leverage different physics backends to simulate vehicles with diverse sizes, scales, and physical properties across various terrains. With robust training and real-world fine-tuning, our model enables precise adaptation to different vehicles, even in the wild and under large state estimation errors. In real-world experiments, AnyCar shows both few-shot and zero-shot generalization across a wide range of vehicles and environments, where our model, combined with a sampling-based MPC, outperforms specialist models by up to 54%. These results represent a key step toward building a foundation model for agile wheeled robot control. We will also open-source our framework to support further research.
Federated learning (FL) is a popular approach that enables organizations to train machine learning models without compromising data privacy and security. As the field of FL continues to grow, it is crucial to have a thorough understanding of the topic, current trends and future research directions for information systems (IS) researchers. Consequently, this paper conducts a comprehensive computational literature review on FL and presents the research landscape. By utilizing advanced data analytics and leveraging the topic modeling approach, we identified and analyzed the most prominent 15 topics and areas that have influenced the research on FL. We also proposed guiding research questions to stimulate further research directions for IS scholars. Our work is valuable for scholars, practitioners, and policymakers since it offers a comprehensive overview of state-of-the-art research on FL.
A pedagogical introduction to restricted Schur polynomials with applications to heavy operators
2024-09-24
Robert de Mello Koch, Minkyoo Kim, Augustine Larweh Mahu
Recent advances in the study of microstates for 1/16-BPS black holes have inspired renewed interest in the analysis of heavy operators. For these operators, traditional techniques that work effectively in the planar limit are no longer applicable. Methods that are sensitive to finite N effects are required. In particular, trace relations that connect different multi-trace operators must be carefully considered. A powerful approach to tackling this challenge, which utilizes the representation theory of the symmetric group, is provided by restricted Schur polynomials. In this review, we develop these methods with the goal of providing the background needed for their application to 1/16-BPS black holes.
The Roles of Generative Artificial Intelligence in Internet of Electric Vehicles
2024-09-24
Hanwen Zhang, Dusit Niyato, Wei Zhang, Changyuan Zhao, Hongyang Du, Abbas Jamalipour, Sumei Sun, Yiyang Pei
With the advancement of generative artificial intelligence (GenAI) models, their capability to generate content is seeing significant enhancement, leading to widespread applications in the field of data generation and forecasting. Furthermore, GenAI has strong capabilities in data modeling and analysis, which enhances Internet of electric vehicles (IoEV) applications in various aspects. In this paper, we investigate and survey applications of GenAI in the IoEV. Specifically, we categorize GenAI for IoEV into four different layers namely, EV's battery layer, individual electric vehicle (EV) layer, smart grid with EV layer, and security layer. We first introduce various GenAI techniques used in each layer of IoEV applications. Subsequently, public datasets available for training the GenAI models are summarized. Finally, we provide recommendations for future directions. This survey not only categorizes the applications of GenAI in IoEV across different layers but also serves as a valuable resource for researchers and practitioners by highlighting the design and implementation challenges within each layer. Furthermore, it provides a roadmap for future research directions, enabling the development of more robust and efficient IoEV systems through the integration of advanced GenAI techniques.
LSAST -- Enhancing Cybersecurity through LLM-supported Static Application Security Testing
2024-09-24
Mete Keltek, Ziyue Li
In the fast-evolving landscape of cybersecurity, Large Language Models (LLMs) play a pivotal role, continually improving their ability to analyze software code. This paper introduces a novel approach to vulnerability scanning by integrating conservative SAST (Static Application Security Testing) scanners with LLM capabilities, resulting in the creation of LSAST (LLM-supported Static Application Security Testing). Our approach significantly enhances the performance of LLMs in vulnerability scanning, establishing a new standard in this field. We benchmark LSAST's efficiency and compare its results with a state-of-the-art LLM. Additionally, we address the inherent drawbacks of LLMs in vulnerability scanning: their reliance on static training datasets, which leads to the exclusion of the latest vulnerabilities, and the privacy concerns associated with sending code to third-party LLM providers. To mitigate these issues, we utilize an open-source LLM to ensure privacy and employ a novel approach to gather relevant vulnerability information, thereby equipping the LLM with up-to-date knowledge.
Sequential Learning in the Dense Associative Memory
2024-09-24
Hayden McAlister, Anthony Robins, Lech Szymanski
Sequential learning involves learning tasks in a sequence, and proves challenging for most neural networks. Biological neural networks regularly conquer the sequential learning challenge and are even capable of transferring knowledge both forward and backwards between tasks. Artificial neural networks often totally fail to transfer performance between tasks, and regularly suffer from degraded performance or catastrophic forgetting on previous tasks. Models of associative memory have been used to investigate the discrepancy between biological and artificial neural networks due to their biological ties and inspirations, of which the Hopfield network is perhaps the most studied model. The Dense Associative Memory, or modern Hopfield network, generalizes the Hopfield network, allowing for greater capacities and prototype learning behaviors, while still retaining the associative memory structure. We investigate the performance of the Dense Associative Memory in sequential learning problems, and benchmark various sequential learning techniques in the network. We give a substantial review of the sequential learning space with particular respect to the Hopfield network and associative memories, as well as describe the techniques we implement in detail. We also draw parallels between the classical and Dense Associative Memory in the context of sequential learning, and discuss the departures from biological inspiration that may influence the utility of the Dense Associative Memory as a tool for studying biological neural networks. We present our findings, and show that existing sequential learning methods can be applied to the Dense Associative Memory to improve sequential learning performance.
LLM-Cure: LLM-based Competitor User Review Analysis for Feature Enhancement
2024-09-24
Maram Assi, Safwat Hassan, Ying Zou
The exponential growth of the mobile app market underscores the importance of constant innovation and rapid response to user demands. As user satisfaction is paramount to the success of a mobile application (app), developers typically rely on user reviews, which represent user feedback that includes ratings and comments to identify areas for improvement. However, the sheer volume of user reviews poses challenges in manual analysis, necessitating automated approaches. Existing automated approaches either analyze only the target apps reviews, neglecting the comparison of similar features to competitors or fail to provide suggestions for feature enhancement. To address these gaps, we propose a Large Language Model (LLM)-based Competitive User Review Analysis for Feature Enhancement) (LLM-Cure), an approach powered by LLMs to automatically generate suggestion s for mobile app feature improvements. More specifically, LLM-Cure identifies and categorizes features within reviews by applying LLMs. When provided with a complaint in a user review, LLM-Cure curates highly rated (4 and 5 stars) reviews in competing apps related to the complaint and proposes potential improvements tailored to the target application. We evaluate LLM-Cure on 1,056,739 reviews of 70 popular Android apps. Our evaluation demonstrates that LLM-Cure significantly outperforms the state-of-the-art approaches in assigning features to reviews by up to 13% in F1-score, up to 16% in recall and up to 11% in precision. Additionally, LLM-Cure demonstrates its capability to provide suggestions for resolving user complaints. We verify the suggestions using the release notes that reflect the changes of features in the target mobile app. LLM-Cure achieves a promising average of 73% of the implementation of the provided suggestions.
Federated Large Language Models: Current Progress and Future Directions
2024-09-24
Yuhang Yao, Jianyi Zhang, Junda Wu, Chengkai Huang, Yu Xia, Tong Yu, Ruiyi Zhang, Sungchul Kim, Ryan Rossi, Ang Li, Lina Yao, Julian McAuley, Yiran Chen, Carlee Joe-Wong
Large language models are rapidly gaining popularity and have been widely adopted in real-world applications. While the quality of training data is essential, privacy concerns arise during data collection. Federated learning offers a solution by allowing multiple clients to collaboratively train LLMs without sharing local data. However, FL introduces new challenges, such as model convergence issues due to heterogeneous data and high communication costs. A comprehensive study is required to address these challenges and guide future research. This paper surveys Federated learning for LLMs (FedLLM), highlighting recent advances and future directions. We focus on two key aspects: fine-tuning and prompt learning in a federated setting, discussing existing work and associated research challenges. We finally propose potential research directions for federated LLMs, including pre-training and how LLMs can further enhance federated learning.
The systematic errors are inevitable in Gaia published astrometric data. Lindegren et al. (L21) proposed a global recipe to correct for the GEDR3 parallax zero point offset, which did not consider the Galactic plane. The applicability of their correction model to the Galactic plane remains uncertain. We attempt to have an independent investigation into the sample dependence of the L21 correction, and its applicability to the Galactic plane. We collect various samples, including quasars, binaries, and sources with parallaxes from other surveys or methods, to validate the L21 correction, especially in the Galactic plane. We conclude that the L21 correction exhibits sample dependence, and does not apply effectively to the Galactic plane. We present a new parallax bias correction applying to the Galactic plane, offering improvements over the existing L21 correction. The correction difference between L21 and this work can go up to 0.01 mas within certain ranges of magnitude and colour. This work provides an additional recipe for users of Gaia parallaxes, especially for sources located near the Galactic plane.
A Survey of Stance Detection on Social Media: New Directions and Perspectives
2024-09-24
Bowen Zhang, Genan Dai, Fuqiang Niu, Nan Yin, Xiaomao Fan, Hu Huang
In modern digital environments, users frequently express opinions on contentious topics, providing a wealth of information on prevailing attitudes. The systematic analysis of these opinions offers valuable insights for decision-making in various sectors, including marketing and politics. As a result, stance detection has emerged as a crucial subfield within affective computing, enabling the automatic detection of user stances in social media conversations and providing a nuanced understanding of public sentiment on complex issues. Recent years have seen a surge of research interest in developing effective stance detection methods, with contributions from multiple communities, including natural language processing, web science, and social computing. This paper provides a comprehensive survey of stance detection techniques on social media, covering task definitions, datasets, approaches, and future works. We review traditional stance detection models, as well as state-of-the-art methods based on large language models, and discuss their strengths and limitations. Our survey highlights the importance of stance detection in understanding public opinion and sentiment, and identifies gaps in current research. We conclude by outlining potential future directions for stance detection on social media, including the need for more robust and generalizable models, and the importance of addressing emerging challenges such as multi-modal stance detection and stance detection in low-resource languages.
Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
2024-09-24
Anthony M. Smaldone, Yu Shee, Gregory W. Kyro, Chuzhi Xu, Nam P. Vu, Rishab Dutta, Marwa H. Farag, Alexey Galda, Sandeep Kumar, Elica Kyoseva, Victor S. Batista
The nexus of quantum computing and machine learning - quantum machine learning - offers the potential for significant advancements in chemistry. This review specifically explores the potential of quantum neural networks on gate-based quantum computers within the context of drug discovery. We discuss the theoretical foundations of quantum machine learning, including data encoding, variational quantum circuits, and hybrid quantum-classical approaches. Applications to drug discovery are highlighted, including molecular property prediction and molecular generation. We provide a balanced perspective, emphasizing both the potential benefits and the challenges that must be addressed.
Theoretical insights into the role of lattice fluctuations on the excited behavior of lead halide perovskites
2024-09-24
Yoonjae Park, Rohit Rana, Daniel Chabeda, Eran Rabani, David T. Limmer
Unravelling the role of charge-lattice interactions on the optoelectronic properties in lead halide perovskites is of great interest due to their unique photophysical properties. While there is broad consensus on the importance of the structural deformations and polar fluctuations on the behavior of charge carriers and quasiparticles, the strongly anharmonic nature of these fluctuations and their strong interactions render theoretical descriptions of lead halides challenging. In this Account, we review our recent efforts to understand how the soft, polar lattice of this class of materials alter their quasiparticle binding energies and fine structure, charge mobilities, and lifetimes of phonons and excess charges. This is reviewed in the context of the effective models we have built, and the novel theoretical methods we have developed to understand bulk crystalline materials, as well as nanostructures, and lower dimensionality lattices. The perspective we detail provides both some design principles for optoelectronic materials and a set of theoretical tools to study them when charge-lattice interactions are important.
Transfer learning for financial data predictions: a systematic review
2024-09-24
V. Lanzetta
Literature highlighted that financial time series data pose significant challenges for accurate stock price prediction, because these data are characterized by noise and susceptibility to news; traditional statistical methodologies made assumptions, such as linearity and normality, which are not suitable for the non-linear nature of financial time series; on the other hand, machine learning methodologies are able to capture non linear relationship in the data. To date, neural network is considered the main machine learning tool for the financial prices prediction. Transfer Learning, as a method aimed at transferring knowledge from source tasks to target tasks, can represent a very useful methodological tool for getting better financial prediction capability. Current reviews on the above body of knowledge are mainly focused on neural network architectures, for financial prediction, with very little emphasis on the transfer learning methodology; thus, this paper is aimed at going deeper on this topic by developing a systematic review with respect to application of Transfer Learning for financial market predictions and to challenges/potential future directions of the transfer learning methodologies for stock market predictions.
The integration of tools in augmenting large language models presents a novel approach toward enhancing the efficiency and accuracy of these models in handling specific, complex tasks. This paper delves into the methodology,challenges, and developments in the realm of teaching LLMs to use external tools, thereby pushing the boundaries of their capabilities beyond pre-existing knowledge bases. We introduce a standardized paradigm for tool integration guided by a series of functions that map user instructions to actionable plans and their execution, emphasizing the significance of understanding user intent, tool selection, and dynamic plan adjustment. Our exploration reveals the various challenges encountered, such as tool invocation timing, selection accuracy, and the need for robust reasoning processes. In addressing these challenges, we investigate techniques within the context of fine-tuning and incontext learning paradigms, highlighting innovative approaches to ensure diversity, augment datasets, and improve generalization.Furthermore, we investigate a perspective on enabling LLMs to not only utilize but also autonomously create tools, which may redefine their role from mere tool users to tool creators. Finally,we reproduced Chameleon's results on ScienceQA and analyzed the code structure.
Core prominence as a signature of restarted jet activity in the LOFAR radio-galaxy population
2024-09-23
Dhanya G. Nair, Raffaella Morganti, Marisa Brienza, Beatriz Mingo, Judith H. Croston, Nika Jurlin, Timothy W. Shimwell, Joseph R. Callingham, Martin J. Hardcastle
(abridged) Characterizing duty cycles of recurrent phases of dormancy and activity in supermassive black holes in active galactic nuclei is crucial in understanding impact of energy released on host galaxies and their evolution. However, identifying sources in quiescent and restarted phases is challenging. Our goal is to identify and characterize a substantial sample of radio galaxies in restarted phase and explore core prominence as a signature of this activity. We expand our prior study from a $30\,\mathrm{deg^2}$ area in Lockman Hole to a larger $424\,\mathrm{deg^2}$ region in Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) field using visually selected core-dominated radio galaxies. We used 144 MHz LOFAR survey images to identify galaxies with restarting jets. By applying selection criteria, including radio core dominance, low surface brightness extended emission, spectral index properties, and morphology, we found 69 candidate restarted radio galaxies. These candidates show diverse intrinsic morphology, spanning FRI, FRII, core-with-halo, and asymmetric forms, suggesting different progenitors. Among these, nine galaxies exhibit ultra-steep spectrum extended emission combined with high radio core prominence, representing previous and current epochs of jet activity. This subset supports a model where the switch-on and switch-off mechanism occurs with fast duty cycle. The restarted candidates span radio luminosities from log$_{10}$(L$_\mathrm{144 MHz}$/$\mathrm{WHz^{-1}}$) = 23.24 to 26.80, with sizes between 88 and 1659 kpc, including 16 giant radio galaxies. Their total stellar content aligns with massive elliptical galaxies. Our findings at $z<0.4$ suggest that many restarting galaxies are not found in rich cluster environments, consistent with broader radio-galaxy population. This study confirms core prominence as an effective parameter for selecting restarted radio sources.
Spectroscopic Observations of the Galex Nearby Young Star Survey Sample. 1. Nearby Moving Group Candidates
2024-09-23
Navya Nagananda, Laura Vican, Ben Zuckerman, David Rodriguez, Alexander Binks, Joel Kastner
The GALEX Nearby Young Star Search (GALNYSS) yielded the identification of more than 2000 late-type stars that, based on their ultraviolet and infrared colors and pre-Gaia proper motions, are potentially of age < 200 Myr and lie within ~120 pc of Earth. We present the results of a campaign of medium- and high-resolution optical spectroscopy of 471 GALNYSS stars aimed at confirming their youth and their potential membership in nearby young stellar moving groups. We present radial velocity (RV), Li absorption, and H-$\alpha$ emission measurements for these spectroscopically observed GALNYSS stars, and assess their Li absorption and optical emission-line properties and infrared excesses. Our RV measurements are combined with literature and Gaia DR3 RV measurements and Gaia DR3 astrometry and photometry to obtain the spatiokinematics and color-magnitude positions of GALNYSS stars. We use these results to assess membership in the TW Hya, Tuc-Hor, Carina, Columba, and Argus Associations and the $\beta$-PMG and AB Dor moving groups. We have identified 132 stars as candidate members of these seven groups; roughly half of these candidates are newly identified on the basis of data presented here. At least one-third of the 132 candidates are spectroscopic and/or photometric binaries and/or have comoving (visual) binary companions in Gaia DR3. The contingent of young, low-mass stars in the solar vicinity we identify here should provide excellent subjects for future direct imaging exoplanet surveys and studies of the early evolution of low-mass stars and their planetary progeny.
Astrophysical Transients in Time Domain and Multi-Messenger Astronomy: a review
2024-09-23
Maria D. Caballero-Garcia
The wide fields of view, high sensitivity, and broad energy coverage of current X-ray and gamma-ray satellites, coupled with the high cadence observational strategy of some of them (recently Swift and Fermi) have been ideal for carrying out unprecedented studies of the variability properties of different classes of Galactic and extra-Galactic high-energy sources. These classes of objects range from nearby flaring stars to the most distant Active Galactic Nuclei (AGNs). In this paper we focus on some of the most energetic events, i.e. those powered by accretion (black hole binaries, ultra-luminous X-rays and Active Galactic Nuclei) until those leading to the first detections of GravitationalWaves (GWs), i.e. the Gamma-ray Bursts (GRBs), passing through the controversial Inter-Mediate Mass Black-Holes (IMBHs). We show the importance of X-ray and gamma-ray emission for the determination of the properties (mass, spin, inclination, height of the corona) of the compact objects residing in systems powered by accretion and the role of the LIGO and VIRGO interferometers in the case of the (compact objects) binary mergers. Gravitational waves allow the determination of the properties of the non-light-emitting compact objects (black-holes; BHs) in binary mergers (BH-BH) that otherwise would be nearly impossible. We show that for the case of neutron star (NS) mergers (NS-BH or NS-NS) electro-magnetic (EM) emission is still possible, and very powerful, being the responsible for the X-ray and gamma-ray emission of many GRBs (kilonovae). Very recently these transients have been discovered to show X-ray and gamma-ray variability patterns that could lead to very important insights into the properties of the binary mergers that originated them, opening a new view for their study.
MRI Radiomics for IDH Genotype Prediction in Glioblastoma Diagnosis
2024-09-23
Stanislav Kozák
Radiomics is a relatively new field which utilises automatically identified features from radiological scans. It has found a widespread application, particularly in oncology because many of the important oncological biomarkers are not visible to the naked eye. The recent advent of big data, including in medical imaging, and the development of new ML techniques brought the possibility of faster and more accurate oncological diagnosis. Furthermore, standardised mathematical feature extraction based on radiomics helps to eliminate possible radiologist bias. This paper reviews the recent development in the oncological use of MRI radiomic features. It focuses on the identification of the isocitrate dehydrogenase (IDH) mutation status, which is an important biomarker for the diagnosis of glioblastoma and grade IV astrocytoma.
Radio Signatures of Star-Planet Interactions, Exoplanets, and Space Weather
2024-09-23
J. R. Callingham, B. J. S. Pope, R. D. Kavanagh, S. Bellotti, S. Daley-Yates, M. Damasso, J. -M. Grießmeier, M. Güdel, M. Günther, M. M. Kao, B. Klein, S. Mahadevan, J. Morin, J. D. Nichols, R. A. Osten, M. Pérez-Torres, J. S. Pineda, J. Rigney, J. Saur, G. Stefánsson, J. D. Turner, H. Vedantham, A. A. Vidotto, J. Villadsen, P. Zarka
Radio detections of stellar systems provide a window onto stellar magnetic activity and the space weather conditions of extrasolar planets, information that is difficult to attain at other wavelengths. There have been recent advances observing auroral emissions from radio-bright low-mass stars and exoplanets largely due to the maturation of low-frequency radio instruments and the plethora of wide-field radio surveys. To guide us in placing these recent results in context, we introduce the foremost local analogues for the field: Solar bursts and the aurorae found on Jupiter. We detail how radio bursts associated with stellar flares are foundational to the study of stellar coronae, and time-resolved radio dynamic spectra offers one of the best prospects of detecting and characterising coronal mass ejections from other stars. We highlight the prospects of directly detecting coherent radio emission from exoplanetary magnetospheres, and early tentative results. We bridge this discussion to the field of brown dwarf radio emission, in which their larger and stronger magnetospheres are amenable to detailed study with current instruments. Bright, coherent radio emission is also predicted from magnetic interactions between stars and close-in planets. We discuss the underlying physics of these interactions and implications of recent provisional detections for exoplanet characterisation. We conclude with an overview of outstanding questions in theory of stellar, star-planet interaction, and exoplanet radio emission, and the prospects of future facilities in answering them.
A Sub-Millisecond Fourier and Wavelet Based Model to Extract Variable Candidates from the NEOWISE Single-Exposure Database
2024-09-23
Matthew Paz
This paper presents VARnet, a capable signal processing model for rapid astronomical timeseries analysis. VARnet leverages wavelet decomposition, a novel method of Fourier feature extraction via the Finite-Embedding Fourier Transform (FEFT), and deep learning to detect faint signals in light curves, utilizing the strengths of modern GPUs to achieve sub-millisecond single-source runtime. We apply VARnet to the NEOWISE Single-Exposure Database, which holds nearly 200 billion apparitions over 10.5 years of infrared sources on the entire sky. This paper devises a pipeline in order to extract variable candidates from the NEOWISE data, serving as a proof of concept for both the efficacy of VARnet and methods for an upcoming variability survey over the entirety of the NEOWISE dataset. We implement models and simulations to synthesize unique light curves to train VARnet. In this case, the model achieves an F1 score of $0.91$ over a 4-class classification scheme on a validation set of real variable sources present in the infrared. With $\sim2000$ points per light curve on a GPU with 22GB of VRAM, VARnet produces a per-source processing time of $<53\mu s$. We confirm that our VARnet is sensitive and precise to both known and previously undiscovered variable sources. These methods prove promising for a complete future survey of variability with WISE, and effectively showcase the power of the VARnet model architecture.
Exploring Requirements Elicitation from App Store User Reviews Using Large Language Models
2024-09-23
Tanmai Kumar Ghosh, Atharva Pargaonkar, Nasir U. Eisty
Mobile applications have become indispensable companions in our daily lives. Spanning over the categories from communication and entertainment to healthcare and finance, these applications have been influential in every aspect. Despite their omnipresence, developing apps that meet user needs and expectations still remains a challenge. Traditional requirements elicitation methods like user interviews can be time-consuming and suffer from limited scope and subjectivity. This research introduces an approach leveraging the power of Large Language Models (LLMs) to analyze user reviews for automated requirements elicitation. We fine-tuned three well-established LLMs BERT, DistilBERT, and GEMMA, on a dataset of app reviews labeled for usefulness. Our evaluation revealed BERT's superior performance, achieving an accuracy of 92.40% and an F1-score of 92.39%, demonstrating its effectiveness in accurately classifying useful reviews. While GEMMA displayed a lower overall performance, it excelled in recall (93.39%), indicating its potential for capturing a comprehensive set of valuable user insights. These findings suggest that LLMs offer a promising avenue for streamlining requirements elicitation in mobile app development, leading to the creation of more user-centric and successful applications.
The emergence of magnetic flux from the deep convection zone plays an important role in the solar magnetism, such as the generation of active regions and triggering of various eruptive phenomena, including jets, flares, and coronal mass ejections. To investigate the effects of magnetic twist on flux emergence, we performed numerical simulations of flux tube emergence using the radiative magnetohydrodynamic code R2D2, and conducted a systematic survey on the initial twist. Specifically, we varied the twist of the initial tube both positively and negatively from zero to twice the critical value for kink instability. As a result, regardless of the initial twist, the flux tube was lifted by the convective upflow and reached the photosphere to create sunspots. However, when the twist was too weak, the photospheric flux was quickly diffused and not retained long as coherent sunspots. The degree of magnetic twist measured in the photosphere conserved the original twist relatively well, and was comparable to actual solar observations. Even in the untwisted case, a finite amount of magnetic helicity was injected into the upper atmosphere because the background turbulence added helicity. However, when the initial twist exceeded the critical value for kink instability, the magnetic helicity normalized by the total magnetic flux was found to be unreasonably larger than the observations, indicating that the kink instability of the emerging flux tube may not be a likely scenario for the formation of flare-productive active regions.
MUSEQuBES: The kinematics of OVI-bearing gas in and around low-redshift galaxies
2024-09-23
Sayak Dutta, Sowgat Muzahid, Joop Schaye, Sean Johnson, Hsiao-Wen Chen, Sebastiano Cantalupo
We present a detailed study of the kinematics of OVI-bearing gas around 60 low-mass (median log(M*/Msun)~8.9) galaxies at low redshift (0.1 < z < 0.7) using background quasars (median impact parameter $\approx115$ kpc) as part of the MUSE Quasar-fields Blind Emitters Survey (MUSEQuBES). We find that the majority of the OVI absorbers detected within the virial radius have line-of-sight velocities smaller than the escape velocities and are thus consistent with being gravitationally bound, irrespective of the halo mass. However, the fraction of such absorbers declines at larger impact parameters. The Doppler $b$ parameter and the velocity width ($\Delta v_{90}$) of the OVI absorbers exhibit large scatter inside the virial radius of the host galaxies, but the scatter declines sharply at impact parameter $D \gtrsim 2R_{\rm vir}$. For high-mass galaxies (log(M*/Msun)>9), OVI absorption displays a larger kinematic spread, quantified by the pixel-velocity two-point correlation function (TPCF). However, this difference disappears for the isolated galaxies when the pixel velocities are scaled by the galaxy's circular velocity. We do not find any significant difference between the TPCF of isolated and group galaxies when the stellar mass is controlled for. A significant fraction of groups (4/6) with four or more member galaxies do not show any detectable OVI absorption, likely due to the passive nature of nearest galaxies.
Disentangling transients and their host galaxies with Scarlet2: A framework to forward model multi-epoch imaging
2024-09-23
Charlotte Ward, Peter Melchior, Matt L. Sampson, Colin J. Burke, Jared Siegel, Benjamin Remy, Sufia Birmingham, Emily Ramey, Sjoert van Velzen
Many science cases for wide-field time-domain surveys rely on accurate identification and characterization of the galaxies hosting transient and variable objects. In the era of the Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory the number of known transient and variable sources will grow by orders of magnitude, and many of these sources will be blended with their host galaxies and neighboring galaxies. A diverse range of applications - including the classification of nuclear and non-nuclear sources, identification of potential host galaxies, extraction of host galaxy SEDs without requiring a transient-free reference image, and combined analysis of photometry from multiple surveys - will benefit from a flexible framework to model time-domain imaging of transients. We describe a time-domain extension of the Scarlet2 scene modeling code for multi-epoch, multi-band, and multi-resolution imaging data to extract simultaneous transient and host galaxy models. Scarlet2 leverages the benefits of data-driven priors on galaxy morphology, is fully GPU compatible, and can jointly model multi-resolution data from ground and space-based surveys. We demonstrate the method on simulated LSST-like supernova imaging, low-resolution Zwicky Transient Facility imaging of tidal disruption events, and Hyper Suprime Cam imaging of variable AGN out to z = 4 in the COSMOS fields. We show that Scarlet2 models provide accurate transient and host galaxy models as well as accurate measurement of host-transient spatial offsets, and demonstrate future applications to the search for 'wandering' massive black holes.
MUSEQuBES: The column density, covering fraction, and mass of OVI-bearing gas in and around low-redshift galaxies
2024-09-23
Sayak Dutta, Sowgat Muzahid, Joop Schaye, Sean Johnson, Nicolas F. Bouché, Hsiao-Wen Chen, Sebastiano Cantalupo
We present a study of OVI-bearing gas around 247 low-mass (median log(M*/Msun)~8.7) galaxies at low redshifts (0.1 < z < 0.7) using background quasars as part of the MUSE Quasar-fields Blind Emitters Survey (MUSEQuBES). We find that the average OVI column density, ${\rm log}_{10}$ = $14.14^{+0.09}_{-0.10}$, measured within the virial radius for our sample, is significantly lower than for L_* galaxies. Combining 253 star-forming galaxies (mostly more massive) from the literature with 176 star-forming galaxies from MUSEQuBES, we find that both and the average covering fraction peak at log(M*/Msun)~9.5. The virial temperature corresponding to this stellar mass is ideal for OVI production via collisional ionization. However, we argue that photoionization and/or non-equilibrium processes are necessary to produce the OVI associated with low-mass, dwarf galaxies (log(M*/Msun)<9). The average OVI mass within the virial radius of dwarf galaxies is measured to be $10^{5.2_{-0.1}^{+0.1}}$ Msun. The characteristic normalized impact parameter at which the OVI covering fraction drops to half of its peak value is the largest (~1.1) for galaxies with stellar mass log(M*/Msun)~9.5. We report the presence of a highly ionized metal floor with ${\rm log}_{10}(N({\rm OVI})/{\rm cm}^{-2}) = 13.2$ outside the virial radius of dwarf galaxies inferred from median spectral stacking.
KiDS-1000: weak lensing and intrinsic alignment around luminous red galaxies
2024-09-23
Maria Cristina Fortuna, Andrej Dvornik, Henk Hoekstra, Nora Elisa Chisari, Marika Asgari, Maciej Bilicki, Catherine Heymans, Hendrik Hildebrandt, Koen Kuijken, Angus H. Wright, Ji Yao
We study the properties of the Luminous Red Galaxies (LRGs) selected from the 4th data release of the Kilo Degree Survey (KiDS-1000) via galaxy-galaxy lensing of the background galaxies from KiDS-1000. We use a halo model formalism to interpret our measurements and obtain estimates of the halo masses and the satellite fractions of the LRGs, resulting in halo masses $2.7 \times 10^{12} h^{-1} {\rm M}_{\odot}
Are all starbursts equal? Star-formation-rate profiles in merger versus secular starbursts
2024-09-23
Mallory D. Thorp, Sara L. Ellison, Ana Galicia
Galaxy interactions can trigger drastic changes in the resolved star-forming properties of their constituents, but it remains unclear as to whether those changes are discernible from secular starburst triggers. In this Letter we investigate whether or not post-merger galaxies create unique star-forming trends on a kiloparsec scale. We present radial trends in star-formation-rate (SFR) surface density ($\Sigma_{SFR}$) for 150 post-merger galaxies with moderate to extremely heightened global SFRs using observations from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. We juxtapose these profiles with those of noninteracting galaxies (excluding both galaxy pairs and post-merger galaxies) with similarly enhanced global SFRs. Post-merger galaxies have a much stronger central starburst than isolated galaxies with similar global star-formation enhancements. Indeed, isolated starburst galaxies (SBs) lack a marked central enhancement and instead show a fairly uniform enhancement in $\Sigma_{SFR}$ with radius. Moreover, the difference in central star formation between post-merger galaxies and noninteracting galaxies is more radially extended and pronounced when the global enhancement in star formation is larger. We conclude that post-merger galaxies create a unique signature in their resolved star-forming properties that is distinct from secular processes that can trigger similar global SFR enhancements.
The Palomar twilight survey of 'Ayló'chaxnim, Atiras, and comets
2024-09-23
B. T. Bolin, F. J. Masci, M. W. Coughlin, D. A. Duev, Ž. Ivezić, R. L. Jones, P. Yoachim, T. Ahumada, V. Bhalerao, H. Choudhary, C. Contreras, Y. -C. Cheng, C. M. Copperwheat, K. Deshmukh, C. Fremling, M. Granvik, K. K. Hardegree-Ullman, A. Y. Q. Ho, R. Jedicke, M. Kasliwal, H. Kumar, Z. -Y. Lin, A. Mahabal, A. Monson, J. D. Neill, D. Nesvorný, D. A. Perley, J. N. Purdum, R. Quimby, E. Serabyn, K. Sharma, V. Swain
Near-sun sky twilight observations allow for the detection of asteroid interior to the orbit of Venus (Aylos), the Earth (Atiras), and comets. We present the results of observations with the Palomar 48-inch telescope (P48)/Zwicky Transient Facility (ZTF) camera in 30 s r-band exposures taken during evening astronomical twilight from 2019 Sep 20 to 2022 March 7 and during morning astronomical twilight sky from 2019 Sep 21 to 2022 Sep 29. More than 46,000 exposures were taken in evening and morning astronomical twilight within 31 to 66 degrees from the Sun with an r-band limiting magnitude between 18.1 and 20.9. The twilight pointings show a slight seasonal dependence in limiting magnitude and ability to point closer towards the Sun, with limiting magnitude slightly improving during summer. In total, the one Aylo, (594913) 'Ayl\'o'chaxnim, and 4 Atiras, 2020 OV1, 2021 BS1, 2021 PB2, and 2021 VR3, were discovered in evening and morning twilight observations. Additional twilight survey discoveries also include 6 long-period comets: C/2020 T2, C/2020 V2, C/2021 D2, C/2021 E3, C/2022 E3, and C/2022 P3, and two short-period comets: P/2021 N1 and P/2022 P2 using deep learning comet detection pipelines. The P48/ZTF twilight survey also recovered 11 known Atiras, one Aylo, three short-period comes, two long-period comets, and one interstellar object. Lastly, the Vera Rubin Observatory will conduct a twilight survey starting in its first year of operations and will cover the sky within 45 degrees of the Sun. Twilight surveys such as those by ZTF and future surveys will provide opportunities for discovering asteroids inside the orbits of Earth and Venus.
Identification and Localization of Cometary Activity in Solar System Objects with Machine Learning
2024-09-23
Bryce T. Bolin, Michael W. Coughlin
In this chapter, we will discuss the use of Machine Learning methods for the identification and localization of cometary activity for Solar System objects in ground and in space-based wide-field all-sky surveys. We will begin the chapter by discussing the challenges of identifying known and unknown active, extended Solar System objects in the presence of stellar-type sources and the application of classical pre-ML identification techniques and their limitations. We will then transition to the discussion of implementing ML techniques to address the challenge of extended object identification. We will finish with prospective future methods and the application to future surveys such as the Vera C. Rubin Observatory.
Gravitational Waves and Black Hole perturbations in Acoustic Analogues
2024-09-23
Chiara Coviello
In the present Master's thesis, I describe the research I conducted during my Master's program on the topic of analogue gravity. This line of research was initiated by Bill Unruh, who established an analogy between hydrodynamic flow with a supersonic region and black holes. One possibility to exploit this hydrodynamics/gravity analogy is to create analogue black holes within Bose-Einstein condensates. At low temperatures, phonons-low energy excitations-behave like a massless scalar field in an emergent acoustic metric determined by the condensate. An acoustic black hole is created by transonic fluid, and quantum fluctuations at the acoustic horizon lead to thermal radiation of phonons, akin to Hawking radiation. This emission has been numerically simulated and experimentally verified in Bose-Einstein condensates. The goal of my Master's thesis is to design a system in which an acoustic horizon is excited by a gravitational wave-like perturbation. The thesis is divided into two main parts: the first reviews essential topics of general relativity, quantum field theory in curved spacetimes and analogue gravity; while the second presents my results. Firstly, I propose a method to reproduce a gravitational wave perturbation on a flat background acoustic metric emergent from a Bose-Einstein condensate. Secondly, I demonstrate how to implement an impinging gravitational wave-like perturbation at an acoustic horizon. I then analyze how the horizon responds to this analogue gravitational wave and discuss the implications of my work, including potential studies on shear viscosity and entropy density of the perturbed acoustic horizon. Notably, these interesting research directions could be explored in experiments conducted with ultra-cold quantum gas platforms.
To enhance workspace awareness for mixed-presence meetings with large displays, previous work propose digital cues to share gestures, gaze, or entire postures. While such cues were demonstrated useful in horizontal or smaller workspaces, efforts have focused on isolated elements in controlled settings. It is unknown what needs would emerge with a more realistic setting and how they could be addressed with workspace awareness cues. In this paper, we report on the results of a focus group, centred around users' perceptions while testing a mixed-presence scenario on wall-sized displays. We analyse the gathered comments using Gutwin and Greenberg's workspace awareness framework to identify the most relevant needs. Our results lead to a refinement of the original framework for wall-sized displays and in particular to a categorization into three types of workspace awareness components (i) the Environment, (ii) Actions and (iii) Attention.
Skeletal Data Matching and Merging from Multiple RGB-D Sensors for Room-Scale Human Behaviour Tracking
2024-09-23
Adrien Coppens, Valérie Maquil
A popular and affordable option to provide room-scale human behaviour tracking is to rely on commodity RGB-D sensors %todo: such as the Kinect family of devices? as such devices offer body tracking capabilities at a reasonable price point. While their capabilities may be sufficient for applications such as entertainment systems where a person plays in front of a television, RGB-D sensors are sensitive to occlusions from objects or other persons that might be in the way in more complex room-scale setups. To alleviate the occlusion issue but also in order to extend the tracking range and strengthen its accuracy, it is possible to rely on multiple RGB-D sensors and perform data fusion. Unfortunately, fusing the data in a meaningful manner raises additional challenges related to the calibration of the sensors relative to each other to provide a common frame of reference, but also regarding skeleton matching and merging when actually combining the data. In this paper, we discuss our approach to tackle these challenges and present the results we achieved, through aligned point clouds and combined skeleton lists. These results successfully enable unobtrusive and occlusion-resilient human behaviour tracking at room scale, that may be used as input for interactive applications as well as (possibly remote) collaborative systems.
SMILE: Discriminating milli-lens systems in a pilot project
2024-09-23
F. M. Pötzl, C. Casadio, G. Kalaitzidakis, D. Álvarez-Ortega, A. Kumar, V. Missaglia, D. Blinov, M. Janssen, N. Loudas, V. Pavlidou, A. C. S. Readhead, K. Tassis, P. N. Wilkinson, J. A. Zensus
Dark Matter (DM) remains poorly probed on critical, sub-galactic scales, where predictions from different models diverge in terms of abundance and density profiles of halos. Gravitational lens systems on milli-arcsecond scales (milli-lenses) are expected for a population of dense DM halos, or free-floating supermassive black holes (SMBHs), that might be comprised of primordial black holes (PBHs), in the mass range of $10^6$ to $10^9 M_\odot$. In this paper, we aim to look for milli-lens systems via a systematic search in a large sample of radio-loud AGN observed with very-long-baseline interferometry (VLBI). We present the observational strategy to discriminate milli-lenses from contaminant objects mimicking a milli-lens morphology. In a pilot project, we have investigated VLBI images from 13,828 sources from the Astrogeo VLBI image database and reduced the number of candidates to 40 in a first step. We present here the images and analysis of sensitive follow-up observations with the EVN at 5 and 22 GHz, and streamline our analysis to reject milli-lens candidates. Using constraints such as the surface brightness ratio, conservation of spectral shape, stability of flux ratios over time, and changes in morphology, we can confidently discriminate between milli-lenses and their mimickers. Using the above constraints, we rule out 32 out of our initial 40 candidates as milli-lenses, demonstrating the power of our approach. Also, we find many new candidates for compact symmetric objects, that are thought to be short-lived, jetted radio sources. This serves as a pathfinder for the final sample used for the SMILE (Search for MIlli-LEnses) project, which will allow us to constrain DM models by comparing the results to theoretical predictions. This SMILE sample will consist of $\sim$5,000 sources based on the VLA CLASS survey, including many observations obtained for this project specifically.
Investigating 39 Galactic Wolf-Rayet stars with VLTI/GRAVITY: Uncovering A Long Period Binary Desert
2024-09-23
K. Deshmukh, H. Sana, A. Mérand, J. Bodensteiner, E. Bordier, K. Dsilva, A. J. Frost, E. Gosset, N. Langer, J. -B. Le Bouquin, R. R. Lefever, L. Mahy, L. R. Patrick, M. Reggiani, A. A. C. Sander, T. Shenar, F. Tramper, J. I. Villaseñor, I. Waisberg
Wolf-Rayet stars (WRs) are thought to be the final evolutionary stages of massive stars and immediate progenitors of stellar-mass black holes. Their multiplicity forms an important anchor point in single and binary population models for predicting gravitational-wave progenitors. Recent spectroscopic campaigns have suggested incompatible multiplicity fractions and period distributions for N- and C-rich Galactic WRs (WNs and WCs) at short as well as long orbital periods, in contradiction with evolutionary model predictions. In this work, we employed infrared interferometry using the $K$-band instrument GRAVITY at the VLTI to investigate the multiplicity of WRs at long periods and explore the nature of their companions. We present a survey of 39 Galactic WRs, including 11 WN, 15 WC and 13 H-rich WN (WNh) stars. We detected wide companions with GRAVITY for only four stars: WR 48, WR 89, WR 93 and WR 115. Combining with spectroscopic studies, we arrived at multiplicity fractions of $f^{\rm WN}_{\rm obs} = 0.55\pm0.15$, $f^{\rm WC}_{\rm obs} = 0.40\pm0.13$ and $f^{\rm WNh}_{\rm obs} = 0.23\pm0.12$. In addition, we also found other features in the GRAVITY dataset such as (i) a diffuse extended component in over half the WR sample; (ii) five known spectroscopic binaries resolved in differential phase data and (iii) spatially resolved winds in four stars: WR 16, WR 31a, WR 78 and WR 110. Our survey reveals a lack of intermediate (few 100s d) and long- (few years to decades) period WR systems. The 200-d peak in the period distributions of WR+OB and BH+OB binaries predicted by Case B mass-transfer binary evolution models is not seen in our data. The rich companionship of their O-type progenitors in this separation range suggest that the WR progenitor stars expand and interact with their companions, most likely through unstable mass-transfer, resulting in either a short-period system or a merger.
A Comprehensive Survey with Critical Analysis for Deepfake Speech Detection
2024-09-23
Lam Pham, Phat Lam, Tin Nguyen, Hieu Tang, Huyen Nguyen, Alexander Schindler, Canh Vu
Thanks to advancements in deep learning, speech generation systems now power a variety of real-world applications, such as text-to-speech for individuals with speech disorders, voice chatbots in call centers, cross-linguistic speech translation, etc. While these systems can autonomously generate human-like speech and replicate specific voices, they also pose risks when misused for malicious purposes. This motivates the research community to develop models for detecting synthesized speech (e.g., fake speech) generated by deep-learning-based models, referred to as the Deepfake Speech Detection task. As the Deepfake Speech Detection task has emerged in recent years, there are not many survey papers proposed for this task. Additionally, existing surveys for the Deepfake Speech Detection task tend to summarize techniques used to construct a Deepfake Speech Detection system rather than providing a thorough analysis. This gap motivated us to conduct a comprehensive survey, providing a critical analysis of the challenges and developments in Deepfake Speech Detection. Our survey is innovatively structured, offering an in-depth analysis of current challenge competitions, public datasets, and the deep-learning techniques that provide enhanced solutions to address existing challenges in the field. From our analysis, we propose hypotheses on leveraging and combining specific deep learning techniques to improve the effectiveness of Deepfake Speech Detection systems. Beyond conducting a survey, we perform extensive experiments to validate these hypotheses and propose a highly competitive model for the task of Deepfake Speech Detection. Given the analysis and the experimental results, we finally indicate potential and promising research directions for the Deepfake Speech Detection task.
Probing $Λ$CDM through the Weyl potential and machine learning forecasts
2024-09-23
Rubén Arjona, Savvas Nesseris, Isaac Tutusaus, Daniel Sobral Blanco, Camille Bonvin
For years, the cosmological constant $\Lambda$ and cold dark matter (CDM) model ($\Lambda\text{CDM}$) has stood as a cornerstone in modern cosmology and serves as the predominant theoretical framework for current and forthcoming surveys. However, the latest results shown by the Dark Energy Spectroscopic Instrument (DESI), along other cosmological data, show hints in favor of an evolving dark energy. Given the elusive nature of dark energy and the imperative to circumvent model bias, we introduce a novel null test, derived from Noether's theorem, that uses measurements of the Weyl potential (the sum of the spatial and temporal distortion) at different redshifts. In order to assess the consistency of the concordance model we quantify the precision of this null test through the reconstruction of mock catalogs based on $\Lambda\text{CDM}$ using forthcoming survey data, employing Genetic Algorithms, a machine learning technique. Our analysis indicates that with forthcoming LSST-like and DESI-like mock data our consistency test will be able to rule out several cosmological models at around 4$\sigma$ and help to check for tensions in the data.
We survey a selection of Yasha Eliashberg's contributions to the philosophy of the h-principle, with a focus on the simplification of singularities and its applications.
Predicting Expert Evaluations in Software Code Reviews
2024-09-23
Yegor Denisov-Blanch, Igor Ciobanu, Simon Obstbaum, Michal Kosinski
Manual code reviews are an essential but time-consuming part of software development, often leading reviewers to prioritize technical issues while skipping valuable assessments. This paper presents an algorithmic model that automates aspects of code review typically avoided due to their complexity or subjectivity, such as assessing coding time, implementation time, and code complexity. Instead of replacing manual reviews, our model adds insights that help reviewers focus on more impactful tasks. Calibrated using expert evaluations, the model predicts key metrics from code commits with strong correlations to human judgments (r = 0.82 for coding time, r = 0.86 for implementation time). By automating these assessments, we reduce the burden on human reviewers and ensure consistent analysis of time-consuming areas, offering a scalable solution alongside manual reviews. This research shows how automated tools can enhance code reviews by addressing overlooked tasks, supporting data-driven decisions and improving the review process.
Spaces of Bridgeland stability conditions in representation theory
2024-09-23
Anna Barbieri
The space of Bridgeland stability conditions is a complex manifold that can be attached to a triangulated category, of which it encodes some homological properties. These notes are an introduction to this topic, with a focus on examples from representation theory, and review the example of the Bridgeland-Smith correspondence for some quiver categories from marked surfaces.
2024-09-23
Bjarne Vincent, Daniel Henry, Abhishek Kumar, Valéry Botton, Alban Pothérat, Sophie Miralles
This work identifies the physical mechanisms at play in the different flow regions along an Eckart acoustic streaming jet by means of numerical simulation based on a novel modeling of the driving acoustic force including attenuation effects. The flow is forced by an axisymmetric beam of progressive sound waves attenuating over a significant part of a closed cylindrical vessel where the jet is confined. We focus on the steady, axisymmetric and laminar regime. The jet typically displays a strong acceleration close to the source before reaching a peak velocity. At further distances from the transducer, the on-axis jet velocity smoothly decays before reaching the opposite wall. For each of these flow regions along the jet, we derive scaling laws for the on-axis velocity with the magnitude of the acoustic force and the diffraction of the driving acoustic beam. These laws highlight the different flow regimes along the jet and establish a clear picture of its spatial structure, able to inform the design of experimental or industrial setups involving Eckart streaming jets.
AIM 2024 Sparse Neural Rendering Challenge: Methods and Results
2024-09-23
Michal Nazarczuk, Sibi Catley-Chandar, Thomas Tanay, Richard Shaw, Eduardo Pérez-Pellitero, Radu Timofte, Xing Yan, Pan Wang, Yali Guo, Yongxin Wu, Youcheng Cai, Yanan Yang, Junting Li, Yanghong Zhou, P. Y. Mok, Zongqi He, Zhe Xiao, Kin-Chung Chan, Hana Lebeta Goshu, Cuixin Yang, Rongkang Dong, Jun Xiao, Kin-Man Lam, Jiayao Hao, Qiong Gao, Yanyan Zu, Junpei Zhang, Licheng Jiao, Xu Liu, Kuldeep Purohit
This paper reviews the challenge on Sparse Neural Rendering that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2024. This manuscript focuses on the competition set-up, the proposed methods and their respective results. The challenge aims at producing novel camera view synthesis of diverse scenes from sparse image observations. It is composed of two tracks, with differing levels of sparsity; 3 views in Track 1 (very sparse) and 9 views in Track 2 (sparse). Participants are asked to optimise objective fidelity to the ground-truth images as measured via the Peak Signal-to-Noise Ratio (PSNR) metric. For both tracks, we use the newly introduced Sparse Rendering (SpaRe) dataset and the popular DTU MVS dataset. In this challenge, 5 teams submitted final results to Track 1 and 4 teams submitted final results to Track 2. The submitted models are varied and push the boundaries of the current state-of-the-art in sparse neural rendering. A detailed description of all models developed in the challenge is provided in this paper.
Probing the rest-frame wavelength dependence of Quasar variability : Insights from the Zwicky Transient Facility Survey
2024-09-23
P. Patel, P. Arévalo, P. Lira, Mouyuan Sun, S. Bernal, M. L. Martínez-Aldama
Context. Quasar variability can potentially unlock crucial insights into the accretion process. Understanding how this variability is influenced by wavelength is crucial for validating and refining quasar variability models. Aims. This paper aims to enhance the understanding of the dependence of variability on rest-frame wavelength by isolating the variance on different time scales in well-defined wavelength bins and examining the Corona-heated Accretion-disk (CHAR) model. Methods. We investigated the relation between variance and rest-frame wavelength using optical g and r-band light curves from the Zwicky Transient Facility (ZTF) Data Release 15 for 5000 quasars within narrow ranges of black hole mass ($M_{BH}$) and Eddington ratio ($R_{Edd}$). A spectral model taking into account disk continuum emission, Balmer transitions and Fe II pseudo-continuum emission, and other emission lines is necessary to best interpret the variance spectrum. Results. Our analysis indicates a strong anti-correlation between median variance and rest-frame wavelength for quasars with $M_{BH} = 10^{8}$ and $R_{Edd} = 10^{-1}$ at different timescales. This anti-correlation is more pronounced at shorter timescales. The results align well with a bending power-law Power Spectrum Density (PSD) model with both the damping timescale and the high-frequency slope of the PSD depending on wavelength. The accurate predictions provided by the CHAR model on the variance spectrum across most timescales studied showcases its potential in constraining temperature variations within the accretion disk. Key words. accretion, accretion discs; galaxies:active; quasars: supermassive black holes
Exactly solvable Schrödinger operators related to the confluent equation
2024-09-23
Jan Dereziński, Jinyeop Lee
Our paper investigates one-dimensional Schr\"odinger operators defined as closed operators on $L^2(\mathbb{R})$ or $L^2(\mathbb{R}_+)$ that are exactly solvable in terms of confluent functions (or, equivalently, Whittaker functions). We allow the potentials to be complex. They fall into three families: Whittaker operators (or radial Coulomb Hamiltonians), Schr\"odinger operators with Morse potentials, and isotonic oscillators. For each of them, we discuss the corresponding basic holomorphic family of closed operators and the integral kernel of their resolvents. We also describe transmutation identities that relate these resolvents. These identities interchange spectral parameters with coupling constants across different operator families. A similar analysis is performed for one-dimensional Schr\"odinger operators solvable in terms of Bessel functions (which are reducible to special cases of Whittaker functions). They fall into two families: Bessel operators and Schr\"odinger operators with exponential potentials. To make our presentation self-contained, we include a short summary of the theory of closed one-dimensional Schr\"odinger operators with singular boundary conditions. We also provide a concise review of special functions that we use.
Multi-modal generative AI has received increasing attention in both academia and industry. Particularly, two dominant families of techniques are: i) The multi-modal large language model (MLLM) such as GPT-4V, which shows impressive ability for multi-modal understanding; ii) The diffusion model such as Sora, which exhibits remarkable multi-modal powers, especially with respect to visual generation. As such, one natural question arises: Is it possible to have a unified model for both understanding and generation? To answer this question, in this paper, we first provide a detailed review of both MLLM and diffusion models, including their probabilistic modeling procedure, multi-modal architecture design, and advanced applications to image/video large language models as well as text-to-image/video generation. Then, we discuss the two important questions on the unified model: i) whether the unified model should adopt the auto-regressive or diffusion probabilistic modeling, and ii) whether the model should utilize a dense architecture or the Mixture of Experts(MoE) architectures to better support generation and understanding, two objectives. We further provide several possible strategies for building a unified model and analyze their potential advantages and disadvantages. We also summarize existing large-scale multi-modal datasets for better model pretraining in the future. To conclude the paper, we present several challenging future directions, which we believe can contribute to the ongoing advancement of multi-modal generative AI.
A key result of the string phenomenology framework is the two swampland conjectures (SCs), namely that the values of the scalar field excursion and gradient of the potential have to be $\mathcal{O}(1)$ in Planck units. Using the first data release from the Dark Energy Spectroscopic Instrument (DESI) survey and model-independent reconstructions of the SCs at late times, via a particular machine learning approach known as the genetic algorithms (GA), we find that the reconstructions of the SCs are, in fact, in agreement with their theoretical values of $\mathcal{O}(1)$. Specifically, the reconstruction of the second SC is in fact several sigmas away from zero, thus indicating a very steep potential in contrast to recent model-specific analyses, assuming exponential potentials. Our approach is based solely on model-independent reconstructions of cosmological observables, such as the angular diameter distance and the Hubble expansion history, thus it is readily applicable to the forthcoming data from Stage IV surveys and will help settle in the near future the issue on the observational falsification, or not, of the two conjectures.
Records from the S-Matrix Marathon: Observables in Expanding Universes
2024-09-23
Paolo Benincasa, Mathieu Giroux, Holmfridur S. Hannesdottir, Sebastian Mizera, Celina Pasiecznik, Francisco Vazão
Observables in expanding universes are crucial to understand the physics of the early universe. In these lectures, we review some recent progress in understanding their mathematical structure and extract the physics encoded in them. After discussing the most salient features of an expanding background and their consequences for defining an observable, we focus on the so-called Bunch--Davies wavefunctional. We analyze its analytic properties on general grounds and introduce an integral representation for it in perturbation theory for a special class of scalar toy models. We discuss both the diagrammatics associated to the usual Feynman rules and combinatorial rules on the graphs, which generate a representation free of spurious poles. Such combinatorial rules find their origin in the combinatorics of the cosmological polytopes of which we provide a gentle introduction to its definition and its main features. Finally, the combinatorics of the cosmological polytopes turns out to determine the combinatorics of a special class of nestohedra that encode the asymptotic behaviour of the cosmological integrals. We provide a general description of such structures and behaviour, which is of crucial importance to understand the infrared divergences which plague observables in an expanding background. These notes are based on a series of lectures held during the S-Matrix Marathon workshop at the Institute for Advanced Study on 11--22 March 2024.
Non-Linear Supergravity and Inflationary Cosmology
2024-09-23
Ignatios Antoniadis, Emilian Dudas, Fotis Farakos, Augusto Sagnotti
We review a series of developments concerning non-linear realizations of supersymmetry coupled to supergravity, with emphasis on some applications to inflationary Cosmology.
Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely
2024-09-23
Siyun Zhao, Yuqing Yang, Zilong Wang, Zhiyuan He, Luna K. Qiu, Lili Qiu
Large language models (LLMs) augmented with external data have demonstrated remarkable capabilities in completing real-world tasks. Techniques for integrating external data into LLMs, such as Retrieval-Augmented Generation (RAG) and fine-tuning, are gaining increasing attention and widespread application. Nonetheless, the effective deployment of data-augmented LLMs across various specialized fields presents substantial challenges. These challenges encompass a wide range of issues, from retrieving relevant data and accurately interpreting user intent to fully harnessing the reasoning capabilities of LLMs for complex tasks. We believe that there is no one-size-fits-all solution for data-augmented LLM applications. In practice, underperformance often arises from a failure to correctly identify the core focus of a task or because the task inherently requires a blend of multiple capabilities that must be disentangled for better resolution. In this survey, we propose a RAG task categorization method, classifying user queries into four levels based on the type of external data required and primary focus of the task: explicit fact queries, implicit fact queries, interpretable rationale queries, and hidden rationale queries. We define these levels of queries, provide relevant datasets, and summarize the key challenges and most effective techniques for addressing these challenges. Finally, we discuss three main forms of integrating external data into LLMs: context, small model, and fine-tuning, highlighting their respective strengths, limitations, and the types of problems they are suited to solve. This work aims to help readers thoroughly understand and decompose the data requirements and key bottlenecks in building LLM applications, offering solutions to the different challenges and serving as a guide to systematically developing such applications.
Unsourced Random Access: A Recent Paradigm for Massive Connectivity
2024-09-23
Mert Ozates, Mohammad Javad Ahmadi, Mohammad Kazemi, Tolga M. Duman
The sixth generation and beyond communication systems are expected to enable communications of a massive number of machine-type devices. The traffic generated by some of these devices will significantly deviate from those in conventional communication scenarios. For instance, for applications where a massive number of cheap sensors communicate with a base station (BS), the devices will only be sporadically active and there will be no coordination among them or with the BS. For such systems requiring massive random access solutions, a new paradigm called unsourced random access (URA) has been proposed. In URA, all the users employ the same codebook and there is no user identity during the data transmission phase. The destination is only interested in the list of messages being sent from the set of active users. In this survey, we provide a comprehensive overview of existing URA solutions with an emphasis on the state-of-the-art, covering both algorithmic and information-theoretic aspects. Moreover, we provide future research directions and challenges, and describe some potential methods of addressing them.
This work proposes a kinodynamic motion planning technique for collaborative object transportation by multiple mobile manipulators in dynamic environments. A global path planner computes a linear piecewise path from start to goal. A novel algorithm detects the narrow regions between the static obstacles and aids in defining the obstacle-free region to enhance the feasibility of the global path. We then formulate a local online motion planning technique for trajectory generation that minimizes the control efforts in a receding horizon manner. It plans the trajectory for finite time horizons, considering the kinodynamic constraints and the static and dynamic obstacles. The planning technique jointly plans for the mobile bases and the arms to utilize the locomotion capability of the mobile base and the manipulation capability of the arm efficiently. We use a convex cone approach to avoid self-collision of the formation by modifying the mobile manipulators admissible state without imposing additional constraints. Numerical simulations and hardware experiments showcase the efficiency of the proposed approach.
Investigating Ultra-Large Large-Scale Structures: Potential Implications for Cosmology
2024-09-23
Alexia M. Lopez, Roger G. Clowes, Gerard M. Williger
Large-scale structure (LSS) studies in cosmology map and analyse matter in the Universe on the largest scales. Understanding the LSS can provide observational support for the Cosmological Principle (CP) and the Standard Cosmological Model ($\Lambda$CDM). In recent years, many discoveries have been made of LSSs that are so large that they become difficult to understand within $\Lambda$CDM. Reasons for this are: they potentially challenge the CP, (i.e. the scale of homogeneity); and their formation and origin are not fully understood. In this article we review two recent LSS discoveries: the Giant Arc (GA, $\sim 1$ Gpc) and the Big Ring (BR, $\sim 400$ Mpc). Both structures are in the same cosmological neighbourhood -- at the same redshift $z \sim 0.8$ and with a separation on the sky of only $\sim 12^\circ$. Both structures exceed the often-cited scale of homogeneity (Yadav+ 2010), so individually and together, these two intriguing structures raise more questions for the validity of the CP and potentially hint at new physics beyond the Standard Model. The GA and BR were discovered using a novel method of mapping faint matter at intermediate redshifts, interpreted from the MgII absorption doublets seen in the spectra of background quasars.
Automatic Traffic Sign Recognition is paramount in modern transportation systems, motivating several research endeavors to focus on performance improvement by utilizing large-scale datasets. As the appearance of traffic signs varies across countries, curating large-scale datasets is often impractical; and requires efficient models that can produce satisfactory performance using limited data. In this connection, we present 'FUSED-Net', built-upon Faster RCNN for traffic sign detection, enhanced by Unfrozen Parameters, Pseudo-Support Sets, Embedding Normalization, and Domain Adaptation while reducing data requirement. Unlike traditional approaches, we keep all parameters unfrozen during training, enabling FUSED-Net to learn from limited samples. The generation of a Pseudo-Support Set through data augmentation further enhances performance by compensating for the scarcity of target domain data. Additionally, Embedding Normalization is incorporated to reduce intra-class variance, standardizing feature representation. Domain Adaptation, achieved by pre-training on a diverse traffic sign dataset distinct from the target domain, improves model generalization. Evaluating FUSED-Net on the BDTSD dataset, we achieved 2.4x, 2.2x, 1.5x, and 1.3x improvements of mAP in 1-shot, 3-shot, 5-shot, and 10-shot scenarios, respectively compared to the state-of-the-art Few-Shot Object Detection (FSOD) models. Additionally, we outperform state-of-the-art works on the cross-domain FSOD benchmark under several scenarios.
Explainable and Human-Grounded AI for Decision Support Systems: The Theory of Epistemic Quasi-Partnerships
2024-09-23
John Dorsch, Maximilian Moll
In the context of AI decision support systems (AI-DSS), we argue that meeting the demands of ethical and explainable AI (XAI) is about developing AI-DSS to provide human decision-makers with three types of human-grounded explanations: reasons, counterfactuals, and confidence, an approach we refer to as the RCC approach. We begin by reviewing current empirical XAI literature that investigates the relationship between various methods for generating model explanations (e.g., LIME, SHAP, Anchors), the perceived trustworthiness of the model, and end-user accuracy. We demonstrate how current theories about what constitutes good human-grounded reasons either do not adequately explain this evidence or do not offer sound ethical advice for development. Thus, we offer a novel theory of human-machine interaction: the theory of epistemic quasi-partnerships (EQP). Finally, we motivate adopting EQP and demonstrate how it explains the empirical evidence, offers sound ethical advice, and entails adopting the RCC approach.
AIM 2024 Challenge on Video Saliency Prediction: Methods and Results
2024-09-23
Andrey Moskalenko, Alexey Bryncev, Dmitry Vatolin, Radu Timofte, Gen Zhan, Li Yang, Yunlong Tang, Yiting Liao, Jiongzhi Lin, Baitao Huang, Morteza Moradi, Mohammad Moradi, Francesco Rundo, Concetto Spampinato, Ali Borji, Simone Palazzo, Yuxin Zhu, Yinan Sun, Huiyu Duan, Yuqin Cao, Ziheng Jia, Qiang Hu, Xiongkuo Min, Guangtao Zhai, Hao Fang, Runmin Cong, Xiankai Lu, Xiaofei Zhou, Wei Zhang, Chunyu Zhao, Wentao Mu, Tao Deng, Hamed R. Tavakoli
This paper reviews the Challenge on Video Saliency Prediction at AIM 2024. The goal of the participants was to develop a method for predicting accurate saliency maps for the provided set of video sequences. Saliency maps are widely exploited in various applications, including video compression, quality assessment, visual perception studies, the advertising industry, etc. For this competition, a previously unused large-scale audio-visual mouse saliency (AViMoS) dataset of 1500 videos with more than 70 observers per video was collected using crowdsourced mouse tracking. The dataset collection methodology has been validated using conventional eye-tracking data and has shown high consistency. Over 30 teams registered in the challenge, and there are 7 teams that submitted the results in the final phase. The final phase solutions were tested and ranked by commonly used quality metrics on a private test subset. The results of this evaluation and the descriptions of the solutions are presented in this report. All data, including the private test subset, is made publicly available on the challenge homepage - https://challenges.videoprocessing.ai/challenges/video-saliency-prediction.html.
Shannon Bounds for Quadratic Rate-Distortion Problems
2024-09-23
Michael Gastpar, Erixhen Sula
The Shannon lower bound has been the subject of several important contributions by Berger. This paper surveys Shannon bounds on rate-distortion problems under mean-squared error distortion with a particular emphasis on Berger's techniques. Moreover, as a new result, the Gray-Wyner network is added to the canon of settings for which such bounds are known. In the Shannon bounding technique, elegant lower bounds are expressed in terms of the source entropy power. Moreover, there is often a complementary upper bound that involves the source variance in such a way that the bounds coincide in the special case of Gaussian statistics. Such pairs of bounds are sometimes referred to as Shannon bounds. The present paper puts Berger's work on many aspects of this problem in the context of more recent developments, encompassing indirect and remote source coding such as the CEO problem, originally proposed by Berger, as well as the Gray-Wyner network as a new contribution.
Enhancing Photometric Redshift Catalogs Through Color-Space Analysis: Application to KiDS-Bright Galaxies
2024-09-23
Priyanka Jalan, Maciej Bilicki, Wojciech A. Hellwing, Angus H. Wright, Andrej Dvornik, Catherine Heymans, Hendrik Hildebrandt, Shahab Joudaki, Konrad Kuijken, Constance Mahony, Szymon Jan Nakoneczny, Mario Radovich, Jan Luca van den Busch, Mijin Yoon
We present a method to refine photometric redshift galaxy catalogs by comparing their color-space matching with overlapping spectroscopic calibration data. We focus on cases where photometric redshifts (photo-$z$) are estimated empirically. Identifying galaxies that are poorly represented in spectroscopic data is crucial, as their photo-$z$ may be unreliable due to extrapolation beyond the training sample. Our approach uses a self-organizing map (SOM) to project a multi-dimensional parameter space of magnitudes and colors onto a 2-D manifold, allowing us to analyze the resulting patterns as a function of various galaxy properties. Using SOM, we compare the Kilo-Degree Survey bright galaxy sample (KiDS-Bright), limited to $r<20$ mag, with various spectroscopic samples, including the Galaxy And Mass Assembly (GAMA). Our analysis reveals that GAMA under-represents KiDS-Bright at its faintest ($r\gtrsim19.5$) and highest-redshift ($z\gtrsim0.4$) ranges, however no strong trends in color or stellar mass. By incorporating additional spectroscopic data from the SDSS, 2dF, and early DESI, we identify SOM cells where photo-$z$ are estimated suboptimally. We derive a set of SOM-based criteria to refine the photometric sample and improve photo-$z$ statistics. For the KiDS-Bright sample, this improvement is modest: exclusion of the least represented 20% of the sample reduces photo-$z$ scatter by less than 10%. We conclude that GAMA, used for KiDS-Bright photo-$z$ training, is sufficiently representative for reliable redshift estimation across most of the color space. Future spectroscopic data from surveys such as DESI should be better suited for exploiting the full improvement potential of our method.
Gamma-ray bursts (GRBs) are the most luminous transients in the universe. The interaction of the relativistic jet with the circumburst medium produces an afterglow and generates multiwavelength emission. In this work, we present simultaneous multiband photometry of GRB~240825A with the Multi-channel Photometric Survey Telescope (Mephisto) and analyze its temporal and spectral properties. The measurement began 128 seconds after the GRB trigger and continued until the fourth day when the afterglow essentially diminished and the measured brightness was close to that of the host galaxy. Based on the multiband light curves in the $uvgriz$ bands, we find that the optical flux density satisfies $F_{\nu,{\rm obs}}\propto t^{-1.34}\nu^{-2.48}$ with a spectral index of $2.48$ much larger than those of most other GRBs. To reconcile the measured much softer spectral energy distribution (SED) with that predicted by the standard afterglow model, an extra host-galaxy extinction of $E_{B-V}\sim(0.37-0.57)$ mag is required. We interpreted this excess as arising from a dense circumburst medium. We further find that the SED of the optical afterglow hardened as the afterglow decayed and the color excess $E_{B-V}$ decreased $\sim0.21$ mag in the first 3000 seconds. Finally, we analyze the properties of the host galaxy of GRB~240825A based on data from the SDSS, PanSTARRS and HSC-SSP surveys. For a host redshift of $z=0.659$, the stellar mass and star formation rate of the host galaxy are estimated to be $\log(M_*/M_\odot)=10.0^{+0.3}_{-0.3}$ and $\log({\rm SFR}/M_{\odot}{\rm yr}^{-1})= 0.6^{+0.8}_{-3.3}$, respectively, pointing to a gas-rich, star-forming, medium-size galaxy.
Quantifying Context Bias in Domain Adaptation for Object Detection
2024-09-23
Hojun Son, Arpan Kusari
Domain adaptation for object detection (DAOD) aims to transfer a trained model from a source to a target domain. Various DAOD methods exist, some of which minimize context bias between foreground-background associations in various domains. However, no prior work has studied context bias in DAOD by analyzing changes in background features during adaptation and how context bias is represented in different domains. Our research experiment highlights the potential usability of context bias in DAOD. We address the problem by varying activation values over different layers of trained models and by masking the background, both of which impact the number and quality of detections. We then use one synthetic dataset from CARLA and two different versions of real open-source data, Cityscapes and Cityscapes foggy, as separate domains to represent and quantify context bias. We utilize different metrics such as Maximum Mean Discrepancy (MMD) and Maximum Variance Discrepancy (MVD) to find the layer-specific conditional probability estimates of foreground given manipulated background regions for separate domains. We demonstrate through detailed analysis that understanding of the context bias can affect DAOD approach and foc
This review paper, written for the second edition of the Handbook of Markov Chain Monte Carlo, provides an introduction to the study of convergence analysis for Markov chain Monte Carlo (MCMC), aimed at researchers new to the field. We focus on methods for constructing bounds on the distance between the distribution of a Markov chain at a given time and its stationary distribution. Two widely-used approaches are explored: the coupling method and the L2 theory of Markov chains. For the latter, we emphasize techniques based on conductance and isoperimetric inequalities. Additionally, we briefly discuss strategies for identifying slow convergence in Markov chains.
Fine Tuning Text-to-Image Diffusion Models for Correcting Anomalous Images
2024-09-23
Hyunwoo Yoo
Since the advent of GANs and VAEs, image generation models have continuously evolved, opening up various real-world applications with the introduction of Stable Diffusion and DALL-E models. These text-to-image models can generate high-quality images for fields such as art, design, and advertising. However, they often produce aberrant images for certain prompts. This study proposes a method to mitigate such issues by fine-tuning the Stable Diffusion 3 model using the DreamBooth technique. Experimental results targeting the prompt "lying on the grass/street" demonstrate that the fine-tuned model shows improved performance in visual evaluation and metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Frechet Inception Distance (FID). User surveys also indicated a higher preference for the fine-tuned model. This research is expected to make contributions to enhancing the practicality and reliability of text-to-image models.
Constructing Nearby Commuting Matrices for Ogata's Theorem on Macroscopic Observables
2024-09-23
David Herrera
Resolving a conjecture of von Neumann, Ogata's theorem in arXiv:1111.5933 showed the highly nontrivial result that arbitrarily many matrices corresponding to macroscopic observables with $N$ sites and a fixed site dimension $d$ are asymptotically nearby commuting observables as $N \to \infty$. We develop a method to construct nearby commuting matrices for normalized highly reducible representations of $su(2)$ whose multiplicities of irreducible subrepresentations exhibit a certain monotonically decreasing behavior. We then provide a constructive proof of Ogata's theorem for site dimension $d=2$ with explicit estimates for how close the nearby observables are. Moreover, motivated by the application to time-reversal symmetry explored in arXiv:1012.3494, our construction has the property that real macroscopic observables are asymptotically nearby real commuting observables. This thesis also contains an introduction to the prerequisite matrix analysis for understanding the proof of the results gotten, a primer on the functional analysis of $C^\ast$-algebras frequently used in the literature of almost commuting operators, a detailed discussion of the history and main results in the problem of almost commuting matrices, a review of the basics of measurement of observables in finite dimensional quantum mechanical systems, and a discussion of the uncertainty principle for non-commuting bounded operators with some results obtained for observables with nearby commuting approximants.
New Upper Bounds on the Minimal Domination Numbers of High-Dimensional Hypercubes
2024-09-22
Zachary DeVivo, Robert K. Hladky
We briefly review known results on upper bounds for the minimal domination number $\gamma_n$ of a hypercube of dimension $n$, then present a new method for constructing dominating sets. Write $n =2^{\hat{n}}-1 +{\check{n}}$ with $0\leq {\check{n}}<2^{\hat{n}}$. Our construction applies to all $n$ lying within the expanding wedge $\theta({\hat{n}}) \leq {\check{n}} < 2^{{\hat{n}}}$, where $\theta$ is a specific, easily computable function with the asymptotic property $\theta(a) \sim 2^{a/2}$. For all $n$ within the smaller wedge $\theta({\hat{n}}) \leq {\check{n}} < 2^{{\hat{n}}-2}$, the resulting upper bound on $\gamma_n$ betters those previously known.
Rational inner functions on the polydisk -- a survey
2024-09-22
Greg Knese
Rational inner functions are a generalization of finite Blaschke products to several variables. In this article we survey a variety of results about rational inner functions related to interpolation, sums of squares formulas, and boundary behavior. We mostly focus on two variables however in the final section we discuss higher dimensions.
A Review of Scalable and Privacy-Preserving Multi-Agent Frameworks for Distributed Energy Resource Control
2024-09-22
Xiang Huo, Hao Huang, Katherine R. Davis, H. Vincent Poor, Mingxi Liu
Distributed energy resources (DERs) are gaining prominence due to their advantages in improving energy efficiency, reducing carbon emissions, and enhancing grid resilience. Despite the increasing deployment, the potential of DERs has yet to be fully explored and exploited. A fundamental question restrains the management of numerous DERs in large-scale power systems, "How should DER data be securely processed and DER operations be efficiently optimized?" To address this question, this paper considers two critical issues, namely privacy for processing DER data and scalability in optimizing DER operations, then surveys existing and emerging solutions from a multi-agent framework perspective. In the context of scalability, this paper reviews state-of-the-art research that relies on parallel control, optimization, and learning within distributed and/or decentralized information exchange structures, while in the context of privacy, it identifies privacy preservation measures that can be synthesized into the aforementioned scalable structures. Despite research advances in these areas, challenges remain because these highly interdisciplinary studies blend a wide variety of scalable computing architectures and privacy preservation techniques from different fields, making them difficult to adapt in practice. To mitigate this issue, this paper provides a holistic review of trending strategies that orchestrate privacy and scalability for large-scale power system operations from a multi-agent perspective, particularly for DER control problems. Furthermore, this review extrapolates new approaches for future scalable, privacy-aware, and cybersecure pathways to unlock the full potential of DERs through controlling, optimizing, and learning generic multi-agent-based cyber-physical systems.
$Mesiri$:Mephisto Early Supernovae Ia Rapid Identifier
2024-09-22
Zhang Lunwei, Wang Zhenyu, Liu Dezi, Fang Yuan, Chen Bingqiu, Kumar Brajesh, Er Xinzhong, Liu Xiaowei
The early time observations of Type Ia supernovae (SNe Ia) play a crucial role in investigating and resolving longstanding questions about progenitor stars and the explosion mechanisms of these events. Colors of supernovae (SNe) in the initial days after the explosion can help differentiate between different types of SNe. However, the use of true color information to identify SNe Ia at the early-time explosion is still in its infancy. The Multi-channel Photometric Survey Telescope (Mephisto) is a photometric survey telescope equipped with three CCD cameras, capable of simultaneously imaging the same patch of sky in three bands (\emph{u, g, i} or \emph{v, r, z}), yielding real-time colors of astronomical objects. In this paper, we introduce a new time-series classification tool named Mephisto Early Supernovae Ia Rapid Identifier (\emph{\texttt{Mesiri}}), which for the first time, utilizes real-time color information to distinguish early-time SNe Ia from core-collapse supernovae (CCSNe). \emph{\texttt{Mesiri}} is based on the deep learning approach and can achieve an accuracy of $96.75\pm0.79$\%, and AUC of $98.87\pm0.53$\% in case of single epoch random observation before the peak brightness. These values reach towards perfectness if additional data points on several night observations are considered. The classification with real-time color significantly outperforms that with pseudo-color, especially at the early time, i.e., with only a few points of observations. The architecture of BiLSTM shows the best performance than the others that have been tested in this work.
Blockchain Based Information Security and Privacy Protection: Challenges and Future Directions using Computational Literature Review
2024-09-22
Gauri Shankar, Md Raihan Uddin, Saddam Mukta, Prabhat Kumar, Shareeful Islam, A. K. M. Najmul Islam
Blockchain technology is an emerging digital innovation that has gained immense popularity in enhancing individual security and privacy within Information Systems (IS). This surge in interest is reflected in the exponential increase in research articles published on blockchain technology, highlighting its growing significance in the digital landscape. However, the rapid proliferation of published research presents significant challenges for manual analysis and synthesis due to the vast volume of information. The complexity and breadth of topics, combined with the inherent limitations of human data processing capabilities, make it difficult to comprehensively analyze and draw meaningful insights from the literature. To this end, we adopted the Computational Literature Review (CLR) to analyze pertinent literature impact and topic modelling using the Latent Dirichlet Allocation (LDA) technique. We identified 10 topics related to security and privacy and provided a detailed description of each topic. From the critical analysis, we have observed several limitations, and several future directions are provided as an outcome of this review.
Large Model Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends
2024-09-22
Yuntao Wang, Yanghe Pan, Quan Zhao, Yi Deng, Zhou Su, Linkang Du, Tom H. Luan
Large Model (LM) agents, powered by large foundation models such as GPT-4 and DALL-E 2, represent a significant step towards achieving Artificial General Intelligence (AGI). LM agents exhibit key characteristics of autonomy, embodiment, and connectivity, allowing them to operate across physical, virtual, and mixed-reality environments while interacting seamlessly with humans, other agents, and their surroundings. This paper provides a comprehensive survey of the state-of-the-art in LM agents, focusing on the architecture, cooperation paradigms, security, privacy, and future prospects. Specifically, we first explore the foundational principles of LM agents, including general architecture, key components, enabling technologies, and modern applications. Then, we discuss practical collaboration paradigms from data, computation, and knowledge perspectives towards connected intelligence of LM agents. Furthermore, we systematically analyze the security vulnerabilities and privacy breaches associated with LM agents, particularly in multi-agent settings. We also explore their underlying mechanisms and review existing and potential countermeasures. Finally, we outline future research directions for building robust and secure LM agent ecosystems.
We surveyed Australian finance professionals and tested whether there are statistically significant differences in promotional propensity according to gender identity. The findings indicate men and women are equally likely to ask for promotion, however, 'gifted advancements' account for the higher statistical frequency of promotions among men. These gender-based differences in behaviors have been overlooked in existing research on promotion. We call for a standardized framework for the development of promotion policies to address this industry-wide problem.
The Ability of Large Language Models to Evaluate Constraint-satisfaction in Agent Responses to Open-ended Requests
2024-09-22
Lior Madmoni, Amir Zait, Ilia Labzovsky, Danny Karmon
Generative AI agents are often expected to respond to complex user requests that have No One Right Answer (NORA), e.g., "design a vegetarian meal plan below 1800 calories". Such requests may entail a set of constraints that the agent should adhere to. To successfully develop agents for NORA scenarios, an accurate automatic evaluation framework is essential, and specifically - one capable of validating the satisfaction of constraints in the agent's response. Recently, large language models (LLMs) have been adopted as versatile evaluators for many NORA tasks, but their ability to evaluate constraint-satisfaction in generated text remains unclear. To study this, we develop and release a novel Arithmetic Constraint-Satisfaction (ACS) benchmarking dataset. The dataset consists of complex user requests with corresponding constraints, agent responses and human labels indicating each constraint's satisfaction level in the response. A unique property of this dataset is that validating many of its constraints requires reviewing the response as a whole (in contrast to many other benchmarks that require the validation of a single independent item). Moreover, it assesses LLMs in performing reasoning, in-context data extraction, arithmetic calculations, and counting. We then benchmark both open and proprietary LLMs on evaluating constraint-satisfaction, and show that most models still have a significant headroom for improvement, and that errors primarily stem from reasoning issues. In addition, most models exhibit a skewed constraint-satisfaction prediction pattern, with higher accuracy where the ground-truth label is "satisfied". Lastly, few-shot prompting for our task proved to be rather challenging, since many of the studied models showed a degradation in performance when it was introduced.
Machine Learning algorithms for optimization of magnetocaloric effect in all-d-metal Heusler alloys
2024-09-22
Danil Baigutlin, Vladimir Sokolovskiy, Vasiliy Buchelnikov, Sergey Taskaev
This study examines the application of machine learning algorithms, specifically the Random Forest regression model, to optimize the magnetocaloric effect in all-d-metal Heusler alloys. The model was trained using descriptors related to the mean properties of individual atoms, the properties of simple compounds in their ground state, and measures of chemical disorder. It demonstrated high accuracy in predicting structural properties, while exhibiting moderate accuracy in predicting magnetic properties. To identify optimal alloy compositions, a genetic algorithm was used to find those with the greatest differences in magnetization during martensitic transitions. Using this combined approach, the Ni-Co-Mn-Ti alloy system was thoroughly explored, resulting in the discovery of an alloy with a maximum magnetization difference. These results are consistent with previous research based on Density Functional Theory (DFT) and highlight the effectiveness of integrating machine learning with genetic algorithms for the discovery of new materials with outstanding magnetocaloric properties. The study emphasizes the need for further refinement of models capable of accurately predicting complex magnetic interactions, which is essential for fully leveraging the potential of all-d-metal Heusler alloys in practical applications.
2024-09-22
Gautam Chinta, Jay Jorgenson, Anders Karlsson, Lejla Smajlović
This paper illustrates the utility of the heat kernel on $\mathbb{Z}$ as the discrete analogue of the Gaussian density function. It is the two-variable function $K_{\mathbb{Z}}(t,x)=e^{-2t}I_{x}(2t)$ involving a Bessel function and variables $x\in\mathbb{Z}$ and real $t\geq 0$. Like its classic counterpart it appears in many mathematical and physical contexts and has a wealth of applications. Some of these will be reviewed here, concerning Bessel integrals, trigonometric sums, hypergeometric functions and asymptotics of discrete models appearing in statistical and quantum physics. Moreover, we prove a new local limit theorem for sums of integer-valued random variables, obtain novel special values of the spectral zeta function of Bethe lattices, and provide a discussion on how $e^{-2t}I_{x}(2t)$ could be useful in differential privacy.
Large Language Models (LLMs) have shown significant potential as judges for Machine Translation (MT) quality assessment, providing both scores and fine-grained feedback. Although approaches such as GEMBA-MQM has shown SOTA performance on reference-free evaluation, the predicted errors do not align well with those annotated by human, limiting their interpretability as feedback signals. To enhance the quality of error annotations predicted by LLM evaluators, we introduce a universal and training-free framework, $\textbf{MQM-APE}$, based on the idea of filtering out non-impactful errors by Automatically Post-Editing (APE) the original translation based on each error, leaving only those errors that contribute to quality improvement. Specifically, we prompt the LLM to act as 1) $\textit{evaluator}$ to provide error annotations, 2) $\textit{post-editor}$ to determine whether errors impact quality improvement and 3) $\textit{pairwise quality verifier}$ as the error filter. Experiments show that our approach consistently improves both the reliability and quality of error spans against GEMBA-MQM, across eight LLMs in both high- and low-resource languages. Orthogonal to trained approaches, MQM-APE complements translation-specific evaluators such as Tower, highlighting its broad applicability. Further analysis confirm the effectiveness of each module and offer valuable insights into evaluator design and LLMs selection. The code will be released to facilitate the community.
Signatures of chaos can be understood by studying quantum systems whose classical counterpart is chaotic. However, the concepts of integrability, non-integrability and chaos extend to systems without a classical analogue. Here, we first review the classical route from order into chaos. Since nature is fundamentally quantum, we discuss how chaos manifests in the quantum domain. We briefly describe semi-classical methods, and discuss the consequences of chaos in quantum information processing. We review the quantum version of Lyapunov exponents, as quantified by the out-of-time ordered correlators (OTOC), Kolmogorov-Sinai (KS) entropy and sensitivity to errors. We then review the study of signatures of quantum chaos using quantum tomography. Classically, if we know the dynamics exactly, as we maintain a constant coarse-grained tracking of the trajectory, we gain exponentially fine-grained information about the initial condition. In the quantum setting,as we track the measurement record with fixed signal-to-noise, we gain increasing information about the initial condition. In the process, we have given a new quantification of operator spreading in Krylov subspaces with quantum state reconstruction. The study of these signatures is not only of theoretical interest but also of practical importance.
Currently, the rapid development of computer vision and deep learning has enabled the creation or manipulation of high-fidelity facial images and videos via deep generative approaches. This technology, also known as deepfake, has achieved dramatic progress and become increasingly popular in social media. However, the technology can generate threats to personal privacy and national security by spreading misinformation. To diminish the risks of deepfake, it is desirable to develop powerful forgery detection methods to distinguish fake faces from real faces. This paper presents a comprehensive survey of recent deep learning-based approaches for facial forgery detection. We attempt to provide the reader with a deeper understanding of the current advances as well as the major challenges for deepfake detection based on deep learning. We present an overview of deepfake techniques and analyse the characteristics of various deepfake datasets. We then provide a systematic review of different categories of deepfake detection and state-of-the-art deepfake detection methods. The drawbacks of existing detection methods are analyzed, and future research directions are discussed to address the challenges in improving both the performance and generalization of deepfake detection.
Survey Data Integration for Distribution Function Estimation
2024-09-22
Jeremy Flood, Sayed Mostafa
Integration of probabilistic and non-probabilistic samples for the estimation of finite population totals (or means) has recently received considerable attention in the field of survey sampling; yet, to the best of our knowledge, this framework has not been extended to cumulative distribution function (CDF) estimation. To address this gap, we propose a novel CDF estimator that integrates data from probability samples with data from (potentially large) nonprobability samples. Assuming that a set of shared covariates are observed in both samples, while the response variable is observed only in the latter, the proposed estimator uses a survey-weighted empirical CDF of regression residuals trained on the convenience sample to estimate the CDF of the response variable. Under some regularity conditions, we show that our CDF estimator is both design-consistent for the finite population CDF and asymptotically normally distributed. Additionally, we define and study a quantile estimator based on the proposed CDF estimator. Furthermore, we use both the bootstrap and asymptotic formulae to estimate their respective sampling variances. Our empirical results show that the proposed CDF estimator is robust to model misspecification under ignorability, and robust to ignorability under model misspecification. When both assumptions are violated, our residual-based CDF estimator still outperforms its 'plug-in' mass imputation and naive siblings, albeit with noted decreases in efficiency.
Impact of Large-Scale Structure Systematics on Cosmological Parameter Estimation
2024-09-21
Humna Awan, Eric Gawiser, Javier Sanchez, Ignacio Sevilla-Noarbe, the LSST Dark Energy Science Collaboration
Large near-future galaxy surveys offer sufficient statistical power to make our cosmology analyses data-driven, limited primarily by systematic errors. Understanding the impact of systematics is therefore critical. We perform an end-to-end analysis to investigate the impact of some of the systematics that affect large-scale structure studies by doing an inference analysis using simulated density maps with various systematics; these include systematics caused by photometric redshifts (photo-$z$s), Galactic dust, structure induced by the telescope observing strategy and observing conditions, and incomplete covariance matrices. Specifically, we consider the impacts of incorrect photo-$z$ distributions (photometric biases, scatter, outliers; spectroscopic calibration biases), dust map resolution, incorrect dust law, selecting none or only some contaminant templates for deprojection, and using a diagonal covariance matrix instead of a full one. We quantify the biases induced by these systematics on cosmological parameter estimation using tomographic galaxy angular power spectra, with a focus on identifying whether the maximum plausible level of each systematic has an adverse impact on the estimation of key cosmological parameters from a galaxy clustering analysis with Rubin Observatory Legacy Survey of Space and Time (LSST). We find photo-$z$ systematics to be the most pressing out of the systematics investigated, with spectroscopic calibration biases leading to the greatest adverse impact while helpfully being flagged by a high $\chi^2$ value for the best fit model. Larger-than-expected photo-$z$ scatter, on the other hand, has a significant impact without necessarily indicating a poor fit. In contrast, in the analysis framework used in this work, biases from observational systematics and incomplete covariance matrices are comfortably subdominant.
A prospectus on the surface metrology of seborrheic keratoses
2024-09-21
Nicole Werpachowski, Juliette Nutovits, Therese Limbana, John Goncalves, Alina Bridges, Brian Lee Beatty
Background: Human skin texture has yet to be quantified for diagnostic purposes. Here, the surface metrology of seborrheic keratoses is investigated with an optical profiler. Materials and Methods: Dermatologic specimens of 7 cadavers were prepared. Specimens were molded with polyvinyl siloxane and casts prepared with resin, which were scanned using a 3D white light optical profiler. Each scan produced 48 variables, categorized into 3 groups for each location: control, lesion center, and lesion edge. Images of the histopathology slides for suspected seborrheic keratoses were reviewed by a dermatopathologist. Results: The parameters under investigation included border versus center of keratoses, age, sex, lesion location, degree of sun exposure, and cause of death. Although some parameters differ between individuals and age groups, the majority of differences identified between the roughness parameters measured are a result of sex, sun exposure, and histological diagnosis, listed in order of increasing importance. Histological diagnosis provided the most significant, definitive number of individual measurements of areas of roughness in seborrheic keratoses in comparison to other parts of the skin (regardless if those were controls or a different pigmented lesion aside from seborrheic keratoses). Conclusions: This study demonstrates that there are quantifiable patterns of surface textures that can be compared between keratotic and standard normal skin surfaces. These findings suggest this method has the potential to be applied as a noninvasive adjunct to current methods of dermatological diagnosis.
A High-Resolution Spectroscopic Survey of Directly Imaged Companion Hosts: I. Determination of diagnostic stellar abundances for planet formation and composition
2024-09-21
Aneesh Baburaj, Quinn M. Konopacky, Christopher A. Theissen, Sarah Peacock, Lori Huseby, Benjamin Fulton, Roman Gerasimov, Travis S. Barman, Kielan K. W. Hoch
We present the first results of an extensive spectroscopic survey of directly imaged planet host stars. The goal of the survey is the measurement of stellar properties and abundances of 15 elements (including C, O, and S) in these stars. In this work, we present the analysis procedure and the results for an initial set of five host stars, including some very well-known systems. We obtain C/O ratios using a combination of spectral modeling and equivalent width measurements for all five stars. Our analysis indicates solar C/O ratios for HR 8799 (0.59 $\pm$ 0.11), 51 Eri (0.54 $\pm$ 0.14), HD 984 (0.63 $\pm$ 0.14), and GJ 504 (0.54 $\pm$ 0.14). However, we find a super-solar C/O (0.81 $\pm$ 0.14) for HD 206893 through spectral modeling. The ratios obtained using the equivalent width method agree with those obtained using spectral modeling but have higher uncertainties ($\sim$0.3 dex). We also calculate the C/S and O/S ratios, which will help us to better constrain planet formation, especially once planetary sulfur abundances are measured using JWST. Lastly, we find no evidence of highly elevated metallicities or abundances for any of our targets, suggesting that a super metal-rich environment is not a prerequisite for large, widely separated gas planet formation. The measurement of elemental abundances beyond carbon and oxygen also provides access to additional abundance ratios, such as Mg/Si, which could aid in further modeling of their giant companions.
Shock breakouts from compact CSM surrounding core-collapse SN progenitors may contribute significantly to the observed $\gtrsim10$ TeV neutrino background
2024-09-21
E. Waxman, T. Wasserman, E. Ofek, A. Gal-Yam
Growing observational evidence suggests that enhanced mass loss from the progenitors of core-collapse supernovae (SNe) is common during $\sim1$ yr preceding the explosion, creating an optically thick circum-stellar medium (CSM) shell at $\sim10^{14.5}$ cm radii. We show that if such mass loss is indeed common, then the breakout of the SN shock through the dense CSM shell produces a neutrino flux that may account for a significant fraction of the observed $\gtrsim10$ TeV neutrino background. The neutrinos are created within a few days from the explosion, during and shortly after the shock breakout, which produces also large UV (and later X-ray) luminosity. The compact size and large UV luminosity imply a pair production optical depth of $\sim10^4$ for $>100$ GeV photons, naturally accounting for the lack of a high-energy gamma-ray background accompanying the neutrino background. SNe producing $>1$ neutrino event in a 1 km$^2$ detector are expected at a rate of $\lesssim0.1$/yr. A quantitative theory describing the evolution of the electromagnetic spectrum during a breakout, as the radiation-mediated shock is transformed into a collisionless one, is required to enable (i) using data from upcoming surveys that will systematically detect large numbers of young, $<1$ d old SNe, to determine the pre-explosion mass loss history of the SN progenitor population, and (ii) a quantitative determination of the neutrino luminosity and spectrum.
With the increasing integration of large lauguage models (LLMs) in education, there is growing interest in using AI agents to support student learning in creative tasks. This study presents an interactive Mentor Agent system named Mentigo, which is designed to assist middle school students in the creative problem solving (CPS) process. We created a comprehensive dataset of real classroom interactions between students and mentors, which include the structured CPS task management, diverse guidance techniques, personalized feedback mechanisms. Based on this dataset, we create agentic workflow for the Mentigo system. The system's effectiveness was evaluated through a comparative experiment with 12 students and reviewed by five expert teachers. The Mentigo system demonstrated significant improvements in student engagement and creative outcomes. The findings provide design implications for leveraging LLMs to support CPS and offer insights into the application of AI mentor agents in educational contexts.
Current Trends and Future Directions for Sexual Health Conversational Agents (CAs) for Youth: A Scoping Review
2024-09-21
Jinkyung Katie Park, Vivek Singh, Pamela Wisniewski
Conversational Agents (CAs, chatbots) are systems with the ability to interact with users using natural human dialogue. While much of the research on CAs for sexual health has focused on adult populations, the insights from such research may not apply to CAs for youth. The study aimed to comprehensively evaluate the state-of-the-art research on sexual health CAs for youth. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we synthesized peer-reviewed studies specific to sexual health CAs designed for youth over the past 14 years. We found that most sexual health CAs were designed to adopt the persona of health professionals to provide general sexual and reproductive health information for youth. Text was the primary communication mode in all sexual health CAs, with half supporting multimedia output. Many sexual health CAs employed rule-based techniques to deliver pre-written expert knowledge on sexual health; yet most sexual health CAs did not have the safety features in place. While youth appreciated accessibility to non-judgmental and confidential conversations about sexual health topics, they perceived current sexual health CAs provided limited sexual health information that is not inclusive of sexual and/or gender minorities. Our review brings to light sexual health CAs needing further development and evaluation and we identify multiple important areas for future work. While the new trend of large language models (LLMs) based CAs can make such technologies more feasible, the privacy and safety of the systems should be prioritized. Finally, best practices for risk mitigation and ethical development of sexual health CAs with and for youth are needed.
Accretion physics studies the process of gravitational capture of ambient matter by massive stars. The background processes are very challenging to observe and measure due to the extreme conditions in these systems. Numerical simulations play a crucial role in accretion physics because they provide the only practical method to model the complex processes occurring in accretion disks. In this review, we outline different branches of numerical simulations, such as hydrodynamic simulations, magnetohydrodynamic simulations, and Monte-Carlo simulations, and their methodology, and we discuss possible implications for modeling accretion physics around black holes, neutron stars, and protoplanetary disks.
Adversarial and Reactive Traffic Agents for Realistic Driving Simulation
2024-09-21
Joshua Ransiek, Philipp Reis, Eric Sax
Despite advancements in perception and planning for autonomous vehicles (AVs), validating their performance remains a significant challenge. The deployment of planning algorithms in real-world environments is often ineffective due to discrepancies between simulations and real traffic conditions. Evaluating AVs planning algorithms in simulation typically involves replaying driving logs from recorded real-world traffic. However, agents replayed from offline data are not reactive, lack the ability to respond to arbitrary AV behavior, and cannot behave in an adversarial manner to test certain properties of the driving policy. Therefore, simulation with realistic and potentially adversarial agents represents a critical task for AV planning software validation. In this work, we aim to review current research efforts in the field of adversarial and reactive traffic agents, with a particular focus on the application of classical and adversarial learning-based techniques. The objective of this work is to categorize existing approaches based on the proposed scenario controllability, defined by the number of reactive or adversarial agents. Moreover, we examine existing traffic simulations with respect to their employed default traffic agents and potential extensions, collate datasets that provide initial driving data, and collect relevant evaluation metrics.
In the era of large language models (LLMs), a vast amount of conversation logs will be accumulated thanks to the rapid development trend of language UI. Conversation Analysis (CA) strives to uncover and analyze critical information from conversation data, streamlining manual processes and supporting business insights and decision-making. The need for CA to extract actionable insights and drive empowerment is becoming increasingly prominent and attracting widespread attention. However, the lack of a clear scope for CA leads to a dispersion of various techniques, making it difficult to form a systematic technical synergy to empower business applications. In this paper, we perform a thorough review and systematize CA task to summarize the existing related work. Specifically, we formally define CA task to confront the fragmented and chaotic landscape in this field, and derive four key steps of CA from conversation scene reconstruction, to in-depth attribution analysis, and then to performing targeted training, finally generating conversations based on the targeted training for achieving the specific goals. In addition, we showcase the relevant benchmarks, discuss potential challenges and point out future directions in both industry and academia. In view of current advancements, it is evident that the majority of efforts are still concentrated on the analysis of shallow conversation elements, which presents a considerable gap between the research and business, and with the assist of LLMs, recent work has shown a trend towards research on causality and strategic tasks which are sophisticated and high-level. The analyzed experiences and insights will inevitably have broader application value in business operations that target conversation logs.
Decision-change Informed Rejection Improves Robustness in Pattern Recognition-based Myoelectric Control
2024-09-21
Shriram Tallam Puranam Raghu, Dawn MacIsaac, Erik Scheme
Post-processing techniques have been shown to improve the quality of the decision stream generated by classifiers used in pattern-recognition-based myoelectric control. However, these techniques have largely been tested individually and on well-behaved, stationary data, failing to fully evaluate their trade-offs between smoothing and latency during dynamic use. Correspondingly, in this work, we survey and compare 8 different post-processing and decision stream improvement schemes in the context of continuous and dynamic class transitions: majority vote, Bayesian fusion, onset locking, outlier detection, confidence-based rejection, confidence scaling, prior adjustment, and adaptive windowing. We then propose two new temporally aware post-processing schemes that use changes in the decision and confidence streams to better reject uncertain decisions. Our decision-change informed rejection (DCIR) approach outperforms existing schemes during both steady-state and transitions based on error rates and decision stream volatility whether using conventional or deep classifiers. These results suggest that added robustness can be gained by appropriately leveraging temporal context in myoelectric control.
SN 2023tsz: A helium-interaction driven supernova in a very low-mass galaxy
2024-09-21
B. Warwick, J. Lyman, M. Pursiainen, D. L. Coppejans, L. Galbany, G. T. Jones, T. L. Killestein, A. Kumar, S. R. Oates, K. Ackley, J. P. Anderson, A. Aryan, R. P. Breton, T. W. Chen, P. Clark, V. S. Dhillon, M. J. Dyer, A. Gal-Yam, D. K. Galloway, C. P. Gutiérrez, M. Gromadzki, C. Inserra, F. Jiménez-Ibarra, L. Kelsey, R. Kotak, T. Kravtsov, H. Kuncarayakti, M. R. Magee, K. Matilainen, S. Mattila, T. E. Müller-Bravo, M. Nicholl, K. Noysena, L. K. Nuttall, P. O'Brien, D. O'Neill, E. Pallé, T. Pessi, T. Petrushevska, G. Pignata, D. Pollacco, F. Ragosta, G. Ramsay, A. Sahu, D. K. Sahu, A. Singh, J. Sollerman, E. Stanway, R. Starling, D. Steeghs, R. S. Teja, K. Ulaczyk
SN 2023tsz is a Type Ibn supernova (SNe Ibn) discovered in an extremely low-mass host. SNe Ibn are an uncommon subtype of stripped-envelope core-collapse SNe. They are characterised by narrow helium emission lines in their spectra and are believed to originate from the collapse of massive Wolf-Rayet (WR) stars, though their progenitor systems still remain poorly understood. In terms of energetics and spectrophotometric evolution, SN 2023tsz is largely a typical example of the class, although line profile asymmetries in the nebular phase are seen, which may indicate the presence of dust formation or unshocked circumstellar material. Intriguingly, SN 2023tsz is located in an extraordinarily low-mass host galaxy that is in the 2nd percentile for SESN host masses and star formation rates (SFR). The host has a radius of 1.0 kpc, a $g$-band absolute magnitude of $-12.73$, and an estimated metallicity of $\log(Z_{*}/Z_{\odot}$) = $-1.56$. The SFR and metallicity of the host galaxy raise questions about the progenitor of SN 2023tsz. The low SFR suggests that a star with sufficient mass to evolve into a WR would be uncommon in this galaxy. Further, the very low-metallicity is a challenge for single stellar evolution to enable H and He stripping of the progenitor and produce a SN Ibn explosion. The host galaxy of SN 2023tsz adds another piece to the ongoing puzzle of SNe Ibn progenitors, and demonstrates that they can occur in hosts too faint to be observed in contemporary sky surveys at a more typical SN Ibn redshift.
Obliviate: Neutralizing Task-agnostic Backdoors within the Parameter-efficient Fine-tuning Paradigm
2024-09-21
Jaehan Kim, Minkyoo Song, Seung Ho Na, Seungwon Shin
Parameter-efficient fine-tuning (PEFT) has become a key training strategy for large language models. However, its reliance on fewer trainable parameters poses security risks, such as task-agnostic backdoors. Despite their severe impact on a wide range of tasks, there is no practical defense solution available that effectively counters task-agnostic backdoors within the context of PEFT. In this study, we introduce Obliviate, a PEFT-integrable backdoor defense. We develop two techniques aimed at amplifying benign neurons within PEFT layers and penalizing the influence of trigger tokens. Our evaluations across three major PEFT architectures show that our method can significantly reduce the attack success rate of the state-of-the-art task-agnostic backdoors (83.6%$\downarrow$). Furthermore, our method exhibits robust defense capabilities against both task-specific backdoors and adaptive attacks. Source code will be obtained at https://github.com/obliviateARR/Obliviate.
Window-based Channel Attention for Wavelet-enhanced Learned Image Compression
2024-09-21
Heng Xu, Bowen Hai, Yushun Tang, Zhihai He
Learned Image Compression (LIC) models have achieved superior rate-distortion performance than traditional codecs. Existing LIC models use CNN, Transformer, or Mixed CNN-Transformer as basic blocks. However, limited by the shifted window attention, Swin-Transformer-based LIC exhibits a restricted growth of receptive fields, affecting the ability to model large objects in the image. To address this issue, we incorporate window partition into channel attention for the first time to obtain large receptive fields and capture more global information. Since channel attention hinders local information learning, it is important to extend existing attention mechanisms in Transformer codecs to the space-channel attention to establish multiple receptive fields, being able to capture global correlations with large receptive fields while maintaining detailed characterization of local correlations with small receptive fields. We also incorporate the discrete wavelet transform into our Spatial-Channel Hybrid (SCH) framework for efficient frequency-dependent down-sampling and further enlarging receptive fields. Experiment results demonstrate that our method achieves state-of-the-art performances, reducing BD-rate by 18.54%, 23.98%, 22.33%, and 24.71% on four standard datasets compared to VTM-23.1.
Higgs Boson Searches at the LHC Beyond the Standard Model
2024-09-21
André Sopczak
The latest results of Higgs boson searches beyond the Standard Model from the ATLAS and CMS experiments are reviewed. This includes searches for additional neutral, charged, and double charged Higgs-like bosons, searches for dark matter produced in association with a Higgs boson, and searches for new physics in Higgs boson production and decay processes. Interpretations are given within the hMSSM, a special parameterization of the Minimal Supersymmetric extension of the Standard Model, in which the mass of the lightest Higgs boson is set to the value of 125 GeV measured at the LHC.
SMART-RAG: Selection using Determinantal Matrices for Augmented Retrieval
2024-09-21
Jiatao Li, Xinyu Hu, Xiaojun Wan
Retrieval-Augmented Generation (RAG) has greatly improved large language models (LLMs) by enabling them to generate accurate, contextually grounded responses through the integration of external information. However, conventional RAG approaches, which prioritize top-ranked documents based solely on query-context relevance, often introduce redundancy and conflicting information. This issue is particularly evident in unsupervised retrieval settings, where there are no mechanisms to effectively mitigate these problems, leading to suboptimal context selection. To address this, we propose Selection using Matrices for Augmented Retrieval (SMART) in question answering tasks, a fully unsupervised and training-free framework designed to optimize context selection in RAG. SMART leverages Determinantal Point Processes (DPPs) to simultaneously model relevance, diversity and conflict, ensuring the selection of potentially high-quality contexts. Experimental results across multiple datasets demonstrate that SMART significantly enhances QA performance and surpasses previous unsupervised context selection methods, showing a promising strategy for RAG.
On the Electromagnetic Field of a Focusing Charged Particle Beam and Its Two-Dimensional Approximation
2024-09-21
Yi-Kai Kan, Ji Qiang
The analytical expressions for the electromagnetic potential generated from a focusing charged particle beam are indispensable in various beam physics problems. In this article, we review the derivation from scratch and point out the necessary assumptions made in it.
Chauhan Weighted Trajectory Analysis of combined efficacy and safety outcomes for risk-benefit analysis
2024-09-20
Utkarsh Chauhan, Daylen Mackey, John R Mackey
Analyzing and effectively communicating the efficacy and toxicity of treatment is the basis of risk benefit analysis (RBA). More efficient and objective tools are needed. We apply Chauhan Weighted Trajectory Analysis (CWTA) to perform RBA with superior objectivity, power, and clarity. We used CWTA to perform 1000-fold simulations of RCTs using ordinal endpoints for both treatment efficacy and toxicity. RCTs were simulated with 1:1 allocation at defined sample sizes and hazard ratios. We studied the simplest case of 3 levels each of toxicity and efficacy and the general case of the advanced cancer trial, with efficacy graded by five RECIST 1.1 health statuses and toxicity by the six-point CTCAE scale (6 x 5 matrix). The latter model was applied to a real-world dose escalation phase I trial in advanced cancer. Simulations in both the 3 x 3 and the 6 x 5 advanced cancer matrix confirmed that drugs with both superior efficacy and toxicity profiles synergize for greater statistical power with CWTA-RBA. The CWTA-RBA 6 x 5 matrix reduced sample size requirements over CWTA efficacy-only analysis. Application to the dose finding phase I clinical trial provided objective, statistically significant validation for the selected dose. CWTA-RBA, by incorporating both drug efficacy and toxicity, provides a single test statistic and plot that analyzes and effectively communicates therapeutic risks and benefits. CWTA-RBA requires fewer patients than CWTA efficacy-only analysis when the experimental drug is both more effective and less toxic. CWTA-RBA facilitates the objective and efficient assessment of new therapies throughout the drug development pathway. Furthermore, several advantages over competing tests in communicating risk-benefit will assist regulatory review, clinical adoption, and understanding of therapeutic risks and benefits by clinicians and patients alike.
Learning Recourse Costs from Pairwise Feature Comparisons
2024-09-20
Kaivalya Rawal, Himabindu Lakkaraju
This paper presents a novel technique for incorporating user input when learning and inferring user preferences. When trying to provide users of black-box machine learning models with actionable recourse, we often wish to incorporate their personal preferences about the ease of modifying each individual feature. These recourse finding algorithms usually require an exhaustive set of tuples associating each feature to its cost of modification. Since it is hard to obtain such costs by directly surveying humans, in this paper, we propose the use of the Bradley-Terry model to automatically infer feature-wise costs using non-exhaustive human comparison surveys. We propose that users only provide inputs comparing entire recourses, with all candidate feature modifications, determining which recourses are easier to implement relative to others, without explicit quantification of their costs. We demonstrate the efficient learning of individual feature costs using MAP estimates, and show that these non-exhaustive human surveys, which do not necessarily contain data for each feature pair comparison, are sufficient to learn an exhaustive set of feature costs, where each feature is associated with a modification cost.
Simulation of stochastic spatially-extended systems is a challenging problem. The fundamental quantities in these models are individual entities such as molecules, cells, or animals, which move and react in a random manner. In big systems, accounting for each individual is inefficient. If the number of entities is large enough, random effects are negligible, and often partial differential equations (PDEs) are used in which the fluctuations are neglected. When the system is heterogeneous, so that the number of individuals is large in certain regions and small in others, the PDE description becomes inaccurate in certain regions. To overcome this problem, the so-called hybrid schemes have been proposed that couple a stochastic description in parts of the domain with its mean field limit in the others. In this chapter, we review the different formulations of this approach and our recent contributions to overcome several of the limitations of previous schemes, including the extension of the concept to multiscale models of cell populations.
High-dimensional learning of narrow neural networks
2024-09-20
Hugo Cui
Recent years have been marked with the fast-pace diversification and increasing ubiquity of machine learning applications. Yet, a firm theoretical understanding of the surprising efficiency of neural networks to learn from high-dimensional data still proves largely elusive. In this endeavour, analyses inspired by statistical physics have proven instrumental, enabling the tight asymptotic characterization of the learning of neural networks in high dimensions, for a broad class of solvable models. This manuscript reviews the tools and ideas underlying recent progress in this line of work. We introduce a generic model -- the sequence multi-index model -- which encompasses numerous previously studied models as special instances. This unified framework covers a broad class of machine learning architectures with a finite number of hidden units, including multi-layer perceptrons, autoencoders, attention mechanisms; and tasks, including (un)supervised learning, denoising, contrastive learning, in the limit of large data dimension, and comparably large number of samples. We explicate in full detail the analysis of the learning of sequence multi-index models, using statistical physics techniques such as the replica method and approximate message-passing algorithms. This manuscript thus provides a unified presentation of analyses reported in several previous works, and a detailed overview of central techniques in the field of statistical physics of machine learning. This review should be a useful primer for machine learning theoreticians curious of statistical physics approaches; it should also be of value to statistical physicists interested in the transfer of such ideas to the study of neural networks.
Instruct-Tuning Pretrained Causal Language Models for Ancient Greek Papyrology and Epigraphy
2024-09-20
Eric Cullhed
This article presents an experiment in fine-tuning a pretrained causal language model (Meta's Llama 3.1 8B Instruct) for aiding in three fundamental tasks of philological research: chronological and geographic attribution as well as text restoration in ancient Greek inscriptions and documentary papyri. Using a prompt-based instruct approach, the fine-tuned models surpass the state of the art in key metrics. For inscriptions, the models achieve a lower average character error rate (CER) of 22.5% (vs. 26.3%), while closely matching top-1 accuracy (60.9% vs. 61.8%) and top-20 accuracy (77.5% vs. 78.3%) for sequences up to 10 characters. They also provide a practical advantage by ignoring spaces during reconstruction, aligning better with the scriptio continua typically used in ancient written artifacts. In geographic attribution, the model outperforms previous benchmarks with a top-1 accuracy of 75.0% (vs. 70.8%) and a top-3 accuracy of 83.7% (vs. 82.1%). For dating, it achieves an average deviation of 26.2 years (vs. 29.3) and a median deviation of 1 year (vs. 3) from the actual date range. The models also set new baselines for documentary papyri, with a CER of 16.3%, a top-1 accuracy of 71.3%, and top-20 of 85.0% in text reconstruction; a top-1 accuracy of 66.4% and top-3 of 79.9% in geographic attribution; and, in chronological attribution, a deviation of 21.7 years from the actual termini post/ante quem, with a median deviation of 0 years.
Direct Two-Dimensional Goniometric Steering of Vacuum Electrospray Ion Beams for Angular Time-of-Flight Studies
2024-09-20
Zach Ulibarri, Elaine Petro
Here we present a novel method for two-dimensional steering of vacuum electrospray ionization beams to better understand their angular properties. Utilizing an externally wetted tungsten needle with the ionic liquid 1-Ethyl-3-methylimidazolium tetrafluoroborate (EMI-BF$_4$), we employ a dual-axis goniometer to achieve in-vacuo beam steering in both pitch and yaw. This setup enables detailed angular-dependent time-of-flight (TOF) measurements of molecular species within the ESI plume as a function of plume angle. Our findings reveal significant variations in the relative abundance of monomers, dimers, trimers, and heavy species across different beam angles, with monomers being at a relative minimum and fragments, trimers, and heavy species being at a relative maximum at the beam center. At higher angles, the relative monomer abundance increases, but at the plume extremities heavy species and trimer fragments begin to rise. The ability to accurately aim the ion beam dramatically enhances the signal-to-noise ratio for various diagnostic tools, underscoring the utility of this system for both scientific studies and practical laboratory applications. This two-dimensional steering capability offers a robust framework for future investigations into ESI plume dynamics and composition, enabling more precise characterizations that are critical for optimizing ESI-based propulsion systems and other applications.
Predicting the Number of Radio Sources Seen by Both VLASS and LSST
2024-09-20
Alex Tellez, Yjan Gordon, Keith Bechtol
Radio surveys typically sample extragalactic sources in higher redshift regimes than is typical for optical surveys, resulting in many radio sources not having a detected optical counterpart. Over the next decade the Legacy Survey of Space and Time (LSST) will be performing the deepest ($i < 26.4\,$mag) wide-area optical survey to date increasing the fraction of radio sources for which we have optical data. In this Research Note we use the Hyper Suprime-Cam survey to analyse how the fraction of radio sources in the Very Large Array Sky Survey (VLASS) with optical detections varies as a function of $i$-band magnitude and extrapolate to predict the number of optical counterparts we expect LSST to detect. Assuming a final VLASS point source depth of $S_{3\,\text{GHz}}\lesssim350\,\mu$Jy, we expect LSST to identify optical counterparts to $\sim 10^6$ radio sources in VLASS.
On the Impact of Bounded Rationality in Strategic Data Gathering
2024-09-20
Anju Anand, Emrah Akyol
We consider the problem of estimation from survey data gathered from strategic and boundedly-rational agents with heterogeneous objectives and available information. Particularly, we consider a setting where there are three different types of survey responders with varying levels of available information, strategicness, and cognitive hierarchy: i) a non-strategic agent with an honest response, ii) a strategic agent that believes everyone else is a non-strategic agent and that the decoder also believes the same, hence assumes a naive estimator, i.e., level-1 in cognitive hierarchy, iii) and strategic agent that believes the population is Poisson distributed over the previous types, and that the decoder believes the same. We model each of these scenarios as a strategic classification of a 2-dimensional source (possibly correlated source and bias components) with quadratic distortion measures and provide a design algorithm. Finally, we provide our numerical results and the code to obtain them for research purposes at https://github.com/strategic-quantization/bounded-rationality.
Ferroelectric and Multiferroic Properties of Quasi-2D Organic Charge-Transfer Salts
2024-09-20
Michael Lang, Peter Lunkenheimer, Owen Ganter, Steve Winter, Jens Müller
In conventional ferroelectrics the electric dipoles are generated by off-center displacements of ions. In recent years, a new type of so-called electronic ferroelectrics has attracted great attention, where the polarization is driven by electronic degrees of freedom. Of particular interest are materials with strong electronic correlations, featuring a variety of intriguing phenomena and instabilities, which may interact with or even induce electronic ferroelectricity. In this review, we will focus on the class of strongly correlated charge-transfer salts, where electronic ferroelectricity was suggested by theory and has been confirmed by numerous experiments. The paper summarizes some basic physical properties of various relevant quasi-two dimensional salts and gives some background on the experimental tools applied to establish ferroelectricity. We discuss the key experimental observations, including the exciting discovery of multiferroicity, and provide some theoretical considerations on the magnetoelectric couplings that are of relevance here.
In this short Topical Review, we look at something typically considered trivial, but not given formally elsewhere -- the behaviour of first multiple fermions then multiple bosons at a beamsplitter, and, extending from this, the behaviour of multiple fermions and multiple bosons in Mach-Zehnder interferometers (MZIs). We hope that by showing how to go from mathematically-simple but unintuitive quantum field theory to a phenomenological description, this Review will help researchers and students in the field build a stronger intuition for the behaviour of quantum particles.
Kinematic analysis of $\mathbf{z = 4.3}$ galaxies in the SPT2349$-$56 protocluster core
2024-09-20
Aparna Venkateshwaran, Axel Weiss, Nikolaus Sulzenauer, Karl Menten, Manuel Aravena, Scott C. Chapman, Anthony Gonzalez, Gayathri Gururajan, Christopher C. Hayward, Ryley Hill, Cassie Reuter, Justin S. Spilker, Joaquin D. Vieira
SPT2349$-$56 is a protocluster discovered in the 2500 deg$^2$ South Pole Telescope (SPT) survey. In this paper, we study the kinematics of the galaxies found in the core of SPT2349$-$56 using high-resolution (1.55 kpc spatial resolution at $z = 4.303$) redshifted [CII] 158-$\mu$m data. Using the publicly available code 3D-Barolo, we analyze the seven far-infrared (FIR) brightest galaxies within the protocluster core. Based on conventional definitions for the detection of rotating discs, we classify six sources to be rotating discs in an actively star-forming protocluster environment, with weighted mean $V_{\mathrm{rot}}/\sigma_{\mathrm{disp}} = 4.5 \pm 1.3$. The weighted mean rotation velocity ($V_{\mathrm{rot}}$) and velocity dispersion ($\sigma_{\mathrm{disp}}$) for the sample are $ 357.1 \pm 114.7$ km s$^{-1}$ and $43.5 \pm 23.5$ km s$^{-1}$, respectively. We also assess the disc stability of the galaxies and find a mean Toomre parameter of $Q_\mathrm{T} = 0.9 \pm 0.3$. The galaxies show a mild positive correlation between disc stability and dynamical support. Using the position-velocity maps, we find that five sources further classify as disturbed discs, and one classifies as a strictly rotating disc. Our sample joins several observations at similar redshift with high $V/\sigma$ values, with the exception that they are morphologically disturbed, kinematically rotating and interacting galaxies in an extreme protocluster environment.
Impacting Atmospheres: How Late-Stage Pollution Alters Exoplanet Composition
2024-09-20
Emilia Vlahos, Yayaati Chachan, Vincent Savignac, Eve J. Lee
Atmospheric composition of exoplanets is often considered as a probe of the planet's formation condition. How exactly the initial chemical memory may be altered from the birth to the final state of the planet, however, remains unknown. Here, we develop a simple model of pollution of planetary atmosphere by the vaporization of infalling planetesimal of varying sizes and composition (SiO$_2$ inside 1 au and H$_2$O outside 1 au), following their trajectory and thermal evolution through the upper advective and radiative layers of a sub-Neptune class planet during the late stage of disk evolution. We vary the rate of pollution by changing the solid content of the disk and by dialing the level of disk gas depletion which in turn determines the rate of planetary migration. We find that pollution by silicate grains will always be limited by the saturation limit set by the thermal state of the atmosphere. By contrast, pollution by water ice can lead to $\sim$2--4 orders of magnitude variation in the atmospheric water mass fraction depending on the solid and gas content of the disk. Both cases suggest that post-formation pollution can erase the initial compositional memory of formation. Post-formation pollution can potentially transform sub-Neptunes with H/He-dominated envelope that initially formed beyond the iceline to waterworlds (water-enriched envelope) when the disk gas is depleted by $\gtrsim$2 orders of magnitude, allowing gentle migration. We additionally discuss the expected C/O ratio profile under pollution by water and refractory carbon species.
The Statistics and Environments of Hostless Supernovae
2024-09-20
Yu-Jing Qin, Ann Zabludoff, Iair Arcavi, Nathan Smith, Yakov Faerman, Dan Maoz
Transient surveys routinely detect supernovae (SNe) without obvious host galaxies. To understand the demographics of these "hostless" SNe and to constrain the possible host properties, we identify 161 SNe reported to the Transient Name Server since 2016 that do not have hosts cataloged from pre-explosion wide-field galaxy surveys. Using forced aperture photometry, we detect excess flux around only 56 of these SNe. Both thermonuclear and core-collapse (CC) SNe are present in our sample. Compared to flux-limited SNe samples with known hosts, superluminous supernovae (SLSNe), particularly hydrogen-deficient SLSNe, are over-represented here relative to all other SNe types; among CC SNe, there is also a higher fraction of interacting SNe than non-interacting. On the low-luminosity side, seven SNe have host absolute magnitude upper limits fainter than M_g=-12, about 1 per cent of the Small Magellanic Cloud's luminosity; the faintest limits are close to the luminosity of globular clusters or ultra-faint dwarf galaxies (M_g~-8). Fitting multi-band forced photometry, 11 SNe have host stellar masses <10^6 Msun assuming quiescent hosts, and 13 SNe have host stellar masses <10^5 Msun assuming star-forming hosts. The spatial distribution of hostless SNe indicates that the majority are not associated with known galaxy groups and clusters, ruling out intracluster stellar light as the primary contributor of such SNe. Hostless Type Ia SNe tend to be more luminous and slow-fading than SNe Ia with known host galaxies, implying a hidden population of low-mass and star-forming hosts. We conclude that any undetected host galaxies are likely star-forming dwarfs in the field.
Measurement of the Higgs boson mass and width using the four-lepton final state in proton-proton collisions at $\sqrt{s}$ = 13 TeV
2024-09-20
CMS Collaboration
A measurement of the Higgs boson mass and width via its decay to two Z bosons is presented. Proton-proton collision data collected by the CMS experiment, corresponding to an integrated luminosity of 138 fb$^{-1}$ at a center-of-mass energy of 13 TeV is used. The invariant mass distribution of four leptons in the on-shell Higgs boson decay is used to measure its mass and contrain its width. This yields the most precise single measurement of the Higgs boson mass to date, 125.04 $\pm$ 0.12 GeV, and an upper limit on the width $\Gamma_\mathrm{H}$ $\lt$ 330 MeV at 95% confidence level. A combination of the on- and off-shell Higgs boson production decaying to four leptons is used to determine the Higgs boson width, assuming that no new virtual particles affect the production, a premise that is tested by adding new heavy particles in the gluon fusion loop model. This result is combined with a previous CMS analysis of the off-shell Higgs boson production with decay to two leptons and two neutrinos, giving a measured Higgs boson width of 3.0$^{+2.0}_{-1.5}$ MeV, in agreement with the standard model prediction of 4.1 MeV. The strength of the off-shell Higgs boson production is also reported. The scenario of no off-shell Higgs boson production is excluded at a confidence level corresponding to 3.8 standard deviations.
Planet Mass and Metallicity: The Exoplanets and Solar System Connection
2024-09-20
Mark R. Swain, Yasuhiro Hasegawa, Daniel P. Thorngren, Gael M. Roudier
Theoretical studies of giant planet formation suggest that substantial quantities of metals - elements heavier than hydrogen and helium - can be delivered by solid accretion during the envelope-assembly phase. This metal enhancement process is believed to diminish as a function of planet mass, leading to predictions for a mass-metallicity relationship. This picture is supported by the abundance of CH$_4$ in solar system giant planets, which is unaffected by condensation, unlike H$_2$O. However, all of the solar system giants exhibit some evidence for stratification of metals outside of their cores. In this context, two fundamental questions are whether metallicity of giant planets inferred from observations of the outer envelope layers represents their bulk metallicities, and if not, how are metals distributed within these planets. Comparing the mass-metallicity relationship inferred for solar system giants with various tracers of exoplanet metallicity has yielded a range of results. There is evidence of a solar-system-like mass-metallicity trend using bulk density estimates of exoplanets. However, transit-spectroscopy-based tracers of exoplanet metallicity, which probe only the outer layers of the envelope, are less clear about a mass-metallicity trend and radial composition gradients. The large number of known exoplanets enables statistical characterization. We develop a formalism for comparing both the metallicity inferred for the outer envelope and the metallicity inferred using the bulk density and show this combination may offer insights into metal stratification within planetary envelopes. Thus, future exoplanet observations with JWST and Ariel will be able to shed light on the conditions governing radial composition gradients in exoplanets and, perhaps, provide information about the factors controlling stratification and convection in our solar system gas giants.
Stance detection is the task of determining the viewpoint expressed in a text towards a given target. A specific direction within the task focuses on cross-target stance detection, where a model trained on samples pertaining to certain targets is then applied to a new, unseen target. With the increasing need to analyze and mining viewpoints and opinions online, the task has recently seen a significant surge in interest. This review paper examines the advancements in cross-target stance detection over the last decade, highlighting the evolution from basic statistical methods to contemporary neural and LLM-based models. These advancements have led to notable improvements in accuracy and adaptability. Innovative approaches include the use of topic-grouped attention and adversarial learning for zero-shot detection, as well as fine-tuning techniques that enhance model robustness. Additionally, prompt-tuning methods and the integration of external knowledge have further refined model performance. A comprehensive overview of the datasets used for evaluating these models is also provided, offering valuable insights into the progress and challenges in the field. We conclude by highlighting emerging directions of research and by suggesting avenues for future work in the task.
Toward Interactive Optimization of Source Code Differences: An Empirical Study of Its Performance
2024-09-20
Tsukasa Yagi, Shinpei Hayashi
A source code difference (diff) indicates changes made by comparing new and old source codes, and it can be utilized in code reviews to help developers understand the changes made to the code. Although many diff generation methods have been proposed, existing automatic methods may generate nonoptimal diffs, hindering reviewers from understanding the changes. In this paper, we propose an interactive approach to optimize diffs. Users can provide feedback for the points of a diff that should not be matched but are or parts that should be matched but are not. The edit graph is updated based on this feedback, enabling users to obtain a more optimal diff. We simulated our proposed method by applying a search algorithm to empirically assess the number of feedback instances required and the amount of diff optimization resulting from the feedback to investigate the potential of this approach. The results of 23 GitHub projects confirm that 92\% of nonoptimal diffs can be addressed with less than four feedback actions in the ideal case.
Constraining Primordial Non-Gaussianity with Density-Split Clustering
2024-09-20
James Morawetz, Enrique Paillas, Will J. Percival
Obtaining tight constraints on primordial non-Gaussianity (PNG) is a key step in discriminating between different models for cosmic inflation. The constraining power from large-scale structure (LSS) measurements is expected to overtake that from cosmic microwave background (CMB) anisotropies with the next generation of galaxy surveys including the Dark Energy Spectroscopic Instrument (DESI) and Euclid. We consider whether Density-Split Clustering (DSC) can help improve PNG constraints from these surveys for local, equilateral and orthogonal types. DSC separates a surveyed volume into regions based on local density and measures the clustering statistics within each environment. Using the Quijote simulations and the Fisher information formalism, we compare PNG constraints from the standard halo power spectrum, DSC power spectra and joint halo/DSC power spectra. We find that the joint halo/DSC power spectra outperform the halo power spectrum by factors of $\sim$ 1.4, 8.8, and 3.6 for local, equilateral and orthogonal PNG, respectively. This is driven by the higher-order information that DSC captures on small scales. We find that applying DSC to a halo field does not allow sample variance cancellation on large scales by providing multiple tracers of the same volume with different local PNG responses. Additionally, we introduce a Fourier space analysis for DSC and study the impact of several modifications to the pipeline, such as varying the smoothing radius and the number of density environments and replacing random query positions with lattice points.
Self-organized attractoring in locomoting animals and robots: an emerging field
2024-09-20
Bulcsú Sándor, Claudius Gros
Locomotion may be induced on three levels. On a classical level, actuators and limbs follow the sequence of open-loop top-down control signals they receive. Limbs may move alternatively on their own, which implies that interlimb coordination must be mediated either by the body or via decentralized inter-limb signaling. In this case, when embodiment is present, two types of controllers are conceivable for the actuators of the limbs, local pacemaker circuits and control principles based on self-organized embodiment. The latter, self-organized control, is based on limit cycles and chaotic attractors that emerge within the feedback loop composed of controller, body, and environment. For this to happen, the sensorimotor loop must be locally closed, e.g. via propriosensation. Here we review the progress made within the framework of self-organized embodiment, with a particular focus on the concept of attractoring. This concept characterizes situations when sets of attractors combining discrete and continuous spectra are available as motor primitives for higher-order control schemes, such as kick control. In particular, we show that a simple generative principle allows for the robust formulation of self-organized embodiment. Based on the recurrent alternation between measuring the actual status of an actuator and providing a target for the actuator to achieve in the next step, we find that the mechanism leads to compliant locomotion for a range of simulated and real-world robots, which include barrel- and sphere-shaped agents, as well as wheeled and legged robots.
A Law of One's Own: The Inefficacy of the DMCA for Non-Consensual Intimate Media
2024-09-20
Li Qiwei, Shihui Zhang, Samantha Paige Pratt, Andrew Timothy Kasper, Eric Gilbert, Sarita Schoenebeck
Non-consensual intimate media (NCIM) presents internet-scale harm to individuals who are depicted. One of the most powerful tools for requesting its removal is the Digital Millennium Copyright Act (DMCA). However, the DMCA was designed to protect copyright holders rather than to address the problem of NCIM. Using a dataset of more than 54,000 DMCA reports and over 85 million infringing URLs spanning over a decade, this paper evaluates the efficacy of the DMCA for NCIM takedown. Results show less than 50% of infringing URLs are removed from website hosts in 60 days, and Google Search takes a median of 11.7 days to deindex infringing content. Across web hosts, only 4% of URLs are removed within the first 48 hours. Additionally, the most frequently reported domains for non-commercial NCIM are smaller websites, not large platforms. We stress the need for new laws that ensure a shorter time to takedown that are enforceable across big and small platforms alike.
Tackling fluffy clouds: field boundaries detection using time series of S2 and/or S1 imagery
2024-09-20
Foivos I. Diakogiannis, Zheng-Shu Zhou, Jeff Wang, Gonzalo Mata, Dave Henry, Roger Lawes, Amy Parker, Peter Caccetta, Rodrigo Ibata, Ondrej Hlinka, Jonathan Richetti, Kathryn Batchelor, Chris Herrmann, Andrew Toovey, John Taylor
Accurate field boundary delineation is a critical challenge in digital agriculture, impacting everything from crop monitoring to resource management. Existing methods often struggle with noise and fail to generalize across varied landscapes, particularly when dealing with cloud cover in optical remote sensing. In response, this study presents a new approach that leverages time series data from Sentinel-2 (S2) and Sentinel-1 (S1) imagery to improve performance under diverse cloud conditions, without the need for manual cloud filtering. We introduce a 3D Vision Transformer architecture specifically designed for satellite image time series, incorporating a memory-efficient attention mechanism. Two models are proposed: PTAViT3D, which handles either S2 or S1 data independently, and PTAViT3D-CA, which fuses both datasets to enhance accuracy. Both models are evaluated under sparse and dense cloud coverage by exploiting spatio-temporal correlations. Our results demonstrate that the models can effectively delineate field boundaries, even with partial (S2 or S2 and S1 data fusion) or dense cloud cover (S1), with the S1-based model providing performance comparable to S2 imagery in terms of spatial resolution. A key strength of this approach lies in its capacity to directly process cloud-contaminated imagery by leveraging spatio-temporal correlations in a memory-efficient manner. This methodology, used in the ePaddocks product to map Australia's national field boundaries, offers a robust, scalable solution adaptable to varying agricultural environments, delivering precision and reliability where existing methods falter. Our code is available at https://github.com/feevos/tfcl.
Spectropolarimetric modeling of interacting Type II supernovae. Application to early-time observations of SN1998S
2024-09-20
Luc Dessart, Douglas C. Leonard, Sergiy S. Vasylyev, D. John Hillier
High-cadence surveys of the sky are revealing that a large fraction of red-supergiant (RSG) stars, which are progenitors of Type II-Plateau (II-P) supernovae (SNe), explode within circumstellar material (CSM). Such SNe II-P/CSM exhibit considerable diversity, with interaction signatures lasting from hours to days, potentially merging with the Type IIn subclass for which longer-duration interaction typically occurs. To tackle this growing sample of transients and to understand the pre-SN mass loss histories of RSGs, we train on the highest quality, spectropolarimetric observations of a young Type IIn SN taken to date: Those of SN1998S at ~5d after explosion. We design an approach based on a combination of radiation hydrodynamics with HERACLES and polarized radiative transfer with CMFGEN and LONG_POL. The adopted asymmetries are based on a latitudinal, depth- and time-independent, scaling of the density of 1D models of SNe II-P/CSM (e.g., model r1w6b with a `wind' mass-loss rate of 0.01Msun/yr used for SN2023ixf). For a pole-to-equator density ratio of five, we find that the polarization reaches, and then remains for days, at a maximum value of 1.0, 1.4, and 1.8% as the CSM extent is changed from 6, to 8 and 10x10^14cm. The polarization is independent of wavelength away from funnel-shaped depolarizations within emission lines. Our models implicate a significant depolarization at line cores, which we use to constrain the interstellar polarization of SN1998S. Our 2D, prolate ejecta models with moderate asymmetry match well the spectropolarimetric observations of SN1998S at 5d, supporting a polarization level of about ~2%. This study provides a framework for interpreting future spectropolarimetric observations of SNe II-P/CSM and SNe IIn and fostering a better understanding of the origin of their pre-SN mass loss.
Constraints on $f(R)$ gravity from tSZE-selected SPT galaxy clusters and weak lensing mass calibration from DES and HST
2024-09-20
S. M. L. Vogt, S. Bocquet, C. T. Davies, J. J. Mohr, F. Schmidt, C. -Z. Ruan, B. Li, C. Hernández-Aguayo, S. Grandis, L. E. Bleem, M. Klein, T. Schrabback, M. Aguena, D. Brooks, D. L. Burke, A. Campos, A. Carnero Rosell, J. Carretero, M. Costanzi, L. N. da Costa, M. E. S. Pereira, J. De Vicente, P. Doel, S. Everett, I. Ferrero, J. Frieman, J. García-Bellido, M. Gatti, G. Giannini, D. Gruen, R. A. Gruendl, S. R. Hinton, D. L. Hollowood, S. Lee, M. Lima, J. L. Marshall, J. Mena-Fernández, R. Miquel, J. Myles, M. Paterno, A. Pieres, A. A. Plazas Malagón, C. L. Reichardt, A. K. Romer, S. Samuroff, A. Sarkar, E. Sanchez, I. Sevilla-Noarbe, M. Smith, E. Suchyta, M. E. C. Swanson, G. Tarle, V. Vikram, N. Weaverdyck, J. Weller
We present constraints on the $f(R)$ gravity model using a sample of 1,005 galaxy clusters in the redshift range $0.25 - 1.78$ that have been selected through the thermal Sunyaev-Zel'dovich effect (tSZE) from South Pole Telescope (SPT) data and subjected to optical and near-infrared confirmation with the Multi-component Matched Filter (MCMF) algorithm. We employ weak gravitational lensing mass calibration from the Dark Energy Survey (DES) Year 3 data for 688 clusters at $z < 0.95$ and from the Hubble Space Telescope (HST) for 39 clusters with $0.6 < z < 1.7$. Our cluster sample is a powerful probe of $f(R)$ gravity, because this model predicts a scale-dependent enhancement in the growth of structure, which impacts the halo mass function (HMF) at cluster mass scales. To account for these modified gravity effects on the HMF, our analysis employs a semi-analytical approach calibrated with numerical simulations. Combining calibrated cluster counts with primary cosmic microwave background (CMB) temperature and polarization anisotropy measurements from the Planck2018 release, we derive robust constraints on the $f(R)$ parameter $f_{R0}$. Our results, $\log_{10} |f_{R0}| < -5.32$ at the 95 % credible level, are the tightest current constraints on $f(R)$ gravity from cosmological scales. This upper limit rules out $f(R)$-like deviations from general relativity that result in more than a $\sim$20 % enhancement of the cluster population on mass scales $M_\mathrm{200c}>3\times10^{14}M_\odot$.
As an essential branch of recommender systems, sequential recommendation (SR) has received much attention due to its well-consistency with real-world situations. However, the widespread data sparsity issue limits the SR model's performance. Therefore, researchers have proposed many data augmentation (DA) methods to mitigate this phenomenon and have achieved impressive progress. In this survey, we provide a comprehensive review of DA methods for SR. We start by introducing the research background and motivation. Then, we categorize existing methodologies regarding their augmentation principles, objects, and purposes. Next, we present a comparative discussion of their advantages and disadvantages, followed by the exhibition and analysis of representative experimental results. Finally, we outline directions for future research and summarize this survey. We also maintain a repository with a paper list at \url{https://github.com/KingGugu/DA-CL-4Rec}.
Contextualized AI for Cyber Defense: An Automated Survey using LLMs
2024-09-20
Christoforus Yoga Haryanto, Anne Maria Elvira, Trung Duc Nguyen, Minh Hieu Vu, Yoshiano Hartanto, Emily Lomempow, Arathi Arakala
This paper surveys the potential of contextualized AI in enhancing cyber defense capabilities, revealing significant research growth from 2015 to 2024. We identify a focus on robustness, reliability, and integration methods, while noting gaps in organizational trust and governance frameworks. Our study employs two LLM-assisted literature survey methodologies: (A) ChatGPT 4 for exploration, and (B) Gemma 2:9b for filtering with Claude 3.5 Sonnet for full-text analysis. We discuss the effectiveness and challenges of using LLMs in academic research, providing insights for future researchers.
A Survey on Moral Foundation Theory and Pre-Trained Language Models: Current Advances and Challenges
2024-09-20
Lorenzo Zangari, Candida M. Greco, Davide Picca, Andrea Tagarelli
Moral values have deep roots in early civilizations, codified within norms and laws that regulated societal order and the common good. They play a crucial role in understanding the psychological basis of human behavior and cultural orientation. The Moral Foundation Theory (MFT) is a well-established framework that identifies the core moral foundations underlying the manner in which different cultures shape individual and social lives. Recent advancements in natural language processing, particularly Pre-trained Language Models (PLMs), have enabled the extraction and analysis of moral dimensions from textual data. This survey presents a comprehensive review of MFT-informed PLMs, providing an analysis of moral tendencies in PLMs and their application in the context of the MFT. We also review relevant datasets and lexicons and discuss trends, limitations, and future directions. By providing a structured overview of the intersection between PLMs and MFT, this work bridges moral psychology insights within the realm of PLMs, paving the way for further research and development in creating morally aware AI systems.
Rutile RuO$_2$ has been posited as a potential $d$-wave altermagnetism candidate, with a predicted significant spin splitting up to 1.4 eV. Despite accumulating theoretical predictions and transport measurements, direct spectroscopic observation of spin splitting has remained elusive. Here, we employ spin- and angle-resolved photoemission spectroscopy to investigate the band structures and spin polarization of thin-film and single-crystal RuO$_2$. Contrary to expectations of altermagnetism, our analysis indicates that RuO$_2$'s electronic structure aligns with those predicted under non-magnetic conditions, exhibiting no evidence of the hypothesized spin splitting. Additionally, we observe significant in-plane spin polarization of the low-lying bulk bands, which is antisymmetric about the high-symmetry plane and contrary to the $d$-wave spin texture due to time-reversal symmetry breaking in altermagnetism. These findings definitively challenge the altermagnetic order previously proposed for rutile RuO$_2$, prompting a reevaluation of its magnetic properties.
Efficiency gain in association studies based on population surveys by augmenting outcome data from the target population
2024-09-20
Tommi Härkänen, Sangita Kulathinal, Arya Panthalanickal Vijayakumar
Routinely collected nation-wide registers contain socio-economic and health-related information from a large number of individuals. However, important information on lifestyle, biological and other risk factors is available at most for small samples of the population through surveys. A majority of health surveys lack detailed medical information necessary for assessing the disease burden. Hence, traditionally data from the registers and the surveys are combined to have necessary information for the survey sample. Our idea is to base analyses on a combined sample obtained by adding a (large) sample of individuals from the population to the survey sample. The main objective is to assess the bias and gain in efficiency of such combined analyses with a binary or time-to-event outcome. We employ (i) the complete-case analysis (CCA) using the respondents of the survey, (ii) analysis of the full survey sample with both unit- and item-nonresponse under the missing at random (MAR) assumption and (iii) analysis of the combined sample under mixed type of missing data mechanism. We handle the missing data using multiple imputation (MI)-based analysis in (ii) and (iii). We utilize simulated as well as empirical data on ischemic heart disease obtained from the Finnish population. Our results suggested that the MI methods improved the efficiency of the estimates when we used the combined data for a binary outcome, but in the case of a time-to-event outcome the CCA was at least as good as the MI using the larger datasets, in terms of the the mean absolute and squared errors. Increasing the participation in the surveys and having good statistical methods for handling missing covariate data when the outcome is time-to-event would be needed for implementation of the proposed ideas.
Concept-Based Explanations in Computer Vision: Where Are We and Where Could We Go?
2024-09-20
Jae Hee Lee, Georgii Mikriukov, Gesina Schwalbe, Stefan Wermter, Diedrich Wolter
Concept-based XAI (C-XAI) approaches to explaining neural vision models are a promising field of research, since explanations that refer to concepts (i.e., semantically meaningful parts in an image) are intuitive to understand and go beyond saliency-based techniques that only reveal relevant regions. Given the remarkable progress in this field in recent years, it is time for the community to take a critical look at the advances and trends. Consequently, this paper reviews C-XAI methods to identify interesting and underexplored areas and proposes future research directions. To this end, we consider three main directions: the choice of concepts to explain, the choice of concept representation, and how we can control concepts. For the latter, we propose techniques and draw inspiration from the field of knowledge representation and learning, showing how this could enrich future C-XAI research.
LLMs have demonstrated commendable performance across diverse domains. Nevertheless, formulating high-quality prompts to assist them in their work poses a challenge for non-AI experts. Existing research in prompt engineering suggests somewhat scattered optimization principles and designs empirically dependent prompt optimizers. Unfortunately, these endeavors lack a structural design, incurring high learning costs and it is not conducive to the iterative updating of prompts, especially for non-AI experts. Inspired by structured reusable programming languages, we propose LangGPT, a structural prompt design framework. Furthermore, we introduce Minstrel, a multi-generative agent system with reflection to automate the generation of structural prompts. Experiments and the case study illustrate that structural prompts generated by Minstrel or written manually significantly enhance the performance of LLMs. Furthermore, we analyze the ease of use of structural prompts through a user survey in our online community.
The discovery of phosphine in Venus' atmosphere provides lessons for the search for life. The detection has survived all challenges and has acquired independent support from archival data from PVP. The presence of phosphine in Venus' oxidising environment is perplexing, and comprehensive studies rule out all known abiotic sources. More data is needed to understand the origin of phosphine, leading to JCMT-Venus, a long term atmospheric monitoring programme. This can find how phosphine varies in relation to other species providing clues to its origin. We present the latest JCMT-Venus results. The discovery and subsequent papers were explicit that they did not constitute evidence for life, only of phosphine. Media and public reaction to the discovery and its implications provide lessons for future life searches, as does the reaction of the scientific community. How this was handled by the team, media, and general public will be reviewed.
2024-09-20
Tianyu Liu, Xiao-Bin Qiang, Hai-Zhou Lu, X. C. Xie
One of the most celebrated accomplishments of modern physics is the description of fundamental principles of nature in the language of geometry. As the motion of celestial bodies is governed by the geometry of spacetime, the motion of electrons in condensed matter can be characterized by the geometry of the Hilbert space of their wave functions. Such quantum geometry, comprising of Berry curvature and quantum metric, can thus exert profound influences on various properties of materials. The dipoles of both Berry curvature and quantum metric produce nonlinear transport. The quantum metric plays an important role in flat-band superconductors by enhancing the transition temperature. The uniformly distributed momentum-space quantum geometry stabilizes the fractional Chern insulators and results in the fractional quantum anomalous Hall effect. We here review in detail quantum geometry in condensed matter, paying close attention to its effects on nonlinear transport, superconductivity, and topological properties. Possible future research directions in this field are also envisaged.
First Step Towards Matter Power Spectrum Reconstruction with Stage-III Weak Gravitational Lensing Surveys
2024-09-20
Jeger C. Broxterman, Konrad Kuijken
Weak gravitational lensing (WL) surveys provide insight into the matter distribution over an extensive range of scales. Current WL results are in mild tension with cosmic microwave background measurements from the early Universe. Reconstructing the matter power spectrum from their measurements instead of condensing the information into a single cosmological parameter may help locate the origin of these differences. To investigate the cosmic shear measurements of Stage-III WL surveys, we compare their tomographic data by assuming a simple parametric model for the matter power spectrum. The model allows the comparison of surveys with different characteristics and, in an agnostic approach, gives insight into the shape of the matter power spectrum preferred by the data without assuming a cosmological model. For the matter power spectrum, we assume a double power law model in scale factor and wavenumber. The best-fit amplitude and exponents are inferred in an MCMC analysis. We identify the scales to which the data is most sensitive. We test the sensitivity to different assumptions of the intrinsic alignment strength. We find that Stage-III surveys' constraining power on the power spectrum shape and evolution is still limited. Most information can be summarised as an overall amplitude at a pivot point in wave number and scale factor, while constraints on the power law indices are considerably weaker. Nevertheless, all surveys prefer a weaker rate of growth from $z=$ 0.5 to 0.1 than predicted. The assumed intrinsic alignment strength is found to have no significant impact on the measured parameters and goodness of fit. Direct estimates of the matter power spectrum from Stage-III weak lensing surveys can, in principle, be used to locate the physical origin of the $S_8$ tension. We present a simple methodology for the first steps in this direction but find that current constraints are still weak.
Contextual Compression in Retrieval-Augmented Generation for Large Language Models: A Survey
2024-09-20
Sourav Verma
Large Language Models (LLMs) showcase remarkable abilities, yet they struggle with limitations such as hallucinations, outdated knowledge, opacity, and inexplicable reasoning. To address these challenges, Retrieval-Augmented Generation (RAG) has proven to be a viable solution, leveraging external databases to improve the consistency and coherence of generated content, especially valuable for complex, knowledge-rich tasks, and facilitates continuous improvement by leveraging domain-specific insights. By combining the intrinsic knowledge of LLMs with the vast, dynamic repositories of external databases, RAG achieves a synergistic effect. However, RAG is not without its limitations, including a limited context window, irrelevant information, and the high processing overhead for extensive contextual data. In this comprehensive work, we explore the evolution of Contextual Compression paradigms, providing an in-depth examination of the field. Finally, we outline the current challenges and suggest potential research and development directions, paving the way for future advancements in this area.
Field theories and quantum methods for stochastic reaction-diffusion systems
2024-09-20
Mauricio J. del Razo, Tommaso Lamma, Wout Merbis
Complex systems are composed of many particles or agents that move and interact with one another. The underlying mathematical framework to model many of these systems must incorporate the spatial transport of particles and their interactions, as well as changes to their copy numbers, all of which can be formulated in terms of stochastic reaction-diffusion processes. The probabilistic representation of these processes is complex because of combinatorial aspects arising due to nonlinear interactions and varying particle numbers. This review presents the main field theory representations of stochastic reaction-diffusion systems, which handle these issues `under-the-hood'. First, we focus on bringing techniques familiar to theoretical physicists -- such as second quantization, Fock space, and path integrals -- back into the classical domain of reaction-diffusion systems. We demonstrate how various field theory representations can all be unified under a single basis-independent representation. We then extend existing quantum-based methods and notation to work directly on the level of the unifying representation, and we illustrate how they can be used to consistently obtain previous known results, such as numerical discretizations and relations between model parameters at multiple scales. Throughout the work, we contextualize how these representations mirror well-known models of chemical physics depending on their spatial resolution, as well as the corresponding macroscopic limits. The framework presented here may find applications in a diverse set of scientific fields, including physical chemistry, theoretical ecology, epidemiology, game theory and socio-economical models of complex systems. The presentation is done in a self-contained educational and unifying manner such that it can be followed by researchers across several fields.
Recent Advancement of Emotion Cognition in Large Language Models
2024-09-20
Yuyan Chen, Yanghua Xiao
Emotion cognition in large language models (LLMs) is crucial for enhancing performance across various applications, such as social media, human-computer interaction, and mental health assessment. We explore the current landscape of research, which primarily revolves around emotion classification, emotionally rich response generation, and Theory of Mind assessments, while acknowledge the challenges like dependency on annotated data and complexity in emotion processing. In this paper, we present a detailed survey of recent progress in LLMs for emotion cognition. We explore key research studies, methodologies, outcomes, and resources, aligning them with Ulric Neisser's cognitive stages. Additionally, we outline potential future directions for research in this evolving field, including unsupervised learning approaches and the development of more complex and interpretable emotion cognition LLMs. We also discuss advanced methods such as contrastive learning used to improve LLMs' emotion cognition capabilities.
Beyond the binary: Limitations and possibilities of gender-related speech technology research
2024-09-20
Ariadna Sanchez, Alice Ross, Nina Markl
This paper presents a review of 107 research papers relating to speech and sex or gender in ISCA Interspeech publications between 2013 and 2023. We note the scarcity of work on this topic and find that terminology, particularly the word gender, is used in ways that are underspecified and often out of step with the prevailing view in social sciences that gender is socially constructed and is a spectrum as opposed to a binary category. We draw attention to the potential problems that this can cause for already marginalised groups, and suggest some questions for researchers to ask themselves when undertaking work on speech and gender.
Measuring Mass of Gas in Central Galaxies using weak lensing and satellite kinematics in MOND
2024-09-20
Li Ma, Ziwen Zhang, Huiyuan Wang, Xufen Wu
In Milgrom's modified Newtonian dynamics (MOND) framework, the dynamical mass of a galaxy is fully determined by its baryonic matter distribution. We fit the distribution of cold and hot gas halos, focusing on hot gas, around SDSS central galaxies using weak lensing signals from the DECaLS survey in MOND. The central galaxies are classified into two samples, the total galaxies and star-forming galaxies. We find that hot gas halo densities nearly follow Plummer's profile for both samples across all mass bins. The rotation curves of the galaxy samples are also demonstrated. The efficiency of converting gas into stars, $M_*/(M_* + M_{\rm g})$, is between 0.3 and 0.8 in all mass bins of the star-forming sample, which is higher than in the total sample. We also calculate gas mass using the satellite kinematics method in MOND. A constant, mildly radial anisotropy or isotropy in satellite motion leads to good agreement between the satellite kinematics and weak lensing methods. Combining both methods, we observe a baryonic mass to line-of-sight velocity dispersion of satellites ($M_{\rm b}$-$\sigma_{\rm s}$) relation. In addition, we examine more sophisticated models in MOND, including external field effects and Osipkov-Merritt anisotropy profiles, and find them unnecessary. A simple, mildly radial constant anisotropic model or an isotropic model, even when isolated, already aligns with observations. The strong concordance between the two methods suggests that weak lensing signals reliably measure the dynamical mass of central galaxies and can constrain the distribution of missing baryons in galaxy clusters.
A Survey of 5G-Based Positioning for Industry 4.0: State of the Art and Enhanced Techniques
2024-09-20
Karthik Muthineni, Alexander Artemenko, Josep Vidal, Montse Nájar
The fifth generation (5G) mobile communication technology integrates communication, positioning, and mapping functionalities as an in-built feature. This has drawn significant attention from industries owing to the capability of replacing the traditional wireless technologies used in industries with 5G infrastructure that can be used for both connectivity and positioning. To this end, we identify the Automated Guided Vehicle (AGV) as a primary use case to benefit from the 5G functionalities. Given that there have been various works focusing on 5G positioning, it is necessary to analyze the existing works about their applicability with AGVs in industrial environments and provide insights to future research. In this paper, we present state of the art in 5G-based positioning, with a focus on key features, such as Millimeter Wave (mmWave) system, Massive Multiple Input Multiple Output (MIMO), Ultra-Dense Network (UDN), Device-to-Device (D2D) communication, and Reconfigurable Intelligent Surface (RIS). Moreover, we present the shortcomings in the current state of the art. Additionally, we propose enhanced techniques that can complement the accuracy of 5G-based positioning in controlled industrial environments.
Re examination of the effect of Pairing Gaps on Gamow Teller Strength Distributions and beta decay Rates
2024-09-20
Jameel Un Nabi, Muhammad Riaz, Arslan Mehmood
Beta decay is one of the key factors to understand the r process and evolution of massive stars. The Gamow Teller (GT) transitions drive the beta decay process. We employ the proton neutron quasiparticle random phase approximation (on QRPA) model to calculate terrestrial and stellar beta decay rates for 50 top ranked nuclei possessing astrophysical significance according to a recent survey. The model parameters of the on QRPA model affect the predicted results of beta decay. The current study investigates the effect of nucleon nucleon pairing gaps on charge changing transitions and the associated beta decay rates. Three different values of pairing gaps, namely TF, 3TF and 5TF, were used in our investigation. It was concluded that both GT strength distributions and half lives are sensitive to pairing gap values. The 3TF pairing gap scheme, in our chosen nuclear model, resulted in best prediction with around 80 percent of the calculated half-lives within a factor 10 of the measured ones. The 3TF pairing scheme also led to calculation of biggest beta decay rates in stellar matter.
Statistical properties and cosmological applications of fast radio bursts
2024-09-20
Qin Wu, Fa-Yin Wang
Fast radio burst (FRB) is a type of extragalactic radio signal characterized by millisecond duration, extremely high brightness temperature, and large dispersion measure. It remains a mystery in the universe. Advancements in instrumentation have led to the discovery of 816 FRB sources and 7622 bursts from 67 repeating FRBs now. This field is undergoing rapid development, rapidly advancing our understanding of the physics of FRBs as new observational data accumulates. The accumulation of data has also promoted our exploration of our universe. In this review, we summarize the statistical analysis and cosmological applications using large samples of FRBs, including the energy functions, the waiting time distributions of repeating FRBs, the probe of "missing baryons" and circumgalactic medium in the universe, measurements of cosmological parameters, exploration of the epoch of reionization history, and study of the gravitational lensing of FRBs.
Exoplanet accretion monitoring spectroscopic survey (ENTROPY) I. Evidence for magnetospheric accretion in the young isolated planetary-mass object 2MASS J11151597+1937266
2024-09-18
Gayathri Viswanath, Simon C. Ringqvist, Dorian Demars, Markus Janson, Mickaël Bonnefoy, Yuhiko Aoyama, Gabriel-Dominique Marleau, Catherine Dougados, Judit Szulágyi, Thanawuth Thanathibodee
Accretion among planets is a poorly understood phenomenon, due to lack of both observational and theoretical studies. Detection of emission lines from accreting gas giants facilitate detailed investigations into this process. This work presents a detailed analysis of Balmer lines from one of the few known young, planetary-mass objects with observed emission, the isolated L2 dwarf 2MASS J11151597+1937266 with a mass 7-21 Mj and age 5-45 Myr, located at 45+-2 pc. We obtained the first high-resolution (R~50,000) spectrum of the target with VLT/UVES, a spectrograph in the near-UV to visible wavelengths (3200-6800 AA). We report resolved H3-H6 and He I (5875.6 AA) emission in the spectrum. Based on the asymmetric line profiles of H3 and H4, 10% width of H3 (199+-1 km/s), tentative He I 6678 AA emission and indications of a disk from MIR excess, we confirm ongoing accretion at this object. Using the Gaia update of the parallax, we revise its temperature to 1816+-63 K and radius to 1.5+-0.1 Rj. Analysis of observed H I profiles using 1D planet-surface shock model implies a pre-shock gas velocity of v0=120(+80,-40) km/s and a pre-shock density of log(n0/cm^-3)=14(+0,-5). Pre-shock velocity points to a mass of 6(+8,-4) Mj for the target. Combining the H I line luminosities and planetary Lline-Lacc scaling relations, we derive a mass accretion rate of 1.4(+2.8,-0.9)x10^-8 Mj/yr.
Disruption of a massive molecular cloud by a supernova in the Galactic Centre: Initial results from the ACES project
2024-09-18
M. Nonhebel, A. T. Barnes, K. Immer, J. Armijos-Abendaño, J. Bally, C. Battersby, M. G. Burton, N. Butterfield, L. Colzi, P. García, A. Ginsburg, J. D. Henshaw, Y. Hu, I. Jiménez-Serra, R. S. Klessen, F. -H. Liang, S. N. Longmore, X. Lu, S. Martín, F. Nogueras-Lara, M. A. Petkova, J. E. Pineda, V. M. Rivilla, Á. Sánchez-Monge, M. G. Santa-Maria, H. A. Smith, Y. Sofue, M. C. Sormani, V. Tolls, D. L. Walker, Q. D. Wang, G. M. Williams, F. -W. Xu
The Milky Way's Central Molecular Zone (CMZ) differs dramatically from our local solar neighbourhood, both in the extreme interstellar medium conditions it exhibits (e.g. high gas, stellar, and feedback density) and in the strong dynamics at play (e.g. due to shear and gas influx along the bar). Consequently, it is likely that there are large-scale physical structures within the CMZ that cannot form elsewhere in the Milky Way. In this paper, we present new results from the Atacama Large Millimeter/submillimeter Array (ALMA) large programme ACES (ALMA CMZ Exploration Survey) and conduct a multi-wavelength and kinematic analysis to determine the origin of the M0.8$-$0.2 ring, a molecular cloud with a distinct ring-like morphology. We estimate the projected inner and outer radii of the M0.8$-$0.2 ring to be 79" and 154", respectively (3.1 pc and 6.1 pc at an assumed Galactic Centre distance of 8.2 kpc) and calculate a mean gas density $> 10^{4}$ cm$^{-3}$, a mass of $\sim$ $10^6$ M$_\odot$, and an expansion speed of $\sim$ 20 km s$^{-1}$, resulting in a high estimated kinetic energy ($> 10^{51}$ erg) and momentum ($> 10^7$ M$_\odot$ km s$^{-1}$). We discuss several possible causes for the existence and expansion of the structure, including stellar feedback and large-scale dynamics. We propose that the most likely cause of the M0.8$-$0.2 ring is a single high-energy hypernova explosion. To viably explain the observed morphology and kinematics, such an explosion would need to have taken place inside a dense, very massive molecular cloud, the remnants of which we now see as the M0.8$-$0.2 ring. In this case, the structure provides an extreme example of how supernovae can affect molecular clouds.
Large Language Models' (LLM) reasoning can be improved using test-time aggregation strategies, i.e., generating multiple samples and voting among generated samples. While these improve performance, they often reach a saturation point. Refinement offers an alternative by using LLM-generated feedback to improve solution quality. However, refinement introduces 3 key challenges: (1) Excessive refinement: Uniformly refining all instances can over-correct and reduce the overall performance. (2) Inability to localize and address errors: LLMs have a limited ability to self-correct and struggle to identify and correct their own mistakes. (3) Insufficient refinement: Deciding how many iterations of refinement are needed is non-trivial, and stopping too soon could leave errors unaddressed. To tackle these issues, we propose MAgICoRe, which avoids excessive refinement by categorizing problem difficulty as easy or hard, solving easy problems with coarse-grained aggregation and hard ones with fine-grained and iterative multi-agent refinement. To improve error localization, we incorporate external step-wise reward model (RM) scores. Moreover, to ensure effective refinement, we employ a multi-agent loop with three agents: Solver, Reviewer (which generates targeted feedback based on step-wise RM scores), and the Refiner (which incorporates feedback). To ensure sufficient refinement, we re-evaluate updated solutions, iteratively initiating further rounds of refinement. We evaluate MAgICoRe on Llama-3-8B and GPT-3.5 and show its effectiveness across 5 math datasets. Even one iteration of MAgICoRe beats Self-Consistency by 3.4%, Best-of-k by 3.2%, and Self-Refine by 4.0% while using less than half the samples. Unlike iterative refinement with baselines, MAgICoRe continues to improve with more iterations. Finally, our ablations highlight the importance of MAgICoRe's RMs and multi-agent communication.
Reporting Non-Consensual Intimate Media: An Audit Study of Deepfakes
2024-09-18
Li Qiwei, Shihui Zhang, Andrew Timothy Kasper, Joshua Ashkinaze, Asia A. Eaton, Sarita Schoenebeck, Eric Gilbert
Non-consensual intimate media (NCIM) inflicts significant harm. Currently, victim-survivors can use two mechanisms to report NCIM - as a non-consensual nudity violation or as copyright infringement. We conducted an audit study of takedown speed of NCIM reported to X (formerly Twitter) of both mechanisms. We uploaded 50 AI-generated nude images and reported half under X's "non-consensual nudity" reporting mechanism and half under its "copyright infringement" mechanism. The copyright condition resulted in successful image removal within 25 hours for all images (100% removal rate), while non-consensual nudity reports resulted in no image removal for over three weeks (0% removal rate). We stress the need for targeted legislation to regulate NCIM removal online. We also discuss ethical considerations for auditing NCIM on social platforms.
The Low-Redshift Lyman Continuum Survey: The Roles of Stellar Feedback and ISM Geometry in LyC Escape
2024-09-18
Sophia R. Flury, Anne E. Jaskot, Alberto Saldana-Lopez, M. S. Oey, John Chisholm, Ricardo Amorín, Omkar Bait, Sanchayeeta Borthakur, Cody Carr, Henry C. Ferguson, Mauro Giavalisco, Matthew Hayes, Timothy Heckman, Alaina Henry, Zhiyuan Ji, Lena Komarova, Floriane Leclercq, Alexandra Le Reste, Stephan McCandliss, Rui Marques-Chaves, Göran Östlin, Laura Pentericci, Swara Ravindranath, Michael Rutkowski, Claudia Scarlata, Daniel Schaerer, Trinh Thuan, Maxime Trebitsch, Eros Vanzella, Anne Verhamme, Bingjie Wang, Gábor Worseck, Xinfeng Xu
One of the fundamental questions of cosmology is the origin and mechanism(s) responsible for the reionization of the Universe beyond $z\sim6$. To address this question, many studies over the past decade have focused on local ($z\sim0.3$) galaxies which leak ionizing radiation (Lyman continuum or LyC). However, line-of-sight effects and data quality have prohibited deeper insight into the nature of LyC escape. To circumvent these limitations, we analyze stacks of a consolidated sample of {\it HST}/COS observations of the LyC in 89 galaxies at $z\sim0.3$. From fitting of the continuum, we obtain information about the underlying stellar populations and neutral ISM geometry. We find that most LyC non-detections are not leaking appreciable LyC ($f_{esc}^{\rm LyC}<1$\%) but also that exceptional cases point to spatial variations in the LyC escape fraction $f_{esc}^{\rm LyC}$. Stellar populations younger than 3 Myr lead to an increase in ionizing feedback, which in turn increases the isotropy of LyC escape. Moreover, mechanical feedback from supernovae in 8-10 Myr stellar populations is important for anisotropic gas distributions needed for LyC escape. While mechanical feedback is necessary for any LyC escape, high $f_{esc}^{\rm LyC}$ ($>5$\%) also requires a confluence of young stars and ionizing feedback. A two-stage burst of star formation could facilitate this optimal LyC escape scenario.
Applications of Knowledge Distillation in Remote Sensing: A Survey
2024-09-18
Yassine Himeur, Nour Aburaed, Omar Elharrouss, Iraklis Varlamis, Shadi Atalla, Wathiq Mansoor, Hussain Al Ahmad
With the ever-growing complexity of models in the field of remote sensing (RS), there is an increasing demand for solutions that balance model accuracy with computational efficiency. Knowledge distillation (KD) has emerged as a powerful tool to meet this need, enabling the transfer of knowledge from large, complex models to smaller, more efficient ones without significant loss in performance. This review article provides an extensive examination of KD and its innovative applications in RS. KD, a technique developed to transfer knowledge from a complex, often cumbersome model (teacher) to a more compact and efficient model (student), has seen significant evolution and application across various domains. Initially, we introduce the fundamental concepts and historical progression of KD methods. The advantages of employing KD are highlighted, particularly in terms of model compression, enhanced computational efficiency, and improved performance, which are pivotal for practical deployments in RS scenarios. The article provides a comprehensive taxonomy of KD techniques, where each category is critically analyzed to demonstrate the breadth and depth of the alternative options, and illustrates specific case studies that showcase the practical implementation of KD methods in RS tasks, such as instance segmentation and object detection. Further, the review discusses the challenges and limitations of KD in RS, including practical constraints and prospective future directions, providing a comprehensive overview for researchers and practitioners in the field of RS. Through this organization, the paper not only elucidates the current state of research in KD but also sets the stage for future research opportunities, thereby contributing significantly to both academic research and real-world applications.
The ubiquity of top-rich final states in the context of beyond the Standard Model (BSM) searches has led to their status as extensively studied signatures at the LHC. Over the past decade, numerous endeavours have been undertaken in the literature to develop methods for efficiently distinguishing boosted top quark jets from QCD jets. Although cut-based strategies for boosted top tagging, which rely on substructure information from fat jets resulting from the hadronic decay of boosted top quarks, were introduced in the literature as early as 2008, recent years have witnessed a surge in the utilization of machine learning-based approaches for the classification of top-jets from QCD jets. The review focuses on the present status of boosted top tagging and its application for BSM searchers.
Optimising the Trade-Off Between Type I and Type II Errors: A Review and Extensions
2024-09-18
Andrew P Grieve
In clinical studies upon which decisions are based there are two types of errors that can be made: a type I error arises when the decision is taken to declare a positive outcome when the truth is in fact negative, and a type II error arises when the decision is taken to declare a negative outcome when the truth is in fact positive. Commonly the primary analysis of such a study entails a two-sided hypothesis test with a type I error rate of 5% and the study is designed to have a sufficiently low type II error rate, for example 10% or 20%. These values are arbitrary and often do not reflect the clinical, or regulatory, context of the study and ignore both the relative costs of making either type of error and the sponsor's prior belief that the drug is superior to either placebo, or a standard of care if relevant. This simplistic approach has recently been challenged by numerous authors both from a frequentist and Bayesian perspective since when resources are constrained there will be a need to consider a trade-off between type I and type II errors. In this paper we review proposals to utilise the trade-off by formally acknowledging the costs to optimise the choice of error rates for simple, point null and alternative hypotheses and extend the results to composite, or interval hypotheses, showing links to the Probability of Success of a clinical study.
Probing the Possible Causes of the Transit Timing Variation for TrES-2b in TESS Era
2024-09-18
Shraddha Biswas, D. Bisht, Ing-Guey Jiang, Devesh P. Sariya, Kaviya Parthasarathy
Nowadays, transit timing variations (TTVs) are proving to be a very valuable tool in exoplanetary science to detect exoplanets by observing variations in transit times. To study the transit timing variation of the hot Jupiter, TrES-2b, we have combined 64 high-quality transit light curves from all seven sectors of NASA's Transiting Exoplanet Survey Satellite (TESS) along with 60 best-quality light curves from the ground-based facility Exoplanet Transit Database (ETD) and 106 mid-transit times from the previous works. From the precise transit timing analysis, we have observed a significant improvement in the orbital ephemerides, but we did not detect any short period TTVs that might result from an additional body. The inability to detect short-term TTVs further motivates us to investigate long-term TTVs, which might be caused by orbital decay, apsidal precession, Applegate mechanism, and $R{\phi}$mer effect and the orbital decay appeared to be a better explanation for the observed TTV with $\Delta BIC$ = 4.32. The orbital period of the hot Jupiter TrES-2b appears to be shrinking at a rate of $-5.58 \pm 1.81$ ms/yr. Assuming this decay is primarily caused by tidal dissipation within the host star, we have subsequently calculated the stellar tidal quality factor value to be 9900, which is 2 to 3 orders of magnitude smaller than the theoretically predicted values for other hot-Jupiter systems and its low value indicates more efficient tidal dissipation within the host star. Additional precise photometric and radial velocity observations are required to pinpoint the cause of the change in the orbital period.
A Survey-Based Quantitative Analysis of Stress Factors and Their Impacts Among Cybersecurity Professionals
2024-09-18
Sunil Arora, John D. Hastings
This study investigates the prevalence and underlying causes of work-related stress and burnout among cybersecurity professionals using a quantitative survey approach guided by the Job Demands-Resources model. Analysis of responses from 50 cybersecurity practitioners reveals an alarming reality: 44% report experiencing severe work-related stress and burnout, while an additional 28% are uncertain about their condition. The demanding nature of cybersecurity roles, unrealistic expectations, and unsupportive organizational cultures emerge as primary factors fueling this crisis. Notably, 66% of respondents perceive cybersecurity jobs as more stressful than other IT positions, with 84% facing additional challenges due to the pandemic and recent high-profile breaches. The study finds that most cybersecurity experts are reluctant to report their struggles to management, perpetuating a cycle of silence and neglect. To address this critical issue, the paper recommends that organizations foster supportive work environments, implement mindfulness programs, and address systemic challenges. By prioritizing the mental health of cybersecurity professionals, organizations can cultivate a more resilient and effective workforce to protect against an ever-evolving threat landscape.
2024-09-18
Codruţ-Andrei Diaconu, Konrad Heidler, Jonathan L. Bamber, Harry Zekollari
The more than 200,000 glaciers outside the ice sheets play a crucial role in our society by influencing sea-level rise, water resource management, natural hazards, biodiversity, and tourism. However, only a fraction of these glaciers benefit from consistent and detailed in-situ observations that allow for assessing their status and changes over time. This limitation can, in part, be overcome by relying on satellite-based Earth Observation techniques. Satellite-based glacier mapping applications have historically mainly relied on manual and semi-automatic detection methods, while recently, a fast and notable transition to deep learning techniques has started. This chapter reviews how combining multi-sensor remote sensing data and deep learning allows us to better delineate (i.e. map) glaciers and detect their temporal changes. We explain how relying on deep learning multi-sensor frameworks to map glaciers benefits from the extensive availability of regional and global glacier inventories. We also analyse the rationale behind glacier mapping, the benefits of deep learning methodologies, and the inherent challenges in integrating multi-sensor earth observation data with deep learning algorithms. While our review aims to provide a broad overview of glacier mapping efforts, we highlight a few setups where deep learning multi-sensor remote sensing applications have a considerable potential added value. This includes applications for debris-covered and rock glaciers that are visually difficult to distinguish from surroundings and for calving glaciers that are in contact with the ocean. These specific cases are illustrated through a series of visual imageries, highlighting some significant advantages and challenges when detecting glacier changes, including dealing with seasonal snow cover, changing debris coverage, and distinguishing glacier fronts from the surrounding sea ice.
Shannon Entropy is better Feature than Category and Sentiment in User Feedback Processing
2024-09-18
Andres Rojas Paredes, Brenda Mareco
App reviews in mobile app stores contain useful information which is used to improve applications and promote software evolution. This information is processed by automatic tools which prioritize reviews. In order to carry out this prioritization, reviews are decomposed into features like category and sentiment. Then, a weighted function assigns a weight to each feature and a review ranking is calculated. Unfortunately, in order to extract category and sentiment from reviews, its is required at least a classifier trained in an annotated corpus. Therefore this task is computational demanding. Thus, in this work, we propose Shannon Entropy as a simple feature which can replace standard features. Our results show that a Shannon Entropy based ranking is better than a standard ranking according to the NDCG metric. This result is promising even if we require fairness by means of algorithmic bias. Finally, we highlight a computational limit which appears in the search of the best ranking.
Optimizing Redshift Distribution Inference through Joint Self-Calibration and Clustering-Redshift Synergy
2024-09-18
Weilun Zheng, Kwan Chuen Chan, Haojie Xu, Le Zhang, Ruiyu Song
Accurately characterizing the true redshift (true-$z$) distribution of a photometric redshift (photo-$z$) sample is critical for cosmological analyses in imaging surveys. Clustering-based techniques, which include clustering-redshift (CZ) and self-calibration (SC) methods--depending on whether external spectroscopic data are used--offer powerful tools for this purpose. In this study, we explore the joint inference of the true-$z$ distribution by combining SC and CZ (denoted as SC+CZ). We derive simple multiplicative update rules to perform the joint inference. By incorporating appropriate error weighting and an additional weighting function, our method shows significant improvement over previous algorithms. We validate our approach using a DES Y3 mock catalog. The true-$z$ distribution estimated through the combined SC+CZ method is generally more accurate than using SC or CZ alone. To account for the different constraining powers of these methods, we assign distinct weights to the SC and CZ contributions. The optimal weights, which minimize the distribution error, depend on the relative constraining strength of the SC and CZ data. Specifically, for a spectroscopic redshift sample that represents 1% of the photo-$z$ sample, the optimal combination reduces the total error by 20% (40%) compared to using CZ (SC) alone, and it keeps the bias in mean redshift [$\Delta \bar{z} / (1 + z) $] at the level of 0.3%. Furthermore, when CZ data is only available in the low-$z$ range and the high-$z$ range relies solely on SC data, SC+CZ enables consistent estimation of the true-$z$ distribution across the entire redshift range. Our findings demonstrate that SC+CZ is an effective tool for constraining the true-$z$ distribution, paving the way for clustering-based methods to be applied at $z\gtrsim 1$.
Putting Data at the Centre of Offline Multi-Agent Reinforcement Learning
2024-09-18
Claude Formanek, Louise Beyers, Callum Rhys Tilbury, Jonathan P. Shock, Arnu Pretorius
Offline multi-agent reinforcement learning (MARL) is an exciting direction of research that uses static datasets to find optimal control policies for multi-agent systems. Though the field is by definition data-driven, efforts have thus far neglected data in their drive to achieve state-of-the-art results. We first substantiate this claim by surveying the literature, showing how the majority of works generate their own datasets without consistent methodology and provide sparse information about the characteristics of these datasets. We then show why neglecting the nature of the data is problematic, through salient examples of how tightly algorithmic performance is coupled to the dataset used, necessitating a common foundation for experiments in the field. In response, we take a big step towards improving data usage and data awareness in offline MARL, with three key contributions: (1) a clear guideline for generating novel datasets; (2) a standardisation of over 80 existing datasets, hosted in a publicly available repository, using a consistent storage format and easy-to-use API; and (3) a suite of analysis tools that allow us to understand these datasets better, aiding further development.
Black Hole Accretion is all about Sub-Keplerian Flows
2024-09-18
Sandip Kumar Chakrabarti
We review the advantages of fitting with a Two Component Advective Flow (TCAF) which uses only four physical parameters. We then present the results of hydrodynamic simulations to highlight the fact that the primary component of a black hole accretion remains the sub-Keplerian or the low angular momentum flow independent of whether we have a high, intermediate or low mass X-ray binary. Every aspect of spectral and timing properties, including the disk-jet connection could be understood well only if such a component is present along with a Keplerian component of variable size and accretion rate.
The use of data-driven methods in fluid mechanics has surged dramatically in recent years due to their capacity to adapt to the complex and multi-scale nature of turbulent flows, as well as to detect patterns in large-scale simulations or experimental tests. In order to interpret the relationships generated in the models during the training process, numerical attributions need to be assigned to the input features. One important example are the additive-feature-attribution methods. These explainability methods link the input features with the model prediction, providing an interpretation based on a linear formulation of the models. The SHapley Additive exPlanations (SHAP values) are formulated as the only possible interpretation that offers a unique solution for understanding the model. In this manuscript, the additive-feature-attribution methods are presented, showing four common implementations in the literature: kernel SHAP, tree SHAP, gradient SHAP, and deep SHAP. Then, the main applications of the additive-feature-attribution methods are introduced, dividing them into three main groups: turbulence modeling, fluid-mechanics fundamentals, and applied problems in fluid dynamics and heat transfer. This review shows thatexplainability techniques, and in particular additive-feature-attribution methods, are crucial for implementing interpretable and physics-compliant deep-learning models in the fluid-mechanics field.
End-to-End Learning of Transmitter and Receiver Filters in Bandwidth Limited Fiber Optic Communication Systems
2024-09-18
Søren Føns Nielsen, Francesco Da Ros, Mikkel N. Schmidt, Darko Zibar
This paper investigates the application of end-to-end (E2E) learning for joint optimization of pulse-shaper and receiver filter to reduce intersymbol interference (ISI) in bandwidth-limited communication systems. We investigate this in two numerical simulation models: 1) an additive white Gaussian noise (AWGN) channel with bandwidth limitation and 2) an intensity modulated direct detection (IM/DD) link employing an electro-absorption modulator. For both simulation models, we implement a wavelength division multiplexing (WDM) scheme to ensure that the learned filters adhere to the bandwidth constraints of the WDM channels. Our findings reveal that E2E learning greatly surpasses traditional single-sided transmitter pulse-shaper or receiver filter optimization methods, achieving significant performance gains in terms of symbol error rate with shorter filter lengths. These results suggest that E2E learning can decrease the complexity and enhance the performance of future high-speed optical communication systems.
SWEET-Cat: A view on the planetary mass-radius relation
2024-09-18
S. G. Sousa, V. Adibekyan, E. Delgado-Mena, N. C. Santos, B. Rojas-Ayala, S. C. Barros, O. D. S. Demangeon, S. Hoyer, G. Israelian, A. Mortier, B. M. T. Soares, M. Tsantaki
SWEET-Cat (Stars With ExoplanETs Catalogue) was originally introduced in 2013, and since then, the number of confirmed exoplanets has increased significantly. A crucial step for a comprehensive understanding of these new worlds is the precise and homogeneous characterization of their host stars. We used a large number of high-resolution spectra to continue the addition of new stellar parameters for planet-host stars in SWEET-Cat following the new detection of exoplanets listed both at the Extrasolar Planets Encyclopedia and at the NASA exoplanet archive. We obtained high-resolution spectra for a significant number of these planet-host stars, either observed by our team or collected through public archives. For FGK stars, the spectroscopic stellar parameters were derived for the spectra following the same homogeneous process using ARES+MOOG as for the previous SWEET-Cat releases. The stellar properties are combined with the planet properties to study possible correlations that could shed more light into the star-planet connection studies. We increase the number of stars with homogeneous parameters by 232 ($\sim$ 25\% - from 959 to 1191). We then focus on the exoplanets with both mass and radius determined to review the mass-radius relation where we find consistent results with the ones previously reported in the literature. For the massive planets we also revisit the radius anomaly where we confirm a metallicity correlation for the radius anomaly already hinted in previous results.
2024-09-18
Michael Taylor, Carlos Areia, Kath Burton, Charles Watkinson
The use of Wikipedia citations in scholarly research has been the topic of much inquiry over the past decade. A cross-publisher study (Taylor & Francis and University of Michigan Press) convened by Digital Science was established in late 2022 to explore author sentiment towards Wikipedia as a trusted source of information. A short survey was designed to poll published authors about views and uses of Wikipedia and explore how the increased addition of research citations in Wikipedia might help combat misinformation in the context of increasing public engagement with and access to validated research sources. With 21,854 surveys sent, targeting 40,402 papers mentioned in Wikipedia, a total of 750 complete surveys from 60 countries were included in this analysis. In general, responses revealed a positive sentiment towards research citation in Wikipedia and the researcher engagement practices. However, our sub analysis revealed statistically significant differences when comparison articles vs books and across disciplines, but not open vs closed access. This study will open the door to further research and deepen our understanding of authors perceived trustworthiness of the representation of their research in Wikipedia.
In this survey article, we review some conceptual approaches to the cyclic category $\Lambda$, as well as its description as a crossed simplicial group. We then give a new proof of the model structure on cyclic sets, work through the details of the generalized Reedy structure on cyclic spaces, and introduce model structures for cyclic Segal spaces and cyclic 2-Segal spaces.
Haptic-ACT: Bridging Human Intuition with Compliant Robotic Manipulation via Immersive VR
2024-09-18
Kelin Li, Shubham M Wagh, Nitish Sharma, Saksham Bhadani, Wei Chen, Chang Liu, Petar Kormushev
Robotic manipulation is essential for the widespread adoption of robots in industrial and home settings and has long been a focus within the robotics community. Advances in artificial intelligence have introduced promising learning-based methods to address this challenge, with imitation learning emerging as particularly effective. However, efficiently acquiring high-quality demonstrations remains a challenge. In this work, we introduce an immersive VR-based teleoperation setup designed to collect demonstrations from a remote human user. We also propose an imitation learning framework called Haptic Action Chunking with Transformers (Haptic-ACT). To evaluate the platform, we conducted a pick-and-place task and collected 50 demonstration episodes. Results indicate that the immersive VR platform significantly reduces demonstrator fingertip forces compared to systems without haptic feedback, enabling more delicate manipulation. Additionally, evaluations of the Haptic-ACT framework in both the MuJoCo simulator and on a real robot demonstrate its effectiveness in teaching robots more compliant manipulation compared to the original ACT. Additional materials are available at https://sites.google.com/view/hapticact.
Out-of-distribution (OOD) detection aims to detect test samples outside the training category space, which is an essential component in building reliable machine learning systems. Existing reviews on OOD detection primarily focus on method taxonomy, surveying the field by categorizing various approaches. However, many recent works concentrate on non-traditional OOD detection scenarios, such as test-time adaptation, multi-modal data sources and other novel contexts. In this survey, we uniquely review recent advances in OOD detection from the problem scenario perspective for the first time. According to whether the training process is completely controlled, we divide OOD detection methods into training-driven and training-agnostic. Besides, considering the rapid development of pre-trained models, large pre-trained model-based OOD detection is also regarded as an important category and discussed separately. Furthermore, we provide a discussion of the evaluation scenarios, a variety of applications, and several future research directions. We believe this survey with new taxonomy will benefit the proposal of new methods and the expansion of more practical scenarios. A curated list of related papers is provided in the Github repository: https://github.com/shuolucs/Awesome-Out-Of-Distribution-Detection
Exascale Quantum Mechanical Simulations: Navigating the Shifting Sands of Hardware and Software
2024-09-18
Ravindra Shinde, Claudia Filippi, Anthony Scemama, William Jalby
The era of exascale computing presents both exciting opportunities and unique challenges for quantum mechanical simulations. While the transition from petaflops to exascale computing has been marked by a steady increase in computational power, the shift towards heterogeneous architectures, particularly the dominant role of graphical processing units (GPUs), demands a fundamental shift in software development strategies. This review examines the changing landscape of hardware and software for exascale computing, highlighting the limitations of traditional algorithms and software implementations in light of the increasing use of heterogeneous architectures in high-end systems. We discuss the challenges of adapting quantum chemistry software to these new architectures, including the fragmentation of the software stack, the need for more efficient algorithms (including reduced precision versions) tailored for GPUs, and the importance of developing standardized libraries and programming models.
Bots are software systems designed to support users by automating a specific process, task, or activity. When such systems implement a conversational component to interact with the users, they are also known as conversational agents. Bots, particularly in their conversation-oriented version and AI-powered, have seen their adoption increase over time for software development and engineering purposes. Despite their exciting potential, ulteriorly enhanced by the advent of Generative AI and Large Language Models, bots still need to be improved to develop and integrate into the development cycle since practitioners report that bots add additional challenges that may worsen rather than improve. In this work, we aim to provide a taxonomy for characterizing bots, as well as a series of challenges for their adoption for Software Engineering associated with potential mitigation strategies. To reach our objectives, we conducted a multivocal literature review, reviewing both research and practitioner's literature. Through such an approach, we hope to contribute to both researchers and practitioners by providing first, a series of future research routes to follow, second, a list of strategies to adopt for improving the use of bots for software engineering purposes, and third, enforce a technology and knowledge transfer from the research field to the practitioners one, that is one of the primary goal of multivocal literature reviews.
Law-based and standards-oriented approach for privacy impact assessment in medical devices: a topic for lawyers, engineers and healthcare practitioners in MedTech
2024-09-18
Yuri R. Ladeia, David M. Pereira
Background: The integration of the General Data Protection Regulation (GDPR) and the Medical Device Regulation (MDR) creates complexities in conducting Data Protection Impact Assessments (DPIAs) for medical devices. The adoption of non-binding standards like ISO and IEC can harmonize these processes by enhancing accountability and privacy by design. Methods: This study employs a multidisciplinary literature review, focusing on GDPR and MDR intersection in medical devices that process personal health data. It evaluates key standards, including ISO/IEC 29134 and IEC 62304, to propose a unified approach for DPIAs that aligns with legal and technical frameworks. Results: The analysis reveals the benefits of integrating ISO/IEC standards into DPIAs, which provide detailed guidance on implementing privacy by design, risk assessment, and mitigation strategies specific to medical devices. The proposed framework ensures that DPIAs are living documents, continuously updated to adapt to evolving data protection challenges. Conclusions: A unified approach combining European Union (EU) regulations and international standards offers a robust framework for conducting DPIAs in medical devices. This integration balances security, innovation, and privacy, enhancing compliance and fostering trust in medical technologies. The study advocates for leveraging both hard law and standards to systematically address privacy and safety in the design and operation of medical devices, thereby raising the maturity of the MedTech ecosystem.
This brief review is based on a lecture given by one of the authors at the international youth conference AYSS-2023. It is devoted to multimessenger astronomy, which studies astrophysical objects and phenomena using various particles and waves that bring information from space. The messengers include electromagnetic and gravitational waves, neutrinos, and cosmic rays. We discuss new opportunities that open up with the combined use of several carriers of information. Combination of data obtained through various observation channels allows one to obtain more complete and accurate information about the processes occurring in the Universe, and even to use it for studying fundamental physics.
Optical Label-Free Microscopy Characterization of Dielectric Nanoparticles
2024-09-18
Berenice Garcia Rodriguez, Erik Olsén, Fredrik Skärberg, Giovanni Volpe, Fredrik Höök, Daniel Sundås Midtvedt
In order to relate nanoparticle properties to function, fast and detailed particle characterization, is needed. The ability to characterize nanoparticle samples using optical microscopy techniques has drastically improved over the past few decades; consequently, there are now numerous microscopy methods available for detailed characterization of particles with nanometric size. However, there is currently no ``one size fits all'' solution to the problem of nanoparticle characterization. Instead, since the available techniques have different detection limits and deliver related but different quantitative information, the measurement and analysis approaches need to be selected and adapted for the sample at hand. In this tutorial, we review the optical theory of single particle scattering and how it relates to the differences and similarities in the quantitative particle information obtained from commonly used microscopy techniques, with an emphasis on nanometric (submicron) sized dielectric particles. Particular emphasis is placed on how the optical signal relates to mass, size, structure, and material properties of the detected particles and to its combination with diffusivity-based particle sizing. We also discuss emerging opportunities in the wake of new technology development, with the ambition to guide the choice of measurement strategy based on various challenges related to different types of nanoparticle samples and associated analytical demands.
From Group Psychology to Software Engineering Research to Automotive R&D: Measuring Team Development at Volvo Cars
2024-09-18
Lucas Gren, Christian Jacobsson
From 2019 to 2022, Volvo Cars successfully translated our research discoveries regarding group dynamics within agile teams into widespread industrial practice. We wish to illuminate the insights gained through the process of garnering support, providing training, executing implementation, and sustaining a tool embraced by approximately 700 teams and 9,000 employees. This tool was designed to empower agile teams and propel their internal development. Our experiences underscore the necessity of comprehensive team training, the cultivation of a cadre of trainers across the organization, and the creation of a novel software solution. In essence, we deduce that an automated concise survey tool, coupled with a repository of actionable strategies, holds remarkable potential in fostering the maturation of agile teams, but we also share many of the challenges we encountered during the implementation.
Recently, AI systems have made remarkable progress in various tasks. Deep Reinforcement Learning(DRL) is an effective tool for agents to learn policies in low-level state spaces to solve highly complex tasks. Researchers have introduced Intrinsic Motivation(IM) to the RL mechanism, which simulates the agent's curiosity, encouraging agents to explore interesting areas of the environment. This new feature has proved vital in enabling agents to learn policies without being given specific goals. However, even though DRL intelligence emerges through a sub-symbolic model, there is still a need for a sort of abstraction to understand the knowledge collected by the agent. To this end, the classical planning formalism has been used in recent research to explicitly represent the knowledge an autonomous agent acquires and effectively reach extrinsic goals. Despite classical planning usually presents limited expressive capabilities, PPDDL demonstrated usefulness in reviewing the knowledge gathered by an autonomous system, making explicit causal correlations, and can be exploited to find a plan to reach any state the agent faces during its experience. This work presents a new architecture implementing an open-ended learning system able to synthesize from scratch its experience into a PPDDL representation and update it over time. Without a predefined set of goals and tasks, the system integrates intrinsic motivations to explore the environment in a self-directed way, exploiting the high-level knowledge acquired during its experience. The system explores the environment and iteratively: (a) discover options, (b) explore the environment using options, (c) abstract the knowledge collected and (d) plan. This paper proposes an alternative approach to implementing open-ended learning architectures exploiting low-level and high-level representations to extend its knowledge in a virtuous loop.
Benchmarking the spectroscopic masses of 249 evolved stars using asteroseismology with TESS
2024-09-18
Sai Prathyusha Malla, Dennis Stello, Benjamin T. Monet, Daniel Huber, Marc Hon, Timothy R. Bedding, Claudia Reyes, Daniel R. Hey
One way to understand planet formation is through studying the correlations between planet occurrence rates and stellar mass. However, measuring stellar mass in the red giant regime is very difficult. In particular, the spectroscopic masses of certain evolved stars, often referred to as "retired A-stars", have been questioned in the literature. Efforts to resolve this mass controversy using spectroscopy, interferometry and asteroseismology have so far been inconclusive. A recent ensemble study found a mass-dependent mass offset, but the result was based on only 16 stars. With NASA's Transiting Exoplanet Survey Satellite (TESS), we expand the investigation of the mass discrepancy to a total of 92 low-luminosity stars, synonymous with the retired A-stars. We measure their characteristic oscillation frequency, $\mathrm{\nu}_{\mathrm{max}}$, and the large frequency separation, $\mathrm{\Delta\nu}$, from their TESS photometric time series. Using these measurements and asteroseismic scaling relations, we derive asteroseismic masses and compare them with spectroscopic masses from five surveys, to comprehensively study the alleged mass-dependent mass offset. We find a mass offset between spectroscopy and seismology that increases with stellar mass. However, we note that adopting the seismic mass scale does not have a significant effect on the planet occurrence-mass-metallicity correlation for the so-called retired A-stars. We also report seismic measurements and masses for 157 higher luminosity giants (mostly helium-core-burning) from the spectroscopic surveys.
Current audio-visual representation learning can capture rough object categories (e.g., ``animals'' and ``instruments''), but it lacks the ability to recognize fine-grained details, such as specific categories like ``dogs'' and ``flutes'' within animals and instruments. To address this issue, we introduce DETECLAP, a method to enhance audio-visual representation learning with object information. Our key idea is to introduce an audio-visual label prediction loss to the existing Contrastive Audio-Visual Masked AutoEncoder to enhance its object awareness. To avoid costly manual annotations, we prepare object labels from both audio and visual inputs using state-of-the-art language-audio models and object detectors. We evaluate the method of audio-visual retrieval and classification using the VGGSound and AudioSet20K datasets. Our method achieves improvements in recall@10 of +1.5% and +1.2% for audio-to-visual and visual-to-audio retrieval, respectively, and an improvement in accuracy of +0.6% for audio-visual classification.
Revolutionizing Pharmaceutical Manufacturing: Advances and Challenges of 3D Printing System and Control
2024-09-18
Rahul Kumar, Vikram Singh, Priya Gupta
The advent of 3D printing has transformed the pharmaceutical industry, enabling precision drug manufacturing with controlled release profiles, dosing, and structural complexity. Additive manufacturing (AM) addresses the growing demand for personalized medicine, overcoming limitations of traditional methods. This technology facilitates tailored dosage forms, complex geometries, and real-time quality control. Recent advancements in drop-on-demand printing, UV curable inks, material science, and regulatory frameworks are discussed. Despite opportunities for cost reduction, flexibility, and decentralized manufacturing, challenges persist in scalability, reproducibility, and regulatory adaptation. This review provides an in-depth analysis of the current state of AM in pharmaceutical manufacturing, exploring recent developments, challenges, and future directions for mainstream integration.
Accretion disc outbursts are re-occurring events observed in various astrophysical systems, including X-ray binaries and cataclysmic variables. These outbursts are characterized by a sudden increase in luminosity due to various instabilities in the accretion disc. We need to investigate the time-dependent accretion flow models to understand the mechanisms driving these outbursts. Time-dependent models incorporate the disc's time evolution and can capture the build-up of instabilities. This review aims to give a basic overview of accretion disc outburst and stability analysis. The paper highlights the necessity of considering the hierarchy of different timescales, dynamical, viscous, and thermal, when investigating the instabilities occurring in the accretion disc. The importance and observational implications of studying these accretion disc outbursts are also discussed.
k-mer-based approaches to bridging pangenomics and population genetics
2024-09-18
Miles D. Roberts, Olivia Davis, Emily B. Josephs, Robert J. Williamson
Many commonly studied species now have more than one chromosome-scale genome assembly, revealing a large amount of genetic diversity previously missed by approaches that map short reads to a single reference. However, many species still lack multiple reference genomes and correctly aligning references to build pangenomes is challenging, limiting our ability to study this missing genomic variation in population genetics. Here, we argue that $k$-mers are a crucial stepping stone to bridging the reference-focused paradigms of population genetics with the reference-free paradigms of pangenomics. We review current literature on the uses of $k$-mers for performing three core components of most population genetics analyses: identifying, measuring, and explaining patterns of genetic variation. We also demonstrate how different $k$-mer-based measures of genetic variation behave in population genetic simulations according to the choice of $k$, depth of sequencing coverage, and degree of data compression. Overall, we find that $k$-mer-based measures of genetic diversity scale consistently with pairwise nucleotide diversity ($\pi$) up to values of about $\pi = 0.025$ ($R^2 = 0.97$) for neutrally evolving populations. For populations with even more variation, using shorter $k$-mers will maintain the scalability up to at least $\pi = 0.1$. Furthermore, in our simulated populations, $k$-mer dissimilarity values can be reliably approximated from counting bloom filters, highlighting a potential avenue to decreasing the memory burden of $k$-mer based genomic dissimilarity analyses. For future studies, there is a great opportunity to further develop methods to identifying selected loci using $k$-mers.
3D Gaussian Splatting has emerged as a powerful 3D scene representation technique, capturing fine details with high efficiency. In this paper, we introduce a novel voting-based method that extends 2D segmentation models to 3D Gaussian splats. Our approach leverages masked gradients, where gradients are filtered by input 2D masks, and these gradients are used as votes to achieve accurate segmentation. As a byproduct, we discovered that inference-time gradients can also be used to prune Gaussians, resulting in up to 21% compression. Additionally, we explore few-shot affordance transfer, allowing annotations from 2D images to be effectively transferred onto 3D Gaussian splats. The robust yet straightforward mathematical formulation underlying this approach makes it a highly effective tool for numerous downstream applications, such as augmented reality (AR), object editing, and robotics. The project code and additional resources are available at https://jojijoseph.github.io/3dgs-segmentation.
WALLABY Pilot Survey: HI source-finding with a machine learning framework
2024-09-18
Li Wang, O. Ivy Wong, Tobias Westmeier, Chandrashekar Murugeshan, Karen Lee-Waddell, Yuanzhi. Cai, Xiu. Liu, Austin Xiaofan Shen, Jonghwan Rhee, Helga Dénes, Nathan Deg, Peter Kamphuis, Barbara Catinella
The data volumes generated by the WALLABY atomic Hydrogen (HI) survey using the Australiian Square Kilometre Array Pathfinder (ASKAP) necessitate greater automation and reliable automation in the task of source-finding and cataloguing. To this end, we introduce and explore a novel deep learning framework for detecting low Signal-to-Noise Ratio (SNR) HI sources in an automated fashion. Specfically, our proposed method provides an automated process for separating true HI detections from false positives when used in combination with the Source Finding Application (SoFiA) output candidate catalogues. Leveraging the spatial and depth capabilities of 3D Convolutional Neural Networks (CNNs), our method is specifically designed to recognise patterns and features in three-dimensional space, making it uniquely suited for rejecting false positive sources in low SNR scenarios generated by conventional linear methods. As a result, our approach is significantly more accurate in source detection and results in considerably fewer false detections compared to previous linear statistics-based source finding algorithms. Performance tests using mock galaxies injected into real ASKAP data cubes reveal our method's capability to achieve near-100% completeness and reliability at a relatively low integrated SNR~3-5. An at-scale version of this tool will greatly maximise the science output from the upcoming widefield HI surveys.
Semi-Supervised Learning (SSL) has become a preferred paradigm in many deep learning tasks, which reduces the need for human labor. Previous studies primarily focus on effectively utilising the labelled and unlabeled data to improve performance. However, we observe that how to select samples for labelling also significantly impacts performance, particularly under extremely low-budget settings. The sample selection task in SSL has been under-explored for a long time. To fill in this gap, we propose a Representative and Diverse Sample Selection approach (RDSS). By adopting a modified Frank-Wolfe algorithm to minimise a novel criterion $\alpha$-Maximum Mean Discrepancy ($\alpha$-MMD), RDSS samples a representative and diverse subset for annotation from the unlabeled data. We demonstrate that minimizing $\alpha$-MMD enhances the generalization ability of low-budget learning. Experimental results show that RDSS consistently improves the performance of several popular SSL frameworks and outperforms the state-of-the-art sample selection approaches used in Active Learning (AL) and Semi-Supervised Active Learning (SSAL), even with constrained annotation budgets.
2024-09-18
Yuichiro Aoyama, Oswin So, Augustinos D. Saravanos, Evangelos A. Theodorou
This paper provides an overview, analysis, and comparison of second-order dynamic optimization algorithms, i.e., constrained Differential Dynamic Programming (DDP) and Sequential Quadratic Programming (SQP). Although a variety of these algorithms has been proposed and used successfully, there exists a gap in understanding the key differences and advantages, which we aim to provide in this work. For constrained DDP, we choose methods that incorporate nonlinear programming techniques to handle state and control constraints, including Augmented Lagrangian (AL), Interior Point, Primal Dual Augmented Lagrangian (PDAL), and Alternating Direction Method of Multipliers. Both DDP and SQP are provided in single- and multiple-shooting formulations, where constraints that arise from dynamics are encoded implicitly and explicitly, respectively. As a byproduct of the review, we also propose a single-shooting PDAL DDP which is robust to the growth of penalty parameters and performs better than the normal AL variant. We perform extensive numerical experiments on various systems with increasing complexity to investigate the quality of the solutions, the levels of constraint violation, iterations for convergence, and the sensitivity of final solutions with respect to initialization. The results show that DDP often has the advantage of finding better local minima, while SQP tends to achieve better constraint satisfaction. For multiple-shooting formulation, both DDP and SQP can enjoy informed initial guesses, while the latter appears to be more advantageous in complex systems. It is also worth highlighting that DDP provides favorable computational complexity and feedback gains as a byproduct of optimization.
"A Woman is More Culturally Knowledgeable than A Man?": The Effect of Personas on Cultural Norm Interpretation in LLMs
2024-09-18
Mahammed Kamruzzaman, Hieu Nguyen, Nazmul Hassan, Gene Louis Kim
As the deployment of large language models (LLMs) expands, there is an increasing demand for personalized LLMs. One method to personalize and guide the outputs of these models is by assigning a persona -- a role that describes the expected behavior of the LLM (e.g., a man, a woman, an engineer). This study investigates whether an LLM's understanding of social norms varies across assigned personas. Ideally, the perception of a social norm should remain consistent regardless of the persona, since acceptability of a social norm should be determined by the region the norm originates from, rather than by individual characteristics such as gender, body size, or race. A norm is universal within its cultural context. In our research, we tested 36 distinct personas from 12 sociodemographic categories (e.g., age, gender, beauty) across four different LLMs. We find that LLMs' cultural norm interpretation varies based on the persona used and the norm interpretation also varies within a sociodemographic category (e.g., a fat person and a thin person as in physical appearance group) where an LLM with the more socially desirable persona (e.g., a thin person) interprets social norms more accurately than with the less socially desirable persona (e.g., a fat person). We also discuss how different types of social biases may contribute to the results that we observe.
2024-09-18
Quang Tien Dam, Tri Tung Nguyen Nguyen, Dinh Tuan Tran, Joo-Ho Lee
Pain is a more intuitive and user-friendly way of communicating problems, making it especially useful in rehabilitation nurse training robots. While most previous methods have focused on classifying or recognizing pain expressions, these approaches often result in unnatural, jiggling robot faces. We introduce PainDiffusion, a model that generates facial expressions in response to pain stimuli, with controllable pain expressiveness and emotion status. PainDiffusion leverages diffusion forcing to roll out predictions over arbitrary lengths using a conditioned temporal U-Net. It operates as a latent diffusion model within EMOCA's facial expression latent space, ensuring a compact data representation and quick rendering time. For training data, we process the BioVid Heatpain Database, extracting expression codes and subject identity configurations. We also propose a novel set of metrics to evaluate pain expressions, focusing on expressiveness, diversity, and the appropriateness of model-generated outputs. Finally, we demonstrate that PainDiffusion outperforms the autoregressive method, both qualitatively and quantitatively. Code, videos, and further analysis are available at: \href{https://damtien444.github.io/paindf/}{https://damtien444.github.io/paindf/}.
Reionization relics in the cross-correlation between the Ly$α$ forest and 21 cm intensity mapping in the post-reionization era
2024-09-18
Paulo Montero-Camacho, Catalina Morales-Gutiérrez, Yao Zhang, Heyang Long, Yi Mao
The tumultuous effects of ultraviolet photons that source cosmic reionization, the subsequent compression and shock-heating of low-density regions, and the modulation of baryons in shallow potential wells induced by the passage of ionization fronts, collectively introduce perturbations to the evolution of the intergalactic medium in the post-reionization era. These enduring fluctuations persist deep into the post-reionization era, casting a challenge upon precision cosmology endeavors targeting tracers in this cosmic era. Simultaneously, these relics from reionization also present a unique opportunity to glean insights into the astrophysics that govern the epoch of reionization. In this work, we propose a first study of the cross-correlation of \lya forest and 21 cm intensity mapping, accounting for the repercussions of inhomogeneous reionization in the post-reionization era. We investigate the ability of SKA $\times$ DESI-like, SKA $\times$ MUST-like, and PUMA $\times$ MUST-like instrumental setups to achieve a high signal-to-noise ratio (SNR) in the redshift range $3.5 \leq z \leq 4$. Moreover, we assess how alterations in integration time, survey area, and reionization scenarios impact the SNR. Furthermore, we forecast the cross-correlation's potential to constrain cosmological parameters under varying assumptions: considering or disregarding reionization relics, marginalizing over reionization astrophysics, and assuming perfect knowledge of reionization. Notably, our findings underscore the remarkable capability of a futuristic PUMA $\times$ MUST-like setup, with a modest 100-hour integration time over a 100 sq. deg. survey, to constrain the ionization efficiency error to $\sigma_\zeta = 3.42 $.
Hertzsprung gap stars in nearby galaxies and the Quest for Luminous Red Novae Progenitors
2024-09-17
Hugo Tranin, Nadejda Blagorodnova, Viraj Karambelkar, Paul J. Groot, Steven Bloemen, Paul M. Vreeswijk, Daniëlle Pieterse, Jan van Roestel
After the main sequence phase, stars more massive than 2.5 M$_\odot$ rapidly evolve through the Hertzsprung gap as yellow giants and supergiants (YSG), before settling into the red giant branch. Identifying YSG in nearby galaxies is crucial for pinpointing progenitors of luminous red novae (LRNe) - astrophysical transients attributed to stellar mergers. In the era of extensive transient surveys like the Vera Rubin Observatory's LSST, this approach offers a new way to predict and select common envelope transients. This study investigates potential progenitors and precursors of LRNe by analysing Hubble Space Telescope (HST) photometry of stellar populations in galaxies within 20 Mpc to identify YSG candidates. Additionally, we use ZTF and MeerLICHT/BlackGEM to identify possible precursors, preparing for future observations by the LSST. We compiled a sample of 369 galaxies with HST exposures in the F475W, F555W, F606W, and F814W filters. We identified YSG candidates using MESA stellar evolution tracks and statistical analysis of color-magnitude diagrams (CMDs). Our sample includes 246,573 YSG candidates with masses between 3 and 20 $M_\odot$ and is affected by various contaminants, such as foreground stars and extinguished main-sequence stars. After excluding foreground stars using Gaia proper motions, contamination is estimated at 1.7\% from foreground stars and 20\% from extinction affecting main-sequence stars. Combining our YSG candidates with time-domain catalogs yielded several interesting candidates. Notably, we identified 12 LRN precursor candidates for which followup is encouraged. We highlight the importance of monitoring future transients that match YSG candidates to avoid missing potential LRNe and other rare transients. LSST will be a game changer in the search for LRN progenitors and precursors, discovering over 300,000 new YSG and 100 precursors within 20 Mpc.
An Empirical Study of Sensitive Information in Logs
2024-09-17
Roozbeh Aghili, Heng Li, Foutse Khomh
Software logs, generated during the runtime of software systems, are essential for various development and analysis activities, such as anomaly detection and failure diagnosis. However, the presence of sensitive information in these logs poses significant privacy concerns, particularly regarding Personally Identifiable Information (PII) and quasi-identifiers that could lead to re-identification risks. While general data privacy has been extensively studied, the specific domain of privacy in software logs remains underexplored, with inconsistent definitions of sensitivity and a lack of standardized guidelines for anonymization. To mitigate this gap, this study offers a comprehensive analysis of privacy in software logs from multiple perspectives. We start by performing an analysis of 25 publicly available log datasets to identify potentially sensitive attributes. Based on the result of this step, we focus on three perspectives: privacy regulations, research literature, and industry practices. We first analyze key data privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), to understand the legal requirements concerning sensitive information in logs. Second, we conduct a systematic literature review to identify common privacy attributes and practices in log anonymization, revealing gaps in existing approaches. Finally, we survey 45 industry professionals to capture practical insights on log anonymization practices. Our findings shed light on various perspectives of log privacy and reveal industry challenges, such as technical and efficiency issues while highlighting the need for standardized guidelines. By combining insights from regulatory, academic, and industry perspectives, our study aims to provide a clearer framework for identifying and protecting sensitive information in software logs.
Time Series Foundation Models (TSFMs) have recently garnered attention for their ability to model complex, large-scale time series data across domains such as retail, finance, and transportation. However, their application to sensitive, domain-specific fields like healthcare remains challenging, primarily due to the difficulty of fine-tuning these models for specialized, out-of-domain tasks with scarce publicly available datasets. In this work, we explore the use of Parameter-Efficient Fine-Tuning (PEFT) techniques to address these limitations, focusing on healthcare applications, particularly ICU vitals forecasting for sepsis patients. We introduce and evaluate two selective (BitFit and LayerNorm Tuning) and two additive (VeRA and FourierFT) PEFT techniques on multiple configurations of the Chronos TSFM for forecasting vital signs of sepsis patients. Our comparative analysis demonstrates that some of these PEFT methods outperform LoRA in terms of parameter efficiency and domain adaptation, establishing state-of-the-art (SOTA) results in ICU vital forecasting tasks. Interestingly, FourierFT applied to the Chronos (Tiny) variant surpasses the SOTA model while fine-tuning only 2,400 parameters compared to the 700K parameters of the benchmark.
We study the observable spectral and temporal properties of kilonova remnants analytically, and showcase quantitative differences with respect to supernova remnants. We provide detection prospects of kilonova remnants in the context of ongoing radio surveys. We find that there is a good chance to expect 10s of such objects in future surveys with a flux threshold of $\sim 0.1$ mJy. Kilonova remnants from a postulated population of long lived supramassive neutron star remnants of neutron star mergers are even more likely to be detected as they are extremely bright and peak earlier. For ongoing survey with threshold of $\sim$ mJy, we expect to find 10-100s of such objects if they are a significant fraction of total kilonova population. Considering that there are no promising such kilonovae candidates in current surveys, we constrain the fraction of such extreme kilonova to be no more than 30 percent of the overall kilonovae rate, depending on the details of ejecta mass and external density distribution.
Colour-Based Disentangling of Mira Variables and Ultra-Cool Dwarfs
2024-09-17
Aleksandra Avdeeva, Kefeng Tan, Santosh Joshi, Dana Kovaleva, Harinder P. Singh, Ali Luo, Oleg Malkov
Despite having different astronomical characteristics, the studies of mira variables and ultra-cool dwarfs frequently show similar red colors, which could cause leading to photometric misclassification. This study uses photometric data from the WISE, 2MASS, and Pan-STARRS surveys to construct color-based selection criteria for red dwarfs, brown dwarfs, and Mira variables. On analyzing the color indices, we developed empirical rules that separate these objects with an overall classification accuracy of approximately 91%-92%. While the differentiation between red dwarfs and both Mira variables and brown dwarfs is effective, challenges remain in distinguishing Mira variables from brown dwarfs due to overlapping color indices. The robustness of our classification technique was validated by a bootstrap analysis, highlighting the significance of color indices in large photometric surveys for stellar classification.
Modeling a frustrated Ising square lattice with the D-Wave Quantum Annealer
2024-09-17
C. Marin, A. Fontana, V. Bellani, F. Pederiva, A. Quaranta, F. Rossella, A. Salamon, G. Salina
The Ising model with nearest-neighbor interactions on a two-dimensional (2D) square lattice is one of the simplest models for studying ferro-magnetic to para-magnetic transitions. Extensive results are available in the literature for this model, which has become a paradigm for the study of magnetic phase transitions in materials, both theoretically and numerically. After a brief review of the main results obtained with a classical computer, we show how to implement on the D- Wave quantum annealer a more complex Ising model with the addition of competing antiferromagnetic interactions between the diagonal next-to-nearest neighbors with two coupling constants J1 and J2. The dynamics of this system, owing to frustration, are richer than those of the simple Ising model and exhibit a third striped (or antiferromagnetic) phase in addition to the ferro- and para-magnetic phases. In this work, we observed all three phases on the D-Wave hardware, studied the behavior of the solution with different annealing parameters, such as the chain strength and annealing time, and showed how to identify the phase transition by varying the ratio between the ferromagnetic and antiferromagnetic couplings. The same system is studied on a classical computer, with the possibility of taking into account the temperature (fixed on D-Wave) as a free parameter and to explore the full phase diagram: some comparative conclusions with D-Wave are drawn.
Biometric recognition systems, known for their convenience, are widely adopted across various fields. However, their security faces risks depending on the authentication algorithm and deployment environment. Current risk assessment methods faces significant challenges in incorporating the crucial factor of attacker's motivation, leading to incomplete evaluations. This paper presents a novel human-centered risk evaluation framework using conjoint analysis to quantify the impact of risk factors, such as surveillance cameras, on attacker's motivation. Our framework calculates risk values incorporating the False Acceptance Rate (FAR) and attack probability, allowing comprehensive comparisons across use cases. A survey of 600 Japanese participants demonstrates our method's effectiveness, showing how security measures influence attacker's motivation. This approach helps decision-makers customize biometric systems to enhance security while maintaining usability.
Evidence for gravitational self-lensing of the central supermassive black hole binary in the Seyfert galaxy NGC 1566
2024-09-17
Wolfram Kollatschny, Doron Chelouche
It is generally accepted that all massive galaxies host supermassive black holes (BHs) in their center and that mergers of two galaxies lead to the formation of BH binaries. The most interesting among them comprise the mergers in their final state, that is to say with parsec (3.2 light years) or sub-parsec orbital separations. It is possible to detect these systems with binary self-lensing. Here we report the potential detection of a central supermassive BH binary in the active galaxy (AGN) NGC1566 based on a microlensing outburst. The light curve of the outburst - based on observations with the All Sky Automated Survey for SuperNovae - lasted from the beginning of 2017 until the beginning of 2020. The steep symmetric light curve as well as its shape look very different with respect to normal random variations in AGN. However, the observations could be easily reproduced with a best-fit standard microlensing light curve. Based on the light curve, we derived a characteristic timescale of 155 days. During the outburst, the continuum as well as the broad line intensities varied; however, the narrow emission lines did not. This is an indication that the lensing object orbits the AGN nucleus between the broad line region (BLR) and the narrow line region (NLR), that is, at a distance on the order of 250 light days. The light curve can be reproduced by a lens with a BH mass of 5*10^{5} M_solar. This implies a mass ratio to the central AGN on the order of 1 to 10.
The emerging field of smart agriculture leverages the Internet of Things (IoT) to revolutionize farming practices. This paper investigates the transformative potential of Long Range (LoRa) technology as a key enabler of long-range wireless communication for agricultural IoT systems. By reviewing existing literature, we identify a gap in research specifically focused on LoRa's prospects and challenges from a communication perspective in smart agriculture. We delve into the details of LoRa-based agricultural networks, covering network architecture design, Physical Layer (PHY) considerations tailored to the agricultural environment, and channel modeling techniques that account for soil characteristics. The paper further explores relaying and routing mechanisms that address the challenges of extending network coverage and optimizing data transmission in vast agricultural landscapes. Transitioning to practical aspects, we discuss sensor deployment strategies and energy management techniques, offering insights for real-world deployments. A comparative analysis of LoRa with other wireless communication technologies employed in agricultural IoT applications highlights its strengths and weaknesses in this context. Furthermore, the paper outlines several future research directions to leverage the potential of LoRa-based agriculture 4.0. These include advancements in channel modeling for diverse farming environments, novel relay routing algorithms, integrating emerging sensor technologies like hyper-spectral imaging and drone-based sensing, on-device Artificial Intelligence (AI) models, and sustainable solutions. This survey can guide researchers, technologists, and practitioners to understand, implement, and propel smart agriculture initiatives using LoRa technology.
Towards Ethical Personal AI Applications: Practical Considerations for AI Assistants with Long-Term Memory
2024-09-17
Eunhae Lee
One application area of long-term memory (LTM) capabilities with increasing traction is personal AI companions and assistants. With the ability to retain and contextualize past interactions and adapt to user preferences, personal AI companions and assistants promise a profound shift in how we interact with AI and are on track to become indispensable in personal and professional settings. However, this advancement introduces new challenges and vulnerabilities that require careful consideration regarding the deployment and widespread use of these systems. The goal of this paper is to explore the broader implications of building and deploying personal AI applications with LTM capabilities using a holistic evaluation approach. This will be done in three ways: 1) reviewing the technological underpinnings of LTM in Large Language Models, 2) surveying current personal AI companions and assistants, and 3) analyzing critical considerations and implications of deploying and using these applications.
Fast radio bursts as a probe of gravity on cosmological scales
2024-09-17
Dennis Neumann, Robert Reischke, Steffen Hagstotz, Hendrik Hildebrandt
We explore the potential for improving constraints on gravity by leveraging correlations in the dispersion measure derived from Fast Radio Bursts (FRBs) in combination with cosmic shear. Specifically, we focus on Horndeski gravity, inferring the kinetic braiding and Planck mass run rate from a stage-4 cosmic shear mock survey alongside a survey comprising $10^4$ FRBs. For the inference pipeline, we utilise hi_class to predict the linear matter power spectrum in modified gravity scenarios, while non-linear corrections are modelled with HMcode, including feedback mechanisms. Our findings indicate that FRBs can disentangle degeneracies between baryonic feedback and cosmological parameters, as well as the mass of massive neutrinos. Since these parameters are also degenerate with modified gravity parameters, the inclusion of FRBs can enhance constraints on Horndeski parameters by up to $40$ percent, despite being a less significant measurement. Additionally, we apply our model to current FRB data and use the uncertainty in the $\mathrm{DM}-z$ relation to impose limits on gravity. However, due to the limited sample size of current data, constraints are predominantly influenced by theoretical priors. Despite this, our study demonstrates that FRBs will significantly augment the limited set of cosmological probes available, playing a critical role in providing alternative tests of feedback, cosmology, and gravity. All codes used in this work are made publically available.
Strategic Insights in Human and Large Language Model Tactics at Word Guessing Games
2024-09-17
Matīss Rikters, Sanita Reinsone
At the beginning of 2022, a simplistic word-guessing game took the world by storm and was further adapted to many languages beyond the original English version. In this paper, we examine the strategies of daily word-guessing game players that have evolved during a period of over two years. A survey gathered from 25% of frequent players reveals their strategies and motivations for continuing the daily journey. We also explore the capability of several popular open-access large language model systems and open-source models at comprehending and playing the game in two different languages. Results highlight the struggles of certain models to maintain correct guess length and generate repetitions, as well as hallucinations of non-existent words and inflections.
Measurements of polarization and spin correlation and observation of entanglement in top quark pairs using lepton+jets events from proton-proton collisions at $\sqrt{s}$ = 13 TeV
2024-09-17
CMS Collaboration
Measurements of the polarization and spin correlation in top quark pairs ($\mathrm{t\bar{t}}$) are presented using events with a single electron or muon and jets in the final state. The measurements are based on proton-proton collision data from the LHC at $\sqrt{s}$ = 13 TeV collected by the CMS experiment, corresponding to an integrated luminosity of 138 fb$^{-1}$. All coefficients of the polarization vectors and the spin correlation matrix are extracted simultaneously by performing a binned likelihood fit to the data. The measurement is performed inclusively and in bins of additional observables, such as the mass of the $\mathrm{t\bar{t}}$ system and the top quark scattering angle in the $\mathrm{t\bar{t}}$ rest frame. The measured polarization and spin correlation are in agreement with the standard model. From the measured spin correlation, conclusions on the $\mathrm{t\bar{t}}$ spin entanglement are drawn by applying the Peres-Horodecki criterion. The standard model predicts entangled spins for $\mathrm{t\bar{t}}$ states at the production threshold and at high masses of the $\mathrm{t\bar{t}}$ system. Entanglement is observed for the first time in events at high $\mathrm{t\bar{t}}$ mass, where a large fraction of the $\mathrm{t\bar{t}}$ decays are space-like separated, with an expected and observed significance of above 5 standard deviations.
If two agents disagree in their decisions, we may suspect they are not both correct. This intuition is formalized for evaluating agents that have carried out a binary classification task. Their agreements and disagreements on a joint test allow us to establish the only group evaluations logically consistent with their responses. This is done by establishing a set of axioms (algebraic relations) that must be universally obeyed by all evaluations of binary responders. A complete set of such axioms are possible for each ensemble of size N. The axioms for $N = 1, 2$ are used to construct a fully logical alarm - one that can prove that at least one ensemble member is malfunctioning using only unlabeled data. The similarities of this approach to formal software verification and its utility for recent agendas of safe guaranteed AI are discussed.
Stretchable Electrohydraulic Artificial Muscle for Full Motion Ranges in Musculoskeletal Antagonistic Joints
2024-09-17
Amirhossein Kazemipour, Ronan Hinchet, Robert K. Katzschmann
Artificial muscles play a crucial role in musculoskeletal robotics and prosthetics to approximate the force-generating functionality of biological muscle. However, current artificial muscle systems are typically limited to either contraction or extension, not both. This limitation hinders the development of fully functional artificial musculoskeletal systems. We address this challenge by introducing an artificial antagonistic muscle system capable of both contraction and extension. Our design integrates non-stretchable electrohydraulic soft actuators (HASELs) with electrostatic clutches within an antagonistic musculoskeletal framework. This configuration enables an antagonistic joint to achieve a full range of motion without displacement loss due to tendon slack. We implement a synchronization method to coordinate muscle and clutch units, ensuring smooth motion profiles and speeds. This approach facilitates seamless transitions between antagonistic muscles at operational frequencies of up to 3.2 Hz. While our prototype utilizes electrohydraulic actuators, this muscle-clutch concept is adaptable to other non-stretchable artificial muscles, such as McKibben actuators, expanding their capability for extension and full range of motion in antagonistic setups. Our design represents a significant advancement in the development of fundamental components for more functional and efficient artificial musculoskeletal systems, bringing their capabilities closer to those of their biological counterparts.
CO spectra of the ISM in the Host Galaxies of the Most Luminous WISE-Selected AGNs
2024-09-17
Lee R. Martin, Andrew W. Blain, Tanio Díaz-Santos, Roberto J. Assef, Chao-Wei Tsai, Hyunsung D. Jun, Peter R. M. Eisenhardt, Jingwen Wu, Andrey Vayner, Román Fernández Aranda
We present observations of mid-J J=4-3 or J=5-4 carbon monoxide (CO) emission lines and continuum emission from a sample of ten of the most luminous log(L/L_solar)~14 Hot Dust-Obscured Galaxies (Hot DOGs) discovered by the Wide-field Infrared Survey Explorer (WISE) with redshifts up to 4.6. We uncover broad spectral lines (FWHM~400 km/s) in these objects, suggesting a turbulent molecular interstellar medium (ISM) may be ubiquitous in Hot DOGs. A halo of molecular gas, extending out to a radius of 5 kpc is observed in W2305-0039, likely supplied by 940 km/s molecular outflows. W0831+0140 is plausibly the host of a merger between at least two galaxies, consistent with observations made using ionized gas. These CO(4-3) observations contrast with previous CO(1-0) studies of the same sources: the CO(4-3) to CO(1-0) luminosity ratios exceed 300 in each source, suggesting that the lowest excited states of CO are underluminous. These findings show that the molecular gas in Hot DOGs is consistently turbulent, plausibly a consequence of AGN feedback, triggered by galactic mergers.
Latent mixed-effect models for high-dimensional longitudinal data
2024-09-17
Priscilla Ong, Manuel Haußmann, Otto Lönnroth, Harri Lähdesmäki
Modelling longitudinal data is an important yet challenging task. These datasets can be high-dimensional, contain non-linear effects and time-varying covariates. Gaussian process (GP) prior-based variational autoencoders (VAEs) have emerged as a promising approach due to their ability to model time-series data. However, they are costly to train and struggle to fully exploit the rich covariates characteristic of longitudinal data, making them difficult for practitioners to use effectively. In this work, we leverage linear mixed models (LMMs) and amortized variational inference to provide conditional priors for VAEs, and propose LMM-VAE, a scalable, interpretable and identifiable model. We highlight theoretical connections between it and GP-based techniques, providing a unified framework for this class of methods. Our proposal performs competitively compared to existing approaches across simulated and real-world datasets.
2024-09-17
Javier de Lucas, Xavier Rivas, Tomasz Sobczak
k-Contact geometry appeared as a generalisation of contact geometry to analyse field theories. This work provides a new insightful approach to k-contact geometry by devising a theory of k-contact forms and proving that the kernel of a k-contact form is locally equivalent to a distribution of corank k that is distributionally maximally non-integrable and admits k commuting Lie symmetries: a so-called k-contact distribution. Compact manifolds admitting a global k-contact form are analysed, we give necessary topological conditions for their existence, k-contact Lie groups are defined and studied, we extend the Weinstein conjecture for the existence of closed orbits of Reeb vector fields in compact manifolds to the k-contact setting after studying compact low-dimensional manifolds endowed with a global k-contact form, and we provide some physical applications of some of our results. Polarisations for k-contact distributions are introduced and it is shown that a polarised k-contact distribution is locally diffeomorphic to the Cartan distribution of the first-order jet bundle over a fibre bundle of order k, which is a globally defined polarised k-contact distribution. Then, we relate k-contact manifolds to presymplectic and k-symplectic manifolds on fibre bundles of larger dimension and define for the first time types of submanifolds in k-contact geometry. We also review the theory of Hamiltonian k-vector fields, studying Hamilton-De Donder-Weyl equations in general and in Lie groups, which are here studied in an unprecedented manner. A theory of k-contact Hamiltonian vector fields is developed, which describes the theory of characteristics for Lie symmetries for first-order partial differential equations in a k-contact Hamiltonian manner. Our new Hamiltonian k-contact techniques are illustrated by analysing Hamilton-Jacobi and Dirac equations.
Audio language models can understand audio inputs and perform a range of audio-related tasks based on instructions, such as speech recognition and audio captioning, where the instructions are usually textual prompts. Audio language models are mostly initialized from pre-trained audio encoders and large language models (LLMs). Although these pre-trained components were developed to support multiple languages, audio-language models are trained predominantly on English data, which may limit their usability to only English instructions or English speech inputs. First, this paper examines the performance of existing audio language models in an underserved language using Thai as an example. This paper demonstrates that, despite being built on multilingual backbones, audio language models do not exhibit cross-lingual emergent abilities to low-resource languages. Second, this paper studies data mixture for developing audio language models that are optimized for a target language as well as English. In addition. this paper integrates audio comprehension and speech instruction-following capabilities into a single unified model. Our experiments provide insights into data mixture for enhancing instruction-following capabilities in both a low-resource language and English. Our model, Typhoon-Audio, outperforms existing open-source audio language models by a considerable margin, and it is comparable to state-of-the-art Gemini-1.5-Pro in both English and Thai languages.
Multi-modal Generative Models in Recommendation System
2024-09-17
Arnau Ramisa, Rene Vidal, Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott Sanner, Mahesh Sathiamoorthy, Atoosa Kasrizadeh, Silvia Milano, Francesco Ricci
Many recommendation systems limit user inputs to text strings or behavior signals such as clicks and purchases, and system outputs to a list of products sorted by relevance. With the advent of generative AI, users have come to expect richer levels of interactions. In visual search, for example, a user may provide a picture of their desired product along with a natural language modification of the content of the picture (e.g., a dress like the one shown in the picture but in red color). Moreover, users may want to better understand the recommendations they receive by visualizing how the product fits their use case, e.g., with a representation of how a garment might look on them, or how a furniture item might look in their room. Such advanced levels of interaction require recommendation systems that are able to discover both shared and complementary information about the product across modalities, and visualize the product in a realistic and informative way. However, existing systems often treat multiple modalities independently: text search is usually done by comparing the user query to product titles and descriptions, while visual search is typically done by comparing an image provided by the customer to product images. We argue that future recommendation systems will benefit from a multi-modal understanding of the products that leverages the rich information retailers have about both customers and products to come up with the best recommendations. In this chapter we review recommendation systems that use multiple data modalities simultaneously.
JWST PRIMER: A lack of outshining in four normal z =4-6 galaxies from the ALMA-CRISTAL Survey
2024-09-17
N. E. P. Lines, R. A. A. Bowler, N. J. Adams, R. Fisher, R. G. Varadaraj, Y. Nakazato, M. Aravena, R. J. Assef, J. E. Birkin, D. Ceverino, E. da Cunha, F. Cullen, I. De Looze, C. T. Donnan, J. S. Dunlop, A. Ferrara, N. A. Grogin, R. Herrera-Camus, R. Ikeda, A. M. Koekemoer, M. Killi, J. Li, D. J. McLeod, R. J. McLure, I. Mitsuhashi, P. G. Pérez-González, M. Relano, M. Solimano, J. S. Spilker, V. Villanueva, N. Yoshida
We present a spatially resolved analysis of four star-forming galaxies at $z = 4.44-5.64$ using data from the JWST PRIMER and ALMA-CRISTAL surveys to probe the stellar and inter-stellar medium properties on the sub-kpc scale. In the $1-5\,\mu{\rm m}$ JWST NIRCam imaging we find that the galaxies are composed of multiple clumps (between $2$ and $\sim 8$) separated by $\simeq 5\,{\rm kpc}$, with comparable morphologies and sizes in the rest-frame UV and optical. Using BAGPIPES to perform pixel-by-pixel SED fitting to the JWST data we show that the SFR ($\simeq 25\,{\rm M}_{\odot}/{\rm yr}$) and stellar mass (${\rm log}_{10}(M_{\star}/{\rm M}_{\odot}) \simeq 9.5$) derived from the resolved analysis are in close ($ \lesssim 0.3\,{\rm dex}$) agreement with those obtained by fitting the integrated photometry. In contrast to studies of lower-mass sources, we thus find a reduced impact of outshining of the older (more massive) stellar populations in these normal $z \simeq 5$ galaxies. Our JWST analysis recovers bluer rest-frame UV slopes ($\beta \simeq -2.1$) and younger ages ($\simeq 100\,{\rm Myr}$) than archival values. We find that the dust continuum from ALMA-CRISTAL seen in two of these galaxies correlates, as expected, with regions of redder rest-frame UV slopes and the SED-derived $A_{\rm V}$, as well as the peak in the stellar mass map. We compute the resolved IRX-$\beta$ relation, showing that the IRX is consistent with the local starburst attenuation curve and further demonstrating the presence of an inhomogeneous dust distribution within the galaxies. A comparison of the CRISTAL sources to those from the FirstLight zoom-in simulation of galaxies with the same $M_{\star}$ and SFR reveals similar age and colour gradients, suggesting that major mergers may be important in the formation of clumpy galaxies at this epoch.
The ALMA-CRISTAL Survey: Spatially-resolved Star Formation Activity and Dust Content in 4 < z < 6 Star-forming Galaxies
2024-09-17
Juno Li, Elisabete Da Cunha, Jorge González-López, Manuel Aravena, Ilse De Looze, N. M. Förster Schreiber, Rodrigo Herrera-Camus, Justin Spilker, Ken-ichi Tadaki, Loreto Barcos-Munoz, Andrew J. Battisti, Jack E. Birkin, Rebecca A. A. Bowler, Rebecca Davies, Tanio Díaz-Santos, Andrea Ferrara, Deanne B. Fisher, Jacqueline Hodge, Ryota Ikeda, Meghana Killi, Lilian Lee, Daizhong Liu, Dieter Lutz, Ikki Mitsuhashi, Thorsten Naab, Ana Posses, Monica Relaño, Manuel Solimano, Hannah Übler, Stefan Anthony van der Giessen, Vicente Villanueva
Using a combination of HST, JWST, and ALMA data, we perform spatially resolved spectral energy distributions (SED) fitting of fourteen 4
Leveraging Reviewer Experience in Code Review Comment Generation
2024-09-17
Hong Yi Lin, Patanamon Thongtanunam, Christoph Treude, Michael W. Godfrey, Chunhua Liu, Wachiraphan Charoenwet
Modern code review is a ubiquitous software quality assurance process aimed at identifying potential issues within newly written code. Despite its effectiveness, the process demands large amounts of effort from the human reviewers involved. To help alleviate this workload, researchers have trained deep learning models to imitate human reviewers in providing natural language code reviews. Formally, this task is known as code review comment generation. Prior work has demonstrated improvements in this task by leveraging machine learning techniques and neural models, such as transfer learning and the transformer architecture. However, the quality of the model generated reviews remain sub-optimal due to the quality of the open-source code review data used in model training. This is in part due to the data obtained from open-source projects where code reviews are conducted in a public forum, and reviewers possess varying levels of software development experience, potentially affecting the quality of their feedback. To accommodate for this variation, we propose a suite of experience-aware training methods that utilise the reviewers' past authoring and reviewing experiences as signals for review quality. Specifically, we propose experience-aware loss functions (ELF), which use the reviewers' authoring and reviewing ownership of a project as weights in the model's loss function. Through this method, experienced reviewers' code reviews yield larger influence over the model's behaviour. Compared to the SOTA model, ELF was able to generate higher quality reviews in terms of accuracy, informativeness, and comment types generated. The key contribution of this work is the demonstration of how traditional software engineering concepts such as reviewer experience can be integrated into the design of AI-based automated code review models.
2024-09-17
Manthan Patel, Jonas Frey, Deegan Atha, Patrick Spieler, Marco Hutter, Shehryar Khattak
Autonomous robot navigation in off-road environments requires a comprehensive understanding of the terrain geometry and traversability. The degraded perceptual conditions and sparse geometric information at longer ranges make the problem challenging especially when driving at high speeds. Furthermore, the sensing-to-mapping latency and the look-ahead map range can limit the maximum speed of the vehicle. Building on top of the recent work RoadRunner, in this work, we address the challenge of long-range (100 m) traversability estimation. Our RoadRunner (M&M) is an end-to-end learning-based framework that directly predicts the traversability and elevation maps at multiple ranges (50 m, 100 m) and resolutions (0.2 m, 0.8 m) taking as input multiple images and a LiDAR voxel map. Our method is trained in a self-supervised manner by leveraging the dense supervision signal generated by fusing predictions from an existing traversability estimation stack (X-Racer) in hindsight and satellite Digital Elevation Maps. RoadRunner M&M achieves a significant improvement of up to 50% for elevation mapping and 30% for traversability estimation over RoadRunner, and is able to predict in 30% more regions compared to X-Racer while achieving real-time performance. Experiments on various out-of-distribution datasets also demonstrate that our data-driven approach starts to generalize to novel unstructured environments. We integrate our proposed framework in closed-loop with the path planner to demonstrate autonomous high-speed off-road robotic navigation in challenging real-world environments. Project Page: https://leggedrobotics.github.io/roadrunner_mm/
We present a review of the work L. Raymond from 1995. The review aims at making this work more accessible and offers adaptations of some statements and proofs. In addition, this review forms an applicable framework for the complete solution of the Dry Ten Martini Problem for Sturmian Hamiltonians as appears in the work arXiv:2402.16703 by R. Band, S. Beckus and R. Loewy. A Sturmian Hamiltonian is a one-dimensional Schr\"odinger operator whose potential is a Sturmian sequence multiplied by a coupling constant, $V\in\mathbb{R}$. The spectrum of such an operator is commonly approximated by the spectra of designated periodic operators. If $V>4$, then the spectral bands of the periodic operators exhibit a particular combinatorial structure. This structure provides a formula for the integrated density of states. Employing this, it is shown that if $V>4$, then all the gaps, as predicted by the gap labelling theorem, are there.
Efficient Computation of Whole-Body Control Utilizing Simplified Whole-Body Dynamics via Centroidal Dynamics
2024-09-17
Junewhee Ahn, Jaesug Jung, Yisoo Lee, Hokyun Lee, Sami Haddadin, Jaeheung Park
In this study, we present a novel method for enhancing the computational efficiency of whole-body control for humanoid robots, a challenge accentuated by their high degrees of freedom. The reduced-dimension rigid body dynamics of a floating base robot is constructed by segmenting its kinematic chain into constrained and unconstrained chains, simplifying the dynamics of the unconstrained chain through the centroidal dynamics. The proposed dynamics model is possible to be applied to whole-body control methods, allowing the problem to be divided into two parts for more efficient computation. The efficiency of the framework is demonstrated by comparative experiments in simulations. The calculation results demonstrate a significant reduction in processing time, highlighting an improvement over the times reported in current methodologies. Additionally, the results also shows the computational efficiency increases as the degrees of freedom of robot model increases.
This research dives into exploring the dark mode effects on students of a university. Research is carried out implementing the dark mode in e-Learning sites and its impact on behavior of the users. Students are spending more time in front of the screen for their studies especially after the pandemic. The blue light from the screen during late hours affects circadian rhythm of the body which negatively impacts the health of humans including eye strain and headache. The difficulty that students faced during the time of interacting with various e-Learning sites especially during late hours was analyzed using different techniques of HCI like survey, interview, evaluation methods and principles of design. Dark mode is an option which creates a pseudo inverted adaptable interface by changing brighter elements of UI into a dim-lit friendly environment. It is said that using dark mode will lessen the amount of blue light emitted and benefit students who suffer from eye strain. Students' interactions with dark mode were investigated using a survey, and an e-learning site with a dark mode theme was created. Based on the students' comments, researchers looked into the effects of dark mode on HCI in e-learning sites. The findings indicate that students have a clear preference for dark mode: 79.7% of survey participants preferred dark mode on their phones, and 61.7% said they would be interested in seeing this feature added to e-learning websites.
Enhancing Security Testing Software for Systems that Cannot be Subjected to the Risks of Penetration Testing Through the Incorporation of Multi-threading and and Other Capabilities
2024-09-17
Matthew Tassava, Cameron Kolodjski, Jordan Milbrath, Jeremy Straub
The development of a system vulnerability analysis tool (SVAT) for complex mission critical systems (CMCS) produced the software for operation and network attack results review (SONARR). This software builds upon the Blackboard Architecture and uses its a rule-fact logic to assess model networks to identify potential pathways that an attacker might take through them via the exploitation of vulnerabilities within the network. The SONARR objects and algorithm were developed previously; however, performance was insufficient for analyzing large networks. This paper describes and analyzes the performance of a multi-threaded SONARR algorithm and other enhancements which were developed to increase SONARR's performance and facilitate the analysis of large networks.
Technical Upgrades to and Enhancements of a System Vulnerability Analysis Tool Based on the Blackboard Architecture
2024-09-17
Matthew Tassava, Cameron Kolodjski, Jeremy Straub
A system vulnerability analysis technique (SVAT) for the analysis of complex mission critical systems (CMCS) that cannot be taken offline or subjected to the risks posed by traditional penetration testing was previously developed. This system uses path-based analysis of vulnerabilities to identify potential threats to system security. Generalization logic building on the Blackboard Architecture's rule-fact paradigm was implemented in this system, the software for operation and network attack results review (SONARR). This paper presents an overview of additional functionality that has been added to this tool and the experimentation that was conducted to analyze their efficacy and the performance benefits of the new in-memory processing capabilities of the SONARR algorithm. The results of the performance tests and their relation to networks' architecture are discussed. The paper concludes with a discussion of avenues of future work, including the implementation of multithreading, additional analysis metrics like confidentiality, integrity, and availability, and improved heuristic development.
A modified recursive transfer matrix algorithm for radiation and scattering computation of multilayer spheres
2024-09-17
Jianing Zhang
We discusses the electromagnetic scattering and radiation problems of multilayered spheres, reviewing the historical expansion of the Lorentz-Mie theory and the numerical stability issues encountered in handling multilayered spheres. By combining recursive methods with the transfer matrix method, we propose a modified transfer matrix algorithm designed for the stable and efficient calculation of electromagnetic scattering coefficients of multilayered spheres. The new algorithm simplifies the recursive formulas by introducing Debye potentials and logarithmic derivatives, effectively avoiding numerical overflow issues associated with Bessel functions under large complex variables. Numerical test results demonstrate that this algorithm offers superior stability and applicability when dealing with complex cases such as thin shells and strongly absorbing media.
2024-09-17
Newshaw Bahreyni, Carlo Cafaro, Leonardo Rossetti
Influence network of events is a view of the universe based on events that may be related to one another via influence. The network of events form a partially-ordered set which, when quantified consistently via a technique called chain projection, results in the emergence of spacetime and the Minkowski metric as well as the Lorentz transformation through changing an observer from one frame to another. Interestingly, using this approach, the motion of a free electron as well as the Dirac equation can be described. Indeed, the same approach can be employed to show how a discrete version of some of the features of Euclidean geometry, including directions, dimensions, subspaces, Pythagorean theorem, and geometric shapes can emerge. In this paper, after reviewing the essentials of the influence network formalism, we build on some of our previous works to further develop aspects of Euclidean geometry. Specifically, we present the emergence of geometric shapes, a discrete version of the Parallel postulate, the dot product, and the outer (wedge product) in 2+1 dimensions. Finally, we show that the scalar quantification of two concatenated orthogonal intervals exhibits features that are similar to those of the well-known concept of geometric product in geometric Clifford algebras.
Advanced Halide Scintillators: From the Bulk to Nano
2024-09-17
Vojtech Vanecek, Katerina Decka, Eva Mihokova, Vaclav Cuba, Robert Kral, Martin Nikl
Halide scintillators play a crucial role in the detection of ionizing radiation since the discovery of scintillation in NaI:Tl in 1948. The discovery of NaI:Tl motivated the research and development (R&D) of halide scintillators resulting in the development of CsI:Tl, CsI:Na, CaF$_2$:Eu, etc. Later, the R&D shifted toward oxide materials due to their high mechanical and chemical stability, good scintillation properties, and relative ease of bulk single crystal growth. However, the development in crystal growth technology allowed for the growth of high-quality single crystals of hygroscopic and mechanically fragile materials including SrI$_2$ and LaBr$_3$. Scintillators based on these materials exhibit excellent performance and push the limits of inorganic scintillators. These results motivated intense research of a large variety of halide-based scintillators. Moreover, materials based on lead halide perovskites found application in the fields of photovoltaics, solid-state lighting, and lasers. The first studies show also the significant potential of lead halide perovskites as ultrafast scintillators in the form of NCs. The purpose of this review is to summarize the R&D in the field of halide scintillators during the last decade and to highlight perspectives for future development.
Opportunities and challenges of mRNA technologies in development of Dengue Virus Vaccine
2024-09-17
Xiaoyang Liu, Daniel Salmon
Dengue virus (DENV) is a mosquito-borne virus with a significant human health concern. With 390 million infections annually and 96 million showing clinical symptoms, severe dengue can lead to life-threatening conditions like dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). The only FDA-approved vaccine, Dengvaxia, has limitations due to antibody-dependent enhancement (ADE), necessitating careful administration. The recent pre-approval of TAK-003 by WHO in 2024 highlights ongoing efforts to improve vaccine options. This review explores recent advancements in dengue vaccine development, emphasizing potential utility of mRNA-based vaccines. By examining current clinical trial data and innovations, we aim to identify promising strategies to address the limitations of existing vaccines and enhance global dengue prevention efforts.
Preference Tuning with Human Feedback on Language, Speech, and Vision Tasks: A Survey
2024-09-17
Genta Indra Winata, Hanyang Zhao, Anirban Das, Wenpin Tang, David D. Yao, Shi-Xiong Zhang, Sambit Sahu
Preference tuning is a crucial process for aligning deep generative models with human preferences. This survey offers a thorough overview of recent advancements in preference tuning and the integration of human feedback. The paper is organized into three main sections: 1) introduction and preliminaries: an introduction to reinforcement learning frameworks, preference tuning tasks, models, and datasets across various modalities: language, speech, and vision, as well as different policy approaches, 2) in-depth examination of each preference tuning approach: a detailed analysis of the methods used in preference tuning, and 3) applications, discussion, and future directions: an exploration of the applications of preference tuning in downstream tasks, including evaluation methods for different modalities, and an outlook on future research directions. Our objective is to present the latest methodologies in preference tuning and model alignment, enhancing the understanding of this field for researchers and practitioners. We hope to encourage further engagement and innovation in this area.
Interplay of Electron Trapping by Defect Midgap State and Quantum Confinement to Optimize Hot Carrier Effect in a Nanowire Structure
2024-09-17
Imam Makhfudz, Hamidreza Esmaielpour, Yaser Hajati, Gregor Koblmüller, Nicolas Cavassilas
Hot carrier effect, a phenomenon where charge carriers generated by photon absorption remain energetic by not losing much energy, has been one of the leading strategies in increasing solar cell efficiency. Nanostructuring offers an effective approach to enhance hot carrier effect via the spatial confinement, as occurring in a nanowire structure. The recent experimental study by Esmaielpour et al. [ACS Applied Nano Materials 7, 2817 (2024)] reveals a fascinating non-monotonic dependence of the hot carrier effect in nanowire array on the diameter of the nanowire, contrary to what might be expected from quantum confinement alone. We show that this non-monotonic behavior can be explained by a simple model for electron energy loss that involves two principal mechanisms. First, electron-phonon scattering, that increases with nanowire diameter, leading to hot carrier effect that decreases with increasing diameter. Second, electron capture by a defect level within band gap, that is, a midgap state, that decreases with nanowire diameter, leading to hot carrier effect that increases with increasing diameter. The two mechanisms balance at a certain diameter corresponding to optimal hot carrier effect. Our result offers a guideline to optimize hot carrier effect in nanowire solar cells and ultimately their efficiency by adjusting the dimensions and micro-structural properties of nanowires.
Noise-aware Dynamic Image Denoising and Positron Range Correction for Rubidium-82 Cardiac PET Imaging via Self-supervision
2024-09-17
Huidong Xie, Liang Guo, Alexandre Velo, Zhao Liu, Qiong Liu, Xueqi Guo, Bo Zhou, Xiongchao Chen, Yu-Jung Tsai, Tianshun Miao, Menghua Xia, Yi-Hwa Liu, Ian S. Armstrong, Ge Wang, Richard E. Carson, Albert J. Sinusas, Chi Liu
Rb-82 is a radioactive isotope widely used for cardiac PET imaging. Despite numerous benefits of 82-Rb, there are several factors that limits its image quality and quantitative accuracy. First, the short half-life of 82-Rb results in noisy dynamic frames. Low signal-to-noise ratio would result in inaccurate and biased image quantification. Noisy dynamic frames also lead to highly noisy parametric images. The noise levels also vary substantially in different dynamic frames due to radiotracer decay and short half-life. Existing denoising methods are not applicable for this task due to the lack of paired training inputs/labels and inability to generalize across varying noise levels. Second, 82-Rb emits high-energy positrons. Compared with other tracers such as 18-F, 82-Rb travels a longer distance before annihilation, which negatively affect image spatial resolution. Here, the goal of this study is to propose a self-supervised method for simultaneous (1) noise-aware dynamic image denoising and (2) positron range correction for 82-Rb cardiac PET imaging. Tested on a series of PET scans from a cohort of normal volunteers, the proposed method produced images with superior visual quality. To demonstrate the improvement in image quantification, we compared image-derived input functions (IDIFs) with arterial input functions (AIFs) from continuous arterial blood samples. The IDIF derived from the proposed method led to lower AUC differences, decreasing from 11.09% to 7.58% on average, compared to the original dynamic frames. The proposed method also improved the quantification of myocardium blood flow (MBF), as validated against 15-O-water scans, with mean MBF differences decreased from 0.43 to 0.09, compared to the original dynamic frames. We also conducted a generalizability experiment on 37 patient scans obtained from a different country using a different scanner.
What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts
2024-09-17
Shuaiyu Chen, T. Clifton Green, Huseyin Gulen, Dexin Zhou
We examine how large language models (LLMs) interpret historical stock returns and compare their forecasts with estimates from a crowd-sourced platform for ranking stocks. While stock returns exhibit short-term reversals, LLM forecasts over-extrapolate, placing excessive weight on recent performance similar to humans. LLM forecasts appear optimistic relative to historical and future realized returns. When prompted for 80% confidence interval predictions, LLM responses are better calibrated than survey evidence but are pessimistic about outliers, leading to skewed forecast distributions. The findings suggest LLMs manifest common behavioral biases when forecasting expected returns but are better at gauging risks than humans.
A search for persistent radio sources toward repeating fast radio bursts discovered by CHIME/FRB
2024-09-17
Adaeze L. Ibik, Maria R. Drout, Bryan M. Gaensler, Paul Scholz, Navin Sridhar, Ben Margalit, Casey J. Law, Tracy E. Clarke, Shriharsh P. Tendulkar, Daniele Michilli, Tarraneh Eftekhari, Mohit Bhardwaj, Sarah Burke-Spolaor, Shami Chatterjee, Amanda M. Cook, Jason W. T. Hessels, Franz Kirsten, Ronniy C. Joseph, Victoria M. Kaspi, Mattias Lazda, Kiyoshi W. Masui, Kenzie Nimmo, Ayush Pandhi, Aaron B. Pearlman, Ziggy Pleunis, Masoud Rafiei-Ravandi, Kaitlyn Shin, Kendrick M. Smith
The identification of persistent radio sources (PRSs) coincident with two repeating fast radio bursts (FRBs) supports FRB theories requiring a compact central engine. However, deep non-detections in other cases highlight the diversity of repeating FRBs and their local environments. Here, we perform a systematic search for radio sources towards 37 CHIME/FRB repeaters using their arcminute localizations and a combination of archival surveys and targeted observations. Through multi-wavelength analysis of individual radio sources, we identify two (20181030A-S1 and 20190417A-S1) for which we disfavor an origin of either star formation or an active galactic nucleus in their host galaxies and thus consider them candidate PRSs. We do not find any associated PRSs for the majority of the repeating FRBs in our sample. For 8 FRB fields with Very Large Array imaging, we provide deep limits on the presence of PRSs that are 2--4 orders of magnitude fainter than the PRS associated with FRB\,20121102A. Using Very Large Array Sky Survey imaging of all 37 fields, we constrain the rate of luminous ($\gtrsim$10$^{40}$ erg s$^{-1}$) PRSs associated with repeating FRBs to be low. Within the context of FRB-PRS models, we find that 20181030A-S1 and 20190417A-S1 can be reasonably explained within the context of magnetar, hypernebulae, gamma-ray burst afterglow, or supernova ejecta models -- although we note that both sources follow the radio luminosity versus rotation measure relationship predicted in the nebula model framework. Future observations will be required to both further characterize and confirm the association of these PRS candidates with the FRBs.
Enhancing the Reliability of LiDAR Point Cloud Sampling: A Colorization and Super-Resolution Approach Based on LiDAR-Generated Images
2024-09-17
Sier Ha, Honghao Du, Xianjia Yu, Jian Song, Tomi Westerlund
In recent years, Light Detection and Ranging (LiDAR) technology, a critical sensor in robotics and autonomous systems, has seen significant advancements. These improvements include enhanced resolution of point clouds and the capability to provide 360{\deg} low-resolution images. These images encode various data such as depth, reflectivity, and near-infrared light within the pixels. However, an excessive density of points and conventional point cloud sampling can be counterproductive, particularly in applications such as LiDAR odometry, where misleading points and degraded geometry information may induce drift errors. Currently, extensive research efforts are being directed towards leveraging LiDAR-generated images to improve situational awareness. This paper presents a comprehensive review of current deep learning (DL) techniques, including colorization and super-resolution, which are traditionally utilized in conventional computer vision tasks. These techniques are applied to LiDAR-generated images and are analyzed qualitatively. Based on this analysis, we have developed a novel approach that selectively integrates the most suited colorization and super-resolution methods with LiDAR imagery to sample reliable points from the LiDAR point cloud. This approach aims to not only improve the accuracy of point cloud registration but also avoid mismatching caused by lacking geometry information, thereby augmenting the utility and precision of LiDAR systems in practical applications. In our evaluation, the proposed approach demonstrates superior performance compared to our previous work, achieving lower translation and rotation errors with a reduced number of points.
Survey of Orion Disks with ALMA (SODA) III: Disks in wide binary systems in L1641 and L1647
2024-09-17
Giulia Ricciardi, Sierk E. van Terwisga, Veronica Roccatagliata, Alvaro Hacar, Thomas Henning, Walter Del Pozzo
Aims. The goal of this work is to comprehensively characterize the impact of stellar multiplicity on Class II disks in the L1641 and L1647 regions of Orion A (~1-3 Myr), part of the Survey of Orion Disks with ALMA (SODA). We characterize the protostellar multiplicity using the Atacama Large Millimeter/submillimeter Array (ALMA), the ESO-VISTA, and Hubble Space telescopes. The resulting sample of 65 multiple systems represents the largest catalogue of wide binary systems to date (projected separation >1000 AU), allowing a more robust statistical characterization of the evolution and properties of protoplanetary disks. Methods. The disk population was observed in continuum with ALMA at 225 GHz, with a median rms of 1.5 Mearth. Combining these data (resolution ~1.1arcsec ) with the ESO-VISTA near-infrared survey of the Orion A cloud (resolution ~0.7arcsec ), multiple systems are assembled and selected by an iterative inside-out search in projected separation (>1000 AU). Results. We identify 61 binary systems, 3 triple systems, and one quadruple system. The separation range is between 1000 and 10^4 AU. The dust mass distributions inferred with the Kaplan-Meier estimator yield a median mass of 3.23+0.6-0.4 Mearth for primary disks and 3.88+0.3-0.3 Mearth for secondary disks.
2024-09-17
M. Janickyj, P. MacCarron, J. Yose, R. Kenna
Social groups have been studied throughout history to understand how different configurations impact those within them. Along with this came the interest in investigating social groups of both fictional and mythological works. Over the last decade these social groups have been studied through the lens of network science allowing for a new level of comparison between these stories. We use this approach to focus on the attributes of the characters within these networks, specifically looking at their gender. With this we review how the female populations within various narratives and to some extent the societies they are based in are portrayed. Through this we find that although there is not a trend of all narratives of the same origin having similar levels of representation some are noticeably better than others. We also observe which narratives overall prioritise important female characters and which do not.
2024-09-17
Noor Aftab, Xunhe, Zhang, David R. Mittelman, Dennis di Cicco, Sean Walker, David H. Sliski, Julia Homa, Colin Holm-Hansen, Mary Putman, David Schiminovich, Arne Henden, Gary Walker
The Mittelman-di Cicco-Walker (MDW) H$\alpha$ Sky Survey is an autonomously-operated and ongoing all-sky imaging survey in the narrowband H$\alpha$ wavelength. The survey was founded by amateur astronomers, and is presented here in its first stage of refinement for rigorous scientific use. Each field is exposed through an H$\alpha$ filter with a 3nm bandwidth for a total of four hours, with a pixel scale of 3.2 arcsec. Here, we introduce the first Data Release of the MDW H$\alpha$ Survey (Data Release 0, or DR0), spanning 238 fields in the region of Orion (~3100 deg$^2$). DR0 includes: calibrated mean fields, star-removed mean fields, a point source catalog matched to Data Release 1 of the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS1) and the INT Galactic Plane Survey (IGAPS), and mosaics.
Oscillations of black hole spacetimes exhibit divergent behavior toward the bifurcation sphere and spatial infinity. This divergence can be understood as a consequence of the geometry in these spacetime regions. In contrast, black-hole oscillations are regular when evaluated toward the event horizon and null infinity. Hyperboloidal surfaces naturally connect these regions, providing a geometric regularization of time-harmonic oscillations called quasinormal modes (QNMs). This review traces the historical development of the hyperboloidal approach to QNMs. We discuss the physical motivation for the hyperboloidal approach and highlight current developments in the field.
A VLBI Calibrator Grid at 600MHz for Fast Radio Transient Localizations with CHIME/FRB Outriggers
2024-09-17
Shion Andrew, Calvin Leung, Alexander Li, Kiyoshi W. Masui, Bridget C. Andersen, Kevin Bandura, Alice P. Curtin, Jane Kaczmarek, Adam E. Lanman, Mattias Lazda, Juan Mena-Parra, Daniele Michilli, Kenzie Nimmo, Aaron B. Pearlman, Mubdi Rahman, Vishwangi Shah, Kaitlyn Shin, Haochen Wang
The Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) Project has a new VLBI Outrigger at the Green Bank Observatory (GBO), which forms a 3300km baseline with CHIME operating at 400-800MHz. Using 100ms long full-array baseband "snapshots" collected commensally during FRB and pulsar triggers, we perform a shallow, wide-area VLBI survey covering a significant fraction of the Northern sky targeted at the positions of compact sources from the Radio Fundamental Catalog. In addition, our survey contains calibrators detected from two 1s long trial baseband snapshots for a deeper survey with CHIME and GBO. In this paper, we present the largest catalog of compact calibrators suitable for 30-milliarcsecond-scale VLBI observations at sub-GHz frequencies to date. Our catalog consists of 200 total calibrators in the Northern Hemisphere that are compact on 30-milliarcsecond scales with fluxes above 100mJy. This calibrator grid will enable the precise localization of hundreds of FRBs a year with CHIME/FRB-Outriggers.
Testing the thermal Sunyaev-Zel'dovich power spectrum of a halo model using hydrodynamical simulations
2024-09-17
Emma Ayçoberry, Pranjal R. S., Karim Benabed, Yohan Dubois, Elisabeth Krause, Tim Eifler
Statistical properties of LSS serve as powerful tools to constrain the cosmological properties of our Universe. Tracing the gas pressure, the tSZ effect is a biased probe of mass distribution and can be used to test the physics of feedback or cosmological models. Therefore, it is crucial to develop robust modeling of hot gas pressure for applications to tSZ surveys. Since gas collapses into bound structures, it is expected that most of the tSZ signal is within halos produced by cosmic accretion shocks. Hence, simple empirical halo models can be used to predict the tSZ power spectra. In this study, we employed the HMx halo model to compare the tSZ power spectra with those of several hydrodynamical simulations: the Horizon suite and the Magneticum simulation. We examined various contributions to the tSZ power spectrum across different redshifts, including the one- and two-halo term decomposition, the amount of bound gas, the importance of different masses and the electron pressure profiles. Our comparison of the tSZ power spectrum reveals discrepancies that increase with redshift. We find a 20% to 50% difference between the measured and predicted tSZ angular power spectrum over the multipole range $\ell=10^3-10^4$. Our analysis reveals that these differences are driven by the excess of power in the predicted two-halo term at low k and in the one-halo term at high k. At higher redshifts (z~3), simulations indicate that more power comes from outside the virial radius than from inside suggesting a limitation in the applicability of the halo model. We observe differences in the pressure profiles, despite the fair level of agreement on the tSZ power spectrum at low redshift with the default calibration of the halo model. In conclusion, our study suggests that the properties of the halo model need to be carefully controlled against real or mock data to be proven useful for cosmological purposes.
CuRIOS-ED: The Technology Demonstrator for the CubeSats for Rapid Infrared and Optical Surveys Mission
2024-09-17
Hannah Gulick, Jessica R. Lu, Aryan Sood, Steven V. W. Beckwith, Joshua S. Bloom, Kodi Rider, Dan Werthimer, Wei Liu, Guy Nir, Harrison Lee, Jeremy McCauley
The rise of time-domain astronomy including electromagnetic counterparts to gravitational waves, gravitational microlensing, explosive phenomena, and even astrometry with Gaia, are showing the power and need for surveys with high-cadence, large area, and long time baselines to study the transient universe. A constellation of SmallSats or CubeSats providing wide, instantaneous sky coverage down to 21 Vega mag at optical wavelengths would be ideal for addressing this need. We are assembling CuRIOS-ED (CubeSats for Rapid Infrared and Optical Survey--Exploration Demo), an optical telescope payload which will act as a technology demonstrator for a larger constellation of several hundred 16U CubeSats known as CuRIOS. In preparation for CuRIOS, CuRIOS-ED will launch in late 2025 as part of the 12U Starspec InspireSat MVP payload. CuRIOS-ED will be used to demonstrate the StarSpec ADCS pointing capabilities to <1" and to space-qualify a commercial camera package for use on the full CuRIOS payload. The CuRIOS-ED camera system will utilize a Sony IMX455 CMOS detector delivered in an off-the-shelf Atik apx60 package which we modified to be compatible with operations in vacuum as well as the CubeSat form factor, power, and thermal constraints. By qualifying this commercial camera solution, the cost of each CuRIOS satellite will be greatly decreased (~100x) when compared with current space-qualified cameras with IMX455 detectors. We discuss the CuRIOS-ED mission design with an emphasis on the disassembly, repackaging, and testing of the Atik apx60 for space-based missions. Characterization of the apx60's read noise, dark current, patterned noise, and thermal behavior are reported for a range of temperatures (-35 C to 40 C) and exposure times (0.001s to 30 s). Additionally, we comment on preliminary environmental testing results from a successful thermal vacuum test.
The LOFAR Two Metre Sky Survey Data Release 2: Probabilistic Spectral Source Classifications and Faint Radio Source Demographics
2024-09-17
A. B. Drake, D. J. B. Smith, M. J. Hardcastle, P. N. Best, R. Kondapally, M. I. Arnaudova, S. Das, S. Shenoy, K. J. Duncan, H. J. A. Röttgering, C. Tasse
We present an analysis of 152,355 radio sources identified in the second data release of the LOFAR Two Metre Sky Survey (LoTSS-DR2) with Sloan Digital Sky Survey (SDSS) spectroscopic redshifts in the range 0.00 < z < 0.57. Using Monte Carlo simulations we determine the reliability of each source exhibiting an excess in radio luminosity relative to that predicted from their Ha emission, and, for a subset of 124,023 sources we combine this measurement with a full BPT analysis. Using these two independent diagnostics we determine the reliability of each source hosting a supermassive black hole of high or low Eddington-scaled accretion rate, and combine the measurements to determine the reliability of sources belonging to each of four physical classes of objects: star forming galaxies (SFGs), radio-quiet active galactic nuclei (RQAGN), and high- or low-excitation radio galaxies (HERGs or emission-line LERGs). The result is a catalogue which enables user-defined samples of radio sources with a reliability threshold suited to their science goal e.g. prioritising purity or completeness. Here we select high-confidence samples of radio sources (>90% reliability) to report: 38,588 radio-excess AGN in the LoTSS DR2 sample (362 HERGs, and 12,648 emission-line LERGs), together with 38,729 SFGs, and 18,726 RQAGN. We validate these results through comparison to literature using independent emission-line measurements, and to widely-adopted WISE photometric selection techniques. While our use of SDSS spectroscopy limits our current analysis to ~4 percent of the LoTSS-DR2 catalogue, our method is directly applicable to data from the forthcoming WEAVE-LOFAR survey which will obtain over a million spectra of 144 MHz selected sources.
Equity considerations in COVID-19 vaccine allocation modelling: a literature review
2024-09-17
Eva Rumpler, Marc Lipsitch
We conducted a literature review of COVID-19 vaccine allocation modelling papers, specifically looking for publications that considered equity. We found that most models did not take equity into account, with the vast majority of publications presenting aggregated results and no results by any subgroup (e.g. age, race, geography, etc). We then give examples of how modelling can be useful to answer equity questions, and highlight some of the findings from the publications that did. Lastly, we describe seven considerations that seem important to consider when including equity in future vaccine allocation models.
The VIRUS-dE Survey II: Cuspy and round halos in dwarf ellipticals -- A result of early assembly?
2024-09-17
Mathias Lipka, Jens Thomas, Roberto Saglia, Ralf Bender, Maximilian Fabricius, Christian Partmann
We analyze the dark matter (DM) halos of a sample of dwarf Ellitpicals (dE) and discuss cosmological and evolutionary implications. Using orbit modeling we recover their density slopes and, for the first time, the halo flattening. We find the `cusp-core' tension is mild, on average dEs have central slopes slightly below the Navarro Frenk White (NFW) predictions. However, the measured flattenings are still more spherical than cosmological simulations predict. Unlike brighter ETGs the total density slopes of dEs are shallower, and their average DM density does not follow their scaling relation with luminosity. Conversely, dE halos are denser and the densities steeper than in LTGs. We find average DM density and slope are strongly correlated with the environment and moderately with the angular momentum. Central, non-rotating dEs have dense and cuspy halos, whereas rotating dEs in Virgo's outskirts are more cored and less dense. This can be explained by a delayed formation of the dEs in the cluster outskirts, or alternatively, by the accumulated baryonic feedback the dEs in the outskirts have experienced during their very different star formation history. Our results suggest halo profiles are not universal (they depend on assembly conditions) and they evolve only mildly due to internal feedback. We conclude dEs in the local Universe have assembled at a higher redshift than local spirals. In these extreme conditions (e.g. star-formation, halo assembly) were very different, suggesting no new dEs are formed at present.
The VIRUS-dE Survey I: Stars in dwarf elliptical galaxies - 3D dynamics and radially resolved stellar initial mass functions
2024-09-16
Mathias Lipka, Jens Thomas, Roberto Saglia, Ralf Bender, Maximilian Fabricius, Gary J. Hill, Matthias Kluge, Martin Landriau, Ximena Mazzalay, Eva Noyola, Taniya Parikh, Jan Snigula
We analyse the stellar structure of a sample of dwarf ellipticals (dE) inhabiting various environments within the Virgo cluster. Integral-field observations with a high spectral resolution allow us to robustly determine their low velocity dispersions ($\sim25$ km s$^{-1}$) and higher-order kinematic moments out to the half-light radius. We find the dEs exhibit a diversity in ages with the younger dEs being less enhanced than the older, suggesting a complex star formation history for those dEs that recently entered Virgo while others have been quenched shortly after reionization. Orbit-superposition modeling allowed us to recover viewing angles, stellar mass-to-light ratios (with gradients), as well as the intrinsic orbit structure. We find that the angular momentum of the dEs is strongly suppressed compared to ordinary early-type galaxies and correlates with the environment. Flattened dEs are so because of a suppressed kinetic energy perpendicular to their equatorial plane. Combining population and dynamical modeling results, we find an age-dependent stellar initial mass function (IMF) or, alternatively, evidence for a more extended star formation history for those galaxies that have had higher initial mass and/or inhabited lower density environments. dEs appear to have a spatially homogeneous stellar structure but the state they were `frozen' in as they stopped forming stars varies dramatically according to their initial conditions.
Constraints on axions from patchy screening of the cosmic microwave background
2024-09-16
Samuel Goldstein, Fiona McCarthy, Cristina Mondino, J. Colin Hill, Junwu Huang, Matthew C. Johnson
The resonant conversion of cosmic microwave background (CMB) photons into axions within large-scale structure induces an anisotropic spectral distortion in CMB temperature maps. Applying state-of-the-art foreground cleaning techniques to $\textit{Planck}$ CMB observations, we construct maps of axion-induced "patchy screening" of the CMB. We cross-correlate these maps with data from the $\textit{unWISE}$ galaxy survey and find no evidence of axions. We constrain the axion-photon coupling, $g_{a\gamma\gamma} \lesssim 2 \times 10^{-12}~{\rm GeV}^{-1}$, at the 95% confidence level for axion masses in the range $10^{-13}~{\rm eV} \lesssim m_a \lesssim 10^{-12}~{\rm eV}$. These constraints are competitive with the tightest astrophysical axion limits in this mass range and are inferred from robust population-level statistics, which makes them complementary to existing searches that rely on modeling of individual systems.
Pennsieve: A Collaborative Platform for Translational Neuroscience and Beyond
2024-09-16
Zack Goldblum, Zhongchuan Xu, Haoer Shi, Patryk Orzechowski, Jamaal Spence, Kathryn A Davis, Brian Litt, Nishant Sinha, Joost Wagenaar
The exponential growth of neuroscientific data necessitates platforms that facilitate data management and multidisciplinary collaboration. In this paper, we introduce Pennsieve - an open-source, cloud-based scientific data management platform built to meet these needs. Pennsieve supports complex multimodal datasets and provides tools for data visualization and analyses. It takes a comprehensive approach to data integration, enabling researchers to define custom metadata schemas and utilize advanced tools to filter and query their data. Pennsieve's modular architecture allows external applications to extend its capabilities, and collaborative workspaces with peer-reviewed data publishing mechanisms promote high-quality datasets optimized for downstream analysis, both in the cloud and on-premises. Pennsieve forms the core for major neuroscience research programs including NIH SPARC Initiative, NIH HEAL Initiative's PRECISION Human Pain Network, and NIH HEAL RE-JOIN Initiative. It serves more than 80 research groups worldwide, along with several large-scale, inter-institutional projects at clinical sites through the University of Pennsylvania. Underpinning the SPARC.Science, Epilepsy.Science, and Pennsieve Discover portals, Pennsieve stores over 125 TB of scientific data, with 35 TB of data publicly available across more than 350 high-impact datasets. It adheres to the findable, accessible, interoperable, and reusable (FAIR) principles of data sharing and is recognized as one of the NIH-approved Data Repositories. By facilitating scientific data management, discovery, and analysis, Pennsieve fosters a robust and collaborative research ecosystem for neuroscience and beyond.
A Host Galaxy Morphology Link Between Quasi-Periodic Eruptions and Tidal Disruption Events
2024-09-16
Olivier Gilbert, John J. Ruan, Michael Eracleous, Daryl Haggard, Jessie C. Runnoe
The physical processes that produce X-ray Quasi-Periodic Eruptions (QPEs) recently discovered from the nuclei of several low-redshift galaxies are mysterious. Several pieces of observational evidence strongly suggest a link between QPEs and Tidal Disruption Events (TDE). Previous studies also reveal that the morphologies of TDE host galaxies are highly concentrated, with high Sersic indicies, bulge-to-total light (B/T) ratios, and stellar surface mass densities relative to the broader galaxy population. We use these distinctive properties to test the link between QPEs and TDEs, by comparing these parameters of QPE host galaxies to TDE host galaxies. We employ archival Legacy Survey images of a sample of 9 QPE host galaxies and a sample of 13 TDE host galaxies, and model their surface brightness profiles. We show that QPE host galaxies have high Sersic indices of ~3, high B/T ratios of ~0.5, and high surface mass densities of ~10^10 Msun kpc^-2. These properties are similar to TDE host galaxies, but are in strong contrast to a mass- and redshift-matched control sample of galaxies. We also find tentative evidence that the central black holes in both QPE and TDE host galaxies are undermassive relative to their stellar mass. The morphological similarities between QPE and TDE host galaxies at the population level add to the mounting evidence of a physical link between these phenomena, and favor QPE models that also invoke TDEs.
XLM for Autonomous Driving Systems: A Comprehensive Review
2024-09-16
Sonda Fourati, Wael Jaafar, Noura Baccar, Safwan Alfattani
Large Language Models (LLMs) have showcased remarkable proficiency in various information-processing tasks. These tasks span from extracting data and summarizing literature to generating content, predictive modeling, decision-making, and system controls. Moreover, Vision Large Models (VLMs) and Multimodal LLMs (MLLMs), which represent the next generation of language models, a.k.a., XLMs, can combine and integrate many data modalities with the strength of language understanding, thus advancing several information-based systems, such as Autonomous Driving Systems (ADS). Indeed, by combining language communication with multimodal sensory inputs, e.g., panoramic images and LiDAR or radar data, accurate driving actions can be taken. In this context, we provide in this survey paper a comprehensive overview of the potential of XLMs towards achieving autonomous driving. Specifically, we review the relevant literature on ADS and XLMs, including their architectures, tools, and frameworks. Then, we detail the proposed approaches to deploy XLMs for autonomous driving solutions. Finally, we provide the related challenges to XLM deployment for ADS and point to future research directions aiming to enable XLM adoption in future ADS frameworks.
Real-Time Whole-Body Control of Legged Robots with Model-Predictive Path Integral Control
2024-09-16
Juan Alvarez-Padilla, John Z. Zhang, Sofia Kwok, John M. Dolan, Zachary Manchester
This paper presents a system for enabling real-time synthesis of whole-body locomotion and manipulation policies for real-world legged robots. Motivated by recent advancements in robot simulation, we leverage the efficient parallelization capabilities of the MuJoCo simulator to achieve fast sampling over the robot state and action trajectories. Our results show surprisingly effective real-world locomotion and manipulation capabilities with a very simple control strategy. We demonstrate our approach on several hardware and simulation experiments: robust locomotion over flat and uneven terrains, climbing over a box whose height is comparable to the robot, and pushing a box to a goal position. To our knowledge, this is the first successful deployment of whole-body sampling-based MPC on real-world legged robot hardware. Experiment videos and code can be found at: https://whole-body-mppi.github.io/
The Debiased Near-Earth Object Population from ATLAS Telescopes
2024-09-16
Rogerio Deienno, Larry Denneau, David Nesvorný, David Vokrouhlický, William F. Bottke, Robert Jedicke, Shantanu Naidu, Steven R. Chesley, Davide Farnocchia, Paul W. Chodas
This work is dedicated to debias the Near-Earth Objects (NEO) population based on observations from the Asteroid Terrestrial-impact Last Alert System (ATLAS) telescopes. We have applied similar methods used to develop the recently released NEO model generator (NEOMOD), once debiasing the NEO population using data from Catalina Sky Survey (CSS) G96 telescope. ATLAS is composed of four different telescopes. We first analyzed observational data from each of all four telescopes separately and later combined them. Our results highlight main differences between CSS and ATLAS, e.g., sky coverage and survey power at debiasing the NEO population. ATLAS has a much larger sky coverage than CSS, allowing it to find bright NEOs that would be constantly "hiding" from CSS. Consequently, ATLAS is more powerful than CSS at debiasing the NEO population for H $\lesssim$ 19. With its intrinsically greater sensitivity and emphasis on observing near opposition, CSS excels in the debiasing of smaller objects. ATLAS, as an all sky survey designed to find imminent hazardous objects, necessarily spends a significant fraction of time looking at places on the sky where objects do not appear, reducing its power for debiasing the population of small objects. We estimate a NEO population completeness of $\approx$ 88%$^{+3\%}_{-2\%}$ for H $<$ 17.75 and $\approx$ 36%$^{+1\%}_{-1\%}$ for H $<$ 22.25. Those numbers are similar to previous estimates (within error bars for H $<$ 17.75) from CSS, yet, around 3% and 8% smaller at their face values, respectively. We also confirm previous finding that the $\nu_6$ secular resonance is the main source of small and faint NEOs at H = 28, whereas the 3:1 mean motion resonance with Jupiter dominates for larger and brighter NEOs at H = 15.
Hydrodynamic hovering of swimming bacteria above surfaces
2024-09-16
Pyae Hein Htet, Debasish Das, Eric Lauga
Flagellated bacteria are hydrodynamically attracted to rigid walls, yet past work shows a 'hovering' state where they swim stably at a finite height above surfaces. We use numerics and theory to reveal the physical origin of hovering. Simulations first show that hovering requires an elongated cell body and results from a tilt away from the wall. Theoretical models then identify two essential asymmetries: the response of width-asymmetric cells to active flows created by length-asymmetric cells. A minimal model reconciles near and far-field hydrodynamics, capturing all key features of hovering.
Taming Diffusion Models for Image Restoration: A Review
2024-09-16
Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön
Diffusion models have achieved remarkable progress in generative modelling, particularly in enhancing image quality to conform to human preferences. Recently, these models have also been applied to low-level computer vision for photo-realistic image restoration (IR) in tasks such as image denoising, deblurring, dehazing, etc. In this review paper, we introduce key constructions in diffusion models and survey contemporary techniques that make use of diffusion models in solving general IR tasks. Furthermore, we point out the main challenges and limitations of existing diffusion-based IR frameworks and provide potential directions for future work.
Phys3DGS: Physically-based 3D Gaussian Splatting for Inverse Rendering
2024-09-16
Euntae Choi, Sungjoo Yoo
We propose two novel ideas (adoption of deferred rendering and mesh-based representation) to improve the quality of 3D Gaussian splatting (3DGS) based inverse rendering. We first report a problem incurred by hidden Gaussians, where Gaussians beneath the surface adversely affect the pixel color in the volume rendering adopted by the existing methods. In order to resolve the problem, we propose applying deferred rendering and report new problems incurred in a naive application of deferred rendering to the existing 3DGS-based inverse rendering. In an effort to improve the quality of 3DGS-based inverse rendering under deferred rendering, we propose a novel two-step training approach which (1) exploits mesh extraction and utilizes a hybrid mesh-3DGS representation and (2) applies novel regularization methods to better exploit the mesh. Our experiments show that, under relighting, the proposed method offers significantly better rendering quality than the existing 3DGS-based inverse rendering methods. Compared with the SOTA voxel grid-based inverse rendering method, it gives better rendering quality while offering real-time rendering.
Baking Relightable NeRF for Real-time Direct/Indirect Illumination Rendering
2024-09-16
Euntae Choi, Vincent Carpentier, Seunghun Shin, Sungjoo Yoo
Relighting, which synthesizes a novel view under a given lighting condition (unseen in training time), is a must feature for immersive photo-realistic experience. However, real-time relighting is challenging due to high computation cost of the rendering equation which requires shape and material decomposition and visibility test to model shadow. Additionally, for indirect illumination, additional computation of rendering equation on each secondary surface point (where reflection occurs) is required rendering real-time relighting challenging. We propose a novel method that executes a CNN renderer to compute primary surface points and rendering parameters, required for direct illumination. We also present a lightweight hash grid-based renderer, for indirect illumination, which is recursively executed to perform the secondary ray tracing process. Both renderers are trained in a distillation from a pre-trained teacher model and provide real-time physically-based rendering under unseen lighting condition at a negligible loss of rendering quality.
The CRAFT Coherent (CRACO) upgrade I: System Description and Results of the 110-ms Radio Transient Pilot Survey
2024-09-16
Z. Wang, K. W. Bannister, V. Gupta, X. Deng, M. Pilawa, J. Tuthill, J. D. Bunton, C. Flynn, M. Glowacki, A. Jaini, Y. W. J. Lee, E. Lenc, J. Lucero, A. Paek, R. Radhakrishnan, N. Thyagarajan, P. Uttarkar, Y. Wang, N. D. R. Bhat, C. W. James, V. A. Moss, Tara Murphy, J. E. Reynolds, R. M. Shannon, L. G. Spitler, A. Tzioumis, M. Caleb, A. T. Deller, A. C. Gordon, L. Marnoch, S. D. Ryder, S. Simha, C. S. Anderson, L. Ball, D. Brodrick, F. R. Cooray, N. Gupta, D. B. Hayman, A. Ng, S. E. Pearce, C. Phillips, M. A. Voronkov, T. Westmeier
We present the first results from a new backend on the Australian Square Kilometre Array Pathfinder, the Commensal Realtime ASKAP Fast Transient COherent (CRACO) upgrade. CRACO records millisecond time resolution visibility data, and searches for dispersed fast transient signals including fast radio bursts (FRB), pulsars, and ultra-long period objects (ULPO). With the visibility data, CRACO can localise the transient events to arcsecond-level precision after the detection. Here, we describe the CRACO system and report the result from a sky survey carried out by CRACO at 110ms resolution during its commissioning phase. During the survey, CRACO detected two FRBs (including one discovered solely with CRACO, FRB 20231027A), reported more precise localisations for four pulsars, discovered two new RRATs, and detected one known ULPO, GPM J1839-10, through its sub-pulse structure. We present a sensitivity calibration of CRACO, finding that it achieves the expected sensitivity of 11.6 Jy ms to bursts of 110 ms duration or less. CRACO is currently running at a 13.8 ms time resolution and aims at a 1.7 ms time resolution before the end of 2024. The planned CRACO has an expected sensitivity of 1.5 Jy ms to bursts of 1.7 ms duration or less, and can detect 10x more FRBs than the current CRAFT incoherent sum system (i.e., 0.5-2 localised FRBs per day), enabling us to better constrain the FRB emission mechanism model and use them as cosmological probes.
Controllability and Inverse Problems for Parabolic Systems with Dynamic Boundary Conditions
2024-09-16
S. E. Chorfi, L. Maniar
This review surveys previous and recent results on null controllability and inverse problems for parabolic systems with dynamic boundary conditions. We aim to demonstrate how classical methods such as Carleman estimates can be extended to prove null controllability for parabolic systems and Lipschitz stability estimates for inverse problems with dynamic boundary conditions of surface diffusion type. We mainly focus on the substantial difficulties compared to static boundary conditions. Finally, some conclusions and open problems will be mentioned.
2024-09-16
Rosario Fazio, Jonathan Keeling, Leonardo Mazza, Marco Schirò
These Lecture Notes discuss the recent theoretical advances in the understanding of open quantum many-body physics in platforms where both dissipative and coherent processes can be tuned and controlled to a high degree. We start by reviewing the theoretical frameworks and methods used to describe and tackle open quantum many-body systems. We then discuss the use of dissipative processes to engineer many-body stationary states with desired properties and the emergence of dissipative phase transitions arising out of the competition between coherent evolution and dissipation. We review the dynamics of open quantum many body systems in the presence of correlated many-body dissipative processes, such as heating and many-body losses. Finally we provide a different perspective on open quantum many-body systems by looking at stochastic quantum trajectories, relevant for the case in which the environment represents a monitoring device, and the associated measurement-induced phase transitions.
FreeDSM and the Gaia4Sustaniability project: a light pollution meter based on IoT technologies
2024-09-16
Mario Casado Diez
Light pollution is a growing environmental issue that affects astronomy, ecosystems, human health. To address this, we introduce the Free Dark Sky Meter (FreeDSM), an affordable IoT-based photometer designed for continuous light pollution monitoring. FreeDSM uses an ESP32 microcontroller with integrated sensors for light, temperature, and humidity, and operates on an open-source platform. Data from multiple devices are centralized and processed using the Gambons model, which leverages Gaia satellite data for accurate real-time assessments of natural light levels. This project is part of the Gaia4Sustainability initiative.
Efficient transport controlled by biharmonic frictional driving
2024-09-16
Martin Maza-Cuello, Diego Maza
Dry friction has been proposed as a rectifying mechanism allowing mass transport over a vibrating surface, even when vibrations are horizontal and unbiased. It has been suggested that the drift velocity will always saturate when the energy of the input oscillation increases, leading to a vanishing efficiency that would hinder the applicability of this phenomenon. Contrary to this conjecture, in this work we experimentally demonstrate that, by carefully controlling the forcing oscillations, this system can maintain a finite transport efficiency for any input energy. A minimal friction model explains the observed dependencies of the drift velocity on the signal parameters in the case of biharmonic base oscillations, which can be extended to obtain efficiency estimates for any periodic excitation.
2024-09-16
T. A. Semenikhin, M. V. Kornilov, M. V. Pruzhinskaya, A. D. Lavrukhina, E. Russeil, E. Gangler, E. E. O. Ishida, V. S. Korolev, K. L. Malanchev, A. A. Volnova, S. Sreejith
In the task of anomaly detection in modern time-domain photometric surveys, the primary goal is to identify astrophysically interesting, rare, and unusual objects among a large volume of data. Unfortunately, artifacts -- such as plane or satellite tracks, bad columns on CCDs, and ghosts -- often constitute significant contaminants in results from anomaly detection analysis. In such contexts, the Active Anomaly Discovery (AAD) algorithm allows tailoring the output of anomaly detection pipelines according to what the expert judges to be scientifically interesting. We demonstrate how the introduction real-bogus scores, obtained from a machine learning classifier, improves the results from AAD. Using labeled data from the SNAD ZTF knowledge database, we train four real-bogus classifiers: XGBoost, CatBoost, Random Forest, and Extremely Randomized Trees. All the models perform real-bogus classification with similar effectiveness, achieving ROC-AUC scores ranging from 0.93 to 0.95. Consequently, we select the Random Forest model as the main model due to its simplicity and interpretability. The Random Forest classifier is applied to 67 million light curves from ZTF DR17. The output real-bogus score is used as an additional feature for two anomaly detection algorithms: static Isolation Forest and AAD. While results from Isolation Forest remained unchanged, the number of artifacts detected by the active approach decreases significantly with the inclusion of the real-bogus score, from 27 to 3 out of 100. We conclude that incorporating the real-bogus classifier result as an additional feature in the active anomaly detection pipeline significantly reduces the number of artifacts in the outputs, thereby increasing the incidence of astrophysically interesting objects presented to human experts.
Precise Tool to Target Positioning Widgets (TOTTA) in Spatial Environments: A Systematic Review
2024-09-16
Mine Dastan, Michele Fiorentino, Antonio E. Uva
TOTTA outlines the spatial position and rotation guidance of a real/virtual tool (TO) towards a real/virtual target (TA), which is a key task in Mixed Reality applications. The task error can have critical consequences regarding safety, performance, and quality, such as in surgical implantology or industrial maintenance scenarios. The TOTTA problem lacks a dedicated study and is scattered across different domains with isolated designs. This work contributes to a systematic review of the TOTTA visual widgets, studying 70 unique designs from 24 papers. TOTTA is commonly guided by visual overlap an intuitive, pre-attentive 'collimation' feedback of simple-shaped widgets: Box, 3D Axes, 3D Model, 2D Crosshair, Globe, Tetrahedron, Line, and Plane. Our research discovers that TO and TA are often represented with the same shape. They are distinguished by topological elements (e.g., edges, vertices, faces), colors, transparency levels, and added shapes, widget quantity, and size. Meanwhile, some designs provide continuous 'during manipulation feedback' relative to the distance between TO and TA by text, dynamic color, sonification, and amplified graphical visualization. Some approaches trigger discrete 'TA reached feedback,' such as color alteration, added sound, TA shape change, and added text. We found a lack of golden standards, including in testing procedures, as current ones are limited to partial sets with different and incomparable setups (different target configurations, avatar, background, etc.). We also found a bias in participants: right-handed, young male, non-color impaired.
In this survey, we consider Banach spaces of analytic functions in one and several complex variables for which: (i) polynomials are dense, (ii) point-evaluations on the domain are bounded linear functionals, and (iii) the shift operators are bounded for each variable. We discuss the problem of determining the shift-cyclic functions in such a space, i.e., functions whose polynomial multiples form a dense subspace. The problem of determining shift-cyclic functions in certain analytic function spaces is known to be intimately connected to some deep problems in other areas of mathematics, such as the dilation completeness problem and even the Riemann hypothesis. What makes determining shift-cyclic functions so difficult is that often we need to employ techniques that are specific to the space in consideration. We therefore cover several different function spaces that have frequently appeared in the past such as the Hardy spaces, Dirichlet-type spaces, complete Pick spaces and Bergman spaces. We highlight the similarities and the differences between shift-cyclic functions among these spaces and list some important general properties that shift-cyclic functions in any given analytic function space must share. Throughout this discussion, we also motivate and provide a large list of open problems related to shift-cyclicity.
2024-09-16
Matteo Scandella, Michelangelo Bin, Thomas Parisini
Learning models of dynamical systems characterized by specific stability properties is of crucial importance in applications. Existing results mainly focus on linear systems or some limited classes of nonlinear systems and stability notions, and the general problem is still open. This article proposes a kernel-based nonlinear identification procedure to directly and systematically learn stable nonlinear discrete-time systems. In particular, the proposed method can be used to enforce, on the learned model, bounded-input-bounded-state stability, asymptotic gain, and input-to-state stability properties, as well as their incremental counterparts. To this aim, we build on the reproducing kernel theory and the Representer Theorem, which are suitably enhanced to handle stability constraints in the kernel properties and in the hyperparameters' selection algorithm. Once the methodology is detailed, and sufficient conditions for stability are singled out, the article reviews some widely used kernels and their applicability within the proposed framework. Finally, numerical results validate the theoretical findings showing, in particular, that stability may have a beneficial impact in long-term simulation with minimal impact on prediction.
This paper introduces a novel reference-free (RF) audio quality metric called the RF-Generative Machine Listener (RF-GML), designed to evaluate coded mono, stereo, and binaural audio at a 48 kHz sample rate. RF-GML leverages transfer learning from a state-of-the-art full-reference (FR) Generative Machine Listener (GML) with minimal architectural modifications. The term "generative" refers to the model's ability to generate an arbitrary number of simulated listening scores. Unlike existing RF models, RF-GML accurately predicts subjective quality scores across diverse content types and codecs. Extensive evaluations demonstrate its superiority in rating unencoded audio and distinguishing different levels of coding artifacts. RF-GML's performance and versatility make it a valuable tool for coded audio quality assessment and monitoring in various applications, all without the need for a reference signal.
A Survey Of High Mass Star Forming Regions In The Line Of Singly Deuterated Ammonia NH2D
2024-09-16
E. Trofimova, I. Zinchenko, P. Zemlyanukha, M. Thomasson
The present survey represents a continuation of our study of high mass star forming regions in the lines of deuterated molecules, the first results of which were published in Trofimova et al. (2020). This paper presents the results of observations of 50 objects in the line of ortho modification of singly deuterated ammonia NH$_2$D $1_{11}^s - 1_{01}^a$ at frequency 85.9 GHz, carried out with the 20-m radio telescope of the Onsala Space Observatory (Sweden). This line is detected in 29 sources. The analysis of obtained data, as well as the fact that gas density in the investigated sources, according to independent estimates, is significantly lower than the critical density for this NH$_2$D transition, indicate non-LTE excitation of NH$_2$D. Based on non-LTE modeling, estimates of the relative content of the NH$_2$D molecule and the degree of deuterium enrichment were obtained, and the dependencies of these parameters on temperature and velocity dispersion were analyzed with and without taking into account detection limits assuming the same gas density in all sources. An anti-correlation between the NH$_2$D relative abundances and the kinetic temperature is revealed in the temperature range 15-50K. At the same time, significant decrease in the ratio of the NH$_2$D/NH$_3$ abundances with increasing temperature, predicted by the available chemical models, is not observed under the adopted assumptions. An anti-correlation was also revealed between the relative content of the main isotopologue of ammonia NH$_3$ and the velocity dispersion, while no statistically significant correlation with the kinetic temperature of sources in the same temperature range was found.
PrePaMS: Privacy-Preserving Participant Management System for Studies with Rewards and Prerequisites
2024-09-16
Echo Meißner, Frank Kargl, Benjamin Erb, Felix Engelmann
Taking part in surveys, experiments, and studies is often compensated by rewards to increase the number of participants and encourage attendance. While privacy requirements are usually considered for participation, privacy aspects of the reward procedure are mostly ignored. To this end, we introduce PrePaMS, an efficient participation management system that supports prerequisite checks and participation rewards in a privacy-preserving way. Our system organizes participations with potential (dis-)qualifying dependencies and enables secure reward payoffs. By leveraging a set of proven cryptographic primitives and mechanisms such as anonymous credentials and zero-knowledge proofs, participations are protected so that service providers and organizers cannot derive the identity of participants even within the reward process. In this paper, we have designed and implemented a prototype of PrePaMS to show its effectiveness and evaluated its performance under realistic workloads. PrePaMS covers the information whether subjects have participated in surveys, experiments, or studies. When combined with other secure solutions for the actual data collection within these events, PrePaMS can represent a cornerstone for more privacy-preserving empirical research.
TCDformer-based Momentum Transfer Model for Long-term Sports Prediction
2024-09-16
Hui Liu, Jiacheng Gu, Xiyuan Huang, Junjie Shi, Tongtong Feng, Ning He
Accurate sports prediction is a crucial skill for professional coaches, which can assist in developing effective training strategies and scientific competition tactics. Traditional methods often use complex mathematical statistical techniques to boost predictability, but this often is limited by dataset scale and has difficulty handling long-term predictions with variable distributions, notably underperforming when predicting point-set-game multi-level matches. To deal with this challenge, this paper proposes TM2, a TCDformer-based Momentum Transfer Model for long-term sports prediction, which encompasses a momentum encoding module and a prediction module based on momentum transfer. TM2 initially encodes momentum in large-scale unstructured time series using the local linear scaling approximation (LLSA) module. Then it decomposes the reconstructed time series with momentum transfer into trend and seasonal components. The final prediction results are derived from the additive combination of a multilayer perceptron (MLP) for predicting trend components and wavelet attention mechanisms for seasonal components. Comprehensive experimental results show that on the 2023 Wimbledon men's tournament datasets, TM2 significantly surpasses existing sports prediction models in terms of performance, reducing MSE by 61.64% and MAE by 63.64%.
Quantum walks of correlated photons in non-Hermitian photonic lattices
2024-09-16
Mingyuan Gao, Chong Sheng, Yule Zhao, Runqiu He, Liangliang Lu, Wei Chen, Kun Ding, Shining Zhu, Hui Liu
Entanglement entropy characterizes the correlation of multi-particles and unveils the crucial features of open quantum systems. However, the experimental realization of exploring entanglement in non-Hermitian systems remains a challenge. In parallel, quantum walks have offered the possibility of studying the underlying mechanisms of non-Hermitian physics, which includes exceptional points, the non-Hermitian skin effect, and non-Bloch phase transitions. Unfortunately, these studies have only involved and prevailingly focused on the behavior of a single particle. Here, we propose and experimentally realize quantum walks of two indistinguishable photons in engineered non-Hermitian photonic lattices. We have successfully observed the unidirectional behavior of quantum walks in the bulk far from the edges induced by the skin effect. Moreover, we experimentally reveal the suppression of entanglement that is caused by the skin effect in non-Hermitian systems. Our study may facilitate a deep understanding of entanglement in open quantum many-body systems that are far from thermal equilibrium.
Trustworthiness in Retrieval-Augmented Generation Systems: A Survey
2024-09-16
Yujia Zhou, Yan Liu, Xiaoxi Li, Jiajie Jin, Hongjin Qian, Zheng Liu, Chaozhuo Li, Zhicheng Dou, Tsung-Yi Ho, Philip S. Yu
Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs). While much of the current research in this field focuses on performance optimization, particularly in terms of accuracy and efficiency, the trustworthiness of RAG systems remains an area still under exploration. From a positive perspective, RAG systems are promising to enhance LLMs by providing them with useful and up-to-date knowledge from vast external databases, thereby mitigating the long-standing problem of hallucination. While from a negative perspective, RAG systems are at the risk of generating undesirable contents if the retrieved information is either inappropriate or poorly utilized. To address these concerns, we propose a unified framework that assesses the trustworthiness of RAG systems across six key dimensions: factuality, robustness, fairness, transparency, accountability, and privacy. Within this framework, we thoroughly review the existing literature on each dimension. Additionally, we create the evaluation benchmark regarding the six dimensions and conduct comprehensive evaluations for a variety of proprietary and open-source models. Finally, we identify the potential challenges for future research based on our investigation results. Through this work, we aim to lay a structured foundation for future investigations and provide practical insights for enhancing the trustworthiness of RAG systems in real-world applications.
Advancing flight physics through natural adaptation and animal learning
2024-09-16
Ariane Gayout, David Lentink
Fluid dynamics, and flight in particular, is a domain where organisms challenge our understanding of its physics. Integrating the current knowledge of animal flight, we propose to revisit the use of live animals to study physical phenomena. After a short description of the physics of flight, we examine the broad literature on animal flight focusing on studies of living animals. We start out reviewing the diverse animal species studied so far and then focus on the experimental techniques used to study them quantitatively. Our network analysis reveals how the three clades of animals performing powered flight - insects, birds and bats - are studied using substantially different combinations of measurement techniques. We then combine these insights with a new paradigm for increasing our physical understanding of flight. This paradigm relies on the concept of Animal Learning, where animals are used as probes to study fluid phenomena and variables involved in flight, harnessing their natural adaptability.
Magnetization dependent anisotropic topological properties in EuCuP
2024-09-16
Jian Yuan, Xianbiao Shi, Hong Du, Xia Wang, Jinguang Cheng, Baotian Wang, Ruidan Zhong, Shihao Zhang, Yanfeng Guo
The correlation between magnetism and nontrivial topological band structure serves as a unique venue for discovering exotic topological properties. Combining magnetotransport measurements and first-principles calculations, we unveil herein that the hexagonal EuCuP holds topologically trivial state in the paramagnetic structure, while strong magnetization dependent anisotropic topological states in the spin-polarization structures. Specifically, it hosts a trivial topological state in the in-plane spin-polarization structure, while a Weyl semimetal state in the out-of-plane spin-polarization structure. Our scaling analysis suggests that the intrinsic Berry curvature in the spin-polarization structures can account for the observed large anisotropic anomalous Hall effect. First-principles calculations show that the magnetization and the spin-orbit coupling simultaneously play essential roles for the appearance of the four pairs of Weyl points in the out-of-plane spin-polarization structure. Our work therefore establishes in EuCuP the intimate relation between magnetism and the nontrivial topological states, which would be instructive for future study on this key issue of topological physics.
Lunar References Systems, Frames and Time-scales in the context of the ESA Programme Moonlight
2024-09-16
Agnes Fienga, Nicolas Rambaux, Krzysztof Sosnica
Lunar reference systems represent a fundamental aspect of lunar exploration. This paper presents a review of the topic in the context of the ESA lunar programme, MoonLight. This paper describes the current state of the art in the definition of the lunar reference frame and introduces TCL, a lunar time scale based on IAU resolutions. It also proposes several possible implementations of this time scale for orbiting and ground-based clocks. Finally, it provides an assessment of the improvement of the lunar reference frame that would result from the addition of lunar retro-reflectors on the Moon surface and the use of orbiter altimetry. This document is an appendix dedicated to lunar reference system definition of a more global document dedicated to the presentation of new concepts in orbit determination and time synchronization of a lunar radio navigation system.
The chirality-induced spin selectivity (CISS) effect has garnered significant interest in the field of molecular spintronics due to its potential for creating spin-polarized electrons without the need for a magnet. Recent studies devoted to CISS effects in various chiral materials demonstrate exciting prospects for spintronics, chiral recognition, and quantum information applications. Several experimental studies have confirmed the applicability of chiral molecules towards spin-filtering properties, influencing spin-polarized electron transport, and photoemission. Researchers aim to predict CISS phenomena and apply this concept to practical applications by compiling experimental results and enhancing understanding of the CISS effect. To expand the possibilities of spin manipulation and create new opportunities for spin-based technologies, researchers are diligently exploring different chiral organic and inorganic materials for probing the CISS effect. This ongoing research holds promise for developing novel spin-based technologies and advancing the understanding of the intricate relationship between chirality and electron spin. This review showcases the remarkable CISS effect and its impact on spintronics, as well as its relevance in various other scientific areas.
Altermagnets and beyond: Nodal magnetically-ordered phases
2024-09-16
Tomas Jungwirth, Rafael M. Fernandes, Jairo Sinova, Libor Smejkal
The recent discovery of altermagnets has opened new perspectives in the field of ordered phases in condensed matter. In strongly-correlated superfluids, the nodal p-wave and d-wave ordered phases of $^{3}$He and cuprates play a prominent role in physics for their rich phenomenology of the symmetry-breaking order parameters. While the p-wave and d-wave superfluids have been extensively studied over the past half a century, material realizations of their magnetic counterparts have remained elusive for many decades. This is resolved in altermagnets, whose recent discovery was driven by research in the field of spintronics towards highly scalable information technologies. Altermagnets feature d, g or i-wave magnetic ordering, with a characteristic alternation of spin polarization and spin-degenerate nodes. Here we review how altermagnetism can be identified from symmetries of collinear spin densities in crystal lattices, and can be realized at normal conditions in a broad family of insulating and conducting materials. We highlight salient electronic-structure signatures of the altermagnetic ordering, discuss extraordinary relativistic and topological phenomena that emerge in their band structures, and comment on strong-correlation effects. We then extend the discussion to non-collinear spin densities in crystals, including the prediction of p-wave magnets, and conclude with a brief summary of the reviewed physical properties of the nodal magnetically-ordered phases.
Hydrostatic and chemical pressure driven crossover from commensurate to the incommensurate state of the Weyl semimetal Mn$_{3+x}$Sn$_{1-x}$
2024-09-16
K. Bhattacharya, A. K. Bharatwaj, C. Singh, R. Gupta, R. Khasanov, S. Kanungo, A. K. Nayak, M. Majumder
The observation of large intrinsic anomalous Hall conductivity (AHC) in the non-collinear antiferromagnetic (AFM) phase of the Weyl semimetal Mn$_3$Sn generates enormous interest in uncovering the entanglement between the real space magnetic ordering and the momentum space band structure. Previous studies show that changes in the magnetic structure induced by the application of hydrostatic and chemical pressure can significantly affect the AHC of Mn$_{3+x}$Sn$_{1-x}$ system. Here, we employ the muon spin relaxation/rotation ($\mu^+$SR) technique to systematically investigate the evolution of different magnetic states in the Mn$_{3+x}$Sn$_{1-x}$ as a function of hydrostatic and chemical pressure. We find two muon sites experimentally, which is also supported by our \textit{ab initio} calculations. Our $\mu^+$SR experiments affirm that the $x = 0.05$ compound exhibits a commensurate magnetic state throughout the magnetically ordered phase below the Neel temperature $T_N \approx 420$~K in ambient pressure. In contrast, we observe an incommensurate magnetic state below $T_{IC} \sim 175$~K when a hydrostatic pressure of 1.5~GPa is applied. A similar transition from the commensurate to incommensurate state is also found with chemical pressure for $x = 0.04$ and $x = 0.03$, using $\mu^+$SR and elastic neutron scattering experiments. Using band structure calculations, we have shown the emergence of Fermi nesting in Mn$_3$Sn and the subsequent development of incommensurate magnetic ordering under hydrostatic/chemical pressure.
Integrating Experiment with Theory to Determine the Structure of Electrode-Electrolyte Interfaces
2024-09-16
Lalith Krishna Samanth Bonagiri, Amir Farokh Payam, Narayana R. Aluru, Yingjie Zhang
Electrode-electrolyte interfaces are crucial for electrochemical energy conversion and storage. At these interfaces, the liquid electrolytes form electrical double layers (EDLs). However, despite more than a century of active research, the fundamental structure of EDLs remains elusive to date. Experimental characterization and theoretical calculations have both provided insights, yet each method by itself only offers incomplete or inexact information of the multifaceted EDL structure. Here we provide a survey of the mainstream approaches for EDL quantification, with a particular focus on the emerging 3D atomic force microscopy (3D-AFM) imaging which provides real-space atomic-scale EDL structures. To overcome the existing limits of EDL characterization methods, we propose a new approach to integrate 3D-AFM with classical molecular dynamics (MD) simulation, to enable realistic, precise, and high-throughput determination and prediction of EDL structures. As examples of real-world application, we will discuss the feasibility of using this joint experiment-theory method to unravel the EDL structure at various carbon-based electrodes for supercapacitors, batteries, and electrocatalysis. Looking forward, we believe 3D-AFM, future versions of scanning probe microscopy, and their integration with theory offer promising platforms to profile liquid structures in many electrochemical systems.
This thesis investigates the role of the quantum gravity cut-off for effective field theories (EFTs) coupled to Einstein gravity, with an emphasis on its implications at low energies within the context of the Swampland program. Part I reviews the relevant aspects of string theory compactifications in different number of spacetime dimensions and with different amounts of supersymmetry preserved. In Part II a model-independent approach is employed so as to determine the maximum regime of validity of any such EFT, identifying the species scale as the natural candidate for the quantum gravity cut-off. We review various arguments proposed in the literature as well as include several new considerations on the matter. Part III provides a systematic study of this framework in string theory compactifications, yielding significant agreement with the previous perturbative and non-perturbative analysis. We also analyze various applications of this concept within the Swampland program, including the purported phenomenon of Emergence. Finally, in Part IV we explore the most immediate implications that this picture would have in the infrared regime, thus uncovering intriguing universal properties associated to the aforementioned energy scale, such as precise lower bounds on its exponential decay rates as well as certain patterns holding within the infinite distance corners of moduli space. The thesis includes new results scattered over the different chapters therein, which have not appeared in the author's original publications.
Neutrino theory: open questions and future opportunities
2024-09-16
Raymond R. Volkas
The subtitle of my talk is ``The quest for understanding the origin of neutrino masses''. After reviewing why the discovery of neutrino masses is also the discovery of New Physics, the substance of the talk details mechanisms for generating Majorana neutrino masses and implications for experimental searches and/or cosmology. I review high-scale seesaw, low-scale seesaw and radiative mechanisms, asking at every turn how testable the scenario is. While it is clear that determining the origin of neutrino masses -- knowing what Lagrangian to put into textbooks -- is a distant and ambitious goal, I end with experimental advances that we can reasonably hope for that would constitute progress.
This paper provides a comprehensive survey of sentiment analysis within the context of artificial intelligence (AI) and large language models (LLMs). Sentiment analysis, a critical aspect of natural language processing (NLP), has evolved significantly from traditional rule-based methods to advanced deep learning techniques. This study examines the historical development of sentiment analysis, highlighting the transition from lexicon-based and pattern-based approaches to more sophisticated machine learning and deep learning models. Key challenges are discussed, including handling bilingual texts, detecting sarcasm, and addressing biases. The paper reviews state-of-the-art approaches, identifies emerging trends, and outlines future research directions to advance the field. By synthesizing current methodologies and exploring future opportunities, this survey aims to understand sentiment analysis in the AI and LLM context thoroughly.
ANNZ+: an enhanced photometric redshift estimation algorithm with applications on the PAU Survey
2024-09-16
Imdad Mahmud Pathi, John Y. H. Soo, Mao Jie Wee, Sazatul Nadhilah Zakaria, Nur Azwin Ismail, Carlton M. Baugh, Giorgio Manzoni, Enrique Gaztanaga, Francisco J. Castander, Martin Eriksen, Jorge Carretero, Enrique Fernandez, Juan Garcia-Bellido, Ramon Miquel, Cristobal Padilla, Pablo Renard, Eusebio Sanchez, Ignacio Sevilla-Noarbe, Pau Tallada-Crespí
ANNZ is a fast and simple algorithm which utilises artificial neural networks (ANNs), it was known as one of the pioneers of machine learning approaches to photometric redshift estimation decades ago. We enhanced the algorithm by introducing new activation functions like tanh, softplus, SiLU, Mish and ReLU variants; its new performance is then vigorously tested on legacy samples like the Luminous Red Galaxy (LRG) and Stripe-82 samples from SDSS, as well as modern galaxy samples like the Physics of the Accelerating Universe Survey (PAUS). This work focuses on testing the robustness of activation functions with respect to the choice of ANN architectures, particularly on its depth and width, in the context of galaxy photometric redshift estimation. Our upgraded algorithm, which we named ANNZ+, shows that the tanh and Leaky ReLU activation functions provide more consistent and stable results across deeper and wider architectures with > 1 per cent improvement in root-mean-square error ($\sigma_{\textrm{RMS}}$) and 68th percentile error ($\sigma_{68}$) when tested on SDSS data sets. While assessing its capabilities in handling high dimensional inputs, we achieved an improvement of 11 per cent in $\sigma_{\textrm{RMS}}$ and 6 per cent in $\sigma_{68}$ with the tanh activation function when tested on the 40-narrowband PAUS dataset; it even outperformed ANNZ2, its supposed successor, by 44 per cent in $\sigma_{\textrm{RMS}}$. This justifies the effort to upgrade the 20-year-old ANNZ, allowing it to remain viable and competitive within the photo-z community today. The updated algorithm ANNZ+ is publicly available at https://github.com/imdadmpt/ANNzPlus.
Redshift Drift fluctuations from N-body simulations
2024-09-16
Pedro Bessa, Valerio Marra, Tiago Castro
Measurements of the redshift drift -- the real time variation of the redshift of distance sources -- are expected in the next couple of decades using next generation facilities such as the ANDES spectrograph at the ELT and the SKAO survey. The unprecedented precision of such observations will demand precise theoretical and numerical modeling of the effect in the standard $\Lambda$CDM cosmology. In this work, we use the Gadget4 $N$-body code to simulate the redshift drift and its fluctuations in $\Lambda$CDM cosmologies, deriving the corresponding power spectra from a simulation with $1024^3$ particles in a $1\textrm{Gpc}\,h^{-1}$ box. Our results provide an estimate for the distribution and amplitude of the fluctuations and the spectra, which match previous work in the literature using Einstein-Boltzmann solvers to within an order of magnitude. Our work provides a methodology for performing statistical analysis of the redshift drift effect and deriving its fluctuation power spectra from future large scale surveys.
Artificial Intelligence-Based Opportunistic Coronary Calcium Screening in the Veterans Affairs National Healthcare System
2024-09-16
Raffi Hagopian, Timothy Strebel, Simon Bernatz, Gregory A Myers, Erik Offerman, Eric Zuniga, Cy Y Kim, Angie T Ng, James A Iwaz, Sunny P Singh, Evan P Carey, Michael J Kim, R Spencer Schaefer, Jeannie Yu, Amilcare Gentili, Hugo JWL Aerts
Coronary artery calcium (CAC) is highly predictive of cardiovascular events. While millions of chest CT scans are performed annually in the United States, CAC is not routinely quantified from scans done for non-cardiac purposes. A deep learning algorithm was developed using 446 expert segmentations to automatically quantify CAC on non-contrast, non-gated CT scans (AI-CAC). Our study differs from prior works as we leverage imaging data across the Veterans Affairs national healthcare system, from 98 medical centers, capturing extensive heterogeneity in imaging protocols, scanners, and patients. AI-CAC performance on non-gated scans was compared against clinical standard ECG-gated CAC scoring. Non-gated AI-CAC differentiated zero vs. non-zero and less than 100 vs. 100 or greater Agatston scores with accuracies of 89.4% (F1 0.93) and 87.3% (F1 0.89), respectively, in 795 patients with paired gated scans within a year of a non-gated CT scan. Non-gated AI-CAC was predictive of 10-year all-cause mortality (CAC 0 vs. >400 group: 25.4% vs. 60.2%, Cox HR 3.49, p < 0.005), and composite first-time stroke, MI, or death (CAC 0 vs. >400 group: 33.5% vs. 63.8%, Cox HR 3.00, p < 0.005). In a screening dataset of 8,052 patients with low-dose lung cancer-screening CTs (LDCT), 3,091/8,052 (38.4%) individuals had AI-CAC >400. Four cardiologists qualitatively reviewed LDCT images from a random sample of >400 AI-CAC patients and verified that 527/531 (99.2%) would benefit from lipid-lowering therapy. To the best of our knowledge, this is the first non-gated CT CAC algorithm developed across a national healthcare system, on multiple imaging protocols, without filtering intra-cardiac hardware, and compared against a strong gated CT reference. We report superior performance relative to previous CAC algorithms evaluated against paired gated scans that included patients with intra-cardiac hardware.
Deep Graph Anomaly Detection: A Survey and New Perspectives
2024-09-16
Hezhe Qiao, Hanghang Tong, Bo An, Irwin King, Charu Aggarwal, Guansong Pang
Graph anomaly detection (GAD), which aims to identify unusual graph instances (nodes, edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its significance in a wide range of applications. Deep learning approaches, graph neural networks (GNNs) in particular, have been emerging as a promising paradigm for GAD, owing to its strong capability in capturing complex structure and/or node attributes in graph data. Considering the large number of methods proposed for GNN-based GAD, it is of paramount importance to summarize the methodologies and findings in the existing GAD studies, so that we can pinpoint effective model designs for tackling open GAD problems. To this end, in this work we aim to present a comprehensive review of deep learning approaches for GAD. Existing GAD surveys are focused on task-specific discussions, making it difficult to understand the technical insights of existing methods and their limitations in addressing some unique challenges in GAD. To fill this gap, we first discuss the problem complexities and their resulting challenges in GAD, and then provide a systematic review of current deep GAD methods from three novel perspectives of methodology, including GNN backbone design, proxy task design for GAD, and graph anomaly measures. To deepen the discussions, we further propose a taxonomy of 13 fine-grained method categories under these three perspectives to provide more in-depth insights into the model designs and their capabilities. To facilitate the experiments and validation, we also summarize a collection of widely-used GAD datasets and empirical comparison. We further discuss multiple open problems to inspire more future high-quality research. A continuously updated repository for datasets, links to the codes of algorithms, and empirical comparison is available at https://github.com/mala-lab/Awesome-Deep-Graph-Anomaly-Detection.
An Independent Measure of the Kinematic Dipole from SDSS
2024-09-16
Prabhakar Tiwari, Dominik J. Schwarz, Gong-Bo Zhao, Ruth Durrer, Martin Kunz, Hamsa Padmanabhan
We utilize the Sloan Digital Sky Survey (SDSS) extended Baryon Oscillation Spectroscopic Survey (eBOSS) and Baryon Oscillation Spectroscopic Survey (BOSS) catalogs with precise spectroscopic redshifts to estimate the kinematic redshift dipole caused by the proper motion of the Solar system. We find that the velocity extracted from the kinematic dipole is consistent with Cosmic Microwave Background inferred values. Although the small sky coverage and limited number density of the SDSS sources constrain us from obtaining precise and robust measurements, we leverage the redshift dipole method to estimate the kinematic dipole. The velocity measurements in this study are insensitive to intrinsic clustering, associated with the source count dipole. The kinematic dipole measured in this work and its consistency with CMB values do not guarantee isotropy at large scales. The anisotropy (excess dipole) measured with the NRAO VLA Sky Survey (NVSS) and the WISE Catalog (CatWISE) could be due to the intrinsic distribution of galaxies. The results in this work focus solely on the kinematic dipole term.
ROS2WASM: Bringing the Robot Operating System to the Web
2024-09-16
Tobias Fischer, Isabel Paredes, Michael Batchelor, Thorsten Beier, Jesse Haviland, Silvio Traversaro, Wolf Vollprecht, Markus Schmitz, Michael Milford
The Robot Operating System (ROS) has become the de facto standard middleware in robotics, widely adopted across domains ranging from education to industrial applications. The RoboStack distribution has extended ROS's accessibility by facilitating installation across all major operating systems and architectures, integrating seamlessly with scientific tools such as PyTorch and Open3D. This paper presents ROS2WASM, a novel integration of RoboStack with WebAssembly, enabling the execution of ROS 2 and its associated software directly within web browsers, without requiring local installations. This approach significantly enhances reproducibility and shareability of research, lowers barriers to robotics education, and leverages WebAssembly's robust security framework to protect against malicious code. We detail our methodology for cross-compiling ROS 2 packages into WebAssembly, the development of a specialized middleware for ROS 2 communication within browsers, and the implementation of a web platform available at www.ros2wasm.dev that allows users to interact with ROS 2 environments. Additionally, we extend support to the Robotics Toolbox for Python and adapt its Swift simulator for browser compatibility. Our work paves the way for unprecedented accessibility in robotics, offering scalable, secure, and reproducible environments that have the potential to transform educational and research paradigms.
Based on the correspondence between circuit Laplacian and Schrodinger equation, recent investigations have shown that classical electric circuits can be used to simulate various topological physics and the Schrodinger's equation. Furthermore, a series of quantum-inspired information processing have been implemented by using classical electric circuit networks. In this review, we begin by analyzing the similarity between circuit Laplacian and lattice Hamiltonian, introducing topological physics based on classical circuits. Subsequently, we provide reviews of the research progress in quantum-inspired information processing based on the electric circuit, including discussions of topological quantum computing with classical circuits, quantum walk based on classical circuits, quantum combinational logics based on classical circuits, electric-circuit realization of fast quantum search, implementing unitary transforms and so on.
We present a study of the detached eclipsing binary TV~Mon using spectra from the LAMOST medium-resolution survey and ASAS-SN, CoRoT photometry. We applied multiple-epochs spectral fitting to derive RV and spectral parameters. The analysis of eclipses in CoRoT data told us relative sizes of the stellar components and almost edge-on circular orbit. Combining spectral and photometrical solution we estimated masses and radii of the components: $M_{A,B}=2.063\pm0.033,~0.218\pm0.004~M_\odot$, $R_{A,B}=2.427\pm0.014,~2.901\pm0.016~R_\odot$. SED analysis and Gaia parallax allowed us to get estimation of temperatures $T_{A,B}=7624^{+194}_{-174},~5184^{+130}_{-123}$ K and distance $d=907\pm11$ pc. We identified three $\delta$ Scuti type pulsation frequencies in primary component, while we also suspect TV~Mon having a long period variability with period $P_{\rm long}\sim128$ days and spot activity in secondary component. This system experienced intensive mass transfer and mass ratio reversal in the past, currently showing no signs of mass transfer in the spectra. The low mass component will lose its outer envelope and shrink to the helium white dwarf, which mass and orbital period are in good agreement with evolutionary models predictions.
Research evolution of metal organic frameworks: A scientometric approach with human-in-the-loop
2024-09-16
Xintong Zhao, Kyle Langlois, Jacob Furst, Yuan An, Xiaohua Hu, Diego Gomez Gualdron, Fernando Uribe-Romo, Jane Greenberg
This paper reports on a scientometric analysis bolstered by human in the loop, domain experts, to examine the field of metal organic frameworks (MOFs) research. Scientometric analyses reveal the intellectual landscape of a field. The study engaged MOF scientists in the design and review of our research workflow. MOF materials are an essential component in next generation renewable energy storage and biomedical technologies. The research approach demonstrates how engaging experts, via human in the loop processes, can help develop a comprehensive view of a field research trends, influential works, and specialized topics.
The Smart Grid (SG) is a critical energy infrastructure that collects real-time electricity usage data to forecast future energy demands using information and communication technologies (ICT). Due to growing concerns about data security and privacy in SGs, federated learning (FL) has emerged as a promising training framework. FL offers a balance between privacy, efficiency, and accuracy in SGs by enabling collaborative model training without sharing private data from IoT devices. In this survey, we thoroughly review recent advancements in designing FL-based SG systems across three stages: generation, transmission and distribution, and consumption. Additionally, we explore potential vulnerabilities that may arise when implementing FL in these stages. Finally, we discuss the gap between state-of-the-art FL research and its practical applications in SGs and propose future research directions. These focus on potential attack and defense strategies for FL-based SG systems and the need to build a robust FL-based SG infrastructure. Unlike traditional surveys that address security issues in centralized machine learning methods for SG systems, this survey specifically examines the applications and security concerns in FL-based SG systems for the first time. Our aim is to inspire further research into applications and improvements in the robustness of FL-based SG systems.
We propose a novel approach to assess the public's coping behavior during the COVID-19 outbreak by examining the emotions. Specifically, we explore (1) changes in the public's emotions with the COVID-19 crisis progression and (2) the impacts of the public's emotions on their information-seeking, information-sharing behaviors, and compliance with stay-at-home policies. We base the study on the appraisal tendency framework, detect the public's emotions by fine-tuning a pre-trained RoBERTa model, and cross-analyze third-party behavioral data. We demonstrate the feasibility and reliability of our proposed approach in providing a large-scale examination of the publi's emotions and coping behaviors in a real-world crisis: COVID-19. The approach complements prior crisis communication research, mainly based on self-reported, small-scale experiments and survey data. Our results show that anger and fear are more prominent than other emotions experienced by the public at the pandemic's outbreak stage. Results also show that the extent of low certainty and passive emotions (e.g., sadness, fear) was related to increased information-seeking and information-sharing behaviors. Additionally, high-certainty (e.g., anger) and low-certainty (e.g., sadness, fear) emotions during the outbreak correlated to the public's compliance with stay-at-home orders.
Notas sobre Teoría de colas y algunas aplicaciones
2024-09-16
Carlos E. Martínez-Rodríguez
This paper presents a comprehensive review of stochastic processes, with a particular focus on Markov chains and jump processes. The main results related to queuing systems are analyzed. Additionally, conditions that ensure the stability, or ergodicity, of such systems are presented. The paper also discusses stability results for queuing networks and their extension to visiting systems. Finally, key contributions concerning the Probability Generating Function, an essential tool in the analysis of the aforementioned processes, are introduced. The review is conducted from the perspective of queuing theory, grounded in the Kendall-Lee notation, emphasizing stability results and the computation of performance measures based on the specific characteristics of each process.
A Novel Optimal Transport-Based Approach for Interpolating Spectral Time Series: Paving the Way for Photometric Classification of Supernovae
2024-09-16
M. Ramirez, G. Pignata, Francisco Förster, Santiago Gonzáles-Gaitán, Claudia P. Gutiérrez, B. Ayala, Guillermo Cabrera-Vives, Márcio Catelan, A. M. Muñoz Arancibia, J. Pineda-García
This paper introduces a novel method for creating spectral time series, which can be used for generating synthetic light curves for photometric classification but also for applications like K-corrections and bolometric corrections. This approach is particularly valuable in the era of large astronomical surveys, where it can significantly enhance the analysis and understanding of an increasing number of SNe, even in the absence of extensive spectroscopic data. methods: By employing interpolations based on optimal transport theory, starting from a spectroscopic sequence, we derive weighted average spectra with high cadence. The weights incorporate an uncertainty factor, for penalizing interpolations between spectra with significant epoch differences and with poor match between the synthetic and observed photometry. results: Our analysis reveals that even with phase difference of up to 40 days between pairs of spectra, optical transport can generate interpolated spectral time series that closely resemble the original ones. Synthetic photometry extracted from these spectral time series aligns well with observed photometry. The best results are achieved in the V band, with relative residuals less than 10% for 87% and 84% of the data for type Ia and II, respectively. For the B, g, R and r bands the relative residuals are between 65% and 87% within the previously mentioned 10% threshold for both classes. The worse results correspond to the i and I bands where, in the case, of SN~Ia the values drop to 53% and 42%, respectively. conclusions: We introduce a new method to construct spectral time series for individual SN starting from a sparse spectroscopic sequence, demonstrating its capability to produce reliable light curves that can be used for photometric classification.
Centralization potential of automotive E/E architectures
2024-09-16
Lucas Mauser, Stefan Wagner
Current automotive E/E architectures are subject to significant transformations: Computing-power-intensive advanced driver-assistance systems, bandwidth-hungry infotainment systems, the connection of the vehicle with the internet and the consequential need for cyber-security drives the centralization of E/E architectures. A centralized architecture is often seen as a key enabler to master those challenges. Available research focuses mostly on the different types of E/E architectures and contrasts their advantages and disadvantages. There is a research gap on guidelines for system designers and function developers to analyze the potential of their systems for centralization. The present paper aims to quantify centralization potential reviewing relevant literature and conducting qualitative interviews with industry practitioners. In literature, we identified seven key automotive system properties reaching limitations in current automotive architectures: busload, functional safety, computing power, feature dependencies, development and maintenance costs, error rate, modularity and flexibility. These properties serve as quantitative evaluation criteria to estimate whether centralization would enhance overall system performance. In the interviews, we have validated centralization and its fundament - the conceptual systems engineering - as capabilities to mitigate these limitations. By focusing on practical insights and lessons learned, this research provides system designers with actionable guidance to optimize their systems, addressing the outlined challenges while avoiding monolithic architecture. This paper bridges the gap between theoretical research and practical application, offering valuable takeaways for practitioners.
SPORES-HWO. II. Limits on Planetary Companions of Future High-contrast Imaging Targets from $>$20 Years of HIRES and HARPS Radial Velocities
2024-09-16
Caleb K. Harada, Courtney D. Dressing, Stephen R. Kane, Sarah Blunt, Jamie Dietrich, Natalie R. Hinkel, Zhexing Li, Eric Mamajek, Malena Rice, Noah W. Tuchow, Emma V. Turtelboom, Robert A. Wittenmyer
Future large, space-based observatories with starlight suppression technology, e.g., the Habitable Worlds Observatory (HWO), will directly image and characterize nearby Earth-like exoplanets. Prior limits on planet masses and system architectures from radial velocity (RV) measurements of potential exo-Earth hosts are critical to the success of HWO's science goals. Here, we present a uniform analysis of archival RVs from HIRES/Keck and HARPS/ESO of the most promising targets for the HWO exo-Earth survey. We analyze RVs and stellar activity indicators of 90 stars in the NASA ExEP Mission Star List and SPORES-HWO Catalog, finding 33 Keplerian signals associated with known planets and 12 signals associated with stellar activity. We also identify 5 new RV signals that we classify as either planet candidates or sources requiring confirmation, noting that the RV observations are biased toward cooler and less active stars. Assessing the sensitivity of the HIRES and HARPS data, we calculate RV limits ranging from $K_{\rm RV} = 0.6 \,{\rm m\,s}^{-1}$ (HD 10700) to $371 \,{\rm m\,s}^{-1}$ (HD 17925) in the middle of the conservative habitable zone (HZ), corresponding to projected planet masses of $5.4 \,{\rm M_\oplus}$ and $10.6 \,{\rm M_{Jup}}$ for those stars. The median HZ sensitivity limit of our sample is $M_{\rm p} \sin i \simeq 66 \,{\rm M_\oplus}$. This work demonstrates the need for future extreme precision radial velocity (EPRV) monitoring of high-priority targets for the next generation of DI missions that will search for habitable extrasolar systems. We advocate for the use of these results in developing future EPRV strategies.
The entropy of strongly coupled Yukawa fluids is discussed from several perspectives. First, it is demonstrated that a vibrational paradigm of atomic dynamics in dense fluids can be used to obtain a simple and accurate estimate of the entropy without any adjustable parameters. Second, it is explained why a quasiuniversal value of the excess entropy of simple fluids at the freezing point should be expected, and it is demonstrated that a remaining very weak dependence of the freezing point entropy on the screening parameter in the Yukawa fluid can be described by a simple linear function. Third, a scaling of the excess entropy with the freezing temperature is examined, a modified form of the Rosenfeld-Tarazona scaling is put forward, and some consequences are briefly discussed. Fourth, the location of the Frenkel line on the phase diagram of Yukawa systems is discussed in terms of the excess entropy and compared with some predictions made in the literature. Fifth, the excess entropy scaling of the transport coefficients (self-diffusion, viscosity, and thermal conductivity) is reexamined using the contemporary datasets for the transport properties of Yukawa fluids. The results could be of particular interest in the context of complex (dusty) plasmas, colloidal suspensions, electrolytes, and other related systems with soft pairwise interactions.
The Sun's Birth Environment: Context for Meteoritics
2024-09-16
Steve Desch, Núria Miret-Roig
Meteorites trace planet formation in the Sun's protoplanetary disk, but they also record the influence of the Sun's birth environment. Whether the Sun formed in a region like Taurus-Auriga with ~10^2 stars, or a region like the Carina Nebula with ~10^6 stars, matters for how large the Sun's disk was, for how long and from how far away it accreted gas from the molecular cloud, and how it acquired radionuclides like 26Al. To provide context for the interpretation of meteoritic data, we review what is known about the Sun's birth environment. Based on an inferred gas disk outer radius ~50-90 AU, radial transport in the disk, and the abundances of noble gases in Jupiter's atmosphere, the Sun's molecular cloud and protoplanetary disk were exposed to an ultraviolet flux G0 ~30-3000 during its birth and first ~10 Myr of evolution. Based on the orbits of Kuiper Belt objects, the Solar System was subsequently exposed to a stellar density ~100 Msol/pc^3 for ~100 Myr, strongly implying formation in a bound cluster. These facts suggest formation in a region like the outskirts of the Orion Nebula, perhaps 2 pc from the center. The protoplanetary disk might have accreted gas for many Myr, but a few x10^5 yr seems more likely. It probably inherited radionuclides from its molecular cloud, enriched by inputs from supernovae and especially Wolf-Rayet star winds, and acquired a typical amount of 26Al.
SpectrAx: Spectral Search of Axion-Like Particles Using Multi-Band Observations of Galaxy Clusters from SKA, SO, CMB-S4 and eROSITA
2024-09-16
Harsh Mehta, Suvodip Mukherjee
The existence of axions or Axion-Like Particles (ALPs) has been predicted by various Beyond Standard Model (BSM) theories, and the proposed photon-ALP interaction is one of the ways to probe them. Such an interaction will lead to photon-ALP resonant conversion in galaxy clusters, resulting in a polarized spectral distortion in the CMB along the cluster line of sight. The estimation of this signal from galaxy clusters requires an estimation of the electron density and magnetic field in galaxy clusters. We have developed a new Bayesian framework \texttt{SpectrAx} that can use observations from different electromagnetic bands such as radio, CMB, optical, and X-ray to infer the astrophysical properties of a galaxy cluster, such as cluster its redshift, electron density and magnetic field, along with the BSM physics such as ALPs. By using the simulated data for upcoming CMB surveys such as Simons Observatory (SO) and CMB-S4 in combination with Square Kilometer Array (SKA) and extended ROentgen Survey with an Imaging Telescope Array (eROSITA) we demonstrate the capability in accurately inferring the ALPs coupling strength along with the radial profile of electron density and magnetic field from galaxy clusters. The application of this framework to the data from future surveys by combining SKA+SO+eROSITA and SKA+CMB-S4+eROSITA will make it possible for the first time to explore both astrophysics and BSM physics from low-redshift galaxy clusters using a multi-band approach.
Spatially Resolved Kinematics of SLACS Lens Galaxies. I: Data and Kinematic Classification
2024-09-16
Shawn Knabel, Tommaso Treu, Michele Cappellari, Anowar J. Shajib, Chih-Fan Chen, Vardha N. Bennert
We obtain spatially resolved kinematics with the Keck Cosmic Web Imager (KCWI) integral-field spectrograph for a sample of 14 massive (11 < log10 M* < 12) lensing early-type galaxies (ETGs) at redshifts z=0.15-0.35 from the Sloan Lens ACS (SLACS) survey. We integrate within the galaxy effective radius and examine the rotational and dispersion velocities, showing that 11/14 are quantitatively classified as slow rotators in comparison with local galaxy surveys. Of key interest is the ability of this data to enable the precision required for cosmological inference with lensing time delays on scales of 1-2% uncertainty. The dataset is unprecedented for galaxy-scale lens galaxies, in terms of signal-to-noise ratio, sampling, and calibration. We test sources of systematic error and identify primary contributions from choice of stellar template library and wavelength range of the spectral fit. Systematics are quantified at the spatial bin level, resulting in systematic error at 3% and positive spatial covariance of 2%. We examine the effects of integration of the kinematic maps within circular apertures of different sizes and compare with SDSS single-aperture velocity dispersions. The most recent velocity dispersion estimates from SDSS spectra are found to be biased by a factor of 5.3% with respect to KCWI data, and to underestimate uncertainties. We examine correlations between scaling relations and show the correlations to agree with previous SLACS analysis with no statistically significant disagreement. A follow-up paper will present Jeans modeling and discuss the context of these observations within broader studies of galaxy evolution and cosmology.
Approximation by Fourier sums on the classes of generalized Poisson integrals
2024-09-16
Anatoly Serdyuk, Tetiana Stepaniuk
We present a survey of results related to the solution of Kolmogorov--Nikolsky problem for Fourier sums on the classes of generalized Poisson integrals $C^{\alpha,r}_{\beta,p}$, which consists in finding of asymptotic equalities for exact upper boundaries o f uniform norms of deviations of partial Fourier sums on the classes of $2\pi$--periodic functions $C^{\alpha,r}_{\beta,p}$, which are defined as convolutions of the functions, which belong to the unit balls pf the spaces $L_{p}$, $1\leq p\leq \infty$, with generalized Poisson kernels $$ P_{\alpha,r,\beta}(t)=\sum\limits_{k=1}^{\infty}e^{-\alpha k^{r}}\cos \big(kt-\frac{\beta\pi}{2}\big), \ \alpha>0, r>0, \ \beta\in \mathbb{R}.$$
We give the full representation theory of the gravitational extended corner symmetry group in two-dimensions. This includes projective representations, which correspond to representations of the quantum corner symmetry group. We begin with a review of Mackey's theory of induced representations and then proceed to its application to the corner symmetries. The field representations, induced from the irreducible representations of the special linear group are worked out first. The little group method is then applied to the extended corner symmetry group to obtain the irreducible unitary representations. Finally, we focus on projective representations and their application to the description of local subsystems.
Inferring stellar parameters and their uncertainties from high-resolution spectroscopy using invertible neural networks
2024-09-16
Nils Candebat, Giuseppe Germano Sacco, Laura Magrini, Francesco Belfiore, Mathieu Van-der-Swaelmen, Stefano Zibetti
Context: New spectroscopic surveys will increase the number of astronomical objects requiring characterization by over tenfold.. Machine learning tools are required to address this data deluge in a fast and accurate fashion. Most machine learning algorithms can not estimate error directly, making them unsuitable for reliable science. Aims: We aim to train a supervised deep-learning algorithm tailored for high-resolution observational stellar spectra. This algorithm accurately infer precise estimates while providing coherent estimates of uncertainties by leveraging information from both the neural network and the spectra. Methods: We train a conditional Invertible Neural Network (cINN) on observational spectroscopic data obtained from the GIRAFFE spectrograph (HR10 and HR21 setups) within the Gaia-ESO survey. A key features of cINN is its ability to produce the Bayesian posterior distribution of parameters for each spectrum. By analyzing this distribution, we inferred parameters and their uncertainties. Several tests have been applied to study how parameters and errors are estimated. Results: We achieved an accuracy of 28K in $T_{\text{eff}}$, 0.06 dex in $\log g$, 0.03 dex in $[\text{Fe/H}]$, and between 0.05 dex and 0.17 dex for the other abundances for high quality spectra. Accuracy remains stable with low signal-to-noise ratio spectra. The uncertainties obtained are well within the same order of magnitude. The network accurately reproduces astrophysical relationships both on the scale of the Milky Way and within smaller star clusters. We created a table containing the new parameters generated by our cINN. Conclusion: This neural network represents a compelling proposition for future astronomical surveys. These coherent derived uncertainties make it possible to reuse these estimates in other works as Bayesian priors and thus present a solid basis for future work.
Unveiling the Diversity of Type IIn Supernovae via Systematic Light Curve Modeling
2024-09-16
C. L. Ransome, V. A. Villar
Type IIn supernovae (SNeIIn) are a highly heterogeneous subclass of core-collapse supernovae, spectroscopically characterized by signatures of interaction with a dense circumstellar medium (CSM). Here we systematically model the light curves of 142 archival SNeIIn using MOSFiT (the Modular Open Source Fitter for Transients). We find that the observed and inferred properties of SNIIn are diverse, but there are some trends. The typical SN CSM is dense ($\sim$10$^{-12}$gcm$^{-3}$) with highly diverse CSM geometry, with a median CSM mass of $\sim$1M$_\odot$. The ejecta are typically massive ($\gtrsim10$M$_\odot$), suggesting massive progenitor systems. We find positive correlations between the CSM mass and the rise and fall times of SNeIIn. Furthermore there are positive correlations between the rise time and fall times and the $r$-band luminosity. We estimate the mass-loss rates of our sample (where spectroscopy is available) and find a high median mass-loss rate of $\sim$10$^{-2}$M$_\odot$yr$^{-1}$, with a range between 10$^{-4}$--1M$_\odot$yr$^{-1}$. These mass-loss rates are most similar to the mass loss from great eruptions of luminous blue variables, consistent with the direct progenitor detections in the literature. We also discuss the role that binary interactions may play, concluding that at least some of our SNeIIn may be from massive binary systems. Finally, we estimate a detection rate of 1.6$\times$10$^5$yr$^{-1}$ in the upcoming Legacy Survey of Space and Time at the Vera C. Rubin Observatory.
LASERS: LAtent Space Encoding for Representations with Sparsity for Generative Modeling
2024-09-16
Xin Li, Anand Sarwate
Learning compact and meaningful latent space representations has been shown to be very useful in generative modeling tasks for visual data. One particular example is applying Vector Quantization (VQ) in variational autoencoders (VQ-VAEs, VQ-GANs, etc.), which has demonstrated state-of-the-art performance in many modern generative modeling applications. Quantizing the latent space has been justified by the assumption that the data themselves are inherently discrete in the latent space (like pixel values). In this paper, we propose an alternative representation of the latent space by relaxing the structural assumption than the VQ formulation. Specifically, we assume that the latent space can be approximated by a union of subspaces model corresponding to a dictionary-based representation under a sparsity constraint. The dictionary is learned/updated during the training process. We apply this approach to look at two models: Dictionary Learning Variational Autoencoders (DL-VAEs) and DL-VAEs with Generative Adversarial Networks (DL-GANs). We show empirically that our more latent space is more expressive and has leads to better representations than the VQ approach in terms of reconstruction quality at the expense of a small computational overhead for the latent space computation. Our results thus suggest that the true benefit of the VQ approach might not be from discretization of the latent space, but rather the lossy compression of the latent space. We confirm this hypothesis by showing that our sparse representations also address the codebook collapse issue as found common in VQ-family models.
Continual Learning of Conjugated Visual Representations through Higher-order Motion Flows
2024-09-16
Simone Marullo, Matteo Tiezzi, Marco Gori, Stefano Melacci
Learning with neural networks from a continuous stream of visual information presents several challenges due to the non-i.i.d. nature of the data. However, it also offers novel opportunities to develop representations that are consistent with the information flow. In this paper we investigate the case of unsupervised continual learning of pixel-wise features subject to multiple motion-induced constraints, therefore named motion-conjugated feature representations. Differently from existing approaches, motion is not a given signal (either ground-truth or estimated by external modules), but is the outcome of a progressive and autonomous learning process, occurring at various levels of the feature hierarchy. Multiple motion flows are estimated with neural networks and characterized by different levels of abstractions, spanning from traditional optical flow to other latent signals originating from higher-level features, hence called higher-order motions. Continuously learning to develop consistent multi-order flows and representations is prone to trivial solutions, which we counteract by introducing a self-supervised contrastive loss, spatially-aware and based on flow-induced similarity. We assess our model on photorealistic synthetic streams and real-world videos, comparing to pre-trained state-of-the art feature extractors (also based on Transformers) and to recent unsupervised learning models, significantly outperforming these alternatives.
2024-09-15
Didem Unat, Ilyas Turimbetov, Mohammed Kefah Taha Issa, Doğan Sağbili, Flavio Vella, Daniele De Sensi, Ismayil Ismayilov
In recent years, GPUs have become the preferred accelerators for HPC and ML applications due to their parallelism and fast memory bandwidth. While GPUs boost computation, inter-GPU communication can create scalability bottlenecks, especially as the number of GPUs per node and cluster grows. Traditionally, the CPU managed multi-GPU communication, but advancements in GPU-centric communication now challenge this CPU dominance by reducing its involvement, granting GPUs more autonomy in communication tasks, and addressing mismatches in multi-GPU communication and computation. This paper provides a landscape of GPU-centric communication, focusing on vendor mechanisms and user-level library supports. It aims to clarify the complexities and diverse options in this field, define the terminology, and categorize existing approaches within and across nodes. The paper discusses vendor-provided mechanisms for communication and memory management in multi-GPU execution and reviews major communication libraries, their benefits, challenges, and performance insights. Then, it explores key research paradigms, future outlooks, and open research questions. By extensively describing GPU-centric communication techniques across the software and hardware stacks, we provide researchers, programmers, engineers, and library designers insights on how to exploit multi-GPU systems at their best.
A Survey of Out-of-distribution Generalization for Graph Machine Learning from a Causal View
2024-09-15
Jing Ma
Graph machine learning (GML) has been successfully applied across a wide range of tasks. Nonetheless, GML faces significant challenges in generalizing over out-of-distribution (OOD) data, which raises concerns about its wider applicability. Recent advancements have underscored the crucial role of causality-driven approaches in overcoming these generalization challenges. Distinct from traditional GML methods that primarily rely on statistical dependencies, causality-focused strategies delve into the underlying causal mechanisms of data generation and model prediction, thus significantly improving the generalization of GML across different environments. This paper offers a thorough review of recent progress in causality-involved GML generalization. We elucidate the fundamental concepts of employing causality to enhance graph model generalization and categorize the various approaches, providing detailed descriptions of their methodologies and the connections among them. Furthermore, we explore the incorporation of causality in other related important areas of trustworthy GML, such as explanation, fairness, and robustness. Concluding with a discussion on potential future research directions, this review seeks to articulate the continuing development and future potential of causality in enhancing the trustworthiness of graph machine learning.
A Comprehensive Survey of PID and Pure Pursuit Control Algorithms for Autonomous Vehicle Navigation
2024-09-15
Harshit Jain, Priyal Babel
The autonomous driving industry is experiencing unprecedented growth, driven by rapid advancements in technology and increasing demand for safer, more efficient transportation. At the heart of this revolution are two critical factors: lateral and longitudinal controls, which together enable vehicles to track complex environments with high accuracy and minimal errors. This paper provides a detailed overview of two of the field's most commonly used and stable control algorithms: proportional-integral-derivative (PID) and pure pursuit. These algorithms have proved useful in solving the issues of lateral (steering) and longitudinal (speed and distance) control in autonomous vehicles. This survey aims to provide researchers, engineers, and industry professionals with an in depth understanding of these fundamental control algorithms, their current applications, and their potential to shape the future of autonomous driving technology.
Causal Inference with Large Language Model: A Survey
2024-09-15
Jing Ma
Causal inference has been a pivotal challenge across diverse domains such as medicine and economics, demanding a complicated integration of human knowledge, mathematical reasoning, and data mining capabilities. Recent advancements in natural language processing (NLP), particularly with the advent of large language models (LLMs), have introduced promising opportunities for traditional causal inference tasks. This paper reviews recent progress in applying LLMs to causal inference, encompassing various tasks spanning different levels of causation. We summarize the main causal problems and approaches, and present a comparison of their evaluation results in different causal scenarios. Furthermore, we discuss key findings and outline directions for future research, underscoring the potential implications of integrating LLMs in advancing causal inference methodologies.
With the rapid development of marine engineering projects such as marine resource extraction and oceanic surveys, underwater visual imaging and analysis has become a critical technology. Unfortunately, due to the inevitable non-linear attenuation of light in underwater environments, underwater images and videos often suffer from low contrast, blurriness, and color degradation, which significantly complicate the subsequent research. Existing underwater image enhancement methods often treat the haze and color cast as a unified degradation process and disregard their independence and interdependence, which limits the performance improvement. Here, we propose a Vision Transformer (ViT)-based network (referred to as WaterFormer) to improve the underwater image quality. WaterFormer contains three major components: a dehazing block (DehazeFormer Block) to capture the self-correlated haze features and extract deep-level features, a Color Restoration Block (CRB) to capture self-correlated color cast features, and a Channel Fusion Block (CFB) to capture fusion features within the network. To ensure authenticity, a soft reconstruction layer based on the underwater imaging physics model is included. To improve the quality of the enhanced images, we introduce the Chromatic Consistency Loss and Sobel Color Loss to train the network. Comprehensive experimental results demonstrate that WaterFormer outperforms other state-of-the-art methods in enhancing underwater images.
The Galaxy Activity, Torus, and Outflow Survey (GATOS). (VI): Polycyclic Aromatic Hydrocarbon Emission in the Central Regions of Three Seyferts
2024-09-15
Lulu Zhang, Ismael García-Bernete, Chris Packham, Fergus R. Donnan, Dimitra Rigopoulou, Erin K. S. Hicks, Ric I. Davies, Taro T. Shimizu, Almudena Alonso-Herrero, Cristina Ramos Almeida, Miguel Pereira-Santaella, Claudio Ricci, Andrew J. Bunker, Mason T. Leist, David J. Rosario, Santiago García-Burillo, Laura Hermosa Muñoz, Francoise Combes, Masatoshi Imanishi, Alvaro Labiano, Donaji Esparza-Arredondo, Enrica Bellocchi, Anelise Audibert, Lindsay Fuller, Omaira González-Martín, Sebastian Hönig, Takuma Izumi, Nancy A. Levenson, Enrique López-Rodríguez, Daniel Rouan, Marko Stalevski, Martin J. Ward
We analyze JWST MIRI/MRS IFU observations of three Seyferts and showcase the intriguing polycyclic aromatic hydrocarbon (PAH) emission characteristics in regions of $\sim 500\,\rm pc$ scales over or around their active galactic nuclei (AGN). Combining the model predictions and the measurements of PAH features and other infrared emission lines, we find that the central regions containing a high fraction of neutral PAHs with small sizes, e.g., those in ESO137-G034, are in highly heated environments, due to collisional shock heating, with hard and moderately intense radiation fields. Such environments are proposed to be associated with inhibited growth or preferential erosion of PAHs, decreasing the average PAH size and the overall abundance of PAHs. We additionally find that the central regions containing a high fraction of ionized PAHs with large sizes, e.g., those in MCG-05-23-016, are likely experiencing severe photo-ionization because of the radiative effects from the radiative shock precursor besides the AGN. The severe photo-ionization can contribute to the ionization of all PAHs and further destruction of small PAHs. Overall, different Seyferts, even different regions in the same galaxy, e.g., those in NGC\,3081, can contain PAH populations of different properties. Specifically, Seyferts that exhibit similar PAH characteristics to ESO137-G034 and MCG-05-23-016 also tend to have similar emission line properties to them, suggesting that the explanations for PAH characteristics of ESO137-G034 and MCG-05-23-016 may also apply generally. These results have promising application in the era of JWST, especially in diagnosing different (i.e., radiative, and kinetic) AGN feedback modes.
The Galaxy Activity, Torus, and Outflow Survey (GATOS). (IV): Exploring Ionized Gas Outflows in Central Kiloparsec Regions of GATOS Seyferts
2024-09-15
Lulu Zhang, Chris Packham, Erin K. S. Hicks, Ric I. Davies, Taro T. Shimizu, Almudena Alonso-Herrero, Laura Hermosa Muñoz, Ismael García-Bernete, Miguel Pereira-Santaella, Anelise Audibert, Enrique López-Rodríguez, Enrica Bellocch, Andrew J. Bunker, Francoise Combes, Tanio Díaz-Santos, Poshak Gandhi, Santiago García-Burillo, Begoña García-Lorenzo, Omaira González-Martín, Masatoshi Imanishi, Alvaro Labiano, Mason T. Leist, Nancy A. Levenson, Cristina Ramos Almeida, Claudio Ricci, Dimitra Rigopoulou, David J. Rosario, Marko Stalevski, Martin J. Ward, Donaji Esparza-Arredondo, Dan Delaney, Lindsay Fuller, Houda Haidar, Sebastian Hönig, Takuma Izumi, Daniel Rouan
Utilizing JWST MIRI/MRS IFU observations of the kiloparsec scale central regions, we showcase the diversity of ionized gas distributions and kinematics in six nearby Seyfert galaxies included in the GATOS survey. Specifically, we present spatially resolved flux distribution and velocity field maps of six ionized emission lines covering a large range of ionization potentials ($15.8-97.1$ eV). Based on these maps, we showcase the evidence of ionized gas outflows in the six targets, and find some highly disturbed regions in NGC\,5728, NGC\,5506, and ESO137-G034. We propose AGN-driven radio jets plausibly play an important role in triggering these highly disturbed regions. With the outflow rates estimated based on [Ne~{\footnotesize V}] emission, we find the six targets tend to have ionized outflow rates converged to a narrower range than previous finding. These results have important implication for the outflow properties in AGN of comparable luminosity.
The rapid development of LLMs brings both convenience and potential threats. As costumed and private LLMs are widely applied, model copyright protection has become important. Text watermarking is emerging as a promising solution to AI-generated text detection and model protection issues. However, current text watermarks have largely ignored the critical need for injecting different watermarks for different users, which could help attribute the watermark to a specific individual. In this paper, we explore the personalized text watermarking scheme for LLM copyright protection and other scenarios, ensuring accountability and traceability in content generation. Specifically, we propose a novel text watermarking method PersonaMark that utilizes sentence structure as the hidden medium for the watermark information and optimizes the sentence-level generation algorithm to minimize disruption to the model's natural generation process. By employing a personalized hashing function to inject unique watermark signals for different users, personalized watermarked text can be obtained. Since our approach performs on sentence level instead of token probability, the text quality is highly preserved. The injection process of unique watermark signals for different users is time-efficient for a large number of users with the designed multi-user hashing function. As far as we know, we achieved personalized text watermarking for the first time through this. We conduct an extensive evaluation of four different LLMs in terms of perplexity, sentiment polarity, alignment, readability, etc. The results demonstrate that our method maintains performance with minimal perturbation to the model's behavior, allows for unbiased insertion of watermark information, and exhibits strong watermark recognition capabilities.
From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare
2024-09-15
Ming Li, Pengcheng Xu, Junjie Hu, Zeyu Tang, Guang Yang
Federated learning holds great potential for enabling large-scale healthcare research and collaboration across multiple centres while ensuring data privacy and security are not compromised. Although numerous recent studies suggest or utilize federated learning based methods in healthcare, it remains unclear which ones have potential clinical utility. This review paper considers and analyzes the most recent studies up to May 2024 that describe federated learning based methods in healthcare. After a thorough review, we find that the vast majority are not appropriate for clinical use due to their methodological flaws and/or underlying biases which include but are not limited to privacy concerns, generalization issues, and communication costs. As a result, the effectiveness of federated learning in healthcare is significantly compromised. To overcome these challenges, we provide recommendations and promising opportunities that might be implemented to resolve these problems and improve the quality of model development in federated learning with healthcare.
High Definition Map Mapping and Update: A General Overview and Future Directions
2024-09-15
Benny Wijaya, Kun Jiang, Mengmeng Yang, Tuopu Wen, Yunlong Wang, Xuewei Tang, Zheng Fu, Taohua Zhou, Diange Yang
Along with the rapid growth of autonomous vehicles (AVs), more and more demands are required for environment perception technology. Among others, HD mapping has become one of the more prominent roles in helping the vehicle realize essential tasks such as localization and path planning. While increasing research efforts have been directed toward HD Map development. However, a comprehensive overview of the overall HD map mapping and update framework is still lacking. This article introduces the development and current state of the algorithm involved in creating HD map mapping and its maintenance. As part of this study, the primary data preprocessing approach of processing raw data to information ready to feed for mapping and update purposes, semantic segmentation, and localization are also briefly reviewed. Moreover, the map taxonomy, ontology, and quality assessment are extensively discussed, the map data's general representation method is presented, and the mapping algorithm ranging from SLAM to transformers learning-based approaches are also discussed. The development of the HD map update algorithm, from change detection to the update methods, is also presented. Finally, the authors discuss possible future developments and the remaining challenges in HD map mapping and update technology. This paper simultaneously serves as a position paper and tutorial to those new to HD map mapping and update domains.
In recent years, there has been a surge in the publication of clinical trial reports, making it challenging to conduct systematic reviews. Automatically extracting Population, Intervention, Comparator, and Outcome (PICO) from clinical trial studies can alleviate the traditionally time-consuming process of manually scrutinizing systematic reviews. Existing approaches of PICO frame extraction involves supervised approach that relies on the existence of manually annotated data points in the form of BIO label tagging. Recent approaches, such as In-Context Learning (ICL), which has been shown to be effective for a number of downstream NLP tasks, require the use of labeled examples. In this work, we adopt ICL strategy by employing the pretrained knowledge of Large Language Models (LLMs), gathered during the pretraining phase of an LLM, to automatically extract the PICO-related terminologies from clinical trial documents in unsupervised set up to bypass the availability of large number of annotated data instances. Additionally, to showcase the highest effectiveness of LLM in oracle scenario where large number of annotated samples are available, we adopt the instruction tuning strategy by employing Low Rank Adaptation (LORA) to conduct the training of gigantic model in low resource environment for the PICO frame extraction task. Our empirical results show that our proposed ICL-based framework produces comparable results on all the version of EBM-NLP datasets and the proposed instruction tuned version of our framework produces state-of-the-art results on all the different EBM-NLP datasets. Our project is available at \url{https://github.com/shrimonmuke0202/AlpaPICO.git}.
A Comprehensive Methodological Survey of Human Activity Recognition Across Divers Data Modalities
2024-09-15
Jungpil Shin, Najmul Hassan, Abu Saleh Musa Miah1, Satoshi Nishimura
Human Activity Recognition (HAR) systems aim to understand human behaviour and assign a label to each action, attracting significant attention in computer vision due to their wide range of applications. HAR can leverage various data modalities, such as RGB images and video, skeleton, depth, infrared, point cloud, event stream, audio, acceleration, and radar signals. Each modality provides unique and complementary information suited to different application scenarios. Consequently, numerous studies have investigated diverse approaches for HAR using these modalities. This paper presents a comprehensive survey of the latest advancements in HAR from 2014 to 2024, focusing on machine learning (ML) and deep learning (DL) approaches categorized by input data modalities. We review both single-modality and multi-modality techniques, highlighting fusion-based and co-learning frameworks. Additionally, we cover advancements in hand-crafted action features, methods for recognizing human-object interactions, and activity detection. Our survey includes a detailed dataset description for each modality and a summary of the latest HAR systems, offering comparative results on benchmark datasets. Finally, we provide insightful observations and propose effective future research directions in HAR.
Bayesian predictive synthesis is useful in synthesizing multiple predictive distributions coherently. However, the proof for the fundamental equation of the synthesized predictive density has been missing. In this technical report, we review the series of research on predictive synthesis, then fill the gap between the known results and the equation used in modern applications. We provide two proofs and clarify the structure of predictive synthesis.
Conditional sampling within generative diffusion models
2024-09-15
Zheng Zhao, Ziwei Luo, Jens Sjölund, Thomas B. Schön
Generative diffusions are a powerful class of Monte Carlo samplers that leverage bridging Markov processes to approximate complex, high-dimensional distributions, such as those found in image processing and language models. Despite their success in these domains, an important open challenge remains: extending these techniques to sample from conditional distributions, as required in, for example, Bayesian inverse problems. In this paper, we present a comprehensive review of existing computational approaches to conditional sampling within generative diffusion models. Specifically, we highlight key methodologies that either utilise the joint distribution, or rely on (pre-trained) marginal distributions with explicit likelihoods, to construct conditional generative samplers.
The characteristic timescale at which the variability of active galactic nuclei (AGNs) turns from red noise to white noise can probe the accretion physics around supermassive black holes (SMBHs). A number of works have studied the characteristic timescale of quasars and obtained quite different scaling relations between the timescale and quasar physical properties. One possible reason for the discrepancies is that the characteristic timescale can be easily underestimated if the light curves are not long enough. In this work, we construct well-defined AGN samples to observationally test the relationships between the characteristic timescale and AGN properties obtained by previous works. Our samples eliminate the effects of insufficient light-curve lengths. We confirm that the timescale predictions \citep{Zhou2024} of the Corona Heated Accretion disk Reprocessing model are consistent with our timescale measurements. The timescale predictions by empirically relations \citep[e.g.,][]{Kelly2009} are systematically smaller than our measured ones. Our results provide further evidence that AGN variability is driven by thermal fluctuations in SMBH accretion disks. Future flagship time-domain surveys can critically test our conclusions and reveal the physical nature of AGN variability.
A Survey of Foundation Models for Music Understanding
2024-09-15
Wenjun Li, Ying Cai, Ziyang Wu, Wenyi Zhang, Yifan Chen, Rundong Qi, Mengqi Dong, Peigen Chen, Xiao Dong, Fenghao Shi, Lei Guo, Junwei Han, Bao Ge, Tianming Liu, Lin Gan, Tuo Zhang
Music is essential in daily life, fulfilling emotional and entertainment needs, and connecting us personally, socially, and culturally. A better understanding of music can enhance our emotions, cognitive skills, and cultural connections. The rapid advancement of artificial intelligence (AI) has introduced new ways to analyze music, aiming to replicate human understanding of music and provide related services. While the traditional models focused on audio features and simple tasks, the recent development of large language models (LLMs) and foundation models (FMs), which excel in various fields by integrating semantic information and demonstrating strong reasoning abilities, could capture complex musical features and patterns, integrate music with language and incorporate rich musical, emotional and psychological knowledge. Therefore, they have the potential in handling complex music understanding tasks from a semantic perspective, producing outputs closer to human perception. This work, to our best knowledge, is one of the early reviews of the intersection of AI techniques and music understanding. We investigated, analyzed, and tested recent large-scale music foundation models in respect of their music comprehension abilities. We also discussed their limitations and proposed possible future directions, offering insights for researchers in this field.
As AI systems become more advanced, ensuring their alignment with a diverse range of individuals and societal values becomes increasingly critical. But how can we capture fundamental human values and assess the degree to which AI systems align with them? We introduce ValueCompass, a framework of fundamental values, grounded in psychological theory and a systematic review, to identify and evaluate human-AI alignment. We apply ValueCompass to measure the value alignment of humans and language models (LMs) across four real-world vignettes: collaborative writing, education, public sectors, and healthcare. Our findings uncover risky misalignment between humans and LMs, such as LMs agreeing with values like "Choose Own Goals", which are largely disagreed by humans. We also observe values vary across vignettes, underscoring the necessity for context-aware AI alignment strategies. This work provides insights into the design space of human-AI alignment, offering foundations for developing AI that responsibly reflects societal values and ethics.
Possible anti-correlations between pulsation amplitudes and the disk growth of Be stars in giant-outbursting Be X-ray binaries
2024-09-15
Masafumi Niwano, Michael M. Fausnaugh, Ryan M. Lau, Kishalay De, Roberto Soria, George R. Ricker, Roland Vanderspek, Michael C. B. Ashley, Nicholas Earley, Matthew J. Hankins, Mansi M. Kasliwal, Anna M. Moore, Jamie Soon, Tony Travouillon, Mahito Sasada, Ichiro Takahashi, Yoichi Yatsu, Nobuyuki Kawai
The mechanism of X-ray outbursts in Be X-ray binaries remains a mystery, and understanding their circumstellar disks is crucial for a solution of the mass-transfer problem. In particular, it is important to identify the Be star activities (e.g., pulsations) that cause mass ejection and, hence, disk formation. Therefore, we investigated the relationship between optical flux oscillations and the infrared (IR) excess in a sample of five Be X-ray binaries. Applying the Lomb-Scargle technique to high-cadence optical light curves from the Transiting Exoplanet Survey Satellite (TESS), we detected several significant oscillation modes in the 3 to 24 hour period range for each source. We also measured the IR excess (a proxy for disk growth) of those five sources, using J-band light curves from Palomar Gattini-IR. In four of the five sources, we found anti-correlations between the IR excess and the amplitude of the main flux oscillation modes. This result is inconsistent with the conventional idea that non-radial pulsations drive mass ejections. We propose an alternative scenario where internal temperature variations in the Be star cause transitions between pulsation-active and mass-ejection-active states.
Finding Passive Galaxies in HI-MaNGA: The Impact of Star-Formation Rate Indicator
2024-09-15
Nora Salem, Karen Masters, David Stark, Anubhav Sharma
HI-rich galaxies typically have high star-formation rates (SFR), but there exist interesting HI-rich and low star-forming (low-SF) galaxies. Previous work on a sample of these galaxies identified from HI-MaNGA (HI follow-up to the MaNGA survey) using an infrared indicator of specific-SFR (sSFR; namely W2-W3~<2) could find no single physical process to explain their unusual behaviour. The method by which galaxies are identified as low sSFR may be important in this conclusion. In this Research Note, we explore how an HI-rich, low sSFR sample of HI-MaNGA galaxies differs using H alpha, single stellar population, and ultraviolet estimators of SFR. We find that samples are statistically similar to each other so long as W2-W3~<2 is interpreted as corresponding to sSFR<10^{-11.15} yr^{-1}.
Recent advances in deep learning and language models for studying the microbiome
2024-09-15
Binghao Yan, Yunbi Nam, Lingyao Li, Rebecca A. Deek, Hongzhe Li, Siyuan Ma
Recent advancements in deep learning, particularly large language models (LLMs), made a significant impact on how researchers study microbiome and metagenomics data. Microbial protein and genomic sequences, like natural languages, form a language of life, enabling the adoption of LLMs to extract useful insights from complex microbial ecologies. In this paper, we review applications of deep learning and language models in analyzing microbiome and metagenomics data. We focus on problem formulations, necessary datasets, and the integration of language modeling techniques. We provide an extensive overview of protein/genomic language modeling and their contributions to microbiome studies. We also discuss applications such as novel viromics language modeling, biosynthetic gene cluster prediction, and knowledge integration for metagenomics studies.
In this paper we present a new machine learning workflow with unsupervised learning techniques to identify domains within atomic force microscopy images obtained from polymer films. The goal of the workflow is to identify the spatial location of the two types of polymer domains with little to no manual intervention and calculate the domain size distributions which in turn can help qualify the phase separated state of the material as macrophase or microphase ordered or disordered domains. We briefly review existing approaches used in other fields, computer vision and signal processing that can be applicable for the above tasks that happen frequently in the field of polymer science and engineering. We then test these approaches from computer vision and signal processing on the AFM image dataset to identify the strengths and limitations of each of these approaches for our first task. For our first domain segmentation task, we found that the workflow using discrete Fourier transform or discrete cosine transform with variance statistics as the feature works the best. The popular ResNet50 deep learning approach from computer vision field exhibited relatively poorer performance in the domain segmentation task for our AFM images as compared to the DFT and DCT based workflows. For the second task, for each of 144 input AFM images, we then used an existing porespy python package to calculate the domain size distribution from the output of that image from DFT based workflow. The information and open source codes we share in this paper can serve as a guide for researchers in the polymer and soft materials fields who need ML modeling and workflows for automated analyses of AFM images from polymer samples that may have crystalline or amorphous domains, sharp or rough interfaces between domains, or micro or macrophase separated domains.
Analysis of flexible traffic control method in SDN
2024-09-15
Marta Szymczyk
The aim of this paper is to analyze methods of flexible control in SDN networks and to propose a self-developed solution that will enable intelligent adaptation of SDN controller performance. This work aims not only to review existing solutions, but also to develop an approach that will increase the efficiency and adaptability of network management. The project uses a modern type of machine learning, Reinforcement Learning, which allows autonomous decisions of a network that learns based on its choices in a dynamically changing environment, which is most similar to the way humans learn. The solution aims not only to improve the network's performance, but also its flexibility and real-time adaptability - flexible traffic control.
A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theorem
2024-09-14
Weijie J. Su
Differential privacy is widely considered the formal privacy for privacy-preserving data analysis due to its robust and rigorous guarantees, with increasingly broad adoption in public services, academia, and industry. Despite originating in the cryptographic context, in this review paper we argue that, fundamentally, differential privacy can be considered a \textit{pure} statistical concept. By leveraging a theorem due to David Blackwell, our focus is to demonstrate that the definition of differential privacy can be formally motivated from a hypothesis testing perspective, thereby showing that hypothesis testing is not merely convenient but also the right language for reasoning about differential privacy. This insight leads to the definition of $f$-differential privacy, which extends other differential privacy definitions through a representation theorem. We review techniques that render $f$-differential privacy a unified framework for analyzing privacy bounds in data analysis and machine learning. Applications of this differential privacy definition to private deep learning, private convex optimization, shuffled mechanisms, and U.S.~Census data are discussed to highlight the benefits of analyzing privacy bounds under this framework compared to existing alternatives.
This paper introduces a size-adaptable robotic endoscope design, which aims to improve the efficiency and comfort of colonoscopy. The robotic endoscope proposed in this paper combines the expansion mechanism and the external drive system, which can adjust the shape according to the different pipe diameters, thus improving the stability and propulsion force during propulsion. As an actuator in the expansion mechanism, flexible bellows can provide a normal force of 3.89 N and an axial deformation of nearly 10mm at the maximum pressure, with a 53% expansion rate in the size of expandable tip. In the test of the locomotion performance of the prototype, we obtained the relationship with the propelling of the prototype by changing the friction coefficient of the pipe and the motor angular velocity. In the experiment with artificial bowel tissues, the prototype can generate a propelling force of 2.83 N, and the maximum linear speed is 29.29 m/s in average, and could produce effective propulsion when it passes through different pipe sizes. The results show that the prototype can realize the ability of shape adaptation in order to obtain more propulsion. The relationship between propelling force and traction force, structural optimization and miniaturization still need further exploration.
One missing piece in Vision and Language: A Survey on Comics Understanding
2024-09-14
Emanuele Vivoli, Andrey Barsky, Mohamed Ali Souibgui, Artemis LLabres, Marco Bertini, Dimosthenis Karatzas
Vision-language models have recently evolved into versatile systems capable of high performance across a range of tasks, such as document understanding, visual question answering, and grounding, often in zero-shot settings. Comics Understanding, a complex and multifaceted field, stands to greatly benefit from these advances. Comics, as a medium, combine rich visual and textual narratives, challenging AI models with tasks that span image classification, object detection, instance segmentation, and deeper narrative comprehension through sequential panels. However, the unique structure of comics -- characterized by creative variations in style, reading order, and non-linear storytelling -- presents a set of challenges distinct from those in other visual-language domains. In this survey, we present a comprehensive review of Comics Understanding from both dataset and task perspectives. Our contributions are fivefold: (1) We analyze the structure of the comics medium, detailing its distinctive compositional elements; (2) We survey the widely used datasets and tasks in comics research, emphasizing their role in advancing the field; (3) We introduce the Layer of Comics Understanding (LoCU) framework, a novel taxonomy that redefines vision-language tasks within comics and lays the foundation for future work; (4) We provide a detailed review and categorization of existing methods following the LoCU framework; (5) Finally, we highlight current research challenges and propose directions for future exploration, particularly in the context of vision-language models applied to comics. This survey is the first to propose a task-oriented framework for comics intelligence and aims to guide future research by addressing critical gaps in data availability and task definition. A project associated with this survey is available at https://github.com/emanuelevivoli/awesome-comics-understanding.
TX-Gen: Multi-Objective Optimization for Sparse Counterfactual Explanations for Time-Series Classification
2024-09-14
Qi Huang, Sofoklis Kitharidis, Thomas Bäck, Niki van Stein
In time-series classification, understanding model decisions is crucial for their application in high-stakes domains such as healthcare and finance. Counterfactual explanations, which provide insights by presenting alternative inputs that change model predictions, offer a promising solution. However, existing methods for generating counterfactual explanations for time-series data often struggle with balancing key objectives like proximity, sparsity, and validity. In this paper, we introduce TX-Gen, a novel algorithm for generating counterfactual explanations based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). TX-Gen leverages evolutionary multi-objective optimization to find a diverse set of counterfactuals that are both sparse and valid, while maintaining minimal dissimilarity to the original time series. By incorporating a flexible reference-guided mechanism, our method improves the plausibility and interpretability of the counterfactuals without relying on predefined assumptions. Extensive experiments on benchmark datasets demonstrate that TX-Gen outperforms existing methods in generating high-quality counterfactuals, making time-series models more transparent and interpretable.
Innovative schemes for Correlation Plenoptic Imaging
2024-09-14
Gianlorenzo Massaro, Francesco Di Lena, Augusto Garuccio, Francesco V. Pepe, Milena D'Angelo
CPI is a novel imaging modality capable of addressing the intrinsic limitations of conventional plenoptic imaging - namely, the resolution loss and the sacrificed change of perspective, - while guaranteeing the typical advantages of plenotpic imaging: 3D imaging, refocusing of acquired pictures, in post-processing, and depth of field extension. In this work, we review a recently developed CPI scheme, named correlation plenoptic imaging between arbitrary planes, and derive the algorithm for image refocusing.
Dressed atom revisited: Hamiltonian-independent treatment of the radiative cascade
2024-09-14
Francesco V. Pepe, Karolina Słowik
The dressed atom approach provides a tool to investigate the dynamics of an atom-laser system by fully retaining the quantum nature of the coherent mode. In its standard derivation, the internal atom-laser evolution is described within the rotating-wave approximation, which determines a doublet structure of the spectrum and the peculiar fluorescence triplet in the steady state. However, the rotating wave approximation may fail to apply to atomic systems subject to femtosecond light pulses, light-matter systems in the strong-coupling regime or sustaining permanent dipole moments. This work aims to demonstrate how the general features of the steady-state radiative cascade are affected by the interaction of the dressed atom with propagating radiation modes. Rather than focusing on a specific model, we analyze how these features depend on the parameters characterizing the dressed eigenstates in arbitrary atom-laser dynamics, given that a set of general hypotheses is satisfied. Our findings clarify the general conditions in which a description of the radiative cascade in terms of transition between dressed states is self-consistent. We provide a guideline to determine the properties of photon emission for any atom-laser interaction model, which can be particularly relevant when the model should be tailored to enhance a specific line. We apply the general results to the case in which a permanent dipole moment is a source of low-energy emission, whose frequency is of the order of the Rabi coupling.
PIP-Loco: A Proprioceptive Infinite Horizon Planning Framework for Quadrupedal Robot Locomotion
2024-09-14
Aditya Shirwatkar, Naman Saxena, Kishore Chandra, Shishir Kolathaya
A core strength of Model Predictive Control (MPC) for quadrupedal locomotion has been its ability to enforce constraints and provide interpretability of the sequence of commands over the horizon. However, despite being able to plan, MPC struggles to scale with task complexity, often failing to achieve robust behavior on rapidly changing surfaces. On the other hand, model-free Reinforcement Learning (RL) methods have outperformed MPC on multiple terrains, showing emergent motions but inherently lack any ability to handle constraints or perform planning. To address these limitations, we propose a framework that integrates proprioceptive planning with RL, allowing for agile and safe locomotion behaviors through the horizon. Inspired by MPC, we incorporate an internal model that includes a velocity estimator and a Dreamer module. During training, the framework learns an expert policy and an internal model that are co-dependent, facilitating exploration for improved locomotion behaviors. During deployment, the Dreamer module solves an infinite-horizon MPC problem, adapting actions and velocity commands to respect the constraints. We validate the robustness of our training framework through ablation studies on internal model components and demonstrate improved robustness to training noise. Finally, we evaluate our approach across multi-terrain scenarios in both simulation and hardware.
Students frequently make mistakes while solving mathematical problems, and traditional error correction methods are both time-consuming and labor-intensive. This paper introduces an innovative \textbf{V}irtual \textbf{A}I \textbf{T}eacher system designed to autonomously analyze and correct student \textbf{E}rrors (VATE). Leveraging advanced large language models (LLMs), the system uses student drafts as a primary source for error analysis, which enhances understanding of the student's learning process. It incorporates sophisticated prompt engineering and maintains an error pool to reduce computational overhead. The AI-driven system also features a real-time dialogue component for efficient student interaction. Our approach demonstrates significant advantages over traditional and machine learning-based error correction methods, including reduced educational costs, high scalability, and superior generalizability. The system has been deployed on the Squirrel AI learning platform for elementary mathematics education, where it achieves 78.3\% accuracy in error analysis and shows a marked improvement in student learning efficiency. Satisfaction surveys indicate a strong positive reception, highlighting the system's potential to transform educational practices.
AMBER -- Advanced SegFormer for Multi-Band Image Segmentation: an application to Hyperspectral Imaging
2024-09-14
Andrea Dosi, Massimo Brescia, Stefano Cavuoti, Mariarca D'Aniello, Michele Delli Veneri, Carlo Donadio, Adriano Ettari, Giuseppe Longo, Alvi Rownok, Luca Sannino, Maria Zampella
Deep learning has revolutionized the field of hyperspectral image (HSI) analysis, enabling the extraction of complex and hierarchical features. While convolutional neural networks (CNNs) have been the backbone of HSI classification, their limitations in capturing global contextual features have led to the exploration of Vision Transformers (ViTs). This paper introduces AMBER, an advanced SegFormer specifically designed for multi-band image segmentation. AMBER enhances the original SegFormer by incorporating three-dimensional convolutions to handle hyperspectral data. Our experiments, conducted on the Indian Pines, Pavia University, and PRISMA datasets, show that AMBER outperforms traditional CNN-based methods in terms of Overall Accuracy, Kappa coefficient, and Average Accuracy on the first two datasets, and achieves state-of-the-art performance on the PRISMA dataset.
In tandem with the recent advancements in foundation model research, there has been a surge of generative music AI applications within the past few years. As the idea of AI-generated or AI-augmented music becomes more mainstream, many researchers in the music AI community may be wondering what avenues of research are left. With regards to music generative models, we outline the current areas of research with significant room for exploration. Firstly, we pose the question of foundational representation of these generative models and investigate approaches towards explainability. Next, we discuss the current state of music datasets and their limitations. We then overview different generative models, forms of evaluating these models, and their computational constraints/limitations. Subsequently, we highlight applications of these generative models towards extensions to multiple modalities and integration with artists' workflow as well as music education systems. Finally, we survey the potential copyright implications of generative music and discuss strategies for protecting the rights of musicians. While it is not meant to be exhaustive, our survey calls to attention a variety of research directions enabled by music foundation models.
Asymptotic analysis in problems with fractional processes
2024-09-14
P. Chigansky, M. Kleptsyna
Some problems in the theory and applications of stochastic processes can be reduced to solving integral equations. Such equations, however, rarely have explicit solutions. Useful information can be obtained by means of their asymptotic analysis with respect to relevant parameters. This paper is a brief survey of some recent progress in the study of such equations related to processes with fractional covariance structure.
Security and Privacy Perspectives of People Living in Shared Home Environments
2024-09-14
Nandita Pattnaik, Shujun Li, Jason R. C. Nurse
Security and privacy perspectives of people in a multi-user home are a growing area of research, with many researchers reflecting on the complicated power imbalance and challenging access control issues of the devices involved. However, these studies primarily focused on the multi-user scenarios in traditional family home settings, leaving other types of multi-user home environments, such as homes shared by co-habitants without a familial relationship, under-studied. This paper closes this research gap via quantitative and qualitative analysis of results from an online survey and content analysis of sampled online posts on Reddit. It explores the complex roles of shared home users, which depend on various factors unique to the shared home environment, e.g., who owns what home devices, how home devices are used by multiple users, and more complicated relationships between the landlord and people in the shared home and among co-habitants. Half (50.7%) of our survey participants thought that devices in a shared home are less secure than in a traditional family home. This perception was found statistically significantly associated with factors such as the fear of devices being tampered with in their absence and (lack of) trust in other co-habitants and their visitors. Our study revealed new user types and relationships in a multi-user environment such as ExternalPrimary-InternalPrimary while analysing the landlord and shared home resident relationship with regard to shared home device use. We propose a threat actor model for shared home environments, which has a focus on possible malicious behaviours of current and past co-habitants of a shared home, as a special type of insider threat in a home environment. We also recommend further research to understand the complex roles co-habitants can play in navigating and adapting to a shared home environment's security and privacy landscape.
The exponential growth of open-source package ecosystems, particularly NPM and PyPI, has led to an alarming increase in software supply chain poisoning attacks. Existing static analysis methods struggle with high false positive rates and are easily thwarted by obfuscation and dynamic code execution techniques. While dynamic analysis approaches offer improvements, they often suffer from capturing non-package behaviors and employing simplistic testing strategies that fail to trigger sophisticated malicious behaviors. To address these challenges, we present OSCAR, a robust dynamic code poisoning detection pipeline for NPM and PyPI ecosystems. OSCAR fully executes packages in a sandbox environment, employs fuzz testing on exported functions and classes, and implements aspect-based behavior monitoring with tailored API hook points. We evaluate OSCAR against six existing tools using a comprehensive benchmark dataset of real-world malicious and benign packages. OSCAR achieves an F1 score of 0.95 in NPM and 0.91 in PyPI, confirming that OSCAR is as effective as the current state-of-the-art technologies. Furthermore, for benign packages exhibiting characteristics typical of malicious packages, OSCAR reduces the false positive rate by an average of 32.06% in NPM (from 34.63% to 2.57%) and 39.87% in PyPI (from 41.10% to 1.23%), compared to other tools, significantly reducing the workload of manual reviews in real-world deployments. In cooperation with Ant Group, a leading financial technology company, we have deployed OSCAR on its NPM and PyPI mirrors since January 2023, identifying 10,404 malicious NPM packages and 1,235 malicious PyPI packages over 18 months. This work not only bridges the gap between academic research and industrial application in code poisoning detection but also provides a robust and practical solution that has been thoroughly tested in a real-world industrial setting.
Generative AI in Data Center Networking: Fundamentals, Perspectives, and Case Study
2024-09-14
Yinqiu Liu, Hongyang Du, Dusit Niyato, Jiawen Kang, Zehui Xiong, Yonggang Wen, Dong In Kim
Generative AI (GenAI), exemplified by Large Language Models (LLMs) such as OpenAI's ChatGPT, is revolutionizing various fields. Central to this transformation is Data Center Networking (DCN), which not only provides the computational power necessary for GenAI training and inference but also delivers GenAI-driven services to users. This article examines an interplay between GenAI and DCNs, highlighting their symbiotic relationship and mutual advancements. We begin by reviewing current challenges within DCNs and discuss how GenAI contributes to enhancing DCN capabilities through innovations, such as data augmentation, process automation, and domain transfer. We then focus on analyzing the distinctive characteristics of GenAI workloads on DCNs, gaining insights that catalyze the evolution of DCNs to more effectively support GenAI and LLMs. Moreover, to illustrate the seamless integration of GenAI with DCNs, we present a case study on full-lifecycle DCN digital twins. In this study, we employ LLMs equipped with Retrieval Augmented Generation (RAG) to formulate optimization problems for DCNs and adopt Diffusion-Deep Reinforcement Learning (DRL) for optimizing the RAG knowledge placement strategy. This approach not only demonstrates the application of advanced GenAI methods within DCNs but also positions the digital twin as a pivotal GenAI service operating on DCNs. We anticipate that this article can promote further research into enhancing the virtuous interaction between GenAI and DCNs.
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by challenges in social communication, repetitive behavior, and sensory processing. One important research area in ASD is evaluating children's behavioral changes over time during treatment. The standard protocol with this objective is BOSCC, which involves dyadic interactions between a child and clinicians performing a pre-defined set of activities. A fundamental aspect of understanding children's behavior in these interactions is automatic speech understanding, particularly identifying who speaks and when. Conventional approaches in this area heavily rely on speech samples recorded from a spectator perspective, and there is limited research on egocentric speech modeling. In this study, we design an experiment to perform speech sampling in BOSCC interviews from an egocentric perspective using wearable sensors and explore pre-training Ego4D speech samples to enhance child-adult speaker classification in dyadic interactions. Our findings highlight the potential of egocentric speech collection and pre-training to improve speaker classification accuracy.
Quantum data encoding as a distinct abstraction layer in the design of quantum circuits
2024-09-14
Gabriele Agliardi, Enrico Prati
Complex quantum circuits are constituted by combinations of quantum subroutines. The computation is possible as long as the quantum data encoding is consistent throughout the circuit. Despite its fundamental importance, the formalization of quantum data encoding has never been addressed systematically so far. We formalize the concept of quantum data encoding, namely the format providing a representation of a data set through a quantum state, as a distinct abstract layer with respect to the associated data loading circuit. We survey existing encoding methods and their respective strategies for classical-to-quantum exact and approximate data loading, for the quantum-to-classical extraction of information from states, and for quantum-to-quantum encoding conversion. Next, we show how major quantum algorithms find a natural interpretation in terms of data loading. For instance, the Quantum Fourier Transform is described as a quantum encoding converter, while the Quantum Amplitude Estimation as an extraction routine. The new conceptual framework is exemplified by considering its application to quantum-based Monte Carlo simulations, thus showcasing the power of the proposed formalism for the description of complex quantum circuits. Indeed, the approach clarifies the structure of complex quantum circuits and enables their efficient design.
Exact Posterior Mean and Covariance for Generalized Linear Mixed Models
2024-09-14
Tonglin Zhang
A novel method is proposed for the exact posterior mean and covariance of the random effects given the response in a generalized linear mixed model (GLMM) when the response does not follow normal. The research solves a long-standing problem in Bayesian statistics when an intractable integral appears in the posterior distribution. It is well-known that the posterior distribution of the random effects given the response in a GLMM when the response does not follow normal contains intractable integrals. Previous methods rely on Monte Carlo simulations for the posterior distributions. They do not provide the exact posterior mean and covariance of the random effects given the response. The special integral computation (SIC) method is proposed to overcome the difficulty. The SIC method does not use the posterior distribution in the computation. It devises an optimization problem to reach the task. An advantage is that the computation of the posterior distribution is unnecessary. The proposed SIC avoids the main difficulty in Bayesian analysis when intractable integrals appear in the posterior distribution.
Real-Time Stochastic Terrain Mapping and Processing for Autonomous Safe Landing
2024-09-14
Kento Tomita, Koki Ho
Onboard terrain sensing and mapping for safe planetary landings often suffer from missed hazardous features, e.g., small rocks, due to the large observational range and the limited resolution of the obtained terrain data. To this end, this paper develops a novel real-time stochastic terrain mapping algorithm that accounts for topographic uncertainty between the sampled points, or the uncertainty due to the sparse 3D terrain measurements. We introduce a Gaussian digital elevation map that is efficiently constructed using the combination of Delauney triangulation and local Gaussian process regression. The geometric investigation of the lander-terrain interaction is exploited to efficiently evaluate the marginally conservative local slope and roughness while avoiding the costly computation of the local plane. The conservativeness is proved in the paper. The developed real-time uncertainty quantification pipeline enables stochastic landing safety evaluation under challenging operational conditions, such as a large observational range or limited sensor capability, which is a critical stepping stone for the development of predictive guidance algorithms for safe autonomous planetary landing. Detailed reviews on background and related works are also presented.
Consistent Spectral Clustering in Hyperbolic Spaces
2024-09-14
Sagar Ghosh, Swagatam Das
Clustering, as an unsupervised technique, plays a pivotal role in various data analysis applications. Among clustering algorithms, Spectral Clustering on Euclidean Spaces has been extensively studied. However, with the rapid evolution of data complexity, Euclidean Space is proving to be inefficient for representing and learning algorithms. Although Deep Neural Networks on hyperbolic spaces have gained recent traction, clustering algorithms or non-deep machine learning models on non-Euclidean Spaces remain underexplored. In this paper, we propose a spectral clustering algorithm on Hyperbolic Spaces to address this gap. Hyperbolic Spaces offer advantages in representing complex data structures like hierarchical and tree-like structures, which cannot be embedded efficiently in Euclidean Spaces. Our proposed algorithm replaces the Euclidean Similarity Matrix with an appropriate Hyperbolic Similarity Matrix, demonstrating improved efficiency compared to clustering in Euclidean Spaces. Our contributions include the development of the spectral clustering algorithm on Hyperbolic Spaces and the proof of its weak consistency. We show that our algorithm converges at least as fast as Spectral Clustering on Euclidean Spaces. To illustrate the efficacy of our approach, we present experimental results on the Wisconsin Breast Cancer Dataset, highlighting the superior performance of Hyperbolic Spectral Clustering over its Euclidean counterpart. This work opens up avenues for utilizing non-Euclidean Spaces in clustering algorithms, offering new perspectives for handling complex data structures and improving clustering efficiency.
Bounding the probability of causality under ordinal outcomes
2024-09-14
Hanmei Sun, Chengfeng Shi, Qiang Zhao
The probability of causation (PC) is often used in liability assessments. In a legal context, for example, where a patient suffered the side effect after taking a medication and sued the pharmaceutical company as a result, the value of the PC can help assess the likelihood that the side effect was caused by the medication, in other words, how likely it is that the patient will win the case. Beyond the issue of legal disputes, the PC plays an equally large role when one wants to go about explaining causal relationships between events that have already occurred in other areas. This article begins by reviewing the definitions and bounds of the probability of causality for binary outcomes, then generalizes them to ordinal outcomes. It demonstrates that incorporating additional mediator variable information in a complete mediation analysis provides a more refined bound compared to the simpler scenario where only exposure and outcome variables are considered.
Symmetry operations and Critical Behaviour in Classical to Quantum Stochastic Processes
2024-09-14
Gustavo Montes, Soham Biswas, Thomas Gorin
Construction of quantum analogs starting from classical stochastic processes have been previously introduced. In this paper, we generate a large class of self contained quantum extensions by symmetry operations. We show that the relaxation processes for different quantum extensions are different and that is supported by the measure of coherence, the the probability of reaching the equilibrium, decay of the domain walls and purity. However, the coherence measure based on the L1-norm does not capture the speed of the relaxation process. We also show that the finite size scaling of coherence exists for both short and long times.
In recent years, Large Language Models (LLMs) have achieved remarkable success and have been widely used in various downstream tasks, especially in the tasks of the software engineering (SE) field. We find that many studies combining LLMs with SE have employed the concept of agents either explicitly or implicitly. However, there is a lack of an in-depth survey to sort out the development context of existing works, analyze how existing works combine the LLM-based agent technologies to optimize various tasks, and clarify the framework of LLM-based agents in SE. In this paper, we conduct the first survey of the studies on combining LLM-based agents with SE and present a framework of LLM-based agents in SE which includes three key modules: perception, memory, and action. We also summarize the current challenges in combining the two fields and propose future opportunities in response to existing challenges. We maintain a GitHub repository of the related papers at: https://github.com/DeepSoftwareAnalytics/Awesome-Agent4SE.
The effect of cosmic web filaments on galaxy evolution
2024-09-13
Callum J. O'Kane, Ulrike Kuchner, Meghan E. Gray, Alfonso Aragón-Salamanca
Galaxy properties are known to be affected by their environment. This is well established for the extremes of the density scales, between the high-density cluster environment and the low-density field. It is however not fully understood how the intermediate-density regime of cosmic web filaments affects galaxy evolution. We investigate this environmental effect using a mass complete sample of 23,441 galaxies in the Sloan Digital Sky Survey DR8 Main Galaxy Sample (${M}_{\text{Stellar}} > 10^{9.91} \text{M}_{\odot}$). We define 6 environments, probing different density regimes and representing unique stages in the structure formation process, comparing the differences in star formation activity and morphology between them. We find that galaxies in filaments tend to be less star forming and favour more early-type morphologies than those in the field. These differences persist when considering stellar mass-matched samples, suggesting that this is a consequence of the environment. We further investigate whether these trends are a result of the large scale or local environment through constructing samples matched both in stellar mass and local galaxy density. We find that when also matching in local galaxy density, the differences observed between the filament and field population vanishes, concluding that the environmental effect of filaments can be entirely parameterised by a local galaxy density index. We find that differences can still be seen in comparisons with the interiors of clusters, suggesting these are unique environments which can impart additional physical processes not characterised by local galaxy density.
This survey article, written for the Encyclopedia of Mathematical Physics, 2nd edition, is devoted to the remarkable family of operators introduced by Charles Dunkl and to their $q$-analogues discovered by Ivan Cherednik. The main focus is on the r\^ole of these operators in studying integrable many-body systems such as the Calogero-Moser and the Ruijsenaars systems. To put these constructions into a wider context, we indicate their relationship with the theory of the rational Cherednik algebras and double affine Hecke algebras. While we do not include proofs, references to the original research articles are provided, accompanied by brief historical comments.
Predicting High Magnification Events in Microlensed Quasars in the Era of LSST using Recurrent Neural Networks
2024-09-13
Joshua Fagin, Eric Paic, Favio Neira, Henry Best, Timo Anguita, Martin Millon, Matthew O'Dowd, Dominique Sluse, Georgios Vernardos
Upcoming wide field surveys such as the Rubin Observatory's Legacy Survey of Space and Time (LSST) will monitor thousands of strongly lensed quasars over a 10-year period. Many of these monitored quasars will undergo high magnification events (HMEs) through microlensing as the accretion disk crosses a caustic, places of infinite magnification. Microlensing allows us to map the inner regions of the accretion disk as it crosses a caustic, even at large cosmological distances. The observational cadences of LSST are not ideal for probing the inner regions of the accretion disk, so there is a need to predict HMEs as early as possible to trigger high-cadence multi-band or spectroscopic follow-up observations. Here we simulate a diverse and realistic sample of 10-year quasar microlensing light curves to train a recurrent neural network (RNN) to predict HMEs before they occur by classifying the location of the peaks at each time step. This is the first deep learning approach to predict HMEs. We give estimates at how well we expect to predict HME peaks during LSST and benchmark how our metrics change with different cadence strategies. With LSST-like observations, we can predict approximately 55% of HME peaks corresponding to tens to hundreds per year and a false positive rate of around 20% compared to the number of HMEs. Our network can be continuously applied throughout the LSST survey, providing crucial alerts to optimize follow-up resources.
Nearly thirty years after its inception, the field of DNA-programmed colloidal self-assembly has begun to realize its initial promise. In this review, we summarize recent developments in designing effective interactions and understanding the dynamic self-assembly pathways of DNA-coated nanoparticles and microparticles, as well as how these advances have propelled tremendous progress in crystal engineering. We also highlight exciting new directions showing that new classes of subunits combining nanoparticles with DNA origami can be used to engineer novel multicomponent assemblies, including structures with self-limiting, finite sizes. We conclude by providing an outlook on how recent theoretical advances focusing on the kinetics of self-assembly could usher in new materials-design opportunities, like the possibility of retrieving multiple distinct target structures from a single suspension or accessing new classes of materials that are stabilized by energy dissipation, mimicking self-assembly in living systems.
The connection of polaritonic chemistry with the physics of a spin glass
2024-09-13
Dominik Sidler, Michael Ruggenthaler, Angel Rubio
Polaritonic chemistry has garnered increasing attention in recent years due to pioneering experimental results, which show that site- and bond-selective chemistry at room temperature is achievable through strong collective coupling to field fluctuations in optical cavities. Despite these notable experimental strides, the underlying theoretical mechanisms remain unclear. In this focus review, we highlight a fundamental theoretical link between the seemingly unrelated fields of polaritonic chemistry and spin glasses, exploring its profound implications for the theoretical framework of polaritonic chemistry. Specifically, we present a mapping of the dressed electronic structure problem under collective vibrational strong coupling to the iconic Sherrington-Kirkpatrick model of spin glasses. This mapping uncovers a collectively induced instability in the dressed electronic structure (spontaneous replica symmetry breaking), which could provide the long-sought seed for significant local chemical modifications in polaritonic chemistry. This mapping paves the way to incorporate, adjust and probe numerous spin glass concepts in polaritonic chemistry, such as frustration, aging dynamics, excess of thermal fluctuations, time-reversal symmetry breaking or stochastic resonances. Ultimately, the mapping also offers fresh insights into the applicability of spin glass theory beyond condensed matter systems and it suggests novel theoretical directions such as polarization glasses with explicitly time-dependent order parameter functions.
Predicting Trust In Autonomous Vehicles: Modeling Young Adult Psychosocial Traits, Risk-Benefit Attitudes, And Driving Factors With Machine Learning
2024-09-13
Robert Kaufman, Emi Lee, Manas Satish Bedmutha, David Kirsh, Nadir Weibel
Low trust remains a significant barrier to Autonomous Vehicle (AV) adoption. To design trustworthy AVs, we need to better understand the individual traits, attitudes, and experiences that impact people's trust judgements. We use machine learning to understand the most important factors that contribute to young adult trust based on a comprehensive set of personal factors gathered via survey (n = 1457). Factors ranged from psychosocial and cognitive attributes to driving style, experiences, and perceived AV risks and benefits. Using the explainable AI technique SHAP, we found that perceptions of AV risks and benefits, attitudes toward feasibility and usability, institutional trust, prior experience, and a person's mental model are the most important predictors. Surprisingly, psychosocial and many technology- and driving-specific factors were not strong predictors. Results highlight the importance of individual differences for designing trustworthy AVs for diverse groups and lead to key implications for future design and research.
User Identity Linkage on Social Networks: A Review of Modern Techniques and Applications
2024-09-13
Caterina Senette, Marco Siino, Maurizio Tesconi
In an Online Social Network (OSN), users can create a unique public persona by crafting a user identity that may encompass profile details, content, and network-related information. As a result, a relevant task of interest is related to the ability to link identities across different OSNs. Linking users across social networks can have multiple implications in several contexts both at the individual level and at the group level. At the individual level, the main interest in linking the same identity across social networks is to enable a better knowledge of each user. At the group level, linking user identities through different OSNs helps in predicting user behaviors, network dynamics, information diffusion, and migration phenomena across social media. The process of tying together user accounts on different OSNs is challenging and has attracted more and more research attention in the last fifteen years. The purpose of this work is to provide a comprehensive review of recent studies (from 2016 to the present) on User Identity Linkage (UIL) methods across online social networks. This review aims to offer guidance for other researchers in the field by outlining the main problem formulations, the different feature extraction strategies, algorithms, machine learning models, datasets, and evaluation metrics proposed by researchers working in this area. The proposed overview takes a pragmatic perspective to highlight the concrete possibilities for accomplishing this task depending on the type of available data.
IMMERTWIN: A Mixed Reality Framework for Enhanced Robotic Arm Teleoperation
2024-09-13
Florent P. Audonnet, Ixchel G. Ramirez-Alpizar, Gerardo Aragon-Camarasa
We present IMMERTWIN, a mixed reality framework for enhance robotic arm teleoperation using a closed-loop digital twin as a bridge for interaction between the user and the robotic system. We evaluated IMMERTWIN by performing a medium-scale user survey with 26 participants on two robots. Users were asked to teleoperate with both robots inside the virtual environment to pick and place 3 cubes in a tower and to repeat this task as many times as possible in 10 minutes, with only 5 minutes of training beforehand. Our experimental results show that most users were able to succeed by building at least a tower of 3 cubes regardless of the robot used and a maximum of 10 towers (1 tower per minute). In addition, users preferred to use IMMERTWIN over our previous work, TELESIM, as it caused them less mental workload. The project website and source code can be found at: https://cvas-ug.github.io/immertwin
Some recent developments on isometric immersions via compensated compactness and gauge transforms
2024-09-13
Siran Li
We survey recent developments on the analysis of Gauss--Codazzi--Ricci equations, the first-order PDE system arising from the classical problem of isometric immersions in differential geometry, especially in the regime of low Sobolev regularity. Such equations are not purely elliptic, parabolic, or hyperbolic in general, hence calling for analytical tools for PDEs of mixed types. We discuss various recent contributions -- in line with the pioneering works by G.-Q. Chen, M. Slemrod, and D. Wang [Proc. Amer. Math. Soc. (2010); Comm. Math. Phys. (2010)] -- on the weak continuity of Gauss--Codazzi--Ricci equations, the weak stability of isometric immersions, and the fundamental theorem of submanifold theory with low regularity. Two mixed-type PDE techniques are emphasised throughout these developments: the method of compensated compactness and the theory of Coulomb--Uhlenbeck gauges.
The $M_\bullet$-$σ_e$ relation for local type 1 AGNs and quasars
2024-09-13
J. Molina, L. C. Ho, K. K. Knudsen
We analyzed MUSE observations of 42 local $z<0.1$ type 1 active galactic nucleus (AGN) host galaxies taken from the Palomar-Green quasar sample and the close AGN reference survey. Our goal was to study the relation between the black hole mass ($M_\bullet$) and bulge stellar velocity dispersion ($\sigma_e$) for type 1 active galaxies. The sample spans black hole masses of $10^{6.0}-10^{9.2}\,M_\odot$, bolometric luminosities of $10^{42.9}-10^{46.0}\,$erg$\,$s$^{-1}$, and Eddington ratios of 0.006-1.2. We avoided AGN emission by extracting the spectra over annular apertures. We modeled the calcium triplet stellar features and measured stellar velocity dispersions of $\sigma_* = 60-230\,$km$\,$s$^{-1}$ for the host galaxies. We find $\sigma_*$ values in agreement with previous measurements for local AGN host galaxies, but slightly lower compared with those reported for nearby X-ray-selected type 2 quasars. Using a novel annular aperture correction recipe to estimate $\sigma_e$ from $\sigma_*$ that considers the bulge morphology and observation beam-smearing, we estimate flux-weighted $\sigma_e = 60-250\,$km$\,$s$^{-1}$. If we consider the bulge type when estimating $M_\bullet$, we find no statistical difference between the distributions of AGN hosts and the inactive galaxies on the $M_\bullet - \sigma_e$ plane for $M_\bullet \lesssim 10^8\,M_\odot$. Conversely, if we do not consider the bulge type when computing $M_\bullet$, we find that both distributions disagree. We find no correlation between the degree of offset from the $M_\bullet - \sigma_e$ relation and Eddington ratio for $M_\bullet \lesssim 10^8\,M_\odot$. The current statistics preclude firm conclusions from being drawn for the high-mass range. We argue these observations support notions that a significant fraction of the local type 1 AGNs and quasars have undermassive black holes compared with their host galaxy bulge properties.
Characterization of M51 supernova remnants with the imaging spectrometer SITELLE
2024-09-13
Billy Gamache, Laurent Drissen, Carmelle Robert, Mykola Posternak
We present preliminary results of a detailed 3D study of supernova remnants in the nearby spiral M51 using data from the SIGNALS survey obtained with the imaging Fourier transform spectrometer SITELLE at the Canada-France-Hawaii telescope (CFHT). Data cubes covering the entire galaxy were gathered in three spectral ranges: SN3 (647-685 nm, R = 5000), SN2 (482-513 nm, R = 600) and SN1 (363-386 nm, R = 1000). The spectral resolution of the SN3 cube allows a precise, spatially resolved measurement of the velocity dispersion of each object. While most of the SNRs were known from previous surveys based on imagery and long-slit spectroscopy, we now provide 2D line flux and kinematic maps for all of them and found 20 new candidates. Most of the SNRs show velocity dispersions ($\sigma$) in the range 30-80 km/s, which is typical for middle-aged SNRs. Finally, we compare the properties of SNRs with those of thousands of HII regions included in the same dataset.
Visual Language Tracking (VLT) enhances tracking by mitigating the limitations of relying solely on the visual modality, utilizing high-level semantic information through language. This integration of the language enables more advanced human-machine interaction. The essence of interaction is cognitive alignment, which typically requires multiple information exchanges, especially in the sequential decision-making process of VLT. However, current VLT benchmarks do not account for multi-round interactions during tracking. They provide only an initial text and bounding box (bbox) in the first frame, with no further interaction as tracking progresses, deviating from the original motivation of the VLT task. To address these limitations, we propose a novel and robust benchmark, VLT-MI (Visual Language Tracking with Multi-modal Interaction), which introduces multi-round interaction into the VLT task for the first time. (1) We generate diverse, multi-granularity texts for multi-round, multi-modal interaction based on existing mainstream VLT benchmarks using DTLLM-VLT, leveraging the world knowledge of LLMs. (2) We propose a new VLT interaction paradigm that achieves multi-round interaction through text updates and object recovery. When multiple tracking failures occur, we provide the tracker with more aligned texts and corrected bboxes through interaction, thereby expanding the scope of VLT downstream tasks. (3) We conduct comparative experiments on both traditional VLT benchmarks and VLT-MI, evaluating and analyzing the accuracy and robustness of trackers under the interactive paradigm. This work offers new insights and paradigms for the VLT task, enabling a fine-grained evaluation of multi-modal trackers. We believe this approach can be extended to additional datasets in the future, supporting broader evaluations and comparisons of video-language model capabilities.
Automating child speech analysis is crucial for applications such as neurocognitive assessments. Speaker diarization, which identifies ``who spoke when'', is an essential component of the automated analysis. However, publicly available child-adult speaker diarization solutions are scarce due to privacy concerns and a lack of annotated datasets, while manually annotating data for each scenario is both time-consuming and costly. To overcome these challenges, we propose a data-efficient solution by creating simulated child-adult conversations using AudioSet. We then train a Whisper Encoder-based model, achieving strong zero-shot performance on child-adult speaker diarization using real datasets. The model performance improves substantially when fine-tuned with only 30 minutes of real train data, with LoRA further improving the transfer learning performance. The source code and the child-adult speaker diarization model trained on simulated conversations are publicly available.
Topological gauge theory of vortices in type-III superconductors
2024-09-13
M. C. Diamantini, C. A. Trugenberger, V. M. Vinokur
Traditional superconductors fall into two categories, type-I, expelling magnetic fields, and type-II, into which magnetic fields exceeding a lower critical field $H_{\rm c1}$ penetrate in form of Abrikosov vortices. Abrikosov vortices are characterized by two spatial scales, the size of the normal core, $\xi$, where the superconducting order parameter is suppressed and the London penetration depth $\lambda$, describing the scale at which circulating superconducting currents forming vortices start to noticeably drop. Here we demonstrate that a novel type-III superconductivity, realized in granular media in any dimension hosts a novel vortex physics. Type-III vortices have no cores, are logarithmically confined and carry only a gauge scale $\lambda$. Accordingly, in type-III superconductors $H_{\rm c1}=0$ at zero temperature and the Ginzburg-Landau theory must be replaced by a topological gauge theory. Type-III superconductivity is destroyed not by Cooper pair breaking but by vortex proliferation generalizing the Berezinskii-Kosterlitz-Thouless mechanism to any dimension.
AutoIRT: Calibrating Item Response Theory Models with Automated Machine Learning
2024-09-13
James Sharpnack, Phoebe Mulcaire, Klinton Bicknell, Geoff LaFlair, Kevin Yancey
Item response theory (IRT) is a class of interpretable factor models that are widely used in computerized adaptive tests (CATs), such as language proficiency tests. Traditionally, these are fit using parametric mixed effects models on the probability of a test taker getting the correct answer to a test item (i.e., question). Neural net extensions of these models, such as BertIRT, require specialized architectures and parameter tuning. We propose a multistage fitting procedure that is compatible with out-of-the-box Automated Machine Learning (AutoML) tools. It is based on a Monte Carlo EM (MCEM) outer loop with a two stage inner loop, which trains a non-parametric AutoML grade model using item features followed by an item specific parametric model. This greatly accelerates the modeling workflow for scoring tests. We demonstrate its effectiveness by applying it to the Duolingo English Test, a high stakes, online English proficiency test. We show that the resulting model is typically more well calibrated, gets better predictive performance, and more accurate scores than existing methods (non-explanatory IRT models and explanatory IRT models like BERT-IRT). Along the way, we provide a brief survey of machine learning methods for calibration of item parameters for CATs.
Theory of Mind (ToM) significantly impacts human collaboration and communication as a crucial capability to understand others. When AI agents with ToM capability collaborate with humans, Mutual Theory of Mind (MToM) arises in such human-AI teams (HATs). The MToM process, which involves interactive communication and ToM-based strategy adjustment, affects the team's performance and collaboration process. To explore the MToM process, we conducted a mixed-design experiment using a large language model-driven AI agent with ToM and communication modules in a real-time shared-workspace task. We find that the agent's ToM capability does not significantly impact team performance but enhances human understanding of the agent and the feeling of being understood. Most participants in our study believe verbal communication increases human burden, and the results show that bidirectional communication leads to lower HAT performance. We discuss the results' implications for designing AI agents that collaborate with humans in real-time shared workspace tasks.
Recent advances in types and extent of medical imaging technologies has led to proliferation of multimodal quantitative imaging data in cancer. Quantitative medical imaging data refer to numerical representations derived from medical imaging technologies, such as radiology and pathology imaging, that can be used to assess and quantify characteristics of diseases, especially cancer. The use of such data in both clinical and research setting enables precise quantifications and analyses of tumor characteristics that can facilitate objective evaluation of disease progression, response to therapy, and prognosis. The scale and size of these imaging biomarkers is vast and presents several analytical and computational challenges that range from high-dimensionality to complex structural correlation patterns. In this review article, we summarize some state-of-the-art statistical methods developed for quantitative medical imaging data ranging from topological, functional and shape data analyses to spatial process models. We delve into common imaging biomarkers with a focus on radiology and pathology imaging in cancer, address the analytical questions and challenges they present, and highlight the innovative statistical and machine learning models that have been developed to answer relevant scientific and clinical questions. We also outline some emerging and open problems in this area for future explorations.
A Review on Flood Risk Conceptual Frameworks and Development of Hierarchical Structures for Assessment Criteria
2024-09-13
Nazgol Tabasi, Mohammad Fereshtehpour, Bardia Roghani
Climate change and rapid urbanization have led to more frequent and severe flooding, causing significant damage. The existing literature on flood risk encompasses a variety of dimensions, such as physical, economic, social, political, environmental, infrastructural, and managerial aspects. This paper aims to provide an extensive review of proposed conceptual frameworks and their components used in flood risk assessment. For this purpose, Initially, conceptual frameworks were extracted to configure the components of flood risk including hazard, vulnerability, exposure, resilience, and susceptibility. Subsequently, a comprehensive set of criteria from the literature were identified, addressing risk components. In this paper, the risk conceptual framework is defined by the intersection of vulnerability and hazard. Vulnerability, shaped by exposure and susceptibility, can be reduced by enhancing resiliency, which includes coping and adaptive capacities. In total, 102 criteria/subcriteria were identified and classified into three hierarchical structures of hazard, susceptibility, and resilience. Finally, flood risk assessment methods were reviewed, with an emphasis on their applicability and characteristics. The review highlighted the strengths and limitations of various methods, providing a comprehensive overview of their suitability for different scenarios. The outcomes of this review could serve as a valuable reference for professionals involved in flood risk assessment, aiding in the identification of the most appropriate risk concepts, assessment criteria, and suitable methods for quantification based on the specific study area and data availability.
Dark Energy Survey: 2.1% measurement of the Baryon Acoustic Oscillation scale from the final dataset
2024-09-13
Juan Mena-Fernández, Dark Energy Survey Collaboration
Here, we present the angular diameter distance measurement obtained from the measurement of the Baryonic Acoustic Oscillation (BAO) feature using the completed Dark Energy Survey (DES) data, summarizing the main results of [Phys. Rev. D 110, 063514] and [Phys. Rev. D 110, 063515]. We use a galaxy sample optimized for BAO science in the redshift range 0.6 < z < 1.2, with an effective redshift of $z_{\rm eff}$ = 0.85. Our consensus measurement constrains the ratio of the angular distance to the sound horizon scale to $D_M(z_{\rm eff})/r_d$ = 19.51 $\pm$ 0.41. This measurement is found to be 2.13$\sigma$ below the angular BAO scale predicted by Planck. To date, it represents the most precise measurement from purely photometric data, and the most precise from any Stage-III experiment at such high redshift. The analysis was performed blinded to the BAO position and is shown to be robust against analysis choices, data removal, redshift calibrations and observational systematics.
2024-09-13
Jide Alaga, Jonas Schuett, Markus Anderljung
Over the past year, artificial intelligence (AI) companies have been increasingly adopting AI safety frameworks. These frameworks outline how companies intend to keep the potential risks associated with developing and deploying frontier AI systems to an acceptable level. Major players like Anthropic, OpenAI, and Google DeepMind have already published their frameworks, while another 13 companies have signaled their intent to release similar frameworks by February 2025. Given their central role in AI companies' efforts to identify and address unacceptable risks from their systems, AI safety frameworks warrant significant scrutiny. To enable governments, academia, and civil society to pass judgment on these frameworks, this paper proposes a grading rubric. The rubric consists of seven evaluation criteria and 21 indicators that concretize the criteria. Each criterion can be graded on a scale from A (gold standard) to F (substandard). The paper also suggests three methods for applying the rubric: surveys, Delphi studies, and audits. The purpose of the grading rubric is to enable nuanced comparisons between frameworks, identify potential areas of improvement, and promote a race to the top in responsible AI development.
Another look at qualitative properties of eigenvalues using effective Hamiltonians
2024-09-13
Idriss Mazari-Fouquer
The goal of this paper is to review several qualitative properties of well-known eigenvalue problems using a different perspective based on the theory of effective Hamiltonians, working exclusively on the Hopf-Cole transform of the equation. We revisit some monotonicity results as well as the derivation of several scaling limits by means of the Donsker-Varadhan formula, and we point out several differences between the case of quadratic Hamiltonians and non-quadratic ones.
Acoustic identification of individual animals with hierarchical contrastive learning
2024-09-13
Ines Nolasco, Ilyass Moummad, Dan Stowell, Emmanouil Benetos
Acoustic identification of individual animals (AIID) is closely related to audio-based species classification but requires a finer level of detail to distinguish between individual animals within the same species. In this work, we frame AIID as a hierarchical multi-label classification task and propose the use of hierarchy-aware loss functions to learn robust representations of individual identities that maintain the hierarchical relationships among species and taxa. Our results demonstrate that hierarchical embeddings not only enhance identification accuracy at the individual level but also at higher taxonomic levels, effectively preserving the hierarchical structure in the learned representations. By comparing our approach with non-hierarchical models, we highlight the advantage of enforcing this structure in the embedding space. Additionally, we extend the evaluation to the classification of novel individual classes, demonstrating the potential of our method in open-set classification scenarios.
The infrared luminosity of retired and post-starburst galaxies: A cautionary tale for star formation rate measurements
2024-09-13
Vivienne Wild, Natalia Vale Asari, Kate Rowlands, Sara L. Ellison, Ho-Hin Leung, Christy Tremonti
In galaxies with significant ongoing star formation there is an impressively tight correlation between total infrared luminosity (L$_{TIR}$) and H$\alpha$ luminosity (L$_{H\alpha}$), when H$\alpha$ is properly corrected for stellar absorption and dust attenuation. This long-standing result gives confidence that both measurements provide accurate estimates of a galaxy's star formation rate (SFR), despite their differing origins. To test the extent to which this holds in galaxies with lower specific SFR (sSFR=SFR/Mgal, where Mgal is the stellar mass), we combine optical spectroscopy from the Sloan Digital Sky Survey (SDSS) with multi-wavelength (FUV to FIR) photometric observations from the Galaxy And Mass Assembly survey (GAMA). We find that L$_{TIR}$/L$_{H\alpha}$increases steadily with decreasing H$\alpha$ equivalent width (W$_{H\alpha}$, a proxy for sSFR), indicating that both luminosities cannot provide a valid measurement of SFR in galaxies below the canonical star-forming sequence. For both `retired galaxies' and `post-starburst galaxies', L$_{TIR}$/L$_{H\alpha}$ can be up to a factor of 30 larger than for star-forming galaxies. The smooth change in L$_{TIR}$/L$_{H\alpha}$, irrespective of star formation history, ionisation or heating source, dust temperature or other properties, suggests that the value of L$_{TIR}$/L$_{H\alpha}$ is given by the balance between star-forming regions and ambient interstellar medium contributing to both L$_{TIR}$ and L$_{H\alpha}$. While L$_{H\alpha}$ can only be used to estimate the SFR for galaxies with W$_{H\alpha}$ > 3A (sSFR $\gtrsim 10^{-11.5}$/yr), we argue that the mid- and far-infrared can only be used to estimate the SFR of galaxies on the star-forming sequence, and in particular only for galaxies with W$_{H\alpha}$ >10 A (sSFR $\gtrsim 10^{-10.5}$/yr). We find no evidence for dust obscured star-formation in post-starburst galaxies.
Is there a genetic relationship between chondrules and matrix?
2024-09-13
Elishevah van Kooten, Adrian Brearley, Denton Ebel, Conel Alexander, Marina Gemma, Dominik Hezel
Chondritic components such as chondrules and matrix are the key time capsules that can help us understand the evolution and dynamics of the protoplanetary disk from which the Solar System originated. Knowledge of where and how these components formed and to what extent they were transported in the gaseous disk provides major constraints to astrophysical models that investigate planet formation. Here, we explore whether chondrules and matrix are genetically related to each other and formed from single reservoirs per chondrite group or if every chondrite represents a unique proportion of components transported from a small number of formation reservoirs in the disk. These static versus dynamic disk interpretations of cosmochemical data have profound implications for the accretion history of the planets in the Solar System. To fully understand the relationship between chondrules and matrix and their potential complementarity, we dive into the petrological nature and origin of matrix, the chemical and isotopic compositions of chondrules and matrix and evaluate these data considering the effect of secondary alteration observed in chondrites and the potential complexity of chondrule formation. Even though we, the authors, have used different datasets and arrived at differing interpretations of chondrule-matrix relationships in the past, this review provides clarity on the existing data and has given us new directions towards future research that can resolve the complementarity debate.
2024-09-13
Anfeng Xu, Biqiao Zhang, Shuyu Kong, Yiteng Huang, Zhaojun Yang, Sangeeta Srivastava, Ming Sun
Keyword spotting (KWS) is an important speech processing component for smart devices with voice assistance capability. In this paper, we investigate if Kolmogorov-Arnold Networks (KAN) can be used to enhance the performance of KWS. We explore various approaches to integrate KAN for a model architecture based on 1D Convolutional Neural Networks (CNN). We find that KAN is effective at modeling high-level features in lower-dimensional spaces, resulting in improved KWS performance when integrated appropriately. The findings shed light on understanding KAN for speech processing tasks and on other modalities for future researchers.
Deep learning-based shot-domain seismic deblending
2024-09-13
Jing Sun, Song Hou, Vetle Vinje, Gordon Poole, Leiv-J Gelius
To streamline fast-track processing of large data volumes, we have developed a deep learning approach to deblend seismic data in the shot domain based on a practical strategy for generating high-quality training data along with a list of data conditioning techniques to improve performance of the data-driven model. We make use of unblended shot gathers acquired at the end of each sail line, to which the access requires no additional time or labor costs beyond the blended acquisition. By manually blending these data we obtain training data with good control of the ground truth and fully adapted to the given survey. Furthermore, we train a deep neural network using multi-channel inputs that include adjacent blended shot gathers as additional channels. The prediction of the blending noise is added in as a related and auxiliary task with the main task of the network being the prediction of the primary-source events. Blending noise in the ground truth is scaled down during the training and validation process due to its excessively strong amplitudes. As part of the process, the to-be-deblended shot gathers are aligned by the blending noise. Implementation on field blended-by-acquisition data demonstrates that introducing the suggested data conditioning steps can considerably reduce the leakage of primary-source events in the deep part of the blended section. The complete proposed approach performs almost as well as a conventional algorithm in the shallow section and shows great advantage in efficiency. It performs slightly worse for larger traveltimes, but still removes the blending noise efficiently.
Secure Offloading in NOMA-Aided Aerial MEC Systems Based on Deep Reinforcement Learning
2024-09-13
Hongjiang Lei, Mingxu Yang, Ki-Hong Park, Gaofeng Pan
Mobile edge computing (MEC) technology can reduce user latency and energy consumption by offloading computationally intensive tasks to the edge servers. Unmanned aerial vehicles (UAVs) and non-orthogonal multiple access (NOMA) technology enable the MEC networks to provide offloaded computing services for massively accessed terrestrial users conveniently. However, the broadcast nature of signal propagation in NOMA-based UAV-MEC networks makes it vulnerable to eavesdropping by malicious eavesdroppers. In this work, a secure offload scheme is proposed for NOMA-based UAV-MEC systems with the existence of an aerial eavesdropper. The long-term average network computational cost is minimized by jointly designing the UAV's trajectory, the terrestrial users' transmit power, and computational frequency while ensuring the security of users' offloaded data. Due to the eavesdropper's location uncertainty, the worst-case security scenario is considered through the estimated eavesdropping range. Due to the high-dimensional continuous action space, the deep deterministic policy gradient algorithm is utilized to solve the non-convex optimization problem. Simulation results validate the effectiveness of the proposed scheme.
Wake and force characteristics of an oscillating cylinder in inline steady currents are investigated numerically over a wide parameter space of dimensionless oscillation amplitude ($A^* = 0.01 - 0.50$) and wavelength ($\lambda^* = 0.4 - 25$) at a fixed Reynolds number $Re = 500$. Fundamental issues addressed in this study are the interactions of wakes induced by steady approaching flow and cylinder oscillations and the influences of the governing parameters of $A^$ and $\lambda^$ on such interactions. Whilst the collinear flow is dominated by wakes induced by cylinder oscillation at $\lambda^* \leq 1.5$ and steady current at $\lambda^* \geq 10$, it exhibits characteristics of nonlinear interactions of wakes induced by the cylinder oscillation and steady current at $\lambda^* = 1.5 - 10$, such as the formation of multiple synchronized modes interleaved with desynchronized modes. The synchronized mode varies with both $\lambda^$ and $A^$, forming an inclined Arnold's tongue across $\lambda^-A^$ space. There is a wide variability of the vortex shedding pattern in each synchronized mode. Variations of different hydrodynamic force coefficients with $\lambda^$ and $A^$ are investigated with physical interpretations based on the wake characteristics. The applicability of the Morison equation in predicting inline force fluctuations is examined. We found that the Morison equation shows reasonable accuracy only for a small range of $\lambda^* \leq 1.5$. Beyond this range, its performance deteriorates due to the influence of steady current on wake characteristics.
Incorporating Procedural Fairness in Flag Submissions on Social Media Platforms
2024-09-13
Yunhee Shim, Shagun Jhaver
Flagging mechanisms on social media platforms allow users to report inappropriate posts/accounts for review by content moderators. These reports are pivotal to platforms' efforts toward regulating norm violations. This paper examines how platforms' design choices in implementing flagging mechanisms influence flaggers' perceptions of content moderation. We conducted a survey experiment asking US respondents (N=2,936) to flag inappropriate posts using one of 54 randomly assigned flagging implementations. After flagging, participants rated their fairness perceptions of the flag submission process along the dimensions of consistency, transparency, and voice (agency). We found that participants perceived greater transparency when flagging interfaces included community guidelines and greater voice when they incorporated a text box for open-ended feedback. Our qualitative analysis highlights user needs for improved accessibility, educational support for reporting, and protections against false flags. We offer design recommendations for building fairer flagging systems without exacerbating the cognitive burden of submitting flags.
Toward satisfactory public accessibility: A crowdsourcing approach through online reviews to inclusive urban design
2024-09-13
Lingyao Li, Songhua Hu, Yinpei Dai, Min Deng, Parisa Momeni, Gabriel Laverghetta, Lizhou Fan, Zihui Ma, Xi Wang, Siyuan Ma, Jay Ligatti, Libby Hemphill
As urban populations grow, the need for accessible urban design has become urgent. Traditional survey methods for assessing public perceptions of accessibility are often limited in scope. Crowdsourcing via online reviews offers a valuable alternative to understanding public perceptions, and advancements in large language models can facilitate their use. This study uses Google Maps reviews across the United States and fine-tunes Llama 3 model with the Low-Rank Adaptation technique to analyze public sentiment on accessibility. At the POI level, most categories -- restaurants, retail, hotels, and healthcare -- show negative sentiments. Socio-spatial analysis reveals that areas with higher proportions of white residents and greater socioeconomic status report more positive sentiment, while areas with more elderly, highly-educated residents exhibit more negative sentiment. Interestingly, no clear link is found between the presence of disabilities and public sentiments. Overall, this study highlights the potential of crowdsourcing for identifying accessibility challenges and providing insights for urban planners.
Input-to-State Stable Coupled Oscillator Networks for Closed-form Model-based Control in Latent Space
2024-09-13
Maximilian Stölzle, Cosimo Della Santina
Even though a variety of methods (e.g., RL, MPC, LQR) have been proposed in the literature, efficient and effective latent-space control of physical systems remains an open challenge. A promising avenue would be to leverage powerful and well-understood closed-form strategies from control theory literature in combination with learned dynamics, such as potential-energy shaping. We identify three fundamental shortcomings in existing latent-space models that have so far prevented this powerful combination: (i) they lack the mathematical structure of a physical system, (ii) they do not inherently conserve the stability properties of the real systems. Furthermore, (iii) these methods do not have an invertible mapping between input and latent-space forcing. This work proposes a novel Coupled Oscillator Network (CON) model that simultaneously tackles all these issues. More specifically, (i) we show analytically that CON is a Lagrangian system - i.e., it presses well-defined potential and kinetic energy terms. Then, (ii) we provide formal proof of global Input-to-State stability using Lyapunov arguments. Moving to the experimental side, (iii) we demonstrate that CON reaches SoA performance when learning complex nonlinear dynamics of mechanical systems directly from images. An additional methodological innovation contributing to achieving this third goal is an approximated closed-form solution for efficient integration of network dynamics, which eases efficient training. We tackle (iv) by approximating the forcing-to-input mapping with a decoder that is trained to reconstruct the input based on the encoded latent space force. Finally, we leverage these four properties and show that they enable latent-space control. We use an integral-saturated PID with potential force compensation and demonstrate high-quality performance on a soft robot using raw pixels as the only feedback information.
The overestimation of equipartition magnetic field strengths from synchrotron emission using synthetically observed galaxies
2024-09-13
Tara Dacunha, Sergio Martin-Alvarez, Susan E. Clark, Enrique Lopez-Rodriguez
Understanding the role that magnetic fields play on the stage of galaxy formation requires accurate methods for inferring the properties of extragalactic magnetic fields. Radio synchrotron emission has been the most promising avenue to infer magnetic field strengths across galaxies, with the application of a central assumption: that galactic cosmic rays are in energy equipartition with the magnetic field. In this work, we leverage flexible synthetic observations of a high-resolution magnetohydrodynamic simulation of a Milky Way-like galaxy to review whether true equipartition is capable of reproducing radio observations of galaxies, and investigate its impact on the inference of magnetic field strengths when varying the properties and density distribution of the cosmic rays. We find that imposing equipartition (regardless of scale length) results in cosmic ray electron densities that are unable to generate either the amplitude or the shape of the radio intensity profiles typically observed in spiral galaxies. Instead, observationally motivated smooth distributions of cosmic ray electrons across the galaxy provide a remarkable match to observations. We further demonstrate that assuming equipartition with those mock observations can lead to significant overestimation of the magnetic field strength ($\sim10-50\times$). This overestimation varies with cosmic ray electron densities, cosmic ray spectrum power-law index, and galactic environment, aggravated in inter-arm regions and attenuated in star-forming regions. Our results promote caution when assuming equipartition in observations, and suggest that additional theoretical and numerical work is required to leverage the upcoming generation of radio observations poised to revolutionize our understanding of astrophysical magnetic fields.
Exploring Dimensionality Reduction of SDSS Spectral Abundances
2024-09-13
Qianyu Fan
High-resolution stellar spectra offer valuable insights into atmospheric parameters and chemical compositions. However, their inherent complexity and high-dimensionality present challenges in fully utilizing the information they contain. In this study, we utilize data from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) within the Sloan Digital Sky Survey IV (SDSS-IV) to explore latent representations of chemical abundances by applying five dimensionality reduction techniques: PCA, t-SNE, UMAP, Autoencoder, and VAE. Through this exploration, we evaluate the preservation of information and compare reconstructed outputs with the original 19 chemical abundance data. Our findings reveal a performance ranking of PCA < UMAP < t-SNE < VAE < Autoencoder, through comparing their explained variance under optimized MSE. The performance of non-linear (Autoencoder and VAE) algorithms has approximately 10\% improvement compared to linear (PCA) algorithm. This difference can be referred to as the "non-linearity gap." Future work should focus on incorporating measurement errors into extension VAEs, thereby enhancing the reliability and interpretability of chemical abundance exploration in astronomical spectra.
A Systematic Review on Process Mining for Curricular Analysis
2024-09-13
Daniel Calegari, Andrea Delgado
Educational Process Mining (EPM) is a data analysis technique that is used to improve educational processes. It is based on Process Mining (PM), which involves gathering records (logs) of events to discover process models and analyze the data from a process-centric perspective. One specific application of EPM is curriculum mining, which focuses on understanding the learning program students follow to achieve educational goals. This is important for institutional curriculum decision-making and quality improvement. Therefore, academic institutions can benefit from organizing the existing techniques, capabilities, and limitations. We conducted a systematic literature review to identify works on applying PM to curricular analysis and provide insights for further research. From the analysis of 22 primary studies, we found that results can be classified into five categories concerning the objectives they pursue: the discovery of educational trajectories, the identification of deviations in the observed behavior of students, the analysis of bottlenecks, the analysis of stopout and dropout problems, and the generation of recommendation. Moreover, we identified some open challenges and opportunities, such as standardizing for replicating studies to perform cross-university curricular analysis and strengthening the connection between PM and data mining for improving curricular analysis.
Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech
2024-09-13
Pan-Pan Jiang, Jimmy Tobin, Katrin Tomanek, Robert L. MacDonald, Katie Seaver, Richard Cave, Marilyn Ladewig, Rus Heywood, Jordan R. Green
Project Euphonia, a Google initiative, is dedicated to improving automatic speech recognition (ASR) of disordered speech. A central objective of the project is to create a large, high-quality, and diverse speech corpus. This report describes the project's latest advancements in data collection and annotation methodologies, such as expanding speaker diversity in the database, adding human-reviewed transcript corrections and audio quality tags to 350K (of the 1.2M total) audio recordings, and amassing a comprehensive set of metadata (including more than 40 speech characteristic labels) for over 75\% of the speakers in the database. We report on the impact of transcript corrections on our machine-learning (ML) research, inter-rater variability of assessments of disordered speech patterns, and our rationale for gathering speech metadata. We also consider the limitations of using automated off-the-shelf annotation methods for assessing disordered speech.
Quantitative Insights into Language Model Usage and Trust in Academia: An Empirical Study
2024-09-13
Minseok Jung, Aurora Zhang, Junho Lee, Paul Pu Liang
Language models (LMs) are revolutionizing knowledge retrieval and processing in academia. However, concerns regarding their misuse and erroneous outputs, such as hallucinations and fabrications, are reasons for distrust in LMs within academic communities. Consequently, there is a pressing need to deepen the understanding of how actual practitioners use and trust these models. There is a notable gap in quantitative evidence regarding the extent of LM usage, user trust in their outputs, and issues to prioritize for real-world development. This study addresses these gaps by providing data and analysis of LM usage and trust. Specifically, our study surveyed 125 individuals at a private school and secured 88 data points after pre-processing. Through both quantitative analysis and qualitative evidence, we found a significant variation in trust levels, which are strongly related to usage time and frequency. Additionally, we discover through a polling process that fact-checking is the most critical issue limiting usage. These findings inform several actionable insights: distrust can be overcome by providing exposure to the models, policies should be developed that prioritize fact-checking, and user trust can be enhanced by increasing engagement. By addressing these critical gaps, this research not only adds to the understanding of user experiences and trust in LMs but also informs the development of more effective LMs.
Negative charge transfer energy in correlated compounds
2024-09-13
Robert J. Green, George A. Sawatzky
In correlated compounds containing cations in high formal oxidation states (assigned by assuming that anions attain full valence shells), the energy of ligand to cation charge transfer can become small or even negative. This yields compounds with a high degree of covalence and can lead to a self-doping of holes into the ligand states of the valence band. Such compounds are of particular topical interest, as highly studied perovskite oxides containing trivalent nickel or tetravalent iron are negative charge transfer systems, as are nickel-containing lithium ion battery cathode materials. In this report, we review the topic of negative charge transfer energy, with an emphasis on plots and diagrams as analysis tools, in the spirit of the celebrated Tanabe-Sugano diagrams which are the focus of this Special Topics Issue.
Incorporation of Verifier Functionality in the Software for Operations and Network Attack Results Review and the Autonomous Penetration Testing System
2024-09-13
Jordan Milbrath, Jeremy Straub
The software for operations and network attack results review (SONARR) and the autonomous penetration testing system (APTS) use facts and common properties in digital twin networks to represent real-world entities. However, in some cases fact values will change regularly, making it difficult for objects in SONARR and APTS to consistently and accurately represent their real-world counterparts. This paper proposes and evaluates the addition of verifiers, which check real-world conditions and update network facts, to SONARR. This inclusion allows SONARR to retrieve fact values from its executing environment and update its network, providing a consistent method of ensuring that the operations and, therefore, the results align with the real-world systems being assessed. Verifiers allow arbitrary scripts and dynamic arguments to be added to normal SONARR operations. This provides a layer of flexibility and consistency that results in more reliable output from the software.
Characterizing the Molecular Gas in Infrared Bright Galaxies with CARMA
2024-09-13
Katherine Alatalo, Andreea O. Petric, Lauranne Lanz, Kate Rowlands, Vivian U, Kirsten L. Larson, Lee Armus, Loreto Barcos-Muñoz, Aaron S. Evans, Jin Koda, Yuanze Luo, Anne M. Medling, Kristina E. Nyland, Justin A. Otter, Pallavi Patil, Fernando Peñaloza, Diane Salim, David B. Sanders, Elizaveta Sazonova, Maya Skarbinski, Yiqing Song, Ezequiel Treister, C. Meg Urry
We present the CO(1-0) maps of 28 infrared-bright galaxies from the Great Observatories All-Sky Luminous Infrared Galaxy Survey (GOALS) taken with the Combined Array for Research in Millimeter Astronomy (CARMA). We detect 100GHz continuum in 16 of 28 galaxies, which trace both active galactic nuclei (AGNs) and compact star-forming cores. The GOALS galaxies show a variety of molecular gas morphologies, though in the majority of cases, the average velocity fields show a gradient consistent with rotation. We fit the full continuum SEDs of each of the source using either MAGPHYS or SED3FIT (if there are signs of an AGN) to derive the total stellar mass, dust mass, and star formation rates of each object. We adopt a value determined from luminous and ultraluminous infrared galaxies (LIRGs and ULIRGs) of $\alpha_{\rm CO}=1.5^{+1.3}_{-0.8}~M_\odot$ (K km s$^{-1}$ pc$^2)^{-1}$, which leads to more physical values for $f_{\rm mol}$ and the gas-to-dust ratio. Mergers tend to have the highest gas-to-dust ratios. We assume the cospatiality of the molecular gas and star formation, and plot the sample on the Schmidt-Kennicutt relation, we find that they preferentially lie above the line set by normal star-forming galaxies. This hyper-efficiency is likely due to the increased turbulence in these systems, which decreases the freefall time compared to star-forming galaxies, leading to "enhanced" star formation efficiency. Line wings are present in a non-negligible subsample (11/28) of the CARMA GOALS sources and are likely due to outflows driven by AGNs or star formation, gas inflows, or additional decoupled gas components.
Recent Trends in Modelling the Continuous Time Series using Deep Learning: A Survey
2024-09-13
Mansura Habiba, Barak A. Pearlmutter, Mehrdad Maleki
Continuous-time series is essential for different modern application areas, e.g. healthcare, automobile, energy, finance, Internet of things (IoT) and other related areas. Different application needs to process as well as analyse a massive amount of data in time series structure in order to determine the data-driven result, for example, financial trend prediction, potential probability of the occurrence of a particular event occurrence identification, patient health record processing and so many more. However, modeling real-time data using a continuous-time series is challenging since the dynamical systems behind the data could be a differential equation. Several research works have tried to solve the challenges of modelling the continuous-time series using different neural network models and approaches for data processing and learning. The existing deep learning models are not free from challenges and limitations due to diversity among different attributes, behaviour, duration of steps, energy, and data sampling rate. This paper has described the general problem domain of time series and reviewed the challenges of modelling the continuous time series. We have presented a comparative analysis of recent developments in deep learning models and their contribution to solving different difficulties of modelling the continuous time series. We have also identified the limitations of the existing neural network model and open issues. The main goal of this review is to understand the recent trend of neural network models used in a different real-world application with continuous-time data.
Are Existing Road Design Guidelines Suitable for Autonomous Vehicles?
2024-09-13
Yang Sun, Christopher M. Poskitt, Jun Sun
The emergence of Autonomous Vehicles (AVs) has spurred research into testing the resilience of their perception systems, i.e. to ensure they are not susceptible to making critical misjudgements. It is important that they are tested not only with respect to other vehicles on the road, but also those objects placed on the roadside. Trash bins, billboards, and greenery are all examples of such objects, typically placed according to guidelines that were developed for the human visual system, and which may not align perfectly with the needs of AVs. Existing tests, however, usually focus on adversarial objects with conspicuous shapes/patches, that are ultimately unrealistic given their unnatural appearances and the need for white box knowledge. In this work, we introduce a black box attack on the perception systems of AVs, in which the objective is to create realistic adversarial scenarios (i.e. satisfying road design guidelines) by manipulating the positions of common roadside objects, and without resorting to `unnatural' adversarial patches. In particular, we propose TrashFuzz , a fuzzing algorithm to find scenarios in which the placement of these objects leads to substantial misperceptions by the AV -- such as mistaking a traffic light's colour -- with overall the goal of causing it to violate traffic laws. To ensure the realism of these scenarios, they must satisfy several rules encoding regulatory guidelines about the placement of objects on public streets. We implemented and evaluated these attacks for the Apollo, finding that TrashFuzz induced it into violating 15 out of 24 different traffic laws.
Learning incomplete factorization preconditioners for GMRES
2024-09-12
Paul Häusner, Aleix Nieto Juscafresa, Jens Sjölund
In this paper, we develop a data-driven approach to generate incomplete LU factorizations of large-scale sparse matrices. The learned approximate factorization is utilized as a preconditioner for the corresponding linear equation system in the GMRES method. Incomplete factorization methods are one of the most commonly applied algebraic preconditioners for sparse linear equation systems and are able to speed up the convergence of Krylov subspace methods. However, they are sensitive to hyper-parameters and might suffer from numerical breakdown or lead to slow convergence when not properly applied. We replace the typically hand-engineered algorithms with a graph neural network based approach that is trained against data to predict an approximate factorization. This allows us to learn preconditioners tailored for a specific problem distribution. We analyze and empirically evaluate different loss functions to train the learned preconditioners and show their effectiveness to decrease the number of GMRES iterations and improve the spectral properties on our synthetic dataset. The code is available at https://github.com/paulhausner/neural-incomplete-factorization.
A review of compact geodesic orbit manifolds and the g.o. condition for $\SU(5)/\s(\U(2)\times \U(2))$
2024-09-12
Andreas Arvanitoyeorgos, Nikolaos Panagiotis Souris, Marina Statha
Geodesic orbit manifolds (or g.o. manifolds) are those Riemannian manifolds $(M,g)$ whose geodesics are integral curves of Killing vector fields. Equivalently, there exists a Lie group $G$ of isometries of $(M,g)$ such that any geodesic $\gamma$ has the simple form $\gamma(t)=e^{tX}\cdot p$, where $e$ denotes the exponential map on $G$. The classification of g.o. manifolds is a longstanding problem in Riemannian geometry. In this brief survey, we present some recent results and open questions on the subject focusing on the compact case. In addition we study the geodesic orbit condition for the space $\SU(5)/\s(\U(2)\times \U(2))$.
In the pursuit of scalable and fault-tolerant quantum computing architectures, photonic-based quantum computers have emerged as a leading frontier. This article provides a comprehensive overview of advancements in photonic quantum computing, developed by leading industry players, examining current performance, architectural designs, and strategies for developing large-scale, fault-tolerant photonic quantum computers. It also highlights recent groundbreaking experiments that leverage the unique advantages of photonic technologies, underscoring their transformative potential. This review captures a pivotal moment of photonic quantum computing in the noisy intermediate-scale quantum (NISQ) era, offering insights into how photonic quantum computers might reshape the future of quantum computing.
We present a review of absolute root-mean-square charge radii of stable nuclei up to $Z=32$, which includes a previously overlooked uncertainty in the combined analysis of muonic x-ray and electron scattering experiments. From these \textit{reference radii} and isotope shift measurements, we obtain those of 12 mirror pairs with a traceable and realistic uncertainty budget. The difference in radii between mirror nuclei is found to be proportional to the isospin asymmetry, confirming recent calculations by Novario \emph{et al.} [PRL~130, 032501]. From the fitted proportionality constant and its uncertainty, the radii of 73 previously unknown mirror partners are predicted. These are useful e.g. for benchmarking atomic and nuclear theory, calibrating entire chains, and as an input to nuclear beta-decay calculations. The radii of $(T=1,T_z=0)$ nuclei are interpolated assuming negligible isospin symmetry breaking. This completes a model-independent, high-precision extraction of the charge and weak radii of all nuclei involved in the testing of the unitarity of the CKM matrix.
Gaussian process (GP) methods have been widely studied recently, especially for large-scale systems with big data and even more extreme cases when data is sparse. Key advantages of these methods consist in: 1) the ability to provide inherent ways to assess the impact of uncertainties (especially in the data, and environment) on the solutions, 2) have efficient factorisation based implementations and 3) can be implemented easily in distributed manners and hence provide scalable solutions. This paper reviews the recently developed key factorised GP methods such as the hierarchical off-diagonal low-rank approximation methods and GP with Kronecker structures. An example illustrates the performance of these methods with respect to accuracy and computational complexity.
Mental Stress Detection and Performance Enhancement Using FNIRS and Wrist Vibrator Biofeedback
2024-09-12
Anita Beigzadeh, Vahid Yazdnian, Kamaledin Setarehdan
Any person in his/her daily life activities experiences different kinds and various amounts of mental stress which has a destructive effect on their performance. Therefore, it is crucial to come up with a systematic way of stress management and performance enhancement. This paper presents a comprehensive portable and real-time biofeedback system that aims at boosting stress management and consequently performance enhancement. For this purpose, a real-time brain signal acquisition device, a wireless vibration biofeedback device, and a software-defined program for stress level classification have been developed. More importantly, the entire system has been designed to present minimum time delay by propitiously bridging all the essential parts of the system together. We have presented different signal processing and feature extraction techniques for an online stress detection application. Accordingly, by testing the stress classification section of the system, an accuracy of 83% and a recall detecting the true mental stress level of 92% was achieved. Moreover, the biofeedback system as integrity has been tested on 20 participants in the controlled experimental setup. Experiment evaluations show promising results of system performances, and the findings reveal that our system is able to help the participants reduce their stress level by 55% and increase their accuracy by 24.5%. It can be concluded from the observations that all primary premises on stress management and performance enhancement through reward learning are valid as well.
Large Language Models (LLMs) demonstrate impressive capabilities across various fields, yet their increasing use raises critical security concerns. This article reviews recent literature addressing key issues in LLM security, with a focus on accuracy, bias, content detection, and vulnerability to attacks. Issues related to inaccurate or misleading outputs from LLMs is discussed, with emphasis on the implementation from fact-checking methodologies to enhance response reliability. Inherent biases within LLMs are critically examined through diverse evaluation techniques, including controlled input studies and red teaming exercises. A comprehensive analysis of bias mitigation strategies is presented, including approaches from pre-processing interventions to in-training adjustments and post-processing refinements. The article also probes the complexity of distinguishing LLM-generated content from human-produced text, introducing detection mechanisms like DetectGPT and watermarking techniques while noting the limitations of machine learning enabled classifiers under intricate circumstances. Moreover, LLM vulnerabilities, including jailbreak attacks and prompt injection exploits, are analyzed by looking into different case studies and large-scale competitions like HackAPrompt. This review is concluded by retrospecting defense mechanisms to safeguard LLMs, accentuating the need for more extensive research into the LLM security field.
EDGE-INFERNO: Simulating every observable star in faint dwarf galaxies and their consequences for resolved-star photometric surveys
2024-09-12
Eric P. Andersson, Martin P. Rey, Andrew Pontzen, Corentin Cadiou, Oscar Agertz, Justin I. Read, Nicolas F. Martin
Interpretation of data from faint dwarf galaxies is made challenging by observations limited to only the brightest stars. We present a major improvement to tackle this challenge by undertaking zoomed cosmological simulations that resolve the evolution of all individual stars more massive than $0.5\,{\rm M}_{\odot}$, thereby explicitly tracking all observable stars for the Hubble time. For the first time, we predict observable color-magnitude diagrams and the spatial distribution of $\approx 100,000$ stars within four faint ($M_{\star} \approx 10^5 \, \,{\rm M}_{\odot}$) dwarf galaxies directly from their cosmological initial conditions. In all cases, simulations predict complex light profiles with multiple components, implying that typical observational measures of structural parameters can make total V-band magnitudes appear up to 0.5 mag dimmer compared to estimates from simulations. Furthermore, when only small ($\lessapprox100$) numbers of stars are observable, shot noise from realizations of the color-magnitude diagram introduces uncertainties comparable to the population scatter in, e.g., total magnitude, half-light radius, and mean iron abundance measurements. Estimating these uncertainties with fully self-consistent mass growth, star formation and chemical enrichment histories paves the way for more robust interpretation of dwarf galaxy data.
Super-slowly rotating Ap (ssrAp) stars: Spectroscopic study
2024-09-12
G. Mathys, D. L. Holdsworth, M. Giarrusso, D. W. Kurtz, G. Catanzaro, F. Leone
To gain better understanding of the Ap stars with the longest rotation periods, we obtained high resolution spectra of a sample of super-slowly rotating Ap (ssrAp) star candidates identified by a TESS photometric survey, to confirm that they are indeed Ap stars, to check that their v sin i values are compatible with super-slow rotation, and to obtain a first estimate of their magnetic field strengths. We determined whenever possible their mean magnetic field modulus, their mean quadratic magnetic field, and an upper limit of their projected equatorial velocities. Eighteen of the 27 stars studied are typical Ap stars; most of the other nine appear to be misclassified. One of the Ap stars is not a slow rotator; it must be seen nearly pole-on. The properties of the remaining 17 are compatible with moderately to extremely long rotation periods. Eight new stars with resolved magnetically split lines in the visible range were discovered; their mean magnetic field modulus and their mean quadratic magnetic field were measured. The mean quadratic field could also be determined in five more stars. Five new spectroscopic binaries containing an Ap star were identified. Among the misclassified stars, one SB2 system with two similar, sharp-lined Am components was also discovered. The technique that we used to carry out a search for ssrAp star candidates using TESS data is validated, but appears limited by uncertainties in the spectral classification of Ap stars. The new magnetic field measurements obtained as part of this study lend further support to the tentative conclusions of our previous studies: the absence of periods longer than ~150 d in stars with magnetic fields stronger than ~7.5 kG, the lower rate of occurrence of super-slow rotation for field strengths less than ~2 kG than in the range ~3-7.5 kG, and the deficiency of slowly rotating Ap stars with field strengths between ~2 and ~3 kG.
Unleashing Worms and Extracting Data: Escalating the Outcome of Attacks against RAG-based Inference in Scale and Severity Using Jailbreaking
2024-09-12
Stav Cohen, Ron Bitton, Ben Nassi
In this paper, we show that with the ability to jailbreak a GenAI model, attackers can escalate the outcome of attacks against RAG-based GenAI-powered applications in severity and scale. In the first part of the paper, we show that attackers can escalate RAG membership inference attacks and RAG entity extraction attacks to RAG documents extraction attacks, forcing a more severe outcome compared to existing attacks. We evaluate the results obtained from three extraction methods, the influence of the type and the size of five embeddings algorithms employed, the size of the provided context, and the GenAI engine. We show that attackers can extract 80%-99.8% of the data stored in the database used by the RAG of a Q&A chatbot. In the second part of the paper, we show that attackers can escalate the scale of RAG data poisoning attacks from compromising a single GenAI-powered application to compromising the entire GenAI ecosystem, forcing a greater scale of damage. This is done by crafting an adversarial self-replicating prompt that triggers a chain reaction of a computer worm within the ecosystem and forces each affected application to perform a malicious activity and compromise the RAG of additional applications. We evaluate the performance of the worm in creating a chain of confidential data extraction about users within a GenAI ecosystem of GenAI-powered email assistants and analyze how the performance of the worm is affected by the size of the context, the adversarial self-replicating prompt used, the type and size of the embeddings algorithm employed, and the number of hops in the propagation. Finally, we review and analyze guardrails to protect RAG-based inference and discuss the tradeoffs.
Massive Star Formation Starts in Sub-virial Dense Clumps Unless Resisted by Strong Magnetic Fields
2024-09-12
Ke Wang, Yueluo Wang, Fengwei Xu
The initial conditions are critical for understanding high-mass star formation, but are not well observed. Built on our previous characterization of a Galaxy-wide sample of 463 candidate high-mass starless clumps (HMSCs), here we investigate the dynamical state of a representative subsample of 44 HMSCs (radii 0.13-1.12 pc) using GBT NH3 (1,1) and (2,2) data from the Radio Ammonia Mid-Plane Survey (RAMPS) pilot data release. By fitting the two NH3 lines simultaneously, we obtain velocity dispersion, gas kinetic temperature, NH3 column density and abundance, Mach number, and virial parameter. Thermodynamic analysis reveals that most HMSCs have Mach number $<$5, inconsistent to what have been considered in theoretical models. All but one (43/44) of the HMSCs are gravitationally bound with virial parameter $\alpha_{\mathrm{vir}} < 2$. Either these massive clumps are in collapsing or magnetic field strengths of 0.10-2.65 mG (average 0.51 mG) would be needed to support them against collapsing. The estimated B-field strength correlates tightly with density, $B_{\rm est}/{\rm mG}=0.269\,(n_{\rm H_2}/10^4\,{\rm cm^{-3}})^{0.61}$, with a similar power-law index as found in observations, but a factor of 4.6 higher in strength. For the first time, the initial dynamical state of high-mass formation regions has been statistically constrained to be sub-virial, in contradictory to theoretical models in virial equilibrium, and in agreement with the lack of observed massive starless cores. The findings urge future observations to quantify the magnetic field support in the prestellar stage of massive clumps, which are rarely explored so far, towards a full understanding of the physical conditions that initiate massive star formation.
We review measures of street network structure proposed in the recent literature, establish their relevance to practice, and identify open challenges facing researchers. These measures' empirical values vary substantially across world regions and development eras, indicating street networks' geometric and topological heterogeneity.
Privacy-preserving federated prediction of pain intensity change based on multi-center survey data
2024-09-12
Supratim Das, Mahdie Rafie, Paula Kammer, Søren T. Skou, Dorte T. Grønne, Ewa M. Roos, André Hajek, Hans-Helmut König, Md Shihab Ullaha, Niklas Probul, Jan Baumbacha, Linda Baumbach
Background: Patient-reported survey data are used to train prognostic models aimed at improving healthcare. However, such data are typically available multi-centric and, for privacy reasons, cannot easily be centralized in one data repository. Models trained locally are less accurate, robust, and generalizable. We present and apply privacy-preserving federated machine learning techniques for prognostic model building, where local survey data never leaves the legally safe harbors of the medical centers. Methods: We used centralized, local, and federated learning techniques on two healthcare datasets (GLA:D data from the five health regions of Denmark and international SHARE data of 27 countries) to predict two different health outcomes. We compared linear regression, random forest regression, and random forest classification models trained on local data with those trained on the entire data in a centralized and in a federated fashion. Results: In GLA:D data, federated linear regression (R2 0.34, RMSE 18.2) and federated random forest regression (R2 0.34, RMSE 18.3) models outperform their local counterparts (i.e., R2 0.32, RMSE 18.6, R2 0.30, RMSE 18.8) with statistical significance. We also found that centralized models (R2 0.34, RMSE 18.2, R2 0.32, RMSE 18.5, respectively) did not perform significantly better than the federated models. In SHARE, the federated model (AC 0.78, AUROC: 0.71) and centralized model (AC 0.84, AUROC: 0.66) perform significantly better than the local models (AC: 0.74, AUROC: 0.69). Conclusion: Federated learning enables the training of prognostic models from multi-center surveys without compromising privacy and with only minimal or no compromise regarding model performance.
Deep Learning for Personalized Electrocardiogram Diagnosis: A Review
2024-09-12
Cheng Ding, Tianliang Yao, Chenwei Wu, Jianyuan Ni
The electrocardiogram (ECG) remains a fundamental tool in cardiac diagnostics, yet its interpretation traditionally reliant on the expertise of cardiologists. The emergence of deep learning has heralded a revolutionary era in medical data analysis, particularly in the domain of ECG diagnostics. However, inter-patient variability prohibit the generalibility of ECG-AI model trained on a population dataset, hence degrade the performance of ECG-AI on specific patient or patient group. Many studies have address this challenge using different deep learning technologies. This comprehensive review systematically synthesizes research from a wide range of studies to provide an in-depth examination of cutting-edge deep-learning techniques in personalized ECG diagnosis. The review outlines a rigorous methodology for the selection of pertinent scholarly articles and offers a comprehensive overview of deep learning approaches applied to personalized ECG diagnostics. Moreover, the challenges these methods encounter are investigated, along with future research directions, culminating in insights into how the integration of deep learning can transform personalized ECG diagnosis and enhance cardiac care. By emphasizing both the strengths and limitations of current methodologies, this review underscores the immense potential of deep learning to refine and redefine ECG analysis in clinical practice, paving the way for more accurate, efficient, and personalized cardiac diagnostics.
Charting the Nanohertz Gravitational Wave Sky with Pulsar Timing Arrays
2024-09-12
Reginald Christian Bernardo, Kin-Wang Ng
In the summer of 2023, the pulsar timing arrays (PTAs) announced a compelling evidence for the existence of a nanohertz stochastic gravitational wave background (SGWB). Despite this breakthrough, however, several critical questions remain unanswered: What is the source of the signal? How can cosmic variance be accounted for? To what extent can we constrain nanohertz gravity? When will individual supermassive black hole binaries become observable? And how can we achieve a stronger detection? These open questions have spurred significant interests in PTA science, making this an opportune moment to revisit the astronomical and theoretical foundations of the field, as well as the data analysis techniques employed. In this review, we focus on the theoretical aspects of the SGWB as detected by PTAs. We provide a comprehensive derivation of the expected signal and its correlation, presented in a pedagogical manner, while also addressing current constraints. Looking ahead, we explore future milestones in the field, with detailed discussions on emerging theoretical considerations such as cosmic variance, the cumulants of the one- and two-point functions, subluminal gravitational waves, and the anisotropy and polarization of the SGWB.
The field of quickest change detection (QCD) concerns design and analysis of algorithms to estimate in real time the time at which an important event takes place and identify properties of the post-change behavior. The goal is to devise a stopping time adapted to the observations that minimizes an $L_1$ loss. Approximately optimal solutions are well known under a variety of assumptions. In the work surveyed here we consider the CUSUM statistic, which is defined as a one-dimensional reflected random walk driven by a functional of the observations. It is known that the optimal functional is a log likelihood ratio subject to special statical assumptions. The paper concerns model free approaches to detection design, considering the following questions: 1. What is the performance for a given functional of the observations? 2. How do the conclusions change when there is dependency between pre- and post-change behavior? 3. How can techniques from statistics and machine learning be adapted to approximate the best functional in a given class?
Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion Models
2024-09-12
Nikolai L. Kühne, Astrid H. F. Kitchen, Marie S. Jensen, Mikkel S. L. Brøndt, Martin Gonzalez, Christophe Biscio, Zheng-Hua Tan
Automatic speech recognition (ASR) systems are known to be vulnerable to adversarial attacks. This paper addresses detection and defence against targeted white-box attacks on speech signals for ASR systems. While existing work has utilised diffusion models (DMs) to purify adversarial examples, achieving state-of-the-art results in keyword spotting tasks, their effectiveness for more complex tasks such as sentence-level ASR remains unexplored. Additionally, the impact of the number of forward diffusion steps on performance is not well understood. In this paper, we systematically investigate the use of DMs for defending against adversarial attacks on sentences and examine the effect of varying forward diffusion steps. Through comprehensive experiments on the Mozilla Common Voice dataset, we demonstrate that two forward diffusion steps can completely defend against adversarial attacks on sentences. Moreover, we introduce a novel, training-free approach for detecting adversarial attacks by leveraging a pre-trained DM. Our experimental results show that this method can detect adversarial attacks with high accuracy.
Modeling Human Responses by Ordinal Archetypal Analysis
2024-09-12
Anna Emilie J. Wedenborg, Michael Alexander Harborg, Andreas Bigom, Oliver Elmgreen, Marcus Presutti, Andreas Råskov, Fumiko Kano Glückstad, Mikkel Schmidt, Morten Mørup
This paper introduces a novel framework for Archetypal Analysis (AA) tailored to ordinal data, particularly from questionnaires. Unlike existing methods, the proposed method, Ordinal Archetypal Analysis (OAA), bypasses the two-step process of transforming ordinal data into continuous scales and operates directly on the ordinal data. We extend traditional AA methods to handle the subjective nature of questionnaire-based data, acknowledging individual differences in scale perception. We introduce the Response Bias Ordinal Archetypal Analysis (RBOAA), which learns individualized scales for each subject during optimization. The effectiveness of these methods is demonstrated on synthetic data and the European Social Survey dataset, highlighting their potential to provide deeper insights into human behavior and perception. The study underscores the importance of considering response bias in cross-national research and offers a principled approach to analyzing ordinal data through Archetypal Analysis.
DNN-based workflow for attenuating seismic interference noise and its application to marine towed streamer data from the Northern Viking Graben
2024-09-12
Jing Sun, Song Hou, Alaa Triki
To separate seismic interference (SI) noise while ensuring high signal fidelity, we propose a deep neural network (DNN)-based workflow applied to common shot gathers (CSGs). In our design, a small subset of the entire to-be-processed data set is first processed by a conventional algorithm to obtain an estimate of the SI noise (from now on called the SI noise model). By manually blending the SI noise model with SI-free CSGs and a set of simulated random noise, we obtain training inputs for the DNN. The SI-free CSGs can be either real SI-free CSGs from the survey or SI-attenuated CSGs produced in parallel with the SI noise model from the conventional algorithm depending on the specific project. To enhance the DNN's output signal fidelity, adjacent shots on both sides of the to-be-processed shot are used as additional channels of the input. We train the DNN to output the SI noise into one channel and the SI-free shot along with the intact random noise into another. Once trained, the DNN can be applied to the entire data set contaminated by the same types of SI in the training process, producing results efficiently. For demonstration, we applied the proposed DNN-based workflow to 3D seismic field data acquired from the Northern Viking Graben (NVG) of the North Sea, and compared it with a conventional algorithm. The studied area has a challenging SI contamination problem with no sail lines free from SI noise during the acquisition. The comparison shows that the proposed DNN-based workflow outperformed the conventional algorithm in processing quality with less noise residual and better signal preservation. This validates its feasibility and value for real processing projects.
Mapping Technical Safety Research at AI Companies: A literature review and incentives analysis
2024-09-12
Oscar Delaney, Oliver Guest, Zoe Williams
As artificial intelligence (AI) systems become more advanced, concerns about large-scale risks from misuse or accidents have grown. This report analyzes the technical research into safe AI development being conducted by three leading AI companies: Anthropic, Google DeepMind, and OpenAI. We define safe AI development as developing AI systems that are unlikely to pose large-scale misuse or accident risks. This encompasses a range of technical approaches aimed at ensuring AI systems behave as intended and do not cause unintended harm, even as they are made more capable and autonomous. We analyzed all papers published by the three companies from January 2022 to July 2024 that were relevant to safe AI development, and categorized the 61 included papers into eight safety approaches. Additionally, we noted three categories representing nascent approaches explored by academia and civil society, but not currently represented in any papers by the three companies. Our analysis reveals where corporate attention is concentrated and where potential gaps lie. Some AI research may stay unpublished for good reasons, such as to not inform adversaries about security techniques they would need to overcome to misuse AI systems. Therefore, we also considered the incentives that AI companies have to research each approach. In particular, we considered reputational effects, regulatory burdens, and whether the approaches could make AI systems more useful. We identified three categories where there are currently no or few papers and where we do not expect AI companies to become more incentivized to pursue this research in the future. These are multi-agent safety, model organisms of misalignment, and safety by design. Our findings provide an indication that these approaches may be slow to progress without funding or efforts from government, civil society, philanthropists, or academia.
2024-09-12
Pedro J. Villasana T., Lars Villemoes, Janusz Klejsa, Per Hedelin
We consider audio decoding as an inverse problem and solve it through diffusion posterior sampling. Explicit conditioning functions are developed for input signal measurements provided by an example of a transform domain perceptual audio codec. Viability is demonstrated by evaluating arbitrary pairings of a set of bitrates and task-agnostic prior models. For instance, we observe significant improvements on piano while maintaining speech performance when a speech model is replaced by a joint model trained on both speech and piano. With a more general music model, improved decoding compared to legacy methods is obtained for a broad range of content types and bitrates. The noisy mean model, underlying the proposed derivation of conditioning, enables a significant reduction of gradient evaluations for diffusion posterior sampling, compared to methods based on Tweedie's mean. Combining Tweedie's mean with our conditioning functions improves the objective performance. An audio demo is available at https://dpscodec-demo.github.io/.
Fate of Boltzmann's breathers: kinetic theory perspective
2024-09-12
P. Maynar, M. I. García de Soria, D. Guéry-Odelin, E. Trizac
The dynamics of a system composed of elastic hard particles confined by an isotropic harmonic potential are studied. In the low-density limit, the Boltzmann equation provides an excellent description, and the system does not reach equilibrium except for highly specific initial conditions: it generically evolves towards and stays in a breathing mode. This state is periodic in time, with a Gaussian velocity distribution, an oscillating temperature and a density profile that oscillates as well. We characterize this breather in terms of initial conditions, and constants of the motion. For low but finite densities, the analysis requires to take into account the finite size of the particles. Under well-controlle approximations, a closed description is provided, which shows how equilibrium is reached at long times. The (weak) dissipation at work erodes the breather's amplitude, while concomitantly shifting its oscillation frequency. An excellent agreement is found between Molecular Dynamics simulation results and the theoretical predictions for the frequency shift. For the damping time, the agreement is not as accurate as for the frequency and the origin of the discrepancies is discussed.
A Comprehensive Survey on Deep Multimodal Learning with Missing Modality
2024-09-12
Renjie Wu, Hu Wang, Hsiang-Ting Chen
During multimodal model training and reasoning, data samples may miss certain modalities and lead to compromised model performance due to sensor limitations, cost constraints, privacy concerns, data loss, and temporal and spatial factors. This survey provides an overview of recent progress in Multimodal Learning with Missing Modality (MLMM), focusing on deep learning techniques. It is the first comprehensive survey that covers the historical background and the distinction between MLMM and standard multimodal learning setups, followed by a detailed analysis of current MLMM methods, applications, and datasets, concluding with a discussion about challenges and potential future directions in the field.
From Uncertainty to Clarity: Uncertainty-Guided Class-Incremental Learning for Limited Biomedical Samples via Semantic Expansion
2024-09-12
Yifei Yao, Hanrong Zhang
In real-world clinical settings, data distributions evolve over time, with a continuous influx of new, limited disease cases. Therefore, class incremental learning is of great significance, i.e., deep learning models are required to learn new class knowledge while maintaining accurate recognition of previous diseases. However, traditional deep neural networks often suffer from severe forgetting of prior knowledge when adapting to new data unless trained from scratch, which undesirably costs much time and computational burden. Additionally, the sample sizes for different diseases can be highly imbalanced, with newly emerging diseases typically having much fewer instances, consequently causing the classification bias. To tackle these challenges, we are the first to propose a class-incremental learning method under limited samples in the biomedical field. First, we propose a novel cumulative entropy prediction module to measure the uncertainty of the samples, of which the most uncertain samples are stored in a memory bank as exemplars for the model's later review. Furthermore, we theoretically demonstrate its effectiveness in measuring uncertainty. Second, we developed a fine-grained semantic expansion module through various augmentations, leading to more compact distributions within the feature space and creating sufficient room for generalization to new classes. Besides, a cosine classifier is utilized to mitigate classification bias caused by imbalanced datasets. Across four imbalanced data distributions over two datasets, our method achieves optimal performance, surpassing state-of-the-art methods by as much as 53.54% in accuracy.
Contrasting Statistical Phase Estimation with the Variational Quantum Eigensolver in the era of Early Fault Tolerant Quantum Computation
2024-09-12
Ming-Zhi Chung, Andreas Thomasen, Henry Liao, Ryosuke Imai
In this review, we give an overview of the proposed applications in the early-FTQC (EFTQC) era. Starting from the error correction architecture for EFTQC device, we first review the recently developed space-time efficient analogue rotation (STAR) architecture \cite{akahoshiPartiallyFaultTolerantQuantum2024}, which is a partially fault-tolerant error correction architecture. Then, we review the requirements of an EFTQC algorithm. In particular, the class of ground state energy estimation (GSEE) algorithm known as the statistical phase estimation algorithm (SPE) is studied. We especially cast our attention on two SPE-type algorithms, the step-function filter-based variant by Lin and Tong (LT22) \cite{Lin:2021rwb} and Gaussian Filter \cite{Wang:2022gxu}. Based on the latter, we introduce the Gaussian Fitting algorithm, which uses an alternative post-processing procedure compared to \cite{Wang:2022gxu}. Finally, we systematically simulate the aforementioned algorithms and Variational Quantum Eigensolver (VQE) using the 1-uCJ ansatz with different shot counts. Most importantly, we perform noisy simulations based on the STAR architecture. We find that for estimating the ground state energy of the 4-qubit $H_2$ Hamiltonian in the STO-3G basis, SPE becomes more advantageous over VQE when the physical error rate is sufficiently low.
The field of Computer Vision (CV) has faced challenges. Initially, it relied on handcrafted features and rule-based algorithms, resulting in limited accuracy. The introduction of machine learning (ML) has brought progress, particularly Transfer Learning (TL), which addresses various CV problems by reusing pre-trained models. TL requires less data and computing while delivering nearly equal accuracy, making it a prominent technique in the CV landscape. Our research focuses on TL development and how CV applications use it to solve real-world problems. We discuss recent developments, limitations, and opportunities.
Music auto-tagging in the long tail: A few-shot approach
2024-09-12
T. Aleksandra Ma, Alexander Lerch
In the realm of digital music, using tags to efficiently organize and retrieve music from extensive databases is crucial for music catalog owners. Human tagging by experts is labor-intensive but mostly accurate, whereas automatic tagging through supervised learning has approached satisfying accuracy but is restricted to a predefined set of training tags. Few-shot learning offers a viable solution to expand beyond this small set of predefined tags by enabling models to learn from only a few human-provided examples to understand tag meanings and subsequently apply these tags autonomously. We propose to integrate few-shot learning methodology into multi-label music auto-tagging by using features from pre-trained models as inputs to a lightweight linear classifier, also known as a linear probe. We investigate different popular pre-trained features, as well as different few-shot parametrizations with varying numbers of classes and samples per class. Our experiments demonstrate that a simple model with pre-trained features can achieve performance close to state-of-the-art models while using significantly less training data, such as 20 samples per tag. Additionally, our linear probe performs competitively with leading models when trained on the entire training dataset. The results show that this transfer learning-based few-shot approach could effectively address the issue of automatically assigning long-tail tags with only limited labeled data.
FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments
2024-09-12
Devansh Dhrafani, Yifei Liu, Andrew Jong, Ukcheol Shin, Yao He, Tyler Harp, Yaoyu Hu, Jean Oh, Sebastian Scherer
Robust depth perception in visually-degraded environments is crucial for autonomous aerial systems. Thermal imaging cameras, which capture infrared radiation, are robust to visual degradation. However, due to lack of a large-scale dataset, the use of thermal cameras for unmanned aerial system (UAS) depth perception has remained largely unexplored. This paper presents a stereo thermal depth perception dataset for autonomous aerial perception applications. The dataset consists of stereo thermal images, LiDAR, IMU and ground truth depth maps captured in urban and forest settings under diverse conditions like day, night, rain, and smoke. We benchmark representative stereo depth estimation algorithms, offering insights into their performance in degraded conditions. Models trained on our dataset generalize well to unseen smoky conditions, highlighting the robustness of stereo thermal imaging for depth perception. We aim for this work to enhance robotic perception in disaster scenarios, allowing for exploration and operations in previously unreachable areas. The dataset and source code are available at https://firestereo.github.io.
Visualization in Motion in Video Games for Different Types of Data
2024-09-12
Federica Bucchieri, Lijie Yao, Petra Isenberg
We contribute an analysis of situated visualizations in motion in video games for different types of data, with a focus on quantitative and categorical data representations. Video games convey a lot of data to players, to help them succeed in the game. These visualizations frequently move across the screen due to camera changes or because the game elements themselves move. Our ultimate goal is to understand how motion factors affect visualization readability in video games and subsequently the players' performance in the game. We started our work by surveying the characteristics of how motion currently influences which kind of data representations in video games. We conducted a systematic review of 160 visualizations in motion in video games and extracted patterns and considerations regarding was what, and how visualizations currently exhibit motion factors in video games.
2024-09-12
Lijie Yao, Anastasia Bezerianos, Romain Vuillemot, Petra Isenberg
Competitive sports coverage increasingly includes information on athlete or team statistics and records. Sports video coverage has traditionally embedded representations of this data in fixed locations on the screen, but more recently also attached representations to athletes or other targets in motion. These publicly used representations so far have been rather simple and systematic investigations of the research space of embedded visualizations in motion are still missing. Here we report on our preliminary research in the domain of professional and amateur swimming. We analyzed how visualizations are currently added to the coverage of Olympics swimming competitions and then plan to derive a design space for embedded data representations for swimming competitions. We are currently conducting a crowdsourced survey to explore which kind of swimming-related data general audiences are interested in, in order to identify opportunities for additional visualizations to be added to swimming competition coverage.
Entropy Contractions in Markov Chains: Half-Step, Full-Step and Continuous-Time
2024-09-12
Pietro Caputo, Zongchen Chen, Yuzhou Gu, Yury Polyanskiy
This paper considers the speed of convergence (mixing) of a finite Markov kernel $P$ with respect to the Kullback-Leibler divergence (entropy). Given a Markov kernel one defines either a discrete-time Markov chain (with the $n$-step transition kernel given by the matrix power $P^n$) or a continuous-time Markov process (with the time-$t$ transition kernel given by $e^{t(P-\mathrm{Id})}$). The contraction of entropy for $n=1$ or $t=0+$ are characterized by the famous functional inequalities, the strong data processing inequality (SDPI) and the modified log-Sobolev inequality (MLSI), respectively. When $P=KK^*$ is written as the product of a kernel and its adjoint, one could also consider the ``half-step'' contraction, which is the SDPI for $K$, while the ``full-step'' contraction refers to the SDPI for $P$. The work [DMLM03] claimed that these contraction coefficients (half-step, full-step, and continuous-time) are generally within a constant factor of each other. We disprove this and related conjectures by working out a number of different counterexamples. In particular, we construct (a) a continuous-time Markov process that contracts arbitrarily faster than its discrete-time counterpart; and (b) a kernel $P$ such that $P^{m+1}$ contracts arbitrarily better than $P^m$. Hence, our main conclusion is that the four standard inequalities comparing five common notions of entropy and variance contraction are generally not improvable. In the process of analyzing the counterexamples, we survey and sharpen the tools for bounding the contraction coefficients and characterize properties of extremizers of the respective functional inequalities. As our examples range from Bernoulli-Laplace model, random walks on graphs, to birth-death chains, the paper is also intended as a tutorial on computing MLSI, SDPI and other constants for these types of commonly occurring Markov chains.
A Deep Dive Into How Open-Source Project Maintainers Review and Resolve Bug Bounty Reports
2024-09-12
Jessy Ayala, Steven Ngo, Joshua Garcia
Researchers have investigated the bug bounty ecosystem from the lens of platforms, programs, and bug hunters. Understanding the perspectives of bug bounty report reviewers, especially those who historically lack a security background and little to no funding for bug hunters, is currently understudied. In this paper, we primarily investigate the perspective of open-source software (OSS) maintainers who have used \texttt{huntr}, a bug bounty platform that pays bounties to bug hunters who find security bugs in GitHub projects and have had valid vulnerabilities patched as a result. We address this area by conducting three studies: identifying characteristics through a listing survey ($n_1=51$), their ranked importance with Likert-scale survey data ($n_2=90$), and conducting semi-structured interviews to dive deeper into real-world experiences ($n_3=17$). As a result, we categorize 40 identified characteristics into benefits, challenges, helpful features, and wanted features. We find that private disclosure and project visibility are the most important benefits, while hunters focused on money or CVEs and pressure to review are the most challenging to overcome. Surprisingly, lack of communication with bug hunters is the least challenging, and CVE creation support is the second-least helpful feature for OSS maintainers when reviewing bug bounty reports. We present recommendations to make the bug bounty review process more accommodating to open-source maintainers and identify areas for future work.
In open-source software (OSS), software vulnerabilities have significantly increased. Although researchers have investigated the perspectives of vulnerability reporters and OSS contributor security practices, understanding the perspectives of OSS maintainers on vulnerability management and platform security features is currently understudied. In this paper, we investigate the perspectives of OSS maintainers who maintain projects listed in the GitHub Advisory Database. We explore this area by conducting two studies: identifying aspects through a listing survey ($n_1=80$) and gathering insights from semi-structured interviews ($n_2=22$). Of the 37 identified aspects, we find that supply chain mistrust and lack of automation for vulnerability management are the most challenging, and barriers to adopting platform security features include a lack of awareness and the perception that they are not necessary. Surprisingly, we find that despite being previously vulnerable, some maintainers still allow public vulnerability reporting, or ignore reports altogether. Based on our findings, we discuss implications for OSS platforms and how the research community can better support OSS vulnerability management efforts.
3D Radiation-Hydrodynamical Simulations of Shadows on Transition Disks
2024-09-12
Shangjia Zhang, Zhaohuan Zhu
Shadows are often observed in transition disks, which can result from obscuring by materials closer to the star, such as a misaligned inner disk. While shadows leave apparent darkened emission as observational signatures, they have significant dynamical impact on the disk. We carry out 3D radiation hydrodynamical simulations to study shadows in transition disks and find that the temperature drop due to the shadow acts as an asymmetric driving force, leading to spirals in the cavity. These spirals have zero pattern speed following the fixed shadow. The pitch angle is given by tan$^{-1}$($c_s$/$v_\phi$) (6$^{\circ}$ if $h/r$=0.1). These spirals transport mass through the cavity efficiently, with $\alpha \sim 10^{-2}$ in our simulation. Besides spirals, the cavity edge can also form vortices and flocculent streamers. When present, these features could disturb the shadow-induced spirals. By carrying out Monte Carlo Radiative Transfer simulations, we show that these features resemble those observed in near-infrared scattered light images. In the vertical direction, the vertical gravity is no longer balanced by the pressure gradient alone. Instead, an azimuthal convective acceleration term balances the gravity-pressure difference, leading to azimuthally periodic upward and downward gas motion reaching 10% of the sound speed, which can be probed by ALMA line observations.
Undecidability and incompleteness in quantum information theory and operator algebras
2024-09-12
Isaac Goldbring
We survey a number of incompleteness results in operator algebras stemming from the recent undecidability result in quantum complexity theory known as $\operatorname{MIP}^*=\operatorname{RE}$, the most prominent of which is the G\"odelian refutation of the Connes Embedding Problem. We also discuss the very recent use of $\operatorname{MIP}^*=\operatorname{RE}$ in refuting the Aldous-Lyons conjecture in probability theory.
A Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE): XVI. The ubiquity of truncated star-forming disks across the Virgo cluster environment
2024-09-12
C. R. Morgan, M. L. Balogh, A. Boselli, M. Fossati, C. Lawlor-Forsyth, E. Sazonova, P. Amram, M. Boquien, J. Braine, L. Cortese, P. Côté, J. C. Cuillandre, L. Ferrarese, S. Gwyn, G. Hensler, Junais, J. Roediger
We examine the prevalence of truncated star-forming disks in the Virgo cluster down to $M_* \simeq 10^7 ~\text{M}_{\odot}$. This work makes use of deep, high-resolution imaging in the H$\alpha$+[NII] narrow-band from the Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE) and optical imaging from the Next Generation Virgo Survey (NGVS). To aid in understanding the effects of the cluster environment on star formation in Virgo galaxies, we take a physically-motivated approach to define the edge of the star-forming disk via a drop-off in the radial specific star formation rate profile. Comparing with the expected sizes of normal galactic disks provides a measure of how truncated star-forming disks are in the cluster. We find that truncated star-forming disks are nearly ubiquitous across all regions of the Virgo cluster, including beyond the virial radius (0.974 Mpc). The majority of truncated disks at large clustercentric radii are of galaxies likely on first infall. As the intra-cluster medium density is low in this region, it is difficult to explain this population with solely ram-pressure stripping. A plausible explanation is that these galaxies are undergoing starvation of their gas supply before ram-pressure stripping becomes the dominant quenching mechanism. A simple model of starvation shows that this mechanism can produce moderate disk truncations within 1-2 Gyr. This model is consistent with `slow-then-rapid' or `delayed-then-rapid' quenching, where the early starvation mode drives disk truncations without significant change to the integrated star formation rate, and the later ram-pressure stripping mode rapidly quenches the galaxy. The origin of starvation may be in the group structures that exist around the main Virgo cluster, which indicates the importance of understanding pre-processing of galaxies beyond the cluster virial radius.
JWST Reveals Bulge-Dominated Star-forming Galaxies at Cosmic Noon
2024-09-12
Chloë E. Benton, Erica J. Nelson, Tim B. Miller, Rachel Bezanson, Justus Gibson, Abigail I Hartley, Marco Martorano, Sedona H. Price, Katherine A. Suess, Arjen van der Wel, Pieter van Dokkum, John R. Weaver, Katherine E. Whitaker
Hubble Space Telescope imaging shows that most star-forming galaxies at cosmic noon -- the peak of cosmic star formation history -- appear disk-dominated, leaving the origin of the dense cores in their quiescent descendants unclear. With the James Webb Space Telescope's (JWST) high-resolution imaging to 5 {\mu}m, we can now map the rest-frame near-infrared emission, a much closer proxy for stellar mass distribution, in these massive galaxies. We selected 70 star-forming galaxies with 10$<$log(M)$<$12 and 1.5$<$z$<$3 in the CEERS survey and compare their morphologies in the rest-frame optical to those in the rest-frame near-IR. While the bulk of these galaxies are disk-dominated in 1.5 {\mu}m (rest-frame optical) imaging, they appear more bulge-dominated at 4.4 {\mu}m (rest-frame near-infrared). Our analysis reveals that in massive star-forming galaxies at z$\sim$2, the radial surface brightness profiles steepen significantly, from a slope of $\sim$0.3/dex at 1.5 {\mu}m to $\sim$1.4/dex at 4.4 {\mu}m within radii $<$ 1 kpc. Additionally, we find their total flux contained within the central 1 kpc is approximately 7 times higher in F444W than in F150W. In rest-optical emission, a galaxy's central surface density appears to be the strongest indicator of whether it is quenched or star-forming. Our most significant finding is that at redder wavelengths, the central surface density ratio between quiescent and star-forming galaxies dramatically decreases from $\sim$10 to $\sim$1. This suggests the high central densities associated with galaxy quenching are already in place during the star-forming phase, imposing new constraints on the transition from star formation to quiescence.
Illuminating dark objects with dark matter lampshades
2024-09-12
Joseph Bramante, Melissa D. Diamond, J. Leo Kim
We demonstrate a new technique to search for dark compact objects. When dark matter comprising a dark compact object interacts with photons, the compact object can disperse light traveling though it. As these objects pass between the Earth and a distant star, they act as "lampshades" that dim the star. We examine how dimming effects from clumps of dark matter in the galaxy could be searched for in microlensing surveys, which measure the brightness of stars as a function of time. Using the EROS-2 and OGLE surveys, we show that a dimming analysis of existing data can be used to constrain dark sectors, and could be used to discover dark matter in compact objects.
Cross-correlation of Luminous Red Galaxies with ML-selected AGN in HSC-SSP: Unobscured AGN residing in more massive halos
2024-09-12
Rodrigo Córdova Rosado, Andy D. Goulding, Jenny E. Greene, Grayson C. Petter, Ryan C. Hickox, Nickolas Kokron, Michael A. Strauss, Jahmour J. Givans, Yoshiki Toba, Cassandra Starr Henderson
Active galactic nuclei (AGN) are the signposts of black hole growth, and likely play an important role in galaxy evolution. An outstanding question is whether AGN of different spectral types indicate different evolutionary stages in the coevolution of black holes and galaxies. We present the angular correlation function between an AGN sample selected from the Hyper Suprime Camera Subaru Strategic Program (HSC-SSP) optical + Wide-field Infrared Survey Explorer (WISE) mid-IR photometry, and a luminous red galaxy (LRG) sample from HSC-SSP. We investigate AGN clustering strength as a function of their luminosity and spectral features across three independent HSC fields totaling $\sim600\,{\rm deg^{2}}$, for $z\in0.6-1.2$ and AGN with $L_{6\mu m}>3\times10^{44}{\rm\,erg\,s^{-1}}$. There are $\sim28,500$ AGN and $\sim1.5$ million LRGs in our primary analysis. We determine the inferred average halo mass for the full AGN sample ($M_h \approx 10^{12.9}h^{-1}M_\odot$), and note that it does not evolve significantly as a function of redshift (over this narrow range) or luminosity. We find that, on average, unobscured AGN ($M_h \approx10^{13.3}h^{-1}M_\odot$) occupy $\sim4.5\times$ more massive halos than obscured AGN ($M_h \approx10^{12.6}h^{-1}M_\odot$), at $5\sigma$ statistical significance using 1-D uncertainties, and at $3\sigma$ using the full covariance matrix, suggesting a physical difference between unobscured and obscured AGN, beyond the line-of-sight viewing angle. Furthermore, we find evidence for a halo mass dependence on reddening level within the Type I AGN population, which could support the existence of a previously claimed dust-obscured phase in AGN-host galaxy coevolution. However, we also find that even quite small systematic shifts in the redshift distributions of the AGN sample could plausibly explain current and previously observed differences in $M_{h}$.
Feature Importance in Pedestrian Intention Prediction: A Context-Aware Review
2024-09-11
Mohsen Azarmi, Mahdi Rezaei, He Wang, Ali Arabian
Recent advancements in predicting pedestrian crossing intentions for Autonomous Vehicles using Computer Vision and Deep Neural Networks are promising. However, the black-box nature of DNNs poses challenges in understanding how the model works and how input features contribute to final predictions. This lack of interpretability delimits the trust in model performance and hinders informed decisions on feature selection, representation, and model optimisation; thereby affecting the efficacy of future research in the field. To address this, we introduce Context-aware Permutation Feature Importance (CAPFI), a novel approach tailored for pedestrian intention prediction. CAPFI enables more interpretability and reliable assessments of feature importance by leveraging subdivided scenario contexts, mitigating the randomness of feature values through targeted shuffling. This aims to reduce variance and prevent biased estimations in importance scores during permutations. We divide the Pedestrian Intention Estimation (PIE) dataset into 16 comparable context sets, measure the baseline performance of five distinct neural network architectures for intention prediction in each context, and assess input feature importance using CAPFI. We observed nuanced differences among models across various contextual characteristics. The research reveals the critical role of pedestrian bounding boxes and ego-vehicle speed in predicting pedestrian intentions, and potential prediction biases due to the speed feature through cross-context permutation evaluation. We propose an alternative feature representation by considering proximity change rate for rendering dynamic pedestrian-vehicle locomotion, thereby enhancing the contributions of input features to intention prediction. These findings underscore the importance of contextual features and their diversity to develop accurate and robust intent-predictive models.
SimulBench: Evaluating Language Models with Creative Simulation Tasks
2024-09-11
Qi Jia, Xiang Yue, Tianyu Zheng, Jie Huang, Bill Yuchen Lin
We introduce SimulBench, a benchmark designed to evaluate large language models (LLMs) across a diverse collection of creative simulation scenarios, such as acting as a Linux terminal or playing text games with users. While these simulation tasks serve as effective measures of an LLM's general intelligence, they are seldom incorporated into existing benchmarks. A major challenge is to develop an evaluation framework for testing different LLMs fairly while preserving the multi-round interactive nature of simulation tasks between users and AI. To tackle this issue, we suggest using a fixed LLM as a user agent to engage with an LLM to collect dialogues first under different tasks. Then, challenging dialogue scripts are extracted for evaluating different target LLMs. To facilitate automatic assessment on \DataName{}, GPT-4 is employed as the evaluator, tasked with reviewing the quality of the final response generated by the target LLMs given multi-turn dialogue scripts. Our comprehensive experiments indicate that these simulation tasks continue to pose a significant challenge with their unique natures and show the gap between proprietary models and the most advanced open LLMs. For example, GPT-4-turbo outperforms LLaMA-3-70b-Chat on 18.55\% more cases.
E-commerce platforms have a vast catalog of items to cater to their customers' shopping interests. Most of these platforms assist their customers in the shopping process by offering optimized recommendation carousels, designed to help customers quickly locate their desired items. Many models have been proposed in academic literature to generate and enhance the ranking and recall set of items in these carousels. Conventionally, the accompanying carousel title text (header) of these carousels remains static. In most instances, a generic text such as "Items similar to your current viewing" is utilized. Fixed variations such as the inclusion of specific attributes "Other items from a similar seller" or "Items from a similar brand" in addition to "frequently bought together" or "considered together" are observed as well. This work proposes a novel approach to customize the header generation process of these carousels. Our work leverages user-generated reviews that lay focus on specific attributes (aspects) of an item that were favorably perceived by users during their interaction with the given item. We extract these aspects from reviews and train a graph neural network-based model under the framework of a conditional ranking task. We refer to our innovative methodology as Dynamic Text Snippets (DTS) which generates multiple header texts for an anchor item and its recall set. Our approach demonstrates the potential of utilizing user-generated reviews and presents a unique paradigm for exploring increasingly context-aware recommendation systems.
Despite the mounting anticipation for the quantum revolution, the success of Quantum Machine Learning (QML) in the Noisy Intermediate-Scale Quantum (NISQ) era hinges on a largely unexplored factor: the generalization error bound, a cornerstone of robust and reliable machine learning models. Current QML research, while exploring novel algorithms and applications extensively, is predominantly situated in the context of noise-free, ideal quantum computers. However, Quantum Circuit (QC) operations in NISQ-era devices are susceptible to various noise sources and errors. In this article, we conduct a Systematic Mapping Study (SMS) to explore the state-of-the-art generalization bound for supervised QML in NISQ-era and analyze the latest practices in the field. Our study systematically summarizes the existing computational platforms with quantum hardware, datasets, optimization techniques, and the common properties of the bounds found in the literature. We further present the performance accuracy of various approaches in classical benchmark datasets like the MNIST and IRIS datasets. The SMS also highlights the limitations and challenges in QML in the NISQ era and discusses future research directions to advance the field. Using a detailed Boolean operators query in five reliable indexers, we collected 544 papers and filtered them to a small set of 37 relevant articles. This filtration was done following the best practice of SMS with well-defined research questions and inclusion and exclusion criteria.
The Impact of Shear on Disk Galaxy Star Formation Rates
2024-09-11
Xena L. Fortune-Bashee, Jiayi Sun, Jonathan C. Tan
Determining the physical processes that control galactic-scale star formation rates is essential for an improved understanding of galaxy evolution. The role of orbital shear is currently unclear, with some models expecting reduced star formation rates (SFRs) and efficiencies (SFEs) with increasing shear, e.g., if shear stabilizes gas against gravitational collapse, while others predicting enhanced rates, e.g., if shear-driven collisions between giant molecular clouds (GMCs) trigger star formation. Expanding on the analysis of 16 galaxies by Suwannajak, Tan, & Leroy (2014), we assess the shear dependence of SFE per orbital time ($\epsilon_\mathrm{orb}$) in 49 galaxies selected from the PHANGS-ALMA survey. In particular, we test a prediction of the shear-driven GMC collision model that $\epsilon_\mathrm{orb}\propto(1-0.7\beta)$, where $\beta\equiv{d}\:\mathrm{ln}\:v_\mathrm{circ}/d\:\mathrm{ln}\:r$, i.e., SFE per orbital time declines with decreasing shear. We fit the function $\epsilon_\mathrm{orb}=\epsilon_\mathrm{orb,\,0}(1-\alpha_\mathrm{CC}\beta)$ finding $\alpha_\mathrm{CC}\simeq0.76\pm0.16$; an alternative fit with $\epsilon_\mathrm{orb}$ normalized by the median value in each galaxy yields $\alpha_\mathrm{CC}^*=0.80\pm0.15$. These results are in good agreement with the prediction of the shear-driven GMC collision theory. We also examine the impact of a galactic bar on $\epsilon_\mathrm{orb}$ finding a modest decrease in SFE in the presence of bar, which can be attributed to lower rates of shear in these regions. We discuss the implications of our results for the GMC life cycle and environmental dependence of star formation activity.
Low temperature ferroelectric state in strontium titanate microcrystals using in situ multi-reflection Bragg coherent X-ray diffraction imaging
2024-09-11
David Yang, Sung Soo Ha, Sungwook Choi, Jialun Liu, Daniel Treuherz, Nan Zhang, Zheyi An, Hieu Minh Ngo, Muhammad Mahmood Nawaz, Ana F. Suzana, Longlong Wu, Gareth Nisbet, Daniel G. Porter, Hyunjung Kim, Ian K. Robinson
Strontium titanate is a classic quantum paraelectric oxide material that has been widely studied in bulk and thin films. It exhibits a well-known cubic-to-tetragonal antiferrodistortive phase transition at 105 K, characterized by the rotation of oxygen octahedra. A possible second phase transition at lower temperature is suppressed by quantum fluctuations, preventing the onset of ferroelectric order. However, recent studies have shown that ferroelectric order can be established at low temperatures by inducing strain and other means. Here, we used in situ multi-reflection Bragg coherent X-ray diffraction imaging to measure the strain and rotation tensors for two strontium titanate microcrystals at low temperature. We observe strains induced by dislocations and inclusion-like impurities in the microcrystals. Based on radial magnitude plots, these strains increase in magnitude and spread as the temperature decreases. Pearson's correlation heatmaps show a structural transition at 50 K, which we associate with the formation of a low-temperature ferroelectric phase in the presence of strain. We do not observe any change in local strains associated with the tetragonal phase transition at 105 K.
Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches
2024-09-11
Jamal Al-Karaki, Muhammad Al-Zafar Khan, Marwan Omar
The rising use of Large Language Models (LLMs) to create and disseminate malware poses a significant cybersecurity challenge due to their ability to generate and distribute attacks with ease. A single prompt can initiate a wide array of malicious activities. This paper addresses this critical issue through a multifaceted approach. First, we provide a comprehensive overview of LLMs and their role in malware detection from diverse sources. We examine five specific applications of LLMs: Malware honeypots, identification of text-based threats, code analysis for detecting malicious intent, trend analysis of malware, and detection of non-standard disguised malware. Our review includes a detailed analysis of the existing literature and establishes guiding principles for the secure use of LLMs. We also introduce a classification scheme to categorize the relevant literature. Second, we propose performance metrics to assess the effectiveness of LLMs in these contexts. Third, we present a risk mitigation framework designed to prevent malware by leveraging LLMs. Finally, we evaluate the performance of our proposed risk mitigation strategies against various factors and demonstrate their effectiveness in countering LLM-enabled malware. The paper concludes by suggesting future advancements and areas requiring deeper exploration in this fascinating field of artificial intelligence.
Inverse Constrained Reinforcement Learning (ICRL) is the task of inferring the implicit constraints followed by expert agents from their demonstration data. As an emerging research topic, ICRL has received considerable attention in recent years. This article presents a categorical survey of the latest advances in ICRL. It serves as a comprehensive reference for machine learning researchers and practitioners, as well as starters seeking to comprehend the definitions, advancements, and important challenges in ICRL. We begin by formally defining the problem and outlining the algorithmic framework that facilitates constraint inference across various scenarios. These include deterministic or stochastic environments, environments with limited demonstrations, and multiple agents. For each context, we illustrate the critical challenges and introduce a series of fundamental methods to tackle these issues. This survey encompasses discrete, virtual, and realistic environments for evaluating ICRL agents. We also delve into the most pertinent applications of ICRL, such as autonomous driving, robot control, and sports analytics. To stimulate continuing research, we conclude the survey with a discussion of key unresolved questions in ICRL that can effectively foster a bridge between theoretical understanding and practical industrial applications.
Euclid preparation. Deep learning true galaxy morphologies for weak lensing shear bias calibration
2024-09-11
Euclid Collaboration, B. Csizi, T. Schrabback, S. Grandis, H. Hoekstra, H. Jansen, L. Linke, G. Congedo, A. N. Taylor, A. Amara, S. Andreon, C. Baccigalupi, M. Baldi, S. Bardelli, P. Battaglia, R. Bender, C. Bodendorf, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, F. J. Castander, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, C. Colodro-Conde, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, J. Dinis, M. Douspis, F. Dubath, X. Dupac, S. Dusini, M. Farina, S. Farrens, F. Faustini, S. Ferriol, S. Fotopoulou, M. Frailis, E. Franceschi, S. Galeotta, B. Gillis, C. Giocoli, A. Grazian, F. Grupp, L. Guzzo, S. V. H. Haugan, W. Holmes, I. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, S. Ilić, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, M. Kilbinger, B. Kubik, K. Kuijken, M. Kümmel, M. Kunz, H. Kurki-Suonio, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, K. Markovic, M. Martinelli, N. Martinet, F. Marulli, R. Massey, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, G. Meylan, M. Moresco, L. Moscardini, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, Z. Sakr, A. G. Sánchez, B. Sartoris, P. Schneider, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, P. Tallada-Crespí, D. Tavagnacco, H. I. Teplitz, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, E. A. Valentijn, L. Valenziano, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, G. Zamorani, E. Zucca, A. Biviano, M. Bolzonella, E. Bozzo, C. Burigana, M. Calabrese, D. Di Ferdinando, J. A. Escartin Vigo, R. Farinelli, J. Gracia-Carpio, S. Matthew, N. Mauri, A. Pezzotta, M. Pöntinen, V. Scottez, M. Tenti, M. Viel, M. Wiesmann, Y. Akrami, V. Allevato, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, M. Ballardini, A. Blanchard, L. Blot, S. Borgani, S. Bruton, R. Cabanac, A. Calabro, G. Cañas-Herrera, A. Cappi, F. Caro, C. S. Carvalho, T. Castro, K. C. Chambers, S. Contarini, A. R. Cooray, G. Desprez, A. Díaz-Sánchez, J. J. Diaz, S. Di Domizio, H. Dole, S. Escoffier, A. G. Ferrari, P. G. Ferreira, I. Ferrero, A. Finoguenov, A. Fontana, F. Fornari, L. Gabarra, K. Ganga, J. García-Bellido, T. Gasparetto, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, C. M. Gutierrez, A. Hall, H. Hildebrandt, J. Hjorth, A. Jimenez Muñoz, S. Joudaki, J. J. E. Kajava, V. Kansal, D. Karagiannis, C. C. Kirkpatrick, A. M. C. Le Brun, J. Le Graet, L. Legrand, J. Lesgourgues, T. I. Liaudat, A. Loureiro, J. Macias-Perez, G. Maggio, M. Magliocchetti, C. Mancini, F. Mannucci, R. Maoli, J. Martín-Fleitas, C. J. A. P. Martins, L. Maurin, R. B. Metcalf, M. Miluzio, P. Monaco, A. Montoro, A. Mora, C. Moretti, G. Morgante, Nicholas A. Walton, L. Pagano, L. Patrizii, V. Popa, D. Potter, I. Risso, P. -F. Rocci, M. Sahlén, E. Sarpa, A. Schneider, M. Sereno, P. Simon, A. Spurio Mancini, J. Stadel, K. Tanidis, C. Tao, N. Tessore, G. Testera, R. Teyssier, S. Toft, S. Tosi, A. Troja, M. Tucci, C. Valieri, J. Valiviita, D. Vergani, G. Verza, P. Vielzeuf
To date, galaxy image simulations for weak lensing surveys usually approximate the light profiles of all galaxies as a single or double S\'ersic profile, neglecting the influence of galaxy substructures and morphologies deviating from such a simplified parametric characterization. While this approximation may be sufficient for previous data sets, the stringent cosmic shear calibration requirements and the high quality of the data in the upcoming Euclid survey demand a consideration of the effects that realistic galaxy substructures have on shear measurement biases. Here we present a novel deep learning-based method to create such simulated galaxies directly from HST data. We first build and validate a convolutional neural network based on the wavelet scattering transform to learn noise-free representations independent of the point-spread function of HST galaxy images that can be injected into simulations of images from Euclid's optical instrument VIS without introducing noise correlations during PSF convolution or shearing. Then, we demonstrate the generation of new galaxy images by sampling from the model randomly and conditionally. Next, we quantify the cosmic shear bias from complex galaxy shapes in Euclid-like simulations by comparing the shear measurement biases between a sample of model objects and their best-fit double-S\'ersic counterparts. Using the KSB shape measurement algorithm, we find a multiplicative bias difference between these branches with realistic morphologies and parametric profiles on the order of $6.9\times 10^{-3}$ for a realistic magnitude-S\'ersic index distribution. Moreover, we find clear detection bias differences between full image scenes simulated with parametric and realistic galaxies, leading to a bias difference of $4.0\times 10^{-3}$ independent of the shape measurement method. This makes it relevant for stage IV weak lensing surveys such as Euclid.
Using Neural Network Models to Estimate Stellar Ages from Lithium Equivalent Widths: An EAGLES Expansion
2024-09-11
George Weaver, Robin D. Jeffries, Richard J. Jackson
We present an Artificial Neural Network (ANN) model of photospheric lithium depletion in cool stars (3000 < Teff / K < 6500), producing estimates and probability distributions of age from Li I 6708A equivalent width (LiEW) and effective temperature data inputs. The model is trained on the same sample of 6200 stars from 52 open clusters, observed in the Gaia-ESO spectroscopic survey, and used to calibrate the previously published analytical EAGLES model, with ages 2 - 6000 Myr and -0.3 < [Fe/H] < 0.2. The additional flexibility of the ANN provides some improvements, including better modelling of the "lithium dip" at ages < 50 Myr and Teff ~ 3500K, and of the intrinsic dispersion in LiEW at all ages. Poor age discrimination is still an issue at ages > 1 Gyr, confirming that additional modelling flexibility is not sufficient to fully represent the LiEW - age - Teff relationship, and suggesting the involvement of further astrophysical parameters. Expansion to include such parameters - rotation, accretion, and surface gravity - is discussed, and the use of an ANN means these can be more easily included in future iterations, alongside more flexible functional forms for the LiEW dispersion. Our methods and ANN model are provided in an updated version 2.0 of the EAGLES software.
Grand Unification at the Cosmological Collider with Chemical Potential
2024-09-11
Arushi Bodas, Edward Broadberry, Raman Sundrum
We introduce a tree-level chemical potential mechanism for spin-1 particles within cosmological collider physics, allowing them to be detected in primordial non-Gaussianities for masses above the inflationary Hubble scale. We apply this mechanism to orbifold grand unification and the massive unification partners of the standard model gauge bosons. Our mechanism requires at least a pair of massive vector fields which are singlets of the standard model, a condition which is satisfied in the classic "trinification" scenario. Assuming that the gauge hierarchy problem is solved by supersymmetry, gauge coupling running points to unification partners at ~ $10^{15}$ GeV. We show that, within high-scale inflation, chemical potential enhancement can lead to observably strong signals for trinification partners in future cosmological surveys.
The PIPER Survey. II. The Globular Cluster Systems of Low Surface Brightness Galaxies in the Perseus Cluster
2024-09-11
Steven R. Janssens, Duncan A. Forbes, Aaron J. Romanowsky, Jonah Gannon, Joel Pfeffer, Warrick J. Couch, Jean P. Brodie, William E. Harris, Patrick R. Durrell, Kenji Bekki
We present Hubble Space Telescope ACS/WFC and WFC3/UVIS imaging for a sample of 50 low surface brightness (LSB) galaxies in the $\sim$10$^{15}$ M$_{\odot}$ Perseus cluster, which were originally identified in ground-based imaging. We measure the structural properties of these galaxies and estimate the total number of globular clusters (GCs) they host. Around half of our sample galaxies meet the strict definition of an ultra-diffuse galaxy (UDG), while the others are UDG-like but are either somewhat more compact or slightly brighter. A small number of galaxies reveal systems with many tens of GCs, rivalling some of the richest GC systems known around UDGs in the Coma cluster. We find the sizes of rich GC systems, in terms of their half-number radii, extending to $\sim$1.2 times the half-light radii of their host galaxy on average. The mean colours of the GC systems are the same, within the uncertainties, as those of their host galaxy stars. This suggests that GCs and galaxy field stars may have formed at the same epoch from the same enriched gas. It may also indicate a significant contribution from disrupted GCs to the stellar component of the host galaxy as might be expected in the 'failed galaxy' formation scenario for UDGs.
A test of the conjectured critical black-hole formation -- null geodesic correspondence: The case of self-gravitating scalar fields
2024-09-11
Shahar Hod
It has recently been conjectured [A. Ianniccari {\it et al.}, Phys. Rev. Lett. {\bf 133}, 081401 (2024)] that there exists a correspondence between the critical threshold of black-hole formation and the stability properties of null circular geodesics in the curved spacetime of the collapsing matter configuration. In the present compact paper we provide a non-trivial test of this intriguing conjecture. In particular, using analytical techniques we study the physical and mathematical properties of self-gravitating scalar field configurations that possess marginally-stable (degenerate) null circular geodesics. We reveal the interesting fact that the {\it analytically} calculated critical compactness parameter ${\cal C}^{\text{analytical}}\equiv{\text{max}_r}\{m(r)/r\}=6/25$, which signals the appearance of the first (marginally-stable) null circular geodesic in the curved spacetime of the self-gravitating scalar fields, agrees quite well (to within $\sim10\%$) with the exact compactness parameter ${\cal C}^{\text{numerical}}\equiv\text{max}_t\{\text{max}_r\{m(r)/r\}\}\simeq0.265$ which is computed {\it numerically} using fully non-linear numerical simulations of the gravitational collapse of scalar fields at the threshold of black-hole formation [here $m(r)$ is the gravitational mass contained within a sphere of radius $r$].
In our ever-evolving world, new data exhibits a long-tailed distribution, such as e-commerce platform reviews. This necessitates continuous model learning imbalanced data without forgetting, addressing the challenge of long-tailed class-incremental learning (LTCIL). Existing methods often rely on retraining linear classifiers with former data, which is impractical in real-world settings. In this paper, we harness the potent representation capabilities of pre-trained models and introduce AdaPtive Adapter RouTing (APART) as an exemplar-free solution for LTCIL. To counteract forgetting, we train inserted adapters with frozen pre-trained weights for deeper adaptation and maintain a pool of adapters for selection during sequential model updates. Additionally, we present an auxiliary adapter pool designed for effective generalization, especially on minority classes. Adaptive instance routing across these pools captures crucial correlations, facilitating a comprehensive representation of all classes. Consequently, APART tackles the imbalance problem as well as catastrophic forgetting in a unified framework. Extensive benchmark experiments validate the effectiveness of APART. Code is available at: https://github.com/vita-qzh/APART
Trust Dynamics in Human-Autonomy Interaction: Uncover Associations between Trust Dynamics and Personal Characteristics
2024-09-11
Hyesun Chung, X. Jessie Yang
While personal characteristics influence people's snapshot trust towards autonomous systems, their relationships with trust dynamics remain poorly understood. We conducted a human-subject experiment with 130 participants performing a simulated surveillance task aided by an automated threat detector. A comprehensive pre-experimental survey collected data on participants' personal characteristics across 12 constructs and 28 dimensions. Based on data collected in the experiment, we clustered participants' trust dynamics into three types and assessed differences among the three clusters in terms of personal characteristics, behaviors, performance, and post-experiment ratings. Participants were clustered into three groups, namely Bayesian decision makers, disbelievers, and oscillators. Results showed that the clusters differ significantly in seven personal characteristics: masculinity, positive affect, extraversion, neuroticism, intellect, performance expectancy, and high expectations. The disbelievers tend to have high neuroticism and low performance expectancy. The oscillators tend to have higher scores in masculinity, positive affect, extraversion and intellect. We also found significant differences in the behaviors and post-experiment ratings among the three groups. The disbelievers are the least likely to blindly follow the recommendations made by the automated threat detector. Based on the significant personal characteristics, we developed a decision tree model to predict cluster types with an accuracy of 70%.
Recent Trends of Multimodal Affective Computing: A Survey from NLP Perspective
2024-09-11
Guimin Hu, Yi Xin, Weimin Lyu, Haojian Huang, Chang Sun, Zhihong Zhu, Lin Gui, Ruichu Cai
Multimodal affective computing (MAC) has garnered increasing attention due to its broad applications in analyzing human behaviors and intentions, especially in text-dominated multimodal affective computing field. This survey presents the recent trends of multimodal affective computing from NLP perspective through four hot tasks: multimodal sentiment analysis, multimodal emotion recognition in conversation, multimodal aspect-based sentiment analysis and multimodal multi-label emotion recognition. The goal of this survey is to explore the current landscape of multimodal affective research, identify development trends, and highlight the similarities and differences across various tasks, offering a comprehensive report on the recent progress in multimodal affective computing from an NLP perspective. This survey covers the formalization of tasks, provides an overview of relevant works, describes benchmark datasets, and details the evaluation metrics for each task. Additionally, it briefly discusses research in multimodal affective computing involving facial expressions, acoustic signals, physiological signals, and emotion causes. Additionally, we discuss the technical approaches, challenges, and future directions in multimodal affective computing. To support further research, we released a repository that compiles related works in multimodal affective computing, providing detailed resources and references for the community.
The microbiome science of composting and human excrement composting: a review
2024-09-11
Jeff Meilander, J. Gregory Caporaso
Linear waste management systems are unsustainable and contribute to environmental degradation, economic inequity, and health disparities. Among the array of environmental challenges stemming from anthropogenic impacts, the management of human excrement (human feces and urine) stands as a significant concern. Over two billion people do not have access to adequate sanitation resulting in a global public health crisis. Composting is the microbial biotechnology aimed at cycling organic waste, including human excrement, for improved public health, agricultural productivity and safety, and environmental sustainability. Applications of modern microbiome-omics and related technologies have vast capacity to support continued advances in composting science and praxis. In this article, we review literature focused on applications of microbiome technologies to study composting systems and reactions. The studies we survey generally fall into the categories of animal manure composting, food and landscaping waste composting, biosolids composting, and human excrement composting. We review experiments utilizing microbiome technologies to investigate strategies for enhancing pathogen suppression and accelerating the biodegradation of organic matter. Additionally, we explore studies focused on the bioengineering potential of microbes as inoculants to facilitate degradation of toxins such as pharmaceuticals or per- and polyfluoroalkyl substances (PFAS). The findings from these studies underscore the importance of advancing our understanding of composting processes through the integration of emerging microbiome-omics technologies. We conclude that work to-date has demonstrated exciting basic and applied science potential from studying compost microbiomes, with promising implications for enhancing global environmental sustainability and public health.
Demo: SGCode: A Flexible Prompt-Optimizing System for Secure Generation of Code
2024-09-11
Khiem Ton, Nhi Nguyen, Mahmoud Nazzal, Abdallah Khreishah, Cristian Borcea, NhatHai Phan, Ruoming Jin, Issa Khalil, Yelong Shen
This paper introduces SGCode, a flexible prompt-optimizing system to generate secure code with large language models (LLMs). SGCode integrates recent prompt-optimization approaches with LLMs in a unified system accessible through front-end and back-end APIs, enabling users to 1) generate secure code, which is free of vulnerabilities, 2) review and share security analysis, and 3) easily switch from one prompt optimization approach to another, while providing insights on model and system performance. We populated SGCode on an AWS server with PromSec, an approach that optimizes prompts by combining an LLM and security tools with a lightweight generative adversarial graph neural network to detect and fix security vulnerabilities in the generated code. Extensive experiments show that SGCode is practical as a public tool to gain insights into the trade-offs between model utility, secure code generation, and system cost. SGCode has only a marginal cost compared with prompting LLMs. SGCode is available at: http://3.131.141.63:8501/.
The Role of Explainable AI in Revolutionizing Human Health Monitoring
2024-09-11
Abdullah Alharthi, Ahmed Alqurashi, Turki Alharbi, Mohammed Alammar, Nasser Aldosari, Houssem Bouchekara, Yusuf Shaaban, Mohammad Shoaib Shahriar, Abdulrahman Al Ayidh
The complex nature of disease mechanisms and the variability of patient symptoms present significant obstacles in developing effective diagnostic tools. Although machine learning has made considerable advances in medical diagnosis, its decision-making processes frequently lack transparency, which can jeopardize patient outcomes. This underscores the critical need for Explainable AI (XAI), which not only offers greater clarity but also has the potential to significantly improve patient care. In this literature review, we conduct a detailed analysis of analyzing XAI methods identified through searches across various databases, focusing on chronic conditions such as Parkinson's, stroke, depression, cancer, heart disease, and Alzheimer's disease. The literature search revealed the application of 9 trending XAI algorithms in the field of healthcare and highlighted the pros and cons of each of them. Thus, the article is concluded with a critical appraisal of the challenges and future research opportunities for XAI in human health monitoring.
PSZ2 G282.28+49.94, a recently discovered analogue of the famous Bullet Cluster
2024-09-11
I. Bartalucci, M. Rossetti, W. Boschin, M. Girardi, M. Nonino, E. Baraldi, M. Balboni, D. Coe, S. De Grandi, F. Gastaldello, S. Ghizzardi, S. Giacintucci, C. Grillo, D. Harvey, L. Lovisari, S. Molendi, T. Resseguier, G. Riva, T. Venturi, A. Zitrin
We present a detailed study of the gas and galaxy properties of the cluster PSZ2 G282.28+49.94 detected in the Planck all-sky survey. The intracluster medium (ICM) of this object at z=0.56 exhibits a cometary-like shape. Combining Chandra and TNG observations, we characterised the spatially resolved thermodynamical properties of the gas and the spatial and velocity distribution of 73 galaxy members. The cluster structure is quite complex with an elongated core region containing the two brightest cluster galaxies and one dense group to the south-east. Since there is no velocity difference between the core and the south-east group, we suggest the presence of a merger along the plane of the sky. This structure is related to complex X-ray and radio features, and thus the merger has likely been caught during the post-merger phase. Comparing the distribution of the ICM and of member galaxies, we find a large offset of $\sim 350$ kpc between the position of the X-ray peak and the centre of a concentration of galaxies, preceding it in the likely direction of motion. This configuration is similar to the famous Bullet Cluster, leading us to dub PSZ2 G282.28+49.94 the "Planck bullet", and represents an ideal situation to provide astrophysical constraints to the self-interaction cross-section ($\sigma/m$) of dark matter particles. These results illustrate the power of a multi-wavelength approach to probe the merging scenario of such complex and distant systems.
Mechanisms for belief elicitation without ground truth
2024-09-11
Niklas Valentin Lehmann
This review article examines the challenge of eliciting truthful information from multiple individuals when such information cannot be verified against an objective truth, a problem known as information elicitation without verification (IEWV). This article reviews over 25 mechanisms designed to incentivize truth-telling in such scenarios, and their effectiveness in empirical studies. The analysis finds that although many mechanisms theoretically ensure truthfulness as a Bayesian Nash Equilibrium, empirical evidence of such mechanisms working in practice is very limited and generally weak. Consequently, more empirical research is needed to validate mechanisms. Given that many mechanisms are very complex and cannot be easily conveyed to research subjects, this review suggests that simpler, more intuitive mechanisms may be easier to test and apply.
Alignment of Diffusion Models: Fundamentals, Challenges, and Future
2024-09-11
Buhua Liu, Shitong Shao, Bao Li, Lichen Bai, Zhiqiang Xu, Haoyi Xiong, James Kwok, Sumi Helal, Zeke Xie
Diffusion models have emerged as the leading paradigm in generative modeling, excelling in various applications. Despite their success, these models often misalign with human intentions, generating outputs that may not match text prompts or possess desired properties. Inspired by the success of alignment in tuning large language models, recent studies have investigated aligning diffusion models with human expectations and preferences. This work mainly reviews alignment of diffusion models, covering advancements in fundamentals of alignment, alignment techniques of diffusion models, preference benchmarks, and evaluation for diffusion models. Moreover, we discuss key perspectives on current challenges and promising future directions on solving the remaining challenges in alignment of diffusion models. To the best of our knowledge, our work is the first comprehensive review paper for researchers and engineers to comprehend, practice, and research alignment of diffusion models.
Negative Sampling in Recommendation: A Survey and Future Directions
2024-09-11
Haokai Ma, Ruobing Xie, Lei Meng, Fuli Feng, Xiaoyu Du, Xingwu Sun, Zhanhui Kang, Xiangxu Meng
Recommender systems aim to capture users' personalized preferences from the cast amount of user behaviors, making them pivotal in the era of information explosion. However, the presence of the dynamic preference, the "information cocoons", and the inherent feedback loops in recommendation make users interact with a limited number of items. Conventional recommendation algorithms typically focus on the positive historical behaviors, while neglecting the essential role of negative feedback in user interest understanding. As a promising but easy-to-ignored area, negative sampling is proficients in revealing the genuine negative aspect inherent in user behaviors, emerging as an inescapable procedure in recommendation. In this survey, we first discuss the role of negative sampling in recommendation and thoroughly analyze challenges that consistently impede its progress. Then, we conduct an extensive literature review on the existing negative sampling strategies in recommendation and classify them into five categories with their discrepant techniques. Finally, we detail the insights of the tailored negative sampling strategies in diverse recommendation scenarios and outline an overview of the prospective research directions toward which the community may engage and benefit.
The early Solar System and its meteoritical witnesses
2024-09-11
Emmanuel Jacquet, Cornelis Dullemond, Joanna Drążkowska, Steven Desch
Meteorites, and in particular primitive meteorites (chondrites), are irreplaceable probes of the solar protoplanetary disk. We review their essential properties and endeavour to place them in astrophysical context. The earliest solar system solids, refractory inclusions, may have formed over the innermost au of the disk and have been transported outward by its expansion or turbulent diffusion. The age spread of chondrite components may be reconciled with the tendency of drag-induced radial drift if they were captured in pressure maxima, which may account for the non-carbonaceous/carbonaceous meteorite isotopic dichotomy. The solid/gas ratio around unity witnessed by chondrules, if interpreted as nebular (non-impact) products, suggests efficient radial concentration and settling at such locations, conducive to planetesimal formation by the streaming instability. The cause of the pressure bumps, e.g. Jupiter or condensation lines, remains to be ascertained.
A Survey of Anomaly Detection in In-Vehicle Networks
2024-09-11
Övgü Özdemir, M. Tuğberk İşyapar, Pınar Karagöz, Klaus Werner Schmidt, Demet Demir, N. Alpay Karagöz
Modern vehicles are equipped with Electronic Control Units (ECU) that are used for controlling important vehicle functions including safety-critical operations. ECUs exchange information via in-vehicle communication buses, of which the Controller Area Network (CAN bus) is by far the most widespread representative. Problems that may occur in the vehicle's physical parts or malicious attacks may cause anomalies in the CAN traffic, impairing the correct vehicle operation. Therefore, the detection of such anomalies is vital for vehicle safety. This paper reviews the research on anomaly detection for in-vehicle networks, more specifically for the CAN bus. Our main focus is the evaluation of methods used for CAN bus anomaly detection together with the datasets used in such analysis. To provide the reader with a more comprehensive understanding of the subject, we first give a brief review of related studies on time series-based anomaly detection. Then, we conduct an extensive survey of recent deep learning-based techniques as well as conventional techniques for CAN bus anomaly detection. Our comprehensive analysis delves into anomaly detection algorithms employed in in-vehicle networks, specifically focusing on their learning paradigms, inherent strengths, and weaknesses, as well as their efficacy when applied to CAN bus datasets. Lastly, we highlight challenges and open research problems in CAN bus anomaly detection.
Cyber Deception: State of the art, Trends and Open challenges
2024-09-11
Pedro Beltrán López, Manuel Gil Pérez, Pantaleone Nespoli
The growing interest in cybersecurity has significantly increased articles designing and implementing various Cyber Deception (CYDEC) mechanisms. This trend reflects the urgent need for new strategies to address cyber threats effectively. Since its emergence, CYDEC has established itself as an innovative defense against attackers, thanks to its proactive and reactive capabilities, finding applications in numerous real-life scenarios. Despite the considerable work devoted to CYDEC, the literature still presents significant gaps. In particular, there has not been (i) a comprehensive analysis of the main components characterizing CYDEC, (ii) a generic classification covering all types of solutions, nor (iii) a survey of the current state of the literature in various contexts. This article aims to fill these gaps through a detailed review of the main features that comprise CYDEC, developing a comprehensive classification taxonomy. In addition, the different frameworks used to generate CYDEC are reviewed, presenting a more comprehensive one. Existing solutions in the literature using CYDEC, both without Artificial Intelligence (AI) and with AI, are studied and compared. Finally, the most salient trends of the current state of the art are discussed, offering a list of pending challenges for future research.
A Perspective on AI-Guided Molecular Simulations in VR: Exploring Strategies for Imitation Learning in Hyperdimensional Molecular Systems
2024-09-11
Mohamed Dhouioui, Jonathan Barnoud, Rhoslyn Roebuck Williams, Harry J. Stroud, Phil Bates, David R. Glowacki
Molecular dynamics simulations are a crucial computational tool for researchers to understand and engineer molecular structure and function in areas such as drug discovery, protein engineering, and material design. Despite their utility, MD simulations are expensive, owing to the high dimensionality of molecular systems. Interactive molecular dynamics in virtual reality (iMD-VR) has recently been developed as a 'human-in-the-loop' strategy, which leverages high-performance computing to accelerate the researcher's ability to solve the hyperdimensional sampling problem. By providing an immersive 3D environment that enables visualization and manipulation of real-time molecular motion, iMD-VR enables researchers and students to efficiently and intuitively explore and navigate these complex, high-dimensional systems. iMD-VR platforms offer a unique opportunity to quickly generate rich datasets that capture human experts' spatial insight regarding molecular structure and function. This paper explores the possibility of employing user-generated iMD-VR datasets to train AI agents via imitation learning (IL). IL is an important technique in robotics that enables agents to mimic complex behaviors from expert demonstrations, thus circumventing the need for explicit programming or intricate reward design. We review the utilization of IL for manipulation tasks in robotics and discuss how iMD-VR recordings could be used to train IL models for solving specific molecular 'tasks'. We then investigate how such approaches could be applied to the data captured from iMD-VR recordings. Finally, we outline the future research directions and potential challenges of using AI agents to augment human expertise to efficiently navigate conformational spaces, highlighting how this approach could provide valuable insight across domains such as materials science, protein engineering, and computer-aided drug design.
Deriving Cosmological Parameters from the Euclid mission
2024-09-11
Davide Sciotti
The Euclid mission is a visionary project undertaken by the European Space Agency (ESA) to probe the universe's evolution and geometry by surveying the position and gravitational shape distortion of billions of galaxies. These observations bear the potential to offer unprecedented measurements of the cosmological parameters, thereby advancing our understanding of the cosmos. This work revolves around the central theme of quantifying the constraining power of the upcoming Euclid 3$\times$2pt photometric survey, accounting for several factors which have been neglected to this date in the official forecasts, especially more subtle sources of uncertainty which need to be included in the forecast (and data) analysis due to the precision of the observations. First, we include and study the impact of super-sample covariance, a source of sample variance coming from the incomplete sampling of the density and shear field Fourier modes caused by the limited survey volume. Second, we examine the effect of scale cuts, translating them from Fourier to harmonic space through the use of the BNT transform, which offers an efficient way of separating angular scales for the cosmic shear signal. This analysis allows quantifying and mitigating the bias coming from the uncertainty on our modelling of small scales. These updated forecasts, validated against the reference Euclid ones, provide insights into the expected precision achieved on the cosmological and nuisance parameters, for a variety of survey settings and for the inclusion of different realistic systematics, such as multiplicative shear bias, magnification bias, uncertainty in the mean of the redshift distribution and so on.
2024-09-11
Victoria Bollo, Martin Zwaan, Celine Peroux, Aleksandra Hamanowicz, Jianhang Chen, Simon Weng, Rob J. Ivison, Andrew Biggs
The ALMACAL survey is based on a database of reprocessed ALMA calibration scans suitable for scientific analysis, observed as part of regular PI observations. We present all the data accumulated from the start of ALMA operations until May 2022 for 1047 calibrator fields across the southern sky spanning ALMA Bands 3 to 10 (~ 84 - 950 GHz), so-called ALMACAL-22. Encompassing over 1000 square arcmin and accumulating over 2000 hours of integration time, ALMACAL is not only one of the largest ALMA surveys to date, but it continues to grow with each new scientific observation. We outline the methods for processing and imaging a subset of the highest-quality data ('pruned sample'). Using deconvolution techniques within the visibility data (uv plane), we created data cubes as the final product for further scientific analysis. We describe the properties and shortcomings of ALMACAL and compare its area and sensitivity with other sub-millimetre surveys. Notably, ALMACAL overcomes limitations of previous sub-millimetre surveys, such as small sky coverage and the effects of cosmic variance. Moreover, we discuss the improvements introduced by the latest version of this dataset that will enhance our understanding of dusty star-forming galaxies, extragalactic absorption lines, active galactic nucleus physics, and ultimately the evolution of molecular gas.
A Fine-grained Sentiment Analysis of App Reviews using Large Language Models: An Evaluation Study
2024-09-11
Faiz Ali Shah, Ahmed Sabir, Rajesh Sharma
Analyzing user reviews for sentiment towards app features can provide valuable insights into users' perceptions of app functionality and their evolving needs. Given the volume of user reviews received daily, an automated mechanism to generate feature-level sentiment summaries of user reviews is needed. Recent advances in Large Language Models (LLMs) such as ChatGPT have shown impressive performance on several new tasks without updating the model's parameters i.e. using zero or a few labeled examples. Despite these advancements, LLMs' capabilities to perform feature-specific sentiment analysis of user reviews remain unexplored. This study compares the performance of state-of-the-art LLMs, including GPT-4, ChatGPT, and LLama-2-chat variants, for extracting app features and associated sentiments under 0-shot, 1-shot, and 5-shot scenarios. Results indicate the best-performing GPT-4 model outperforms rule-based approaches by 23.6% in f1-score with zero-shot feature extraction; 5-shot further improving it by 6%. GPT-4 achieves a 74% f1-score for predicting positive sentiment towards correctly predicted app features, with 5-shot enhancing it by 7%. Our study suggests that LLM models are promising for generating feature-specific sentiment summaries of user reviews.
The IXPE View of Neutron Star Low-Mass X-ray Binaries
2024-09-11
Francesco Ursini, Andrea Gnarini, Fiamma Capitanio, Anna Bobrikova, Massimo Cocchi, Alessandro Di Marco, Sergio Fabiani, Ruben Farinelli, Fabio La Monaca, John Rankin, Mary Lynne Saade, Juri Poutanen
Low-mass X-ray binaries hosting weakly magnetized neutron stars (NS-LMXBs) are among the brightest sources in the X-ray sky. Since 2021, the Imaging X-ray Polarimetry Explorer (IXPE) has provided new measurements of the X-ray polarization of these sources. IXPE observations have revealed that most NS-LMXBs are significantly polarized in the X-rays, providing unprecedented insight into the geometry of their accretion flow. In this review paper, we summarize the first results obtained by IXPE on NS-LMXBs, the emerging trends within each class of sources (atoll/Z), and possible physical interpretations.
Deep Learning Techniques for Hand Vein Biometrics: A Comprehensive Review
2024-09-11
Mustapha Hemis, Hamza Kheddar, Sami Bourouis, Nasir Saleem
Biometric authentication has garnered significant attention as a secure and efficient method of identity verification. Among the various modalities, hand vein biometrics, including finger vein, palm vein, and dorsal hand vein recognition, offer unique advantages due to their high accuracy, low susceptibility to forgery, and non-intrusiveness. The vein patterns within the hand are highly complex and distinct for each individual, making them an ideal biometric identifier. Additionally, hand vein recognition is contactless, enhancing user convenience and hygiene compared to other modalities such as fingerprint or iris recognition. Furthermore, the veins are internally located, rendering them less susceptible to damage or alteration, thus enhancing the security and reliability of the biometric system. The combination of these factors makes hand vein biometrics a highly effective and secure method for identity verification. This review paper delves into the latest advancements in deep learning techniques applied to finger vein, palm vein, and dorsal hand vein recognition. It encompasses all essential fundamentals of hand vein biometrics, summarizes publicly available datasets, and discusses state-of-the-art metrics used for evaluating the three modes. Moreover, it provides a comprehensive overview of suggested approaches for finger, palm, dorsal, and multimodal vein techniques, offering insights into the best performance achieved, data augmentation techniques, and effective transfer learning methods, along with associated pretrained deep learning models. Additionally, the review addresses research challenges faced and outlines future directions and perspectives, encouraging researchers to enhance existing methods and propose innovative techniques.
Cross-Refine: Improving Natural Language Explanation Generation by Learning in Tandem
2024-09-11
Qianli Wang, Tatiana Anikina, Nils Feldhus, Simon Ostermann, Sebastian Möller, Vera Schmitt
Natural language explanations (NLEs) are vital for elucidating the reasoning behind large language model (LLM) decisions. Many techniques have been developed to generate NLEs using LLMs. However, like humans, LLMs might not always produce optimal NLEs on first attempt. Inspired by human learning processes, we introduce Cross-Refine, which employs role modeling by deploying two LLMs as generator and critic, respectively. The generator outputs a first NLE and then refines this initial explanation using feedback and suggestions provided by the critic. Cross-Refine does not require any supervised training data or additional training. We validate Cross-Refine across three NLP tasks using three state-of-the-art open-source LLMs through automatic and human evaluation. We select Self-Refine (Madaan et al., 2023) as the baseline, which only utilizes self-feedback to refine the explanations. Our findings from automatic evaluation and a user study indicate that Cross-Refine outperforms Self-Refine. Meanwhile, Cross-Refine can perform effectively with less powerful LLMs, whereas Self-Refine only yields strong results with ChatGPT. Additionally, we conduct an ablation study to assess the importance of feedback and suggestions. Both of them play an important role in refining explanations. We further evaluate Cross-Refine on a bilingual dataset in English and German.
Diophantine stability for curves over finite fields
2024-09-11
Francesc Bars, Joan Carles Lario
We carry out a survey on curves defined over finite fields that are Diophantine stable; that is, with the property that the set of points of the curve is not altered under a proper field extension. First, we derive some general results of such curves and then we analyze several families of curves that happen to be Diophantine stable.
Recent state-of-the-art authorship attribution methods learn authorship representations of texts in a latent, non-interpretable space, hindering their usability in real-world applications. Our work proposes a novel approach to interpreting these learned embeddings by identifying representative points in the latent space and utilizing LLMs to generate informative natural language descriptions of the writing style of each point. We evaluate the alignment of our interpretable space with the latent one and find that it achieves the best prediction agreement compared to other baselines. Additionally, we conduct a human evaluation to assess the quality of these style descriptions, validating their utility as explanations for the latent space. Finally, we investigate whether human performance on the challenging AA task improves when aided by our system's explanations, finding an average improvement of around +20% in accuracy.
Neutron skin and its effects in heavy-ion collisions
2024-09-11
Meng-Qi Ding, De-Qing Fang, Yu-Gang Ma
Neutron skin is an exotic phenomena in unstable nuclei. The various effects in nuclear reactions caused by the neutron skin and also its relation with the properties of nuclear structure are reviewed in this article. Based on numerous studies with theoretical models, strong correlations have been found between the neutron skin thickness and neutron removal cross section, neutron/proton yield ratio, t/\ch{^3He} yield ratio, neutron-proton momentum difference, isoscaling parameter, photon production, reaction cross sections for neutron induced reactions, charge-changing cross sections difference of mirror nuclei, astrophysical $S$-factor, and other quantities in nuclear reactions induced by neutron-rich nuclei. Moreover, the relationships between neutron skin thickness and some properties of nuclear structure, such as $\alpha$-cluster formation, $\alpha$ decay, nuclear surface, nuclear temperature, and proton radii difference of mirror nuclei, have also been investigated. It also has been shown that the neutron skin plays a crucial role in relativistic heavy-ion collisions. Experimentally, an unstable nucleus with neutron skin can be generated by radioactive nuclear beam facilities, and the thickness of neutron skin could be extracted by measuring the sensitive probes, which further helps giving stringent constraints on the equation of state of asymmetric nuclear matter and properties of neutron stars.
2024-09-11
Federica Bucchieri, Lijie Yao, Petra Isenberg
We contribute a systematic review of situated visualizations in motion in the context of video games. Video games produce rich dynamic datasets during gameplay that are often visualized to help players succeed in a game. Often these visualizations are moving either because they are attached to moving game elements or due to camera changes. We want to understand to what extent this motion and contextual game factors impact how players can read these visualizations. In order to ground our work, we surveyed 160 visualizations in motion and their embeddings in the game world. Here, we report on our analysis and categorization of these visualizations.
A Unified Phenomenology of Ion Heating in Low-$β$ Plasmas: Test-Particle Simulations
2024-09-11
Zade Johnston, Jonathan Squire, Romain Meyrand
We argue that two prominent theories of ion heating in low-$\beta$ collisionless plasmas -- stochastic and quasi-linear heating -- represent similar physical processes in turbulence with different normalized cross helicities. To capture both, we propose a simple phenomenology based on the power in scales at which critically balanced fluctuations reach their smallest parallel scale. Simulations of test ions interacting with turbulence confirm our scalings across a wide range of different ion and turbulence properties, including with a steep ion-kinetic transition range as relevant to the solar wind.
The Blending ToolKit: A simulation framework for evaluation of galaxy detection and deblending
2024-09-11
Ismael Mendoza, Andrii Torchylo, Thomas Sainrat, Axel Guinot, Alexandre Boucaud, Maxime Paillasa, Camille Avestruz, Prakruth Adari, Eric Aubourg, Biswajit Biswas, James Buchanan, Patricia Burchat, Cyrille Doux, Remy Joseph, Sowmya Kamath, Alex I. Malz, Grant Merz, Hironao Miyatake, Cécile Roucelle, Tianqing Zhang, the LSST Dark Energy Science Collaboration
We present an open source Python library for simulating overlapping (i.e., blended) images of galaxies and performing self-consistent comparisons of detection and deblending algorithms based on a suite of metrics. The package, named Blending Toolkit (BTK), serves as a modular, flexible, easy-to-install, and simple-to-use interface for exploring and analyzing systematic effects related to blended galaxies in cosmological surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST). BTK has three main components: (1) a set of modules that perform fast image simulations of blended galaxies, using the open source image simulation package GalSim; (2) a module that standardizes the inputs and outputs of existing deblending algorithms; (3) a library of deblending metrics commonly defined in the galaxy deblending literature. In combination, these modules allow researchers to explore the impacts of galaxy blending in cosmological surveys. Additionally, BTK provides researchers who are developing a new deblending algorithm a framework to evaluate algorithm performance and make principled comparisons with existing deblenders. BTK includes a suite of tutorials and comprehensive documentation. The source code is publicly available on GitHub at https://github.com/LSSTDESC/BlendingToolKit.
Neural Algorithmic Reasoning with Multiple Correct Solutions
2024-09-11
Zeno Kujawa, John Poole, Dobrik Georgiev, Danilo Numeroso, Pietro Liò
Neural Algorithmic Reasoning (NAR) aims to optimize classical algorithms. However, canonical implementations of NAR train neural networks to return only a single solution, even when there are multiple correct solutions to a problem, such as single-source shortest paths. For some applications, it is desirable to recover more than one correct solution. To that end, we give the first method for NAR with multiple solutions. We demonstrate our method on two classical algorithms: Bellman-Ford (BF) and Depth-First Search (DFS), favouring deeper insight into two algorithms over a broader survey of algorithms. This method involves generating appropriate training data as well as sampling and validating solutions from model output. Each step of our method, which can serve as a framework for neural algorithmic reasoning beyond the tasks presented in this paper, might be of independent interest to the field and our results represent the first attempt at this task in the NAR literature.
Analysis of Potential Biases and Validity of Studies Using Multiverse Approaches to Assess the Impacts of Government Responses to Epidemics
2024-09-11
Jeremy D. Goldhaber-Fiebert
We analyze the methodological approach and validity of interpretation of using national-level time-series regression analyses relating epidemic outcomes to policies that estimate many models involving permutations of analytic choices (i.e., a "multiverse" approach). Specifically, we evaluate the possible biases and pitfalls of interpretation of a multiverse approach to the context of government responses to epidemics using the example of COVID-19 and a recently published peer-reviewed paper by Bendavid and Patel (2024) along with the subsequent commentary that the two authors published discussing and interpreting the implications of their work. While we identify multiple potential errors and sources of biases in how the specific analyses were undertaken that are also relevant for other studies employing similar approaches, our most important finding involves constructing a counterexample showing that causal model specification-agnostic multiverse analyses can be incorrectly used to suggest that no consistent effect can be discovered in data especially in cases where most specifications estimated with the data are far from causally valid. Finally, we suggest an alternative approach involving hypothesis-drive specifications that explicitly account for infectiousness across jurisdictions in the analysis as well as the interconnected ways that policies and behavioral responses may evolve within and across these jurisdictions.
The young exoplanetary system TOI-4562: Confirming the presence of a third body in the system
2024-09-11
V. Fermiano, R. K. Saito, V. D. Ivanov, C. Caceres, L. A. Almeida, J. Aires, J. C. Beamin, D. Minniti, T. Ferreira, L. Andrade, B. W. Borges, L. de Almeida, F. Jablonski, W. Schlindwein
Young planetary systems represent an opportunity to investigate the early stages of (exo)planetary formation because the gravitational interactions have not yet significantly changed the initial configuration of the system. TOI-4562 b is a highly eccentric temperate Jupiter analogue orbiting a young F7V-type star of $<700$ Myr in age with an orbital period of $P_{orb} \sim 225$ days and an eccentricity of $e=0.76$, and is one of the largest known exoplanets to have formed in situ. We observed a new transit of TOI-4562 b using the 0.6-m Zeiss telescope at the Pico dos Dias Observatory (OPD/LNA) in Minas Gerais, Brazil, and combine our data with Transiting Exoplanet Survey Satellite (TESS) and archive data, with the aim being to improve the ephemerides of this interesting system. The $O-C$ diagram for the new ephemeris is consistent with the presence of a giant planet in an outer orbit around TOI-4562. TOI-4562 c is a planet with a mass of $M=5.77 M_{Jup}$, an orbital period of $P_{orb}= 3990$ days, and a semi-major axis of $a = 5.219$ AU. We report the discovery of TOI-4562 c, the exoplanet with the longest orbital period discovered to date via the transit timing variation (TTV) method. The TOI-4562 system is in the process of violent evolution with intense dynamical changes - judging by its young age and high eccentricity - and is therefore a prime target for studies of formation and evolution of planetary systems.
Fermi Blazars in the Zwicky Transient Facility Survey: Properties of Large Optical Variations
2024-09-11
Si-Si Sun, Zhongxiang Wang, Shun-hao Ji
We analyze the optical light-curve data, obtained with the Zwicky Transient Facility (ZTF) survey, for 47 gamma-ray blazars monitored by the Large Area Telescope onboard {\it the Fermi Gamma-ray Space Telescope (Fermi)}. These 47 sources are selected because they are among the Fermi blazars with the largest optical variations in the ZTF data. Two color-magnitude variation patterns are seen in them, one being redder to stable when brighter (RSWB; in 31 sources) and the other being stable when brighter (in 16 sources). The patterns fit with the results recently reported in several similar studies with different data. Moreover, we find that the colors in the stable state of the sources share similar values, which (after corrected for the Galactic extinction) of most sources are in a range of 0.4--0.55. This feature could be intrinsic and may be applied in, for example, the study of intragalactic medium. We also determine the turning points for the sources showing the RSWB pattern, after which the color changes saturate and become stable. We find a correlation between optical fluxes and gamma-ray fluxes at the turning points. The physical implications of the correlation remain to be investigated, probably better with a sample of high-quality gamma-ray flux measurements.
Dataset of Tensile Properties for Sub-sized Specimens of Nuclear Structural Materials
2024-09-11
Longze Li, John W. Merickel, Yalei Tang, Rongjie Song, Joshua E. Rittenhouse, Aleksandar Vakanski, Fei Xu
Mechanical testing with sub-sized specimens plays an important role in the nuclear industry, facilitating tests in confined experimental spaces with lower irradiation levels and accelerating the qualification of new materials. The reduced size of specimens results in different material behavior at the microscale, mesoscale, and macroscale, in comparison to standard-sized specimens, which is referred to as the specimen size effect. Although analytical models have been proposed to correlate the properties of sub-sized specimens to standard-sized specimens, these models lack broad applicability across different materials and testing conditions. The objective of this study is to create the first large public dataset of tensile properties for sub-sized specimens used in nuclear structural materials. We performed an extensive literature review of relevant publications and extracted over 1,000 tensile testing records comprising 54 parameters including material type and composition, manufacturing information, irradiation conditions, specimen dimensions, and tensile properties. The dataset can serve as a valuable resource to investigate the specimen size effect and develop computational methods to correlate the tensile properties of sub-sized specimens.
What is the Role of Small Models in the LLM Era: A Survey
2024-09-10
Lihu Chen, Gaël Varoquaux
Large Language Models (LLMs) have made significant progress in advancing artificial general intelligence (AGI), leading to the development of increasingly large models such as GPT-4 and LLaMA-405B. However, scaling up model sizes results in exponentially higher computational costs and energy consumption, making these models impractical for academic researchers and businesses with limited resources. At the same time, Small Models (SMs) are frequently used in practical settings, although their significance is currently underestimated. This raises important questions about the role of small models in the era of LLMs, a topic that has received limited attention in prior research. In this work, we systematically examine the relationship between LLMs and SMs from two key perspectives: Collaboration and Competition. We hope this survey provides valuable insights for practitioners, fostering a deeper understanding of the contribution of small models and promoting more efficient use of computational resources. The code is available at https://github.com/tigerchen52/role_of_small_models
Electron Microscopy-based Automatic Defect Inspection for Semiconductor Manufacturing: A Systematic Review
2024-09-10
Enrique Dehaerne, Bappaditya Dey, Victor Blanco, Jesse Davis
In this review, automatic defect inspection algorithms that analyze Electron Microscope (EM) images of Semiconductor Manufacturing (SM) products are identified, categorized, and discussed. This is a topic of critical importance for the SM industry as the continuous shrinking of device patterns has led to increasing defectivity and a greater prevalence of higher-resolution imaging tools such as EM. These aspects among others threaten to increase costs as a result of increased inspection time-to-solution and decreased yield, respectively. Relevant research papers were systematically identified in four popular publication databases in January 2024. A total of 103 papers were selected after screening for novel contributions relating to automatic EM image analysis algorithms for semiconductor defect inspection. These papers were then categorized based on the inspection tasks they addressed, their evaluation metrics, and the type of algorithms used. A notable finding from this categorization is that reference-based defect detection algorithms were the most popular algorithm type until 2020 when deep learning-based inspection algorithms became more popular, especially for defect classification. Furthermore, four broader research questions were discussed to come to the following conclusions: (i) the key components of inspection algorithms are set up, pre-processing, feature extraction, and final prediction; (ii) the maturity of the manufacturing process affects the data availability and required sensitivity of inspection algorithms; (iii) key challenges for these algorithms relate to the desiderata of minimizing time-to-solution which pushes for high imaging throughput, reducing manual input during algorithm setup, and higher processing throughput; and (iv) three promising directions for future work are suggested based on gaps in the reviewed literature that address key remaining limitations.
2024-09-10
Nicolas Perrault, Qi Heng Ho, Morteza Lahijanian
Sampling-based motion planners (SBMPs) are effective for planning with complex kinodynamic constraints in high-dimensional spaces, but they still struggle to achieve real-time performance, which is mainly due to their serial computation design. We present Kinodynamic Parallel Accelerated eXpansion (Kino-PAX), a novel highly parallel kinodynamic SBMP designed for parallel devices such as GPUs. Kino-PAX grows a tree of trajectory segments directly in parallel. Our key insight is how to decompose the iterative tree growth process into three massively parallel subroutines. Kino-PAX is designed to align with the parallel device execution hierarchies, through ensuring that threads are largely independent, share equal workloads, and take advantage of low-latency resources while minimizing high-latency data transfers and process synchronization. This design results in a very efficient GPU implementation. We prove that Kino-PAX is probabilistically complete and analyze its scalability with compute hardware improvements. Empirical evaluations demonstrate solutions in the order of 10 ms on a desktop GPU and in the order of 100 ms on an embedded GPU, representing up to 1000 times improvement compared to coarse-grained CPU parallelization of state-of-the-art sequential algorithms over a range of complex environments and systems.
A photochemical PHO network for hydrogen-dominated exoplanet atmospheres
2024-09-10
Elspeth K. H. Lee, Shang-Min Tsai, Julianne I. Moses, John M. C. Plane, Channon Visscher, Stephen J. Klippenstein
Due to the detection of phosphine PH3 in the Solar System gas giants Jupiter and Saturn, PH3 has long been suggested to be detectable in exosolar substellar atmospheres too. However, to date, a direct detection of phosphine has proven to be elusive in exoplanet atmosphere surveys. We construct an updated phosphorus-hydrogen-oxygen (PHO) photochemical network suitable for simulation of gas giant hydrogen-dominated atmospheres. Using this network, we examine PHO photochemistry in hot Jupiter and warm Neptune exoplanet atmospheres at Solar and enriched metallicities. Our results show for HD 189733b-like hot Jupiters that HOPO, PO and P2 are typically the dominant P carriers at pressures important for transit and emission spectra, rather than PH3. For GJ1214b-like warm Neptune atmospheres our results suggest that at Solar metallicity PH3 is dominant in the absence of photochemistry, but is generally not in high abundance for all other chemical environments. At 10 and 100 times Solar, small oxygenated phosphorus molecules such as HOPO and PO dominate for both thermochemical and photochemical simulations. The network is able to reproduce well the observed PH3 abundances on Jupiter and Saturn. Despite progress in improving the accuracy of the PHO network, large portions of the reaction rate data remain with approximate, uncertain or missing values, which could change the conclusions of the current study significantly. Improving understanding of the kinetics of phosphorus-bearing chemical reactions will be a key undertaking for astronomers aiming to detect phosphine and other phosphorus species in both rocky and gaseous exoplanetary atmospheres in the near future.
Asymptotic Hodge Theory in String Compactifications and Integrable Systems
2024-09-10
Jeroen Monnee
In this thesis we study the framework of asymptotic Hodge theory and its applications in both the string landscape and the landscape of 2d integrable field theories. We show how this mathematical framework allows for a general characterization of the asymptotic behaviour of physical couplings in low-energy effective theories coming from string theory, and apply this knowledge to investigate the finiteness and geometric structure of the string landscape landscape. At the same time, we find that the defining equations of variations of Hodge structure also arise in the context of certain integrable field theories, which opens the way to finding new classes of very general solutions to said models. Part I reviews the relevant aspects of type IIB / F-theory flux compactifications and the resulting landscape of 4d low-energy effective $\mathcal{N}=1$ supergravity theories. Part II provides an in-depth discussion on asymptotic Hodge theory, including detailed explanations on the nilpotent orbit theorem of Schmid, and the multi-variable Sl(2)-orbit theorem of Cattani, Kaplan, and Schmid. This part of the thesis also contains new results regarding the multi-variable bulk reconstruction procedure, which have not appeared in the author's previous publications. Part III concerns the application of the aforementioned results to study the finiteness of the F-theory flux landscape. Additionally, motivated by recent advances in the field of o-minimal geometry and the theory of unlikely intersections, we propose three conjectures which aim to address finer features of the flux landscape. Part IV investigates two corners of the landscape of 2d integrable non-linear sigma-models, namely the $\lambda$-deformed gauged WZW model and the critical bi-Yang-Baxter model. Notably, it is shown that asymptotic Hodge theory can be used to find broad classes of solutions these models.
Design, scientific goals, and performance of the SCExAO survey for planets around accelerating stars
2024-09-10
Mona El Morsy, Thayne Currie, Masayuki Kuzuhara, Jeffrey Chilcote, Olivier Guyon, Taylor L. Tobin, Timothy Brandt, Qier An, Kyohoon Anh, Danielle Bovie, Vincent Deo, Tyler Groff, Ziying Gu, Markus Janson, Nemanja Jovanovic, Yiting Li, Kellen Lawson, Julien Lozi, Miles Lucas, Christian Marois, Naoshi Murakami, Eric Nielsen, Barnaby Norris, Nour Skaf, Motohide Tamura, William Thompson, Taichi Uyama, Sebastien Vievard
We describe the motivation, design, and early results for our 42-night, 125 star Subaru/SCExAO direct imaging survey for planets around accelerating stars. Unlike prior large surveys, ours focuses only on stars showing evidence for an astrometric acceleration plausibly due to the dynamical pull of an unseen planet or brown dwarf. Our program is motivated by results from a recent pilot program that found the first planet jointly discovered from direct imaging and astrometry and resulted in a planet and brown dwarf discovery rate substantially higher than previous unbiased surveys like GPIES. The first preliminary results from our program reveal multiple new companions; discovered planets and brown dwarfs can be further characterized with follow-up data, including higher-resolution spectra. Finally, we describe the critical role this program plays in supporting the Roman Space Telescope Coronagraphic Instrument, providing a currently-missing list of targets suitable for the CGI technological demonstration without which the CGI tech demo risks failure.
The Intrinsic Flattening of Extragalactic Stellar Disks
2024-09-10
Jeremy Favaro, Stéphane Courteau, Sébastien Comerón, Connor Stone
Highly inclined (edge-on) disk galaxies offer the unique perspective to constrain their intrinsic flattening, $c/a$, where $c$ and $a$ are respectively the vertical and long radial axes of the disk measured at suitable stellar densities. The ratio $c/a$ is a necessary quantity in the assessment of galaxy inclinations, three-dimensional structural reconstructions, total masses, as well as a constraint to galaxy formation models. 3.6 micron maps of 133 edge-on spiral galaxies from the Spitzer Survey of Stellar Structure in Galaxies (S4G) and its early-type galaxy extension are used to revisit the assessment of $c/a$ free from dust extinction and away from the influence of a stellar bulge. We present a simple definition of $c/a$ and explore trends with other galactic physical parameters: total stellar mass, concentration index, total HI mass, mass of the central mass concentration, circular velocity, model-dependent scales, as well as Hubble type. Other than a dependence on early/late Hubble types, and a related trend with light concentration, no other parameters were found to correlate with the intrinsic flattening of spiral galaxies. The latter is mostly constant with $\langle c/a \rangle$ = 0.124 $\pm$ 0.001 (stat) $\pm$ 0.033 (intrinsic/systematic) and greater for earlier types.
2024-09-10
Anthony J. Taylor, Steven L. Finkelstein, Dale D. Kocevski, Junehyoung Jeon, Volker Bromm, Ricardo O. Amorin, Pablo Arrabal Haro, Bren E. Backhaus, Micaela B. Bagley, Eduardo Bañados, Rachana Bhatawdekar, Madisyn Brooks, Antonello Calabro, Oscar A. Chavez Ortiz, Yingjie Cheng, Nikko J. Cleri, Justin W. Cole, Kelcey Davis, Mark Dickinson, Callum Donnan, James S. Dunlop, Richard S. Ellis, Vital Fernandez, Adriano Fontana, Seiji Fujimoto, Mauro Giavalisco, Andrea Grazian, Jingsong Guo, Nimish P. Hathi, Benne W. Holwerda, Michaela Hirschmann, Kohei Inayoshi, Jeyhan S. Kartaltepe, Yana Khusanova, Anton M. Koekemoer, Vasily Kokorev, Rebecca L. Larson, Gene C. K. Leung, Ray A. Lucas, Derek J. McLeod, Lorenzo Napolitano, Masafusa Onoue, Fabio Pacucci, Casey Papovich, Pablo G. Pérez-González, Nor Pirzkal, Rachel S. Somerville, Jonathan R. Trump, Stephen M. Wilkins, L. Y. Aaron Yung, Haowen Zhang
We present a sample of 50 H-alpha detected broad-line active galactic nuclei (BLAGN) at redshifts 3.50), independent of the contributions of emission lines to the broadband photometry. We construct the black hole (BH) mass function at 3.5
MAMBO -- An empirical galaxy and AGN mock catalogue for the exploitation of future surveys
2024-09-10
X. López-López, M. Bolzonella, L. Pozzetti, M. Salvato, L. Bisigello, A. Feltre, I. E. López, A. Viitanen, V. Allevato, A. Bongiorno, G. Girelli, J. Buchner, S. Charlot, F. Ricci, C. Schreiber, G. Zamorani
Aims. We present MAMBO, a flexible and efficient workflow to build empirical galaxy and Active Galactic Nuclei (AGN) mock catalogues that reproduce the physical and observational properties of these sources. Methods. We start from simulated dark matter (DM) haloes, to preserve the link with the cosmic web, and we populate them with galaxies and AGN using abundance matching techniques. We follow an empirical methodology, using stellar mass functions (SMF), host galaxy AGN mass functions and AGN accretion rate distribution functions studied at different redshifts to assign, among other properties, stellar masses, the fraction of quenched galaxies, or the AGN activity (demography, obscuration, multiwavelength emission, etc.). Results. As a proof test, we apply the method to a Millennium DM lightcone of 3.14 $\rm deg^2$ up to redshift $z=10$ and down to stellar masses $\mathcal{M} \gtrsim 10^{7.5} \, M_\odot$. We show that the AGN population from the mock lightcone here presented reproduces with good accuracy various observables, such as state-of-the-art luminosity functions in the X-ray up to $z \sim 7$ and in the ultraviolet up to $z \sim 5$, optical/NIR colour-colour diagrams, and narrow emission line diagnostic diagrams. Finally, we demonstrate how this catalogue can be used to make useful predictions for large surveys. Using \textit{Euclid} as a case example, we compute, among other forecasts, the expected surface densities of galaxies and AGN detectable in the \textit{Euclid} $H_{\rm E}$ band. We find that \textit{Euclid} might observe (on $H_{\rm E}$ only) about $10^{7}$ and $8 \times 10^{7}$ Type 1 and 2 AGN respectively, and $2 \times 10^{9}$ galaxies at the end of its 14 679 $\rm deg^2$ Wide survey, in good agreement with other published forecasts.
Modeling Image Tone Dichotomy with the Power Function
2024-09-10
Axel Martinez, Gustavo Olague, Emilio Hernandez
The primary purpose of this paper is to present the concept of dichotomy in image illumination modeling based on the power function. In particular, we review several mathematical properties of the power function to identify the limitations and propose a new mathematical model capable of abstracting illumination dichotomy. The simplicity of the equation opens new avenues for classical and modern image analysis and processing. The article provides practical and illustrative image examples to explain how the new model manages dichotomy in image perception. The article shows dichotomy image space as a viable way to extract rich information from images despite poor contrast linked to tone, lightness, and color perception. Moreover, a comparison with state-of-the-art methods in image enhancement provides evidence of the method's value.
Insuring Uninsurable Risks from AI: The State as Insurer of Last Resort
2024-09-10
Cristian Trout
Many experts believe that AI systems will sooner or later pose uninsurable risks, including existential risks. This creates an extreme judgment-proof problem: few if any parties can be held accountable ex post in the event of such a catastrophe. This paper proposes a novel solution: a government-provided, mandatory indemnification program for AI developers. The program uses risk-priced indemnity fees to induce socially optimal levels of care. Risk-estimates are determined by surveying experts, including indemnified developers. The Bayesian Truth Serum mechanism is employed to incent honest and effortful responses. Compared to alternatives, this approach arguably better leverages all private information, and provides a clearer signal to indemnified developers regarding what risks they must mitigate to lower their fees. It's recommended that collected fees be used to help fund the safety research developers need, employing a fund matching mechanism (Quadratic Financing) to induce an optimal supply of this public good. Under Quadratic Financing, safety research projects would compete for private contributions from developers, signaling how much each is to be supplemented with public funds.
Human Perception of LLM-generated Text Content in Social Media Environments
2024-09-10
Kristina Radivojevic, Matthew Chou, Karla Badillo-Urquiola, Paul Brenner
Emerging technologies, particularly artificial intelligence (AI), and more specifically Large Language Models (LLMs) have provided malicious actors with powerful tools for manipulating digital discourse. LLMs have the potential to affect traditional forms of democratic engagements, such as voter choice, government surveys, or even online communication with regulators; since bots are capable of producing large quantities of credible text. To investigate the human perception of LLM-generated content, we recruited over 1,000 participants who then tried to differentiate bot from human posts in social media discussion threads. We found that humans perform poorly at identifying the true nature of user posts on social media. We also found patterns in how humans identify LLM-generated text content in social media discourse. Finally, we observed the Uncanny Valley effect in text dialogue in both user perception and identification. This indicates that despite humans being poor at the identification process, they can still sense discomfort when reading LLM-generated content.
A Systematic Approach to Crossing Numbers of Cartesian Products with Paths
2024-09-10
Zayed Asiri, Ryan Burdett, Markus Chimani, Michael Haythorpe, Alex Newcombe, Mirko H. Wagner
Determining the crossing numbers of Cartesian products of small graphs with arbitrarily large paths has been an ongoing topic of research since the 1970s. Doing so requires the establishment of coincident upper and lower bounds; the former is usually demonstrated by providing a suitable drawing procedure, while the latter often requires substantial theoretical arguments. Many such papers have been published, which typically focus on just one or two small graphs at a time, and use ad hoc arguments specific to those graphs. We propose a general approach which, when successful, establishes the required lower bound. This approach can be applied to the Cartesian product of any graph with arbitrarily large paths, and in each case involves solving a modified version of the crossing number problem on a finite number (typically only two or three) of small graphs. We demonstrate the potency of this approach by applying it to Cartesian products involving all 133 graphs $G$ of orders five or six, and show that it is successful in 128 cases. This includes 60 cases which a recent survey listed as either undetermined, or determined only in journals without adequate peer review.
Intrinsic alignments (IAs) of galaxies/halos observed via galaxy imaging survey, combined with redshift information, offer a novel probe of cosmology as a tracer of tidal force field of large-scale structure. In this paper, we present a perturbation theory based model for the redshift-space power spectra of galaxy/halo IAs that can keep the impact of Finger-of-God damping effect, known as a nonlinear systematics of redshift-space distortions, under control. Focusing particularly on galaxy/halo density and IA cross power spectrum, we derive analytically the explicit expressions for the next-to-leading order corrections. Comparing the model predictions with $N$-body simulations, we show that these corrections indeed play an important role for an unbiased determination of the growth-rate parameter, and hence the model proposed here can be used for a precision test of gravity on cosmological scales.
2024-09-10
Laura Georgescu, James Fox, Anna Gautier, Michael Wooldridge
Quadratic Voting (QV) is a social choice mechanism that addresses the "tyranny of the majority" of one-person-one-vote mechanisms. Agents express not only their preference ordering but also their preference intensity by purchasing $x$ votes at a cost of $x^2$. Although this pricing rule maximizes utilitarian social welfare and is robust against strategic manipulation, it has not yet found many real-life applications. One key reason is that the original QV mechanism does not limit voter budgets. Two variations have since been proposed: a (no-budget) multiple-issue generalization and a fixed-budget version that allocates a constant number of credits to agents for use in multiple binary elections. While some analysis has been undertaken with respect to the multiple-issue variation, the fixed-budget version has not yet been rigorously studied. In this work, we formally propose a novel fixed-budget multiple-issue QV mechanism. This integrates the advantages of both the aforementioned variations, laying the theoretical foundations for practical use cases of QV, such as multi-agent resource allocation. We analyse our fixed-budget multiple-issue QV by comparing it with traditional voting systems, exploring potential collusion strategies, and showing that checking whether strategy profiles form a Nash equilibrium is tractable.
Cosmological gravity on all scales IV: 3x2pt Fisher forecasts for pixelised phenomenological modified gravity
2024-09-10
Sankarshana Srinivasan, Daniel B Thomas, Peter L. Taylor
Stage IV large scale structure surveys are promising probes of gravity on cosmological scales. Due to the vast model-space in the modified gravity literature, model-independent parameterisations represent useful and scalable ways to test extensions of $\Lambda$CDM. In this work we use a recently validated approach of computing the non-linear $3\times 2$pt observables in modified gravity models with a time-varying effective gravitational constant $\mu$ and a gravitational slip $\eta$ that is binned in redshift to produce Fisher forecasts for an LSST Y10-like survey. We also include in our modelling an effective nulling scheme for weak-lensing by applying the BNT transformation that localises the weak-lensing kernel enabling well-informed scale cuts. We show that the combination of improved non-linear modelling and better control of the scales that are modelled/cut yields high precision constraints on the cosmological and modified gravity parameters. We find that 4 redshift bins for $\mu$ of width corresponding to equal incremental $\Lambda$CDM growth is optimal given the state-of-the-art modelling and show how the BNT transformation can be used to mitigate the impact of small-scale systematic effects, such as baryonic feedback.
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling
2024-09-10
Alex Glyn-Davies, Arnaud Vadeboncoeur, O. Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami
Variational inference (VI) is a computationally efficient and scalable methodology for approximate Bayesian inference. It strikes a balance between accuracy of uncertainty quantification and practical tractability. It excels at generative modelling and inversion tasks due to its built-in Bayesian regularisation and flexibility, essential qualities for physics related problems. Deriving the central learning objective for VI must often be tailored to new learning tasks where the nature of the problems dictates the conditional dependence between variables of interest, such as arising in physics problems. In this paper, we provide an accessible and thorough technical introduction to VI for forward and inverse problems, guiding the reader through standard derivations of the VI framework and how it can best be realized through deep learning. We then review and unify recent literature exemplifying the creative flexibility allowed by VI. This paper is designed for a general scientific audience looking to solve physics-based problems with an emphasis on uncertainty quantification.
Social Mediation through Robots -- A Scoping Review on Improving Group Interactions through Directed Robot Action using an Extended Group Process Model
2024-09-10
Thomas H. Weisswange, Hifza Javed, Manuel Dietrich, Malte F. Jung, Nawid Jamali
Group processes refer to the dynamics that occur within a group and are critical for understanding how groups function. With robots being increasingly placed within small groups, improving these processes has emerged as an important application of social robotics. Social Mediation Robots elicit behavioral change within groups by deliberately influencing the processes of groups. While research in this field has demonstrated that robots can effectively affect interpersonal dynamics, there is a notable gap in integrating these insights to develop coherent understanding and theory. We present a scoping review of literature targeting changes in social interactions between multiple humans through intentional action from robotic agents. To guide our review, we adapt the classical Input-Process-Output (I-P-O) models that we call "Mediation I-P-O model". We evaluated 1633 publications, which yielded 89 distinct social mediation concepts. We construct 11 mediation approaches robots can use to shape processes in small groups and teams. This work strives to produce generalizable insights and evaluate the extent to which the potential of social mediation through robots has been realized thus far. We hope that the proposed framework encourages a holistic approach to the study of social mediation and provides a foundation to standardize future reporting in the domain.
A tutorial on automatic differentiation with complex numbers
2024-09-10
Nicholas Krämer
Automatic differentiation is everywhere, but there exists only minimal documentation of how it works in complex arithmetic beyond stating "derivatives in $\mathbb{C}^d$" $\cong$ "derivatives in $\mathbb{R}^{2d}$" and, at best, shallow references to Wirtinger calculus. Unfortunately, the equivalence $\mathbb{C}^d \cong \mathbb{R}^{2d}$ becomes insufficient as soon as we need to derive custom gradient rules, e.g., to avoid differentiating "through" expensive linear algebra functions or differential equation simulators. To combat such a lack of documentation, this article surveys forward- and reverse-mode automatic differentiation with complex numbers, covering topics such as Wirtinger derivatives, a modified chain rule, and different gradient conventions while explicitly avoiding holomorphicity and the Cauchy--Riemann equations (which would be far too restrictive). To be precise, we will derive, explain, and implement a complex version of Jacobian-vector and vector-Jacobian products almost entirely with linear algebra without relying on complex analysis or differential geometry. This tutorial is a call to action, for users and developers alike, to take complex values seriously when implementing custom gradient propagation rules -- the manuscript explains how.
2024-09-10
Daniel A. Messenger, April Tran, Vanja Dukic, David M. Bortz
The weak form is a ubiquitous, well-studied, and widely-utilized mathematical tool in modern computational and applied mathematics. In this work we provide a survey of both the history and recent developments for several fields in which the weak form can play a critical role. In particular, we highlight several recent advances in weak form versions of equation learning, parameter estimation, and coarse graining, which offer surprising noise robustness, accuracy, and computational efficiency. We note that this manuscript is a companion piece to our October 2024 SIAM News article of the same name. Here we provide more detailed explanations of mathematical developments as well as a more complete list of references. Lastly, we note that the software with which to reproduce the results in this manuscript is also available on our group's GitHub website https://github.com/MathBioCU .
In recent years robots have become an important part of our day-to-day lives with various applications. Human-robot interaction creates a positive impact in the field of robotics to interact and communicate with the robots. Gesture recognition techniques combined with machine learning algorithms have shown remarkable progress in recent years, particularly in human-robot interaction (HRI). This paper comprehensively reviews the latest advancements in gesture recognition methods and their integration with machine learning approaches to enhance HRI. Furthermore, this paper represents the vision-based gesture recognition for safe and reliable human-robot-interaction with a depth-sensing system, analyses the role of machine learning algorithms such as deep learning, reinforcement learning, and transfer learning in improving the accuracy and robustness of gesture recognition systems for effective communication between humans and robots.
Blind source separation in 3rd generation gravitational-wave detectors
2024-09-10
Francesca Badaracco, Biswajit Banerjee, Marica Branchesi, Andrea Chincarini
Third generation and future upgrades of current gravitational-wave detectors will present exquisite sensitivities which will allow to detect a plethora of gravitational wave signals. Hence, a new problem to be solved arises: the detection and parameter estimation of overlapped signals. The problem of separating and identifying two signals that overlap in time, space or frequency is something well known in other fields (e.g. medicine and telecommunication). Blind source separation techniques are all those methods that aim at separating two or more unknown signals. This article provides a methodological review of the most common blind source separation techniques and it analyses whether they can be successfully applied to overlapped gravitational wave signals or not, while comparing the limits and advantages of each method.
JADES: Measuring reionization properties using Lyman-alpha emission
2024-09-10
Gareth C. Jones, Andrew J. Bunker, Aayush Saxena, Santiago Arribas, Rachana Bhatawdekar, Kristan Boyett, Stefano Carniani, Stephane Charlot, Emma Curtis-Lake, Kevin Hainline, Benjamin D. Johnson, Nimisha Kumari, Michael V. Maseda, Hans-Walter Rix, Brant E. Robertson, Sandro Tacchella, Hannah Übler, Christina C. Williams, Chris Willott, Joris Witstok, Yongda Zhu
Ly$\alpha$ is the transition to the ground state from the first excited state of hydrogen (the most common element). Resonant scattering of this line by neutral hydrogen greatly impedes its emergence from galaxies, so the fraction of galaxies which show Ly$\alpha$ is a tracer of the neutral fraction of the intergalactic medium (IGM), and thus the history of reionization. In previous works, we used early JWST/NIRSpec data from the JWST Advanced Deep Extragalactic Survey (JADES) to classify and characterise Ly$\alpha$ emitting galaxies (LAEs). This survey is now approaching completion, and the current sample is nearly an order of magnitude larger. From a sample of 784 galaxies in JADES at $4.0
Quantum computers are promising tools for the simulation of many-body systems, and among those, QCD stands out by its rich phenomenology. Every simulation starts with a codification, and here we succently review a newly developed compact encoding based on the identification between registers and particles; the quantum memory is divided into registers, and to each we associate a Hilbert space of dimension the number of degrees of freedom of the codified particles. In this way we gain an exponential compression over direct encodings for a low number of particles with many degrees of freedom. As an example we apply this encoding on a two-register memory and implement antisymmetrization and exponentiation algorithms.
Sifting the debris: Patterns in the SNR population with unsupervised ML methods
2024-09-10
F. Bufano, C. Bordiu, T. Cecconello, M. Munari, A. Hopkins, A. Ingallinera, P. Leto, S. Loru, S. Riggi, E. Sciacca, G. Vizzari, A. De Marco, C. S. Buemi, F. Cavallaro, C. Trigilio, G. Umana
Supernova remnants (SNRs) carry vast amounts of mechanical and radiative energy that heavily influence the structural, dynamical, and chemical evolution of galaxies. To this day, more than 300 SNRs have been discovered in the Milky Way, exhibiting a wide variety of observational features. However, existing classification schemes are mainly based on their radio morphology. In this work, we introduce a novel unsupervised deep learning pipeline to analyse a representative subsample of the Galactic SNR population ($\sim$ 50% of the total) with the aim of finding a connection between their multi-wavelength features and their physical properties. The pipeline involves two stages: (1) a representation learning stage, consisting of a convolutional autoencoder that feeds on imagery from infrared and radio continuum surveys (WISE 22$\mu$m, Hi-GAL 70 $\mu$m and SMGPS 30 cm) and produces a compact representation in a lower-dimensionality latent space; and (2) a clustering stage that seeks meaningful clusters in the latent space that can be linked to the physical properties of the SNRs and their surroundings. Our results suggest that this approach, when combined with an intermediate uniform manifold approximation and projection (UMAP) reprojection of the autoencoded embeddings into a more clusterable manifold, enables us to find reliable clusters. Despite a large number of sources being classified as outliers, most clusters relate to the presence of distinctive features, such as the distribution of infrared emission, the presence of radio shells and pulsar wind nebulae, and the existence of dust filaments.
Note on the existence of classical solutions of derivative semilinear models for one dimensional wave equation
2024-09-10
Takiko Sasaki, Hiroyuki Takamura
This note is a supplement with a new result to the review paper by Takamura [13] on nonlinear wave equations in one space dimension. We are focusing here to the long-time existence of classical solutions of semilinear wave equations in one space dimension, especially with derivative nonlinear terms of product-type. Our result is an extension of the single component case, but it is meaningful to provide models as possible as many to cover the optimality of the general theory. The proof is based on the classical iteration argument of the point-wise estimate of the solution.
Planetary nebulae seen with TESS: New and revisited short-period binary central star candidates from Cycles 1 to 4
2024-09-10
Alba Aller, Jorge Lillo-Box, David Jones
High-precision and high-cadence photometric surveys such as Kepler or TESS are making huge progress not only in the detection of new extrasolar planets but also in the study of a great number of variable stars. This is the case for central stars of planetary nebulae (PNe), which have similarly benefited from the capabilities of these missions, increasing the number of known binary central stars and helping us to constrain the relationship between binarity and the complex morphologies of their host PNe. In this paper, we analyse the TESS light curves of a large sample of central stars of PNe with the aim of detecting signs of variability that may hint at the presence of short-period binary nuclei. We analysed 62 central stars of true, likely, or possible PNe and modelled the detected variability through an MCMC approach accounting for three effects: reflection, ellipsoidal modulations, and Doppler beaming. Among the 62 central stars, only 38 are amenable for this study. The remaining 24 show large contamination from nearby sources preventing an optimal analysis. Also, eight targets are already known binary central stars, which we revisit here with the new high precision of the TESS data. In addition, we find that 18 further central stars show clear signs of periodic variability in the TESS data, probably resulting from different physical effects compatible with the binary scenario. We propose them as new candidate binary central stars. We also discuss the origin of the detected variability in each particular case by using the TESS_localize algorithm. Finally, 12 targets show no or only weak evidence of variability at the sensitivity of TESS. Our study demonstrates the power of space-based photometric surveys in searching for close binary companions of central stars of PNe.
High-Performance Few-Shot Segmentation with Foundation Models: An Empirical Study
2024-09-10
Shijie Chang, Lihe Zhang, Huchuan Lu
Existing few-shot segmentation (FSS) methods mainly focus on designing novel support-query matching and self-matching mechanisms to exploit implicit knowledge in pre-trained backbones. However, the performance of these methods is often constrained by models pre-trained on classification tasks. The exploration of what types of pre-trained models can provide more beneficial implicit knowledge for FSS remains limited. In this paper, inspired by the representation consistency of foundational computer vision models, we develop a FSS framework based on foundation models. To be specific, we propose a simple approach to extract implicit knowledge from foundation models to construct coarse correspondence and introduce a lightweight decoder to refine coarse correspondence for fine-grained segmentation. We systematically summarize the performance of various foundation models on FSS and discover that the implicit knowledge within some of these models is more beneficial for FSS than models pre-trained on classification tasks. Extensive experiments on two widely used datasets demonstrate the effectiveness of our approach in leveraging the implicit knowledge of foundation models. Notably, the combination of DINOv2 and DFN exceeds previous state-of-the-art methods by 17.5% on COCO-20i. Code is available at https://github.com/DUT-CSJ/FoundationFSS.
S-dual of Hamiltonian $\mathbf G$ spaces and relative Langlands duality
2024-09-10
Hiraku Nakajima
The S-dual $(\mathbf G^\vee\curvearrowright\mathbf M^\vee)$ of the pair $(\mathbf G\curvearrowright\mathbf M)$ of a smooth affine algebraic symplectic manifold $\mathbf M$ with hamiltonian action of a complex reductive group $\mathbf G$ was introduced implicitly in [arXiv:1706.02112] and explicitly in [arXiv:1807.09038] under the cotangent type assumption. The definition was a modification of the definition of Coulomb branches of gauge theories in [arXiv:1601.03586]. It was motivated by the S-duality of boundary conditions of 4-dimensional $\mathcal N=4$ super Yang-Mills theory, studied by Gaiotto and Witten [arXiv:0807.3720]. It is also relevant to the relative Langlands duality proposed by Ben-Zvi, Sakellaridis and Venkatesh. In this article, we review the definition and properties of S-dual.
Mechanistic-statistical model for the expansion of ash dieback
2024-09-10
Coralie Fritsch, Marie Grosdidier, Anne Gégout-Petit, Benoit Marçais
Hymenoscyphus fraxineus is an invasive forest fungal pathogen that induces severe dieback in European ash populations. The spread of the disease has been closely monitored in France by the forest health survey system. We have developed a mechanisticstatistical model that describes the spread of the disease. It takes into account climate (summer temperature and spring rainfall), pathogen population dynamics (foliar infection, Allee effect induced by limited sexual partner encounters) and host density. We fitted this model using available disease reports. We estimated the parameters of our model, first identifying the appropriate ranges for the parameters, which led to a model reduction, and then using an adaptive multiple importance sampling algorithm for fitting. The model reproduces well the propagation observed in France over the last 20 years. In particular, it predicts the absence of disease impact in the south-east of the country and its weak development in the Garonne valley in south-west France. Summer temperature is the factor with the highest overall effect on disease spread, and explains the limited impact in southern France. Among the different temperature indices tested, the number of summer days with temperatures above 28{\textdegree}C gave the best qualitative behavior and the best fit. In contrast, the Allee effect and the heterogeneity of spring precipitation did not strongly affect the overall expansion of H. fraxineus in France and could be neglected in the modeling process. The model can be used to infer the average annual dispersal of H. fraxineus in France.
Market Reaction to News Flows in Supply Chain Networks
2024-09-10
Hiroyasu Inoue, Yasuyuki Todo
This study examines whether positive news about firms increases their stock prices and, moreover, whether it increases stock prices of the firms' suppliers and customers, using a large sample of publicly listed firms across the world and another of Japanese listed firms. The level of positiveness of each news article is determined by FinBERT, a natural language processing model fine-tuned specifically for financial information. Supply chains of firms across the world are identified mostly by financial statements, while those of Japanese firms are taken from large-scale firm-level surveys. We find that positive news increases the change rate of stock prices of firms mentioned in the news before its disclosure, most likely because of diffusion of information through informal channels. Positive news also raises stock prices of the firms' suppliers and customers before its disclosure, confirming propagation of market values through supply chains. In addition, we generally find a larger post-news effect on stock prices of the mentioned firms and their suppliers and customers than the pre-news effect. The positive difference between the post- and pre-news effects can be considered as the net effect of the disclosure of positive news, controlling for informal information diffusion. However, the post-news effect on suppliers and customers in Japan is smaller than the pre-news effect, a result opposite to those from firms across the world. This notable result is possibly because supply chain links of Japanese firms are stronger than global supply chains while such knowledge is restricted to selected investors.
This paper discusses a generalization of virtual knot theory that we call multi-virtual knot theory. Multi-virtual knot theory uses a multiplicity of types of virtual crossings. As we will explain, this multiplicity is motivated by the way it arises first in a graph-theoretic setting in relation to generalizing the Penrose evaluation for colorings of planar trivalent graphs to all trivalent graphs, and later by its uses in a virtual knot theory. As a consequence, the paper begins with the graph theory as a basis for our constructions, and then proceeds to the topology of multi-virtual knots and links. The second section of the paper is a review of our previous work (See arXiv:1511.06844). The reader interested in seeing our generalizations of the original Penrose evaluation, can begin this paper at the beginning and see the graph theory. A reader primarily interested in multi-virtual knots and links can begin reading in section 4 with references to the earlier part of the paper.
Structural Robustness and Vulnerability of Networks
2024-09-10
Alice C. Schwarze, Jessica Jiang, Jonny Wray, Mason A. Porter
Networks are useful descriptions of the structure of many complex systems. Unsurprisingly, it is thus important to analyze the robustness of networks in many scientific disciplines. In applications in communication, logistics, finance, ecology, biomedicine, and many other fields, researchers have studied the robustness of networks to the removal of nodes, edges, or other subnetworks to identify and characterize robust network structures. A major challenge in the study of network robustness is that researchers have reported that different and seemingly contradictory network properties are correlated with a network's robustness. Using a framework by Alderson and Doyle~\cite{Alderson2010}, we categorize several notions of network robustness and we examine these ostensible contradictions. We survey studies of network robustness with a focus on (1)~identifying robustness specifications in common use, (2)~understanding when these specifications are appropriate, and (3)~understanding the conditions under which one can expect different notions of robustness to yield similar results. With this review, we aim to give researchers an overview of the large, interdisciplinary body of work on network robustness and develop practical guidance for the design of computational experiments to study a network's robustness.
VBIT: Towards Enhancing Privacy Control Over IoT Devices
2024-09-10
Jad Al Aaraj, Olivia Figueira, Tu Le, Isabela Figueira, Rahmadi Trimananda, Athina Markopoulou
Internet-of-Things (IoT) devices are increasingly deployed at home, at work, and in other shared and public spaces. IoT devices collect and share data with service providers and third parties, which poses privacy concerns. Although privacy enhancing tools are quite advanced in other applications domains (\eg~ advertising and tracker blockers for browsers), users have currently no convenient way to know or manage what and how data is collected and shared by IoT devices. In this paper, we present VBIT, an interactive system combining Mixed Reality (MR) and web-based applications that allows users to: (1) uncover and visualize tracking services by IoT devices in an instrumented space and (2) take action to stop or limit that tracking. We design and implement VBIT to operate at the network traffic level, and we show that it has negligible performance overhead, and offers flexibility and good usability. We perform a mixed-method user study consisting of an online survey and an in-person interview study. We show that VBIT users appreciate VBIT's transparency, control, and customization features, and they become significantly more willing to install an IoT advertising and tracking blocker, after using VBIT. In the process, we obtain design insights that can be used to further iterate and improve the design of VBIT and other systems for IoT transparency and control.
NLP-Powered Repository and Search Engine for Academic Papers: A Case Study on Cyber Risk Literature with CyLit
2024-09-10
Linfeng Zhang, Changyue Hu, Zhiyu Quan
As the body of academic literature continues to grow, researchers face increasing difficulties in effectively searching for relevant resources. Existing databases and search engines often fall short of providing a comprehensive and contextually relevant collection of academic literature. To address this issue, we propose a novel framework that leverages Natural Language Processing (NLP) techniques. This framework automates the retrieval, summarization, and clustering of academic literature within a specific research domain. To demonstrate the effectiveness of our approach, we introduce CyLit, an NLP-powered repository specifically designed for the cyber risk literature. CyLit empowers researchers by providing access to context-specific resources and enabling the tracking of trends in the dynamic and rapidly evolving field of cyber risk. Through the automatic processing of large volumes of data, our NLP-powered solution significantly enhances the efficiency and specificity of academic literature searches. We compare the literature categorization results of CyLit to those presented in survey papers or generated by ChatGPT, highlighting the distinctive insights this tool provides into cyber risk research literature. Using NLP techniques, we aim to revolutionize the way researchers discover, analyze, and utilize academic resources, ultimately fostering advancements in various domains of knowledge.
Generative AI for Requirements Engineering: A Systematic Literature Review
2024-09-10
Haowei Cheng, Jati H. Husen, Sien Reeve Peralta, Bowen Jiang, Nobukazu Yoshioka, Naoyasu Ubayashi, Hironori Washizaki
Context: Generative AI (GenAI) has emerged as a transformative tool in software engineering, with requirements engineering (RE) actively exploring its potential to revolutionize processes and outcomes. The integration of GenAI into RE presents both promising opportunities and significant challenges that necessitate systematic analysis and evaluation. Objective: This paper presents a comprehensive systematic literature review (SLR) analyzing state-of-the-art applications and innovative proposals leveraging GenAI in RE. It surveys studies focusing on the utilization of GenAI to enhance RE processes while identifying key challenges and opportunities in this rapidly evolving field. Method: A rigorous SLR methodology was used to analyze 27 carefully selected primary studies in-depth. The review examined research questions pertaining to the application of GenAI across various RE phases, the models and techniques used, and the challenges encountered in implementation and adoption. Results: The most salient findings include i) a predominant focus on the early stages of RE, particularly the elicitation and analysis of requirements, indicating potential for expansion into later phases; ii) the dominance of large language models, especially the GPT series, highlighting the need for diverse AI approaches; and iii) persistent challenges in domain-specific applications and the interpretability of AI-generated outputs, underscoring areas requiring further research and development. Conclusions: The results highlight the critical need for comprehensive evaluation frameworks, improved human-AI collaboration models, and thorough consideration of ethical implications in GenAI-assisted RE. Future research should prioritize extending GenAI applications across the entire RE lifecycle, enhancing domain-specific capabilities, and developing strategies for responsible AI integration in RE practices.
FAST Ultra-Deep Survey (FUDS): Data Release for FUDS0
2024-09-10
Hongwei Xi, Bo Peng, Lister Staveley-Smith, Bi-Qing For, Bin Liu, Dejian Ding
We have used the Five-hundred-meter Aperture Spherical radio Telescope (FAST) to make a blind ultra-deep survey for neutral hydrogen (HI). We present the complete results from the first of six fields (FUDS0). This observation of 95 hours allowed us to achieve a high sensitivity ($\sim 50~\mu$Jy beam$^{-1}$) and a high frequency resolution (22.9 kHz) over an area of 0.72 deg$^2$. We detected 128 galaxies in HI distributed over the redshift range of $0 0.38$, where no unlensed HI emission has previously been directly detected. Estimates of completeness and reliability are presented for the catalog. Consistency of continuum and HI flux estimates with NVSS and AUDS, respectively, confirms the accuracy of calibration method and data reduction pipeline developed for the full FUDS survey.
Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review
2024-09-10
Neha Prakriya, Jui-Nan Yen, Cho-Jui Hsieh, Jason Cong
Large Language Model (LLM) pretraining traditionally relies on autoregressive language modeling on randomly sampled data blocks from web-scale datasets. We take inspiration from human learning techniques like spaced repetition to hypothesize that random data sampling for LLMs leads to high training cost and low quality models which tend to forget data. In order to effectively commit web-scale information to long-term memory, we propose the LFR (Learn, Focus, and Review) pedagogy, a new dynamic training paradigm which focuses and repeatedly reviews complex data blocks at systematic intervals based on the model's learning pace and progress. LFR records the model perplexities for different data blocks and frequently revisits blocks with higher perplexity which are more likely to be forgotten. We pretrain the GPT-2 models (124M - 1.5B) from scratch on the OpenWebText dataset using LFR. We test on downstream tasks from the language modeling, question answering, translation, and problem solving domains to achieve consistently lower perplexity and higher accuracy than the baseline OpenAI models, while obtaining a 20x pretraining speed-up.
A Census of the beta Pic Moving Group and Other Nearby Associations with Gaia
2024-09-09
K. L. Luhman
I have used the third data release of the Gaia mission to improve the reliability and completeness of membership samples in the beta Pic moving group (BPMG) and other nearby associations with ages of 20-50 Myr (Sco Body, Carina, Columba, chi1 For, Tuc-Hor, IC 2602, IC 2391, NGC 2547). I find that Carina, Columba, and chi1 For are physically related and coeval, and that Carina is the closest fringe of a much larger association. Similarly, Tuc-Hor and IC 2602 form a coeval population that is spatially and kinematically continuous. Both results agree with hypotheses from Gagne et al. (2021). I have used the new catalogs to study the associations in terms of their initial mass functions, X-ray emission, ages, and circumstellar disks. For instance, using the model for Li depletion from Jeffries et al. (2023), I have derived an age of 24.7+0.9/-0.6 Myr for BPMG, which is similar to estimates from previous studies. In addition, I have used infrared photometry from the Wide-field Infrared Survey Explorer to check for excess emission from circumstellar disks among the members of the associations, which has resulted in a dramatic increase in the number of known disks around M stars at ages of 30-50 Myr and a significant improvement in measurements of excess fractions for those spectral types and ages. Most notably, I find that the W3 excess fraction for M0-M6 initially declines with age to a minimum in BPMG <0.015), increases to a maximum in Carina/Columba chi1 For (0.041+0.009/-0.007, 34 Myr), and declines again in the oldest two associations (40-50 Myr). The origin of that peak and the nature of the M dwarf disks at >20 Myr are unclear.
High-Speed Outflows and Dusty Disks during the AGB to PN Transition: The PANORAMA survey
2024-09-09
Raghvendra Sahai, Javier Alcolea, Bruce Balick, Eric G. Blackman, Valentin Bujarrabal, Arancha Castro-Carrizo, Orsola De Marco, Joel Kastner, Hyosun Kim, Eric Lagadec, Chin-Fei Lee, Laurence Sabin, M. Santander-Garcia, Carmen Sánchez Contreras, Daniel Tafoya, Toshiya Ueta, Wouter Vlemmings, Albert Zijlstra
As mass-losing asymptotic giant branch (AGB) stars evolve to planetary nebulae (PNe), the mass outflow geometries transform from nearly spherical to extreme aspherical. The physical mechanisms governing this transformation are widely believed to be linked to binarity and the associated production of disks and fast jets during transitional (post-AGB) evolutionary stages. We are carrying out a systematic ALMA survey ($P$re-planet$A$ry $N$ebulae high-angular-res$O$lution su$R$vey with $A$L$MA$ or PANORAMA) of a representative sample of bipolar and multipolar post-AGB objects. We have obtained high angular-resolution (0".1-0".4) observations of the CO(3--2) and/or 6--5 emission in order to probe the spatio-kinematic structure of the collimated outflows and the central disk/torii. The results are remarkable, generally showing the presence of bipolar or multipolar high-velocity outflows, dense toroidal waists, and in one case, a geometrically-thin circular ring around the central bipolar nebula. A high degree of point-symmetry characterizes the morphology of the mass ejecta. In this contribution, we present these and other highlights from our survey. We aim to use 2D/3D radiative transfer modeling in order to derive accurate outflow momenta, masses and mass-loss rates for our sample, and build hydrodynamical models that can explain the observed spatio-kinematic structures. These results will then be used to distinguish between different classes of PN-shaping binary interaction models.
Computer-aided tumor detection has shown great potential in enhancing the interpretation of over 80 million CT scans performed annually in the United States. However, challenges arise due to the rarity of CT scans with tumors, especially early-stage tumors. Developing AI with real tumor data faces issues of scarcity, annotation difficulty, and low prevalence. Tumor synthesis addresses these challenges by generating numerous tumor examples in medical images, aiding AI training for tumor detection and segmentation. Successful synthesis requires realistic and generalizable synthetic tumors across various organs. This chapter reviews AI development on real and synthetic data and summarizes two key trends in synthetic data for cancer imaging research: modeling-based and learning-based approaches. Modeling-based methods, like Pixel2Cancer, simulate tumor development over time using generic rules, while learning-based methods, like DiffTumor, learn from a few annotated examples in one organ to generate synthetic tumors in others. Reader studies with expert radiologists show that synthetic tumors can be convincingly realistic. We also present case studies in the liver, pancreas, and kidneys reveal that AI trained on synthetic tumors can achieve performance comparable to, or better than, AI only trained on real data. Tumor synthesis holds significant promise for expanding datasets, enhancing AI reliability, improving tumor detection performance, and preserving patient privacy.
Investigating the effects of housing instability on depression, anxiety, and mental health treatment in childhood and adolescence
2024-09-09
Rachael Zehrung, Di Hu, Yawen Guo, Kai Zheng, Yunan Chen
Housing instability is a widespread phenomenon in the United States. In combination with other social determinants of health, housing instability affects children's overall health and development. Drawing on data from the 2022 National Survey of Children's Health, we employed multiple logistic regression models to understand how sociodemographic factors, especially housing instability, affect mental health outcomes and treatment access for youth aged 6-17 years. Our results show that youth facing housing instability have a higher likelihood of experiencing anxiety (OR: 1.42, p<0.001) and depression (OR: 1.57, p<0.001). Furthermore, youth experiencing both mental health conditions and housing instability are significantly less likely to receive mental health services in the past year, indicating the substantial barriers they face in accessing mental health care. Based on our findings, we highlight opportunities for digital mental health interventions to provide children experiencing housing instability with more accessible and consistent mental health services.
Cosmological constraints from angular homogeneity scale measurements
2024-09-09
Xiaoyun Shao, Carlos A. P. Bengaly, Rodrigo S. Gonçalves, Gabriela C. Carvalho, Jailson Alcaniz
In this paper, we obtain new measurements of the angular homogeneity scale ($\theta_H$) from the BOSS DR12 and eBOSS DR16 catalogs of Luminous Red Galaxies of the Sloan Digital Sky Survey. Considering the flat $\Lambda$CDM model, we use the $\theta_H(z)$ data to constrain the matter density parameter ($\Omega_{m0}$) and the Hubble constant ($H_{0}$). We find $H_0 = 65.7 \pm 7.0$ km s$^{-1}$ Mpc$^{-1}$ and $\Omega_{m0}>0.293$. By combining the $\theta_H$ measurements with current Baryon Acoustic Oscillations (BAO) and Type Ia Supernova (SN) data, we obtain $H_{0}= 69.9^{+4.9}_{-4.5}$ km s$^{-1}$ Mpc$^{-1}$ and $\Omega_{m0} = 0.294^{+0.013}_{-0.015}$ ($\theta_H$ + BAO) and $H_{0}=70.7^{+5.2}_{-4.1}$ km s$^{-1}$ Mpc$^{-1}$ and $\Omega_{m0}=0.299 \pm 0.022$ ($\theta_H$ + SN). We show that $\theta_H$ measurements help break the BAO and SN degeneracies concerning $H_0$, as they do not depend on the sound horizon scale at the drag epoch or the SN absolute magnitude value obtained from the distance ladder method. Hence, despite those constraints being loose compared to other probes, $\theta_H$ data may provide an independent cosmological probe of $H_0$ in light of the Hubble tension. For completeness, we also forecast the constraining power of future $\theta_H$ data via Monte Carlo simulations. Considering a relative error of the order of 1$\%$, we obtain competitive constraints on $\Omega_{m0}$ and $H_0$ ($\approx 5\%$ error) from the joint analysis with current SN and BAO measurements.
The cosmological analysis of X-ray cluster surveys: VI. Inference based on analytically simulated observable diagrams
2024-09-09
M. Kosiba, N. Cerardi, M. Pierre, F. Lanusse, C. Garrel, N. Werner, M. Shalak
The number density of galaxy clusters across mass and redshift has been established as a powerful cosmological probe. Cosmological analyses with galaxy clusters traditionally employ scaling relations. However, many challenges arise from this approach as the scaling relations are highly scattered, may be ill-calibrated, depend on the cosmology, and contain many nuisance parameters with low physical significance. In this paper, we use a simulation-based inference method utilizing artificial neural networks to optimally extract cosmological information from a shallow X-ray survey of galaxy clusters, solely using count rates (CR), hardness ratios (HR), and redshifts. This procedure enables us to conduct likelihood-free inference of cosmological parameters $\Omega_{\mathrm{m}}$ and $\sigma_8$. We analytically generate simulations of galaxy cluster distribution in a CR, HR space in multiple redshift bins based on totally random combinations of cosmological and scaling relation parameters. We train Convolutional Neural Networks (CNNs) to retrieve the cosmological parameters from these simulations. We then use neural density estimation (NDE) neural networks to predict the posterior probability distribution of $\Omega_{\mathrm{m}}$ and $\sigma_8$ given an input galaxy cluster sample. The 1 $\sigma$ errors of our density estimator on one of the target testing simulations are 1000 deg$^2$: 15.2% for $\Omega_{\mathrm{m}}$ and 10.0% for $\sigma_8$; 10000 deg$^2$: 9.6% for $\Omega_{\mathrm{m}}$ and 5.6% for $\sigma_8$. We also compare our results with Fisher analysis. We demonstrate, as a proof of concept, that it is possible to calculate cosmological predictions of $\Omega_{\mathrm{m}}$ and $\sigma_8$ from a galaxy cluster population without explicitly computing cluster masses and even, the scaling relation coefficients, thus avoiding potential biases resulting from such a procedure. [abridged]
Simulated performance of energy-resolving detectors towards exoplanet imaging with the Habitable Worlds Observatory
2024-09-09
Sarah Steiger, Laurent Pueyo, Emiel H. Por, Pin Chen, Rémi Soummer, Raphaël Pourcelot, Iva Laginja, Vanessa P. Bailey
One of the primary science goals of the Habitable Worlds Observatory (HWO) as defined by the Astro2020 decadal survey is the imaging of the first Earth-like planet around a Sun-like star. A key technology gap towards reaching this goal are the development of ultra-low-noise photon counting detectors capable of measuring the incredibly low count rates coming from these planets which are at contrasts of $\sim 1 \times 10^{-10}$. Superconducting energy-resolving detectors (ERDs) are a promising technology for this purpose as, despite their technological challenges, needing to be cooled below their superconducting transition temperature ($< 1\mathrm{K}$), they have essentially zero read noise, dark current, or clock-induced charge, and can get the wavelength of each incident photon without the use of additional throughput-reducing filters or gratings that spread light over many pixels. The use of these detectors on HWO will not only impact the science of the mission by decreasing the required exposure times for exo-Earth detection and characterization, but also in a wavefront sensing and control context when used for starlight suppression to generate a dark zone. We show simulated results using both an EMCCD and an ERD to ``dig a dark zone'' demonstrating that ERDs can achieve the same final contrast as an EMCCD in about half of the total time. We also perform a simple case study using an exposure time calculator tool called the Error Budget Software (EBS) to determine the required integration times to detect water for HWO targets of interest using both EMCCDs and ERDs. This shows that once a dark zone is achieved, using an ERD can decrease these exposure times by factors of 1.5--2 depending on the specific host star properties.
This review provides a detailed analysis of the advancements in unmanned aerial vehicle (UAV) detection and classification systems from 2020 to today. It covers various detection methodologies such as radar, radio frequency, optical, and acoustic sensors, and emphasizes their integration via sophisticated sensor fusion techniques. The fundamental technologies driving UAV detection and classification are thoroughly examined, with a focus on their accuracy and range. Additionally, the paper discusses the latest innovations in artificial intelligence and machine learning, illustrating their impact on improving the accuracy and efficiency of these systems. The review concludes by predicting further technological developments in UAV detection, which are expected to enhance both performance and reliability.
CCAT: A status update on the EoR-Spec instrument module for Prime-Cam
2024-09-09
Rodrigo Freundt, Yaqiong Li, Doug Henke, Jason Austermann, James R. Burgoyne, Scott Chapman, Steve K. Choi, Cody J. Duell, Zach Huber, Michael Niemack, Thomas Nikola, Lawrence Lin, Dominik A. Riechers, Gordon Stacey, Anna K. Vaskuri, Eve M. Vavagiakis, Jordan Wheeler, Bugao Zou
The Epoch of Reionization Spectrometer (EoR-Spec) is an upcoming Line Intensity Mapping (LIM) instrument designed to study the evolution of the early universe (z = 3.5 to 8) by probing the redshifted [CII] 158 $\mu$m fine-structure line from aggregates of galaxies. The [CII] emission is an excellent tracer of star formation since it is the dominant cooling line from neutral gas heated by OB star light and thus can be used to probe the reionization of the early Universe due to star formation. EoR-Spec will be deployed on Prime-Cam, a modular direct-detection receiver for the 6-meter Fred Young Submillimeter Telescope (FYST), currently under construction by CPI Vertex Antennentechnik GmbH and to be installed near the summit of Cerro Chajnantor in the Atacama Desert. This instrument features an image plane populated with more than 6500 Microwave Kinetic Inductance Detectors (MKIDs) that are illuminated by a 4-lens optical design with a cryogenic, scanning Fabry-Perot Interferometer (FPI) at the pupil of the optical system. The FPI is designed to provide a spectral resolving power of $R\sim100$ over the full spectral range of 210--420 GHz. EoR-Spec will tomographically survey the E-COSMOS and E-CDFS fields with a depth of about 4000 hours over a 5 year period. Here we give an update on EoR-Spec's final mechanical/optical design and the current status of fabrication, characterization and testing towards first light in 2026.
Challenges and Opportunities of Teaching Data Visualization Together with Data Science
2024-09-09
Shri Harini Ramesh, Fateme Rajabiyazdi
With the increasing amount of data globally, analyzing and visualizing data are becoming essential skills across various professions. It is important to equip university students with these essential data skills. To learn, design, and develop data visualization, students need knowledge of programming and data science topics. Many university programs lack dedicated data science courses for undergraduate students, making it important to introduce these concepts through integrated courses. However, combining data science and data visualization into one course can be challenging due to the time constraints and the heavy load of learning. In this paper, we discuss the development of teaching data science and data visualization together in one course and share the results of the post-course evaluation survey. From the survey's results, we identified four challenges, including difficulty in learning multiple tools and diverse data science topics, varying proficiency levels with tools and libraries, and selecting and cleaning datasets. We also distilled five opportunities for developing a successful data science and visualization course. These opportunities include clarifying the course structure, emphasizing visualization literacy early in the course, updating the course content according to student needs, using large real-world datasets, learning from industry professionals, and promoting collaboration among students.
Dust-UV offsets in high-redshift galaxies in the Cosmic Dawn III simulation
2024-09-09
Pierre Ocvirk, Joseph S. W. Lewis, Luke Conaboy, Yohan Dubois, Matthieu Bethermin, Jenny G. Sorce, Dominique Aubert, Paul R. Shapiro, Taha Dawoodbhoy, Joohyun Lee, Romain Teyssier, Gustavo Yepes, Stefan Gottlöber, Ilian T. Iliev, Kyungjin Ahn, Hyunbae Park
We investigate the spatial offsets between dust and ultraviolet (UV) emission in high-redshift galaxies using the Cosmic Dawn III (CoDa III) simulation, a state-of-the-art fully coupled radiation-hydrodynamics cosmological simulation. Recent observations have revealed puzzling spatial disparities between ALMA dust continuum and UV emission as seen by HST and JWST in galaxies at z=5-7, compelling us to propose a physical interpretation of such offsets. Our simulation, which incorporates a dynamical dust model, naturally reproduces these offsets in massive, UV-bright galaxies (log$_{10}$(M$_{\rm{DM}}$/M$_{\odot}$)>11.5, M$_{\rm{AB1500}}$<-20). We find that dust-UV offsets increase with halo mass and UV brightness, reaching up to $\sim 2$ pkpc for the most massive systems, in good agreement with observational data from the ALPINE and REBELS surveys. Our analysis reveals that these offsets primarily result from severe dust extinction in galactic centers rather than a misalignment between dust and stellar mass distributions. The dust remains well-aligned with the bulk stellar component, and we predict the dust continuum should therefore align well with the stellar rest-frame NIR component, less affected by dust attenuation. This study provides crucial insights into the complex interplay between star formation, dust distribution, and observed galaxy morphologies during the epoch of reionization, highlighting the importance of dust in shaping the appearance of early galaxies at UV wavelengths.
RUBIES: a complete census of the bright and red distant Universe with JWST/NIRSpec
2024-09-09
Anna de Graaff, Gabriel Brammer, Andrea Weibel, Zach Lewis, Michael V. Maseda, Pascal A. Oesch, Rachel Bezanson, Leindert A. Boogaard, Nikko J. Cleri, Olivia R. Cooper, Rashmi Gottumukkala, Jenny E. Greene, Michaela Hirschmann, Raphael E. Hviding, Harley Katz, Ivo Labbé, Joel Leja, Jorryt Matthee, Ian McConachie, Tim B. Miller, Rohan P. Naidu, Sedona H. Price, Hans-Walter Rix, David J. Setton, Katherine A. Suess, Bingjie Wang, Katherine E. Whitaker, Christina C. Williams
We present the Red Unknowns: Bright Infrared Extragalactic Survey (RUBIES), providing JWST/NIRSpec spectroscopy of red sources selected across ~150 arcmin$^2$ from public JWST/NIRCam imaging in the UDS and EGS fields. RUBIES novel observing strategy offers a well-quantified selection function: the survey is optimised to reach high (>70%) completeness for bright and red (F150W-F444W>2) sources that are very rare. To place these rare sources in context, we simultaneously observe a reference sample of the 23$ using only the G395M disperser. The RUBIES data reveal a highly diverse population of red sources that span a broad redshift range ($z_{spec}\sim1-9$), with photometric redshift scatter and outlier fraction that are 3 times higher than for similarly bright sources that are less red. This diversity is not apparent from the photometric SEDs. Only spectroscopy reveals that the SEDs encompass a mixture of galaxies with dust-obscured star formation, extreme line emission, a lack of star formation indicating early quenching, and luminous active galactic nuclei. As a first demonstration of our broader selection function we compare the stellar masses and rest-frame U-V colours of the red sources and our reference sample: red sources are typically more massive ($M_*\sim10^{10-11.5} M_\odot$) across all redshifts. However, we find that the most massive systems span a wide range in U-V colour. We describe our data reduction procedure and data quality, and publicly release the reduced RUBIES data and vetted spectroscopic redshifts of the first half of the survey through the DJA.
The MAGPI Survey: Orbital distributions, intrinsic shapes, and mass profiles for MAGPI-like Eagle galaxies using Schwarzschild dynamical models
2024-09-09
Giulia Santucci, Claudia Del P. Lagos, Katherine E. Harborne, Caro Derkenne, Adriano Poci, Sabine Thater, Richard M. McDermid, J. Trevor Mendel, Emily Wisnioski, Scott M. Croom, Anna Ferré-Mateu, Eric G. M. Muller, Jesse van de Sande, Gauri Sharma, Sarah M. Sweet, Takafumi Tsukui, Lucas M. Valenzuela, Glenn van de Ven, Tayyaba Zafar
Schwarzschild dynamical models are now regularly employed in large surveys of galaxies in the local and distant Universe to derive information on galaxies' intrinsic properties such as their orbital structure and their (dark matter and stellar) mass distribution. Comparing the internal orbital structures and mass distributions of galaxies in the distant Universe with simulations is key to understanding what physical processes are responsible for shaping galaxy properties. However it is first crucial to understand whether observationally derived properties are directly comparable with intrinsic ones in simulations. To assess this, we build Schwarzschild dynamical models for MUSE-like IFS cubes (constructed to be like those obtained by the MAGPI survey) of 75 galaxies at z ~ 0.3 from the Eagle simulations. We compare the true particle-derived properties with the galaxies' model-derived properties. In general, we find that the models can recover the true galaxy properties qualitatively well, with the exception of the enclosed dark matter, where we find a median offset of 48%, which is due to the assumed NFW profile not being able to reproduce the dark matter distribution in the inner region of the galaxies. We then compare our model-derived properties with Schwarzschild models-derived properties of observed MAGPI galaxies and find good agreement between MAGPI and Eagle: the majority of our galaxies (57%) have non-oblate shapes within 1 effective radius. More triaxial galaxies show higher fractions of hot orbits in their inner regions and tend to be more radially anisotropic.
Enhancement of fusion reactivities using non-Maxwellian energy distributions
2024-09-09
Ben I. Squarer, Carlo Presilla, Roberto Onofrio
We discuss conditions for the enhancement of fusion reactivities arising from different choices of energy distribution functions for the reactants. The key element for potential gains in fusion reactivity is identified in the functional dependence of the tunnellng coefficient upon the energy, ensuring the existence of a finite range of temperatures for which reactivity of fusion processes is boosted with respect to the Maxwellian case. This is shown, using a convenient parameterization of the tunneling coefficient dependence upon the energy, analytically in the simplified case of a bimodal Maxwell-Boltzmann distribution, and numerically for kappa-distributions. We then consider tunneling potentials progressively better approximating fusion processes, and evaluate in each case the average reactivity in the case of kappa-distributions.
Measuring the speed of light with cosmological observations: current constraints and forecasts
2024-09-09
Jaiane Santos, Carlos Bengaly, Jonathan Morais, Rodrigo S. Gonçalves
We measure the speed of light with current observations, such as Type Ia Supernova, galaxy ages, radial BAO mode, as well as simulations of future redshift surveys and gravitational waves as standard sirens. By means of a Gaussian Process reconstruction, we find that the precision of such measurements can be improved from roughly 6\% to 1.5-2\%, in light of these forthcoming observations. This result demonstrates that we will be able to perform a cosmological measurement of a fundamental physical constant with unprecedented precision, which will help us underpinning if its value is truly consistent with local measurements, as predicted by the standard model of Cosmology.
Valuation system for telescope observation time proposals for the Gemini Observatory
2024-09-09
C. G. Díaz, R. Petrucci, L. V. Ferrero, E. Jofré
The preparation of a telescope observation time proposal is a recurring activity in observational astronomy. It is a necessary investment of time and effort to obtain data to advance a research topic. Therefore, the success of an observation proposal is a condition for progress in observational research. This guide was created to offer a straightforward, practical, and comprehensive resource for applicants who are preparing a Gemini Observatory proposal. It reviews the fundamentals of an observation proposal, including its content and evaluation criteria, to help applicants organize their submissions effectively and improve specific aspects of their presentations. This manuscript is inspired by the recommendations of the User Advisory Council established in the documents ''Criterios de evaluaci\'on de propuestas por parte del NTAC'' (National Time Allocation Committee), ''Consideraciones b\'asicas para la presentaci\'on de propuestas Gemini'', and in experiences of members of the NTAC.
Learning control of underactuated double pendulum with Model-Based Reinforcement Learning
2024-09-09
Niccolò Turcato, Alberto Dalla Libera, Giulio Giacomuzzo, Ruggero Carli, Diego Romeres
This report describes our proposed solution for the second AI Olympics competition held at IROS 2024. Our solution is based on a recent Model-Based Reinforcement Learning algorithm named MC-PILCO. Besides briefly reviewing the algorithm, we discuss the most critical aspects of the MC-PILCO implementation in the tasks at hand.
Environmental dependence on galaxy-halo connections for satellites using HSC weak lensing
2024-09-09
Amit Kumar, Surhud More
We present the luminosity-halo mass relations of satellite (sLHMRs) galaxies in the SDSS redMaPPer cluster catalogue and the effects of the dense cluster environment on subhalo mass evolution. We use data from the Subaru Hyper Suprime-Cam survey Year-3 catalogue of galaxy shapes to measure the weak lensing signal around these satellites. This signal serves as a probe of the matter distribution around the satellites, thereby providing the masses of their associated subhalos. We bin our satellites based on physical observable quantities such as their luminosity or the host cluster's richness, combined with their cluster-centric radial separations. Our results indicate that although more luminous satellites tend to reside in more massive halos, the sLHMRs depend on the distance of the satellite from the cluster centre. Subhalos near the cluster centre (within $<0.3 h^{-1}Mpc$) are stripped of mass. Consequently, the ratio of subhalo mass to luminosity decreases near the cluster centre. For low luminosity galaxies ($L < 10^{10} h^{-2}L_{\odot}$), the lack of evidence of increasing subhalo masses with luminosity shows the impact of tidal stripping. We also present stellar-to-subhalo mass relations (sSHMRs) for our satellite sample evolving at different cluster-centric separations. Inferred sSHMRs in the outer radial bin appear to match that observed for the field galaxies. We show that the sSHMRs from the mock-redMaPPer run on galaxy catalogues generated by the empirical UniverseMachine galaxy formation model are in good agreement with our observational results. Satellites, when binned based on the host cluster's richness, show very little dependence of the subhalo mass on the richness.
A discussion on estimating small bodies taxonomies using phase curves results
2024-09-09
Alvaro Alvarez-Candal
Upcoming large multiwavelength photometric surveys will provide a leap in our understanding of small body populations, among other fields of modern astrophysics. Serendipitous observations of small bodies in different orbital locations allow us to study diverse phenomena related to how their surfaces scatter solar light. In particular, multiple observations of the same object in different epochs permit us to construct their phase curves to obtain absolute magnitudes and phase coefficients. In this work, we tackle a series of long-used relationships associating these phase coefficients with the taxa of small bodies and suggest that some may need to be revised in the light of large-number statistics.
Implications of feedback solutions to the $S_8$ tension for the baryon fractions of galaxy groups and clusters
2024-09-09
Jaime Salcido, Ian G. McCarthy
Recent large-scale structure (LSS) surveys have revealed a persistent tension in the value of $S_8$ compared to predictions from the standard cosmological model. This tension may suggest the need for new physics beyond the standard model, but an accurate characterisation of baryonic effects is essential to avoid biases. Although some studies indicate that baryonic effects are too small to resolve this tension, others propose that more aggressive feedback mechanisms could reconcile differences between cosmic microwave background (CMB) measurements and low-redshift LSS observations. In this paper, we investigate the role of baryonic effects in alleviating the $S_8$ tension. We extend the SP(k) model (Salcido et al. 2023), which was trained on hundreds of cosmological hydrodynamical simulations to map the suppression of the matter power spectrum to the baryon fraction in groups and clusters, to predict the required baryon fraction for a given $P(k)$ suppression. We then compare predictions from recent cosmic shear (weak lensing) analyses with the latest baryon budget measurements from X-ray and weak gravitational lensing studies. Our findings show that studies marginalising over baryonic effects while fixing cosmological parameters to a Planck-like cosmology predict strong $P(k)$ suppression and baryon fractions that are much lower than existing low-redshift baryon budget estimates of galaxy groups and clusters. Conversely, most studies that marginalise over both cosmological parameters and baryonic effects imply baryon fractions that are consistent with observations but lower values of $S_8$ than inferred from the CMB. Unless the observed baryon fractions are biased high by a factor of several, these results suggest that a mechanism beyond baryonic physics alone is required to modify or slow down the growth of structure in the universe in order to resolve the $S_8$ tension.
The Influence of Task and Group Disparities over Users' Attitudes Toward Using Large Language Models for Psychotherapy
2024-09-09
Qihang He, Jiyao Wang, Dengbo He
The population suffering from mental health disorders has kept increasing in recent years. With the advancements in large language models (LLMs) in diverse fields, LLM-based psychotherapy has also attracted increasingly more attention. However, the factors influencing users' attitudes to LLM-based psychotherapy have rarely been explored. As the first attempt, this paper investigated the influence of task and group disparities on user attitudes toward LLM-based psychotherapy tools. Utilizing the Technology Acceptance Model (TAM) and Automation Acceptance Model (AAM), based on an online survey, we collected and analyzed responses from 222 LLM-based psychotherapy users in mainland China. The results revealed that group disparity (i.e., mental health conditions) can influence users' attitudes toward LLM tools. Further, one of the typical task disparities, i.e., the privacy concern, was not found to have a significant effect on trust and usage intention. These findings can guide the design of future LLM-based psychotherapy services.
The Galaxy Activity, Torus, and Outflow Survey (GATOS). V: Unveiling PAH survival and resilience in the circumnuclear regions of AGN with JWST
2024-09-09
I. García-Bernete, D. Rigopoulou, F. R. Donnan, A. Alonso-Herrero, M. Pereira-Santella, T. Shimizu, R. Davies, P. F. Roche, S. García-Burillo, A. Labiano, L. Hermosa Muñoz, L. Zhang, A. Audibert, E. Bellocchi, A. Bunker, F. Combes, D. Delaney, D. Esparza-Arredondo, P. Gandhi, O. González-Martín, S. F. Hönig, M. Imanishi, E. K. S. Hicks, L. Fuller, M. Leist, N. A. Levenson, E. Lopez-Rodriguez, C. Packham, C. Ramos Almeida, C. Ricci, M. Stalevski, M. Villar Martín, M. J. Ward
We analyze JWST MIRI/MRS observations of the infrared PAH bands in the nuclear and circumnuclear regions of local AGN from the GATOS Survey. In this work, we examine the PAH properties in the circumnuclear regions of AGN and AGN-outflows, and compare them to those in star-forming regions and the innermost regions of AGN. This study employs 4.9-28.1 micron sub-arcsecond angular resolution data to investigate the properties of PAH in three nearby sources (DL~30-40 Mpc). Our findings align with previous JWST studies, showing that the central regions of AGN show a larger fraction of neutral PAH molecules (i.e. elevated 11.3/6.2 and 11.3/7.7 PAH ratios) compared to star-forming galaxies. We find that the AGN might affect not only the PAH population in the innermost region but also in the extended regions up to ~kpc scales. By comparing our observations to PAH diagnostic diagrams, we find that, in general, regions located in the projected direction of the AGN-outflow occupy similar positions on the PAH diagnostic diagrams as those of the innermost regions of AGN. Star-forming regions that are not affected by the AGN in these galaxies share the same part of the diagram as Star-forming galaxies. We examine the potential of the PAH-H2 diagram to disentangle AGN versus star-forming activity. Our results suggest that in Sy-like AGN, illumination and feedback from the AGN might affect the PAH population at nuclear and kpc scales, in particular, the ionization state of the PAH grains. However, PAH sizes are rather similar. The carriers of the ionized PAH bands (6.2 and 7.7 micron) are less resilience than those of neutral PAH bands (11.3 micron), which might be particularly important for strongly AGN-host coupled systems. Therefore, caution must be applied when using PAH bands as star-formation rate indicators in these systems even at kpc scales, with the ionized ones being more affected by the AGN.
ForestFlow: cosmological emulation of Lyman-$α$ forest clustering from linear to nonlinear scales
2024-09-09
J. Chaves-Montero, L. Cabayol-Garcia, M. Lokken, A. Font-Ribera, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, S. Cole, A. de la Macorra, S. Ferraro, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, R. Kehoe, D. Kirkby, A. Kremin, A. Lambert, M. Landriau, M. Manera, P. Martini, R. Miquel, A. Muñoz-Gutiérrez, G. Niz, I. Pérez-Ràfols, G. Rossi, E. Sanchez, M. Schubnell, D. Sprayberry, G. Tarlé, B. A. Weaver
On large scales, measurements of the Lyman-$\alpha$ forest offer insights into the expansion history of the Universe, while on small scales, these impose strict constraints on the growth history, the nature of dark matter, and the sum of neutrino masses. This work introduces ForestFlow, a cosmological emulator designed to bridge the gap between large- and small-scale Lyman-$\alpha$ forest analyses. Using conditional normalizing flows, ForestFlow emulates the 2 Lyman-$\alpha$ linear biases ($b_\delta$ and $b_\eta$) and 6 parameters describing small-scale deviations of the 3D flux power spectrum ($P_\mathrm{3D}$) from linear theory. These 8 parameters are modeled as a function of cosmology $\unicode{x2013}$ the small-scale amplitude and slope of the linear power spectrum $\unicode{x2013}$ and the physics of the intergalactic medium. Thus, in combination with a Boltzmann solver, ForestFlow can predict $P_\mathrm{3D}$ on arbitrarily large (linear) scales and the 1D flux power spectrum ($P_\mathrm{1D}$) $\unicode{x2013}$ the primary observable for small-scale analyses $\unicode{x2013}$ without the need for interpolation or extrapolation. Consequently, ForestFlow enables for the first time multiscale analyses. Trained on a suite of 30 fixed-and-paired cosmological hydrodynamical simulations spanning redshifts from $z=2$ to $4.5$, ForestFlow achieves $3$ and $1.5\%$ precision in describing $P_\mathrm{3D}$ and $P_\mathrm{1D}$ from linear scales to $k=5\,\mathrm{Mpc}^{-1}$ and $k_\parallel=4\,\mathrm{Mpc}^{-1}$, respectively. Thanks to its parameterization, the precision of the emulator is also similar for both ionization histories and two extensions to the $\Lambda$CDM model $\unicode{x2013}$ massive neutrinos and curvature $\unicode{x2013}$ not included in the training set. ForestFlow will be crucial for the cosmological analysis of Lyman-$\alpha$ forest measurements from the DESI survey.
Cherenkov Imaged Bio-morphological Features Verify Patient Positioning with Deformable Tissue Translocation in Breast Radiotherapy
2024-09-09
Yao Chen, Savannah M. Decker, Petr Bruza, David J. Gladstone, Lesley A. Jarvis, Brian W. Pogue, Kimberley S. Samkoe, Rongxiao Zhang
Accurate patient positioning is critical for precise radiotherapy dose delivery, as positioning errors can significantly affect treatment outcomes. This study introduces a novel method for tracking loco-regional tissue deformation through Cherenkov image analysis during fractionated breast cancer radiotherapy. The primary goal was to develop and test an algorithm for Cherenkov-based regional position accuracy quantification, specifically for loco-regional deformations, which lack ideal quantification methods in radiotherapy. Blood vessel detection and segmentation were developed in Cherenkov images using a tissue phantom with incremental movements, and later applied to images from fractionated whole breast radiotherapy in human patients (n=10). A combined rigid and non-rigid registration technique was used to detect inter- and intra-fractional positioning variations. This approach quantified positioning variations in two parts: a global shift from rigid registration and a two-dimensional variation map of loco-regional deformation from non-rigid registration. The methodology was validated using an anthropomorphic chest phantom experiment, where known treatment couch translations and respiratory motion were simulated to assess inter- and intra-fractional uncertainties, yielding an average accuracy of 0.83 mm for couch translations up to 20 mm. Analysis of clinical Cherenkov data from ten breast cancer patients showed an inter-fraction setup variation of 3.7 plus minus 2.4 mm relative to the first fraction and loco-regional deformations (95th percentile) of up to 3.3 plus minus 1.9 mm. This study presents a Cherenkov-based approach to quantify global and local positioning variations, demonstrating feasibility in addressing loco-regional deformations that conventional imaging techniques fail to capture.
Robust Real-time Segmentation of Bio-Morphological Features in Human Cherenkov Imaging during Radiotherapy via Deep Learning
2024-09-09
Shiru Wang, Yao Chen, Lesley A. Jarvis, Yucheng Tang, David J. Gladstone, Kimberley S. Samkoe, Brian W. Pogue, Petr Bruza, Rongxiao Zhang
Cherenkov imaging enables real-time visualization of megavoltage X-ray or electron beam delivery to the patient during Radiation Therapy (RT). Bio-morphological features, such as vasculature, seen in these images are patient-specific signatures that can be used for verification of positioning and motion management that are essential to precise RT treatment. However until now, no concerted analysis of this biological feature-based tracking was utilized because of the slow speed and accuracy of conventional image processing for feature segmentation. This study demonstrated the first deep learning framework for such an application, achieving video frame rate processing. To address the challenge of limited annotation of these features in Cherenkov images, a transfer learning strategy was applied. A fundus photography dataset including 20,529 patch retina images with ground-truth vessel annotation was used to pre-train a ResNet segmentation framework. Subsequently, a small Cherenkov dataset (1,483 images from 212 treatment fractions of 19 breast cancer patients) with known annotated vasculature masks was used to fine-tune the model for accurate segmentation prediction. This deep learning framework achieved consistent and rapid segmentation of Cherenkov-imaged bio-morphological features on another 19 patients, including subcutaneous veins, scars, and pigmented skin. Average segmentation by the model achieved Dice score of 0.85 and required less than 0.7 milliseconds processing time per instance. The model demonstrated outstanding consistency against input image variances and speed compared to conventional manual segmentation methods, laying the foundation for online segmentation in real-time monitoring in a prospective setting.
Seeing Through the Mask: Rethinking Adversarial Examples for CAPTCHAs
2024-09-09
Yahya Jabary, Andreas Plesner, Turlan Kuzhagaliyev, Roger Wattenhofer
Modern CAPTCHAs rely heavily on vision tasks that are supposedly hard for computers but easy for humans. However, advances in image recognition models pose a significant threat to such CAPTCHAs. These models can easily be fooled by generating some well-hidden "random" noise and adding it to the image, or hiding objects in the image. However, these methods are model-specific and thus can not aid CAPTCHAs in fooling all models. We show in this work that by allowing for more significant changes to the images while preserving the semantic information and keeping it solvable by humans, we can fool many state-of-the-art models. Specifically, we demonstrate that by adding masks of various intensities the Accuracy @ 1 (Acc@1) drops by more than 50%-points for all models, and supposedly robust models such as vision transformers see an Acc@1 drop of 80%-points. These masks can therefore effectively fool modern image classifiers, thus showing that machines have not caught up with humans -- yet.
Towards Resilient 6G O-RAN: An Energy-Efficient URLLC Resource Allocation Framework
2024-09-09
Rana M. Sohaib, Syed Tariq Shah, Poonam Yadav
The demands of ultra-reliable low-latency communication (URLLC) in ``NextG" cellular networks necessitate innovative approaches for efficient resource utilisation. The current literature on 6G O-RAN primarily addresses improved mobile broadband (eMBB) performance or URLLC latency optimisation individually, often neglecting the intricate balance required to optimise both simultaneously under practical constraints. This paper addresses this gap by proposing a DRL-based resource allocation framework integrated with meta-learning to manage eMBB and URLLC services adaptively. Our approach efficiently allocates heterogeneous network resources, aiming to maximise energy efficiency (EE) while minimising URLLC latency, even under varying environmental conditions. We highlight the critical importance of accurately estimating the traffic distribution flow in the multi-connectivity (MC) scenario, as its uncertainty can significantly degrade EE. The proposed framework demonstrates superior adaptability across different path loss models, outperforming traditional methods and paving the way for more resilient and efficient 6G networks.
Temporal graph neural networks (TGNNs) outperform regular GNNs by incorporating time information into graph-based operations. However, TGNNs adopt specialized models (e.g., TGN, TGAT, and APAN ) and require tailored training frameworks (e.g., TGL and ETC). In this paper, we propose TF-TGN, which uses Transformer decoder as the backbone model for TGNN to enjoy Transformer's codebase for efficient training. In particular, Transformer achieves tremendous success for language modeling, and thus the community developed high-performance kernels (e.g., flash-attention and memory-efficient attention) and efficient distributed training schemes (e.g., PyTorch FSDP, DeepSpeed, and Megatron-LM). We observe that TGNN resembles language modeling, i.e., the message aggregation operation between chronologically occurring nodes and their temporal neighbors in TGNNs can be structured as sequence modeling. Beside this similarity, we also incorporate a series of algorithm designs including suffix infilling, temporal graph attention with self-loop, and causal masking self-attention to make TF-TGN work. During training, existing systems are slow in transforming the graph topology and conducting graph sampling. As such, we propose methods to parallelize the CSR format conversion and graph sampling. We also adapt Transformer codebase to train TF-TGN efficiently with multiple GPUs. We experiment with 9 graphs and compare with 2 state-of-the-art TGNN training frameworks. The results show that TF-TGN can accelerate training by over 2.20 while providing comparable or even superior accuracy to existing SOTA TGNNs. TF-TGN is available at https://github.com/qianghuangwhu/TF-TGN.
2024-09-09
Ben J Draper, Mark J Dunning, David C James
Alternative splicing is crucial in gene regulation, with significant implications in clinical settings and biotechnology. This review article compiles bioinformatics RNA-seq tools for investigating differential splicing; offering a detailed examination of their statistical methods, case applications, and benefits. A total of 22 tools are categorised by their statistical family (parametric, non-parametric, and probabilistic) and level of analysis (transcript, exon, and event). The central challenges in quantifying alternative splicing include correct splice site identification and accurate isoform deconvolution of transcripts. Benchmarking studies show no consensus on tool performance, revealing considerable variability across different scenarios. Tools with high citation frequency and continued developer maintenance, such as DEXSeq and rMATS, are recommended for prospective researchers. To aid in tool selection, a guide schematic is proposed based on variations in data input and the required level of analysis. Additionally, advancements in long-read RNA sequencing are expected to drive the evolution of differential splicing tools, reducing the need for isoform deconvolution and prompting further innovation.
A semi-Lagrangian method for the direct numerical simulation of crystallization and precipitation at the pore scale
2024-09-09
Sarah Perez, Jean-Matthieu Etancelin, Philippe Poncet
This article introduces a new efficient particle method for the numerical simulation of crystallization and precipitation at the pore scale of real rock geometries extracted by X-Ray tomography. It is based on the coupling between superficial velocity models of porous media, Lagrangian description of chemistry using Transition-State-Theory, involving underlying grids. Its ability to successfully compute dissolution process has been established in the past and is presently generalized to precipitation and crystallization by means of adsorption modeling. Numerical simulations of mineral CO2 trapping are provided, showing evidence of clogging/non-clogging regimes, and one of the main results is the introduction of a new non-dimensional number needed for this characterization.
Representational Analysis of Binding in Large Language Models
2024-09-09
Qin Dai, Benjamin Heinzerling, Kentaro Inui
Entity tracking is essential for complex reasoning. To perform in-context entity tracking, language models (LMs) must bind an entity to its attribute (e.g., bind a container to its content) to recall attribute for a given entity. For example, given a context mentioning ``The coffee is in Box Z, the stone is in Box M, the map is in Box H'', to infer ``Box Z contains the coffee'' later, LMs must bind ``Box Z'' to ``coffee''. To explain the binding behaviour of LMs, Feng and Steinhardt (2023) introduce a Binding ID mechanism and state that LMs use a abstract concept called Binding ID (BI) to internally mark entity-attribute pairs. However, they have not directly captured the BI determinant information from entity activations. In this work, we provide a novel view of the Binding ID mechanism by localizing the prototype of BI information. Specifically, we discover that there exists a low-rank subspace in the hidden state (or activation) of LMs, that primarily encodes the order of entity and attribute and which is used as the prototype of BI to causally determine the binding. To identify this subspace, we choose principle component analysis as our first attempt and it is empirically proven to be effective. Moreover, we also discover that when editing representations along directions in the subspace, LMs tend to bind a given entity to other attributes accordingly. For example, by patching activations along the BI encoding direction we can make the LM to infer ``Box Z contains the stone'' and ``Box Z contains the map''.
State-Novelty Guided Action Persistence in Deep Reinforcement Learning
2024-09-09
Jianshu Hu, Paul Weng, Yutong Ban
While a powerful and promising approach, deep reinforcement learning (DRL) still suffers from sample inefficiency, which can be notably improved by resorting to more sophisticated techniques to address the exploration-exploitation dilemma. One such technique relies on action persistence (i.e., repeating an action over multiple steps). However, previous work exploiting action persistence either applies a fixed strategy or learns additional value functions (or policy) for selecting the repetition number. In this paper, we propose a novel method to dynamically adjust the action persistence based on the current exploration status of the state space. In such a way, our method does not require training of additional value functions or policy. Moreover, the use of a smooth scheduling of the repeat probability allows a more effective balance between exploration and exploitation. Furthermore, our method can be seamlessly integrated into various basic exploration strategies to incorporate temporal persistence. Finally, extensive experiments on different DMControl tasks demonstrate that our state-novelty guided action persistence method significantly improves the sample efficiency.
Assessing SPARQL capabilities of Large Language Models
2024-09-09
Lars-Peter Meyer, Johannes Frey, Felix Brei, Natanael Arndt
The integration of Large Language Models (LLMs) with Knowledge Graphs (KGs) offers significant synergistic potential for knowledge-driven applications. One possible integration is the interpretation and generation of formal languages, such as those used in the Semantic Web, with SPARQL being a core technology for accessing KGs. In this paper, we focus on measuring out-of-the box capabilities of LLMs to work with SPARQL and more specifically with SPARQL SELECT queries applying a quantitative approach. We implemented various benchmarking tasks in the LLM-KG-Bench framework for automated execution and evaluation with several LLMs. The tasks assess capabilities along the dimensions of syntax, semantic read, semantic create, and the role of knowledge graph prompt inclusion. With this new benchmarking tasks, we evaluated a selection of GPT, Gemini, and Claude models. Our findings indicate that working with SPARQL SELECT queries is still challenging for LLMs and heavily depends on the specific LLM as well as the complexity of the task. While fixing basic syntax errors seems to pose no problems for the best of the current LLMs evaluated, creating semantically correct SPARQL SELECT queries is difficult in several cases.
A Survey of Multimodal Composite Editing and Retrieval
2024-09-09
Suyan Li, Fuxiang Huang, Lei Zhang
In the real world, where information is abundant and diverse across different modalities, understanding and utilizing various data types to improve retrieval systems is a key focus of research. Multimodal composite retrieval integrates diverse modalities such as text, image and audio, etc. to provide more accurate, personalized, and contextually relevant results. To facilitate a deeper understanding of this promising direction, this survey explores multimodal composite editing and retrieval in depth, covering image-text composite editing, image-text composite retrieval, and other multimodal composite retrieval. In this survey, we systematically organize the application scenarios, methods, benchmarks, experiments, and future directions. Multimodal learning is a hot topic in large model era, and have also witnessed some surveys in multimodal learning and vision-language models with transformers published in the PAMI journal. To the best of our knowledge, this survey is the first comprehensive review of the literature on multimodal composite retrieval, which is a timely complement of multimodal fusion to existing reviews. To help readers' quickly track this field, we build the project page for this survey, which can be found at https://github.com/fuxianghuang1/Multimodal-Composite-Editing-and-Retrieval.
Matching seismic masses for RR Lyrae-type and oscillating red horizontal-branch stars in M4
2024-09-09
László Molnár, Henryka Netzel, Madeline Howell, Csilla Kalup, Meridith Joyce
Globular clusters offer a powerful way to test the properties of stellar populations and the late stages of low-mass stellar evolution. In this paper we study oscillating giant stars and overtone RR Lyrae-type pulsators in the nearest globular cluster, M4, with the help of high-precision, continuous light curves collected by the Kepler space telescope in the K2 mission. We determine the frequency composition of five RRc stars and model their physical parameters with a grid of linear pulsation models. We are able, for the first time, to compare seismic masses of RR Lyrae stars directly to the masses of the very similar red horizontal branch stars in the same stellar population, independently determined from asteroseismic scaling relations. We find a close match, with an average seismic mass of $0.651\pm0.028\,M_\odot$ for RR Lyrae stars and $0.657\pm0.034\,M_\odot$ for red horizontal-branch stars. While the validity of our RR Lyrae masses still relies on the similarity of neighboring horizontal branch subgroups, this result strongly indicates that RRc stars may indeed exhibit high-degree, $l = 8$ and 9 non-radial modes, and modeling these modes can provide realistic mass estimates. We also determine the He content of the cluster to be $Y = 0.266\pm 0.008$, and compare the seismic masses for our sample of RR Lyrae to theoretical mass relations and highlight the limitations of these relations.
Predictive Coding with Spiking Neural Networks: a Survey
2024-09-09
Antony W. N'dri, William Gebhardt, Céline Teulière, Fleur Zeldenrust, Rajesh P. N. Rao, Jochen Triesch, Alexander Ororbia
In this article, we review a class of neuro-mimetic computational models that we place under the label of spiking predictive coding. Specifically, we review the general framework of predictive processing in the context of neurons that emit discrete action potentials, i.e., spikes. Theoretically, we structure our survey around how prediction errors are represented, which results in an organization of historical neuromorphic generalizations that is centered around three broad classes of approaches: prediction errors in explicit groups of error neurons, in membrane potentials, and implicit prediction error encoding. Furthermore, we examine some applications of spiking predictive coding that utilize more energy-efficient, edge-computing hardware platforms. Finally, we highlight important future directions and challenges in this emerging line of inquiry in brain-inspired computing. Building on the prior results of work in computational cognitive neuroscience, machine intelligence, and neuromorphic engineering, we hope that this review of neuromorphic formulations and implementations of predictive coding will encourage and guide future research and development in this emerging research area.
Video anomaly detection (VAD) aims to discover behaviors or events deviating from the normality in videos. As a long-standing task in the field of computer vision, VAD has witnessed much good progress. In the era of deep learning, with the explosion of architectures of continuously growing capability and capacity, a great variety of deep learning based methods are constantly emerging for the VAD task, greatly improving the generalization ability of detection algorithms and broadening the application scenarios. Therefore, such a multitude of methods and a large body of literature make a comprehensive survey a pressing necessity. In this paper, we present an extensive and comprehensive research review, covering the spectrum of five different categories, namely, semi-supervised, weakly supervised, fully supervised, unsupervised and open-set supervised VAD, and we also delve into the latest VAD works based on pre-trained large models, remedying the limitations of past reviews in terms of only focusing on semi-supervised VAD and small model based methods. For the VAD task with different levels of supervision, we construct a well-organized taxonomy, profoundly discuss the characteristics of different types of methods, and show their performance comparisons. In addition, this review involves the public datasets, open-source codes, and evaluation metrics covering all the aforementioned VAD tasks. Finally, we provide several important research directions for the VAD community.
The present study aims to detect helium-3 in nickel-based metal nano-composites doped with zirconia, which exhibited anomalous heat generation when exposed to hydrogen gas at approximately 450{\deg}C. Two complementary analytical techniques were employed: Nuclear Reaction Analysis (NRA) utilizing 1.4 MeV deuteron beams from a tandem accelerator, and Thermal Desorption Spectrometry (TDS) using a quadrupole mass spectrometer. Both methods successfully detected helium-3 in the samples. Given the extreme rarity of this isotope, its presence strongly suggests the occurrence of nuclear reactions within the nickel-containing materials. These findings lend support to the 4H/TSC (4 Hydrogen/Tetrahedral Symmetric Condensate) model, which uniquely predicts helium-3 as one of the primary reaction products.
Modern collaborative Mixed Reality (MR) systems continue to break the boundaries of conventional co-located and remote collaboration and communication. They merge physical and virtual worlds and enable natural interaction, opening up a spectrum of novel opportunities for interpersonal connection. For these connections to be perceived as engaging and positive, collaborators should feel comfortable and experience a sense of belonging. Not having the dedicated devices to smoothly participate in these spaces can hinder this and give users the impression of being left out. To counteract this, we propose to prioritize designing for device inclusivity in MR collaboration, focusing on compensating disadvantages of common non-immersive device classes in cross-device systems.
Macroscopic self-trapping in the dynamical tunneling of a Bose-Einstein condensate
2024-09-09
Sebastian Wüster, Joy Cree, Matthew J. Davis
A Bose-Einstein condensate in a modulated, one-dimensional, anharmonic potential can exhibit dynamical tunneling between islands of regular motion in phase space. With increasingly repulsive atomic interactions, dynamical tunneling is predicted to cease due to self-trapping [S. W\"uster et al. Phys. Rev. Lett. 109 080401 (2012)]. This suppression of tunneling oscillations is related to the same phenomenon that occurs in the two-mode dynamics of a repulsively interacting Bose-Einstein condensate in a double-well potential. Here we present a two-mode model for dynamical tunnelling based on nonlinear Floquet states and examine the range of validity of the approximation. We characterise nonlinear dynamical tunneling for different trap strengths, modulation amplitudes, and effective Planck constants. Using the linear Floquet states we derive an expression for the critical nonlinearity beyond which tunneling ceases. Finally we demonstrate the dynamical instability of selected nonlinear Floquet states and show how to initialise some Floquet states in experiments. Our detailed survey will enable experiments to target accessible parameter regimes for the study of nonlinear dynamical tunneling.
Quantifying azimuthal variations within the interstellar medium of z ~ 0 spiral galaxies with the TYPHOON survey
2024-09-09
Qian-Hui Chen, Kathryn Grasha, Andrew J. Battisti, Emily Wisnioski, Zefeng Li, Hye-Jin Park, Brent Groves, Paul Torrey, Trevor Mendel, Barry F. Madore, Mark Seibert, Eva Sextl, Alex M. Garcia, Jeff A. Rich, Rachael L. Beaton, Lisa J. Kewley
Most star formation in the local Universe occurs in spiral galaxies, but their origin remains an unanswered question. Various theories have been proposed to explain the development of spiral arms, each predicting different spatial distributions of the interstellar medium. This study maps the star formation rate (SFR) and gas-phase metallicity of nine spiral galaxies with the TYPHOON survey to test two dominating theories: density wave theory and dynamic spiral theory. We discuss the environmental effects on our galaxies, considering reported environments and merging events. Taking advantage of the large field of view covering the entire optical disk, we quantify the fluctuation of SFR and metallicity relative to the azimuthal distance from the spiral arms. We find higher SFR and metallicity in the trailing edge of NGC~1365 (by 0.117~dex and 0.068~dex, respectively) and NGC~1566 (by 0.119~dex and 0.037~dex, respectively), which is in line with density wave theory. NGC~2442 shows a different result with higher metallicity (0.093~dex) in the leading edge, possibly attributed to an ongoing merging. The other six spiral galaxies show no statistically significant offset in SFR or metallicity, consistent with dynamic spiral theory. We also compare the behaviour of metallicity inside and outside the co-rotation radius (CR) of NGC~1365 and NGC~1566. We find comparable metallicity fluctuations near and beyond the CR of NGC~1365, indicating gravitational perturbation. NGC~1566 shows the greatest fluctuation near the CR, in line with the analytic spiral arms. Our work highlights that a combination of mechanisms explains the origin of spiral features in the local Universe.
The Complex Emotion Recognition System (CERS) deciphers complex emotional states by examining combinations of basic emotions expressed, their interconnections, and the dynamic variations. Through the utilization of advanced algorithms, CERS provides profound insights into emotional dynamics, facilitating a nuanced understanding and customized responses. The attainment of such a level of emotional recognition in machines necessitates the knowledge distillation and the comprehension of novel concepts akin to human cognition. The development of AI systems for discerning complex emotions poses a substantial challenge with significant implications for affective computing. Furthermore, obtaining a sizable dataset for CERS proves to be a daunting task due to the intricacies involved in capturing subtle emotions, necessitating specialized methods for data collection and processing. Incorporating physiological signals such as Electrocardiogram (ECG) and Electroencephalogram (EEG) can notably enhance CERS by furnishing valuable insights into the user's emotional state, enhancing the quality of datasets, and fortifying system dependability. A comprehensive literature review was conducted in this study to assess the efficacy of machine learning, deep learning, and meta-learning approaches in both basic and complex emotion recognition utilizing EEG, ECG signals, and facial expression datasets. The chosen research papers offer perspectives on potential applications, clinical implications, and results of CERSs, with the objective of promoting their acceptance and integration into clinical decision-making processes. This study highlights research gaps and challenges in understanding CERSs, encouraging further investigation by relevant studies and organizations. Lastly, the significance of meta-learning approaches in improving CERS performance and guiding future research endeavors is underscored.
SCALE at Scale: Cosmological applications of small-scale CMB lensing
2024-09-09
Victor C. Chan, Renée Hložek, Joel Meyers, Alexander van Engelen
The Small-Correlated-Against-Large Estimator (SCALE) for small-scale lensing of the cosmic microwave background (CMB) provides a novel method for measuring the amplitude of CMB lensing power without the need for reconstruction of the lensing field. In our previous study, we showed that the SCALE method can outperform existing reconstruction methods to detect the presence of lensing at small scales ($\ell \gg 3000$). Here we develop a procedure to include information from SCALE in cosmological parameter inference. We construct a precise neural network emulator to quickly map cosmological parameters to desired CMB observables such as temperature and lensing power spectra and SCALE cross spectra. We also outline a method to apply SCALE to full-sky maps of the CMB temperature field, and construct a likelihood for the application of SCALE in parameter estimation. SCALE supplements conventional observables such as the CMB power spectra and baryon acoustic oscillations in constraining parameters that are sensitive to the small-scale lensing amplitude such as the neutrino mass $m_\nu$. We show that including estimates of the small-scale lensing amplitude from SCALE in such an analysis provides enough constraining information to measure the minimum neutrino mass at $4\sigma$ significance in the scenario of minimal mass, and higher significance for higher mass. Finally, we show that SCALE will play a powerful role in constraining models of clustering that generate scale-dependent modulation to the distribution of matter and the lensing power spectrum, as predicted by models of warm or fuzzy dark matter.
Stratified dispersal explains mountain pine beetle's range expansion in Alberta
2024-09-09
Evan C. Johnson, Micah Brush, Mark A. Lewis
The mountain pine beetle (MPB), a destructive pest native to Western North America, has recently extended its range into Alberta, Canada. Predicting the dispersal of MPB is challenging due to their small size and complex dispersal behavior. Because of these challenges, estimates of MPB's typical dispersal distances have varied widely, ranging from 10 meters to 18 kilometers. Here, we use high-quality data from helicopter and field-crew surveys to parameterize a large number of dispersal kernels. We find that fat-tailed kernels -- those which allow for a small number of long-distance dispersal events -- consistently provide the best fit to the data. Specifically, the radially-symmetric Student's t-distribution with parameters {\nu} = 0.012 and {\rho} = 1.45 stands out as parsimonious and user-friendly; this model predicts a median dispersal distance of 60 meters, but with the 95th percentile of dispersers travelling nearly 5 kilometers. The best-fitting mathematical models have biological interpretations. The Student's t-distribution, derivable as a mixture of diffusive processes with varying settling times, is consistent with observations that most beetles fly short distances while few travel far; early-emerging beetles fly farther; and larger beetles from larger trees exhibit greater variance in flight distance. Finally, we explain why other studies have found such a wide variation in the length scale in MPB dispersal, and we demonstrate that long-distance dispersal events are critical for modelling MPB range expansion.
The Dirac Equation Near Centenary: a Contemporary Introduction to the Dirac Equation Consideration
2024-09-09
V. M. Simulik
More then 35 approaches to the Dirac equation derivation are presented. The various physical principles and mathematical methods are used. A review of well-known and not enough known contributions to the problem is given, the unexpected and unconventional derivations are presented as well. Three original approaches to the problem suggested by the author are considered as well. They are (i) the generalization of H. Sallhofer derivation, (ii) the obtaining of the massless Dirac equation from the Maxwell equations in maximally symmetrical form, (iii) the derivation of the Dirac equation with nonzero mass from the relativistic canonical quantum mechanics of the fermion-antifermion spin s=1/2 doublet. Today we are able to demonstrate new features of our derivations given in original papers. In some sense the important role of the Dirac equation in contemporary theoretical physics is demonstrated. A criterion for the usefulness of one or another derivation of the Dirac equation has been established
Online Resynthesis of High-Level Collaborative Tasks for Robots with Changing Capabilities
2024-09-09
Amy Fang, Tenny Yin, Hadas Kress-Gazit
Given a collaborative high-level task and a team of heterogeneous robots and behaviors to satisfy it, this work focuses on the challenge of automatically, at runtime, adjusting the individual robot behaviors such that the task is still satisfied, when robots encounter changes to their abilities--either failures or additional actions they can perform. We consider tasks encoded in LTL^\psi and minimize global teaming reassignments (and as a result, local resynthesis) when robots' capabilities change. We also increase the expressivity of LTL^\psi by including additional types of constraints on the overall teaming assignment that the user can specify, such as the minimum number of robots required for each assignment. We demonstrate the framework in a simulated warehouse scenario.
Analyzing fisher effort -- Gender differences and the impact of Covid-19
2024-09-08
Julia S. Schmid, Sean Simmons, Mark S. Poesch, Pouria Ramazi, Mark A. Lewis
Fishing is a valuable recreational activity in our society. To assess future fishing activity, identifying variables related to differences in fishing activity, such as gender or Covid-19, is helpful. We conducted a Canada-wide email survey of users of an online fishing platform and analyzed responses with a focus on gender, the impact of Covid-19, and variables directly related to fisher effort. Genders (90.1% male and 9.9% female respondents) significantly differed in demographics, socioeconomic status, and fishing skills but were similar in fishing preferences, fisher effort in terms of trip frequency, and travel distance. For almost half of the fishers, Covid-19 caused a change in trip frequency, determined by the activity level and gender of the fisher. A Bayesian network revealed that travel distance was the main determinant of trip frequency and negatively impacted the fishing activity of 61% of the fishers. Fisher effort was also directly related to fishing expertise. The study shows how online surveys and Bayesian networks can help understand the relationship between fishers' characteristics and activity and predict future fishing trends.
Infinite-Length Limit of Spectral Curves and Inverse Scattering
2024-09-08
Niklas Beisert, Kunal Gupta
Integrability equips models of theoretical physics with efficient methods for the exact construction of useful states and their evolution. Relevant tools for classical integrable field models in one spatial dimensional are spectral curves in the case of periodic fields and inverse scattering for asymptotic boundary conditions. Even though the two methods are quite different in many ways, they ought to be related by taking the periodicity length of closed boundary conditions to infinity. Using the Korteweg-de Vries equation and the continuous Heisenberg magnet as prototypical classical integrable field models, we discuss and illustrate how data for spectral curves transforms into asymptotic scattering data. In order to gain intuition and also for concreteness, we review how the elliptic states of these models degenerate into solitons at infinite length.
Advances in Nanoparticle-Based Targeted Drug Delivery Systems for Colorectal Cancer Therapy: A Review
2024-09-08
Mahadi Hasan, Camryn Grace Evett, Jack Burton
Colorectal cancer (CRC) continues to be a significant global health burden, prompting the need for more effective and targeted therapeutic strategies. Nanoparticle-based drug delivery systems have emerged as a promising approach to address the limitations of conventional chemotherapy, offering enhanced specificity, reduced systemic toxicity, and improved therapeutic outcomes. This paper provides an in-depth review of the current advancements in the application of nanoparticles as vehicles for targeted drug delivery in CRC therapy. It covers a variety of nanoparticle types, including liposomes, polymeric nanoparticles, dendrimers, and mesoporous silica nanoparticles (MSNs), with a focus on their design, functionalization, and mechanisms of action. This review also examines the challenges associated with the clinical translation of these technologies and explores future directions, emphasizing the potential of nanoparticle-based systems to revolutionize CRC treatment.
Seven decades of exploring planetary interiors with rotating convection experiments
2024-09-08
Alban Pothérat, Susanne Horn
The interiors of many planets consist mostly of fluid layers. When these layers are subject to superadiabatic temperature or compositional gradients, turbulent convection transports heat and momentum. In addition, planets are fast rotators. Thus, the key process that underpins planetary evolution, the dynamo action, flow patterns and more, is rotating convection. Because planetary interiors are inaccessible to direct observation, experiments offer physically consistent models that are crucial to guide our understanding. If we can fully understand the laboratory model, we may eventually fully understand the original. Experimentally reproducing rotating thermal convection relevant to planetary interiors comes with specific challenges, e.g. modelling the central gravity field of a planet that is parallel to the temperature gradient. Three classes of experiments tackle this challenge. One approach consists of using an alternative central force field, such as the electric force. These are, however, weaker than gravity and require going to space. Another method entails rotating the device fast enough so that the centrifugal force supersedes Earth's gravity. This mimics the equatorial regions of a planet. Lastly, by using the actual lab gravity aligned with the rotation axis, insight into the polar regions is gained. These experiments have been continuously refined during the past seven decades. We review their evolution, from the early days of visualising the onset patterns of convection, over central force field experiments in spacecrafts, liquid metal experiments, to the latest optical velocity mapping of rotating magnetoconvection in sulfuric acid inside high-field magnets. We show how innovative experimental design and emerging experimental techniques advanced our understanding and painted a more realistic picture of planetary interiors, including Earth's liquid metal outer core.
Advances in colored k-mer sets: essentials for the curious
2024-09-08
Camille Marchet
This paper provides a comprehensive review of recent advancements in k-mer-based data structures representing collections of several samples (sometimes called colored de Bruijn graphs) and their applications in large-scale sequence indexing and pangenomics. The review explores the evolution of k-mer set representations, highlighting the trade-offs between exact and inexact methods, as well as the integration of compression strategies and modular implementations. I discuss the impact of these structures on practical applications and describe recent utilization of these methods for analysis. By surveying the state-of-the-art techniques and identifying emerging trends, this work aims to guide researchers in selecting and developing methods for large scale and reference-free genomic data. For a broader overview of k-mer set representations and foundational data structures, see the accompanying article on practical k-mer sets.
Advances in practical k-mer sets: essentials for the curious
2024-09-08
Camille Marchet
This paper provides a comprehensive survey of data structures for representing k-mer sets, which are fundamental in high-throughput sequencing analysis. It categorizes the methods into two main strategies: those using fingerprinting and hashing for compact storage, and those leveraging lexicographic properties for efficient representation. The paper reviews key operations supported by these structures, such as membership queries and dynamic updates, and highlights recent advancements in memory efficiency and query speed. A companion paper explores colored k-mer sets, which extend these concepts to integrate multiple datasets or genomes.
2024-09-08
Xin Jin, Hongyu Zhu, Siyuan Li, Zedong Wang, Zicheng Liu, Chang Yu, Huafeng Qin, Stan Z. Li
As Deep Neural Networks have achieved thrilling breakthroughs in the past decade, data augmentations have garnered increasing attention as regularization techniques when massive labeled data are unavailable. Among existing augmentations, Mixup and relevant data-mixing methods that convexly combine selected samples and the corresponding labels are widely adopted because they yield high performances by generating data-dependent virtual data while easily migrating to various domains. This survey presents a comprehensive review of foundational mixup methods and their applications. We first elaborate on the training pipeline with mixup augmentations as a unified framework containing modules. A reformulated framework could contain various mixup methods and give intuitive operational procedures. Then, we systematically investigate the applications of mixup augmentations on vision downstream tasks, various data modalities, and some analysis \& theorems of mixup. Meanwhile, we conclude the current status and limitations of mixup research and point out further work for effective and efficient mixup augmentations. This survey can provide researchers with the current state of the art in mixup methods and provide some insights and guidance roles in the mixup arena. An online project with this survey is available at \url{https://github.com/Westlake-AI/Awesome-Mixup}.
Seismic monitoring of CO2 plume dynamics using ensemble Kalman filtering
2024-09-08
Grant Bruer, Abhinav Prakash Gahlot, Edmond Chow, Felix Herrmann
Monitoring carbon dioxide (CO2) injected and stored in subsurface reservoirs is critical for avoiding failure scenarios and enables real-time optimization of CO2 injection rates. Sequential Bayesian data assimilation (DA) is a statistical method for combining information over time from multiple sources to estimate a hidden state, such as the spread of the subsurface CO2 plume. An example of scalable and efficient sequential Bayesian DA is the ensemble Kalman filter (EnKF). We improve upon existing DA literature in the seismic-CO2 monitoring domain by applying this scalable DA algorithm to a high-dimensional CO2 reservoir using two-phase flow dynamics and time-lapse full waveform seismic data with a realistic surface-seismic survey design. We show more accurate estimates of the CO2 saturation field using the EnKF compared to using either the seismic data or the fluid physics alone. Furthermore, we test a range of values for the EnKF hyperparameters and give guidance on their selection for seismic CO2 reservoir monitoring.
The Effect of Radiation and Supernovae Feedback on LyC Escape in Local Star-forming Galaxies
2024-09-08
Cody A. Carr, Renyue Cen, Claudia Scarlata, Xinfeng Xu, Alaina Henry, Rui Marques-Chaves, Daniel Schaerer, Ricardo O. Amorín, M. S. Oey, Lena Komarova, Sophia Flury, Anne Jaskot, Alberto Saldana-Lopez, Zhiyuan Ji, Mason Huberty, Timothy Heckman, Göran Ostlin, Omkar Bait, Matthew James Hayes, Trinh Thuan, Danielle A. Berg, Mauro Giavalisco, Sanchayeeta Borthakur, John Chisholm, Harry C. Ferguson, Leo Michel-Dansac, Anne Verhamme, Gábor Worseck
Feedback is widely recognized as an essential condition for Lyman continuum (LyC) escape in star-forming galaxies. However, the mechanisms by which galactic outflows clear neutral gas and dust remain unclear. In this paper, we model the Mg II 2796\r{A}, 2804\r{A} absorption + emission lines in 29 galaxies taken from the Low-z LyC Survey (LzLCS) to investigate the impact of (radiation + mechanical) feedback on LyC escape. Using constraints on Mg$^+$ and photoionization models, we map the outflows' neutral hydrogen content and predict $f_{esc}^{LyC}$ with a multiphase wind model. We measure mass, momentum, and energy loading factors for the neutral winds, which carry up to 10% of the momentum and 1% of the energy in SFR-based deposition rates. We use SED template fitting to determine the relative ages of stellar populations, allowing us to identify radiation feedback dominant systems. We then examine feedback related properties (stellar age, loading factors, etc.) under conditions that optimize feedback efficiency, specifically high star formation rate surface density and compact UV half-light radii. Our findings indicate that the strongest leakers are radiation feedback dominant, lack Mg II outflows, but have extended broad components in higher ionization lines like [O III] 5007\r{A}, as observed by Amor\'in et al. (2024). In contrast, galaxies experiencing supernovae feedback typically exhibit weaker $f_{esc}^{LyC}$ and show evidence of outflows in both Mg II and higher ionization lines. We attribute these findings to rapid or "catastrophic" cooling in the radiation-dominant systems, which, given the low metallicities in our sample, are likely experiencing delayed supernovae.
Lenticular galaxies (S0s) in the local universe are generally absent of recent star formation and lack molecular gas. In this paper, we investigate one massive ($M_*$$\sim$5$\times10^{10}$ M$_\odot$) star-forming S0, PGC 39535, with the Northern Extended Millimeter Array (NOEMA). Using optical data from SDSS-IV MaNGA survey, we find star formation mainly concentrates in the central region of PGC 39535. The total star formation rate estimated using extinction-corrected H$\alpha$ flux is 1.57 M$_\odot$ yr$^{-1}$. Results of NOEMA observation suggest that the molecular gas mainly concentrates in the central regions as a gaseous bar and a ring-like structure, and shows similar kinematics as the stellar and ionized gas components. The total molecular gas mass estimated from CO(1-0) is (5.42$\pm$1.52)$\times$10$^{9}$ M$_{\odot}$. We find PGC 39535 lies on the star-forming main sequence, but falls below Kennicutt-Schmidt relation of spiral galaxies, suggesting that the star formation efficiency may be suppressed by the massive bulge. The existence of a second Gaussian component in the CO spectrum of the central region indicates possible gas flows. Furthermore, our analyses suggest that PGC 39535 resides in the center of a massive group and the derived star formation history indicates it may experience a series of gas-rich mergers over the past 2$\sim$7 Gyr.
Magnetospheric control of ionospheric TEC perturbations via whistler-mode and ULF waves
2024-09-08
Yangyang Shen, Olga P. Verkhoglyadova, Anton Artemyev, Michael D. Hartinger, Vassilis Angelopoulos, Xueling Shi, Ying Zou
The weakly ionized plasma in the Earth's ionosphere is controlled by a complex interplay between solar and magnetospheric inputs from above, atmospheric processes from below, and plasma electrodynamics from within. This interaction results in ionosphere structuring and variability that pose major challenges for accurate ionosphere prediction for global navigation satellite system (GNSS) related applications and space weather research. The ionospheric structuring and variability are often probed using the total electron content (TEC) and its relative perturbations (dTEC). Among dTEC variations observed at high latitudes, a unique modulation pattern has been linked to magnetospheric ultra low frequency (ULF) waves, yet its underlying mechanisms remain unclear. Here using magnetically-conjugate observations from the THEMIS spacecraft and a ground-based GPS receiver at Fairbanks, Alaska, we provide direct evidence that these dTEC modulations are driven by magnetospheric electron precipitation induced by ULF-modulated whistler-mode waves. We observed peak-to-peak dTEC amplitudes reaching ~0.5 TECU (1 TECU is equal to 10$^6$ electrons/m$^2$) with modulations spanning scales of ~5--100 km. The cross-correlation between our modeled and observed dTEC reached ~0.8 during the conjugacy period but decreased outside of it. The spectra of whistler-mode waves and dTEC also matched closely at ULF frequencies during the conjugacy period but diverged outside of it. Our findings elucidate the high-latitude dTEC generation from magnetospheric wave-induced precipitation, addressing a significant gap in current physics-based dTEC modeling. Theses results thus improve ionospheric dTEC prediction and enhance our understanding of magnetosphere-ionosphere coupling via ULF waves.
Difference Between Cyclic and Distributed Approach in Stochastic Optimization for Multi-agent System
2024-09-08
Jiahao Shi, James C. Spall
Many stochastic optimization problems in multi-agent systems can be decomposed into smaller subproblems or reduced decision subspaces. The cyclic and distributed approaches are two widely used strategies for solving such problems. In this manuscript, we review four existing methods for addressing these problems and compare them based on their suitable problem frameworks and update rules.
Stellar reddening map from DESI imaging and spectroscopy
2024-09-08
Rongpu Zhou, Julien Guy, Sergey E. Koposov, Edward F. Schlafly, David Schlegel, Jessica Aguilar, Steven Ahlen, Stephen Bailey, David Bianchi, David Brooks, Edmond Chaussidon, Todd Claybaugh, Kyle Dawson, Axel de la Macorra, Biprateep Dey, Daniel J. Eisenstein, Simone Ferraro, Andreu Font-Ribera, Jaime E. Forero-Romero, Enrique Gaztañaga, Satya Gontcho A Gontcho, Gaston Gutierrez, Klaus Honscheid, Stephanie Juneau, Robert Kehoe, David Kirkby, Theodore Kisner, Anthony Kremin, Andrew Lambert, Martin Landriau, Laurent Le Guillou, Michael E. Levi, Ting S. Li, Marc Manera, Paul Martini, Aaron Meisner, Ramon Miquel, John Moustakas, Adam D. Myers, Jeffrey A. Newman, Gustavo Niz, Nathalie Palanque-Delabrouille, Will J. Percival, Claire Poppett, Francisco Prada, Anand Raichoor, Ashley J. Ross, Graziano Rossi, Eusebio Sanchez, Andrew K. Saydjari, Michael Schubnell, David Sprayberry, Gregory Tarl, Benjamin A. Weaver, Pauline Zarrouk, Hu Zou
We present new Galactic reddening maps of the high Galactic latitude sky using DESI imaging and spectroscopy. We directly measure the reddening of 2.6 million stars by comparing the observed stellar colors in $g-r$ and $r-z$ from DESI imaging with the synthetic colors derived from DESI spectra from the first two years of the survey. The reddening in the two colors is on average consistent with the \cite{fitzpatrick_correcting_1999} extinction curve with $R_\mathrm{V}=3.1$. We find that our reddening maps differ significantly from the commonly used \cite{schlegel_maps_1998} (SFD) reddening map (by up to 80 mmag in $E(B-V)$), and we attribute most of this difference to systematic errors in the SFD map. To validate the reddening map, we select a galaxy sample with extinction correction based on our reddening map, and this yields significantly better uniformity than the SFD extinction correction. Finally, we discuss the potential systematic errors in the DESI reddening measurements, including the photometric calibration errors that are the limiting factor on our accuracy. The $E(g-r)$ and $E(g-r)$ maps presented in this work, and for convenience their corresponding $E(B-V)$ maps with SFD calibration, are publicly available.
On the Need to Monitor Continuous Integration Practices -- An Empirical Study
2024-09-08
Jadson Santos, Daniel Alencar da Costa, Shane McIntosh, Uirá Kulesza
Continuous Integration (CI) encompasses a set of widely adopted practices that enhance software development. However, there are indications that developers may not adequately monitor CI practices. Hence, this paper explores developers' perceptions regarding the monitoring CI practices. To achieve this, we first perform a Document Analysis to assess developers' expressed need for practice monitoring in pull requests comments generated by developers during the development process. After that, we conduct a survey among developers from 121 open-source projects to understand perception of the significance of monitoring seven CI practices in their projects. Finally, we triangulate the emergent themes from our survey by performing a second Document Analysis to understand the extent of monitoring features supported by existing CI services. Our key findings indicate that: 1) the most frequently mentioned CI practice during the development process is ``Test Coverage'' (> 80\%), while ``Build Health'' and ``Time to Fix a Broken Build'' present notable opportunities for monitoring CI practices; 2) developers do not adequately monitor all CI practices and express interest in monitoring additional practices; and 3) the most popular CI services currently offer limited native support for monitoring CI practices, requiring the use of third-party tools. Our results lead us to conclude that monitoring CI practices is often overlooked by both CI services and developers. Using third-party tools in conjunction with CI services is challenging, they monitor some redundant practices and still falls short of fully supporting CI practices monitoring. Therefore, CI services should implement CI practices monitoring, which would facilitate and encourage developers to monitor them.
The circular velocity and halo mass functions of galaxies in the nearby Universe
2024-09-08
Andrei Ristea, Luca Cortese, Brent Groves, A. Fraser-McKelvie, Danail Obreschkow, Karl Glazebrook
The circular velocity function (CVF) of galaxies is a fundamental test of the $\Lambda$ Cold Dark Matter (CDM) paradigm as it traces the variation of galaxy number densities with circular velocity ($v_{\rm{circ}}$), a proxy for dynamical mass. Previous observational studies of the CVF have either been based on \ion{H}{I}-rich galaxies, or encompassed low-number statistics and probed narrow ranges in $v_{\rm{circ}}$. We present a benchmark computation of the CVF between $100-350\ \rm{km\ s^{-1}}$ using a sample of 3527 nearby-Universe galaxies, representative for stellar masses between $10^{9.2}-10^{11.9} \rm{M_{\odot}}$. We find significantly larger number densities above 150 $\rm{km\ s^{-1}}$ compared to results from \ion{H}{I} surveys, pertaining to the morphological diversity of our sample. Leveraging the fact that circular velocities are tracing the gravitational potential of halos, we compute the halo mass function (HMF), covering $\sim$1 dex of previously unprobed halo masses ($10^{11.7}-10^{12.7} \rm{M_{\odot}}$). The HMF for our sample, representative of the galaxy population with $M_{200}\geqslant10^{11.35} \rm{M_{\odot}}$, shows that spiral morphologies contribute 67 per cent of the matter density in the nearby Universe, while early types account for the rest. We combine our HMF data with literature measurements based on \ion{H}{I} kinematics and group/cluster velocity dispersions. We constrain the functional form of the HMF between $10^{10.5}-10^{15.5} \rm{M_{\odot}}$, finding a good agreement with $\Lambda$CDM predictions. The halo mass range probed encompasses 72$\substack{+5 \\ -6}$ per cent ($\Omega_{\rm{M,10.5-15.5}} = 0.227 \pm 0.018$) of the matter density in the nearby Universe; 31$\substack{+5 \\ -6}$ per cent is accounted for by halos below $10^{12.7}\rm{M_{\odot}}$ occupied by a single galaxy.
In this paper we firstly review how to \textit{explicitly} solve a system of $3$ \textit{first-order linear recursions }and outline the main properties of these solutions. Next, via a change of variables, we identify a class of systems of $3$ \textit{first-order nonlinear recursions} which also are \textit{explicitly solvable}. These systems might be of interest for practitioners in \textit{applied} sciences: they allow a complete display of their solutions, which may feature interesting behaviors, for instance be \textit{completely periodic} ("isochronous systems", if the independent variable $n=0,1,2,3...$is considered a \textit{ticking time}), or feature this property \textit{only asymptotically} (as\textit{\ }$n\rightarrow \infty $).
In modern materials science, effective and high-volume data management across leading-edge experimental facilities and world-class supercomputers is indispensable for cutting-edge research. Such facilities and supercomputers are typically utilized by a wide range of researchers across different fields and organizations in academia and industry. However, existing integrated systems that handle data from these resources have primarily focused just on smaller-scale cross-institutional or single-domain operations. As a result, they often lack the scalability, efficiency, agility, and interdisciplinarity, needed for handling substantial volumes of data from various researchers. In this paper, we introduce ARIM-mdx data system, a nationwide data platform for materials science in Japan. The platform involves 8 universities and institutes all over Japan through the governmental materials science project. Currently in its trial phase, the ARIM-mdx data system is utilized by over 800 researchers from around 140 organizations in academia and industry, being intended to gradually expand its reach. The system employs a hybrid architecture, combining a peta-scale dedicated storage system for security and stability with a high-performance academic cloud for efficiency and scalability. Through direct network connections between them, the system achieves 4.7x latency reduction compared to a conventional approach, resulting in near real-time interactive data analysis. It also utilizes specialized IoT devices for secure data transfer from equipment computers and connects to multiple supercomputers via an academic ultra-fast network, achieving 4x faster data transfer compared to the public Internet. The ARIM-mdx data system, as a pioneering nationwide data platform, has the potential to contribute to the creation of new research communities and accelerates innovations.
The origins of lenticular galaxies (S0s) can be classified into two main categories: ``minor mergers" in low-density environments (LDEs) and ``faded spirals" in high-density environments (HDEs). The transitional phase in the evolution of S0s, namely, star-forming lenticular galaxies (SFS0s), can serve as an important probe for analyzing the complex processes involved in the transformation between different galaxy types and the quenching of star formation (SF). We attempt to find the impact of different environments on the global properties and spatially resolved quantities of SFS0s. We selected 71 SFS0s from the SDSS-IV MaNGA Survey, comprising 23 SFS0s in HDEs (SFS0s$\_$HE) and 48 SFS0s in LDEs (SFS0s$\_$LE). We examined the effects of the environment, by studying the global properties, concentration index, and radial profiles of the derived quantities. The varied environments of SFS0s do not lead to any significant difference in global properties (e.g., S$\acute{\rm e}$rsic index). By calculating $CI_{\rm H_{\alpha}/cont}$, we observe that different environments may cause varying concentrations of SF. Specifically, SFS0s$\_$LE, affected by external gas mergers or inflow, exhibit a more centrally concentrated SF (i.e., larger $CI_{\rm H_{\alpha}/cont}$). This trend is further supported by $CI_{\rm SFR, H_{\alpha}}$, which only considers the gas disk of the galaxy. This observation is aligned with the observed shrinking of gas disks in galaxies affected by ram-pressure stripping in HDEs. Furthermore, their $\Sigma_{\rm SFR}$ or resolved sSFR are comparable. On average, SFS0s$\_$LE display significantly higher values for both quantities. Finally, the observed D$_{\rm n}4000$ and gas-phase metallicity gradient correspond well to their assumed origins. However, we did not find a significantly lower gas-phase metallicity in SFS0s$\_$LE. Abridged
The Influence of Demographic Variation on the Perception of Industrial Robot Movements
2024-09-08
Damian Hostettler
The influence of individual differences on the perception and evaluation of interactions with robots has been researched for decades. Some human demographic characteristics have been shown to affect how individuals perceive interactions with robots. Still, it is to-date not clear whether, which and to what extent individual differences influence how we perceive robots, and even less is known about human factors and their effect on the perception of robot movements. In addition, most results on the relevance of individual differences investigate human-robot interactions with humanoid or social robots whereas interactions with industrial robots are underrepresented. We present a literature review on the relationship of robot movements and the influence of demographic variation. Our review reveals a limited comparability of existing findings due to a lack of standardized robot manipulations, various dependent variables used and differing experimental setups including different robot types. In addition, most studies have insufficient sample sizes to derive generalizable results. To overcome these shortcomings, we report the results from a Web-based experiment with 930 participants that studies the effect of demographic characteristics on the evaluation of movement behaviors of an articulated robot arm. Our findings demonstrate that most participants prefer an approach from the side, a large movement range, conventional numbers of rotations, smooth movements and neither fast nor slow movement speeds. Regarding individual differences, most of these preferences are robust to demographic variation, and only gender and age was found to cause slight preference differences between slow and fast movements.
While traditional recommendation techniques have made significant strides in the past decades, they still suffer from limited generalization performance caused by factors like inadequate collaborative signals, weak latent representations, and noisy data. In response, diffusion models (DMs) have emerged as promising solutions for recommender systems due to their robust generative capabilities, solid theoretical foundations, and improved training stability. To this end, in this paper, we present the first comprehensive survey on diffusion models for recommendation, and draw a bird's-eye view from the perspective of the whole pipeline in real-world recommender systems. We systematically categorize existing research works into three primary domains: (1) diffusion for data engineering & encoding, focusing on data augmentation and representation enhancement; (2) diffusion as recommender models, employing diffusion models to directly estimate user preferences and rank items; and (3) diffusion for content presentation, utilizing diffusion models to generate personalized content such as fashion and advertisement creatives. Our taxonomy highlights the unique strengths of diffusion models in capturing complex data distributions and generating high-quality, diverse samples that closely align with user preferences. We also summarize the core characteristics of the adapting diffusion models for recommendation, and further identify key areas for future exploration, which helps establish a roadmap for researchers and practitioners seeking to advance recommender systems through the innovative application of diffusion models. To further facilitate the research community of recommender systems based on diffusion models, we actively maintain a GitHub repository for papers and other related resources in this rising direction https://github.com/CHIANGEL/Awesome-Diffusion-for-RecSys.
We review the theoretical model underpinning the recently reported room-temperature, ambient-pressure superconductivity along line defects on the surface of highly-oriented pyrolytic graphite. The main ingredients for this 1D room-temperature superconductivity are pairing by effective strain gauge fields, the formation of an effective Josephson junction array in its Bose metal state on the surface and the suppression of phase slips by dimensional embedding in an extremely well-conducting 3D bulk structure.
Unified External Stakeholder Engagement and Requirements Strategy
2024-09-08
Ahmed Abdulaziz Alnhari, Rizwan Qureshi
Understanding stakeholder needs is essential for project success, as stakeholder importance varies across projects. This study proposes a framework for early stakeholder identification and continuous engagement throughout the project lifecycle. The framework addresses common organizational failures in stakeholder communication that lead to project delays and cancellations. By classifying stakeholders by influence and interest, establishing clear communication channels, and implementing regular feedback loops, the framework ensures effective stakeholder involvement. This approach allows for necessary project adjustments and builds long-term relationships, validated by a survey of IT professionals. Engaging stakeholders strategically at all stages minimizes misunderstandings and project risks, contributing to better project management and lifecycle outcomes.
In 2023, Sonatype reported a 200\% increase in software supply chain attacks, including major build infrastructure attacks. To secure the software supply chain, practitioners can follow security framework guidance like the Supply-chain Levels for Software Artifacts (SLSA). However, recent surveys and industry summits have shown that despite growing interest, the adoption of SLSA is not widespread. To understand adoption challenges, \textit{the goal of this study is to aid framework authors and practitioners in improving the adoption and development of Supply-Chain Levels for Software Artifacts (SLSA) through a qualitative study of SLSA-related issues on GitHub}. We analyzed 1,523 SLSA-related issues extracted from 233 GitHub repositories. We conducted a topic-guided thematic analysis, leveraging the Latent Dirichlet Allocation (LDA) unsupervised machine learning algorithm, to explore the challenges of adopting SLSA and the strategies for overcoming these challenges. We identified four significant challenges and five suggested adoption strategies. The two main challenges reported are complex implementation and unclear communication, highlighting the difficulties in implementing and understanding the SLSA process across diverse ecosystems. The suggested strategies include streamlining provenance generation processes, improving the SLSA verification process, and providing specific and detailed documentation. Our findings indicate that some strategies can help mitigate multiple challenges, and some challenges need future research and tool enhancement.
Towards Patronizing and Condescending Language in Chinese Videos: A Multimodal Dataset and Detector
2024-09-08
Hongbo Wang, Junyu Lu, Yan Han, Kai Ma, Liang Yang, Hongfei Lin
Patronizing and Condescending Language (PCL) is a form of discriminatory toxic speech targeting vulnerable groups, threatening both online and offline safety. While toxic speech research has mainly focused on overt toxicity, such as hate speech, microaggressions in the form of PCL remain underexplored. Additionally, dominant groups' discriminatory facial expressions and attitudes toward vulnerable communities can be more impactful than verbal cues, yet these frame features are often overlooked. In this paper, we introduce the PCLMM dataset, the first Chinese multimodal dataset for PCL, consisting of 715 annotated videos from Bilibili, with high-quality PCL facial frame spans. We also propose the MultiPCL detector, featuring a facial expression detection module for PCL recognition, demonstrating the effectiveness of modality complementarity in this challenging task. Our work makes an important contribution to advancing microaggression detection within the domain of toxic speech.
Consumer Research with Projective Techniques: A Mixed Methods-Focused Review and Empirical Reanalysis
2024-09-08
Stephen L. France
This article gives an integrative review of research using projective methods in the consumer research domain. We give a general historical overview of the use of projective methods, both in psychology and in consumer research applications, and discuss the reliability and validity aspects and measurement for projective techniques. We review the literature on projective techniques in the areas of marketing, hospitality & tourism, and consumer & food science, with a mixed methods research focus on the interplay of qualitative and quantitative techniques. We review the use of several quantitative techniques used for structuring and analyzing projective data and run an empirical reanalysis of previously gathered data. We give recommendations for improved rigor and for potential future work involving mixed methods in projective techniques.
The popularity of pulsating stars resides in their capacity of determining several crucial and relevant parameters such as heliocentric distances, ages, metallicity gradients and reddening. RR Lyrae stars are old stellar tracers and have been detected in nearly all nearby galaxies that have been searched for these stars, with just a few exceptions of very low mass dwarfs. Less common but also of great importance are Anomalous Cepheids, indicators of either old or intermediate-age population, depending on their stellar origin. Classical Cepheids are only found within young stellar populations, and because of their brighter absolute magnitudes, they can be detected in galaxies farther than the Local Group. This paper presents a concise review built upon the aforementioned pulsating stars in Local Group dwarf galaxies and some of their applications to infer important properties of their host galaxies.
A foundation model enpowered by a multi-modal prompt engine for universal seismic geobody interpretation across surveys
2024-09-08
Hang Gao, Xinming Wu, Luming Liang, Hanlin Sheng, Xu Si, Gao Hui, Yaxing Li
Seismic geobody interpretation is crucial for structural geology studies and various engineering applications. Existing deep learning methods show promise but lack support for multi-modal inputs and struggle to generalize to different geobody types or surveys. We introduce a promptable foundation model for interpreting any geobodies across seismic surveys. This model integrates a pre-trained vision foundation model (VFM) with a sophisticated multi-modal prompt engine. The VFM, pre-trained on massive natural images and fine-tuned on seismic data, provides robust feature extraction for cross-survey generalization. The prompt engine incorporates multi-modal prior information to iteratively refine geobody delineation. Extensive experiments demonstrate the model's superior accuracy, scalability from 2D to 3D, and generalizability to various geobody types, including those unseen during training. To our knowledge, this is the first highly scalable and versatile multi-modal foundation model capable of interpreting any geobodies across surveys while supporting real-time interactions. Our approach establishes a new paradigm for geoscientific data interpretation, with broad potential for transfer to other tasks.
DEPLOYERS: An agent based modeling tool for multi country real world data
2024-09-07
Martin Jaraiz, Ruth Pinacho
We present recent progress in the design and development of DEPLOYERS, an agent-based macroeconomics modeling (ABM) framework, capable to deploy and simulate a full economic system (individual workers, goods and services firms, government, central and private banks, financial market, external sectors) whose structure and activity analysis reproduce the desired calibration data, that can be, for example a Social Accounting Matrix (SAM) or a Supply-Use Table (SUT) or an Input-Output Table (IOT).Here we extend our previous work to a multi-country version and show an example using data from a 46-countries 64-sectors FIGARO Inter-Country IOT. The simulation of each country runs on a separate thread or CPU core to simulate the activity of one step (month, week, or day) and then interacts (updates imports, exports, transfer) with that country's foreign partners, and proceeds to the next step. This interaction can be chosen to be aggregated (a single row and column IO account) or disaggregated (64 rows and columns) with each partner. A typical run simulates thousands of individuals and firms engaged in their monthly activity and then records the results, much like a survey of the country's economic system. This data can then be subjected to, for example, an Input-Output analysis to find out the sources of observed stylized effects as a function of time in the detailed and realistic modeling environment that can be easily implemented in an ABM framework.
2024-09-07
Diego G. Miralles, Jordi Vila-Guerau de Arellano, Tim R. McVicar, Miguel D. Mahecha
Vegetation often understood merely as the result of long-term climate conditions. However, vegetation itself plays a fundamental role in shaping Earth's climate by regulating the energy, water, and biogeochemical cycles across terrestrial landscapes. It exerts influence by altering surface roughness, consuming significant water resources through transpiration and interception, lowering atmospheric CO2 concentration, and controlling net radiation and its partitioning into sensible and latent heat fluxes. This influence propagates through the atmosphere, from microclimate scales to the entire atmospheric boundary layer, subsequently impacting large-scale circulation and the global transport of heat and moisture. Understanding the feedbacks between vegetation and atmosphere across multiple scales is crucial for predicting the influence of land use and cover changes and for accurately representing these processes in climate models. This short review aims to discuss the mechanisms through which vegetation modulates climate across spatial and temporal scales. Particularly, we evaluate the influence of vegetation on circulation patterns, precipitation and temperature, both in terms of trends and extreme events, such as droughts and heatwaves. The main goal is to highlight the state of science and review recent studies that may help advance our collective understanding of vegetation feedbacks and the role they play in climate.
The Dark Energy Camera Magellanic Clouds Emission-Line Survey
2024-09-07
Sean D. Points, Knox S. Long, William P. Blair, Rosa Williams, You-Hua Chu, P. Frank Winkler, Richard L. White, Armin Rest, Chuan-Jui Li, Francisco Valdes
We have used the Dark Energy Camera (DECam) on the CTIO Blanco 4-m telescope to perform a new emission-line survey of the Large Magellanic Cloud (LMC) using narrow-band H-alpha and [SII] filters in addition to a continuum band for use in creating pure emission-line images. We refer to this new survey as DeMCELS, to distinguish it from the earlier Magellanic Cloud Emission Line Survey (MCELS) that has been in service for nearly 25 years. DeMCELS covers $\sim 54$ degrees$^{2}$, encompassing most of the bright optical disk of the LMC. With DECam's pixel size of only 0.27", our DeMCELS survey provides a seeing-limited improvement of 3-5 times over MCELS and is comparable in depth, with surface brightness limits of 3.3E-17 erg cm$^{-2}$ s$^{-1}$ arcsec$^{-2}$ in H-alpha and 2.9E-17 erg cm$^{-2}$ s$^{-1}$ arcsec$^{-2}$ in H-alpha and [SII], respectively. DeMCELS provides detailed morphological information on nebulae of all scales, from the largest supershells to individual [HII] regions and supernova remnants, to bubbles of emission surrounding individual stars, and even to faint structures in the diffuse ionized gas of the LMC. Many complex regions of emission show significant variations in the ratio of [SII] to H-alpha, a sign of the mixture of shocks from stellar winds and/or supernovae with photoionization by embedded hot, young stars. We present the details of the observing strategy and data processing for this survey, and show selected results in comparison with previous data. A companion project for the Small Magellanic Cloud is in progress and will be reported separately. We are making these new data available to the community at large via the NOIRLab's Data Lab site.
Achieving Peak Performance for Large Language Models: A Systematic Review
2024-09-07
Zhyar Rzgar K Rostam, Sándor Szénási, Gábor Kertész
In recent years, large language models (LLMs) have achieved remarkable success in natural language processing (NLP). LLMs require an extreme amount of parameters to attain high performance. As models grow into the trillion-parameter range, computational and memory costs increase significantly. This makes it difficult for many researchers to access the resources needed to train or apply these models. Optimizing LLM performance involves two main approaches: fine-tuning pre-trained models for specific tasks to achieve state-of-the-art performance, and reducing costs or improving training time while maintaining similar performance. This paper presents a systematic literature review (SLR) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. We reviewed 65 publications out of 983 from 2017 to December 2023, retrieved from 5 databases. The study presents methods to optimize and accelerate LLMs while achieving cutting-edge results without sacrificing accuracy. We begin with an overview of the development of language modeling, followed by a detailed explanation of commonly used frameworks and libraries, and a taxonomy for improving and speeding up LLMs based on three classes: LLM training, LLM inference, and system serving. We then delve into recent optimization and acceleration strategies such as training optimization, hardware optimization, scalability and reliability, accompanied by the taxonomy and categorization of these strategies. Finally, we provide an in-depth comparison of each class and strategy, with two case studies on optimizing model training and enhancing inference efficiency. These case studies showcase practical approaches to address LLM resource limitations while maintaining performance.
Beyond Dependencies: The Role of Copy-Based Reuse in Open Source Software Development
2024-09-07
Mahmoud Jahanshahi, David Reid, Audris Mockus
In Open Source Software, resources of any project are open for reuse by introducing dependencies or copying the resource itself. In contrast to dependency-based reuse, the infrastructure to systematically support copy-based reuse appears to be entirely missing. Our aim is to enable future research and tool development to increase efficiency and reduce the risks of copy-based reuse. We seek a better understanding of such reuse by measuring its prevalence and identifying factors affecting the propensity to reuse. To identify reused artifacts and trace their origins, our method exploits World of Code infrastructure. We begin with a set of theory-derived factors related to the propensity to reuse, sample instances of different reuse types, and survey developers to better understand their intentions. Our results indicate that copy-based reuse is common, with many developers being aware of it when writing code. The propensity for a file to be reused varies greatly among languages and between source code and binary files, consistently decreasing over time. Files introduced by popular projects are more likely to be reused, but at least half of reused resources originate from ``small'' and ``medium'' projects. Developers had various reasons for reuse but were generally positive about using a package manager.
OSS License Identification at Scale: A Comprehensive Dataset Using World of Code
2024-09-07
Mahmoud Jahanshahi, David Reid, Adam McDaniel, Audris Mockus
The proliferation of open source software (OSS) has led to a complex landscape of licensing practices, making accurate license identification crucial for legal and compliance purposes. This study presents a comprehensive analysis of OSS licenses using the World of Code (WoC) infrastructure. We employ an exhaustive approach, scanning all files containing ``license'' in their filepath, and apply the winnowing algorithm for robust text matching. Our method identifies and matches over 5.5 million distinct license blobs across millions of OSS projects, creating a detailed project-to-license (P2L) map. We verify the accuracy of our approach through stratified sampling and manual review, achieving a final accuracy of 92.08%, with precision of 87.14%, recall of 95.45%, and an F1 score of 91.11%. This work enhances the understanding of OSS licensing practices and provides a valuable resource for developers, researchers, and legal professionals. Future work will expand the scope of license detection to include code files and references to licenses in project documentation.
Optimizing reaction and transport fluxes in temperature gradient-driven chemical reaction-diffusion systems
2024-09-07
Mohammed Loukili, Ludovic Jullien, Guillaume Baffou, Raphaël Plasson
Temperature gradients represent energy sources that can be harvested to generate steady reaction or transport fluxes. Technological developments could lead to the transfer of free energy from heat sources and sinks to chemical systems for the purpose of extraction, thermal batteries, or nonequilibrium synthesis. We present a theoretical study of 1D chemical systems subjected to temperature gradients. A complete theoretical framework describes the behavior of the system induced by various temperature profiles. An exact mathematical derivation was established for a simple two-compartment model and was generalized to arbitrary reaction-diffusion systems based on numerical models. An experimental system was eventually scaled and tuned to optimize either nonequilibrium chemical transport or reaction. The relevant parameters for this description were identified; they focused on the system symmetry for chemical reaction and transport. Nonequilibrium thermodynamic approaches lead to a description analogous to electric circuits. Temperature gradients lead to the onset of a steady chemical force, which maintains steady reaction-diffusion fluxes moderated by chemical resistance. The system activity was then assessed using the entropy production rate as a measure of its dissipated power. The chemical characteristics of the system can be tuned for general optimization of the nonequilibrium state or for the specific optimization of either transport or reaction processes. The shape of the temperature gradient can be tailored to precisely control the spatial localization of active processes, targeting either precise spatial localization or propagation over large areas. The resulting temperature-driven chemical system can in turn be used to drive secondary processes into either nonequilibrium reaction fluxes or concentration gradients.
SGSeg: Enabling Text-free Inference in Language-guided Segmentation of Chest X-rays via Self-guidance
2024-09-07
Shuchang Ye, Mingyuan Meng, Mingjian Li, Dagan Feng, Jinman Kim
Segmentation of infected areas in chest X-rays is pivotal for facilitating the accurate delineation of pulmonary structures and pathological anomalies. Recently, multi-modal language-guided image segmentation methods have emerged as a promising solution for chest X-rays where the clinical text reports, depicting the assessment of the images, are used as guidance. Nevertheless, existing language-guided methods require clinical reports alongside the images, and hence, they are not applicable for use in image segmentation in a decision support context, but rather limited to retrospective image analysis after clinical reporting has been completed. In this study, we propose a self-guided segmentation framework (SGSeg) that leverages language guidance for training (multi-modal) while enabling text-free inference (uni-modal), which is the first that enables text-free inference in language-guided segmentation. We exploit the critical location information of both pulmonary and pathological structures depicted in the text reports and introduce a novel localization-enhanced report generation (LERG) module to generate clinical reports for self-guidance. Our LERG integrates an object detector and a location-based attention aggregator, weakly-supervised by a location-aware pseudo-label extraction module. Extensive experiments on a well-benchmarked QaTa-COV19 dataset demonstrate that our SGSeg achieved superior performance than existing uni-modal segmentation methods and closely matched the state-of-the-art performance of multi-modal language-guided segmentation methods.
Reliable uncertainty estimation has become a crucial requirement for the industrial deployment of deep learning algorithms, particularly in high-risk applications such as autonomous driving and medical diagnosis. However, mainstream uncertainty estimation methods, based on deep ensembling or Bayesian neural networks, generally impose substantial computational overhead. To address this challenge, a novel paradigm called Evidential Deep Learning (EDL) has emerged, providing reliable uncertainty estimation with minimal additional computation in a single forward pass. This survey provides a comprehensive overview of the current research on EDL, designed to offer readers a broad introduction to the field without assuming prior knowledge. Specifically, we first delve into the theoretical foundation of EDL, the subjective logic theory, and discuss its distinctions from other uncertainty estimation frameworks. We further present existing theoretical advancements in EDL from four perspectives: reformulating the evidence collection process, improving uncertainty estimation via OOD samples, delving into various training strategies, and evidential regression networks. Thereafter, we elaborate on its extensive applications across various machine learning paradigms and downstream tasks. In the end, an outlook on future directions for better performances and broader adoption of EDL is provided, highlighting potential research avenues.
Algorithmic Scenario Generation as Quality Diversity Optimization
2024-09-07
Stefanos Nikolaidis
The increasing complexity of robots and autonomous agents that interact with people highlights the critical need for approaches that systematically test them before deployment. This review paper presents a general framework for solving this problem, describes the insights that we have gained from working on each component of the framework, and shows how integrating these components leads to the discovery of a diverse range of realistic and challenging scenarios that reveal previously unknown failures in deployed robotic systems interacting with people.
Exploring Crowdworkers' Perceptions, Current Practices, and Desired Practices Regarding Using Non-Workstation Devices for Crowdwork
2024-09-07
Senjuti Dutta, Scott Ruoti, Rhema Linder, Alex C. Williams, Anastasia Kuzminykh
Despite a plethora of research dedicated to designing HITs for non-workstations, there is a lack of research looking specifically into workers' perceptions of the suitability of these devices for managing and completing work. In this work, we fill this research gap by conducting an online survey of 148 workers on Amazon Mechanical Turk to explore 1. how crowdworkers currently use their non-workstation devices to complete and manage crowdwork, 2. what challenges they face using those devices, and 3. to what extent they wish they could use those devices if their concerns were addressed. Our results show that workers unanimously favor using a desktop to complete and manage crowdwork. While workers occasionally use smartphones or tablets, they find their suitability marginal at best and have little interest in smart speakers and smartwatches, viewing them as unsuitable for crowdwork. When investigating the reason for these views, we find that the key issue is that non workstation devices lack the tooling necessary to automatically find and accept HITs, tooling that workers view as essential in their efforts to compete with bots in accepting high paying work. To address this problem, we propose a new paradigm for finding, accepting, and completing crowdwork that puts crowdworkers on equal footing with bots in these tasks. We also describe future research directions for tailoring HITs to non workstation devices and definitely answering whether smart speakers and smartwatches have a place in crowdwork.